Repository: DUanalytics/rAnalytics
Branch: master
Commit: ba122862509a
Files: 1497
Total size: 11.2 MB

Directory structure:
gitextract_w2ho1432/

├── .gitignore
├── 0-Practise/
│   ├── day1.R
│   ├── day2.R
│   ├── day3.R
│   ├── first.R
│   ├── htmlimport.R
│   ├── iims2.R
│   ├── import2.R
│   ├── kt1.R
│   ├── lm-sim-test1.R
│   ├── practise.R
│   ├── practise2.R
│   ├── rough.R
│   └── vector.R
├── 0-Rdata/
│   ├── madata.Rdata
│   ├── student.rds
│   ├── student1.rds
│   ├── twitter authentication.Rdata
│   └── twitterauthentication.Rdata
├── 0-class/
│   ├── AR-groceries.R
│   ├── CLUST-customer.R
│   ├── DT-CART-sales.R
│   ├── NAvalues.R
│   ├── autoML1.R
│   ├── bigQuery.R
│   ├── hhe_d01.R
│   ├── hhe_d02.R
│   ├── hhe_d03.R
│   ├── hhe_d04.R
│   ├── hhe_d04b.R
│   ├── lm_AIC.R
│   ├── missingValues.R
│   ├── munaz.csv
│   └── purchaseProb.R
├── 01-IIM/
│   ├── 10a-daily.R
│   ├── 11-analyticLevels.R
│   ├── 11a1-start.R
│   ├── 11a2-packages1.R
│   ├── 11a3-packages2.R
│   ├── 11a4-packages3.R
│   ├── 11a5-packages4.R
│   ├── 11b2-DS1.R
│   ├── 11b3-DS2-factor.R
│   ├── 11b4-DS4-reproduce.R
│   ├── 11b5-DS3.R
│   ├── 11e2_vectors1.R
│   ├── 11e3_matrices1.R
│   ├── 11e4_dataframe1.R
│   ├── 12a3-impexp1.R
│   ├── 12a4-datasets.R
│   ├── 12a4-impexp-xls.R
│   ├── 12e4-impexp-gs.R
│   ├── 13a2-NAvalues.R
│   ├── 13b2-outliers.R
│   ├── 15a2-GPH-basic.R
│   ├── 15a3-GPH-graphs.R
│   ├── 15a4-GPH-advgraphs.R
│   ├── 16b1-GPH-wordcloud.R
│   ├── 16e0-GPH-wordcloud.R
│   ├── 16e3-GPH-wordcloud-text1.R
│   ├── 16e4-GPH-wordcloud-text2.R
│   ├── 17a2-STATS-freqtable.R
│   ├── 17c1-STATS-datapartition.R
│   ├── 17d2-STATS-basicstats.R
│   ├── 18d1-DPLYR-mtcars1.R
│   ├── 18d3-DPLYR-mtcars2.R
│   ├── 18d4-DPLYR-dplyr.R
│   ├── 21a1-SLM-women.R
│   ├── 21a2-SLM-women.R
│   ├── 21a4-SLM-women.R
│   ├── 21a5-SLM-women-A.R
│   ├── 21b1-SLM-sales.R
│   ├── 21b3-SLM-areasales.R
│   ├── 21b4-SLM-salesarea.R
│   ├── 21d2-MLM-mtcars1.R
│   ├── 22a4-MLM-allmodels.R
│   ├── 22c2-MLM-mtcars-olsrr.R
│   ├── 22c3-MLM-cars.R
│   ├── 22c3-MLM-salespromotion.R
│   ├── 22d3-MLM-omni.R
│   ├── 22d4-MLM-sales-TV.R
│   ├── 23c1-LOGR-logR.R
│   ├── 23d1-LGR-gre.R
│   ├── 24b1-LOGR-purchase.R
│   ├── 24c2-LOGR-adult.R
│   ├── 24d2-LOGR-gre.R
│   ├── 24e2-LOGR-general.R
│   ├── 24g1-LOGR-cancer.R
│   ├── 24g2-LOGR-sample1.R
│   ├── 31a1-DT-cart-split.R
│   ├── 31a2-DT-outlook.R
│   ├── 31a3-DT-general.R
│   ├── 31a3-DT-practiseCase.R
│   ├── 31b1-DT-CART-carseats.R
│   ├── 31b2-DT-CART-sales.R
│   ├── 31b3-DT-CART-titanic.R
│   ├── 31b4-DT-CART-loan.R
│   ├── 31b5-DT-CART-gre.R
│   ├── 31b5-DT-loanapproved1.R
│   ├── 31b5-DT-rpart-iris.R
│   ├── 31b7-DT-party.R
│   ├── 31f1-DT-cart2.R
│   ├── 32a4-DT-cart1.R
│   ├── 32a4-DT-rf-4.R
│   ├── 32c2-DT-chaid-usvote.R
│   ├── 32d1-DT-CHAID-usvote.R
│   ├── 32d5-DT-CART-RF.R
│   ├── 32k1-CLS-svm1.R
│   ├── 33g3-CLS-randomForest1.R
│   ├── 34a1-CLUST-clustering.R
│   ├── 34a1-CLUST-samplecase.R
│   ├── 34b2-CLUST-customer.R
│   ├── 34d1-CLUST-creditData.R
│   ├── 34e4-clust-NOC-iris.R
│   ├── 34g1-CLUST-segmentation.R
│   ├── 34h1-CLUST-clustering.R
│   ├── 34h1-CLUST-clustering2.R
│   ├── 34j2-CLUST-NOC.R
│   ├── 35d1-CLUST-hclust1.R
│   ├── 35d2-CLUST-hclust2.R
│   ├── 37a2-AR-eg.R
│   ├── 37b1-AR-groceries.R
│   ├── 37b2-AR-samplecase.R
│   ├── 37b3-AR-groceries-subset.R
│   ├── 37b5-AR-finproducts.R
│   ├── 37b5-AR-groceries.R
│   ├── 37c3-AR-redundant.R
│   ├── 44b1-TS-data.R
│   ├── 44b1-TS-dates.R
│   ├── 44c2-TS-dates-lubridate.R
│   ├── 44c4-TS-xts-data.R
│   ├── 45d2-TS-airpassengers.R
│   ├── 45d3-TS-components-airp.R
│   ├── 45d4-TS-johnson.R
│   ├── 45g3-TS-TTR-ma.R
│   ├── 46e3-TS-auto-arima-johnson.R
│   ├── 55c1-TM-twitter.R
│   ├── 55e1-TM-twitter1.R
│   ├── 61b1-LP-marketingspend.R
│   ├── 61c2-LP-marketingspend-case.R
│   ├── 61d2-LP-tpt.R
│   ├── 61e2-LP-machassign.R
│   ├── 61e5-LP-farmer1.R
│   ├── 77a1-FA-quandl.R
│   ├── 77a2-FA-quandl2.R
│   ├── 77a5-FA-quantmod.R
│   ├── 77a6-FA-quantmod-I-stocks.R
│   ├── 77f2-FA-quantmod1.R
│   ├── 77f3-FA-indianstocks.R
│   ├── 91ab3-Case-student1.R
│   ├── 91b4-Case-dencoCase.R
│   ├── 91b5-Case-denco.R
│   ├── 91g4-CASE-dencoCase.R
│   ├── revision1.R
│   ├── test.R
│   ├── x55a1-TM-tweets.R
│   ├── x55a2-TM-twitter.R
│   └── x55a3-TM-twitter2.R
├── 02-IIMcases/
│   ├── DT_diabetis.R
│   ├── case-denco.R
│   ├── case-dplyr-mtcars.R
│   ├── rev_iima20_1.R
│   ├── revision1.R
│   ├── senitmentTwitter.R
│   └── textMiningSentence.R
├── 03-setup/
│   ├── 11a-start.R
│   ├── 11b-gettingstarted.R
│   ├── 12-packageInstall.R
│   ├── 15a-envrm.R
│   ├── 15b-renv.R
│   ├── 15e-rjava.R
│   ├── 16a-pathconfig.R
│   ├── 17a-rstudio.R
│   ├── 18a-processtime.R
│   ├── 21a-floorceiling1.R
│   ├── 21b-options.R
│   ├── 24a-github.R
│   ├── 25a-help.R
│   ├── 51c-deletefiles.R
│   ├── envVar.R
│   ├── help.R
│   └── pathconfig.R
├── 04-lib/
│   ├── 10a-fBasics.R
│   ├── 11a-pysch.R
│   ├── 21b-installpackages.R
│   ├── 21e-installFmGit.R
│   ├── 21g-packages1.R
│   ├── 31b-datasets.R
│   ├── 31c-datasets.R
│   ├── 41-purrr1.R
│   ├── 42-purr2.R
│   ├── 43-purrr3.R
│   ├── 44-purrr4.R
│   ├── 45-purrr5.R
│   ├── 51-plyr1.R
│   ├── 61-splitapplycombine1.R
│   ├── 62-splitapplycombine2.R
│   ├── 71-broom1.R
│   ├── packages1.R
│   ├── switchr.R
│   ├── useful.R
│   └── useful2.R
├── 05-dataIE/
│   ├── 14a-readcsv.R
│   ├── 14b-readcsv.R
│   ├── 14c-importweb.R
│   ├── 14d-importweb.R
│   ├── 14e-readothers.R
│   ├── 15b-datawrangling.R
│   ├── 20a-importgg.R
│   ├── 21b-googlesheet1.R
│   ├── 22b-ggsheets2.R
│   ├── 31a-export.R
│   ├── 32c-writecsv.R
│   ├── datasets.R
│   └── importExcel.R
├── 06-DS/
│   ├── 0FileList.R
│   ├── 10a-TOC
│   ├── 13b-ds-blank.R
│   ├── 14b-Basic_R_v01.R
│   ├── 14b-objectsmethods.R
│   ├── 14c-ds1.R
│   ├── 15a-objects.R
│   ├── 16b-datatypes.R
│   ├── 16c-basicDT.R
│   ├── 16d-ds1.R
│   ├── 20a-vectors1.R
│   ├── 20b-vectors2.R
│   ├── 20c-vectors3.R
│   ├── 20d-vectors4.R
│   ├── 20f-vectors5.R
│   ├── 20g-vectors6.R
│   ├── 25a-matrices.R
│   ├── 25c-matrices.R
│   ├── 25d-matrices.R
│   ├── 25e-matrices.R
│   ├── 27a-arrays.R
│   ├── 27b-arrays.R
│   ├── 27d-arrays.R
│   ├── 30c-basicdatatypes.R
│   ├── 30d-ds1.R
│   ├── 30e-datatypes.R
│   ├── 33b-df.R
│   ├── 33c-df.R
│   ├── 35a-lists.R
│   ├── 35b-lists.R
│   ├── 35e-lists.R
│   ├── 38a-factors.R
│   ├── 38b-factors.R
│   ├── 38c-factors.R
│   ├── 38e-factors.R
│   └── factors_iims.R
├── 07-excel/
│   ├── excelData.xlsx
│   ├── impExpExcel.R
│   ├── importFmExcel.R
│   ├── importFmGS.R
│   ├── mtcars.xlsx
│   ├── student1.R
│   └── student2.xlsx
├── 08-MBB/
│   ├── 01-MBB.R
│   ├── 02-MBB.R
│   ├── 03-MBB.R
│   ├── 04-MBB.R
│   ├── 05-MBB.R
│   └── 06-MBB.R
├── 10-packages/
│   ├── analyzer.R
│   ├── autoreg.R
│   ├── dttr2.R
│   ├── ggpmisc.R
│   ├── packages.txt
│   ├── timeplyr.R
│   └── tsutils.R
├── 11-stats/
│   ├── 10-statslinks.R
│   ├── 10a-distributions.R
│   ├── 10c-allstats.R
│   ├── 11a-normal.R
│   ├── 11b-normalq.R
│   ├── 11c-normald.R
│   ├── 12a-binomial.R
│   ├── 13a-mean.R
│   ├── 14a-median.R
│   ├── 15a-mode.R
│   ├── 15b-mode.R
│   ├── 16a-range.R
│   ├── 17a-sd.R
│   ├── 18a-covariance.R
│   ├── 19a-correlation.R
│   ├── 20a-coev.R
│   ├── 37a-sample1.R
│   ├── 40a-missing1.R
│   ├── 40b-missing1.R
│   ├── 40c-missing2.R
│   ├── 40d-missing3.R
│   ├── 42a-outlier1.R
│   ├── 43a-outliers1.R
│   ├── 45a-sampling.R
│   ├── 55a-traintest1.R
│   ├── 60a-kurtosis.R
│   ├── 60b-kurtosis.R
│   ├── 64a-skewness.R
│   ├── 64b-skewness.R
│   ├── 71a-xtabs.R
│   ├── ave1.R
│   ├── interactions.R
│   ├── mean1.R
│   ├── mean2.R
│   ├── mean3.R
│   ├── meandev.R
│   ├── meanwt1.R
│   ├── median1.R
│   ├── mode1.R
│   ├── normal_height.R
│   ├── normality.R
│   ├── normality2.R
│   ├── outlier2.R
│   ├── outliers1.R
│   ├── poiss1.R
│   └── quantile1.R
├── 13-statsH/
│   ├── 20b-distributions.R
│   ├── 23b-ztest-bsda.R
│   ├── 25c-tdistribution.R
│   ├── 26b-ttestindep.R
│   ├── 26c-ttestpaired.R
│   ├── 27b-TTS1-case1.R
│   ├── 28b-TTS2-case1.R
│   ├── 28c-TTS1-case3.R
│   ├── 28e-TT-sample1i.R
│   ├── 31b-chisqdistr.R
│   ├── 32b-HT-chisq1.R
│   ├── 32c-HT-chisq2.R
│   ├── 33b-HT-chisq.R
│   ├── 33d-chisqtest1.R
│   ├── 33e-chisqtest2.R
│   ├── 34b-goodnessfit.R
│   ├── datadistr.R
│   ├── htestnd1.R
│   ├── randomdistr.R
│   ├── shadeareainplot.R
│   └── tests1.R
├── 15-sampling/
│   ├── 12b-samples.R
│   ├── 12e-sampleint.R
│   ├── 14b-stratified.R
│   ├── 15b-samplesplit.R
│   ├── 15d-datapartition.R
│   └── 15f-partitionfolds.R
├── 22-summary/
│   ├── 22b-aggregate.R
│   ├── 22c-aggregate2.R
│   ├── 23b-freqdistr1.R
│   ├── 23c-freqdistr2.R
│   ├── 23d-freqdistr3.R
│   ├── 23f-FD.R
│   ├── 23f-freqdistr.R
│   ├── 24b-freqdistr4.R
│   ├── 24f-freqdistr5.R
│   ├── 25g-freqdistr6.R
│   ├── 31c-rowsums1.R
│   ├── 32b-addmargin1.R
│   ├── 32c-margintable1.R
│   ├── 32d-proptable.R
│   ├── 32d-tableprop2.R
│   ├── 35b-crosstab.R
│   ├── 99a-Pskim.R
│   ├── 99a-studentdata1.R
│   └── descriptive.R
├── 23-functions/
│   ├── 21b-rep.R
│   ├── 21c-seq.R
│   ├── 21g-replicate.R
│   ├── 21g-seqdates.R
│   ├── 22b-letters.R
│   ├── 25b-interval.R
│   ├── 25c-midpoint.R
│   ├── 27b-recode-car.R
│   ├── 29b-subset.R
│   ├── 29c-split1.R
│   ├── 29d-splitdata.R
│   ├── 29e-partitiondata.R
│   ├── 31b-rowcol1.R
│   ├── 33b-sortorder.R
│   ├── 33c-order.R
│   ├── 33c-sortorderrank.R
│   ├── 33d-rank.R
│   ├── 34b-castmelt1.R
│   ├── 34c-castmelt2.R
│   ├── 37a-mtcars-subset.R
│   ├── 37b-duplicates1.R
│   ├── 37c-unique.R
│   ├── 38b-scale1.R
│   ├── 41b-randnos1.R
│   ├── 41c-randnos.R
│   ├── 42b-normdist.R
│   ├── 45b-forloop1.R
│   ├── 45e-ifelse2.R
│   ├── 45v-switch1.R
│   ├── 46b-withoutapply.R
│   ├── 46c-applyForCompare.R
│   ├── 46d-applyfamily.R
│   ├── 46e-applytype.R
│   ├── 46f-while1.R
│   ├── 47b-apply1.R
│   ├── 47c-apply.R
│   ├── 47d-apply1.R
│   ├── 47h-tapply1.R
│   ├── 47j-lapply1.R
│   ├── 47m-mapply1.R
│   ├── 47n-mapply2.R
│   ├── 47o-rapply.R
│   ├── 47on-eapply.R
│   ├── 47p-sapply1.R
│   ├── 47q-sapply2.R
│   ├── 47s-tapply2.R
│   ├── 47t-vapply1.R
│   ├── 49b-replicate1.R
│   ├── 49c-replicate.R
│   ├── 49e-by.R
│   ├── 49f-by.R
│   ├── 49g-bywith.R
│   ├── 51b-myfunc.R
│   ├── 51c-functions1.R
│   ├── 53b-cbindrbind1.R
│   ├── 53c-joinDFs.R
│   ├── 53c-merge1.R
│   ├── 54b-combination.R
│   ├── 54d-expandgrid.R
│   ├── 55b-sweep1.R
│   ├── 55d-sweep2.R
│   ├── 56b-outer1.R
│   ├── 56c-outer2.R
│   ├── 57b-stack1.R
│   ├── 58-DF-common.R
│   ├── 58-df-matching1.R
│   ├── 58-df2.R
│   ├── 58-hmisc.R
│   ├── 58-pmatchchar.R
│   ├── 61c-missing1.R
│   ├── 61c-missing2.R
│   ├── 61c-missing3.R
│   ├── 62b-outlier.R
│   └── 62c-outlier2.R
├── 24-Strings/
│   ├── abvn.R
│   ├── latex.R
│   ├── output.txt
│   ├── paste1.R
│   ├── setop1.R
│   ├── strcmpt1.R
│   ├── string1.R
│   ├── strjoin.R
│   ├── strlength.R
│   ├── strman1.R
│   ├── strman2.R
│   ├── strman3.R
│   ├── strman4.R
│   ├── strman5.R
│   ├── strman6.R
│   ├── strman7.R
│   ├── strman9.R
│   ├── strprint1.R
│   ├── strreplace1.R
│   ├── strsearch.R
│   ├── strsplit1.R
│   ├── strsplit2.R
│   ├── strsplit3.R
│   ├── strsplit4.R
│   └── tidyr-strseperate.R
├── 31-graphs/
│   ├── 1bubblechart.R
│   ├── 1bubblechart2.R
│   ├── multipleplots1.R
│   └── tableGrob.R
├── 32-basicGraphs/
│   ├── 10a-graphs.R
│   ├── 10b-graphs.R
│   ├── 12b-graphs2.R
│   ├── 12d-title1.R
│   ├── 12e-text.R
│   ├── 12f-abline.R
│   ├── 12g-legend.R
│   ├── 12k-tick.R
│   ├── 12m-axis1.R
│   ├── 13e-multipleplots1.R
│   ├── 13f-multipleplots.R
│   ├── 13g-subplot.R
│   ├── 15a-graphdata1.R
│   ├── 15b-graph1.R
│   ├── 21b-plot-hist1.R
│   ├── 21c-plot.R
│   ├── 23b-line.R
│   ├── 23c-lines2.R
│   ├── 24b-histogram.R
│   ├── 24c-histogram2.R
│   ├── 25b-barplot.R
│   ├── 25c-barplot2.R
│   ├── 26b-boxplot.R
│   ├── 26c-boxplot2.R
│   ├── 26d-boxplot2.R
│   ├── 27b-pie.R
│   ├── 27c-pie2.R
│   ├── 29b-corrgram1.R
│   ├── 32b-freqdistr.R
│   ├── 33b-dotplot.R
│   ├── 33b-matrixplots.R
│   ├── 37b-scatter.R
│   ├── 42b-intplots1.R
│   ├── 43b-mosaic.R
│   ├── 43c-corrplot.R
│   ├── 43c-ggally.R
│   ├── 44b-textplots.R
│   ├── 45b-violinplot.R
│   ├── ria2g1.R
│   ├── ria2g2.R
│   ├── ria2g3.R
│   └── ria3g3.R
├── 33-AdvGraphs/
│   ├── cowplot1.R
│   ├── donut.R
│   ├── donut2.R
│   ├── esquisse.R
│   ├── lattice.R
│   ├── lattice1.R
│   ├── plotsToWord.R
│   ├── survey.R
│   ├── symbols.R
│   ├── vtree1.R
│   ├── waffle.R
│   └── xxggsubplot.R
├── 33-DT/
│   ├── 0-DTsummary.R
│   ├── 1-dt1.R
│   ├── 2-DT.R
│   └── 3-DT.R
├── 34-ggplots/
│   ├── circbarplot.R
│   ├── gg-apexcharts.R
│   ├── gg-bar1.R
│   ├── gg-bar2.R
│   ├── gg-box2.R
│   ├── gg-boxhist.R
│   ├── gg-boxplot1.R
│   ├── gg-halves.R
│   ├── gg-heatmap.R
│   ├── gg-hist1.R
│   ├── gg-hline.R
│   ├── gg-hvlines.R
│   ├── gg-labelend.R
│   ├── gg-legend1.R
│   ├── gg-line.R
│   ├── gg-pie1.R
│   ├── gg-slope.R
│   ├── gg-slope2.R
│   ├── ggbarplot.R
│   ├── ggbarplots.R
│   ├── ggboxhist.R
│   ├── ggboxplot2.R
│   ├── gghistogram.R
│   ├── ggp2.R
│   ├── ggplot-DU1.R
│   ├── ggplot-legend1.R
│   ├── ggplot3.R
│   ├── ggplot5.R
│   ├── ggplot6.R
│   ├── ggplot7.R
│   └── twoaxis-gg.R
├── 35-tidyverse/
│   ├── 20a-dplyr.R
│   ├── 21a-dplyr-select.R
│   ├── 21b-dplyr-slice1.R
│   ├── 21c-dplyr-mutate1.R
│   ├── 21d-dplyr-summarise.R
│   ├── 21e-dplyr-filter1.R
│   ├── 21f-dplyr-str.R
│   ├── 21g-dplyr-arrange.R
│   ├── 22b-dplyr-seperate1.R
│   ├── 22b-group.R
│   ├── 22c-summarise.R
│   ├── 22g-tibble-rownames.R
│   ├── 25b-magrittr.R
│   ├── 26c-tidyr-DSR1.R
│   ├── 26d-tidyr-DSR-who.R
│   ├── 31b-plyr1.R
│   ├── 32b-plyr-mutate.R
│   ├── 33d-dplyr-joins.R
│   ├── 33f-dplyr-split.R
│   ├── plyr-ddply-gpsum.R
│   ├── tidyr1.R
│   ├── zz-dplyr1.R
│   └── zz-tidy-dataformating.R
├── 41-LM/
│   ├── 10a-lm-women2.R
│   ├── 10b-lm-salesarea2.R
│   ├── 10c-MLR-omni.R
│   ├── 10e-lm-errorplot.R
│   ├── 13b-lm-commands.R
│   ├── 16b-SLM-women2.R
│   ├── 16c-SLM-women1.R
│   ├── 16e-SLM-women-A.R
│   ├── 16f-SLM-women-V.R
│   ├── 16f-SLM-women.R
│   ├── 16m-SLM-women2.R
│   ├── 17a-LM-case1.R
│   ├── 17b-LM-stock1.R
│   ├── 18a-SLM-salesarea.R
│   ├── 18b-SLM-salesarea.R
│   ├── 18c-SLM-salesarea.R
│   ├── 23a-MLM-omni.R
│   ├── 23c-MLM-omni.R
│   ├── 24a-MLM-pcsales.R
│   ├── 25a-MLM-mtcars.R
│   ├── 25c-MLM-mtcars.R
│   ├── 25c-MLM-mtcars1.R
│   ├── 25d-MLM-mtcars-A.R
│   ├── 26a-MLM-airquality.R
│   ├── 27a-MLM-marketing.R
│   ├── 35a-MLM-case1.R
│   ├── 37a-LM-dummy-fireplace.R
│   ├── 37b-dummy1.R
│   ├── 38c-LM-dummy1.R
│   ├── 41c-LM-assumptions.R
│   ├── 42b-LM-linearity.R
│   ├── 42c-LM-normality.R
│   ├── 42d-LM-variance.R
│   ├── 42e-LM-outliers.R
│   ├── 42f-LM-autocorr.R
│   ├── 42g-LM-influentialvariables.R
│   ├── 42h-LM-multicollinearity.R
│   ├── 42j-gvlma.R
│   ├── 43a-LM-graphs.R
│   ├── LM-all-mtcars1.R
│   ├── ProbDist.R
│   ├── Simulation.R
│   ├── confusionmatrix.R
│   ├── contrasts1.R
│   ├── dummies.R
│   ├── homosecadicity.R
│   ├── lm-broom.R
│   ├── lm-dummy1.R
│   ├── lm-housing.R
│   ├── lm-mtcars1.R
│   ├── lm-mtcars2.R
│   ├── lm-plot1.R
│   ├── lm-segments1.R
│   ├── mlm-state77.R
│   ├── multvariate1.R
│   ├── plotcoef1.R
│   └── regrplot1.R
├── 44-LogR/
│   ├── 24c-LR-default.R
│   ├── 24d-LR-default.R
│   ├── 24e-LR-default.R
│   ├── 24g-LR-default-accuracy.R
│   ├── 26b-LR-germancredit.R
│   ├── 27b-LR-gre.R
│   ├── 28b-LR-subscribe.R
│   ├── 28c-LR-subscribe.R
│   ├── 29b-LR-ads.R
│   ├── 31b-LR-income.R
│   ├── 31c-income.R
│   ├── 33b-LR-purchase.R
│   ├── 45b-compareAUC.R
│   ├── 45c-roc-default.R
│   ├── 45e-roc-general.R
│   ├── 45f-roc1.R
│   ├── 45h-roc2.R
│   ├── 46c-accuracy.R
│   ├── 48b-auc1.R
│   ├── 48c-auc1.R
│   ├── 48d-auc.R
│   ├── 48e-auc.R
│   ├── 49c-thresholdvalue.R
│   ├── pdpu.R
│   └── zz--logR.R
├── 51-DT/
│   ├── cls-gen
│   ├── cls1M-cancer.R
│   ├── dt-multiplemodels.R
│   ├── giniIndex.R
│   └── rattle.R
├── 52-CART/
│   ├── 10-CART-gen.R
│   ├── 11-cart-understandsplit.R
│   ├── 12-DT-outlook.R
│   ├── 12-IIMBG-wksp.R
│   ├── 12-IIMJ-wksp.R
│   ├── 12-IIMS-wksp.R
│   ├── 12-IITB-wksp.R
│   ├── CARTR_sales.R
│   ├── CART_Regression Tree v01.R
│   ├── DT-germanCredit.R
│   ├── DT-rpart-claims.R
│   ├── c-dt-rpart-Case-DU1.R
│   ├── c-dt-rpart-iris.R
│   ├── c-dt-rpart-sales1.R
│   ├── cls-cart-churn2.R
│   ├── cls-rpart-plot2.R
│   ├── dt-car.R
│   ├── dt-general.R
│   ├── dt-glaucoma.R
│   ├── dt-ionos1.R
│   ├── dt-iris1.R
│   ├── dt-kyphosis.R
│   ├── dt-loanapproved1.R
│   ├── dt-rpart-du.R
│   ├── dt-rpart-du1.R
│   ├── dt-rpart-du2.R
│   ├── dt-rpart-du3.R
│   ├── dt-rpart-metal.R
│   ├── dt-rpart-student1.R
│   ├── dt-rpart-text1.R
│   ├── dt-rpart-varimp1.R
│   ├── dt-rpart-varimp2.R
│   ├── dt-sleep.R
│   ├── dt-tree-car1.R
│   ├── dt3-eyes.R
│   ├── entropy.R
│   ├── multimodel.R
│   ├── tree-houseprices.R
│   └── zz-test.R
├── 53-splitcriteria/
│   ├── cls-entropy.R
│   ├── dt-rpart-criteria.R
│   ├── splitcriteria1.R
│   ├── splitcriteria2.R
│   └── splitcriteria3.R
├── 54-KNN/
│   ├── knn1_cancer.R
│   ├── knn2.R
│   ├── knn3.R
│   ├── knn4.R
│   ├── knn_diamonds.R
│   └── knn_iris.R
├── 54-NLM/
│   ├── nlm1.R
│   └── nlm2-mtcars.R
├── 55-CHAID/
│   ├── CHAID-nps2.R
│   ├── CHAID-xsell1.R
│   ├── c-dt-chaid-nps.R
│   ├── c-dt-chaid-usvote1.R
│   ├── chaid-attrition.R
│   ├── chaid-cancer.R
│   ├── chaid-usvote.R
│   ├── chaid2.R
│   ├── chaid4.R
│   ├── chisq.R
│   └── chisqtest2.R
├── 56-ctree/
│   ├── CTREE NPS R code v01.R
│   ├── ctree-KyCU.R
│   ├── ctree-airquality.R
│   ├── ctree-churn2.R
│   ├── ctree-clsregr-party.R
│   ├── ctree-clsregr.R
│   ├── ctree-readingskills.R
│   ├── ctree2-iris.R
│   ├── ctreee-iris.R
│   └── dt-ctree-playYes.R
├── 57-GLM/
│   ├── Logr-party.R
│   ├── crossfold.R
│   ├── crossval1.R
│   ├── cv-houseprices.R
│   ├── cv-women1.R
│   ├── cv3.R
│   ├── cvlm2.R
│   ├── glm-affairs1.R
│   ├── glm-affairs2.R
│   ├── glm-cars.R
│   ├── glm-titanic1.R
│   ├── logR1.R
│   ├── logpos1.R
│   ├── logr-mtcars.R
│   ├── logr-mtcars1.R
│   ├── logrMaths.R
│   ├── logreg-iris1.R
│   ├── multinominal.R
│   ├── multinominal2.R
│   ├── multinominal3.R
│   ├── multinominal4.R
│   ├── multinominal5.R
│   ├── multinominal6.R
│   └── nls1.R
├── 57-RF/
│   ├── dt-caret-xxx.R
│   ├── dt-rf-DU3.R
│   ├── dt-rf-eg2.R
│   ├── dt-rf-eg3.R
│   └── dt-rf-kyphosis1.R
├── 57-naive/
│   ├── naivbayes1.R
│   └── naivbayes2.R
├── 61-clust/
│   ├── 10-clust-packages.R
│   ├── 16b-km-withinss.R
│   ├── 17b-clust-noclusters1.R
│   ├── 17c-clust-numbers-iris.R
│   ├── 17d-noc-mclust.R
│   ├── 19b-clust-distances.R
│   ├── 19c-clust-distances.R
│   ├── 19d-clust-scaling.R
│   ├── 20b-clust-plots.R
│   ├── 20c-clust-plots2.R
│   ├── 23b-km-marks1.R
│   ├── 23c-km-marks2.R
│   ├── 23d-km-amap-marks3.R
│   ├── 23e-km-student2.R
│   ├── 24b-clust-women.R
│   ├── 25b-km-iris.R
│   ├── 25c-km-iris2.R
│   ├── 25f-km-iris2.R
│   ├── 26h-km-attitude.R
│   ├── 27c-clust-som1.R
│   ├── 33c-hc-nutrients1.R
│   ├── 33c-hc-vegan-dune1.R
│   ├── 33d-hc-protein.R
│   ├── 33f-hc-marks.R
│   ├── 33g-hc-sample.R
│   ├── 35d-pam-iris.R
│   ├── 35e-pam-nutrient.R
│   ├── 40b-mixedclust1.R
│   ├── 40c-clust-dendgm.R
│   ├── 43b-clust-mixedDataTypes1.R
│   ├── 45c-clustering-exist1.R
│   ├── 45e-clustering-animation1.R
│   ├── 50b-clust-ma1.R
│   ├── 50c-clust-ma2.R
│   ├── 50d-clust-ma3.R
│   ├── 61b-clust-custsegm.R
│   ├── animation2.R
│   ├── clust-allcustering.R
│   ├── clust-case-liberty.R
│   ├── clust-class-differences.R
│   ├── clust-compare.R
│   ├── clust-distance-calc.R
│   ├── clust-distance2.R
│   ├── clust-entropy.R
│   ├── clust-iterations.R
│   ├── clust-kselect.R
│   ├── clustering-seeds-dunn.R
│   ├── clustering-women.R
│   ├── clusters3.R
│   ├── hclust-USarrests.R
│   ├── hier-simplecase.R
│   ├── hier-usarrests.R
│   ├── iris.R
│   ├── kmeans-bankdata.R
│   ├── kmeans-pcalike.R
│   ├── kmeans-plots.R
│   ├── kmeans-randomness.R
│   └── pam1.R
├── 65-AR/
│   ├── 11a-measures1.R
│   ├── 12a-ar-samplecase.R
│   ├── 12b-ar-samplecase2.R
│   ├── 14a-ar-datastr.R
│   ├── 15-ar-groceries.R
│   ├── 15a-ar-Groceries1.R
│   ├── 15b-ar-Groceries.R
│   ├── 16b-groceries-summary.R
│   ├── 16d-ar-groceries-subset.R
│   ├── 16f-ar-groceries-vis.R
│   ├── 16f-ar-groceries-vis2.R
│   ├── 16f-ar-groceries-vis3.R
│   ├── 16f-ar-groceries-vis4.R
│   ├── 16f-ar-groceries-vis5.R
│   ├── 16k-ar-grocery-DT.R
│   ├── 17a-ar-transactionformat.R
│   ├── 17d-ar-matrix-transactions.R
│   ├── 17e-ar-df-transcations.R
│   ├── 17f-ar-csv-transactions.R
│   ├── 17f-ar-csv2-transactions.R
│   ├── 17g-ar-list-transcations.R
│   ├── 17h-ar-dataformats.R
│   ├── 18a-arules1.R
│   ├── 20a-ar-DU1.R
│   ├── 20b-ar-DU2.R
│   ├── 20c-ar-DU3.R
│   ├── 22a-ar-edn.R
│   ├── 22b-ar-elective.R
│   ├── 22d-ar-subjects.R
│   ├── 22e-ar-placement.R
│   ├── 22f-myAR1.R
│   ├── 25a-ar-income.R
│   ├── 25b-ar-medical.R
│   ├── 25c-ar-titanic.R
│   ├── 29a-ar-Adult.R
│   ├── 29b-ar-Adult-NW.R
│   ├── 29c-ar-Adult-Draft.R
│   ├── 30a-ar-Finance1.R
│   ├── 30b-ar-Finance.R
│   ├── 32a-ar-visual.R
│   ├── 33a-ar-redundant.R
│   ├── 33b-redundantrules.R
│   ├── 40a-ar-multilevel-Groceries.R
│   ├── 43a-ar-patterns.R
│   ├── 45a-ar-rulesextract.R
│   ├── 99-ar-NW.R
│   ├── 99-ar-OnlineSales.R
│   ├── 99-ar-basketanalysis2.R
│   ├── 99-ar-policechecks.R
│   ├── AR-Weka
│   ├── EDA-placement1.R
│   ├── ar-case-liberty.R
│   ├── ar-groceries2.R
│   ├── ar-practise.R
│   └── my_basket1.txt
├── 75-OR-LP/
│   ├── 15b-lpsolveAPI.R
│   ├── 15c-lpassign.R
│   ├── 21b-LP-mach-prod.R
│   ├── 21c-LP-mach-prod.R
│   ├── 22b-LP-case1.R
│   ├── 22c-LP-assign-case3.R
│   ├── 22d-LP-Case-carmanufacturing.R
│   ├── 25b-LPassign-job.R
│   ├── 30a-LP-tpt-function.R
│   ├── 31b-LP-tpt1.R
│   ├── 31c-LP-tpt2.R
│   ├── 31d-LP-tpt3.R
│   ├── 33d-proptable.R
│   ├── 41b-pricing.R
│   ├── 51b-LP-marketing.R
│   ├── lp-ss.R
│   ├── model.lp
│   ├── zz-LP-clplite.R
│   └── zz-LP-general.R
├── 77-TS/
│   ├── 11-tsdata.R
│   ├── 12b-TS-add-mult.R
│   ├── 14-ts-zoo.R
│   ├── 16-ts-xts.R
│   ├── 16c-dates-split1.R
│   ├── 16d-dates1.R
│   ├── 16d-ts-xts.R
│   ├── 23b-TS-Case-sales.R
│   ├── 23b-lubridate1.R
│   ├── 24b-Data-DFtoTS.R
│   ├── 24b-timeseries1.R
│   ├── 24c-timeseries2.R
│   ├── 24f-ts-data.R
│   ├── 26b-ts-components-airp.R
│   ├── 26c-ts-components.R
│   ├── 27b-ts-johnson.R
│   ├── 27c-ts-lm-uscons.R
│   ├── 28c-ts-lubridate1.R
│   ├── 31c-TS-airp.R
│   ├── 33b-zoo-ts.R
│   ├── 35b-LSM-beer1.R
│   ├── 38b-tsplots2a.R
│   ├── 38c-tsplots3.R
│   ├── 38f-plot-zz.R
│   ├── 41b-arima1.R
│   ├── 41c-arima2.R
│   ├── 41d-arima-airp.R
│   ├── 41d-arima-jj-nile.R
│   ├── 45b-TS-arima.R
│   ├── 52c-Case1-complete.R
│   ├── 53b-sales-ts.R
│   ├── 55b-ts-case-xxx2.R
│   ├── 55c-ts-case-xxxx.R
│   ├── SMA-nile.R
│   ├── TS-P-fpp.R
│   ├── TS-c02.R
│   ├── TS-data-DU1.R
│   ├── TS-fpp-seasonplot.R
│   ├── TS-kings.R
│   ├── TS-links
│   ├── TS-movag1.R
│   ├── TS-nile.R
│   ├── TS-xts.R
│   ├── TS-zoo.R
│   ├── UDFdates.R
│   ├── blank.R
│   ├── ts-P-highfreq.R
│   ├── ts-P-openair.R
│   ├── ts-P-padr.R
│   ├── ts-beer2.R
│   ├── ts-case1.R
│   ├── ts-case2.R
│   ├── ts-lubridate2.R
│   ├── ts-rollingvalues.R
│   ├── ts-rollingvalues2.R
│   ├── ts-splitdate.R
│   ├── ts-timestamp.R
│   ├── ts-yoy.R
│   └── tsforecast-exp.R
├── 78-nlp/
│   ├── SM-rtexttools1.R
│   ├── TM-zz.R
│   ├── downloadfile.R
│   ├── facebook1.R
│   ├── facebook2.R
│   ├── fms.txt
│   ├── linkedin1.R
│   ├── linkedin3.R
│   ├── pagerank.R
│   ├── readpdf.R
│   ├── rowling.txt
│   ├── rquery_wordcloud.R
│   ├── sentiment-tidyr1.R
│   ├── sentiment2.R
│   ├── textmining-DU1.R
│   ├── tm-worldcloud4.R
│   ├── twitter-hotel.R
│   ├── twitter-keys.R
│   ├── twitter-sentiment2.R
│   ├── twitter1-DU1.R
│   ├── twitter1-DU2.R
│   ├── twitter1-authen.R
│   ├── twitter1.R
│   ├── twitter2.R
│   ├── twitteracct
│   ├── wordcloud1.R
│   ├── wordcloud2.R
│   ├── wordcloud3.R
│   └── worldcloud2.R
├── 78-textdocs/
│   └── vit.txt
├── 80-SIM/
│   ├── montecarlo1.R
│   ├── montecarlo2.R
│   ├── mvsim1.R
│   ├── randomNos.R
│   ├── simLinks
│   └── simple1.R
├── 81-case-sum/
│   ├── 31b-DA-dencoCase.R
│   ├── 31c-DA-dencoCase2.R
│   ├── 31d-dsum-denco.R
│   ├── 31e-dencoCase2.R
│   ├── 31f-dencoCase.R
│   ├── 33c-basicDM-mtcars.R
│   ├── 33c-dplyr-mtcars.R
│   ├── 33f-DA-bakerydata1.R
│   ├── 34b-sales1.R
│   ├── 34c-sales2.R
│   ├── 35b-DA-student1.R
│   ├── 35c-dm-student1.R
│   ├── 36b-dsum-Case1.R
│   ├── 36c-dsum-Case2.R
│   ├── 36f-DSA-case2.R
│   ├── 37b-dsum-iris1.R
│   ├── 38b-dsum-haireyecolor1.R
│   ├── 42b-case-sum-graphs.R
│   └── dataexplore.R
├── 83-MA/
│   ├── CA.R
│   ├── campaign.csv
│   ├── caseStudy_juice.R
│   ├── data1.R
│   ├── graph1.R
│   ├── maregression1.R
│   ├── pricing1.R
│   └── tree1.R
├── 84-HR/
│   └── hr_churn.R
├── 85-RFM/
│   ├── rfm1.R
│   └── rfm3.R
├── 87-FA/
│   ├── 10-FAlinks.R
│   ├── Insurance Loss v01.R
│   ├── InsuranceLosses.csv
│   ├── Packages Pre-requisites_v03.R
│   ├── aapl.csv
│   ├── fa-iitg-dataanalysis.R
│   ├── finTS1.R
│   ├── findata1.R
│   ├── finstmts1.R
│   ├── finstmts2.R
│   ├── finstmts3.R
│   ├── gtrends1.R
│   ├── intrino1.R
│   ├── intrino2.R
│   ├── lag1.R
│   ├── logistic_regression.R
│   ├── qf1.R
│   ├── sentianalysistrading1.R
│   ├── shares1.R
│   ├── shares2.R
│   ├── stock3.R
│   ├── stockanalysis1.R
│   ├── stockanalysis2.RData
│   ├── stocks5.R
│   ├── stocksanalysis3.R
│   ├── stocksanalysis4.R
│   └── volatity1.R
├── 88-Network/
│   ├── NetSciX 2016 Workshop.R
│   ├── network1.R
│   ├── network2.R
│   └── traveltime1.R
├── 89-rVideos/
│   └── clustering-rV.R
├── 92-wksp2/
│   ├── 1a1-start.R
│   ├── 1a3-packages1.R
│   ├── 1b2-ds.R
│   ├── 1b3-factor.R
│   ├── 1d2-basicstats.R
│   ├── 1d2-dm-student1.R
│   ├── 1d3-dencoCase.R
│   ├── 1d4-DA-dencoCase.R
│   ├── 1e-graphs-basic.R
│   ├── 1e2-graphs.R
│   ├── 1e3-advgraphs.R
│   ├── 1f-SLR-women.R
│   ├── 1h1-dplyr.R
│   ├── 1h2-freqtable.R
│   ├── 2a-importExport.R
│   ├── 2b-SLR-salesarea.R
│   ├── 2b-allmodels.R
│   ├── 2b2-SLM-women.R
│   ├── 2b3-SLM-women-A.R
│   ├── 2b4-LM-cars.R
│   ├── 2b4-SLR-women.R
│   ├── 2c3-MLM-salespromotion.R
│   ├── 2c4-MLM-mtcars1.R
│   ├── 2d1-missingvalues.R
│   ├── 2d3-datapartition.R
│   ├── 2e1-logR-purchase.R
│   ├── 2e2-LOGR-adult.R
│   ├── 2e3-LOGR-gre.R
│   ├── 3b1-DT-CART-carseats.R
│   ├── 3b2-DT-CART-R-sales.R
│   ├── 3b3-DT-CART-titanic.R
│   ├── 3b4-DT-CART-R-loan.R
│   ├── 3b5-DT-loanapproved1.R
│   ├── 3b5-DT-rpart-iris.R
│   ├── 3d1-DT-CHAID-usvote.R
│   ├── 3e1-clust-customer.R
│   ├── 3e1-clustering.R
│   ├── 3e2-clust-samplecase.R
│   ├── 3e3-clust-segmentation.R
│   ├── 3e4-clust-noOfclusters.R
│   ├── 4b1-AR-groceries.R
│   ├── 4b2-AR-samplecase.R
│   ├── 4b3-AR-groceries-subset.R
│   ├── 4b5-AR-finproducts.R
│   ├── 4e1-twitter1.R
│   ├── 4e2-wordcloud.R
│   ├── 4e3-worldcloud2.R
│   ├── 4e5-wordcloud3.R
│   ├── 4f2-quantmod1.R
│   ├── 4f3-indianstocks.R
│   ├── 5-wordcloud2-New.R
│   ├── 5b-LP-marketingspend.R
│   ├── 5c2-LP-marketingspend-case.R
│   ├── 5d-wordcloud2.R
│   ├── 5d2-LP-tpt.R
│   ├── 5e2-LP-machassign.R
│   ├── 5e5-LP-farmer1.R
│   ├── 6b1-dates.R
│   ├── 6b1-ts-data.R
│   ├── 6c2-dates-lubridate.R
│   ├── 6d-TS-airpassengers.R
│   ├── 6d-ts-components-airp.R
│   ├── 6d-ts-johnson.R
│   ├── 6d-ts-xts-data.R
│   ├── 6e-TS-auto-arima-johnson.R
│   ├── 6g-ts-TTR-ma.R
│   ├── 8-fa-quandl.R
│   ├── 8-fa-quandl2.R
│   ├── 8-fa-quantmod.R
│   ├── 8-quantmod-I-stocks.R
│   └── zz-practise.R
├── 93-wksp3/
│   ├── Graph-matrixplots.R
│   ├── LMtrainTest.R
│   ├── Links_DAR
│   ├── Links_DAR.R
│   ├── TS-arima-johnson.R
│   ├── TS-components-airpassengers.R
│   ├── TS-data.R
│   ├── TS-dates.R
│   ├── TS-lubridate.R
│   ├── TS-movavg-Nile.R
│   ├── TS-movavg.R
│   ├── assocrule1.R
│   ├── assocrule2.R
│   ├── assocrule3.R
│   ├── decisiontree1.R
│   ├── decisiontree2.R
│   ├── decisiontree3.R
│   ├── decisiontree4.R
│   ├── decisiontree5.R
│   ├── decisiontree5CHAID.R
│   ├── df.R
│   ├── environ.R
│   ├── freqtable.R
│   ├── lm-salesarea.R
│   ├── lm-salesqty.R
│   ├── lm-women-simple.R
│   ├── lm.R
│   ├── logR.R
│   ├── logr-gre.R
│   ├── matrix.R
│   ├── missingvalues.R
│   ├── packages1.R
│   ├── packages2.R
│   ├── stats2.R
│   ├── twitter.R
│   ├── vectors.R
│   ├── wordcloud1.R
│   └── wordcloud2.R
├── 95-studqueries/
│   ├── Sapient_Big Data.R
│   ├── achal1.R
│   ├── achal1.csv
│   ├── achal2.R
│   ├── achal2.csv
│   ├── deepak.R
│   ├── hitesh-dec18.R
│   ├── hitesh1.R
│   ├── hitesh2.R
│   ├── hitesh3.R
│   ├── hitesh4.R
│   ├── hiteshJul18.R
│   ├── lalit1.R
│   ├── meena1
│   ├── meena2.R
│   ├── meena3.R
│   ├── missingValue.R
│   ├── sidana2.R
│   ├── tanviTS1.R
│   └── vivekIIMLN.R
├── 96-cancer/
│   ├── data-cancer.R
│   ├── rf-cancer.R
│   ├── svm-cancer1.R
│   └── svm-examples.R
├── 96-iris/
│   └── sumgraph1.R
├── 96-mtcars/
│   ├── 10b-datastructures.R
│   ├── 11b-mtcars.R
│   ├── 11c-mtcars-filter.R
│   ├── 11d-mtcars-descp.R
│   ├── 11f-mtcars-loops.R
│   ├── 11g-mtcars-sort.R
│   ├── 11h-mtcars-dplyr.R
│   ├── 12d-mtcars-graph1.R
│   ├── 12e-mtcars-graph2.R
│   ├── 12e-mtcars-summarise-dplyr.R
│   ├── 12f-diag-ggplot2-mtcars.R
│   ├── 12f-ggplot2-mtcars.R
│   ├── 13b-mtcars-lm1.R
│   ├── 13c-mtcars-lm2.R
│   ├── 13e-mtcars-lm3.R
│   ├── 14b-mtcars-logr.R
│   ├── 15b-mtcars-DT-class.R
│   ├── 15c-mtcars-DT-anova.R
│   ├── 16b-mtcars-cluster1.R
│   ├── 16c-mtcars-cluster2.R
│   ├── 22f-tidyr-mtcars.R
│   ├── mtcars-clust1.R
│   ├── mtcars-hclust.R
│   └── s1.R
├── 97-artwork/
│   ├── AuctionsData - artwork.csv
│   ├── AuctionsData - set1.csv
│   ├── artwork-cls1.R
│   ├── artwork-descp.R
│   ├── artwork-eda1.R
│   ├── artwork-eda2.R
│   ├── artwork-rought.R
│   ├── artwork1.R
│   ├── artwork2.R
│   ├── artwork4.R
│   ├── awdata1.R
│   ├── density.R
│   └── file2.R
├── 99-GD/
│   ├── aboutSL
│   ├── gradientdescent1.R
│   ├── gradientdescent2.R
│   ├── gradientdescent3.R
│   ├── gradientdescent4.R
│   └── regr1.R
├── 99-HTML/
│   └── aboutUSL
├── 99-Misc/
│   ├── dhmethods.R
│   ├── funcpgm1.R
│   ├── h2o.R
│   └── skimr-package.R
├── 99-json/
│   ├── 21b-json-format.R
│   ├── 23b-xml-import.R
│   ├── 25a-httr1.R
│   ├── 25c-httr2.R
│   ├── json-1.R
│   └── json2.R
├── 99-phd/
│   ├── attendance2.R
│   └── grades.R
├── 99-sports/
│   ├── cricket1.R
│   └── cricket2-york.R
├── 99-weka/
│   ├── cls-ID3.R
│   ├── cls-c45weka.R
│   ├── clsW-iris.R
│   └── clsW-iris2.R
├── MBArules.csv
├── README.md
├── Unsorted/
│   ├── CLT.R
│   ├── R-Exercise.R
│   ├── RCommander.R.R
│   ├── basiclm1.R
│   ├── binomial.R
│   ├── boxplot.R
│   ├── c.R
│   ├── central1.R
│   ├── colstats1.R
│   ├── complextables.R
│   ├── cor1.R
│   ├── crossfold1.R
│   ├── cut1.R
│   ├── cutprety1.R
│   ├── datalevels.R
│   ├── dbconnection.R
│   ├── dec17.R
│   ├── dec17b.R
│   ├── demo1.R
│   ├── density2.R
│   ├── descstatsgraphs1.R
│   ├── dplyr1.R
│   ├── ds1.R
│   ├── env1.R
│   ├── examB.R
│   ├── extra.R
│   ├── fd1.R
│   ├── fd2.R
│   ├── fd3.R
│   ├── fd4.R
│   ├── fd5-means.R
│   ├── googleS.R
│   ├── knitr.R
│   ├── kurtosis.R
│   ├── lm-sales.R
│   ├── lm1-sales.R
│   ├── lm1.R
│   ├── miscscripts.R
│   ├── nd1.R
│   ├── normal.R
│   ├── paneldata1.R
│   ├── plot1.R
│   ├── practise-dec17c.R
│   ├── practise.R
│   ├── rattle1.R
│   ├── rattle2.R
│   ├── rcdr1.R
│   ├── rjava.R
│   ├── rle1.R
│   ├── sample1.R
│   ├── sample2.R
│   ├── scripting1.R
│   ├── skewness1.R
│   ├── skewness2.R
│   ├── skewness3.R
│   ├── smpdist1.R
│   ├── summary1.R
│   ├── sumstats1.R
│   ├── ttest1.R
│   └── vaibhavi.R
├── cacert.pem
├── caseStudies/
│   └── allCases.R
├── data/
│   ├── AuctionsData - set1.csv
│   ├── Churn.csv
│   ├── Computers.csv
│   ├── Dataset1-Media-Example-EDGES.csv
│   ├── Dataset1-Media-Example-NODES.csv
│   ├── Dataset2-Media-User-Example-EDGES.csv
│   ├── Dataset2-Media-User-Example-NODES.csv
│   ├── ItemList.csv
│   ├── MA.RData
│   ├── MBA.csv
│   ├── MBArules.csv
│   ├── MMM_raw_data_v02.csv
│   ├── NPS Data Food Order v01.csv
│   ├── Predict Merchant_Sales v01.csv
│   ├── Prostate_Cancer.csv
│   ├── Rules_20.csv
│   ├── Sales.csv
│   ├── Sales_files/
│   │   ├── 6006907
│   │   ├── frameworks-95aff0b550d3fe338b645a4deebdcb1b.css
│   │   ├── frameworks-b3cd8fa1481bc34c4b18cf307ca75438.js.download
│   │   ├── github-542f291c828bb453339765ba3a54c144.js.download
│   │   └── github-cdaf214b636e7d0581fce94eda9de4bd.css
│   ├── Segmentation_Data v01.csv
│   ├── Social_Network_Ads.csv
│   ├── StudentPassFail.csv
│   ├── StudentTid1.csv
│   ├── StudentTid2.csv
│   ├── airpsng.csv
│   ├── ar14.csv
│   ├── ar14b.csv
│   ├── artwork.rds
│   ├── arulesfin.csv
│   ├── attendance1.csv
│   ├── attendance2.csv
│   ├── attrition.csv
│   ├── badata.Rdata
│   ├── bakery.csv
│   ├── bank.csv
│   ├── binary.csv
│   ├── bitsgoa.csv
│   ├── cclogr.csv
│   ├── clscredit.csv
│   ├── clsplay.csv
│   ├── clust_custseg.csv
│   ├── dar1.csv
│   ├── dar1w.csv
│   ├── dar1w.csv.arff
│   ├── dar2.csv
│   ├── dar3.csv
│   ├── dar3a.csv
│   ├── dar3b.csv
│   ├── data1.R
│   ├── data4cluster2.csv
│   ├── data_clus_2.csv
│   ├── dataiitb.csv
│   ├── dateformat1.R
│   ├── denco.csv
│   ├── dhiraj.csv
│   ├── dtdata.csv
│   ├── fintransactions.csv
│   ├── grades.csv
│   ├── groceries.csv
│   ├── heart_tidy.csv
│   ├── hhe.txt
│   ├── iimS.xlsx
│   ├── iima.csv
│   ├── iimc1.csv
│   ├── iimtrichy.csv
│   ├── iitgfa.csv
│   ├── iitgfa.xlsx
│   ├── iitgfa2.xlsx
│   ├── iris.csv
│   ├── iris.xlsx
│   ├── irisF.csv
│   ├── irisT.csv
│   ├── itemlist1
│   ├── km5_c2.csv
│   ├── logr2.csv
│   ├── msales.csv
│   ├── mtcars.csv
│   ├── mtcars.sas7bdat
│   ├── mtcars.xlsx
│   ├── mtcars1.csv
│   ├── mtcarsF.csv
│   ├── mtcarsT.csv
│   ├── mushrooms.csv
│   ├── my_basket
│   ├── myexcel.xlsx
│   ├── myitems1.csv
│   ├── myrules1.csv
│   ├── mytextcars.txt
│   ├── myworkbook.xlsx
│   ├── node1.csv
│   ├── onsen.csv
│   ├── pumba.csv
│   ├── rep2.csv
│   ├── rep4.csv
│   ├── rep5.csv
│   ├── revision1.csv
│   ├── rules.csv
│   ├── rulesR.csv
│   ├── s1.csv
│   ├── salesdata.csv
│   ├── salesdatamonth.csv
│   ├── salesslr.csv
│   ├── slr1.csv
│   ├── splitData1.R
│   ├── stock.csv
│   ├── stock1.csv
│   ├── stock1.txt
│   ├── student.csv
│   ├── student1.csv
│   ├── student1.xlsx
│   ├── student2.xlsx
│   ├── student3.xlsx
│   ├── student3a.xlsx
│   ├── studentdata.R
│   ├── studentdata.csv
│   ├── studentdata2.csv
│   ├── studentdata3.txt
│   ├── studentdata4.csv
│   ├── students.csv
│   ├── students3.csv
│   ├── talltransactions.csv
│   ├── tendulkar.csv
│   ├── titanic.csv
│   ├── titanic.raw.rdata
│   ├── women.sav
│   └── ximb.csv
├── dates/
│   ├── 11-date1.R
│   ├── 12-dates-seq.R
│   ├── 12c-dates-seq2.R
│   ├── 13-dates-format.R
│   ├── 14-dates-format2.R
│   ├── 15-date-subset-arithmetic.R
│   ├── 17-time-chron.R
│   ├── 17c-time-posixt.R
│   ├── 19-datetime-lubridate.R
│   ├── 19c-datetime-lubridate.R
│   ├── 19d-datetime-lubridate.R
│   ├── 19e-datetime-lubridate.R
│   ├── 19f-datetime-lubridate.R
│   └── 30-datetime-zzz.R
├── download/
│   ├── fms.txt
│   ├── iris.csv
│   ├── iris.xlsx
│   ├── rowling.txt
│   └── vector.R
├── iim.txt
├── misc/
│   ├── 1-ds.R
│   ├── cswr.R
│   ├── fms.txt
│   ├── importcsv.R
│   ├── mysqlR.R
│   ├── nest.R
│   └── timeszones.R
├── munaz.csv
├── mycars.csv
├── myexcelcars.xlsx
├── myrules1.csv
├── practise1.R
├── rAnalytics.Rproj
├── report/
│   ├── knit2.R
│   ├── knitr-minimal.R
│   ├── sample1.R
│   └── sample2.R
├── trg/
│   ├── bennett1.R
│   ├── bennett3.R
│   ├── d1-fmssrcc.R
│   ├── d2a-fmssrcc.R
│   ├── d2b-fmssrcc.R
│   ├── iima-d1.R
│   ├── iima-d3.R
│   ├── iima-d4.R
│   ├── iimkpg-d6.R
│   ├── iimkpv-d1.R
│   ├── iimkpv-d4.R
│   └── ximb-r.R
└── twitter authentication.Rdata

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
.Rproj.user
.Rhistory
.RData
.Ruserdata
*.json
.httr-oauth


================================================
FILE: 0-Practise/day1.R
================================================
# Day 1

library(ISLR)
data('Default')
str(Default)
LR1 = glm(default ~ ., family='binomial', data=Default)
summary(LR1) #leave income
LR2 = glm(default ~ student + balance, family='binomial', data=Default)
summary(LR2)
#
range(Default$balance)

ndata3 = Default[c(1,60,700),]
predict(LR2,newdata=ndata3, type='response' )

#mtcars
str(mtcars)






#vectors, arrays, matrix, list, factor, dataframe

x = 1:5
x1 = c('a','b')
m1 = matrix(1:24, nrow=6)
m1
list1 = list(x, x1, m1)
list1
class(women)
women
str(women)
?women
women
head(women)
tail(women,n=3)
head(women, n=3)
names(women)
summary(women)
dim(women)
data()
library(MASS)
x = women$height
x
plot(x)
mean(x)
sd(x) ; var(x)
max(x)
median(x)
x
sort(x, decreasing = T)
table(x)
quantile(x)
x
seq(0,1,.1)
quantile(x, c(.1, .5, .8))
quantile(x,seq(0,1,.1) )
summary(x)
min(x); max(x)
boxplot(x)
abline(h= c(min(x), max(x),mean(x)+1, median(x)), col=1:5, lwd=4)
#


# LM
head(women)
names(women)
model1 = lm(weight ~ height, data=women)
plot(women)
?lm
#options(scipen=999)
summary(model1)
model1
#y = mx + c
y = 3.45 * x + - 87
women$height
fitted(model1)
cbind(women, fitted(model1))
residuals(model1)
cbind(women, fitted(model1), residuals(model1), diff= fitted(model1) - women$weight)
sqrt(sum(residuals(model1)^2)/nrow(women))
cbind(women, fitted(model1))
range(women$height)
new1= data.frame(height=c(57, 60.5,70))
p1=predict(model1, newdata = new1)
cbind(new1, p1)

#mtcars----
names(mtcars)
?mtcars
mtmodel_1 = lm(mpg ~ wt, data=mtcars )
mtmodel_2 = lm(mpg ~ wt + disp, data=mtcars )
mtmodel_3 = lm(mpg ~ wt + disp + cyl, data=mtcars )
mtmodel_4 = lm(mpg ~ ., data=mtcars )
summary(mtmodel_1)  #.745
summary(mtmodel_2)  #.766
summary(mtmodel_3)  #.766
summary(mtmodel_4)  #.807
AIC(mtmodel_1, mtmodel_2,mtmodel_3,mtmodel_4)
summary(mtmodel_4)  #.807
step(lm(mpg ~ ., data=mtcars ))
mtmodel_5= lm(mpg ~ wt + qsec + am, data=mtcars)
summary(mtmodel_5) #Adjusted R-squared:  0.834 


#

attendance = 1:20
marks = 1:20
summary(lm(marks ~ attendance))
cbind(attendance, marks)
cor(attendance, marks)
#

#y = mx + c
x
y = 3.45 * x + - 87
x
head(women)
(y = 4.45 * 58 - 87)
plot(women)
abline(model1, col='red', lwd=4)
abline(v=64) ; abline(h=150)



x2 = floor(runif(1000, 50, 100))
x2
x2a= sort(x2)
x2a[1000/2]
median(x2)
sort(x)

t1= table(x2)
sort(t1, decreasing = T)

x1 = rep(10,10)
x1
sd(x1)




dim(mtcars)
mtlogmodel = glm(am ~ hp + wt, family='binomial', data=mtcars)
summary(mtlogmodel)
p1=predict(mtlogmodel, newdata=mtcars, type='response')
p2= round(p1, 3)
p3 = ifelse(p2<0.5,0,1)
cbind(mtcars$am, mtcars$hp, mtcars$wt, p2,p3, truefalse= mtcars$am == p3)




================================================
FILE: 0-Practise/day2.R
================================================
# Day 3 - Online batch of MA

#attach function of R
women
names(women)
height
attach(women)
height
weight
women$height

#List
g <- "My First List"  #scalar
h <- c(25, 26, 18, 39)  # numeric vector
j <- matrix(1:10, nrow=5) #matrix
k <- c("one", "two", "three") # character vector
mylist <- list(title=g, ages=h, j, k, women)
mylist

mylist[[2]]
mylist[[5]]


# plot
plot(x=height, y=weight, type='b', lty=5, pch=11, fg='red', bg='green', col.axis='purple', cex=1.5, cex.axis=2)
title(main='Henry Harvin', sub=' MA Course')


================================================
FILE: 0-Practise/day3.R
================================================
attach(mtcars)
plot(wt, mpg)
abline(lm(mpg~wt))
title("Regression of MPG on Weight")
detach(mtcars)

dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
plot(dose, drugA, type="l")
plot(dose, drugA, type="b")

par(no.readonly = T)
opar <- par(no.readonly=TRUE)
plot(dose, drugA, type="b")
par(lty=2, pch=17)
plot(dose, drugA, type="b")
plot(dose, drugB, type="b")
par(opar)
plot(dose, drugA, type="b")
plot(dose, drugA, type="b",fg='red', col='purple', col.axis='green')

library(RColorBrewer)
n <- 7
mycolors <- brewer.pal(n, "Set1")
barplot(rep(1,n), col=mycolors)
barplot(rep(1,n), col=1:7)

n <- 10
mycolors <- rainbow(n)
pie(rep(1, n), labels=mycolors, col=mycolors)
mygrays <- gray(0:n/n)
pie(rep(1, n), labels=mygrays, col=mygrays)


================================================
FILE: 0-Practise/first.R
================================================
# First File in R
x1 <- c(1, 5, 4, 9, 0) # <- is assignment x to have value 1,5,4,9,0
#control + enter
x2 = c(1, 5, 4, 9, 0)
x1
x2

x = c(1,2,3,4,5,6,7,8,9,10)
x
x = 1:100
x
x = runif(100, 50, 200)
?runif
x
x = rnorm(100, mean=50, sd=10)
x
trunc(x)
round(x,1)
floor(x)
ceiling(x)
hist(x)
as.integer(x)
plot(density(x))
abline(v=50)
?runif
head(x)
class(x);mode(x)
typeof(x)
summary(x)


# Types of Data Structures in R




================================================
FILE: 0-Practise/htmlimport.R
================================================
#Installing the web scraping package rvest
#install.packages("rvest")
library(rvest)
#Specifying the url for desired website to be scrapped
url <- 'http://pgdbablog.wordpress.com/2015/12/10/pre-semester-at-iim-calcutta/'


#Reading the HTML code from the website
webpage <- read_html(url)

#Know about the selector gadget
vignette("selectorgadget")

#Using CSS selectors to scrap the post date
post_date_html <- html_nodes(webpage,'.entry-date')

post_date_html <- html_nodes(webpage,'.published , .entry-title')
#Converting the post date to text
post_date <- html_text(post_date_html)

#Verify the date captured
post_date



url="www.imdb.com"

#Using the CSS selector (using ‘www.imdb.com’ website in this example)
rating_html=html_nodes(webpage,'.imdb-rating')   #’.imdb-rating’ is taken from CSS selector

#Converting the rating data to text
rating <- html_text(rating_html)

#Check the rating captured
rating

html <- read_html("http://www.imdb.com/title/tt1490017/")
cast <- html_nodes(html, "#titleCast .itemprop")
length(cast)
#> [1] 30
cast[1:2]


html <- read_html("http://www.imdb.com/title/tt1490017/")
cast <- html_nodes(html, ".quicklink")
length(cast)
#> [1] 15

html_text(cast)



url="https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats"
typeof(url)
length(url)
links= ".stats_female_male_ratio , .stats_pc_intl_students , .stats_student_staff_ratio , .stats_number_students , .ranking-institution-title"

html <- read_html("https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats")
cast <- html_nodes(html, ".stats_female_male_ratio")
length(cast)
#> [1] 30
cast[1:2]
cast
ranks <- html_nodes(url, ".ranking-institution-title")




library(rvest)

URL <- "https://www.soccerstats.com/latest.asp?league=netherlands" #Feed page

WS <- read_html (URL) #reads webpage into WS variable

URLs <- WS %>% html_nodes ("a:nth-child(1)") %>% html_attr("href")         %>% as.character() # Get the CSS nodes & extract the URLs 

URLs <- paste0("http://www.soccerstats.com/",URLs) 

oversdf <- data.frame(URLs=URLs)

rownames(oversdf)  #returns a vector of row names in the overs data.frame:

URLs <-subset(oversdf, grepl("pmatch", oversdf$URLs),stringsAsFactors       =       FALSE)

write.csv(URLs,file=paste(getwd(),"/sportURLs.csv",sep=""),row.names=FALSE)

Catcher1 <- data.frame(FMatch=character(),TotalGoals=character    (),stringsAsFactors = FALSE)

##################################
#start of workaround
n<-nrow(URLs)
URLs2<-character()
for (i in 1:n) {
  URLs2[i]<-as.character(URLs[i,1])
}


library(dplyr)
library(rvest)
web = read_html("https://news.google.com/?hl=en-IN&gl=IN&ceid=IN:en")

web %>% html_nodes(".VDXfz") %>% html_text()

library(rvest)
library(purr)
url_base =html("https://www.cochranelibrary.com/cdsr/table-of-contents/2018/11")
#map_df(1:4)

page = read_html(url_base)
page %>% data.frame(paper = html_text(html_nodes(".search-result-doi"))) %>% df4


url2 = "http://www.espncricinfo.com/india/content/player/28081.html"
library(rvest)
library(curl)
msd = read_html(url2)
msd

msd2 <- msd %>% html_nodes("table") %>% .[1] %>% html_table(fill=T)
msd2
str(msd2)

#-----
url3 = "https://www.timeshighereducation.com/world-university-rankings/2019/world-ranking#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats"
the = read_html(url3)
the

the3 <- the %>% html_nodes("table") %>% .[1] %>% html_table(fill=T)
the3
str(msd2)

html_

================================================
FILE: 0-Practise/iims2.R
================================================
# data structures in R

#way of storing and manipulating data in any programming languages

#vector----
x = c(1,2,3,6) #control + enter or Run button
#vector of single data type
x  #print x
class(x)
x2 = c('a',"b", 'Dhiraj',3)  #character vector
x2
class(x2)
?c  #help
mean(x)  #mean of x vector values
sum(x)
(x3 = 1:100)  #create a vector and then print
x3
class(x3)
x4 = seq(1,100,3)  #sequence
x4
?seq
x5 = list(1,3,5,'a')
x5
class(x5)
#data types - numeric, integer, character, logical
x6 = c(T, T, F, F,F)
length(x6)
x6
class(x6)
#matrix-----



















#dataframe-----



================================================
FILE: 0-Practise/import2.R
================================================
#web scrapping
#https://www.analyticsvidhya.com/blog/2017/03/beginners-guide-on-web-scraping-in-r-using-rvest-with-hands-on-knowledge/


#Loading the rvest package
library('rvest')

#Specifying the url for desired website to be scrapped
url <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature'

#Reading the HTML code from the website
webpage <- read_html(url)
webpage

#Using CSS selectors to scrap the rankings section
rank_data_html <- html_nodes(webpage,'.text-primary')

#Converting the ranking data to text
rank_data <- html_text(rank_data_html)

#Let's have a look at the rankings
head(rank_data)

#Data-Preprocessing: Converting rankings to numerical
rank_data<-as.numeric(rank_data)

#Let's have another look at the rankings
head(rank_data)

#[1] 1 2 3 4 5 6


#Using CSS selectors to scrap the title section
title_data_html <- html_nodes(webpage,'.lister-item-header a')

#Converting the title data to text
title_data <- html_text(title_data_html)

#Let's have a look at the title
head(title_data)

#[1] "Sing"          "Moana"         "Moonlight"     "Hacksaw Ridge"
#[5] "Passengers"    "Trolls"

#Using CSS selectors to scrap the description section
description_data_html <- html_nodes(webpage,'.ratings-bar+ .text-muted')

#Converting the description data to text
description_data <- html_text(description_data_html)

#Let's have a look at the description data
head(description_data)

#Data-Preprocessing: removing '\n'
description_data<-gsub("\n","",description_data)

#Let's have another look at the description data 
head(description_data)


================================================
FILE: 0-Practise/kt1.R
================================================
?which

LETTERS
which(LETTERS == "K")  #11th alphabet

marks = c(10,30,40, 60)
which(marks > 30)  #position
marks[which(marks > 30)]  #which of thos valuees

names = c('kanika','dhiraj', 'tanvi','poonam','dhananjay', 'upen')
#which names have dh
grep('[dh]', names, ignore.case=T)
names[grep('[dh]', names, ignore.case=T)]

mtcars
which(mtcars$mpg > 25)
mtcars[which(mtcars$mpg > 25),]


j = c(2,3,4,5,6,7,8,9,23,24,22,1,10)
j > 6
which(j > 6)
j[ which(j > 6) ]


?grep

which(ll <- c(TRUE, FALSE, TRUE, NA, FALSE, FALSE, TRUE)) #> 1 3 7
names(ll) <- letters[seq(ll)]
which(ll)
which((1:12)%%2 == 0) # which are even?
which(1:10 > 3, arr.ind = TRUE)


================================================
FILE: 0-Practise/lm-sim-test1.R
================================================
#LM Simulation

library(car)
library(PerformanceAnalytics)
library(corrgram)
library(corrplot)

#Data
set.seed(1234); x1 <- rnorm(100, 70, 10) + rnorm(100)
set.seed(1234); x2 <- runif(100, 60, 90)
set.seed(1234); x3 <- .75 * x1 + rnorm(10,7,2)
set.seed(1234); x4 = factor(sample(c('M','F'), size=100, replace=T, prob=c(.7,.3)))
set.seed(1234); x5 <- runif(100, 30,50) - rnorm(20,5,2)

set.seed(1234); error <- rnorm(100,15,3)
alias(fit)
y = 5 + 1.2 * x1 + 0.005 * x2 -  x3 - .6 * x5  + error
df = data.frame(y, x1, x2, x3, x4, x5)
head(df)
str(df)

#LM
fit = lm(y ~ x1 + x2 + x3 + x4 + x5, data= df)
alias(fit)  #any built in correlation
x6 = .1*x1
alias(lm(y ~ x1 + x2 + x6)) #x6 related to x1

attributes(fit)
attributes(summary(fit))
summary(fit)
summary(fit)$r.squared
summary(fit)$adj.r.squared
summary(fit)$sigma # SEE

#Model worthiness
anova(fit)

#Interpret
names(fit)
fit$residuals   #resid(fit)
fitted(fit)  # predict(fit, df)

#assumptions
#A1:Linearity IV-DV----
car::crPlots(lm(formula, data=df))
corrgram::corrgram(df)
corfit = cor(df[,-5])
corrplot::corrplot(cor(df[,-5]), method="pie")
corrplot::corrplot(cor(df[,-5]), method="number")
PerformanceAnalytics::chart.Correlation(df[,-5], histogram=TRUE, pch=19)

#A : Normality of Residues---
car::qqPlot(fit)

#A2:Autocollinearity
car::durbinWatsonTest(fit)
#if pvalues < 0.05, autocorr exists

#A4:Homoscedascity of Residuals
car::ncvTest(fit)
plot(fit, which=1)


#MultiCollinearity----
car::vif(fit) # variance inflation factors 
sqrt(vif(fit)) > 2 # problem?
#remove variable one by one start from highest vif value


# Assessing Outliers
car::outlierTest(fit) # Bonferonni p-value for most extreme obs
car::qqPlot(fit, main="QQ Plot") #qq plot for studentized resid car::leveragePlots(fit) # leverage plots

#Influencing Values
plot(fit,which=4)
car::influencePlot(fit)
avPlots(fit)



#Model2
df
summary(fit)
fit2 = lm(y ~ x1 + x3 + x5, data= df)
summary(fit2)

#Model Compare
anova(fit2,fit)  #do not reject Ho ie. Model1 is better
AIC(fit2, fit) # whichever lower AIC is better



================================================
FILE: 0-Practise/practise.R
================================================
#List
x; m1; a1; df1
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist2 = list(k, mylist)
mylist2
mylist
mylist[1]
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages

#List end


#Factors
df1
# category type - ordered or unordered
#gender,  course, color - unordered
#grades, division, position,likertscale, ratings
summary(df1)
(grades = sample(c('A', 'B', 'C'),size=10, replace=T, prob=c(.4,.3,.3)))

df1$grades = grades
df1
summary(df1)
df1$gender = factor(df1$gender)
summary(df1)
df1$grades =factor(df1$grades, ordered=T)
df1$grades
aggregate(df1$age, by=list(df1$grades), mean)
aggregate(df1$age, by=list(df1$gender), mean)
aggregate(df1$age, by=list(df1$course), mean)
(df1$grades =factor(df1$grades, ordered=T, levels=c('C','B','A')))

(division = sample(c('Excellent', 'Very Good', 'Sat'),size=10, replace=T, prob=c(.4,.3,.3)))
division
summary(division)
Fdivision = factor(division)
summary(Fdivision)
Fdivision2 = factor(division, ordered=T, levels=c('Sat', 'Very Good', 'Excellent'))
summary(Fdivision2)
Fdivision2
Fdivision3 = factor(division, ordered=T)
summary(Fdivision3)
Fdivision3
#factors end


#Data Frame
(rollno = 1:10)
(sname = paste('Student',1:10,sep='-'))
(age = floor(runif(10, 20, 30)))
(gender = c(rep('M',5),rep('F',5)))
(course = sample(c('Engg','Medical','MBA'), 10, replace=T, prob=c(.3, .4, .3)))
table(course)
(married = sample(c(TRUE, FALSE), 10, replace=T))
table(married)
rollno; sname; age ; gender; course; married

(df1 = data.frame(rollno, sname, age , gender, course, married))
df1[1:2,3:4]
df1$sname
df1[df1$married==T,  ]
df1[df1$course=='Engg' & df1$age > 25,  ]
df1[df1$married==T & df1$course=='Engg' & df1$age > 25, c('sname') ]
?aggregate
aggregate(df1$age, by=list(df1$gender), FUN=mean)
aggregate(df1$age, by=list(df1$course), FUN=mean)
aggregate(df1$age, by=list(df1$course, df1$gender), FUN=mean)


df1
summary(df1)

#DF end








#Array
?array
array(data = NA, dim = length(data), dimnames = NULL)
#Coys - 5, Products-3, Locations-4
ceiling(3.2);

(salesfig = floor(runif(60, 70, 100)))
(a1 = array(data = salesfig, dim = c(4,3,5), dimnames = list(paste('Loc',1:4),paste('Prod',1:3),paste('Coy',1:5))))
apply(a1,1, sum)# sum locationwise
apply(a1,2, sum)
apply(a1,3, sum)
(ma1 = apply(a1,c(1,3), sum))
colSums(ma1)
rowSums(ma1)

apply(a1,c(2,3), sum)




#arrayend


#Matrix
#row x columns
?rnorm
set.seed(1234)
(x = trunc(runif(24,100,500)))
(m1 = matrix(data=x, nrow=4,dimnames = list(c('delhi','mumbai','noida','chennai'),paste('Prod',1:6,sep="-"))))
colMeans(m1);rowMeans(m1)
colSums(m1); rowSums(m1)
pie(x=rowMeans(m1))
barplot(rowMeans(m1))  # barplot for locations
barplot(colMeans(m1))  #barlplot for products
barplot(colMeans(m1), horiz = T)
barplot(colMeans(m1), horiz = T, col=1:6)
m1
#Subset a Matrix
m1[ , 1:2]
m1[ ,c(1,4)]
m1[c(1,3) ,c(1,4)]
m1[c('delhi','mumbai'),c('Prod-3')]
m1[m1 > 300]
m1
m1[c('delhi'),]
sd(m1[c('delhi'),])
sum(m1[c('delhi','mumbai'),c('Prod-3','Prod-4')])

#m end

(m2 = matrix(data=x, nrow=4, byrow = T))
(m3= matrix(x, ncol=4 ))
(m4 = matrix(c(1,2,3,4), nrow=2, ncol=4,byrow=T))
m1




# Vectors
snames = string (single char, multiple char)
marks1 = numeric(integer, decimal)
married - true or F
gender = categories
snames = c('student1', "student2", 'student3')
snames
class(snames)
marks = c(10, 20 ,30)
marks
class(marks)
married = c(TRUE, FALSE, TRUE)
married
class(married)
snames; marks; married
(age = c(30,35,26))
age = c(30,35,26)
age
?class

#subsetting a Vector
(x1 = 1:100)
(x2 = seq(50,100,3))
?seq
x1
x1[10]
x1[20:30]
x2[2:5]
x1[x1 > 50]
x2
x2 > 70
x2[(x2 > 70) | (x2 < 60)]
x2[c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,  TRUE,  TRUE,  TRUE,TRUE,  TRUE,  TRUE,  TRUE,  TRUE,  TRUE,  TRUE)]
x2
#operations on the vector
x3 = c('A','B','C')
x3[c(1,3)]
x3[c(TRUE, FALSE, TRUE)]
x3[(x3=='A') | (x3=='C')]

x2[(x2 > 60) & (x2 < 70)]
x2[x2==69]
x2==69

x2
length(x2)
length(x1)
mean(x2)
sum(x2)/length(x2)
sum(x2)
median(x2)
x2

set.seed(1)
(x4 = trunc(runif(20,5,100)))
median(x4)
(shirtcolors = c("red",'blue','green','blue','green', 'blue'))
mean(shirtcolors)
table(shirtcolors)

sort(x4)

mtcars$mpg
mean(mtcars$mpg)
women
data()
?mtcars

snames

x = rnorm(1000000, 50,10)
head(x)
mean(x)
hist(x)


================================================
FILE: 0-Practise/practise2.R
================================================
#misc practise

x = 1:5
data.entry(x)


================================================
FILE: 0-Practise/rough.R
================================================
# Rough Work
?cat
?dput
?dget
?dump
?write
?write.table
?save
?detach
?attach
?dir
?ls
?rm
?attr
?attributes

data1 = c(3, 60, 19, 9, 4 , 5)

labels1 = c('Building training sets', 'Cleaning and Organising Data', 'Collecting Data sets', 'Mining data for patterns', 'Refining Algorithms', 'Others')
pie(data1, labels= pielabels)

barplot(data1)
pie(data1,labels=NA,   clockwise=TRUE,
    col=rainbow(6),  border="white",  radius=1.2,
    cex=0.8,  main="Average Time Spent by Data Scientists")
legend("bottomright",legend=pielabels,bty="n", # horiz = T,
       fill=rainbow(6))
barplot(data1, col=rainbow(6), names.arg=pielabels, 
        cex.names = 1, horiz=T, angle=90, 
        main="Average Time Spent by Data Scientists"
)
text(1:6, data1, labels= pielabels)
?barplot



browsers<-read.table("browsers.txt",header=TRUE)
browsers<-data1
browsers
pielabels <- sprintf("%s = %3.1f%s", labels1, browsers, "%")
pielabels
?sprintf
library(RColorBrewer)
pie(browsers,
    labels=NA,
    clockwise=TRUE,
    col=brewer.pal(6,"Set1"),
    border="white",
    radius=1,
    cex=0.8,
    main="Percentage Share of Internet Browser usage")
legend("bottomleft",legend=labels1,bty="n",
       fill=brewer.pal(6,"Set1"))


?strde
states


================================================
FILE: 0-Practise/vector.R
================================================
#Data Structure - Vectors

x = c(1,5,7,8,4)
x2 <- c(2,5,7,8,4)
x
x2
x4 = c('M','F','M','F','M')
x4
(x5 = 1:100)
(x6 = seq(1,100,by=3))

marks = rnorm(60, mean=60, sd=10)
marks
plot(density(marks))
matrix(marks, ncol=6)








================================================
FILE: 0-class/AR-groceries.R
================================================
# Association Rules - Groceries data set ####

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries') #different format - transaction format
Groceries
#Structure of Groceries
str(Groceries)
Groceries
arules::LIST(Groceries[1:3])  #another view
arules::inspect(Groceries[1:3])
arules::inspect(Groceries[c(1000,5000,9000)])
arules::inspect(Groceries[sample(1:nrow(Groceries), size=3)])
nrow(Groceries)
#Find Frequent Itemset
#.01 * 9835; A + B + C = 3 items, A + B + C + D : 4 items
frequentItems = eclat(Groceries, parameter = list(supp = 0.01, minlen= 3, maxlen = 3))
#frequentItems = eclat (Groceries, parameter = list(minlen= 3))
inspect(frequentItems[1:3])   #correct lib to used
frequentItems
inspect(frequentItems[])  #inspect for printing the itemssets
options(digits=3)
#inspect(frequentItems[100:122])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries, topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
options(digits=2)#output decimal places to 2 digits
rules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=T)
inspect (rulesl[1:5])
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) 
rules2  #no of rules
rules #earlier rules
inspect(rules2[1:15])

#Find out what events were influenced by a given event - from already created rules
subset1 = subset(rules2, subset=rhs %in% "whole milk")
inspect(subset1[1:5]) # rhs has milk
subset1 = subset(rules2, subset=rhs %in% 'bottled beer' )
inspect(subset1) #rhs has beer
#inspect(rules2)
subset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2) # all items  %ain%
subset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )
inspect(subset2a) # any of the items %in%

#RHS, Confidence, sort by Lift
subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .5, by = 'lift', decreasing = T)
inspect(subset3)  #sometimes there may be no rules, change few parameters
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4)
subset4b = subset(rules2, subset=rhs %in% 'bottled beer'  )
inspect(subset4b) #no such rules

library(arulesViz) #install first
#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf
#Visualizing The Rules -----
subset1
rules2
inspect(subset2)
plot(subset1[1:2]) 
plot(subset1[1:2], measure=c("support", "lift"), shading="confidence")
#change the axis
plot(rules2[1:100], measure=c("support", "lift"), jitter=0,shading="confidence")
#

#Find what factors influenced an event ‘X’ - create fresh Rules
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.7), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
inspect(rules3[1:5])
inspect(rules3)


#rhs as it is, lhs to have tropical fruit or herbs
rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)
plot(subset4) 


#legend to filter
#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.


#summarise Association Rules / Market Basket analysis
#load libraries - arules, arulesViz
#load dataset in Transaction Format eg Groceries
#Find frequentitems set
#Find rules as per parameters
#Parameters - min support, min confidence, minlen, maxlen
#sort - confidence, lift, count, support
#subset of rules - lhs, rhs, confidence, all, any item
#plot of the rules 
#find interesting rules  - high lift, high confidence
#put strategy in place - location, bundle, discounts, advertisement


================================================
FILE: 0-class/CLUST-customer.R
================================================
# HH MA example  - customer

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)

names(data)
summary(data)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200, nstart = 1,  method = c("euclidean"))

k1$centers  # group means
###Fetch size/n of obs for the groups
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
#write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")


================================================
FILE: 0-class/DT-CART-sales.R
================================================
# CART Models - HH Case Study - Regression
library(rpart)
library(rpart.plot)
library(forecast)


library(gsheet)
url='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'
data = as.data.frame(gsheet2tbl(url))
str(data)

# Summarize the dataset
summary(data)
names(data)
# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(data), size = 0.7*nrow(data))
Index

# Create Train dataset
train= data[Index, ]
nrow(train)

# Create Test dataset
test = data[-Index, ]
nrow(test)
nrow(test) + nrow(train)

########################### Modeling #################################

trainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = "anova")
trainModel
mean(train$Annual_Sales)

# Plot the Regression Tree
rpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)

#cp selection
printcp(trainModel)
trainModel_prune = prune(trainModel, cp=0.01)
rpart.plot(trainModel_prune)

#Predict and check accuracy
predictSales_test = predict(trainModel_prune, newdata = test, type = "vector")
predictSales_test  #vector to print values of sales predicted

library(forecast)
# Validate RMSE and MAPE calculation with a function in R
ModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)
ModelAccuarcy
#RMSE should be as less as possible



================================================
FILE: 0-class/NAvalues.R
================================================
# Missing values

#missing values are indicate NA  keyword
x = c(1, ,3)  #wrong way to create missing values
x = c(NA, 1, NA, 2,3, NA)  #introducing missing values
x
x1=x  #make a copy
x1

is.na(x)
sum(is.na(x))
sum(c(T,F,T,F,F))
x
mean(x)  #this will not work if NA values are present

?mean
mean(x, na.rm=T)
(1+2+3)/3

sum(x,na.rm=T)/3
x
x[is.na(x)]  #list out missing values
mean(x, na.rm=T)
x[is.na(x)] = mean(x, na.rm=T)  #replace these missing values with mean of other values
x
cbind(x,x1)  #see where values have been filled
x1 = c(4,6,8,9)
length(x1[x1 >= 6])
sum(x1 >= 6)
x1 >= 6

x2 = rnorm(100000, mean=50, sd=5)  #normal distributed values
x2
length(x2)
posn=sample(100000, size=30)
posn
x2[posn] = NA
summary(x2)
is.na(x2)
sum(is.na(x2))
mean(x2)
mean(x2, na.rm=T)
x2[is.na(x2)] = mean(x2, na.rm=T)
sum(is.na(x2))

#install this library
library(VIM)
?sleep
data(sleep, package='VIM')
head(sleep) #first few rows of sleep
dim(sleep)  #dimensions of sleep data
complete.cases(sleep)  # which row have complete data in T/ F
sum(complete.cases(sleep))  # no of rows have which no missing data
sum(!complete.cases(sleep))  # no of rows which have missing data
sleep[complete.cases(sleep),]  #rows which are complete 42
sleep[!complete.cases(sleep),] #rows which have missing values 20
summary(sleep)
colSums(is.na(sleep))  #which column how many data missing
rowSums(is.na(sleep))  #which row how many data missing
head(sleep)
df= sleep #make a copy of sleep data
complete.cases(df) #complete cases
mean(df$Dream, na.rm=T) #mean of Dream Col
sum(is.na(df$Dream))
sum(!is.na(df$Dream))
df$Dream
summary(df)
df[ , 4:5]; df[ ,c(1,3,5)]
df[is.na(df$Dream), 'Dream'] #missing values in Dream Column
df[is.na(df$Dream), "Dream"] = mean(df$Dream, na.rm=T)  #find and replace
df$Dream
sum(df$Dream == 1.972)
#use mice package
library(mice)

#https://www.analyticsvidhya.com/blog/2016/03/tutorial-powerful-packages-imputing-missing-values/
#MICE, Amelia, missForest, Hmisc, mi



================================================
FILE: 0-class/autoML1.R
================================================
# Auto ML in R

pacman::p_load(caret, randomForest)
set.seed(123)
n = 100

sData <- data.frame(X1=rnorm(n), X2= rnorm(n), Y = rbinom(n, 1, 0.5))
sData

target <- 'Y'
#hyper parameters
ctrl <- trainControl(method='cv', number=5)
tune_grid <- expand.grid(.mtry = 2.5)

aModel1 <- train(sData[, setdiff(names(sData), target)], sData[, target], method='rf', trControl = ctrl, tuneGrid = tune_grid)

aModel1
#predictions
new_data = data.frame(X1=0.1, X2 = -0.2)
predictions = predict(aModel1 , newdata = new_data)
print(aModel1)

#---------
library(lares)

# The data we'll use is the Titanic dataset
data(dft)
df <- subset(dft, select = -c(Ticket, PassengerId, Cabin))
df

r <- h2o_automl(df, y = Survived, max_models = 1, impute = FALSE, target = "TRUE")



#H2o----
library(h2o)
library(caTools)
mt = mtcars

index_mt = sample.split(Y = mt$mpg, SplitRatio = .7)
index_mt
train_mt = mt[index_mt, ]
test_mt = mt[!index_mt, ]
sapply(list(train_mt, test_mt), dim)

y <- 'mpg'
x <- setdiff(names(train_mt), y)
x; y

aModel2 <- h2o.automl(x=x, y = y, training_frame = train_mt, max_models = 5, seed=1)


================================================
FILE: 0-class/bigQuery.R
================================================
# big query
#https://bigrquery.r-dbi.org/dev/
#install.packages("bigrquery")

library(bigrquery)

library(DBI)

billing <- bq_test_project() # replace this with your project ID 
sql <- "SELECT year, month, day, weight_pounds FROM `publicdata.samples.natality`"

tb <- bq_project_query(billing, sql)
bq_table_download(tb, n_max = 10)
con <- dbConnect(
  bigrquery::bigquery(),
  project = "publicdata",
  dataset = "samples",
  billing = billing
)

con
dbListTables(con)

dbGetQuery(con, sql, n = 10)


================================================
FILE: 0-class/hhe_d01.R
================================================
#HHE Batch : Nov 2023
#05Nov2023
mtcars
#control+enter   to run a line and move ahead
#customise shortcuts 

#vector----

x1 = c(1,4,6) #control + enter -> modify shortcuts
x1
print(x1)

x1[2]

x1[c(1,3)]

x2 <- c(4,35,34,77)
x2
print(x2)

class(x2)

#vector, matrics, dataframes - data structure
mean(x1)
1:10
X2

(x3 = 100:200)  #assign and print
min(x3)
max(x3)
sd(x3)

x5 = c('A', "Chaman", "Divya", 'mUNAZ')
class(x5)
x5
x5[3]
toupper(x5)
tolower(x5)

#matrix----
50:60
length(50:61)
?matrix

m1 = matrix(data=50:61,ncol=3, byrow=T, dimnames = list(c('R1','R2','R3','R4'), c('C1','C2','C3')))
m1
colSums(m1)
colMeans(m1)  
rowMeans(m1)

#array----
#will do later
?array
array(data=1:3, dim=c(2,4,2,2))

#dataframe----
rollno = c('S01','S02')
name = c('Dhiraj','Munaz')
age = c(55, 42)
sapply(list(rollno, name,age), class)

df = data.frame(rollno, name, age)
df
write.csv(df, file='./0-class/munaz.csv', row.names=F, na='' )
?read.csv
df1 = read.csv(file='./0-class/munaz.csv')
df1

#list----

ls()
list1 = list(df, x1,m1, age)
list1
list1[1]
list1[4]

#factor----
mtcars
?mtcars
?factor
class(mtcars)
str(mtcars)

mt = mtcars
str(mt)
dim(mt)
head(mt)
tail(mt)
summary(mt)

boxplot(mt$mpg)
hist(mt$mpg)
lines(mt$mpg)

barplot(table(mt$cyl), col=1:3)
barplot(table(mt$cyl), col=c('red','green','yellow'))
pie(table(mt$cyl), col=c('red','green','yellow'))
?pie

table(mt$cyl)
mt$cyl
table(mt$cyl, mt$am)

#dplyr-----

library(dplyr)
?mtcars
names(mt)
mt %>% group_by(gear) %>% count()
mt %>% group_by(gear) %>% tally()
mt %>% group_by(gear) %>% summarise(n=n())
mt %>% group_by(gear) %>% summarise(n=n(), meanMPG = mean(mpg, na.rm=T), minMPG = min(mpg, na.rm=T), maxMPG = max(mpg, na.rm=T))

mt %>% group_by(gear,am) %>% summarise(n=n(), meanMPG = mean(mpg, na.rm=T), minMPG = min(mpg, na.rm=T), maxMPG = max(mpg, na.rm=T))

mt %>% arrange(mpg)
mt %>% arrange(gear, -mpg) %>% select(gear, mpg)
mt %>% group_by(gear)  %>% slice_max(mpg, n=1)
mt %>% group_by(gear)  %>% slice_min(mpg, n=1)
mt %>% group_by(gear)  %>% slice_max(hp, n=2)

mt %>% group_by(gear) %>% summarise(n=n(), meanMPG = mean(mpg, na.rm=T), meanWT = mean(wt, na.rm=T))

mt %>% group_by(gear, am)  %>% tally() 


================================================
FILE: 0-class/hhe_d02.R
================================================
# HHE Batch :  MA with R ;  Munaz
#day2------

#vectors-----
(rollNo = 1:100)
paste('student',1:100)
(name = paste('student', sprintf('%003d',1:100)))
set.seed(1234)
(gender = sample(x=c('M','F'), size=100, replace=T, prob=c(.6, .4)))
table(gender)
prop.table(table(gender))
courses = c('BBA', 'MBA', 'PHD')
course = sample(x=courses, size=100, replace=T, prob = c(.5, .3, .2))
course
table(course)
prop.table(table(course))
#marketing, operations
(marketing  = round(rnorm(n=100, mean=65, sd=7)))
mean(marketing); sd(marketing)
?distributions
hist(marketing)
(operations = round(runif(n=100, min = 55, max=90)))
range(operations)
hist(operations)
grades = c('A','B','C')
(project = sample(x=grades, size=100, replace=T))
(project = factor(project, ordered=T, levels = c('C', 'B','A')))

varNames = list(rollNo, name, gender, course, marketing, operations, project)
sapply(varNames, length)
?sapply(varNames, IQR)
sapply(varNames, length)

?apply
#matrix------
(m1 = matrix(data= 1:24, nrow=4, ncol=6))


#array -----
(a1 = array(data = 1:24, dim = c(4,6)))
(a2 = array(data = 1:24, dim = c(3,4,2)))

#factor-----
(courses = factor(x=c('BBA', 'MBA', 'PHD')))
(likertScale = factor(x=c('Excellent','Good','Fair','Poor')))
(likertScale2 = factor(x=c('Excellent','Good','Fair','Poor'), ordered = T))
(likertScale2 = factor(x=c('Excellent','Good','Fair','Poor'), ordered = T, levels = c('Poor', 'Fair', 'Good','Excellent')))

#data frame-------
sapply(list(rollNo, name, gender, course), length)
df = data.frame(rollNo, name, gender, course, marketing, operations, project)
head(df)
str(df)
summary(df)
df$name
df$gender = factor(gender)
df$course = factor(course)
df$project
summary(df)
tail(df)

library(dplyr)
#mean marks of subjects - gender wise
df %>%  group_by(gender) %>% summarise(meanMarketing  = mean(marketing, na.rm=T), meanOperations = mean(operations, na.rm=T), count = n())


df %>% group_by(gender, course)  %>% summarise(meanM = mean(marketing, na.rm=T), maxM = max(marketing), minM = min(marketing))
names(df)
df %>% group_by(gender)  %>% summarise(across(.cols=marketing:operations, .fns=mean, .names='mean_{col}'))
df %>% group_by(gender) %>% summarise(across(.cols=c(marketing, operations), .fns = list(mean=mean, min=min, max=max), na.rm=T, .names="{fn}_{col}"))

df %>% group_by(gender) %>% summarise(across(where(is.numeric), .fns = list(mean=mean, max=max), na.rm=T, .names="{col}_{fn}"))
df %>% group_by(gender) %>% summarise(across(where(is.numeric), .fns = list(mean=mean, max=max), .unpack=T))

cols = c('marketing', 'operations')
df
df %>% mutate(across(all_of(cols), round,-1))
df %>% mutate(across(where(is.double) & !c('marketing'), round,-1))
x=1234566677.999
round(x,0)
trunc(x,2)
floor(x)
ceiling(x)
?grepl
gsub('4','99999999',x)
#automatically unpack
x='munaz'
gsub('z','j',x)
#list --

#ggplot-------
library(ggplot2)
head(mpg)
?mpg
?ggplot
ggplot(mpg, aes(x=displ, y=hwy, colour = class)) + geom_point() 
head(mtcars)
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point(color='red')
ggplot(mtcars, aes(x=wt, y=mpg, color=factor(gear), shape=factor(cyl), size=hp)) + geom_point()

ggplot(mtcars, aes(x=wt, y=mpg, color=factor(gear), shape=factor(cyl), size=hp)) + geom_point() + geom_text(aes(label=paste0('(',wt,',',mpg,')')), hjust=1.5, size=3)  + facet_wrap(am ~ vs, labeller = label_both)
?facet_wrap

#end of day2---------

================================================
FILE: 0-class/hhe_d03.R
================================================
#HHE MAR - Day3
library(dplyr)   #load the library

#with and within-----

df = mtcars

#with(DF, expr) : new Col
#within(DF, newCol <- expr) : new DF
names(df)
?mtcars
head(df)
?with
(newMPG <- with(df, mpg * 1.5))
21 * 1.5
class(newMPG)
newMPG
(DF2 <- within(df, newMPG2 <- mpg * 1.5))
head(DF2)
class(DF2)

#table-----
df$gear
table(df$gear)
names(df)
table(df$carb)

#mutate------
df$mpg
range(df$mpg)
mean(df$mpg)
median(df$mpg)
sort(df$mpg)[8] # [32/4]
quantile(df$mpg,c(0, .25, .5, .75,1))
df %>% summary()
df %>% mutate(gear = factor(gear)) %>% summary()

df %>% mutate_at(c('cyl','vs', 'am','carb', 'gear'), as.factor) %>% summary()
df %>% mutate(across(c(cyl, vs, am, carb), .fns= as.factor))  %>% str()
df %>% mutate(across(c(2,8:11), .fns= as.factor))   %>% summary()
names(df)
head(df)
df %>% mutate(across(c(mpg, wt, hp, qsec), .fns= round)) %>% head()

cols = c('mpg', 'hp')  #list of cols
df %>% mutate(across(all_of(cols), .fns= round)) %>% head()

#only numeric & mpg & wt
df %>% mutate(across(where(is.double) & c(mpg, wt), .fns= round)) %>% head()

#summarise----purr style
df %>% group_by(gear)  %>% summarise(across(all_of(cols), ~mean(.x, na.rm=T)))
df %>% group_by(gear)  %>% summarise(across(all_of(cols), list(mean=mean, sd=sd)))

#names of output-----
df %>% group_by(gear)  %>% summarise(across(all_of(cols), list(mean=mean, sd=sd), .names = '{.col}.{.fn}'))
df %>% group_by(gear)  %>% summarise(across(all_of(cols), list(mean=mean, sd=sd), .names = '{.fn}.{.col}'))

df %>% filter(if_any(.cols = c(1:11), .fns= is.character))
df %>% filter(if_any(.cols = c(1:11), .fns= is.integer))
df %>% filter(if_any(.cols = c(1:11), .fns= is.numeric))

df %>% filter(if_all(.cols = c(1,3,4,5), .fns= is.numeric))

df %>% select_if(is.numeric)  %>% names()
df %>% select_if(where(is.numeric))  %>% names()
df %>% purrr::discard(~is.numeric(.))  %>% names()
df %>% purrr::discard(~ !is.numeric(.))  %>% names()
df %>% purrr::keep(~ is.numeric(.))  %>% names()
df %>% purrr::keep(~ is.factor(.))  %>% names()
df %>% mutate_at(c('cyl','vs', 'am','carb'), as.factor) %>% purrr::keep(~ is.factor(.))  %>% names()
df %>% select_if(is.integer)

#groupby-------
names(df)
head(df)
sum1 <- df %>% group_by(gear)  %>% summarise(count =n(), meanMPG = mean(mpg, na.rm=T), minMPG = min(mpg), maxMPG = max(mpg), meanWT = mean(wt, na.rm=T))
sum1

library(ggplot2)
sum1 %>% tidyr::pivot_longer(cols=count:meanWT, names_to = 'stats')  %>% mutate(gear = factor(gear)) %>% ggplot(., aes(x = gear, y=value, fill=gear)) + geom_bar(stat='identity') + geom_text(aes(label=round(value,1))) + facet_wrap(stats ~., scales='free')


#1st car in each gear group, highest mpg
df %>% group_by(gear) %>% arrange(gear, -mpg) %>% select(gear, mpg,wt, hp)  %>% top_n(n=3, wt=mpg)

df %>% filter((mpg > 30 & am == 1) | wt < 2)

df %>% slice(5:7)

sample(x=df, size=4)
?slice_sample

slice_sample(df, n=5)
slice_min(df, order_by = mpg, n=1)



df %>% select(gear, mpg, hp, disp) %>% group_by(gear)  %>% summarise(across(everything(), mean))
df  %>% group_by(gear)  %>% summarise(across(c(mpg, hp, disp, qsec), mean), .groups = 'drop')

#arrange-------



#missingValues------
v1 = c(1, NA, 3, 4, NA, 6)
v1
sum(is.na(v1))
mean(v1)
mean(v1, na.rm=T)

v1[is.na(v1)] = 99
v1


df2 = df
df2[sample(1:32, size=10), 'mpg'] = NA
df2[sample(1:32, size=10), 'hp'] = NA
colSums(is.na(df2))
str(df2)

df2 %>% select(mpg, hp, gear)
df2 %>% mutate(mpg = ifelse(is.na(mpg), mean(mpg, na.rm=T), mpg))  %>% select(mpg, hp, gear)


#statistics-----
summary(df)
names(df)
numCols = c('mpg','hp','disp','qsec', 'wt')
cor(df[numCols])
cor(df$wt, df$mpg)

sapply(df[,numCols], sd)
sapply(df[,numCols], mean)
sapply(df[,numCols], quantile)
sapply(df[,numCols], fivenum)
fivenum(df$wt)  #min, lowerHinge, median, upperHinge, max
boxplot(df$wt, main=paste('Five Number Summary Box Plot \n min, lowerHinge, median, upperHinge, max \n', paste(fivenum(df$wt), collapse=',')))
abline(h=fivenum(df$wt))
boxplot(wt ~ am, data=df, ann=T, col=c('red','green'))

library(e1071)
skewness(df$wt)
kurtosis(df$wt)


library(Rfast)
colkurtosis(as.matrix(df[,numCols]))

#devtools::install_github('smin95/smplot')
#devtools::install_github('smin95/smplot2', force=T)

library(smplot2)
?smplot2


================================================
FILE: 0-class/hhe_d04.R
================================================
# HHE Day-4 : 19 Nov 2023 : Munaz
# Supervised and Non-Supervised Learning
# Hypothesis Testing
# Linear & Logistic Regression
# Market Basket & Clustering
# Missing Values
# Customer Churn & Customer Purchase Probability
# autoML

#SLM--------
women
?women
head(women)
cov(women)
cor(women)
plot(women)

?lm
lm1 <- lm(formula = weight ~ height, data=women)
lm1
# weight = coeff + slope * height
# wt = -87.52 + 3.45 * ht
# y = c + mx
range(women$height)
library(dplyr)
women %>% arrange(height)
hist(women$height)
colMeans(women)
head(women)
-87.52 + 3.45 * 58
plot(lm1)
summary(lm1)
residuals(lm1)
nrow(women)
cbind(women, residuals(lm1), fitted.values(lm1))


#MLM-----
head(mtcars)
?mtcars
#predict mpg(DV) on other IVs(wt, hp, gear)
mt2 = mtcars %>% mutate(gear = factor(gear))  %>% select(mpg, wt, hp, gear)
summary(mt2)
lm2 = lm(formula = mpg ~ wt + hp + gear, data = mt2)
lm2
# mpg = 34.87 - 3.2 * wt + -0.034 * hp  #G3
# mpg = 34.87 - 3.2 * wt + -0.034 * hp + 1.26 * gear #G4
# mpg = 34.87 - 3.2 * wt + -0.034 * hp + 1.86 * gear #G5

mt3 = mtcars %>% select(mpg, wt, hp, disp)
head(mt3)

lm3 <- lm(formula = mpg ~ wt + hp + disp, data = mt3)
lm3
summary(lm3)

lm4 <- lm(formula = mpg ~ wt + hp, data = mt3)
lm4
summary(lm4)
plot(mt3$mpg, mt3$hp)

lm5 <- lm(mpg ~ ., data=mtcars)
summary(lm5)



#train & Test ----
noYes = sample(x=c('No','Yes'), size=100, replace=T, prob=c(.6, .4))
noYes = sample(x=c(0,1), size=100, replace=T, prob=c(.6, .4))

table(noYes)

prop.table(table(noYes))

salary = round(rnorm(n=100, mean=60, sd=10),0)
salary

df = data.frame(salary, noYes)
df
#df$noYes = factor(df$noYes)
summary(df)
# Splitting dataset
library(caTools)
library(ROCR)
#split DF into 70% and 30% rows
?sample.split
split <- sample.split(df$noYes, SplitRatio = 0.7)
split
prop.table(table(split))

train_reg <- subset(df, split == "TRUE")
test_reg <- subset(df, split == "FALSE")
dim(train_reg)
dim(test_reg)
train_reg
prop.table(table(train_reg$noYes))
prop.table(table(test_reg$noYes))

# Training model
logistic_model <- glm(noYes ~ salary, data = train_reg,  family = "binomial")
logistic_model

# Summary
summary(logistic_model)
#https://www.geeksforgeeks.org/logistic-regression-in-r-programming/

predict_reg <- predict(logistic_model, test_reg, type = "response")
test_reg

#https://www.statology.org/logistic-regression-in-r/
#practise this-----
#https://www.geeksforgeeks.org/automated-machine-learning-for-supervised-learning-using-r/
predict_reg
cbind(test_reg, predict_reg)
# Changing probabilities
predict_reg2 <- ifelse(predict_reg > 0.5, 1, 0)
cbind(test_reg, predict_reg, predict_reg2)

library(caret)
confusionMatrix(factor(test_reg$noYes), factor(predict_reg2))
(13+5)/30


================================================
FILE: 0-class/hhe_d04b.R
================================================
# day4 - MAR - Munaz
#plan - Modeling

#What is Linear Regression ? , What does it do ?, How do we run the model in R ? How do we check if the model is good ?
#general eq : y = c + mx  ; y = c + b1x1 + b2x2 ....
#x's are IV, y is IV, c - constant/intercept; m/b1/b2 - slope/coeficients

women
?women
head(women)
#Does height depent on weight or vice-versa ?
#create relation ship between height & weight
lm1 = lm(weight ~ height, data=women)
summary(lm1)
print(lm1)
plot(weight ~ height, data=women, main='Height & Weight of Women')
abline(lm1)  #line of prediction
#as height increase, weight increases almost linearly
predict(lm1, newdata = data.frame(height = c(50, 65.5, 75.5, 100)))

#prediction for ranges beyond IV is wrong
range(women$height)


#multiple Linear Regression ------
df = mtcars[, c('mpg', 'wt','hp', 'drat')]
head(df)
lm2 = lm(mpg ~ wt + hp + drat, data=df)
print(lm2)
summary(lm2)
sapply(df, range)
(df1A = data.frame(wt= c(2,3), hp=c(100, 200), drat = c(3,4)))
predict(lm2, newdata = df1A)  #mpg predicted
cbind(df1A, mpgPredicted = predict(lm2, newdata = df1A))

library(car)
avPlots(lm2)
#about plot
#angle of the line in each plot matches the sign of coef from estimate equation
coef(lm2)
#x- single IV, y - DV (mpg)
#blue line - association bet IV & DV, keeping other IVs constant
#labelled points (2) - largest residuals & largest partial leverage (contribution of individual IV to total leverage/change)

#summary
summary(lm2)
#R2, Adjs R2, Coeff & their p value, FStat p Value
lm2$residuals   # diff betw predicted and actual DV
(lm2_residuals = residuals(lm2))
hist(lm2_residuals)   #it should look like normal
qqnorm(lm2_residuals)  #it should be straight line
qqline(lm2_residuals)   #few portions around the straight line

#multi-collinearity
library(ggcorrplot)
df_subset = df[, c('wt', 'hp', 'drat')]
(corr_matrix= round(cor(df_subset),2))
ggcorrplot(corr_matrix, hc.order=T, type='lower', lab=T)
#there is strong corr (< .8) , then keep only 1

#compare model----
anova(lm2)
#anova(model1, model2)  #if pvalue < .05, choose model1

#another way to check assumptions
plot(lm2)

par(mfrow=c(2,2))
plot(lm2)
#5key assumptions-----
#linear relationship, Multivariate normality, Little or no Multicollinearity, No Auto Correlation, Homoscedasticity

library(ggfortify)
autoplot(lm2)
par(mfrow=c(1,1))
#residual vs Fitted - Linear Relationship
plot(lm2, 1)
#Normal Q-Q : Normally Distributed Residuals
plot(lm2, 2)
#Scale-Location (Spread) : Homoeneity of variance of residuals. it should b horizontal line
plot(lm2, 3)
#Residuals vs Leverage : Influential cases (outliers)
plot(lm2, 5)
#Cooks Distance
plot(lm2, 4)  #3 extreme
plot(lm2, 4, id.n=5)   #top 5 extreme values



================================================
FILE: 0-class/lm_AIC.R
================================================
# AIC Linear Regression

#https://bookdown.org/steve_midway/DAR/model-selection.html

mtcars.lm1 <- lm(mpg ~ disp, data=mtcars)
summary(mtcars.lm1)
broom::glance(mtcars.lm1)

mtcars.lm2 <- lm(mpg ~ disp + wt, data=mtcars)
broom::glance(mtcars.lm2)


mtcars.lm3 <- lm(mpg ~ disp + wt + hp, data=mtcars)
broom::glance(mtcars.lm3)

mtcars.lm4 <- lm(mpg ~ disp + wt + hp + cyl, data=mtcars) 
broom::glance(mtcars.lm4)

mtcars.lm5 <- lm(mpg ~ disp + wt + hp + cyl + gear, data=mtcars)
broom::glance(mtcars.lm5)

AIC(mtcars.lm1, mtcars.lm2, mtcars.lm3, mtcars.lm4, mtcars.lm5)
sapply(list(mtcars.lm1, mtcars.lm2, mtcars.lm3, mtcars.lm4, mtcars.lm5), broom::glance)
#http://www.sthda.com/english/articles/38-regression-model-validation/158-regression-model-accuracy-metrics-r-square-aic-bic-cp-and-more/
library(MASS)
data(mtcars)
summary(car_model <- lm(mpg ~., data = mtcars))
step_car <- stepAIC(car_model, trace = TRUE, direction= "both")
#The goal is to have the combination of variables that has the lowest AIC or lowest residual sum of squares (RSS).
step_car
#The last line is the final model that we assign to step_car object.
library(stargazer)
stargazer(car_model, step_car, type = "text")

#----
library(olsrr)
step_plot <- ols_step_both_aic(car_model)

plot(step_plot)
?ols_step_both_aic


================================================
FILE: 0-class/missingValues.R
================================================
#missing values

rm(list = ls())
is.na(x) # returns TRUE of x is missing
x <- NA
is.na(x) # returns TRUE of x is missing

y <- c(1,2,3,NA)
is.na(y) # returns a vector (F F F T)

#Recoding Values to Missing
mt = mtcars
mt$v1 = 99
head(mt)
# recode 99 to missing for variable v1
# select rows where v1 is 99 and recode column v1
mt$v1[mt$v1==99] <- NA
mt

#Excluding Missing Values from Analyses
x <- c(1,2,NA,3)
mean(x) # returns NA
mean(x, na.rm=TRUE) # returns 2
mt
mt[c(1,4,10,15,30), 'v1'] = 100
mt
mt[complete.cases(mt),]

library(VIM)
sleep
is.na(sleep)
summary(sleep)
sum(is.na(sleep))
colSums(is.na(sleep))
rowSums(is.na(sleep))
complete.cases(sleep)
sleep[complete.cases(sleep),]

aggr(sleep, prop = F, numbers = T)


library(visdat)
vis_miss(sleep)


================================================
FILE: 0-class/munaz.csv
================================================
"rollno","name","age"
"S01","Dhiraj",55
"S02","Munaz",42
"S03","HHE",30


================================================
FILE: 0-class/purchaseProb.R
================================================
# customer purchase probability

#https://www.masterdataanalysis.com/r/using-r-predict-customer-will-buy/


================================================
FILE: 01-IIM/10a-daily.R
================================================
#daily practise File
#keep yourself updated with common R commands and Modeling Techniques

================================================
FILE: 01-IIM/11-analyticLevels.R
================================================
#all levels of analytics - Descriptive, Diagnostic, Predictive, Prescriptive

#import from ggsheet
library(gsheet)
slr1 = "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2023826519"
df = as.data.frame(gsheet2tbl(slr1))
names(df) = c('area','sales')
head(df)
df
#X-area in sqft, Y-sales in some unit currency
str(df)
linearmodel = lm(sales ~ area, data=df)
plot(df$area, df$sales)
abline(lm(sales ~ area, data=df), col='red')
cor(df$area, df$sales)
cov(df$area, df$sales)
summary(linearmodel)
#Ho: (F Test) : No relationship between Y and any X
#Ha:  There is relationship between Y and at least one X
# p < 0.05 Reject Ho in favour of Ha
coef(linearmodel)
#Y = 0.96 + 1.66 * X
range(df$area) #value of X to be betw this range : interpolation not extrapolation 
(new2 = data.frame(area=c(1.5,2,3,4,5)))

(p2sales= predict(linearmodel, newdata= new2))
cbind(new2, p2sales)

summary(linearmodel)
head(df)
#residual
(sales = 0.96 + 1.66 * 2.2)
(r = 5.6 - 4.61)
plot(df$area, df$sales)
abline(lm(sales ~ area, data=df), col='red')
abline(v=2.2,h=c(5.6, 4.61))
cbind(resid(linearmodel))
fitted(linearmodel) - df$sales

#assumptions
plot(linearmodel)

#descriptive ------
df
head(df)
summary(df)
hist(df$area)
hist(df$sales)
range(df$sales)
range(df$area)

#diagnostic -----
cor(df)
cov(df)

#predictive -------
model = lm(sales ~ area, data = df)
model
summary(model)
testdata = data.frame(area=c(3,4))
predicted = predict(model, newdata = testdata)
cbind(testdata, predicted)
#prescriptive-----
pacman::p_load(lpSolveAPI)
#we want min sales to be 11 units where area can be between 2 & 5 units

#model : (sales = 0.96 + 1.66 * area)

#First we create an empty model x.
11/1.66  - .96  #5.6 units of area required 
#max: 0.96 + 1.66 * area
lprec1 <- make.lp(0, 1)
lprec1
set.objfn(lprec1, c(1.66))
lprec1



#maximise it
lp.control(lprec1, sense="max")
lprec1
set.bounds(lprec1, lower = c(2), columns = c(1))
lprec1
set.bounds(lprec1, upper = c(4.5), columns = c(1))
lprec1
ColNames <- c("area")
dimnames(lprec1)[[2]] <- list(ColNames)
lprec1
solve(lprec1)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec1)
get.variables(lprec1)
get.constraints(lprec1)
print(lprec1)

#another way------
# Set 0 constraints and 1 decision variables
lprec2 <- make.lp(nrow = 0, ncol = 1)
# Set the type of problem we are trying to solve
lp.control(lprec2, sense="max")
# Set type of decision variables
set.type(lprec2, 1, type=c("real"))
## Set the coefficients of the decision variables -> C
C <- c(1.66)
# Set objective function coefficients vector C
set.objfn(lprec2, C)
lprec2
# Add constraints
add.constraint(lprec2, c(1) , "<=", 4.5)
lprec2
add.constraint(lprec2, c(1) , ">=", 2)
lprec2
solve(lprec2)
get.objective(lprec2)
.96 + 1.66 * get.objective(lprec2)
# Get the decision variables values
get.variables(lprec2)
# Get the value of the objective function
get.objective(lprec2)

# Note that the default boundaries on the decision variable are c(0, 0, 0) and c(Inf, Inf, Inf)
get.bounds(lprec2)
# Boundaries can be set with following function
#lpSolveAPI::set.bounds()
set.bounds(lprec2, lower=3, upper=NULL, columns = 1)

#contain a constant, but this constant does not change the optimization problem or the optimal solution.

4.5 * 1.66 + .96


================================================
FILE: 01-IIM/11a1-start.R
================================================
#initial commands

# assign
x1 = 3 #press control + enter to run the line
x2 <- 3 # same 
x3 < - 3  #incorrect
#which is better
x1
x2
y
ls() #variables in env
women

?AirPassengers
data() # datasets available for use

library()  # libraries currently loaded

?mean  #help
help(mean)
??mean  #search through other sources
x=0:100000000
x
x <- c(0:10, 50,100,200)
x
mean(x)
xm <- mean(x)
xm
mean(x, trim = 0.10)
x=c(1,1,1,1,5,5,5,5,7,7)
mean(x)
mean(x, trim=.3)
x=c(1,5,5)
mean(x)
c(mean(x), mean(x, trim = 0.10))

1:10
1:10000000
x=c(1,34,5)
x
?c
version #version of R

Sys.Date() # todays date

getwd()  # working directory

methods(class='matrix')  #methods available for a class of object

plot(10:100) #basic command to plot
plot(women)

#vector
#how to run command
#check datasets
#install library
#check which libraries available
#.....



================================================
FILE: 01-IIM/11a2-packages1.R
================================================
#List of packages to be installed

#Installing
listOfPackages = c('P1','P2')
install.packages(listOfPackages)

#first install package pacman
#loading multiple packages
library(pacman)
pacman::p_load(listOfPackages)

#install and load : first time only
pacman::p_install(listOfPackages)

#unload packages
pacman::p_unload(listOfPackages)

#remove packages from PC
pacman::p_delete(listOfPackages)

#datasets packages from packages
pacman::p_data(listOfPackages)

#installing
library(devtools)   #installing from github/dev env


#data manipulation-----
library(dplyr)  #summarise
library(data.table)  #new DS for manipulating large data
ibrary(tidyverse)  # data manipulation

#linear modeling
library(olsrr)


#decision tree-----
library(rpart)
library(rpart.plot)
library(randomForest)
library(CHAID)  #library for performing CHAID decision tree
install.packages("CHAID", repos="http://R-Forge.R-project.org")
library("mlbench") #data for DT
library(partykit)
library(party)
library(ISLR)  #data for DT

#association rule-----
library(arules)
library(arulesViz)


#clustering-----
library(cluster)    # clustering algorithms
library(factoextra) # clustering visualization
library(dendextend) # for comparing two dendrograms
library(fpc) 
library(NbClust) # finding the optimal number of clusters
library(amap)
library(flexclust)


#statistics-----
library(survey) #data for stats
library(catspec)  #categories
library(survival)  #data for statistics
library(MASS)
library(caret)  #train & test, accuracy
library(caTools)  #sampling
library(Hmisc)
library(e10171)
library(BSDA)  #ztest
library(gmodels)
library(questionr)
library(fBasics)
library(outliers)
library(car)
library(mice)   #missing value
library(mosaic)



#timeseries-----
tsp1 <- c('timeseries','tseries','zoo','xts','quantmod','TTR','lubridate')
pacman::p_install(listOfPackages)
library(timeSeries)
library(tseries)
library(zoo)
library(xts)
library(quantmod)
library(TTR)
library(lubridate)
library(forecast)
library(astsa)
#graphs-----
library(ggplot2)
library(gridExtra) #ggplot graph combin
library(RColorBrewer) #color combinations
library(corrgram)  #corrrgram
library(lattice)

#import/export-----
library(openxlsx)   #excel
library(gsheet) #google sheet
library(readxl) #read excel files
library(xlsx)  #read/write xlsx : difficult to use
library(rJava)   #java reqd for some packages

#reproduce data (keep it small)----
recode_mtcars <- dput(head(mtcars))
dput(droplevels(iris[1:4, ])) #for df with factors
#copy to/from excel: clipboard
fromexcel <- dput(read.table("clipboard",sep="\t",header=TRUE))

#text mining-----
library(wordcloud2)
library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)
library(syuzhet)
library(twitterR)
library(ROAuth)
library(RTextTools)
library(rvest)

#misc----
library(rattle)  #multi purpose
library(vcd) #data
library(VIM)
library(mice)


#others


================================================
FILE: 01-IIM/11a3-packages2.R
================================================
# Packages installation

#List avl packages
library()


#Total Avl Packages
nrow(available.packages())

#Install Package amap
install.packages('amap')

#Load package
library(amap)

#Find functions in package
library(help=amap)

#Help wrt a package
help(package='amap')  #see on right side pane

#Unload---
install.packages('tm')

library(tm)
library(VIM)
search()
detach('package:tm', unload=TRUE)
detach(package:VIM, unload = T) 
search()


#----------------- Part I Over
#Detach Multiple Packages
(detpkg = c('plyr','tm'))
library('plyr') #load lib
library('tm')  #load lib
search()  #check if loaded
Vectorize(detach)(name=paste0("package:", detpkg), unload=TRUE, character.only=TRUE)  #code to detach
search()  # confirm if removed
#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.

#Detach from memory all packages
rm(list = ls(all = TRUE))
sessionInfo()


#Remove Packages ----- uninstall 
remove.packages("tm")
require('tm')# check if unistalled



#multiple packages

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}
packagelist <- c('NLP','tm', 'lubridate')
ipak(packagelist)

#  package lists
pkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')
pkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')
pkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')
pkg4 = c('arules' , 'arulesViz')
pkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')
install.packages("CHAID", repos="http://R-Forge.R-project.org")


library(lubridate)

#multiple load 
easypackages::libraries(pkg1)


#easypackages
#install.packages('easypackages')
library(easypackages)

#Install Multiple Packages
packages("plyr", "psych", "tm")
libraries("plyr", "psych", "tm")



#lubripack
install.packages('lubripack')  #NA for some versions
#install older version of R packages
#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages
#https://rdrr.io/github/Espanta/lubripack/

require(devtools)
install_github("Espanta/lubripack")
library(lubripack)
lubripack("plyr", "psych", "tm", "quantmod")



pack <- available.packages()
pack["ggplot2","Depends"]
pack["ggplot2","Imports"]
pack["data.table","Depends"]

packrat:::recursivePackageDependencies("ggplot2",lib.loc = .libPaths()[1])
tools::dependsOnPkgs('ggplot2')
tools::dependsOnPkgs('dplyr')
tools::dependsOnPkgs("ggplot2",installed=available.packages())
library(rusk)
#Remove Package
remove.packages('quantmod')
library(quantmod)



#This will remove all/Detach all  packages
library(mise)
search()
#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)
mise(pkgs=T)
search()



# list all packages where an update is available
old.packages()

# update all available packages
update.packages()

# update, without prompts for permission/clarification
update.packages(ask = FALSE)


================================================
FILE: 01-IIM/11a4-packages3.R
================================================

pinstall <- c('rpart','rpart.plot', 'catools', 'caret','arules','arulesViz')

install.packages(pinstall)



================================================
FILE: 01-IIM/11a5-packages4.R
================================================
#Install packages for Data Analytics Course

#install package
install.packages('packageName')
#multiple packages
install.packages(c('package1', 'package2'))

#load library( oneby one)
#library(package1)
library(dplyr)
#check invironment
ls()

.libPaths() # get library location
library()   # see all packages installed
search()    # see packages currently loaded
#remove library from environment
detach("package:dplyr", unload = TRUE)
search()    # see packages currently loaded
#dplyr gone

#install java from web
#https://www.java.com/en/download/
#restart your laptop and then run this file to install

packages1 = c('rJava','xlsx','dplyr','plyr','tidyr', 'gsheet','psych', 'tm', 'ggplot2','quantmod', 'data.table','car', 'VIM')
packages2 = c('lubridate','ISLR','amap','arules','arulesViz','forecast','rattle','rpart','rpart.plot','ts','zoo','twitterR','ROAuth','partykit','strucchange','didrooRFM','curl','syuzhet','stringr','RTextTools','e1071','wordcloud','caret','catools','olsrr')					

#Installation steps :  #one by change to packages1, package2, packages3
list.of.packages = packages1
new.packages = list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
new.packages
if(length(new.packages)) install.packages(new.packages)
#next set
list.of.packages = packages2
new.packages = list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
#if no installation takes place that means they have been installed or they are not available for the version of R you are working

# some packages have to be installed from other repositories
install.packages("CHAID",repos="http://R-Forge.R-project.org")


================================================
FILE: 01-IIM/11b2-DS1.R
================================================
# Data Structures in R

#control+enter when you are in the line to execute
# Vectors-----
c(2,4,6)
?seq
seq(2,10,.5)
seq(by=.5, from=2,to=3)
rep(1:3,times=4)
rep(1:3,each=4)

rep(c(3,6,7,2),each=4)
rep(c(3,6,7,2), times=4)

?rep

x=1:10   #create seq of nos from 1 to 10
x
(x1 <- 1:20)  # brackets - assign & print

(x1=1:30)
(x2=c(1,2,13,4,5))
class(x2)

(x3=c('a',"ABC"))
class(x3)
(x3=letters[1:10])
class(x3)
LETTERS[1:26]
(x3b = c('a',"Henry",4))#should not combine numeric and character
class(x3b)

(x4=c(T,FALSE,TRUE,T,F))  #logical
class(x4)
class(c(3,5))
(x5a = c(3,5.5))
class(x5a)
as.integer(x5a)

(x5=c(3L,5L, 100L))
class(x5)
x5a = c(3,5)
class(x5a)
(x5b = c(1, 'a',T, 4L))
class(x5b)
#blank variable ?

x=3.5677
trunc(x)
round(x)
floor(x)
ceiling(x)

#access elements
?seq
(x6 = seq(0,100,by=3))
seq(0,100,3)
seq(to=100,from=0,by=3)
seq(1,5,2)
?seq
#[1]  0  2  4  6  8 10
ls()  #variables in my environment
x6
length(x6)
x6[1]; x6[21]

x6[1:5]
x6[10:20]
x6[ seq(1,length(x6), 2)]
x6
x6[3]  # access 3rd element
#[1] 4
x6[c(2, 4)]     # access 2nd and 4th element
x6[-1]          # access all but 1st element
x6[-c(1:10, 15:20)]
x6[c(2, -4)]    # cannot mix positive and negative integers
#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts
x6[c(2.4, 3.54)]    # real numbers are truncated to integers
x6[c(2,3)]

x6[-c(1,5,20)]
x6
x6[x6 > 30]

x6[x6 > 30 & x6 < 40]  # 31-39
x6[x6 != 30]
#or | and is &  !
length(x6)
x6
x6[-(length(x6)-1)]
x2
(x7 = c(x6, x2))

#------
#modify
x6
set.seed(1234)
(x6 = sample(1:50))
(x6b = sort(sample(1:50)))
sort(x6)
sort(x6[-c(1,2)])
sort(x6, decreasing=T)
x6
rev(x6)

seq(-3, 10, by=.2)
x6[-c(1:12)]
x6
x6[x6> 30 & x6 < 40]
(x = -3:2)
x6
x6[2:10] <- 99; x6        # modify 2nd element
x6[x6 > 30 & x6 < 40] = 999
x6


x6
x7 = x6[1:4]; x7      # truncate x to first 4 elements

1:5
#equal partitions within a range
(x = seq(1,5, length.out = 15))
x
x = NULL
x
#NULL
x[4]
#NULL
?distribution
?rnorm
(x = rnorm(100))
plot(density(x))
abline(v=c(-3,0,3))
mean(x)
(x1 = rnorm(100, mean=50, sd=5))
plot(density(x1))
abline(v=mean(x1),h=0.04)
hist(x1, breaks=7)
hist(x1)
hist(x1, freq=F)
lines(density(x1), col=2)
summary(x1)
quantile(x1)
quantile(x1, seq(0,1,.25))
quantile(x1,c(.1, .5, .8))
quantile(x1,seq(0,1,.01))
stem(x1)

#Matrix-----
100:111
length(100:111)
matrix(1,ncol=3, nrow=4)
(m1 = matrix(100:111, nrow=4))
(m2 = matrix(100:111, ncol=3, byrow=T))

x=101:124
length(x)
matrix(x, ncol=6)
class(m1)
attributes(m1)
dim(m1)
m1

# access elements of matrix
m1[1,]
m1[,1]
m1[,1, drop=F]
m1[,-1]  #remove 1st column
m1[1,2:3]
m1[c(1,3),]
m1[,-c(1,3), drop=F]
m1[m1> 105 & m1 < 108]

#names of cols and rows
m1

paste("C","D",sep="-")
paste("C",1:100,sep="-")
paste("C",1:3,sep='')
(colnames(m1) = paste('C',1:3, sep=''))
m1
(rownames(m1) = paste("R",1:4, sep=''))
m1
attributes(m1)
m1[,c('C1','C3')]
m1[,c(1,3)]
#Vector to Matrix
(m3 = 1:24)
m3
dim(m3)= c(6,4)
m3

#access elements
m2
m2[1,]  #first row
m2[c(1,3,4),]  #1st,3rd,4th row

m2[,1]  #first col
m2[,2:3] # 2nd to 3rd coln

m2[c(1,2),c(2,3)]
m2[,]
m2[-2,] # exclude 2nd row
m2
m2[1:5] # matrix is like vector
m2
m2[c(TRUE,F,T,F),c(F, T, T)] #logical indexing
m2[m2 > 5 & m2 < 10]

m1
m1[1:2,1:2]
m1[c('R1','R2'),c('C1','C2')]
m1[1:2,]
m1[c(T,T,F,F),]
m1

#modify Vector
m2
m2[2,2]
m2[2,2] = 10
m2
m2[,2] = 10
m2
m2[m2> 107] = 9999
m2
rbind(m2, c(50,60,70))
rbind(m2,m2)
m2
cbind(m2, c(55,65,75,85))
m2m2= cbind(m2,m2)
m2m2
m2
cbind(m2,m2)
rbind(m2,m2)
#row and col wise summary

m1
colSums(m1)
rowSums(m1)
colMeans(m1)
rowMeans(m1)

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN="*" ) #colwise

#addmargins
m1
?addmargins
addmargins(m1,margin=1,sum) #colwise function
addmargins(m1,1,sd) #colwise function

addmargins(m1,2,mean) #rowwise function
addmargins(m1,c(1,2),mean) #row & col wise function
?addmargins
(M1sum= addmargins(m1,c(1,2),list(list(mean,sum,max, min), list(var,sd, max, min)))) #row & col wise function
round(M1sum,0)

#Array-----
length(100:123)
4*3*2
#2 coys, 3 products, 4 locations sold qty
(a1 = array(100:123, dim=c(4,3,2)))
(loc = paste('loc', 1:4,sep='-'))
(product = paste('p', 1:3,sep='@'))
(coy = paste('coy', 1:2,sep='%'))
dimnames(a1) = list(loc, product, coy)
a1
apply(a1,1, sum) #locationwise
apply(a1,2, sum) #productwise
apply(a1,c(1,2), sum) #product-location wise
apply(a1,c(2,3), sum) #product-coy wise
apply(a1,c(1,3), sum) #coy-location
apply(a1,3, sum) #coywise
sum(a1) #total

#DataFrame----
#create Vectors to be combined into DF
(rollno = 1:30)
(sname = paste('student',1:30,sep=''))
(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
(marks1 = floor(rnorm(30,mean= 50,sd=10)))
(marks2 = ceiling(rnorm(30,40,5)))
(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))
rollno; sname; gender
marks1 ; marks2; course

#create DF
df1= data.frame(rollno, sname, gender, marks1, marks2, course, stringsAsFactors = F)
str(df1) #structure of DF
head(df1) #top 6 rows
head(df1,n=3) #top 3 rows
tail(df1) #last 6 rows
class(df1) # DF
summary(df1) #summary
nrow(df1) 
dim(df1)
length(df1)
df1$course
df1$gender = factor(df1$gender)
df1$course = factor(df1$course)
#df1$sname = as.character(df1$sname)
str(df1)
summary(df1)
boxplot(marks1 ~ gender + course, data=df1)

df1  #full data
df1$gender  # one column
head(df1[ , c(2,4)]) #multiple columns
df1[1:10 ,] #select rows, all columns
df1[1:5,1:4]
#as per conditionis
df1[ marks1 > 50 & gender=='F', c('rollno', 'sname','gender', 'marks1')]
df1[ marks1 > 50 & gender=='F', c(1,2)]
df1[ marks1 > 50 | gender=='F', ]

names(df1)  # names of columns
dim(df1)  #Dimensions

aggregate(df1$marks1, by=list(df1$gender), FUN=sum)
aggregate(marks1 ~ gender, data=df1, FUN=max)
aggregate(cbind(marks1, marks2) ~ gender, data=df1, FUN=max)


(df2 = aggregate(cbind(marks1,marks2) ~ gender + course, data=df1, FUN=mean))
df2

df1





#List -----
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages





#Factor -----

(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))
summary(grades)
table(grades)
(gradesFactor = factor(grades))
summary(gradesFactor)

(gradesFactorOrdered = factor(grades, ordered=T))
summary(gradesFactorOrdered)

(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))
summary(gradesFactorOrderedLevels)
gradesFactor
gradesFactorOrdered
gradesFactorOrderedLevels
pie(c(10,15,17))
pie(summary(gradesFactorOrderedLevels))
barplot(summary(gradesFactorOrderedLevels), col=1:4)

class(grades)
class(gradesFactorOrdered)
class(gradesFactorOrderedLevels)



# Object Properties
#vector
v1= 1:100
class(v1) ; typeof(v1)
v2=letters[1:10]
class(v2) ; typeof(v2)
length(v2)
summary(v1)

#matrix
m1= matrix(1:24,nrow=6)
class(m1)
summary(m1)
dim(m1)
str(m1)

#Array
a1 =array(1:24, dim=c(4,3,2))
class(a1)
str(a1)
dim(a1)
summary(a1)



#DF
#data() #built in datasets
df1= iris 
str(df1)
summary(df1)
class(df1); dim(df1)
nrow(df1) ; names(df1) ;NROW(df1)
colnames(df1)
rownames(df1)

#list
list1 = list(v1,m1,a1,df1)
str(list1)

#Statistical Description
library(Hmisc)
describe(df1)


#Next Topics
x= c(123.2234, 33333.544, 43243.8442)
floor(x)
ceiling(x)
trunc(x)
round(x,-2)
round(x, digits = 5)


================================================
FILE: 01-IIM/11b3-DS2-factor.R
================================================
#Factors
#categories without (eg Gender) Order or with (eg Grades) Orders

(grades = sample(c(LETTERS[1:4]), size=30, replace=T, prob=c(.4,.2,.3,.1 )))
summary(grades)
(gradesF = factor(grades))
summary(gradesF)
table(grades)
table(gradesF)
class(gradesF)
(gradesFO = factor(grades, ordered=T))
(gradesFO1 = factor(grades, ordered=T, levels=c('B','C','A','D')))
summary(gradesFO1)

(marks = ceiling(rnorm(30, mean=60, sd=5)))
(gender = factor(sample(c('M', 'F'), size=30, replace=T)))
(student1 = data.frame(marks, gender, gradesFO1))
boxplot( marks ~ gradesFO1, data=student1)      
boxplot( marks ~ gradesFO1 + gender, data=student1)      

boxplot(marks)
summary(marks)
abline(h = summary(marks))
quantile(marks)


================================================
FILE: 01-IIM/11b4-DS4-reproduce.R
================================================
#Reproducible Code

mtcars 
head(mtcars)
recode_mtcars <- dput(head(mtcars))
newdf <- recode_mtcars
newdf
#if Df has factors 
recode_iris <- dput(droplevels(iris[1:4, ]))
recode_iris

#One other caveat for dput is that it will not work for keyed data.table objects or for grouped tbl_df (class grouped_df) from dplyr. In these cases you can convert back to a regular data frame before sharing, dput(as.data.frame(my_data))


#links
#https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example

#trick
library(devtools)
source_url("https://raw.github.com/rsaporta/pubR/gitbranch/reproduce.R")

rec_mtcars2 <- reproduce(mtcars, cols=1:4)
?reproduce
#copy and paste the script

df2 <- structure(list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 6, 8, 4), disp = c(160,160, 108, 258, 360, 225, 360, 145, 301, 121), hp = c(110, 110,93, 110, 175, 105, 245, 175, 335, 109)), class = "data.frame", row.names = c("Mazda RX4","Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout","Valiant", "Duster 360", "Ferrari Dino", "Maserati Bora", "Volvo 142E")) 
df2



#o quickly create a dput of your data you can just copy (a piece of) the data to your clipboard and run the following in R:

#for data in Excel:
#go to excel and copy data
df5 <- dput(read.table("clipboard",sep="\t",header=TRUE))
df5

#for data in a txt file:
#go to text file seperated by space
df6 <- dput(read.table("clipboard",sep="",header=TRUE))
df6


#--------
library(reprex)
(y <- 1:4)
mean(y)
reprex()
#------------
#now copy crom viewer and send it 
#https://www.tidyverse.org/help/
#https://stackoverflow.com/help/minimal-reproducible-example


================================================
FILE: 01-IIM/11b5-DS3.R
================================================
#Data Structures - II
#Home Work

#Arrays
length(100:123)
4*3*2
#2 coys, 3 products, 4 locations sold qty
(a1 = array(100:123, dim=c(4,3,2)))
(loc = paste('loc', 1:4,sep='-'))
(product = paste('p', 1:3,sep='@'))
(coy = paste('coy', 1:2,sep='%'))
dimnames(a1) = list(loc, product, coy)
a1
apply(a1,1, sum) #locationwise
apply(a1,2, sum) #productwise
apply(a1,c(1,2), sum) #product-location wise
apply(a1,c(2,3), sum) #product-coy wise
apply(a1,c(1,3), sum) #coy-location
apply(a1,3, sum) #coywise
sum(a1) #total



#Lists
#List -----
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages


================================================
FILE: 01-IIM/11e2_vectors1.R
================================================
#Case in Vector in R
#Vector is single dim

#creations of Vectors------
#Vector values from 1 to 100
x1 = 1:100
x1

#vector of values 1, 5, 11, 30
x2 = c(1, 5, 11, 30)
x2

#vector with integer values starting from 5 to 60 with a step of 3
x3 = seq(from=5, to=60, by=3)
x3

#vector with following values using rep & times: 1 2 3 1 2 3 1 2 3
x4 = rep(c(1,2,3), times=3)
x4

#vector with following values using rep & each : 5 5 5 8 8 8 9 9 9
x5 = rep(c(5,8,9), each=3)
x5

#vector with following values using seq & length.out : 10.0 12.5 15.0 17.5 20.0
x6 = seq(from=10, to=20, length.out=5)
x6

#vector with values between 25 and 100 and number of values drawn from another vector 
x7a = 60:75
length(x7a)
x7b = seq(from=10, to=20, along.with = x7a)
x7b
length(x7b)

#multiple functions-------
#vector drawn randomly from seq of values from 10 to 50
#sort the data; print the data in reverse order
x8a = 10:50
x8a
x8b = sample(x8a)
x8b

sort(x8b)
rev(x8b)

#print the 5th to 10th element
x8b[5:10]
#print every alternate element between 4th and 12 element
x8b[seq(4,12,2)]
#print values between 30 & 40 and their location also
x8b[x8b >=30 & x8b <=40]
which(x8b >=30 & x8b <=40)
x8b

#basic stats - mean, median of values
mean(x8b)
median(x8b)
#min and max
min(x8b)
max(x8b)
#range of values
range(x8b)

#plots : histogram
hist(x8b)
#with 5 bars only
hist(x8b, breaks=5)

#vector of categorical values of Male(M) & Female(F)
y1 = c('M','F','M','M')
y1

#vector of 100 with M & F, randomly selected
y2a= c('M','F')
y2a

y2b = sample(x=y2a, size=100, replace=T)
y2b
table(y2b)  #count of M & F

# Male % approx 60%
y2c = sample(x=y2a, size=100, replace=T, prob=c(.6,.4))
y2c
table(y2c)  #count of M & F
prop.table(table(y2c))  #count in Proportion

#barplot & pie
barplot(table(y2c), col=1:2)
pie(table(y2c), col=1:2)

#list only males
y2c[y2c=='M']

#mode
library(modeest)
mlv(y2c, method = "mfv")

#other functions-----
#normal distribution values - 100000
n1 = rnorm(n=10000)
n1

#mean and std dev
mean(n1)
sd(n1)

#plot & histogram
hist(n1)
plot(density(n1))

hist(n1, freq=F); lines(density(n1), col='red')

#uniform distribution values - 100 students with marks between 62 and 90
n2 = runif(n=100, min=65, max=90)
n2
range(n2)

#round the marks to 1 decimal place
n2 = round(n2,digits=1)
n2

#mean and std dev, median, mode,summary, quantile
mean(n2)
sd(n2)
median(n2)
modeest::mlv(n2, method = "mfv")  #mode
range(n2)
summary(n2)
quantile(n2)  #4 quantiles
quantile(n2, p=c(.1, .7))  #10th & 70th percentile

library(moments)
moments::skewness(n2)
moments::kurtosis(n2)

#plot & histogram
hist(n2)
boxplot(n2)
plot(density(n2))
hist(n2, freq=F); lines(density(n2), col='red')

#how many values between 65 & 72
sum(n2 > 65 & n2 < 72)

#mean of marks of students who scored between 75 & 80
mean(n2[n2 > 75 & n2 < 80])

#class interval : 50-65, 65-75, 75-85, > 85
breaks1 = c(0,50,65,75,85,100)
n2cut = cut(n2, breaks= breaks1)
table(n2cut)

#increase marks by 5 for students who scored < 75
n2
n2[n2 < 75]
which(n2 < 75)
n2[n2 < 75] =   n2[n2 < 75] + 5
n2[n2 < 80]

#all of who have scored > 80, make them 82
n2[n2 > 80] =   82
n2
n2[n2 == 82]


#end
str(n2)
summary(n2)
length(n2)
range(n2)
class(n2)
?mean #help


================================================
FILE: 01-IIM/11e3_matrices1.R
================================================
#Matrices in R
#Matrix is a two dimensional data structure in R programming. Matrix is similar to vector but additionally contains the dimension attribute. All attributes of an object can be checked with the attributes() function (dimension can be checked directly with the dim() function).
#vector ----
(v1 = 1:24)

dim(v1)
attributes(v1)
class(v1)

#matrices
(m1 = matrix(data = 1:24, nrow=4))  #bycolumn
(m2 = matrix(data = 1:24, nrow=4, byrow=T))  #by row
(m3 = matrix(data = 1:24, ncol=4))  #by column, ncol

#what to do - filter, sum, other stats
length(m1)
length(m3)  #no of elements
dim(m1)
dim(m3) #dimensions rows * cols
attributes(m1)  #properties
class(m1)  #matrix, array

m1
dimnames(m1)= list(c('Jan','Feb','Mar','Apr'), paste('coy',1:6, sep=''))
m1 #4 months, 6 coys
m3
month.abb  #month abbreviations
#another way of giving names to row & col
colnames(m3) =  paste('coy',1:4, sep='')
rownames(m3)= month.abb[1:6]
m3  #6 months, 4 coys

m1
m1[1,]  #first row
m1[, 2]  #first row
m1[,2 , drop=F]  #see it is as column
m1[1:2,]  #first 2 rows
m1[1, 2:3] #first row, 2 to 3rd col
m1[c(1,3),c(2,3,5)]  #what is this
m1[,-1]  #skip first column
m1[,-c(2,5)]  #what is this

m1[1:24]  #matrix as vector
class(m1[1:24])  #integer


m1
m1 > 5 & m1 < 15  #which position are True & False
m1[m1 > 5 & m1 < 15] # values
m1[m1 > 5 & m1 < 15] = 100  #change values
m1

#join matrics
cbind(m1, m3)  #matching row & cols numbers
cbind(m1, m2)
rbind(m1,m2)

#stats
m1
colSums(m1)
rowSums(m1)
colMeans(m1)
rowMeans(m1)

m3
t(m3)  #transpose
sweep(m3, MARGIN=1, STATS=c(2,3,4,5,6,7), FUN='+')  #row + x
sweep(m3, MARGIN=2, STATS=c(1.2,1.3,1.4,1.5), FUN='*')  #col * x

#addmargins
m3
addmargins(m3, margin=1, sum)  #create a row with sum of columns
addmargins(m3, margin=2, sum)  #create a col with sum of rows
addmargins(m3, margin=c(1,2), mean)
M3functions = addmargins(m3, margin=c(1,2), list(list(mean, sum, max, min), list(var, sd, max, min)))
M3functions
round(M3functions,1)


#array is more than 2 dim matrix, same data type
4*3*2
length(100:123)
#2 coy, 3 products, 4 months
(a1 = array(100:123, dim=c(4,3,2)))
dimnames(a1) = list(list('Jan','Feb','Mar','Apr'), list('P1','P2','P3'), list('Coy1','Coy2'))
a1

apply(a1, MARGIN=1, FUN=sum)  #rows : month sales
apply(a1, MARGIN=2, FUN=sum)  #cols : product sales
apply(a1, MARGIN=3, FUN=sum)  #matrix : coy sales

apply(a1, MARGIN=c(2,3), FUN=sum)  #cols : product  & coy sales
apply(a1, MARGIN=c(1,3), FUN=mean)

#matrix & array - > 2 dim
# same datatype


================================================
FILE: 01-IIM/11e4_dataframe1.R
================================================
#Data Frame in R

#A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column. ... The data stored in a data frame can be of numeric, factor or character type.

mtcars
class(mtcars)

#lets us create a DF of 50 students
sizeN=50
#rollno, name, gender, grade, marks1, marks2
(rollno = 1:50)
(name = paste('Student', 101:150, sep='_'))
sex = c('M','F')
(gender = sample(x=sex, size=sizeN, replace=T, prob=c(.6,.4)))
table(gender)
(courses = c('BBA', 'MBA'))
(course = sample(x=courses, size=sizeN, replace=T, prob=c(.4,.6)))

(gradetypes = LETTERS[1:4])
(grades = sample(x=gradetypes, size=sizeN, replace=T, prob=c(.2,.3,.4, .1)))
table(grades)
(marks1 = trunc(rnorm(n=sizeN, mean=75, sd=5 ))) 
(marks2 = trunc(runif(n=sizeN, min=60, max=95 ))) 

vectorList = list (rollno, name, gender, course, grades, marks1, marks2)
mapply(length, vectorList)  #all are of length 50

#df
df1 = data.frame(rollno, name, gender, course, grades, marks1, marks2)
head(df1) #first 6
tail(df1,n=5)  #last 5 
class(df1)  #dataframe
summary(df1)  #summarys
str(df1)  #structure of data
attributes(df1)
names(df1) # names of cols
dim(df1) # dimensions 50 * 6
length(df1)  #no of columns

#selected 
df1[1:4, 1:5]
df1[1:5, c('rollno', 'name', 'gender')]
df1[df1$gender == 'M', c('rollno', 'name','gender')]
df1[df1$gender == 'M' & df1$marks1 > 80, c('rollno', 'name','gender')]
df1[df1$grades %in% c('A','C') & df1$marks2 > 80 & df1$marks1 < 75, c('rollno', 'name','gender', 'grades', 'marks1', 'marks2')]

#summary
aggregate(df1$marks1, by= list(df1$gender), FUN=max)
aggregate(cbind(marks1, marks2) ~ gender + course , data=df1, FUN=mean)

#other
df1[order(df1$marks1),] #sort by marks1
df1[order(df1$gender, df1$marks1),] #sort by gender marks1

#factors
(genderF = factor(gender))  #nominal data
(courseF = factor(course))  #course data
(gradesOF = factor(grades, ordered=T, levels = c('C','B','A','D')))
#D is highest

barplot(table(grades), col=1:4)
barplot(table(gradesOF), col=1:4)

summary(df1)

df2 = data.frame(rollno, name, genderF, courseF, gradesOF, marks1, marks2)
summary(df2)
#see the summary statistics


================================================
FILE: 01-IIM/12a3-impexp1.R
================================================
# Read Data into R Environment
#CSV Files---- local or network
#Read from CSV file in PC
head(mtcars)
rownames(mtcars)
write.csv(mtcars, "./data/mtcarsF.csv", row.names=F) #without rownames
write.csv(mtcars, "./data/mtcarsT.csv", row.names=T) #with rownames

head(iris)
write.csv(iris, "./data/irisF.csv", row.names=F) #without rownames
write.csv(iris, "./data/irisT.csv", row.names=T)  #with rownames

#go to folder data and open these csv files
read.csv(file='./data/irisF.csv')

readiris = read.csv(file="./data/irisF.csv", header = TRUE,sep = ",")
readiris
readmtcars = read.csv(file="./data/mtcarsF.csv", header = TRUE,sep = ",")
head(readmtcars)
str(readmtcars)
class(readmtcars)
head(readmtcars)

read2 = read.table(file="./data/iris.csv", header = TRUE,sep = ",")
str(read2); class(read2)
head(read2)
read3 = read.delim(file="./data/iris.csv", header = TRUE,sep = ",")
str(read3) ; class(read3)
head(read3)

#difference is use of specify delimeter(read.csv takes default as comma)
#or location is different from Project Folders, or want to search for the file
read4 = read.csv(file=file.choose())
str(read4)
head(read4)


# From URL : Read CSV from Web----
read_web1 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')
head(read_web1)

library(data.table)
read_web2 = fread("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv")
head(read_web2)
class(read_web2)

#Text file from Web-----
read_txt = read.table("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/test.txt", header = FALSE)
head(read_txt)

#other ways - google sheets, excel
#end here

================================================
FILE: 01-IIM/12a4-datasets.R
================================================
#data sets in R
#https://lgatto.github.io/IntroMachineLearningWithR/example-datasets.html#edgar-andersons-iris-data

#Iris
data(iris)
data(mtcars)
#
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("MSnbsase", "pRoloc")) ## software
biocLite("pRolocdata") ## date

library("ggplot2")
data(diamonds)

library("mlbench")
data(Sonar)

#housing values
library("MASS")
data(Boston)

#customer churn
library("C50")
data(churn)
dim(churnTrain)


pacman::p_load(datarium)
data(package='datarium')
data('marketing', package='datarium')
marketing
#it is a dataset containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. The first three columns are the advertising budget in thousands of dollars along with the fourth column as sales. The advertising experiment has been repeated 200 times. Hence, it has 200 rows.

================================================
FILE: 01-IIM/12a4-impexp-xls.R
================================================
# Read Data into R Environment - to/fro XLS

#Excel----
#Create a excel file with data in 2 sheets
# first row contains variable names
#C:\Program Files\Java\jre1.8.0_261
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_261')
library(xlsx)
library(rJava)
write.xlsx2(mtcars, file='./data/mtcars.xlsx', sheetName = 'mtcars1', row.names=F, append=F)
df_excel1 = read.xlsx( "./data/mtcars.xlsx", 1)
df_excel1

head(iris)
write.xlsx2(iris, file='./data/mtcars.xlsx', sheetName = 'iris1', row.names=F, append=T)
df_excel2 = read.xlsx( "./data/mtcars.xlsx", 2)
df_excel2

write.xlsx2(iris, file='./data/mtcars.xlsx', sheetName = 'iris2', row.names=F, append=T)
df_excel2b = read.xlsx( "./data/mtcars.xlsx", 'iris2')
df_excel2b
#see the excel sheet in data folder: windows explorer

library(readxl)  #for reading only, faster
readxl::excel_sheets("./data/mtcars.xlsx")  #names of sheets in workbook
df_rxl1 <- readxl::read_excel( "./data/mtcars.xlsx", 'iris1')
df_rxl2 <- readxl::read_excel( "./data/mtcars.xlsx", 2)

head(df_rxl1)
head(df_rxl2)
readxl::read_excel( "./data/mtcars.xlsx", 2, skip=1)


?`readxl-package`
?read_excel
#end here

================================================
FILE: 01-IIM/12e4-impexp-gs.R
================================================
#import Data from Google Sheets

#google sheets #publically shared
url1 = 'docs.google.com/spreadsheets/d/1I9mJsS5QnXF2TNNntTy-HrcdHmIF9wJ8ONYvEJTXSNo'

library(gsheet)  #public gsheets
data1 = gsheet2tbl(url1)
head(data1)

url2 = "https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=216113907"

data2 = gsheet2tbl(url2)
data2

#private  gsheets
library(googlesheets4)
url1 = 'docs.google.com/spreadsheets/d/1I9mJsS5QnXF2TNNntTy-HrcdHmIF9wJ8ONYvEJTXSNo'
gsDF = googlesheets4::read_sheet(url1, sheet=1) #give permissions
#accept the popup
head(gsDF)



================================================
FILE: 01-IIM/13a2-NAvalues.R
================================================
# Missing values

#missing values are indicate NA  keyword
x = c(1, ,3)  #wrong way to create missing values
x = c(NA, 1, NA, 2,3, NA)  #introducing missing values
x
x1=x  #make a copy
x1

is.na(x)
sum(is.na(x))
sum(c(T,F,T,F,F))
x
mean(x)  #this will not work if NA values are present

?mean
mean(x, na.rm=T)
(1+2+3)/3

sum(x,na.rm=T)/3
x
x[is.na(x)]  #list out missing values
mean(x, na.rm=T)
x[is.na(x)] = mean(x, na.rm=T)  #replace these missing values with mean of other values
x
cbind(x,x1)  #see where values have been filled
x1 = c(4,6,8,9)
length(x1[x1 >= 6])
sum(x1 >= 6)
x1 >= 6

x2 = rnorm(100000, mean=50, sd=5)  #normal distributed values
x2
length(x2)
posn=sample(100000, size=30)
posn
x2[posn] = NA
summary(x2)
is.na(x2)
sum(is.na(x2))
mean(x2)
mean(x2, na.rm=T)
x2[is.na(x2)] = mean(x2, na.rm=T)
sum(is.na(x2))

#install this library
library(VIM)
?sleep
data(sleep, package='VIM')
head(sleep) #first few rows of sleep
dim(sleep)  #dimensions of sleep data
complete.cases(sleep)  # which row have complete data in T/ F
sum(complete.cases(sleep))  # no of rows have which no missing data
sum(!complete.cases(sleep))  # no of rows which have missing data
sleep[complete.cases(sleep),]  #rows which are complete 42
sleep[!complete.cases(sleep),] #rows which have missing values 20
summary(sleep)
colSums(is.na(sleep))  #which column how many data missing
rowSums(is.na(sleep))  #which row how many data missing
head(sleep)
df= sleep #make a copy of sleep data
complete.cases(df) #complete cases
mean(df$Dream, na.rm=T) #mean of Dream Col
sum(is.na(df$Dream))
sum(!is.na(df$Dream))
df$Dream
summary(df)
df[ , 4:5]; df[ ,c(1,3,5)]
df[is.na(df$Dream), 'Dream'] #missing values in Dream Column
df[is.na(df$Dream), "Dream"] = mean(df$Dream, na.rm=T)  #find and replace
df$Dream
sum(df$Dream == 1.972)
#use mice package
library(mice)

#https://www.analyticsvidhya.com/blog/2016/03/tutorial-powerful-packages-imputing-missing-values/
#MICE, Amelia, missForest, Hmisc, mi



================================================
FILE: 01-IIM/13b2-outliers.R
================================================
#Outliers
#https://www.statsandr.com/blog/outliers-detection-in-r/
#outliers--------------------------
#An outlier is a value or an observation that is distant from other observations, that is to say, a data point that differs significantly from other data points

data <- ggplot2::mpg
head(data)
data$hwy
summary(data$hwy)
range(data$hwy)

(breaks1 = sqrt(nrow(data)))  #no of bins/ break point
hist(data$hwy,  xlab = "hwy",   main = "Histogram of hwy",  breaks = breaks1)

#ggplot2
library(ggplot2)

ggplot(data) +  aes(x = hwy) + geom_histogram(bins = 30L, fill = "#0c4c8a") + theme_minimal()
# there seems to be a couple of observations higher than all other observations (see the bar on the right side of the plot).


#boxplot-----
boxplot(data$hwy,  ylab = "hwy")
ggplot(data) +  aes(x = "", y = hwy) +   geom_boxplot(fill = "#0c4c8a") + theme_minimal()
#there are 2 potential outliers (see the 2 points above the vertical line, at the top of the boxplot).
#box plot visualizes a quantitative variable by displaying five common location summary (minimum, median, first and third quartiles and maximum) and any observation that was classified as a suspected outlier using the interquartile range (IQR) criterion. The IQR criterion means that all observations < Q1 - 1.5IQR and > Q3 + 1.5IQR are classified as potential outliers

#IQR is the difference between the third and first quartile) are considered as potential outliers by R. 
#extract the values of the potential outliers based on the IQR criterion
boxplot.stats(data$hwy)$out
#row no
out <- boxplot.stats(dat$hwy)$out
out_ind <- which(dat$hwy %in% c(out))
out_ind
dat[out_ind, ]  #complete data

#print the values
boxplot(dat$hwy,ylab = "hwy", main = "Boxplot of highway miles per gallon"
)
mtext(paste("Outliers: ", paste(out, collapse = ", ")))


#This method of outliers detection is based on the percentiles. With the percentiles method, all observations that lie outside the interval formed by the 2.5 and 97.5 percentiles will be considered as potential outliers. Other percentiles such as the 1 and 99, or the 5 and 95 percentiles can also be considered to construct the interval.
#The values of the lower and upper percentiles (and thus the lower and upper limits of the interval) can be computed with the quantile() function:
(lower_bound <- quantile(data$hwy, 0.025))
(upper_bound <- quantile(dat$hwy, 0.975))
#all observations below 14 and above 35.175 will be considered as potential outliers. The row numbers of the observations outside of the interval can then be extracted with the which() function:
(outlier_ind <- which(data$hwy < lower_bound | data$hwy > upper_bound))
#values of highway miles per gallon can be printed:
data[outlier_ind, "hwy"]
data[outlier_ind, ]

#There are 11 potential outliers according to the percentiles method. To reduce this number, you can set the percentiles to 1 and 99:
  
(lower_bound <- quantile(data$hwy, 0.01))
(upper_bound <- quantile(data$hwy, 0.99))

(outlier_ind <- which(dat$hwy < lower_bound | dat$hwy > upper_bound))


#more
#https://www.statsandr.com/blog/outliers-detection-in-r/#introduction
#https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
#https://www.r-bloggers.com/2020/01/how-to-remove-outliers-in-r/
#https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba

================================================
FILE: 01-IIM/15a2-GPH-basic.R
================================================
# Basic plots
#plot, histogram, pie, boxplot, linechart, correlation plot

#plot
women
plot(women)

(x= seq(1,10))
(x =c(1,2,3,4,5,6,7,8,9,10))
y= seq(11,20)
plot(x,y)

plot(x=women$height, y=women$weight)
plot(women)
plot(mtcars)
pairs(mtcars[,1:3])
?plot

plot(women, type='p', pch=4, col='blue')
plot(women, type='l')
plot(women, type='b')
plot(women, type='b', pch=18, lty=1, col=2, lwd=4)
plot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)

#more features with plot
plot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title', sub='Sub Title')
#see cheat sheet on base graphs
mtcars$cyl
#plot(x=mtcars$wt, y=mtcars$mpg, col=mtcars$gear, pch=c(4,6,8), cex=c(1,2))
#as.numeric(levels(as.factor(mtcars$cyl)))
plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)

#boxplot-----
str(women)
boxplot(women$height)
summary(women$height)

abline(h=c(58, 62,65,68,72))
text(x=1.2, y=c(58, 62,65,68,72) ,c('Min','1Q', 'Median','Mean','3Q','Max'), col='red' )

x= rnorm(100000, mean=40, sd=5)
boxplot(x, notch=T)
(sum1= as.vector(summary(x)))
abline(h=sum1)
text(x=1.3, y=sum1 ,c('Min','1Q', 'Median','Mean','3Q','Max'), col='red' )

hist(x)
range(x)
hist(x, breaks=c(0,40,50,60,100))
plot(density(x))

?hist
#draw lines on plot for number summary
summary(women)
quantile(women$height)
quantile(women$height, seq(0,1,.1))
quantile(women$height, seq(0,1,.01))
stem(women$height)
boxplot(women$height, col='green')
abline(h=quantile(women$height))
text(1, quantile(women$height), labels=c('min','1Q','median','3Q','max'))

#histogram
hist(women$height)
hist(women$height, breaks=10)
hist(women$height, breaks=5, col=1:5)

#histogram2
(x = rnorm(100,50,10))
hist(x)

hist(x, freq=F, col=1:10)
lines(density(x))

#density plot : shape of data
plot(density(x), col='red')

#pie
gender= sample(c('M','F'), size=100, replace=T)
table(gender)
pie(table(gender))
x = c(10,20,40,50)
pie(x)
xlabels = c('A ','B ','C ','D ')
x/sum(x)
(labels2 = paste(xlabels, round(x/sum(x),2) * 100 , sep='-'))
(labels3 = paste0(labels2,"%%"))
(labels2 = paste0(xlabels, round(x/sum(x),2) * 100, '%'))

pie(x, labels=labels2)
x
#barplot
barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

#correlation plot
pairs(women)
cor(women$height,women$weight)
cov(women$height, women$weight)
head(mtcars)
?mtcars

cor(mtcars)
names(mtcars)
pairs(mtcars)
pairs(mtcars[1:4])
options(digits=4)
pairs(mtcars[c('mpg', 'wt','hp')])



================================================
FILE: 01-IIM/15a3-GPH-graphs.R
================================================
# Combined Plots

#plot, histogram, pie, boxplot, linechart, correlation plot

#plot
women
?women
str(women)
plot(women)
plot(x=women$height, y=women$weight)
?plot
plot(women, type='p', pch=17)
plot(women, type='l')
plot(women, type='b', pch=18, lty=2, col=2)
plot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)
data()

#more features with plot
plot(y=women$height, x=women$weight, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title- PDU', sub='Sub Title')
#see cheat sheet on base graphs

plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)
abline(h = c(130, 150), col='green') 
abline(v=c(62, 66, 70), col='blue')
abline(v=women$height, col='purple')

#boxplot
boxplot(women$height)
boxplot(df$marks1)

abline(h=c(58, 62,65,68,72))

#draw lines on plot for number summary
summary(women)
quantile(women$height)
boxplot(women$height, col='green')
abline(h=quantile(women$height))


#histogram
hist(women$height)
hist(women$height, breaks=10)
hist(women$height, breaks=5, col=1:5)
hist(df$marks2, breaks=3)
#histogram2
?rnorm
x = rnorm(n=100000,mean=50,sd=10)
hist(x)

hist(x, freq=F, col=1:5)
lines(density(x))

#density plot : shape of data
plot(density(x), col='red')


#pie
x = c(10,20,40,50)
pie(x)
xlabels = c('A','B','C','D')
pie(x, labels=xlabels)
pie(x, labels=paste(round(x/sum(x) * 100,0),'%') )

x
#barplot
barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

#correlation plot
pairs(women)
cor(women$height,women$weight)

names(mtcars)
cor(mtcars)
pairs(mtcars)
options(digits=4)
pairs(mtcars[1:4])


================================================
FILE: 01-IIM/15a4-GPH-advgraphs.R
================================================
#Advanced Graphs
library(corrgram)
cor(mtcars[1:4])
corrgram(mtcars[1:4], order=TRUE, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt,    main="Car Milage Data in PC2/PC1 Order")

#alternative of box plot

boxplot(mpg ~ cyl, data=mtcars)

library(corrplot)
relationship=cor(mtcars)
relationship
corrplot(relationship)
corrplot(relationship, type="upper")

#-----

library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
x1; x2; x3
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"),   col="gold")
title("Violin Plots of Miles Per Gallon")
abline(h=c(15,20))


================================================
FILE: 01-IIM/16b1-GPH-wordcloud.R
================================================
#Word Cloud
#word & Freq; built in data sets

#-----
# World Cloud
#http://stat.ethz.ch/R-manual/R-devel/library/base/html/strsplit.html
#https://stackoverflow.com/questions/4350440/split-a-column-of-a-data-frame-to-multiple-columns

library(stringr)
library(wordcloud)
library(RColorBrewer)
library(tm)
library(SnowballC)
library(RCurl)
library(XML)


#How to use strsplit
strsplit('IIT-Gawahati', "-")
strsplit('IIT Gawahati', " ")

library(gsheet)
url = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=0'
#check for dashtype, they are not always same
df1 = as.data.frame(gsheet2tbl(url))
df1
head(df1)
df1$wordexplanation
base::strsplit(df1$wordexplanation,'-')  #output as list need in DF

#Different Methods split and put in dataframe objects

#Stringr - str_split
stringr::str_split('IIT-Guwahati', "-")

#Method
(out1 = strsplit(as.character('IIT - Guwahati'),'-')) #if the word was not character
head(df1)
out = strsplit(df1$wordexplanation,'-') 
head(out)

t(sapply(out[1:5], '['))
df2=data.frame(t(sapply(out[1:200], '[')))

head(df2)
df= cbind(df1,df2)
head(df)
names(df)[c(2,3)] = c('word','explanation') #rename columns
head(df)

# create random frequencies for all words in a col
df$freq = floor(runif(100, 1,100))
head(df)

#Method for word cloud
library(wordcloud)
par(mar = c(1, 1, 1, 1))
wc1 = wordcloud(df$word, df$freq, random.order=T)
par(mar = c(1, 1, 1, 1))
wc2 = wordcloud(df$word, df$freq, random.order=F, colors = topo.colors(10))
par(mar = c(1, 1, 1, 1))
wc3 = wordcloud(df$word, df$freq, scale=c(4,.1), min.freq=2, max.words=50,  random.order=F)
wc4 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F)


#------

library(RColorBrewer)
library(tm)
pal <- brewer.pal(8,"Dark2")
wc5 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F, colors=pal)


#load the function rquery.wordcloud
source('http://www.sthda.com/upload/rquery_wordcloud.r')

res <- rquery.wordcloud(filePath, type ="file", lang = "english",min.freq = 1,  max.words = 200)

# Reds color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english",colorPalette = "Reds")
# RdBu color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english", colorPalette = "RdBu")
# use unique color
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  colorPalette = "black")


tdm <- res$tdm
freqTable <- res$freqTable
# Show the top10 words and their frequency
head(freqTable, 10)

# Bar plot of the frequency for the top10
barplot(freqTable[1:10,]$freq, las = 2, 
        names.arg = freqTable[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")

findFreqTerms(tdm, lowfreq = 4)

findAssocs(tdm, terms = "freedom", corlimit = 0.3)

url = "http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software"
rquery.wordcloud(x=url, type="url")


#------
#wordcloud2

#install.packages('wordcloud2')
library(wordcloud2)
df = data.frame(word=c('mdi','iim','imt'),freq=c(20,23,15))
df
par(mar=c(0,0,0,0))
wordcloud2(df)

head(demoFreq)
dim(demoFreq)
par(mar=c(0,0,0,0))
wordcloud2(demoFreq)

par(mar=c(0,0,0,0))
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")

names(demoFreq)
par(mar=c(0,0,0,0))
wordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6,   rotateRatio = 1)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = pi/6,    rotateRatio = 0.9)
par(mar=c(0,0,0,0))
wordcloud2(demoFreqC, size = 2,  color = "random-light", backgroundColor = "grey")
wordcloud2(demoFreqC, size = 2, minRotation = -pi/6, maxRotation = -pi/6,  rotateRatio = 1)

# Color Vector
?wordcloud2
colorVec = rep(c('red', 'skyblue'), length.out=nrow(demoFreq))
wordcloud2(demoFreq, color = colorVec, fontWeight = "bold")

wordcloud2(demoFreq, color = ifelse(demoFreq[, 2] > 20, 'red', 'skyblue'))


#Example2 -----
#Word Cloud 2

#(https://www.r-graph-gallery.com/the-wordcloud2-library/)

# library : install it first
library(wordcloud2) 

# have a look to the example dataset
head(demoFreq)
dim(demoFreq)
str(demoFreq)
#wordcloud
wordcloud2(demoFreq, size=1.6)
head(demoFreq[order(-demoFreq$freq),])
?wordcloud2

#create your own set of words
word = c('marketing','consumer', 'dhiraj','price','business','iimkashipur', 'sunder','vignesh', 'jyoti','finance', 'operations')
freq = c(30,20,15,36,15,13,11,44,13,44,34)
df1 = data.frame(word, freq)
#rownames(df1)= word
head(df1)
#df1 = head(demoFreq)
wordcloud2(df1, size=.4)
?wordcloud2
# Gives a proposed palette
wordcloud2(demoFreq, size=1.6, color='random-dark')
wordcloud2(df1, size=1, color='random-dark')

# or a vector of colors. vector must be same length than input data
wordcloud2(demoFreq, size=1.6, color=rep_len( c("green","blue"), nrow(demoFreq) ) )
# Change the background color
wordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor="black")


# Change the shape:
wordcloud2(demoFreq, size = 0.7, shape = 'star')
#It is possible to change the shape of the wordcloud. Several shapes are available within the package: ‘circle’ (default), ‘cardioid’, ‘diamond’ (alias of square), ‘triangle-forward’, ‘triangle’, ‘pentagon’, and ‘star’).
wordcloud2(df1, size = 0.7, shape = 'pentagon')


#It is also possible to use any image you have as a mask! Just insert the image in the current working directory and use it as in the code below
# Change the shape using your image - not working
#wordcloud2(demoFreq, figPath = "peace.png", size = 1.5, color = "skyblue", backgroundColor="black")


#rotation
ww=wordcloud2(demoFreq, size = 2.3, minRotation = -pi/6, maxRotation = -pi/6, rotateRatio = 1)
ww

#chinese
demoFreqC
head(demoFreqC)
wordcloud2(demoFreqC, size = 2, fontFamily = "微软雅黑", color = "random-light", backgroundColor = "grey")

wordcloud2(demoFreqC, size = 2, color = "random-light", backgroundColor = "grey")
?demoFreqC

#Try this... not working as of now
#https://unicode.org/charts/PDF/U0900.pdf :  0900–097F
V2 = c('<U+9000><U+9001>', '<U+9002><U+9003>', '<U+900D><U+900F>', '<U+6570><U+636E>')
V1 = c(2000, 1000, 500,1000)
hindi = data.frame(V2, V1)
hindi
wordcloud2(hindi, size = 2, color = "random-light", backgroundColor = "grey")



#notworking : clear the plot area

#The lettercloud function allows to use a letter or a word as a shape for the wordcloud.
letterCloud( demoFreq, word = "R", color='random-light' , backgroundColor="blue")
letterCloud( demoFreq, word = "PEACE", color="white", backgroundColor="pink")


#see the link : https://www.r-graph-gallery.com

#Example3 -----
#wordcloud2

#install.packages('wordcloud2')
library(wordcloud2)

#eg1
df = data.frame(word=factor(c('mdi','iim','imt','fms')),freq=c(20,23,105, 30))
df
wordcloud2(df)

#built in data set
head(demoFreq)
nrow(demoFreq)
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")
names(demoFreq)
wordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6,   rotateRatio = 1)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = pi/6,    rotateRatio = 0.9)

wordcloud2(demoFreqC, size = 2,  color = "random-light", backgroundColor = "grey")
wordcloud2(demoFreqC, size = 2, minRotation = -pi/6, maxRotation = -pi/6,  rotateRatio = 1)

# Color Vector

colorVec = rep(c('red', 'skyblue'), length.out=nrow(demoFreq))
wordcloud2(demoFreq, color = colorVec, fontWeight = "bold")

wordcloud2(demoFreq, color = ifelse(demoFreq[, 2] > 20, 'red', 'skyblue'))


#
#eg2 : subset these words
str(demoFreq)
df3 = demoFreq[ sample(1:nrow(demoFreq), size=10, replace=T), ]
df3
wordcloud2(df3)
df3=fix(df3)
df3
wordcloud2(df3)



#Example4------
##http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know

#https://www.r-graph-gallery.com/196-the-wordcloud2-library/

# library #install this
library(wordcloud2) 

# have a look to the example dataset
head(demoFreq)
par(mar = c(1, 1, 1, 1))
wordcloud2(demoFreq, size=1)
#create your set of words and freq
df = data.frame(word=c('IIMS','IIMK','IIMKPV','IIMA','IIMC'),freq=c(70,13,35,40,21))
df
wordcloud2(df, size = 1)



# Gives a proposed palette
wordcloud2(demoFreq, size=1.6, color='random-dark')

# or a vector of colors. vector must be same length than input data
wordcloud2(demoFreq, size=1.6, color=rep_len( c("green","blue"), nrow(demoFreq) ) )

# Change the background color
wordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor="black")

# Change the shape:
wordcloud2(demoFreq, size = 0.7, shape = 'star')
head(demoFreq)
?wordcloud2
# Change the shape using your image
wordcloud2(demoFreq, figPath = "india.jpg", size = 1.5, color = "skyblue", backgroundColor="black")



#myeg-----
word = factor(c('Assets', 'Liabilities', 'Expenses', 'CashFlow', 'BottomLine','ProfitandLoss', 'Strategy', 'Planning', 'Forecast','Business'))
length(word)
(freq= c(27,35,32,31,33,24,26,27,28,29))
#freq = as.integer(runif(length(word), 50,100))
(df2 = data.frame(word, freq))
row.names(df2) = word

head(df2)
wordcloud2(df2)
wordcloud2(df2, color = ifelse(df2[, 2] > 30, 'red', 'skyblue'))



================================================
FILE: 01-IIM/16e0-GPH-wordcloud.R
================================================
#Word Cloud in R

library(wordcloud)

?wordcloud
#wordcloud(words,freq,scale=c(4,.5),min.freq=3,max.words=Inf, random.order=TRUE, random.color=FALSE, rot.per=.1, colors="black",ordered.colors=FALSE,use.r.layout=FALSE, fixed.asp=TRUE)
(words1 = c(letters, LETTERS, 0:9))
length(words1)
(freq1 = seq(1, 1000, len = 62))
wordcloud(words= words1, freq=freq1)

(words2 = c('Dhiraj','Vishnu', 'Nayak', 'Aishwarya', 'Kavita'))
(freq2 = runif(n=length(words2), min=10, max=50))
par(mar=c(0,0,0,0))
cbind(words2, freq2)
wordcloud(words= words2, freq=freq2)

#another library - data frame
library(wordcloud2)
?wordcloud2  
#wordcloud2(data, size = 1, minSize = 0, gridSize =  0, fontFamily = 'Segoe UI', fontWeight = 'bold', color = 'random-dark', backgroundColor = "white", minRotation = -pi/4, maxRotation = pi/4, shuffle = TRUE, rotateRatio = 0.4, shape = 'circle', ellipticity = 0.65, widgetsize = NULL, figPath = NULL, hoverFunction = NULL)

wordcloud2(demoFreq)
wordcloud2(demoFreq, size = 2)

wordcloud2(demoFreq, size = 1,shape = 'pentagon')
wordcloud2(demoFreq, size = 1,shape = 'star')

wordcloud2(demoFreq, size = 2,  color = "random-light", backgroundColor = "grey")

defect = paste('defect', 1:100, sep='_')
freq = round(rnorm(n=100, mean=40, sd=10))
quality = data.frame(defect, freq)
wordcloud2(quality, size = 1,shape = 'star')


(iimstudents = data.frame(word=words2, freq=freq2))
par(mar=c(0,0,0,0))
wordcloud2(iimstudents, size=.5, shuffle = F)


#https://cran.r-project.org/web/packages/wordcloud2/vignettes/wordcloud.html
#https://www.r-graph-gallery.com/wordcloud.html

================================================
FILE: 01-IIM/16e3-GPH-wordcloud-text1.R
================================================
# World Cloud 2
library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

# Read the text file from file
#text = readLines(file.choose())
text = readLines(con= file("iim.txt"))
text
text[1]
# Load the data as a corpus
docs = Corpus(VectorSource(text))
docs

#Text transformation
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
docs
#Cleaning Text
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
docs
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
docs <- tm_map(docs, stemDocument)


#Document Matrix
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Generate Word Cloud
set.seed(1234)
par(mar=c(0,0,0,0))
wordcloud(words = d$word, freq = d$freq, min.freq = 1,    max.words=200, random.order=FALSE, rot.per=0.35,        colors=brewer.pal(8, "Dark2"))

findFreqTerms(dtm, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
head(d, 10)

#Plot Freq
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,       col ="lightblue", main ="Most frequent words",    ylab = "Word frequencies")

#Example2------

# Word Cloud

##http://dni-institute.in/blogs/colorful-word-cloud-using-r/
# tm for text mining
# SnowballC for text stemming
# wordcloud for generating word cloud images
# RCurl and XML packages to download and parse web pages
# RColorBrewer for color palettes


library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

source('http://www.sthda.com/upload/rquery_wordcloud.r')
#or #source('./TM/rquery_wordcloud.R')
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
#filePath2 <- "./data/martin-luther-king-i-have-a-dream-speech.txt"

res<-rquery.wordcloud(filePath, type ="file", lang = "english")
#res<-rquery.wordcloud(filePath2, type ="file", lang = "english")



================================================
FILE: 01-IIM/16e4-GPH-wordcloud-text2.R
================================================
#Word Cloud Text File

Step 1 : Install and load the required packages
# Load
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")

#Step 2: Create/Read a text file------
#read from a text file 
#text <- readLines(file.choose())
#or
# Read the text file from internet
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text <- readLines(filePath)

#Step 3 : Text mining------
# Load the data as a corpus
docs <- Corpus(VectorSource(text))
docs
inspect(docs)

#replace certain characters
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
docs
inspect(docs)

# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)

#Step 4 : Build a term-document matrix-----
#Document matrix is a table containing the frequency of the words. Column names are words and row names are documents. The function TermDocumentMatrix() from text mining package can be used as follow :
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Step 5 : Generate the Word cloud-----
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,  max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
#words : the words to be plotted
# freq : their frequencies
# min.freq : words with frequency below min.freq will not be plotted
# max.words : maximum number of words to be plotted
# random.order : plot words in random order. If false, they will be plotted in decreasing frequency
# rot.per : proportion words with 90 degree rotation (vertical text)
# colors : color words from least to most frequent. Use, for example, colors =“black” for single color.
#Misc
findFreqTerms(dtm, lowfreq = 4)
#correlation of freedom with 
findAssocs(dtm, terms = "freedom", corlimit = 0.3)

#barplot of words
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,  col ="lightblue", main ="Most frequent words",  ylab = "Word frequencies")

#library wordcloud2
par(mar=c(0,0,0,0))
wordcloud2::wordcloud2(d)
wordcloud2::wordcloud2(d, shape='star')


#refer this
#http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know

================================================
FILE: 01-IIM/17a2-STATS-freqtable.R
================================================
# Frequency Distribution

#Discrete Cat Data
(attend = c('A','P','P','A','P','A'))
table(attend)
cbind(table(attend))  # A & P on left side

#Discrete Numeric Data

color=c('Blue','Green','Magenta','Green','Black','Blue','Black')
x2 = table(color)
x2
x2a = cbind(x2)
x2a
hist(x2a)  # not relevant 
barplot(x2a)  # not good
barplot(x2a, beside=T)  # Better
unique(color) ; length(unique(color))
barplot(x2a, beside=T, col=rainbow(length(unique(color))))
pie(x2a)

#Continuous Data 
set.seed(1234)
x3 = runif(100,0,150)  # 0 to 150 marks range, 100 values 
x3
x3 = ceiling(x3)  #round to higher value
x3
range(x3)
# Divide range into step of 15 ie 10 levels
breaks = seq(0,150,by=15)
breaks
length(breaks)
x3
#x3[1] = 60; x3[2] = 75
x3.cut = cut(x3, breaks)
x3.cut
table(x3.cut)
cbind(table(x3.cut))  #see it vertically

#give intervals a character values a, b..
(x3.cut = cut(x3, breaks, labels=letters[1:10]))
#(x3.cut = cut(x3, breaks, labels=letters[1:length(breaks)-1]))
x3.cut

(x3a = table(x3.cut))
(x3b = cbind(x3a))

#plot these freq Table: which is better
hist(x3b)
pie(x3b)
barplot(x3b, beside=T)
barplot(x3b, beside=T, names.arg =rownames(x3b))
plot(x3b)


# and so..on like previous eg




================================================
FILE: 01-IIM/17c1-STATS-datapartition.R
================================================
#partition the data into train and test set
mtcars
nrow(mtcars)
#train-70%, test-30%
(myvalues = 1:32)
selected = sample(x=myvalues, size=.7 * 32)
length(selected)

index = sample(x=1:nrow(mtcars), size=.7 * nrow(mtcars), replace=F)
index
mtcars[c(1,4),]  #first & 4th row no
mtcars[-c(1,4),] #exclude 1 & 4

train= mtcars[index,]
test= mtcars[-index,]
nrow(train)
nrow(test)
nrow(train) + nrow(test)

#
(rollnos = 1:50)
rollnos[rollnos > 20]
rollnos[10:15]
set.seed(1234)
(select = sample(x=rollnos, size=.75 * length(rollnos)))
length(select)
#
#40 students - M & F
(gender = c('M','F','M'))
(gender = sample(x=c('M','F'), size=40, replace=T))
(gender = sample(x=c('M','F'), size=10000, replace=T, prob=c(.6, .4)))
table(gender)
prop.table(table(gender))



#-------

## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)

train <- mtcars[train_ind, ]
test <- mtcars[-train_ind, ]

# -----
library(caTools)
set.seed(101) 
sample = sample.split(mtcars$am, SplitRatio = .75)
sample
train = subset(mtcars, sample == TRUE)
test  = subset(mtcars, sample == FALSE)
train
; test
nrow(train); nrow(test)
table(train$am); table(test$am)

mtcars$id <- 1:nrow(mtcars)
train <- mtcars %>% dplyr::sample_frac(.75)
test  <- dplyr::anti_join(mtcars, train, by = 'id')

library(caret)
intrain<-createDataPartition(y=factor(mtcars$am),p=0.7,list=FALSE)
intrain
training<-mtcars[intrain,]
testing<-mtcars[-intrain,]
training  
testing
table(training$am)
table(testing$am)


#data partition is used for modeling when you want to divide data in to parts - Train and Test.
#caret package is most popular


library(dplyr)
mtcars %>% group_by(am) %>% sample_n(5)
mtcars %>% group_by(am) %>% sample_n(.2)










================================================
FILE: 01-IIM/17d2-STATS-basicstats.R
================================================
# Basic Stats
x = ceiling(rnorm(10000, mean=60, sd=20))
mean(x)
median(x)
#there is no mode function for mode stats
table(x)
sort(table(x), decreasing=T)


#mode
library(modeest)
mlv(x,method='shorth')

#quantile
quantile(x)
quantile(x,seq(.1,1,by=.1)) #decile
quantile(x,seq(.01,1,by=.01)) #percentile

ccdlibrary(e1071)                    # load e1071 

plot(density(x))    #density plot
e1071::skewness(x)                # apply the skewness 
kurtosis(x)

sd(x); var(x)
cov(women$weight, women$height)
cor(women$height, women$height)

stem(x)

#Freq Table
library(fdth)  #fast way of creating FT
ftable1 = fdt(x)
ftable1



================================================
FILE: 01-IIM/18d1-DPLYR-mtcars1.R
================================================
#Data Summarisation using Dplyr 
#dataset - mtcars

#dplyr - mtcars
#install.packages('dplyr')
library(dplyr) #install this library


head(mtcars)
names(mtcars)

#Plan how would you like to summarise this dataset

#dplyr - mtcars
library(dplyr)
df = mtcars
df$carnames = rownames(mtcars)
df = cbind(cars=rownames(mtcars), mtcars)
df %>% mutate(add_c)
head(df)
head(df)
#Filter----
filter(df, cyl == 8) %>% select(carnames, mpg,cyl)

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)

#Select rows 
#by rownumber
filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

#slice-----
slice(mtcars, 1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))


#mutate----
#create new columns based on existing columns
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col

#GroupBy summary

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))
mtcars %>% group_by(am) %>% summarise(MEANMPG = mean(mpg), MAXWT= max(wt))
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)

#select rows on the basis of sample

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)

#Rows having least mpg (last 2)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))



================================================
FILE: 01-IIM/18d3-DPLYR-mtcars2.R
================================================
# Data Summarisation - dplyr
#Home work

#Data Summarisation using Dplyr 
#dataset - mtcars

#dplyr - mtcars
#install.packages('dplyr')
library(dplyr) #install this library

head(mtcars)
names(mtcars)

#Plan how would you like to summarise this dataset


#Filter----

filter(mtcars, cyl == 6)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))



#mutate----
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col
transmute(mtcars, displ_l = disp / 61.0237) #removes other cols
mutate(mtcars, cyl = NULL) #do not display cyl


#slice-----
slice(mtcars, 1L)
slice(mtcars, n())

slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

(by_cyl <- group_by(mtcars, cyl)) # ???
slice(by_cyl, 1:2)

#structure----
tbl_df(mtcars) # convert to tbl class
glimpse(mtcars)  # dense summary of tbl data
View(mtcars) # spreasheet like form base pacakge



#rownames----
df = tibble::rownames_to_column(df, var='cars')
df2 = mtcars
names(df)

#has
tibble::has_rownames(mtcars)
tibble::has_rownames(df)

#remove rownames  
#see without rownames
head(df2[1:5])
tibble::remove_rownames(df2)

#rowid as column
tibble::rowid_to_column(df, var = "rowid")

#column to rownames
head(df[1:5])
tibble::column_to_rownames(df, var = "cars")

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))


#summarise----
summarise(mtcars, mean(disp))  
summarise(group_by(mtcars, cyl), mean(disp)) 
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear) %>% summarise_all(c("min", "max"))
mtcars %>% group_by(am, gear) %>% summarise_all(funs(med = median))


mtcars %>% group_by(cyl, am) %>% summarise(max(mpg), mean(hp))
?mtcars
#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))





mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)


#------------------------------------
#unsortd----
dplyr::tbl_df(iris)
print(dplyr::tbl_df(mtcars), n=20)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=11)
tbl_df(mtcars) %>% print(n = Inf)
tbl_df(mtcars) %>% print(width = Inf)
tbl_df(mtcars) %>% as.data.frame(mtcars)

glimpse(mtcars)
df = mtcars
row.names(df) = NULL
df %>% select(mpg)
#head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
  filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)
distinct(mtcars)
df  = data.frame(a=c(2,2),b=c(2,2))
df
distinct(df)

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)
slice(mtcars,10:14)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))
#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

df = data.frame(marks=c(1,2,3,7,1))
cbind(df, dplyr::mutate_each(df, funs(min_rank)))

mtcars %>% lead() %>% lag()
dplyr::n(mtcars)
select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df
rename(df, marks2 = marks)
df %>% mutate(marks2 = marks + 2, marks3 = marks + 4)
df %>% transmute(marks2 = marks + 2, marks3 = marks + 4)

library(nycflights13)
data(flights)


destinations <- group_by(flights, dest)
destinations
summarise(destinations,
          planes = n_distinct(tailnum),
          flights = n()
)


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter


# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)



#gr

================================================
FILE: 01-IIM/18d4-DPLYR-dplyr.R
================================================
#dplyr - mtcars
library(dplyr)
#library(tidyverse)
#Filter----

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()-2))



#mutate----
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col
transmute(mtcars, displ_l = disp / 61.0237) #removes other cols
mutate(mtcars, cyl = NULL) #do not display cyl


#slice-----
slice(mtcars, 1L)
mtcars %>% slice(1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

(by_cyl <- group_by(mtcars, cyl)) # ???
slice(by_cyl, 1:2)
mtcars %>% group_by(cyl) %>% slice(1:2)
#structure----
tbl_df(mtcars) # convert to tbl class
glimpse(mtcars)  # dense summary of tbl data
View(mtcars) # spreasheet like form base pacakge

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt), min(wt))


#summarise----
summarise(mtcars, mean(disp))  
summarise(group_by(mtcars, cyl), mean(disp)) 
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))  %>% as.data.frame()
mtcars %>% group_by(am, gear)%>% summarise_all(list(med = median))



#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))



#summarise if : 
mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
str(iris)  #Species is a factor
iris %>% summarise_all(mean)

iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)


#------------------------------------
#unsorted----
dplyr::tbl_df(iris)  #all rows not displayed
print(dplyr::tbl_df(mtcars), n=20)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=11)
tbl_df(mtcars) %>% print(n = Inf)  #all rows
tbl_df(mtcars) %>% print(width = Inf)
tbl_df(mtcars) %>% as.data.frame(mtcars)

glimpse(mtcars)
df = mtcars
row.names(df) = NULL  #remove rownames
df %>% select(mpg)
#head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)

#distinct rows
distinct(mtcars)
(df3  = data.frame(a=c(2,2,3),b=c(2,2,1)))
distinct(df3)

#sampling
sample_frac(mtcars, 0.2, replace=F)
sample_n(mtcars, 2, replace=F)
#%>% select(mpg)
slice(mtcars,10:14)
sort(mtcars$mpg, decreasing = T)
top_n(mtcars,-2, mpg)  #least 2 mpg

select(mtcars, mpg) %>% arrange(desc(mpg))

#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

df= mtcars
df$names = rownames(mtcars)
head(df)
df %>% select(1:5,12) %>% arrange(mpg)

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

(df4 = data.frame(marks=c(1,2,2,3,7,1,100)))
cbind(df4, dplyr::mutate_all(df4, funs(min_rank)))

#shift the columns
mtcars %>% lead() 
mtcars %>% lag()
mtcars %>% summarise(n())

select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df

df = women
rename(df, HeightWomen = height)
df %>% mutate(height2 = height + 2, weight2 = weight + 4)
#does not show orginal columns
df %>% transmute(height2 = height + 2, weight2 = weight + 4)

library(nycflights13)
data(flights)
head(flights)
destinations <- group_by(flights, dest)
destinations
summarise(destinations, planes = n_distinct(tailnum), flights = n())


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter

#error ???????
# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)






================================================
FILE: 01-IIM/21a1-SLM-women.R
================================================
#topics ----
#factors, env, import/export. package install
#rep, recode, split, partition, subset, loops, cast & melt
#missing values. duplicates, apply
#graphs - bar, multiple line, pie, box, corrgram
# predict weight for certain height

women
head(women)
?women
dim(women)
dim(women)
plot(women)
cor(women)  #strength & direction of relations -1 to 0 to +1
cor(women$height, women$weight)
cov(women$height, women$weight)
#is height the cause of change in weight ???? - do linear regression
fit = lm(weight ~ height,data = women)
summary(fit)
range(women$height)
y = mx + c
weight = m * height + c
weight = 3.45 * height + - 87
#cannot do exterpoliation, only interpolate withing IV range

(ndata = data.frame(height = c(60.5, 70.5, 71.5)))
(predictedwt = predict(fit, newdata = ndata))
cbind(ndata, predictedwt)
3.45 * 60.5 + - 87
resid(fit)
fitted(fit)
cbind(women, fitted(fit), resid(fit))
#assumptions

plot(fit)

#multiple linear regression
head(mtcars)
df <- mtcars %>% select(mpg, wt, hp)
head(df)
fit2 = lm(mpg ~ wt + hp, data = df)
summary(fit2)
mpg = 37 + -3.87 * wt + - 0.03 * hp
37 + -3.87 * 2.620 + - 0.03 * 110
resid(fit2)
df$mpg
fitted(fit2)
cbind(df$mpg, fitted(fit2), resid(fit2), df$mpg - fitted(fit2))

#cpp, java.. object oriented - correlation
residuals(fit2)
df$mpg - fitted(fit2)  #predicted values
resid(fit2) == residuals(fit2)

plot(fit2)
women
df2 = women
df3 = rbind(df2, c(73, 500))
fit2b = lm(weight ~ height, data = df3)
summary(fit2b)
summary(fit)
plot(fit2b)
df3


================================================
FILE: 01-IIM/21a2-SLM-women.R
================================================
# Regression Analysis
# Simple Linear with 1 IV and 1 DV

data(women)
women
names(women)
str(women)

cov(women$height, women$weight)
#69 : which show positive relationship between height and weight

cor(women$height, women$weight)
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight, type='b')
#lm( y ~ x , data)
abline(lm(weight ~ height, data=women), col='red')
names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)  # creating a model
summary(fit1)  #summary of the Model

#Ho: (F Test) : No relationship between Y and any X
#Ha:  There is relationship between Y and at least one X
# p < 0.05 Reject Ho in favour of Ha
#linear relationship between X & Y axist

attributes(fit1)  #output of model

#coefficients
coef(fit1)
#p values for b0 & b1 are significant as it is < 0.05
#pvalue1 = 1.71e-09 < 0.05
#pvalue2 = 1.09e-14 < 0.05

coef(fit1)
#Y = -87 + 3.4 * X  # no extrapolations
(Y = -87 + 3.4 * 20)  # weight cannot be negative
range(women$height)
(Y = -87 + 3.4 * 58)
(Y = -87 + 3.4 * 61.5)

(Y = -87 + 3.4 * 72)
range(women$height)
women


#R^2
(summary(fit1))$r.squared  #0.991
(summary(fit1))$adj.r.squared  #0.9903
#99% of variation in weight is explained by Height
#Good Linear Model for Prediction


#FStats : p value
(summary(fit1))$fstatistic 
#pvalue : 1.09e-14  < 0.05 : Model exists
#There is at least 1 IV which explains variation in Y (DV)


#Prediction : for height = 65, 66

(new1 = data.frame(height=c(65,66,66.5)))
new1
(p1=predict(fit1, newdata = new1))
cbind(new1, p1)
#136.7333 140.1833

(new2 = data.frame(height=c(60,69)))
(p2=predict(fit1, newdata = new2))
cbind(new2, p2)

?predict
predict(fit1, newdata = new1, interval='confidence')
predict(fit1, newdata = new1, interval='prediction')

women
women$weight
fitted(fit1) # predicted values of all original Xs
predict(fit1, newdata = data.frame(women$height)) # same as above for single X

new3 = data.frame(women$height)  # DF using height of original data
cbind(women, fitted(fit1), residuals(fit1))  # compare women, predicted, errors
residuals(fit1)  # diff between actual and predicted values of weight
summary(fit1)
summary(residuals(fit1))

plot(fit1)
cor(fitted(fit1), women$weight)
summary(fit1)


================================================
FILE: 01-IIM/21a4-SLM-women.R
================================================

fit = lm(weight ~ height, data=women)
summary(fit)
range(women$height)
(ndata = data.frame(height= c(58.5, 60.7)))
(p = predict(fit, newdata = ndata))
cbind(ndata, p)
plot(fit)

sum((fitted(fit) - women$weight)^2)


================================================
FILE: 01-IIM/21a5-SLM-women-A.R
================================================
# Simple Linear Regression : Built in Data Set Women
# Check for assumptions of Regression in the data Set
women
?women
str(women)
fit = lm(weight ~ height, data=women)
?plot
#Initial Checks
cor(women$height, women$weight)
#there is Strong and Positive Relationship between height and weight
plot(women$height, women$weight)
par(mfrow=c(1,1))
plot(fit, which=2)
plot(women)

str(women)
head(women)
women[,2]
fitted(fit)
cbind(women, fitted(fit), predicted=3.45 * women$height - 87)
cbind(women, fitted(fit), residue=fitted(fit)-women$weight, resid(fit))
y = 3.4 * height - 87
plot(residuals(fit))
hist(women$height, breaks=4)
hist(residuals(fit))

hist(residuals(fit), freq=F)
lines(density(residuals(fit)))

#Prediction
ndata = data.frame(height=52.5)
predict(fit, newdata=ndata, type='response')


#Assumptions Regression
#Linearity----
#Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.
# component + residual plot 
plot(women$height, fitted(fit))
plot(residuals(fit) ~ fitted(fit))
plot(fit, which=1)
#residuals should be randomly distributed and not increase or decrease 


#Normality----
#Normality of residuals. The residual errors are assumed to be normally distributed.
plot(density(resid(fit)))
plot(fit, which=2)

#Homoscedasticity----
#Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity - opposite of heteroscedasticity)
plot(fit, which=3)
#No funnel shape, random distribution of residuals
plot(fit, which=4)

#Auto-Correlation----
library(car)
#Independence of residuals error terms. (Not dependent on previous values)
durbinWatsonTest(fit)
#pvalue < 0; Ho that there is no correlation (r2=0) is accepted

#Outliers
plot(fit, which=4)
summary(fit)
#potential outliers are highlighted 1, 14, 15 row
women[c(1,14,15),]

#Lets remove these values and then find R2
fit2 = lm(weight ~ height, data=women[-c(1,14,15),])
summary(fit2)
(summary(fit))$r.squared
(summary(fit2))$r.squared
AIC(fit, fit2) #lower value of AIC is better


#Potential Problems
#Non-linearity of the outcome - predictor relationships
#Heteroscedasticity: Non-constant variance of error terms.
#Auto Collinearity, Multi-collinearity
#Presence of influential values in the data that can be:
#Outliers: extreme values in the outcome (y) variable
#High-leverage points: extreme values in the predictors (x) variable

#All these assumptions and potential problems can be checked by producing some diagnostic plots visualizing the residual errors.

plot(fit1)


================================================
FILE: 01-IIM/21b1-SLM-sales.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion

#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
omni1 = data.frame(sales, price, promotion)
head(omni1)
str(omni1)

#2nd Method : CSV file
omni2 = read.csv(file.choose())

#3rd Method : gsheet 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
omni3 = as.data.frame(gsheet::gsheet2tbl(url))

#Make one of data frames active
omni = omni1
head(omni)

?lm  #see help of LM
#Simple Linear Model would look like this
slr1 = lm(formula = sales ~ price, data=omni) # sales depend on price of item
slr2 = lm(formula = sales ~ promotion, data=omni) # sales depend on promotion exp
summary(slr1)
summary(slr2)


#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
mlrmodel1 = lm(sales ~ price + promotion, omni)
#mlrmodel1 = lm(omni, sales ~ price + promotion)
?lm
#how to give parameter values in different sequence, use arguments names if in different order
mlrmodel1 = lm( data=omni, formula = sales ~ price + promotion)

range(omni$sales)
summary(mlrmodel1)  # summary statistics IMP STEP
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals

coef(mlrmodel1) #coefficients b1, b2
#anova(mlrmodel1) #seeing from anova model

head(omni)

plot(y=omni$sales, x=omni$promotion)

#Predicted Values----
dim(omni)
fitted(mlrmodel1)
cbind(omni, fitted(mlrmodel1), residuals(mlrmodel1))
summary(mlrmodel1)

names(omni)
#create a dataframe of new sample values
(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))
range(omni$price); range(omni$promotion)
predict(mlrmodel1, newdata=ndata1)
cbind(ndata1, Predict=predict(mlrmodel1, newdata=ndata1, predict='response'))






#R2 and Adjs R2
names(mlrmodel1)
summary(mlrmodel1)
summary(mlrmodel1)$r.squared
summary(mlrmodel1)$adj.r.squared

#Manual Calculation of Adjs R2
(r2 = summary(mlrmodel1)$r.squared)
k = 2 # no of IVs
(n = nrow(omni)) # sample size
(adjr2 = 1 - ( (1 - r2) * ((n - 1)/ (n - k - 1))))


# Fstatistics
summary(mlrmodel1)$fstatistic[1]  # from output of model

(df1 = k) ;  (df2 = n-k-1)  
qf(.95, df1, df2)  # from table wrt df1 & df2
#Model Fstats > table(Fstat) 

# Pvalue of Model
fstat = summary(mlrmodel1)$fstatistic 
pf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE) 
# this is  < 0.05 : Significant
# 

#Plots of the Modle
plot(mlrmodel1,1)  # no pattern, equal variance
plot(mlrmodel1,2)  # Residues are normally distributed
plot(mlrmodel1,3)
plot(mlrmodel1,4)  # tells outliers which affect model


# Confidence Intervals


#Fitted values : Predicting on IVs using model
fitted(mlrmodel1)
residuals(mlrmodel1) 
mlrmodel1$residuals
cbind(omni$sales, fitted(mlrmodel1), omni$sales - fitted(mlrmodel1), residuals(mlrmodel1))

#sqrt(sum((residuals(mlrmodel1)^2)))
names(mlrmodel1)
summary(mlrmodel1)




#Diagnostics Test for Checking Assumptions 
#Should be Linear relationship between Residuals Vs Ypi, X1i, X2i
cbind(fitted(mlrmodel1), residuals(mlrmodel1))
plot(cbind(fitted(mlrmodel1), residuals(mlrmodel1)))
#not quadratic
plot(cbind(omni$price, residuals(mlrmodel1)))
plot(cbind(omni$promotion, residuals(mlrmodel1)))
#May indicate quadratic term of IVs


#Train and Test Data


# RMSE


omni
names(omni)
mlr2 = lm(sales ~ price + promotion, data= omni)
summary(mlr2)
new1=data.frame(price=60:70, promotion=400)
predict(mlr2, newdata = new1)
cbind(new1,predict(mlr2, newdata = new1) )




================================================
FILE: 01-IIM/21b3-SLM-areasales.R
================================================
# SLR Area vs Sales
#https://www.statisticshowto.datasciencecentral.com/excel-regression-analysis-output-explained/

#import from ggsheet
library(gsheet)
slr1 = "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2023826519"
df = as.data.frame(gsheet2tbl(slr1))

head(df)
#X-area in sqft, Y-sales in some unit currency
str(df)
linearmodel = lm(Y ~ X, data=df)
plot(df$X, df$Y)
abline(lm(Y ~ X, data=df), col='red')
cor(df$X, df$Y)
cov(df$X, df$Y)
summary(linearmodel)
#Ho: (F Test) : No relationship between Y and any X
#Ha:  There is relationship between Y and at least one X
# p < 0.05 Reject Ho in favour of Ha
coef(linearmodel)
#Y = 0.96 + 1.66 * X
range(df$X) #value of X to be betw this range : interpolation not extrapolation 
(new2 = data.frame(X=c(1.5,2,3,4,5)))

(p2sales= predict(linearmodel, newdata= new2))
cbind(new2, p2sales)

summary(linearmodel)
head(df)
#residual
(Y = 0.96 + 1.66 * 2.2)
(r = 5.6 - 4.61)
plot(df$X, df$Y)
abline(lm(Y ~ X, data=df), col='red')
abline(v=2.2,h=c(5.6, 4.61))
cbind(resid(linearmodel))
fitted(linearmodel) - df$Y

#assumptions
plot(linearmodel)


#second case
#for women dataset; make a linear model
#predict weight for ht=62.5 and 66.5
head(women)
names(women)
range(women$height)
df1 = women
head(df1)
model1 = lm(weight ~ height, data = df1)
summary(model1)
#F-stat pvalue < 0.05 : Lin Model Exists
#Coeff : Both are significant
#if ht is increased by 1 unit, wt incr 3.45 units
#Multiple r2 : 99% of variation in Wt is due to Ht
#Assumptions : plot ; linearity, normality, equal variance, outliers, no autocorrelation, no multi-collinearity
#predict
(ndata2 = data.frame(height=c(62.5, 66.5)))
(p2wt = predict( level=.95,interval= 'confidence', newdata= ndata2, object=model1))
cbind(ndata2, p2wt)
?predict




================================================
FILE: 01-IIM/21b4-SLM-salesarea.R
================================================
#Simple Linear Regression - Case Study
# Regression : Areas vs Sales
#Given data of area and sales, predict value for sales for specific areas eg : (1.5,2,3,4,5)
#manual way of doing regression see online URL


#Create/ Import Data-----
#Method1 : creating data from Vectors
#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y
X = c(1.7,1.6,2.8,5.6,1.3,2.2,1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )
df1 = data.frame(X,Y)
head(df1)

#2nd method of importing data
#import from ggsheet  #pickup the correct url
library(gsheet)
area1 = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
df2 = as.data.frame(gsheet::gsheet2tbl(area1))
str(df2)
head(df2)

#Third method of importing data from csv
df3 = read.csv('./data/slr1.csv')
str(df3)

#Method4 for importing from CSV file with choose location
df4 = read.csv(file.choose())
str(df4)

# Use Vector Data or method used to import data
#make one of the DF active
df = df1
df
#simple stats
mean(df$X); mean(df$Y)
sum(df$X); sum(df$Y)
sd(df$X) ; var(df$Y)
cov(df$X,df$Y); cov(df$Y,df$X)
cor(df$X,df$Y) ; cor(df$Y,df$X)
#cor.test(df$X,df$Y)

#some plots to understand pattern
plot(df$X, df$Y)  #simple command to plot : Next with features
plot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)+1), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)+ 1), col='red',pch=17)
?plot
abline(lm(Y ~ X,data=df1), lty=1, lwd=2, col='green') # with regression line
abline(v=c(3,5, min(df$X), max(df$X)),h=c(6,10, min(df$Y), max(df$Y)), col=c('red','blue','green','yellow')) # few straight lines at x & y axis
range(df$X)
#Model
fit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X
fit1
summary(fit1)
range(df$X)
(newdata = data.frame(X= c(2.5,3.2)))
predict(fit1, newdata = newdata)


#few extras
names(fit1) # attributes(fit1)
system.time(lm(Y ~ X, data=df)) #time taken to compute linear regression
coef(fit1)  # Coefficients of Equation Y = mX + C
fitted(fit1) # predicted values for all X in orginal data
predict(fit1, newdata=data.frame(X))
residuals(fit1) # diff between actual and predicted values - residuals
(R= df$Y - fitted(fit1))
plot(density(residuals(fit1)))
#residuals should be less :Diff of Y actual - Y predicted
#abline(h=coef(fit1)[1])

summary(fit1)  # summary statistics of Linear Model(LM)
#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

names(fit1)  #output variables names of LM
names(summary(fit1))
#u can select them to see output

summary(fit1)$r.squared
coef(fit1)[2]  # slope or beta
fitted(fit1)
#combine the data with Ypredicted, errors
cbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))

#Mathematical Equation and predictions
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4
#predict for area = 4
#using equation
summary(fit1)
coef(fit1)
(Y = coef(fit1)[1] + coef(fit1)[2] * 4)
#using model
range(df$X)
predict(fit1, newdata= data.frame(X=c(4,5)))
(new1 = data.frame(X=c(4,5,3,2)))

predicted2= predict(fit1, newdata= new1)
cbind(new1, predicted2)
#prediction can only be interpolated not extrapolated
range(df$X)  #min to max value of X: area

#select new data given in the case
(new1 = data.frame(X=c(1.5,2,3,4,5)))
#sample data for X for prediction, should be between the range of X values
predict(fit1, newdata= new1) # Predict Function for 4 values of X
#columnbind with input and predicted values
cbind(new1, predictedY = predict(fit1, newdata= new1) )

library(forecast) #install it first
?accuracy(fit1)
#RMSE is generally used and should be least when selecting models

anova(fit1)
summary(fit1)$r.squared  # R2 from Model
summary(fit1)$sigma  #Residual Std Error SD along the LM Line

#---------------------------------------#Assumptions--------
#Assumption : Graphical Analysis : IMP STEP
plot(fit1)
par(mfrow=c(2,2))
plot(fit1)
par(mfrow=c(1,1))

plot(fit1, which=1)
# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y
plot(df)
abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
?car::durbinWatsonTest
#pvalue > 0 : Do not reject Ho. that means there is no autocorrelation

#Normality of residuals
resid(fit1)
#qqplot(fitted(fit1),resid(fit1) )
plot(fit1, which=2)
#points to be around the straight line

#Equal Variance : 4th Assumption : homoscedasticity
plot(fit1, which=1)
#no funnel shape to show hetero-cedasticity

#Outlier Analysis
plot(fit1, which=4)
#abline(h=c(.5))
#no value of cooks distance > .5 : no data to be removed
#outlier values can affect the model

#see all diagnostic plots together
par(mfrow=c(2,2))
#multiple frames per row : rowwise filling, 2 rows, 2 columns
plot(fit1)
par(mfrow=c(1,1))

#SUMMARY ------
summary(fit1)$r.squared  # 90% variation in Y explained by X
summary(fit1)
#F Stats pvalue < 0.05: Model exists : At least 1 indep variable has strong relationship with Dependent variable (Y)
#pvalue of Coef (X) < 0.05 : Significant X


#End of Simple Linear Regression
#Do different SLR on different data sets
#Learn what to do if there are violations of assumptions




df # dataset being used for LM
fit= lm(Y ~ X, data=df) #model creation
summary(fit)  #summary of linear model
plot(fit)  #diagnostic plots
predict(fit, newdata=data.frame(X=mean(df$X)))
#Multiple R2 explains the variation, model fitness



================================================
FILE: 01-IIM/21d2-MLM-mtcars1.R
================================================
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr) #install it first

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
summary(model)
#model <- lm(mpg ~ hp + wt , data = mtcars)
#summary(model)
k <- ols_step_all_possible(model)
#plot(k)
k
summary(lm(mpg ~ wt, data=mtcars))
summary(lm(mpg ~ wt+ hp, data=mtcars))
summary(lm(mpg ~ hp + wt + qsec, data=mtcars))


#library(olsrr)
fit = lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k = ols_step_all_possible(fit)
plot(k)
k
summary(lm(mpg ~ wt, data= train))
summary(lm(mpg ~ wt + hp, data= train))
finalmodel = lm(mpg ~ wt + hp, data= train)
library(gvlma)
gvmodel = gvlma(finalmodel)
gvmodel

finalmodel = lm(mpg ~ wt + hp, data= train)
(predictedvalues = predict(finalmodel, ndata=test))
cbind(test$mpg, predictedvalues)



================================================
FILE: 01-IIM/22a4-MLM-allmodels.R
================================================
# All models - This code performs all modeling in quick method.
# for details go detail code.

library(dplyr)

#Linear Regression
head(women)
head(mtcars)
#predict weight for new height
plot(women)
plot(women, ylim=c(0, 160), xlim=c(0,90))
fit1 = lm(weight ~ height, data=women)
summary(fit1)
range(women$height)
(ndata1=data.frame(height=c(59.5, 62.5)))
(predicted1 = predict(fit1, newdata= ndata1))
cbind(ndata1, predicted1)
plot(fit1) #check for assumptions
par(mfrow=c(2,2))
plot(fit1)
par(mfrow=c(1,1))


#Logistic Regression
data2 = read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
head(data2)
str(data2)
summary(data2)
data2$rank = factor(data2$rank)
data2$admit = factor(data2$admit)
table(data2$rank, data2$admit) #2 way table
xtabs(~admit + rank, data = data2)
#create Logistic Model
fit2 <- glm(admit ~ gre + gpa + rank,data=data2,family="binomial")
summary(fit2)
(ndata2 = sample_n(data2, 3))  #pick up sample rows
#Predict admit for input data
(predicted2=predict(fit2,newdata=ndata2, type="response"))
(predictedclass2=ifelse(predicted2 > .5, 1,0))
cbind(ndata2, predicted2, predictedclass2)

#Data Partition
#see detailed case
#----------------------------------------------------



#Decision Tree - Classification 
url3 = 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/titanic_train.csv'
data3a = read.csv(url3)
head(data3a)
names(data3a)
data3 = data3a[,c(2,3,5,6,7)]  #select few columns only
head(data3)

#install & load libraries
library(rpart)
library(rpart.plot)

#Decision Tree
fit3 = rpart(Survived ~ ., data = data3, method = 'class')
fit3
rpart.plot(fit3, extra = 106, cex=.8,nn=T)  #plot
printcp(fit3) #select complexity parameter
prunetree3 = prune(fit3, cp=.014) #make tree smaller
rpart.plot(prunetree3, cex=.8,nn=T)
prunetree3
table(data3$survived)
#Predict class category or probabilities
(ndata3 = sample_n(data3,4))
predicted3 = predict(prunetree3, newdata=ndata3, type='class')
predicted3b= predict(prunetree3, newdata=ndata3, type='prob')
cbind(ndata3, predicted3, predicted3b)
#predict on test set
url3b ='https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/titanic_test.csv'  #test set
data3b = read.csv(url3b)
head(data3b)
names(data3b)
data3b = data3b[, c('Pclass','Sex','Age','SibSp')]
#select few columns only
head(data3b)



#similarly Regression Tree can be made



#Clustering----
#install.packages("amap")
##Read the data in the file
url4 = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data4 = as.data.frame(gsheet2tbl(url4))
head(data4)
summary(data4)
str(data4)
nrow(data4)
###Verify the data
colnames(data4)
apply(data4, 2, FUN= class)  #are all numeric
fit4 = kmeans(data4[,-1],centers=3)
fit4$centers  # group means
fit4$size  #how rows in which cluster
fit4$withinss  #math difference within each cluster; Which is more cohesive gp?
fit4$cluster
table(fit4$cluster)
cluster2 = data4[ fit4$cluster == 2,]
head(cluster2)
cluster2[-1] %>% summarise_all(mean)
write.csv(cluster2, file = "./data/data4cluster2.csv")


## Association Rules - Groceries data set----
library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries')
Groceries
arules::LIST(Groceries[1:6]) #different format
#Find Frequent Itemset
frequentItems = eclat(Groceries,parameter=list(supp = 0.01,minlen=3, maxlen=5)) 
inspect(frequentItems[1:10])
frequentItems
inspect(sort(frequentItems,by="count",decreasing=TRUE)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = apriori(Groceries, parameter = list(supp = 0.001, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
#similary it can be done for lift and support
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf

#subset -----
#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.
#Find what factors influenced an event ‘X’ :
#Find out what events were influenced by a given event
subset1a = subset(rules, subset=rhs %in% "whole milk")
inspect(subset1a[1:10])
subset1b = subset(rules, subset=rhs %in% 'bottled beer' )
inspect(subset1b)  #no such rule with beer on rhs, change some parameters to such rules 
#inspect(rules)
#Items in : all or any
subset2a = subset(rules, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2a) #all items in 1 rule
subset2b = subset(rules, subset=lhs %in% c('baking powder','soda') )
inspect(subset2b[1:5]) #any of the items in the rule
#rhs- beer, confidence , sort by lift
subset3a = subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3a)
subset4 = subset(rules, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' ) # lhs- beer, rhs- milk
inspect(subset4)

#Visualizing The Rules -----
plot(subset1[1:10]) 
plot(subset1[1:10], measure=c("support", "lift"), shading="confidence")

#

#install.packages('wordcloud2')
library(wordcloud2)

df = data.frame(word=c('cbap','cmap','iim','imt','calcutta'),freq=c(20,23,15,10,13))
df
wordcloud2(df)


head(demoFreq)
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")
names(demoFreq)


================================================
FILE: 01-IIM/22c2-MLM-mtcars-olsrr.R
================================================
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr)
head(mtcars)
names(mtcars)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_all_possible(model)
plot(k)
k
summary(lm(mpg ~ wt, data=mtcars))
summary(lm(mpg ~ wt + hp + qsec, data=mtcars))



================================================
FILE: 01-IIM/22c3-MLM-cars.R
================================================


#http://r-statistics.co/Linear-Regression.html
head(cars) 
fit2=lm(dist ~ speed, data=cars)
summary(fit2)
predict(fit2, newdata=data.frame(speed=c(5,8)))
plot(fit2)
nrow(cars)


index = sample(1:nrow(cars), size=0.8 * nrow(cars))
index
length(index)
length(unique(index))
traindata = cars[index,]
traindata
testdata = cars[-index,]
testdata
nrow(traindata) + nrow(testdata)

model1 = lm(dist ~ speed, data = traindata)
coef(model1)
(P1 = predict(model1, newdata = testdata))
df_test = cbind(testdata, P1)
cor(df_test$dist, P1)
(error = testdata$dist - P1)

AIC(model1)
BIC(model1)
(MAPE_error = mean(abs(error)/testdata$dist))

library(forecast)
accuracy(model1)
accuracy(error)

#https://www.ritchieng.com/machine-learning-evaluate-linear-regression-model/
#https://www.guru99.com/r-decision-trees.html
#http://www.sthda.com/english/articles/35-statistical-machine-learning-essentials/141-cart-model-decision-tree-essentials/
  

================================================
FILE: 01-IIM/22c3-MLM-salespromotion.R
================================================
#Multiple Linear Regression : DV vs more than 1 IVs
#sales Qty vs price & promotion
#Predict Sales Qty from Price and Promotion of the Product

#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
#Create a DF from 3 variables
omni1 = data.frame(sales,price,promotion)
head(omni1)

#2nd Method : from CSV file
#omni2 = read.csv(file.choose())

#3rd Method : from gsheet 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
omni3 = as.data.frame(gsheet::gsheet2tbl(url))
head(omni3)
#Make one of data frames active
omni = omni1
head(omni)
str(omni)
nrow(omni)
dim(omni)
#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
fit2 = lm(sales ~ price + promotion, data=omni)

# summary statistics of model IMP STEP
summary(fit2)
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals
#F Stats pvalue = 2.86e-10 < 0.05 : Model Exists
#At least 1 IV can be used to predict sales
names(summary(fit2))
summary(fit2)$adj.r.squared  # Adjt R2 here > .6 
#>74% of variation in sales is explained by price and promotion

#coefficients b1, b2
coef(fit2)
summary(fit2)
#price  : -53 , pvalue = 9.2e-09 < 0.05 *** : Significant
#keeping promotion constant, if price is increased by 1 unit, salesqty decreases by 53 units
#promotion  : +3.6 , pvalue = 9.82e-06 < 0.05 ***: Significant
#keeping price constant, if promotion is increased by 1 unit, salesqty increases by 3 units

fitted(fit2)#predicted sales values for input/ actual price and promtion
omni$sales #actual sales
residuals(fit2) #diff between predicted and actual sales
summary(residuals(fit2))
summary(fit2)
#Predict SalesQty for new combination of Values----

#create a dataframe of new sample values
range(omni$price) ; range(omni$promotion)
(ndata2 = data.frame(price=c(60,70,72), promotion=c(300,400,350)))
(p2sales = predict(fit2, newdata=ndata2, type='response'))
cbind(ndata2, p2sales)
head(omni)
#Assumptions
par(mfrow=c(2,2))
plot(fit2)
par(mfrow=c(1,1))
plot(fit2)
plot(fit2,which=1)  # no pattern, equal variance
plot(fit2,2)  # Residuals are normally distributed
plot(fit2,3)  # No hetero-scedascity
plot(fit2,4)  # tells outliers which affect model
omni[c(11,14,15),]

fit3 = lm(sales ~ price + promotion, data=omni[-c(11,14,15),])
plot(fit3,4)
summary(fit2)
summary(fit3)

#End of Multiple Linear Regression

#when variables are large, select only significant variables
#Model with higher R2 to be selected
#other measures of model selection : AIC, BIC, RMSE
#Dataset can be divided into train(70%) and test(30%) set to check the accuracy

#create model with t





#questions
fit2
summary(fit2)
head(omni)
cbind(omni, predict(fit2, newdata = data.frame(omni$price, omni$promotion)))
cbind(omni, fitted(fit2))
cbind(omni, fitted(fit2), omni$sales - fitted(fit2), residuals(fit2))


#divided data into parts
#training set  =70%
#test set - 30%
#
head(women)
nrow(women)
library(dplyr)
women %>% sample_frac(.7) -> train1


================================================
FILE: 01-IIM/22d3-MLM-omni.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion

#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
omni1 = data.frame(sales, price, promotion)
head(omni1)
str(omni1)
#Make one of data frames active
omni = omni1
head(omni)
options(scipen=999)
model2 = lm(sales ~ price + promotion, data = omni)
summary(model2)
#F-stats : pvalue < .05 : LM exists : Y is related to one of the Xs
#Adj R2 > .6 : 74% of variation in price is captured by price and promotion
#coeff : all are significant 
#keeping price constant, if we increase promotion by 1 unit, sales increase by 3.6 units
range(omni$price)
dim(omni)
sdata <- omni %>% sample_n(2)
sdata
ndata3 = data.frame(price, promotion)

cbind(sdata, predict(model2, newdata= sdata))

plot(model2)
plot(model2, which=4)
omni[14,]
model2b = lm(sales ~ price + promotion, data = omni[-14,])
summary(model2b)

AIC(model2b)
AIC(model2)


================================================
FILE: 01-IIM/22d4-MLM-sales-TV.R
================================================
#Linear Model : Sales - TV, Radio, Newspaper

library(dplyr)
df = read.table("https://online.stat.psu.edu/onlinecourses/sites/stat508/files/lesson01/Advertising.data", header = T)
df
head(df)
cor(df)
cov(df)
plot(df$TV, df$Sales)
plot(df$Radio, df$Sales)
plot(df$Newspaper, df$Sales)

linModel = lm(Sales ~ TV + Radio + Newspaper, data=df)
summary(linModel)
#Newspaper is not significant
linModel2 = lm(Sales ~ TV + Radio, data=df)
summary(linModel2)
#all are significant; Adjust R2 > .89

anova(linModel, linModel2)
AIC(linModel, linModel2)

s1 <- sample_n(df,2)
predict(linModel2, newdata=s1, type='response')
df


================================================
FILE: 01-IIM/23c1-LOGR-logR.R
================================================
# Logistic Regression
#data()  # datasets available for use in R

# Load the textbook R package
library(ISLR)   #install the package
?Default   # data set
# Load in the credit data
data("Default")
# See the properties
str(Default)
class(Default)
head(Default)
names(Default)
dim(Default)
summary(Default)

# How many people actual default?
(tmp = table(Default$default))
paste(333/10000,' people default in payment of credit card')
#How many student default
?table
table(Default$default, Default$student, dnn=c("default","students"))
ftable(Default$default, Default$student)
t1= table(Default$default, Default$student, dnn=c("default","students"))
addmargins(t1)
table(Default$student)


?glm
#Multiple Logistic Regression
logit1 = glm(default ~ income + balance + student, family='binomial', data=Default)
summary(logit1)
exp(coef(logit1))
#No Rs
#income is not significant - remove it

logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit2)
coef(logit2)
exp(coef(logit2))
anova(logit2, logit1)
AIC(logit2, logit1)
#if pvalue < 0.05, logit1 is better model otherwise logit2
#use logit2

# Predict : use sample values
head(Default)
seq(1, 10000,500)
Default[c(1,501),]

ndata1= Default[seq(1, 10000,500),]
ndata1
nrow(Default[seq(1, 10000,500),])
10000/500

library(dplyr)
(ndata = (slice(Default, seq(1,n(),500))))
ndata
slice(Default, seq(1,n(),1000))  # another way
head(ndata)

addmargins(prop.table(table(Default$default,Default$student)))
0.2817/0.9667; 0.0127/0.0333

options(digits=10)

fitted.results = predict(logit2, newdata=ndata,type='response')
fitted.results = round(fitted.results,4)
head(fitted.results)
fitted.results
cbind(ndata, fitted.results)

ndata
p2 <- ndata %>% mutate(predictnew = ifelse(fitted.results < 0.5, 'No','Yes'))
cbind(p2,fitted.results)
fitted.results
ifelse(fitted.results < 0.05, 0,1)


(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))
(fitted.results2 <- predict(logit2, newdata=ndata2,type='response'))
cbind(ndata2, fitted.results2)

Default
xtabs( ~ default + student, data=Default)
table(Default$default, Default$student)
ftable(Default$default, Default$student)

#Accuracy of Model
library(caret)
set.seed(3456)
str(Default)
trainIndex <- createDataPartition(Default$default, p = .67, list = FALSE,   times = 1)

Train <- Default[ trainIndex,]
Test  <- Default[-trainIndex,]
head(Train)
head(Test)

# Logistic Regression Model
model = glm(default ~ student, data=Default, family='binomial')
Test$model_prob <- predict(model, Test, type = "response")
head(Test)
Test <- Test  %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))
head(Test)
Test <- Test %>% mutate(accurate = 1*(default == default_pred))
sum(Test$accurate)/nrow(Test)                         
#96% Accuracy


================================================
FILE: 01-IIM/23d1-LGR-gre.R
================================================
#Logistic Regresion : GRE
#https://stats.idre.ucla.edu/r/dae/logit-regression/
#A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable


inputData <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(inputData)
inputData
dim(inputData)
summary(inputData)
sapply(inputData, sd)  # sd(inputData$gre)
str(inputData)
data= inputData  # make a copy for futher analysis

data$rank = factor(data$rank)
data$admit = factor(data$admit)
summary(data)

## 2way contingency table of cat outcome and predictors we want
## to make sure there are not 0 cells
xtabs(~ admit + rank, data = data)
xtabs(~ cyl + gear + am + vs , data = mtcars)

head(data)
#create Logistic Model
mylogit <- glm(admit ~ gre + gpa + rank, data = data, family = "binomial")

summary(mylogit)
#gre,gpa, rank are statistically significant, 
#For every one unit change in gre, the log odds of admission (versus non-admission) increases by 0.002.
#For a one unit increase in gpa, the log odds of being admitted to graduate school increases by 0.804.
#The indicator variables for rank have a slightly different interpretation. For example, having attended an undergraduate institution with rank of 2, versus an institution with a rank of 1, changes the log odds of admission by -0.675.

## odds ratios only
exp(coef(mylogit))
range(data$gre); range(data$gpa)
head(data)
data[data$admit==1,]

(ndata4 = data.frame(gre=c(379,520), gpa=c(3.71,4), rank=factor(c(4,1))))
(p3 =predict(mylogit, newdata=ndata4, type="response"))
p3
cbind(ndata4, p3)
(p3b=ifelse(p3 < .5, 0, 1))
cbind(ndata4, p3, p3b)

#divide data into 2 parts : train 70% & test 30%
# train
nrow(data) * .7
index = sample(nrow(data), size=nrow(data)* .7, replace=F)
length(index)
train = data[index, ]
dim(train)
# test
test = data[-index, ]
dim(test)

# create model using train set data
mylogit2 = glm(admit ~ gre + gpa + rank, data = train, family = "binomial")
summary(mylogit2)
# predict on test set data
(ptest = predict(mylogit2, newdata = test, type='response'))
# prob -> 0, 1 using if else statements
AIC(mylogit); AIC(mylogit2) #comparing models
(ptestb=ifelse(ptest < .5, 0, 1))
# test$admin compare with predicted admit values
compare = cbind(test, ptestb)
head(compare)
# confusion matrix
(cm=table(compare$admit, compare$ptestb))
(73 + 8) / (73 + 6 + 33 + 8)  #Accuracy of model

library(caret)
confusionMatrix(cm)

#---------------------------------



#Predict admit for input data
prob=predict(mylogit,type=c("response"))
cbind(data, prob)

#cutoff value
library(InformationValue)
(optCutOff <- optimalCutoff(data$admit, prob)[1] ) #.46
confusionMatrix(data$admit, prob, threshold = optCutOff)
(accuracy = (247+38)/ (sum(247+38+89+26))) # .715
confusionMatrix(data$admit, prob, threshold = .7)
(accuracy = (272+2)/ (sum(272+2+125+1))) #.685

confusionMatrix(data$admit, prob, threshold = .2)


## view data frame
library(dplyr)
sample_n(data,size=1)
(newdata1 = data.frame(gre=450, gpa=3.7, rank=factor(3) ))
(newdata1$admitPredicted <- predict(mylogit, newdata = newdata1, type = "response"))
(newdata1$admitClass = ifelse(newdata1$admitPredicted > .46,1,0))
newdata1  #b=not admitted to institute

#End of Logistic Regression
#also check for assumptions of residues, VIF, Multi-collinearity
#Parition the data into train and test



library(caret)
Index <- createDataPartition(y=data$admit, p=0.70, list=FALSE)
head(Index)
nrow(data)
trainData = data[Index ,]
testData = data[-Index, ]
table(data$admit); prop.table(table(data$admit))
summary(trainData$admit); summary(testData$admit)
nrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)
prop.table(table(trainData$admit))
prop.table(table(testData$admit))
#same promotion of admit in test and train
str(testData)
#now construct a model with train and then test on testdata



================================================
FILE: 01-IIM/24b1-LOGR-purchase.R
================================================
# Logistic Regression : Predict Purchase

# Import the dataset
#df1 = read.csv('./data/logr2.csv')
#head(df1)

url="https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=120271978"
library(gsheet)
df2 = as.data.frame(gsheet2tbl(url))
head(df2)
dim(df2)
dataset=df2  #or df2 if data is imported from google sheets
head(dataset)
str(dataset)
summary(dataset)
dim(dataset)
View(dataset)
dataset$gender = factor(dataset$gender)
summary(dataset)
# Split the dataset into the Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(2000)
split = sample.split(dataset$purchased, SplitRatio = 0.75)
split
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

dim(dataset); dim(training_set); dim(test_set)
names(dataset)
prop.table(table(training_set$purchased))
prop.table(table(test_set$purchased))
#------------------------------------
# Logisitic Model on Training Set
logitmodel1 = glm(purchased ~ gender + age + salary, family = binomial,  data = training_set)
summary(logitmodel1)
#AIC - 209.13
# gender not insignificant dropped here
logitmodel2 = glm(purchased ~ age + salary, family = binomial, data = training_set)
summary(logitmodel2)
#AIC - 209.95
#summary(logitmodel2)$coefficient  # they are in log terms
head(training_set)
#predict on sample data
test_set2 = data.frame(age=c(40,65), gender=c('Male', 'Female'), salary=c(40000, 50000))
test_set2
(prob_pred2 = predict(logitmodel2, type = 'response', newdata = test_set2))
cbind(test_set2, prob_pred2)
#age=65 person likely to purchase

# Predicting the Test set results from testset
head(test_set)
prob_pred = predict(logitmodel2, type = 'response', newdata = test_set)
summary(prob_pred)
head(cbind(test_set,prob_pred ),10)

#if prob > 0.5 make it 1, else 0
y_pred = ifelse(prob_pred > 0.5, 1, 0)
head(cbind(test_set$purchased, y_pred),100)

# Making the Confusion Matrix
cm = table(test_set[,5], y_pred)
cm
library(caret)
caret::confusionMatrix(cm)

names(dataset)
#predict binary classification

#84% accuracy in predict whether customer will purchase the product on the basis of IV variables - age & salary
coef(logitmodel1)
coef(logitmodel2)


================================================
FILE: 01-IIM/24c2-LOGR-adult.R
================================================
#Logistic Regression : Binary Cls : 0 or 1

#Case Study :  predict if an individual will earn more than $50K using logistic regression based on demographic variables available in the adult data.

#Steps
# Import the data
# Check for class bias
# Create training and test samples
# Compute information value to find out important variables
# Build logit models and predict on test data
# Do model diagnostics

#Data Import ----
#from URL
inputData <- read.csv("http://rstatistics.net/wp-content/uploads/2015/09/adult.csv")
head(inputData)
str(inputData)
names(inputData)
removeColumns = c('FNLWGT','EDUCATION')

data= inputData[,-which(names(inputData) %in% removeColumns)]
names(data)

data$ABOVE50K = factor(data$ABOVE50K)

#train and test sets
library(caret)
Index <- createDataPartition(y=data$ABOVE50K, p=0.70, list=FALSE)
head(Index)
nrow(data)
trainData = data[Index ,]
testData = data[-Index, ]
table(data$ABOVE50K); prop.table(table(data$ABOVE50K))
summary(trainData$ABOVE50K); summary(testData$ABOVE50K)
nrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)
prop.table(table(trainData$ABOVE50K))
prop.table(table(testData$ABOVE50K))
str(testData)


#Logistic Regression on selected columns
names(data)
logitMod <- glm(ABOVE50K ~ RELATIONSHIP + AGE + CAPITALGAIN + OCCUPATION , data=trainData, family='binomial')
summary(logitMod)
AIC(logitMod)

#Check the probabilities predicted for test data - 2 methods
predicted <- plogis(predict(logitMod, testData))  
head(predicted)
predicted <- predict(logitMod, testData, type="response")
head(predicted)

#what should be the cutoff value between 0 and 1 to categorise them into 0 or 1, so that accuracy is high (correct splitting)

#cutoff value
library(InformationValue)
(optCutOff <- optimalCutoff(testData$ABOVE50K, predicted)[1] )

#Confusion Matrix
(cm1= confusionMatrix(testData$ABOVE50K, predicted, threshold = optCutOff))
#0 classified as 0, 1 classified as 1



#diagnostics

car::vif(logitMod)
#all X variables in the model to have VIF below 4.

misClassError(testData$ABOVE50K, predicted, threshold = optCutOff)
#Misclassification error is the percentage mismatch of predcited vs actuals, irrespective of 1’s or 0’s. The lower the misclassification error, the better is your model.




ROC
#Receiver Operating Characteristics Curve traces the percentage of true positives accurately predicted by a given logit model as the prediction probability cutoff is lowered from 1 to 0. For a good model, as the cutoff is lowered, it should mark more of actual 1’s as positives and lesser of actual 0’s as 1’s. So for a good model, the curve should rise steeply, indicating that the TPR (Y-Axis) increases faster than the FPR (X-Axis) as the cutoff score decreases. Greater the area under the ROC curve, better the predictive ability of the model.

plotROC(testData$ABOVE50K, predicted)



















#http://r-statistics.co/Logistic-Regression-With-R.html
#https://rpubs.com/H_Zhu/235617




================================================
FILE: 01-IIM/24d2-LOGR-gre.R
================================================
#Logistic Regresion : GRE
#https://stats.idre.ucla.edu/r/dae/logit-regression/
#A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable


inputData <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(inputData)
dim(inputData)
inputData
summary(inputData)
#sd of all columns 
sd(inputData$admit)
sd(inputData[,1])
sd(inputData[,2])
sd(inputData[,3])
sd(inputData[,4])

for (i in 1:4) { print(sd(inputData[,i]))}
#use apply functions
sapply(inputData, sd)
lapply(inputData, sd)
quantile(inputData$gre)
quantile(inputData$gre, c(.1,.2, .3, .7,.8,1))
quantile(inputData$gre, seq(0,1,.1))
quantile(inputData$gre, seq(0,1,.01))

lapply(inputData, quantile)
sapply(inputData, quantile)

?sapply

str(inputData)
data= inputData  # make a copy for futher analysis
data$rank = as.factor(data$rank)
data$admit = factor(data$admit)
str(data)
## 2way contingency table of cat outcome and predictors we want
## to make sure there are not 0 cells
table(data$rank, data$admit)
#which rank of institute are more successful (in nos/ %) in getting admitted - 2 /1
(t1= xtabs(~ admit + rank, data = data))
addmargins(t1)
prop.table(t1,2)
?prop.table
#xtabs(~ gear + cyl + am , data=mtcars)
#create Logistic Model
head(data)
mylogit = glm(admit ~ gre + gpa + rank, data = data, family = "binomial")

summary(mylogit)
#gre,gpa, rank are statistically significant, Star in each variable rows
#For every one unit change in gre, the log odds of admission (versus non-admission) increases by 0.002.
#For a one unit increase in gpa, the log odds of being admitted to graduate school increases by 0.804. (keeping others constants)
#The indicator variables for rank have a slightly different interpretation. For example, having attended an undergraduate institution with rank of 2, versus an institution with a rank of 1, changes the log odds of admission by -0.675.

## odds ratios only
exp(coef(mylogit))
library(dplyr)
set.seed(1234)
(ndata1 = sample_n(data, 3))
(p1=predict(mylogit,newdata=ndata1, type=c("response")))

#Predict admit for input data
(ndata2 = data.frame(gre=c(600, 700), gpa=c(2,3), rank=factor(c(1,1))))
(p2= predict(mylogit,newdata=ndata2, type=c("response")))

cbind(ndata1, p1, predict=ifelse(p1 < .5, 0, 1))
cbind(ndata2, p2, predict=ifelse(p2 < .5, 0, 1))

#predict for original values
(p3 = predict(mylogit, newdata=data, type='response'))
nrow(data)
(predicted3 = factor(ifelse(p3 < .5, 0, 1)))
(actual = data$admit)
df = data.frame(predicted3, actual)
#cutoff value
head(df)
df %>% mutate(same = predicted3 == actual)
caret::confusionMatrix(df$predicted3, df$actual)
(254+30)/(19+97+254+30)
?confusionMatrix

## view data frame
library(dplyr)
sample_n(data,size=1)
(newdata1 = data.frame(gre=450, gpa=3.7, rank=factor(3) ))
(newdata1$admitPredicted <- predict(mylogit, newdata = newdata1, type = "response"))
(newdata1$admitClass = ifelse(newdata1$admitPredicted > .46,1,0))
newdata1  #b=not admitted to institute

#End of Logistic Regression
#also check for assumptions of residues, VIF, Multi-collinearity
#Parition the data into train and test



library(caret)
Index <- createDataPartition(y=data$admit, p=0.70, list=FALSE)
head(Index)  #rownumbers for T
Index ; length(Index)
length(Index)/ nrow(data)

nrow(data)
trainData = data[Index , ]
nrow(trainData)
testData = data[-Index, ]
nrow(testData)
nrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)
table(data$admit); prop.table(table(data$admit)) #original table
prop.table(table(trainData$admit))
prop.table(table(testData$admit))
summary(trainData$admit); summary(testData$admit)
#proportion of Admit (0 and 1) are same/similar in train and test set
#this is partition of data
#trainData & testData
log1 = glm(admit ~ gre + gpa + rank, data=trainData, family='binomial')
summary(log1)

car::vif(log1)
#we will use all variables 
#predict on test set 
(testData$predictNew = predict(log1, newdata = testData, type='response'))

library(InformationValue) #finds optimal cutoff values
(optCutOff <- optimalCutoff(testData$admit, testData$predictNew)[1] )
str(testData)

#find class with optimal & .5 cutoff value
testData2 = cbind(testData, predictClass1 = factor(ifelse(testData$predictNew < optCutOff, 0, 1)) , predictClass2 = factor(ifelse(testData$predictNew < .5, 0, 1)))
caret::confusionMatrix(testData2$admit, testData2$predictClass1)  #better
caret::confusionMatrix(testData2$admit, testData2$predictClass2)

head(testData2)

misClassError(testData2$admit, testData2$predictNew, threshold = optCutOff)
plotROC(testData2$admit, testData2$predictNew)
Concordance(testData2$admit, testData2$predictNew)

#now construct a model with train and then test on testdata
#http://r-statistics.co/Logistic-Regression-With-R.html
#logistic Regression



library(InformationValue)
(optCutOff <- optimalCutoff(data$admit, prob)[1] ) #.46
confusionMatrix(data$admit, prob, threshold = optCutOff)
(accuracy = (247+38)/ (sum(247+38+89+26))) # .715
confusionMatrix(data$admit, prob, threshold = .7)
(accuracy = (272+2)/ (sum(272+2+125+1))) #.685

confusionMatrix(data$admit, prob, threshold = .2)


print(summary(am.data))

#end



================================================
FILE: 01-IIM/24e2-LOGR-general.R
================================================
#generic LogRegession


#load data
data <- read.csv(....)

#create training and validation data from given data
install.packages('caTools')
library(caTools)

set.seed(88)
split <- sample.split(data$class, SplitRatio = 0.75)

#get training and test data
train <- subset(data, split == TRUE)
test <- subset(data, split == FALSE)

#logistic regression model (remove ID coln if any)
model <- glm (class ~ ., data = train, family = binomial)
summary(model)
predict <- predict(model, type = 'response')
#confusion matrix
table(train$class, predict > 0.5)

#ROCR Curve
library(ROCR)
ROCRpred <- prediction(predict, train$class)
ROCRperf <- performance(ROCRpred, 'tpr','fpr')
plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))

#plot glm with any xvar and class 
library(ggplot2)
ggplot(train, aes(x=xvar, y=class)) + geom_point() + 
  stat_smooth(method="glm", family="binomial", se=FALSE)

================================================
FILE: 01-IIM/24g1-LOGR-cancer.R
================================================
#logistic Regression
#https://www.machinelearningplus.com/machine-learning/logistic-regression-tutorial-examples-r/
# Load data
# install.packages('mlbench')
library(mlbench)

data(BreastCancer, package="mlbench")
bc <- BreastCancer[complete.cases(BreastCancer), ]  # keep complete rows

# remove id column
bc <- bc[,-1]

# convert to numeric
for(i in 1:9) {
  bc[, i] <- as.numeric(as.character(bc[, i]))
}

# Change Y values to 1's and 0's
bc$Class <- ifelse(bc$Class == "malignant", 1, 0)
bc$Class <- factor(bc$Class, levels = c(0, 1))

# Prep Training and Test data.
library(caret)
'%ni%' <- Negate('%in%')  # define 'not in' func
options(scipen=999)  # prevents printing scientific notations.
set.seed(100)
trainDataIndex <- createDataPartition(bc$Class, p=0.7, list = F)
trainData <- bc[trainDataIndex, ]
testData <- bc[-trainDataIndex, ]

# Class distribution of train data
table(trainData$Class)
table(testData$Class)
prop.table(table(trainData$Class))
prop.table(table(testData$Class))

# Down Sample
set.seed(100)
down_train <- downSample(x = trainData[, colnames(trainData) %ni% "Class"], y = trainData$Class)

table(down_train$Class)

?downSample
# Up Sample (optional)
set.seed(100)
up_train <- upSample(x = trainData[, colnames(trainData) %ni% "Class"],
                     y = trainData$Class)

table(up_train$Class)

# Build Logistic Model
logitmod <- glm(Class ~ Cl.thickness + Cell.size + Cell.shape, family = "binomial", data=down_train)

summary(logitmod)

pred <- predict(logitmod, newdata = testData, type = "response")
pred

# Recode factors
y_pred_num <- ifelse(pred > 0.5, 1, 0)
y_pred <- factor(y_pred_num, levels=c(0, 1))
y_act <- testData$Class

# Accuracy
mean(y_pred == y_act)  # 94%


================================================
FILE: 01-IIM/24g2-LOGR-sample1.R
================================================
#generic LogRegession


#load data
data <- read.csv(....)
data = mtcars

#create training and validation data from given data
install.packages('caTools')
library(caTools)

set.seed(88)
#which category to be predicted: transmission 0/1
data$am = factor(data$am)
table(data$am)

split <- sample.split(data$am, SplitRatio = 0.75)
split
#get training and test data
train <- subset(data, split == TRUE)
test <- subset(data, split == FALSE)
table(train$am); table(test$am)


#logistic regression model (remove ID coln if any)
model <- glm (am ~ ., data = train, family = binomial)
summary(model)
predict <- predict(model, type = 'response')
#confusion matrix
table(train$am, predict > 0.5)

#ROCR Curve
library(ROCR)
ROCRpred <- prediction(predict, train$am)
ROCRperf <- performance(ROCRpred, 'tpr','fpr')
plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))

#plot glm with any xvar and class 
library(ggplot2)
ggplot(train, aes(x=mpg, y=am)) + geom_point() +   stat_smooth(method="glm", family="binomial", se=FALSE)


================================================
FILE: 01-IIM/31a1-DT-cart-split.R
================================================
#Understanding Splitting and selection of variables

#install the libraries
pacman::p_load(rpart, rpart.plot)
#library(rpart); library(rpart.plot)
#---

(gender = c(rep('M', 60), rep('F', 40)))
(play = c(rep(c('Yes','No'), c(30,30)), rep(c('Yes','No'), c(20,20))))
students = data.frame(gender, play)  
head(students)  
(t1=table(students$gender, students$play))
prop.table(t1)
addmargins(t1)  # 50% of each category play and don't play
dtree1 = rpart(play ~ gender, data=students, control = list(cp=-1, minsplit=5))
dtree1
rpart.plot(dtree1)
#no decision tree as proportion is 50%

#----------------------
(gender2 = c(rep('M', 60), rep('F', 40)))
(play2 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))
students2 = data.frame(gender2, play2)  
head(students2)  
(t2=table(students2$gender2, students2$play2))
addmargins(t2)
prop.table(t2)  #more % of males play
prop.table(t1)
addmargins(prop.table(t2))
#how many play - .6, 60% from 100% values

dtree2 = rpart(play2 ~ gender2, data=students2, control = list(cp=-1, minsplit=5))
dtree2
rpart.plot(dtree2, extra=104, nn=T)
rpart.plot(dtree2)
table(students2$play2)
rpart.plot(dtree2, extra=104, cex=1)
rpart.plot(dtree2, extra=104, yesno=2, left=F, xflip=T, yflip=T,faclen=3, cex=1.5)
predict(dtree2, newdata = data.frame(gender2='M'), type='class')
predict(dtree2, newdata = data.frame(gender2='M'), type='prob')


#explore below document
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
prop.table(t2,1) #40/60
#majority play (at root node)

#Eg3----------------------

(gender3 = c(rep('M', 60), rep('F', 40)))
(play3 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))
(married3 = c(rep(c('Md','Sg'), c(50,10)), rep(c('Md','Sg'), c(10,30))))


students3 = data.frame(gender3, play3, married3)  
head(students3)  
(t3a= table(students3$play3, students3$gender3))
(t3b= table(students3$play3, students3$married3))
addmargins(t3a)
addmargins(t3b)

dtree3 = rpart(play3 ~ gender3 + married3, data=students3, control = list(cp=-1, minsplit=5))
dtree3
rpart.plot(dtree3)
addmargins(prop.table(table(students3$play3, students3$married3)))

#decision tree as proportion of M is not 50%
table(students3$play3)
#majority play (at root node)

#rpart(y~x+z, data=df, parms=list(split='gini'))


#Variable with lower Gini Index value, should be chosen as a variable that gives best split. The next step would be to take the results from the split and further partition.  
table(students3$play3)

table(students3$married3)
#married Variable : Gini Index
table(students3$play3, students3$married3)
(gini_md_sg = 1 - (30/40)^2 - (10/40)^2)
(gini_md_md = 1 - (10/60)^2 - (50/60)^2)
(gini_md = gini_md_sg + gini_md_md)

#gender Variable : Gini Index
table(students3$play3, students3$gender3)
(gini_gd_m = 1 - (20/60)^2 - (40/60)^2)
(gini_gd_f = 1 - (20/40)^2 - (20/40)^2)
(gini_gd = gini_gd_m + gini_gd_f)
gini_md < gini_gd
#gini_md is lower hence should be selected as split variable

#After Splitting : look at left tree ie. Single students
#singles = split(students3, married3="Sg")


================================================
FILE: 01-IIM/31a2-DT-outlook.R
================================================
#Decision Tree - Book Eg : Weather - Predict Play

outlook =c('Sunny', 'Sunny', 'Overcast', 'Rain','Rain', 'Rain','Overcast', 'Sunny', 'Sunny','Rain','Sunny', 'Overcast','Overcast','Rain')
temperature = c( 'Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild')
humidity = c('Weak','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Strong')
wind = c( 'High','High','High','High','Normal','Normal','Normal','High','Normal','Normal','Normal','High','Normal','High')
play = c('No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No')

game = data.frame(outlook, temperature, humidity, wind, play)
head(game)

library(rpart)
library(rpart.plot)
dt_game1 = rpart(play ~ . , data=game) 
dt_game1
#no tree 
#cp=-1

dt_game2 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=1, minbucket = 1))
dt_game2
rpart.plot(dt_game2,cex=.8, nn=T)

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "gini"))
dt_game3
rpart.plot(dt_game3,cex=.8, nn=T)

?rpart

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "information"))
dt_game3
attributes(dt_game3)
rpart.plot(dt_game3,cex=.8, nn=T)
dt_game3$parms
dt_game3$variable.importance
dt_game3$method
dt_game3$cptable
dt_game3$control
dt_game3$splits
dt_game3$numresp
dt_game3$y
game$play
dt_game3$ordered






================================================
FILE: 01-IIM/31a3-DT-general.R
================================================
#Basic Steps in CART

#Collect Data
#Identify if it is Classification or Regression Model
#Write the formula : y ~ x` + x2  ....

#load libraries #install packages - rpart, rpart.plot, RColorBrewer
library(rpart)# doing classification
library(rpart.plot) # visualising the tree
#model= rpart(y  ~ ., method='class', data= , control=rpart.control(minsplit=4, cp=0.00001))
rpart.plot()

#Set any control Parameters
control_1 = rpart.control(cp = 0.005, minsplit=10, minbucket = 5)
control_2 = rpart.control(cp = -1)

# # Finding how people take decisions to buy products
# Creating Decision Tree

#Students : Gender - (Male & Female) buy a product
#Variable Gender
set.seed(123)
n=1000
gender = sample(x=c('Male','Female'), size=n, replace=T, prob=c(0.5,0.5) )
head(gender)
table(gender)

#Variable- Buy : Decision
set.seed(135)
buy = sample(x=c('Buy','NotBuy'), size=n, replace=T, prob=c(.41,.59) )
head(buy)
table(buy)
prop.table(table(buy))

#create Data Frame
students1 = data.frame(gender, buy)
head(students1)

xtabs(~ gender + buy,data = students1)

#Table
table(students1)
prop.table(table(students1))
addmargins(prop.table(table(students1)))
(t1= table(students1$gender, students1$buy))
addmargins(t1)
prop.table(table(students1$gender, students1$buy))
addmargins(prop.table(table(students1$gender, students1$buy))
)

#Model1
head(students1)
fit = rpart(buy ~ gender, data= students1)
fit
table(students1$buy)
dev.off()
rpart.plot(fit,nn=T)

fit1 = rpart(buy ~ gender, data=students1, minsplit=4, minbucket=2)
#'minsplit' is min 4 obsv reqd to split a node
#'minbucket' determines the minimal number of observations per leaf ('minbucket') 

fit1  #print(fit1)

table(students1$gender, students1$buy)
head(students1)
library(rpart.plot)
rpart.plot(fit1, main='Classification Tree', nn=T, type=4, extra=104)

fit1

predict(fit1, newdata = data.frame(gender='Female'))

predict(fit1, newdata = data.frame(gender='Female'), type=c('class'))
predict(fit1, newdata = data.frame(gender=c('Male','Female','Male')), type='class')

#---- Part -2 Add another column----
set.seed(1234)
married = sample(x=c('Married','Single'), size=1000, replace=T, prob=c(0.6,0.4) )
table(married)
#new data frame
students2 = data.frame(gender, married, buy)
rownames(students2) = rollno
head(students2)
str(students2)
prop.table(ftable(students2))
table(students2$buy)  # Majority - Don't Buy
addmargins(prop.table(table(students2)))
#write.csv(students2, 'dtdata.csv')

# Model2
#library(rpart)
names(students2)
head(students2)
fit2 = rpart(buy ~ gender + married, data=students2, minsplit=10, cp=-1)
#fit2 = rpart(buy ~ gender + married, data=students2, minsplit=10)

summary(fit2)
fit2
rpart.plot(fit2,type=2,extra=104, tweak=1.2, under=T, shadow=c('brown', 'green','red'), nn=T)
fit2
prp(fit2)
prp(fit2, main="An Example",
    type=4, fallen=T, branch=.3, round=0, leaf.round=9,
    clip.right.labs=F, under.cex=1,
    box.palette="GnYlRd",
    prefix="Student\n", branch.col="gray", branch.lwd=2,
    extra=101, under=T, lt=" < ", ge=" >= ", cex.main=1.5)

prp(fit2, branch.type=5)
labels(fit2)


#Plot----
library(RColorBrewer)
rpart.plot::rpart.plot(fit2, main='Classification Tree')
rpart.plot::rpart.plot(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE)

rpart.plot::prp(fit2,fallen.leaves = F)
prp(fit2, type=2)


#Predict

predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='prob')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='class')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='vector')

predict(fit2, newdata = data.frame(gender='Male', married='Married'))

testdata = data.frame(gender=c('Male','Male','Female','Female'), married=c('Married','Single','Married','Single'))
testdata

(p1 = predict(fit2, newdata = testdata, type='vector'))  #node/level 
#play=2, notplay=1
(p2 = predict(fit2, newdata = testdata, type='class')) #factor
(p3 = predict(fit2, newdata = testdata, type='prob')) # prob

cbind(testdata, p1, p2, p3)
#level number, class frequencies, probabilities
predict(fit2, newdata= testdata, type = "matrix")

head(students2)

#Parameters Setting : CP
printcp(fit2)
getOption('digits')
plotcp(fit2)
names(fit2)

fit2$where  #which row at which node no

students2[1:5,]
cbind(students2, nodeno=rownames(fit2$frame) [ fit2$where])
fit2
rpart.plot(fit2)
pfit=  prune(fit2, cp=0.02) # from cptable  
pfit
rpart.plot(pfit)

#Interactive----
new.tree <- prp(fit2, snip=TRUE)$obj # interactively trim the tree
prp(new.tree) # display the new tree
#click on quit 












#using iris dataset
set.seed(123)
irisclass = rpart(Species ~ ., data = iris, control = control_1)
irisclass
rpart.plot(irisclass)
printcp(irisclass)
#--------
irisclass2 = rpart(Species ~ ., data = iris, control = control_2)
irisclass2
rpart.plot(irisclass2, cex=.8)


#----------------------
#regression Tree-----
names(iris)  #which is numeric column - Length
irisregression = rpart(Sepal.Length ~ ., data = iris, method="anova", control = control_1)
irisregression
rpart.plot(irisregression)
printcp(irisregression)
pruneRT = prune(irisregression, cp=.008)
rpart.plot(pruneRT)



================================================
FILE: 01-IIM/31a3-DT-practiseCase.R
================================================
#DT - simple case
#https://www.gormanalysis.com/blog/decision-trees-in-r-using-rpart/
#https://en.wikipedia.org/wiki/Staged_crash
#RearEnd crash - Fraud

library(rpart)
library(rpart.plot)
train <- data.frame(
  ClaimID = c(1,2,3),
  RearEnd = c(TRUE, FALSE, TRUE),
  Fraud = c(TRUE, FALSE, TRUE)
)
train
mytree <- rpart(  Fraud ~ RearEnd,   data = train,  method = "class" )

mytree
#Notice the output shows only a root node. This is because rpart has some default parameters that prevented our tree from growing. Namely minsplit and minbucket. minsplit is “the minimum number of observations that must exist in a node in order for a split to be attempted” and minbucket is “the minimum number of observations in any terminal node”. See what happens when we override these parameters.

mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class", 
  minsplit = 2, 
  minbucket = 1
)
train
mytree

library(rattle)
library(rpart.plot)
library(RColorBrewer)

# plot mytree
fancyRpartPlot(mytree, caption = NULL)
#The decision tree correctly identified that if a claim involved a rear-end collision, the claim was most likely fraudulent. By default, rpart uses gini impurity to select splits when performing classification. (If you’re unfamiliar read this article.) You can use information gain instead by specifying it in the parms parameter.
#https://www.gormanalysis.com/blog/magic-behind-constructing-a-decision-tree
mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class",
  parms = list(split = 'information'), 
  minsplit = 2, 
  minbucket = 1
)

mytree

#-----
#Now suppose our training set looked like this..

train <- data.frame(
  ClaimID = c(1,2,3),
  RearEnd = c(TRUE, FALSE, TRUE),
  Fraud = c(TRUE, FALSE, FALSE)
)

train
mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class", 
  minsplit = 2, 
  minbucket = 1
)

mytree
#again no tree

#Once again we’re left with just a root node. Internally, rpart keeps track of something called the complexity of a tree. The complexity measure is a combination of the size of a tree and the ability of the tree to separate the classes of the target variable. If the next best split in growing a tree does not reduce the tree’s overall complexity by a certain amount, rpart will terminate the growing process. This amount is specified by the complexity parameter, cp, in the call to rpart(). Setting cp to a negative amount ensures that the tree will be fully grown.
mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class", 
  minsplit = 2, 
  minbucket = 1, 
  cp = -1
)

fancyRpartPlot(mytree, caption = NULL)
#This is not always a good idea since it will typically produce over-fitted trees, but trees can be pruned back as discussed later in this article.
#You can also weight each observation for the tree’s construction by specifying the weights argument to rpart().
mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class", 
  minsplit = 2, 
  minbucket = 1,
  weights = c(0.4, 0.4, 0.2)
)

fancyRpartPlot(mytree, caption = NULL)


train <- data.frame(
ClaimID = 1:7,
RearEnd = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE),
Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),
Fraud = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE))

train
#let’s grow our decision tree, restricting it to one split by setting the maxdepth argument to 1.

mytree <- rpart(
  Fraud ~ RearEnd + Whiplash, 
  data = train, 
  method = "class",
  maxdepth = 1, 
  minsplit = 2, 
  minbucket = 1
)

fancyRpartPlot(mytree, caption = NULL)
#rpart has determined that RearEnd was the best variable for identifying a fraudulent claim. BUT there was one fraudulent claim in the training dataset that was not a rear-end collision. If the insurance company wants to identify a high percentage of fraudulent claims without worrying too much about investigating non-fraudulent claims they can set the loss matrix to penalize claims incorrectly labeled as fraudulent three times less than claims incorrectly labeled as non-fraudulent.
lossmatrix <- matrix(c(0,1,3,0), byrow = TRUE, nrow = 2)
lossmatrix

mytree <- rpart(
  Fraud ~ RearEnd + Whiplash, 
  data = train, 
  method = "class",
  maxdepth = 1, 
  minsplit = 2, 
  minbucket = 1,
  parms = list(loss = lossmatrix)
)

fancyRpartPlot(mytree, caption = NULL)
#Now our model suggests that Whiplash is the best variable to identify fraudulent claims. What I just described is known as a valuation metric and its up to the discretion of the insurance company to decide on it



================================================
FILE: 01-IIM/31b1-DT-CART-carseats.R
================================================
# Decision Trees : - regression tree
#install and load this library
library(ISLR)
data(Carseats)
?Carseats
data = Carseats
head(data)

#Libraries for Decision Tree
library(rpart)
library(rpart.plot)

#Model
tree1 = rpart(Sales ~ . , data=data, method='anova' )
tree1
rpart.plot(tree1, cex=.6,nn=T)

#this is large tree, so prune it: check cp
printcp(tree1)
#cp value should be chosen such that xerror is least
prunetree = prune(tree1, cp=0.03)
prunetree
#here we have selected a different value to simplify the tree
mean(data$Sales)
prunetree
str(data)
library(dplyr)
data %>% filter(ShelveLoc == 'Good') %>% summarise(mean(Sales))
mean(data[data$ShelveLoc == 'Good', 'Sales'], na.rm=T)

rpart.plot(prunetree,  nn=T)
#Interpretation
#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2

#improve the plot
rpart.plot(prunetree, nn=T, cex=.8, type=4)
#read this document to improve the plot
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
#http://www.milbo.org/rpart-plot/prp.pdf

library(dplyr)
#Predict for test value
(testdata = sample_n(data,2))
(predictedSales=predict(prunetree, newdata=testdata, type='vector'))
cbind(testdata, predictedSales)
#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class
(predict(prunetree, newdata=testdata, type='vector'))
#ERROR - use vector

#see online help here
#https://www.datacamp.com/community/tutorials/decision-trees-R



================================================
FILE: 01-IIM/31b2-DT-CART-sales.R
================================================
# CART Models - HH Case Study - Regression
library(rpart)
library(rpart.plot)
library(forecast)


library(gsheet)
url='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'
data = as.data.frame(gsheet2tbl(url))
str(data)

# Summarize the dataset
summary(data)
names(data)
# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(data), size = 0.7*nrow(data))
Index

# Create Train dataset
train= data[Index, ]
nrow(train)

# Create Test dataset
test = data[-Index, ]
nrow(test)
nrow(test) + nrow(train)

########################### Modeling #################################

trainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = "anova")
trainModel
mean(train$Annual_Sales)

# Plot the Regression Tree
rpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)

#cp selection
printcp(trainModel)
trainModel_prune = prune(trainModel, cp=0.01)
rpart.plot(trainModel_prune)

#Predict and check accuracy
predictSales_test = predict(trainModel_prune, newdata = test, type = "vector")
predictSales_test  #vector to print values of sales predicted

library(forecast)
# Validate RMSE and MAPE calculation with a function in R
ModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)
ModelAccuarcy
#RMSE should be as less as possible



================================================
FILE: 01-IIM/31b3-DT-CART-titanic.R
================================================
# Decision Tree - Classification
#we want predict for combination of input variables, is a person likely to survive or not
library(dplyr)
#import data from online site
path = 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/titanic_train.csv'
titanic <- read.csv(path)
head(titanic)
names(titanic)
data = titanic[,c(2,3,5,6,7)]  #select few columns only
head(data)
dim(data)
#load libraries
library(rpart)
library(rpart.plot)
str(data)
#Decision Tree
names(data)
table(data$Survived)
342/891
str(data)
data$Pclass = factor(data$Pclass)
str(data)
table(data$Sex)
table(data$Pclass)
table(data$Pclass, data$Survived)
head(data)
fit <- rpart(Survived ~ ., data = data, method = 'class')
#fit1 <- rpart(Survived ~ Pclass + Sex + Age + Sibsp, data=data, method='class')
fit
rpart.plot(fit)
rpart.plot(fit, extra = 104, cex=.6, nn=T)  #plot
nrow(data)
table(data$Survived)
342/891; 549/891
table(data$Sex)
head(data)
printcp(fit) #select complexity parameter
prunetree2 = prune(fit, cp=.018)
rpart.plot(prunetree2, cex=.8,nn=T, extra=104)
prunetree2
nrow(data)
table(data$Survived)
# predict for Female, pclass=3, siblings=2, what is the chance of survival

#Predict class category or probabilities
(testdata = sample_n(data,2))
predict(prunetree2, newdata=testdata, type='class')
predict(prunetree2, newdata=testdata, type='prob')
str(data)
testdata2 = data.frame(Pclass=factor(2), Sex=factor('male'), Age=15, SibSp=2)
testdata2
predict(prunetree2, newdata = testdata2, type='class')
predict(prunetree2, newdata = testdata2, type='prob')

#Use decision trees for predicting
#customer is likely to buy a product or not with probabilities
#customer is likely to default on payment or not with probabilities
#Student is likely to get selected, cricket team likely to win etc

#Imp steps
#select columns for prediction
#load libraries, create model y ~ x1 + x2 
#prune the tree with cp value
#plot the graph
#predict for new cases

#rpart, CART, classification model
#regression decision = predict numerical value eg sales



================================================
FILE: 01-IIM/31b4-DT-CART-loan.R
================================================
#Decision Tree - Not completed
#(https://rpubs.com/fabiorocha5150/decisiontreemodel)

url='https://raw.githubusercontent.com/fabiorcampos/Bank-Marketing/master/data/bank-full.csv'
data= read.table(url,sep=';', header = T )
head(data)
dim(data)
str(data)
names(data)
df1 = data
df1
str(df1)
hist(df1$age)
hist(df1$balance)
table(df1$marital)
barplot(table(df1$marital), col=1:3, beside=T)
barplot(table(df1$loan), col=1:3, beside=T)



================================================
FILE: 01-IIM/31b5-DT-CART-gre.R
================================================
#Decision Tree -  GRE
library(dplyr)
library(rpart)
library(rpart.plot)
library(ggplot2)

#https://stats.idre.ucla.edu/r/dae/logit-regression/
#A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable

df1 <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(df1)
table(df1$admit)
273/400
dim(df1)
data = df1
data[, c('admit','rank')] = lapply(data[, c('admit','rank')], as.factor)
str(data)
table(data$admit)
table(data$rank)
table(data$rank, data$admit)
prop.table(table(data$rank, data$admit))
prop.table(table(data$admit))
#68% dont get admitted

#Model-1 -----
#Stage1------
library(caret)
index = createDataPartition(y=data$admit, p=.75, list=F)
train = data[ index, ]
test = data[-index, ]
sapply(list(train, test), dim)  #rows & cols

dtree1 <- rpart(admit ~ ., data=train)
dtree1
rpart.plot(dtree1, cex=.8, nn=T, extra=104)

#variable importance
dtree1$variable.importance
#An overall measure of variable importance is the sum of the goodness of split measures for each split for which it was the primary variable…

#predict on test----
Pdtree1 <- predict(dtree1, type='class', newdata=test)
Pdtree1  #class predicted
sum1 <- table(Pdtree1, test$admit, dnn=c('Predicted','Actual'))
caret::confusionMatrix(sum1)

#M1:Stage2------
printcp(dtree1)
dtree1prune = prune(dtree1, cp=.010417)
dtree1prune
rpart.plot(dtree1prune, cex=.8, nn=T, extra=104)
dtree1prune$variable.importance

Pdtree1B <- predict(dtree1prune, type='class', newdata=test)
Pdtree1B  #class predicted
(sum1B <- table(Pdtree1B, test$admit, dnn=c('Predicted','Actual')))
caret::confusionMatrix(sum1B)  #some improvement
caret::confusionMatrix(sum1)
head()
#better plot----
rpart.plot(dtree1prune, branch.lty=3, nn=T, type=4, cex=.8)

prp(dtree1prune, extra=6, branch.col = 'green')
#?prp for more details

library(rattle)
fancyRpartPlot(dtree1prune)

#printing rules----
rpart.plot::rpart.rules(dtree1prune)
rpart.utils::rpart.subrules.table(dtree1prune)
rattle::asRules(dtree1prune)

#Stage-3 ----
#68% dont get admitted
dtree2 <- rpart(admit ~ ., data=train, parms=list(prior=c(.7,.3)))
dtree2
rpart.plot(dtree2)
printcp(dtree2)
dtree2$variable.importance
dtree2prune = prune(dtree2, cp=.010213)
Pdtree2B <- predict(dtree2prune, type='class', newdata=test)
Pdtree2B  #class predicted
(sum2B <- table(Pdtree2B, test$admit, dnn=c('Predicted','Actual')))
caret::confusionMatrix(sum2B)  #is there improvement
caret::confusionMatrix(sum1B)


#links to read----
#https://www.gormanalysis.com/blog/decision-trees-in-r-using-rpart/
  

================================================
FILE: 01-IIM/31b5-DT-loanapproved1.R
================================================
# Decision Tree # loanapproved = age + job + house + credit

library(rpart)
library(rpart.plot)
library(dplyr)

loanapproved = sample(x=c('Yes','No'), size=50, replace=T)
age = runif(50,30,60)
house = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.4,.6))
job = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.6,.4))
credit = ceiling(rnorm(50,100, 10))
loan = data.frame(loanapproved, age,job,house, credit)
head(loan)

ctree = rpart(loanapproved ~ ., data=loan)
ctree
rpart.plot(ctree, cex=1)

ndata = data.frame(age=c(45,55), house=c("No",'Yes'), job=c("Yes",'No'), credit=c(90,100))
ndata
(p1=predict(ctree, newdata=ndata,type='class'))

(p2=predict(ctree, newdata=ndata,type='prob'))
(p3=predict(ctree, newdata=ndata,type='matrix'))
cbind(ndata, p1, p2, p3)


#class Tree

loan_tree = rpart(loanapproved ~ ., data=loan, method='class', control=rpart.control(minsplit=5, cp=-1))
loan_tree
rpart.plot(loan_tree)
rpart.plot(loan_tree,  type = 4,fallen.leaves = T, cex = 0.6)
rpart.plot(loan_tree,type=2, extra=104, cex=1, tweak=1, under=T, shadow=c('brown', 'green','red'), nn=T)

printcp(loan_tree)


plot(loan_tree)
text(loan_tree)



================================================
FILE: 01-IIM/31b5-DT-rpart-iris.R
================================================
#CART Regression Tree

#Load Libraries
library(rpart)  #does only binary splits; CART
library(rpart.plot)

#DataSet
str(iris)
head(iris)

table(iris$Species)

library(dplyr)
sample_n(iris, 3)
sample_frac(iris, .1)
# Classification Tree
summary(iris)
set.seed(1234)
#Predict Species
ctree = rpart(Species ~ ., method='class', data=iris)
ctree
rpart.plot(ctree, main='Classification Tree', nn=T, type=4, cex=1,extra=104)
printcp(ctree)
ctreeprune = prune(ctree, cp=0.44)
ctreeprune
rpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)
# Regression Tree - Predict Continuous Value Length
#Predict Sepal.Length
rtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method="anova", data=iris )
rtree
rpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)

#another way to plot
plot(rtree, uniform=TRUE, main="Regression Tree for Sepal Length")
text(rtree, use.n=TRUE, cex = 1)
rtree

#CP
printcp(rtree)
rsq.rpart(rtree)  #r2 on anova only

rtreeprune = prune(rtree, cp=0.023)
rpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)



#https://analytics4all.org/2016/11/23/r-decision-trees-regression/


#Predict : Class  : Category of Flower
str(iris)
testData1  = data.frame ( Sepal.Length = 5, Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(ctree, testData1, method = "class")  #setosa


#Predict : Continous Value : Length

testData2  = data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(rtree, testData2, method = "anova") #5.17



================================================
FILE: 01-IIM/31b7-DT-party.R
================================================
#Decision Tree 2
#libraries - partykit, strucchange

#ctree - Classification and Regression
library(partykit) # use this to model, can also use party but that will give different output
?airquality
head(airquality)
sum(is.na(airquality)) 
summary(airquality)
### regression----
airq <- subset(airquality, !is.na(Ozone))
sum(is.na(airq)) 
summary(airq)
airct <- ctree(Ozone ~ ., data = airq)
airct  
plot(airct)
plot(airct, type='simple')
plot(as.simpleparty(airct))   #when partykit is used to model

library("strucchange")
strucchange::sctest(airct, node = 1)
strucchange::sctest(airct, node = c(2,7,4,9))

df1 = airq[airq$Temp <= 82 & airq$Wind <= 6.9, ]
head(df1)

boxplot(df1$Ozone)
(ndata = airq[3,])
predict(airct, newdata = ndata)

predict(airct)
mean((airq$Ozone - predict(airct))^2)  #error
nodeids(airct,2)
nodeids(airct)
nodeids(airct, terminal=T)
nodeids(airct, from=2, terminal=T)
nodeids(airct,1,5)

range(airq$Ozone)
#party
#party::ctree(Ozone ~ ., data = airq)
head(iris)
table(iris$Species)
library(partykit)
### classification ---
irisct <- partykit::ctree(Species ~ .,data = iris)
irisct
plot(irisct, type='simple')
strucchange::sctest(irisct, node = 1)
strucchange::sctest(irisct, node = c(2,3,4))
#7 not significant
nodeapply(irisct, ids = nodeids(irisct), function(n) info_node(n)$p.value)




table(predict(irisct), iris$Species)
### estimated class probabilities, a list
iris[1:10,]
(tr <- predict(irisct, newdata = iris[1:10,], type = "prob"))
(tr <- predict(irisct, newdata = iris, type = "prob"))

(tr2 <- predict(irisct, newdata = iris[1:10,], type = "response"))
head(iris)


summary(lm(mpg ~ wt + hp, data=mtcars))
mtctree = ctree(mpg ~ wt + hp, data=mtcars)
mtctree
plot(mtctree)



#Me



================================================
FILE: 01-IIM/31f1-DT-cart2.R
================================================
#outlook

outlook =c('Sunny', 'Sunny', 'Overcast', 'Rain','Rain', 'Rain','Overcast', 'Sunny', 'Sunny','Rain','Sunny', 'Overcast','Overcast','Rain')
temperature = c( 'Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild')
humidity = c('Weak','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Strong')
wind = c( 'High','High','High','High','Normal','Normal','Normal','High','Normal','Normal','Normal','High','Normal','High')
play = c('No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No')

game = data.frame(outlook, temperature, humidity, wind, play)
head(game)

library(rpart)
library(rpart.plot)
dt_game1 = rpart(play ~ . , data=game) 
dt_game1
#no tree 
#cp=-1

dt_game2 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=1, minbucket = 1))
dt_game2
rpart.plot(dt_game2,cex=.8, nn=T)

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "gini"))
dt_game3
rpart.plot(dt_game3,cex=.8, nn=T)

?rpart

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "information"))
dt_game3
attributes(dt_game3)
rpart.plot(dt_game3,cex=.8, nn=T)
dt_game3$parms
dt_game3$variable.importance
dt_game3$method
dt_game3$cptable
dt_game3$control
dt_game3$splits
dt_game3$numresp
dt_game3$y
game$play
dt_game3$ordered






================================================
FILE: 01-IIM/32a4-DT-cart1.R
================================================
#Decision Tree

#CART : Classification and Regression Tree
library(rpart)
library(rpart.plot)
library(dplyr)
#model= rpart(y  ~ ., method='class', data= , control=rpart.control(minsplit=4, cp=0.00001))  #method=anova for regression tree
rpart.plot()
#predict(model, newdata = ...., type='class' or 'prob')

#--------------------
#we want predict for combination of input variables, is a person likely to survive or not

#import data from online site
path = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'
titanic = read.csv(path)
head(titanic)
names(titanic)
data1 = titanic[,c(2,3,5,6,7)]  #select few columns only
head(data1)
str(data1)
table(data1$pclass); table(data1$sex)
sapply(data1[c(1,2,3,5)], table)

#load libraries
library(rpart)
library(rpart.plot)
names(data1)
#Decision Tree- classification tree : predict survived 0 or 1
fit1 = rpart(Survived ~ . , data = data1, method = 'class')
fit1
rpart.plot(fit1, cex=.8, nn=T)  #plot

printcp(fit1) #select complexity parameter
#which has least xerror 
prunetree1 = prune(fit1, cp=.014)
# here we select a different value, no of splits will reduce
par(mfrow=c(1,2))
rpart.plot(prunetree1, extra=104, cex=.8,nn=T)
rpart.plot(prunetree1, cex=.8,nn=T)
par(mfrow=c(1,1))
rpart.plot(prunetree1, extra=104, cex=.8,nn=T)
prunetree1
nrow(data1)
table(data1$survived)
809/1309
prunetree1$variable.importance
table(data1$sex, data1$survived)
#Predict class category or probabilities

(testdata1 = sample_n(data1,2))
predict(prunetree1, newdata=testdata1, type='class')
predict(prunetree1, newdata=testdata1, type='prob')

#Use decision trees for predicting
#customer is likely to buy a product or not with probabilities
#customer is likely to default on payment or not with probabilities
#Student is likely to get selected, cricket team likely to win etc

#Imp steps
#select columns for prediction
#load libraries, create model
#prune the tree with cp value
#plot the graph
#predict for new cases

#----------------
#regression Tree
#Predict Numerical Value
library(ISLR)
data(Carseats)
data2 = Carseats
head(data2)

#Libraries for Decision Tree
library(rpart)
library(rpart.plot)
library(dplyr)

#Model
fit2 = rpart(Sales ~ . , data=data2, method='anova' )
fit2
rpart.plot(fit2, cex=.8, nn=T)

#this is large tree, so prune it: check cp
printcp(fit2)
#cp value should be chosen such that xerror is least
prunetree2 = prune(fit2, cp=0.05)
#here we have selected a different value to simplify the tree

prunetree2
rpart.plot(prunetree2, nn=T)
#Interpretation
#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2

#improve the plot
rpart.plot(prunetree2, nn=T, cex=.8, type=4)
#read this document to improve the plot
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
#http://www.milbo.org/rpart-plot/prp.pdf

#Predict for test value
(testdata2 = sample_n(data2,2))
testdata2
(predictedSales=predict(prunetree2, newdata=testdata2, type='vector'))
cbind(testdata2, predictedSales)
#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class
(predict(prunetree2, newdata=testdata2, type='class'))

#End of DT : 
#other libraries to DT - CHAID, ctree - rparty
#Read applications of DT in Business
#https://www.quora.com/What-are-some-practical-business-uses-of-decision-trees
#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466856/
#https://hbr.org/1964/07/decision-trees-for-decision-making
#https://www.researchoptimus.com/blog/uses-of-decision-trees-in-business-data-mining/

================================================
FILE: 01-IIM/32a4-DT-rf-4.R
================================================
#https://lgatto.github.io/IntroMachineLearningWithR/supervised-learning.html#random-forest

library("mlbench")
data(Sonar)
datatable(Sonar)


library("rpart") ## recursive partitioning
m <- rpart(Class ~ ., data = Sonar,
           method = "class")
library("rpart.plot")

#RF
library(caret)
set.seed(12)
model <- train(Class ~ .,  data = Sonar, method = "ranger") 
print(model)
rpart.plot(m)
p <- predict(m, Sonar, type = "class")
table(p, Sonar$Class)
plot(model)


================================================
FILE: 01-IIM/32c2-DT-chaid-usvote.R
================================================
#CHAID - dataset USvote #multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)  #library for performing CHAID decision tree
#used when all variables are categories
#Dataset
data(USvote)  #from lib CHAID
?USvote
head(USvote)
str(USvote)
#Quick CHAID analysis
set.seed(101)
nrow(USvote)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
chaidModel = chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))
#minsplit	Number of observations in splitted response at which no further split is desired.
#minbucket Minimum number of observations in terminal nodes.
#minprob	Mininimum frequency of observations in terminal nodes.
print(chaidModel)
plot(chaidModel)
?chaid

================================================
FILE: 01-IIM/32d1-DT-CHAID-usvote.R
================================================
#CHAID - dataset USvote #multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
#you may need partykit from CRAN also
library(CHAID)  #library for performing CHAID decision tree
#Dataset
data(USvote)  #from lib CHAID
?USvote
head(USvote)
str(USvote)
#Quick CHAID analysis
set.seed(101)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
head(sample1)
str(sample1)
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 10, minsplit=20, minprob=0))

?chaid
print(chaidModel)
plot(chaidModel)
sample1 %>% filter(marstat=='married') %>% tally()
#sample1 %>% filter(ager=='18-24') %>% tally
dim(sample1)
chaidModel2 <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=20, minprob=0, maxheight=2))
plot(chaidModel2) 

#Summary
#More than 2 splits
#used in Marketing / Business 
#Net Promoter Score, Feedback, Where all variables are categories

================================================
FILE: 01-IIM/32d5-DT-CART-RF.R
================================================
# Classification using DT and RF

#load libraries
library(ggplot2)
library(randomForest)
library(rpart)
library(rpart.plot)
library(pander)
library(caTools)
#library(rattle)
#library(RColorBrewer)
library(plyr)


#import data from online site
path = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'
titanic <- read.csv(path)
head(titanic)
names(titanic)
data1 = titanic[,c(2,3,5,6,7)]  #select few columns only
head(data1)
dim(data1)
str(data1)
summary(data1)

#split data - Train and Test : equal proportion of survived
set.seed(1234)
split1 <- sample.split(data1$survived, SplitRatio = 0.75)
train1 <- subset(data1,split1==TRUE)
test1 <- subset(data1,split1==FALSE)
dim(train1); dim(test1); dim(data1)

#Visualise data
ggplot(train1, aes(x=survived, fill=sex)) + geom_bar(width=.85, colour='darkgreen') + scale_fill_brewer() + labs(x='Survived-Y/N', y='Survived Count - Genderwise') + ggtitle("Titanic : Gender Vs Survived")

#CART
names(data)
dtree1 = rpart(survived ~ . , data=train1, method='class')
dtree1

#Performance on Training Data
predict_train1 = predict(dtree1, newdata=train1, type='class')
confmatrix_train1 = table(train1$survived, predict_train1)
confmatrix_train1
accuracy_train1 = (confmatrix_train1[1,1] + confmatrix_train1[2,2]) / sum(confmatrix_train1)
accuracy_train1  # 81%

#Performance on Test Data
predict_test1 = predict(dtree1, newdata=test1, type='class')
confmatrix_test1 = table(test1$survived, predict_test1)
confmatrix_test1
accuracy_test1 = (confmatrix_test1[1,1] + confmatrix_test1[2,2]) / sum(confmatrix_test1)
accuracy_test1  # 82%

#plot the tree
prp(dtree1)
rpart.plot(dtree1, extra=104,nn=T)


#using RF-----
#remove missing values
data2= data1  #copy of data1
sum(is.na(data2))
dim(data2)
colSums(is.na(data2))
#age has 263 missing values : Remove column age or impute
knitr::kable(colSums(is.na(data2))) #another view

#use rpart to predict age through anova method
predictageModel = rpart(age ~ . ,data=data2[!is.na(data2$age),], method='anova') #uses 1046 rows 
predictageModel
data2[is.na(data2$age), 'age'] <- predict(predictageModel, newdata = data2[is.na(data2$age),], type='vector')
colSums(is.na(data2)) #missing values of age removed
summary(data2)

#check for class: survived should be a factor
sapply(data2, class)
data2$survived = as.factor(data2$survived)

#split data
#split data - Train and Test : equal proportion of survived
set.seed(1234)
split2 <- sample.split(data2$survived, SplitRatio = 0.75)
train2 <- subset(data2,split2==TRUE)
test2 <- subset(data2,split2==FALSE)
dim(train2); dim(test2); dim(data2)
#check for class: survived should be a factor
sapply(train2, class)
sapply(test2, class)


#Model
rf1 = randomForest(survived ~ . , data=train2, importanct=TRUE, ntree =2000)
# error - DV to be factors , ntree ~ 500 (Less) to create unique trees.
importance(rf1) #sex is most significant
varImpPlot(rf1)
rf1
##Out of Bag(OOB) error is the overall classification error in the above model : > 15%
plot(rf1)
legend('topright', colnames(rf1$err.rate), col=1:3, fill=1:3)
#he black line shows the overall error rate which falls below 20%. The red and green lines show the error rate for ‘died’ and ‘survived’ respectively. We can see that right now we’re much more successful predicting death than we are survival.


#Predict
predict_test2 = predict(rf1, newdata=test2, type='class')
confmatrix_test2 = table(test2$survived, predict_test2)
confmatrix_test2
accuracy_test2 = (confmatrix_test2[1,1] + confmatrix_test2[2,2]) / sum(confmatrix_test2)
accuracy_test2  # 85%




#------
library(mice)
summary(data1)
data3 = data1
set.seed(1234)
names(data3)
mice_mod <- mice(data3[, !names(data3) %in% c('survived')], method='rf') 
mice_output <- complete(mice_mod)
mice_output
data3$age <-  mice_output$age
summary(data3)


#plot RF
#https://stats.stackexchange.com/questions/41443/how-to-actually-plot-a-sample-tree-from-randomforestgettree
devtools::install_github('araastat/reprtree')
library(reprtree)
reprtree:::plot.getTree(model)
getTree(rf1, 1, labelVar=TRUE)
rf1$forest$treemap


================================================
FILE: 01-IIM/32k1-CLS-svm1.R
================================================
#SVM using caret package
#http://dataaspirant.com/2017/01/19/support-vector-machine-classifier-implementation-r-caret-package/

library(caret)

#data Import / Properties
heart_df <- read.csv("./data/heart_tidy.csv", sep = ',', header = FALSE)
str(heart_df)
head(heart_df)
dim(heart_df)
anyNA(heart_df) #no missing values
summary(heart_df)
#convert target variable to category - factor
heart_df[["V14"]] = factor(heart_df[["V14"]])

#Partition data : 14th col is DV
set.seed(3033)
intrain <- createDataPartition(y = heart_df$V14, p= 0.7, list = FALSE)
training <- heart_df[intrain,]
testing <- heart_df[-intrain,]
dim(training); dim(testing)

#model
#setting model parameters
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3233)
svm_Linear <- train(V14 ~., data = training, method = "svmLinear",   trControl=trctrl,preProcess = c("center", "scale"), tuneLength = 10)

svm_Linear

#Predict
test_pred <- predict(svm_Linear, newdata = testing)
test_pred
confusionMatrix(test_pred, testing$V14 )


================================================
FILE: 01-IIM/33g3-CLS-randomForest1.R
================================================
# Random Forests
#http://rpubs.com/Jeffery/titanic

#import data from online site
path = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'
titanic <- read.csv(path)
head(titanic)
names(titanic)
str(titanic)

#Random Forest
#Random forests are based on a simple idea: 'the wisdom of the crowd'. 

# In Decision Tree you learned how to use it to make a binary prediction. 
# To improve our technique, we can train a group of Decision Tree classifiers, 
# each on a different random subset of the train set. To make a prediction, 
# we just obtain the predictions of all individuals trees, 
# then predict the class that gets the most votes. 
# This technique is called Random Forest.

#Import Data
library(dplyr)

data_train <- read.csv("https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_train.csv")
head(data_train)
#ignore first column
data_train <- select(data_train,-1)

###Or

#data_train <- read.csv("https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_train.csv") %>%  select(-1)

#data_test <- read.csv("https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_test.csv")
#data_test <- select(data_test,-1)



#Cross Validation
#One way to evaluate the performance of a model is to train it on a number of different smaller datasets 
#and evaluate them over the other smaller testing set.


# RandomForest(formula, ntree=n, mtry=FALSE, maxnodes = NULL)
# Arguments:
# - Formula: Formula of the fitted model
# - ntree: number of trees in the forest
# - mtry: Number of candidates draw to feed the algorithm. By default, it is the square of the number of columns.
# - maxnodes: Set the maximum amount of terminal nodes in the forest
# - importance=TRUE: Whether independent variables importance in the random forest be assessed

#Grid Search definition
#the model will be evaluated over all the combination you pass in the function, using cross-validation.


#In order to lower the computational cost.
#Use Random Search Definition

#install.packages(c("randomForest","caret","e1071"))
library(randomForest)
library(caret)
library(e1071)


# trainControl(method = "cv", number = n, search ="grid")
# arguments
# - method = "cv": The method used to resample the dataset. 
# - number = n: Number of folders to create
# - search = "grid": Use the search grid method.


trControl <- trainControl(method = "cv", number = 10, search = "grid")

# train(formula, df, method = "rf", metric= "Accuracy", trControl = trainControl(), tuneGrid = NULL)
# argument
# - `formula`: Define the formula of the algorithm
# - `method`: Define which model to train. Note, at the end of the tutorial, there is a list of all the models that can be trained
# - `metric` = "Accuracy": Define how to select the optimal model
# - `trControl = trainControl()`: Define the control parameters
# - `tuneGrid = NULL`: Return a data frame with all the possible combination

set.seed(1234)
# Run the model
rf_default <- train(survived~., data = data_train,method = "rf", metric = "Accuracy",  trControl = trControl)
# Print the results
print(rf_default)

#Search best mtry

tuneGrid <- expand.grid(.mtry = c(1: 10))
tuneGrid
rf_mtry <- train(survived~.,  data = data_train, method = "rf", metric = "Accuracy", tuneGrid = tuneGrid, trControl = trControl, importance = TRUE,   nodesize = 14, ntree = 300)

print(rf_mtry)

#The best value was stored in 
rf_mtry$bestTune$mtry

best_mtry <- rf_mtry$bestTune$mtry 
best_mtry


store_maxnode <- list()
tuneGrid <- expand.grid(.mtry = best_mtry)
for (maxnodes in c(5: 15)) {
  set.seed(1234)
  rf_maxnode <- train(survived~.,
                      data = data_train,
                      method = "rf",
                      metric = "Accuracy",
                      tuneGrid = tuneGrid,
                      trControl = trControl,
                      importance = TRUE,
                      nodesize = 14,
                      maxnodes = maxnodes,
                      ntree = 300)
  current_iteration <- toString(maxnodes)
  store_maxnode[[current_iteration]] <- rf_maxnode
}
results_mtry <- resamples(store_maxnode)
summary(results_mtry)



store_maxtrees <- list()
for (ntree in c(250, 300, 350, 400, 450, 500, 550, 600, 800, 1000, 2000)) {
  set.seed(5678)
  rf_maxtrees <- train(survived~.,
                       data = data_train,
                       method = "rf",
                       metric = "Accuracy",
                       tuneGrid = tuneGrid,
                       trControl = trControl,
                       importance = TRUE,
                       nodesize = 14,
                       maxnodes = 24,
                       ntree = ntree)
  key <- toString(ntree)
  store_maxtrees[[key]] <- rf_maxtrees
}
results_tree <- resamples(store_maxtrees)
summary(results_tree)

####For optimised result
# 
# ntree =800: 800 trees will be trained
# mtry=4: 4 features is chosen for each iteration
# maxnodes = 24: Maximum 24 nodes in the terminal nodes (leaves)

fit_rf <- train(survived~.,
                data_train,
                method = "rf",
                metric = "Accuracy",
                tuneGrid = tuneGrid,
                trControl = trControl,
                importance = TRUE,
                nodesize = 14,
                ntree = 800,
                maxnodes = 24)

prediction <-predict(fit_rf, data_test)


confusionMatrix(prediction, data_test$survived)

#accuracy of 80%

varImp(fit_rf)

================================================
FILE: 01-IIM/34a1-CLUST-clustering.R
================================================
#Clustering
#sample data, iris, no of clusters
library(cluster)
library(fpc)
library(dplyr)
#kmeans -
#sample Data
marks = data.frame(sub1=c(0,1,2,4,5,4,6,7),sub2=c(0,1,2,3,3,4,5,5))
marks
#with 1 column
marks[,1]
km12 = kmeans(marks[,1], center=3)
#analyse output
km12

km12$cluster  #which row is assigned to which cluster No
cbind(marks[,1], km12$cluster)
km12$size  #size of each cluster
km12$centers  #average of each variable for each cluster
km12$iter  #no cycles, optimal cluster was found

marks %>% mutate(cluster = km12$cluster) %>% group_by(cluster) %>%  summarise(mean(sub1))

#Distances
km12$withinss
km12$tot.withinss  #sum of withinss
km12$betweenss
km12$tot.withinss + km12$betweenss
km12$totss
km12$iter


#now do it for both the columns
marks
km23 = kmeans(marks, center=3)
#sqrt((x1-x2)^2 + (y1-y2)^2)
km23
km23$centers

km23$betweenss
km23$tot.withinss
km23$withinss
km23$betweenss/ (km23$betweenss + km23$tot.withinss)
km23$betweenss; km23$betweenss/ (km23$totss) #same as below
km23  #within cluster sum of sq by cluster
# should be high for good clustering

#plots
plot(marks)
plot(marks,col=km23$cluster,cex=1.5)
abline(v=c(5.2, 0, 1.5), h=c(4,0,1.5))
km23$centers
points(km23$center,col=1:2,pch=8,cex=2)  #center point
#see the change in columns : use PCA
plotcluster(marks, km23$cluster)
clusplot(marks, clus=km23$cluster, color=TRUE, shade=TRUE, labels=2, lines=1, plotchar=T, span=T,main="Cluster Plot")
?clusplot

#iris dataset

#Plot1----
library(cluster)
library(fpc)

data(iris)
head(iris)
dim(iris)
data = iris[, -5] # without known classification 
# Kmeans cluster analysis
iriskm1 =  kmeans(data, centers=3)
plotcluster(data, iriskm1$cluster)

# More complex : PCA method: 2 dim
clusplot(data, iriskm1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
iris[c(16,19),]

# another method
with(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) 
with(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) 


#Finding optimal No of Clusters
iris
table(iris$Species)
data = iris[-5]  #remove target column
head(data)

km1= kmeans(data,centers=1)
km1$tot.withinss; km1$withinss

km2= kmeans(data,centers=2)
km2$tot.withinss; km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss ; km3$withinss


km4= kmeans(data,centers=4)
km4$tot.withinss; km4$withinss

km5= kmeans(data,centers=5)
km5$tot.withinss;km5$withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km5$tot.withinss ; km5$tot.withinss
data[km5$cluster==4,]

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")  
#avg -  distance Dij between two clusters Ci and Cj is the mean of the distances between the pair of points x and y

kiris = kmeans(data, centers=3)
kiris$centers
irisclusters = cbind(iris$Species, data, kiris$cluster)
head(irisclusters)

library(dplyr)
iris %>% group_by(Species) %>% summarise_all(mean)
kiris$centers

#setsoa - Cluster1, etc
#grouping may not be perfect, but close to orginal classification
#this way we can do Customer, Product Segmentation
#Other Clustering Techniques - Hierarchical etc





#Scaling in Clusters: marks of different max
set.seed(1234); marks50 = ceiling(runif(100, 1,50)) 
set.seed(1234); marks500 = ceiling(runif(100, 250,500)) 

students1= data.frame(marks50, marks500)
head(students1)

skm1 = kmeans(students1, centers=3)
skm1$centers
skm1$withinss

#scaling gives equal importance to all variables
#avoid obtaining clusters that are dominated by variables having the largest amount of variation

students2 = scale(students1)
head(students2)
skm2 = kmeans(students2, centers=3)
skm2$centers
skm2$withinss

par(mfrow=c(1,2))
plot(students1$marks50, students1$marks500, col=skm1$cluster)
plot(students1$marks50, students1$marks500, col=skm2$cluster)
#better cluster in 2nd case
par(mfrow=c(1,1))



================================================
FILE: 01-IIM/34a1-CLUST-samplecase.R
================================================
# Clustering

set.seed(1234)
subject1 = trunc(rnorm(30, mean=60, sd=15))
range(subject1)
subject1
marks = data.frame(subject1)
head(marks)
marks
sort(marks$subject1)
k2 = kmeans(marks, centers=2)
k2
k2$size
k2$iter
cbind(marks, k2$cluster) #which data row into which cluster
length(marks[k2$cluster==1,])
marks[k2$cluster==2,]
marks[k2$cluster==1,]

mean(marks[k2$cluster==1,])
mean(marks[k2$cluster==2,])
k2$centers

k2a = kmeans(marks, centers=c(50,70))
k2a
k2a$centers

#create marks of students
#perform clustering to group students with similar marks

head(iris)
head(mtcars)
marks
subject
subject1 = trunc(rnorm(30, mean=60, sd=15))
subject1
scale(subject1)
cbind(subject1, scale(subject1))
?scale

scale(mtcars)


================================================
FILE: 01-IIM/34b2-CLUST-customer.R
================================================
# HH MA example  - customer

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)

names(data)
summary(data)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200,nstart = 1,  method = c("euclidean"))

k1$centers  # group means
###Fetch size/n of obs for the groups
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")


================================================
FILE: 01-IIM/34d1-CLUST-creditData.R
================================================
#Clustering - Credit Data
library(dplyr)
library(ggplot2)
library(gridExtra)
library(cluster)    # clustering algorithms
library(factoextra) # clustering algorithms & visualization

link="https://online.stat.psu.edu/onlinecourses/sites/stat508/files/lesson01/Credit.data"

df = read.table(link, header=T)
head(df)
sapply(df, class)
(num_cols <- unlist(lapply(df, is.numeric)) )
data <- df[ , num_cols]
head(data)

#group them---
km3 = kmeans(data, centers=3)
km3
km3$centers

km3$cluster
table(km3$cluster)
data[km3$cluster==1,]

library(cluster)    # clustering algorithms
library(factoextra) # clustering algorithms & visualization
fviz_cluster(km3, data = data)
head(data)
data %>% mutate(cluster = km3$cluster, rnames = row.names(data)) %>%  ggplot(aes(Income, Age, color = factor(cluster), label = rnames)) +   geom_text()
data %>% mutate(cluster = km3$cluster, rnames = row.names(data)) %>%  ggplot(aes(Education, Age, color = factor(cluster), label = rnames)) +   geom_text()

#how many clusters to select ?
#read..



#cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated.
#centers: A matrix of cluster centers.
#totss: The total sum of squares.


#Plotting distances
#select sample
(data2 <- data %>% sample_n(10))

distance <- get_dist(data2)
fviz_dist(distance, gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))



#------
#withinss: Vector of within-cluster sum of squares, one component per cluster.
#tot.withinss: Total within-cluster sum of squares, i.e. sum(withinss).
#betweenss: The between-cluster sum of squares, i.e. $totss-tot.withinss$.
#size: The number of points in each cluster.
#-----
(data3 <- data %>% sample_n(100))

#visualising clusters----
k2 <- kmeans(data3, centers = 2, nstart = 25)
k3 <- kmeans(data3, centers = 3, nstart = 25)
k3
k4 <- kmeans(data3, centers = 4, nstart = 25)
k5 <- kmeans(data3, centers = 5, nstart = 25)

# plots to compare
p2 <- fviz_cluster(k2, geom = "point", data = data3) + ggtitle("k = 2")
p3 <- fviz_cluster(k3, geom = "point", data = data3) + ggtitle("k = 3")
p4 <- fviz_cluster(k4, geom = "point", data = data3) + ggtitle("k = 4")
p5 <- fviz_cluster(k5, geom = "point", data = data3) + ggtitle("k = 5")

library(gridExtra)
grid.arrange(p2, p3, p4, p5, nrow = 2)
#which cluster has good seperation and cohesion

#elbow method to find number of clusters
set.seed(123)
fviz_nbclust(data3, kmeans, method = "wss")

# compute gap statistic
set.seed(123)
gap_stat <- clusGap(data3, FUN = kmeans, nstart = 25,K.max = 10, B = 50)
print(gap_stat, method = "firstmax")
fviz_gap_stat(gap_stat)




#read more----
#https://uc-r.github.io/kmeans_clustering

#Hierarchical Clustering
library(tidyverse)  # data manipulation
library(cluster)    # clustering algorithms
library(factoextra) # clustering visualization
library(dendextend) # for comparing two dendrograms

head(data)
data4 <- dplyr::sample_n(data, 50)
dim(data4)

# Dissimilarity matrix
d1 <- dist(data4, method = "euclidean")
# Hierarchical clustering using Complete Linkage
hc1 <- hclust(d1, method = "complete" )
# Plot the obtained dendrogram
plot(hc1, cex = 0.6, hang = -1)

# Cut tree into 4 groups
sub_grp4 <- cutree(hc1, k = 4)
table(sub_grp4)
plot(sub_grp4)
data4 %>%   mutate(cluster = sub_grp4) %>%   head

#plot clusters with original dendrogram
plot(hc1, cex = 0.6)
rect.hclust(hc1, k = 4, border = 2:5)

fviz_cluster(list(data = data4, cluster = sub_grp4))

#compare Dendrograms with 2 methods
# Compute distance matrix
res.dist <- dist(data4, method = "euclidean")

# Compute 2 hierarchical clusterings
hc2a <- hclust(res.dist, method = "complete")
hc2b <- hclust(res.dist, method = "ward.D2")

# Create two dendrograms
dend2a <- as.dendrogram (hc2a)
dend2b <- as.dendrogram (hc2b)

tanglegram(dend2a, dend2b)


#Optimal no of clusters
fviz_nbclust(data4, FUN = hcut, method = "wss")
fviz_nbclust(data4, FUN = hcut, method = "silhouette")
gap_stat <- clusGap(data4, FUN = hcut, nstart = 25, K.max = 10, B = 50)
fviz_gap_stat(gap_stat)
head(data4)

#read
#https://uc-r.github.io/hc_clustering
#https://www.datacamp.com/community/tutorials/hierarchical-clustering-R

================================================
FILE: 01-IIM/34e4-clust-NOC-iris.R
================================================
#Optimal Number of Clusters in data
#Reduce total within ss
iris
dim(iris)
head(iris)
table(iris$Species)

data = iris[-5]
head(data)

km1= kmeans(data,centers=1)
km1$withinss
km1$tot.withinss

km2= kmeans(data,centers=2)
km2$tot.withinss
km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss
km3$withinss

km4= kmeans(data,centers=4)
km4$tot.withinss

km5= kmeans(data,centers=5)
km5$tot.withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")
?NbClust
kiris = kmeans(data, centers=3)
kiris$centers

cbind(iris$Species, data, kiris$cluster)
aggregate(cbind(Sepal.Length, Sepal.Width) ~ Species, data=iris, mean)
table(iris$Species)
table(kiris$cluster)

names(mtcars)
data2 = mtcars[c('mpg','disp','hp','wt')]
head(data2)
nc = NbClust(data2, distance="euclidean",min.nc=2, max.nc=15, method="average")
det(as.matrix(mtcars))
?na.action
km3= kmeans(data,centers=3)
km3$tot.withinss

cbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)

#we select no clusters at elbow point
#adding more clusters does not significantly reduce total withinss


kiris = kmeans(data, centers=3)
kiris$centers
plot(data$Sepal.Length, data$Sepal.Width, col=kiris$cluster)


================================================
FILE: 01-IIM/34g1-CLUST-segmentation.R
================================================
# HH MA example  - customer

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)
dim(data)
names(data)
summary(data)
str(data)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200)
?Kmeans
k1$centers  # group means
###Fetch size/n of obs for the groups
attributes(k1)
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")


================================================
FILE: 01-IIM/34h1-CLUST-clustering.R
================================================
## Clustering

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)

names(data)
summary(data)
str(data)
sapply(data, class)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k3 = amap::Kmeans(data[,-1],centers=3, iter.max = 200)

str(data)

k3$centers  # group means
###Fetch size/n of obs for the groups
k3$size
###Fetch sum of squared  for the groups
k3$withinss
###Fetch the cluster for each obs
#k1$cluster
k3$cluster
k1$centers
k3$cluster[9000:9800]
table(k3$cluster)
k3$size
data_clus_2 <- data[ k3$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "data_clus_2.csv")


================================================
FILE: 01-IIM/34h1-CLUST-clustering2.R
================================================
#clustering - segmentation into groups

(salary = rnorm(20, mean=60, sd=10))
(age = rnorm(20, mean=30, sd=5))
df1 = data.frame(salary, age)
head(df1)
#Euclidean Distance 
k3 = kmeans(df1, centers=3)
k3
k3$cluster
k3$withinss
final = cbind(df1, cluster=k3$cluster)
final[order(final$cluster),]
plot(df1, col=k3$cluster, cex=k3$cluster, pch=k3$cluster)
points(k3$centers, pch=18, cex=3, col=k3$cluster)
k3$centers
head(df1)
sapply(df1, range)
df2= scale(df1)

k3b = kmeans(df2, centers=3)
k3b
cbind(k3b$cluster, k3$cluster)
k3b$centers
df2;df1
df1$cluster = k3b$cluster
head(df1)
df1$cluster1 = k3$cluster
head(df1)

library(dplyr)
df1 %>% group_by(cluster) %>% summarise(mean(salary), mean(age))


================================================
FILE: 01-IIM/34j2-CLUST-NOC.R
================================================
#Optimal Number of Clusters in data
#Reduce total within ss
library(NbClust)

iris
dim(iris)
head(iris)
table(iris$Species)

data = iris[-5]
head(data)

km1= kmeans(data,centers=1)
km1$withinss
km1$tot.withinss

km2= kmeans(data,centers=2)
km2$tot.withinss
km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss

km4= kmeans(data,centers=4)
km4$tot.withinss

km5= kmeans(data,centers=5)
km5$tot.withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss

withinss = c(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss , km5$tot.withinss)
noclusters = c(1,2,3,4,5)
plot(x=noclusters, y=withinss, type='b')

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")

?NbClust
kiris = kmeans(data, centers=3)
kiris$centers

cbind(iris$Species, data, kiris$cluster)
aggregate(cbind(Sepal.Length, Sepal.Width) ~ Species, data=iris, mean)

names(mtcars)
data2 = mtcars[c('mpg','disp','hp','wt')]
head(data2)
nc = NbClust(data2, distance="euclidean",min.nc=2, max.nc=15, method="average")
det(as.matrix(mtcars))
?na.action
km3= kmeans(data,centers=3)
km3$tot.withinss

cbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)

#we select no clusters at elbow point
#adding more clusters does not significantly reduce total withinss


================================================
FILE: 01-IIM/35d1-CLUST-hclust1.R
================================================
# RinA - Hierarchical Clustering

#Packages Required - flexclust,cluster, NbClust
#Dataset - fkexclust::nutrient
#data(nutrient, package="flexclust")

#Hclust - calculate first distances and then do clustering
#no of clusters not required

library(flexclust)#----
data(nutrient)
str(nutrient)
head(nutrient,4)
dim(nutrient)

#Calculating Distances
#Distance, Dissimilarity, Proxmity
#Euclidean Distance
d = dist(nutrient)
d[1:10]
d
(dm = as.matrix(d)[1:4, 1:4])  #see only few values
dm[1,2]
nutrient[1:2,]
#dist Beef & burger

#Manually Calculating
paste('sqrt (', paste(nutrient[1,], nutrient[2,], sep="-", collapse=" ^2 + "), ")")
sqrt ( (340-245)^2 + (20-21)^2 + (28-17) ^2 + (9-9) ^2 + (2.6-2.7) )
dist(nutrient[c(1,2),])
#manuall & by algo is same

#-----
library(cluster)
#Pairwise distances 
daisy(nutrient[c(1,2),])  #same distance
daisy(nutrient)

#Scaling-----
#put all data into 1 scale - zscore
#Scaling Data - reduce of particular variable
data(nutrient, package='flexclust')

row.names(nutrient) = tolower(row.names(nutrient))
head(nutrient)
nutrient.scaled = scale(nutrient)  #zscore
head(nutrient.scaled)
d = dist(nutrient.scaled)
head(d)

#Perform HClustering-----
fit.average = hclust(d, method = 'average')
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')


#Eg
#Smaller No of Clusters

library(NbClust)
devAskNewPage(ask=T)#ask for next plot
nc = NbClust(nutrient.scaled, distance='euclidean', min.nc=2, max.nc=15, method='average')

devAskNewPage(ask=F)
par(mfrow=c(1,1))
barplot(nc$Best.n[1, ], xlab='No of Clusters', ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')

?NbClust
#Eg : Final Cluster Solution, Select 5
clusters = cutree (fit.average, k=5)
sort(clusters)
table(clusters)
aggregate(nutrient, by=list(cluster=clusters), median)

aggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters), median)

plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering \n5 Cluster Solution')
rect.hclust(fit.average, k=5)

library(rattle)
str(wine)
head(wine)
df = scale(wine[-1])
df

wssplot <- function(data, nc=15, seed=1234){
  wss <- (nrow(data)-1)*sum(apply(data,2,var))
  for (i in 2:nc){
    set.seed(seed)
    wss[i] <- sum(kmeans(data, centers=i)$withinss)}
  plot(1:nc, wss, type="b", xlab="Number of Clusters",
       ylab="Within groups sum of squares")}

wssplot(df)

library(NBClust)
set.seed(1234)
devAskNewPage(ask=T)
nc = NbClust(df, min.nc =2, max.nc=15, method='kmeans')
table(nc$Best.nc[1,])
par(mfrow=c(1,1))
barplot(table(nc$Best.nc[1,]), xlab='No of Clusters', ylab='No of Criteria',
        main='No of Clusters Chosen by 26 Criteria')

set.seed(1234)
fit.km  = kmeans(df,3, nstart=25)
fit.km
fit.km$size

fit.km$centers

aggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)

ct.km = table(wine$Type, fit.km$cluster)
ct.km

library(flexclust)
randIndex(ct.km)

library(cluster)
?pam
library(cluster)
set.seed(1234)
fit.pam = pam(wine[-1], k=3, stand=T)
fit.pam
fit.pam$medoids
clusplot(fit.pam, main='Bivariate Cluster Plot')
ct.pam = table(wine$Type, fit.pam$clustering)
ct.pam
randIndex(ct.pam)

#Eg
library(fMultivar)
set.seed(1234)
df = rnorm2d(1000, rho=.5)
df = as.data.frame(df)
df
plot(df, main='Bivariate Normal Distribution with rho=0.5')
wssplot(df)
library(NbClust)
nc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')

library(ggplot2)
library(cluster)
fit = pam(df, k=2)
fit
df$clustering = factor(fit$clustering)

par(mfrow=c(1,1))
ggplot(data=df, aes(x=V1,y=V2, color=clustering, shape=clustering)) +
  geom_point() +  ggtitle('Clustering of Bivariate Normal Data')
plot(nc$All.index[ ,4], type='o',     ylab='CCC', xlab='Number of    clusters', col='blue')

================================================
FILE: 01-IIM/35d2-CLUST-hclust2.R
================================================
#simple case of hierarchial clustering

library(flexclust)
data(nutrient, package='flexclust')
head(nutrient)
nutrient.scaled = scale(nutrient)  #zscore
head(nutrient.scaled)
d = dist(nutrient.scaled)
d  # diagonal matrix with distance values
head(d)
rownames(nutrient)

#Perform HClustering-----
fit.average = hclust(d, method = 'average')
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(fit.average, k=5)
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(fit.average, k=3, border='green')
plot(fit.average, hang=-1, xlab=NA, sub=NA, labels=rownames(nutrient), cex=.5, main='Average Linkage Clustering')
rect.hclust(fit.average, k=2, border='purple')
?rect.hclust
plot(fit.average, hang=-1, cex=.8, main='H Clustering')
rect.hclust(fit.average, h=3, border='blue')

library('dendextend')
## Example:
dend <- as.dendrogram(hclust(dist(USArrests), "ave"))
d1=color_branches(dend,k=5, col = c(3,1,1,4,1)) #k1-3; k2,k3,k5-1; k4-4
plot(d1) # selective coloring of branches :)
d2=color_branches(d1,k=5) # auto-coloring 5 clusters of branches.
plot(d2)
# More examples are in ?color_branches


================================================
FILE: 01-IIM/37a2-AR-eg.R
================================================
# Association Rule - Simple Example Case
# read this pdf for help
#https://cran.r-project.org/web/packages/arules/arules.pdf

#libraries
library(arules)
library(arulesViz)

#Create Data

#Method3 Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
itemlist
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist   #items in transactions
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
summary(tdata3)

tdata=tdata3

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
itemlist
image(tdata)

#Analysis
freqitems = eclat(tdata) #default support=.1
freqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))
freqitems
inspect(freqitems)

itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)

#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules[1:5])
inspect(rules)

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

rules2 = rules
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)

# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './myrules1.csv')

#Visualisation
plot(rules)



================================================
FILE: 01-IIM/37b1-AR-groceries.R
================================================
# Association Rules - Groceries data set ####

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries') #different format - transaction format
Groceries
#Structure of Groceries
str(Groceries)
Groceries
arules::LIST(Groceries[1:3])  #another view
arules::inspect(Groceries[1:3])
arules::inspect(Groceries[c(1000,5000,9000)])
arules::inspect(Groceries[sample(1:nrow(Groceries), size=3)])
nrow(Groceries)
#Find Frequent Itemset
#.01 * 9835; A + B + C = 3 items, A + B + C + D : 4 items
frequentItems = eclat(Groceries, parameter = list(supp = 0.01, minlen= 3, maxlen = 3))
#frequentItems = eclat (Groceries, parameter = list(minlen= 3))
inspect(frequentItems[1:3])   #correct lib to used
frequentItems
inspect(frequentItems[])  #inspect for printing the itemssets
options(digits=3)
#inspect(frequentItems[100:122])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries, topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
options(digits=2)#output decimal places to 2 digits
rules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=T)
inspect (rulesl[1:5])
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) 
rules2  #no of rules
rules #earlier rules
inspect(rules2[1:15])

#Find out what events were influenced by a given event - from already created rules
subset1 = subset(rules2, subset=rhs %in% "whole milk")
inspect(subset1[1:5]) # rhs has milk
subset1 = subset(rules2, subset=rhs %in% 'bottled beer' )
inspect(subset1) #rhs has beer
#inspect(rules2)
subset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2) # all items  %ain%
subset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )
inspect(subset2a) # any of the items %in%

#RHS, Confidence, sort by Lift
subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .5, by = 'lift', decreasing = T)
inspect(subset3)  #sometimes there may be no rules, change few parameters
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4)
subset4b = subset(rules2, subset=rhs %in% 'bottled beer'  )
inspect(subset4b) #no such rules

library(arulesViz) #install first
#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf
#Visualizing The Rules -----
subset1
rules2
inspect(subset2)
plot(subset1[1:2]) 
plot(subset1[1:2], measure=c("support", "lift"), shading="confidence")
#change the axis
plot(rules2[1:100], measure=c("support", "lift"), jitter=0,shading="confidence")
#

#Find what factors influenced an event ‘X’ - create fresh Rules
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.7), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
inspect(rules3[1:5])
inspect(rules3)


#rhs as it is, lhs to have tropical fruit or herbs
rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)
plot(subset4) 


#legend to filter
#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.


#summarise Association Rules / Market Basket analysis
#load libraries - arules, arulesViz
#load dataset in Transaction Format eg Groceries
#Find frequentitems set
#Find rules as per parameters
#Parameters - min support, min confidence, minlen, maxlen
#sort - confidence, lift, count, support
#subset of rules - lhs, rhs, confidence, all, any item
#plot of the rules 
#find interesting rules  - high lift, high confidence
#put strategy in place - location, bundle, discounts, advertisement


================================================
FILE: 01-IIM/37b2-AR-samplecase.R
================================================
# Association Rule - Simple Example Case
# read this pdf for help
#https://cran.r-project.org/web/packages/arules/arules.pdf

#libraries
library(arules)
library(arulesViz)


#Create Data

#Method3 Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
itemlist
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
summary(tdata3)

tdata=tdata3

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
itemlist
image(tdata)

#Analysis
freqitems = eclat(tdata) #default support=.1
freqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))

freqitems
inspect(freqitems)

support(items(freqitems[1:2]), transactions=tdata)
inspect(freqitems[1])
inspect(items(freqitems[1]))

itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)
write.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')


#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules[1:5])
inspect(rules)
write.csv(as.data.frame(inspect(rules)),'rules1.csv')
#sort rules by support
rules_s = sort(rules, by="support", decreasing=TRUE )
inspect(rules_s)
inspect(rules_s[1:5])  #itemsset having high support

#sort rules by confidence
rules_c = sort(rules, by="confidence", decreasing=TRUE )
inspect(rules_c)
inspect(rules_c[1:5])  #itemsset having high confidence

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

#Quality Data of Rules
quality(rules_c) 

#Redundant Rules
inspect(rules)
(redundant = which(is.redundant(rules)))
inspect(rules[c(8,9,10,11,12,14,14)])

inspect(rules[redundant])
inspect(rules)
write.csv(as(rules,"data.frame"), file='./data/rulesR.csv')

#Remove Redundant Rules
rulesNR <- rules[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now
inspect(rulesNR)

#Rules with LHS and RHS: single or combination
rules2= rulesNR
inspect(rules2)
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)



# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './data/myrules1.csv')

#Visualisation
plot(rules)


================================================
FILE: 01-IIM/37b3-AR-groceries-subset.R
================================================
#AR - Groceries - Subset

#Subsetting rules and itemsets
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,   maxlen=5, target='rules' ))
rules
#target='rules' # to mine for rules)


inspect(sort(rules, by="confidence", decreasing = T)[1:5])

#subset conditions
#rhs should be ‘bottled beer’
#confidence should be above .7
#results should be sorted by lift

#Subset1----
inspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7),   by = 'lift', decreasing = T))

#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”

# Another rule with different parameters
#subset2----
canned_rules <- apriori(Groceries,parameter = list(support=.001,  confidence=.01, maxlen=5, target='rules' ))

inspect(subset(canned_rules, subset=lhs %ain% c("liquor", "red/blush wine") & rhs %in% 'canned beer' ))
#no rule found or it could be significant low % less than 1 in 100 people would do

#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

#either side lhs or rhs
#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95
#subset3----
inspect(subset(rules, subset=items %ain% c("whole milk","yogurt") & confidence >.95))

#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9
inspect(subset(rules, subset=lhs %ain% c("whole milk","yogurt") & confidence >.9))

#Subset4----
#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9

inspect(subset(rules, subset= lhs %pin% "bread" & rhs %in% "whole milk" & confidence > .9))


#Subset5----
#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs
inspect(subset(rules, subset= lhs %ain% c("flour","whole milk") & confidence>.7))



#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.
rules <- apriori(Groceries,  parameter = list(support=.001, conf = .01, minlen=2, maxlen=2, target='rules'))

#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:

inspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))

crossTable(Groceries)['canned beer','bottled beer']

#the probability of a consecutive purchase (confidence) is pretty small: ~3%
#this is despite both bottled beer and canned beer being pretty popular purchases
eclat(Groceries, maxlen=1)
singleitems = eclat (Groceries, parameter = list(supp = 0.05, maxlen = 2)) 
inspect(singleitems)
inspect(sort (singleitems, by="count", decreasing=TRUE)[1:25])

crossTable(Groceries)['canned beer','canned beer']
crossTable(Groceries)['bottled beer','bottled beer']

crossTable(Groceries)['bottled beer','canned beer']
crossTable(Groceries)['canned beer','bottled beer']

quality(rules)$chi  <- interestMeasure(rules, measure='chi', significance=T, Groceries)
quality(rules)$chi[4785:4786]
#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes.



================================================
FILE: 01-IIM/37b5-AR-finproducts.R
================================================
# AR data for Finance

library("arules")

set.seed(101)
transactionID = sample(1:500, 1000, replace=T)
transactionID
table(transactionID)

finproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')
length(finproducts)
item = sample(finproducts,1000, replace=T)
item
orders = data.frame(transactionID, item)
head(orders)

library(dplyr)
orders %>% group_by(transactionID) %>% tally() %>% as.data.frame()

write.csv(orders, "./data/fintransactions.csv")

ordertrans <- arules::read.transactions(
  file = "./data/fintransactions.csv",  format = "single",
  sep = ",",  cols=c("transactionID","item"),  rm.duplicates = T
)
inspect(ordertrans[1:5])
ordertrans
summary(ordertrans)
str(ordertrans)
head(ordertrans)


#find frequent item set
frequentItems <- eclat (ordertrans, parameter = list(supp = 0.005, minlen= 2, maxlen = 5)) 
inspect(frequentItems[1:10])
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:15])
#support(A&B) = n(A&B)/ N

frequentItems

itemFrequencyPlot (ordertrans,topN = 15,type="absolute")
itemFrequencyPlot(ordertrans, topN = 10, type='relative')
abline(h=0.15)

#Create Rules
rules1 <- arules::apriori(ordertrans, parameter = list(supp = 0.005, conf = 0.5))
rules1
write.csv(inspect(rules1, './data/rules.csv'))
inspect(rules1[1:5])

rules1L = sort (rules1, by="lift", decreasing=TRUE)
inspect (rules1L[1:5])


#How To Control The Number Of Rules in Output ?
rules2 = apriori (ordertrans, parameter = list (supp = 0.005, conf = 0.5, minlen=3, maxlen=5)) # minlen = 3 & maxlen limits the elements in a rule to 3 & 5
inspect(rules2)

rules3 = apriori(ordertrans)  #use default values  no rules


#Find what factors influenced an event ‘X’


rules4 <- apriori(data=ordertrans, parameter=list (supp=0.001,conf = 0.08), appearance = list (default="lhs",rhs="Credit Card"), control = list (verbose=F))  #verbose=F no log
inspect(rules4[1:15])

#Find out what events were influenced by a given event
rules5 <- apriori (data=ordertrans, parameter=list (supp=0.001,conf = 0.05,minlen=2), appearance = list (default="rhs",lhs="General Insurance"), control = list (verbose=F)) 
inspect(rules5)

inspect( subset( rules5, subset = rhs %pin% c("Bonds") ))
inspect( subset( rules5, subset = rhs %pin% c("FD") ))


#Control lhs and rhs
rules6 <- apriori(ordertrans, parameter=list(support =0.00001, confidence =0.05, minlen=2, maxlen=5), appearance = list(lhs=c("Debit Card"), rhs=c("Credit Card"), default="none"))
inspect(rules6)

# commands for subset 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

inspect(subset(rules1, subset=items %ain% c("Mobile Banking") & confidence >.95))
inspect(subset(rules1, subset=lhs %ain% c("Mobile Banking") & confidence >.9))

inspect(subset(rules1, subset= lhs %pin% "Debit Card" & rhs %in% "Savings Account" & confidence > .7))

#Export Rules into a table
library(data.table)
rules_dt <- data.table( lhs = labels( lhs(rules2) ), 
                        rhs = labels( rhs(rules2) ), 
                        quality(rules2) )[ order(-lift), ]
rules_dt
DT::datatable(rules_dt)  # wrapper for datatables
write.csv(rules_dt, './data/arulesfin.csv')


#Visualizing The Rules -----
library(arulesViz)
inspect(rules1)
plot(rules1, measure=c("support", "lift"), shading="confidence")
plot(rules1, measure=c("support", "confidence"), shading="lift")

plot(rules1,method="graph",engine='interactive', shading="confidence") 



================================================
FILE: 01-IIM/37b5-AR-groceries.R
================================================
# Association Rules - Groceries data set ####
#load the libraries
#load the data set - transaction format built in - Groceries
#frequent items - min support
#rules - minsp, minconf
#support, confidence and lift : sort, filter, interpret
# total transactions - 100
# burger - 80
# pepsi - 50
# pepsi + burger - 40
#pepsi -> burger :4/5
#burger -> pepsi : 4/8

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it; reqd for Groceries
data('Groceries')
Groceries

#Structure of Groceries
str(Groceries)
Groceries
arules::LIST(Groceries[1:6])  #another view
arules::inspect(Groceries[1:5])
arules::inspect(Groceries[100])

length(Groceries)
#LIST(Groceries[length(Groceries)-5:length(Groceries)])

#Find Frequent Itemset
frequentItems = eclat(Groceries, parameter = list(supp = 0.01, minlen= 2, maxlen = 5)) 
.01 * 9835
inspect(frequentItems)
frequentItems
inspect(frequentItems[10:15])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 20, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = arules::apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
options(digits=3)
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=TRUE)
inspect (rulesl[1:5])
inspect (rulesl)

#which items have strong confidence and lift 
rules
#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=6)) 
inspect(rules2[1:50])
#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially - milk and whole milk for pin in milk
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

#Find out what events were influenced by a given event
subset1 = subset(rules2, subset=rhs %in% "whole milk")  # whole milk in rhs
inspect(subset1[1:10])  #show only 1st 10 rows ; change it to show more
#if no such pattern, no output or index out of range will be printed
subset1 = subset(rules2, subset=rhs %in% 'root vegetables' )   #this item in rhs
inspect(subset1[1:10])
subset2a = subset(rules2, subset=lhs %ain% c('baking powder','soda') )  #all items in lhs
inspect(subset2a)
subset2b = subset(rules2, subset=lhs %in% c('baking powder','soda') )  #any of these in lhs
inspect(subset2b[1:5])  #first 5 rules
inspect(subset2b)
subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3)
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4[1:5])

subset5 = subset(rules2, subset=(rhs %pin% 'milk' | lhs %pin% 'milk') & confidence > .8)   #this item in rhs
inspect(subset5[c(1:10, 20:30)])

#Visualizing The Rules -----
#(https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf)
plot(subset1[1:10]) 
inspect(subset1[1:10])
plot(subset1[1:10], measure=c("support", "lift"), shading="confidence")
plot(subset1, method = "two-key plot")
plot(subset1, method = "grouped")
plot(subset1[1:5], method = "paracoord")
inspect(subset1[1:5])
#
#we can create subset conditions at the time of creation of rules
#Find what factors influenced an event ‘X’
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
inspect(rules3[1:5])
inspect(rules3)


rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)

#end of AR
#understand the measures of AR - support, confidence, lift
#apply the rules; find frequent items, sort rules, subset rules on conditions ; plot rule; interpret them; bring changes to your strategy



================================================
FILE: 01-IIM/37c3-AR-redundant.R
================================================
# Association Rule - Simple Example Case
# read this pdf for help
#https://cran.r-project.org/web/packages/arules/arules.pdf

#libraries
library(arules)
library(arulesViz)

#Create Data

#Method Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
itemlist
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
summary(tdata3)

tdata=tdata3

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
itemlist
image(tdata)

#Analysis
freqitems = eclat(tdata) #default support=.1
freqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))

freqitems
inspect(freqitems)

support(items(freqitems[1:2]), transactions=tdata)
inspect(freqitems[1])
inspect(items(freqitems[1]))

itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)
write.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')


#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules[1:5])
inspect(rules)
write.csv(as.data.frame(inspect(rules)),'rules1.csv')
#sort rules by support
rules_s = sort(rules, by="support", decreasing=TRUE )
inspect(rules_s)
inspect(rules_s[1:5])  #itemsset having high support

#sort rules by confidence
rules_c = sort(rules, by="confidence", decreasing=TRUE )
inspect(rules_c)
inspect(rules_c[1:5])  #itemsset having high confidence

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

#Quality Data of Rules
quality(rules_c) 

#Redundant Rules
inspect(rules)
(redundant = which(is.redundant(rules)))
inspect(rules[c(8,9,10,11,12,14,14)])
redundant
red_rules = inspect(rules[redundant])
red_rules
inspect(rules)
write.csv(as(rules,"data.frame"), file='./data/rulesR.csv')

#Remove Redundant Rules
rulesNR <- rules[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now
inspect(rulesNR)

#Rules with LHS and RHS: single or combination
rules2= rulesNR
inspect(rules2)
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)



# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './data/myrules1.csv')

#Visualisation
plot(rules)

================================================
FILE: 01-IIM/44b1-TS-data.R
================================================
# create a time series data

#first create a vector of numerical values
# 36 observations 
set.seed(1234)
(sales = round(runif(36, 0,100)))
length(sales)

#This data can be daily, weekly, monthly, quarter, yearly data
#create yearly time series : start year 1980

#Yearly----
(ysales = ts(sales, frequency = 1)) #annual -1, month-12, daily-365

(yearlysales = ts(sales, start=c(1980), frequency=1))
yearlysales
(yearlysales1 = ts(sales, start=c(1980,3), frequency=1)) 
# 3rd yr from 1980
plot(yearlysales)

#find the year when sales was > 50
yearlysales1[ yearlysales1 > 50]
class(yearlysales1)
methods(class=ts)
methods(class=data.frame)
yearlysales1
(w1= window(yearlysales1, start=1985, end=1990))
plot(w1)

#Quarterly -----
12/4  # freq=4
(qtrsales = ts(sales, start=c(1980), frequency=4))
plot(qtrsales)
#list data from Qtr3 1980 to  1985
window(qtrsales, start=c(1980, 3), end=c(1985, 2))


#Monthly -----
12/12  # freq=12 start month=Apr/ 1990
(monsales = ts(sales, start=c(1990,4), frequency=12))
plot(monsales)
window(monsales, start=c(1991, 3), end=c(1992,4))

#create data from Feb 2000 to Nov 2002
(monsales1 = ts(sales, start=c(2000,2), end=c(2003,3), frequency=12)) #recycling of elements beyond given sales value
monsales1
str(monsales1)
length(monsales1)

#see subset of sales data : May 2000 to Aug 2001
window(monsales1, start=c(2000, 5), end=c(2001, 8))

#Daily TS
sales2 = ceiling(rnorm(365, mean=100, sd=10))
sales2  #vector of numeric values : convert to TS
#YYYY,day
(dailysales = ts(sales2, start=c(2017), frequency=365))

(dailysales = ts(sales2, start=c(2017,10), frequency=365))
window(dailysales, start=c(2017,50), end=c(2017,100))
mean(window(dailysales, start=c(2017,50), end=c(2017,100)))
head(sales2)
plot(dailysales)
class(dailysales)

#quarterly
sales3 = floor(rnorm(16, mean=200, sd = 12))
(qtrsales = ts(sales3, start = c(2018,1), frequency = 4))
plot(qtrsales)

#weekly
weekdata = ceiling(rnorm(52, mean=100, sd=10))
(weeksales = ts(weekdata, start = c(2018,1), frequency = 52))
plot(weeksales)

#year, qtr, month, week, daily

#fortnight
fortnightdata = ceiling(rnorm(12*2, mean=100, sd=10))
(fortnightsales = ts(fortnightdata, start = c(2018,1), frequency = 24))
plot(fortnightsales)

dailysales
diff(dailysales)
monsales
monthplot(monsales)
time(monsales)
methods(class='ts')
#timeseries intro


================================================
FILE: 01-IIM/44b1-TS-dates.R
================================================
#Data Format in R 
#When we import data into R, dates and times are usually stored as character or factor by default due to symbols such as “-”, “:” and “/”

Sys.Date()  #"2020-06-06"  : YYYY-MM-DD  : %Y-%m-%b
Sys.time() #"2020-06-06 14:22:54 IST" : %h:%m:%s timezone
cdate1='15/August/1947'
cdate1
cdate1 + 1  #error
class(cdate1)
cdate2 = c('15-Aug-1947', "26-Jan-1950", "01-Oct-2018")
cdate2

class(cdate1) ; class(cdate2)
#arithmetic operation like add, diff, day, month

#convert to Dates
#Default Format of Date in R
?as.Date()

#See/ Convert Date format
as.Date('2020-12-25')  #: year - month - day

#Default Format : Year-Month-Date : %Y-%m-%d
(date3a= as.Date('2020-12-25', format='%Y-%m-%d'))
class(date3a)

(date3b= as.Date('25-2020-12', format='%d-%Y-%m')) #: 25-%d
class(date3b)
date3b

#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan) 
#%d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

#convert cdate1 cdate2 to date formats
cdate1 #"15/August/1947
date1 = as.Date(cdate1, format='%d/%B/%Y')
date1
class(date1)
date1 + 1
#
cdate2 #"15-Aug-1947" "26-Jan-1950"
date2 = as.Date(cdate2, format='%d-%b-%Y')
date2
date2 + 1:3
#

#one more practise on date format
date3 =as.Date('25Mar20',format("%d%b%y"))
class(date3)
date3 + 2
as.character(date3, format='%A %Y')

#Date to Characters
class(date3)
(cdate2 = as.character(date3))
class(cdate2)

#excel date columns
#01 Jan 2018, 01/Jan/18, 01-Jan 2018, 2018-Jan-01, 2018 01 jan
#this is wrong
#The “Date” class means dates are stored as the number of days since January 1, 1970, with negative values for earlier dates. We can use the as.numeric function to view the raw values.

date1
as.numeric(date1)
date2
as.numeric(date2)
refDate = as.Date('1970-01-01')   #center point
#all dates before this will have - values, after this will have + numeric values
as.numeric(refDate)

ktbday = as.Date('1993-01-07')
as.numeric(ktbday)


as.numeric(Sys.Date())
Sys.Date() - as.Date('1970-01-01') 

#Sequence of Dates
#Create Sequence of Dates

#Next day after certain date
as.Date('2018-12-01') + 1
#Sequence of Dates
as.Date('2018-12-01') + 0:14
as.Date('2018-12-01') + 0:33

#alternative Dates
seq(1,30,2)
as.Date('2018-12-01') + seq(0,29,7)


#system date
Sys.Date()
#next 15 days after todays date
Sys.Date() + 1:15

##increment/ decrement dates 
(startdt = as.Date("2018-12-01", format="%Y-%m-%d"))
(next20days = startdt + 1:20) # start : 20 days course
startdt - 1
(prev20days = startdt - c(0:19)) # before : 20 days course
rev(prev20days)



#Data Arithmetic
# date Arithmetic and subsets

#start course on YYYY-MM-DD for 30 days
(course1 = as.Date("2018-12-01") + 0:29)

#Difference in dates
course1
length(course1)  #no of days
min(course1)  # first date
max(course1)  # last date
range(course1) # start to end
mean(course1)  # center of the course period
median(course1) # middle date

#sd(course1) #no meaning

#subset / select dates
course1
#1st and 5th dates
course1[c(1,5)] # 1st & 5th date 
#1st and 5th dates
course1[1] ; course1[5]

#duration from 8th date to first date
(duration1 = course1[8] - course1[1])
#total duration
(duration2 = max(course1) - min(course1)+ 1)

#duration since independence
(independencedays = Sys.Date() - as.Date('15-08-1947', '%d-%m-%Y'))
independencedays/365  #years

as.character(Sys.Date(), format="%Y--%m--/%d %A")
course1
as.character(course1, format="%A")
paste(course1 , as.character(course1, format="%A"), sep=":: ")

#day on which you were born
dob = "14/08/1994"
dob_date = as.Date(dob,format='%d/%m/%Y')
dob_date
as.character(dob_date, format="%A")

#get all staturday and sundays for year
sdate = '2019-01-01'
sdate1 = as.Date(sdate)
sdate2 = sdate1 + 0:364
as.character(sdate2, format='%A')



================================================
FILE: 01-IIM/44c2-TS-dates-lubridate.R
================================================
#Package Lubridate https://data.library.virginia.edu/working-with-dates-and-time-in-r-using-the-lubridate-package/
# Date Functions - Deal with Date & Time

#lubridate provides a series of functions that are a permutation of the letters “m”, “d” and “y” to represent the ordering of month, day and year. For example, if our data has a column of dates such as May 11, 1996, our dates are ordered month-day-year. Therefore we would use the mdy function to transform the column to a date object. If our dates were in the order of, say, year-month-day, we would use the ymd function. lubridate provides functions for every permutation of “m”, “d”, “y”.
  
  
#Eg1
library(lubridate)
(date8a = lubridate::ymd("20110604"))
(date8 = ymd("20110604"))
class(date8)
date8 + 1:10
#date in different format seq
mdy("06-04-2011") #mon-date-year
dmy("04/06/2011") #date-mon-year

#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

begin = c("May 11, 1996", "September 12, 2001", "July 1, 1988")
end = c("7/8/97","10/23/02","1/4/91")
class(begin)  ## [1] "character"
class(end) ## [1] "character"

(begin = mdy(begin))
## [1] "1996-05-11" "2001-09-12" "1988-07-01"
class(begin)

(end = mdy(end))
## [1] "1997-07-08" "2002-10-23" "1991-01-04"

class(begin) ; class(end)## [1] "Date"


#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.

begin1 = c("May 11, 1996 12:05", "September 12, 2001 1:00", "July 1, 1988 3:32")
end1 = c("7/8/97 8:00","10/23/02: 12:00","1/4/91 2:05")
(begin1a = mdy_hm(begin1))
(end1a = mdy_hm(end1))

class(begin1a) ; class(end1a) ## [1] "POSIXct" "POSIXt"
begin1a ; as.numeric(begin1a)
end1a ; as.numeric(end1a)

#class is now “POSIXct”. “POSIXct” represents the number of seconds since the beginning of 1970. If a date is before 1970, the number of seconds is negative. 

#Notice also the the letters “UTC” have been appended to the date-times. UTC is short for Universal Coordinated Time. it’s basically the time standard by which the world regulates clocks. If we prefer we can specify a time zone when formatting dates by using the tz argument. Here’s how we can specify the Eastern Time Zone in the United States when formatting our dates.

(begin1b = mdy_hm(begin1, tz = "US/Eastern"))
(begin1c = mdy_hm(begin1, tz = "Asia/Calcutta"))

#use the OlsonNames function to see a character vector of all time zone names
OlsonNames()
Sys.timezone()

#read in times without dates using the functions ms, hm, or hms, where again “h”, “m”, and “s” stand for “hours”, “minutes”, and “seconds”

time1 = c("1:13", "0:58", "1:01")
time2 = c("12:23:11", "09:45:31", "12:05:22")
time3 = c("2:14", "2:16", "3:35")

(time1a <- ms(time1)) ## [1] "1M 13S" "58S"    "1M 1S"
(time2a <- hms(time2)) ## [1] "12H 23M 11S" "9H 45M 31S"  "12H 5M 22S"
(time3a <- hm(time3)) ## [1] "2H 14M 0S" "2H 16M 0S" "3H 35M 0S"



arrive <- ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
arrive
leave <- ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
leave

leave - arrive  # Time Difference

#Setting and Extracting information
#Functions

#Eg
second(arrive)
second(arrive) = 25  #change
second(arrive)
arrive
minute(arrive)
hour(arrive)
day(arrive)
wday(arrive)
wday(arrive)
wday(arrive, label = TRUE)

week(arrive)

month(arrive)
year(arrive)
tz(arrive)

#Time Intervals
#save an interval of time as an Interval class object
(student1 <- interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                     ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                         ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))



================================================
FILE: 01-IIM/44c4-TS-xts-data.R
================================================
# xts  - create object and export data

library(xts)

#create matrix : 1 col for 1 share
(stockprices = matrix(c(100,103, 105, 205, 210, 207, 530, 500, 535), ncol=3, byrow = F))
stockprices

(cdtindex = c('25-09-2018', '27-09-2018', '28-09-2018'))

#convert to date format
(dtindex = as.Date(cdtindex, format="%d-%m-%Y"))
#now we have index + matrix for xts object

(ts_xts = xts(x=stockprices, order.by=dtindex))
#this series is not continuous can have missing dates

ts_xts
colnames(ts_xts) = c("SBI", "ICICI", 'HDFC')
ts_xts
#-----
#matrix data extract
coredata(ts_xts)
#extract dates
index(ts_xts)


#write to csv file
write.zoo(ts_xts, "./data/zoodata.csv")

================================================
FILE: 01-IIM/45d2-TS-airpassengers.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=3)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object

plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregular
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years
plot(AirPassengers)
(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change

#STL
?stl
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
AirPassengers
stl1 = stl(AirPassengers, s.window = 'periodic')
monthplot(AirPassengers) #max traffic in Jun/ Jul across years
monthplot(LogAirPassengers)
monthplot(stl1, choice='seasonal') # less in winters, more in summers
monthplot(stl1, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(stl1, choice='remainder') # irregular components
?monthplot

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(stl1, choice='seasonal')
monthplot(stl1, choice='trend')
monthplot(stl1, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast) #install the library
# Forecast # adjust for multiplicative model
?ets
fit2b = ets(AirPassengers, model='MAM')
fit2b
tail(AirPassengers)
(f2b=forecast(fit2b, 12))
head(f2b)$mean

plot(f2b)


================================================
FILE: 01-IIM/45d3-TS-components-airp.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=2)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object

plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregual
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years

(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change


#STL
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
monthplot(AirPassengers) #max traffic in Jun/ Jul across years
monthplot(fit, choice='seasonal') # less in winters, more in summers
monthplot(fit, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(fit, choice='remainder') # irregular components

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(fit, choice='seasonal')
monthplot(fit, choice='trend')
monthplot(fit, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast)
# Forecast # adjust for multiplicative model
fit2b = ets(AirPassengers, model='MAM')
fit2b
(f2b=forecast(fit2b, 12))
head(f2b)$mean



================================================
FILE: 01-IIM/45d4-TS-johnson.R
================================================
#Johnson Case - TS
#time series analysis
#plot, decompose, forecast, 
JohnsonJohnson
?JohnsonJohnson

monthplot(JohnsonJohnson)
quarters.Date(Sys.Date())

stl_jj = stl(JohnsonJohnson, s.window = 'periodic')
plot(stl_jj)
plot(JohnsonJohnson)
monthplot(stl_jj, choice='seasonal')
monthplot(stl_jj, choice='trend')
monthplot(stl_jj, choice='remainder')

library(forecast)
ets_jj = ets(JohnsonJohnson, model = "MAM")
forecast(ets_jj,h=3)
plot(forecast(ets_jj,h=3))


#---
require(stats); require(graphics)
JJ <- log10(JohnsonJohnson)
plot(JohnsonJohnson)
plot(JJ)

## This example gives a possible-non-convergence warning on some
## platforms, but does seem to converge on x86 Linux and Windows.
(fit <- StructTS(JJ, type = "BSM"))
tsdiag(fit)
sm <- tsSmooth(fit)
plot(cbind(JJ, sm[, 1], sm[, 3]-0.5), plot.type = "single",
     col = c("black", "green", "blue"))
abline(h = -0.5, col = "grey60")

monthplot(fit)

================================================
FILE: 01-IIM/45g3-TS-TTR-ma.R
================================================
#Time Series - SMA
#packages used - TTR and forecast

library(forecast)
#MA
#https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/ma
head(wineind)
?wineind
wineind
class(wineind)
str(wineind)
plot(wineind)
sma3 <- ma(wineind,order=3)
?ma  #function of forecast
sma3
head(wineind,n=3)
head(sma3, n=3)
sum(head(wineind,n=3)) / 3

lines(sma3,col="red")
sma3F <- ma(wineind,order=3, centre=F)
head(sma3F)


#centered
(v1 = runif(25,50,80))
(x = ts(v1)) #convert to Time Series Objects without any date
forecast::ma(x, order=2, centre=T)

plot(wineind)
csma12 <- ma(wineind,order=12, centre=T)
csma12
lines(csma12,col="red")



#method1
#https://rpubs.com/ajaydecis/ts5
kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
kings
kingstimeseries <- ts(kings)
kingstimeseries
plot.ts(kingstimeseries)
#install.packages("TTR")
library("TTR")
(kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8))
?SMA
plot.ts(kingstimeseriesSMA8)

library(forecast)
fit <- HoltWinters(kingstimeseries, beta=FALSE, gamma=FALSE)
fit1<- ets(kingstimeseries)
?ets
forecast(fit, 3)
plot(forecast(fit, 3)) 

forecast(fit1, 3)
plot(forecast(fit1, 3)) 




#Method2
#https://cran.r-project.org/web/packages/smooth/vignettes/sma.html
require(smooth)
require(Mcomp)
sma(M3$N2457$x, h=18, silent=FALSE)
sma(M3$N2568$x, h=18)


================================================
FILE: 01-IIM/46e3-TS-auto-arima-johnson.R
================================================
#Times Series Analysis 
# is the price of Johnson and Johnson shares change over time
# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year
# Can you forecast what future share prices will be and to what degree of accuracy

#dataset - Johnson
#Quarterly earnings per Johnson Shares
#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA

JohnsonJohnson

library(forecast)

#ets auto select best predicton model
?ets
#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time

fit1 = ets(JohnsonJohnson)
fit1
#alpha - trend
#beta  = seasonal
#gamma  - irregular

JohnsonJohnson
head(JohnsonJohnson)
tail(JohnsonJohnson)

(f1= forecast(fit1,h=10))  # 
?forecast.ets
plot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3)  # linetype for forecast area
#shaded portion is confidence intervals area

par(mfrow=c(1,1))

# ARIMA Forecasting : 
#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.

#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg

f2 = auto.arima(JohnsonJohnson)
summary(f2)
tail(JohnsonJohnson)  # last few values
forecast(f2,h=5)
plot(forecast(f2,h=5))

#ARIMA Forecasting  : compare two datasets
library(tseries)
plot(JohnsonJohnson)
ndiffs(JohnsonJohnson)
plot(diff(JohnsonJohnson))

plot(Nile)
plot(diff(Nile))
ndiffs(Nile)

#-----
djj = diff(JohnsonJohnson)
plot(djj)

dnile = diff(Nile)
plot(dnile)

#----
adf.test(djj)
#if pv < 0.05 accept Alt Hypothesis that series is stationary

#Model Selection 
#parameters p, d , q
# d = no of diffs applied to make the series stationary
#https://people.duke.edu/~rnau/arimrule.htm
Acf(dnile)
#Trail off to zero : Zero after lag ; 0,1(p)
#Zero after lag q : Trails off to zero ; 1(q), 0 
#Trails off to zero : Trial off to zero : 0,0
#Nile - 1 large auto correlation at lag 1 : 
#Nile - pacf trails off to zero as the lags gets bigger
?arima
Pacf(dnile)

fit3 = arima(Nile, order=c(0,1,1)) # p,d,q
fit3

(fit3b = arima(Nile, order=c(1,1,1)))


#Model Test
qqnorm(fit3$residuals) # residuals ND
qqline(fit3$residuals)

#auto correl = 0 : check
Box.test(fit3$residuals, type='Ljung-Box')
#Test auto corr : H0= r=0 (True)

#Forecast
forecast(fit3,4)

Nile
#Auto ARIMA
plot(Nile)

library(forecast)
#forecast::auto.arima()
fit4 = auto.arima(Nile)
fit4

forecast(fit4,5)
plot(forecast(fit4,5))



================================================
FILE: 01-IIM/55c1-TM-twitter.R
================================================
#new Twitter
#Learning Business Analytics  #businessanalytics   using R  @R_Programming  at @IIMBodhGaya  with @curtisakshay  by @henryharvin_in 

# load twitter library - the rtweet library is recommended now over twitteR
pacman::p_load(rtweet, ggplot2, dplyr, tidytext)

# whatever name you assigned to your created app
appname <- "DUtwitterProject"

#keys
consumerKey="sDToDBULOI4wokzrVEiHPEi7o"
consumerSecret="w3sjSpmUOh9JglcHth9gCNB2ur4MElgVbL0ayhXBNWyFDIyAWf"
AccessToken="14366551-3sCiFAUo8Gj72FaxzQ0kWGG4NFvI0BxWAXUbpYT9l"
AccessTokenSecret="mZf2xtm2Y0O0vj8KSUpD09TccYb6ul6CnrbNKzdbkWZwh"

# create token named "twitter_token"
twitter_token <- create_token(
  app = appname,
  consumer_key = consumerKey,
  consumer_secret = consumerSecret,
  access_token = AccessToken,
  access_secret = AccessTokenSecret)
#check token
get_token()
#search will Returns Twitter statuses matching a user provided search query. ONLY RETURNS DATA FROM THE PAST 6-9 DAYS. To return more than 18,000 statuses in a single call, set "retryonratelimit" to TRUE.
#DelhiPolls2020
searchString1 = '#DelhiElection'   # change this to see others eg analytics...
## search for 50 tweets using the #sirmaur hashtag
tweets1 <- search_tweets(q = searchString1, n = 50)
# view the first 3 rows of the dataframe
head(tweets1[1:5], n = 3)
tail(tweets1[1:5], n = 3)
tweets1$text
names(tweets1)  #variable names
tweets1$text  #tweet
tweets1[1]
#https://twitter.com/hashtag/sirmaur?lang=en   #see tweets here

# find recent tweets but ignore retweets 
tweets1B <- search_tweets(searchString1, n = 50, include_rts = FALSE)
# view top 2 rows of data
head(tweets1B[1:5], n = 2)

#screen name : who tweeted
head(tweets1B$screen_name)
unique(tweets1B$screen_name)
tweets1B[1, 'text']
# who are the users tweeting with searchString
users1 <- search_users(searchString1, n = 20)
users1[1:5]

# how many locations are represented
length(unique(users$location))
## [1] 311

users1 %>%  ggplot(aes(location)) + geom_bar() + coord_flip() +  labs(x = "Count",   y = "Location",  title = "Twitter users - unique locations ")

#show in order of count : top 5
users1 %>%   count(location, sort = TRUE) %>%   mutate(location = reorder(location, n)) %>%   top_n(5, wt=n) %>%   ggplot(aes(x = location, y = n)) + geom_col() +   coord_flip() +   labs(x = "Count",   y = "Location", title = "Where Twitter users are from - unique locations ")
#since some rows are same value, they will have same rank, hence more than 5 may be displayed

#remove any rows with missing locations and then plot
users1 %>% count(location, sort = TRUE) %>% mutate(location = reorder(location,n)) %>%   na.omit() %>%   top_n(5) %>%   ggplot(aes(x = location,y = n)) +
  geom_col() +   coord_flip() +   labs(x = "Location",  y = "Count",       title = "Twitter users - unique locations ")
#------------ 

#@IIM_Sirmaur
searchString2 = 'IIM_Sirmaur'   # change this to see others
## search for tweets using the searchString hashtag
tweets2 <- search_tweets(q = searchString2, n = 50, retryonratelimit = T)
# view the first 3 rows of the dataframe
head(tweets2[1:5], n = 3)

searchString3 = 'dupadhyaya'  #change it your twitter id
searchString3 = 'KILANIAGAURAV'

tweets3 <- search_tweets(q = searchString3, n=100, retryonratelimit = T)
tweets3  #there may be no data
tweets3[,2:5]
search_tweets(q="kanikatiwari8", n=100, include_rts = T)

searchString4 = "businessanalytics"
tweets4 <- search_tweets(searchString4, n=50,lang="en")
head(tweets4[1:5])


## perform search for lots of tweets
rt <- search_tweets( "trump OR president OR potus", n = 100, retryonratelimit = TRUE)
## plot time series of tweets frequency
head(rt)
summary(rt$created_at)  #all for one day
ts_plot(rt, by = "mins")

#multiple searches
ds <- Map("search_tweets",  c("\"data science\"", "rstats OR python"),  n = 1000)
## bind by row whilst preserving users data
ds <- do_call_rbind(ds)
ds[,1:5]
dim(ds)  #approx 2000 rows, 90 columns 
ts_plot(ds, by = "days")

#https://rtweet.info/
#https://cran.r-project.org/web/packages/rtweet/rtweet.pdf
#https://www.earthdatascience.org/courses/earth-analytics/get-data-using-apis/use-twitter-api-r/
?search_tweets  
#
## search for 10,000 tweets sent from the US
rt <- search_tweets(  "lang:en", geocode = lookup_coords("usa"), n = 10000)

## create lat/lng variables using all available tweet and profile geo-location data
rt <- lat_lng(rt)

## plot state boundaries
par(mar = c(0, 0, 0, 0))
maps::map("state", lwd = .25)

## plot lat and lng points onto state map
with(rt, points(lng, lat, pch = 20, cex = .75, col = rgb(0, .3, .7, .75)))

#----
## random sample for 30 seconds (default)
rt <- stream_tweets("",timeout=30)
rt
summary(rt)
rt$text
## stream tweets from london for 60 seconds
rt <- stream_tweets(lookup_coords("london, uk"), timeout = 60)
#needs api key... which may be on payment mode

#----
## stream london tweets for a week (60 secs x 60 mins * 24 hours *  7 days)
stream_tweets(  "realdonaldtrump,trump",  timeout = 10,  file_name = "tweetsabouttrump.json",  parse = FALSE
)

## read in the data as a tidy tbl data frame
djt <- parse_stream("tweetsabouttrump.json")
djt
djt$text
## get user IDs of accounts followed by CNN
cnn_fds <- get_friends("cnn")
get_friends('dupadhyaya')
## lookup data on those accounts
cnn_fds_data <- lookup_users(cnn_fds$user_id)
lookup_users(get_friends('dupadhyaya')$user_id)
# get user IDs of accounts following CNN
cnn_flw <- get_followers("cnn", n = 75)
get_followers("dupadhyaya", n = 100)


## lookup data on those accounts
cnn_flw_data <- lookup_users(cnn_flw$user_id)
lookup_users(get_friends('dupadhyaya')$user_id)
## how many total follows does cnn have?
cnn <- lookup_users("cnn")

## get them all (this would take a little over 5 days)
#cnn_flw <- get_followers(   "cnn", n = cnn$followers_count, retryonratelimit = TRUE)

# get user IDs of accounts followed by CNN
tmls <- get_timelines(c("cnn", "BBCWorld", "foxnews"), n = 10)

## plot the frequency of tweets for each user over time
tmls %>%  dplyr::filter(created_at > "2019-10-29") %>%
  dplyr::group_by(screen_name) %>%
  ts_plot("days", trim = 1L) +
  ggplot2::geom_point() +
  ggplot2::theme_minimal() +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom",
    plot.title = ggplot2::element_text(face = "bold")) +
  ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses posted by news organization",
    subtitle = "Twitter status (tweet) counts aggregated by day from October/November 2017",
    caption = "\nSource: Data collected from Twitter's REST API via rtweet"
  )

#Get the 30 most recently favorited statuses by JK Rowling.

jkr <- get_favorites("jk_rowling", n = 30)
jkr
#Search for 10 users with the rstats hashtag in their profile bios.

## search for users with #rstats in their profiles
usrs <- search_users("#rstats", n = 100)
usrs
#Posting (tweeting from R console) or reading direct messages require additional permissions
#If you’d like to post Twitter statuses, follow or unfollow accounts, and/or read your direct messages, you’ll need to create your own Twitter app
#To create your own Twitter app, follow the instructions in the authorization vignette on obtaining and using access tokens
#https://rtweet.info/articles/auth.html

================================================
FILE: 01-IIM/55e1-TM-twitter1.R
================================================
#Twitter 1 - Configure Tweets and Download them
#@dupadhyaya  #Working using my Keys
#---Learning Business Analytics  #businessanalytics   using R  @R_Programming  at @IIMBodhGaya  with @curtisakshay  by @henryharvin_in 

#Load libraries
library("curl")
library("twitteR")
library("ROAuth")
library("syuzhet") #library for sentiment analysis - comparison

download.file(url="http://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")

#https://apps.twitter.com/
#different for each account
# consumerKey="uRDuync3BziwQnor1MZFBKp0x"
# consumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"
# AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
# AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"
consumerKey='uRDuync3BziwQnor1MZFBKp0x'
consumerSecret='t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV'
AccessToken='14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW'
AccessTokenSecret='3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz'

#Common for all accounts except the keys

cred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')

cred$handshake(cainfo="cacert.pem") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt
save(cred, file="twitter authentication.Rdata") # store this to avoid asking again
#Load saved authentication cert
load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)

#type 1 : Yes 
search.string <- "#businessanalytics"
search.string <- "#marketinganalytics"

no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets
tweets[1:10]
#Another Topics
search.string <- "#indvsaus"
#search.string <- "#asiacup"

no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets[1:5]

#My Tweets : will change if you use your own account
homeTimeline(n=15)  #my tweets
mentions(n=15)   # my tags
mentions(n=5)

#for user - 
(tweets = userTimeline("rajatkadam1998", n=10))
userTimeline("rahulg_IITK", n=5)

#------------------------------------
?userTimeline
tweets = userTimeline("realDonaldTrump", n=100)
tweets = userTimeline("imVkohli", n=100)
#where he has been tagged also.#mentions(n=15)
#english
tweets[1:5]
n.tweet <- length(tweets)
n.tweet
tweets.df = twListToDF(tweets) 
head(tweets.df)
summary(tweets.df)

#Remove hashtags & unnecessary characters
tweets.df2 <- gsub("http.*","",tweets.df$text)
tweets.df2 <- gsub("https.*","",tweets.df2)
tweets.df2 <- gsub("#.*","",tweets.df2)
tweets.df2 <- gsub("@.*","",tweets.df2)

head(tweets.df2)
#-----
library("syuzhet") #library for sentiment analysis - comparison
word.df <- as.vector(tweets.df2)
word.df
emotion.df <- get_nrc_sentiment(word.df)
emotion.df2 <- cbind(tweets.df2, emotion.df) 
head(emotion.df2)
emotion.df2
word.df
#-----
sent.value <- get_sentiment(word.df)
sent.value
word.df[44]
tweets[c(16,62)]
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(tweets,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)
tweets[13]


#Text Mining / Twitter Analysis
#Finding out sentiments
#looking for keywords used in the tweets
#wordcloud for these words
#


================================================
FILE: 01-IIM/61b1-LP-marketingspend.R
================================================
#LP in R : Marketing Spend
#https://analyticsprofile.com/business-analytics/how-to-optimise-digital-marketing-spend-using-linear-programming-in-r/
  

================================================
FILE: 01-IIM/61c2-LP-marketingspend-case.R
================================================
# LP - Marketing Spend
## Code to solve LP

#install.packages("linprog")
library(linprog)

Max_ROI = c(0.07, 0.03, 0.15, 0.12, 0.05) #Objective Function

Contraint_Vector = c(5000, -500, 0, -200, 0, 0, -300, 900, -100, 2500) #Constraints

#Decision variables under constraints 
Decision_Var <- rbind(
  c(1,1,1,1,1), 
  c(-1, 0, 0, 0, 0), 
  c(-0.05, 0.95, -0.05, -0.05, -0.05), 
  c(-0, -1, 0, 0, 0), 
  c(0.5, 0.5, -0.5, -0.5, 0.5), 
  c(0, 0, 1, -2.5, 0), 
  c(0, 0, 0, -1, 0), 
  c(0, 0, 0, 1, 0), 
  c(0, 0, 0, 0, -1),
  c(2, 0.3, 1.8, 0.9, 2)
)
Decision_Var
solveLP(Max_ROI, Contraint_Vector, Decision_Var, TRUE)



================================================
FILE: 01-IIM/61d2-LP-tpt.R
================================================
# LP - Transportation Problem 
#https://docs.google.com/spreadsheets/d/1G6-iPDoD_i4THQAHwBeOLeiTfuqn7a6Q7MrOg9v1C5U/edit#gid=166724984
#https://cran.r-project.org/web/packages/lpSolve/lpSolve.pdf
library(lpSolve)

(costs <- matrix (c(3,1,5,2,5,4),ncol=2))  #cost fm F to W
(row.signs <- c("<", "<", "<"))
(row.rhs <- c(45, 60, 35)) #factories product
(col.signs <- rep (">", 2))
(col.rhs <- c(50, 60))  #warehouse demand
#edit(costs)
# Run
lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)
## Not run: Success: the objective function is 7790

lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)$solution

#Eg2
(cost <- matrix(c(3,1,5,2,5,4),ncol=2))
lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )$solution
lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )
?lp.transport

library(lpSolve)
x <- matrix(c(.91,.32,.86,.14,.59,.36,.67,.34,.87,.56,.10,.09),ncol=3,byrow=T)
x
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3)) 
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3) )$solution
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3) )$object



================================================
FILE: 01-IIM/61e2-LP-machassign.R
================================================
#----------------------------------------------#
#Another Method
#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf
#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd

library(lpSolveAPI)

#First we create an empty model x.
?make.lp
#max: x + y - 50
lprec <- make.lp(0, 2)
lprec
set.objfn(lprec, c(1, 1))
lprec
#maximise it
lp.control(lprec, sense="max")
lprec

#set.type(lprec, c(1,2), type = c("integer"))
lprec
#50x + 24y <= 2400
add.constraint(lprec, c(50,24), "<=", 2400)
lprec
#30x + 33y <= 2100
add.constraint(lprec, c(30,33), "<=", 2100)
lprec
#x >= 45
#add.constraint(lprec, c(1,0), ">=", 45)
#lprec
#y >= 5
#add.constraint(lprec, c(0,1), ">=", 5)
#lprec
#50 >= x + y
add.constraint(lprec, c(1,1), ">=", 50)
lprec

set.bounds(lprec, lower = c(45, 5), columns = c(1, 2))
lprec

#set.bounds(lprec, upper = 48.98, columns = 4)
#RowNames <- c("MachineA", "MachineB","InitalA", "InitalB","TotalInitial")
RowNames <- c("MachineA", "MachineB","TotalInitial")
ColNames <- c("ProductX", "ProductY")
dimnames(lprec) <- list(RowNames, ColNames)
lprec
solve(lprec)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec)
get.variables(lprec)
get.constraints(lprec)
plot(lprec)
print(lprec)


================================================
FILE: 01-IIM/61e5-LP-farmer1.R
================================================
#Farmer Problem in LP
#A farmer plans to plant two crops, A and B. The cost of cultivating crop A is $40/acre, whereas the cost of crop B is $60/acre. The farmer has a maximum of $7400 available for land cultivation. Each acre of crop A requires 20 labor-hours and each acre of crop B requires 25 labor-hours. The farmer has a maximum of 3300 labor-hours available. If she expects to make a profit of $150/acre on crop A and $200/acre on crop B, how many acres of each crop should she plant in order to maximize her profit?
  
library(lpSolveAPI)

#First we create an empty model x.
?make.lp
#two variables ie. crops A & B: find which crop to be grown how much to max profit
lprecF1 <- make.lp(0, 2)
lprecF1
#Profit :: 150A + 200B
set.objfn(lprecF1, c(150, 200))
lprecF1
#Change from min to max problem
lp.control(lprecF1, sense="max")
lprecF1

#answer required in integer or real no for A & B: default Real
lprecF1
#1st Constraint : Budget Avl
#40x + 60y <= 7400
add.constraint(lprecF1, c(40,60), "<=", 7400)
lprecF1
#2nd constraint : Labour Hours Avl
#20x + 25y <= 3300
add.constraint(lprecF1, c(20,25), "<=", 3300)
lprecF1
#set lower limits : A & B > 0
set.bounds(lprecF1, lower = c(0, 0), columns = c(1, 2))
lprecF1
#upper bounds can also be set only for 1 or more columns
#set.bounds(lprec, upper = c(200), columns = 2)
ColNames <- c("CropA", "CropB")
RowNames <- c("Budget", "Labor")
dimnames(lprecF1) <- list(RowNames, ColNames)
lprecF1
solve(lprecF1)   #if 0  then solution found
#get.dual.solution(lprec)
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B
150* 65 + 200 * 80
get.constraints(lprecF1) #constraints of budget & labor used
plot(lprecF1)  # print graphical output : only when type is real
#if type is integer, the plot will not work
print(lprecF1)  #see the model


#add more constraints like water
#35x + 40y <= 10000
add.constraint(lprecF1, c(5,10), "<=", 1000)
lprecF1
#delete.constraint(lprecF1, 3)
solve(lprecF1)   #if 0  then solution found
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B
get.constraints(lprecF1) #constraints of budget & labor used
#how much of funds, labour and water used

#deleter water constraint - water
delete.constraint(lprecF1, 3)
lprecF1
#setting integer value
set.type(lprecF1, c(1,2), type = c("integer"))
lprecF1
solve(lprecF1)   #if 0  then solution found
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B


#http://lpsolve.sourceforge.net/5.5/R.htm
?lp
?lp.assign
?lp.object

?lp.transport
?print.lp


================================================
FILE: 01-IIM/77a1-FA-quandl.R
================================================
# Finance Stock Analysis
#Stock Download

library(Quandl)
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
ICICI
HDFC = Quandl("NSE/HDFCBANK",collapse="daily",start_date="2017-09-01",type="raw")
HDFC
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI

allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
dim(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)
#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#Library to store data in xlsx files
Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre1.8.0_191")
library(xlsx) #needs rJava

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
(df1 <- allstocks %>% select(Stock, Open, Close))
df1 = as.data.frame(df1)
write.xlsx(df1, './data/iitgfa.xlsx', sheetName = "IITG12", append = T)


#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
#store this data into DF
(df2 <- allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean) )
write.csv(df2, './data/iitgfa.csv')

#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx#using-xlsx-package


#write.xlsx(USArrests, file = "myworkbook.xlsx", #check with this file sheetName = "USA-ARRESTS", append = FALSE)
df2 = as.data.frame(df2)
write.xlsx(df2, './data/iitgfa.xlsx', sheetName = "IITG2", append = T)


options(dplyr.print_max = 1e9)  #print all rows
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)

allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 01-IIM/77a2-FA-quandl2.R
================================================
# Finance Stock Analysis

#Install Packages 
#pckgs<-c("Quandl","Sweep","tidyverse")
#install.packages(pckgs,dependencies = TRUE)

library(Quandl)
# library(tidyverse)
# library(ggplot2)
# library(stringr)
# library(plyr)
# library(stringr)
# library(gridExtra)

#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)
#ICICI SBI PNB  price band of Rs 200 to Rs 500. 
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI
#combine them
allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)

#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
allstocks %>% select(Stock, Open, Close)

#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)
options(dplyr.print_max = 1e9)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)
allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 01-IIM/77a5-FA-quantmod.R
================================================
#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/
#Stock Analysis  

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2018-01-01")
end <- as.Date("2018-12-01")

# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the
# quantmod function getSymbols, and pass a string as a first argument to
# identify the desired ticker symbol, pass 'yahoo' to src for Yahoo!
# Finance, and from and to specify date ranges

# The default behavior for getSymbols is to load data directly into the
# global environment, with the object being named after the loaded ticker
# symbol. This feature may become deprecated in the future, but we exploit
# it now.

getSymbols("AAPL", src = "yahoo", from = start, to = end)

# What is AAPL?
class(AAPL)
head(AAPL)
tail(AAPL)
plot(AAPL[, "AAPL.Close"], main = "AAPL")
candleChart(AAPL[1:10,], up.col = "black", dn.col = "red", theme = "white")
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
#30-Nov-2018	180.29	180.33	177.03	178.58	178.58	3,94,83,800

# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google is
# held by a holding company called Alphabet, Inc., which is the company
# traded on the exchange and uses the ticker symbol GOOG).
getSymbols(c("MSFT", "GOOG"), src = "yahoo", from = start, to = end)
MSFT
# Create an xts object (xts is loaded with quantmod) that contains closing
# prices for AAPL, MSFT, and GOOG
stocks = as.xts(data.frame(AAPL = AAPL[, "AAPL.Close"], MSFT = MSFT[, "MSFT.Close"], GOOG = GOOG[, "GOOG.Close"]))
head(stocks)
class(stocks)

# Create a plot showing all series as lines; must use as.zoo to use the zoo
# method for plot, which allows for multiple series to be plotted on same
# plot
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

plot(as.zoo(stocks[, c("AAPL.Close", "MSFT.Close")]), screens = 1, lty = 1:2,  xlab = "Date", ylab = "Price")
par(new = TRUE)
plot(as.zoo(stocks[, "GOOG.Close"]), screens = 1, lty = 3, xaxt = "n", yaxt = "n", xlab = "", ylab = "")
axis(4)
mtext("Price", side = 4, line = 3)
legend("topleft", c("AAPL (left)", "MSFT (left)", "GOOG"), lty = 1:3, cex = 0.5)


# Get pipe operator!
if (!require("magrittr")) {
  install.packages("magrittr")
  library(magrittr)
}
stock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% 
  t %>% as.xts

head(stock_return)

plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Return")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

stock_change = stocks %>% log %>% diff
head(stock_change)

plot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = "Date", ylab = "Log Difference")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)  


candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addSMA(n = 20)



start = as.Date("2010-01-01")
getSymbols(c("AAPL", "MSFT", "GOOG"), src = "yahoo", from = start, to = end)
# The subset argument allows specifying the date range to view in the chart.
# This uses xts style subsetting. Here, I'm using the idiom
# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is
# the start date, and the date on the right-hand side is the end date. If
# either is left blank, either the earliest date or latest date in the
# series is used (as appropriate). This method can be used for any xts
# object, say, AAPL
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = 20)

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = c(20, 50, 200))


================================================
FILE: 01-IIM/77a6-FA-quantmod-I-stocks.R
================================================
#Indian Stocks

# Indian Stocks
#stocks2 - Quantitive Financial Modeling
#https://cran.r-project.org/web/packages/quantmod/quantmod.pdf
# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

#TS objects are 3 types in R : ts, zoo, xts

start <- as.Date("2019-01-01")
end <- as.Date("2019-06-31")
getSymbols("SBIN.NS", src = "yahoo", from = start, to = end)
?getSymbols
# What is SBI?
class(SBIN.NS)
head(SBIN.NS)
tail(SBIN.NS)
#plot only closing prices
plot(SBIN.NS[, "SBIN.NS.Close"], main = "SBIN.NS")
plot(SBIN.NS[, "SBIN.NS.Close"],na.action= na.omit, main = "SBIN.NS")
plot(tail(SBIN.NS[, "SBIN.NS.Close"]), main='Last 6 days rates')
#plot candle Chart
candleChart(SBIN.NS[1:50,], up.col = "black", dn.col = "red", theme = "white")

?write.csv
#ICICIBANK.NS
#TATAMOTORS.NS
getSymbols(c("ICICIBANK.NS", "TATAMOTORS.NS"), src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"]))
stocks
stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"], ICICI = ICICIBANK.NS[, "ICICIBANK.NS.Close"], TATAMOTORS = TATAMOTORS.NS[, "TATAMOTORS.NS.Close"]))
head(stocks)
class(stocks)
#in zoo format you can have multiple TS
plot(as.zoo(stocks), screens = 1, lty = 1:3, col=1:3, main='Indian Stock Rates', xlab = "Date", ylab = "Price")
legend("topright", c("SBIN", "ICICI", "TATATMOTORS"), lty = 1:3, col=1:3, cex = 0.5)

#end of analysis
# read this for more analysis :
#https://cran.r-project.org/web/packages/quantmod/quantmod.pdf

stock = SBIN.NS[,'SBIN.NS.Close']
head(stock)
class(stock)
stock1=as.data.frame(stock)
stock1$sdate = row.names(stock1)
head(stock1)

write.csv(stock1, 'data/stock1.csv', row.names=F)
write.table(stock, 'data/stock1.txt', col.names=T)

library(tibble)
library(dplyr)
stock %>% rownames_to_column('sdate')


================================================
FILE: 01-IIM/77f2-FA-quantmod1.R
================================================
#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/
#Stock Analysis  

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2017-01-01")
end <- as.Date("2018-10-01")

# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the quantmod function getSymbols, and pass a string as a first argument to identify the desired ticker symbol, pass 'yahoo' to src for Yahoo! Finance, and from and to specify date ranges
# The default behavior for getSymbols is to load data directly into the global environment, with the object being named after the loaded ticker symbol. This feature may become deprecated in the future, but we exploit it now.

getSymbols("AAPL", src = "yahoo", from = start, to = end)

# What is AAPL?
class(AAPL)
head(AAPL)
tail(AAPL)
plot(AAPL[, "AAPL.Close"], main = "AAPL")
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")


# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google isheld by a holding company called Alphabet, Inc., which is the company traded on the exchange and uses the ticker symbol GOOG).
getSymbols(c("MSFT", "GOOG"), src = "yahoo", from = start, to = end)

# Create an xts object (xts is loaded with quantmod) that contains closing prices for AAPL, MSFT, and GOOG
stocks = as.xts(data.frame(AAPL = AAPL[, "AAPL.Close"], MSFT = MSFT[, "MSFT.Close"], GOOG = GOOG[, "GOOG.Close"]))
head(stocks)
tail(stocks)

# Create a plot showing all series as lines; must use as.zoo to use the zoo method for plot, which allows for multiple series to be plotted on same plot
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

plot(as.zoo(stocks[, c("AAPL.Close", "MSFT.Close")]), screens = 1, lty = 1:2,  xlab = "Date", ylab = "Price")
par(new = TRUE)
plot(as.zoo(stocks[, "GOOG.Close"]), screens = 1, lty = 3, xaxt = "n", yaxt = "n", xlab = "", ylab = "")
axis(4)
mtext("Price", side = 4, line = 3)
legend("topleft", c("AAPL (left)", "MSFT (left)", "GOOG"), lty = 1:3, cex = 0.5)


# Get pipe operator!
if (!require("magrittr")) {
  install.packages("magrittr")
  library(magrittr)
}
stock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>%  t %>% as.xts

head(stock_return)

plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Return")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

stock_change = stocks %>% log %>% diff
head(stock_change)

plot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = "Date", ylab = "Log Difference")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)  


candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addSMA(n = 20)


#------
start = as.Date("2010-01-01")
getSymbols(c("AAPL", "MSFT", "GOOG"), src = "yahoo", from = start, to = end)
# The subset argument allows specifying the date range to view in the chart.
# This uses xts style subsetting. Here, we r using the idiom
# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is
# the start date, and the date on the right-hand side is the end date. If
# either is left blank, either the earliest date or latest date in the series is used (as appropriate). This method can be used for any xts object, say, AAPL

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = 20)

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = c(20, 50, 200))



================================================
FILE: 01-IIM/77f3-FA-indianstocks.R
================================================
# Indian Stocks

library(quantmod)
start <- as.Date("2017-01-01")
end <- as.Date("2018-10-01")
getSymbols("SBIN.NS", src = "yahoo", from = start, to = end)


# What is SBI?
class(SBIN.NS)
head(SBIN.NS)
tail(SBIN.NS)
plot(SBIN.NS[, "SBIN.NS.Close"], main = "SBIN.NS")
candleChart(SBIN.NS, up.col = "black", dn.col = "red", theme = "white")


#ICICIBANK.NS
#TATAMOTORS.NS
quantmod::getSymbols(c("ICICIBANK.NS", "TATAMOTORS.NS"), src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"]))
stocks
stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"], ICICI = ICICIBANK.NS[, "ICICIBANK.NS.Close"], TATAMOTORS = TATAMOTORS.NS[, "TATAMOTORS.NS.Close"]))
head(stocks)

plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("SBIN", "ICICI", "TATATMOTORS"), lty = 1:3, cex = 0.5)



================================================
FILE: 01-IIM/91ab3-Case-student1.R
================================================
# Data Manipulation : Academic Data

#Method1 : gsheet
library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174"
student1 = as.data.frame(gsheet2tbl(url))

#Method2 : read.csv
student2 = read.csv('./data/student1.csv')
names(student2)

#save the imported data object into a new object
student=student1  # or student2
str(student)
names(student)
df1= student
lm(df1$btechmarks ~ df1$attnd, data=df1)

(colnames = names(df1))
#attributes(df1) = NULL
#df1 = as.data.frame(df1)
attributes(df1)
attr(df1, which='spec') = NULL
#names(df1) = colnames
str(df1)
#class of each column
class(df1$gender)
sapply(df1, class)

# convert character to factor
factorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')

df1[factorcols] = lapply(df1[factorcols] ,factor)
sapply(df1, class)
str(df1)


# Now do summarisation
#attach(df1)
names(df1)
table(df1$gender)
#combine it in single command 
(l1= lapply(df1[factorcols],factor,ordered=TRUE)) #ordering is not necessary for all
sapply(l1,table)

table(df1$gender)
table(df1$batchyr)
sapply(df1[factorcols],table)


sapply(lapply(df1[factorcols],factor,ordered=TRUE), table)  #Method1


sapply(df1[factorcols], table)   #Method2
sapply(df1[c('gender', 'cat', 'class12')], table)
?lapply
str(df1)
# Numeric Cols
sapply(df1, is.numeric)
(numcols = sapply(df1, is.numeric))
class(numcols)
(numcols = names(df1[numcols]))
#remove rollno
(numcols = numcols[-1])
head(df1[numcols])
colSums(df1[numcols])
colSums(df1[numcols],dims=1)
colMeans(df1[numcols],dims=1)
numcols[c(1,3)]
colMeans(df1[c('age','java', 'cpp')])
names(df1)
colMeans(df1[numcols[c(1:3)]])

names(df1)
#Look for other summarisation and grouping
aggregate(df1[numcols], by=list(df1$br), FUN=mean)
aggregate(cbind(sem1, sem2) ~ br + gender, data=student, FUN=mean)

aggregate(java + cbnst ~ br, data=df1, FUN=mean)  #sum of java & cbnst wrt branch
aggregate( cbnst ~ gender, data=df1, FUN=mean)
aggregate( cbnst ~ gender + br, data=df1, FUN=mean)



#using dplyr package
library(dplyr)

#Top 2 students from each Branch---- 
df1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)

# Average Fees Paid by Batch Yr----
df1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))

names(df1)
# Avg BTech Marks and Min Marks in Java : Group by Gender, Granch and Final Grade ----
df1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))

#Count by Gender ----
df1 %>%   group_by(gender) %>%  tally()

#Filter by Final Grade = B : select only few columns ----
df1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)

#sample : select few rows on randowm basis ----
df1 %>% sample_frac(0.2, replace = TRUE)  %>% select(rollno, sname)  # % of Total
df1 %>% sample_frac(0.2, replace = F)  %>% select(rollno, sname) # % of total : replace should be False to have non repeated rows
df1 %>% sample_n(3, replace = F)  %>% select(rollno, sname) # select only 3 rows

#few rows : 10 to 15
slice(df1, 10:15)

library(tidyr) #Reshape
df1long <- df1 %>% select(sname, java, cbnst)
df1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) 

head(df1long)
distinct(df1long)

tidyr::unite(df1, 'rollnoname', c(rollno, sname), sep="-") %>% select(1:5)%>% head


# data
str(df1$dob)
as.Date("5-Apr-91", '%d-%b-%y')
df1$dob = as.Date(df1$dob, '%d-%b-%y')
df1 %>% select(sname, dob) %>% tidyr::separate(dob, c("y", "m", "d"))


# Rows
df1 %>% slice(1:n():5)
slice(df1, 1:5)

df1 %>% slice(1:n())
df1 %>% slice(1:10)
slice(df1, n()-10: n())  #different way

names(df1)
#defaults to the last variable in the tbl
df1 %>% group_by(gender) %>% top_n(2)  #Rank not data on last column
# top_n(n = 5, wt = x)
df1 %>% group_by(gender) %>% top_n(2,wt=class10)  #Rank not data wrt class10 marks

df1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values
df1 %>% group_by(gender) %>% tally(java)  #total count
df1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)

#Selecting Columns
df1 %>% select(1:5)
df1 %>% select(contains('java'))
df1 %>% select(starts_with('btech'))
df1 %>% select(ends_with('s'))
df1 %>% select(everything())
df1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)
names(df1)


# Summarise
df1 %>% summarise(avgjava = mean(java))
df1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))
df1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))

df1 %>% group_by(cat, gender) %>% count(class12, finalgrade)




#Summary Functions
v1 = df1$btechmarks
v1
v1 %>% first
v1 %>% last
v1 %>% sd
v1 %>% mean
v1 %>% n_distinct()


# Combine Data Sets

(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))
(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))

dplyr::left_join(a, b, by = "x1")
#Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")
#Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")
#Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")
#Join data. Retain all values, all rows.

dplyr::semi_join(a, b, by = "x1")
#All rows in a that have a match in b.

cbind(a,b)

dplyr::anti_join(a, b, by = "x1")
#All rows in a that do not have a match in b.

#-----
y = data.frame(x1=c('A','B','C'), x2=c(1,2,3))
z = data.frame(x1=c('B','C','D'), x2=c(2,3,4))
cbind(y,z)

dplyr::intersect(y, z)  # B&C
#Rows that appear in both y and z.

dplyr::union(y, z)
#Rows that appear in either or both y and z.

dplyr::setdiff(y, z)
#Rows that appear in y but not z.

#Bind
dplyr::bind_rows(y, z)
#Append z to y as new rows.
dplyr::bind_cols(y, z)
#Append z to y as new columns.
#Caution: matches rows by position




================================================
FILE: 01-IIM/91b4-Case-dencoCase.R
================================================
# Case Study - Denco  
#Manufacturing Firm with sales data of partnum and customer with region wise sales

# Should know - import, table, dplyr, aggregate etc

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1) #see if data is loaded and check its structure
head(sales1)
#read file : Method2 : when location is not in project folder
sales2 = read.csv(file.choose())
str(sales2)
head(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)


# whichever you read the data, store it in sales object
sales = sales1  # keeping a backuph
head(sales)  #top 6 rows
str(sales)  # structure
class(sales) # class type - Df
dim(sales)  # rows & columns
summary(sales) # summary- colname, type
names(sales) # col names
head(sales) # first 6 values
tail(sales) # last 6 values

# Some other functions
unique(sales$custname)  # unique customer names
length(unique(sales$custname)) # no of unique customers
length(unique(sales$region )) # no of distinct regions

#Information Required----
# 1. Who are the most loyal Customers - What to do : Improve repeated sales, Target customers with low sales Volumes
# 2. Which customers contribute the most to their revenue : How do I retain these customers & target incentives
# 3a. What part numbers bring in to significant portion of revenue : Maximise revenue from high value parts
# 3b. What parts have the highest profit margin : What parts are driving profits & what parts need to build further


#dplyr
names(sales)
library(dplyr)  #use this library to do summarisation

#Case-1 : Loyal Customers----
# Finding Frequency and sort them in descending order
names(sales)
(t1=table(sales$custname))  # freq table for all customers
class(t1); length(t1)
head(t1) # top 6 but not sorted
t2= sort(t1,decreasing=T )
head(t2)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

#other methods through dplyr
library(dplyr)
sales %>% count(custname, sort=TRUE) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase


#Case-2 : Customer- Highest Revenue---
sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=5)
#Ans2: Triumph Insulation gave max revenue

# save this object and then analyse
salesrevenue = sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))

#dply uses tibble format and does not show all rows/cols by default
options(tibble.width = Inf) # displays all columns.
options(tibble.print_max = Inf) # to show all the rows.
salesrevenue[1:5,] # first 5 rows
#Ans2: here also Triump insulation gives max revenue = 35592K

#Case3 : Most Profitable PartsNums ----
# Look for freq, revenue and/ or profit margin
head(sales)
# Summarise by Part Num for frequency
sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))  %>% head(n=5)
#Ans3a: Part Num- 764821000 was purchased max times -   122

# Summarise Partnum for Profit(margin) : sum(profit)
names(sales)
sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()
#Ans 3b: Part Num - 733648000 gave max margin profit - 11003367

# Summarise Partnum for revenue
#(practise it )


#Case : Extra ----
#Find Regions wise sales

(salesregionrevenue = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue)))

#Some Graphs related to Data Summarised
pie(x = salesregionrevenue$Revenue, labels=unique(sales$region))
barplot(salesregionrevenue$Revenue, col=1:4)

# Filter Data----
#Rows where revenue > 700000 : show only custname, revenue
sales %>% filter(revenue >= 700000 ) %>% select(custname, revenue)

#select 1% rows on random basis
sales %>% sample_frac(.01)  %>% select(custname, revenue)

#select 10 rows on random basis and sort them regionwise
sales %>% sample_n(10)  %>% select(custname, region) %>% arrange(region)
sample_n(sales, 10) # similar method

#End of session for Denco Case - Manufacturing Firm
#Descriptive Analysis
#Loyal Customers, Customer giving max revenue
#Profitable Partnums - Freq, Revenue and Margin



================================================
FILE: 01-IIM/91b5-Case-denco.R
================================================
#Case Study - Manufacturing Coy

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)
dim(sales3)
library(dplyr)
names(sales3)
#loyal : customer + count ----
length(unique(sales3$custname))
sales3 %>% group_by(custname) %>% summarise(n=n())  %>% arrange(desc(n)) %>% head(5)
t1=table(sales3$custname)
tail(sort(t1),5)
#customer + revenue-----
sales3 %>% group_by(custname) %>% summarise(totalRev=sum(revenue))  %>% arrange(desc(totalRev)) %>% head(5)

#partno + revenue -----
names(sales3)
sales3 %>% group_by(partnum) %>% summarise(totalRev=sum(revenue))  %>% arrange(desc(totalRev)) %>% head(5)

#partno + margin ------
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(5)
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(totalMar) %>% head(5)


#top n=2 from each region : total revenue
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T))
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% filter(region =='9x-Export') %>% arrange(desc(totRev)) %>% head(2)

sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% top_n(n=2, wt=totRev)
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% top_n(n=-3, wt=totRev)

df = mtcars
head(mtcars)
rownames(mtcars)
df$carnames = rownames(df)
head(df)
df %>% arrange(mpg)  %>% select(carnames, mpg, wt)
df %>% top_n(n=2, wt=mpg)  

#details about high margin parts
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(7)
hspno <- sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(7) %>% pull(partnum)
hspno
sales3 %>% filter(partnum %in% hspno) 
sales3 %>% filter(partnum %in% hspno) %>% group_by(partnum, region) %>% summarise(n=n())

table(sales3$region)
#parts sold in East and china region : | or %in%
sales3 %>% filter(region == '01-East' | region == '09-China') %>% nrow()
sales3 %>% filter(region %in% c("01-East" ,'09-China')) %>% nrow()


#plots
#box plot
boxplot(mtcars$mpg)
boxplot(mpg ~ gear, data=mtcars)
boxplot(sales3$margin)
boxplot(margin ~ region, data = sales3)
range(sales3$margin)
hist(sales3$margin)
#bar plot
bpdata <- sales3 %>% group_by(region) %>% summarise(TotRev= sum(revenue))
barplot(bpdata$TotRev)


?barplot
#hist

#pie
pie(bpdata$TotRev)

library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) + geom_point()
ggplot(data=mtcars, aes(x=wt, y=mpg, color=factor(gear), shape=factor(cyl), size=hp)) + geom_point()
ggplot(data = mtcars, aes(x=factor(gear), fill=factor(cyl))) + geom_bar(stat='count')  + facet_grid(am ~ carb) + geom_text(stat='count', aes(label=..count..), vjust=-1)

#reshape data
library(reshape2)
dflong <- df %>% group_by(gear, cyl) %>% summarise(meanMPG = mean(mpg))
dflong
dcast(data=dflong, formula = gear ~ cyl, value.var = 'meanMPG')


sales3
head(sales3)
names(sales3)
dflong2 <- sales3 %>% group_by(custname,region) %>% summarise(sum2=sum(revenue))
head(dflong2)

dlong22=dcast(data=dflong2, formula= custname ~ region, value.var = 'sum2')

head(dlong22)
dlong23 <- melt(data=dlong22, id.vars = 'custname')
head(dlong23)

rollno =1:10
(subject = sample(paste('S',1:5), size=10, replace=T))
students = data.frame(rollno, subject)
head(students)
students$marks = trunc(rnorm(10,mean=60, sd=10))
students
sub1 = trunc(rnorm(10,mean=60, sd=10))
sub2 = trunc(rnorm(10,mean=60, sd=10))
sub3 = trunc(rnorm(10,mean=60, sd=10))
sub4 = trunc(rnorm(10,mean=60, sd=10))
sub5 = trunc(rnorm(10,mean=60, sd=10))

students2 = data.frame(rollno, sub1, sub2, sub3, sub4, sub5)
students2
melt(data=students2, id.vars = 'rollno')


================================================
FILE: 01-IIM/91g4-CASE-dencoCase.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1)

#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)


#using gsheet
library(gsheet)
denco2 = as.data.frame(gsheet2tbl(url))
str(denco2)

#head(sales1,n=7)
#names(sales1)

# whichever you read the data, store it in sales object
sales = sales3  # keeping a backup
str(sales)
class(sales)
str(sales)
summary(sales)

str(sales)
dim(sales) #dimensios of DF rows & colnum
unique(sales$custname)
length(unique(sales$custname))
length(unique(sales$region ))

# aggregation
aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
df1 = aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
head(df1)
str(df1)

df1=df1[order(df1$x, decreasing=TRUE),]
head(df1,10)

head(df1[order(df1$x, decreasing=TRUE),], 5)

aggregate(sales$revenue, by=list(sales$region), FUN=mean)
df2= aggregate(formula=revenue ~ region, data=sales, FUN=sum)
df2[order(df2$revenue, decreasing=F),]


#Aggregate Formula
(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))
head(df2[order(df2$revenue,decreasing=T),],10)

#List
list1= tapply(sales$revenue, sales$custname, FUN=sum)
head(list1)
list1
head(sort(list1, decreasing=T))
summary(df3)
str(df3)

#dplyr
names(sales)

library(dplyr)

sales %>% filter(margin > 10000) %>% arrange(region, desc(revenue))
filter(sales, margin > 1000000)

sales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)

names(sales)
sales %>% group_by(custname) %>% 
  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))


library(data.table)
dt1 = as.data.table(sales)
dt2 = dt1[, sum(revenue), by=custname]
names(dt2)
dt1[, dt1[, sum(revenue), by=custname]]
#dt1[, order( decreasing = T)]

# Select
library(sqldf)
df5 =sqldf("Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc ")
head(df5)

# Frequency --------
names(sales)
t1=table(sales$custname)
class(t1)
length(t1)
head(t1)
t2= sort(t1,decreasing=T )
head(t2)
head(sort(table(sales$custname), decreasing=T), n=10)
tail(sort(table(sales$custname), decreasing=T), n=10)

#xtab
head(sort(xtabs(~ custname, sales), decreasing=T))

#
library(dplyr)
sales %>% dplyr::count(custname, sort=TRUE)

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))



#plyr
df2a= plyr::count(sales, c('custname'))
str(df2a); names(df2a)
head(df2a[order(-df2a$freq),])

# Summarise by Part Num

df3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)
head(df3a)
str(df3a)
df3a[order(-df3a$x),][1:6,]
head(df3a[order(-df3a$x),])

df3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)
head(df3b)
head(df3b[order(df3b$revenue, decreasing=T),])

sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))


# which parts have highest Profit : partno - sum(profit)
names(sales)
df4a = aggregate(margin ~ partnum, data=sales, FUN=sum)
aggregate(margin ~ partnum, data=sales, FUN=sum)
head(df4a[order(df4a$margin, decreasing = T),])

sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()



================================================
FILE: 01-IIM/revision1.R
================================================
#Revision of Topics

mtcars
?mtcars   #help on mtcars
class(mtcars)
x= 1:5
class(x)
x
y=c(1, 3.5,5)
y
class(y)
class(as.integer(y))
as.integer(y)
z=c(1L, 3L, 5L)
z
class(z)
str(mtcars)
?str
summary(mtcars)

#vectors----
x = 1:5
x
x1 = c(1,2,3,4,5)
x1
(x3 = c(2,4,36,3))
x3
print(x3)
x3
x3[2]
plot(x3)
(x4 = rnorm(n=100, mean=60, sd=10))
plot(x4)
plot(density(x4))
hist(x4)
hist(x4, breaks=10, col=1:10)
range(x4)
min(x4); max(x4)
mean(x4) ; median(x4)
boxplot(x4)
sort(x4)
sort(x4, decreasing = TRUE)
plot(sort(x4), type='b') #line and point
plot(x4, type='l')
x4[x4>65]
mean(x4[x4 > 65])  #mean of values more than 65
x4
x4[1:10]
x4[91:100]
x4[-c(1:10)]
x4[x4 > 65]
length(x4[x4 > 65])
sum(x4 > 65)
rev(x4)
sum(x4[x4 > 65])
x4[x4 > 60 & x4 < 70]
sum(x4)
#
#matrices----
(data = c(10,30,40,44,22,55))
(m1 = matrix(data = data, nrow=2))
(m2 = matrix(data = data, nrow=2, byrow=T))
rownames(m1) = c('R1','R2')
m1
colnames(m1) = c('C1','C2','C3')
m1
m1[1,]
m1['R1', ]
m1[,3]
m1
m1[,2:3]
m1[, c('C1','C3')]
colSums(m1)
rowSums(m1)
colMeans(m1)
rowMeans(m1)
apply(m1, 1, FUN=min)
m1
apply(m1[, 'C2', drop=F], 2, FUN=max)

#dataframe----
(rollno = paste('IIMLN', 1:13, sep='-'))
(name = paste('Student',1:13, sep=' & '))
(age = round(runif(13, min=24, max=32),2))
(marks = trunc(rnorm(13, mean=60, sd=10)))
set.seed(34)
(gender =  sample(c('M','F'), size=13, replace=T, prob=c(.7, .3)))
table(gender)
prop.table(table(gender))
(x=c(-14.35, 14.35, -14.55, 15.35))
floor(x) ;  ceiling(x) ; trunc(x)
set.seed(55)
(grade =  sample(c('Ex','Good','Sat'), size=13, replace=T, prob=c(.6, .3,.1)))
table(grade)
prop.table(table(grade))
sapply(list(rollno, name, age, marks, gender, grade), length)
(students = data.frame(rollno, name, age, marks, gender, grade, stringsAsFactors = F ))

write.csv(students,'data/students.csv' , row.names = F )

df1 = read.csv('data/students.csv')
df1

df2 = read.csv('https://raw.githubusercontent.com/DUanalytics/rAnalytics/master/data/students.csv')
df2

df3 = read.csv(file.choose())
df3

students
#install.packages("dplyr")


class(students)
summary(students)
str(students)
students$gender = factor(students$gender)
str(students)
students$grade = factor(students$grade, ordered=T, levels=c('Ex','Sat','Good'))
str(students)
table(students$gender)
table(students$grade)

students
library(dplyr)  #loading library

students %>% group_by(gender)  %>% tally()
students %>% group_by(gender)  %>% summarise(mean(age), n(), min(marks), max(marks))
students %>% group_by(gender, grade)  %>% summarise(mean(age))



#dplyr - mtcars
library(dplyr)
#library(tidyverse)
#Filter----
mtcars
filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()-2))


#day2:21Feb20------
library(dplyr)
mtcars
table(mtcars$cyl)
summary(mtcars$cyl)
mtcars %>% group_by(cyl)  %>% tally()
mtcars %>% group_by(cyl)  %>% summarise(COUNT = n())
xtabs( ~ cyl, data=mtcars)
ftable(mtcars$cyl)
#gear & cyl
table(mtcars$cyl, mtcars$gear)
table(mtcars$cyl, mtcars$gear, dnn=c('cylinder','gear'))
mtcars %>% group_by(cyl, gear) %>% tally()
mtcars %>% group_by(cyl, gear) %>% summarise(COUNT = n())
mtcars %>% group_by(cyl, mpg) %>% summarise(COUNT = n()) %>% as.data.frame()

xtabs(  ~ cyl + gear, data=mtcars)
ftable(mtcars$cyl, mtcars$gear)
table(mtcars$cyl, mtcars$gear, mtcars$am, dnn=c('Cyl','Gear','AutoManual'))

df = mtcars
head(df)   
tail(df)
df$am= ifelse(df$am==0, 'Auto', 'Manual')
df
mtcars %>% mutate(TxType = ifelse(am==0, 'Auto', 'Manual'))
mtcars %>% mutate(TxType = ifelse(am==0, 'Auto', 'Manual')) %>% group_by(TxType) %>% summarise(COUNT = n())
mtcars                  
df = mtcars
df$mpg
df[,'mpg']
df
head(df)
df = df %>% mutate(TxType = ifelse(am==0, 'Auto', 'Manual'))
head(df)
#increase mileage by 10%
df$mpg * 1.1
#add mpg + wt into new column MPGWT
df$mpg  + df$wt
df$MPGWT = df$mpg * 1.1  + df$wt
head(df)
df
#top 2 cars from mpg from each gear type : use group_by & top_n
?top_n
df %>% group_by(gear) %>% top_n(n=2, wt=mpg) %>% select(gear, mpg)
df %>% group_by(gear) %>% arrange(-mpg)  %>% select(gear, mpg)
df %>% group_by(gear) %>% top_n(n=2, wt=-mpg) %>% select(gear, mpg)

#list out details of any 2 cars picked randomly: then do 25% of the cars
df %>% sample_n(2)
df %>% sample_frac(.25)
#sort on mpg
df %>% sample_frac(.25)  %>%  arrange(mpg)
#ascending gear, descending mpg
df %>% sample_frac(.25)  %>%  arrange(gear, desc(mpg))

sort(df$mpg)
df[ order(df$mpg), ]
df %>% slice(1)
df %>% slice(10:15)

#find mean of mpg, wt, hp, disp for each gear type
df %>% group_by(gear)  %>% summarise_at(c('mpg','wt', 'hp','disp'),c(mean))
df %>% group_by(gear)  %>% summarise_at(c('mpg','wt', 'hp','disp'),c(mean, median))
df %>% select(gear, mpg, wt, hp,disp) %>% group_by(gear) %>% summarise_all(mean)
#find min and max of wt for each gear type
df %>% select(mpg, wt, gear) %>% group_by(gear) %>% summarise_each(c(min, max))


#graphs----
hist(df$mpg)
barplot(table(df$gear), col=1:3)
L1 <- paste(round(table(df$gear)/nrow(df) * 100),'%')

pie(table(df$gear), labels=c('gear3','G4','G5'))
pie(table(df$gear), labels=L1, col=1:3)
plot(df$wt, df$mpg)

library(ggplot2)
library(reshape2)
#install.packages('ggplot2')  #this is in simple R
#install.packages('reshape2')

(rollno = paste('IIM',1:10, sep='_'))
(name = paste('SName',1:10, sep=' '))
(gender = sample(c('M','F'), size=10, replace=T, prob=c(.7,.3)))
(program = sample(c('BBA','MBA'), size=10, replace = T))
(marketing = trunc(rnorm(10, mean=60, sd=10)))
(operations = trunc(rnorm(10, mean=70, sd=5)))
(finance = trunc(rnorm(10, mean=55, sd=12)))
students <- data.frame(rollno, name, gender, program, marketing, finance, operations, stringsAsFactors = F)
students
head(students)
#students %>% group_by(gender, program)  %>% summarise(mean(marketing), mean(finance), mean(operations))

(meltSum1 <- melt(students, id.vars=c('rollno','name', 'gender','program'), variable.name = 'subject', value.name ='marks'))
meltSum1
head(meltSum1)
sum2 <- meltSum1 %>% group_by(program, gender, subject) %>% summarise(MeanMarks = mean(marks))
head(sum2)
ggplot(data=sum2, aes(x=gender, y=MeanMarks, fill=gender)) + geom_bar(stat='identity') + facet_grid(program ~ subject)
#



students
head(students, n=2)
students %>% group_by(gender) %>% summarise(COUNT = n())
ggplot(students %>% group_by(gender) %>% summarise(COUNT = n()), aes(x=gender, y=COUNT, fill=gender)) + geom_bar(stat='identity') + geom_text(aes(label=COUNT)) + labs(title='Gender Wise Count')
#stacked bar
ggplot(students %>% group_by(program, gender) %>% summarise(COUNT = n()), aes(x=gender, y=COUNT, fill=program)) + geom_bar(stat='identity') + geom_text(aes(label=COUNT)) + labs(title='Gender Wise - Program Count')
#side by side
ggplot(students %>% group_by(program, gender) %>% summarise(COUNT = n()), aes(x=gender, y=COUNT, fill=program)) + geom_bar(stat='identity',position = position_dodge2(.7)) + geom_text(aes(label=COUNT), position = position_dodge2(.7)) + labs(title='Gender Wise - Program Count')
#Subject - Program - Gender - Mean Marks 
names(students)
names(meltSum1)
head(meltSum1)
ggplot(meltSum1 %>% group_by(program, gender, subject) %>% summarise(meanMarks = round(mean(marks))), aes(x=gender, y=meanMarks, fill=program)) + geom_bar(stat='identity',position = position_dodge2(.7)) + geom_text(aes(label=meanMarks), position = position_dodge2(.7)) + labs(title='Subject - Program - Gender - Mean Marks ') + facet_grid(~ subject)

ggplot(mtcars, aes(x=wt, y=mpg, size=hp, color=factor(gear), shape=factor(am))) + geom_point()
#
ggplot(students, aes(x=gender, y=..count..)) + geom_bar(stat='count')


#
(dcastSum1 <- dcast(meltSum1, rollno + name ~ variable, value.var='value'))
?recast
recast(data=students,  gender ~ variable, fun.aggregate=mean )




#day3 : 23 Feb -----
(x = c(1,2,4,5))
sum(x)
#(x2 = c(1,2,,4, ,5))   #error
(x2 = c(1,2,NA,4,NA,5))
sum(x2)  # NA
sum(x2, na.rm=T) #12
?sum
length(x2)
is.na(x2)
sum(c(T,F,T,F,T))
sum(is.na(x2))
sum(is.na(x2))/length(x2)  #% perc of missing values
x2
mean(x2, na.rm=T)
x2[is.na(x2)]
x2[c(F,F,T,F,T,T)]
x2[is.na(x2)] = mean(x2, na.rm=T)
x2

library(VIM)
data(sleep)
sleep
?sleep
head(sleep)
tail(sleep)
str(sleep)
dim(sleep)
length(sleep)
summary(sleep)
(x=200:300)
quantile(x)
quantile(x, seq(0,1,.25))
quantile(x, seq(0,1,.1))
quantile(x, seq(0,1,.01))

head(sleep)
is.na(sleep)
sum(is.na(sleep))
colSums(is.na(sleep))
rowSums(is.na(sleep))
complete.cases(sleep)
sum(complete.cases(sleep))
sleep[complete.cases(sleep), ]
sleep[!complete.cases(sleep), ]
(xy = colSums(is.na(sleep)))
xy[xy > 0]
(c1 <- names(xy[xy > 0]))
sleep[ , c1]
sleep %>% select(c1) %>% length()
sleep %>% select(-c1)  %>% length()

`%notin%` <- Negate(`%in%`)
c2 <- names(sleep) %notin% c1
sleep[ , c2]

#data partitioning
(x = trunc(rnorm(100, mean=60, sd=15)))
set.seed(134)
s1 <- sample(x, size=70)
length(s1)
sum(s1)

s2 <- sample(x, size=.7 * length(x))
length(s2)
x

mtcars
mtcars %>% sample_n(24)
mtcars %>% sample_frac(.7)
dim(mtcars); nrow(mtcars)
(index = sample(1:nrow(mtcars), size=.7 * nrow(mtcars)))
mtcars[ index, ]
dim(mtcars[index, ])
mtcars[ -index, ]

#clusterpackages <- c('factoextra', 'dendextend','NbClust', 'cluster','fpc', 'amap','animation')
#arulepackages <- c('arules','arulesViz')
#classpackages <- c('rpart','rpart.plot')
#iepackages <- c('gsheet', 'readxl', 'rJava', 'xlsx')
#statspackages <- c('modeest','fdth','e1071','caTools', 'caret' )
#tspackages <- c('timeSeries','xts','zoo','forecast','TTR','quantmod', 'lubridate','smooth','Mcomp')
#tmpackages <- c('rtweet',"curl", 'twitterR', 'ROAuth', 'syuzhet','wordcloud', 'wordcloud2')
#lppackages <- c('lpSolve', 'linprog', 'lpSolveAPI')

#install.packages(pinstall)  #replace pinstall with name of vector of package list

#Multiple Install
list.of.packages <- plist #substitute plist with name of list of packages
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

#load if avl, install if not avl---
#Load if available, install packages if not available in the system & then load
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}
#---------------------------------------------
#data partition
library(caTools)
sample = sample.split(Y=mtcars$am, SplitRatio = .7)
sample
table(sample)
prop.table(table(sample))
(y1= mtcars[sample==T,])  #TRUE set
y2= mtcars[sample==F,]  #FALSE set
table(y1$am) ; prop.table(table(y1$am))
table(y2$am) ; prop.table(table(y2$am))

library(caret)
(intrain <- createDataPartition(y = mtcars$am, p=.7, list=F))
train <- mtcars[ intrain, ]
test <- mtcars[ - intrain, ]
prop.table(table(train$am)) ; prop.table(table(test$am))
           


#linear regression----

women
head(women)
model = lm(weight ~ height, data=women)
summary(model)
#y = mx + c
#weight = 3.45 * height + - 87.51
plot(x=women$height, y=women$weight)
abline(model, col=2)
residuals(model)
women$weight
predWt <- predict(model, newdata=women, type='response')
head(women)
3.45 * 58 - 87
cbind(women, predWt, res = women$weight - predWt, res2= residuals(model))
summary(model)
sqrt(sum(residuals(model)^2))  #SSE

(ndata = data.frame(height=c(66.5, 75.8)))
predict(model, newdata=ndata, type='response')
confint(model)

#case2 : LR -----
link = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
library(gsheet)
df = as.data.frame(gsheet2tbl(link))
head(df)
model2 = lm(Y ~ X, data =df)
plot(df$X, df$Y)
abline(model2)

#write your inferences
#R2
#does model exists
#coeff values
#SSE value
summary(model2)
resid(model2)
range(df$X)
ndata2 = data.frame(X=c(3,4))
predict(model2, newdata=ndata2, type='response')
#assumptions 
plot(model)


#MLM - promotion sales
link2 = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"

df2 = as.data.frame(gsheet2tbl(link2))
head(df2)
plot(df2$price, df2$sqty)
plot(df2$promotion, df2$sqty)

model3 <- lm(sqty ~ price + promotion     , data=df2)
summary(model3)
plot(model3)
plot(df2$price, df2$sqty)
abline(model3, col=2)
plot(df2$promotion, df2$sqty)
abline(model3, col=4)
range(df2$price) ; range(df2$promotion)
(ndata3 = data.frame(price=c(60, 75), promotion=c(300, 500)))
predict(model3, newdata=ndata3, type='response')
plot(model3)

#decision trees-----
# Decision Tree - Classification
#we want predict for combination of input variables, is a person likely to survive or not

#import data from online site
path = 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/titanic_train.csv'
titanic <- read.csv(path)
head(titanic)
names(titanic)
data = titanic[,c(2,3,5,6,7)]  #select few columns only
head(data)
dim(data)
#load libraries
library(rpart)
library(rpart.plot)
str(data)
#Decision Tree
names(data)
table(data$Survived)
prop.table(table(data$Survived))

str(data)
data$Pclass = factor(data$Pclass)
fit <- rpart(Survived ~ ., data = data, method = 'class')
fit
rpart.plot(fit, extra = 104, cex=.8,nn=T)  #plot
head(data)
printcp(fit) #select complexity parameter
prunetree2 = prune(fit, cp=.017544)
rpart.plot(prunetree2, cex=1,nn=T, extra=104)
prunetree2
nrow(data)
table(data$Survived)
# predict for Female, pclass=3, siblings=2, what is the chance of survival

#Predict class category or probabilities
(testdata = sample_n(data,2))
predict(prunetree2, newdata=testdata, type='class')
predict(prunetree2, newdata=testdata, type='prob')
str(data)
testdata2 = data.frame(Pclass=factor(2), Sex=factor('male'), Age=15, SibSp=2)
testdata2
predict(prunetree2, newdata = testdata2, type='class')
predict(prunetree2, newdata = testdata2, type='prob')

#Use decision trees for predicting
#customer is likely to buy a product or not with probabilities
#customer is likely to default on payment or not with probabilities
#Student is likely to get selected, cricket team likely to win etc

#Imp steps
#select columns for prediction
#load libraries, create model y ~ x1 + x2 
#prune the tree with cp value
#plot the graph
#predict for new cases

#rpart, CART, classification model
#regression decision = predict numerical value eg sales

#Clustering ------
set.seed(1234)
(marks1 = trunc(rnorm(n=30, mean=70, sd=8)))
sum(marks1)
df5 <- data.frame(marks1=marks1)
head(df5)
#
km3 <- kmeans(df5, centers=3)
attributes(km3)
km3$cluster
km3$centers
km3$size
sort(df5$marks1)
cbind(df5, km3$cluster)  #which row which cluster
df5$cluster = km3$cluster
head(df5)
df5 %>% arrange(cluster)
dist(df5[1:5,])
#-----------------------------------------
set.seed(1234)
(marks1 = trunc(rnorm(n=30, mean=70, sd=8)))
(marks2 = trunc(rnorm(n=30, mean=120, sd=10)))
df6 <- data.frame(marks1, marks2)
head(df6)
#
km3 <- kmeans(df6, centers=5)
attributes(km3)
km3$cluster
km3$centers
km3$size
cbind(df6, km3$cluster)  #which row which cluster
df6$cluster = km3$cluster
head(df6)
df6 %>% arrange(cluster)
df6[1:5,]
dist(df6[1:5,])
plot(df6$marks1, df6$marks2, col=df6$cluster)

#scale data and then do clustering
head(df6)
(df6B <- scale(df6[, c('marks1','marks2')]))
head(df6B)
#
km3B <- kmeans(df6B, centers=5)
attributes(km3B)
km3B$cluster
km3B$centers
km3B$size
cbind(df6B, km3B$cluster)  #which row which cluster
head(df6)
df6$newcluster = km3B$cluster
head(df6)
df6B %>% arrange(cluster)
df6[1:5,]
dist(df6[1:5,])
plot(df6$marks1, df6$marks2, col=df6$cluster)
plot(df6$marks1, df6$marks2, col=df6$newcluster)





#Factor Extra - hclust

library("factoextra")
# Compute hierarchical clustering and cut into 4 clusters
res <- hcut(USArrests, k = 4, stand = TRUE)
res
# Visualize
k_colors = c("#00AFBB","#2E9FDF", "#E7B800", "#FC4E07")
fviz_dend(res, rect = TRUE, cex = 0.5, k_colors=c('red','green','blue','yellow')  )

# Optimal number of clusters for k-means
library("factoextra")
my_data <- scale(USArrests)
fviz_nbclust(my_data, kmeans, method = "gap_stat")



#----wordcloud-----


#install.packages('wordcloud2')
library(wordcloud2)
df = data.frame(word=c('iit','mdi','iim','imt'),freq=c(50,20,23,15))
df
par(mar=c(0,0,0,0))
wordcloud2(df)

head(demoFreq)
dim(demoFreq)
par(mar=c(0,0,0,0))
wordcloud2(demoFreq)

par(mar=c(0,0,0,0))
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")

names(demoFreq)
par(mar=c(0,0,0,0))
wordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6,   rotateRatio = 1)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = pi/6,    rotateRatio = 0.9)
par(mar=c(0,0,0,0))
wordcloud2(demoFreqC, size = 3,  color = "random-light", backgroundColor = "grey")
wordcloud2(demoFreqC, size = 2, minRotation = -pi/6, maxRotation = -pi/6,  rotateRatio = 1)

# Color Vector
?wordcloud2
colorVec = rep(c('red', 'skyblue'), length.out=nrow(demoFreq))
wordcloud2(demoFreq, color = colorVec, fontWeight = "bold")

wordcloud2(demoFreq, color = ifelse(demoFreq[, 2] > 10, 'red', 'skyblue'))


#Example2 -----
#Word Cloud 2

#(https://www.r-graph-gallery.com/the-wordcloud2-library/)

# library : install it first
library(wordcloud2) 

# have a look to the example dataset
head(demoFreq)
dim(demoFreq)
str(demoFreq)
#wordcloud
wordcloud2(demoFreq, size=1.6)
head(demoFreq[order(-demoFreq$freq),])
?wordcloud2

#create your own set of words
word = c('marketing','consumer', 'dhiraj','price','business','iimkashipur', 'sunder','vignesh', 'jyoti','finance', 'operations')
freq = c(30,20,15,36,15,13,11,44,13,44,34)
df1 = data.frame(word, freq)
#rownames(df1)= word
head(df1)
#df1 = head(demoFreq)
wordcloud2(df1, size=.4)
?wordcloud2
# Gives a proposed palette
wordcloud2(demoFreq, size=1.6, color='random-dark')
wordcloud2(df1, size=1, color='random-dark')

# or a vector of colors. vector must be same length than input data
wordcloud2(demoFreq, size=1.6, color=rep_len( c("green","blue"), nrow(demoFreq) ) )
# Change the background color
wordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor="black")



================================================
FILE: 01-IIM/test.R
================================================

mtcars
print(tail(mtcars))
iris
print(head(iris))
women
plot(women)


lapply(mtcars, FUN=mean)
sapply(mtcars, FUN=mean)


================================================
FILE: 01-IIM/x55a1-TM-tweets.R
================================================
#TWeets


Learning Business Analytics  #businessanalytics   using R  @R_Programming  at @IIMBodhGaya  with @curtisakshay  by @henryharvin_in 


================================================
FILE: 01-IIM/x55a2-TM-twitter.R
================================================
#Twitter 1 - Configure Tweets and Download them
#@dupadhyaya  #Working using my Keys
#Load libraries
library("curl")
library("twitteR")
library("ROAuth")
library("syuzhet") #library for sentiment analysis - comparison

download.file(url="http://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")

#https://apps.twitter.com/   create developer account here to use ur own keys
#different for each account

consumerKey="sDToDBULOI4wokzrVEiHPEi7o"
consumerSecret="w3sjSpmUOh9JglcHth9gCNB2ur4MElgVbL0ayhXBNWyFDIyAWf"
AccessToken="14366551-3sCiFAUo8Gj72FaxzQ0kWGG4NFvI0BxWAXUbpYT9l"
AccessTokenSecret="mZf2xtm2Y0O0vj8KSUpD09TccYb6ul6CnrbNKzdbkWZwh"

#Common for all accounts except the keys

cred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')

#IMPORTANT------
cred$handshake(cainfo="cacert.pem") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt
save(cred, file="twitter authentication.Rdata") # store this to avoid asking again
#Load saved authentication cert
load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)
#type 1 : Yes : Direct Authentication

search.string <- "#businessanalytics"
#search.string <- "#marketinganalytics"
no.of.tweets <- 100
tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets
tweets[1:10]
#Another Topics
#search.string <- "#indvseng"
search.string <- "#asiacup"
no.of.tweets <- 100
tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets[1:5]
?searchTwitter
#My Tweets : will change if you use your own account
homeTimeline(n=15)  #my tweets
mentions(n=5)   # my tags

#for user - 
(tweets = userTimeline("galactico_jyoti", n=10))
userTimeline("galactico_jyoti", n=5)

#------------------------------------
?userTimeline
tweets = userTimeline("realDonaldTrump", n=100)
#english
tweets[1:5]
(n.tweet <- length(tweets))
n.tweet
tweets.df = twListToDF(tweets) 
head(tweets.df)
summary(tweets.df)

#Remove hashtags & unnecessary characters
tweets.df2 <- gsub("http.*","",tweets.df$text)
tweets.df2 <- gsub("https.*","",tweets.df2)
tweets.df2 <- gsub("#.*","",tweets.df2)
tweets.df2 <- gsub("@.*","",tweets.df2)

head(tweets.df2)
#-----
library("syuzhet") #library for sentiment analysis - comparison
word.df <- as.vector(tweets.df2)
word.df
emotion.df <- get_nrc_sentiment(word.df)
emotion.df
word.df[1]
emotion.df2 <- cbind(tweets.df2, emotion.df) 
head(emotion.df2)

#-----
sent.value <- get_sentiment(word.df)
?get_sentiment
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(tweets,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)



================================================
FILE: 01-IIM/x55a3-TM-twitter2.R
================================================

#https://cran.r-project.org/web/packages/rtweet/vignettes/intro.html
#https://rtweet.info/
#https://www.rdocumentation.org/packages/rtweet/versions/0.4.0
#https://rud.is/books/21-recipes/
#https://rud.is/books/21-recipes/harvesting-tweets.html

library(rtweet)
library(dplyr)


#Two methods of authentical - Browser and Access Token
## autheticate via web browser
## install httpuv if not already
if (!requireNamespace("httpuv", quietly = TRUE)) {
  install.packages("httpuv")
}
twitter_token <- create_token(
  app = "DUrtweet",
  consumer_key = "0glwhLXXAahrusA3Dxvhou82M",
  consumer_secret = "bItULC8KINiNFIWcMBDekO6IztIoHeQ8yD6STqxbvIBxRCePZx")

rt1 <- search_tweets("business analytics", n = 100, token = twitter_token)
rt1
rtweet::write_as_csv(rt1, 'E:/data/tweets1.csv')

get_followers(user= 'dupadhyaya', n = 100, page = "-1", parse = TRUE, token =twitter_token)
get_friends(user= 'dupadhyaya', page = "-1", parse = TRUE, token =twitter_token)
duT = get_timeline(user= 'dupadhyaya', n = 200, max_id = NULL, home = FALSE, parse = TRUE,check = TRUE, usr = TRUE, token =twitter_token )
duT
users_data(duT)
duT[1,]$source

# lookup vector of 1 or more user_id or screen_name
users = c('rahulg_IITK', 'Saurabh84522682', 'Dharmes13753606','dupadhyaya', 'narendramodi', 'imvirat', 'iimahmedabad', 'rahulgandhi')

usr_df <- lookup_users(users)
usr_df
as.data.frame(usr_df)
# view tweet data for these users via tweets_data()
tweets_data(usr_df)

users2 <- c('imvirat')
usr_df2 <- lookup_users(users2)
usr_df2
# view tweet data for these users via tweets_data()
tweets_data(usr_df2)

#get_mentions: Get mentions for the authenticating user.
dugt = get_mentions()
dugt
get_mentions(n = 200, since_id = NULL, max_id = NULL, parse = TRUE, token = NULL)

tweets_with_users(rt)
su = search_users('businessanalytics',n=100, parse=F)
head(su)
users_with_tweets(su)

#get_collections-----
viratT = get_collections(user ='iamvirat', status_id = NULL, n = 200, cursor = NULL, parse = TRUE, token = NULL)

str(viratT)
personid='dupadhyaya'
personid='imVkohli'

## get user IDs of accounts followed by CNN
du_fds <- get_friends(personid)
du_fds
## lookup data on those accounts
du_fds_data <- lookup_users(du_fds$user_id)
du_fds_data
rtweet::write_as_csv(du_fds_data, 'E:/data/tweets2.csv')




## check to see if the token is loaded
identical(twitter_token, get_token())  #not working
#-----------------------------
#Access token/secret method
create_token(
  app = "DUrtweet",
  consumer_key = "0glwhLXXAahrusA3Dxvhou82M",
  consumer_secret = "bItULC8KINiNFIWcMBDekO6IztIoHeQ8yD6STqxbvIBxRCePZx",
  access_token = "14366551-KKd2B8pb5as86RKNf6sgULoM2aoL1JxspMzstXH8h",
  access_secret = "6s3lIn4YtppBLnj9H4xsHvtXWAHeywTfaqcwmfBWfWcO5")

## check to see if the token is loaded
identical(twitter_token, get_token())  #not working



## search for 18000 tweets using the rstats hashtag
rt <- search_tweets(
  "#rstats", n = 180000, include_rts = FALSE
)
#----
rt

#-----
## plot time series of tweets
ts_plot(rt, "3 hours") +
  ggplot2::theme_minimal() +
  ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) +
  ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of #rstats Twitter statuses from past 9 days",
    subtitle = "Twitter status (tweet) counts aggregated using three-hour intervals",
    caption = "\nSource: Data collected from Twitter's REST API via rtweet"
  )

#-----
## search for 250,000 tweets containing the word data
rt <- search_tweets(
  "data", n = 2500, retryonratelimit = TRUE
)

#-----
## search for 10,000 tweets sent from the US
rt <- search_tweets(
  "lang:en", geocode = lookup_coords("usa"), n = 10
)

## create lat/lng variables using all available tweet and profile geo-location data
rt <- lat_lng(rt)

## plot state boundaries
par(mar = c(0, 0, 0, 0))
maps::map("state", lwd = .25)

## plot lat and lng points onto state map
with(rt, points(lng, lat, pch = 20, cex = .75, col = rgb(0, .3, .7, .75)))


#------
## random sample for 30 seconds (default)
rt <- stream_tweets("")

#------
## stream tweets from london for 60 seconds
rt <- stream_tweets(lookup_coords("london, uk"), timeout = 60)


#------
## stream london tweets for a week (60 secs x 60 mins * 24 hours *  7 days)
stream_tweets(
  "realdonaldtrump,trump",
  timeout = 60 * 60 * 24 * 7,
  file_name = "tweetsabouttrump.json",
  parse = FALSE
)

## read in the data as a tidy tbl data frame
djt <- parse_stream("tweetsabouttrump.json")

#------
## get user IDs of accounts followed by CNN
cnn_fds <- get_friends("cnn")

## lookup data on those accounts
cnn_fds_data <- lookup_users(cnn_fds$user_id)

#------
## get user IDs of accounts following CNN
cnn_flw <- get_followers("cnn", n = 75000)

## lookup data on those accounts
cnn_flw_data <- lookup_users(cnn_flw$user_id)

#----
## how many total follows does cnn have?
cnn <- lookup_users("cnn")

## get them all (this would take a little over 5 days)
cnn_flw <- get_followers(
  "cnn", n = cnn$followers_count, retryonratelimit = TRUE)

#-----
## get user IDs of accounts followed by CNN
tmls <- get_timelines(c("cnn", "BBCWorld", "foxnews"), n = 3200)

## plot the frequency of tweets for each user over time
tmls %>%
  dplyr::filter(created_at > "2017-10-29") %>%
  dplyr::group_by(screen_name) %>%
  ts_plot("days", trim = 1L) +
  ggplot2::geom_point() +
  ggplot2::theme_minimal() +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom",
    plot.title = ggplot2::element_text(face = "bold")) +
  ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses posted by news organization",
    subtitle = "Twitter status (tweet) counts aggregated by day from October/November 2017",
    caption = "\nSource: Data collected from Twitter's REST API via rtweet"
  )

#------
jkr <- get_favorites("jk_rowling", n = 3000)


#------
## search for users with #rstats in their profiles
usrs <- search_users("#rstats", n = 1000)



#-----
#trends
sf <- get_trends("san francisco")


#-----
## quick overview of rtweet functions
vignette("auth", package = "rtweet")

#----
## quick overview of rtweet functions
vignette("intro", package = "rtweet")

#------
## working with the stream
vignette("stream", package = "rtweet")


#-------
## working with the stream
vignette("FAQ", package = "rtweet")

  




neilfws <- get_timeline("neilfws", n = 100)
neilfws %>%
  glimpse()

================================================
FILE: 02-IIMcases/DT_diabetis.R
================================================
#Decision Tree : Similar eg in Python
#https://www.kaggle.com/aungpyaeap/diabetes-test-using-decision-tree

url='https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/diabetes.csv'

pacman::p_load(rpart, rpart.plot, dplyr)
data = read.csv(url)
head(data)
summary(data)
data %>% group_by(Outcome)  %>% summarise(meanAge = mean(Age, na.rm=T), max(Glucose), max(BloodPressure))

names(data)
table(data$Outcome)
 500/(500 + 268)
str(data)

data$Outcome = factor(data$Outcome)

fit <- rpart(Outcome ~ ., data = data, method = 'class')
print(fit)

rpart.plot(fit)
rpart.plot(fit, extra = 104, cex=.6, nn=T)  #plot

printcp(fit) #select complexity parameter
prunetree2 = prune(fit, cp=.017)
rpart.plot(prunetree2, cex=.5,nn=T, extra=104)
prunetree2

(testdata = sample_n(data,2))
predict(prunetree2, newdata=testdata, type='class')
predict(prunetree2, newdata=testdata, type='prob')
str(data)

testdata2 = data.frame(Pregnancies=2, Glucose=130, BloodPressure=80, SkinThickness=30, Insulin=20, BMI=30, DiabetesPedigreeFunction=.6, Age=40)
testdata2

predict(prunetree2, newdata = testdata2, type='class')
predict(prunetree2, newdata = testdata2, type='prob')



#divide data into train and test-----
library(caret)
index = createDataPartition(y=data$Outcome, p=.75, list=F)
train = data[ index, ]
test = data[-index, ]
sapply(list(train, test), dim)  #rows & cols

dtree1 <- rpart(Outcome ~ ., data=train)
dtree1
rpart.plot(dtree1, cex=.8, nn=T, extra=104)
dtree1$variable.importance

#predict on test----
Pdtree1 <- predict(dtree1, type='class', newdata=test)
Pdtree1  #class predicted
length(Pdtree1)
cbind(test$Outcome, Pdtree1)
(summary1 <- table(Pdtree1, test$Outcome, dnn=c('Predicted','Actual')))
caret::confusionMatrix(summary1)
(102+38)/192


#revise
#data import
#partition/ split data
#create model
#predict model on test
#confusion matrix
#accuracy
#predict on unknown IVs
#visual plot

================================================
FILE: 02-IIMcases/case-denco.R
================================================
#Case Study - Manufacturing Coy

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)
dim(sales3)
library(dplyr)
names(sales3)
#find out Loyal Customers, Customers who gave high revenue, High Revenue Part Nos, PartNos which give highest Margin; #top n=2 from each region- total revenue
#parts sold in East and china region

sales3
names(sales3)
sales3 %>% group_by(partnum) %>% summarise(MARGIN = sum(margin)) %>% arrange(desc(MARGIN))  %>% head(5)
sales3 %>% group_by(region) %>% summarise(MARGIN = sum(margin)) %>% arrange(desc(MARGIN))  %>% head(2)
sales3 %>% group_by(region,partnum) %>% summarise(MARGIN = sum(margin)) %>% arrange(region, desc(MARGIN))
sales3 %>% group_by(region,partnum) %>% summarise(MARGIN = sum(margin)) %>% top_n(-2, MARGIN)
dim(sales3)
regionSales <- sales3 %>% group_by(region) %>% summarise(MARGIN = sum(margin)) %>% arrange(desc(MARGIN))
regionSales
barplot(regionSales$MARGIN)
sales3 %>% group_by(region) %>% summarise(MARGIN = sum(margin))  
sales3
head(sales3)
sales3 %>% group_by(custname) %>% summarise(REVENUE = sum(revenue, na.rm=T), COUNT = n())
sales3 %>% group_by(custname) %>% summarise(REVENUE = sum(revenue, na.rm=T), COUNT = n()) %>% top_n(5, REVENUE)
sales3 %>% group_by(custname) %>% summarise(REVENUE = sum(revenue, na.rm=T), COUNT = n()) %>% top_n(5, COUNT)






library(ggplot2)
ggplot(data=regionSales, aes(x=region, y=MARGIN)) + geom_bar(stat='identity')


================================================
FILE: 02-IIMcases/case-dplyr-mtcars.R
================================================
#dplyr - mtcars
library(dplyr)
mtcars
head(mtcars)

#Filter----

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)
mtcars %>% filter(cyl == 8) %>% summarise(meanMPG = mean(mpg))

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 2, n()-2))
mtcars

#mutate----
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col
transmute(mtcars, displ_l = disp / 61.0237) #removes other cols
mutate(mtcars, cyl = NULL) #do not display cyl


#slice-----
slice(mtcars, 1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

(by_cyl <- group_by(mtcars, cyl)) # ???
slice(by_cyl, 1:2)

#structure----
tbl_df(mtcars) # convert to tbl class
glimpse(mtcars)  # dense summary of tbl data
View(mtcars) # spreasheet like form base pacakge



#rownames----
df = tibble::rownames_to_column(df, var='cars')
df2 = mtcars
names(df)

#has
tibble::has_rownames(mtcars)
tibble::has_rownames(df)

#remove rownames  
#see without rownames
head(df2[1:5])
tibble::remove_rownames(df2)

#rowid as column
tibble::rowid_to_column(df, var = "rowid")

#column to rownames
head(df[1:5])
tibble::column_to_rownames(df, var = "cars")

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))


#summarise----
summarise(mtcars, mean(disp))  
summarise(group_by(mtcars, cyl), mean(disp)) 
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))
mtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))



#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))





mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)


#------------------------------------
#unsortd----
dplyr::tbl_df(iris)
print(dplyr::tbl_df(mtcars), n=20)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=11)
tbl_df(mtcars) %>% print(n = Inf)
tbl_df(mtcars) %>% print(width = Inf)
tbl_df(mtcars) %>% as.data.frame(mtcars)

glimpse(mtcars)
df = mtcars
row.names(df) = NULL
df %>% select(mpg)
#head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
  filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)
distinct(mtcars)
df  = data.frame(a=c(2,2),b=c(2,2))
df
distinct(df)

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)
slice(mtcars,10:14)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))
#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

df = data.frame(marks=c(1,2,3,7,1))
cbind(df, dplyr::mutate_each(df, funs(min_rank)))

mtcars %>% lead() %>% lag()
dplyr::n(mtcars)
select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df
rename(df, marks2 = marks)
df %>% mutate(marks2 = marks + 2, marks3 = marks + 4)
df %>% transmute(marks2 = marks + 2, marks3 = marks + 4)

library(nycflights13)
data(flights)


destinations <- group_by(flights, dest)
destinations
summarise(destinations,
          planes = n_distinct(tailnum),
          flights = n()
)


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter


# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)





================================================
FILE: 02-IIMcases/rev_iima20_1.R
================================================
#Revision Session - IIMA - Sep 2020

#vectors-----
x = 1:10000
class(x)
(marks = rnorm(n=100, mean=60, sd=10))

#matrices------
m1 = matrix(data = 1:100, ncol=10)
m1
colSums(m1)

#array--------

#list---------


#dataframe-----
rollno= 1:30
(sname = paste('IIM Student', 1:30))
(marks= rnorm(n=30, mean=60, sd=5))
df = data.frame(rollno, sname, marks)
head(df)
write.csv(df, './data/s1.csv', row.names = F)
#factors-------
sex = c('M','F')
(gender = sample(sex, size=30, replace=T))
df$gender = gender
head(df)
df$gender
summary(df)
df$gender = factor(df$gender)
summary(df)
#read/write - CSV, googlesheets



================================================
FILE: 02-IIMcases/revision1.R
================================================
#Revision - IIM

#data Structures - vectors, matrix, dataframe, factors
(vector1  = c('a','b','E', 'Dhiraj', 'Vishnu', 'Aurshi', 'Kavita', 'Nayak'))
vector1[1:5]
(vector2 = trunc(rnorm(n=24, mean=60, sd=10)))
mean(vector2)
hist(vector2)
length(vector2)/5
(matrix1 = matrix(data=vector2, nrow=6))
dim(matrix1)
colSums(matrix1)
colMeans(matrix1)
rowSums(matrix1)
addmargins(matrix1, FUN=c(mean, sum))
#prop.table, margin.table, lapply, mapply, sapply, tapply

#dataframe 
(rollno = 1:5)
(name = paste('S',1:5, sep='_'))
(gender = factor(sample(x=c('M','F'), size=5, replace=T, prob=c(.5,.5))))
(marks = round(runif(n=5, min=50, max=90)))
(grades = factor(sample(x=c('A','B', 'C'), size=5, replace=T), ordered=T, levels = c('C','B','A')))
(df1 = data.frame(rollno, name, gender, marks, grades))
head(df1)
str(df1)
summary(df1)
dim(df1)

#summary with dplyr
library(dplyr)
df1 %>% group_by(gender) %>% summarise(count=n(), meanMarks = mean(marks, na.rm=T))

#graphs
hist(df1$marks)
barplot(table(df1$gender))
boxplot(x=df1$marks)

#import/export data - csv, excel, googlesheet
write.csv(df1, 'data/revision1.csv')
list.files('data')
#-----------------------------------------------------------
#Modeling - Linear, Logistic, Decision Tree, Clustering, 
#partitioning the data data
df2 = mtcars[,c('mpg','hp','wt','am')]
head(df2)
df2$am = factor(df2$am)

#predict mpg ~ hp + wt + am
#split the data into train(70%) and test(30%)
set.seed(1234)
index1 = sample(x=1:nrow(df2), size=.7 * nrow(df2), replace=F)
index1
train1 = df2[index1, ] ; dim(train1)
test1 = df2[-index1, ] ; dim(test1)
LM1 = lm(mpg ~ ., data=train1)
summary(LM1)
attributes(summary(LM1))
summary(LM1)$adj.r.squared

#am is not significant, remove it
LM2 = lm(mpg ~ . - am, data=train1)
summary(LM2)
#equation is mpg = 37 + - 0.033 * hp  +  - 3.74 * wt
predictedMPG = predict(LM2, newdata=test1, type='response')
originalMPG = test1$mpg

library(caret)  #library for accuracy
MAE(predictedMPG, originalMPG)
RMSE(predictedMPG, originalMPG)
R2(predictedMPG, originalMPG, form = "traditional")

#------------------------------------------
#predict prob of car being auto(0) or manual(1)
library(caret)
set.seed(1234)
index2 <- createDataPartition(y=df2$am, p=0.7, list=FALSE)
train2 <- df2[index2,] ; dim(train2)
test2 <-  df2[-index2,] ; dim(test2)
table(train2$am)
table(test2$am)
prop.table(table(train2$am))
prop.table(table(test2$am)) #approx same / similar, less if rows are less
str(train2)

LGM1 = glm(am ~ ., data=train2, family='binomial')
#error if data is less
summary(LGM1)
#here model is not good, as no variable is significant, still we will continue

(predictedAM = predict(LGM1, newdata = test2, type='response'))
((predictedAM_class = ifelse(predictedAM < 0.5, 0, 1)))

caret::confusionMatrix(data= factor(test2$am), factor(predictedAM_class))
#approx 75% accuracy

#decision tree 
#predict if car is auto or manual using DT
#partition similar to logistic regression
train2 ; test2

set.seed(1234)
DT1 = rpart(am ~ ., data=train2, method='class')
DT1
rpart.plot(DT1, extra=104, nn=T)
printcp(DT1)  #no pruning required here as tree is small

predictedAM_DT = predict(DT1, newdata = test2, type='class')

caret::confusionMatrix(data= test2$am, factor(predictedAM_DT))
#accuracy ~ 1 (100%)
cbind(original = test2$am, predicted=predictedAM_DT)

#regression tree
#predict mpg on other factors
set.seed(1234)
DT2 = rpart(mpg ~ ., data=train2, method='anova')
DT2
rpart.plot(DT2, nn=T)
printcp(DT2)  #no pruning required here as tree is small

(predictedMPG_DT = predict(DT2, newdata = test2, type='vector'))

caret::RMSE(test1$mpg, predictedMPG_DT)
cbind(original=test1$mpg, predicted=predictedMPG_DT)

#clustering
#group cars into 3 groups
df3 = mtcars[, c('mpg','hp','wt')]
lapply(df3, range)
df3_scaled = scale(df3)
head(df3_scaled)
?scale
head(df3)

km3A = kmeans(df3, centers=3)
km3A$cluster  
cbind(df3, cno = km3A$cluster) %>% arrange(cno)
km3A$centers

km3B = kmeans(df3_scaled, centers=3)
km3B$cluster  
cbind(df3, cno = km3B$cluster) %>% arrange(cno)
km3B$centers
df3 %>% group_by(cnoB= km3B$cluster) %>% summarise_all(mean)
km3A$centers 

#why scaling ----

#hierarchical clustering
head(df3_scaled)
(d=dist(df3_scaled))
as.matrix(dist(df3_scaled[1:5]))

HCLUST_avg = hclust(d, method = 'average')
HCLUST_avg
plot(HCLUST_avg, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(HCLUST_avg, k=5, border='red')  #5 clusters

plot(HCLUST_avg, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(HCLUST_avg, k=3, border='green')  #3 clusters

#using height
plot(HCLUST_avg, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(HCLUST_avg, h=3, border='blue')

#dendrogram
library('dendextend')
## Example:
dend <- as.dendrogram(hclust(d, "ave"))
dcol1=color_branches(dend,k=3, col = c(3,2,1))
plot(dcol1) # selective coloring of branches :)


#no of clusters
library(NbClust)
nc = NbClust(df3_scaled, distance='euclidean', min.nc=2, max.nc=10, method='average')

km5_new = kmeans(df3_scaled, centers=5)

clusplot(x=df3_scaled, km5_new$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
cbind(rownames(df3), cno=km5_new$cluster) %>% as.data.frame() %>% arrange(cno)



#end here.


================================================
FILE: 02-IIMcases/senitmentTwitter.R
================================================
#Sentiment - twitter

# Tweeter Download and Analysis
#connect all libraries
library(plyr)
library(dplyr)
library(stringr)
library(ggplot2)
library(tidytext)
library(rtweet) 
library("syuzhet")  #sentiment analysis

# whatever name you assigned to your created app
appname <- "DUtwitterProject"

#keys
consumerKey="sDToDBULOI4wokzrVEiHPEi7o"
consumerSecret="w3sjSpmUOh9JglcHth9gCNB2ur4MElgVbL0ayhXBNWyFDIyAWf"
AccessToken="14366551-3sCiFAUo8Gj72FaxzQ0kWGG4NFvI0BxWAXUbpYT9l"
AccessTokenSecret="mZf2xtm2Y0O0vj8KSUpD09TccYb6ul6CnrbNKzdbkWZwh"

# create token named "twitter_token"
twitter_token <- create_token(
  app = appname,
  consumer_key = consumerKey,
  consumer_secret = consumerSecret,
  access_token = AccessToken,
  access_secret = AccessTokenSecret)
#check token
get_token()
users = c("cnn", "BBCWorld", "realDonaldTrump")
famousUsers = lookup_users(users)
tweets_data(famousUsers)
#searchString1 = 'realDonaldTrump'
#download tweets in json file
#tweets1 <- search_tweets(q = searchString1, n = 50, retryonratelimit = T)
stream_tweets("realdonaldtrump",   timeout = 30,  file_name = "tweetsabouttrump.json",  parse = FALSE)
?stream_tweets
#https://cran.r-project.org/web/packages/rtweet/vignettes/intro.html
## read in the data as a tidy tbl data frame
djt <- parse_stream("tweetsabouttrump.json")
dim(djt)
ts_plot(djt,'secs') #frequency of tweets over a specified interval of time.
summary(djt[1:5])
names(djt[1:5])
djt$text[1:5]
#Remove hashtags & unnecessary characters
tweets2a <- gsub("http.*","",djt$text)  #https
tweets2a[1:4]
tweets2b <- gsub("https.*","",tweets2a)  #https
tweets2b[1:4]
tweets2c <- gsub("#.*","",tweets2b) #others # topics
tweets2c[1:4]
tweets2d <- gsub("@.*","",tweets2c)  #others @ names
tweets2d[1:4]

head(tweets2d)
#-----
word.df <- as.vector(tweets2d)
word.df[1:2]
#library(syuzhet)
emotion.df <- get_nrc_sentiment(word.df)
emotion.df2 <- cbind(tweets2d, emotion.df) 
head(emotion.df2)

#-----
sent.value <- get_sentiment(word.df)
sent.value
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(djt$text,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)
barplot(table(category_senti))
#----


================================================
FILE: 02-IIMcases/textMiningSentence.R
================================================
#Text Mining

#Loading Packages
library(tm)
library(wordcloud)
library(RColorBrewer)
#Now in order to process or clean the text using tm package, you need to first convert this plain text data into a format called corpus which can then be processed by the tm package. A corpus is a collection of documents (although in our case we only have one).Following is the command to convert .txt file into a corpus. 

text = "Do not let anyone or anything outside of your own control tell you or suggest to you what will make you happy. That decision of what is happiness is up to you and you alone to create and cherish"
speech <- Corpus(VectorSource(text))
speech
#speech <- Corpus(VectorSource(speech_txt))
#To see the first few documents in the text file, type the R command:
inspect(speech)[1:10]
?tm_map
(speech_data<-tm_map(speech,stripWhitespace))
(speech_data<-tm_map(speech_data,tolower))
speech_data<-tm_map(speech_data,removeNumbers)
speech_data<-tm_map(speech_data,removePunctuation)
speech_data<-tm_map(speech_data,removeWords, stopwords('english'))
speech_data<-tm_map(speech_data, removeWords, c('and','the','our','that','for','are','also','more','has','must','have','should','this','with'))

#Create a Term Document Matrix

#It is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to words in the collection and columns correspond to documents.

#Now we can create a word cloud even without a TDM. But the advantage of using this here is to take a look at the frequency of words.
tdm_speech<-TermDocumentMatrix (speech_data) #Creates a TDM
tdm_speech
TDM1 <- as.matrix(tdm_speech) #Convert this into a matrix format
TDM1
v = sort(rowSums(TDM1), decreasing = TRUE) #Gives you the frequencies for every word
head(v)
summary(v)
#summary(v) will give us the distribution of the frequency of words. So we can take a look at the least and max number of times a word has occurred. This helps us set the “max.words” parameter in the next step.

#Create your first word cloud!
par(mar=c(0,0,0,0))
set.seed(1234)
wordcloud (speech_data, scale=c(3,0.5), max.words=15, random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, 'Dark2'))

#Scale controls the difference between the largest and smallest font, max.words is required to limit the number of words in the cloud (if you omit this R will try to squeeze every unique word into the diagram), rot.per is the percentage of vertical text, and colors provides a wide choice of symbolizing your data. look at the topics that were brushed upon in the speech. If you use bigger text documents (I would suggest you try with some short stories, your favorite plays or any tweets data trends on the latest topic) then you can set your max.words as 50 or 100 depending on which range you want your word cloud to be.


#http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know

#words which occur at least 4 times
findFreqTerms(TDM1, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")


#--------
link3 <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text <- readLines(link3)
text 
# Load the data as a corpus
docs <- Corpus(VectorSource(text))
docs
inspect(docs)
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument  

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))
findFreqTerms(dtm, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")
#http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know

================================================
FILE: 03-setup/11a-start.R
================================================
# R Environment

# The operators <- and = assign into the environment in which they are # evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

#Control + Enter to execute Line by line
x1 = 1:5
x1
x2 <- 1:5
x2
(x3 = 1:5)  # assign and print to screen



================================================
FILE: 03-setup/11b-gettingstarted.R
================================================
# Getting Started- Basics/Shortcuts in R
# For Help
?mean

sessionInfo()
library(dplyr)
sessionInfo()

detach("package:dplyr", unload=TRUE)
sessionInfo()

library(arules)
sessionInfo()



================================================
FILE: 03-setup/12-packageInstall.R
================================================
#packages install

#Total Avl Packages
nrow(available.packages()) #C
#16000

#install r tools

install.packages('pacman')

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}



#  package lists
pkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')
ipak(pkg1)

#timeseries
pkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')
ipak(pkg2)

pkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')
ipak(pkg3)

#association rules
pkg4 = c('arules' , 'arulesViz')
ipak(pkg4)


#textmining
pkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud', 'wordcloud2', 'NLP','tm')
ipak(pkg5)

#git packages
install.packages("CHAID", repos="http://R-Forge.R-project.org")

#processmining
pkg6 = c('bupar', 'bupaR', 'edeaR', 'processmapR', 'processanimateR', 'processcheckR', 'eventdataR', 'xesreadR', 'propro', 'petrinetR', 'daqapo', 'heuristicminR', 'pm4py') 
ipak(pkg6)

#devtools::install_github("bupaverse/processmapR")
pkg6b = c('bupaverse/processmapR','bupaverse/logbuildR', 'bupaverse/docker', 'bupaverse/bupaR', 'gertjanssenswillen/petrinetR')
devtools::install_github(pkg6b)
devtools::install_github("gertjanssenswillen/petrinetR")


================================================
FILE: 03-setup/15a-envrm.R
================================================
# Workspace & Environment

# Identify the directory
getwd()

# List all the objects in directory
dir()

# How to run the code-- Press Control Key and then Press Enter

# "#" is used to comment a particular section in R
#comment
#comment with bookmarks
#topic2----
# To clear the console--- Press Control and then L Key

getwd()         # print the current working directory
setwd("E:\\analytics\\projects\\ranalytics")  # set working directory
getwd()
setwd("E:\\analytics\\projects\\graphs")  # set working directory
getwd()
setwd("E:\\analytics\\projects\\ranalytics")  # set working directory

ls()        # list the objects in the current workspace

x=1:10
ls()

# To Clear the enviornment list or unload the objects
remove(list = ls())


rm(x)                         # remove an object from workspace
rm(a,b)                       # remove multiple object from workspace
rm(list=ls(all=TRUE))         # clear workspace
ls()

#--------------------------------------------------



================================================
FILE: 03-setup/15b-renv.R
================================================
# Sys Env

## whether HOST is set will be shell-dependent e.g. Solaris' csh does not.
Sys.getenv(c("R_HOME", "R_PAPERSIZE", "R_PRINTCMD", "HOST","JAVA_HOME"))

names(s <- Sys.getenv()) # all settings (the values could be very long)
head(s, 12)# using the Dlist print() method

## Language and Locale settings -- but rather use Sys.getlocale()
Sys.getlocale()

#version of R
version



================================================
FILE: 03-setup/15e-rjava.R
================================================
#rJava Settings


#install java https://java.com/en/download/win10.jsp


library(xlsx)
library(rJava)

Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_291') # for 64-bit

library(xlsx)
library(rJava)


================================================
FILE: 03-setup/16a-pathconfig.R
================================================
# Lib paths

.libPaths()

file.exists("~/.Rprofile")
file.edit("~/.Rprofile")
# Add these lines to Rprofile
# .First = function() cat("\n Welcome Dhiraj to R ")
# .Last = function() cat("\n Thanks for working in R")
# R_LIBS="E:/rWork/rLibrary"
# .libPaths()
file.edit("~/.Renviron")


================================================
FILE: 03-setup/17a-rstudio.R
================================================
# rstudio

#Tools -> Global Options
#code -> editing -> Soft Wrap

# themes

#fonts

#colors

#size of Fonts


================================================
FILE: 03-setup/18a-processtime.R
================================================
# Execution Time
proc.time()
g <- rnorm(100000)
h <- rep(NA, 100000)

# Start the clock!
ptm <- proc.time()

# Loop through the vector, adding one
for (i in 1:100000){
  h[i] <- g[i] + 1
}

# Stop the clock
proc.time() - ptm


#M2
(ptm <- proc.time())
h <- g + 1
proc.time()
(proc.time() - ptm)

#user  system elapsed 
#0.00    0.02    0.01
?proc.time

# M3 system.time
quickadd <- function(g){ 
  return(g+1)
}

slowadd <- function(g){
  h <- rep(NA, length(g))
  for (i in 1:length(g)){
    h[i] <- g[i] + 1
  }
  return(h)
}

system.time(a <- slowadd(g))
#user  system elapsed 
#0.34    0.00    0.34

system.time(a <- quickadd(g))
#user  system elapsed 
#0       0       0
#
#M5


# M3
system.time(x < 1:100000000)


system.time({
  # Do something that takes time
  x <- 1:100000
  for (i in seq_along(x))  x[i] <- x[i]+1
})

#>    user  system elapsed 
#>   0.144   0.002   0.153
#The output means it took 0.153 seconds to run
# the block of code.


#M5
sleep_for_a_minute <- function() { Sys.sleep(10) }

start_time <- Sys.time()
sleep_for_a_minute()
end_time <- Sys.time()

end_time - start_time
# Time difference of 1.000327 mins
# 
# 
# Benchmark ----
library("benchmarkme")
library("pryr")
Sys.info()
method1 = function(n) {
  vec = NULL # Or vec = c()
  for(i in seq_len(n))
    vec = c(vec, i)
  vec
}
method2 = function(n) {
  vec = numeric(n)
  for(i in seq_len(n))
    vec[i] = i
  vec
}
method3 = function(n) seq_len(n)
n=100
method3(100)

library("compiler")
library("memoise")
library("microbenchmark")
microbenchmark(times = 100, unit = "s", 
               method1(n=5000), 
               method2(n=5000), 
               method3(n=5000))

message("dhiraj")


================================================
FILE: 03-setup/21a-floorceiling1.R
================================================
# R Tips1

#round----
round(14.5378, digits=2) # 14.54
round(14.5378, digits=1) # 14.5
round(14.5378) #15

#Significant----
signif(pi, digits=3) #3.14
signif(pi, digits=5) #3.14

#floor----
floor(3.14) #3
floor(3.56) #3

#ceiling----
ceiling(3.14) #4
ceiling(3.56) #3

pi
#truncate----
trunc(3.141593) #integer
trunc(314.1) #integer


#log----
logb(8, base=2) #3
logb(100, base=10) #3
log(100,base=2)
log(1000,2)

log2(100)
2^log2(100)

log(100,base=10)
log(1000,base=10)
log10(1000)
10^log(1000, base=10)  #antilog

#natural log -----
#to the base e=2.71
log(5)  #without base is natural log
log(5, base=2.71)
log(5, base=exp(1))

#antilog of natural log
exp(1.614)
2.71^log(5)
2.71^1.614


================================================
FILE: 03-setup/21b-options.R
================================================
#Options
#Allow the user to set and examine a variety of global options which affect the way in which R computes and displays its results.
#https://stat.ethz.ch/R-manual/R-devel/library/base/html/options.html


options() #what options avl
op = options()  #save options to reset later

getOption(x='width')
getOption(x='digits')
(x=pi)
#set options
options(digits=3)
x #3.14 instead of 3.14153
getOption(x='digits')

#reset options
options(op)
x

#
options('scipen')
#integer. A penalty to be applied when deciding to print numeric values in fixed or exponential notation. Positive values bias towards fixed and negative towards scientific notation

(x1=123.123456789123456789)
options(scipen = 999) #turn off
x1
options(scipen = 0) #turn on
x1

(x2 = c(1.810032e+09, 4.12345, 5) )
options("scipen"=-100, "digits"=4)
x2 #scientific
options("scipen"=100, "digits"=4)
x2  #min 4 digits shown with no scientific
options("scipen"=100, "digits"=3)
x2  #min 3 digits are shown



options(scipen = 5)
x1
options(scipen = -4)
x1

format(1810032000, scientific = FALSE)
format(1810032000, scientific = T)




#graphical parameters-----
par()
#parameters which can be changed
par(no.readonly = T)

stop('test it')



================================================
FILE: 03-setup/24a-github.R
================================================
# github

================================================
FILE: 03-setup/25a-help.R
================================================
# Misc Commands
#https://www.r-project.org/help.html

#library for help-----
library(swirl)  # for online help
swirl()

#datasets-----
data()  # to see in built data sets

#? for help----
?mtcars
?mean

#help----
help(package="MASS")
help(rlm, package="MASS") # function in MASS package

help(package='dplyr')

#example on functions----
example(lm)
example(mean)


#vignette----
#Many packages include vignettes, which are discursive documents meant to illustrate and explain facilities in the package
browseVignettes()
#window opens in browser
browseVignettes(package="survival") #browser
vignette(package='survival') # text form
vignette("timedep")
vignette("timedep", package="survival")
vignette(package='dplyr')
browseVignettes(package="dplyr") #browser


#demo-----
#extended code demonstrations (“demos”). 
#demo() lists all demos for all packages in your library,
#demo(package="package-name")
demo(package='survival')  #no demo
demo(package='stats')  #avl demos
demo("nlm", package="stats")
demo(graphics)

#apropos-----
#apropos() function searches for objects, including functions, directly accessible in the current R session that have names that include a specified character string.
apropos('lm')

#help.search()----
#help.search() function scans the documentation for packages installed in your library.
help.search("^lm")

#help.start()-----
#help.start() starts and displays a hypertext based version of R’s online documentation in your default browser 
help.start()



#TeachingDemos library
#https://www.rdocumentation.org/packages/TeachingDemos/versions/2.10


================================================
FILE: 03-setup/51c-deletefiles.R
================================================
# Delete Files from command

write.csv(mtcars, 'mcars.csv')
file.exists('mcars.csv')
unlink('mcars.csv')
file.exists('mcars.csv')

write.csv(mtcars, './data/mcars2.csv')
file.exists('./data/mcars2.csv')
unlink('./data/mcars2.csv')
file.exists('./data/mcars2.csv')

#fileremove----
write.csv(mtcars, 'mcars3.csv')
file.exists('mcars3.csv')
file.remove('mcars3.csv')
file.exists('mcars3.csv')




================================================
FILE: 03-setup/envVar.R
================================================
#Env Variables
Sys.getenv("PATH")
Sys.getenv("JAVA_HOME")
Sys.setenv(JAVA_HOME='C\\Program Files\\Java\\jre1.8.0_291\\bin')
#C:\Program Files\Java\jre1.8.0_291\bin


options(device="RStudioGD") #display in r studio
options(device = "quartz") # display in popup
windows();(mpg ~ wt, mtcars)


================================================
FILE: 03-setup/help.R
================================================
# Misc Commands

library(swirl)  # for online help
swirl()

data()  # to see in built data sets

mean(mtcars$mgp)
?mean

??mean


================================================
FILE: 03-setup/pathconfig.R
================================================
# Lib paths

.libPaths()
file.exists("~/.Rprofile")
file.edit("~/.Rprofile")
# Add these lines to Rprofile
# .First = function() cat("\n Welcome Dhiraj to R ")
# .Last = function() cat("\n Thanks for working in R")
# R_LIBS="E:/rWork/rLibrary"
# .libPaths()
file.edit("~/.Renviron")


================================================
FILE: 04-lib/10a-fBasics.R
================================================
#Package fBasics
#http://math.furman.edu/~dcs/courses/math47/R/library/fBasics/html/015A-BasicStatistics.html

library(fBasics)
#skewness	returns value of skewness,
skewness(mtcars)

#kurtosis	returns value of kurtosis,
kurtosis(mtcars)

#basicStats	computes an overview of basic statistical values,
basicStats(mtcars)

#rowStats	calculates row statistics,
rowStats(mtcars,FUN=mean)

#colStats	calculates column statistics,
colStats(mtcars, FUN=median)

#rowAvgs	calculates row means,
rowAvgs(mtcars)

#colAvgs	calculates column means,
colAvgs(mtcars)

#rowVars	calculates row variances,
rowVars(mtcars)

#colVars	calculates column variances,

#rowStdevs	calculates row standard deviations,

#colStdevs	calculates column standard deviations,

#rowSkewness	calculates row skewness,

#colSkewness	calculates column skewness,

#rowKurtosis	calculates row kurtosis,

#colKurtosis	calculates column kurtosis,
rowCumsums	calculates row cumulated Sums,
colCumsums	calculates column cumulated Sums.

================================================
FILE: 04-lib/11a-pysch.R
================================================
#Descriptive Values of data set

library(psych)
describe(mtcars)


================================================
FILE: 04-lib/21b-installpackages.R
================================================
#Install packages for Data Analytics Course


packages1 = c('rJava','xlsx','dplyr')
packages2 = c('plyr', 'psych', 'tm', 'quantmod')
packages3 = c('lubridate')
packages4 = c('dplyr','plyr','data.table','tidyr','gsheet','car','ggplot2','ISLR','amap','arules','arulesViz','forecast','rattle','CHAID','rpart','rpart.plot','ts','zoo','twitterR','ROAuth','RGtk2','partykit','strucchange','didrooRFM','curl','syuzhet','stringr','RTextTools','e10171','wordcloud','caret','catools','olsrr','xlsr','rJava')					


list.of.packages <- packages4
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
new.packages
if(length(new.packages)) install.packages(new.packages)


================================================
FILE: 04-lib/21e-installFmGit.R
================================================
#install packages from Git Hub

#lubripack
install.packages('lubripack')  #NA for some versions
#install older version of R packages
#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages
#https://rdrr.io/github/Espanta/lubripack/

install.packages('devtools')  #install library for download from git

require(devtools) #small diff from library
#does not give error if package not avl

install_github("Espanta/lubripack")

#this package installes multiple pacakges
library(lubripack)
lubripack("plyr", "psych", "tm", "quantmod",'olsrr','lattice','caret','rpart','arules','arulesViz','Matrix','outliers')
#if already installed it will just inform TRUE



================================================
FILE: 04-lib/21g-packages1.R
================================================
# Packages installation
#https://www.rstudio.com/products/rpackages/

#List avl packages
library()

#Total Avl Packages
nrow(available.packages())

#Install Package amap
install.packages('amap')

#Load package
library(amap)
library(dplyr)

#Find functions in package
library(help=amap)

#Help wrt a package
help(package='amap')  #see on right side pane


#Unload---
install.packages('tm')

library(tm)
library(VIM)
search()
detach('package:tm', unload=TRUE)
detach(package:VIM, unload = T) 
search()



#Detach Multiple Packages
(detpkg = c('plyr','tm'))
library('plyr') #load lib
library('tm')  #load lib
search()  #check if loaded
Vectorize(detach)(name=paste0("package:", detpkg), unload=TRUE, character.only=TRUE)  #code to detach
search()  # confirm if removed
#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.

#Detach from memory all packages
rm(list = ls(all = TRUE))
sessionInfo()


#Remove Packages ----- uninstall 
remove.packages("tm")
require('tm')# check if unistalled



#multiple packages

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}
packagelist <- c('NLP','tm', 'lubridate')
ipak(packagelist)

#  package lists
pkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')
pkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')
pkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')
pkg4 = c('arules' , 'arulesViz')
pkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')
install.packages("CHAID", repos="http://R-Forge.R-project.org")


library(lubridate)

#multiple load 
easypackages::libraries(pkg1)


#easypackages
#install.packages('easypackages')
library(easypackages)

#Install Multiple Packages
packages("plyr", "psych", "tm")
libraries("plyr", "psych", "tm")






pack <- available.packages()
pack["ggplot2","Depends"]
pack["ggplot2","Imports"]
pack["data.table","Depends"]

packrat:::recursivePackageDependencies("ggplot2",lib.loc = .libPaths()[1])
tools::dependsOnPkgs('ggplot2')
tools::dependsOnPkgs('dplyr')
tools::dependsOnPkgs("ggplot2",installed=available.packages())
library(rusk)
#Remove Package
remove.packages('quantmod')
library(quantmod)



#Detach all mice package
library(mise)
search()
#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)
mise(pkgs=T)
search()



# list all packages where an update is available
old.packages()

# update all available packages
update.packages()

# update, without prompts for permission/clarification
update.packages(ask = FALSE)


================================================
FILE: 04-lib/31b-datasets.R
================================================
# Data Sets
#https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html

#built in datasets in base
mtcars
women
data()  # available Data Sets with loaded libraries

#package having datasets
library(datasets) # no need to install it, just load
data(package='datasets')

#Datasets in particular Package
data(package = "plyr")
data(package ="MASS")


#find the names of data sets in all installed packages, you can use the following. .packages(TRUE) gives all packages available in the library location path lib.loc. Since the data sets in the base and stats packages have been moved to the datasets package, we need to account for that by taking them out with setdiff().


## names of all packages sans base and stats
pkgs <- setdiff(.packages(TRUE), c("base", "stats"))
pkgs

## get the names of all the data sets
dsets <- data(package = pkgs)$result[, "Item"]
## look at the first few in our result
head(dsets)


#data frame containing the name, class, dimensions, and title of each data set found in a package.
vcdExtra::datasets("plyr")
vcdExtra::datasets(c("plyr", "dplyr"))
vcdExtra::datasets("datasets")




================================================
FILE: 04-lib/31c-datasets.R
================================================
#Datasets

?datasets                     # Using R's built in data sets
data()

library(help=datasets)

data(mtcars)                 
cars <- mtcars  
levels(mtcars$gear)

# Viewing data set
mtcars    #Console Window         
View(mtcars)#new tab                 
head(mtcars,10) #top 10 records
head(mtcars) # top 6
tail(mtcars)  # last 6                 

str(mtcars)   #structure of dataset                 

names(mtcars) # column names of DF



================================================
FILE: 04-lib/41-purrr1.R
================================================
#purrr
#https://www.weirdfishes.blog/blog/practical-purrr/

df = mtcars

library(purrr)
library(dplyr)

#functions

mu = c(50,60, 70)
#3 lists of ND with different means, n=10, sd=1
mu %>% map(rnorm, n=10)
sigma = c(2,3,4)
#3 lists of 10 nos with different means and sd
map2(mu, sigma, rnorm, n=10)

n = c(10,20,30)
#ND lists of different values of n, mu, sd
list(n, mu, sigma ) %>% pmap(rnorm)

#different functions with different arguments
f <- c("runif", "rnorm", "rpois")
param <- list(
  list(min = -1, max = 1), 
  list(sd = 5), 
  list(lambda = 10)
)
#To handle this case, you can use invoke_map():
invoke_map(f, param, n = 5)
invoke_map(f, param, n = 5) %>% str()


================================================
FILE: 04-lib/42-purr2.R
================================================
#purrr

df = mtcars

library(purrr)
library(dplyr)

df %>% split(.$cyl)

df %>% split(.$cyl)  %>% walk(print)


df %>% split(.$am)  %>% mean(mpg)

library(ggplot2)
plots <- mtcars %>% split(.$cyl) %>%  map(~ggplot(., aes(mpg, wt)) + geom_point())
plots$`4`

paths <- stringr::str_c(names(plots), ".pdf")
paths
pwalk(list(paths, plots), ggsave, path = tempdir())
tempdir()


#keep
iris %>%   keep(is.factor) %>%   str()
iris %>%   discard(is.factor) %>% str()

#some
x <- list(1:5, letters, list(10))
x %>% some(is_character)
x %>% some(is_numeric)
x %>% every(is_numeric)
x %>% every(is_vector)

#detect
x <- sample(10)
x

x %>% detect(~ . > 5)
x %>% detect_index(~ . > 5)

#take elements from the start or end of a vector while a predicate is true:
x %>% head_while(~ . > 5)
x %>% tail_while(~ . > 5)

#accumulate
x <- sample(10)
x
x %>% accumulate(`+`)
x %>% accumulate(`*`)
x %>% accumulate(`/`)
x %>% accumulate(`-`)
#https://r4ds.had.co.nz/iteration.html


================================================
FILE: 04-lib/43-purrr3.R
================================================
#purrr3

library(purrr)
library(dplyr)

car_data <- transform(aggregate(. ~ cyl,  data = subset(mtcars, hp > 100), FUN = function(x) round(mean(x, 2))),  kpl = mpg*0.4251)
car_data

#If we don’t want to save intermediate results, composing multiple functions becomes super import

mtcars %>% subset(hp > 100) %>%   aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%  transform(kpl = mpg %>% multiply_by(0.4251))
#error on multiply_by

devtools::install_github("jennybc/repurrrsive")

ghr <- repurrrsive::gh_repos
head(ghr)

map_dbl(ghr, length)
map_dbl(ghr, ~length(.x))
map(1:3, ~runif(2, max = .x))

map(1:3, runif, min = 3, max = 6)
map(1:3, function(n) { runif(n, min = 3, max = 6) })

map(ghr, ~map(.x, length))

ghr %>% map_chr(~map_chr(.x, ~.x$owner$login)[1]) %>% set_names(ghr, .) %>%   pluck("jennybc")

#map2_xxx() (analogous to map_xxx())
# pmap() (with which you can map over howerver many elements you want)
# lmap() (for mapping with funcions that take and return lists)
# imap() (for mapping over a list and its names, just like we did above)
# map_at()/map_if() (functions that allow you to filter which elements get mapped)



================================================
FILE: 04-lib/44-purrr4.R
================================================
#purrr4

#purrr is designed to help with “functional programming”, which you can take broadly as trying to use functions (preferably “pure” ones) to accomplish most of your complex and repetitive tasks (don’t copy and paste more then 3 times - H. Wickham)


library(purrr)
library(dplyr)

map(1:3, ~runif(2, max = .x))

shades <- colors()[1:10]

for (i in seq_along(shades)){  print(shades[i])}
a <-  lapply(shades, print)
a
b <-  map(shades, print)

#general
#map("Lists to apply function to","Function to apply across lists","Additional parameters")

map(mtcars, mean, na.rm = T)
mtcars_means <- map(mtcars, mean, na.rm = T)
class(mtcars_means)

#map_TYPE returns an object of class TYPE, e.g.
#map_lgl returns logical objects
#map_df returns data frames, etc.
df1= map_df(mtcars, mean, na.rm = T)
class(df1)
df1

#map can also be extended to deal with multiple input lists
#map applies a function over one list.
#map2 applies a function over combinations of two lists in the form
#map2(list1, list2, ~function(.x,.y), ...)
map2_chr(c('one','two','red','blue'), c('fish'), paste)


z <- 10
foo <- function(x,y) {
    z <- x + y
    return(z) }

foo(x = 2,y = 3)
z #not affected

a <- 10
z =0
foo <- function(x,y) {
  z <- x + y + a
  return(z) }
foo(x = 2, y = 3)
a; z
#recommend you avoid using global variables inside functions: it
#“anonymous” functions in :make anonymous functions with ~
map2_chr(c('one','two','red','blue'), c('fish'), ~paste(.x,.y))

cvfoo <- function(x){ sd(x) / mean(x) }
map(mtcars, cvfoo)
#MULTIPLE LISTS USING PMAP

library(ggplot2)
library(dplyr)
dmonds <- diamonds %>%  slice(1:4)

pmap_foo <- function(list1, list2 , list3){
  paste0("Diamond #", list1, " sold for $", list2," and was ", list3, " carats") }

pmap(list(list1 = 1:nrow(dmonds), list2 = dmonds$price, list3 = dmonds$carat), pmap_foo)


================================================
FILE: 04-lib/45-purrr5.R
================================================
#purrr5
#https://adv-r.hadley.nz/functionals.html

library(purrr)
library(dplyr)

triple <- function(x) x * 3
map(1:3, triple)

map_chr(mtcars, typeof)
map_lgl(mtcars, is.double)

n_unique <- function(x) length(unique(x))
map_int(mtcars, n_unique)
map_dbl(mtcars, mean)

pair <- function(x) c(x, x)
map_dbl(1:2, pair)
map_dbl(1:2, as.character)
#in either case, it’s often useful to switch back to map(), because map() can accept any type of output. That allows you to see the problematic output, and figure out what to do with it.
map(1:2, pair)

#anonymous functions
map_dbl(mtcars, function(x) length(unique(x)))
map_dbl(mtcars, ~ length(unique(.x)))
map_dbl(mtcars, ~ mean(.x))
#This works because all purrr functions translate formulas, created by ~ (pronounced “twiddle”), into functions. 
x <- map(1:3, ~ runif(2))
str(x)

x <- list(1:5, c(1:10, NA))
map_dbl(x, ~ mean(.x, na.rm = TRUE))
map_dbl(x, mean, na.rm = TRUE)

trims <- c(0, 0.1, 0.2, 0.5)
x <- rcauchy(1000)
map_dbl(trims, mean, x = x)



by_cyl <- split(mtcars, mtcars$cyl)
by_cyl
by_cyl %>%   map(~ lm(mpg ~ wt, data = .x)) %>%   map(coef) %>%  map_dbl(2)
#base
by_cyl %>%  lapply(function(data) lm(mpg ~ wt, data = data)) %>%  lapply(coef) %>%  vapply(function(x) x[[2]], double(1))
models <- lapply(by_cyl, function(data) lm(mpg ~ wt, data = data))
vapply(models, function(x) coef(x)[[2]], double(1))



#modify - same format as input
df <- data.frame(  x = 1:3,  y = 6:4 )
map(df, ~ .x * 2)
modify(df, ~ .x * 2) #temp
df <- modify(df, ~ .x * 2)
df


#	List	Atomic	Same type	Nothing
# One argument	map()	map_lgl(), …	modify()	walk()
# Two arguments	map2()	map2_lgl(), …	modify2()	walk2()
# One argument + index	imap()	imap_lgl(), …	imodify()	iwalk()
# N arguments	pmap()	pmap_lgl(), …	—	pwalk()

================================================
FILE: 04-lib/51-plyr1.R
================================================
# split - apply - combine

#https://vita.had.co.nz/papers/plyr.pdf
a*ply(.data, .margins, .fun, ..., .progress = "none")
d*ply(.data, .variables, .fun, ..., .progress = "none")
l*ply(.data, .fun, ..., .progress = "none")
#a = array, d = data frame, l = list, and _ means the output is discarded.
#The first argument is the .data which will be split up, processed and recombined. The second argument, .variables or .margins, describes how to split up the input into pieces. The third argument, .fun, is the processing function, and is applied to each piece in turn. All further arguments are passed on to the processing function. If you omit .fun the individual pieces

# input\output  array  dataframe list      discarded
# array         aaply   adply    alply     a_ply
# dataframe     daply   ddply    dlply     d_ply
# list          laply   ldply    llply     l_ply
#.progress argument controls display of a progress bar,
#all arguments start with “.”. This prevents name clashes with the arguments of the processing function, and helps to visually delineate arguments that control the repetition from arguments that control the individual steps. Some functions in base R use all uppercase argument names for this purpose,

shape <- function(x) if (is.vector(x)) length(x) else dim(x)
shape(mtcars)
x <- array(1:24, 2:4)
library(plyr)
shape(aaply(x, 2, function(y) 0))
shape(aaply(x, 2, function(y) rep(1,5)))
shape(aaply(x, 2, function(y) matrix(0,nrow=5,ncol=6)))
shape(aaply(x, 1, function(y) matrix(0,nrow=5,ncol=6)))


================================================
FILE: 04-lib/61-splitapplycombine1.R
================================================
#plyr, dplyr, 

#http://krlmlr.github.io/pdlyr/vignettes/pdlyr.html

#https://coolbutuseless.bitbucket.io/2018/03/03/split-apply-combine-my-search-for-a-replacement-for-group_by---do/#split-apply-combine---prehistoric-times---split-lapply-do.callrbind-...


#plyr::ddply(mtcars, 'cyl', do(mean(.))
complex_func <- function(x) { mean(x)}            
result_df <- mtcars %>%  group_by(cyl) %>%  do(complex_func(.)) %>% ungroup() 
            

================================================
FILE: 04-lib/62-splitapplycombine2.R
================================================
#split apply combine

df = mtcars
baseball
library(plyr)
baseball.1 <- ddply(baseball, .(id), transform, cyear = year - min(year) +1)
head(baseball.1)

ddply(df, .(cyl), filter, mpg > mean(mpg))              
ddply(.data = df,.variables = c('cyl'), .fun = function(p){ summarize(p, m1 = mean(mpg))} )
df %>% dplyr::group_by(cyl) %>% dplyr::summarize(meanMPG = mean(mpg))
ddply(df, .(cyl), filter, mpg >= min(mpg))
ddply(df, .(cyl), filter, mpg >= max(mpg))


================================================
FILE: 04-lib/71-broom1.R
================================================

library(tidyr)
library(dplyr)
library(broom)
mdply(expand.grid(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 10)
mtcars %>%   group_by(cyl) %>%  summarise(hp_mean = list(tidy(lm(hp ~ 1, data  = .))))

apply_lm <- function(df){ tidy(lm(data = df, hp ~ 1)) }
d <- mtcars %>%   group_by(cyl) %>% nest() %>%  mutate(hp_mean = purrr::map(.f = apply_lm, . = data)) 
d
d %>% unnest(hp_mean)

mtcars %>%  group_by(cyl) %>% dplyr::group_split() %>% map_dfr(~lm(hp ~ 1, data = .) %>% tidy())

#https://data-se.netlify.com/2018/12/10/new-split-apply-combine-variant-in-dplyr-group-split/
library(dplyr)


library(magrittr)
library(dplyr)
mtcars %>%  filter(wt > 2) %$%  cor.test(hp, mpg)

all_letters <- c(letters, LETTERS) %>%  sort %T>% write.csv(file = "all_letters.csv")
read.csv("all_letters.csv") %>% head()

#%T>% (tee operator) allows you to forward a value into a side-effect-producing function while keeping the original lhs value intact. In other words: the tee operator works like %>%, except the return values is lhs itself, and not the result of the rhs function/expression.


================================================
FILE: 04-lib/packages1.R
================================================
# Packages installation

#List avl packages
library()


#Total Avl Packages
nrow(available.packages())

#Install Package amap
install.packages('amap')

#Load package
library(amap)

#Find functions in package
library(help=amap)

#Help wrt a package
help(package='amap')  #see on right side pane


#Unload---
install.packages('tm')

library(tm)
library(VIM)
search()
detach('package:tm', unload=TRUE)
detach(package:VIM, unload = T) 
search()



#Detach Multiple Packages
(detpkg = c('plyr','tm'))
library('plyr') #load lib
library('tm')  #load lib
search()  #check if loaded
Vectorize(detach)(name=paste0("package:", detpkg), unload=TRUE, character.only=TRUE)  #code to detach
search()  # confirm if removed
#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.

#Detach from memory all packages
rm(list = ls(all = TRUE))
sessionInfo()


#Remove Packages ----- uninstall 
remove.packages("tm")
require('tm')# check if unistalled



#multiple packages

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}
packagelist <- c('NLP','tm', 'lubridate')
ipak(packagelist)

#  package lists
pkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')
pkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')
pkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')
pkg4 = c('arules' , 'arulesViz')
pkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')
install.packages("CHAID", repos="http://R-Forge.R-project.org")


library(lubridate)

#multiple load 
easypackages::libraries(pkg1)


#easypackages
#install.packages('easypackages')
library(easypackages)

#Install Multiple Packages
packages("plyr", "psych", "tm")
libraries("plyr", "psych", "tm")



#lubripack
install.packages('lubripack')  #NA for some versions
#install older version of R packages
#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages
#https://rdrr.io/github/Espanta/lubripack/

require(devtools)
install_github("Espanta/lubripack")
library(lubripack)
lubripack("plyr", "psych", "tm", "quantmod")



pack <- available.packages()
pack["ggplot2","Depends"]
pack["ggplot2","Imports"]
pack["data.table","Depends"]

packrat:::recursivePackageDependencies("ggplot2",lib.loc = .libPaths()[1])
tools::dependsOnPkgs('ggplot2')
tools::dependsOnPkgs('dplyr')
tools::dependsOnPkgs("ggplot2",installed=available.packages())
library(rusk)
#Remove Package
remove.packages('quantmod')
library(quantmod)



#Detach all mice package
library(mise)
search()
#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)
mise(pkgs=T)
search()



# list all packages where an update is available
old.packages()

# update all available packages
update.packages()

# update, without prompts for permission/clarification
update.packages(ask = FALSE)

================================================
FILE: 04-lib/switchr.R
================================================
#switchR for switching between libraries
#https://cran.r-project.org/web/packages/switchr/switchr.pdf

library(switchr)

#To switch to a different library, creating it if necessary, use the switchTo function:
  
switchTo("mynewlib")
#Then work normally, when you wish to switch back to your original library, use switchBack:
switchBack()

#-----------
.libPaths2()

#.libPaths2(fulllp, exclude.site = TRUE)

defaultRepos()

dep_repos('dplyr')

install_packages(pkgs, repos, versions = NULL, verbose = FALSE, ...)

library_paths(swtichr)
packages(dplyr)


================================================
FILE: 04-lib/useful.R
================================================





#library - useful
library(useful)
k1 <- kmeans(x=iris[, 1:4], centers=3)
plot(k1)
plot(k1, data=iris)

toCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')
find.case(toCheck, 'upper')
find.case(toCheck, 'lower')


data(iris)
hartiganResults <- FitKMeans(iris[, -ncol(iris)])
PlotHartigan(hartiganResults)

data=mtcars
#ForceDataFrame(data)
indexToPosition(3, 2)
indexToPosition(c(1, 4, 5, 7, 9), 3)
indexToPosition(1:16, 4)
indexToPosition(c(1, 3, 5, 6, 8, 10, 11, 13, 15), 5)

x=mtcars
left(x, c = 5L)

theDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)
df <- moveToFront(theDF, c('B', 'C'))
df
moveToFront(theDF, c('C', 'B'))
moveToFront(theDF, c('C', 'C'))
moveToBack(theDF, c('C', 'C'))
moveToBack(theDF, c('C', 'B'))
moveToBack(theDF, c('C', 'C'))


plot(sunspot.year)
plot(sunspot.year, acf=TRUE)

positionToIndex(1, 2, 2)
positionToIndex(row=c(1, 1, 2, 1, 3), col=c(1, 2, 2, 3, 3), nrow=3)
positionToIndex(rep(1:4, 4), rep(1:4, each=4), nrow=4)
positionToIndex(rep(c(1, 3, 5), 3), rep(1:3, each=3), nrow=5)


theDF <- data.frame(A=1:10, B=1:10)
reclass(theDF) <- 'newclass'
class(theDF)
theDF <- reclass(theDF, 'another')
class(theDF)

right(diamonds) # displays all rows and only the last 5 columns

myData <- data.frame(Upper=LETTERS, lower=letters)
myData
shift.column(data=myData, columns="lower")
shift.column(data=myData, columns="lower", len=2)
shift.column(data=myData, columns=c("lower", "Upper"), len=3, up=T)
shift.column(data=myData, columns=c("lower", "Upper"), len=3, up=F)


theDF <- data.frame(A=1:10, B=1:10, C=1:10)
theDF[c(1, 4, 6), c(1)] <- NA
theDF[c(3, 4, 8), c(3)] <- NA
theDF
simple.impute.data.frame(theDF)
simple.impute.data.frame(theDF, mean)
simple.impute.data.frame(theDF, constant(4))

subOut(c("Hello", "(parens)", "Excited! Mark"))
subOut(c("Hello", "(parens)", "Excited! Mark"), specialChars=c("!", "("))

subSpecials(c("Hello", "(parens)", "Excited! Mark"))
subSpecials(c("Hello", "(parens)", "Excited! Mark"), specialChars=c("!", "("))
subSpecials(c("Hello", "(parens)", "Excited! Mark"),
            c("This is a period. And this is an asterisk *"), specialChars=c("!", "("))
subSpecials(c("Hello", "(parens)", "Excited! Mark"),
            c("This is a period. And this is an asterisk *"), specialChars=c("!", "(", "*"))

================================================
FILE: 04-lib/useful2.R
================================================
#useful Functions

library(useful)


binary.flip(c(1,1,0,1,0,0,1))

classdf(mtcars, cols=c(1:5))
classdf(mtcars)

bottomleft(mtcars, r = 5L, c = 5L)
bottomright(mtcars, r = 5L, c = 5L)
topright(mtcars)
topleft(mtcars)

fortify(acf(sunspot.year, plot=FALSE))
fortify(pacf(sunspot.year, plot=FALSE))

k1 <- kmeans(x=iris[, 1:4], centers=3)
hold <- fortify(k1, data=iris)
head(hold)
hold2 <- fortify(k1)
head(hold2)

MapToInterval(1:10, start=0, stop=1)
mapping(1:10, start=0, stop=1)
mapping(0:100, start=min(mtcars$mpg), stop=max(mtcars$mpg))

require(scales)
vect <- c(1000, 1500, 23450, 21784, 875003780)
multiple(vect)
multiple(vect, extra=dollar)
multiple(vect, extra=identity)

require(ggplot2)
data(diamonds)
ggplot(diamonds, aes(x=x, y=y, color=price*100)) + geom_point() + 
  scale_color_gradient2(labels=multiple)


vect <- c(mean, mode, mean)
vect2 <- c(mean, mode, max)
vect3 <- c(mean, mean)
compare.list(vect, vect)
compare.list(vect, vect2)


theDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)
theDF
colsToFront(theDF, c('B', 'C'))
colsToFront(theDF, c('C', 'B'))
colsToFront(theDF, c('C', 'C'))
colsToBack(theDF, c('C', 'C'))
colsToBack(theDF, c('C', 'B'))
colsToBack(theDF, c('C', 'C'))

theDF <- data.frame(A=1:10, B=1:10, C=1:10)
theDF[c(1, 4, 6), c(1)] <- NA
theDF
theDF[c(3, 4, 8), c(3)] <- NA
theDF
simple.impute(theDF, constant(4))

library(scales)
vect <- c(1000, 1500, 23450, 21784, 875003780)
multiple_format()(vect)
multiple_format(extra=dollar)(vect)
multiple_format(extra=identity)(vect)
multiple_format()(mtcars$mpg)(multiple=kgs)
class(mtcars$mpg)
multiple(vect, multiple='K', digits=2, prefix='Rs')
?multiple_format

load_packages(c('ggplot2', 'dplyr'))

toCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 
             'little with space', 'MIXED with SPACE', '17')
numeric.case(toCheck)


require(scales)
vect <- c(1000, 1500, 23450, 21784, 875003780)
multiple.comma(vect)
multiple.comma(vect, multiple="k")
multiple.comma(vect, multiple="h")

# NOT RUN {
toCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')
lower.case(toCheck)
# }


#library - useful
library(useful)
k1 <- kmeans(x=iris[, 1:4], centers=3)
plot(k1)
plot(k1, data=iris)

toCheck <- c('BIG', 'little', 'Mixed', 'BIG WITH SPACE', 'little with space', 'MIXED with SPACE')
find.case(toCheck, 'upper')
find.case(toCheck, 'lower')


data(iris)
hartiganResults <- FitKMeans(iris[, -ncol(iris)])
PlotHartigan(hartiganResults)

data=mtcars
#ForceDataFrame(data)
indexToPosition(3, 2)
indexToPosition(c(1, 4, 5, 7, 9), 3)
indexToPosition(1:16, 4)
indexToPosition(c(1, 3, 5, 6, 8, 10, 11, 13, 15), 5)

x=mtcars
left(x, c = 5L)

theDF <- data.frame(A=1:10, B=11:20, C=1:10, D=11:20)
df <- moveToFront(theDF, c('B', 'C'))
df
moveToFront(theDF, c('C', 'B'))
moveToFront(theDF, c('C', 'C'))
moveToBack(theDF, c('C', 'C'))
moveToBack(theDF, c('C', 'B'))
moveToBack(theDF, c('C', 'C'))


plot(sunspot.year)
plot(sunspot.year, acf=TRUE)

positionToIndex(1, 2, 2)
positionToIndex(row=c(1, 1, 2, 1, 3), col=c(1, 2, 2, 3, 3), nrow=3)
positionToIndex(rep(1:4, 4), rep(1:4, each=4), nrow=4)
positionToIndex(rep(c(1, 3, 5), 3), rep(1:3, each=3), nrow=5)


theDF <- data.frame(A=1:10, B=1:10)
reclass(theDF) <- 'newclass'
class(theDF)
theDF <- reclass(theDF, 'another')
class(theDF)

right(diamonds) # displays all rows and only the last 5 columns

myData <- data.frame(Upper=LETTERS, lower=letters)
myData
shift.column(data=myData, columns="lower")
shift.column(data=myData, columns="lower", len=2)
shift.column(data=myData, columns=c("lower", "Upper"), len=3, up=T)
shift.column(data=myData, columns=c("lower", "Upper"), len=3, up=F)


theDF <- data.frame(A=1:10, B=1:10, C=1:10)
theDF[c(1, 4, 6), c(1)] <- NA
theDF[c(3, 4, 8), c(3)] <- NA
theDF
simple.impute.data.frame(theDF)
simple.impute.data.frame(theDF, mean)
simple.impute.data.frame(theDF, constant(4))

subOut(c("Hello", "(parens)", "Excited! Mark"))
subOut(c("Hello", "(parens)", "Excited! Mark"), specialChars=c("!", "("))

subSpecials(c("Hello", "(parens)", "Excited! Mark"))
subSpecials(c("Hello", "(parens)", "Excited! Mark"), specialChars=c("!", "("))
subSpecials(c("Hello", "(parens)", "Excited! Mark"),
            c("This is a period. And this is an asterisk *"), specialChars=c("!", "("))
subSpecials(c("Hello", "(parens)", "Excited! Mark"),
            c("This is a period. And this is an asterisk *"), specialChars=c("!", "(", "*"))

================================================
FILE: 05-dataIE/14a-readcsv.R
================================================
# Read csv

# How to read CSV File

#read.csv(file, header = TRUE, sep = ",", quote = "\"", dec = ".", fill = TRUE, comment.char= "")

read1 = read.csv(file="./data/iris.csv",header = TRUE,sep = ",")
str(read1)
class(read1)

read2 = read.table(file="./data/iris.csv",header = TRUE,sep = ",")
str(read2)
class(read2)

read3 = read.delim(file="./data/iris.csv",header = TRUE,sep = ",")
str(read3)
class(read3)


================================================
FILE: 05-dataIE/14b-readcsv.R
================================================
# reading from file

#used generally when u don't want to path to the file
#or location is different from Project Folders, or want to search for the file
read1 = read.csv(file=file.choose())
str(read1)


================================================
FILE: 05-dataIE/14c-importweb.R
================================================
# read from Internet 

#read.csv(url("http://some.where.net/data/foo.csv"))
#url is optional

df2 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')
df2

read.csv(url("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv"))

df3= read.csv("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv")
df3


#using library
library(data.table)
ad <- fread("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv")
head(ad)


#library(readr)


#Using Curl package
library(RCurl)


================================================
FILE: 05-dataIE/14d-importweb.R
================================================
#Loading the rvest package
library('rvest')

#Specifying the url for desired website to be scrapped
lego_movie <- read_html("http://www.imdb.com/title/tt1490017/")

#Reading the HTML code from the website
lego_movie %>%
  html_node("strong span") %>%
  html_text() %>%
  as.numeric()
#> [1] 7.9

lego_movie %>%
  html_nodes("#titleCast .itemprop span") %>%
  html_text()



#Table in webpage
library(XML)
airline = "http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines"

airline.table = XML::readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

airline.table

chess = "http://ratings.fide.com/top.phtml?list=men"
chess.table = XML::readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)
chess.table[,1:5]
names(chess.table)
?readHTMLTable


================================================
FILE: 05-dataIE/14e-readothers.R
================================================
# reading data from other formats 
# txt, 
# spss, sas




================================================
FILE: 05-dataIE/15b-datawrangling.R
================================================
drinks = data.frame(ingredient=c('amulya', 'pepsi',
                                'maaza','vodka'), sweetness=c(7,9,4,2), fizziness=c(8,1,8,1),
                   type=c('energy', 'health','cold','hard'))
                   
dist()

url <- "http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html"
tbls_xml <- readHTMLTable(url)
library(RCurl)
library(rjson)
library(XML)
library(rvest)
library(plyr)
tbls_xml
tbls_read <- url %>% html_nodes("table") %>% html_table(fill = TRUE)
tbls <- html_nodes(url, "table")
####################
#                  #
#    Exercise 1    #
#                  #
####################

url <- getURL("https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-1/data.csv")

####################
#                  #
#    Exercise 2    #
#                  #
####################

csv_file <- read.csv(text = url)

####################
#                  #
#    Exercise 3    #
#                  #
####################

url <- getURL("https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.txt")

txt_file <- read.table(text = url)
txt_file
####################
#                  #
#    Exercise 4    #
#                  #
####################

url <- getURL("https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.json")

json_file <- fromJSON(url)

####################
#                  #
#    Exercise 5    #
#                  #
####################

url <- getURL("https://raw.githubusercontent.com/VasTsak/r-exercises-dw/master/part-2/data.xml")
xml_file <- ldply(xmlToList(url), data.frame)

####################
#                  #
#    Exercise 6    #
#                  #
####################

url <- read_html("http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html")

####################
#                  #
#    Exercise 7    #
#                  #
####################

tbls <- html_nodes(url, "table")
tbls
####################
#                  #
#    Exercise 8    #
#                  #
####################

tbls_read <- url %>%
  html_nodes("table") %>%
  html_table(fill = TRUE)
tbls_read
####################
#                  #
#    Exercise 9    #
#                  #
####################

url <- "http://www.worldatlas.com/articles/largest-cities-in-europe-by-population.html"
tbls_xml <- readHTMLTable(url)

####################
#                  #
#    Exercise 10   #
#                  #
####################

df_pop <- htmltab(doc = url, which = "//th[text() = 'Rank']/ancestor::table")


================================================
FILE: 05-dataIE/20a-importgg.R
================================================
#Importing from ggsheet
#install.packages('gsheet')
library(gsheet)

regr1 = "https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534"

logr1 = "https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=560796239"

df1 = as.data.frame(gsheet2tbl(regr1))
df1
df2 = as.data.frame(gsheet2tbl(logr1))
str(df)
df2
summary(df1)
summary(df2)



#
docurl = "https://docs.google.com/spreadsheets/d/"
sheeturl = paste0("1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k","/edit#gid=")
sheetname = "560796239"
fullurl = paste0(docurl, sheeturl, sheetname)
fullurl
df = as.data.frame(gsheet::gsheet2tbl(fullurl))
summary(df)
str(df)


================================================
FILE: 05-dataIE/21b-googlesheet1.R
================================================
#R and Google Sheets
#https://cran.r-project.org/web/packages/googlesheets/vignettes/basic-usage.html

library(googlesheets)
library(tidyverse)

#(my_sheets <- gs_ls())
#gs_title(), gs_key(), gs_url(), and gs_gs() return a registered sheet as a googlesheet object, which is the first argument to practically every function in this package. Likewise, almost every function returns a freshly registered googlesheet object, ready to be stored or piped into the next command.

#this will take you to R and require ur approval
(my_sheets <- gs_ls())
my_sheets %>% glimpse()
?gs_ls
gs_ls("googleRdata") #see if the sheet exists
mygs = gs_title("googleRdata") #Register the sheet
mygs  # details of registered sheet
mygs %>% gs_browse()  #go browser and see the online file
mygs %>% gs_browse(ws=2)
mygs %>% gs_browse(ws='test')

# read from online sheet
testdata <- mygs %>%  gs_read(ws = "test")
str(testdata)
head(testdata)

#read only certain cells
mygs %>% gs_read(ws = 2, range = "A1:D8")
mygs %>% gs_read(ws = "test", range = cell_rows(1:4))
mygs %>% gs_read(ws = "test", range = cell_rows(10:15), col_names = FALSE)
mygs %>% gs_read(ws = "test", range = cell_cols(1:4))
mygs %>% gs_read(ws = "test", range = cell_limits(c(1, 4), c(5, NA)))


#Add a new worksheet to an existing Google Sheet
mygs <- mygs %>%  gs_ws_new(ws_title = "mtcars", input = head(mtcars),  trim = TRUE, verbose = FALSE)
mymtcars <- mygs %>% gs_read(ws = 'mtcars')
mymtcars


#Rename or delete worksheets
#gs_ws_delete() and gs_ws_rename() to delete the mtcars worksheet and rename the iris worksheets, respectively:

#re-register sheet / refresh
mygs <- mygs %>% gs_gs()
#be careful 
mygs <- mygs %>%  gs_ws_delete(ws = 'test2') 

#data from R object to google sheets
iris_ss <- mygs %>%   gs_ws_new(ws_title = "iris", input = iris,trim = TRUE, verbose = FALSE)
iris_ss %>% gs_read(ws = 'iris')

#my data
train_ss <- mygs %>%   gs_ws_new(ws_title = "train1", input = data,trim = TRUE, verbose = FALSE)


#Make new Sheets from local delimited files or Excel workbooks
iris %>%  head(5) %>%  write.csv("iris.csv", row.names = FALSE)
iris_ss <- gs_upload("iris.csv")


#New sheet
foo <- gs_new("foo") %>% 
  gs_ws_rename(from = "Sheet1", to = "edit_cells") %>% 
  gs_ws_new("add_row")
#two sheets created one renamed
foo


================================================
FILE: 05-dataIE/22b-ggsheets2.R
================================================
#https://googlesheets4.tidyverse.org/


#install.packages("devtools")
library(devtools)
devtools::install_github("tidyverse/googlesheets4")
library(googledrive)
library(googlesheets4)
#give authorisations
(qsthe <- drive_get("AccreditionQSTHE"))
qsthe
sheets_get(qsthe)
sheet1 = sheets_get("1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc")
sheet1$spreadsheet_id
sheet1$sheets
sheet2 = read_sheet("1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc", sheet = 2)
sheet2
str(sheet2)
(df <- sheets_cells("1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc", range = "A1:E7"))


================================================
FILE: 05-dataIE/31a-export.R
================================================
#export data

mtcars
write.csv(mtcars, file='./data/mtcars.csv')

#list files
dir('./data')
list.files('./data')

file.exists("./data/mtcars.csv")
file.exists('./data/dhiraj.csv')
df= read.csv('./data/mtcars.csv')
str(df)

#Add arguments at saving
names(mtcars)
head(mtcars)
#Do not create columns for row names
write.csv(mtcars, file='./data/mtcars1.csv', row.names = F)

#help
?write.csv


================================================
FILE: 05-dataIE/32c-writecsv.R
================================================
# Write to csv file



================================================
FILE: 05-dataIE/datasets.R
================================================

#base
data() # current available data sets

#
mtcars
iris
volcano



#packages
#M1
library(titanic)
titanic::titanic_train
#M2
data('titanic_train', package='titanic')
titanic_train
data('titanic_test', package='titanic')
titanic_test

lapply(list(titanic_train,titanic_test), dim )



## Create dummy variables for categorical variables
library(dummies)
titanic_data <- dummy.data.frame(titanic_train, names=c("Pclass","Sex","Embarked"), sep="_")


================================================
FILE: 05-dataIE/importExcel.R
================================================
#download data from web in excel

pacman::p_load('xlsx', 'readxl', 'dplyr','rio')
version
url = 'https://www.scimagojr.com/countryrank.php?out=xls'
url2 = 'https://www.scimagojr.com/journalrank.php?out=csv'

temp = tempfile(fileext = ".xlsx")
dataURL <- url
download.file(dataURL, destfile=temp, mode='wb')

test <- readxl::read_excel(temp, sheet =1)
head(test)

temp = tempfile(fileext = ".")
dataURL <- url2
download.file(dataURL, destfile=temp, mode='wb')

#test <- readxl::read_excel(temp, sheet =1)
test <- read.csv(temp)

head(test)


file1 <- read.csv(file.choose(), sep=';')
head(file1)
names(file1)


================================================
FILE: 06-DS/0FileList.R
================================================
# List of Files in this folder

# Data Structures in R
# Use Help
# Packages
# PathConfig


================================================
FILE: 06-DS/10a-TOC
================================================
#Files in this folder related

# Data Structures in R
# Use Help
# Packages
# PathConfig

================================================
FILE: 06-DS/13b-ds-blank.R
================================================
# Data Structures in R

#Vectors----




#Matrix----





#Arrays----







#List----

#Factors----

#DataFrame----



================================================
FILE: 06-DS/14b-Basic_R_v01.R
================================================


#BASICS ABOUT R =================================

x<-10+20                  # Simple Math
x


10+20 -> x                # "<-" Assignment Operator
x                         # Print the x value
y = 1:10                  # Assign values
y
1:250                     # Prints range of numbers 
print("My First Program") # Prints data in console


#R objects =================================

x <- 1:1  # put the numbers 1-10 in the variable X - Assignment operator (= or <-)
1:10 -> x1                     # you can also assign the same
x1
y <- c(11,12,13,14,15)         # concate nate function , puts the numbers into y
a<- b<- c <-3                  # multiple assignments
x+y                            # adds corresponding elements in x & y
x*2                            # Multiplication by each element with 2

a
b
c

# DATA TYPES ============================================

# Numeric -------------------------------------------
1.7                        # Decimal mark is a dot
options(scipen=999)
7.8e3                      # One can use scientific notation
-1/0                       # Infinity is marked as inf or -inf 

4/6799                     # Formatting numbers
format(4/6799, sci = TRUE) # Formatting numbers
format(4/6799, digits = 2) # Formatting numbers

round(pi, 2)                # Rounding


# Logical -----------------------------------------------

2 == 5       #is 2 equal to 5?

(2 != 2)     # ! means negation
!(2 == 2)    # ! means negation

(2 == 2) & (5 == 5)     # & - logical conjunction  
(2 == 2) | (5 != 5)     # | - logical alternative 

# Character ------------------------------------------------------

"Meeting in with  R"                                                  # strings should be enclosed with ' or "
paste("I learn R", 'and use R')    # Concatenation of strings


# Special --------------------------------------------------

1/0                    # Inf, -Inf
-1/0                   # Inf, -Inf
log(-1)                # Not a number
NA                     # NA - Missing values
NULL                   # NULL


# Factor --------------------------------------------------
f1 = c("girl", "boy", "boy", "girl", "boy")
f1
class(f1)
f <- factor(c("girl", "boy", "boy", "girl", "boy" , "child")) 
f
class(f)
# Categorical variables

# DATA STRUCTURES ===========================================

# Vectors # set of objects of the same type, one dimensional --------------------------------------------

my_vector <- c(-1, 2, 6, 6.7, 2, 0.45, 2, 4)    # defined with a c() function (c for concatenate), vectors are vertical)  # Numeric vector

mode(my_vector)
typeof(my_vector)
class(my_vector)
is.numeric(my_vector)



is.character(my_vector)

is.data.frame(my_vector)

x5 <- c(5, 4, 2, 3, 1,6,8,9)   #concatenation
x7 <- c(3, 4, 2)
x8 <- c("HA", "DL","MH","AP")  # Character Vector

x7+x5                          # sum of vectors
data <- c(x5,x7)               # concatenation of vectors
data

(my_vector2 <- 2 * my_vector)  # Computations with numeric vectors
my_vector * c(2, 3)           
my_vector + c(2, 3, 4)

# Character vector
(my_char_vector <- c(4, 2, "Hello, world!"))

mode(my_char_vector)
typeof(my_char_vector)
class(my_char_vector)
is.numeric(my_char_vector)

# Maths operations
sqrt(my_vector)        # NaN (not a number) produced as a resulting of squarting negative values
log(my_vector)         # etc.
sum(my_vector)         # sum
prod(my_vector)        # product


# Useful functions
length(my_vector)     # vector's length
unique(my_vector)     # distinct values of the vector
head(my_vector, 3)    # displays first 3 elements of my_vector
tail(my_vector, 2)    # displays last 2 elements of my_vector
sort(my_vector)       # sorts my_vector
order(my_vector)      # returns the order of my_vector
rev(my_vector)        # reverts the order of elements in my_vector
which.max(my_vector)  # return position of the max value
which.min(my_vector)  # return position of the min value
rank(my_vector)       # rank elements

#()[]

# Indexing
my_vector[3]
my_vector[-2]
my_vector[1:4]
my_vector[-(1:7)]
my_vector[-c(2,4,7,5)]
my_vector[my_vector > 3]
which(my_vector > 3)
my_vector[my_vector > 0 & my_vector < 4]
my_vector[my_vector %in% 5:7]
match(my_vector, 1:3)
my_vector[c(rank(my_vector)) == 2]
my_vector[order(my_vector)]

my_vector2 = c("a", "b")
my_vector = c(1,2)
# Combining two vectors
c(my_vector, my_vector2)
cbind(my_vector, my_char_vector)
rbind(my_vector, my_char_vector)
cbind(my_char_vector,my_vector)
rbind(my_char_vector,my_vector)

# Sequences
4:7
seq(0, 1, length.out = 16)
t =seq(1, 9, by = 2)
t
seq(1, 8, by = 2) 
seq(9, 1, by = -2) 
seq(17) 

all(seq(17) == 1:17)

1:10 -> x1
x1

rep(1:4, 2)
rep(1:4, each = 2)      
rep(1:4, c(2, 1, 2, 3))
rep(1:4, each = 2, len = 10)  


rep(1:4, each = 2, times = 3)


# Matrix --------------mxn--------------------------------

# Defined with a matrix() function with 3 arguments: 
#vector of values, number of rows, number of columns

(my_matrix <- matrix(c(4, 2, 7, 9), nrow=2, ncol=2))
(my_matrix <- matrix(c(1, 2, 3, 4), 4, 1))
(my_matrix <- matrix(c(1, 2, 3, 4), 2, 2, byrow=TRUE))

#transpose
myMat_Transposed <- t(my_matrix)


my_matrix[1, 2]
my_matrix[, 1]
my_matrix[1, ]

dim(my_matrix)                # Dimesnion of matrix
det(my_matrix)                # Determinant
eigen(my_matrix)              # Eigenvalues/Eigenvectors
(t(my_matrix) %*% my_matrix)  # Transpose and multiply

rowSums(my_matrix)            # Sum of rows
colSums(my_matrix)            # Sum of colums
rowMeans(my_matrix)           # Avgs of rows
colMeans(my_matrix)           # Avgs of colums


# Array -------------------------------------------------

# Defined with a array() function with 3 arguments: 
#vector of values, dimensions, dimension names

our_dim_names <- list(dimension1 = c("A", "B", "C", "D"), 
                      dimension2 = c("a", "b", "c"),
                      dimension3 = c("1", "2"))

(my_array <- array(data = 1:24, dim = c(4,3,2), 
                   dimnames = our_dim_names))

dim(my_array)

# Indexing
my_array[3, 2, 1]
my_array[3, 2, ]
my_array[3, , ]


# List ------------------------------------------------

# Set of objects that can have different types # Defined with the list() function
(my_list <- list(name = c("Analytics", "Labs"), 
                 age = 3, 
                 club = "Bulls", 
                 matrix = my_matrix))

(my_list2 <- list(c("Analytics", "Labs"), 
                  3, "Bulls",
                  my_matrix))



mode(my_list)

typeof(my_list)
class(my_list)

# Indexing
dim(list)
my_list$name
my_list[1]
my_list[[1]]
my_list[[1]][2]
names(my_list2)      #lists all names of the list


# Data frame ---------------------------------------------

# table with the same type within a 
#column and different types between columns 
# defined with a data.frame() function

my_df <- data.frame(id = c(1, 2, 3), 
                    name = c("Analytics", "a", "b"), 
                    Goals = c(50, 49, 25))

my_df 

dim(my_df)

str(my_df)
summary(my_df)
















# Workspace & Environment -----------------------------------------------------

getwd()                       # Get working directory
setwd("C:/")                  # set working directory
ls()                          # List elements of the environment

rm(x)                         # remove an object from workspace
rm(a,b)                       # remove multiple object from workspace
rm(list=ls())                 # clear workspace

# Ctrl - L ... Clear Console


# Installing & managing packages -------------------------------------------------------------------------------------------


install.packages("ggplot2")   # TO install packages # download packages from CRAN and install in R
library("ggplot2")            # make package available ; often used for loading in scripts
require("ggplot2")            # prefered for loading in functions; may be better
library(help="ggplot2")       # brings up documentation  in editor window
update.packages("ggplot2")    # check for pacakge updates; do it regularly
detach("package:ggplot2", unload=TRUE) # Unload/Remove  packages # by default, all loaded packges are unloaded when R quits.
remove.packages("ggplot2")    # TO permanantly remove it.   

#Datasets -----------------------------------------------------------------------------------------------------------------------
?datasets                     # Using R's built in data sets

library(help=datasets)
library(datasets)

data(mtcars)                 # Loading mtcars data set
cars <-mtcars                # Save the data into workspace
detach(package:datasets)     # To remove the datasets package

# Viewing data set
mtcars                       # Total data set in console
View(mtcars)                 # Viewing dataset in spreadsheet
head(mtcars,10)              # Viewing top-10 observations (default: top-6)
tail(mtcars)                 # Viewing bottom 10 observations
str(mtcars)                  # Viewing data dictionary
names(mtcars)                # Viewing column names

v1 = mtcars$disp
newvar <- mtcars$disp + mtcars$hp
v1 <- mtcars$mpg             # Assigning single variable from mtcars data to v1
v2 <- mtcars$cyl
v3 <- mtcars$disp
v4 <- mtcars$hp

mtcars1<-rbind(v1,v2,v3,v4) # Combined as rows #Horizontal joins
head(mtcars1)
mtcars2<-cbind(v1,v2,v3,v4) # Combined as columns # Vertical joins

# DATA IMPORT =================================================================

setwd("E:\\HHges - Mkt Anyts\\Marketing Analytics\\R Programming")




# Import from csv -------------------------------------------------------------

# Specify file
file <- paste0(path,"\\", "Car_data_cf.csv")
data_cf <- read.csv(file)


# DATA EXPORT =================================================================

# Export to csv ---------------------------------------------------------------

file <- paste0(path, "MyRClassDemo.csv")        # Specify file
write.csv(cars, file)                              # Export file
dir(path)

# Export to RData file --------------------------------------------------------

path <- getwd()                                       # Save entire environment
save.image(file = paste0(path, "environment.RData"))  
save(fpe, file = paste0(path, "data.RData"))         # Save single R objects
load(file = paste0(path, "data.RData"))               # Load RData file(s)

# Let's Import and build a function -----------------------------------

setwd("E:\\HHges - Mkt Anyts\\Marketing Analytics\\R Programming")
mydata<-read.csv("sample_file.csv")
View(mydata)
str(mydata)
modelname <- mydata$Age
modelname
# user written function for creating descriptive statistics
mystats <- function(x) {
  nmiss<-sum(is.na(x))
  a <- x[!is.na(x)]
  m <- mean(a)
  n <- length(a)
  s <- sd(a)
  min <- min(a)
  p1<-quantile(a,0.01)
  p5<-quantile(a,0.05)
  p10<-quantile(a,0.10)
  q1<-quantile(a,0.25)
  q2<-quantile(a,0.5)
  q3<-quantile(a,0.75)
  p90<-quantile(a,0.90)
  p95<-quantile(a,0.95)
  p99<-quantile(a,0.99)
  max <- max(a)
  UC <- m+3*s
  LC <- m-3*s
  outlier_flag<- max>UC | min<LC
  return(c(n=n, nmiss=nmiss, outlier_flag=outlier_flag, mean=m, stdev=s,min = min, p1=p1,p5=p5,p10=p10,q1=q1,q2=q2,q3=q3,p90=p90,p95=p95,p99=p99,max=max, UC=UC, LC=LC ))
}

vars <- c("Age"	,	"Sales" )
str(mydata[vars])
stats<-t(data.frame(apply(mydata[vars], 2, mystats)))
View(stats)

## OUTLIERS

mydata$Sales[mydata$Sales>971169.4] <- 971169.4
vars <- c("Age"	,	"Sales" )
str(mydata[vars])
stats<-t(data.frame(apply(mydata[vars], 2, mystats)))
View(stats)

# Writing Summary stats to external file

write.csv(stats, file = "stats.csv")


## Missing value treatment
mydata <- mydata[!is.na(mydata$Cust_id),] # dropping obs where Gender=missing
install.packages("Hmisc")
require(Hmisc)
?apply
mydata1<-data.frame(apply(mydata[vars],2,  function(x) impute(x, mean))) #Imputing missings with mean for IV's



mydata$Location
aggdata1 <- aggregate(x=mydata[,3],
by = list( mydata$Location),
                     FUN = mean,
                     na.rm = TRUE)
aggdata1


#*************************************/

install.packages("swirl")
library(swirl)


#**********************************/


######################################## Loops & Advanced R #####################

#initialize a variable
N <- 10

#check if this variable * 5 is > 40
if (N * 5 > 40){
  print("This is easy!")
} else {
  print ("It's not easy!")
}



#initialize a vector
y <- c(99,45,34,65,76,23)

#print the first 4 numbers of this vector
for(i in 1:4){
  print (y[i])
}



for (i in seq(0, 10, 2)) print(i)


#initialize a condition
Age <- 12

#check if age is less than 17
while(Age < 17){
  print(Age)
  Age <- Age + 1 #Once the loop is executed, this code breaks the loop
}

# Creating Histogram

cars
head(cars)
cars$dist

hist(cars$speed)
plot(cars$dist)
plot(cars$speed)

hist(cars$dist, col = "Grey", border = "green")

data()

data(mtcars)

data(iris)

head(iris, 5)

head(mtcars, 5)

rm(iris)

mtcars

str(mtcars)

# Summary of the data
summary(mtcars$mpg)
hist(mtcars$wt)
hist(mtcars$hp, col = "green", main = "Histogram of HP")

mtcars

str(mtcars)


#Creating new variable basis the already existing ####
str(iris)
Iris1 <- transform(iris,
                   sumx = Sepal.Length + Sepal.Width,
                   meanx = (Sepal.Width + Sepal.Length) / 2)

iris <- transform(iris,
                  sumx = Sepal.Length - Sepal.Width,
                  meanx = (Sepal.Width + Sepal.Length) / 2)
rm(Iris)

# Converting from one data type to another ####
a <- c(1, 2, 3)
a
is.numeric(a)
is.vector(a)
a <- as.character(a)
a
is.numeric(a)
is.vector(a)
is.character(a)


# Transposing a dataset ####
cars
t(cars)


#  Aggregating data ####
options(digits = 3)
attach(mtcars)
summary(mtcars)
summary(mtcars$disp)
head(mtcars)
?aggregate
aggdata <- aggregate(mtcars,
                     by = list(cyl, gear),
                     FUN = mean,
                     na.rm = TRUE)
aggdata
detach(mtcars)

mtcars




# one way table ####
library(vcd)
Arthritis
table(Arthritis$Improved)
mytable <- with(Arthritis, table(Improved))
mytable # frequencies
prop.table(mytable) # proportions
options(digits = 3)
prop.table(mytable) * 100 # percentages
addmargins(mytable)

# two way table ####
mytable <- xtabs( ~ Treatment + Improved, data = Arthritis)
mytable # frequencies

#Two way table using CrossTable ####
install.packages("gmodels")
library(gmodels)
CrossTable(Arthritis$Treatment, Arthritis$Improved)


================================================
FILE: 06-DS/14b-objectsmethods.R
================================================

#Methods to handle objects
x = 1:5
methods(class=class(x))

methods(class=class(mtcars))


#seq
1:5
rev(x)
match(5, x)


#all combinations
(df=expand.grid(x=c('A','B','C','D'), y=1:3))

choose(6,4)

weighted.mean(c(1,2), c(3,4))
(1*3 + 2*4)/(3+4)

df2 = data.frame(x=c(1,2), y=c(3,4), z=c(5,6))
stars(df2[,1:2])
stars(df2[,1:3])

filled.contour((df2))
?image
image(df2)

df2 = mtcars[1:5,1:8]


================================================
FILE: 06-DS/14c-ds1.R
================================================
# Data Structures in R

#Vectors----
v1 = c('A', 'B','C')   #create a vector
v1   #print the vector
class(v1)  #print the class of vector
(v2 = c(1,2,3))
v2
class(v2)
(v3 = c(TRUE, FALSE, TRUE))
class(v3)
V3a <- c(T,F,T)
v4 = 100:200
length(v4)
v4
v5 = seq(1,10,2)
v4; v5
v4
v4[-c(1,3,4)]
v4[v4 > 150]
v7 = c('A','C')
v1
v1[!v1 %in% v7]
v1
v1['A']  #wrong/ error
v1[1]
v1


v4(v8 = 10:15)

names(v8) = c('E','F','G','H','I','J')
v8
v8['G']
v8[c('G','I')]

v8[3:5]

str(v8)
str(v1)
class(v8)
class(v1)

#Dataframe----
rollno=c(1,2,3)
name=c('Rohit','Lalit', 'Hitesh')
course=c('MBA','BBA','MCA')
dept=c('Dept1','Dept1','Dept2')
marks=floor(rnorm(3,50,10))
?rnorm
rnorm(3,50,10)         
students = data.frame(rollno, name, course, dept, marks)
students
class(students)
str(students)
summary(students)          


df = data.frame(rollno=c(1,2,3), name=c('Rohit','Lalit', 'Hitesh'), course=c('MBA','BBA','MCA'), dept=c('Dept1','Dept1','Dept2'),marks=floor(runif(3,50,100)))
df
class(df)
#df = fix(df)
df
str(df)
class(df)
summary(df)

#List
listL1 = list('dhiraj', v1, students, mymatrix, myarray)
listL1


#?ls
ls()
rm(list=ls())
ls()


(mylist1 = list( 1, df, v4))

?matrix
'a':'Z'
mym = matrix(c('a','b',2,'d'), ncol=2)
mym
1:24
(mymatrix = matrix(1:24,ncol=6,byrow=T))
#(mymatrix = matrix(11:34,nrow=6,byrow=T))
length(11:35)
mymatrix[,1]
mymatrix[2:4,3:4]
mymatrix[5,3]
mymatrix
?matrix
(mymatrix = matrix(1:24,ncol=4, byrow=TRUE))
rows1= c('R_1','R_2')
1:6
rn = paste('R',1:6, sep='-')
cn = paste('C',1:4, sep='-')
dimnames(mymatrix) = list(c(rn), c(cn))
mymatrix
?dimnames
dimnames(mymatrix) = list(c(paste('R',1:6, sep='_')), c(paste('C',1:4,sep='')))
mymatrix
paste('C',1:4, sep='')
mymatrix[c('R-1'),]
mymatrix[,c('C-3')]
mymatrix[c('R-1','R-3'),]
mymatrix[c(1,3),]

mymatrix
colSums(mymatrix)
rowSums(mymatrix)
rowMeans(mymatrix)
colMeans(mymatrix)

#--
(myarray = array(101:124, dim=c(4,3,2)))

(myarray = array(1:24, dim=c(3,2,4)))


(myarray = array(1:24, dim=c(4,3,2), dimnames = list(c('S1','S2','S3','S4'), c('Sub1','Sub2','Sub3'), c('Dept1','Dept2'))))
myarray
apply(myarray,3,sum)
apply(myarray,2,mean)
apply(myarray,3,sum)
apply(myarray,c(2,3),mean)
apply(myarray,c(1,3),sd)
apply(myarray,c(1,2),max)



# Factors
name = c('S1','S2','S3','S4','S5')
course = c('PHD', 'MTECH', 'BTECH','BTECH','PHD')
gender = c('M', 'F', 'M', 'F','M')
grades = c('A','B','C','A','F')
marks =  c(runif(5, 50, 100))
df = data.frame(name, course, gender, grades, marks)
df
str(df)
df[1:2,]
df[,1:3]
df$name = as.character(df$name)
str(df)
df$grades = factor(df$grades, ordered=F, levels=c('A','B','C','D','E','F'))
str(df)
df$grades
df$gender = factor(df$gender, ordered=T, levels=c('M','F'))

df$gender
#x



#

students
df
df=fix(df)
df[3:4,1:2]
df
df$course
df$name
df$gender = c('M','F','M','M')
df
str(df)
df$gender = factor(df$gender)
str(df)
df$grades = c('A', 'B', 'A', 'C')
df
str(df)
df$grades = factor(df$grades, ordered=T)
str(df)
df$grades
df$grades = factor(df$grades, ordered=T, levels=c('C','B','A'))
df$grades



table(df$course,df$gender)
mymatrix
apply(mymatrix, 1, sum)  
apply(mymatrix, 2, sum)  
apply(mymatrix, 2, sd)  
myarray
apply(myarray, 1, sum)  
df
?apply
df
tapply(df$marks, df$gender, mean)
?tapply
df
df$gender = NULL
df
df[df$dept == 'Dept1',]
df[df$marks >= 80,]







================================================
FILE: 06-DS/15a-objects.R
================================================
# Objects
# 

m1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)
colnames(m1) = c('sub1','sub2','sub3','sub4')
rownames(m1) = paste('R',1:10,sep='')

a1= array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )
a1

df1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))
df1

# Lists

list1 = list(sub1=10:1, sub2=rep(5,3), sub3=rep(c(5,6),4),sub4=seq_len(length.out=10))
list1

list2 = list(num=1:10, vec=c(1:5, 4:5, 6:8, NA, 9, 12, 17), lg=log(1:5))
list2

#Data Frame df3
newnum = c(2:5, 4:5, 6:8, 9,17)
fac1 = factor(c(rep("A", 3), rep("B", 3), rep("C", 3), rep("D",2)))
fac2 = gl(n=2, k=1, length=11, labels = month.abb[1:2])
newnum
fac2
fac1
df3 = data.frame(response = newnum, pred1 = fac1, pred2 = fac2)
df3
#rm(list=ls())

student1 <- readRDS("student1.rds")
# Dataframe student
str(student1)
s1 = student1[,c('br', 'city','java','dbms', 'dwm','vlsi', 'cpp', 'cbnst')]
str(s1)
student1[,c(15:22)]


================================================
FILE: 06-DS/16b-datatypes.R
================================================
# R Programming
?mean
x <- c(0:10, 50)
x
x[c(seq(1,12,2))]
?c
seq(2,100,2)

?seq
xm <- mean(x)
xm
mean(x, trim = 0.10)

# Scalar
(x <- 1)
x = 1
# Vector
c(1:5)
x = c(1,2,3,4,10,5,5,3,2,5,7,7,8,9)
mean(x); median(x)
sort(x)

table(x)

library(modeest)
mlv(x, method = "lientz", bw = 0.2)

ratings =c(rep('Good',5), rep('Poor',3))
ratings
table(ratings)
barplot(table(ratings))



rollno = c(10,22,44,23)
sales = runif(12, 50, 100)
sales
plot(sales, type='b')
?plot

?runif


str(x)
y = c('abhinav', 'Rishi', 'Shreya')
str(y)
z = c(TRUE, FALSE, TRUE, FALSE)
str(z)


abinav=c(1,3,6,8,6,3,100,rep(NA,5))
abinav
str(abinav)
is.na(abinav)
anyNA(abinav)
mean(abinav,na.rm=T)

mean(x=abinav)
mean(x=abinav,trim=0.4, na.rm=T)
x = c(TRUE, FALSE, TRUE)
mean(x)
mean(c(1,0,1))

#Categorical Data
product = c('maggi', 'topramen', 'hakka')
product
shirtcolor = c('green', 'red', 'blue', 'black')
shirtcolor
rating = c('Excellent', 'Very Good', 'Good', 'Average', 'Poor' )
rating
str(rating)
performance = c( 1, 2, 3, 4, 5)
str(performance)

decision = c('buy', 'sell')
decisionTF = c(TRUE, FALSE )
str(decision); str(decisionTF)
grades = c('A','C','B','D','E','B')
grades
grades1 = factor(grades, ordered=T, levels=c('E','D','A','B','C'))
grades1
itemssold = c(10, 20, 30, 25)


#Nominal Data
product

#Ordinal Data
performance
#need to create order
performance=c(1,2,5,2,5,2,5,2,5)
length(performance)
performance
str(performance)
performance[9] = 100
performance

performance1 = factor(performance, ordered=TRUE, levels=c(4,2,5,1,3,8,100))
performance1[1] = 100

table(performance1)
performance1



rating
rev(rating)
rating1 = factor(rating, ordered=T, levels=rev(rating))
rating1

grades
grades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))
grades1
summary(grades1)

# -----
#vectors, factors
#list matrix
?matrix
mdat <- matrix(c(1,2,3,11,12,13), nrow = 2, ncol = 3)
mdat[1,1] ='dhiraj'              
mdat               
(x = 1:24)               
(matx = matrix(x, nrow=6, byrow=TRUE))               
?data.frame



(salemon = month.abb[1:12])
salemon = factor(salemon, ordered=T,levels=month.abb[1:12])
salemon
(sales1 = runif(12, min=50, max=100))
salemon; sales1
(adamt = runif(12, min=12, max=25))
(salesmonthwise = data.frame(salemon, sales, adamt))
rownames(salesmonthwise) = month.abb[1:12]
salesmonthwise
colnames(salesmonthwise) = c('col1','col2','col3')

salesmonthwise[1:5,1:3]
?head
head(salesmonthwise,n=3)
head(salesmonthwise[c(1,3)],n=3)
head(salesmonthwise[c('col1','col2')],n=3)
salesmonthwise[c('Jan'),c('col1','col2')]
salesmonthwise[salesmonthwise$col3 < 20,c(1,2,3)]

colSums(salesmonthwise[-1])
rowSums(salesmonthwise[-1])

# Day3

# Data Properties
set.seed(10)
?rnorm
sales1 = rnorm(100, mean=50, sd=10)
sales1
range(sales1)
hist(sales1)
plot(density(sales1))
abline(v=50)

set.seed(1)
sales3 = runif(10,1,5)
sales3
mean(sales3)
set.seed(1)
sales4 = runif(10,1,5)
sales4
mean(sales4)


names = c('A', 'B', 'C')
?sample
v1= sample(x=names, size=4, replace = FALSE, prob = c(.2,.3,.5))
v1
table(v1)


sales2 = rnorm(100, mean=60, sd=12)
sales2
plot(density(sales2))
abline(v=60)
sales3= c(1,5,2,5,2,3,6,7,8,4,5)
sales3
#


shirtcolor = c('green', 'red', 'blue', 'black')
sellitems = sample(shirtcolor, 20, replace=T, prob=c(.4,.3,.2,.1)) 
sellitems
summary(sellitems)
table(sellitems)

grades = c('A','C','B', 'D','E', 'B', 'A', 'B', 'A', 'C','F')
grades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))
grades1


#Location / Central Tendency
mean(sales1)
mean(sales2)
mean(sellitems)
mean(grades)
mean(sales3)

#Middle Values
median(sales1)
median(sales2)
median(sales3)
sort(sales3)
quantile(sales3)

median(sellitems)
median(grades1)


#Mode
mode(sales1)  # error
library(modeest)
mlv(sales1, method='mfv')
mlv(sales2, method='mfv')
mlv(sales3, method='mfv')
mlv(sellitems, method='mfv')
mlv(grades1, method='mfv')
table(sale3)
table(sellitems)
table(grades1)
which.max(table(grades1))



# Graphical Way
mean(sales1)
hist(sales1)
plot(density(sales1))
abline(v=50)

sales2 = rnorm(100, mean=60, sd=12)

sales2
range(sales2)
hist(sales2, prob=T)
?hist
lines(density(sales2), col='red')
abline(v=60, col="green")
abline(h=.02, col="red")

# Combine Plots
plot(density(sales1), col='green', ylim=c(0,0.05))
lines(density(sales2), col="blue", lwd=2)
abline(v=c(50,60), col=c('green', 'blue'), lwd=3)


================================================
FILE: 06-DS/16c-basicDT.R
================================================
#Basic Data Types

#Numeric ----
#Decimal values are called numerics in R. It is the default computational data type. If we assign a decimal value to a variable x as follows, x will be of numeric type.

x = 10.5       # assign a decimal value 
x              # print the value of x [1] 10.5 
class(x)       # print the class name of x [1] "numeric"

#Furthermore, even if we assign an integer to a variable k, it is still being saved as a numeric value.

(k = 1) #[1] 1 
class(k)       # print the class name of k [1] "numeric"
is.integer(k)  # is k an integer? FALSE

#INTEGER----
#invoke as.integer function 

(y = as.integer(3) )  # print the value of y [1] 3 
class(y)       # print the class name of y 
[1] "integer" 
is.integer(y)  # is y an integer? [1] TRUE

#coerce a numeric value into an integer with the same as.integer function.
as.integer(3.14)    # coerce a numeric value [1] 3

#And we can parse a string for decimal values in much the same way.
as.integer("5.27")  # coerce a decimal string [1] 5

as.integer(TRUE)    # the numeric value of TRUE [1] 1 
as.integer(FALSE)   # the numeric value of FALSE [1] 0

#CHARACTER----
#A character object is used to represent string values in R. We convert objects into character values with the as.character() function:
  
(x = as.character(3.14) )   # print the character string [1] "3.14" 
class(x)       # print the class name of x [1] "character"

fname = "Joe"; lname ="Smith" 
paste(fname, lname) #concatenate character values



#LOGICAL----
#A logical value is often created via comparison between variables.
x = 1; y = 2   # sample values 
z = x > y      # is x larger than y? 
z              # print the logical value [1] FALSE 
class(z)       # print the class name of z [1] "logical"

#Standard logical operations are "&" (and), "|" (or), and "!" (negation).
u = TRUE; v = FALSE 
u & v          # u AND v [1] FALSE 
u | v          # u OR v [1] TRUE 
!u             # negation of u [1] FALSE


#COMPLEX
#A complex value in R is defined via the pure imaginary value i.
(z = 1 + 2i)     # create a complex number, print the value of z 1+2i 
class(z)       # print the class name of z [1] "complex"


================================================
FILE: 06-DS/16d-ds1.R
================================================
# Data Structures in R

#Vectors----
v1 = c('A', 'B','C')   #create a vector
v1   #print the vector
class(v1)  #print the class of vector
(v2 = c(1,2,3))
v2
class(v2)
(v3 = c(TRUE, FALSE, TRUE))
class(v3)
V3a <- c(T,F,T)
v4 = 100:200
length(v4)
v4
v5 = seq(1,10,2)
v4; v5
v4
v4[-c(1,3,4)]
v4[v4 > 150]
v7 = c('A','C')
v1
v1[!v1 %in% v7]
v1
v1['A']  #wrong/ error
v1[1]
v1


v4(v8 = 10:15)

names(v8) = c('E','F','G','H','I','J')
v8
v8['G']
v8[c('G','I')]

v8[3:5]

str(v8)
str(v1)
class(v8)
class(v1)

#Dataframe----
rollno=c(1,2,3)
name=c('Rohit','Lalit', 'Hitesh')
course=c('MBA','BBA','MCA')
dept=c('Dept1','Dept1','Dept2')
marks=floor(rnorm(3,50,10))
?rnorm
rnorm(3,50,10)         
students = data.frame(rollno, name, course, dept, marks)
students
class(students)
str(students)
summary(students)          


df = data.frame(rollno=c(1,2,3), name=c('Rohit','Lalit', 'Hitesh'), course=c('MBA','BBA','MCA'), dept=c('Dept1','Dept1','Dept2'),marks=floor(runif(3,50,100)))
df
class(df)
#df = fix(df)
df
str(df)
class(df)
summary(df)

#List
listL1 = list('dhiraj', v1, students, mymatrix, myarray)
listL1


#?ls
ls()
rm(list=ls())
ls()


(mylist1 = list( 1, df, v4))

?matrix
'a':'Z'
mym = matrix(c('a','b',2,'d'), ncol=2)
mym
1:24
(mymatrix = matrix(1:24,ncol=6,byrow=T))
#(mymatrix = matrix(11:34,nrow=6,byrow=T))
length(11:35)
mymatrix[,1]
mymatrix[2:4,3:4]
mymatrix[5,3]
mymatrix
?matrix
(mymatrix = matrix(1:24,ncol=4, byrow=TRUE))
rows1= c('R_1','R_2')
1:6
rn = paste('R',1:6, sep='-')
cn = paste('C',1:4, sep='-')
dimnames(mymatrix) = list(c(rn), c(cn))
mymatrix
?dimnames
dimnames(mymatrix) = list(c(paste('R',1:6, sep='_')), c(paste('C',1:4,sep='')))
mymatrix
paste('C',1:4, sep='')
mymatrix[c('R-1'),]
mymatrix[,c('C-3')]
mymatrix[c('R-1','R-3'),]
mymatrix[c(1,3),]

mymatrix
colSums(mymatrix)
rowSums(mymatrix)
rowMeans(mymatrix)
colMeans(mymatrix)

#--
(myarray = array(101:124, dim=c(4,3,2)))

(myarray = array(1:24, dim=c(4,3,2), dimnames = list(c('S1','S2','S3','S4'), c('Sub1','Sub2','Sub3'), c('Dept1','Dept2'))))
myarray
apply(myarray,1,sum)
apply(myarray,2,mean)
apply(myarray,3,sum)
apply(myarray,c(2,3),mean)
apply(myarray,c(1,3),sd)
apply(myarray,c(1,2),max)



# Factors
name = c('S1','S2','S3','S4','S5')
course = c('PHD', 'MTECH', 'BTECH','BTECH','PHD')
gender = c('M', 'F', 'M', 'F','M')
grades = c('A','B','C','A','F')
marks =  c(runif(5, 50, 100))
df = data.frame(name, course, gender, grades, marks)
df
str(df)
df[1:2,]
df[,1:3]
df$name = as.character(df$name)
str(df)
df$grades = factor(df$grades, ordered=F, levels=c('A','B','C','D','E','F'))
str(df)
df$grades
df$gender = factor(df$gender, ordered=T, levels=c('M','F'))

df$gender
#x



#

students
df
df=fix(df)
df[3:4,1:2]
df
df$course
df$name
df$gender = c('M','F','M','M')
df
str(df)
df$gender = factor(df$gender)
str(df)
df$grades = c('A', 'B', 'A', 'C')
df
str(df)
df$grades = factor(df$grades, ordered=T)
str(df)
df$grades
df$grades = factor(df$grades, ordered=T, levels=c('C','B','A'))
df$grades



table(df$course,df$gender)
mymatrix
apply(mymatrix, 1, sum)  
apply(mymatrix, 2, sum)  
apply(mymatrix, 2, sd)  
myarray
apply(myarray, 1, sum)  
df
?apply
df
tapply(df$marks, df$gender, mean)
?tapply
df
df$gender = NULL
df
df[df$dept == 'Dept1',]
df[df$marks >= 80,]







================================================
FILE: 06-DS/20a-vectors1.R
================================================
# Vectors

#sequence of data elements of the same basic type

#Scalar
x1 = 1
x2 <- 2
x1
x2
(x3 = 3) #assign and print

#Vector----
# Combining vectors with c()----
x = c(1, 5, 4, 9, 0)
typeof(x)  #[1] "double"
length(x)   #[1] 5

#Vector using : operator
x = 1:7; x   #[1] 1 2 3 4 5 6 7
y <- 2:-2; y  #[1]  2  1  0 -1 -2

#Vector using seq command
seq(1, 3, by=0.2)          # specify step size
#[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
seq(1, 5, length.out=4)    # specify length of the vector 4parts from 5
#[1] 1.00 2.33 3.667 5.00

seq(1,5, along.with = c(1,2,13,15,6,7,8,20))
x=c(1,2,13,15,6,7,8,20,30,60); length(x)
x1=110:150
length(x1)
seq(0,1, length.out = length(x1))
seq(0,1, along.with = x1)

seq(0,1, length.out = 60)

#Character/ String ----
(x3=c('S1','S2',"S3"))
class(x3)
#Numeric ----
(x4=1:10)
class(x4)
#Logical----
(x5=c(TRUE, FALSE, TRUE))
class(x4)
(x5b=c(F, T, T, F))
class(x5b)



#Vector Coercian ----
#if even 1 character, whole vector converted to character
x = c(1, 5.4, TRUE, "hello")
x  #[1] "1"     "5.4"   "TRUE"  "hello"
typeof(x)  #[1] "character"


#Vector Recycling ----
#If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u

u = c(10, 20, 30) 
v = c(1, 2, 3, 4, 5, 6, 7, 8, 9) 
u + v 
#[1] 11 22 33 14 25 36 17 28 39
#1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...

#Vector Index ----
u[1]

u[-3]  #negative index :strip the member whose position has the same absolute value as the negative index.

#Numeric Index Vector ----
s = c("aa", "bb", "cc", "dd", "ee") 
s[c(2, 3)] 
s[-c(4,5)]

#cannot mix + & -
s[c(-2,3)]

#Duplicate Index
s[c(2,3,2)]

#Range Index
s[c(2:5)]
s[2:5]
#Out of Range Index
s[c(10)]

#Logical Index Vector
#Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.
s = c("aa", "bb", "cc", "dd", "ee") #retrieve 2nd & 4th member
L = c(FALSE, TRUE, FALSE, TRUE, FALSE) 
s[L] 
#[1] "bb" "dd"

x = 1:10
x[x<5]  #elements satisfying condition


#Named Vector Members ----
#We can assign names to vector members.
(v = c("Mary", "Sue") )
names(v) = c("First", "Last")  #Name the members
v 
#Retrieve the first member by its name.
v["First"]   #[1] "Mary"
#everse the order with a character string index vector.
v[c("Last", "First")] 


#DELETE Vector----
(x = 1:10)
x = NULL  #delete
x

#Vector ARITHMETIC----
# Create two vectors.
v1 = c(3,8,4,5,0,11)
v2 = c(4,11,0,8,1,2)

(add.result = v1+v2) # Vector addition.
(sub.result = v1-v2) # Vector subtraction.
(multi.result = v1*v2) # Vector multiplication.
(divi.result = v1/v2) # Vector division.
(v1/2)


#Sort
v = c(3,8,4,5,0,11, -9, 304)
sort(v)
sort(v, decreasing=T)



#READ values
nums = scan()  #type the numbers with space
nums
names = scan(what='character')
names


#Missing Values
x = c(1,4,7,NA,12,19,15,21,20)
mean(x)
mean(x, na.rm=T)
is.na(x)
x[!is.na(x)] #non missing values


================================================
FILE: 06-DS/20b-vectors2.R
================================================
# Vectors 2

x <- vector()
# with a length and type
vector("character", length = 10)

================================================
FILE: 06-DS/20c-vectors3.R
================================================
#Datatypes:  string- name, location, department
# Number--- age, salary, weight, height
# Boolean/Logical---- TRUE/FALSE



snames = c('student1', "student2", 'student3')
class(snames)
marks = c(10, 20 ,30,50 ,60,40)
marks
class(marks)
married = c(TRUE, FALSE, TRUE)
married
class(married)
snames; marks; married
(age = c(30,35,26))
age = c(30,35,26)
age



#subsetting a Vector
(x1 = 1:100)
(x2 = seq(50,100,3))
?seq
?class
x3 = seq(from= 100, to= 200, by=2)

x1
x1[10]
x1[20:30]
x2[2:5]
x1[x1 > 50]
x2
x2 > 70
x2[(x2 > 70) | (x2 < 60)]
x2[c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,  TRUE,  TRUE,  TRUE,TRUE,  TRUE,  
     TRUE,  TRUE,  TRUE,  TRUE,  TRUE)]
x2
#operations on the vector
x3 = c('A','B','C')
x3[c(1,3)]
x3[c(TRUE, FALSE, TRUE)]
x3[(x3=='A') | (x3=='C')]

x2[(x2 > 60) & (x2 < 70)]
x2[x2==69]
x2==69

x2
length(x2)
length(x1)
mean(x2)
sum(x2)/length(x2)
sum(x2)
median(x2)
x2

set.seed(1)

X5 <- sample(1:10,50,replace = TRUE )
(x4 = trunc(runif(20,5,100)))
median(x4)
(shirtcolors = c("red",'blue','green','blue','green', 'blue'))
mean(shirtcolors)
table(shirtcolors)

sort(x4)





test_matrix <- matrix(data=marks,nrow = 3, byrow = TRUE)

test_list <- list(snames, marks, married)

class(test_list)

qt <- c("Time", 24, "October", TRUE, 3.33) 

class(qt)



================================================
FILE: 06-DS/20d-vectors4.R
================================================
# Functions for Vectors

x = 1:10

#Confirmation Functions
is.numeric(x)
is.character(x)
is.complex(x)
is.integer(x)
is.factor(x)
is.date(x)
is.logical(x)  

#Change the DAta type of Objects 
as.numeric(x)
as.character(x)
as.complex(x)
as.integer(x)
as.factor(x)
as.Date(x)
as.logical()


================================================
FILE: 06-DS/20f-vectors5.R
================================================
# Vectors

#sequence of data elements of the same basic type

#Scalar

#Vector----
x = c(1, 5, 4, 9, 0)
typeof(x)  #[1] "double"
length(x)   #[1] 5

#Vector using : operator
x = 1:7; x   #[1] 1 2 3 4 5 6 7
y <- 2:-2; y  #[1]  2  1  0 -1 -2

#Vector using seq command
seq(1, 3, by=0.2)          # specify step size
#[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
seq(1, 5, length.out=4)    # specify length of the vector 4parts from 5
#[1] 1.00 2.33 3.667 5.00


#Character/ String ----

#Numeric ----


#Logical----






# Combining vectors with c()----




#Vector Coercian ----
x = c(1, 5.4, TRUE, "hello")
x  #[1] "1"     "5.4"   "TRUE"  "hello"
typeof(x)  #[1] "character"


#Vector Recycling ----
#If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u

u = c(10, 20, 30) 
v = c(1, 2, 3, 4, 5, 6, 7, 8, 9) 
u + v 
#[1] 11 22 33 14 25 36 17 28 39
#1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...

#Vector Index ----
u[1]

u[-3]  #negative index :strip the member whose position has the same absolute value as the negative index.

#Numeric Index Vector ----
s = c("aa", "bb", "cc", "dd", "ee") 
s[c(2, 3)] 
s[-c(4,5)]

#cannot mix + & -
s[c(-2,3)]

#Duplicate Index
s[c(2,3,2)]

#Range Index
s[c(2:5)]
s[2:5]
#Out of Range Index
s[c(10)]

#Logical Index Vector
#Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.
s = c("aa", "bb", "cc", "dd", "ee") #retrieve 2nd & 4th member
L = c(FALSE, TRUE, FALSE, TRUE, FALSE) 
s[L] 
#[1] "bb" "dd"

x = 1:10
x[x<5]  #elements satisfying condition


#Named Vector Members ----
#We can assign names to vector members.
(v = c("Mary", "Sue") )
names(v) = c("First", "Last")  #Name the members
v 
#Retrieve the first member by its name.
v["First"]   #[1] "Mary"
#everse the order with a character string index vector.
v[c("Last", "First")] 


#DELETE Vector----
(x = 1:10)
x = NULL  #delete
x



#Vector ARITHMETIC----
# Create two vectors.
v1 = c(3,8,4,5,0,11)
v2 = c(4,11,0,8,1,2)

(add.result = v1+v2) # Vector addition.
(sub.result = v1-v2) # Vector subtraction.
(multi.result = v1*v2) # Vector multiplication.
(divi.result = v1/v2) # Vector division.
(v1/2)


#Sort
v = c(3,8,4,5,0,11, -9, 304)
sort(v)
sort(v, decreasing=T)



#READ values
nums = scan()  #type the numbers with space
nums
names = scan(what='character')
names


#Missing Values
x = c(1,4,7,NA,12,19,15,21,20)
mean(x)
mean(x, na.rm=T)
is.na(x)
x[!is.na(x)] #non missing values



# Vectors 2

x <- vector()
# with a length and type
vector("character", length = 10)

================================================
FILE: 06-DS/20g-vectors6.R
================================================
# Value Generation

#seq


#rep

#rnorm

#runif

#sample

#months

#alphabets


================================================
FILE: 06-DS/25a-matrices.R
================================================

# Matrices
m1 = matrix(1:12, ncol=3, byrow=T)

class(m1)
#[1] "matrix"
attributes(m1)
#$dim
#[1] 4 3
dim(m1)
#[1] 4 3

#names of cols and rows
m1
(colnames(m1) = paste('C',1:3, sep=''))
(rownames(m1) = paste('R',1:4, sep=''))
m1

#Vector to Matrix
(m3 = 1:24)
dim(m3)= c(6,4)
m3

#access elements
m2 = m1
m2[1,]  #first row
m2[c(1,3,4),]  #1st,3rd,4th row

m2[,1]  #first col
m2[,2:3] # 2nd to 3rd coln

m2[c(1,2),c(2,3)]
m2[,]
m2[-2,] # exclude 2nd row
m2[1:5] # matrix is like vector
m2[c(TRUE,FALSE),c(2,3)] #logical indexing
m2[m2 > 5]

#modify Vector
m2[2,2]
m2[2,2] = 10
m2
m2[m2> 10] = 99
m2
rbind(m2, c(50,60,70))
cbind(m2, c(55,65,75,85))

#row and col wise summary
m1
colSums(m1); rowSums(m1)
colMeans(m1); rowMeans(m1)

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN="*" ) #colwise

#addmargins
m1
addmargins(m1,1,sum) #colwise function
addmargins(m1,2,mean) #rowwise function
addmargins(m1,c(1,2),mean) #row & col wise function
addmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function



================================================
FILE: 06-DS/25c-matrices.R
================================================
#matrix

library(matlib)

A <- matrix( c(5, 1, 0,
               3,-1, 2,
               4, 0,-1), nrow=3, byrow=TRUE)
A
det(A)
inv(A)
diag(A)
t(A)
A * 2
solve(A)


================================================
FILE: 06-DS/25d-matrices.R
================================================
# Matrix
#col or rownames, sweep, addmargins
(m1 = matrix(1:12, nrow=4))

class(m1)
attributes(m1)

#names of cols and rows
m1
(colnames(m1) = paste('C',1:3, sep=''))
(rownames(m1) = paste('R',1:4, sep=''))
m1


#row and col wise summary
m1
colSums(m1); rowSums(m1)
colMeans(m1); rowMeans(m1)

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN="*" ) #colwise

#addmargins
m1
addmargins(m1,1,sum) #colwise function
addmargins(m1,2,mean) #rowwise function
addmargins(m1,c(1,2),mean) #row & col wise function
addmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function



================================================
FILE: 06-DS/25e-matrices.R
================================================
# Matrices

================================================
FILE: 06-DS/27a-arrays.R
================================================
# Arrays
# 2 states ; Each State has 3 districts : Each District has 4 cities
state = c('state1', 'state2')
district = c('district1','district2', 'district3')
city = c('city1', 'city2', 'city3', 'city4')
pincode = 101:124
citylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))
citylist

#state1  district1 city1 101
#state1  district1 city2 102
state = paste('state',1:4,sep='')
state2= rep(state,6)


colnames(citylist)
rownames(citylist)
dimnames(citylist)
length(citylist)
nrow(citylist)
ncol(citylist)
dim(citylist)
class(citylist)

citylist[3,2,1]
citylist[1,1,1] # city1, district1, state1, 
citylist[1,,] # city1 of all states & district
citylist[,,1] # State1

apply(citylist, c(1), sum)  # citywise
apply(citylist, c(2), sum)  # districtwise
apply(citylist, c(3), sum) # statewise
apply(citylist, c(2,3), sum) # all cities - district & statewise
apply(citylist, c(1,2,3), length) # statewise

?xtabs

## This is already a contingency table in array form.
DF <- as.data.frame(UCBAdmissions)

## Now 'DF' is a data frame with a grid of the factors and the counts
## in variable 'Freq'.
DF
## Nice for taking margins ...
xtabs(Freq ~ Gender + Admit, DF)
xtabs( ~ Gender + Admit )
## And for testing independence ...
summary(xtabs(Freq ~ ., DF))



================================================
FILE: 06-DS/27b-arrays.R
================================================
# Arrays
# 2 states ; Each State has 3 districts : Each District has 4 cities
state = c('state1', 'state2')
district = c('district1','district2', 'district3')
city = c('city1', 'city2', 'city3', 'city4')
pincode = 101:124
citylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))
citylist
colnames(citylist)
rownames(citylist)
dimnames(citylist)
length(citylist)
nrow(citylist)
ncol(citylist)
dim(citylist)
class(citylist)

citylist[3,2,1]
citylist[1,1,1] # city1, district1, state1, 
citylist[1,,] # city1 of all states & district
citylist[,,1] # State1

apply(citylist, c(1), sum)  # citywise
apply(citylist, c(2), sum)  # districtwise
apply(citylist, c(3), sum) # statewise
apply(citylist, c(2,3), sum) # all cities - district & statewise
apply(citylist, c(1,2,3), length) # statewise



================================================
FILE: 06-DS/27d-arrays.R
================================================
# Arrays
# 2 states ; Each State has 3 districts : Each District has 4 cities
state = c('state1', 'state2')
district = c('district1','district2', 'district3')
city = c('city1', 'city2', 'city3', 'city4')
pincode = 101:124
citylist = array(pincode, c(4,3,2) , dimnames = list(city, district ,state))
citylist

state1  district1 city1 101
state1  district1 city2 102
state = paste('state',1:4,sep='')
state2= rep(state,6)


colnames(citylist)
rownames(citylist)
dimnames(citylist)
length(citylist)
nrow(citylist)
ncol(citylist)
dim(citylist)
class(citylist)

citylist[3,2,1]
citylist[1,1,1] # city1, district1, state1, 
citylist[1,,] # city1 of all states & district
citylist[,,1] # State1

apply(citylist, c(1), sum)  # citywise
apply(citylist, c(2), sum)  # districtwise
apply(citylist, c(3), sum) # statewise
apply(citylist, c(2,3), sum) # all cities - district & statewise
apply(citylist, c(1,2,3), length) # statewise

?xtabs

## This is already a contingency table in array form.
DF <- as.data.frame(UCBAdmissions)

## Now 'DF' is a data frame with a grid of the factors and the counts
## in variable 'Freq'.
DF
## Nice for taking margins ...
xtabs(Freq ~ Gender + Admit, DF)
xtabs( ~ Gender + Admit )
## And for testing independence ...
summary(xtabs(Freq ~ ., DF))


================================================
FILE: 06-DS/30c-basicdatatypes.R
================================================
#Basic Data Types

#Numeric ----
#Decimal values are called numerics in R. It is the default computational data type. If we assign a decimal value to a variable x as follows, x will be of numeric type.

x = 10.5       # assign a decimal value 
x              # print the value of x [1] 10.5 
class(x)       # print the class name of x [1] "numeric"

#Furthermore, even if we assign an integer to a variable k, it is still being saved as a numeric value.

(k = 1) #[1] 1 
class(k)       # print the class name of k [1] "numeric"
is.integer(k)  # is k an integer? FALSE

#INTEGER----
#invoke as.integer function 

(y = as.integer(3) )  # print the value of y [1] 3 
class(y)       # print the class name of y 
[1] "integer" 
is.integer(y)  # is y an integer? [1] TRUE

#coerce a numeric value into an integer with the same as.integer function.
as.integer(3.14)    # coerce a numeric value [1] 3

#And we can parse a string for decimal values in much the same way.
as.integer("5.27")  # coerce a decimal string [1] 5

as.integer(TRUE)    # the numeric value of TRUE [1] 1 
as.integer(FALSE)   # the numeric value of FALSE [1] 0

#CHARACTER----
#A character object is used to represent string values in R. We convert objects into character values with the as.character() function:
  
(x = as.character(3.14) )   # print the character string [1] "3.14" 
class(x)       # print the class name of x [1] "character"

fname = "Joe"; lname ="Smith" 
paste(fname, lname) #concatenate character values



#LOGICAL----
#A logical value is often created via comparison between variables.
x = 1; y = 2   # sample values 
z = x > y      # is x larger than y? 
z              # print the logical value [1] FALSE 
class(z)       # print the class name of z [1] "logical"

#Standard logical operations are "&" (and), "|" (or), and "!" (negation).
u = TRUE; v = FALSE 
u & v          # u AND v [1] FALSE 
u | v          # u OR v [1] TRUE 
!u             # negation of u [1] FALSE


#COMPLEX
#A complex value in R is defined via the pure imaginary value i.
(z = 1 + 2i)     # create a complex number, print the value of z 1+2i 
class(z)       # print the class name of z [1] "complex"


================================================
FILE: 06-DS/30d-ds1.R
================================================
# data strucutres 

#Vectors----
v1 = c(1,2,3,4,5,4)
class(v1)
v1
v1[v1==4]=7
v1
?class
v2= c('A','B','C','D','e')
v2
class(v2)
v3 =c(TRUE,FALSE,T,F)
class(v3)
v2[3]
v3[4]
v4= c(1,2, 'A')
v4
v5 = c(T,2,F,'A')
v5
class(v4)
v6=c('A', 2,'B')
v6
v1
set.seed(100)
v7 = rnorm(100, mean=60, sd=15)
v7
mean(v7)
sd(v7)
?rnorm
hist(v7)
hist(v7, breaks=15, col=1:15)
hist(v7,freq=F, col='yellow', main='Pune Univ', sub='MBA' , xlab='Marks', ylab='Density of Counts', xlim=c(0, 100), ylim=c(0,0.03))
lines(density(v7), col='red', lty=4, lwd=3)

#title(main=' Pune University', sub='MBA')

#Matrix----
length(100:123)
(m1 =matrix(100:123, ncol=6, byrow = T))
(m2 =matrix(c(100:123), ncol=4, byrow = T))
m2[1 , ]
m2[ , 2]
m2[1:2 , ]
m2
m2[, c(1,4)]
m2[c(2,6), c(1,4)] # 2 & 6th row, 1st & 4th col
m2[2:6, 1:4]
colSums(m2)
colMeans(m2)
rowSums(m2)
rowMeans(m2)
mean(m2[,2])

#x



#Array----
length(100:123)
4*3*2
(a1 = array(100:123, dim=c(4,3,2)))
?array
(loc = paste('loc', 1:4,sep='-'))
(product = paste('p', 1:3,sep='@'))
(coy = paste('coy', 1:2,sep='%'))
dimnames(a1) = list(loc, product, coy)
a1
apply(a1,1, sum)
apply(a1,2, sum)
apply(a1,c(1,2), sum)
apply(a1,c(2,3), sum)
apply(a1,c(1,3), sum)

apply(a1,3, sum)
sum(a1)

#Data Frame----
(rollno = 1:30)
(sname = paste('student',1:30,sep=''))
(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
(marks = floor(rnorm(30, 50,10)))
(marks2 = ceiling(rnorm(30,40,5)))
(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))

df1= data.frame(rollno, sname, gender, marks, marks2, course)
head(df1,n=3)
head(df1,3)
?head
tail(df1)
str(df1)
class(df1)
summary(df1)
df1
df1$gender
df1$marks
df1[ , c(2,4)]
df1[1:10 ,]
df1[ marks > 50 & gender=='F', c('rollno', 'sname')]
df1[ marks > 50 & gender=='F', c(1,2)]
df1[ marks > 50 | gender=='F', ]
names(df1)  # names of columns
dim(df1)  #Dimensions

aggregate(df1$marks, by=list(df1$gender), FUN=max)
df2 = aggregate(cbind(marks,marks2) ~ gender + course, data=df1, FUN=max)
df2
pie(x=df2$marks)
barplot(df2$marks, col=1:2)
?aggregate

#using dplyr package
library(dplyr)


#


#List----

#Factors----

(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))
table(grades)
class(grades)
gradesF = factor(grades)
grades
gradesF
gradesF1 = factor(grades, ordered=T)
gradesF1
gradesF2 = factor(grades, ordered=T, levels=c('D','C','B','A'))
gradesF2
x = 1:4
sort(x)
rev(x)




?list
(v1 = 1:30)
(m1 = matrix(1:24, nrow=6))
(a1 = array(1:24, dim=c(4,3,2)))
(df1 = data.frame(rollno=c(1:5), sname=c(paste('S',1:5, sep='-'))))
class(v1); class(m1); class(a1); class(df1)
L1 = list(v1, m1, a1, df1)  # List
L1
L1[1]; L1[2] ; L1[3]; L1[4]
(v2 = c('a','b'))
L2 = list(v2, L1)
L2



================================================
FILE: 06-DS/30e-datatypes.R
================================================
# R Programming
?mean
x <- c(0:10, 50)
x
x[c(seq(1,12,2))]
?c
seq(2,100,2)

?seq
xm <- mean(x)
xm
mean(x, trim = 0.10)

# Scalar
(x <- 1)
x = 1
# Vector
c(1:5)
x = c(1,2,3,4,10,5,5,3,2,5,7,7,8,9)
mean(x); median(x)
sort(x)

table(x)

library(modeest)
mlv(x, method = "lientz", bw = 0.2)

ratings =c(rep('Good',5), rep('Poor',3))
ratings
table(ratings)
barplot(table(ratings))



rollno = c(10,22,44,23)
sales = runif(12, 50, 100)
sales
plot(sales, type='b')
?plot

?runif


str(x)
y = c('abhinav', 'Rishi', 'Shreya')
str(y)
z = c(TRUE, FALSE, TRUE, FALSE)
str(z)


abinav=c(1,3,6,8,6,3,100,rep(NA,5))
abinav
str(abinav)
is.na(abinav)
anyNA(abinav)
mean(abinav,na.rm=T)

mean(x=abinav)
mean(x=abinav,trim=0.4, na.rm=T)
x = c(TRUE, FALSE, TRUE)
mean(x)
mean(c(1,0,1))

#Categorical Data
product = c('maggi', 'topramen', 'hakka')
product
shirtcolor = c('green', 'red', 'blue', 'black')
shirtcolor
rating = c('Excellent', 'Very Good', 'Good', 'Average', 'Poor' )
rating
str(rating)
performance = c( 1, 2, 3, 4, 5)
str(performance)

decision = c('buy', 'sell')
decisionTF = c(TRUE, FALSE )
str(decision); str(decisionTF)
grades = c('A','C','B','D','E','B')
grades
grades1 = factor(grades, ordered=T, levels=c('E','D','A','B','C'))
grades1
itemssold = c(10, 20, 30, 25)


#Nominal Data
product

#Ordinal Data
performance
#need to create order
performance=c(1,2,5,2,5,2,5,2,5)
length(performance)
performance
str(performance)
performance[9] = 100
performance

performance1 = factor(performance, ordered=TRUE, levels=c(4,2,5,1,3,8,100))
performance1[1] = 100

table(performance1)
performance1



rating
rev(rating)
rating1 = factor(rating, ordered=T, levels=rev(rating))
rating1

grades
grades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))
grades1
summary(grades1)

# -----
#vectors, factors
#list matrix
?matrix
mdat <- matrix(c(1,2,3,11,12,13), nrow = 2, ncol = 3)
mdat[1,1] ='dhiraj'              
mdat               
(x = 1:24)               
(matx = matrix(x, nrow=6, byrow=TRUE))               
?data.frame



(salemon = month.abb[1:12])
salemon = factor(salemon, ordered=T,levels=month.abb[1:12])
salemon
(sales1 = runif(12, min=50, max=100))
salemon; sales1
(adamt = runif(12, min=12, max=25))
(salesmonthwise = data.frame(salemon, sales, adamt))
rownames(salesmonthwise) = month.abb[1:12]
salesmonthwise
colnames(salesmonthwise) = c('col1','col2','col3')

salesmonthwise[1:5,1:3]
?head
head(salesmonthwise,n=3)
head(salesmonthwise[c(1,3)],n=3)
head(salesmonthwise[c('col1','col2')],n=3)
salesmonthwise[c('Jan'),c('col1','col2')]
salesmonthwise[salesmonthwise$col3 < 20,c(1,2,3)]

colSums(salesmonthwise[-1])
rowSums(salesmonthwise[-1])

# Day3

# Data Properties
set.seed(10)
?rnorm
sales1 = rnorm(100, mean=50, sd=10)
sales1
range(sales1)
hist(sales1)
plot(density(sales1))
abline(v=50)

set.seed(1)
sales3 = runif(10,1,5)
sales3
mean(sales3)
set.seed(1)
sales4 = runif(10,1,5)
sales4
mean(sales4)


names = c('A', 'B', 'C')
?sample
v1= sample(x=names, size=4, replace = FALSE, prob = c(.2,.3,.5))
v1
table(v1)


sales2 = rnorm(100, mean=60, sd=12)
sales2
plot(density(sales2))
abline(v=60)
sales3= c(1,5,2,5,2,3,6,7,8,4,5)
sales3
#


shirtcolor = c('green', 'red', 'blue', 'black')
sellitems = sample(shirtcolor, 20, replace=T, prob=c(.4,.3,.2,.1)) 
sellitems
summary(sellitems)
table(sellitems)

grades = c('A','C','B', 'D','E', 'B', 'A', 'B', 'A', 'C','F')
grades1 = factor(grades, ordered=T, levels=c('F','E','D','C','B', 'A'))
grades1


#Location / Central Tendency
mean(sales1)
mean(sales2)
mean(sellitems)
mean(grades)
mean(sales3)

#Middle Values
median(sales1)
median(sales2)
median(sales3)
sort(sales3)
quantile(sales3)

median(sellitems)
median(grades1)


#Mode
mode(sales1)  # error
library(modeest)
mlv(sales1, method='mfv')
mlv(sales2, method='mfv')
mlv(sales3, method='mfv')
mlv(sellitems, method='mfv')
mlv(grades1, method='mfv')
table(sale3)
table(sellitems)
table(grades1)
which.max(table(grades1))



# Graphical Way
mean(sales1)
hist(sales1)
plot(density(sales1))
abline(v=50)

sales2 = rnorm(100, mean=60, sd=12)

sales2
range(sales2)
hist(sales2, prob=T)
?hist
lines(density(sales2), col='red')
abline(v=60, col="green")
abline(h=.02, col="red")

# Combine Plots
plot(density(sales1), col='green', ylim=c(0,0.05))
lines(density(sales2), col="blue", lwd=2)
abline(v=c(50,60), col=c('green', 'blue'), lwd=3)


================================================
FILE: 06-DS/33b-df.R
================================================
# Data Frames

#(rollno= c('MBA-01', 'MBA-02'))
(rollno= paste('MBA',1:60,sep='-'))

#(name = c('Student1', 'Student2'))
(name= paste('Student',1:60,sep='-'))

set.seed(1234)
(gender = sample(c('M','F'),size=60, replace=T, prob=c(.4, .6)))
table(gender)
prop.table(table(gender))

(marks = rnorm(60, mean=60, sd=15))
(grades= sample(c('Excellent','Good','Poor'), size=60, replace = T, prob=c(.2, .3, .5)))
table(grades)
grades = factor(grades, levels=c('Poor','Good','Excellent'), ordered=T)
grades
summary(grades)


df = data.frame(rollno, name, gender,marks, grades, stringsAsFactors = F)
df
head(df, n=3); tail(df)
str(df)
summary(df)
df$gender =factor(df$gender)
str(df)
summary(df)
dim(df)
class(df)

aggregate(df$marks, by=list(df$gender), FUN=sum)
aggregate(df$marks, by=list(df$gender), FUN=mean)
aggregate(marks ~ gender, data=df, FUN=mean)
aggregate(marks ~ gender + grades, data=df, FUN=mean)

df$marks2 = runif(60,50,100)
head(df)

aggregate(cbind(marks, marks2) ~ gender , data=df, FUN=mean)


df
df[ df$gender=='M',]
df[ df$gender=='M', c(1,2,4)]
df[ df$gender=='M', c('rollno', 'gender')]




================================================
FILE: 06-DS/33c-df.R
================================================
# Data Frames

================================================
FILE: 06-DS/35a-lists.R
================================================
# Lists
# 
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages


================================================
FILE: 06-DS/35b-lists.R
================================================
# Lists

g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages



================================================
FILE: 06-DS/35e-lists.R
================================================
# Lists
# 
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages


================================================
FILE: 06-DS/38a-factors.R
================================================
# Factors
# 
#nominal
diabetes = c('Type1','Type2','Type1','Type1')
gender = c( 1,2,1,1,2) # 1-M, 2-F
#ordinal
clsposn = c(1 , 2 , 3 , 1)
status = c('Poor','Improved', 'Excellent', 'Improved')

diabetes ; gender ; clsposn ; status
class(diabetes) ; class(gender) ; class(clsposn) ; class(status)

# order of values don't matter
fdiabetes = factor(diabetes)
class(fdiabetes)
fdiabetes
summary(fdiabetes)
summary(diabetes)

# add another Type; converts into character
fdiabetes = c(fdiabetes, 'Type3')
fdiabetes
class(fdiabetes)

# Add another Level Properly
fdiabetes = factor(diabetes)
levels(fdiabetes)
fdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))
fdiabetes
levels(fdiabetes) = c(levels(fdiabetes),'Type4')
fdiabetes
levels(fdiabetes)[5] = 'Type5'
fdiabetes
summary(fdiabetes)
fdiabetes[4] = c('Type3')
fdiabetes
fdiabetes[5] = c('Type6')  # Invalid as Type6 is not a level
fdiabetes
fdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level
fdiabetes
#NA created
#
sum (is.na (fdiabetes))  
table (fdiabetes, exclude=NULL)
table (fdiabetes, exclude=NA)

is.na(fdiabetes)
# Remove Null Values
fdiabetes = na.omit(fdiabetes)
fdiabetes
fdiabetes[1] = c('Type4')
summary(fdiabetes)

#Drop unused Levels
droplevels.factor(fdiabetes)
fdiabetes

levels(fdiabetes) = factor(fdiabetes)
fdiabetes


table(fdiabetes)
droplevels(table(fdiabetes))
# 1-M, 2-F
gender = c( 1,2,1,1,2)
fgender = factor(gender, levels=c(1,2),
           labels=c('M', 'F') )
gender ; fgender

head(airquality)
aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))
aq
summary(airquality$Month)
summary(aq$Month)
aq <- subset(aq, Month != "Jul")
summary(aq$Month)
table(           aq $Month)
table(droplevels(aq)$Month)
droplevels(aq)$Month


# Nominal - Integer Values
gender = c( 1,2,1,1,2) # 1-M, 2-F
fgender = factor(gender)
fgender
summary(fgender)

fgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))
fgender2
summary(fgender2)



#ordinal
clsposn = c(1 , 2 , 3 , 1)
class(clsposn)
summary(clsposn)
mode(clsposn)
fclsposn = factor(clsposn)
fclsposn

summary(fclsposn)
levels(fclsposn)

fclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, 
        labels=c('First','Second','Third','Fourth', 'Fifth'))
fclsposn2
summary(fclsposn2)
levels(fclsposn2)


fclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])
fclsposn3
print(levels(fclsposn3))

fclsposn2
fclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))
fclsposn4

class(fclsposn4)


status = c('Poor','Improved', 'Excellent', 'Improved')

fstatus = factor(status)
fstatus
# unordered Factors can be releveled - Making Poor first
fstatus2 = relevel(fstatus, 'Poor')
fstatus2

fstatus3 = factor(status, ordered=T, 
        levels=c('Poor', 'Improved', 'Excellent') )
fstatus3
summary(fstatus3)
plot(fstatus3)
plot(status)
plot(table(status))


library(gdata)
f <- factor(c("A", "B", "C", "D"))[1:3]
f
drop.levels(f)

l <- list(f=f, i=1:3, c=c("A", "B", "D"))
l
drop.levels(l)

df <- as.data.frame(l)
df
str(df)
str(drop.levels(df, reorder = F))


================================================
FILE: 06-DS/38b-factors.R
================================================
# Factors
# 
#nominal
diabetes = c('Type1','Type2','Type1','Type1')
gender = c( 1,2,1,1,2) # 1-M, 2-F
#ordinal
clsposn = c(1 , 2 , 3 , 1)
status = c('Poor','Improved', 'Excellent', 'Improved')

diabetes ; gender ; clsposn ; status
class(diabetes) ; class(gender) ; class(clsposn) ; class(status)

# order of values don't matter
fdiabetes = factor(diabetes)
class(fdiabetes)
fdiabetes
summary(fdiabetes)
summary(diabetes)

# add another Type; converts into character
fdiabetes = c(fdiabetes, 'Type3')
fdiabetes
class(fdiabetes)

# Add another Level Properly
fdiabetes = factor(diabetes)
levels(fdiabetes)
fdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))
fdiabetes
levels(fdiabetes) = c(levels(fdiabetes),'Type4')
fdiabetes
levels(fdiabetes)[5] = 'Type5'
fdiabetes
summary(fdiabetes)
fdiabetes[4] = c('Type3')
fdiabetes
fdiabetes[5] = c('Type6')  # Invalid as Type6 is not a level
fdiabetes
fdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level
fdiabetes
#NA created
#
sum (is.na (fdiabetes))  
table (fdiabetes, exclude=NULL)
table (fdiabetes, exclude=NA)

is.na(fdiabetes)
# Remove Null Values
fdiabetes = na.omit(fdiabetes)
fdiabetes
fdiabetes[1] = c('Type4')
summary(fdiabetes)

#Drop unused Levels
droplevels.factor(fdiabetes)
fdiabetes

levels(fdiabetes) = factor(fdiabetes)
fdiabetes


table(fdiabetes)
droplevels(table(fdiabetes))
# 1-M, 2-F
gender = c( 1,2,1,1,2)
fgender = factor(gender, levels=c(1,2),
           labels=c('M', 'F') )
gender ; fgender

head(airquality)
aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))
aq
summary(airquality$Month)
summary(aq$Month)
aq <- subset(aq, Month != "Jul")
summary(aq$Month)
table(           aq $Month)
table(droplevels(aq)$Month)
droplevels(aq)$Month


# Nominal - Integer Values
gender = c( 1,2,1,1,2) # 1-M, 2-F
fgender = factor(gender)
fgender
summary(fgender)

fgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))
fgender2
summary(fgender2)



#ordinal
clsposn = c(1 , 2 , 3 , 1)
class(clsposn)
summary(clsposn)
mode(clsposn)
fclsposn = factor(clsposn)
fclsposn

summary(fclsposn)
levels(fclsposn)

fclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, 
        labels=c('First','Second','Third','Fourth', 'Fifth'))
fclsposn2
summary(fclsposn2)
levels(fclsposn2)


fclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])
fclsposn3
print(levels(fclsposn3))

fclsposn2
fclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))
fclsposn4

class(fclsposn4)


status = c('Poor','Improved', 'Excellent', 'Improved')

fstatus = factor(status)
fstatus
# unordered Factors can be releveled - Making Poor first
fstatus2 = relevel(fstatus, 'Poor')
fstatus2

fstatus3 = factor(status, ordered=T, 
        levels=c('Poor', 'Improved', 'Excellent') )
fstatus3
summary(fstatus3)
plot(fstatus3)
plot(status)
plot(table(status))


library(gdata)
f <- factor(c("A", "B", "C", "D"))[1:3]
f
drop.levels(f)

l <- list(f=f, i=1:3, c=c("A", "B", "D"))
l
drop.levels(l)

df <- as.data.frame(l)
df
str(df)
str(drop.levels(df, reorder = F))


================================================
FILE: 06-DS/38c-factors.R
================================================
#Factor -----

(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))
table(grades)
(gradesFactor = factor(grades))
(gradesFactorOrdered = factor(grades, ordered=T))
(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))
class(grades)
class(gradesFactorOrdered)
class(gradesFactorOrderedLevels)


================================================
FILE: 06-DS/38e-factors.R
================================================
# Factors
# 
#nominal
diabetes = c('Type1','Type2','Type1','Type1')
gender = c( 1,2,1,1,2) # 1-M, 2-F
#ordinal
clsposn = c(1 , 2 , 3 , 1)
status = c('Poor','Improved', 'Excellent', 'Improved')

diabetes ; gender ; clsposn ; status
class(diabetes) ; class(gender) ; class(clsposn) ; class(status)

# order of values don't matter
fdiabetes = factor(diabetes)
class(fdiabetes)
fdiabetes
summary(fdiabetes)
summary(diabetes)

# add another Type; converts into character
fdiabetes = c(fdiabetes, 'Type3')
fdiabetes
class(fdiabetes)

# Add another Level Properly
fdiabetes = factor(diabetes)
levels(fdiabetes)
fdiabetes = factor(fdiabetes, levels = c(levels(fdiabetes), 'Type3'))
fdiabetes
levels(fdiabetes) = c(levels(fdiabetes),'Type4')
fdiabetes
levels(fdiabetes)[5] = 'Type5'
fdiabetes
summary(fdiabetes)
fdiabetes[4] = c('Type3')
fdiabetes
fdiabetes[5] = c('Type6')  # Invalid as Type6 is not a level
fdiabetes
fdiabetes[1] = c('Type6') # Invalid changing an existing value to non existent level
fdiabetes
#NA created
#
sum (is.na (fdiabetes))  
table (fdiabetes, exclude=NULL)
table (fdiabetes, exclude=NA)

is.na(fdiabetes)
# Remove Null Values
fdiabetes = na.omit(fdiabetes)
fdiabetes
fdiabetes[1] = c('Type4')
summary(fdiabetes)

#Drop unused Levels
droplevels.factor(fdiabetes)
fdiabetes

levels(fdiabetes) = factor(fdiabetes)
fdiabetes


table(fdiabetes)
droplevels(table(fdiabetes))
# 1-M, 2-F
gender = c( 1,2,1,1,2)
fgender = factor(gender, levels=c(1,2),
           labels=c('M', 'F') )
gender ; fgender

head(airquality)
aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))
aq
summary(airquality$Month)
summary(aq$Month)
aq <- subset(aq, Month != "Jul")
summary(aq$Month)
table(           aq $Month)
table(droplevels(aq)$Month)
droplevels(aq)$Month


# Nominal - Integer Values
gender = c( 1,2,1,1,2) # 1-M, 2-F
fgender = factor(gender)
fgender
summary(fgender)

fgender2 = factor(gender, levels=c(1,2), labels=c('M','F'))
fgender2
summary(fgender2)



#ordinal
clsposn = c(1 , 2 , 3 , 1)
class(clsposn)
summary(clsposn)
mode(clsposn)
fclsposn = factor(clsposn)
fclsposn

summary(fclsposn)
levels(fclsposn)

fclsposn2 = factor(clsposn, levels=c(1,2,3,4,5), ordered=T, 
        labels=c('First','Second','Third','Fourth', 'Fifth'))
fclsposn2
summary(fclsposn2)
levels(fclsposn2)


fclsposn3 = factor(fclsposn2,levels(fclsposn2)[c(5,3,4,2,1)])
fclsposn3
print(levels(fclsposn3))

fclsposn2
fclsposn4 <- factor(fclsposn2, levels=rev(levels(fclsposn2)))
fclsposn4

class(fclsposn4)


status = c('Poor','Improved', 'Excellent', 'Improved')

fstatus = factor(status)
fstatus
# unordered Factors can be releveled - Making Poor first
fstatus2 = relevel(fstatus, 'Poor')
fstatus2

fstatus3 = factor(status, ordered=T, 
        levels=c('Poor', 'Improved', 'Excellent') )
fstatus3
summary(fstatus3)
plot(fstatus3)
plot(status)
plot(table(status))


library(gdata)
f <- factor(c("A", "B", "C", "D"))[1:3]
f
drop.levels(f)

l <- list(f=f, i=1:3, c=c("A", "B", "D"))
l
drop.levels(l)

df <- as.data.frame(l)
df
str(df)
str(drop.levels(df, reorder = F))


================================================
FILE: 06-DS/factors_iims.R
================================================
#factors

(colorset = c('Red','Green', 'Blue'))
set.seed(1234)
tshirts = sample(colorset, size=50, replace=T, prob=c(.5, .3,.2)) 
table(tshirts)
prop.table(table(tshirts))
class(tshirts)
summary(tshirts)
#convert to Factors
FactorTshirts = factor(tshirts, ordered=F)
FactorTshirts
class(FactorTshirts)
summary(FactorTshirts)

likertScale = c('Excellent','Good','Satisfactory','Poor')
feedback = sample(likertScale, size=50, replace=T, prob=c(.4, .3,.2,.1))
feedback
summary(feedback)
table(feedback)
barplot(table(feedback))
#convert to ordered Factor
FactorFeedback = factor(feedback, ordered=T, levels = c('Excellent', 'Good','Satisfactory','Poor'))
barplot(table(FactorFeedback))
summary(FactorFeedback)


================================================
FILE: 07-excel/impExpExcel.R
================================================
#Import and Export : Excel
#Java should be configured properly


head(mtcars)

library(rJava)
library(xlsx)
#first export - append =F ; make new
write.xlsx2(mtcars, file='07-excel/mtcars.xlsx', sheetName = 'mtcars1', row.names=F, append=F)
df_excel1 = read.xlsx( "07-excel/mtcars.xlsx", 1)
df_excel1

#now make append =T
write.xlsx2(iris, file='07-excel/mtcars.xlsx', sheetName = 'iris1', row.names=F, append=T)
df_excel2 = read.xlsx( "07-excel/mtcars.xlsx", 2)
df_excel2

write.xlsx2(iris, file='07-excel/mtcars.xlsx', sheetName = 'iris2', row.names=F, append=T)
df_excel2b = read.xlsx( "07-excel/mtcars.xlsx", 'iris2')
df_excel2b

#see the excel sheet in data folder: windows explorer

================================================
FILE: 07-excel/importFmExcel.R
================================================
#Import from Excel
#import only
library(readxl)

data1 = read_excel(file.choose(),1)
data1

data2 = read_excel('07-excel/excelData.xlsx',1)
head(data2)

data3 = read_excel('07-excel/excelData.xlsx','denco')
head(data3)

data4 = read_excel('07-excel/excelData.xlsx',2)
head(data4)


================================================
FILE: 07-excel/importFmGS.R
================================================
#Google Sheet to R

#Create a google Sheet
#add or upload data into the sheet
#click on Share - make link sharing on (ie Public sharing)
#copy the url link of the active sheet which you want to import data from
#eg

library(gsheet)

url1 = 'docs.google.com/spreadsheets/d/1I9mJsS5QnXF2TNNntTy-HrcdHmIF9wJ8ONYvEJTXSNo'

library(gsheet)
data1 = gsheet2tbl(url1)

url2 = "https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=216113907"

data2 = gsheet2tbl(url2)
data2


#export to GS not possible unless you set your API credentials


================================================
FILE: 07-excel/student1.R
================================================
# R to Excel 
# Data Analysis in Excel


rollno = paste('S',1:30,sep='-')
name = paste0('Student',1:30, ' - surname')
name
gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3))
course = sample(c('BTech','MTech','Phd'), size=30, prob=c(.5,.3,.2), replace=T)
marks = floor(rnorm(30,mean=50,sd=20))
age = floor(runif(30, 20,40))
married = sample(c(TRUE, FALSE), size=30, replace=T, prob=c(.2, .8))

df = data.frame(rollno, name, gender, age, course, marks, married)
df

#subset the data
sample(nrow(df),3)
df1= df[sample(nrow(df), 3), ]
df1
(df2 = subset(df, age >= 20 & age < 27, select=c(rollno, name)))
(df2b = subset(df, marks >= 55 & marks < 70, select=c(name, marks)))

#dplyr
library(dplyr)
(df3= sample_n(df, 10))  # in numbers
(df4 = sample_frac(df, .5))  #in %

df5= df[ which(gender=='F' | age > 25),]

#Save this data for export
write.csv(df, './data/students3.csv')

library(xlsx)  #rJava also to be installed at this stage

Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre1.8.0_144")
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_181') # for 64-bit


library(rJava)
library(xlsx) #needs rJava

write.xlsx(df, './data/student3a.xlsx', sheetName = "IITG1", row.names = F, append=T)
write.xlsx(df2, './data/student3a.xlsx', sheetName = "IITG2", row.names = F, append=T)


#Open the xls file in Excel



================================================
FILE: 08-MBB/01-MBB.R
================================================
# MBB - R Module
# Analytics
# Analytics Definition and Applications of Analytics
# slide on levels of Analytics

# R Introduction-----
# About R: R Installation
# Why R
#https://www.dataquest.io/blog/learn-r-for-data-science
#https://data-flair.training/blogs/why-learn-r/
#https://www.business-science.io/business/2020/12/17/six-reasons-to-use-R-for-business-2021.html


# R Environment and R Studio ; R Studio Overview/Features-----
#https://www.rstudio.com/about/
#https://www.rstudio.com/resources/cheatsheets/
#https://wp.stolaf.edu/it/r-vs-rstudio/
#https://www.rstudio.org/links/ide_cheat_sheet
#https://github.com/rstudio/cheatsheets/raw/master/rstudio-ide.pdf

# Data Import and Export------
# Importing through CSV; Importing through Excel; Importing through CSV URL;Importing through Text File; Importing through Google Sheets; Data Export

#https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf
#https://www.rstudio.com/wp-content/uploads/2016/10/r-cheat-sheet-3.pdf

# Operators in R------
##(https://www.tutorialspoint.com/r/r_operators.htm)

# Assignment operator; Arithmetic operators; Relational operators; Logical operators

# Data Types and Data Structures------

# Data Types; Vectors; Matrices; Arrays; List; Data Frames


# Handling Data in R


# Handling packages in R


# Package Installation; Package Loading; Unloading Package; Removing Package; Updating Packages


# Subsetting Data in R
# Square Bracket Notation; Subset Function ; Which Function with bracket Notations


# Data Management in R-------
# Renaming Variables; Adding/Removing : Rows/Columns; Reshaping the data; Sorting and Transpose; Merging the data; Splitting the data


# Basic Statistics-----
# Descriptive Statistics
# Understanding Different Statistical Functions in R


# Data Manipulation in R-----
# Apply Family in R
# Using Apply functions to manage data in R
# Aggregate and Table Command
# Aggregate Command ; Table Command
# Data Manipulation in R
# dplyr functions

# Graphical Representation of Data------
# Summarising the data graphically
# Scatter Plot; Box Plot; Histogram; Bar Graph; Pie Chart


# Extra Topics
# Advanced Graphs------
# Data Visualization using GGPlot Library
# Text Mining------
# Text Mining Overview, Applications of TM, Steps for TM in R, creating Word Cloud using diff packages


================================================
FILE: 08-MBB/02-MBB.R
================================================
#01-MBB  : Initial Setup

#git----
#create a git profile to follow, manage your online repositories
#https://github.com/  (signup- preferebly use personal id)
#https://github.com/DUanalytics/rAnalytics  (star this repository)
#https://github.com/dupadhyaya  (you follow others profile)

#download & install----
#https://cran.r-project.org/bin/windows/base/  (R Base) : latest 4.0.5
#choose latest version of your OS
#install this as Administrator in local system/laptop

#https://www.rstudio.com/products/rstudio/download/  (choose free version of your OS)
#https://www.rstudio.com/products/rstudio/#rstudio-desktop
#install this and read how to use

#https://ourcodingclub.github.io/tutorials/intro-to-r/


#Create a Empty Project
#https://swcarpentry.github.io/r-novice-gapminder/02-project-intro/

#Project-----
#create a folder in D or E Drive : analytics (small)
#File -> New Project -> New Directory -> New Project -> Directory Name in a folder of your choice (Preferably use D or E)
#names this folder - mbbAnalytics

#see on right side of R Studio - mbbAnalytics should be visible
#https://www.rstudio.com/products/rstudio/features/  (read these)

#on lower left pane type
#version  (and it will tell which version of R are you working)

#on source windows (top left)
#version   (and press Control + Enter)

#command to execute a line in Source Windows is Control + Enter

#start working on the project


#remember ------
#R programming is not for traditional programming but for statistics and data analysis. 


================================================
FILE: 08-MBB/03-MBB.R
================================================
## Data Import and Export------
#inbuilt Data sets
data() #names of datasets 
mtcars
iris
str(mtcars)  #structure
summary(mtcars)#summary
names(mtcars) #names of variables/ columns
#many other ways - nrow, dim, attributes

#export as csv
write.csv(mtcars, 'mycars.csv', row.names = F)
?write.csv  #help on command
#a file with extension will be created which you can see under files tab (right bottom window of R Studio)
dir()
dir(pattern = '*.csv')

# Importing through CSV-----
newcars <- read.csv(file='mycars.csv')
newcars  #see the data 

#Importing through Excel ------
install.packages('readxl')  #install this package
#copy a simple excel file into the folder of project eg. myexcelcars.xlsx
newexcelcars <- readxl::read_excel(path='myexcelcars.xlsx',sheet=1)
newexcelcars

#Importing through CSV URL ------
url = 'https://raw.githubusercontent.com/DUanalytics/rAnalytics/master/data/denco.csv'
dataURL <- read.csv(url)
dataURL

#Importing through Text File-----
url2 = 'https://raw.githubusercontent.com/DUanalytics/rAnalytics/master/data/mytextcars.txt'
dataText = read.table(file=url2, sep='', header = T)
dataText
head(dataText)

#Importing through Google Sheets----
#docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2051155174

library(googlesheets4)  #install this library if not available
urlGS = 'https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU'
dataGS <- googlesheets4::read_sheet(ss=urlGS, sheet = 'student1')
dataGS

#Data Export------
#data preferably should be exported as csv files
?write.csv

#to excel : read this (needs some java setup)
#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx
#https://stackoverflow.com/questions/19414605/export-data-from-r-to-excel


#read from online sources and stackoverflow
#https://stackoverflow.com/
#http://www.sthda.com/
#https://www.listendata.com/p/r-programming-tutorials.html
#https://www.guru99.com/r-tutorial.html
#https://www.statmethods.net/r-tutorial/index.html
#https://www.manning.com/books/r-in-action
#https://github.com/kabacoff/RiA2
#https://github.com/DUanalytics/eBooks  (my collection)


#

================================================
FILE: 08-MBB/04-MBB.R
================================================
#04-MBB 

#data structures
#vector-----
age = c(34, 25, 56, 25, 70)
age
mean(age)
length(age)
min(age)
max(age)
range(age)
median(age)

(gender = c(rep('F',5), rep('M',10), rep('O',3)))
table(gender)
barplot(table(gender), col=1:3)

#dataframe
#rollno, name, age, gender
(rollno = 1:5)
(name = paste('student',1:5, sep='_'))
(age = round(rnorm(n=5, mean=40, sd=3),0))
(gender = sample(c('M', "F"), size=5, replace=T))
(student = data.frame(rollno, name, age, gender))

str(student)
str(mtcars)

student$name
mean(student$age)
head(student,n=2)
nrow(student)
student

(history = round(rnorm(n=5, mean=50, sd=3),0))
(maths = round(rnorm(n=5, mean=60, sd=2),0))
(science = round(rnorm(n=5, mean=55, sd=5),0))
(marks = data.frame(rollno, history, maths, science))
marks
marks2 <- marks %>% filter(rollno !=5)
marks2
head(student)
head(marks)

(data1 <- merge(student, marks, by.x='rollno', by.y='rollno'))
(data2 <- merge(student, marks2, by.x='rollno', by.y='rollno', all.x=F))
(data2 <- merge(student, marks2, by.x='rollno', by.y='rollno', all.x=T))

#matrix, list, arrays
(matrix1 = matrix(c(1:24), nrow=4, ncol=6))
colSums(matrix1)
rowSums(matrix1)
colMeans(matrix1)
?apply

#dplyr
library(dplyr)

mtcars
?mtcars
head(mtcars)

mtcars %>% group_by(am) %>% summarise(meanMPG = mean(mpg, na.rm=T))
mtcars %>% group_by(am) %>% summarise(meanMPG = mean(mpg, na.rm=T), meanWT = mean(wt, na.rm=T), maxHP = max(hp), minDisp = min(disp), count = n())

mtcars %>% filter(mpg > 26)
mtcars %>% filter(mpg < 26, vs == 0)
mtcars %>% filter(mpg < 26, vs == 0) %>% mutate(newmpg = mpg * 1.1 )

quantile(mtcars$mpg)

boxplot(mtcars$mpg)
mtcars %>% group_by(am,gear, carb) %>% summarise(meanMPG = mean(mpg, na.rm=T), meanWT = mean(wt, na.rm=T), maxHP = max(hp), minDisp = min(disp), count = n())




================================================
FILE: 08-MBB/05-MBB.R
================================================
#MBB - 05

# Subsetting Data in R
# Square Bracket Notation; Subset Function ; Which Function with bracket Notations

mtcars
#filter on rows but conditions works on columns
mtcars[ mtcars$mpg > 25,]
mtcars[ mtcars$mpg > 25 & mtcars$cyl == 4,]
mtcars[ mtcars$mpg > 25 | mtcars$cyl == 4,]
#selected columns
mtcars[, 1:3]
mtcars[, c(1,4,7)]
mtcars[, c('mpg','cyl')]
mtcars[ mtcars$mpg > 23 & mtcars$mpg < 27, 1:3]


# Data Management in R-------
# Renaming Variables; Adding/Removing : Rows/Columns; Reshaping the data; Sorting and Transpose; Merging the data; Splitting the data
names(mtcars)
mycars <- mtcars # copy
mycars
names(mycars)[1]
head(mycars)
names(mycars)[1:2] <- c('mympg','mycyl') 

#reshape ------
rollno = 1:5
(subject1 = round(rnorm(n=5, mean=60, sd=3)))
(subject2 = round(rnorm(n=5, mean=70, sd=3)))
sMarks <- data.frame(rollno, subject1, subject2)
sMarks
#change orientation
sMarksLong = reshape2::melt(data=sMarks, id.vars=c('rollno'))
head(sMarksLong)
sMarksWide = reshape2::dcast(data=sMarksLong, rollno ~ variable, value.var='value') 
head(sMarksWide)

# Basic Statistics-----
mean(sMarks$subject1)
sd(sMarks$subject1)


# Descriptive Statistics
# Understanding Different Statistical Functions in R
library(psych)
psych::describe(sMarks)

# Data Manipulation in R-----
# Apply Family in Rp
# Using Apply functions to manage data in R
apply(mtcars, MARGIN=1, FUN='mean')  #rowwise 1
apply(mtcars, MARGIN=2, FUN='mean')  #columns 2

# Aggregate and Table Command
# Aggregate Command ; Table Command
# Data Manipulation in R
# dplyr functions

# Graphical Representation of Data------
# Summarising the data graphically
# Scatter Plot; Box Plot; Histogram; Bar Graph; Pie Chart

plot(mtcars$wt, mtcars$mpg)
boxplot(mtcars$wt)
barplot(table(mtcars$cyl))
pie(table(mtcars$cyl))

# Extra Topics
# Advanced Graphs------
# Data Visualization using GGPlot Library
library(ggplot2)

head(mtcars)
plot(mtcars$wt, mtcars$mpg)
ggplot(data=mtcars, aes(x=wt, y=mpg)) + geom_point()
ggplot(data=mtcars, aes(x=wt, y=mpg, color=factor(gear))) + geom_point()
ggplot(data=mtcars, aes(x=wt, y=mpg, color=factor(gear), size=hp, shape=factor(am))) + geom_point()
ggplot(data=mtcars, aes(x=factor(gear), y=mpg, fill=factor(gear))) + geom_boxplot()

#http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization
  

# Text Mining------
# Text Mining Overview, Applications of TM, Steps for TM in R, creating Word Cloud using diff packages


================================================
FILE: 08-MBB/06-MBB.R
================================================
#dplyr functions

library(dplyr)

mtcars
names(mtcars)
hist(mtcars$mpg)
mtcars %>% arrange(mpg)
mtcars %>% arrange(desc(mpg))
mtcars %>% arrange(gear, desc(mpg)) %>% select(gear, mpg)
mtcars %>% group_by(gear, cyl)  %>% dplyr::summarise(meanMPG = mean(mpg, na.rm=T))

top_n(x=mtcars, n=1, wt=mpg)
top_n(x=mtcars, n=-1, wt=mpg)
top_n(x=mtcars, n=2, wt=mpg)

table(mtcars$gear)
barplot(table(mtcars$gear))
mtcars %>% group_by(gear) %>% top_n(n=-1, wt=mpg)  %>% select(gear, mpg) %>% arrange(gear)
mtcars %>% group_by(gear,am) %>% top_n(n=-1, wt=mpg)  %>% select(gear, mpg) %>% arrange(gear)



================================================
FILE: 10-packages/analyzer.R
================================================
#analyzer
#https://cran.r-project.org/web/packages/analyzer/analyzer.pdf

library(analyzer)

explainer(mtcars)

anderson.test(mtcars$mpg)

tb <- mtcars
tb$cyl <- as.factor(tb$cyl)
tb$vs <- as.factor(tb$vs)
out <- association(tb, categorical = c("cyl", "vs"))
out
# To use the methodMats parameter, create a matrix like this
methodMats <- out$method_used
methodMats["mpg", "disp"] <- methodMats["disp", "mpg"] <- "spearman"

out <- association(tb, categorical = c("cyl", "vs"), methodMats = methodMats)
out
rm(tb)

CCassociation(tb)
consoleBoxplot(x=mtcars$mpg)


explainer(mtcars)
explainer(mtcars$mpg) #same as explainer.numeric(mtcars$mpg)
# for factor
explainer(as.factor(mtcars$cyl)) #same as explainer.factor(as.factor(mtcars$cyl))


alphabets <- as.factor(sample(LETTERS[1:5], 50, replace = TRUE))
explainer(alphabets)
#rm(alphabets)

GenerateReport(dtpath = mtcars,
               catVars = c("cyl", "vs", "am", "gear"),
               yvar = "vs", model = "binClass",
               output_format = NULL,
               title = "Report",
               output_dir = 'E:/analytics/output/', # pass the output directory
               interactive.plots = FALSE)

kurtosis(mtcars)
linedivider(20)


norm_test_fun(mtcars$mpg)
norm_test_fun(mtcars$mpg, method = "shapiro",
              pval = 0.05, xn = "mpg", bin = TRUE)

# creating the plot
p <- plottr(mtcars)
plot(p$mpg)
plot(p$hp)


p <- plotNA(airquality)
# function to show the 'analyzerPlot' class plot
plot(p)
p1 <- plotNA(airquality, order = FALSE)
plot(p1)


# simple use for one variable
?plottr
p <- plottr(mtcars$mpg)
# To display the plot
plot(p$x)
# With complete dataframe and assuming 'mpg' as a dependent variable
p <- plottr(mtcars, yvar = "mpg", yclass = "numeric")
plot(p$wt)


================================================
FILE: 10-packages/autoreg.R
================================================
# autoreg

library(autoReg)


library(survival)
fit=coxph(Surv(time,status)~rx+logWBC,data=anderson)
adjustedPlot(fit)
adjustedPlot(fit,xnames="rx",se=TRUE,type="plot")
adjustedPlot(fit,xnames="rx",se=TRUE)


================================================
FILE: 10-packages/dttr2.R
================================================
#dttr2
#https://cran.r-project.org/web/packages/dttr2/dttr2.pdf
library(dttr2)

chk::chk_identical(Sys.time(), "UTC")

chk_time(hms::as_hms("10:00:10"))
try(chk_time(1))

dtt_add_units(as.Date("1999-12-31"), units="days",n=2)

dtt_adjust_tz(as.POSIXct("1970-01-01", tz = "Etc/GMT+8"), tz = "UTC")

dtt_aggregate(as.Date(c("1992-01-01", "1991-02-02", "1991-03-03")), "years")

dtt_complete(as.Date(c(x="2002-01-01", from="2001-01-03", to="2001-01-01")))

dtt_date(1L)
dtt_date(-1)
dtt_date("2000-01-01")
as.Date(as.POSIXct("2019-05-01", tz = "Etc/GMT-8"))
dtt_date(as.POSIXct("2019-05-01", tz = "Etc/GMT-8"))
dtt_date(hms::as_hms("23:59:59"))
dtt_date(hms::as_hms("24:00:00"))
dtt_date_from_ints(year = 1972L, month = 1L, day = 1L)

dtt_days_in_month(as.Date(c("2000-02-11", "2001-02-01")))

dtt_excel_to_date(42370)


================================================
FILE: 10-packages/ggpmisc.R
================================================
#ggpmisc

library(ggpmisc)
library(tibble)
ggplot(lynx, as.numeric = FALSE) + geom_line() +  stat_peaks(colour = "red") +   stat_peaks(geom = "text", colour = "red", angle = 66,   hjust = -0.1, x.label.fmt = "%Y") + ylim(NA, 8000)
formula <- y ~ poly(x, 2, raw = TRUE)


ggplot(cars, aes(speed, dist)) + geom_point() +   stat_poly_line(formula = formula) +   stat_poly_eq(use_label(c("eq", "R2", "P")),  formula = formula, parse = TRUE) +  labs(x = expression("Speed, "*x~("mph")), y = expression("Stopping distance, "*y~("ft")))

formula <- y ~ x
ggplot(PlantGrowth, aes(group, weight)) +  stat_summary(fun.data = "mean_se") +  stat_fit_tb(method = "lm", method.args = list(formula = formula),  tb.type = "fit.anova", tb.vars = c(Term = "term", "df", "M.S." = "meansq","italic(F)" = "statistic", "italic(p)" = "p.value"), tb.params = c("Group" = 1, "Error" = 2), table.theme = ttheme_gtbw(parse = TRUE)) +  labs(x = "Group", y = "Dry weight of plants") +   theme_classic()


================================================
FILE: 10-packages/packages.txt
================================================
# packages in R

https://cran.r-project.org/web/packages/available_packages_by_date.html


================================================
FILE: 10-packages/timeplyr.R
================================================
# time dplyr
#https://cran.r-project.org/web/packages/timeplyr/timeplyr.pdf

library(timeplyr)
library(dplyr)
library(nycflights13)
library(lubridate)
flights

flights %>%
  fdistinct(dest) %>%
  farrange(desc(dest))


# Create a calendar for the current year
from <- floor_date(today(), unit = "year")
from
to <- ceiling_date(today(), unit = "year", change_on_boundary = TRUE) - days(1)
to
my_seq <- time_seq(from, to, time_by = "day")
calendar(my_seq)

flights %>%  duplicate_rows()

# Duplicate flights with the same tail number and departure time
flights %>%  duplicate_rows(tailnum, dep_time)


iris %>%  fcount()
iris %>%  fadd_count(name = "count") %>%   fslice_head(n = 10)

# Sorted by group
starwars %>%  fcount(hair_color)


================================================
FILE: 10-packages/tsutils.R
================================================
#tsutils

#https://cran.r-project.org/web/packages/tsutils/tsutils.pdf
#https://kourentzes.com/forecasting/2019/01/14/r-package-tsutils/
#https://kourentzes.com/forecasting/2016/10/15/abc-xyz-analysis-for-forecasting/
library(tsutils)

x <- abs(matrix(cumsum(rnorm(5400,0,1)),36,150))
x
z <- abc(x)
print(z)
plot(z)

abc(x, prc = c(0.2, 0.3, 0.5))

abcxyz(abc(x),xyz(x,type="cv"))

referrals
cmav(referrals,outplot=TRUE)

coxstuart(referrals)

decomp(referrals)

decomp(AirPassengers)

x1 <- rnorm(10)
lagmatrix(x1,c(0,1,-1,2))
lagmatrix(AirPassengers,c(0,1,2))


x2 <- c(rep(0,5),rnorm(100),rep(0,5))
x2
leadtrail(x2, rm=NULL, lead=c(T,T), trail=c(T,T))

?plotSthief(AirPassengers)
plotSthief(AirPassengers, labels=c(T,T,T))          

seasplot(referrals,outplot=1)
Sthief(AirPassengers)
trendtest(referrals,TRUE)

wins(x, p = 0.05)

z2 <- xyz(x,m=12)
print(z2)
plot(z2)

cma(x)

x5 = rnorm(n=50, mean=50, sd=5)
x5
abc(x5)

x6 = c(1:10)
x6
dim(x6)
dim(x6) = c(2,5)
x6
abc(x6)
plot(abc(x6))

cmav(referrals)


================================================
FILE: 11-stats/10-statslinks.R
================================================
#Stats Links
#https://ugoproto.github.io/ugo_r_doc/A_Hands-on_Introduction_to_Statistics_with_R,_Notes/
  

================================================
FILE: 11-stats/10a-distributions.R
================================================
# Distribution
#http://www.stat.umn.edu/geyer/old/5101/rlook.html

?Distributions
# The functions for the density/mass function, cumulative distribution function, quantile function and random variate generation are named in the form dxxx, pxxx, qxxx and rxxx respectively.
#
#Every distribution that R handles has four functions. There is a root name, for example, the root name for the normal distribution is norm. This root is prefixed by one of the letters

# p for "probability", the cumulative distribution function (c. d. f.)
# q for "quantile", the inverse c. d. f.
# d for "density", the density function (p. f. or p. d. f.)
# r for "random", a random variable having the specified distribution

# For the beta distribution see dbeta.
# 
# For the binomial (including Bernoulli) distribution see dbinom.
# 
# For the Cauchy distribution see dcauchy.
# 
# For the chi-squared distribution see dchisq.
# 
# For the exponential distribution see dexp.
# 
# For the F distribution see df.
# 
# For the gamma distribution see dgamma.
# 
# For the geometric distribution see dgeom. (This is also a special case of the negative binomial.)
# 
# For the hypergeometric distribution see dhyper.
# 
# For the log-normal distribution see dlnorm.
# 
# For the multinomial distribution see dmultinom.
# 
# For the negative binomial distribution see dnbinom.
# 
# For the normal distribution see dnorm.
# 
# For the Poisson distribution see dpois.
# 
# For the Student's t distribution see dt.
# 
# For the uniform distribution see dunif.
# 
# For the Weibull distribution see dweibull.
#
# For less common distributions of test statistics see pbirthday, dsignrank, ptukey and dwilcox (and see the ‘See Also’ section of cor.test).

================================================
FILE: 11-stats/10c-allstats.R
================================================
#Basic Stats Tests


#binomial distribution------
#MCQ - 1 out 5 correct; Prob of having <= 4 correct ans : 12 Questions

#exactly 4 answers
dbinom(x=4, size=12, prob=.2)

#which is sum of 0,1,2,3,4 right ans
(pl4 <- dbinom(x=0, size=12, prob=.2)+ dbinom(x=1, size=12, prob=.2) + dbinom(x=2, size=12, prob=.2) + dbinom(x=3, size=12, prob=.2) + dbinom(x=4, size=12, prob=.2))

#another method
pbinom(q=4, size=12, prob=.2)
#prob is .92 for ans 4 or less right answers
#------------------------------------------
#Poission Distribution-----
#12 cars cross bridge / min on an avg; Find prob of having >= 17 cars/ min

#prob of <=16 cars
p16Lcars <- ppois(q=16, lambda=12, lower.tail = T)
1- p16Lcars

#another method
(p17Mcars <- ppois(q=16, lambda=12, lower.tail = F))
#approx p=.10 that 17 or more cars cross/ min

#------------------------------
#uniform Distribution-------
#10 random nos from uniform distribtion
runif(n=10, min=50, max=100)




================================================
FILE: 11-stats/11a-normal.R
================================================
#Normal Distribution

#pnorm is the R function that calculates the c. d. f.

#F(x) = P(X <= x)
#where X is normal. Optional arguments described on the on-line documentation specify the parameters of the particular normal distribution.
#Both of the R commands in the box below do exactly the same thing.


pnorm(27.4, mean=50, sd=20)
pnorm(27.4, 50, 20)

#what is the probability of x being < 27.4 from distribution having mean 50 and sd 20
#They look up P(X < 27.4) when X is normal with mean 50 and standard deviation 20.


#Question: Suppose widgit weights produced at Acme Widgit Works have weights that are normally distributed with mean 17.46 grams and variance 375.67 grams. What is the probability that a randomly chosen widgit weighs more then 19 grams?
  
#Question Rephrased: What is P(X > 19) when X has the N(17.46, 375.67) distribution?
  
# Caution: R wants the s. d. as the parameter, not the variance. We'll need to take a square root!

#Answer:
1 - pnorm(19, mean=17.46, sd=sqrt(375.67))



================================================
FILE: 11-stats/11b-normalq.R
================================================
#Normal - inverse - qnorm

#qnorm is the R function that calculates the inverse c. d. f. F-1 of the normal distribution The c. d. f. and the inverse c. d. f. are related by

#p = F(x)  :x = F-1(p)
#So given a number p between zero and one, qnorm looks up the p-th quantile of the normal distribution. As with pnorm, optional arguments specify the mean and standard deviation of the distribution
#Example :Question: Suppose IQ scores are normally distributed with mean 100 and standard deviation 15. What is the 95th percentile of the distribution of IQ scores?
#Question Rephrased: What is F-1(0.95) when X has the N(100, 152) distribution?
qnorm(0.95, mean=100, sd=15)


================================================
FILE: 11-stats/11c-normald.R
================================================
#Normal Distribution - Density

#Density
#dnorm is the R function that calculates the p. d. f. f of the normal distribution. As with pnorm and qnorm, optional arguments specify the mean and standard deviation of the distribution.

#There's not much need for this function in doing calculations, because you need to do integrals to use any p. d. f., and R doesn't do integrals. In fact, there's not much use for the "d" function for any continuous distribution (discrete distributions are entirely another matter, for them the "d" functions are very useful, see the section about dbinom).

#For an example of the use of pnorm, see the following section.

#Random Variates
#rnorm is the R function that simulates random variates having a specified normal distribution. As with pnorm, qnorm, and dnorm, optional arguments specify the mean and standard deviation of the distribution.

x <- rnorm(1000, mean=100, sd=15)
hist(x, probability=TRUE)
xx <- seq(min(x), max(x), length=100)
lines(xx, dnorm(xx, mean=100, sd=15))

#This generates 1000 i. i. d. normal random numbers (first line), plots their histogram (second line), and graphs the p. d. f. of the same normal distribution (third and forth lines).

================================================
FILE: 11-stats/12a-binomial.R
================================================
# The Binomial Distribtion
#http://www.stat.umn.edu/geyer/old/5101/rlook.html

#dbinom is the R function that calculates the p. f. of the binomial distribution. Optional arguments described on the on-line documentation specify the parameters of the particular binomial distribution.

#Both of the R commands in the box below do exactly the same thing.

dbinom(27, size=100, prob=0.25)
dbinom(27, 100, 0.25)


#They look up P(X = 27) when X is has the Bin(100, 0.25) distribution
#Example Question: Suppose widgits produced at Acme Widgit Works have probability 0.005 of being defective. Suppose widgits are shipped in cartons containing 25 widgits. What is the probability that a randomly chosen carton contains exactly one defective widgit?
  
#Question Rephrased: What is P(X = 1) when X has the Bin(25, 0.005) distribution?
dbinom(1, 25, 0.005)

  
  Answer:
  
  

================================================
FILE: 11-stats/13a-mean.R
================================================
#mean

mtcars
head(mtcars)
mean(mtcars$mpg)

x=c(1:10,NA,100)
x
mean(x)
mean(x, na.rm=T)
mean(x, na.rm=T, trim=.1)
mean(2:10)


================================================
FILE: 11-stats/14a-median.R
================================================
#Median

head(mtcars)
median(mtcars$mpg)

x=c(1:10,NA,100)
x
median(x)
median(x, na.rm=T)
median(x, na.rm=T, trim=.1)
median(2:10)
2:10


================================================
FILE: 11-stats/15a-mode.R
================================================
#mode

mtcars
head(mtcars)
(t1= table(mtcars$cyl))

max(table(mtcars$cyl))

which.max(table(mtcars$cyl))

names(t1)[which(t1==max(t1))]

subset(mtcars, cyl==8)
subset(mtcars, cyl==names(t1)[which(t1==max(t1))])


#two modes
#Testing if there are two or more numbers with same frequncy
x <- c(8,2,7,1,2,9,8,2,10,9,8)
sort(x)
##  [1]  1  2  2  2  7  8  8  8  9  9 10
#Mode
table(x)
names(table(x))[table(x)==max(table(x))]
## [1] "2" "8"


================================================
FILE: 11-stats/15b-mode.R
================================================
# Mode with Package
#https://cran.r-project.org/web/packages/modeest/modeest.pdf

library(modeest)
#
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("genefilter")

mlv(mtcars$cyl,method='shorth')
mlv(mtcars$cyl, method = "discrete")

#M <- mlv(x, method = "discrete")
#plot(M)
#table(mtcars$cyl)


================================================
FILE: 11-stats/16a-range.R
================================================
#Range

range(mtcars$mpg)

min(mtcars$mpg)
max(mtcars$mpg)


================================================
FILE: 11-stats/17a-sd.R
================================================
#Standard Deviation

sd(mtcars$mpg)

var(mtcars$mpg)
sqrt(var(mtcars$mpg))



================================================
FILE: 11-stats/18a-covariance.R
================================================
#Covariance

cov(mtcars$mpg, mtcars$wt)

cov(mtcars$wt, mtcars$mpg)

#direction of relationship. magnitude has no value


================================================
FILE: 11-stats/19a-correlation.R
================================================
#Correlation

cor(mtcars$mpg, mtcars$wt)

cor(mtcars$wt, mtcars$mpg)

#strength and direction of relationship


================================================
FILE: 11-stats/20a-coev.R
================================================
#Coefficient of Variance
#Measures of dispersion—such as range, variance, standard deviation, and coefficient of variation

sd(mtcars$mpg) / mean(mtcars$mpg)

library(sjstats)
cv(mtcars$mpg)
?cv


================================================
FILE: 11-stats/37a-sample1.R
================================================
# Sample Methods

#Base Package
letters[1:5]
(L1 = letters[1:26])
(L2 = LETTERS[1:26])
(M1 = month.abb[1:12])
(M2 = month.name[1:12])

sample(L1, 2)
sample(L1, 30)  #error
sample(L1, 30, replace=T)  
sample(M1)
sample(M1, 5)

set.seed(10)
sample(M1,5)
set.seed(10)
sample(M1,5)
sample(M1,5)  # diff output w/o same seeding

(grades= LETTERS[1:5])
(sgrades = sample(grades, 20, replace=T, prob=c(0.4,0.2,0.2,0.1,0.1)))
table(sgrades)



prop.table(table(sgrades))
(sgrades = sample(grades, 20, replace=T, prob=c(0.4,0.2,0.2,0.1,0.1)))
table(sgrades)
prop.table(table(sgrades))


#Different Concept
sample(2, nrow(mtcars), replace=T, prob=c(0.8,0.2))

#Dplyr
library(dplyr)
dim(mtcars)
sample_frac(mtcars, 0.2)
sample_n(mtcars,5)




library(caTools)
set.seed(1000)
split = sample.split(mtcars$mpg, SplitRatio = 0.80)
train = subset(mtcars, split==TRUE)
test = subset(mtcars, split==FALSE)
head(train)
head(test)


================================================
FILE: 11-stats/40a-missing1.R
================================================
# Missing Values
# Create, Detect, Replace, Remove, Impute, Visualise
v1 = 1:5
v1
(v1 = c(1,2,NA,NA,5))
is.na(v1)
v1=NULL
v1  # remove all values

(v1 = c(1,2,NA,NA,5))
sum(c(TRUE, FALSE,TRUE))
sum(is.na(v1))  # sum TRUE values ie missing values

mean(v1)  # cannot calculate if NA values in vector
mean(v1, na.rm=T)  # remove and calc
(1+2+5)/3

(v1a = na.omit(v1))  # omit missing values and put in new vector
sum(v1a) # now you can sum on non NA values
?na.omit

anyNA(v1) # is there any NA value
?anyNA
#impute missing values of vector with mean value of non NA values
v1
mean(v1, na.rm=T)  # this value to be used
v1[is.na(v1)]
v1[c(FALSE,F,T,T,F)]
v1
is.na(v1)
v1[is.na(v1)] = mean(v1, na.rm=T)
v1

#Use VIM package to load sleep dataset with NA values in rows & col
library(VIM)
data(sleep, package='VIM')
summary(sleep)# see NA values in cols
head(sleep)
dim(sleep)
complete.cases(sleep) #all complete rows without any missing values
sum(complete.cases(sleep)) # count non NA rows/ complete 42
sum(!complete.cases(sleep))  #count NA rows 20
mean(complete.cases(sleep))  #42/62

# total cases = 62, complete=42  
sleep[complete.cases(sleep),] # list rows which are complete
sleep[!complete.cases(sleep),] # list rows which have NA values

# Particular Column
is.na(sleep$Dream)
head(sleep)
sum(is.na(sleep$Dream))
mean(is.na(sleep$Dream))  # 12/62

# How many missing values
sum(is.na(sleep))  #total in data frame

summary(sleep)

names(sleep)# check columname
colSums(is.na(sleep))  #NA values in each column
rowSums(is.na(sleep))  #NA values in each row
head(sleep)
as.data.table(sleep)  #data.table format
is.na(sleep[1:3,]) # first 3 rows
head(sleep,n=3)

#Tabulate
library(mice)
mice::md.pattern(sleep)
#42 rows without any missing values # 2 rows with 1 NA NonD

#Visualisation
VIM::aggr(sleep, prop=F, numbers=T)
#NonD max NA values 
VIM::aggr(sleep, prop=T)  # Proportion
VIM::matrixplot(sleep) # rowno and missing values in red
VIM::marginmatrix(sleep)
VIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c("darkgray", "red", "blue"))  # finding relationship between NA value


#Exploration
x = as.data.frame(abs(is.na(sleep)))
head(sleep, na=5)
head(x, n=5)
y = x[which(apply(x, 2, sum) > 0)]
cor(y)  #Dream - NonD 0.9
cor(sleep, y, use="pairwise.complete.obs")
#ignore NA, 
#

#Listwise Deletion
options(digits=3)
na.omit(sleep)
cor(na.omit(sleep))
cor(sleep, use="complete.obs")

fit = lm(Dream ~ Span + Gest, data= na.omit(sleep))
summary(fit)


#imputations Packages - mice, Amelia, mi : mice, with, pool

imp = mice::mice(sleep, seed=1234)
?mice
 
# explore more


================================================
FILE: 11-stats/40b-missing1.R
================================================
#misssing data

#NA
z = c(1, 2, NA, 8 , 3, NA, 3)
z
is.na(z)

x = c('A',NA,'B')
is.na(x)

# NULL
x1 = c(1, NULL, 3)
x1
is.null(x1)

#NULL cannot be part of vector
x= 1/0
y = x + 2
is.nan(x)
is.nan(y)
 
  

================================================
FILE: 11-stats/40c-missing2.R
================================================
# Missing Values

df = read.csv(file='./data/dsstudents2.csv')
df
str(df)
summary(df)
is.na(df)
anyNA(df)
table(is.na(df))
all(is.na(df))
all(is.na(df)==F)
all(is.na(df)==T)
all(is.na(df)==F & is.na(df)==T )
df
complete.cases(df)
!complete.cases(df)
sum(complete.cases(df))
sum(!complete.cases(df))

df[complete.cases(df),][1:2]
mean(df$excel)
mean(df$excel, na.rm=T)
mean(df$excel, na.rm=T, trim=.2)
mean(df$excel, na.rm=T, trim=.4)
cat(df$excel, trim=.2)

df$excel
na.omit(df$excel)
na.omit(df)[1:2]

is.na(df)
colSums(is.na(df))  # apply to numeric M, DF
#there is no colsums  (small S)
apply(df,2, function(x) sum(is.na(x)))
apply(is.na(df), 2, sum)

funModeling::df_status(df,2)
VIM::aggr(df)
Amelia::missmap(df)
length(which(is.na(df['excel']==T)))  # particular col
sum(is.na(df$excel))
mean(df$excel,na.rm=T)
df$excel
df$excel[is.na(df$excel)] = mean(df$excel,na.rm=T)
df$excel
ma


library(mice)
library(purrr)
library(dplyr)
# map_df with purrr
x=purrr::map_df(df, function(x) sum(is.na(x)))
x
x[5:14]

#dplyr::describe(df)
na_count <-sapply(df, 
        function(y) sum(length(which(is.na(y)))))
#should give you a list with the counts for each column.

(na_count <- data.frame(na_count))


================================================
FILE: 11-stats/40d-missing3.R
================================================
# NA Values

is.finite(10^(305:310))
#[1] TRUE TRUE TRUE TRUE FALSE FALSE

2 / 0
#[1] Inf

4 - Inf
#[1] -Inf

#divide any real number by infinity, you get zero.
#But what if you divide infinity by infinity?
Inf / Inf
#[1] NaN

# Removing bad values - NA, NaN infiite
#NULL, NA, or NaN values.

apply(df, 2, function(x) any(is.na(x)))

#Will test the condition by column.

#In the case that you would like to test for both conditions,
#you can add the pipe operator, |.

any(is.na(x) | is.infinite(x))

#It reads, "Does any member of the variable x have 
#the value NA or -Inf or Inf?"

#if you want to learn the names of the column with these values,
#assign the output of our apply function above to a variable, 
#and subset the column names by that new variable:
  
indx <- apply(df, 2, function(x) any(is.na(x) | is.infinite(x)))
colnames[indx]



# Missing Values Plot
v1 = ceiling(rnorm(100,60,20))
v1[sample(1:40, 10, replace=F)] = NA
v1
x=v1
library(reshape2)
library(ggplot2)
library(dplyr)

ggplot_missing <- function(x){
  x %>% is.na %>% melt %>%  ggplot(data = ., aes(x = X2, y = X1)) +
    geom_raster(aes(fill = value)) + scale_fill_grey(name = "",
    labels = c("Present","Missing")) + theme_minimal() + 
    theme(axis.text.x = element_text(angle=45, vjust=0.5)) + 
    labs(x = "Variables in Dataset",  y = "Rows / observations")
}
#Let’s test it out
ggplot_missing(x)


#Function	    Inf	  –Inf	NaN	  NA
#is.finite()	FALSE	FALSE	FALSE	FALSE
#is.infinite()TRUE	TRUE	FALSE	FALSE
#is.nan()	    FALSE	FALSE	TRUE	FALSE
#is.na()	    FALSE	FALSE	TRUE	TRUE
#R actually considers NaN to be numeric, 
#so you can use NaN in calculations. The outcome of those calculations is always NaN,

NaN + 4
#[1] NaN

================================================
FILE: 11-stats/42a-outlier1.R
================================================
# Outlier Analysis
#outlier not predicted well in by fitted regression model

marks1 = c(1:80, 100)
marks2 = c(1:80,100, 1000)
summary(marks1)
summary(marks2)

par(mfrow=c(1,2))  # multiple frames per row
boxplot(marks1)
(q1= quantile(marks1, c(.0,.25,.50,.75,1)))
abline(h=q1, col=1:6)
text(1, q1+2, labels=c('min','1Q','median','3Q','max'), col=1:5)
title(main='Marks1')

boxplot(marks2)
(q2= quantile(marks2, c(.0,.25,.50,.75,1)))
abline(h=q2, col=1:6)
text(1, q2+2, labels=c('min','1Q','median','3Q','max'), col=1:5)
title(main='Marks2')

mean(marks1); mean(marks2)  # diff in means due to outlier
; mean(marks2, trim=.1)# trim extreme values
median(marks1); median(marks2)  # not much diff

# when you have large values how to identify outlier rows
#Identify---
set.seed(482)
(y = c(rnorm(100,50,10), 150, 170,200))
boxplot(y)
identify(rep(1, length(y)), y, labels = seq_along(y))
y[c(101,102,103)]

# How do outlier effect Regressions : Identify them

names(mtcars)
mtcarslm = lm(mpg ~ wt + cyl + hp + am + gear, data=mtcars)
boxplot(mtcars)
mtv1 = c('hp', 'disp')  # few variables only
boxplot(mtcars[mtv1])
boxplot(mtcars[c(-mtv1)])
ix= match(mtv1,names(mtcars))
boxplot(mtcars[,-ix])
?match
library(car)
car::outlierTest(mtcarslm)

car::influencePlot(mtcarslm)



#Extras -----
library(outliers)
set.seed(1234)
x = rnorm(10)
chisq.out.test(x)
chisq.out.test(x,opposite=TRUE)

car::outlierTest(fit, labels=names(rstud))
car::outlierTest(lm(prestige~income+education, data=Duncan))

#car::avPlots(mtcarslm, ask=F, id.method='identify')

library(outliers)
#https://cran.r-project.org/web/packages/outliers/outliers.pdf
set.seed(1234)
y=rnorm(100)
outlier(y)
outlier(y,opposite=TRUE)
y=rm.outlier(y, fill = FALSE, median = FALSE, opposite = FALSE)
rm.outlier(y, fill = FALSE, median = FALSE, opposite = FALSE)
#scores(y, type = c("z"), prob = NA, lim = NA)

set.seed(1234)
y=rnorm(100)
dim(y) <- c(20,5) # convert to matrix
y
outlier(y) #in each column
outlier(y,opposite=TRUE)
apply(y, c(2), range)
boxplot(y)
scores(y, type = c("z", "t", "chisq", "iqr", "mad"), prob = NA, lim = NA)



#Univariate
url <- "http://rstatistics.net/wp-content/uploads/2015/09/ozone.csv"  
# alternate source:  https://raw.githubusercontent.com/selva86/datasets/master/ozone.csv
inputData <- read.csv(url)  # import data
outlier_values <- boxplot.stats(inputData$pressure_height)$out  
outlier_values
boxplot(inputData$pressure_height, main="Pressure Height", boxwex=0.1)
mtext(paste("Outliers: ", paste(outlier_values, collapse=", ")), cex=0.6)





fit2 = lm(weight ~ height + I(height^2), data=women)
par(mfrow=c(2,2)) ;plot(fit2)
par(mfrow=c(1,1)) ; plot(fit2, which=4)
?plot
fit3 = lm(weight ~ height + I(height^2), data=women[-c(13,15),])
fit3
par(mfrow=c(1,1)) ; plot(fit3, which=4)



#Bivariate
url <- "http://rstatistics.net/wp-content/uploads/2015/09/ozone.csv"
ozone <- read.csv(url)
# For categorical variable
boxplot(ozone_reading ~ Month, data=ozone, main="Ozone reading across months")  # clear pattern is noticeable.
boxplot(ozone_reading ~ Day_of_week, data=ozone, main="Ozone reading for days of week")  # this may not be significant, as day of week variable is a subset of the month var.





x <- ozone$pressure_height
qnt <- quantile(x, probs=c(.25, .75), na.rm = T)
caps <- quantile(x, probs=c(.05, .95), na.rm = T)
H <- 1.5 * IQR(x, na.rm = T)
x[x < (qnt[1] - H)] <- caps[1]
x[x > (qnt[2] + H)] <- caps[2]



#----

library(DMwR)
sampledata = mtcars[1:3]
outlier.scores = lofactor(sampledata, k=2)
outlier.scores
plot(density(outlier.scores))
order(outlier.scores, decreasing=T)[1:5]  # rownows
sampledata[c(6,4,25,3,28),]

marks = c(1:80,100, 1000)
marks2 = c(1:80, 100)
marks
boxplot(marks2)
summary(marks)
boxplot(marks)
outlier.scores2 = lofactor(marks,1)
outlier.scores2
plot(density(outlier.scores2))
order(outlier.scores2, decreasing=T)[1:2]  # rownows
marks[c(82,81)]
marks


================================================
FILE: 11-stats/43a-outliers1.R
================================================
#Outliers
#http://rstudio-pubs-static.s3.amazonaws.com/7901_72d4736f412e4414b1aeffbd1d5c69e9.html

(data= df[11:14])  # numeric values
cat(names(data))

(sds <- apply(data, 2, sd, na.rm = TRUE))


findOutlier <- function(data, cutoff = 2) {
  ## Calculate the sd
  sds <- apply(data, 2, sd, na.rm = TRUE)
  ## Identify the cells with value greater than cutoff * sd (column wise)
  result <- mapply(function(d, s) {
    which(d > cutoff * s)
  }, data, sds)
  result
}

outliers <- findOutlier(data)
outliers


================================================
FILE: 11-stats/45a-sampling.R
================================================
#sampling
n=10
set.seed(1)

a=rep(0:1,5)
b=rep(0,10)
c=rep(1,10)
dataset=matrix(cbind(a,b,c),nrow=10,ncol=3)
dataset
sample(dataset, n, replace=T)
mysample <- sample(length(dataset), n, replace=T)
mysample


================================================
FILE: 11-stats/55a-traintest1.R
================================================
# data into train and test set

#Case-1---
df = mtcars
dim(df)  # 32 11

#Sample Indexes
set.seed(1234)
indexes = sample(1:nrow(df), size=0.2*nrow(df))
# Split data
test = df[indexes,]
dim(test)  # 6 11
train = df[-indexes,]
dim(train) # 26 11

#Case-2----
## 75% of the sample size
(smp_size <- floor(0.75 * nrow(df)))

## set the seed to make your partition reproducible
set.seed(1234)
train_ind <- sample(seq_len(nrow(df)), size = smp_size)

train <- df[train_ind, ]
test <- df[-train_ind, ]
train
test

#Case-3---
require(caTools)
set.seed(1234) 
sample = sample.split(df$mpg, SplitRatio = .75)
train3 = subset(df, sample == TRUE)
test3  = subset(df, sample == FALSE)
dim(train3)
dim(test3)

#Case-4----
bound <- floor((nrow(df)/4)*3)         #define % of training and test set

df <- df[sample(nrow(df)), ]           #sample rows 
df.train4 <- df[1:bound, ]              #get training set
df.test4 <- df[(bound+1):nrow(df), ]    #get test set
dim(df.train4)
dim(df.test4)

#Case-5----
library(dplyr)
df5 = df
rownames(df5) = NULL  #as they were numeric
head(df5)
train5 <-sample_frac(df5, 0.7)
sid <- as.numeric(rownames(train5)) 
sid
test5 <- df5[-sid,]
dim(train5); dim(test5)

#Case-6----
df6 = df
df6$id <- 1:nrow(df6)  # create id var
train6 <- df6 %>% dplyr::sample_frac(.75)
test6  <- dplyr::anti_join(df6, train, by = 'id')
dim(train6); dim(test6)

#Case-7 ----
set.seed(1234)
split7=sample.split(df$mpg,SplitRatio=2/3)
set.seed(1234)
split7a=sample.split(df,SplitRatio=2/3)
split7a
train7=subset(df,split7==TRUE)
test7=subset(df,split7==FALSE)
dim(train7); dim(test7)
train7a=subset(df,split7a==TRUE)
test7a=subset(df,split7a==FALSE)
dim(train7a); dim(test7a)




library(ISLR)
df = Default
names(df)

library(dplyr)
df <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df %>% separate(x, c("A", "B"))

================================================
FILE: 11-stats/60a-kurtosis.R
================================================
# kurtosis

# kurtosis describes the tail shape of the data distribution.
#Normal distribution - 0 kurtosis and thus the standard tail shape. It is said to be mesokurtic. 
#Negative kurtosis would indicate a thin-tailed data distribution, and is said to be platykurtic. 
#Positive kurtosis would indicate a fat-tailed distribution, and is said to be leptokurtic
library(e1071)                    # load e1071 
duration = faithful$eruptions     # eruption durations 
hist(duration)
plot(density(duration))
kurtosis(duration)                # apply the kurtosis function 
#[1] -1.5116

#Answer :The kurtosis of eruption duration is -1.5116, which indicates that eruption duration distribution is platykurtic. This is consistent with the fact that its histogram is not bell-shaped.



================================================
FILE: 11-stats/60b-kurtosis.R
================================================
#Kurtosis using fBasics

library(fBasics)

#rowKurtosis	calculates row kurtosis,
rowKurtosis(mtcars)

#colKurtosis	calculates column kurtosis,
colKurtosis(mtcars)


================================================
FILE: 11-stats/64a-skewness.R
================================================
#skewness
#the skewness is a measure of symmetry. As a rule, negative skewness indicates that the mean of the data values is less than the median, and the data distribution is left-skewed. Positive skewness would indicate that the mean of the data values is larger than the median, and the data distribution is right-skewed.

library(e1071)                    # load e1071 
duration = faithful$eruptions     # eruption durations 
plot(density(duration))
e1071::skewness(duration)                # apply the skewness 
function #[1] -0.41355

#Answer :The skewness of eruption duration is -0.41355. It indicates that the eruption duration distribution is skewed towards the left.


================================================
FILE: 11-stats/64b-skewness.R
================================================
#Skewness

library(PerformanceAnalytics)
skew <-  PerformanceAnalytics::skewness(mtcars$mpg)
skew  # Right Skewed
hist(mtcars$mpg)
plot(density(mtcars$mpg))
abline(v=c(mean(mtcars$mpg), median(mtcars$mpg)), col=2:3, lwd=2:3)

       

================================================
FILE: 11-stats/71a-xtabs.R
================================================
#Frequency

df = mtcars
names(df)
catcols = c('cyl','vs','am','gear','carb')

df[,catcols] = lapply(df[,catcols], factor)
str(df)

#count gears
table(df$gear)
#count gears and cyl
table(df$gear, df$cyl)
(t1 <- table(df$gear, df$cyl, dnn=c('Gear','Cylinder')))
t1

#margin wise summary from table
margin.table(t1)
margin.table(t1,1)
margin.table(t1,2)
margin.table(t1,c(1,2))
?margin.table

#addmargins
addmargins(t1)
addmargins(t1,margin=1, FUN=mean)
addmargins(t1,margin=2, FUN=sum)

#prop table
prop.table(t1)
prop.table(t1) * 100           
(pt1 <- round(prop.table(t1)* 100))

addmargins(pt1)


#xtabs
xtabs( ~ cyl + gear, data=df)

#Crosstable(gmodules)
gmodels::CrossTable(df$cyl, df$gear)

#ftable
ftable(df$cyl, df$gear)

library(survey)

#gmodels::Crosstab( t1, row.vars='Cylinder', col.vars='Gear')

head(titanic)
library(catspec)
catspec::ctab(Titanic)
catspec::ctab(t1)
catspec::ctab(occupationalStatus)

library(survival)
data("logan")
str(logan)
catspec::ctab(table(logan$education, logan$race))


================================================
FILE: 11-stats/ave1.R
================================================
#AVE

# ave is tapply's friendly cousin. It returns results
# in a form that you can plug straight back into your data frame

(dfr <- data.frame(a=1:20, f=rep(LETTERS[1:5], each=4)))
(means <- tapply(dfr$a, dfr$f, mean))
##  A    B    C    D    E 
## 2.5  6.5 10.5 14.5 18.5 

## great, but putting it back in the data frame is another line:

(dfr$m <- means[dfr$f])
dfr

#-----------
(dfr$m2 <- ave(dfr$a, dfr$f, FUN=mean)) # NB argument name FUN is needed!
dfr

#---------
#There is nothing in the base package that works like ave
#for whole data frames (as by is like tapply for data frames). 
#But you can fudge it:
  
dfr$foo <- ave(1:nrow(dfr), dfr$f, FUN=function(x) {
    x <- dfr[x,]
    sum(x$m*x$m2)
  })
dfr


================================================
FILE: 11-stats/interactions.R
================================================
# Margin Tables 

# Election Results
parties1 = c('bjp', 'congress', 'sp', 'aap')
states1 = c('up', 'delhi','haryana', 'punjab','uk')

# Factor interactions
(comb = interaction(parties, states))
comb
mps = ceiling(runif(16,30,50))
cbind(comb, mps)

?gl
parties2= gl(n=4, k=1, length=4, labels=parties1 )
states2= gl(n=5, k=1, length=4, labels=states1 )
# Longer object to multiple of shorter object length : keep length=4
# order not reqed, only once k=1, 
(gl(n=4, k=2, length=8, labels=parties ))  # random order, twice rep

comb2= interaction(parties2, states2, sep='-')
df1 = data.frame(comb2, mps)
str(df1)
df1  # some combination may be missing

# not very good
parties3= gl(n=4, k=5, length=20, labels=parties1 )
states3= gl(n=5, k=4, length=20, labels=states1 )
cbind(parties3, states3)
(df2 = data.frame(parties3, states3))
table(df2)


================================================
FILE: 11-stats/mean1.R
================================================
# Mean

set.seed(1234)
x = rnorm(100, 50, 10)  # 100 random nos mean-50, Sd-10
x = ceiling(x)
x
meanx = sum(x) / length(x)
meanx
mean(x)
summary(x)

#  Trimming Values
x
set.seed(1234)
x = ceiling(runif(100,50,100)) # Extreme values
x
mean(x)
mean(x, trim = 1)

# NA values - Remove
x[c(1,20,30)] = NA
x
length(na.omit(x))  #3 values
mean(x) # error
mean(x, na.rm=T)

# Trim
x <- c(0:10, 50)
x
xm <- mean(x)
c(xm, mean(x, trim = 0.10))  # with & without Trim


================================================
FILE: 11-stats/mean2.R
================================================
# Summary1

library(MASS)                 # load the MASS package 
school = painters$School      # the painter schools 
c_school = school == "C"      # the logical index vector
c_school
#which school has the highest mean composition score
c_painters = painters[c_school, ]  # child data set
c_painters
mean(c_painters$Composition) 

tapply(painters$Composition, painters$School, mean)


================================================
FILE: 11-stats/mean3.R
================================================
#mean3

set.seed(1234)
x = ceiling(runif(100,50,100))  # 100 uniform dist nos
x
mean(x)

#sampling mean
(s1 = sample(x,10))
mean(s1)
?sample

(s2= sample(x,10, replace=T))
mean(s2)

for i in 1:10 {
  
}

================================================
FILE: 11-stats/meandev.R
================================================
# Mean Deviation
x = c(3, 6, 6, 7, 8, 11, 15, 16)
(mx = mean(x))
(mdev = x - mx)
abs(mdev)
(mabsdev = sum(abs(mdev)))/ length(x)
mad(x) # Direct from Formula



#Marks (out of 25): 7, 4, 10, 9, 15, 12, 7, 9, 7
# Calculate MD about Mean, Median, Mode
#http://www.emathzone.com/tutorials/basic-statistics/mean-deviation-and-its-coefficient.html

x = c(7, 4, 10, 9, 15, 12, 7, 9, 7)
mean(x)
median(x)
library(modeest)
md = mlv(x,  method = "mfv")

(MDmean = sum(abs(x - mean(x)))/ length(x))
(CoeffMDMean = MDmean/ mean(x) )

(MDmedian = sum(abs(x - median(x)))/ length(x))
(CoeffMDabtMedian = MDmedian/ median(x) )

md$M
(MDmode = sum(abs(x - md$M))/ length(x))
(CoeffMDabtMode = MDmode/ md$M)




================================================
FILE: 11-stats/meanwt1.R
================================================
#mean Weighted

wt = c(5,  5,  4,  1 )
rwt = wt/ sum(wt)
pie(rwt)
rwt  # relative weight of each subject
x = c(3.7,3.3,3.5,2.8)  #marks of subject
(xm = weighted.mean(x, rwt)) # Grade Point Average

(xm2 = sum(x * rwt))


================================================
FILE: 11-stats/median1.R
================================================
#Median

================================================
FILE: 11-stats/mode1.R
================================================
#mode1


x = c(2,3,4,4,5,5)
library(modeest)
m =mlv(x,  method = "mfv")
m
getmode(x)

fivenum(x)


================================================
FILE: 11-stats/normal_height.R
================================================
# creating normal plot
# Men's heights are normally distributed with a population mean of 69.0 inches
# and a population standard deviation of 2.8 inches

population_mean <- 69
population_sd <- 2.8
sd_to_fill <- 1
lower_bound <- population_mean - population_sd * sd_to_fill
upper_bound <- population_mean + population_sd * sd_to_fill

# Generates equally spaced values within 4 standard deviations of the mean
# This is used to connect the points on the curve so the more points the better
x <- seq(-4, 4, length = 1000) * population_sd + population_mean

# Returns the height of the probably distribution at each of those points
y <- dnorm(x, population_mean, population_sd)

# Generate the plot, where:
# - type: the type of plot to be drawn where "n" means do not plot the points
# - xlab: the title of the x axis
# - ylab: the title of the y axis
# - main: the overall title for the plot
# - axes: when false it suppresses the axis automatically generated by the high level plotting function so that we can create custom axis
plot(x, y, type="n", xlab = "Height (inches)", ylab = "", main = "Distribution of Men's Heights", axes = FALSE)

# Connect all of the points with each other to form the bell curve
lines(x, y)

# Returns a vector of boolean values representing whether the x value is between the two bounds then
# filters the values so that only the ones within the bounds are returned
bounds_filter <- x >= lower_bound & x <= upper_bound
x_within_bounds <- x[bounds_filter]
y_within_bounds <- y[bounds_filter]

# We want the filled in area to extend all the way down to the y axis which is why these two lines are necessary
# It makes the first point in the polygon (lower_bound, 0) and the last point (upper_bound, 0)
x_polygon <- c(lower_bound, x_within_bounds, upper_bound)
y_polygon <- c(0, y_within_bounds, 0)

polygon(x_polygon, y_polygon, col = "red")

# Now determine the probability that someone falls between the two bounds so we can display it above the curve
# Remember that pnorm returns the probability that a normally distributed random number will be less than the given number
probability_within_bounds <- pnorm(upper_bound, population_mean, population_sd) - pnorm(lower_bound, population_mean, population_sd)

# Concatenate the various values so we can display it on the curve
text <- paste("p(", lower_bound, "< height <", upper_bound, ") =", signif(probability_within_bounds, digits = 3))

# Display the text on the plot. The default "side" parameter is 3, representing the top of the plot.
mtext(text)

# Add an axis to the current plot, where:
# - side: which side of the plot the axis should be drawn on where 1 represents the bottom
# - at: the points at which the tick-marks are to be drawn
# - pos: the coordinate at which the axis line is to be drawn
sd_axis_bounds = 5
axis_bounds <- seq(-sd_axis_bounds * population_sd + population_mean, sd_axis_bounds * population_sd + population_mean, by = population_sd)
axis(side = 1, at = axis_bounds, pos = 0)



================================================
FILE: 11-stats/normality.R
================================================
# Normality

#https://stats.stackexchange.com/questions/52293/r-qqplot-how-to-see-whether-data-are-normally-distributed

#Method1 -----------
library(moments)
library(nortest)
library(e1071)





set.seed(777)
x <- rnorm(250,10,1)

# skewness and kurtosis, they should be around (0,3)
skewness(x)
kurtosis(x)

plot(density(x))
# Shapiro-Wilks test
shapiro.test(x)

# Kolmogorov-Smirnov test
ks.test(x,"pnorm",mean(x),sqrt(var(x)))

# Anderson-Darling test
ad.test(x)

# qq-plot: you should observe a good fit of the straight line
qqnorm(x)
qqline(x)

# p-plot: you should observe a good fit of the straight line
probplot(x, qdist=qnorm)

# fitted normal density
f.den <- function(t) dnorm(t,mean(x),sqrt(var(x)))
curve(f.den,xlim=c(6,14))
hist(x,prob=T,add=T)

# Method 2
library(car)

qqPlot(lm(prestige ~ income + education + type, data=Duncan), 
       envelope=.99)

================================================
FILE: 11-stats/normality2.R
================================================
#Normality Test

qqnorm(CO2$uptake)
qqline(CO2$uptake)


ggplot2::qplot(sample=CO2$uptake,
                data=CO2)
ggplot2::qplot(sample=CO2$uptake,
               data=CO2, color=factor(Treatment))

library(car)
car::qqPlot(lm(prestige ~ income + education + type,
               data=Duncan), envelope=.99)


shapiro.test(CO2$uptake)

nortest::ad.test(CO2$uptake)

mvoutlier::aq.plot(CO2[ c('uptake', 'conc')])
mvoutlier::aq.plot(CO2[ c('uptake')])


================================================
FILE: 11-stats/outlier2.R
================================================
# Outliers 2

#http://mantra.edina.ac.uk/practicals/MANTRA-R-Practical-Intro.pdf

#different methods for checking apparent outliers
with(riverquality,which(Temp<3))# the result is given as just the row number
which(riverquality$Temp<3)
riverquality$Temp<3  # prints the entire column
#as a set of TRUE, FALSE or NA responses for each cell, and involves counting
#which value meets the TRUE criterion. 


================================================
FILE: 11-stats/outliers1.R
================================================
# Outliers
# Outliers
#http://r-statistics.co/Outlier-Treatment-With-R.html

library(outliers)
x = c(1:10,45,46,47,99,100,101)
chisq.out.test(x)
outlier(x)
# Inject outliers into data.
cars1 <- cars[1:30, ]  # original data
cars_outliers <- data.frame(speed=c(19,19,20,20,20), dist=c(190, 186, 210, 220, 218))  # introduce outliers.
cars2 <- rbind(cars1, cars_outliers)  # data with outliers.
cars2
outlier(cars2)
# Plot of data with outliers.
par(mfrow=c(1, 2))
plot(cars2$speed, cars2$dist, xlim=c(0, 28), ylim=c(0, 230), main="With Outliers", xlab="speed", ylab="dist", pch="*", col="red", cex=2)
abline(lm(dist ~ speed, data=cars2), col="blue", lwd=3, lty=2)

# Plot of original data without outliers. Note the change in slope (angle) of best fit line.
plot(cars1$speed, cars1$dist, xlim=c(0, 28), ylim=c(0, 230), main="Outliers removed \n A much better fit!", xlab="speed", ylab="dist", pch="*", col="red", cex=2)
abline(lm(dist ~ speed, data=cars1), col="blue", lwd=3, lty=2)

# Detect outliers Univariate approach ---------------------------------------


================================================
FILE: 11-stats/poiss1.R
================================================
# Poission Distribution

?poisson
# 12 cars crossing a bridge per minute on average, find the probability of having 
# 17 or more cars crossing the bridge in a particular minute.
ppois(16, lambda=12)   # lower tail sixteen or less cars 
ppois(16, lambda=12, lower=FALSE)   # upper tail seventeen or more cars 

?ppois
# Q1: 1 yr - 18 accidents, P(X=2) Exact
lambda = 18/12 # accidents / month
q=2
ppois(q, lambda, lower.tail = T)

# at least 1 accident P(X>=1)
1 - ppois(q=0, lambda)
ppois(q=0, lambda, lower.tail = F)


#probability that there are more than 2 accidents in a particular month
ppois(q=2, lambda, lower.tail = F)   # right side
#1 - (P(0) + P(1) + P(2))
(ppois(q=c(0,1,2,3,4,5), lambda, lower.tail = T))  # cumulative
1-ppois(q=c(2), lambda, lower.tail = T)
#Table gives us cumulative probability.

# mean=3 ; P(X = 4) = P(X ≤ 4) − P(X ≤ 3) = 0.815 − 0.647 = 0.168
lambda=3
ppois(q=4, lambda, lower.tail = T) - ppois(q=3, lambda, lower.tail = T)
#P(X=4 exactly)

#Q: Patients = 6/hr, lambda=3/30min ; X=4
lambda=6/2; q=4
ppois(q,lambda) - ppois(q-1,lambda) #0.1680314

#Q : Outrages  = 4/hr ; X=3, x>=3
lambda=4 ; q=3
ppois(q,lambda) - ppois(q-1,lambda) #0.195
ppois(q,lambda, lower.tail = F) #0.566

#Q : Trucks : exactly 5
lambda= 3 * 2 ; q=5
ppois(q,lambda) - ppois(q-1,lambda) #0.160

#Q: Messages arrive at a switching centre at random and 
#at an average rate of 1.2 per second.
#(a) Find the probability of 5 messages arriving in a 2-sec interval.
lambda = 1.2*2 ; q=5
ppois(q,lambda, lower.tail = T) - ppois(q-1,lambda, lower.tail = T) #0.06
exp(-lambda) * (lambda)^q / factorial(q)
#0.060


#Q three or more will fail within ten years?
(lambda = 5000000 * (10^-6))
ppois(q=2, lambda, lower.tail = F)  #0.875348
1- ppois(q=2, lambda, lower.tail = T)  #0.875348
#0.875

================================================
FILE: 11-stats/quantile1.R
================================================
# Quantile

set.seed(1234)
x = ceiling(rnorm(100,50,10))
x
#Quartile
quantile(x , c(0.25, 0.5, 0.75))
sort(x)
quantile(x)
IQR(x)
Q1 = quantile(x , 0.25)
(Q2 = quantile(x , 0.5))
Q3 = quantile(x , 0.75)
(iqr = Q3 - Q1)
#Q2 - 46.5

#Decile
quantile(x, seq(.1,1,by=0.1))
#50 = 46.5

#Percentile
quantile(x, seq(.01,1,by=0.01))
# P50 = 46.5

# Summary
summary(x)

fivenum(x)

summary(x)

plot(density(x))
hist(x)


================================================
FILE: 13-statsH/20b-distributions.R
================================================
# Table Values in R
?distributions
#http://www.stat.umn.edu/geyer/old/5101/rlook.html
#https://cran.r-project.org/doc/contrib/Ricci-distributions-en.pdf
#https://stats.idre.ucla.edu/r/modules/probabilities-and-distributions/
  #

#z-----
?dnorm
rnorm
pnorm
qnorm




#t----
?dt
rt
pt
qt



#chisq----






================================================
FILE: 13-statsH/23b-ztest-bsda.R
================================================
# Hypothesis Test in R

#Z Test
#https://www.rdocumentation.org/packages/BSDA/versions/1.2.0/topics/z.test
#https://www.investopedia.com/terms/z/z-test.asp


library(BSDA)

#z.test(x, y = NULL, alternative = "two.sided", mu = 0, sigma.x = NULL, sigma.y = NULL, conf.level = 0.95)




================================================
FILE: 13-statsH/25c-tdistribution.R
================================================
# t distribution
#Plot of Student Distribution
?qt
#Density, distribution function, quantile function and random generation 
#for the t distribution with df degrees of freedom 

#Usage
dt(x, df, ncp, log = FALSE)
pt(q, df, ncp, lower.tail = TRUE, log.p = FALSE)
qt(p, df, ncp, lower.tail = TRUE, log.p = FALSE)
rt(n, df, ncp)

#Arguments
#x, q	 vector of quantiles.
#p	 vector of probabilities.
#n	 number of observations. If length(n) > 1, the length is taken to be the number required.
#df degrees of freedom (> 0, maybe non-integer). df = Inf is allowed.
#ncp	non-centrality parameter delta; currently except for rt(), only for abs(ncp) <= 37.62. If omitted, use the central t distribution.
#log, log.p	logical; if TRUE, probabilities p are given as log(p).
#lower.tail	logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].

data_vector <- c(63, 75, 84, 58, 52, 96, 63, 55, 76, 83)
data_vector
(dfs <- length(data_vector)-1)   #dof
dfs
x_ <- seq(-8,8,0.1)  # x axis points
x_
y <- dt(x_,dfs)  # y axis points density (t) func
y
# sign level=0.05, p = 1 -0.05 = 0.95 : 95% of values (area) from left 
t.val <- qt(1-0.05,df=dfs)
t.val   # on x axis

plot(x_,y,type='l',lwd=3,col='blue',xlab='x',
     ylab='Density',main='Student distribution \n 9 Degree of Freedom')

abline(v=0)
text(0,0.1, labels=' Center Line', srt=90)

abline(v=t.val,lwd=2,col='red')
text(t.val,0.2, labels='Line at tval', srt=90, col='red')
dt(t.val,dfs)  # point where t.val line touches the plot
mtext(1,at=t.val,text=round(t.val,2))
points(t.val,dt(t.val,dfs),lwd=3,col='red')
text(t.val,dt(t.val,dfs), labels= ' Intersect')



================================================
FILE: 13-statsH/26b-ttestindep.R
================================================
#t-test Independent : Difference Between Means
#two-sample t-test

#links
#http://www.sthda.com/english/wiki/unpaired-two-samples-t-test-in-r#r-function-to-compute-unpaired-two-samples-t-test
#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independent-t.html
#http://www.r-tutor.com/elementary-statistics/inference-about-two-populations/population-mean-between-two-independent-samples


#Criteria
#simple random sampling.
#The samples are independent.
#Each population is at least 20 times larger than its respective sample.
#The sampling distribution is approximately normal, which is generally the case if any of the following conditions apply.
#   The population distribution is normal.
#   The population data are symmetric, unimodal, without outliers, and the sample size is 15 or less.
#   The population data are slightly skewed, unimodal, without outliers, and the sample size is 16 to 40.
#   The sample size is greater than 40, without outliers.

#http://stattrek.com/hypothesis-test/difference-in-means.aspx?Tutorial=AP
#Hypothesis #H0: μ1 = μ2 Ha: μ1 ≠ μ2
(class1 = sample(60:100,25))
(class2 = sample(60:100,30))

#determine whether the difference between means found in the sample is significantly 
#different from the hypothesized difference between means ?t.test
#t.test(x, y ,  alternative = c("two.sided", "less", "greater"),  mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, ...)

(ttest2s1 = t.test(x=class1, y=class2))

ttest2s1$statistic ; ttest2s1$p.value ;ttest2s1$conf.int
names(ttest2s1)

(n1 = length(class1)); (n2=length(class2))
qt(1-.05/2,df=min(n1,n2))

#Equal Variance
(ttest2s1 = t.test(x=class1, y=class2, var.equal = T))

#Unequal Variance
(ttest2s1 = t.test(x=class1, y=class2, var.equal = F))


# Smokers
nonsmokers = c(18,22,21,17,20,17,23,20,22,21)
smokers = c(16,20,14,21,20,18,13,15,17,21)

#sample size < 30
t.test(nonsmokers,smokers) # 2 sample
t.test(nonsmokers, mu=20)  #1 sample
(n1 = length(nonsmokers))
(n2 = length(smokers))
dfs = n1 + n2 -2 
qt(1-.05/2,df=dfs)
qt(.05/2,df=dfs)
t.test(nonsmokers,smokers)
#2.5 > absolute(2.1)
# t calc > t-table : pval < 0.03 : Reject Ho
# Diff in means != 0

par(mfrow=c(1,2))               # set graphics window to plot side by side
plot(density(nonsmokers), main="nonsmokers")
plot(density(smokers), main="smokers")
par(mfrow=c(1,1))
boxplot(nonsmokers, smokers, ylab="Scores on Digit Span Task",     # plot and label y-axis
        names=c("nonsmokers","smokers"),                           # group names on x-axis
        main="Digit Span Performance by\n Smoking Status")         # main title

# 1 Tail, Equal variance
t.test(nonsmokers, smokers, alternative="greater", var.equal=T)


# Formula Method
scores = c(nonsmokers, smokers)
groups = c("nonsmokers","smokers")        # Make sure these are in the right order!
groups = rep(groups, times=c(10,10))      # 10 nonsmokers, 10 smokers
mj.data = data.frame(groups, scores)
mj.data
by(scores, groups, mean)
boxplot(scores ~ groups)     
t.test(scores ~ groups, data=mj.data) 

#
wilcox.test(nonsmokers, smokers)

# One Tail Tests ---------
#Null hypothesis: μ1 - μ2 >= 7 
#Alternative hypothesis: μ1 - μ2 < 7


================================================
FILE: 13-statsH/26c-ttestpaired.R
================================================
# t test - paired

before = c(50,60,55,70,60)
after = c(55,64,61,74,58)
t.test(x=before, y=after, paired=T)
#Ho: Diff = 0 , Ha: Diff != 0
# Manual Calculation
(d = before-after)
n = length(before)
(dmean = mean(d))
(tcalc = (dmean - 0)/(sd(d)/sqrt(n-1)))
qt(1-0.05/2,df=length(before)-1)
qt(0.05/2,df=length(before)-1)
#tcalc lies between qt values : Accept Ho : True diff = 0
t.test(x=before, y=after, paired=T,alternative='two.sided', mu=0)

# Ho: d = 3
t.test(x=before, y=after, paired=T,alternative='two.sided', mu=3)



# Example 2 : Training
before= c(95,89,76,92,91,53, 67, 88, 75, 85, 90, 85, 87, 85, 85, 68, 81, 84, 71, 46, 75, 80)
after = c(90,85,73,90,90,53,68,90,78,89,95,83,83,83,82,65,79,83,60,47,77,83)
# Did training help in scores
# Matched Pair : Same People - before and after training scores
t.test(x=before, y=after, paired=T,mu=0,alternative='two.sided')
# Accept Ho: No significant changes
#Manually
d = before - after
n = length(before)
dmean = mean(d)
(d.sd = sqrt(sum((d - dmean)^2)/(n-1)))
(se = d.sd/sqrt(n))
mu=0
(tcalc = (mean(d) - mu)/ se)
t.test(before, after, paired=T, mu=0, alternative='two.sided')
qt(1-0.05/2,df=n-1); qt(0.05/2,df=n-1)
#tcalc lies between two extremes (not in rejection) : Accept Ho
# p value > 0.05;  p-value (0.205) is greater than the signif level (0.05), we cannot reject Ho.
# conf Interval : mean of diff lies betw conf intervals

meanx= 70
sdx = 10
nx=10
x1 = rnorm(n=n,mean=meanx, sd=sdx)
x1
mean(x1)
sd(x1)
x2 = rnorm(10)
x2 = sdx*(x2-mean(x2)) /sd(x2)+70
mean(x2)
sd(x2)
help(distributions)


================================================
FILE: 13-statsH/27b-TTS1-case1.R
================================================
# Hypothesis 1 sample Test

# Q : 1 sample ------------------------------------------------------------
#A random sample of 900 members has a mean 3.4 cm. 
#Can it be reasonably regarded as sample from a
#large population of mean 3.25 cm and SD 2.61 cms
#Ho : μ = 3.25  ; Ha : μ ≠ 3.25
library(TeachingDemos) 
za= qnorm(1-.05/2)
x=rnorm(1000,mean=3.25,sd=2.61)
plot(density(x))
z.test(3.4,mu=3.25,sd=2.61,n=900,conf.level = .975,
       alternative="two.sided")

zc = (3.4 - 3.25)/(2.61/sqrt(900))
abline(v=c(za,-za))
abline(v=zc, col='red')
#zc (calc) lies betw -za & za. Do not reject Ho

#One Sample z-test
#data:  3.4
#z = 1.7241, n = 900.000, Std. Dev. = 2.610, Std. Dev. of the sample
#mean = 0.087, p-value = 0.08468
#alternative hypothesis: true mean is not equal to 3.25
#95 percent confidence interval:  3.229483 3.570517
#sample estimates: mean of 3.4          3.4 

#[ |Z* = 1.724|   <   Zα/2 = 1.9599 : Accept Ho ]
#Sample mean(3.4) is falling in between the 95% Confidence Interval calculated : [3.229483, 3.570517]



# Q2 ----------------------------------------------------------------------
#Manufacturer claims that the average mileage of scooters of his company is 40 km/litre.
#A random sample of 38 scooters of the company showed an average mileage of 42 km/litre. Test the claim of the manufacturer on the assumption that the mileage of scooter is normally distributed with SD of 2 km/litre
#Ho : μ = 40  Ha : μ ≠ 40
qnorm(1-.05/2) #[1] 1.959964
z.test(42,mu=40,sd=2,n=38,conf.level = .95,
       alternative="two.sided")
#Sample mean(42) is falling in between the 95% Confidence Interval calculated : [41.36, 42.63]
#z = 6.1644, n = 38.00000, Std. Dev. = 2.00000, Std. Dev. of the sample mean = 0.32444, p-value = 7.074e-10

za2 = qnorm(1-.05/2, mean=40,sd=2,lower.tail=T)
za1 = qnorm(.05/2, mean=40,sd=2,lower.tail=T)
za1; za2
x = rnorm(1000,mean=40,sd=2)
plot(density(x))
abline(v=c(40,42))

mtext(side=1,at=40,text='Mean at 40',col='green')
text(x=42,y=.1,labels='Sample Mean at 42', col='purple', 
     srt=90, offset=0.5)
?text
abline(v=c(za1,za2), col='red')
text(x=za1-.5,y=0.1,labels='Left Bound', srt=90)
text(x=za2-.5,y=0.1,labels='Right Bound', srt=90)

# Sample mean lies between left and right bound

#One Sample z-test
#data:  42
#z = 6.1644, n = 38.00000, Std. Dev. = 2.00000, Std. Dev. of the sample mean = 0.32444, p-value = 7.074e-10
#alternative hypothesis: true mean is not equal to 40
#95 percent confidence interval:  41.3641 42.6359
#sample estimates: mean of 42  


# Conclusion[ |Z* = 6.61 |   <   Zα/2 = 1.9599 : Accept Ho ]
#Sample mean(42) is falling in between the 95% Confidence Interval calculated : [41.36, 42.63]


# Q3 ----------------------------------------------------------------------



#A stenographer claims that she can type at the rate of 120 words per min. 
#Can we reject her claim on the basis of 100 trials in which she demonstrates a mean of 116 words with a SD of 15 words ? Use 5% level of significance
#Ho : μ = 120   Ha : μ ≠ 120
qnorm(1-.05/2)#[1] 1.959964
z.test(116,mu=120,sd=15,n=100,conf.level = .95,alternative="two.sided")
?z.test

#[ |Z* =-2.667 |   >   Zα/2 = 1.9599 : Reject Ho ]
# use x as vector of values instead of one mean value
x = c(117,120,110,123,125, 114, 118,123, 117, 125)
length(x); mean(x) ; sd(x)
z.test(x,mu=120,sd=sd(x),n=length(x),conf.level = .95,alternative="two.sided")

# Q4 ----------------------------------------------------------------------


#Mean life of a sample of 400 fluorescent bulbs produced by a company is found to be 1570 hours with a SD of 150 h. Test the hypothesis that the mean life of the bulbs produced by the company is 1600 against the alternative hypothesis that it is greater than 1600 hours at 1% levels of significance
#Ho : μ = 1600  Ha : μ > 1600   : One Tail, Left Tail
qnorm(1-.01) #[1] 2.326348

z.test(x=1570,mu=1600,sd=150,n=400,conf.level = .95,alternative="greater")

#[ abs |Z* =-4 |   >   Zα/2 = 2.33    : Reject Ho ]

#Sample mean(1570) is not falling in between the 95% Confidence Interval calculated : [1557.664,   Inf ]
# Ha Mean is higher than 1600




================================================
FILE: 13-statsH/28b-TTS2-case1.R
================================================
# Hypothesis Tests - t for n < 30

# Q1 ----------------------------------------------------------------------

#Random Sample of size 20 from a normal population has mean 42 and SD = 5. Test the Hypothesis that the population mean is 45. Use 5% level of significance
#Ho : μ = 45  Ha : μ ≠ 45   : Two Tail
qt(.975,df=19) #[1] 2.093024
#Calculate Z* since ND and SD is given.

z.test(42,mu=45,alternative="two.sided",n=20,sd=5)
 
# t* > tα/2  : -2.683 > 2.093  : Reject Ho


# Q4 ----------------------------------------------------------------------

#The average breaking strength of steel rods is specified to be 18.5 thousand per kg. For this a sample for 14 rods was tested. The mean and SD obtained were 17.85 and 1.955 respectively. Test the significance of the deviation.
#Ho : μ = 18.5  Ha : μ ≠ 18.5 (Two Tailed t test) 

#n=14, x̅ = 17.85 , s=1.955 , μ = 18.5 

# Result : Ho accepted


# Q5 ----------------------------------------------------------------------
#The nine items of a sample had the following items. 45,47,50,52,48,47,49,53,51 .
#  Does the mean of nine items differ significantly from the assumed population mean of 47.5
#N = 9 (N<30) we use t-test.  Ho : μ = 47.5  Ha : μ ≠ 47.5 (Two Tailed t test) 
qt(.975,df=8) #[1] 2.306004
x = c(45,47,50,52,48,47,49,53,51)
t.test(x, alternative="two.sided",mu=47.5,conf.level = .95)
t.test(x,mu=47.5,conf.level = .95)
t.test(x,mu=47.5)
qt(p=.95+.05/2,df=length(x)-1)
mean(x)
#|t* = 1.8452 | < tα/2  : Do not reject Ho
#Sample Mean between confidence interval


# Q6 ----------------------------------------------------------------------


#A drug manufacturer has installed a machine which automatically fills 5 gm of drug in each phial. A random sample of  fills was taken and it was found to contain 5.02 gm on an average in phial. The sd of sample was 0.002 gms. SD of sample was 0.002 gms, Test at 5% level of significance if the adjustment in the machine is in order.
#Ho : μ = 5  Ha : μ ≠ 5 (Two Tailed t test) 

# Solve this????


#P value > 0.05 (significance level) : Do not reject Ho


# Q7 ----------------------------------------------------------------------

#Lifetime of Electric bulbs for a random sample of 10 from a large consignment gave the following data
#4.2 , 4.6, 3.9, 4.1, 5.2, 3.8, 3.9, 4.3, 4.4, 5.6  (in thousands 1000s)
#Can we accept the hypothesis that the average lifetime of bulb is 4000 hours
#Ho : μ = 4000  Ha : μ ≠ 4000 (Two Tailed t test) 
x = c(4.2 , 4.6, 3.9, 4.1, 5.2, 3.8, 3.9, 4.3, 4.4, 5.6)
t.test(x*1000, alternative="two.sided",mu=4000)
# t* < t(table) : for df=9  at alpha=0.05 : Ho accepted


# Q8 ----------------------------------------------------------------------
#Machine is designed to produce insulating washers for electrical devices of average thickness of 0.025 cm. A random sample of 10 washers was found to have a mean thickness of 0.024 cm with a SD of 0.002 cm. Test the significance of the deviation at 5%
#Ho : μ = 0.025  Ha : μ ≠ 0.025 (Two Tailed t test) 
# Solve This ????

#Result :Ho accepted


================================================
FILE: 13-statsH/28c-TTS1-case3.R
================================================
# Hypothesis Tests - 2 Sample : Dependent Samples : Paired t-tests---------------------------------------------
options(digits=2)
# Check for Difference Between Two Population Means: Different Hypothesis
#l. Ho: mu1  = mu2   Ha : mu1 != mu2 
#2. Ho: mu1 >= mu2   Ha : mu1 < mu2 
#3. Ho: mu1 <= mu2   Ha : mu1 > mu2 
# Independent Samples  Drawn from Different Population-----------------------------------------------------

# Dependent Samples -------------------------------------------------------
# Drawn from Same Population
#There are many experimental settings where each subject in the study is in both the
#treatment and control group



#Sample size < 30 and/or Population SD unknown- t tests ####
#Dependent Samples 
# Cases (a) Sample Size are equal n1 = n1 (b) two sample are not independent but same observations are paired together
# Drug Given to same set of patients; Measurements done before & after the drug
# Training Given to same set of Students; Performance Measures before & after training
# or students performance in 2 subjects
#Ho : mu1  = mu2 ie the process/ drug / training / performance is not effective 
#Ha : mu1 != mu2 ie Effect is noticed
#di = yi = xi # difference in observations of ith observation 
#compute dbar = sum(di)/n  # mean difference
#compute Pooled Variance Sv^2 = 1/(n-1)* sum((di - dbar)^2)

# Q7.47
#To test whether a course in statistics improved performance a similar test was given
#to 12 particaipants.
x = c(44,40,61,52,32,44,70,41,67,72,53,72)
y = c(53,38,69,57,46,39,73,48,73,74,60,78)
x;y
#Using R formula for t test
t.test(x,y,alternative='two.sided',paired=T) # check ?t.test
ttable = qt(1-0.05/2,length(x)) ; ttable # Table value of t (critical value)
ta = ttable
#Ho: x1bar = x2bar
#Ha: x1bar != x2bar
n=length(x)
d = y -x
d ; d^2
sum(d^2)
dbar = mean(d); dbar
S= sqrt((1/(n-1))* sum((d - dbar)^2)) ; S # SD is Pooled SD since the sample are dependent  
tcalc = abs(dbar)/ (S / sqrt(n)) ; tcalc
if(abs(tcalc) > ta) paste("Ho is rejected, Difference Significant ",";Course Improved Performance" ,abs(tcalc) ,'>',ta) else paste("Failed to Reject Ho ; No Difference in Mean Values " ,abs(tcalc) ,'<',ta )



# Question 2 @ 7.48 -------------------------------------------------------

# You are given marks obtained by 11 students in two tests one before
# and one after special coaching. Do the data reveal that special coaching is effective
#In either of these situations we can’t use two-sample t-tests since the independence assumption is not valid. Instead we need to
#use a paired t-test. This can be done using the option paired =TRUE.
x=c(23,20,19,21,18,20,18,17,23,16,19)
y=c(24,19,22,18,20,22,20,20,23,20,17)
t.test(x,y,alternative='two.sided',paired=T)
# p < 0.3 : Ho is accepted



# Question @ 7.49  ---------------------------------------------------------------
#A certain stimulus when administered to each of the 12 patients
#result in the following increase of BP
d= x = c(5,2,8,-1,3,0,-2,1,5,0,4,6) # these are values of d = y-x
#Can it be concluded that the stimulus when will in General
#Calculate dbar, S (SD)
dbar = mean(d)
n = length(d)
S= sqrt((1/(n-1))* sum((d - dbar)^2)) ; S # SD is Pooled SD since the sample are dependent  
tcalc = dbar / (S / sqrt(n)) ;tcalc
ttable = qt(.975,n-1) ; ta=ttable
identical(tcalc, ttable) # checking for equality
compare::compare(1, 2, ignoreNames=T) # compare equality
if(abs(tcalc) > ta) paste("Ho is rejected" ,abs(tcalc) ,'>',ta) else paste("Failed to Reject Ho : Accept Ho ::" ,abs(tcalc) ,'<',ta )
# Ho is rejected: There is significant different by stimulus on BP



# Question 3 --------------------------------------------------------------

#Ex. A study was performed to test whether cars get better mileage on premium gas than
#on regular gas. Each of 10 cars was first filled with either regular or premium gas,
#decided by a coin toss, and the mileage for that tank was recorded. The mileage was
#recorded again for the same cars using the other kind of gasoline. We use a paired ttest
#to determine whether cars get significantly better mileage with premium gas
reg = c(16, 20, 21, 22, 23, 22, 27, 25, 27, 28)
prem = c(19, 22, 24, 24, 25, 25, 26, 26, 28, 32)
t.test(prem,reg,alternative="greater", paired=TRUE)
qt(1-0.05/2,length(reg))
# The results show that the t-statistic is equal to 4.47 > 2.2 and the p-value is 0.00075. Since
#the p-value is very low, we reject the null hypothesis. There is strong evidence of a
#mean increase in gas mileage between regular and premium gasoline.



# Question : Independent --------------------------------------------------

#Ex. 6 subjects were given a drug (treatment group) and an additional 6 subjects a
#placebo (control group). Their reaction time to a stimulus was measured (in ms). We
#want to perform a two-sample t-test for comparing the means of the treatment and
#control groups.
#Let mu1 be the mean of the population taking medicine and mu2 the mean of the untreated
#population. Here the hypothesis of interest can be expressed as:
#H0: mu1- mu2=0
#Ha: mu1- mu2<0
#Here we will need to include the data for the treatment group in x and the data for the
#control group in y. We will also need to include the options alternative="less", mu=0.
#Finally, we need to decide whether or not the standard deviations are the same in both groups

================================================
FILE: 13-statsH/28e-TT-sample1i.R
================================================
# Hypothesis Tests - 2 Sample ---------------------------------------------
options(digits=2)
# Check for Difference Between Two Population Means: Different Hypothesis
#l. Ho: mu1  = mu2   Ha : mu1 != mu2 
#2. Ho: mu1 >= mu2   Ha : mu1 < mu2 
#3. Ho: mu1 <= mu2   Ha : mu1 > mu2 
#http://www.stat.columbia.edu/~martin/W2024/R2.pdf

# Dependent Samples -------------------------------------------------------
# Drawn from Same Population

# Independent Samples -----------------------------------------------------
# Drawn from Different Population


#Sample size < 30 and/or Population SD unknown ####
#Independent Samples

#Q1: (7.43) Two salesman A and B are working in certain district. 
#From a sample survey conducted by the head office, the following result were obtained.
#State whether there is any significant difference in the average sales between the two salesman
#Salesman A : n1=20 (No of Sales), x1bar = 170 (Average Sales), sd1 = 20 (Std Dev)
#Salesman B : n2=18 (No of Sales), x2bar = 205 (Average Sales), sd2 = 25 (Std Dev)
# No of samples each Data < 30 : Use t test (as Populations are different)
#t.test to be use in r, but sample values are not given, only summary
n1=20;x1bar = 170; sd1 = 20 ; # Salesman A
n2=18;x2bar = 205; sd2 = 25 ; #Salesman B
#Ho: x1bar = x2bar
#Ha: x1bar != x2bar
df1=n1-1 ; df2=n2-1
paste("Salesman A -",n1,x1bar,sd1,df1)
paste("Salesman B -",n2,x2bar,sd2,df2)

dfc = df1 + df2
alpha = 0.05  # assume if not given
cl = 1 - alpha/2  # confidence level since it is two tailed


ttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom
#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD
Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD
paste("combined df=",dfc,"alpha=",alpha, "cl=",cl, 'S pooled =',Sv )

tcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t 
(170-205)/(23*sqrt((1/19+1/17)))


paste(' t-test =', tcalc,' ttable = ', ttable)
ta=ttable # to make it atomic
#round(ta,digits=4); round(ttable,digits=4)
if(abs(tcalc) > ta) paste("Ho is rejected" ,abs(tcalc) ,'>',ta) else paste("Failed to Reject Ho" ,abs(tcalc) ,'<',ta )


# Question2 ---------------------------------------------------------------

# A statistics teacher wants to compare his two classes to see if they performed any differently on
# the tests he gave that semester. Class A had 25 students with an average score of 70, standard
# deviation 15. Class B had 20 students with an average score of 74, standard deviation 25. Using
# alpha 0.05, did these two classes perform differently on the tests?
n1=25; x1bar=70; sd1=15;
n1=20; x2bar=74; sd2=25;

#Ho: x1bar = x2bar
#Ha: x1bar != x2bar
df1=n1-1 ; df2=n2-1
paste("Class A -",n1,x1bar,sd1,df1)
paste("Class B -",n2,x2bar,sd2,df2)

dfc = df1 + df2
alpha = 0.05  # assume if not given
cl = 1 - alpha/2  # confidence level since it is two tailed

ttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom
#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD
Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD
paste("combined df=",dfc,"alpha=",alpha, "cl=",cl )

tcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t 
(70-74)/(20.34*sqrt((1/25+1/19)))
paste('S pooled =',Sv,' t-test =', tcalc,' ttable = ', ttable)
ta=ttable # to make it atomic
#round(ta,digits=4); round(ttable,digits=4)
if(abs(tcalc) > ta) paste("Ho is rejected" ,abs(tcalc) ,'>',ta) else paste("Failed to Reject Ho" ,abs(tcalc) ,'<',ta )

#-----------------


# Question 3 --------------------------------------------------------------

# @7.45 : Below are the given gain of weiths in lbs of lions on two diet X and Y
#Teat at 5% significance level whether the two dietes differ significantly in increasing weight  
x1 = x = c(25,32,30,32,24,14,32)
x2 = y = c(24,34,22,30,42,31,40,30,32,35)
# Step 1 : Frame Hypothesis #Ho: x1bar = x2bar ; Ha: x1bar != x2bar

x1bar = mean(x1); x2bar = mean(x2)
n1 = length(x1) ; n2 = length(x2)
paste('x1bar= ',x1bar,' x2bar= ',x2bar,' Sample size x= ',n1,' Sample Size y= ',n2)
sum((x1 - x1bar)^2); sum((x2 - x2bar)^2)
#sd1 = sd(x1) ; sd2 = sd(x2) #for testing
sd1a = sqrt(sum((x1 - x1bar)^2)/n1); sd2a = sqrt(sum((x2 - x2bar)^2)/n2) # manually calc sd
paste("X : SD - Using R=", sd1,' X-Calc= ',sd1a,"Y : SD - Using R=", sd2,' Y-Calc= ',sd2a)
df1=n1-1 ; df2=n2-1
paste("Diet X -",n1,x1bar,sd1,df1)
paste("Diet Y -",n2,x2bar,sd2,df2)


dfc = df1 + df2
alpha = 0.05  # assume if not given
cl = 1 - alpha/2  # confidence level since it is two tailed
paste("combined df=",dfc,"alpha=",alpha, "cl=",cl )

ttable = qt(cl, dfc ) # table value for conf level/alpha & degree of freedom
#Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(n1+n2-2)) # pooled SD
Sv = sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2)) # pooled SD
sqrt(((df1 * sd1^2) + (df2 * sd2^2))/(df1+df2))

tcalc = (x1bar - x2bar)/ (Sv * sqrt(1/n1 + 1/n2)) # calculated value of t 
(27-32)/(6.4*sqrt((1/6+1/9)))

paste('S pooled =',Sv,' t-test =', tcalc,' ttable = ', ttable)
ta=ttable # to make it atomic
#round(ta,digits=4); round(ttable,digits=4)
if(abs(tcalc) > ta) paste("Ho is rejected" ,abs(tcalc) ,'>',ta) else paste("Failed to Reject Ho : Accept Ho ::" ,abs(tcalc) ,'<',ta )

t.test(x1,x2,alternative='two.sided',paired=F,var.equal=T)






================================================
FILE: 13-statsH/31b-chisqdistr.R
================================================
# Chi Square Distribution
x <- rchisq(100, 5) 
hist(x, prob=TRUE, ylim=c(0,0.2)) 
curve( dchisq(x, df=1), col='blue', add=TRUE) 
curve( dchisq(x, df=5), col='green', add=TRUE) 
curve( dchisq(x, df=10), col='red', add=TRUE ) 

x <- rchisq(100, 5) 
hist(x, prob=TRUE, ylim=c(0,0.2)) 
for (i in 1:10) {
  curve( dchisq(x, df=i), col=sample(rainbow(10)), lwd=1, add=TRUE) 
}
?curve



# Example 2

x <- rchisq(100, 5)
hist(x, prob=TRUE, ylim=c(0,0.2))
curve( dchisq(x, df=5), col='green', add=TRUE)
curve( dchisq(x, df=10), col='red', add=TRUE )

#It may be easier to compare the therotical curve to a density estimate rather than the histogram:
lines( density(x), col='orange')


#Here is one way to dynamically change the df to see how the fit compares:
  
tmpfun <- function(x,df=1) {
    hist(x, prob=TRUE)
    curve( dchisq(x, df=df), col='blue', add=TRUE )
  }

TeachingDemos::tkexamp( tmpfun(x), list( df=list('slider', from=1, to=15)))


#Another approach that may give a better feel for the fit is the hanging rootogram:
  
library(vcd)

tmp <- hist(x, plot=FALSE)
rootogram( tmp$counts, diff(pchisq( tmp$breaks, df=5 )*length(x) ) )
rootogram( tmp$counts, diff(pchisq( tmp$breaks, df=10 )*length(x) ) )

tmpfun2 <- function(x, df=1) {
  tmp <- hist(x, plot=FALSE)
  rootogram( tmp$counts, diff(pchisq(tmp$breaks, df=df)*length(x) ) )
}

TeachingDemos::tkexamp( tmpfun2(x), list( df=list('slider', from=1, to=15)))


================================================
FILE: 13-statsH/32b-HT-chisq1.R
================================================
# Chi Square Test 
# About Chi Square Test
# https://stat.ethz.ch/R-manual/R-devel/library/stats/html/chisq.test.html

# Goodness of Fit : Discrepancy between observed & Expected ####
# Q1 @ 7.65 ---------------------------------------------------------------

#The following table gives the number of aircraft accidents that 
#occured during the various days of the week. Find whether
#accidents are uniformly distributed over the week. 
#Given value of chi2a at 5% sign for 6 df = 12.59
chi2a = 12.59  # Given using the table
?chisq.test
# days starting from Sun to Sat
x = accidents = c(14,16,8,12,11,9,14)
x
qchisq(.95, df=length(x)-1) # Using R here alpha/2 is not done
paste('Samples -', length(x) ,'Sum -', sum(x),'Mean ', mean(x))
#chi2 = sum((Oi - Ei)^2/Ei)
Ei=mean(x) ; Ei # Expected value is mean of the week
Oi = x # Observed values
chi2 = sum((Oi - Ei)^2/Ei)
chi2
if(abs(chi2) > chi2a) paste("Ho is rejected" ,abs(chi2) ,'>',chi2a) else paste("Failed to Reject Ho : Accept Ho ::" ,abs(chi2) ,'<',chi2a )
# There is not difference in O & E values of accidents with alpha=5%

chisq.test(x)
# df=6, X2= 4 which < given Chi2, p value > .5 : Accept Ho


# Q2 : @7.68 --------------------------------------------------------------
# The theory predicts that the proportion of beans in the four groups A, B, C, D
#should be 11:4:3:2. In an experiment it was observed that the number of fours groups
# A,B,C,D are 1070,430,330,170. Does the result of the experiment support theory
# chi2 for 3 df at 5% level = 7.815
Oi = c(1070,430,330,170) # Observed Values
ei = c(11,4,3,2) # Expected Proportion
totalei = sum(ei) # for relative frequencies
totalOi = sum(Oi); total # total Values  ; 
Ei= ei/totalei * total ; Ei # Expected Values based on ratios 
Oi ; Ei
chi2 = sum((Oi - Ei)^2/Ei); chi2
chi2a = 7.815 # Given for 3 df at 5% level = 7.815
qchisq(.95, df=length(Oi)-1) # Using R here alpha/2 is not done

if(abs(chi2) > chi2a) paste("Ho is rejected" ,abs(chi2) ,'>',chi2a) else paste("Failed to Reject Ho : Accept Ho ::" ,abs(chi2) ,'<',chi2a )
# There is not difference in O & E values  with alpha=5%
# Experiment does not support theory 

chisq.test(Ei,Oi)
# df=9, chi2= 10 which is > given table Chi2(7.8- Given), p value < .5 : Reject Ho


================================================
FILE: 13-statsH/32c-HT-chisq2.R
================================================
# Chi squared Tests 2
# http://stattrek.com/chi-square-test/independence.aspx?Tutorial=AP

# Q1 ----------------------------------------------------------------------

#Ho: Gender and voting preferences are independent. 
#Ha: Gender and voting preferences are not independent.

#Formulate an analysis plan. For this analysis, the significance level is 0.05.

#Analyze sample data. Applying the chi-square test for independence to sample data, 
#we compute the degrees of freedom, the expected frequency counts, and the chi-square test statistic. 
#Based on the chi-square statistic and the degrees of freedom, we determine the P-value.
# Find Chisq (table values from R command alpha=.05 ie CL = .95 & df = 7)
qchisq(.95, df=7)
#dchisq(x, df, ncp = 0, log = FALSE)
#pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
#qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
#rchisq(n, df, ncp = 0)
?chisq.test
?Chisquare
chisq.test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), 
    rescale.p = FALSE, simulate.p.value = FALSE, B = 2000)


# Chi Squared

# Q1 ----------------------------------------------------------------------
#http://www.r-tutor.com/elementary-statistics/goodness-fit/chi-squared-test-independence

#In the built-in data set survey, the Smoke column records the students smoking habit, 
#while the Exer column records their exercise level. 
#The allowed values in Smoke are "Heavy", "Regul" (regularly), "Occas" (occasionally) and "Never". 
#As for Exer, they are "Freq" (frequently), "Some" and "None".
#We can tally the students smoking habit against the exercise level with the table function in R. 
#The result is called the contingency table of the two variables.
library(MASS)       # load the MASS package 
tbl = table(survey$Smoke, survey$Exer) 
tbl                 # the contingency table 
#Test the hypothesis whether the students smoking habit is independent of their exercise level at .05 significance level.
#We apply the chisq.test function to the contingency table tbl, and found the p-value to be 0.4828.
chisq.test(tbl)
#as the p-value 0.4828 is greater than the .05 significance level, we do not reject the null hypothesis that the smoking habit is independent of the exercise level of the students.

#The warning message found in the solution above is due to the small cell values in the contingency table. To avoid such warning, we combine the second and third columns of tbl, and save it in a new table named ctbl. Then we apply the chisq.test function against ctbl instead.

ctbl = cbind(tbl[,"Freq"], tbl[,"None"] + tbl[,"Some"]) 
ctbl 
chisq.test(ctbl)


# Q2 ----------------------------------------------------------------------

#http://courses.statistics.com/software/R/Rchisq.htm

# q3 ----------------------------------------------------------------------

#http://www.statisticshowto.com/what-is-a-chi-square-statistic/
  
# Q3 ----------------------------------------------------------------------

#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html

row1 = c(91,90,51)                   # or col1 = c(91,150,109)
row2 = c(150,200,155)                # and col2 = c(90,200,198)
row3 = c(109,198,172)                # and col3 = c(51,155,172)
data.table = rbind(row1,row2,row3)   # and data.table = cbind(col1,col2,col3)
data.table
chisq.test(data.table)

#If the data are available in an electronic document, like this one, it can be entered into R using the scan() function.
ls()  # list all current objects
rm(list=ls())                             # Clean up a bit first!
the.data = scan()   # input from scan
#1:   91            90             51
#4:  150           200            155
#7:  109           198            172
#10: 
#  Read 9 items
the.data                                  # scan produces a vector
#[1]  91  90  51 150 200 155 109 198 172
data.matrix = matrix(the.data, byrow=T, nrow=3)
data.matrix                               # which we convert to a matrix
chisq.test(data.matrix)$statistic         # keeping the output brief

#X-squared  25.08597

dimnames(data.matrix) = list(Age=c("lt.45","45.to.59","ge.60"), Freq=c("Monthly","Occasionally","Never"))



# Q5 ----------------------------------------------------------------------

#http://stattrek.com/chi-square-test/independence.aspx
#Ho: Gender and voting preferences are independent. 
#Ha: Gender and voting preferences are not independent.
male=c(200,150,50)
female=c(250,300,50)
M = as.table(rbind(male,female))
dimnames(M) <- list(gender = c("M", "F"),
                    party = c("BJP","Congress", "Independent"))

Xcalc = chisq.test(M) # prints test summary
Xcalc
Xtable = qchisq(.95, df=(2-1)*(3-1)) ; Xtable
Xcalc$observed   # observed counts (same as M)
Xcalc$expected   # expected counts under the null
Xcalc$residuals  # Pearson residuals
Xcalc$stdres     # standardized residuals
# X-squared = 16.204 > Xtable= 5.991465, then Reject Ho


#http://web.pdx.edu/~newsomj/da1/ho_chisq.pdf


================================================
FILE: 13-statsH/33b-HT-chisq.R
================================================
#Chi Square Test

#In the built-in data set survey, the Smoke column records the students smoking habit, while the Exer column records their exercise level. The allowed values in Smoke are "Heavy", "Regul" (regularly), "Occas" (occasionally) and "Never". As for Exer, they are "Freq" (frequently), "Some" and "None".

#We can tally the students smoking habit against the exercise level with the table function in R. The result is called the contingency table of the two variables.

library(MASS)       # load the MASS package 
tbl = table(survey$Smoke, survey$Exer) 
tbl                 # the contingency table 

#Test the hypothesis whether the students smoking habit is independent of their exercise level at .05 significance level.

#We apply the chisq.test function to the contingency table tbl, and found the p-value to be 0.4828.

chisq.test(tbl) 
#As the p-value 0.4828 is greater than the .05 significance level, we do not reject the null hypothesis that the smoking habit is independent of the exercise level of the students.
ctbl = cbind(tbl[,"Freq"], tbl[,"None"] + tbl[,"Some"]) 
ctbl 

================================================
FILE: 13-statsH/33d-chisqtest1.R
================================================
# Chi Squared Tests
# Goodness of Fit
#Links------
#http://www.r-tutor.com/elementary-statistics/goodness-fit/multinomial-goodness-fit
#http://math.hws.edu/javamath/ryan/ChiSquare.html


#The test is applied when you have one categorical variable from a single population. It is used to determine 
#whether sample data are consistent with a hypothesized distribution.

# When to use 
#1 Simple Random Sampling
#2 Variable is categorical - Count of grades A, B,C ; Colors etc
#3 min freq 5

# Hypothesis -----------

#Ho: data consistent with a specified distribution. (Observed as per prob)
#Ha: data are not consistent with a specified distribution.

#Interpret results -----------
# If P-value < significance level (0.05), we cannot accept the null hypothesis.


# School Grades
grades = c(50,45,5) # grades - cat variable
expgrades = c(0.3, 0.6,0.1) # theoretical distribution
chisq.test(grades) # exp calc from grades itself not correct
(ctest = chisq.test(x=grades, p=expgrades))
ctest$statistic
(q= qchisq(1-.05,df=2))
ctest$method

x <- rchisq(1000, 5) 
hist(x, prob=TRUE, ylim=c(0,0.2)) 
curve( dchisq(x, df=2), col='blue', add=TRUE)
abline(v=c(q,ctest$statistic), col=c('green','red'))
text(ctest$statistic-1, 0+0.1,srt=90, cex=0.75,
     labels = 'Test Statistics right of Table Line')


# Data from Table
HairEyeColor  # Margin Table of Hair vs Eye Colors : Count
(eye = margin.table(HairEyeColor,2))
eye
(ctest2 = chisq.test(x=eye, p=c(0.5,0.25,0.15,0.1)))
ctest2
qchisq(1-.05,df=3)
ctest2$p.value # < 0.05
ctest2$statistic # > chisq table value 
# Reject Ho : Data is not expected

# Data from Data Frame


================================================
FILE: 13-statsH/33e-chisqtest2.R
================================================
# Chi Square Test------------

#two categorical variables from a single population.
#Used to determine whether there is a significant association between the two variables.

#egs ------
#determine whether gender is related to voting preference.
# Smoking habits related to gender

# Hypothesis --------
#Ho: Variable A and Variable B are independent. 
#Ha: Variable A and Variable B are not independent.

# Interpret Values
#If the sample findings are unlikely, given the null hypothesis, 
#the researcher rejects the null hypothesis. Typically, this involves 
#comparing the P-value to the significance level, 
#and rejecting the null hypothesis when the P-value is less than the significance level.



#Test of Independence -----------
# When to use ------------
#1 Sampling method is simple random sampling.
#2 Variables under study are each categorical.
#3 Sample data are displayed in a contingency table, 
#   the expected frequency count for each cell of the table is at least 5.


# Link1 ------------
#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html
#http://stattrek.com/chi-square-test/independence.aspx?Tutorial=AP
#http://www.r-tutor.com/elementary-statistics/goodness-fit/chi-squared-test-independence

# Enter Data -----------
row1 = c(91,90,51)                   # or col1 = c(91,150,109)
row2 = c(150,200,155)                # and col2 = c(90,200,198)
row3 = c(109,198,172)                # and col3 = c(51,155,172)
data.table = rbind(row1,row2,row3)   # and data.table = cbind(col1,col2,col3)
data.table

chisq.test(data.table)




# Data From Data Table -----------
UCBAdmissions 
ftable(UCBAdmissions, row.vars=c("Admit"))
str(UCBAdmissions)
deptB = UCBAdmissions[,,2]           # all rows, all cols, but only of layer 2
deptB

chisq.test(deptB)
#Yate's correction for continuity is applied by default when a 2x2 table is entered into the chi square procedure.

chisq.test(deptB)$expected
chisq.test(deptB)$resid
chisq.test(deptB)$statistic



#https://ww2.coastal.edu/kingw/statistics/R-tutorials/independ.html
# Data From Data Frame ----------
# The data frame "survey" in the "MASS" package contains responses from 237 statistics
# students to a series of questions, some of which resulted in categorical answers. 
# For one item, the students were asked to fold their arms, and the recorded result was 
# which arm was on top: right, left, or neither. 
# Let's see how this variable relates to the sex of the student. 
#Ho: Folding Habits is not related to Gender (Independent)
#Ha : Folding Habits relate to Gender (Not Independent) Males do it this way, F do it this


data(survey, package="MASS")
str(survey)# Interpret the results
# p-value > 0.05 : Accept Ho : Sex and Folding are Independent (not related)

table(survey$Sex, survey$Fold)
(ct3 = chisq.test(x=survey$Sex, y=survey$Fold))           
ct3$observed; ct3$expected

rbind(ct3$observed, ct3$expected)
ct3$residuals


# Example Elections
#the.data = scan() # Manually entering data
(result = matrix(c(200,150,50,250,300,50), byrow=T, ncol=3))
dimnames(result) = list(gender=c('male','female'), party=c('bjp','cong','aap') )
result
#Ho : Gender and Party preference is independent
#Ha : Gender and Party Preference is related (not independent)
(ct4 = chisq.test(x=result))
ct4$observed; ct4$expected
ct4$statistic
ct4$p.value
qchisq(1-.05,df=(2-1)*(3-1))
#pvalue < 0.005 : Reject Ho : Gender -Party are related
#calc > table : Rejection Region

ct4$residuals
#Standardized residual = (observed count – expected count) / √expected count


================================================
FILE: 13-statsH/34b-goodnessfit.R
================================================



# Chi Sq - Goodness of Fit
#From the library Mass, select the survey dataset. 
library(MASS)
data(survey)
str(survey)

#What are the levels of Smokers. 
levels(survey$Smoke)


#Create frequency distribution using table command.
smoke.freq = table(survey$Smoke)
round(prop.table(smoke.freq)* 100,2)



#Percentage distribution of a given sample data is as follows. 
#Heavy   Never   Occas   Regul 
#4.5%   79.5%    8.5%    7.5%
#Carryout a Chi Square Test of Goodness of of Fit.  
#Determine whether the sample data in survey supports Distribution at .05 significance level
# Ho: Sample Data follows the distribution
# Ha: Sample Data does not fit the distribution
(smoke.prob = round(prop.table(smoke.freq),3))
sum(smoke.prob)
smoke.prob = c(.045, .795, .085, .075)
sum(smoke.prob)

smoke.prob
chisq.test(smoke.freq, p=smoke.prob) 

#As the p-value 0.991 is greater than the .05 significance level,
# we do not reject the null hypothesis that the sample data in survey
#  supports the campus-wide smoking statistics.
#

================================================
FILE: 13-statsH/datadistr.R
================================================
# Distribution of Data
data2 = c(3,5,7,5,3,2,6,8,5,6,9,4,5,7,3,4)
data2
length(data2)
summary(data2)
str(data2)
table(data2)
names(table(data2))
hist(data2, labels=T, ylim=c(0,8),main='Histogram of Data'
     ,ylab='Frequency', xlab='Size of Data'
     , col='gray')
#breaks	 :the n+1 cell boundaries 
#(= breaks if that was a vector). These are the nominal breaks, not with the boundary fuzz.
hist(x, breaks = "Sturges",
     freq = NULL, probability = !freq,
     include.lowest = TRUE, right = TRUE,
     density = NULL, angle = 45, col = NULL, border = NULL,
     main = paste("Histogram of" , xname),
     xlim = range(breaks), ylim = NULL,
     xlab = xname, ylab,
     axes = TRUE, plot = TRUE, labels = FALSE,
     nclass = NULL, warn.unused = TRUE)

hist(data2, label=T, ylim=c(0,8), breaks=3)
hist(data2, label=T, ylim=c(0,8), breaks=7)
hist(data2, label=T, ylim=c(0,8), breaks=2:9) # same
hist(data2, label=T, ylim=c(0,8), breaks=c(2,3,4,5,6,7,8,9)) # same
hist(data2, label=T, ylim=c(0,8), breaks=c(2,4,5,6,9)) # different
#unequal width range; convert to density
hist(data2, label=T,breaks=c(2,4,5,6,9)) # different
?hist
hist(data2, label=T, ylim=c(0,0.3), breaks=c(2,4,5,6,9))

# Density
table(data2)
df2 = data.frame(t(table(data2)))
df2
df2$pf = round(df2$Freq/sum(df2$Freq),2)
df2$cpf = round(cumsum(df2$pf),2)
df2
dens = density(data2)
names(dens)
str(dens)
hist(data2, freq=F,      labels=T)
lines(density(data2), lty=2, col='red')
#lines(density(data2), type='rectangular')

abline(h=df2$pf,col='green')
#text(2:9,df2$pf,labels=df2$pf,col='blue', cex=.5)
#text(dens$x ,dens$y,labels=df2$pf,col='blue', cex=.5)

?lines

plot(density(data2))

plot(density(data2), main=' Density Plot', xlab=' Size of Class')

hist(data2, freq=F, ylim=c(0,0.3))
lines(density(data2), lty=2)


================================================
FILE: 13-statsH/htestnd1.R
================================================
# Hypo Test on ND data
#https://www.r-bloggers.com/hypothesis-testing-on-normally-distributed-data-in-r/
#interested in knowing if the average value of a certain parameter differs significantly from 
#a given value within a well defined confidence level: 
#Ho : U = Uo
#Ha : U != Uo
#mutually Exclusive Hypothesis
#First, we should check what kind of distribution our data follows. 
#Roughly speaking, if the number of samples is > 30 we can plot 
#a histogram to get a visual grasp of the distribution and then 
#run a few simple function, 
#to assess the skewness and kurtosis of the distribution. 

The code below can be used to perform a z-test under the following
# assumptions:
# 1. The data is normally distributed
# 2. Samples are iid
# 
# Remember that:
# 1.Low pvalue: strong empirical evidence against h0
# 2.High pvalue: little or 'no' empirical evidence against h0
#
# Generally, as a rule of thumb pvalue < 0.05 can be considered low 
# however it depends on many factors and I'd rather not dive into 
# this :)

# Data to perform the test on
data_vector <- c(63, 75, 84, 58, 52, 96, 63, 55, 76, 83)

# Left tail test
# H0: mu >= mu0
# H1: mu < mu0
t.test.left <- function(data, mu0, alpha)
{
  t.stat <- (mean(data) - mu0) / (sqrt(var(data) / length(data)))
  dof <- length(data) - 1
  t.critical <- qt(alpha, df= dof) #Es alpha 0.05 -> -1.64 (df=Inf)
  p.value <- pt(t.stat, df= dof)
  
  if(t.stat <= t.critical)
  {
    print("Reject H0")
  }
  else
  {
    print("Accept H0")
  }
  print('T statistic')
  print(t.stat)
  print('T critical value')
  print(t.critical)
  print('P value')
  print(p.value)
  print("#####################")
  
  return(t.stat)
}

t.test.left(data_vector, 73, 0.05)

# Right tail test
# H0: mu <= mu0
# H1: mu > mu0
t.test.right <- function(data, mu0, alpha)
{
  t.stat <- (mean(data) - mu0) / (sqrt(var(data) / length(data)))
  dof <- length(data) - 1
  t.critical <- qt(1-alpha, df= dof) #Es alpha 0.05 -> 1.64 (df=Inf)
  p.value <- 1 - pt(t.stat, df= dof)
  
  if(t.stat >= t.critical)
  {
    print("Reject H0")
  }
  else
  {
    print("Accept H0")
  }
  print('T statistic')
  print(t.stat)
  print('T critical value')
  print(t.critical)
  print('P value')
  print(p.value)
  print("#####################")
  
  return(t.stat)
}

t.test.right(data_vector, 73, 0.05)


# Two tail z test
# H0: mu = mu0
# H1: mu != mu0
t.test.twoTails <- function(data, mu0, alpha)
{
  t.stat <- abs((mean(data) - mu0)) / (sqrt(var(data) / length(data)))
  dof <- length(data) - 1
  t.critical <- qt(1-alpha/2, df= dof) #Es alpha 0.05 -> -1.9599 (df=Inf)
  p.value <- 2*(1-pt(t.stat, df= dof))
  
  if(t.stat >= t.critical)
  {
    print("Reject H0")
  }
  else
  {
    print("Accept H0")
  }
  print('T statistic')
  print(t.stat)
  print('T critical values')
  print(c(-t.critical,t.critical))
  print('P value')
  print(p.value)
  print("#####################")
  
  return(t.stat)
}

t.test.twoTails(data_vector, 73, 0.05)

#Plot of Student Distribution
dfs <- length(data_vector)-1
x_ <- seq(-8,8,0.1)
y <- dt(x_,dfs)
t.val <- qt(1-0.05,df=dfs)
plot(x_,y,type='l',lwd=3,col='blue',xlab='x',
     ylab='Density',main='Student distribution \n 9 Degree of Freedom')
abline(v=0)
abline(v=t.val,lwd=2,col='red')
points(t.val,dt(t.val,dfs),lwd=3,col='red')


================================================
FILE: 13-statsH/randomdistr.R
================================================
# Random Numbers

#uniformly distributed (flat) random numbers, use runif(). 
#By default, its range is from 0 to 1.
runif(1)

# Get a vector of 4 numbers
runif(4)

# Get a vector of 3 numbers from 0 to 100
runif(3, min=0, max=100)


# Get 3 integers from 0 to 100
# Use max=101 because it will never actually equal 101
floor(runif(3, min=0, max=101))

# This will do the same thing
sample(1:100, 3, replace=TRUE)

# To generate integers WITHOUT replacement:
sample(1:100, 3, replace=FALSE)



#Generate a random number between 5.0 and 7.5

x1 <- runif(1, 5.0, 7.5)
x1

x2 <- runif(10, 5.0, 7.5)
x2



x3 <- sample(1:10, 1)
x3

x4 <- sample(1:10, 5, replace=T)
x4

x5 <- sample(1:40, 6, replace=F)
x5

sample(state.name, 10)

sample(state.name, 52)  # error


?runif
?sample
?.Random.seed



# Random Numbers - Normal Distribution
rnorm(4)
#> [1] -2.3308287 -0.9073857 -0.7638332 -0.2193786

# Use a different mean and standard deviation
rnorm(4, mean=50, sd=10)
#> [1] 59.20927 40.12440 44.58840 41.97056

# To check that the distribution looks right, make a histogram of the numbers
x <- rnorm(400, mean=50, sd=10)
hist(x)


# Sample
#Another approach to randomization is the sample function,
#which pulls elements from an object (such as a vector) of defined values or, alternatively, can be specified to select cases from a string of integers. The function also has the option of specifying whether replacement will be used or not.

?sample
sample(x, size, replace = FALSE, prob = NULL)
#If x has length 1, is numeric (in the sense of is.numeric)
#and x >= 1, sampling via sample takes place from 1:x.


RandomSample <- sort(sample(99, 100, replace=TRUE))
RandomSample

RandomSample <- sort(sample(1:99, 100, replace=TRUE))
RandomSample

# same Random nos with set.seed
set.seed(100)
RandomSample <- sort(sample(99, 100, replace=TRUE))
RandomSample
set.seed(100)
RandomSample <- sort(sample(99, 100, replace=TRUE))
RandomSample

# Conditions in Sample
# sample()'s surprise -- example
x <- 1:10
sample(x[x >  8]) # length 2
sample(x[x >  9]) # oops -- length 10!
sample(x[x > 10]) # length 0

# Resample
resample <- function(x) x[sample.int(length(x))]
resample(x[x >  8]) # length 2
resample(x[x >  8]) # length 2

resample(x[x >  9]) # length 1
resample(x[x > 10]) # length 0


# the list of party-goers
dwarves <- c("Fíli", "Kíli", "Balin", "Dwalin", "Óin", "Glóin", "Bifur", "Bofur", "Bombur", "Ori", "Nori", "Dori", "Thorin") 
# draw a sorted sample of 5 without replacement
Party <- sort(sample(dwarves, 5))
# print the names
Party


================================================
FILE: 13-statsH/shadeareainplot.R
================================================
x_mean <- c(1, 2, 3, 4)
y_mean <- c(1, 1, 1, 1)

y_max <- c(4, 4, 4, 4)
y_min <- c(-4, -4, -4, -4)


x_shade <- c(2, 3, 4)

y_max_shade <- c(4, 4, 4)
y_min_shade <- c(-4, -4, -4)

plot(x=rep(x_mean, 3), y=c(y_mean, y_max, y_min), bty='n', type="n" )
rect(2,-4,4,4,col = rgb(0.5,0.5,0.5,1/4))
arrows(x0=x_mean, y0=y_min, x1=x_mean, y1=y_max, length=0)
points( x=x_mean, y=y_mean, pch=16)




# ------------

xx <- c(1:50)
yy <- rnorm(50)
n <- 50
hline <- 0
plot (yy ~ xx, type="n", axes=FALSE, ann=FALSE)
text(x=xx,y=min(yy)+max(yy),labels='a')
polygon(c(xx[1], xx, xx[n]), c(min(yy), yy, min(yy)),    
        col=rgb(1, 0, 0,0.5), border=NA)


#------normal plot
x=seq(-4,4,length=200)
x
y=dnorm(x)
plot(x,y,type="l", lwd=2, col="blue")
x=seq(-1,1,length=100)
y=dnorm(x)
polygon(c(-1,x,1),c(0,y,0),col="gray")
abline(v=c(-1,1))


#--------------

par.default <- par(no.readonly=TRUE)
par(mar=c(5,6,4,2)+0.1)
par(pty="s")

# create data
x <- 1:100
Exp <- 50 + x*0.1
Con.High <- Exp + 5 + rnorm(100)
Con.Low <- Exp - 5 + rnorm(100)
One.Run <- Exp + rnorm(100,0,2)

# create plot
plot(Exp~x,type="l",
     xlab= "Time Step",
     ylab="Populatioh Size (N)",
     lty="dashed",
     ylim=range(One.Run,Exp,Con.High,Con.Low),
     cex.lab=1.5,las=1,cex.axis=1.5)

# add polygon and overlplot the lines
polygon(c(x,rev(x)),c(Con.Low,rev(Con.High)),col="thistle",border=NA)
lines(x,Exp,lty="solid",col="purple",lwd=1.5)
lines(x,One.Run)


par(par.default)
rm(list=ls())

#-------------

# Create data for the area to shade
cord.x <- c(-3,seq(-3,-1,0.01),-1) 
cord.y <- c(0,dnorm(seq(-3,-1,0.01)),0) 

# Make a curve
curve(dnorm(x,0,1), xlim=c(-3,3), main='Standard Normal') 

# Add the shaded area.
polygon(cord.x,cord.y,col='skyblue')


================================================
FILE: 13-statsH/tests1.R
================================================

#H0: no significant difference
#H1: significant difference




t.test(A,B)
#reject null hypothesis
?t.test
t.test(A,B, alternative='two.sided', conf.level = 0.95)
qt(df=9,p=.05)

t.test(A,B, alternative='less', conf.level = 0.95)
qt(df=9,p=.05)
t.test(A,B, alternative='greater', conf.level = 0.95)
qt(df=9,p=.05)

(t= (500 - 560)/ sqrt(100^2/10 + 121^2/10))
n1=10 ; n2=10; s1=100; s2=121
(PV = ((n2-1)*s1 + (n2-1)*s2))/(n1 + n2 -2) 
(PS = sqrt(PV))
(t= (500 - 560)/ (Sv * sqrt(1/n1 + 1/n2)))

df= n1+n2-1
qt(df=n1+n2-1, p=0.975)


================================================
FILE: 15-sampling/12b-samples.R
================================================
# Samples from original set
options(scipen=99)

#rollno, product no etc
(rollno=1:10)

#pick 4 random values from x
sample(x=rollno, size=4)
#run again : different values
sample(x=rollno, size=4)

#if same values are required use set.seed
set.seed(1234) ;sample(x=rollno, size=4)
set.seed(1234) ;sample(x=rollno, size=4)


#size more than the length of set of values
sample(x=c('Male', 'Female'), size=10, replace=TRUE)

#probability of occurance is known 
gender = sample(x=c('Male', 'Female'), size=10, replace=TRUE, prob=c(.6,.4))
table(gender)
prop.table(table(gender))

#proportion will not be exactly same as asked due to small size
prop.table(table(sample(x=c('Male', 'Female'), size=1000, replace=TRUE, prob=c(.6,.4))))  # now proportion as required



================================================
FILE: 15-sampling/12e-sampleint.R
================================================
#Samples of integer numbers

#draw out samples for integer nos (max=n) 
sample.int(n=10, size=100, replace=T)

sample.int(n=100, size=10)



================================================
FILE: 15-sampling/14b-stratified.R
================================================
# Stratied Sampling

#Divide data set into groups/strata and select samples from each group

df=mtcars
#group var- cyl, vs, am, gear, carb


#dplyr
library(dplyr)
df %>%  group_by(am, gear) %>%  summarise(count=n())
df %>%  group_by(am, gear) %>%  sample_n(1)



#
library(splitstackshape)
df %>% group_by(am,vs) %>% summarise(count=n()) 
#select 2 from each group
set.seed(1)
#am=0,1; vs=0,1
(stratified(df, c("am", "vs"), 2))
#gear-3,4,5 ; am=0,1
df %>% group_by(gear,am) %>% summarise(count=n()) 
(stratified(df, c("gear", "am"), 1))


#
library(sampling)
summary(df)
names(df)
df %>% group_by(cyl,am) %>% summarise(count=n()) 
interaction(df$cyl, df$am)
levels(factor(df$cyl)); levels(factor(df$am))
#3 x 2 = 6 combinations : vector selecting size from each strat- size
strata(data= df, stratanames = c('cyl', 'am'), size=c(1,2,1,2,1,1), method='srswor')



================================================
FILE: 15-sampling/15b-samplesplit.R
================================================
# Sample Split


library(caTools)
df=mtcars

(train_rows = sample.split(df$am, SplitRatio=0.7))

(train_a =df[train_rows == TRUE, ])
#both methods are same - above and below
(train = df[ train_rows,])
train == train_a #check is same

table(train$am)
prop.table(table(train$am))
nrow(train)
(test  = df[!train_rows,])
table(test$am)
prop.table(table(test$am))
nrow(test)

nrow(train) + nrow(test)
#Both the proportions are approx same


#use with gears
(train_rows2 = sample.split(df$gear, SplitRatio=0.75))

(train2 = df[ train_rows2,])
table(train2$gear)
prop.table(table(train2$gear))
nrow(train2)

(test2  = df[!train_rows2,])
table(test2$gear)
prop.table(table(test2$gear))
nrow(test2)

#Both the proportions are approx same

nrow(train2) + nrow(test2)


================================================
FILE: 15-sampling/15d-datapartition.R
================================================
# Data Frame Split / Partition
#https://www.rdocumentation.org/packages/caret/versions/6.0-80/topics/createDataPartition

library(caret)
df=mtcars

?createDataPartition
# considering response variable as strata
(partition1= createDataPartition(y = factor(df$am), p = 0.75))
#this will return list type of object : set1 for train
length(partition1$Resample1)/32  #approx 0.75

# y :a vector of outcomes. 
# times :the number of partitions to create
# p : the percentage of data that goes to training
# list : logical - should the results be in a list (TRUE) or a matrix with the number of rows equal to floor(p * length(y)) and times columns.
# groups : for numeric y, the number of breaks in the quantiles (see below)

(partition2= createDataPartition(y = factor(df$am), p = 0.75, list=F))  #this will print index no of rows selected
(trainset = df[partition2,])  #train set  
(testset = df[-partition2,])  #test set  

nrow(trainset); nrow(testset)
table(trainset$am);table(testset$am)
#similar proportions of df$am : this should be a factor

#--------

# No of equal partitions for vector
createDataPartition(df$mpg, times=2)
createDataPartition(df$hp, times=3)


================================================
FILE: 15-sampling/15f-partitionfolds.R
================================================
#Create Partition and Folds

data(oil)
head(oilType)
str(oilType)
oilType

length(oilType)
#Two Groups - equal numbers
createDataPartition(oilType, 2)

#Three Groups - equal numbers
createDataPartition(oilType, 3)
oilSamples = createDataPartition(oilType, 3)
str(oilSamples)  #list items 50 each
lapply(oilSamples,FUN=length)


#Create two partitions and plot them together
x = rnorm(100, mean=60, sd=5)
(x1 = createDataPartition(x, list=F)) #p=0.5 default
length(x1)

plot(density(x[x1]), col=2)
rug(x[x1], col=2)
points(density(x[-x1]), type = "l", col = 4)
rug(x[-inA], col = 4)


#Two Parts
createResample(oilType, 2)
createResample(oilType, 3)

#Folds
createFolds(oilType, 5)
createFolds(oilType, 3, FALSE)

createFolds(rnorm(21))  #default fold=10

#Time Slices : shift in phases
createTimeSlices(1:9, 5, 1, fixedWindow = FALSE)
createTimeSlices(1:9, 5, 1, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)

createTimeSlices(1:15, 5, 3)
createTimeSlices(1:15, 5, 3, skip = 2)
createTimeSlices(1:15, 5, 3, skip = 3)


#Group Fold... 
set.seed(131)
groups = sort(sample(letters[1:4], size = 20, replace = TRUE))
groups
table(groups)
folds = groupKFold(groups)
lapply(folds, function(x, y) table(y[x]), y = groups)
# }

================================================
FILE: 22-summary/22b-aggregate.R
================================================
# Aggregate

student1 <- readRDS("./0-Rdata/student1.rds")
str(student1)
dim(student1)
names(student1)
summary(student1)
student1$br
student1submarks = student1[c("java","dbms", "dwm","vlsi","cpp","cbnst")]

str(student1submarks)

# for each gp of btechmarks, average other columns
aggregate(student1submarks, list(student1$btechmarks), mean, na.rm=T)

# Use dot notation 
# Grouping variables of java and dbms
aggregate(. ~ java + dbms, student1submarks, mean)

# Find the means of the numeric variables with the grouping var
head(aggregate(. ~ student1$btechmarks, student1submarks, mean))

# aggregate(formula, data, FUN) # use aggregate.formula for a one-to-one aggregation of “airquality” by the mean of “Ozone” to the grouping variable “Day“.
head(aggregate(java ~ dbms, student1submarks, mean))
student1submarks$java[student1submarks$dbms==31]
student1submarks$java[student1submarks$dbms==40]
aggregate(dbms ~ java, student1submarks, mean)


aggregate(student1submarks, list(student1$btechmarks), mean, na.rm=T)
str(student1submarks)
aggregate(student1submarks[c(2,3,4)], list(student1$btechmarks), mean, na.rm=T)

aggregate(student1submarks$java, list(dbms=student1submarks$dbms), mean, na.rm=T)
aggregate(student1submarks$java, list(dbms=student1submarks$dbms), sd, na.rm=T)

aggregate(cbind(java, dbms) ~ student1$btechmarks, student1submarks, mean)

head(aggregate(student1$btechmarks ~ ., student1submarks, mean)) # bteck last
head(aggregate(. ~ student1$btechmarks, student1submarks, mean)) # btech first

lm(java ~ dbms+vlsi, data = student1submarks)






================================================
FILE: 22-summary/22c-aggregate2.R
================================================
#aggregate2

?ChickWeight
data = ChickWeight
str(ChickWeight)
dim(data)
unique(data$Chick)
unique(data$Diet)

library(ggplot2)
ggplot(data=data, aes(x=Time, y=weight, group=Chick, colour=Chick)) +
  geom_line() +
  geom_point()
#mean wt depending on diet
aggregate(data$weight, list(diet=data$Diet), mean)

#aggregate on time
aggregate(data$weight, list(time=data$Time), mean)

aggregate(data$weight, list(time=data$Time, diet=data$Diet), mean)

#to see the weights over time across different diets
ggplot(data) + geom_line(aes(x=Time, y=weight, colour=Chick)) +
  facet_wrap(~Diet) +
  guides(col=guide_legend(ncol=3))



#Eg1
values <- data.frame(value = c("a", "a", "a", "a", 
                               "a", "b", "b", "b", "c", 
                               "c", "c", "c"))
nr.of.appearances <- aggregate(x = values, 
    by = list(unique.values = values$value), FUN = length)
nr.of.appearances


#Eg2
dates <- data.frame(date = as.Date("2016-01-01", format = "%Y-%m-%d") + 0:729)
dates
last.day <- aggregate(x = dates["date"],by = list(month = substr(dates$date, 1, 7)), FUN = max)
last.day 


options(digits=3)
attach(mtcars)
aggregate(mtcars, by=list(cylinder= cyl,gears = gear), FUN=mean, na.rm=TRUE)



================================================
FILE: 22-summary/23b-freqdistr1.R
================================================
# Frequency Distribution

#Discrete Cat Data
attend = c('A','P','P','A','P','A')
table(attend)
cbind(table(attend))  # A & P on left side

#Discrete Numeric Data

color=c('Blue','Green','Magenta','Green','Black','Blue','Black')
x2 = table(color)
x2
x2a = cbind(x2)
x2a
hist(x2a)  # not relevant 
barplot(x2a)  # not good
barplot(x2a, beside=T)  # Better
unique(color) ; length(unique(color))
barplot(x2a, beside=T, col=rainbow(length(unique(color))))
pie(x2a)

#Continuous Data 
set.seed(1234)
x3 = runif(100,0,150)  # 0 to 150 marks range, 100 values 
x3
x3 = ceiling(x3)  #round to higher value
x3
range(x3)
# Divide range into step of 15 ie 10 levels
breaks = seq(0,150,by=15)
breaks
length(breaks)
x3
#x3[1] = 60; x3[2] = 75
x3.cut = cut(x3, breaks)
x3.cut
x3.cut = cut(x3, breaks, labels=letters[1:10])
x3.cut
x3.cut = cut(x3, breaks, labels=letters[1:length(breaks)-1])
x3.cut

?cut
x3a = table(x3.cut)
x3a
x3b = cbind(x3a)
x3b
hist(x3b)
pie(x3b)
barplot(x3b, beside=T)
barplot(x3b, beside=T, names.arg =rownames(x3b))
plot(x3b)

#2----
# Frequency 2
# Relative Frequency
#http://www.r-tutor.com/elementary-statistics/qualitative-data/relative-frequency-distribution-qualitative-data


#3----
#FD3





# cum relative Freq Graph
duration = faithful$eruptions 
breaks = seq(1.5, 5.5, by=0.5) 
duration.cut = cut(duration, breaks, right=FALSE) #closed on left
duration.freq = table(duration.cut) 
duration.cumfreq = cumsum(duration.freq) 
duration.cumfreq
duration.cumrelfreq = duration.cumfreq / nrow(faithful)

cumrelfreq0 = c(0, duration.cumrelfreq) 
cumrelfreq0
plot(breaks, cumrelfreq0, 
     main="Old Faithful Eruptions",  # main title 
     xlab="Duration minutes", 
     ylab="Cumulative eruption proportion") 
lines(breaks, cumrelfreq0)        # join the points


#Eg3
Fn = ecdf(duration) 
?ecdf
plot(Fn, 
     main="Old Faithful Eruptions", 
     xlab="Duration minutes", 
     ylab="Cumulative eruption proportion")


# Table of Value - Mean Median Mode

# Observation Data  -------------
marks = c(43, 60, 37, 48, 65, 48, 57, 78, 31, 59, 50)
marks1 = scan() 
# copy column of values from Excel 
#and paste & enter after the last value
marks1

mean(marks) # 52.36
median(marks) # 50

estimate_mode <- function(x) {
  d <- density(x)
  d$x[which.max(d$y)]
}
estimate_mode(marks)
#50.89

# Quantile, Percentile, --------
quantile(marks)
sort(marks)
quantile(marks, c(0,.1,.2,.25, .4, .5, .75, .9, 1))
quantile(marks, c(0,.09,.18,.27, .36, .45, .55, .64, .73, .82, .91, 1))
IQR(marks)
quantile(marks, c(.25,.75))
(iqr = quantile(marks, .75) - quantile(marks,.25))

summary(marks)


# Discrete Data

#marks2 = scan()

marks2a =  c(3.5, 4.5, 5.5, 6.5, 7.5,8.5, 9.5)
freq2 = c(3,7,22,60,85,32,8)
cbind(marks2a, freq)  # see how it looks - excel
df2 = data.frame(marks2a, freq2)
df2
df2$marksfreq = df2$marks2a * df2$freq2
df2
sum(df2$marksfreq)
meanmarks2a = sum(df2$marksfreq)/ sum(df2$freq2)
meanmarks2a
midfreq = sum(freq2)/2
df2$cumfreq = cumsum(df2$freq2)
df2
# Median
df2$marks2a[df2$cumfreq > midfreq]  # X which are more than mid value
# use min to select least value
median2 = min(df2$marks2a[df2$cumfreq > midfreq]) # 
median2

#mode
max(df2$freq2)
which(df2$freq2 == max(df2$freq2))  # index No
df2$marks2[5]

mode2 = df2$marks2[ which(df2$freq2 == max(df2$freq2))]
mode2

# real data is not like this - it is observations
#create data from the table given
x2a = rep(marks2a, freq2)
x2a  # total obsverations
table(x2a)
cbind(table(x2a)) # Looks like the orginal table
mean(x2a)
median(x2a)
estimate_mode(x2a)


# Discrete Data - Cat Labels -------------
?scan
#marks2b = scan(what = character())
marks2b =c ("A", "B", "C", "D", "E", "F", "G") 
freq2
x2b = rep(marks2b, freq2)
x2b
table(x2b)
cbind(table(x2b))  # looks similar

mean(x2b) # will not work becoz data is not numeric
# Median  - E
median(x2b) # mid value if arranged in series
length(x2b)
mid = ceiling(length(x2b)/2) 
mid
x2b[mid]
order(x2b)[mid]
x2b[order(x2b)[mid]]

# Mode - max freq

estimate_mode(x2b)  # does not work
density(x2b)  # because u cannot calc density 

#simples 
table(x2b)
rownames(table(x2b))
colnames(table(x2b))  # does not work

max(table(x2b))
mode2b1 = rownames(table(x2b))[which(table(x2b) == max(table(x2b)))]
mode2b1

#Method3
freq <- tapply(x2b,x2b,length)
table(x2b)
names(freq)[which.max(freq)]


# continuous Data

#class = scan(what='character')
class3 =c("0-10","10-20","20-30","30-40","40-50","50-60", "60-70")
class3
#freq3 = scan()
freq3 = c(3,  7, 22, 60, 85, 32,  8)
as.numeric(unlist(strsplit(class3,"-")))  # long method
midx3 = c(5,15,25,35,45,55,65)
cbind(class3, midx3, freq3)  #midx3 is also character
df3 = data.frame(class3, midx3, freq3)
df3
df3$midxfreq = df3$midx3 * df3$freq3
df3

# and so..on like previous eg

# create observation data from contiuous series
x3 = rep(midx3, freq3)
x3
sum(x3)
table(x3)
cbind(table(x3))  # looks like original table

mean(x3)
median(x3)
estimate_mode(x3)


================================================
FILE: 22-summary/23c-freqdistr2.R
================================================
#Freq Distribution2

color= sample(c('Red','Blue','Green','Black'), size=100, prob=c(.3,.2,.1,.4), replace=T)
table(color)

cbind(table(color))
transform(table(color))

hist(color)  #error as values are not numeric

barplot(cbind(table(color)), beside=T)

#Continuous
#Continuous Data 
set.seed(1234)
marks = ceiling(runif(100,0,150))  # 0 to 150 marks range, 100 values 
range(marks)
h=hist(marks)
plot(h)
h$counts
h$mids
cbind(h$mids, h$counts) #Mid Value & count


# Divide range into step of 15 ie 10 levels
breaks = seq(0,150,by=15)
marks.cut = cut(marks, breaks, right=FALSE)
marks.table = table(marks.cut)
transform(marks.table)

#Cumulative Freq
marks.cumfreq = cumsum(marks.table)
plot(marks.cumfreq, type='b')

#Package
library(plyr)
count(color)
count(marks.cut)



================================================
FILE: 22-summary/23d-freqdistr3.R
================================================
#Frequency Distribution 3

marks = ceiling(runif(100,0,150))  # 0 to 150 marks
#Package----
library(fdth)
#https://cran.r-project.org/web/packages/fdth/fdth.pdf
(tb = fdt(marks))
# Histograms
plot(tb) # Absolute frequency histogram
plot(tb, main='My title')
plot(tb, x.round=3, col='darkgreen')
plot(tb, xlas=2)
plot(tb, x.round=3, xlas=2, xlab=NULL)
plot(tb, v=TRUE, cex=.8,x.round=3,xlas=2, xlab=NULL,  col=rainbow(11))
plot(tb, type='fh') # Absolute frequency histogram
plot(tb, type='rfh') # Relative frequency histogram
plot(tb, type='rfph') # Relative frequency (%) histogram
plot(tb, type='cdh') # Cumulative density histogram
plot(tb, type='cfh') # Cumulative frequency histogram
plot(tb,  type='cfph') # Cumulative frequency (%) histogram
# Polygons
plot(tb, type='fp') # Absolute frequency polygon
plot(tb, type='rfp') # Relative frequency polygon
plot(tb, type='rfpp') # Relative frequency (%) polygon
plot(tb, type='cdp') # Cumulative density polygon
plot(tb, type='cfp') # Cumulative frequency polygon
plot(tb, type='cfpp') # Cumulative frequency (%) polygon

# Density
plot(tb, type='d') # Density
# Summary
tb
summary(tb) # the same
print(tb) # the same
show(tb) # the same

summary(tb, format=TRUE) # It can not be what you want to publications!
summary(tb,  format=TRUE,
        pattern='%.2f') # Huumm ..., good, but ... Can it be better?
summary(tb,  col=c(1:2, 4, 6),format=TRUE,  pattern='%.2f') # Yes, it can!

x=marks
range(x) # To know x
summary(fdt(x,  start=1, end=9,  h=1),col=c(1:2, 4, 6),    format=TRUE,   pattern='%d') # Is it nice now?

# The fdt.object
tb[['table']] # Stores the feq. dist. table (fdt)
tb[['breaks']] # Stores the breaks of fdt
tb[['breaks']]['start'] # Stores the left value of the first class
tb[['breaks']]['end'] # Stores the right value of the last class
tb[['breaks']]['h'] # Stores the class interval
as.logical(tb[['breaks']]['right']) # Stores the right option

# Theoretical curve and fdt
y <- rnorm(1e5,  mean=5, sd=1)
tb <- fdt(y,  k=100)
plot(tb, type='d', # density
     col=heat.colors(100))
curve(dnorm(x,mean=5, sd=1),n=1e3, add=TRUE, lwd=4)



#
#

================================================
FILE: 22-summary/23f-FD.R
================================================
# FD

#Eg2 https://cnx.org/contents/yTlmgmzh@4/Frequency-Relative-Frequency-a
# Entering the data : 20 records
hours.worked = c(5, 6, 3, 3, 2, 4, 7, 5, 2, 3, 5, 6, 
                 5, 4, 4, 3, 5, 2, 5, 3)

# A general frequency table
table(hours.worked)
## hours.worked
## 2 3 4 5 6 7 
## 3 5 3 6 2 1

# Relative frequency table
length(hours.worked)
table(hours.worked)/length(hours.worked)
## hours.worked
##    2    3    4    5    6    7 
## 0.15 0.25 0.15 0.30 0.10 0.05

# To get cumulative frequencies, we need to put
# the hours into different intervals
x = table(cut(hours.worked, breaks = c(1:7)))
x
# Cumulative frequencies
cumsum(x)
## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] 
##     3     8    11    17    19    20

# Cumulative relative frequencies
cumsum(x)/length(hours.worked)
## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] 
##  0.15  0.40  0.55  0.85  0.95  1.00




================================================
FILE: 22-summary/23f-freqdistr.R
================================================
# freq dist

#Eg1
library(MASS)                 # load the MASS package 
school = painters$School      # the painter schools 
school.freq = table(school)   # apply the table function
school
school.freq
head(painters)
nrow(painters) # no of rows/ records
school.relfreq = school.freq / nrow(painters)
school.relfreq
cbind(school.relfreq) 
round(cbind(school.relfreq),2)
school2 = data.frame(round(cbind(school.relfreq),2))
hist(school2$school.relfreq)

#Cumulative Relative Frequency
school2$cum = cumsum(as.numeric(school2$school.relfreq))
school2
plot(school2$cum, type='l')




================================================
FILE: 22-summary/24b-freqdistr4.R
================================================
# FD with DF
library(fdth)

mdf <- data.frame(X1=rep(LETTERS[1:4], 25), X2=as.factor(rep(1:10, 10)),Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA), Y2=rnorm(100, 60, 4), Y3=rnorm(100, 50, 4), Y4=rnorm(100, 40, 4))
mdf
(tb <- fdt(mdf))
# Histograms
plot(tb, v=TRUE)
plot(tb, col=rainbow(8))
plot(tb, type='fh')
dev.off()
par(mar=c(1,1,1,1))
#graphics.off()
plot(tb, type='rfh')
plot(tb,type='rfph')
plot(tb,
     type='cdh')
plot(tb,
     type='cfh')
plot(tb,
     type='cfph')
# Poligons
plot(tb,
     v=TRUE,
     type='fp')
plot(tb,
     type='rfp')
plot(tb,
     type='rfpp')
plot(tb,
     type='cdp')
plot(tb,
     type='cfp')
plot(tb,
     type='cfpp')


================================================
FILE: 22-summary/24f-freqdistr5.R
================================================
# FD - categories

library(fdth)
## Categorical
x <- sample(x=letters[1:5],  size=5e2,rep=TRUE)
x
(fdt.c <- fdt_cat(x))
(fdt.c <- fdt_cat(x,  sort=FALSE))


#----
# Data.frame: multivariated with two categorical
##================================================
mdf <- data.frame(c1=sample(LETTERS[1:3], 1e2, rep=TRUE), c2=as.factor(sample(1:10, 1e2, rep=TRUE)), n1=c(NA, NA, rnorm(96, 10, 1), NA, NA), n2=rnorm(100, 60, 4),n3=rnorm(100, 50, 4))
head(mdf)
(fdt.c <- fdt_cat(mdf))
(fdt.c <- fdt_cat(mdf, dec=FALSE))
(fdt.c <- fdt_cat(mdf,sort=FALSE))
(fdt.c <- fdt_cat(mdf,  by='c1'))#error


#----
x <- matrix(sample(x=letters[1:10],  size=100, rep=TRUE), nc=2, dimnames=list(NULL, c('c1', 'c2')))
x
head(x)
(fdt.c <- fdt_cat(x))



================================================
FILE: 22-summary/25g-freqdistr6.R
================================================
# Freq Distr
library(freqdist)

# Create Sample Data Frame
data <- c(1,2,3,4,4)
data <- as.data.frame(data)
# Run Frequency Distribution Command on Data Frame
freqdist(data)


================================================
FILE: 22-summary/31c-rowsums1.R
================================================
# Row and Column Sums / Means

?rowSums
M = matrix(1:12, nrow=4)
M
rowSums(M)
cbind(M,rowSums(M))
addmargins(M)
addmargins(M,2)
margin.table(M,1)
cbind(M,margin.table(M,1))

colSums(M)
cbind(M,colSums(M)) # Check ?
rbind(M,colSums(M))
addmargins(M, 1)
margin.table(M,2)
rbind(M,margin.table(M,2))

# Means
rowMeans(M)
cbind(M, rowMeans(M))
addmargins(M,2,FUN=list(list(mean, sum, sd, var, median)))
addmargins(M,c(1,2),FUN=list(list(mean, sum, sd),
                             list(var, median)))

?colMeans(M)
rbind(M,colMeans(M))
addmargins(M,1, FUN=list(mean))

(hostel = matrix(rep(c(TRUE, FALSE), 12), nrow=3))
rowSums(hostel)
colSums(hostel)
rowMeans(hostel)
colMeans(hostel)

#data frame
df = as.data.frame(M)
class(df)
rowSums(df)


?addmargins

?rowsum
#rowsum - Colsum - M/ F based on gp variable
rowsum(x=mtcars, group=mtcars$cyl, reorder = T)

rowsum(x=mtcars, group=mtcars$hp, reorder = T)
# cyl - 4,6,8 ; cyl - sum 

?colsum  # NA

colSums()

colMeans()

rowMeans()

rowSums()

rownames(x, do.NULL = TRUE, prefix = "row")
rownames(x) <- value

row.names()

colnames(x) <- value
?colnames(mtcars,do.NULL = T, prefix='col')
colnames(mtcars)
x = mtcars



m0 <- matrix(NA, 4, 0)
rownames(m0)

m2 <- cbind(1, 1:4)
colnames(m2) = colnames(m2, do.NULL = FALSE, prefix='col')
colnames(m2)

colnames(m2) = NULL
colnames(m2)
# do.NULL - if F & names are empty, names are created
# if T, names are not empty, names are not created
colnames(m2) = colnames(m2, do.NULL = FALSE, prefix='sub')
colnames(m2)
m2
(m2 = cbind(m2, 1:4))
is.na(colnames(m2))
colnames(m2)[3] = NULL

colnames(m2)
colnames(m2) = colnames(m2, do.NULL = TRUE, prefix='subject')
colnames(m2)


colnames(m2) <- c("x","Y")
colnames(m2)

rownames(m2)
rownames(m2) <- rownames(m2, do.NULL = FALSE, prefix = "Obs.")
m2

?setNames
x= setNames(data.frame(matrix(ncol = 3, nrow = 0)), 
         c("name", "age", "gender"))
str(x)



================================================
FILE: 22-summary/32b-addmargin1.R
================================================
# Add Margins

marks = matrix(1:12, nrow=3)
marks
addmargins(marks)
addmargins(marks, FUN=sd)
addmargins(marks, FUN=list(sum, sd))
addmargins(marks, FUN=list(list(sum, sd), list(var, median)))
addmargins(marks, FUN=list(SUM=sum, StdDev = sd))  # no spaces



set.seed(1)
x <- sample( 1:7, 20, replace = TRUE)
y <- sample( 1:7, 20, replace = TRUE)
table(x,y)
tx <- addmargins( table(x, y) )
print(tx, zero.print = ".")




years <- sample(c("2015", "2016", "2017"), 177, replace = TRUE)
matches <- sample(c("test", "oneday"), 177, replace = TRUE)
teams <- sample(c("india", "pakistan", "england", "australia"), 177, replace = TRUE)
dfcricket = data.frame(years, matches, teams)
dfcricket
(A <- table(years, matches, teams))
(B = table(dfcricket))  # this also works
addmargins(A)
addmargins(B)

ftable(A)
ftable(addmargins(A))

# Non-commutative functions - note differences between resulting tables:
ftable(addmargins(A, c(1, 3),
                  FUN = list(Sum = sum, list(Min = min, Max = max))))
ftable(addmargins(A, c(3, 1),
                  FUN = list(list(Min = min, Max = max), Sum = sum)))

# Weird function needed to return the N when computing percentages
sqsm <- function(x) sum(x)^2/100  # user defd function

(C <- table(teams, matches))
apply(C, 2, sum)/100
sweep(C, 2, apply(C, 2, sum)/100, "/")
?sweep
round(sweep(C, 2, apply(C, 2, sum)/100, "/"), 1)


round(sweep(addmargins(C, 1, list(list(All = sum, N = sqsm))), 2,
            apply(C, 2, sum)/100, "/"), 1)
round(sweep(addmargins(C, 2, list(list(All = sum, N = sqsm))), 1,
            apply(C, 1, sum)/100, "/"), 1)

# A total over Bee requires formation of the Bee-margin first:
mC <-  addmargins(C, 2, FUN = list(list(Total = sum)))
round(ftable(sweep(addmargins(mC, 1, list(list(All = sum, N = sqsm))), 2,
                   apply(mC, 2, sum)/100, "/")), 1)


# Example 2


(M = matrix( 1:12, ncol=3))
addmargins(M)
addmargins(M,margin = 1)
addmargins(M, FUN=mean)
addmargins(M, FUN=list(list(mean,sum), list(sd, median)))




================================================
FILE: 22-summary/32c-margintable1.R
================================================
# Margin Tables
#14 Sep 17
parties1 = c('bjp', 'congress', 'sp', 'aap')
length(parties1)
states1 = c('up', 'delhi','haryana', 'punjab','uk')
length(states1)


(col1 = rep(parties1, each=5))
length(col1)

(col2 = rep(states1, times=4 ))
length(col2)
cbind(col1,col2)
party = factor(col1)
state = factor(col2)
#xtabs(col1 ~ col2)
set.seed(1111)
(mps = ceiling(runif(20,25,50)))
sum(mps)
(df1 = data.frame(party, state, mps))
table(df1$party)
table(df1) # not relevant here...
ftable(df1)
str(col1)
#tabulate(df1$col1)  # one coln factor type

#table(df1)
table(df1$party)
table(df1$state)
table(df1[,c('party', 'state')])
table(df1[,c('party', 'state')], dnn=list('Political Parties', 
            'States of India'))

xtabs(mps ~ party + state, data=df1)   #formula method
xtabs(mps ~  state + party , data=df1)   #formula method

statesel = c('up', 'delhi')  # vector of state names (few)
xtabs(mps ~ party + state, data=df1, subset= state %in% statesel)
xtabs(mps ~ party + state, data=df1, 
  subset=c(TRUE,  TRUE, FALSE, FALSE, FALSE,  TRUE,  TRUE, FALSE, FALSE, FALSE,
         TRUE,  TRUE, FALSE, FALSE, FALSE,  TRUE,  TRUE, FALSE, FALSE ))

state %in% statesel
xtabs(mps ~ party + state, data=df1, subset= state %in% statesel,
      drop.unused.levels = T)

partysel = c('bjp', 'aap')
xtabs(mps ~ party + state, data=df1, subset= party %in% partysel)
xtabs(mps ~ party + state, data=df1, subset= party %in% partysel,
      drop.unused.levels = T)

xtabs(mps ~ party + state, data=df1, subset= state %in% statesel & 
      party %in% partysel, drop.unused.levels = T)



ftable(df1)  # where they fought and won
ftable(df1$state, df1$party, df1$mps)  # change order
ftable(party ~ state, data = df1)  # formula method
ftable(party ~ mps, data = df1)  # formula method
ftable(df1, row.vars = 3, col.vars=1) # specify rows and cols to use
ftable(df1, row.vars = 2, col.vars=1) # specify rows and cols to use


# Margins -----------
matresults = xtabs(mps ~ party + state, data=df1)
matresults   # rows - parties, col- states
?margin.table
str(matresults)
margin.table(matresults, margin=NULL)  # total MPS
margin.table(matresults, margin=1)  # total MPS in party Rows
matresults   # rows - parties, col- states

margin.table(matresults, margin=2)  # total MPS in states Cols

# if matresults was an array : dimension=3
margin.table(matresults, margin=3)  # error here

#Prop Table----
prop.table(matresults, margin=NULL)  # proportion of total
round(prop.table(matresults, margin=NULL),2)  # proportion of total
sum(round(prop.table(matresults, margin=NULL),2))  # proportion of total
# total to 1

(p1 = round(prop.table(matresults, margin=1),2)) 
# proportion of total Row
rowSums(p1)  # equal to 1  : rowise sum
addmargins(p1, 2, sum)
colSums(p1) # not equal to 1 

(p2 = round(prop.table(matresults, margin=2),2)) 
# proportion of total Col
rowSums(p2)  # not equal to 1  
colSums(p2) #  equal to 1  colwise sum
addmargins(p2,1,sum)
p2

addmargins(p2)
addmargins(p2,1) # 
addmargins(p2,2) # 



================================================
FILE: 22-summary/32d-proptable.R
================================================
#prop.table


================================================
FILE: 22-summary/32d-tableprop2.R
================================================
# Proportion Table

(M = matrix( 1:12, ncol=3))
prop.table(M)
round(prop.table(M),2)
round(prop.table(M,1),2)
round(prop.table(M,2),2)

addmargins(prop.table(M))
addmargins(round(prop.table(M),2))
addmargins(round(prop.table(M),2),1)
addmargins(round(prop.table(M),2),2)
addmargins(round(prop.table(M),2),3)  # no 3rd dim

margin.table(round(prop.table(M),2))
margin.table(round(prop.table(M),2),1)   # summary of row - 4
margin.table(round(prop.table(M),2),2)   #summary of col  - 3





================================================
FILE: 22-summary/35b-crosstab.R
================================================
# Crosstab
#http://rstudio-pubs-static.s3.amazonaws.com/6975_c4943349b6174f448104a5513fed59a9.html

ID <- seq(1:177)
Age <- sample(c("0-15", "16-29", "30-44", "45-64", "65+"), 177, replace = TRUE)
Sex <- sample(c("Male", "Female"), 177, replace = TRUE)
Country <- sample(c("England", "Wales", "Scotland", "N. Ireland"), 177, replace = TRUE)
Health <- sample(c("Poor", "Average", "Good"), 177, replace = TRUE)
Survey <- data.frame(Age, Sex, Country, Health)
head(Survey)

library(catspec)
?ctab
ftable(Titanic)
str(Titanic)
ctab(Titanic)
ctab(Titanic,type='r')
ctab(Titanic,type=c('r','n'))
ctab(Titanic,type=c("n","c","t","r"),style="w")
mytab<-ftable(Titanic,row.vars=c(1,3),type="r")
mytab
ctab(mytab)
newtab = ctab(mytab, type='r')
newtab
summary(newtab)

# Data Frame ------------
# 
library(survival)
data(logan)
attach(logan)
str(logan)
class(logan) #"data.frame"
head(logan)
ctab(occupation)
ctab(occupation,addmargins=TRUE)
ctab(occupation,style="w",type="c")
ctab(occupation,style="l",type="n")
ctab(occupation,style="l",type="c")

z<-ctab(occupation,addmargins=TRUE,style="l")
z
print(z,addmargins=FALSE,dec.places=5)
summary(z)


================================================
FILE: 22-summary/99a-Pskim.R
================================================
# Data description ----
#http://cmdlinetips.com/2018/03/skimr-a-r-package-to-skim-summary-data-effortlessly/

# install.packages("devtools")
devtools::install_github("ropenscilabs/skimr")

library(gapminder)
library(dplyr)
library(skimr)

x = seq(1000)
y = rnorm(1000,mean=3)
df = data.frame(x,y)
skim(df)

skim(gapminder, lifeExp, gdpPercap) 
skim(mtcars)

funs <- list(
  min = min,
  max = max,
  mean = mean
)
skim_with(numeric = funs, append = FALSE)

gapminder %>% 
  filter(year==2007) %>%
  select(continent,lifeExp,gdpPercap)%>%
  group_by(continent) %>%
  skim()

# Restore defaults
skim_with_defaults()


================================================
FILE: 22-summary/99a-studentdata1.R
================================================
# Initialise Data
# 

#Roll No
rollno = seq(1,100)
rollno

#Subjects
subjects = data.frame(subid= c('STATS','MATHS','EXCEL', 'RPGM', 'PYTHON'),
            maxmks=c(150,150,100,100,100))
subjects

#Date of Birth - Sequence
dob= seq(as.Date("1994/1/1"), by = "week", length.out = 100)
length(dob)

#Course Names
course = c('BSCDS','MSCDS', 'PGDDS', 'BTCSDA')
course

#Allot Courses to all Students in groups
scourse = rep(x=c('BSCDS','MSCDS', 'PGDDS', 'BTCSDA'), c(20,30,15,35) )
length(scourse)
table(scourse)

#Gender
set.seed(1234)
gender = sample(c('M','F'), size=100, replace=TRUE, prob=c(0.8,0.2))
table(gender)

# Data frame for Master Data
student = data.frame(rollno=rollno, name='XYZ', dob=dob,
        gender= gender, course=scourse, batch=c(2016,2017))
student
str(student)

subscore = data.frame(expand.grid(rollno = student$rollno, 
                    subid = subjects$subid), score=0)
str(subscore)
table(subscore$subid)
length(subscore$rollno) # 100 students x 5 subjects = 500 rows
head(subscore)


#subscore$score[subscore$score >= 0 & subscore$subid %in% c('STATS','MATHS')] = round(rnorm(100, mean=110, sd=10))
subjects$subid
#STATS (110,10), MATHS(100,20), EXCEL(80,15), RPGM(70,12), PYTHON(75,10)
subscore$score[subscore$score >= 0 & subscore$subid == c('STATS') ] =
  round(rnorm(100, mean=110, sd=10))
subscore$score[subscore$score >= 0 & subscore$subid == c('MATHS') ] =
  round(rnorm(100, mean=100, sd=20))
subscore$score[subscore$score >= 0 & subscore$subid == c('EXCEL') ] =
  round(rnorm(100, mean=80, sd=15))
subscore$score[subscore$score >= 0 & subscore$subid == c('RPGM') ] =
  round(rnorm(100, mean=70, sd=12))
subscore$score[subscore$score >= 0 & subscore$subid == c('PYTHON') ] =
  round(rnorm(100, mean=75, sd=10))

summary(subscore)
aggregate(subscore$score, by=list(subscore$subid), mean)
head(subscore,70)


library(reshape2)

dfcast = dcast(subscore, rollno ~ subid) # Good Function
melt(dfcast) # Reverse back to original

total = merge(student, dfcast, by='rollno')
total
options(digits=4)
head(total)
aggregate(total[c(7:11)], by=list(total$course, total$batch), FUN=mean)
aggregate(total[c(7:11)], by=list(total$course), FUN=mean)
aggregate(total[c(7:11)], by=list(total$batch), FUN=mean)
aggregate(total[c(7:11)], by=list(total$gender), FUN=mean)
str(total)
library(lubridate)
total$age = as.period(interval(total$dob, Sys.Date()), unit='year')$year
str(total)
aggregate(total[c(7:11)], by=list(total$age), FUN=mean)
write.csv(total, file='studentdata.csv', row.names = F)
?write.csv
# Clustering
# 
# 
mydata = total

library(ggplot2)
ggplot(mydata, aes(STATS, RPGM, color=gender)) + geom_point()
set.seed(20)
sdataCluster <- kmeans(mydata[, 7:11], 5, nstart = 20)
sdataCluster      
sdata$cluster = sdataCluster$cluster
head(sdata[c(1,7:13)])

str(mydata)
mydata2 = mydata[-c(1:6)]
str(mydata2)
# Prepare Data
mydata2 <- na.omit(mydata2) # listwise deletion of missing
head(mydata2)
mydata3 <- scale(mydata2) # standardize variables
head(mydata3)
mydata = mydata3

# No of Clusters
# Determine number of clusters
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata, 
                                     centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares")

# K-Means Cluster Analysis
fit <- kmeans(mydata, 7) # 7 cluster solution
# get cluster means 
aggregate(mydata,by=list(fit$cluster),FUN=mean)
# append cluster assignment
mydata <- data.frame(mydata, fit$cluster)
head(mydata)



# Ward Hierarchical Clustering
d <- dist(mydata, method = "euclidean") # distance matrix
fit <- hclust(d, method="ward.D") 
plot(fit) # display dendogram
groups <- cutree(fit, k=7) # cut tree into 5 clusters
# draw dendogram with red borders around the 5 clusters 
rect.hclust(fit, k=7, border="red")
head(mydata)


================================================
FILE: 22-summary/descriptive.R
================================================
#descriptive 

library(mosaic)
favstats (~ mtcars$mpg)
favstats(~ mtcars$mpg, na.rm = TRUE)


================================================
FILE: 23-functions/21b-rep.R
================================================
# Value generation with : repeat
#rep
?rep
#rep(x, ...)
#rep.int(x, times)
#rep_len(x, length.out)
#times -repeat each element/ vector
#length.out - length of the output vector
#each - x is repeated each times

#1, 10 times
rep(1,times=10)
#1,2,3 : 5 times
rep(c(1,2,3),times=5)
#1- 4 times, 2-4 times, 3 - 4 times
rep(c(1,2,3),each=4)
#(1-4times : 2-times...) - 5 times
rep(1:3, each=4, times=5)
#length of vector 20: 1,2,3,4,5 - 5 times 
(y1=rep(c(1,2,3,4,5),length.out=20, times=3))
length(y1)

rep(1:3, each=4, len=20) #len is abbreviation of length.out 

#1-4times, 2-3 times, 3-2 times
rep(1:3, times=c(4,3,2))


================================================
FILE: 23-functions/21c-seq.R
================================================
# Value Generation with seq

#seq
seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),
    length.out = NULL, along.with = NULL)
#by - number: increment of the sequence.
#length.out - desired length of the sequence. A non-negative number, which for seq and seq.int will be rounded up if fractional.
# along.with - take the length from the length of this argument.
(v1 = c(8,9,10))
seq(1, 50, by=3)
seq(1, by=3, along.with=v1)
seq(from=0, to=50, length.out=5) #divide range 0 to 50 into 5 equal parts



#seq_along : no in seq as per another vector
(a <- c(8, 9, 10))
(b <- c(9, 10))
(c <- 10)

#seq_along----
seq_along(a)
# [1] 1 2 3
seq_along(b)
# [1] 1 2
seq_along(c)
# [1] 1

x= c(8,9,10)
#seq_along(x) takes a vector for x, and it creates a sequence upto the count of elements in the vector.
#seq_along(x) is the same as seq_len(length(x))
seq_along(x)
seq_len(length(x))

#seq_len----
#seq_len(y) takes numeric for y, it creates a sequence upto the number y

#seq_len(x) is the same as seq_len(x[1])
seq_len(x)
seq_len(x[1])  #no in seq upto first position of vector
#When using seq_len, it will give you:
#A warning, if x has more than 1 element.
#An error, if x[1] is not numeric.
#An error, if x is a frame.


#sample

#months
month.abb[1:5]
month.name[1:5]

#alphabets
LETTERS[1:10]
letters[5:15]


================================================
FILE: 23-functions/21g-replicate.R
================================================
# Replicates

?replicate

# 5 values/rows, 4 columns
set.seed(1234)
(m1=cbind(rnorm(5), rnorm(5),rnorm(5),rnorm(5)))
set.seed(1234)
(m2=replicate(4, rnorm(5)))
sum(m1)
sum(m2)


================================================
FILE: 23-functions/21g-seqdates.R
================================================
#Value Generation  - year/months

month.abb[1:12]
month.name[1:10]
month
seq.Date(from='2018/01/07', to='2018/01/07')
month[4]

seq(from, to, by, length.out = NULL, along.with = NULL, ...)
#from / to - start date(reqd), end date(Optional)
#by	increment of the sequence. Optional. See ‘Details’.
#length.out	- integer, optional. Desired length of the sequence.
#along.with	- take the length from the length of this argument.

## first days of years
seq(as.Date("2000/1/1"), as.Date("2018/7/1"), "years")
## by month
seq(as.Date("2018/1/1"), by = "month", length.out = 7)
## quarters
seq(as.Date("2018/1/1"), as.Date("2018/7/1"), by = "quarter")
seq(as.Date("2018/7/1"), as.Date("2018/7/22"), by = "day")


================================================
FILE: 23-functions/22b-letters.R
================================================
# Value generation - letters

#LETTERS and letters (for uppercase and lowercase respectively).
letters[1:4]
LETTERS[seq( from = 1, to = 10 )]
letters[seq( from = 1, to = 10 )]
letters[1:10]
letters[c(1,5,10,26)]


================================================
FILE: 23-functions/25b-interval.R
================================================
# Find Interval

table<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), 
                   habitat=c(1,2,3,4,5,6))
table
table$size <- findInterval(table$population, c(0, 500, 1000, 2000, 3000, 5000), rightmost.closed = TRUE)
table


================================================
FILE: 23-functions/25c-midpoint.R
================================================
# Mid Point


df<- data.frame(lon1=c(-4.568,-4.3980), 
                lat1=c(59.235,56.369), 
                lon2=c(-5.123,-4.698), 
                lat2=c(60.258,59.197) )
df
df$midlong <- apply(df[,c(1,3)], 1, mean) 
df$midlat <- apply(df[,c(2,4)], 1, mean) 
df 



#
library(geosphere) 
#library(midpoint)

(p1 <- matrix(c(df$lon1,df$lat1), ncol=2  ) )
(p2 <- matrix(c(df$lon2,df$lat2), ncol=2  ) )

(midpoint <- as.data.frame(midPoint(p1,p2) ) )


================================================
FILE: 23-functions/27b-recode-car.R
================================================
# Recode
# 
#using car package : recode
(marks = ceiling(runif(10,40,100)))
#The lo keyword tells recode to start the range at the lowest value.
#The hi keyword tells recode to end the range at the highest value.

(markscode <- car::recode(marks, "lo:70=1; 71:80=2;else = 3"))


#now code 1 to 6
(markscode = car::recode(markscode, "1=6"))

# straight method
markscode[markscode==6] = 5
markscode



================================================
FILE: 23-functions/29b-subset.R
================================================
# Subset
#subset( ) function is the easiest way to select variables and observations.

mtcars
# using subset function 
newdata = subset(mtcars, mpg >=20 & mpg < 30,  select=c(mpg, disp))
newdata
#we select cars with mpg >= 20   and < 10 
#we keep variables mpg and disp

# using subset function (part 2)
names(mtcars)
head(mtcars)
newdata = subset(mtcars, cyl == 6 & disp > 150,select=mpg:wt)
newdata

newdata2 = mtcars[ order(mtcars$mpg), c('mpg','wt')]
head(newdata2)

subset(airquality, select = Ozone:Wind)
subset(airquality, Temp > 80, select = c(Ozone, Temp))
subset(airquality, Temp > 80, select = - Temp)

subset(airquality, Month == 8 & Temp > 90) # better
airquality[airquality$Month == 8 & airquality$Temp > 90, ]



================================================
FILE: 23-functions/29c-split1.R
================================================
# Splitting Data Frames

mtcars
# display first 2 cols, split by course
split(mtcars[c(1,2)], mtcars$am)




================================================
FILE: 23-functions/29d-splitdata.R
================================================
# Split data
#partitioning data(train/test) is different from split - 
mtcars
names(mtcars)
df=mtcars[,c('mpg','cyl','disp','hp','wt','gear','am')]

#Case-1----
names(df)

#1a-split by column values
split(df, df$am)

#1b- split -no of rows randomly selected rows between 1 to 5
split(df, sample(1:5, nrow(df), replace=T))
#sample() will assign equal probability to each group.


#Case-2----
#split into bins , 4 here
nrow(df)
stopifnot(nrow(df) %% 4 == 0)
df2    <- df[order(runif(nrow(df))), ]
(bins  <- rep(1:4, nrow(df2) / 4))
split(df2, bins)

#Case-3----
#library(githubinstall)
#githubinstall("minDiff")
library(minDiff)
#split into groups with criteria avg values of mpg, wt
names(df)
df3 <- create_groups(df, criteria_scale = c("mpg",'wt'), sets_n = 4, repetitions = 10)
df3
df3[order(df3$newSet),]

#now split the data and find mean values
(groups = split(df3, df3$newSet))
sapply(groups, FUN=function(i) sapply(i[, c('mpg','wt')], mean))
sapply(groups, function(x) colMeans(x[c('mpg','wt')]))

#rejoin the groups
library(dplyr)
bind_rows(groups) %>%  group_by(newSet) %>%  summarise_all(funs(mean))

#bind the splitted lists
library(data.table)
rbindlist(groups)


#Case-4----
set.seed(1234)
split(df, sample(rep(1:2, 16)))



#Case-5 ----
library(plyr)
dlply(df, "am", identity)
#spliting by multiple columns values
dlply(df,c( "am",'cyl'), identity)

dlply(df, c("cyl", "am"))


#Case-6----
#slow
df %>% group_by(am) %>% do(data = (.)) %>% select(data) %>% map(identity) 


================================================
FILE: 23-functions/29e-partitiondata.R
================================================
# Data Set Partition

#random basis

df = mtcars
nrow(df)

sample(x=1:32, size=10, replace=F)
sample(x=32, size=10, replace=F)

indexnos = sample(x=1:32, size=10, replace=F)
train1= df[indexnos,]

#other indexnos
test1 = df[-indexnos]
train1 
test1

#----
## 75% of the sample size
smp_size <- floor(0.75 * nrow(df))

## set the seed to make your partition reproducible
set.seed(123)
seq_len(nrow(df))  #generate a seq of number upto no of rows
indexnos = sample(seq_len(nrow(df)), size = smp_size)

train2 = df[indexnos, ]
test2 = df[-indexnos, ]
train2
test2

#Using Package
require(caTools)
set.seed(121) 
#sample.split  - preserves relative ratios in each set
gender = sample(c('M','F'), size=100, replace=T, prob=c(.6,.4))
marks = floor(rnorm(100,mean=50,sd=10))
students = data.frame(gender, marks)
table(students$gender)
prop.table(table(students$gender))

samples = sample.split(students$gender, SplitRatio = 0.75)
table(samples)
set1 = subset(students, samples == TRUE)
table(tra$gender)
prop.table(table(set1$gender))

set2 = subset(students, samples == FALSE)
prop.table(table(set2$gender))

#proportion is almost same


#Method
library(caret)
table(df$am)
prop.table(table(df$am))
?createDataPartition
trainnos = createDataPartition(y=df$am , p=0.7,list=FALSE)
trainnos
train4 = df[trainnos,]
test4 =  df[-trainnos,]
train4
test4
prop.table(table(train4$am))
prop.table(table(test4$am))



library(dplyr)
set.seed(275) #to get repeatable data

data.train <- sample_frac(Default, 0.7)

train_index <- as.numeric(rownames(data.train))
data.test <- Default[-train.index, ]


================================================
FILE: 23-functions/31b-rowcol1.R
================================================
#Row Column Summary
# array dimnames = list(row.names, col.names, matrix.names)

#Eg10
a4 = array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )
a4
#, , s1    d1 d2 d3 c1  1  5  9 c2  2  6 10 c3  3  7 11 c4  4  8 12
#, , s2    d1 d2 d3 c1 13 17 21 c2 14 18 22 c3 15 19 23 c4 16 20 24
x = a4


rowMeans(x,dims=1)
rowMeans(x,dims=2)

rowSums(x,dims=1)
rowSums(x,dims=2)

colMeans(x,dims=1)
colMeans(x,dims=2)

colSums(x,dims=1)
colSums(x,dims=2)

# Data Frame and Matrix
df1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))
df1
x = df1

data = c
m1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)
colnames(m1) = c('sub1','sub2','sub3','sub4')
rownames(m1) = paste('R',1:10,sep='')

x=m1

rowMeans(x,dims=1)
rowMeans(x,dims=2)

rowSums(x,dims=1)
rowSums(x,dims=2)

colMeans(x,dims=1)
colMeans(x,dims=2)
colMeans(x,dims=0)

colSums(x,dims=1)
colSums(x,dims=2)

col




================================================
FILE: 23-functions/33b-sortorder.R
================================================
#Sort Order and Rank

set.seed(123)
#Vector
(marks = ceiling(runif(11,5,10)))

sort(marks)
sort(marks, decreasing = TRUE)
rev(sort(marks))

order(marks) #index values
marks[order(marks)] #this is marks
marks[order(-marks)] #this is marks


#DF
(df1=mtcars)
df[order(mtcars$mpg),c(1:5)]
df[order(mtcars$mpg, decreasing=T),c(1:5)]
df[order(mtcars$cyl, -mtcars$mpg),c('cyl','mpg','wt','hp')]

#rank
rank(marks)


================================================
FILE: 23-functions/33c-order.R
================================================
#Sort Order Rank


================================================
FILE: 23-functions/33c-sortorderrank.R
================================================
# Sort Order Rank

set.seed(123)
(marks = ceiling(runif(11,5,10)))

# sort
sort(marks)

sort(marks, decreasing = TRUE)
sort(-marks)  # wrong
rev(sort(marks))

# NA values and sort later ----

# order
order(marks) # indices
marks
marks[c(6,7,10)]
marks[order(marks)]  # similar to sort(x)
order(marks, decreasing = T)
order(-marks)  # decreasing, this works

length(marks)
marks2 = marks
#insert missing value
marks2[5] = NA  # check with NA Value  : display, position
marks2
order(marks2)  # NA is last
order(marks2, na.last = NA)  # removed
length(marks)  # length is same
is.na(marks2)  # logical vector if NA
anyNA(marks2)  # is there is any NA

order(marks2, na.last = NA)  # 5 removed
order(marks2, na.last = FALSE)   # 5 is first
order(marks2, na.last = TRUE)   # 5 is last
length(order(marks2, na.last = TRUE))   # 5 is last

length(order(marks2, na.last = NA))   # 5 is not there

# rank handles tied values
?rank
marks   # 10-3 times, 8 & - 2 times
sort(marks)
table(marks)

#[1]  6  6  7  8  8  9  9 10 10 10 10
#     1  2  3  4  5  6  7  8  8  9 10  
rank(marks) # ties method = avg, first, last, random , max, min
?rank
table(marks)  # check freq
marks
rank(marks, ties.method='first') # 2-6s - 1,2
#6-1, 6-2, 7-3, 8-4, 8-5, 9-6, 9-7, 10-8, 10-9, 10-10, 10-11
rank(marks, ties.method='average')  # default 2-6's - 5
#all 6s : (1+2)/2 = 3/2=1.5 ; (8+9+10+11)/4= 37/3 = 9.5
rank(marks, ties.method='max')  # 2 6's - 7
#Two 6 -1,2 -> 2 (max): Four 10s - 8,9,10,11 -> 11
rank(marks, ties.method='random')  # put them in unique order
sort(rank(marks, ties.method='random'))

table(marks)
marks

marks2  #  Now check with NA values

sort(marks2)  #NA value missing
length(marks2)
length(sort(marks2))   # 1 less

marks2

# NA values - sort and rank
sort(marks2, na.last=TRUE)  # NA last - 11
sort(marks2, na.last=FALSE)  # NA first  - 11
sort(marks2, na.last='keep')  # Nothing . not shown - 10
length(sort(marks2, na.last='keep'))

marks2
length(marks2)
rank(marks2, na.last=TRUE)  # No effect - avg; NA - largest 11
length(rank(marks2, na.last=TRUE))  # No effect

rank(marks2, na.last=FALSE)  # No effect - Avg; NA - smallest 1
rank(marks2, na.last='keep')  # NA shown as NA


mean(marks2)  # does not work with NA values
mean(marks2, na.rm=T)  # add na.rm=T


================================================
FILE: 23-functions/33d-rank.R
================================================
#Sort Order Rank


================================================
FILE: 23-functions/34b-castmelt1.R
================================================
# Data Frame

# Wide Format to Long Format 
rollno = c(17010,17045,17012,17087,17057,17056,17032,17084,17078,17018,17013)
lname  = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')
name  = c('Achal','Apoorva','Goldie','Hitesh','Kaustav','Meena','Rashmi','Shruti','Shubham','Vijay','Lalit')

course = c('PGDDS','PGDDS','PGDDS','PGDDS','MSCDS','PGDDS','MSCDS','PGDDS','PGDDS','PGDDS','PGDDS')
stats = ceiling(runif(11,50,100))
excel = ceiling(runif(11,50,100))
rpgm = ceiling(runif(11,50,100))

students = data.frame(rollno, name, course, stats, excel, rpgm, stringsAsFactors = F)
students
students$course = factor(students$course)
str(students)
# data ready
head(students)

library(reshape2)
students2 <- melt(students, id.vars=c("rollno",'name','course'),
          measure.vars=c("stats", "excel", "rpgm"),
         variable.name="subject", value.name="marks")
students2


# Long to Short
students3 = students2
head(students3)
students4 = dcast(students3, rollno + name + course ~ subject, value.var='marks')
students4 

students5 = dcast(students3, course ~ subject, value.var = 'marks', mean)
students5

students6 = dcast(students3, name ~ subject, value.var = 'marks', mean)
students6  # name to factor

students7 = dcast(students3, rollno + name ~ course + subject, value.var='marks')
students7 # not meaninful here

?dcast


?cast



# Examples
#Air quality example
names(airquality) <- tolower(names(airquality))
head(airquality)
aqm <- melt(airquality, id=c("month", "day"), na.rm=TRUE)
head(aqm)

acast(aqm, day ~ month ~ variable)
acast(aqm, month ~ variable, mean)
acast(aqm, month ~ variable, mean, margins = TRUE)
dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))

library(plyr) # needed to access . function
acast(aqm, variable ~ month, mean, subset = .(variable == "ozone"))
acast(aqm, variable ~ month, mean, subset = .(month == 5))

#Chick weight example
names(ChickWeight) <- tolower(names(ChickWeight))
chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE)

dcast(chick_m, time ~ variable, mean) # average effect of time
dcast(chick_m, diet ~ variable, mean) # average effect of diet
acast(chick_m, diet ~ time, mean) # average effect of diet & time

# How many chicks at each time? - checking for balance
acast(chick_m, time ~ diet, length)
acast(chick_m, chick ~ time, mean)
acast(chick_m, chick ~ time, mean, subset = .(time < 10 & chick < 20))

acast(chick_m, time ~ diet, length)

dcast(chick_m, diet + chick ~ time)
acast(chick_m, diet + chick ~ time)
acast(chick_m, chick ~ time ~ diet)
acast(chick_m, diet + chick ~ time, length, margins="diet")
acast(chick_m, diet + chick ~ time, length, drop = FALSE)

#Tips example
dcast(melt(tips), sex ~ smoker, mean, subset = .(variable == "total_bill"))

ff_d <- melt(french_fries, id=1:4, na.rm=TRUE)
acast(ff_d, subject ~ time, length)
acast(ff_d, subject ~ time, length, fill=0)
dcast(ff_d, treatment ~ variable, mean, margins = TRUE)
dcast(ff_d, treatment + subject ~ variable, mean, margins="treatment")
if (require("lattice")) {
  lattice::xyplot(`1` ~ `2` | variable, dcast(ff_d, ... ~ rep), aspect="iso")
}


================================================
FILE: 23-functions/34c-castmelt2.R
================================================
# Cast and Melt
#https://www.r-statistics.com/2012/01/aggregation-and-restructuring-data-from-r-in-action/
  
(rollno = rep(c(10,11,12,13),4))
sname = rep(c('Achal','Apoorva','Goldie','Hitesh'),4)
(examunit = rep(c('U1', 'U2', 'U3', 'U4'),each= 4))
#(examunit = rep(c(1,2,3,4), each=4))
set.seed(1234)
(rpgm = ceiling(rnorm(4*4,60,10)))
set.seed(1234)
(sql  = ceiling(rnorm(4*4,65,10)))
df1 = data.frame(rollno, sname, examunit, rpgm, sql)
df1  # 16 rows, 4 students, 4 Units, 2 subjects(cols)

# Melt the data : wide to Long (more rows)

md = reshape::melt(df1, id=c('rollno', 'sname', 'examunit'))
head(md)
md
md[md$rollno==13,]
summary(md)

# Cast
# rollno + sname -> subjectwise mean
reshape::cast(md, rollno + sname ~ variable, mean)
reshape::cast(md, rollno + sname ~ variable, sum)



# unitwise mean
reshape::cast(md, examunit ~ variable)
reshape::cast(md, examunit ~ variable, sum)

head(md)
# unitwse mean per student
# cat(names(md))
# aggregate(md[-2], by=list(rollno,exam), value , FUN=mean )
# ?aggregate
df1[df1$rollno==10,]
# rollno -> subjectwise summary mean
reshape::cast(md, rollno ~ variable, mean)
# rollno -> unitwise summary (all subjects)
reshape::cast(md, rollno ~ examunit, mean)  # correct
# alongwith names
reshape::cast(md, rollno + sname ~ examunit, mean)  # correct
# rollno -> unitwise summary all subject
reshape::cast(md, rollno ~ examunit, mean, value='value') # this is also correct
# rollno - subject - units....
reshape::cast(md, rollno + variable ~ examunit, FUN=mean, value='value')

# initial data -> melt -> cast -> initial 
reshape::cast(md, rollno + sname + examunit  ~  variable, sum)
length(md); nrow(md)
head(md)
# Without aggregation  ----
reshape::cast(md, rollno + sname + examunit ~ variable)
reshape::cast(md, rollno + sname + variable ~ examunit)
reshape::cast(md, rollno + sname ~ variable ~ examunit)  # list type

library(reshape)
# Better way to do it
md2 = reshape::melt(df1, id.vars=c("rollno",'sname', 'examunit'),
           measure.vars=c("rpgm", "sql"),
           variable.name="subject", value.name="marks")
str(md2)
head(md2)  # with proper labeling

reshape::cast(md2, rollno + sname ~ subject, mean)
reshape::cast(md2, rollno + sname ~ subject, sum) # for all units
reshape::cast(md2, examunit ~ subject, mean, value='marks')  # for all students
reshape::cast(md2, examunit ~ subject, sum, value='marks')  # for all students



================================================
FILE: 23-functions/37a-mtcars-subset.R
================================================
#Subset - mtcars

# Subset
#subset( ) function is the easiest way to select variables and observations.

mtcars
# using subset function 
(newdata = subset(mtcars, mpg >=20 & mpg < 30))
(newdata = subset(mtcars, mpg >=20 & mpg < 30, select=c(mpg, disp)))
(newdata <- subset(mtcars, cyl == 6 & disp > 150,select=mpg:wt))


================================================
FILE: 23-functions/37b-duplicates1.R
================================================
# Find Duplicates

x <- c(9:20, 1:5, 3:7, 0:8)
## extract unique elements
x

(xu <- x[!duplicated(x)])

## similar, same elements but different order:
(xu2 <- x[!duplicated(x, fromLast = TRUE)])

## xu == unique(x) but unique(x) is more efficient
stopifnot(identical(xu,  unique(x)),
          identical(xu2, unique(x, fromLast = TRUE)))

duplicated(iris)[140:143]

duplicated(iris3, MARGIN = c(1, 3))
anyDuplicated(iris) ## 143

anyDuplicated(x)
anyDuplicated(x, fromLast = TRUE)


subset(df, !duplicated(subset(df, select=c(allrl, RAW.PVAL))))

================================================
FILE: 23-functions/37c-unique.R
================================================
# Unique

================================================
FILE: 23-functions/38b-scale1.R
================================================
# Scale
#standardization of the data
#scale is generic function whose default method centers and/or scales the columns of a numeric matrix.
#https://t4tutorials.com/decimal-scale-normalization-in-data-mining/

x = c(1,3,5,7,9,2,4,6,8,10)
(x1 = scale(x, scale=F, center=F))
as.vector(x1)
(xc = scale(x, scale=F, center=T))
as.vector(xc)
(xc = scale(x, scale=F, center=5.5))
xc1 = scale(x, scale=F, center=4)
as.vector(xc1)

(xs = scale(x, scale=T, center=F))
as.vector(round(xs,2))
(xs1 = (x - mean(x)/ sd(x)))
(rmse = sqrt(sum(x^2)/(length(x)-1)))
round((xs2 = (x/ rmse)),2)
(xs3 = (x))

(xsc = scale(x, scale=T, center=T))
as.vector(round(xsc,2))

cbind(x, round(x1,2), round(xc,2), round(xs,2),round(xsc,2))
x
(x2 = x - mean(x))
round((x2 - mean(x2))/ sd(x2),2)


as.vector(xs)
round((zscore = (x - mean(x))/ sd(x)),2)

as.vector(round(xsc,2))

(xc= scale(x, scale=F, center=4))
cbind(x,-4,xc)
(xs = scale(x, scale=T, center=F))
x
(xs = scale(x, scale=T, center=F))
(xsm = sqrt(sum(x^2)/(length(x)-1)))

cbind(x,round(xs,2), round(xsm,2))


round(scale(x,center=min(x),scale=diff(range(x)))[,1],2)
round((x - min(x))/ (max(x) - min(x)),2)
(xC = x - mean(x))
round((xS = xC / sqrt(sum(xC^2)/(length(xC)-1))),2)

#Scale by SD - if there are 2 columns

scale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE))

data(women)
women
scale(women)
#Scale by SD - if there are 2 columns
scale(women, center = TRUE, scale = apply(women, 2, sd, na.rm = TRUE))

(ws = scale(women, center = T, scale = c(max(women$height), max(women$weight))))
cbind(women, round(ws,2))

(ws2 = scale(women, center = T, scale = c(100, 200)))
cbind(women, round(ws2,2))









x

set.seed(1234)
x = rnorm(100,50,10)
scale(x)
hist(x)

hist(scale(x))

scale(x, center=T)
scale(x, center=2)
plot(scale(x, center=2))  # irrelevant

(marks1 = ceiling(runif(10,50,100)))
(marks2 = ceiling(runif(10,50,150)))
scale(marks1)
scale(marks1)[,1]
round(scale(marks1)[,1],2)
round(scale(marks2)[,1],2)

round(scale(marks1)[,1],2)

round(scale(marks1, center=F)[,1],2)
round(scale(marks2, center=F)[,1],2) 

round(scale(marks1, center=c(0,1))[,1],2)




#
s = sort(rexp(100))
s
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
range01(s)


#
library("scales")
s=marks1
rescale(s)

rescale(marks1)
rescale(s, to=c(0,10))
rescale(s, from=c(0, max(s)))

x=marks1
round(scale(x,center=min(x),scale=diff(range(x)))[,1],3)



# Example Data
x = sample(-100:100, 50)

#Normalized Data
normalized = (x-min(x))/(max(x)-min(x))

# Histogram of example data and normalized data
par(mfrow=c(1,2))
hist(x,          breaks=10, xlab="Data",            col="lightblue", main="")
hist(normalized, breaks=10, xlab="Normalized Data", col="lightblue", main="")
par(mfrow=c(1,1))


x <- matrix(1:10, ncol = 2)
x
(centered.x <- scale(x, scale = FALSE))
centered.scaled.x <- scale(x, center = TRUE, scale = TRUE)
cov(centered.scaled.x)


================================================
FILE: 23-functions/41b-randnos1.R
================================================
# Random Numbers


#Case ----
randu$x
y <- qunif(ppoints(length(randu$x)))
qqplot(randu$x,y)
randu


================================================
FILE: 23-functions/41c-randnos.R
================================================
# Random Numbers

#sample
sample(x=1:10, size=3)
sample(x=c(1.5, 2.5, 3, 6.7), size=2)

sample(x=1:10, size=15)  #error size is more than sample size
sample(x=1:10, size=15, replace=T) 
sample.int(n=100, size=30, replace=F) #nos between 1:n


#sample from vector values
month.abb[1:12]
sample(x=month.abb[1:12], size=3, replace=T)
sample(x=c('M','F'), size=10, replace=T)


================================================
FILE: 23-functions/42b-normdist.R
================================================
# Normal Distribution Data

rnorm(10)
?rnorm
#rnorm(n, mean = 0, sd = 1)

(x=rnorm(10, mean=5, sd=2))


================================================
FILE: 23-functions/45b-forloop1.R
================================================
# For Loop

for ( i in 1:10)
{  print(i)
}

print(1:10)


# controlling loop
#
for (i in 1:10)
{ 
  if ( i==3)
  {
    next
  }
  print(i)
}
# 3 was not printed


x = 0
for (i in 1:10)
{
  x = x + i
  print(c(i,x))
}

x = 1
for (v in c(3, 4, 7, 2))
{
  x = x*v
}
x

x = c(1,2,3)
for (v in c(4:6))
{
  print(c(x, v))
}
x

#create vector elements
x <- NULL
for(i in 1:5){
  x[i] <- i * 2
}
x
#substitute 2 and 3 position with zero values
for(i in 2:3){
  x[i] <- 0
}
x




x = c(1,2,3)
for (v in c(4:6))
{
  x = x * v
  print(x)
}
x

#-----------
y = 0
for (x in c(1,2,3)) {
  for (v in c(4:6))
  {
    y[x] = x * v
    print(paste(x,v, x))
  }
}
x;v; y

# ---------
x = 0
for (i in seq(4))
{
  for (j in seq(i))
  {
    x = x+i*j
    print(paste(i,j,x))
  }
}
x
1*1 + 2*1 + 2*2 + 3*1 + 3*2 + 3*3 + 4*1 + 4*2 + 4*3 + 4*4 #65

1*1 + 1*2 + 1*3 + 1*4
2*1 + 2*2 + 2*3 + 2*4
3*1 + 3*2 + 3*3 + 3*4
4*1 + 4*2 + 4*3 + 4*4

# ---------

#http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat600/Notes/R_introduction.pdf

x <- c(2,5,3,9,8,11,6);
count <- 0 ;
for (val in x) 
{ if(val %% 2 == 0)
  count =  count+1 ;  } 
print(count)


================================================
FILE: 23-functions/45e-ifelse2.R
================================================
#if else
<<<<<<< HEAD
as.numeric(TRUE)

as.numeric(FALSE)
1==1
1< 1

toCheck = 1
if ( toCheck ==1) 
{ 
  print("hello")
}
tocheck=2
tocheck
check.bool <- function(x)
{if ( x ==1) 
  { 
    print("hello")
  } else
  { 
    print("No is not 1")
  }
}
check.bool(1)
check.bool(0)



# ifelse

ifelse(1 == 1, 'Yes', 'No')


# Compound Test
#
a = c(1,1,0,1)
b = c(2,1,0,1)
ifelse( a== 1 & b == 1, 'Yes', 'No')
# check each element of a with corr element of b
ifelse( a== 1 && b == 1, 'Yes', 'No')
# only first element checked
=======

var <- c("Private", "Private", "?", "Private")
df <- data.frame(var)
df$var[which(df$var == "?")] = "Private"
df


#
L3 <- LETTERS[1:3]
dat1 <- data.frame(x=rep(1,6),y=rep(1:3,2), fac=sample(L3, 6, replace=TRUE))
dat1

dat1[dat1$x==1 & dat1$y==1 , 1:2] <- NA
dat1

# Run from start
cond <- with(dat1, x==1 & y==1)
cond
is.na(dat1$x) <- cond
is.na(dat1$y) <- cond
dat1


#

table<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), 
                   habitat=c(1,2,3,4,5,6))
table1 = table
## Solution 1 
table$size[table$population<500]<-1
table$size[table$population>=500 & table$population<1000]<-2
table$size[table$population>=1000 & table$population<2000]<-3
table$size[table$population>=2000 & table$population<3000]<-4
table$size[table$population>=3000 & table$population<=5000]<-5
table


## Solution 2 Nested ifelse
table = table1
table$size1<-ifelse(table$population<500,1,
                    ifelse(table$population>=500 & table$population<1000,2,
                           ifelse(table$population>=1000 & table$population<2000,3,
                                  ifelse(table$population>=2000 & table$population<3000,4,5
                                  ))))

table

#

table<- data.frame(population=c(100, 300, 5000, 2000, 900, 2500), 
                   habitat=c(1,2,3,4,5,6))
table
table$size <- findInterval(table$population, c(0, 500, 1000, 2000, 3000, 5000), rightmost.closed = TRUE)
table
#>>>>>>> 50c058778f06851438b41e2cc02aecbdfb90c903

x=c(1,NA,2,3)
dplyr::if_else(x%%2==0, "Multiple of 2", "Not a multiple of 2", "Missing")


#sqldf package----
df=data.frame(k=c(2,NA,3,4,5))
#library(sqldf)
sqldf::sqldf(
  "SELECT *,
  CASE WHEN (k%2)=0  THEN 'Multiple of 2'
  WHEN  k is NULL  THEN 'Missing'
  ELSE 'Not a multiple of 2'
  END AS T
  FROM df"
)


================================================
FILE: 23-functions/45v-switch1.R
================================================
#Switch

use.switch <- function(x)
{
  switch(x,'a' = 'First','b' = 'Second','c' =  'Third','other')
}

use.switch('a')
use.switch('b')
use.switch('other')
use.switch('6')
use.switch(6)  # nothing returned

switch(2, 'Red', 'Blue', 'Green')
colors = c('Red', 'Blue', 'Green')
switch(2,colors)  # does not work.. elements not vectorised

#collist = as.list(colors)
#collist
#switch(2, collist)

#-----

xlist = c("A","B", "C")
value = "B"
?alist
(myList <- alist(print(xlist[1]), print(xlist[2]), print(xlist[3]), print("Other !!!")))
(myList <- setNames(myList, c(xlist, '')))  # give names to list, last is blank

?do.call
do.call(switch, c(EXPR = value, myList))
#Examples:
value = "D"
do.call(switch, c(EXPR = value, myList))
#[1] "Other !!!"
value = "A"
do.call(switch, c(EXPR = value, myList))
#[1] "A"
value = "C"
do.call(switch, c(EXPR = value, myList))
#[1] "C"
value = "B"
do.call(switch, c(EXPR = value, myList))
#[1] "B"


================================================
FILE: 23-functions/46b-withoutapply.R
================================================
# Before Apply
#https://csgillespie.github.io/efficientR/programming.html#the-apply-family

(m1 = matrix(1:20, nrow=4))

#mean of each row: manually
mean(m1[1,]); mean(m1[2,]); mean(m1[3,]); mean(m1[4,])
#mean of each row: for loop
for (i in 1:nrow(m1)) {
  print(mean(m1[i,]))
}
#apply command
apply(m1,1,mean)

#apply for columns
apply(m1, MARGIN = 2, mean)


# with apply
apply(m1, 2, mean)

# mean using addmargins
addmargins(m1,1,mean)
addmargins(m1,2,mean)
addmargins(m1,c(1,2),mean)
addmargins(m1)


#using for loop
for( i in 1:4) {
  print(mean(m1[i,]))
}


nrow(m1)
ncol(m1)

for( i in 1:nrow(m1)) {
  print(paste0('Mean of row', i , ' is ', mean(m1[i,])))
}

for( i in 1:ncol(m1)) {
  print(paste0('Mean of Col', i , ' is ', mean(m1[,i])))
}

# Better way to do
apply(m1, 1, mean)  # row means
apply(m1, 2, mean)  # col means



================================================
FILE: 23-functions/46c-applyForCompare.R
================================================
#apply LAPPLY SAPPLY


#lapply - When you want to apply a function to each element of a list in turn and get a list back.

#This is the workhorse of many of the other *apply functions. 
(x <- list(a = 1, b = 1:3, c = 10:100) )
lapply(x, FUN = length) 

lapply(x, FUN = sum) 


#sapply ------
#sapply - When you want to apply a function to each element of a list in turn,
#but you want a vector back, rather than a list.

#If you find yourself typing unlist(lapply(...)), stop and consider sapply.

x <- list(a = 1, b = 1:3, c = 10:100)
#Compare with above; a named vector, not a list 
sapply(x, FUN = length)  
#a  b  c   
#1  3 91

sapply(x, FUN = sum)   
#a    b    c    
#1    6 5005 

#In more advanced uses of sapply it will attempt to coerce the result to a multi-dimensional array, if appropriate. For example, if our function returns vectors of the same length, 
#sapply will use them as columns of a matrix:
  
sapply(1:5,function(x) rnorm(3,x))
#If our function returns a 2 dimensional matrix, 
#sapply will do essentially the same thing,
#treating each returned matrix as a single long vector:

func2 = function(x){
  rnorm(3,x)
}
func2(1)

sapply(1:5, func2)

for (i in 1:5) {
print(matrix(i,2,2))
}

matrix(5, 2,2)

sapply(1:5,function(x) matrix(x,2,2))
#Unless we specify simplify = "array", in which case it will use
#the individual matrices to build a multi-dimensional array:
  
sapply(1:5,function(x) matrix(x,2,2), simplify = "array")
#Each of these behaviors is of course contingent on our function returning 
#vectors or matrices of the same length or dimension.



summary <- function(x) {
  c(mean(x, na.rm = TRUE),
    median(x, na.rm = TRUE),
    sd(x, na.rm = TRUE),
    mad(x, na.rm = TRUE),
    IQR(x, na.rm = TRUE))
}

summary <- function(x) {
  funs <- c(mean, median, sd, mad, IQR)
  lapply(funs, function(f) f(x, na.rm = TRUE))
}

lapply(mtcars, function(x) length(unique(x)))
Filter(function(x) !is.numeric(x), mtcars)
integrate(function(x) sin(x) ^ 2, 0, pi)

formals(function(x = 4) g(x) + h(x))
environment(function(x = 4) g(x) + h(x))


================================================
FILE: 23-functions/46d-applyfamily.R
================================================
# Apply Family


================================================
FILE: 23-functions/46e-applytype.R
================================================
# Apply Functions

#Data Structure
# vector
(v1 = 1:20)

#Array ----
(a1 = array(1:24, dim=c(4,3,2), 
    dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) ))

#Matrix ----
seq_len(length.out=5)
m1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),
        seq_len(length.out=10) ), byrow=F, ncol =4)
colnames(m1) = c('sub1','sub2','sub3','sub4')
rownames(m1) = paste('R',1:10,sep='')
m1

#DataFrame ----
(df1 = data.frame(sub1=10:1, sub2=5, 
        sub3=rep(c(5,6),5), 
        sub4=seq_len(length.out=10)))

#List ----
(list1 = list(sub1=10:1, sub2=rep(5,3), 
      sub3=rep(c(5,6),4),
      sub4=seq_len(length.out=10)))

#print objects
v1
a1
m1
df1
list1

?melt
#apply --------
?apply
#apply(X, MARGIN, FUN, ...)
#X - array or matrix MARGIN=1 (Rows) ; 2 (Columns)
#FUN = mean, median, UDF, .... ... Others eg. na.rm=T
#Applies a function over the margins

#array
(X=a1)
apply(X, 1, sum)  # dimension=1 : c
apply(X, 1, mean)  # dimension=1 : c
X
apply(X, 2, sum)  # d
apply(X, 3, sum) # s
apply(X,c(1,2), sum)  # c & d
apply(X,c(1,2,3), sqrt)  # original  : c d s 
apply(X,c(1,2,3), sum)
apply(X,c(2,3), sum)  # sum over 2 & 3rd dim : d & s


class(apply(X, 1, sum))   # Integer Vector
addmargins(X,1, sum)  # row margin=1
addmargins(X,2, sum)  # col margin=1
addmargins(X,3, sum)  # sum s1 + s2
addmargins(X,c(1,2), sum)  # row & col
addmargins(X,c(1,3), sum)  # sum rows of s1 & s2
addmargins(X,c(2,3), sum)  # sum cols of s1 & s2
class(X)
m1
(m2=matrix(1:12,nrow=3))

addmargins(m2,c(1,2), 
    FUN=list(list(sum,sd), 
  list(mean,var,IQR, sum)))  # row & col
head(mtcars)

#https://www.r-bloggers.com/r-tutorial-on-the-apply-family-of-functions/
  
apply(X, 2, sum)  # dim2 : d
#d1  d2  d3   68 100 132

apply(X, 3, sum)  # dim3 s
#s1  s2   78 222

apply(X,c(3,2), sum)  # s & d : see switch of col & row
#   d1 d2 d3 
#s1 10 26 42 
#s2 58 74 90

apply(X,c(2,3), sum)   # d & s
#   s1 s2 
#d1 10 58 
#d2 26 74 
#d3 42 90

apply(X,c(1,2), sum) # c & d
#   d1 d2 d3 
#c1 14 22 30 
#c2 16 24 32 
#c3 18 26 34 
#c4 20 28 36

apply(X,c(1,3), sum)  # c & s
#    s1 s2 
#c1 15 51 
#c2 18 54 
#c3 21 57 
#c4 24 60

##matrix------- 2 dim
(X=m1)
apply(X, 1, sum) # R's
#R1  R2  R3  R4  R5  R6  R7  R8  R9 R10
#21  22  21  22  21  22  21  22  21  22

apply(X,2,sum) # sub's
#sub1 sub2 sub3 sub4
#55   50   55   55

apply(X,3,sum) # no 3rd dim
#Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed

apply(X,c(3,2), sum)  #Error
apply(X,c(2,3), sum)  #Error

apply(X,c(1,2), sum) # original matrix
#sub1 sub2 sub3 sub4
#[1,]   10    5    5    1

apply(X,c(1,3), sum)  #error no 3rd dim

apply(X,sum)  # error - margins not specified

#dataframe -------------
# this acts like a matrix as all values are numeric
(X=df1)
apply(X, 1, sum)
#[1] 21 22 21 22 21 22 21 22 21 22

apply(X,2,sum)
#sub1 sub2 sub3 sub4    55   50   55   55

apply(X,3,sum)#  Error in if (d2 == 0L) { : missing value where TRUE/FALSE needed
apply(X,c(3,2), sum) #    Error
apply(X,c(2,3), sum) #    Error

apply(X,c(1,2), sum)  # original DF
#sub1 sub2 sub3 sub4
#[1,]   10    5    5    1

apply(X,c(1,3), sum)   #error

apply(X,sum)   #error no dime

#NA Values

m1[5] = NA
# use other function also
X=m1
apply(X,1,mean)
#R1   R2   R3   R4   R5   R6   R7   R8   R9  R10
#5.25 5.50 5.25 5.50   NA 5.50 5.25 5.50 5.25 5.50

apply(X,1,mean,na.rm=T)  # better
#R1   R2   R3   R4   R5   R6   R7   R8   R9  R10
#5.25 5.50 5.25 5.50 5.00 5.50 5.25 5.50 5.25 5.50

apply(X,2,mean)
#sub1 sub2 sub3 sub4
#NA  5.0  5.5  5.5

apply(X,2,mean,na.rm=T)  # better
#sub1 sub2 sub3 sub4
#5.44 5.00 5.50 5.50
    


?lapply # apply over list or vector
#lapply ---------
#lapply(X, FUN, na.rm=T, ...)
(X=list1)
lapply(X,FUN=mean, na.rm=T)
dim(list1)  # list does not have any dimensions
apply(list1, FUN=sum)  # this will give error for apply

#Egs: NA Values
X[[1]][5] = NA
lapply(X,FUN=mean, na.rm=F)


lapply(X,FUN=mean, na.rm=T)

#Unlist  - output as vector
unlist(lapply(X,FUN=mean, na.rm=T))

# simply output of list apply function
# sapply ----- 
?sapply  # returns a vector, matrix or array if appropriate
#like lapply - simplify output
sapply(list1,mean)  # for list
sapply(df1, mean)
sapply(m1, mean)
a1
sapply(a1, sum)
sapply(a1, sum, simplify = 'array')

sapply(v1, summary) # not relevant here

# mapply -----
# mapply - function to multiple list or vector args
?mapply(a1, sum)
mapply(rep, times=1:4, x = 1:4)  # list
rep(1:4,times=1:4)
mapply(rep,1:5,4)  # matrix 1 2 3 4

mapply(FUN=rep, each=1, x = v1, y=v1)
list1
rep(v1,each=1)
rep(m1,each=1)
rep(list1,each=1)

e1 = 1:3
e2 = 6:8
e3 = 10:12
#function : x * y
#1st elements : a = e1 : FUN to each elements
#2nd elements : b = e2: takes this as arguments
#3rd elements : c = e3: takes this as arguments
# arguments are recycled if required
mapply(FUN=function(x,y) x+y, e1, e2 )
e1+e2
mapply(FUN=function(x,y,z) x+y+z, e1, e2 , e3)
e1+e2+e3

mapply(FUN=function(x,y,z) mean(x*y)/ z, e1, e2 , e3)



mapply(FUN=mean, x = list(a1,m1))
mean(a1) ; mean(m1)


mapply(function(x, y) x + y,
       x=c(a =  1, b = 2, c = 3),  # names from first
       y=c(A = 10, B = 0, C = -10))

mapply(function(x, y) x ^ y, USE.NAMES = F, # no names
       x=c(a =2, b = 3, c = 4), 
       y=c(A =2, B = 3, C = 4))


# vapply ------
?vapply # similar to sapply - pre spcfd return type & better speed

vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)
vapply(df1, FUN=fivenum,
       c(Min. = 0, "1st Qu." = 0, Median = 0, "3rd Qu." = 0, Max. = 0))
vapply(df1, FUN=mean, FUN.VALUE=c(mean=0))
vapply(1:5, identity)
## [1] 1 2 3 4 5
vapply(integer(), identity)
## integer(0



#tapply
(df1a = df1)
df1a$course = sample(c('MSC','PG'),10, prob=c(.7,.3), replace=T)
df1a
tapply(X=df1a$sub1, INDEX=df1a$course, mean)
df1a$gender = sample(c('M','F'),10, prob=c(.6,.4), replace=T)
tapply(X=df1a$sub1, INDEX=df1a$gender, mean)
# 2 indices
tapply(X=df1a$sub1, INDEX=list(df1a$gender,df1a$course), mean)
# 3 indices
df1a$hostel = sample(c('Y','N'),10, prob=c(.5,.5), replace=T)
tapply(X=df1a$sub1, INDEX=list(df1a$hostel), mean)
tapply(X=df1a$sub1, INDEX=list(df1a$hostel,df1a$gender), mean)
tapply(X=df1a$sub1, INDEX=list(df1a$hostel,df1a$course), mean)

tapply(X=df1a$sub1, INDEX=list(df1a$hostel, df1a$gender,df1a$course), sum)
tapply(X=df1a$sub1, INDEX=list(df1a$hostel, df1a$gender,df1a$course), length)

df1a

?aggregate
aggregate(x, by, FUN, ..., simplify = TRUE, drop = TRUE)
aggregate(formula, data, FUN,
          subset, na.action = na.omit)
#Aggregate --------
cat(names(mtcars))
aggregate(mtcars[c('disp', 'hp', 'wt')],
          by=list(mtcars$cyl,mtcars$gear),
          FUN=mean, na.rm=TRUE, simplify = T)
aggregate(mtcars[c('disp', 'hp', 'wt')],
          by=list(Cylinder=mtcars$cyl,Gears=mtcars$gear),
          FUN=mean, na.rm=TRUE, simplify = T)[,1:3]

aggregate(mtcars[c('cyl', 'mpg','wt')],
          by=list(mtcars$cyl),
          FUN=mean)
aggregate(cyl ~ . , data= mtcars, FUN='mean') 
# formula  variables ~ groupingvars
aggregate(. ~ cyl , data= mtcars, FUN='mean') 
aggregate(. ~ cyl + am , data= mtcars, FUN='mean')
aggregate(. ~ cyl + am , data= mtcars, subset=(wt < 15), FUN='mean')

aggregate(am ~ cyl , data= mtcars, FUN='mean') #' FUN in single quote
# cyl ~ all variables
aggregate(cyl ~ mpg + wt, data= mtcars, FUN='mean',
          subset=(wt > 4)) 
aggregate(cyl  ~ mpg  + wt,
      data=subset(mtcars,wt>=5 & gear==c(3,4)), FUN='mean')
summary(mtcars$cyl)

head(df1a)
# some colns are non-numeric, exclude them
aggregate(x=df1a[c('sub1', 'sub2', 'sub3')], by=list(df1a$course), FUN=mean)
(dftemp = aggregate(x=df1a[c('sub1', 'sub2', 'sub3')], 
          by=list(df1a$course, df1a$hostel, df1a$gender), FUN=mean))
dftemp[order(dftemp$Group.1, dftemp$Group.2, dftemp$Group.3),]

(dftemp = aggregate(x=df1a[c('sub1', 'sub2', 'sub3')], 
      by=list(gpcourse = df1a$course, gphostel=df1a$hostel,gpgender= df1a$gender), FUN=mean))
dftemp[order(dftemp$gpcourse, dftemp$gpgender, 
             dftemp$gphostel),]
dftemp[order(dftemp$gpcourse, dftemp$gpgender, 
             dftemp$gphostel),][,c('gphostel', 'sub1')]
dftemp[order(dftemp$gpcourse, dftemp$gpgender, 
             dftemp$gphostel),][,c(3,2,1,4,5,6)]


================================================
FILE: 23-functions/46f-while1.R
================================================
# while loop

x = 1
while (x <= 5)
{
  print(x)
  x = x + 1
}


================================================
FILE: 23-functions/47b-apply1.R
================================================
#apply
# https://davetang.org/muse/2013/05/22/using-aggregate-and-apply-in-r/
#apply functions over array margins
#apply(X, MARGIN, FUN, ...)
#make up some dataframe
df <- data.frame(first = c(1:10), second = c(11:20))
df
apply(df , 2, mean)  #col
apply(df, 1, mean)  # row


s1 = student1[c(17:23)]
apply(student, 1, mean)  # not all are numeric
apply(s1,1,mean)
apply(s1,2,mean)


a4 = array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )
a4

m1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)
colnames(m1) = c('sub1','sub2','sub3','sub4')
rownames(m1) = paste('R',1:10,sep='')

df1 = data.frame(sub1=10:1, sub2=5, sub3=rep(c(5,6),5), sub4=seq_len(length.out=10))
df1

X=m1
apply(X,1,sum)
apply(X,2,sum)
apply(X,3,sum)
apply(X,c(3,2), sum)
apply(X,c(2,3), sum)

apply(X,c(1,2), sum)
apply(X,c(1,3), sum)
apply(X,0,sum)

apply(X,sum)
apply(X,0,sum)
apply(X,4,sum)
apply(X,c(1,2),sum)
apply(X,c(1,2,3),sum)

# NA Values
m1[5] = NA
X=m1
m1
apply(X,1,mean)
apply(X,1,mean,na.rm=T)
apply(X,2,mean)
apply(X,2,mean,na.rm=T)


================================================
FILE: 23-functions/47c-apply.R
================================================
# Apply Commands

# apply Functions
# #apply ----
#create matrix####
m1 = matrix(1:10,nrow=5)
m1

# create sample data of Matrix : Use ND data : 3 cols, 30 rows
m2 <- matrix(data=cbind(rnorm(30, 0), rnorm(30, 2), rnorm(30, 5)), nrow=30, ncol=3)
m2

# apply is used for structured data - matrix, dataframe with same number of coln & rows
# https://www.r-bloggers.com/using-apply-sapply-lapply-in-r/
# apply Mean on Rowise on all 3 Column
m1
apply(m1, 1, mean)
apply(m1, 2, mean)
# apply function on particular values : count number of negative values
apply(m2, 2, function(x) length(x[x<0]))
m2
apply(m2, 2, function(x) is.numeric(x))
apply(m2, 1, function(x) is.character(x))
apply(m2, 2, function(x) is.vector(x))

apply(m2, 2, function(x) mean(x[x>0]))

# sapply
sapply(1:3, function(x) x^2)

sapply(m[1,], function(x) x^2)
sapply(m[,2], function(x) x^2)

# lapply - return list rather than a vector
lapply(1:3, function(x) x^2)
unlist(lapply(1:3, function(x) x^2))

mean(m[,1])
mean(m[,3])

#https://www.r-bloggers.com/the-apply-command-101/

# tricks
sapply(1:3, function(x) mean(m[,x]))
# dataset name pass as y 
sapply(1:3, function(x, y) mean(y[,x]), y=m)

# Means of various colns
meanset = c( mean(m[,1]), mean(m[,2]), mean(m[,3]))
meanset

# fiven no summary five number summary (minimum, lower-hinge, median, upper-hinge, maximum) for the input data

sum1 = apply(m,2,fivenum)
sum1
?fivenum
mean(m[,2])
sum2 = apply(m,2,summary)
sum2

# rowmeans
rowMeans(m)
colMeans(m)

# trimming data
mean(m[,1])
mean(m[,1],trim=0.5)


a1 = array(1:(3*4*2), dim=c(3,4,2))
a1
#5 rows, 4 cols, 3 matrices
apply(a1, 1, sum)
apply(a1, 2, sum)
apply(a1, c(3), sum)


apply(a1, c(1,2), sum)
apply(a1, c(2,3), sum)
apply(a1, c(1,2,3), sum)
apply(a1, c(1,3), sum)



================================================
FILE: 23-functions/47d-apply1.R
================================================
#apply APPLY

#apply - When you want to apply a function to the rows or columns of a matrix (and higher-dimensional analogues); not generally advisable for data frames as it will coerce to a matrix first.

# Two dimensional matrix
(M <- matrix(seq(1,16), 4, 4))

# apply min to rows
apply(M, 1, min)
#[1] 1 2 3 4

# apply max to columns
apply(M, 2, max)
[1]  4  8 12 16

# 3 dimensional array
(M <- array( seq(32), dim = c(4,4,2)))

# Apply sum across each M[*, , ] - i.e Sum across 2nd and 3rd dimension
apply(M, 1, sum)
# Result is one-dimensional
#[1] 120 128 136 144
apply(M, 2, sum)

apply(M, 3, sum)

# Apply sum across each M[*, *, ] - i.e Sum across 3rd dimension
apply(M, c(1,2), sum)
# Result is two-dimensional
#[,1] [,2] [,3] [,4]
#[1,]   18   26   34   42
#[2,]   20   28   36   44
#[3,]   22   30   38   46
#[4,]   24   32   40   48


================================================
FILE: 23-functions/47h-tapply1.R
================================================
# apply TAPPLY

#tapply - For when you want to apply a function to subsets of a vector 
#and the subsets are defined by some other vector, usually a factor.

#The black sheep of the *apply family, of sorts. 
#The help file's use of the phrase "ragged array" can be a bit confusing, 
#but it is actually quite simple.


(x <- 1:20)
#A factor (of the same length!) defining groups:
(y <- factor(rep(letters[1:5], each = 4)))
#Add up the values in x within each subgroup defined by y:
cbind(x, y)

tapply(x, y, sum)  
#a  b  c  d  e  
#10 26 42 58 74 

#More complex examples can be handled where the subgroups are 
#defined by the unique combinations of a list of several factors.
#tapply is similar in spirit to the split-apply-combine
#functions that are common in R (aggregate, by, ave, ddply, etc.)
#Hence its black sheep status.

tapply(iris$Sepal.Length, iris$Species, mean)
tapply(mtcars$mpg, mtcars$cyl, sd)


================================================
FILE: 23-functions/47j-lapply1.R
================================================
#lapply
#lapply = apply a function over a list
#for reproducibility
#write function to sample 10 numbers
#from a Poisson distribution according to lambda
f <- function(l){
  rpois(10, l)
}
f(10)
f(1)
set.seed(123)
#save into draws
draws <- lapply(1:5,f)
draws


list1 = list(sub1=10:1, sub2=rep(5,3), sub3=rep(c(5,6),4),sub4=seq_len(length.out=10))
list1
X=list1
lapply(X,FUN=mean, na.rm=T)
X[[1]][5] = NA
lapply(X,FUN=mean, na.rm=F)
lapply(X,FUN=mean, na.rm=T)
unlist(lapply(X,FUN=mean, na.rm=T))

lapply(a1, mean)


================================================
FILE: 23-functions/47m-mapply1.R
================================================
# Mapply

# vector
(v1 = 1:20)
(v2 = 12:15)
# array
#List ----
(list1 = list(sub1=10:1, sub2=rep(5,3), 
              sub3=rep(c(5,6),4),
              sub4=seq_len(length.out=10)))
mapply(v1,v2,sum)

# mapply -----
# mapply - function to multiple list or vector args
#mapply(a1, sum)

# one way of writing - creating a list 
#1 -1 ; 2-2,2  ; 3- 3,3,3 ; 4-4,4,4,4
mapply(rep, times=1:4, x = 1:4)  # list
rep(1:4,times=1:4)
mapply(rep,1:5,4)  # matrix 1 2 3 4
# 1 2 3 4 5
# 1 2 3 4 5
# 1 2 3 4 5

mapply(FUN=rep, each=1, x = v1, y=v1)
#list1
rep(v1,each=1)
rep(m1,each=1)
rep(list1,each=1)

e1 = 1:3
e2 = 6:8
e3 = 10:12
#function : x * y
#1st elements : a = e1 : FUN to each elements
#2nd elements : b = e2: takes this as arguments
#3rd elements : c = e3: takes this as arguments
# arguments are recycled if required
mapply(FUN=function(x,y) x+y, e1, e2 )
e1+e2
mapply(FUN=function(x,y,z) x+y+z, e1, e2 , e3)
e1+e2+e3

mapply(FUN=function(x,y,z) mean(x*y)/ z, e1, e2 , e3)



mapply(FUN=mean, x = list(a1,m1))
mean(a1) ; mean(m1)


mapply(function(x, y) x + y,
       x=c(a =  1, b = 2, c = 3),  # names from first
       y=c(A = 10, B = 0, C = -10))

mapply(function(x, y) x ^ y, USE.NAMES = F, # no names
       x=c(a =2, b = 3, c = 4), 
       y=c(A =2, B = 3, C = 4))

================================================
FILE: 23-functions/47n-mapply2.R
================================================
# Apply - mapply

#‘multivariate’ apply. Its purpose is to be able to vectorize arguments 
#to a function
#that is not usually accepting vectors as arguments.

# Create a 4x4 matrix
Q1 <- matrix(c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4)),4,4)

# Print `Q1`
(Q1)

# Or use `mapply()`
(Q2 <- mapply(rep,1:4,5))
#same length of 4 translates to matrix
# Print `Q2`
print(Q2)


# List -----
list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))
mapply(rep, 1:4, 4:1)
# different length translates to list

# ------
#https://leemendelowitz.github.io/blog/mapply.html
noise <- function(n, mean, std) {
  rnorm(n, mean, std)
}
noise(5, 1, 2)  # 5 random normally distributed values with mean, sd
#noise function is not vectorized. mapply gives us a way
mapply(noise, 1:5, 1:5, 2)  # pairs (1,1), (2,2)

#http://www.studytrails.com/r/core/control_structures_r_apply_functions_2/

2^3 ;  4^2  #= 8 and 16
mapply(function(x,y) x^y, x=c(2,4), y=c(3,2))
#call a function FUN over vectors or lists one index at a time. 
#arguments x and y are recycled if they are of different lengths
mapply(function(x,y){x^y},c(a=2,b=3),c(A=3,B=4))
mapply(function(x,y){x^y},c(a=2,b=3),c(A=3,B=4),USE.NAMES=FALSE)

mapply(function(x,y,z,k){(x+k)^(y+z)},x= c(a=2,b=3),
       y=c(A=3,B=4),MoreArgs=list(z=1,k=2))
#values z and k are 1 and 2 respectively. So the first evaluation of function gives (2+2)^(3+1) and the second gives (3+2)^(4+1)


================================================
FILE: 23-functions/47o-rapply.R
================================================
#apply RAPPLY

#rapply - For when you want to apply a function to each element 
#of a nested list structure, recursively.

#rapply is best illustrated with a user-defined function to apply:
#Append ! to string, otherwise increment

myFun <- function(x){
  if (is.character(x)){
  return(paste(x,"!",sep=""))  # add character
  }
  else{
  return(x + 1)  #inc by 1
  }
}

#A nested list structure
(l <- list(a = list(a1 = "Boo", b1 = 2, c1 = "Eeek"), b = 3, c = "Yikes", 
   d = list(a2 = 1, b2 = list(a3 = "Hey", b3 = 5))))


#Result is named vector, coerced to character           
rapply(l,myFun)

#Result is a nested list like l, with values altered
rapply(l, myFun, how = "replace")


================================================
FILE: 23-functions/47on-eapply.R
================================================
# eapply
# like lapply except that rather than applying a function to 
# every element in a list, it applies a function to every 
# element in an environment. For example if you want to find
# a list of user defined functions in the global environment:


A<-c(1,3,5,7,9)
B<-c(0,3,6,9,12)
C<-list(x=1, y=2)
D<-function(x){x+1}
ls()
lsf.str()

eapply(.GlobalEnv, is.function)
#$D - TRUE being function , $A- FALSE
#if you are building a lot of packages or 
#create a lot of environments it may come in handy.
unlist(eapply(.GlobalEnv, is.vector))


================================================
FILE: 23-functions/47p-sapply1.R
================================================
#lapply sapply

lapply(1:3, function(x) x^2)
unlist(lapply(1:3, function(x) x^2))

#Passing simplify=FALSE to sapply will also give you a list:
  
sapply(1:3, function(x) x^2, simplify=F)
sapply(1:3, function(x) x^2)

m = matrix(1:12, ncol=3)

mean(m[,c(1,2,3)])
for (i in 1:3) print(mean(m[,c(i)]))

sapply(1:3, function(x) mean(m[,x]))

sapply(c(1,2,3), function(x) mean(m[,x]))

sapply(1:3, function(x, y) mean(y[,x]), y=m)


sapply(vec, f)
#input is a vector. 
#output is a vector/matrix, where element i is f(vec[i]), giving you a matrix if f has a multi-element output
lapply(vec, f)
#: same as sapply, but output is a list?
apply(matrix, 1/2, f): 
#input is a matrix. output is a vector, 
#where element i is f(row/col i of the matrix)

tapply(vector, grouping, f)
#: output is a matrix/array, where an element in the matrix/array is the value of f at a grouping g of the vector, and g gets pushed to the row/col names

by(dataframe, grouping, f)
#: let g be a grouping. apply f to each column of the group/dataframe. pretty print the grouping and the value of f at each column.

aggregate(matrix, grouping, f)
#: similar to by, but instead of pretty printing the output, aggregate sticks everything into a dataframe.
#https://stackoverflow.com/questions/3505701/grouping-functions-tapply-by-aggregate-and-the-apply-family


?Map
Map(sum, 1:5, 1:5, 1:5)
Map(sum, 1:5, 1:5, 1:5, simplify2array=F)
Map(sum, 1:5, 11:15, 21:25, simplify=T)



================================================
FILE: 23-functions/47q-sapply2.R
================================================
# SAPPLY

#List
list2 = list(num=1:10, vec=c(1:5, 4:5, 6:8, NA, 9, 12, 17), lg=log(1:5))
list2

sapply(list2, FUN=mean, na.rm=T)

# DF
str(student1)
sapply(draws, mean)
dim(student1)
s1 = student1[,c('java','dbms', 'dwm','vlsi', 'cpp', 'cbnst')]
sapply(s1,mean)
mode(sapply(s1,mean))
class(sapply(s1,mean))

mode(s1)
class(s1)
str(s1)

#List
l <- list(a = 1:100, b = 11:20)
l
l.mean <- sapply(l, mean)
l.mean

#difference with lapply?
#lapply always returns a list. 
#sapply (if it can) simplifies the results
lapply(draws,mean)
#get same result as sapply
unlist(lapply(draws,mean))


================================================
FILE: 23-functions/47s-tapply2.R
================================================
#tapply

#Data Frame df3
newnum = c(2:5, 4:5, 6:8, 9,17)
fac1 = factor(c(rep("A", 3), rep("B", 3), rep("C", 3), rep("D",2)))
fac2 = gl(n=2, k=1, length=11, labels = month.abb[1:2])
newnum
fac2
fac1
df3 = data.frame(response = newnum, pred1 = fac1, pred2 = fac2)
df3
X = df3
tapply(X=newnum, INDEX= fac1, FUN=NULL)
tapply(X=newnum, INDEX = fac1, FUN = sum)
tapply(X=newnum, INDEX = list(fac1,fac2), FUN = sum)
tapply(X=newnum, INDEX=fac1, FUN=median)

mode(tapply(X=newnum, INDEX=fac1, FUN=median, simplify=F))

?tapply
with(df3, tapply(response, INDEX=pred1, FUN=sum) )

================================================
FILE: 23-functions/47t-vapply1.R
================================================
#apply - VAPPLY

#vapply - When you want to use sapply but perhaps need to squeeze some more speed out of your code.

#For vapply, you basically give R an example of what sort of thing 
#your function will return, which can save some time 
#coercing returned values to fit in a single atomic vector.

(x <- list(a = 1, b = 1:3, c = 10:100))

#Note that since the advantage here is mainly speed, this
# example is only for illustration. We're telling R that
# everything returned by length() should be an integer of 
# length 1. 
vapply(x, FUN = length, FUN.VALUE = 0L) 
#a  b  c  
#1  3 91

================================================
FILE: 23-functions/49b-replicate1.R
================================================
#replicate
#
?replicate
replicate(n, expr, simplify = "array")
replicate(3,4)
#[1] 4 4 4
replicate(4,3)
#[1] 3 3 3 3
x = c(1:5)
replicate(3,x)
replicate(x,3)  # Invalid length
?replicate
rnorm(5)
replicate(4, rnorm(5))
j=0
(x<-unlist(replicate(5, for(i in 1:5){
  print(paste(' Hello Dhiraj: i- ' , i,sep=': '))
  })))

replicate(5, 'Hello')
replicate(5, mean(1:5))

seq(1,5,1)
replicate(5, seq(1,5,1))


rep(1:4,len=20)
replicate(1:4, length=20)
?replicate

(x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE)))

# compute the list mean for each list element
lapply(x, mean)

# median and quartiles for each list element
1:3/4
lapply(x, quantile, probs = 1:3/4)

sapply(x, quantile) # on list items

(i39 <- sapply(3:9, seq) ) # list of vectors


sapply(i39, fivenum)

vapply(i39, fivenum,
       c(Min. = 0, "1st Qu." = 0, Median = 0, "3rd Qu." = 0, Max. = 0))

## sapply(*, "array") -- artificial example
(v <- structure(10*(5:8), names = LETTERS[1:4]))

f2 <- function(x, y) outer(rep(x, length.out = 3), y)
(a2 <- sapply(v, f2, y = 2*(1:5), simplify = "array"))
a.2 <- vapply(v, f2, outer(1:3, 1:5), y = 2*(1:5))
stopifnot(dim(a2) == c(3,5,4), all.equal(a2, a.2),
          identical(dimnames(a2), list(NULL,NULL,LETTERS[1:4])))

hist(replicate(100, mean(rexp(10))))







## use of replicate() with parameters:
foo <- function(x = 1, y = 2) c(x, y)

# does not work: bar <- function(n, ...) replicate(n, foo(...))
bar <- function(n, x) replicate(n, foo(x = x))
bar(5, x = 3)



data1 = c(3, 60, 19, 9, 4 , 5)

labels1 = c('Building training sets', 'Cleaning and Organising Data', 'Collecting Data sets', 'Mining data for patterns', 'Refining Algorithms', 'Others')
pie(data1, labels= labels1)

barplot(data1)
pie(data1,labels=NA,   clockwise=TRUE,
    col=rainbow(6),  border="white",  radius=1.2,
    cex=0.8,  main="Average Time Spent by Data Scientists")
legend("bottomright",legend=pielabels,bty="n", # horiz = T,
       fill=rainbow(6))
barplot(data1, col=rainbow(6), names.arg=pielabels, 
        cex.names = 1, horiz=T, angle=90, 
        main="Average Time Spent by Data Scientists"
)
text(1:6, data1, labels= labels1)
?barplot



browsers<-read.table("browsers.txt",header=TRUE)
browsers<-data1
browsers
pielabels <- sprintf("%s = %3.1f%s", labels1, browsers, "%")
pielabels
?sprintf
library(RColorBrewer)
pie(browsers,
    labels=NA,
    clockwise=TRUE,
    col=brewer.pal(6,"Set1"),
    border="white",
    radius=1,
    cex=0.8,
    main="Percentage Share of Internet Browser usage")
legend("bottomleft",legend=labels1,bty="n",
       fill=brewer.pal(6,"Set1"))


================================================
FILE: 23-functions/49c-replicate.R
================================================
#replicate
#
?replicate
replicate(n, expr, simplify = "array")

replicate(4, rnorm(5))


my.fun = function() {
  
  for (i in 1:1000) {
    ...
    for (j in 1:20) {
      ...
    }
  }
  
  return(output)
  
}

rep(1:4,len=20)
replicate(1:4,len=20)


================================================
FILE: 23-functions/49e-by.R
================================================
# Apply Family - by
# used for DF split by factors
mtcars
# we can take am, gear or carb as factors
by(data, INDICES, FUN, ..., simplify = TRUE)
split(mtcars, mtcars$am)
mtcars$am; mtcars$mpg
by(mtcars$mpg,mtcars$am, FUN=mean )

# wrapper for tapply
by(warpbreaks[, 1:2], warpbreaks[,"tension"], summary)
by(warpbreaks[, 1:2], warpbreaks[,"tension"], mean)  # will not work


# what works and what not
by(iris$Sepal.Width , iris$Species , summary )
by(iris, iris$Species, length)
by(x=mtcars, mtcars$am, function(x) mean(x))  # will not work
by(iris, iris$Species, mean) # will not work
by(iris$Sepal.Width, iris$Species, mean)


# DF or matrix
df=data.frame(a=c(1:15),b=c(1,1,2,2,2,2,3,4,4,4,5,5,6,7,7))
#sum of all values of a grouped by values of b. 
#That is, sum of all values of a where b=1, sum of all values of a where b is 2 and so on
by(df,factor(df$b),sum) # by(df,df$b,sum) also works
df
df2=data.frame(a=c(1:15),k=c(1:15),b=c(1,1,2,2,2,2,3,4,4,4,5,5,6,7,7))
by(df2,factor(df2$b),sum)
df2

?by
# mtcars
bo1 = by(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], summary)
#bo2 = by(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], summary, simplify=T)
str(bo1)
#str(bo2);
by(data= mtcars[,'mpg'], INDICES=mtcars[,'cyl'], max)
by(data= mtcars[,'disp'], INDICES=mtcars[,c('vs','am')], mean)

by(data= mtcars[,c('disp','wt')], INDICES=mtcars[,c('vs','am')], mean)  # not work
aggregate(mtcars[,c('disp','wt')], mtcars[,c('vs','am')], mean)

by(data= mtcars[,c('disp','wt')], INDICES=mtcars[,c('vs','am')], summary)


================================================
FILE: 23-functions/49f-by.R
================================================
#by - data frame - matrix form

?by
#by(data, INDICES, FUN, ..., simplify = TRUE)

by(mtcars, INDICES=mtcars[,'am'],  FUN=summary, simplify=T)
by(mtcars, INDICES=mtcars[,c('am','cyl')],  FUN=summary, simplify=T)

by(mtcars, INDICES=mtcars[,c('am','cyl')],FUN=colMeans, simplify=T)
by(mtcars, INDICES=mtcars[,'am'],  FUN=colMeans, simplify=T)



================================================
FILE: 23-functions/49g-bywith.R
================================================
#with by
#functions that can help write simpler and more efficient code.

#by function, as stated in the documentation can be though, 
#as a "wrapper" for tapply. The power of by arises when we want to compute a task that tapply can't handle. One example is this code:

(ct <- tapply(iris$Sepal.Width , iris$Species , summary ))
(cb <- by(iris$Sepal.Width , iris$Species , summary ))


#ct and cb, we "essentially" have the same results and the only differences are in how they are shown and 
#the different class attributes, respectively by for cb and array for ct.

tapply(iris, iris$Species, summary )
#Error in tapply(iris, iris$Species, summary) : 
#  arguments must have same length
#we want to calculate the summary of all variable in iris along the factor Species":
#but R just can't do that because it does not know how to handle.

#by function R dispatch a specific method for data frame class and
#then let the summary function works even if the length of the first argument (and the type too) are different.

#Sums the 1st elements, the 2nd elements, etc. 
mapply(sum, 1:5, 1:5, 1:5) 
[1]  3  6  9 12 15
#To do rep(1,4), rep(2,3), etc.
mapply(rep, 1:4, 4:1)   


# object of class by that along Species (say, for each of them) computes the summary of each variable.
# 
# Note that if the first argument is a data frame, 
# the dispatched function must have a method for that class of objects. 
# For example is we use this code with the mean
#function we will have this code that has no sense at all:

#if the first argument is a data frame, the dispatched function 
#must have a method for that class of objects.
#For example is we use this code with the mean function we will have this code that has no sense at all:
  
by(iris, iris$Species, mean)
# cannot have mean of all variables
split(iris, iris$Species)

(bywork <- by(iris, iris$Species, summary ))

by(iris, iris$Species, length)

iris
# with----------------------

rm(list=ls())
?with  # temp opening data
with(data=mtcars, mean(mpg)) # list, df

with(data=mtcars,c(mean(mpg), mean(wt))) # list, df
# within : modifies the data 
df2 = within(data=mtcars, c(mean(hp), mean(wt)))
df2
with(data=mtcars, aggregate(by=list(cyl), FUN=mean))
by(x=mtcars, mtcars$am, function(x) mean(x))


load('./data/du3.RData')
str(df2)
by(df2, df2$course, FUN=summary)
by(df2[,'sql'], df2$gender, FUN=function(x) mean(x))

names(df2)
#with ------
#applys an expression to a dataset. I
# with(data, expression)
# example applying a t-test to a data frame mydata 
with(mydata, t.test(y ~ group))




#by -----
#by( ) function applys a function to each level of a factor or factors. It is similar to BY processing in SAS.

# by(data, factorlist, function)
# example obtain variable means separately for
# each level of byvar in data frame mydata 
by(mydata, mydata$byvar, function(x) mean(x))


================================================
FILE: 23-functions/51b-myfunc.R
================================================
x = C(0.90,0.95,0.975,0.99,0.995)
log2 <-
function(x) log(x,base=3)
cummedian = function(x) {
  tmp = seq_along(x)
  for (i in 1:length(tmp)) tmp[i] = median(x[1:i])
  print(tmp)
  }
cummedian(df1)

interest = function(p,r,t=1) return((p * r * t) / 100 )

mapply(function(p,r,t=1) return((p * r * t) / 100 ),p,r)


cl2 = c(0.90,0.95,0.975,0.99,0.995)
qnorm(cl2)






t.single = function(obs.mean, mu, SD, n) {
  t.obt = (obs.mean - mu) / (SD / sqrt(n))
  p.value = pt(abs(t.obt), df=n-1, lower.tail=F)
  print(c(t.obt = t.obt, p.value = p.value))
  warning("P-value is one-sided. Double for two-sided.")
}

ttest <- function(sample_mean, pop_mean, sample_sd, n) {
  t <- (sample_mean - pop_mean) / (sample_sd/sqrt(n))
}


# Blank Function
my.fun = function() {
  for (i in 1:1000) {
    ...
    for (j in 1:20) {
      ...
    }
  }
  return(output)
}


================================================
FILE: 23-functions/51c-functions1.R
================================================
#Functions

# Commands 
args()
function()
formals()
lsf.str()
body()

# Create function
x=2 ; y = 3
(sumofnos = x + y)

(x1 = c(1:10))
(sumofnos = sum(x1))

sumofnos = sum(x1)

fsum1 <- function(x1) {
  sumofnos = sum(x1)/length(x1)
}
fsum1
fsum1(1:10)

# Code - Extract Function
# run above function lines
fsum1(x1)  # no output
(sumout = fsum1(x1))  # output seen : value assigned to a variable

# print output from function call
fsum2 <- function(x1) {
  sumofnos = sum(x1)
  return(sumofnos)
}

fsum2(x1)  # output seen

x1=1:10 ; y1=100:200

sum2a <- function(x3, y3) {
  sumofnos = sum(x3) * mean(y3)
  return(sumofnos)
  }
sum2a(x1, y1)








# another way.. printing values inside a function
fsum3 <- function(x1) {
  sumofnos = sum(x1)
  print(sumofnos)  # this is for printing
  print(paste('Sum of object is ',sumofnos, sep=' xxxx '))
  cat(paste('Sum of my object is ',sumofnos, sep='- '))
  }
fsum3(x1)

# Check structure of function
body(fsum3)
body(fsum1)
args('fsum3')
2**2; 2^2
args(bxp)  # args of built in function
body(bxp)  # 
# create a new function

# Area of Circle
areacircle = pi * r^2

areacircle <- function(constant=pi, r) {
  areacircle = constant * r^2
  return(areacircle)
  }
# pi is constant neednot be givien
areacircle( ,4)
areacircle(r=4)

areacircle(r=4, constant=4)
body(areacircle)
args(areacircle)

library(plyr)
# Structure of functions
lsf.str()
search()
ls()
lsf.str(pos=-1)  # relate to functions
lsf.str(pos=1)  # relate to functions
lsf.str(pos=2)  # relate to 2nd package in order found in search()
lsf.str(pos=4)

save(areacircle, fsum1, file='./data/dudata1.Rdata')
# Clear Invironment
rm(list=ls())
load('./data/dudata1.RData')  # Rdata RData are same in windows
ls()
lsf.str()
search()
lsf.str(pos=1)


# R Commands and objects name into character strings
args(areacircle)
(ch1 = deparse(args(areacircle)))
str(ch1)

# make result of function but donot print
areacircle(r=6)

areacircle2 <- function(constant=3.14, r) {
  areacircle = constant * r^2
  invisible(areacircle)
}
areacircle2(r=5)  # no output shown
(ch = areacircle2(r=5))  # assign and print

# Print command and options
pi
print(pi)
print(pi, digits = 2)
?print
sname = c('Achal Kumar','Apoorva Karn',
          'Goldie Sahni','Hitesh Mann',
          'Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')
sname
print(sname)
print(sname, quote=F)


================================================
FILE: 23-functions/53b-cbindrbind1.R
================================================
# cbind rbind

cbind()  # easier than matrix command
# retain row and column names 
marks1 = ceiling(runif(11, 4, 10))
marks2 = ceiling(runif(11, 4, 10))

cbind(marks1, marks2)
(m1 = matrix(c(marks1, marks2), ncol=2))
# rownames gone

(m2 = as.matrix(cbind(marks1, marks2)))
str(m2)  #??
m2[[1]]
m2[1]
m2[,1]
m2$marks

#(m3 = cbind(marks1, marks2))
#(m3 = as.numeric(cbind(marks1, marks2))


#(m3 = as.matrix(as.numeric(cbind(marks1, marks2))   , ncol=2))

#str(m3)

# DF from cbind and rbind
# 
(df1= cbind.data.frame(marks1, marks2))
str(df1)

(df2=rbind.data.frame(marks1,marks2))


================================================
FILE: 23-functions/53c-joinDFs.R
================================================
#Join Data Frame

#same size
women1 = women
women2 = women
cbind(women1, women2)
data.frame(women, women)

rbind(women1, women2)


================================================
FILE: 23-functions/53c-merge1.R
================================================
#Merge

?merge

df2= merge(mtcars, mtcars)
df2
dim(mtcars)
dim(df2)
df3= merge(mtcars, mtcars, by.x = names(mtcars))
dim(df3)

# merge2

rollno = c(17010,17045,17012,17087,17057,17056,17032,17084,17078,17018,17013)
sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')
course =  c('PGDDS','PGDDS','PGDDS','PGDDS','MSCDS','PGDDS','MSCDS','PGDDS','PGDDS','PGDDS','PGDDS')
df1 = data.frame(rollno, sname, course)

gender = c('M','F','M','M','M','F','M','F','M','M','M')
exp =  c(3,3.5,14,5,0,1,1,15,1,3,8) # Experience
hostel =  c(FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,TRUE)
df2 = data.frame(rollno, gender, exp, hostel)
df1; df2
merge(df1,df2)


# Other joins
# DF of marks of subjec1
ID = c(17010,17045,17012,1)
SEX = c('M','F','M','T')
SUB1 = c(50,60, 70,80)
(df3 = data.frame(ID, SEX, SUB1))
merge(df1, df3)  # incorrect
merge(df1, df3, all=F)  

merge(df1, df3, by.x = c('rollno'), by.y=c('ID'))

# use two columns to join
df1$gender = gender
merge(df1, df3, by.x = c('rollno','gender'), by.y=c('ID','SEX'))
df3

merge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all=TRUE)
merge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all=FALSE)
merge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all.x=TRUE)
merge(df1, df3, by.x = c('rollno'), by.y=c('ID'), all.y=TRUE)


================================================
FILE: 23-functions/54b-combination.R
================================================
# Combination of Elements
#combinations of the elements of x taken m at a time
combn(x, m, FUN = NULL, simplify = TRUE, ...)

combn(letters[1:4], 2)

(m <- combn(10, 5, min))   # minimum value in each combination

mm <- combn(15, 6, function(x) matrix(x, 2, 3))

stopifnot(round(choose(10, 5)) == length(m),
          c(2,3, round(choose(15, 6))) == dim(mm))

## Different way of encoding points:
combn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins = 4)

## Compute support points and (scaled) probabilities for a
## Multivariate-Hypergeometric(n = 3, N = c(4,3,2,1)) p.f.:
# table.mat(t(combn(c(1,1,1,1,2,2,2,3,3,4), 3, tabulate, nbins = 4)))

## Assuring the identity
for(n in 1:7)
  for(m in 0:n) stopifnot(is.array(cc <- combn(n, m)),
                          dim(cc) == c(m, choose(n, m)))

#data frame from all combinations of the supplied vectors or factors.
expand.grid(..., KEEP.OUT.ATTRS = TRUE, stringsAsFactors = TRUE)

expand.grid(height = seq(60, 80, 5), 
            weight = seq(100, 300, 50),
            sex = c("Male","Female"))

x <- seq(0, 10, length.out = 100)
y <- seq(-1, 1, length.out = 20)
d1 <- expand.grid(x = x, y = y)
d1
(d2 <- expand.grid(x = x, y = y, KEEP.OUT.ATTRS = FALSE))
object.size(d1) - object.size(d2)
##-> 5992 or 8832 (on 32- / 64-bit platform)


# Unique Combinations
x = 1:10
y = letters[1:5]
(df= expand.grid(X=x, Y=y)) 
df[order(df$X), ]


================================================
FILE: 23-functions/54d-expandgrid.R
================================================
# Expand Grid
expand.grid(height = seq(60, 80, 5), weight = seq(100, 300, 50),
            sex = c("Male","Female"))

x <- seq(0, 10, length.out = 100)
y <- seq(-1, 1, length.out = 20)
d1 <- expand.grid(x = x, y = y)
d1
d2 <- expand.grid(x = x, y = y, KEEP.OUT.ATTRS = FALSE)
d2
object.size(d1) - object.size(d2)
##-> 5992 or 8832 (on 32- / 64-bit platform)


================================================
FILE: 23-functions/55b-sweep1.R
================================================
#Sweep

#method signature is similar to 'apply' 
#though 'sweep' requires one more parameter, 'STATS'.
#'sweep' returns an array of the same shape as the input array, 
#whereas the result returned by 'apply' depends on the function passed in.
#https://stackoverflow.com/questions/3444889/how-to-use-the-r-function-sweep
# e.g., use 'sweep' to express a given matrix in terms of distance from 
# the respective column mean

# create some data:
(M = matrix( 1:12, ncol=3))

M+1
M+c(1,2)
M
M+c(1,2,3)
cbind(M, M+c(1,2,3))



# calculate column-wise mean for M
(dx = colMeans(M))
colSums(M)  # and not colsums

# now 'sweep' that summary statistic from M
sweep(M, 2, dx, FUN="-")
M ; dx

#      [,1] [,2] [,3]
#[1,] -1.5 -1.5 -1.5
#[2,] -0.5 -0.5 -0.5
#[3,]  0.5  0.5  0.5
#[4,]  1.5  1.5  1.5
sweep(M, 1, dx, FUN="-")   # Warning  - no recycle, less nos 3 and 4
(dy = rowMeans(M))
sweep(M, 1, dy, FUN="-")

M+1
M+c(1,2)
M
M+c(1,2,3)
cbind(M, M+c(1,2,3))


================================================
FILE: 23-functions/55d-sweep2.R
================================================
#sweep

#The basic idea is to sweep through an 
#array row- or column-wise and return a modified array.

#you have a matrix and want to standardize it column-wise:

(dataPoints <- matrix(4:15, nrow = 4))

# Find means per column with `apply()`
(dataPoints_means <- apply(dataPoints, 2, mean))

# Find standard deviation with `apply()`
(dataPoints_sdev <- apply(dataPoints, 2, sd))

# Center the points 
(dataPoints_Trans1 <- sweep(dataPoints, 2, dataPoints_means,"-"))
print(dataPoints_Trans1)
##      [,1] [,2] [,3]
## [1,] -1.5 -1.5 -1.5
## [2,] -0.5 -0.5 -0.5
## [3,]  0.5  0.5  0.5
## [4,]  1.5  1.5  1.5
# Return the result
dataPoints_Trans1
##      [,1] [,2] [,3]
## [1,] -1.5 -1.5 -1.5
## [2,] -0.5 -0.5 -0.5
## [3,]  0.5  0.5  0.5
## [4,]  1.5  1.5  1.5
# Normalize
(dataPoints_Trans2 <- sweep(dataPoints_Trans1, 2, dataPoints_sdev, "/"))

# Return the result
dataPoints_Trans2
##            [,1]       [,2]       [,3]
## [1,] -1.1618950 -1.1618950 -1.1618950
## [2,] -0.3872983 -0.3872983 -0.3872983
## [3,]  0.3872983  0.3872983  0.3872983
## [4,]  1.1618950  1.1618950  1.1618950

#NB: for this simple example the same result can of course be achieved more easily by
apply(dataPoints, 2, scale)


?attitude
dim(attitude)
head(attitude)
(med.att <- apply(attitude, 2, median))
?sweep(data.matrix(attitude), 2, med.att)  
# subtract the column medians

## More sweeping:
#Create a array of values 1 to 24 with 3 dimension - 4,3,2
(A <- array(1:24, dim = 4:2))

## no warnings in normal use
#Subtract 5 from each row value
sweep(A, 1, 5)
(A.min <- apply(A, 1, min))  # == 1:4
sweep(A, 1, A.min)
apply(A, 1:2, median)
sweep(A, 1:2, apply(A, 1:2, median))

## warnings when mismatch
sweep(A, 1, 1:3)  # STATS does not recycle
sweep(A, 1, 6:1)  # STATS is longer

## exact recycling:
sweep(A, 1, 1:2)  # no warning
sweep(A, 1, as.array(1:2))  # warning
as.array(1,2)


================================================
FILE: 23-functions/56b-outer1.R
================================================
# Outer

A<-c(1,3,5,7,9)
B<-c(0,3,6,9,12)
?pmax  # parallel max
#(1,0)->1 ; (3,3)-> 3 ; (5,6)->6

mapply(FUN=pmax, A, B)
#[1]  1  3  6  9 12

outer(A,B, pmax)  # every combination of every element
# only useful for linear algebra type things. 
# However, it can be used much like mapply to apply a
# function to two vectors of inputs. 
# The difference is that mapply will apply the function 
# to the first two elements and then the second two etc, 
# whereas outer will apply the function to every combination
# of one element 
# from the first vector and one from the second. For example:

================================================
FILE: 23-functions/56c-outer2.R
================================================
#Outer
#
?outer
outer(1:4, 4:6)
1:4 %o% 4:6
outer(1:4, 4:6, FUN=paste)
x=1:4 ; y=4:6 ; z=c(10,11,12)
x;y;z
outer(x,y, FUN='*')
outer(x,y, FUN=paste)
outer(x,y, FUN='+')
outer(x,y, FUN='-')
#(Y= matrix(-y,ncol=3,nrow=4,dimnames=list(NULL,c('y1','y2','y3'))))
#(X=matrix(x,ncol=3,nrow=4,byrow=F,dimnames=list(NULL,c('x1','x2','x3'))))
x;y
#cbind(Y,X) 
outer(x,-y, FUN=paste)
outer(x,y, FUN='-')
outer(x,y, FUN='^')
outer(x,rep('^',3),y, FUN=paste0)

outer(x,y, FUN='/')  # what is this
z = 


================================================
FILE: 23-functions/57b-stack1.R
================================================
# Stack
# 
classA = c(10,14,15)
classB = c(20,25,27)
classC = c(13,15,17)
classmarks = data.frame(classA, classB, classC)
(s1 = stack(classmarks))

(s2= stack(classmarks, select=c("classA", "classC")))
unstack(s2)


================================================
FILE: 23-functions/58-DF-common.R
================================================
#find common between DF
#http://www.cookbook-r.com/Manipulating_data/Comparing_data_frames/

dfA <- data.frame(Subject=c(1,1,2,2), Response=c("X","X","X","X"))
dfA

dfB <- data.frame(Subject=c(1,2,3), Response=c("X","Y","X"))
dfB

dfC <- data.frame(Subject=c(1,2,3), Response=c("Z","Y","Z"))
dfC

dfA$Coder <- "A"
dfB$Coder <- "B"
dfC$Coder <- "C"
dfA
df <- rbind(dfA, dfB, dfC)                    # Stick them together
df
df <- df[,c("Coder", "Subject", "Response")]  # Reorder the columns to look nice
df

# Find the rows which have duplicates in a different group.
dupRows <- dupsBetweenGroups(df, "Coder")
dupRows
cbind(df, dup=dupRows)
#find the rows that are unique within each group:
cbind(df, unique=!dupRows)
#Store the results in df
dfDup <- cbind(df, dup=dupRows)
dfDup
dfA1 <- subset(dfDup, Coder=="A", select=-Coder)
dfA1


#2 df 
# 1 with few unique rows
# 2 table with multiple rows of same data
# common column is not same but part of it is same

a <-c('a','b','c','d','e')
b <-c('a','b','d','e', NA)
a[!(a %in% b)]
a[(a %in% b)]

ab <- data.frame(a,b)
ab$a[!(ab$a %in% ab$b)]
ab$a[(ab$a %in% ab$b)]

#

set.seed(111)       # so can replicate results
x <- matrix(runif(500), ncol=2)
x
y <- matrix(runif(2000), ncol=2)
y
library(Hmisc)
w <- find.matches(x, y, maxmatch=5, tol=c(.02,.03))
?find.matches
head(x)
head(y)
w
w$matches[1:5,]
w$distance[1:5,]
# Find first x with 3 or more y-matches
num.match <- apply(w$matches, 1, function(x)sum(x > 0))
j <- ((1:length(num.match))[num.match > 2])[1]
x[j,]
y[w$matches[j,],]


summary(w)


#
library(plyr)
# count the occurrences of each id in the baseball dataframe, then get the subset with a freq >25
head(baseball)
longterm <- subset(count(baseball, "id"), freq > 25)
longterm
# Select only rows from these longterm players from the baseball dataframe
# (match would default to match on shared column names, but here was explicitly set "id")
bb_longterm <- match_df(baseball, longterm, on="id")
bb_longterm[1:5,]

#-------------

dupsbetween
#https://rdrr.io/github/vapniks/mergeutils/man/dupsBetweenGroups.html

install.packages("remotes")
remotes::install_github("vapniks/mergeutils", force=T)
library(mergeutils)
dupsBetweenDFs(dfs, by = NULL, matchall = FALSE)
dupsBetweenDFs(mtcars, mtcars, by=wt)



================================================
FILE: 23-functions/58-df-matching1.R
================================================
#how to rows in 1st DF from column values in 2nd DF

library(gsheet)
library(dplyr)

url1 = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=2077624779'

df1 = data.frame(gsheet2tbl(url1))
head(df1)

url2 = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=943100206'
df2 = data.frame(gsheet2tbl(url2))
head(df2)
names(df1); names(df2)
head(df2)
df2$stock
df1[which(df2$stock %in% df1$stockname), ]
df1

sqldf("select * from df1 where stockname LIKE '%HDFC%'")
sqldf("select stock from df2")

sqldf("select * from df1 where stockname in (select stock from df2)")
sqldf("select * from df1 where stockname like in (select stock from df2)")

df1[(sapply(df1$stockname, grepl, df2$stock) %>% rowSums() %>% as.logical()) * (1:nrow(x)), ]
# not working

library(dplyr)
library(fuzzyjoin)
head(df2)
regex_join(  mutate_if(df1, is.factor, as.character), mutate_if(df2, is.factor, as.character),  by = c("stockname" = "stock") )
#https://stackoverflow.com/questions/45638726/how-to-query-a-dataframe-using-a-column-of-other-dataframe-in-r

#
library(magrittr)  # To use the pipe, "%>%"
df1 %>% data.table::setDT()  # To return the result as a table easily
m2 = df1[(sapply(df2$stock, grepl, df1$stockname) %>% rowSums() %>% as.logical()) * (1:nrow(df1)), ]
as.data.frame(m2)
df2$stock


#sqldf
sqldf("select df1.stockname, df2.stock FROM df1 JOIN df2 on df1.stockname LIKE '%' || df2.stock || '%'")

#-----
library(data.table)
mtcars[rownames(mtcars) %like% "Merc", ]
iris[iris$Species %like% "osa", ]


#------
mtcars[grep("Merc", rownames(mtcars)), ]
iris[grep("osa", iris$Species), ]


#-----
library(stringr)
library(dplyr)

CO2 %>%  filter(str_detect(Treatment, "non"))


#-----
require(sqldf)
df <- data.frame(name = c('bob','robert','peter'),id=c(1,2,3))
df
sqldf("select * from df where name LIKE '%er%'")



================================================
FILE: 23-functions/58-df2.R
================================================

t1 <- read.table(text = "ID1     Name1     Type1     Name2
1       A1         T1        B1
2       A2         T2        B2
3       A3         T1        B3_a
4       A4         T3        B4_a", header = T)

t1

t2 <- read.table(text = "NameBank     TypeBank
A1             T1
A2             T2
A3             T1
A4             T3
B1             T1
B2             T4
B3             T2
B4             T3", header = T)

t2
t1;t2

library(dplyr)
#charmatch which seeks matches for the elements of its first argument among those of its second.:
t2[1,]
t1[1,]
chrs <- charmatch(t2$NameBank, t1$Name2)
chrs
cbind(
  t1[chrs[!is.na(chrs)],],
  t2[which(!is.na(chrs)),]
)

t1$Type2 <- NA_character_
t1
library(stringr)
for( row in seq_len( nrow( t2 ) ) ) {
  t1$Type2[ substr( t1$Name2, 0, nchar( t2$NameBank[row] ) ) == t2$NameBank[row] ] <- t2$TypeBank[row]
}


================================================
FILE: 23-functions/58-hmisc.R
================================================

library(Hmisc)
find.matches(x, y, tol=rep(0, ncol(y)), scale=tol, maxmatch=10)

?find.matches

y <- rbind(c(.1, .2),c(.11, .22), c(.3, .4), c(.31, .41), c(.32, 5))
x <- rbind(c(.09,.21), c(.29,.39))
y
x
w <- find.matches(x, y, maxmatch=5, tol=c(.05,.05))
w
w$matches
w$distance
x;y
w1 <- find.matches(x, y, tol=c(.1,.01))
w1

find.matches(y, x, maxmatch=5, tol=c(.05,.05))


# NOT RUN {
agrep("lasy", "1 lazy 2")
agrep("lasy", c(" 1 lazy 2", "1 lasy 2"), max = list(sub = 0))
agrep("laysy", c("1 lazy", "1", "1 LAZY"), max = 2)
agrep("laysy", c("1 lazy", "1", "1 LAZY"), max = 2, value = TRUE)
agrep("laysy", c("1 lazy", "1", "1 LAZY"), max = 2, ignore.case = TRUE)
# }

agrep("dhiraj", 'Dhiraj Upadhyaya')
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'))
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'), ignore.case = T)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ'), max.distance = 0)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 0)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 1)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 2)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 3)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 6)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = 3, value=T)
#max expressed at absolute nos
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = .1, value=T)
#expressed as decimal
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (del=2), value=T)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (cost=3), value=T)
agrep("dhiraj", c('Dhiraj Upadhyaya','Dheeraj', 'Dhiru','D Upadhyaya', 'Dhiraj', 'DHIRAJ', 'dhiraj'), max.distance = (inser=3), value=T)



library(RecordLinkage)

ClosestMatch2 = function(string, stringVector){
  distance = levenshteinSim(string, stringVector);
  stringVector[distance == max(distance)]
}
word <- 'test'
words <- c('Teest','teeeest','New York City','yeast','text','Test')
ClosestMatch2(word,words)


================================================
FILE: 23-functions/58-pmatchchar.R
================================================
#partial matching
#https://astrostatistics.psu.edu/su07/R/html/base/html/pmatch.html

pmatch("m",   c("mean", "median", "mode")) # returns NA
pmatch("med", c("mean", "median", "mode")) # returns 2

pmatch(c("", "ab", "ab"), c("abc", "ab"), dup = FALSE)
pmatch(c("", "ab", "ab"), c("abc", "ab"), dup = TRUE)
## compare
charmatch(c("", "ab", "ab"), c("abc", "ab"))

charmatch("", "")                             # returns 1
charmatch("m",   c("mean", "median", "mode")) # returns 0
charmatch("med", c("mean", "median", "mode")) # returns 2



================================================
FILE: 23-functions/61c-missing1.R
================================================
# Missing Values
# Create, Detect, Replace, Remove, Impute, Visualise
library(VIM)

(v1 = c(1,2,NA,NA,5))
is.na(v1)
v1=NULL
v1  # remove all values
(v1 = c(1,2,NA,NA,5,6,7,NA))
sum(is.na(v1))  # sum TRUE values ie missing values
mean(v1)  # cannot calculate if NA values in vector
mean(v1, na.rm=T)  # remove and calc
(v1a = na.omit(v1))  # omit missing values and put in new vector
v1a
mean(v1a)
sum(v1a) # now you can sum on non NA values
?na.omit

anyNA(v1) # is there any NA value
anyNA(v1a)
?anyNA
#impute missing values of vector with mean value of non NA values
v1
mean(v1, na.rm=T)  # this value to be used
v1[is.na(v1)]
v1[is.na(v1)] = mean(v1, na.rm=T)
v1

#Use VIM package to load sleep dataset with NA values in rows & col
library(VIM)
data(sleep, package='VIM')
summary(sleep)# see NA values in cols
head(sleep)
dim(sleep)
complete.cases(sleep) #all complete rows without any missing values
sum(complete.cases(sleep)) # count non NA rows/ complete 42
sum(!complete.cases(sleep))  #count NA rows 20
mean(complete.cases(sleep))  #42/62
# total cases = 62, complete=42  
sleep[complete.cases(sleep),] # list rows which are complete
sleep[!complete.cases(sleep),] # list rows which have NA values

# Particular Column
is.na(sleep$Dream)
sum(is.na(sleep$Dream))
mean(is.na(sleep$Dream))  # 12/62

# How many missing values
sum(is.na(sleep))  #total in data frame
names(sleep)# check columname
colSums(is.na(sleep))  #NA values in each column
rowSums(is.na(sleep))  #NA values in each row
head(sleep)
as.data.table(sleep)  #data.table format
is.na(sleep[1:3,]) # first 3 rows
head(sleep,n=3)

#Tabulate
library(mice)
mice::md.pattern(sleep)
#42 rows without any missing values # 2 rows with 1 NA NonD

#Visualisation
VIM::aggr(sleep, prop=F, numbers=T)
#NonD max NA values 
VIM::aggr(sleep, prop=T)  # Proportion
VIM::matrixplot(sleep) # rowno and missing values in red
VIM::marginmatrix(sleep)
VIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c("darkgray", "red", "blue"))  # finding relationship between NA value


#Exploration
x = as.data.frame(abs(is.na(sleep)))
head(sleep, na=5)
head(x, n=5)
y = x[which(apply(x, 2, sum) > 0)]
cor(y)  #Dream - NonD 0.9
cor(sleep, y, use="pairwise.complete.obs")
#ignore NA, 
#

#Listwise Deletion
options(digits=3)
na.omit(sleep)
cor(na.omit(sleep))
cor(sleep, use="complete.obs")

fit = lm(Dream ~ Span + Gest, data= na.omit(sleep))
summary(fit)


#imputations Packages - mice, Amelia, mi : mice, with, pool

imp = mice::mice(sleep, seed=1234)
?mice

# explore more


================================================
FILE: 23-functions/61c-missing2.R
================================================
#Missing Values - Visualisation  

library(VIM)
#Visualisation
VIM::aggr(sleep, prop=F, numbers=T)  #Total nos
#NonD max NA values 
VIM::aggr(sleep, prop=T)  # Proportion
VIM::matrixplot(sleep) # rowno and missing values in red
#The continuous variable is rescaled and represented by grayscale colors. Lighter colors indicate lower values and darker colors suggest larger values
names(sleep)
VIM::barMiss(sleep)
VIM::histMiss(sleep[,3:8])
sleep$BodyWgt
#Click in in the left margin to switch to the previous variable or in the right margin to switch to the next variable.
#To regain use of the VIM GUI and the R console, click anywhere else in the graphics window.
VIM::spineMiss(sleep[c('Gest','Dream')])
VIM::marginmatrix(sleep)
VIM::scattmatrixMiss(sleep)
VIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c("darkgray", "red", "blue"))  # finding relationship between NA value



(shadow = as.data.frame(abs(is.na(sleep))))


#Exploration
x = as.data.frame(abs(is.na(sleep)))
head(sleep, na=5)
head(x, n=5)
y = x[which(apply(x, 2, sum) > 0)]
cor(y)  #Dream - NonD 0.9
cor(sleep, y, use="pairwise.complete.obs")
#ignore NA, 
#

#Listwise Deletion
options(digits=3)
na.omit(sleep)
cor(na.omit(sleep))
cor(sleep, use="complete.obs")

fit = lm(Dream ~ Span + Gest, data= na.omit(sleep))
summary(fit)


#imputations Packages - mice, Amelia, mi : mice, with, pool

imp = mice::mice(sleep, seed=1234)
imp
?mice

# explore more


================================================
FILE: 23-functions/61c-missing3.R
================================================
# Missing values

x = c(NA, 1, NA, 2)
is.na(x)
mean(x)
mean(x, na.rm=T)
x[is.na(x)] = mean(x, na.rm=T)
x
sum(is.na(x))


x2 = rnorm(100000, mean=50, sd=5)
length(x2)
posn=sample(100000, size=30)
x2[posn] = NA
sum(is.na(x2))


library(VIM)

head(sleep)
complete.cases(sleep)
sleep[complete.cases(sleep),]
sleep[!complete.cases(sleep),]
colSums(is.na(sleep))
rowSums(is.na(sleep))
 

================================================
FILE: 23-functions/62b-outlier.R
================================================
#Outliers
#libaries - outliers,car
easypackages::libraries('outliers','car')

#Outlier
x = c(50:70,99, 150,150)
mean(x)
mean(x[x < 90])
mean(50:70)

#Box Plot
set.seed(482)
y = rnorm(100)
boxplot(y)
stripchart(y, vertical = TRUE,  method = "jitter", add = TRUE, pch = 20, col = 'blue', jitter=.05)
?stripchart
(pts = identify(rep(1, length(y)), y, labels = seq_along(y)))
#Click on outlier value and press FINISH button on Plot tab
#click on line along dashed lines
y[pts]


#Case
# Inject outliers into data.
head(cars)
cars1 <- cars[1:30, ]
cars_outliers <- data.frame(speed=c(19,19,20,20,20), dist=c(190, 186, 210, 220, 218))
cars2 <- rbind(cars1, cars_outliers)
tail(cars2)

# Plot of data with outliers.
par(mfrow=c(1, 2))
plot(cars2$speed, cars2$dist, xlim=c(0, 28), ylim=c(0, 230), main="With Outliers", xlab="speed", ylab="dist", pch="*", col="red", cex=2)
abline(lm(dist ~ speed, data=cars2), col="blue", lwd=3, lty=2)

# Plot of original data without outliers. Note the change in slope (angle) of best fit line.
plot(cars1$speed, cars1$dist, xlim=c(0, 28), ylim=c(0, 230), main="Outliers removed \n A much better fit!", xlab="speed", ylab="dist", pch="*", col="red", cex=2)
abline(lm(dist ~ speed, data=cars1), col="blue", lwd=3, lty=2)
#Notice the change in slope of the best fit line after removing the outliers. Had we used the outliers to train the model(left chart), our predictions would be exagerated (high error) for larger values of speed because of the larger slope.

#-------------------
#Now proceed with Outlier Analsis with Univariate, Bivariate, Multivariate


#Univariate Analysis

#add some extreme values to MPG variable
x = c(mtcars$mpg, c(50,55,90))
(outlier_values = boxplot.stats(x)$out)  # outlier values.
boxplot(x, main="MPG", boxwex=0.1)
mtext(paste("Outliers: ", paste(outlier_values, collapse=", ")), cex=0.6)

#outlier package
outliers::outlier(x)
outliers::scores(x,type="z", prob=0.95)

#Bivariate Analysis
#Visualize in box-plot of the X and Y, for categorical X’s
#What is the inference? The change in the level of boxes suggests that GEAR seem to have an less impact in MPG than CYL. Any outliers in respective categorical level show up as dots outside the whiskers of the boxplot.

#For continuous variable (convert to categorical if needed.)
par(mfrow=c(1,2))
boxplot(mpg ~ factor(gear), data=mtcars, main="Boxplot for MPG vs GEAR")
boxplot(mpg ~ factor(cyl), data=mtcars, main="Boxplot for MPG vs CYL")
#one outlier in CYL-8 boxplot with MPG

par(mfrow=c(1,1))
boxplot(mpg ~ factor(cyl), data=mtcars, main="Boxplot for MPG vs CYL")
#merge strip chart by add=T
stripchart(mpg ~ factor(cyl), vertical = TRUE, data = mtcars, method = "jitter", add = TRUE, pch = 20, col = 'blue')


#Multivariate Analysis (LM)
mod = lm(mpg ~ ., data=mtcars)
(cooksd = cooks.distance(mod))
cooksd[cooksd > 4 * mean(cooksd)]

#Influence measures- In general use, those observations that have a cook’s distance greater than 4 times the mean may be classified as influential. This is not a hard boundary.

plot(cooksd, pch="*", cex=2, main="Influential Obs by Cooks distance")  # plot cook's distance
abline(h = 4*mean(cooksd, na.rm=T), col="red")  # add cutoff line
text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),""), col="red")  # add labels
#rows affected

(influential <- names(cooksd)[(cooksd > 4*mean(cooksd, na.rm=T))])
mtcars[influential, ]
colMeans(mtcars)
#disp & hp very high in Ford, qsec very high & hp very low in Merc 230

#Using Diagnostic Plot in LM
plot(mod,which=4)

#using outlier test - most extreme outlier
car::outlierTest(mod)

#outlier package-----
outliers::outlier(mtcars)
colMeans(mtcars)
#scores
#Compute the normalised scores based on “z”, “t”, “chisq” etc
#Find out observations that lie beyond a given percentile based on a given score.

outliers::scores(mtcars,type="z", prob=0.95)
#prob=.95 within 95% interval


#Methods to Resolve----
#Imputing
#Capping
x = mtcars$mpg
outliers::outlier(x)
(qnt = quantile(x, probs=c(.25, .75), na.rm = T))
(caps = quantile(x, probs=c(.05, .95), na.rm = T))
(H = 1.5 * IQR(x, na.rm = T))
boxplot(x,ylim=c(0,45))
text(1, 39,"Outlier")
#which values is more than 1.5 IQR
x[x > (qnt[2] + H)] #replace with 95% value
x[x < (qnt[1] - H)] <- caps[1]
x[x > (qnt[2] + H)] <- caps[2]
outliers::outlier(x) #next may come up
x[x > (qnt[2] + H)] # but here not it is < 1.5 IQR

#Prediction


#Advanced methods/ packages in R for outliers



================================================
FILE: 23-functions/62c-outlier2.R
================================================
#Outliers Advance

#http://www.rdatamining.com/examples/outlier-detection


library(DMwR)
# remove "Species", which is a categorical column
iris2 <- iris[,1:4]
outlier.scores <- lofactor(iris2, k=5)
plot(density(outlier.scores))

# pick top 5 as outliers
outliers <- order(outlier.scores, decreasing=T)[1:5]
# who are outliers
print(outliers)
#[1] 42 107 23 110 63


#Visualize Outliers with Plots
#Next, we show outliers with a biplot of the first two principal components.

n <- nrow(iris2)
labels <- 1:n
labels[-outliers] <- "."
biplot(prcomp(iris2), cex=.8, xlabs=labels)


#Visualization of outliers in a biplot of the 1st two principal components
#We can also show outliers with a pairs plot as below, where outliers are labeled with "+" in red.

pch <- rep(".", n)
pch[outliers] <- "+"
col <- rep("black", n)
col[outliers] <- "red"
pairs(iris2, pch=pch, col=col)

#Parallel Computation of LOF Scores
#Package Rlof provides function lof(), a parallel implementation of the LOF algorithm. Its usage is similar to the above lofactor(), but lof() has two additional features of supporting multiple values of k and several choices of distance metrics. Below is an example of lof(). 
#how many friends u have

library(Rlof)
outlier.scores <- lof(iris2, k=5)
# try with different number of neighbors (k = 5,6,7,8,9 and 10)
outlier.scores <- lof(iris2, k=c(5:10))
outlier.scores
plot(density(outlier.scores))
# pick top 10 as outliers
outliers <- order(outlier.scores, decreasing=T)[1:10]
# who are outliers
print(outliers)
#[1]  42 192 342 492 642 792 107  23 257 407
iris2[outliers,]
iris2



#--------------------------------------------

#https://gist.github.com/revodavid/162943fcc49af20f0a978f9dfa5aa162

library(VIM)
library(ggplot2)
library(ggthemes)
library(OutliersO3)
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
update_geom_defaults("bar",   list(fill = "grey70", colour = "grey40"))
s1 <- O3prep(stackloss)
O3s1 <- O3plotT(s1)
O3s1$gO3

s2 =O3prep(mtcars)
O3s2 = O3plotT(s2)
O3s2$gO3


================================================
FILE: 24-Strings/abvn.R
================================================
# Abbreviations

head(USArrests)
str(USArrests)
# rows as strings

# name of States
states = rownames(USArrests)
head(states)

# substr
(s1= substr(x = states, start=1, stop=4))

# Find duplicates
duplicated(s1)
sum(duplicated(s1), na.rm=TRUE)
sum(duplicated(s1))
sum(duplicated(s1), na.rm=FALSE)
# sums only TRUE values

table(duplicated(s1))

length(s1)
length(unique(s1))

# Better
(states2 = abbreviate(states))
head(states2)
# remove rownames for convenience
names(states2) = NULL
states2
sum(duplicated(states2))

# Change length of abbreviation to 5
(states3 = abbreviate(states, minlength = 5))
names(states3) = NULL
states3



================================================
FILE: 24-Strings/latex.R
================================================
library(Hmisc)
d1 = data.frame(drugId = c(11, 22, 33), drugInfo = c("$36.",
                                                     "2 for $11", "50% sale"), stringsAsFactors = FALSE)
d1$drugInfo = gsub("\\$", "\\\\$", d1$drugInfo)
d1$drugInfo = gsub("\\%", "\\\\%", d1$drugInfo)
d1

latex(d1, rowname = NULL, colheads = c("Drug ID", "Drug Price"),
      file = "")


================================================
FILE: 24-Strings/output.txt
================================================
 Programming with data is fun with R

================================================
FILE: 24-Strings/paste1.R
================================================
# Paste Functions
## When passing a single vector, paste0 and paste work like as.character.
paste0(1:12)
paste(1:12)        # same
as.character(1:12) # same

## If you pass several vectors to paste0, they are concatenated in a
## vectorized way.
(nth <- paste0(1:12, c("st", "nd", "rd", rep("th", 9))))

## paste works the same, but separates each input with a space.
## Notice that the recycling rules make every input as long as the longest input.
paste(month.abb, "is the", nth, "month of the year.")
paste(month.abb, letters)

## You can change the separator by passing a sep argument
## which can be multiple characters.
paste(month.abb, "is the", nth, "month of the year.", sep = "_*_")

## To collapse the output into a single string, pass a collapse argument.
paste0(nth, collapse = ", ")

## For inputs of length 1, use the sep argument rather than collapse
paste("1st", "2nd", "3rd", collapse = ", ") # probably not what you wanted
paste("1st", "2nd", "3rd", sep = ", ")

## You can combine the sep and collapse arguments together.
paste(month.abb, nth, sep = ": ", collapse = "; ")

## Using paste() in combination with strwrap() can be useful
## for dealing with long strings.
(title <- paste(strwrap(
  "Stopping distance of cars (ft) vs. speed (mph) from Ezekiel (1930)",
  width = 15), collapse = "\n"))
plot(dist ~ speed, cars, main = title)


================================================
FILE: 24-Strings/setop1.R
================================================
#set Operations

union()  # set union
intersect()  # Intersection
setdiff()  # set difference
setequal()  # eualt sets
identical()  # exact equality
is.element() # is element
#%in%()  # contains
sort()  # sorting
paste(rep()) # repetition


(first1 = c('Achal','Apoorva','Goldie','Hitesh','Kaustav','Meena','Rashmi','Shruti','Shubham','Vijay','Lalit'))
(second = c('Kumar','Karn','Sahni','Mann','Chatterjee','Srisha','Ranjan','Sinha','Pujan','Singh','Sahni'))
(first2 = c('Achal','Apoorva','Kaustav','Rashmi','Shruti','Vijay','Lalit','Varun','Dhiraj'))

# union -------

union(first1, second)  #nothing
union(first1, first2)

set1 =c('some', 'random', 'words', 'some')
set2 =c('some', 'many', 'none', 'few')
union(set1, set2)

set3 = c('A','B','C','E')
set4 = c('B','D','F','P')
union(set3, set4)  # remove duplicates

#intersect
intersect(first1, first2)  # common names
intersect(set3,set4)

#setdiff
setdiff(set3,set4)
first1 ; first2
setdiff(first1, first2) # check ? what is there in 1st & not in 2
setdiff(first2, first1) # check ? what is there in 2nd & not in 1

# set equal  : same elements
setequal(first1, first1)
setequal(first1, first2)

set4 = c('A','B','C','E')
set5 = c('B','A','E','C')
setequal(set4, set5)
set6 = c('B','A','E','F')
setequal(set4, set6)

# identical : element by element
identical(set4,set5)
set7 = c('A','B','C','E')
identical(set4, set7)

# check if element is contained in ..

is.element('A', set4)
is.element('Dhiraj', first2)
is.element('Varun',  first1)
is.element('Varun',  first2)

'Dhiraj' %in% first2   # %in% operator
'A' %in% set4

# sort
sort(first1)
sort(set5)
sort(first1, decreasing = T)
sort(first1, decreasing = F)


# Repetition

paste(rep('x',4), collapse='')  # xxxx
paste(rep('x',4))  # 'x' 'x' 'x' 'x'


================================================
FILE: 24-Strings/strcmpt1.R
================================================
# String Computations

# name of States
states = rownames(USArrests)
head(states)

# Histogram
nchar(states)
hist(nchar(states))
hist(nchar(states), main='Histogram',
     xlab='No of Characters in US State Names')


# Find no of vowels in Names

#no match = -1
# positions of a's
position_a = gregexpr(pattern='a', text=states, ignore.case = T)
position_a

str(position_a)  # positions a occured 1 3 6
states[1]
head(states,n=10)
position_a[c(1,7)] # 1st -1,3,5,7; 7th -1

# how many a's
(num_a = sapply(position_a, function(x) ifelse(x[1] > 0, length(x), 0)))
x = c(1,3,0)
sapply(x, function(x) ifelse(x[1] > 0, length(x), 0))
x = c('dhiraj', 'upadhyaya')
(num_a = sapply(position_a, function(x) ifelse(x[1] > 0, length(x), 0)))
head(position_a)

# Same command - stringr
library(stringr)
str_count(states,'a')  # only small case : no option for this
# transform to upper/lower case and then count
str_count(tolower(states), 'a')

# Same can be done for other vowels a, e,i,o, u
vowels = c('a', 'e', 'i', 'o', 'u')
# vector for storing results of positions
num_vowels = vector(mode='integer', length=5)  # blank int vector
num_vowels
# find vowels in each name
#num_aux = str_count(tolower(states), 'e')  # check for e
#sum(num_aux)

for(j in seq_along(vowels)) {
  num_aux = str_count(tolower(states), vowels[j])
  num_vowels[j] = sum(num_aux)
}
num_vowels
names(num_vowels) = vowels
num_vowels

# sort
sort(num_vowels, decreasing = T)

# draw barplot
barplot(num_vowels, main='No of vowels in US States Names',
      border=NA, ylim=c(0,80))


================================================
FILE: 24-Strings/string1.R
================================================
#Strings http://gastonsanchez.com/Handling_and_Processing_Strings_in_R.pdf
# names of states
states = rownames(USArrests)
# substr
substr(x = states, start = 1, stop = 4)

# abbreviate state names
states2 = abbreviate(states)
states2

# remove vector names (for convenience)
names(states2) = NULL
states2


# abbreviate state names with 5 letters
abbreviate(states, minlength = 5)


# size (in characters) of each name
state_chars = nchar(states)
# longest name
states[which(state_chars == max(state_chars))]

# get states names with 'k'
grep(pattern = "k", x = states, value = TRUE)

# get states names with 'w'
grep(pattern = "w", x = states, value = TRUE)

# get states names with 'w' or 'W'
grep(pattern = "[wW]", x = states, value = TRUE)

# get states names with 'w'
grep(pattern = "w", x = tolower(states), value = TRUE)

# get states names with 'W'
grep(pattern = "W", x = toupper(states), value = TRUE)

# get states names with 'w'
grep(pattern = "w", x = states, value = TRUE, ignore.case = TRUE)

# histogram
hist(nchar(states), main = "Histogram",
     xlab = "number of characters in US State names")

# position of a's
positions_a = gregexpr(pattern = "a", text = states, ignore.case = TRUE)

# how many a's?
num_a = sapply(positions_a, function(x) ifelse(x[1] > 0, length(x), 0))
num_a

# load stringr (remember to install it first)
library(stringr)

# total number of a's
str_count(states, "a")


================================================
FILE: 24-Strings/strjoin.R
================================================
# Strings Join
library(stringr)
str_c("Letter: ", letters)
str_c("Letter", letters, sep = ": ")
str_c(letters, " is for", "...")
str_c(letters[-26], " comes before ", letters[-1])

str_c(letters, collapse = "")
str_c(rev(letters), collapse = "")

str_c(letters, collapse = ", ")


================================================
FILE: 24-Strings/strlength.R
================================================
# String Length

# name of States
states = rownames(USArrests)
head(states)

# size of characters
length(states)  # length of vector
(state_chars = nchar(states))

# Longest name
max(state_chars)
state_chars
states[which(state_chars == max(state_chars))]


================================================
FILE: 24-Strings/strman1.R
================================================
# String Manipulations

?paste
paste (..., sep = " ", collapse = NULL)
paste0(..., collapse = NULL)

#paste
(PI = paste('The Life of ', pi))
# default sep space ' '

(IloveR = paste('I', 'love', 'R', sep='-'))

# different lengths
paste('X', 1:5, sep='.')

# paste with collapse  after each paste what to do ?
paste(1:3, c('!', '?', "+"), sep='', collapse='')
paste(1:3, c('!', '?', "+"), sep='')

paste(1:3, c('!', '?', "+"), sep='%%', collapse='***')
paste(1:3, c('!', '?', "+"), sep='%%')

# after end of each character

paste(1:3,c('a','b','c'), c('A','B','C'))
# explore with sep & collapse

# handle NA  - use as character
(evalue = paste(" The value of 'e' is ", exp(1), NA))

# paste0
#eq of paste(..., sep="", collapse)
paste0('lets', 'collapse', 'all', 'these', 'words')
paste('lets', 'collapse', 'all', 'these', 'words')
paste('lets', 'collapse', 'all', 'these', 'words', collapse='') # check this

paste('lets', 'collapse', 'all', 'these', 'words', sep='-', collapse='*') # check this



================================================
FILE: 24-Strings/strman2.R
================================================
# String Manipulations

nchar()  # no of characters
tolower()  # convert to lowercase
toupper()  # convert to uppercase
casefold() # case folding
chartr()  # character translation
abbreviate()  # abbreviation
substr()  # substrings of a character vector

# ---

================================================
FILE: 24-Strings/strman3.R
================================================
# String Manipulations2


nchar()  # no of characters
tolower() # convert to lowercase
toupper() # convert to uppercase
casefold() # case folding
chartr()  # character translation
abbreviate()  # abbreviation
substring() # substrings of a character vector
substr()  # substrings of a character vector


#nchar -------------
(c1 = c('How', 'Many', 'Characters?'))
(nchar(c1))
length(c1)

(c2= c('How Many Characters?'))  # white spaces also counted

(nchar(c2))  # white spaces also counted
length(c2)  # no of elements

#tolower
tolower(c1)

toupper(c1)

casefold(c1)
casefold(c1, upper=TRUE)  #opposite
c3 = c('DHIRAJ','UPADHYAYA')
casefold(c3)  # lower case
casefold(c3, upper=TRUE)


chartr( old= 'a',new= 'A', x= c1)  # find a, replace with A
chartr('A', 'a', c3)
chartr('RA', 'a', c3)  # RA is longer than a

sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni')
chartr('a', 'A', sname)  #multiple replacements

?abbreviate
abbreviate(names.arg, minlength = 4, use.classes = TRUE,
           dot = FALSE, strict = FALSE,
           method = c("left.kept", "both.sides"), named = TRUE)
sname
abbreviate(sname)

colors[1:4]
(some_colors = colors[1:4])
(colors1 = abbreviate(some_colors))

(colors2 = abbreviate(some_colors, minlength = 5))
(colors2 = abbreviate(some_colors, minlength = 5, method='both.sides'))

(aname2 = abbreviate(sname, minlength = 5, method='both.sides'))


# Replace by substr
?substr
substr(x, start, stop)
substring(text, first, last = 1000000L)
substr(x, start, stop) <- value
substring(text, first, last = 1000000L) <- value

substr('bcdef', start=2, stop=4) # extract cde
(x= month.abb[1:6])
substr(x,2,2) <- '#'
x

y= c('may', 'the', 'force', 'be', 'with', 'you')
substr(y,2,3) <- ":)"
y

# replace with recycling
z= c('may', 'the', 'force', 'be', 'with', 'you')
substr(z, 2,1) <- c('#', '@')
substr(z, 2,2) <- c('#', '@')
z

# substring - similar to substr
substring('ABCDEF', 2, 4)
substr('ABCDEF', 2, 4)

substring('ABCDEF', 1:6, 1:6)  # each character

# multiple replacement with recycling ???? check
(text = c('more', 'emotions', 'are', 'better', 'than', 'less'))
substring(text, 1:3) <- c('', 'zzz')
text
(text1 = c('dhiraj','more', 'emotions', 'are', 'better', 'than', 'less'))
substring(text1, 1:3) <- c('', 'yyz')
text1

(sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni'))
substring(sname, 1:4) <- c('', 'sods')
sname

(sname = c('Achal Kumar','Apoorva Karn','Goldie Sahni','Hitesh Mann','Kaustav Chatterjee','Meena Srisha Valavala','Rashmi Ranjan Mangaraj','Shruti Sinha','Shubham Pujan','Vijay Pal Singh','Lalit Sahni'))
substring(sname, 2:5) <- c('', 'MUIT')
sname



================================================
FILE: 24-Strings/strman4.R
================================================
# Set Operations


union()# set union
intersect() #intersection
setdiff() #set difference
setequal() #equal sets
identical() #exact equality
is.element() #is element
#%in%() #contains
sort() #sorting
paste(rep()) #repetition

# Union  total, no repeats
set1 = c('A', 'B', 'C', 'D')
set2 = c('A', 'C', 'F' , 'G')

union(set1,set2)  # no repeats

#intersect  common
intersect(set1, set2)
intersect(set2, set1)

# setdiff wrt to each other
setdiff(set1, set2)  # set1 not in set2
setdiff(set2, set1)   # set2 not in set1

#setequal

setequal(set1, set2)
set3 = set1
setequal(set1, set3)

#identical
set1
set4 = c('B','C', 'D', 'A') # same as set1 but in different order
identical(set1, set2)
identical(set1, set3)
identical(set1, set4) # not same element by element
setequal(set1, set4) # but same elements present

#is.element
# three vectors
set10 = c("some", "stuff", "to", "play", "with")
elem1 = "play"
elem2 = "crazy"

is.element(elem1, set10)
is.element('A', set1)
is.element('P', set1)


# %in% operator
elem1 %in% set10
'A' %in% set1
'P' %in% set1

# sort
sort(set10)
set4
sort(set4)
sort(set4, decreasing=T)

set12 = c("today", "produced", "example", "beautiful", "1", "nicely")
sort(set12) # alpha - numbers first
sort(set12, decreasing=T)

# rep()

paste(rep('x', 4), collapse='')
paste(rep('x', 4))



================================================
FILE: 24-Strings/strman5.R
================================================
#Stringr 5
library(stringr)
detach("package:stringr", unload=TRUE)

# Drawbacks

# some text vector
(text = c("one", "two", "three", NA, "five"))

# how many characters in each string?
nchar(text)
## [1] 3 3 5 2 4   ? NA should be left blank instead of 2


# this works fine
paste("University", "of", "California", "Berkeley")
## [1] "University of California Berkeley"
# this works fine too
paste("University", "of", "California", "Berkeley")

# this is weird
paste("University", "of", "California", "Berkeley", NULL)
## [1] "University of California Berkeley "

# this is ugly
paste("University", "of", "California", "Berkeley", NULL, character(0),
      "Go Bears!")
## [1] "University of California Berkeley Go Bears!"

#The objects NULL and character(0)
#have zero length, yet when included inside paste() they are treated as an empty string "".

#install.packages('stringr')
library(stringr)

#Function Description Similar to
str_c() #string concatenation paste()
str_length() #number of characters nchar()
str_sub()# extracts substrings substring()
str_dup() #duplicates characters none
str_trim() #removes leading and trailing whitespace none
str_pad() #pads a string none
str_wrap() #wraps a string paragraph strwrap()
str_trim() #trims a string none

#str_c
# default usage
str_c("May", "The", "Force", "Be", "With", "You")
## [1] "MayTheForceBeWithYou"

# removing zero length objects
str_c("May", "The", "Force", NULL, "Be", "With", "You", character(0))
## [1] "MayTheForceBeWithYou"
paste("May", "The", "Force", NULL, "Be", "With", "You", character(0))
#[1] "May The Force  Be With You "
#: zero length arguments like
#NULL and character(0) are silently removed by str c().

# changing separator
str_c("May", "The", "Force", "Be", "With", "You", sep = "_")
## [1] "May_The_Force_Be_With_You"

# synonym function 'str_join'
str_join("May", "The", "Force", "Be", "With", "You", sep = "-")
## [1] "May-The-Force-Be-With-You"

#string length

# some text (NA included)
some_text = c("one", "two", "three", NA, "five")
# compare 'str_length' with 'nchar'
nchar(some_text)
## [1] 3 3 5 2 4  check it is same
str_length(some_text)
## [1] 3 3 5 NA 4

# some factor
some_factor = factor(c(1, 1, 1, 2, 2, 2), labels = c("good", "bad"))
some_factor
## [1] good good good bad bad bad
## Levels: good bad
# try 'nchar' on a factor

nchar(some_factor)
## Error: ’nchar()’ requires a character vector
# now compare it with 'str_length'
str_length(some_factor)
## [1] 4 4 4 3 3 3

# Extract Strings -------
str_sub(string, start = 1L, end = -1L)

# some text
lorem = "Lorem Ipsum"
# apply 'str_sub'
str_sub(lorem, start = 1, end = 5)
## [1] "Lorem"
# equivalent to 'substring'
substring(lorem, first = 1, last = 5)
## [1] "Lorem"
# another example
str_sub("adios", 1:3)
## [1] "adios" "dios" "ios"  ???

# can work with negative indices

# some strings
resto = c("brasserie", "bistrot", "creperie", "bouchon")
# 'str_sub' with negative positions
str_sub(resto, start = -4, end = -1)
## [1] "erie" "trot" "erie" "chon"
# compared to substring (useless)
substring(resto, first = -4, last = -1)
## [1] "" "" "" ""

# extracting sequentially  ???
lorem
seq_len(nchar(lorem))
str_sub(lorem, seq_len(nchar(lorem)))

substring(lorem, seq_len(nchar(lorem)))

# reverse substrings with negative positions
str_sub(lorem, -seq_len(nchar(lorem)))

substring(lorem, -seq_len(nchar(lorem)))

#We can use str sub() not only for extracting subtrings but also for replacing substrings:
# replacing 'Lorem' with 'Nullam'
lorem = "Lorem Ipsum"
str_sub(lorem, 1, 5) <- "Nullam"
lorem

# replacing with negative positions
lorem = "Lorem Ipsum"
str_sub(lorem, -1) <- "Nullam"
lorem

# multiple replacements
lorem = "Lorem Ipsum"
str_sub(lorem, c(1, 7), c(5, 8)) <- c("Nullam", "Enim")
lorem
## [1] "Nullam Ipsum" "Lorem Enimsum"


#Duplicates
str_dup(string, times)
# default usage
str_dup("hola", 3)

# use with differetn 'times'
str_dup("adios", 1:3)
## [1] "adios" "adiosadios" "adiosadiosadios"

# use with a string vector
words = c("lorem", "ipsum", "dolor", "sit", "amet")
str_dup(words, 2)
## [1] "loremlorem" "ipsumipsum" "dolordolor" "sitsit" "ametamet"
str_dup(words, 1:5)
str_dup(words, 1:15) # recycle


#Padding
str_pad(string, width, side = "left", pad = " ")

# default usage
str_pad("hola", width = 7)
## [1] " hola"

# pad both sides
str_pad("adios", width = 7, side = "both")
## [1] " adios "
# left padding with '#'
str_pad("hashtag", width = 8, pad = "#")
## [1] "#hashtag"

# pad both sides with '-'
str_pad("hashtag", width = 9, side = "both", pad = "-")
## [1] "-hashtag-"

#string wrap
str_wrap(string, width = 80, indent = 0, exdent = 0)

# quote (by Douglas Adams)
some_quote = c(
  "I may not have gone",
  "where I intended to go,",
  "but I think I have ended up",
  "where I needed to be")
# some_quote in a single paragraph
some_quote = paste(some_quote, collapse = " ")
some_quote
# display paragraph with width=30
cat(str_wrap(some_quote, width = 30))

# display paragraph with first line indentation of 2
cat(str_wrap(some_quote, width = 30, indent = 2), "\n")

# display paragraph with following lines indentation of 3
cat(str_wrap(some_quote, width = 30, exdent = 3), "\n")


#Trimming with str trim() --------
str_trim(string, side = "both")

# text with whitespaces
bad_text = c("This", " example ", "has several ", " whitespaces ")

# remove whitespaces on the left side
str_trim(bad_text, side = "left")

## [1] "This" "example " "has several " "whitespaces "

# remove whitespaces on the right side

str_trim(bad_text, side = "right")
## [1] "This" " example" "has several" " whitespaces"

# remove whitespaces on both sides
str_trim(bad_text, side = "both")

#Word extraction with word() ---------
word(string, start = 1L, end = start, sep = fixed(" "))

# some sentence
(change = c("Be the change", "you want to be"))

# extract first word
word(change, 1)

## [1] "Be" "you"
# extract second word
word(change, 2)
## [1] "the" "want"

# extract last word
word(change, -1)
## [1] "change" "be"

# extract all but the first words
word(change, 2, -1)
## [1] "the change" "want to be"

word(change, c(1,2), c(3,4))  # ???





================================================
FILE: 24-Strings/strman6.R
================================================
# String Manipulations 6
# Regular Expressions
#A regular expression (a.k.a. regex) is a special text string for describing a certain amount
#of text. This “certain amount of text” receives the formal name of pattern. Hence we say
#that a regular expression is a pattern that describes a set of strings.

# Meta Characters  . \ | ( ) [ { $ * + ?
#escape them with a backslash \. In R, however, we
#need to escape them with a double backslash \\
# Metacharacter Literal meaning Escape in R
# . the period or dot \\.
# $ the dollar sign \\$
# * the asterisk or star \\*
# + the plus sign \\+
# ? the question mark \\?
# | the vertical bar or pipe symbol \\|
# \ the backslash \\\\
# ^ the caret \\^
# [ the opening square bracket \\[
# ] the closing square bracket \\]
# { the opening curly bracket \\{
# } the closing curly bracket \\}
# ( the opening round bracket \\(
# ) the closing round bracket \\)

#dollar sign $ with an empty string ""
# string
money = "$money"
# the naive but wrong way
sub(pattern = "$", replacement = "", x = money)
## [1] "$money"

# the usual (in other languages) yet wrong way in R
sub(pattern = "\$", replacement = "", x = money)

# the right way in R
sub(pattern = "\\$", replacement = "", x = money)
## [1] "money"

# silly egs
# dollar
sub("\\$", "", "$Peace-Love")
## [1] "Peace-Love"
# dot
sub("\\.", "", "Peace.Love")
## [1] "PeaceLove"
# plus
sub("\\+", "", "Peace+Love")
## [1] "PeaceLove"
# caret
sub("\\^", "", "Peace^Love")
## [1] "PeaceLove"
# vertical bar
sub("\\|", "", "Peace|Love")
## [1] "PeaceLove"
# opening round bracket
sub("\\(", "", "Peace(Love)")
## [1] "PeaceLove)"

# closing round bracket
sub("\\)", "", "Peace(Love)")
## [1] "Peace(Love"
# opening square bracket
sub("\\[", "", "Peace[Love]")
## [1] "PeaceLove]"
# closing square bracket
sub("\\]", "", "Peace[Love]")
## [1] "Peace[Love"
# opening curly bracket
sub("\\{", "", "Peace{Love}")

## [1] "PeaceLove}"
# closing curly bracket
sub("\\}", "", "Peace{Love}")
## [1] "Peace{Love"
# double backslash
sub("\\\\", "", "Peace\\Love")
## [1] "PeaceLove"



# Sequences ------------
# Anchor Description
# \\d match a digit character
# \\D match a non-digit character
# \\s match a space character
# \\S match a non-space character
# \\w match a word character
# \\W match a non-word character
# \\b match a word boundary
# \\B match a non-(word boundary)
# \\h match a horizontal space
# \\H match a non-horizontal space
# \\v match a vertical space
# \\V match a non-vertical space

# Digits and non digits
# replace digit with '_' first
sub("\\d", "_", "the dandelion war 2010")
## [1] "the dandelion war _010"

gsub("\\d", "_", "the dandelion war 2010")
## [1] "the dandelion war ____"  all digits

# replace non-digit with '_'
sub("\\D", "_", "the dandelion war 2010")
## [1] "_he dandelion war 2010"
gsub("\\D", "_", "the dandelion war 2010")
## [1] "__________________2010"


#Spaces and non-spaces -----
# replace space with '_'
sub("\\s", "_", "the dandelion war 2010")
## [1] "the_dandelion war 2010"
gsub("\\s", "_", "the dandelion war 2010")
## [1] "the_dandelion_war_2010"

# replace non-space with '_'
sub("\\S", "_", "the dandelion war 2010")
## [1] "_he dandelion war 2010"
gsub("\\S", "_", "the dandelion war 2010")
## [1] "___ _________ ___ ____"

#Words and non-words
# replace word with '_'
sub("\\b", "_", "the dandelion war 2010")
## [1] "_the dandelion war 2010"
gsub("\\b", "_", "the dandelion war 2010")
## [1] "_t_h_e_ _d_a_n_d_e_l_i_o_n_ _w_a_r_ _2_0_1_0_"


# replace non-word with '_'
sub("\\B", "_", "the dandelion war 2010")
## [1] "t_he dandelion war 2010"
gsub("\\B", "_", "the dandelion war 2010")
## [1] "t_he d_an_de_li_on w_ar 2_01_0"

#Word boundaries and non-word-boundaries wherever word is there
# replace word boundary with '_'
sub("\\w", "_", "the dandelion war 2010")
## [1] "_he dandelion war 2010"
gsub("\\w", "_", "the dandelion war 2010")
## [1] "___ _________ ___ ____"

# replace non-word-boundary with '_'
sub("\\W", "_", "the dandelion war 2010")
## [1] "the_dandelion war 2010"
gsub("\\W", "_", "the dandelion war 2010")
## [1] "the_dandelion_war_2010"



# Character Classes ---------

# Anchor Description
# [aeiou] match any one lower case vowel
# [AEIOU] match any one upper case vowel
# [0123456789] match any digit
# [0-9] match any digit (same as previous class)
# [a-z] match any lower case ASCII letter
# [A-Z] match any upper case ASCII letter
# [a-zA-Z0-9] match any of the above classes
# [^aeiou] match anything other than a lowercase vowel
# [^0-9] match anything other than a digit

# some string
transport = c("car", "bike", "plane", "boat")
# look for 'e' or 'i'
grep(pattern = "[ei]", transport, value = TRUE)
## [1] "bike" "plane"

# some numeric strings
numerics = c("123", "17-April", "I-II-III", "R 3.0.1")
# match strings with 0 or 1
grep(pattern = "[01]", numerics, value = TRUE)
## [1] "123" "17-April" "R 3.0.1"
# match any digit
grep(pattern = "[0-9]", numerics, value = TRUE)
## [1] "123" "17-April" "R 3.0.1"
# negated digit
grep(pattern = "[^0-9]", numerics, value = TRUE)
## [1] "17-April" "I-II-III" "R 3.0.1"


# POSIX Characters
#Closely related to the regex character classes we have what is known as POSIX character
#classes. In R, POSIX character classes are represented with expressions inside double brackets
#[[ ]].
# Class Description
# [[:lower:]] Lower-case letters
# [[:upper:]] Upper-case letters
# [[:alpha:]] Alphabetic characters ([[:lower:]] and [[:upper:]])
# [[:digit:]] Digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
# [[:alnum:]] Alphanumeric characters ([[:alpha:]] and [[:digit:]])
# [[:blank:]] Blank characters: space and tab
# [[:cntrl:]] Control characters
# [[:punct:]] Punctuation characters: ! ” # % & ’ ( ) * + , - . / : ;
# [[:space:]] Space characters: tab, newline, vertical tab, form feed,
# carriage return, and space
# [[:xdigit:]] Hexadecimal digits: 0-9 A B C D E F a b c d e f
# [[:print:]] Printable characters ([[:alpha:]], [[:punct:]] and space)
# [[:graph:]] Graphical characters ([[:alpha:]] and [[:punct:]])


# la vie (string)
la_vie = "La vie en #FFC0CB (rose);\nCes't la vie! \ttres jolie"
# if you print 'la_vie'
print(la_vie)
## [1] "La vie en #FFC0CB (rose);\nCes't la vie! \ttres jolie"
# if you cat 'la_vie'
cat(la_vie)
## La vie en #FFC0CB (rose);
## Ces't la vie! tres jolie


# remove space characters
gsub(pattern = "[[:blank:]]", replacement = "", la_vie)
## [1] "Lavieen#FFC0CB(rose);\nCes'tlavie!tresjolie"
# remove digits
gsub(pattern = "[[:punct:]]", replacement = "", la_vie)
## [1] "La vie en FFC0CB rose\nCest la vie \ttres jolie"
# remove digits
gsub(pattern = "[[:xdigit:]]", replacement = "", la_vie)
## [1] "L vi n # (ros);\ns't l vi! \ttrs joli"
# remove printable characters
gsub(pattern = "[[:print:]]", replacement = "", la_vie)
## [1] "\n\t"
# remove non-printable characters
gsub(pattern = "[^[:print:]]", replacement = "", la_vie)
## [1] "La vie en #FFC0CB (rose);Ces't la vie! tres jolie"


# remove graphical characters
gsub(pattern = "[[:graph:]]", replacement = "", la_vie)
## [1] " \n \t "
# remove non-graphical characters
gsub(pattern = "[^[:graph:]]", replacement = "", la_vie)
## [1] "Lavieen#FFC0CB(rose);Ces'tlavie!tresjolie"



# Quantifiers  ---
#These are used when we want to match a certain number of characters that meet certain criteria

# Quantifier Description
# ? The preceding item is optional and will be matched at most once
# * The preceding item will be matched zero or more times
# + The preceding item will be matched one or more times
# {n} The preceding item is matched exactly n times
# {n,} The preceding item is matched n or more times
# {n,m} The preceding item is matched at least n times, but not more than m times


# people names
people = c("rori", "emilia", "matteo", "mehmet", "filipe", "anna", "tyler",
           "rasmus", "jacob", "youna", "flora", "adi")
# match 'm' at most once
grep(pattern = "m?", people, value = TRUE)
## [1] "rori" "emilia" "matteo" "mehmet" "filipe" "anna" "tyler"
## [8] "rasmus" "jacob" "youna" "flora" "adi"
# match 'm' exactly once
grep(pattern = "m{1}", people, value = TRUE, perl = FALSE)
## [1] "emilia" "matteo" "mehmet" "rasmus"
# match 'm' zero or more times, and 't'
grep(pattern = "m*t", people, value = TRUE)
## [1] "matteo" "mehmet" "tyler"

# match 't' zero or more times, and 'm'
grep(pattern = "t*m", people, value = TRUE)
## [1] "emilia" "matteo" "mehmet" "rasmus"
# match 'm' one or more times
grep(pattern = "m+", people, value = TRUE)
## [1] "emilia" "matteo" "mehmet" "rasmus"
# match 'm' one or more times, and 't'
grep(pattern = "m+.t", people, value = TRUE)
## [1] "matteo" "mehmet"
# match 't' exactly twice
grep(pattern = "t{2}", people, value = TRUE)
## [1] "matteo"


# Reg Exp Functions 
# Function Purpose Characteristic
# grep() finding regex matches which elements are matched (index or value)
# grepl() finding regex matches which elements are matched (TRUE & FALSE)
# regexpr() finding regex matches positions of the first match
# gregexpr() finding regex matches positions of all matches
# regexec() finding regex matches hybrid of regexpr() and gregexpr()
# sub() replacing regex matches only first match is replaced
# gsub() replacing regex matches all matches are replaced
# strsplit() splitting regex matches split vector according to matches

# in Stringr
# str_detect() Detect the presence or absence of a pattern in a string
# str_extract() Extract first piece of a string that matches a pattern
# str_extract all() Extract all pieces of a string that match a pattern
# str_match() Extract first matched group from a string
# str_match all() Extract all matched groups from a string
# str_locate() Locate the position of the first occurence of a pattern in a string
# str_locate all() Locate the position of all occurences of a pattern in a string
# str_replace() Replace first occurrence of a matched pattern in a string
# str_replace all() Replace all occurrences of a matched pattern in a string
# str_split() Split up a string into a variable number of pieces
# str_split fixed() Split up a string into a fixed number of pieces
str_function(string, pattern)

# Complimentary
# Function Purpose Characteristic
# regmatches() extract or replace matches use with data from regexpr(),
# gregexpr() or regexec()
# match() value matching finding positions of (first) matches
# pmatch() partial string matching finding positions
# charmatch() similar to pmatch() finding positions

# Accessory Functions accepting regex
# apropos() find objects by (partial) name
# browseEnv() browse objects in environment
# glob2rx() change wildcard or globbing pattern into Regular Expression
# help.search() search the help system
# list.files() list the files in a directory/folder


================================================
FILE: 24-Strings/strman7.R
================================================
#String Manipulations 7

#Pattern Finding Functions

#Finding a match
grep(pattern, text)
grepl(pattern, text)
regexpr(pattern, text)
gregexpr(pattern, text)
regexec(pattern, text)

# some text
text = c("one word", "a sentence", "you and me", "three two one")
# pattern
pat = "one"
# default usage
grep(pat, text)
## [1] 1 4

# with 'value' (showing matched text)
grep(pat, text, value = TRUE)
## [1] "one word" "three two one"

# with 'invert' (showing unmatched parts)
grep(pat, text, invert = TRUE)
## [1] 2 3
# same with 'values'
grep(pat, text, invert = TRUE, value = TRUE)
## [1] "a sentence" "you and me"


# some text
text = c("one word", "a sentence", "you and me", "three two one")
# pattern
pat = "one"
# default usage
grepl(pat, text)
## [1] TRUE FALSE FALSE TRUE

# location 
# some text
text = c("one word", "a sentence", "you and me", "three two one")
# default usage
regexpr("one", text)
## [1] 1 -1 -1 11
## attr(,"match.length")
## [1] 3 -1 -1 3
## attr(,"useBytes")
## [1] TRUE


# some text
text = c("one word", "a sentence", "you and me", "three two one")
# pattern
pat = "one"
# default usage
gregexpr(pat, text)

# some text
text = c("one word", "a sentence", "you and me", "three two one")
# pattern
pat = "one"
# default usage
regexec(pat, text)

# handy function to extract matched term
x = regexpr(pat, text)
substring(text, x, x + attr(x, "match.length") - 1)
## [1] "one" "" "" "one"
# with NA
regexpr(pat, c(text, NA))
## [1] 1 -1 -1 11 NA
## attr(,"match.length")
## [1] 3 -1 -1 3 NA


#6.2 Pattern Replacement Functions ----
sub(pattern, replacement, text)
gsub(pattern, replacement, text)


# string
Rstring = c("The R Foundation",
            "for Statistical Computing",
            "R is FREE software",
            "R is a collaborative project")
# substitute 'R' with 'RR'
sub("R", "RR", Rstring)
## [1] "The RR Foundation" "for Statistical Computing"
## [3] "RR is FREE software" "RR is a collaborative project"

Rstring = c("The R Foundation",
            "for Statistical Computing",
            "R is FREE software",
            "R is a collaborative project")
# substitute
gsub("R", "RR", Rstring)
## [1] "The RR Foundation" "for Statistical Computing"
## [3] "RR is FRREE software" "RR is a collaborative project"

strsplit(x, split) # same as strsplit(text, pattern)

# a sentence
sentence = c("R is a collaborative project with many contributors")
# split into words
strsplit(sentence, " ")
## [[1]]
## [1] "R" "is" "a" "collaborative"
## [5] "project" "with" "many" "contributors"

# telephone numbers
tels = c("510-548-2238", "707-231-2440", "650-752-1300")
# split each number into its portions
strsplit(tels, "-")

# some objects
some_objs = c("pen", "pencil", "marker", "spray")
# detect phones
str_detect(some_objs, "pen")
## [1] TRUE TRUE FALSE FALSE
# select detected macthes
some_objs[str_detect(some_objs, "pen")]
## [1] "pen" "pencil"

# some strings
strings = c("12 Jun 2002", " 8 September 2004 ", "22-July-2009 ",
            "01 01 2001", "date", "02.06.2000",
            "xxx-yyy-zzzz", "$2,600")
# date pattern (month as text)
dates = "([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})"
# detect dates
str_detect(strings, dates)
## [1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE


# tweets about 'Paris'
paris_tweets = c(
  "#Paris is chock-full of cultural and culinary attractions",
  "Some time in #Paris along Canal St.-Martin famous by #Amelie",
  "While you're in #Paris, stop at cafe: http://goo.gl/yaCbW",
  "Paris, the city of light")
# hashtag pattern
hash = "#[a-zA-Z]{1,}"
# extract (first) hashtag
str_extract(paris_tweets, hash)
## [1] "#Paris" "#Paris" "#Paris" NA
#As you can see, the output of str extract() is a vector of same length as string. Those
#elements that don’t match the pattern are indicated as NA. Note that str extract() only
#matches the first pattern: it didn’t extract the hashtag "#Amelie"

# extract (all) hashtags
str_extract_all(paris_tweets, "#[a-zA-Z]{1,}")  #??? where was Paris

# string vector
strings = c("12 Jun 2002", " 8 September 2004 ", "22-July-2009 ",
            "01 01 2001", "date", "02.06.2000",
            "xxx-yyy-zzzz", "$2,600")
# date pattern (month as text)
dates = "([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})"
# extract first matched group
str_match(strings, dates)

# tweets about 'Paris'
paris_tweets = c(
  "#Paris is chock-full of cultural and culinary attractions",
  "Some time in #Paris along Canal St.-Martin famous by #Amelie",
  "While you're in #Paris, stop at cafe: http://goo.gl/yaCbW",
  "Paris, the city of light")
# match (all) hashtags in 'paris_tweets'
str_match_all(paris_tweets, "#[a-zA-Z]{1,}")


# locate position of (first) hashtag
str_locate(paris_tweets, "#[a-zA-Z]{1,}")
## start end
## [1,] 1 6
## [2,] 14 19
## [3,] 17 22
## [4,] NA NA

# locate (all) hashtags in 'paris_tweets'
str_locate_all(paris_tweets, "#[a-zA-Z]{1,}")


# city names
cities = c("San Francisco", "Barcelona", "Naples", "Paris")
# replace first matched vowel
str_replace(cities, "[aeiou]", ";")
## [1] "S;n Francisco" "B;rcelona" "N;ples" "P;ris"

#Now, suppose that we want to replace the first consonant in each name. We just need to
#modify the pattern with a negated class:
# replace first matched consonant
str_replace(cities, "[^aeiou]", ";")
## [1] ";an Francisco" ";arcelona" ";aples" ";aris"


# city names
cities = c("San Francisco", "Barcelona", "Naples", "Paris")
# replace all matched vowel
str_replace_all(cities, pattern = "[aeiou]", ";")
## [1] "S;n Fr;nc;sc;" "B;rc;l;n;" "N;pl;s" "P;r;s"

# replace all matched consonants
str_replace_all(cities, pattern = "[^aeiou]", ";")
## [1] ";a;;;;a;;i;;o" ";a;;e;o;a" ";a;;e;" ";a;i;"


# a sentence
sentence = c("R is a collaborative project with many contributors")
# split into words
str_split(sentence, " ")
## [[1]]
## [1] "R" "is" "a" "collaborative"
## [5] "project" "with" "many" "contributors"

# telephone numbers
tels = c("510-548-2238", "707-231-2440", "650-752-1300")
# split each number into its portions
str_split(tels, "-")

# string
flavors = c("chocolate", "vanilla", "cinnamon", "mint", "lemon")
# split by vowels
str_split(flavors, "[aeiou]")


# split by first vowel
str_split(flavors, "[aeiou]", n = 2)

# string
flavors = c("chocolate", "vanilla", "cinnamon", "mint", "lemon")
# split flavors into 2 pieces
str_split_fixed(flavors, "n", 2)

# split favors into 3 pieces
str_split_fixed(flavors, "n", 3)


================================================
FILE: 24-Strings/strman9.R
================================================
# String Manipulation 9

# function that reverses a string by characters
reverse_chars <- function(string)
{
  # split string by characters
  string_split = strsplit(string, split = "")
  # reverse order
  rev_order = nchar(string):1
  # reversed characters
  reversed_chars = string_split[[1]][rev_order]
  # collapse reversed characters
  paste(reversed_chars, collapse="")
}
#Let’s test our reversing function with a character and numeric vectors:
  # try 'reverse_chars'
reverse_chars("abcdefg")
## [1] "gfedcba"
# try with non-character input
reverse_chars(12345)
## Error: non-character argument
reverse_chars('12345')


# reversing a string by characters
reverse_chars <- function(string)
{
  string_split = strsplit(as.character(string), split = "")
  reversed_split = string_split[[1]][nchar(string):1]
  paste(reversed_split, collapse="")
}


# example with one word
reverse_chars("atmosphere")
## [1] "erehpsomta"
# example with a several words
reverse_chars("the big bang theory")
## [1] "yroeht gnab gib eht"

# try 'reverse_chars'
reverse_chars("abcdefg")
## [1] "gfedcba"
# try with non-character input
reverse_chars(12345)
## [1] "54321"

# reverse vector (by characters)
lapply(c("the big bang theory", "atmosphere"), reverse_chars)
## [[1]]
## [1] "yroeht gnab gib eht"
##
## [[2]]
## [1] "erehpsomta"

# function that reverses a string by words
reverse_words <- function(string)
{
  # split string by blank spaces
  string_split = strsplit(as.character(string), split = " ")
  # how many split terms?
  string_length = length(string_split[[1]])
  # decide what to do
  if (string_length == 1) {
    # one word (do nothing)
    reversed_string = string_split[[1]]
  } else {
    # more than one word (collapse them)
    reversed_split = string_split[[1]][string_length:1]
    reversed_string = paste(reversed_split, collapse = " ")
  }
  # output
  return(reversed_string)
}


# examples
reverse_words("atmosphere")
## [1] "atmosphere"
reverse_words("the big bang theory")
## [1] "theory bang big the"

# reverse vector (by words)
lapply(c("the big bang theory", "atmosphere"), reverse_words)
## [[1]]
## [1] "theory bang big the"
##
## [[2]]
## [1] "atmosphere"


#Email
# pattern
email_pat = "^([a-z0-9_\\.-]+)@([\\da-z\\.-]+)\\.([a-z\\.]{2,6})$"
# string that matches
grepl(pattern = email_pat, x = "gaston@abc.com")
## [1] TRUE

# another string that matches
grep(pattern = email_pat, x = "gaston.sanchez@research-center.fr")
## [1] 1
# unmatched email (TLD too long)
grep(pattern = email_pat, x = "gaston@abc.something")
## integer(0)


# potential email addresses
emails = c(
  "simple@example.com",
  "johnsmith@email.gov",
  "marie.curie@college.edu",
  "very.common@example.com",
  "a.little.lengthy.but.ok@dept.example.com",
  "disposable.style.email.with+symbol@example.com",
  "not_good@email.address")

# detect pattern
str_detect(string=emails, pattern=email_pat)
## [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE


================================================
FILE: 24-Strings/strprint1.R
================================================
# Printing Characters
print()  # generic printing
noquotes() # print with no quotes
cat() # concatenation
format() # special formats
toStrings() # convert to string
sprintf() # print as per c format

# print ------------

# text string
mystring = " Programming with data is fun"

print(mystring)
# quoted by default

# noquote -------------
noquote(mystring)

(no_quote1 = noquote(c('some', 'quoted', 'text', '!%~(&=')))

class(no_quote1) #  noquote
class(noquote)  # function 
#text character
is.character(no_quote1) # TRUE
no_quote1[2:3]  # 2nd & 3rd string


# concatenate -----------
?cat
cat(... , file = "", sep = " ", fill = FALSE, labels = NULL,
    append = FALSE)
# Accepts several types of data -> character
# default sep space

mystring
cat(mystring) # without quote
# prints to screen or file, no line indicator[1]

# 2 or more strings
cat(mystring, 'with R')

# cat with sep
cat(mystring, 'with R', sep=' =) ')
cat(1:10, sep="-")
#passing vectors, taken as seperate arguments
cat(month.abb[1:4], sep='#')
# break long strings  fill=30  string width
cat('Loooooooong strings', 'can be displayed', 'in a nice format',
    "by using the 'fill ' argument ", fill=30)
cat('Loooooooong strings', 'can be displayed', 'in a nice format',
    "by using the 'fill ' argument ", fill=50)
cat('Loooooooong strings', 'can be displayed', 'in a nice format',
    "by using the 'fill ' argument ", fill=5) # takes min of string

# output to a file
cat(mystring, 'with R', file='strings/output.txt')

# Encoding strings with format() ----------
# preety printing

#default usage
format(13.7)
format(13.1343232)
format(13.134323245544545)
# upto 5 decimal places
# arguments width, trim(padding), justify(text only), digits, scientific

format(13.7, nsmall = 3) # padding upto 3 digits
format(13.7, nsmall = 5)

format(c(6, 13.1), digits=2)
format(c(6, 13.1), digits=3) # max digits
format(c(6, 13.1), digits=1)  # no effect
format(c(6, 13.1), digits=5)  # no effect
format(c(6, 13.12345), digits=5)

format(c(6, 13.1), digits=2, nsmall=2)

format(c(6, 13.1), digits=2, nsmall=4)
format(c(6, 13.12345), digits=5, nsmall=3)  #shorten the display, pad if reqd

# pad - makes length same

#justify ----------------

format(c('A', 'BB', 'CCC'), width=5, justify='centre')

format(c('A', 'BB', 'CCC'), width=6, justify='left')

format(c('A', 'BB', 'CCC'), width=3, justify='right')

format(c('A', 'BB', 'CCC'), width=2, justify='none')

#digits
format(1/1:5, digits=2)
format(1/1:5, digits=5)

format( format(1/1:5, digits=2), width=6, justify='c') # center
format( format(1/1:5, digits=2), width=8, justify='r') # center
format( format(1/1:5, digits=10), width=8, justify='r') # center

# big mark
format(123456, big.mark = ',')
format(123456, big.interval = ',')
?big.mark
format(12345.678,big.mark=",",scientific=FALSE)
format(12345.678,big.mark=",",scientific=TRUE)

library(scales)    
comma_format()(1000000)

#sprintf - c styling
?sprintf
sprintf(fmt, ...)
gettextf(fmt, ..., domain = NULL)

sprintf('%f', pi)
sprintf('%.3f', pi)  # 3 decimal places
sprintf('%1.0f', pi)  # 1 integer & 0 decimal
sprintf('%5.1f', pi) # decimal notation 1 decimals
sprintf('%5.2f', pi) # decimal notation 2 decimals
sprintf('%05.1f', pi) # decimal notation 1 decimals inital padding
sprintf('%05.1f', 1234567.12345) # decimal notation 1 decimals inital padding

sprintf('%+f', 1234567.12345) # decimal notation 1 decimals inital padding

sprintf('% f', 1234567.12345) # prefix a space
sprintf('%-10f', 1234567.12345) # left justified
n1 = 12345678.12345678 ; n2=87654321.87654321
sprintf('%-10f', n1,'%+10f', n2)  # only 1 print variable

sprintf('%e', 1234567.12345) # exponential format e
sprintf('%E', 1234567.12345) # exponential format E
sprintf('%g', 1234567.12345) # significant digits default 6
sprintf('%.3g', 1234567.12345) # significant digits default 6
sprintf('%.8g', 123456789123.12345) # significant digits default 6


# Convert objects to strings

toString(17.04)

#combine 2 objects to single string sepearated by commas
toString(c(17.04, 1978))

# combine objects
toString(c('Bonjour', 123, TRUE, NA, log(exp(1))))

# use of width max 
toString(c('One', 'two', '3333333', 444), width=8)
toString(c('One', 'two', '3333333', 444), width=20)
toString(c('One', 'two', '3333333', 444))


# Printing methods

print(1:5)
as.character(1:5)
cat(1:5, sep='-')
paste(1:5)
paste(1:5, collapse='')
toString(1:5)
noquote(as.character(1:5))


# extras
prettyNum(c(123,1234),big.mark=",")


================================================
FILE: 24-Strings/strreplace1.R
================================================

x = '0_0_01-09-2013_0'
gsub('_', ',',x)
gsub('_', ',',x, fixed=T)
sub('_', ',',x)  # only 1st occurance
library(stringr)
str_replace_all(x, "_", ",")

library(stringi)
stri_replace_all(x, ",", fixed="_")


#----
library(tidyverse)
text_string = "developer"

# This works fine
x %>% str_replace_all(c('_' =',')) 

# But this is not what I want
x %>% str_replace_all(c('_' =", ", '-' = "/"))


casefold('DHiraj Upadhyaya', upper=T)
casefold('DHiraj Upadhyaya', upper=F)


chartr('a', 'A', 'This is a boring string')
chartr("_", ",", x)


================================================
FILE: 24-Strings/strsearch.R
================================================
# Selecting from Strings

# name of States
states = rownames(USArrests)
head(states)

# Select states with k
pattern1 = 'k'
grep(pattern = pattern1, x = states, value = T)

pattern1 = 'w'
grep(pattern = pattern1, x = states, value = T)

# w or W
pattern1 = '[wW]'
grep(pattern = pattern1, x = states, value = T)

# ignore case
pattern1 = 'w'
grep(pattern = pattern1, x = tolower(states), value = T)

pattern1 = 'W'
grep(pattern = pattern1, x = toupper(states), value = T)

pattern1 = 'w'
grep(pattern = pattern1, x = states, ignore.case=T, value = T)


================================================
FILE: 24-Strings/strsplit1.R
================================================
# String - Splitting Columns

library(splitstackshape)
concat.test
# Sample data
temp <- head(concat.test)
temp

## Split the "Likes" column
cSplit(temp, "Likes")

## Split the "Likes" and "Hates" columns --
##   they have different delimiters...
cSplit(temp, c("Likes", "Hates"), c(",", ";"))

## Split "Siblings" into a long form...
cSplit(temp, "Siblings", ",", direction = "long")

head(temp)
## Split "Siblings" into a long form, not removing whitespace
cSplit(temp, "Siblings", ",", direction = "long", stripWhite = FALSE)
cSplit(temp, "Siblings", ",", direction = "long", stripWhite = T)


## Split a vector
(y <- c("a_b_c", "a_b", "c_a_b"))
cSplit(data.frame(y), "y", "_")




#---
library(splitstackshape)
wordexplantion
splitstackshape::cSplit(df, wordexplantion, "-")

before <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))  
before
do.call(rbind, stringr::str_split(before$type, '_and_'))


#-----
library(data.table) ## v 1.9.6+ 
setDT(before)[, paste0("type", 1:2) := tstrsplit(type, "_and_")]
before


#before <- data.frame(attr = c(1,30,4,6), type=c('foo_and_bar','foo_and_bar_2'))  
out <- strsplit(as.character(before$type),'_and_') 
do.call(rbind, out)


#---
ibrary(dplyr)
library(tidyr)

before <- data.frame(
  attr = c(1, 30 ,4 ,6 ), 
  type = c('foo_and_bar', 'foo_and_bar_2')
)

before %>%
  separate(type, c("foo", "bar"), "_and_")


================================================
FILE: 24-Strings/strsplit2.R
================================================
#String Split

(x=c('10-20','20-30','30-40','40-50'))
(x1 = strsplit(x,"-"))
(x2 = unlist(x1))
(x3 = as.numeric(x2))
(x4 = unique(x3))
?strsplit
x4
x4[-length(x4)]
diff(x4)
diff(x4)/2

(x5 = x4[-length(x4)] + diff(x4)/2)


# Mid Point
a <- seq(0,50,10)
a
a[-length(a)] + diff(a)/2



================================================
FILE: 24-Strings/strsplit3.R
================================================
#
library(splitstackshape)

campus = c('AUHR_M', 'AUHR_M')
institute = c('ABS', 'ASET')
programs = c('BBA, MBA, MBA-HR', 'BTECH_CSE, BTECH_ME, BTECH_CE, MTECH, PHD')
courses = data.frame(campus,institute, programs)
courses

## Split the "programs" column
cSplit(courses, "programs")

##   they have different delimiters...
#cSplit(temp, c("Likes", "Hates"), c(",", ";"))

## Split "Siblings" into a long form...
cSplit(courses, "programs", ",", direction = "long")
#solved
cSplit(courses, "programs", ",", direction = "long", stripWhite = F)


#split a occurance of a character or string




================================================
FILE: 24-Strings/strsplit4.R
================================================

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1M-sAZqHHyuzzwW7vUwXoCw2JVwUiWki7LdFCgAyOccc/edit#gid=1470469521"

df = as.data.frame(gsheet2tbl(url))
head(df)

df1 = df[,2:4]
head(df1)
unlist(strsplit(df1$name, "\n"))[1]
df2= vapply(strsplit(df1$name, "\n", fixed = TRUE), "[", "", 1)
head(df2,n=20)
strsplit(df2[1:20], "United Kingdom")
vapply(strsplit(df1[1:20], c("United Kingdom","United States"), fixed = TRUE), "[", "", 1)



word <- c('apple-orange-strawberry','chocolate')
sapply(strsplit(word, "-"), head, 1)

df3= head(df2)
head(df3); names(df3)
names(df3)
df3
df4 = data.frame(do.call(rbind, strsplit(df3, split = "United Kingdom")))
df4

word <- c('JNUIndia','IITIndia','OxfordUnited Kingdom','HarvardUSA')
strsplit(word, split=c('India'))
strsplit(word, split=c('United Kingdom'))
strsplit(word, split=c('India','United Kingdom'))
strsplit(word, split=c('India|United Kingdom|USA'))

sapply(strsplit(word, split=c('India|United Kingdom|USA')),head,1)
?sapply
vapply(strsplit(word,split=c('India|United Kingdom|USA'), fixed = F), "[", 1)

vapply(strsplit(word, split=c('India|United Kingdom|USA')), fixed = TRUE, "[", "", 1)

word2 <- c('JNU India','IIT India','Oxford UnitedKingdom','Harvard USA')

Split <- strsplit(word,split=c('India|United Kingdom|USA') , fixed = F)
Split
Dates <- sapply(Split, "[", 1)
Rates <- sapply(Split, "[", 2)
Dates
Rates

Split <- strsplit(word2, split=" " , fixed = F)
Split
Dates <- sapply(Split, "[", 1)
Rates <- sapply(Split, "[", 2)
Dates
Rates



sapply(strsplit(word, split=c("India")), head, 1)
?strsplit
       

================================================
FILE: 24-Strings/tidyr-strseperate.R
================================================
#Strings using dplyr
#http://tidyr.tidyverse.org/reference/separate.html
# separate(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ...)

library(dplyr)
library(tidyr)
df1 <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df1
(df1 %>% separate(x, c("A", "B")))


# If every row doesn't split into the same number of pieces, use
# the extra and file arguments to control what happens
df2 <- data.frame(x = c("a", "a b", "a b c", NA))
df2
df2 %>% separate(x, c("a", "b"))
#> Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3].
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].

# The same behaviour but no warnings
df2 %>% separate(x, c("a", "b"), extra = "drop", fill = "right")

# Another option:
df2 %>% separate(x, c("a", "b"), extra = "merge", fill = "left")

# If only want to split specified number of times use extra = "merge"
df3 <- data.frame(x = c("x: 123", "y: error: 7"))
df3 %>% separate(x, c("key", "value"), ": ", extra = "merge")

#>   key    value
#> 1   x      123
#> 2   y error: 7



================================================
FILE: 31-graphs/1bubblechart.R
================================================
#bubble Chart - ggplot

library(ggplot2)
library(data.table)
#http://brooksandrew.github.io/simpleblog/articles/advanced-data-table/#method-1-in-line


DTmtcars = data.table(mtcars)

#this will create new column in the Table
#DTmtcars[, mpg_biased_mean:=mean(mpg)), by=cyl] 
#this will not
DTmtcars[, .(mean_mpg=mean(mpg)), by=cyl] 
#mean mpg and count by cyl
DTmtcars[, .(count=.N, mean_mpg=mean(mpg)), by=cyl] 
#group by cyl and gear and calc count & mean mpg and sort by gear, cyl
DT1 <- DTmtcars[ order(gear,cyl) , .(count=.N, mean_mpg=round(mean(mpg),2)), by=list(gear, cyl)] 
DT1

#simple point with size as count
ggplot(DT1, aes(x=factor(gear), y=factor(cyl), size=count)) + geom_point()
#change the point 
ggplot(DT1, aes(x=factor(gear), y=factor(cyl))) + geom_point(aes(size=count, colour = 'black', fill='cornsilk'))

ggplot(DT1, aes(x=factor(gear), y=factor(cyl))) + geom_point(aes(size=count, alpha=mean_mpg), fill='green', colour = 'black', shape=21) + scale_size_area(name='Mean MPG', max_size = 20, guide=F) + geom_text(aes(y=as.numeric(factor(cyl)) - sqrt(count)/22 , label=count, size=mean_mpg), vjust=1.2) + labs(x='Gear', y='Cylinder', title='Bubble Chart in GGplot') + scale_alpha(guide=F)+  theme(plot.subtitle=element_text(size=12, hjust=0.5, face="italic", color="blue"))-> g
DT1
g = g + labs( subtitle='Using mtcars data set and data.table Nos show the count of Gear & Cyl: See Details in the table \n Density of Colour indicates Mileage Levels, Number Labels and size of Circle indicate the count of Cars in the category of Gear & Cyl ' ) 

#
grid.arrange(tableGrob(DT1), g, ncol=2, widths=c(.2,.8))

grid.arrange( tableGrob(DT1, theme=ttheme_default()), g, ncol = 2, widths = c(.5, 1))

#no legend : + theme(legend.position="none")


#https://flowingdata.com/2010/11/23/how-to-make-bubble-charts/

crime <- read.csv("http://datasets.flowingdata.com/crimeRatesByState2005.tsv", header=TRUE, sep="\t")

symbols(crime$murder, crime$burglary, circles=crime$population)

radius <- sqrt( crime$population/ pi )
symbols(crime$murder, crime$burglary, circles=radius)
symbols(crime$murder, crime$burglary, circles=radius, inches=0.35, fg="white", bg="red", xlab="Murder Rate", ylab="Burglary Rate")

symbols(crime$murder, crime$burglary, circles=radius, inches=0.35, xlab="Murder Rate", ylab="Burglary Rate", fg='red', bg=rgb(255,255,255,50,maxColorValue=255))
symbols(crime$murder, crime$burglary, squares=sqrt(crime$population), inches=0.5)
text(crime$murder, crime$burglary, crime$state, cex=0.5)
?text


library(ggplot2)
qplot(murder, burglary, data = crime, label = state, colour='red', size = 50*population)+ geom_text(colour = "black", hjust=0,vjust=1, size=3)+  xlim(c(0,10))


================================================
FILE: 31-graphs/1bubblechart2.R
================================================
#bubble chart -using mtcars

#base plot

symbols(mtcars$mpg, mtcars$wt, mtcars$hp, inches=.2, xlab="MPG", ylab
        
        p3
p4 = p3 + labs(x='MPG', y='Weight', title='Bubble Chart in GGplot using MTCARS dataset')  +  theme(plot.subtitle=element_text(size=12, hjust=0.5, face="italic", color="blue"))
p4
p5 = p4 + labs( subtitle='Size of Point is proportional to HP/2' ) 
p5

#Another bubble chart showing count of values
library(dplyr)
mtcars %>% group_by(gear, cyl) %>% summarise(count=n()) -> df1
df1
df1[,1:2] <- lapply(df1[,1:2], as.factor)

str(df1)

g1 <-  ggplot(df1, aes(x=gear, y=cyl) )  + geom_point(aes(size=count), fill='green', colour = 'black', shape=21)
g1
g2 = g1 + scale_size_area(name='Count', max_size = 20, guide=F) 
g2
g3 = g2 + geom_text(aes(y=as.numeric(cyl) - sqrt(count)/20 , label=count, size=count/2), vjust=1.5)
g3
g4= g3 + labs(x='Gear', y='Cylinder', title='Bubble Chart in GGplot') 
g4
g5 = g4 + labs( subtitle='Using mtcars data set and data.table Nos show the count of Gear & Cyl: See Details in the table \n Number Labels and size of Circle indicate the count of Cars in the category of Gear & Cyl ' ) +  theme(plot.subtitle=element_text(size=12, hjust=0.5, face="italic", color="blue"))
g5
g6 = g5 + annotate(geom='text', x=2, y=3.2, label='Max cars with Gears=3 & Cylinder=8', size=4)
g6
g7 = g6 + geom_segment(mapping=aes(x=2, y=3.2, xend=1, yend=3), arrow=arrow(), size=1, colour="blue") 
g7
#
library(gridExtra)
grid.arrange(tableGrob(df1), g7, ncol=2, widths=c(.2,.8))

grid.arrange( tableGrob(DT1, theme=ttheme_default()), g7, ncol = 2, widths = c(.5, 1))


library(sp)

# NOT RUN {
data(meuse)
head(meuse)
coordinates(meuse) <- c("x", "y") # promote to SpatialPointsDataFrame
df2 = df1
coordinates(df2) <- c('x','y')
bubble(df2, "count", maxsize = 2.5, main = "cadmium concentrations (ppm)",   key.entries = 2^(-1:4))
bubble(meuse, "zinc", main = "zinc concentrations (ppm)",
       key.entries =  100 * 2^(0:4))
# }

================================================
FILE: 31-graphs/multipleplots1.R
================================================
#multiple Plots

library(ggplot2)
p1 <- qplot(mpg, wt, data = mtcars, colour = cyl)
p2 <- qplot(mpg, data = mtcars) + ggtitle("title")
p3 <- qplot(mpg, data = mtcars, geom = "dotplot")
p4 <-
  p1 + facet_wrap( ~ carb, nrow = 1) + theme(legend.position = "none") +
  ggtitle("facetted plot")
p4

grid.arrange(p1, p2, nrow = 1)

gl=list(p1,p2,p3,p4)
grid.arrange(
  grobs = gl,
  widths = c(2, 1, 1),
  layout_matrix = rbind(c(1, 2, NA),
                        c(3, 3, 4))
)

library(grid)
g <- ggplotGrob(qplot(1, 1) + theme(plot.background = element_rect(colour = "black")))
g
qplot(1:10, 1:10) +
  annotation_custom(grob = g, xmin = 1, xmax = 5, ymin = 5, ymax = 10  ) +
  annotation_custom(grob = rectGrob(gp = gpar(fill = "white")), xmin = 7.5,  xmax = Inf,ymin = -Inf,ymax = 5  )


library(gtable)
g2 <- ggplotGrob(p2)
g3 <- ggplotGrob(p3)
g <- rbind(g2, g3, size = "first")
g$widths <- unit.pmax(g2$widths, g3$widths)
grid.newpage()
grid.draw(g)


library(ggpubr)
p1 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
  geom_point()
p2 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
  geom_point() + facet_wrap(~ cyl, ncol = 2, scales = "free") +
  guides(colour = "none") +
  theme()

ggarrange(p1, p2, widths = 1:2)

grid.arrange( p3, p3, p3, nrow = 1,  top = "Title of the page",
  bottom = textGrob("this footnote is right-justified", gp = gpar(fontface = 3, fontsize = 9),  hjust = 1,   x = 1  ))


grid.arrange( tableGrob(mtcars[1:4, 1:4]),  p2, ncol = 2, widths = c(1.5, 1),
  clip = FALSE )


================================================
FILE: 31-graphs/tableGrob.R
================================================
#https://cran.r-project.org/web/packages/gridExtra/vignettes/tableGrob.html

library(gridExtra)
library(grid)
d <- head(iris[,1:3])
grid.table(d)

d[2,3] <- "this is very wwwwwide"
d[1,2] <- "this\nis\ntall"
colnames(d) <- c("alpha*integral(xdx,a,infinity)",
                 "this text\nis high", 'alpha/beta')

tt <- ttheme_default(colhead=list(fg_params = list(parse=TRUE)))
grid.table(d, theme=tt)


tt1 <- ttheme_default()
tt2 <- ttheme_minimal()
tt3 <- ttheme_minimal(
  core=list(bg_params = list(fill = blues9[1:4], col=NA),
            fg_params=list(fontface=3)),
  colhead=list(fg_params=list(col="navyblue", fontface=4L)),
  rowhead=list(fg_params=list(col="orange", fontface=3L)))

grid.arrange(
  tableGrob(iris[1:4, 1:2], theme=tt1),
  tableGrob(iris[1:4, 1:2], theme=tt2),
  tableGrob(iris[1:4, 1:2], theme=tt3),
  nrow=1)


t1 <- ttheme_default(core=list(
  fg_params=list(fontface=c(rep("plain", 4), "bold.italic")),
  bg_params = list(fill=c(rep(c("grey95", "grey90"),
                              length.out=4), "#6BAED6"),
                   alpha = rep(c(1,0.5), each=5))
))
grid.newpage()
grid.table(iris[1:5, 1:3], theme = t1)


tt1 <- ttheme_default()
tt2 <- ttheme_default(core=list(fg_params=list(hjust=1, x=0.9)),
                      rowhead=list(fg_params=list(hjust=1, x=0.95)))
tt3 <- ttheme_default(core=list(fg_params=list(hjust=0, x=0.1)),
                      rowhead=list(fg_params=list(hjust=0, x=0)))
grid.arrange(
  tableGrob(mtcars[1:4, 1:2], theme=tt1),
  tableGrob(mtcars[1:4, 1:2], theme=tt2),
  tableGrob(mtcars[1:4, 1:2], theme=tt3),
  nrow=1)


g <- g2 <- tableGrob(iris[1:4, 1:3], cols = NULL, rows=NULL)
g2$widths <- unit(rep(1/ncol(g2), ncol(g2)), "npc")
grid.arrange(rectGrob(), rectGrob(), nrow=1)
grid.arrange(g, g2, nrow=1, newpage = FALSE)


d1 <- PlantGrowth[1:3,1, drop=FALSE]
d2 <- PlantGrowth[1:2,1:2]

g1 <- tableGrob(d1)
g2 <- tableGrob(d2)

haligned <- gtable_combine(g1,g2, along=1)
valigned <- gtable_combine(g1,g2, along=2)
grid.newpage()
grid.arrange(haligned, valigned, ncol=2)


library(gtable)
g <- tableGrob(iris[1:4, 1:3], rows = NULL)
g <- gtable_add_grob(g,
                     grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),
                     t = 2, b = nrow(g), l = 1, r = ncol(g))
g <- gtable_add_grob(g,
                     grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),
                     t = 1, l = 1, r = ncol(g))
grid.draw(g)


================================================
FILE: 32-basicGraphs/10a-graphs.R
================================================
# Combined Plots

#plot, histogram, pie, boxplot, linechart, correlation plot

#plot
women
plot(women)
plot(women, type='p', pch=17)
plot(women, type='l')
plot(women, type='b', pch=18, lty=2, col=2)
plot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)

#more features with plot
plot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title', sub='Sub Title')
#see cheat sheet on base graphs

plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)


#boxplot
boxplot(women$height)
abline(h=c(58, 62,65,68,72))

#draw lines on plot for number summary
summary(women)
quantile(women$height)
boxplot(women$height, col='green')
abline(h=quantile(women$height))


#histogram
hist(women$height)
hist(women$height, breaks=10)
hist(women$height, breaks=5, col=1:5)

#histogram2
x = rnorm(100,50,100)
hist(x)

hist(x, freq=F, col=1:5)
lines(density(x))

#density plot : shape of data
plot(density(x), col='red')


#pie
x = c(10,20,40,50)
pie(x)
xlabels = c('A','B','C','D')
pie(x, labels=xlabels)

#barplot
barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

#correlation plot
pairs(women)
cor(women$height,women$weight)

head(mtcars)
cor(mtcars)
pairs(mtcars)
options(digits=4)
pairs(mtcars[1:4])


#end




================================================
FILE: 32-basicGraphs/10b-graphs.R
================================================
#More Graphs

# Simple Dotplot
dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,   main="Gas Milage for Car Models",  xlab="Miles Per Gallon")

# Stacked Bar Plot with Colors and Legend
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",  xlab="Number of Gears", col=c("darkblue","red"),legend = rownames(counts))

# Grouped Bar Plot
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",  xlab="Number of Gears", col=c("darkblue","red"),  legend = rownames(counts), beside=TRUE)


#scatterplot
# Basic Scatterplot Matrix
pairs(~mpg+disp+drat+wt,data=mtcars,   main="Simple Scatterplot Matrix")

library(car)   #install this
scatterplot(mpg ~ wt, data=mtcars)
scatterplot(mpg ~ wt | factor(cyl), data=mtcars)


#Violin, 

# Violin Plots----
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), col="gold")
title("Violin Plots of Miles Per Gallon")

# Pie Chart with Percentages
slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct) # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie(slices,labels = lbls, col=rainbow(length(lbls)),  main="Pie Chart of Countries")


# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,   main="Pie Chart of Countries ")



# Boxplot of MPG by Car Cylinders 
boxplot(mpg~cyl,data=mtcars, main="Car Milage Data", xlab="Number of Cylinders", ylab="Miles Per Gallon")


boxplot(mpg ~ cyl * am, data=mtcars, notch=TRUE,  col=(c("gold","darkgreen")),  main="MTCars MPG", xlab="cyl & vs")
#ignore warnings


#end


================================================
FILE: 32-basicGraphs/12b-graphs2.R
================================================


women
plot(women)
plot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title', sub='Sub Title')

boxplot(women$height)
abline(h=c(58, 62,65,68,72))
?boxplot
summary(women)

quantile(women$height)

hist(women$height, breaks=10)

x = rnorm(100,50,100)
hist(x)
hist(x, freq=F)
lines(density(x))
plot(density(x), col='red')

#pie
x = c(10,20,40,50)
pie(x)
xlabels = c('A','B','C','D')
pie(x, labels=xlabels)

barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

pairs(women)
head(mtcars)
pairs(mtcars)
cor(women$height,women$weight)
options(digits=4)
plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)



mosaic::qdist("norm", p = 0.95, mean = 0, sd = 1)


================================================
FILE: 32-basicGraphs/12d-title1.R
================================================
#titles

title(main="main title", sub="sub-title", 
      xlab="x-axis label", ylab="y-axis label")

# Add a red title and a blue subtitle. Make x and y 
# labels 25% smaller than the default and green. 
plot(x=1)
title(main="My Title", col.main="red", 
      sub="My Sub-title", col.sub="blue", 
      xlab="My X label", ylab="My Y label",
      col.lab="green", cex.lab=0.75)


================================================
FILE: 32-basicGraphs/12e-text.R
================================================
#text in graphs

#Text can be added to graphs using the text( ) and mtext( ) functions. text( ) places text within the graph while mtext( ) places text in one of the four margins.

text(location, "text to place", pos, ...)
mtext("text to place", side, line=n, ...)

# Example of labeling points
attach(mtcars)
plot(wt, mpg, main="Milage vs. Car Weight", 
     xlab="Weight", ylab="Mileage", pch=18, col="blue")
text(wt, mpg, row.names(mtcars), cex=0.6, pos=4, col="red")


================================================
FILE: 32-basicGraphs/12f-abline.R
================================================
#abline

abline(h=yvalues, v=xvalues)

Other graphical parameters (such as line type, color, and width) can also be specified in the abline( ) function.

# add solid horizontal lines at y=1,5,7 
abline(h=c(1,5,7))
# add dashed blue verical lines at x = 1,3,5,7,9
abline(v=seq(1,10,2),lty=2,col="blue")

Note: You can also use the grid( ) function to add reference lines.


================================================
FILE: 32-basicGraphs/12g-legend.R
================================================
#Legend

counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", xlab="Number of Gears")
barplot(counts, beside=T, legend=T, main="Car Distribution", xlab="Number of Gears",   args.legend = list(x='right',bty='n'))
palette()  # current settings
# 3 series in this example 3,4,5
palette(gray(seq(0.5,1,len=3)))
palette()
palette('default')
palette(gray(seq(0.5,1,len=3)))
barplot(counts, beside=T, legend=T, col=palette(),  args.legend = list(x='right',bty='n'))
#Other Method
barplot(counts, beside=T, col=palette())
legend(x= 'topright',legend = rownames(counts), fill=palette(),    title='No of Gears')
?legend

legend(x, y = NULL, legend, fill = NULL, col = par("col"), border = "black", lty, lwd, pch,   angle = 45, density = NULL, bty = "o", bg = par("bg"),    box.lwd = par("lwd"), box.lty = par("lty"), box.col = par("fg"),  pt.bg = NA, cex = 1, pt.cex = cex, pt.lwd = lwd, xjust = 0, yjust = 1, x.intersp = 1, y.intersp = 1,  adj = c(0, 0.5), text.width = NULL, text.col = par("col"), text.font = NULL, merge = do.lines && has.pch, trace = FALSE,  plot = TRUE, ncol = 1, horiz = FALSE, title = NULL,  inset = 0, xpd, title.col = text.col, title.adj = 0.5, seg.len = 2)

# Other Color Palettes - rainbow, heat.colors, terrain.colors, 
# topo.colors, cm.colors, gray
palette(c('red','blue','green'))
barplot(counts, beside=T, col=palette())
legend(x= 2, y=15,legend = rownames(counts), fill=palette(),  title='No of Gears')
# When plot with lines/ points , col=par('col'); pch= plotting symbol
#bty = border n,o, # 

#links

#(http://www.sthda.com/english/wiki/add-legends-to-plots-in-r-software-the-easiest-way)


================================================
FILE: 32-basicGraphs/12k-tick.R
================================================
#tick marks

# Add minor tick marks
library(Hmisc)
minor.tick(nx=n, ny=n, tick.ratio=n)


nx is the number of minor tick marks to place between x-axis major tick marks.
ny does the same for the y-axis. tick.ratio is the size of the minor tick mark relative to the major tick mark. The length of the major tick mark is retrieved from par("tck").


================================================
FILE: 32-basicGraphs/12m-axis1.R
================================================
#axis Labels and titles in R

plot(faithful, main = "Eruptions of Old Faithful",
     xlab = "Eruption time (min)",
      ylab = "Waiting time to next eruption (min)")

# Specify axis options within plot() 
plot(x, y, main="title", sub="subtitle",
     xlab="X-axis label", ylab="y-axix label",
     xlim=c(xmin, xmax), ylim=c(ymin, ymax))



#axis

axis(side, at=, labels=, pos=, lty=, col=, las=, tck=, ...)

# side	an integer indicating the side of the graph to draw the axis (1=bottom, 2=left, 3=top, 4=right)
# at	a numeric vector indicating where tic marks should be drawn
# labels	a character vector of labels to be placed at the tickmarks 
# (if NULL, the at values will be used)
# pos	the coordinate at which the axis line is to be drawn. 
# (i.e., the value on the other axis where it crosses)
# lty	line type
# col	the line and tick mark color
# las	labels are parallel (=0) or perpendicular(=2) to axis
# tck	length of tick mark as fraction of plotting region (negative number is outside graph, positive number is inside, 0 suppresses ticks, 1 creates gridlines) default is -0.01
# (...)


# A Silly Axis Example

# specify the data 
x <- c(1:10); y <- x; z <- 10/x

# create extra margin room on the right for an axis 
par(mar=c(5, 4, 4, 8) + 0.1)

# plot x vs. y 
plot(x, y,type="b", pch=21, col="red", 
     yaxt="n", lty=3, xlab="", ylab="")

# add x vs. 1/x 
lines(x, z, type="b", pch=22, col="blue", lty=2)

# draw an axis on the left 
axis(2, at=x,labels=x, col.axis="red", las=2)

# draw an axis on the right, with smaller text and ticks 
axis(4, at=z,labels=round(z,digits=2),
     col.axis="blue", las=2, cex.axis=0.7, tck=-.01)

# add a title for the right axis 
mtext("y=1/x", side=4, line=3, cex.lab=1,las=2, col="blue")

# add a main title and bottom and left axis labels 
title("An Example of Creative Axes", xlab="X values",
      ylab="Y=X")

================================================
FILE: 32-basicGraphs/13e-multipleplots1.R
================================================
#multiple plots

# 4 figures arranged in 2 rows and 2 columns
attach(mtcars)
par(mfrow=c(2,2))
plot(wt,mpg, main="Scatterplot of wt vs. mpg")
plot(wt,disp, main="Scatterplot of wt vs disp")
hist(wt, main="Histogram of wt")
boxplot(wt, main="Boxplot of wt")


# 3 figures arranged in 3 rows and 1 column
attach(mtcars)
par(mfrow=c(3,1)) 
hist(wt)
hist(mpg)

hist(disp)


# One figure in row 1 and two figures in row 2
attach(mtcars)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
hist(wt)
hist(mpg)
hist(disp)


# One figure in row 1 and two figures in row 2
# row 1 is 1/3 the height of row 2
# column 2 is 1/4 the width of the column 1 
attach(mtcars)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), 
       widths=c(3,1), heights=c(1,2))
hist(wt)
hist(mpg)
hist(disp)



# Add boxplots to a scatterplot
par(fig=c(0,0.8,0,0.8), new=TRUE)
plot(mtcars$wt, mtcars$mpg, xlab="Car Weight",
     ylab="Miles Per Gallon")
par(fig=c(0,0.8,0.55,1), new=TRUE)
boxplot(mtcars$wt, horizontal=TRUE, axes=FALSE)
par(fig=c(0.65,1,0,0.8),new=TRUE)
boxplot(mtcars$mpg, axes=FALSE)
mtext("Enhanced Scatterplot", side=3, outer=TRUE, line=-3)



================================================
FILE: 32-basicGraphs/13f-multipleplots.R
================================================
#Multiple Plots
#https://stackoverflow.com/questions/2564258/plot-two-graphs-in-same-plot-in-r

x= rnorm(100, 50,10)
y= rnorm(100, 70, 15)
x1= runif(100, 50,100)
x2= runif(100, 70, 120)
y1 = runif(100, 100,200)
y2 = runif(100, 100,200)


#plot
#plot(first thing to plot)
#plot(Second thing to plotm, add=T)


#same x coord
matplot(x, cbind(y1,y2),type="l",col=c("red","green"),lty=c(1,1))


#Diff x coordinates
x1  <- seq(-2, 2, 0.05)
x2  <- seq(-3, 3, 0.05)
y1 <- pnorm(x1)
y2 <- pnorm(x2,1,1)

plot(x1,y1,ylim=range(c(y1,y2)),xlim=range(c(x1,x2)), type="l",col="red")
lines(x2,y2,col="green")


#next to each other
par(mfrow=c(1,2))
plot(x)
plot(y) 
par(mfrow=c(1,1))


#
plot(x1, y1,col='red')
points(x2,y2,col='blue')
lines(x2,y2,col='green')


#----
kx = matrix( c(21,50,80,41), nrow=2 )
x
y = matrix( c(1,2,1,2), nrow=2 )
y
plot(x, y, col=c("red","blue"))

#ggplot
require(ggplot2)
df= data.frame(x, y1,y2)

ggplot(df, aes(x,y1)) +                    # basic graphical object
  geom_line(colour="red")   # first layer

ggplot(df, aes(x)) +                    # basic graphical object
  geom_line(aes(y=y1), colour="red") +  # first layer
  geom_line(aes(y=y2), colour="green")  # second layer


================================================
FILE: 32-basicGraphs/13g-subplot.R
================================================
#subplot
#https://www.programiz.com/r-programming/subplot
#put two or more graphs in a single plot.
par()
#mfrow can be used to specify the number of subplot we need
max.temp <- c(20, 25, 20, 27, 33, 22, 28)
max.temp    # a vector used for plotting


par(mfrow=c(1,2))    # set the plotting area into a 1*2 array

barplot(max.temp, main="Barplot")
pie(max.temp, main="Piechart", radius=1)
#same phenomenon can be achieved with the graphical parameter mfcol.
#only difference between the two is that, mfrow fills in the subplot region row wise while mfcol fills it column wise.

Temperature <- airquality$Temp
Ozone <- airquality$Ozone
#rowfirst
par(mfrow=c(2,2))
#par(mfcol=c(2,2))


hist(Temperature)
boxplot(Temperature, horizontal=TRUE)
hist(Ozone)
boxplot(Ozone, horizontal=TRUE)

#more control using fig
# make labels and margins smaller
par(cex=0.7, mai=c(0.1,0.1,0.2,0.1))

Temperature <- airquality$Temp

# define area for the histogram
par(fig=c(0.1,0.7,0.3,0.9))
hist(Temperature)

# define area for the boxplot
par(fig=c(0.8,1,0,1), new=TRUE)
boxplot(Temperature)

# define area for the stripchart
par(fig=c(0.1,0.67,0.1,0.25), new=TRUE)
stripchart(Temperature, method="jitter")


================================================
FILE: 32-basicGraphs/15a-graphdata1.R
================================================
# Data for graphs Plot using Business Data

# Data
set.seed(100)
sales = floor(rnorm(100, 80, 20))

set.seed(101)
coy = sample(c('coy1','coy2','coy3'), size=100, replace=T) 
coy = factor(coy)

set.seed(102)
adtv = ceiling(rnorm(100,10,5))

set.seed(103)
adweb = ceiling(rnorm(100,7,2))

set.seed(104)
loc = sample(c('delhi','noida'), size=100, replace=T) 

set.seed(105)
salesman = factor(sample(c(1,2,3), size=100, replace=T))

set.seed(106)
product = factor(sample(c('TV','Friz','Car'), size=100, replace=T))

df1 = data.frame(coy,sales,adtv,adweb,loc,product,salesman)
head(df1)
str(df1)

save(df1, file='./data/badata.Rdata')



================================================
FILE: 32-basicGraphs/15b-graph1.R
================================================
# Plot using Business Data
df1
#use df1 : load(file='./data/ba.Rdata')
# Data
load(file='./data/badata.Rdata')
df1
attach(df1)
#scatter Plot
plot(adtv, sales)
plot(x=adtv, y=adweb, pch=15, type='p', col='yellow')

abline(lm(adweb ~ adtv))

car::scatterplot(sales ~ coy, data=df1, legend.columns=T)
car::scatterplot(sales ~ adtv, data=df1, legend.columns=T)

car::scatterplotMatrix(~ sales + adtv + adweb, data=df1)
plot(sales, adtv)
plot(sales, adweb)
plot(adtv, adweb)
pairs( ~ sales + adtv + adweb, data=df1)



#Corrgrams
library(corrgram)
names(df1)
cor(df1[c('sales','adtv','adweb')])
corrgram::corrgram(df1[c('sales','adtv','adweb')])
nvars = c('sales', 'adtv', 'adweb')
corrgram::corrgram(df1[nvars], order=T, lower.panel = panel.shade, upper.panel = panel.pie, diag.panel = panel.minmax, text.panel = panel.txt, main='Corrgram Plot')

corrgram::corrgram(df1[nvars], order=T, panel = panel.ellipse, diag.panel = panel.minmax, text.panel = panel.txt, main='Corrgram Plot - Ellipse')
#color - magnitude, direction = +/1, pie- value, min/ max values
corrgram::corrgram(df1[nvars], order=T, lower.panel = panel.pts, upper.panel = panel.conf, diag.panel=panel.density, main='Corrgram Plot - Ellipse')

corrgram::corrgram(df1[nvars], order=T, lower.panel = corrgram::panel.ellipse, upper.panel = panel.bar, diag.panel=panel.minmax, col.regions=colorRampPalette(c('red','blue','green')), main='Corrgram Plot')

corrgram::corrgram(df1[nvars], order=T, upper.panel=panel.cor)


#Mosaic Plot
library(vcd)
data(Titanic)
vcd::mosaic(Titanic, shade=T, legend=T)
names(df1)
XS= structable(~ coy + loc + product + salesman, data=df1)
df3= as.table(XS) ; is.array(XS) 
df3
vcd::mosaic(df3,color='skyblue', shade=T, main="Main", sub='Sub Title', xlab='Product')
vcd::assoc(df3)


# Plots
mean(sales)
plot(sales, pch=18)
plot(density(sales))
hist(sales, bin=10)
library(ggplot2)
plot(x=Aadv1, y=Asales1)
#ggplot(data=df1 + aes(x=df1$Aadv1, y=df1$Acoy1)) + geom_point()
ggplot(data=mtcars) + aes(x=wt, y=mpg) + geom_point()
names(df1)
p1 <- ggplot(df1, aes(sales, adtv)) +
  geom_point(data = df1[coy == 'coy1',]) 
p1



qplot(df1$sales, geom="histogram", bins=12)
?qplot
names(df1)
ggplot(data=df1) + aes(y=sales, x=adtv, + geom_point(binwidth=5, colour="black", fill="white") )  #not working
df1


#Histogram
hist(df1$sales)

hist(df1$sales, freq=F, col=1:12, breaks=5)
?hist
lines(density(df1$sales), lwd=2, col='red')

str(df1)
#box plots
boxplot(df1$sales)
boxplot(df1$sales ~ df1$coy + df1$loc)

boxplot(df1$sales ~ df1$coy, varwidth=T, col=1:3)



#Dot Chart
dotchart(df1$sales)  #not effective here
dotchart(df1$sales, group=df1$coy, color=df1$coy, pch=10:13)  



#add another column and convert to dataframe
names(df1)
head(df1)
aggregate(df1$sales, by=list(coy), FUN=mean)
(adf1 = aggregate(formula = sales ~ coy, data=df1, FUN=mean))

pie(adf1$sales, label=adf1$coy)
barplot(adf1$sales,names.arg=adf1$coy)
abline(h=adf1[adf1$coy=='coy1',]$sales)

barplot(adf1$sales,names.arg=adf1$coy, horiz = T)
abline(v=adf1[adf1$coy=='coy1',]$sales)

barplot(adf1$sales,names.arg=adf1$coy, beside=T, legend=rownames(adf1), col=1:3)
abline(v=adf1[adf1$coy=='coy1',]$sales)





================================================
FILE: 32-basicGraphs/21b-plot-hist1.R
================================================
# Plotting with Factors

#create a data frame
marks = floor(rnorm(50, 50,10))
gender1 = sample(c('M','F'), 50, replace=T, prob=c(.4,.6))
gender1
gender2 = factor(gender1,ordered=T, level=c('M','F'))
student1 = data.frame(marks, gender1)
student2 = data.frame(marks, gender2)
str(student1)
str(student2)
boxplot(student1)  #marks & gender1
boxplot(marks ~ gender1, data=student1)  # better format
boxplot(student1$marks ~ student1$gender1)

boxplot(marks ~ gender2, data=student2)  # see the order of printing M then F

#add another column
mstatus = sample(c('Married','Single'), 50, replace=T, prob=c(.6,.4))
mstatus = factor(mstatus)
student3 = data.frame(marks, gender2, mstatus)
boxplot( marks ~ gender2 + mstatus, data=student3)

# use ggplot to enhance the graph
hist(student3$marks)
# can we fill with colors for gender or married in histogram
library(ggplot2)
ggplot(student3)  #nothing
names(student3)
?barplot
(t1= table(student3$gender2))
(t2= table(student3$gender2, student3$mstatus))

barplot(height=t1, col=1:2)
barplot(height=t1, col=4:5, horiz=T)
barplot(height=t1, col=6:7, besides=T, horiz=T)

barplot(height=t2, col=3:4)
barplot(height=t2, col=6:9, beside=T)

#set ylim to fit legend
barplot(height=t2, col=6:9, beside=T, ylim=c(0,max(t2) * 1.3))
title(main='Bar Plot of Married and Gender Status', sub='See the distibution')

legend("topright", legend = c("M-Single", "M-Married","F-Single", "F-Married"),   fill = 6:9, horiz = T)


#Using GGplot2
library(ggplot2)

ggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='stack') + labs(title='Distribution of Gender & Married status')

#dodge postion  . side wise
ggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='dodge') + labs(title='Distribution of Gender & Married status')

#fill postion  . utilised full column length
ggplot(students3, aes(x=gender2, fill=mstatus)) + geom_bar(position='fill') + labs(title='Distribution of Gender & Married status')
ggplot(students3, aes(x=mstatus, fill=gender2)) + geom_bar(position='fill') + labs(title='Distribution of Gender & Married status')



#Histogram and distribution
#http://ggplot2.tidyverse.org/reference/geom_bar.html

hist(student3$marks)
ggplot(student3, aes(x=marks)) + geom_histogram(bins=15)
g1a = ggplot(student3, aes(x=marks))
g1b = geom_histogram(bins=15)
g1a + g1b


g2a = ggplot(student3, aes(x=marks))
g2b = geom_bar()
g2a + g2b

g2c = geom_bar(aes(fill = gender2))
g2a + g2c

g2d = geom_bar(aes(fill = mstatus))
g2a + g2d

g2e = geom_bar(aes(fill = mstatus)) +  theme(legend.position = "top")
g2a + g2e

g2e = geom_bar(aes(fill = mstatus), position = position_stack(reverse = TRUE)) +  coord_flip() +  theme(legend.position = "top")

 fill=gender2)) + geom_bar(position='stack', binwidth = 1.3) + scale_x_continuous('marks') + scale_y_continuous('Frequency')



data(Salaries, package='car')
Salaries


================================================
FILE: 32-basicGraphs/21c-plot.R
================================================
#Plot
#https://www.programiz.com/r-programming/plot-function
#simplest case, we can pass in a vector and we will get a scatter plot of magnitude vs index. But generally, we pass in two vectors 
#and a scatter plot of these points are plotted.
plot(c(1,2),c(3,5))
plot(1)
x <- seq(-pi,pi,0.1)

plot(x, sin(x))

plot(x, sin(x),
     main="The Sine Function",
     ylab="sin(x)")
#options
#"p" - points
# "l" - lines
# "b" - both points and lines
# "c" - empty points joined by lines
# "o" - overplotted points and lines
# "s" and "S" - stair steps
# "h" - histogram-like vertical lines
# "n" - does not produce any points or lines
plot(x, sin(x),
     main="The Sine Function",
     ylab="sin(x)",
     type="l",
     col="blue")
#Overlaying plot
plot(x, sin(x),
     main="Overlaying Graphs",
     ylab="",
     type="l",
     col="blue")

lines(x,cos(x), col="red")

legend("topleft",
       c("sin(x)","cos(x)"),
       fill=c("blue","red")
)


================================================
FILE: 32-basicGraphs/23b-line.R
================================================
#Line Charts

x <- c(1:5); y <- x # create some data 
par(pch=22, col="red") # plotting symbol and color 
par(mfrow=c(2,4)) # all plots on one page 
opts = c("p","l","o","b","c","s","S","h") 
for(i in 1:length(opts)){ 
  heading = paste("type=",opts[i]) 
  plot(x, y, type="n", main=heading) 
  lines(x, y, type=opts[i]) 
}


================================================
FILE: 32-basicGraphs/23c-lines2.R
================================================
#multiple lines plot

#base
# Create the data for the chart. rainfall two years
year1 <- c(7,12,28,3,41)
names(year1) = month.abb[6:10]
year2 <- c(14,7,6,19,3)

# store in file(file = "line_chart_2_lines.jpg")
# Plot the bar chart.
plot(year1,type = "o",col = "red", xlab = "Month/Year", ylab = "Rain fall",  main = "Rain fall chart")
lines(year2, type = "o", col = "blue")
legend("topleft", col=c('red','blue'), pch=1, legend=c('year1','year2')) # legend()
#dev.off() # Save the file.
?legend





#wide format
dat <- matrix(runif(40,1,20),ncol=4) # make data
dat
matplot(dat, type = c("b"),pch=1,col = 1:4) #plot
legend("topleft", legend = 1:4, col=1:4, pch=1) # optional legend


#ggplot
# dummy data
set.seed(45)
df <- data.frame(x=rep(1:5, 9), val=sample(1:100, 45), 
     variable=rep(paste0("category", 1:9), each=5))
# plot
df
ggplot(data = df, aes(x=x, y=val)) + geom_line(aes(colour=variable))


#lattice
library(lattice)
xyplot(val~x,type=c('l','p'),groups= variable,data=df,auto.key=T)


================================================
FILE: 32-basicGraphs/24b-histogram.R
================================================
#Histogram

#Histograms
#You can create histograms with the function hist(x) where x is a numeric vector of values to be plotted. The option freq=FALSE plots probability densities instead of frequencies. The option breaks= controls the number of bins.
?hist
hist(x, breaks = "Sturges",
     freq = NULL, probability = !freq,
     include.lowest = TRUE, right = TRUE,
     density = NULL, angle = 45, col = NULL, border = NULL,
     main = paste("Histogram of" , xname),
     xlim = range(breaks), ylim = NULL,
     xlab = xname, ylab,
     axes = TRUE, plot = TRUE, labels = FALSE,
     nclass = NULL, warn.unused = TRUE, ...)
# Simple Histogram
hist(mtcars$mpg)

# Colored Histogram with Different Number of Bins
hist(mtcars$mpg, breaks=12, col="red")
# 12 bars
hist(mtcars$mpg, breaks=7, xlim=c(10,40), ylim=c(0,20) ,
     col=rainbow(7), labels=T, 
     main=paste('Histogram of MPG'))

# Add a Normal Curve 
x <- mtcars$mpg 
h<-hist(x, breaks=10, col="lightblue",  xlab="Miles per Gallon", 
   xaxt='n', labels=T , main="Histogram with Normal Curve") 
x
axis(1, at=h$mids,labels=h$mids,line=.25, col.axis="red", cex.axis=0.7, las=1)
# add a title for the x axis 
mtext("Interval of 2 mpg", side=1, line=2, 
      cex.lab=1,las=1, col="blue", at=30)
# another mtext
mtext("Interval of 2 mpg", side=1, line=4, at=17,
      cex.lab=1,las=1, col="green")

xfit<-seq(min(x),max(x),length=40) 
xfit
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) 
yfit
h$breaks ; h$mids ; h$counts; 
h$mids[1:2] ; length(x); diff(h$mids[1:2]); diff(h$mids[1:2]) * length(x)
yfit <- yfit*diff(h$mids[1:2])*length(x) 
yfit
lines(xfit, yfit, col="blue", lwd=2)

# Air Passengers
str(AirPassengers)
head(AirPassengers)
hist(AirPassengers)
hist(AirPassengers, main="Histogram for Air Passengers", 
     xlab="Passengers", border="blue", col="green", 
     xlim=c(100,700),las=2, breaks=5)
hist(AirPassengers, breaks=c(100, 300, 500, 700))
hist(AirPassengers, breaks=c(100, seq(200,700, 150))) # Prob automatically
#start at 100 on the x-axis, and from values 200 to 700, make the bins 150 wide

hist(AirPassengers, main="Histogram for Air Passengers", 
     xlab="Passengers",border="blue", col="green", 
     xlim=c(100,700), las=1,breaks=5,prob = TRUE)
lines(density(AirPassengers)) 
#Get a density curve to go along with your AirPassengers histogram
hist.info = hist(AirPassengers)
summary(hist.info)
hist.info
hist(AirPassengers, freq=F)
hist2 = hist(AirPassengers, plot=F)
hist2$density
sum(hist2$density)
sum(hist2$density * diff(hist2$breaks))
#curve(dnorm(AirPassengers, mean=mean(AirPassengers), sd=sd(AirPassengers)),
#add=TRUE, col='darkblue', lwd=2)

# Faithfull--------------
colors = c("red", "yellow", "green", "violet", "orange", 
           "blue", "pink", "cyan") 
hist(faithful$eruptions    # apply the hist function 
       ,right=FALSE    # intervals closed on the left 
       ,col=colors     # set the color palette 
       ,main="Old Faithful Eruptions" # the main title 
       ,xlab="Duration minutes")       # x-axis label




# Kernel Density Plot
d <- density(mtcars$mpg) # returns the density data 
plot(d) # plots the results

# Filled Density Plot
d <- density(mtcars$mpg)
plot(d, main="Kernel Density of Miles Per Gallon")
polygon(d, col="red", border="blue")

# Compare MPG distributions for cars with 
# 4,6, or 8 cylinders
library(sm)
attach(mtcars)

# create value labels 
cyl.f <- factor(cyl, levels= c(4,6,8),
                labels = c("4 cylinder", "6 cylinder", "8 cylinder")) 

# plot densities 
sm.density.compare(mpg, cyl, xlab="Miles Per Gallon")
title(main="MPG Distribution by Car Cylinders")

# add legend via mouse click
colfill<-c(2:(2+length(levels(cyl.f)))) 
legend(locator(1), levels(cyl.f), fill=colfill)



================================================
FILE: 32-basicGraphs/24c-histogram2.R
================================================
# Histogram2
#https://www.programiz.com/r-programming/histogram
str(airquality)
airquality$Temp
Temperature = airquality$Temp
hist(Temperature)
# 9 cells equally spaced; Height of cell = no of observations falling in the cell
# main title, xlab, ylab, xlim, ylim, col, freq
# histogram with added parameters

hist(Temperature,
     main="Maximum daily temperature ",
     xlab="Temperature in deg F",
     xlim=c(50,100),
     col="darkmagenta",
     freq=FALSE
)
# yaxis labeled density instead of freq. total area =1
# return value of hist()
h = hist(Temperature)
h
#breaks-places where the breaks occur,
#counts-the number of observations falling in that cell,
#density-the density of cells, mids-the midpoints of cells,
#xname-the x argument name and
#equidist-a logical value indicating if the breaks are equally spaced or not.
#
h <- hist(Temperature,ylim=c(0,40))
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5))

#Define no of breaks
hist(Temperature, breaks=4, main="With breaks=4")
hist(Temperature, breaks=20, main="With breaks=20")

#With manual Breaks
hist(Temperature,
     main="Maximum daily temperature",
     xlab="Temperature in degrees Fahrenheit",
     xlim=c(50,100),
     col="chocolate",
     border="brown",
     breaks=c(55,60,70,75,80,100)
)



================================================
FILE: 32-basicGraphs/25b-barplot.R
================================================
# Bar Plot
# Simple Bar Plot 
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution",  xlab="Number of Gears")
#which cars have max type of gears

# Simple Horizontal Bar Plot with Added Labels 
counts <- table(mtcars$gear)
counts
barplot(counts, main="Car Distribution", horiz=TRUE,
        names.arg=c("3 Gears", "4 Gears", "5 Gears"))

# Stacked Bar Plot with Colors and Legend
counts <- table(mtcars$vs, mtcars$gear)
counts
barplot(counts, main="Car Distribution by Gears and VS",
        xlab="Number of Gears", col=c("darkblue","red"),
        legend = rownames(counts))
#what is distribution of Engine Type for each gear type
#which type of engines are max in gear3

# Grouped Bar Plot
counts <- table(mtcars$vs, mtcars$gear)
counts
barplot(counts, main="Car Distribution by Gears and VS",
        xlab="Number of Gears", col=c("darkblue","red"),
        legend = rownames(counts), beside=TRUE)

# Fitting Labels 
par(las=2) # make label text perpendicular to axis
par(mar=c(5,8,4,2)) # increase y-axis margin.

counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", horiz=TRUE, names.arg=c("3 Gears", "4 Gears", "5 Gears"), cex.names=0.8)



================================================
FILE: 32-basicGraphs/25c-barplot2.R
================================================
#Bar Plot2
#
heights1 = c(5, 5, 4.5, 4, 4, 4, 4.5, 2, 4, 4) # Your data
mydata <- matrix(heights1, ncol = 2, byrow = TRUE,
     dimnames = list(c("Spanish", "English", "Hindi", 
     "Arabic", "Body Lang"), c("Reading/Writing", "Speaking")))
#matrix( ,  , , list(rowname,s colnames))
colors <- c("darkblue", "red") # Define the colors you're using

barplot(t(mydata), beside = TRUE, col = colors, 
        ylim = c(0, 6), axes = FALSE,
        xlab = "Language starting with mostly used",
        main = "Languages (Verbal & Non-verbal)")
axis(2, at = 0:5, labels = 0:5)
legend("topright", colnames(mydata), fill = colors, bty = "n")
legend("topleft", c("reading/Writing", "Speaking"), col=c("darkblue","red"), lwd=10)

#2

##Create data
x = replicate(2, sample(letters[1:2], 100, rep=T))
apply(x, 2, table)

##Create plot
barplot(matrix(c(5, 3, 8, 9), nr=2), beside=TRUE, 
        col=c("aquamarine3", "coral"), 
        names.arg=LETTERS[1:2], 
        ylim=range(0,12))

##Add text at coordinates: x=2, y=6
##Use trial and error to place them 
text(2, 6, "A label")
text(5, 10, "Another label")

#3
tt = structure(c(7L, 13L, 24L, 30L, 30L, 38L, 35L, 45L, 37L, 
    43L, 38L, 59L, 33L, 45L, 37L, 58L), .Dim = c(2L, 8L), 
   .Dimnames = structure(list(param = c("A","B"), 
   xvar = c("5", "6", "7", "8", "9", "10", "11", "12")), 
 .Names = c("param", "xvar")), class = "table")
tt
bb <- barplot(tt, col=c("grey60","grey80"))
text(bb,tt[1,]-4,labels=tt[1,],cex=.8)
text(bb,colSums(tt)-4,labels=tt[2,],cex=0.8)
str(tt)

library(ggplot2)
ggplot(as.data.frame(tt),aes(x=factor(xvar),y=Freq,fill=param)) +
  geom_bar(stat="identity",position="stack")+
  geom_text(aes(label=Freq),position="stack",vjust=1)+
  scale_fill_manual(values=c("grey60","grey80"))+
  theme_bw()


#4
students = c(10,5,15,
             25,20,30,
             12,14,17)
stusummary = matrix(students, byrow=T,ncol=3, 
    dimnames = list(c('DS','ANM', 'LAW'), 
     c('sem1','sem3','sem5') ) )
stusummary
barplot(stusummary)    
barplot(stusummary, beside=T , col=rainbow(nrow(stusummary)) 
        ,legend=rownames(stusummary)) 

#
barplot(stusummary, beside=T , 
    col=rainbow(nrow(stusummary)) )

legend('top', legend=rownames(stusummary), horiz=T,
       fill=rainbow(nrow(stusummary)))

#Invert       
barplot(t(stusummary))
barplot(t(stusummary), beside=T)
barplot(stusummary, beside=T , 
        col=rainbow(nrow(t(stusummary))) )
legend('top', legend=colnames(t(stusummary)), horiz=T,
       fill=rainbow(ncol(t(stusummary))))


#4
max.temp <- c(22, 27, 26, 24, 23, 26, 28)
barplot(max.temp)
# barchart with added parameters

barplot(max.temp,
        main = "Maximum Temperatures in a Week",
        xlab = "Degree Celsius",
        ylab = "Day",
        names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
        col = "darkred",
        horiz = TRUE)

barplot(max.temp,
        main = "Maximum Temperatures in a Week",
        ylab = "Degree Celsius",
        xlab = "Day",
        names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
        col = "yellow",
        horiz = F)
?barplot
max.temp2 <- c(20, 25, 20, 27, 33, 22, 28)
temp = matrix(c(max.temp, max.temp2), byrow=F, ncol=2)
colnames(temp) = c('Jan', 'Feb')
temp
barplot(temp, beside=T, xlab='Days', ylab='Degree C', cex.names = 0.6,
  cex.axis = 0.5, ylim=range(0, max(temp)+5),names.arg = rep(c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),2)
        )
title(main='Avg Day Temp of Month',sub='Jan & Feb')
text(x=c(1:7,9:15)+.5, temp+1, temp, col='red', cex=0.5)
axis(1, at= c(4, 12), pos=-3, labels=c('Jan','Feb') , lwd=0)
?axis


================================================
FILE: 32-basicGraphs/26b-boxplot.R
================================================
# Box Plots

# Boxplot of MPG by Car Cylinders 
boxplot(mpg~cyl,data=mtcars, main="Car Milage Data", 
        xlab="Number of Cylinders", ylab="Miles Per Gallon")

# Notched Boxplot of Tooth Growth Against 2 Crossed Factors
# boxes colored for ease of interpretation 
boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, 
        col=(c("gold","darkgreen")),
        main="Tooth Growth", xlab="Suppliment and Dose")
#In the notched boxplot, if two boxes' notches do not overlap this is ‘strong evidence’ their medians differ 

# Notched Boxplot of Tooth Growth Against 2 Crossed Factors
# boxes colored for ease of interpretation 
boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, 
        col=(c("gold","darkgreen")),
        main="Tooth Growth", xlab="Suppliment and Dose")








# BoxPlot 3 -------------
data    <- c(0.4, 0.7, 0.75, 0.82, 0.9)
data
endaxis <- c(0, 1)  # endpoints of axis
datamm  <- c(min(data), max(data))
boxplot(data, horizontal = TRUE, range = 0, ylim = endaxis,
        axes = FALSE, col = "grey", add = FALSE)
arrows(endaxis, 1,  datamm, 1,  code = 1, angle = 90, length = 0.1)
valuelabels <- c(endaxis[1], round(fivenum(data)[2], digits = 2) ,
                 round(fivenum(data)[4], digits = 2), endaxis[2]  ) 

# Boxplot 4 ----------

boxplot(data)
abline(h = min(data), col = "Blue")
mtext(4,at = min(data), text='Min' , las=2)

abline(h = max(data), col = "Yellow")
text(x=1.2, y=max(data), labels= 'Max', col='Yellow')

abline(h = median(data), col = "Green")
text(x=.8, y=median(data), labels= 'Median', col='Green')

abline(h = mean(data), col = "Violet")
text(x=.8, y=mean(data), labels= 'Mean', col='Violet')

abline(h = quantile(data, c(0.25, 0.75)), col = "Red")
text(x=1.5, y=quantile(data, c(0.25, 0.75)), 
     labels= c('1QR','3QR'), col='Red')


data    <- c(0.4, 0.7, 0.75, 0.82, 0.9)
boxplot(data)
axis(4, at=summary(data), labels = names(summary(data)), 
las=2, line = -3, cex.lab=.5,col="blue")
text(x=1.25, y=summary(data), labels = summary(data), col='red', cex=.5)

text(x=1, y=c(min(data)-.01, max(data)+.01),  labels = c('Outliers - Low', 'Outliers - High'), col='red', cex=.5)

boxplot(mtcars$mpg)
axis(4, at=summary(mtcars$mpg), labels = names(summary(mtcars$mpg)), las=2, line = -3, cex.lab=.5,col="blue")
text(x=1.25, y=summary(mtcars$mpg), labels = summary(mtcars$mpg), col='red', cex=.5)
text(x=1, y=c(min(mtcars$mpg)-.01, max(mtcars$mpg)+.01),  labels = c('Outliers - Low', 'Outliers - High'), col='red', cex=.5)
     
     

================================================
FILE: 32-basicGraphs/26c-boxplot2.R
================================================
# Box Plot2
#his function takes in any number of numeric vectors, 
#drawing a boxplot for each vector
#
str(airquality)
boxplot(airquality$Ozone)
#median is more dispersed. We can also notice two outliers at the higher extreme.
#Additional Para - main, xlab, ylab, col, horiz=T, notch=T
boxplot(airquality$Ozone,
        main = "Mean ozone in parts per billion ",
        xlab = "Parts Per Billion",
        ylab = "Ozone",
        col = "orange",
        border = "brown",
        horizontal = TRUE,
        notch = TRUE
)

#Return Value of boxplot
b <- boxplot(airquality$Ozone)
b
#n-the number of observation the boxplot is drawn with (notice that NA's are not taken into account)
#conf-upper/lower extremes of the notch, out-value of the outliers
#group-a vector of the same length as out whose elements indicate to which group the outlier belongs and
#names-a vector of names for the group

#Multiple Box Plots
#Ozone, Temp
ozone = airquality$Ozone
temp = airquality$Temp
#generate normal distribution with the same mean and standard deviation and plot them side by side for comparison.
ozone_norm <- rnorm(200,mean=mean(ozone, na.rm=TRUE), sd=sd(ozone, na.rm=TRUE))
temp_norm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd=sd(temp, na.rm=TRUE))
boxplot(ozone, ozone_norm, temp, temp_norm,
        main = "Multiple boxplots for comparision",
        at = c(1,2,4,5),
        names = c("ozone", "normal", "temp", "normal"),
        las = 2,
        col = c("orange","red"),
        border = "brown",
        horizontal = TRUE,
        notch = TRUE
)

#Boxplot with Formula
boxplot(Temp~Month,
        data=airquality,
        main="Different boxplots for each month",
        xlab="Month Number",
        ylab="Degree Fahrenheit",
        col="orange",
        border="brown"
)
#month number 7 (July) is relatively hotter than the rest.
#

================================================
FILE: 32-basicGraphs/26d-boxplot2.R
================================================
#understanding box plot
#https://www.shmoop.com/basic-statistics-probability/box-whisker-plots.html


#eg1----
marks = c(53,65,68,69,70,72,72,79,83,84,85,87,89,90,94)
quantile(marks)
sort(marks)
boxplot(marks, horizontal = T)


#eg2----
#mtcars

(mileage = mtcars$mpg)

boxplot(mileage)


boxplot(mileage, ylim=c(0,45))
quantile(mileage)
IQR(mileage)
range(mileage)

#quartiles
abline(h=quantile(mileage), col='green')
text(1-.3, quantile(mileage)+.2, labels=c('Q0','Q1','Q2','Q3','Q4'), col='green')

quantile(mileage)[c(2,4)]
(LL = quantile(mileage)[c(2)] - IQR(mileage))
(UL = quantile(mileage)[c(4)] + IQR(mileage))
abline(h=c(LL, UL), col='red', lwd=2)
text(1+.3, c(LL, UL)+.2, labels=c('Q1-IQR', 'Q3+IQR'), col='red')
text(1,0,label='beyond this value - consider them outliers', col='red')
#beyond this value - consider them outliers

#min/max
abline(h=range(mileage)+.1, col='blue', lwd=2)
text(1+.4, range(mileage)+.3, labels=c('min', 'max'), col='blue')

#mean/ median
abline(h=c(mean(mileage)+.1, median(mileage)+.1), col='brown', lwd=2)
text(c(1+.3,1), c(mean(mileage)-.2, median(mileage)+.2), labels=c(paste('MEAN',round(mean(mileage))), paste('MEDIAN',round(median(mileage),2))), col='brown')

# eg2------

mileage = c(rnorm(100, mean=20, sd=5), rnorm(100,mean=30,sd=5), rnorm(1000,mean=35,sd=5))
plot(density(mileage))
e1071::skewness(mileage)

#left skewed

boxplot(mileage, ylim=c(0,60), notch=T, col='yellow', main='Box Plot')
rug(jitter(mileage, amount = 0.01), side = 2, col = "light blue")
quantile(mileage)
IQR(mileage)
range(mileage)

#quartiles
abline(h=quantile(mileage), col='green')
text(1-.3, quantile(mileage)+.2, labels=c('Q0','Q1','Q2','Q3','Q4'), col='green')

quantile(mileage)[c(2,4)]
(LL = quantile(mileage)[c(2)] - IQR(mileage))
(UL = quantile(mileage)[c(4)] + IQR(mileage))
abline(h=c(LL, UL), col='red', lwd=2)
text(1+.3, c(LL, UL)+.2, labels=c('Q1-IQR', 'Q3+IQR'), col='red')
text(1,0,label='beyond this value - consider them outliers', col='red')
#beyond this value - consider them outliers

#min/max
abline(h=range(mileage)+.1, col='blue', lwd=2)
text(1+.4, range(mileage)+.3, labels=c('min', 'max'), col='blue')

#mean/ median
abline(h=c(mean(mileage)+.1, median(mileage)+.1), col='brown', lwd=2)
text(c(1+.3,1), c(mean(mileage)-.2, median(mileage)+.2), labels=c(paste('MEAN',round(mean(mileage))), paste('MEDIAN',round(median(mileage),2))), col='brown')

hist(mileage, freq=F)
rug(jitter(mileage))     
lines(density(mileage))


================================================
FILE: 32-basicGraphs/27b-pie.R
================================================
#Pie
par()
# Simple Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")

# Pie Chart with Percentages
slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct) # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie(slices,labels = lbls, col=rainbow(length(lbls)),
    main="Pie Chart of Countries")

# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,  main="Pie Chart of Countries ")

# Pie Chart from data frame with Appended Sample Sizes
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls,   main="Pie Chart of Species\n (with sample sizes)")



================================================
FILE: 32-basicGraphs/27c-pie2.R
================================================
# Pie2
expenditure=c(600,300,150,100,200)
names(expenditure)=c('Housing','Food','Cloths','Entertainment',
            'Other') 
expenditure
pie(expenditure)

pie(expenditure,
    labels=as.character(expenditure),
    main="Monthly Expenditure Breakdown",
    col=c("red","orange","yellow","blue","green"),
    border="brown",
    clockwise=TRUE
)


================================================
FILE: 32-basicGraphs/29b-corrgram1.R
================================================
#correloggram

# First Correlogram Example

library(corrgram)
cor(mtcars[1:4])
corrgram(mtcars[1:4], order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Car Milage Data in PC2/PC1 Order")
cor(mtcars[1:5])
corrgram(mtcars[1:5], order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt)



#https://www.statmethods.net/advgraphs/correlograms.html


# Second Correlogram Example
library(corrgram)
corrgram(mtcars[1:5], order=TRUE, lower.panel=panel.ellipse,   upper.panel=panel.pts, text.panel=panel.txt,
         diag.panel=panel.minmax)



================================================
FILE: 32-basicGraphs/32b-freqdistr.R
================================================
# Frequency Distribution

duration = faithful$eruptions 
range(duration)
breaks = seq(1.5, 5.5, by=0.5)# half-integer sequence 
breaks 
duration.cut = cut(duration, breaks, right=FALSE)
#left side not closed [1.5-2.0) ie 1.5 to 1.9999
#value of 2 will go into [2-2.5) interval
duration.cut
duration.freq = table(duration.cut)
duration.freq
cbind(duration.freq) 


# Relative Freq
duration.relfreq = duration.freq / nrow(faithful)
duration.relfreq

old = options(digits=1) 
duration.relfreq 
options(old) # restore
duration.relfreq 
cbind(duration.freq, duration.relfreq) 

# Cumulative freq
duration.cumfreq = cumsum(duration.freq)

#graphically-----
#using histogram
h = hist(faithful$eruptions)
h$breaks
h$counts
#compare with
cbind(duration.freq) 
h$mids


#right=T
duration.cut.R = cut(duration, breaks, right=T)
##value of 2 will go into (1.5-2] interval
duration.cut.R
duration.freq.R = table(duration.cut.R)
duration.freq.R
cbind(duration.freq.R) 
h$counts  #same



================================================
FILE: 32-basicGraphs/33b-dotplot.R
================================================
# Simple Dotplot
dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,
         main="Gas Milage for Car Models", 
         xlab="Miles Per Gallon")

# Dotplot: Grouped Sorted and Colored
# Sort by mpg, group and color by cylinder 
x <- mtcars[order(mtcars$mpg),] # sort by mpg
x$cyl <- factor(x$cyl) # it must be a factor
x$color[x$cyl==4] <- "red"
x$color[x$cyl==6] <- "blue"
x$color[x$cyl==8] <- "darkgreen"	
dotchart(x$mpg,labels=row.names(x),cex=.7,groups= x$cyl,
         main="Gas Milage for Car Models\ngrouped by cylinder",
         xlab="Miles Per Gallon", gcolor="black", color=x$color)


================================================
FILE: 32-basicGraphs/33b-matrixplots.R
================================================
# Matrix Plots
# Multiple Series on one Graph

table(iris$Species) # is data.frame with 'Species' factor
iS <- iris$Species == "setosa"
iV <- iris$Species == "versicolor"
iS;iV
op <- par(bg = "bisque")
matplot(c(1, 8), c(0, 4.5), type =  "n", xlab = "Length", 
        ylab = "Width",  main = "Petal and Sepal Dimensions in Iris Blossoms")
matpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = "sS", 
          col = c(2,4))
matpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = "vV", 
          col = c(2,4))
legend(1, 4, c("    Setosa Petals", "    Setosa Sepals",
               "Versicolor Petals", "Versicolor Sepals"),
       pch = "sSvV", col = rep(c(2,4), 2))

# Example 2

a <- rnorm(100)
b <- 2*a + 3
c <- 3*a + 2
a;b;c
matplot(a, cbind(b,c), pch=1:2, col=c(2,4))
legend("bottomright", inset=.05, legend=c("b", "c"), 
       pch=1:2, col=c(2,4), horiz=F)



# Example 3
x = 1:10
A = c(15, 36, 54, 60, 68, 71, 73, 75, 78, 78)
B = c(20, 49, 58, 69, 75, 80, 83, 86, 88, 89)
C = c(24, 58, 68, 75, 83, 90, 93, 93, 95, 96)
Performance = data.frame(A,B,C)
matplot(x,Performance, type="o", pch=c(1,2,3), col=c("red","green","blue"))
legend("bottomright", inset=.05, legend=c("A", "B",'C'),
      pch=1:3, col=c("red","green","blue"), horiz=F)


================================================
FILE: 32-basicGraphs/37b-scatter.R
================================================
#Scatter Plot

#The basic function is plot(x, y), where x and y are numeric vectors denoting the (x,y) points to plot.

# Simple Scatterplot
attach(mtcars)
plot(wt, mpg, main="Scatterplot Example", 
     xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
# Add fit lines
abline(lm(mpg~wt), col="red") # regression line (y~x) 
lines(lowess(wt,mpg), col="blue") # lowess line (x,y)

# Enhanced Scatterplot of MPG vs. Weight 
# by Number of Car Cylinders 
library(car) 
scatterplot(mpg ~ wt | cyl, data=mtcars, 
            xlab="Weight of Car", ylab="Miles Per Gallon", 
            main="Enhanced Scatter Plot", 
            labels=row.names(mtcars))


# Basic Scatterplot Matrix
pairs(~mpg+disp+drat+wt,data=mtcars, 
      main="Simple Scatterplot Matrix")

# Scatterplot Matrices from the lattice Package 
library(lattice)
splom(mtcars[c(1,3,5,6)], groups=cyl, data=mtcars,
      panel=panel.superpose, 
      key=list(title="Three Cylinder Options",
               columns=3,
               points=list(pch=super.sym$pch[1:3],
                           col=super.sym$col[1:3]),
               text=list(c("4 Cylinder","6 Cylinder","8 Cylinder"))))


# Scatterplot Matrices from the car Package
library(car)
scatterplot.matrix(~mpg+disp+drat+wt|cyl, data=mtcars,
                   main="Three Cylinder Options")


# Scatterplot Matrices from the glus Package 
library(gclus)
dta <- mtcars[c(1,3,5,6)] # get data 
dta.r <- abs(cor(dta)) # get correlations
dta.col <- dmat.color(dta.r) # get colors
# reorder variables so those with highest correlation
# are closest to the diagonal
dta.o <- order.single(dta.r) 
cpairs(dta, dta.o, panel.colors=dta.col, gap=.5,
       main="Variables Ordered and Colored by Correlation" )

# High Density Scatterplot with Binning
library(hexbin)
x <- rnorm(1000)
y <- rnorm(1000)
bin<-hexbin(x, y, xbins=50) 
plot(bin, main="Hexagonal Binning")

# High Density Scatterplot with Color Transparency 
pdf("c:/scatterplot.pdf") 
x <- rnorm(1000)
y <- rnorm(1000) 
plot(x,y, main="PDF Scatterplot Example", col=rgb(0,100,0,50,maxColorValue=255), pch=16)
dev.off()


# 3D Scatterplot
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, main="3D Scatterplot")


# 3D Scatterplot with Coloring and Vertical Drop Lines
library(scatterplot3d) 
attach(mtcars) 
scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
              type="h", main="3D Scatterplot")


# 3D Scatterplot with Coloring and Vertical Lines
# and Regression Plane 
library(scatterplot3d) 
attach(mtcars) 
s3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
                    type="h", main="3D Scatterplot")
fit <- lm(mpg ~ wt+disp) 
s3d$plane3d(fit)

# Spinning 3d Scatterplot
library(rgl)

plot3d(wt, disp, mpg, col="red", size=3)

# Another Spinning 3d Scatterplot
library(Rcmdr)
attach(mtcars)
scatter3d(wt, disp, mpg)



================================================
FILE: 32-basicGraphs/42b-intplots1.R
================================================
# Interactive Plots
load('./data/badata.Rdata')

head(df1)
plot(x=df1$adtv, y=df1$sales)
identify(x=df1$adtv, y=df1$sales, labels=row.names(df1))
#click on the points and finish
coords = locator(type='l')
coords



#gobi
library(rggobi)


================================================
FILE: 32-basicGraphs/43b-mosaic.R
================================================
#Mosaic Plots
#For extended mosaic plots, use mosaic(x, condvar=, data=) where x is a table or formula, condvar= is an optional conditioning variable, and data= specifies a data frame or a table. Include shade=TRUE to color the figure, and legend=TRUE to display a legend for the Pearson residuals.

# Mosaic Plot  ----------
library(vcd)
mosaic(HairEyeColor, shade=TRUE, legend=TRUE)


#Association Plots -------------
#To produce an extended association plot use assoc(x, row_vars, col_vars)
#where x is a contingency table, row_vars is a vector of integers 
#giving the indices of the variables to be used for the rows, and 
#col_vars is a vector of integers giving the indices of the variables
#to be used for the columns of the association plot.

# Association Plot Example
library(vcd)
assoc(HairEyeColor, shade=TRUE)


================================================
FILE: 32-basicGraphs/43c-corrplot.R
================================================
# some more advance graphs

#install corrplot
library(corrplot)
relationship=cor(mtcars)
relationship
corrplot(relationship, type = "upper", order = "hclust",  tl.col = "black", tl.srt = 45)

#install library
library("PerformanceAnalytics")
my_data <- mtcars[, c(1,3,4,5,6,7)]
chart.Correlation(my_data, histogram=TRUE, pch=19)


# Get some colors
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(x = res, col = col, symm = TRUE)



================================================
FILE: 32-basicGraphs/43c-ggally.R
================================================

data(trees)
trees

library(GGally)
ggpairs(data=trees, columns=1:3, title="trees data")
fit_1  <- lm(Volume ~ Girth, data = trees) 
summary(fit_1) 


================================================
FILE: 32-basicGraphs/44b-textplots.R
================================================
# Text Plots

library('txtplot')
txtplot(cars[,1], cars[,2], xlab = "speed", ylab = "distance")


================================================
FILE: 32-basicGraphs/45b-violinplot.R
================================================
# Violin Plots
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
x1
x2
x3
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), 
        col="gold")
title("Violin Plots of Miles Per Gallon")


================================================
FILE: 32-basicGraphs/ria2g1.R
================================================
# ----------------------------------------------------#
# Getting started with graphs                         #
# requires that the Hmisc and RColorBrewer  packages  #
# have been installed                                 #
# install.packages(c("Hmisc", "RColorBrewer"))        #
#-----------------------------------------------------#

#par(ask=TRUE)
opar <- par(no.readonly=TRUE) # make a copy of current settings

attach(mtcars) # be sure to execute this line

plot(wt, mpg)
abline(lm(mpg~wt))
title("Regression of MPG on Weight")

# Input data for drug example
dose  <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)

plot(dose, drugA, type="b")

opar <- par(no.readonly=TRUE) # make a copy of current settings
par(lty=2, pch=17)            # change line type and symbol
plot(dose, drugA, type="b")   # generate a plot
par(opar)                     # restore the original settings 

plot(dose, drugA, type="b", lty=3, lwd=3, pch=15, cex=2)


# choosing colors
library(RColorBrewer)
n <- 7
mycolors <- brewer.pal(n, "Set1")
barplot(rep(1,n), col=mycolors)

n <- 10
mycolors <- rainbow(n)
pie(rep(1, n), labels=mycolors, col=mycolors)
mygrays <- gray(0:n/n)
pie(rep(1, n), labels=mygrays, col=mygrays)


# Listing 3.1 - Using graphical parameters to control graph appearance	
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(pin=c(2, 3))
par(lwd=2, cex=1.5)
par(cex.axis=.75, font.axis=3)
plot(dose, drugA, type="b", pch=19, lty=2, col="red")
plot(dose, drugB, type="b", pch=23, lty=6, col="blue", bg="green")
par(opar)				


# Adding text, lines, and symbols
plot(dose, drugA, type="b",  
     col="red", lty=2, pch=2, lwd=2,
     main="Clinical Trials for Drug A", 
     sub="This is hypothetical data", 
     xlab="Dosage", ylab="Drug Response",
     xlim=c(0, 60), ylim=c(0, 70))


# Listing 3.2 - An Example of Custom Axes
x <- c(1:10)
y <- x
z <- 10/x
opar <- par(no.readonly=TRUE)
par(mar=c(5, 4, 4, 8) + 0.1)
plot(x, y, type="b",
     pch=21, col="red",
     yaxt="n", lty=3, ann=FALSE)
lines(x, z, type="b", pch=22, col="blue", lty=2)
axis(2, at=x, labels=x, col.axis="red", las=2)
axis(4, at=z, labels=round(z, digits=2),
     col.axis="blue", las=2, cex.axis=0.7, tck=-.01)
mtext("y=1/x", side=4, line=3, cex.lab=1, las=2, col="blue")
title("An Example of Creative Axes",
      xlab="X values",
      ylab="Y=X")
par(opar)


# Listing 3.3 - Comparing Drug A and Drug B response by dose
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(lwd=2, cex=1.5, font.lab=2)
plot(dose, drugA, type="b",
     pch=15, lty=1, col="red", ylim=c(0, 60),
     main="Drug A vs. Drug B",
     xlab="Drug Dosage", ylab="Drug Response")
lines(dose, drugB, type="b",
      pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
library(Hmisc)
minor.tick(nx=3, ny=3, tick.ratio=0.5)
legend("topleft", inset=.05, title="Drug Type", c("A","B"),
       lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
par(opar)


# Example of labeling points
attach(mtcars)
plot(wt, mpg,
     main="Mileage vs. Car Weight",
     xlab="Weight", ylab="Mileage",
     pch=18, col="blue")
text(wt, mpg,
     row.names(mtcars),
     cex=0.6, pos=4, col="red")
detach(mtcars)


# View font families 
opar <- par(no.readonly=TRUE)
par(cex=1.5)
plot(1:7,1:7,type="n")
text(3,3,"Example of default text")
text(4,4,family="mono","Example of mono-spaced text")
text(5,5,family="serif","Example of serif text")
par(opar)


# Combining graphs
attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(wt,mpg, main="Scatterplot of wt vs. mpg")
plot(wt,disp, main="Scatterplot of wt vs. disp")
hist(wt, main="Histogram of wt")
boxplot(wt, main="Boxplot of wt")
par(opar)
detach(mtcars)

attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(3,1))
hist(wt)
hist(mpg)
hist(disp)
par(opar)
detach(mtcars)

attach(mtcars)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
hist(wt)
hist(mpg)
hist(disp)
detach(mtcars)


attach(mtcars)
layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE),
       widths=c(3, 1), heights=c(1, 2))
hist(wt)
hist(mpg)
hist(disp)
detach(mtcars)


# Listing 3.4 - Fine placement of figures in a graph
opar <- par(no.readonly=TRUE)
par(fig=c(0, 0.8, 0, 0.8))
plot(mtcars$mpg, mtcars$wt,
     xlab="Miles Per Gallon",
     ylab="Car Weight")
par(fig=c(0, 0.8, 0.55, 1), new=TRUE)
boxplot(mtcars$mpg, horizontal=TRUE, axes=FALSE)
par(fig=c(0.65, 1, 0, 0.8), new=TRUE)
boxplot(mtcars$wt, axes=FALSE)
mtext("Enhanced Scatterplot", side=3, outer=TRUE, line=-3)
par(opar)

================================================
FILE: 32-basicGraphs/ria2g2.R
================================================
#---------------------------------------------------------------#
# Basic graphs - 6
# https://github.com/kabacoff/RiA2/blob/master/Ch06%20Basic%20Graphs.R #
# requires packages vcd, plotrix, sm, vioplot to be installed   #
# install.packages(c("vcd", "plotrix", "sm", "vioplot"))        #
#---------------------------------------------------------------#

#par(ask=TRUE)
opar <- par(no.readonly=TRUE) # save original parameter settings

library(vcd)
counts <- table(Arthritis$Improved)
counts


# Listing 6.1 - Simple bar plot
# vertical barplot
barplot(counts, 
        main="Simple Bar Plot",
        xlab="Improvement", ylab="Frequency")
# horizontal bar plot   
barplot(counts, 
        main="Horizontal Bar Plot", 
        xlab="Frequency", ylab="Improvement", 
        horiz=TRUE)


# obtain 2-way frequency table
library(vcd)
counts <- table(Arthritis$Improved, Arthritis$Treatment)
counts


# Listing 6.2 - Stacked and grouped bar plots 
# stacked barplot
barplot(counts, 
        main="Stacked Bar Plot",
        xlab="Treatment", ylab="Frequency", 
        col=c("red", "yellow","green"),            
        legend=rownames(counts)) 

# grouped barplot                       
barplot(counts, 
        main="Grouped Bar Plot", 
        xlab="Treatment", ylab="Frequency",
        col=c("red", "yellow", "green"),
        legend=rownames(counts), beside=TRUE)


# Listing 6.3 - Bar plot for sorted mean values
states <- data.frame(state.region, state.x77)
means <- aggregate(states$Illiteracy, by=list(state.region), FUN=mean)
means

means <- means[order(means$x),]  
means

barplot(means$x, names.arg=means$Group.1) 
title("Mean Illiteracy Rate")  


# Listing 6.4 - Fitting labels in bar plots
par(las=2)                # set label text perpendicular to the axis
par(mar=c(5,8,4,2))       # increase the y-axis margin
counts <- table(Arthritis$Improved) # get the data for the bars

# produce the graph
barplot(counts, 
        main="Treatment Outcome", horiz=TRUE, cex.names=0.8,
        names.arg=c("No Improvement", "Some Improvement", "Marked Improvement")
)
par(opar)


# Spinograms
library(vcd)
attach(Arthritis)
counts <- table(Treatment,Improved)
spine(counts, main="Spinogram Example")
detach(Arthritis)


# Listing 6.5 - Pie charts
par(mfrow=c(2,2))                             
slices <- c(10, 12,4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")

pie(slices, labels = lbls, 
    main="Simple Pie Chart")

pct <- round(slices/sum(slices)*100)                      
lbls <- paste(lbls, pct) 
lbls <- paste(lbls,"%",sep="")
pie(slices,labels = lbls, col=rainbow(length(lbls)),
    main="Pie Chart with Percentages")

library(plotrix)                                               
pie3D(slices, labels=lbls,explode=0.1,
      main="3D Pie Chart ")

mytable <- table(state.region)                                   
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls, 
    main="Pie Chart from a dataframe\n (with sample sizes)")

par(opar)


# Fan plots
library(plotrix)
slices <- c(10, 12,4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")   
fan.plot(slices, labels = lbls, main="Fan Plot")


# Listing 6.6 - Histograms
# simple histogram                                                        1
hist(mtcars$mpg)

# colored histogram with specified number of bins        
hist(mtcars$mpg, 
     breaks=12, 
     col="red", 
     xlab="Miles Per Gallon", 
     main="Colored histogram with 12 bins")

# colored histogram with rug plot, frame, and specified number of bins 
hist(mtcars$mpg, 
     freq=FALSE, 
     breaks=12, 
     col="red", 
     xlab="Miles Per Gallon", 
     main="Histogram, rug plot, density curve")  
rug(jitter(mtcars$mpg)) 
lines(density(mtcars$mpg), col="blue", lwd=2)

# histogram with superimposed normal curve (Thanks to Peter Dalgaard)  
x <- mtcars$mpg 
h<-hist(x, 
        breaks=12, 
        col="red", 
        xlab="Miles Per Gallon", 
        main="Histogram with normal curve and box") 
xfit<-seq(min(x),max(x),length=40) 
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) 
yfit <- yfit*diff(h$mids[1:2])*length(x) 
lines(xfit, yfit, col="blue", lwd=2)
box()


# Listing 6.7 - Kernel density plot
d <- density(mtcars$mpg) # returns the density data  
plot(d) # plots the results 

d <- density(mtcars$mpg)                                  
plot(d, main="Kernel Density of Miles Per Gallon")       
polygon(d, col="red", border="blue")                     
rug(mtcars$mpg, col="brown") 


# Listing 6.8 - Comparing kernel density plots
par(lwd=2)                                                       
library(sm)
attach(mtcars)

# create value labels 
cyl.f <- factor(cyl, levels= c(4, 6, 8),                               
                labels = c("4 cylinder", "6 cylinder", "8 cylinder")) 

# plot densities 
sm.density.compare(mpg, cyl, xlab="Miles Per Gallon")                
title(main="MPG Distribution by Car Cylinders")

# add legend via mouse click
colfill<-c(2:(2+length(levels(cyl.f)))) 
cat("Use mouse to place legend...","\n\n")
legend(locator(1), levels(cyl.f), fill=colfill) 
detach(mtcars)
par(lwd=1)


# parallel box plots
boxplot(mpg~cyl,data=mtcars,
        main="Car Milage Data", 
        xlab="Number of Cylinders", 
        ylab="Miles Per Gallon")


# notched box plots
boxplot(mpg~cyl,data=mtcars, 
        notch=TRUE, 
        varwidth=TRUE,
        col="red",
        main="Car Mileage Data", 
        xlab="Number of Cylinders", 
        ylab="Miles Per Gallon")


# Listing 6.9 - Box plots for two crossed factors
# create a factor for number of cylinders
mtcars$cyl.f <- factor(mtcars$cyl,
                       levels=c(4,6,8),
                       labels=c("4","6","8"))

# create a factor for transmission type
mtcars$am.f <- factor(mtcars$am, 
                      levels=c(0,1), 
                      labels=c("auto","standard"))

# generate boxplot
boxplot(mpg ~ am.f *cyl.f, 
        data=mtcars, 
        varwidth=TRUE,
        col=c("gold", "darkgreen"),
        main="MPG Distribution by Auto Type", 
        xlab="Auto Type")


# Listing 6.10 - Violin plots

library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4] 
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, 
        names=c("4 cyl", "6 cyl", "8 cyl"), 
        col="gold")
title("Violin Plots of Miles Per Gallon")


# dot chart
dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,
         main="Gas Mileage for Car Models", 
         xlab="Miles Per Gallon")


# Listing 6.11 - Dot plot grouped, sorted, and colored
x <- mtcars[order(mtcars$mpg),]                      
x$cyl <- factor(x$cyl)                                 
x$color[x$cyl==4] <- "red"                              
x$color[x$cyl==6] <- "blue"
x$color[x$cyl==8] <- "darkgreen" 
dotchart(x$mpg,
         labels = row.names(x),                               
         cex=.7, 
         pch=19,                                              
         groups = x$cyl,                                       
         gcolor = "black",
         color = x$color,
         main = "Gas Mileage for Car Models\ngrouped by cylinder",
         xlab = "Miles Per Gallon")

================================================
FILE: 32-basicGraphs/ria2g3.R
================================================
#---------------------------------------------------------------------#
# R in Action (2nd ed): Chapter 11                            #
# Intermediate graphs                                #
# requires packages car, scatterplot3d, gclus, hexbin, IDPmisc, Hmisc, # 
# corrgram, vcd, rlg to be installed                         #
# install.packages(c("car", "scatterplot3d", "gclus", "hexbin", "IDPmisc", "Hmisc",  #
#  "corrgram", "vcd", "rld"))                        #
#------------------------------------------------------#

#par(ask=TRUE)
opar <- par(no.readonly=TRUE) # record current settings

# Listing 11.1 - A scatter plot with best fit lines
attach(mtcars)                                                     
plot(wt, mpg, 
     main="Basic Scatterplot of MPG vs. Weight",       
     xlab="Car Weight (lbs/1000)", 
     ylab="Miles Per Gallon ", pch=19)
abline(lm(mpg ~ wt), col="red", lwd=2, lty=1)            
lines(lowess(wt, mpg), col="blue", lwd=2, lty=2)
detach(mtcars)

# Scatter plot with fit lines by group
library(car) 
scatterplot(mpg ~ wt | cyl, data=mtcars, lwd=2,
            main="Scatter Plot of MPG vs. Weight by # Cylinders", 
            xlab="Weight of Car (lbs/1000)", 
            ylab="Miles Per Gallon", id.method="identify",
            legend.plot=TRUE, labels=row.names(mtcars), 
            boxplots="xy")


# Scatter-plot matrices
pairs(~ mpg + disp + drat + wt, data=mtcars, 
      main="Basic Scatterplot Matrix")

library(car)
library(car)
scatterplotMatrix(~ mpg + disp + drat + wt, data=mtcars,
                  spread=FALSE, smoother.args=list(lty=2),
                  main="Scatter Plot Matrix via car Package")


# high density scatterplots
set.seed(1234)
n <- 10000
c1 <- matrix(rnorm(n, mean=0, sd=.5), ncol=2)
c2 <- matrix(rnorm(n, mean=3, sd=2), ncol=2)
mydata <- rbind(c1, c2)
mydata <- as.data.frame(mydata)
names(mydata) <- c("x", "y")

with(mydata,
     plot(x, y, pch=19, main="Scatter Plot with 10000 Observations"))

with(mydata,
     smoothScatter(x, y, main="Scatter Plot colored by Smoothed Densities"))

library(hexbin)
with(mydata, {
  bin <- hexbin(x, y, xbins=50)
  plot(bin, main="Hexagonal Binning with 10,000 Observations")
})


# 3-D Scatterplots
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt, disp, mpg,
              main="Basic 3D Scatter Plot")

scatterplot3d(wt, disp, mpg,
              pch=16,
              highlight.3d=TRUE,
              type="h",
              main="3D Scatter Plot with Vertical Lines")

s3d <-scatterplot3d(wt, disp, mpg,
                    pch=16,
                    highlight.3d=TRUE,
                    type="h",
                    main="3D Scatter Plot with Vertical Lines and Regression Plane")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)
detach(mtcars)

# spinning 3D plot
library(rgl)
attach(mtcars)
plot3d(wt, disp, mpg, col="red", size=5)

# alternative
library(car)
with(mtcars,
     scatter3d(wt, disp, mpg))


# bubble plots
attach(mtcars)
r <- sqrt(disp/pi)
symbols(wt, mpg, circle=r, inches=0.30,
        fg="white", bg="lightblue",
        main="Bubble Plot with point size proportional to displacement",
        ylab="Miles Per Gallon",
        xlab="Weight of Car (lbs/1000)")
text(wt, mpg, rownames(mtcars), cex=0.6)
detach(mtcars)


# Listing 11.2 - Creating side by side scatter and line plots
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
t1 <- subset(Orange, Tree==1)
plot(t1$age, t1$circumference,
     xlab="Age (days)",
     ylab="Circumference (mm)",
     main="Orange Tree 1 Growth")
plot(t1$age, t1$circumference,
     xlab="Age (days)",
     ylab="Circumference (mm)",
     main="Orange Tree 1 Growth",
     type="b")
par(opar)


# Listing 11.3 - Line chart displaying the growth of 5 Orange trees over time
Orange$Tree <- as.numeric(Orange$Tree)
ntrees <- max(Orange$Tree)
xrange <- range(Orange$age)
yrange <- range(Orange$circumference)
plot(xrange, yrange,
     type="n",
     xlab="Age (days)",
     ylab="Circumference (mm)"
)
colors <- rainbow(ntrees)
linetype <- c(1:ntrees)
plotchar <- seq(18, 18+ntrees, 1)
for (i in 1:ntrees) {
  tree <- subset(Orange, Tree==i)
  lines(tree$age, tree$circumference,
        type="b",
        lwd=2,
        lty=linetype[i],
        col=colors[i],
        pch=plotchar[i]
  )
}
title("Tree Growth", "example of line plot")
legend(xrange[1], yrange[2],
       1:ntrees,
       cex=0.8,
       col=colors,
       pch=plotchar,
       lty=linetype,
       title="Tree"
)                                          


# Correlograms
options(digits=2)
cor(mtcars)

library(corrgram)
corrgram(mtcars, order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Corrgram of mtcars intercorrelations")

corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,
         upper.panel=panel.pts, text.panel=panel.txt,
         diag.panel=panel.minmax,
         main="Corrgram of mtcars data using scatter plots
         and ellipses")

cols <- colorRampPalette(c("darkgoldenrod4", "burlywood1",
                           "darkkhaki", "darkgreen"))
corrgram(mtcars, order=TRUE, col.regions=cols,
         lower.panel=panel.shade,
         upper.panel=panel.conf, text.panel=panel.txt,
         main="A Corrgram (or Horse) of a Different Color")


# Mosaic Plots
ftable(Titanic)
library(vcd)
mosaic(Titanic, shade=TRUE, legend=TRUE)

library(vcd)
mosaic(~Class+Sex+Age+Survived, data=Titanic, shade=TRUE, legend=TRUE)


# type= options in the plot() and lines() functions
x <- c(1:5)
y <- c(1:5)
par(mfrow=c(2,4))
types <- c("p", "l", "o", "b", "c", "s", "S", "h")
for (i in types){
  plottitle <- paste("type=", i)
  plot(x,y,type=i, col="red", lwd=2, cex=1, main=plottitle)
}


================================================
FILE: 32-basicGraphs/ria3g3.R
================================================
#----------------------------------------------------------#
# R in Action (2nd ed): Chapter 19                         #
# Advanced graphics with ggplot2                           #
# requires packages ggplot2, RColorBrewer, gridExtra,      #
#   and car (for datasets)                                 #
# install.packages(c("ggplot2", "gridExtra",               # 
#      "RColorBrewer", "car"))                             #
#----------------------------------------------------------#

#par(ask=TRUE)

# Basic scatterplot
library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) +
  geom_point() +
  labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")


# Scatter plot with additional options
library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) +
  geom_point(pch=17, color="blue", size=2) +
  geom_smooth(method="lm", color="red", linetype=2) +
  labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")


# Scatter plot with faceting and grouping
data(mtcars)
mtcars$am <- factor(mtcars$am, levels=c(0,1),
                    labels=c("Automatic", "Manual"))
mtcars$vs <- factor(mtcars$vs, levels=c(0,1),
                    labels=c("V-Engine", "Straight Engine"))
mtcars$cyl <- factor(mtcars$cyl)


library(ggplot2)
ggplot(data=mtcars, aes(x=hp, y=mpg,
                        shape=cyl, color=cyl)) +
  geom_point(size=3) +
  facet_grid(am~vs) +
  labs(title="Automobile Data by Engine Type",
       x="Horsepower", y="Miles Per Gallon")

# Using geoms
data(singer, package="lattice")
ggplot(singer, aes(x=height)) + geom_histogram()

ggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()

data(Salaries, package="car")
library(ggplot2)
ggplot(Salaries, aes(x=rank, y=salary)) +
  geom_boxplot(fill="cornflowerblue",
               color="black", notch=TRUE)+
  geom_point(position="jitter", color="blue", alpha=.5)+
  geom_rug(side="l", color="black")


# Grouping
library(ggplot2)
data(singer, package="lattice")
ggplot(singer, aes(x=voice.part, y=height)) +
  geom_violin(fill="lightblue") +
  geom_boxplot(fill="lightgreen", width=.2)

data(Salaries, package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=salary, fill=rank)) +
  geom_density(alpha=.3)

ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,
                     shape=sex)) + geom_point()

ggplot(Salaries, aes(x=rank, fill=sex)) +
  geom_bar(position="stack") + labs(title='position="stack"')

ggplot(Salaries, aes(x=rank, fill=sex)) +
  geom_bar(position="dodge") + labs(title='position="dodge"')

ggplot(Salaries, aes(x=rank, fill=sex)) +
  geom_bar(position="fill") + labs(title='position="fill"')


# Placing options
ggplot(Salaries, aes(x=rank, fill=sex))+ geom_bar()

ggplot(Salaries, aes(x=rank)) + geom_bar(fill="red")

ggplot(Salaries, aes(x=rank, fill="red")) + geom_bar()


# Faceting
data(singer, package="lattice")
library(ggplot2)
ggplot(data=singer, aes(x=height)) +
  geom_histogram() +
  facet_wrap(~voice.part, nrow=4)

library(ggplot2)
ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,
                     shape=rank)) + geom_point() + facet_grid(.~sex)

data(singer, package="lattice")
library(ggplot2)
ggplot(data=singer, aes(x=height, fill=voice.part)) +
  geom_density() +
  facet_grid(voice.part~.)


# Adding smoothed lines
data(Salaries, package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +
  geom_smooth() + geom_point()

ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary,
                          linetype=sex, shape=sex, color=sex)) +
  geom_smooth(method=lm, formula=y~poly(x,2),
              se=FALSE, size=1) +
  geom_point(size=2)


# Modifying axes
data(Salaries,package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
                   labels=c("Assistant\nProfessor",
                            "Associate\nProfessor",
                            "Full\nProfessor")) +
  scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
                     labels=c("$50K", "$100K", "$150K", "$200K")) +
  labs(title="Faculty Salary by Rank and Sex", x="", y="")


# Legends
data(Salaries,package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
                   labels=c("Assistant\nProfessor",
                            "Associate\nProfessor",
                            "Full\nProfessor")) +
  scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
                     labels=c("$50K", "$100K", "$150K", "$200K")) +
  labs(title="Faculty Salary by Rank and Gender",
       x="", y="", fill="Gender") +
  theme(legend.position=c(.1,.8))


# Scales
ggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +
  geom_point(shape=21, color="black", fill="cornsilk") +
  labs(x="Weight", y="Miles Per Gallon",
       title="Bubble Chart", size="Engine\nDisplacement")

data(Salaries, package="car")
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
  scale_color_manual(values=c("orange", "olivedrab", "navy")) +
  geom_point(size=2)

ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
  scale_color_brewer(palette="Set1") + geom_point(size=2)

library(RColorBrewer)
display.brewer.all()


# Themes
data(Salaries, package="car")
library(ggplot2)
mytheme <- theme(plot.title=element_text(face="bold.italic",
                                         size="14", color="brown"),
                 axis.title=element_text(face="bold.italic",
                                         size=10, color="brown"),
                 axis.text=element_text(face="bold", size=9,
                                        color="darkblue"),
                 panel.background=element_rect(fill="white",
                                               color="darkblue"),
                 panel.grid.major.y=element_line(color="grey",
                                                 linetype=1),
                 panel.grid.minor.y=element_line(color="grey",
                                                 linetype=2),
                 panel.grid.minor.x=element_blank(),
                 legend.position="top")

ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  labs(title="Salary by Rank and Sex", 
       x="Rank", y="Salary") +
  mytheme


# Multiple graphs per page
data(Salaries, package="car")
library(ggplot2)
p1 <- ggplot(data=Salaries, aes(x=rank)) + geom_bar()
p2 <- ggplot(data=Salaries, aes(x=sex)) + geom_bar()
p3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) + geom_point()

library(gridExtra)
grid.arrange(p1, p2, p3, ncol=3)


# Saving graphs
ggplot(data=mtcars, aes(x=mpg)) + geom_histogram()
ggsave(file="mygraph.pdf")

================================================
FILE: 33-AdvGraphs/cowplot1.R
================================================
# Cow Plot
library(cowplot)

#adding xlim and ylim to align axis.
p1 = qplot(x = 1, y = mpg, data = mtcars, xlab = "", geom = 'boxplot') + 
  coord_flip() +
  ylim(min(mtcars$mpg),max(mtcars$mpg))

p2 = qplot(x = mpg, data = mtcars, geom = 'histogram')+
  xlim(min(mtcars$mpg),max(mtcars$mpg))

#result
plot_grid(p1, p2, labels = c("A", "B"), align = "v",ncol = 1)


================================================
FILE: 33-AdvGraphs/donut.R
================================================
# Doughnut

# load GGplot2
library(ggplot2)

# Create test data.
dat = data.frame(count=c(10, 60, 30), category=c("A", "B", "C"))

# Add addition columns, needed for drawing with geom_rect.
dat$fraction = dat$count / sum(dat$count)
dat = dat[order(dat$fraction), ]
dat$ymax = cumsum(dat$fraction)
dat$ymin = c(0, head(dat$ymax, n=-1))

# Make the plot
p1 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +
  geom_rect() +
  coord_polar(theta="y") +
  xlim(c(0, 4)) +
  theme(panel.grid=element_blank()) +
  theme(axis.text=element_blank()) +
  theme(axis.ticks=element_blank()) +
  annotate("text", x = 0, y = 0, label = "My Ring plot !") +
  labs(title="")
p1


================================================
FILE: 33-AdvGraphs/donut2.R
================================================
# Donut 2
# 
library(ggplot2)

# Create test data.
dat = data.frame(count=c(10, 60, 30), category=c("A", "B", "C"))

# Add addition columns, needed for drawing with geom_rect.
dat$fraction = dat$count / sum(dat$count)
dat = dat[order(dat$fraction), ]
dat$ymax = cumsum(dat$fraction)
dat$ymin = c(0, head(dat$ymax, n=-1))

p1 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +
  geom_rect() +
  coord_polar(theta="y") +
  xlim(c(0, 4)) +
  labs(title="Basic ring plot")

p2 = ggplot(dat, aes(fill=category, ymax=ymax, ymin=ymin, xmax=4, xmin=3)) +
  geom_rect(colour="grey30") +
  coord_polar(theta="y") +
  xlim(c(0, 4)) +
  theme_bw() +
  theme(panel.grid=element_blank()) +
  theme(axis.text=element_blank()) +
  theme(axis.ticks=element_blank()) +
  labs(title="Customized ring plot")


library(gridExtra)
#png("ring_plots_1.png", height=4, width=8, units="in", res=120)
grid.arrange(p1, p2, nrow=1)
#dev.off()


================================================
FILE: 33-AdvGraphs/esquisse.R
================================================
#adv web based graphs
#https://towardsdatascience.com/tableau-esque-drag-and-drop-gui-visualization-in-r-901ee9f2fe3f

library(esquisse)
#loading tidyverse to read input
library(tidyverse)
# loading itunesr for retrieving itunes review data that we will use in this analysis
library(itunesr)
#loading the magical esquisse library
library(esquisse)
# Flipkart Reviews
reviews <- getReviews(742044692,'in',1)
reviews
#converting Rating to numeric type
reviews$Rating <- as.numeric(reviews$Rating)
#let us say we want to see if there's any correlation between rating and review length
reviews$len <- nchar(reviews$Review)
#let the magic begin
esquisse::esquisser(reviews)

esquisse::esquisser(mtcars)


#copied code
ggplot(data = reviews) +
  aes(x = len, y = Rating) +
  geom_point(color = '#e31a1c') +
  labs(title = 'Ratings vs Reviews Length',
       x = 'Length of Review',
       y = 'Rating associated with Review',
       caption = 'Data Source: App Store',
       subtitle = 'Flipkart iOS App Reviews') +
  theme_linedraw()

ggplot(data = mtcars) +
  aes(x = mpg, y = hp) +
  geom_point(color = '#e31a1c') +
  labs(title = 'MPG vs HP',
       x = 'MPG',
       y = 'HP',
       caption = 'mtcars dataset',
       subtitle = 'R Programming') +
  theme_linedraw()


================================================
FILE: 33-AdvGraphs/lattice.R
================================================
# Lattice
# 
# http://www.statmethods.net/advgraphs/trellis.html
# 


================================================
FILE: 33-AdvGraphs/lattice1.R
================================================
# Lattice Package - improves base graphs
library(lattice)
head(df1)
names(df1)
lattice::densityplot(~ df1$sales)
lattice::densityplot(~ df1$sales | df1$coy)
lattice::densityplot(~ df1$sales | df1$coy, layout=c(1,3))
#lattice::densityplot(df1$loc ~ df1$sales | df1$coy, layout=c(1,3))
str(df1)
lattice::bwplot(df1$loc ~ df1$sales | df1$coy, layout=c(1,3))
lattice::xyplot(df1$adtv ~ df1$adweb | df1$loc )
lattice::cloud(df1$adweb ~ df1$salesman * df1$loc )
lattice::dotplot(df1$sales ~ df1$adtv | df1$coy, layout=c(1,3) )
lattice::splom(df1[c(sales, adtv, coy),] )

#formula Method
#


================================================
FILE: 33-AdvGraphs/plotsToWord.R
================================================
#plots to Word
#https://davidgohel.github.io/officer/articles/offcran/graphics.html

library(officer)
#officer is not producing the image, it only provides functions to add a produced image into a Word document.
library(devEMF)
library(magrittr)
library(ggplot2)


# ggplot example
gg <- ggplot(mtcars, aes(x = mpg , y = wt, colour = qsec)) + geom_point() + theme_minimal()

# produce an emf file containing the ggplot
filename <- tempfile(fileext = ".emf")
emf(file = filename, width = 6, height = 7)
print(gg)
dev.off()

read_docx() %>% 
  body_add_img(src = filename, width = 6, height = 7) %>% 
  print(target = "E:/graphs/demo_emf.docx")


#------




#-----
#PowerPoint documents and graphics
Raster images and vector graphic images are added in a PowerPoint document as a shape on the current slide. In PowerPoint, images and text can not be mixed in the same shape (or placeholder). officer provides a function ph_with to let users add images into a PowerPoint document:
  
#Again those functions accept various image formats: png, jpeg and emf.

read_pptx() %>% 
  add_slide(layout = "Title and Content", master = "Office Theme") %>% 
  ph_with(external_img(src = filename, width = 6, height = 7), 
          location = ph_location_type(type = "body"), use_loc_size = FALSE ) %>% 
  add_slide(layout = "Title Only", master = "Office Theme") %>% 
  ph_with(external_img(src = filename, width = 6, height = 7), 
          location = ph_location(left = 0, top = 0, width = 6, height = 7) ) %>% 
  print(target = "E:/graphs/demo_emf.pptx")


================================================
FILE: 33-AdvGraphs/survey.R
================================================

library(survey)
data(api)
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, 
                    fpc = ~fpc)
opar<-par(mfrow=c(1,3))
svyhist(~enroll, dstrat, main="Survey weighted",col="purple",ylim=c(0,1.3e-3))
hist(apistrat$enroll,  main="Sample unweighted",col="purple",prob=TRUE,ylim=c(0,1.3e-3))
hist(apipop$enroll,  main="Population",col="purple",prob=TRUE,ylim=c(0,1.3e-3))

par(mfrow=c(1,1))
svyboxplot(enroll~stype,dstrat)
svyboxplot(enroll~1,dstrat)
par(opar)


================================================
FILE: 33-AdvGraphs/symbols.R
================================================
require(stats); require(grDevices)
x <- 1:10
y <- sort(10*runif(10))
z <- runif(10)
z3 <- cbind(z, 2*runif(10), runif(10))
x;y;z
symbols(x, y, thermometers = cbind(.5, 1, z), inches = .5, fg = 1:10)
symbols(x, y, thermometers = z3, inches = FALSE)
text(x, y, apply(format(round(z3, digits = 2)), 1, paste, collapse = ","),
     adj = c(-.2,0), cex = .75, col = "purple", xpd = NA)
symbols(x, y , circles=z, inches=.2)

## Note that  example(trees)  shows more sensible plots!
N <- nrow(trees)
with(trees, {
  ## Girth is diameter in inches
  symbols(Height, Volume, circles = Girth/24, inches = FALSE,
          main = "Trees' Girth") # xlab and ylab automatically
  ## Colours too:
  op <- palette(rainbow(N, end = 0.9))
  symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N,
          fg = "gray30", main = "symbols(*, circles = Girth/16, bg = 1:N)")
  palette(op)
})

head(trees)
with(trees, symbols(Height, Volume, circles = Girth/24, inches = FALSE,
                    main = "Trees' Girth") )
op <- palette(rainbow(N, end = 0.9)) 
with(trees, symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N, fg = "gray30", main = "symbols(*, circles = Girth/16, bg = 1:N)"))


================================================
FILE: 33-AdvGraphs/vtree1.R
================================================
#Vtree
#https://cran.r-project.org/web/packages/vtree/vtree.pdf

library(vtree)
#other packages needed
pack = c('vtree','glue','tibble','purrr','XML','colorspace','stringi', 'lazyeval','Rcpp','V8','DiagrammeRsvg','rsvg')

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}
ipak(pack)
#packages loaded

FakeData

# A single-level hierarchy
vtree(FakeData,"Severity")
str(FakeData)
# A two-level hierarchy
vtree(FakeData,"Severity Sex")
# A two-level hierarchy with pruning of some values of Severity
vtree(FakeData,"Severity Sex",prune=list("Severity"=c("Moderate","NA")))

# Rename some nodes
vtree(FakeData,"Severity Sex",labelnode=list(Sex=(c("Male"="M","Female"="F"))))
# Rename a variable
vtree(FakeData,"Severity Sex",labelvar=c(Severity="How bad?"))
# Show legend. Put labels on the same line as counts and percentages
vtree(FakeData,"Severity Sex Viral",sameline=TRUE,showlegend=TRUE)
# Using the summary parameter to list ID numbers (truncated to 40 characters) in specific
vtree(FakeData,"Severity Sex",summary="id \nid = %list% %var=Severity% %trunc=40%")


grVizToPNG(g, width = NULL, height = NULL, folder = ".")


#mtcars
vtree(mtcars,"am")
vtree(mtcars,"am gear")
vtree(mtcars,"am gear cyl")

vtree(mtcars,"am", horiz = F)
#vtree(mtcars,"am", summary)

vtree(mtcars,"am gear",horiz=FALSE,plain=TRUE)
vtree(mtcars,"am gear",showlegend=TRUE,shownodelabels=FALSE)

vtree(mtcars,"am gear",sameline=TRUE)
vtree(mtcars,"am gear",sameline=TRUE,showvarnames=FALSE)
vtree(mtcars,"am gear", splitwidth=15, lsplitwidth=8)

vtree(FakeData,"Severity Sex",prune=list(Severity=c("Mild","Moderate")))

vtree(FakeData,"Severity Sex",prunebelow=list(Severity=c("Mild","Moderate")))

vtree(FakeData,"Severity Sex",horiz=T,labelvar=c(Severity="Severity on day 1"),labelnode=list(Sex=c(Male="M",Female="F")))

vtree(FakeData,"Severity",horiz=T,  text=list(Severity=c(Mild="*Includes first-time visits*")))


#Displaying summary statistics in nodes
vtree(FakeData,"Severity",summary="Score %mean%",horiz=T)
vtree(mtcars,"cyl",summary="mpg %mean%",horiz=T)
vtree(mtcars,"cyl am",summary="mpg %mean%",horiz=T)

vtree(FakeData,"Severity",horiz=FALSE,showvarnames=FALSE, summary=c( "Score \nScore: mean (SD)\n %mean% (%SD%)", "Pre \n\nPre: range\n %min%, %max%"))

vtree(mtcars,"cyl",horiz=F,showvarnames=FALSE, summary=c( "mpg \n mpg: mean (SD)\n %mean% (%SD%)", "wt \n\n wt: range\n %min%, %max%"))

vtree(FakeData,"Severity",summary="Viral \nViral %npct%",horiz=T,showvarnames=FALSE)
str(FakeData)
vtree(mtcars,"gear",summary="am \nam %npct%",horiz=FALSE,showvarnames=FALSE)
table(mtcars$am)
table(mtcars$am, mtcars$gear)
#T=1, F=0:TRUE / FALSE

#Venn Diagram
vtree(FakeData,"Ind1 Ind2 Ind3 Ind4",Venn=TRUE)

sequence <- with(FakeData,paste(Ind1,Ind2,Ind3,Ind4))
sequence
vtree(FakeData,"Ind1 Ind2 Ind3 Ind4",Venn=TRUE,seq=TRUE,
     palette=c(Ind1=1,Ind2=2,Ind3=3,Ind4=4))


================================================
FILE: 33-AdvGraphs/waffle.R
================================================
#waffle charts

library(ggplot2)
library(dplyr)
library(waffle)
#https://github.com/hrbrmstr/waffle
#https://towardsdatascience.com/three-key-charts-for-visualizing-proportion-data-4cf58c23d513

count <- mtcars %>%   group_by(cyl) %>%   summarise(n = n()) %>%  mutate(percent = round(n/sum(n)*100))
case_counts <- count$percent
names(case_counts) <- count$cyl
waffle(case_counts)


#-------
library(waffle)
library(magrittr)
library(hrbrthemes)
library(ggplot2)
library(dplyr)
library(waffle)
data.frame(
  parts = factor(rep(month.abb[1:3], 3), levels=month.abb[1:3]),
  vals = c(10, 20, 30, 6, 14, 40, 30, 20, 10),
  col = rep(c("blue", "black", "red"), 3),
  fct = c(rep("Thing 1", 3),
          rep("Thing 2", 3),
          rep("Thing 3", 3))
) -> xdf
xdf
xdf %>%
  count(parts, wt = vals) %>%
  ggplot(aes(fill = parts, values = n)) +
  geom_waffle(n_rows = 20, size = 0.33, colour = "white", flip = TRUE) +
  scale_fill_manual(
    name = NULL,
    values = c("#a40000", "#c68958", "#ae6056"),
    labels = c("Fruit", "Sammich", "Pizza")
  ) +
  coord_equal() +
  theme_ipsum_rc(grid="") +
  theme_enhance_waffle()


================================================
FILE: 33-AdvGraphs/xxggsubplot.R
================================================
#ggsubplot
#https://rdrr.io/github/garrettgman/ggsubplot/

#install.packages("remotes")
remotes::install_github("garrettgman/ggsubplot")
library(ggsubplot)
# Not run: ggplot(nasa) +
map_americas +
  geom_subplot(aes(long, lat, group = id,
                   subplot = geom_point(aes(surftemp, temperature), size = 1/4))) +
  coord_map()


================================================
FILE: 33-DT/0-DTsummary.R
================================================
#data Table 
#types of techniquest/ links/ commands

#(http://brooksandrew.github.io/simpleblog/articles/advanced-data-table/#method-1-in-line )


================================================
FILE: 33-DT/1-dt1.R
================================================
#data.table

library(data.table)
DTmtcars = data.table(mtcars)

str(DTmtcars)

#group by

DTmtcars[ , mean(mpg), by=gear]
DTmtcars[ , .(MeanMPG = mean(mpg)), by=list(gear,cyl)]

#count of cars in the category
DTmtcars[ , .(MeanMPG = mean(mpg), n=.N), by=list(gear,cyl)]

DTmtcars[ , .SD[, mean(mpg)], by=gear]
DTmtcars[ , .SD[, mean(mpg),by=gear], by=cyl]
DTmtcars[ , .SD[, .(mean(mpg), n=.N),by=gear], by=cyl]
DTmtcars[ , .SD[, .(mean(mpg), n=.N),by=gear], by=list(cyl,am)]


#sort
DTmtcars
DTmtcars[ sort(mpg),mean(mpg) , by=cyl]



DTmtcars[,`:=`(avg_mpg_cyl=mean(mpg), Ncyl=.N), by=cyl]
DTmtcars[,.(avg_mpg_cyl=mean(mpg), Ncyl=.N), by=cyl]

DTmtcars
DTmtcars[,.(Ncylgear=.N, avg_mpg_cyl_gear=mean(mpg)), by=.(cyl, gear)]

setkey(DTmtcars, cyl, gear)  
DTmtcars


#inrease no of rows
dt <- data.table(mtcars)
dt <- dt[sample(1:.N, 100000, replace=T), ] # increase # of rows in mtcars
dt$gear <- sample(1:300, nrow(dt), replace=T) # adding in more cateogries
dt

uid <- unique(dt$gear)
system.time(dt[, dt[!gear %in% (uid[.GRP]), mean(mpg), by=cyl] , by=gear][order(cyl, gear)])
dt[, dt[!gear %in% (uid[.GRP]), mean(mpg), by=cyl] , by=gear][order(cyl, gear)]
mtcars
#????
DTmtcars[,{
  vbar = sum(hp)
  n = .N
  .SD[,.(n, .N, sum_in_gear_cyl=sum(hp), sum_in_cyl=vbar), by=gear]
} , by=cyl]


================================================
FILE: 33-DT/2-DT.R
================================================
#Data.table

#combining tables

library(data.table)
(rollno = paste('S',10:20,sep=''))
(sname = paste('Student',1:11,sep=''))
students = data.table(rollno, sname)
students

(rollno2 = paste('S',10:15,sep=''))
(sub1 = round(rnorm(6, mean=60, sd=10)))
(marks = data.table(rollno2, sub1))

#join
students[ marks, on= .(rollno = rollno2)]

#roll ?
students[ marks, on= .(rollno = rollno2), roll=T]

#cbind
cbind(students, students)
rbind(students, students)


#reshape
(rollno3 = paste('S',11:20,sep=''))
(sname3 = paste('Student',1:10,sep=''))
(sub1 =  round(rnorm(10, mean=60, sd=10)))
(sub2 =  round(rnorm(10, mean=70, sd=5)))

students3 = data.table(rollno3, sname3, sub1, sub2)
students3
#melt
DTmelt <- melt(students3, id.vars= c('rollno3', 'sname3'), measure.vars = c('sub1','sub2'), variable.name='subject', value.name = 'marks')
DTmelt

#wide/ spread
dcast(DTmelt, formula = rollno3 + sname3 ~ subject, value.var = 'marks')


================================================
FILE: 33-DT/3-DT.R
================================================
#data.table - subset data

library(data.table)
DTmtcars = data.table(mtcars)

DTmtcars

DTmtcars[  , .SD, mpg > 25]

#sum groupwise for mpg > 25

#first row
DTmtcars[  , .SD[1], mpg > 25]
DTmtcars[  , .SD[.N], mpg > 25]
#last row

#condition based
DTmtcars[ mpg > 20 & am == 1,  ]

DTmtcars[ mpg > 20, .(sumHP=sum(hp)), by='gear']

#nested groups
DTmtcars[ mpg > 20, .(sumHP=sum(hp)), by=c('gear', 'cyl')]
DTmtcars[ mpg > 20, .(sumHP=sum(hp), meanMPG = mean(mpg)), by=c('gear', 'cyl')]

#group and sort
DTmtcars[ mpg > 20, .(sumHP=sum(hp)), by=gear]
DTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)]

DTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)][ order(sumHP),]
DTmtcars[ mpg > 20, .(sumHP=sum(hp)), keyby= .(gear)][ order(-sumHP),]



DTmtcars[order(-rank(gear), mpg)]

DTmtcars[order(-rank(gear), cyl), .(sumHP=sum(hp)), by=c('gear','cyl')]

DTmtcars[am == 0, tx :='Auto']
DTmtcars
DTmtcars[am == 1, tx1 := 'Manual']

DTmtcars[ , tx := ifelse(am==0, 'Auto', 'Manual')]
DTmtcars


DTmtcars[ , mpg]
DTmtcars[ , mpg, ] #like vector
DTmtcars[ , c(1), ]  #column 

DTmtcars[ , .(mpg,hp)  ]  #working

DTmtcars[am==1 , .(mpg,hp)  ]  #working

setorder(DTmtcars, cyl,-gear)
DTmtcars[ , .(cyl, gear, mpg)]

#group Min/ Max/ Optima
DTmtcars[ , .SD[which.max(mpg)], by=cyl]
DTmtcars[ , .SD[which(mpg > 25)], by=cyl]
DTmtcars[ , .SD[which.min(mpg)], by=cyl]
DTmtcars[ , .SD[which.min(mpg)], by=c('cyl','gear')]


================================================
FILE: 34-ggplots/circbarplot.R
================================================
# Circular Bar Plot

library(ggplot2)

# make data
data=data.frame(group=c("A ","B ","C ","D ") , value=c(33,62,56,67) )

# Usual bar plot :
ggplot(data, aes(x = group, y = value ,fill = group )) + 
  geom_bar(width = 0.85, stat="identity")

# Circular one
ggplot(data, aes(x = group, y = value ,fill = group)) + 
  geom_bar(width = 0.85, stat="identity") +    
  
  # To use a polar plot and not a basic barplot
  coord_polar(theta = "y") +    
  
  #Remove useless labels of axis
  xlab("") + ylab("") +
  
  #Increase ylim to avoid having a complete circle
  ylim(c(0,75)) + 
  
  #Add group labels close to the bars :
  geom_text(data = data, hjust = 1, size = 3, aes(x = group, y = 0, label = group)) +
  
  #Remove useless legend, y axis ticks and y axis text
  theme(legend.position = "none" , axis.text.y = element_blank() , axis.ticks = element_blank())


================================================
FILE: 34-ggplots/gg-apexcharts.R
================================================
#Charts

#https://dreamrs.github.io/apexcharter/index.html

#install.packages("apexcharter")
#Or install the development version from GitHub with:
  
# install.packages("devtools")
#devtools::install_github("dreamRs/apexcharter")

library(apexcharter)

data("mpg", package = "ggplot2")
n_manufac <- dplyr::count(mpg, manufacturer)

apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))


data("economics", package = "ggplot2")
apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed)) %>%   ax_stroke(width = 1)


#%%%
apexchart(ax_opts = list(  chart = list(    type = "line"  ),  stroke = list( curve = "smooth"  ),
  grid = list(borderColor = "#e7e7e7",    row = list(colors = c("#f3f3f3", "transparent"),      opacity = 0.5    )  ),
  dataLabels = list(    enabled = TRUE  ),
  markers = list(style = "inverted", size = 6),
  series = list(    list(      name = "High",      data = c(28, 29, 33, 36, 32, 32, 33)),
    list(name = "Low",data = c(12, 11, 14, 18, 17, 13, 13) )  ),
  title = list( text = "Average High & Low Temperature", align = "left"  ),  xaxis = list(categories = month.abb[1:7]  ),
  yaxis = list(
    title = list(text = "Temperature"),
    labels = list(      formatter = htmlwidgets::JS("function(value) {return value + '°C';}")
    )
  )
))


================================================
FILE: 34-ggplots/gg-bar1.R
================================================
#81 BARPLOT WITH VARIABLE WIDTH
library(ggplot2)

# make data
data=data.frame(group=c("A ","B ","C ","D ") , value=c(33,62,56,67) , number_of_obs=c(100,500,459,342))

# Calculate the future positions on the x axis of each bar (left border, central position, right border)
data$right=cumsum(data$number_of_obs) + 30*c(0:(nrow(data)-1))
data$left=data$right - data$number_of_obs 

# Plot
ggplot(data, aes(ymin = 0)) + 
  geom_rect(aes(xmin = left, xmax = right, ymax = value, colour = group, fill = group)) +
  xlab("number of obs") + ylab("value")



================================================
FILE: 34-ggplots/gg-bar2.R
================================================
#bar plot in ggplot2
#mtcars

library(ggplot2)
library(dplyr)
mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar()

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl))) 


mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)))

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position='dodge')

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE))

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE)) + coord_flip() +  theme(legend.position = "top")

mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity')
        
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=gear)) + geom_text(aes(label = n), vjust = -0.3)  

library(ggpubr)
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=factor(gear))) + geom_text(aes(label = n), vjust = -0.3) + theme_pubclean()                             


ggplot(data=mtcars, aes(x=factor(gear), fill=factor(cyl))) +  geom_bar(aes(y = (..count..)/sum(..count..)))

ggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear)))

ggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear))) + facet_wrap(~cyl)

#not correctly drawn.  use proportions
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(gear))) + geom_bar(stat='identity') + geom_text(aes(label = n), vjust = -0.3) + coord_polar("y", start = 0) +  ggpubr::fill_palette("jco")+ theme_void()                          #http://www.sthda.com/english/articles/32-r-graphics-essentials/133-plot-one-variable-frequency-graph-density-distribution-and-more/


mtcars %>% count(gear) %>% mutate(gearProp = n / nrow(mtcars)) %>% ggplot(., aes(x=gear, y=gearProp)) + geom_bar(stat='identity')
mtcars %>% select(gear) %>% table() %>% prop.table() %>% as.data.frame() -> y
class(y)
ggplot(mtcars, aes(x = gear)) +   geom_bar(aes(y = (..count..)/sum(..count..)))
ggplot(mtcars, aes(gear)) + geom_bar(aes(y = (..count..)/sum(..count..))) + scale_y_continuous(labels=scales::percent) +  ylab("relative frequencies")

ggplot(mtcars, aes(gear, group=am)) + geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +  scale_y_continuous(labels=scales::percent) +  ylab("relative frequencies") +  facet_grid(~am) + scale_fill_manual(values = c("red", "green", "yellow"))

ggplot(mtcars, aes(x=gear,  group=am)) +  geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") + geom_text(aes( label = scales::percent(round(..prop..,3)), y= ..prop.. ), stat= "count", vjust = -.5) + labs(y = "Percent", fill="Transmission") + facet_grid(~am) +   scale_y_continuous(labels = scales::percent)


ggplot(mtcars, aes(x = gear, fill = factor(cyl))) + geom_bar(stat = "bin")
#x should be continuous

my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)



# Outside bars
mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) +  geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=n), vjust=-0.3, size=3.5)+   theme_minimal()
# Inside bars
mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) +  geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=n), vjust=1.6, size=3.5)+   theme_minimal()

#x should be continuous
ggplot(mtcars, aes(x=cyl))+ geom_bar(stat="bin", fill="steelblue")+ theme_minimal()


mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, colour=factor(gear))) +  geom_bar(stat="identity", fill="white")+   geom_text(aes(label=n), vjust=1.6, size=3.5) ->p
p
# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic()

mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, fill=factor(gear))) +  geom_bar(stat="identity")+   geom_text(aes(label=n), vjust=1.6, size=3.5) -> q
q
# Use custom color palettes
q+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
q+scale_fill_brewer(palette="Dark2")
# Use grey scale
q + scale_fill_grey()
q + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

#legend position
# Change bar fill colors to blues
q + scale_fill_brewer(palette="Blues")
q + theme(legend.position="top")
q + theme(legend.position="bottom")
# Remove legend
q + theme(legend.position="none")



#multiple groups
# Stacked barplot with multiple groups
mtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) +  geom_bar(stat="identity")
# Use position=position_dodge()
mtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) +  geom_bar(stat="identity", position=position_dodge())

#Paired
mtcars %>% group_by(gear, vs) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) +  geom_bar(stat="identity", position=position_dodge()) + geom_text(aes(label=n), vjust=1.6, color="white",   position = position_dodge(0.9), size=3.5)+
  scale_fill_brewer(palette="Paired")+   theme_minimal()

library(dplyr)
mtcars %>% group_by(gear, vs) %>% count() %>% arrange(gear,desc(vs)) -> df
df
library(plyr)
# Calculate the cumulative sum of len for each dose
df1 <- ddply(df, c('gear'),  transform, label_ypos=cumsum(n))
head(df1)
library(ggplot2)
df1 %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) +  geom_bar(stat="identity") + geom_text(aes(y=label_ypos, label=n), vjust=1.6, color="white", size=3) + scale_fill_brewer(palette="Paired")+   theme_minimal()



================================================
FILE: 34-ggplots/gg-box2.R
================================================


df <- data.frame(State = LETTERS[1:3],
                Y = sample(1:10, 30, replace = TRUE),
                X = rep(1:10, 3))
df
library(ggplot2)

# You can plot geom_histogram or bar (pre-counted stats)
ggplot(df, aes(X, Y)) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_grid(State ~ .)
# Or you can plot similar figure with geom_rect
ggplot(df)  +
  geom_rect(aes(xmin = X - 0.4, xmax = X + 0.4, ymin = 0, ymax = Y)) +
  facet_grid(State ~ .)

ggplot(df)  +
  geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4)) +
  geom_boxplot(aes(X, Y)) +
  coord_flip() +
  facet_grid(State ~ .)

ggplot(df)  +
  geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4),
            fill = "blue", color = "black") +
  geom_boxplot(aes(X, Y), alpha = 0.7, fill = "salmon2") +
  coord_flip() +
  facet_grid(State ~ .) +
  theme_classic() +
  scale_y_continuous(breaks = 1:max(df$X))


ggplot(iris, aes(x = Sepal.Width)) + 
  geom_histogram(binwidth = 0.05) +
  geom_boxplot(aes(x = 3, y = Sepal.Width))

library(gridExtra)

a <- ggplot(iris, aes(x = Sepal.Width)) + 
  geom_histogram(binwidth = 0.05) 

b <- ggplot(iris, aes(x = "", y = Sepal.Width)) + 
  geom_boxplot() + 
  coord_flip()

grid.arrange(a,b,nrow=2)

a <- ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 0.1) 

b <- ggplot(mtcars, aes(x = "", y = mpg)) + geom_boxplot() +  coord_flip()

grid.arrange(a,b,nrow=2)


my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)

================================================
FILE: 34-ggplots/gg-boxhist.R
================================================
# ggplot
#https://stackoverflow.com/questions/4551582/combination-boxplot-and-histogram-using-ggplot2
# box and histogram together

require(ggplot2)
require(grid)

fig1 <- ggplot(data = mtcars, aes(x = 1, y = mpg)) +
  geom_boxplot( ) +  coord_flip() +
  scale_y_continuous(expand = c(0,0), limit = c(10, 35))
fig1
fig2 <- ggplot(data = mtcars, aes(x = mpg)) +
  geom_histogram(binwidth = 1) +
  scale_x_continuous(expand = c(0,0), limit = c(10, 35))

grid.draw(rbind(ggplotGrob(fig1),  ggplotGrob(fig2), size = "first"))



================================================
FILE: 34-ggplots/gg-boxplot1.R
================================================
# ggplot

marks1 = runif(100, 50,100)
marks2 = rnorm(100, 60,10)
marks3 = rnorm(100, 70,15)
df = data.frame(marks1, marks2, marks3)

head(stack(df))

library(ggplot2)
ggplot(stack(df[,c(1,2,3)]), aes(x = ind, y = values)) + geom_boxplot() +   theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust=1)) +
  labs(title = "Boxplots of columns") + labs(x = "", y = "Values") + 
  scale_y_continuous(breaks = seq(1, 10, by = 1))



================================================
FILE: 34-ggplots/gg-halves.R
================================================

#https://cran.r-project.org/web/packages/gghalves/vignettes/gghalves.html

library(gghalves)

ggplot(iris, aes(y = Sepal.Width)) +
  geom_half_boxplot() +
  geom_half_point_panel(aes(x = 0.5, color = Species), range_scale = .5)


ggplot(iris, aes(x = Species, y = Sepal.Width)) +
  geom_half_boxplot()

ggplot(iris, aes(x = Species, y = Sepal.Width)) +
  geom_half_boxplot(side = "r", center = TRUE, errorbar.draw = FALSE)


library(ggbeeswarm)
ggplot(iris, aes(x = Species, y = Sepal.Width)) +
  geom_half_boxplot() +
  geom_beeswarm(beeswarmArgs = list(side = 1))



================================================
FILE: 34-ggplots/gg-heatmap.R
================================================
#ggplot - Heatmap Graph


================================================
FILE: 34-ggplots/gg-hist1.R
================================================


library(ggplot2)
histogram <- ggplot(data=iris, aes(x=Sepal.Width))
histogram + geom_histogram(binwidth=0.2, color="black", aes(fill=Species)) + 
  xlab("Sepal Width") +  ylab("Frequency") + ggtitle("Histogram of Sepal Width")

ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 3, aes(fill=factor(cyl)))


library(ggplot2)
density <- ggplot(data=iris, aes(x=Sepal.Width))
density + geom_histogram(binwidth=0.2, color="black", fill="steelblue", aes(y=..density..)) +  geom_density(stat="density", alpha=I(0.2), fill="blue") +
  xlab("Sepal Width") +  ylab("Density") + ggtitle("Histogram & Density Curve")

ggplot(mtcars, aes(x=mpg, fill=factor(cyl))) + geom_histogram(binwidth =5,  aes(y=..density..)) + geom_density(stat="density", alpha=I(.2), fill="blue") 


ggplot(aes(x = price ) , data = diamonds) + 
  geom_histogram(aes(fill=cut), binwidth=1500, colour="grey20", lwd=0.2) +  stat_bin(binwidth=1500, geom="text", colour="white", size=3.5,  aes(label=..count.., group=cut), position=position_stack(vjust=0.5)) +  scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))

ggplot(data=mtcars, aes(x=mpg)) + geom_histogram(aes(fill=factor(cyl)),binwidth=5, lwd=.5) + stat_bin(binwidth=5, geom="text", colour="black", size=4,  aes(label=..count.., group=factor(cyl)), position=position_stack(vjust=0.5))


ggplot(aes(x = price ), data = diamonds) +   geom_histogram(aes(fill = cut ), binwidth=1500, colour="grey20", lwd=0.2) +  stat_bin(binwidth=1500, geom="text", colour="red", size=3.5, aes(label=..count.., group=cut, y=0.8*(..count..))) +  scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))


library(ggplot2)
s <- ggplot(mpg, aes(manufacturer, fill = class))
nrow(mpg)
s + geom_bar(position = "stack") +  theme(axis.text.x = element_text(angle=90, vjust=1)) +  geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5),size=4)

ggplot(mpg, aes(manufacturer, fill=class)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5))+  theme(axis.text.x = element_text(angle=90, vjust=1)) 


ggplot(mtcars, aes(mpg, fill=gear)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5)) +  theme(axis.text.x = element_text(angle=90, vjust=1)) 


ggplot(data=mtcars, aes(mpg, fill=factor(gear))) + geom_histogram(breaks=seq(20,35,3)) + stat_bin(geom='text', aes(label=..count.., y=1*(..count..)), position= position_stack(vjust=0.5), breaks=seq(20,35,3))


================================================
FILE: 34-ggplots/gg-hline.R
================================================
# Geom Hline

pacman::p_load(dplyr, ggplot2)

#create data frame 
df <- data.frame( x=c(1, 3, 3, 4, 5, 5, 6, 9, 12, 15),
                 y=c(13, 14, 14, 12, 17, 21, 22, 28, 30, 31))

#create scatterplot with horizontal line at y=20
g1 <- df %>% ggplot(., aes(x=x, y=y)) +  geom_point() + geom_hline(yintercept=20)
g1 + annotate("text", x=9, y=20.5, label="Some text")

g1 +  annotate("text", x=10, y=21.5, label="Some text", size=15, color="blue")


g1 + annotate("text", x=10, y=19, label="Some text", size=15, color="blue") +  annotate("text", x=10, y=21, label="More text", size=9, color="red")

# avg line------
ggplot(df, aes(x=x, y=y)) +  geom_point() + geom_hline(yintercept = mean(df$y, na.rm=TRUE))

ggplot(df, aes(x=x, y=y)) +   geom_point() + geom_hline(yintercept = mean(df$y, na.rm=TRUE), color='blue', lty='dashed', lwd=2)


#-----------
df <- data.frame(y=c(1:10),x=c(1:10))
h <- 7.1
plot1 <- ggplot(df, aes(x=x,y=y)) + geom_point() 
plot2 <- plot1+ geom_hline(aes(yintercept=h))

plot1 + geom_hline(aes(yintercept=h)) +   geom_text(aes(0,h,label = h, vjust = -1))

plot1 + geom_hline(aes(yintercept=h), colour="#BB0000", linetype="dashed") +   geom_text(aes( 0, h, label = h, vjust = -1), size = 3)


================================================
FILE: 34-ggplots/gg-hvlines.R
================================================
#geom_lines

#https://allancameron.github.io/geomtextpath/reference/geom_textabline.html
pacman::p_load(dplyr, ggplot2, geomtextpath)

ggplot(mtcars, aes(mpg, disp)) +  geom_point() +
  geom_texthline(yintercept = 200, label = "displacement threshold",   hjust = 0.8, color = "red4") +
  geom_textvline(xintercept = 20, label = "consumption threshold", hjust = 0.8,    linetype = 2, vjust = 1.3, color = "blue4") +  geom_textabline(slope = 15, intercept = -100, label = "partition line", color = "green4", hjust = 0.6, vjust = -0.2)

#-----------------
S1 <- mtcars %>% group_by(gear, am)  %>% summarise(avgMPG = mean(mpg, na.rm=T))
(S1am <- mtcars %>% group_by(am)  %>% summarise(avgMPG = mean(mpg, na.rm=T)))


g1 <- S1 %>% ggplot(., aes(x=factor(gear), y=avgMPG, fill=factor(gear))) + geom_bar(stat='identity') + facet_grid(am ~. , scales='free')
g1 + geom_texthline(data=S1am, aes(yintercept = avgMPG, label = round(avgMPG)),   hjust = 0.8, color = "red4")

(S2 <- mtcars %>% group_by(gear, am, cyl)  %>% summarise(avgMPG = mean(mpg, na.rm=T)))
(S2amcyl <- mtcars %>% group_by(am, cyl)  %>% summarise(avgMPG = mean(mpg, na.rm=T)))


g2 <- S2 %>% ggplot(., aes(x=factor(gear), y=avgMPG, fill=factor(gear))) + geom_bar(stat='identity') + facet_grid(am ~ cyl , scales='free')
g2
g2 + geom_texthline(data=S2amcyl, aes(yintercept = avgMPG, label = paste('Avg MPG of am-', am, '/ cyl-', cyl, ":", round(avgMPG))),   hjust = 0.8, color = "red4") + guides(fill='none')



================================================
FILE: 34-ggplots/gg-labelend.R
================================================
library(tidyverse)
library(ggrepel)

theme_set(theme_bw())

iris
df_long <- iris %>% tidyr::pivot_longer(cols=Petal.Length:Sepal.Width, names_to = 'variable', values_to = 'value')
head(df_long)

plot <- ggplot(df_long, aes(x=Species, y=value, group=variable)) + geom_line(aes(color=variable)) + geom_point() + theme(legend.position = 'top')
plot

(data_ends <- df_long %>% filter(Species == 'virginica'))
plot + geom_text_repel(aes(label=value), data=data_ends, face='plain', color='black', size=3)


(plot2 <- ggplot(df_long, aes(x=Species, y=value, group=variable)) + geom_line(aes(color=variable)) + geom_point())

plot2 + geom_text_repel(aes(label=variable), data=data_ends, face='plain', color='black', size=3)


================================================
FILE: 34-ggplots/gg-legend1.R
================================================
#ggplot scale

library(ggplot2)
V1<-rnorm(10)
V2<-rnorm(10)
V3<-rnorm(10)
DF<-data.frame(V1,V2,V3)
DF               

ggplot(DF,aes(x=V1,y=V2,size=V3))+
  geom_point(fill='red',shape=21)+
  theme_bw()+
  scale_size(range=c(1,5))+
  theme(legend.justification=c(1,0), legend.position=c(1,0))

#Option 2: Use facet_wrap(...) if you can. This way you only get one legend for the set of plots.

#Option 3: Use color and size, and then hide the size legend altogether.

ggplot(DF,aes(x=V1,y=V2,size=V3, color=V3))+
  geom_point(shape=20)+
  theme_bw()+
  scale_size(range=c(2,10), guide="none")+
#  scale_color_gradient(high="green", low="yellow", guide="none")  
scale_color_gradient(high="#ff0000", low="#ffffcc")  


#-----
library(ggplot2)
basic=ggplot(mtcars, aes(wt, mpg, colour = factor(cyl), shape = factor(vs) )) + geom_point()
basic

basic+labs( colour = "Gp-1 : Type of Eng",   shape = "Gp-2 : Cylinders")

#do not display 1 legend
basic + guides(shape=FALSE)

#1: no legend
basic + theme(legend.position = "none")

#2: around the plot
basic + theme(legend.position = "bottom")

#3: inside the plot
basic + theme(
  legend.position = c(.95, .95),
  legend.justification = c("right", "top"),
  legend.box.just = "right",
  legend.margin = margin(6, 6, 6, 6)
)

# custom box around legend
basic + theme(
  legend.box.background = element_rect(color="red", size=1),
  legend.box.margin = margin(116, 6, 6, 6)
)

# custom the key
basic + theme(legend.key = element_rect(fill = "white", colour = "black"))

# custom the text
basic + theme(legend.text = element_text(size = 8, colour = "red"))

# custom the title
basic + theme(legend.title = element_text(face = "bold"))


#-----
library(ggplot2)
library(grid)
library(gridExtra)
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
(d <- ggplot(dsamp, aes(carat, price)) +
    geom_point(aes(colour = clarity)) +
    theme(legend.position = c(0.06, 0.75))
)

library(lemon)

reposition_legend(d, 'top left')

#multiple legends
d2 <- d + aes(shape=cut) + 
  theme(legend.box.background = element_rect(fill='#fffafa'),
        legend.background = element_blank())
reposition_legend(d2, 'left')

reposition_legend(d + theme_classic(), 'top left')

reposition_legend(d + theme_bw(), 'top left', x=0.002, y=1-0.002)

reposition_legend(d + theme_bw(), 'top left', offset=0.002)

d2 <- d + facet_grid(.~cut)
gtable_show_names(d2)
reposition_legend(d2, 'top left', panel = 'panel-3-1')
reposition_legend(d + facet_wrap(~cut, ncol=3), 'top left', panel='panel-3-2')

d3 <- d + facet_wrap(~cut, ncol=3) + scale_color_discrete(guide=guide_legend(ncol=3))
reposition_legend(d3, 'center', panel='panel-3-2')
#https://cran.r-project.org/web/packages/lemon/vignettes/legends.html


================================================
FILE: 34-ggplots/gg-line.R
================================================
#ggplot - Line Graph
#http://www.cookbook-r.com/Graphs/Bar_and_line_graphs_(ggplot2)/

dat <- data.frame(
  time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")),
  total_bill = c(14.89, 17.23)
)
dat

# Load the ggplot2 package
library(ggplot2)

# Basic line graph
ggplot(data=dat, aes(x=time, y=total_bill, group=1)) +  geom_line()
## This would have the same result as above
ggplot(data=dat, aes(x=time, y=total_bill)) + geom_line(aes(group=1))

# Add points
ggplot(data=dat, aes(x=time, y=total_bill, group=1)) +  geom_line() + geom_point()

# Change color of both line and points
# Change line type and point type, and use thicker line and larger points
# Change points to circles with white fill
ggplot(data=dat, aes(x=time, y=total_bill, group=1)) +   geom_line(colour="red", linetype="dashed", size=1.5) +   geom_point(colour="red", size=4, shape=21, fill="white")

#---------------
# Change the y-range to go from 0 to the maximum value in the total_bill column, and change axis labels
ggplot(data=dat, aes(x=time, y=total_bill, group=1)) +  geom_line() +
  geom_point() +  expand_limits(y=0) +   xlab("Time of day") + ylab("Total bill") +  ggtitle("Average bill for 2 people")


#------
dat1 <- data.frame(  sex = factor(c("Female","Female","Male","Male")),  time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),  total_bill = c(13.53, 16.81, 16.24, 17.42))
dat1
#time: x-axis, sex: color fill/ line color, total_bill: y-axis.

# Basic line graph with points
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex)) +  geom_line() + geom_point()

# Map sex to color
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, colour=sex)) +  geom_line() +  geom_point()

# Map sex to different point shape, and use larger points
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex)) +  geom_line() +  geom_point()


# Use thicker lines and larger points, and hollow white-filled points
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex)) +  geom_line(size=1.5) +   geom_point(size=3, fill="white") + scale_shape_manual(values=c(22,21))

ggplot(data=dat1, aes(x=sex, y=total_bill, group=time, shape=time, color=time)) +  geom_line() +  geom_point()


#--- final graph
# A line graph
ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + 
  geom_line(aes(linetype=sex), size=1) +     # Set linetype by sex
  geom_point(size=3, fill="white") +         # Use larger points, fill with white
  expand_limits(y=0) +                       # Set y range to include 0
  scale_colour_hue(name="Sex of payer",      # Set legend title
                   l=30)  +                  # Use darker colors (lightness=30)
  scale_shape_manual(name="Sex of payer",
                     values=c(22,21)) +      # Use points with a fill color
  scale_linetype_discrete(name="Sex of payer") +
  xlab("Time of day") + ylab("Total bill") + # Set axis labels
  ggtitle("Average bill for 2 people") +     # Set title
  theme_bw() +
  theme(legend.position=c(.7, .4))           # Position legend inside
# This must go after theme_bw

#----------------------- With a numeric x-axis
When the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical. In this data set, the dose is a numeric variable with values 0.5, 1.0, and 2.0. It might be useful to treat these values as equal categories when making a graph.
datn <- read.table(header=TRUE, text='
supp dose length
  OJ  0.5  13.23
  OJ  1.0  22.70
  OJ  2.0  26.06
  VC  0.5   7.98
  VC  1.0  16.77
  VC  2.0  26.14
')
datn
#With x-axis treated as continuous: A simple graph might put dose on the x-axis as a numeric value. It is possible to make a line graph this way, but not a bar graph.

ggplot(data=datn, aes(x=dose, y=length, group=supp, colour=supp)) +  geom_line() +  geom_point()

#----------With x-axis treated as categorical
#If you wish to treat it as a categorical variable instead of a numeric one, it must be converted to a factor. This can be done by modifying the data frame, or by changing the specification of the graph.

# Copy the data frame and convert dose to a factor
datn2 <- datn
datn2$dose <- factor(datn2$dose)
ggplot(data=datn2, aes(x=dose, y=length, group=supp, colour=supp)) +  geom_line() + geom_point()

# Use the original data frame, but put factor() directly in the plot specification
ggplot(data=datn, aes(x=factor(dose), y=length, group=supp, colour=supp)) +  geom_line() +  geom_point()

#bar # Use the original data frame, but put factor() directly in the plot specification
ggplot(data=datn, aes(x=factor(dose), y=length, fill=supp)) + geom_bar(stat="identity", position=position_dodge())


================================================
FILE: 34-ggplots/gg-pie1.R
================================================
#pie with ggplot2

library(ggplot2)

df <- data.frame(  group = c("Male", "Female", "Child"), value = c(25, 25, 50))
head(df)

# Barplot
df
bp<- ggplot(df, aes(x="", y=value, fill=group)) +  geom_bar(width = 1, stat = "identity")
bp

# Barplot
bp<- ggplot(df, aes(x="", y=value, fill=group))+   geom_bar(width = 1, stat = "identity")
bp

pie <- bp + coord_polar("y", start=0)
pie
df
# Use custom color palettes
pie + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))

# use brewer color palettes
pie + scale_fill_brewer(palette="Dark2")

#factor variable
ggplot(PlantGrowth, aes(x=factor(1), fill=group))+ geom_bar(width = 1)+  coord_polar("y")

library(scales)
pie + geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]),     label = percent(value/100)), size=5)

pie + geom_text(aes(y = value/3 + c(0, cumsum(value)[-length(value)]), label = percent(value/100)), size=5) + scale_fill_brewer("Blues") +  theme(axis.text.x=element_blank())



#-----
count.data <- data.frame(   class = c("1st", "2nd", "3rd", "Crew"),
  n = c(325, 285, 706, 885),   prop = c(14.8, 12.9, 32.1, 40.2) )
count.data

# Add label position
count.data <- count.data %>%   arrange(desc(class)) %>%   mutate(lab.ypos = cumsum(prop) - 0.5*prop) 
count.data


mycols <- c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")

ggplot(count.data, aes(x = "", y = prop, fill = class)) +   geom_bar(width = 1, stat = "identity", color = "white") +   coord_polar("y", start = 0)+  geom_text(aes(y = lab.ypos, label = prop), color = "white")+   scale_fill_manual(values = mycols) +   theme_void()

#https://www.datanovia.com/en/blog/how-to-create-a-pie-chart-in-r-using-ggplot2/
ggplot(count.data, aes(x = 2, y = prop, fill = class)) +  geom_bar(stat = "identity", color = "white") +   coord_polar(theta = "y", start = 0)+   geom_text(aes(y = lab.ypos, label = prop), color = "white")+   scale_fill_manual(values = mycols) +   theme_void()+   xlim(0.5, 2.5)


library(esquisse)
esquisse::esquisser()


ggplot(mtcars, aes(x=carb, y=..count.., fill=carb)) + geom_bar(stat='count') + coord_polar(theta='y', start=0)

mtcars %>% group_by(carb) %>% summarise(count=n()) %>% arrange(count) %>% mutate(cumSUM = cumsum(count)) %>%  mutate(prop = count / sum(count)) %>% mutate(lab.ypos = cumsum(count) - .6 * prop) %>%  ggplot(., aes(x="", y=count, fill=factor(carb)))+   geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) + labs(fill='Carburators') + geom_text(aes(y=lab.ypos, label=round(prop*100,0)), color='white') 
("y", start=0)

================================================
FILE: 34-ggplots/gg-slope.R
================================================
#ggplot - Slope Graph

library(ggplot2)
library(scales) #label the points nicely by putting commas in the numbers.

months <-24
year1 <-c(200,175, 190)
year3 <-c(150,100, 90)
group <-c("Group C", "Group B", "Group A")
a <- data.frame(year1,year3,group)
a

#Labels
l11<-paste(a$group,comma_format()(round(a$year1/(3600*24*30.5))),sep="\n")
l13<-paste(a$group,comma_format()(round(a$year3/(3600*24*30.5))),sep="\n")
l11
l13
a

#slope
p <- ggplot(a) + geom_segment(aes(x=0, xend=months, y=year1, yend=year3), size=.75)
p

#blank themes
p<-p + theme(panel.background = element_blank())
p<-p + theme(panel.grid=element_blank())
p<-p + theme(axis.ticks=element_blank())
p<-p + theme(axis.text=element_blank())
p<-p + theme(panel.border=element_blank())
p

#Set the axis labels and limits
p<-p + xlab("") + ylab("Amount Used")
p<-p + theme(axis.title.y=theme_text(vjust=3))
p<-p + xlim((0-12),(months+12))
p<-p + ylim(0,(1.2*(max(a$year3,a$year1))))
p


#Label the slope
p<-p + geom_text(label=l13, y=a$year3, x=rep.int(months,length(a)),hjust=-0.2,size=3.5)
p<-p + geom_text(label=l11, y=a$year1, x=rep.int( 0,length(a)),hjust=1.2,size=3.5)
p

#Label the columns
p<-p + geom_text(label="Year 1", x=0,     y=(1.1*(max(a$year3,a$year1))),hjust= 1.2,size=5)
p<-p + geom_text(label="Year 3", x=months,y=(1.1*(max(a$year3,a$year1))),hjust=-0.1,size=5)
p




#links
#https://rpubs.com/walkerke/slopegraph



================================================
FILE: 34-ggplots/gg-slope2.R
================================================
#ggplot - slope graph
#https://rdrr.io/github/leeper/slopegraph/man/ggslopegraph.html
#https://github.com/leeper/slopegraph
#https://rdrr.io/github/leeper/slopegraph/man/
require("ggplot2")
library(ggslopegraph) #na from CRAN
install.packages("remotes")
remotes::install_github("leeper/slopegraph", force=T)

#---------------
library("slopegraph")
data(cancer)
## Tufte's Cancer Graph (to the correct scale)
data(cancer)
cancer
ggslopegraph(cancer, col.lines = 'gray',  xlabels = c('5 Year','10 Year','15 Year','20 Year'))

## Tufte's GDP Graph
data(gdp)
ggslopegraph(gdp, col.line='gray', xlabels = c('1970','1979'),              main = 'Current Receipts of Goverment\nas a Percentage of Gross Domestic Product') + 
  theme_bw()

## Ranking of U.S. State populations
data(states)
ggslopegraph(states,  main = 'Relative Rank of U.S. State Populations, 1790-1870',  yrev = TRUE)

cls <- rep("black", nrow(states))
cls[rownames(states) == "South Carolina"] <- "red"
cls[rownames(states) == "Tennessee"] <- "blue"
ggslopegraph(states, main = 'Relative Rank of U.S. State Populations, 1790-1870', yrev = TRUE, col.lines = cls, col.lab = cls)

## ranking of U.S. Bachelors Degrees fields
data(bachelors)
bachelors[] <- lapply(bachelors, function(x) rank(x))
names(bachelors) <- substring(names(bachelors), 3, 7)
ggslopegraph(bachelors, offset.x = 0, xlim = c(1, 25), col.num = NA, labpos.left = NULL)


#
slopegraph(gdp, col.lines = 'gray', col.lab = "black", xlabels = c('1970','1979'),   main = 'Current Receipts of Goverment as a Percentage of Gross Domestic Product')


#
data(states)
cols <- `[<-`(rep("black", 37), 7, "red")
slopegraph(states, xlim = c(-1, 12), ylim = c(37,0), offset.x = 0.06,           col.lines = cols, col.lab = cols,            main = 'Relative Rank of U.S. State Populations, 1790-1870')


#---
data(states)
cols <- `[<-`(rep("black", 37), 7, "red")
ggslopegraph(states, offset.x = 0.06, yrev = TRUE,
             col.lines = cols, col.lab = cols, 
             main = 'Relative Rank of U.S. State Populations, 1790-1870') +
  theme_bw()   




#Slope Graph 2----------------------
# basic graph
ggslopegraph2(cancer2, Year, Survival, Type,  title = "Estimates of Percent Survival Rates")

# with further customization
ggslopegraph2(cancer2, Year, Survival, Type, title = "Estimates of Percent Survival Rates",   linecolor = "black",      linethickness = 1,  subtitle = NULL,   caption = NULL)

# demonstrating linecolor recyclng
ggslopegraph2(cancer2, Year, Survival, Type, 
title = "Estimates of Percent Survival Rates", subtitle = "Based on: Edward Tufte, Beautiful Evidence, 174, 176.",  caption = "DU",          linecolor = c("black", "red", "grey"), linethickness = .5)
#https://rdrr.io/github/leeper/slopegraph/man/ggslopegraph2.html

#---- segment
data(gdp)
gdp
head(segmentize(gdp))
segmentize(gdp)


================================================
FILE: 34-ggplots/ggbarplot.R
================================================
#81 BARPLOT WITH VARIABLE WIDTH
library(ggplot2)

# make data
data=data.frame(group=c("A ","B ","C ","D ") , value=c(33,62,56,67) , number_of_obs=c(100,500,459,342))

# Calculate the future positions on the x axis of each bar (left border, central position, right border)
data$right=cumsum(data$number_of_obs) + 30*c(0:(nrow(data)-1))
data$left=data$right - data$number_of_obs 

# Plot
ggplot(data, aes(ymin = 0)) + 
  geom_rect(aes(xmin = left, xmax = right, ymax = value, colour = group, fill = group)) +
  xlab("number of obs") + ylab("value")



================================================
FILE: 34-ggplots/ggbarplots.R
================================================
#bar plot in ggplot2
#mtcars

library(ggplot2)
library(dplyr)
mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar()

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl))) 


mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)))

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position='dodge')

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE))

mtcars %>% ggplot(., aes(x=factor(gear))) + geom_bar(aes(fill=factor(cyl)), position = position_stack(reverse = TRUE)) + coord_flip() +  theme(legend.position = "top")

mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity')
        
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=gear)) + geom_text(aes(label = n), vjust = -0.3)  

library(ggpubr)
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n)) + geom_bar(stat='identity', aes(fill=factor(gear))) + geom_text(aes(label = n), vjust = -0.3) + theme_pubclean()                             


ggplot(data=mtcars, aes(x=factor(gear), fill=factor(cyl))) +  geom_bar(aes(y = (..count..)/sum(..count..)))

ggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear)))

ggplot(data=mtcars, aes(x=factor(gear))) + geom_bar(aes(y = ..prop.., group = 1, fill=factor(gear))) + facet_wrap(~cyl)

#not correctly drawn.  use proportions
mtcars %>% group_by(gear) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(gear))) + geom_bar(stat='identity') + geom_text(aes(label = n), vjust = -0.3) + coord_polar("y", start = 0) +  ggpubr::fill_palette("jco")+ theme_void()                          #http://www.sthda.com/english/articles/32-r-graphics-essentials/133-plot-one-variable-frequency-graph-density-distribution-and-more/


mtcars %>% count(gear) %>% mutate(gearProp = n / nrow(mtcars)) %>% ggplot(., aes(x=gear, y=gearProp)) + geom_bar(stat='identity')
mtcars %>% select(gear) %>% table() %>% prop.table() %>% as.data.frame() -> y
class(y)
ggplot(mtcars, aes(x = gear)) +   geom_bar(aes(y = (..count..)/sum(..count..)))
ggplot(mtcars, aes(gear)) + geom_bar(aes(y = (..count..)/sum(..count..))) + scale_y_continuous(labels=scales::percent) +  ylab("relative frequencies")

ggplot(mtcars, aes(gear, group=am)) + geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +  scale_y_continuous(labels=scales::percent) +  ylab("relative frequencies") +  facet_grid(~am) + scale_fill_manual(values = c("red", "green", "yellow"))

ggplot(mtcars, aes(x=gear,  group=am)) +  geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") + geom_text(aes( label = scales::percent(round(..prop..,3)), y= ..prop.. ), stat= "count", vjust = -.5) + labs(y = "Percent", fill="Transmission") + facet_grid(~am) +   scale_y_continuous(labels = scales::percent)


ggplot(mtcars, aes(x = gear, fill = factor(cyl))) + geom_bar(stat = "bin")
#x should be continuous

my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)



# Outside bars
mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) +  geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=n), vjust=-0.3, size=3.5)+   theme_minimal()
# Inside bars
mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n)) +  geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=n), vjust=1.6, size=3.5)+   theme_minimal()

#x should be continuous
ggplot(mtcars, aes(x=cyl))+ geom_bar(stat="bin", fill="steelblue")+ theme_minimal()


mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, colour=factor(gear))) +  geom_bar(stat="identity", fill="white")+   geom_text(aes(label=n), vjust=1.6, size=3.5) ->p
p
# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic()

mtcars %>% count(gear) %>% ggplot(., aes(x=gear,y=n, fill=factor(gear))) +  geom_bar(stat="identity")+   geom_text(aes(label=n), vjust=1.6, size=3.5) -> q
q
# Use custom color palettes
q+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
q+scale_fill_brewer(palette="Dark2")
# Use grey scale
q + scale_fill_grey()
q + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

#legend position
# Change bar fill colors to blues
q + scale_fill_brewer(palette="Blues")
q + theme(legend.position="top")
q + theme(legend.position="bottom")
# Remove legend
q + theme(legend.position="none")



#multiple groups
# Stacked barplot with multiple groups
mtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) +  geom_bar(stat="identity")
# Use position=position_dodge()
mtcars %>% group_by(gear, cyl) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(cyl))) +  geom_bar(stat="identity", position=position_dodge())

#Paired
mtcars %>% group_by(gear, vs) %>% count() %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) +  geom_bar(stat="identity", position=position_dodge()) + geom_text(aes(label=n), vjust=1.6, color="white",   position = position_dodge(0.9), size=3.5)+
  scale_fill_brewer(palette="Paired")+   theme_minimal()

library(dplyr)
mtcars %>% group_by(gear, vs) %>% count() %>% arrange(gear,desc(vs)) -> df
df
library(plyr)
# Calculate the cumulative sum of len for each dose
df1 <- ddply(df, c('gear'),  transform, label_ypos=cumsum(n))
head(df1)
library(ggplot2)
df1 %>% ggplot(., aes(x=gear, y=n, fill=factor(vs))) +  geom_bar(stat="identity") + geom_text(aes(y=label_ypos, label=n), vjust=1.6, color="white", size=3) + scale_fill_brewer(palette="Paired")+   theme_minimal()



================================================
FILE: 34-ggplots/ggboxhist.R
================================================
# ggplot
#https://stackoverflow.com/questions/4551582/combination-boxplot-and-histogram-using-ggplot2
# box and histogram together

require(ggplot2)
require(grid)

fig1 <- ggplot(data = mtcars, aes(x = 1, y = mpg)) +
  geom_boxplot( ) +  coord_flip() +
  scale_y_continuous(expand = c(0,0), limit = c(10, 35))
fig1
fig2 <- ggplot(data = mtcars, aes(x = mpg)) +
  geom_histogram(binwidth = 1) +
  scale_x_continuous(expand = c(0,0), limit = c(10, 35))

grid.draw(rbind(ggplotGrob(fig1),  ggplotGrob(fig2), size = "first"))



================================================
FILE: 34-ggplots/ggboxplot2.R
================================================


df <- data.frame(State = LETTERS[1:3],
                Y = sample(1:10, 30, replace = TRUE),
                X = rep(1:10, 3))
df
library(ggplot2)

# You can plot geom_histogram or bar (pre-counted stats)
ggplot(df, aes(X, Y)) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_grid(State ~ .)
# Or you can plot similar figure with geom_rect
ggplot(df)  +
  geom_rect(aes(xmin = X - 0.4, xmax = X + 0.4, ymin = 0, ymax = Y)) +
  facet_grid(State ~ .)

ggplot(df)  +
  geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4)) +
  geom_boxplot(aes(X, Y)) +
  coord_flip() +
  facet_grid(State ~ .)

ggplot(df)  +
  geom_rect(aes(xmin = 0, xmax = Y, ymin = X - 0.4, ymax = X + 0.4),
            fill = "blue", color = "black") +
  geom_boxplot(aes(X, Y), alpha = 0.7, fill = "salmon2") +
  coord_flip() +
  facet_grid(State ~ .) +
  theme_classic() +
  scale_y_continuous(breaks = 1:max(df$X))


ggplot(iris, aes(x = Sepal.Width)) + 
  geom_histogram(binwidth = 0.05) +
  geom_boxplot(aes(x = 3, y = Sepal.Width))

library(gridExtra)

a <- ggplot(iris, aes(x = Sepal.Width)) + 
  geom_histogram(binwidth = 0.05) 

b <- ggplot(iris, aes(x = "", y = Sepal.Width)) + 
  geom_boxplot() + 
  coord_flip()

grid.arrange(a,b,nrow=2)

a <- ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 0.1) 

b <- ggplot(mtcars, aes(x = "", y = mpg)) + geom_boxplot() +  coord_flip()

grid.arrange(a,b,nrow=2)


my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ggplot(mtcars, aes(x=cyl, y=mpg , group=gear)) + geom_dotplot(aes(color = gear, fill = gear), binaxis = 'y', stackdir = 'center') + scale_color_manual(values = my3cols) + scale_fill_manual(values = my3cols)

================================================
FILE: 34-ggplots/gghistogram.R
================================================


library(ggplot2)
histogram <- ggplot(data=iris, aes(x=Sepal.Width))
histogram + geom_histogram(binwidth=0.2, color="black", aes(fill=Species)) + 
  xlab("Sepal Width") +  ylab("Frequency") + ggtitle("Histogram of Sepal Width")

ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 3, aes(fill=factor(cyl)))


library(ggplot2)
density <- ggplot(data=iris, aes(x=Sepal.Width))
density + geom_histogram(binwidth=0.2, color="black", fill="steelblue", aes(y=..density..)) +  geom_density(stat="density", alpha=I(0.2), fill="blue") +
  xlab("Sepal Width") +  ylab("Density") + ggtitle("Histogram & Density Curve")

ggplot(mtcars, aes(x=mpg, fill=factor(cyl))) + geom_histogram(binwidth =5,  aes(y=..density..)) + geom_density(stat="density", alpha=I(.2), fill="blue") 


ggplot(aes(x = price ) , data = diamonds) + 
  geom_histogram(aes(fill=cut), binwidth=1500, colour="grey20", lwd=0.2) +  stat_bin(binwidth=1500, geom="text", colour="white", size=3.5,  aes(label=..count.., group=cut), position=position_stack(vjust=0.5)) +  scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))

ggplot(data=mtcars, aes(x=mpg)) + geom_histogram(aes(fill=factor(cyl)),binwidth=5, lwd=.5) + stat_bin(binwidth=5, geom="text", colour="black", size=4,  aes(label=..count.., group=factor(cyl)), position=position_stack(vjust=0.5))


ggplot(aes(x = price ), data = diamonds) +   geom_histogram(aes(fill = cut ), binwidth=1500, colour="grey20", lwd=0.2) +  stat_bin(binwidth=1500, geom="text", colour="red", size=3.5, aes(label=..count.., group=cut, y=0.8*(..count..))) +  scale_x_continuous(breaks=seq(0,max(diamonds$price), 1500))


library(ggplot2)
s <- ggplot(mpg, aes(manufacturer, fill = class))
nrow(mpg)
s + geom_bar(position = "stack") +  theme(axis.text.x = element_text(angle=90, vjust=1)) +  geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5),size=4)

ggplot(mpg, aes(manufacturer, fill=class)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5))+  theme(axis.text.x = element_text(angle=90, vjust=1)) 


ggplot(mtcars, aes(mpg, fill=gear)) + geom_bar(position='stack') + geom_text(stat='count', aes(label=..count..), position=position_stack(vjust=0.5)) +  theme(axis.text.x = element_text(angle=90, vjust=1)) 


ggplot(data=mtcars, aes(mpg, fill=factor(gear))) + geom_histogram(breaks=seq(20,35,3)) + stat_bin(geom='text', aes(label=..count.., y=1*(..count..)), position= position_stack(vjust=0.5), breaks=seq(20,35,3))


================================================
FILE: 34-ggplots/ggp2.R
================================================
# Graph 1
library(ggplot2)
sales
salesg = sales
ggplot(data = salesg)+ aes(x=sdate,y=pies) +
  geom_point() + labs( title ='Sales data of Pies', x='Date', y='Sales')
# not working
salesg
#Stepwise
g1= ggplot(data = salesg, aes(x=sdate,y=pies)) 
g2= g1+ geom_point()
g3= g2 + labs( title ='Sales data of Pies', x='Date', y='Sales')
g3  #run

g1= ggplot(data = salesg, aes(x=sdate,y=pies)) 
g2= geom_point()
g3= labs( title ='Sales data of Pies', x='Date', y='Sales')
g=g1+g2+g3  #run
g


#Part 2
g2b = geom_point(pch=17, color='blue', size=2)
g3b = geom_smooth(method='lm', color='red', linetype=3)
gA = g1 + g2b + g3b
gA

names(salesg)
#Facegrid
f1 = ggplot(salesg, aes(x=sdate, y=pies, shape=daywk, col=daywk))
f2 = geom_point(size=3)
f= f1+f2
f




================================================
FILE: 34-ggplots/ggplot-DU1.R
================================================
#ggplot
names(df1)
range(df1$sales)

#Histogram
ggplot(df1, aes(x = df1$sales)) + geom_histogram()

g1= ggplot(df1, aes(x = df1$sales))
g2=geom_histogram()
(g1+g2)
g2=geom_bar(color='green')
(g1+g2)

g2=ggplot(df1, aes(y = df1$Asales1, x=Acoy1)) + geom_boxplot(color='blue') 
g2

g2=ggplot(df1, aes(y = df1$Asales1, x=Acoy1)) + geom_violin(color='yellow', fill='green')
g2

g1= ggplot(df1, aes(x = df1$Asales1))
g2=geom_density(color='green')
(g1+g2)

g2=geom_rug(color='green')
(g1+g2)

ggplot_build(g1)$data

x
# Overlay
h1= ggplot(df1, aes(y = df1$Asales1, x=df1$Acoy1))
h2 = geom_boxplot(fill='blue', color='green', notch = T)
h3 = geom_point(position='jitter', color='red', alpha=.5)
h4 = geom_rug(sides="trbl", color='black')
(h1+h2)
(h1+h2+h3)
(h1+h2+h3+h4)
(h1+h3)

#Overlay2
f1 = ggplot(df1, aes(y = df1$Asales1, x=df1$Acoy1))
f2 = geom_violin(fill='lightblue', color='green')
f3 = geom_boxplot(fill='lightgreen', width=.5)
(f1+f2)
(f1+f2+f3)


#Grouping
e1= ggplot(data=df1, aes(x=Asales1, fill=Acoy1))
e2 = geom_density(alpha=.3)
(e1+e2)

names(df1)
d1 = ggplot(data=df1, aes(y=Asales1, x=Asalesman1, color=Acoy1, shape=Aloc1 ))
d2 = geom_point() 
(d1 + d2)

#Grouped Bar
aggregate(df1$Asales1, by=list(Acoy1,Aloc1), FUN=NROW)
(df2= aggregate(Asales1 ~ Acoy1 + Aloc1, FUN=NROW))
c1 = ggplot(data=df1, aes( x=Acoy1, fill=Aloc1)) + geom_bar(position = 'stack') + labs(title='position=stack')
(c1) #stack on top
c2 = ggplot(data=df1, aes(x=Acoy1, fill=Aloc1)) + geom_bar(position = 'dodge') + labs(title='position=dodge/ beside')
(c2) #side

c3 = ggplot(data=df1, aes(x=Acoy1, fill=Aloc1)) + geom_bar(position = 'fill') + labs(title='position=fill')
(c3)  #full


#Faceting
b1 = ggplot(data=df1, aes(x=Asales1))
b1
b2 = geom_histogram(bins=10) 
b3 = facet_wrap(~ Acoy1, nrow=1)
(b1 + b2 + b3)

ggplot(df1, aes(x=Aadv1, y=Asales1, color=Aloc1, shape=factor(Asalesman1))) + geom_point() + facet_grid(.~ Aloc1)

summary(df1)
z1 = ggplot(data=df1, aes(x=Aadv1, fill=Aloc1))
z2 = geom_density()
z3 = facet_wrap(Acoy1 ~ Asalesman1)
(z1+z2)
(z1+z2+z3)

# Smooth Lines
y1 = ggplot(data=df1, aes(x=Aadv1, y=Asales1)) + geom_smooth() + geom_point()
y2 = ggplot(data=df1, aes(x=Aadv1, y=Asales1, linetype=Asalesman1))
(y3 = y2 + geom_smooth() + geom_point())

#axis 
q1 = ggplot(data=df1, aes(x=Acoy1, y=Asales1, fill=Aloc1))
(q2 = q1 + geom_boxplot())

q3x= scale_x_discrete(breaks=c('coy1','coy2','coy3'), labels=c("Company1", "Company2", "Company3"))
q3y= scale_y_continuous(breaks=c(50,60,70,100,150), labels=c("50K", "60K", "70K", "100K", "150K"))
q4 = labs(title="Sales Details of 3 Companies Citywise", x="",y="")
(q2+q3x)
(q2+q3x+q3y)
(q2+q3x+q3y + q4)

#Legends
q4b = labs(title="Sales Details of 3 Companies Citywise", x="",y="", fill=Acoy1)
q5 = theme(legend.position = c(.5,.8))
(q2+q3x+q3y+q4b+q5)

# Scales
r1 = ggplot(data=df1, aes(x=Aadv1, y=Asales1, size=Aadv2))
r2 = geom_point(shape=21, color='red', fill='green')
r3 = labs(x='Adv1 Amount', y="Sales Amount", title="Sales vs Ad1 Amount - Ad2/ Salesman Shape")
(r1+r2+r3)
names(df1)
r1a = ggplot(data=df1, aes(x=Aadv1, y=Asales1, size=Asalesman1)) #discrete
(r1a + r2 + r3)
#warning
ggplot(data=df1, aes(x=Aadv1, y=Asales1, color=Asalesman1)) + scale_color_manual(values=c('red','blue','yellow')) + geom_point(size=2)

ggplot(data=df1, aes(x=Aadv1, y=Asales1, color=Asalesman1)) + scale_color_brewer(palette="Set3") + geom_point(size=3)
#preset colors


#Multiple Graphs

(df3 = aggregate(Asales1 ~ Acoy1, data=df1, FUN=sum))

q4a= ggplot(data=df2, aes(x="", y=Asales1, fill=Acoy1))
q4b = geom_bar(width=1,stat='identity')
(q4a + q4b)
(q4a + coord_polar("y", start=0))

library(gridExtra)
q1= ggplot(data=df1, aes(x=Aadv1)) + geom_bar()
q2= ggplot(data=df1, aes(x=Aadv2)) + geom_bar()
q3= ggplot(data=df1, aes(x=Asales1, y=Aadv1)) + geom_point()
q4= ggplot(data=df2, aes(x="", y=Asales1, fill=Acoy1)) + geom_bar(width=1,stat='identity')

gridExtra::grid.arrange(q1, q2, q3, ncol=3)
gridExtra::grid.arrange(q1, q2, q3, q4, nrow=2, ncol=2)


ggplot(data=df1, aes(x=Aadv1, y=Asales1, linetype=Asalesman1, shape=Acoy1)) + geom_smooth() + geom_point()


#Plot
ggplot(df1, aes(x = df1$Asales1, y=df1$Aadv1)) + geom_point()
y1 = ggplot(df1, aes(y = df1$Asales1, x=df1$Aadv1)) 
y2=  geom_point(size=3, pch=17, color='blue')


ggplot(df1, aes(y = df1$Asales1, x=df1$Aadv1)) + geom_point(size=3, pch=17, color='blue') + facet_grid(Adv2 ~ Acoy1)



p1 <- ggplot(df1, aes(Aadv1, Aadv2)) +
  geom_point(data = df1[df1$Asales1 > 50,]) 
p1

p2 <- ggplot(df1, aes(Aadv1, Aadv2)) +
  geom_point(data = df1[df1$Asales1 > 50,], aes(size=Asales1)) 
p2
names(df1)
p3 <- ggplot(df1, aes(Aadv1, Aadv2)) + geom_point(data = df1[Asalesman1 ==1,]) 
p3

p4 <- ggplot(df1, aes(Aadv1, Aadv2)) +
  geom_point(data = df1[Asales1 > 70,], aes(color=Asalesman1)) 
p4


================================================
FILE: 34-ggplots/ggplot-legend1.R
================================================
#ggplot scale

library(ggplot2)
V1<-rnorm(10)
V2<-rnorm(10)
V3<-rnorm(10)
DF<-data.frame(V1,V2,V3)
DF               

ggplot(DF,aes(x=V1,y=V2,size=V3))+
  geom_point(fill='red',shape=21)+
  theme_bw()+
  scale_size(range=c(1,5))+
  theme(legend.justification=c(1,0), legend.position=c(1,0))

#Option 2: Use facet_wrap(...) if you can. This way you only get one legend for the set of plots.

#Option 3: Use color and size, and then hide the size legend altogether.

ggplot(DF,aes(x=V1,y=V2,size=V3, color=V3))+
  geom_point(shape=20)+
  theme_bw()+
  scale_size(range=c(2,10), guide="none")+
#  scale_color_gradient(high="green", low="yellow", guide="none")  
scale_color_gradient(high="#ff0000", low="#ffffcc")  


#-----
library(ggplot2)
basic=ggplot(mtcars, aes(wt, mpg, colour = factor(cyl), shape = factor(vs) )) + geom_point()
basic

basic+labs( colour = "Gp-1 : Type of Eng",   shape = "Gp-2 : Cylinders")

#do not display 1 legend
basic + guides(shape=FALSE)

#1: no legend
basic + theme(legend.position = "none")

#2: around the plot
basic + theme(legend.position = "bottom")

#3: inside the plot
basic + theme(
  legend.position = c(.95, .95),
  legend.justification = c("right", "top"),
  legend.box.just = "right",
  legend.margin = margin(6, 6, 6, 6)
)

# custom box around legend
basic + theme(
  legend.box.background = element_rect(color="red", size=1),
  legend.box.margin = margin(116, 6, 6, 6)
)

# custom the key
basic + theme(legend.key = element_rect(fill = "white", colour = "black"))

# custom the text
basic + theme(legend.text = element_text(size = 8, colour = "red"))

# custom the title
basic + theme(legend.title = element_text(face = "bold"))


#-----
library(ggplot2)
library(grid)
library(gridExtra)
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
(d <- ggplot(dsamp, aes(carat, price)) +
    geom_point(aes(colour = clarity)) +
    theme(legend.position = c(0.06, 0.75))
)

library(lemon)

reposition_legend(d, 'top left')

#multiple legends
d2 <- d + aes(shape=cut) + 
  theme(legend.box.background = element_rect(fill='#fffafa'),
        legend.background = element_blank())
reposition_legend(d2, 'left')

reposition_legend(d + theme_classic(), 'top left')

reposition_legend(d + theme_bw(), 'top left', x=0.002, y=1-0.002)

reposition_legend(d + theme_bw(), 'top left', offset=0.002)

d2 <- d + facet_grid(.~cut)
gtable_show_names(d2)
reposition_legend(d2, 'top left', panel = 'panel-3-1')
reposition_legend(d + facet_wrap(~cut, ncol=3), 'top left', panel='panel-3-2')

d3 <- d + facet_wrap(~cut, ncol=3) + scale_color_discrete(guide=guide_legend(ncol=3))
reposition_legend(d3, 'center', panel='panel-3-2')
#https://cran.r-project.org/web/packages/lemon/vignettes/legends.html


================================================
FILE: 34-ggplots/ggplot3.R
================================================
# ggplot
# 
#http://www.r-graph-gallery.com/209-the-options-of-barplot/


================================================
FILE: 34-ggplots/ggplot5.R
================================================

library(ggplot2)
dat<-rnorm(80)
dat<-data.frame(dat)
p<-ggplot(dat, aes(x=dat))+geom_histogram()
p<-p+geom_rug(sides="b", colour="blue")
p


p + geom_rug(sides = "b", aes(y = 0), position = "jitter", colour = "blue")
#using some more obvious data:
  
dat <- c(rep(1, 50), rep(2, 50))
dat <- data.frame(dat)



================================================
FILE: 34-ggplots/ggplot6.R
================================================
# GG Plot
#
data(mtcars)
names(mtcars)
hist(mtcars$mpg)

library(ggplot2)
ggplot(mtcars, aes(x = mtcars$mpg)) # nothing
ggplot(mtcars, aes(x = mtcars$mpg)) + geom_histogram()

housing = read.csv('./data/landdata-states.csv')
# Complex
head(housing[,c('Home.Value', 'Date')])
plot(Home.Value ~ Date, data=subset(housing, State='MA'))

points(Home.Value ~ Date, data=subset(housing, State='TX'), col='red')
legend(1975, 400000, c('MA', 'TX'), title='State', col=c('black', 'red'), pch=c(1,1))

ggplot(subset(housing, State %in% c('MA', 'TX')), 
       aes(x=Date, y=Home.Value, color=State)) + geom_point()

ggplot(subset(mtcars, gear %in% c(3,4)), aes(x=cyl, y=gear, col=factor(am))) + geom_point()

help.search("geom_", package = "ggplot2")
data("airquality")
str(airquality)
str(faithful)
str(sleep)
sleep
AirPassengers
housing
str(housing)
hp2001Q1 = subset(housing, Date == 2001.25)
dim(hp2001Q1)

ggplot(hp2001Q1, aes(y=Structure.Cost, x=Land.Value)) + geom_point()
#Eg1
hp2001Q1 <- subset(housing, Date == 2001.25)
ggplot(hp2001Q1, aes(y = Structure.Cost, x = Land.Value)) +
  geom_point()
ggplot(hp2001Q1, aes(y =Structure.Cost, x = log(Land.Value))) +   geom_point()

#Eg2
ggplot(mtcars, aes(y = mpg, x = wt)) +
  geom_point()

#Eg1
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
p1 + geom_point(aes(color = Home.Value)) +
  geom_line(aes(y = pred.SC))

#Eg1
p1 +
  geom_point( aes(color = Home.Value )) +
  geom_smooth()

#Eg1
p1 +
  geom_text(aes( label=State), size=3)

#Eg2 : ggrepel
library(ggrepel)
p1 +
  geom_point() +
  geom_text_repel( aes(label=State), size=3)

p1 +
  geom_point (aes (size=2),
              color='red')

#Eg
p1 +
  geom_point(aes (color = Home.Value, shape=region))
             
#Eg2 : R in Action @19.2
library(ggplot2)
ggplot(data = mtcars, aes(x=wt, y=mpg)) +
#  geom_point() +
#geom_point(pch=17, col='blue', size=2)
geom_smooth(method='lm', color='red', linetype=2) +
  labs(title = "Automobile Data", x="Weight", y="Miles per Gallon")


#Eg3: RinA : Grouping
mydata=mtcars
mydata$am = factor(mydata$am, levels=c(0,1), labels=c('Automatic', 'Manual'))
mydata$vs = factor(mydata$vs, levels=c(0,1), labels=c('V-Engine', 'Straight Engine'))
levels(mydata$cyl)
mydata$cyl = factor(mydata$cyl)

str(mydata)
ggplot(data=mtcars, aes(x=hp, y=mpg, shape=cyl, col=cyl)) +   scale_shape_identity() +  geom_point(size=1) +   facet_grid(am~vs) +  labs(title=' Automobile Data by Engine Type', x ='Horsepower', y='Miles per Gallon')


#Eg4 RiA : Singer
data(singer, package='lattice')
#histogram
ggplot(singer, aes(x =height)) + geom_histogram()
#boxplot
ggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()

#Eg5a: RiA : Salaries
data(Salaries, package='carData')
Salaries
ggplot(Salaries, aes(x=rank, y=salary)) +
  geom_boxplot( fill='cornflowerblue', color='black', notch=T) +
  geom_point(position='jitter', color='blue', alpha=.5) +
  geom_rug(sides='l', color='black')

#Interpretations
#1- salaries of 3 ranks differ from each other (No overlap in the box plot notches)
#2 variance in salaries increases with greater rank, with larger range of salaries for full professors
# 3 : salaries of 3 professors is so large to make them outlier's (3 black dots)

?singer
head(singer)
table(singer)
str(singer)
data('Salaries', package='car')
str(Salaries)
head(Salaries)
?Salaries




#Eg1
data(Salaries, package='carData' )
ggplot( data=Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  scale_x_discrete( breaks=c('AsstProf', 'AssocProf', 'Prof'), labels= c('Assistant \n Professor', 'Associate \n Professor', 'Full \n Professor' )) +
  scale_y_continuous(breaks = c(5000, 10000, 15000, 20000), labels =c('Rs 50K', 'Rs 100k', 'Rs 150K', 'Rs 200K')) +
#  labs(title= 'Faculty Salary by Rank and Sex', x=' Salaries', y=' Ranks')

#Eg1
labs(title= 'Faculty Salary by Rank and Sex', y=' Salaries', x=' Ranks', fill='Gender') +
theme(legend.position =c(.1, .8))
      
ggplot( data=Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  scale_x_discrete( breaks=c('AsstProf', 'AssocProf', 'Prof'), labels= c('Assistant \n Professor', 'Associate \n Professor', 'Full \n Professor' )) +
  scale_y_continuous(breaks = c(5000, 10000, 15000, 20000), labels =c('Rs 50K', 'Rs 100k', 'Rs 150K', 'Rs 200K')) +
  labs(title= 'Faculty Salary by Rank and Sex', y=' Salaries', x=' Ranks', fill='Gender') +
  theme(legend.position =c(.1, .8))



#Eg1a
ggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +
  geom_point(shape=21, color='black', fill='cornsilk') +
  labs(x='Weight', y='Miles per Gallon' , title='Bubble Chart', size='Engine \n Displacement')


Eg1b
data(Salaries, package='carData')
ggplot(data=Salaries, aes(x=yrs.since.phd, y = salary, color=rank)) +
  #scale_color_manual(values=c('orange', 'olivedrab', 'navy')) +
  scale_color_brewer(palette='Set1') +       
  geom_point(size=2)
       

mytheme <- theme(plot.title=element_text(face="bold.italic",
                                         size="14", color="brown"),
                 axis.title=element_text(face="bold.italic",
                                         size=10, color="brown"),
                 axis.text=element_text(face="bold", size=9,
                                        color="darkblue"),
                 panel.background=element_rect(fill="white",
                                               color="darkblue"),
                 panel.grid.major.y=element_line(color="grey",
                                                 linetype=1),
                 panel.grid.minor.y=element_line(color="grey",
                                                 linetype=2),
                 panel.grid.minor.x=element_blank(),
                 legend.position="top")

ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
  geom_boxplot() +
  labs(title="Salary by Rank and Sex", 
       x="Rank", y="Salary") +
  mytheme

#Eg1
data(Salaries, package="car")
p1 <- ggplot(data=Salaries, aes(x=rank)) +
  geom_bar()
p2 <- ggplot(data=Salaries, aes(x=sex)) +
  geom_bar()
p3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +
  geom_point()
library(gridExtra)
grid.arrange(p1, p2, p3, ncol=3)


#Eg1
ggplot(data=mtcars, aes(x=mpg)) + geom_histogram()
ggsave(file="mygraph.pdf")



library(playwith)
library(lattice)
playwith(
  xyplot(mpg~wt|factor(cyl)*factor(am),
         data=mtcars, subscripts=TRUE,
         type=c("r", "p"))
)


================================================
FILE: 34-ggplots/ggplot7.R
================================================

#ggplot bar

library(ggplot2)

(p1 <- ggplot(mtcars, aes(x=cyl)) + geom_bar())

library(dplyr)
(p2 <- mtcars %>% group_by(cyl) %>% tally %>% ggplot(., aes(x=cyl, y=n)) + geom_bar(stat='identity'))

library(gridExtra)
grid.arrange(p1,p2, ncol=2)


mtcars %>% group_by(cyl) %>% tally %>% ggplot(., aes(x=factor(cyl), y=n)) + geom_bar(stat='identity')  + labs(title="Main Title", x='Cylinders', y='Nos') + coord_flip()

ggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) + geom_point() + geom_smooth()

ggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) + geom_point() + geom_smooth() + theme(legend.position="none") + labs(title="legend.position='none'")  # remove legend


ggplot(mtcars, aes(x=cyl)) + geom_bar(fill='darkgoldenrod2') +
  theme(panel.background = element_rect(fill = 'steelblue'),
        panel.grid.major = element_line(colour = "firebrick", size=2),
        panel.grid.minor = element_line(colour = "blue", size=1))


ggplot(mtcars, aes(x=cyl)) + geom_bar(fill="firebrick") + theme(plot.background=element_rect(fill="steelblue"), plot.margin = unit(c(2, 4, 1, 3), "cm")) # top, right, bottom, left


================================================
FILE: 34-ggplots/twoaxis-gg.R
================================================
#two axis - ggplot2

library (ggplot2)
library(tidyverse)

(year = seq(2016,2020))
(students = c(1000,1200, 1500, 1300,2000))
(fac = c(100,120, 150, 130,180))

library(ggplot2)
(df = data.frame(year, students, fac))
g= ggplot() + geom_bar(data=df, aes(x=year,y=fac,fill=year, alpha=.4) , stat='identity') + geom_line(data=df, aes(x=year, y=students/10 ), colour='red') +  scale_y_continuous(sec.axis=sec_axis(~.*10, name="students")) + guides(fill='none', alpha='none')

g + theme(axis.text.y.right=element_text(colour="red"),
      axis.title.y.right=element_text(colour="red"),
      axis.text.y=element_text(colour="#346D9D"),
      axis.title.y=element_text(colour="#346D9D"),
      axis.title.x=element_blank(),
      legend.position="bottom")


================================================
FILE: 35-tidyverse/20a-dplyr.R
================================================
# Data Manipulation  through dplyr package
# understand iris, mtcars, women datasets and dplyr package

library(dplyr)
dplyr::tbl_df(iris)
print(dplyr::tbl_df(mtcars), n=15)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=5)
tbl_df(iris) %>% print(n = Inf)
tbl_df(iris) %>% print(width = Inf)
tbl_df(iris) %>% as.data.frame(iris)

library(dplyr)
# glimpse
glimpse(iris)
glimpse(mtcars)
str(iris)
View(iris)
?mtcars
data()
mtcars['mpg']
df = mtcars
row.names(df) = NULL
df %>% select(mpg)
head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)
distinct(mtcars)
df  = data.frame(a=c(2,2),b=c(2,2))
df
distinct(df)

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)
slice(mtcars,10:14)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))
#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

df = data.frame(marks=c(1,2,3,7,1))
cbind(df, dplyr::mutate_each(df, funs(min_rank)))

mtcars %>% lead() %>% lag()
dplyr::n(mtcars)
select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df
rename(df, marks2 = marks)
df %>% mutate(marks2 = marks + 2, marks3 = marks + 4)
df %>% transmute(marks2 = marks + 2, marks3 = marks + 4)

library(nycflights13)
data(flights)


destinations <- group_by(flights, dest)
destinations
summarise(destinations,
          planes = n_distinct(tailnum),
          flights = n()
)


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter


# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)



#Default dataset
#
Default %>% group_by(balance) %>% arrange(desc(balance)) %>% top_n(5, balance)
Default %>% group_by(balance) %>% arrange(balance) %>% top_n(5, balance)

Default %>% group_by(balance) %>% arrange(balance) %>% filter(row_number() %in% c(1:3,n()-5:n()))

================================================
FILE: 35-tidyverse/21a-dplyr-select.R
================================================
# Select only selected variabels

iris4 = iris
head(iris4)
head(iris <- tbl_df(iris)) # so it prints a little nicer
select(iris, starts_with("Petal"))  # column name starts with
select(iris, ends_with("Width"))
select(iris, contains("etal"))
select(iris, matches(".t."))
select(iris, Petal.Length, Petal.Width)
vars <- c("Petal.Length", "Petal.Width")
select(iris, one_of(vars))

df <- as.data.frame(matrix(runif(100), nrow = 10))
df
df <- tbl_df(df[c(3, 4, 7, 1, 9, 8, 5, 2, 6, 10)])
df
select(df, V4:V6)
select(df, num_range("V", 4:6))


#select-----



================================================
FILE: 35-tidyverse/21b-dplyr-slice1.R
================================================
# Slice
# does not work with DBMS

library(dplyr)
slice(mtcars, 1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

mtcars
(by_cyl <- group_by(mtcars, cyl)) # ???
nrow(by_cyl)

slice(by_cyl, 1:2)


================================================
FILE: 35-tidyverse/21c-dplyr-mutate1.R
================================================
# Mutate : add new, Add new without old
#Mutate adds new variables and preserves existing; 
#transmute drops existing variables.

library(dplyr)

mutate(mtcars, displ_l = disp / 61.0237)
transmute(mtcars, displ_l = disp / 61.0237)

mutate(mtcars, cyl = NULL)


================================================
FILE: 35-tidyverse/21d-dplyr-summarise.R
================================================
# summarise
#Summarise multiple values to a single value.

library(dplyr)

summarise(mtcars, mean(disp))
summarise(group_by(mtcars, cyl), mean(disp))
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


================================================
FILE: 35-tidyverse/21e-dplyr-filter1.R
================================================
# Filter

library(dplyr)

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))


================================================
FILE: 35-tidyverse/21f-dplyr-str.R
================================================
# Data Manipulation using tidyr
#
salesd = sales
library(dplyr)
tbl_df(salesd) # convert to tbl class

glimpse(salesd)  # dense summary of tbl data

View(salesd) # spreasheet like format

salesd %>% group_by(daywk) # no summary till formula

salesd %>% group_by(daywk) %>% summarise(avg=mean(cakes)) %>% arrange(avg)

summarise_each(salesd, funs(mean))
count(salesd, cookies)

#Grouping  # need to use function to do some action
group_by(salesd, daywk)
salesd %>% group_by(daywk) %>%  summarise(avg = mean(cookies))


#Reshaping
#spread
head(tidyr::separate(data= salesd, col=sdate, into= c('y','m','d'), sep='-'))
head(tidyr::unite(data=salesd[,c('sdate','daywk')] , col='New Column',sep='::'), remove=T)
names(salesd)
head(tidyr::spread(salesd[1:5,c('sdate','pies','daywk')],sdate, pies, drop=T, fill='NoSale' ))
head(tidyr::spread(salesd[1:5,c('sdate','pies')],sdate, pies, drop=T, fill='NoSale' ))



dplyr::arrange(salesd, daywk)
head(salesd)


#Filter
dplyr::filter(salesd, daywk == 'Thursday')

dplyr::filter(salesd, cakes > 100 & pies < 40)

#mutate
mutate(salesd, incpies = pies * 1.5)  %>% filter(incpies > 90)


================================================
FILE: 35-tidyverse/21g-dplyr-arrange.R
================================================
#arrange

library(dplyr)
# sort in particular order
arrange(mtcars, +cyl, -disp)
arrange(mtcars, desc(disp))


================================================
FILE: 35-tidyverse/22b-dplyr-seperate1.R
================================================
#Seperate using dplry
#http://cmdlinetips.com/2018/03/how-to-split-text-in-a-column-in-data-frame-in-r/

#’s separate function is the best option to separate a column or split a column of text the way you want. Let us see some simple examples of using tidyr’s separate function.

library(dplyr)
library(tidyr)
df <- data.frame(file_name = c("1_jan_2018.csv",
                               "2_feb_2018.csv", 
                               "3_mar_2018.csv"))

df
df %>% separate(file_name, c("ID", "Month","Year", "rest"))
#tidyr automatically found that the delimiters are underscore and dot and separted the single column to four columns with the names specified

df %>% separate(file_name,c("ID", "Month","Year"))
#want only part of text in a column.

df %>% separate(file_name, 
                c("ID", "Month","Year"),
                extra='drop')

df %>% tidyr::separate(file_name, 
                       c("ID"), extra='drop')

df %>%   tidyr::separate(file_name, c("ID", "Month","Year"),extra='drop') %>%   select('Month')


df %>%  tidyr::separate(file_name, c("ID", "Month","Year"), extra='drop') %>%
  tidyr::unite('ID_Month',c('ID','Month'))


================================================
FILE: 35-tidyverse/22b-group.R
================================================
# dplyr - group by

library(dplyr)

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg))

#subgroups
mtcars %>% group_by(am, gear) %>% summarise(mean(mpg))

#subgroups
mtcars %>% group_by(am, gear) %>% summarise(mean(mpg)) %>% ungroup()


================================================
FILE: 35-tidyverse/22c-summarise.R
================================================
#Summarise with Group

library(dplyr)
head(mtcars)
mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))

#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))
mtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))



#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))





mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)



================================================
FILE: 35-tidyverse/22g-tibble-rownames.R
================================================
# dplyr row names
library(dplyr)

head(iris)
iris %>% head()

head(mtcars)
mtcars[1:2]
mtcars %>% select(mpg,wt)
df = mtcars
df2 = mtcars
head(df)
df = tibble::rownames_to_column(df, var='cars')
names(df)
df %>% select(mpg,wt,cars)

#has
tibble::has_rownames(mtcars)
tibble::has_rownames(df)

#remove rownames  
#see without rownames
head(df2[1:5])
tibble::remove_rownames(df2)



#rowid as column
tibble::rowid_to_column(df, var = "rowid")

#column to rownames
head(df[1:5])
tibble::column_to_rownames(df, var = "cars")


================================================
FILE: 35-tidyverse/25b-magrittr.R
================================================
# Pipe Symbols in tidyverse

library(magrittr)
#
library(magrittr)

car_data <- 
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print

#not a good alternative
car_data <- 
  transform(aggregate(. ~ cyl, 
                      data = subset(mtcars, hp > 100), 
                      FUN = function(x) round(mean(x, 2))), 
            kpl = mpg*0.4251)
car_data

#anonymous functions
head(car_data,1)
tail(car_data,1)
car_data %>%
  (function(x) {
    if (nrow(x) > 2) 
      rbind(head(x, 1), tail(x, 1))
    else x
  })

#shorthand notation of above
car_data %>%
{ 
  if (nrow(.) > 0)
    rbind(head(., 1), tail(., 1))
  else .
}

#useful but less used
1:10 %>% (substitute(f(), list(f = sum)))

#tee----
#returns the left-hand side value, and not the result of the right-hand side operation.
set.seed(1234)
rnorm(200) %>%
  matrix(ncol = 2) %T>%
  plot %>% # plot usually does not return anything. 
  colSums
set.seed(1234)
x=rnorm(200) 
m=matrix(x, ncol=2)  #tee applied after this to skip 1 function for print
plot(m)
colSums(m)



#$operator-----
iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) %$%
  cor(Sepal.Length, Sepal.Width)

data.frame(z = rnorm(100)) %$% 
  ts.plot(z)



#%<>%----
#shorthand notation for expressions like foo <- foo %>% bar %>% baz, which boils down to foo %<>% bar %>% baz

mpgwt <- mtcars %>% filter(mpg > 25) %>% select(mpg, wt) %>% print
mpgwt <- mpgwt %>% transform(mpg = mpg * 2)  %>% print
mpgwt %<>% transform(mpg = mpg * 2) %>% print

#aliases ------
set.seed(1234)
rnorm(1000)    %>%
  multiply_by(5) %>%
  add(5)         %>%
  { 
    cat("Mean:", mean(.), 
        "Variance:", var(.), "\n")
    head(.)
  }

#alternative
set.seed(1234)
rnorm(100) %>% `*`(5) %>% `+`(5) %>% 
{
  cat("Mean:", mean(.), "Variance:", var(.),  "\n")
  head(.)
}


================================================
FILE: 35-tidyverse/26c-tidyr-DSR1.R
================================================
# tidyr 

library(tidyr)
#devtools::install_github("garrettgman/DSR")
library(DSR)
head(table1)
head(table2)
head(table3)
head(table4)
head(table5)
head(table6)


table1$cases

#spread----
library(tidyr)
head(table2)
spread(table2, type, count)
?spread


#gather-----
head(table4)
gather(table4, "year", "cases", 2:3)
head(table4)
gather(table4, "year", "population", 2:3)

gather(table4, "year", "population", -1)

?gather
#seperate----
#turns a single character column into multiple columns by splitting the values of the column wherever a separator character appears.
?separate
head(table3)
separate(table3, rate, into = c("cases", "population"))
# specify character
separate(table3, rate, into = c("cases", "population"), sep = "/")
#integer position
head(table3)
separate(table3, year, into = c("century", "year"), sep = 2)


#unite----
head(table6)
unite(table6, "new", century, year, sep = "")
?unite


================================================
FILE: 35-tidyverse/26d-tidyr-DSR-who.R
================================================
# tidyr 

library(tidyr)
#devtools::install_github("garrettgman/DSR")
library(DSR)
data(who)
#View(who)
#who data set in the DSR package contains cases of tuberculosis (TB) reported between 1995 and 2013 sorted by country, age, and gender. 

dim(who) #60 columns
names(who)
head(who[1:10])


#gather-----
head(names(who),10)
who1 <- gather(who, "code", "value", 5:60)
head(who1,10)

#seperate----
#default sep _
who2 <- separate(who1, code, c("new", "var", "sexage"))
head(who2)
table(who2$sexage)

#second pass will split sexage after the first character to create a sex column and an age column.
#split after 1st character : f014 - f and 014
who3 <- separate(who2, sexage, c("sex", "age"), sep = 1)
head(who3)


#spread----
#move the rel, ep, sn, and sp keys into their own column names with spread().
head(names(who3))
head(who3[c('var')])
table(who3$var)
who4 <- spread(who3, var, value)
head(who4)

dim(who4)
names(who4)


================================================
FILE: 35-tidyverse/31b-plyr1.R
================================================
#Plyr Package


library(plyr)
#make some example data
dd = data.frame(matrix(rnorm(216),72,3), 
                c(rep("A",24),rep("B",24),rep("C",24)),
                c(rep("J",36),rep("K",36)))
colnames(dd) <- c("v1", "v2", "v3", "dim1", "dim2")
dd
head(dd)
aggregate(dd$v1, by=list(dd$dim1, dd$dim2), FUN=mean)
aggregate(v1 ~ dim1 + dim2 , data=dd, FUN=mean)

aggregate(cbind(v1, v2) ~ dim1 + dim2 , data=dd, FUN=mean)

#ddply is the plyr function

plyr::ddply(dd, c("dim1","dim2"), function(df) mean(df$v1))

ddply(dd, c("dim1","dim2"), function(df)c(mean(df$v1), mean(df$v2), mean(df$v3), sd(df$v1), sd(df$v2), sd(df$v3)))




#
set.seed(1)
d <- data.frame(year = rep(2000:2002, each = 3),
                count = round(runif(9, 0, 20)))
print(d)

library(plyr)
ddply(d, "year", function(x) {
  mean.count <- mean(x$count)
  sd.count <- sd(x$count)
  cv <- sd.count/mean.count
  data.frame(cv.count = cv)
})


ddply(d, "year", summarise, mean.count = mean(count)) #mean of years
ddply(d, "year", transform, total.count = sum(count)) # yearwise sum also
d
cumsum(d$count)


ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),
      cv = sigma/mu)

par(mfrow = c(1, 3), mar = c(2, 2, 1, 1), oma = c(3, 3, 0, 0))
d_ply(d, "year", transform, plot(count, main = unique(year), type = "o"))
mtext("count", side = 1, outer = TRUE, line = 1)
mtext("frequency", side = 2, outer = TRUE, line = 1)

#----
baseball.dat <- subset(baseball, year > 2000) # data from the plyr package
head(baseball.dat)
x <- ddply(baseball.dat, c("year", "team"), summarize, homeruns = sum(hr))
head(x)

#http://seananderson.ca/2013/12/01/plyr/


#Speed
system.time(ddply(baseball, "id", summarize, length(year)))
system.time(tapply(baseball$year, baseball$id,function(x) length(x)))

#alternative
library(data.table)
dt <- data.table(baseball, key = "id")
system.time(dt[, length(year), by = list(id)])


#http://philogrammer.com/2016-12-03/aggregte/
library(plyr)
aggregate(price ~ cut, diamonds, each(mean, median))


================================================
FILE: 35-tidyverse/32b-plyr-mutate.R
================================================
#Mutate

library(plyr)
df=mtcars

#Case-ddply?----
ddply(df, .(am), mutate,  mgp2 = mpg*2 + 1)
ddply(df, .(mpg), mutate,  mgp2 = mpg*2 + 1)
base2


================================================
FILE: 35-tidyverse/33d-dplyr-joins.R
================================================
# DM - dplyr - joins

# Combine Data Sets

(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))
(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))

dplyr::left_join(a, b, by = "x1")
#Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")
#Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")
#Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")
#Join data. Retain all values, all rows.

dplyr::semi_join(a, b, by = "x1")
#All rows in a that have a match in b.

cbind(a,b)

dplyr::anti_join(a, b, by = "x1")
#All rows in a that do not have a match in b.

#-----
y = data.frame(x1=c('A','B','C'), x2=c(1,2,3))
z = data.frame(x1=c('B','C','D'), x2=c(2,3,4))
cbind(y,z)

dplyr::intersect(y, z)  # B&C
#Rows that appear in both y and z.

dplyr::union(y, z)
#Rows that appear in either or both y and z.

dplyr::setdiff(y, z)
#Rows that appear in y but not z.

#Bind
dplyr::bind_rows(y, z)
#Append z to y as new rows.
dplyr::bind_cols(y, z)
#Append z to y as new columns.
#Caution: matches rows by position




================================================
FILE: 35-tidyverse/33f-dplyr-split.R
================================================
# data
str(df1$dob)
as.Date("5-Apr-91", '%d-%b-%y')
df1$dob = as.Date(df1$dob, '%d-%b-%y')
df1 %>% select(sname, dob) %>% tidyr::separate(dob, c("y", "m", "d"))


library(tidyr) #Reshape
df2 = df1
df2$name = rownames(df1)  # col a column which is unique name
head(df2)
df2long <- df2 %>% select(name, cyl, carb) %>% tidyr::gather(key = name, value = name, cyl, carb) 
head(df1long)
distinct(df1long)

tidyr::unite(df1, 'rollnoname', c(rollno, sname), sep="-") %>% select(1:5)%>% head



================================================
FILE: 35-tidyverse/plyr-ddply-gpsum.R
================================================
# Group Summaries



#Case- ----
#dplyr
df = mtcars[,c('cyl','am','hp','wt','mpg')]

# Note the use of the '.' function to allow
# group and sex to be used without quoting
ddply(df, .(cyl, am), summarize,  mean = round(mean(wt), 2),sd = round(sd(hp), 2))

#Case-
# An example using a formula for .variables
ddply(df[1:15,], ~ am, nrow)

# Applying two functions; nrow and ncol
ddply(df, .(am), c("nrow", "ncol"))

base2 <- ddply(baseball, .(id), mutate,
               career_year = year - min(year) + 1
)

================================================
FILE: 35-tidyverse/tidyr1.R
================================================
# Data Manipulation using tidyr
#
salesd = sales
library(dplyr)
tbl_df(salesd) # convert to tbl class

glimpse(salesd)  # dense summary of tbl data

View(salesd) # spreasheet like format

salesd %>% group_by(daywk) # no summary till formula

salesd %>% group_by(daywk) %>% summarise(avg=mean(cakes)) %>% arrange(avg)

summarise_each(salesd, funs(mean))
count(salesd, cookies)

#Grouping  # need to use function to do some action
group_by(salesd, daywk)
salesd %>% group_by(daywk) %>%  summarise(avg = mean(cookies))


#Reshaping
#spread
head(tidyr::separate(data= salesd, col=sdate, into= c('y','m','d'), sep='-'))
head(tidyr::unite(data=salesd[,c('sdate','daywk')] , col='New Column',sep='::'), remove=T)
names(salesd)
head(tidyr::spread(salesd[1:5,c('sdate','pies','daywk')],sdate, pies, drop=T, fill='NoSale' ))
head(tidyr::spread(salesd[1:5,c('sdate','pies')],sdate, pies, drop=T, fill='NoSale' ))



dplyr::arrange(salesd, daywk)
head(salesd)


#Filter
dplyr::filter(salesd, daywk == 'Thursday')

dplyr::filter(salesd, cakes > 100 & pies < 40)

#mutate
mutate(salesd, incpies = pies * 1.5)  %>% filter(incpies > 90)


================================================
FILE: 35-tidyverse/zz-dplyr1.R
================================================
# Data Manipulation  through dplyr

library(dplyr)
dplyr::tbl_df(iris)
print(dplyr::tbl_df(mtcars), n=15)
#print(dplyr::tbl_df(mtcars), width=5)
tbl_df(iris) %>% print(n = Inf)
tbl_df(iris) %>% print(width = Inf)
tbl_df(iris) %>% as.data.frame(iris)

library(dplyr)
# glimpse
glimpse(iris)
glimpse(mtcars)
str(iris)
View(iris)
?mtcars
data()
mtcars['mpg']
df = mtcars
row.names(df) = NULL
df %>% select(mpg)
head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 

filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)
distinct(mtcars)
df  = data.frame(a=c(2,2),b=c(2,2))
df
distinct(df)

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)
slice(mtcars,10:14)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))
#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

df = data.frame(marks=c(1,2,3,7,1))
cbind(df, dplyr::mutate_each(df, funs(min_rank)))

mtcars %>% lead() %>% lag()
dplyr::n(mtcars)
select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df
rename(df, marks2 = marks)
df %>% mutate(marks2 = marks + 2, marks3 = marks + 4)
df %>% transmute(marks2 = marks + 2, marks3 = marks + 4)

library(nycflights13)
data(flights)


destinations <- group_by(flights, dest)
destinations
summarise(destinations,
          planes = n_distinct(tailnum),
          flights = n()
)


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter


# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)



#Default dataset
#
Default %>% group_by(balance) %>% arrange(desc(balance)) %>% top_n(5, balance)
Default %>% group_by(balance) %>% arrange(balance) %>% top_n(5, balance)

Default %>% group_by(balance) %>% arrange(balance) %>% filter(row_number() %in% c(1:3,n()-5:n()))

================================================
FILE: 35-tidyverse/zz-tidy-dataformating.R
================================================
#http://garrettgman.github.io/tidying/



================================================
FILE: 41-LM/10a-lm-women2.R
================================================
# Regression Analysis

data(women)
women
str(women)
cov(women$height, women$weight)
#69 : which show positive relationship between height and weight
cor(women$height, women$weight)
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight)
abline(lm(weight ~ height, data=women), col='red')
names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)
summary(fit1)

#coefficients
coef(fit1)
#p values for b0 & b1 are significant as it is < 0.05
#pvalue1 = 1.71e-09 < 0.05
#pvalue2 = 1.09e-14 < 0.05

#R2
(summary(fit1))$r.squared  #0.991
(summary(fit1))$adj.r.squared  #0.9903
#99% of variation in weight is explained by Height
#Good Linear Model for Prediction


#FStats : p value
(summary(fit1))$fstatistic 
#pvalue : 1.09e-14  < 0.05 : Model exists
#There is at least 1 IV which explains variation in Y (DV)

range(women$height)
#Prediction : for height = 65, 66
(new1 = data.frame(height=c(65,66)))
predict(fit1, newdata = new1, type='response')
#136.7333 140.1833

predict(fit1, newdata = new1, interval='confidence')
predict(fit1, newdata = new1, interval='prediction')

set.seed(10)
(df$age = floor(runif(15, 20,30)))
set.seed(10)
(age = floor(runif(15, 20,30)))
df$age == age
cbind(df$age, age, df$age-age)
df$var2 = floor(runif(15, 30,40))
df
df$age2 = age
df[-c(1:2)]
df = df[-4]
df

#y = mx + c
#fit1= lm(formula= weight ~ height + age +var2 , data= df)
summary(fit1)
abline(fit1)
df
cor(df$weight, df$height)
df=women
df$predict = predict(fit1, newdata=data.frame(height=women$height))
df
abline(v=60)
abline(h=119)



================================================
FILE: 41-LM/10b-lm-salesarea2.R
================================================
# SLR Area vs Sales
#import from ggsheet
library(gsheet)
area1 = "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2023826519"
df2 = as.data.frame(gsheet::gsheet2tbl(area1))
str(df2)
df = df2
fit2 = lm(Y ~ X, data=df)
new2 = data.frame(X=c(1.5,2,3,4,5))
new2
predict(fit2, newdata= new2)


================================================
FILE: 41-LM/10c-MLR-omni.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion

#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
omni1 = data.frame(sales, price, promotion)
head(omni1)
str(omni1)

#2nd Method : CSV file
omni2 = read.csv(file.choose())

#3rd Method : gsheet 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
omni3 = as.data.frame(gsheet::gsheet2tbl(url))

#Make one of data frames active
omni = omni1
head(omni)

?lm  #see help of LM
#Simple Linear Model would look like this
slr1 = lm(formula = sales ~ price, data=omni) # sales depend on price of item
slr2 = lm(formula = sales ~ promotion, data=omni) # sales depend on promotion exp
summary(slr1)
summary(slr2)


#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
mlrmodel1 = lm(sales ~ price + promotion, omni)
#mlrmodel1 = lm(omni, sales ~ price + promotion)
?lm
#how to give parameter values in different sequence, use arguments names if in different order
mlrmodel1 = lm( data=omni, formula = sales ~ price + promotion)

range(omni$sales)
summary(mlrmodel1)  # summary statistics IMP STEP
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals

coef(mlrmodel1) #coefficients b1, b2
#anova(mlrmodel1) #seeing from anova model

head(omni)

plot(y=omni$sales, x=omni$promotion)

#Predicted Values----
dim(omni)
fitted(mlrmodel1)
cbind(omni, fitted(mlrmodel1), residuals(mlrmodel1))
summary(mlrmodel1)

names(omni)
#create a dataframe of new sample values
(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))
range(omni$price); range(omni$promotion)
predict(mlrmodel1, newdata=ndata1)
cbind(ndata1, Predict=predict(mlrmodel1, newdata=ndata1, predict='response'))






#R2 and Adjs R2
names(mlrmodel1)
summary(mlrmodel1)
summary(mlrmodel1)$r.squared
summary(mlrmodel1)$adj.r.squared

#Manual Calculation of Adjs R2
(r2 = summary(mlrmodel1)$r.squared)
k = 2 # no of IVs
(n = nrow(omni)) # sample size
(adjr2 = 1 - ( (1 - r2) * ((n - 1)/ (n - k - 1))))


# Fstatistics
summary(mlrmodel1)$fstatistic[1]  # from output of model

(df1 = k) ;  (df2 = n-k-1)  
qf(.95, df1, df2)  # from table wrt df1 & df2
#Model Fstats > table(Fstat) 

# Pvalue of Model
fstat = summary(mlrmodel1)$fstatistic 
pf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE) 
# this is  < 0.05 : Significant
# 

#Plots of the Modle
plot(mlrmodel1,1)  # no pattern, equal variance
plot(mlrmodel1,2)  # Residues are normally distributed
plot(mlrmodel1,3)
plot(mlrmodel1,4)  # tells outliers which affect model


# Confidence Intervals


#Fitted values : Predicting on IVs using model
fitted(mlrmodel1)
residuals(mlrmodel1) 
mlrmodel1$residuals
cbind(omni$sales, fitted(mlrmodel1), omni$sales - fitted(mlrmodel1), residuals(mlrmodel1))

#sqrt(sum((residuals(mlrmodel1)^2)))
names(mlrmodel1)
summary(mlrmodel1)




#Diagnostics Test for Checking Assumptions 
#Should be Linear relationship between Residuals Vs Ypi, X1i, X2i
cbind(fitted(mlrmodel1), residuals(mlrmodel1))
plot(cbind(fitted(mlrmodel1), residuals(mlrmodel1)))
#not quadratic
plot(cbind(omni$price, residuals(mlrmodel1)))
plot(cbind(omni$promotion, residuals(mlrmodel1)))
#May indicate quadratic term of IVs


#Train and Test Data


# RMSE


omni
names(omni)
mlr2 = lm(sales ~ price + promotion, data= omni)
summary(mlr2)
new1=data.frame(price=60:70, promotion=400)
predict(mlr2, newdata = new1)
cbind(new1,predict(mlr2, newdata = new1) )




================================================
FILE: 41-LM/10e-lm-errorplot.R
================================================
#LM - Sample Cases

#Case1----
#Different Data
x <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)
y <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92)
mod2 <- lm(y ~ x)
plot(x, y, xlim=c(min(x)-5, max(x)+5), ylim=c(min(y)-10, max(y)+10))
abline(mod2, lwd=2)
res <- signif(residuals(mod2), 5)
pre <- predict(mod2)
segments(x, y, x, pre, col="red")
library(calibrate)
textxy(x, y, res, cx=0.7)

================================================
FILE: 41-LM/13b-lm-commands.R
================================================

fit = lm(weight ~ height, data = women)
attributes(fit)
attributes(summary(fit))


================================================
FILE: 41-LM/16b-SLM-women2.R
================================================
# Regression Analysis
# Simple Linear with 1 IV and 1 DV

data(women)
women
names(women)
str(women)

cov(women$height, women$weight)
#69 : which show positive relationship between height and weight

cor(women$height, women$weight)
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight)
abline(lm(weight ~ height, data=women), col='red')
names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)  # creating a model
summary(fit1)  #summary of the Model

#Ho: (F Test) : No relationship between Y and any X
#Ha:  There is relationship between Y and at least one X
# p < 0.05 Reject Ho in favour of Ha

attributes(fit1)  #output of model

#coefficients
coef(fit1)
#p values for b0 & b1 are significant as it is < 0.05
#pvalue1 = 1.71e-09 < 0.05
#pvalue2 = 1.09e-14 < 0.05

coef(fit1)
#Y = -87 + 3.4 * X  # no extrapolations
(Y = -87 + 3.4 * 20)  # weight cannot be negative
(Y = -87 + 3.4 * 58)
(Y = -87 + 3.4 * 61.5)
(Y = -87 + 3.4 * 72)
range(women$height)
women


#R^2
(summary(fit1))$r.squared  #0.991
(summary(fit1))$adj.r.squared  #0.9903
#99% of variation in weight is explained by Height
#Good Linear Model for Prediction


#FStats : p value
(summary(fit1))$fstatistic 
#pvalue : 1.09e-14  < 0.05 : Model exists
#There is at least 1 IV which explains variation in Y (DV)


#Prediction : for height = 65, 66
(new1 = data.frame(height=c(65,66)))
new1
(p1=predict(fit1, newdata = new1))
cbind(new1, p1)
#136.7333 140.1833

(new2 = data.frame(height=c(60,69)))
(p2=predict(fit1, newdata = new2))
cbind(new2, p2)

?predict
predict(fit1, newdata = new1, interval='confidence')
predict(fit1, newdata = new1, interval='prediction')

women
women$weight
fitted(fit1) # predicted values of all original Xs
predict(fit1, newdata = data.frame(women$height)) # same as above for single X

new3 = data.frame(women$height)  # DF using height of original data
cbind(women, fitted(fit1), residuals(fit1))  # compare women, predicted, errors
residuals(fit1)  # diff between actual and predicted values of weight
summary(fit1)
summary(residuals(fit1))

plot(fit1)


x1 = c(1,2,3)
x2 = c(4,5,6)
x1
x2
cbind(x1,x2)
rbind(x1,x2)


================================================
FILE: 41-LM/16c-SLM-women1.R
================================================
# Simple Linear Regression : Built in Data Set Women
# Predit Weight of heights of women - 60.5, 75.5

#Check the Data set
women 
str(women)

cov(women$height, women$weight)
#69 : which show positive relationship between height and weight

cor(women$height, women$weight)
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight)
abline(lm(weight ~ height, data=women), col='red')
names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)  # creating a model
summary(fit1)  #summary of the Model


#Predict
fit = lm(weight ~ height, data=women)
(ndata = data.frame(height = c(60.5, 75.5)))
predictedwt = predict(fit, newdata = ndata)
cbind(ndata, predictedwt)

#End : Simple way to predict in SLM

================================================
FILE: 41-LM/16e-SLM-women-A.R
================================================
# Simple Linear Regression : Built in Data Set Women
# Check for assumptions of Regression in the data Set

str(women)
fit = lm(weight ~ height, data=women)

#Initial Checks
cor(women$height, women$weight)
#there is Strong and Positive Relationship between height and weight
plot(women$height, women$weight)
plot(fit)

#Assumptions Regression
#Linearity----
#Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.
# component + residual plot 
plot(women$height, fitted(fit))
plot(residuals(fit) ~ fitted(fit))
plot(fit, which=1)
#residuals should be randomly distributed and not increase or decrease 


#Normality----
#Normality of residuals. The residual errors are assumed to be normally distributed.
plot(density(resid(fit)))
plot(fit, which=2)

#Homoscedasticity----
#Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity - opposite of heteroscedasticity)
plot(fit, which=3)
#No funnel shape, random distribution of residuals
plot(fit, which=4)

#Auto-Correlation----
#Independence of residuals error terms. (Not dependent on previous values)
durbinWatsonTest(fit)
#pvalue < 0; Ho that there is no correlation (r2=0) is accepted

#Outliers
plot(fit, which=4)
#potential outliers are highlighted 1, 14, 15 row
women[c(1,14,15),]

#Lets remove these values and then find R2
fit2 = lm(weight ~ height, data=women[-c(1,14,15),])
summary(fit2)
(summary(fit))$r.squared
(summary(fit2))$r.squared
AIC(fit, fit2) #lower value of AIC is better


#Potential Problems
#Non-linearity of the outcome - predictor relationships
#Heteroscedasticity: Non-constant variance of error terms.
#Presence of influential values in the data that can be:
#Outliers: extreme values in the outcome (y) variable
#High-leverage points: extreme values in the predictors (x) variable

#All these assumptions and potential problems can be checked by producing some diagnostic plots visualizing the residual errors.




================================================
FILE: 41-LM/16f-SLM-women-V.R
================================================
#Simple Linear Regression : Validation of the Model - women
#Is it good Model for Prediction, Are errors large, 
#Are Coefficient of variables significant

str(women)
fit1 = lm(weight ~ height, data=women)
summary(fit1)

#R2----
#Coefficient of determination : between 0 and 1; higher the better
(summary(fit1))$r.squared  #0.991
(summary(fit1))$adj.r.squared  #0.9903
#99% of variation in weight is explained by Height
#Good Linear Model for Prediction

#HypoTests----
#Hypothesis Tests for Linear coefficient b1
#if b1=0, no relationship between y and x's
#coefficients
coef(fit1)
#p values for b1 are significant as it's p value < 0.05 and coef > 0
summary(fit1)

#FTest----
#Overall Model Test
#FStats : p value
(summary(fit))$fstatistic 
summary(fit)
#p-value of FStats is 1.9e-14 << 0; Significant Model
# Theres is atleast one IV which has significant relationship with DV

#SSE
#std sum of sq errors should be min (0)
residuals(fit)
sum(residuals(fit))


================================================
FILE: 41-LM/16f-SLM-women.R
================================================
# Regression Analysis
data()
data(women)
women
str(women)
cov(women$height, women$weight)
cor(women$height, women$weight)
plot(women)
names(women)  #x- IV, y -DV
plot(women, xlim=c(0,75), ylim=c(0,180))
df = women
df
fit1 = lm(height ~ weight, data=women)
summary(fit1)

set.seed(10)
(df$age = floor(runif(15, 20,30)))
set.seed(10)
(age = floor(runif(15, 20,30)))
df$age == age
cbind(df$age, age, df$age-age)
df$var2 = floor(runif(15, 30,40))
df
df$age2 = age
df[-c(1:2)]
df = df[-4]
df

#y = mx + c
fit1= lm(formula= weight ~ height + age +var2 , data= df)
summary(fit1)
abline(fit1)
df
cor(df$weight, df$height)
df=women
df$predict = predict(fit1, newdata=data.frame(height=women$height))
df
abline(v=60)
abline(h=119)



================================================
FILE: 41-LM/16m-SLM-women2.R
================================================
#Simple Linear Regression 
#Predicting on variable only  : DV

data(women)
str(women)

#Batch of 15 women students have certain weights
#Predict weight of next women 

wt = women$weight

#Plot the weight
plot(wt)
mean(wt)  #This is what we predict normally
text(wt+2, labels = wt, cex=.6, col='green')
abline(h=mean(wt), col='red')
text(3,mean(wt)+2,label=paste('Predicited Wt', round(mean(wt))), col='red', cex=.7)

#error in predicting : draw line
segments(x0=1:15, y0=mean(wt), x1=1:15, y=wt, col='purple')
#Put Lables
e = mean(wt) - wt
text(1:15, wt+5, labels=round(e,1), cex=.8, col='orange')

#sum of errors
sum(e)  # approx zero

#this is why sum of squares is taken
sum(e^2)  # SSE
(rmse = sqrt(sum(e^2)/length(wt)))  #root mean square
# difference between predicted and actual values of DV
# Regression tries to minimise this error by creating a model based on best fit line
# if some indepedent variable is added there is likelihood of this error decreasing

#weight not dependent on any variable - mean(wt)
fit0 = lm(wt ~ 1)
summary(fit0)
#here the intercept is 137 which is also the mean weight
attributes(fit0)





#read this http://www.learnbymarketing.com/tutorials/explaining-the-lm-summary-in-r/



================================================
FILE: 41-LM/17a-LM-case1.R
================================================
# Regression Analysis
# 
sales = read.csv('./data/bowl.csv')
names(sales)
summary(sales)
?gsub
head(sales)
#sales$bowlprice = as.numeric(gsub("\\$", "", sales$bowlprice))
summary(sales)
names(sales)
sales1 = sales[,1:2]
names(sales1)
cor(sales1$bowls, sales1$bowlprice)

fit1 = lm(bowls ~ bowlprice, data=sales1)

summary(fit1)
head(sales1)
new1 = data.frame(bowlprice=c(7,8,9))
predict(fit1, newdata=new1)

plot(x=sales1$bowlprice, y= sales1$bowls)
sales2 = sales1[order(sales1$bowlprice),]
plot(x=sales2$bowlprice, y= sales2$bowls)
abline(fit1)




================================================
FILE: 41-LM/17b-LM-stock1.R
================================================
# Regression on Stock Prices
#Stock Prices

Yprice = c(80.13, 79.57, 79.93, 81.69, 80.82, 81.02)
Xprice = c(72.86, 72.88, 71.72, 71.54, 71, 71.78)
data = data.frame(Yprice, Xprice)
plot(y=data$Yprice, x=data$Xprice)
LinearR.lm = lm(Yprice ~ Xprice, data = data)
coef(LinearR.lm)
LinearR.lm
summary(LinearR.lm)$r.squared


================================================
FILE: 41-LM/18a-SLM-salesarea.R
================================================
#Simple Linear Regression : IIT Gauhati
# Regression : Areas vs Sales

#Method1 : creating data from Vectors
#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y
X = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )

#create a data frame from X & Y
#df1 =
#first few values

#2nd method of importing data
#import from ggsheet  #pickup the correct url
library(gsheet)

#df2 = 
#first few values

#Third method of importing data from CSV file
#df3 =
#first few values

#Method4 for importing from CSV file with choose
#df4 = read.csv(file.choose())
#first few values

# Use Vector Data or method used to import data
#make one of the DF active
#df = 

#Find mean, sum of X & Y, Covariance, Correlation

#plot X & Y

#draw abline/ prediction line


#Linear Model fit= lm(Y~X, data=)


#Print the Model, Summary, Coeff, Residuals


#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

#output of variable Names

#Print Slope value


#combine the data with Ypredicted, errors

# Predictions
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4
(Y = coef(fit1)[1] + coef(fit1)[2] * 4)  # for X=4 using values from output of LM 

fitted(fit1)
cbind(df, fitted(fit1))  # combine data with predicted values
range(df$X)  #min to max value of X: area

# Create a Data Frame with sample values of X



#sample data for X for prediction, should be between the range of X values
#prediction data should be in the form of Data Frame

#Predict

predict(fit1, newdata= new1) # Predict Function for 4 values of X
#fitted(fit1) is equal to predict function using original X values

df$X
predictX = predict(fit1, newdata= data.frame(X=df$X))
cbind(df, predictX, fitted(fit1))

library(forecast)
accuracy(fit1)


summary(fit1)
summary(fit1)$sigma  #Residual Std Error SD along the LM Line


#Assumption : Graphical Analysis : IMP STEP
plot(df$X, df$Y) # plot of Y & X
plot(x=df$X, y=residuals(fit1)) # Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y
abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
#pvalue > 0 : Do not reject Ho that there is no correlation


#Normality of residuals
hist(residuals(fit1)) #distribution of Residuals

hist(residuals(fit1), freq=F)
lines(density(residuals(fit1)))

#histogram values to show how hist rectangle are created
(h=hist(residuals(fit1)))
names(h)
cbind(h$breaks, h$counts)

#Normality Plot to check assumptions of LM
qqnorm(residuals(fit1))
qqline(residuals(fit1))

#Equal Variance : 4th Assumption : homoscedasticity
plot(y=residuals(fit1), x=df$X)
abline(h=0)  #Variance across all x values is almost constant


#Outlier Analysis

boxplot(residuals(fit1), names=c('Residuals'))
identify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))

car::outlierTest(fit1)

car::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))




================================================
FILE: 41-LM/18b-SLM-salesarea.R
================================================
# Regression : Areas vs Sales

#Method1 : creating data from Vectors
#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y
X = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )
df1 = data.frame(X,Y)
head(df1)

#2nd method of importing data
#import from ggsheet  #pickup the correct url
library(gsheet)
area1 = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
df2 = as.data.frame(gsheet::gsheet2tbl(area1))
str(df2)
head(df2)

#Third method of importing data
df3 = read.csv('./data/slr1.csv')
str(df3)

#Method4 for importing from CSV file
df4 = read.csv(file.choose())
str(df4)

# Use Vector Data or method used to import data
#make one of the DF active
df = df1

mean(df$X); mean(df$Y)
sum(df$X); sum(df$Y)
sd(df$X) ; var(df$Y)
cov(df$X,df$Y); cov(df$Y,df$X)
cor(df$X,df$Y) ; cor(df$Y,df$X)
#cor.test(df$X,df$Y)

plot(df$X, df$Y)  #simple command to plot : Next with features
plot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)), col='red',pch=10)

abline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line
abline(v=c(3,5),h=c(6,10), col=c('red','blue')) # few straight lines at x & y axis

#Model
fit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X
fit1
summary(fit1)

fitted(fit1) # predicted values for all X in orginal data
residuals(fit1)

names(fit1) # attributes(fit1)
system.time(lm(Y ~ X, data=df))
coef(fit1)  # Coefficients of Equation Y = mX + C
residuals(fit1)  # Diff of Y actual - Y predicted
#abline(h=coef(fit1)[1])

summary(fit1)  # summary statistics of Linear Model(LM)
#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

names(fit1)  #output variables names of LM
coef(fit1)
#terms(fit1)
coef(fit1)[2]  # slope or beta
fitted(fit1)
#combine the data with Ypredicted, errors
cbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))

# Predictions----
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4
(Y = coef(fit1)[1] + coef(fit1)[2] * 4)  # for X=4 using values from output of LM 
coef(fit1)[2]
new1 = data.frame(X=c(4))
predict(fit1, newdata= new1)


fitted(fit1)
cbind(df, fitted(fit1))  # combine data with predicted values
range(df$X)  #min to max value of X: area


new1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values
new1 #prediction data should be inform of Data Frame

predict(fit1, newdata= new1) # Predict Function for 4 values of X


#fitted(fit1) is equal to predict function using original X values
cbind(new1, predictedY = predict(fit1, newdata= new1) )

plot(fit1)

library(forecast)
accuracy(fit1)

#Variation  # Errors in the model 
#Calculating R2 manually
(SSE = sum((df1$Y - df1$predict)^2))
(SSR = sum((df1$predict - mean(df1$Y))^2))
#11.2 + 105.74
(SST = sum((df1$Y - mean(df1$Y))^2))
anova(fit1)
qt(0.95+.025, 14-2)

anova(fit1)
(r2 = SSR/SST)  # R2 from Calculation ANOVA
summary(fit1)$r.squared  # R2 from Model

summary(fit1)
summary(fit1)$sigma  #Residual Std Error SD along the LM Line


#Assumption : Graphical Analysis : IMP STEP
plot(fit1, which=1)
plot(fit1)

# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y

abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
#pvalue > 0 : Do not reject Ho that there is no correlation


#Normality of residuals
hist(residuals(fit1)) #distribution of Residuals

hist(residuals(fit1), freq=F)
lines(density(residuals(fit1)))

#histogram values to show how hist rectangle are created
(h=hist(residuals(fit1)))
names(h)
cbind(h$breaks, h$counts)

#Normality Plot to check assumptions of LM
qqnorm(residuals(fit1))
qqline(residuals(fit1))

#Normality Plot can also be used with Std Values also
residuals(fit1)
rstandard(fit1)
sales.stdres = rstandard(fit1)
qqnorm(sales.stdres, ylab="Standardized Residuals", xlab="Normal Scores", main="Checking normality of Residuals") 
qqline(sales.stdres)

stem(residuals(fit1))

#Equal Variance : 4th Assumption : homoscedasticity
plot(y=residuals(fit1), x=df$X)
abline(h=0)  #Variance across all x values is almost constant


#Outlier Analysis
df
df[14,]
df$Y
residuals(fit1)
boxplot( df$Y)
boxplot( fitted(fit1))

boxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))
abline(h=c(4.1, 5.97, -1.87))

boxplot(residuals(fit1), names=c('Residuals'))
identify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))

car::outlierTest(fit1)
df[14,]

car::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))

fit1


================================================
FILE: 41-LM/18c-SLM-salesarea.R
================================================
# Regression : Areas vs Sales

#Method1 : creating data from Vectors
#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y
X = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )
df1 = data.frame(X,Y)
head(df1)

#2nd method of importing data
#import from ggsheet  #pickup the correct url
library(gsheet)
area1 = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
df2 = as.data.frame(gsheet::gsheet2tbl(area1))
str(df2)
head(df2)

#Third method of importing data
df3 = read.csv('./data/slr1.csv')
str(df3)

#Method4 for importing from CSV file
df4 = read.csv(file.choose())
str(df4)

# Use Vector Data or method used to import data
#make one of the DF active
df = df1

mean(df$X); mean(df$Y)
sum(df$X); sum(df$Y)
sd(df$X) ; var(df$Y)
cov(df$X,df$Y); cov(df$Y,df$X)
cor(df$X,df$Y) ; cor(df$Y,df$X)
#cor.test(df$X,df$Y)

plot(df$X, df$Y)  #simple command to plot : Next with features
plot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)), col='red',pch=10)

abline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line
abline(v=c(3,5),h=c(6,10), col=c('red','blue')) # few straight lines at x & y axis
#Model
fit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X
fit1
fitted(fit1)

names(fit1)
system.time(lm(Y ~ X, data=df))
coef(fit1)  # Coefficients of Equation Y = mX + C
residuals(fit1)  # Diff of Y actual - Y predicted
#abline(h=coef(fit1)[1])

summary(fit1)  # summary statistics of Linear Model(LM)
#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

names(fit1)  #output variables names of LM
coef(fit1)
#terms(fit1)
coef(fit1)[2]  # slope or beta
fitted(fit1)
#combine the data with Ypredicted, errors
cbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))

# Predictions----
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4
(Y = coef(fit1)[1] + coef(fit1)[2] * 4)  # for X=4 using values from output of LM 
new1 = data.frame(X=c(4))
predict(fit1, newdata= new1)





fitted(fit1)
cbind(df, fitted(fit1))  # combine data with predicted values
range(df$X)  #min to max value of X: area


new1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values
new1 #prediction data should be inform of Data Frame

predict(fit1, newdata= new1) # Predict Function for 4 values of X


#fitted(fit1) is equal to predict function using original X values
cbind(new1, predictedY = predict(fit1, newdata= new1) )

library(forecast)
accuracy(fit1)

#Variation  # Errors in the model 
#Calculating R2 manually
(SSE = sum((df1$Y - df1$predict)^2))
(SSR = sum((df1$predict - mean(df1$Y))^2))
#11.2 + 105.74
(SST = sum((df1$Y - mean(df1$Y))^2))
anova(fit1)
qt(0.95+.025, 14-2)

anova(fit1)
(r2 = SSR/SST)  # R2 from Calculation ANOVA
summary(fit1)$r.squared  # R2 from Model

summary(fit1)
summary(fit1)$sigma  #Residual Std Error SD along the LM Line


#Assumption : Graphical Analysis : IMP STEP
plot(fit1, which=1)
plot(fit1)

# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y

abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
#pvalue > 0 : Do not reject Ho that there is no correlation


#Normality of residuals
hist(residuals(fit1)) #distribution of Residuals

hist(residuals(fit1), freq=F)
lines(density(residuals(fit1)))

#histogram values to show how hist rectangle are created
(h=hist(residuals(fit1)))
names(h)
cbind(h$breaks, h$counts)

#Normality Plot to check assumptions of LM
qqnorm(residuals(fit1))
qqline(residuals(fit1))

#Normality Plot can also be used with Std Values also
residuals(fit1)
rstandard(fit1)
sales.stdres = rstandard(fit1)
qqnorm(sales.stdres, ylab="Standardized Residuals", xlab="Normal Scores", main="Checking normality of Residuals") 
qqline(sales.stdres)

stem(residuals(fit1))

#Equal Variance : 4th Assumption : homoscedasticity
plot(y=residuals(fit1), x=df$X)
abline(h=0)  #Variance across all x values is almost constant


#Outlier Analysis
df
df[14,]
df$Y
residuals(fit1)
boxplot( df$Y)
boxplot( fitted(fit1))

boxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))
abline(h=c(4.1, 5.97, -1.87))

boxplot(residuals(fit1), names=c('Residuals'))
identify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))

car::outlierTest(fit1)
df[14,]

car::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))

fit1


================================================
FILE: 41-LM/23a-MLM-omni.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion

#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
#Create a DF from 3 variables
#omni1 = data.frame(   ,    ,   )

#Check if it has been created


#2nd Method : CSV file
#omni2 = read.csv(file.choose())

#3rd Method : gsheet 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
omni3 = as.data.frame(gsheet::gsheet2tbl(url))

#Make one of data frames active
omni = omni1

?lm  #see help of LM
#Simple Linear Model would look like this
slr1 = lm(formula = sales ~ price, data=omni) # sales depend on price of item
slr2 = lm(formula = sales ~ promotion, data=omni) # sales depend on promotion exp
summary(slr1)
summary(slr2)


#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
mlrmodel1 = lm(formula = sales ~ price + promotion, data=omni)
#how to give parameter values in different sequence, use arguments names if in different order
mlrmodel1 = lm( data=omni, formula = sales ~ price + promotion)

# summary statistics of model IMP STEP

#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals

#coefficients b1, b2



#Predicted Values----

#create a dataframe of new sample values
(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))

#cbind the values





#Plots of the Modle
plot(mlrmodel1,1)  # no pattern, equal variance
plot(mlrmodel1,2)  # Residuals are normally distributed
plot(mlrmodel1,3)
plot(mlrmodel1,4)  # tells outliers which affect model










================================================
FILE: 41-LM/23c-MLM-omni.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion
#Prerquistes - Lin Modeling, lm, output

#Omni Store
#creating data using Vector
sqty= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59,79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
sales1 = data.frame(sqty, price, promotion)
head(sales1)
str(sales1)
# find change in Sales Qty for 1 unit change in price and promotion

#2nd Method : CSV file : file not in project folder
sales2 = read.csv(file.choose())

#3rd Method : gsheet library
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
sales3 = as.data.frame(gsheet::gsheet2tbl(url))
str(sales3)

#4th Method : CSV file : file in project folder
sales4 = read.csv('./data/salesqty.csv')
str(sales4)

sapply(list(sales1,sales2,sales3, sales4), dim)
sapply(list(sales1,sales2,sales3, sales4), names)

#Make one of data frames active
sales = sales1
head(sales)

?lm  #see help of LM
#Simple Linear Model would look like this
slr1 = lm(formula = sqty ~ price, data=sales) # sales depend on price of item
summary(slr1)

slr2 = lm(formula = sqty ~ promotion, data=sales) # sales depend on promotion exp
summary(slr2)
AIC(slr1, slr2)  # slr1 better with 1 IV


#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
mlrmodel1 = lm(sqty ~ price + promotion, sales)

#how to give parameter values in different sequence, use arguments names if in different order
mlrmodel1a = lm( data=sales, formula = sqty ~ price + promotion)


range(sales$sqty)  # range of sales qty
summary(mlrmodel1)  # summary statistics IMP STEP
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals

coef(mlrmodel1) #coefficients b1, b2
#anova(mlrmodel1) #seeing from anova model
attributes(mlrmodel1)
head(sales)

#coplot(sqty ~ price | promotion, data=sales)


#Predicted Values----
dim(sales)
fitted(mlrmodel1)  #predicted values for various input combinations of IV
cbind(sales, fitted(mlrmodel1), residuals(mlrmodel1))
summary(mlrmodel1)
coef(mlrmodel1)
5837 + 59 * -53 + 200 * 3.651
names(omni)
range(sales$price); range(sales$promotion)
#create a dataframe of new sample values
(ndata1 = data.frame(price=c(69,98), promotion=c(500,559)))
predict(mlrmodel1, newdata=ndata1)
cbind(ndata1, Predict=predict(mlrmodel1, newdata=ndata1, predict='response'))



#R2 and Adjs R2
names(mlrmodel1)
summary(mlrmodel1)
summary(mlrmodel1)$r.squared

#Manual Calculation of Adjs R2
(r2 = summary(mlrmodel1)$r.squared)
k = 2 # no of IVs
(n = nrow(sales)) # sample size
(adjr2 = 1 - ( (1 - r2) * ((n - 1)/ (n - k - 1))))


# Fstatistics
summary(mlrmodel1)$fstatistic[1]  # from output of model

(df1 = k) ;  (df2 = n-k-1)  
qf(.95, df1, df2)  # from table wrt df1 & df2
#Model Fstats > table(Fstat) 

# Pvalue of Model
fstat = summary(mlrmodel1)$fstatistic 
pf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE) 
# this is  < 0.05 : Significant
# 

#Plots of the Model
plot(mlrmodel1,1)  # no pattern, equal variance
plot(mlrmodel1,2)  # Residues are normally distributed
plot(mlrmodel1,3)
plot(mlrmodel1,4)  # tells outliers which affect model
#most significant is row 14

# Confidence Intervals


#Fitted values : Predicting on IVs using model
fitted(mlrmodel1)
residuals(mlrmodel1) 
mlrmodel1$residuals
cbind(sales$sqty, fitted(mlrmodel1), sales$sqt - fitted(mlrmodel1), residuals(mlrmodel1))

#sqrt(sum((residuals(mlrmodel1)^2)))
names(mlrmodel1)
summary(mlrmodel1)




#Diagnostics Test for Checking Assumptions 
#Should be Linear relationship between Residuals Vs Ypi, X1i, X2i
cbind(fitted(mlrmodel1), residuals(mlrmodel1))
plot(cbind(fitted(mlrmodel1), residuals(mlrmodel1)))
#not quadratic
plot(cbind(sales$price, residuals(mlrmodel1)))
plot(cbind(sales$promotion, residuals(mlrmodel1)))
#May indicate quadratic term of IVs


#Train and Test Data
#since this data is small full data is used for train and test
#otherwise train = 70% of data

# RMSE
library(Metrics)
mlrmodel1
rmse(sales$sqty, fitted(mlrmodel1))

slr1
rmse(sales$sqty, fitted(slr1))
slr2
rmse(sales$sqty, fitted(slr2))

AIC(mlrmodel1, slr1, slr2)
#Which is best prediction model - with price and promotion

# Extra: Predict for other ranges
head(sales) ; names(sales)
mlr2 = lm(sqty ~ price + promotion, data= sales)
summary(mlr2)
new1=data.frame(price=60:70, promotion=400)
predict(mlr2, newdata = new1)
#summary of IV values and predicted values
cbind(new1,predict2 = predict(mlr2, newdata = new1) )




================================================
FILE: 41-LM/24a-MLM-pcsales.R
================================================
#Multiple Linear Regression
#
sales3 = read.csv('./data/pcsales.csv', skip=1)
summary(sales3)
sales3$salescapita = as.numeric(gsub("\\$", "", sales3$salescapita))
summary(sales3)
sales4 = sales3[-1]
names(sales4)
fit2 = lm(salescapita ~ ., data=sales4)
summary(fit2)

fit3 = lm(salescapita ~ . -unemrate, data=sales4)
summary(fit3)
#this is ok
head(sales4)
new1 = data.frame(pop=8, pcsales=200, gnp=5000, unemrate=5, eduspend=5)
predict(fit3, newdata=new1)


================================================
FILE: 41-LM/25a-MLM-mtcars.R
================================================
#MLR using mtcars dataset
#mtcars
mtcars # mtcars data set
data()  # available dataset in R through libraries
?mtcars  # help on mtcars

dim(mtcars)
names(mtcars)
head(mtcars)
?mtcars

mtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear +carb , data = mtcars)
summary(mtcarsfit1)

mtcarsfit2 = lm(mpg ~ . , data = mtcars)
summary(mtcarsfit2)



library(MASS)
(model1a = lm(mpg ~ ., data=mtcars))
(model1b = lm(mpg ~ 1, data=mtcars))

summary(model1a)
summary(model1b)


stepboth = stepAIC(model1a,   direction='both')
fit1= lm(mpg ~ wt + qsec + am, data=mtcars)
summary(fit1)


stepfwd  = stepAIC(model1b,   direction='forward', scope=list(upper=model1a,lower=model1b))

stepback = stepAIC(model1a,   direction='backward')
?stepAIC


#-----
?step # base package

lm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)
model.aic.backward <- step(lm.full, direction = "backward", trace = 1)
summary(model.aic.backward)

lm.null = lm(mpg ~ 1, data=mtcars)
model.aic.forward <- step(lm.null, direction = "forward", trace = 1,scope = ~  wt + cyl + gear + am +disp)

summary(model.aic.forward)


#----
fit1 <- lm(y ~ ., data)
fit2 <- lm(y ~ 1, data)
stepAIC(fit1,direction="backward")
stepAIC(fit2,direction="forward",scope=list(upper=fit1,lower=fit2))
stepAIC(fit2,direction="both",scope=list(upper=fit1,lower=fit2))


(model1a = lm(mpg ~ disp + cyl + hp + drat + wt +
                qsec + carb, data=mtcars))
(model1b = lm(mpg ~ 1, data=mtcars))



================================================
FILE: 41-LM/25c-MLM-mtcars.R
================================================
#MLR using mtcars dataset
#mtcars
mtcars # mtcars data set
data()  # available dataset in R through libraries
?mtcars  # help on mtcars

dim(mtcars)
names(mtcars)
head(mtcars)
?mtcars

mtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear +carb , data = mtcars)
summary(mtcarsfit1)

mtcarsfit2 = lm(mpg ~ . , data = mtcars)
summary(mtcarsfit2)



library(MASS)
(model1a = lm(mpg ~ ., data=mtcars))
(model1b = lm(mpg ~ 1, data=mtcars))

summary(model1a)
summary(model1b)


stepboth = stepAIC(model1a,   direction='both')
fit1= lm(mpg ~ wt + qsec + am, data=mtcars)
summary(fit1)


stepfwd  = stepAIC(model1b,   direction='forward', scope=list(upper=model1a,lower=model1b))

stepback = stepAIC(model1a,   direction='backward')
?stepAIC


#-----
?step # base package

lm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)
model.aic.backward <- step(lm.full, direction = "backward", trace = 1)
summary(model.aic.backward)

lm.null = lm(mpg ~ 1, data=mtcars)
model.aic.forward <- step(lm.null, direction = "forward", trace = 1,scope = ~  wt + cyl + gear + am +disp)

summary(model.aic.forward)


#----
fit1 <- lm(y ~ ., data)
fit2 <- lm(y ~ 1, data)
stepAIC(fit1,direction="backward")
stepAIC(fit2,direction="forward",scope=list(upper=fit1,lower=fit2))
stepAIC(fit2,direction="both",scope=list(upper=fit1,lower=fit2))


(model1a = lm(mpg ~ disp + cyl + hp + drat + wt +
                qsec + carb, data=mtcars))
(model1b = lm(mpg ~ 1, data=mtcars))



================================================
FILE: 41-LM/25c-MLM-mtcars1.R
================================================
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_all_possible(model)
plot(k)
k
summary(lm(mpg ~ wt, data=mtcars))
summary(lm(mpg ~ wt+ hp, data=mtcars))


================================================
FILE: 41-LM/25d-MLM-mtcars-A.R
================================================
# Model Assumptions in Linear Regression
# Diagnostics (https://www.statmethods.net/stats/rdiagnostics.html)

# Assume that we are fitting a multiple linear regression
# on the MTCARS data
library(car)
fit <- lm(mpg~disp+hp+wt+drat, data=mtcars)

# Assessing Outliers----
outlierTest(fit) # Bonferonni p-value for most extreme obs
qqPlot(fit, main="QQ Plot") #qq plot for studentized resid 
leveragePlots(fit) # leverage plots


# Influential Observations----
# added variable plots 
car::avPlots(fit)

# Cook's D plot
# identify D values > 4/(n-k-1) 
cutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) 
plot(fit, which=4, cook.levels=cutoff)

# Influence Plot 
influencePlot(fit,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )


# Normality of Residuals----
# qq plot for studentized resid - 
qqPlot(fit, main="QQ Plot")
# distribution of studentized residuals - should be normal
library(MASS)
residuals(fit) # standardise them
sresid <- studres(fit) 
hist(sresid, freq=FALSE, main="Distribution of Studentized Residuals")
plot(density(sresid))

xfit<-seq(min(sresid),max(sresid),length=40) 
yfit<-dnorm(xfit) 
lines(xfit, yfit)

#Non-constant Error Variance ---
# Evaluate homoscedasticity. Shouldhave constant variance
# non-constant error variance test
car::ncvTest(fit)
# plot studentized residuals vs. fitted values 
spreadLevelPlot(fit)
#spread is scattered

#Multi-collinearity ----
# Evaluate Collinearity
vif(fit) # variance inflation factors 
sqrt(vif(fit)) > 2 # if > 2, there is aproblem?


#Nonlinearity----
# Evaluate Nonlinearity
# component + residual plot 
crPlots(fit)
# Ceres plots 
ceresPlots(fit)

#Non-independence of Errors----
# Test for Autocorrelated Errors
durbinWatsonTest(fit)


#Additional Diagnostic Help----
#The gvlma( ) function in the gvlma package, performs a global validation of linear model assumptions as well separate evaluations of skewness, kurtosis, and heteroscedasticity.

# Global test of model assumptions
library(gvlma)
gvmodel <- gvlma(fit) 
summary(gvmodel)


#(http://r-statistics.co/Assumptions-of-Linear-Regression.html)
#Simple Linear





#Multiple Linear



#http://r-statistics.co/Assumptions-of-Linear-Regression.html










================================================
FILE: 41-LM/26a-MLM-airquality.R
================================================
# Linear Regression - Data set - Air Quality


data(airquality)# to call the data
attach(airquality)
head(airquality,10)# to see first 10 rows
#attach () function makes the data available to the R search path.

#Summary function gives you the range, quartiles, median and mean for numerical variables and table with frequencies for categorical variable.
summary(airquality)

#Data visualization 
#boxplot to visualize the daily temperature for month 5, 6, 7, 8 and 9.
month5=subset(airquality,Month=5)
month6=subset(airquality,Month=6)
month7=subset(airquality,Month=7)
month8=subset(airquality,Month=8)
month9=subset(airquality,Month=9)

par(mfrow = c(1,2))  # 3 rows and 2 columns
boxplot((month5$Temp~airquality$Day),main='Month 5',col=rainbow(3))
boxplot((month6$Temp~airquality$Day),main='Month 6',col=rainbow(3))
boxplot((month7$Temp~airquality$Day),main='Month 7',col=rainbow(3))
boxplot((month8$Temp~airquality$Day),main='Month 8',col=rainbow(3))
boxplot((month9$Temp~airquality$Day),main='Month 9',col=rainbow(3))

par(mfrow = c(1,1))                                                                
#histogram to see the distribution of temperature data.
hist(airquality$Temp,col=rainbow(2))

#scatter plot to see if there is a linear pattern between the ‘temperature rise’ and other variables.
                                                                
plot(airquality$Temp ~ airquality$Day + airquality$Solar.R + airquality$Wind + airquality$Ozone, col='blue')
                      
#It seems that solar.R , Ozone, and wind have a linear pattern with temperature. Solar and Ozone have a positive relationship and wind has a negative one.  I use Co-plot to see the effect of wind and solar radiations combined on Temperature

head(airquality[,c('Ozone','Solar.R', 'Wind')])
coplot(Ozone ~ Solar.R | Wind, panel=panel.smooth, airquality, col= 'green')

# Data Cleaning ----
#Missing Values
sapply(airquality,function(x){sum(is.na(x))})

#Impute Missing Values
airquality$Ozone[is.na(airquality$Ozone)]=mean(airquality$Ozone,na.rm=T)

airquality$Solar.R[is.na(airquality$Solar.R)]=mean(airquality$Solar.R,na.rm=T)

sapply(airquality,function(x){sum(is.na(x))})

#Correlation
library(corrplot)

o=corrplot(cor(airquality),method='number') 
# this method can be changed try using method=’circle’
corrplot(cor(airquality),method='circle') 

#Model
Model_lm1=lm(Temp~.,data=airquality)
summary(Model_lm1)
AIC(Model_lm1)

#Best Model based on AIC
Model_lm_best=step(Model_lm1)
summary(Model_lm_best)
plot(Model_lm_best,col='blue')


# VIF
library(fmsb)
Model_lm1=lm(Temp~ Ozone+Solar.R+Month,data=airquality)
VIF(lm(Month ~ Ozone+Solar.R,data=airquality))
VIF(lm(Ozone ~ Solar.R+Month, data=airquality))
VIF(lm(Solar.R ~ Ozone +Month, data=airquality))
#As a general rule, VIF < 5 is acceptable (VIF = 1 means there is no multicollinearity), and VIF > 5 is not acceptable and we need to check our model.

#In our example, VIF < 5 and hence there is no need of any additional verification needed.

================================================
FILE: 41-LM/27a-MLM-marketing.R
================================================
#MLR in R


#http://www.sthda.com/english/articles/40-regression-analysis/168-multiple-linear-regression-in-r/
  
  

================================================
FILE: 41-LM/35a-MLM-case1.R
================================================
#Set Working Directory

#Read the data file
DefaultData<-read.csv("./regr/MMM_raw_data_v02.csv")

# Cars Sold analysis

#Check if the data is populated/imported properly
head(DefaultData)
tail(DefaultData)

#Check the summary of the file
summary(DefaultData)

#Generate plot of Dependent variable
plot(DefaultData$Unit_Sold)

#Check the quantile to find out the outlier limit
quantile(DefaultData$Unit_Sold, c(0,0.05,0.1,0.25,0.5,0.75,0.90,0.95,0.99,0.995,1))

#Creating the Capped Losses column with 768 cap
DefaultData$CappedUnitSold<-ifelse(DefaultData$Unit_Sold>768,768,DefaultData$Unit_Sold)

#Check if Capped Losses column has been created properly or not
summary(DefaultData)
names(DefaultData)

#Create new object deleting Losses and S.no.
DefaultData3<-DefaultData[,-c(2)]

#Check the headings of the new object

names(DefaultData3)
head(DefaultData3)

#Generate plots to see the relation between the independent variables and the dependent variable
plot(DefaultData3$Print_Spend,DefaultData3$CappedUnitSold)

plot(DefaultData3$Radio_Spend,DefaultData3$CappedUnitSold)
plot(DefaultData3$TV_Spend,DefaultData3$CappedUnitSold)

names(DefaultData3)


#Install car Package for vif (Multicollinearity)
#install.packages("car")

#Load the car package
library("car")

#Create linear function for vif
vif_data<-lm(CappedUnitSold~
               Print_Spend+
               Radio_Spend+
               TV_Spend+
               Promotion_Dummy,data=DefaultData3)

#Check Vif, vif>2 means presence of multicollinearity
vif(vif_data)

#Run Linear Regression w/o Years_Drv_Exp
lin_r1<-lm(CappedUnitSold~
             Print_Spend+
             Radio_Spend+
             TV_Spend+
             Promotion_Dummy,data=DefaultData3)

#Let's look at the results
summary(lin_r1)

fitted(lin_r1)
summary(lin_r1)$r.squared #R-square Value 


##Generate the diagnostic plots
plot(lin_r1)

meancnt1=DefaultData3
meancnt1$pred = 56.7185191 + 
  0.0098758*DefaultData3$Print_Spend+0.2493764*DefaultData3$Radio_Spend



meancnt1$res = meancnt1$pred - meancnt1$CappedUnitSold
meancnt1$abs_res = abs(meancnt1$res)
meancnt1$mape = 100*meancnt1$abs_res/meancnt1$CappedUnitSold;
meancnt1$mape

# Plot Y-pred vs. Error to ensure there is no pattern
plot(meancnt1$pred,meancnt1$res)



================================================
FILE: 41-LM/37a-LM-dummy-fireplace.R
================================================
# Dummy Variables in Regression

value = c(84.4,77.4,75.7,85.9,79.1,70.4,75.8,85.9,78.5,79.2,86.7,79.3,74.5,83.8,76.8)
area = c(2.00,1.71,1.45,1.76,1.93,1.20,1.55,1.93,1.59,1.50,1.90,1.39,1.54,1.89,1.59)
fireplace= c('Yes','No','No','Yes','No','Yes','Yes','Yes','Yes','Yes','Yes','Yes','No','Yes','No')

fireplace = factor(fireplace)
house = data.frame(value, area, fireplace)
str(house)

mlrhouse1 = lm(value ~ area + fireplace, data=house)
summary(mlrhouse1)

#Equation : value = 50 + 16.1 * area + 3.85 * 1 : for fireplace
#Equation : value = 16.1 * area  : for no fireplace

head(house)
predict(mlrhouse1, newdata=head(house), predict='response')

(value1 = 50 + 16.1 * 2 + 1 * 1)  # Fireplace
(value2 = 50 + 16.1 * 1.71 )   #no fireplace


================================================
FILE: 41-LM/37b-dummy1.R
================================================
# Dummy Variables

df = mtcars
summary(df)
(df$cyl = factor(df$cyl))
(df$am = factor(df$am))

M1 = lm(mpg ~ wt + cyl , data=df)
summary(M1)
summary(df$cyl)
data.frame(wt=c(2,3), cyl=factor(c(4,6)))
predict(M1, newdata = data.frame(wt=c(2,3), cyl=factor(c(4,6))) )


summary(df)
y4 = 33.99 + -3.2 * wt + 0
y6 = 33.99 + -3.2 * wt + -4.2 * cyl6  #(=1)
y8 = 33.99 + -3.2 * wt + -6.07 * cyl8 #(=1)

head(df)
y4 = 

================================================
FILE: 41-LM/38c-LM-dummy1.R
================================================
# Dummy Variables
# How categorical Values are handled in Linear Regression

df = mtcars
summary(df)
df$cyl = factor(df$cyl)
df$am = factor(df$am)

M1 = lm(mpg ~ wt + cyl , data=df)
summary(M1)

summary(df$cyl)

data.frame(wt=c(2,3), cyl=factor(c(4,6)))
predict(M1, newdata = data.frame(wt=c(2,2,2), cyl=factor(c(4,6, 8))) )
str(df)

summary(df)
#Predicting in case of Dummy Variables : Manual
y = 33.99 + -3.2 * wt + (0) * cyl4 + (-4.1) * cyl6 + (-6.071) * cyl8
wt=2 ; cyl4=1; cyl6=0; cyl8=0 #value predicted = 27.48
y4 = 33.99 + -3.2 * wt + 0  # this is for cyl=4
cyl6=1
(y6 = 33.99 + -3.2 * wt + -4.2 * cyl6)  #cyl = 6 (=1)

y8 = 33.99 + -3.2 * wt + -6.07 * cyl8 #cyl = 8(=1)

head(df)


================================================
FILE: 41-LM/41c-LM-assumptions.R
================================================
# Model Assumptions in Linear Regression
# Diagnostics (https://www.statmethods.net/stats/rdiagnostics.html)
#(http://www.sthda.com/english/articles/39-regression-model-diagnostics/161-linear-regression-assumptions-and-diagnostics-in-r-essentials/)


#The regression has five key assumptions:
#1.Linear relationship.
#Multivariate normality.
#No or little multicollinearity.
#No auto-correlation.
#Homoscedasticity.

btech -->  java , cpp, maths
sales figures over 1 year


# Assume that we are fitting a multiple linear regression
# on the MTCARS data
library(car)
fit <- lm(mpg~disp+hp+wt+drat, data=mtcars)
# Assessing Outliers
outlierTest(fit) # Bonferonni p-value for most extreme obs
qqPlot(fit, main="QQ Plot") #qq plot for studentized resid 
leveragePlots(fit) # leverage plots

# Influential Observations
# added variable plots 
av.Plots(fit)
# Cook's D plot
# identify D values > 4/(n-k-1) 
cutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) 
plot(fit, which=4, cook.levels=cutoff)
# Influence Plot 
influencePlot(fit,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )


# Normality of Residuals
# qq plot for studentized resid
qqPlot(fit, main="QQ Plot")
# distribution of studentized residuals
library(MASS)
sresid <- studres(fit) 
hist(sresid, freq=FALSE, 
     main="Distribution of Studentized Residuals")
xfit<-seq(min(sresid),max(sresid),length=40) 
yfit<-dnorm(xfit) 
lines(xfit, yfit)

#Non-constant Error Variance ---
# Evaluate homoscedasticity. Should have constant variance
# non-constant error variance test
ncvTest(fit)
# plot studentized residuals vs. fitted values 
spreadLevelPlot(fit)

#Multi-collinearity ----
# Evaluate Collinearity
vif(fit) # variance inflation factors 
sqrt(vif(fit)) > 2 # problem?

summary(fit)
#Nonlinearity----
# Evaluate Nonlinearity
# component + residual plot 
crPlots(fit)
# Ceres plots 
ceresPlots(fit)

#Non-independence of Errors----
# Test for Autocorrelated Errors
durbinWatsonTest(fit)


#Additional Diagnostic Help----
#The gvlma( ) function in the gvlma package, performs a global validation of linear model assumptions as well separate evaluations of skewness, kurtosis, and heteroscedasticity.

# Global test of model assumptions
library(gvlma)
gvmodel <- gvlma(fit) 
summary(gvmodel)

#Plot command
plot(fit)

#(http://r-statistics.co/Assumptions-of-Linear-Regression.html)
#Simple Linear

#Multiple Linear

#http://r-statistics.co/Assumptions-of-Linear-Regression.html


================================================
FILE: 41-LM/42b-LM-linearity.R
================================================
#Nonlinearity

# Evaluate Nonlinearity
plot(fit,1)

# component + residual plot 
crPlots(fit)
# Ceres plots 
ceresPlots(fit)

#The regression model is linear in parameters
#An example of model equation that is linear in parameters 
#Y=a+(β1*X1)+(β2*X2^2)

#Assumption 2----
#The mean of residuals is zero
#How to check?
#Check the mean of the residuals. If it zero (or very close), then this assumption is held true for that model. This is default unless you explicitly make amends, such as setting the intercept term to zero.

fit2 <- lm(dist ~ speed, data=cars)
mean(fit2$residuals)
#Since the mean of residuals is approximately zero, this assumption holds true for this model.


================================================
FILE: 41-LM/42c-LM-normality.R
================================================
#Normality

#If the error terms are non- normally distributed, confidence intervals may become too wide or narrow. Once confidence interval becomes unstable, it leads to difficulty in estimating coefficients based on minimization of least squares. Presence of non – normal distribution suggests that there are a few unusual data points which must be studied closely to make a better model.

#How to check
#QQ plot (shown below). You can also perform statistical tests of normality such as Kolmogorov-Smirnov test, Shapiro-Wilk test

# Normality of Residuals
# qq plot for studentized resid
qqPlot(fit, main="QQ Plot")
# distribution of studentized residuals
library(MASS)
sresid <- studres(fit) 
hist(sresid, freq=FALSE, 
     main="Distribution of Studentized Residuals")
xfit<-seq(min(sresid),max(sresid),length=40) 
yfit<-dnorm(xfit) 
lines(xfit, yfit)

================================================
FILE: 41-LM/42d-LM-variance.R
================================================
#Variance

#The presence of non-constant variance in the error terms results in heteroskedasticity. Generally, non-constant variance arises in presence of outliers or extreme leverage values. Look like, these values get too much weight, thereby disproportionately influences the model’s performance. When this phenomenon occurs, the confidence interval for out of sample prediction tends to be unrealistically wide or narrow.

#How to check
#Look at residual vs fitted values plot. If heteroskedasticity exists, the plot would exhibit a funnel shape pattern 

#Non-constant Error Variance
# Evaluate homoscedasticity
# non-constant error variance test
ncvTest(fit)
# plot studentized residuals vs. fitted values 
spreadLevelPlot(fit)

#Homoscedasticity of residuals or equal variance
#Top Left and bottom-Left Plots show how the residuals vary as the fitted values increase.

par(mfrow=c(2,2))  # set 2 rows and 2 column plot layout
fit3 <- lm(mpg ~ disp, data=mtcars)  # linear model
plot(fit3)

#first plot (top-left), as the fitted values along x increase, the residuals decrease and then increase. This pattern is indicated by the red line, which should be approximately flat if the disturbances are homoscedastic. The plot on the bottom left also checks this, and is more convenient as the disturbance term in Y axis is standardized.

#another model

fit4 = lm(dist ~ speed, data=cars[1:20, ])  #  linear model
plot(fit4) #homoscedascity present

#M3
library(car)
data(cars)
lmMod <- lm(dist ~ speed, data=cars) # initial model
lmtest::bptest(lmMod)
car::ncvTest(lmMod) 

#Box Transformation

distBCMod <- caret::BoxCoxTrans(cars$dist)
print(distBCMod)
cars <- cbind(cars, dist_new=predict(distBCMod, cars$dist)) # append the transformed variable to cars
head(cars) # view the top 6 rows
lmMod_bc <- lm(dist_new ~ speed, data=cars)
lmtest::bptest(lmMod_bc)
#With a p-value of 0.91, we fail to reject the null hypothesis (that variance of residuals is constant) and therefore infer that ther residuals are homoscedastic. Lets check this graphically as well.

plot(lmMod_bc)

#much flatter line and an evenly distributed residuals in the top-left plot. So the problem of heteroscedsticity is solved and the case is closed. If you have any question post a comment below.


# M4----
#https://cran.r-project.org/web/packages/olsrr/vignettes/heteroskedasticity.html

library(olsrr)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_score(model)


#Holm’s p value Adjustment
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'holm')

#Bonferroni p value Adjustment
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'bonferroni')

#Use independent variables of the model and perform multiple tests
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)

#Use independent variables of the model
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
ols_test_breusch_pagan(model, rhs = TRUE)

#Use fitted values of the model
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
ols_test_breusch_pagan(model)

#Use independent variables of the model
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_score(model, rhs = TRUE)

#Specify variables
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_score(model, vars = c('disp', 'hp'))

#F Test Use fitted values of the model
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_f(model)

#Use independent variables of the model
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_f(model, rhs = TRUE)

#Specify variables
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_f(model, vars = c('disp', 'hp'))


================================================
FILE: 41-LM/42e-LM-outliers.R
================================================
#Outliers

# Assessing Outliers
outlierTest(fit) # Bonferonni p-value for most extreme obs
qqPlot(fit, main="QQ Plot") #qq plot for studentized resid 
leveragePlots(fit) # leverage plots

================================================
FILE: 41-LM/42f-LM-autocorr.R
================================================
#Non-independence of Errors

# Test for Autocorrelated Errors
durbinWatsonTest(fit)


#No autocorrelation of residuals
#This is applicable especially for time series data. Autocorrelation is the correlation of a time Series with lags of itself. When the residuals are autocorrelated, it means that the current value is dependent of the previous (historic) values and that there is a definite unexplained pattern in the Y variable that shows up in the disturbances.

par(mfrow=c(1,1))
#M1:Using acf plot
# Method 1: Visualise with acf plot
library(ggplot2)
data(economics)
fit1 <- lm(pce ~ pop, data=economics)
acf(fit1$residuals)  # highly autocorrelated from the picture.

fit2 = lm(weight ~ height, data = women)
acf(fit2$residuals)  # no autocorrelation


# Method 2: Runs test to test for randomness
lawstat::runs.test(fit1$residuals)
#=>   Runs Test - Two sided  # Ho: Random Ha: Relation
#With a p-value < 0.05, we reject the null hypothesis that it is random. This means there is a definite pattern in the residuals.

# Method 3: Durbin-Watson test
lmtest::dwtest(fit1)
#=>   Durbin-Watson test
#pvalue < 0.05; Auto Corr > 0 ie there is auto corr

lmtest::dwtest(fit2)
#pvalue < 0.05 but the dat is cross sectional

#How to rectify?----
# Add lag1 of residual as an X variable to the original model. This can be conveniently done using the slide function in DataCombine package.

library(DataCombine)
econ_data <- data.frame(economics, resid_mod1=fit1$residuals)
econ_data_1 <- slide(econ_data, Var="resid_mod1", NewVar = "lag1", slideBy = -1)
econ_data_2 <- na.omit(econ_data_1)
fit1a <- lm(pce ~ pop + lag1, data=econ_data_2)

lmtest::dwtest(fit1a)
#now p-value > 0.05 ; hence Ho accept, Auto corr=0


================================================
FILE: 41-LM/42g-LM-influentialvariables.R
================================================
# Influential Observations

# added variable plots 
av.Plots(fit)
# Cook's D plot
# identify D values > 4/(n-k-1) 
cutoff <- 4/((nrow(mtcars)-length(fit$coefficients)-2)) 
plot(fit, which=4, cook.levels=cutoff)
# Influence Plot 
influencePlot(fit,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )

================================================
FILE: 41-LM/42h-LM-multicollinearity.R
================================================
#Multi-collinearity

# Evaluate Collinearity
vif(fit) # variance inflation factors 
sqrt(vif(fit)) > 2 # problem?

================================================
FILE: 41-LM/42j-gvlma.R
================================================
# Global test of model assumptions

library(gvlma)
gvmodel <- gvlma(fit) 
summary(gvmodel)

================================================
FILE: 41-LM/43a-LM-graphs.R
================================================
# Linear Regression - Graphs for Visualise


fit = lm(weight ~ height, data=women)

#Base Package
par(mfrow=c(2,2))
plot(fit, ask=FALSE)
par(mfrow=c(1,1))


library(ggfortify)  #ggplotbased
autoplot(fit)


#Cooks Distance
#measures howm much the Predicted value of DV changes for all the obsversations in the sample when a particular observation is excluded from the sample for estimation of regression
car::avPlots(fit)
car::influencePlot(fit)


================================================
FILE: 41-LM/LM-all-mtcars1.R
================================================
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_all_possible(model)
plot(k)
k
summary(lm(mpg ~ wt, data=mtcars))
summary(lm(mpg ~ wt+ hp, data=mtcars))


================================================
FILE: 41-LM/ProbDist.R
================================================
# Prob Distri
face = seq(1:6)
#face = 1:6 will also work.
face
#You will see the six die faces.
p = c(1, 1, 1, 1, 1, 1) / 6
#This assigns a uniform distribution to the die faces; each gets a probability of 1/6
data.frame(face, p)
#We see a tabular description of the distribution. 

#PROBABILITY SPIKE PLOT FOR ONE DIE
plot(face, p, type="h", xlim=c(0,6), ylim=c(0,1))
points(face, p, pch=16, cex=2)
#We see a probability spike plot of the distribution.This resembles a probability histogram with very thin bars.
#In the ‘points’ command, ‘pch’ may be thought of as “point character.” ‘pch=16’ gives us filled-in circular disks at the points.
# If we do ‘pch=15’, for example, we get boxes at the points.
#‘cex’ controls the size of the disks, boxes, etc.
points(face, p, pch=15, cex=3.5, col="red") 

#----
#SAMPLING FROM A UNIFORM DISTRIBUTION
#Let’s roll our die.
sample(face, size=1)
#• It is assumed that we have a uniform distribution. As we will see later, we
#can type ‘sample(face, size=1, prob=p)’, where p is our probability
#distribution, uniform or otherwise.
#> (Repeat the above command a few times. You should get a variety of results.)

#• Let’s roll a pair of dice and get their sum.

sum(sample(face, size=2, replace=T))
#• We want to sample with replacement, because we want doubles to be possible.
# The distribution of totals is not uniform!
#  > (Repeat the above command a few times. You should get a variety of results.)
sample(face, size=100, replace=T)
#• Our die is rolled 100 times.
#• How do the numbers of ‘1’s, ‘2’s, etc. compare?
#• ‘replace=T’ means that we sample with replacement. It is OK for us to get the same number more than once.
#DESCRIBING SAMPLE RESULTS
#• Let’s store some sample results and do a frequency table, a relative frequency
#table, a histogram, and a pie chart.
results = sample(face, size=100, replace=T)
results
sort(results)
table(results)
#• We obtain a frequency table of our results.
table(results) / length(results)
#• We obtain a relative frequency table of our results.
#• Note: length(results) is 100 here. 
#Page R9.3
hist(results, prob=T, breaks=seq(-0.5, 6.5, by=1), ylim=c(0,1))
#• We see a relative frequency histogram of our results.
#• For example, the interval from 3.5 to 4.5 corresponds to “4.”
#• This should resemble the (theoretical) probability distribution for one die.
lines(c(0.5,6.5), c(1/6,1/6), col="blue")
#• This adds a blue line from the point 0.5, 16 to 6.5, 16
#The line corresponds to the (theoretical) probability distribution.
pie(table(results), face)
#• We see a pie chart.
#A NON-UNIFORM DISTRIBUTION
x = c(2, 4, 8, 16)
p2 = c(1/8, 1/8, 1/4, 1/2)
#• This assigns a non-uniform distribution to the x values.
data.frame(x, p2)
#• We see a tabular description of the distribution.
plot(x, p2, type="h", xlim=c(0,20), ylim=c(0,1))
points(x, p2, pch=16, cex=2)
#• We see a probability spike plot of the distribution.
results2 = sample(x, size=100, replace=T, prob=p2)
#• ‘prob=p2’ indicates that p2 contains our probabilities for the x values.
results2
#What do you notice?
sort(results2)
#What do you notice?
table(results2)
#We obtain a frequency table of our results.
#What do you notice?
table(results2) / length(results2)
#We obtain a relative frequency table of our results.
length(results2) 
hist(results2, prob=T, breaks=seq(-0.5, 16.5, by=1), ylim=c(0,1))
#We see a relative frequency histogram of our results.
#For example, the interval from 3.5 to 4.5 corresponds to “4.” 
#Page R9.4
#MEAN (OR EXPECTED VALUE) OF A DISTRIBUTION
data.frame(x, p2)
#• We see a tabular description of the distribution.
#• Let’s find the mean (or expected value) of this distribution. It’s a “zigzag.”
the.mean = sum(x*p2)
#The formula is: μ, or E X( ) = x 
 P x( ).
#We use ‘the.mean’ to avoid conflict with the ‘mean’ command.
the.mean
#VARIANCE AND STANDARD DEVIATION OF A DISTRIBUTION
#Let’s find the variance of this distribution.
the.var = sum(x^2 * p2) - the.mean^2
#• The formula is:   2 = E X2 ( ) 
 μ2 = E X2 ( ) 
 E X( )  2x2   P x( )   
 μ2.
#• ‘p2’ is the name of the probability vector; there is no squaring here!
#  • Also, 2 = E X( ) 
 μ2  = ( ) x 
 μ2  P x( ), so we could do:
the.var = sum(((x - the.mean)^2) * p2)
the.var
#Let’s find the standard deviation of this distribution.
the.sd = sqrt(the.var)
the.sd
#COMPARING SAMPLE RESULTS WITH THEORETICAL RESULTS
#• How do our sample mean, variance, and standard deviation compare with the
#[theoretical] mean, variance, and standard deviation for the distribution?
results2
#• This was our sample of size 100 from the distribution.
mean(results2)
#• Compare this sample mean with the.mean, the theoretical mean of thedistribution.
var(results2)
#We do want the sample variance here.
#• Compare this sample variance with the.var, the theoretical variance of thedistribution.
sd(results2)
#• Compare this sample standard deviation with the.sd, the theoretical
#standard deviation of the distribution. 


================================================
FILE: 41-LM/Simulation.R
================================================
# Simulation
x = c(1, 3, 5)
px = c(0.6, 0.3, 0.1)
draws = sample(x, size = 500, replace = TRUE, prob = px)
draws
hist(draws, breaks = seq(1, 5, by = 0.25), main = "1000 discrete draws")
var(draws)
draws = sample(x, size = 4 * 500,replace = TRUE, prob = px)
draws = matrix(draws, 4)
drawmeans = apply(draws, 2, mean)
hist(drawmeans, breaks = seq(1, 5, by = 0.25), main = "1000 means of 4 draws")

var(drawmeans)
drawmeans = apply(matrix(sample(x, size = 16 * 500, replace = TRUE, prob = px), 16), 2, mean)
hist(drawmeans, breaks = seq(1, 5, by = 0.25), main = "1000 means of 16 draws")
var(drawmeans)


-----------
  p.die <- rep(1/6,6)
sum(p.die)
die <- 1:6
sample(die, size=10, prob=p.die, replace=T)
s <- table(sample(die, size=1000, prob=p.die, replace=T))
lbls = sprintf("%0.1f%%", s/sum(s)*100)
barX <- barplot(s, ylim=c(0,200))
text(x=barX, y=s+10, label=lbls)

generate.sample.means <- function(n) {
  sample.means <- numeric()
  for (i in 1:1000) { 
    sample.means <- append(sample.means, sum(sample(die, size=n, prob=p.die, replace=T))/n)
  }
  return (sample.means)
}
sample.means <- generate.sample.means(100)
plot(density(sample.means), main="Distribution of sample means",xlab="sample mean", col="orange")



================================================
FILE: 41-LM/confusionmatrix.R
================================================
#Confusion Matrix
#https://cran.r-project.org/web/packages/heuristica/vignettes/confusion-matrix.html
#
library(heuristica)
lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
                levels = rev(lvs))
pred <- factor(
  c(
    rep(lvs, times = c(54, 32)),
    rep(lvs, times = c(27, 231))),               
  levels = rev(lvs))

xtab <- table(pred, truth)
# load Caret package for computing Confusion matrix
library(caret) 
confusionMatrix(xtab)


================================================
FILE: 41-LM/contrasts1.R
================================================

# Coding for Regression Variables
# https://stats.idre.ucla.edu/r/modules/coding-for-categorical-variables-in-regression-models/
# 
hsb2 <- read.csv("https://stats.idre.ucla.edu/stat/data/hsb2.csv")
str(hsb2)

# creating the factor variable
hsb2$race.f <- factor(hsb2$race)
is.factor(hsb2$race.f)
summary(lm(write ~ race.f, data = hsb2))

summary(lm(write ~ factor(race), data = hsb2))


hsb2 <- within(hsb2, {
  race.ct <- C(race.f, treatment)
  print(attributes(race.ct))
})

hsb2 <- within(hsb2, {
  race.ch <- C(race.f, helmert)
  print(attributes(race.ch))
})

summary(lm(write ~ race.ct, data = hsb2))
summary(lm(write ~ race.ch, data = hsb2))

hsb2 <- within(hsb2, {
  race.ch1 <- C(race.f, helmert, 3)
  print(attributes(race.ch1))
})

summary(lm(write ~ race.ch1, data = hsb2))

(a <- contrasts(hsb2$race.f))

(contrasts(hsb2$race.f) <- contr.treatment(4))
summary(lm(write ~ race.f, data = hsb2))

(contrasts(hsb2$race.f) <- contr.treatment(4, base = 2))
summary(lm(write ~ race.f, data = hsb2))

summary(lm(write ~ I(race.f == 1) + I(race.f == 3) + I(race.f == 4), data = hsb2))

(contrasts(hsb2$race.f) <- contr.helmert(4))
summary(lm(write ~ race.f, data = hsb2))


================================================
FILE: 41-LM/dummies.R
================================================

library(dummies)

# example data
df1 <- data.frame(id = 1:4, year = 1991:1994)

df1 <- cbind(df1, dummy(df1$year, sep = "_"))

df1



library(mlr)
df <- data.frame(var = sample(c("A", "B", "C"), 10, replace = TRUE))
df
createDummyFeatures(df, cols = "var")

year=sample(c(1957,1958,1959,1960),size=10,replace=T, prob=c(.4,.2,.2,.2))
(data = data.frame(year))
                  
                
data$factor_year_1 <- factor ( with ( data, ifelse ( ( year == 1957 ), 1 , 0 ) ) )
data

ifelse(year == 1957, 1, 0)



================================================
FILE: 41-LM/homosecadicity.R
================================================
set.seed(5)

N  = 500; b0 = 3 ; b1 = 0.4
s2 = 5 ; g1 = 1.5 ; g2 = 0.015

x        = runif(N, min=0, max=100)
y_homo   = b0 + b1*x + rnorm(N, mean=0, sd=sqrt(s2            ))
y_hetero = b0 + b1*x + rnorm(N, mean=0, sd=sqrt(exp(g1 + g2*x)))

mod.homo   = lm(y_homo~x)
mod.hetero = lm(y_hetero~x)
plot(mod.homo)
plot(mod.hetero)
fit=mod.homo
# Evaluate homoscedasticity
# non-constant error variance test
ncvTest(fit)
# plot studentized residuals vs. fitted values 
spreadLevelPlot(fit)

library(gvlma)
gvmodel <- gvlma(fit) 
summary(gvmodel)


================================================
FILE: 41-LM/lm-broom.R
================================================
#Broom Package for output view of LM
#http://varianceexplained.org/r/broom-intro/

================================================
FILE: 41-LM/lm-dummy1.R
================================================
#Regression with Dummy Variables
mycars <- mtcars
mycars$cyl <- factor(mycars$cyl)
contrasts(mycars$cyl)
fit <- lm( mpg ~ wt+cyl, data=mycars, x=T )
summary(fit)
?lm
fit$contrasts
head(fit$x)
cbind(mycars$mpg, fitted(fit))

# 2 Dummy Variables
mycars <- mtcars
mycars$cyl = factor(mycars$cyl)
mycars$am  = factor(mycars$am)
contrasts(mycars$cyl)
contrasts(mycars$am)
fit2 <- lm( mpg ~ wt + cyl + am, data=mycars, x=T )
summary(fit2)
head(fit2$x)
cbind(mycars$mpg, fitted(fit))


================================================
FILE: 41-LM/lm-housing.R
================================================
# Housing Rent on factors

house = read.table("http://www.rossmanchance.com/iscam2/data/housing.txt", header = T, sep = "\t")
house

attach(house)
names(house)
fit = lm(price ~ sqft + City + bedrooms + baths)
summary(fit)

panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  r <- cor(x, y)
  txt <- format(c(r, 0.123456789), digits = digits)[1]
  txt <- paste0(prefix, txt)
  text(0.5, 0.5, txt)
}
pairs(cbind(price, sqft, bedrooms, baths), lower.panel = panel.cor, pch = 18)

summary(lm(price ~ sqft))

summary(lm(price ~ bedrooms))

par(mfrow = c(1, 2))
plot(lm(price ~ sqft)$fitted, lm(price ~ sqft)$resid, xlab = "fitted w sqft", 
     ylab = "resid", pch = 18)
abline(h = 0)
plot(lm(price ~ bedrooms)$fitted, lm(price ~ bedrooms)$resid, xlab = "fitted w bed", 
     ylab = "resid", pch = 18)
abline(h = 0)

par(mfrow = c(1, 2))
plot(lm(log(price) ~ sqft)$fitted, lm(log(price) ~ sqft)$resid , xlab = "fitted w sqft", 
     ylab = "resid for ln", pch = 18)
abline(h = 0)
plot(lm(log(price) ~ bedrooms)$fitted, lm(log(price) ~ bedrooms)$resid, xlab = "fitted w bed", 
     ylab = "resid for ln", pch = 18)
abline(h = 0)
summary(lm(log(price) ~ sqft + bedrooms))

summary(lm(log(price) ~ sqft))


predict(lm(log(price) ~ sqft), newdata = data.frame(sqft = 2000), interval = "confidence")
predict(lm(log(price) ~ sqft), newdata = data.frame(sqft = 2000), interval = "prediction")

par(mfrow=c(1,1))
plot(sqft, log(price), pch = 18)
sqftlm = lm(log(price) ~ sqft)
abline(sqftlm, col = "red")
newX = seq(min(sqft), max(sqft), 1)
prd.CI = predict(sqftlm, newdata = data.frame(sqft = newX), interval = "confidence", 
                 level = 0.95)
lines(newX, prd.CI[, 2], col = "blue", lty = 2)
lines(newX, prd.CI[, 3], col = "blue", lty = 2)
prd.PI = predict(sqftlm, newdata = data.frame(sqft = newX), interval = "prediction", 
                 level = 0.95)
lines(newX, prd.PI[, 2], col = "green", lty = 3)
lines(newX, prd.PI[, 3], col = "green", lty = 3)



================================================
FILE: 41-LM/lm-mtcars1.R
================================================
# LR with MT cars
# Forward and Backward

#Data----
str(mtcars)
names(mtcars)
dim(mtcars)
table(mtcars$am)
addmargins(table(mtcars$am))

#Blank Model----
(mtcars.lm = lm(mpg ~ 1, data=mtcars))
summary(mtcars.lm)

#Find Best Predictor----
add1(mtcars.lm, mtcars, test='F')

#Add Best Predictor----
mtcars.lm = lm(mpg ~ wt, data=mtcars)
summary(mtcars.lm)

#Next Best Predictor
add1(mtcars.lm, mtcars, test='F')

mtcars.lm = lm(mpg ~ wt + cyl, data=mtcars)
summary(mtcars.lm)

add1(mtcars.lm, mtcars, test='F')
#None to be added


# compare models
fit1 <- lm(mpg ~ wt, data=mtcars)
fit2 <- lm(mpg ~ wt + cyl, mtcars)
anova(fit1, fit2)  #more,less
#pv < 0.05, adding a variable has significant effect on R2

AIC(fit1,fit2)  #fit2 with wt & cyl better

# Other useful functions 
coefficients(fit) # model coefficients
confint(fit, level=0.95) # CIs for model parameters 
fitted(fit) # predicted values
residuals(fit) # residuals
anova(fit) # anova table 
vcov(fit) # covariance matrix for model parameters 
influence(fit) # regression diagnostics

# diagnostic plots 
layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page 
plot(fit)






#Backward
#
fit.full <- lm(mpg ~ wt + cyl + hp + am ,data=mtcars)
summary(fit.full)
drop1(fit.full, test='F')
drop1(update(fit.full, ~ . -am), test = "F")
drop1(update(fit.full, ~ . -am - hp), test = "F")


#http://rstudio-pubs-static.s3.amazonaws.com/2899_a9129debf6bd47d2a0501de9c0dc583d.html

# K-fold cross-validation
library(DAAG)
cv.lm(data=mtcars, fit2, m=3, plotit=F) # 3 fold cross-validation
#mse = 7.09
#assess R2 shrinkage via K-fold cross-validation.
#
#
# Assessing R2 shrinkage using 10-Fold Cross-Validation 

fit <- lm(mpg ~wt + cyl,data=mtcars) 

library(bootstrap)
# define functions 
theta.fit <- function(x,y){lsfit(x,y)}
theta.predict <- function(fit,x){cbind(1,x)%*%fit$coef} 

# matrix of predictors
X <- as.matrix(mtcars[c("wt","cyl")])
# vector of predicted values
y <- as.matrix(mydata[c("mpg")]) 

results <- crossval(X,y,theta.fit,theta.predict,ngroup=10)
cor(y, fit$fitted.values)**2 # raw R2 
cor(y,results$cv.fit)**2 # cross-validated R2

names(mtcars)
# Stepwise Regression
library(MASS)
fit <- lm(mpg ~ wt + cyl + hp + am ,data=mtcars)
step <- stepAIC(fit, direction="both")
step$anova # display results


Alternatively, you can perform all-subsets regression using the leaps( ) function from the leaps package. In the following code nbest indicates the number of subsets of each size to report. Here, the ten best models will be reported for each subset size (1 predictor, 2 predictors, etc.).

# All Subsets Regression
library(leaps)
attach(mydata)
leaps<-regsubsets(mpg ~ wt + cyl + hp + am,data=mtcars,nbest=10)
# view results 
summary(leaps)
# plot a table of models showing variables in each model.
# models are ordered by the selection statistic.
plot(leaps,scale="r2")
# plot statistic by subset size 
library(car)
subsets(leaps, statistic="rsq")
Other options for plot( ) are bic, Cp, and adjr2.
Other options for plotting with 
subset( ) are bic, cp, adjr2, and rss

The relaimpo package provides measures of relative importance for each of the predictors in the model. See help(calc.relimp) for details on the four measures of relative importance provided.

# Calculate Relative Importance for Each Predictor
library(relaimpo)
calc.relimp(fit,type=c("lmg","last","first","pratt"),rela=TRUE)

# Bootstrap Measures of Relative Importance (1000 samples) 
boot <- boot.relimp(fit, b = 1000, type = c("lmg", 
      "last", "first", "pratt"), rank = TRUE, 
                    diff = TRUE, rela = TRUE)
booteval.relimp(boot) # print result
plot(booteval.relimp(boot,sort=TRUE)) # plot result


library(rms)
mydata = as.matrix(mtcars)
ols.full <- ols(mpg ~ mpg + wt  , data = mtcars)

fastbw(ols.full, rule = "p", sls = 0.1)
mtcars$cylgr <- with(mtcars,interaction(cyl,gear == "4"))
regular_ols <- ols(mpg ~ wt + cyl + gear + cylgr, data=mtcars)
regular_ols
fastbw(regular_ols, rule='p', sls=0.1)
?ols

lm.full = lm(mpg ~ wt + cyl + gear + am +disp, data=mtcars)
model.aic.backward <- step(lm.full, direction = "backward", trace = 1)
summary(model.aic.backward)
lm.null = lm(mpg ~ 1, data=mtcars)
model.aic.forward <- step(lm.null, direction = "forward", trace = 1,
      scope = ~  wt + cyl + gear + am +disp)
summary(model.aic.forward)

model.aic.both <- step(lm.null, direction ="both",
trace = 1, scope =  ~ wt + cyl + gear + am +disp)
summary(model.aic.both)





================================================
FILE: 41-LM/lm-mtcars2.R
================================================
# Multiple Regression in R
mtcars
(model1 = lm(mpg ~ disp + cyl + hp
            + drat + wt + qsec + carb, data=mtcars))

summary(model1)
anova(model1)
vcov(model1)

layout(matrix(c(1,2,3,4),2,2))
plot(model1)

(model1 = lm(mpg ~ disp + cyl + hp
             + drat + wt + qsec + carb, data=mtcars))
summary(model1)
(model2 = lm(mpg ~ disp + cyl + wt , data=mtcars))
summary(model2)

anova(model1, model2)
anova(model2, model1)

(model3 = lm(mpg ~ cyl + wt , data=mtcars))
summary(model3)

anova(model3, model2)

plot(model3)

anova(model3, model1)
anova(model1, model3)


#Interaction Effects
(model4 = lm(mpg ~ cyl + wt + cyl*wt, data=mtcars))
summary(model4)

anova(model3, model4)

#AIC
library(MASS)
(model1a = lm(mpg ~ disp + cyl + hp
             + drat + wt + qsec + carb, data=mtcars))
step = stepAIC(model1a, direction='both')

step$anova


#Polynomial Regression
(model5 = lm(mpg ~ cyl + wt + I(wt^2), data=mtcars))
summary(model5)



#LOOCV
library(ISLR)
library(boot)
str(Auto)
(model1 = glm(mpg ~ horsepower, data = Auto))
mse_loocv = cv.glm(Auto , model1)
mse_loocv

par(mfrow=c(1,1))
#Kfold
library(DAAG)
(model2 = lm(mpg ~ horsepower, data=Auto))
(mse_fold = cv.lm(Auto, model2, m=3))
summary(mse_fold)
n=nrow(Auto)
sumcv = sum(mse_fold$cvpred)
(msecalc = sqrt(n/sumcv))
attributes(mse_fold)$ms


================================================
FILE: 41-LM/lm-plot1.R
================================================
# Graph Practise
?text

plot(mtcars$wt, mtcars$mpg, main=' MPG vs Weight',
     xlab='Weight' , ylab='Miles per Gallon', pch=18, col='blue', las=1)
text(mtcars$wt, mtcars$mpg, cex=0.6, pos=4, col='red')
text(mtcars$wt, mtcars$mpg, rownames(mtcars), cex=0.6, pos=4, col='red')
text(mtcars$wt, mtcars$mpg, 1:length(mtcars), cex=0.6, pos=2, col='blue')

plot(1:10, pch=10)
text(1:10, 1:10, 1:10)
text(1:10, 1:10, 1:10, pos=1, col='blue')
text(1:10, 1:10, 1:10, pos=2, cex=0.75, col='green')
text(1:10, 1:10, 1:10, pos=3, cex=1.25, col='red')
text(1:10, 1:10, 1:10, pos=4, cex=0.8, col='purple')


?plot
?text
?legend

# Data
x <- c(1,2,3,4,5)
y <- 2*x

# Plot
plot(x,y)

# Grid
grid(10,10)

data(iris)
str(iris)
iris[iris$Species=='setosa',]
dfsetosa = iris[iris$Species=='setosa',][1:2]
dfsetosa
plot(y=dfsetosa$Sepal.Length, x=dfsetosa$Sepal.Width)
fit=lm(Sepal.Length ~ Sepal.Width, data=dfsetosa)
summary(fit)
plot(fit, which=1)


================================================
FILE: 41-LM/lm-segments1.R
================================================
#Segments

plot(y=mtcars$mpg, x=mtcars$wt, xlim=range(mtcars$wt), ylim=range(mtcars$mpg))
abline(fit)
segments(mtcars$wt, mtcars$mpg, mtcars$wt, fitted(fit), lty=2, lwd=2,col=1:nrow(mtcars))
text(mtcars$wt+.02, mtcars$mpg+.5, labels = round(residuals(fit)), cex=.8)



#Covariance in R
cov(mtcars$mpg,mtcars$wt)
cov(mtcars$wt,mtcars$mpg)
#Covariance manually

sum( (mtcars$wt - mean(mtcars$wt)) * (mtcars$mpg - mean(mtcars$mpg)) )/ (length(mtcars$wt) - 1)
     

#Diagnostic Plots
plot(fitted(fit), residuals(fit), pch=20, xlab='Fitted', ylab='Residual')
sdres = sd(residuals(fit))
for (j in -3:3)
  abline(h = j * sqrt(var(resid(fit))), col=abs(j) + 1)


#Plot - Residuals vs Fitted values with horz lines at 3,2,1 SDEV of residuals


ix = which(abs(resid(fit)) > 2 * sdres)
text(fitted(fit)[ix], resid(fit)[ix], ix, pos=4)
cbind(mtcars[ix, c('wt', 'mpg')], fit=round(fitted(fit)[ix],1), resid=round(residuals(fit)[ix],1) )
rm(sdres, ix)

#ND
stem(residuals(fit),scale=2)
qqnorm(residuals(fit))
qqline(residuals(fit))
?shapiro.test(residuals(fit))
#Ho data is Normal Distribution; Ha - not ND

shapiro.test(rnorm(100, mean = 5, sd = 3)) # ND : p > 0.05
shapiro.test(runif(100, min = 2, max = 4)) # not ND : p < 0.05


#hat values for Influencing Observations
h = hatvalues(fit)
head(h)
hi.lev = which( h > 3 * mean(h))
hi.lev
mtcars[hi.lev,]

plot(mtcars$wt, h)
plot(mtcars$wt, mtcars$mpg)

fit2 = lm(mtcars$mpg ~ mtcars$wt, data=mtcars, subset = - hi.lev)
summary(fit2)
round(coef(fit)); round(coef(fit2))

sqrt(sum(residuals(fit)^2)/ length(residuals(fit)))
1 - (sum(residuals(fit)^2)/ sum((mtcars$mpg - mean(mtcars$mpg))^2))
summary(fit)


(pred=predict(fit, data.frame(wt = mean(mtcars$wt)), se.fit=T))
str(pred)
pred
round(pred$fit + qt(c(.025, .975), pred$df) * pred$se.fit, 1)

pframe = data.frame(wt = seq(min(mtcars$wt), max(mtcars$wt), by=1))
pframe
pred.c = predict(fit, pframe, interval='confidence', level=.95)
str(pred.c)
pred.c

#error

#pred.p = predict(fit2,pframe, interval='prediction', level=.95)
#pred.p

#plot
plot(pframe$wt, pred.c[, 'fit'], type='n')
lines(pframe$wt, pred.c[,'fit'], col=1, lwd=2)
lines(pframe$wt, pred.c[,'lwr'], col=2, lwd=2)
lines(pframe$wt, pred.c[,'upr'], col=3, lwd=2)


zone

================================================
FILE: 41-LM/mlm-state77.R
================================================
# Multiple Regression Part 1 - 16 Jan 16
# http://stats.stackexchange.com/questions/59250/how-to-interpret-the-output-of-the-summary-method-for-an-lm-object-in-r
# http://feliperego.github.io/blog/2015/10/23/Interpreting-Model-Output-In-R

#We will be using the dataset state.x77, which is part of the state datasets available in R. (Additional information 
#about the dataset can be obtained by running help(state.x77).)

# Ex1
# a. Load the state datasets.
# b. Convert the state.x77 dataset to a dataframe.
# c. Rename the Life Exp variable to Life.Exp, and HS Grad to HS.Grad. (This avoids problems with referring to these variables when specifying a model.)
data(state) 
state.x77
str(state.x77)
class(state.x77)
# convert to DF
state77 = data.frame(state.x77)
class(state77)
# Rename
names(state77)
names(state.x77)
names(state77)[4] = 'Life.Exp'
names(state77)[6] = 'HS.Grad'

# 
# Ex2
# Suppose we wanted to enter all the variables in a first-order 
#linear regression model with Life Expectancy as the dependent
#variable. Fit this model
# 
str(state77)
model= lm(Life.Exp ~ . , data= state77)  # . means all
summary(model)
# 
# ex3
# Suppose we wanted to remove the Income, Illiteracy, and
#Area variables from the model in Exercise 2. 
#Use the update function to fit this model.
str(state77) 
model2 = update(model, . ~ . - Income - Illiteracy - Area)
#model2 = lm(Life.Exp ~ Population + Murder, HS.Grad, Frost, data=state77)
summary(model2)

# Exercise 4
# Let’s assume that we have settled on a model that has HS.Grad and
#Murder as predictors. Fit this model.
#
model3 = lm(Life.Exp ~ Murder+ HS.Grad, data = state77)
summary(model3)

# Exercise 5
# Add an interaction term to the model in Exercise 4 
#(3 different ways).
#
head(state77[,c('Murder', 'HS.Grad')])
model4a = lm(Life.Exp ~ HS.Grad + Murder + HS.Grad:Murder, data=state77)
summary(model4a)

model4b = lm(Life.Exp ~ HS.Grad * Murder, data=state77)
summary(model4b)

model4c = lm(Life.Exp ~ (HS.Grad + Murder)^2, data=state77)
summary(model4c)


# Exercise 6
# For this and the remaining exercises in this set 
#we will use the model from Exercise 4. Obtain 95% confidence intervals for 
#the coefficients of the two predictor variables.

confint(model3, level=0.95)

# Exercise 7
# Predict the Life Expectancy for a state where 55% of the population are High School graduates, and the murder rate is 8 per 100,000.
#
predict(model3, data.frame(HS.Grad=55, Murder=8))

# Exercise 8
# 
# Obtain a 98% confidence interval for the mean Life Expectancy in a state where 55% 
#of the population are High School graduates, and the murder rate is 8 per 100,000.
#
formula(model3)
coefficients(model3)

predict(model3, data.frame(HS.Grad=55, Murder=8), interval='confidence', level=0.98)
# Ex9
# Obtain a 98% confidence interval for the Life Expectancy of a person living in a state where 55% of the population are High School graduates, and the murder rate is 8 per 100,000.
#
predict(model3, data.frame(HS.Grad=55, Murder=8), interval='confidence')

# Ex10
# Since our model only has two predictor variables, we can generate 
#a 3D plot of our data and the fitted regression plane. Create this plot.

require(rgl)
# create a DF with all combinations of factor variables of HS.Grad & Murder
plotdat = expand.grid(HS.Grad = seq(34,70,by=2), Murder=seq(1,16,by=1))
summary(plotdat)
plotdat$pred1 = predict(model3, newdata = plotdat)
plotdat$pred1

with(state77, plot3d(HS.Grad, Murder, Life.Exp, col='red', size=2
    ,xlab='High School Grad', ylab='Murder Rate', zlab=' Life Expectancy'
    ,main("3D Scatter Plot "), type='p'))
?plot3d

with(plotdat, surface3d(unique(HS.Grad), unique(Murder), pred1
    , alpha=0.5, front='line', back='line'))
?surface3d

# Misc -----------------
plot(model3)
#http://data.princeton.edu/R/linearModels.html

require(broom)
lmfit <- lm(mpg ~ wt, mtcars)
summary(lmfit)  
tidy(lmfit)
is.data.frame(tidy(lmfit))

================================================
FILE: 41-LM/multvariate1.R
================================================
# Linear Modeling http://data.princeton.edu/R/readingdata.html

fpe <- read.table("http://data.princeton.edu/wws509/datasets/effort.dat")
names(fpe)
str(fpe)
boxplot(fpe$setting,  col='lavender')
attach(fpe)
plot(effort, change,pch=21, bg='gold')
title('Scatter Plot of change vs Effort', col.main='#3366CC')
identify(effort, change, row.names(fpe), ps=9)
row.names(fpe)
fpe
pairs(fpe)

# Linear Model
lmfit = lm(change ~ setting + effort)
lmfit
(lmfit2 = lm(change ~ setting:effort))
(lmfit3 = lm(change ~ setting * effort))
summary(lmfit)
summary(lmfit, correlation = T)
anova(lmfit)
plot(lmfit)
fitted(lmfit)
coef(lmfit)
residuals(lmfit)
# Factors & Covariates
unique(effort)
(effortg = cut(effort, breaks = c(-1,4,14,100)))
effortg = cut(effort, breaks = c(-1,4,14,100), label=c('weak', 'moderate', 'strong'))
effortg # <4, 4-15, > 15 : weak, moderate, strong
covfit = lm(change ~ setting + effortg)
covfit
options(contrasts=c('contr.treatment','contr.poly'))

anova(covfit)
lm(change ~ log(setting) + effort)

lm(change ~ poly(setting,3) + effort)

library(splines)
setting.bs = bs(setting, knots = c(66,74,84))
setting.bs

setting.ns = ns(setting, df=5)
setting.ns
splinefit = lm(change ~ ns(setting, knot = median(setting)) + 
                 ns(effort, knot = median(effort)))
splinefit


# Other Options
X = cbind(1, effort, setting)
X
solve(t(X)  %*%  X ) %*% t(X) %*% change

# GLM
cuse <- read.table("http://data.princeton.edu/wws509/datasets/cuse.dat", header=TRUE)
cuse
attach(cuse)
lrfit = glm(cbind(using, notUsing) ~ age + education + wantsMore, family = binomial)
lrfit
(noMore = wantsMore== 'no')
hiEduc = education == 'high'
hiEduc

glm( cbind(using, notUsing) ~ age + hiEduc + noMore, family = binomial)
1- pchisq(29.92,10)

lrfit = glm(cbind(using, notUsing) ~ age * noMore + hiEduc, family = binomial)
lrfit
summary(lrfit)

# Updating Models
lrfit0 = update(lrfit, ~. - age:noMore)
anova(lrfit0, lrfit)

anova(lrfit, test='Chisq')

# Model Selection
drop1(lrfit, test='Chisq')

add1(lrfit, ~. ^2, test='Chisq')
search = step(lrfit, ~ . ^2)  # ???
str(cuse)
?step


================================================
FILE: 41-LM/plotcoef1.R
================================================

library(car) # for the example data
data(Duncan) # example data
m1 = lm(prestige ~ income + education + type, data=Duncan)
library(arm) # for coefplot()
coefplot(m1, vertical=FALSE, mar=c(5.5,2.5,2,2))

require(rpart)
model <- lm(Petal.Width ~ ., data = iris)

plot_coeffs(model)
coefplot(model)


data("Mroz", package = "car")
fm <- glm(lfp ~ ., data = Mroz, family = binomial)
coefplot(fm)
?coefplot


library("arm")
data("Mroz", package = "car")
M1<-      glm(lfp ~ ., data = Mroz, family = binomial)
M2<- bayesglm(lfp ~ ., data = Mroz, family = binomial)
M3<-      glm(lfp ~ ., data = Mroz, family = binomial(probit))
coefplot(M2, xlim=c(-2, 6),            intercept=TRUE)
coefplot(M1, add=TRUE, col.pts="red",  intercept=TRUE)
coefplot(M3, add=TRUE, col.pts="blue", intercept=TRUE, offset=0.2)



================================================
FILE: 41-LM/regrplot1.R
================================================

x <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)
y <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92) # plot scatterplot and the regression line

data("women")
x = women$height
y = women$weight
mod1 <- lm(y ~ x)
plot(x, y, xlim=c(min(x)-5, max(x)+5), ylim=c(min(y)-10, max(y)+10))
abline(mod1, lwd=2)


# calculate residuals and predicted values
res <- signif(residuals(mod1), 5)
pre <- predict(mod1) # plot distances between points and the regression line
segments(x, y, x, pre, col="red")

# add labels (res values) to points
library(calibrate)
textxy(x, y, res, cx=0.7)

# Difference Data Set
x <- c(173, 169, 176, 166, 161, 164, 160, 158, 180, 187)
y <- c(80, 68, 72, 75, 70, 65, 62, 60, 85, 92) # plot scatterplot and the regression line


#Women
fit = lm(weight ~ height, women) 
plot(fit)


y <- qunif(ppoints(length(randu$x)))
qqplot(randu$x,y)
randu


================================================
FILE: 44-LogR/24c-LR-default.R
================================================
# Logistic Regression
#data()  # datasets available for use in R

# Load the textbook R package
library(ISLR)   #install the package
?Default   # data set
# Load in the credit data
data("Default")
# See the properties
str(Default)
class(Default)
head(Default)
names(Default)
dim(Default)
summary(Default)

# How many people actual default?
(tmp = table(Default$default))
paste(333/10000,' people default in payment of credit card')
#How many student default
?table
table(Default$default, Default$student, dnn=c("default","students"))
ftable(Default$default, Default$student)
t1= table(Default$default, Default$student, dnn=c("default","students"))
addmargins(t1)
table(Default$student)


?glm
#Multiple Logistic Regression
logit1 = glm(default ~ income + balance + student, family='binomial', data=Default)
summary(logit1)
exp(coef(logit1))
#No Rs
#income is not significant - remove it

logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit2)
coef(logit2)
exp(coef(logit2))
anova(logit2, logit1)
#if pvalue < 0.05, logit1 is better model otherwise logit2

# Predict : use sample values
head(Default)
seq(1, 10000,500)
Default[c(1,501),]

ndata1= Default[seq(1, 10000,500),]
ndata1
nrow(Default[seq(1, 10000,500),])
10000/500

library(dplyr)
(ndata = (slice(Default, seq(1,n(),500))))
ndata
slice(Default, seq(1,n(),1000))  # another way
head(ndata)

addmargins(prop.table(table(Default$default,Default$student)))
0.2817/0.9667; 0.0127/0.0333

options(digits=10)

fitted.results = predict(logit2, newdata=ndata,type='response')
fitted.results = round(fitted.results,4)
head(fitted.results)
fitted.results
cbind(ndata, fitted.results)

ndata
p2 <- ndata %>% mutate(predictnew = ifelse(fitted.results < 0.5, 'No','Yes'))
cbind(p2,fitted.results)
fitted.results
ifelse(fitted.results < 0.05, 0,1)


(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))
(fitted.results2 <- predict(logit2, newdata=ndata2,type='response'))
cbind(ndata2, fitted.results2)

Default
xtabs( ~ default + student, data=Default)
table(Default$default, Default$student)
ftable(Default$default, Default$student)

#Accuracy of Model
library(caret)
set.seed(3456)
str(Default)
trainIndex <- createDataPartition(Default$default, p = .67, list = FALSE,   times = 1)

Train <- Default[ trainIndex,]
Test  <- Default[-trainIndex,]
head(Train)
head(Test)

# Logistic Regression Model
model = glm(default ~ student, data=Default, family='binomial')
Test$model_prob <- predict(model, Test, type = "response")
head(Test)
Test <- Test  %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))
head(Test)
Test <- Test %>% mutate(accurate = 1*(default == default_pred))
sum(Test$accurate)/nrow(Test)                         
#96% Accuracy




================================================
FILE: 44-LogR/24d-LR-default.R
================================================
# Logistic Regression

# Load the textbook R package
library(ISLR)
?Default
# Load in the credit data
#Check the structure of Data
#Data Structures
#
head(Default)
str(Default)
summary(Default)

names(Default)
# How many people actual default?

#Plot
Default$default

plot(Default$balance, Default$income, col=Default$default)
#Model
logitb = glm(default ~ balance, data=Default, family='binomial')
summary(logitb)
coef(logitb)

exp(coef(logitb))
range(Default$balance)
b1= logitb$coefficients[2]
exp(b1)^1  # due to 1 unit increase in balance 
exp(b1)^100  # due to 100 unit increase in balance
exp(b1)^1000  # due to 1000 unit increase in balance


exp(confint(logitb))

#Assessing impact of predictors on Probability of Outcome

# Create test data between the range
predict(logit, newdata = data.frame(balance=c(0,100,500,1000,1500,1800,2500)), type=c('response'))

head(Default)

top_n(Default, 5, balance)
Default %>% arrange(balance) %>% slice(1:5)
#top_n(Default, 5, desc(balance))
Default %>% arrange(balance) %>% slice(seq(1,n(),1000))


# Create a new dummy variable for students
#Default$studentD <- 0  # put all zeros
#Default$studentD[Default$student=="Yes"] <- 1  # replace 0 wherever yes is there
#Multiple Logistic Regression
logit = glm(default ~ income + balance + student, family='binomial', data=Default)
summary(logit2)
exp(coef(logit2))

logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit)
exp(coef(logit))


Default %>% group_by(student) %>% arrange(student, balance) 



# Predict
head(Default)
ndata2 = data.frame(income=c(40000), balance=c(700), student=c('Yes'))
predict(logit, newdata=ndata2, predict='response')

(ndata = (slice(Default, seq(1,n(),500))))

slice(Default, seq(1,n(),1000))
head(ndata)
addmargins(prop.table(table(Default$default,Default$student)))
0.2817/0.9667; 0.0127/0.0333
options(digits=10)

fitted.results = predict(logit, newdata=ndata,type='response')

fitted.results
cbind(ndata, fitted.results)
ndata
ndata %>% mutate(predict = ifelse(fitted.results < 0.5, 0,1))

fitted.results
ifelse(fitted.results < 0.05, 0,1)


(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))
(fitted.results2 <- predict(logit, newdata=ndata2,type='response'))



#Accuracy of Model
library(caret)
set.seed(3456)
str(Default)
trainIndex <- createDataPartition(Default$default, p = .67,
        list = FALSE,   times = 1)

Train <- Default[ trainIndex,]
Test  <- Default[-trainIndex,]
head(Train)
head(Test)

# Logistic Regression Model
model = glm(default ~ student, data=Default, family='binomial')
Test$model_prob <- predict(model, Test, type = "response")
head(Test)
Test <- Test  %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))
head(Test)
Test <- Test %>% mutate(accurate = 1*(default == default_pred))
sum(Test$accurate)/nrow(Test)                         
#96% Accuracy

?createDataPartition


================================================
FILE: 44-LogR/24e-LR-default.R
================================================
# Logistic Regression
data()  # datasets available for use in R

# Load the textbook R package
library(ISLR)
?Default   # data set
# Load in the credit data
data("Default")
str(Default)
class(Default)
head(Default)
names(Default)
balance
attach(Default)
balance
str(Default)
Default
dim(Default)
#write.csv(Default,'./data/cclogr.csv')
#Data Structures

head(Default)
str(Default)
summary(Default)

names(Default)
# How many people actual default?
tmp = table(default)
tmp
333/9667

(tmp[[2]]/tmp[[1]])*100
tmp[[2]]/dim(Default)[1] * 100

#Plot
library(ggplot2); library(gridExtra)
## Loading required package: grid
x = qplot(x=balance, y=income, color=default, shape=default, geom='point')+scale_shape(solid=FALSE)
y = qplot(x=default, y=balance, fill=default, geom='boxplot')+guides(fill=FALSE)
z = qplot(x=default, y=income, fill=default, geom='boxplot')+guides(fill=FALSE)
# Plot
x
grid.arrange(y, z, nrow=1)

#Model
logitb = glm(default ~ balance, data=Default, family='binomial')
summary(logitb)
coef(logitb)

exp(coef(logitb))
range(Default$balance)
b1= logitb$coefficients[2]
exp(b1)^1  # due to 1 unit increase in balance 
exp(b1)^100  # due to 100 unit increase in balance
exp(b1)^1000  # due to 1000 unit increase in balance


exp(confint(logitb))

#Assessing impact of predictors on Probability of Outcome

# Create test data between the range
predict(logit, newdata = data.frame(balance=c(0,100,500,1000,1500,1800,2500)), type=c('response'))

head(Default)

top_n(Default, 5, balance)
Default %>% arrange(balance) %>% slice(1:5)
#top_n(Default, 5, desc(balance))
Default %>% arrange(balance) %>% slice(seq(1,n(),1000))

#student
str(Default)
logits = glm(default ~ student, data=Default, family='binomial')
summary(logits)


# Create a new dummy variable for students
#Default$studentD <- 0  # put all zeros
#Default$studentD[Default$student=="Yes"] <- 1  # replace 0 wherever yes is there
#head(Default)

#logit <- glm(default ~ studentD, data=Default, family='binomial')
#summary(logit)
#predict(logit, newdata = data.frame(studentD=c(0)), type=c('response'))
#predict(logit, newdata = data.frame(studentD=c(1)), type=c('response'))


logits <- glm(default ~ student, data=Default, family='binomial')
summary(logits)
predict(logits, newdata = data.frame(student=c('Yes')), type=c('response'))
predict(logits, newdata = data.frame(student=c('No')), type=c('response'))


class(Default)
#Multiple Logistic Regression
logit1 = glm(default ~ income + balance + student, family='binomial', data=Default)
summary(logit1)
exp(coef(logit1))

logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit2)
exp(coef(logit2))


Default %>% group_by(student) %>% arrange(student, balance) 



# Predict
head(Default)
#ndata2 = data.frame(balance=c(700), student=c('Yes'))
#ndata2
#predict(logit2, newdata=ndata2, predict='response')
seq(1, 10000,500)
Default[c(1,501),]
Default[seq(1, 10000,500),]

library(dplyr)
(ndata = (slice(Default, seq(1,n(),500))))
ndata
slice(Default, seq(1,n(),1000))
head(ndata)
addmargins(prop.table(table(Default$default,Default$student)))
0.2817/0.9667; 0.0127/0.0333
options(digits=10)

fitted.results = predict(logit2, newdata=ndata,type='response')
fitted.results
head(fitted.results)
fitted.results
cbind(ndata, fitted.results)
ndata
ndata %>% mutate(predict = ifelse(fitted.results < 0.5, 'No','Yes'))

fitted.results
ifelse(fitted.results < 0.05, 0,1)


(ndata2 = data.frame(student=c('Yes','No'), balance=mean(Default$balance), income=mean(Default$income)))
(fitted.results2 <- predict(logit, newdata=ndata2,type='response'))



#Accuracy of Model
library(caret)
set.seed(3456)
str(Default)
trainIndex <- createDataPartition(Default$default, p = .67,
        list = FALSE,   times = 1)

Train <- Default[ trainIndex,]
Test  <- Default[-trainIndex,]
head(Train)
head(Test)

# Logistic Regression Model
model = glm(default ~ student, data=Default, family='binomial')
Test$model_prob <- predict(model, Test, type = "response")
head(Test)
Test <- Test  %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))
head(Test)
Test <- Test %>% mutate(accurate = 1*(default == default_pred))
sum(Test$accurate)/nrow(Test)                         
#96% Accuracy

?createDataPartition

Default
head(Default)
logr1 = glm(default ~ student + balance + income, data=Default, family='binomial')
summary(logr1)
logr2 = glm(default ~ student + balance , data=Default, family='binomial')
summary(logr2)
ndata3 = Default[seq(1,nrow(Default), 1000), ]
ndata3
(p1 = predict(logr2, newdata = ndata3, type='response'))
cbind(ndata3, p1, p2= ifelse(p1 < 0.5, 'No', 'Yes'))


================================================
FILE: 44-LogR/24g-LR-default-accuracy.R
================================================
#Logistic Regression - German Credit

library(caret)
library(ISLR)
data(Default) ; head(Default); str(Default)
data = Default
data$default = ifelse(data$default=="No",0,1)
head(data)

Train <- createDataPartition(data$default, p=0.6, list=FALSE)
training <- data[ Train, ]
testing <- data[ -Train, ]


head(training); dim(training); dim(testing)
str(training) ; str(training$default)

#Model
logR1 = glm(default ~ income + balance + student, family='binomial', data=training)
predict(logR1, newdata=testing, type="response")



actuals = testing$default
preds = predict(logR1, newdata=testing, type="response")
#best threshold (or cutoff) point to be used in glm models is the point which maximises the specificity and the sensitivity.  This threshold point might not give the highest prediction in your model, but it wouldn't be biased towards positives or negatives.

#-------
library(InformationValue)
(optCutOff <- optimalCutoff(actuals, preds)[1] )

summary(preds)
pred_default = ifelse(preds > optCutOff, 1, 0)

library(caret)
confusionMatrix(actuals, pred_default)
(t1=table(testing$default, pred_default))
accuracy1= caret::confusionMatrix(t1)
#-------
cutoff2 =0.5
pred_default2 = ifelse(preds > cutoff2, 1, 0)
confusionMatrix(actuals, pred_default2)
(t2=table(actuals, pred_default2))
accuracy2= caret::confusionMatrix(t2)
t1;t2
accuracy1; accuracy2
(TP1 = sum(diag(t1))/ sum(t1))
(TP2 = sum(diag(t2))/ sum(t2))
accuracy2


================================================
FILE: 44-LogR/26b-LR-germancredit.R
================================================
# Logisitic Regression : German Credit
#https://www.r-bloggers.com/evaluating-logistic-regression-models/
#
library(caret)
data(GermanCredit)
?GermanCredit
str(GermanCredit)
Train <- createDataPartition(GermanCredit$Class, p=0.6, list=FALSE)
training <- GermanCredit[ Train, ]
testing <- GermanCredit[ -Train, ]

#Model
mod_fit <- train(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + CreditHistory.Critical,  data=training, method="glm", family="binomial")

exp(coef(mod_fit$finalModel))

predict(mod_fit, newdata=testing)
predict(mod_fit, newdata=testing, type="prob")

mod_fit_one <- glm(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own +   CreditHistory.Critical, data=training, family="binomial")
predict(mod_fit_one, newdata=testing)

mod_fit_two <- glm(Class ~ Age + ForeignWorker, data=training, family="binomial")


anova(mod_fit_one, mod_fit_two, test ="Chisq")

library(lmtest)
lrtest(mod_fit_one, mod_fit_two)

library(pscl)
pR2(mod_fit_one)  # look for 'McFadden'
library(MKmisc)
HLgof.test(fit = fitted(mod_fit_one), obs = training$Class)
library(ResourceSelection)
hoslem.test(training$Class, fitted(mod_fit_one), g=10)

library(survey)
regTermTest(mod_fit_one, "ForeignWorker")
## Wald test for ForeignWorker
##  in glm(formula = Class ~ Age + ForeignWorker + Property.RealEstate + 
##     Housing.Own + CreditHistory.Critical, family = "binomial", 
##     data = training)
## F =  0.949388  on  1  and  594  df: p= 0.33027
regTermTest(mod_fit_one, "CreditHistory.Critical")
## Wald test for CreditHistory.Critical
##  in glm(formula = Class ~ Age + ForeignWorker + Property.RealEstate + 
##     Housing.Own + CreditHistory.Critical, family = "binomial", 
##     data = training)
## F =  16.67828  on  1  and  594  df: p= 5.0357e-05
#Variable ImportanceTo assess the relative importance of individual predictors in the model, we can also look at the absolute value of the t-statistic for each model parameter. This technique is utilized by the varImp function in the caret package for general and generalized linear models.

varImp(mod_fit)
## glm variable importance
## 
##                        Overall
## CreditHistory.Critical  100.00
## Property.RealEstate      57.53
## Housing.Own              50.73
## Age                      22.04
## ForeignWorker             0.00

red = predict(mod_fit, newdata=testing)
accuracy <- table(pred, testing[,"Class"])
sum(diag(accuracy))/sum(accuracy)
## [1] 0.705
pred = predict(mod_fit, newdata=testing)
confusionMatrix(data=pred, testing$Class)

ibrary(pROC)
# Compute AUC for predicting Class with the variable CreditHistory.Critical
f1 = roc(Class ~ CreditHistory.Critical, data=training) 
plot(f1, col="red")
## 
## Call:
## roc.formula(formula = Class ~ CreditHistory.Critical, data = training)
## 
## Data: CreditHistory.Critical in 180 controls (Class Bad) < 420 cases (Class Good).
## Area under the curve: 0.5944
library(ROCR)
# Compute AUC for predicting Class with the model
prob <- predict(mod_fit_one, newdata=testing, type="response")
pred <- prediction(prob, testing$Class)
perf <- performance(pred, measure = "tpr", x.measure = "fpr")
plot(perf)
auc <- performance(pred, measure = "auc")
auc <- auc@y.values[[1]]
auc
## [1] 0.6540625

ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)

mod_fit <- train(Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + 
                   CreditHistory.Critical,  data=GermanCredit, method="glm", family="binomial",
                 trControl = ctrl, tuneLength = 5)

pred = predict(mod_fit, newdata=testing)
confusionMatrix(data=pred, testing$Class)

================================================
FILE: 44-LogR/27b-LR-gre.R
================================================
# ROCC AUC
#https://stats.idre.ucla.edu/r/dae/logit-regression/

mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(mydata)
summary(mydata)
dim(mydata)
str(mydata)
table(mydata$admit)
sd(mydata$gre)
sapply(mydata, sd)

## two-way contingency table of categorical outcome and predictors we want
## to make sure there are not 0 cells
xtabs(~admit + rank, data = mydata)
str(mydata)
#convert rank into factors
mydata$rank <- factor(mydata$rank)
str(mydata)
?glm
model1 <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")
summary(model1)
#another model without rank
model2 <- glm(admit ~ gre + gpa , data = mydata, family = "binomial")
summary(model2)
AIC(mylogit)
AIC(model2)

## CIs using profiled log-likelihood
confint(model1)

## CIs using standard errors
confint.default(model1)

#Rank Significance - Wald Test
?wald.test
library(aod)
aod::wald.test(b = coef(model1), Sigma = vcov(mylogit), Terms = 4:6)
l <- cbind(0, 0, 0, 1, -1, 0)
wald.test(b = coef(mylogit), Sigma = vcov(mylogit), L = l)

#----
## odds ratios only
exp(coef(mylogit))

## odds ratios and 95% CI
exp(cbind(OR = coef(mylogit), confint(mylogit)))


prob=predict(mylogit,type=c("response"))
mydata$prob=prob

#Test with new data
newdata1 <- with(mydata, data.frame(gre = mean(gre), gpa = mean(gpa), rank = factor(1:4)))
## view data frame
newdata1
newdata1$rankP <- predict(model1, newdata = newdata1, type = "response")
newdata1
newdata2 <- with(mydata, data.frame(gre = rep(seq(from = 200, to = 800, length.out = 100),4), gpa = mean(gpa), rank = factor(rep(1:4, each = 100))))
newdata3 <- cbind(newdata2, predict(mylogit, newdata = newdata2, type = "link", se = TRUE))
newdata3
newdata3 <- within(newdata3, {
  PredictedProb <- plogis(fit)
  LL <- plogis(fit - (1.96 * se.fit))
  UL <- plogis(fit + (1.96 * se.fit))
})
head(newdata3)
library(ggplot2)

ggplot(newdata3, aes(x = gre, y = PredictedProb)) + geom_ribbon(aes(ymin = LL,
  ymax = UL, fill = rank), alpha = 0.2) + geom_line(aes(colour = rank), size = 1)
## view first few rows of final dataset
head(newdata3)

#----
with(mylogit, null.deviance - deviance)
with(mylogit, df.null - df.residual)
with(mylogit, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE))
logLik(mylogit)

library(pROC)
g <- roc(admit ~ prob, data = mydata)
plot(g)    


#best fit point ----
coords(g, "best")
mydata$prediction=ifelse(prob>=0.3126844,1,0)
caret::confusionMatrix(mydata$prediction,mydata$admit)
with(mydata, table(admit,gre))


#ROC Curve
#C:\Program Files\Java\jre1.8.0_144
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144') 
library(rJava)
library(Deducer)
modelfit <- glm(formula=admit ~ gre + gpa, family=binomial(), data=mydata, na.action=na.omit)
rocplot(modelfit)


#Another way to plot ROC
library(ROCR)
pred <- prediction(prob, mydata$admit)    
perf <- performance(pred, measure = "tpr", x.measure = "fpr")     
plot(perf, col=rainbow(7), main="ROC curve Admissions", xlab="Specificity", ylab="Sensitivity")    
abline(0, 1) #add a 45 degree line
(auc = performance(pred, "auc"))


================================================
FILE: 44-LogR/28b-LR-subscribe.R
================================================
# Logistic Regression

subscribe = read.csv('./data/subscribe.csv')

summary(subscribe)
table(subscribe$subyn)
fitlr = glm(subyn ~ age, data=subscribe, family= binomial())
summary(fitlr)

coef(fitlr)
exp(coef(fitlr))
new1 = data.frame(age=24)
predict(fitlr, newdata=new1, type='response')



================================================
FILE: 44-LogR/28c-LR-subscribe.R
================================================
# Logistic Regression
#https://www.youtube.com/watch?v=AVx7Wc1CQ7Y

# Read data file
mydata <- read.csv(file.choose(), header = T)
str(mydata)
mydata$admit <- as.factor(mydata$admit)
mydata$rank <- as.factor(mydata$rank)

# Two-way table of factor variables
xtabs(~admit + rank, data = mydata)

# Partition data - train (80%) & test (20%)
set.seed(1234)
dim(mydata)
ind = sample(2, nrow(mydata), replace = T, prob = c(0.8, 0.2))
ind
?sample
(train = mydata[ind==1,])
(test = mydata[ind==2,])
dim(train)
dim(test)
# Logistic regression model
mymodel <- glm(admit ~ gpa + rank, data = train, family = 'binomial')
summary(mymodel)

# Prediction
p1 <- predict(mymodel, train, type = 'response')
head(p1)
head(train)

# Misclassification error - train data
pred1 <- ifelse(p1>0.5, 1, 0)
tab1 <- table(Predicted = pred1, Actual = train$admit)
tab1
1 - sum(diag(tab1))/sum(tab1)

# Misclassification error - test data
p2 <- predict(mymodel, test, type = 'response')
pred2 <- ifelse(p2>0.5, 1, 0)
tab2 <- table(Predicted = pred2, Actual = test$admit)
tab2
1 - sum(diag(tab2))/sum(tab2)

# Goodness-of-fit test
with(mymodel, pchisq(null.deviance - deviance, df.null-df.residual, lower.tail = F))


================================================
FILE: 44-LogR/29b-LR-ads.R
================================================
# Logistic Regression
# Import the dataset

dataset = read.csv('./regr/Social_Network_Ads.csv')
#dataset = dataset[, 3:5]
View(dataset)
# Split the dataset into the Training set and Test set

#install.packages('caTools')
library(caTools)
?caTools
set.seed(2000)
split = sample.split(dataset$Purchased, SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)
dim(training_set); dim(test_set)
names(training_set)
# Feature Scaling
#training_set[, 1:2] = scale(training_set[, 1:2])
#test_set[, 1:2] = scale(test_set[, 1:2])

# Fitting Logistic Regression to the Training set
classifier = glm(formula = Purchased ~ as.factor(Gender)+Age+EstimatedSalary,family = binomial, data = training_set)

summary(classifier)

# Fitting Logistic Regression to the Training set - Age being insignificant is dropped here
classifier = glm(formula = Purchased ~ as.factor(Gender)+EstimatedSalary, family = binomial, data = training_set)

summary(classifier)
summary(classifier)$coefficient


# Predicting the Test set results
prob_pred = predict(classifier, type = 'response', newdata = test_set)
df_prob_pred = as.data.frame(prob_pred)

head(df_prob_pred)
y_pred = ifelse(prob_pred > 0.5, 1, 0)
y_pred

# Making the Confusion Matrix
cm = table(test_set[,5], y_pred)
cm
caret::confusionMatrix(cm)


================================================
FILE: 44-LogR/31b-LR-income.R
================================================
#logistic Regression
#https://www.guru99.com/r-generalized-linear-model.html



library(dplyr)
data_adult <-
  read.csv("https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/data_adult.csv")
glimpse(data_adult)
str(data_adult)

continuous <-select_if(data_adult, is.numeric)
summary(continuous)

# Histogram with kernel density curve
library(ggplot2)
ggplot(continuous, aes(x = hours.per.week)) +
  geom_density(alpha = .2, fill = "#FF6666")

quantile(data_adult$hours.per.week)
top_one_percent <- quantile(data_adult$hours.per.week, .99)
top_one_percent

data_adult_drop <-data_adult %>%
  filter(hours.per.week < top_one_percent)
dim(data_adult_drop)

#std only numeric columns
data_adult_rescale <- data_adult_drop %>%
  mutate_if(is.numeric, funs(as.numeric(scale(.))))
head(data_adult_rescale)


# Select categorical column
(charcols <- data.frame(select_if(data_adult_rescale, is.character)))
ncol(charcols)
data_adult_rescale[names(charcols)]
data_adult_rescale[names(charcols)] = lapply(data_adult_rescale[names(charcols)],factor)
factor <- data.frame(select_if(data_adult_rescale, is.factor))

ncol(factor)
summary(data_adult_rescale)
names(factor)
library(ggplot2)
# Create graph for each column
graph <- lapply(names(factor),
                function(x) 
                  ggplot(factor, aes(get(x))) +
                  geom_bar() +
                  theme(axis.text.x = element_text(angle = 90)))
graph


#Feature Engg
recast_data <- data_adult_rescale %>%
  select(-X) %>%  mutate(education = factor(ifelse(education == "Preschool" | education == "10th" | education == "11th" | education == "12th" | education == "1st-4th" | education == "5th-6th" | education == "7th-8th" | education == "9th", "dropout", ifelse(education == "HS-grad", "HighGrad", ifelse(education == "Some-college" | education == "Assoc-acdm" | education == "Assoc-voc", "Community",  ifelse(education == "Bachelors", "Bachelors",ifelse(education == "Masters" | education == "Prof-school", "Master", "PhD")))))))


recast_data %>%  group_by(education) %>%
  summarize(average_educ_year = mean(educational.num),
  count = n()) %>%  arrange(average_educ_year)

# Change level marry
recast_data <- recast_data %>%  mutate(marital.status = factor(ifelse(marital.status == "Never-married" | marital.status == "Married-spouse-absent", "Not_married", ifelse(marital.status == "Married-AF-spouse" | marital.status == "Married-civ-spouse", "Married", ifelse(marital.status == "Separated" | marital.status == "Divorced", "Separated", "Widow")))))

table(recast_data$marital.status)

# Plot gender income
ggplot(recast_data, aes(x = gender, fill = income)) +
  geom_bar(position = "fill") +  theme_classic()

# Plot origin income
ggplot(recast_data, aes(x = race, fill = income)) +
  geom_bar(position = "fill") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90))

# box plot gender working time
ggplot(recast_data, aes(x = gender, y = hours.per.week)) +
  geom_boxplot() +   stat_summary(fun.y = mean, geom = "point",  size = 3, color = "steelblue") +  theme_classic()

# Plot distribution working time by education
ggplot(recast_data, aes(x = hours.per.week)) +
  geom_density(aes(color = education), alpha = 0.5) +
  theme_classic()

anova <- aov(hours.per.week~education, recast_data)
summary(anova)

library(ggplot2)
ggplot(recast_data, aes(x = age, y = hours.per.week)) +
  geom_point(aes(color = income),
             size = 0.5) +
  stat_smooth(method = 'lm',
              formula = y~poly(x, 2),
              se = TRUE,
              aes(color = income)) +
  theme_classic()

library(GGally)
# Convert data to numeric
corr <- data.frame(lapply(recast_data, as.integer))
# Plot the graph
ggcorr(corr,method = c("pairwise", "spearman"),nbreaks = 6, hjust = 0.8,label = TRUE,label_size = 3,color = "grey50")

set.seed(1234)
create_train_test <- function(data, size = 0.8, train = TRUE) {
  n_row = nrow(data)
  total_row = size * n_row
  train_sample <- 1: total_row
  if (train == TRUE) {
    return (data[train_sample, ])
  } else {
    return (data[-train_sample, ])
  }
}
data_train <- create_train_test(recast_data, 0.8, train = TRUE)
data_test <- create_train_test(recast_data, 0.8, train = FALSE)
dim(data_train)

formula <- income ~ .
logit <- glm(formula, data = data_train, family = 'binomial')
summary(logit)

lapply(logit, class)[1:3]
logit$aic

predict <- predict(logit, data_test, type = 'response')
# confusion matrix
table_mat <- table(data_test$income, predict > 0.5)
table_mat

accuracy_Test <- sum(diag(table_mat)) / sum(table_mat)
accuracy_Test

precision <- function(matrix) {
  # True positive
  tp <- matrix[2, 2]
  # false positive
  fp <- matrix[1, 2]
  return (tp / (tp + fp))
}

recall <- function(matrix) {
  # true positive
  tp <- matrix[2, 2]# false positive
  fn <- matrix[2, 1]
  return (tp / (tp + fn))
}

prec <- precision(table_mat)
prec
rec <- recall(table_mat)
rec

f1 <- 2 * ((prec * rec) / (prec + rec))
f1

library(ROCR)
ROCRpred <- prediction(predict, data_test$income)
ROCRperf <- performance(ROCRpred, 'tpr', 'fpr')
plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2, 1.7))

formula_2 <- income~age: hours.per.week + gender: hours.per.week + .
logit_2 <- glm(formula_2, data = data_train, family = 'binomial')
predict_2 <- predict(logit_2, data_test, type = 'response')
table_mat_2 <- table(data_test$income, predict_2 > 0.5)
precision_2 <- precision(table_mat_2)
recall_2 <- recall(table_mat_2)
f1_2 <- 2 * ((precision_2 * recall_2) / (precision_2 + recall_2))
f1_2


================================================
FILE: 44-LogR/31c-income.R
================================================
#Log R - income class
##http://r-statistics.co/Logistic-Regression-With-R.html


================================================
FILE: 44-LogR/33b-LR-purchase.R
================================================
# Logistic Regression : Predict Purchase

# Import the dataset
df1 = read.csv('./data/logr2.csv')
head(df1)

url="https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=120271978"
library(gsheet)
df2 = as.data.frame(gsheet2tbl(url))
head(df2)

dataset=df1  #or df2 if data is imported from google sheets
head(dataset)
str(dataset)
summary(dataset)
dim(dataset)
View(dataset)
dataset$gender = factor(dataset$gender)


# Split the dataset into the Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(2000)

split = sample.split(dataset$purchased, SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

dim(dataset); dim(training_set); dim(test_set)
names(dataset)


# Logisitic Model on Training Set
logitmodel1 = glm(purchased ~ gender + age + salary, family = binomial,  data = training_set)
summary(logitmodel1)

# gender not insignificant dropped here
logitmodel2 = glm(purchased ~ age + salary, family = binomial, data = training_set)
summary(logitmodel2)

#summary(logitmodel2)$coefficient  # they are in log terms

#predict on sample data
test_set2 = data.frame(age=c(40,65), salary=c(40000, 50000))
(prob_pred2 = predict(logitmodel2, type = 'response', newdata = test_set2))
cbind(test_set2, prob_pred2)
#age=65 person likely to purchase

# Predicting the Test set results from testset
head(test_set)
prob_pred = predict(logitmodel2, type = 'response', newdata = test_set)
summary(prob_pred)
head(cbind(test_set,prob_pred ),10)

#if prob > 0.5 make it 1, else 0
y_pred = ifelse(prob_pred > 0.5, 1, 0)
head(cbind(test_set$purchased, y_pred),15)

# Making the Confusion Matrix
cm = table(test_set[,5], y_pred)
cm
caret::confusionMatrix(cm)

#ROCR Curve
library(ROCR)
ROCRpred <- prediction(y_pred, test_set$purchased)
ROCRperf <- performance(ROCRpred, 'tpr','fpr')
plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))



================================================
FILE: 44-LogR/45b-compareAUC.R
================================================
# ROC Area : 2 models
#uc-r.github.i/logistic_regression#multi

library(tidyverse) # formating output
library(ISLR)
data("Default")
head(Default)

df2 = Default
#convert default to binary coded : No-0, Yes-1
df2$default = ifelse(df2$default=='No',0,1)
head(df2$default)
#train Data
set.seed(100)
index = sample(0:nrow(df2), .6 * nrow(df2), replace=F)
index
df2Train = df2[index,]

#test Data
df2Test = df2[-index,]
nrow(df2Train) ; nrow(df2Test)

names(df2)
summary(df2)
#Models on Train Data
rocmodel1 = glm(default ~ balance, data=df2Train, family='binomial')
rocmodel2 = glm(default ~ student, data=df2Train, family='binomial')
rocmodel3 = glm(default ~ balance + income + student, data=df2Train, family='binomial')
tidy(rocmodel1)
tidy(rocmodel2)
tidy(rocmodel3)

#Model Evaluation and Comparison
#ANOVA----
anova(rocmodel1, rocmodel3, test='Chisq') #1 < 2 for variables
#M3 is better as pvalue < 0.05
#McFadden : Pseudo R2 ----
list(M1=pscl::pR2(rocmodel1)['McFadden'],
     M2=pscl::pR2(rocmodel2)['McFadden'],
     M3=pscl::pR2(rocmodel3)['McFadden'])
#M3 has highest value > -.4 


#Apply models to Test Data
test.predicted.m1 = predict(rocmodel1, newdata= df2Test, type='response')
test.predicted.m2 = predict(rocmodel2, newdata= df2Test, type='response')
test.predicted.m3 = predict(rocmodel3, newdata= df2Test, type='response')

#now compare the outputs by making confusion matrix 
list(M1 = table(df2Test$default, test.predicted.m1 > 0.5) %>% prop.table,
     M2 = table(df2Test$default, test.predicted.m2 > 0.5) %>% prop.table,
     M3 = table(df2Test$default, test.predicted.m3 > 0.5) %>% prop.table)
#M2 does not predict default cases (1) accurately
#M1 & M3 are almost same
#let us understand misclassification rate instead of accuracy
options(digits=4)
head(test.predicted.m1)
head(df2Test)
df2Test %>% 
  mutate(m1.pred = ifelse(test.predicted.m1 > 0.5, 1, 0), 
         m2.pred = ifelse(test.predicted.m2 > 0.5, 1, 0), 
         m3.pred = ifelse(test.predicted.m3 > 0.5, 1, 0)) %>%
  summarise(m1.error = mean(default != m1.pred),
            m2.error = mean(default != m2.pred),
            m3.error = mean(default != m3.pred))

head(df2Test)
table(df2Test$default, test.predicted.m1 > 0.5) %>% addmargins()
table(df2Test$default, test.predicted.m1 > 0.5) %>% prop.table  %>% addmargins()

#Plots
library(ROCR)
par(mfrow=c(1,3))
prediction(test.predicted.m1, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()
prediction(test.predicted.m2, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()
prediction(test.predicted.m3, df2Test$default) %>% performance(measure = 'tpr', x.measure = 'fpr') %>% plot()

#AUC
prediction(test.predicted.m1, df2Test$default) %>% performance(measure='auc') %>% .@y.values
#M1 has larger AUC : better
prediction(test.predicted.m2, df2Test$default) %>% performance(measure='auc') %>% .@y.values
prediction(test.predicted.m3, df2Test$default) %>% performance(measure='auc') %>% .@y.values
#M1 & M3 are almost same

model1 = glm(default1 ~ balance + income + student, data=dfTrain, family='binomial')
model1
caret::varImp(model1)

================================================
FILE: 44-LogR/45c-roc-default.R
================================================

library(ISLR)
data("Default")
head(Default)

df = Default
#convert default to binary coded : No-0, Yes-1
df$default1 = ifelse(df$default=='No',0,1)
#or
df$default2 = factor(df$default, levels = c('No','Yes'), labels=c(0,1))
summary(df)

#plot sigmoid curve
library(tidyverse)
library(ggplot2)
df %>% ggplot(aes(balance, default1))+ geom_point(alpha=.15) + geom_smooth(method='glm', method.args=list(family='binomial')) + ylab("Prob of Default")


head(df$default1)
#train Data
set.seed(100)
index = sample(0:nrow(df), .7 * nrow(df), replace=F)
index
dfTrain = df[index,]

#test Data
dfTest = df[-index,]
nrow(dfTrain) ; nrow(dfTest)

names(df)
summary(df)
#Model on Train Data
model1 = glm(default1 ~ balance + income + student, data=dfTrain, family='binomial')
model1
caret::varImp(model1)

exp(coef(model1))

#sample case :
balance=1500 ; income= 40000 ; student=1
c= as.numeric(coef(model1))
c[1:4]
(ex = exp( c[1] + balance * c[2] + income * c[3] + student * c[4]))  #student
ex/(1+ex)
(ex = exp( c[1] + balance * c[2] + income * c[3] + 0 * c[4]))  #Non student
ex/(1+ex)
ex/(1+ex)/2


#predict 
(new1 = data.frame(balance=1500, income= 40000, student=c('Yes','No')))
predict(model1, newdata = new1, type='response')
#same values as above manual calc
#being student, prob of default decreases by 50%

p1 = predict(model1, newdata = dfTest, type='response')
head(p1)

#model fitness
pscl::pR2(model1)['McFadden']
#good fit > .3

broom::augment(model1)
library(broom)
model1_data <- augment(model1) %>% mutate(index= 1:n())
ggplot(model1_data, aes(index, .std.resid, color=default1)) + geom_point(alpha=.5) + geom_abline(h=3)
plot(model1, which=4, id.n=15)

df[c(4160,652,9539, 9874, 9523),]  #influential values top5



================================================
FILE: 44-LogR/45e-roc-general.R
================================================
# ROC Curve
#https://rocr.bioinf.mpi-sb.mpg.de

library(ROCR)

pred = prediction(predictions, labels)
perf = performacne(pred, measure='tpr', x.measure='fpr')
plot(perf, col=rainbow(10))


================================================
FILE: 44-LogR/45f-roc1.R
================================================
# ROC Curve

set.seed(1)
N = 2000
P = .01

rare_success = sample(c(TRUE, FALSE), N, replace=TRUE, prob=c(P,1-P))
table(rare_success)

guess_not = rep(0,N)
table(guess_not)

library(pROC)
plot(roc(rare_success, guess_not), print.auc=T)


simp_roc = function(labels, scores) {
  labels = labels[order(scores, decreasing=T)]
  data.frame(TPR=cumsum(labels)/sum(labels), FPR=cumsum(!labels)/sum(!labels), labels) }

simp_roc = simp_roc(rare_success, guess_not)
simp_roc

with(simp_roc, lines(1-FPR, TPR, col='blue', lty=2))


================================================
FILE: 44-LogR/45h-roc2.R
================================================
# ROCR

library(ROCR)
data(ROCR.simple)

summary(ROCR.simple)
class(ROCR.simple)
ROCR.simple

pred = prediction(ROCR.simple$predictions, ROCR.simple$labels)
pred
perf = performance(pred, 'tpr', 'fpr')
plot(perf)


================================================
FILE: 44-LogR/46c-accuracy.R
================================================
# ROC Characteristics

actual = c(1,1,0,1,0,1,1,0)
#Model1 and Model2 probability values
M1 = c(0.75,0.8,0.4,0.25,0.45,0.35,0.65,0.15)
M2 = c(0.9,0.85,0.15,0.45,0.05,0.45,0.7,0.2)
(m=cbind(actual,M1,M2))

#threshold values T1
T1=0.5
predictM1T1 = ifelse(M1 > T1,1,0)
predictM2T1 = ifelse(M2 > T1,1,0)
(m1 = cbind(m, predictM1T1, predictM2T1))
#crosstable of actual vs predicted for each model
(t111= table(m1[,1],m1[,4]))  #xtable actual vs predict 1
(t211 = table(m1[,1],m1[,5]))

(ma = matrix(c('tp','fp','fn','tn'),nrow=2))
# tp – True Positive
# fp – False Positive
# fn – False Negative
# tn – True Negative
# 
# Accuracy = (tp+tn)/Total
(accuracy = sum(diag(t111))/ sum(t111))
# True Positive Rate (tpr) = tp/(tp+fn)
(tpr = t111[1,1]/sum(t111[1,1] + t111[2,2]))
(tpr = t111[1,1]/sum(diag(t111)))

# False Positive Rate (fpr) = fp/(tn+fp)

# Sensitivity (Recall) = tpr

# Specificity = tnr = tn/(fp+tn)


#library caret - easier way to do: pass table to function
library(caret)
caret::confusionMatrix(table(m1[,1],m1[,4]))
caret::confusionMatrix(table(m1[,1],m1[,5]))
caret::confusionMatrix(t111)
caret::confusionMatrix(t211)


#Different threshold T2=1
#2nd Stage
T2=1
predictM1T2 = ifelse(M1 > T2,1,0)
predictM2T2 = ifelse(M2 > T2,1,0)
(m2 = cbind(m, predictM1T2, predictM2T2))
#crosstable of actual vs predicted for each model
(t122= table(m2[,1],m2[,4]))  #xtable actual vs predict 1
# no value for column 1 (predicted); actual has
(t222 = table(m2[,1],m2[,5]))
caret::confusionMatrix(t122) #does not work becos no 2nd col
caret::confusionMatrix(table(m2[,1],m2[,4]))
# this threshold values does not classify well so will be for value of T=0

#3rd threshold values = 0.8
T3=0.8
predictM1T3 = ifelse(M1 > T3,1,0)
predictM2T3 = ifelse(M2 > T3,1,0)
(m3 = cbind(m, predictM1T3, predictM2T3))
#crosstable of actual vs predicted for each model
(t133= table(m3[,1],m3[,4]))  #xtable actual vs predict 1
# no value for column 1 (predicted); actual has
(t233 = table(m3[,1],m3[,5]))
caret::confusionMatrix(t133) #does not work becos no 2nd col
caret::confusionMatrix(t233)
# this threshold values does not classify well so will be for value of T=0

#4th threshold value = 0.3
T4=0.3
predictM1T4 = ifelse(M1 > T4,1,0)
predictM2T4 = ifelse(M2 > T4,1,0)
(m4 = cbind(m, predictM1T4, predictM2T4))
#crosstable of actual vs predicted for each model
(t144= table(m4[,1],m4[,4]))  #xtable actual vs predict 1
# no value for column 1 (predicted); actual has
(t244 = table(m4[,1],m4[,5]))
caret::confusionMatrix(t144) 
caret::confusionMatrix(t244)

#what is the right value


================================================
FILE: 44-LogR/48b-auc1.R
================================================
# AUC Logistic Regression
#https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/


category <- c(1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0)
category
prediction <- rev(seq_along(category))
prediction
prediction[9:10] <- mean(prediction[9:10])
prediction

library(pROC)
roc_obj <- roc(category, prediction)
auc(roc_obj)
## Area under the curve: 0.825
roc_df <- data.frame(
  TPR=rev(roc_obj$sensitivities), 
  FPR=rev(1 - roc_obj$specificities), 
  labels=roc_obj$response, 
  scores=roc_obj$predictor)

rectangle <- function(x, y, width, height, density=12, angle=-45, ...) {
  polygon(c(x,x,x+width,x+width), c(y,y+height,y+height,y), 
          density=density, angle=angle, ...)
}


roc_df <- transform(roc_df, 
                    dFPR = c(diff(FPR), 0),
                    dTPR = c(diff(TPR), 0))


plot(0:10/10, 0:10/10, type='n', xlab="FPR", ylab="TPR")
abline(h=0:10/10, col="lightblue")
abline(v=0:10/10, col="lightblue")

with(roc_df, {
  mapply(rectangle, x=FPR, y=0,   
         width=dFPR, height=TPR, col="green", lwd=2)
  mapply(rectangle, x=FPR, y=TPR, 
         width=dFPR, height=dTPR, col="blue", lwd=2)
  
  lines(FPR, TPR, type='b', lwd=3, col="red")
})



simple_auc <- function(TPR, FPR){
  # inputs already sorted, best scores first 
  dFPR <- c(diff(FPR), 0)
  dTPR <- c(diff(TPR), 0)
  sum(TPR * dFPR) + sum(dTPR * dFPR)/2
}

with(roc_df, simple_auc(TPR, FPR))
## [1] 0.825

rank_comparison_auc <- function(labels, scores, plot_image=TRUE, ...){
  score_order <- order(scores, decreasing=TRUE)
  labels <- as.logical(labels[score_order])
  scores <- scores[score_order]
  pos_scores <- scores[labels]
  neg_scores <- scores[!labels]
  n_pos <- sum(labels)
  n_neg <- sum(!labels)
  M <- outer(sum(labels):1, 1:sum(!labels), 
             function(i, j) (1 + sign(pos_scores[i] - neg_scores[j]))/2)
  
  AUC <- mean (M)
  if (plot_image){
    image(t(M[nrow(M):1,]), ...)
    library(pROC)
    with( roc(labels, scores),
          lines((1 + 1/n_neg)*((1 - specificities) - 0.5/n_neg), 
                (1 + 1/n_pos)*sensitivities - 0.5/n_pos, 
                col="blue", lwd=2, type='b'))
    text(0.5, 0.5, sprintf("AUC = %0.4f", AUC))
  }
  
  return(AUC)
}


rank_comparison_auc(labels=as.logical(category), scores=prediction)

auc_probability <- function(labels, scores, N=1e7){
  pos <- sample(scores[labels], N, replace=TRUE)
  neg <- sample(scores[!labels], N, replace=TRUE)
  # sum( (1 + sign(pos - neg))/2)/N # does the same thing
  (sum(pos > neg) + sum(pos == neg)/2) / N # give partial credit for ties
}

auc_probability(as.logical(category), prediction)


================================================
FILE: 44-LogR/48c-auc1.R
================================================
# AUC 

library(pROC)

auc(predictions$survived, predictions$pred)

?aSAH

# load the dataset
data(aSAH)

# Gender, outcome and set
with(aSAH, table(gender, outcome))

summary(aSAH$s100b)

head(aSAH)
auc(aSAH$outcome, aSAH$s100b)
#Area under the curve: 0.7314

================================================
FILE: 44-LogR/48d-auc.R
================================================
#https://stackoverflow.com/questions/4903092/calculate-auc-in-r


library(data.table)
library(mltools)

actuals <- c(0, 0, 1, 1)
preds <- c(.1, .3, .3, .9)


auc_roc(preds, actuals)  # 0.875

auc_roc(preds, actuals, returnDT=TRUE)


================================================
FILE: 44-LogR/48e-auc.R
================================================
#LR Model Checking
#https://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it

cls = c('P', 'P', 'N', 'P', 'P', 'P', 'N', 'N', 'P', 'N', 'P','N', 'P', 'N', 'N', 'N', 'P', 'N', 'P', 'N')
table(cls)
score = c(0.9, 0.8, 0.7, 0.6, 0.55, 0.51, 0.49, 0.43, 0.42, 0.39, 0.33, 0.31, 0.23, 0.22, 0.19, 0.15, 0.12, 0.11, 0.04, 0.01)

cbind(cls, score)
pos = score[cls == 'P']
neg = score[cls == 'N']

cbind(cls, score)
set.seed(14)
for( i in 1:100 ){
rnorm(10)
mean(rnorm(10))
print(mean(rnorm(10)))
}

replicate(100, mean(rnorm(10)))
sample(pos, size=1) > sample(neg, size=1)

#create values of True and false such that pos count > neg
p = replicate(50000, sample(pos, size=1) > sample(neg, size=1))
head(p); table(p)
prop.table(table(p))
mean(p)
#True=1, False=0 mean= 0.67926. Quite close, isn't it?



# use ROCR package for drawing ROC curves and calculating AUC.

library('ROCR')

pred = prediction(score, cls)
pred
roc = performance(pred, "tpr", "fpr")
roc

plot(roc, lwd=2, colorize=TRUE)
lines(x=c(0, 1), y=c(0, 1), col="black", lwd=1)

auc = performance(pred, "auc")
auc = unlist(auc@y.values)
auc


================================================
FILE: 44-LogR/49c-thresholdvalue.R
================================================
#http://ethen8181.github.io/machine-learning/unbalanced/unbalanced.html



================================================
FILE: 44-LogR/pdpu.R
================================================
#logisitic regression

library(ISLR)
?Default
data(Default)
head(Default,n=30)
str(Default)
head(Default[Default$default=='Yes',])

table(Default$student)
table(Default$default)
t1=table(Default$default, Default$student)
addmargins(t1)
addmargins(t1, FUN = mean)


library(dplyr)
Default %>% group_by(student, default) %>% summarise(n())
Default %>% group_by(student, default) %>% summarise(mean(income), mean(balance))

?glm

#logistic model
logit1 = glm(default ~ income + balance + student, family='binomial', data=Default)

#output is different from linear regression
#predict prob from 0 to 1
summary(logit1)

#are all IV signicant - No (income)
logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit2)

(ndata1 = data.frame(balance = mean(Default$balance), student="Yes"))
(p1=predict(logit2, newdata = ndata1, type='response') )

(ndata1 = data.frame(balance = 1486, student="Yes"))
(p1=predict(logit2, newdata = ndata1, type='response') )


================================================
FILE: 44-LogR/zz--logR.R
================================================
acc = read.csv("path to data")
View(acc)

set.seed(1)
index  <- sample(1:nrow(acc), round(0.75*nrow(acc)))
train  <- acc[index,]
test   <- acc[-index,]
fitTrn <- glm(isOneday~., data=train, family=binomial(link="logit")) 
fitted.results <- predict(fitTrn, newdata=test, type='response')

library(ROCR)
p   <- predict(fitTrn, newdata=test, type="response")
pr  <- prediction(p, test$isOneday)
prf <- performance(pr, measure="tpr", x.measure="fpr")
auc <- performance(pr, measure="auc")

================================================
FILE: 51-DT/cls-gen
================================================
Classification

Links
https://predictoanalycto.wordpress.com/2014/05/30/classification-trees-with-r-programming-language-part-1/
https://www.dezyre.com/data-science-in-r-programming-tutorial/decision-tree-tutorial
http://ipub.com/id3-with-data-tree/



================================================
FILE: 51-DT/cls1M-cancer.R
================================================

#Wisconsin Breast Cancer
loc='http://archive.ics.uci.edu/ml/machine-learning-databases/'
ds='breast-cancer-wisconsin/breast-cancer-wisconsin.data'
url = paste(loc,ds,sep='')
breast = read.table(url, sep=',' , header=F, na.strings='?' )
names(breast)
#[1] "V1" "V2" "V3" "V4" ..
#'data.frame': 699 obs. of  11 variables:  $ V1 : int  1000025 1002945 $ V2 : int  5 5 3 6
names(breast) = c('ID' , 'clumpThickness', 'sizeUniformity', 'shapeUniformity', 'maginalAdhesion', 'singleEpthelialCellSize', 'bareNuclei', 'blandChromatin', 'normalNucleoli', 'mitosis', 'class')
df = breast[-1]
head(df)
df$class = factor(df$class, levels=c(2,4), labels = c('benign', 'malignant'))
head(df$class)
set.seed(1234)
train = sample(nrow(df), 0.7*nrow(df) )
length(train)
df.train = df[train, ]
head(df.train[,c(1,10)])
df.validate= df[-train, ]
nrow(df.validate)
table(df.train$class)
table(df.validate$class)

?glm

#Eg1
fit.logit = glm(class ~. , data = df.train, family = binomial() )
summary(fit.logit)
prob = predict (fit.logit, df.validate, type='response')
prob
logit.pred = factor(prob > .5, level=c(F,T), labels=c('benign', 'malignant') )
logit.pred
logit.perf = table(df.validate$class, logit.pred, dnn=c('Actual', 'Predicted') )
logit.perf
table(df.validate$class)


#Eg
library(rpart)
set.seed(1234)
str(df.train)
str(df.validate)
dtree = rpart(class ~ ., data=df.train, method= 'class', parms = list(split='information') )
dtree$cptable
print(dtree)
plotcp(dtree)
abline(v=1)
summary(dtree)
dtree.pruned = prune(dtree, cp =.01)
dtree.pruned
library(rpart.plot)
prp(dtree.pruned, type=2, extra = 104, fallen.leaves=T, main='Decision Tree')
dtree.pred = predict(dtree.pruned, df.validate, type='class')
dtree.perf = table(df.validate$class, dtree.pred, dnn=c('Actual', 'Predicted') )
dtree.perf
201/210

#Eg
library(party)
fit.ctree = ctree(class ~ ., data = df.train)
plot(fit.ctree, main='Conditional Inference Tree')
ctree.pred = predict(fit.ctree, df.validate, type='response')
ctree.perf = table(df.validate$class, ctree.pred, dnn=c('Actual', 'Predicted') )
ctree.perf

#Eg
library(randomForest)
set.seed(1234)
fit.forest = randomForest(class ~ ., data=df.train, na.action=na.roughfix, important=T)
fit.forest
importance(fit.forest, type=2)
forest.pred = predict( fit.forest, df.validate)
forest.perf = table(df.validate$class, forest.pred, dnn=c('Actual', 'Predicted') )
forest.perf
(126+73)

#Eg2 : cforest() :: party package
#Eg2 : cforest() :: party package
library(party)
cforest(Species ~ ., data=iris, controls=cforest_control(mtry=2, mincriterion=0))
Random Forest using Conditional Inference Trees Number of trees:  500  Response:  Species  Inputs:  Sepal.Length, Sepal.Width, Petal.Length, Petal.Width  Number of observations:  150
rf <- randomForest(Species ~ ., data=iris)
getTree(rf, 1, labelVar=TRUE)

#Eg3
data(mtcars)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE, importance=TRUE)
plot(mtcars.rf, log="y")
varImpPlot(mtcars.rf)
#Eg4
set.seed(1)
data(iris)
iris.rf <- randomForest(Species ~ ., iris, proximity=TRUE, keep.forest=FALSE)
MDSplot(iris.rf, iris$Species)

#Eg1
library(e1071)
set.seed(1234)
fit.svm = svm(class ~., data = df.train)
fit.svm
svm.pred = predict(fit.svm, na.omit(df.validate))
svm.perf = table(na.omit (df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted') )
svm.perf


#Eg2
set.seed(1234)
tuned = tune.svm(class ~., data = df.train, gamma = 10^ (-6:1), cost(10^(-10:10)))

                 tuned
fit.svm = svm(class ~., data=df.train, gamma=0.01, cost=1 )
svm.pred = predict(fit.svm, na.omit(df.validate))
svm.perf = table(na.omit (df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted') )
svm.perf
                 

================================================
FILE: 51-DT/dt-multiplemodels.R
================================================
#Classification - DT

#Load Libraries
library(tidyverse)
library(caret)
library(rpart)

library(rpart)
#Create Model
model <- rpart(Species ~., data = iris)
par(xpd = NA) # otherwise on some devices the text is clipped
plot(model)
text(model, digits = 3)

#Print the Rules
print(model, digits = 2)
  
#Predict
newdata <- data.frame(
  Sepal.Length = 6.5, Sepal.Width = 3.0,
  Petal.Length = 5.2, Petal.Width = 2.0
)
model %>% predict(newdata, "class") 


#Case-2----
# Load the data and remove NAs
data("PimaIndiansDiabetes2", package = "mlbench")
PimaIndiansDiabetes2 <- na.omit(PimaIndiansDiabetes2)

# Inspect the data
sample_n(PimaIndiansDiabetes2, 3)

# Split the data into training and test set
set.seed(123)

training.samples <- PimaIndiansDiabetes2$diabetes %>% 
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- PimaIndiansDiabetes2[training.samples, ]
test.data <- PimaIndiansDiabetes2[-training.samples, ]

#----
# Build the model
set.seed(123)
model1 <- rpart(diabetes ~., data = train.data, method = "class")
# Plot the trees
par(xpd = NA) # Avoid clipping the text in some device
plot(model1)
text(model1, digits = 3)

#Predict
# Make predictions on the test data
predicted.classes <- model1 %>% 
  predict(test.data, type = "class")
head(predicted.classes)

# Compute model accuracy rate on test data
mean(predicted.classes == test.data$diabetes)

#caret package----
# Fit the model on the training set
set.seed(123)
model2 <- caret::train(
  diabetes ~., data = train.data, method = "rpart",
  trControl = trainControl("cv", number = 10),
  tuneLength = 10
)
# Plot model accuracy vs different values of
# cp (complexity parameter)
plot(model2)

# Print the best tuning parameter cp that
# maximizes the model accuracy
model2$bestTune

# Plot the final tree model
par(xpd = NA) # Avoid clipping the text in some device
plot(model2$finalModel)
text(model2$finalModel,  digits = 3)

# Decision rules in the model
model2$finalModel

# Make predictions on the test data
predicted.classes <- model2 %>% predict(test.data)
# Compute model accuracy rate on test data
mean(predicted.classes == test.data$diabetes)


#----
#regression tree
# Load the data
data("Boston", package = "MASS")
# Inspect the data
sample_n(Boston, 3)
# Split the data into training and test set
set.seed(123)
training.samples <- Boston$medv %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- Boston[training.samples, ]
test.data <- Boston[-training.samples, ]

# Fit the model on the training set
set.seed(123)
model <- train(
  medv ~., data = train.data, method = "rpart",
  trControl = trainControl("cv", number = 10),
  tuneLength = 10
)
# Plot model error vs different values of
# cp (complexity parameter)
plot(model)
# Print the best tuning parameter cp that
# minimize the model RMSE
model$bestTune

# Plot the final tree model
par(xpd = NA) # Avoid clipping the text in some device
plot(model$finalModel)
text(model$finalModel, digits = 3)

# Decision rules in the model
model$finalModel
# Make predictions on the test data
predictions <- model %>% predict(test.data)
head(predictions)
# Compute the prediction error RMSE
RMSE(predictions, test.data$medv)


#Conditional Tree
# Load the data
data("PimaIndiansDiabetes2", package = "mlbench")
pima.data <- na.omit(PimaIndiansDiabetes2)

# Split the data into training and test set
set.seed(123)
training.samples <- pima.data$diabetes %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- pima.data[training.samples, ]
test.data <- pima.data[-training.samples, ]

#-
library(party)
set.seed(123)
model <- train(
  diabetes ~., data = train.data, method = "ctree2",
  trControl = trainControl("cv", number = 10),
  tuneGrid = expand.grid(maxdepth = 3, mincriterion = 0.95 )
)
plot(model$finalModel)
model$finalModel

# Make predictions on the test data
predicted.classes <- model %>% predict(test.data)
# Compute model accuracy rate on test data
mean(predicted.classes == test.data$diabetes)



================================================
FILE: 51-DT/giniIndex.R
================================================
#Gini Index
student = c('Yes', 'Yes','Yes','No','No','No','No', 'Yes','No')
length(student)
hostel = c(T,T,F,F,T,T,F,T,T)
length(hostel)
target = c('Yes','Yes','No','Yes','No','No','No', 'Yes','No')
length(target)

(df = data.frame(student, hostel, target))
str(df)
#Gini Index
table(df$target)
(gini_target = 1 - ((5/9)^2 + (4/9)^2))
#1 - 41/81 = 59/81
# 0.49

#Gini Index
table(df$student, df$target)
xtabs(~ student + target, data = df)
(gini_NonStudent_Target = 1 - ((4/5)^2 + (1/5)^2))  #8/25
(gini_Student_Target = 1 - ((1/4)^2 + (3/4)^2)) #3/8
table(df$student)
(gini_Student = gini_target - ((5/9)* gini_NonStudent_Target + (4/9) * gini_Student_Target))
#0.149

#Gini Index - hostel
table(df$hosel, df$target)
xtabs(~ hostel + target, data = df)
(gini_NonHostel_Target = 1 - ((2/3)^2 + (1/3)^2))  # 4/9
(gini_Hostel_Target = 1 - ((3/6)^2 + (3/6)^2)) # 1/2
table(df$hostel)
(gini_Hostel = gini_target - ((3/9)* gini_NonHostel_Target + (6/9) * gini_Hostel_Target))
#0.012
gini_Student > gini_Hostel
#higher Gain by Student Variable, best splitting variable

#check by rpart
library(rpart)
library(rpart.plot)
(dt_rpart1 = rpart(target ~ student + hostel, data=df))
#no splitting

(dt_rpart2 = rpart(target ~ student + hostel, data=df, cp=-1, minsplit=2))
rpart.plot(dt_rpart2)
#splitting by student
dt_rpart2$variable.importance
summary(dt_rpart2)

info.gain.rpart(dt_rpart) #
rc1 = rpart.control(cp = 0.005, minsplit=2, minbucket = 1,maxcompete = 2, maxdepth = 1)
(dt_rpart3 = rpart(target ~ student + hostel, data=df, control=rc1))
rpart.plot(dt_rpart3)


#randomForest
library(randomForest)
(dt_rf = randomForest(target ~ student + hostel, data=df))
importance(dt_rf)
varImpPlot(dt_rf,type=2)


edarf::variable_importance(dt_rf, data=df)

library(caret)
head(df)
dt_caret = train( target ~ student + hostel, data=df,method="rpart", cp=-1)
dt_caret

library(CHAID)
dt_chaid = chaid(target ~ hostel + student, data=df)
dt_chaid
plot(dt_chaid)

library(party)
dt_party = ctree(target ~ hostel + student, data=df)
dt_party
plot(dt_party)

#https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf

df_mtcars = rpart(am ~ ., data=mtcars)
df_mtcars
df_mtcars$variable.importance



================================================
FILE: 51-DT/rattle.R
================================================
# Rattle
#Eg1
loc='http://archive.ics.uci.edu/ml/machine-learning-databases/'
ds = 'pima-indians-diabetes/pima-indians-diabetes.data'
url = paste(loc, ds, sep='')
diabetes = read.table(url, sep=',' , header=F)
names(diabetes) = c('npregant', 'plasma', 'bp', 'triceps', 'insulin', 'bmi', 'pedigree', 'age', 'class')
diabetes$class = factor(diabetes$class, levels = c(0,1) , labels = c('normal', 'diabetic'))
library(rattle)
rattle()

cv = matrix(c(145, 50, 8,27, nrow=2))
cv
performance(as.table(cv))


================================================
FILE: 52-CART/10-CART-gen.R
================================================
#Basic Steps in CART

#Collect Data
#Identify if it is Classification or Regression Model
#Write the formula : y ~ x` + x2  ....

#load libraries 
library(rpart)
library(rpart.plot)
model= rpart(y  ~ ., method='class', data= , control=rpart.control(minsplit=4, cp=0.00001))
rpart.plot()

#Set any control Parameters
control_1 = rpart.control(cp = 0.005, minsplit=10, minbucket = 5)
control_2 = rpart.control(cp = -1)

#using iris dataset
set.seed(123)
irisclass = rpart(Species ~ ., data = iris, control = control_1)
irisclass
rpart.plot(irisclass)
printcp(irisclass)
#--------
irisclass2 = rpart(Species ~ ., data = iris, control = control_2)
irisclass2
rpart.plot(irisclass2, cex=.8)


#----------------------
#regression Tree-----
names(iris)  #which is numeric column - Length
irisregression = rpart(Sepal.Length ~ ., data = iris, method="anova", control = control_1)
irisregression
rpart.plot(irisregression)
printcp(irisregression)
pruneRT = prune(irisregression, cp=.008)
rpart.plot(pruneRT)


================================================
FILE: 52-CART/11-cart-understandsplit.R
================================================
#Understanding Splitting and selection of variables

library(rpart)
library(rpart.plot)
#---

(gender = c(rep('M', 60), rep('F', 40)))
(play = c(rep(c('Yes','No'), c(30,30)), rep(c('Yes','No'), c(20,20))))
students = data.frame(gender, play)  
head(students)  
(t1=table(students$gender, students$play))
prop.table(t1)

dtree1 = rpart(play ~ gender, data=students, control = list(cp=-1, minsplit=5))
dtree1
rpart.plot(dtree1)
#no decision tree as proportion is 50%

#----------------------
(gender2 = c(rep('M', 60), rep('F', 40)))
(play2 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))
students2 = data.frame(gender2, play2)  
head(students2)  
(t2=table(students2$gender2, students2$play2))
addmargins(t2)
prop.table(t2)  #more % of males play
prop.table(t1)
addmargins(prop.table(t2))
#how many play - .6, 60% from 100% values

dtree2 = rpart(play2 ~ gender2, data=students2, control = list(cp=-1, minsplit=5))
dtree2
rpart.plot(dtree2)
rpart.plot(dtree2, extra=104)
rpart.plot(dtree2, extra=104, yesno=2, left=F, xflip=T, yflip=T,faclen=3, cex=1.5)
#explore below document
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
prop.table(t2,1) #40/60
#majority play (at root node)

#Eg3----------------------

(gender3 = c(rep('M', 60), rep('F', 40)))
(play3 = c(rep(c('Yes','No'), c(40,20)), rep(c('Yes','No'), c(20,20))))
(married3 = c(rep(c('Md','Sg'), c(50,10)), rep(c('Md','Sg'), c(10,30))))


students3 = data.frame(gender3, play3, married3)  
head(students3)  
(t3a= table(students3$play3, students3$gender3))
(t3b= table(students3$play3, students3$married3))
addmargins(t3a)
addmargins(t3b)

dtree3 = rpart(play3 ~ gender3 + married3, data=students3, control = list(cp=-1, minsplit=5))
dtree3
rpart.plot(dtree3)
addmargins(prop.table(table(students3$play3, students3$married3)))

#decision tree as proportion of M is not 50%
table(students3$play3)
#majority play (at root node)

#rpart(y~x+z, data=df, parms=list(split='gini'))


#Variable with lower Gini Index value, should be chosen as a variable that gives best split. The next step would be to take the results from the split and further partition.  
table(students3$play3)

table(students3$married3)
#married Variable : Gini Index
table(students3$play3, students3$married3)
(gini_md_sg = 1 - (30/40)^2 - (10/40)^2)
(gini_md_md = 1 - (10/60)^2 - (50/60)^2)
(gini_md = gini_md_sg + gini_md_md)

#gender Variable : Gini Index
table(students3$play3, students3$gender3)
(gini_gd_m = 1 - (20/60)^2 - (40/60)^2)
(gini_gd_f = 1 - (20/40)^2 - (20/40)^2)
(gini_gd = gini_gd_m + gini_gd_f)
gini_md < gini_gd
#gini_md is lower hence should be selected as split variable

#After Splitting : look at left tree ie. Single students
#singles = split(students3, married3="Sg")


================================================
FILE: 52-CART/12-DT-outlook.R
================================================
#example in many books
outlook =c('Sunny', 'Sunny', 'Overcast', 'Rain','Rain', 'Rain','Overcast', 'Sunny', 'Sunny','Rain','Sunny', 'Overcast','Overcast','Rain')
temperature = c( 'Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild')
humidity = c('Weak','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Weak','Weak','Strong','Strong','Weak','Strong')
wind = c( 'High','High','High','High','Normal','Normal','Normal','High','Normal','Normal','Normal','High','Normal','High')
play = c('No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No')

game = data.frame(outlook, temperature, humidity, wind, play)
head(game)

library(rpart)
library(rpart.plot)
dt_game1 = rpart(play ~ . , data=game) 
dt_game1
#no tree 
#cp=-1

dt_game2 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=1, minbucket = 1))
dt_game2
rpart.plot(dt_game2,cex=.8, nn=T)

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "gini"))
dt_game3
rpart.plot(dt_game3,cex=.8, nn=T)

?rpart

#make tree smaller
dt_game3 = rpart(play ~ . , data=game, control=rpart.control(cp = -1, minsplit=3, minbucket = 2, split = "information"))
dt_game3
attributes(dt_game3)
rpart.plot(dt_game3,cex=.8, nn=T)
dt_game3$parms
dt_game3$variable.importance
dt_game3$method
dt_game3$cptable
dt_game3$control
dt_game3$splits
dt_game3$numresp
dt_game3$y
game$play
dt_game3$ordered






================================================
FILE: 52-CART/12-IIMBG-wksp.R
================================================
#IIM - Bodh Gaya
#Feb 2020

#iimbg

#vectors ----
(x = rnorm(100, mean=60,sd=15))
x[ seq(1,100,2)]
hist(x)
hist(x, breaks=10)
hist(x, breaks=c(10,40,80,100))
plot(x)
plot(density(x))

#matrices
(data = round(rnorm(24, mean=40,sd=3)))
trunc(34.6)
floor(c(15.4, 15.7, -15.4))
trunc(c(15.4, 15.7, -15.4))
ceiling(c(15.4, 15.7, -15.4))
round(c(15.4, 15.7, -15.4))
data
(m1 = matrix(data, nrow=4))
(m2 = matrix(data, nrow=4, byrow=T))
m1
m2
colSums(m1)
rowSums(m1)
colMeans(m1)
rowMeans(m1)
m1[,c(1,4)]
m1[c(3,2),c(1,4)]

#DF
mtcars
str(mtcars)
summary(mtcars)
df = mtcars
names(df)
?mtcars
df$cyl = factor(df$cyl)
str(df)
df[,c('cyl','vs','am','gear','carb')] = lapply(df[,c('cyl','vs','am','gear','carb')], factor)
str(df)
summary(df)
table(df$cyl)
barplot(table(df$cyl), col=1:3)
pie(table(df$cyl), col=1:3)

table(df$gear, df$am, dnn=c('gear','am'))
table(df$gear, df$am, df$cyl, dnn=c('gear','am', 'cyl'))
?aggregate()
aggregate(cbind(mpg , wt) ~ cyl, data = df, mean)
aggregate(cbind(mpg , wt) ~ cyl + gear, data = df, mean)
#mean mpg & wt for each gear and cyl type

#factors
#without order
set.seed(1234)
(gender = sample(c('M','F'), size=100000, replace=T, prob=c(.7,.3)))
table(gender)
prop.table(table(gender))
barplot(table(gender))
pie(table(gender))

#grades
set.seed(1234)
(grades = sample(c('E','G','S','P'), size=100000, replace=T, prob=c(.4,.3,.2,.1)))
table(grades)
prop.table(table(grades))
pie(table(grades))
barplot(table(grades))
grades2 = factor(grades, ordered=T, levels =c('E','G','S','P'))
str(grades2)
barplot(table(grades2))


#dplyr----
library(dplyr)
df= mtcars
df[,c('cyl','vs','am','gear','carb')] = lapply(df[,c('cyl','vs','am','gear','carb')], factor)
str(df)
#find mean mpg & wt wrt to each gear & cyl type
df %>% group_by(cyl, gear) %>% summarise(meanWt = mean(wt, na.rm=T), meanMPG = mean(mpg, na.rm=T), maxHP = max(hp), min(wt), max(wt), n())
df %>% filter(mpg > 25)
df %>% filter(mpg > 25 & gear %in% c(4))

df
#ggplots
ggplot(df, aes(x=wt, y=mpg)) + geom_point()
ggplot(df, aes(x=wt, y=mpg)) + geom_point(aes(color=am))
ggplot(df, aes(x=wt, y=mpg)) + geom_point(aes(color=am, size=hp))
ggplot(df, aes(x=wt, y=mpg)) + geom_point(aes(color=am, size=hp, shape=carb))

df %>% group_by(cyl, gear)  %>% summarise(CNT = n())
ggplot(df %>% group_by(cyl, gear)  %>% summarise(CNT = n()), aes(x=cyl, y=CNT, fill=gear)) + geom_bar(stat='identity')
ggplot(df %>% group_by(cyl, gear)  %>% summarise(CNT = n()), aes(x=cyl, y=CNT, fill=gear)) + geom_bar(stat='identity', position= position_dodge2(.7)) + geom_text(aes(label=CNT), position=position_dodge2(.7))

ggplot(df %>% group_by(cyl, gear, carb, am)  %>% summarise(CNT = n()), aes(x=cyl, y=CNT, fill=gear)) + geom_bar(stat='identity', position= position_dodge2(.7)) + geom_text(aes(label=CNT), position=position_dodge2(.7)) + facet_grid(carb ~ am, scale='free')


ggplot(df, aes(x='', y=mpg)) + geom_boxplot()
ggplot(df, aes(x=gear, y=mpg, fill=gear)) + geom_boxplot()
ggplot(df, aes(x=gear, y=mpg, fill=gear)) + geom_boxplot() + facet_grid(am ~ cyl)

#simple linear regression

women
head(women)
plot(women)
cor(women)
cov(women$height, women$weight)
cor(women$height, women$weight)

model = lm(weight ~ height, data = women)
model
summary(model)
range(women$height)
residuals(model)
plot(model)

predict(model, new=data.frame(height=77))
-87 + 3.45 * 77

#mtcars
head(mtcars)
model2 = lm(mpg ~ wt + hp + qsec, data = mtcars)
summary(model2)
model3 = lm(mpg ~ wt , data = mtcars)



#load the data
#missing Values
#correlation between IV
#form Linear Model
#Check for Model Summary - F Stats, R2, Coeff P value
#check for assumptions
#predict & find accuracy
summary(model3)


#missing values

x = c(3,5,4,5)
x
mean(x)
x1 = c(4,5, ,6)
x2 = c(5,5,NA,20)
x2
mean(x2)
mean(x2, na.rm=T)
is.na(x2)
sum(is.na(x2))

x2[is.na(x2)] = mean(x2, na.rm=T)
x2
library(VIM)
data(sleep)
str(sleep)
summary(sleep)
names(sleep)
is.na(sleep$Dream)
sum(is.na(sleep$Dream))
colSums(is.na(sleep))
rowSums(is.na(sleep))
complete.cases(sleep)
sleep[complete.cases(sleep),]


#---

mtcars ; proportion of am to be maintained
train_MTC  - 70%
test_MTC  - 30%

set.seed(34)
library(caret)
split1 <- ...
train_MTC  <-  ....
test_MTC <- ....
table(train_MTC$am)
table(test_MTC$test)


x3


================================================
FILE: 52-CART/12-IIMJ-wksp.R
================================================
#IIM Jammu - Online - Oct 2019


mtcars
n=1000
(rollno = 1:n-1)
(name = paste('Student',1:n,sep=''))
(gender = c(rep('M',n/2), rep('F',n/2)))
(marks = rnorm(n, mean=50, 10))
(area = sample(c('Mktg','Fin',"HR","Ops"), size=n, replace=T))

df = data.frame(rollno, name, gender, marks, area)
df

table(df$gender)
table(df$area)
barplot(table(df$gender))
barplot(table(df$area), col=1:4)
table(df$area, df$gender)
mean(df$marks)

df
df[df$gender == "M",]
df[df$gender == "M","marks"]
mean(df[df$gender == "M","marks"])

library(dplyr)
df %>% group_by(gender, area) %>% summarise(MM = mean(marks, na.rm=T), n())

df[df$name == 'Student100',]
hist(df$marks)
h=hist(df$marks,5)
h


================================================
FILE: 52-CART/12-IIMS-wksp.R
================================================
#IIM Sirmaur  - Nov 2019

#Case Study - Manufacturing Coy

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)
dim(sales3)
library(dplyr)
names(sales3)
#loyal : customer + count ----
length(unique(sales3$custname))
sales3 %>% group_by(custname) %>% summarise(n=n())  %>% arrange(desc(n)) %>% head(5)
t1=table(sales3$custname)
tail(sort(t1),5)
#customer + revenue-----
sales3 %>% group_by(custname) %>% summarise(totalRev=sum(revenue))  %>% arrange(desc(totalRev)) %>% head(5)

#partno + revenue -----
names(sales3)
sales3 %>% group_by(partnum) %>% summarise(totalRev=sum(revenue))  %>% arrange(desc(totalRev)) %>% head(5)

#partno + margin ------
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(5)
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(totalMar) %>% head(5)


#top n=2 from each region : total revenue
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T))
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% filter(region =='9x-Export') %>% arrange(desc(totRev)) %>% head(2)

sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% top_n(n=2, wt=totRev)
sales3 %>% group_by(region, custname) %>% summarise(totRev = sum(revenue, na.rm=T)) %>% top_n(n=-3, wt=totRev)

df = mtcars
head(mtcars)
rownames(mtcars)
df$carnames = rownames(df)
head(df)
df %>% arrange(mpg)  %>% select(carnames, mpg, wt)
df %>% top_n(n=2, wt=mpg)  

#details about high margin parts
sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(7)
hspno <- sales3 %>% group_by(partnum) %>% summarise(totalMar=sum(margin))  %>% arrange(desc(totalMar)) %>% head(7) %>% pull(partnum)
hspno
sales3 %>% filter(partnum %in% hspno) 
sales3 %>% filter(partnum %in% hspno) %>% group_by(partnum, region) %>% summarise(n=n())

table(sales3$region)
#parts sold in East and china region : | or %in%
sales3 %>% filter(region == '01-East' | region == '09-China') %>% nrow()
sales3 %>% filter(region %in% c("01-East" ,'09-China')) %>% nrow()


#plots
#box plot
boxplot(mtcars$mpg)
boxplot(mpg ~ gear, data=mtcars)
boxplot(sales3$margin)
boxplot(margin ~ region, data = sales3)
range(sales3$margin)
hist(sales3$margin)
#bar plot
bpdata <- sales3 %>% group_by(region) %>% summarise(TotRev= sum(revenue))
barplot(bpdata$TotRev)


?barplot
#hist

#pie
pie(bpdata$TotRev)

library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) + geom_point()
ggplot(data=mtcars, aes(x=wt, y=mpg, color=factor(gear), shape=factor(cyl), size=hp)) + geom_point()
ggplot(data = mtcars, aes(x=factor(gear), fill=factor(cyl))) + geom_bar(stat='count')  + facet_grid(am ~ carb) + geom_text(stat='count', aes(label=..count..), vjust=-1)

#reshape data
library(reshape2)
dflong <- df %>% group_by(gear, cyl) %>% summarise(meanMPG = mean(mpg))
dflong
dcast(data=dflong, formula = gear ~ cyl, value.var = 'meanMPG')


sales3
head(sales3)
names(sales3)
dflong2 <- sales3 %>% group_by(custname,region) %>% summarise(sum2=sum(revenue))
head(dflong2)

dlong22=dcast(data=dflong2, formula= custname ~ region, value.var = 'sum2')

head(dlong22)
dlong23 <- melt(data=dlong22, id.vars = 'custname')
head(dlong23)

rollno =1:10
(subject = sample(paste('S',1:5), size=10, replace=T))
students = data.frame(rollno, subject)
head(students)
students$marks = trunc(rnorm(10,mean=60, sd=10))
students
sub1 = trunc(rnorm(10,mean=60, sd=10))
sub2 = trunc(rnorm(10,mean=60, sd=10))
sub3 = trunc(rnorm(10,mean=60, sd=10))
sub4 = trunc(rnorm(10,mean=60, sd=10))
sub5 = trunc(rnorm(10,mean=60, sd=10))

students2 = data.frame(rollno, sub1, sub2, sub3, sub4, sub5)
students2
melt(data=students2, id.vars = 'rollno')






#Missing Values

#identified by NA

x = c(3,4,NA, 4,2,NA)
x

is.na(x)
sum(is.na(x))
mean(x)
mean(x, na.rm=T)
x[is.na(x)]
x[is.na(x)] = mean(x, na.rm=T)
x

library(VIM)
sleep
head(sleep)
dim(sleep)
str(sleep)
summary(sleep)
is.na(sleep)
sum(is.na(sleep))
colSums(is.na(sleep))    
rowSums(is.na(sleep))
complete.cases(sleep)
head(sleep)
sleep[complete.cases(sleep),]
sleep[!complete.cases(sleep),]
df = sleep
colSums(is.na(df))
df$Dream
df$Dream[ is.na(df$Dream)] = mean(df$Dream, na.rm=T)
df$Dream



x=1:100
x
quantile(x)
x = rnorm(100, mean=60, sd=10)
x
sort(x)
quantile(x)v


#------
#reading from excel
library(readxl)
?readxl
readxl_example()
readxl_example("clippy.xls")
xlsx_example <- readxl_example("datasets.xlsx")
read_excel(xlsx_example)
excel_sheets(xlsx_example)
read_excel(xlsx_example, sheet = "chickwts")
read_excel(xls_example, sheet = 4)
read_excel(xlsx_example, n_max = 3)  #rows
read_excel(xlsx_example, range = "C1:E4")
read_excel(xlsx_example, range = cell_rows(1:4))
read_excel(xlsx_example, range = cell_cols("B:D"))
read_excel(xlsx_example, range = "mtcars!B1:D5")
#https://readxl.tidyverse.org/
read_excel(file.choose(), sheet=1)
read_excel(file.choose(), sheet='IIMK')
read_excel(file.choose(), sheet='IIMKPV')
read_excel('C:\\Users\\du\\Documents\\CBAP.xlsx',sheet=2)

iris_xl <- readxl_example("datasets.xlsx") %>% 
  read_excel(sheet = "iris") %>% 
  write_csv("iris-raw.csv")
#https://readxl.tidyverse.org/articles/articles/readxl-workflows.html

#check libraries to be loaded
path <- readxl_example("datasets.xlsx")
path %>%   excel_sheets() %>%   set_names() %>% 
  purr::map(read_excel, path = path)


================================================
FILE: 52-CART/12-IITB-wksp.R
================================================
#IIT Mumbai/ Bombay

data5
#Linear Regression
model1 = lm(y ~ x1 + x2, data=data5)
#rmse

#logistic
model2 = glm(y ~ x1 + x2 , data = data5)
#confusion matrix

#classification DT
model3 = rpart( y ~ x1 + x2, data = data5, method='class') #class
#confusion matrix
model3b = rpart( y ~ x1 + x2, data =data5, method='anova') #num

#
predict( model, newdata = ndata1 , type = 'response'/'class')

#-----
women
?women
names(women)
str(women)

model1 = lm(weight ~ height, data = women)
summary(model1)

y = mx + c
height= c(1)
weight <- 3.45 * height - 87
weight
ndata1 = data.frame(height=c(63,66, 68 ))
p1= predict(model1, newdata = ndata1, type='response')
cbind(ndata1, p1 )
range(women$height)

head(mtcars)
str(mtcars)

model2 = lm(mpg ~ wt + hp + disp, data=mtcars)
summary(model2)

model3 = lm(mpg ~ wt + hp , data=mtcars)
summary(model3)


#clustering
kmeans
mtcars$mpg
dim(mtcars)
str(mtcars) #structure
nrow(mtcars)
ncol(mtcars)
class(mtcars)
?mtcars
summary(mtcars)
str(mtcars)
model3=kmeans(mtcars$mpg, centers=3) 
model3
model3$cluster
cbind(mtcars$mpg, model3$cluster)
model3$centers

model4=kmeans(mtcars[,c('mpg','wt')], centers=3) 
model4
model4$cluster
df2= cbind(mtcars[,c('mpg','wt')], cluster2= model4$cluster)
head(df2)
library(dplyr)
df2 %>% arrange(cluster2)
df2 %>% group_by(cluster2) %>% summarise(n())
model4$centers


df = mtcars
df
head(df)
df$cyl = factor(df$cyl)
str(df)
df$cyl

# decision trees using rpart

install.packages(c('rpart','rpart.plot'))
library(rpart)
library(rpart.plot)

head(mtcars)


#------
#exercise

#create a dataframe of these columns for 1000 students
#rollno, sname, gender, age, marks1, mark2, passfail
#passfail - 0 fail, 1 pass
n=1000
(rollno = 1:n)
(sname = paste('Student', 1:n, sep='_'))
(gender = sample(c('M','F'), size=n, replace=T, prob=c(.35, .65))   ) #M, F; 65% females
table(gender)
prop.table(table(gender))
(age =  trunc(runif(n, 20, 30)))    #between 20 and 30 , uniform distribution
(marks1 = round(rnorm(n, mean=65, sd=10))  ) #mean=65, sd=10 , normally distributed
(marks2 = round(rnorm(n, mean=70, sd=8),2))  # mean=70, sd=8, normally distributed
(course = sample(c('btech','mtech','phd'), size=n, replace=T, prob=c(.7, .2, .1)))
(passfail = sample(c(0,1), size=n, replace=T, prob=c(.4,.6)) )  #Pass, Fail on placement drive, 60% in pass
#give the data frame name as dataiitb 
dataiitb = data.frame(rollno, sname, gender, age, course, marks1, marks2, passfail)
head(dataiitb)
write.csv(dataiitb, './data/dataiitb.csv') #subdirectory data in current repository
#find properties of data like 
#first few and last few rows
head(dataiitb)
tail(dataiitb)
#dimensions
dim(dataiitb)
#no of rows and columns
nrow(dataiitb); ncol(dataiitb)
#summary
summary(dataiitb)
#structure
str(dataiitb)
#class of data
class(dataiitb)
#import from online csv
data2 = read.csv('https://raw.githubusercontent.com/dupadhyaya/hheanalytics/master/data/dataiitb.csv')
head(data2)

#find gender mean of marks1 and marks2
library(dplyr)
data2 %>% group_by(gender) %>% summarise(meanM1 = mean(marks1), meanM2 = mean(marks2))

sum1 <- data2 %>% group_by(gender, course) %>% summarise(meanM1= mean(marks1), minA = min(age), sdA = sd(age), count=n()) %>% arrange(gender, desc(sdA))
sum1

#arrange the data in the order of gender, sdA

#who are the top 2 students from each gender from marks of subject2
data2 %>% group_by(gender) %>% arrange(gender, marks2) %>% top_n(2, marks2)

#make a barplot gender distribution
barplot(height=c(10,20))
table(data2$gender)
barplot(height = table(data2$gender), col=1:2)
barplot(height = table(data2$course))
hist(data2$marks1, breaks=5, col=1:5)
plot(density(data2$marks1))
plot(density(data2$age))

?barplot
#create a linear model of predicting marks1 from age
fit1 = lm(marks2 ~ age + marks1, data=data2)
summary(fit1)

#create a logistic model to predict passfail from marks1, marks2, gender
fit2 = glm(passfail ~ marks1 + marks2 + gender + course, data=data2, family='binomial')
?glm
summary(fit2)
#clustering
fit5 = kmeans(data2[, c('age','marks1','marks2')], centers = 4)
fit5$centers

#create a decision tree using previous case of logistic model. Predict passfail for sample case of gender, marks1, marks2
fit3 = rpart(passfail ~ marks1 + marks2 + gender, data=data2,method='class' )
summary(fit3)library
rpart.plot(fit3)
#create a decision tree - regression model : predict age on gender, marks1, marks2


================================================
FILE: 52-CART/CARTR_sales.R
================================================
# CART Models

library(rpart)
library(rpart.plot)
library(forecast)
#install.packages("https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip", repos=NULL)
#library(RGtk2)
#install.packages("rattle")
#library(rattle)


# Set the working directory to folder where you have placed the Input Data


MerSales2 = read.csv(file = "./data/msales.csv", skip=1, header = T)
MerSales= MerSales2
dim(MerSales)
# Summarize the dataset
summary(MerSales)
str(MerSales)
# Look at the average Annual_Sales()
for(i in 2:ncol(MerSales))
{
  if(length(unique(MerSales[,i])) <= 5)
  {
    AnnualSales = aggregate(x =  MerSales$sales, by = list(MerSales[,i]), FUN = mean)
    print(colnames(MerSales)[i])
    print(AnnualSales)
    print("*******************************************")
  }
}

aggregate(MerSales$sales, by=list(MerSales$loc), FUN=sum)
# Make a copy of the Original Dataset
MerSalesUncapped = MerSales

# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(MerSalesUncapped), size = 0.7*nrow(MerSalesUncapped))
Index
length(Index)
# Create Train dataset
MerSalesTrainUncapped = MerSalesUncapped[Index, ]
nrow(MerSalesTrainUncapped)
summary(object = MerSalesTrainUncapped)

# Create Test dataset
MerSalesTestUncapped = MerSalesUncapped[-Index, ]
nrow(MerSalesTestUncapped)
summary(object = MerSalesTestUncapped)
#divide data into 2 parts - Train (70%), Test(30%)

########################### Modeling ############################

# Build a full model with default settings
set.seed(123) # To ensure reproducibility of xerrors (cross validated errors while estimating complexity paramter for tree pruning)

CartFullModel = rpart(sales ~ . , data = MerSalesTrainUncapped[,-1], method = "anova")
CartFullModel
summary(object = CartFullModel)


# Plot the Regression Tree
rpart.plot(CartFullModel, type = 4,fallen.leaves = T,nn=T, cex = 1)

mean(MerSalesTrainUncapped$sales)

title("CartFullModel") # Enlarge the plot by clicking on Zoom button in Plots Tab on R Studio

# fancyRpartPlot() function to plot the same model
# Expand the plot window in R Studio to see a presentable output
fancyRpartPlot(model = CartFullModel, main = "CartFullModel", cex = 0.6) 
?fancyRpartPlot
# The following code also produces the same output, but in a windowed form
windows()
fancyRpartPlot(model = CartFullModel, main = "CartFullModel", cex = 0.6)


# The CP table also shows us valueable information in terms of pruning the tree (which is explained later in the code)
 # => The complexity parameter "CP" specifies how the cost of a tree C(T) is penalized by the number of terminal nodes |T|.
 # Hence, Small "CP" results in larger trees and potential overfitting, large CP" in small trees and potential underfitting.
 # => The "rel error" is 1- RSquare, similar to linear regression. This is the error on the observations used to estimate the model. The last node value of rel error suggests the R-Square of the model, if this happens to be the model
 # => The "xerror" is is the error on the observations from cross validation data, which happens to be a 10-Fold Cross Validation. Particpants need to note that, in order to reproduce the "xerror" values, they must use the same set.seed() number each time
 # => Root Node Error is given by sum((Dependent - Mean(Dependent))^2), i.e.
 # sum((InsDataTrainUncapped$Losses-mean(InsDataTrainUncapped$Losses))^2
 
printcp(CartFullModel)

# This produces a plot which may help particpants to look for a model depending on R-Square values produced at various splits
rsq.rpart(x = CartFullModel)

fit4 = prune(CartFullModel, cp=.014)
fit4
rpart.plot(fit4, nn=T, cex=1)

########################### Using CP to expand / Prune the tree #################################################
# Lets change rpart.control() to specify certain attributes for tree building
RpartControl = rpart.control(cp = 0.005)
set.seed(123)
names(MerSales)
CartModel_1 = rpart(formula = sales ~ . , 
                    data = MerSalesTrainUncapped[,-1], 
                    method = "anova", control = RpartControl)

CartModel_1

CartModel_1$where
trainingnodes = rownames(CartModel_1$frame) [ CartModel_1$where]
trainingnodes

summary(CartModel_1)
rpart.plot(x = CartModel_1, type = 4,cex = 0.6)
printcp(x = CartModel_1)
rsq.rpart(x = CartModel_1)




####################### Some Extra pointers in R ###########################################


# Model Evaluation Measures on test dataset using the finalized (pruned model)
# Use predict() the get the predicted values for the testset using the finalized model

# Intermediate Model: Finalize CartFullModel (Based on Tree size i.e. Depth, Variables included as well as the R-Square produced)
# Predict on testset
CartFullModelPredictTest = predict(CartFullModel, newdata = MerSalesTestUncapped, type = "vector")
CartFullModelPredictTest[1]
MerSales[2,'sales']
# Calculate RMSE and MAPE manually
# Participants can calculate RMSE and MAPE using various available functions in R, but that may not
# communicate effectively the mathematical aspect behind the calculations

# RMSE
Act_vs_Pred = CartFullModelPredictTest - MerSalesTestUncapped$Annual_Sales # Differnce
Act_vs_Pred_Square = Act_vs_Pred^2 # Square
Act_vs_Pred_Square_Mean = mean(Act_vs_Pred_Square) # Mean
Act_vs_Pred_Square_Mean_SqRoot = sqrt(Act_vs_Pred_Square_Mean) # Square Root
Act_vs_Pred_Square_Mean_SqRoot

# MAPE
Act_vs_Pred_Abs = abs(CartFullModelPredictTest - MerSalesTestUncapped$sales) # Absolute Differnce
Act_vs_Pred_Abs_Percent = Act_vs_Pred_Abs/MerSalesTestUncapped$sales # Percent Error
Act_vs_Pred_Abs_Percent_Mean = mean(Act_vs_Pred_Abs_Percent)*100 # Mean
Act_vs_Pred_Abs_Percent_Mean
library(forecast)
# Validate RMSE and MAPE calculation with a function in R
UncappedModelAccuarcy = forecast::accuracy(f = CartFullModelPredictTest, x = MerSalesTestUncapped$sales)
UncappedModelAccuarcy



windows()
fancyRpartPlot(model = CartModel_1, main = "Final CART Regression Tree", cex = 0.6, sub = "Model 12")


================================================
FILE: 52-CART/CART_Regression Tree v01.R
================================================
# CART Models - HH Case Study

library(rpart)
library(rpart.plot)
library(forecast)
#install.packages("https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip", repos=NULL)
#library(RGtk2)
#install.packages("rattle")
#library(rattle)

library(gsheet)
url='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'
data = as.data.frame(gsheet2tbl(url))
str(data)
# or Load from CSV File
Mer_Sales = read.csv(file = "./data/Predict Merchant_Sales v01.csv", header = T)

Mer_Sales = data

# Summarize the dataset
summary(Mer_Sales)
names(Mer_Sales)
# Look at the average Annual_Sales()
for(i in 2:ncol(Mer_Sales))
{
  if(length(unique(Mer_Sales[,i])) <= 5)
  {
    Annual_Sales=aggregate(x=Mer_Sales$Annual_Sales,by=list(Mer_Sales[,i]),FUN=mean)
    print(colnames(Mer_Sales)[i])
    print(Annual_Sales)
    print("****************************************")
  }
}
head(Mer_Sales)
summary(Mer_Sales)
str(Mer_Sales)
# Make a copy of the Original Dataset
Mer_SalesUncapped = Mer_Sales
dim(Mer_SalesUncapped)
# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(Mer_SalesUncapped), size = 0.7*nrow(Mer_SalesUncapped))
Index

# Create Train dataset
Mer_SalesTrainUncapped = Mer_SalesUncapped[Index, ]
nrow(Mer_SalesTrainUncapped)
summary(Mer_SalesTrainUncapped)

# Create Test dataset
Mer_SalesTestUncapped = Mer_SalesUncapped[-Index, ]
nrow(Mer_SalesTestUncapped)
summary(object = Mer_SalesTestUncapped)


########################### Modeling #################################

head(Mer_SalesTrainUncapped)
# Build a full model with default settings
set.seed(123) # To ensure reproducibility of xerrors (cross validated errors while estimating complexity paramter for tree pruning)
summary(Mer_SalesTestUncapped)
CartFullModel = rpart(Annual_Sales ~ . , data = Mer_SalesTrainUncapped[,-1], method = "anova")
CartFullModel
mean(Mer_SalesTrainUncapped$Annual_Sales)
summary(object = CartFullModel)
summary(Mer_SalesTestUncapped[,'Annual_Sales'])


names(Mer_SalesTrainUncapped)
# Plot the Regression Tree
rpart.plot(x = CartFullModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)
title("CartFullModel") # Enlarge the plot by clicking on Zoom button in Plots Tab on R Studio

# fancyRpartPlot() function to plot the same model
# Expand the plot window in R Studio to see a presentable output
fancyRpartPlot(model = CartFullModel, main = "CartFullModel", cex = 0.6) 

# The following code also produces the same output, but in a windowed form
#windows()
#fancyRpartPlot(model = CartFullModel, main = "CartFullModel", cex = 0.6)


# The CP table also shows us valueable information in terms of pruning the tree (which is explained later in the code)
 # => The complexity parameter "CP" specifies how the cost of a tree C(T) is penalized by the number of terminal nodes |T|.
 # Hence, Small "CP" results in larger trees and potential overfitting, large CP" in small trees and potential underfitting.
 # => The "rel error" is 1- RSquare, similar to linear regression. This is the error on the observations used to estimate the model. The last node value of rel error suggests the R-Square of the model, if this happens to be the model
 # => The "xerror" is is the error on the observations from cross validation data, which happens to be a 10-Fold Cross Validation. Particpants need to note that, in order to reproduce the "xerror" values, they must use the same set.seed() number each time
 # => Root Node Error is given by sum((Dependent - Mean(Dependent))^2), i.e.
 # sum((InsDataTrainUncapped$Losses-mean(InsDataTrainUncapped$Losses))^2
 
printcp(x = CartFullModel)
ptree = prune(CartFullModel, cp=0.027)
rpart.plot(ptree)
plotcp(CartFullModel)

# This produces a plot which may help particpants to look for a model depending on R-Square values produced at various splits
rsq.rpart(x = CartFullModel)

#### Using CP to expand / Prune the tree ####################
# Lets change rpart.control() to specify certain attributes for tree building
RpartControl = rpart.control(cp = 0.027)
set.seed(123)
CartModel_1 = rpart(formula = Annual_Sales ~ . , 
                    data = Mer_SalesTrainUncapped[,-1], 
                    method = "anova", control = RpartControl)

CartModel_1
summary(CartModel_1)
rpart.plot(x = CartModel_1, type = 4,fallen.leaves = T, cex = 0.6)
printcp(x = CartModel_1)
plotcp(CartModel_1)
rsq.rpart(x = CartModel_1)




####### Some Extra pointers in R #####################


# Model Evaluation Measures on test dataset using the finalized (pruned model)
# Use predict() the get the predicted values for the testset using the finalized model

# Intermediate Model: Finalize CartFullModel (Based on Tree size i.e. Depth, Variables included as well as the R-Square produced)
# Predict on testset
CartFullModelPredictTest = predict(CartFullModel, newdata = Mer_SalesTestUncapped, type = "vector")
CartFullModelPredictTest
# Calculate RMSE and MAPE manually
# Participants can calculate RMSE and MAPE using various available functions in R, but that may not
# communicate effectively the mathematical aspect behind the calculations

# RMSE
Act_vs_Pred = CartFullModelPredictTest - Mer_SalesTestUncapped$Annual_Sales # Differnce
Act_vs_Pred_Square = Act_vs_Pred^2 # Square
Act_vs_Pred_Square_Mean = mean(Act_vs_Pred_Square) # Mean
Act_vs_Pred_Square_Mean_SqRoot = sqrt(Act_vs_Pred_Square_Mean) # Square Root
Act_vs_Pred_Square_Mean_SqRoot

# MAPE
Act_vs_Pred_Abs = abs(CartFullModelPredictTest - Mer_SalesTestUncapped$Annual_Sales) # Absolute Differnce
Act_vs_Pred_Abs_Percent = Act_vs_Pred_Abs/Mer_SalesTestUncapped$Annual_Sales # Percent Error
Act_vs_Pred_Abs_Percent_Mean = mean(Act_vs_Pred_Abs_Percent)*100 # Mean
Act_vs_Pred_Abs_Percent_Mean

library(forecast)
# Validate RMSE and MAPE calculation with a function in R
UncappedModelAccuarcy = accuracy(f = CartFullModelPredictTest, x = Mer_SalesTestUncapped$Annual_Sales)
UncappedModelAccuarcy


Lift = 100/ 400 / ((200 /
                      400) * (160/400))AIC(CartModel_1)
#Needs library rtk
windows()
fancyRpartPlot(model = CartModel_1, main = "Final CART Regression Tree", cex = 0.6, sub = "Model 12")


================================================
FILE: 52-CART/DT-germanCredit.R
================================================
#Analysis of German Credit Data

#Decision Tree
#https://online.stat.psu.edu/stat508/book/export/html/796
#https://cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf
#https://cran.r-project.org/other-docs.html

#Data
library(gsheet)
library(dplyr)
link='https://docs.google.com/spreadsheets/d/1n2hGjaqTMBLBBvVif04ViLX14_Mf_ww27TqUMlNxdOM/edit#gid=1792335381'
#
df = as.data.frame(gsheet2tbl(link))
head(df)
dim(df)
data = df
table(data$default)

#some loans do default - Can we predict which people characteristics default
names(data)
data[,c('default','age','property')]
lapply(data[,c('default','age','amount')], FUN=class)


german_credit <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data")
german_credit
head(german_credit)
colnames(german_credit) <- c("chk_acct", "duration", "credit_his", "purpose",  "amount", "saving_acct", "present_emp", "installment_rate", "sex", "other_debtor", "present_resid", "property", "age", "other_install", "housing", "n_credits", "job", "n_people", "telephone", "foreign", "response")
head(german_credit)
names(german_credit)
head(german_credit[,c('response','property','age','amount')])
dim(german_credit)
table(german_credit$response)  #1-good load-700
range(data$credit_amount)



================================================
FILE: 52-CART/DT-rpart-claims.R
================================================
#DT using Rpart
#https://gormanalysis.com/decision-trees-in-r-using-rpart/

#define a problem. There’s a common scam amongst motorists where a person will slam on his breaks in heavy traffic with the intention of being rear-ended. The person will then file an insurance claim for personal injury and damage to his vehicle, alleging that the other driver was at fault. Suppose we want to predict which of an insurance company’s claims are fraudulent using a decision tree

train <- data.frame(ClaimID = c(1,2,3), RearEnd = c(TRUE, FALSE, TRUE), Fraud = c(TRUE, FALSE, TRUE))

train

library(rpart) #load the rpart package

mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class")
mytree
#output shows only a root node. This is because rpart has some default parameters that prevented our tree from growing. Namely: minsplit and minbucket. minsplit is “the minimum number of observations that must exist in a node in order for a split to be attempted” and minbucket is “the minimum number of observations in any terminal node”. See what happens when we override these parameters.

mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class", minsplit = 2, minbucket = 1)
mytree

library(rattle)
library(rpart.plot)
library(RColorBrewer)
fancyRpartPlot(mytree)

#decision tree correctly identified that if a claim involved a rear-end collision, the claim was most likely fraudulent.

#By default, rpart uses gini impurty to select splits when performing classification. (If you’re unfamiliar with this metric, read this article.) You can use information gain instead by specifying it in the parms parameter.
#https://gormanalysis.com/magic-behind-constructing-a-decision-tree/

mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class", parms = list(split = 'information'), minsplit = 2, minbucket = 1)
mytree

train <- data.frame(ClaimID = c(1,2,3),  RearEnd = c(TRUE, FALSE, TRUE),  Fraud = c(TRUE, FALSE, FALSE))
train

mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class", minsplit = 2, minbucket = 1)
mytree
rpart.plot(mytree)
#Once again we’re left with just a root node. Internally, rpart keeps track of something called the complexity of a tree. The complexity measure is a combination of the size of a tree and the ability of the tree to separate the classes of the target variable. If the next best split in growing a tree does not reduce the tree’s overall complexity by a certain amount, rpart will terminate the growing process. This amount is specified by the complexity parameter, cp, in the call to rpart. Setting cp to a negative amount ensures that the tree will be fully grown.

mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class", minsplit = 2, minbucket = 1, cp=-1)

fancyRpartPlot(mytree)

#This is not always a good idea since it will typically produce over-fitted trees, but trees can be pruned back as discussed later in this article.

#You can also weight each observation for the tree’s construction by specifying the weights argument to rpart.

train
mytree <- rpart(Fraud ~ RearEnd, data = train, method = "class", minsplit = 2, minbucket = 1, weights = c(.4, .4, .2))
mytree
fancyRpartPlot(mytree)

#One of the best ways to identify a fraudulent claim is to hire a private investigator to monitor the activities of a claimant. Since private investigators don’t work for free, the insurance company will have to strategically decide which claims to investigate. To do this, they can use a decision tree model based off some initial features of the claim. If the insurance company wants to aggressively investigate claims (i.e. investigate a lot of claims), they can train their decision tree in a manner that will penalize incorrectly labeled fraudulent claims more than it penalizes incorrectly labeled non-fraudulent claims.

#To alter the default, equal penalization of mislabeled target classes set the loss component of the parms parameter to a matrix where the (i,j) element is the penalty for misclassifying an i as a j. (The loss matrix must have 0s in the diagonal). For example, consider the following training data.

train <- data.frame(ClaimID = c(1,2,3,4,5,6,7), RearEnd = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),Fraud = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE))
train
#grow our decision tree, restricting it to one split by setting the maxdepth argument to 1.

mytree <- rpart(Fraud ~ RearEnd + Whiplash, data = train, method = "class", maxdepth = 1, minsplit = 2, minbucket = 1)
mytree
fancyRpartPlot(mytree)

#rpart has determined that RearEnd was the best variable for identifying a fraudulent claim. BUT there was one fraudulent claim in the training set that was not the case of a rear-end collision. If the insurance company wants to identify a high percentage of fraudulent claims without worrying too much about investigating non-fraudulent claims they can set the loss matrix to penalize claims incorrectly labeled as fraudulent three times less than claims incorrectly labeled as non-fraudulent.

lossmatrix <- matrix(c(0,1,3,0), byrow=TRUE, nrow=2)
lossmatrix

mytree <- rpart(Fraud ~ RearEnd + Whiplash, data = train, method = "class", maxdepth = 1, minsplit = 2, minbucket = 1,parms = list(loss = lossmatrix))
fancyRpartPlot(mytree)
#Now our model suggests that Whiplash is the best vairable to identify fraudulent claims. What I just described is known as an error measure and its up to the discretion of the insurance company to decide on it.
#http://work.caltech.edu/library/042.html


#Now let’s see how rpart interacts with factor variables. Suppose the insurance company hires an investigator to assess the activity level of claimants. Activity levels can be Very Active, Active, Inactive, or Very Inactive.

train <- data.frame(ClaimID = c(1,2,3,4,5,6,7,8,9,10),RearEnd = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE), Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE),  Activity = factor(c("active", "very active", "very active", "inactive", "very inactive", "inactive", "very inactive", "active", "active", "very active"),levels=c("very inactive", "inactive", "active", "very active"), ordered=TRUE),Fraud = c(FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE))
train

#Grow a full tree
mytree <- rpart(Fraud ~ RearEnd + Whiplash + Activity, data = train, method = "class", minsplit = 2, minbucket = 1, cp=-1)

fancyRpartPlot(mytree)
# view the importance of each variable in the model by calling the variable.importance attribute of the resulting rpart object. From the rpart documentation, “An overall measure of variable importance is the sum of the goodness of split measures for each split for which it was the primary variable…”
mytree$variable.importance
#When rpart grows a tree it performs 10-fold cross validation on the data. To see the cross validation results use the printcp() function.
printcp(mytree)
#The rel error of each iteration of the tree is the fraction of mislabeled elements in the iteration relative to the fraction of mislabeled elements in the root. In this example, 50% of training cases are fraudulent. The first splitting criteria is “Is the claimant very active?”, which separates the data into a set of three cases, all of which are fraudulent and a set of seven cases of which two are fraudulent. Labeling the cases at this point would produce an error rate of 20% which is 40% of the root node error rate (i.e. it’s 60% better). The cross validation error rates and standard deviations are displayed in the columns xerror and xstd respectively.

#As a rule of thumb, it’s best to prune a decision tree using the cp of smallest tree that is within one standard deviation of the tree with the smallest xerror. In this example, the best xerror is 0.4 with standard deviation .25298. So, we want the smallest tree with xerror less than 0.65298. This is the tree with cp = 0.2, so we’ll want to prune our tree with a cp slightly greater than than 0.2.

mytree <- prune(mytree, cp=.21)

fancyRpartPlot(mytree)

#use our decision tree to predict fraudulent claims on an unseen dataset using the predict() function.

test <- data.frame(ClaimID = c(1,2,3,4,5,6,7,8,9,10),RearEnd = c(FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE), Whiplash = c(FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE), Activity = factor(c("inactive", "very active", "very active", "inactive", "very inactive", "inactive", "very inactive", "active", "active", "very active"),levels=c("very inactive", "inactive", "active", "very active"),  ordered=TRUE))
test

test$FraudClass <- predict(mytree, newdata = test, type="class") #Returns the predicted class
test$FraudProb <- round(predict(mytree, newdata = test, type="prob"),2) #Returns a matrix of predicted probabilities
test


================================================
FILE: 52-CART/c-dt-rpart-Case-DU1.R
================================================
# # Finding how people take decisions to buy products
# Creating Decision Tree
#install packages - rpart, rpart.plot, RColorBrewer

library(rpart)# doing classification
library(rpart.plot) # visualising the tree

#Students : Gender - (Male & Female) buy a product
#Rownames
rollno = paste('S',1:1000, sep='')
rollno

#Variable Gender
set.seed(100)
gender = sample(x=c('Male','Female'), size=1000, replace=T, prob=c(0.5,0.5) )
head(gender)
table(gender)

#Variable- Buy : Decision
set.seed(100)
buy = sample(x=c('Buy','NotBuy'), size=1000, replace=T, prob=c(.41,.59) )
head(buy)
table(buy)
prop.table(table(buy))

#create Data Frame
students1 = data.frame(gender, buy)
rownames(students1) = rollno
head(students1)

xtabs(~ gender + buy,data = students1)

#Table
table(students1)
prop.table(table(students1))
addmargins(prop.table(table(students1)))
(t1= table(students1$gender, students1$buy))
addmargins(t1)
prop.table(table(students1$gender, students1$buy))
addmargins(prop.table(table(students1$gender, students1$buy))
)

#Model1
head(students1)
fit = rpart(buy ~ gender, data= students1)
fit
table(students1$buy)
rpart.plot(fit,nn=T)

fit1 = rpart(buy ~ gender, data=students1, minsplit=4, minbucket=2)
#'minsplit' is min 4 obsv reqd to split a node
#'minbucket' determines the minimal number of observations per leaf ('minbucket') 

fit1  #print(fit1)

table(students1$gender, students1$buy)
head(students1)
library(rpart.plot)
rpart.plot(fit1, main='Classification Tree', nn=T, type=4, extra=104)

fit1

predict(fit1, newdata = data.frame(gender='Female'))

predict(fit1, newdata = data.frame(gender='Female'), type=c('class'))
predict(fit1, newdata = data.frame(gender=c('Male','Female','Male')), type='class')

#---- Part -2 Add another column----
set.seed(1234)
married = sample(x=c('Married','Single'), size=1000, replace=T, prob=c(0.6,0.4) )
table(married)
#new data frame
students2 = data.frame(gender, married, buy)
rownames(students2) = rollno
head(students2)
str(students2)
prop.table(ftable(students2))
table(students2$buy)  # Majority - Don't Buy
addmargins(prop.table(table(students2)))
#write.csv(students2, 'dtdata.csv')

# Model2
#library(rpart)
names(students2)
head(students2)
fit2 = rpart(buy ~ gender + married, data=students2, minsplit=10, cp=-1)
#fit2 = rpart(buy ~ gender + married, data=students2, minsplit=10)

summary(fit2)
fit2
rpart.plot(fit2,type=2,extra=104, tweak=1.2, under=T, shadow=c('brown', 'green','red'), nn=T)
fit2
prp(fit2)
prp(fit2, main="An Example",
    type=4, fallen=T, branch=.3, round=0, leaf.round=9,
    clip.right.labs=F, under.cex=1,
    box.palette="GnYlRd",
    prefix="Student\n", branch.col="gray", branch.lwd=2,
    extra=101, under=T, lt=" < ", ge=" >= ", cex.main=1.5)

prp(fit2, branch.type=5)
labels(fit2)


#Plot----
library(RColorBrewer)
rpart.plot::rpart.plot(fit2, main='Classification Tree')
rpart.plot::rpart.plot(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE)

rpart.plot::prp(fit2,fallen.leaves = F)
prp(fit2, type=2)


#Predict

predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='prob')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='class')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='vector')

predict(fit2, newdata = data.frame(gender='Male', married='Married'))

testdata = data.frame(gender=c('Male','Male','Female','Female'), married=c('Married','Single','Married','Single'))
testdata

(p1 = predict(fit2, newdata = testdata, type='vector'))  #node/level 
#play=2, notplay=1
(p2 = predict(fit2, newdata = testdata, type='class')) #factor
(p3 = predict(fit2, newdata = testdata, type='prob')) # prob

cbind(testdata, p1, p2, p3)
#level number, class frequencies, probabilities
predict(fit2, newdata= testdata, type = "matrix")

head(students2)

#Parameters Setting : CP
printcp(fit2)
getOption('digits')
plotcp(fit2)
names(fit2)

fit2$where  #which row at which node no

students2[1:5,]
cbind(students2, nodeno=rownames(fit2$frame) [ fit2$where])
fit2
rpart.plot(fit2)
pfit=  prune(fit2, cp=0.02) # from cptable  
pfit
rpart.plot(pfit)

#Interactive----
new.tree <- prp(fit2, snip=TRUE)$obj # interactively trim the tree
prp(new.tree) # display the new tree
#click on quit 



================================================
FILE: 52-CART/c-dt-rpart-iris.R
================================================
#CART Regression Tree

#Load Libraries
library(rpart)  #does only binary splits; CART
library(rpart.plot)

#DataSet
str(iris)
head(iris)
summary(iris)
names(iris)
# Classification Tree
summary(iris)
set.seed(1234)
#Predict Species
ctree = rpart(Species ~ ., method='class', data=iris)
ctree
rpart.plot(ctree)
rpart.plot(ctree, main='Classification Tree', nn=T, type=4, extra=104, cex=1.2)

printcp(ctree)

ctreeprune = prune(ctree, cp=0.44)
ctreeprune
rpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)

# Regression Tree - Predict Continuous Value Length
#Predict Sepal.Length
rtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method="anova", data=iris )
rtree
rpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)

#another way to plot
plot(rtree, uniform=TRUE, main="Regression Tree for Sepal Length")
text(rtree, use.n=TRUE, cex = 1)

#CP
printcp(rtree)
rsq.rpart(rtree)  #r2 on anova only

rtreeprune = prune(rtree, cp=0.023)
rpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)



#https://analytics4all.org/2016/11/23/r-decision-trees-regression/


#Predict : Class  : Category of Flower
str(iris)
testData1  = data.frame ( Sepal.Length = 5, Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(ctree, testData1, method = "class")  #setosa


#Predict : Continous Value : Length

testData2  = data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(rtree, testData2, method = "anova") #5.17



================================================
FILE: 52-CART/c-dt-rpart-sales1.R
================================================
# CART Models - Regression Trees - Merchent Sales
#Predict the mean sales from the data; create decision tree
library(rpart)  # CART algo
library(rpart.plot)# visualising decision tree
library(forecast)# calculating accuracy

msales = read.csv(file = "./data/msales.csv", skip=1, header = T)
dim(msales)
# Summarize the dataset
summary(msales)
str(msales)
# See Summary values of sales
aggregate(msales$sales,by=list(msales$zone), FUN = mean)

# Random Sampling : Train and Test
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(msales), size = 0.7*nrow(msales))
Index
length(Index)

#divide data into 2 parts - Train (70%), Test(30%)
# Create Train dataset
train = msales[Index, ]
nrow(train); dim(train)

# Create Test dataset
test = msales[-Index, ]
nrow(test); dim(test)

#### Modeling ############
# Build a full model with default settings
set.seed(123)
CartModel=rpart(sales ~ . ,data=train[,-1], method="anova")
CartModel
summary(CartModel)

# Plot the Regression Tree
rpart.plot(CartModel,type=4,fallen.leaves=T,nn=T, cex=1)
mean(msales$sales)
printcp(CartModel)
rsq.rpart(CartModel)  #only for Regression Tree

prune1 = prune(CartModel, cp=.014)
prune1
rpart.plot(prune1, nn=T, cex=1)

#### Using CP to expand / Prune the tree ###
#Start fresh Model
RpartControl = rpart.control(cp = 0.005)
set.seed(123)
CartModel_1 = rpart(sales ~ ., data = msales[,-1], 
      method = "anova", control = RpartControl)
CartModel_1

CartModel_1$where
msales[987,'sales']
CartModel_1$frame
trainingnodes = rownames(CartModel_1$frame) [ CartModel_1$where]
trainingnodes

summary(CartModel_1)
rpart.plot(CartModel_1, type = 4,cex = 0.6)
printcp(CartModel_1)
rsq.rpart(CartModel_1)


#Validation / Test Accuracy
PredictTest=predict(CartModel,newdata=test,type="vector")
PredictTest1=predict(CartModel_1,newdata=test,type="vector")
PredictTest1
length(PredictTest)
msales[2,'sales']
cbind(test$sales, PredictTest, PredictTest1)

# Calculate RMSE and MAPE 
library(forecast)
# Validate RMSE and MAPE calculation with a function in R
length(train$sales)
ModelAccuracy = accuracy(PredictTest,test$sales)
ModelAccuracy1 = accuracy(PredictTest1,test$sales)
rbind(ModelAccuracy,ModelAccuracy1)

#select model with least error


================================================
FILE: 52-CART/cls-cart-churn2.R
================================================
# Churn Case : CART

telecom <- read.csv("http://www.dataminingconsultant.com/data/churn.txt", header=TRUE, sep=",")


================================================
FILE: 52-CART/cls-rpart-plot2.R
================================================
#CART Plot
#



library(rpart.plot)
tree <- rpart(Kyphosis~., data=kyphosis)
prp(tree, extra=7) # left graph
library(plotmo)
plotmo(tree, type="prob", nresponse="present") # middle graph
# type="prob" is passed to predict()
plotmo(tree, type="prob", nresponse="present", # right graph
       type2="image", ngrid2=200, # type2="image" for an image plot
       pt.col=ifelse(kyphosis$Kyphosis=="present", "red", "lightblue"))
labels(tree)


================================================
FILE: 52-CART/dt-car.R
================================================
#DT - car

library(caret)
library(rpart.plot)

data_url <- c("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data")
download.file(url = data_url, destfile = "car.data")

car_df <- read.csv("car.data", sep = ',', header = FALSE)

str(car_df)
dim(car_df)
head(car_df)
#all are categories, no normalisation required
set.seed(3033)
intrain = createDataPartition(y=car_df$V7, p=0.7, list=F)
intrain
training = car_df[intrain,]
testing = car_df[-intrain,]

dim(training)
dim(testing)
#---------------
anyNA(car_df)
summary(car_df)

#---- Training DT classifier with criterion as information gain
#Training DT classifier with criterion as Info Gain

trctrl = trainControl(method ='repeatedcv', number = 10, repeats=3)
set.seed(333)
dtree_fit = train(V7 ~ ., data= training, method='rpart', parms= list(split='information'), trControl=trctrl, tuneLength = 10)

dtree_fit

#Plot DT
prp(dtree_fit$finalModel, box.palette = 'Reds', tweak=1.2)


#---- Prediction
testing[1,]
predict(dtree_fit, newdata = testing[1,])

test_pred = predict(dtree_fit, newdata = testing)
test_pred
confusionMatrix(test_pred, testing$V7)

#---- Gini Index
set.seed(3333)
dtree_fit_gini = train(V7 ~ . , data = training, method = 'rpart', 
                       parms = list(split='gini'), trControl=trctrl, tuneLength=10)
dtree_fit_gini
prp(dtree_fit_gini$finalModel, box.palette = 'Blues', tweak=1.2)

#Prediction
test_pred_gini = predict(dtree_fit_gini, newdata=testing)
confusionMatrix(test_pred_gini, testing$V7)



================================================
FILE: 52-CART/dt-general.R
================================================
# Decision Tree 2
#not working

refund=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')
married=c('S','M','S','M','D','M','D','S','M','S')
income=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)
cheat=c('No','No','No','No','Yes','No','No','Yes','No','Yes')

train= data.frame(refund, married, income, cheat)
train

library(rpart)
fit1 = rpart(cheat ~ ., data=train,cp=0)
fit1

library(rpart.plot)
rpart.plot(fit1)



================================================
FILE: 52-CART/dt-glaucoma.R
================================================
#https://www.r-project.org/conferences/useR-2009/slides/Hothorn+Zeileis.pdf

library(ipred)
data(data=GlaucomaMVF , package = "ipred")

library("rpart")
head(GlaucomaMVF)
g_rpart <- rpart(Class ~ ., data = GlaucomaMVF)
print(g_rpart)
plot(g_rpart)
text(g_rpart)

library("partykit")
g_party <- as.party(g_rpart)
print(g_party, header = FALSE)
print(g_party, header = T)
plot(g_party, type = "simple")
plot(g_party, type = "extended")



================================================
FILE: 52-CART/dt-ionos1.R
================================================
#Ionosphere
#https://eight2late.wordpress.com/2016/02/16/a-gentle-introduction-to-decision-trees-using-r/
#
#set working directory if needed (modify path as needed)
#setwd(“C:/Users/.....”)
#load required libraries – rpart for classification and regression trees
library(rpart)
#mlbench for Ionosphere dataset
library(mlbench)
#load Ionosphere
data(Ionosphere)
str(Ionosphere)
#set seed to ensure reproducible results
set.seed(42)
#split into training and test sets
Ionosphere[,'train'] <- ifelse(runif(nrow(Ionosphere))<0.8,1,0)
#separate training and test sets
trainset <- Ionosphere[Ionosphere$train==1,]
testset <- Ionosphere[Ionosphere$train==0,]
#get column index of train flag
trainColNum <- grep("train",names(trainset))
#remove train flag column from train and test sets
trainset <- trainset[,-trainColNum]
testset <- testset[,-trainColNum]


#get column index of predicted variable in dataset
typeColNum <- grep("Class",names(Ionosphere))
typeColNum
#build model
rpart_model <- rpart(Class~.,data = trainset, method="class")
#plot tree
plot(rpart_model);text(rpart_model)


#…and the moment of reckoning
rpart_predict <- predict(rpart_model,testset[,-typeColNum],type="class")
mean(rpart_predict==testset$Class)
#confusion matrix
table(pred=rpart_predict,true=testset$Class)

#cost-complexity pruning
printcp(rpart_model)

# get index of CP with lowest xerror
opt <- which.min(rpart_model$cptable[,"xerror"])
#get its value
cp <- rpart_model$cptable[opt, "CP"]
cp

#prune tree
pruned_model <- prune(rpart_model,cp)
#plot tree
plot(pruned_model);text(pruned_model)


================================================
FILE: 52-CART/dt-iris1.R
================================================
# Iris
#
set.seed(1234)
iris.rp = rpart(Species ~ ., method='class', data=iris, 
                control=rpart.control(minsplit=4, cp=0.00001))
printcp(iris.rp)
iris.rp

iris.rp = rpart(Species ~ ., method='anova', data=iris, 
                control=rpart.control(minsplit=4, cp=0.00001))
printcp(iris.rp)



================================================
FILE: 52-CART/dt-kyphosis.R
================================================
#
#https://www.statmethods.net/advstats/cart.html
# Classification Tree with rpart

library(rpart)
?kyphosis
# grow tree 
fit <- rpart(Kyphosis ~ Age + Number + Start,
             method="class", data=kyphosis)
head(kyphosis)  #DV Kyphosis
summary(kyphosis)
printcp(fit) # display the results 
plotcp(fit) # visualize cross-validation results 
summary(fit) # detailed summary of splits

library(tree)
summary(tree( Kyphosis ~ Age + Number + Start, data=kyphosis))


# plot tree 
plot(fit, uniform=TRUE,  main="Classification Tree for Kyphosis")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

# create attractive postscript plot of tree 
post(fit, file = "tree.ps", 
     title = "Classification Tree for Kyphosis")


# prune the tree 
#pfit <- prune(fit, cp=   fit$cptable[which.min(fit$cptable[,"xerror"]),"CP"])
pfit <- prune(fit, cp=0.01)

pfit

# plot the pruned tree 
plot(pfit, uniform=TRUE,  main="Pruned Classification Tree for Kyphosis")
text(pfit, use.n=TRUE, all=TRUE, cex=.8)
post(fit, file = "ptree.ps",   title = "Pruned Classification Tree for Kyphosis")



# Regression Tree Example
library(rpart)
?cu.summary
# grow tree 
fit <- rpart(Mileage~Price + Country + Reliability + Type,             method="anova", data=cu.summary)

printcp(fit) # display the results 
plotcp(fit) # visualize cross-validation results 
summary(fit) # detailed summary of splits

# create additional plots 
par(mfrow=c(1,2)) # two plots on one page 
rsq.rpart(fit) # visualize cross-validation results  	

# plot tree 
plot(fit, uniform=TRUE, main="Regression Tree for Mileage ")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

# create attractive postcript plot of tree 
post(fit, file = "c:/tree2.ps",  title = "Regression Tree for Mileage ")


# prune the tree 
pfit<- prune(fit, cp=0.01160389) # from cptable   

# plot the pruned tree 
plot(pfit, uniform=TRUE,     main="Pruned Regression Tree for Mileage")
text(pfit, use.n=TRUE, all=TRUE, cex=.8)
post(pfit, file = "c:/ptree2.ps",  title = "Pruned Regression Tree for Mileage")



# Conditional Inference Tree for Kyphosis
library(party)
fit <- ctree(Kyphosis ~ Age + Number + Start,  data=kyphosis)
plot(fit, main="Conditional Inference Tree for Kyphosis")

# Conditional Inference Tree for Mileage
library(party)
fit2 <- ctree(Mileage~Price + Country + Reliability + Type, data=na.omit(cu.summary))


# Random Forest prediction of Kyphosis data
library(randomForest)
fit <- randomForest(Kyphosis ~ Age + Number + Start,   data=kyphosis)
print(fit) # view results 
importance(fit) # importance of each predictor


================================================
FILE: 52-CART/dt-loanapproved1.R
================================================
# Decision Tree 
# loanapproved = age + job + house + credit

loanapproved = sample(x=c('Yes','No'), size=50, replace=T)
age = runif(50,30,60)
house = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.4,.6))
job = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.6,.4))
credit = ceiling(rnorm(50,100, 10))

loan = data.frame(loanapproved, age,job, credit)
loan
head(loan)
library(rpart)
table(loan$loanapproved, loan$credit)

loan_tree = rpart(loanapproved ~ ., data=loan, method='class', control=rpart.control(minsplit=5, cp=.09))
loan_tree
plot(loan_tree)
text(loan_tree)

library(rpart.plot)
rpart.plot(loan_tree)
rpart.plot(loan_tree,  type = 4,fallen.leaves = T, cex = 0.6)
rpart.plot(loan_tree,type=2, extra=104, cex=1, tweak=1, under=T, shadow=c('brown', 'green','red'), nn=T)

printcp(loan_tree)



================================================
FILE: 52-CART/dt-rpart-du.R
================================================
# Decision Tree - DU Example
library(rpart)
library(rpart.plot)
library(caret) # variable imp

#Case-1----
#Student Data  Create Data
(rollno = paste('S',1:30, sep=''))
set.seed(1234)
(gender = sample(x=c('Male', 'Female'), size=30, replace=T, prob=c(0.5,0.5) ))
table(gender)
set.seed(1234)
(play = sample(x=c('Play','NotPlay'), size=30, replace=T, prob=c(17/30,13/30) ))
table(play)
prop.table(table(play))

students1 = data.frame(gender, play)
rownames(students1) = rollno
students1
table(students1)

#Model1
set.seed(1234)
fit1 = rpart(play ~ gender, data=students1, minsplit=5, cp=-1)
print(fit1)
varImp(fit1)
options(digits=4)
rpart.plot(fit1, nn=T)
(t1=table(students1$play))
prop.table(t1)

(t2= table(students1$gender, students1$play))
prop.table(t2)
prop.table(t2,1)


#Predict
predict(fit1, newdata = data.frame(gender=c('Female')))
predict(fit1, newdata = data.frame(gender=c('Male', 'Female','Male')))
predict(fit1, newdata = data.frame(gender=c('Male', 'Female','Male')), type='class')

#Case-2----
#Create Dataset
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )
table(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )
table(married)

set.seed(1234)
play = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,20/30) )
table(play)

students2 = data.frame(gender, married, play)
#rownames(students2) = rollno
head(students2)
ftable(students2)

#Model2
set.seed(1234)
fit2 = rpart(play ~ gender + married, data=students2, minsplit=5, cp=-1)
fit2
summary(fit2)
varImp(fit2)

#Plot
rpart.plot::rpart.plot(fit2, nn=T, main='Model2 : Classification Tree')

rpart.plot::rpart.plot(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE)

rpart.plot::prp(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE, fallen.leaves = T)


#Predict

predict(fit2, newdata = data.frame(gender='Male', married='Married'))
testdata = data.frame(gender=c( 'Male','Male','Female','Female'), married=c('Married', 'Single','Married','Single'))
testdata

(p1 = predict(fit2, newdata = testdata, type='vector'))
(p2 = predict(fit2, newdata = testdata, type='class'))
(p3 = predict(fit2, newdata = testdata, type='prob'))
cbind(testdata, p1, p2, p3)
predict(fit2, newdata= testdata, type = "matrix")

# Accuracy rate
(pdata = predict(fit2, newdata=students2, type='class'))
table(students2$play, pdata)
sum(students2$play==pdata)/length(pdata)
caret::confusionMatrix(factor(students2$play), pdata)

#Location of each data point
fit2$where
rownames(fit2$frame) [fit2$where]
?fit2$frame
?rpart
path.rpart(fit2, c(2,3), pretty = 0, print.it = TRUE)
residuals(fit2, type = c("usual"))
#reduce/ snip the nodes
rpart.plot(snip.rpart(fit2, c(2,3,4,5)))

#Pruning
printcp(fit2)
plotcp(fit2)
fit2a = prune(fit2, cp=0)
fit2a
rpart.plot(fit2a)

#Case-3----
#Create Dataset
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.7,0.3) )
table(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.2,0.8) )
table(married)

set.seed(1111)
play = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(20/30,10/30) )
table(play)
set.seed(1234)
education = sample(x=c('school','graduate', 'pg'), size=30, replace=T, prob=c(0.3,0.4,.3) )
table(education)
set.seed(1234)
hostel = sample(x=c(TRUE, FALSE), size=30, replace=T, prob=c(.3,.7))
table(hostel)

set.seed(1234)
marks = floor(rnorm(30,70,20))
mean(marks)

students3 = data.frame(gender, married, education, hostel, marks,play)
with(students3, ftable(education, hostel, gender, married,play))
set.seed(1234)
fit3 = rpart(play ~ . , data=students3, minsplit=5, cp=-1)
fit3
varImp(fit3) #at root level 

printcp(fit3)
(bestcp= fit3$cptable[which.min(fit3$cptable[,'xerror']),'CP'])
fit3b = prune(fit3, cp=0)
fit3b
#fit3b.pruned =  prune(fit3, cp=bestcp)
rpart.plot(fit3b, nn=T, cex=.8)
fit3b$where
path.rpart(fit3b, nodes=c(1,2,7,13), pretty =0, print.it = TRUE)
testdata1= data.frame(gender='Male', married='Married',
     education='school', hostel=TRUE, marks=60)
predict(fit3b, newdata = testdata1 )


#Case-4----
#Create Dataset V is numeric CV
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )
table(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )
table(married)

set.seed(1234)
play = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,25/30) )
table(play)

set.seed(1234)
income = ceiling(runif(30, 50,100) )
mean(income)

students4 = data.frame(income,gender, married, play)
rownames(students4) = rollno
head(students4)
str(students4)

#Model4
fit4 = rpart(income ~ gender + married + play, data=students4, minsplit=5, cp=-1, method='anova')
fit4
rpart.plot(fit4)

#complexity Parameter
printcp(fit4)
rsq.rpart(fit4)

#Predict
(ndata= data.frame(gender='Male', married='Single', play='Play'))
predict(fit4, newdata=ndata, type='vector')
round(residuals(fit4),2)
students4b = cbind(head(students4), predicted=predict(fit4, newdata=head(students4)))
diff = round(students4b$income - students4b$predicted,2)
cbind(rownames(students4b), diff)

#Case-5 ----
#: Divide and conquer

head(students2)
fit2
rpart.plot(fit2, nn=T)
node1= students2
write.csv(node1, "./data/node1.csv")
table(node1$play)
prop.table(table(node1$play))
node1[,1:3] = lapply(node1[,c(1:3)], factor)
node1
str(node1)
(node123=split(node0, node0$married))

(node2= node123$Single)
table(node2$play)
prop.table(table(node2$play))

(node3= node123$Married)
(node4 = split(node2, node2$gender)$Female)
(node5 = split(node2, node2$gender)$Male)
nrow(node1); nrow(node2); nrow(node3);nrow(node4);nrow(node5)


================================================
FILE: 52-CART/dt-rpart-du1.R
================================================
# # Finding how people take decisions to buy products
# Creating Decision Tree

library(rpart)
library(rplart.plot)

#Students : Gender - (Male & Female) buy a product
#Rownames
rollno = paste('S',1:1000, sep='')
rollno

#Variable Gender
set.seed(100)
gender = sample(x=c('Male','Female'), size=1000, replace=T, prob=c(0.5,0.5) )
head(gender)
table(gender)

#Variable- Buy : Decision
set.seed(3000)
buy = sample(x=c('Buy','NotBuy'), size=1000, replace=T, prob=c(.49,.51) )
head(buy)
table(buy)
prop.table(table(buy))

#create Data Frame
students1 = data.frame(gender, buy)
rownames(students1) = rollno
head(students1)

#Table
table(students1)
prop.table(table(students1))
addmargins(prop.table(table(students1)))
(t1= table(students1$gender, students1$buy))
addmargins(t1)
prop.table(table(students1$gender, students1$buy))
addmargins(prop.table(table(students1$gender, students1$buy))
)
#Model1
fit1 = rpart(buy ~ gender, data=students1,minsplit=4, minbucket=2)
#'minsplit' is 20 and determines the minimal number of observations per leaf ('minbucket') 
fit1  #print(fit1)
table(students1$gender, students1$buy)
head(students1)
library(rpart.plot)
rpart.plot(fit1, main='Classification Tree', nn=T, type=4, extra=104)

fit1
predict(fit1, newdata = data.frame(gender='Female'))
predict(fit1, newdata = data.frame(gender='Female'), type=c('class'))
predict(fit1, newdata = data.frame(gender=c('Male','Female','Male')), type='class')

#---- Part -2 Add another column
set.seed(5000)
married = sample(x=c('Married','Single'), size=1000, replace=T, prob=c(0.5,0.5) )
table(married)
students2 = data.frame(gender, married, buy)
rownames(students2) = rollno
head(students2)
str(students2)
prop.table(ftable(students2))
table(students2$buy)  # Majority - Don't Buy
addmargins(prop.table(table(students2)))
#write.csv(students2, 'dtdata.csv')

# Model2
#library(rpart)
fit2 = rpart(buy ~ gender + married, data=students2, minsplit=12)
summary(fit2)
fit2
rpart.plot(fit2,type=2,extra=104, tweak=1.2, under=T, shadow=c('brown', 'green','red'), nn=T)
fit2
prp(fit2)
prp(fit2, main="An Example",
    type=4, fallen=T, branch=.3, round=0, leaf.round=9,
    clip.right.labs=F, under.cex=1,
    box.palette="GnYlRd",
    prefix="Student\n", branch.col="gray", branch.lwd=2,
    extra=101, under=T, lt=" < ", ge=" >= ", cex.main=1.5)

prp(fit2, branch.type=5)
labels(fit2)
#Interactive

new.tree <- prp(fit2, snip=TRUE)$obj # interactively trim the tree
prp(new.tree) # display the new tree

#Plot----
library(RColorBrewer)
#library(rattle)

rpart.plot::rpart.plot(fit2, main='Classification Tree')
rpart.plot::rpart.plot(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE)

rpart.plot::prp(fit2,fallen.leaves = F)
prp(fit2, type=2)


#Predict
#
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='prob')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='class')
predict(fit2, newdata = data.frame(gender='Male', married='Married'), type='vector')

predict(fit2, newdata = data.frame(gender='Male', married='Married'))
?predict


testdata = data.frame(gender=c('Male','Male','Female','Female'), married=c('Married','Single','Married','Single'))
testdata
                               
(p1 = predict(fit2, newdata = testdata, type='vector'))  #node/level 
#play=2, notplay=1
(p2 = predict(fit2, newdata = testdata, type='class')) #factor
(p3 = predict(fit2, newdata = testdata, type='prob')) # prob

cbind(testdata, p1, p2, p3)
#level number, class frequencies, probabilities
predict(fit2, newdata= testdata, type = "matrix")

head(students2)

#Parameters Setting : CP
printcp(fit2)
getOption('digits')
plotcp(fit2)
names(fit2)

fit2$where  #which row at which nodeno

students2[1:5,]
cbind(students2, nodeno=rownames(fit2$frame) [ fit2$where])
pfit=  prune(fit2, cp=0.077) # from cptable  
pfit
rpart.plot(pfit)

#do some changes to pfit

#--------------------------------------------------------

#add column with 3 classes and numeric and logical
set.seed(105)
education = sample(x=c('school','graduate', 'pg'), size=1000, replace=T, prob=c(0.3,0.4,.3) )
education; table(education)
set.seed(106)
hostel = sample(x=c(TRUE, FALSE), size=1000, replace=T, prob=c(.3,.7))
table(hostel)
set.seed(107)
marks = floor(runif(1000,50,100))
mean(marks)
students3 = data.frame(gender, married, education, hostel,marks,buy)
with(students3, ftable(education, hostel, gender, married,buy))

# Model3
fit3a = rpart(buy ~ ., data=students3)
fit3a
rpart.plot::rpart.plot(fit3a, main='Classification Tree')

#Model3b : change some parameters minbucket, minsplit
fit3b = rpart(buy ~ ., data=students3, minsplit=4, minbucket=2) #control= rpart.control(cp=0.00001))#use low cp
fit3b
rpart.plot::rpart.plot(fit3b, main='Classification Tree', cex=.6, type=3, extra=104, nn=T)
rpart.plot::prp(fit3b)
#rattle::fancyRpartPlot(model = fit3b, main = "Final CART Regression Tree", cex = 0.6, sub = "Model3")
prp(fit3b,box.col=c("Grey", "Orange")[fit3b$frame$yval],varlen=0,faclen=0, type=1,extra=4,under=TRUE, tweak=1.2)


#Lets see CP
plotcp(fit3b)
printcp(fit3b, digits = getOption("digits") - 5)
(bestcp= fit3b$cptable[which.min(fit3b$cptable[,'xerror']),'CP'])
#but this is at root node only, select next better 
bestcp = 0.01

prp(fit3b)
fit3b2 = rpart(buy ~ ., data=students3, minsplit=4, minbucket=2, control= rpart.control(cp=0.001))
fit3b2
prp(fit3b2)
fit3b.pruned = prune(fit3b, cp=bestcp)
fit3b.pruned
prp(fit3b.pruned)

rpart.plot(fit3b.pruned,cex=.6, extra=101, type=1)


#Predict Model3
fit3b.pruned$where
fit3b.pruned
path.rpart(fit3b.pruned, nodes=c(1,4,10,43), pretty = 0, print.it = TRUE)
testdata1= data.frame(gender='Male', married='Married', education='school', hostel=TRUE, marks=60)
testdata1
predict(fit3b.pruned, newdata = testdata1 )



# now practise with Marketing Data



================================================
FILE: 52-CART/dt-rpart-du2.R
================================================
# Decision Tree 2 : Not Working

refund=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')
married=c('S','M','S','M','D','M','D','S','M','S')
income=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)
cheat=c('No','No','No','No','Yes','No','No','Yes','No','Yes')
train= data.frame(refund, married, income, cheat)
train
library(rpart)
fit1 = rpart(cheat ~ ., data=train, cp=-1, minsplit=3)
fit1
library(rpart.plot)
rpart.plot(fit1)


================================================
FILE: 52-CART/dt-rpart-du3.R
================================================
# Simple Example in CT

train1 = data.frame(rollno=c(1,2,3), gender=c('M','M','F'),
                    play=c('Play','NoPlay','Play'))
train1

library(rpart)
?rpart
mytree1 = rpart (play ~ gender, data=train1, method='class', minsplit=2, cp=-1)
mytree1
rpart.plot(mytree1)

#more data
train2 = data.frame(rollno=c(1,2,3,4), gender=c('M','M','F','F'), play=c('Play','NoPlay','Play','Play'))
rpart (play ~ gender, data=train2, method='class')
rpart (play ~ gender, data=train2, method='class',
       minsplit=1, minbucket=1)
train2

# Part -2 : increase data points
train3 = data.frame(rollno=c(1,2,3,4,5,6,7),  gender=c('M','M','F','M','F','F','M'), play=c('Play','Play','NoPlay','Play','Play','NoPlay','Play'))
train3
mytree3 = rpart (play ~ gender, data=train3, method='class',
        minsplit=2, minbucket=1 )
#method - class uses gini index
mytree3
train3
path.rpart(mytree3, nodes=c(1,2,3), pretty = 0, print.it = TRUE)
mytree3$where
predict(mytree3, newdata=data.frame(gender='F'),
        type = c("class"))

table(train3$gender,train3$play)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
fancyRpartPlot(mytree3)
predict(mytree3, newdata=data.frame(gender='M'))


# Another method to do 
mytree3a = rpart (play ~ gender, data=train3, method='class',
                 minsplit=2, minbucket=1 )
fancyRpartPlot(mytree3a)

#cp usage
mytree4 = rpart (play ~ gender, data=train3, method='class',
            minsplit=2, minbucket=1, cp=-1 )
mytree4
fancyRpartPlot(mytree4)

# first DF
mytree1b = rpart (play ~ gender, data=train1, method='class',
                 minsplit=2, minbucket=1, cp=-1 )
mytree1b

# Add more columsn to train3
train3$married = c('Married','Single','Married',
            'Married','Married','Single','Single')
train3
mytree4b = rpart (play ~ gender + married, data=train3,
        method='class', minsplit=2, minbucket=1)
fancyRpartPlot(mytree4b)

mytree4b = rpart (play ~ gender + married, data=train3,
          method='class', minsplit=2, minbucket=1, cp=-1 )
mytree4b
fancyRpartPlot(mytree4b)
table(train3$play, train3$gender)
mytree4b$where
train3
# Giving Weights
mytree4c = rpart (play ~ gender + married, data=train3, method='class',
                  minsplit=2, minbucket=1)
fancyRpartPlot(mytree4c)
mytree4c = rpart (play ~ gender, data=train3, method='class',
        weights=c(.3,.1,.1,.1,.1,.1,.2),  minsplit=2, minbucket=1)
fancyRpartPlot(mytree4c)

predict(mytree4b, newdata=data.frame(gender='M',
                  married='Single'))
printcp(mytree4b)


================================================
FILE: 52-CART/dt-rpart-metal.R
================================================
# Decision Tree

library(rpart)
raw.orig <- read.csv(file='E:/dataSets/DM/rsei212_chemical.txt', header=T, sep="\t")
str(raw.orig)
head(raw.orig)
# Keep the dataset small and tidy
# The Dataverse: hdl:1902.1/21235
raw = subset(raw.orig, select=c("Metal","OTW","AirDecay","Koc"))

row.names(raw) = raw.orig$CASNumber
raw = na.omit(raw);
raw
frmla = Metal ~ OTW + AirDecay + Koc

# Metal: Core Metal (CM); Metal (M); Non-Metal (NM); Core Non-Metal (CNM)

fit = rpart(frmla, method="class", data=raw)

printcp(fit) # display the results
plotcp(fit) # visualize cross-validation results
summary(fit) # detailed summary of splits

# plot tree
plot(fit, uniform=TRUE, main="Classification Tree for Chemicals")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

# tabulate some of the data
table(subset(raw, Koc>=190.5)$Metal)



================================================
FILE: 52-CART/dt-rpart-student1.R
================================================
#Classification

data <- read.csv("./data/studentdata.csv")
head(data)
data$class = sample(c('Pass','Fail'), size=100, replace=TRUE, prob=c(0.6,0.4))
str(data)
head(data[c(1:4,13)])
data1 = data[-c(2,3)]
str(data1)
head(data1)
library(rpart)
set.seed(1234)
train = sample(nrow(data1), 0.7*nrow(data1) )
data1.train = data1[train, ]
data1.train
data1.validate= data1[-train, ]
table(data1.train$class)
table(data1.validate$class)

#attribute selectio
dtree = rpart(class ~ ., data=data1.train, method= 'class', parms =
                list(split='information') )
print(dtree)
summary(dtree)
dtree$cptable
plotcp(dtree)
dtree.pruned = prune(dtree, cp =.01)
library(rpart.plot)

prp(dtree.pruned, type=2, extra = 104,
    fallen.leaves=T, main='Decision Tree')
dtree.pred = predict(dtree.pruned, data1.validate, type='class')
dtree.perf = table(data1.validate$class, dtree.pred, dnn=c('Actual', 'Predicted') )
dtree.perf


#Method2  : errors
#Eg  Character Value not supported
library(party)
fit.ctree = party::ctree(class ~ ., data = data1.train)
plot(fit.ctree)
#Logistics
fit.logit = glm(class ~. , data = data1.train, family = binomial() )


================================================
FILE: 52-CART/dt-rpart-text1.R
================================================
# Text Rpart
library(rpart)
freen.tr <- rpart(y ~ ., freeny)
par(xpd = TRUE)
plot(freen.tr)
text(freen.tr, use.n = TRUE, all = TRUE)
labels(freen.tr, digits = 4, minlength = 1L, pretty, collapse = TRUE)


================================================
FILE: 52-CART/dt-rpart-varimp1.R
================================================
#DT - Variable Imp 1

library(rpart)
library(rpart.plot)
library(caret)

#Case-2----
#Create Dataset
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )
table(gender)
gender = factor(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )
table(married)
married = factor(married)
set.seed(1234)
play = factor(sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,20/30) ))
table(play)

students2 = data.frame(gender, married, play)
#rownames(students2) = rollno
head(students2)
ftable(students2)

#Model2
set.seed(1234)
fit2 = rpart(play ~ gender + married, data=students2, minsplit=5, cp=-1)
fit2 ; rpart.plot(fit2)
summary(fit2)
names(fit2)
library(caret)
library(caret)
varImp(fit2)


#Plot
rpart.plot::rpart.plot(fit2, nn=T, main='Model2 : Classification Tree')

rpart.plot::rpart.plot(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE)

rpart.plot::prp(fit2, extra=104, box.palette="GnBu", branch.lty=3, shadow.col="gray", nn=TRUE, fallen.leaves = T)


#Predict

predict(fit2, newdata = data.frame(gender='Male', married='Married'))
testdata = data.frame(gender=c( 'Male','Male','Female','Female'), married=c('Married', 'Single','Married','Single'))
testdata

(p1 = predict(fit2, newdata = testdata, type='vector'))
(p2 = predict(fit2, newdata = testdata, type='class'))
(p3 = predict(fit2, newdata = testdata, type='prob'))
cbind(testdata, p1, p2, p3)
predict(fit2, newdata= testdata, type = "matrix")


#Variable Importance

set.seed(1234)
data(mtcars)
library(randomForest)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE,
                          importance=TRUE)
varImpPlot(mtcars.rf)



str(students2)
students2.rf <- randomForest(play ~ gender + married, data=students2, ntree=1000, keep.forest=FALSE,importance=TRUE)
?randomForest
varImpPlot(students2.rf)
importance(students2.rf)

#Mean Decrease in Accuracy the number or proportion of observations that are incorrectly classified by removing the feature (or values from the feature) in question from the model?"




================================================
FILE: 52-CART/dt-rpart-varimp2.R
================================================
# Decision Tree - Variable Importance - rpart

library(rpart)
library(rpart.plot)
library(caret)

#Case-3----
#Create Dataset
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.7,0.3) )
table(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.2,0.8) )
table(married)

set.seed(1111)
play = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(20/30,10/30) )
table(play)
set.seed(1234)
education = sample(x=c('school','graduate', 'pg'), size=30, replace=T, prob=c(0.3,0.4,.3) )
table(education)
set.seed(1234)
hostel = sample(x=c(TRUE, FALSE), size=30, replace=T, prob=c(.3,.7))
table(hostel)

set.seed(1234)
marks = floor(rnorm(30,70,20))
mean(marks)

students3 = data.frame(gender, married, education, hostel, marks,play)
with(students3, ftable(education, hostel, gender, married,play))
set.seed(1234)
fit3 = rpart(play ~ . , data=students3, minsplit=5, cp=-1)
fit3
varImp(fit3) #at root level 
summary(fit3)
rpart.plot(fit3)

#Split DF - daply & split, subset
library(plyr)
x=daply(students3, .(gender), function(x) return(x))
x
(split1 = split(students3, marks >=76))
split1$'FALSE'
split(students3, marks >=76)$'FALSE'
split(students3, marks >=76)[['TRUE']]

s3Left = as.data.frame(split1$'FALSE')
s3Right = as.data.frame(split1$'TRUE')
#left node
fitL2 = rpart(play ~ ., data = s3Left, minsplit=5, cp=-1)
fitL2
rpart.plot(fitL2)
varImp(fitL2) # other variable are not important
printcp(fitL2)

fitL2b = rpart(play ~ ., data = s3Left, minsplit=5, cp=.1)
fitL2b
varImp(fitL2b) # other variable are not important
rpart.plot(fitL2b)

#right side
s3Right = as.data.frame(split1$'TRUE')
fitR2 = rpart(play ~ ., data = s3Right, minsplit=5, cp=-1)
fitR2
rpart.plot(fitR2)
varImp(fitR2) # other variable are not important
printcp(fitR2)

fitL2b = rpart(play ~ ., data = s3Left, minsplit=5, cp=.1)
fitL2b
varImp(fitL2b) # other variable are not important
rpart.plot(fitL2b)




#Case-4----
#Create Dataset V is numeric CV
set.seed(1234)
gender = sample(x= c('Male','Female'), size=30, replace=T, prob=c(0.4,0.6) )
table(gender)
set.seed(1234)
married = sample(x= c('Married','Single'), size=30, replace=T, prob=c(0.3,0.7) )
table(married)

set.seed(1234)
play = sample(x= c('Play','NotPlay'), size=30, replace=T, prob=c(10/30,25/30) )
table(play)

set.seed(1234)
income = ceiling(runif(30, 50,100) )
mean(income)

students4 = data.frame(income,gender, married, play)
rownames(students4) = rollno
head(students4)
str(students4)

#Model4
fit4 = rpart(income ~ gender + married + play, data=students4, minsplit=5, cp=-1, method='anova')
fit4
rpart.plot(fit4)

================================================
FILE: 52-CART/dt-sleep.R
================================================
#Sleep Data Set

library(ggplot2)
data("msleep")
?msleep
str(msleep)
df = msleep[, c(3,4,6,10,11)]
df
str(df)
head(df)

#Build a Regression Tree
#Predicting Sleeping Hours based on other IVs
library(rpart)
library(rpart.plot)
rtree1= rpart(sleep_total ~ . , data=df, method='anova')
rtree1
rpart.plot(rtree1, type=3, digits=3, fallen.leaves = T)

#dataset could test data instead of df
p1 = predict(rtree1, df)
p1
#Lower MAE the better
MAE = function(actual, predicted) { mean(abs(actual - predicted))}
MAE(df$sleep_total, p1)



================================================
FILE: 52-CART/dt-tree-car1.R
================================================
library(caret)
library(rpart.plot)

data_url <- c("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data")
download.file(url = data_url, destfile = "car.data")

car_df <- read.csv("car.data", sep = ',', header = FALSE)

str(car_df)
dim(car_df)
head(car_df)
#all are categories, no normalisation required
set.seed(3033)
intrain = createDataPartition(y=car_df$V7, p=0.7, list=F)
intrain
training = car_df[intrain,]
testing = car_df[-intrain,]

dim(training)
dim(testing)
#---------------
anyNA(car_df)
summary(car_df)

#---- Training DT classifier with criterion as information gain
#Training DT classifier with criterion as Info Gain

trctrl = trainControl(method ='repeatedcv', number = 10, repeats=3)
set.seed(333)
dtree_fit = train(V7 ~ ., data= training, method='rpart', parms= list(split='information'), trControl=trctrl, tuneLength = 10)

dtree_fit

#Plot DT
prp(dtree_fit$finalModel, box.palette = 'Reds', tweak=1.2)


#---- Prediction
testing[1,]
predict(dtree_fit, newdata = testing[1,])

test_pred = predict(dtree_fit, newdata = testing)
test_pred
confusionMatrix(test_pred, testing$V7)

#---- Gini Index
set.seed(3333)
dtree_fit_gini = train(V7 ~ . , data = training, method = 'rpart', 
                       parms = list(split='gini'), trControl=trctrl, tuneLength=10)
dtree_fit_gini
prp(dtree_fit_gini$finalModel, box.palette = 'Blues', tweak=1.2)

#Prediction
test_pred_gini = predict(dtree_fit_gini, newdata=testing)
confusionMatrix(test_pred_gini, testing$V7)


================================================
FILE: 52-CART/dt3-eyes.R
================================================
#https://www.r-project.org/conferences/useR-2009/slides/Hothorn+Zeileis.pdf

#Working

library(ipred)
data(data=GlaucomaMVF , package = "ipred")
head(GlaucomaMVF)
summary(GlaucomaMVF)

library("rpart")
g_rpart <- rpart(Class ~ ., data = GlaucomaMVF)
print(g_rpart)
plot(g_rpart)
text(g_rpart)

library("partykit")
g_party <- partykit::as.party(g_rpart)
print(g_party, header = FALSE)
print(g_party, header = T)
plot(g_party, type = "simple")
plot(g_party, type = "extended")


================================================
FILE: 52-CART/entropy.R
================================================
# Entropy

library(entropy)

buys <- c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes", "yes", "yes", "yes", "no")

freqs <- table(buys)/length(buys)
freqs
#Same this way
prop.table(table(buys))

# calculate shannon-entropy
-sum(freqs * log2(freqs))
#[1] 0.940286

entropy.empirical(freqs, unit="log2")
#0.940286


================================================
FILE: 52-CART/multimodel.R
================================================
#Multiple Models
#
#A model with a binary response.
library(titanic)
data("Titanic")
str(Titanic)
titanic = data.frame(Titanic)

library(rpart)
library(rpart.plot)
binary.model <- rpart(Survived∼., data=titanic, cp=.02)

rpart.plot(binary.model)
rpart.plot(binary.model, type=1)
rpart.plot(binary.model, type=2)
rpart.plot(binary.model, extra=1)
rpart.plot(binary.model, extra=2)
rpart.plot(binary.model, extra=100)




#A model with a continuous response (an anova model).
str(cu.summary)
anova.model <- rpart(Mileage∼., data=cu.summary)
rpart.plot(anova.model, extra=0)
rpart.plot(anova.model, extra=1)
rpart.plot(anova.model, extra=100)
rpart.plot(anova.model, extra=101)

#Each node shows- the predicted value,
#- the percentage of observations in the node.

#A model with a multi-class response.
multi.class.model <- rpart(Reliability ~ ., data=cu.summary)
rpart.plot(multi.class.model)
rpart.plot(multi.class.model, extra=0)

#Each node shows
#- the predicted class (Much worse, worse, ..., Much better),
#- the predicted probability of each class,
#- the percentage of observations in the node.



================================================
FILE: 52-CART/tree-houseprices.R
================================================
# tree
# http://www.di.fc.ul.pt/~jpn/r/tree/tree.html
# https://rpubs.com/JanpuHou/242986
library(tree)
set.seed(1234)

real.state <- read.table(file="https://raw.githubusercontent.com/jbryer/CompStats/master/Data/cadata.dat",head=TRUE)
tree.model= tree(log(MedianHouseValue) ~ Longitude + Latitude, data=real.state)
plot(tree.model)
text(tree.model, cex=.75)

price.decibels = quantile(real.state$MedianHouseValue, 0:10/10)
price.decibels
cut.prices = cut(real.state$MedianHouseValue, price.decibels, include.lowest = T)
cut.prices
plot(real.state$Longitude, real.state$Latitude, col=grey(10:2/11)  [cut.prices], pch=20, xlab='Longitude', ylab='Latitude')
partition.tree(tree.model, ordvars = c('Longitude','Latitude'), add=T)
?partition.tree

summary(tree.model)


================================================
FILE: 52-CART/zz-test.R
================================================


#method = one of "anova", "poisson", "class" or "exp". 
#If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is
#x

#Different Control Parameters
#cp, minsplit, minbucket, maxdepth, maxcompete, xval,
control_1 = rpart.control(cp = 0.005, minsplit=3, minbucket = 2)
control_1 = rpart.control(cp = 0.005, minsplit=10, minbucket = 5)

m1 = rpart(play ~ . , data=game, x=T, model=T, control=control_1)
m1
m1$frame
rpart.plot(m1, nn=T)
m1$model
class(m1$model)

summary(m1)
print(m1)

#
library(rattle)
library(RColorBrewer)
fancyRpartPlot(m1)


================================================
FILE: 53-splitcriteria/cls-entropy.R
================================================
# Entropy

buys <- c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes", "yes", "yes", "yes", "no")

credit <- c("fair", "excellent", "fair", "fair", "fair", "excellent", "excellent", "fair", "fair", "fair", "excellent", "excellent", "fair", "excellent")

student <- c("no", "no", "no","no", "yes", "yes", "yes", "no", "yes", "yes", "yes", "no", "yes", "no")

income <- c("high", "high", "high", "medium", "low", "low", "low", "medium", "low", "medium", "medium", "medium", "high", "medium")

age <- c(25, 27, 35, 41, 48, 42, 36, 29, 26, 45, 23, 33, 37, 44) # we change the age from categorical to numeric

df = data.frame(age, student, income, credit, buys)
df

#M1
freqs <- table(buys)/length(buys)
freqs
sum(freqs)
#info(freqs)  #[1] 0.940286

#M2
# calculate shannon-entropy
-sum(freqs * log2(freqs))    #[1] 0.940286

#M3
entropy::entropy.empirical(freqs, unit="log2")  #[1] 0.940286

6/16
10/16
6/8; 2/8


================================================
FILE: 53-splitcriteria/dt-rpart-criteria.R
================================================

# Regression Tree Example
library(rpart)

# grow tree 
fit <- rpart(Mileage~Price + Country + Reliability + Type, 
             method="anova", data=cu.summary)

printcp(fit) # display the results 
plotcp(fit) # visualize cross-validation results 
summary(fit) # detailed summary of splits

# create additional plots 
par(mfrow=c(1,2)) # two plots on one page 
rsq.rpart(fit) # visualize cross-validation results  	

par(mfrow=c(1,1)) # two plots on one page 

# plot tree 
plot(fit, uniform=TRUE, 
     main="Regression Tree for Mileage ")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

# create attractive postcript plot of tree 
post(fit, file = "c:/tree2.ps", 
     title = "Regression Tree for Mileage ")
# prune the tree 
pfit<- prune(fit, cp=0.01160389) # from cptable   

# plot the pruned tree 
plot(pfit, uniform=TRUE, 
     main="Pruned Regression Tree for Mileage")
text(pfit, use.n=TRUE, all=TRUE, cex=.8)
post(pfit, file = "c:/ptree2.ps", 
     title = "Pruned Regression Tree for Mileage")



#Gini and Information Gain Method

fmla = Mileage~Price + Country + Reliability + Type
data = cu.summary
gini_model<-rpart(fmla,data = data, 
                  parms = list(split = "gini"),
                  control = list(minsplit=10))

rpart.plot(gini_model,main="Gini Split Criteria")

info_model<-rpart(fmla,data = data, 
                  parms = list(split = "information"),
                  control = list(minsplit=10))

rpart.plot(info_model,main="InfoGain Split Criteria")


================================================
FILE: 53-splitcriteria/splitcriteria1.R
================================================
#Decision Tree - CART - Gini Index
# Gini Index

library(rpart)
library(rpart.plot)

graduate=c('Yes','No' ,'No', 'Yes','No','No', 'Yes','No','No','No')
married=c('S','M','S','M','D','M','D','S','M','S')
income=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)
buy=c('No','No','No','No','Yes','No','No','Yes','No','Yes')
(train= data.frame(graduate, married, income, buy))

#uploading datat
mytrain %>%  gs_ws_new(ws_title = "sample", input = train,  trim = TRUE, verbose = FALSE)


#rpart(y~x+z, data=df, parms=list(split='gini'))
library(rpart)

fit1a = rpart(cheat ~ married + refund, data=train, parms=list(split='gini'),  minsplit=4, minbucket=2, cp=-1)
fit1a
rpart.plot(fit1a)

#Split on Married or Refund : why : Gini index
t1a=table(cheat, married)
addmargins(t1a)
#CART has only binary split, so one of labels of married has to be combined with other
(m1 = matrix(c(4,0,3,3), byrow=F, nrow=2, dimnames=list(c('No','Yes'), c('M','SD'))))
#Gini Index
#Married M
(GMM = 1 - (4/4)^2 - (0/4)^2 )
(GMSD = 1 - (3/6)^2 - (3/6)^2)
(Gmarried = (4/10) * GMM + (7/10)* GMSD)
#Refund R
(t1b = table(cheat, refund))
(GRN = 1 - (4/7)^2 - (3/4)^2)
(GRY = 1 - (3/3)^2 - (0/3)^2)
(Grefund = (7/10) * GRN + (3/10)* GRY)
(GIM = 1 - Gmarried) ; (GIR = 1 - Grefund)
#married has lower value of GI, so married columns selected

(t1b = table(cheat, income)) # not done this way
income2 = ifelse(income < 78, 'Yes', 'No')
(t1b2 = table(cheat, income2)) # this way
(GIY = 1 - (4/7)^2 - (3/4)^2)
(GIN = 1 - (3/3)^2 - (0/3)^2)
(Gincome = (7/10)*GIY + (3/10)*GIN)
(GII = 1 - Gincome)
paste(GIM, GIR, GII, sep= ' ')
min(GIM, GIR, GII)
#Gini Index married column is least


fit2a = rpart(cheat ~ ., data=train, method='class', parms=list(split='gini'),  minsplit=2, minbucket=1, cp=-1)
fit2a
rpart.plot(fit2a)



fit2b = rpart(cheat ~ ., data=train, method= 'class', parms =   list(split='information'),  minsplit=2, minbucket=1, cp=-1 )
fit2b
rpart.plot(fit2b, nn=T)

fit3a = rpart(income ~ ., data=train, method= 'anova', parms =  list(split='gini'),  minsplit=2, minbucket=1, cp=-1 )
fit3a
rpart.plot(fit3a)

fit3b = rpart(income ~ ., data=train, method= 'anova', parms =  list(split='information'),  minsplit=2, minbucket=1, cp=-1 )
fit3b
rpart.plot(fit3b, nn=T)


library(ineq)
ineq(cheat,type = c("Gini"), na.rm = TRUE)
Gini(x, corr = FALSE, na.rm = TRUE)

library(ineq)
your_data = train
coeff= NULL
for (i in colnames(your_data[])){
  coeff= c(coeff,round(ineq(your_data[,i],type = 'Gini'),4))
}
coeff
data_coeff = data.frame(cbind(coeff,colnames(your_data[,-1])))
colnames(data_coeff) = c("Coeff","Colnames")
data_coeff
If you want it by for each rows try this :
  
  your_new_data = as.data.frame(t(your_data[,-1]), row.names =T)

colnames(your_new_data) = your_data[,1]









library(lawstat)
data(popdata)
gini.index(popdata[,"pop1900"])
gini.index(popdata[,"pop1972"])
gini.index(income)
gini.index(married)
gini.index(refund)

library(DescTools)

Gini(names(t1),t1)

library(rpart)
fit1 = rpart(cheat ~ ., data=train)
fit1
library(rpart.plot)
rpart.plot(fit1)


================================================
FILE: 53-splitcriteria/splitcriteria2.R
================================================
# Split Criteria

library(rpart)
library(rpart.plot)

(target = rep(c(1,0),times=c(5,5)))
(var1 = c(0,0,0,0,1,1,1,0,1,0))
(var2 = c(34,45,54,41,47,51,29,0,65,41))
data = data.frame(target, var1, var2)

set.seed(1234)
fit = rpart(target ~ var1 + var2, data= data, parms=list(split='gini'),  minsplit=4, minbucket=2,cp=.2)
fit
rpart.plot(fit)
printcp(fit)

table(data$target, data$var1, dnn=c('target', 'var1'))
table(data$target, data$var2, dnn=c('target', 'var2'))

================================================
FILE: 53-splitcriteria/splitcriteria3.R
================================================
# Decision Tree - Splitting Criteria

# Gini Index

library(rpart)
library(rpart.plot)

education=factor(c(3,2,2,3,2,2,3,2,2,2))
married=factor(c('S','M','S','M','M','M','S','S','M','S'))
income=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)
buy=factor(c(0,0,0,0,1,0,0,1,0,1))
(data= data.frame(buy, education, married, income))

fit = rpart(buy ~ education + married, data=data, parms=list(split='gini'),  minsplit=4, minbucket=2,cp=-1)
fit
rpart.plot(fit)
printcp(fit)

fit1 = rpart(buy ~ education + married + income, data=data, parms=list(split='gini'),  minsplit=4, minbucket=2,cp=.2)
fit1
rpart.plot(fit1)
printcp(fit1)

fit2 = rpart(buy ~ education + married + income, data=data, parms=list(split='gini'),  minsplit=4, minbucket=2,cp=-1)
fit2
rpart.plot(fit2)
printcp(fit2)


fit2$where  #which row at which node no
data[1:5,]
cbind(data, nodeno=rownames(fit2$frame) [ fit2$where])





================================================
FILE: 54-KNN/knn1_cancer.R
================================================
# KNN algo Analytics Vidya
#https://www.analyticsvidhya.com/blog/2015/08/learning-concept-knn-algorithms-programming/
#prc = read.csv(file=file.choose(), stringsAsFactors = F)
prc = read.csv('https://raw.githubusercontent.com/dupadhyaya/Ranalytics/master/data/Prostate_Cancer.csv', stringsAsFactors=F)
head(prc)
str(prc)
prc = prc[-1] # remove id column
str(prc)
table(prc$diagnosis_result) # table of cancer diagnosis Malignant or Benign 
prc$diagnosis = factor(prc$diagnosis_result,level=c('B','M'), labels=c('Benign','Malignant'))
str(prc)
prc[1:10,c('diagnosis_result','diagnosis')]
prc[1:10,c(1,10)]
table(prc$diagnosis)
round(prop.table(table(prc$diagnosis))*100,digits=1)

#Normalising Data

normalise <- function(x) {
  (x - min(x))/ (max(x) - min(x))
}
a = normalise(prc$radius)
prc_n = as.data.frame(lapply(prc[2:9],normalise))
head(prc_n)
summary(prc_n$radius) # data has been normalised Min is 0, Max is 1, Median 0.5, Mean may vary
summary(prc_n)

# Divide the data into Training and Test Data
prc_train = prc_n[1:65,] # all columns to be included
prc_test = prc_n[66:100,]
# Extract labels from prc
prc_train_labels = prc[1:65,1]
prc_test_labels = prc[66:100,1]
prc_train_labels;prc_test_labels

# Training the Model
library(class)
prc_test_pred = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=10) # k = sqrt(obsvn=100)
prc_test_pred
data.frame(prc_test_labels,prc_test_pred) # compare the output

# Evaluate
library(gmodels)
CrossTable(x=prc_test_labels, y = prc_test_pred, prop.chisq = F) # Confusion Matrix
# B-B : 7 ; M:M- 15 - correct prediction 7 + 15 = 22 out of 35
(accuracy = 22/35)

# Improving Algorithm
# Training the Model
prc_test_pred1 = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=5) # k = sqrt(obsvn=100)
prc_test_pred1
data.frame(prc_test_labels,prc_test_pred1) # compare the output

# Evaluate
CrossTable(x=prc_test_labels, y = prc_test_pred1, prop.chisq = F) # Confusion Matrix
# B-B : 9 ; M:M- 15 - correct prediction 9 + 15 = 24 out of 35
(accuracy1 = 24/35)

# Training the Model
#k  is therefore just the number of neighbors "voting" on the test example's class.
# if k=1, class of the training sample, k=3, mode of 3 closest samples
prc_test_pred2 = knn(train= prc_train, test=prc_test, cl = prc_train_labels, k=3) # k = sqrt(obsvn=100)
prc_test_pred2
data.frame(prc_test_labels,prc_test_pred2) # compare the output

# Evaluate
CrossTable(x=prc_test_labels, y = prc_test_pred2, prop.chisq = F ) # Confusion Matrix
# B-B : 7 ; M:M- 15 - correct prediction 7 + 15 = 22 out of 35
(accuracy2 = 22/35) # no more improvement





================================================
FILE: 54-KNN/knn2.R
================================================
# Classification K knn

train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
knn(train, test, cl, k = 3, prob=TRUE)
attributes(.Last.value)


================================================
FILE: 54-KNN/knn3.R
================================================
# K neigbour - glass

library(kknn)  #Weighted k-Nearest Neighbors
#kknn(formula = formula(train), train, test, na.action = na.omit(),    k = 7, distance = 2, kernel = "optimal", ykernel = NULL, scale=TRUE,     contrasts = c('unordered' = "contr.dummy", ordered = "contr.ordinal"))
#kknn.dist(learn, valid, k = 10, distance = 2)

#https://cran.r-project.org/web/packages/kknn/kknn.pdf
data(glass)
str(glass)
#A data frame with 214 observations, where the problem is to predict the type of glass in terms of
#their oxide content (i.e. Na, Fe, K, etc). The study of classification of types of glass was motivated
#by criminological investigation. At the scene of the crime, the glass left can be used as evidence...
#if it is correctly identified!

data("ionosphere")
str(ionosphere)
# This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased
# array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts.
# See the paper for more details. The targets were free electrons in the ionosphere. "Good" radar
# returns are those showing evidence of some type of structure in the ionosphere. "Bad" returns are
# those that do not; their signals pass through the ionosphere.
# Received signals were processed using an autocorrelation function whose arguments are the time of
# a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in
# this database are described by 2 attributes per pulse number, corresponding to the complex values
# returned by the function resulting from the complex electromagnetic signal.
# A data frame with 351 observations on the following 35 variables. The first 34 continuous covariables
# are used for the prediction. The 35th attribute is either g ("good") or b ("bad") according to
# the definition summarized above. This is a binary classification task.

data(miete)
str(miete)

# Spectral Clustering
#specClust(data=miete, centers=NULL, nn = 7, method = "symmetric", gmax=NULL, ...)
## S3 method for class 'specClust'
plot(x, ...)


================================================
FILE: 54-KNN/knn4.R
================================================
# Knn4
#http://archive.ics.uci.edu/ml/
#https://www3.nd.edu/~steve/computing_with_data/17_Refining_kNN/refining_knn.html

================================================
FILE: 54-KNN/knn_diamonds.R
================================================
##because diamonds dataset is in ggplot2 package
library(ggplot2)
##load data
data(diamonds)

##store it as data frame
dia <- data.frame(diamonds)

##create a random number equal 90% of total number of rows
ran <- sample(1:nrow(dia),0.9 * nrow(dia))

##the normalization function is created
nor <-function(x) { (x -min(x))/(max(x)-min(x))   }

##normalization function is created
dia_nor <- as.data.frame(lapply(dia[,c(1,5,6,7,8,9,10)], nor))

##training dataset extracted
dia_train <- dia_nor[ran,]

##test dataset extracted
dia_test <- dia_nor[-ran,]
##the 2nd column of training dataset because that is what we need to predict about testing dataset
##also convert ordered factor to normal factor
dia_target <- as.factor(dia[ran,2])

##the actual values of 2nd couln of testing dataset to compaire it with values that will be predicted
##also convert ordered factor to normal factor
test_target <- as.factor(dia[-ran,2])

##run knn function
library(class)
pr <- knn(dia_train,dia_test,cl=dia_target,k=20)

##create the confusion matrix
tb <- table(pr,test_target)

##check the accuracy
accuracy <- function(x){sum(diag(x)/(sum(rowSums(x)))) * 100}
accuracy(tb)

#In this model we have tried to predict the “cut” variable since it is a categorical variable and we know knn works better for classification problems. we have not taken the 3rd and 4th column into consideration since they were also categorical and would cause ambiguity because other predictive variables are integers. Note that there are ways to run knn on mixture of categorical and integer variables, but we are not discussing it here. The rest of the procedure is same as the iris dataset, and in the end we get the accurate result 71% of the times.

================================================
FILE: 54-KNN/knn_iris.R
================================================
#KNN using iris

df <- data(iris) ##load data
head(iris) ## see the studcture
##Generate random numbers that is 90% of the total number of rows in dataset.
ran <- sample(1:nrow(iris), 0.9 * nrow(iris)) 
ran
##the normalization function is created
nor <-function(x) { (x -min(x))/(max(x)-min(x))   }
nor
##Run nomalization on first 4 coulumns of dataset because they are the predictors
iris_norm <- as.data.frame(lapply(iris[,c(1,2,3,4)], nor))
head(iris_norm)
summary(iris_norm)

##extract training set
iris_train <- iris_norm[ran,] 
head(iris_train)

##extract testing set
iris_test <- iris_norm[-ran,] 
head(iris_test)

##extract 5th column of train dataset because it will be used as 'cl' argument in knn function.
iris_target_category <- iris[ran,5]

##extract 5th column if test dataset to measure the accuracy
iris_test_category <- iris[-ran,5]

##load the package class
library(class)

##run knn function
pr <- knn(train=iris_train,test=iris_test,cl=iris_target_category,k=13)

##create confusion matrix
tab <- table(pr,iris_test_category)
tab

##this function divides the correct predictions by total number of predictions that tell us how accurate teh model is.

accuracy <- function(x){sum(diag(x)/(sum(rowSums(x)))) * 100}
accuracy(tab)


#read more from here
#https://towardsdatascience.com/k-nearest-neighbors-algorithm-with-examples-in-r-simply-explained-knn-1f2c88da405c


#-----------------------------

================================================
FILE: 54-NLM/nlm1.R
================================================
#Non - Linear Regression in R
#https://www.r-bloggers.com/first-steps-with-non-linear-regression-in-r/

#simulate some data
set.seed(20160227)
x = seq(0,50,1)
y = ((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(51,0,1)
y
#for simple models nls find good starting values for the parameters even if it throw a warning
m = nls(y~a*x/(b+x))
summary(m)

#get some estimation of goodness of fit
cor(y,predict(m))


#plot
plot(x,y)
lines(x,predict(m),lty=2,col="red",lwd=3)


================================================
FILE: 54-NLM/nlm2-mtcars.R
================================================
# NLM using mtcars
# https://stats.stackexchange.com/questions/142443/simple-non-linear-regression-problem
#Load data----

data(mtcars)

# Scatterplot with log-transformation
plot(mpg ~ disp, data=mtcars)
plot(log(mpg)~disp, data = mtcars, las = 1, pch = 16, xlab = "Displacement", ylab = "Log(Miles/Gallon)")

# Linear regression with log-transformation

log.mod <- lm(log(mpg)~disp, data = mtcars)

# Prediction intervals

newframe <- data.frame(disp = seq(min(mtcars$disp), max(mtcars$disp), length = 1000))

pred <- predict(log.mod, newdata = newframe, interval = "prediction")
pred
# Plot prediction intervals on log scale

plot(log(mpg)~disp
     , data = mtcars
     , ylim = c(2, 4)
     , las = 1
     , pch = 16
     , main = "Log scale"
     , xlab = "Displacement", ylab = "Log(Miles/Gallon)")

lines(pred[,"fit"]~newframe$disp, col = "steelblue", lwd = 2)
lines(pred[,"lwr"]~newframe$disp, lty = 2)
lines(pred[,"upr"]~newframe$disp, lty = 2)

# Plot prediction intervals on original scale

plot(mpg~disp
     , data = mtcars
     , ylim = c(8, 38)
     , las = 1
     , pch = 16
     , main = "Original scale"
     , xlab = "Displacement", ylab = "Miles/Gallon")

lines(exp(pred[,"fit"])~newframe$disp, col = "steelblue", lwd = 2)
lines(exp(pred[,"lwr"])~newframe$disp, lty = 2)
lines(exp(pred[,"upr"])~newframe$disp, lty = 2)

# Quadratic regression on original scale

quad.lm <- lm(mpg~poly(disp, 2), data = mtcars)

# Prediction intervals

newframe <- data.frame(disp = seq(min(mtcars$disp), max(mtcars$disp), length = 1000))

pred <- predict(quad.lm, newdata = newframe, interval = "prediction")

# Plot prediction intervals on log scale

plot(mpg~disp
     , data = mtcars
     , ylim = c(7, 36)
     , las = 1
     , pch = 16
     , main = "Original scale"
     , xlab = "Displacement", ylab = "Miles/Gallon")

lines(pred[,"fit"]~newframe$disp, col = "steelblue", lwd = 2)
lines(pred[,"lwr"]~newframe$disp, lty = 2)
lines(pred[,"upr"]~newframe$disp, lty = 2)

================================================
FILE: 55-CHAID/CHAID-nps2.R
================================================
###
library("CHAID")
#convert data to factors

##Read the data in the file
cust_data<-read.csv("./data/NPS Data Food Order v01.csv")
str(cust_data)
dim(cust_data)
head(cust_data)
oldnames=names(cust_data)
oldnames
df= cust_data


# Changing Names
newnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city', 'paymode', 'items', 'gender','mode', 'squality','sdely','smenu', 'srecom', 'sdperson')
length(newnames)
names(df) = newnames
apply(df,2, class)
df2 =lapply(df[,c('npscat', 'dely', 'srecom', 'years','calls')], factor)
df2= as.data.frame(df2)
table(df2$npscat)
fit <- chaid(npscat ~ dely + srecom + years + calls, data=df2)
plot(fit, main="Conditional Inference Tree for NPS ")
plot(fit, gp = gpar(fontsize = 5), inner_panel=node_inner,
     ip_args=list( abbreviate = FALSE,id = FALSE))
summary(fit)
partykit:::.list.rules.party(fit)
names(df2)
cbind(predict(fit, newdata=df2[1:10,]), df2$npscat[1:10])
nodeids(fit,1)
nodeids(fit, from=4, TERMINAL=TRUE)
?nodeids

print(fit)

df2
summary(df2$years)
summary(df2)
unique(df2$years)


================================================
FILE: 55-CHAID/CHAID-xsell1.R
================================================
# CHAID 2
#http://dni-institute.in/blogs/reading-data-in-r/
  
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)
library(help=CHAID)
#install.packages("CHAID",   repos="http://R-Forge.R-project.org")
termCrosssell1 <- read.csv(file="http://dni-institute.in/blogs/wp-content/uploads/2016/07/termCrosssell.csv")

termCrosssell = termCrosssell1
save(termCrosssell,file='madata.Rdata')
names(termCrosssell)

table(termCrosssell$housing)  #multiple classes
summary(termCrosssell[,c('marital','education', 'y')])
dim(termCrosssell)



dt.chaid  <- chaid(y~ marital+education , 
   control = chaid_control(minprob = 0.001,
   minsplit = 500, minbucket = 200), data=termCrosssell)

plot(dt.chaid)

plot(dt.chaid, uniform = T, compress = T, margin = 0.2, branch = 0.3)
# Label on Decision Tree
text(dt.chaid)
summary(dt.chaid)
print(dt.chaid)




# ?chaid
head(termCrosssell[,c('y','marital', 'education')])
ftable(termCrosssell[,c('y','education', 'marital')])

#Chisquare test 
#education to y  or marital to y which is more dependent
(te1= table(termCrosssell[,c('education','y')]))
(tm2= table(termCrosssell[,c('marital','y')]))
#Ho: Xcell  is independent of their education level (No association)
#H1 : There is relationship : Some association
(ce1= chisq.test(t1))
#Ho: Xcell  is independent of their marital level 
(cm2=chisq.test(t2))
ce1$statistic;cm2$statistic
ce1$p.value;cm2$p.value


================================================
FILE: 55-CHAID/c-dt-chaid-nps.R
================================================
###
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library("CHAID")
#convert data to factors

##Read the data in the file
cust_data<-read.csv("./data/nps.csv")
str(cust_data)
dim(cust_data)
head(cust_data)
oldnames=names(cust_data)
oldnames
df= cust_data


# Changing Names
newnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city', 'paymode', 'items', 'gender','mode', 'squality','sdely','smenu', 'srecom', 'sdperson')
length(newnames)
names(df) = newnames
apply(df,2, class)
df2 =lapply(df[,c('npscat', 'dely', 'srecom', 'years','calls')], factor)
df2= as.data.frame(df2)
table(df2$npscat)
fit <- chaid(npscat ~ dely + srecom + years + calls, data=df2)
plot(fit, main="Conditional Inference Tree for NPS ")
plot(fit, gp = gpar(fontsize = 5), inner_panel=node_inner,
     ip_args=list( abbreviate = FALSE,id = FALSE))
summary(fit)



partykit:::.list.rules.party(fit)
names(df2)
cbind(predict(fit, newdata=df2[1:10,]), df2$npscat[1:10])
nodeids(fit,1)
nodeids(fit, from=4, TERMINAL=TRUE)
?nodeids

print(fit)

df2
summary(df2$years)
summary(df2)
unique(df2$years)



================================================
FILE: 55-CHAID/c-dt-chaid-usvote1.R
================================================
#CHAID - dataset USvote
#multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
require(dplyr);require(ggplot2);theme_set(theme_bw()) ;require(CHAID); require(purrr) ; require(caret)
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)  #library for performing CHAID decision tree
#Dataset
data(USvote)
?USvote


head(USvote)
str(USvote)
names(USvote)
summary(USvote)
dim(USvote)

#Quick CHAID analysis
set.seed(101)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))
print(chaidModel)
plot(chaidModel)


#Train and Test Set
require(caTools)
set.seed(101) 
sample = sample.split(USvote$vote3, SplitRatio = .75)
train = subset(USvote, sample == TRUE)
test  = subset(USvote, sample == FALSE)
dim(train); dim(test)

#control Parameters
ctrl <- chaid_control(minsplit = 100, minbucket = 10, minprob = 0.001)
ctrl_1 <- chaid_control(minprob=.002, maxheight = 4, minsplit=2000, minbucket=700)
#parameters - minsplit, minbucket, minprob,  
ctrl_1  #see the control values
chaidModel_1 <- chaid(vote3 ~ ., data = train, control = ctrl_1)

#summary(chaidModel_1)
print(chaidModel_1)
plot(chaidModel_1)
plot(chaidModel_1, type='simple')

#Variable Selection
chisq.test(train$vote3, train$marstat)  #least
#this is why 1st split happens on marstat
chisq.test(train$vote3, train$ager)
chisq.test(train$vote3, train$empstat)


#Plots
plot(chaidModel_1,main = "Testing Graphical Options",
     gp = gpar(fontsize = 8),  type = "simple")

plot(chaidModel_1,  main = "Testing More Graphical Options",
     gp = gpar(col = "blue",  lty = "solid",   lwd = 3,  fontsize = 10
     )
)


#Predict
p1a = predict(chaidModel_1, type='response')
head(p1a)
#“response”, “prob”, “quantile”, “density”, “node”
p1b = predict(chaidModel_1, type='prob')
head(p1b)
p1c = predict(chaidModel_1, type='node')
head(p1c)
#quantile & density for numeric responses




sum(complete.cases(train))# there are some missing values
sum(!complete.cases(train))# there are some missing values

length(p1a); length(train$vote3)


#Confusion Matrix
library(caret)
confusionMatrix(p1a, train[complete.cases(train),]$vote3)


#Now change different parameters of chaid and predict accuracy
#Select best model
ctrl_2 <- chaid_control(minprob=.01, minsplit=1000)
#parameters - minsplit, minbucket, minprob,  
ctrl_2  #see the control values
chaidModel_2 <- chaid(vote3 ~ ., data = train, control = ctrl_2)
p2a = predict(chaidModel_2, type='response')
confusionMatrix(p2a, train[complete.cases(train),]$vote3)
#

#Compare models and select with higher accuracy




================================================
FILE: 55-CHAID/chaid-attrition.R
================================================
# CHAID - multiple splits

library(CHAID)
library(rsample)
library(plyr)
library(dplyr)

attrition %>% select_if(is.factor) %>% ncol
## [1] 15
attrition %>% select_if(is.numeric) %>% ncol
## [1] 16

attrition %>%  select_if(function(col) length(unique(col)) <= 5 & is.integer(col)) %>%   head

attrition %>%  select_if(function(col) length(unique(col)) <= 10 & is.integer(col)) %>%   head


#


















#create a data set
income = floor(runif(1000, 50, 100))
gender = sample(c('M','F'), size=1000, replace=T, prob=c(.6,.4))
married = sample(c('Married','Single', 'Divorce'), size=1000, replace=T, prob=c(.3,.4,.3))
buy = sample(c('Buy','DontBuy'), size=1000, replace=T, prob=c(.6,.4))

df = data.frame(income, gender, married, buy)
head(df)
str(df)
fcols = c('income','gender', 'married', 'buy')
df[fcols] = lapply(df[fcols], factor)
lapply(df,class)

table(df$gender, df$buy)

#Run the model
chaidmodel_1 = chaid(buy ~ gender + married + income, data=df)
#summary(chaidmodel_1)
print(chaidmodel_1)
plot(chaidmodel_1)

#no plot or tree: some control parameters to be modified

#Modify the parameters
ctrl_1 = chaid_control(minsplit = 10, minprob = 0.01)
chaidmodel_2 = chaid(buy ~ incomee + gender + married, data=df, control = ctrl_1)
plot(chaidmodel_2)
print(chaidmodel_2)

chaidmodel = chaid(buy ~ ., data=df)
?chaid


#https://www.r-bloggers.com/chaid-and-r-when-you-need-explanation-may-15-2018/
require(rsample)
data(attrition)
str(attrition)
names(attrition)


================================================
FILE: 55-CHAID/chaid-cancer.R
================================================
#CHAID on Breast Cancer

data("BreastCancer", package = "mlbench")
#install.packages("CHAID", repos="http://R-Forge.R-project.org")

library("CHAID")
b_chaid <- chaid(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, data = BreastCancer)
plot(b_chaid)
dim(BreastCancer)

attach(BreastCancer)
table(BreastCancer$class, BreastCancer$Cell.size)
table(table(BreastCancer$Cell.size), BreastCancer$class)
table(BreastCancer$Cell.size, BreastCancer$class)


================================================
FILE: 55-CHAID/chaid-usvote.R
================================================
#CHAID1

### fit tree to
###  subsample
USvote
library(CHAID)
set.seed(290875)
data(USvote)
attach(USvote)
?USvote
head(USvote)
str(USvote)
names(USvote)
summary(USvote)
dim(USvote)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))
print(chaidModel)
plot(chaidModel)


#Train and Test Set
require(caTools)
set.seed(101) 
sample = sample.split(USvote$vote3, SplitRatio = .75)
train = subset(USvote, sample == TRUE)
test  = subset(USvote, sample == FALSE)
dim(train); dim(test)

#control Parameters
ctrl1 <- chaid_control(minsplit = 100, minbucket = 10, minprob = 0.001)
ctrl2 <- chaid_control(minprob=.002,maxheight = 4, minsplit=2000, minbucket=700)
#parameters - minsplit, minbucket, minprob,  
ctrl2  #see the control values
chaidModel_1 <- chaid(vote3 ~ ., data = train, control = ctrl2)

#summary(chaidModel_1)
print(chaidModel_1)
plot(chaidModel_1)
plot(chaidUS1, type='simple')
chisq.test(train$vote3, train$marstat)  #least
#this is why 1st split happens on marstat
chisq.test(train$vote3, train$ager)
chisq.test(train$vote3, train$empstat)


#Plots
plot(chaidModel_1,main = "Testing Graphical Options",
  gp = gpar(fontsize = 8),  type = "simple")

plot(chaidModel_1,  main = "Testing More Graphical Options",
  gp = gpar(col = "blue",  lty = "solid",   lwd = 3,  fontsize = 10
  )
)


#Predict
p1 = predict(chaidModel_1)
head(p1)
sum(complete.cases(train))# there are some missing values
sum(!complete.cases(train))# there are some missing values

length(p1); length(train$vote3)


#Confusion Matrix
library(caret)
confusionMatrix(p1, train[complete.cases(train),]$vote3)


# aggregation
unique(USvote$vote3)
plyr::count(USvote, c('gender','vote3'))
plyr::count(USvote, c('marstat','vote3'))
(t1=ftable(USvote$vote3, USvote$gender))
(t2=ftable(USvote$vote3, USvote$marstat))
prop.table(t2)
(t3=ftable(USvote$vote3, USvote$educr))
prop.table(t3)
(t4=ftable(USvote$vote3, USvote$ager))

chisq.test(t1)
chisq.test(t2)
chisq.test(t3)
chisq.test(t4)


chisq.test(t3)

#https://select-statistics.co.uk/blog/chaid-chi-square-automatic-interaction-detector/
  

#selecting the best variable
names(USvote)
dplyr::count(USvote, vote3 , gender)
library(dplyr)
USvote %>% count(vote3, gender)
USvote %>% group_by(vote3, gender) %>%  summarise(number = n())
USvote %>% group_by(vote3, gender) %>%  tally()


(t1=ftable(USvote$vote3, USvote$gender))
(t2=ftable(USvote$vote3, USvote$ager))
(t3=ftable(USvote$vote3, USvote$empstat))
(t4=ftable(USvote$vote3, USvote$educr))
(t4=ftable(USvote$vote3, USvote$marstat))
chisq.test(t1)

library(EnQuireR)
data(tea)
chisq.desc(tea,c(1,2),c(3,4))

chisq.desc(tea,c(1,2),c(3,4),report=TRUE)

================================================
FILE: 55-CHAID/chaid2.R
================================================
#CHAID
#
gender = c(rep('Female',10),rep('Male',20))
gender
play = c(rep('Yes',2),rep('No',8),rep('Yes',13),rep('No',7) )
play
df1 = data.frame(gender, play)
df1
table(df1)
?chisq.test(table(df1))
chisq.test(gender,play)


((-1)^2 / 7) ^ 0.5
((1)^2 / 8) ^ 0.5
((-2)^2 / 8) ^ 0.5



================================================
FILE: 55-CHAID/chaid4.R
================================================
# CHAID - Own eg

set.seed(1234)
gender = sample(c('Male', 'Female'), 200, replace=T,prob = c(0.7,0.3))
gender
set.seed(1234)
play = sample(c('Play', 'NoPlay'), 200, replace=T,prob = c(0.4,0.6))
play
df = data.frame(gender, play)
table(df)
#CART - classification
fit1 = rpart(play ~ gender, data=df)
fit1
# not a good plot
plot(fit1)
text(fit1)
rpart.plot(fit1)
prop.table(table(df))
addmargins(prop.table(table(df)))
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')))

?rpart::predict.rpart
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),
  type='vector')
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),
        type='class')
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),   type='vector')
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')), type='prob')
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),  type='vector')
predict(fit1, newdata=data.frame(gender=c('Male','Female','Male')),
        type='matrix')

        
printcp(fit1)
prune(fit1, cp=0.74)
prune(fit1, cp=1)
prune(fit1, cp=0)

library(rattle)
fancyRpartPlot(fit1)
rpart.plot::prp(fit1, type=3)
rpart.plot::prp(fit1, type=1)
rpart.plot::prp(fit1, extra=2)
rpart.plot::prp(fit1, extra=3)
rpart.plot::prp(fit1, extra=4, cex=2)


# Vary min split and see 
fit1a <- rpart(play ~ gender,data=df, 
    control=rpart.control(minsplit=210,minbucket=10, cp=0))
prp(fit1a)

fit1.new = prp(fit1,snip=TRUE)$obj
# some error coming


#----  Chisq
chisq.test(table(df))
#Ho reject: Not Independent : But Dependent : Only Males Play
library(CHAID)
?chaid
df.chaid= CHAID::chaid(play ~ gender, data = df)
plot(df.chaid)

df.chaid = CHAID::chaid(play ~ gender, data=df, 
        control = chaid_control(minprob = 0.001,
        minsplit=10, minbucket = 5))
plot(df.chaid, uniform=T, compress=T, margin=0.4, branch=0.5)

str(df.chaid)
summary(dt.chaid)
# prp will not work here as it is not rpart object










library(party)
ctree1 = party::ctree(play ~ gender, data=df)
plot(ctree1)

set.seed(1111)
marks = sample(c(60,70, 80,90), 200, replace=T,prob = c(0.3,0.4,.2,.1))
marks
df2 = data.frame(gender, play, marks)
ctree2 = party::ctree(marks ~ play + gender, data=df2)
plot(ctree2)
dim(df2)
head(df2)
library(rpart)
library(rpart.plot)
tree2a = rpart::rpart(marks ~ play + gender, data=df2)
tree2a
plot(tree2a)

tree2a = rpart::rpart(marks ~ play + gender, data=df2,
          control=rpart.control(minsplit=4, cp=0.001), method='anova')
tree2a
rpart.plot(tree2a)
library(rattle)
fancyRpartPlot(tree2a)
table(df2$play)
table(df2$play, df2$gender)
prp(tree2a)

tree2a = rpart::rpart(play ~ marks + gender, data=df2,
      control=rpart.control(minsplit=4, cp=0.001))
tree2a
prp(tree2a)
new.tree.1 <- prp(tree2a,snip=TRUE)$obj # interactively trim the tree
prp(new.tree.1) # display the new tree


================================================
FILE: 55-CHAID/chisq.R
================================================
#Chi Square
library(MASS)
str(Cars93)
names(Cars93)
car.data = Cars93[,c('AirBags','Type')]
table(car.data)
car.data.table =table(car.data$AirBags, car.data$Type)
car.data.table
chisq.test(car.data.table)


#---- 2nd Eg
library(MASS)       # load the MASS package 
tbl = table(survey$Smoke, survey$Exer) 
tbl                 # the contingency table 
chisq.test(tbl) 
ctbl = cbind(tbl[,"Freq"], tbl[,"None"] + tbl[,"Some"]) 
ctbl 
chisq.test(ctbl) 


================================================
FILE: 55-CHAID/chisqtest2.R
================================================
#CHisquare test 
(result = matrix(c(200,150,50,250,300,50), byrow=T, ncol=3))
dimnames(result) = list(gender=c('male','female'),party=c('bjp','cong','aap') )
(ct= chisq.test(result))
summary(ct)
ct$statistic; ct$p.value
ct$observed; ct$expected
addmargins(result,c(1,2),sum)
addmargins(prop.table(result,1),c(2),sum)
addmargins(prop.table(result,2),c(1),sum)

prop.table(result,2)
prop.table(result,1)


================================================
FILE: 56-ctree/CTREE NPS R code v01.R
================================================
###

#install.packages("party")
library(party)   #better than partykit
#install.packages("partykit")
library(partykit)

##Read the data in the file
cust_data<-read.csv("./data/NPS Data Food Order v01.csv")
str(cust_data)
dim(cust_data)
head(cust_data)
cust_data$NPS_Category
cust_data$NPS_Score

# Conditional Inference Tree for Default_On_Payment
fit <- party::ctree(  NPS_Category~
               Timely_delivery	+
               No_of_calls_in_an_Order	+
               Loyalty_Years	+
               C_Marital_Status	+
               Tier_City	+
               Mode_of_Payment	+
               No_of_Items_in_Order	+
               Gender	+
               Order_Mode	+
               Score_on_Food_Quality	+
               Score_on_Order_Logistics	+
               Menu_Presentation_Score	+
               Score_on_Recommendations	+
               Score_on_Delivery_Person_Friendliness
             , 
             data=cust_data )
fit
plot(fit)
length(fit)

# Conditional Inference Tree for Default_On_Payment by specifying the limits
fit <- ctree(  NPS_Category~
                 Timely_delivery	+
                 No_of_calls_in_an_Order	+
                 Loyalty_Years	+
                 C_Marital_Status	+
                 Tier_City	+
                 Mode_of_Payment	+
                 No_of_Items_in_Order	+
                 Gender	+
                 Order_Mode	+
                 Score_on_Food_Quality	+
                 Score_on_Order_Logistics	+
                 Menu_Presentation_Score	+
                 Score_on_Recommendations	+
                 Score_on_Delivery_Person_Friendliness
               , 
               data=cust_data,
               control = ctree_control(maxdepth = 4))

#plot(fit, main="Conditional Inference Tree for NPS ")

plot(fit, gp = gpar(fontsize = 5),     # font size changed to 6
     inner_panel=node_inner,
     ip_args=list(
       abbreviate = FALSE, 
       id = FALSE)
)

###detailed results including splits
summary(fit)


partykit:::.list.rules.party(fit)

# Method2 ------
oldnames=names(cust_data)
oldnames
df= cust_data
# Changing Names
newnames = c('custid', 'nps', 'npscat', 'dely', 'calls', 'years', 'married', 'city',
'paymode', 'items', 'gender','mode', 'squality','sdely','smenu',
'srecom', 'sdperson')
length(newnames)
names(df) = newnames
df
table(df$npscat)
fit2a <- partykit::ctree(npscat ~ .- custid - nps, data=df)
#length(fit2a)  #length of tree for partykit only
plot(fit2a, main="Conditional Inference Tree for NPS ")
plot(fit2a, gp = gpar(fontsize = 5), inner_panel=node_inner,
     ip_args=list( abbreviate = FALSE,id = FALSE))
summary(fit2a)
fit2b <- ctree(npscat ~ .- custid - nps, data=df, control = ctree_control(maxdepth = 4))
plot(fit2b, type = "simple")
print(fit2b)

#list.rules.party(fit2b)
names(df)
predict(fit2b, newdata=df[1:10,])

df$npscat[1:10]


================================================
FILE: 56-ctree/ctree-KyCU.R
================================================
# Condition Tree 

# Conditional Inference Tree for Kyphosis
library(party)
str(kyphosis)
fit <- ctree(Kyphosis ~ Age + Number + Start, 
             data=kyphosis)
plot(fit, main="Conditional Inference Tree for Kyphosis")


library(partykit)
str(kyphosis)
fit <- partykit::ctree(Kyphosis ~ Age + Number + Start, 
             data=kyphosis)
plot(fit, main="Conditional Inference Tree for Kyphosis")
sctest(fit, node = c(1,2,3))





# Conditional Inference Tree for Mileage
library(party)
str(cu.summary)
fit2 <- ctree(Mileage~Price + Country + Reliability + Type, 
              data=na.omit(cu.summary))
plot(fit2, main="Conditional Inference Tree for cu.summary - DV numeric")


library("strucchange")
strucchange::sctest(fit2, node = 1)
strucchange::sctest(fit2, node = c(2,7,4,9))



================================================
FILE: 56-ctree/ctree-airquality.R
================================================
#ctree using Air Quality
#https://www.r-bloggers.com/package-party-conditional-inference-trees/

#
library(party)
data(airquality)
head(airquality)
airq <- subset(airquality, !is.na(Ozone))
summary(lm(Ozone ~., data=airq))
head(airq)
air.ct <- party::ctree(Ozone ~ ., data = airq)
air.ct
plot(air.ct)
nodes(air.ct, 1)

nodes(air.ct, 5)
nodes(air.ct, 7)
?ctree

Solar.R = c(rep(0,6))
Wind = c(rep(c(5,12),3))
Temp = c(rep(70,6))
Month = Day = c(rep(0,6))

new.airq = data.frame(Solar.R, Wind, Temp, Month, Day)
new.airq
sapply(airq[,-1], class)
sapply(new.airq, class)
class(new.airq$Solar.R) = 'integer'
class(new.airq$Temp) = 'integer'
class(new.airq$Month) = 'integer'
class(new.airq$Day) = 'integer'

cbind(new.airq, predict(air.ct, newdata = new.airq))
predict(air.ct, newdata = new.airq, type = "node")
airq$node = predict(air.ct, type = "node")
head(airq)
where(air.ct)



================================================
FILE: 56-ctree/ctree-churn2.R
================================================
# Decision Tree - Loan Default or Churn
# age, gender, status, 

library(rpart)
# Part -2 : increase data points
age = floor(runif(50,30,60))
age
gender = sample(c('M','F'), size=50, replace=T)
gender
churn = sample(c('Yes','No'), size=50, replace=T, prob=c(.6,.4))
churn
mobilecust = data.frame(age, gender, churn)
str(mobilecust)


mytree1 = rpart (churn ~ age + gender, data=mobilecust, method='class', minsplit=10, minbucket=5)

summary(mytree1)
plotcp(mytree1)
plot(mytree1)
library(rpart.plot)
library(RColorBrewer)
fancyRpartPlot(mytree1)



#Case2  : Larger Data
#https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/
churn2 = read.csv('./data/churn.csv') 
summary(churn2)
sapply(churn2, function(x) sum(is.na(x)))
churn2 <- churn2[complete.cases(churn2), ]
names(churn2)
tree2 <- ctree(Churn ~ Contract + tenure  +PaperlessBilling, data=churn2)
plot(tree2)
fancyRpartPlot(tree2)


================================================
FILE: 56-ctree/ctree-clsregr-party.R
================================================
#ctree - Classification and Regression
library(party) # use this to model, can also use party but that will give different output

head(airquality)
### regression----
airq <- subset(airquality, !is.na(Ozone))
airct <- party::ctree(Ozone ~ ., data = airq)
airct  
plot(airct)
plot(airct, type='simple')
plot(as.simpleparty(airct))   #when partykit is used to model

library("strucchange")
strucchange::sctest(airct, node = 1)
strucchange::sctest(airct, node = c(2,7,4,9))



predict(airct)
mean((airq$Ozone - predict(airct))^2)  #error
nodeids(airct,2)
nodeids(airct)
nodeids(airct, terminal=T)
nodeids(airct, from=2, terminal=T)
nodeids(airct,1,5)

range(airq$Ozone)
#party
#party::ctree(Ozone ~ ., data = airq)
head(iris)
library(partykit)
### classification ---
irisct <- partykit::ctree(Species ~ .,data = iris)
irisct
plot(irisct, type='simple')
strucchange::sctest(irisct, node = 1)
strucchange::sctest(irisct, node = c(2,3,4))
#7 not significant
nodeapply(irisct, ids = nodeids(irisct), function(n) info_node(n)$p.value)




table(predict(irisct), iris$Species)
### estimated class probabilities, a list
(tr <- predict(irisct, newdata = iris[1:10,], type = "prob"))
(tr <- predict(irisct, newdata = iris, type = "prob"))

(tr2 <- predict(irisct, newdata = iris[1:10,], type = "response"))
head(iris)
      

================================================
FILE: 56-ctree/ctree-clsregr.R
================================================
#ctree - Classification and Regression
library(partykit) # use this to model, can also use party but that will give different output

head(airquality)
### regression----
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq)
airct  
plot(airct)
plot(airct, type='simple')
plot(as.simpleparty(airct))   #when partykit is used to model

library("strucchange")
strucchange::sctest(airct, node = 1)
strucchange::sctest(airct, node = c(2,7,4,9))



predict(airct)
mean((airq$Ozone - predict(airct))^2)  #error
nodeids(airct,2)
nodeids(airct)
nodeids(airct, terminal=T)
nodeids(airct, from=2, terminal=T)
nodeids(airct,1,5)

range(airq$Ozone)
#party
#party::ctree(Ozone ~ ., data = airq)
head(iris)
library(partykit)
### classification ---
irisct <- partykit::ctree(Species ~ .,data = iris)
irisct
plot(irisct, type='simple')
strucchange::sctest(irisct, node = 1)
strucchange::sctest(irisct, node = c(2,3,4))
#7 not significant
nodeapply(irisct, ids = nodeids(irisct), function(n) info_node(n)$p.value)




table(predict(irisct), iris$Species)
### estimated class probabilities, a list
(tr <- predict(irisct, newdata = iris[1:10,], type = "prob"))
(tr <- predict(irisct, newdata = iris, type = "prob"))

(tr2 <- predict(irisct, newdata = iris[1:10,], type = "response"))
head(iris)
      

================================================
FILE: 56-ctree/ctree-readingskills.R
================================================
# Classification : Dataset - readingSkills
?readingSkills
head(readingSkills)
library(party)
str(readingSkills)
readingSkills

# Create the input data frame.
input.dat <- readingSkills[c(1:105),]
?readingSkills

# Give the chart file a name.
#png(file = "decision_tree.png")

# Create the tree.
output.tree <- ctree( nativeSpeaker ~ age + shoeSize + score,  data = input.dat)
output.tree

# Plot the tree.
plot(output.tree)

# Save the file.
#dev.off()



================================================
FILE: 56-ctree/ctree2-iris.R
================================================
# Classification ####
# Decision Treee Eg 1 ####
#http://www.rdatamining.com/examples/decision-tree

library("party")
str(iris)
#Call function ctree to build a decision tree. The first parameter is a formula, which defines a target variable and a list of independent variables.

iris_ctree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)
print(iris_ctree)
plot(iris_ctree)
plot(iris_ctree,type='simple')




# 2nd Example
head(iris)
table(iris$Species, predict(iris.ct), dnn = c("Actual species", "Predicted species"))
new.iris
predict(iris.ct, newdata = new.iris)
predict(iris.ct, newdata = new.iris, type = "node")
#https://www.r-bloggers.com/package-party-conditional-inference-trees/
#



================================================
FILE: 56-ctree/ctreee-iris.R
================================================
# Classification - Party(Ctree) + IRIS
#https://predictoanalycto.wordpress.com/2014/05/30/classification-trees-with-r-programming-language-part-1/

#Load Libraries


str(iris)
#decision tree for classifying the flowers as one of the three Species : “setosa”, “versicolor”, “virginica”
#Attribute X - Condition - Either A or B or ...
#partition data set into two parts: Training  and Testing
#CART,  ID3, C4.5 are most widely used. ctree() uses CART algorithm.

set.seed(1234)
ind <- sample(2, nrow(iris), replace = TRUE, prob = c(0.7,0.3))
ind # 70% samples rows go as Trg, 
trainData <- iris[ind == 1,]
testData <- iris[ind == 2,]
head(trainData) ; head(testData)

library(party) #loads the library party
myFormula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length  + Petal.Width
iris_tree <- ctree(myFormula, data = trainData) #ctree is a part of party package
table(predict(iris_tree), trainData$Species)

#setosa class are correctly classified. 3 objects belonging to versicolor class are classified as virginica and 1 object belonging to virgnica class has been classified as versicolor. 
#These values can differ depending on the seed that you had set previously

print(iris_tree) #To see how plain text version of our classification tree 
plot(iris_tree) #To see the plot of our tree

plot(iris_tree, type = "simple")
# for a condensed decision tree n = 19 y = (0, 0.842, 0.158) means that this node contains 19 training instances
#and they belong to versicolor with a # probability of 0.842

testPred <- predict (iris_tree, newdata = testData)
# Predicts the class name of the testData instances
table(testPred, testData$Species)
# Plots the predicted instances class against actual classes

# Most of the algorithm work similarly. Most of them involve following steps:
#   
# Find split location(attribute) in the dataset that minimizes a certain measure of node impurity.
# Repeat the above step on rest of the attributes until no attribute is left on which split can be achieved or model provides required amount of accuracy on unseen data.
# It is not necessary to use all the attributes of objects in making a decision tree, sometimes less number of attribute tests can also provide satisfactory results.
# Measures of node impurity:
#   
# Entropy = -\sum_{i=0}^{c-1}p(i|t)\log_{2}p(i|t)
# Gini = 1-\sum_{i=0}^{c-1}[p(i|t)]^{2}
# Classification error = 1-max_{i}[p(i|t)]




================================================
FILE: 56-ctree/dt-ctree-playYes.R
================================================
#Classification - Decision Trees - Distances
#ctree

play = read.csv('./data/clsplay.csv', row.names=1)
credit = read.csv('./data/clscredit.csv')
play
str(play)
credit
names(play)
# Case-1 : Play
library(party) 
ctc = ctree_control(minsplit = 1, minbucket = 1)
treeplay <- ctree(play ~ outlook + temperature + humidity + wind, data = play,controls = ctc )

?ctree
treeplay
plot(treeplay)
predict(treeplay, )

table(predict(treeplay), play$play)
table(predict(iris_tree), trainData$Species)

#working Example
ls <- data.frame(y = gl(3, 50, labels = c("A", "B", "C")),
      x1 = rnorm(150) + rep(c(1, 0, 0), c(50, 50, 50)),
       x2 = runif(150))
ls
names(ls)
ct= ctree(y ~ x1 + x2, data = ls)
plot(ct)
predict(ct, ls)  #predict for training data
predict(ct, newdata = ls[c(1, 51, 101),], type = "prob")
predict(ct, newdata = ls[c(1, 51, 101),], type = "response")
predict(ct, newdata = ls[c(1, 51, 101),], type = "node")


data("airquality", package = "datasets")
airq <- subset(airquality, !is.na(Ozone))
(airctparty <- party::ctree(Ozone ~ ., data = airq,
   controls = party::ctree_control(maxsurrogate = 3)))
plot(airctparty)
mean((airq$Ozone - predict(airctparty))^2)
?ctree


================================================
FILE: 57-GLM/Logr-party.R
================================================
#Log Regression
#https://visualstudiomagazine.com/articles/2016/12/01/logistic-regression-using-r.aspx
#
Age <- c(1,5,3,2,6,3,7,4,2,4)
Edu <- c(4,8,7,5,7,2,5,5,3,7)
Party <- c("red","red","red","red","red", "blue","blue","blue","blue","blue")
mydf <- data.frame(Age,Edu,Party)
mydf$Party <- factor(mydf$Party, levels=c("red","blue"))
mydf

plot(mydf$Age, mydf$Edu, xlim=c(0,9), ylim=c(0,9),
     xaxs="i", yaxs="i", col=c("red","blue")[mydf$Party],pch=20, cex=2)

axis(side=1, at=c(0:9))
axis(side=2, at=c(0:9))
abline(h=0:9,v=0:9, col="gray", lty=3)

mymodel = glm(Party ~ Age + Edu, data=mydf, family="binomial")
summary(mymodel)
age=1; edu=4
(z = 3.5566 + (0.9939 * 1) + (-1.3191 * 4)) #  -0.7259
(z = 3.5566 + 0.9939 * age - 1.3191 * edu)
(p = 1 / (1 + exp(-z)))  #0.3261
contrasts(mydf$Party)  # blue=1
#If p > 0.5, the predicted value is the second possible value, "blue

predict(mymodel, mydf, type="response")


if (p <= 0.5) { 
  cat("predicted party = red \n") 
  } else {
  cat("predicted party = blue \n")
  }
#predicted party = red
#2nd way to predict
b0 = mymodel$coefficients[[1]]
b1 = mymodel$coefficients[[2]]
b2 = mymodel$coefficients[[3]]
(z = b0 + (b1 * 1) + (b2 * 4))
(p = 1 / (1 + exp(-z)))

#3rd way to predict
nd = with(mydf, data.frame(Age=1, Edu=4))
nd
nd$pred = predict(mymodel, newdata=nd, type="response")
nd


================================================
FILE: 57-GLM/crossfold.R
================================================
# Cross Validation using Auto dataset

split(mtcars[,1:4] , f = rep_len(1:4, nrow(mtcars) ) )

library(ISLR)
library(boot)
ISLR::Auto
attach(Auto)
(model1 = glm(mpg ~ horsepower, data =Auto))
(mse_loocv = cv.glm(Auto, model1))

(model2 = lm(mpg ~ horsepower, data=Auto))
(mse_fold = cv.glm(Auto, model1, K=3))

library(DAAG)
mydata=women
(fit = lm(weight ~ height, data=mydata))
plot( mydata$height , mydata$weight)
DAAG::CVlm(data=mydata, form.lm=formula(
       weight ~ height), m=3, plotit="Observed" )


#Eg2
library(DAAG)
mydata=Auto
(fit = lm(mpg ~ horsepower, data=mydata))
plot( y=mydata$horsepower , mydata$mpg)
nrow(Auto)
DAAG::CVlm(data=mydata, form.lm=fit, m=3, plotit="Observed" )



================================================
FILE: 57-GLM/crossval1.R
================================================
# load the libraries
library(caret)
library(klaR)
# load the iris dataset
data(iris)
# define an 80%/20% train/test split of the dataset
split=0.80
trainIndex <- createDataPartition(iris$Species, p=split, list=FALSE)
data_train <- iris[ trainIndex,]
data_test <- iris[-trainIndex,]
# train a naive bayes model
model <- NaiveBayes(Species~., data=data_train)
# make predictions
x_test <- data_test[,1:4]
y_test <- data_test[,5]
predictions <- predict(model, x_test)
# summarize results
confusionMatrix(predictions$class, y_test)


# load the library

# load the library
library(caret)
# load the iris dataset
data(iris)
# define training control
train_control <- trainControl(method="cv", number=10)
# fix the parameters of the algorithm
grid <- expand.grid(.fL=c(0), .usekernel=c(FALSE))
# train the model
model <- train(Species~., data=iris, trControl=train_control, method="nb", tuneGrid=grid)
# summarize results
print(model)

ated k-fold Cross Validation in RR

# load the library
library(caret)
# load the iris dataset
data(iris)
# define training control
train_control <- trainControl(method="repeatedcv", number=10, repeats=3)
# train the model
model <- train(Species~., data=iris, trControl=train_control, method="nb")
# summarize results
print(model)
1
2
3
4
5
6
7
8
9
10

# load the library
library(caret)
# load the iris dataset
data(iris)
# define training control
train_control <- trainControl(method="repeatedcv", number=10, repeats=3)
# train the model
model <- train(Species~., data=iris, trControl=train_control, method="nb")
# summarize results
print(model)


================================================
FILE: 57-GLM/cv-houseprices.R
================================================
#CV - houseprices
#Eg1
str(houseprices)
?houseprices
(fit = lm(sale.price ~ ., data=houseprices))
summary(fit)
attr(fit,"ms")

(fit1 <- lm(sale.price ~ area, data=houseprices))
(cv1 <- DAAG::CVlm(houseprices, fit1, plotit=TRUE, m=4))
attr(cv1, "ms")
anova(fit1)
summary(fit1)
(673 + 1849 + 6070 + 6287)/4

(fit2 <- lm(sale.price ~ bedrooms, data=houseprices))
(cv2 <- DAAG::CVlm(houseprices, fit2, plotit=TRUE, m=4))
attr(cv2, "ms")
anova(fit2)
summary(fit2)
(40 + 954 + 2781 + 4563)/4


================================================
FILE: 57-GLM/cv-women1.R
================================================
# CV using women dataset

women

mydata=women

(fit1 = lm(weight ~ height, data=mydata))
summary(fit1)
(RSS <- c(crossprod(fit1$residuals)))
(MSE <- RSS / length(fit1$residuals))
(RMSE <- sqrt(MSE))
(sig2 <- RSS / fit1$df.residual)
predict(fit1)
library(DAAG)
(fit1 = lm(weight ~ height, data=mydata))

(cvfit1 = DAAG::CVlm(data=mydata, form.lm=lm(weight ~ height,
              data=mydata), m=3, plotit="Observed" ))
predict(fit1)
attributes(cvfit1a)
(76.9 + 8.29 + 15.9)/15

cvfit1a$cvpred
women$height #58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
women$weight #115 117 120 123 126 129 132 135 139 142 146 150 154 159 164


# Model2

(fit2 = lm(weight ~ I(height^2), data=mydata))
summary(fit2)

(RSS <- c(crossprod(fit2$residuals)))
(MSE <- RSS / length(fit2$residuals))
(RMSE <- sqrt(MSE))
(sig2 <- RSS / fit2$df.residual)
predict(fit2)
library(DAAG)

(fit2 = lm(weight ~ I(height^2), data=mydata))
summary(fit2)
#(cvfit2a = DAAG::CVlm(data=mydata, form.lm=fit2 ))
(cvfit2a = DAAG::CVlm(data=mydata, form.lm=lm(weight ~ height + I(height^2), data=mydata),m=3, dots = TRUE, seed=29, plotit=TRUE, printit=TRUE))
(cvfit2a = DAAG::CVlm(data=mydata, form.lm=fit2, dots = TRUE, seed=29, plotit=TRUE, printit=TRUE))


(15.4 + 1.66 + 3.88 )/3
summary(cvfit2a)
cvfit2a$cvpred


s1 = caTools::sample.split(mydata, SplitRatio = .75)
s1
df=mydata
stopifnot(nrow(df) %% N == 0)
df    <- df[order(runif(nrow(df))), ]
bins  <- rep(1:N, nrow(df) / N)
split(df, bins)
N= nrow(df)
split(df, sample(1:N, nrow(df), replace=T))


n <- 3
nr <- nrow(df)
nr
split1 = split(df, rep(1:ceiling(nr/n), each=n, length.out=nr))
split1
split1$`1`
(fit1 = lm(weight ~ height, data=split1$`1`))
summary(fit1)


CVres1= c(-0.154, 1.9,2.8, 4.69, 6.59)
CVres1^2
sum(CVres1^2)
sum(CVres1^2)/5



================================================
FILE: 57-GLM/cv3.R
================================================
library(gRapfa)
data(Wheeze)
gRapfa::cross.validate(Data=Wheeze, K = 10, crit = NULL, beagle = TRUE, dir='')

library(adabag)
data(churn)
trainset = churn[1:100,]
churn.baggingcv = bagging.cv(churn ~ .,
                             v=10, data=trainset, mfinal=10)
churn.baggingcv$confusion

churn.baggingcv$error


================================================
FILE: 57-GLM/cvlm2.R
================================================

library(caret)

data(diamonds)
?diamonds

mydata = diamonds
model <- train(
  price ~ ., mydata,
  method = "lm",
  trControl = trainControl(
    method = "cv", number = 10,
    verboseIter = TRUE
  )
)
summary(model)

fit = lm(price ~ . , data=mydata)
summary(fit)
AIC(model,fit)



library(caret)
# load the iris dataset
data(iris)
# define training control
train_control <- trainControl(method="cv", number=10)
# fix the parameters of the algorithm
grid <- expand.grid(.fL=c(0), .usekernel=c(FALSE))
grid
?expand.grid
# train the model
model <- train(Species~., data=iris, trControl=train_control, method="nb", tuneGrid=grid)
# summarize results
print(model)


library(DAAG)
houseprices
cv.lm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
      m = 3, dots = FALSE, seed = 29, plotit = c("Observed","Residual"),
      main="Small symbols show cross-validation predicted values",
      legend.pos="topleft", printit = TRUE)
CVlm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
    m = 3, dots = FALSE, seed = 29, plotit = c("Observed","Residual"),
    main="Small symbols show cross-validation predicted values",
    legend.pos="topleft", printit = TRUE)



CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
     plotit="Observed")
CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
     plotit="Residual")
out <- CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
            plotit="Observed")
out
out[c("ms","df")]

mydata=women
str(mydata)

fit = lm(weight ~ height, data=mydata)
plot( mydata$height , mydata$weight)
abline(fit)
summary(fit)

CV1 =CVlm(data=mydata, m=3, form.lm=formula(weight ~ height), plotit="Observed" )

CV2 = CVlm(data=mydata, m=4, form.lm=formula(weight ~ height), plotit="Observed" )

attributes(CV2)$ms


================================================
FILE: 57-GLM/glm-affairs1.R
================================================
#Affairs,
#https://github.com/marianov24/RAffairsDataAnalysis/blob/master/Affairs_George_Maria.Rmd

# Loading all the required libraries
library("dplyr")
library("ggplot2")
library("car") # Contains the scatterplotMatrix function
#install.packages("boot")
library("boot") # Perform crossvalidation
#install.packages("tree")
library("tree")
library("randomForest")
library(pROC) # Useful for computing and plotting classifer metrics

# install.packages("AER")
library(AER) # Contains Affairs dataset 

# Loading the Affairs dataset of AER package and saving it into a local variable
data("Affairs")
Affairs.data <- Affairs

# Displaying the first few rows of the dataset
head(Affairs.data)
# Displaying the summary of the Affairs.data dataset 
summary(Affairs.data)
# Displaying the structure of the Affairs.data dataset 
str(Affairs.data)

# Finding the proportion of male and female respondents
Affairs.data %>%  
  group_by(gender) %>% 
  summarise(total_participants = n()) %>% 
  ungroup() %>%
  mutate(prop_gender = total_participants/sum(total_participants))

# Plotting the frequency of participants based on gender
ggplot(Affairs.data, aes(gender))  + geom_bar(width = 0.2) +
  xlab("Gender") + ylab("Frequency") + 
  ggtitle("Number of participants based on gender")

# Calculating the average age of the participants
Affairs.data %>%  
  summarise(avg_age = mean(age, na.rm = TRUE))

# Plotting the frequency of participants based on occupation
ggplot(Affairs.data, aes(occupation)) + geom_bar(width = 0.2) +
  xlab("Occupation") + ylab("Frequency") + 
  ggtitle("Number of participants based on occupation") +
  scale_x_continuous(breaks=c(1, 2, 3, 4, 5, 6, 7), 
                     labels=c("Class 1", "Class 2", "Class 3", "Class 4",
                              "Class 5", "Class 6", "Class 7"))


# Plotting the frequency of participants based on education
ggplot(Affairs.data, aes(education)) + geom_bar(width = 0.2) +
  xlab("Education") + ylab("Frequency") + 
  ggtitle("Number of participants based on education") +
  scale_x_continuous(breaks=c(9, 12, 14, 16, 17, 18, 20), 
                     labels=c("Grade School", "High School Graduate", 
                              "Some college", "College graduate",
                              "Some graduate work", "Masters degree",
                              "Advanced Degree")) + coord_flip()


# Creating a binary variable haveaffair denoting whether a participant have
# an affair or not
# If the number of affairs are greater than 0, binary variable is set to 1
Affairs.data$haveaffair[Affairs.data$affairs  > 0] <- 1
# If the number of affairs is equal to 0, binary variable is set to 0
Affairs.data$haveaffair[Affairs.data$affairs == 0] <- 0

# Converting the binary response variable to factor datatype with labels 
# No and Yes for levels 0 and 1 respectively.
Affairs$haveaffair <- factor(Affairs.data$haveaffair,
                             levels=c(0,1),
                             labels=c("No","Yes"))

# Displaying the count of the new binary variable
table(Affairs$haveaffair)

#Fitting a logistic regression model twith haveaffair as the response variable
# and all other personal characteristics as predictor variables
fit.allpredictors <- glm(haveaffair ~ gender + age + yearsmarried + children +
                           religiousness + education + occupation +rating,
                         data=Affairs.data,family=binomial())

# Loading the bestglm package
#install.packages("bestglm")
library("bestglm")

# Creating a new column y, response variable to fit bestglm
Affairs.data$y <- Affairs.data$haveaffair
# Rearranging the columns of Affairs.data dataset to fit bestglm
Affairs.for.bestglm <- Affairs.data[,c("gender","age","yearsmarried","children",
                                       "religiousness", "education",
                                       "occupation", "rating", "y")]

# Using bestglm to perform subset model selection 
set.seed(1)
fit.reduced <- bestglm(Affairs.for.bestglm, family = binomial,
                       method = "exhaustive")

# Displaying the summary statistic of the Best Model
fit.reduced$BestModel

# Displaying the summary statistics of the fitted model.
summary(fit.allpredictors)


# Creating an artificial test dataset
testdata <- data.frame(yearsmarried=mean(Affairs.data$yearsmarried),
                       religiousness=mean(Affairs.data$religiousness),
                       rating=c(1, 2, 3, 4, 5))

# Creating a new column prob to the test data containing the predicted 
# probabilities
testdata$prob <- predict(fit.reduced$BestModel, testdata, type="response")
# Displaying the test data
testdata

# Plotting rating of marriage vs probability of having an affair
ggplot(testdata, aes(rating, prob)) + geom_point() + geom_line() +
  xlab("Rating") + ylab("Probabilty of having an affair") + 
  ggtitle("Rating of marriage vs probability of having an affair") +
  scale_x_continuous(breaks=c(1, 2, 3, 4, 5), 
    labels=c("Very unhappy", "Somewhat unhappy",
    "Average", "Happier than avaerage", "Very happy"))




================================================
FILE: 57-GLM/glm-affairs2.R
================================================
#Log Regression
#
library(AER)
data(Affairs)
data(Affairs, package='AER')
mydata=Affairs
summary(mydata)
dim(mydata)
x=mydata$affairs
table(x)
round(prop.table(table(x)),3)
round(prop.table(table(x))*100,1)
x=mydata$gender
round(prop.table(table(x))*100)

x=mydata$children
round(prop.table(table(x))*100)

median(mydata$age)

# Convert No of Affairs - Yes/ No
mydata$ynaffair[mydata$affairs > 0 ] = 1
mydata$ynaffair[mydata$affairs == 0 ] = 0
mydata$ynaffair = factor(mydata$ynaffair,
          levels=c(0,1), labels=c('No','Yes'))
table(mydata$ynaffair)

# ready for Log Regr
fit.full = glm(ynaffair ~ gender + age + yearsmarried + children + religiousness + education + occupation + rating, data=mydata, family=binomial())
summary(fit.full)

#Modified Model
fit.reduced = glm(ynaffair ~ age + yearsmarried + religiousness
               + rating, data=mydata, family=binomial())
summary(fit.reduced)

#Compare
anova(fit.reduced, fit.full, test='Chisq')


coef(fit.reduced)
exp(coef(fit.reduced))

exp(confint(fit.reduced))

1.106^10

testdata = data.frame(rating=c(1,2,3,4,5), age=mean(mydata$age),
              yearsmarried=mean(mydata$yearsmarried), 
              religiousness = mean(mydata$religiousness))
testdata

testdata$predict = predict(fit.reduced, newdata=testdata, type='response')
testdata

#Age varies
testdata = data.frame(rating=mean(mydata$rating), age=seq(17,57,10),
                      yearsmarried=mean(mydata$yearsmarried), 
                      religiousness = mean(mydata$religiousness))
testdata

testdata$predict = predict(fit.reduced, newdata=testdata, type='response')
testdata

# Overdispersion

deviance(fit.reduced)/ df.residual(fit.reduced)

fit.b = glm(ynaffair ~ age + yearsmarried + religiousness
                  + rating, data=mydata, family=binomial())
fit.qb= glm(ynaffair ~ age + yearsmarried + religiousness
                  + rating, data=mydata, family=quasibinomial())
pchisq(summary(fit.qb)$dispersion * fit.b$df.residual, fit.b$df.residual, lower=F)



================================================
FILE: 57-GLM/glm-cars.R
================================================
# CV - cars

plot(speed~dist, data=cars, main = "Cars" , xlab = "Stopping
Distance", ylab = "Speed")

#Step-2
glm.fit = glm(speed~dist, data=cars)
degree=1:5
cv.error5=rep(0,5)
for(d in degree){ 
  glm.fit = glm(speed~poly(dist, d), data=cars) 
  cv.error5[d] = cv.glm(cars,glm.fit,K=5)$delta[1]
}
glm.fit
par(mfrow=c(2,2))
plot(glm.fit)
cv.error5
plot(cv.error5)
lines(cv.error5)
par(mfrow=c(1,1))
plot(speed~dist, data=cars, main = "Cars" , xlab
     = "Stopping Distance", ylab = "Speed")


================================================
FILE: 57-GLM/glm-titanic1.R
================================================
# R Bloggers Site
#Log Regression 2
training.data.raw <- read.csv('./../../rDataSets/titanic/train.csv',header=T,na.strings=c(""))

sapply(training.data.raw,function(x) sum(is.na(x)))

sapply(training.data.raw, function(x) length(unique(x)))

library(Amelia)
Amelia::missmap(training.data.raw, main = "Missing values vs observed")

data <- subset(training.data.raw,select=c(2,3,5,6,7,8,10,12))

data$Age[is.na(data$Age)] <- mean(data$Age,na.rm=T)

is.factor(data$Sex)
is.factor(data$Embarked)
unique(data$Sex)
contrasts(data$Sex)

unique(data$Embarked) 
contrasts(data$Embarked)

data <- data[!is.na(data$Embarked),]
rownames(data) <- NULL

# Split the data in order
train <- data[1:800,]
test <- data[801:889,]

model <- glm(Survived ~.,family=binomial(link='logit'),data=train)
log(2e-16)
exp(5.45e-06)
summary(model)

anova(model, test="Chisq")


================================================
FILE: 57-GLM/logR1.R
================================================
#Clear All
rm(list=ls())

# Load libraries
library(datasets)
library(caTools)
library(nnet)

# Load the dataset
dataset <- iris 
#Structure of the data
str(dataset)

#Split data into train and test
set.seed(123)
split <- sample.split(dataset$Species,SplitRatio = 0.80)
split
train <- subset(dataset,split==TRUE)
test <- subset(dataset,split==FALSE)
dim(dataset)
dim(train)
dim(test)

# Use logistic regression and the one-vs-all strategy to build a classification model.
multinomModel <- multinom(Species~.,data=train)
summary (multinomModel) # model summary

# Predict on Test Data
(predicted_scores <- predict (multinomModel, test, "class")) # predict on new data

predicted_scores <- predict (multinomModel, test, "probs") # predict on new data
predicted_scores
predictions <- apply(predicted_scores, 1, which.max)
predictions
names(test)[apply(predicted_scores, 1, which.max)]
predictions[which(predictions=="1")] <- unique(dataset$Species)[1]
predictions
unique(dataset$Species)[1]
predictions[which(predictions=="2")] <- unique(dataset$Species)[2]
predictions[which(predictions=="3")] <- unique(dataset$Species)[3]
test$prediction <- predictions
test$prediction
test$Species
# summarize accuracy
table(test$prediction, test$Species)
(10 + 9 + 10)/((10 + 9 + 10 + 1))


df <- data.frame(a = rnorm(5), b = rnorm(5), c = rnorm(5), x = rnorm(5), y = rnorm(5), z = rnorm(5))
df
df2 = df[,4:6]
df2
names(df2)[apply(df2, 1, which.max)]


================================================
FILE: 57-GLM/logpos1.R
================================================
# Log Regression - Poission
#
data(breslow.dat, package='robust')
names(breslow.dat)
mydata = breslow.dat
summary(mydata)
summary(mydata[c(6,7,8,10)])

opar = par(no.readonly = TRUE)
par(mfrow=c(1,2))
attach(mydata)
hist(sumY, breaks=20, xlab='Seizure Count', main='Distribution of Seizures')
boxplot(sumY  ~ Trt, xlab='Treatment', main='Group Comparisons')
par(opar)


fit = glm(sumY ~ Base + Age + Trt, data=mydata, family=poisson())
summary(fit)


coef(fit)
exp(coef(fit))

deviance(fit)/ df.residual(fit)
qcc::qcc.overdispersion.test(   mydata$sumY, type='poisson')

fit.od = glm(sumY ~ Base +
               Age + Trt, data = mydata,
             family = quasipoisson() )
summary(fit)


================================================
FILE: 57-GLM/logr-mtcars.R
================================================
# Logistic Regression

# Contingency Table for Tx of Cars
data(mtcars)
?mtcars
(t= table(mtcars$am))
(amt = addmargins(t))
(ppt = prop.table(t))
p1 = 19/32   #automatic Probability
# Prob that am=Manual
(p0a = 1 - 19/32 )
# Odds Ratio
(p1 = 19/13 )
(p2 = 13/19 )

#glm
attach(mtcars)
(base = glm(am ~ 1, data=mtcars, family = binomial()))
summary(base)
anova(base)

#Fit 1
(fit1 = glm(am ~ mpg + disp + hp + wt, data
            = mtcars, family = binomial() ))
summary(fit1)
anova(fit1)

layout(matrix(c(1,2,3,4), 2,2))
plot(fit1)


#Fit2
(fit2 = glm(am ~ mpg + hp + wt, data
            = mtcars, family = binomial() ))
summary(fit2)

AIC(fit1, fit2)

anova(fit2)
layout(matrix(c(1,2,3,4), 2,2))
plot(fit2)

anova(fit1, fit2)

#Fit3
(fit3 = glm(am ~ hp + wt, data=mtcars, family = binomial()))
summary(fit3)
anova(fit3)
plot(fit3)
anova(fit2, fit3)
AIC(fit2,fit3)


#Final Model
newdata = data.frame(wt=2, hp=150)
predict(fit3, newdata, type='response')


================================================
FILE: 57-GLM/logr-mtcars1.R
================================================
# Logistic Regression - mtcars

(base = glm(am ~ 1, data=mtcars,family = binomial()))
exp(-0.3795)
(odds = exp(-0.3795))
anova(base)
(fit1 = glm(am ~ mpg + disp + hp + wt, data = mtcars, family = binomial() ))
fit1
summary(fit1)
par(mar=c(1,1,1,1))
layout(matrix(c(1,2,3,4), 2,2))
plot(fit1)


(fit2 = glm(am ~ mpg + hp + wt, data = mtcars,
            family = binomial() ))
fit2


(fit3 = glm(am ~ hp + wt, data=mtcars,  family = binomial()))

exp(8.124)
(p = exp(8.124)/ (1 + exp(8.124)))


newdata =  data.frame(wt=2.780, hp=109)
newdata =  data.frame(wt=3.845, hp=175)

predict(fit3, newdata = newdata, type='response')
newdata =  data.frame(wt=mtcars$wt, hp=mtcars$hp)
p1=predict(fit3, newdata = newdata, type='response')


(p1 = predict(fit3, newdata = mtcars[c('wt','hp')], type='response'))
(p1= round(p1,2))
(p1a =ifelse(p1 > 0.5, 1, 0))
cbind(mtcars$am, mtcars$wt, mtcars$hp, p1, p1a)
(xtab = table(mtcars$am, p1a))
caret::confusionMatrix(xtab)

df= mtcars
#Manually calc probabilities
calcp = function(df, x1, x2) {
df$y = 18.86 + (0.0362 * df[,x1]) - (8.083 * df[,x2])
df$p = exp(p)/ (1 + exp(p))
#print(p)
#return(y)
}
calcp('df', 'hp','wt')
df
cbind(p1,p2)


d <- data.frame(hp=mtcars$hp, wt=mtcars$wt)
d
testing <- function (d, x1,x2) {
  d$y = 18.86 + (0.0362 * d[[x1]]) - (8.083 * df[[x2]])
  d$p = exp(d$y)/ (1 + exp(d$y))
  #return (d[[p]])
  d$p
}

print (testing(d, 'hp','wt')) 
d


================================================
FILE: 57-GLM/logrMaths.R
================================================
# General Logistic Regression
# Subscribe y ~ age
constant = -26.52
agecoeff = 0.78
age = c(35,36,25,26,45, 46)
(y = constant * 1 + agecoeff * age)
round(y,2)
(p = exp(y) / ((exp(y) + 1)))
round(p,3)
round(diff(p),3)


# Subscribe y ~ age  + woman
# 
constant = -26.46
agecoeff = 0.787
womancoeff = -0.55
age = c(35,36,36,36)
gender = c('F','M','F','M')
woman = c(1,0,1,0)
(y = constant * 1 + agecoeff * age + womancoeff * woman)

round(y,3)
(p = exp(y) / ((exp(y) + 1)))
round(p,3)
round(diff(p),3)


================================================
FILE: 57-GLM/logreg-iris1.R
================================================
#Logistic Regression using Iris Dataset
#06 Nov 17
#http://rstudio-pubs-static.s3.amazonaws.com/230133_fb9cb3c35ca345d0b9e5726ed3f45a21.html
library(datasets)
ir_data<- iris
head(ir_data)
str(ir_data)
levels(ir_data$Species)
sum(is.na(ir_data))

ir_data<-ir_data[1:100,]
head(ir_data)
unique(ir_data$Species)

set.seed(100)
samp<-sample(1:100,80)
ir_test<-ir_data[samp,]
ir_ctrl<-ir_data[-samp,]

library(ggplot2); library(GGally)
pairs(ir_test)
ggpairs(ir_test)

# Model Fitting
y<-ir_test$Species; x<-ir_test$Sepal.Length
glfit<-glm(y~x, family = 'binomial')

summary(glfit)

newdata<- data.frame(x=ir_ctrl$Sepal.Length)
predicted_val<-predict(glfit, newdata, type="response")
prediction<-data.frame(ir_ctrl$Sepal.Length, ir_ctrl$Species,predicted_val)
prediction

qplot(prediction[,1], round(prediction[,3]), col=prediction[,2], xlab = 'Sepal Length', ylab =
        'Prediction using Logistic Reg.')




# Other Examples on IRIS
data(iris)
fit <- lm(Petal.Width ~ Petal.Length, data=iris)
class(fit)
methods(class=class(fit))
summary(fit) # show results

coefficients(fit) # model coefficients
predict(fit) # fitted predictions
predict(fit, newdata=data.frame(Petal.Length=seq(1, 2, by=0.1)))
confint(fit, level=0.95) # CIs for model parameters 
fitted(fit) # predicted values
residuals(fit) # residuals
anova(fit) # anova table 
influence(fit) # regression diagnostics

par(mfrow=c(2,2))
par(mar=c(2,2,2,2))
plot(fit)


#Multiple Regression
fit2 <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width, data=iris)
summary(fit2) # show results
anova(fit, fit2)

fit2int <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width + Petal.Length:Sepal.Length, data=iris)
anova(fit2, fit2int)

tail(model.matrix(~ Species, data=iris))
(model.matrix(~ Species, data=iris))
tail(iris)

fit3 <- lm(Petal.Width ~ Petal.Length + Sepal.Length + Sepal.Width + Species, data=iris)
summary(fit3)

anova(fit2, fit3)

library(xtable)
print(xtable(fit3), type="html")


iris2 <- iris
iris2$virginica <- iris$Species == "virginica"
fit4 <- glm(virginica ~ Petal.Width + Petal.Length + Sepal.Length + Sepal.Width, data=iris2, family=binomial)

plot(fit4)


fit5 <- glm(round(Petal.Width) ~ Petal.Length + Sepal.Length + Sepal.Width, data=iris2, family=poisson)
plot(fit5)

summary(fit5)

#Plotting a linear regression with confidence and prediction intervals.

plot(Petal.Width ~ Petal.Length, col=c("black", "red", "blue")[Species], pch=(15:17)[Species], xlab="Petal Length (cm)", ylab="Petal Width (cm)", data=iris)
newx <- data.frame(Petal.Length=seq(min(iris$Petal.Length), max(iris$Petal.Length), length.out=100))
conf.interval <- predict(fit, newdata=newx, interval="confidence")
pred.interval <- predict(fit, newdata=newx, interval="prediction")
lines(conf.interval[, "fit"] ~ newx[, 1], lty=1, lw=3)
lines(conf.interval[, "lwr"] ~ newx[, 1], lty=2)
lines(conf.interval[, "upr"] ~ newx[, 1], lty=2)
lines(pred.interval[, "lwr"] ~ newx[, 1], lty=3)
lines(pred.interval[, "upr"] ~ newx[, 1], lty=3)
legend("topleft", legend=c(levels(iris$Species), "CI", "PI"), col=c("black", "red", "blue", "black", "black"), pch=c(15:17, -1, -1), lty=c(-1, -1, -1, 2, 3))



#Multi Class https://stackoverflow.com/questions/22009871/how-to-perform-multi-class-classification-using-svm-of-e1071-package-in-r
#
# install.packages( 'e1071' )
library( 'e1071' )
data( iris )
model <- svm( iris$Species~., iris )
summary(model)
res <- predict( model, newdata=iris )
res
head(cbind(res, iris))


================================================
FILE: 57-GLM/multinominal.R
================================================
#Multinominal Regression

require(foreign)
require(nnet)
require(ggplot2)
require(reshape2)
#Eg1
ml <- read.dta("https://stats.idre.ucla.edu/stat/data/hsbdemo.dta")
str(ml)
with(ml, table(ses, prog))
with(ml, do.call(rbind, tapply(write, prog, function(x) c(M = mean(x), SD = sd(x)))))
tapply(ml$write, ml$prog, function(x) c(M = mean(x), SD = sd(x)))

ml$prog2 <- relevel(ml$prog, ref = "academic")
unique(ml$prog2)
test <- multinom(prog2 ~ ses + write, data = ml)       

summary(test)       

z <- summary(test)$coefficients/summary(test)$standard.errors
z

# 2-tailed z test
p <- (1 - pnorm(abs(z), 0, 1)) * 2
p

## extract the coefficients from the model and exponentiate
exp(coef(test))

head(pp <- fitted(test))

dses <- data.frame(ses = c("low", "middle", "high"), write = mean(ml$write))
predict(test, newdata = dses, "probs")

dwrite <- data.frame(ses = rep(c("low", "middle", "high"), each = 41), write = rep(c(30:70),
                                                                                   3))
head(dwrite)

## store the predicted probabilities for each value of ses and write
pp.write <- cbind(dwrite, predict(test, newdata = dwrite, type = "probs", se = TRUE))

## calculate the mean probabilities within each level of ses
by(pp.write[, 3:5], pp.write$ses, colMeans)


## melt data set to long for ggplot2
lpp <- melt(pp.write, id.vars = c("ses", "write"), value.name = "probability")
head(lpp)  # view first few rows


## plot predicted probabilities across write values for each level of ses
## facetted by program type
ggplot(lpp, aes(x = write, y = probability, colour = ses)) + geom_line() + facet_grid(variable ~
                                                                                        ., scales = "free")


================================================
FILE: 57-GLM/multinominal2.R
================================================
#Multinominal 2

#https://www.princeton.edu/~otorres/LogitR101.pdf
#
# Loading the required packages
library(foreign)
library(nnet)
library(stargazer)

# Getting the sample data from UCLA
#mydata = read.dta("http://www.ats.ucla.edu/stat/data/hsb2.dta")
mydata <- read.dta("https://stats.idre.ucla.edu/stat/data/hsbdemo.dta")

# Checking the output (dependent) variable
table(mydata$ses)


# By default the first category is the reference.
# To change it so ‘middle’ is the reference type
mydata$ses2 = relevel(mydata$ses, ref = "middle")
unique(mydata$ses2)

# Running the multinomial logit model using the multinom() function
multi1 = multinom(ses2 ~ science + socst + female, data=mydata)

summary(multi1)

library(stargazer)
stargazer(multi1, type="html", out="multi1.htm")
stargazer(multi1, type="text",out="multi1.txt")
multi1.rrr = exp(coef(multi1))
multi1.rrr


library(stargazer)
stargazer(multi1, type="html", coef=list(multi1.rrr), p.auto=FALSE, out="multi1rrr.htm")


allmean <- data.frame(science=rep(mean(mydata$science),2),
                      socst=rep(mean(mydata$socst),2),
                      female = c("male","female"))
allmean

allmean[, c("pred.prob")] <- predict(multi1, newdata=allmean, type="probs")

allmean

allmean[, c("pred.prob")] <- predict(multi1, newdata=allmean, type="class")
allmean


================================================
FILE: 57-GLM/multinominal3.R
================================================
#multinomial3
#https://www.r-bloggers.com/how-to-multinomial-regression-models-in-r/
#
n <- 1000
df1 <- data.frame(x1=runif(n,0,100),x2=runif(n,0,100))
head(df1)
df1 <- transform(df1, y=1+ifelse(100 - x1 - x2 + rnorm(n,sd=10) < 0, 0,
       ifelse(100 - 2*x2 + rnorm(n,sd=10) < 0, 1, 2)),   set="Original")
head(df1)
df1
# Load the neural network package and fit the model
library(nnet)
mod <- multinom(y ~ x1 + x2, df1)

predict(mod)

predict(mod,df1,"probs")


# Function to predict multinomial logit choice model outcomes
# model = nnet class multinomial model
# newdata = data frame containing new values to predict
predictMNL <- function(model, newdata) {
  
  # Only works for neural network models
  if (is.element("nnet",class(model))) {
    # Calculate the individual and cumulative probabilities
    probs <- predict(model,newdata,"probs")
    cum.probs <- t(apply(probs,1,cumsum))
    
    # Draw random values
    vals <- runif(nrow(newdata))
    
    # Join cumulative probabilities and random draws
    tmp <- cbind(cum.probs,vals)
    
    # For each row, get choice index.
    k <- ncol(probs)
    ids <- 1 + apply(tmp,1,function(x) length(which(x[1:k] < x[k+1])))
    
    # Return the values
    return(ids)
  }
}


# Make up some new data
n <- 200
df <- data.frame(x1=runif(n,0,100),
                 x2=runif(n,0,100),
                 set="Model")
y2 <- predictMNL(mod,df)
df2 <- cbind(df,y=y2)



================================================
FILE: 57-GLM/multinominal4.R
================================================
#multinominal 4
#https://stats.stackexchange.com/questions/9715/how-to-set-up-and-estimate-a-multinomial-logit-model-in-r
#
library("nnet")
data("Fishing", package = "mlogit")
fishing.mu <- multinom(mode ~ income, data = Fishing)
sum.fishing <- summary(fishing.mu) # gives a table of outcomes by covariates for coef and SE
str(sum.fishing)
# now get the p values by first getting the t values
pt(abs(sum.fishing$coefficients / sum.fishing$standard.errors),
   df=nrow(Fishing)-6,lower=FALSE)


================================================
FILE: 57-GLM/multinominal5.R
================================================
#Clear All
rm(list=ls())

# Load libraries
library(datasets)
library(caTools)
library(nnet)

# Load the dataset
dataset <- iris 
#Structure of the data
str(dataset)

#Split data into train and test
set.seed(123)
split <- sample.split(dataset$Species,SplitRatio = 0.80)
train <- subset(dataset,split==TRUE)
test <- subset(dataset,split==FALSE)

# Use logistic regression and the one-vs-all strategy to build a classification model.
multinomModel <- multinom(Species~.,data=train)
summary (multinomModel) # model summary

# Predict on Test Data
predicted_scores <- predict (multinomModel, test, "probs") # predict on new data
predictions <- apply(predicted_scores , 1, which.max)
predictions[which(predictions=="1")] <- unique(dataset$Species)[1]
predictions[which(predictions=="2")] <- unique(dataset$Species)[2]
predictions[which(predictions=="3")] <- unique(dataset$Species)[3]
test$prediction <- predictions

# summarize accuracy
table(test$prediction, test$Species)


================================================
FILE: 57-GLM/multinominal6.R
================================================
# MUltinominal Regression
# Students Selecting a Stream
dept = c('an', 'law', 'ds')
student = c('s',1:50,sep='')
sex = c('M','F')
set.seed(1234)
maths = ceiling(runif(50, 55,95))
set.seed(1234)
stream = sample(dept, 50, replace=T)
set.seed(1234)
gender = sample(sex, 50, replace=T)
admission = data.frame(stream, gender, maths )
admission
aggregate(admission$maths, by=list(stream,gender), mean )
aggregate(admission$maths, by=list(stream,gender), length )
aggregate(admission$maths, by=list(stream), length )
str(admission)
unique(admission$stream)
#base is an
#
library(nnet)
mmodel1 <- multinom( stream ~ gender + maths ,data= admission)
summary (mmodel1) # model summary


================================================
FILE: 57-GLM/nls1.R
================================================
# Non Linear
#simulate some data
set.seed(20160227)
x<-seq(0,50,1)
y<-((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(51,0,1)
#for simple models nls find good starting values for the parameters even if it throw a warning
m<-nls(y~a*x/(b+x))
#get some estimation of goodness of fit
cor(y,predict(m))

[1] 0.9496598

#plot
plot(x,y)
lines(x,predict(m),lty=2,col="red",lwd=3)


#simulate some data, this without a priori knowledge of the parameter value
y<-runif(1,5,15)*exp(-runif(1,0.01,0.05)*x)+rnorm(51,0,0.5)
#visually estimate some starting parameter values
plot(x,y)
#from this graph set approximate starting values
a_start<-8 #param a is the y value when x=0
b_start<-2*log(2)/a_start #b is the decay rate
#model
m<-nls(y~a*exp(-b*x),start=list(a=a_start,b=b_start))
#get some estimation of goodness of fit
cor(y,predict(m))

#plot the fit
lines(x,predict(m),col="red",lty=2,lwd=3)


library(deSolve)
#simulating some population growth from the logistic equation and estimating the parameters using nls
log_growth <- function(Time, State, Pars) {
  with(as.list(c(State, Pars)), {
    dN <- R*N*(1-N/K)
    return(list(c(dN)))
  })
}
#the parameters for the logisitc growth
pars  <- c(R=0.2,K=1000)
#the initial numbers
N_ini  <- c(N=1)
#the time step to evaluate the ODE
times <- seq(0, 50, by = 1)
#the ODE
out   <- ode(N_ini, times, log_growth, pars)
#add some random variation to it
N_obs<-out[,2]+rnorm(51,0,50)
#numbers cannot go lower than 1
N_obs<-ifelse(N_obs<1,1,N_obs)
#plot
plot(times,N_obs)


#find the parameters for the equation
SS<-getInitial(N_obs~SSlogis(times,alpha,xmid,scale),data=data.frame(N_obs=N_obs,times=times))
SS


================================================
FILE: 57-RF/dt-caret-xxx.R
================================================
# Decision Tree - caret pacakage
#The caret library is really useful because you can easily apply different models and compare their performance. It can call rpart but uses a slightly different syntax to include all features.

library(caret)
library(randomForest)
library(data.table)

mt <- data.table(mtcars)

tr <- train(x=mt[,-'hp', with=FALSE], y = mt[, hp], method='rpart')
tr2 <- train(x=mt[,-'hp', with=FALSE], y = mt[, hp], method='rf')

plot(tr$finalModel)
text(tr$finalModel)



================================================
FILE: 57-RF/dt-rf-DU3.R
================================================
# Random Forests
#https://www.r-bloggers.com/variable-importance-plot-and-variable-selection/

set.seed(1)
n=500
library(clusterGeneration)
library(mnormt)
S=genPositiveDefMat("eigen",dim=15)
S
S=genPositiveDefMat("unifcorrmat",dim=15)
S
X=rmnorm(n,varcov=S$Sigma)
X
library(corrplot)
corrplot(cor(X), order = "hclust")

P=exp(Score)/(1+exp(Score))
P=exp(S)/(1+exp(S))

Y=rbinom(n,size=1,prob=P)
df=data.frame(Y,X)
allX=paste("X",1:ncol(X),sep="")
names(df)=c("Y",allX)
#The variable importance plot is obtained by growing some trees,

require(randomForest)
fit=randomForest(factor(Y)~., data=df)
(VI_F=importance(fit))



================================================
FILE: 57-RF/dt-rf-eg2.R
================================================
#random Forests in R
#https://www.r-bloggers.com/how-to-implement-random-forests-in-r/

# Data Source: https://archive.ics.uci.edu/ml/machine-learning-databases/car/

#install.packages("randomForest")
library(randomForest)

df1 = read.csv('https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data')
head(df1)
data1 = df1
head(data1)
str(data1)
names(data1) = c("BuyingPrice", "Maintenance", "NumDoors", "NumPersons", "BootSpace" , "Safety" , "Condition")
summary(data1)
head(data1)

# Split into Train and Validation sets
# Training Set : Validation Set = 70 : 30 (random)
set.seed(100)
train <- sample(nrow(data1), 0.7*nrow(data1), replace = FALSE)
TrainSet <- data1[train,]
ValidSet <- data1[-train,]
summary(TrainSet)
summary(ValidSet)

summary(TrainSet)
summary(ValidSet)

# Create a Random Forest model with default parameters
model1 <- randomForest(Condition ~ ., data = TrainSet, importance = TRUE)
model1

# Fine tuning parameters of Random Forest model
model2 <- randomForest(Condition ~ ., data = TrainSet, ntree = 500, mtry = 6, importance = TRUE)
model2

# Predicting on train set
predTrain <- predict(model2, TrainSet, type = "class")
# Checking classification accuracy
table(predTrain, TrainSet$Condition)

# Predicting on Validation set
predValid <- predict(model2, ValidSet, type = "class")
# Checking classification accuracy
mean(predValid == ValidSet$Condition)                    
table(predValid,ValidSet$Condition)

# To check important variables
importance(model2)        
varImpPlot(model2)

# Using For loop to identify the right mtry for model
a=c()
i=5
for (i in 3:8) {
  model3 <- randomForest(Condition ~ ., data = TrainSet, ntree = 500, mtry = i, importance = TRUE)
  predValid <- predict(model3, ValidSet, type = "class")
  a[i-2] = mean(predValid == ValidSet$Condition)
}

a

plot(3:8,a)


#compare with dtree
# Compare with Decision Tree

#install.packages("rpart")
#install.packages("caret")
#install.packages("e1071")

library(rpart)
library(caret)
library(e1071)

model_dt = train(Condition ~ ., data = TrainSet, method = "rpart")
model_dt_1 = predict(model_dt, data = TrainSet)
table(model_dt_1, TrainSet$Condition)

mean(model_dt_1 == TrainSet$Condition)

# Running on Validation Set
model_dt_vs = predict(model_dt, newdata = ValidSet)
table(model_dt_vs, ValidSet$Condition)

mean(model_dt_vs == ValidSet$Condition)


================================================
FILE: 57-RF/dt-rf-eg3.R
================================================
#https://datascienceplus.com/random-forests-in-r/ 
require(randomForest)
require(MASS)#Package which contains the Boston housing dataset
attach(Boston)
set.seed(101)

dim(Boston)

#training Sample with 300 observations
train=sample(1:nrow(Boston),300)
?Boston  #to search on the dataset

Boston.rf=randomForest(medv ~ . , data = Boston , subset = train)
Boston.rf
plot(Boston.rf)

oob.err=double(13)
test.err=double(13)

#mtry is no of Variables randomly chosen at each split
for(mtry in 1:13) 
{
  rf=randomForest(medv ~ . , data = Boston , subset = train,mtry=mtry,ntree=400) 
  oob.err[mtry] = rf$mse[400] #Error of all Trees fitted
  
  pred<-predict(rf,Boston[-train,]) #Predictions on Test Set for each Tree
  test.err[mtry]= with(Boston[-train,], mean( (medv - pred)^2)) #Mean Squared Test Error
  
  cat(mtry," ") #printing the output to the console
  
}
test.err
oob.err

matplot(1:mtry , cbind(oob.err,test.err), pch=19 , col=c("red","blue"),type="b",ylab="Mean Squared Error",xlab="Number of Predictors Considered at each Split")
legend("topright",legend=c("Out of Bag Error","Test Error"),pch=19, col=c("red","blue"))



================================================
FILE: 57-RF/dt-rf-kyphosis1.R
================================================
# Random Forests

Random Forest prediction of Kyphosis data
library(randomForest)
fit <- randomForest(Kyphosis ~ Age + Number + Start,   data=kyphosis)
print(fit) # view results 
importance(fit) # importance of each predictor
head(kyphosis)
?kyphosis
plot(fit)
# Importance of each predictor.
print(importance(fit,type = 2)) 

#EWg2
Load the party package. It will automatically load other
# required packages.
library(party)
library(randomForest)

# Create the forest.
output.forest <- randomForest(nativeSpeaker ~ age + shoeSize + score,   data = readingSkills)

# View the forest results.
print(output.forest) 
# Importance of each predictor.
print(importance(output.forest,type = 2)) 



================================================
FILE: 57-naive/naivbayes1.R
================================================
# Naive Bayes
#https://www.r-bloggers.com/naive-bayes-classification-in-r-part-1/


library(e1071)
model <- naiveBayes(am ~ ., data = mtcars)
class(model)
summary(model)
print(model)

preds <- predict(model, newdata = mtcars)
preds
conf_matrix <- table(preds, mtcars$am)



#----
## Categorical data only:
data(HouseVotes84, package = "mlbench")
head(HouseVotes84)
model <- naiveBayes(Class ~ ., data = HouseVotes84)
predict(model, HouseVotes84[1:10,])
predict(model, HouseVotes84[1:10,], type = "raw")

pred <- predict(model, HouseVotes84)
table(pred, HouseVotes84$Class)

## using laplace smoothing:
model <- naiveBayes(Class ~ ., data = HouseVotes84, laplace = 3)
pred <- predict(model, HouseVotes84[,-1])
table(pred, HouseVotes84$Class)


## Example of using a contingency table:
data(Titanic)
m <- naiveBayes(Survived ~ ., data = Titanic)
m
predict(m, as.data.frame(Titanic))

## Example with metric predictors:
data(iris)
m <- naiveBayes(Species ~ ., data = iris)
## alternatively:
m <- naiveBayes(iris[,-5], iris[,5])
m
table(predict(m, iris), iris[,5])




================================================
FILE: 57-naive/naivbayes2.R
================================================
# Naive Bayes 
#https://www.r-bloggers.com/understanding-naive-bayes-classifier-using-r/


#Getting started with Naive Bayes
#Install the package
#install.packages(“e1071”)
#Loading the library
library(e1071)
?naiveBayes #The documentation also contains an example implementation of Titanic dataset
#Next load the Titanic dataset
data('Titanic')
#Save into a data frame and view it
Titanic_df=as.data.frame(Titanic)

#Creating data from table
repeating_sequence=rep.int(seq_len(nrow(Titanic_df)), Titanic_df$Freq) #This will repeat each combination equal to the frequency of each combination
rep.int(c(1,2,3),c(5,6,7))
repeating_sequence

#Create the dataset by row repetition created
Titanic_dataset=Titanic_df[repeating_sequence,]
#We no longer need the frequency, drop the feature

#Fitting the Naive Bayes model
Naive_Bayes_Model=naiveBayes(Survived ~., data=Titanic_dataset)
#What does the model say? Print the model summary
Naive_Bayes_Model


#Prediction on the dataset
NB_Predictions=predict(Naive_Bayes_Model,Titanic_dataset)
#Confusion matrix to check accuracy
table(NB_Predictions,Titanic_dataset$Survived)


#improvement
Getting started with Naive Bayes in mlr
#Install the package
#install.packages(“mlr”)
#Loading the library
library(mlr)

#Create a classification task for learning on Titanic Dataset and specify the target feature
task = makeClassifTask(data = Titanic_dataset, target = "Survived")
#Initialize the Naive Bayes classifier
selected_model = makeLearner("classif.naiveBayes")
#Train the model
NB_mlr = train(selected_model, task)

#Read the model learned  
NB_mlr$learner.model


#Predict on the dataset without passing the target feature
predictions_mlr = as.data.frame(predict(NB_mlr, newdata = Titanic_dataset[,1:3]))

##Confusion matrix to check accuracy
table(predictions_mlr[,1],Titanic_dataset$Survived)
Titanic_dataset$Freq=NULL


================================================
FILE: 61-clust/10-clust-packages.R
================================================
#Packages for Clustering

#install.packages('easypackages')
library(easypackages)
clustpackages = c('dplyr','amap','NbClust','gsheet', 'cluster', 'factoextra','fpc', 'devtools', 'vegan', 'kohonen','datasets','ggplot2', 'flexclust','tsne','ISLR','reshape','corrplot','fMultivar','animation','plyr','switchr')
easypackages::packages(clustpackages)
install_github("pablo14/clusplus")
easypackages::libraries(c(clustpackages,'clusplus'))

                        

================================================
FILE: 61-clust/16b-km-withinss.R
================================================
# Clustering - Video Lesson

library(cluster) #computing pam, analyzing cluster silhouettes
library(factoextra) # visualizing clusters using ggplot2 plotting system
library(NbClust) # finding the optimal number of clusters
#Case-1: One Numeric Variable
#libraries
library(cluster)
library(factoextra)
library(NbClust)
#create data
set.seed(1234)
marks1 = ceiling(runif(20,50,90))
df1 = data.frame(marks1)
df1
#Perform Clustering
set.seed(1234)
km1 = kmeans(df1,3,nstart=5)
#nstart option attempts multiple initial configurations and reports on the best one. For example, adding nstart=25 will generate 25 initial random centroids and choose the best one for the algorithm

km1
(df1a = cbind(df1, cluster=km1$cluster))
df1a[ order(df1a$cluster), ]

#Avl components
str(km1)
attributes(km1)
# Calculate distances between each point
#stats::dist(df1$marks1, method = "euclidean")
m1 = as.matrix(stats::dist(df1$marks1, method = "euclidean"))
m1
rownames(m1) = df1$marks1
colnames(m1) = df1$marks1
m1
#distance between 1st and 2nd 
sqrt((diff(df1$marks1[c(1,20)]))^2)
useful::topright(m1)

# distance calculated by Algo
#within : inside a cluster : sum of sq differences of each point from centeriod
km1$withinss
#cluster 1 : 
km1$withinss[1]
#calculate manually
(c1 = df1[km1$cluster==1,])
(m1 = km1$centers[1])
sum((c1 - m1)^2)

#sum all within cluster sum sq differences
km1$tot.withinss
km1$withinss ; sum(km1$withinss)

#between each cluster centerioids
km1$betweenss
km1$centers
km1$centers
diff(km1$centers)
(comb1 = combn(km1$centers,2))
(diff1 = apply(comb1, 2, diff))
diff1^2
sum(diff1^2)

c(diff(km1$centers))
sum(as.numeric(dist(km1$centers))^2)

sum((abs(apply(combn(km1$centers,2), 2, diff))^2))

#total distance : tot.within + between
km1$totss
km1$tot.withinss + km1$betweenss

#Other Output of Algo
#Centeriod
km1$centers
#Size of each cluster
km1$size
#Total SS Distance
km1$totss

#Plot the Clusters
#dim of plot should > 2
fviz_cluster(km1, data = df1, geom = "point", stand = FALSE, ellipse.type = "norm") #Less than 2 Not work
plot(df1$marks1, col=km1$cluster)

library(cluster)
clusplot(df1, km1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
#optimal number of clusters
#Package
set.seed(123)
fviz_nbclust(df1, kmeans, method = "wss") + geom_vline(xintercept = 3, linetype = 2)
nb <- NbClust(df1, distance = "euclidean", min.nc = 2,               max.nc = 10, method = "complete", index ="all") #Not Working

#Manual way
set.seed(1234)
(kmc1 = kmeans(df1,1)) ; (kmc2 = kmeans(df1,2)); (kmc3 = kmeans(df1,3)) ; (kmc4 = kmeans(df1,4));(kmc5 = kmeans(df1,5))
kmc1$tot.withinss; kmc5$tot.withinss
(wss = sapply(1:5, function(k){kmeans(df1, k )$tot.withinss}))
plot(1:5, wss, type="b")
abline(v = 3, lty =2)



#doing with 2 variables
(df2=women)
km2 = kmeans(df2, 3)
str(km2)
df2$cluster=factor(km2$cluster)
fviz_cluster(km2, data = df2[1:2], geom = "point", stand = FALSE, ellipse.type = "norm") # 2 cols required

clusplot(df2, km2$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
animation::kmeans.ani(df2[1:2],3)
(centers=as.data.frame(km2$centers))

g= ggplot(data=df2[1:3], aes(x=height, y=weight, color=cluster,size=10 )) + geom_point()
g
g + geom_point(data=centers, aes(x=height,y=weight, colour=unique(df2$cluster)), size=67, alpha=.3) + theme(legend.position = "none")



#links
#https://rstudio-pubs-static.s3.amazonaws.com/309962_f68823cbde484d0bace3fd37ab76482a.html

example(kmeans)



================================================
FILE: 61-clust/17b-clust-noclusters1.R
================================================
# Clustering - Number of Clusters
library(NbClust)
#https://www.rdocumentation.org/packages/NbClust/versions/3.0/topics/NbClust

income = ceiling(runif(100,60,100))
age = ceiling(rnorm(100, 35,5))

df = data.frame(income, age)
head(df)
data = scale(df)

fit.km3 = kmeans(data,centers=3)
fit.km3$tot.withinss
fit.km4 = kmeans(data,centers=4)
fit.km4$tot.withinss
fit.km10 = kmeans(data,centers=10)
fit.km10$tot.withinss

# Second set of data
n = 100; g = 6 
set.seed(g)
d <- data.frame(x = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))), y = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))))
d
plot(d)

data=d

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean", 
              min.nc=2, max.nc=15, method="average")
par(opar)
table(nc$Best.n[1,])
barplot(table(nc$Best.n[1,]), xlab="Numer of Clusters", ylab="Number of Criteria", main="Number of Clusters Chosen by 26 Criteria") 

#Method2
wss <- (nrow(data)-1)*sum(apply(data,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data, centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares")


#Method : usin PAM
library(fpc)
pamk.best <- pamk(data)
cat("number of clusters estimated by optimum average silhouette width:", pamk.best$nc, "\n")
plot(pam(data, pamk.best$nc))


#Method we could also do:Not Working
library(fpc)
asw <- numeric(20)
for(k in 2:20) asw[[k]] = pam(data, k)$silinfo$avg.width
k.best = which.max(asw)
cat("silhouette-optimal number of clusters:", k.best, "\n")

#Method----
require(vegan)
fit <- cascadeKM(scale(data, center = TRUE,  scale = TRUE), 1, 10, iter = 1000)
plot(fit, sortg = TRUE, grpmts.plot = TRUE)
calinski.best <- as.numeric(which.max(fit$results[2,]))
cat("Calinski criterion optimal number of clusters:", calinski.best, "\n")
# 3 clusters!

#Method : Bayesian
library(mclust)
# at least 1 model and up 20.
d_clust <- Mclust(as.matrix(data), G=1:20)
d_clust
m.best <- dim(d_clust$z)[2]
cat("model-based optimal number of clusters:", m.best, "\n")
# 4 clusters
plot(d_clust)

#Method----
library(apcluster)
?negDistMat
data
d.apclus <- apcluster(negDistMat(r=2), data)
cat("affinity propogation optimal number of clusters:", length(d.apclus@clusters), "\n")
# 
heatmap(d.apclus)
plot(d.apclus, data)


#Method
library(cluster)
clusGap(data, kmeans, 10, B = 100, verbose = interactive())


#Method
library(NbClust)
nb <- NbClust(data,  distance = "euclidean",  min.nc=2, max.nc=15, method = "kmeans",  index = "alllong", alphaBeale = 0.1)
#diss="NULL" remove
hist(nb$Best.nc[1,], breaks = max(na.omit(nb$Best.nc[1,])))


#Method
d_dist <- dist(as.matrix(data))   # find distance matrix 
plot(hclust(d_dist)) 
clusters <- identify(hclust(d_dist))
clusters


#Method
library(apcluster)
apclus = apcluster(negDistMat(r=2), data)
show(apclus)

sim = corSimMat(data, method="spearman")
apclus = apcluster(s=sim)
apclus


#Method - Weka
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144')
library(RWeka)

# Print a list of available options for the X-Means algorithm
WOW("XMeans")
WPM("refresh-cache")
WPM("list-packages", "installed")
WPM("list-packages", "available")
WPM("install-package", 'XMeans')
WOW("XMeans")
# Create a Weka_control object which will specify our parameters
weka_ctrl <- Weka_control(
  I = 1000,  # max no. of overall iterations
  M = 1000,  # max no. of iterations in the kMeans loop
  L = 20,    # min no. of clusters
  H = 150,   # max no. of clusters
  D = "weka.core.EuclideanDistance", # dist metric Euclidean
  C = 0.4,   # cutoff factor ???
  S = 12     # random number seed (for reproducibility)
)

# Run the algorithm on your data, d
x_means <- XMeans(data, control = weka_ctrl)

# Assign cluster IDs to original data set
data$xmeans.cluster <- x_means$class_ids
data$xmeans.cluster


#Method
set.seed(2)
x=matrix(rnorm(50*2), ncol=2)
hc.complete = hclust(dist(x), method="complete")
plot(hc.complete)
cutree(hc.complete,k = 2)
cutree(hc.complete,h = 4.9)


#
library(pvclust)
library(MASS)
?pvclust
data(Boston)
boston.pv <- pvclust(Boston)
plot(boston.pv)


================================================
FILE: 61-clust/17c-clust-numbers-iris.R
================================================
#Optimal Number of Clusters in data
#Reduce total within ss
iris
table(iris$Species)

data = iris[-5]
head(data)

km1= kmeans(data,centers=1)
km1$tot.withinss

km2= kmeans(data,centers=2)
km2$tot.withinss
km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss


km4= kmeans(data,centers=4)
km4$tot.withinss

km5= kmeans(data,centers=5)
km5$tot.withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")

kiris = kmeans(data, centers=3)
kiris$centers

cbind(iris$Species, data, kiris$cluster)


names(mtcars)
data2 = mtcars[c('mpg','disp','hp','wt')]
head(data2)
nc = NbClust(data2, distance="euclidean",min.nc=2, max.nc=15, method="average")
det(as.matrix(mtcars))
?na.action
km3= kmeans(data,centers=3)
km3$tot.withinss

cbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)

#we select no clusters at elbow point
#adding more clusters does not significantly reduce total withinss


================================================
FILE: 61-clust/17d-noc-mclust.R
================================================
#Nos of clusters
#https://datascienceplus.com/finding-optimal-number-of-clusters/


================================================
FILE: 61-clust/19b-clust-distances.R
================================================
# Various Distance in Clustering


================================================
FILE: 61-clust/19c-clust-distances.R
================================================
# Clustering - Video Lesson

library(cluster) #computing pam, analyzing cluster silhouettes
library(factoextra) # visualizing clusters using ggplot2 plotting system
library(NbClust) # finding the optimal number of clusters
#Case-1: One Numeric Variable
#libraries
library(cluster)
library(factoextra)
library(NbClust)
#create data
set.seed(1234)

income = ceiling(runif(20,10000,300000))
children = round(runif(20,0,5),0)
rent = ceiling(rnorm(20,15000,2000))
df2 = data.frame(income, children, rent)

#Perform Clustering
set.seed(1234)
km2 = kmeans(df2,3)
km2
df2a = cbind(df2, cluster=km2$cluster)
df2a
df2a[ order(df2a$cluster), ]

#Avl components
attributes(km2)
# Calculate distances between each point
#stats::dist(df1$marks1, method = "euclidean")
m2 = as.matrix(stats::dist(df2, method = "euclidean"))
m2
# distance calculated by Algo
#within
km2$withinss
km2$tot.withinss
#between
km2$betweenss
#Other Output of Algo
#Centeriod
km2$centers
#Size of each cluster
km2$size
#Total SS Distance
km2$totss

#Plot the Clusters
fviz_cluster(km2, data = df2, geom = "point", stand = FALSE, ellipse.type = "norm") #work more data reqd
plot(df2$income, col=km2$cluster)

library(cluster)
clusplot(df2, km2$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)

#optimal number of clusters
#Package
set.seed(123)
fviz_nbclust(df2, kmeans, method = "wss") + geom_vline(xintercept = 3, linetype = 2)
#nb <- NbClust(df2, distance = "euclidean", min.nc = 2,               max.nc = 10, method = "complete", index ="all") #Not Working

#Manual way
set.seed(1234)
(kmc1 = kmeans(df2,1)) ; (kmc2 = kmeans(df2,2)); (kmc3 = kmeans(df2,3)) ; (kmc4 = kmeans(df2,4));(kmc5 = kmeans(df2,5))
kmc2$tot.withinss; kmc5$tot.withinss
(wss = sapply(1:5, function(k){kmeans(df2, k )$tot.withinss}))
plot(1:5, wss, type="b")
abline(v = 3, lty =2)

#Scale the data and then do Kmeans
df3 = data.frame(scale(df2))
class(df3)
head(df3)
km3 = kmeans(df3, 3)
km3
km3$tot.withinss
#Plot the Clusters
fviz_cluster(km3, data = df3, geom = "point", stand = FALSE, ellipse.type = "norm") #work more data reqd
plot(df3$income, col=km3$cluster)

#Package
set.seed(123)
fviz_nbclust(df3, kmeans, method = "wss")
(wss3 = sapply(1:5, function(k){kmeans(df3, k )$tot.withinss}))
plot(1:5, wss3, type="b")
abline(v = 3, lty =2)

fviz_nbclust(df3, kmeans, method = "silhouette")


#fields-------------------------------
library(fields)
x <- c(3,4,5) ; y <- c(6,7,8,9)
x;y
rdist(x, y)
#rows on x ; columns on y
i=1; j=1
for (i in 1:3) { 
  for (j in 1:4) {
    d <- abs(x[i]-y[j])
   # cat(paste(d," "))
    cat(d, ' ')
  }
}


================================================
FILE: 61-clust/19d-clust-scaling.R
================================================
# Scaling the data for Clustering

set.seed(1234); marks10 = ceiling(runif(100, 5,10)) 
set.seed(1234); marks500 = ceiling(runif(100, 250,500)) 

students1= data.frame(marks10, marks500)
head(students1)

km1 = kmeans(students1, centers=3)
km1$centers; km1$withinss
#scaling gives equal importance to all variables
#avoid obtaining clusters that are dominated by variables having the largest amount of variation

students2 = scale(students1)
km2 = kmeans(students2, centers=3)
km2$centers; km2$withinss

par(mfrow=c(1,2))
plot(students1$marks10, students1$marks500, col=km1$cluster)
plot(students1$marks10, students1$marks500, col=km2$cluster)
par(mfrow=c(1,1))


================================================
FILE: 61-clust/20b-clust-plots.R
================================================
# Plotting in Clusters


#Plot1----
library(cluster)   #plotting
library(fpc)

data(iris)
data = iris[, -5] # without known classification 
# Kmeans cluster analysis
km1 =  kmeans(data, centers=3)
plotcluster(data, km1$cluster)

#Plot2
# More complex
clusplot(data, km1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
?clusplot
#plot3
with(iris, pairs(data, col=c(1:3)[km1$cluster])) 

#plot4
X <- data.frame(c1=c(0,1,2,4,5,4,6,7),c2=c(0,1,2,3,3,4,5,5))
km <- kmeans(X, center=2)
plot(X,col=km$cluster)
points(km$center,col=1:2,pch=8,cex=1)





#Plot5----
library(cluster)
library(HSAUR)
data(pottery)
km    <- kmeans(pottery,3)
dissE <- daisy(pottery) 
dE2   <- dissE^2
sk2   <- silhouette(km$cl, dE2)
plot(sk2)





set.seed(1234); (age = ceiling(rnorm(50, 45, 10)))
set.seed(1234);(income = ceiling(rnorm(50, 100000, 10000)))
set.seed(1234);(children = sample(c(1,2,3), size=50, replace=T, prob=c(.4,.3,.2)))
customers = data.frame(age, income, children)
head(customers)

# No of Clusters
library(NbClust)
nc = NbClust(customers, distance="euclidean", 
             min.nc=2, max.nc=15, method="average")
#without scaling
km1 = kmeans(customers, centers=3)
km1$centers


#Fan Plot
library(ape)
library(cluster) 
data(mtcars)
plot(hclust(dist(mtcars)))

plot(as.phylo(hclust(dist(mtcars))),type="fan")


#
# Load data
data(mtcars)
# Compute distances and hierarchical clustering
dd <- dist(scale(mtcars), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
library(factoextra)
fviz_dend(hc, cex = 0.5)
#You can use the arguments main, sub, xlab, ylab to change plot titles as follow:
  
fviz_dend(hc, cex = 0.5,  main = "Dendrogram - ward.D2",  xlab = "Objects", ylab = "Distance", sub = "")
#To draw a horizontal dendrogram, type this:
  
fviz_dend(hc, cex = 0.5, horiz = TRUE)

fviz_dend(hc, k = 4, # Cut in four groups
          cex = 0.5, # label size
          k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
          color_labels_by_k = TRUE, # color labels by groups
          rect = TRUE, # Add rectangle around groups
          rect_border = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"), 
          rect_fill = TRUE)

fviz_dend(hc, k = 4,                 # Cut in four groups
          cex = 0.5,                 # label size
          k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
          color_labels_by_k = TRUE,  # color labels by groups
          ggtheme = theme_gray()     # Change theme
)
fviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups
          k_colors = "jco")

fviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups
          k_colors = "jco", horiz=T)

fviz_dend(hc, k = 4, cex = 0.4, horiz = TRUE,  k_colors = "jco", 
          rect = TRUE, rect_border = "jco", rect_fill = TRUE)

fviz_dend(hc, cex = 0.5, k = 4, 
          k_colors = "jco", type = "circular")

require("igraph")
fviz_dend(hc, k = 4, k_colors = "jco",
          type = "phylogenic", repel = TRUE)

require("igraph")
fviz_dend(hc, k = 4, # Cut in four groups
          k_colors = "jco",
          type = "phylogenic", repel = TRUE,
          phylo_layout = "layout.gem")


#zoom in dendrogram
fviz_dend(hc)
fviz_dend(hc, xlim = c(1, 20), ylim = c(0, 5))
fviz_dend(hc, xlim = c(0, 10), ylim = c(-2, 5))

#subtree
# Create a plot of the whole dendrogram,
# and extract the dendrogram data
dend_plot <- fviz_dend(hc, k = 4, # Cut in four groups
                       cex = 0.5, # label size
                       k_colors = "jco")
dend_plot
dend_data <- attr(dend_plot, "dendrogram") # Extract dendrogram data
dend_data
# Cut the dendrogram at height h = 10
dend_cuts <- cut(dend_data, h = 10)
# Visualize the truncated version containing
# two branches
fviz_dend(dend_cuts$upper)
attributes(dend_cuts)
dend_cuts
#Plot subtree 1
fviz_dend(dend_cuts$lower[[1]], main = "Subtree 1")
# Plot subtree 2
fviz_dend(dend_cuts$lower[[2]], main = "Subtree 2")
fviz_dend(dend_cuts$lower[[3]])
# Plot the whole dendrogram
print(dend_plot)

fviz_dend(dend_cuts$lower[[2]], type = "circular")

#
pdf("dendrogram.pdf", width=30, height=15)            # Open a PDF
p <- fviz_dend(hc, k = 4, cex = 1, k_colors = "jco" ) # Do plotting
print(p)
dev.off()                                             # Close the PDF
#http://www.sthda.com/english/articles/28-hierarchical-clustering-essentials/92-visualizing-dendrograms-ultimate-guide/
  
#using chain operat
data <- scale(mtcars)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)
#R code for creating a dendrogram using chaining operator:
library(dendextend)
dend  
mtcars[1:8,] %>% # data
  scale %>% # Scale the data
  dist %>% # calculate a distance matrix, 
  hclust(method = "ward.D2") %>% # Hierarchical clustering 
  as.dendrogram %>% # Turn the object into a dendrogram.
  plot

#-
library(dendextend)
# 1. Create a customized dendrogram
mycols <- c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07")
dend <-  as.dendrogram(hc) %>%
  set("branches_lwd", 5) %>% # Branches line width
  set("branches_k_color", mycols, k = 4) %>% # Color branches by groups
  set("labels_colors", mycols, k = 4) %>%  # Color labels by groups
  set("labels_cex", 0.8) # Change label size
# 2. Create plot 
fviz_dend(dend, horiz=T) 

#radial Plot
library(dendextend)
library(circlize)

# create a dendrogram
hc <- hclust(dist(datasets::mtcars))
dend <- as.dendrogram(hc)

# modify the dendrogram to have some colors in the branches and labels
dend <- dend %>% 
  color_branches(k=4) %>% 
  color_labels

# plot the radial plot
par(mar = rep(0,4))
# circlize_dendrogram(dend, dend_track_height = 0.8) 
circlize_dendrogram(dend, labels_track_height = NA, dend_track_height = .2) 
#increase the size of windows 


#https://rdrr.io/cran/GOplot/man/GOCluster.html
library(GOplot)

#-------

#dendrogram with distance
mat <- matrix(abs(rnorm(100)),10,10)
dimnames(mat) <- list(letters[1:10],letters[1:10])
ma2 <- as.dist(mat)
hc <- hclust(ma2)
dend <- as.dendrogram(hc)

par(mfrow=c(1,2))

plot(dend)

require(circlize)
require(dendextend)

#Get the heights for each branch
heights <- round(get_branches_heights(dend, sort=FALSE), 1)

#Get max height
maxHeight= max(heights)

#Set label and dendrogram height for cicular dendrogram
labelHeight=0.1
dendHeight=0.8

#Draw the circular dendrogram
circlize_dendrogram(dend, facing="outside", labels=TRUE, labels_track_height=labelHeight, dend_track_height=dendHeight)

#Create tick co-ordinates and values for the new axis
#We have to enure that we don't overlap the label plot region (height specified by labelHeight), nor the central region of the plot (1-(dendHeight+labelHeight))
ticks <- seq(from=(1-(dendHeight+labelHeight)), to=(1-labelHeight), length.out=5)
values <- round(rev(seq(from=0, to=maxHeight, length.out=5)), 1)

#Add the new axis
require(plotrix)
ablineclip(h=0, v=ticks, col="black", x1=1-(dendHeight+labelHeight), x2=1-labelHeight, y1=0, y2=0.04, lwd=1.5)
text(ticks, 0+0.08, values, cex=0.8)
text((1-labelHeight)-(((1-labelHeight)-(1-(dendHeight+labelHeight)))/2), 0+0.14, "Height", cex=0.8)



#https://en.proft.me/2017/01/29/exploring-hierarchical-clustering-r/
#Parallel coordinates are a common way of visualizing high-dimensional geometry and analyzing multivariate data. The lines show the relationship between the axes, much like scatterplots, and the patterns that the lines form indicate the relationship. 
# get nice colors
library(colorspace)

iris2 <- iris[,-5]
species_labels <- iris[,5]
species_col <- rev(rainbow_hcl(3))[as.numeric(species_labels)]
MASS::parcoord(iris2, col = species_col, var.label = TRUE, lwd = 2)
title("Parallel coordinates plot of the Iris data")
legend("top", cex = 1, legend = as.character(levels(species_labels)), fill = unique(species_col), horiz = TRUE)

#We can see that the Setosa species are distinctly different from Versicolor and Virginica (they have lower petal length and width). But Versicolor and Virginica cannot easily be separated based on measurements of their sepal and petal width/length.



================================================
FILE: 61-clust/20c-clust-plots2.R
================================================
#Clustering Plots

### some sample data
require(vegan)
data(dune)

# kmeans
kclus <- kmeans(dune,centers= 4, iter.max=1000, nstart=10000)

# distance matrix
dune_dist <- dist(dune)

# Multidimensional scaling
cmd <- cmdscale(dune_dist)
head(cmd)

# plot MDS, with colors by groups from kmeans
groups <- levels(factor(kclus$cluster))
ordiplot(cmd, type = "n")
cols <- c("steelblue", "darkred", "darkgreen", "pink")
for(i in seq_along(groups)){
  points(cmd[factor(kclus$cluster) == groups[i], ], col = cols[i], pch = 16)
}

# add spider and hull
ordispider(cmd, factor(kclus$cluster), label = TRUE)
ordihull(cmd, factor(kclus$cluster), lty = "dotted")



#Plot2----
#install.packages("devtools") ## To be able to download packages from github
library(devtools)
#install_github("pablo14/clusplus")
library(clusplus)

## Create k-means model with 3 clusters
fit_mtcars=kmeans(mtcars,3)

## Call the function
plot_clus_coord(fit_mtcars, mtcars)


================================================
FILE: 61-clust/23b-km-marks1.R
================================================
#Clustering - Simple dataset - Marks in 2 subjects

A=c(1,1.5,3,5,3.5,4.5,3.5)
B=c(1,2,4,7,5,5,4.5)
length(A); length(B)
marks=data.frame(A,B)
marks
?kmeans
(c1 = kmeans(marks, centers=3))  #??
cbind(marks, c1$cluster)
plot(marks, pch=10,col = c1$cluster)
c1$centers
points(c1$centers, col = 1:3, pch = 8, cex = 3)

c1$iter #?

#C1- 1, 2 : (1.3, 1.5)
#C2- 3, 4, 5, 6, 7  : (3.9, 5.1)
#
#Specify Coordinates for Centers
mcenters = marks[c(1,5),]
mcenters
(c2a <- kmeans(marks, centers=mcenters))
c2a
cbind(marks, c2a$cluster)
matrix(c(1,1,5,7), ncol=2)
?matrix
(c2b <- kmeans(marks, centers=matrix(c(1,1,5,7), ncol=2)))

c2a
cbind(marks,c2a$cluster)
c2a$centers
aggregate(marks,by=list(c2a$cluster),FUN=mean)
c2a
c2a$iter

library(dplyr)
marks
marks %>% group_by(c2a$cluster) %>% summarise_all(funs(sum, mean, median, n()))


# Distances
x1=marks[1,]; x2=marks[2,]
x1;x2
sqrt(sum((x1-x2)^2))
sqrt(1.25)
dist(rbind(x1,x2))
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
for (i in 1:7)
  print(paste(i, round(euc.dist(marks[i,], marks[1,]),2),sep='-'))

ref1 = marks[1,]; ref1
ref2 = marks[4,]; ref2

(d1= apply(marks,1,function(x)sqrt(sum((x-ref1)^2))))
(d2= apply(marks,1,function(x)sqrt(sum((x-ref2)^2))))
df=cbind(marks, d1,d2)
df

apply(df, 1, function(x) max(which(x == min(x, na.rm = TRUE))))
df
apply(df[,c(3,4)],1, min)
df3 <-transform(df, mind1d2=apply(df[,c(3,4)],1, min, na.rm = TRUE))
df3




gender = c('M','F','M')
gender
genderF = factor(gender)
genderF
grades = c('A','B','C')
grades
gradesF = factor(grades)
gradesF
gradesF1 = factor(grades, ordered=T)
gradesF1
gradesF2 = factor(grades, ordered=T, levels=c('C','B','A'))
gradesF2
marks = rnorm(3, 50,10)
df = data.frame(genderF, gradesF, marks)
df
str(df)



================================================
FILE: 61-clust/23c-km-marks2.R
================================================
#Clustering - Simple dataset - Marks in 2 subjects

A=c(1,1.5,3,5,3.5,4.5,3.5)
B=c(1,2,4,7,5,5,4.5)
marks=data.frame(A,B)
marks
?kmeans
(c1 = kmeans(marks,centers=3))  #??
#no of clusters or initial distinct cluster centers
c1$iter
cbind(marks, c1$cluster)
c1$centers

plot(marks, pch=10,col = c1$cluster)
c1$centers
points(c1$centers, col = 1:3, pch = 8, cex = 3)

c1$iter #?

#C1- 1, 2 : (1.3, 1.5)
#C2- 3, 4, 5, 6, 7  : (3.9, 5.1)
#
#Specify Coordinates for Centers
mcenters = marks[c(1,5),]
mcenters
(c2a <- kmeans(marks, centers=mcenters))
cbind(marks, c2a$cluster)
matrix(c(1,1,5,7), ncol=2)
?matrix
(c2b <- kmeans(marks, centers=matrix(c(1,1,5,7), ncol=2)))

c2a
cbind(marks,c2a$cluster)
c2a$centers
aggregate(marks,by=list(c2a$cluster),FUN=mean)
c2a
c2a$iter

library(dplyr)
marks
marks %>% group_by(c2a$cluster) %>% summarise_all(funs(sum, mean, median, n()))


# Distances
x1=marks[1,]; x2=marks[2,]
x1;x2
sqrt(sum((x1-x2)^2))
sqrt(1.25)
dist(rbind(x1,x2))
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
for (i in 1:7)
  print(paste(i, round(euc.dist(marks[i,], marks[1,]),2),sep='-'))

ref1 = marks[1,]; ref1
ref2 = marks[4,]; ref2

(d1= apply(marks,1,function(x)sqrt(sum((x-ref1)^2))))
(d2= apply(marks,1,function(x)sqrt(sum((x-ref2)^2))))
df=cbind(marks, d1,d2)
df

apply(df, 1, function(x) max(which(x == min(x, na.rm = TRUE))))
df
apply(df[,c(3,4)],1, min)
df3 <-transform(df, mind1d2=apply(df[,c(3,4)],1, min, na.rm = TRUE))
df3




gender = c('M','F','M')
gender
genderF = factor(gender)
genderF
grades = c('A','B','C')
grades
gradesF = factor(grades)
gradesF
gradesF1 = factor(grades, ordered=T)
gradesF1
gradesF2 = factor(grades, ordered=T, levels=c('C','B','A'))
gradesF2
marks = rnorm(3, 50,10)
df = data.frame(genderF, gradesF, marks)
df
str(df)



================================================
FILE: 61-clust/23d-km-amap-marks3.R
================================================
#Clustering using amap package

library(amap)
A=c(1,1.5,3,5,3.5,4.5,3.5)
B=c(1,2,4,7,5,5,4.5)
marks=data.frame(A,B)
marks
?amap::Kmeans
#Kmeans(x, centers, iter.max = 10, nstart = 1, method = "euclidean")
(c1a =amap::Kmeans(marks,centers= 2))
(c2a =amap::Kmeans(marks,centers= marks[c(1,4),] ))
anova(c1a, cs2a)
c1a$cluster

library(fpc)
?cluster.stats
cluster.stats(c1a$cluster, c2a$cluster)


================================================
FILE: 61-clust/23e-km-student2.R
================================================
# Clustering- 

data <- read.csv("./data/studentdata.csv")
str(data)
data1 = data[-(1:6)]
sdata1 = data.frame(scale(data[-(1:6)]))
str(sdata1)
# K-Means
k.means.fit <- kmeans(sdata1, 5) # k = 5 clusters
attributes(k.means.fit)

# Centroids:
k.means.fit$centers

# Clusters:
k.means.fit$cluster
# Cluster size:
k.means.fit$size

wssplot <- function(data, nc=15, seed=1234){
  wss <- (nrow(data)-1)*sum(apply(data,2,var))
  for (i in 2:nc){
    set.seed(seed)
    wss[i] <- sum(kmeans(data, centers=i)$withinss)}
  plot(1:nc, wss, type="b", xlab="Number of Clusters",
       ylab="Within groups sum of squares")}

wssplot(sdata1, nc=6) 

library(cluster)
clusplot(sdata1, k.means.fit$cluster,  color=TRUE, shade=TRUE,
         main='2D representation of the Cluster solution',
        labels=2, lines=0)
# which student in which cluster
table(data1[,1],k.means.fit$cluster)

# Hierarchical
d <- dist(sdata1, method = "euclidean") # Euclidean distance matrix.
H.fit <- hclust(d, method="ward.D2")

plot(H.fit) # display dendogram
groups <- cutree(H.fit, k=5) # cut tree into 5 clusters
# draw dendogram with red borders around the 5 clusters
rect.hclust(H.fit, k=5, border="red") 
table(data1[,1],groups)


# Clustering 2 Method

library(NbClust)
nb = NbClust(sdata1,
             distance='euclidean', min.nc=2,
             max.nc=15, method='average')
par(mfrow=c(1,1))
barplot(nb$Best.n[1, ], xlab='No of Clusters', 
        ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')
library(flexclust)
d = dist(sdata1)
fit.average = hclust(d, method = 'average')
plot(fit.average, hang=-1, cex=.8,
     main='Average Linkage Clustering')
clusters = cutree(fit.average, k=2)
sort(clusters)
table(clusters)
aggregate(data1,
          by=list(cluster=clusters), median)
aggregate(as.data.frame(sdata1),
          by=list(cluster=clusters), median)
x=2
plot(fit.average, hang=-1, cex=.8, main=paste( 'Average Linkage Clustering \n', x,   'Cluster Solution'))
rect.hclust(fit.average, k=2)


#Pam
#
str(data)
fit.pam = pam(data[-(1:6)], k=2, stand=T)
fit.pam
fit.pam$medoids
clusplot(fit.pam, main='Bivariate Cluster Plot')
ct.pam = table(data$course,
               fit.pam$clustering)
ct.pam
randIndex(ct.pam)



================================================
FILE: 61-clust/24b-clust-women.R
================================================
#Clustering on women data set
#http://dni-institute.in/blogs/k-means-clustering-algorithm-explained/
library(cluster)
library(fpc)

data(women)
df = women
set.seed(101)
wc1 = kmeans(df, centers=3, iter.max=1)
wc1

set.seed(101)
wc2 = kmeans(df, centers=3, iter.max=5)
wc2

# Plot
plot(df,col=wc2$cluster)
points(wc2$center,col=1:2,pch=8,cex=1)

fpc::plotcluster(df, wc2$cluster)

#plot(df, wc2)
cluster::clusplot(df, wc2$cluster, color=TRUE, shade=TRUE,        labels=3, lines=1)
points(wc2$centers, col = 1:2, pch = 15, cex = 5)

with(df, pairs(df, col=c(1:3)[wc2$cluster])) 



================================================
FILE: 61-clust/25b-km-iris.R
================================================
# Clustering - IRIS data set

head(iris)
str(iris$Species)
table(iris$Species)
irisFeatures = iris[,-5]
head(irisFeatures)
iriskm1 = kmeans(irisFeatures,centers=3)
iriskm1
iriskm1$centers
colMeans(irisFeatures[iriskm1$cluster==1,])
iriskm1$iter
(iris[iriskm1$cluster==1,])

iriskm1$size # no of rows in in each cluster
iriskm1$cluster # row no to clusters
plot(irisFeatures$Sepal.Length, col=iriskm1$cluster)
plot(irisFeatures$Sepal.Length, col=1:3)
x= cbind(irisFeatures, cl=iriskm1$cluster, cat=iris$Species)
head(x)
x
iriskm1  # understand the output

#Method2 - Scaling 
irisFeatures2 = irisFeatures
scaleiris = scale(irisFeatures2) # scaling dataset if they are in different range
head(scaleiris) #use this scaleiris for kmeans and plots; this becomes data
irisKM = kmeans(scaleiris, 2)
irisKM$cluster

library(fpc)
library(cluster)
clusplot(scaleiris, irisKM$cluster, color= T, lines=0)
# using PCA technique - finds most Principal Components

#Method3
irisKM2 = kmeans(scaleiris, 3, iter.max= 5)
irisKM2
clusplot(scaleiris, irisKM2$cluster, color=T, lines=0)  



================================================
FILE: 61-clust/25c-km-iris2.R
================================================
#Clustering - iris Data Set

?iris
head(iris)
library(dplyr)
str(iris)
iris %>% group_by(Species) %>% summarise_all(mean)

head(iris[,1:4])

irisFeatures = iris[-5]
head(irisFeatures)
names(irisFeatures)

iriskm1 = kmeans(irisFeatures,3)

iriskm1$size # no of rows in in each cluster
iriskm1$cluster # row no to clusters

plot(irisFeatures$Sepal.Length, col=iriskm1$cluster)
plot(irisFeatures$Sepal.Width, col=iriskm1$cluster)

head(irisFeatures)
iriskm1$cluster
cbind(irisFeatures,  iriskm1$cluster, iris$Species)


km  # understand the output

#Method2 - Scaling 
irisFeatures2 = irisFeatures
scaleiris = scale(irisFeatures2) # scaling dataset if they are in different range
head(scaleiris) #use this scaleiris for kmeans and plots; this becomes data
irisKM = kmeans(scaleiris, 3)
irisKM$cluster

library(fpc)
library(cluster)
clusplot(scaleiris, irisKM$cluster, color= T, lines=0)
# using PCA technique - finds most Principal Components

#Method3
irisKM2 = kmeans(scaleiris, 3, iter.max= 5)
irisKM2
clusplot(scaleiris, irisKM2$cluster, color=T, lines=0)  


================================================
FILE: 61-clust/25f-km-iris2.R
================================================
# Clustering 1 ####
# Types - k menas with kmeans
# kmediods with pam() and pamk()
#hierarchical clustering
# density based clustering with DBScan
#KMEANS

set.seed(8953)
iris2 = iris
iris2
head(iris2)
iris2$Species = NULL  # remove values
str(iris2)
(kmeans.result = kmeans(iris2,3))
head(iris2)
#check clustering against class labels Species
kmeans.result$cluster
table(iris$Species,kmeans.result$cluster)
#class setosa is distinct, other 2 are overlapping
plot(iris2[c("Sepal.Length", "Sepal.Width")], col = kmeans.result$cluster)
points(kmeans.result$centers[, c("Sepal.Length", "Sepal.Width")],
       col = 1:3, pch = 8, cex = 2) # plot cluster centers


================================================
FILE: 61-clust/26h-km-attitude.R
================================================
#Clustering - kmeans 

library(datasets)
data(attitude)
attitude

# Subset the attitude data
dat = attitude[,c(3,4)]

# Plot subset data
plot(dat, main = "% of favourable responses to
     Learning and Privilege", pch =20, cex =2)

# Perform K-Means with 2 clusters
set.seed(7)
km2 = kmeans(dat, 2, nstart=100)
km2$withinss
km2$tot.withinss

# Plot results
plot(dat, col =(km2$cluster +1) , main="K-Means result with 2 clusters", pch=20, cex=2)

km3 = kmeans(dat, 3, nstart=100)
km3$withinss
km3$tot.withinss

# Check for the optimal number of clusters given the data

mydata <- dat
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15)
    wss[i] <- sum(kmeans(mydata, centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares", main="Assessing the Optimal Number of Clusters with the Elbow Method", pch=20, cex=2)


# Perform K-Means with the optimal number of clusters identified from the Elbow method
set.seed(7)
km6 = kmeans(dat, 6, nstart=100)
km10 = kmeans(dat, 10, nstart=100)

# Examine the result of the clustering algorithm
km6    
km1$tot.withinss; km2$tot.withinss;
km3$tot.withinss; km4$tot.withinss

# Plot results
plot(dat, col =(km6$cluster +1) , main="K-Means result with 6 clusters", pch=20, cex=2)

?kmeans


================================================
FILE: 61-clust/27c-clust-som1.R
================================================
# Creating Self-organising maps in R
#https://www.shanelynn.ie/self-organising-maps-for-customer-segmentation-using-r/


# Load the kohonen package 
require(kohonen)
# Create a training data set (rows are samples, columns are variables
# Here I am selecting a subset of my variables available in "data"
data_train <- data[, c(2,4,5,8)]
# Change the data frame with training data to a matrix
# Also center and scale all variables to give them equal importance during
# the SOM training process. 
data_train_matrix <- as.matrix(scale(data_train))
# Create the SOM Grid - you generally have to specify the size of the 
# training grid prior to training the SOM. Hexagonal and Circular 
# topologies are possible
som_grid <- somgrid(xdim = 20, ydim=20, topo="hexagonal")
# Finally, train the SOM, options for the number of iterations,
# the learning rates, and the neighbourhood are available
som_model <- som(data_train_matrix, 
                 grid=som_grid, 
                 rlen=500, 
                 alpha=c(0.05,0.01), 
                 keep.data = TRUE )

#Training progress for SOM
plot(som_model, type="changes")

================================================
FILE: 61-clust/33c-hc-nutrients1.R
================================================
# RinA - Hierarchical Clustering

#Packages Required - flexclust,cluster, NbClust
#Dataset - fkexclust::nutrient
#data(nutrient, package="flexclust")

library(flexclust)----
data(nutrient)
str(nutrient)
head(nutrient,4)
dim(nutrient)

#Calculating Distances
#Distance, Dissimilarity, Proxmity
#Euclidean Distance
d = dist(nutrient)
d[1:10]
d
(dm = as.matrix(d)[1:4, 1:4])  #see only few values
dm[1,2]
nutrient[1:2,]
#dist Beef & burger

#Manually Calculating
paste('sqrt (', paste(nutrient[1,], nutrient[2,], sep="-", collapse=" ^2 + "), ")")
sqrt ( (340-245)^2 + (20-21)^2 + (28-17) ^2 + (9-9) ^2 + (2.6-2.7) )
dist(nutrient[c(1,2),])
#manuall & by algo is same

#-----
library(cluster)
#Pairwise distances 
daisy(nutrient[c(1,2),])  #same distance
daisy(nutrient)

#Scaling-----
#Scaling Data - reduce of particular variable
data(nutrient, package='flexclust')

row.names(nutrient) = tolower(row.names(nutrient))
head(nutrient)
nutrient.scaled = scale(nutrient)  #zscore
head(nutrient.scaled)
d = dist(nutrient.scaled)
head(d)

#Perform HClustering-----
fit.average = hclust(d, method = 'average')
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')


#Eg
#Smaller No of Clusters

library(NbClust)
devAskNewPage(ask=T)#ask for next plot
nc = NbClust(nutrient.scaled, distance='euclidean', min.nc=2, max.nc=15, method='average')
devAskNewPage(ask=F)
par(mfrow=c(1,1))
barplot(nc$Best.n[1, ], xlab='No of Clusters', ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')

?NbClust
#Eg : Final Cluster Solution, Select 5
clusters = cutree (fit.average, k=5)
sort(clusters)
table(clusters)
aggregate(nutrient, by=list(cluster=clusters), median)

aggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters), median)

plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering \n5 Cluster Solution')
rect.hclust(fit.average, k=5)

library(rattle)
str(wine)
head(wine)
df = scale(wine[-1])
df

wssplot <- function(data, nc=15, seed=1234){
  wss <- (nrow(data)-1)*sum(apply(data,2,var))
  for (i in 2:nc){
    set.seed(seed)
    wss[i] <- sum(kmeans(data, centers=i)$withinss)}
  plot(1:nc, wss, type="b", xlab="Number of Clusters",
       ylab="Within groups sum of squares")}

wssplot(df)

library(NBClust)
set.seed(1234)
devAskNewPage(ask=T)
nc = NbClust(df, min.nc =2, max.nc=15, method='kmeans')
table(nc$Best.nc[1,])
par(mfrow=c(1,1))
barplot(table(nc$Best.nc[1,]), xlab='No of Clusters', ylab='No of Criteria',
        main='No of Clusters Chosen by 26 Criteria')

set.seed(1234)
fit.km  = kmeans(df,3, nstart=25)
fit.km
fit.km$size

fit.km$centers

aggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)

ct.km = table(wine$Type, fit.km$cluster)
ct.km

library(flexclust)
randIndex(ct.km)

library(cluster)
?pam
library(cluster)
set.seed(1234)
fit.pam = pam(wine[-1], k=3, stand=T)
fit.pam
fit.pam$medoids
clusplot(fit.pam, main='Bivariate Cluster Plot')
ct.pam = table(wine$Type, fit.pam$clustering)
ct.pam
randIndex(ct.pam)

#Eg
library(fMultivar)
set.seed(1234)
df = rnorm2d(1000, rho=.5)
df = as.data.frame(df)
df
plot(df, main='Bivariate Normal Distribution with rho=0.5')
wssplot(df)
library(NbClust)
nc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')

library(ggplot2)
library(cluster)
fit = pam(df, k=2)
fit
df$clustering = factor(fit$clustering)

par(mfrow=c(1,1))
ggplot(data=df, aes(x=V1,y=V2, color=clustering, shape=clustering)) +
  geom_point() +
  ggtitle('Clustering of Bivariate Normal Data')
plot(nc$All.index[ ,4], type='o',
     ylab='CCC', xlab='Number of
     clusters', col='blue')



================================================
FILE: 61-clust/33c-hc-vegan-dune1.R
================================================
# Clustering
#http://cc.oulu.fi/~jarioksa/opetus/metodi/sessio3.pdf

library(vegan)  #decostand(df, "hell"), 3)
data(dune)
str(dune)
dim(dune) # 30 speicies, 20 obsvn
summary(dune)
?dune

d = vegdist(dune)
d
#par(mfros=c(1,3))
(csin = hclust(d, method='single'))
plot(csin)
plot(csin, hang=-1)
(ccom = hclust(d, method='complete'))
plot(ccom)
plot(ccom, hang=-1)

caver = hclust(d, method='aver')
plot(caver)
plot(caver, hang=-1)
#
vegemite(dune, caver)
#
plot(csin, hang=-1)
rect.hclust(csin,3)
plot(ccom, hang=-1)
rect.hclust(ccom,3)
plot(caver, hang=-1)
rect.hclust(caver,3)
#
c1 = cutree(ccom,3)
c1
table(c1)

table(c1, cutree(csin,3))
table(c1, cutree(caver,3))
#
ord = cmdscale(d)
ord
ordiplot(ord)

ordihull(ord, c1, lty=3)
ordispider(ord, c1, col='blue', label=T)
ordiellipse(ord, c1, col='red')

ordiplot(ord, dis='si')
ordiplot(ord, cutree(caver, 3))
ordiplot(ord, dis='si')
ordicluster(ord, csin)
#
ordiplot(ord, dis='sites')
ordicluster(ord, caver)
ordiplot(ord, dis='si')
ordicluster(ord, caver, prune=2)

#
#Reordering a Dendrogram
den = as.dendrogram(caver)
x = scores(ord, display ='sites', choices=1)
oden = reorder(den, x)
plot(den)
plot(oden)
vegemite(dune, oden)

tabasco(dune, caver)
tabasco(dune, caver, Rowv=F)
tabasco(dune, oden, Rowv=F)
tabasco(dune, oden, Colv=F)
#
# Minimum Spanning Tree
mst = spantree(d)
ordiplot(ord, dis='si')
lines(mst, ord)
plot(mst, type='t')
#
#Cophenetic Distance
plot(d, cophenetic(csin), asp=1)
abline(0,1)
plot(d, cophenetic(ccom), asp=1)
abline(0,1)
plot(d, cophenetic(caver), asp=1)
abline(0,1)

cor(d, cophenetic(csin))
cor(d, cophenetic(ccom))
cor(d, cophenetic(caver))

#Intepretation of Classes
data(dune.env)
str(dune.env)
c1 = factor(c1)
Moist = with(dune.env, as.numeric(as.character(Moisture)))
with(dune.env, as.numeric(Moisture))
boxplot(Moist ~ c1, notch = T)
anova(lm(Moist ~ c1))
anova( rda(Moist ~ c1))
with(dune.env, table(c1, Management))
#
library(labdsv)
const(dune, c1)
importance(dune, c1)
mod = indval(dune, as.numeric(c1))
names(mod)
mod$maxcls
mod$pval
summary(mod)
summary(mod, type='long')
#
#Optimisd Clustering at Given Level
ckm = kmeans(decostand(dune, 'hell'), 3)
ckm$cluster
ordiplot(ord, dis='si')
ordihull(ord, ckm$cluster, col='red')
# Optimal no of clusters
ccas = cascadeKM(decostand(dune, 'hell'), 2, 15)
plot(ccas, sortq=T)
#
#Fuzzy Clustering
library(cluster)
cfuz = fanny(d, 3, memb.exp = 1.7)
names(cfuz)
ordiplot(ord, dis='si')
ordiplot(ord, dis='si', type='n')
stars(cfuz$membership, locatio=ord, draw.segm=T, add=T, scale=F,len=0.1)
ordihull(ord, cfuz$clustering, col='blue')

#
df = iris[-5]
cluster = kmeans(decostand(df,'hell'), 3)
cluster$cluster
ordiplot(cluster, dis="si")
ordihull(ord, ckm$cluster, col="red")

ccas <- cascadeKM(decostand(df, "hell"), 2, 15)
plot(ccas, sortq=TRUE)


================================================
FILE: 61-clust/33d-hc-protein.R
================================================
#Clustering - Protein Consumption

#Study case I: EUROPEAN PROTEIN CONSUMPTION
#25 European countries and their protein intakes (in percent) from nine major food sources (p = 9)

url = 'http://www.biz.uiowa.edu/faculty/jledolter/DataMining/protein.csv'
food = read.csv(url)
head(food)

#----
#clustering on just Red and White meat (p=2) and k=3 clusters.
names(food)

set.seed(123) ## to fix the random starting clusters
grpMeat = kmeans(food[,c("WhiteMeat","RedMeat")], centers=3, nstart=10)
grpMeat
grpMeat$cluster

## list of cluster assignments
(o=order(grpMeat$cluster))
food[o,]  #only white and red meat
grpMeat$cluster[o]

data.frame(food$Country[o],grpMeat$cluster[o])

#cluster assignment on a scatter plot:
  
plot(food$Red, food$White, type="n", xlim=c(3,19), xlab="Red Meat", ylab="White Meat")
text(x=food$Red, y=food$White, labels=food$Country, col=grpMeat$cluster+1) #color as per cluster no


#cluster on all nine protein groups and prepare the program to create seven clusters. 

set.seed(123)
grpProtein = kmeans(food[,-1], centers=7, nstart=10)

o=order(grpProtein$cluster)
data.frame(country=food$Country[o],cluster=grpProtein$cluster[o])

#Plot
library(cluster)
clusplot(food[,-1], grpProtein$cluster, main='2D representation of the Cluster solution', color=TRUE, shade=TRUE, labels=2, lines=0)

# Use agnes function for computing H clust
foodagg=agnes(food, diss=FALSE, metric="euclidian")
plot(foodagg, main='Dendrogram') ## dendrogram
groups = cutree(foodagg, k=4) # cut tree into 3 clusters
rect.hclust(foodagg, k=4, border="red") 


#
library(cluster)
clusplot(food[,-1], grpProtein$cluster, main='2D representation of the Cluster solution', color=TRUE, shade=TRUE, labels=2, lines=0)


================================================
FILE: 61-clust/33f-hc-marks.R
================================================
#Hierarchical Clustering 

#Marks
A=c(1,1.5,3,5,3.5,4.5,3.5)
B=c(1,2,4,7,5,5,4.5)
marks=data.frame(A,B)
marks

hc <- hclust(dist(marks), "ave")
plot(hc)
plot(hc, hang = -1)




================================================
FILE: 61-clust/33g-hc-sample.R
================================================
#Hierarchical Clustering

hclust()
set.seed(123)
marks = ceiling(rnorm(6, mean=60, sd=10))
m = matrix(marks, nrow=6)
rownames(m) = paste("s",1:6,sep='')
colnames(m) = c('sub1')
m

(d= dist(m))
clust_h = hclust(d)
clust_h
plot(clust_h)
plot(clust_h, hang=-1, cex=.8, main='Average Linkage Clustering')
rect.hclust(clust_h, k=2)
rect.hclust(clust_h, k=3, x=3)
rect.hclust(clust_h, k=3, which=3)
rect.hclust(clust_h, k=3, which=2, border=5)
rect.hclust(clust_h, k=3, which=1, border=6)
rect.hclust(clust_h, k=3, which=c(1,3), border=7)
rect.hclust(clust_h, h=5, which=c(1,2), border=9)

clust_h$height
attributes(clust_h)
names(clust_h)

?rect.hclust

#----
set.seed(123)
x = ceiling(rnorm(20*5, 65,15))
m2 = matrix(x, ncol=5)
rownames(m2) = paste('s',1:20,sep='')
colnames(m2) = paste('sub',1:5,sep='')
head(m2)

#------
d2 = dist(m2)
head(d2)
d2
m2_hclust = hclust(d2)
plot(m2_hclust)
rect.hclust(clust_h, k=4)

plot(m2_hclust, hang=-1,cex=.8)
rect.hclust(m2_hclust, h=60, border=5)

plot(m2_hclust, hang=-1,cex=.8)
rect.hclust(clust_h, k=4, which=2, border='red')

plot(m2_hclust, hang=-1,cex=.8)
abline(h=80,col=3)
rect.hclust(clust_h, h=40, which=c(1,2))

#-----
hca <- hclust(dist(USArrests))
plot(hca,hang=-1)
rect.hclust(hca, k = 3, border = "red")
rect.hclust(hca, h = 50, which = c(2,3,4,7), border = 3:4)

x <- rect.hclust(hca, h = 50, which = c(2,7), border = 3:4)
x
#------

library(fpc)
plotcluster(m, clust_h2$cluster)
plotcluster(m, clust_h2$cluster)
#---
# Ward Hierarchical Clustering with Bootstrapped p values
library(pvclust)
fit <- pvclust(m2, method.hclust="ward.D2",  method.dist="euclidean")
plot(fit) # dendogram with p values
# add rectangles around groups highly supported by the data
pvrect(fit, alpha=.95)

#------
library(factoextra)

clust_h2 = hkmeans(m, 3)
clust_h2
clust_h2$centers
cbind(m, clust_h2$cluster)
# Visualize the tree
fviz_dend(clust_h2, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE)
# Visualize the final clusters
fviz_cluster(clust_h2, palette = "jco", repel = TRUE, ggtheme = theme_classic())
#needs 2 variables at least
#




# comparing 2 cluster solutions
library(fpc)
cluster.stats(d, fit1$cluster, fit2$cluster)


================================================
FILE: 61-clust/35d-pam-iris.R
================================================
# Clustering 2 : K Mediods ####
#http://www.rdatamining.com/docs/data-clustering-with-r
# cluster is represented with its center in k means algo
#but with object closest to the center of the cluster in the k mediods clustering
# more robusst than k means in presence of outliers
#PAM and CLARA in package cluster, pamk in fpc (cant choose k)
library(fpc)
iris2 = iris
iris2$Species = NULL # making it pure numeric DF
pamk.result = pamk(iris2)
str(iris2)
pamk.result$nc # no of clusters
table(pamk.result$pamobject$clustering,iris$Species)
# two clusters - setso and mixture of versi& virgin
layout(matrix(c(1,2),1,2)) # 2 graphs per page
plot(pamk.result$pamobject)
# left - 2 dim clusplot of 2 clusters & lines shows distance betw clusters
# right - silhouetters higher s more cluster
layout(matrix(1)) # reverse layout

#pam
library(cluster)
pam.result = pam(iris2,3) # k=3
table(pam.result$clustering,iris$Species)
# cluster 1 seperated from 2 & 3
# cluster 2 mainly made of versicolor
# cluster 3 mainly of virigina
layout(matrix(c(1,2),1,2))
plot(pam.result)
layout(matrix(1)) # reverse layout
# 3 Clusters identified 

# Hierarchical Clustering ####
set.seed(2835)
idx = sample(1:dim(iris)[1],40)
idx
irisSample = iris[idx,]
# remove class label
irisSample$Species = NULL
# hierarchical clustering
hc = hclust((dist(irisSample)),method='ave')
# plot clusters
plot(hc,hang=-1,labels = iris$Species[idx])
# cut tree into 3 clusters
rect.hclust(hc,k=3)
# get clusterids
groups = cutree(hc,k=3)

# Density Based Clustering
#DBSCAN min points, reachability
library(fpc)
iris2 = iris[-5] # remove class tags
str(iris2)
ds = dbscan(iris2, eps=0.42, MinPts = 5)
ds
table(ds$cluster,iris$Species)
# 1 to 3 identified clusters; 0 due to noise
plot(ds,iris2)
plot(ds,iris2[c(1,2)])
plotcluster(iris2, ds$cluster)


# Prediction with Clustering Model
set.seed(435)
idx = sample(1:nrow(iris),10)
idx
# remove class label
new.data = iris[idx,-5]
head(new.data)
# add random noise
new.data = new.data + matrix(runif(10*4,min=0,max=0.2),nrow=10,ncol=4)
head(new.data)
#label new data
pred = predict(ds,iris2, new.data)
pred
table(pred, iris$Species[idx]) # check cluster labels
# 3 + 3 + 2 = 8 out of 10 objects assigned with correct classes labels
plot(iris2[c(1,4)],col=1 + ds$cluster)
points(new.data[c(1,4)], pch='+',col=1+pred,cex=3)


================================================
FILE: 61-clust/35e-pam-nutrient.R
================================================
# R in Action
library(flexclust)
data(nutrient)
str(nutrient)
head(nutrient,4)
#Calculating Distances
#Distance, Dissimilarity, Proxmity
?dist
data(nutrient, package="flexclust")
head(nutrient,4)
d = dist(nutrient)
d[1:4]
dm = as.matrix(d)[1:4, 1:4]
dm[1,2]
nutrient[1,1]
#dist Beef & burger
paste('sqrt (', paste(nutrient[1,], nutrient[2,], sep="-", collapse=" ^2 + "), ")")
dist(nutrient[c(1,2),])
daisy(nutrient[c(1,2),])
library(cluster)

#Eg
data(nutrient, package='flexclust')
row.names(nutrient) = tolower(row.names(nutrient))
nutrient.scaled = scale(nutrient)
d = dist(nutrient.scaled)
fit.average = hclust(d, method = 'average')
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering')
par(mfrow=c(1,1))
#Eg
#Smaller No of Clusters
library(NbClust)
devAskNewPage(ask=T)
nc = NbClust(nutrient.scaled, distance='euclidean', min.nc=2, max.nc=15, method='average')
barplot(nc$Best.n[1, ], xlab='No of Clusters', ylab='No of Criteria', main='No of Clusters Chosen by 26 Criteria')

?NbClust
#Eg : Final Cluster Solution
clusters = cutree (fit.average, k=5)
sort(clusters)
table(clusters)
aggregate(nutrient, by=list(cluster=clusters), median)
aggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters), median)
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering \n5 Cluster Solution')
rect.hclust(fit.average, k=5)

library(rattle)
str(wine)
head(wine)
df = scale(wine[-1])
df

wssplot <- function(data, nc=15, seed=1234){
  wss <- (nrow(data)-1)*sum(apply(data,2,var))
  for (i in 2:nc){
    set.seed(seed)
    wss[i] <- sum(kmeans(data, centers=i)$withinss)}
  plot(1:nc, wss, type="b", xlab="Number of Clusters",
       ylab="Within groups sum of squares")}

wssplot(df)

library(NBClust)
set.seed(1234)
devAskNewPage(ask=T)
nc = NbClust(df, min.nc =2, max.nc=15, method='kmeans')
table(nc$Best.nc[1,])
par(mfrow=c(1,1))
barplot(table(nc$Best.nc[1,]), xlab='No of Clusters', ylab='No of Criteria',
        main='No of Clusters Chosen by 26 Criteria')

set.seed(1234)
fit.km  = kmeans(df,3, nstart=25)
fit.km
fit.km$size

fit.km$centers

aggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)

ct.km = table(wine$Type, fit.km$cluster)
ct.km

library(flexclust)
randIndex(ct.km)

library(cluster)
?pam
library(cluster)
set.seed(1234)
fit.pam = pam(wine[-1], k=3, stand=T)
fit.pam
fit.pam$medoids
clusplot(fit.pam, main='Bivariate Cluster Plot')
ct.pam = table(wine$Type, fit.pam$clustering)
ct.pam
randIndex(ct.pam)

#Eg
library(fMultivar)
set.seed(1234)
df = rnorm2d(1000, rho=.5)
df = as.data.frame(df)
df
plot(df, main='Bivariate Normal Distribution with rho=0.5')
wssplot(df)
library(NbClust)
nc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')

library(ggplot2)
library(cluster)
fit = pam(df, k=2)
fit
df$clustering = factor(fit$clustering)

par(mfrow=c(1,1))
ggplot(data=df, aes(x=V1,y=V2, color=clustering, shape=clustering)) +
  geom_point() +
  ggtitle('Clustering of Bivariate Normal Data')
plot(nc$All.index[ ,4], type='o',
     ylab='CCC', xlab='Number of
     clusters', col='blue')

s

================================================
FILE: 61-clust/40b-mixedclust1.R
================================================
#Clustering
#https://www.listendata.com/2016/01/cluster-analysis-with-r.html
# Loading data
data<-iris[,-c(5)]

# To standarize the variables 
data = scale(data) 

# Assessing cluster tendency
if(!require(clustertend)) install.packages("clustertend")
library(clustertend)
# Compute Hopkins statistic for the dataset
set.seed(123)
hopkins(data, n = nrow(data)-1)
#Since the H value = 0.1815 which is far below the threshold 0.5, it is highly clusterable

###########################################################################
####################### K Means clustering ################################
###########################################################################

# K-mean - Determining optimal number of clusters
# NbClust Package : 30 indices to determine the number of clusters in a dataset
# If index = 'all' - run 30 indices to determine the optimal no. of clusters
# If index = "silhouette" - It is a measure to estimate the dissimilarity between clusters.
# A higher silhouette width is preferred to determine the optimal number of clusters

if(!require(NbClust)) install.packages("NbClust")
nb <- NbClust(data,  distance = "euclidean", min.nc=2, max.nc=15, method = "kmeans",  index = "silhouette")
nb$All.index
nb$Best.nc

#Method II : Same Silhouette Width analysis with fpc package
library(fpc)
pamkClus <- pamk(data, krange = 2:15, criterion="multiasw", ns=2, critout=TRUE)
pamkClus$nc
cat("number of clusters estimated by optimum average silhouette width:", pamkClus$nc, "\n")

#Method III : Scree plot to determine the number of clusters
wss <- (nrow(data)-1)*sum(apply(data,2,var))
for (i in 2:15) {
  wss[i] <- sum(kmeans(data,centers=i)$withinss)
} 
plot(1:15, wss, type="b", xlab="Number of Clusters",ylab="Within groups sum of squares")

# K-Means Cluster Analysis
fit <- kmeans(data,pamkClus$nc)

# get cluster means
aggregate(data,by=list(fit$cluster),FUN=mean)

# append cluster assignment
data <- data.frame(data, clusterid=fit$cluster)

###########################################################################
####################### Hierarchical clustering############################
###########################################################################

# Hierarchical clustering - Determining optimal number of clusters
library(NbClust)
res<-NbClust(data, diss=NULL, distance = "euclidean", min.nc=2, max.nc=6,
             method = "ward.D2", index = "kl")
res$All.index
res$Best.nc

# Ward Hierarchical Clustering
d <- dist(data, method = "euclidean")
fit <- hclust(d, method="ward.D2")
plot(fit) # display dendogram

# cluster assignment (members)
groups <- cutree(fit, k=2)
data = cbind(data,groups)

# draw dendogram with red borders around the 2 clusters
rect.hclust(fit, k=2, border="red")



================================================
FILE: 61-clust/40c-clust-dendgm.R
================================================
# Dendrograms - Hierarchical Clustering


# Load data
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
hc

#plot(x, labels = NULL, hang = 0.1, main = "Cluster dendrogram", sub = NULL,   xlab = NULL, ylab = "Height")

# Default plot
plot(hc)

# Put the labels at the same height: hang = -1
plot(hc, hang = -1, cex = 0.6)

# Convert hclust into a dendrogram and plot
hcd <- as.dendrogram(hc)
# Default plot
plot(hcd, type = "rectangle", ylab = "Height")

# Triangle plot
plot(hcd, type = "triangle", ylab = "Height")

# Zoom in to the first dendrogram
plot(hcd, xlim = c(1, 20), ylim = c(1,8))

# Define nodePar
nodePar <- list(lab.cex = 0.6, pch = c(NA, 19), 
                cex = 0.7, col = "blue")
# Customized plot; remove labels
plot(hcd, ylab = "Height", nodePar = nodePar, leaflab = "none")

# Horizontal plot
plot(hcd,  xlab = "Height",
     nodePar = nodePar, horiz = TRUE)

# Change edge color
plot(hcd,  xlab = "Height", nodePar = nodePar, 
     edgePar = list(col = 2:3, lwd = 2:1))

# install.packages("ape")
library("ape")
# Default plot
plot(as.phylo(hc), cex = 0.6, label.offset = 0.5)


Cladogram
plot(as.phylo(hc), type = "cladogram", cex = 0.6, 
     label.offset = 0.5)

# Unrooted
plot(as.phylo(hc), type = "unrooted", cex = 0.6,
     no.margin = TRUE)

# Fan
plot(as.phylo(hc), type = "fan")

# Radial
plot(as.phylo(hc), type = "radial")

# Cut the dendrogram into 4 clusters
colors = c("red", "blue", "green", "black")
clus4 = cutree(hc, 4)
plot(as.phylo(hc), type = "fan", tip.color = colors[clus4],
     label.offset = 1, cex = 0.7)

# Change the appearance
# change edge and label (tip)
plot(as.phylo(hc), type = "cladogram", cex = 0.6,
     edge.color = "steelblue", edge.width = 2, edge.lty = 2,
     tip.color = "steelblue")

install.packages("ggdendro")

library("ggplot2")
library("ggdendro")

# Visualization using the default theme named theme_dendro()
ggdendrogram(hc)

# Rotate the plot and remove default theme
ggdendrogram(hc, rotate = TRUE, theme_dendro = FALSE)

# Build dendrogram object from hclust results
dend <- as.dendrogram(hc)
# Extract the data (for rectangular lines)
# Type can be "rectangle" or "triangle"
dend_data <- dendro_data(dend, type = "rectangle")
# What contains dend_data
names(dend_data)

# Extract data for line segments
head(dend_data$segments)

# Extract data for labels
head(dend_data$labels)

# Plot line segments and add labels
p <- ggplot(dend_data$segments) + 
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend))+
  geom_text(data = dend_data$labels, aes(x, y, label = label),
            hjust = 1, angle = 90, size = 3)+
  ylim(-3, 15)
print(p)


data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)

dend <- USArrests[1:5,] %>% # data
  scale %>% # Scale the data
  dist %>% # calculate a distance matrix, 
  hclust(method = "ward.D2") %>% # Hierarchical clustering 
  as.dendrogram # Turn the object into a dendrogram.
plot(dend)

library(dendextend)
Create a dendrogram and plot it
dend <- USArrests[1:5,] %>%  scale %>% 
  dist %>% hclust %>% as.dendrogram
dend %>% plot

# Change the labels, and then plot:
dend %>% set("labels", c("a", "b", "c", "d", "e")) %>% plot

# Change color and size for labels
dend %>% set("labels_col", c("green", "blue")) %>% # change color
  set("labels_cex", 2) %>% # Change size
  plot(main = "Change the color \nand size") # plot

# Color labels by specifying the number of cluster (k)
dend %>% set("labels_col", value = c("green", "blue"), k=2) %>% 
  plot(main = "Color labels \nper cluster")
abline(h = 2, lty = 2)






library(pvclust)
data(lung) # 916 genes for 73 subjects
set.seed(1234)
result <- pvclust(lung[1:100, 1:10], method.dist="cor", 
                  method.hclust="average", nboot=10)
# Default plot of the result
plot(result)
pvrect(result)

# pvclust and dendextend
result %>% as.dendrogram %>% 
  set("branches_k_color", k = 2, value = c("purple", "orange")) %>%
  plot
result %>% text
result %>% pvrect


#circular -----
iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
library(colorspace) # get nice colors
species_col <- rev(rainbow_hcl(3))[as.numeric(species_labels)]
d_iris <- dist(iris2) # method="man" # is a bit better
hc_iris <- hclust(d_iris, method = "complete")
iris_species <- rev(levels(iris[,5]))

library(dendextend)
dend <- as.dendrogram(hc_iris)
# order it the closest we can to the order of the observations:
dend <- rotate(dend, 1:150)
par(mar = rep(0,4))
par(mar = rep(1,4))
par(mar =c(1,1,1,1))
dend <- hang.dendrogram(dend,hang_height=0.1)
circlize_dendrogram(dend)


================================================
FILE: 61-clust/43b-clust-mixedDataTypes1.R
================================================
#Clustetering - College Data

set.seed(1680) # for reproducibility

library(dplyr) # for data cleaning
library(ISLR) # for college dataset
library(cluster) # for gower similarity and pam
library(tsne) # for t-SNE plot
library(ggplot2) # for visualization

college_clean <- College %>%  mutate(name = row.names(.), accept_rate = Accept/Apps,  isElite = cut(Top10perc, breaks = c(0, 50, 100),labels = c("Not Elite", "Elite"),include.lowest = TRUE)) %>% mutate(isElite = factor(isElite)) %>% select(name, accept_rate, Outstate, Enroll, Grad.Rate, Private, isElite)

glimpse(college_clean)

# Remove college name before clustering

gower_dist <- daisy(college_clean[, -1],metric = "gower",
        type = list(logratio = 3))
gower_dist
# Check attributes to ensure the correct methods are being used
# (I = interval, N = nominal)
# Note that despite logratio being called, 
# the type remains coded as "I"

summary(gower_dist)
## 301476 dissimilarities, summarized :
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0018601 0.1034400 0.2358700 0.2314500 0.3271400 0.7773500 
## Metric :  mixed ;  Types = I, I, I, I, N, N 
## Number of objects : 777

gower_mat <- as.matrix(gower_dist)
gower_mat

# Output most similar pair
college_clean[which(gower_mat == min(gower_mat[gower_mat != min(gower_mat)]),
        arr.ind = TRUE)[1, ], ]

# Output most dissimilar pair
college_clean[which(gower_mat == max(gower_mat[gower_mat != max(gower_mat)]),
        arr.ind = TRUE)[1, ], ]


# Calculate silhouette width for many k using PAM

sil_width <- c(NA)
for(i in 2:10){
  pam_fit <- pam(gower_dist,
                 diss = TRUE,
                 k = i)
  sil_width[i] <- pam_fit$silinfo$avg.width
}

# Plot sihouette width (higher is better)
plot(1:10, sil_width,
     xlab = "Number of clusters",
     ylab = "Silhouette Width")
lines(1:10, sil_width)


pam_fit <- pam(gower_dist, diss = TRUE, k = 3)

pam_results <- college_clean %>%
  dplyr::select(-name) %>%
  mutate(cluster = pam_fit$clustering) %>%
  group_by(cluster) %>%
  do(the_summary = summary(.))

pam_results$the_summary

college_clean[pam_fit$medoids, ]


#tsne_obj <- tsne(gower_dist, is_distance = TRUE)
tsne_obj <- tsne(gower_dist)

tsne_data <- tsne_obj$Y %>%
  data.frame() %>%
  setNames(c("X", "Y")) %>%
  mutate(cluster = factor(pam_fit$clustering),
         name = college_clean$name)

ggplot(aes(x = X, y = Y), data = tsne_data) +
  geom_point(aes(color = cluster))

tsne_data %>%
  filter(X > 15 & X < 25,
         Y > -15 & Y < -10) %>%
  left_join(college_clean, by = "name") %>%
  collect %>%
  .[["name"]]


================================================
FILE: 61-clust/45c-clustering-exist1.R
================================================
#CLustering - Does Cluster exist
#install.packages("seriation")
library(seriation)

d_iris <- dist(iris[,-5])
VAT(d_iris)
iVAT(d_iris)


================================================
FILE: 61-clust/45e-clustering-animation1.R
================================================
#Clustering and Animation
#https://www.guru99.com/r-k-means-clustering.html

library(dplyr)
PATH <-"https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/Computers.csv"
data = read.csv(PATH)
data = read.csv(file.choose())
names(data)
dim(data)
#Remove certain columns
df <- data %>% select(-c(X, cd, multi, premium))
glimpse(df)

summary(df)
#scale and remove unscaled columns
rescale_df <- df %>%
  mutate(price_scal = scale(price),
         hd_scal = scale(hd),
         ram_scal = scale(ram),
         screen_scal = scale(screen),
         ads_scal = scale(ads),
         trend_scal = scale(trend)) %>%
  select(-c(price, speed, hd, ram, screen, ads, trend))

head(rescale_df)

#kmeans & animation
set.seed(2345)
library(animation)

kmeans.ani(rescale_df[2:5], 5)
#see the plot area on right hand side of rstudio
# Step 1: R randomly chooses three points
# Step 2: Compute the Euclidean distance and draw the clusters. You have one cluster in green at the bottom left, one large cluster colored in black at the right and a red one between them.
# Step 3: Compute the centroid, i.e. the mean of the clusters
# Repeat until no data changes cluster

pc_cluster <-kmeans(rescale_df, 5)

# The list pc_cluster contains seven interesting elements:
# pc_cluster$cluster: Indicates the cluster of each observation
# pc_cluster$centers: The cluster centres
# pc_cluster$totss: The total sum of squares
# pc_cluster$withinss: Within sum of square. The number of components return is equal to `k`
# pc_cluster$tot.withinss: Sum of withinss
# pc_clusterbetweenss: Total sum of square minus Within sum of square
# pc_cluster$size: Number of observation within each cluster

kmean_withinss <- function(k) {
  cluster <- kmeans(rescale_df, k)
  return (cluster$tot.withinss)
}
kmean_withinss(2)

# Set maximum cluster 
max_k <-20 
# Run algorithm over a range of k 
wss <- sapply(2:max_k, kmean_withinss)

# Create a data frame to plot the graph
elbow <-data.frame(2:max_k, wss)

# Plot the graph with gglop
library(ggplot2)
ggplot(elbow, aes(x = X2.max_k, y = wss)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(1, 20, by = 1))

#From the graph, you can see the optimal k is seven, where the curve is starting to have a diminishing return. Once you have our optimal k, you re-run the algorithm with k equals to 7 and evaluate the clusters.

pc_cluster_2 <-kmeans(rescale_df, 7)

pc_cluster_2$cluster
pc_cluster_2$centers
pc_cluster_2$size

(center <-pc_cluster_2$centers)

library(tidyr)

# create dataset with the cluster number

cluster <- c(1: 7)
(center_df <- data.frame(cluster, center))

# Reshape the data

center_reshape <- gather(center_df, features, values, price_scal: trend_scal)
head(center_reshape)

library(RColorBrewer)
# Create the palette
hm.palette <-colorRampPalette(rev(brewer.pal(10, 'RdYlGn')),space='Lab')

# Plot the heat map
ggplot(data = center_reshape, aes(x = features, y = cluster, fill = values)) +
  scale_y_continuous(breaks = seq(1, 7, by = 1)) +
  geom_tile() +
  coord_equal() +
  scale_fill_gradientn(colours = hm.palette(90)) +
  theme_classic()



================================================
FILE: 61-clust/50b-clust-ma1.R
================================================
#Clustering - offer - marketing campaign

library(gsheet)
?gsheet2tbl
fullurl_1 = "https://docs.google.com/spreadsheets/d/13tWJP6fcWNE7FObz4Cn_-_R0us7qIFialUy1m9nwBYY/edit#gid=1772535900"
offers = as.data.frame(gsheet::gsheet2tbl(fullurl_1))
dim(offers)


fullurl_2 = "https://docs.google.com/spreadsheets/d/13tWJP6fcWNE7FObz4Cn_-_R0us7qIFialUy1m9nwBYY/edit#gid=788072611"
transactions = as.data.frame(gsheet::gsheet2tbl(fullurl_2))
dim(transactions)


#Step-1: # Create transaction matrix (a pivot table like in Excel way!)
library(reshape)
head(transactions)
pivot = melt(transactions[1:2])
head(pivot)


pivot2 = (cast(pivot,value ~ custlastname, fill=0, fun.aggregate=function(x) length(x)))
head(pivot2)

pivot3 = cbind(offers,pivot2[-1])
head(pivot3)

#can save the data for future use
# write.csv(file="pivot.csv",pivot) # to save your data

cluster.data = pivot3[,8:length(pivot3)]
names(cluster.data)
cluster.data2 = t(cluster.data) #transpose
head(cluster.data2)

#Step-2
library(cluster)
D=daisy(cluster.data2, metric='gower')

#After the creation of a distance matrix, we implement a Ward’s hierarchical clustering procedure:
  
H.fit = hclust(D, method="ward.D2")
plot(H.fit) # display dendrogram
groups = cutree(H.fit, k=4) # cut tree into 4 clusters
# draw dendogram with red borders around the 4 clusters
rect.hclust(H.fit, k=4, border="red") 
# 2D representation of the Segmentation:
clusplot(cluster.data2,groups, color=TRUE, shade=TRUE, labels=2, lines=0, main= 'Customer segments')

?clusplot


================================================
FILE: 61-clust/50c-clust-ma2.R
================================================
# Clustering For Customer Segmentation

set.seed(1234); (age = ceiling(rnorm(50, 45, 10)))
set.seed(1234);(income = ceiling(rnorm(50, 100000, 10000)))
set.seed(1234);(children = sample(c(1,2,3), size=50, replace=T, prob=c(.4,.3,.2)))
customers = data.frame(age, income, children)
head(customers)

# No of Clusters
library(NbClust)
nc = NbClust(customers, distance="euclidean", 
             min.nc=2, max.nc=15, method="average")
#without scaling
km1 = kmeans(customers, centers=3)
km1$centers

#----
#------------------------------------------------------------------------
#plots
library(cluster)
cluster::clusplot(customers, km1$cluster, color=TRUE, shade=TRUE, 
         labels=2, lines=0)

#https://blog.datascienceheroes.com/short-lesson-on-cluster-analysis/
library(clusplus)
plot_clus_coord(km1, customers)





#case1 : only age
age1 = data.frame(age)
clust1 = kmeans(age1, 3)
clust1$cluster
combined = cbind(age1, clust1$cluster)
plot(age1$age)
plot(age1$age, col=clust1$cluster, pch=c(11,12,14))

#case1 : only income
income1 = data.frame(income)
clust1a = kmeans(income, 4)
clust1a$cluster
(combined = cbind(income1, clust1a$cluster))
combined[order(clust1a$cluster),]
plot(income1$income, col=clust1a$cluster, pch=c(11,12,14,15))




df2 = data.frame(age, income)
clust2 = kmeans(df2, 4)
clust2$cluster
combined2 = cbind(df2, clust2$cluster)
head(combined2)
plot(x=df2$age, y=df2$income, col=clust2$cluster, pch=c(11,12,14,17))
df2
df2[clust2$cluster==3,]



(gender = sample(c(1,2), size=1000, replace=T, prob=c(.6,.4)))
table(gender)
prop.table(table(gender))


================================================
FILE: 61-clust/50d-clust-ma3.R
================================================
# HH MA example 
#setwd("")
#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
names(data)
summary(data)
##
##
##
##
cust_data<-read.csv("./data/Segmentation_Data v01.csv")
cust_data = data
###Verify the data
colnames(cust_data)
class(cust_data$Age)
apply(cust_data, 2, FUN= class)

dim(cust_data)
head(cust_data)
summary(cust_data)
str(cust_data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(cust_data)

k1<-amap::Kmeans(cust_data[,-c(1)],centers=3, iter.max = 200,nstart = 1, 
           method = c("euclidean"))
k1<-amap::Kmeans(cust_data[,-c(1)],3)


k1$centers  # group means
###Fetch size/n of obs for the groups
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
dim(cust_data)
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- cust_data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")

names(mtcars)
colnames(mtcars)
rownames(mtcars)


================================================
FILE: 61-clust/61b-clust-custsegm.R
================================================
#Clustering - Customer Segmentation
#Import Data using Google Sheets
#library - gsheet, corrplot, dplyr
#Link : http://archive.ics.uci.edu/ml/datasets/Wholesale+customers https://subhayo.wordpress.com/2017/08/15/customer-segmentation-using-k-means-clustering/

easypackages::libraries( 'dplyr', 'gsheet', 'corrplot')
#library(dplyr);library(gsheet); library(corrplot)

#import Data
url="https://docs.google.com/spreadsheets/d/1y6a6neeU-nH7zsAyDuyyy8GbPyGaxYfwpWcjIT4gInU/edit#gid=251433617"
data1 = as.data.frame(gsheet2tbl(url))
#data = read.csv(file.choose())
head(data1)  
cdata1 = data1

#Read the input file
w = cor(cdata1)
corrplot(w)
corrplot(w,method="number")
#check for strong cor = Grocery & Detergents

#Choosing no of clusters - The elbow method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn’t give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the “elbow criterion”.

set.seed(123)
k.max = 15
head(cdata1)  #skip 1st & 2nd col
#scale the data and put in matrix : all will be numeric
sdata1 = as.matrix(scale(cdata1[,(3:8)]))
head(sdata1)
wss = sapply(1:k.max, function(k){
  kmeans(sdata1, k, nstart=50,iter.max = 15 )$tot.withinss
  })

plot(1:k.max, wss,
      type="b", pch = 19, frame = FALSE, 
      xlab="Number of clusters",
      ylab="Sum of squares")
abline(v=c(3:5))
#rom the above plot we can see that 3 or 5 is the optimal number of clusters, as we can see that after these numbers the curve remains less changing.

#create 3 clusters
#use scaled data sdata1
set.seed(123)
km3 = kmeans(sdata1, 3)
km3
set.seed(456)
#create 5 clusters
km5 = kmeans(sdata1, 5)
km5

#Cluster Analysis & Interpretation
head(km3$cluster)
cluster_data = cbind(cdata1, C3= km3$cluster, C5=km5$cluster)
head(cluster_data)
#join them into 1 data frame

(C3sum <- cluster_data %>% select(-c(C5, Channel, Region)) %>% group_by(C3) %>% summarise_all(mean) %>% print(width=Inf, n = Inf))

# Cluster 2 – highest fresh-products.
C3sum %>% arrange(Fresh)

# Cluster 3 – low spenders.
C3sum %>% select(-1) %>% rowSums %>% cbind()

# Cluster 1 – highest milk, grocery, detergents_papers spenders.
C3sum %>% replace(is.na(.), 0) %>% mutate(Rsum = rowSums(.[-1])) %>% arrange(-Rsum)
C3sum  #find max of each column and tell its cluster no
max.col(C3sum,ties.method="first")  #column no, we want name
colnames(C3sum)[max.col(C3sum,ties.method="first")]
(C3sumT = t(C3sum))
colnames(C3sumT) = paste("cluster",1:3,sep='')
C3sumT
#Cluster No with highest in Col...
cbind(rownames(C3sumT), colnames(C3sumT)[max.col(C3sumT,ties.method="first")])

#Analyse Cluster 5

(C5sum <- cluster_data %>% select(-c(C3, Channel, Region)) %>% group_by(C5) %>% summarise_all(mean) %>% print(width=Inf, n = Inf))

# Cluster 3 – low spenders
rowSums(C5sum)
cbind(rowSums(C5sum[-1]), C5sum$C5)
C5sum %>% replace(is.na(.), 0) %>% mutate(Rsum = rowSums(.[-1])) %>% arrange(-Rsum)
# Cluster 1 – mediocre spenders 55681
# Cluster 3 – low spenders 20087

# Cluster 4 – highest Fresh spenders
C5sum %>% arrange(-Fresh)
# Cluster 4 – highest- Fresh, milk, Frozen, Delcassen
(C5sumT = t(C5sum))
colnames(C5sumT) = paste("cluster",1:5,sep='')
C5sumT
#Cluster No with highest in Col...
cbind(rownames(C5sumT), colnames(C5sumT)[max.col(C5sumT,ties.method="first")])


#cluster 2 from km5 : save as csv file and send for more analysis 
km5_c2 = subset(cluster_data, C5==2)
write.csv(km5_c2, './data/km5_c2.csv')


#Hierarchical Clustering
#http://inseaddataanalytics.github.io/INSEADAnalytics/CourseSessions/Sessions45/ClusterAnalysisReading.html

#Kmeans requires the user to define how many segments to create, while Hierarchical Clustering does not.
#Hierarchial Clustering method, as we do not know for now how many segments there are in our data. Hierarchical clustering is a method that also helps us visualise how the data may be clustered together. It generates a plot called the Dendrogram which is often helpful for visualization 

head
# Run hclust algorithm
head(cdata1)
hc1 =  hclust(dist(cdata1))
dim(cdata1)

library(ggdendro)
dendrogram1  <- ggdendrogram(hc1, rotate = TRUE)
dendrogram1
# Write plot to disk
library(ggplot2)
ggsave("dendrogram.png",dendrogram1, width = 16, height = 9)

plot(hc1)
plot(hc1, cex = 0.6, hang = -1)
rect.hclust(hc1, k=3, border="red") 

#Cut the Hcluster into 5 groups
hc5 = cutree(hc1, k=5) # cut tree into 5 clusters
#which data into which cluster
hc5_data = cbind(cdata1, hc5)
head(hc5_data)
hc5_data %>% group_by(hc5) %>% select(-c(1,2)) %>% summarise_all(mean)
C5sum


================================================
FILE: 61-clust/animation2.R
================================================
library(animation)
df = mtcars
data = df[c('mpg','wt')]
str(data)
#kmeans.ani - function provides a demo of the k-Means cluster algorithm for data containing only two variables (columns).
plot(scale(data))
km = kmeans(scale(data),3)
plot(scale(data), col=1:km$cluster)
plotcluster(data, km$cluster)
plotcluster(scale(data), km$cluster)
clusplot(data, km$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
clusplot(scale(data), km$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)


km2= kmeans.ani(scale(data), 3)
km2
km$cluster == km2$cluster
cbind(km$cluster, km2$cluster)
km$tot.withinss 
km$centers;km2$centers

================================================
FILE: 61-clust/clust-allcustering.R
================================================
#https://github.com/mhahsler/Introduction_to_Data_Mining_R_Examples/blob/master/chap8.R


#' This code covers chapter 8 of _"Introduction to Data Mining"_
#' by Pang-Ning Tan, Michael Steinbach and Vipin Kumar.
#' __See [table of contents](https://github.com/mhahsler/Introduction_to_Data_Mining_R_Examples#readme) for code examples for other chapters.__

#' [Michael Hahsler](http://michael.hahsler.net).



#' ruspini_scaled data is in package cluster. It is a very simple data set with well separated clusters.
data(ruspini, package="cluster")
#' Shuffle rows
ruspini <- ruspini[sample(1:nrow(ruspini)),]
plot(ruspini)
head(ruspini)

#' Scale each column in the data to zero mean and unit standard deviation (z-scores). This prevents one attribute with a large range to dominate the others for the distance calculation.
ruspini_scaled <- scale(ruspini)
plot(ruspini_scaled)

#' # Clustering methods
#' ## k-means Clustering
#'
#' Assumes Euclidean distances. We use k=10 clusters and run the algorithm 10 times with random initialized centroids. The best result is returned.
km <- kmeans(ruspini_scaled, centers=4, nstart=10)
km
km$iter
plot(ruspini_scaled, col=km$cluster)
points(km$centers, pch=3, cex=2) # this adds the centroids
text(km$centers, labels=1:4, pos=2) # this adds the cluster ID
#' Alternative plot from package cluster (uses principal components analysis for >2 dimensions)
library(cluster)
clusplot(ruspini_scaled, km$cluster)

#' Inspect the centroids (cluster profiles)
km$centers

#+ fig.height=3, fig.width=10
def.par <- par(no.readonly = TRUE) # save default, for resetting...
layout(t(1:4)) # 4 plots in one
for(i in 1:4) barplot(km$centers[i,], ylim=c(-2,2), main=paste("Cluster", i))
par(def.par)  #- reset to default

#' Find data for a single cluster
#'#' All you need is to select the rows corresponding to the cluster. The next
#' example plots all data points of cluster 1
cluster1 <- ruspini_scaled[km$cluster==1,]
head(cluster1)
plot(cluster1, xlim = c(-2,2), ylim = c(-2,2))

#' Try 10 clusters
plot(ruspini_scaled, col=kmeans(ruspini_scaled, centers=10)$cluster)

#' ## Hierarchical Clustering
#'
#' dist defaults to method="Euclidean"
d <- dist(ruspini_scaled)
#' We cluster using complete link
hc <- hclust(d, method="complete")

#' Dendrogram
plot(hc)
rect.hclust(hc, k=4)

plot(as.dendrogram(hc), leaflab="none") # plot dendrogram without leaf labels

#' More plotting options for dendrograms, including plotting
#' parts of large dendrograms can be found [here.](https://rpubs.com/gaston/dendrograms)

cluster_complete <- cutree(hc, k=4)
plot(ruspini_scaled, col=cluster_complete)

#' Try 10 clusters
plot(ruspini_scaled, col=cutree(hc, k=10))

#' Clustering with single link
hc_single <- hclust(d, method="single")
plot(hc_single)
rect.hclust(hc_single, k=4)

cluster_single <- cutree(hc_single, k=4)
plot(ruspini_scaled, col=cluster_single)

#' Try 10 clusters
plot(ruspini_scaled, col=cutree(hc_single, k=10))

#' ## Density-based clustering with DBSCAN

library(dbscan)

#' Parameters: minPts is often chosen as dimensionality of the data +1.
#' Decide on epsilon using the knee in the kNN distance plot
#' (seems to be around eps = .25).
kNNdistplot(ruspini_scaled, k = 3)
abline(h=.25, col="red")

#' run dbscan
db <- dbscan(ruspini_scaled, eps=.25, minPts=3)
db
str(db)
plot(ruspini_scaled, col=db$cluster+1L)
#' __Note:__ 0 is not a color so we add 1 to cluster (0 is black now).
#'
#' Alternative visualization from package dbscan
hullplot(ruspini_scaled, db)

#'
#' Play with eps (neighborhood size) and MinPts (minimum of points needed for core cluster)

#'
#' ## Gaussian Mixture Models
library(mclust)

#' Mclust uses Bayesian Information Criterion (BIC) to find the
#' number of clusters (model selection). BIC uses the likelihood and a
#' penalty term to guard against overfitting.
m <- Mclust(ruspini_scaled)
summary(m)
plot(m, what = "classification")

#' Rerun with a fixed number of 4 clusters
m <- Mclust(ruspini_scaled, G=4)
summary(m)
plot(m, what = "classification")

#' ## Spectral clustering
library("kernlab")
cluster_spec <- specc(ruspini_scaled, centers = 4)
plot(ruspini_scaled, col=cluster_spec)


#' # Internal Cluster Validation
#'
#' ## Compare the Clustering Quality
#'
#' Look at the within.cluster.ss and the avg.silwidth

#library(fpc)
#' Note: I do not load fpc since the NAMESPACE overwrites dbscan.

fpc::cluster.stats(d, km$cluster)
#cluster.stats(d, cluster_complete)
#cluster.stats(d, cluster_single)

#' Read `? cluster.stats` for an explanation of all the available indices.

sapply(list(
  km=km$cluster,
  hc_compl=cluster_complete,
  hc_single=cluster_single),
  FUN=function(x)
    fpc::cluster.stats(d, x))[c("within.cluster.ss","avg.silwidth"),]

#' ## Silhouette plot
library(cluster)
plot(silhouette(km$cluster, d))
#' __Note:__ The silhouette plot does not show correctly in R Studio if you have too many objects (bars are missing). I will work when you open a new plotting device with `windows()`, `x11()` or `quartz()`.
#'

#' ## Find Optimal Number of Clusters for k-means
plot(ruspini_scaled)

set.seed(1234)
ks <- 2:10

#' ### Within Sum of Squares
#' Use within sum of squares and look for the knee (nstart=5 repeats k-means 5 times and returns the best solution)
WSS <- sapply(ks, FUN=function(k) {
  kmeans(ruspini_scaled, centers=k, nstart=5)$tot.withinss
})
plot(ks, WSS, type="l")
abline(v=4, col="red", lty=2)

#' ### Average Silhouette Width
#' Use average silhouette width (look for the max)
ASW <- sapply(ks, FUN=function(k) {
  fpc::cluster.stats(d, kmeans(ruspini_scaled, centers=k, nstart=5)$cluster)$avg.silwidth
})
plot(ks, ASW, type="l")

ks[which.max(ASW)]
abline(v=ks[which.max(ASW)], col="red", lty=2)

#' ### Dunn Index
#' Use Dunn index (another internal measure given by min. separation/ max. diameter)
DI <- sapply(ks, FUN=function(k) {
  fpc::cluster.stats(d, kmeans(ruspini_scaled, centers=k, nstart=5)$cluster)$dunn
})
plot(ks, DI, type="l")
ks[which.max(DI)]
abline(v=ks[which.max(DI)], col="red", lty=2)


#' ### Gap Statistic
#' Compares the change in within-cluster dispersion with that expected
#' from a null model (see `? clusGap`).
#' The default method is to
#' choose the smallest k such that its value Gap(k) is not more
#' than 1 standard error away from the first local maximum.
library(cluster)
k <- clusGap(ruspini_scaled, FUN = kmeans,  nstart = 10, K.max = 10)
k
plot(k)


#' __Note:__ these methods can also be used for hierarchical clustering.
#'
#' There have been many other methods and indices proposed to determine
#' the number of clusters.
#' See, e.g.,  package [NbClust](https://cran.r-project.org/package=NbClust).
#'

#' ## Visualize the Distance Matrix
#'
#' Visualizing the unordered distance matrix does not show much structure.

plot(ruspini_scaled)
d <- dist(ruspini_scaled)

library(seriation)
pimage(d)

#' Reorder using cluster labels
pimage(d, order=order(km$cluster))

#' Use dissplot which rearranges clusters, adds cluster labels,
#'  and shows average dissimilarity in the lower half of the plot.
dissplot(d, labels=km$cluster, options=list(main="k-means with k=4"))
dissplot(d, labels=db$cluster+1, options=list(main="DBSCAN"))
#' Spot the problem data points for DBSCAN (we use +1 so the noise is now cluster #1)
#'
#' Misspecified k
dissplot(d, labels=kmeans(ruspini_scaled, centers=3)$cluster)
dissplot(d, labels=kmeans(ruspini_scaled, centers=9)$cluster)


#' # External Cluster Validation
#'
#' External cluster validation uses ground truth information. That is,
#' the user has an idea how the data should be grouped. This could be a know
#' class label not provided to the clustering algorithm.
#'
#' We use an artificial data set with known groups here. First, we need to
#' cluster the new data. We do k-means and hierarchical clustering.

library(mlbench)
shapes <- mlbench.smiley(n=500, sd1 = 0.1, sd2 = 0.05)
plot(shapes)

#' Prepare data
truth <- as.integer(shapes$class)
shapes <- scale(shapes$x)

plot(shapes)

#' Find optimal number of Clusters for k-means
ks <- 2:20

#' Use within sum of squares (look for the knee)
WSS <- sapply(ks, FUN=function(k) {
  kmeans(shapes, centers=k, nstart=10)$tot.withinss
})
plot(ks, WSS, type="l")
#' looks like 6 clusters
km <- kmeans(shapes, centers=6, nstart = 10)
plot(shapes, col=km$cluster)

#' Hierarchical clustering: single-link because of the mouth
d <- dist(shapes)
hc <- hclust(d, method="single")

#' Find optimal number of clusters
ASW <- sapply(ks, FUN=function(k) {
  fpc::cluster.stats(d, cutree(hc, k))$avg.silwidth
})
plot(ks, ASW, type="l")
#' 4 clusters
hc_4 <- cutree(hc, 4)
plot(shapes, col=hc_4)

#' Spectral Clustering
library("kernlab")
spec <- specc(shapes, centers = 4)
plot(shapes, col=spec)


#' Compare with ground truth with the corrected (=adjusted) Rand index (ARI),
#' the variation of information (VI) index, entropy and purity.
#
#' define entropy and purity
entropy <- function(cluster, truth) {
  k <- max(cluster, truth)
  cluster <- factor(cluster, levels = 1:k)
  truth <- factor(truth, levels = 1:k)
  m <- length(cluster)
  mi <- table(cluster)
  
  cnts <- split(truth, cluster)
  cnts <- sapply(cnts, FUN = function(n) table(n))
  p <- sweep(cnts, 1, rowSums(cnts), "/")
  p[is.nan(p)] <- 0
  e <- -p * log(p, 2)
  sum(rowSums(e, na.rm = TRUE) * mi/m)
}

purity <- function(cluster, truth) {
  k <- max(cluster, truth)
  cluster <- factor(cluster, levels = 1:k)
  truth <- factor(truth, levels = 1:k)
  m <- length(cluster)
  mi <- table(cluster)
  
  cnts <- split(truth, cluster)
  cnts <- sapply(cnts, FUN = function(n) table(n))
  p <- sweep(cnts, 1, rowSums(cnts), "/")
  p[is.nan(p)] <- 0
  
  sum(apply(p, 1, max) * mi/m)
}

#' calculate measures (for comparison we also use random "clusterings"
#' with 4 and 6 clusters)
random4 <- sample(1:4, nrow(shapes), replace = TRUE)
random6 <- sample(1:6, nrow(shapes), replace = TRUE)

r <- rbind(
  kmeans = c(
    unlist(fpc::cluster.stats(d, km$cluster, truth, compareonly = TRUE)),
    entropy = entropy(km$cluster, truth),
    purity = purity(km$cluster, truth)
  ),
  hc = c(
    unlist(fpc::cluster.stats(d, hc_4, truth, compareonly = TRUE)),
    entropy = entropy(hc_4, truth),
    purity = purity(hc_4, truth)
  ),
  spec = c(
    unlist(fpc::cluster.stats(d, spec, truth, compareonly = TRUE)),
    entropy = entropy(spec, truth),
    purity = purity(spec, truth)
  ),
  random4 = c(
    unlist(fpc::cluster.stats(d, random4, truth, compareonly = TRUE)),
    entropy = entropy(random4, truth),
    purity = purity(random4, truth)
  ),
  random6 = c(
    unlist(fpc::cluster.stats(d, random6, truth, compareonly = TRUE)),
    entropy = entropy(random6, truth),
    purity = purity(random6, truth)
  )
)
r

#' Hierarchical clustering found the perfect clustering.
#'
#' Read `? cluster.stats` for an explanation of all the available indices.


#'
#' # Related Topics
#' ## Outlier Removal
#' It is often useful to remove outliers prior to clustering.
#' A density based method to identify outlier is LOF (Local Outlier Factor).
#' It is related to dbscan and compares the density around a point with the
#' densities around its neighbors. The LOF value for a regular data
#' point is 1. The larger the LOF value gets, the more likely the point is and
#' outlier.
library(dbscan)
lof <- lof(ruspini_scaled, k = 3)
lof
plot(ruspini_scaled, pch = ".", main = "LOF (k=3)")
points(ruspini_scaled, cex = (lof-1)*3, pch = 1, col="red")

#' Find outliers (find the knee)
plot(sort(lof), type = "l")
abline(h = 1.3, col = "red")
plot(ruspini_scaled[lof < 1.3,], main = "Data and outliers")
points(ruspini_scaled[lof >= 1.3,], col = "grey", pch = 4)


#' There are many other outlier removal strategies available. See, e.g., package
#' [outliers](https://cran.r-project.org/package=outliers).
#'
#' ## Clustering Tendency
#' Most clustering algorithms will always produce a clustering, even if the
#' data does not contain a cluster structure. It is typically good to check
#' cluster tendency before attempting to cluster the data.
#'
#' We use again the smiley data.
d_shapes <- dist(shapes)

library(seriation)
#' Visual Analysis for Cluster Tendency Assessment (VAT) reorders the
#' objects to show potential clustering tendency as a block structure
#' (dark blocks along the main diagonal).
VAT(d_shapes)

#' iVAT uses largest distances in all possible paths between the objects
#' instead of the distances to make the block structure better visible.
iVAT(d_shapes)

#' Both plots show a strong cluster structure with 4 clusters.
#'
#' Compare with random data.
data_random <- matrix(runif(2*500), ncol=2, dimnames = list(NULL, c("x", "y")))
plot(data_random)

d_random <- dist(data_random)
VAT(d_random)
iVAT(d_random)
#' There is very little structure visible.

================================================
FILE: 61-clust/clust-case-liberty.R
================================================
# Liberty Case for Clustering

custid = 1:12
trans = c(5,10,15,2,18,9,14,8,7,9,1,6)
totpurch = c(450,800,900,50, 900, 200, 500, 300, 250, 1000, 30, 700)
income = c(90,82,77,30, 60, 45, 82, 22, 90, 80,60, 80)
df = data.frame(custid, trans, totpurch, income)
apply(df, 2, FUN=length)
#Right no of clusters
#Centeriod for the clusters




================================================
FILE: 61-clust/clust-class-differences.R
================================================
#Simple Case - classification and clustering
#This could be done by finding the similarity between two hair lengths and keep them in the same group if the similarity is less(Difference of hair length is less). The same process could continue until all the hair length properly grouped into two categories.
#http://dataaspirant.com/2016/09/24/classification-clustering-alogrithms/
#sample data
set.seed(123)
hairlength = ceiling(c(rnorm(25, mean=12, sd=4), (rnorm(25, mean=25, sd=5))))
hairlength
gender = factor(rep(c('M','F'), each=25))                     
gender
hair = data.frame(hairlength, gender)
table(hair$gender)
aggregate(hairlength ~ gender, data=hair, mean)

#classification-----
library(rpart); library(rpart.plot)
tree = rpart(gender ~ hairlength, data= hair)
tree
rpart.plot(tree)
predict(tree, newdata = data.frame(hairlength=22), type='class')
#

#Clustering-----
names(hair)
data = hair$hairlength
km2 = kmeans(data, centers=2, nstart=3)
km2
km2$centers
km2$cluster
plot(data, col=km2$cluster)
#clusters do not indicate whether they relate to male or female

#hierarchial clustering
hc = hclust(dist(data))
plot(hc)
plot(hc, cex = 0.6)
rect.hclust(hc, k = 2, border = 4:5)
rect.hclust(hc, k = 3, border = 3:5)
ct = cutree(hc, k = 2)
mean(data[ct==1])
mean(data[ct==2])

?rect.hclust

library(ape)
library(cluster) 
plot(as.phylo(hc),type="fan")


================================================
FILE: 61-clust/clust-compare.R
================================================
# compare different clustering methods using cluster.stat from the fpc package.

library(fpc)
#Perform the following steps to compare clustering methods.

#install.packages("fpc")

#You then need to use hierarchical clustering with the single method to cluster customer data and generate the object hc_single

single_c = hclust(dist(iris[,1:4]), method="single")
plot(single_c)

hc_single = cutree(single_c, k=3)

#Use hierarchical clustering with the complete method to cluster customer data and generate the object hc_complete

complete_c = hclust(dist(iris[,1:4]), method="complete")

(hc_complete = cutree(complete_c, k=3))

#You can then use k-means clustering to cluster customer data and generate the object km

set.seed(1234)
km = kmeans(iris[1:4], 3, iter.max=1000, algorithm=c("Forgy"))

#Next, retrieve the cluster validation statistics of either clustering method:
  
cs = cluster.stats(dist(iris[1:4]), km$cluster)
cs
#Most often, we focus on using within.cluster.ss and avg.silwidth to validate the clustering method. The within.cluster.ss measurement stands for the within clusters sum of squares, and avg.silwidth represents the average silhouette width.

#within.cluster.ss measurement shows how closely related objects are in clusters; the smaller the value, the more closely related objects are within the cluster.
#avg.silwidth is a measurement that considers how closely related objects are within the cluster and how clusters are separated from each other. The silhouette value usually ranges from 0 to 1; a value closer to 1 suggests the data is better clustered.
cs[c("within.cluster.ss","avg.silwidth")]

#Finally, we can generate the cluster statistics of each clustering method and list them in a table:

cluster.stats(dist(iris[1:4]), km$cluster)[c("within.cluster.ss","avg.silwidth")] 
cluster.stats(dist(iris[1:4]), hc_single)[c("within.cluster.ss","avg.silwidth")] 
cluster.stats(dist(iris[1:4]), hc_complete)[c("within.cluster.ss","avg.silwidth")] 


sapply(list(kmeans = km$cluster, hc_single = hc_single, hc_complete = hc_complete),  function(c) cluster.stats(dist(iris[1:4]), c)[c("within.cluster.ss","avg.silwidth")])

        
#https://en.proft.me/2017/01/29/exploring-hierarchical-clustering-r/


================================================
FILE: 61-clust/clust-distance-calc.R
================================================
# Distance Calcuation
#https://stackoverflow.com/questions/27082378/how-to-compute-distances-between-centroids-and-data-matrix-for-kmeans-algorithm

myEuclid <- function(points1, points2) {
  distanceMatrix <- matrix(NA, nrow=dim(points1)[1], ncol=dim(points2)[1])
  for(i in 1:nrow(points2)) {
    distanceMatrix[,i] <- sqrt(rowSums(t(t(points1)-points2[i,])^2))
  }
  distanceMatrix
}


myKmeans <- function(x, centers, distFun, nItter=10) {
  clusterHistory <- vector(nItter, mode="list")
  centerHistory <- vector(nItter, mode="list")
  
  for(i in 1:nItter) {
    distsToCenters <- distFun(x, centers)
    clusters <- apply(distsToCenters, 1, which.min)
    centers <- apply(x, 2, tapply, clusters, mean)
    # Saving history
    clusterHistory[[i]] <- clusters
    centerHistory[[i]] <- centers
  }
  
  list(clusters=clusterHistory, centers=centerHistory)
}


mat <- matrix(rnorm(100), ncol=2)
mat
centers <- mat[sample(nrow(mat), 5),]
centers
theResult <- myKmeans(mat, centers, myEuclid, 10)
theResult

theResult2 <- kmeans(mat, centers, 10, algorithm="Forgy")
theResult2

par(mfrow=c(2,2))
for(i in 1:4) {
  plot(mat, col=theResult$clusters[[i]], main=paste("itteration:", i), xlab="x", ylab="y")
  points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))
}

for(i in 5:8) {
  plot(mat, col=theResult$clusters[[i]], main=paste("itteration:", i), xlab="x", ylab="y")
  points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))
}


for(i in 9:10) {
  plot(mat, col=theResult$clusters[[i]], main=paste("itteration:", i), xlab="x", ylab="y")
  points(theResult$centers[[i]], cex=3, pch=19, col=1:nrow(theResult$centers[[i]]))
}


myCor <- function(points1, points2) {
  return(1 - ((cor(t(points1), t(points2))+1)/2))
}

theResult <- myKmeans(mat, centers, myCor, 10)



================================================
FILE: 61-clust/clust-distance2.R
================================================
#Clustering Example

#Packages - library(fields)

(income = c(68,72,100))
(customers = data.frame(income))

#which 2 customers are similar
(cm =combn(income, 2))
diff(c(68,72))
(d1= apply(cm, 2, diff))
rbind(cm,d1 )
#C1 & C2 are closer/ similar

#Now use euclidean distance
sqrt((68-72)^2); sqrt((68-100)^2); sqrt((100-72)^2)
fields::rdist(customers)
#Most Similar are 1 and 2 - diff is 4 units

#add one more factor
(age = c(25, 70, 28))
(customers2 = data.frame(income, age))
#which 2 customers are similar
# minus sign will cancel plus values, so use euclidean distance
dist(customers)
#sqrt(sum((x1-x2)^2 + (y1-y2)^2))
sqrt(sum((68 - 72)^2 + (25 - 70) ^2))
rdist(customers2)

#Most Similar now are C1 and C3


#when scales are different is recommended to standardise values
scale(customers2)
customers2
colMeans(customers2)
?scale
x = customers$income

scale1 = function(x) (x - min(x))/ (max(x) - min(x))
scale2 = function(x) (x - mean(x))/ sd(x)
scale1(x)
scale2(x)
scale(x)
rescale(x, to=c(0,1))
rescale(x, to=c(0,100))
apply(customers2, 2, FUN = rescale)
apply(customers, 2, FUN = rescale)


library(BBmisc)
normalize(x, method = "standardize", range = c(0, 1), margin = 1, on.constant = "quiet")


================================================
FILE: 61-clust/clust-entropy.R
================================================
#Entropy in Clustering
#https://www.rdocumentation.org/packages/NMF/versions/0.21.0/topics/purity

library(NMF)
# NOT RUN {
# generate a synthetic dataset with known classes: 50 features, 18 samples (5+5+8)
n <- 50; counts <- c(5, 5, 8);
V <- syntheticNMF(n, counts)
V
cl <- unlist(mapply(rep, 1:3, counts))
head(cl)
cl
# perform default NMF with rank=2
x2 <- nmf(V, 2)
purity(x2, cl)
entropy(x2, cl)
# perform default NMF with rank=2
x3 <- nmf(V, 3)
purity(x3, cl)
entropy(x3, cl)
# }

================================================
FILE: 61-clust/clust-iterations.R
================================================
#Check how clustering changes due to No of Iterations

iris
ciris = iris[-5]

km3 = kmeans(ciris, 3, iter.max=3, nstart=3, trace=T)
km3
km3$iter
?kmeans
km3 = kmeans(ciris, 3, iter.max = 15)
km3
km3$iter

library(ISLR)
data(package='ISLR')
head(Default)
cDefault = Default[c(3:4)]
head(cDefault)
dim(cDefault)
sDefault = scale(cDefault)
km3_default = kmeans(sDefault, 4)
km3_default$centers


================================================
FILE: 61-clust/clust-kselect.R
================================================
# Choosing K


# OutL2 is the dataset we are working with
iris
summary(iris)
df = iris[-5]
head(df)
# We take the first iteration
wss <- sum(kmeans(df,centers=1)$withinss)
wss

k1 = kmeans(df,3)
k1

k1$withinss  # k=3
k1$tot.withinss  # sum for all k
#tot.withinss = sum(withinss)
(tot.withinss = sum(k1$withinss))

k1$betweenss #1-2, 1-3, 2-3
k1$tot.withinss #as above
k1$totss #
#totss = tot.withinss + betweenss
(totss = k1$tot.withinss + k1$betweenss)


k1$size
k1$iter
k1$centers


# We take iteration 2 to 15
for (i in 2:15) wss[i] <- sum(kmeans(df,centers=i)$withinss)
wss

# We plot the 15 withinss values. One for each k
plot(1:15, wss, type="b", xlab="Number of Clusters",ylab="Within groups sum of squares")


library(cluster)
set.seed(1234)
fit.pam2 = pam(df, k=3, stand=TRUE)
fit.pam2$medoids
clusplot(fit.pam2, main='Bivariate Cluster Plot')
#plot of 1st two PC derived from PCA
summary(fit.pam2)
names(fit.pam2)




================================================
FILE: 61-clust/clustering-seeds-dunn.R
================================================
#Clustering - seeds data set
#https://www.datacamp.com/community/tutorials/hierarchical-clustering-R


set.seed(786)
file_loc <- 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/seeds.csv'
seeds_df <- read.csv(file_loc,header = FALSE)

feature_name <- c('area','perimeter','compactness','length.of.kernel','width.of.kernal','asymmetry.coefficient','length.of.kernel.groove','type.of.seed')
colnames(seeds_df) <- feature_name

str(seeds_df)
summary(seeds_df)
any(is.na(seeds_df))
seeds_df[!complete.cases(seeds_df),]
seeds_label <- seeds_df$type.of.seed
seeds_df$type.of.seed <- NULL
str(seeds_df)

seeds_df_sc <- as.data.frame(scale(seeds_df))
summary(seeds_df_sc)

dist_mat <- dist(seeds_df_sc, method = 'euclidean')

hclust_avg <- hclust(dist_mat, method = 'average')
plot(hclust_avg)
#cut the dendrogram in order to create the desired number of clusters. Since in this case you already know that there could be only three types of wheat you will choose the number of clusters to be k = 3, or as you can see in the dendrogram h = 3 you get three clusters. You will use R's cutree() function to cut the tree with hclust_avg as one parameter and the other parameter as h = 3 or k = 3.
cut_avg <- cutree(hclust_avg, k = 3)

plot(hclust_avg)
rect.hclust(hclust_avg , k = 3, border = 2:6)
abline(h = 3, col = 'red')

library(dendextend)
avg_dend_obj <- as.dendrogram(hclust_avg)
avg_col_dend <- color_branches(avg_dend_obj, h = 3)
plot(avg_col_dend)


library(dplyr)
seeds_df_cl <- mutate(seeds_df, cluster = cut_avg)
count(seeds_df_cl,cluster)
#how many observations were assigned in each cluster. Note that in reality from the labeled data you had 70 observations for each variety of wheat.

#rend between two features based on the clustering that you did in order to extract more useful insights from the data cluster-wise. As an exercise, you can analyze the trend between wheat's perimeter and area cluster-wise with the help of ggplot2 package

library(ggplot2)
ggplot(seeds_df_cl, aes(x=area, y = perimeter, color = factor(cluster))) + geom_point()

#all the varieties of wheat there seems to be a linear relationship between their perimeter and area.
#already have the true labels for this dataset, you can also consider cross-checking your clustering results using the table() function.
table(seeds_df_cl$cluster,seeds_label)

#see three groups with 55 elements or more. Overall, you can say that your clusters adequately represent the different types of seeds because originally you had 70 observations for each variety of wheat. The larger groups represent the correspondence between the clusters and the actual types.
#n many cases you don't actually have the true labels. In those cases, as already discussed, you can go for other measures like maximizing Dunn's index. You can calculate dunn's index by using the dunn() function from the clValid library. Also, you can consider doing cross validation of the results by making train and test sets, just like you do in any other machine learning algorithm, and then doing the clustering when you do have the true labels.

#---------------
library(clValid)
data(mouse)
express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) <- mouse$ID[1:25]
## hierarchical clustering
Dist <- dist(express,method="euclidean")
clusterObj <- hclust(Dist, method="average")
nc <- 2 ## number of clusters      
cluster <- cutree(clusterObj,nc)
dunn(Dist, cluster)

#-----
hclust_avg <- hclust(dist_mat, method = 'average')
cut_avg <- cutree(hclust_avg, k = 3)
plot(hclust_avg)
as.matrix(dist_mat)
head(seeds_df)
dunn(distance = dist_mat, clusters=cut_avg, Data = seeds_df, method = "euclidean")   
dunn(distance = dist_mat, clusters=cut_avg)
#distance The distance matrix (as a matrix object) of the clustered observations. Required if Data is NULL.
#clusters- An integer vector indicating the cluster partitioning
#Data -The data matrix of the clustered observations. Required if distance is NULL.
#method -The metric used to determine the distance matrix. Not used if distance is provided.

#https://kkulma.github.io/2017-05-10-cluster-validation-in-unsupervised-machine-learning/
#Dunn Index represents the ratio of the smallest distance between observations not in the same cluster to the largest intra-cluster distance. As you can imagine, the nominator should be maximised and the denomitor minimised, therefore the index should be maximized. 
#  0 is bad and 1 is good.
#  Minimize the inter-similarities and maximize the intra similarities between the clusters by a quotient object function as a clustering quality measure.


================================================
FILE: 61-clust/clustering-women.R
================================================
#Clustering with Women Data set
#http://dni-institute.in/blogs/k-means-clustering-algorithm-explained/


================================================
FILE: 61-clust/clusters3.R
================================================
# Non Existent Clusters

library(fMultivar)
set.seed(1234)
df = rnorm2d(1000, rho=.5)
df = as.data.frame(df)
head(df)
plot(df, main='Pivariable ND with rho = 0.5')
# seems to be no cluster

#Function wssplot
wssplot <- function(data, nc=15, seed=1234){
  wss <- (nrow(data)-1)*sum(apply(data,2,var))
  for (i in 2:nc){
    set.seed(seed)
    wss[i] <- sum(kmeans(data, centers=i)$withinss)}
    plot(1:nc, wss, type="b", xlab="Number of Clusters",
       ylab="Within groups sum of squares")
  }

#Determine no of clusters present
library(NbClust)
wssplot(df)

nc = NbClust(df, min.nc=2, max.nc=15, method='kmeans')
nc

================================================
FILE: 61-clust/hclust-USarrests.R
================================================
#Factor Extra - hclust

library("factoextra")
# Compute hierarchical clustering and cut into 4 clusters
res <- hcut(USArrests, k = 4, stand = TRUE)
res
# Visualize
k_colors = c("#00AFBB","#2E9FDF", "#E7B800", "#FC4E07")
fviz_dend(res, rect = TRUE, cex = 0.5, k_colors=c('red','green','blue','yellow')  )

# Optimal number of clusters for k-means
library("factoextra")
my_data <- scale(USArrests)
fviz_nbclust(my_data, kmeans, method = "gap_stat")


#
res3 <- hcut(USArrests, k = 3, stand = TRUE)
res3
# Visualize
fviz_dend(res3, rect = TRUE, cex = 0.5,  k_colors = c("#00AFBB","#2E9FDF", "#E7B800"))



#links
#http://www.sthda.com/english/wiki/factoextra-r-package-easy-multivariate-data-analyses-and-elegant-visualization
#https://www.analyticsvidhya.com/blog/2019/05/beginners-guide-hierarchical-clustering/


================================================
FILE: 61-clust/hier-simplecase.R
================================================
#hier clust - HW

df = data.frame(x= c(1,4,8,6,3,10,7,11,13,2), y= c(5,9,15,2,12, 7,7, 4,10,13))
nrow(df)
rownames(df) = paste('A',1:10,sep='')     
df                  

hc1 = hclust(dist(df))
attributes(hc1)
plot(hc1, hang=-1)

hc2 = hclust(dist(df), method = "ward.D2")
plot(hc2, hang=-1)

hc3 = hclust(dist(df), method = "ave")
plot(hc3, hang=-1)

plot(as.dendrogram(hc3), cex = 0.6,  horiz = TRUE)

# Cut tree into 4 groups
grp <- cutree(hc3, k = 3)
# Number of members in each cluster
table(grp)
# Get the names for the members of cluster 2
rownames(df)[grp == 2]

plot(hc3, cex = 0.6)
rect.hclust(hc3, k = 3, border = 2:5)
# Cut agnes() tree into 4 groups
ct = cutree(hc3, k = 2)


plot(hc3, cex = 0.6)
rect.hclust(hc3, k = 3, border = 2:5)

#best number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",  min.nc=2, max.nc=15, method="average")


================================================
FILE: 61-clust/hier-usarrests.R
================================================
#Hierarchical Clustering
#http://www.sthda.com/english/wiki/print.php?id=237

#if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/factoextra")
#Install cluster and dendextend packages as follow:
install.packages("cluster")
install.packages("dendextend")
#Load the packages :
library(cluster)
library(dendextend)
library(factoextra)

# Load the data set
data("USArrests")

# Remove any missing value (i.e, NA values for not available)
# That might be present in the data
df <- na.omit(USArrests)
head(df)

# View the firt 6 rows of the data
head(df, n = 6)

#Before hierarchical clustering, we can compute some descriptive statistics:
  
desc_stats <- data.frame(
    Min = apply(df, 2, min), # minimum
    Med = apply(df, 2, median), # median
    Mean = apply(df, 2, mean), # mean
    SD = apply(df, 2, sd), # Standard deviation
    Max = apply(df, 2, max) # Maximum
  )
desc_stats <- round(desc_stats, 1)
head(desc_stats)

#Note that the variables have a large different means and variances. This is explained by the fact that the variables are measured in different units; Murder, Rape, and Assault are measured as the number of occurrences per 100 000 people, and UrbanPop is the percentage of the state’s population that lives in an urban area.

#They must be standardized (i.e., scaled) to make them comparable. Recall that, standardization consists of transforming the variables such that they have mean zero and standard deviation one. You can read more about standardization in the following article: distance measures and scaling.

#As we don’t want the hierarchical clustering result to depend to an arbitrary variable unit, we start by scaling the data using the R function scale() as follow:
  
df <- scale(df)
head(df)
#----
#There are different functions available in R for computing hierarchical clustering. The commonly used functions are:
  
#hclust() [in stats package] and agnes() [in cluster package] for agglomerative hierarchical clustering (HC)
#diana() [in cluster package] for divisive HC

#hclust() function is the built-in R function [in stats package] for computing hierarchical clustering.

#The simplified format is: hclust(d, method = "complete")
#d a dissimilarity structure as produced by the dist() function.
#method: The agglomeration method to be used. Allowed values is one of “ward.D”, “ward.D2”, “single”, “complete”, “average”, “mcquitty”, “median” or “centroid”.

#The dist() function is used to compute the Euclidean distance between observations. Finally, observations are clustered using Ward’s method.

# Dissimilarity matrix
d <- dist(df, method = "euclidean")

# Hierarchical clustering using Ward's method
res.hc <- hclust(d, method = "ward.D2" )

# Plot the obtained dendrogram
plot(res.hc, cex = 0.6, hang = -1)

#The R function agnes() [in cluster package] can be also used to compute agglomerative hierarchical clustering. The R function diana() [ in cluster package ] is an example of divisive hierarchical clustering.

# Agglomerative Nesting (Hierarchical Clustering)
library(cluster)
agnes(x, metric = "euclidean", stand = FALSE, method = "average")

# DIvisive ANAlysis Clustering
diana(x, metric = "euclidean", stand = FALSE)

#x: data matrix or data frame or dissimilarity matrix. In case of matrix and data frame, rows are observations and columns are variables. In case of a dissimilarity matrix, x is typically the output of daisy() or dist().
#metric: the metric to be used for calculating dissimilarities between observations. Possible values are “euclidean” and “manhattan”.
#stand: if TRUE, then the measurements in x are standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable’s mean value and dividing by the variable’s mean absolute deviation
#method: The clustering method. Possible values includes “average”, “single”, “complete”, “ward”.
#The function agnes() returns an object of class “agnes” (see ?agnes.object) which has methods for the functions: print(), summary(), plot(), pltree(), as.dendrogram(), as.hclust() and cutree().
#The function diana() returns an object of class “diana” (see ?diana.object) which has also methods for the functions: print(), summary(), plot(), pltree(), as.dendrogram(), as.hclust() and cutree().
#Compared to other agglomerative clustering methods such as hclust(), agnes() has the following features:
  
#  It yields the agglomerative coefficient (see agnes.object) which measures the amount of clustering structure found
#Apart from the usual tree it also provides the banner, a novel graphical display (see plot.agnes).

#R code for computing agnes
library("cluster")
# Compute agnes()
res.agnes <- agnes(df, method = "ward")
# Agglomerative coefficient
res.agnes$ac
## [1] 0.934621
# Plot the tree using pltree()
pltree(res.agnes, cex = 0.6, hang = -1,
       main = "Dendrogram of agnes") 

#It’s also possible to draw AGNES dendrogram using the function plot.hclust() and the function plot.dendrogram() as follow:
  
# plot.hclust()
plot(as.hclust(res.agnes), cex = 0.6, hang = -1)
# plot.dendrogram()
plot(as.dendrogram(res.agnes), cex = 0.6,  horiz = TRUE)

#R code for computing diana
# Compute diana()
res.diana <- diana(df)
# Plot the tree
pltree(res.diana, cex = 0.6, hang = -1, main = "Dendrogram of diana")

# Divise coefficient; amount of clustering structure found
res.diana$dc
## [1] 0.8514345

#As for plotting AGNES dendrogram, the functions plot.hclust() and plot.dendrogram() can be used as follow:
  
# plot.hclust()
plot(as.hclust(res.diana), cex = 0.6, hang = -1)
# plot.dendrogram()
plot(as.dendrogram(res.diana), cex = 0.6, horiz = TRUE)





#------------
#Cut the dendrogram into different groups
#The height of the cut to the dendrogram controls the number of clusters obtained. It plays the same role as the k in k-means clustering.

#The function cutree() is used and it returns a vector containing the cluster number of each observation:
#n the dendrogram displayed above, each leaf corresponds to one observation. As we move up the tree, observations that are similar to each other are combined into branches, which are themselves fused at a higher height.

#The height of the fusion, provided on the vertical axis, indicates the (dis)similarity between two observations. The higher the height of the fusion, the less similar the observations are.

#Note that, conclusions about the proximity of two observations can be drawn only based on the height where branches containing those two observations first are fused. We cannot use the proximity of two observations along the horizontal axis as a criteria of their similarity.

#In order to identify sub-groups (i.e. clusters), we can cut the dendrogram at a certain height as described in the next section.
#The height of the cut to the dendrogram controls the number of clusters obtained. It plays the same role as the k in k-means clustering.

#The function cutree() is used and it returns a vector containing the cluster number of each observation:
  
# Cut tree into 4 groups
grp <- cutree(res.hc, k = 4)
# Number of members in each cluster
table(grp)
# Get the names for the members of cluster 1
rownames(df)[grp == 1]

#It’s also possible to draw the dendrogram with a border around the 4 clusters. The argument border is used to specify the border colors for the rectangles:
  
plot(res.hc, cex = 0.6)
rect.hclust(res.hc, k = 4, border = 2:5)

#using the function fviz_cluster() [in factoextra], we can also visualize the result in a scatter plot. Observations are represented by points in the plot, using principal components. A frame is drawn around each cluster.

library(factoextra)
fviz_cluster(list(data = df, cluster = grp))


#The function cutree() can be used also to cut the tree generated with agnes() and diana() as follow:

# Cut agnes() tree into 4 groups
cutree(res.agnes, k = 4)

# Cut diana() tree into 4 groups
cutree(as.hclust(res.diana), k = 4)

#Hierarchical clustering and correlation based distance
#The different functions for hierarchical clustering use Euclidean distance measures as default metric. It’s also possible to use correlation-based distance measures. Firstly, pairwise correlation matrix between items is computed using the function cor() which can calculate either “pearson”, “spearman” or “kendall” correlation #method. Next, the correlation matrix is converted as a distance matrix and finally clustering can be computed on the resulting distance matrix.

res.cor <- cor(t(df), method = "pearson")
d.cor <- as.dist(1 - res.cor)
plot(hclust(d.cor, method = "ward.D2"), cex = 0.6)


#What type of distance measures should we choose?
#The choice of dissimilarity measures is very important, as it has a strong influence on the resulting dendrogram.

#In many of the examples described above, we used Euclidean distance as the dissimilarity measure. Depending on the type of the data and the researcher questions, other dissimilarity measures might be preferred such as correlation-based distance.

#Correlation-based distance considers two observations to be similar if their features are highly correlated, even though the observed values may be far apart in terms of Euclidean distance.

#If we want to identify clusters of observations with the same overall profiles regardless of their magnitudes, then we should go with correlation-based distance as a dissimilarity measure. This is particularly the case in gene expression data analysis, where we might want to consider genes similar when they are “up” and “down” together. It is also the case, in marketing if we want to identify group of shoppers with the same preference in term of items, regardless of the volume of items they bought.

#If Euclidean distance is chosen, then observations with high values of features will be clustered together. The same holds true for observations with low values of features.


#Note that, when the data are standardized, there is a functional relationship between the Pearson correlation coefficient r(x,y) and the Euclidean distance.

#Comparing two dendrograms
#We’ll use the package dendextend which contains many functions for comparing two dendrograms, including: dend_diff(), tanglegram(), entanglement(), all.equal.dendrogram(), cor.dendlist().

#The function tanglegram() and cor.dendlist() are described in this section.

#A random subset of the dataset will be used in the following example. The function sample() is used to randomly select 10 observations among the 50 observations contained in the data set

# Subset containing 10 rows
set.seed(123)
ss <- sample(1:50, 10)
df <- df[ss,]
#In the R code below, we’ll start by computing pairwise distance matrix using the function dist(). Next, hierarchical clustering (HC) is computed using two different linkage methods (“average” and “ward.D2”). Finally the results of HC are transformed as dendrograms:
  
library(dendextend)
# Compute distance matrix
res.dist <- dist(df, method = "euclidean")

# Compute 2 hierarchical clusterings
hc1 <- hclust(res.dist, method = "average")
hc2 <- hclust(res.dist, method = "ward.D2")

# Create two dendrograms
dend1 <- as.dendrogram (hc1)
dend2 <- as.dendrogram (hc2)

# Create a list of dendrograms
dend_list <- dendlist(dend1, dend2)
# Tanglegram
#The function tanglegram() plots two dendrograms, side by side, with their labels connected by lines. It can be used for visually comparing two methods of Hierarchical clustering as follow:

tanglegram(dend1, dend2)

#Note that “unique” nodes, with a combination of labels/items not present in the other tree, are highlighted with dashed lines.

#The quality of the alignment of the two trees can be measured using the function entanglement(). The output of tanglegram() can be customized using many other options as follow:
  
tanglegram(dend1, dend2, highlight_distinct_edges = FALSE, # Turn-off dashed lines
 common_subtrees_color_lines = FALSE, # Turn-off line colors
 common_subtrees_color_branches = TRUE, # Color common branches 
 main = paste("entanglement =", round(entanglement(dend_list), 2))
  )
#Correlation matrix between a list of dendrogram
#The function cor.dendlist() is used to compute “Baker” or “Cophenetic” correlation matrix between a list of trees.

# Cophenetic correlation matrix
cor.dendlist(dend_list, method = "cophenetic")

# Baker correlation matrix
cor.dendlist(dend_list, method = "baker")


#The correlation between two trees can be also computed as follow:

# Cophenetic correlation coefficient
cor_cophenetic(dend1, dend2)
## [1] 0.9646883
# Baker correlation coefficient
cor_bakers_gamma(dend1, dend2)
## [1] 0.9622885
#It’s also possible to compare simultaneously multiple dendrograms. A chaining operator %>% (available in dendextend) is used to run multiple function at the same time. It’s useful for simplifying the code:
  
# Subset data
set.seed(123)
ss <- sample(1:150, 10 )
# Create multiple dendrograms by chaining
dend1 <- df %>% dist %>% hclust("com") %>% as.dendrogram
dend2 <- df %>% dist %>% hclust("single") %>% as.dendrogram
dend3 <- df %>% dist %>% hclust("ave") %>% as.dendrogram
dend4 <- df %>% dist %>% hclust("centroid") %>% as.dendrogram
# Compute correlation matrix
dend_list <- dendlist("Complete" = dend1, "Single" = dend2,       "Average" = dend3, "Centroid" = dend4)
cors <- cor.dendlist(dend_list)
# Print correlation matrix
round(cors, 2)

# Visualize the correlation matrix using corrplot package
library(corrplot)
corrplot(cors, "pie", "lower")


================================================
FILE: 61-clust/iris.R
================================================


data(iris)
library(dplyr)
library(tidyr)

# The easy way, if your data allows, is to tidy the data, find the max in
# each group, and join the results back in
iris_id = iris %>% mutate(id=1:nrow(.))
iris_id
iris_id %>%
  gather('attribute', 'value', Sepal.Length:Petal.Width) %>%
  group_by(id) %>%
  summarize(max_attribute=max(value)) %>%
  right_join(iris_id, by='id') %>%
  head(3)


================================================
FILE: 61-clust/kmeans-bankdata.R
================================================
#kmeans - bank data
#https://www.linkedin.com/pulse/decision-making-using-unsupervised-learning-data-r-k-means-pandian/

#bank loan data and picked up two factors Age and Income \
set.seed(123)
age = round(runif(100,20,70),2)
income = round(rnorm(100,100000,20000),2)
bank = data.frame(age, income)
head(bank)

bankdata = scale(bank)
for(i in 1:15){
   wss[i]<-sum(kmeans(bankdata,centers = i)$withinss)
}
plot(wss, type='b')
#From the plot of cluster sizes we can see that K values of 4, 5, 6 and 7 might have the potential. After K=7 the sizes become too small and may be the smaller clusters  may be just sub sets of some larger cluster and may not possess unique characteristics. For the reaming study we have parked at these values of K.
abline(v=7)
km7 = kmeans(bankdata, centers=7)
km7
library(ggvis)
bank %>% ggvis(~age, ~income)
bankdata %>% ggvis(x = ~age, y = ~income)
bank %>% ggvis(x = ~age, y = ~income,fill=~factor(km7$cluster)) %>% layer_points()

require(lattice)
xyplot(income ~age| as.factor(km7$cluster),groups= as.factor(km7$cluster),bank, pch= 20)

#PCA
library(cluster)
clusplot(bank,km7$cluster,color=TRUE,shade=TRUE,labels = 2,lines=0)

#symbols
?symbols
km7$tot.withinss
head(bankdata)
class(bankdata)
bankdata[,2]
bankdata[,1:2]
plot(bankdata)
km7$withinss
km7$centers

km7a = kmeans(bank,7)

(symboldata = as.data.frame(cbind(km7a$centers, sradius=sqrt(km7a$withinss))))
head(trees)

with(symboldata, symbols(age,income, circles=sradius, inches = .5))

with(bank, plot(age,income))
par(new=TRUE)
points(km7a$centers, col='red', pch=18)
par(new=TRUE)
with(symboldata, symbols(age,income, circles=sradius, inches = 1), axis=F, xlim=c(20,70), ylim=c(60000, 140000))

with(symboldata, symbols(age, income, circles = sradius/24, inches = FALSE,
                    main = "Trees' Girth") )


with(trees, symbols(Height, Volume, circles = Girth/24, inches = FALSE,
                    main = "Trees' Girth") )


symbols(x=bankdata[,1], y=bankdata[,2], circles=km7$tot.withinss,inches=0.25, fg="black", bg="blue", xlab="Age", ylab="Income",main="K=4")

text(k4$age, k4$income, k4$sqrt_withinss,pos=4,col="dark green",cex=1.2)




================================================
FILE: 61-clust/kmeans-pcalike.R
================================================
#kmeans : PCA like technique

#https://www.biostars.org/p/15669/

library(fpc)
library(ggplot2)
data=mtcars
kclust=kmeans(data,centers=3)
kclust$cluster <- as.factor(kclust$cluster)
kclust
d=dist(data, method = "euclidean") 
fit=cmdscale(d,eig=TRUE, k=2) # k is the number of dim
fit

p = ggplot(data.frame(data), aes(fit$points[,1], fit$points[,2], color =  factor(kclust$cluster))) 
p <- p + theme(axis.title.y = element_text(size = rel(1), angle = 90))
p <- p + theme(axis.title.x = element_text(size = rel(1), angle = 00))
p= p + theme(axis.text=element_text(size=10,angle=90),axis.title=element_text(size=20,face="bold")) + geom_point(size=4)
p= p + theme(legend.text = element_text(size = 10, colour = "black"))
p= p + theme(legend.title = element_text(size = 10, colour = "black"))
p= p  + theme(legend.key.size = unit(1.5, "cm"))
p


#-----


================================================
FILE: 61-clust/kmeans-plots.R
================================================
#kmeans plots

df=iris
m=as.matrix(cbind(df$Petal.Length, df$Petal.Width),ncol=2)

#Now we will do the actual clustering.

cl=(kmeans(m,3))

#The cl object contains a number of interesting attributes associated with the model.

cl$size
cl$withinss

#Next we do a bit of data formatting and preparation for subsequent calls to graph the data.  Notice that we add the cluster information back to our original data frame.  This is a good organization of the data and also a requirement for working with ggplot2 which is designed to use data frames.

df$cluster=factor(cl$cluster)
centers=as.data.frame(cl$centers)

#The following graph color codes the points by cluster.  We also add the centers and a semi transparent halo around the center to emphasize the place of the center… and its role in classifying the observations into clusters.

library(ggplot2)
centers
ggplot(data=df, aes(x=Petal.Length, y=Petal.Width, color=cluster )) +  geom_point() +  geom_point(data=centers, aes(x=V1,y=V2, color='Center'))  + geom_point(data=centers, aes(x=V1,y=V2, color='Center'), size=52, alpha=.3)

library(sqldf)
sqldf('select Species, cluster, count(*) from df group by Species, Cluster')


#oultiers
df2 = sqldf('select * from df where (Species || cluster) in (select Species || cluster from df group by Species, Cluster having count(*) < 10)')
df2

#sqldf('select Species || cluster from df group by Species, Cluster having count(*) < 10')

last_plot()
last_plot() +  geom_point(data=df2, aes(x=Petal_Length, y=Petal_Width, shape=5, alpha=.7, size=4.5), legend=FALSE) 


================================================
FILE: 61-clust/kmeans-randomness.R
================================================
#kmeans - everytime new values of withinss
#https://rpubs.com/mohammadshadan/273129
# Set up 2 x 3 plotting grid

par(mfrow = c(2, 3))

# Set seed
set.seed(1)
(x = iris[,1:2])
for(i in 1:6) {
  # Run kmeans() on x with three clusters and one start
  km.out <- kmeans(x, centers=3, nstart=1)
  
  # Plot clusters
  plot(x, col = km.out$cluster, 
       main = km.out$tot.withinss, 
       xlab = "", ylab = "")
}
url <- "http://s3.amazonaws.com/assets.datacamp.com/production/course_1903/datasets/WisconsinCancer.csv"

#Download the data: wisc.df
wisc.df <- read.csv(url)
head(wisc.df)
# Convert the features of the data: wisc.data
wisc.data <- as.matrix(wisc.df[3:32])

# Set the row names of wisc.data
row.names(wisc.data) <- wisc.df$id
head(wisc.data)
# Create diagnosis vector
diagnosis <- as.numeric(wisc.df$diagnosis == "M")
head(diagnosis)
colnames(wisc.data)
length(grep("_mean", colnames(wisc.data)))
colnames(wisc.data)[(grep("_mean", colnames(wisc.data)))]
table(diagnosis)

# Execute PCA, scaling if appropriate: wisc.pr
wisc.pr <- prcomp(wisc.data, scale=TRUE)

# Look at summary of results
summary(wisc.pr)

# Create a biplot of wisc.pr
biplot(wisc.pr)
# Scatter plot observations by components 1 and 2
plot(wisc.pr$x[, c(1, 2)], col = (diagnosis + 1), 
     xlab = "PC1", ylab = "PC2")
# Repeat for components 1 and 3
plot(wisc.pr$x[, c(1, 3)], col = (diagnosis + 1), 
     xlab = "PC1", ylab = "PC3")
# Do additional data exploration of your choosing below (optional)
plot(wisc.pr$x[, c(1, 4)], col = (diagnosis + 1), 
     xlab = "PC1", ylab = "PC3")

par(mfrow = c(1, 2))

# Calculate variability of each component
pr.var <- wisc.pr$sdev^2

# Variance explained by each principal component: pve
pve <- pr.var/sum(pr.var)

# Plot variance explained for each principal component
plot(pve, xlab = "Principal Component", 
     ylab = "Proportion of Variance Explained", 
     ylim = c(0, 1), type = "b")

# Plot cumulative proportion of variance explained
plot(cumsum(pve), xlab = "Principal Component", 
     ylab = "Cummulative Proportion of Variance Explained", 
     ylim = c(0, 1), type = "b")

wisc.pr$rotation[rownames(wisc.pr$rotation)=="concave.points_mean",]

wisc.pr$rotation[rownames(wisc.pr$rotation)=="concave.points_mean",][1]

round(colMeans(wisc.data),1)

# Scale the wisc.data data: data.scaled
data.scaled <- scale(wisc.data)

# Calculate the (Euclidean) distances: data.dist
data.dist <- dist(data.scaled)

# Create a hierarchical clustering model: wisc.hclust
wisc.hclust <- hclust(data.dist, method="complete")
plot(wisc.hclust)

# Cut tree so that it has 4 clusters: wisc.hclust.clusters
wisc.hclust.clusters <- cutree(wisc.hclust, k=4)

# Compare cluster membership to actual diagnoses
table(wisc.hclust.clusters, diagnosis)


================================================
FILE: 61-clust/pam1.R
================================================
# Partitioning around Mediods (PAM)


library(cluster)
set.seed(1234)
data(wine, package='rattle.data')
head(wine)
fit.pam = pam(wine[-1,], k=3, stand=TRUE)
fit.pam$medoids
clusplot(fit.pam, main='Bivariate Cluster Plot')
#plot of 1st two PC derived from PCA
summary(fit.pam)
names(fit.pam)





================================================
FILE: 65-AR/11a-measures1.R
================================================
(Lift = 100/ 400 / ((200 /
                      400) * (160/400)))
.25/.40
0.75 * 0.9
.75/.9


================================================
FILE: 65-AR/12a-ar-samplecase.R
================================================
# Association Rule - Simple Example Case

#libraries
library(arules)
library(arulesViz)

#Create Data
#Method1: format basket
itemlist1 = paste('I1,I2,I5','I2,I4', 'I2,I3','I1,I2,I4','I1,I3','I2,I3','I1,I3','I1,I2,I3,I5','I1,I2,I3', sep="\n");
itemlist1
write(itemlist1, file = "./data/itemlist1");
tdata1 = read.transactions("./data/itemlist1", format = "basket", sep=",");
summary(tdata1)
tdata1
inspect(tdata1)

#Method2
itemlist2 <- data.frame(
  TID = c(1,1,1,2,2,3,3,4,4,4,5,5,6,6,7,7,8,8,8,8,9,9,9), 
  item=c('I1','I2','I5', 'I2','I4', 'I2','I3','I1','I2','I4','I1','I3','I2','I3','I1','I3','I1','I2','I3','I5','I1','I2','I3')
)
itemlist2
tdata2 <- as(split(itemlist2[,"item"], itemlist2[,"TID"]), "transactions")
tdata2
inspect(tdata2)
summary(tdata2)
image(tdata2)

#Method3 Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
summary(tdata3)
inspect(tdata3)

#Method4 : DF ----
T1 = c(T,T,F,F,T); T2 = c(T,F,F,T,F); T3 = c(F,T,T,F,T)
T4 = c(T,T,F,T,F); T5 = c(T,F,T,F,T); T6 = c(F,T,T,F,T)
T7 = c(T,F,T,F,T); T8 = c(T,T,T,F,T); T9 = c(T,T,T,F,T)
df = data.frame(rbind(T1,T2,T3,T4,T5,T6,T7,T8,T9))
colnames(df)=c('I1','I2','I3','I4','I5')
df
tdata4 = as(df, "transactions")
summary(tdata4)
tdata4
inspect(tdata4)

tdata=tdata1

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
image(tdata)

#Analysis
freqitems = eclat(tdata)
freqitems
inspect(freqitems)
itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)

#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.1, conf = 0.6, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules)

#sort rules by support
rules_s = sort(rules, by="support", decreasing=TRUE )
inspect(rules_s)
inspect(rules_s[1:5])  #itemsset having high support

#sort rules by confidence
rules_c = sort(rules, by="confidence", decreasing=TRUE )
inspect(rules_c)
inspect(rules_c[1:5])  #itemsset having high confidence

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

#Quality Data of Rules
quality(rules_c) 

#Redundant Rules
inspect(rules)
(redundant = which(is.redundant(rules)))
inspect(rules[c(8,9,10,11,12,14,14)])
inspect(rules[redundant])
inspect(rules)
write.csv(as(rules,"data.frame"), file='./data/rulesR.csv')

#Remove Redundant Rules
rulesNR <- rules[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now
inspect(rulesNR)

#Rules with LHS and RHS: single or combination
rules2= rulesNR
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)

# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './data/myrules1.csv')

#Visualisation
plot(rules)

#This interactve plot will open in another window
#use menu options to check 
plot(rules[1:5],method="graph",engine='interactive', shading="confidence") 

#plot with unified interface, the no of items contained in the rule
plot(rules, method = "two-key plot")

#plot with lift on the y-axis. identify all rules with high lift. 
plot(rules, measure = c("support", "lift"), shading = "confidence")

#Matrix Plots
subrules <- rules[quality(rules)$confidence > 0.8]
inspect(subrules)
plot(subrules, method = "matrix", measure = "lift")
plot(subrules, method = "matrix", engine='3d', measure = "lift")

#Grouped
plot(rules, method = "grouped")
#The group of most interesting rules according to lift (the default measure) are shown in the top-left corner of the plot.
plot(subrules, method = "graph")

plot(subrules, method = "paracoord", control = list(reorder = TRUE))

inspect(rules[14])
plot(rules[14], method = "doubledecker", data = tdata)

#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf


#extra
# freq items

freqitems = eclat(tdata, parameter = list(supp = 0.001))
freqitems
inspect(freqitems)

freqitems1 = eclat(tdata, parameter = list(supp = 0.1))
freqitems1
inspect(freqitems1)

freqitems2 = eclat(tdata, parameter = list(supp = 0.2))
freqitems2
inspect(freqitems2)

freqitems3 = eclat(tdata, parameter = list(supp = 0.3))
freqitems3
inspect(freqitems3)

## Create rules from the itemsets
rules3b <- ruleInduction(freqitems3, tdata, confidence = .6)
rules3b
inspect(rules3b)


#calculate confidence and lift manually
inspect(head(rules, n = 1, by ="lift"))
itemsets <- unique(generatingItemsets(rules))
itemsets
inspect(itemsets)
(itemsets.df <- as(itemsets, "data.frame"))

frequentItemsets <- itemsets.df[with(itemsets.df, order(-support,items)),]
names(frequentItemsets)[1] <- "itemset"
itemsets.df
itemFrequency(items(rules))
freqitems = eclat(tdata)
inspect(freqitems)


================================================
FILE: 65-AR/12b-ar-samplecase2.R
================================================
#AR - sample Case
library(arules)

#Data
#Method1: format basket
itemlist1 = paste('I1,I2,I5','I2,I4', 'I2,I3','I1,I2,I4','I1,I3','I2,I3','I1,I3','I1,I2,I3,I5','I1,I2,I3', sep="\n");
itemlist1
write(itemlist1, file = "./data/itemlist1");
tdata1 = read.transactions("./data/itemlist1", format = "basket", sep=",");
summary(tdata1)
inspect(tdata1);

#Modeling
library(arules)
tdata1
inspect(abbreviate(tdata1))  #smaller names of items

fitems = eclat(tdata1, parameter = list(supp = 0.0))
inspect(fitems)
inspect(sort(fitems, by='support'))
rules = apriori(tdata1, list(supp = 0.10, conf=.60, minlen=2) )

inspect(rules )
inspect(head(rules, n = 5, by ="lift"))
inspect(head(rules, n = 5, by ="support"))

rules2 = apriori(tdata1, list(supp = 0.2, conf=.8) )
inspect(rules2)
support(items(fitems), tdata1)


#Part 2
rules3 <- ruleInduction(fitems, tdata1, confidence = 0)
inspect(rules3)

#Other Measures
#https://rdrr.io/cran/arules/man/interestMeasure.html
measures = c('lift', 'confidence', 'support', 'conviction', 'count', 'coverage', 'doc', 'importance')
interestMeasure(rules,measure=measures, tdata1)

inspect(fitems[is.maximal(fitems)])

length(fitems)
LIST(fitems)
size(fitems)
inspect(fitems)
supportingTransactions(fitems, tdata1)
unique(fitems)
support(fitems, tdata1)
subset(rules,  lhs %in% c("I3",'I1') & lift > 1)
subset(rules,  lhs %oin% c("I1",'I2') & lift > 1)

#Cross Table - Good
crossTable(tdata1, measure = c("count"), sort = FALSE)
crossTable(tdata1, measure = c("lift"), sort = FALSE)
crossTable(tdata1, measure = c("support"), sort = FALSE)
DATAFRAME(fitems)
DATAFRAME(rules)
DATAFRAME(rules, separate = TRUE, setStart = '', itemSep = ' + ', setEnd = '')

hits(tdata1, iter = 16L, tol = NULL, type = "absolute")
itemFrequency(tdata1)
is.closed(fitems)
inspect(fitems[is.closed(fitems)])
inspect(fitems[is.significant(rules,tdata1, method='fisher')])

coverage(rules, transactions = tdata1)
cbind(quality(rules), coverage = coverage(rules))

#aggregate(rules, by='level2')

#Combine trans, rules
aComb <- c(a1, a2)
rComb <- c(r1, r2) 
rUnion <- union(r1,r2)

#Add items
g2 <- addComplement(tdata1, c("I1","I2","I3","I4"))
inspect(g2)

## use a custom label for the complement-item
g2=addComplement(tdata1, labels="I5", complementLabels=c('No-I5'))
inspect(g2)


#
## Example 1: Manual decoding
## get code
iLabels <- itemLabels(tdata1)
head(iLabels)

## get undecoded list and decode in a second step
list <- LIST(tdata1, decode = FALSE)
list

decode(list, itemLabels = iLabels)
inspect(tdata1)



#Random Items
## create a random item as a matrix
randomItem <- sample(c(TRUE, FALSE), size=length(tdata1),replace=TRUE)
randomItem <- as.matrix(randomItem)
colnames(randomItem) <- "random item"
head(randomItem, 3)

## add the random item to Groceries
g3 <- merge(tdata1, randomItem)
nitems(tdata1)
nitems(g3)
inspect(head(g3))


#----
#https://rdrr.io/cran/arules/f/inst/doc/arules.pdf
random.transactions(c('I1','I2','I3', 'I4','I5'))
items5 = random.transactions(15,10, method='agrawal')
inspect(items5)
trans <- random.transactions(nItems = 20, nTrans = 1000,  lambda = 5, iProb = seq(0.2,0.0001, length=20))
summary(size(trans))
summary(trans)


fi = eclat(trans)
LIST(fi)

image(trans)
itemMatrix(trans)
class(trans)
inspect(trans[1:5])

itemFrequencyPlot(trans,topN = 5,type="absolute")
itemFrequencyPlot(trans,topN = 5,type="relative", horiz=T)

#Construct the Rules
rules8 = apriori(trans, parameter = list(supp = 0.02, conf = 0.7, minlen=2))
itemFrequencyPlot(items(rules8))

inspect(rules8)



#
AR in R : simple steps
#eclat
library(arules)
library(arulesViz)
library(igraph)
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144')
library(iplots)
data <- read.transactions('file', sep=',')
tdata1
itemsets <- eclat(tdata, parameter = list(supp = 0.1, minlen=2, maxlen = 3))
inspect(itemsets)
plot(itemsets, method="graph", control=list(type="items"))


================================================
FILE: 65-AR/14a-ar-datastr.R
================================================
# Data Structures for AR in R

data("Groceries")
mydata = Groceries
str(mydata)
dim(mydata)
length (mydata)
size(mydata)
LIST(mydata[1:5]) 
inspect(mydata[1:5])
head(mydata)
mode(mydata)
mydata[1:5]
mydata

summary(rules)

#Transactions to List
LIST(mydata[1:5])
LIST(mydata[1:5])[[1]]
LIST(mydata[1:5])[[1]][1]

#Transactions to Data Frame
mydf = as(mydata,"data.frame")
str(mydf)
head(mydf$items)

#Data Frame to Transactions





#Measures
quality(rules)$improvement <- interestMeasure(rules, measure = "improvement")
quality(rules)



#Making Rules For Continuous Data
discretize (x, method="cluster", categories=3) 


# Calculates affinity - the 'nxn' Jaccard Index affinity matrix
affinity(mydata) 

# Adds "Item 1" to all transactions in transDat
transDat_c <- addComplement(mydata, "meat") 
transDat_c
inspect(transDat_c)

# Duplicate Rules
duplicated(rules) # find out if any rule is duplicated

sum(duplicated(rules))


================================================
FILE: 65-AR/15-ar-groceries.R
================================================
# Market Basket / Association Rule Analysis 
# Find Relationship between items


================================================
FILE: 65-AR/15a-ar-Groceries1.R
================================================
# Association Rule - Set 3 ###
#http://www.salemmarafi.com/code/market-basket-analysis-with-r/
#if this, then that”.
# The Groceries Dataset

# Load the libraries
library(arules)
library(arulesViz)
library(datasets)
data(Groceries)
# Load the data set : Items set structure - List Type; Cannot see properly
Groceries
#str(Groceries)
head(Groceries)
inspect(Groceries[1:5])

# Create an item frequency plot for the top 20 items
itemFrequencyPlot(Groceries,topN=20,type="absolute")
#set the min support to 0.001 , minimum conf of 0.8 ; show top 5 rules
# Get the rules
rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))

# Show the top 5 rules, but only 2 digits
options(digits=5)
inspect(rules[1:5])
#[3] if someone buys yogurt & cereals, they are 81% likely to buy whole milk too.
summary(rules)
# 410 rules, Most rules are 4 items long
# Sort Rules The first issue we see here is that the rules are not sorted. Often we will want the most relevant rules first. Lets say we wanted to have the most likely rules. We can easily sort by confidence by executing the following code.

rules<-sort(rules, by="lift", decreasing=TRUE)
inspect(head(rules))
#Rule 4/ other is perhaps excessively long. Lets say you wanted more concise rules. That is also easy to do by adding a “maxlen” parameter to your apriori function:
rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8,maxlen=3))
inspect(head(rules))

# Redundancies ####
subset.matrix <- is.subset(rules, rules)
subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA
redundant <- colSums(subset.matrix, na.rm=T) >= 1
rules.pruned <- rules[!redundant]
rules<-rules.pruned
inspect(rules)
# Target Items
#Now that we know how to generate rules, limit the output, lets say we wanted to target items to generate rules. There are two types of targets we might be interested in that are illustrated with an example of “whole milk”:
#  What are customers likely to buy before buying whole milk
#What are customers likely to buy if they purchase whole milk?
#This essentially means we want to set either the Left Hand Side and Right Hand Side. This is not difficult to do with R!
# 1st part
rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.08), 
               appearance = list(default="lhs",rhs="whole milk"),
               control = list(verbose=F))
rules<-sort(rules, decreasing=TRUE,by="confidence")
inspect(rules[1:5])
#Likewise, we can set the left hand side to be “whole milk” and find its antecedents.
#Note the following:
#  We set the confidence to 0.15 since we get no rules with 0.8
#We set a minimum length of 2 to avoid empty left hand side items
rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.15,minlen=2), appearance = list(default="rhs",lhs="whole milk"),
               control = list(verbose=F))
rules<-sort(rules, decreasing=TRUE,by="confidence")
inspect(rules)

# Visualisation ####
#The last step is visualization. Lets say you wanted to map out the rules in a graph. We can do that with another library called “arulesViz”.
library(arulesViz)
# Interactive Graph - Seperate Window
inspect(rules[1:5])
plot(rules[1:5],method="graph",engine='interactive',shading=NA)



# Groceries Example 2 ####
#http://rstatistics.net/association-mining-with-r/
library (arules)
library (arulesViz)
library (datasets)
data (Groceries) # Load the data set
str(Groceries)
# Change to any data Frame
myDataFrame= Groceries
transDat <- as (myDataFrame, "transactions") # convert to 'transactions' class
inspect (transDat) # view the observations
length (transDat) # get number of observations
size (transDat) # number of items in each observation
LIST(transDat) # convert 'transactions' to a list, note the LIST in CAPS

#Lets Apply Apriori Algorithm
#For illustrative purpose, Lets continue to work with ‘Groceries’ dataset from ‘arules’ package.
frequentItems <- eclat (Groceries, parameter = list(supp = 0.07, maxlen = 15)) # calculates support for frequent items
itemFrequencyPlot (Groceries,topN=10,type="absolute") # plot frequent items

#A low support and high confidence helps to extract strong relationship even for less overall co-occurrences in data.
rules <- apriori (Groceries, parameter = list(supp = 0.001, conf = 0.5)) # Min Support as 0.001, confidence as 0.8.

#quality(rules) # show the support, lift and confidence for all rules
head(quality(rules))
# Show the top 5 rules, but only 2 digits
options (digits=2)
inspect (rules[1:5])

rules <- sort (rules, by="confidence", decreasing=TRUE) # 'high-confidence' rules.
#How To Control The Number Of Rules in Output ?
#Adjust the maxlen and conf  arguments in the apriori statement to control the number of rules generated. Use your best judgement here.
rules <- apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, maxlen=3)) # maxlen = 3 limits the elements in a rule to 3

#To get ‘strong‘ rules, increase the value of ‘conf’ parameter. To get ‘longer‘ rules, increase ‘maxlen’
#How To Remove Redundant Rules ?
#Use the below code to find out and filter the redundant rules.
redundant <- which (colSums (is.subset (rules, rules)) > 1) # get redundant rules in vector
rules <- rules[-redundant] # remove redundant rules

#How to Find Rules Related To Given Item/s ? This method is the core of ‘Market basket analysis’ that is useful to make recommendations of new items to your users. 
#This can be achieved by modifying the ‘appearance’ parameter in the apriori() function. For example,
#Find what factors influenced an event ‘X’
#To find out what customers had purchased before buying ‘Whole Milk’. This will help you understand the patterns that led to the purchase of ‘whole milk’.
rules <- apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.08), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F)) # get rules that lead to buying 'whole milk'

#Find out what events were influenced by a given event

#In this case: the Customers who bought ‘Whole Milk’ also bought. In the equation, ‘whole milk’ is in LHS (left hand side).                  
rules <- apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.15,minlen=2), appearance = list (default="rhs",lhs="whole milk"), control = list (verbose=F)) # those who bought 'milk' also bought..

#Remove redundancies
#Sort the rules, filter the redundant ones and show the Top 7 Rules.

rules <- sort (rules, decreasing=TRUE,by="confidence")

redundant <- which (colSums(is.subset(rules, rules)) > 1) # get redundant rules in vector

rules <- rules[-redundant] # remove redundant rules inspect (rules[1:7])
rules
#Making Rules For Continuous Data
#If you try to make rules on continuous variables, each value will be treated as distinct item, causing undesirable explosion of rules. So, convert the continuous variables to factors, which can be easily done using discretize() function.
#discretize (x, method="cluster", categories=3) # method can make cuts in equal "intervals", "frequency", "cluster", "fixed"

#Visualizing The Rules

# Interactive Plot

plot (rules[1:25],method="graph",interactive=TRUE,shading="confidence") # feel free to expand and move around the objects in this plot

plot (rules, measure=c("support", "lift"), shading="confidence")


affinity(transDat) # Calculates affinity - the 'nxn' Jaccard Index affinity matrix
transDat_c <- addComplement(transDat, "Item 1") # Adds "Item 1" to all transactions in transDat
duplicated(rules) # find out if any rule is duplicated


================================================
FILE: 65-AR/15b-ar-Groceries.R
================================================
# Association Rules - Groceries data set ####

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries')
Groceries

#Structure of Groceries
str(Groceries)
Groceries
inspect(Groceries[1:5])  #view
LIST(Groceries[1:6])  #another view

#Find Frequent Itemset
frequentItems = eclat (Groceries, parameter = list(supp = 0.01, minlen= 2, maxlen = 5)) 
inspect(frequentItems)
frequentItems
inspect(frequentItems[10:15])
#inspect(frequentItems[100:122])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.2)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5))
rules
options (digits=2)
inspect (rules[1:5])

#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=TRUE)
inspect (rulesl[1:5])
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) 
inspect(rules2[1:5])

# Are there any duplicate/ Redundant Rules 
#https://rdrr.io/cran/arules/man/is.redundant.html

sum(is.redundant(rules2))
(redundant = which(is.redundant(rules2)))
inspect(rules2[redundant])
#inspect(subset(rules2, subset=lhs %ain% c('citrus fruit','rice') & rhs %in% 'whole milk' ))
#remove it
rulesNR = rules2[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now



#Find what factors influenced an event ‘X’
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
inspect(rules3[1:5])
inspect(rules3)

#Find out what events were influenced by a given event
subset1 = subset(rules2, appearance = list (default="lhs",rhs="whole milk"))
subset1 = subset(rules2, subset=rhs %in% 'bottled beer' )
inspect(subset1)
inspect(rules2)
subset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2)
subset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )
inspect(subset2a)




subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3)
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4)

#Visualizing The Rules -----
plot(subset1[1:10]) 
plot(subset1[1:10], measure=c("support", "lift"), shading="confidence")

#


rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)


================================================
FILE: 65-AR/16b-groceries-summary.R
================================================
#Association Rule - Analysis

library(arules)
library(datasets)
data(Groceries)

summary(Groceries)
#there were 9,835 transactions altogether
#169 different items were bought during the month
#the most frequently bought item was “whole milk”: 2’513 purchases
#there were 2159 single item baskets, the biggest basket included 32 items
#median basket included 3 items; mean had 4.4 items.

inspect(Groceries[1:5])
itemLabels(Groceries)[1:10] # [1:10] can be dropped to show all items

par(mfrow=c(1,2))

#Plot - Most Frequent
itemFrequencyPlot(Groceries,
                  type="relative",
                  topN=10, # can be changed to the number of interest
                  horiz=TRUE,
                  col='steelblue3',
                  xlab='',
                  main='Item frequency, relative')

itemFrequencyPlot(Groceries,
                  type="absolute",
                  topN=10,
                  horiz=TRUE,
                  col='steelblue3',
                  xlab='',
                  main='Item frequency, absolute')


#Plot - Least Frequent
par(mar=c(2,10,2,2), mfrow=c(1,2))
table(unlist(LIST(Groceries)))
sort(table(unlist(LIST(Groceries))))

barplot(sort(table(unlist(LIST(Groceries))))[1:10]/9835,
        horiz=TRUE,
        las=1,
        col='steelblue3',
        xlab='',
        main='Frequency, relative')

barplot(sort(table(unlist(LIST(Groceries))))[1:10],
        horiz=TRUE,
        las=1,
        col='steelblue3',
        xlab='',
        main='Frequency, absolute')
crossTable(Groceries, measure='lift',sort=T)[1:5,1:5]


#Contingency Table
tbl <- crossTable(Groceries)
#tbl  #too big list
tbl[1:5,1:5]

tbl['whole milk','whole milk']  #count of milk
tbl['whole milk','flour']  #milk with flour
#number of occasions when these two items were purchased together.

tbl <- crossTable(Groceries, sort=TRUE)
tbl[1:5,1:5]


#Xtable with lift
crossTable(Groceries, measure='lift',sort=T)[1:5,1:5]
#whole milk” goes well with all products, but “soda”.
#judged by lift, we are on the way to claim that soda is a substitute for “whole milk” for some people: they tend to buy either one or the other, but buying them together is a relatively rare event
crossTable(Groceries, measure='chi')['whole milk', 'soda']
#low p-value excludes possibility that lift less than 1 is due to chance.

================================================
FILE: 65-AR/16d-ar-groceries-subset.R
================================================
#AR - Groceries - Subset

#Subsetting rules and itemsets
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,
        maxlen=5, target='rules' ))
#target='rules' # to mine for rules)

inspect(sort(rules, by="confidence", decreasing = T)[1:5])

#subset conditions
#rhs should be ‘bottled beer’
#confidence should be above .7
#results should be sorted by lift

#Subset1----
inspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7),
     by = 'lift', decreasing = T))
#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”


# Another rule with different parameters
#subset2----
canned_rules <- apriori(Groceries,parameter = list(support=.001,
  confidence=.01, maxlen=5, target='rules' ))
inspect(subset(canned_rules, subset=lhs %ain% c("liquor", "red/blush wine") & rhs %in% 'canned beer' ))
#no rule found or it could be significant low % less than 1 in 100 people would do

# 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

#either side lhs or rhs
#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95
#subset3----
inspect(subset(rules, subset=items %ain% c("whole milk","yogurt") & confidence >.95))

#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9
inspect(subset(rules, subset=lhs %ain% c("whole milk","yogurt") & confidence >.9))

#Subset4----
#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9

inspect(subset(rules, subset= lhs %pin% "bread" & rhs %in% "whole milk" & confidence > .9))


#Subset5----
#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs
inspect(subset(rules, subset= lhs %ain% c("flour","whole milk") & confidence>.7))



#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.
rules <- apriori(Groceries,  parameter = list(support=.001,
    conf = .01, minlen=2, maxlen=2, target='rules'))

#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:

inspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))
crossTable(Groceries)['canned beer','bottled beer']

#the probability of a consecutive purchase (confidence) is pretty small: ~3%
#this is despite both bottled beer and canned beer being pretty popular purchases

crossTable(Groceries)['canned beer','canned beer']
crossTable(Groceries)['bottled beer','bottled beer']

quality(rules)$chi  <- interestMeasure(rules, measure='chi', significance=T, Groceries)
quality(rules)$chi[4785:4786]
#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes.

================================================
FILE: 65-AR/16f-ar-groceries-vis.R
================================================
#Association Rule - Visualisation

library(arules)
library(datasets)
data(Groceries)

#
rules <- apriori(Groceries,  parameter = list(support=.001,
              conf = .01, minlen=2, maxlen=2, target='rules'))

#Visually mining rules. Both rules and itemsets can be visualized with the help of arulesViz library. The power of the plot() function from arulesViz library comes from interactive argument. Remember, as a general rule we want rules with both high support and high confidence.

plot(rules, interactive = T)

#interactive-rules-choice With the help of this function we can visually mine rules by:
#1. Selecting rectangular area by clicking twice on the plot.
#2. Then clicking inspect

#This would produce rules found in that region, e.g.:



inspectDT(rules)
 

================================================
FILE: 65-AR/16f-ar-groceries-vis2.R
================================================
# AR - Visualisation

library(arules)
library(datasets)
data(Groceries)
library(arulesViz)
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,
                                             maxlen=5, target='rules' ))


plot(rules, measure = c("support", "lift"), shading = "confidence")
plot(rules, measure = c("confidence", "lift"), shading = "support")


#Twokey plot. Here support and confidence are used for the x and y-axes and the color of the points is used to indicate “order,” i.e., the number of items contained in the rule. Two-key plots can be produced using the unified interface by:
plot(rules, method = "two-key plot")

#order and support have a very strong inverse relationship, which is a known fact for association rules



#Interactive
sel <- plot(rules, measure=c("support", "lift"), shading = "confidence", interactive = TRUE)
#Inspecting individual rules by selecting them and clicking the inspect button.
#Inspecting sets of rules by selecting a rectangular region of the plot and clicking the inspect button.
#Zooming into a selected region (zoom in/zoom out buttons).
#Filtering rules using the measure used for shading by clicking the filter button and selecting a cut-off point in the color key. All rules with a measure lower than the cut-off point will be filtered.
#Returning the last selection for further analysis (end button).




================================================
FILE: 65-AR/16f-ar-groceries-vis3.R
================================================
# AR - Visualisation - Matrix

library(arules)
library(datasets)
data(Groceries)
library(arulesViz)
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,                   maxlen=5, target='rules' ))


#Matrix-based visualization techniques organize the antecedent and consequent itemsets on the x and y-axes, respectively. A selected interest measure is displayed at the intersection of the antecedent and consequent of a given rule. If no rule is available for a antecedent/consequent combination the intersection area is left blank.

subrules <- rules[quality(rules)$confidence > 0.8]
subrules
inspect(subrules)
plot(subrules, method = "matrix", measure = "lift")

# not much space for long labels in the plot, we only show numbers as labels for rows and columns and the complete itemsets are printed to the terminal for look-up

plot(subrules, method = "matrix3D", measure = "lift")



#Grouped matrix-based visualization
#Matrix-based visualization is limited in the number of rules it can visualize effectively since large sets of rules typically also have large sets of unique antecedents/consequents.
summary(rules)
plot(rules, method = "grouped")

plot(rules, method = "grouped", control = list(k = 50))

sel <- plot(rules, method = "grouped", interactive = TRUE)


================================================
FILE: 65-AR/16f-ar-groceries-vis4.R
================================================
#AR - Graph Based Visualisation

library(arules)
library(datasets)
library(arulesViz)

data(Groceries)

rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,
                       maxlen=5, target='rules' ))

#visualize association rules using vertices and edges where vertices annodated with item labels represent items, and itemsets or rules are reptesented as a second set of vertices. Items are connected with itemsets/rules using arrows. For rules arrows pointing from items to rule vertices indicate LHS items and an arrow from a rule to an item indicates the RHS. Interest measures are typically added to the plot by using color or size of the vertices representing the itemsets/rules.

#Graph-based visualization offers a very clear representation of rules but they tend to easily become cluttered and thus are only viable for very small sets of rule

subrules2 <- head(rules, n = 10, by = "lift")

plot(subrules2, method = "graph")


#only useful for small set of rules. To explore large sets of rules with graphs, advanced interactive features like zooming, filtering, grouping and coloring nodes are needed

saveAsGraph(head(rules, n = 1000, by = "lift"), file = "rules.graphml")


================================================
FILE: 65-AR/16f-ar-groceries-vis5.R
================================================
# Parallel coordinates plot

library(arules)
library(datasets)
library(arulesViz)

data(Groceries)

rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,
                                             maxlen=5, target='rules' ))
subrules2 <- head(rules, n = 10, by = "lift")
plot(subrules2, method = "paracoord")

#The width of the arrows represents support and the intensity of the color represent confidence. It is obvious that for larger rule sets visual analysis becomes difficult since with an increasing number of rules also the number
#of crossovers between the lines increases

#reduce crossover by reorder
plot(subrules2, method = "paracoord", control = list(reorder = TRUE))


================================================
FILE: 65-AR/16k-ar-grocery-DT.R
================================================
#Association Rule - Interactive Data Table

library(arules)
library(datasets)
data(Groceries)

rules <- apriori(Groceries, parameter = list(support=.002, confidence=.7,
           maxlen=5, target='rules' ))
inspectDT(rules)
#see on right side

plotly_arules(rules)# depreciated
#may not work

================================================
FILE: 65-AR/17a-ar-transactionformat.R
================================================
# Data Formats : Conversion to Transactions

library(arules)
#Matrix----
library(arules)
m = matrix(c(1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1,1,0,1,0,1), ncol = 5)
m
dimnames(m) <-  list(paste("Tr",c(1:6), sep = ""), c("a","b","c","d","e" ))
m
trans1 <-  as(m, "transactions")
trans1
inspect(trans1)
rules1= apriori(trans1)
inspect(rules1[1:5])

#Data Frame----
df <- data.frame(
  age = as.factor(c(6,8,7,6,9,5)), 
  grade = as.factor(c(1,3,1,1,4,1)))  

#note: all attributes have to be factors ####
df
trans2 <- as(df, "transactions") 
inspect(trans2)
image(trans2)




#List-----




#csv----


================================================
FILE: 65-AR/17d-ar-matrix-transactions.R
================================================
# Creating Transaction Data  ####
#http://stackoverflow.com/questions/20758814/convert-data-frame-in-r-to-transactions-or-an-itemmatrix
#example 1: creating transactions from a matrix ####
library(arules)
a_matrix <- matrix(c(1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1), ncol = 5)
#set dim names ####
a_matrix

dimnames(a_matrix) <-  list(
  c("a","b","c","d","e"),
  paste("Tr",c(1:5), sep = ""))

a_matrix
#coerce ####

trans2 <-  as(a_matrix, "transactions")
trans2
inspect(trans2)
#example 2: creating transactions from data.frame ####

a_df <- data.frame(
  age = as.factor(c(6,8,7,6,9,5)), 
  grade = as.factor(c(1,3,1,1,4,1)))  
#note: all attributes have to be factors ####

a_df
#coerce ####

trans3 <- as(a_df, "transactions") 
inspect(trans3)
image(trans3)
data2 = read.csv("dar2.csv")

data2
data2t = as(data2,'transactions')
inspect(data2t)

data3 = read.csv("dar3.csv")
#data3t = as(data3,'transactions')
data3t = read.transactions("dar3a.csv", format = "single", sep = ",")
str(data3t)
head(data3t)
inspect(data3t)
library(datasets)
itemFrequencyPlot(data3t,topN=10,type='absolute')
rules3 = apriori(data3t, parameter=list(minlen=2))
rules3.sorted = sort(rules3, by='lift')
inspect(rules3.sorted)



================================================
FILE: 65-AR/17e-ar-df-transcations.R
================================================
# DF to Transactions

#example 2: creating transactions from data.frame ####

a_df <- data.frame(
  age = as.factor(c(6,8,7,6,9,5)), 
  grade = as.factor(c(1,3,1,1,4,1)))  
#note: all attributes have to be factors ####

a_df
#coerce ####

trans3 <- as(a_df, "transactions") 
inspect(trans3)
image(trans3)





================================================
FILE: 65-AR/17f-ar-csv-transactions.R
================================================
# CSV to Transaction Format
pacman::p_load(arules, arulesViz)
data2 = read.csv("./data/dar2.csv")
data2
data2t = as(data2,'transactions')
inspect(data2t)

data3 = read.csv("./data/dar3.csv")
head(data3)
#data3t = as(data3,'transactions')
data3t = read.transactions("./data/dar3a.csv", format = "single", sep = ",")
str(data3t)
head(data3t)
inspect(data3t)
library(datasets)
itemFrequencyPlot(data3t,topN=10,type='absolute')
rules3 = apriori(data3t, parameter=list(minlen=2))
rules3.sorted = sort(rules3, by='lift')
inspect(rules3.sorted)

================================================
FILE: 65-AR/17f-ar-csv2-transactions.R
================================================
# AR Data Formats and Conversion

# Data Format ----

library("arules")

set.seed(101)
transactionID = sample(1:500, 1000, replace=T)
transactionID
item = paste("item", sample(1:50, 1000, replace=T),sep = "")
item
orders <- data.frame(transactionID, item)
head(orders)
orders [ order(orders$transactionID),]
write.csv(orders, "./data/talltransactions.csv")



ordertrans <- arules::read.transactions(
  file = "./data/talltransactions.csv",
  format = "single",
  sep = ",",
  cols=c("transactionID","item"),
  rm.duplicates = T
)
ordertrans
inspect(ordertrans[1:5])
?read.transactions
summary(ordertrans)
str(ordertrans)
head(ordertrans)

# Create an item frequency plot for the top 20 items
itemFrequencyPlot(ordertrans,topN=20,type="absolute")

#minsupport to 0.001 , min conf of 0.8 ; show top 5 rules
rules <- apriori(ordertrans, parameter = list(supp = 0.003, conf = 0.8))

# Show the top 5 rules, but only 2 digits
options(digits=2)
inspect(rules[1:5])

summary(rules)

rules<-sort(rules, by="confidence", decreasing=TRUE)
inspect(head(rules))


rules2 <- apriori(ordertrans, parameter = list(supp = 0.001, conf = 0.8,maxlen=3))
inspect(head(rules2))

library(arulesViz)
# Interactive Graph - Seperate Window
plot(rules2,method="graph",interactive=T,shading=NA)










# DataFrame directly to transactions
# Converting to a Matrix ####
summary(orders)
orders$const = TRUE

# Remove duplicates
dim(orders) #1,000 x 3
orders <- unique(orders)
dim(orders) #979 x 3

?reshape
# Need to reshape the matrix
ordersmatprep <- reshape(data = orders,
                           idvar = "transactionID",
                           timevar = "item",
                           direction = "wide")
head(ordersmatprep[,1:3])

# Drop the transaction ID
ordermatrix <- as.matrix(ordersmatprep[,-1])
head(ordermatrix[,1:3])


# Clean up the missing values to be FALSE
ordermatrix[is.na(ordermatrix)] <- FALSE
head(ordermatrix[,1:3])
dim(ordermatrix)

# Clean up names
colnames(ordermatrix)
colnames(ordermatrix) <- gsub(x=colnames(ordermatrix),
       pattern="const\\.", replacement="")
colnames(ordermatrix)

ordertrans2 <- as(ordermatrix,"transactions")
summary(ordertrans2)


================================================
FILE: 65-AR/17g-ar-list-transcations.R
================================================
#List to Transactions

a_list <- list(
  c("a","b","c"),
  c("a","b"),
  c("a","b","d"),
  c("c","e"),
  c("a","b","d","e")
)

## set transaction names
names(a_list) <- paste("Tr",c(1:5), sep = "")
a_list

## coerce into transactions
trans1 <- as(a_list, "transactions")
inspect(trans1)

## analyze transactions
summary(trans1)
image(trans1)
inspect(trans1)



================================================
FILE: 65-AR/17h-ar-dataformats.R
================================================
# Basket Analysis - Converting into Transactions format
# 
#
library(arules)
library(arulesViz)

## convert to transaction
trans <- as(df, "transactions")
trans
summary(trans)
inspect(trans)


#DF---
#creating transactions from data.frame
a_df <- data.frame(
  age   = as.factor(c(6, 8, NA, 9, 16)), 
  grade = as.factor(c("A", "C", "F", NA, "C")),
  pass  = c(TRUE, TRUE, FALSE, TRUE, TRUE))  
## note: factors are translated differently to logicals and NAs are ignored
a_df

## coerce  ## convert to transaction
trans3 <- as(a_df, "transactions") 
inspect(trans3)
as(trans3, "data.frame")
image(trans3)

# M3 : format basket
my_data = paste("1,2","1","2,3", sep="\n");
my_data
write(my_data, file = "./data/my_basket");
trans = read.transactions("./data/my_basket", format = "basket", sep=",");
inspect(trans);

#-----------------
## example 4: creating transactions from a data.frame with 
## transaction IDs and items (by converting it into a list of transactions first) 
a_df3 <- data.frame(
  TID = c(1,1,2,2,2,3), 
  item=c("a","b","a","b","c", "b")
)
a_df3
trans4 <- as(split(a_df3[,"item"], a_df3[,"TID"]), "transactions")
trans4
inspect(trans4)
image(trans4)
## Note: This is very slow for large datasets. It is much faster to read transactions in this format from disk using read.transactions() with format = "single".





#--------------------

#  creating transactions from a matrix
a_matrix <- matrix(c( 1,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,1,1),ncol=5)
a_matrix
## set dim names
dimnames(a_matrix) <- list(c("a","b","c","d","e"), paste("Tr",c(1:5), sep = ""))
a_matrix

## coerce
trans2 <- as(a_matrix, "transactions")
trans2
inspect(trans2)



================================================
FILE: 65-AR/18a-arules1.R
================================================
# Documentation on arules

library(arules)





#----



#----


#significant
#LHS andthe RHS depend on each other. 
data("Income")
rules <- apriori(Income, parameter = list(support = 0.5))
is.significant(rules, Income)
inspect(rules[is.significant(rules, Income)])

#super-subset-----
#memory intensive- looks for each element in x which elements in y are supersets or subsets.
data("Adult")
set <- eclat(Adult, parameter = list(supp = 0.8))
inspect(set)
### find the supersets of each itemset in set
is.superset(set, set)
is.superset(set, set, sparse = FALSE)
is.subset(set,set)


#itemCoding----
data("Adult")
## Example 1: Manual decoding
## get code
iLabels <- itemLabels(Adult)
head(iLabels)
## get undecoded list and decode in a second step
list <- LIST(Adult[1:5], decode = FALSE)
list

decode(list, itemLabels = iLabels)
## Example 2: Manually create an itemMatrix
data <- list(
  c("income=small", "age=Young"),
  c("income=large", "age=Middle-aged")
)
iM <- encode(data, iLabels)
iM
inspect(iM)
## use the itemMatrix to create transactions
as(iM, "transactions")
## Example 3: use recode
## select first 100 transactions and all education-related items
sub <- Adult[1:100, itemInfo(Adult)$variables == "education"]
itemLabels(sub)
image(sub)
## recode to match Adult again
sub.recoded <- recode(sub, match = Adult)
image(sub.recoded)

#itemFrequency
data("Adult")
itemFrequency(Adult, type = "relative")


#itemFrequencyPlot
data(Adult)
## the following example compares the item frequencies
## of people with a large income (boxes) with the average in the data set
Adult.largeIncome <- Adult[Adult %in%  "income=large"]
## simple plot
itemFrequencyPlot(Adult.largeIncome)
## plot with the averages of the population plotted as a line
## (for first 72 variables/items)
itemFrequencyPlot(Adult.largeIncome[, 1:72],population = Adult[, 1:72])
## plot lift ratio (frequency in x / frequency in population)
## for items with a support of 20% in the population
itemFrequencyPlot(Adult.largeIncome, population = Adult, support = 0.2,
                  lift = TRUE, horiz = TRUE)





#itemsetsclass-----
data("Adult")
## Mine frequent itemsets with Eclat.
fsets <- eclat(Adult, parameter = list(supp = 0.5))
## Display the 5 itemsets with the highest support.
fsets.top5 <- sort(fsets)[1:5]
inspect(fsets.top5)
## Get the itemsets as a list
as(items(fsets.top5), "list")
## Get the itemsets as a binary matrix
as(items(fsets.top5), "matrix")
## Get the itemsets as a sparse matrix, a ngCMatrix from package Matrix.
## Warning: for efficiency reasons, the ngCMatrix you get is transposed
as(items(fsets.top5), "ngCMatrix")


#LIST----
data(Adult)
### default coercions (same as as(Adult[1:5], "list"))
LIST(Adult[1:5])
### coercion without item decoding: uses column n
LIST(Adult[1:5], decode = FALSE)


#match----
data("Adult")
## get unique transactions, count frequency of unique transactions
## and plot frequency of unique transactions
vals <- unique(Adult)
vals
cnts <- tabulate(match(Adult, vals))
cnts
plot(sort(cnts, decreasing=TRUE))
## find all transactions which are equal to transaction 10 in Adult
which(Adult %in% Adult[10])
## for transactions we can also match directly with itemLabels.
## Find in the first 10 transactions the ones which
## contain age=Middle-aged (see help page for class itemMatrix)
Adult[1:10] %in% "age=Middle-aged"
inspect(Adult[1:3])
## find all transactions which contain items that partially match "age=" (all here).
Adult[1:10] %pin% "age="
## find all transactions that only include the item "age=Middle-aged" (none here).
Adult[1:10] %oin% "age=Middle-aged"
## find al transaction which contain both items "age=Middle-aged" and "sex=Male"
Adult[1:10] %ain% c("age=Middle-aged", "sex=Male")



#merge----
data("Groceries")
## create a random item as a matrix
randomItem <- sample(c(TRUE, FALSE), size=length(Groceries),replace=TRUE)
randomItem
randomItem <- as.matrix(randomItem)
randomItem
colnames(randomItem) <- "random item"
head(randomItem, 3)
## add the random item to Groceries
g2 <- merge(Groceries, randomItem)
nitems(Groceries)
nitems(g2)
inspect(head(g2,3))  #added to 3rd transaction

#Mushroom----
data("Mushroom")
inspect(head(Mushroom,3))
head(LIST(Mushroom),3)

#predit----
data("Adult")
## sample
small <- sample(Adult, 500)
head(LIST(small))
large <- sample(Adult, 5000)
head(LIST(large))

## cluster a small sample
d_jaccard <- dissimilarity(small)
head(d_jaccard)
hc <- hclust(d_jaccard)
hc
l <- cutree(hc, k=4)

## predict labels for a larger sample
labels <- predict(small, large, l)
## plot the profile of the 1. cluster
itemFrequencyPlot(large[labels==1, itemFrequency(large) > 0.2])


#random.transactions----


#read.PMML-----
library(pmml)
data("Groceries")
rules <- apriori(Groceries, parameter=list(support=0.001))
rules <- head(rules, by="lift")
rules
inspect(rules)
### save rules as PMML
write.PMML(rules, file = "rules.xml")
### read rules back
rules2 <- read.PMML("rules.xml")
rules2
### compare rules
inspect(rules[1])
inspect(rules2[1])
### clean up
unlink("rules.xml")


#read.transactions----
## create a demo file using basket format for the example
data <- paste(
  "# this is some test data",
  "item1, item2",
  "item1",
  "item2, item3",
  sep="\n")
cat(data)
write(data, file = "demo_basket")
## read demo data (skip comment line)
tr <- read.transactions("demo_basket", format = "basket", sep=",", skip = 1)
inspect(tr)
## create a demo file using single format for the example
## column 1 contains the transaction ID and column 2 contains one item
data <- paste(
  "trans1 item1",
  "trans2 item1",
  "trans2 item2",
  sep ="\n")
cat(data)
write(data, file = "demo_single")
## read demo data
tr <- read.transactions("demo_single", format = "single", cols = c(1,2))
inspect(tr)
## tidy up
unlink("demo_basket")
unlink("demo_single")

# rule-induction-----
data("Adult")
## find all closed frequent itemsets
closed_is <- apriori(Adult,  parameter = list(target = "closed frequent itemsets", support = 0.4))
closed_is
## use rule induction to produce all closed association rules
closed_rules <- ruleInduction(closed_is, Adult,control = list(verbose = TRUE))
## X&Y are already closed, check that X is also closed
closed_rules[is.element(lhs(closed_rules), items(closed_is))]
## inspect the resulting closed rules
summary(closed_rules)
inspect(head(closed_rules, by = "lift"))

## use lattice of frequent itemsets
ec <- eclat(Adult, parameter = list(support = 0.4))
rec <- ruleInduction(ec)
rec
inspect(head(rec))


#rule-class----
data("Adult")
## Mine rules.
rules <- apriori(Adult, parameter = list(support = 0.4))
## Select a subset of rules using partial matching on the items
## in the right-hand-side and a quality measure
rules.sub <- subset(rules, subset = rhs %pin% "sex" & lift > 1.3)
## Display the top 3 support rules
inspect(head(rules.sub, n = 3, by = "support"))
## Display the first 3 rules
inspect(rules.sub[1:3])
## Get labels for the first 3 rules
labels(rules.sub[1:3])
labels(rules.sub[1:3], itemSep = " + ", setStart = "", setEnd="",
       ruleSep = " ---> ")

#sample----
data("Adult")
## sample with replacement
s <- sample(Adult, 50, replace = TRUE)
s


#setOperations----
data("Adult")
## mine some rules
r <- apriori(Adult)
## take 2 subsets
r1 <- r[1:10]
r2 <- r[6:15]
union(r1,r2)
intersect(r1,r2)
setequal(r1,r2)


#size----
data("Adult")
summary(size(Adult))


#sort----
data("Adult")
## Mine rules with APRIORI
rules <- apriori(Adult, parameter = list(supp = 0.6))
rules_by_lift <- sort(rules, by = "lift")
inspect(head(rules))
inspect(head(rules_by_lift))
## A faster/less memory consuming way to get the top 5 rules according to lift
inspect(head(rules, n = 5, by = "lift"))


#subset-----
#subset works on the rows/itemsets/rules of x
#%in% (selectitemsets matching any given item), %ain% (select only itemsets matching all given item), %oin% (select only itemsets matching only the given item), and %pin% (%in% with partial matching)

data("Adult")
rules <- apriori(Adult)
## select all rules with item "marital-status=Never-married" in
## the right-hand-side and lift > 2
rules.sub <- subset(rules, subset = rhs %in% "marital-status=Never-married" & lift > 2)
## use partial matching for all items corresponding to the variable
## "marital-status"
rules.sub <- subset(rules, subset = rhs %pin% "marital-status=")
## select only rules with items "age=Young" and "workclass=Private" in the left-hand-side
rules.sub <- subset(rules, subset = lhs %ain% c("age=Young", "workclass=Private"))

#SunBai----
data(SunBai)
summary(SunBai)
inspect(SunBai)
transactionInfo(SunBai)


#support----
data("Income")
## find and some frequent itemsets
itemsets <- eclat(Income)[1:5]
## inspect the support returned by eclat
inspect(itemsets)
## count support in the database
support(items(itemsets), Income)


#supportingTransactions----
data <- list(
  c("a","b","c"),
  c("a","b"),
  c("a","b","d"),
  c("b","e"),
  c("b","c","e"),
  c("a","d","e"),
  c("a","c"),
  c("a","b","d"),
  c("c","e"),
  c("a","b","d","e")
)
data <- as(data, "transactions")
## mine itemsets
f <- eclat(data, parameter = list(support = .2, minlen=3))
inspect(f)
## find supporting Transactions
st <- supportingTransactions(f, data)
st
as(st, "list")

#tidLists-class----
## Create transaction data set.
data <- list(
  c("a","b","c"),
  c("a","b"),
  c("a","b","d"),
  c("b","e"),
  c("b","c","e"),
  c("a","d","e"),
  c("a","c"),
  c("a","b","d"),
  c("c","e"),
  c("a","b","d","e")
)
data <- as(data, "transactions")
data
## convert transactions to transaction ID lists
tl <- as(data, "tidLists")
tl
inspect(tl)
dim(tl)
dimnames(tl)
## inspect visually
image(tl)


#transactions-class-----
## example 1: creating transactions form a list
a_list <- list(
  c("a","b","c"),
  c("a","b"),
  c("a","b","d"),
  c("c","e"),
  c("a","b","d","e")
)
## set transaction names
names(a_list) <- paste("Tr",c(1:5), sep = "")
a_list
## coerce into transactions
trans1 <- as(a_list, "transactions")
## analyze transactions
summary(trans1)
image(trans1)
## example 2: creating transactions from a matrix
a_matrix <- matrix(c(
  1,1,1,0,0,
  1,1,0,0,0,
  1,1,0,1,0,
  0,0,1,0,1,
  1,1,0,1,1
), ncol = 5)

## set dim names
dimnames(a_matrix) <- list(c("a","b","c","d","e"),
                           paste("Tr",c(1:5), sep = ""))
a_matrix
## coerce
trans2 <- as(a_matrix, "transactions")
trans2
inspect(trans2)
## example 3: creating transactions from data.frame
a_df <- data.frame(
  age = as.factor(c(6, 8, NA, 9, 16)),
  grade = as.factor(c("A", "C", "F", NA, "C")),
  pass = c(TRUE, TRUE, FALSE, TRUE, TRUE))
## note: factors are translated differently to logicals and NAs are ignored
a_df
## coerce
trans3 <- as(a_df, "transactions")
inspect(trans3)
as(trans3, "data.frame")

## example 4: creating transactions from a data.frame with
## transaction IDs and items (by converting it into a list of transactions first)
a_df3 <- data.frame(
  TID = c(1,1,2,2,2,3), item=c("a","b","a","b","c", "b")
)
a_df3
trans4 <- as(split(a_df3[,"item"], a_df3[,"TID"]), "transactions")
trans4
inspect(trans4)
## Note: This is very slow for large datasets. It is much faster to
## read transactions in this format from disk using read.transactions()## with format = "single".
## example 5: create transactions from a dataset with numeric variables## using discretization.
data(iris)
irisDisc <- discretizeDF(iris)
head(irisDisc)
trans5 <- as(irisDisc, "transactions")
trans5
inspect(head(trans5))


#unique----
data("Adult")
r1 <- apriori(Adult[1:1000], parameter = list(support = 0.5))
r2 <- apriori(Adult[1001:2000], parameter = list(support = 0.5))
## Note that this produces a collection of rules from two sets
r_comb <- c(r1, r2)
r_comb <- unique(r_comb)
r_comb


#weclat-----
data(SunBai)
SunBai
## weights are stored in transactionInfo
transactionInfo(SunBai)
## mine weighted support itemsets using transaction support in SunBai
s <- weclat(SunBai, parameter = list(support = 0.3),
            control = list(verbose = TRUE))
inspect(sort(s))
## create rules using weighted support (satisfying a minimum
## weighted confidence of 90%).
r <- ruleInduction(s, confidence = .9)
inspect(r)

#write----
data("Epub")
head(Epub)
## write the formated transactions to screen (basket format)
write(head(Epub))
## write the formated transactions to screen (single format)
write(head(Epub), format="single")

## write the formated result to file in CSV format
write(Epub, file = "data.csv", format="single", sep = ",")
## write rules in CSV format
rules <- apriori(Epub, parameter=list(support=0.0005, conf=0.8))
write(rules, file = "data.csv", sep = ",")
unlink("data.csv") # tidy up  delete files

#[----
data(Adult)
Adult
## select first 10 transactions
Adult[1:10]
## select first 10 items for first 100 transactions
Adult[1:100, 1:10]
## select the first 100 transactions for the items containing
## "income" or "age=Young" in their labels
Adult[1:100, c("income=small", "income=large" ,"age=Young")]


================================================
FILE: 65-AR/20a-ar-DU1.R
================================================
# Association Rules Set 4 ####

library(arules)
df <- data.frame(
  age   = as.factor(c(6, 6, 8, 8, NA, 9, 16)),
  grade = as.factor(c("A", "C", "C", "C", "F", NA, "C")),
  pass  = c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE)) 
df
## convert to transaction
trans3 <- as(df, "transactions")
summary(trans3)
inspect(trans3)

# MBA Example
mydata = read.csv("./data/MBA.csv")
mydata
# See first 10 observations
head(mydata, n=10)

# Split data
dt <- split(mydata$Products, mydata$ID)
dt
# Loading arules package
if(!require(arules)) install.packages("arules")

# Convert data to transaction level
dt2 = as(dt,"transactions")
dt2
summary(dt2)
inspect(dt2)

# Most Frequent Items
itemFrequency(dt2, type = "relative")
itemFrequencyPlot(dt2,topN = 5)

# aggregated data
rules = apriori(dt2, parameter=list(support=0.005, confidence=0.8))
rules = apriori(dt2, parameter=list(support=0.005, confidence=0.8, minlen = 3))
rules = apriori(dt2, parameter=list(support=0.005, confidence=0.8, maxlen = 4))

#Convert rules into data frame
rules3 = as(rules, "data.frame")
write(rules, "MBArules.csv", sep=",")

# Show only particular product rules
inspect( subset( rules, subset = rhs %pin% "Product H" ))

# Show the top 10 rules
options(digits=2)
inspect(rules[1:10])

# Get Summary Information
summary(rules)

# Sort by Lift
rules<-sort(rules, by="lift", decreasing=TRUE)

# Remove Unnecessary Rules
subset.matrix <- is.subset(rules, rules)
subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA
redundant <- colSums(subset.matrix, na.rm=T) >= 1
which(redundant)
rules.pruned <- rules[!redundant]
rules<-rules.pruned

#Clean Rules
rules3$rules=gsub("\\{", "", rules3$rules)
rules3$rules=gsub("\\}", "", rules3$rules)
rules3$rules=gsub("\"", "", rules3$rules)

#Split the rule
if(!require(splitstackshape))library(splitstackshape)
Rules4=cSplit(rules3, "rules","=>")
names(Rules4)[names(Rules4) == 'rules_1'] <- 'LHS'
Rules5=cSplit(Rules4, "LHS",",")
Rules6=subset(Rules5, select= -c(rules_2))
names(Rules6)[names(Rules6) == 'rules_3'] <- 'RHS'

# What are customers likely to buy before they purchase "Product A"
rules<-apriori(data=dt, parameter=list(supp=0.001,conf = 0.8), 
               appearance = list(default="lhs",rhs="Product A"),
               control = list(verbose=F))
rules<-sort(rules, decreasing=TRUE,by="confidence")
inspect(rules[1:5])

# What are customers likely to buy if they purchased "Product A"
rules<-apriori(data=dt, parameter=list(supp=0.001,conf = 0.8), 
               appearance = list(default="rhs",lhs="Product A"),
               control = list(verbose=F))
rules<-sort(rules, decreasing=TRUE,by="confidence")
rules
inspect(rules)



================================================
FILE: 65-AR/20b-ar-DU2.R
================================================
# My Basket 

library(package = "arules")
mypurchase = read.csv("./ar/my_basket1.txt", sep=" ")


v = read.table(file = file('stdin'))
transactions = read.transactions(file = file("stdin"), format = "basket", sep = ",")
#a,b
#a,b,c
#
#
mybasket = read.csv(file='./ar/my_basket1.txt')
v = read.table(file = file('stdin'))
transactions = read.transactions(file = file("stdin"), format = "basket", sep = ",")


================================================
FILE: 65-AR/20c-ar-DU3.R
================================================
#My Basket 3
#
set.seed(101)
transactionID = sample(1:10, 50, replace=T)
transactionID
items = sample(c('milk', 'bread', 'butter', 'eggs'),size=50, replace=T, prob=c(.4,.25,.2,.15))
items
orders <- data.frame(transactionID, items)
head(orders)

write.csv(orders, "./data/myitems1.csv")



ordertrans <- arules::read.transactions(
  file = "./data/myitems1.csv",
  format = "single",
  sep = ",",
  cols=c("transactionID","items"),
  rm.duplicates = T
)
inspect(ordertrans[1:5])
?read.transactions
summary(ordertrans)
str(ordertrans)
head(ordertrans)

# Create an item frequency plot for the top 20 items
itemFrequencyPlot(ordertrans,topN=20,type="absolute")

#minsupport to 0.001 , min conf of 0.8 ; show top 5 rules
rules <- apriori(ordertrans, parameter = list(supp = 0.001, conf = 0.8))

# Show the top 5 rules, but only 2 digits
options(digits=2)
inspect(rules[1:5])

summary(rules)

rules<-sort(rules, by="confidence", decreasing=TRUE)
inspect(head(rules))



================================================
FILE: 65-AR/22a-ar-edn.R
================================================
# Association Rule 14
# data set having Gradution, Attendance, Assignment, Unit Test and Univ Result Results
# Aim - to develop Association Rule 
options(digits=2)
edndata = read.csv(file='ar14.csv',header=T, stringsAsFactors = T)

# Check Properties of Data Set 
str(edndata)
dim(edndata)
names(edndata)
# load library
library(arules)
# Find Association Rules with Default Settings
rules = apriori(edndata)
inspect(rules)

# Custom Rules
rules = apriori(edndata, parameter = list(minlen=2,supp=.005,conf=0.8),
                list(rhs=c("result=Poor"),default="lhs"), control=list(verbose=F))
rules.sorted = sort(rules, by="lift")
inspect(rules.sorted)



# Taking data as numerical values and then converting it into categorical values
edndata14b = read.csv(file='ar14b.csv',header=T)
str(edndata14b)
breaks = seq(50,100,by=10)
# Table values of Attendance
attnd.cut = cut(edndata14b$attendance,breaks,right=F)
attnd.freq = table(attnd.cut) ; attnd.freq

edndata14b[edndata14b >= 70] = 'Good'
edndata14b[edndata14b >= 60 & edndata14b < 70 ] = 'Avg'
edndata14b[edndata14b < 60] = 'Poor'
str(edndata14b)
#edndata14b$graduation = factor(edndata14b$graduation) # one column at a time
edndata14b[] <- lapply( edndata14b, factor) # All Colns of DF
str(edndata14b)

rules14b = apriori(edndata14b)
inspect(rules14b)
head(edndata14b)
# Custom Rules result = 'Poor'
arules14b = apriori(edndata14b, parameter = list(minlen=2,supp=.005,conf=0.8),
                list(rhs=c('result=Poor'),default="lhs"), control=list(verbose=F))
<<<<<<< HEAD
arules14b.sorted = sort(arules14b, by="lift")
inspect(arules14b.sorted)
quality(arules14b.sorted)
head(inspect(arules14b.sorted))



# Plotting Rules
# Visualising
library(arulesViz)
plot(arules14b)

plot(arules14b, method="graph", control=list(type="items"))

plot(arules14b, method="paracoord", control=list(reorder=TRUE))
=======
rules.sorted = sort(rules, by="lift")
inspect(rules.sorted)
quality(rules.sorted)
head(inspect(rules.sorted))

>>>>>>> 0b50577bb0f052107564367a2845372e98756506


================================================
FILE: 65-AR/22b-ar-elective.R
================================================
# Subject Choices of Electives
#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer
library(arules)
datar2 <- read.csv("dar3b.csv", header = TRUE, row.names=1)
str(datar2)
datar2 <- as.matrix(datar2)
str(datar2)
head(datar2)
#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.
#IMP If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.
#Next, I convert onsen matrix into transaction class.
datar2.trans <- as(datar2, "transactions")
str(datar2.trans)
summary(datar2.trans) # check what’s in the transaction data.
#With inspect(), check each transaction’s items.
inspect(datar2.trans)

#want to know frequency of items, I can plot as below;
itemFrequencyPlot(datar2.trans, ylim=c(0,1))
#Making association rules ####
#Now make association rules.
options(digits=2)
datar2.rule <- apriori(datar2.trans, parameter = list(maxlen=3, support=0.04, confidence=0.8, ext=TRUE))


#apriori() is a function to make association rules.
#maxlen means a number of rules (in this case, number of effects of an datar2) in a association rule.
#support means how often it happens (or probability of an event). 0.04 is not so rare, not too often.
#And confidence means how often it occurs under specific situations. If this is so small, it may be not be worth making association rules in the first place.
#Now, check summary of the association rules  made, with summary().
#check a number of rules and how many rules each association rule has.
#datar2.rule = sort(datar2.rule, by='confidence', decreasing=T)
datar2.rule = sort(datar2.rule, by='lift', decreasing=T)

summary(datar2.rule)
inspect(sort(subset(datar2.rule, subset= (support >0.5))))

#In order to inspect the contents of association rules, I can do it as below;
inspect(datar2.rule[1:5])

#If I want to extract specific association rules, I use subset() as below.
#In this case, I want the result to have “comnskills” as the rule body and more than 1 lift.

rule1 <- subset(datar2.rule, subset=(rhs %in% "elective6") & (lift>1.0))
rule1
inspect(rule1)


rule2 <- subset(datar2.rule, subset=(rhs %in% c('elective3','elective4')) & (lift>1.5))
rule2 = sort(rule2, by='confidence', decreasing=T)
inspect(rule2)

rule3 <- subset(datar2.rule, subset=(rhs %in% "elective1") & (support >0.5))
rule3 = sort(rule3, by='support', decreasing=T)
inspect(rule3)


================================================
FILE: 65-AR/22d-ar-subjects.R
================================================
# Association Rule
dataar1 = read.csv("dar1.csv")
str(dataar1)
?write.csv
write.csv(dataar1,file='dar1w.csv',row.names = F)
(dataar1 = dataar1[-7])
library(arules)
str(dataar1)
# change to factors for Association Rule
#dataar1 = lapply(dataar1,function(x) as.factor(x))
rules = apriori(dataar1)
inspect(rules)
rules
# more than 300 rules generated
# Refining Rules
rhsrule = c('java=Yes','cpp=Yes')
rules = apriori(dataar1, parameter = list(minlen=2,supp=.05,conf=0.8),
                list(rhs=rhsrule,default="lhs"), control=list(verbose=F))
rules.sorted = sort(rules,by='lift')
inspect(rules.sorted)

# Prune Redundant Rules
subset.matrix = is.subset(rules.sorted,rules.sorted)
subset.matrix
subset.matrix[lower.tri(subset.matrix,diag=T)] = NA
subset.matrix
redundant <- colSums(subset.matrix,na.rm=T) >=1
subset.matrix
redundant
which(redundant)
rules.pruned = rules.sorted[!redundant]
inspect(rules.pruned)
# Visualising
library(arulesViz)
plot(rules)
plot.new()
plot(rules,method='graph',control=list(type='items'))
plot(rules,method='paracoord',control=list(reorder=T))

# Using Weka Library ####
#Using Weka 
library(RWeka)
library(rattle)
rattle()
Apriori()

================================================
FILE: 65-AR/22e-ar-placement.R
================================================
# Data set - Placement Selection Criteria
#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer
# About data set
library(arules)
datar2 <- read.csv("./data/dar2.csv", header = TRUE, row.names=1)
str(datar2)
datar2 <- as.matrix(datar2)
str(datar2)
head(datar2)
#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.
#IMP If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.
#Next, I convert onsen matrix into transaction class.
datar2.trans <- as(datar2, "transactions")
str(datar2.trans)
summary(datar2.trans) # check what’s in the transaction data.
#With inspect(), check each transaction’s items.
inspect(datar2.trans)

#want to know frequency of items, I can plot as below;
itemFrequencyPlot(datar2.trans, ylim=c(0,1))
#Making association rules ####
#Now make association rules.
options(digits=2)
datar2.rule <- apriori(datar2.trans, parameter = list(maxlen=3, support=0.04, confidence=0.8, ext=TRUE))

datar2.rule = sort(datar2.rule, by='confidence', decreasing=T)

summary(datar2.rule)

#inspect
inspect(datar2.rule[1:5])

#extract specific association rules, use subset
#subset - commnskills  and lift > 1

rule1 <- subset(datar2.rule, subset=(rhs %in% "comnskills") & (lift>1.0))
rule1
inspect(rule1[1:5])

rule2 <- subset(datar2.rule, subset=(rhs %in% c('extraco','gender'))  & (lift>1.7))
rule2
inspect(rule2[1:5])

rule3 <- subset(datar2.rule, subset=(rhs %in% "select") & lhs %in% "relocwilling" & (lift>1.0))
rule3
inspect(rule3)


================================================
FILE: 65-AR/22f-myAR1.R
================================================
# Presentation to Guide
dataar1 = read.csv("./data/dar1.csv")
str(dataar1)
#?write.csv
#write.csv(dataar1,file='dar1w.csv',row.names = F)
dataar1 = dataar1[-7]
library(arules)
str(dataar1)
# change to factors for Association Rule
#dataar1 = lapply(dataar1,function(x) as.factor(x))
rules = apriori(dataar1)
inspect(rules[1:5])
rules
# more than 300 rules generated
# Refining Rules
rhsrule = c('java=Yes','cpp=Yes')
rules = apriori(dataar1, parameter = list(minlen=2,supp=.05,conf=0.8),
                list(rhs=rhsrule,default="lhs"), control=list(verbose=F))
rules.sorted = sort(rules,by='lift')
inspect(rules.sorted[1:5])

# Prune Redundant Rules
sum(is.redundant(rules.sorted))
(redundant = which(is.redundant(rules.sorted)))
rulesNR <- rules[-redundant] 
inspect(rulesNR[1:5])
length(rulesNR)

# Visualising
library(arulesViz)
plot(rules)
plot.new()
plot(rules,method='graph',control=list(type='items'))
plot(rules,method='paracoord',control=list(reorder=T))

# Using Weka Library ####
#Using Weka 
library(RWeka)
library(rattle)
rattle()
Apriori()


================================================
FILE: 65-AR/25a-ar-income.R
================================================
#AR - Income

data("Income")

## mine some rules with the consequent "language in home=english"
rules <- apriori(Income, parameter = list(support = 0.5),  appearance = list(rhs = "language in home=english", default = "lhs"))

## for better comparison we sort the rules by confidence and add Bayado's improvement
rules <- sort(rules, by = "confidence")
quality(rules)$improvement <- interestMeasure(rules, measure = "improvement")
inspect(rules)
is.redundant(rules)

## redundant rules
inspect(rules[is.redundant(rules)])

## non-redundant rules
inspect(rules[!is.redundant(rules)])



================================================
FILE: 65-AR/25b-ar-medical.R
================================================
# Onsen
#http://tallman-world.tumblr.com/post/111511141517/rhow-to-make-association-rules-on-r-all-integer
#“onsen_name” is a name of onsen (hot spring) and the other columns mean effects that people can get from it.
library(arules)
onsen <- read.csv("./data/onsen.csv", header = TRUE, row.names=1)
onsen <- as.matrix(onsen)
#If all the columns except the row name are two-valued data (0 or 1), matrix class is easier to deal with in association rules.
#If all the columns are string data, I just convert the data.frame into transaction data without converting it into matrix data.
#Next, I convert onsen matrix into transaction class.
onsen
onsen.trans <- as(onsen, "transactions")
summary(onsen.trans) # check what’s in the transaction data.
#With inspect(), check each transaction’s items.
inspect(onsen.trans)
#want to know frequency of items, I can plot as below;
itemFrequencyPlot(onsen.trans, ylim=c(0,1))
#Making association rules ####
#Now that I am fully prepared, I make association rules.
onsen.rule <- apriori(onsen.trans, parameter = list(maxlen=4, support=0.04, confidence=0.55, ext=TRUE))

#apriori() is a function to make association rules.
#maxlen means a number of rules (in this case, number of effects of an onsen) in a association rule.
#support means how often it happens (or probability of an event). 0.04 is not so rare, not too often.
#And confidence means how often it occurs under specific situations. If this is so small, it may be worth making association rules in the first place.
#Now, I check summary of the association rules I’ve made, with summary().
#check a number of rules and how many rules each association rule has.
summary(onsen.rule)

#In order to inspect the contents of association rules, I can do it as below;
inspect(onsen.rule)

#If I want to extract specific association rules, I use subset() as below.
#In this case, I want the result to have “nerve_pain” as the rule body and more than 1 lift.

nerve_pain_and_more_than_1_lift <- subset(onsen.rule, subset=(rhs %in% "nerve_pain") & (lift>1.0))


================================================
FILE: 65-AR/25c-ar-titanic.R
================================================
# Association Rule Example 1 ####
#http://www.rdatamining.com/examples/association-rules
load("./data/titanic.raw.rdata")
titanic1 = titanic.raw
str(titanic1)

#write.csv(titanic.raw,file="titanic.csv")
titanic2 = read.csv("./data/titanic.csv")
str(titanic2)

titanic = titanic1
head(titanic)
#titanic = titanic[-1]  # not reqd from rdata
head(titanic)
str(titanic)

library(arules)

rules = apriori(titanic, control=list(verbose=F))
rules = apriori(titanic)

options(digits=3)
inspect(rules)
rules
inspect(rules[1:5])
quality(rules) = round(quality(rules), digits=3)
quality(rules[1:5])

rules = apriori(titanic, parameter = list(minlen=2,supp=.005,conf=0.8),  list(rhs=c("Survived=No","Survived=Yes"),default="lhs"), control=list(verbose=F))

rules.sorted = sort(rules, by="lift")

inspect(rules.sorted[1:5])

options(digits=2)


# Prune Redundant
rules.pruned <- rules[!is.redundant(rules)]
rules.redundant <- rules[is.redundant(rules)]
?is.redundant
inspect(rules.pruned)
rules.pruned
inspect(rules.redundant)
rules.pruned.sorted = sort(rules.pruned, by="lift")
inspect(rules.pruned.sorted)

# Visualising
library(arulesViz)
plot(rules.pruned.sorted)

plot(rules.pruned.sorted, method="graph", control=list(type="items"))

plot(rules.pruned.sorted, method="paracoord", control=list(reorder=TRUE))



================================================
FILE: 65-AR/29a-ar-Adult.R
================================================
# Association Rule Adult ----
data("Adult")
head(Adult)
LIST(Adult[1:5])
rulesA1 = apriori(Adult)
inspect(rulesA1[1:5])
inspect(rulesA1[1:5], ruleSep="---->", itemSep="+", setStart = "", setEnd ="",   linebreak = FALSE)


# Change to any data Frame
myDataFrame= Groceries
#transDat <- as (myDataFrame, "transactions") 
transDat
inspect (transDat[1:10]) 
inspect (transDat[10]) 

?inspect
length (transDat)
size (transDat)
LIST(transDat) 


================================================
FILE: 65-AR/29b-ar-Adult-NW.R
================================================
#my Work
library(arules)
data("AdultUCI")
AdultUCI
str(AdultUCI)
Adult = as(AdultUCI,'transactions')
# error here as some colns are logical or factor
data1 = read.csv(file.choose())
data1
data1a = as(data1,'transactions')
data1a
#trans = read.transactions('data1',format='basket',sep=',')
inspect(data1a)


================================================
FILE: 65-AR/29c-ar-Adult-Draft.R
================================================
# Rough Work on Association Rules
library(arules) 
data("AdultUCI") 
AdultUCI$ID <- 1:nrow(AdultUCI) 
dim(AdultUCI)
str(AdultUCI)
names(AdultUCI)
summary(AdultUCI)
unique(AdultUCI$`marital-status`)
head(AdultUCI[,c('ID','marital-status')])
lst1 <- split(AdultUCI[,"ID"], AdultUCI[,"marital-status"]) 
#head(lst1)
as(lst1, "transactions") 
#transactions in sparse format with 
# 7 transactions (rows) and 
# 48842 items (columns) 



================================================
FILE: 65-AR/30a-ar-Finance1.R
================================================
# AR in Financial Domain

finproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')

finproducts


================================================
FILE: 65-AR/30b-ar-Finance.R
================================================
# AR data for Finance

library("arules")

set.seed(101)
transactionID = sample(1:500, 1000, replace=T)
transactionID

finproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')
length(finproducts)
item = sample(finproducts,1000, replace=T)
item
orders = data.frame(transactionID, item)
head(orders)

write.csv(orders, "./data/fintransactions.csv")

ordertrans <- arules::read.transactions(
  file = "./data/fintransactions.csv",  format = "single",
  sep = ",",  cols=c("transactionID","item"),  rm.duplicates = T
)
inspect(ordertrans[1:5])
ordertrans
summary(ordertrans)
str(ordertrans)
head(ordertrans)


#find frequent item set
frequentItems <- eclat (ordertrans, parameter = list(supp = 0.005, minlen= 2, maxlen = 5)) 
inspect(frequentItems[1:10])
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:15])
#support(A&B) = n(A&B)/ N

frequentItems
inspect(frequentItems[1:20], by='support', decreasing=F)  #view by support increasing order
inspect(frequentItems)  #view all

itemFrequencyPlot (ordertrans,topN = 15,type="absolute")
itemFrequencyPlot(ordertrans, topN = 10, type='relative')
abline(h=0.15)

rules1 <- arules::apriori(ordertrans, parameter = list(supp = 0.005, conf = 0.5))
rules1
write.csv(inspect(rules1, 'rules.csv'))
inspect(rules1[1:5])
quality(rules1) 
head(quality(rules1))
options (digits=2) # set no of digits
inspect (rules1[1:5])
rules1C = sort (rules1, by="confidence", decreasing=TRUE)
inspect(rules1C[1:5])
rules1L = sort (rules1, by="lift", decreasing=TRUE)
inspect (rules1L[1:5])
rules1S = sort (rules1, by="support", decreasing=TRUE)
inspect (rules1S[1:5])
rules1N = sort (rules1, by="count", decreasing=TRUE)
inspect (rules1N[1:5])


#How To Control The Number Of Rules in Output ?
rules2 <- apriori (ordertrans, parameter = list (supp = 0.005, conf = 0.5, minlen=3, maxlen=5)) # minlen = 3 & maxlen limits the elements in a rule to 3 & 5
inspect(rules2)

rules3 = apriori(ordertrans)  #use default values  no rules
rules3 = apriori(ordertrans, parameter=list(support=0.002))  #use default values  no rules

#How To Remove Redundant Rules ?
#A->B, C->B, A&C ->B  : Then A&C -> B is redundant if value of life <= 1st two
#A,B,C -> D; A&B -> D having same sp & conf : has lower lift
is.redundant(rules3)
sum(is.redundant(rules3))
(redundant = which(is.redundant(rules3)))
inspect(rules3[redundant])
length(redundant)
inspect(rules3[redundant][1:10])
rulesNR = rules3[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now

#Another method union of lhs & rhs
redundant3 = which (colSums (is.subset (rules3, rules3)) > 1) 
redundant3
length(redundant3)
rules3NR = rules3[-redundant3]
inspect(rules3NR[1:10])

itemFrequency(ordertrans)
#Find what factors influenced an event ‘X’
rules4 <- apriori(data=ordertrans, parameter=list (supp=0.001,conf = 0.08), appearance = list (default="lhs",rhs="Credit Card"), control = list (verbose=F))  #verbose=F no log
inspect(rules4[1:15])

#Find out what events were influenced by a given event

rules5 <- apriori (data=ordertrans, parameter=list (supp=0.001,conf = 0.05,minlen=2), appearance = list (default="rhs",lhs="General Insurance"), control = list (verbose=F)) 
inspect(rules5)
inspect( subset( rules5, subset = rhs %pin% c("Bonds") ))
inspect( subset( rules5, subset = rhs %pin% c("FD") ))


#Control lhs and rhs
rules6 <- apriori(ordertrans, parameter=list(support =0.00001, confidence =0.05, minlen=2, maxlen=5), appearance = list(lhs=c("Debit Card"), rhs=c("Credit Card"), default="none"))
inspect(rules6)

#Export Rules into a table
library(data.table)
rules_dt <- data.table( lhs = labels( lhs(rules2) ), 
                        rhs = labels( rhs(rules2) ), 
                        quality(rules2) )[ order(-lift), ]
rules_dt
DT::datatable(rules_dt)  # wrapper for datatables
write.csv(rules_dt, './data/arulesfin.csv')


#Visualizing The Rules -----
library(arulesViz)
inspect(rules1)
plot(rules1, measure=c("support", "lift"), shading="confidence")
plot(rules1, measure=c("support", "confidence"), shading="lift")

plot(rules1,method="graph",engine='interactive', shading="confidence") 
#extra window
plot(rules1,method="graph",engine='interactive',shading=NA)
interestMeasure(rules1, transactions=ordertrans, measure=c('support','count','lift'))
quality(rules1)
plot(rules1, shading="order", control=list(main = "Two-key plot"), col=4:6)
inspect(rules1)
plot(rules2, shading="order", control=list(main = "Two-key plot"), col=1:4)
inspect(rules2)

sel = plot(rules1, measure=c("support", "confidence"), shading="lift", engine='interactive') #use mouse to do selection
#https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf


#Matrix Based Visualisation
subrules1 <- rules1[quality(rules1)$confidence > 0.8]
subrules1
plot(subrules1, method="matrix", measure="lift")
plot(subrules1, method="matrix", measure="lift", control=list(reorder=TRUE))
plot(subrules1, method="matrix", engine='3d', measure="lift")

# Group based
plot(rules1, method="grouped")

plot(subrules1, method="graph", control=list(type="itemsets"))

plot(subrules1, method="paracoord")
plot(rules1[1], method="doubledecker", data = ordertrans)
#The area of blocks gives the support and the height of the “yes” blocks is proportional to the confidence for the rules consisting of the antecedent items marked as “yes.”


================================================
FILE: 65-AR/32a-ar-visual.R
================================================

library(arules)
library(arulesViz)
data(Groceries)
rules <- apriori(Groceries, parameter=list(support=0.001, confidence=0.8))
inspect(rules[1:5])
## Scatterplot
## -----------
plot(rules)

## Scatterplot with custom colors
library(colorspace) # for sequential_hcl
plot(rules, control = list(col=sequential_hcl(100)))
plot(rules, col=sequential_hcl(100))
plot(rules, col=grey.colors(50, alpha =.8))


## See all control options using verbose
plot(rules, verbose = TRUE)

plot(rules, engine = "htmlwidget")
plot(rules, method = "two-key plot")


subrules <- subset(rules, lift>5)
subrules
## 2D matrix with shading
plot(subrules, method="matrix")
## 3D matrix
plot(subrules, method="matrix", engine = "3d")
## Matrix with two measures
plot(subrules, method="matrix", shading=c("lift", "confidence"))


plot(rules, method="grouped matrix")
plot(rules, method="grouped matrix", col = grey.colors(10),   gp_labels = gpar(col = "blue", cex=1, fontface="italic"))


## Graphs only work well with very few rules
subrules2 <- sample(subrules, 25)

plot(subrules2, method="graph")
## Custom colors
plot(subrules2, method="graph",  nodeCol = grey.colors(10), edgeCol = grey(.7), alpha = 1)
## igraph layout generators can be used (see ? igraph::layout_)
plot(subrules2, method="graph", layout=igraph::in_circle())
plot(subrules2, method="graph",
     layout=igraph::with_graphopt(spring.const=5, mass=50))

## Parallel coordinates plot
## -------------------------
plot(subrules2, method="paracoord")
plot(subrules2, method="paracoord", reorder=TRUE)

## Note: only works for a single rule
oneRule <- sample(rules, 1)
inspect(oneRule)
plot(oneRule, method="doubledecker", data = Groceries)


## Itemsets
## --------
itemsets <- eclat(Groceries, parameter = list(support = 0.02, minlen=2))
plot(itemsets)
plot(itemsets, method="graph")
plot(itemsets, method="paracoord", alpha=.5, reorder=TRUE)


## Add more quality measures to use for the scatterplot
## ----------------------------------------------------
quality(itemsets) <- interestMeasure(itemsets, trans=Groceries)
head(quality(itemsets))
plot(itemsets, measure=c("support", "allConfidence"), shading="lift")



library(plotly)
data(Groceries)
rules <- apriori(Groceries, parameter=list(support=0.001, confidence=0.8))
rules
# interactive scatter plot visualization
plotly_arules(rules)
plotly_arules(rules, measure = c("support", "lift"), shading = "confidence")
plotly_arules(rules, method = "two-key plot")

# add jitter, change color and markers and add a title
plotly_arules(rules, jitter = 10,
              marker = list(opacity = .7, size = 10, symbol = 1),
              colors = c("blue", "green"))
# save a plot as a html page
p <- plotly_arules(rules)
htmlwidgets::saveWidget(p, "arules.html", selfcontained = FALSE)
browseURL("arules.html")
# Note: selfcontained seems to make the browser slow.
# interactive matrix visualization
plotly_arules(rules, method = "matrix")

#Graph
#
data("Groceries")
rules <- apriori(Groceries, parameter=list(support=0.01, confidence=0.5))
saveAsGraph(rules, "rules.graphml")
## clean up
unlink("rules.graphml")

================================================
FILE: 65-AR/33a-ar-redundant.R
================================================
# Redundant Rules
# 

# Case 1: Income ----
data("Income")

## mine some rules with the consequent "language in home=english"
rules <- apriori(Income, parameter = list(support = 0.5), appearance = list(rhs = "language in home=english", default = "lhs"))
inspect(rules[1:5])

## comparison :sort the rules by confidence and add Bayado's improvement
rules2 <- sort(rules, by = "confidence")
quality(rules2)$improvement <- interestMeasure(rules2, measure = "improvement")
inspect(rules2)
is.redundant(rules2)

## redundant rules
inspect(rules2[is.redundant(rules2)])

## non-redundant rules
inspect(rules2[!is.redundant(rules2)])



#Case-2 :
transactions = Groceries
rules<-apriori(transactions,parameter = list(support=0.001,confidence=0.8,minlen=3))
rules
summary(rules)
rules
#Method_1
subset.matrix <- is.subset(rules,rules)
subset.matrix[lower.tri(subset.matrix,diag=T)] <- NA
redundant <- colSums(subset.matrix,na.rm=T) >= 1
redundant
rules[which(redundant)]
rules2 <- rules[!redundant]
rules2
rules[redundant]

#Method_2
is.redundant(rules)
inspect(rules[!is.redundant(rules)])



#Method X ----

subsetRules <- which(colSums(is.subset(rules, rules)) > 1) # get subset rules in vector
length(subsetRules)  #> 3913
rules <- rules[-subsetRules] # remove subset rules. 


================================================
FILE: 65-AR/33b-redundantrules.R
================================================
# Redundant Rules 2


================================================
FILE: 65-AR/40a-ar-multilevel-Groceries.R
================================================
#Multi Level AR 
#https://rdrr.io/cran/arules/man/aggregate.html

data(Groceries)

head(itemInfo(Groceries))
## aggregate by level2: items will become labels at level2
## Note that the number of items is therefore reduced to 55
Groceries_level2 <- aggregate(Groceries, by = "level2")
Groceries_level2
head(itemInfo(Groceries_level2)) ## labels are alphabetically sorted!


## compare orginal and aggregated transactions
inspect(head(Groceries, 2))
inspect(head(Groceries_level2, 2))

itemLabels(Groceries)
## create lables manually (organize items by the first letter)
mylevels <- toupper(substr(itemLabels(Groceries), 1, 1))
head(mylevels)

Groceries_alpha <- aggregate(Groceries, by = mylevels)
Groceries_alpha
inspect(head(Groceries_alpha, 2))

## aggregate rules ## Note: you could also directly mine rules from aggregated transactions toget support, lift and support
rules <- apriori(Groceries, parameter=list(supp=0.005, conf=0.5))
rules
inspect(rules[1])

rules_level2 <- aggregate(rules, by = "level2")
inspect(rules_level2[1])

## mine multi-level rules:
## (1) add aggregate items. These items are followed by a *
Groceries_multilevel <- addAggregate(Groceries, "level2")
summary(Groceries_multilevel)
inspect(head(Groceries_multilevel))

rules <- apriori(Groceries_multilevel, 
                 parameter = list(support = 0.01, conf = .9))
inspect(head(rules, by = "lift"))
## this contains many spurous rules of type 'item X => aggregare of item X'
## with a confidence of 1 and high lift.

## filter spurious rules resulting from the aggregation 
rules <- filterAggregate(rules)
inspect(head(rules, by = "lift"))



================================================
FILE: 65-AR/43a-ar-patterns.R
================================================
#Association Rule Learning and the Apriori Algorithm
#http://statistical-research.com/association-rule-learning-and-the-apriori-algorithm/
#Often Association Rule Learning is used to analyze the “market-basket” for retailers.  Traditionally, this simply looks at whether a person has purchased an item or not and can be seen as a binary matrix.
library("arules");

library("arulesViz");
#The random data is generated in such a way where there is correlation and has correlated items.
patterns = random.patterns(nItems = 100);
patterns
summary(patterns);

trans = random.transactions(nItems = 100, nTrans = 150, method = "agrawal",  patterns = patterns);
image(trans);
options(digits=2)
itemFrequencyPlot(trans, support = 0.1, cex.names=0.8)
#Sample Transactions from data Set ####
#However, a transaction dataset will usually be available using the approach described in “Data Frames and Transactions“.  
#The rules can then be created using the apriori function on the transaction dataset.
rules = apriori(trans, parameter=list(support=0.05, confidence=0.8));

rules;
inspect(rules)
itemFrequency(trans,type='absolute')
?itemFrequency
#Once the rules have been created a researcher can then review and filter the rules down to a manageable subset.  This can be done in a variety of ways using both graphs and by simply inspecting the rules.

inspect(head(sort(rules, by="lift"),3));

plot(rules);

head(quality(rules));

plot(rules, measure=c("support","lift"), shading="confidence");

plot(rules, shading="order", control=list(main ="Two-key plot"));

#trim down the rules to the ones that are important.

sel = plot(rules, measure=c("support","lift"), shading="confidence", interactive=TRUE);

subrules = rules[quality(rules)$confidence > 0.8];

subrules
# Once again we can now subset the rules to get a visual.  In these graphs we can see the two parts to an association rule: the antecedent (IF) and the consequent (THEN).  These patterns are found by determining frequent patterns in the data and these are identified by the support and confidence.  The support indicates how frequently the items appear in the dataset.  The confidence indicates the number of times the IF/THEN statement on the data are true.  These IF/THEN statements can be visualized by the following graph:

# --------------------
WRITE(rules, file = "", sep = ",", quote = TRUE, col.names = NA)


================================================
FILE: 65-AR/45a-ar-rulesextract.R
================================================
# Association Rules Extraction


library(arules)
library(arulesViz)
library(datasets)

rules = apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))

# Show the top 5 rules, but only 2 digits
options(digits=5)

#summary of rule
summary(rules)

#rules with measures
inspect(rules[1:5])
#numerical values of measures
quality(rules[1:5])

#Levels of Rules
str(rhs(rules)@itemInfo)


rulelength = length(rules)

rulelabels = labels(rules)
print(rulelabels[1:5])

cut = unlist(strsplit(rulelabels,"=> "))[seq(2,2*rulelength,by=2)]
print(cut[1:15])



#RHS of rule
rules@rhs@itemInfo$labels[rules@rhs@data@i+1]

#Subset of Rules based on items
sub_rules <- rules[3]
inspect(sub_rules)
sub_trans = subset(Groceries, items %in% unlist(as(lhs(sub_rules), "list")))
sub_trans
inspect(sub_trans[1:5])


item <- "whole milk"
arules::subset(rules, rhs %in% "whole milk")
rules.sub <- subset(rules, lhs %in% item & size(lhs)==1)
inspect(rules.sub)


# rules to DF
df <- as(rules, 'data.frame')
head(df)
#can save now


================================================
FILE: 65-AR/99-ar-NW.R
================================================
# Interactive Association rule Analysis
#https://www.r-bloggers.com/interactive-association-rules-exploration-app/
library('devtools')
install_github('brooksandrew/Rsenal')
library('Rsenal')
?Rsenal::arulesApp
source_gist(id='706a28f832a33e90283b')
# Not Working Now - check after update
library('devtools')
library('shiny')
library('arules')
library('arulesViz')
source_gist(id='706a28f832a33e90283b')


================================================
FILE: 65-AR/99-ar-OnlineSales.R
================================================
# Association Rule - Online Sales

library(tidyverse)
library(readxl)
library(knitr)
library(ggplot2)
library(lubridate)
library(arules)
library(arulesViz)
library(plyr)


#retail <- read_excel('Online_retail.xlsx')
retail1 <- readxl::read_excel('E:/rWork/rDataSets/Onlineretail.xlsx')

retail <- retail[complete.cases(retail1), ]
summary(retail)
retail %>% plyr::mutate(Description = as.factor(Description))
retail %>% plyr::mutate(Country = as.factor(Country))
retail$Date <- as.Date(retail$InvoiceDate)
retail$Time <- format(retail$InvoiceDate,"%H:%M:%S")
retail$InvoiceNo <- as.numeric(as.character(retail$InvoiceNo))
dplyr::glimpse(retail)


retail$Time <- as.factor(retail$Time)
retail %>%
  ggplot(aes(x=Time)) + 
  geom_histogram(stat="count",fill="indianred")


detach("package:plyr", unload=TRUE)
retail %>% 
  group_by(InvoiceNo) %>% 
  summarize(n_items = mean(Quantity)) %>%
  ggplot(aes(x=n_items))+
  geom_histogram(fill="indianred", bins = 100000) + 
  geom_rug()+
  coord_cartesian(xlim=c(0,80))



retail %>%
  group_by(StockCode, Description) %>% 
  summarize(count = n()) %>% 
  arrange(desc(count))
#error
retail %>%
  ggplot(aes(x=reorder(Description,count), y=count))+
  geom_bar(stat="identity",fill="indian red")+
  coord_flip()


retail_sorted <- retail[order(retail$CustomerID),]
library(plyr)
itemList <- ddply(retail,c("CustomerID","Date"), 
                  function(df1)paste(df1$Description, 
                                     collapse = ","))
head(itemList)

itemList$CustomerID <- NULL
itemList$Date <- NULL
colnames(itemList) <- c("items")
head(itemList)

write.csv(itemList,"./data/market_basket.csv", quote = FALSE, row.names = TRUE)

#AR Analysis
tr <- read.transactions('./data/market_basket.csv', format = 'basket', sep=',')
head(tr)
LIST(tr[1:5])
summary(tr)

itemFrequencyPlot(tr, topN=10, type='absolute')
#graphics error
dev.off()
graphics.off()
par(mar=c(1,1,1,1))

rules <- apriori(tr, parameter = list(supp=0.001, conf=0.8))
rules <- sort(rules, by='confidence', decreasing = TRUE)
summary(rules)

inspect(rules[1:10])

topRules <- rules[1:10]
plot(topRules)

plot(topRules, method="graph")

plot(topRules, method = "grouped")




================================================
FILE: 65-AR/99-ar-basketanalysis2.R
================================================
#Market Basket Analysis2
#https://datascienceplus.com/a-gentle-introduction-on-market-basket-analysis%E2%80%8A-%E2%80%8Aassociation-rules/


================================================
FILE: 65-AR/99-ar-policechecks.R
================================================
# AR - Police Checks
#http://michael.hahsler.net/research/arules_RUG_2015/demo/#slides-code-and-about-the-author

if(!file.exists("./data/SQF 2012.csv")) {
  download.file("http://michael.hahsler.net/research/arules_RUG_2015/demo/Stop-and-Frisk-2012.zip","./data/Stop-and-Frisk-2012.zip")
  unzip("./data/Stop-and-Frisk-2012.zip")
}

dat <- read.csv("./data/SQF 2012.csv")
dim(dat)


data <- list(
  c("a","b","c"),
  c("a","b"),
  c("a","b","d"),
  c("b","e"),
  c("b","c","e"),
  c("a","d","e"),
  c("a","c"),
  c("a","b","d"),
  c("c","e"),
  c("a","b","d","e")
)

t <- as(data, "transactions")
t
## Mine itemsets with tidLists.
f <- eclat(data, parameter = list(support = 0, tidLists = TRUE))

?tidLists
f
tidLists(f)
## Get dimensions of the tidLists.
dim(tidLists(f))

## Coerce tidLists to list.
as(tidLists(f), "list")

## Inspect visually.
image(tidLists(f))

##Show the Frequent itemsets and respectives supports
inspect(f)


================================================
FILE: 65-AR/AR-Weka
================================================
=== Run information ===

Scheme:       weka.associations.Apriori -N 10 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -1.0 -c -1
Relation:     dar1w-weka.filters.unsupervised.attribute.Remove-R7
Instances:    50
Attributes:   6
              java
              cpp
              dbms
              dwm
              cbnst
              os
=== Associator model (full training set) ===


Apriori
=======

Minimum support: 0.55 (27 instances)
Minimum metric <confidence>: 0.9
Number of cycles performed: 9

Generated sets of large itemsets:

Size of set of large itemsets L(1): 6

Size of set of large itemsets L(2): 6

Size of set of large itemsets L(3): 3

Best rules found:

 1. java=Yes dbms=Yes 27 ==> cpp=Yes 27    conf:(1)
 2. dbms=Yes dwm=Yes 27 ==> cpp=Yes 27    conf:(1)
 3. java=Yes 29 ==> cpp=Yes 28    conf:(0.97)
 4. java=Yes 29 ==> dwm=Yes 28    conf:(0.97)
 5. cpp=Yes dbms=Yes 28 ==> java=Yes 27    conf:(0.96)
 6. java=Yes cpp=Yes 28 ==> dbms=Yes 27    conf:(0.96)
 7. java=Yes dwm=Yes 28 ==> cpp=Yes 27    conf:(0.96)
 8. java=Yes cpp=Yes 28 ==> dwm=Yes 27    conf:(0.96)
 9. cpp=Yes dbms=Yes 28 ==> dwm=Yes 27    conf:(0.96)
10. java=Yes 29 ==> dbms=Yes 27    conf:(0.93)


================================================
FILE: 65-AR/EDA-placement1.R
================================================
#AR Placement Results

require(arules)
require(arulesViz)
library(dplyr)
library(ggplot2)
options(digits=3)

#sample columns for placement
#select, gender, grade, comskills, itskills
n=1000
(select = sample(x=c('Placed', 'NotPlaced'), size=n, replace=T, prob=c(.6,.4)))
(gender = sample(x=c('M', 'F'), size=n, replace=T, prob=c(.6,.4)))
(grade = sample(x=c('A','B','C'), size=n, replace=T, prob=c(.3,.5,.2)))
table(grade)
(comskills = sample(x=c('Good','Sat','Poor'), size=n, replace=T, prob=c(.3,.5,.2)))
table(comskills)
(itskills = sample(x=c('Advanced','Basic','No'), size=n, replace=T, prob=c(.4,.4,.2)))
table(itskills)

students = data.frame(gender, grade, comskills,itskills,stringsAsFactors = T)
summary(students)
studentsT = as(students, "transactions")

#include Results------
studentsPlacement = cbind(students, select)
head(studentsPlacement)
summary(studentsPlacement)
#cluster Rules## cluster association rules
studentsPlacementT = as(studentPlacement, "transactions")


#---------------------------
#without Results
studentsT
(s1 <- sample(studentsT, 50))
(a1 <- affinity(s1))
#Affinity between the two items i and j is affinity
image(a1)

d_jaccard1 <- dissimilarity(s1, which = "items")
hc1=hclust(d_jaccard1, method = "ward.D2")
plot(hc1, main = "Dendrogram for items")
plot(hc1, cex = 1, hang = -1)
rect.hclust(hc1, k = 4, border = 2:5) #around 4 clusters with colors 2:5
rect.hclust(hc1, k = 2, border = 7:8)
plot(hc1, cex = 1, hang = -1)
rect.hclust(hc1, h = 1, border = 2:5)

plot(hc1, cex = 1, hang = -1)
rect.hclust(hc1, k = 4, border = 2:5)
assign1 <- cutree(hc1, k=4)
table(assign1)
assign1
itemFrequencyPlot(s1[assign1==1], population=s1, topN=5)

## calculate affinity-based distances between transactions and do hclust
#which student is similar to other
s1
d_affinity1b <- dissimilarity(s1, method = "affinity")
hc1b <- hclust(d_affinity1b, method = "ward.D2")
plot(hc1b, main = "Dendrogram for Transactions (Affinity)", hang=1)
rect.hclust(hc1b, k=4, border = 2:5)
students[c(301,89),]
assign1b <- cutree(hc1b, k=4)
table(assign1b)
assign1b
#table(assign1b ==1, assign1b ==2)
inspect(s1[assign1b==1])
itemFrequencyPlot(s1[assign1b==1], population=s1, topN=5)

#with results--------
studentsPlacementT
LIST(studentsPlacementT[1:5])
itemFrequency(studentsPlacementT)
itemFrequencyPlot(studentsPlacementT, type='absolute')

rules1 <- apriori(studentsPlacementT, parameter=list(support=0.005))
inspect(rules1)
#appearance1 = list(items = c("select=Placed", "comskills=Good"))
appearance1 = list(default = "lhs", rhs=c("select=Placed", "select=NotPlaced"))
rules2 <- apriori(studentsPlacementT, parameter=list(minlen=2, supp=0.003,conf=.6), appearance = appearance1)
inspect(rules2)

## use affinity to cluster rules we need to supply the transactions (or affinities) from the (sample).
s2 <- sample(studentsPlacementT, 50)
d_affinity2 <- dissimilarity(rules2, method = "affinity", args = list(transactions = s2))
d_affinity2
hc2a <- hclust(d_affinity2, method = "ward.D2")
plot(hc2a, main = "Dendrogram for Rules (Affinity)")
## create 4 groups and inspect the rules in the first group.
assign2a <- cutree(hc2a, k = 3)
assign2a
inspect(rules2[assign2a == 1])





#graphs------------------------
ggplot(studentsPlacement, aes(x=select, fill=select)) + geom_bar(stat='count') + geom_text(stat='count',aes(label=..count..), size=rel(7)) + ggtitle('Overall Placement Count ') + theme(legend.position = 'top')

ggplot(studentsPlacement, aes(x=select, fill=gender)) + geom_bar(stat='count') + ggrepel::geom_text_repel(stat='count',aes(label=..count..), size=rel(4), vjust=0, position=position_stack()) + ggtitle('Gender Wise : Placement Count ') + theme(legend.position = 'top')

ggplot(studentsPlacement, aes(x=select, fill=gender)) + geom_bar(stat='count') + ggrepel::geom_text_repel(stat='count',aes(label=..count..), size=rel(4), vjust=0, position=position_stack()) + ggtitle('Gender - Grade Skill Wise : Placement Count ') + theme(legend.position = 'top') + facet_grid(~ grade)

ggplot(studentPlacement, aes(x=select, fill=gender)) + geom_bar(stat='count', position=position_stack()) + ggrepel::geom_text_repel(stat='count',aes(label=..count..), size=rel(4), vjust=0, position=position_stack()) + ggtitle('Gender - Communication - IT Skill Wise : Placement Count ') + theme(legend.position = 'top') + facet_grid(itskills ~ comskills, labeller = label_both)


fset1 = eclat(studentsPlacementT, parameter= list(support=.05))
head(inspect(fset1))
summary(fset1)

itemFrequencyPlot(studentsPlacementT)
(singleItems <- fset1[size(items(fset1)) == 1])#single iteme
inspect(singleItems)
(singleSupport <- quality(singleItems)$support)
inspect(fset1[size(items(fset1)) == 2]) #2 items
inspect(fset1[size(items(fset1)) == 3]) #3 items 
inspect(fset1[size(items(fset1)) == 4]) #4 items 

inspect(items(fset1[1:5]))
names(singleSupport) <- unlist(LIST(items(singleItems),decode = FALSE))
singleSupport
head(singleSupport, n = 5)

rules = rules2
DF_rules = DATAFRAME(rules)
DF_rules
DATAFRAME(rules, setStart = '', itemSep = ' + ', setEnd = '')

inspect(head(rules, by = "conf"))
as(rules, "data.frame")
DATAFRAME(rules)
DATAFRAME(rules, separate = TRUE)
#swperate LHS and RHS
DATAFRAME(rules, separate = FALSE)
#seperate parameters
DATAFRAME(rules, separate = TRUE, setStart = '', itemSep = ' + ', setEnd = '')


(fsetsNotPlaced <- subset(fset1, subset = items %pin% "NotPlaced"))
inspect(sort(fsetsNotPlaced[size(fsetsNotPlaced) >= 3]))

crossTable(studentsPlacementT, measure="support", sort=TRUE)[1:5,1:5]
crossTable(studentsPlacementT, measure="count", sort=TRUE)
crossTable(studentsPlacementT, measure="probability", sort=TRUE)
crossTable(studentsPlacementT, measure="lift", sort=TRUE)[1:5,1:5]


#other functions
#combine rules
r1 <- apriori(studentsPlacementT[1:500], parameter=list(support=0.005))
r2 <- apriori(studentsPlacementT[400:1000], parameter=list(support=0.005))
(totalRules = c(r1,r2))
(unionRules = union(r1, r2))  #error
(quality(r2) <- cbind(quality(r2), coverage = coverage(r2)))
# support of the left-hand-side of the rule
inspect(r2)


#discritise data
(cgpa = round(runif(n=n, min=5, max=9.9),2))
CGPA = discretize(cgpa, method = "cluster", breaks = 3, labels = c('Low','Medium','High'))
#discretizeDF(iris)
studentsPlacement2 = cbind(studentPlacement, CGPA)
head(studentsPlacement2)

dissimilarity(studentsPlacementT[1:10], y = NULL, method = NULL, args = NULL,  which = "transactions")
dissimilarity(studentsPlacementT[1:10], y = NULL, method = NULL, args = NULL,  which = "associations")
duplicated(studentsPlacementT)
studentsPlacement[duplicated(studentsPlacementT),]

#this if there are levels
#aggregate(studentsPlacementT, by='level2')

hits(studentPlacementT)
#visually inspect binary incidence matrices,
image(studentsPlacementT[1:100])

inspect(rules3[10], ruleSep = "---->", itemSep = " + ", setStart = "", setEnd ="",  linebreak = FALSE)

## calculate several measures
m <- interestMeasure(rules2, c("support","confidence", "oddsRatio", "leverage"), transactions = studentPlacementT)
m

#significant
is.significant(rules2, transactions=studentsPlacementT, method = "fisher", alpha = 0.001, adjust = "bonferroni")

studentsPlacementT
iLabels <- itemLabels(studentsPlacementT)
iLabels
list <- LIST(studentsPlacementT[1:5], decode = FALSE)
list
decode(list, itemLabels = iLabels)

itemFrequencyPlot(x=studentsPlacementT, type = "absolute")
itemFrequencyPlot(x=studentsPlacementT, type = "absolute", lift=T, population=studentPlacementT)
itemFrequencyPlot(x=studentsPlacementT, type = "absolute", support = .05,population=studentPlacementT, horiz=T)


itemIntersect(x, y)
inspect(itemUnion(items(fset1[1:2]), items(fset1[3:4])))
inspect(itemSetdiff(items(fset1[1:2]), items(fset1[3:4])))
inspect(itemIntersect(items(fset1[1:2]), items(fset1[3:4])))

#get unique transactions, count frequency of unique transactions and plot frequency of unique transactions
(vals <- unique(studentsPlacementT))
(cnts <- tabulate(match(studentsPlacementT, vals)))
plot(sort(cnts, decreasing=TRUE))
## find all transactions which are equal to transaction 10 in Adult
which(studentsPlacementT %in% studentsPlacementT[10])
studentsPlacementT[1:10] %in% "gender=M"
## find all transactions which contain items that partially match "gender=" (all here).
studentsPlacementT[1:10] %pin% "=Placed"
## find all transactions that only include the item "age=Middle-aged" (none here).
studentsPlacementT[1:10] %oin% "select=Placed"
## find al transaction which contain both items "age=Middle-aged" and "sex=Male"
studentsPlacementT[1:10] %ain% c("select=Placed", "comskills=Good")
head(students)
head(studentsPlacement)
studentsPlacement[studentsPlacementT %ain% c("select=Placed", "comskills=Good"),]


#Predicts the membership (nearest neighbor) of new data to clusters represented by medoids or labeled examples.



#https://cran.r-project.org/web/packages/arules/arules.pdf


================================================
FILE: 65-AR/ar-case-liberty.R
================================================
# Transactions from Liberty Store
# Create AR for the data with 33% support level & 66% conf

barley = c(1,NA, 1, NA, 1, NA, 1, NA, 1, 1, NA, 1)
corn = c(1, 1, NA, 1, NA, NA, NA, NA, 1, 1, 1, 1)
gram = c(1, NA, 1,1,1,1,NA, NA, 1,1, NA, NA)
millet = c(1,1,NA, NA, 1, NA, 1,1,1,NA,1,1)
rice = c(1,1,1,1,1,1,1,1,NA,1,1,1)
wheat = c(1,1,1,1,NA,1,1,1,NA,1,1,1)
sapply(list(barley, corn, gram, millet, rice, wheat), length)

stores = data.frame(barley,corn,millet, rice, wheat)
apply(stores, 2, length)
apply(stores, 2, is.na)
colSums(!is.na(stores))

(stores1= as.data.frame(apply(stores, 2, as.factor)))

library(arules)
stores2= as(stores1, 'transactions')
inspect(stores2)

fitems1 = arules::eclat(stores2, parameter = list(support=0.33, minlen=2, maxlen=4))
inspect(fitems1)
rules1 = ruleInduction(fitems1, stores2, confidence=.7)
rules1
inspect(rules1)


# Part 2

rules2 = apriori(stores2, parameter =list(supp=0.33, conf=.7, minlen=2, maxlen=6))
inspect(rules2)


#Sort
(top.support <- sort(rules2, decreasing = TRUE, na.last = NA, by = "support"))
inspect(head(top.support,10))

top.confidence <- sort(rules2, decreasing = TRUE, na.last = NA, by = "confidence")
inspect(head(top.confidence,10))

# lhs, rhs
rules3 <- apriori(stores2, parameter=list(supp = 0.1, conf = 0.8),  appearance = list(lhs = c("wheat=1", "millet=1"),default = "rhs"))
top.lift3 = sort(rules3, decreasing = TRUE, na.last = NA, by = "lift")
inspect(top.lift3)
inspect(head(subset(top.lift3, lhs %pin% "wheat"), 3))

rules4 <- apriori(stores2, parameter=list(supp = 0.001, conf = 0.3),  appearance = list(lhs = c("wheat=1") , rhs= c('rice=1')))
top.lift4 = sort(rules4, decreasing = TRUE, na.last = NA, by = "lift")
inspect(top.lift4)
inspect(head(subset(top.lift4, lhs %pin% "wheat"), 3))




as(rules2, "data.frame")


================================================
FILE: 65-AR/ar-groceries2.R
================================================
# Association Rule - Groceries
#http://datascienceplus.com/implementing-apriori-algorithm-in-r/

df_groceries <- read.csv("./data/groceries.csv")
str(df_groceries)
head(df_groceries)
#Implementing Market Basket Analysis using Apriori Algorithm
dim(df_groceries)
#read transactions

df_sorted <- df_groceries[order(df_groceries$Member_number),]
df_sorted

#convert member number to numeric
df_sorted$Member_number <- as.numeric(df_sorted$Member_number)
str(df_sorted)
head(df_sorted)
#convert item description to categorical format
df_sorted$itemDescription <- as.factor(df_sorted$itemDescription)
str(df_sorted)
#convert dataframe to transaction format using ddply; 

#if(sessionInfo()['basePkgs']=="dplyr" | sessionInfo()['otherPkgs']=="dplyr"){
#  detach(package:dplyr, unload=TRUE)
#}
library(dplyr)

#group all the items that were bought together; by the same customer on the same date
library(plyr)
?ddply

df_itemList <- ddply(df_groceries, c("Member_number","Date"), function(df1)paste(df1$itemDescription,collapse = ","))

#remove member number and date
df_itemList$Member_number <- NULL
df_itemList$Date <- NULL
str(df_itemList)
colnames(df_itemList) <- c("itemList")
str(df_itemList)
head(df_itemList,n=20)
df_itemList
#write to csv format
write.csv(df_itemList,"./data/ItemList.csv", row.names = TRUE)

#-------------------- association rule mining algorithm : apriori ---#

#load package required
library(arules)

#convert csv file to basket format - transactions
txn = read.transactions(file="./data/ItemList.csv", rm.duplicates= TRUE, format="basket",sep=",",cols=1);
txn = read.transactions(file.choose(), rm.duplicates= TRUE, format="basket",sep=",",cols=1);
txn
inspect(txn)
#remove quotes from transactions
txn@itemInfo$labels <- gsub("\"","",txn@itemInfo$labels)


#run apriori algorithm
basket_rules <- apriori(txn,parameter = list(minlen=2,sup = 0.01, conf = 0.7, target="rules"))
#basket_rules <- apriori(txn,parameter = list(minlen=2,sup = 0.00001, conf = 0.01, target="rules"),appearance = list(lhs = "CLEMENTINES")))
inspect(basket_rules)  # no rule

#check if tm is attched; if yes then detach
if(sessionInfo()['basePkgs']=="tm" | sessionInfo()['otherPkgs']=="tm"){
  detach(package:sentiment, unload=TRUE)
  detach(package:tm, unload=TRUE)
}


#view rules
inspect(basket_rules)

#convert to datframe and view; optional
df_basket <- as(basket_rules,"data.frame")
df_basket$confidence <- df_basket$confidence * 100
df_basket$support <- df_basket$support * nrow(df)

write.csv(df_basket,"Rules_20.csv",row.names = FALSE)

#plot the rules
library(arulesViz)
plot(basket_rules)

set.seed(8000)
plot(basket_rules, method = "grouped", control = list(k = 5))

plot(basket_rules[1:10,], method="graph", control=list(type="items"))

plot(basket_rules[1:10,], method="paracoord",  control=list(alpha=.5, reorder=TRUE))

itemFrequencyPlot(txn, topN = 5)

plot(basket_rules[1:10,],measure=c("support","lift"),shading="confidence",interactive=T)



================================================
FILE: 65-AR/ar-practise.R
================================================
#AR

library(arules)
library(arulesViz)
library(dplyr)
library(ggplot2)


data("Groceries")
## add a complement-items for "whole milk" and "other vegetables"
g2 <- addComplement(Groceries, c("whole milk", "other vegetables"))
inspect(head(g2, 3))
LIST(head(g2, 3))

LIST(head(g2, 3))
## use a custom label for the complement-item
g3 <- addComplement(g2, "coffee", "NO coffee")
LIST(head(g3, 3))

LIST(head(Groceries, 3))
LIST(head(g2, 3))
LIST(head(g3, 16))

data("AdultUCI")
head(AdultUCI)
adult2 <- AdultUCI[c('workclass', 'education', 'marital-status')]
Adult1 <- as(adult2, 'transactions')
Adult1
LIST(Adult1[1:5])

d <- dist(USArrests, method='euclidean')
class(d)
round(as.matrix(d)[1:5,1:5],2)

hc <- hclust(d)
plot(hc)
cutree(hc, k = 1:5) #k = 1 is trivial
cutree(hc, h = 250)

## Compare the 2 and 4 grouping:
g24 <- cutree(hc, k = c(2,4))
rect.hclust(hc, k=2, border = 2)
rect.hclust(hc, k=4, border = 3)
abline(h = 200, col = 'yellow')
g24
table(grp2 = g24[,"2"], grp4 = g24[,"4"])
#from Cluster 1 of grp21, 14 stayin grp41, 2 in grp44
#from 24 values of grp22, 14 stayin grp42, 2 in grp43

library(dplyr)
library(ggplot2)
library(dendextend)
avg_dend_obj <- as.dendrogram(hc)
avg_col_dend <- color_branches(avg_dend_obj, h = 100)
plot(avg_col_dend)
cut_avg <- cutree(hc, k = 3)
seeds_df_cl <- mutate(USArrests, cluster = cut_avg)
seeds_df_cl
count(seeds_df_cl,cluster)
ggplot(seeds_df_cl, aes(x=Murder, y = Assault, color = factor(cluster))) + geom_point()
table(seeds_df_cl$cluster,seeds_label)
#https://www.datacamp.com/community/tutorials/hierarchical-clustering-R


================================================
FILE: 65-AR/my_basket1.txt
================================================
'milk', 'bread'
'bread', 'milk'
'milk', 'butter'

================================================
FILE: 75-OR-LP/15b-lpsolveAPI.R
================================================
#lpSolveAPI
#(https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf)
#https://www.rdocumentation.org/packages/lpSolveAPI/versions/5.5.2.0-17

pacman::p_load(lpSolveAPI)
#addcolumn-----
#Add a column to an lpSolve linear program model object
add.column(lprec, x, indices)
lps.model <- make.lp(4, 0)
lps.model
add.column(lps.model, c(6,2,4,9))
lps.model
add.column(lps.model, c(3,1,5), indices = c(1,2,4))
lps.model

#add.constraint-----
add.constraint(lprec, xt, type = c("<=", "=", ">="), rhs, indices, lhs)
lps.model <- make.lp(0, 4); lps.model
set.objfn(lps.model, rep(1, 4)); lps.model
add.constraint(lps.model, c(6,2,4,9), "<=", 50); lps.model
add.constraint(lps.model, c(3,1,5), 2, 75, indices = c(1,2,4)); lps.model


#add.SOS----
#Add A Special Ordered Set Constraint
add.SOS(lprec, name, type, priority, columns, weights)


#deletecolumn----
delete.column(lprec, columns)
lps.model <- make.lp(4, 0);lps.model
x <- c(6,2,4,9)
add.column(lps.model, x); lps.model
y <- c(3,1,5) ;ind <- c(1,2,4)
add.column(lps.model, y, ind); lps.model
delete.column(lps.model, 1) ; lps.model


#deleteconstraint----
delete.constraint(lprec, constraints)
lps.model <- make.lp(0, 4) ; lps.model
set.objfn(lps.model, rep(1, 4)); lps.model
xt <- c(6,2,4,9)
add.constraint(lps.model, xt, "<=", 50); lps.model
yt <- c(3,1,5) ;ind <- c(1,2,4)
add.constraint(lps.model, yt, 2, 75, ind); lps.model
delete.constraint(lps.model, 1); lps.model

#delete lp----
delete.lp(lprec)

#dim ----
dim(x) <- value
lps.model <- make.lp(4, 0) ; lps.model
x <- c(6,2,4,9)
add.column(lps.model, x); lps.model
y <- c(3,1,5) ; ind <- c(1,2,4)
add.column(lps.model, y, ind) ; lps.model
dim(lps.model)


#dimnames.lpExtPtr -----
dimnames(x) ; dimnames(x) <- value

lps.model <- make.lp(4, 0) ; lps.model
x <- c(6,2,4,9)
add.column(lps.model, x); lps.model
y <- c(3,1,5) ; ind <- c(1,2,4)
add.column(lps.model, y, ind) ; lps.model
dimnames(lps.model) <- list(c("alpha", "bravo", "charlie", "delta"),
                            c("whiskey", "tango"))
lps.model

#get.basis-----
get.basis(lprec, nonbasic = FALSE)
lps.model <- make.lp(3, 3); lps.model
## build and solve model ##
get.basis(lps.model)

#getbounds----
get.bounds(lprec, columns = 1:n)
lps.model <- make.lp(3, 3); lps.model
get.bounds(lps.model)


#getbranchmode
get.branch.mode(lprec, columns = 1:n, as.char = TRUE)

lps.model <- make.lp(3, 3); lps.model
get.branch.mode(lps.model)

#getcolumn -----
get.column(lprec, column)

lps.model <- make.lp(4, 0)
x <- c(6,2,4,9)
add.column(lps.model, x); lps.model
y <- c(3,1,5) ;ind <- c(1,2,4)
add.column(lps.model, y, ind); lps.model
get.column(lps.model, 2)

#getconstraint type-----
get.constr.type(lprec, constraints = 1:m, as.char = TRUE)
lps.model <- make.lp(0, 3); lps.model
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 15); lps.model
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 15); lps.model
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40); lps.model
get.constr.type(lps.model)

#getconstraint value-----
get.constr.value(lprec, side = c("rhs", "lhs"), constraints = 1:m)

#getconstraints----
get.constraints(lprec)

lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3)); lps.model
solve(lps.model)
get.constraints(lps.model)

#getdualsolution----
get.dual.solution(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3)) ; lps.model
solve(lps.model)
get.dual.solution(lps.model)

#get.kind ----
get.kind(lprec, columns = 1:n)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
get.kind(lps.model)


get.mat(lprec, i, j)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
get.mat(lps.model, 2, 2)


get.objective(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.objective(lps.model)


get.primal.solution(lprec, orig = FALSE)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.primal.solution(lps.model)

get.rhs(lprec, constraints = 1:m)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
get.rhs(lps.model)

get.sensitivity.obj(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.sensitivity.obj(lps.model)


get.sensitivity.objex(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.sensitivity.objex(lps.model)


get.sensitivity.rhs(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.sensitivity.rhs(lps.model)


get.solutioncount(lprec)


get.total.iter(lprec)


get.total.nodes(lprec)

get.type(lprec, columns = 1:n, as.char = TRUE)

lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
set.type(lps.model, 2, "binary")
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.type(lps.model, 3, "integer")
set.objfn(lps.model, c(-3,-4,-3))
get.type(lps.model)

get.variables(lprec)
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)
get.variables(lps.model)


guess.basis(lprec, guess)

lp.control(lprec, ..., reset = FALSE)


make.lp(nrow = 0, ncol = 0, verbose = "neutral")
lps.model <- make.lp(4, 3)


name.lp(lprec, name)

lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
name.lp(lps.model, "Simple LP")
name.lp(lps.model)


#plot.lpExtPtr
#print.lpExtPtr

read.lp(filename, type = c("lp", "mps", "freemps"), verbose = "neutral",        options)

#resize.lp
resize.lp(lprec, nrow, ncol)
lps.model <- make.lp(4, 0)
resize.lp(lps.model, 4, 2) ; lps.model
x <- c(6,2,4,9)
add.column(lps.model, x); lps.model
y <- c(3,1,5) ;ind <- c(1,2,4)
add.column(lps.model, y, ind); lps.model


#row.add.mode(lprec, state)


#select.solution(lprec, solution)

#set.basis(lprec, basis, nonbasic = FALSE, default = FALSE)

#set.bounds----

set.bounds(lprec, lower = NULL, upper = NULL, columns = 1:n)
lps.model <- make.lp(0, 4)
set.bounds(lps.model, lower = rep(-1.0, 4)); lps.model
set.bounds(lps.model, upper = 1:4);lps.model
set.bounds(lps.model, lower = rep(0.0, 4), upper = rep(1.0, 4))
lps.model


#set.branch.mode(lprec, columns, modes)


#set.branch.weights(lprec, weights)


#set.column(lprec, column, x, indices)

lps.model <- make.lp(4, 2)
set.column(lps.model, 2, c(6,2,4,9)); lps.model
set.column(lps.model, 1, c(3,1,5), indices = c(1,2,4)); lps.model

#set.constr.type
set.constr.type(lprec, types, constraints = 1:m)
lps.model <- make.lp(4, 2)
x <- c(6,2,4,9)
set.column(lps.model, 2, x); lps.model
y <- c(3,1,5); ind <- c(1,2,4)
set.column(lps.model, 1, y, ind); lps.model
set.constr.type(lps.model, rep("<=", 4)) ; lps.model


set.constr.value(lprec, rhs = NULL, lhs = NULL, constraints = 1:m)


set.mat(lprec, i, j, value)


lps.model <- make.lp(4, 2)
x <- c(6,2,4,9)
set.column(lps.model, 2, x)
y <- c(3,1,5) ;ind <- c(1,2,4)
set.column(lps.model, 1, y, ind)
set.constr.type(lps.model, rep("<=", 4)); lps.model
set.mat(lps.model, 3, 2, 4.5)
lps.model


set.objfn(lprec, obj, indices)
lps.model <- make.lp(2, 4)
set.objfn(lps.model, c(1,2,3,4))
set.objfn(lps.model, c(5,7,6), indices = c(1,2,4))

set.rhs(lprec, b, constraints = 1:m)
lps.model <- make.lp(4, 2)
x <- c(6,2,4,9)
set.column(lps.model, 2, x)
y <- c(3,1,5)
ind <- c(1,2,4)
set.column(lps.model, 1, y, ind)
set.rhs(lps.model, c(10, 20, 40, 80))
lps.model


set.row(lprec, row, xt, indices)
lps.model <- make.lp(2, 4); lps.model
set.row(lps.model, 2, c(6,2,4,9)); lps.model
set.row(lps.model, 1, c(3,1,5), indices = c(1,2,4)); lps.model


set.semicont(lprec, columns, sc = TRUE)


set.type(lprec, columns, type = c("integer", "binary", "real"))
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
set.type(lps.model, 2, "binary")
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.type(lps.model, 3, "integer")
set.objfn(lps.model, c(-3,-4,-3))
get.type(lps.model)
lps.model


#solve.lpExtPtr
lps.model <- make.lp(0, 3)
xt <- c(6,2,4)
add.constraint(lps.model, xt, "<=", 150)
xt <- c(1,1,6)
add.constraint(lps.model, xt, ">=", 0)
xt <- c(4,5,4)
add.constraint(lps.model, xt, "=", 40)
set.objfn(lps.model, c(-3,-4,-3))
solve(lps.model)

# 0: "optimal solution found"
# 1: "the model is sub-optimal"
# 2: "the model is infeasible"
# 3: "the model is unbounded"
# 4: "the model is degenerate"
# 5: "numerical failure encountered"
# 6: "process aborted"
# 7: "timeout"
# 9: "the model was solved by presolve"
# 10: "the branch and bound routine failed"
# 11: "the branch and bound was stopped because of a break-at-first or break-at-value"
# 12: "a feasible branch and bound solution was found"
# 13: "no feasible branch and bound solution was found"


write.lp(lprec, filename, type = c("lp", "mps", "freemps"),
         use.names = c(TRUE, TRUE))


================================================
FILE: 75-OR-LP/15c-lpassign.R
================================================
#lp.assign
#https://www.rdocumentation.org/packages/lpSolve/versions/5.6.13/topics/lp.assign
#https://rdrr.io/cran/lpSolve/src/R/lp.assign.R


#Interface to lp\_solve linear/integer programming system specifically for solving assignment problems

library(lpSolve)

#lp.assign (cost.mat, direction = "min", presolve = 0, compute.sens = 0)
#This is a particular integer programming problem. All the decision variables are assumed to be integers; each row has the constraint that its entries must add up to 1 (so that there is one 1 and the remaining entries are 0) and each column has the same constraint. This is assumed to be a minimization problem.


assign.costs <- matrix (c(2, 7, 7, 2, 7, 7, 3, 2, 7, 2, 8, 10, 1, 9, 8, 2), 4, 4)
assign.costs
#      [,1] [,2] [,3] [,4]
# [1,]    2    7    7    1
# [2,]    7    7    2    9
# [3,]    7    3    8    8
# [4,]    2    2   10    2

lp.assign (assign.costs)
## Not run: Success: the objective function is 8
lp.assign (assign.costs)$solution


================================================
FILE: 75-OR-LP/21b-LP-mach-prod.R
================================================
#LP Cases - Machine A & B, Products X & Y
#see Mindmap
#Equations
#50x + 24y <= 2400
#30x + 33y <= 2100
#x >= 45
#y >= 5
#max: x + y - 50
#50 >= x + y
library(lpSolve)
f.obj <- c(1, 1)
f.con <- matrix (c(50, 24, 30, 33, 1, 0, 0, 1, 1, 1), ncol=2, byrow=TRUE)
f.con
f.dir <- c("<=", "<=", ">=", ">=", ">=")
f.rhs <- c(2400, 2100, 45, 5,50)
cbind(f.con, f.dir, f.rhs)
#
lp ("max", f.obj, f.con, f.dir, f.rhs)
lp ("max", f.obj, f.con, f.dir, f.rhs)$solution
#x=45, y=6.25


#----------------------------------------------#
#Another Method
#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf
#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd

library(lpSolveAPI)

#First we create an empty model x.

#max: x + y - 50
lprec <- make.lp(0, 2)
lprec
set.objfn(lprec, c(1, 1))
lprec
#maximise it
lp.control(lprec, sense="max")
lprec

set.type(lprec, c(1,2), type = c("integer"))
lprec
#50x + 24y <= 2400
add.constraint(lprec, c(50,24), "<=", 2400)
lprec
#30x + 33y <= 2100
add.constraint(lprec, c(30,33), "<=", 2100)
lprec
#x >= 45
#add.constraint(lprec, c(1,0), ">=", 45)
#lprec
#y >= 5
#add.constraint(lprec, c(0,1), ">=", 5)
#lprec
#50 >= x + y
add.constraint(lprec, c(1,1), ">=", 50)
lprec

set.bounds(lprec, lower = c(45, 5), columns = c(1, 2))
lprec

#set.bounds(lprec, upper = 48.98, columns = 4)
#RowNames <- c("MachineA", "MachineB","InitalA", "InitalB","TotalInitial")
RowNames <- c("MachineA", "MachineB","TotalInitial")
ColNames <- c("ProductX", "ProductY")
dimnames(lprec) <- list(RowNames, ColNames)
lprec
solve(lprec)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec)
get.variables(lprec)
get.constraints(lprec)
plot(lprec)
print(lprec)


================================================
FILE: 75-OR-LP/21c-LP-mach-prod.R
================================================
#----------------------------------------------#
#Another Method
#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf
#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd

library(lpSolveAPI)

#First we create an empty model x.

#max: x + y - 50
lprec <- make.lp(0, 2)
lprec
set.objfn(lprec, c(1, 1))
lprec
#maximise it
lp.control(lprec, sense="max")
lprec

set.type(lprec, c(1,2), type = c("integer"))
lprec
#50x + 24y <= 2400
add.constraint(lprec, c(50,24), "<=", 2400)
lprec
#30x + 33y <= 2100
add.constraint(lprec, c(30,33), "<=", 2100)
lprec
#x >= 45
#add.constraint(lprec, c(1,0), ">=", 45)
#lprec
#y >= 5
#add.constraint(lprec, c(0,1), ">=", 5)
#lprec
#50 >= x + y
add.constraint(lprec, c(1,1), ">=", 50)
lprec

set.bounds(lprec, lower = c(45, 5), columns = c(1, 2))
lprec

#set.bounds(lprec, upper = 48.98, columns = 4)
#RowNames <- c("MachineA", "MachineB","InitalA", "InitalB","TotalInitial")
RowNames <- c("MachineA", "MachineB","TotalInitial")
ColNames <- c("ProductX", "ProductY")
dimnames(lprec) <- list(RowNames, ColNames)
lprec
solve(lprec)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec)
get.variables(lprec)
get.constraints(lprec)
plot(lprec)
print(lprec)

================================================
FILE: 75-OR-LP/22b-LP-case1.R
================================================
# Assignement Problem
# Set up objective.
library(lpSolve)
ex.obj <- c( 592, 381, 273, 55, 48, 37, 23)
# The constraint needs to be a matrix.
ex.con <- matrix (c(3534, 2356, 1767, 589, 528, 451, 304), nrow=1)
#
# Create the sign and the right-hand side of the constraint.#
ex.sign <- "<="
ex.rhs <- 119567
#
# Require all seven variables to be integers. In general this
# vector will contain the indices of the integer variables.
#
ex.int <- 1:7
#
# Solve the linear program. By default print only the objective
#
library(lpSolve)
?lp
lp1 = lp("max", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=ex.int)
#Success: the objective function is 19979
#lp1 -  Interface to lp\_solve linear/integer programming system
# Solve and print the vector of optimal values.
#
lp("max", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=ex.int)$solution
#[1] 32 2 1 0 0 0 0
# Require only variables 1 and 3 to be integer
lp("max", ex.obj, ex.con, ex.sign, ex.rhs, int.vec=c(1, 3))$solution
#[1] 33.00 1.25 0.00 0.00 0.00 0.00 0.00




================================================
FILE: 75-OR-LP/22c-LP-assign-case3.R
================================================
# Assignment Problem : Minimise costs 
#lp2
#
library(lpSolve)
assign.costs <- matrix (c(2, 7, 7, 2, 7, 7, 3, 2, 7, 2, 8, 10, 1, 9, 8, 2), 4, 4)
assign.costs

lp.assign(assign.costs)
## Not run: Success: the objective function is 8
lp.assign (assign.costs)$solution
## Not run:


#------------------------ ------------------
#
# Set up problem: 
# Max = x1 + 9 x2 + x3 subject to
# x1 + 2 x2 + 3 x3 <= 9
# 3 x1 + 2 x2 + 2 x3 <= 15
#
f.obj <- c(1, 9, 1)
f.con <- matrix (c(1, 2, 3, 3, 2, 2), nrow=2, byrow=TRUE)
f.dir <- c("<=", "<=")
f.rhs <- c(9, 15)
#
# Now run.
#
lp ("max", f.obj, f.con, f.dir, f.rhs)
## Not run: Success: the objective function is 40.5
lp ("max", f.obj, f.con, f.dir, f.rhs)$solution
## Not run: [1] 0.0 4.5 0.0

# ----------------
#The same problem using the dense constraint approach:

f.con.d <- matrix (c(rep (1:2,each=3), 
        rep (1:3, 2), t(f.con)), ncol=3)
f.con.d
lp("max", f.obj, ,f.dir, f.rhs, dense.const=f.con.d)
## Not run: Success: the objective function is 40.5

# Get sensitivities
#
lp ("max", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$sens.coef.from
## Not run: [1] -1e+30 2e+00 -1e+30
lp ("max", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$sens.coef.to
## Not run: [1] 4.50e+00 1.00e+30 1.35e+01
#
# Right now the dual values for the constraints and the variables are
# combined, constraints coming first. So in this example...
#
lp ("max", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals
## Not run: [1] 4.5 0.0 -3.5 0.0 -10.5
#
# ...the duals of the constraints are 4.5 and 0, and of the variables,
# -3.5, 0.0, -10.5. Here are the lower and upper limits on these:
#
lp ("max", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals.from
## Not run: [1] 0e+00 -1e+30 -1e+30 -1e+30 -6e+00
lp ("max", f.obj, f.con, f.dir, f.rhs, compute.sens=TRUE)$duals.to
## Not run: [1] 1.5e+01 1.0e+30 3.0e+00 1.0e+30 3.0e+00
#
# Run again, this time requiring that all three variables be integer
#
lp ("max", f.obj, f.con, f.dir, f.rhs, int.vec=1:3)
## Not run: Success: the objective function is 37
lp ("max", f.obj, f.con, f.dir, f.rhs, int.vec=1:3)$solution
## Not run: [1] 1 4 0
#
# You can get sensitivities in the integer case, but they're harder to
# interpret.
#
lp ("max", f.obj, f.con, f.dir, f.rhs, int.vec=1:3, compute.sens=TRUE)$duals
## Not run: [1] 1 0 0 7 0
#
# Here's an example in which we want more than one solution to a problem
# in which all variables are binary: the 8-queens problem,
# with dense constraints.
#
chess.obj <- rep (1, 64)
q8 <- make.q8 ()
chess.dir <- rep (c("=", "<"), c(16, 26))
chess.rhs <- rep (1, 42)
lp ('max', chess.obj, , chess.dir, chess.rhs, dense.const = q8,
    all.bin=TRUE, num.bin.solns=3)



================================================
FILE: 75-OR-LP/22d-LP-Case-carmanufacturing.R
================================================
#LP car manufacturing
#A car company produces 2 models, model A and model B. Long-term projections indicate an expected demand of at least 100 model A cars and 80 model B cars each day. Because of limitations on production capacity, no more than 200 model A cars and 170 model B cars can be made daily. To satisfy a shipping contract, a total of at least 200 cars much be shipped each day. If each model A car sold results in a $2000 loss, but each model B car produces a $5000 profit, how many of each type should be made daily to maximize net profits?

#Problem
#X= number of model A car Y=number of model B car
#As we are going for the maximization the equation will be
#-2000x+5000y=Z
#And the constraints are
# x>=100 {Demand of Model A}
# y>=80 {Demand of Model B}
# x<=200 {Model A’s Production Limit}
# y<=170 {Model B’s Production Limit}
# x+y>=200 {Transportation Contract}

f.con<-matrix(c(1,0,0,1,1,0,0,1,1,1),nrow = 5,byrow = TRUE) #Matrix of Constraints i.e x>=100 is convered as (1,0) where in 100 is used in f.rhs
f.con
f.obj<-c(-2000,5000) #Objective function
f.dir <- c(">=",">=","<=", "<=",">=") #Direction
f.rhs <- c(100,80,200,170,200) #RHS of the matrix
lps1<-lp ("max", f.obj, f.con, f.dir, f.rhs,compute.sens = TRUE) #LPP Function
lps1$solution #Solution

#x and y wherein we have to produce 100 units of Model A and 170 of Model B on daily basis for a optimum production planning


================================================
FILE: 75-OR-LP/25b-LPassign-job.R
================================================
#lpassign - Case
#https://stackoverflow.com/questions/21149134/assignment-algorthms-in-r-lp-assign

#matrix m below where each row is a student and each column is a job and 1 means the student's top choice, 2 means the second choice and so on. 9 means the student did not rank the job. There were 3 students and 4 tasks so we added a dummy student, U, of all 9s for the last row so that the number of students and tasks are the same. We assume the objective is to minimize the sum of the ranks. Below we see that the best assignment is to assign student 1 to job C, student 2 to job D, student 3 to job A and the unassigned row slurps up job B

library(lpSolve)

m <- matrix(c(3, 2, 1, 9, 2, 3, 2, 9, 1, 9, 3, 9, 9, 1, 9, 9), 4,  dimnames = list(c(1, 2, 3, "U"), c("A", "B", "C", "D")))
m
fm <- lp.assign(m)

#fm$solution contains the solution, a matrix of the same dimensions as m with 0 and 1 entries.
fm$solution

student <- rownames(m)
ix <- round(fm$solution %*% seq_len(ncol(m)))
job <- colnames(m)[ifelse(ix == 0, NA, ix)]
data.frame(student, job)


================================================
FILE: 75-OR-LP/30a-LP-tpt-function.R
================================================
#LP - Transport
library(lpSolve)

?lp.transport

================================================
FILE: 75-OR-LP/31b-LP-tpt1.R
================================================
# Transport Problem :
# Assisn costs to each source to each destination 
costs <- matrix (10000, 8, 5) # Give max value to all by default
costs  # max costs denote no route, route not to be selected
# not one by one add lower/actual costs
costs[4,1] <- costs[-4,5] <- 0
costs
costs[1,2] <- costs[2,3] <- costs[3,4] <- 7; 
costs
costs[1,3] <- costs[2,4] <- 7.7
costs
costs[5,1] <- costs[7,3] <- 8;
costs
costs[1,4] <- 8.4; 
costs
costs[6,2] <- 9
costs[8,4] <- 10; 
costs[4,2:4] <- c(.7, 1.4, 2.1)
costs
# Set up constraint signs and right-hand sides.
row.signs <- rep ("<", 8)  # repeat the sign 8 times/ colns
row.rhs <- c(200, 300, 350, 200, 100, 50, 100, 150)
col.signs <- rep (">", 5)  # repeat the sign 5 times/ rows
col.rhs <- c(250, 100, 400, 500, 200)
# Run
#lp.transport
#lp.transport (cost.mat, direction="min", row.signs, row.rhs, col.signs,
#              col.rhs, presolve=0, compute.sens=0, integers = 1:(nc*nr) )

lp.transport(costs, "min", row.signs, row.rhs, col.signs, col.rhs)
## Not run: Success: the objective function is 7790 - Min cost of tpt
lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)$solution
# this is matrix how source will connect to destination
# 


================================================
FILE: 75-OR-LP/31c-LP-tpt2.R
================================================
#http://stats.stackexchange.com/questions/136608/constrained-assignment-problem-linear-programming-genetic-algorithm-etc

#As an example, suppose we have 3 shops and 2 supply centers with distances between them as x1,1x1,1 = 2.8, x1,2x1,2 = 5.4, x1,3x1,3 = 1.4, x2,1x2,1 = 4.2, x2,2x2,2 = 3.0, x2,3x2,3 = 6.3. Then

library(Rsymphony)
obj <- c(2.8, 5.4, 1.4, 4.2, 3.0, 6.3, 0, 0)
mat <- matrix(c(1,0,0,1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,-3,0,1,0,0,0,0,-3,1), nrow = 6)
dir <- c("==", "==", "==", "<=", "<=", "<=")
rhs <- c(1, 1, 1, 0, 0, 2)
mat
max <- FALSE
types <- "B"
fac.loc <- Rsymphony_solve_LP(obj, mat, dir, rhs, types = types, max = max, write_lp = TRUE)
fac.loc

tells us that we should open both supply centers and 
supply center 1 will supply shops 1 and 3 and supply center 2 will supply shop 2, for a total distance of 7.2.


================================================
FILE: 75-OR-LP/31d-LP-tpt3.R
================================================

#Transportation Problem with Costs

(costs <- matrix(c(3,1,5,2,5,4),ncol=2))
row.signs <- c("<", "<", "<")
row.rhs <- c(45, 60, 35)
col.signs <- rep (">", 2)
col.rhs <- c(50, 60)
cbind(costs, row.signs, row.rhs)
rbind(costs, col.signs, col.rhs)

#Model
lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)
(m1=lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)$solution)
colSums(m1)
rowSums(m1)


#Model2
#Shorter Method
(cost <- matrix(c(3,1,5,2,5,4),ncol=2))
(m2=lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )$solution)
lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )

# Using Sweep function
m2
costs
objvalue=sweep(m2,c(1,2),costs,"*" )
sum(objvalue)



================================================
FILE: 75-OR-LP/33d-proptable.R
================================================
#prop.table


================================================
FILE: 75-OR-LP/41b-pricing.R
================================================
#LP - Pricing


================================================
FILE: 75-OR-LP/51b-LP-marketing.R
================================================
#LP - Marketing

#Final Model
#Max : 750 * x1 + 1000 * x2
#Subject To
#x1 + x2  <= 10
#x1 + 2 * x2  <= 15
#4*x1 + 3* x2 <= 25
#x1 >=  0
#x2 >= 0

library(lpSolveAPI)
(lprec <- make.lp(0, 2))
set.objfn(lprec, c(750, 1000)) ; lprec
lp.control(lprec, sense="max"); lprec #maximise it
set.type(lprec, c(1,2), type = c("integer")); lprec
add.constraint(lprec, c(1,1), "<=", 10) ;lprec
add.constraint(lprec, c(1,2), "<=", 15) ;lprec
add.constraint(lprec, c(4,3), "<=", 25) ;lprec
set.bounds(lprec, lower = c(0, 0), columns = c(1, 2));lprec
ColNames <- c("Notebook", "Desktop")
RowNames <- c("Allocated", "ChipsAvl","AssemblyTime")
dimnames(lprec) <- list(RowNames, ColNames);lprec
#Solve
solve(lprec)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec)
get.variables(lprec)
get.constraints(lprec)
get.sensitivity.obj(lprec)
#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf

print.lpExtPtr(lprec)
#real constraints
set.type(lprec, c(1,2), type = c("real")); lprec
#should be real to draw graph
solve(lprec)

plot.lpExtPtr(lprec)
name.lp(lprec,'Chips Allocation'); lprec



================================================
FILE: 75-OR-LP/lp-ss.R
================================================
#LP - SS

# #MINIMIZE
# -50*x + 20*y + 0
# SUBJECT TO
# _C1: 2 x - y >= -5
# _C2: 3 x + y >= 3
# _C3: 2 x - 3 y <= 12
# VARIABLES
# x Continuous
# y Continuous



library(linprog)
f.obj <- c(-50, 20)
f.con <- matrix(c(2,-1, 3, 1, 2, -3), nrow = 3, byrow = TRUE)
f.dir <- c(">=", ">=", "<=")
f.rhs <- c(-5, 3, 12)
solveLP(cvec = f.obj, bvec = f.rhs, Amat = f.con, maximum = T, const.dir = f.dir)

library(lpSolve)
results <- lp(direction = "max", objective.in = f.obj, const.mat = f.con,   const.dir = f.dir, const.rhs = f.rhs)

str(results)
results$solution
cat("The optimal solution is:", results$solution, "\nAnd the optimal objective function value is:", results$objval)


#eg
#Minimise Z = 2x + y  st - 6x + 2y >= 18, 3x + 3y  > 21
#2x + 4y >= 16 ; x >= 0, y >=0

f.obj <- c(2, 1)
f.con <- matrix(c(6,2, 3, 3, 2, 4), nrow = 3, byrow = TRUE)
f.dir <- c(">=", ">", ">=")
f.rhs <- c(18, 21, 16)
solveLP(cvec = f.obj, bvec = f.rhs, Amat = f.con, maximum = F, const.dir = f.dir)

library(lpSolve)
results <- lp(direction = "max", objective.in = f.obj, const.mat = f.con,   const.dir = f.dir, const.rhs = f.rhs)


pacman::p_load(gMOIP)
A <-matrix(c(1,2,1,2,0,-2,-1,2,3),ncol=3,byrow=TRUE)
A
A <- matrix(c(1,-1,-1,-1,0,1,1,0), ncol = 2, byrow = TRUE)
A
b <- c(1,-4,3,4)
obj <- c(98,50)
plotPolytope( A,  b, obj,  type = rep("c", ncol(A)), crit = "min",  faces = rep("c", ncol(A)), plotFaces = TRUE,  plotFeasible = TRUE, plotOptimum = FALSE,  labels = NULL)

#Ss------
#https://tnmutusva.github.io/course/lpp.html
#To plot the feasible region we will use the plotPolytope command.Define the model as max/min subject to A x<=b, x>=0. A is the matrix of coefficients on the left hand side of the inequality signs. b represents the coeffients on the right had side of the coefficients. obj represents the coefficients of the objective function. To use plotPolytope all the constraints must be less or equal to constraints. If there are any greater or equal to contraints first multiply them by -1 to get the required form. 
#6x + 2y >= 18 -> -6x - 2y <= -18 
#3x + 3y >= 21 -> -3x - 3y <= -21
#2x + 4y >= 16 -> -2x - 4y <= -16
#Z=2x+y
A <- matrix(c(-6,-2,-3,-3,-2,-4), ncol = 2, byrow = TRUE)
A
b <- c(-18,-21,-16)
obj <- c(2,1)
plotPolytope( A,  b, obj,  type = rep("c", ncol(A)), crit = "min",  faces = rep("c", ncol(A)), plotFaces = TRUE,  plotFeasible = TRUE, plotOptimum = FALSE,  labels = 'coord')

plotPolytope( A,  b, obj,  type = rep("c", ncol(A)), crit = "min",  faces = rep("c", ncol(A)), plotFaces = TRUE,  plotFeasible = TRUE, plotOptimum = T,  labels = 'coord')


================================================
FILE: 75-OR-LP/model.lp
================================================
/* Objective function */
max: +2000 C1 +2500 C2 +5000 C3 +3500 C4 +2000 C5 +2500 C6 +5000 C7 +3500 C8 +2000 C9 +2500 C10 +5000 C11
 +3500 C12;

/* Constraints */
+C1 +C2 +C3 +C4 <= 10;
+C5 +C6 +C7 +C8 <= 8;
+C9 +C10 +C11 +C12 <= 12;
+400 C1 +300 C2 +200 C3 +500 C4 <= 5000;
+400 C5 +300 C6 +200 C7 +500 C8 <= 4000;
+400 C9 +300 C10 +200 C11 +500 C12 <= 8000;
+C1 +C5 +C9 <= 18;
+C2 +C6 +C10 <= 10;
+C3 +C7 +C11 <= 5;
+C4 +C8 +C12 <= 20;


================================================
FILE: 75-OR-LP/zz-LP-clplite.R
================================================
#LP - Package clplite
#https://github.com/yixuan/clplite

library('githubinstall')
githubinstall('clplite')

library(clplite)
obj = c(1, 0, 0, 0, 2, 0, 0, -1)
A = matrix(c(
  3,   1,   0,  -2,  -1,   0,    0,   -1,
  0,   2, 1.1,   0,   0,   0,    0,    0,
  0,   0,   1,   0,   0,   1,    0,    0,
  0,   0,   0, 2.8,   0,   0, -1.2,    0,
  5.6,   0,   0,   0,   1,   0,    0,  1.9
), nrow = 5, byrow = TRUE)
constr_lb = c(2.5, -Inf,  4, 1.8,  3)
constr_ub = c(Inf,  2.1,  4,   5, 15)
var_lb    = c(2.5,   0,   0,   0, 0.5,   0,   0,   0)
var_ub    = c(Inf, 4.1, Inf, Inf,   4, Inf, Inf, 4.3)

clp_solve(obj, A, constr_lb, constr_ub, var_lb, var_ub, max = TRUE)
clp_solve(obj, A, constr_lb, constr_ub, var_lb, var_ub, max = FALSE)


================================================
FILE: 75-OR-LP/zz-LP-general.R
================================================
#https://github.com/rewyllys/LinearOptimizationUsingR/blob/master/SolvingLinOptProbsUsinglpSolveAPI.doc

Example 1:  A Simple Airline Optimization Problem 
The first example (Video 1 of Part 1 of Unit 8) is called the "AirlineSimple" situation and is presented as follows:
  "To illustrate how linear optimization works in revenue management, let us consider a simple example:  a flight from New York to Los Angeles.  In this flight, there are two types of economy fares:  Early Bird [i.e., discount] fares that cost $238, and Last Minute [i.e., regular] fares that cost $617.
"In this flight, a Boeing 757 is used that has 166 economy seats.  Demand for these prices has been forecasted using analytics tools, looking at historical data and incorporating models like time series or linear regression.  Clearly, forecasts have errors, and therefore, we need to assess the sensitivity of our decisions to these errors.
"To illustrate the use of linear optimization, we assume that demand has already been forecasted.  We'll illustrate how our decisions on how many discount seats to sell vary as the demand forecasts vary.  If the demand for regular seats is 50, and for discounted fares is 150, and the capacity is 166 seats, then the optimal allocation is going to be to sell the 50 seats to satisfy the regular demand, and then we allocate the remaining 116 seats to the discounted fare class.  If the regular demand increases to 100 seats, then we allocate these 100 seats to these customers, and only 66 seats to discounted fare customers.  Finally, if the regular demand increases to 200, then we allocate all of our capacity, 166 seats, to these customers.  While this seems simple, what happens if we have 100 flights with connections in tens of fares?"
Part 1 of Unit 8 of "The Analytics Edge" has thus set up the objective in this problem as being the maximization of revenue from selling seats on this flight at either the Regular Fare, $617, or the Discount Fare, $238.  The key to seeing what variables are involved in a linear optimization problem is to examine the function that encapsulates the objective; this function must involve the variables whose values are to be determined in such a way as to optimize (to maximize, in this case, or in other cases, to minimize) the whole objective function.
In this first example, there are two variables in the objective function:  the number of seats to be sold at $617 and the number to be sold at $238.  If we represent the number of seats to be sold at the Regular Fare as xR and the number to be sold at the Discount Fare as xD, then the objective function to be maximized can be written as:
  617*xR + 238*xD
There are three constraints. One of them is that the aircraft used has 166 seats.  The other two are that the airline forecasts the demand for Regular seats as 100 and the demand for Discount seats as 150 on a typical instance of the flight.  These constraints can be written as shown next.  Note that in writing out each constraint it is important to include all the variables, even if the coefficient of one or more of them is 0 in a particular constraint.
1*xR + 1*xD <=  166
1*xR + 0*xD <=  100
0*xR + 1*xD <=  150
We see that there are three constraints, and that each involves the two variables in the objective.  The interpretation of these considerations in lpSolveAPI is that we need, first, to define a model along the following lines:
  AirlineSimple <-  make.lp(3,2)
Further, the constraints must be entered into the model as columns in an array.  In this example there are two variables, so the array has two columns.  In lpSolveAPI, these two columns are entered as shown next:
  set.column(AirlineSimple, 1, c(1, 1, 0))
set.column(AirlineSimple, 2, c(1, 0, 1))
The inequalities are handled as follows:
  set.constr.type(AirlineSimple, c("<=", "<=", "<="))
set.rhs(AirlineSimple, c(166, 100, 150))
The objective is entered into the model as follows:
  set.objfn(AirlineSimple, c(617, 238))
Since the default sense of what is to be done with the objective function is, in lpSolveAPI, to minimize it, we need to specify that here the objective is to be maximized:
  lp.control(AirlineSimple, sense="max")
When we have reached this point in constructing our model, it can be helpful to have lpSolveAPI print out its understanding of the model.  To do this, we simply issue, at the R prompt, the name of the model, which in this case is "AirlineSimple".
AirlineSimple
Here is what the AirlineSimple model looks like when the steps being detailed here are actually run in R:
  Model name: 
  C1    C2         
Maximize   617   238         
R1           1     1  <=  166
R2           1     0  <=  100
R3           0     1  <=  150
Kind       Std   Std         
Type      Real  Real         
Upper      Inf   Inf         
Lower        0     0         

If the printed-out model looks like what we intended it to be, we are ready to tell lpSolveAPI to solve the problem:
  solve(AirlineSimple)
This instruction will return the value 0 if lpSolvAPI is able to find a solution.  To see the details of the solution and the value of the objective, we issue the following commands:
  get.objective(AirlineSimple)
get.variables(AirlineSimple)
The script provided by "The Analytics Edge" concludes by saying that the objective value turns out to be $77,408, and that the optimal values of the variables turn out to be:  xR = 100 and xD = 66.
Below, between the two long rows of ###s, is the script provided in "The Analytics Edge".  (As you might expect, in R scripts any line beginning with # is not executed by R.)
My recommendation on how to run this script is to open, on your computer, a copy of RStudio, which is my favorite open-source GUI for using R.  You can obtain a free copy of RStudio from
http://www.rstudio.com/
  To use this script, you should: (i) copy and paste it into the upper left-hand portion of the RStudio window; and then (ii) select the entire script and click on the button labelled "Run" in the menu bar of this upper left-hand portion.
####################################################################################
#             R Script for the AirlineSimple Problem  

# The following is the R script as provided in Part 1 of Unit 8 of 
# "The Analytics Edge".

# In this script file, we show how the airline revenue management 
# problems discussed in this lecture [i.e., in the videos in the 
# Airline Revenue Management section (Part 1) of Unit 8 of "The 
# Analytics Edge"] can be solved in R using the "lpSolveAPI" package.
# This package allows you to solve linear optimization problems in 
# R using the lp_solve library, which is an open-source 
# optimization solver.

# If you are solving larger and more complex optimization problems,
# lp_solve might not be powerful enough to solve your problems. There 
# are other optimization packages available in R that rely on commercial
# solvers, such as cplexAPI and Rcplex. For more information, see the
# CRAN Optimization page: 
# http://cran.us.r-project.org/web/views/Optimization.html

# First, you need to install and load the lpSolveAPI package

install.packages("lpSolveAPI")
library(lpSolveAPI)

# Now let's start by solving the simple airline problem with just 
# two decision variables: the number of regular seats to offer, and 
# the number of discount seats to offer. 

# The first step is to create a model, which takes as arguments the
# number of constraints in your model, and the number of decision
# variables in your model. We have three constraints (one capacity
# constraint and two demand constraints) and two decision variables.

AirlineSimple = make.lp(3,2)

# Now we need to set up the constraints and objective. The best way to 
# do this using the lpSolveAPI package is by viewing the constraints in
# a matrix format. Our objective and three constraints are as follows:
# max         617*R + 238*D
# subject to    1*R +   1*D <= 166
#               1*R +   0*D <= 100
#               0*R +   1*D <= 150	

# So the first column in our constraint matrix is c(1,1,0), and the
# second column in our constraint matrix is c(1,0,1). We also need to
# indicate that these are less-than-or-equal constraints, and set the
# right-hand-side values to c(166,100,150):

set.column(AirlineSimple, 1, c(1,1,0))
set.column(AirlineSimple, 2, c(1,0,1))
set.constr.type(AirlineSimple, c("<=","<=","<="))
set.rhs(AirlineSimple, c(166,100,150))

# And our objective coefficients are c(617,238):

set.objfn(AirlineSimple, c(617,238))

# The default setting is minimize for the objective, so we need to 
# tell R that we are maximizing our objective:

lp.control(AirlineSimple,sense='max')

# If we want to take a look at our model, we can just type:

AirlineSimple

# You should see that there is a row called "Lower" in the model 
# output -- the values of zero indicate that our decision variables 
# are constrained to be non-negative, which is the default setting.
# If you want to change the lower or upper bounds for a decision
# variable, you can do so with the set.bounds function. (Next week 
# we will learn about integer and binary decision variables -- to 
# change a decision variable to one of these types, you can use 
# the set.type function.)

# Now we are ready to solve our model:

solve(AirlineSimple)

# An output of zero means that the model was successfully solved. 
# You can look at the optimal objective function value or optimal
# decision variable values with get.objective and get.variables:

get.objective(AirlineSimple)
get.variables(AirlineSimple)

# You should get an objective value of 77408 and decision variable values of 100 and 66, just as we did in the lecture.

#########################################################################

Example 2:  An Airline Optimization Problem that Includes Connecting Flights

Video 7 of Part 1 of Unit 8 of "The Analytics Edge" introduces a more complex problem, that of optimizing the revenue from a flight that includes an intermediate destination.  Tickets for this flight are sold for the entire flight (viz., from airport 1 to airport 3), or for either of two shorter flights:  to or from the intermediate destination (viz., from airport 1 to airport 2, or from airport 2 to airport 3).  

Here is what the transcript of Video 7 says about this more complex situation:
  
  "So far, we've only considered optimizing the fares for a single route.  In this video, we'll change our optimization formulation to include connecting flights.  Now, instead of just being able to go from JFK in New York to LAX in Los Angeles, let's suppose that the plane stops in Dallas at the Dallas Fort Worth airport.  We still are just using one plane, but the passengers can now fly from New York to Dallas, Dallas to Los Angeles, or from New York to Los Angeles by just staying on the plane in Dallas.

"So how does our optimization problem change?  We now have six types of seats that we can offer: the original two types, regular and discount from New York to LA, and four new types.  We can sell both regular and discount seats from New York to Dallas, and regular and discount seats from Dallas to Los Angeles.  We know the price of each type of ticket as well as the forecasted demand for each type of ticket.  

"We also know that we have a capacity of 166 seats on our plane for each leg of the trip.  There's room for 166 passengers on the plane from New York to Dallas, or the first leg of the trip.  Then the passengers with a final destination of Dallas will get off the plane and the passengers flying from Dallas to LA will get on the plane.  On the second leg of the trip, flying from Dallas to LA, we also have a capacity of 166 seats.  So we need to remember that the passengers flying from New York to LA will take up capacity on both legs of the trip, while the other types of passengers will take up capacity on only one leg of the trip.

"What are our decisions now?  They're the number of regular tickets to sell for each type, and the number of discount tickets to sell for each type.  So in total, we have six decisions to make.  Now, let's define our objective.  As before, it's to maximize the total revenue.  This is the sum of the price of the ticket times the number of seats of that type we sell, for each type of ticket.  And as before, we have two types of constraints:  capacity constraints and demand constraints.

"For the capacity constraints, the airline shouldn't sell more seats than the capacity of the plane, for each leg of the trip.  So we need two capacity constraints here: one for the New York to Dallas leg and one for the Dallas to LA leg.  Note that the New York to LA passengers have to be counted on both legs of the trip.  So the first constraint accounts for all passengers that need to be on the plane when it flies from New York to Dallas, and the second constraint accounts for all passengers that need to be on the plane when it flies from Dallas to LA.  

"We also need six demand constraints, one for each type of ticket.  The number of seats sold should not exceed the forecasted demand for each type.  And last, we can't sell a negative number of seats, so we have our non-negativity constraints to prevent the variables from being negative." 

The remainder of the discussion of this problem, the AirlineConnecting problem, is essentially as presented in a script provided in the course, "The Analytics Edge".   In the hope of contributing additional clarity and understanding, I have changed the names of the variables and added comments in various places.

The situation in this problem is that there are two classes of tickets, and each class can be sold for any of 3 different flights.  Thus, there are six variables to be considered.  We can denote these variables as follows:  xRjl represents a Regular fare from JFK to LAX; xDjl represents a Discount fare from JFK to LAX; xRjd represents a Regular fare from JFK to DFW; xDjd represents a Discount fare from JFK to DFW; xRdl represents a Regular fare from DFW to LAX; and xDdl represents a Discount fare from DFW to LAX.

The objective is to maximize the revenue from this function:
  
  428*xRjl  + 190*xDjl + 642*xRjd + 224*xDjd + 512*xRdl + 190*xDdl

From additional information provided in the statement of the problem in "The Analytics Edge", we have the following forecasts of demand: for Regular seats from JFK to LAX, 80; for Discount seats from JFK to LAX, 120;  for Regular seats from JFK to DFW, 75; for Discount seats from JFK to DFW, 100;  for Regular seats from DFW to LAX, 60; and for Discount seats from DFW to LAX, 110.

The constraints can be shown as follows: 
  
  legs 1 & 2          leg 1 only          leg 2 only
col 1       col 2       col 3        col 4       col 5        col 6        RHS
subject to   1*xRjl + 1*xDjl + 1*xRjd + 1*xDjd +0*xRdl + 0*xDdl  <=  166			
1*xRjl + 1*xDjl + 0*xRjd + 0*xDjd +1*xRdl + 1*xDdl  <=  166             
1*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 0*xDdl  <=    80
0*xRjl + 1*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 0*xDdl  <=  120
0*xRjl + 0*xDjl + 1*xRjd + 0*xDjd +0*xRdl + 0*xDdl  <=    75
0*xRjl + 0*xDjl + 0*xRjd + 1*xDjd +0*xRdl + 0*xDdl  <=  100
0*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +1*xRdl + 0*xDdl  <=    60
0*xRjl + 0*xDjl + 0*xRjd + 0*xDjd +0*xRdl + 1*xDdl  <=  110

This list of constraints makes it clear that the AirlineConnecting model needs to have 8 rows and 6 columns.  To build this model using lpSolveAPI, we issue the following command in R;

AirlineConnecting = make.lp(8,6)

To detail the columns of this model using lpSolveAPI, we issue the following commands:
  
  set.columns(AirlineConnecting, 1, c(1,1,1,0,0,0,0,0))
set.columns(AirlineConnecting, 2, c(1,1,0,1,0,0,0,0))
set.columns(AirlineConnecting, 3, c(1,0,0,0,1,0,0,0))
set.columns(AirlineConnecting, 4, c(1,0,0,0,0,1,0,0))
set.columns(AirlineConnecting, 5, c(0,1,0,0,0,0,1,0))
set.columns(AirlineConnecting, 6, c(0,1,0,0,0,0,0,1))
set.constr.type(AirlineConnecting, rep("<=",8))
set.rhs(AirlineConnecting, c(166,166,80,120,75,100,60,110))

For the remaining steps in setting up the AirlineConnecting model, we issue:
  
  set.objfn(AirlineConnecting, c(428,190,642,224,512,190))
lp.control(AirlineConnecting, sense="max")

To check on the model we have created, we can type AirlineConnecting.

Airline Connecting

Here is how the model should look:
  Model name: 
  C1    C2    C3    C4    C5    C6         
Maximize   428   190   642   224   512   190         
R1           1     1     1     1     0     0  <=  166
R2           1     1     0     0     1     1  <=  166
R3           1     0     0     0     0     0  <=   80
R4           0     1     0     0     0     0  <=  120
R5           0     0     1     0     0     0  <=   75
R6           0     0     0     1     0     0  <=  100
R7           0     0     0     0     1     0  <=   60
R8           0     0     0     0     0     1  <=  110
Kind       Std   Std   Std   Std   Std   Std         
Type      Real  Real  Real  Real  Real  Real         You should get an objective function value of 120514 and decision variable values of 80, 0, 75, 11, 60, 26. 
Upper      Inf   Inf   Inf   Inf   Inf   Inf         
Lower        0     0     0     0     0     0  

The remaining steps are to issue the following commands:
  
  solve(AirlineConnecting)
get.objective(AirlineConnecting)
get.variables(AirlineConnecting)

You should get an objective function value of $120,514 and decision variable values of 80, 0, 75, 11, 60, 26. 

Below, between the two long rows of ###s, is the script provided in "The Analytics Edge" for the AirlineConnecting problem.
########################################################################

# The following commands set up and solve the more sophisticated airline
# problem with six decision variables.

# First, you need to install and load the lpSolveAPI package:

install.packages("lpSolveAPI")
library(lpSolveAPI)

# We can distinguish the three different trips as: JFK-LAX, "jl"; 
# JFK-DFW, "jd"; and DFW-LAX, "dl".
# The objective to be maximized is: 
# 428*Rjl + 190*Djl + 642*Rjd + 224*Djd + 512*Rdl + 190*Ddl

# legs 1 & 2 leg 1 only leg 2 only
# col 1 col 2 col 3 col 4 col 5 col 6 RHS
# subject to 1*Rjl + 1*Djl + 1*Rjd + 1*Djd +0*Rdl + 0*Ddl <= 166 
# 1*Rjl + 1*Djl + 0*Rjd + 0*Djd +1*Rdl + 1*Ddl <= 166 
# 1*Rjl + 0*Djl + 0*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 80
# 0*Rjl + 1*Djl + 0*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 120
# 0*Rjl + 0*Djl + 1*Rjd + 0*Djd +0*Rdl + 0*Ddl <= 75
# 0*Rjl + 0*Djl + 0*Rjd + 1*Djd +0*Rdl + 0*Ddl <= 100
# 0*Rjl + 0*Djl + 0*Rjd + 0*Djd +1*Rdl + 0*Ddl <= 60
# 0*Rjl + 0*Djl + 0*Rjd + 0*Djd +0*Rdl + 1*Ddl <= 110

AirlineConnecting = make.lp(8,6)

set.column(AirlineConnecting, 1, c(1,1,1,0,0,0,0,0))
set.column(AirlineConnecting, 2, c(1,1,0,1,0,0,0,0))
set.column(AirlineConnecting, 3, c(1,0,0,0,1,0,0,0))
set.column(AirlineConnecting, 4, c(1,0,0,0,0,1,0,0))
set.column(AirlineConnecting, 5, c(0,1,0,0,0,0,1,0))
set.column(AirlineConnecting, 6, c(0,1,0,0,0,0,0,1))
set.constr.type(AirlineConnecting, rep("<=",8))
set.rhs(AirlineConnecting, c(166,166,80,120,75,100,60,110))

set.objfn(AirlineConnecting, c(428,190,642,224,512,190))
lp.control(AirlineConnecting,sense='max')

# To check on the model we have created, type AirlineConnecting.

AirlineConnecting

solve(AirlineConnecting)
get.objective(AirlineConnecting)
get.variables(AirlineConnecting)

# You should get an objective function value of 120514 and 
# decision variable values of 80, 0, 75, 11, 60, 26.

# For additional examples, we direct you to the lpSolveAPI User's Guide:
# http://www.icesi.edu.co/CRAN/web/packages/lpSolveAPI/vignettes/lpSolveAPI.pdf

###########################################################################

Example 3:  A Problem Involving the Optimization of Beamlets of Radioactivity to Attack                  	              Cancerous Tumors
Part 2 of Unit 8 of "The Analytics Edge" deals with the optimizing the administration of radiation therapy treatments for cancer patients.  The transcript of Video 1 of Part 2 of Unit 8 says in part:
  "Cancer can be treated using radiation therapy, where beams of high-energy photons are fired into the patient to kill cancerous cells.  This is a very common form of treatment for many types of cancers, and in the United States, about half of all cancer patients undergo some form of radiation therapy.  Radiation therapy has a history going back to the late 1800s.  X-rays were discovered by Wilhelm Rontgen in 1895, who was later awarded the first Nobel Prize in Physics.  Shortly after the discovery, X-rays started being used to treat skin cancers.  This image shows an x-ray of Rontgen's wife's hand.  You can see the bones in her hand as well as her wedding ring on her finger.  A few years later in 1898, radium was discovered by Marie and Pierre Curie.  They won the Nobel Prize for this discovery in 1911.  Radium started being used to treat cancer, as well as other diseases.  
"Later in the 1900s, the first radiation delivery machines, called linear accelerators, were developed.  Then computed tomography, or CT scans, were invented in 1971.  These discoveries led to the invention of Intensity Modulated Radiation Therapy, or IMRT, in the early 1980s.  The invention of IMRT significantly improved the ability of radiation therapy to target cancerous cells.  
"To reach the tumor, radiation passes through healthy tissue and therefore damages both healthy and cancerous tissue.  This damage to healthy tissue can lead to undesirable side effects that reduce post-treatment quality of life.  For example, blistering and burning of skin can occur because of the damage to healthy skin cells.  For this reason, we want the dose to fit the tumor as closely as possible to reduce the dose to healthy tissue.  This became possible with the invention of IMRT.  In IMRT, the intensity profile of each beam is non-uniform.  Before IMRT, each beam had to have the same intensity, so the tumor could not be targeted very well.  But by using non-uniform intensity profiles, the radiation dose can better fit the tumor.  Let's see what this looks like.  












In this image, we have a person's body outlined in black, and then the target, or tumor, and two critical structures also outlined.  We would like to maximize the radiation to the target, while minimizing the dose to healthy tissue,  and especially to the critical structures."
From Video 2 of Part 2 of Unit 8 of "The Analytics Edge" provides further discussion of how IMRT works.  The transcript of this video says in part:
  "The data are collected in the treatment-planning process, which starts from a CT scan, like the one you see here, on the right.  Using a CT scan, a radiation oncologist contours, or draws outlines around the tumor and various critical structures.  In [the CT] image, the oncologist would contour structures like the parotid glands, the largest of the saliva glands, and the brain.
"Then, each structure is discretized into voxels, or volume elements, which are typically four millimeters in dimension.  The second image here shows a closer view of the brain.  You can see the small squares, or voxels.  Here, they're two-dimensional, but in reality they would be three-dimensional.  Now, we can compute how much dose each beamlet, or piece of the beam, delivers to each voxel."
"We'll start with a small example.  Suppose we have nine voxels and six beamlets.  Our voxels can be categorized into three types:  the tumor voxels, which are colored pink here; the spinal cord voxel, colored dark green; and other healthy tissue voxels, colored light green.  So we have four tumor voxels, one spinal cord voxel, and four other healthy tissue voxels.  We have two beams that are each split into three beamlets.  Beam 1 is composed of beamlets 1, 2, and 3, and comes in from the right.  Beam 2 is composed of beamlets 4, 5, and 6, and comes in from the top.
"Our objective is to minimize the total dose to healthy tissue, both to the spinal cord and to the other healthy tissue.  We have two types of constraints.  The first is that the dose to the tumor voxels must be at least 7 Gray, which is the unit of measure for radiation.  Our second constraint is that the dose to the spinal cord voxel can't be more than 5 Gray, since we want to be careful to protect the spinal cord.  
"We know the dose that each beamlet gives to each voxel at unit intensity.  This table shows the dose that each beamlet in Beam 1 gives to the voxels.  Remember that this is at unit intensity.  If we double the intensity of the beamlet, we double the doses.  The dose to each voxel can depend on how far the beamlet has to travel, or the type of tissue that the beamlet has to travel through.  Similarly, we know the dose that each beamlet in Beam 2 gives to each voxel, again at unit intensity.
"The dose depends on the direction of the beam and what it travels through.  Putting these tables together, we can write out our optimization problem."

"Our decision variables are the intensities of each beamlet.  We'll call them: x1, the intensity for beamlet 1; x2, the intensity for beamlet 2; x3, the intensity for beamlet 3; etc., all the way up through x6.  As we mentioned before, our objective is to minimize the total dose to the healthy tissue, including the spinal cord.  So we want to minimize the total dose beamlet 1 gives to healthy tissue, which is (1 + 2)*x1, plus the total dose beamlet 2 gives to healthy tissue, which is (2 + 2.5)*x2, plus the total dose beamlet 3 gives to healthy tissue, which is 2.5*x3.  Now for beamlets 4, 5, and 6, beamlet 4 just gives one dose to healthy tissue; beamlet 5, 2*x5; and then beamlet 6 gives (1 + 2 + 1)*x6.  
"Now for our constraints.  First, we need to make sure that each voxel of the tumor gets a dose of at least 7.  Let's start with the  tumor voxel in the top row.  So 2*x1 + x5 needs to be greater than or equal to 7.  For the tumor voxel in the second row, we have x2 + 2*x4, also greater than or equal to 7.  For the two tumor voxels in the bottom row, we have 1.5*x3 + x4, greater than or equal to 7.  And 1.5*x_3 + x5, greater than or equal to 7.  Then for the spinal cord, we need to make sure that 2*x2 + 2*x5 is less than or equal to 5.  And lastly, we just need to make sure that all of our decision variables are non-negative."
Just below, between the long rows of ###s, is the R script that I developed to provide a linearly optimized solution to the IMRT problem using the lpSolveAPI function in R:
######################################################################

# Script for Solving the "Analytics Edge" IMRT Linear Optimization
# Problem in R

# In this script file, we show how the Intensity Modulated Radiation
# Therapy problem discussed in "The Analytics Edge" (specifically, 
# in the videos in the IMRT part of Unit 8 of "The Analytics Edge")
# can be solved in R using the "lpSolveAPI" package. This package 
# allows you to solve linear optimization problems in R using the 
# lp_solve library, which is an open-source optimization solver.

# If you are solving larger and more complex optimization problems, 
# lp_solve might not be powerful enough to solve your problems. 
# There are other optimization packages available in R that rely 
#on commercial solvers, such as cplexAPI and Rcplex.  
# For more information, see the CRAN Optimization page: 
# http://cran.us.r-project.org/web/views/Optimization.html

# First, you need to install and load the lpSolveAPI package:

install.packages("lpSolveAPI")
library(lpSolveAPI)

# The IMRT problem is how to minimize the amount of radiation 
# delivered to the spinal cord and other healthy tissue while 
# ensuring that at least the minimum needed radiation reaches the 
# tumor.  The target region of the human body is divided into 
# volume elements called voxels, which are typically 4mm in each 
# dimension.  The first step is to create a model, which takes as 
# arguments the number of constraints in your model, and the number 
# of decision variables in your model.

# In the IMRT problem, we deal with 9 voxels of body matter, arranged 
# in a 3 x 3 array.  In the top row, we have voxels 1, 2, 3; in the 
# middle row, voxels 4, 5, 6; and in the bottom row, voxels 7, 8, 9.  

# The healthy voxels are 1, 3, 5 (spinal cord), 6, and 9.  
# The tumor voxels are 2, 4, 7, and 8.
# The modulatable beamlets i, (i in 1:6), have intensities 
# x1, x2, . . ., x6.

# For beams of UNIT intensity, the beamlets deliver varying amounts 
# of radiation, measured in Grays, to the voxels as follows. 
# beamlet 1: 1 dose to voxel 1, 2 doses to voxel 2, 2 doses to 
# voxel 3
# beamlet 2: 1 dose to voxel 4, 2 doses to voxel 5, 2.5 doses to 
# voxel 6
# beamlet 3: 1.5 doses to voxel 7, 1.5 doses to voxel 8, 2.5 doses to 
# voxel 9
# beamlet 4: 1 dose to voxel 1, 2 doses to voxel 4, 1 dose to voxel 7
# beamlet 5: 1 dose to voxel 2, 2 doses to voxel 5, 1 dose to voxel 8
# beamlet 6: 1 dose to voxel 3, 2 doses to voxel 6, 1 doses to 
# voxel 9

# Our decision variables are the intensities of each beamlet.  We'll 
# call them:  x1, the intensity for beamlet 1; x2, the intensity for 
# beamlet 2; x3, the intensity for beamlet 3; etc., all the way up 
# through x6.  As we mentioned before, our objective is to minimize 
# the total dose to the healthy tissue, including the spinal cord. 
# Hence, we want to minimize the total dose beamlet 1 gives to 
# healthy tissue, which is (1 + 2)*x1, plus the total dose beamlet 2 
# gives to healthy tissue, which is (2 + 2.5)*x2, plus the total dose 
# beamlet 3 gives to healthy tissue, which is 2.5*x3.  
# For beamlets 4, 5, and 6: beamlet 4 just gives one dose to healthy 
# tissue, 1*x4; beamlet 5 gives 2 doses, 2*x5; and beamlet 6 gives 
# 1 + 2 + 1 = 4 doses, 4*x6.  

# Next, we consider our constraints.  First, we need to make sure 
# that each voxel of the tumor gets a dose of at least 7.  Let's 
# start  with the tumor voxel in the top row: 2*x1 + 1*x5 needs to 
# be greater than or equal to 7.  For the tumor voxel in the second 
# row, we have x2 + 2*x4, which also needs to be greater than or 
# equal  to 7.  For the first tumor voxel in the bottom row, we need 
# 1.5*x3 + 1*x4 to be greater than or equal to 7.  For the second 
# tumor voxel in the bottom row, we need 1.5*x3 + 1*x5 to be greater 
# than or equal to 7.  

# For the spinal cord, we need to make sure that 2*x2 + 2*x5 is less 
# than or equal to 5.  

# Finally, we need to make sure that all of our decision variables 
# are non-negative:  x1, x2, . . ., x6 >= 0.

# Writing all these considerations out in the form of inequalities, 
# we have:
# 2*x1 + 0*x2 +   0*x3 + 0*x4 + 1*x5 + 0*x6  >= 7
# 0*x1 + 1*x2 +   0*x3 + 2*x4 + 0*x5 + 0*x6  >= 7
# 0*x1 + 0*x2 + 1.5*x3 + 1*x4 + 0*x5 + 0*x6  >= 7 
# 0*x1 + 0*x2 + 1.5*x3 + 0*x4 + 1*x5 + 0*x6  >= 7
# 0*x1 + 2*x2 +   0*x3 + 0*x4 + 2*x5 + 0*x6  <= 5
# x1, x2, . . ., x6 >= 0  (Note: This non-negativity constraint is built into the lpSolveAPI function.)

IMRT <- make.lp(5,6)

set.column(IMRT, 1, c(2,0,0,0,0))
set.column(IMRT, 2, c(0,1,0,0,2))
set.column(IMRT, 3, c(0,0,1.5,1.5,0))
set.column(IMRT, 4, c(0,2,1,0,0))
set.column(IMRT, 5, c(1,0,0,1,2))
set.column(IMRT, 6, c(0,0,0,0,0))
set.constr.type(IMRT, c(">=",">=",">=",">=","<="))
set.rhs(IMRT, c(7,7,7,7,5))

# The objective is to minimize the total number of Grays of radiation 
# delivered to the healthy tissue: 
# (1+2)*x1 + (2+2.5)*x2 + 2.5*x3 + 1*x4 + 2*x5 + (1+2+1)*x6

set.objfn(IMRT, c(3,4.5,2.5,1,2,4))

# By default, the lpSolveAPI function assumes that the desired sense
# of the objective is minimization.  However, no harm is done by 
# making the sense explicit. (If the sense were maximization, we 
# would write sense="max" in lp.control.)

lp.control(IMRT, sense="min") 

# To check on our model, we can type IMRT.

IMRT

# Model name: 
#             C1    C2    C3    C4    C5    C6       
# R1           2     0     0     0     1      0  >=  7
# R2           0     1     0     2     0      0  >=  7
# R3           0     0   1.5     1     0      0  >=  7
# R4           0     0   1.5     0     1      0  >=  7
# R5           0     2     0     0     2      0  <=  5
# Kind        Std   Std   Std   Std   Std    Std       
# Type       Real  Real  Real  Real  Real   Real       
# Upper       Inf   Inf   Inf   Inf   Inf    Inf       
# Lower        0     0     0     0     0      0     

solve(IMRT)
get.objective(IMRT)
get.variables(IMRT)

# You should get an objective value of 22.75 total Grays, and 
# beamlet intensities of 2.25, 0, 3, 3.5, 2.5, 0.

######################################################################

Example 4:  A Linear Optimization Problem Involving the Amounts of Certain Cargoes to be    		 Asssigned to Certain Wagons (i.e., Boxcars) of a Train
This problem was first presented on the World-Wide Web as "Linear programming in R: an lpSolveAPI Example", written by Bart and posted on July 14, 2012.iii  Its URL is:
  http://www.r-bloggers.com/linear-programming-in-r-an-lpsolveapi-example/
  Bart's post indicates that an R script is available at:  http://fishyoperations.com/r/linear-programming-in-r-an-lpsolveapi-example/uploads/lpsolve_example.R.  However, this site appears to be no longer available on the Web.
Bart states the problem as follows: 
"A trading company is looking for a way to maximize profit per transportation of their goods. The company has a train available with 3 wagons. When stocking the wagons they can choose between 4 types of cargo, each with its own specifications."
The following data concerning the wagons and the cargoes are available:
Train Wagon
Weight Capacity 
(in tonnes)
Space Capacity 
(in cubic metres)
w1
w2
w3
10
8
12
5000
4000
8000
Cargo Type
Cargo Availability 
(in tonnes)
Cargo Volume 
(in cubic metres)
Cargo Profit 
(per tonne)
c1
c2
c3
c4
18
10
5
20
400
300
200
500
2000
2500
5000
3500

The variables in this problem are more complicated than those in Examples 1 - 3 of this discussion.  Here the variables consist of pairings of wagon and cargo.  Since there are 3 types of wagons and 4 types of cargo, there are 12 pairings to be considered.  We shall represent them by xij, where i = type of wagon and j = type of cargo:   x11, x12,  . . . , x14, x21, . . . , x31, . . . , x34.
The goal of the company is to maximize the profit from each trip by its three wagons, so the objective function is to maximize the following:
2000*x11 + 2500*x12 + 5000*x13 + 3500*x14 +2000*x21 + 2500*x22
+ 5000*x23 + 3500*x24 + 2000*x31 + 2500*x32 + 5000*x33 + 3500*x34
There are constraints due to the weight capacities of the wagons, constraints due to the space capacities of the wagons, and further constraints due to the availability of the cargoes.
The weight constraints can be expressed as:
x11 + x12 + x13 + x14  <=  10
x21 + x22 + x23 + x24  <=   8
x31 + x32 + x33 + x34  <=  12
The space capacity constraints can be expressed as:
400*x11 + 300*x12 + 200*x13 +500*x14  <=  5000
400*x21 + 300*x22 + 200*x23 + 500*x24  <=  4000
400*x31 + 300*x32 + 200*x33 + 500*x34  <=  8000
The cargo availability constraints can be expressed as:
x11 + x21 + x31   <=  18
x12 + x22 + x32   <=  10
x13 + x23 + x33   <=    5
x14 + x24 + x34   <=  20
Next, we need to combine the statements of these constraints into columns corresponding to the 12 variables, so that we can easily see how to enter the correct values into the model when we use the set.column function:
x11     x12     x13     x14     x21    x22     x23     x24     x31     x32     x33     x34          
1         1         1         1         0         0        0         0         0         0         0          0      <=       10
0         0         0         0         1         1        1         1         0         0         0          0      <=         8
0         0         0         0         0         0        0         0         1         1         1          1      <=        12           
400     300     200     500         0         0        0         0         0         0         0          0       <=    5000
0         0         0         0     400     300    200     500         0         0         0          0       <=    4000
0         0         0         0         0         0        0         0     400     300     200      500       <=    8000
1         0         0         0         1         0        0         0         1         0         0          0       <=        18
0         1         0         0         0         1        0         0         0         1         0          0       <=        10
0         0         1         0         0         0        1         0         0         0         1          0       <=          5
0         0         0         1         0         0        0         1         0         0         0          1       <=        20   

Now we are ready to use the make.lp and set.column functions:
wagoncargo <- make.lp(10, 12)
set.column(wagoncargo, 1, c(1, 0, 0, 400, 0, 0, 1, 0, 0, 0))
set.column(wagoncargo, 2, c(1, 0, 0, 300, 0, 0, 0, 1, 0, 0))
set.column(wagoncargo, 3, c(1, 0, 0, 200, 0, 0, 0, 0, 1, 0))
set.column(wagoncargo, 4, c(1, 0, 0, 500, 0, 0, 0, 0, 0, 1))
set.column(wagoncargo, 5, c(0, 1, 0, 0, 400, 0, 1, 0, 0, 0))
set.column(wagoncargo, 6, c(0, 1, 0, 0, 300, 0, 0, 1, 0, 0))
set.column(wagoncargo, 7, c(0, 1, 0, 0, 200, 0, 0, 0, 1, 0))
set.column(wagoncargo, 8, c(0, 1, 0, 0, 500, 0, 0, 0, 0, 1))
set.column(wagoncargo, 9, c(0, 0, 1, 0, 0, 400, 1, 0, 0, 0))
set.column(wagoncargo, 10, c(0, 0, 1, 0, 0, 300, 0, 1, 0, 0))
set.column(wagoncargo, 11, c(0, 0, 1, 0, 0, 200, 0, 0, 1, 0))
set.column(wagoncargo, 12, c(0, 0, 1, 0, 0, 500, 0, 0, 0, 1))

Next, we set the inequalities, the right-hand side of the model, the objective function, and the lp.control function:

set.constr.type(wagoncargo, rep("<=", 10))
set.rhs(wagoncargo, c(10, 8, 12, 5000, 4000, 8000, 18, 10, 5, 20))
set.objfn(c(2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500))
lp.control(wagoncargo, sense="max")

To check on whether the model looks correct, we can issue the command:

wagoncargo

The remaining commands to be issued are:

solve(wagoncargo)
get.objective(wagoncargo)
get.variables(wagoncargo)

This finishes the discussion of Example 4 and how to solve it.  Immediately below, between the long rows of ###s, is an R script for solving Example 4.  Since the discussion above has been extensive, in the script below I have supplied a terser set of comments.

########################################################################

# We begin by installing the lpSolveAPI package and loading it into R.

install.packages("lpSolveAPI")
library(lpSolveAPI)

# The variables in this problem consist of pairings of wagon and cargo. 
# Since there are 3 types of wagons and 4 types of cargo, there are 12 
# pairings to be considered.  They are represented by xij, where 
# i = type of wagon and j = type of cargo:   
# x11, x12,  . . . , x14, x21, . . . , x31, . . . , x34. 

# The goal of the company is to maximize the profit from each trip by 
# its three wagons, so the objective function is to maximize the 
# following: 
# 2000*x11 + 2500*x12 + 5000*x13 + 3500*x14 +2000*x21 + 2500*x22 
# + 5000*x23 + 3500*x24 + 2000*x31 + 2500*x32 + 5000*x33 + 3500*x34 

# The weight constraints can be expressed as: 
# x11 + x12 + x13 + x14  <=  10 
# x21 + x22 + x23 + x24  <=   8 
# x31 + x32 + x33 + x34  <=  12 

# The space capacity constraints can be expressed as: 
# 400*x11 + 300*x12 + 200*x13 + 500*x14  <=  5000 
# 400*x21 + 300*x22 + 200*x23 + 500*x24  <=  4000 
# 400*x31 + 300*x32 + 200*x33 + 500*x34  <=  8000 

# The cargo availability constraints can be expressed as: 
# x11 + x21 + x31   <=  18 
# x12 + x22 + x32   <=  10 
# x13 + x23 + x33   <=   5 
# x14 + x24 + x34   <=  20 

# Now we are ready to use the make.lp and set.column functions: 

wagoncargo <- make.lp(10, 12) 
set.column(wagoncargo, 1, c(1, 0, 0, 400, 0, 0, 1, 0, 0, 0)) 
set.column(wagoncargo, 2, c(1, 0, 0, 300, 0, 0, 0, 1, 0, 0)) 
set.column(wagoncargo, 3, c(1, 0, 0, 200, 0, 0, 0, 0, 1, 0)) 
set.column(wagoncargo, 4, c(1, 0, 0, 500, 0, 0, 0, 0, 0, 1)) 
set.column(wagoncargo, 5, c(0, 1, 0, 0, 400, 0, 1, 0, 0, 0)) 
set.column(wagoncargo, 6, c(0, 1, 0, 0, 300, 0, 0, 1, 0, 0)) 
set.column(wagoncargo, 7, c(0, 1, 0, 0, 200, 0, 0, 0, 1, 0)) 
set.column(wagoncargo, 8, c(0, 1, 0, 0, 500, 0, 0, 0, 0, 1)) 
set.column(wagoncargo, 9, c(0, 0, 1, 0, 0, 400, 1, 0, 0, 0)) 
set.column(wagoncargo, 10, c(0, 0, 1, 0, 0, 300, 0, 1, 0, 0)) 
set.column(wagoncargo, 11, c(0, 0, 1, 0, 0, 200, 0, 0, 1, 0)) 
set.column(wagoncargo, 12, c(0, 0, 1, 0, 0, 500, 0, 0, 0, 1)) 

# Next, we set the inequalities, the right-hand side of the model, 
# the objective function, and the lp.control function: 

set.constr.type(wagoncargo, rep("<=", 10)) 
set.rhs(wagoncargo, c(10, 8, 12, 5000, 4000, 8000, 18, 10, 5, 20)) 
set.objfn(wagoncargo, c(2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500, 2000, 2500, 5000, 3500)) 

lp.control(wagoncargo, sense="max") 

# To check on whether the model looks correct, we issue the command: 

wagoncargo 

# The remaining commands to be issued are: 

solve(wagoncargo) 
get.objective(wagoncargo) 
get.variables(wagoncargo)

# For the maximized profit per trip, you should get $107,500.
# For the loadings of wagons with cargoes, you should get:
# x11 = 0, x12 = 5, x13 = 5, x14 = 0; i.e., in wagon 1, 5 tonnes each 
# of cargo types 2 and 3
# x21 = 0, x22 = 0, x23 = 0, x24 = 8; i.e., in wagon 2, 8 tonnes of 
# cargo type 4
# x31 = 0, x32 = 0, x33 = 0, x34 = 12; i.e., in wagon 3, 12 tonnes of 
# cargo type 4

########################################################################

With Example 4, this discussion of how to perfom linear optimization by using the lpSolveAPI function in R has finished.  I hope the discussion and the examples have been of interest and assistance to fellow R enthusiasts.



Additional Resources on lpSolveAPI

An excellent short resource is "lpSolveAPI Package Users Guide"iv by Kjell Konis, the maintainer of lpSolveAPI.  The URL of this guide is:  

http://www.icesi.edu.co/CRAN/web/packages/lpSolveAPI/vignettes/lpSolveAPI.pdf

Detailed information about the lpSolveAPI packagev is available at:

http://cran.cnr.berkeley.edu/web/packages/lpSolveAPI/lpSolveAPI.pdf

================================================
FILE: 77-TS/11-tsdata.R
================================================
# create a time series data

#first create a vector of numerical values
# 36 observations 
set.seed(1234)
(sales = round(runif(3*12)*100,0))
length(sales)

#This data can be daily, weekly, monthly, quartler, yearly data
#create yearly time series : start year 1980

#Yearly----
(yearlysales = ts(sales, start=c(1980), frequency=1))
(yearlysales1 = ts(sales, start=c(1980,3), frequency=1)) 
# 3rd yr from 1980

#Quarterly -----
12/4  # freq=4
(qtrsales = ts(sales, start=c(1980), frequency=4))
plot(qtrsales)
#list data from Qtr3 1980 to  1985
window(qtrsales, start=c(1980, 3), end=c(1985, 2))


#Monthly -----
12/12  # freq=12 start month=Mar/ 1990
(monsales = ts(sales, start=c(1990,3), frequency=12))

#create data from Feb 2000 to Nov 2002
(monsales1 = ts(sales, start=c(2000,2), end=c(2002,11), frequency=12))
length(monsales1)

#see subset of sales data : May 2000 to Aug 2001
window(monsales1, start=c(2000, 5), end=c(2001, 8))





================================================
FILE: 77-TS/12b-TS-add-mult.R
================================================
# TS - Log of Values

#additive ZA = T + S + I (+ e)
#multiplicative ZM = T * S +* I (+ e)
T=4 ; S=5; I=6
(A = T + S + I)
(M = T * S * I)

#multiplicative to additive
log(M)
log(T)+ log(S) + log(I)

log(32); 5 * log(2); log(2) + log(2) + log(2) + log(2) + log(2) 



================================================
FILE: 77-TS/14-ts-zoo.R
================================================
# zoo package for TS data


================================================
FILE: 77-TS/16-ts-xts.R
================================================
#xts
#creating XTS objects

library(xts)
#xts - matrix of observations combined with an index of corresponding dates and times.

#xts = matrix + times The main xts constructor takes a number of arguments, but the two most important are x for the data and order.by for the index. x must be a vector or matrix. order.by is a vector which must be the same length or number of rows as x, be a proper time or date object (very important!), and be in increasing order.

# Create the object data using 5 random numbers
(data = rnorm(5))

# Create dates as a Date class object starting from 2016-01-01
(dates = seq(as.Date("2016-01-01"), length = 5, by = "days"))

# Use xts() 
(sales5 = xts(x = data, order.by = dates))


#creating XTS objects

(xts1 = xts(x=1:10, order.by=Sys.Date()-1:10))
#see the index column / rownames

(data = rnorm(5))
(dates1 = seq(as.Date("2017-05-01"),length=5,by="days"))
(xts2 = xts(x=data, order.by=dates))

# Create bday (1899-05-08) - reference period using a POSIXct date class object
# Create xts object with datetime index and add a new attribute called born
(xts3 = xts(x=rnorm(10),  order.by=as.POSIXct(Sys.Date()+1:10), born=as.POSIXct("1899-05-08")))
#without time
(xts4 <- xts(x=1:10, order.by=Sys.Date()+1:10))

?xts











bday <- as.POSIXct("1899-05-08")


(hayek <- xts(x = data, order.by = dates, born = bday))
attributes(hayek)


================================================
FILE: 77-TS/16c-dates-split1.R
================================================
# Split Data into parts

#Split date and time seperately
#M1----
(tdatetime = Sys.time())
(splitTime <- sapply(strsplit(as.character(tdatetime), " "), "[", 2))  #time
(splitDate <- sapply(strsplit(as.character(tdatetime), " "), "[", 1))  #date

#  M2----
(df <- data.frame(datetime = c( "13:11:2013 15:39" , "13:11:2013 16:15" , "13:11:2013 17:52" ) ))
(new <- do.call( rbind , strsplit( as.character( df$datetime ) , " " ) ))

#M3----
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%H:%M:%S")
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%Y:%m:%d")

#Time Stamp to POSIXT
td = 1377986400
as.POSIXct(1377986400,origin="1970-01-01")
(td1 = as.POSIXct(as.numeric(td),origin="1970-01-01"))


datetime2 <- as.POSIXct('4/17/2008 16:01', format='%m/%d/%Y %H:%M') 
(datetime2split <- strsplit(format(datetime2, "%d %m %Y %H %M"), ' ') )

(x2 = as.POSIXct(td1, format='%m/%d/%Y %H:%M'))
(y2 <- strsplit(format(x2, "%d %m %Y %H %M"), ' ') )

#-----
# In R, there are two basic data types around date and time in R. One is Date, which contains only the date information like 2016–01–10. Another is POSIXct, which has both date and time information like 2016–01–10 10:45:20.

library(dplyr)
dateposix = seq(1377986400, 1377986420, by=1)
df = data.frame(dateposix)
(df$datetime = as.POSIXct(as.numeric(df$dateposix),origin="1970-01-01"))
library(lubridate)
head(df)
df$dateposix2 = format(unclass(df$datetime), scientific=F)
df <- df %>% mutate(year=year(datetime), month = month(datetime), day= day(datetime), hour=hour(datetime), min=minute(datetime))
head(format(df, scientific=F))

split1 =strsplit(format(df$datetime, "%d %m %Y %H %M"), ' ') #list form
matrix(as.numeric(unlist(split1)), ncol=5, byrow = T) 
do.call(rbind, split1)
as.Date(with(df$datetime, ISOdate(Year, Month, Day)))

#-----

do.call(rbind, spl) 

# using seperate in tidyr
library(dplyr)
head(df)
df1 = df['datetime']
head(df1)
df1 %>% separate(datetime, into=c('Date', 'Time'), sep=' ')
(year=as.numeric (format(df1$datetime,"%Y")))
(month=as.numeric (format(df1$datetime,"%m")))
(day=as.numeric (format(df1$datetime,"%d")))
(hour=as.numeric (format(df1$datetime,"%H")))
(min =as.numeric (format(df1$datetime,"%M")))
data.frame(df1,cbind(year, month, day, hour, min))

#method
(dt = df$datetime)  #vector
(df2 = df['datetime'])
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144') 
library(qdap)
df2a = colSplit(df2$datetime, c("Date", "Time"), ",")
df2a

#-----
head(df2)
list <- strsplit(df2$datetime, " ")
library("plyr")
df <- ldply(list)
colnames(df2) <- c('datetime', 'date', 'time')




#-----
DF <- readr::read_csv(
  'ID,Date1,Date2
  1,"21-03-16 8:36","22-03-16 12:36"
  1,"23-03-16 9:36","24-03-16 01:36"
  1,"22-03-16 10:36","25-03-16 11:46"
  1,"23-03-16 11:36","28-03-16 10:16"'
)
DF

library(dplyr)
library(lubridate)
library(hms)
DF
DF %>%  mutate_at(vars(Date1, Date2), dmy_hm) %>% 
  mutate_at(vars(Date1, Date2), funs("date" = date(.), "time" = as.hms(.)))

DF %>%  mutate_at(vars(Date1, Date2), dmy_hm) %>% 
  mutate_at(vars(Date1, Date2), funs("date" = date(.), "time" = as.hms(.))) %>%   select(-Date1, -Date2)


================================================
FILE: 77-TS/16d-dates1.R
================================================
#Handling and creating Dates in R

#date in yyyy-mm-dd
dates1a = c('2018-21-4', '2018-29-4')
class(dates1a)
?as.Date
as.Date(dates1a)  # error
dates1a
dates1b = as.Date(dates1a, format='%Y-%d-%m')
class(dates1b)
dates1b
dates1b + 1
as.Date('2018-04-29') + 1
as.Date('2018-09-16') + 1:15
as.Date('2018-09-16') + seq(1,10,2)


#default format %Y-%m-%d
dates2a = c('2018-4-21')
class(dates2a)  # character so far
(dates2b = as.Date(dates2a))  # convert to date
class(dates2b)  # date now

format(dates2b, format=('%d-%m-%Y')) # print in different format
format(dates2b, format=('%d / %B - %Y')) # print in different format
format(dates2b, format=('%d / %b - %Y')) # print in different format

dates2b


#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan)
#, %d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

format(Sys.Date(), "%A")

format(Sys.Date(), format="%A %a %d  %m %b %B %y %Y") # another format

pumba = c('09-07-1993', '08-08-1994', '04-11-1994')
class(pumba)
pumba1 = as.Date(pumba, '%d-%m-%Y')
format(pumba1, '%A')

format(as.Date('18-04-1994', '%d-%m-%Y'), '%A')

#System Date
Sys.Date()
format(Sys.Date(), format="%A: %d %B")

#increment/ decrement dates 
startdt = as.Date("2018-13-09", format="%Y-%d-%m")
(course = startdt + 0:20) # start : 20 days course

cat(format(course, format="%B-%d"))
months(course)
weekdays(course)
quarters(course)
paste(quarters(course),'2018',sep='-')


#Date to character
dates2b; class(dates2b)
(dtoc = as.character(dates2b))
class(dtoc)

#one more practise on date format
dates3a =as.Date('30Apr18',format("%d%b%y"))
class(dates3a)

#Difference in dates
course
min(course)  # first date
max(course)  # last date
range(course) # start to end
mean(course)  # avg date ?
median(course) # middle date
course
course[c(1,5)] # 1st & 5th date 
course[1] ; course[9]
(duration = course[8] - course[1])
(duration = max(course) - min(course))
(duration1 = Sys.Date() - as.Date('14-08-1994', '%d-%m-%Y'))


#Date of Birth
dob = as.Date('14-08-1994', '%d-%m-%Y')
dob


dob1= dob + seq(1,1000,30)  # create 1000 dates
length(dob1)
Sys.Date()
as.numeric((Sys.Date() - dob)/365)  #: Years
#par(mfrow=c(1,1))
boxplot(as.numeric((Sys.Date() - dob1)/365))# : Years
as.numeric((Sys.Date() - dob1)/365)# : Years
mean(as.numeric((Sys.Date() - dob1)/365))# : Years
(duration2 = max(course) - min(course))

#Sequence of Dates----
seq(1,10,2)
#seq(from, to, by, length.out = NULL, along.with = NULL, ...)
?seq
#by years : from start date to end date
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), "years")
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), "months")
seq(as.Date("2017/1/1"), by = "days", length.out = 6)

(students = c('ashish','chirag'))

## by month : 6 months
seq(as.Date("2017/1/1"), by = "month", length.out = 6)
seq(as.Date("2017/1/1"), by = "month", along.with = c(3,2,43,5,6,7))  # 
seq(as.Date("2017/1/1"), by = "month", along.with = students)  # 
seq(as.Date("2017/1/1"), by = "quarter", along.with = students)  # 

seq(as.Date("2017/1/1"), by = "month", length.out = length(c(3,2,43))) 


## quarters
seq(as.Date("2017/1/1"), as.Date("2018/1/1"), by = "quarter")

#Find 7th of all months between two dates
(dateseqF = seq(as.Date("2017/1/7"), as.Date("2018/1/7"), by = "1 month"))  # order
# dates in opposite order
(dateseq = seq(as.Date("2018/1/7"), as.Date("2017/1/7"), by = "-2 month"))  # reverse order
# then reverse them
rev(dateseq)# correct the order
format(rev(dateseq), "%A : %d-%b")

#another Sequence : using another package
chron::seq.dates("01/01/2017", "12/31/2017", by = "months")
chron::seq.dates("01/01/2017", "12/31/2017", by = "weeks")



================================================
FILE: 77-TS/16d-ts-xts.R
================================================
#xts time series

#Time Series
class(AirPassengers)
head(AirPassengers,10)
AirPassengers
#subset
window(AirPassengers, start=c(1950,3), end=c(1954,5))

#XTS series-----
airp.xts = as.xts(AirPassengers)
class(airp.xts)
#subset - looks at index values
airp.xts["1954-03/1954-09"] # range month-year
airp.xts["1954"]  #only 1954 year
airp.xts["/1954-09"] # start to  month-year
airp.xts["1954-09"] # for a particular period

airp.xts['1954-03/']  # March 1954 to the end of the data set
airp.xts['1954-03/1954']  # March 1954to the end of 1954
airp.xts['/'] # the whole data set
airp.xts['/1954'] # the beginning of the data through 1954
airp.xts['1954-01-03'] # just the 3rd of January 1954
#above will not work since it is monthly ts: create daily ts for this

#daily XTS 
(daily.xts = xts(x=1:40, order.by=Sys.Date()+1:40))
daily.xts["2018-10-28"]

#extra week data
first(daily.xts,'1 week') #Extract first 1 week
daily.xts
index(daily.xts)
.indexwday(daily.xts)
cbind(daily.xts, .indexwday(daily.xts))

#depends on sysdate
first(daily.xts,'2 week') #Extract first 2 weeks

#Get first 3 days of the 2nd last week of data
first(last(xts4,'2 week'),'3 days') 



================================================
FILE: 77-TS/23b-TS-Case-sales.R
================================================
#TS analysis
#https://stepupanalytics.com/time-series-forecasting-using-r/

library(gsheet)

url = "https://docs.google.com/spreadsheets/d/1D9WaU_CZUyLiZv0TyjFX8pgdJ1WIOh99n37BWvwRAz4/edit#gid=1372342646"

sales = as.data.frame(gsheet2tbl(url))
head(sales)

library(forecast)

data = ts(sales[,2], start = c(2003,1), frequency = 12)
head(data)
data
plot(data, xlab = "Years", ylab = "Sales")

library(tseries)
adf.test(data, alternative = "stationary")
#Null Hypo : non stationary : it is non-stationary here

plot(diff(data), ylab = "1st Order Difference of Sales")

data1 = diff(data)
data1

adf.test(data1, alternative = "stationary")
plot(diff(data1), ylab = "2nd Order Difference of Sales")

data2 = diff(data1)
data2

adf.test(data2, alternative = "stationary")

plot(log10(data), ylab = "Log(Sales)")

data10 = log10(data)
data10

adf.test(data10, alternative = "stationary")

plot(diff(data10), ylab = "1st Order Difference of Log(Sales)")
data11 = diff(data10)
plot(diff(data11), ylab = "2nd Order Difference of Log(Sales)")
data12 = diff(data11)

adf.test(data12, alternative = "stationary")

acf(ts(diff(diff(data))), main = "ACF Sales")

pacf(ts(diff(diff(data))), main = "PACF Sales")

ARIMAfit = arima(diff(data), c(2,2,0))
summary(ARIMAfit)

ARIMAautofit = auto.arima(diff(data), approximation = TRUE, trace = TRUE)
summary(ARIMAautofit)

acf(ts(diff(diff(log10(data)))), main = "ACF Log(Sales)")
pacf(ts(diff(diff(log10(data)))), main = "PACF Log(Sales)")

ARIMAautofit2 = auto.arima(diff(log10(data)), approximation = TRUE, trace = TRUE)
summary(ARIMAautofit2)

pred = predict(ARIMAautofit, n.ahead = 36)
pred

pred$pred
pred$se

plot(forecast(ARIMAautofit, h = 36))

acf(ts(ARIMAautofit$residuals), main = "ACF Residual Of Sales")
pacf(ts(ARIMAautofit$residuals), main = "PACF Residual Of Sales")

autoexp_fit = ets(data10)
summary(autoexp_fit)

pred_ets = predict(autoexp_fit, h = 36)
plot(forecast(autoexp_fit))
summary(pred_ets)
acf(autoexp_fit$residuals, lag.max = 20, main = "ACF Residual Of Sales")


================================================
FILE: 77-TS/23b-lubridate1.R
================================================
# Package Lubridate
# Date Functions - Date & Time

#Eg1
library(lubridate)
dt=lubridate::ymd("20110604")
dt + 1:10
lubridate::mdy("06-04-2011")
lubridate::dmy("04/06/2011")
#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

#Eg1
#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.
OlsonNames()
?Sys.timezone()
Sys.timezone()

arrive <- ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
arrive
leave <- ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
leave

leave - arrive  # Time Difference

#Setting and Extracting information
#Functions

#Eg
second(arrive)
second(arrive) = 25  #change
second(arrive)
arrive
minute(arrive)
hour(arrive)
day(arrive)
wday(arrive)
wday(arrive)
wday(arrive, label = TRUE)

week(arrive)

month(arrive)
year(arrive)
tz(arrive)

#Time Intervals
#save an interval of time as an Interval class object
(student1 <- interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                   ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                 ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))


================================================
FILE: 77-TS/24b-Data-DFtoTS.R
================================================
# Times Series 
#DF to TS

Lines <- "Dates   Bajaj_close Hero_close
3/14/2013   1854.8  1669.1
3/15/2013   1850.3  1684.45
3/18/2013   1812.1  1690.5
3/19/2013   1835.9  1645.6
3/20/2013   1840    1651.15
3/21/2013   1755.3  1623.3
3/22/2013   1820.65 1659.6
3/25/2013   1802.5  1617.7
3/26/2013   1801.25 1571.85
3/28/2013   1799.55 1542"
Lines

library(zoo)
z <- read.zoo(text = Lines, header = TRUE, format = "%m/%d/%Y")
z

DF <- read.table(text = Lines, header = TRUE)
z2 <- read.zoo(DF, format = "%m/%d/%Y")
z2

zz <- z
time(zz) <- 1:nrow(zz)
zz

as.ts(z)
as.ts(zz)
#The first has a time index which is the number of days since the Epoch (January 1, 1970) and will have NAs for missing days and the second will have 1, 2, 3, ... as the time index and no NAs.

#Monthly Series
z.m <- as.zooreg(aggregate(z, as.yearmon, mean), freq = 12)
as.ts(z.m)

#time_ser=ts(data,frequency=4,start=c(1954,2))


================================================
FILE: 77-TS/24b-timeseries1.R
================================================
# Time Series
#https://rpubs.com/ryankelly/tsa6

library(astsa, quietly=TRUE, warn.conflicts=FALSE)
require(knitr)
library(ggplot2)
kings<-scan('http://robjhyndman.com/tsdldata/misc/kings.dat', skip=3)
kings
(kings = ts(kings))

births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")

(births <- ts(births, frequency = 12, start = c(1946, 1)))

plot.ts(kings)  
plot.ts(births)


gift <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
(gift<- ts(gift, frequency=12, start=c(1987,1)))

plot.ts(gift)

(logGift = log(gift))
plot.ts(logGift)


#Simple Moving Average
library(TTR)
(kingsSMA3 = SMA(kings, n=3))
plot.ts(kingsSMA3)

(kingsSMA8 = SMA(kings, n=8))
plot.ts(kingsSMA8)

(birthsComp <- decompose(births))
plot(birthsComp)

(birthsSeasonAdj <- births - birthsComp$seasonal)
plot(birthsSeasonAdj)



#
rain <- ts(scan("http://robjhyndman.com/tsdldata/hurst/precip1.dat",skip=1), start=c(1813))
head(rain)
rain
plot.ts(rain)

rainF <- HoltWinters(rain, beta=FALSE, gamma = FALSE)
rainF

plot(rainF)





#Forecasts using Exponential Smoothing
#Simple Exponential Smoothing
rain <- ts(scan("http://robjhyndman.com/tsdldata/hurst/precip1.dat",skip=1), start=c(1813))
plot.ts(rain)

library(forecast)

rainF8 <- forecast:::forecast.HoltWinters(rainF, h=8)
forecast:::plot.forecast(rainF8)
acf(rainF8$residuals, na.action = na.pass)
rainF8$residuals
Box.test(rainF8$residuals, lag=20, type='Ljung-Box')

plot.ts(rainF8$residuals)


skirts <- ts(scan("http://robjhyndman.com/tsdldata/roberts/skirts.dat",skip=5), start=c(1866))
plot.ts(skirts)

skirtsF <- HoltWinters(skirts, gamma=F)
skirtsF
skirtsF$SSE
plot(skirtsF)


# Forecast into the future
skirtsF19 <- forecast:::forecast.HoltWinters(skirtsF, h=19)
forecast:::plot.forecast(skirtsF19)

dev.off(); graphics.off()
par(mar=c(3,2,2,2))
par(mfrow=c(3, 1))
acf(skirtsF19$residuals, lag.max=20, na.action = na.pass)
Box.test(skirtsF19$residuals, lag=20, type='Ljung-Box')

plot.ts(skirtsF19$residuals)

par(mfrow=c(1, 1))
#Eg2
giftLog <- log(gift) # take natural log
plot.ts(giftLog)


giftLogF <- HoltWinters(giftLog)
giftLogF

giftLogF$SSE
plot(giftLogF)

giftLogF48 <- forecast:::forecast.HoltWinters(giftLogF, h=48) # predict 48 months ahead
forecast:::plot.forecast(giftLogF48)

par(mar=c(3,2,2,2))

#Test
acf(giftLogF48$residuals, lag.max=20, na.action = na.pass)

Box.test(giftLogF48$residuals, lag=20, type='Ljung-Box')
plot.ts(giftLogF48$residuals)



================================================
FILE: 77-TS/24c-timeseries2.R
================================================
# Mov Average
#https://www.otexts.org/fpp/6/2

admission = c(100,110,115,130,150,145,160,125,123,134)
length(admission)
admyr = ts(admission, start=2001)
admyr
(100+110+115)/3
forecast::ma(admyr, order=3)
plot(admyr, main="Yearly Admissions", ylab="Admissions", xlab="Year")
lines(ma(admyr,3),col="red")

admyr1 <- window(admyr,start=2005)
admyr1

ma4 <- ma(admyr1, order=4, centre=FALSE)
ma4
ma2x4 <- ma(admyr1, order=4, centre=TRUE)
ma2x4




abbshares <- scan(file="")

library(pracma)
plot(abbshares, type = "l", col = 1, ylim = c(15, 30),
     main = "Types of moving averages", sub = "Mid 2011--Mid 2012",
     xlab = "Days", ylab = "ABB Shares Price (in USD)")
y <-  pracma::movavg(abbshares, 50, "s"); lines(y, col = 2)
y <- pracma::movavg(abbshares, 50, "t"); lines(y, col = 3)
y <- pracma::movavg(abbshares, 50, "w"); lines(y, col = 4)
y <- pracma::movavg(abbshares, 50, "m"); lines(y, col = 5)
y <- pracma::movavg(abbshares, 50, "e"); lines(y, col = 6)
y <- pracma::movavg(abbshares, 50, "r"); lines(y, col = 7)
grid()
legend(120, 29, c("original data", "simple", "triangular", "weighted",
     "modified", "exponential", "running"),
     col = 1:7, lty = 1, lwd = 1, box.col = "gray", bg = "white")

#x
elecs = read.csv(file='./data/elect.csv')
elecs1 = elecs$Sales
electsales = ts(elecs1, start= 1989)
electsales

forecast::ma(elecsales, order=3)
forecast::ma(elecsales, order=5)


================================================
FILE: 77-TS/24f-ts-data.R
================================================
#ts9
# save a numeric vector containing 72 monthly observations # from Jan 2009 to Dec 2014 as a time series object
myvector = round(runif(6*12)*100,0)
myvector
myts <- ts(myvector, start=c(2009, 1), end=c(2014, 12), frequency=12)
myts
# subset the time series (June 2014 to December 2014)
myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))
myts2
# plot series
plot(myts)


#Eg2
sales= c(18,33,41,7,34,35,24,25,24,21,25,20, 22,31,40,29,25,21,22,54,31,25,26,35)
tsales = ts(sales, start=c(2003,1), frequency=12)
tsales
plot(tsales)
plot(tsales, type='o', pch=19)
start(tsales)
end(tsales)
frequency(tsales)
tsales.subset = window(tsales, start=c(2003,5), end=c(2004,6))
tsales.subset

#Yearly Series

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,2), end=c(2014,11), frequency=3)
myts
myts2 <- window(myts, start=c(2010), end=c(2013))
myts2


# quarterly 

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,1), end=c(2014,11), frequency=3)
myts
str(myts)
plot(myts, type='o', pch=19)
text()

plot.ts(myts, type='o', pch=19, xy.labels=F)

axis(1, myts$Date, format(myts$Date, "%b %d"), cex.axis = .7)


myts <- ts(myvector, start=c(2009,2), end=c(2014,9), frequency=3)
myts
str(myts)


myts2 <- window(myts, start=c(2010), end=c(2013))
myts2
plot(myts, type='o', pch=19)
str(myts2)



myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))
plot(myts,type='o', pch=19)
text()
plot(myts2,type='o', pch=19)


library(zoo)
x.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = "-"))
x.Date
?zoo
x <- zoo(rnorm(24), x.Date)
x
plot(x)



#If we want different tick locations, we can suppress the default axis plotting and add our own:
  
plot(x, xaxt = "n")
axis(1, at = time(x), labels = FALSE)
#Or combine them:
  
plot(x)
axis(1, at = time(x), labels = FALSE)

## weekly ticks
plot(x)
times <- time(x)
ticks <- seq(times[1], times[length(times)], by = "weeks")
axis(1, at = ticks, labels = FALSE, tcl = -0.3)


library(zoo)
library(lattice)

# create test data
z <- zooreg(1:83, start = as.Date("2009-04-01"), deltat = 7)

xyplot(z)




# Time series plots with good X axis labels
library(zoo)
# data
today = Sys.Date()
dates = as.Date((today-500):today)
dates
z = zoo (100+cumsum(rnorm(501)), dates)
z
# method1 : default X axis labels do not look good
?plot.zoo
plot(z)
?plot.ts
plot(ts(z))
time(z)
# method 2 : Lattice
library(lattice)
?xyplot.zoo
xyplot(z)
xyplot(z, lwd=2, col="tomato")

# method 3 : XTS
library(xts)
?plot.xts
plot(as.xts(z))
plot(as.xts(z), auto.grid=F, major.format="%b %y", las=2)

# method 4 : Base graph
timeline = time(z)
summary(timeline)
index = seq(from=1, to=length(timeline), 90) # put ticks every 90 days
plot(z, xaxt="n")
axis(side=1, at=timeline[index], label=format(timeline[index], "%b %y"), cex.axis=0.8)

# method 5 : ggplot
library(ggplot2)
library(scales)
?date_breaks
df = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))
head(df)
# default plot
ggplot(df, aes(x=date, y=value)) + geom_line()
# formatted
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"))
# custom breaks
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"), breaks=date_breaks("3 months"))


================================================
FILE: 77-TS/26b-ts-components-airp.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=2)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object

plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregual
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years

(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change


#STL
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
monthplot(AirPassengers) #max traffic in Jun/ Jul across years
monthplot(fit, choice='seasonal') # less in winters, more in summers
monthplot(fit, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(fit, choice='remainder') # irregular components

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(fit, choice='seasonal')
monthplot(fit, choice='trend')
monthplot(fit, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast)
# Forecast # adjust for multiplicative model
fit2b = ets(AirPassengers, model='MAM')
fit2b
(f2b=forecast(fit2b, 12))
head(f2b)$mean



================================================
FILE: 77-TS/26c-ts-components.R
================================================
# Time Series Components

# TS data components : Level + Irregular + Seasonal
# Level changing - Trend ; Irregular are variations 
#http://slideplayer.com/1507363/5/images/5/Time-Series+Components.jpg
#http://www.grroups.com/uploads_media/4a47a0db6e60853dedfcfdf08a5ca249%20%285%29.png
#http://images.slideplayer.com/31/9794753/slides/slide_4.jpg
#http://rstatistics.net/wp-content/uploads/2014/09/Additive-Decomposition-of-Time-series.png
#https://www.frontiersin.org/files/Articles/144575/fpsyg-06-00727-HTML/image_m/fpsyg-06-00727-g002.jpg
#https://image.slidesharecdn.com/operationsmanagement-forecasting-150217234022-conversion-gate02/95/operations-management-forecasting-11-638.jpg?cb=1432630382




================================================
FILE: 77-TS/27b-ts-johnson.R
================================================
#Times Series Analysis 
# is the price of Johnson and Johnson shares change over time
# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year
# Can you forecast what future share prices will be and to what degree of accuracy

#dataset - Johnson
#Quarterly earnings per Johnson Shares
#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA

JohnsonJohnson

library(forecast)

#ets auto select best predicton model
?ets
#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time

fit1 = ets(JohnsonJohnson)
fit1
#alpha - trend
#beta  = seasonal
#gamma  - irregular

JohnsonJohnson
head(JohnsonJohnson)
tail(JohnsonJohnson)

(f1= forecast(fit1,h=10))  # 
?forecast.ets
plot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3)  # linetype for forecast area
#shaded portion is confidence intervals area

par(mfrow=c(1,1))

# ARIMA Forecasting : 
#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.

#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg

f2 = auto.arima(JohnsonJohnson)
summary(f2)
tail(JohnsonJohnson)  # last few values
forecast(f2,h=5)


#ARIMA Forecasting  : compare two datasets
library(tseries)
plot(JohnsonJohnson)
ndiffs(JohnsonJohnson)
plot(diff(JohnsonJohnson))

plot(Nile)
plot(diff(Nile))
ndiffs(Nile)

#-----
djj = diff(JohnsonJohnson)
plot(djj)

dnile = diff(Nile)
plot(dnile)

#----
adf.test(djj)
#if pv < 0.05 accept Alt Hypothesis that series is stationary

#Model Selection 
#parameters p, d , q
# d = no of diffs applied to make the series stationary
#https://people.duke.edu/~rnau/arimrule.htm
Acf(dnile)
#Trail off to zero : Zero after lag ; 0,1(p)
#Zero after lag q : Trails off to zero ; 1(q), 0 
#Trails off to zero : Trial off to zero : 0,0
#Nile - 1 large auto correlation at lag 1 : 
#Nile - pacf trails off to zero as the lags gets bigger
?arima
Pacf(dnile)

fit3 = arima(Nile, order=c(0,1,1)) # p,d,q
fit3

(fit3b = arima(Nile, order=c(1,1,1)))


#Model Test
qqnorm(fit3$residuals) # residuals ND
qqline(fit3$residuals)

#auto correl = 0 : check
Box.test(fit3$residuals, type='Ljung-Box')
#Test auto corr : H0= r=0 (True)

#Forecast
forecast(fit3,4)

Nile
#Auto ARIMA
library(forecast)
#forecast::auto.arima()
fit4 = auto.arima(Nile)
fit4

forecast(fit4,5)
plot(forecast(fit4,5))



================================================
FILE: 77-TS/27c-ts-lm-uscons.R
================================================
# Time Series - Regression

data("usconsumption")
#https://www.otexts.org/fpp/4/8
?usconsumption

str(usconsumption)
head(usconsumption)
fit.ex3 <- tslm(consumption ~ income, data=usconsumption)
plot(usconsumption, ylab="% change in consumption and income",
     plot.type="single", col=1:2, xlab="Year")
legend("topright", legend=c("Consumption","Income"),
       lty=1, col=c(1,2), cex=.9)
plot(consumption ~ income, data=usconsumption, 
     ylab="% change in consumption", xlab="% change in income")
abline(fit.ex3)
summary(fit.ex3)



fcast <- forecast(fit.ex3, newdata=data.frame(income=c(-1,1)))
fcast
plot(fcast, ylab="% change in consumption", xlab="% change in income")

?austa
austa
par(mfrow=c(1,1)); par(mar=c(3,2,2,2))
fit.ex4 <- tslm(austa ~ trend)
fit.ex4

(f <- forecast(fit.ex4, h=5,level=c(80,95)))

plot(f, ylab="International tourist arrivals to Australia (millions)",
     xlab="t")
lines(fitted(fit.ex4),col="blue")
summary(fit.ex4)

graphics.off()
par(mar=c(3,5,2,1))
par(mfrow=c(2,2))
res3 <- ts(resid(fit.ex3),s=1970.25,f=4)
res3
plot.ts(res3,ylab="res (Consumption)")
abline(0,0)
Acf(res3)
res4 <- resid(fit.ex4)
res4
plot(res4,ylab="res (Tourism)")
abline(0,0)
Acf(res4)

par(mfrow=c(2,1))

tslm(AirPassengers ~ guinearice)
plot(AirPassengers)
plot(guinearice)


================================================
FILE: 77-TS/28c-ts-lubridate1.R
================================================
#Package Lubridate

#functions which allow you to specify the order in which year, month and day components appear in date


library(lubridate)
ymd("20170601")
mdy("06-01-2017")
dmy("01/06/2017")
dmy("01-06-2017")

Sys.timezone(location=T)
#282- Asia/Kolkata
('Asia/Kolkatta')

(arrive <- ymd_hms("2018-04-09 14:00:00", tz = "Asia/Calcutta"))
(leave <- ymd_hms("2018-04-14 13:00:00", tz = "Asia/Calcutta"))
(leave2 <- ymd_hms("2018-04-14 13:00:00", tz = "GMT"))
leave - arrive
leave2- arrive
#Check the seconds for arrival
second(arrive)
#change seconds in arrival data
second(arrive) <- 25
arrive

#Check which date/day/month I arrived
mday(arrive) # arrived on 9 of the month
wday(arrive) ##  in no start from Sun-1, Mon-2 ..
wday(arrive, label = TRUE)  # arrived on Mon
month(arrive)
month(arrive, label=T, abbr=T)
month(arrive, label=T, abbr=F)
year(arrive)


#practise upto here  
#Students to look up other practise exercises for Data below


meeting <- ymd_hms("2011-07-01 09:00:00", tz = "Pacific/Auckland")
## "2011-07-01 09:00:00 NZST"
with_tz(meeting, "America/Chicago")
## "2011-06-30 16:00:00 CDT"

mistake <- force_tz(meeting, "America/Chicago")
## "2011-07-01 09:00:00 CDT"
with_tz(mistake, "Pacific/Auckland")
## "2011-07-02 02:00:00 NZST"
## 
auckland <- interval(arrive, leave)
auckland
## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST
auckland <- arrive %--% leave
## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST
auckland
tdiff = arrive - leave
tdiff

jsm <- interval(ymd(20110720, tz = "Pacific/Auckland"), ymd(20110831, tz = "Pacific/Auckland"))
jsm
int_overlaps(jsm, auckland) ## TRUE
setdiff(auckland, jsm) ## 2011-06-04 12:00:00 NZST–2011-07-20 NZST

minutes(2) # period
## 2 minutes
dminutes(2) # duration
## 120s (~2 minutes)
dhours(2)
seconds(2)
dyears(2)
leap_year(2011)
## FALSE
ymd(20110101) + dyears(1)
## "2012-01-01 UTC"
ymd(20110101) + years(1)
## "2012-01-01 UTC"

leap_year(2012)
## TRUE
ymd(20120101) + dyears(1)
## "2012-12-31 UTC"
ymd(20120101) + years(1)
## "2013-01-01 UTC"

meetings <- meeting + weeks(0:5)
meetings
## [1] "2011-07-01 09:00:00 NZST" "2011-07-08 09:00:00 NZST"
## [3] "2011-07-15 09:00:00 NZST" "2011-07-22 09:00:00 NZST"
## [5] "2011-07-29 09:00:00 NZST" "2011-08-05 09:00:00 NZST"

periods = meeting + days(1:5)
periods


second(arrive)
## 0
second(arrive) <- 25
arrive
## "2017-06-01 12:00:25 IST"
second(arrive) <- 0
wday(arrive)
## 5
wday(arrive, label = TRUE)
## Thurs



meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meeting

with_tz(meeting, "America/Chicago")
mistake <- force_tz(meeting, "America/Chicago")
with_tz(mistake, "Asia/Calcutta")
mistake


udit <- interval(arrive, leave)
udit
nakul <- arrive %--% leave
nakul

dhiraj <- interval(ymd(20170702, tz = "Asia/Calcutta"),
                   ymd(20170704, tz = "Asia/Calcutta"))
dhiraj
udit
#Will  visit overlap with and his travels
int_overlaps(udit, dhiraj) ## TRUE

#For what part of Dhiraj's' visit will Udit will be there?
setdiff(udit, dhiraj)
setdiff(dhiraj, udit)

minutes(2) # period
## 2 minutes
dminutes(2) # duration
## 120s (~2 minutes)
eminutes(2)


leap_year(2011)
## FALSE
ymd(20110101) + dyears(1)
## "2012-01-01 UTC"
ymd(20110101) + years(1)
## "2012-01-01 UTC"

leap_year(c(2012, 2017))
## TRUE FALSE
ymd(20170101) + dyears(1)
## "2018-01-01"
ymd(20170101) + years(1)
## "2018-01-01"
## 

meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meetings <- meeting + weeks(0:5)
meetings

friend = interval(ymd(20170705, tz = "Asia/Calcutta"),
                ymd(20170715, tz = "Asia/Calcutta"))
friend
mypd = interval(ymd(20170702, tz = "Asia/Calcutta"),
                          ymd(20170803, tz = "Asia/Calcutta"))
mypd
int_overlaps(friend, mypd)

#For what part of Dhiraj's' visit will Udit will be there?
setdiff(friend, mypd)


?interval
mypd
meetings %within% mypd

meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meetings <- meeting + weeks(0:5)
meetings
meetings %within% mypd

mypd / edays(1)
mypd / ddays(1)
mypd / ddays(2)
mypd / dminutes(1)
mypd / dseconds(1)
mypd / months(1)
mypd / years(1)


as.period(mypd %% months(1))
as.period(mypd)

last_day <- function(date) {
  ceiling_date(date, "month") - days(1)
}
# try last_day(ymd(20000101) + months(0:11))

last_day(ymd(20000101))
last_day(ymd(20000101) + months(0:11))


================================================
FILE: 77-TS/31c-TS-airp.R
================================================
# Time Series ts1
#https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/

data(AirPassengers)
class(AirPassengers)
#This tells you that the data series is in a time series format
str(AirPassengers)
head(AirPassengers); tail(AirPassengers)
start(AirPassengers)
#This is the start of the time series
end(AirPassengers)
#[1] 1960 12
#This is the end of the time series
frequency(AirPassengers)  # $[1] 12
#The cycle of this time series is 12months in a year
summary(AirPassengers)


# Sec 2 -------------------------------------------------------------------
AirPassengers
#The number of passengers are distributed across the spectrum
plot(AirPassengers)
#This will plot the time series
abline(reg=lm(AirPassengers~time(AirPassengers)))
# This will fit in a line


# Sec3 --------------------------------------------------------------------

cycle(AirPassengers)
#This will print the cycle across years.
plot(aggregate(AirPassengers,FUN=mean))
#This will aggregate the cycles and display a year on year trend
boxplot(AirPassengers~cycle(AirPassengers))
#Box plot across months will give us a sense on seasonal effect


plot(aggregate(AirPassengers,FUN=mean))
plot(log(AirPassengers))
abline(reg=lm(log(AirPassengers) ~ time(AirPassengers)))

plot(diff (log(AirPassengers)))
abline(h=0, col='red')
abline(reg=lm(diff(log(AirPassengers)) ~ time(AirPassengers)))


#Model
acf(AirPassengers)
acf(diff (log(AirPassengers)))
pacf(diff (log(AirPassengers)))

fit = arima(log(AirPassengers), c(0,1,1), seasonal =
              list(order = c(0,1,1), period = 12) )
pred = predict(fit, n.ahead = 10 * 12)
pred1 = 2.718 * pred$pred
ts.plot(AirPassengers, pred1, log='y', lty=c(1,3))



================================================
FILE: 77-TS/33b-zoo-ts.R
================================================
# Zoo Package
#Regular and Irregular Time Series
# normal ts commands creates date in series/ sequence

set.seed(1234)
options(digits=2)
(sales = round(rnorm(30,50,10),0))  #normal distributed values


library(zoo)
?zooreg
(z1 = zooreg(sales))  #nothing much done here only type of object changed
class(z1)

(z2 = zooreg(sales, start=as.Date("2016-01-01")))

sales
length(2000:2017) ; length(sales)
(z3 = zooreg(sales, start=c(2000,3), end=c(2017,3), freq=12, calendar=T))
?zoo  - used for converting ts to zoo format
#(z3 = zoo(sales, start=c(2000,3), end=c(2017,3), freq=12, calendar=T)) # error

(sales4 = zooreg(sales, start=c(2016,2), freq=12))

#compare ts and zoo
(ts(sales,start=c(2016,2), freq=12))
as.zoo(ts(x, start=c(2016,2), freq=12))

zoo(sales, seq(2016.0, 2017.5, by=1/12), freq=12)
zoo(sales, seq(2016.3, 2017.7, by=1/12), freq=12)
zoo(sales, c(2016.1, 2016.4, 2016.13))  #irregular TS

zoo(sales, yearqtr(seq(2016.25, 2017.5, by=1/4)))
zooreg(sales, start=yearmon(2016), end=yearmon(2017))

zooreg(sales[(1:5)], end = yearmon(2017))  #only end date specified, works backwards
(z7= zooreg(sales, end = yearmon(2018.10)))  #.10 - Feb, .25 - Apr

frequency(z7)
delta(z7)
cycle(z7)
cycle(z[-(3:4)])
lag(z, k=-1)
diff(z)
x <- c(1, 2, 3, 6)
zz <- zoo(x, x)
zz
zr <- as.zooreg(zz)
lag(zz, k = -1)
lag(zr, k = -1)
diff(zz)
diff(zr)



dd <- as.Date(c("2016-01-01", "2016-01-02", "2000-01-03", "2000-01-04"))
zrd <- as.zooreg(zoo(sales, dd))
zrd
str(zrd)
plot(zrd)


================================================
FILE: 77-TS/35b-LSM-beer1.R
================================================
# LSM
library(fpp)
data(beer)
beer
str(beer)

#Model Parabola
lbeer<-log(beer)
lbeer
t<-seq(1956,1995.2,length=length(beer))
t
t2<-t^2
t2
plot(lbeer)
lm(lbeer~ t + t2)
lines(lm(lbeer~t+t2)$fit,col='red',lwd=3)



#Model2
lbeer<-log(beer)
t<-seq(1956,1995.2,length=length(beer))
t2<-t^2
sin.t<-sin(2*pi*t)
cos.t<-cos(2*pi*t)
plot(lbeer)
lines(lm(lbeer~t+t2+sin.t+cos.t)$fit,col=4)
summary(lm(lbeer ~ t+t2+sin.t+cos.t))


================================================
FILE: 77-TS/38b-tsplots2a.R
================================================
# Plot 2 TS together
graphics.off()
plot(AirPassengers)
plot(guinearice)
ts.plot(..., gpars = list())
set.seed(1)
x = ts(rnorm(20), frequency = 4, start = c(1959, 2))
y = ts(rnorm(20), frequency = 4, start = c(1959, 2))
ts.plot(x, y, gpars = list(col = c("black", "red")))


#Eg2
set.seed(1)
xz = zoo(ts(rnorm(20), frequency = 4, start = c(1959, 2)))
yz = zoo(ts(rnorm(20), frequency = 4, start = c(1959, 2)))
# Basic approach
plot(xz)
lines(yz, col = "red")
# Panels
plot.zoo(cbind(xz, yz))
# Overplotted
plot.zoo(cbind(xz, yz),
         plot.type = "single", col = c("red", "blue"))


#GGPLOT
library(ggplot2)

dta <- data.frame(year = 2012,
                  month = rep(seq(1,9),each=10),
                  day = sample(seq(1,20),90,replace=T),
                  Group = sample(c('A','B'),90,replace=T),
                  Value = seq(1,90)
)

dta$Date <- apply(dta[,c('year','month','day')],1,paste,sep='',collapse='-')
dta$Date <- as.Date(dta$Date)

qplot(Date,Value,data=dta,geom='line',color=Group)



#Eg4
temp<- rnorm(365, 5, 10)
mort<- rnorm(365, 300, 45)
poll<- rpois(365,  lambda=76)
date<-seq(as.Date('2011-01-01'),as.Date('2011-12-31'),by = 1)
df<-data.frame(date,mort,poll,temp)
df
library(ggplot2)
library(reshape2)

df_melt = reshape2::melt(df, id.vars = 'date')
ggplot(df_melt, aes(x = date, y = value)) + 
  geom_line() + 
  facet_wrap(~ variable, scales = 'free_y', ncol = 1)

ggplot(df_melt, aes(x = date, y = value)) + 
  geom_line() + 
  facet_wrap(~ variable, nrow=1, scales = 'free_y')


df = within(df, {
  temp[61:90] = temp[61:90] + runif(30, 30, 50)
  mort[61:90] = mort[61:90] + runif(30, 300, 500)
})
df_melt = reshape2::melt(df, id.vars = 'date')
ggplot(df_melt, aes(x = date, y = value)) + 
  geom_line() + 
  facet_wrap(~ variable, scales = 'free_y', ncol = 1)



#Eg5
library(zoo)
z <- read.zoo(df)
# classic graphics in separate and single plots
plot(z)
plot(z, screen = 1)
# lattice graphics in separate and single plots
library(lattice)
xyplot(z)
xyplot(z, screen = 1)
# ggplot2 graphics in separate and single plots
library(ggplot2)
autoplot(z) + facet_free()
autoplot(z, facet = NULL)

# Eg6
tseries::seqplot.ts(AirPassengers, guinearice)
data(USeconomic)
x <- ts.union(log(M1), log(GNP), rs, rl)
head(x)
m.ar <- ar(x, method = "ols", order.max = 5)
y <- predict(m.ar, x, n.ahead = 200, se.fit = FALSE)
seqplot.ts(x, y)

#Eg7
components_airp <- decompose(AirPassengers)
components_airp
plot(components_airp)
ts.plot(components_airp$seasonal,y=NULL,
        gpars = list(col = c("red", "blue"), ylab = "Seasonality",
                     main = "Seasonality in Search Term Popularity"))


================================================
FILE: 77-TS/38c-tsplots3.R
================================================
# plot the graph in R
set.seed(15)
a = c(1,2,-2,4,2,3,1,0,3,4,2,3,1)
b = a + rnorm(length(a), sd = 0.4)
plot(ts(b), col="#f44e2e", lwd=3)
lines(a, col="#27ccc0", lwd=3)
a
round(b,1)
# compute using the R language
corr = ccf(a,b)
corr


================================================
FILE: 77-TS/38f-plot-zz.R
================================================
#ts8
#https://www.otexts.org/fpp/6/1
par(mfrow=c(2,2))
plot(hsales,xlab="Year",ylab="Monthly housing sales (millions)")
plot(ustreas,xlab="Day",ylab="US treasury bill contracts")
plot(elec,xlab="Year",ylab="Australian monthly electricity production")
plot(diff(dj),xlab="Day",ylab="Daily change in Dow Jones index")


================================================
FILE: 77-TS/41b-arima1.R
================================================
#ARIMA

set.seed(1)

# set number of observations
(my.n <- 5000)

# set model's parameters
(my.model <- list(ar = 0.5, ma = -0.1))
(my.sd <- 1)

# simulate model
(my.ts <- arima.sim(n = my.n, model = my.model , sd = my.sd))

library(ggplot2)

# set df
temp.df <- data.frame(y = unclass(my.ts), date = Sys.Date() + 1:my.n)
head(temp.df, n=10)
p <- ggplot(temp.df, aes(x = date, y = y))
p <- p + geom_line(size=0.5)
print(p)

head(temp.df)
# estimate arima model
my.arima <- arima(my.ts, order = c(1,0,1))

# print result
print(coef(my.arima))

print(summary(my.arima))


# read file
my.f <- 'data/SP500.csv'
df.SP500 <- read.csv(my.f, header=F, col.names = c('price'))
head(df.SP500)
names(df.SP500)
calc.ret <- function(P) {
  # calculates arithmetic returns from a vector of prices
  #  # Args:
  #   P - vector of prices (numeric)
  #  # Returns:  #   A vector of returns
  
  my.length <- length(P)
  ret <- c(NA, P[2:my.length]/P[1:(my.length - 1)] - 1)
  return(ret)
}
head(df.SP500)

# set return column
(df.SP500$ret <- calc.ret(df.SP500$price))

library(tseries)
print(adf.test(na.omit(df.SP500$ret)))
  
print(adf.test(df.SP500$price))

library(forecast)

# estimate arima model with automatic identification
my.autoarima <- auto.arima(x = df.SP500$ret)

# print result
print(my.autoarima)

# forecast model
print(forecast(my.autoarima, h = 5))


================================================
FILE: 77-TS/41c-arima2.R
================================================
#arima
Nile
lag(Nile,1)
?lag
yearmon(Nile)

Acf(Nile)
acf(Nile)
pacf(Nile)
Pacf(Nile)

#Ensure that the TS is stationery
library(forecast)
library(tseries)
plot(Nile)
ndiffs(Nile)
dNile = diff(Nile)
dNile
plot(dNile)
adf.test(dNile)

#Ensure a reasonable model(s) (possible values of p & q)
Acf(dNile)
Pacf(dNile)

#Fit the model
arima(ts, order=c(p,d,q))
      fit = arima(Nile, order=c(0,1,1))
      fit
accuracy(fit)
qqnorm(fit$residuals)
qqline(fit$residuals)
Box.test(fit$residuals,
         type='Ljung-Box')
#Once final model has been chosen, it can be used to make forecast of Future Values.
forecast(fit, 3)
plot(forecast(fit,3), xlab='Year', ylab='Annual Flow')

#eg
library(forecast)
str(sunspots)
head(sunspots)
fit = auto.arima(sunspots)
fit
forecast(fit,3)
accuracy(fit)



================================================
FILE: 77-TS/41d-arima-airp.R
================================================
# Time Series Arima ts2
# https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/
#library(forecast)
library(tseries)
adf.test(diff(log(AirPassengers)), alternative="stationary", k=0)
#series is stationary enough to do any kind of time series modelling.
#Next step is to find the right parameters to be used in the ARIMA model. 
#ACF Plots

acf(log(AirPassengers))
acf(diff(log(AirPassengers)))
pacf(diff(log(AirPassengers)))
(fit <- arima(log(AirPassengers), c(0, 1, 1),seasonal = list(order = c(0, 1, 1), period = 12)))
pred <- predict(fit, n.ahead = 10*12)
ts.plot(AirPassengers,2.718^pred$pred, log = "y", lty = c(1,3))



================================================
FILE: 77-TS/41d-arima-jj-nile.R
================================================
#Times Series Analysis 
# is the price of Johnson and Johnson shares change over time
# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year
# Can you forecast what future share prices will be and to what degree of accuracy

#dataset - Johnson
#Quarterly earnings per Johnson Shares
#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA

JohnsonJohnson
?JohnsonJohnson
library(forecast)

#ets auto select best predicton model
?ets
#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time

fit1 = ets(JohnsonJohnson)
fit1
#alpha - trend
#beta  = seasonal
#gamma  - irregular

JohnsonJohnson
head(JohnsonJohnson)
tail(JohnsonJohnson)

(f1= forecast(fit1,h=10))  # 
?forecast.ets
par(mfrow=c(1,1))
plot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3)  # linetype for forecast area
#shaded portion is confidence intervals area

par(mfrow=c(1,1))

# ARIMA Forecasting : 
#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.

#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg

f2 = auto.arima(JohnsonJohnson)
summary(f2)
tail(JohnsonJohnson)  # last few values
forecast(f2,h=5)

#Auto ARIMA on Nile DataSet

Nile
#Auto ARIMA
library(forecast)
#forecast::auto.arima()
fit4 = auto.arima(Nile)
fit4

forecast(fit4,h=5)
plot(forecast(fit4,h=5))
#forecasting is generally done for limited periods



================================================
FILE: 77-TS/45b-TS-arima.R
================================================
#Time Series - Arima ts4
#http://stat.ethz.ch/R-manual/R-devel/library/stats/html/arima.html

arima(lh, order = c(1,0,0))
arima(lh, order = c(3,0,0))
arima(lh, order = c(1,0,1))

arima(lh, order = c(3,0,0), method = "CSS")

arima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)))
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)),
      method = "CSS") # drops first 13 observations.
# for a model with as few years as this, we want full ML

arima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron) - 1920)

## presidents contains NAs
## graphs in example(acf) suggest order 1 or 3
require(graphics)
(fit1 <- arima(presidents, c(1, 0, 0)))
nobs(fit1)
tsdiag(fit1)
(fit3 <- arima(presidents, c(3, 0, 0)))  # smaller AIC
tsdiag(fit3)
BIC(fit1, fit3)
## compare a whole set of models; BIC() would choose the smallest
AIC(fit1, arima(presidents, c(2,0,0)),
    arima(presidents, c(2,0,1)), # <- chosen (barely) by AIC
    fit3, arima(presidents, c(3,0,1)))

## An example of ARIMA forecasting:
predict(fit3, 3)



================================================
FILE: 77-TS/52c-Case1-complete.R
================================================
#TS4 Time Series
#http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/zeitreihen/sommer03/ts_r_intro.pdf
tui <- read.csv("tui.csv", header=T, dec=",", sep=";")
tui  #dataset is stored as a matrix–object with the name tui
str(tui)
plot(tui[,5],type="l") # A chart with the closing values

plot(tui[,5], type="l",lwd=2, col="red", xlab="time", ylab="closing values",main="TUI AG", ylim=c(0,60) )

#plot differences of the logarithms of the returns.
plot(diff(log(tui[,5])),type="l")

# investigate distributional properties. A first
#step would be to draw a histogram and to compare it with e.g. the density of the ND

hist(diff(tui[,4]),prob=T,ylim=c(0,0.6),xlim=c(-5,5),col="red")
lines(density(diff(tui[,4])),lwd=2)

mu<-mean(diff(tui[,4]))
sigma<-sd(diff(tui[,4]))

x<-seq(-4,4,length=100)
y<-dnorm(x,mu,sigma)
lines(x,y,lwd=2,col="blue")

qqnorm(diff(tui[,4]))
abline(0,1)

x<-diff(log(tui[,5]))
ks.test(x,"pnorm",mean(x),sd(x))

shapiro.test(x)

# Linear Filtering of Time Series ------------------
# plot the closing values of the TUI shares and the averages in different colours
#library(ts) # NA for 3.3.2
library(timeSeries)
plot(tui[,5],type="l")
tui.1 <- filter(tui[,5],filter=rep(1/5,5))
tui.2 <- filter(tui[,5],filter=rep(1/25,25))
tui.3 <- filter(tui[,5],filter=rep(1/81,81))
lines(tui.1,col="red")
lines(tui.2,col="purple")
lines(tui.3,col="blue")

# Decomposition of Time Series ------------------
#http://astro.temple.edu/~buck/Econ616/Kernel/beer.csv




beer<-read.csv("beer.csv",header=T,dec=",",sep=";")
beer<-ts(beer[,1],start=1956,freq=12)
plot(stl(log(beer),s.window="periodic"))

#2.3 Regression analysis -------------------------------------------------
lbeer<-log(beer)
t<-seq(1956,1995.2,length=length(beer))
t2<-t^2
plot(lbeer)
lm(lbeer~t+t2)
lines(lm(lbeer~t+t2)$fit,col=2,lwd=2)

lbeer<-log(beer)
t<-seq(1956,1995.2,length=length(beer))
t2<-t^2
sin.t<-sin(2*pi*t)
cos.t<-cos(2*pi*t)
plot(lbeer)
lines(lm(lbeer~t+t2+sin.t+cos.t)$fit,col=4)

summary(lm(lbeer ~ t+t2+sin.t+cos.t))



# Exponential Smoothing --------------------------------------------------

beer <- read.csv("beer.csv",header=T,dec=",",sep=";")
beer<-ts(beer[,1],start=1956,freq=12)
HoltWinters(beer)
# This performs the Holt–Winters procedure on the beer – dataset. As result, it
#displays a list with e.g. the smoothing parameters (which should be α ≈ 0.076,β ≈ 0.07 and γ ≈ 0.145 in this case).

plot(beer)
lines(HoltWinters(beer)$fitted,col="red") # ERROR

beer.hw<-HoltWinters(beer)

predict(beer.hw,n.ahead=12)
plot(beer,xlim=c(1956,1999))
lines(predict(beer.hw,n.ahead=48),col=2)

# ARIMA–Models-------------------
#Analysis of Autocorrelations and Partial Autocorrelations ----------------------

sim.ar<-arima.sim(list(ar=c(0.4,0.4)),n=1000)
sim.ma<-arima.sim(list(ma=c(0.6,-0.4)),n=1000)
par(mfrow=c(2,2))
acf(sim.ar,main="ACF of AR(2) process")
acf(sim.ma,main="ACF of MA(2) process")
pacf(sim.ar,main="PACF of AR(2) process")
pacf(sim.ma,main="PACF of MA(2) process")

#arima(data,order=c(p,d,q))

data(LakeHuron)
fit<-arima(LakeHuron,order=c(1,0,1))
fit<-arima(LakeHuron,order=c(1,0,1))
tsdiag(fit) # NOT VISIBLE

Box.test(fit$residuals,lag=1)

# Prediction
fit<-arima(LakeHuron,order=c(1,0,1))
LH.pred<-predict(fit,n.ahead=8)

par(mfrow=c(1,1))
plot(LakeHuron,xlim=c(1875,1980),ylim=c(575,584))
LH.pred<-predict(fit,n.ahead=8)
lines(LH.pred$pred,col="red")
lines(LH.pred$pred+2*LH.pred$se,col="red",lty=3)
lines(LH.pred$pred-2*LH.pred$se,col="red",lty=3)



================================================
FILE: 77-TS/53b-sales-ts.R
================================================
#-----------------------------------------Time Series Forecasting---------------------------------------#



#Analytical Problem: To forecast the Sales (in thoushand units) of a Automobile company for the next 36 months

#----------------------------------Preparing the environment--------------------------------------------#

list.of.packages <- c("forecast", "ggplot2","MASS","caTools","sqldf","tseries")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages, repos="http://cran.rstudio.com/")
library(forecast)
library(tseries)

#-----------------------------Setting the working directory-------------------------------------------#

Path<-"C:/Ganesha_Accenture/Ganesha_IVY/R/Gan_B01/03Data"
setwd(Path)
getwd()

data<-read.csv("./data/Sales.csv",header = TRUE)
#data = read.csv(file.choose())
head(data)
TSdata=data#To create a backup of original data


#-------Exploring the data-------------------#
head(TSdata)#
dim(TSdata)#We have 144 time series points (at a date level) and 2 vars(Date, Sales)
str(TSdata)
summary(TSdata)
colSums(is.na(TSdata))
names(TSdata)[c(1:2)]=c("Date","Sales")


#--Transformation of the date data into time series----------#

TSdata=ts(TSdata[,2],start=c(2003,1),frequency=12)
start(TSdata)
end(TSdata)
frequency(TSdata)
StructTS(TSdata)
TSdata

#2003,1 is the start date and 12 is the frequency of the time series (monthly series)
str(TSdata)


#--------------------->plotting the sales 
plot(TSdata,ylab="Sales", xlab="Year",main="Sales between 2003-2014",col="grey")
abline(reg = lm(TSdata~time(TSdata)))
cycle(TSdata)
plot(aggregate(TSdata,FUN=mean))
#This plot displays the year on year trend in the sales from 2003 

#Data has both a trend and drift, i.e. time invariant mean and variance 



#--------------->Differencing the data to remove trend and drift


plot(log10(TSdata),ylab="log(Sales)",xlab="Year",main="log(Sales) between 2003-2014",col="grey")
##Differencing the data to remove trend
plot(diff(TSdata,differences = 1),ylab="Diff(Sales)",xlab="Year",main="Diff(Sales) between 2003-2014",col="grey")
#The differenced data continues to have unequal variance 


#--------------->Differencing+Log transformation the data to remove trend and unequal variance


plot(diff(log10(TSdata),differences = 1),ylab="Diff(Sales)",xlab="Year",main="Diff(Log(Sales)) between 2003-2014",col="grey")

#So, with Log10 and 1 order of differencing makes the series stationary

#----------------->Checking the stationarity of transformed data using the Augmented Dickey-Fuller Test(ADF)

LDTSdata=diff(log10(TSdata),differences = 1)
require(forecast)
adf.test(LDTSdata,alternative="stationary")

#Since, the p-value <0.05, hence, we reject the Ho: Series is Non-Stationary 

kpss.test(LDTSdata)
#creating the ACF and PACF plot
par(mfrow=c(1,2))
acf(diff(log10(TSdata)),main="ACF plot")#ACF PLOT -- Moving Average or q
pacf(diff(log10(TSdata)),main="PACF plot")#PACF PLOT -- Auto Regressive or p
#Running the ARIMA model
ARIMAFit=arima((log10(TSdata)),c(1,1,0))


summary(ARIMAFit)
#Running the ARIMA model-R, gives the best model fit 
require(forecast)
ARIMAFit1=auto.arima(log10(TSdata),approximation=TRUE,trace=TRUE)
?auto.arima
?arima
summary(ARIMAFit1)
ARIMAFit1$residuals
#Predicting the future values
pred=predict(ARIMAFit1,n.ahead=36)
pred

#n.ahead is the no. of time series, we want to predict

##########################################3
##Ploting the observed data and forecasted data together
par(mfrow=c(1,1))
plot(TSdata,type="l",xlim=c(2004,2018),ylim=c(1,1600),xlab="Year",ylab="Sales")
lines(10^(pred$pred),col="red")

###############################################
#plotting the +-2 standard error to range of expected error
plot(TSdata,type="l",xlim=c(2004,2018),ylim=c(1,1600),xlab = "Year",ylab = "Sales")
lines(10^(pred$pred),col="red")
lines(10^(pred$pred+2*pred$se),col="blue")
lines(10^(pred$pred-2*pred$se),col="black")
## then forecast the result
pred = predict(ARIMAFit1, n.ahead = 36)
write.csv(pred,"predict.csv")

## then do the exponential since you had used log earlier.
normal_result=10^pred$pred ## you get the desired result. 
write.csv(normal_result,"finalpredict.csv", row.names = FALSE)
plot(normal_result)

#-------------------------------End of the model-----------------------------------#


================================================
FILE: 77-TS/55b-ts-case-xxx2.R
================================================
# Aggregating TS

data = read.csv(file.choose())
str(data)
head(data)
names(data)
nrow(data)
df= data

df$timestamp2 = as.POSIXct(df$timestamp ,origin="1970-01-01")
head(df)

library(zoo)
library(xts)
ts1 = xts(df[1:3], order.by=df$timestamp2)
class(ts1)

ts1

# use dplyr
library(dplyr)
hourly_summary <- df %>% mutate(hour = as.Date(timestamp2, format="%Y-%m-%d %H")) %>% group_by(house_id, household_id) %>% summarise(hoursum=sum(value),hourlsd = sd(value), hourmean=mean(value))

hourly_summary
class(hourly_summary)
df %>% group_by(house_id, household_id, as.Date(timestamp2, format="%Y-%m-%d") )
aggregate(value ~ house_id + household_id, data=df, FUN=sum)

Data$hour <- as.POSIXlt(dates)$hour  # extract hour of the day



================================================
FILE: 77-TS/55c-ts-case-xxxx.R
================================================
#Analytics Vidya Case
#06 Jul 18


summary(rollmean2)
#ts range : 1377986400 1380578340
rawdata = expand.grid(c(0,1), c(0,1,2), c(1377986400:1380578340))
nrow(rawdata)
value1 = runif(15551646,10,50)
head(value1)

rollmean1 = rollmean(value1,k=60, fill=NA, align='right')
rollsum1 = rollsum(value1,k=60, fill=NA, align='right')
rollsd # not avl
library(TTR)
rollsd1 = runSD(value1,n = 60, sample = FALSE, cumulative = FALSE) 
du1 = cbind(rawdata, value1, rollmean1, rollsd1, rollmean1 + rollsd1)
du2 = cbind(rawdata, value1, rollsd1)
head(du2,n=62)
head(du2)
names(du1) = c('house_id','household_id', 'timestamp', 'value', 'rollmean', 'rollsd', 'checkvalue')
names(du2) = c('house_id','household_id', 'timestamp', 'value','rollsd')

head(du2)
du1$alert = ifelse(du1$value > du1$checkvalue,1,0)
head(du1, n=70)


#Time Series Data
du2ts = xts(du2, order.by= as.POSIXct(du2$timestamp ,origin="1970-01-01"))
du2ts
endpoints(du1ts, on = 'minute') 
endpoints(du1ts, on = 'minute', k=2) 
# Calculate the minute endpoints
ep <- endpoints(du1ts, on = "minute")

# Now calculate the weekly mean and display the results
period.apply(du1[, "value"], INDEX = ep, FUN = mean)
period.apply(du1[, "value"], INDEX = ep, FUN = sd)

rollapply(du1$value, width=60, FUN = sd)
periodicity(du1ts)

du1
#build decision tree
library(rpart)
library(rpart.plot)

================================================
FILE: 77-TS/SMA-nile.R
================================================
# TS - Simple Moving Average
#SMA
# technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends


Nile  # river flow from year 1871 to 1970

plot(Nile)

library(forecast)  # for forecasting
opar = par(no.readonly = T)  # save current parameters
par(mfrow=c(2,2))
(ylim = range(Nile))
plot(Nile, main='Original TS')
head(Nile)
ma(Nile,3)
head(Nile,4)
head(ma(Nile,3))
(1120+1160+963)/3
mean(Nile[1:3]);mean(Nile[2:4]); mean(Nile[3:5]); mean(Nile[4:6])
ma(Nile,3)[1:10]

plot(ma(Nile,3), main='SMA k=3', ylim=ylim)
plot(ma(Nile,7), main='SMA k=7', ylim=ylim)
plot(ma(Nile,15),main='SMA k=15', ylim=ylim)
par(opar)
plot(ma(Nile,3), main='SMA k=3', ylim=ylim)




#mfrow mfcol
mtcars
names(mtcars)
plot(mtcars$mpg)
par(mfrow=c(1,2))
plot(mtcars$mpg)
plot(mtcars$disp)

par(mfrow=c(2,1))
plot(mtcars$mpg)
plot(mtcars$disp)

par(mfrow=c(2,2))
plot(mtcars$mpg)
barplot(table(mtcars$cyl))
pie(table(mtcars$am))
hist(mtcars$disp)


================================================
FILE: 77-TS/TS-P-fpp.R
================================================
#House Sales
3https://www.otexts.org/fpp/6/1
library(fpp)
hsales
data(package='fpp')
par(mfrow=c(2,2))
plot(hsales,xlab="Year",ylab="Monthly housing sales (millions)")
plot(ustreas,xlab="Day",ylab="US treasury bill contracts")
plot(elec,xlab="Year",ylab="Australian monthly electricity production")
plot(diff(dj),xlab="Day",ylab="Daily change in Dow Jones index")
par(mfrow=c(1,1))
fit <- stl(elecequip, s.window=5)
plot(elecequip, col="gray",
     main="Electrical equipment manufacturing",
     ylab="New orders index", xlab="")
lines(fit$time.series[,2],col="red",ylab="Trend")

plot(fit)

monthplot(fit$time.series[,"seasonal"], main="", ylab="Seasonal")

plot(elecequip, col="grey",
     main="Electrical equipment manufacturing",
     xlab="", ylab="New orders index")
lines(seasadj(fit),col="red",ylab="Seasonally adjusted")
ma(elecsales, order=5)
plot(elecsales, main="Residential electricity sales",
     ylab="GWh", xlab="Year")
lines(ma(elecsales,5),col="red")


beer2 <- window(ausbeer,start=1992)
ma4 <- ma(beer2, order=4, centre=FALSE)
ma2x4 <- ma(beer2, order=4, centre=TRUE)

plot(elecequip, ylab="New orders index", col="gray",
     main="Electrical equipment manufacturing (Euro area)")
lines(ma(elecequip, order=12), col="red")

fit <- stl(elecequip, t.window=15, s.window="periodic", robust=TRUE)
plot(fit)


================================================
FILE: 77-TS/TS-c02.R
================================================
#Time Series with CO2 data
#https://www.datacamp.com/community/tutorials/time-series-r

#R packages to be used
library(forecast)
library(TSA)
#ARIMA(p,d,q) process : Autoregressive Integrated Moving Average Models are time series defined by the equation: SARIMA(p,d,q)(P,D,Q) process

# Data from TSA package
data("co2")
#A time series of 468 observations; monthly from 1959 to 1997.
head(co2)
str(co2)
# fitting
fit <- auto.arima(co2)

# Time series plot
plot(fc <- forecast(fit, h = 15))

#%%%%----------------------
#Boardings
data("boardings")
?boardings
# fitting
fit2 <- auto.arima(boardings[,"log.price"])


# forecasting
plot(fc2 <- forecast(fit2, h = 15))

#%%%---------------------------
data('co2', package='datasets')
co2
head(co)
str(co2)
468 * 12


================================================
FILE: 77-TS/TS-data-DU1.R
================================================
# Time Series

# Create Time Series data of Finance Sales
sales = c(18, 33, 41, 7, 34, 35, 24, 25, 24, 21, 25, 20,
          22, 31, 40, 29, 25, 21, 22, 54, 31, 25, 26, 35)
class(sales)
str(sales)
length(sales)
sales ; class(sales)  # vector of sales values

#put in TS without any dates : format of TS
?ts  # help

#ts(data = NA, start = 1, end = numeric(), frequency = 1)
#start - time of 1st obsv - single no / vector of 2 integers
#frequency - no of obvsv per unit of time
#No of obsv per unit time
(tsales2a = ts(sales, start=1, end=12)) #only first 12 considered

(ts(sales, start=1, end=12, freq=1)) #only first 12 considered
(ts(sales, start=1, end=12, freq=2)) #

(ts(sales, start=1, end=12, freq=3)) #
(ts(sales, start=1, end=12, freq=4)) # Qtrly series
(ts(sales, start=1, end=12, freq=5)) #
(ts(sales, start=1, end=12, freq=6)) #

(ts(sales, start=1, end=12, freq=12)) #  12 months * 12 years


(tsales2b = ts(sales, start=1, end=6)) #only first 6
(tsales2c = ts(sales, start=1, end=24)) #all 24 freq=1
(tsales2d = ts(sales, start=1, end=12, frequency = 1))
(tsales2e = ts(sales, start=1, end=12, frequency = 2)) # 1 to 12 : 1 unit divided into 2 : so 24 observations

#Specify data periods in start and end
#specify only start dates & freq
length(sales)

(tsales3a = ts(sales, start=c(2017, 1), frequency=12)) # years 2017 to 2018, 24 values, monthwise 24 observations spread to 2 years
sales2 = c(sales, sales)
length(sales2)
(tsales3a = ts(sales2, start=c(2017, 1), frequency=12)) # years 2017 to 2018, 

(tsales3a = ts(sales2, start=c(2017, 1), frequency=1)) # years 2017 to 2018, 


admission = c(1000, 900, 1200, 3000)
ts1= ts(admission, start=c(2016), end=c(2018), frequency=1)
ts1
plot(ts1)
admission = c(1000, 900, 1200, 3000)
ts2= ts(admission, start=c(2016,7), end=c(2016,10), frequency=12)
ts2
plot(ts1)


ts(sales, start=c(2017, 4), end=c(2018,3), frequency=12)
ts(sales, start=c(2017, 2), frequency=12)  # different start month
ts(sales, start=c(2017, 2), frequency=4)  # different start month
plot(ts(sales, start=c(2017,1), frequency=1))  # different start month



?ts
#Specify start and end date periods
(tsales3b = ts(sales, start=c(2016,4), end=12) ) #error start and end to be of same format
(tsales3b = ts(sales, start=c(2016), end=c(2018))) # default freq=1 once a year 1st 3 values becomes year sales value
# R understands that 2016 is year

(tsales3c = ts(sales, start=c(2016,4), end=c(2018,4), frequency=12)) # period diveded into 24 equal intervals
tsales3c
plot(tsales3c)

# Daily Sales Data
sales2 = ceiling(runif(365, 50,100))
sales2
(dailyTSdata = ts(sales2, start=c(2017,1), frequency=365))
plot(dailyTSdata)
start(dailyTSdata)
end(dailyTSdata)

#Weekly Sales Data  52 weeks in a year
sales3 = ceiling(runif(52, 50,100))
(weeklyTSdata = ts(sales3, start=c(2017,3), frequency=52))
weeklyTSdata
plot(weeklyTSdata)
start(weeklyTSdata); end(weeklyTSdata) ;frequency(weeklyTSdata)

?ts
#Yearly Sales Data : 2000 to 2017 
seq(1,10,2)
?seq
seq(1,20, along.with = c(1:5))
seq(1,20, along.with = sales)  # 24 values

(values= trunc(seq(1,20, along.with = c(2000:2017))))  # take length from 2000 to 2017

(sales4 = floor(rnorm(values, mean=50, sd=10)) ) #pick up length from values

(yearlyTSdata = ts(sales4, start=c(2000), end=c(2017)))
plot(yearlyTSdata)

start(yearlyTSdata); end(yearlyTSdata) ;frequency(yearlyTSdata)

#diff and lag & window in ts
tsales3c
tsales = tsales3c
tsales
range(tsales)
diff(tsales)
diff(tsales,lag=1)
diff(tsales,lag=2)
diff(tsales,lag=6)

cycle(tsales)

tsales
tsales
#subset Time Series with range of dates
(tsales.subset = window(tsales, start=c(2003, 5), end=c(2004, 6))) #error due to range period incorrect

tsales
(tsales.subset = window(tsales, start=c(2016, 5), end=c(2017, 8))) 
#5th month of 2016 to 8th month of 2016

#Another TS with different freq


#dataset---- 
#available for TS Analysis - understand them, they are used for TS analysis
AirPassengers  #passengers travel data - seasonal data
?AirPassengers
plot(AirPassengers)
class(AirPassengers)
JohnsonJohnson  #stock price prediction
nhtemp  # temp variations
Nile  # river level variations
sunspots  #Temp of sun spots variations
ds = list(AirPassengers,JohnsonJohnson,nhtemp,Nile,sunspots)
sapply(ds, class)  # check type of class of each dataset


#end here - do time series analysis - SMA / EXP/ ARIMA

# Move to doing TS analysis - 
# other packages zoo, xts






================================================
FILE: 77-TS/TS-fpp-seasonplot.R
================================================
# Time Series 6


# Section 5 : fpp package -------------------------------------------------
library(fpp)
plot(a10)
# Intro https://www.otexts.org/fpp/using-r

#Using the Workspace menu, choose "Import Dataset" and import the data from the tute1.csv file.
tute1 = read.csv('tute1.csv')
head(tute1)
tail(tute1)
tute1[,2]
tute1[,"Sales"]
tute1[5,]
tute1[1:10,3:4]
tute1[1:20,]
# Data into TS
tute1 <- ts(tute1[,-1], start=1981, frequency=4)
plot(tute1)

# Plot
seasonplot(tute1[,"Sales"])
seasonplot(tute1[,"AdBudget"])
seasonplot(tute1[,"GDP"])
monthplot(tute1[,"Sales"])
monthplot(tute1[,"GDP"])
monthplot(tute1[,"AdBudget"])

#
plot(Sales ~ AdBudget, data=tute1)
plot(Sales ~ GDP, data=tute1)

#
pairs(as.data.frame(tute1))
#
summary(tute1)
#
hist(tute1[,"Sales"])
hist(tute1[,"AdBudget"])
boxplot(tute1[,"Sales"])
boxplot(as.data.frame(tute1))
#
cor.test(tute1[,"Sales"],tute1[,"AdBudget"])


#https://www.otexts.org/fpp/6/1
par(mfrow=c(2,2))
str(hsales) ;head(hsales)
plot(hsales,xlab="Year",ylab="Monthly housing sales (millions)")
str(ustreas)
plot(ustreas,xlab="Day",ylab="US treasury bill contracts")
str(elec)
plot(elec,xlab="Year",ylab="Australian monthly electricity production")
str(dj)
plot(diff(dj),xlab="Day",ylab="Daily change in Dow Jones index")

# Electrical Components
fit <- stl(elecequip, s.window=5)
plot(elecequip, col="gray",
     main="Electrical equipment manufacturing",
     ylab="New orders index", xlab="")
lines(fit$time.series[,2],col="red",ylab="Trend")
plot(fit)
#
monthplot(fit$time.series[,"seasonal"], main="", ylab="Seasonal")

# Seasonally Adjusted Data seasonal component is removed from the original data,
plot(elecequip, col="grey",
     main="Electrical equipment manufacturing",
     xlab="", ylab="New orders index")
lines(seasadj(fit),col="red",ylab="Seasonally adjusted")

# Moving Average
ma(elecsales, order=5)

# Moving Averages of Moving Averages
# apply a moving average to a moving average. One reason for doing this is to make an even-order moving average symmetric.
str(ausbeer)
beer2 <- window(ausbeer,start=1992)
ma4 <- ma(beer2, order=4, centre=FALSE)
ma4
ma2x4 <- ma(beer2, order=4, centre=TRUE)
ma2x4

# Estimating the trend-cycle with seasonal data
plot(elecequip, ylab="New orders index", col="gray",
     main="Electrical equipment manufacturing (Euro area)")
lines(ma(elecequip, order=12), col="red")

# Classication Decomposition
# x is the time series
#https://www.otexts.org/fpp/6/3


# STL Decomposition
fit <- stl(elecequip, t.window=15, s.window="periodic", robust=TRUE)
plot(fit)

x = data1
x
fit <- decompose(x, type="multiplicative")
plot(fit)

# Forecasting with Decomposition
fit <- stl(elecequip, t.window=15, s.window="periodic", robust=TRUE)
eeadj <- seasadj(fit)
plot(naive(eeadj), xlab="New orders index",
     main="Naive forecasts of seasonally adjusted data")

fcast <- forecast(fit, method="naive")
plot(fcast, ylab="New orders index")

# Exercise
#https://www.otexts.org/fpp/6/7

================================================
FILE: 77-TS/TS-kings.R
================================================
# Time Series ts3
#http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html
kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
#Read 42 items
kings
#To store the data in a time series object, we use the ts() function in R.

kingstimeseries <- ts(kings)
kingstimeseries
# Sometimes the time series data set that you have may have been collected at regular intervals that were less than one year, 
#for example, monthly or quarterly. In this case, you can specify the number of times that data was collected per year by using 
#the ‘frequency’ parameter in the ts() function. For monthly time series data, you set frequency=12, while for quarterly time series data, 
#you set frequency=4.

#You can also specify the first year that the data was collected, and the first interval in that year by using the ‘start’ parameter 
#in the ts() function. For example, if the first data point corresponds to the second quarter of 1986, you would set start=c(1986,2).

plot.ts(kingstimeseries)



# Sec2 --------------------------------------------------------------------
#example is a data set of the number of births per month in New York city, from January 1946 to December 1959

births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")
#Read 168 items
birthstimeseries <- ts(births, frequency=12, start=c(1946,1))
birthstimeseries
plot.ts(birthstimeseries)

# Sec3 --------------------------------------------------------------------

souvenir <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
#Read 84 items
souvenirtimeseries <- ts(souvenir, frequency=12, start=c(1987,1))
souvenirtimeseries
plot.ts(souvenirtimeseries)
#In this case, it appears that an additive model is not appropriate for describing this time series, since the size of the seasonal fluctuations and random fluctuations seem to increase with the level of the time series. Thus, we may need to transform the time series in order to get a transformed time series that can be described using an additive model. For example, we can transform the time series by calculating the natural log of the original data:
logsouvenirtimeseries <- log(souvenirtimeseries)
plot.ts(logsouvenirtimeseries)
# size of the seasonal fluctuations and random fluctuations in the log-transformed time series seem to be roughly constant over time, and do not depend on the level of the time series. Thus, the log-transformed time series can probably be described using an additive model.



# Decomposing Time Series -------------------------------------------------

# Decomposing a time series means separating it into its constituent components, which are usually a trend component and an irregular component, and if it is a seasonal time series, a seasonal component.
# http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html#decomposing-time-series
library("TTR")
kingstimeseriesSMA3 <- SMA(kingstimeseries,n=3)
plot.ts(kingstimeseries)
plot.ts(kingstimeseriesSMA3)

kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8)
plot.ts(kingstimeseriesSMA8)
forecast(kingstimeseriesSMA8,3)
plot(forecast(kingstimeseriesSMA8,3))

birthstimeseriescomponents <- decompose(birthstimeseries)
birthstimeseriescomponents$seasonal # get the estimated values of the seasonal component
plot(birthstimeseriescomponents)

# Seasonal Adjusting
birthstimeseriescomponents <- decompose(birthstimeseries)
birthstimeseriesseasonallyadjusted <- birthstimeseries - birthstimeseriescomponents$seasonal

plot(birthstimeseriesseasonallyadjusted)


# Forecasts using Exponential Smoothing - make short-term forecasts for time series data.----------------------------------

rain <- scan("http://robjhyndman.com/tsdldata/hurst/precip1.dat",skip=1)
#Read 100 items
rainseries <- ts(rain,start=c(1813))
plot.ts(rainseries)

rainseriesforecasts <- HoltWinters(rainseries, beta=FALSE, gamma=FALSE)
rainseriesforecasts
rainseriesforecasts$fitted
plot(rainseriesforecasts)
rainseriesforecasts$SSE
HoltWinters(rainseries, beta=FALSE, gamma=FALSE, l.start=23.56)

library("forecast")
#error in code
rainseriesforecasts2 <- forecast.HoltWinters(rainseriesforecasts, h=8)
rainseriesforecasts2
plot.forecast(rainseriesforecasts2)
acf(rainseriesforecasts2$residuals, lag.max=20)
#Box.test(rainseriesforecasts2$residuals, lag=20, type="Ljung-Box")
plot.ts(rainseriesforecasts2$residuals)



================================================
FILE: 77-TS/TS-links
================================================
# Time Series 

Links

https://www.analyticsvidhya.com/blog/2018/08/auto-arima-time-series-modeling-python-r/



================================================
FILE: 77-TS/TS-movag1.R
================================================
# TS Moving Averages 
# Daily to Month to Yearly Averages
# Section 1 ---------------------------------------------------------------


(x = 1:20)
# 3 Year and Past Data
?filter  #Linear Filtering of TS
mav <- function(x,n=3){filter(x,rep(1/n,n), sides=1)}
mav(x)
# First 2 values are NA (Previous Data)
# 5 Year and Past Data
mav <- function(x,n=5){filter(x,rep(1/n,n), sides=1)}
mav(x)
# First 4 valueas are NA
# 3 Year and Past & Present Data : Centered Sides=2
mav <- function(x,n=3){filter(x,rep(1/n,n), sides=2)}
mav(x)
# 1st and Last values are NA

# 5 Year and Past & Present Data : Centered Sides=2
mav <- function(x,n=5){filter(x,rep(1/n,n), sides=2)}
mav(x)
# 1st,2nd and Last 2 values are NA

#https://druedin.com/2012/08/11/moving-averages-in-r/

# Price of milk over consective 6 years
data1 <- c(30,32,42,50,60,90)
plot(data1, type="l", col="blue")
text(1:5, data1, data1, pos=2, col="blue")
# draw line of 3 period moving average Past Periods forecast
mav2 <- function(x,n=3){filter(x,rep(1/n,n), sides=1)}
mav2(data1)
lines(mav2(data1), type="b", col="red")
text(1:5, mav2(data1,3), round(mav2(data1,3),2), pos=3, col="red")

# if NA values
na.omit(mav(c(4,5,4,6,NA,4), 3))

# Section2 ----------------------------------------------------------------
# Using ZOO package
library(zoo)
rollmean(data1,3)


# Section 3 ---------------------------------------------------------------

library(forecast)
?ma
# 3 year moving average
ma(data1,order=3)
plot(data1)
lines(ma(data1,order=3),col='red')
text(data1)



# Section 4 TTR -----------------------------------------------------------
library(TTR)
?MovingAverages
#Simple Moving average for past 3 observations
SMA(data1,3)
data(ttrc)
str(ttrc); dim(ttrc)
head(ttrc)
#SMA over 20month 
sma.20 = SMA(ttrc[,'Close'],20)
sma.20


# Section 5 hydroTSM ------------------------------------------------------
#https://www.rforge.net/doc/packages/hydroTSM/ma.html

library(hydroTSM)
ma(x,3)
# Loading daily streamflows at the station Oca en Ona (Ebro River basin, Spain) ##
data(OcaEnOnaQts)
x <- OcaEnOnaQts

## Daily to Monthly ts
m <- daily2monthly(x, FUN=mean, na.rm=FALSE)
x
m
# Plotting the monthly values
plot(m, xlab="Time")

## Plotting the annual moving average in station 'x'
lines(ma(m, win.len=12), col="blue")



================================================
FILE: 77-TS/TS-nile.R
================================================
# TS Nile
# 
data("Nile")
str(Nile)
Nile
# Plot the Nile data
plot(Nile)
?plot
# Plot the Nile data with xlab and ylab arguments
plot(Nile, xlab = "Year", ylab = "River Volume", type="b", 
      main = "Annual River Nile Volume at Aswan, 1871-1970")
# Plot the Nile data with xlab, ylab, main, and type arguments

#Dataset : Nile
#Time-Series [1:100] from 1871 to 1970:  1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#Time Series: Start = 1871 End = 1970 Frequency = 1 [1] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110
Nile
length(Nile)
Nile[29]
Nile[51:58]
Nile[c(1,98,100)]
which(Nile > 1000)
which(Nile > 1000) + 1870
hist(Nile)
plot(Nile)
#Other Examples on Nile


#Package : Forecast
library(forecast)
opar = par(no.readonly=T)
par(mfrow=c(2,2))
#par(opar)
#Nile Dataset
ylim = c(min(Nile), max(Nile))
plot(Nile, main='Raw time series')
abline(v=c(1892,1900, 1941,1961))
plot(ma(Nile, 3), main='Simple Moving Averages (k=3)', ylim=ylim)
plot(ma(Nile, 7), main='Simple Moving Averages (k=7)', ylim=ylim)
plot(ma(Nile, 15), main='Simple Moving Averages (k=15)', ylim=ylim)

par(opar)

?stl
str(AirPassengers)
AirPassengers
plot(AirPassengers)
AirPassengers_log = log(AirPassengers)
plot( AirPassengers_log, ylab="log(AirPassengers)" )
fit = stl(AirPassengers_log, s.window = 'period')
plot(fit)

fit$time.series
exp(fit$time.series)

par(mfrow=c(1,1))
library(forecast)
monthplot(AirPassengers,xlab="", ylab="")
seasonplot(AirPassengers, year.labels = 'TRUE', main='')


#Eg1a : Dataset - nhtemp
#Mean annual temp in degree F in a city from 1912 to 1971
#No obvious trend, lack seasonal component
library(forecast)
fit = ets(nhtemp, model = 'ANN')
fit

forecast(fit,1)

plot(forecast(fit,1), xlab='Year', ylab=expression(paste("Temp (", degree * F,")" ,)), main=" New Haven Annual Mean Temp")
accuracy(fit)

#Eg1b
#Decompose a TS describing monthly totals (in log 1000) of passengers into additive - trend, seasonal, irregular components ; Predict future travel ; Log values to used
library(forecast)
fit = ets(log(AirPassengers), model='AAA')
fit
accuracy(fit)
pred = forecast(fit, 5)
pred

plot(pred, main ='Forecast for Air Travel', ylab='Log(AirPassengers)', xlab='Time')

pred$mean
pred$mean = exp(pred$mean)
pred$mean

pred$lower = exp(pred$lower)
pred$upper = exp(pred$upper)
p = cbind(pred$mean, pred$lower, pred$upper)
dimnames(p)[[2]] = c("mean", "Lo 80", "Lo 95","Hi 80",  "Hi 95")
p


#Eg1d @15.2.3
library(forecast)
fit =ets(JohnsonJohnson)
fit
plot(forecast(fit), main="Johnson & Johnson Forecasts", ylab="Quarterly Earnings in Dolloars", xlab="TIme", flty=2)

     

================================================
FILE: 77-TS/TS-xts.R
================================================
# Time Series - xts


================================================
FILE: 77-TS/TS-zoo.R
================================================
# Zoo package
#Eg1
library(zoo)
library(lattice)
# create test data
z <- zooreg(1:83, start = as.Date("2009-04-01"), deltat = 7)
head(z)
xyplot(z)


# Time series plots with good X axis labels
library(zoo)
# data
today = Sys.Date()
dates = as.Date((today-500):today)
head(dates)
z = zoo (100+cumsum(rnorm(501)), dates)
cumsum(rnorm(501))
head(z)

# method1 : default X axis labels do not look good
?plot.zoo
plot(z)
?plot.ts
plot(ts(z))

# method 2 : Lattice
library(lattice)
?xyplot.zoo
xyplot(z)
xyplot(z, lwd=2, col="tomato")

# method 3 : XTS
library(xts)
?plot.xts
plot(as.xts(z))
plot(as.xts(z), auto.grid=F, major.format="%b %y", las=2)

# method 4 : Base graph
timeline = time(z)
summary(timeline)
index = seq(from=1, to=length(timeline), 90) # put ticks every 90 days
index
plot(z, xaxt="n")
axis(side=1, at=timeline[index], label=format(timeline[index], "%b %y"), cex.axis=0.8)

# method 5 : ggplot
library(ggplot2)
library(scales)
?date_breaks
df = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))
head(df)
# default plot
ggplot(df, aes(x=date, y=value)) + geom_line()
# formatted
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"))
# custom breaks
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"), breaks=date_breaks("3 months"))



xx.Date <- as.Date((Sys.Date()-500):Sys.Date())
x <- zoo(cumsum(rnorm(501)), xx.Date)
tt=time(x)
plot(x, xaxt ="n")
tt <- time(x)
ix <- seq(1, length(tt), by=60) #every 60 days
fmt <- "%b-%d" # format for axis labels
labs <- format(tt[ix], fmt)
axis(side = 1, at = tt[ix], labels = labs,  cex.axis = 0.7)

#Eg: Set 2
#Eg1
x.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = "-"))
head(x.Date)
x <- zoo(rnorm(24), x.Date)
head(x)
plot(x)
#Eg2
plot(x, xaxt = "n")
axis(1, at = time(x), labels = FALSE)
time(x)
#Eg3
plot(x)
axis(1, at = time(x), labels = FALSE)
#Eg4
plot(x)
times <- time(x)
ticks <- seq(times[1], times[length(times)], by = "weeks")
axis(1, at = ticks, labels = FALSE, tcl = -0.3)


================================================
FILE: 77-TS/UDFdates.R
================================================
# date sequence as required
#https://cran.r-project.org/web/packages/bizdays/bizdays.pdf
#user defined

library(lubridate)
library(bizdays)
#calendar year
from1 = as.Date('2019-01-01') ; to1 = as.Date('2019-12-31')

#all days
d1 = seq(from1, to1, by="1 day")
d1
length(d1)
weekdays(d1)

#only working days
d1 <- seq(from1, to1, by="1 day")
wkd1 <- d1[ ! weekdays(d1) %in% c('Saturday','Sunday') ]
wkd1
length(wkd1)
weekdays(wkd1)
table(weekdays(wkd1))

#------------------------
#last day of week
wkd1= seq(from2, to1, by="1 week")
wkd1
weekdays(wkd1)

#month- last date
m1 = seq(from1, to1, by="1 month")
m1
ceiling_date(m1, "month") - days(1)
weekdays(ceiling_date(m1, "month") - days(1))

#quarter - last date
q1 = seq(from1, to1, by="1 quarter")
q1

diff(q1)
interval(dmy(01112018), today()) %/% months(1) #months between a date and today

#half year - last date
from1; to1
h1 = seq(from1, to1 , by="6 month")
h1

#year 
y1 = seq(from1, to1, by="year")
y1

# all dates
d1
wd1
wd1
m1
q1
h1
y1

D1 <- expand.grid(c('D'), d1)
WD1 <- expand.grid(c('WD'), wd1)
M1 <- expand.grid(c('M'), m1)
Q1 <- expand.grid(c('Q'), q1)
H1 <- expand.grid(c('H'), h1)
Y1 <- expand.grid(c('H'), y1)
rbind(D1, WD1, M1, Q1, H1, Y1)

#------
?difftime
difftime(time1=strptime("06-11-2019", format = "%d-%m-%Y"), time2=strptime("01-11-2018", format = "%d-%m-%Y") ,units="weeks")
#months
(zoo::as.yearmon(x=strptime("06-11-2019", format = "%d-%m-%Y")) - zoo::as.yearmon(x=strptime("01-11-2018", format = "%d-%m-%Y")))  * 12


# quarters
(zoo::as.yearqtr(x=strptime("06-11-2019", format = "%d-%m-%Y")) - zoo::as.yearqtr(x=strptime("01-11-2018", format = "%d-%m-%Y")))  * 4

# years
(lubridate::year(x=strptime("06-11-2019", format = "%d-%m-%Y")) - lubridate::year(x=strptime("01-11-2018", format = "%d-%m-%Y"))) 

#age--------------
age <- function(dob, age.day = today(), units = "years", floor = TRUE) {
  calc.age = interval(dob, age.day) / duration(num = 1, units = units)
  if (floor) return(as.integer(floor(calc.age)))
  return(calc.age)
}

my.dob = as.Date('1967-06-23')
age(my.dob)
age(my.dob, floor = FALSE)
age(my.dob, units = "minutes")
seq(my.dob, length.out = 6, by = "years") #after year gap
age(seq(my.dob, length.out = 6, by = "years"))


#------------
(dates <- c(dmy('01.11.2018'),dmy('05.11.2019')))
span <- dates[1] %--% dates[2] #creating an interval object

#creating period objects 
as.period(span, unit = 'year') 
as.period(span, unit = 'month')
as.period(span, unit = 'day')
#Periods do not accept weeks as units. But you can convert durations to weeks:
as.duration(span)/ dweeks(1)



#https://rdrr.io/cran/lubridate/man/round_date.html
#https://davidcarslaw.github.io/openair/reference/selectByDate.html
library(openair)

kc1 <- importAURN(site = "kc1", year = 2018:2019)
head(kc1)
selectByDate(kc1, day = "weekday", year = 2018, month = 6:9, hour = 7:19)
openair::selectByDate(d1, day='weekday', year=2019, month=3:4)

library(timeDate)
isBizday(d1, holidays = holidayNYSE(), wday = 1:5)
?isBizday()

holidayNYSE()
isBizday(ts(d1), holidayNYSE())
tS[isBizday(tS, holidayNYSE())]


#------------- custom periods
# date sequence as required
#https://cran.r-project.org/web/packages/bizdays/bizdays.pdf

library(lubridate)
library(bizdays)


from1 = as.Date('2019-01-01') ; to1 = as.Date('2019-12-31')
from1 = as.Date('2018-09-01') ; to1 = as.Date('2019-08-31')

#all days
d1 = seq(from1, to1, by="1 day")
d1
length(d1)
weekdays(d1)

#only working days
d1 <- seq(from1, to1, by="1 day")
wkd1 <- d1[ ! weekdays(d1) %in% c('Saturday','Sunday') ]
wkd1
length(wkd1)


#------------------------
from2 = as.Date('2018-12-30')
from2 = as.Date('2018-08-30') + 3

#weekend
wkd1= seq(from2, to1, by="1 week")
wkd1
weekdays(wkd1)

#month- last date
m1 = seq(from1, to1, by="1 month")
m1
ceiling_date(m1, "month") - days(1)

#quarter - last date
q1 = seq(from1, to1, by="1 quarter")
q1

(q2 <- seq(from1, to1 , by="3 month"))
(q2 <- ceiling_date(q2 - days(1), "month"))

#half year - last date
from1; to1
h1 = seq(from1, to1 , by="6 month")
h2 <- h1 + months(5)
h2 <- ceiling_date(h2, 'month') - days(1)

#year 
y1 = seq(from1, to1, by="year")
y1
ceiling_date(y1, "year") - days(1)

(y2 = from1 + months(12) - days(1)) 

# all dates
d1
wd1
wd1
m1
q2
h2
y2

expand.grid(c('D'), d1)
expand.grid(c('WD'), wd1)
expand.grid(c('M'), m1)
expand.grid(c('Q'), q2)
expand.grid(c('H'), h2)
expand.grid(c('H'), y2)


#https://rdrr.io/cran/lubridate/man/round_date.html
#https://davidcarslaw.github.io/openair/reference/selectByDate.html
library(openair)

kc1 <- importAURN(site = "kc1", year = 2018:2019)
head(kc1)
selectByDate(kc1, day = "weekday", year = 2018, month = 6:9, hour = 7:19)
openair::selectByDate(d1, day='weekday', year=2019, month=3:4)

library(timeDate)
isBizday(d1, holidays = holidayNYSE(), wday = 1:5)
?isBizday()

holidayNYSE()
isBizday(ts(d1), holidayNYSE())
tS[isBizday(tS, holidayNYSE())]

================================================
FILE: 77-TS/blank.R
================================================


#Eg2
sales= c(18,33,41,7,34,35,24,25,24,21,25,20, 22,31,40,29,25,21,22,54,31,25,26,35)
tsales = ts(sales, start=c(2003,1), frequency=12)
tsales
plot(tsales)
plot(tsales, type='o', pch=19)
start(tsales)
end(tsales)
frequency(tsales)
tsales.subset = window(tsales, start=c(2003,5), end=c(2004,6))
tsales.subset

#Yearly Series

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,2), end=c(2014,11), frequency=3)
myts
myts2 <- window(myts, start=c(2010), end=c(2013))
myts2


# quarterly 

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,1), end=c(2014,11), frequency=3)
myts
str(myts)
plot(myts, type='o', pch=19)
text()

plot.ts(myts, type='o', pch=19, xy.labels=F)

axis(1, myts$Date, format(myts$Date, "%b %d"), cex.axis = .7)


myts <- ts(myvector, start=c(2009,2), end=c(2014,9), frequency=3)
myts
str(myts)


myts2 <- window(myts, start=c(2010), end=c(2013))
myts2
plot(myts, type='o', pch=19)
str(myts2)



myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))
plot(myts,type='o', pch=19)
text()
plot(myts2,type='o', pch=19)


library(zoo)
x.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = "-"))
x.Date
?zoo
x <- zoo(rnorm(24), x.Date)
x
plot(x)



#If we want different tick locations, we can suppress the default axis plotting and add our own:

plot(x, xaxt = "n")
axis(1, at = time(x), labels = FALSE)
#Or combine them:

plot(x)
axis(1, at = time(x), labels = FALSE)

## weekly ticks
plot(x)
times <- time(x)
ticks <- seq(times[1], times[length(times)], by = "weeks")
axis(1, at = ticks, labels = FALSE, tcl = -0.3)


library(zoo)
library(lattice)

# create test data
z <- zooreg(1:83, start = as.Date("2009-04-01"), deltat = 7)

xyplot(z)




# Time series plots with good X axis labels
library(zoo)
# data
today = Sys.Date()
dates = as.Date((today-500):today)
dates
z = zoo (100+cumsum(rnorm(501)), dates)
z
# method1 : default X axis labels do not look good
?plot.zoo
plot(z)
?plot.ts
plot(ts(z))
time(z)
# method 2 : Lattice
library(lattice)
?xyplot.zoo
xyplot(z)
xyplot(z, lwd=2, col="tomato")

# method 3 : XTS
library(xts)
?plot.xts
plot(as.xts(z))
plot(as.xts(z), auto.grid=F, major.format="%b %y", las=2)

# method 4 : Base graph
timeline = time(z)
summary(timeline)
index = seq(from=1, to=length(timeline), 90) # put ticks every 90 days
plot(z, xaxt="n")
axis(side=1, at=timeline[index], label=format(timeline[index], "%b %y"), cex.axis=0.8)

# method 5 : ggplot
library(ggplot2)
library(scales)
?date_breaks
df = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))
head(df)
# default plot
ggplot(df, aes(x=date, y=value)) + geom_line()
# formatted
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"))
# custom breaks
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"), breaks=date_breaks("3 months"))


================================================
FILE: 77-TS/ts-P-highfreq.R
================================================

library(highfrequency)
#load sample price data
data("sample_tdata");
head(sample_tdata)
ts = sample_tdata$PRICE;

#Previous tick aggregation to the 5-minute sampling frequency:
tsagg5min = aggregatets(ts,on="minutes",k=5);
head(tsagg5min);

#Previous tick aggregation to the 30-second sampling frequency:
tsagg30sec = aggregatets(ts,on="seconds",k=30);
tail(tsagg30sec);


================================================
FILE: 77-TS/ts-P-openair.R
================================================
# Package - openair

library(openair)

# load example data from package
data(mydata)
# trend plot for nox
TheilSen(mydata, pollutant = "nox")

timeAverage(mydata)
head(mydata)
print(tbl_df(mydata), n=100)

mydata %>% tbl_df %>% print(n=40)


## daily average values
daily <- timeAverage(mydata, avg.time = "day")
head(daily)
## daily average values ensuring at least 75 % data capture
## i.e. at least 18 valid hours
# NOT RUN {
daily <- timeAverage(mydata, avg.time = "day", data.thresh = 75)
# }
head(daily)
## 2-weekly averages
# NOT RUN {
fortnight <- timeAverage(mydata, avg.time = "2 week")
# }
head(fortnight)

hour <- timeAverage(mydata, avg.time = "hour")
head(hour)


#----
head(df)
df$date = df$timestamp2
hour2 <- timeAverage(df, avg.time = "hour")
head(hour2)


================================================
FILE: 77-TS/ts-P-padr.R
================================================
# PADR package

library(tidyverse)

library(padr)
top12 <- emergency %>% count(title) %>% arrange(desc(n)) %>% slice(1:12) %>% 
  select(title) %>% inner_join(emergency)
top12
head(emergency)
?emergency

top12 %>% thicken('month', col = 'month') %>% 
  count(title, month) %>%  ggplot(aes(month, n)) +  geom_bar(aes(fill = title), 
           col = "black", stat = 'identity',   position = "fill")

hourly_distribution <- top12 %>% thicken('hour') %>% 
  count(title, time_stamp_hour) %>% 
  pad(group = 'title') %>% fill_by_value(n) 

hourly_distribution

names(df)
df %>% thicken('hour') %>% sum('value') %>% pad(group='household_id')


================================================
FILE: 77-TS/ts-beer2.R
================================================
# Australian Beer
library(ts)

library(gsheet)
df1 = gsheet2tbl('https://docs.google.com/spreadsheets/d/1RCj6uvsu242xDf1l5Qb720DSnOmjnTUG8ety3iYBG90')
beer=df1

beer.ts <- ts(beer, frequency = 12, start = c(1956,1), end = c(1994,12))
beer.ts.qtr <- aggregate(beer.ts, nfrequency=4)
beer.ts.yr <- aggregate(beer.ts, nfrequency=1)
plot.ts(beer.ts[,2], main = "Monthly Beer Production in Australia", xlab = "Year", ylab = "ML")

plot.ts(beer.ts.qtr[,2], main = "Quarterly Beer Production in Australia", xlab = "Year", ylab = "ML")


seasonplot(beer.ts[,2], year.labels = TRUE, year.labels.left=TRUE, col=1:40, pch=19, main = "Monthly Beer Production in Australia - seasonplot", xlab = "Month", ylab = "ML")



================================================
FILE: 77-TS/ts-case1.R
================================================
# Time Series Analysis
#http://r-statistics.co/Time-Series-Analysis-With-R.html



================================================
FILE: 77-TS/ts-case2.R
================================================
# TS Case3


library(astsa)             # then load it (has to be done at the start of each session)
data()              # use this command to view all the loaded data 
?astsa
#Johnson & Johnson data set. It's included in astsa as jj
jj

# data properties
jj[1]       # the first element
#[1] 0.71
jj[84]      # the last element
#[1] 11.61
jj[1:4]     # the first 4 elements
#[1] 0.71 0.63 0.85 0.44
jj[-(1:80)] # everything EXCEPT the first 80 elements
#[1] 16.20 14.67 16.02 11.61
length(jj)  # the number of elements 
#[1] 84
dim(jj)     # but no dimensions ...
#NULL
nrow(jj)    # ... no rows
#NULL
ncol(jj)    # ... and no columns
#NULL
#-- if you want it to be a column vector (in R, a matrix), an easy way to go is:
jjm = as.matrix(jj)
matrix(jjm, ncol=6)
dim(jjm)   
#[1] 84  1 
jjm


#monthly model
options(digits=2)  # the default is 7, but it's more than I want now
?options           # to see your options (it's the help file)
?rnorm             # we're using it on the next line
(zardoz = ts(rnorm(48), start=c(2018,6), frequency=12))  #start from Jun 2018
(oz = window(zardoz, start=c(2018,10), end=c(2019,10)))
(oz = window(zardoz, start=2019, end=c(2019,10)))
time(jj)
time(oz)
time(zardoz)

cycle(oz)
cycle(zardoz) #position of cycle in each obsvn
?cycle

#plot
plot(jj, ylab="Earnings per Share", main="J & J")
plot(jj, type="o", col="blue", lty="dashed")
plot(diff(log(jj)), main="logged and diffed") 


x = -5:5                  # sequence of integers from -5 to 5
y = 5*cos(x)              # guess
par(mfrow=c(3,2))         # multifigure setup: 3 rows, 2 cols
#---  plot:
plot(x, main="plot(x)")
plot(x, y, main="plot(x,y)")
#---  plot.ts:
plot.ts(x, main="plot.ts(x)")
plot.ts(x, y, main="plot.ts(x,y)")
#---  ts.plot:
ts.plot(x, main="ts.plot(x)")
ts.plot(ts(x), ts(y), col=1:2, main="ts.plot(x,y)")  # note- x and y are ts objects 
#---  the help files [? and help() are the same]:
?plot.ts
help(ts.plot)
?par        # might as well skim the graphical parameters help file while you're here


par(mfrow=c(1,1))
k = c(.5,1,1,1,.5)            # k is the vector of weights
(k = k/sum(k))       
#[1] 0.125 0.250 0.250 0.250 0.125
fjj = stats::filter(jj, sides=2, k)  # ?filter for help [but you knew that already]
plot(jj)
lines(fjj, col="red")         # adds a line to the existing plot
lines(lowess(jj), col="blue", lty="dashed")

#... and the result:


dljj = diff(log(jj))        # difference the logged data
plot(dljj)                  # plot it (not shown)

shapiro.test(dljj)          # test for normality 
#Shapiro-Wilk normality test
#data:  dljj 
#W = 0.9725, p-value = 0.07211

#sanity.clause(dljj)         # test for sanity
#dljj is crazy  
#SC = 45.57, p-value = 0.0003    

# I couldn't fool you - there ain't no sanity clause  



par(mfrow=c(2,1))        # set up the graphics 
hist(dljj, prob=TRUE, 12)   # histogram    
lines(density(dljj))     # smooth it - ?density for details 
qqnorm(dljj)             # normal Q-Q plot  
qqline(dljj)             # add a line    

lag1.plot(dljj, 4)  # this is the astsa version of lag.plot in the stats package 

acf2(dljj)   # astsa gives both in one swell foop ... or use acf() and pacf() individually 

plot(dog <- stl(log(jj), "per"))   


================================================
FILE: 77-TS/ts-lubridate2.R
================================================
# Package Lubridate
# Date Functions - Date & Time

#Eg1
library(lubridate)
lubridate::ymd("20110604")
lubridate::mdy("06-04-2011")
lubridate::dmy("04/06/2011")
#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

#Eg1
#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.
OlsonNames()
Sys.timezone()
Sys.timezone(location=F)
arrive <- ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
arrive
leave <- ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
leave

leave - arrive  # Time Difference

#Setting and Extracting information
#Functions

#Eg
second(arrive)
second(arrive) = 25  #change
second(arrive)
arrive
minute(arrive)
hour(arrive)
day(arrive)
wday(arrive)
wday(arrive)
wday(arrive, label = TRUE)

week(arrive)

month(arrive)
year(arrive)
tz(arrive)

#Time Intervals
#save an interval of time as an Interval class object
(student1 <- interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                     ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                         ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))


================================================
FILE: 77-TS/ts-rollingvalues.R
================================================
# Rolling Time Series

library(zoo)
set.seed(0)
nSamples    <- 5000
vecDT       <- rexp(nSamples, 3)
vecTimes    <- cumsum(c(0,vecDT))
vecDrift    <- c(0, rnorm(nSamples, mean = 1/nSamples, sd = 0.01))
vecVals     <- cumsum(vecDrift)
vecZ        <- zoo(vecVals, order.by = vecTimes)
rm(vecDT, vecDrift)

library(xts)
library(TTR)
x <- .xts(vecZ, vecTimes)
xx <- na.locf(cbind(xts(, seq.POSIXt(from=start(x), to=end(x), by='sec')), x))
x$means <- runMean(xx, n=180)
out <- x[!is.na(x[, 1]), ]
tail(out)


tq_mutate_fun_options() %>%
  str()


##  $ zoo                 : chr [1:14] "rollapply" "rollapplyr" "rollmax" "rollmax.default" ...
##  $ xts                 : chr [1:27] "apply.daily" "apply.monthly" "apply.quarterly" "apply.weekly" ...
##  $ quantmod            : chr [1:25] "allReturns" "annualReturn" "ClCl" "dailyReturn" ...
##  $ TTR                 : chr [1:61] "adjRatios" "ADX" "ALMA" "aroon" ...
##  $ PerformanceAnalytics: chr [1:7] "Return.annualized" "Return.annualized.excess" "Return.clean" "Return.cumulative" ..

================================================
FILE: 77-TS/ts-rollingvalues2.R
================================================
#https://www.r-bloggers.com/tidy-time-series-analysis-part-2-rolling-functions/
library(tidyquant)  # Loads tidyverse, tidyquant, financial pkgs, xts/zoo
library(cranlogs)   # For inspecting package downloads over time

library(tidyquant)

custom_stat_fun_2 <- function(x, na.rm = TRUE) {
  # x     = numeric vector
  # na.rm = boolean, whether or not to remove NA's
    m  <- mean(x, na.rm = na.rm)
  s  <- sd(x, na.rm = na.rm)
  hi <- m + 2*s
  lo <- m - 2*s
  ret <- c(mean = m, stdev = s, hi.95 = hi, lo.95 = lo) 
  return(ret)
}



tidyverse_downloads_rollstats <- tidyverse_downloads %>%
  tq_mutate(
    select     = count,
    mutate_fun = rollapply, 
    # rollapply args
    width      = 30,
    align      = "right",
    by.column  = FALSE,
    FUN        = custom_stat_fun_2,
    # FUN args
    na.rm      = TRUE
  )
class(tidyverse_downloads)

tidyverse_downloads_rollstats
print(tbl_df(tidyverse_downloads_rollstats), n=40)
tq_mutate_fun_options() %>% str()

#----
# tidyverse packages (see my laptop stickers from last post) ;)
pkgs <- c(
  "tidyr", "lubridate", "dplyr", 
  "broom", "tidyquant", "ggplot2", "purrr", 
  "stringr", "knitr"
)

# Get the downloads for the individual packages
tidyverse_downloads <- cran_downloads(
  packages = pkgs, 
  from     = "2017-01-01", 
  to       = "2017-06-30") %>%
  tibble::as_tibble() %>%
  group_by(package)

# Visualize the package downloads
tidyverse_downloads %>%
  ggplot(aes(x = date, y = count, color = package)) +
  # Data
  geom_point(alpha = 0.5) +
  facet_wrap(~ package, ncol = 3, scale = "free_y") +
  # Aesthetics
  labs(title = "tidyverse packages: Daily downloads", x = "",
       subtitle = "2017-01-01 through 2017-06-30",
       caption = "Downloads data courtesy of cranlogs package") +
  scale_color_tq() +
  theme_tq() +
  theme(legend.position="none")

# my case
head(data)
head(df)
names(df)
df2 = xts(df[1:4], order.by=df$timestamp2)
names(df2)

custom_stat_fun_3 <- function(x, na.rm = TRUE) {
  # x     = numeric vector
  # na.rm = boolean, whether or not to remove NA's
  m  <- mean(x, na.rm = na.rm)
  s  <- sd(x, na.rm = na.rm)
  hi <- m + 1*s
  lo <- m - 1*s
  ret <- c(mean = m, stdev = s, hi.95 = hi, lo.95 = lo) 
  return(ret)
}
class(df2)


df2_rollstats <- df %>%
  tq_mutate(
    select     = value,
    mutate_fun = rollapply, 
    # rollapply args
    width      = 30,
    align      = "right",
    by.column  = FALSE,
    FUN        = custom_stat_fun_3,
    # FUN args
    na.rm      = TRUE
  )


#timeAverage

================================================
FILE: 77-TS/ts-splitdate.R
================================================
# Split Data into parts

#Split date and time seperately
#M1----
(tdatetime = Sys.time())
(splitTime <- sapply(strsplit(as.character(tdatetime), " "), "[", 2))  #time
(splitDate <- sapply(strsplit(as.character(tdatetime), " "), "[", 1))  #date

#  M2----
(df <- data.frame(datetime = c( "13:11:2013 15:39" , "13:11:2013 16:15" , "13:11:2013 17:52" ) ))
(new <- do.call( rbind , strsplit( as.character( df$datetime ) , " " ) ))

#M3----
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%H:%M:%S")
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%Y:%m:%d")

#Time Stamp to POSIXT
td = 1377986400
as.POSIXct(1377986400,origin="1970-01-01")
(td1 = as.POSIXct(as.numeric(td),origin="1970-01-01"))


datetime2 <- as.POSIXct('4/17/2008 16:01', format='%m/%d/%Y %H:%M') 
(datetime2split <- strsplit(format(datetime2, "%d %m %Y %H %M"), ' ') )

(x2 = as.POSIXct(td1, format='%m/%d/%Y %H:%M'))
(y2 <- strsplit(format(x2, "%d %m %Y %H %M"), ' ') )

#-----
# In R, there are two basic data types around date and time in R. One is Date, which contains only the date information like 2016–01–10. Another is POSIXct, which has both date and time information like 2016–01–10 10:45:20.

library(dplyr)
dateposix = seq(1377986400, 1377986420, by=1)
df = data.frame(dateposix)
(df$datetime = as.POSIXct(as.numeric(df$dateposix),origin="1970-01-01"))
library(lubridate)
head(df)
df$dateposix2 = format(unclass(df$datetime), scientific=F)
df <- df %>% mutate(year=year(datetime), month = month(datetime), day= day(datetime), hour=hour(datetime), min=minute(datetime))
head(format(df, scientific=F))

split1 =strsplit(format(df$datetime, "%d %m %Y %H %M"), ' ') #list form
matrix(as.numeric(unlist(split1)), ncol=5, byrow = T) 
do.call(rbind, split1)
as.Date(with(df$datetime, ISOdate(Year, Month, Day)))
  
#-----

do.call(rbind, spl) 

# using seperate in tidyr
library(dplyr)
head(df)
df1 = df['datetime']
head(df1)
df1 %>% separate(datetime, into=c('Date', 'Time'), sep=' ')
(year=as.numeric (format(df1$datetime,"%Y")))
(month=as.numeric (format(df1$datetime,"%m")))
(day=as.numeric (format(df1$datetime,"%d")))
(hour=as.numeric (format(df1$datetime,"%H")))
(min =as.numeric (format(df1$datetime,"%M")))
data.frame(df1,cbind(year, month, day, hour, min))


================================================
FILE: 77-TS/ts-timestamp.R
================================================
help(DateTimeClasses)
t <- Sys.time()
typeof(t)
t
print(t)
cat(t,"\n")
1390505301
c <- as.POSIXct(t)
c
typeof(c)
print(c)
[1] "2014-01-23 14:28:21 EST"
> cat(c,"\n")
1390505301
>
  >
  > l <- as.POSIXlt(t)
> l
[1] "2014-01-23 14:28:21 EST"
> typeof(l)
[1] "list"
> print(l)
[1] "2014-01-23 14:28:21 EST"
> cat(l,"\n")
Error in cat(list(...), file, sep, fill, labels, append) :
  argument 1 (type 'list') cannot be handled by 'cat'
> names(l)
NULL
> l[[1]]
[1] 21.01023
> l[[2]]
[1] 28
> l[[3]]
[1] 14
> l[[4]]
[1] 23
> l[[5]]
[1] 0
> l[[6]]
[1] 114
> l[[7]]
[1] 4
> l[[8]]
[1] 22
> l[[9]]
[1] 0
>
  > b <- as.POSIXct(l)
> cat(b,"\n")
1390505301

library(bReeze)
data(winddata)
head(winddata)
# format time stamp
head(timestamp(timestamp=winddata[,1]))


#time stamp
x <- 15103
as_date(x, origin = lubridate::origin) #"2011-05-09"

y <- 1442866615
as_datetime(y, origin = lubridate::origin)
#"2015-09-21 20:16:55 UTC"

x=axis.Date(1, as.Date(15103, origin = "1960-10-01"))
x


#----
now <- Sys.time()
now
as.numeric(now)
now + 10  # adds 10 seconds
as.POSIXlt(now)
str(as.POSIXlt(now))
unclass(as.POSIXlt(now))


#----
Jan1 <- strptime("2018-01-01 00:00:00", "%Y-%m-%d %H:%M:%S")
difftime(now, Jan1, unit="week")


#----
help(strftime)
t <- Sys.time()
cat(t,"\n")
(timeStamp <-  strftime(t,"%Y-%m-%d %H:%M:%S"))
typeof(timeStamp)

#----
myData <- data.frame(time=c("2014-01-23 14:28:21","2014-01-23 14:28:55",
                            "2014-01-23 14:29:02","2014-01-23 14:31:18"),
                     speed=c(2.0,2.2,3.4,5.5))
myData
summary(myData)
myData$time[1]
myData$time <- strptime(myData$time,"%Y-%m-%d %H:%M:%S")
typeof(myData$time)
myData$time[1]
myData$time[1][[2]]

N = length(myData$time)
N
myData$time[2:N] - myData$time[1:(N-1)]


================================================
FILE: 77-TS/ts-yoy.R
================================================
#time series YoY differences

pacman::p_load(dplyr, googlesheets4, ggplot2, ggrepel, directlabels, lubridate, RColorBrewer)
# Define the number of colors you want
nb.cols <- 12
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)

gsID = "1G-F9YEH1qJBPtZgGbW3jZhIPfAcD5YgJHM_Fg05OtbU"

df <- googlesheets4::read_sheet(ss=gsID, sheet='admDates')
df
df2 <- df %>% tidyr::pivot_longer(cols=c('formDate','feePaidDate'), names_to = 'dateType', values_to='dateValue') %>% mutate(admYear = paste0('Y',admYear))
df2
df2$dateValue  <- as.Date(df2$dateValue)
df2

#forms-----
formsDF <- df2 %>% filter(dateType == 'formDate')
formsDF %>% group_by(admYear)  %>% summarise(n=n())

df3 <- formsDF %>% group_by(monValue = lubridate::month(dateValue, label=T), admYear)  %>% summarise(n=n())
df3
(df3A <- df3 %>% pivot_wider(names_from = c(admYear), values_from = n))
(df3B <- df3 %>%  group_by(admYear) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear'), values_from = 'cumValue'))

df3 %>%  group_by(admYear) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=cumValue, group=admYear))  + geom_line(aes(color=admYear, group=admYear), size=2) + geom_point() + ggrepel::geom_label_repel(aes(label=cumValue, color=admYear)) + directlabels::geom_dl(aes(label = admYear, color=admYear), method = list(dl.trans(y = y - 0.2),dl.combine(method="first.points", "last.points"), rot=00), cex = 1) 
df3A

yearOrder = c('Y2021', 'Y22Y21', 'Y2022', 'Y22Y20', 'Y2020' )
df3A %>% mutate(Y22Y21 = Y2022-Y2021, Y22Y20 = Y2022-Y2020)  %>%  tidyr::pivot_longer(cols=c('Y2022', 'Y22Y21', 'Y2021', 'Y2020', 'Y22Y20'), names_to = 'YearDiff', values_to = 'value') %>% mutate(YearDiff = factor(YearDiff, ordered=T, levels=yearOrder))  %>% ggplot(., aes(x=monValue, y=value, fill=monValue)) + geom_bar(stat='identity') + facet_grid(YearDiff ~. ) + geom_text(aes(label=paste0(value)))  + scale_fill_manual(values = mycolors) + theme(strip.text = element_text(color = "red"), strip.background = element_rect(fill = "yellow"))

#perc in each Month
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear'), values_from = 'propPerc') 
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=admYear, y=propPerc, fill=monValue)) + geom_bar(stat='identity', position = position_fill(), color='black') + scale_fill_manual(values = mycolors) + coord_flip() + geom_text(aes(label=paste(monValue,'\n', propPerc, '%'), size=propPerc), position = position_fill(.5)) + scale_size(range=c(4,6))
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=propPerc, fill=monValue)) + geom_bar(stat='identity') + facet_grid(admYear ~. ) + geom_text(aes(label=paste0(propPerc,'%')))


#as per level ------

ldf3 <- df2 %>% group_by(monValue = lubridate::month(dateValue, label=T), level, admYear)  %>% summarise(n=n())
ldf3
(ldf3A <- ldf3 %>% pivot_wider(names_from = c(admYear), values_from = n))
(ldf3B <- ldf3 %>%  group_by(admYear, level) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear','level'), values_from = 'cumValue'))

ldf3 %>%  group_by(admYear, level) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=cumValue, group=admYear))  + geom_line(aes(color=admYear, group=admYear), size=2) + geom_point() + facet_grid(level ~. ) + ggrepel::geom_label_repel(aes(label=cumValue, color=admYear)) + directlabels::geom_dl(aes(label = admYear, color=admYear), method = list(dl.trans(y = y - 0.2),dl.combine(method="first.points", "last.points"), rot=00), cex = 1) 
ldf3A

yearOrder = c('Y2021', 'Y22Y21', 'Y2022', 'Y22Y20', 'Y2020' )
ldf3A %>% mutate(Y22Y21 = Y2022-Y2021, Y22Y20 = Y2022-Y2020)  %>%  tidyr::pivot_longer(cols=c('Y2022', 'Y22Y21', 'Y2021', 'Y2020', 'Y22Y20'), names_to = 'YearDiff', values_to = 'value') %>% mutate(YearDiff = factor(YearDiff, ordered=T, levels=yearOrder))  %>% ggplot(., aes(x=monValue, y=value, fill=monValue)) + geom_bar(stat='identity') + facet_grid(YearDiff ~ level ) + geom_text(aes(label=paste0(value)))  + scale_fill_manual(values = mycolors) + theme(strip.text = element_text(color = "red"), strip.background = element_rect(fill = "yellow"))

#perc in each Month
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear', 'level'), values_from = 'propPerc') 
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=admYear, y=propPerc, fill=monValue)) + geom_bar(stat='identity', position = position_fill(), color='black') + scale_fill_manual(values = mycolors) + facet_grid(level ~. ) + coord_flip() + geom_text(aes(label=paste(monValue,'\n', propPerc, '%'), size=propPerc), position = position_fill(.5)) + scale_size(range=c(4,6))
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=propPerc, fill=monValue)) + geom_bar(stat='identity') + facet_grid(admYear ~ level ) + geom_text(aes(label=paste0(propPerc,'%')))


#admDate-----
admDF <- df2 %>% filter(dateType == 'feePaidDate')
admDF %>% group_by(admYear)  %>% summarise(n=n())

df3 <- admDF %>% group_by(monValue = lubridate::month(dateValue, label=T), admYear)  %>% summarise(n=n())
df3
(df3A <- df3 %>% pivot_wider(names_from = c(admYear), values_from = n))
(df3B <- df3 %>%  group_by(admYear) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear'), values_from = 'cumValue'))

df3 %>%  group_by(admYear) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=cumValue, group=admYear))  + geom_line(aes(color=admYear, group=admYear), size=2) + geom_point() + ggrepel::geom_label_repel(aes(label=cumValue, color=admYear)) + directlabels::geom_dl(aes(label = admYear, color=admYear), method = list(dl.trans(y = y - 0.2),dl.combine(method="first.points", "last.points"), rot=00), cex = 1) 
df3A

yearOrder = c('Y2021', 'Y22Y21', 'Y2022', 'Y22Y20', 'Y2020' )
df3A %>% mutate(Y22Y21 = Y2022-Y2021, Y22Y20 = Y2022-Y2020)  %>%  tidyr::pivot_longer(cols=c('Y2022', 'Y22Y21', 'Y2021', 'Y2020', 'Y22Y20'), names_to = 'YearDiff', values_to = 'value') %>% mutate(YearDiff = factor(YearDiff, ordered=T, levels=yearOrder))  %>% ggplot(., aes(x=monValue, y=value, fill=monValue)) + geom_bar(stat='identity') + facet_grid(YearDiff ~. ) + geom_text(aes(label=paste0(value)))  + scale_fill_manual(values = mycolors) + theme(strip.text = element_text(color = "red"), strip.background = element_rect(fill = "yellow"))

#perc in each Month
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear'), values_from = 'propPerc') 
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=admYear, y=propPerc, fill=monValue)) + geom_bar(stat='identity', position = position_fill(), color='black') + scale_fill_manual(values = mycolors) + coord_flip() + geom_text(aes(label=paste(monValue,'\n', propPerc, '%'), size=propPerc), position = position_fill(.5)) + scale_size(range=c(4,6))
df3 %>% group_by(admYear) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=propPerc, fill=monValue)) + geom_bar(stat='identity') + facet_grid(admYear ~. ) + geom_text(aes(label=paste0(propPerc,'%')))


#as per level ------

ldf3 <- df2 %>% group_by(monValue = lubridate::month(dateValue, label=T), level, admYear)  %>% summarise(n=n())
ldf3
(ldf3A <- ldf3 %>% pivot_wider(names_from = c(admYear), values_from = n))
(ldf3B <- ldf3 %>%  group_by(admYear, level) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear','level'), values_from = 'cumValue'))

ldf3 %>%  group_by(admYear, level) %>% mutate(cumValue = cumsum(n))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=cumValue, group=admYear))  + geom_line(aes(color=admYear, group=admYear), size=2) + geom_point() + facet_grid(level ~. ) + ggrepel::geom_label_repel(aes(label=cumValue, color=admYear)) + directlabels::geom_dl(aes(label = admYear, color=admYear), method = list(dl.trans(y = y - 0.2),dl.combine(method="first.points", "last.points"), rot=00), cex = 1) 
ldf3A

yearOrder = c('Y2021', 'Y22Y21', 'Y2022', 'Y22Y20', 'Y2020' )
ldf3A %>% mutate(Y22Y21 = Y2022-Y2021, Y22Y20 = Y2022-Y2020)  %>%  tidyr::pivot_longer(cols=c('Y2022', 'Y22Y21', 'Y2021', 'Y2020', 'Y22Y20'), names_to = 'YearDiff', values_to = 'value') %>% mutate(YearDiff = factor(YearDiff, ordered=T, levels=yearOrder))  %>% ggplot(., aes(x=monValue, y=value, fill=monValue)) + geom_bar(stat='identity') + facet_grid(YearDiff ~ level ) + geom_text(aes(label=paste0(value)))  + scale_fill_manual(values = mycolors) + theme(strip.text = element_text(color = "red"), strip.background = element_rect(fill = "yellow"))

#perc in each Month
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% pivot_wider(names_from = c('admYear', 'level'), values_from = 'propPerc') 
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=admYear, y=propPerc, fill=monValue)) + geom_bar(stat='identity', position = position_fill(), color='black') + scale_fill_manual(values = mycolors) + facet_grid(level ~. ) + coord_flip() + geom_text(aes(label=paste(monValue,'\n', propPerc, '%'), size=propPerc), position = position_fill(.5)) + scale_size(range=c(4,6))
ldf3 %>% group_by(admYear, level) %>%  mutate(propPerc = round(100 * n/ sum(n)))  %>% select(-c(n)) %>% ggplot(., aes(x=monValue, y=propPerc, fill=monValue)) + geom_bar(stat='identity') + facet_grid(admYear ~ level ) + geom_text(aes(label=paste0(propPerc,'%')))


#-----------------
#geom_dl(aes(label = State), method = list(dl.trans(x = x - 0.2), "first.points", cex = 0.8)) 
#geom_dl(aes(label = State), method = list(dl.combine("first.points", "last.points")), cex = 0.8) 


================================================
FILE: 77-TS/tsforecast-exp.R
================================================
# TS Forecasting with Exponential Smoothening

# Simple exponential smoothing
library(forecast)
?nhtemp
#Mean annual Temp in Degree F from 1912 to 1917 : 60 observations
# Types of Smoothening : (level, trend, seasoanal) : A- Additive, None, Z-automatic
# Simple - Level  : model- ANN
# double - level, slope (trend) : model : AAN
# triple - level, slope(trend), seasonal : model - AAA
par(mfrow=c(1,1))
plot(nhtemp)
(fitse = ets(nhtemp, model='ANN'))
(fitse2 = ses(nhtemp))

forecast(fitse,3)
plot(forecast(fitse,c(3)))
accuracy(fitse)

#Holt Exponential Smoothening
TS = level + slope * t + irregular
plot(AirPassengers)
#log model to use additive model
plot(log(AirPassengers))

(fithe = ets(log(AirPassengers), model='AAA'))
(pred = forecast(fithe, 5))
plot(pred, main='Forecast for Air Travel', ylab='Log (Air Passengers)', xlab='Time')
#since log was used, use exp to get predicted values
pred$mean
(pred$mean = exp(pred$mean))
(pred$lower = exp(pred$lower))
(pred$upper = exp(pred$upper))
(p = cbind(pred$mean, pred$lower, pred$upper))

(pred$mean = exp(pred$mean))




#Holt Winters Exponential Smoothening
TS = level + slope * t + s(t) + irregular

fit <- HoltWinters(nhtemp, beta=FALSE, gamma=FALSE)
fit
forecast(fit, 1)
plot(forecast(fit, 1), xlab="Year",   ylab=expression(paste("Temperature (", degree*F,")",)), main="New Haven Annual Mean Temperature")
accuracy(fit)
# Listing 15.5 - Exponential smoothing with level, slope, and seasonal components
fit <- HoltWinters(log(AirPassengers))
fit
accuracy(fit)
pred <- forecast(fit, 5)
pred
plot(pred, main="Forecast for Air Travel",
     ylab="Log(AirPassengers)", xlab="Time")
pred$mean <- exp(pred$mean)
pred$lower <- exp(pred$lower)
pred$upper <- exp(pred$upper)
p <- cbind(pred$mean, pred$lower, pred$upper)
dimnames(p)[[2]] <- c("mean", "Lo 80", "Lo 95", "Hi 80", "Hi 95")
p
# Listing 15.6 - Automatic exponential forecasting with ets()
library(forecast)
fit <- ets(JohnsonJohnson)
fit
plot(forecast(fit), main="Johnson and Johnson Forecasts",
     ylab="Quarterly Earnings (Dollars)", xlab="Time")
# Listing 15.7 - Transforming the time series and assessing stationarity
library(forecast)
library(tseries)
plot(Nile)
ndiffs(Nile)
dNile <- diff(Nile)
plot(dNile)
adf.test(dNile)

library(forecast)
?auto.arima
?arima


================================================
FILE: 78-nlp/SM-rtexttools1.R
================================================
# Text Analysis
library(RTextTools)
library(e1071)


pos_tweets =  rbind(
  c('I love this car', 'positive'),
  c('This view is amazing', 'positive'),
  c('I feel great this morning', 'positive'),
  c('I am so excited about the concert', 'positive'),
  c('He is my best friend', 'positive')
)

neg_tweets = rbind(
  c('I do not like this car', 'negative'),
  c('This view is horrible', 'negative'),
  c('I feel tired this morning', 'negative'),
  c('I am not looking forward to the concert', 'negative'),
  c('He is my enemy', 'negative')
)

test_tweets = rbind(
  c('feel happy this morning', 'positive'),
  c('larry friend', 'positive'),
  c('not like that man', 'negative'),
  c('house not great', 'negative'),
  c('your song annoying', 'negative')
)

tweets = rbind(pos_tweets, neg_tweets, test_tweets)

# build dtm
matrix= create_matrix(tweets[,1], language="english", 
                      removeStopwords=FALSE, removeNumbers=TRUE, 
                      stemWords=FALSE) 

# train the model
mat = as.matrix(matrix)
classifier = naiveBayes(mat[1:10,], as.factor(tweets[1:10,2]) )

# test the validity
predicted = predict(classifier, mat[11:15,]); predicted
table(tweets[11:15, 2], predicted)
recall_accuracy(tweets[11:15, 2], predicted)


# build the data to specify response variable, training set, testing set.
container = create_container(matrix, as.numeric(as.factor(tweets[,2])), trainSize=1:10, testSize=11:15,virgin=FALSE)



models = train_models(container, algorithms=c("MAXENT" , "SVM", "RF", "BAGGING", "TREE"))
#Now, we can classify the testing set using the trained models.

results = classify_models(container, models)
#How about the accuracy?
  
# accuracy table
table(as.numeric(as.factor(tweets[11:15, 2])), results[,"FORESTS_LABEL"])
table(as.numeric(as.factor(tweets[11:15, 2])), results[,"MAXENTROPY_LABEL"])

# recall accuracy
recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"FORESTS_LABEL"])
recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"MAXENTROPY_LABEL"])
recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"TREE_LABEL"])
recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"BAGGING_LABEL"])
recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"SVM_LABEL"])
#To summarize the results (especially the validity) in a formal way:

# model summary
analytics = create_analytics(container, results)
summary(analytics)
head(analytics@document_summary)
analytics@ensemble_summary
#To cross validate the results:
  
N=4
set.seed(2014)
cross_validate(container,N,"MAXENT")
cross_validate(container,N,"TREE")
cross_validate(container,N,"SVM")
cross_validate(container,N,"RF")
#The results can be found on my Rpub page. It seems that maxent reached the same recall accuracy as naive Bayes. The other methods even did a worse job. This is understandable, since we have only a very small data set. To enlarge the training set, we can get a much better results for sentiment analysis of tweets using more sophisticated methods. I will show the results with anther example.

#Sentiment analysis for tweets
#The data comes from victorneo. victorneo shows how to do sentiment analysis for tweets using Python. Here, I will demonstrate how to do it in R.

#Read data:
  
###################
#"load data"  : set it to your file
###################
setwd("D:/Twitter-Sentimental-Analysis-master/")
happy = readLines("./happy.txt")
sad = readLines("./sad.txt")
happy_test = readLines("./happy_test.txt")
sad_test = readLines("./sad_test.txt")

tweet = c(happy, sad)
tweet_test= c(happy_test, sad_test)
tweet_all = c(tweet, tweet_test)
sentiment = c(rep("happy", length(happy) ), 
              rep("sad", length(sad)))
sentiment_test = c(rep("happy", length(happy_test) ), 
                   rep("sad", length(sad_test)))
sentiment_all = as.factor(c(sentiment, sentiment_test))

library(RTextTools)
First, try naive Bayes.

# naive bayes
mat= create_matrix(tweet_all, language="english", 
                   removeStopwords=FALSE, removeNumbers=TRUE, 
                   stemWords=FALSE, tm::weightTfIdf)

mat = as.matrix(mat)

classifier = naiveBayes(mat[1:160,], as.factor(sentiment_all[1:160]))
predicted = predict(classifier, mat[161:180,]); predicted

table(sentiment_test, predicted)
recall_accuracy(sentiment_test, predicted)
Then, try the other methods:
  
  # the other methods
  mat= create_matrix(tweet_all, language="english", 
                     removeStopwords=FALSE, removeNumbers=TRUE, 
                     stemWords=FALSE, tm::weightTfIdf)

container = create_container(mat, as.numeric(sentiment_all),
                             trainSize=1:160, testSize=161:180,virgin=FALSE) #可以设置removeSparseTerms

models = train_models(container, algorithms=c("MAXENT",
                                              "SVM",
                                              #"GLMNET", "BOOSTING", 
                                              "SLDA","BAGGING", 
                                              "RF", # "NNET", 
                                              "TREE" 
))

# test the model
results = classify_models(container, models)
table(as.numeric(as.numeric(sentiment_all[161:180])), results[,"FORESTS_LABEL"])
recall_accuracy(as.numeric(as.numeric(sentiment_all[161:180])), results[,"FORESTS_LABEL"])
Here we also want to get the formal test results, including:
  
  analytics@algorithm_summary: Summary of precision, recall, f-scores, and accuracy sorted by topic code for each algorithm
analytics@label_summary: Summary of label (e.g. Topic) accuracy
analytics@document_summary: Raw summary of all data and scoring
analytics@ensemble_summary: Summary of ensemble precision/coverage. Uses the n variable passed into create_analytics()
Now let’s see the results:
  
  # formal tests
  analytics = create_analytics(container, results)
summary(analytics)

head(analytics@algorithm_summary)
head(analytics@label_summary)
head(analytics@document_summary)
analytics@ensemble_summary # Ensemble Agreement

# Cross Validation
N=3
cross_SVM = cross_validate(container,N,"SVM")
cross_GLMNET = cross_validate(container,N,"GLMNET")
cross_MAXENT = cross_validate(container,N,"MAXENT")

================================================
FILE: 78-nlp/TM-zz.R
================================================
#text mining

library(tidytext)

sentiments
get_sentiments("afinn")
?get_sentiments

get_sentiments("bing")



library(janeaustenr)
library(dplyr)
library(stringr)

tidy_books <- austen_books() %>%
  group_by(book) %>%
  mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", ignore_case = TRUE)))) %>%
  ungroup() %>%
  unnest_tokens(word, text)
#https://www.tidytextmining.com/tidytext.html



================================================
FILE: 78-nlp/downloadfile.R
================================================
# download a text Document 

#download.file
#download.file can be used to download a single file as described by url from the internet and store it in destfile. The url must start with a scheme such as http://, https://,


url2 ="http://52.66.73.63/docs/fms.txt"
download.file(url2, "fms.txt" )
download.file(url2, "./download/fms.txt" )

url3 ="http://52.66.73.63/docs/rowling.txt"
download.file(url3, "./download/rowling.txt" )




#readtext
library(readtext)
readtext("./download/rowling.txt")

#url.show
url.show("http://52.66.73.63/docs/rowling.txt")

#file.show
file.show("./download/rowling.txt")

url = "http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv"
download.file(url, "./download/iris.csv", quiet=TRUE)


library(httr)
url <- "https://rawgit.com/yoke2/dsxref/master/iris.xlsx"
GET(url, write_disk("./download/iris.xlsx", overwrite=TRUE))


library(downloader)
#format is not correct
urlforR= "https://github.com/dupadhyaya/analytics/blob/master/15a-DS/20a-vectors.R"
download(urlforR,"./download/vector.R")
downloader::source_url(urlforR)


================================================
FILE: 78-nlp/facebook1.R
================================================
# Facebook posts
#http://thinktostart.com/analyzing-facebook-with-r/
#errorat authentication
#https://www.listendata.com/2017/03/facebook-data-mining-using-r.html

library(devtools)

install_github("Rfacebook", "pablobarbera", subdir="Rfacebook")

#require (Rfacebook)
library(Rfacebook)
library(RCurl)

require("Rfacebook")
#https://developers.facebook.com/apps/2208824636012038/settings/basic/
  
fb_oauth <- fbOAuth(app_id="2208824636012038", app_secret="xxx",extended_permissions = TRUE)
#Not working

save(fb_oauth, file="fb_oauth")

load("fb_oauth")

================================================
FILE: 78-nlp/facebook2.R
================================================
# Facebook Analytics
#http://thinktostart.com/analyzing-facebook-with-r/
  
#install.packages("devtools")
library(devtools)
install_github("Rfacebook", "pablobarbera", subdir="Rfacebook")

require (Rfacebook)
fb_oauth <- fbOAuth(app_id="289287295005213", app_secret="b624856b1b54d3146e8d9d93b5d667fa",extended_permissions = TRUE)

save(fb_oauth, file="fb_oauth")

load("fb_oauth")
me <- getUsers("me",token=fb_oauth)
my_likes <- getLikes(user="me", token=fb_oauth)

token <- 'YOUR AUTHENTICATION TOKEN'
me <- getUsers("me", token, private_info=TRUE)

getUsers(c("barackobama", "donaldtrump"), token)


================================================
FILE: 78-nlp/fms.txt
================================================
The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.

But much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.

FMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.

Corporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.

We invite you to discover FMS for yourself.

================================================
FILE: 78-nlp/linkedin1.R
================================================
#Linkedin Posts

require(devtools)
#install_github("mpiccirilli/Rlinkedin")
require(Rlinkedin)
in.auth <- inOAuth()

app_name <- "5231725"
consumer_key <- "818i9sxp6fbimd"
consumer_secret <- "uau6nQyST7BP9XtV"

in.auth <- inOAuth(app_name, consumer_key, consumer_secret)
my.connections <- getMyConnections(in.auth)
text <- toString(my.connections$industry)



clean.text <- function(some_txt)
{
  some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
  some_txt = gsub("@\\w+", "", some_txt)
  some_txt = gsub("[[:punct:]]", "", some_txt)
  some_txt = gsub("[[:digit:]]", "", some_txt)
  some_txt = gsub("http\\w+", "", some_txt)
  some_txt = gsub("[ \t]{2,}", "", some_txt)
  some_txt = gsub("^\\s+|\\s+$", "", some_txt)
  some_txt = gsub("amp", "", some_txt)
  # define "tolower error handling" function
  try.tolower = function(x)
  {
    y = NA
    try_error = tryCatch(tolower(x), error=function(e) e)
    if (!inherits(try_error, "error"))
      y = tolower(x)
    return(y)
  }
  
  some_txt = sapply(some_txt, try.tolower)
  some_txt = some_txt[some_txt != ""]
  names(some_txt) = NULL
  return(some_txt)
}

clean_text = clean.text(text)
tweet_corpus = Corpus(VectorSource(clean_text))

tdm = TermDocumentMatrix(tweet_corpus, control = list(removePunctuation = TRUE,stopwords = stopwords("english"), removeNumbers = TRUE, tolower = TRUE))

#install.packages(c("wordcloud","tm"),repos="http://cran.r-project.org")

library(wordcloud)
m = as.matrix(tdm) #we define tdm as matrix
word_freqs = sort(rowSums(m), decreasing=TRUE) #now we get the word orders in decreasing order
dm = data.frame(word=names(word_freqs), freq=word_freqs) #we create our data set
wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, "Dark2")) #and we visualize our data


================================================
FILE: 78-nlp/linkedin3.R
================================================
#Linked In posts - Note Working

# Get a person's name, location, summary, # of connections, and skills & endorsements from LinkedIn

# URL of the LinkedIn page 
user_url <- "https://www.linkedin.com/in/dhiraj-upadhyaya-4b7161a"

# since the information isn't available without being logged in, the web
# scraper needs to log in. Provide your LinkedIn user/pw here (this isn't stored
# anywhere as you can see, it's just used to log in during the scrape session)
username <- "xxxx"
password <- "xxx"

# takes a couple seconds and might throw a warning, but ignore the warning
(linkedin_info <- scrape_linkedin(user_url))

############################

library(rvest)

scrape_linkedin <- function(user_url) {
  linkedin_url <- "http://linkedin.com/"
  pgsession <- html_session(linkedin_url) 
  pgform <- html_form(pgsession)[[1]]
  filled_form <- set_values(pgform,
                            session_key = username, 
                            session_password = password)
  
  submit_form(pgsession, filled_form)
  
  pgsession <- jump_to(pgsession, user_url)
  page_html <- read_html(pgsession)
  
  name <-
    page_html %>% html_nodes("#name") %>% html_text()
  
  location <-
    page_html %>% html_nodes("#location .locality") %>% html_text()
  
  num_connections <-
    page_html %>% html_nodes(".member-connections strong") %>% html_text()
  
  summary <- 
    page_html %>% html_nodes("#summary-item-view") %>% html_text()
  
  skills_nodes <-
    page_html %>% html_nodes("#profile-skills .skill-pill")
  
  skills <-
    lapply(skills_nodes, function(node) {
      num <- node %>% html_nodes(".num-endorsements") %>% html_text()
      name <- node %>% html_nodes(".endorse-item-name-text") %>% html_text()
      data.frame(name = name, num = num)
    })
  
  skills <- do.call(rbind, skills)
  
  
  list(
    name = name,
    location = location,
    num_connections = num_connections,
    summary = summary,
    skills = skills
  ) 
}

(linkedin_info <- scrape_linkedin(user_url))
linkedin_info


================================================
FILE: 78-nlp/pagerank.R
================================================
#Page Rank

#install.packages('igraph')
library(igraph)

mygraph = graph(c('A','B',
                  'A','D',
                  'B','A',
                  'B','B',
                  'B','C',
                  'C','C',
                  'A','D',
                  'C','D',
                  'C','A'), directed=T)
plot(mygraph, cex=1.5)


================================================
FILE: 78-nlp/readpdf.R
================================================
#read pdf documents

#https://data.library.virginia.edu/reading-pdf-files-into-r-for-text-mining/
#https://www.charlesbordet.com/en/extract-pdf/#use-the-tm-package

#install.packages("pdftools")
library(pdftools)
library(dplyr)

#------
library(pdftools)
download.file("https://github.com/Huitziii/crispy-pdf/raw/master/71_PV.62.pdf", "./71_PV.62.pdf")
text <- pdf_text("./71_PV.62.pdf")








files <- list.files(pattern = "pdf$")
files
grepl(".pdf",files)
#pdf_list <- file_vector[grepl(".pdf",file_list)]

pdf_text("placement.pdf")
text <- pdf_text("placement.pdf") %>% strsplit(split = "\n")

text2 <- strsplit(text, "\n")

opinions <- lapply(files, pdf_text)
library(tm)
corp <- Corpus(URISource(files), readerControl = list(reader = readPDF))

#-------------
uri <- paste0("file://", system.file(file.path("doc", "tm.pdf"), package = "tm"))
engine <- if(nzchar(system.file(package = "pdftools"))) {  "pdftools" 
} else {  "ghostscript"}
reader <- readPDF(engine)
pdf <- reader(elem = list(uri = uri), language = "en", id = "id1")
cat(content(pdf)[1])
VCorpus(URISource(uri, mode = ""),readerControl = list(reader = readPDF(engine = "ghostscript")))

#https://cran.r-project.org/web/packages/textmining/textmining.pdf


================================================
FILE: 78-nlp/rowling.txt
================================================
President Faust, members of the Harvard Corporation and the Board of Overseers, members of the faculty, proud parents, and, above all, graduates.

The first thing I would like to say is ‘thank you.’ Not only has Harvard given me an extraordinary honour, but the weeks of fear and nausea I have endured at the thought of giving this commencement address have made me lose weight. A win-win situation! Now all I have to do is take deep breaths, squint at the red banners and convince myself that I am at the world’s largest Gryffindor reunion.
Delivering a commencement address is a great responsibility; or so I thought until I cast my mind back to my own graduation. The commencement speaker that day was the distinguished British philosopher Baroness Mary Warnock. Reflecting on her speech has helped me enormously in writing this one, because it turns out that I can’t remember a single word she said. This liberating discovery enables me to proceed without any fear that I might inadvertently influence you to abandon promising careers in business, the law or politics for the giddy delights of becoming a gay wizard.

You see? If all you remember in years to come is the ‘gay wizard’ joke, I’ve come out ahead of Baroness Mary Warnock. Achievable goals: the first step to self improvement.

Actually, I have wracked my mind and heart for what I ought to say to you today. I have asked myself what I wish I had known at my own graduation, and what important lessons I have learned in the 21 years that have expired between that day and this.

I have come up with two answers. On this wonderful day when we are gathered together to celebrate your academic success, I have decided to talk to you about the benefits of failure. And as you stand on the threshold of what is sometimes called ‘real life’, I want to extol the crucial importance of imagination.

These may seem quixotic or paradoxical choices, but please bear with me.

Looking back at the 21-year-old that I was at graduation, is a slightly uncomfortable experience for the 42-year-old that she has become. Half my lifetime ago, I was striking an uneasy balance between the ambition I had for myself, and what those closest to me expected of me.

I was convinced that the only thing I wanted to do, ever, was to write novels. However, my parents, both of whom came from impoverished backgrounds and neither of whom had been to college, took the view that my overactive imagination was an amusing personal quirk that would never pay a mortgage, or secure a pension. I know that the irony strikes with the force of a cartoon anvil, now.

So they hoped that I would take a vocational degree; I wanted to study English Literature. A compromise was reached that in retrospect satisfied nobody, and I went up to study Modern Languages. Hardly had my parents’ car rounded the corner at the end of the road than I ditched German and scuttled off down the Classics corridor.

I cannot remember telling my parents that I was studying Classics; they might well have found out for the first time on graduation day. Of all the subjects on this planet, I think they would have been hard put to name one less useful than Greek mythology when it came to securing the keys to an executive bathroom.

I would like to make it clear, in parenthesis, that I do not blame my parents for their point of view. There is an expiry date on blaming your parents for steering you in the wrong direction; the moment you are old enough to take the wheel, responsibility lies with you. What is more, I cannot criticise my parents for hoping that I would never experience poverty. They had been poor themselves, and I have since been poor, and I quite agree with them that it is not an ennobling experience. Poverty entails fear, and stress, and sometimes depression; it means a thousand petty humiliations and hardships. Climbing out of poverty by your own efforts, that is indeed something on which to pride yourself, but poverty itself is romanticised only by fools.

What I feared most for myself at your age was not poverty, but failure.
At your age, in spite of a distinct lack of motivation at university, where I had spent far too long in the coffee bar writing stories, and far too little time at lectures, I had a knack for passing examinations, and that, for years, had been the measure of success in my life and that of my peers.

I am not dull enough to suppose that because you are young, gifted and well-educated, you have never known hardship or heartbreak. Talent and intelligence never yet inoculated anyone against the caprice of the Fates, and I do not for a moment suppose that everyone here has enjoyed an existence of unruffled privilege and contentment.

However, the fact that you are graduating from Harvard suggests that you are not very well-acquainted with failure. You might be driven by a fear of failure quite as much as a desire for success. Indeed, your conception of failure might not be too far from the average person’s idea of success, so high have you already flown.

Ultimately, we all have to decide for ourselves what constitutes failure, but the world is quite eager to give you a set of criteria if you let it. So I think it fair to say that by any conventional measure, a mere seven years after my graduation day, I had failed on an epic scale. An exceptionally short-lived marriage had imploded, and I was jobless, a lone parent, and as poor as it is possible to be in modern Britain, without being homeless. The fears that my parents had had for me, and that I had had for myself, had both come to pass, and by every usual standard, I was the biggest failure I knew.

Now, I am not going to stand here and tell you that failure is fun. That period of my life was a dark one, and I had no idea that there was going to be what the press has since represented as a kind of fairy tale resolution. I had no idea then how far the tunnel extended, and for a long time, any light at the end of it was a hope rather than a reality.

So why do I talk about the benefits of failure? Simply because failure meant a stripping away of the inessential. I stopped pretending to myself that I was anything other than what I was, and began to direct all my energy into finishing the only work that mattered to me. Had I really succeeded at anything else, I might never have found the determination to succeed in the one arena I believed I truly belonged. I was set free, because my greatest fear had been realised, and I was still alive, and I still had a daughter whom I adored, and I had an old typewriter and a big idea. And so rock bottom became the solid foundation on which I rebuilt my life.

You might never fail on the scale I did, but some failure in life is inevitable. It is impossible to live without failing at something, unless you live so cautiously that you might as well not have lived at all – in which case, you fail by default.

Failure gave me an inner security that I had never attained by passing examinations. Failure taught me things about myself that I could have learned no other way. I discovered that I had a strong will, and more discipline than I had suspected; I also found out that I had friends whose value was truly above the price of rubies.

The knowledge that you have emerged wiser and stronger from setbacks means that you are, ever after, secure in your ability to survive. You will never truly know yourself, or the strength of your relationships, until both have been tested by adversity. Such knowledge is a true gift, for all that it is painfully won, and it has been worth more than any qualification I ever earned.


================================================
FILE: 78-nlp/rquery_wordcloud.R
================================================
#++++++++++++++++++++++++++++++++++
# rquery.wordcloud() : Word cloud generator
# - http://www.sthda.com
#+++++++++++++++++++++++++++++++++++
# x : character string (plain text, web url, txt file path)
# type : specify whether x is a plain text, a web page url or a file path
# lang : the language of the text
# excludeWords : a vector of words to exclude from the text
# textStemming : reduces words to their root form
# colorPalette : the name of color palette taken from RColorBrewer package, 
# or a color name, or a color code
# min.freq : words with frequency below min.freq will not be plotted
# max.words : Maximum number of words to be plotted. least frequent terms dropped

# value returned by the function : a list(tdm, freqTable)
rquery.wordcloud <- function(x, type=c("text", "url", "file"), 
                             lang="english", excludeWords=NULL, 
                             textStemming=FALSE,  colorPalette="Dark2",
                             min.freq=3, max.words=200)
{ 
  library("tm")
  library("SnowballC")
  library("wordcloud")
  library("RColorBrewer") 
  
  if(type[1]=="file") text <- readLines(x)
  else if(type[1]=="url") text <- html_to_text(x)
  else if(type[1]=="text") text <- x
  
  # Load the text as a corpus
  docs <- Corpus(VectorSource(text))
  # Convert the text to lower case
  docs <- tm_map(docs, content_transformer(tolower))
  # Remove numbers
  docs <- tm_map(docs, removeNumbers)
  # Remove stopwords for the language 
  docs <- tm_map(docs, removeWords, stopwords(lang))
  # Remove punctuations
  docs <- tm_map(docs, removePunctuation)
  # Eliminate extra white spaces
  docs <- tm_map(docs, stripWhitespace)
  # Remove your own stopwords
  if(!is.null(excludeWords)) 
    docs <- tm_map(docs, removeWords, excludeWords) 
  # Text stemming
  if(textStemming) docs <- tm_map(docs, stemDocument)
  # Create term-document matrix
  tdm <- TermDocumentMatrix(docs)
  m <- as.matrix(tdm)
  v <- sort(rowSums(m),decreasing=TRUE)
  d <- data.frame(word = names(v),freq=v)
  # check the color palette name 
  if(!colorPalette %in% rownames(brewer.pal.info)) colors = colorPalette
  else colors = brewer.pal(8, colorPalette) 
  # Plot the word cloud
  set.seed(1234)
  wordcloud(d$word,d$freq, min.freq=min.freq, max.words=max.words,
            random.order=FALSE, rot.per=0.35, 
            use.r.layout=FALSE, colors=colors)
  
  invisible(list(tdm=tdm, freqTable = d))
}

#++++++++++++++++++++++
# Helper function
#++++++++++++++++++++++
# Download and parse webpage
html_to_text<-function(url){
  library(RCurl)
  library(XML)
  # download html
  html.doc <- getURL(url)  
  #convert to plain text
  doc = htmlParse(html.doc, asText=TRUE)
  # "//text()" returns all text outside of HTML tags.
  # We also don’t want text such as style and script codes
  text <- xpathSApply(doc, "//text()[not(ancestor::script)][not(ancestor::style)][not(ancestor::noscript)][not(ancestor::form)]", xmlValue)
  # Format text vector into one character string
  return(paste(text, collapse = " "))
}



================================================
FILE: 78-nlp/sentiment-tidyr1.R
================================================
# Sentiment analysis

library(tidytext)
sentiments
get_sentiments("afinn")
get_sentiments("bing")
get_sentiments("nrc")

#install.packages("janeaustenr")
library(dplyr)
library(stringr)

tidy_books <- austn_books() %>% group_by(book) %>% mutate(linenumber= row_number() )



================================================
FILE: 78-nlp/sentiment2.R
================================================
#Sentiment Analysis

#https://cran.r-project.org/web/packages/SentimentAnalysis/SentimentAnalysis.pdf

library(SentimentAnalysis)

## Not run:
library(tm)
# via vector of strings
corpus <- c("Positive text", "Neutral but uncertain text", "Negative text")
sentiment <- analyzeSentiment(corpus)
sentiment
compareToResponse(sentiment, c(+1, 0, -2))
# via Corpus from tm package
data("crude")
crude

sentiment <- analyzeSentiment(crude)
sentiment
# via DocumentTermMatrix (with stemmed entries)
dtm <- DocumentTermMatrix(VCorpus(VectorSource(c("posit posit", "negat neutral"))))
sentiment <- analyzeSentiment(dtm)
sentiment


d1 <- SentimentDictionary(c("uncertain", "possible", "likely"))
d2 <- SentimentDictionary(c("rather", "intend", "likely"))
cmp <- compareDictionaries(d1, d2)


documents <- c("This is a test", "an one more")
# count words (without stopwords)
countWords(documents)
# count all words (including stopwords)
countWords(documents, removeStopwords=FALSE)


================================================
FILE: 78-nlp/textmining-DU1.R
================================================
# Text Mining
#Load Libraries
library(plyr)
library(ggplot2)
library(RColorBrewer)
library(wordcloud)
library(tm)
library(SnowballC)
library(NLP)

#Folder of text files
(cname = file.path("E:\\rwork\\rProjects\\analytics\\textdocs"))
dir(cname)
#Load texts into R
docs = Corpus(DirSource(cname))
summary(docs)
inspect(docs)

#Make a copy to see all through
docs1 = docs
docs2 = tm_map(docs1, removePunctuation )
docs3 = tm_map(docs2, removeNumbers)
docs4 = tm_map(docs3, tolower)
docs5 = tm_map(docs4, removeWords, stopwords('english'))
docs6 = tm_map(docs5, removeWords, c('brothers','sisters'))
docs7 = tm_map(docs6, stemDocument)
docs8 = tm_map(docs7, stripWhitespace)
docs8 = Corpus(VectorSource(docs8))
docs9 = tm_map(docs8, PlainTextDocument)# due to error
docs9 = Corpus(VectorSource(docs9))
dtm = DocumentTermMatrix(docs9)
dtm
tdm = TermDocumentMatrix(docs9)
tdm


# Frequency
freq  = colSums(as.matrix(dtm))
freq
length(freq)
dtms = removeSparseTerms(dtm, 0.1)
dtms
ord = order(freq)
ord

freq[head(ord)]
freq[tail(ord)]
freq2 = sort(colSums(as.matrix(dtm)), decreasing = T)
freq2
head(freq2, 14)
findFreqTerms(dtm, lowfreq = 4)
wf = data.frame(word=names(freq2), freq=freq2)
head(wf)

# Association
tm::findAssocs(dtms, 'student', corlimit=0.1)
tm::findAssocs(dtms, 'learn', corlimit=0.1)

#Plotting
p = ggplot2::ggplot(subset(head(wf,n=10), freq=3), aes(word, freq))
p = p + geom_bar(stat='identity')
p = p + theme(axis.text.x = element_text(angle=45, hjust=1))
p


#Word Cloud
#library(wordcloud)
set.seed(142)
wordcloud(names(freq2), scale=c(2,0.5), freq2, min.freq=2)
wordcloud(names(freq2), freq2,scale=c(2,0.5),  max.words=10)
wordcloud(names(freq2), freq2, min.freq=3, scale=c(2,0.5),
          colors=brewer.pal(6,'Dark2'))
dark2 = brewer.pal(6, 'Dark2')
par(mar=c(1,1,1,1))
#scale = c(4, 0.2) or with less words : max.words = 30
#gsub() longwords to shorter abbreviation
wordcloud(names(freq2), freq2, max.words=30, rot.per=0.2, 
          scale=c(2,0.5), colors=dark2)
head(freq2)

dtmss= removeSparseTerms(dtm, 0.68)
library(cluster)
d = dist(t(dtmss), method='euclidean')
fit = hclust(d=d, method='ward.D')
fit
plot(fit) # very not proper
groups = cutree(fit, k=5)
groups
rect.hclust(fit, k=5, border='red')

# Clustering
library(fpc)
d = dist(t(dtmss), method='euclidean')
kfit = kmeans(d, 2)
kfit
clusplot(as.matrix(d), kfit$cluster, color=T, shade=T, labels=2, lines=0)



================================================
FILE: 78-nlp/tm-worldcloud4.R
================================================

library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

source('http://www.sthda.com/upload/rquery_wordcloud.r')
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
res<-rquery.wordcloud(filePath, type ="file", lang = "english")
res
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  min.freq = 1,  max.words = 200)

# Reds color palette
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  colorPalette = "Reds")
# RdBu color palette
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  colorPalette = "RdBu")
# use unique color
res<-rquery.wordcloud(filePath, type ="file", lang = "english", colorPalette = "black")


tdm <- res$tdm
freqTable <- res$freqTable


# Show the top10 words and their frequency
head(freqTable, 10)

# Bar plot of the frequency for the top10
barplot(freqTable[1:10,]$freq, las = 2, 
        names.arg = freqTable[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")

findFreqTerms(tdm, lowfreq = 4)

findAssocs(tdm, terms = "freedom", corlimit = 0.3)

url = "http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software"
rquery.wordcloud(x=url, type="url")


================================================
FILE: 78-nlp/twitter-hotel.R
================================================
#https://www.r-bloggers.com/mining-twitter-for-consumer-attitudes-towards-hotels/

#load twitter library
library(twitteR)
# search for all the hilton tweets
hilton.tweets=searchTwitter('@hilton',n=1500)
hilton.tweets=searchTwitter('@narendramodi',n=15)

hilton.tweets
length(hilton.tweets)
class(hilton.tweets)
tweet=hilton.tweets[[1]]
class(tweet)
tweet$getScreenName()
tweet$getText()

library("plyr")
hilton.text=laply(hilton.tweets,function(t)t$getText())
length(hilton.text)
head(hilton.text,5)
# load list of positive and negative words for SIMPLE sentiment analysis

# you would have to download the files from a website I included below - make sure you put in the directory that you will be
# referencing
hu.liu.pos=scan('/Users/marcinkulakowski/Downloads/r/positive-words.txt',what='character',comment.char=';')

hu.liu.neg=scan('/Users/marcinkulakowski/Downloads/r/negative-words.txt',what='character',comment.char=';')
pos.words=c(hu.liu.pos,'upgrade')
neg.words=c(hu.liu.neg,'wtf','wait','waiting','epicfail','mechanical')

# sampling
sample=c("You'reawesomeandIloveyou","Ihateandhateandhate.Soangry.Die!","Impressedandamazed:youarepeerlessinyourachievementofunparalleledmediocrity.")
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
  require(plyr)
  require(stringr)
  
  # we got a vector of sentences. plyr will handle a list
  # or a vector as an "l" for us
  # we want a simple array ("a") of scores back, so we use
  # "l" + "a" + "ply" = "laply":
  scores = laply(sentences, function(sentence, pos.words, neg.words) {
    
    # clean up sentences with R's regex-driven global substitute, gsub():
    sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    # and convert to lower case:
    sentence = tolower(sentence)
    
    # split into words. str_split is in the stringr package
    word.list = str_split(sentence, '\\s+')
    # sometimes a list() is one level of hierarchy too much
    words = unlist(word.list)
    
    # compare our words to the dictionaries of positive & negative terms
    pos.matches = match(words, pos.words)
    neg.matches = match(words, neg.words)
    
    # match() returns the position of the matched term or NA
    # we just want a TRUE/FALSE:
    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)
    
    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
    score = sum(pos.matches) - sum(neg.matches)
    
    return(score)
  }, pos.words, neg.words, .progress=.progress )
  
  scores.df = data.frame(score=scores, text=sentences)
  return(scores.df)
}
> result=score.sentiment(sample,pos.words,neg.words)
> class(result)
[1] "data.frame"
> result$score
[1] 0 0 0
> hilton.scores=score.sentiment(hilton.text,pos.words,neg.words,.progress='text')
> hilton.scores$hotel='Hilton'
> hilton.scores$code='HL'
> hist(hilton.scores$score)
# hilton histogram
> library("ggplot2")
> qplot(hilton.scores$score)
# qplot hilton
# lets search for all other major hotels
# Intercontinental
intercontinental.tweets=searchTwitter('@intercontinental',n=1500)
class(tweet)
intercontinental.text=laply(intercontinental.tweets,function(t)t$getText())
intercontinental.scores=score.sentiment(intercontinental.text,pos.words,neg.words,.progress='text')
intercontinental.scores$hotel='Intercontinental'
intercontinental.scores$code='IC'
# Wyndham
wyndham.tweets=searchTwitter('@wyndham',n=1500)
class(tweet)
wyndham.text=laply(wyndham.tweets,function(t)t$getText())
wyndham.scores=score.sentiment(wyndham.text,pos.words,neg.words,.progress='text')
wyndham.scores$hotel='Wyndham'
wyndham.scores$code='WY'
# Marriott
marriott.tweets=searchTwitter('@marriott',n=1500)
class(tweet)
marriott.text=laply(marriott.tweets,function(t)t$getText())
marriott.scores=score.sentiment(marriott.text,pos.words,neg.words,.progress='text')
marriott.scores$hotel='Marriott'
marriott.scores$code='MI'
# BestWestern
bestwestern.tweets=searchTwitter('@bestwestern',n=1500)
class(tweet)
bestwestern.text=laply(bestwestern.tweets,function(t)t$getText())
bestwestern.scores=score.sentiment(bestwestern.text,pos.words,neg.words,.progress='text')
bestwestern.scores$hotel='Bestwestern'
bestwestern.scores$code='BW'
# Starwood
starwood.tweets=searchTwitter('@starwood',n=1500)
class(tweet)
starwood.text=laply(starwood.tweets,function(t)t$getText())
starwood.scores=score.sentiment(starwood.text,pos.words,neg.words,.progress='text')
starwood.scores$hotel='Starwood'
starwood.scores$code='SW'
# Hyatt
hyatt.tweets=searchTwitter('@hyatt',n=1500)
class(tweet)
hyatt.text=laply(hyatt.tweets,function(t)t$getText())
hyatt.scores=score.sentiment(hyatt.text,pos.words,neg.words,.progress='text')
hyatt.scores$hotel='Hyatt'
hyatt.scores$code='HY'
> all.scores=rbind(intercontinental.scores,wyndham.scores,hilton.scores,marriott.scores,bestwestern.scores,starwood.scores,hyatt.scores)
# Make separate plot for each hotel
> ggplot(data=all.scores)+#ggplotworksondata.frames,always
  geom_bar(mapping=aes(x=score,fill=hotel),binwidth=1)+
  facet_grid(hotel~.)+#makeaseparateplotforeachhotel
  theme_bw()+scale_fill_brewer()#plaindisplay,nicercolors
# Plot
> all.scores$very.pos=as.numeric(all.scores$score>=2)
> all.scores$very.neg=as.numeric(all.scores$score twitter.df=ddply(all.scores,c('hotel','code'),summarise,pos.count=sum(very.pos),neg.count=sum(very.neg))
                                 > twitter.df$all.count=twitter.df$pos.count+twitter.df$neg.count
                                 > twitter.df$score=round(100*twitter.df$pos.count/twitter.df$all.count)
                                 > install.packages("doBy")
                                 > library("doBy")
                                 > orderBy(~-score,twitter.df)
                                 hotel code pos.count neg.count all.count score
                                 1 Bestwestern BW 6 0 6 100
                                 5 Starwood SW 7 0 7 100
                                 6 Wyndham WY 2 0 2 100
                                 3 Hyatt HY 7 1 8 88
                                 2 Hilton HL 15 3 18 83
                                 4 Marriott MI 13 4 17 76
                                 > install.packages("XML")
                                 > library(XML)
                                 > acsi.url='http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Hotels'
                                 # scrape acsi website for scores
                                 > acsi.df=readHTMLTable(acsi.url,header=T,which=1,stringsAsFactors=F)
                                 > acsi.df=acsi.df[,c(1,18)]
                                 > head(acsi.df,1)
                                 > colnames(acsi.df)=c('hotel','score')
                                 > acsi.df$score=as.numeric(acsi.df$score)
                                 > View(acsi.df)




================================================
FILE: 78-nlp/twitter-keys.R
================================================
# Consumer Key (API Key)	uRDuync3BziwQnor1MZFBKp0x
# Consumer Secret (API Secret)	t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV
# 
# Access Token	14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW
# Access Token Secret	3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz


================================================
FILE: 78-nlp/twitter-sentiment2.R
================================================
# Tweeter Download and Analysis
#connect all libraries
library(twitteR)
library(ROAuth)
library(plyr)
library(dplyr)
library(stringr)
library(ggplot2)
#Certificate not Required
#load("twitter authentication.Rdata")
#https://dataaspirant.com/2018/03/22/twitter-sentiment-analysis-using-r/


AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"
ConsumerKey="uRDuync3BziwQnor1MZFBKp0x"
ConsumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"

#---- Alread saved authentication  
load('twitter authentication.Rdata') #Once you launch the code first time, you can start from this line in the future (libraries should be connected)
setup_twitter_oauth(ConsumerKey, ConsumerSecret, AccessToken, AccessTokenSecret)

tweets <- userTimeline("realDonaldTrump", n=200)
#english
tweets[2]
n.tweet <- length(tweets)
n.tweet
tweets.df <- twListToDF(tweets) 
head(tweets.df)
summary(tweets.df)

#Remove hashtags & unnecessary characters
tweets.df2 <- gsub("http.*","",tweets.df$text)
tweets.df2 <- gsub("https.*","",tweets.df2)
tweets.df2 <- gsub("#.*","",tweets.df2)
tweets.df2 <- gsub("@.*","",tweets.df2)

head(tweets.df2)
#-----
library("syuzhet")
word.df <- as.vector(tweets.df2)
emotion.df <- get_nrc_sentiment(word.df)
emotion.df2 <- cbind(tweets.df2, emotion.df) 
head(emotion.df2)

#-----
sent.value <- get_sentiment(word.df)
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(tweets,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)



================================================
FILE: 78-nlp/twitter1-DU1.R
================================================
#Twitter 1 - Configure Tweets and Download them
#@dupadhyaya
#Working using my Keys
library("curl")
library("twitteR")
library("ROAuth")

download.file(url="http://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")

#https://apps.twitter.com/
#different for each account
consumerKey="uRDuync3BziwQnor1MZFBKp0x"
consumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"
AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"

#Common for all accounts except the keys

cred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')

cred$handshake(cainfo="cacert.pem") # it will take you to browser: authorise, copy key
save(cred, file="twitter authentication.Rdata") # store this to avoid asking again

#Load saved authentication cert
load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)

search.string <- "#FifaWorldCup2018"
no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets




================================================
FILE: 78-nlp/twitter1-DU2.R
================================================
# Use Existing Twitter Authentication Stored
# Marketing/ Business Analytics

library("twitteR")
library("ROAuth")

# Certificate not Required
#load("twitter authentication.Rdata")

AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"
ConsumerKey="uRDuync3BziwQnor1MZFBKp0x"
ConsumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"

load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(ConsumerKey, ConsumerSecret, AccessToken, AccessTokenSecret)

search.string <- "#marketinganalytics"
no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets

#My Tweets : will change if you use your own account
homeTimeline(n=15)  #my tweets
mentions(n=15)   # my tags


================================================
FILE: 78-nlp/twitter1-authen.R
================================================
# Twitter - Setting up Authentication




================================================
FILE: 78-nlp/twitter1.R
================================================
# twitter
library(twitteR)
library(ROAuth)
library(RCurl)
library(plyr)
library(stringr)
library(ggplot2)
library(digest)
library(rjson)
library(httr)
library(RJSONIO)
library(tm)
library(scales)
library(lubridate)
library(syuzhet)

options(httr_oauth_cache = T)
access_token='14366551-ymV6A8XM2aXcgGUYNLTBOxAuZGIRLinLb0uzzOYKe'
access_token_secret = 'VEmnsIvcIvfySEXD8eXKe1CLE3ZY7mMBwEIK9fB665SDe'
api_key='L2ch59MKPheK7FsSYFPL4oFdU'
api_secret = 'lvZyPxULK9HzI5CBgdXah5MoQFLsU2EQ6T0NND1FFBb40ovBdW'


#setup_twitter_oauth(consumer_Key, consumer_Secret, access_token, access_secret)
setup_twitter_oauth(api_key,api_secret,access_token, access_token_secret)

tweet = searchTwitter("#Demonitisation OR #Modi",n=500,since='2006-11-08')
view(tweet_df)
class(tweet)
tweet_df = twListToDF(tweet)
tweets.text = lapply(tweet,function(t) ....)

mycorpus = Corpus(VectorSource(tweets.text))
x = as.character(mycorpus)
str(mycorpus)
mycorpus1 = tm_map(mycorpus,stripWhitespace)
mycorpus2 = tm_map(mycorpus1,tolower)
mycorpus3 = tm_map(mycorpus2,removewords,stopwords('english'))
mycorpus4 = tm_map(mycorpus3,removePunctuation)
mycorpus5 = tm_map(mycorpus4,removeNumbers)
mycorpus6 = tm_map(mycorpus5,PlainTextDocument)
data_dtm1 = DocumentTermMatrix(mycorpus6)
inspect(data_dtm1)
frequent = findFreqTerms(data_dtm1,lowfreq=100,highfreq=Inf)
frequent

library(wordcloud)



#Sentimental Analysis


================================================
FILE: 78-nlp/twitter2.R
================================================
# Twitter 1

# install.packages("RColorBrewer")
# install.packages("tm")
# install.packages("wordcloud")
# install.packages('base64enc')
# install.packages('ROAuth')
# install.packages('plyr')
# install.packages('stringr')
# install.packages("openssl")
#install.packages("httpuv")
install.packages('twitteR')
library(openssl)
library(httpuv)
library(RColorBrewer)
library(wordcloud)
library(tm)
library(twitteR)
library(ROAuth)
library(plyr)
library(stringr)
library(base64enc)


download.file(url='http://curl.haxx.se/ca/cacert.pem', destfile='cacert.pem')

reqURL = 'https://api.twitter.com/oauth/request_token'
accessURL = 'https://api.twitter.com/oauth/access_token'
authURL = 'https://api.twitter.com/oauth/authorize'



#Extract  / error
dubatweets1 = searchTwitter(' #businessanalytics', n=3000, lang='en')

#Setup accesstoken for this session

#Copy keys from Twitter Account
consumerKey = 'uRDuync3BziwQnor1MZFBKp0x'
consumerSecret = 't8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV'
setup_twitter_oauth(consumerKey, consumerSecret)


cred <- ROAuth::OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL=reqURL, accessURL=accessURL, authURL=authURL)  #ok

#install.packages("openssl")
#install.packages("httpuv")


setup_twitter_oauth(consumerKey, consumerSecret)
setup_twitter_oauth(api_key, api_secret, access_token, access_token_secret, access_token=NULL, access_secret=NULL)


cred$handshake(cainfo = system.file('CurlSSL', 'cacert.pem', package = 'RCurl'))

# goto web browser - and authorise - note the key and paste it in 

save(cred, file='twitterauthentication.Rdata')
load('twitterauthentication.Rdata') 
setup_twitter_oauth(cred)

dubatweets1 = searchTwitter(' #businessanalytics', n=3000, lang='en')
setup_twitter_oauth(consumerKey,consumerSecret, accessToken=consumerKey, accessTokenSecret=consumerSecret)


================================================
FILE: 78-nlp/twitteracct
================================================
ConsumerKey="uRDuync3BziwQnor1MZFBKp0x"
ConsumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"
AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"


================================================
FILE: 78-nlp/wordcloud1.R
================================================
# World Cloud 2 - error
library(wordcloud)
# Read the text file from internet
filePath = "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text = readLines(filePath)
text

# Load the data as a corpus
docs = Corpus(VectorSource(text))
docs

#Text transformation
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

#Cleaning Text
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
docs <- tm_map(docs, stemDocument)


#Document Matrix
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Generate Word Cloud
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,  colors=brewer.pal(8, "Dark2"))



================================================
FILE: 78-nlp/wordcloud2.R
================================================
# Word Cloud

##http://dni-institute.in/blogs/colorful-word-cloud-using-r/
# tm for text mining
# SnowballC for text stemming
# wordcloud for generating word cloud images
# RCurl and XML packages to download and parse web pages
# RColorBrewer for color palettes

library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

source('http://www.sthda.com/upload/rquery_wordcloud.r')
#or
#source('./TM/rquery_wordcloud.R')
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
filePath2 <- "./data/martin-luther-king-i-have-a-dream-speech.txt"

res<-rquery.wordcloud(filePath, type ="file", lang = "english")
res<-rquery.wordcloud(filePath, type ="file", lang = "english")



================================================
FILE: 78-nlp/wordcloud3.R
================================================
# World Cloud
#http://stat.ethz.ch/R-manual/R-devel/library/base/html/strsplit.html
#https://stackoverflow.com/questions/4350440/split-a-column-of-a-data-frame-to-multiple-columns

library(stringr)
library(wordcloud)
library(RColorBrewer)
library(tm)
library(SnowballC)
library(RCurl)
library(XML)



#How to use strsplit
strsplit('IIT-Gawahati', "-")
strsplit('IIT Gawahati', " ")

library(gsheet)
url = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=0'
#check for dashtype, they are not always same
df1 = as.data.frame(gsheet2tbl(url))
df1
head(df1)
df1$wordexplanation
base::strsplit(df1$wordexplanation,'-')  #output as list need in DF

#Different Methods split and put in dataframe objects

#Stringr - str_split
stringr::str_split('IIT-Guwahati', "-")

#Method
(out1 = strsplit(as.character('IIT - Guwahati'),'-')) #if the word was not character
head(df1)
out = strsplit(df1$wordexplanation,'-') 
head(out)

t(sapply(out[1:5], '['))
df2=data.frame(t(sapply(out[1:200], '[')))

head(df2)
df= cbind(df1,df2)
head(df)
names(df)[c(2,3)] = c('word','explanation') #rename columns
head(df)

# create random frequencies for all words in a col
df$freq = floor(runif(100, 1,100))
head(df)

#Method for word cloud
library(wordcloud)
wc1 = wordcloud(df$word, df$freq, random.order=T)
wc2 = wordcloud(df$word, df$freq, random.order=F, colors = topo.colors(10))
wc3 = wordcloud(df$word, df$freq, scale=c(4,.1), min.freq=2, max.words=50,  random.order=F)
wc4 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F)


#------

library(RColorBrewer)
library(tm)
pal <- brewer.pal(8,"Dark2")
wc5 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F, colors=pal)


#load the function rquery.wordcloud
source('http://www.sthda.com/upload/rquery_wordcloud.r')

res <- rquery.wordcloud(filePath, type ="file", lang = "english",min.freq = 1,  max.words = 200)

# Reds color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english",colorPalette = "Reds")
# RdBu color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english", colorPalette = "RdBu")
# use unique color
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  colorPalette = "black")


tdm <- res$tdm
freqTable <- res$freqTable
# Show the top10 words and their frequency
head(freqTable, 10)

# Bar plot of the frequency for the top10
barplot(freqTable[1:10,]$freq, las = 2, 
        names.arg = freqTable[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")

findFreqTerms(tdm, lowfreq = 4)

findAssocs(tdm, terms = "freedom", corlimit = 0.3)

url = "http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software"
rquery.wordcloud(x=url, type="url")


================================================
FILE: 78-nlp/worldcloud2.R
================================================
# World Cloud 2

# Read the text file from file
text <- readLines(file.choose())
text

# Load the data as a corpus
docs = Corpus(VectorSource(text))
docs

#Text transformation
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

#Cleaning Text
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
docs <- tm_map(docs, stemDocument)


#Document Matrix
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Generate Word Cloud
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

findFreqTerms(dtm, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
head(d, 10)

#Plot Freq
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
       col ="lightblue", main ="Most frequent words",
       ylab = "Word frequencies")


================================================
FILE: 78-textdocs/vit.txt
================================================
VIT was established with the aim of providing quality higher education on par with international standards. It persistently seeks and adopts innovative methods to improve the quality of higher education on a consistent basis.The campus has a cosmopolitan atmosphere with students from all corners of the globe. Experienced and learned teachers are strongly encouraged to nurture the students. The global standards set at VIT in the field of teaching and research spur us on in our relentless pursuit of excellence. In fact, it has become a way of life for us. The highly motivated youngsters on the campus are a constant source of pride. Our Memoranda of Understanding with various international universities are our major strength. They provide for an exchange of students and faculty and encourage joint research projects for the mutual benefit of these universities. Many of our students, who pursue their research projects in foreign universities, bring high quality to their work and esteem to India and have done us proud. With steady steps, we continue our march forward. We look forward to meeting you here at VIT.

History of VIT
The begining VIT as VEC
Open Gallery 
It was established under Section 3 of the University Grants Commission (UGC) Act, 1956, and was founded in 1984 as a self-financing institution called the Vellore Engineering College. The Union Ministry of Human Resources Development conferred University status on Vellore Engineering College in 2001. The University is headed by its founder and Chancellor, Dr. G. Viswanathan, a former Parliamentarian and Minister in the Tamil Nadu Government. In recognition of his service to India in offering world class education, he was conferred an honorary doctorate by the West Virginia University, USA. Sankar Viswanathan, Sekar Viswanathan and G.V. Selvam are the Vice-Presidents; Dr. Anand A. Samuel is the Vice-Chancellor and Dr. V. Raju and Dr. S. Narayanan are the Pro-Vice-Chancellors.

Our Vision
We at VIT will impart futuristic technical education and instil high patterns of discipline through our dedicated staff, who shall set global standards, making our students technologically superior and ethically strong, who in turn shall improve the quality of life of the human race.

Our Mission
Our mission is to educate students from all over India, including those from the local and rural areas, and from other countries, so they become enlightened individuals, improving the living standards of their families, industry and society. We will provide individual attention, world-class quality education and take care of character building.

Quick Facts
Over 2,23,000 appeared for the VIT Engineering Entrance Examination (VITEEE) in 2017
The institution offers 36 Undergraduate, 31 Post graduate, 4 Integrated and 3 Research Programmes.
NAAC has re-accredited VIT with an 'A' grade.
A 360-acre eco-friendly campus with over 50.83 lakh sq.ft. built-up space at Vellore.
A 170 acre eco-friendly second campus at Chennai.
Over 35,000 students, with nearly 1/3rd of them women
Students from 60 countries as well as from every state in India
Over 1400 faculty and 1413 staff.
VIT has been accredited by TCS, Wipro and Cognizant for academic collaboration and placements.
Entered the Limca Book of Records for Campus Recruitment.

================================================
FILE: 80-SIM/montecarlo1.R
================================================
#monte carlo Simulation

#basics of a Monte Carlo simulation are simply to model your problem, and than randomly simulate it until you get an answer

#Normal distribution with a mean of 1 and a standard deviation of 10.
runs <- 100000
sims <- rnorm(runs,mean=1,sd=10)
#write a simulation that samples from this distribution 100,000 times and see how many values are between 3 and 6.
mc.integral <- sum(sims >= 3 & sims <= 6)/runs
mc.integral
#approx 11 %

#Approximating the Binomial Distribution
#We flip a coin 10 times and we want to know the probability of getting more than 3 heads. Now this is a trivial problem for the Binomial distribution, but suppose we have forgotten about this or never learned it in the first place. We can easily solve this problem with a Monte Carlo Simulation. We'll use the common trick of representing tails with 0 and heads with 1, then simulate 10 coin tosses 100,000 times and see how often that happens. 

runs <- 100000
#one.trail simulates a single round of toss 10 coins
#and returns true if the number of heads is > 3
one.trial <- function(){
  sum(sample(c(0,1),10,replace=T)) > 3
}
#now we repeat that trial 'runs' times.
mc.binom <- sum(replicate(runs,one.trial()))/runs
mc.binom
#For our ad hoc Binomial distribution we get mc.binom = 0.8279

#Which we can compare to R's builtin Binomial distribution function
pbinom(3,10,0.5,lower.tail=FALSE) #= 0.8281


================================================
FILE: 80-SIM/montecarlo2.R
================================================
#Monte Carlo
#https://cran.r-project.org/web/packages/MonteCarlo/vignettes/MonteCarlo-Vignette.html
library(MonteCarlo)


================================================
FILE: 80-SIM/mvsim1.R
================================================
#MV Simulation

#Simulating multivariate data structures

#four multivariate normal variables with specified correlations (Sigma = sig), specified means (mu = 0 for all four), and use 'empirical = TRUE' to replicate the exact correlations among the simulated variables.

sig <- matrix(c(1.0, 0.8, 0.5, 0.2,
                0.8, 1.0, 0.5, 0.5,
                0.5, 0.5, 1.0, 0.5,
                0.2, 0.5, 0.5, 1.0), nrow = 4)
sig
library(MASS)
df.4 <- data.frame(mvrnorm(n = 1000, mu = rep(0, 4), Sigma = sig, empirical = TRUE))
detach("package:MASS")
summary(df.4)
ncol(df.4)
nrow(df.4)
head(df.4)
#see the original pattern
round(sig, 2)
round(cor(df.4), 2)


================================================
FILE: 80-SIM/randomNos.R
================================================
#random Nos

#https://bookdown.org/rdpeng/rprogdatascience/simulation.html


================================================
FILE: 80-SIM/simLinks
================================================
#Links


https://web.stanford.edu/class/bios221/labs/simulation/Lab_3_simulation.html
http://rstudio-pubs-static.s3.amazonaws.com/302783_75485bd9eb4646698f534a4833a026e5.html

https://campus.datacamp.com/courses/statistical-inference-and-data-analysis/lab-2-probability?ex=10
http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/simRreg.pdf
http://had.co.nz/stat480/lectures/15-simulation.pdf

https://www.iima.ac.in/c/document_library/get_file?uuid=f514db0c-33a3-495f-ab8f-975a45cd59ac&groupId=585550&filename=Simulationknitr

https://towardsdatascience.com/monte-carlo-simulation-in-r-with-focus-on-financial-data-ad43e2a4aedf

https://cran.r-project.org/web/packages/simulator/simulator.pdf

http://inst-mat.utalca.cl/jornadasbioestadistica2011/doc/CursoCasella/%20UseR-SC-10-B-Part1.pdf
https://cran.r-project.org/web/packages/MonteCarlo/vignettes/MonteCarlo-Vignette.html


================================================
FILE: 80-SIM/simple1.R
================================================
#Simulation

#Simple
#https://it.unt.edu/simple-data-simulationshttps://it.unt.edu/simple-data-simulations

x <- rnorm(n = 10000, mean = 10, sd = 1.5)
hist(x)
mean(x)
sd(x)

#without mean & sd
x <- rnorm(10000)
hist(x)
mean(x)
sd(x)


# other theoretical distributions; such as: 'rbeta' (Beta distribution), 'rexp' (Exponential distribution), 'rchisq' (non-central Chi-Squared distribution), 'rpois' (Poisson distribution), 'rf' (F distribution), 'rt' (Student's t distribution), etc. Each of the distribution functions create random realizations of a theoretical distribution, given the parameters for each distribution (e.g. mean and standard deviation for Normal distributions). Each distribution also has three other functions which provide density, distribution function, and quantile. So, for the Normal distribution(s) there are four standard functions: 'rnorm', 'dnorm' (density), 'pnorm' (distribution function), and 'qnorm' (for quantiles).


#Common Regression-style simulation
x <- rnorm(100, 10, 1.5)
Zx <- scale(x)
Zy <- .8*Zx + rnorm(100, 0, sqrt(1 - (.8^2)))
cor(Zx, Zy)
summary(lm(Zy ~ 0 + Zx))


================================================
FILE: 81-case-sum/31b-DA-dencoCase.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1)
head(sales1)
#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)


#using gsheet
library(gsheet)
denco2 = as.data.frame(gsheet2tbl(url))
str(denco2)

#head(sales1,n=7)
#names(sales1)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backup
str(sales)
class(sales)
str(sales)
?summary
summary(sales)

str(sales)
dim(sales) #dimensios of DF rows & colnum
unique(sales$custname)
length(unique(sales$custname))
length(unique(sales$region ))

# aggregation
aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
df1 = aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
head(df1)
str(df1)

df1=df1[order(df1$x, decreasing=TRUE),]
head(df1,10)

head(df1[order(df1$x, decreasing=TRUE),], 5)

aggregate(sales$revenue, by=list(sales$region), FUN=mean)
df2= aggregate(formula=revenue ~ region, data=sales, FUN=sum)
df2[order(df2$revenue, decreasing=F),]


#Aggregate Formula
(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))
head(df2[order(df2$revenue,decreasing=T),],10)

#List
list1= tapply(sales$revenue, sales$custname, FUN=sum)
head(list1)
list1
head(sort(list1, decreasing=T))
summary(df3)
str(df3)

#dplyr
names(sales)

library(dplyr)

sales %>% filter(margin > 10000) %>% arrange(region, desc(revenue))
filter(sales, margin > 1000000)

sales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)

names(sales)
sales %>% group_by(custname) %>% 
  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))


library(data.table)
dt1 = as.data.table(sales)
dt2 = dt1[, sum(revenue), by=custname]
names(dt2)
dt1[, dt1[, sum(revenue), by=custname]]
#dt1[, order( decreasing = T)]

# Select
library(sqldf)
df5 =sqldf("Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc ")
head(df5)

# Frequency --------
names(sales)
t1=table(sales$custname)
class(t1)
length(t1)
head(t1)
t2= sort(t1,decreasing=T )
head(t2)
head(sort(table(sales$custname), decreasing=T), n=10)
tail(sort(table(sales$custname), decreasing=T), n=10)

#xtab
head(sort(xtabs(~ custname, sales), decreasing=T))

#
library(dplyr)
sales %>% dplyr::count(custname, sort=TRUE)

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))



#plyr
df2a= plyr::count(sales, c('custname'))
str(df2a); names(df2a)
head(df2a[order(-df2a$freq),])

# Summarise by Part Num

df3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)
head(df3a)
str(df3a)
df3a[order(-df3a$x),][1:6,]
head(df3a[order(-df3a$x),])

df3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)
head(df3b)
head(df3b[order(df3b$revenue, decreasing=T),])

sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))


# which parts have highest Profit : partno - sum(profit)
names(sales)
df4a = aggregate(margin ~ partnum, data=sales, FUN=sum)
aggregate(margin ~ partnum, data=sales, FUN=sum)
head(df4a[order(df4a$margin, decreasing = T),])

sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()



================================================
FILE: 81-case-sum/31c-DA-dencoCase2.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1)

#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backuph
head(sales)  #top 6 rows
str(sales)
class(sales)
dim(sales)
summary(sales)
names(sales)
tail(sales)

unique(sales$custname)
length(unique(sales$custname))
length(unique(sales$region ))
#dplyr
names(sales)
library(dplyr)

names(sales)
sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)
#Triumph Insulation gave highest Revenue

df3 = sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))
options(tibble.width = Inf) # displays all columns.
options(tibble.print_max = Inf) # to show all the rows.
df3[15:60,]

# Frequency --------
names(sales)
t1=table(sales$custname)
t1
class(t1); length(t1) ;head(t1)
t2= sort(t1,decreasing=T )
head(t2)


library(dplyr)
sales %>% count(custname, sort=TRUE)
sales %>% count(custname, sort=TRUE) %>% head(n=5)


sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)
# Summarise by Part Num
sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))  %>% head(n=5)
# which parts have highest Profit : partno - sum(profit)
names(sales)
sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()

df4 = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))
pie(x = df4$Revenue)
barplot(df4$Revenue, col=1:4)

sales %>% filter(revenue >= 7771000 ) %>% select(custname, revenue)
sales[, 1:2]

sales %>% sample_frac(.01) 
sales %>% sample_n(5)
sample_n(sales, 5)




================================================
FILE: 81-case-sum/31d-dsum-denco.R
================================================
# Case Study - Denco  
#Manufacturing Firm with sales data of partnum and customer with region wise sales

# Should know - import, table, dplyr, aggregate etc

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1) #see if data is loaded and check its structure

#read file : Method2 : when location is not in project folder
sales2 = read.csv(file.choose())
str(sales2)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backuph
head(sales)  #top 6 rows
str(sales)  # structure
class(sales) # class type - Df
dim(sales)  # rows & columns
summary(sales) # summary- colname, type
names(sales) # col names
head(sales) # first 6 values
tail(sales) # last 6 values

# Some other functions
unique(sales$custname)  # unique customer names
length(unique(sales$custname)) # no of unique customers
length(unique(sales$region )) # no of distinct regions

#Information Required----
# 1. Who are the most loyal Customers - What to do : Improve repeated sales, Target customers with low sales Volumes
# 2. Which customers contribute the most to their revenue : How do I retain these customers & target incentives
# 3a. What part numbers bring in to significant portion of revenue : Maximise revenue from high value parts
# 3b. What parts have the highest profit margin : What parts are driving profits & what parts need to build further


#dplyr
names(sales)
library(dplyr)  #use this library to do summarisation

#Case-1 : Loyal Customers----
# Finding Frequency and sort them in descending order
names(sales)
(t1=table(sales$custname))  # freq table for all customers
class(t1); length(t1)
head(t1) # top 6 but not sorted
t2= sort(t1,decreasing=T )
head(t2)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

#other methods through dplyr
library(dplyr)
sales %>% count(custname, sort=TRUE) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase


#Case-2 : Customer- Highest Revenue---
sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)
#Ans2: Triumph Insulation gave max revenue

# save this object and then analyse
salesrevenue = sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))

#dply uses tibble format and does not show all rows/cols by default
options(tibble.width = Inf) # displays all columns.
options(tibble.print_max = Inf) # to show all the rows.
salesrevenue[1:5,] # first 5 rows
#Ans2: here also Triump insulation gives max revenue = 35592K

#Case3 : Most Profitable PartsNums ----
# Look for freq, revenue and/ or profit margin

# Summarise by Part Num for frequency
sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))  %>% head(n=5)
#Ans3a: Part Num- 764821000 was purchased max times -   122

# Summarise Partnum for Profit(margin) : sum(profit)
names(sales)
sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()
#Ans 3b: Part Num - 733648000 gave max margin profit - 11003367

# Summarise Partnum for revenue
#(practise it )


#Case : Extra ----
#Find Regions wise sales

salesregionrevenue = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))

#Some Graphs related to Data Summarised
pie(x = salesregionrevenue$Revenue, labels=unique(sales$region))
barplot(salesregionrevenue$Revenue, col=1:4)

# Filter Data----
#Rows where revenue > 700000 : show only custname, revenue
sales %>% filter(revenue >= 700000 ) %>% select(custname, revenue)

#select 1% rows on random basis
sales %>% sample_frac(.01)  %>% select(custname, revenue)

#select 10 rows on random basis and sort them regionwise
sales %>% sample_n(10)  %>% select(custname, region) %>% arrange(region)
sample_n(sales, 10) # similar method

#End of session for Denco Case - Manufacturing Firm
#Descriptive Analysis
#Loyal Customers, Customer giving max revenue
#Profitable Partnums - Freq, Revenue and Margin



================================================
FILE: 81-case-sum/31e-dencoCase2.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1)

#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backuph
head(sales)  #top 6 rows
str(sales)
class(sales)
dim(sales)
summary(sales)
names(sales)
tail(sales)

unique(sales$custname)
length(unique(sales$custname))
length(unique(sales$region ))
#dplyr
names(sales)
library(dplyr)

names(sales)
sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=3)
#Triumph Insulation gave highest Revenue

df3 = sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))
options(tibble.width = Inf) # displays all columns.
options(tibble.print_max = Inf) # to show all the rows.
df3[15:60,]

# Frequency --------
names(sales)
t1=table(sales$custname)
t1
class(t1); length(t1) ;head(t1)
t2= sort(t1,decreasing=T )

library(dplyr)
sales %>% count(custname, sort=TRUE) %>% head(n=5)
sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)
# Summarise by Part Num
sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))  %>% head(n=5)
# which parts have highest Profit : partno - sum(profit)
names(sales)
sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()

df4 = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue))
pie(x = df4$Revenue)
barplot(df4$Revenue, col=1:4)

sales %>% filter(revenue >= 7771000 ) %>% select(custname, revenue)
sales[, 1:2]

sales %>% sample_frac(.01) 
sales %>% sample_n(5)
sample_n(sales, 5)




================================================
FILE: 81-case-sum/31f-dencoCase.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
head(sales1)
str(sales1)

#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)
head(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)
head(sales2)
head(sales1)


#head(sales1,n=7)
#names(sales1)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backup
str(sales)
class(sales)
summary(sales)

sales$region = factor(sales$region)
summary(sales)

str(sales)
dim(sales) #dimensios of DF rows & colnum
unique(sales$custname)
length(unique(sales$custname)) #1268
length(unique(sales$region ))
#custname #revenue(sum)

t1= table(sales$custname)



options(digits=7)

# aggregation
aggregate(sales$revenue , by=list(sales$custname), FUN=sum)  #1268 rows
head(aggregate(revenue ~ custname, data=sales, FUN=sum))  #1268 rows
df1= aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
head(df1)
str(df1)

#head(df1[order(df1$x, decreasing=TRUE),], 5)
df1=df1[ order(df1$x, decreasing=TRUE), ] # order and save as original DF
head(df1,n=10)
#Triumph insulation - max revenue

#Region wise revenue
df2= aggregate(formula=revenue ~ region, data=sales, FUN=mean)
head(df2)
head(df2[order(df2$revenue, decreasing=FALSE),], 6)
#China region gave least mean revenue

#Aggregate Formula
(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))
head(df2[order(df2$revenue,decreasing=T),],10)

#List
list1= tapply(sales$revenue, sales$custname, FUN=sum)
head(list1)
list1
head(sort(list1, decreasing=T))
summary(df3)
str(df3)

#dplyr----
names(sales)  #names of variables or columns
library(dplyr)
sales[sales$margin > 1000000, ]  #base functionality
filter(sales, margin > 1000000)
sales %>% filter(margin > 1000000)
sales %>% filter(margin > 1000000) %>% filter(revenue > 2000000) 

sales %>% filter(margin > 100000) %>% arrange(region, desc(revenue))
#custname - region(asc) - revenue(desc) for margins > 100000
filter(sales, margin > 1000000)

sales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)

sales %>% select(custname, region, margin) %>% arrange(desc(margin)) %>% head(n=10)

names(sales)
df5 = (sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)))
sales %>% group_by(custname) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))  %>% head(n=5)
str(df5)
as.data.frame(df5)

sales %>% group_by(region) %>% summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))



library(data.table)
dt1 = as.data.table(sales)
dt2 = dt1[, sum(revenue), by=custname]
names(dt2)
dt1[, dt1[, sum(revenue), by=custname]]
#dt1[, order( decreasing = T)]

# Select
library(sqldf)
df5 =sqldf("Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc ")
head(df5)

# Frequency --------
names(sales)
t1=table(sales$custname)
class(t1)
length(t1)
head(t1)
t2= sort(t1,decreasing=T )
head(t2)
head(sort(table(sales$custname), decreasing=T), n=10)
tail(sort(table(sales$custname), decreasing=T), n=10)

#xtab
head(sort(xtabs(~ custname, sales), decreasing=T))

#
library(dplyr)
sales %>% count(custname, sort=TRUE)
sales %>% group_by(custname) %>% summarise(n = n()) %>% arrange(desc(n))
# chiz bros have been most loyal


#plyr
df2a= plyr::count(sales, c('custname'))
str(df2a); names(df2a)
head(df2a[order(-df2a$freq),])

# Summarise by Part Num

df3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)
head(df3a)
str(df3a)
df3a[order(-df3a$x),][1:6,]
head(df3a[order(-df3a$x),])

df3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)
head(df3b)
head(df3b[order(df3b$revenue, decreasing=T),])

sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))


# which parts gave highest Profit margin : partno - sum(profit)
names(sales)
df4a = aggregate(margin ~ partnum, data=sales, FUN=sum)
aggregate(margin ~ partnum, data=sales, FUN=sum)
head(df4a[order(df4a$margin, decreasing = T),])

sales %>% group_by(partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()


# Graphs
boxplot(sales$revenue)
df6 <- sales %>% group_by(region) %>% summarise(Revenue = sum(revenue))
df6 = as.data.frame(df6)
df6
pie(df6$Revenue, labels = df6$region)
barplot(df6$Revenue, col=1:6, names.arg = df6$region)


================================================
FILE: 81-case-sum/33c-basicDM-mtcars.R
================================================
# Basic Data Manipulation in mtcars
# Adding Columns, Deleting them, Removing them, Selecting them

df3 = mtcars
head(df3)

#adding variables
df3$mpgplus = df3$mpg + 2
head(df3)

#selected variables
varnames = c('mpg', 'wt', 'cyl')
df3[varnames]
#columns to be from any of these
(selected = names(df3) %in% varnames)
df3[selected]

#other than selected columns
df3[-selected]

#remove the column
df3$mpgplus = NULL
head(df3)  


================================================
FILE: 81-case-sum/33c-dplyr-mtcars.R
================================================
# Descriptive Analysis : mtcars  using : dplyr

df1 = mtcars # make a copy and do analysis on df
head(df1)
str(df)
#what ways we can have summary

# convert relevant cols to factor (can be ordered also)
factorcols = c('cyl','vs', 'am', 'gear', 'carb')

df1[factorcols] = lapply(df1[factorcols] ,factor, ordered=T)
sapply(df1, class)
str(df1)
unique(abbreviate(rownames(df1)))

# Now do summarisation
#attach(df1)
names(df1)
table(df1$cyl)  #cylinder wise summary
table(df1$am) #transmission wise

#combine it in single command 
sapply(df1[factorcols],table)


# Find Numeric Cols and numerical summaries on them
sapply(df1, is.numeric)
(numcols = sapply(df1, is.numeric))
class(numcols)
(numcols = names(df1[numcols]))
head(df1[numcols])

colSums(df1[numcols])
colSums(df1[numcols],dims=1)
colMeans(df1[numcols],dims=1)

colMeans(df1[c('mpg','disp', 'hp')])
colMeans(df1[numcols[c(1:3)]])

#Look for other summarisation and grouping
aggregate(df1[numcols], by=list(df1$cyl), FUN=mean)
aggregate(hp + disp ~ am, data=df1, FUN=mean)  #sum of 2 cols wrt 3 col
aggregate(hp + disp ~ am + cyl, data=df1, FUN=mean)  #sum of 2 cols wrt 2 col


#---------------------------------------------------------
#using dplyr package
library(dplyr)
library(tibble)


#Two ways of using dplyr
df1 %>% slice(1:4)  #pipe it
slice(df1, 1:4)  # use object inside command

#dplyr does not display rownames when do manipulation , so
df1 %>% tbl_df()
df1 %>% add_rownames()  #depreciated, will not be used in future
df1 %>% tibble::rownames_to_column()

# add column with abbreviated names
length(unique(abbreviate(rownames(df1))))
df1$name = abbreviate(rownames(df1))
head(df1)  

#all rows having numeric col values > 3
df1 %>%  filter_if(is.numeric, all_vars(. >= 3.5))

#Top mileage from each cyl type, what is its hp
df1 %>% select(cyl, mpg, hp, name) %>% group_by(cyl) %>% arrange(desc(mpg)) %>% top_n(n=1)

# Average Mileage & disp by cyl
df1 %>% group_by(cyl) %>% select(cyl, mpg) %>% summarize(mean_mpg = mean(mpg, na.rm = TRUE))

head(df1)
#groupby cyl,gear, carb: avg mpg, min disp
df1 %>% filter(am == 0) %>% group_by(cyl, gear,carb) %>% summarize(mpg_avg = mean(mpg, na.rm = TRUE), disp_min = min(disp, na.rm = TRUE))

#Count
df1 %>%   group_by(cyl) %>%  tally()
df1 %>%   group_by(cyl, am) %>%  tally()

#Filter
df1 %>% filter(cyl == 4 & mpg > 25) %>% select(name, mpg, cyl, wt)

#sample
df1 %>% sample_frac(0.2, replace = TRUE)  %>% select(name, mpg, cyl) #with replacement
df1 %>% sample_frac(0.2, replace = F)  %>% select(name, mpg, cyl) #w/o replacement
df1 %>% sample_n(3, replace = F)  %>% select(name, mpg, cyl) #numbers instead for %

#few rows
slice(df1, 10:15)

# Rows 
head(df1)
df1 %>% slice(1:5)
slice(df1, 1:5)
df1 %>% slice(-5) # -5th row
df1 %>% slice(-(5:10)) # -5th to 10th
df1 %>% slice(1:(n()-10)) 
df1 %>% slice(seq(1,n(),3)) #every 3rd



names(df1)
#defaults to the last variable in the table
df1 %>% group_by(cyl) %>% top_n(2)  #Rank not data qsec
# top_n(n = 5, wt = x)
df1 %>% group_by(cyl) %>% top_n(2,wt=mpg)  #Rank not data wrt mpg
#21mpg for 6 are 2 rows

df1 %>% top_n(-2, wt=mpg) %>% select(name, mpg,wt) #bottom 2 ranks - see 3 values

df1 %>% group_by(cyl) %>% tally()  #total count

df1 %>% select(name,mpg, cyl) %>% top_n(1, mpg)

#Selecting Columns
names(df1)
df1 %>% select(1:5)
df1 %>% select(contains('s'))  %>% head()
df1 %>% select(starts_with('d')) %>% head()
df1 %>% select(ends_with('s')) %>% head()
df1 %>% select(everything()) %>% head()
#first col is wt
df1 %>% select(wt, everything()) %>% select(1:5) %>% select(-2) %>% head()
names(df1)


# Summarise
df1 %>% summarise(mpg_ave = mean(mpg))
df1 %>% group_by(cyl) %>% summarise(mean_ave = mean(mpg),wt_sum = sum(wt))
df1 %>% group_by(cyl, am) %>% select(numcols) %>% summarise_each( funs(mean))
factorcols
df1 %>% group_by(cyl, vs) %>% count(am, gear)



#Summary Functions
v1 = df1$mpg
v1
v1 %>% first
v1 %>% last
v1 %>% sd
v1 %>% mean
v1 %>% n_distinct()





================================================
FILE: 81-case-sum/33f-DA-bakerydata1.R
================================================
# Data for Graph and Data Manipulation

sales1 = read.csv('./data/bakery.csv')
sales1
str(sales1)
sales1$sdate = as.Date(sales1$sdate, '%d-%m-%y')
str(sales1)

head(sales1)

#library(timeDate)
#weekdays(sales1$sdate)  # date should not be factor
#sales1$daywk = as.character(sales$wkday)
unique(sales1$daywk)
sweekdays = c('Monday','Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')


head(sales1)
sales1$daywk = factor(sales1$daywk, ordered=T, levels=sweekdays)
str(sales1)
head(sales1)

sales= sales1  # make a copy for analysis

str(sales)
head(sales)
save(sales,file='./data/MA.RData')
?save


================================================
FILE: 81-case-sum/34b-sales1.R
================================================
# Data Visualisation and Summarisation

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1JkMVHbbWlxofMy27X-TdBPfy5R6YvrPw_EExARA8Bno"
sales1 = as.data.frame(gsheet2tbl(url))
sales1
sales= sales1 # save a copy of original data

head(sales1)

#missing values in 3 columns
sales1$revporder = floor(sales1$revenue/sales1$orders)
head(sales1)

sales1$revpsalespers = floor(sales1$revenue/sales1$salespers)
head(sales1)


library(dplyr)
sales1 <- mutate(sales1, orderspsalespers = trunc(orders/salespers))
head(sales1)

#data columns ready for summarisation and visualisation

summary(sales1)
sales1$group = factor(sales1$group)

# Distribution of Data
boxplot(sales1$revenue)
head(sales1)
boxplot(sales1$orders, sales1$salespers,names=c('Orders','Salespersons'), col=4:5)

?boxplot

# Summarise

aggregate(sales1$revenue, by=list(sales1$group), FUN=sum)

aggregate(revenue ~ group, data=sales1, sum)

df1=aggregate(revenue ~ group, data=sales1, sum)
pie(x=df1$revenue, labels=df1$group)
labels1 = paste(df1$group, 'Sum-', df1$revenue, round(df1$revenue/ sum(df1$revenue),2) * 100 , '% ' , sep=' ')

pie(x=df1$revenue, labels=labels1, col = rainbow(length(df1$group)))
legend("topright", c("Group1","Group2","Group3"), cex = 0.8,
       fill= rainbow(length(df1$group)))
title(main='Revenue of Sales : Groupwise')

#----
aggregate(cbind(revenue, orders) ~ group, data=sales1, FUN=sum)
df2= aggregate(cbind(salespers, orders) ~ group, data=sales1, FUN=sum)

#simple barpot
barplot(height=sales1$revenue, names.arg = sales1$product, col=1:length(sales1$product))


df2
df2matrix = matrix(c(df2$salespers, df2$orders), byrow = F, ncol=2)
df2matrix
t(df2matrix)
barplot(t(df2matrix))
barplot(t(df2matrix), beside = T, col=1:2, ylab='Numbers', ylim=c(0,max(df2matrix) *1.5), names.arg = df2$group)
legend('topright', c('salespers','orders'), cex = 1, fill = 1:2)
barplot(t(df2matrix), horiz = T, col=c('green','yellow'))

#http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization#barplot-of-counts



aggregate(cbind(revenue, orders) ~ group, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))

aggregate(cbind(revenue, orders) ~ group + cat, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))


#functions specify to column type


# Trend Line and Line Chart
plot(sales1$revporder, xlab="Products", type='o',  col='red', xaxt='n')
mtext(text=sales1$product, side=1, at=1:length(sales$product), line= 1, col=1:length(sales$product))
lines(sales1$orderspsalespers, type='o', col='green')
text(1:length(sales1$product), sales1$revenue, sales1$revenue)
text(1, 80, 'hellow')


================================================
FILE: 81-case-sum/34c-sales2.R
================================================
# Data Summarisation : Sales Data

# Create a DataFrame of 50 rows with following columns and properties

#product company qtysold priceitem

#this will create all combination of product and coys
df=expand.grid(product = c('noodles', 'biscuits', 'wafers', 'chocolates'), coy = c('coy1','coy2','coy3'))
df
#assign random values for qtysold and price for each product/ coy
df$qty = floor(runif(12,50,100))
df$price = floor(rnorm(12,70,15))
head(df)

# Do descriptive Analysis and draw some graphs
# use aggregate or functions from dplyr package
library(dplyr)

#Total Sales Qty : Product wise
aggregate(df$qty, by=list(df$product), sum)
aggregate(qty ~ product, data=df, sum)
df %>% group_by(product) %>% summarise(TotalQty= sum(qty))

#Total Sales Qty : Coy wise
df %>% group_by(coy) %>% summarise(TotalQty= sum(qty))


#Total Revenue : Product and Coy Wise
df %>% mutate(TotalSale = qty * price)
df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale))
df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale))


#Draw comparison plot : Bar and Pie Charts
sum1 <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue))
sum1
barplot(sum1$TotalRevenue, names.arg = sum1$product, col=1:nrow(sum1))

sum1
plabels <- paste(sum1$product, round(sum1$TotalRevenue/sum(sum1$TotalRevenue)* 100, 0) , "%" , sep=' ') 
plabels
pie(sum1$TotalRevenue, labels = plabels, col=1:nrow(sum1))

#Which product generates max revenue
sum1a <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) 
sum1a %>% top_n(1)

#Which coy generated min Revenue
sum2a <- df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) 
sum2a %>% top_n(-1)
sum2a




================================================
FILE: 81-case-sum/35b-DA-student1.R
================================================
# Data Manipulation : Academic Data

#Method1 : gsheet
library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174"
student1 = as.data.frame(gsheet2tbl(url))

#Method2 : read.csv
student2 = read.csv('./data/student1.csv')
names(student2)

#save the imported data object into a new object
student=student1  # or student2
str(student)
df1= student
(colnames = names(df1))
#attributes(df1) = NULL
#df1 = as.data.frame(df1)
attributes(df1)
attr(df1, which='spec') = NULL
#names(df1) = colnames
str(df1)

#class of each column
class(df1$gender)
sapply(df1, class)

# convert character to factor
factorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')

df1[factorcols] = lapply(df1[factorcols] ,factor)
sapply(df1, class)
str(df1)


# Now do summarisation
#attach(df1)
names(df1)
table(df1$gender)
#combine it in single command 
(l1= lapply(df1[factorcols],factor,ordered=TRUE))
sapply(l1,table)

table(df1$gender)
table(df1$batchyr)
sapply(df1[factorcols],table)


sapply(lapply(df1[factorcols],factor,ordered=TRUE), table)  #Method1


sapply(df1[factorcols], table)   #Method2
sapply(df1[c('gender', 'cat', 'class12')], table)
?lapply
str(df1)
# Numeric Cols
sapply(df1, is.numeric)
(numcols = sapply(df1, is.numeric))
class(numcols)
(numcols = names(df1[numcols]))
#remove rollno
(numcols = numcols[-1])
head(df1[numcols])
colSums(df1[numcols])
colSums(df1[numcols],dims=1)
colMeans(df1[numcols],dims=1)
numcols[c(1,3)]
colMeans(df1[c('age','java', 'cpp')])
names(df1)
colMeans(df1[numcols[c(1:3)]])

names(df1)
#Look for other summarisation and grouping
aggregate(df1[numcols], by=list(df1$br), FUN=mean)
aggregate(java + cbnst ~ br, data=df1, FUN=mean)  #sum of java & cbnst wrt branch
aggregate( cbnst ~ gender, data=df1, FUN=mean)
aggregate( cbnst ~ gender + br, data=df1, FUN=mean)



#using dplyr package
library(dplyr)

#Top 2 students from each Branch 
df1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)

# Average Fees Paid by Batch Yr
df1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))

names(df1)
df1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))

#Count
df1 %>%   group_by(gender) %>%  tally()

#Filter
df1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)

#sample
df1 %>% sample_frac(0.2, replace = TRUE)  %>% select(rollno, sname)
df1 %>% sample_frac(0.2, replace = F)  %>% select(rollno, sname)
df1 %>% sample_n(3, replace = F)  %>% select(rollno, sname)

#few rows
slice(df1, 10:15)

library(tidyr) #Reshape
df1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) 
head(df1long)
distinct(df1long)

tidyr::unite(df1, 'rollnoname', c(rollno, sname), sep="-") %>% select(1:5)%>% head


# data
str(df1$dob)
as.Date("5-Apr-91", '%d-%b-%y')
df1$dob = as.Date(df1$dob, '%d-%b-%y')
df1 %>% select(sname, dob) %>% tidyr::separate(dob, c("y", "m", "d"))


# Rows
df1 %>% slice(1:n():5)
slice(df1, 1:5)

df1 %>% slice(1:n())
df1 %>% slice(1:10)
slice(df1, n()-10: n())  #different way

names(df1)
#defaults to the last variable in the tbl
df1 %>% group_by(gender) %>% top_n(2)  #Rank not data
# top_n(n = 5, wt = x)
df1 %>% group_by(gender) %>% top_n(2,wt=class10)  #Rank not data wrt class10 marks

df1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values
df1 %>% group_by(gender) %>% tally(java)  #total count
df1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)

#Selecting Columns
df1 %>% select(1:5)
df1 %>% select(contains('java'))
df1 %>% select(starts_with('btech'))
df1 %>% select(ends_with('s'))
df1 %>% select(everything())
df1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)
names(df1)


# Summarise
df1 %>% summarise(avgjava = mean(java))
df1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))
df1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))

df1 %>% group_by(cat, gender) %>% count(class12, finalgrade)




#Summary Functions
v1 = df1$btechmarks
v1
v1 %>% first
v1 %>% last
v1 %>% sd
v1 %>% mean
v1 %>% n_distinct()


# Combine Data Sets

(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))
(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))

dplyr::left_join(a, b, by = "x1")
#Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")
#Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")
#Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")
#Join data. Retain all values, all rows.

dplyr::semi_join(a, b, by = "x1")
#All rows in a that have a match in b.

cbind(a,b)

dplyr::anti_join(a, b, by = "x1")
#All rows in a that do not have a match in b.

#-----
y = data.frame(x1=c('A','B','C'), x2=c(1,2,3))
z = data.frame(x1=c('B','C','D'), x2=c(2,3,4))
cbind(y,z)

dplyr::intersect(y, z)  # B&C
#Rows that appear in both y and z.

dplyr::union(y, z)
#Rows that appear in either or both y and z.

dplyr::setdiff(y, z)
#Rows that appear in y but not z.

#Bind
dplyr::bind_rows(y, z)
#Append z to y as new rows.
dplyr::bind_cols(y, z)
#Append z to y as new columns.
#Caution: matches rows by position




================================================
FILE: 81-case-sum/35c-dm-student1.R
================================================
# Data Manipulation : Academic Data

#Method1 : gsheet
library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174"
student1 = as.data.frame(gsheet2tbl(url))

#Method2 : read.csv
student2 = read.csv('./data/student1.csv')
names(student2)

#save the imported data object into a new object
student=student1  # or student2
str(student)
names(student)
df1= student
lm(df1$btechmarks ~ df1$attnd, data=df1)

(colnames = names(df1))
#attributes(df1) = NULL
#df1 = as.data.frame(df1)
attributes(df1)
attr(df1, which='spec') = NULL
#names(df1) = colnames
str(df1)
#class of each column
class(df1$gender)
sapply(df1, class)

# convert character to factor
factorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')

df1[factorcols] = lapply(df1[factorcols] ,factor)
sapply(df1, class)
str(df1)


# Now do summarisation
#attach(df1)
names(df1)
table(df1$gender)
#combine it in single command 
(l1= lapply(df1[factorcols],factor,ordered=TRUE)) #ordering is not necessary for all
sapply(l1,table)

table(df1$gender)
table(df1$batchyr)
sapply(df1[factorcols],table)


sapply(lapply(df1[factorcols],factor,ordered=TRUE), table)  #Method1


sapply(df1[factorcols], table)   #Method2
sapply(df1[c('gender', 'cat', 'class12')], table)
?lapply
str(df1)
# Numeric Cols
sapply(df1, is.numeric)
(numcols = sapply(df1, is.numeric))
class(numcols)
(numcols = names(df1[numcols]))
#remove rollno
(numcols = numcols[-1])
head(df1[numcols])
colSums(df1[numcols])
colSums(df1[numcols],dims=1)
colMeans(df1[numcols],dims=1)
numcols[c(1,3)]
colMeans(df1[c('age','java', 'cpp')])
names(df1)
colMeans(df1[numcols[c(1:3)]])

names(df1)
#Look for other summarisation and grouping
aggregate(df1[numcols], by=list(df1$br), FUN=mean)
aggregate(cbind(sem1, sem2) ~ br + gender, data=student, FUN=mean)

aggregate(java + cbnst ~ br, data=df1, FUN=mean)  #sum of java & cbnst wrt branch
aggregate( cbnst ~ gender, data=df1, FUN=mean)
aggregate( cbnst ~ gender + br, data=df1, FUN=mean)



#using dplyr package
library(dplyr)

#Top 2 students from each Branch---- 
df1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)

# Average Fees Paid by Batch Yr----
df1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))

names(df1)
# Avg BTech Marks and Min Marks in Java : Group by Gender, Granch and Final Grade ----
df1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))

#Count by Gender ----
df1 %>%   group_by(gender) %>%  tally()

#Filter by Final Grade = B : select only few columns ----
df1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)

#sample : select few rows on randowm basis ----
df1 %>% sample_frac(0.2, replace = TRUE)  %>% select(rollno, sname)  # % of Total
df1 %>% sample_frac(0.2, replace = F)  %>% select(rollno, sname) # % of total : replace should be False to have non repeated rows
df1 %>% sample_n(3, replace = F)  %>% select(rollno, sname) # select only 3 rows

#few rows : 10 to 15
slice(df1, 10:15)

library(tidyr) #Reshape
df1long <- df1 %>% select(sname, java, cbnst)
df1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) 

head(df1long)
distinct(df1long)

tidyr::unite(df1, 'rollnoname', c(rollno, sname), sep="-") %>% select(1:5)%>% head


# data
str(df1$dob)
as.Date("5-Apr-91", '%d-%b-%y')
df1$dob = as.Date(df1$dob, '%d-%b-%y')
df1 %>% select(sname, dob) %>% tidyr::separate(dob, c("y", "m", "d"))


# Rows
df1 %>% slice(1:n():5)
slice(df1, 1:5)

df1 %>% slice(1:n())
df1 %>% slice(1:10)
slice(df1, n()-10: n())  #different way

names(df1)
#defaults to the last variable in the tbl
df1 %>% group_by(gender) %>% top_n(2)  #Rank not data on last column
# top_n(n = 5, wt = x)
df1 %>% group_by(gender) %>% top_n(2,wt=class10)  #Rank not data wrt class10 marks

df1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values
df1 %>% group_by(gender) %>% tally(java)  #total count
df1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)

#Selecting Columns
df1 %>% select(1:5)
df1 %>% select(contains('java'))
df1 %>% select(starts_with('btech'))
df1 %>% select(ends_with('s'))
df1 %>% select(everything())
df1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)
names(df1)


# Summarise
df1 %>% summarise(avgjava = mean(java))
df1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))
df1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))

df1 %>% group_by(cat, gender) %>% count(class12, finalgrade)




#Summary Functions
v1 = df1$btechmarks
v1
v1 %>% first
v1 %>% last
v1 %>% sd
v1 %>% mean
v1 %>% n_distinct()


# Combine Data Sets

(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))
(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))

dplyr::left_join(a, b, by = "x1")
#Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")
#Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")
#Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")
#Join data. Retain all values, all rows.

dplyr::semi_join(a, b, by = "x1")
#All rows in a that have a match in b.

cbind(a,b)

dplyr::anti_join(a, b, by = "x1")
#All rows in a that do not have a match in b.

#-----
y = data.frame(x1=c('A','B','C'), x2=c(1,2,3))
z = data.frame(x1=c('B','C','D'), x2=c(2,3,4))
cbind(y,z)

dplyr::intersect(y, z)  # B&C
#Rows that appear in both y and z.

dplyr::union(y, z)
#Rows that appear in either or both y and z.

dplyr::setdiff(y, z)
#Rows that appear in y but not z.

#Bind
dplyr::bind_rows(y, z)
#Append z to y as new rows.
dplyr::bind_cols(y, z)
#Append z to y as new columns.
#Caution: matches rows by position




================================================
FILE: 81-case-sum/36b-dsum-Case1.R
================================================
# Data Visualisation and Summarisation

library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1JkMVHbbWlxofMy27X-TdBPfy5R6YvrPw_EExARA8Bno"
sales1 = as.data.frame(gsheet2tbl(url))
sales1
sales= sales1 # save a copy of original data

head(sales1)

#missing values in 3 columns
sales1$revporder = floor(sales1$revenue/sales1$orders)
head(sales1)

sales1$revpsalespers = floor(sales1$revenue/sales1$salespers)
head(sales1)


library(dplyr)
sales1 <- mutate(sales1, orderspsalespers = trunc(orders/salespers))
head(sales1)

#data columns ready for summarisation and visualisation

summary(sales1)
sales1$group = factor(sales1$group)

# Distribution of Data
boxplot(sales1$revenue)
head(sales1)
boxplot(sales1$orders, sales1$salespers,names=c('Orders','Salespersons'), col=4:5)

?boxplot

# Summarise

aggregate(sales1$revenue, by=list(sales1$group), FUN=sum)

aggregate(revenue ~ group, data=sales1, sum)

df1=aggregate(revenue ~ group, data=sales1, sum)
pie(x=df1$revenue, labels=df1$group)
labels1 = paste(df1$group, 'Sum-', df1$revenue, round(df1$revenue/ sum(df1$revenue),2) * 100 , '% ' , sep=' ')

pie(x=df1$revenue, labels=labels1, col = rainbow(length(df1$group)))
legend("topright", c("Group1","Group2","Group3"), cex = 0.8,
       fill= rainbow(length(df1$group)))
title(main='Revenue of Sales : Groupwise')

#----
aggregate(cbind(revenue, orders) ~ group, data=sales1, FUN=sum)
df2= aggregate(cbind(salespers, orders) ~ group, data=sales1, FUN=sum)

#simple barpot
barplot(height=sales1$revenue, names.arg = sales1$product, col=1:length(sales1$product))


df2
df2matrix = matrix(c(df2$salespers, df2$orders), byrow = F, ncol=2)
df2matrix
t(df2matrix)
barplot(t(df2matrix))
barplot(t(df2matrix), beside = T, col=1:2, ylab='Numbers', ylim=c(0,max(df2matrix) *1.5), names.arg = df2$group)
legend('topright', c('salespers','orders'), cex = 1, fill = 1:2)
barplot(t(df2matrix), horiz = T, col=c('green','yellow'))

#http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization#barplot-of-counts



aggregate(cbind(revenue, orders) ~ group, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))

aggregate(cbind(revenue, orders) ~ group + cat, data=sales1, FUN = function(x) c(mn = mean(x), n = length(x) ))


#functions specify to column type


# Trend Line and Line Chart
plot(sales1$revporder, xlab="Products", type='o',  col='red', xaxt='n')
mtext(text=sales1$product, side=1, at=1:length(sales$product), line= 1, col=1:length(sales$product))
lines(sales1$orderspsalespers, type='o', col='green')
text(1:length(sales1$product), sales1$revenue, sales1$revenue)
text(1, 80, 'hellow')


================================================
FILE: 81-case-sum/36c-dsum-Case2.R
================================================
# Data Summarisation : Sales Data

# Create a DF of 50 rows with following columns and properties
#product company qtysold priceitem

#this will create all combination of product and coys
df=expand.grid(product = c('noodles', 'biscuits', 'wafers', 'chocolates'), coy = c('coy1','coy2','coy3'))
df
#assign random values for qtysold and price for each product/ coy
df$qty = floor(runif(12,50,100))
df$price = floor(rnorm(12,70,15))
head(df)

# Do descriptive Analysis and draw some graphs
# use aggregate or functions from dplyr package
library(dplyr)

#Total Sales Qty : Product wise
aggregate(df$qty, by=list(df$product), sum)
aggregate(qty ~ product, data=df, sum)
df %>% group_by(product) %>% summarise(TotalQty= sum(qty))

#Total Sales Qty : Coy wise
df %>% group_by(coy) %>% summarise(TotalQty= sum(qty))


#Total Revenue : Product and Coy Wise
df %>% mutate(TotalSale = qty * price)
df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale))
df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale))


#Draw comparison plot : Bar and Pie Charts
sum1 <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue))
sum1
barplot(sum1$TotalRevenue, names.arg = sum1$product, col=1:nrow(sum1))

sum1
plabels <- paste(sum1$product, round(sum1$TotalRevenue/sum(sum1$TotalRevenue)* 100, 0) , "%" , sep=' ') 
plabels
pie(sum1$TotalRevenue, labels = plabels, col=1:nrow(sum1))

#Which product generates max revenue
sum1a <- df %>% mutate(TotalSale = qty * price) %>% group_by(product) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) 
sum1a %>% top_n(1)

#Which coy generated min Revenue
sum2a <- df %>% mutate(TotalSale = qty * price) %>% group_by(coy) %>% summarise(TotalRevenue= sum(TotalSale)) %>% arrange(desc(TotalRevenue)) 
sum2a %>% top_n(-1)
sum2a



================================================
FILE: 81-case-sum/36f-DSA-case2.R
================================================
# Descriptive Analysis in R

coys = c('Nestle', 'Dabur', 'Patanjali', 'Amul' )
#nestle - noodles, chocolate, coffee
nestle = c('noodles', 'chocolate', 'coffee')
#dabur - toothpaste, chyawanprash
dabur = c('toothpaste', 'chyawanprash')
#patanjali - honey, chyawanprash, coffee, toothpaste
patanjali = c('honey', 'chyawanprash', 'coffee', 'toothpaste')
#amul - butter, icecream
amul = c('butter', 'icecream')
(products = c(nestle, dabur, patanjali, amul))
length(products)
(type = c('T1','T2','T2','T3','T1','T1','T1','T2','T3','T1','T1'))
coys = rep(coys, times=c(3,2,4,2))
coys
cbind(products, coys,type)
#sales in Rs Crores
set.seed(1234)
(y2016 = sample.int(100, 11, replace = F))
set.seed(1234)
(y2017 = sample.int(120, 11, replace = F))
set.seed(1234)
(commission = round(rnorm(11,mean=.02, sd=.003),3))
(ratings=sample(c('Excellent', 'Good','Poor'), size=11, replace=T))
fmcg = data.frame(coys, products, type, y2016, y2017, commission, ratings, stringsAsFactors = F)
fmcg  
summary(fmcg)

#Category columns
fmcg$coys = factor(fmcg$coys, ordered=T)
fmcg$type = factor(fmcg$type)
fmcg$products = factor(fmcg$products)
fmcg$ratings = factor(fmcg$ratings, ordered=T, levels=c('Poor','Good','Excellent'))
summary(fmcg)

#products of each coy
table(fmcg$products)
table(fmcg$products,fmcg$coys )


#summary
#how much is each coy selling 
aggregate(fmcg$y2016, by=list(coys), FUN=sum)
aggregate(y2016 ~ coys, data=fmcg, FUN=sum)
aggregate((y2016 + y2017) ~ coys, data=fmcg, FUN=sum)
aggregate(cbind(y2016,y2017) ~ coys, data=fmcg, FUN=sum)
aggregate(cbind(y2016,y2017) ~ coys + type, data=fmcg, FUN=sum)
(yearcoywise = aggregate(cbind(y2016,y2017) ~ coys, data=fmcg, FUN=sum))
str(yearcoywise)
#margin sums: works on arrays
rownames(yearcoywise) = yearcoywise$coys
yearcoywise = yearcoywise[-1]
yearcoywise

#add.margins
addmargins(as.matrix(yearcoywise),1, FUN=sum)  #
addmargins(as.matrix(yearcoywise),2, FUN=sum)  #
addmargins(as.matrix(yearcoywise),c(1,2), FUN=sum)  
addmargins(as.matrix(yearcoywise),c(1,2), FUN=list(list(sum, mean,min), list(mean,max)))  

#margin.table
margin.table(as.matrix(yearcoywise),1)
margin.table(as.matrix(yearcoywise),2)

#prop.table : for both years : proportional sale
prop.table(as.matrix(yearcoywise)) 
#wrt to each coy
prop.table(as.matrix(yearcoywise),1) 

#wrt to each year
prop.table(as.matrix(yearcoywise),2) 
(pt2=prop.table(as.matrix(yearcoywise),2)) 

#addmargins#total column
addmargins(pt2,1, FUN=sum)

#add values data eg. Taxes in 2016,2017 were 20%, 30%
yearcoywise
#calculate taxes from each coy
(taxes= sweep(yearcoywise, MARGIN=1, STATS=c(.2, .3), FUN='*'))
taxes[which.max(taxes$y2016),]

#graphs
pie(taxes$y2016)
pie(taxes$y2016, labels=rownames(taxes))
pie(taxes$y2016, labels=rownames(taxes))
percvalues = round(taxes$y2016/sum(taxes$y2016),2) * 100
pie(taxes$y2016, labels=paste(rownames(taxes), percvalues, sep=' - '))

#barpot
barplot(height=taxes$y2016, col=1:nrow(taxes))
barplot(height=taxes$y2016, col=1:nrow(taxes), names.arg = row.names(taxes))

#Stacked Bar Graph
barplot(as.matrix(taxes), beside = F, col=rainbow(nrow(taxes))) 
legend("top", row.names(taxes), cex=1.0, bty="n",  fill=rainbow(nrow(taxes)))

#Besides
barplot(as.matrix(taxes), beside = T, col=rainbow(nrow(taxes)), ylab='Amount in Lakh Rs', xlab='Year') 
title(main='Tax Collections from FMCG Coy', sub = 'Product Wise')
legend("topleft", row.names(t(taxes)), cex=1.0, bty="n",  fill=rainbow(nrow(taxes)))

#different format
barplot(t(as.matrix(taxes)), beside = T, ylab='Amount in Lakh Rs', xlab='Year', col=2:3) 
title(main='Tax Collections from FMCG Coy', sub = 'Coy - Year')
legend("topleft", row.names(t(taxes)), cex=1.0, bty="n",  fill=2:3)

#Reshape the data to display in different format
library(reshape2)
yearcoywise2a = data.frame(cbind(yearcoywise), coys=row.names(yearcoywise))
yearcoywise2a

yearcoywise2b <- melt(yearcoywise2a, id.vars ='coys')
yearcoywise2b


#Line Chart
year = 2010:2018
nestle = ceiling(runif(9,50,100))
dabur = floor(runif(9,55,95))
yearwisesales = data.frame(year, nestle, dabur)
yearwisesales


plot(yearwisesales$year,yearwisesales$nestle, type="l", col="green", lwd=5, xlab="Year", ylab="Sales")
lines(yearwisesales$year,yearwisesales$dabur, col="red", lwd=2)
title("Change in Sales over Years - Nestle and Dabur")
legend("topright",c("Nestle","Dabur"), lwd=c(5,2), col=c("green","red"))

#End of Descriptive Analysis




================================================
FILE: 81-case-sum/37b-dsum-iris1.R
================================================
# Descriptive Stats and Graphs
# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics


#Import your data into R----

# Store the data in the variable my_data
my_data <- iris

#Check your data----
# Print the first 6 rows
head(my_data, 6)

#R functions for computing descriptive statistics----

#Descriptive statistics for a single group----

#Measure of central tendency: mean, median, mode----
# Compute the mean value
mean(my_data$Sepal.Length)

# Compute the median value
median(my_data$Sepal.Length)

# Compute the mode
#install.packages("modeest")
require(modeest)
mfv(my_data$Sepal.Length)


#Measure of variablity----

#Range: minimum & maximum----
# Compute the minimum value
min(my_data$Sepal.Length)

# Compute the maximum value
max(my_data$Sepal.Length)

# Range
range(my_data$Sepal.Length)

#Interquartile range---
sort(my_data$Sepal.Length)
quantile(my_data$Sepal.Length)

#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).
quantile(my_data$Sepal.Length, seq(0, 1, 0.1))
IQR(my_data$Sepal.Length)

#Variance and standard deviation----

# Compute the variance
var(my_data$Sepal.Length)
# Compute the standard deviation =
# square root of th variance
sqrt(var(my_data$Sepal.Length))
sd(my_data$Sepal.Length)

#Median absolute deviation----
# Compute the median
median(my_data$Sepal.Length)
# Compute the median absolute deviation : Dev wrt median value
mad(my_data$Sepal.Length)

#Which measure to use?----

# Range. It’s not often used because it’s very sensitive to outliers.
# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.
# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool
# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.
# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.
# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.




#Computing an overall summary of a variable and an entire data frame----

#summary() function----
summary(my_data$Sepal.Length)
summary(my_data, digits = 1)

# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:
#   If the column is a numeric variable, mean, median, min, max and quartiles are returned.
# If the column is a factor variable, the number of observations in each group is returned.



#sapply() function----

# Compute the mean of each column
sapply(my_data[, -5], mean)

# Compute quartiles
sapply(my_data[, -5], quantile)


#stat.desc() function----
#install.packages("pastecs")

# Compute descriptive statistics
library(pastecs)
res <- stat.desc(my_data[, -5])
round(res, 2)

#Case of missing values----
mean(my_data$Sepal.Length, na.rm = TRUE)

#Graphical display of distributions----

#Installation and loading ggpubr----
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
#or install.packages("ggpubr")

library(ggpubr)

#Box plots----
ggboxplot(my_data, y = "Sepal.Length", width = 0.5)

#Histogram----
#Histograms show the number of observations that fall within specified divisions (i.e., bins).
gghistogram(my_data, x = "Sepal.Length", bins = 9, add = "mean")


#Empirical cumulative distribution function (ECDF)----
#ECDF is the fraction of data smaller than or equal to x.
ggecdf(my_data, x = "Sepal.Length")


#Q-Q plots----
ggqqplot(my_data, x = "Sepal.Length")


#Descriptive statistics by groups----

#Frequency tables----
library(dplyr)
group_by(my_data, Species) %>% 
  summarise(count = n(),   mean = mean(Sepal.Length, na.rm = TRUE),
    sd = sd(Sepal.Length, na.rm = TRUE)
  )

library("ggpubr")
# Box plot colored by groups: Species
ggboxplot(my_data, x = "Species", y = "Sepal.Length",
    color = "Species",  palette = c("#00AFBB", "#E7B800", "#FC4E07"))

# Stripchart colored by groups: Species
ggstripchart(my_data, x = "Species", y = "Sepal.Length",
     color = "Species",  palette = c("#00AFBB", "#E7B800", "#FC4E07"),   add = "mean_sd")






================================================
FILE: 81-case-sum/38b-dsum-haireyecolor1.R
================================================
#Descriptive Stats - Hair/ Eye Color

# Hair/eye color data
str(HairEyeColor)
HairEyeColor
apply(HairEyeColor, c(1,2), sum)

df <- as.data.frame(HairEyeColor)
hair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]
rownames(hair_eye_col) <- 1:nrow(hair_eye_col)
head(hair_eye_col)

#Simple frequency distribution: one categorical variable----
# hair/eye variables
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Frequency distribution of hair color
table(Hair)
table(Eye)

#Two-way contingency table: Two categorical variables----
# Compute table and convert as data frame
df <- as.data.frame(table(Hair))
df

# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq")

#Multiway tables: More than two categorical variables----
tbl2 <- table(Hair , Eye)
tbl2
#function xtabs(), which will create cross tabulation of data frames with a formula interface.
xtabs(~ Hair + Eye, data = hair_eye_col)

#Compute table margins and relative frequency----

df <- as.data.frame(tbl2)
head(df)


# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", 
          palette = c("brown", "blue", "gold", "green"))

# position dodge
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", position = position_dodge(),
          palette = c("brown", "blue", "gold", "green"))


#Multiway tables: More than two categorical variables
#Hair and Eye color distributions by sex using xtabs():
xtabs(~Hair + Eye + Sex, data = hair_eye_col)

#ftable when you have more than two variables:
ftable(Sex + Hair ~ Eye, data = hair_eye_col)


#Compute table margins and relative frequency
#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.
#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)
#x: table; margin: index number (1 for rows and 2 for columns)

#compute table margins:
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Hair/Eye color table
he.tbl <- table(Hair, Eye)
he.tbl

# Margin of rows
margin.table(he.tbl, 1)

# Margin of columns
margin.table(he.tbl, 2)

# Frequencies relative to row total
prop.table(he.tbl, 1)

# Table of percentages
round(prop.table(he.tbl, 1), 2)*100

#express the frequencies relative to the grand total, use this:
he.tbl/sum(he.tbl)

================================================
FILE: 81-case-sum/42b-case-sum-graphs.R
================================================
# Descriptive Stats and Graphs
# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics


#Import your data into R----

# Store the data in the variable my_data
my_data <- iris

#Check your data----
# Print the first 6 rows
head(my_data, 6)

#R functions for computing descriptive statistics----

#Descriptive statistics for a single group----

#Measure of central tendency: mean, median, mode----
# Compute the mean value
mean(my_data$Sepal.Length)

# Compute the median value
median(my_data$Sepal.Length)

# Compute the mode
#install.packages("modeest")
require(modeest)
mfv(my_data$Sepal.Length)


#Measure of variablity----

#Range: minimum & maximum----
# Compute the minimum value
min(my_data$Sepal.Length)

# Compute the maximum value
max(my_data$Sepal.Length)

# Range
range(my_data$Sepal.Length)

#Interquartile range---
sort(my_data$Sepal.Length)
quantile(my_data$Sepal.Length)

#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).
quantile(my_data$Sepal.Length, seq(0, 1, 0.1))
IQR(my_data$Sepal.Length)

#Variance and standard deviation----

# Compute the variance
var(my_data$Sepal.Length)
# Compute the standard deviation =
# square root of th variance
sqrt(var(my_data$Sepal.Length))
sd(my_data$Sepal.Length)

#Median absolute deviation----
# Compute the median
median(my_data$Sepal.Length)
# Compute the median absolute deviation : Dev wrt median value
mad(my_data$Sepal.Length)

#Which measure to use?----

# Range. It’s not often used because it’s very sensitive to outliers.
# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.
# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool
# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.
# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.
# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.




#Computing an overall summary of a variable and an entire data frame----

#summary() function----
summary(my_data$Sepal.Length)
summary(my_data, digits = 1)

# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:
#   If the column is a numeric variable, mean, median, min, max and quartiles are returned.
# If the column is a factor variable, the number of observations in each group is returned.



#sapply() function----

# Compute the mean of each column
sapply(my_data[, -5], mean)

# Compute quartiles
sapply(my_data[, -5], quantile)


#stat.desc() function----
#install.packages("pastecs")

# Compute descriptive statistics
library(pastecs)
res <- stat.desc(my_data[, -5])
round(res, 2)

#Case of missing values----
mean(my_data$Sepal.Length, na.rm = TRUE)

#Graphical display of distributions----

#Installation and loading ggpubr----
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
#or install.packages("ggpubr")

library(ggpubr)

#Box plots----
ggboxplot(my_data, y = "Sepal.Length", width = 0.5)

#Histogram----
#Histograms show the number of observations that fall within specified divisions (i.e., bins).
gghistogram(my_data, x = "Sepal.Length", bins = 9, add = "mean")


#Empirical cumulative distribution function (ECDF)----
#ECDF is the fraction of data smaller than or equal to x.
ggecdf(my_data, x = "Sepal.Length")


#Q-Q plots----
ggqqplot(my_data, x = "Sepal.Length")


#Descriptive statistics by groups----

#Frequency tables----
library(dplyr)
group_by(my_data, Species) %>% 
  summarise(count = n(),   mean = mean(Sepal.Length, na.rm = TRUE),
    sd = sd(Sepal.Length, na.rm = TRUE)
  )

library("ggpubr")
# Box plot colored by groups: Species
ggboxplot(my_data, x = "Species", y = "Sepal.Length",
    color = "Species",  palette = c("#00AFBB", "#E7B800", "#FC4E07"))

# Stripchart colored by groups: Species
ggstripchart(my_data, x = "Species", y = "Sepal.Length",
             color = "Species",
             palette = c("#00AFBB", "#E7B800", "#FC4E07"),
             add = "mean_sd")

#Create some data----

# Hair/eye color data
str(HairEyeColor)
HairEyeColor
apply(HairEyeColor, c(1,2), sum)

df <- as.data.frame(HairEyeColor)
hair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]
rownames(hair_eye_col) <- 1:nrow(hair_eye_col)
head(hair_eye_col)

#Simple frequency distribution: one categorical variable----
# hair/eye variables
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Frequency distribution of hair color
table(Hair)
table(Eye)

#Two-way contingency table: Two categorical variables----
# Compute table and convert as data frame
df <- as.data.frame(table(Hair))
df

# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq")

#Multiway tables: More than two categorical variables----
tbl2 <- table(Hair , Eye)
tbl2
#function xtabs(), which will create cross tabulation of data frames with a formula interface.
xtabs(~ Hair + Eye, data = hair_eye_col)

#Compute table margins and relative frequency----

df <- as.data.frame(tbl2)
head(df)


# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", 
          palette = c("brown", "blue", "gold", "green"))

# position dodge
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", position = position_dodge(),
          palette = c("brown", "blue", "gold", "green"))


#Multiway tables: More than two categorical variables
#Hair and Eye color distributions by sex using xtabs():
xtabs(~Hair + Eye + Sex, data = hair_eye_col)

#ftable when you have more than two variables:
ftable(Sex + Hair ~ Eye, data = hair_eye_col)


#Compute table margins and relative frequency
#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.
#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)
#x: table; margin: index number (1 for rows and 2 for columns)

#compute table margins:
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Hair/Eye color table
he.tbl <- table(Hair, Eye)
he.tbl

# Margin of rows
margin.table(he.tbl, 1)

# Margin of columns
margin.table(he.tbl, 2)

# Frequencies relative to row total
prop.table(he.tbl, 1)

# Table of percentages
round(prop.table(he.tbl, 1), 2)*100

#express the frequencies relative to the grand total, use this:
he.tbl/sum(he.tbl)


================================================
FILE: 81-case-sum/dataexplore.R
================================================
# tabulations  
#  http://r-exercises.com/2016/04/20/data-exploration-with-tables-exercises/

#Exercise 1 Basic tabulation of categorical data:
#  This is the first dataset to explore:
Gender <- c("Female","Female","Male","Male")
Restaurant <- c("Yes","No","Yes","No")
Count <- c(220, 780, 400, 600)
DiningSurvey <- data.frame(Gender, Restaurant, Count)
DiningSurvey
# Using the above data, and the table() function, compare the Gender and Restaurant variables in the above dataset.
table(DiningSurvey$Gender)


# Exercise 2 The table() function modified with a logical vector:
# Use the table() function, and logical vector of “Count > 650”, to summarize the DiningSurvey data.

table(DiningSurvey$Count > 650)

# Exercise 3 : The useNA & is.na arguments find missing values:
table(DiningSurvey$Restaurant, useNA = 'always')

# First append the “DiningSurvey” dataset with missing values:
DiningSurvey$Restaurant <- c("Yes", "No", "Yes", NA)
# Apply the “useNA” argument to find missing Restaurant data.

DiningSurvey
# Next, apply the “is.na()” argument to find missing Restaurant data by Gender.
table(DiningSurvey$Gender, is.na(DiningSurvey$Restaurant))


# Exercise 4 : The “exclude =” parameter excludes columns of data:
#  Exclude one of the dataset’s Genders with the “exclude” argument.
table(DiningSurvey$Gender, exclude = 'Male')

# Exercise 5 The “margin.table()” function requires data in array form, and generates tables of marginal frequencies. The margin.table() function summarizes arrays within a given index:
# First, generate array format data:

RentalUnits <- matrix(c(45,37,34,10,15,12,24,18,19),ncol=3,byrow=TRUE)
colnames(RentalUnits) <- c("Section1","Section2","Section3")
rownames(RentalUnits) <- c("Rented","Vacant","Reserved")
RentalUnits <- as.table(RentalUnits)
RentalUnits
# Using the above dataset, and the margin.table() function, find the amount of Occupancy summed over Sections.

margin.table(RentalUnits,1)

#Next, find the amount of Units summed by Section.
margin.table(RentalUnits, 2)

# Exercise 6 : The prop.table() function creates tables of proportions within the dataset:
# With the “RentalUnits” data table, use the “prop.table()” function to create a basic table of proportions.
# Next, find row percentages, and column percentages.
prop.table(RentalUnits)
prop.table(RentalUnits, 1)
prop.table(RentalUnits, 2)


# Exercise 7 : The ftable() function generates multidimensional n-way tables, or “flat” contingency tables:
# Use the ftable() function to summarize the dataset, “RentalUnits”.
ftable(RentalUnits)

# Exercise 8 : The “summary()” function performs an independence test of factors:
# Use “summary()” to perform a Chi-Square Test of Independence, of the “RentalUnits” variables.
summary(RentalUnits)

# Exercise 9 : “as.data.frame()” summarizes frequencies of data arrays.
# Use “as.data.frame()” to list frequencies within the “RentalUnits” array.
as.data.frame(RentalUnits)

# Exercise 10 : The “addmargins()” function creates arbitrary margins on multivariate arrays:
# Use “addmargins()” to append “RentalUnits” with sums.
addmargins(RentalUnits)
# Next, summarize columns with “RentalUnits”.
addmargins(RentalUnits,1)
# Next, summarize rows with “RentalUnits”.
addmargins(RentalUnits,2)
# Finally, combine “addmargins()” and “prop.table()” to summarize proportions within “RentalUnits”. What is statistically inferred about sales of rental units by section?
addmargins(prop.table(RentalUnits, 2))


================================================
FILE: 83-MA/CA.R
================================================
#Perceptual Maps
#http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/113-ca-correspondence-analysis-in-r-essentials/

library("FactoMineR")
library("factoextra")
data(housetasks)
head(housetasks)
housetasks

library("gplots")
# 1. convert the data as a table
dt <- as.table(as.matrix(housetasks))
# 2. Graph
balloonplot(t(dt), main ="housetasks", xlab ="", ylab="",
            label = FALSE, show.margins = FALSE)

chisq <- chisq.test(housetasks)
chisq

#CA(X, ncp = 5, graph = TRUE)

library("FactoMineR")
res.ca <- CA(housetasks, graph = FALSE)

print(res.ca)

# Chi-square statistics
chi2 <- 1944.456
# Degree of freedom
df <- (nrow(housetasks) - 1) * (ncol(housetasks) - 1)
# P-value
pval <- pchisq(chi2, df = df, lower.tail = FALSE)
pval

library("factoextra")
eig.val <- get_eigenvalue(res.ca)
eig.val

fviz_screeplot(res.ca, addlabels = TRUE, ylim = c(0, 50))

fviz_screeplot(res.ca) +
  geom_hline(yintercept=33.33, linetype=2, color="red")

# repel= TRUE to avoid text overlapping (slow if many point)
fviz_ca_biplot(res.ca, repel = T)

fviz_ca_row(res.ca, col.row="steelblue", shape.row = 15)

# Color by cos2 values: quality on the factor map
fviz_ca_row(res.ca, col.row = "cos2",
            gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
            repel = TRUE)


================================================
FILE: 83-MA/campaign.csv
================================================
Sales,Newspaper,TV,Online
16850,1000,500,1500
12010,500,500,500
14740,2000,500,500
13890,1000,1000,1000
12950,1000,500,500
15640,500,1000,1000
14960,1000,1000,1000
13630,500,1500,500

================================================
FILE: 83-MA/caseStudy_juice.R
================================================
#MA

library(s20x)
?s20x
apples.df
data(airpass.df)
airpass.df

## STATS20x data:
data(course.df)
## Single variable summary
with(course.df, summaryStats(Exam))
## Using a formula
summaryStats(Exam ~ Stage1, course.df)

## Using a matrix
X = cbind(rnorm(50), rnorm(50))
summaryStats(X)
## Saving and extracting the information
sumStats = summaryStats(Exam ~ Degree, course.df)
sumStats
## Just the BAs
sumStats['BA', ]
## Just the means
?sumStats



#data 
url = 'https://raw.githubusercontent.com/PacktPublishing/Jupyter-for-Data-Science/master/Chapter01/grapeJuice.csv'

df = read.csv(url)
df
head(df)
boxplot(df$sales, horiz=T, xlab='sales')
hist(df$sales, main ='', xlab='sales' , prob=T)
lines(density(df$sales), lty='dashed',  lwd=2.5, col='red')

(sales_ad_nature = subset(df, ad_type == 0))
(sales_ad_family = subset(df, ad_type == 1))

hist(sales_ad_nature$sales, main= '', xlab ='sales with nature production theme ad', prob=T)
lines(density(sales_ad_nature$sales), lty='dashed', lwd=2.5, col='red')

#test Ho
shapiro.test(sales_ad_nature$sales)
shapiro.test(sales_ad_family$sales)
t.test(sales_ad_nature$sales, sales_ad_family$sales)

pairs(df, col='blue', pch=20)
pairs20x(df)

sales.reg <- lm(sales ~ price + ad_type + price_apple + price_cookies, data=df)
summary(sales.reg)

library(regclass)
regclass::VIF(sales.reg)

(inputData <- data.frame(price=10, ad_type=1, price_apple = 7.6, price_cookies = 9.738))
predict(sales.reg, inputData, interval='p')


================================================
FILE: 83-MA/data1.R
================================================
# Data
#Students of Class IX & X (M & F) play sports
student = paste('S',1:30, sep='')
student
set.seed(1234)
gender = sample(x=c('Male','Female'), size=30, replace=T, prob=c(20/30,10/30) )
gender
set.seed(1234)
class = sample(x=c('IX','X'), size=30, replace=T, prob=c(14/30,16/30) )
class
set.seed(1234)
play = sample(x=c('Yes','No'), size=30, replace=T, prob=c(15/30,15/30) )
play
studentsplay = data.frame(student, gender, class, play)
studentsplay
t1= table(studentsplay$gender,studentsplay$play)
prop.table(t1)

studentsplay

# Pruning only on the basis of Gender
playmodel1 <-rpart(play ~ gender , method="class", data=studentsplay,control=rpart.control(minsplit=2))
playmodel1
printcp(playmodel1)
prp(playmodel1,box.col=c("Grey", "Orange")[playmodel1$frame$yval],varlen=0,
    faclen=0, type=1,extra=4,under=TRUE)

playmodel2 <-rpart(play ~ class + gender, method="class", data=studentsplay,control=rpart.control(minsplit=4))
playmodel2
printcp(playmodel2)
prp(playmodel2,box.col=c("Grey", "Orange")[playmodel2$frame$yval],varlen=0,
    faclen=0, type=1,extra=4,under=TRUE)

playmodel3 <-rpart(play ~ class + gender, method="class", 
          data=studentsplay,control=rpart.control(minsplit=2, cp=0.001))
playmodel3
printcp(playmodel3)
prp(playmodel3,box.col=c("Grey", "Orange")[playmodel3$frame$yval],varlen=0,
    faclen=0, type=1,extra=4,under=TRUE)

Sample_tree<-rpart(play~gender+class, method="class", data=studentsplay, control=rpart.control(minsplit=2, cp=0.001))
Sample_tree



================================================
FILE: 83-MA/graph1.R
================================================
# Graphs for MA

salemonth = month.abb[1:12]
salemonth  # order of variables not correct
salemonth = factor(salemonth, ordered=T, levels=month.abb[1:12])
salemonth
product1 = round(runif(12,10,100),2)
product2 = round(runif(12,10,100),2)
sales = data.frame(salemonth, product1, product2)
str(sales)
# ok
plot(salemonth, product1, data=sales, type='l', col='red')
plot(salemonth, product2, data=sales, type='l', col='red')
par(options)
abline(lm(product2 ~ salemonth))
lines(lowess(product2 ~ salemonth))


#
pairs(sales[,1:3])

#
plot(wt ~ mpg, data=mtcars)
plot(sales$salemonth , sales$product1, type='p', col='red', pch='17')
barplot(product1, data=sales, names.arg=salemonth)
barplot(as.matrix(sales[,c('product1', 'product2')]), beside = T, data=sales, names.arg=salemonth, legend.text = 'Legend')

#
plot(as.numeric(sales$salemonth) , sales$product1, type='l', col='red', xlab='Months', ylab='Products', ylim=c(0,100))
par(new=TRUE)
plot(as.numeric(sales$salemonth) , sales$product2, type='l', col='green', xlab ='Months', lwd='2', ylab="", ylim=c(0,100))


#ggplot2
library(ggplot2)
g = ggplot(sales, aes(as.numeric(salemonth)))  #g
g = g + geom_line(aes(y=product1), color='red')
g = g + geom_line(aes(y=product2), color='green')
g = g + labs(x = "Month",y = "Sales amount", title = "Sales for Different Products")
g

#
b = ggplot(data=sales, aes(x=salemonth, y=product1)) +
  geom_bar(stat="identity")
b
# Two graphs do not work this way.. do some changes
sales
library(reshape2)
sales2 = melt(data=sales, id.vars='salemonth')
sales2

b2 = ggplot(data=sales2, aes(x=salemonth, y=value, fill=variable)) 
b2 = b2 + geom_bar(stat="identity", position='dodge')
b2 
#ok

sales2[salemonth==c('Jan','Feb'),]
b2 = ggplot(data=sales2[salemonth==c('Jan','Feb'),], aes(x=salemonth, y=value, fill=variable)) 
b2 = b2 + geom_bar(stat="identity", position='dodge')
b2 



#wide to long


================================================
FILE: 83-MA/maregression1.R
================================================
#Campaign Analysis

campaign = read.csv("./data/campaign.csv")
campaign
colSums(campaign)
sum(campaign[,-1])
summary(campaign)
(Campaignmodel<-lm(Sales~Newspaper+TV+Online, data=campaign) )

summary(Campaignmodel)

plot(Campaignmodel)

cbind(campaign, predict(Campaignmodel))

testdata = data.frame(Newspaper=500, TV=500, Online=2000)
testdata = data.frame(Newspaper=1000, TV=500, Online=2500)

predict(Campaignmodel, newdata = testdata)
9562 +(1.25*500)+(0.92*500) + (3.53*2000)


================================================
FILE: 83-MA/pricing1.R
================================================
# Pricing Metrics
#https://www.analyticsvidhya.com/blog/2016/07/solving-case-study-optimize-products-price-online-vendor-level-hard/

data = read.csv(file.choose())
str(data)
names(data)
cnames = c('product','avgprice', 'unitcost', 'avgprofit', 'avgunitssold', 'incracq','incsalevol')
names(data) = cnames
summary(data)
dim(data)

library(fdth)
(tb1 = fdt(data$avgprofit))

(tb2 <- fdt(data))

# Histograms
plot(tb) # Absolute frequency histogram
plot(tb, main='My title')



#-----
dim(data)
#250*7
summary(data)
# This is the initial value of the increment vector - all zeros
increment <- array(0,250)
head(increment)
flag = 0
increment_i <- increment
#flag = 1 is a condition when the increment vector remains the same
while (flag == 0) {
  print(find_rev(increment_i))
  increment_iplus1 <- incremental_new(increment_i)
  if (min(increment_iplus1 == increment_i) == 1) {flag = 1}
  increment_i <- increment_iplus1
}
increment_i
find_rev(increment_i)
find_rev(increment)
price <- increment_i
write.csv(price,"price.csv")

#This function tries to get the next best increment vector
incremental_new <- function(initial_increments){
  initial_rev <- find_rev(initial_increments)
  intermediate_rev <- 0
  for(i in 1:250){
    increments <- initial_increments
    if(increments[i] > -0.099) {increments[i] <- increments[i] - 0.01}
    rev <- find_rev(increments)
    if (rev > initial_rev) {final_increments <- increments
    intermediate_rev <- rev
    }
    if(increments[i] < 0.19) {increments[i] <- initial_increments[i] + 0.01}
    rev <- find_rev(increments)
    if (rev > max(initial_rev,intermediate_rev)) {final_increments <- increments}
  }
  return(final_increments)
}
# This function will get us the overall revenue for the given increment vector
find_rev <- function(increment){
  price <- data$Avg_Price_per_unit*(1+increment)
  volumes <- data$Average_units_sold*(1-(data$Increase_sale_volume*increment*10))
  multiplier <- (1-(data$Incremental_acquisition*increment*10))
  total_multiplier <- prod(multiplier)
  profit_wo_multiplier <- 0.05*(sum(price*volumes) - sum(volumes*data$Cost_per_unit))
  profit_w_multiplier <- profit_wo_multiplier*total_multiplier
  net_profit <- sum(profit_w_multiplier)
  return(net_profit)}


================================================
FILE: 83-MA/tree1.R
================================================
# tree
# http://www.di.fc.ul.pt/~jpn/r/tree/tree.html
# https://rpubs.com/JanpuHou/242986
library(tree)
set.seed(1234)

real.state <- read.table(file="https://raw.githubusercontent.com/jbryer/CompStats/master/Data/cadata.dat",head=TRUE)
tree.model= tree(log(MedianHouseValue) ~ Longitude + Latitude, data=real.state)
plot(tree.model)
text(tree.model, cex=.75)

price.decibels = quantile(real.state$MedianHouseValue, 0:10/10)
price.decibels
cut.prices = cut(real.state$MedianHouseValue, price.decibels, include.lowest = T)
cut.prices
plot(real.state$Longitude, real.state$Latitude, col=grey(10:2/11)
     [cut.prices], pch=20, xlab='Longitude', ylab='Latitude')
partition.tree(tree.model, ordvars = c('Longitude','Latitude'), add=T)
?partition.tree

summary(tree.model)


================================================
FILE: 84-HR/hr_churn.R
================================================
#HR Analytics
#Employee churn is the overall turnover in an organization’s staff as existing employees leave and new ones are hired. The churn rate is usually calculated as the percentage of employees leaving the company over a specified time period. Although some staff turnover is inevitable, a high rate of churn is costly.


#https://medium.com/datadriveninvestor/hr-analytics-with-data-science-adc1649f4596
#predict the possible churn of employees in a firm. Using Data Science procedures, companies can predict employees who are likely to leave and also use Exploratory Data Analysis to find patterns that can inform reasons for which employees may decide to leave.
#Read newly saved file
url = 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/attrition.csv'
attrition <- read.csv(url, stringsAsFactors = T)
#Examine data structures
str(attrition)
#Lets start with some correlation relationships
library(polycor)
pacman::p_load(ggplot2, dplyr, reshape2)
attrition_corr <- hetcor(attrition)
attrition_corr

attrition_corr$correlations %>%   melt() %>%   ggplot(., aes(Var1, Var2, fill = value)) +  geom_tile() +   scale_fill_gradient(low = "grey", high = "darkred") +   geom_text(aes(Var1, Var2, label = round(value,2)), size = 2) +  labs(title = "Correlation Matrix", x = "Numeric column", y = "Numeric Column",  fill = "Coefficient Range") + theme(axis.text.x = element_text(vjust = 0.5, angle = 45), plot.title = element_text(face = "bold", hjust = 0.5)) 
#As seen above, this is a correlation analysis to examine relationships between the variables in the dataset.
#we take a look at the ratio of existing staff versus those who left:
#Lets examine the class 
attrition %>%   count(status) %>%  ggplot(., aes(status, n, fill = status)) +   geom_bar(stat = "identity") + ggtitle("Existing staffs vs Exits")

#Next, we move to examine the department with the highest number of employees: 
#Department with highest mumber of employess
attrition %>%   count(dept) %>%  ggplot(., aes(reorder(dept, n), n, fill = n)) +   geom_bar(stat = "identity") + coord_flip() + labs(title = "Deparment staff counts",   x = "Department")

#The sales department clearly has the highest number of staff, with the management department having the least number. With this information, we can attempt to take a look at the department with the highest churn:
#Lets examine the department who has more people who left
attrition %>%   count(status, dept) %>%   filter(status == "left") %>%  ggplot(., aes(reorder(dept,n), n, fill = n)) +   geom_bar(stat = "identity") +   coord_flip() +   ggtitle("Department with Largest Attrition Number") +   xlab("Department")

#It appears that there is a relationship between a department size and the attrition count for that department. To examine that relationship:
#relationship between department size and the churn size
cbind.data.frame(size = attrition %>% count(dept) %>%  select(n), churn = attrition %>%  count(status, dept) %>%  filter(status == "left") %>%  select(n))
#There is a relationship of 0.993

# look at the average working hours across all departments:
#Examine the average working hrs of each dept
attrition %>% group_by(dept) %>%  summarise(n = mean(average_montly_hours)) %>% ggplot(., aes(reorder(dept,n), n, fill = n)) + geom_bar(stat = "identity") +  coord_flip()

#The average working hours have an almost equal average across all departments. Examining the salary distribution across all departments may give us further insights into the dataset:
#Dept with the lowest salary range
attrition %>%  group_by(dept) %>% count(salary, sort = T) %>%  ggplot(., aes(reorder(dept, n), n, fill = salary)) +   geom_bar(stat = "identity") +   coord_flip() +  ggtitle("Salary range by department")


#It can be seen that all departments have a low salary more than other ranges, followed by the medium. The only department having a seeming par between medium and high salary range is the management department. To get deeper insights into churn in each department, it is important that one critically examines the ratio of existing employees and those who left individually by each department:
#ratio of people in a dept versus those who left
attrition %>% group_by(dept) %>% count(status) %>%  mutate(n2 = sum(n)) %>% mutate(n3 = n/n2) %>% ggplot(., aes(x = "", y = n3, fill = status)) +  geom_bar(stat = "identity", width = 2) + coord_polar(theta = "y") +   facet_wrap(~dept) + scale_fill_manual(values=c("#999999", "#E69F00"))


#his kind of visualization does is that it helps us remove the size and carefully take a look at the churn rate in each department. For example, the sales department has the highest number of employees, but the rate of churn is actually smaller compare to the HR department which has the second to least size. Proceeding to examine the average satisfaction level for each department:
#Satisfaction level by department
attrition %>% group_by(dept) %>%   summarise(level = mean(satisfaction_level)) %>%  ggplot(., aes(reorder(dept, level), level, fill = dept)) +  geom_bar(stat = "identity") + coord_flip() +  ggtitle("Satisfaction Level by Department")


#Now, we can look at the ratio of people who left or remained and the number of projects executed:
#Ratio of people who left and are remaining by the number of projects they conducted
attrition %>% 
  group_by(number_project) %>% 
  count(status) %>% 
  ggplot(., aes(factor(number_project), n, fill = status)) +
  geom_bar(stat = "identity")
attrition %>% 
  group_by(number_project) %>% 
  count(status) %>% 
  mutate(n2 = sum(n)) %>% 
  ungroup() %>% 
  mutate(n3 = n/n2) %>% 
  ggplot(., aes(x = "", y = n3, fill = status)) +
  geom_bar(stat = "identity", width = 2) +
  coord_polar(theta = "y") +
  facet_wrap(~number_project) +
  scale_fill_manual(values=c("#999999", "#E69F00"))


#This visual provides a lot of facts about people who left. We can see that people who have projects of 2, 6 and 7 counts have a greater tendency to leave. For example, everyone who has 7 projects left. How insightful!
#Taking it a step further, let us look at the average working hours of people existing and those who left:
  #People who left ap
  ggplot(attrition, aes(average_montly_hours, fill = status)) +
  geom_density(alpha = 0.1) +
  ggtitle("Average Working hours")

#This density provides a lot of information. People who left are being overworked and largely under-worked, while there is a fairly steady distribution for people who are current employees. It is safe to deduce that people who left are those largely under-worked and overworked.
#Finally, we examine the relationship between time spent in the company and the churn status:
  #Lets examine the time spent in the company
  attrition %>% 
  group_by(time_spend_company) %>% 
  count(status) %>% 
  mutate(n2 = sum(n)) %>% 
  ungroup() %>% 
  mutate(n3 = n/n2) %>% 
  ggplot(., aes(x = "", y = n3, fill = status)) +
  geom_bar(stat = "identity", width = 2) +
  coord_polar(theta = "y") +
  facet_wrap(~time_spend_company) +
  scale_fill_manual(values=c("#999999", "#E69F00")) +
  ggtitle("Time Spent in Company")

  
#The pie chart shows that, people who are likely to leave also fall in the range of 3–6 working years in the firm. Having all this information, firms can discern ways to make working more attractive for their employees.

# Machine Learning-----
#  The machine Learning script can be executed and the final score shows an accuracy of about 99% on the test data using a Random Forest model. An ensemble method was employed to find the best model, RF performed better but with a slightly longer computational time:
  
#This project has been able to manipulate an attrition dataset and provided actionable insights into deep problems an organization may be facing in terms of employee churn. It is of great importance, that the HR team takes the findings from the Analytical team to the stakeholders and profer measures to ensure efficient retaining culture.

    

================================================
FILE: 85-RFM/rfm1.R
================================================
#RFM

##Creating Random Sales Data of the format CustomerId (unique to each customer), Sales.Date,Purchase.Value

custid = sample(1000:1999, replace=T,size=10000)
sales.value = abs(round(rnorm(10000,28,13)))
sales=data.frame(custid, sales.value)
head(sales)

head(700*sort(stats::runif(10000)),50)
sales.dates <- as.Date("2016/1/1") + 700*sort(stats::runif(10000))

head(sales.dates, 20)
#generating random dates

sales=cbind(sales,sales.dates)
head(sales,10)
str(sales)
sales1 = sales
summary(sales)
difftime(Sys.Date(), Sys.Date()-3, units="days")

sales$recency=round(as.numeric(difftime(Sys.Date(),sales[,3],units="days")) )
head(sales)

#library(gregmisc)
library(gdata)

##if you have existing sales data you need to just shape it in this format
gdata::rename.vars(sales, from="sales.value", to="purchase.value" )

## Creating Total Sales(Monetization),Frequency, Last Purchase date for each customer
names(sales)
(salesM=aggregate(sales[,2],list(sales$custid),sum))

names(salesM)=c("custid","monetization")
head(salesM, 5)
head(salesM[order(salesM$monetization, decreasing=T),], 5)

#Frequency
salesF=aggregate(sales[,2],list(sales$custid),length)
head(salesF)
names(salesF)=c("custid","freq")
head(salesF)
head(salesF[order(salesF$freq, decreasing=T),], 5)

#Recency
salesR=aggregate(sales[,4],list(sales$custid),min)
head(salesR)
names(salesR)=c("custid","recency")
head(salesR[order(salesR$recency, decreasing=F),], 5)

##Merging R,F,M
summary(sales)
test1=merge(salesF,salesR,"custid")
head(test1)
salesRFM=merge(salesM,test1,"custid")

##Creating R,F,M levels : intervals of 5 : 1-5:A, 6-10:B

salesRFM$rankR=cut(salesRFM$recency, 5,labels=F) #rankR 1 is very recent while rankR 5 is least recent

salesRFM$rankF=cut(salesRFM$freq, 5,labels=F)#rankF 1 is least frequent while rankF 5 is most frequent

salesRFM$rankM=cut(salesRFM$monetization, 5,labels=F)#rankM 1 is lowest sales while rankM 5 is highest sales

head(salesRFM,50)
##Looking at RFM tables
table(salesRFM[,5:6])
table(salesRFM[,6:7])
table(salesRFM[,5:7])

#use quantiles instead of cut


================================================
FILE: 85-RFM/rfm3.R
================================================
# RFM 2

library(didrooRFM)
?didrooRFM
#part 1
TransNo = c('10','11','12','13')
CustomerID = c('Cust1','Cust2', 'Cust3','Cust2')
DateofPurch = as.Date(c('2010-11-1','2008-3-25','2017-3-25', '2016-3-25'))
Amount = c(1000,500, 600, 700)
customerData = data.frame(TransNo,CustomerID,DateofPurch,Amount)
customerData
?didrooRFM
rfm1 = findRFM(customerData)
rfm1$FinalScore
rfm1[5:16]
options(dplyr.width = Inf)
rfm1
findRFM(customerData, recencyWeight = 3, frequencyWeight = 12,
        monetoryWeight = 2)
#
#
#
#Part2



data(customerdata)
#customerdat - "TransNo","CustomerID","DateofPurch","Amount"
TransNo = 10001:20000
head(sales1)
customerdata = cbind(TransNo, sales1)
head(customerdata)
str(customerdata)
#customerdat - "TransNo","CustomerID","DateofPurch","Amount"
names(customerdata)
data()
customerdata = customerdata[,c('TransNo','custid', 'sales.dates', 'sales.value')]
dim(customerdata)
head(customerdata,100)
dim(customerdata)
# Create Customer Data
library(didrooRFM)


findRFM(customerdata, recencyWeight = 4, frequencyWeight = 4,
        monetoryWeight = 4)


================================================
FILE: 87-FA/10-FAlinks.R
================================================
#FA Links
#http://docplayer.net/15135763-Working-with-xts-and-quantmod.html


================================================
FILE: 87-FA/Insurance Loss v01.R
================================================

setwd("E:\\HHges - Mkt Anyts\\BITS Pilani Bussiness Anallytics\\03. Linear Regression")
getwd()

library(dplyr)
library(car)
file.data <- read.csv("InsuranceLosses.csv")

summary(file.data)

set.seed(5)
s1 <- sample(1:nrow(file.data), 0.7*nrow(file.data))
data.train <- file.data[s1,]
data.test <- file.data[-s1,]

head(file.data)
names(file.data)
fit.train <- lm(Losses ~ Age+Years.of.Driving.Experience+
                  Number.of.Vehicles+Gender+
                  as.factor(Married)+Vehicle.Age+                           
                  Fuel_type_D+ 
                  Income   
                , data=data.train[,-c(1)])
library(car)
vif(fit.train)
names(data.train)
fit.train <- lm(Losses ~ Years.of.Driving.Experience+
                  Number.of.Vehicles+Gender+
                  as.factor(Married)+Vehicle.Age+                           
                  Fuel_type_D+ 
                  Income   
                , data=data.train[,-c(1,2)])
vif(fit.train)


summary(fit.train)

fit.final <- lm(Losses ~ 
                Vehicle.Age+                           
                  Fuel_type_D+ 
                  Income   
                , data=data.train[,-c(1)])

summary(fit.final)

fitted(fit.final)
summary(fit.final)$r.squared #R-square Value 



meancnt1=data.test
meancnt1$pred = 
  8.187 + 
  -0.01376*meancnt1$Vehicle.Age+
  0.2405*meancnt1$Fuel_type_D+
  0.01999*meancnt1$Income
  


meancnt1$res = meancnt1$pred - meancnt1$Losses
head(meancnt1)
meancnt1$abs_res = abs(meancnt1$res)
meancnt1$mape = 100*meancnt1$abs_res/meancnt1$Losses
(MAPE_Percentage =mean(meancnt1$mape))

# Plot Y-pred vs. Error to ensure there is no pattern
plot(meancnt1$pred,meancnt1$res)



================================================
FILE: 87-FA/InsuranceLosses.csv
================================================
Policy Number,Age,Years of Driving Experience,Number of Vehicles,Gender,Married,Vehicle Age,Losses,Fuel_type_D,Income
147805,25,8,1,0,Single,5,3500,1,174613
122992,50,32,1,1,Single,15,3462.340629,1,172645.0315
143824,20,1,2,0,Single,3,3462,1,172830
174360,16,0,3,0,Single,4,3333,1,166165
110488,35,15,1,0,Single,9,3332.771801,1,166072.5901
190621,16,0,1,0,Single,15,3250,1,162190
180426,25,7,4,0,Single,1,3227,1,160978
111434,64,45,1,1,Single,13,3226.612176,1,161066.6088
164185,25,6,1,1,Single,8,2879.176458,1,143459.8229
142085,25,7,1,1,Married,11,2879.067137,1,143703.3569
160115,25,8,2,0,Single,3,2879,1,143693
161339,22,4,2,0,Single,5,2879,1,143506
152551,52,34,1,0,Married,5,2852.18873,1,142146.4365
157767,18,0,2,0,Single,2,2852,1,142211
161675,66,48,1,0,Single,10,2850,1,142080
191297,38,20,1,1,Married,3,2814.312522,1,140428.6261
156601,19,2,2,0,Single,0,2814,1,140491
122473,21,4,1,0,Single,0,2700,1,134575
104242,25,8,1,0,Single,5,2600,1,129416
188783,25,7,2,0,Single,0,2535,1,126434
165974,63,43,1,1,Single,10,2534.63946,1,126216.973
115780,23,5,2,0,Single,4,1953.606442,1,97452.3221
112744,22,4,2,0,Single,1,1883.373932,1,93577.6966
145380,21,4,2,0,Single,3,1850.996,1,92212.8
103525,20,3,4,0,Single,0,1848.845387,1,92235.26935
183049,17,0,3,0,Single,2,1802.444618,1,89739.2309
115456,17,0,4,0,Single,1,1776.525239,1,88500.26195
180372,18,0,1,0,Single,0,1772.78492,1,88276.246
103212,22,4,2,0,Single,1,1747.297619,1,87071.88095
190827,22,4,2,0,Single,1,1736.287451,1,86583.37255
105766,21,3,4,0,Single,1,1731.433041,1,86171.65205
135228,19,2,1,0,Single,5,1730.684951,1,86100.24755
184028,19,0,4,0,Single,1,1710.178261,1,85076.91305
108912,18,1,3,0,Single,1,1709.307889,1,85140.39445
130887,17,0,2,0,Single,0,1679.147211,1,83524.36055
115039,21,3,2,0,Single,1,1667.136998,1,82990.8499
168998,21,3,4,0,Single,2,1662.706358,1,82764.3179
177042,20,1,2,1,Single,0,1654.71703,1,82511.8515
193787,22,4,2,0,Single,2,1641.515906,1,81594.7953
118625,24,6,4,0,Single,5,1621.306259,1,80593.31295
131902,22,4,1,0,Single,5,1620.536634,1,80506.8317
164526,19,1,4,0,Single,1,1619.073617,1,80376.68085
118298,17,0,3,0,Single,5,1610.185929,1,80309.29645
126147,21,4,4,0,Single,5,1594.377501,1,79134.87505
155387,17,0,2,0,Married,2,1590.157527,1,79251.87635
187352,19,2,4,0,Single,2,1586.970936,1,78929.5468
133730,23,6,3,0,Single,5,1582.314185,1,78796.70925
176085,18,1,1,0,Single,1,1570.197729,1,78114.88645
147118,17,0,4,0,Single,2,1555.951395,1,77430.56975
159096,23,4,2,0,Single,5,1555.438202,1,77456.9101
149211,18,1,2,0,Single,3,1555.052888,1,77212.6444
128925,20,1,4,1,Single,3,1549.236462,1,77119.8231
120120,17,0,3,0,Married,0,1530.056899,1,76051.84495
175730,20,2,2,0,Single,4,1523.041103,1,75700.05515
188722,22,3,2,1,Single,4,1511.71037,1,75048.5185
186031,16,0,3,0,Married,1,1501.75855,1,74634.9275
157151,17,0,2,1,Single,1,1498.958037,1,74479.90185
109203,24,7,1,0,Single,0,1491.634513,1,74237.72565
118210,24,5,3,1,Single,4,1490.804835,1,74013.24175
132773,17,0,2,0,Single,0,1486.388076,1,73993.4038
185503,20,1,3,1,Single,0,1485.928765,1,73846.43825
177241,22,4,3,0,Single,3,1481.705551,1,73581.27755
162984,18,0,2,0,Single,3,1481.046017,1,73690.30085
139476,16,0,3,0,Married,3,1479.831034,1,73790.5517
128571,22,3,4,0,Single,13,1473.7403,1,73201.015
197370,18,0,4,0,Single,8,1440.526634,1,71747.3317
108978,23,4,3,0,Single,2,1435.870915,1,71410.54575
139687,21,4,2,1,Single,5,1428.120323,1,71021.01615
182122,21,4,2,0,Single,7,1424.841406,1,70953.0703
106191,17,0,1,0,Married,1,1401.284803,1,69515.24015
139807,24,5,2,0,Single,12,1397.488209,1,69361.41045
103107,22,4,2,0,Single,2,1394.085068,1,69128.2534
189951,17,0,3,0,Single,10,1380.980203,1,68756.01015
196688,25,7,1,0,Single,2,1380.915346,1,68583.7673
177159,16,0,3,0,Single,15,1377.539259,1,68326.96295
175402,20,1,2,0,Single,0,1370.142171,1,68225.10855
167635,25,7,4,0,Single,2,1368.54557,1,68141.2785
147525,22,4,3,0,Single,8,1363.388951,1,67953.44755
160461,17,0,4,0,Single,4,1362.991866,1,67818.5933
187753,24,6,3,1,Single,0,1362.170278,1,67699.5139
139500,22,3,1,0,Single,7,1359.573132,1,67476.6566
108695,25,8,3,1,Single,1,1355.412924,1,67404.6462
195256,18,0,1,0,Single,2,1355.15476,1,67333.738
190271,17,0,3,0,Single,15,1350.971873,1,67217.59365
128195,18,0,4,0,Single,13,1350.647537,1,67129.37685
116510,16,0,2,0,Single,2,1345.610516,1,66992.5258
163674,24,5,3,0,Single,7,1341.042514,1,66676.1257
158125,23,4,1,0,Single,0,1340.946549,1,66454.32745
143467,16,0,3,0,Single,1,1326.426656,1,66036.3328
123118,17,0,2,0,Single,8,1322.743329,1,65807.16645
193871,17,0,2,1,Single,3,1319.454126,1,65434.7063
177340,20,3,3,0,Single,9,1318.990738,1,65680.5369
161198,17,0,4,0,Single,9,1317.92769,1,65328.3845
136812,18,0,4,1,Single,1,1314.475359,1,65352.76795
105488,25,6,2,0,Single,1,1313.38813,1,65137.4065
200454,21,3,3,0,Single,8,1308.787234,1,65122.3617
129585,21,4,3,0,Single,1,1305.985998,1,64807.2999
190111,18,0,4,0,Single,2,1301.428122,1,64635.4061
184731,18,1,3,0,Single,0,1299.059765,1,64632.98825
174530,21,3,4,0,Married,2,1297.739892,1,64594.9946
118982,25,8,2,0,Single,8,1297.152399,1,64601.61995
188582,25,6,3,1,Single,0,1296.990069,1,64316.50345
160407,20,2,3,0,Single,11,1296.816028,1,64619.8014
109682,20,2,3,0,Single,7,1291.362186,1,64036.1093
193169,22,3,2,0,Single,13,1291.352892,1,64109.6446
161542,22,3,4,0,Single,2,1289.983352,1,64073.1676
107664,23,4,3,1,Single,3,1288.521549,1,64195.07745
164774,19,2,3,0,Single,9,1287.277428,1,63876.8714
177869,20,1,2,0,Single,10,1286.089237,1,64038.46185
160806,18,1,2,0,Single,1,1283.713451,1,63901.67255
175815,20,1,2,1,Single,0,1281.070759,1,63663.53795
123734,17,0,4,0,Single,10,1280.495256,1,63469.7628
184927,16,0,1,0,Single,11,1278.510959,1,63654.54795
106867,20,1,2,0,Single,12,1270.186638,1,63135.3319
156461,22,5,3,0,Single,1,1269.443421,1,63093.17105
141554,18,0,3,0,Single,15,1268.076011,1,63014.80055
161657,23,6,3,0,Married,0,1267.597166,1,62880.8583
162998,23,4,3,0,Single,9,1265.998313,1,62708.91565
168352,25,7,1,0,Single,12,1265.254483,1,62992.72415
187221,19,1,3,0,Single,4,1262.926707,1,62625.33535
191874,21,2,4,1,Single,3,1262.024405,1,62834.22025
173522,24,7,3,0,Single,15,1261.179325,1,62605.96625
163643,17,0,4,0,Single,15,1260.120154,1,62760.0077
194934,17,0,1,0,Single,10,1255.767314,1,62526.3657
161998,23,5,3,0,Married,0,1250.233231,1,61936.66155
119043,16,0,2,0,Single,4,1248.405899,1,61861.29495
147925,17,0,3,0,Single,8,1243.405151,1,61634.25755
162334,17,0,3,0,Single,12,1243.266651,1,61725.33255
183420,17,0,3,0,Single,6,1242.883674,1,61776.1837
155784,20,1,2,0,Single,7,1240.961967,1,61570.09835
184822,22,5,3,0,Single,7,1239.894273,1,61478.71365
124724,21,4,1,0,Single,13,1239.79541,1,61689.7705
109426,21,2,2,0,Single,15,1238.855434,1,61646.7717
101504,25,7,2,0,Single,12,1237.946994,1,61571.3497
164615,18,0,2,0,Single,14,1231.570261,1,61172.51305
151334,20,3,3,0,Single,8,1231.095742,1,61219.7871
141795,16,0,2,0,Single,7,1229.29243,1,60971.6215
178037,16,0,2,0,Single,6,1227.567261,1,60926.36305
110012,18,0,2,0,Single,0,1226.265111,1,61110.25555
113843,17,0,3,0,Single,10,1226.233875,1,60962.69375
127761,16,0,2,0,Married,0,1225.158785,1,60940.93925
163552,18,1,3,0,Married,2,1223.990411,1,60636.52055
135122,25,6,2,0,Single,2,1223.159682,1,60874.9841
186574,23,5,3,0,Single,8,1218.356458,1,60461.8229
122102,22,4,3,0,Single,6,1216.554442,1,60345.7221
101915,20,2,2,0,Single,4,1208.851512,1,60007.5756
131374,25,7,1,0,Married,3,1208.666417,1,59959.32085
158244,20,3,2,0,Single,0,1207.301401,1,60016.07005
166321,19,2,2,0,Single,12,1207.007954,1,60023.3977
104939,18,0,3,0,Single,2,1204.833206,1,59769.6603
177507,18,0,1,0,Married,4,1204.444856,1,59623.2428
113103,25,6,2,0,Single,0,1204.281553,1,59814.07765
163906,23,5,3,0,Single,14,1204.097279,1,59784.86395
149340,19,2,1,0,Single,10,1203.938094,1,59782.9047
101726,20,1,2,1,Single,4,1201.951572,1,59556.5786
105328,18,0,4,0,Single,13,1201.320791,1,59746.03955
122908,25,8,2,0,Single,11,1200.47256,1,59822.628
191540,25,7,1,0,Married,1,1199.944535,1,59425.22675
112619,17,0,1,0,Single,8,1197.444316,1,59628.2158
119061,20,2,2,0,Single,0,1196.529346,1,59329.4673
146826,19,1,3,0,Married,5,1195.283536,1,59394.1768
125169,23,4,2,0,Single,11,1194.817985,1,59519.89925
186532,22,3,2,0,Married,1,1193.282553,1,59186.12765
184808,23,4,2,0,Single,7,1191.530812,1,59324.5406
141504,24,6,2,1,Single,0,1191.211171,1,59067.55855
157407,18,0,2,0,Single,10,1189.577654,1,59058.8827
103007,25,7,1,0,Single,13,1189.051543,1,58865.57715
142018,25,7,3,0,Single,12,1188.744129,1,58971.20645
155825,18,0,2,0,Single,6,1186.401052,1,58977.0526
105595,20,2,2,0,Married,0,1182.802099,1,58933.10495
188333,16,0,1,0,Single,11,1182.476864,1,58888.8432
148397,19,1,2,0,Single,6,1178.477467,1,58393.87335
125119,19,0,4,0,Married,1,1177.115598,1,58319.7799
164592,22,3,4,0,Single,11,1175.319784,1,58560.9892
104425,16,0,3,0,Single,15,1174.253909,1,58512.69545
123534,19,2,2,0,Single,3,1173.421392,1,58336.0696
141946,25,6,3,0,Single,8,1172.205433,1,58210.27165
171455,17,0,2,0,Married,1,1170.318061,1,58028.90305
142483,24,5,3,0,Single,6,1168.957156,1,58024.8578
170929,24,6,2,1,Single,5,1167.922229,1,57858.11145
109181,24,5,2,0,Married,5,1164.988741,1,57949.43705
118003,20,2,3,0,Married,4,1163.722413,1,57608.12065
100482,16,0,2,0,Single,6,1162.650269,1,57704.51345
110063,22,5,2,0,Single,9,1162.288811,1,57619.44055
139979,19,1,2,1,Single,0,1162.112704,1,57621.6352
149161,22,4,4,0,Single,10,1160.872891,1,57691.64455
109903,18,0,4,0,Single,7,1159.050397,1,57387.51985
167570,23,5,3,0,Single,0,1157.352758,1,57328.6379
194758,18,0,3,0,Married,4,1156.541639,1,57544.08195
128427,25,6,2,0,Single,9,1155.585908,1,57291.2954
111639,20,2,1,0,Single,10,1155.570126,1,57221.5063
120016,16,0,3,0,Single,9,1154.366373,1,57301.31865
145262,23,6,3,0,Single,7,1154.024463,1,57274.22315
135569,24,5,2,0,Single,11,1151.642678,1,57369.1339
132759,22,3,2,0,Married,1,1151.615653,1,57176.78265
139785,20,3,2,0,Single,3,1151.197551,1,57148.87755
191604,20,3,2,0,Single,0,1151.11018,1,57239.509
117437,22,4,4,0,Single,9,1148.295076,1,57153.7538
104479,21,2,4,0,Single,11,1147.154541,1,56934.72705
184721,19,2,4,0,Married,0,1144.362889,1,56680.14445
113495,20,1,2,0,Single,7,1142.6563,1,56546.815
171797,22,4,3,1,Single,3,1141.298387,1,56656.91935
102526,25,6,2,0,Single,10,1136.769929,1,56533.49645
156817,16,0,2,1,Single,4,1133.909051,1,56424.45255
159011,16,0,3,0,Single,12,1133.167261,1,56454.36305
160916,19,1,1,1,Single,0,1131.743655,1,56072.18275
103954,16,0,4,0,Married,4,1130.591985,1,56051.59925
102452,16,0,4,0,Married,1,1130.276363,1,56145.81815
195333,19,1,1,0,Single,0,1129.371234,1,55878.5617
160246,22,3,1,0,Single,9,1128.246122,1,55917.3061
185146,23,4,4,0,Single,6,1126.051162,1,55821.5581
123784,19,2,4,0,Single,13,1125.801015,1,56023.05075
174124,18,1,3,1,Single,5,1124.19002,1,56007.501
154975,17,0,2,0,Single,13,1122.25101,1,55603.5505
190354,16,0,2,1,Single,4,1121.535836,1,55695.7918
150879,25,8,2,0,Single,9,1121.372912,1,55566.6456
181952,25,7,3,0,Married,0,1121.163062,1,55468.1531
162793,18,0,3,0,Single,14,1120.12851,1,55524.4255
190921,16,0,4,0,Single,12,1119.340454,1,55687.0227
114134,16,0,2,0,Single,14,1117.127223,1,55329.36115
113119,18,0,3,0,Single,9,1113.614621,1,55333.73105
161428,24,6,3,0,Married,2,1113.363315,1,55156.16575
146674,16,0,4,0,Single,1,1109.628396,1,55164.4198
141331,18,0,2,1,Single,3,1109.392633,1,54960.63165
142978,20,3,3,0,Single,15,1109.134848,1,55233.7424
111407,16,0,2,0,Single,6,1107.401753,1,55169.08765
196549,20,3,3,0,Single,5,1106.820353,1,55080.01765
195140,19,2,2,0,Single,1,1103.528284,1,54853.4142
129514,17,0,3,1,Single,4,1102.664607,1,54594.23035
187555,25,6,3,0,Single,6,1102.42198,1,54852.099
181830,18,0,3,0,Single,7,1101.32202,1,54762.101
196054,24,6,2,0,Single,15,1099.378637,1,54593.93185
143694,20,3,2,0,Single,4,1099.181643,1,54523.08215
133381,25,7,1,0,Single,4,1098.237869,1,54458.89345
157217,18,0,3,0,Single,13,1096.765535,1,54496.27675
108960,25,6,2,0,Single,3,1096.314301,1,54406.71505
191212,24,6,2,1,Single,0,1096.206474,1,54228.3237
155060,22,3,1,0,Single,4,1095.601585,1,54348.07925
144397,21,2,3,0,Single,13,1095.356069,1,54392.80345
123748,21,4,3,0,Single,15,1095.189428,1,54369.4714
190876,23,4,2,0,Married,2,1094.928187,1,54374.40935
114516,17,0,3,0,Single,11,1094.091621,1,54460.58105
193022,22,4,4,0,Single,7,1093.989056,1,54440.4528
185597,20,3,3,0,Single,12,1093.491213,1,54434.56065
104612,21,2,3,0,Single,6,1093.141561,1,54333.07805
143303,20,1,2,0,Single,8,1092.721727,1,54343.08635
112005,16,0,3,0,Single,8,1092.341774,1,54166.0887
125000,24,6,4,0,Single,6,1091.162554,1,54033.1277
199907,20,1,3,0,Single,4,1090.679449,1,54147.97245
192571,21,4,1,0,Single,8,1089.683475,1,54266.17375
151336,16,0,2,0,Single,0,1087.404136,1,53989.2068
146707,17,0,3,0,Single,9,1087.141546,1,53920.0773
119860,23,5,1,0,Single,15,1086.924905,1,54109.24525
167399,19,1,2,0,Single,12,1084.643825,1,53841.19125
122159,17,0,3,0,Single,12,1084.582349,1,53848.11745
148260,23,6,2,0,Single,7,1084.569504,1,53963.4752
136989,23,4,2,1,Single,0,1084.380091,1,53777.00455
168270,16,0,3,0,Single,7,1083.622202,1,53947.1101
198247,27,7,3,0,Single,5,1080.326184,1,53455.3092
100217,23,6,1,0,Single,13,1079.526481,1,53400.32405
153904,21,3,4,0,Single,9,1078.616179,1,53526.80895
159080,19,0,3,1,Single,0,1078.598204,1,53656.9102
112682,20,3,2,0,Single,9,1078.082268,1,53479.1134
199177,23,6,2,0,Single,8,1077.884502,1,53344.2251
149679,21,3,3,0,Single,12,1077.083376,1,53306.1688
113090,23,6,2,0,Single,9,1076.568147,1,53572.40735
180572,18,1,3,0,Single,8,1073.046511,1,53076.32555
168978,20,3,1,1,Single,3,1072.705985,1,53353.29925
173908,18,0,2,0,Single,7,1071.895675,1,53110.78375
104574,24,6,2,0,Single,15,1070.485093,1,53197.25465
182178,23,5,1,0,Single,6,1070.210869,1,53309.54345
159668,24,6,1,0,Single,12,1070.204776,1,52936.2388
133556,23,5,1,1,Single,1,1069.470213,1,53093.51065
138981,19,0,2,0,Single,8,1069.37391,1,53011.6955
139616,23,5,3,0,Single,6,1068.707903,1,53030.39515
180667,21,4,4,0,Single,7,1067.742432,1,52854.1216
146768,24,7,3,0,Single,7,1067.514121,1,53153.70605
169547,16,0,2,0,Single,5,1066.856405,1,52823.82025
195112,21,4,1,0,Married,4,1066.302464,1,52732.1232
159229,25,6,3,0,Single,9,1065.366034,1,53054.3017
131529,17,0,2,1,Single,4,1064.583565,1,53013.17825
145921,17,0,3,0,Single,10,1063.703625,1,52943.18125
107139,21,4,2,0,Single,7,1061.520829,1,52493.04145
105608,51,31,4,0,Single,2,1061.157458,1,52641.8729
195241,20,2,2,0,Single,6,1060.415929,1,52548.79645
111729,22,3,2,0,Single,2,1058.035625,1,52629.78125
131315,16,0,2,0,Single,9,1057.42047,1,52410.0235
130873,25,8,2,0,Single,9,1055.197653,1,52265.88265
192903,25,8,3,0,Married,5,1053.630447,1,52144.52235
158856,24,7,2,0,Single,6,1052.836142,1,52134.8071
179570,25,8,3,0,Single,15,1052.124636,1,52373.2318
188219,22,4,4,0,Single,1,1050.53842,1,52321.921
113202,17,0,4,0,Single,12,1050.068538,1,51997.4269
163260,18,0,4,0,Single,6,1049.911783,1,51999.58915
147044,21,2,2,0,Married,5,1049.625869,1,51981.29345
195430,23,5,3,0,Single,9,1049.170554,1,51912.5277
112639,16,0,3,0,Single,6,1048.423276,1,51851.1638
142707,20,1,1,0,Single,6,1048.410022,1,52122.5011
196986,16,0,2,0,Single,6,1047.157424,1,52047.8712
164933,20,2,2,0,Single,12,1046.414359,1,52006.71795
190968,16,0,3,0,Single,11,1044.619269,1,51860.96345
176455,22,3,3,0,Single,8,1042.843674,1,51621.1837
100918,19,2,4,0,Single,7,1042.809484,1,51686.4742
171975,18,0,3,0,Single,10,1042.184386,1,51753.2193
122171,20,1,3,0,Single,0,1041.848121,1,51839.40605
175375,19,2,2,0,Single,13,1041.495458,1,51513.7729
156955,25,8,3,0,Single,14,1041.094865,1,51472.74325
180050,24,7,3,0,Single,12,1041.064578,1,51839.2289
163636,21,4,4,0,Single,15,1038.948205,1,51506.41025
167470,17,0,1,1,Single,4,1037.373455,1,51384.67275
160787,18,0,1,0,Single,14,1037.104939,1,51655.24695
125588,25,7,1,0,Single,7,1036.422302,1,51355.1151
152690,25,7,3,0,Single,8,1035.803278,1,51400.1639
120755,21,2,2,0,Single,10,1034.576892,1,51214.8446
117039,17,0,2,0,Single,8,1033.629031,1,51378.45155
101703,25,8,4,0,Single,15,1032.654421,1,51398.72105
117930,18,0,1,0,Single,10,1032.461873,1,51196.09365
132985,22,4,2,0,Single,6,1030.454301,1,51144.71505
101497,23,6,2,0,Single,12,1030.446074,1,51249.3037
109716,33,13,2,0,Single,3,1029.987461,1,51235.37305
132757,24,7,3,0,Single,15,1029.882891,1,51023.14455
131655,21,4,2,0,Single,14,1029.260813,1,51247.04065
103046,19,2,1,1,Single,3,1028.075432,1,50868.7716
138513,18,0,2,0,Single,14,1027.582618,1,50856.1309
103952,25,8,3,0,Single,14,1027.244394,1,51093.2197
119795,20,2,3,0,Single,7,1027.202169,1,50761.10845
132865,18,0,2,0,Single,5,1026.325173,1,51107.25865
162190,16,0,2,0,Single,6,1026.310667,1,50879.53335
189686,22,3,2,0,Married,3,1025.776072,1,50724.8036
170574,25,8,4,0,Single,9,1025.512944,1,50956.6472
179633,16,0,2,0,Single,11,1025.052975,1,50727.64875
112879,25,8,3,0,Single,7,1024.033822,1,50968.6911
154713,17,0,1,0,Single,11,1023.788951,1,50707.44755
120401,22,5,3,0,Single,13,1023.411106,1,50846.5553
118278,24,6,1,0,Married,0,1023.403662,1,50695.1831
199423,21,4,3,0,Married,2,1023.205561,1,50892.27805
153650,18,0,3,0,Single,9,1022.130812,1,50557.5406
145428,25,8,2,1,Single,0,1021.639065,1,50800.95325
157919,21,2,1,0,Single,7,1020.797227,1,50789.86135
103875,17,0,3,0,Single,10,1020.479783,1,50673.98915
129060,25,8,4,0,Single,13,1020.092166,1,50415.6083
174992,19,1,1,0,Single,14,1018.192642,1,50489.6321
114262,17,0,3,0,Single,14,1017.283924,1,50329.1962
102891,42,23,2,0,Single,5,1016.827347,1,50427.36735
158281,21,3,2,0,Single,9,1014.926338,1,50219.3169
151957,25,7,1,0,Single,11,1014.321608,1,50371.0804
197473,32,14,4,0,Single,2,1013.725034,1,50204.2517
186787,53,33,2,0,Single,2,1013.473644,1,50408.6822
166636,20,1,4,0,Single,12,1012.762359,1,50327.11795
137449,25,6,2,0,Single,11,1008.820062,1,50131.0031
152623,20,1,3,1,Single,4,1006.304881,1,50001.24405
187748,20,1,1,1,Single,1,1005.793409,1,50008.67045
171806,19,1,3,0,Single,7,1005.151383,1,49928.56915
128905,20,3,3,0,Single,7,1003.986869,1,49730.34345
165322,17,0,3,0,Single,10,1003.798871,1,49802.94355
157090,17,0,3,0,Single,8,1002.27879,1,49654.9395
135353,22,5,3,1,Single,2,1000.752305,1,49466.61525
186577,19,0,2,0,Single,9,999.9315769,1,49503.57885
182028,25,6,3,0,Single,0,999.9212233,1,49655.06117
108599,21,4,3,0,Single,10,999.8730496,1,49752.65248
125065,18,0,2,0,Single,11,998.9172356,1,49618.86178
119841,26,6,3,0,Single,5,997.4067386,1,49598.33693
182579,22,4,4,0,Single,13,997.2445349,1,49532.22675
115916,51,30,2,0,Single,4,994.8093204,1,49427.46602
189595,18,0,3,0,Single,0,994.5109291,1,49268.54646
182354,22,4,3,1,Single,4,994.4812481,1,49262.06241
101225,17,0,2,0,Married,1,994.0657896,1,49180.28948
125408,24,6,3,0,Single,14,993.2333375,1,49442.66688
145291,24,5,4,0,Single,7,989.980317,1,49254.01585
184598,47,29,1,0,Single,1,989.827214,1,49029.3607
115293,24,5,1,0,Married,0,989.6964115,1,49060.82058
171994,17,0,3,0,Single,12,989.518448,1,48904.9224
119642,24,5,2,0,Single,11,988.0390258,1,49016.95129
152732,24,5,4,0,Single,0,987.0121712,1,49104.60856
147113,22,5,3,0,Single,11,986.7371392,1,49006.85696
146039,24,7,3,0,Single,8,985.8140431,1,49050.70216
193707,24,5,3,0,Single,11,984.9714047,1,48771.57024
196804,22,3,3,1,Single,4,984.5450866,1,48884.25433
127572,25,7,1,0,Single,9,983.6492536,1,48867.46268
111399,57,38,4,0,Single,1,981.6439078,1,48715.19539
138063,21,2,2,0,Single,15,981.3483733,1,48474.41867
129526,25,7,3,0,Single,14,981.1059191,1,48460.29596
136782,25,6,2,0,Single,14,981.002503,1,48571.12515
152143,24,6,3,0,Single,6,980.8848113,1,48574.24057
142718,22,4,2,0,Single,8,980.3384425,1,48462.92213
189426,20,1,3,0,Single,2,980.1310295,1,48667.55148
180629,17,0,4,0,Single,11,979.0879538,1,48639.39769
168651,24,7,4,0,Single,15,978.218947,1,48700.94735
103237,31,13,2,0,Single,1,978.0305203,1,48392.52602
195391,23,4,3,0,Single,15,977.7389017,1,48646.94509
127338,19,2,3,0,Single,9,977.5152074,1,48391.76037
127344,21,3,2,0,Single,14,977.226696,1,48533.3348
155411,23,6,2,1,Single,3,977.006565,1,48578.32825
141133,23,4,2,1,Single,0,976.7366225,1,48513.83113
107600,44,27,2,0,Single,0,975.5526657,1,48261.63329
125869,19,1,2,1,Married,2,974.0882952,1,48182.41476
132872,31,13,3,1,Single,5,974.0050128,1,48223.25064
138635,19,1,4,0,Single,14,973.9247055,1,48262.23528
167680,32,12,4,0,Single,3,973.2740876,1,48126.70438
198289,21,4,2,0,Single,11,972.7204643,1,48281.02322
126066,19,0,3,0,Single,10,972.4744963,1,48339.72482
113217,25,8,2,0,Married,1,971.6615958,1,48192.07979
168613,16,0,2,0,Single,13,971.1937518,1,47994.68759
133124,19,0,4,0,Single,10,970.5309697,1,48144.54849
122479,37,19,3,0,Single,0,970.4087184,1,48292.43592
119111,19,2,3,0,Single,9,970.2005878,1,48209.02939
149732,18,1,4,0,Single,14,967.2427229,1,47900.13615
186854,22,5,3,0,Single,7,967.0016334,1,47856.08167
179032,21,4,4,0,Single,9,966.8027841,1,48093.13921
172577,25,6,1,0,Single,13,966.127914,1,48001.3957
133137,35,14,1,0,Single,5,965.3134906,1,47999.67453
197366,19,2,4,0,Single,10,965.0542936,1,47848.71468
103199,20,3,2,0,Single,2,964.5509037,1,47765.54519
154884,25,7,2,0,Single,1,964.1886481,1,47858.43241
140290,17,0,1,0,Single,13,963.5760233,1,47718.80117
148812,17,0,1,0,Single,15,961.7886561,1,47858.43281
180313,16,0,4,0,Single,14,961.688498,1,47881.4249
170873,23,6,3,0,Single,12,961.5602489,1,47774.01245
122581,16,0,4,0,Single,11,961.4443379,1,47569.2169
156888,25,6,3,0,Single,8,960.0839428,1,47412.19714
174931,19,0,1,0,Single,15,959.6700925,1,47640.50463
129275,42,22,3,0,Single,3,959.66223,1,47740.1115
103318,17,0,2,0,Single,7,959.1959551,1,47699.79776
180875,18,0,3,0,Single,13,958.4658194,1,47509.29097
127839,25,6,3,0,Single,12,955.6538288,1,47482.69144
175721,25,8,2,1,Single,1,955.5054968,1,47468.27484
162178,23,6,4,0,Single,13,953.954434,1,47149.7217
149438,16,0,1,0,Single,13,953.3025049,1,47177.12525
195887,22,3,3,0,Single,6,953.0572637,1,47240.86319
118262,21,4,1,0,Single,10,953.0263777,1,47080.31889
163533,55,35,2,1,Single,5,952.4488681,1,47115.44341
154372,24,5,3,0,Single,12,951.3606455,1,47229.03228
182975,16,0,1,0,Single,8,950.9628027,1,47008.14014
130769,25,6,1,0,Single,11,950.4048689,1,47307.24345
173602,20,3,4,1,Single,6,949.5901163,1,47175.50582
118063,25,7,2,0,Married,3,949.3304039,1,47200.5202
173000,52,34,3,1,Single,0,948.9842316,1,46979.21158
137188,54,35,2,0,Single,0,948.961617,1,47126.08085
197183,19,2,2,0,Single,15,948.6471955,1,46996.35978
108429,23,5,1,0,Single,11,948.1599283,1,46982.99642
142447,54,35,3,0,Single,0,948.1136788,1,47003.68394
150580,25,7,3,0,Single,15,947.8379808,1,46957.89904
122466,17,0,3,0,Single,14,947.6692741,1,46911.46371
192349,54,33,4,0,Single,3,947.134347,1,47010.71735
162980,48,29,3,1,Single,1,946.6285018,1,46745.42509
104756,22,3,1,0,Single,11,946.1029263,1,46820.14632
102346,20,2,2,0,Single,11,945.7624519,1,47019.1226
181011,17,0,3,0,Single,11,945.3620797,1,46899.10399
143099,16,0,2,0,Single,12,944.5853351,1,46795.26676
174446,23,5,1,0,Single,6,944.4645244,1,47012.22622
129426,46,25,2,0,Single,1,944.0640569,1,46946.20285
178021,21,3,3,0,Single,8,943.1746391,1,46820.73196
134930,18,0,1,0,Single,7,942.7213084,1,46637.06542
187036,16,0,2,0,Single,11,942.5701269,1,46812.50635
192780,19,0,3,0,Single,11,941.5145186,1,46624.72593
123936,54,37,1,0,Single,5,941.2388843,1,46651.94422
137285,18,1,2,0,Single,7,941.1540062,1,46658.70031
151004,23,6,3,0,Single,9,941.1118572,1,46824.59286
108052,20,1,1,0,Single,12,939.1788216,1,46400.94108
171515,34,13,1,0,Single,5,939.0853092,1,46574.26546
161468,35,17,2,0,Single,3,938.6415736,1,46536.07868
169018,23,4,4,0,Single,15,938.5448085,1,46572.24043
135168,16,0,3,0,Single,7,938.3568203,1,46443.84102
191234,48,29,3,0,Single,4,938.1577435,1,46441.88718
149230,24,6,3,0,Single,13,938.1062322,1,46537.31161
185852,36,15,2,0,Single,3,937.8639927,1,46498.19964
129263,40,20,3,0,Single,5,937.2636371,1,46457.18186
139962,39,19,1,0,Single,5,937.0531376,1,46317.65688
161120,19,0,4,0,Married,2,937.03371,1,46293.6855
186369,16,0,3,0,Single,11,936.8815469,1,46454.07735
116785,16,0,4,1,Single,0,936.8707277,1,46493.53639
146514,19,1,4,0,Single,15,936.5582036,1,46555.91018
175438,19,2,3,0,Single,12,936.441347,1,46528.06735
189837,19,1,3,0,Single,10,936.2423902,1,46296.11951
163026,16,0,3,0,Single,10,935.9619359,1,46248.0968
123630,17,0,1,1,Single,5,935.8503856,1,46526.51928
137669,24,5,2,0,Single,14,934.7447209,1,46407.23605
188250,20,2,4,0,Single,14,932.6833827,1,46219.16914
172164,22,5,3,0,Single,2,932.3618464,1,46206.09232
193981,25,8,3,0,Single,10,931.7268963,1,46247.34482
145321,21,2,1,0,Single,13,930.9972091,1,46137.86046
105727,25,6,4,0,Single,11,930.8997836,1,46126.98918
172899,21,4,1,0,Single,12,928.8911063,1,46073.55532
121584,22,4,3,0,Married,5,927.8335038,1,45808.67519
195023,18,0,4,0,Single,14,927.7630189,1,45833.15095
137597,21,4,3,0,Single,15,927.4976597,1,45809.88299
123069,19,0,2,0,Single,7,927.3547156,1,45924.73578
165497,21,2,2,0,Single,12,926.8695021,1,45791.47511
163338,24,6,3,0,Married,5,926.5767778,1,46022.83889
174047,19,2,2,0,Single,11,925.0132734,1,45724.66367
174281,57,36,2,0,Single,3,924.9965684,1,45783.82842
193610,33,16,2,0,Single,2,924.3987952,1,45958.93976
183341,17,0,4,0,Single,13,924.3242891,1,45701.21446
156676,52,35,2,0,Single,4,924.0729718,1,45720.64859
139662,23,4,1,0,Single,12,922.892173,1,45587.60865
115679,17,0,4,0,Single,13,921.6483992,1,45708.41996
185548,26,8,3,0,Single,2,921.0948551,1,45745.74276
107699,55,37,3,0,Single,4,920.5813617,1,45782.06809
156275,53,34,3,0,Single,5,919.846849,1,45553.34245
187943,58,38,3,0,Single,3,919.8353211,1,45719.76606
191190,22,5,2,0,Single,7,919.7144451,1,45739.72226
168735,33,12,1,0,Single,4,919.4340912,1,45481.70456
126288,20,3,4,0,Single,14,919.237615,1,45429.88075
178951,17,0,4,1,Single,3,918.7340673,1,45521.70337
110496,59,40,2,0,Single,2,918.2602107,1,45493.01054
131794,21,2,2,0,Single,6,917.9892784,1,45532.46392
132733,21,3,3,0,Single,1,917.9699854,1,45417.49927
122086,52,32,2,0,Single,5,916.7338256,1,45540.69128
182093,17,0,3,1,Single,10,916.4451498,1,45408.25749
175353,20,3,3,0,Single,9,915.7145156,1,45359.72578
123364,33,14,3,0,Single,4,915.5152136,1,45323.76068
169562,17,0,4,0,Single,10,915.2129933,1,45193.64967
165655,21,2,3,0,Married,9,912.3406014,1,45132.03007
158951,23,6,1,0,Single,13,911.5321693,1,45193.60847
194273,24,7,3,0,Single,12,910.9480966,1,45336.40483
115431,17,0,3,0,Single,11,910.6919164,1,45185.59582
150698,22,4,2,0,Single,14,909.7139133,1,45225.69567
110550,19,2,3,0,Single,6,909.7073767,1,45186.36884
198997,31,10,4,0,Single,4,909.6147219,1,45111.7361
150643,17,0,1,0,Single,12,908.9218149,1,44857.09075
109236,20,3,4,0,Single,15,906.6569461,1,45014.84731
155453,58,38,2,0,Single,2,906.6119094,1,44820.59547
156961,16,0,2,0,Single,10,906.6043671,1,45030.21836
166897,25,6,3,0,Married,2,906.2718078,1,45014.59039
164980,59,38,3,0,Single,1,906.1988692,1,45109.94346
131221,24,6,2,0,Single,11,906.1379206,1,44955.89603
112374,16,0,4,0,Married,1,905.766452,1,45059.3226
196174,17,0,2,0,Single,8,905.5988211,1,44872.94106
196673,18,1,1,0,Single,7,904.8401349,1,44664.00675
157023,44,25,2,1,Single,0,904.6842247,1,44901.21124
123071,57,39,4,0,Single,1,904.542576,1,44800.1288
160726,17,0,3,1,Single,9,904.0967949,1,44872.83975
190011,17,0,2,1,Single,1,903.8186993,1,44971.93497
173130,22,4,2,1,Married,2,903.5620212,1,44736.10106
142734,21,3,3,0,Single,12,902.9052832,1,44779.26416
152388,25,6,3,0,Single,13,902.8980685,1,44789.90343
151322,28,10,4,0,Single,3,901.8141232,1,44771.70616
195381,20,1,2,0,Single,13,901.0590477,1,44472.95239
103339,22,5,3,0,Single,9,900.8594808,1,44476.97404
170771,32,11,2,0,Single,4,900.7508798,1,44607.54399
119213,19,2,4,0,Single,10,900.421872,1,44573.0936
155424,24,7,4,0,Single,12,900.3112144,1,44562.56072
151376,22,4,4,0,Single,15,899.5962331,1,44614.81166
118623,19,1,4,0,Single,13,899.4890292,1,44417.45146
181506,20,3,4,0,Single,10,899.4736913,1,44374.68457
154925,24,6,2,0,Single,5,899.4357777,1,44543.78889
169627,20,3,2,0,Married,8,898.9884224,1,44378.42112
116337,33,14,2,1,Single,1,898.8948057,1,44463.74029
156489,58,38,3,0,Single,2,898.8260511,1,44733.30256
130843,55,34,2,0,Single,5,898.5889774,1,44531.44887
161811,21,4,4,1,Single,6,898.1259855,1,44373.29928
112262,23,5,3,0,Single,6,898.0880446,1,44449.40223
199569,22,3,1,0,Single,9,897.3301732,1,44287.50866
197393,34,13,3,0,Single,2,896.7493232,1,44409.46616
113898,45,28,2,0,Single,4,896.7392544,1,44337.96272
128349,29,11,3,0,Single,1,896.0307292,1,44217.53646
179151,20,1,1,0,Single,8,895.8877575,1,44250.38788
199788,25,7,4,0,Married,4,895.8863582,1,44468.31791
112948,18,0,3,0,Single,10,895.8679446,1,44381.39723
151795,16,0,2,0,Single,6,895.8225724,1,44368.12862
167134,28,7,1,0,Single,5,895.4863389,1,44267.31695
123558,39,21,4,0,Single,0,894.1464749,1,44460.32375
186178,21,4,2,0,Single,14,894.0514435,1,44129.57218
133185,24,6,2,0,Single,14,892.8377124,1,44161.88562
117460,26,5,3,0,Single,4,892.7377269,1,44039.88635
184236,54,33,2,0,Single,1,891.7757371,1,44357.78686
117922,21,2,1,0,Married,2,891.5187959,1,44270.9398
185325,21,4,4,0,Single,13,890.2712621,1,43963.56311
101176,23,4,3,0,Single,12,890.0562572,1,44040.81286
129739,23,4,3,0,Single,9,888.8297571,1,44230.48786
175758,60,40,3,0,Single,5,888.5431868,1,44217.15934
120312,24,6,4,0,Single,14,888.3916178,1,44182.58089
182418,24,7,1,0,Single,14,888.0452687,1,44027.26344
151465,37,20,2,0,Single,0,887.8276787,1,43819.38394
179724,25,6,4,0,Single,11,887.7569489,1,43933.84745
193969,45,26,2,1,Single,3,887.3625199,1,44131.126
192264,20,1,2,0,Single,12,887.0470283,1,44082.35142
163248,22,5,3,0,Single,13,886.4901177,1,43920.50589
190851,22,5,2,0,Single,9,886.4107121,1,43928.53561
143662,23,6,2,0,Married,3,886.1880093,1,43922.40047
169738,16,0,2,1,Single,6,884.8413585,1,43869.06793
126747,18,0,3,0,Single,9,884.7424128,1,43793.12064
127645,40,22,3,0,Single,4,884.6429806,1,43784.14903
149280,20,2,2,0,Single,13,884.2743717,1,43795.71859
181057,21,3,2,0,Married,4,883.5604761,1,43589.02381
112586,17,0,3,1,Single,14,881.8866539,1,43529.3327
113611,24,5,1,0,Single,15,880.953005,1,43680.65025
114798,16,0,1,0,Single,15,880.5423223,1,43719.11612
129951,22,4,3,1,Single,15,880.436309,1,43433.81545
130541,25,8,2,0,Single,13,880.3170622,1,43578.85311
113227,39,18,2,1,Single,1,880.3158034,1,43752.79017
126104,50,31,3,0,Single,2,880.0825294,1,43652.12647
105433,25,6,1,1,Single,1,879.8218612,1,43608.09306
179079,23,4,3,0,Single,15,879.2732699,1,43385.6635
162801,21,3,3,0,Single,6,878.9935855,1,43351.67928
142650,61,44,4,0,Single,0,878.8911742,1,43726.55871
184991,22,3,4,0,Single,13,878.7386362,1,43339.93181
100180,18,1,2,0,Single,10,878.6432238,1,43354.16119
102655,21,4,3,0,Single,12,878.0601151,1,43428.00576
172979,36,15,1,1,Single,4,877.9333318,1,43589.66659
142404,18,0,2,1,Single,8,877.6314114,1,43492.57057
157853,58,40,2,0,Single,0,877.1687137,1,43611.43569
140896,17,0,1,0,Single,15,877.0778833,1,43292.89417
187977,48,28,3,0,Single,5,877.0190257,1,43603.95129
147235,24,6,2,0,Single,12,876.8373128,1,43475.86564
155073,53,32,1,1,Single,4,875.5579818,1,43483.89909
140467,50,29,2,0,Single,1,875.2491631,1,43453.45816
109501,22,4,1,0,Single,12,874.4650234,1,43175.25117
161535,19,2,2,1,Single,9,874.335659,1,43283.78295
150930,58,37,4,1,Single,4,874.0454331,1,43252.27166
186820,22,3,2,1,Single,5,872.2668873,1,43292.34437
128690,20,3,4,0,Single,12,872.0708925,1,43154.54463
169504,20,1,2,1,Single,5,872.0054822,1,43067.27411
143938,16,0,3,1,Single,3,871.983205,1,43066.16025
167652,25,7,4,0,Single,9,871.3147058,1,43071.73529
138407,57,39,3,1,Single,0,870.5953191,1,43157.76596
168922,23,6,2,1,Single,7,870.4727891,1,43210.63946
150561,19,2,3,0,Married,3,869.2425615,1,43097.12808
137141,24,5,3,0,Single,12,868.7337283,1,43132.68642
168794,19,2,3,0,Single,9,868.3792568,1,43141.96284
118590,56,35,4,1,Single,0,868.2423373,1,43061.11687
134655,24,5,2,0,Single,11,868.1959057,1,43101.79529
123277,21,4,3,0,Single,5,867.8923141,1,42905.61571
137207,24,5,4,0,Single,11,867.5820474,1,42904.10237
130506,20,1,1,0,Single,13,867.5781431,1,42947.90716
106850,19,0,2,0,Married,5,867.23165,1,43141.5825
175631,58,39,2,0,Single,4,867.1481766,1,43136.40883
104310,19,0,3,0,Married,6,865.4965116,1,42956.82558
195602,33,15,2,0,Single,1,865.3294783,1,42793.47392
166862,25,7,1,1,Single,5,865.1700498,1,42905.50249
114055,28,10,1,1,Single,3,865.1666105,1,43031.33053
163425,24,7,1,0,Single,6,865.1338794,1,42832.69397
196468,33,14,2,0,Single,0,864.8282591,1,42816.41296
146103,23,6,2,0,Single,15,864.624116,1,42903.2058
149049,16,0,2,1,Single,8,863.5664988,1,42607.32494
114641,23,5,2,0,Single,13,863.5322451,1,42975.61226
178117,20,3,2,0,Single,12,862.7117483,1,42828.58742
127638,22,3,1,0,Single,8,862.4872404,1,42778.36202
148888,39,20,1,0,Single,0,862.1750525,1,42682.75263
134971,57,36,1,1,Single,2,861.7717368,1,42768.58684
176375,45,25,2,1,Single,2,861.7152966,1,42785.76483
116098,20,2,3,1,Single,13,861.4828046,1,42742.14023
114426,46,29,2,0,Single,1,861.3950543,1,42836.75272
160342,20,1,3,0,Single,8,861.000057,1,42649.00285
187193,53,33,2,0,Single,0,860.7963518,1,42604.81759
117149,17,0,3,0,Single,12,860.4545437,1,42819.72719
125870,24,7,3,0,Single,12,859.2409475,1,42656.04738
158455,51,32,3,0,Single,1,858.1128834,1,42328.64417
199174,25,7,2,1,Single,1,857.9935307,1,42517.67654
139916,17,0,3,0,Single,8,857.9576175,1,42661.88088
193011,24,5,1,0,Single,9,857.4740156,1,42408.70078
149720,20,1,4,0,Single,9,857.3325509,1,42650.62755
140546,21,3,3,0,Single,13,857.0885895,1,42633.42948
114976,24,5,3,1,Single,0,856.298933,1,42528.94665
101304,19,1,3,0,Married,2,856.1352159,1,42360.7608
114113,45,28,4,0,Single,3,856.1075666,1,42384.37833
169967,40,21,4,0,Single,2,855.9220408,1,42576.10204
137671,19,2,2,0,Single,7,855.3519575,1,42529.59788
105405,16,0,1,0,Single,8,855.2744104,1,42374.72052
156075,21,4,3,0,Single,8,854.6332739,1,42241.6637
101814,18,1,2,1,Single,4,854.6130422,1,42414.65211
148712,17,0,2,0,Single,11,853.5472693,1,42335.36347
172878,17,0,2,1,Single,2,853.5172334,1,42170.86167
132270,34,15,2,0,Single,0,853.1898691,1,42285.49346
132758,22,3,1,0,Single,11,853.178004,1,42331.9002
161478,17,0,1,0,Single,13,852.9917769,1,42150.58885
152189,69,50,4,0,Single,0,852.7946893,1,42243.73447
149377,17,0,3,0,Single,7,852.6772216,1,42423.86108
152657,55,36,2,1,Single,3,852.6038376,1,42426.19188
163450,25,6,3,0,Single,11,851.8635648,1,42090.17824
183501,24,5,2,0,Single,12,851.6316833,1,42121.58417
182324,27,6,4,0,Single,5,851.2897498,1,42273.48749
189194,21,4,4,0,Single,14,849.7975643,1,42220.87822
138691,24,7,2,0,Single,7,849.4587334,1,41934.93667
134822,53,35,2,0,Single,3,849.1458105,1,41940.29053
144281,24,7,2,0,Single,8,848.20531,1,42143.2655
156355,17,0,4,1,Single,9,848.1450321,1,42175.25161
197902,20,1,2,0,Single,11,848.0448585,1,41994.24293
151193,25,6,2,0,Single,8,847.9486516,1,41857.43258
107331,17,0,1,1,Single,10,847.5286139,1,42060.4307
163805,17,0,2,1,Married,2,847.3971276,1,41835.85638
197720,34,15,3,0,Single,2,847.3281865,1,41859.40933
196670,18,0,2,0,Single,9,846.0133552,1,42075.66776
184960,25,8,2,0,Single,6,845.9031137,1,41949.15569
152298,23,4,1,0,Single,10,845.7592784,1,41727.96392
111024,44,24,3,0,Single,2,845.6608289,1,41850.04145
113547,44,25,3,0,Single,3,845.4949707,1,41909.74854
178804,24,5,3,0,Single,10,845.2984939,1,41950.9247
179759,19,0,1,0,Single,13,844.9966518,1,41996.83259
120756,26,6,3,0,Single,3,843.5965041,1,41757.82521
161227,47,27,4,0,Single,4,843.3687661,1,41960.43831
115163,63,46,2,0,Single,0,842.7744806,1,41802.72403
110474,19,1,1,1,Single,5,842.361699,1,41616.08495
155197,22,3,3,0,Single,13,841.8967517,1,41557.83759
171316,25,8,3,0,Married,6,841.7800349,1,41761.00175
196933,17,0,1,0,Single,7,841.6097425,1,41751.48713
125427,42,22,2,0,Single,5,841.2897521,1,41551.48761
108947,34,14,3,1,Single,0,840.4201906,1,41470.00953
134345,39,22,4,0,Single,5,839.7674111,1,41727.37056
103072,18,1,1,1,Married,4,839.6952451,1,41553.76226
187192,25,6,3,0,Single,3,839.6352567,1,41611.76284
118634,16,0,1,1,Single,2,839.4242413,1,41562.21207
108466,20,3,4,0,Single,10,839.3059837,1,41743.29919
126019,17,0,2,0,Married,2,839.0500314,1,41364.50157
192376,58,41,4,0,Single,3,838.9273372,1,41593.36686
129838,39,19,3,1,Single,0,838.8723606,1,41590.61803
127886,25,7,2,0,Married,4,838.6878046,1,41598.39023
183448,18,1,1,1,Single,5,838.4535579,1,41690.6779
128138,25,8,4,0,Married,3,838.391464,1,41602.5732
157165,19,0,3,0,Single,10,837.7497832,1,41287.48916
150786,36,19,3,0,Single,1,837.7117701,1,41420.58851
101731,25,6,3,0,Single,11,837.5364949,1,41409.82475
106139,38,21,1,1,Single,0,837.4720719,1,41470.6036
193285,16,0,2,0,Single,12,837.4157377,1,41471.78689
110661,22,3,4,0,Single,15,837.372063,1,41569.60315
154549,22,3,1,1,Married,1,837.3677817,1,41350.38909
175640,21,2,2,1,Married,3,837.216986,1,41408.8493
105249,22,5,3,0,Single,11,836.5161262,1,41386.80631
127432,28,11,1,0,Single,4,836.2796454,1,41226.98227
150146,44,23,2,0,Single,4,836.218815,1,41323.94075
130871,21,3,4,0,Single,8,835.9234667,1,41467.17334
145887,39,19,2,0,Single,0,835.8613877,1,41583.06939
186803,19,0,1,0,Single,14,835.5043575,1,41291.21788
168145,36,19,2,1,Single,0,834.4249675,1,41473.24838
174033,24,6,2,0,Single,8,834.413299,1,41362.66495
158979,22,3,2,0,Single,7,834.28166,1,41221.083
114039,16,0,3,1,Single,4,834.1661725,1,41491.30863
172471,41,22,3,0,Single,4,833.740771,1,41199.03855
125401,25,7,1,0,Single,13,832.9295306,1,41206.47653
120404,30,10,3,1,Single,3,832.7590839,1,41171.95419
137736,20,2,4,0,Single,11,832.5978179,1,41061.8909
170717,24,5,1,0,Single,12,831.9907195,1,41196.53598
128154,24,6,3,0,Single,6,831.9525677,1,41040.62839
155098,53,35,2,0,Single,4,831.8201575,1,41119.00788
131060,23,5,3,0,Single,12,831.7622185,1,41117.11093
121202,48,27,3,0,Single,1,831.7003993,1,41183.01997
109587,36,17,3,1,Single,3,830.9321942,1,41267.60971
143582,28,7,3,0,Single,3,830.5209925,1,40990.04963
167724,39,19,3,0,Single,4,828.8621037,1,41045.10519
149767,26,6,4,0,Single,3,828.5666144,1,41173.33072
164011,18,0,3,0,Single,11,828.2996803,1,40954.98402
132670,38,21,4,0,Single,3,828.1457095,1,41201.28548
163122,36,19,4,0,Single,3,826.600291,1,40930.01455
167700,16,0,3,1,Single,1,826.1310526,1,41063.55263
166244,33,14,3,1,Single,1,825.9355909,1,40961.77955
124525,24,6,4,0,Single,11,825.5370097,1,41033.85049
129582,40,23,1,1,Single,2,825.530745,1,40696.53725
152556,44,25,2,0,Single,3,825.4557724,1,41046.78862
140879,19,1,3,0,Single,7,825.4040835,1,40780.20418
127518,22,5,4,0,Single,11,825.3943013,1,41027.71507
197555,29,10,1,1,Single,0,825.2772277,1,40860.86139
143639,22,3,2,1,Single,3,825.2313449,1,40709.56725
121347,21,3,4,0,Single,11,824.9399434,1,40700.99717
126940,29,12,4,0,Single,1,824.1849697,1,40783.24849
148188,16,0,3,0,Single,13,823.9431127,1,40935.15564
104069,17,0,2,0,Married,3,823.7885094,1,40892.42547
168411,67,46,3,0,Single,2,823.0331624,1,40647.65812
180420,22,4,4,0,Married,9,822.4705539,1,40734.5277
174925,43,22,3,0,Single,2,822.0191275,1,40825.95638
173111,17,0,3,0,Single,12,821.9540619,1,40828.7031
161672,25,7,3,0,Single,12,821.6369946,1,40863.84973
160543,20,1,3,1,Single,8,821.5817084,1,40754.08542
123905,55,35,3,1,Single,4,821.5480216,1,40537.40108
185565,31,12,1,0,Single,4,820.9288721,1,40710.44361
110704,24,5,2,1,Single,4,820.8498927,1,40643.49464
156743,20,3,2,1,Single,15,820.7664852,1,40611.32426
101900,23,5,4,1,Single,7,820.2508634,1,40575.54317
170526,18,0,4,0,Single,10,820.2432806,1,40546.16403
122970,21,2,3,0,Single,13,819.5961656,1,40724.80828
151731,23,4,2,0,Single,15,819.5266661,1,40441.33331
163505,28,10,4,0,Single,5,819.3876678,1,40697.38339
108018,58,39,1,0,Single,0,818.0944497,1,40344.72249
156090,31,13,1,0,Single,2,817.9641516,1,40538.20758
192644,52,35,2,0,Single,5,817.8884176,1,40336.42088
101100,19,2,2,0,Single,11,817.0889534,1,40418.44767
168139,21,2,4,1,Single,0,816.5543542,1,40615.71771
133653,53,34,4,0,Single,1,815.9700344,1,40330.50172
194779,52,33,1,0,Single,2,815.6127623,1,40236.63812
123300,17,0,4,0,Single,12,815.5316177,1,40414.58089
177744,16,0,2,1,Single,14,814.827043,1,40143.35215
108343,24,6,3,0,Single,15,814.6169377,1,40384.84689
136629,22,3,2,0,Single,7,813.9865215,1,40118.32608
136014,29,11,4,0,Single,1,813.7948792,1,40226.74396
140358,20,2,3,0,Single,13,813.7886128,1,40136.43064
112032,55,36,3,0,Single,1,813.5079191,1,40110.39596
156994,55,38,2,0,Single,2,813.3357101,1,40183.78551
152780,32,13,2,0,Single,0,813.2375101,1,40266.87551
135205,27,8,2,1,Single,0,813.2008888,1,40077.04444
180217,42,24,3,0,Single,3,812.8991652,1,40243.95826
171799,23,5,2,0,Single,11,812.7566703,1,40250.83352
188065,22,4,3,0,Married,3,812.6058671,1,40203.29336
178971,54,33,4,0,Single,2,812.388498,1,40357.4249
144509,29,12,3,0,Single,4,812.2876834,1,40088.38417
117357,19,1,3,0,Single,13,812.2123093,1,40240.61547
156644,53,36,2,1,Single,2,812.0970481,1,40173.85241
128056,17,0,2,1,Single,12,811.6163512,1,40321.81756
143659,23,5,3,1,Single,9,810.8454527,1,40257.27264
195943,28,11,1,0,Single,5,810.8059873,1,40187.29937
148898,22,3,2,1,Single,0,810.6958771,1,40228.79386
113232,18,0,1,0,Single,13,810.3321729,1,40036.60865
174454,57,37,3,0,Single,4,810.3233857,1,40024.16929
170970,18,1,2,1,Married,3,809.9843557,1,39911.21779
139191,22,3,2,0,Single,12,809.5177125,1,40237.88563
186586,19,0,3,0,Single,12,809.1885008,1,40058.42504
159367,28,8,1,1,Single,1,809.1460715,1,39931.30358
167758,21,4,1,0,Single,8,809.0071153,1,39958.35577
193588,66,45,2,0,Single,1,808.9727914,1,39940.63957
122750,16,0,2,0,Single,14,808.8667038,1,40029.33519
132928,35,18,4,1,Single,5,808.8431858,1,40014.15929
114357,21,4,1,1,Single,7,808.7610789,1,40031.05395
112294,24,7,2,1,Single,6,808.6426951,1,40009.13476
116764,40,23,4,0,Single,4,808.310749,1,40135.53745
172421,21,3,2,0,Married,2,807.7350617,1,39828.75309
199078,29,8,3,1,Single,1,807.6592434,1,40120.96217
138615,48,27,3,0,Single,3,807.3712285,1,39901.56143
100303,59,40,1,0,Single,2,807.2839766,1,40111.19883
191802,17,0,2,1,Single,11,807.2224591,1,40070.12296
120595,22,5,4,0,Single,7,807.1660883,1,39956.30442
108669,17,0,1,1,Married,4,806.8782918,1,40034.91459
120355,25,7,2,0,Single,12,806.5279584,1,39932.39792
149422,21,2,2,1,Single,2,805.8564752,1,39806.82376
142167,25,8,3,0,Single,9,805.7545692,1,40038.72846
135519,25,8,1,1,Married,1,805.5978863,1,39822.89432
191329,25,8,2,1,Single,7,805.5687001,1,40073.43501
173015,25,6,2,1,Single,5,805.4390507,1,39759.95254
113746,46,25,1,0,Single,4,805.3226847,1,39703.13424
188465,21,4,2,0,Single,3,804.7640707,1,39868.20354
168182,47,29,3,0,Single,1,804.6557167,1,39776.78584
147112,24,6,2,1,Single,13,803.8944195,1,39903.72098
198419,64,46,1,0,Single,2,803.706458,1,39631.3229
195236,39,20,3,0,Single,0,803.6527948,1,39756.63974
113026,25,7,3,0,Single,15,802.9377782,1,39654.88891
104457,20,3,3,1,Single,6,802.8991513,1,39719.95757
142840,54,33,2,0,Single,3,802.797211,1,39667.86055
164869,21,3,2,0,Single,12,802.6177559,1,39852.8878
178699,18,1,1,0,Single,9,802.0179957,1,39892.89979
122326,35,16,4,1,Single,3,801.9689769,1,39536.44885
165231,23,6,1,0,Single,14,801.8957989,1,39540.78995
136056,33,13,4,0,Single,1,801.5256903,1,39631.28452
113910,21,4,3,0,Married,10,801.4144112,1,39487.72056
124642,23,6,1,0,Single,14,801.2580357,1,39754.90179
133739,58,37,2,0,Single,0,800.9668415,1,39534.34208
131359,22,5,1,0,Single,6,800.9309908,1,39622.54954
187238,43,22,4,0,Single,2,800.8715333,1,39769.57667
188474,17,0,3,0,Single,11,800.8220074,1,39447.10037
196799,16,0,3,1,Single,6,800.818839,1,39707.94195
169116,31,12,1,0,Single,2,800.6210897,1,39466.05449
134296,38,17,1,1,Single,0,800.5456109,1,39567.28055
126133,22,3,2,0,Single,15,800.4356427,1,39454.78214
160190,20,3,2,1,Single,15,800.4307807,1,39681.53904
110569,21,3,2,0,Married,7,800.2390702,1,39606.95351
148876,22,4,2,0,Married,0,800.0111715,1,39419.55858
119863,19,1,4,0,Single,10,799.9744139,1,39692.7207
153238,37,16,2,0,Single,4,799.820527,1,39621.02635
175030,18,0,3,1,Single,10,799.7889888,1,39673.44944
185238,36,16,2,1,Single,3,799.3756691,1,39466.78346
125795,24,5,3,0,Single,12,798.1434548,1,39309.17274
165849,21,4,3,1,Single,15,798.0314367,1,39573.57184
101507,17,0,3,0,Single,6,797.7268973,1,39617.34487
119945,46,25,4,1,Single,5,797.6864221,1,39411.32111
169001,41,23,1,1,Single,0,797.4727074,1,39447.63537
150204,28,7,3,0,Single,1,797.3344963,1,39268.72482
132403,42,23,2,0,Single,2,797.3170676,1,39610.85338
148429,17,0,2,0,Single,9,797.2267243,1,39358.33622
197579,34,14,3,1,Single,4,796.8075628,1,39618.37814
178839,48,28,2,1,Single,0,796.5701342,1,39278.50671
188452,25,6,3,1,Single,9,796.4004917,1,39225.02459
109133,52,31,2,0,Single,1,796.2903449,1,39500.51725
103182,66,46,1,0,Single,1,796.0709274,1,39264.54637
158616,25,6,2,1,Single,8,795.929798,1,39550.4899
167265,21,4,4,0,Single,15,795.6680301,1,39372.40151
170525,20,3,2,0,Married,6,795.5997796,1,39371.98898
114445,57,39,1,1,Single,0,795.1639064,1,39433.19532
160756,25,8,3,0,Single,6,795.014178,1,39480.7089
119439,25,7,1,1,Single,2,794.6637929,1,39176.18965
155627,23,5,3,0,Single,6,794.1462024,1,39256.31012
149194,47,27,1,1,Single,2,793.1536372,1,39186.68186
151623,24,7,2,1,Single,14,793.1202872,1,39059.01436
198256,20,2,3,1,Single,6,793.0381979,1,39227.9099
172244,16,0,4,1,Single,9,792.9503309,1,39218.51655
182724,16,0,4,1,Single,7,792.5986484,1,39175.93242
190965,16,0,4,1,Married,0,792.0112836,1,39248.56418
199893,18,0,3,0,Single,11,791.9230115,1,39022.15058
144806,16,0,2,0,Single,11,791.8835425,1,39108.17713
135782,44,26,3,0,Single,3,791.6829438,1,39073.14719
151382,44,24,3,0,Married,3,791.1675116,1,39217.37558
197889,39,18,1,1,Single,3,791.135206,1,39053.7603
169713,21,2,2,0,Married,0,791.0895425,1,38988.47713
191964,17,0,2,0,Married,10,790.954274,1,39043.7137
129404,25,6,2,0,Single,10,790.9371839,1,39204.8592
185076,53,36,4,0,Single,5,790.2309083,1,38912.54542
133031,37,19,3,0,Single,1,790.1389191,1,38957.94596
109825,41,21,3,1,Single,1,790.0094992,1,39117.47496
192065,29,8,3,0,Married,4,789.8858954,1,39091.29477
155413,24,7,3,0,Single,9,789.6978699,1,38925.8935
152224,21,2,2,0,Single,15,789.4779955,1,39174.89978
140674,29,12,3,0,Married,2,789.2849186,1,39245.24593
128196,16,0,1,0,Single,15,789.2221205,1,39007.10603
101782,29,12,4,1,Single,5,788.8082877,1,39039.41439
110074,38,17,3,1,Single,5,788.7316684,1,39115.58342
116560,16,0,3,1,Married,4,788.6704555,1,39031.52278
111689,21,4,3,0,Single,11,788.3187541,1,39122.93771
120913,19,0,3,0,Married,4,788.1587277,1,38901.93639
113670,50,32,2,0,Single,4,788.0825102,1,39118.12551
107168,54,34,2,0,Married,1,787.9457959,1,39009.2898
177258,23,4,4,1,Single,8,787.9110859,1,38820.5543
117370,23,5,1,1,Single,6,787.7636887,1,38790.18444
198426,37,19,4,0,Single,4,787.4769096,1,39167.84548
112938,16,0,4,1,Single,14,786.1993207,1,38869.96604
149674,69,48,3,0,Single,4,786.1841411,1,38976.20706
104368,60,42,3,0,Single,3,785.3303755,1,38807.51878
185050,19,2,3,0,Single,15,784.806863,1,38707.34315
140023,38,17,2,0,Single,2,784.4685291,1,38907.42646
197623,32,12,2,0,Single,2,784.2140182,1,38755.70091
177965,19,2,3,0,Single,12,783.3686177,1,38758.43089
186812,36,19,4,1,Single,3,783.342,1,38899.1
199432,28,8,4,0,Single,2,783.2022589,1,38838.11295
115626,19,0,2,0,Married,5,783.0149197,1,38736.74599
177309,17,0,2,1,Single,4,782.8205413,1,38796.02707
100236,23,4,4,0,Single,7,782.7024727,1,38557.12364
148396,33,12,3,1,Single,2,782.658998,1,38605.9499
141074,23,6,3,1,Single,5,782.5185902,1,38743.92951
129946,16,0,2,0,Single,9,782.3743275,1,38772.71638
149829,28,11,2,0,Married,2,782.3594009,1,38691.97005
105504,18,0,4,0,Single,9,781.8003476,1,38773.01738
133673,23,4,2,0,Single,11,781.4360444,1,38688.80222
144753,21,4,2,0,Single,12,781.2241448,1,38524.20724
100986,21,2,3,0,Single,15,781.0661993,1,38481.30997
111428,24,6,2,1,Single,6,780.667958,1,38507.3979
186105,23,6,2,0,Single,15,780.4636888,1,38742.18444
189028,59,40,1,0,Single,2,780.2021419,1,38761.1071
188759,21,3,2,0,Single,12,779.7358002,1,38610.79001
137315,52,35,4,1,Single,0,779.6095191,1,38382.47596
194931,18,0,2,0,Married,6,779.3561103,1,38623.80552
159635,25,7,4,1,Single,8,779.3304797,1,38571.52399
161708,33,14,1,0,Single,4,779.1418236,1,38427.09118
140837,22,5,3,0,Single,15,778.7422888,1,38722.11444
180669,18,0,4,0,Married,8,778.6599257,1,38396.99629
159261,26,7,3,1,Single,0,778.5660349,1,38437.30175
198862,23,4,4,0,Single,14,778.543894,1,38399.1947
132293,42,23,4,1,Single,4,778.45885,1,38461.9425
197116,23,6,2,0,Single,14,778.4199705,1,38462.99853
133745,16,0,4,0,Single,12,778.2249222,1,38381.24611
194831,23,4,4,0,Single,9,777.7414191,1,38377.07096
149512,19,2,1,1,Single,9,777.5951773,1,38566.75887
172594,30,13,4,1,Single,0,777.1630942,1,38611.15471
123004,54,36,3,0,Single,3,777.1511103,1,38554.55552
104924,16,0,1,0,Married,15,777.0907015,1,38440.53508
125855,36,15,1,1,Single,1,777.0625289,1,38512.12645
106020,25,7,1,0,Married,1,776.9765728,1,38599.82864
177653,22,3,2,0,Single,15,776.8857949,1,38569.28975
146482,25,6,3,1,Single,11,776.6696572,1,38424.48286
161929,33,15,3,0,Single,4,776.5491355,1,38616.45678
191576,30,10,2,0,Single,1,776.3322799,1,38508.614
189726,17,0,1,1,Single,1,776.2991428,1,38291.95714
171808,22,5,2,0,Single,10,776.2116881,1,38479.58441
140872,22,4,4,1,Single,6,775.9261328,1,38296.30664
107952,21,4,4,0,Single,12,775.6210576,1,38551.05288
184678,18,1,1,0,Married,10,775.5602497,1,38539.01249
100532,44,23,4,0,Single,1,775.2016057,1,38526.08029
178008,35,14,2,1,Single,5,775.0018095,1,38411.09048
171614,21,3,2,1,Single,11,774.9606361,1,38280.03181
121061,43,26,2,0,Single,3,774.4937363,1,38161.68682
105031,25,8,4,0,Single,15,774.2368954,1,38308.84477
138631,20,1,4,1,Single,6,774.1602653,1,38356.01327
169397,58,39,1,0,Single,2,773.6304312,1,38359.52156
128758,56,39,2,0,Single,0,773.3717502,1,38123.58751
188834,21,4,1,1,Single,14,772.9256098,1,38269.28049
185788,16,0,3,0,Single,12,772.7090627,1,38422.45314
132340,25,6,2,1,Single,0,772.6737478,1,38051.68739
108345,20,1,1,0,Single,15,772.6448203,1,38321.24102
174696,23,6,3,0,Single,15,772.2370835,1,38341.85418
136578,21,4,1,1,Single,10,772.0719204,1,38333.59602
104814,53,33,3,1,Single,3,772.0094708,1,38282.47354
173422,21,3,3,0,Single,12,771.488762,1,38143.4381
157449,23,4,1,0,Married,9,771.4163793,1,38255.81897
161381,22,4,4,0,Single,11,771.3682045,1,38011.41022
107370,46,27,1,0,Single,3,771.279313,1,38011.96565
149283,17,0,3,1,Single,0,771.2105558,1,38204.52779
149342,37,17,3,0,Single,5,770.947188,1,38287.3594
125679,27,6,3,1,Single,5,770.667867,1,38179.39335
127336,21,2,1,0,Married,3,770.4316087,1,38311.58044
187752,34,16,3,1,Single,2,770.149042,1,37956.4521
105487,22,3,3,0,Single,13,769.9601678,1,37942.00839
197341,34,13,2,0,Single,1,769.957755,1,38051.88775
115254,63,45,2,0,Single,0,769.8337762,1,38153.68881
109603,17,0,4,0,Married,1,769.569766,1,37999.4883
101763,22,3,3,0,Single,15,769.200494,1,38099.0247
127469,45,24,2,1,Single,3,769.072315,1,37991.61575
155860,23,5,3,1,Single,9,768.8229896,1,38240.14948
175073,53,32,2,0,Single,4,768.5655688,1,38225.27844
118619,18,0,3,1,Single,7,768.4793814,1,38097.96907
125388,23,4,3,0,Single,10,768.4643137,1,38218.21569
181059,24,5,1,1,Single,2,768.3369443,1,37851.84722
115261,23,6,4,1,Single,9,768.3254987,1,38005.27494
198975,16,0,2,0,Married,8,767.522167,1,38138.10835
127388,28,11,4,0,Single,4,767.222576,1,37805.1288
129824,30,11,3,0,Single,3,766.4775796,1,38053.87898
157931,40,23,3,0,Married,3,766.2697688,1,37909.48844
184387,17,0,2,0,Single,10,765.6528906,1,38027.64453
129872,18,0,2,0,Married,6,764.4670012,1,37892.35006
152321,21,3,3,0,Single,7,764.3609836,1,37873.04918
152589,33,15,3,1,Single,2,764.3024432,1,37734.12216
177143,19,1,2,0,Single,12,764.2696764,1,37672.48382
182915,18,0,2,1,Single,7,763.8953612,1,37742.76806
154894,19,1,1,0,Single,12,763.7298567,1,37625.49284
158260,25,7,3,1,Single,6,763.6360013,1,37806.80007
156819,41,20,3,1,Single,0,763.4237607,1,37913.18804
182640,25,7,3,0,Married,10,763.3224726,1,37740.12363
110668,20,1,4,1,Single,11,763.2407384,1,37761.03692
153799,52,33,3,1,Single,2,763.106938,1,37596.3469
143613,41,22,4,1,Single,3,762.8219161,1,37551.09581
196809,24,7,4,0,Single,13,762.8124126,1,37770.62063
104261,20,2,2,1,Single,14,762.7241919,1,37853.2096
119333,40,19,2,0,Single,1,762.4698835,1,37895.49418
190823,68,48,2,0,Single,3,762.3992413,1,37639.96207
146373,34,14,2,0,Single,2,762.1941333,1,37832.70667
195842,65,46,2,0,Single,3,762.1540821,1,37511.70411
118790,34,14,1,0,Single,2,762.0191521,1,37755.95761
140123,18,1,4,0,Single,15,761.9423352,1,37551.11676
116995,17,0,2,0,Married,14,761.9294278,1,37752.47139
155552,49,30,3,0,Single,2,761.7817369,1,37681.08685
183875,20,3,3,1,Single,4,761.7805601,1,37811.02801
108359,20,3,4,1,Single,7,761.1355832,1,37695.77916
111063,25,8,2,0,Single,12,761.0834079,1,37776.1704
161941,18,0,4,0,Married,10,760.7036261,1,37568.18131
150727,16,0,3,1,Married,3,760.6486821,1,37666.43411
143443,22,4,2,0,Married,14,760.2474635,1,37645.37318
118686,23,5,3,0,Single,11,759.9971924,1,37428.85962
147503,25,7,2,1,Single,11,759.9206549,1,37433.03275
114217,41,20,3,0,Single,4,759.8802844,1,37440.01422
160530,57,40,3,0,Single,1,759.7345504,1,37451.72752
121076,17,0,2,1,Single,8,759.4798338,1,37634.99169
109984,37,18,4,0,Single,0,759.2695883,1,37369.47942
116415,33,14,3,1,Single,5,759.082387,1,37708.11935
150215,22,3,3,0,Married,6,758.960996,1,37728.0498
130774,48,30,4,0,Single,4,758.8821771,1,37543.10885
149540,17,0,3,0,Single,15,758.6707456,1,37616.53728
145500,31,11,4,1,Single,2,758.6592321,1,37585.96161
183530,22,5,2,0,Single,11,758.3316514,1,37416.58257
167956,20,3,4,0,Married,0,758.0724015,1,37481.62008
190404,25,6,1,1,Single,10,757.8709012,1,37318.54506
110528,29,8,2,0,Single,1,757.7647701,1,37322.23851
179866,19,1,2,0,Married,7,757.3871116,1,37498.35558
124566,31,11,3,1,Single,4,757.231476,1,37267.5738
108569,17,0,2,0,Single,10,757.0065759,1,37266.3288
105149,25,6,2,1,Single,11,756.6989314,1,37613.94657
182799,42,22,2,0,Single,3,756.6293924,1,37498.46962
160535,18,0,2,1,Single,6,756.4072524,1,37351.36262
189218,19,0,4,0,Single,6,756.3925763,1,37444.62882
166005,25,8,3,1,Married,4,756.143944,1,37341.1972
139868,23,4,4,0,Married,11,755.8973259,1,37286.8663
141672,19,2,2,0,Single,11,755.4257252,1,37220.28626
126765,46,29,2,1,Single,2,755.4199753,1,37359.99877
199620,54,35,2,0,Single,5,755.3288916,1,37558.44458
101780,16,0,2,1,Single,3,754.5744672,1,37346.72336
141485,21,4,2,1,Single,14,754.3540717,1,37483.70359
124714,17,0,2,0,Single,9,754.2445733,1,37188.22867
184258,57,39,2,1,Single,1,754.1085424,1,37287.42712
146576,24,7,3,1,Single,7,753.9013475,1,37202.06738
154587,55,37,3,1,Married,4,753.774245,1,37488.71225
138710,21,2,1,1,Single,3,753.3982901,1,37318.91451
134300,61,40,2,1,Single,3,753.3058236,1,37394.29118
160769,21,4,2,0,Single,14,753.1638178,1,37289.19089
136239,21,4,2,0,Single,9,753.0137799,1,37263.689
153370,25,8,3,0,Single,8,752.9000012,1,37203.00006
133965,44,24,3,0,Single,1,752.6678759,1,37272.3938
189436,56,37,3,1,Single,1,752.4649834,1,37106.24917
161230,20,3,3,1,Single,0,752.3533309,1,37327.66655
151646,68,48,3,1,Single,2,751.6554839,1,37205.7742
155164,22,3,3,1,Single,11,751.6107893,1,37283.53947
116387,59,39,2,1,Single,3,751.4537726,1,37173.68863
101152,30,13,3,1,Single,2,751.4022995,1,37128.11498
167605,50,33,3,1,Single,3,751.3778859,1,37338.8943
116806,20,2,2,0,Single,6,751.1068831,1,37129.34416
104797,24,7,1,0,Single,9,750.7435809,1,37166.17905
124202,39,18,4,0,Single,1,750.7101184,1,37189.50592
121205,25,8,3,0,Single,11,750.7100354,1,37055.50177
198483,22,5,1,0,Married,12,750.6949749,1,36945.74875
173404,50,30,2,0,Single,2,750.1423091,1,36970.11546
158402,21,3,2,1,Single,13,750.1251378,1,37218.25689
182408,55,37,2,0,Married,5,749.5688,1,37125.44
117642,43,22,3,1,Single,4,749.2786847,1,36903.93424
175425,40,20,3,0,Single,0,749.0718397,1,37013.59199
166909,17,0,3,0,Married,8,749.0647745,1,36939.23873
130225,16,0,3,1,Single,7,749.016163,1,37027.80815
172474,22,4,2,0,Single,12,748.3427857,1,37129.13929
158443,53,35,1,1,Single,1,748.0892554,1,36829.46277
102269,21,3,1,1,Single,1,747.9948959,1,37112.7448
165057,16,0,3,0,Married,7,747.9162636,1,36918.81318
112311,37,20,3,0,Single,4,747.8615942,1,37077.07971
167501,28,8,1,0,Single,5,747.8135443,1,36873.67722
167905,23,6,2,0,Single,8,747.6396837,1,36874.98419
141901,38,21,3,0,Single,0,747.5487832,1,36808.43916
104053,29,8,4,1,Single,4,747.5424258,1,37105.12129
140463,25,6,1,0,Single,12,747.5273873,1,37035.36937
192262,21,4,1,0,Single,6,747.4862966,1,36811.31483
181490,21,2,3,1,Single,1,747.4044981,1,37021.22491
129782,20,2,2,0,Single,6,747.1602793,1,36767.01397
140338,45,24,2,1,Single,3,746.962177,1,37073.10885
107194,19,1,3,0,Single,15,746.8935141,1,36854.67571
163677,33,15,3,0,Single,4,746.8129203,1,37005.64602
135258,57,40,1,0,Married,2,746.5266329,1,36995.33165
115268,17,0,3,0,Married,2,746.3830767,1,36952.15384
150373,19,0,4,0,Married,14,745.6941256,1,36684.70628
108190,21,4,4,0,Single,15,745.3988008,1,36767.94004
126069,49,31,3,1,Single,5,745.2307544,1,36942.53772
145282,24,5,2,0,Single,10,745.2238905,1,37041.19453
119478,17,0,4,1,Single,6,744.9886189,1,36711.43095
189311,18,1,3,1,Single,7,744.7415847,1,36968.07924
142640,20,3,2,0,Married,6,744.7098138,1,37017.49069
180809,24,5,3,1,Single,15,744.7075008,1,36909.37504
167598,21,4,2,0,Single,13,744.6703098,1,36799.51549
180444,18,0,1,1,Single,6,744.3208775,1,36912.04388
142715,22,3,3,0,Single,9,744.2801501,1,36725.00751
115565,22,4,3,1,Single,10,743.8411092,1,36640.05546
128105,26,5,2,1,Single,0,743.8404236,1,36694.02118
163845,28,8,4,1,Single,4,743.7252397,1,36661.26199
171787,37,17,3,0,Single,3,743.7041636,1,36736.20818
112974,68,48,3,1,Single,0,743.6526726,1,36723.63363
162671,18,0,3,0,Single,6,743.3368202,1,36870.84101
179221,21,3,4,0,Single,7,742.9845214,1,36766.22607
159582,55,34,3,1,Single,5,742.8816331,1,36718.08166
100964,25,8,4,0,Single,7,742.8293205,1,36551.46603
139614,22,3,3,0,Single,11,742.704017,1,36607.20085
189205,19,2,1,1,Single,10,742.6331885,1,36648.65943
170048,53,35,2,1,Single,4,742.6323394,1,36606.61697
189638,45,28,4,1,Single,2,742.4942154,1,36561.71077
128839,56,39,3,0,Single,1,742.4066248,1,36625.33124
191255,34,15,2,1,Single,1,742.1126203,1,36716.63102
113698,21,3,3,0,Single,13,742.0448449,1,36622.24225
123406,17,0,3,0,Single,12,741.6520164,1,36585.60082
171785,21,2,2,1,Single,10,741.6500184,1,36752.50092
152259,24,5,2,0,Single,11,741.5099286,1,36829.49643
176637,22,4,1,0,Married,8,741.4046857,1,36822.23429
162610,25,6,2,0,Married,13,741.1393669,1,36670.96835
183210,46,29,2,0,Married,5,740.3524966,1,36438.62483
199193,16,0,2,1,Single,7,740.2205245,1,36651.02623
167432,39,20,2,1,Single,1,740.1509003,1,36624.54502
187973,19,2,4,1,Single,6,739.8044903,1,36497.22452
171018,38,21,2,1,Single,4,739.7080323,1,36769.40162
131261,53,36,3,1,Single,3,739.5009409,1,36762.04705
118016,21,3,4,0,Single,10,739.08745,1,36643.3725
136351,39,21,1,1,Single,4,739.0265466,1,36631.32733
162634,29,8,1,0,Single,5,738.9508303,1,36732.54152
158867,17,0,3,1,Single,14,738.9427143,1,36613.13572
104486,20,3,1,0,Single,12,738.7166747,1,36579.83374
176201,36,16,2,0,Single,3,738.4086177,1,36665.43089
190282,58,39,2,1,Single,0,738.3166478,1,36438.83239
192969,28,10,2,0,Single,3,738.3157306,1,36647.78653
188569,26,5,1,0,Married,4,738.1112479,1,36689.5624
185847,67,47,1,0,Single,0,738.0077268,1,36387.38634
136718,21,3,4,1,Single,12,737.9319218,1,36440.59609
162718,51,32,3,0,Single,5,737.9240961,1,36349.20481
166649,24,6,3,0,Single,7,737.570523,1,36310.52615
154287,49,30,3,0,Single,4,737.3769927,1,36648.84964
100945,19,2,2,0,Single,8,737.3161446,1,36371.80723
181199,21,4,3,1,Single,15,737.21522,1,36632.761
101619,20,2,3,0,Married,6,736.8606984,1,36316.03492
170495,34,16,4,1,Single,2,736.6505999,1,36496.53
136117,18,0,3,0,Single,11,736.4336297,1,36478.68149
147685,24,5,2,1,Single,2,736.2592586,1,36233.96293
181095,52,31,1,0,Married,3,736.0551029,1,36211.75515
110469,25,7,2,1,Single,4,735.9151019,1,36255.7551
198779,16,0,4,0,Married,2,735.4850769,1,36259.25385
153709,18,0,2,1,Single,1,735.4720889,1,36289.60445
160854,24,6,3,0,Single,8,735.2600412,1,36552.00206
188905,31,13,2,0,Single,3,735.2008246,1,36433.04123
190989,34,15,1,0,Single,1,735.1929055,1,36161.64528
153116,52,34,2,0,Married,2,735.1736164,1,36248.68082
126897,25,8,1,0,Single,6,735.046617,1,36248.33085
149552,46,29,2,0,Single,0,734.9538041,1,36276.69021
149621,37,16,3,1,Single,5,734.8100542,1,36316.50271
198391,19,1,2,1,Single,1,734.5382363,1,36317.91181
176853,41,24,3,0,Single,2,734.4469905,1,36240.34953
155720,58,39,3,0,Single,4,734.156536,1,36129.8268
150250,25,7,3,1,Single,13,733.5631324,1,36209.15662
173553,51,33,3,1,Single,4,733.5515891,1,36368.57946
189350,22,5,1,0,Married,11,733.2995135,1,36095.97568
191833,25,7,1,0,Married,15,733.1835635,1,36063.17818
193784,38,21,2,1,Married,5,733.1059148,1,36444.29574
101129,25,8,3,0,Single,13,733.0506137,1,36138.53069
126289,16,0,3,1,Single,1,733.0239691,1,36255.19846
170336,45,25,1,0,Single,0,732.981314,1,36169.0657
149662,34,16,4,0,Married,2,732.911493,1,36255.57465
107471,64,45,3,1,Single,4,732.8982638,1,36149.91319
193717,57,37,1,1,Single,0,732.7378461,1,36313.89231
182411,19,2,3,0,Single,10,732.5388838,1,36366.94419
146871,24,7,2,1,Single,8,732.330998,1,36297.5499
139399,35,15,4,0,Married,2,732.1364996,1,36387.82498
140093,22,5,1,0,Married,13,731.936793,1,36231.83965
146254,22,3,4,0,Single,4,731.7796272,1,36228.98136
134194,38,19,3,0,Married,2,731.5071003,1,36349.35502
107345,25,6,2,0,Single,14,731.4462962,1,36075.31481
160128,20,1,2,1,Single,11,731.4175567,1,36190.87784
170260,17,0,2,1,Single,7,731.3380755,1,36037.90378
144971,22,5,3,1,Single,7,731.3215187,1,36239.07594
156612,20,2,2,0,Married,3,730.946049,1,35948.30245
194606,21,3,3,1,Single,12,730.8661549,1,36190.30775
109970,20,3,1,0,Single,14,730.6043258,1,36322.21629
175654,49,28,4,0,Married,3,730.5001882,1,36070.00941
103225,30,13,4,0,Single,4,730.0730083,1,36179.65042
138015,20,3,4,1,Single,14,729.9872438,1,36293.36219
194129,38,17,2,0,Single,1,729.9846534,1,36016.23267
189148,52,34,2,0,Married,3,729.7407166,1,36237.03583
136227,35,14,2,0,Married,5,729.6449966,1,35924.24983
145070,44,23,3,0,Married,2,729.4560945,1,36134.80473
158743,21,4,3,0,Married,8,729.3349608,1,36000.74804
185158,31,13,2,0,Single,3,729.2347634,1,36031.73817
183155,23,5,2,0,Married,6,729.1836728,1,36159.18364
102568,56,38,2,0,Single,5,728.9073294,1,36029.36647
117065,18,0,4,1,Single,6,728.8863497,1,36084.31749
149487,55,36,3,0,Single,2,728.8201517,1,36074.00759
122225,30,12,3,1,Single,0,728.740211,1,36173.01055
125666,22,4,3,1,Single,10,728.724884,1,35953.2442
165695,50,31,1,0,Single,4,728.5124594,1,35918.62297
107299,16,0,3,0,Married,11,728.4154246,1,36120.77123
112346,22,5,2,1,Single,13,728.2709886,1,36011.54943
145856,21,3,3,0,Single,12,728.2566462,1,36010.83231
172287,51,34,2,0,Single,1,728.2463712,1,36174.31856
196949,62,44,3,1,Single,3,728.1737695,1,35881.68848
123654,18,0,1,1,Married,1,728.1487947,1,36178.43974
180944,23,5,1,1,Single,11,728.0642984,1,35826.21492
187419,24,7,3,0,Single,12,727.8987162,1,35899.93581
137693,31,12,1,0,Single,4,727.8923199,1,35818.616
135877,18,0,4,0,Single,12,727.6521363,1,36078.60682
148196,19,1,3,0,Single,12,727.543379,1,35843.16895
116954,52,32,4,1,Single,1,727.4182801,1,36096.91401
190451,39,21,4,1,Single,1,727.4062496,1,35775.31248
173617,18,0,3,0,Married,15,727.3539675,1,35780.69838
137950,34,13,3,0,Married,4,727.2942665,1,36094.71333
134430,24,5,1,0,Single,15,727.2299234,1,35836.49617
155600,16,0,3,1,Single,7,726.9696083,1,36030.48042
179762,20,3,1,1,Single,12,726.9287831,1,35840.43916
106693,23,5,2,0,Single,11,726.7582512,1,35872.91256
135565,24,6,4,0,Married,13,726.3996716,1,36068.98358
197010,24,7,3,1,Married,3,726.2144867,1,35767.72434
143653,48,29,3,0,Single,4,726.1445664,1,36090.22832
187015,38,19,1,0,Single,2,726.0985559,1,35783.9278
137374,41,22,4,1,Single,1,725.6808454,1,35760.04227
150949,24,6,2,0,Single,8,725.5793808,1,36002.96904
121251,21,3,2,0,Single,14,725.3238635,1,35996.19318
175748,56,37,3,0,Single,3,725.0004017,1,35739.02009
160684,23,5,3,1,Single,7,724.8890803,1,35807.45402
163060,60,43,2,0,Single,3,724.8525528,1,35966.62764
178376,22,4,1,0,Married,9,724.7811444,1,35941.05722
129388,22,4,3,1,Single,7,724.6667315,1,35991.33658
166429,52,31,3,0,Married,3,724.4941323,1,35938.70662
155261,28,8,2,0,Married,3,724.4201613,1,35708.00807
177427,21,2,3,0,Single,8,724.4198456,1,35660.99228
189955,24,5,2,0,Single,14,724.3322862,1,35763.61431
179435,17,0,3,0,Married,6,724.3219304,1,35968.09652
171283,36,17,4,1,Single,3,724.1517815,1,35654.58908
165586,33,12,4,1,Single,4,723.9313513,1,35641.56757
117860,20,2,3,1,Single,5,723.7090932,1,35647.45466
193669,18,0,3,0,Single,13,723.6851015,1,35687.25508
187627,24,7,2,1,Single,8,723.358997,1,35756.94985
199346,56,36,4,0,Single,0,723.3344189,1,35956.72095
133604,18,1,3,0,Single,7,723.0670895,1,35902.35448
137319,22,4,4,1,Single,13,722.9855694,1,35837.27847
115716,21,2,2,0,Single,12,722.9591886,1,35597.95943
149268,54,34,2,0,Single,0,722.6879625,1,35697.39813
178291,19,1,2,0,Married,6,722.5777502,1,35790.88751
116370,27,10,1,0,Single,0,722.4834937,1,35632.17469
147093,17,0,2,0,Single,12,721.8671019,1,35738.3551
183713,67,46,2,0,Single,4,721.4294983,1,35667.47492
167244,48,27,4,0,Single,3,721.3954283,1,35469.77142
165791,17,0,2,0,Married,1,721.3547276,1,35502.73638
165972,70,49,3,0,Single,5,721.3353791,1,35851.76896
154901,22,4,4,1,Single,7,721.1868745,1,35468.34373
100968,22,4,4,0,Married,1,721.1489608,1,35513.44804
140755,43,24,4,1,Single,1,721.0038433,1,35505.19217
111406,17,0,3,0,Single,1,720.999411,1,35776.97055
114072,69,48,3,0,Married,1,720.9926207,1,35779.63104
107526,54,35,1,0,Married,0,720.563647,1,35639.18235
197238,17,0,3,0,Single,12,720.5410613,1,35432.05307
117823,44,25,1,0,Single,2,720.5009586,1,35592.04793
198213,30,12,2,0,Married,0,720.3111168,1,35570.55584
181439,60,41,1,1,Single,2,720.2641611,1,35604.20806
106224,16,0,3,0,Married,9,720.0387756,1,35601.93878
119432,51,30,2,1,Single,3,720.0040562,1,35626.20281
132963,65,45,4,0,Single,3,719.882821,1,35409.14105
171209,23,6,2,1,Single,15,719.4288199,1,35620.441
142588,23,4,4,1,Single,15,719.4111251,1,35445.55626
111108,60,43,4,0,Single,5,719.2340931,1,35383.70466
120704,17,0,4,0,Single,13,718.5018398,1,35395.09199
176273,19,2,1,1,Single,8,717.8144118,1,35646.72059
192070,47,30,2,0,Single,0,717.6878712,1,35284.39356
112136,17,0,3,0,Married,9,717.6696252,1,35614.48126
155791,49,30,2,0,Single,3,717.3119185,1,35347.59593
192033,17,0,4,0,Single,7,717.1666177,1,35268.33089
154128,56,35,2,1,Single,5,717.0004191,1,35367.02096
152932,26,9,3,0,Married,0,716.6627127,1,35623.13564
124560,18,0,1,0,Single,11,716.2810493,1,35502.05247
199655,19,0,3,0,Single,12,716.0003598,1,35577.01799
173823,20,3,1,1,Single,9,715.9079639,1,35483.3982
195216,21,2,3,1,Single,8,715.7363036,1,35428.81518
181139,21,4,2,1,Single,15,715.6296193,1,35267.48097
108149,31,13,4,1,Married,2,715.5279536,1,35288.39768
197032,20,2,1,0,Single,14,715.4017418,1,35397.08709
166934,39,18,4,0,Single,5,715.2978066,1,35349.89033
153418,56,35,3,1,Single,5,715.2970434,1,35453.85217
150351,18,0,3,0,Single,12,715.1642346,1,35239.21173
133288,17,0,2,1,Single,12,715.003246,1,35205.1623
125763,44,25,2,1,Single,5,714.9612771,1,35439.06386
156248,21,4,1,0,Single,15,714.8738821,1,35482.69411
171467,21,3,2,0,Married,8,714.1525743,1,35236.62872
176990,25,6,2,0,Single,11,714.116311,1,35140.81555
129660,57,39,3,0,Married,3,713.4269686,1,35134.34843
154528,21,2,3,1,Single,4,713.340243,1,35196.01215
109813,46,28,1,1,Single,5,713.2035955,1,35151.17978
114007,23,4,3,1,Single,2,712.8656995,1,35257.28498
161053,59,40,2,0,Single,1,712.7631408,1,35427.15704
111488,58,39,3,0,Married,2,712.510153,1,35263.50765
107649,25,8,1,0,Single,14,712.2418496,1,35315.09248
109440,25,6,2,1,Single,13,712.1576689,1,35022.88345
170163,29,11,2,1,Single,5,712.0506677,1,35154.53339
163489,42,22,3,1,Single,0,711.7372416,1,35280.86208
179465,16,0,3,0,Single,11,711.6021736,1,35345.10868
136129,22,3,1,1,Married,5,711.319749,1,35045.98745
199668,27,8,3,0,Single,5,710.9679541,1,35178.39771
124204,50,31,2,0,Single,2,710.872881,1,35274.64405
194674,47,27,2,0,Married,5,710.8321571,1,35229.60786
153530,39,22,3,0,Married,4,710.8187758,1,34946.93879
174898,55,37,2,1,Single,2,710.6913874,1,35267.56937
114834,58,38,2,1,Single,3,710.6296673,1,35194.48337
113961,38,21,2,0,Single,1,710.6181282,1,35035.90641
191464,22,3,3,1,Single,7,710.4609954,1,35213.04977
159161,48,30,2,1,Single,4,710.1964161,1,35305.82081
144991,25,7,1,0,Married,10,710.145085,1,35092.25425
117501,16,0,2,0,Single,14,709.9622245,1,35071.11123
159180,23,5,2,0,Single,15,709.7052299,1,35272.2615
101997,30,9,3,1,Single,4,709.0451916,1,35047.25958
155080,24,7,4,1,Single,10,708.9236538,1,35178.18269
195716,34,15,3,0,Married,4,708.9139951,1,35054.69976
140722,59,42,3,0,Single,4,708.7560801,1,34880.80401
196837,54,34,2,0,Married,1,708.6359446,1,35223.79723
186925,43,26,3,1,Single,2,708.5914602,1,35042.57301
161787,46,28,3,0,Single,3,708.5389958,1,34852.94979
171296,31,13,1,1,Single,2,708.3775224,1,35112.87612
121756,25,6,2,0,Single,12,708.2943873,1,35011.71937
171503,18,1,2,0,Married,6,708.2637427,1,34960.18714
172010,30,9,3,1,Single,5,708.2050607,1,35029.25304
194685,21,4,1,0,Married,9,708.1011729,1,34819.05865
137858,52,34,4,0,Single,1,707.9965208,1,35188.82604
129950,16,0,4,0,Single,13,707.985618,1,34992.2809
161298,21,2,2,1,Single,10,707.8560154,1,34938.80077
131041,33,15,1,0,Single,2,707.7098739,1,34840.4937
193733,44,27,2,0,Single,2,707.6464212,1,34938.32106
161914,33,13,2,1,Single,0,707.5019734,1,35020.09867
152174,16,0,4,0,Single,7,707.4290189,1,34943.45095
157903,28,9,1,0,Single,5,707.3793245,1,35067.96623
188846,19,1,2,0,Single,14,706.9268496,1,35093.34248
193485,24,6,2,1,Married,5,706.7631618,1,35113.15809
165061,25,6,3,0,Married,7,706.6349724,1,34879.74862
150535,22,4,2,0,Single,11,706.4166287,1,35051.83144
198961,44,27,2,1,Single,3,706.3421025,1,34946.10513
139693,17,0,2,1,Single,7,706.3116363,1,35010.58182
102630,23,4,2,0,Married,7,706.1884573,1,34863.42287
169709,32,11,3,0,Single,3,706.1728941,1,34867.64471
154782,46,26,3,1,Single,3,706.0207451,1,35069.03726
174345,19,1,3,1,Single,10,705.0733831,1,34791.66916
169185,22,3,3,1,Single,9,704.5082833,1,34835.41417
125470,17,0,2,0,Married,6,704.3709913,1,34897.54957
194958,20,1,2,0,Single,11,704.2772453,1,34923.86227
199181,21,4,3,0,Married,4,704.2167743,1,34921.83872
197594,22,3,3,0,Married,12,703.9341038,1,34974.70519
176346,40,21,4,1,Single,5,703.8309659,1,34934.5483
106695,18,0,2,1,Single,8,703.7133256,1,34678.66628
130419,45,28,2,0,Married,5,703.1923045,1,34674.61523
124975,17,0,1,0,Single,12,703.1834416,1,34647.17208
144032,32,15,3,0,Single,3,703.1167888,1,34724.83944
172662,19,0,1,1,Single,11,703.098246,1,34626.9123
118154,18,0,2,0,Single,6,702.6961237,1,34845.80619
143500,40,23,1,0,Married,4,702.6532417,1,34914.66209
174691,18,0,2,0,Single,15,702.236446,1,34714.8223
114082,16,0,2,0,Single,8,702.1755846,1,34512.77923
161695,53,32,2,0,Single,4,702.1396254,1,34712.98127
178887,50,32,3,0,Married,4,702.1098017,1,34827.49009
183876,35,18,3,0,Married,3,702.0178521,1,34604.89261
163307,30,10,2,1,Single,0,701.8218843,1,34564.09422
165781,25,7,4,0,Single,15,701.7830967,1,34580.15484
129230,51,30,2,1,Single,3,701.7325782,1,34690.62891
195105,48,29,2,1,Married,0,701.4094598,1,34686.47299
132521,16,0,2,0,Single,6,701.2531217,1,34649.65609
158771,16,0,2,0,Married,11,701.2351638,1,34826.75819
102735,23,6,2,1,Single,7,701.2232747,1,34481.16374
157753,23,6,3,1,Single,10,701.2154637,1,34663.77319
136927,17,0,2,1,Single,6,701.0064846,1,34651.32423
172793,23,6,1,1,Single,3,700.8519395,1,34581.59698
152724,23,5,4,1,Single,9,700.8223472,1,34734.11736
113795,44,23,2,0,Married,4,700.8128781,1,34815.64391
198246,45,26,4,0,Single,3,700.7749063,1,34802.74532
197364,18,0,3,1,Single,12,700.5392738,1,34560.96369
134418,16,0,4,0,Single,8,700.4517323,1,34534.58662
195449,19,0,4,1,Married,1,700.394564,1,34657.7282
168751,70,53,3,1,Single,2,700.3607889,1,34647.03945
103317,26,9,2,1,Single,0,700.2866318,1,34548.33159
170327,49,28,4,0,Single,3,700.2664529,1,34793.32265
188774,47,29,3,1,Single,1,699.9012311,1,34445.06156
174879,25,7,3,0,Single,13,699.7989747,1,34418.94874
134146,36,19,1,0,Single,3,699.7955734,1,34631.77867
106391,27,9,2,1,Single,3,699.6290908,1,34493.45454
123075,57,36,3,0,Single,1,699.5215605,1,34485.07803
135715,17,0,4,0,Married,14,699.4333382,1,34394.66691
107692,67,48,2,0,Single,0,699.1471439,1,34570.3572
119649,27,10,3,1,Single,2,699.0681192,1,34743.40596
151238,39,19,3,0,Married,5,699.051582,1,34664.5791
114868,18,0,4,1,Single,13,699.0305029,1,34353.52515
115705,17,0,3,1,Single,6,698.8769957,1,34553.84979
114814,16,0,2,1,Single,5,698.8542541,1,34718.71271
121117,43,25,2,0,Single,4,698.6641127,1,34428.20564
125317,62,44,3,0,Single,1,698.5960213,1,34503.80107
140017,61,41,4,0,Married,4,698.5172302,1,34389.86151
108319,18,1,1,1,Single,10,698.2797078,1,34694.98539
182111,36,17,2,0,Single,3,697.7882878,1,34667.41439
140202,23,5,1,1,Single,15,697.6382677,1,34296.91339
116597,20,2,2,0,Married,11,697.5862202,1,34615.31101
170803,51,30,3,0,Single,2,697.3411895,1,34543.05948
169949,30,11,2,0,Married,3,697.2607911,1,34362.03956
139860,36,16,4,1,Single,4,697.2179608,1,34323.89804
157361,47,29,3,0,Single,0,697.1630111,1,34536.15056
112732,44,27,4,0,Married,4,696.6347494,1,34331.73747
184205,63,45,2,0,Married,2,696.6123044,1,34271.61522
197630,42,24,2,0,Single,5,696.5284014,1,34408.42007
101155,30,13,1,1,Single,1,696.0295489,1,34273.47745
156502,31,12,2,1,Single,4,695.946743,1,34320.33715
162628,22,5,2,1,Married,2,695.7030896,1,34501.15448
155112,24,5,2,0,Married,12,695.6235802,1,34381.17901
120073,23,4,3,0,Single,11,695.4005572,1,34412.02786
178732,16,0,2,0,Single,13,695.3534376,1,34327.67188
183060,18,0,2,0,Married,11,695.3406506,1,34192.03253
152850,27,7,2,1,Single,0,695.1590822,1,34237.95411
115872,18,0,2,1,Single,11,695.1091588,1,34384.45794
164700,18,0,3,0,Single,14,694.7965141,1,34395.82571
152180,64,46,3,0,Single,4,694.6043769,1,34240.21885
177554,23,4,1,0,Married,14,694.5794472,1,34524.97236
112350,25,7,2,0,Married,11,694.561926,1,34244.0963
138771,33,15,1,1,Single,4,694.5450764,1,34253.25382
113044,18,1,3,0,Married,10,694.286633,1,34224.33165
133861,22,4,3,1,Single,12,694.2796779,1,34477.9839
140660,24,7,4,1,Single,11,694.1400539,1,34142.0027
101041,31,14,4,1,Single,3,694.0279665,1,34244.39833
167595,57,39,1,0,Married,4,693.878229,1,34372.91145
104491,49,31,3,1,Single,2,693.7368884,1,34189.84442
198874,70,50,3,0,Single,3,693.584713,1,34139.23565
181116,22,5,1,0,Married,10,693.4669374,1,34076.34687
172014,57,36,3,1,Single,1,693.2775041,1,34372.87521
199120,40,22,2,1,Single,0,693.2217985,1,34258.08993
124285,38,20,3,0,Single,0,693.1753088,1,34239.76544
131184,24,5,3,0,Single,14,693.1694165,1,34390.47083
190123,21,2,3,0,Single,11,693.0582757,1,34421.91379
143377,49,29,4,0,Single,5,692.9896657,1,34144.48329
191555,26,8,2,0,Single,4,692.8702562,1,34414.51281
186802,16,0,3,0,Single,2,692.758983,1,34199.94915
165371,21,3,1,1,Married,4,692.7306542,1,34058.53271
115774,46,26,2,0,Single,3,692.7240723,1,34196.20362
112556,25,6,3,0,Married,15,692.7167535,1,34152.83768
137880,18,0,2,0,Single,12,692.6939638,1,34112.69819
134055,43,25,1,0,Married,0,692.538638,1,34296.9319
159452,35,17,4,1,Single,2,692.5043339,1,34288.2167
196909,24,7,2,1,Single,9,692.2309,1,34282.545
193383,45,26,3,0,Married,1,691.986297,1,34125.31485
120658,33,16,4,1,Single,4,691.7972533,1,34218.86267
106125,16,0,2,0,Married,15,691.6319204,1,34323.59602
124596,46,26,2,1,Single,4,691.5961948,1,34135.80974
143812,17,0,1,0,Married,14,691.1435667,1,34333.17834
120215,16,0,3,1,Married,1,691.0489419,1,34169.4471
160398,29,8,2,1,Single,3,691.0368252,1,34145.84126
170192,45,24,4,0,Single,4,691.0362156,1,34130.81078
146215,27,7,2,0,Single,1,690.8500547,1,34166.50274
163342,19,2,3,0,Married,11,690.6259334,1,34073.29667
188180,42,24,2,1,Single,2,690.3977415,1,34148.88708
178441,19,0,1,1,Single,8,690.3083206,1,34093.41603
147183,20,3,1,1,Single,13,690.2847547,1,33943.23774
162069,38,18,1,0,Married,3,690.219118,1,34115.9559
149876,24,6,1,0,Married,6,690.1699369,1,34229.49685
117634,16,0,2,1,Married,1,690.1442762,1,34290.21381
164085,56,35,3,1,Single,4,690.1345826,1,33994.72913
183961,47,26,3,1,Single,0,690.0031162,1,33933.15581
169677,28,7,2,0,Single,3,689.9700256,1,33914.50128
167179,70,53,3,1,Single,2,689.6666395,1,33943.33198
173859,19,1,3,1,Single,14,689.6378418,1,34073.89209
156380,18,0,3,0,Single,7,689.4130703,1,34230.65352
126198,22,3,2,0,Single,13,689.3821702,1,34115.10851
189088,19,0,3,1,Single,9,689.3640906,1,33962.20453
172614,24,6,2,0,Single,9,689.36232,1,33932.116
121232,43,23,1,0,Married,0,689.2935821,1,34252.67911
159363,19,2,2,0,Single,14,689.154748,1,33953.7374
179212,18,0,3,0,Single,6,688.9480521,1,34031.40261
108228,17,0,2,1,Single,15,688.8547617,1,33980.73809
145682,23,4,2,1,Single,14,688.7446412,1,33950.23206
110198,20,3,3,1,Single,9,688.7438228,1,34200.19114
119010,23,4,3,0,Single,12,688.7317091,1,34151.58546
177873,25,8,4,0,Married,7,688.502446,1,34126.1223
119290,60,41,2,0,Single,2,688.3114093,1,34072.57047
147552,20,3,2,0,Married,8,688.2579359,1,34163.8968
159312,44,27,1,1,Single,4,688.1702964,1,33834.51482
190636,39,18,1,0,Single,5,688.1633261,1,33825.16631
105095,46,27,3,1,Single,4,688.0319335,1,34157.59668
188347,18,0,3,1,Single,6,687.9402382,1,33990.01191
110524,19,0,2,0,Married,12,687.901036,1,33996.0518
193936,19,1,3,1,Single,13,687.7102757,1,33877.51379
170212,31,12,3,0,Single,0,687.4971991,1,33814.85996
119060,30,10,3,1,Single,5,687.3164652,1,34090.82326
177626,22,5,4,0,Single,15,687.1250602,1,34124.25301
114333,56,39,3,1,Married,2,687.0747031,1,34080.73516
114186,18,0,4,0,Single,11,686.9093288,1,33752.46644
122865,19,0,2,1,Single,13,686.6814596,1,34033.07298
173157,34,16,2,0,Married,2,686.6682312,0,33916.41156
141182,48,27,1,1,Single,0,686.6217767,0,33840.08884
186942,19,2,3,0,Single,12,686.5275682,0,34018.37841
148265,20,3,3,1,Single,6,686.5084197,0,33927.42099
131373,51,31,3,1,Single,1,686.4441136,0,34068.20568
109243,59,39,4,1,Married,3,686.404928,0,33990.2464
145086,23,6,3,1,Single,9,686.2576642,0,33817.88321
144886,40,21,2,1,Married,1,686.067487,0,33842.37435
129320,50,33,4,1,Married,0,686.0361749,0,33932.80875
154334,18,1,3,0,Married,14,685.7656083,0,34043.28042
169528,57,40,2,1,Single,0,685.7087943,0,34025.43972
124217,21,3,1,0,Married,0,685.70269,0,33922.1345
113228,16,0,2,0,Married,6,685.5676599,0,34073.383
123935,24,5,2,0,Married,15,685.5472249,0,33919.36125
156634,27,8,2,1,Single,3,685.5365484,0,33783.82742
122035,26,5,3,1,Single,3,685.4844285,0,33814.22143
178987,45,24,4,1,Single,5,685.4408486,0,33831.04243
134245,25,8,2,1,Single,2,685.3157449,0,33699.78725
144958,45,24,4,1,Single,0,685.1928276,0,33918.64138
117928,24,7,3,0,Single,8,685.1143207,0,33703.71604
168111,54,34,2,1,Single,1,685.0277859,0,33796.3893
113488,44,27,1,0,Single,1,684.9800141,0,33746.00071
150946,20,1,3,0,Married,14,684.9732329,0,33896.66165
118920,19,2,2,0,Single,11,684.837681,0,33734.88405
180703,25,6,4,0,Married,7,684.6603654,0,34023.01827
190872,18,0,1,0,Single,9,684.6526982,0,34004.63491
197354,35,15,3,1,Married,0,684.4578628,0,33639.89314
174155,20,2,3,0,Single,11,684.4273231,0,33651.36616
169530,20,1,2,0,Married,14,684.3776358,0,33759.88179
109552,21,4,3,0,Single,14,684.2638013,0,33903.19007
191006,35,15,2,0,Married,1,683.9467174,0,33771.33587
127592,45,24,4,0,Married,5,683.6910719,0,33697.5536
130243,42,21,2,1,Single,4,683.6721296,0,33592.60648
188314,21,3,3,1,Single,3,683.5334385,0,33924.67193
106723,20,3,1,0,Married,10,683.3311595,0,33631.55798
137076,24,6,3,0,Single,15,683.2504772,0,33752.52386
165138,18,0,3,1,Single,9,683.2486593,0,33719.43297
127204,21,3,3,1,Married,2,683.1064293,0,33837.32147
158107,38,17,3,1,Single,1,682.8144216,0,33575.72108
160577,25,8,2,0,Single,14,682.7733509,0,33804.66755
152280,41,23,2,0,Single,4,682.7708517,0,33673.54259
191908,53,36,4,0,Married,4,682.7015018,0,33715.07509
165658,23,4,3,0,Single,13,682.6715142,0,33906.57571
158928,41,23,3,1,Single,1,682.37381,0,33776.6905
193241,25,8,3,0,Married,9,682.3533622,0,33541.66811
165092,47,28,4,0,Single,4,682.2925662,0,33907.62831
165507,23,6,4,0,Single,14,682.0951784,0,33726.75892
141877,50,30,4,1,Single,3,681.9795945,0,33889.97973
165569,18,1,3,0,Married,11,681.9643691,0,33562.21846
185997,19,0,3,1,Single,11,681.7601798,0,33725.00899
166622,43,26,2,0,Married,1,681.7282118,0,33716.41059
182444,20,2,3,1,Single,10,681.7202243,0,33629.01122
160510,22,5,2,1,Married,1,681.6681284,0,33544.40642
192857,23,6,2,0,Single,13,681.6024216,0,33556.12108
192607,24,7,1,0,Single,14,681.6009788,0,33719.04894
100079,20,3,1,0,Married,1,681.5827516,0,33621.13758
177155,24,5,4,0,Single,7,681.5777283,0,33513.88642
175068,20,3,2,1,Single,5,681.5311064,0,33790.55532
135025,44,26,2,0,Married,0,681.5267918,0,33848.33959
166556,22,3,3,0,Married,6,681.5196725,0,33580.98363
134487,27,6,2,0,Married,3,681.3895589,0,33543.47795
165763,54,36,3,0,Married,4,681.3273614,0,33503.36807
185315,25,8,2,1,Married,3,681.2100708,0,33622.50354
137894,49,32,2,0,Married,3,681.1434152,0,33756.17076
156003,45,27,4,0,Married,1,681.0571377,0,33613.85689
129748,22,5,1,1,Single,14,680.9239433,0,33591.19717
194616,16,0,2,0,Married,6,680.6325471,0,33700.62736
110987,53,33,2,0,Single,4,680.6276518,0,33738.38259
189764,16,0,3,1,Single,6,680.6098474,0,33750.49237
118206,26,7,4,0,Married,5,680.2711562,0,33749.55781
146242,39,22,1,0,Married,3,680.1630093,0,33509.15047
188206,25,8,1,0,Married,11,679.8339825,0,33691.69913
136707,17,0,3,0,Single,9,679.790063,0,33457.50315
107925,52,31,2,0,Married,5,679.4670175,0,33709.35088
173702,56,36,2,0,Married,3,679.1324761,0,33481.62381
106553,54,35,1,0,Single,1,679.0530079,0,33738.6504
141076,19,0,2,0,Married,2,678.7030456,0,33709.15228
152819,47,29,2,0,Single,5,678.618111,0,33366.90555
105468,16,0,4,0,Married,5,678.4732977,0,33560.66489
160674,58,38,4,1,Married,1,678.3193772,0,33586.96886
135468,45,26,2,0,Single,3,678.1316539,0,33406.5827
146413,18,0,1,1,Single,3,678.1012495,0,33602.06248
112698,23,5,1,0,Single,6,678.0913473,0,33400.56737
145987,69,50,2,0,Married,3,677.7201767,0,33624.00884
124329,19,2,3,0,Married,8,677.6766583,0,33303.83292
155135,70,49,3,0,Single,4,677.6506049,0,33512.53025
150052,42,25,2,0,Single,5,677.604744,0,33350.2372
102026,18,1,4,0,Married,3,677.5618141,0,33434.09071
162882,32,13,3,0,Married,2,677.5407463,0,33369.03732
166933,17,0,3,1,Single,11,677.3899567,0,33388.49784
142814,23,6,3,0,Single,14,677.2536208,0,33296.68104
116629,18,0,3,1,Single,5,676.9304876,0,33638.52438
174475,18,0,1,0,Married,11,676.8854783,0,33578.27392
161208,16,0,3,1,Single,14,676.7684721,0,33564.42361
135367,58,39,1,1,Single,3,676.5249895,0,33615.24948
124671,17,0,2,0,Single,8,676.4965432,0,33425.82716
105815,17,0,2,1,Married,5,676.4310376,0,33236.55188
171919,32,13,3,0,Married,5,676.3882247,0,33479.41124
181487,62,43,2,1,Single,3,676.2909917,0,33234.54959
124485,17,0,2,1,Single,7,676.2447027,0,33385.23514
116102,64,44,4,0,Single,5,676.2242731,0,33314.21366
170468,23,5,2,0,Single,11,676.0622962,0,33357.11481
148077,20,1,3,1,Single,2,676.0452565,0,33549.26283
158478,32,13,1,1,Single,4,675.9475429,0,33418.37715
169898,24,5,3,0,Single,10,675.8663698,0,33444.31849
102406,33,14,2,1,Single,2,675.8074022,0,33236.37011
136937,29,11,1,0,Single,0,675.7091855,0,33261.45928
148774,48,29,2,1,Single,5,675.685326,0,33251.2663
140444,21,4,2,0,Single,3,675.247383,0,33492.36915
115306,18,0,3,0,Single,14,675.2355254,0,33516.77627
103733,18,0,2,0,Single,11,675.2350349,0,33524.75175
185198,18,1,3,1,Single,11,675.1680806,0,33322.40403
139513,58,40,2,1,Married,0,675.052376,0,33182.6188
129989,38,17,3,1,Single,0,674.9152905,0,33532.76453
102116,33,13,2,1,Single,4,674.8249536,0,33379.24768
114484,16,0,2,1,Single,8,674.7125191,0,33366.62596
119734,38,18,3,0,Single,1,674.6962598,0,33261.81299
169401,19,1,2,1,Single,12,674.6170251,0,33256.85126
185267,41,22,2,0,Single,1,674.064058,0,33420.2029
169516,16,0,4,0,Married,7,673.8552929,0,33491.76465
154675,24,7,2,0,Married,11,673.7799104,0,33378.99552
179326,60,43,2,1,Single,1,673.7004525,0,33135.02263
127064,23,6,2,0,Married,4,673.6614881,0,33235.07441
133176,24,5,3,1,Single,15,673.6116548,0,33255.58274
156319,21,3,2,0,Single,13,673.5593531,0,33121.96766
187794,23,6,2,0,Married,14,673.3593171,0,33345.96586
188800,43,23,1,0,Single,4,673.3541527,0,33358.70764
132277,17,0,1,1,Single,5,673.3517116,0,33241.58558
165502,16,0,1,0,Single,15,673.1939661,0,33179.69831
141656,20,2,3,0,Single,15,673.1513223,0,33214.56612
197436,25,7,3,0,Married,6,673.1208313,0,33136.04157
165520,20,2,1,1,Single,12,672.9064735,0,33266.32368
121973,54,34,4,1,Married,4,672.8481974,0,33352.40987
138021,56,36,3,1,Single,3,672.7048885,0,33400.24443
159118,20,2,2,0,Single,10,672.6785805,0,33329.92903
152728,21,4,3,0,Married,9,672.4982295,0,33253.91148
167797,25,8,2,0,Single,15,672.4941619,0,33040.7081
104438,21,2,1,0,Single,14,672.0778797,0,33135.89399
190370,38,20,2,0,Married,3,671.8734738,0,33198.67369
140425,38,19,2,1,Single,3,671.8160061,0,33329.80031
105932,45,26,2,0,Married,5,671.7997427,0,33036.98714
106336,63,43,2,1,Single,5,671.7035307,0,33103.17654
196720,23,5,3,1,Single,15,671.6230195,0,33040.15098
156987,20,1,3,0,Single,11,671.5744658,0,33265.72329
153354,25,8,1,1,Single,12,671.5230097,0,33245.15049
133512,18,0,3,1,Single,7,671.1728517,0,33122.64259
171346,17,0,2,1,Single,5,671.1452613,0,33159.26307
173062,48,29,3,0,Married,5,670.9693545,0,32993.46773
101564,31,12,1,0,Married,4,670.8921754,0,33165.60877
156849,53,32,3,1,Married,1,670.6590707,0,33010.95354
192582,49,29,1,1,Married,0,670.4684599,0,33092.423
179338,51,34,2,1,Single,0,670.3400985,0,33228.00493
114959,27,10,2,0,Single,0,669.8577239,0,33011.8862
136983,16,0,3,0,Married,14,669.7758392,0,33250.79196
121141,23,5,2,0,Single,15,669.5400051,0,33144.00026
171120,16,0,4,1,Single,15,669.4806249,0,33054.03125
140259,23,6,2,0,Married,9,669.3993572,0,33171.96786
128752,44,27,3,0,Single,3,669.1662155,0,33075.31078
136422,68,49,4,0,Married,2,669.1036894,0,33111.18447
136254,51,32,3,0,Married,4,669.021422,0,33237.0711
113159,22,5,1,1,Single,12,668.9781664,0,33122.90832
103144,58,37,1,0,Married,1,668.946836,0,32872.3418
139445,28,9,1,0,Married,2,668.780303,0,32860.01515
150931,16,0,2,0,Married,14,668.7291417,0,33023.45709
190315,18,0,2,0,Married,8,668.6205091,0,33104.02546
117848,43,25,3,0,Single,2,668.5719867,0,32882.59934
147395,18,0,3,1,Single,8,668.5541986,0,33055.70993
197523,18,0,2,0,Married,14,668.3636007,0,33015.18004
190606,38,19,4,0,Single,1,668.2032758,0,32890.16379
122437,44,25,3,0,Married,5,667.6572673,0,33049.86337
175311,25,8,2,0,Married,3,667.6514268,0,32963.57134
130756,62,42,3,0,Single,0,667.35053,0,32814.5265
190863,51,33,2,0,Single,4,667.3091222,0,32815.45611
124820,40,23,2,1,Single,2,666.9432518,0,32895.16259
122418,26,8,1,1,Single,3,666.6766088,0,32907.83044
143896,37,19,2,0,Single,5,666.5898635,0,32853.49318
144700,19,2,2,0,Single,15,666.5657916,0,32794.28958
134205,30,11,4,1,Single,5,666.4330934,0,32909.65467
103856,43,22,3,0,Single,1,666.3692559,0,32771.4628
140800,52,31,4,1,Single,4,666.0408773,0,33046.04387
188649,24,6,1,0,Married,15,665.9750545,0,32994.75273
149545,29,9,1,0,Single,0,665.9491438,0,32982.45719
160308,44,24,1,0,Married,5,665.8883025,0,33018.41513
156680,16,0,3,1,Single,14,665.824362,0,32890.2181
175578,28,8,2,1,Single,3,665.8071197,0,32943.35599
108484,34,16,2,0,Single,4,665.6719734,0,33005.59867
115371,45,27,3,1,Single,5,665.6410649,0,32744.05325
196619,37,16,2,1,Single,1,665.5563364,0,32711.81682
169022,54,36,3,1,Single,2,665.5022843,0,32714.11422
153908,17,0,1,0,Single,12,665.4768153,0,32914.84077
144251,18,0,2,0,Married,13,665.3737691,0,32698.68846
128033,63,43,2,1,Single,0,665.2165102,0,33018.82551
173775,37,16,1,0,Married,5,665.0776527,0,32908.88264
184774,51,34,2,0,Single,1,664.7626221,0,33024.13111
131066,19,1,2,0,Married,8,664.6908227,0,32914.54114
174568,58,38,3,0,Married,1,664.6459222,0,32855.29611
117900,22,5,1,0,Married,7,664.5934678,0,32647.67339
196300,16,0,2,0,Married,15,664.5913205,0,32875.56603
155029,30,12,4,0,Married,1,664.5255283,0,32738.27642
103049,19,1,2,0,Single,11,664.5184463,0,33009.92232
109415,28,9,3,0,Married,4,664.2565449,0,32940.82725
122434,25,8,3,0,Single,12,664.2423112,0,32702.11556
114318,57,36,2,0,Married,5,664.237318,0,32800.8659
152950,23,5,2,1,Single,8,664.1069867,0,33005.34934
145597,31,11,1,0,Married,5,664.0535613,0,32730.67807
132714,45,24,4,0,Single,4,663.9290693,0,32738.45347
156637,24,7,2,1,Single,8,663.9160877,0,32993.80439
148302,17,0,2,0,Married,0,663.8869849,0,32805.34925
126743,57,40,2,0,Single,3,663.8865517,0,32987.32759
167466,22,3,2,0,Single,13,663.7236283,0,32872.18141
143175,24,5,4,1,Single,6,663.6265959,0,32587.3298
125037,16,0,1,0,Married,5,663.3734231,0,32607.67116
171764,16,0,2,0,Single,12,663.3554871,0,32853.77436
119469,22,4,2,1,Married,4,663.3347343,0,32885.73672
167718,36,16,2,0,Single,4,663.29509,0,32648.7545
194713,32,14,3,1,Single,4,663.1071537,0,32760.35769
179975,23,4,3,0,Single,13,663.0013146,0,32907.06573
110841,50,33,2,0,Married,3,662.9874221,0,32680.37111
152336,58,37,2,1,Single,0,662.9831695,0,32884.15848
149661,23,5,3,0,Single,15,662.93972,0,32638.986
160063,52,33,2,1,Married,1,662.8956138,0,32647.78069
179559,17,0,2,0,Single,11,662.7873582,0,32709.36791
155435,27,6,2,0,Married,5,662.5729489,0,32539.64745
146862,37,16,2,1,Single,1,662.5605733,0,32842.02867
131138,49,30,3,0,Married,4,662.3350856,0,32892.75428
164911,19,2,3,1,Single,10,662.3114573,0,32715.57287
169435,21,4,3,0,Single,14,662.1250573,0,32549.25287
126150,29,9,3,1,Single,3,661.6758331,0,32791.79166
190503,37,19,2,1,Married,3,661.6002501,0,32714.01251
121897,55,37,3,0,Married,5,661.336893,0,32684.84465
118151,21,4,3,1,Single,15,661.3159383,0,32699.79692
162333,33,12,1,0,Married,0,661.1792137,0,32491.96069
146561,20,3,2,0,Single,11,661.1184216,0,32597.92108
141276,25,8,3,0,Married,6,660.7046752,0,32754.23376
171546,53,33,4,0,Married,2,660.4946263,0,32659.73132
142717,31,14,3,1,Single,1,660.2775882,0,32548.87941
196111,19,1,1,0,Single,11,660.0792756,0,32629.96378
113208,29,10,2,1,Married,2,660.0459781,0,32403.29891
106502,39,20,1,0,Married,4,659.9782567,0,32685.91284
162455,19,1,2,0,Single,14,659.8311101,0,32409.55551
187621,24,7,2,1,Married,4,659.7952018,0,32424.76009
115898,17,0,3,0,Married,9,659.7949775,0,32449.74888
105762,68,51,3,1,Single,2,659.7527272,0,32614.63636
159334,19,0,2,0,Single,11,659.6831203,0,32608.15602
116331,21,2,2,0,Married,5,659.6655382,0,32718.27691
113416,25,6,2,1,Single,7,659.5723022,0,32720.61511
103140,24,7,3,0,Married,3,659.249757,0,32397.48785
110968,17,0,1,1,Single,0,659.2477512,0,32601.38756
185133,20,3,2,1,Single,12,658.9425536,0,32375.12768
130761,56,36,4,0,Single,1,658.5876308,0,32537.38154
100804,19,0,3,0,Single,10,658.4688237,0,32336.44119
188865,22,4,4,0,Single,14,658.1316668,0,32392.58334
188331,23,4,3,1,Single,11,658.0923631,0,32557.61816
159360,56,37,3,1,Married,1,658.0898718,0,32632.49359
149871,67,46,1,1,Single,3,658.0763977,0,32405.81989
182552,33,14,3,0,Married,4,657.8315098,0,32628.57549
158364,38,20,3,0,Married,4,657.7115524,0,32341.57762
196591,24,6,2,1,Single,11,657.7052993,0,32367.26497
171377,33,13,4,0,Married,3,657.4021385,0,32277.10693
157733,56,37,4,0,Single,3,657.3356664,0,32662.78332
115844,16,0,3,0,Single,15,657.2997719,0,32636.9886
155307,62,43,3,1,Single,4,657.0786444,0,32631.93222
100484,57,38,3,0,Married,2,657.0095861,0,32435.47931
176075,23,4,4,0,Married,10,656.9649014,0,32443.24507
100969,18,0,2,0,Single,14,656.9475684,0,32575.37842
112299,48,28,2,0,Single,3,656.8840275,0,32644.20138
100618,42,24,3,1,Married,4,656.7647761,0,32530.23881
133232,59,39,2,1,Single,5,656.7219971,0,32339.09986
154022,45,24,3,1,Single,1,656.6655102,0,32554.27551
127347,18,1,4,0,Single,15,656.5910916,0,32461.55458
142903,35,16,3,1,Single,5,656.4117222,0,32423.58611
127635,25,6,4,0,Married,10,656.2419141,0,32408.09571
109613,69,49,2,0,Single,3,656.1977202,0,32466.88601
106186,21,2,1,1,Single,13,656.1548087,0,32460.74044
100739,28,10,3,0,Married,3,656.1247051,0,32595.23526
173544,21,4,4,0,Single,12,655.9999696,0,32426.99848
179788,39,18,3,0,Single,5,655.9321551,0,32462.60776
102929,27,6,2,0,Married,3,655.695717,0,32239.78585
138339,17,0,2,1,Single,11,655.613048,0,32293.6524
135303,29,10,3,0,Married,3,655.4816786,0,32302.08393
142628,50,29,4,1,Married,5,655.3008475,0,32562.04238
109324,32,15,3,0,Married,3,655.2306971,0,32229.53486
122265,20,2,1,0,Married,14,655.2242872,0,32319.21436
125915,25,8,2,1,Single,9,655.1555498,0,32402.77749
133656,22,5,1,0,Single,14,655.1327292,0,32408.63646
186555,69,50,4,1,Married,3,655.0474775,0,32423.37388
187173,64,46,2,0,Single,2,655.0243856,0,32387.21928
162128,29,9,2,1,Single,0,655.0052795,0,32344.26398
120966,26,6,3,0,Married,2,654.5872932,0,32248.36466
188579,44,24,2,1,Single,1,654.484792,0,32414.2396
162623,22,3,1,0,Single,15,654.294492,0,32416.7246
162861,19,0,4,1,Single,12,654.2606407,0,32383.03204
106966,24,7,2,1,Married,2,654.1892676,0,32287.46338
188913,22,5,1,0,Single,8,653.8661986,0,32193.30993
190408,49,29,4,0,Married,0,653.7769381,0,32446.84691
169412,28,7,2,0,Single,4,653.7119739,0,32409.5987
187837,19,1,1,0,Single,7,653.382622,0,32457.1311
144855,36,15,3,0,Single,1,653.2984319,0,32241.9216
104045,23,6,2,1,Married,1,653.2742442,0,32173.71221
191846,49,32,2,1,Single,1,653.224712,0,32156.2356
176657,36,16,4,1,Single,1,652.9423246,0,32336.11623
134067,30,12,3,1,Single,2,652.7468437,0,32362.34219
180064,66,48,2,1,Single,0,652.6104325,0,32228.52163
134517,41,23,2,1,Single,1,652.4612639,0,32044.0632
189545,50,33,2,1,Single,0,652.433378,0,32226.6689
197814,32,11,3,1,Single,5,652.4245763,0,32242.22882
162640,29,8,2,0,Married,0,652.1225397,0,32254.12699
100862,59,40,2,0,Married,2,652.1079977,0,32202.39989
177405,50,32,3,0,Married,1,651.9154879,0,32371.7744
198629,23,5,1,0,Married,8,651.9069317,0,32230.34659
126830,66,49,2,0,Married,3,651.8805216,0,32221.02608
175060,16,0,3,0,Single,12,651.8411728,0,32039.05864
141258,40,23,1,0,Single,3,651.4634054,0,32332.17027
127084,23,4,2,0,Married,2,651.4601572,0,32154.00786
105641,16,0,2,0,Married,13,651.4406586,0,32286.03293
122543,20,3,1,0,Single,14,651.4197408,0,32293.98704
125268,38,18,2,0,Single,3,651.1895254,0,32347.47627
134118,21,2,2,1,Single,11,651.1858695,0,32174.29348
113376,55,37,3,0,Married,0,651.1314961,0,31998.57481
180198,24,6,3,0,Married,6,651.1247737,0,32078.23869
102119,24,5,3,0,Married,8,650.6772051,0,32141.86026
143763,58,37,3,1,Married,3,650.5273182,0,32146.36591
193876,42,23,4,1,Single,4,650.1712786,0,32253.56393
163734,49,29,3,1,Single,2,649.7087321,0,32134.43661
156037,31,12,2,0,Married,1,649.6232377,0,32262.16189
198585,29,8,3,1,Single,4,649.4023528,0,31883.11764
157788,61,40,3,0,Single,0,649.0457704,0,32195.28852
155174,23,6,1,1,Single,8,648.6773297,0,31989.86649
196438,35,17,3,1,Married,3,648.638863,0,31948.94315
157512,18,0,3,1,Single,12,648.5689889,0,31932.44945
184545,63,45,2,1,Married,2,648.5628976,0,32065.14488
185498,24,7,1,0,Single,14,648.4970615,0,32202.85308
184700,38,20,1,0,Married,0,648.4727226,0,32026.63613
102952,51,30,1,1,Single,0,648.3377527,0,32149.88764
110149,36,17,3,1,Single,5,648.2724683,0,31844.62342
154359,35,17,3,1,Single,1,648.2237088,0,32079.18544
116478,22,5,2,0,Single,8,648.0421491,0,32011.10746
125978,20,3,2,0,Married,11,648.0153159,0,32192.7658
133054,18,0,2,0,Married,9,647.851471,0,31846.57355
192887,25,6,1,1,Single,6,647.6694226,0,31915.47113
162448,25,7,3,0,Single,12,647.6636606,0,32021.18303
186744,21,3,2,1,Single,7,647.3982171,0,31984.91086
171129,41,23,3,0,Married,5,647.3515386,0,31879.57693
134255,23,6,3,0,Single,7,647.1741685,0,32055.70843
104893,26,9,3,0,Single,4,647.088197,0,31882.40985
128587,16,0,3,1,Single,14,647.0847879,0,32136.2394
119401,19,0,4,0,Single,15,646.9458306,0,32127.29153
121461,17,0,2,0,Single,15,646.9235949,0,32114.17975
118268,45,27,3,0,Single,3,646.833763,0,32049.68815
109456,47,26,4,1,Married,3,646.6960826,0,32005.80413
177009,27,6,1,0,Married,3,646.6608555,0,31827.04278
182000,39,20,2,1,Married,2,646.6507561,0,32108.53781
157223,23,6,3,0,Single,14,646.6307467,0,32070.53734
150615,24,5,2,0,Single,14,646.3478607,0,32059.39304
129573,23,5,2,0,Married,7,645.5930282,0,31802.65141
111544,23,6,2,1,Married,5,645.4350689,0,31737.75345
130569,21,2,4,0,Married,9,645.388918,0,31832.4459
180072,62,42,2,0,Single,1,645.3861965,0,31670.30983
195854,16,0,1,1,Single,4,645.3645357,0,31835.22679
156206,27,9,2,1,Married,2,645.2374919,0,31676.8746
124872,52,31,1,1,Married,2,645.0260481,0,31851.30241
142664,16,0,2,0,Married,6,644.9014959,0,32029.0748
197293,48,29,2,1,Married,2,644.8848643,0,31789.24322
160748,39,19,2,0,Single,1,644.7900113,0,32033.50057
109646,41,20,1,0,Single,0,644.7784085,0,31642.92043
132596,55,35,1,0,Married,1,644.72313,0,31748.1565
199532,16,0,2,0,Single,11,644.6991757,0,31854.95879
143527,35,14,1,1,Single,0,644.6010832,0,31651.05416
126042,20,1,1,0,Single,12,644.5098594,0,32018.49297
126736,20,3,4,1,Single,14,644.3614642,0,31676.07321
192260,20,1,3,1,Single,14,644.2999437,0,31946.99719
122640,40,22,2,0,Single,0,644.0822542,0,31850.11271
149066,18,0,2,0,Single,14,643.8830466,0,31703.15233
132962,26,9,2,0,Single,4,643.8323624,0,31615.61812
160722,19,0,2,1,Single,5,643.7632811,0,31805.16406
139945,23,4,1,0,Married,6,643.5273912,0,31963.36956
173353,51,32,2,1,Single,0,643.4801329,0,31715.00665
105293,49,28,3,1,Married,5,643.3409511,0,31567.04756
188467,38,20,4,1,Married,5,643.3343761,0,31702.71881
181560,18,0,2,0,Single,8,643.2453717,0,31850.26859
185088,65,48,3,0,Married,3,643.1651214,0,31909.25607
163995,45,25,2,1,Married,1,643.1581009,0,31655.90505
161457,17,0,2,0,Married,13,643.1122901,0,31934.61451
168636,25,6,2,0,Married,6,642.8873908,0,31831.36954
142565,46,27,3,0,Married,0,642.879242,0,31898.9621
178651,19,2,3,0,Single,11,642.8462797,0,31849.31399
167850,18,0,2,1,Single,8,642.8128637,0,31807.64319
157484,54,33,4,1,Single,2,642.790732,0,31927.5366
198552,17,0,2,0,Single,12,642.7209895,0,31548.04948
196221,24,6,2,1,Single,6,642.6161195,0,31533.80598
185804,49,29,3,0,Single,5,642.3640407,0,31880.20204
176111,29,9,1,1,Married,3,642.2300098,0,31890.50049
112869,19,0,3,0,Married,14,642.1196909,0,31648.98455
165268,24,7,4,0,Single,13,642.0626915,0,31868.13458
139411,20,1,3,0,Married,5,642.0558506,0,31602.79253
132221,68,48,2,0,Married,3,642.0489852,0,31868.44926
129476,51,30,3,0,Married,0,641.8876964,0,31891.38482
184119,59,40,2,0,Single,1,641.8233901,0,31759.16951
137967,23,6,3,0,Single,9,641.8082752,0,31730.41376
118850,23,6,1,1,Single,3,641.6075399,0,31676.377
180908,16,0,3,0,Single,13,641.3946703,0,31599.73352
146001,42,24,1,1,Single,3,641.3910282,0,31598.55141
192764,30,13,4,1,Single,4,641.0927553,0,31503.63777
185196,25,8,3,0,Married,6,641.0568299,0,31738.8415
104826,44,27,3,1,Single,3,641.0551113,0,31534.75556
152158,33,13,3,0,Married,2,641.0392555,0,31537.96278
147621,35,18,3,0,Married,1,640.8854143,0,31697.27072
166102,21,3,4,0,Married,10,640.8558315,0,31463.79158
170631,42,23,2,0,Married,1,640.791076,0,31488.5538
188937,40,20,1,1,Single,3,640.738802,0,31507.9401
156254,36,17,2,0,Married,1,640.6990473,0,31477.95237
130378,40,23,4,0,Married,4,640.6807811,0,31595.03906
154693,20,1,2,0,Single,14,640.6504454,0,31782.52227
198411,70,52,3,0,Married,2,640.5796391,0,31671.98196
190032,16,0,1,1,Single,11,640.2131123,0,31785.65562
163464,37,18,4,0,Single,2,639.9843022,0,31656.21511
138028,21,4,2,0,Single,9,639.9576405,0,31661.88203
192946,26,9,4,0,Single,4,639.8119066,0,31553.59533
187735,44,23,3,0,Married,1,639.7393753,0,31522.96877
134967,31,12,3,1,Single,1,639.7120078,0,31564.60039
196944,38,20,3,1,Married,2,639.6023552,0,31557.11776
192254,21,3,2,1,Single,12,639.4551566,0,31655.75783
127602,16,0,2,1,Single,5,639.3834022,0,31725.17011
174163,26,7,2,1,Single,0,639.34696,0,31543.348
114006,16,0,3,0,Married,15,639.3004263,0,31408.02132
125842,70,49,1,0,Single,2,639.261733,0,31744.08665
196746,26,5,3,0,Married,3,639.0417596,0,31693.08798
136526,41,24,2,1,Single,2,638.9886613,0,31622.43307
112013,17,0,2,0,Married,13,638.9174617,0,31472.87309
105522,63,42,3,1,Single,0,638.9009768,0,31386.04884
143952,18,0,3,0,Married,8,638.7920458,0,31346.60229
141263,23,6,2,0,Married,8,638.5858885,0,31410.29443
122103,48,29,3,0,Single,3,638.5384535,0,31388.92268
116224,28,8,3,1,Married,1,638.4408901,0,31662.04451
137938,24,7,1,0,Single,14,638.3784062,0,31477.92031
192531,25,8,2,0,Single,14,638.3443766,0,31541.21883
129136,21,3,2,0,Single,15,638.2598637,0,31705.99319
188287,39,21,4,1,Single,4,638.2435083,0,31671.17542
167455,34,17,3,0,Single,4,638.1785514,0,31311.92757
177551,18,0,3,0,Single,13,638.0311359,0,31627.5568
188658,25,7,2,1,Single,7,638.02869,0,31622.4345
100438,23,4,3,0,Single,14,637.9966337,0,31562.83169
143194,24,5,1,0,Married,12,637.961713,0,31443.08565
186610,42,25,2,1,Married,1,637.8821118,0,31336.10559
131688,22,5,3,0,Married,6,637.7350752,0,31311.75376
189559,19,2,3,0,Single,11,637.5363636,0,31478.81818
153925,17,0,4,1,Single,14,637.2844851,0,31537.22426
189654,31,14,2,1,Married,5,637.2550518,0,31338.75259
183622,54,35,4,1,Single,2,637.2057499,0,31478.2875
151745,25,8,3,1,Single,15,637.1825197,0,31369.12599
157722,59,38,4,1,Single,1,637.1596088,0,31446.98044
147785,16,0,2,1,Single,11,637.006292,0,31599.3146
128152,69,48,4,0,Married,3,636.9997516,0,31272.98758
141395,19,0,3,1,Single,5,636.9476958,0,31433.38479
131837,26,6,1,0,Single,4,636.9060704,0,31319.30352
198609,16,0,4,0,Single,13,636.8510539,0,31484.5527
187541,44,27,2,1,Married,3,636.8107252,0,31370.53626
100887,23,5,3,1,Married,3,636.7934777,0,31324.67389
189920,32,15,1,1,Single,5,636.6797227,0,31516.98614
111103,53,36,1,1,Single,4,636.6652396,0,31539.26198
104884,24,5,4,0,Single,11,636.6335813,0,31400.67907
144076,27,6,3,0,Married,1,636.5105846,0,31235.52923
198632,22,4,3,1,Single,8,636.4555646,0,31369.77823
140233,70,52,2,1,Single,5,636.1406818,0,31347.03409
107217,25,6,3,1,Married,4,636.0503488,0,31541.51744
118575,24,6,2,1,Single,11,636.0165641,0,31344.82821
183345,36,19,2,0,Single,2,635.7843899,0,31410.2195
183861,20,3,4,1,Single,8,635.7349774,0,31549.74887
170165,57,39,3,0,Married,1,635.6589864,0,31471.94932
192419,20,2,1,0,Single,12,635.5338229,0,31388.69115
116287,23,5,3,0,Single,9,635.3993034,0,31458.96517
109240,33,16,2,0,Single,1,635.3621555,0,31169.10778
140139,23,4,4,1,Single,8,635.0319306,0,31344.59653
189270,21,4,4,0,Married,9,634.9963839,0,31255.8192
136376,48,31,4,1,Married,0,634.839158,0,31196.9579
166848,16,0,3,1,Single,12,634.8251684,0,31303.25842
122509,62,45,3,0,Married,0,634.7543215,0,31235.71608
167013,61,42,3,0,Single,3,634.6854911,0,31240.27456
162186,51,31,2,0,Married,3,634.6816208,0,31488.08104
106351,60,40,2,0,Single,3,634.6227028,0,31297.13514
114714,59,40,2,1,Married,3,634.5363021,0,31446.81511
180243,24,5,2,0,Married,12,634.5101073,0,31295.50537
182135,29,8,3,1,Married,2,634.4004256,0,31489.02128
173377,43,25,1,0,Married,2,634.287001,0,31287.35005
165359,18,0,3,0,Married,8,634.1466141,0,31122.33071
116296,24,6,1,0,Married,0,633.9571627,0,31357.85814
100609,47,30,2,1,Married,2,633.9120347,0,31237.60174
133887,39,21,2,1,Married,3,633.8975472,0,31457.87736
115546,16,0,3,0,Married,10,633.6359986,0,31260.79993
133561,20,1,3,0,Single,15,633.366832,0,31408.3416
196068,40,22,2,0,Married,1,633.2255247,0,31425.27624
111560,25,7,3,0,Single,11,633.0663893,0,31404.31947
100767,35,16,2,0,Married,4,633.0470208,0,31320.35104
106584,16,0,2,0,Single,12,633.0287569,0,31440.43785
130165,38,20,1,0,Single,3,632.8459416,0,31309.29708
127154,46,26,3,1,Single,5,632.6400659,0,31110.0033
124766,17,0,2,0,Single,9,632.4444462,0,31028.22231
173764,21,3,2,0,Single,11,632.3787533,0,31132.93767
187247,23,6,2,0,Single,10,632.2749098,0,31036.74549
136016,19,2,2,0,Single,11,632.1200446,0,31022.00223
127394,53,33,3,0,Single,0,631.9767168,0,31008.83584
141683,23,5,4,0,Single,6,631.973488,0,31194.6744
124833,17,0,3,0,Single,11,631.9304981,0,31163.52491
170638,21,4,3,0,Married,1,631.8019488,0,31070.09744
156140,24,7,2,0,Single,12,631.7729612,0,31013.64806
170229,30,10,2,0,Married,0,631.7516049,0,31241.58025
124246,51,31,3,0,Single,5,631.7062428,0,31308.31214
197837,29,11,4,0,Single,0,631.5946979,0,31304.7349
153571,30,11,2,0,Single,0,631.5285866,0,31031.42933
106254,18,0,2,0,Married,12,631.3421248,0,31002.10624
142742,44,24,2,0,Married,2,631.3342652,0,31227.71326
182653,49,29,3,1,Married,3,631.3156236,0,31065.78118
134326,43,22,4,0,Single,3,631.3015297,0,31013.07649
112287,24,5,2,0,Single,13,631.206713,0,31008.33565
179247,16,0,2,0,Single,9,631.1747614,0,31298.73807
155991,21,2,4,0,Single,10,631.1508185,0,31042.54093
196818,23,6,2,0,Single,11,631.0976125,0,31353.88063
153078,26,5,4,0,Married,2,631.0840663,0,31256.20332
128956,52,33,3,0,Married,3,630.997314,0,31037.8657
158944,35,14,4,1,Single,4,630.8403634,0,31225.01817
133625,20,1,4,0,Married,6,630.7948845,0,31260.74423
195132,23,6,3,1,Single,13,630.7366806,0,31299.83403
156007,46,28,1,0,Married,4,630.5057856,0,31096.28928
164636,19,1,2,0,Married,11,630.4480275,0,31238.40138
100627,19,0,3,0,Married,13,630.4318843,0,31044.59422
118649,57,40,4,1,Single,4,630.3374905,0,30988.87453
162072,44,24,2,1,Single,3,630.3165834,0,31086.82917
160549,22,5,3,0,Single,9,630.2061658,0,31090.30829
131674,34,16,4,0,Married,4,630.0057954,0,30908.28977
116153,31,10,2,0,Single,5,629.8601463,0,31079.00732
101987,17,0,3,0,Single,11,629.7962025,0,31020.81013
149238,36,17,3,0,Single,2,629.5130907,0,31076.65454
135272,40,22,1,1,Single,2,629.444886,0,31259.2443
169084,58,40,2,0,Single,4,629.062243,0,31002.11215
177782,17,0,3,0,Single,14,628.8984778,0,31053.92389
145885,44,26,4,0,Single,4,628.8964673,0,31202.82337
116818,22,3,2,0,Single,14,628.6636366,0,30932.18183
199635,48,27,1,0,Married,5,628.6211982,0,30903.05991
164524,23,5,2,1,Single,10,628.5541623,0,31061.70812
172087,57,39,4,1,Married,1,628.4233997,0,30910.16999
116640,35,18,1,0,Married,5,628.2463584,0,30819.31792
138729,23,5,2,1,Single,13,628.1035152,0,31018.17576
124644,22,3,3,0,Single,13,627.9072058,0,30853.36029
166467,23,4,2,1,Single,14,627.7736206,0,31066.68103
102985,23,4,3,0,Married,13,627.699644,0,30961.9822
194325,21,3,4,1,Single,15,627.6955863,0,30828.77932
103801,27,10,3,0,Married,4,627.6512931,0,30810.56466
149436,44,23,2,0,Married,5,627.6014932,0,30931.07466
193790,16,0,4,0,Married,10,627.5152066,0,31098.76033
126653,54,35,4,0,Single,3,627.4470266,0,30946.35133
148998,21,3,2,1,Single,12,627.3027977,0,31020.13989
185444,41,21,2,1,Married,3,627.2721496,0,30772.60748
159498,23,4,2,0,Married,12,627.2246947,0,30834.23474
189220,54,36,2,1,Married,0,627.0833742,0,30760.16871
145069,17,0,4,1,Single,13,627.00749,0,30994.3745
130725,37,19,4,1,Single,4,626.9966239,0,30874.8312
176177,20,3,3,1,Single,14,626.9501344,0,30948.50672
103067,47,29,2,0,Married,1,626.8658744,0,30751.29372
163646,17,0,2,0,Married,12,626.8658074,0,31115.29037
171543,51,31,4,1,Married,5,626.778193,0,30978.90965
179967,70,51,3,1,Single,3,626.5491835,0,31029.45918
154432,68,48,3,1,Married,0,626.5094615,0,30877.47308
181017,22,5,2,1,Single,9,626.4513425,0,31048.56713
199813,18,0,2,1,Single,5,626.3859709,0,30918.29855
175527,32,13,1,0,Married,0,626.3597611,0,30732.98806
185185,49,28,3,1,Married,5,626.3339813,0,30842.69907
174301,38,17,3,1,Married,0,626.2477129,0,31033.38565
189794,26,5,4,0,Single,5,626.2195329,0,30720.97665
173905,30,12,3,0,Single,1,626.2191271,0,30887.95636
155538,58,41,2,0,Married,3,626.1478938,0,30913.39469
101096,18,1,2,0,Single,7,626.0806825,0,30778.03413
134731,32,12,4,0,Single,1,626.0486157,0,30959.43079
114899,68,51,3,0,Single,3,626.0416517,0,31021.08259
193943,52,33,1,0,Married,1,625.962084,0,31040.1042
113259,38,20,1,0,Married,3,625.919221,0,30900.96105
177539,17,0,1,0,Single,13,625.7153342,0,30723.76671
101673,65,48,2,0,Single,3,625.6710232,0,30786.55116
105991,17,0,2,1,Single,11,625.5870582,0,31003.35291
186499,20,1,1,1,Single,12,625.5405704,0,30681.02852
177382,17,0,2,0,Single,10,625.5144383,0,31030.72192
121587,55,35,4,0,Single,4,625.4247816,0,30896.23908
190143,47,27,2,0,Married,0,625.28844,0,30719.422
143559,57,40,1,0,Married,3,625.2493695,0,30838.46848
106213,24,6,3,0,Single,15,625.2450204,0,30788.25102
158938,59,42,2,1,Married,3,625.0589683,0,30672.94842
143877,37,18,3,1,Single,1,625.0330082,0,31021.65041
120106,25,7,3,0,Single,8,625.0268,0,30754.34
152480,16,0,3,1,Single,8,625.00839,0,30886.4195
117351,46,25,2,1,Married,5,624.8275414,0,30849.37707
125254,27,8,2,0,Married,3,624.6832837,0,30702.16419
158826,24,7,4,0,Single,11,624.6176756,0,30998.88378
137426,26,5,4,0,Single,5,624.4043917,0,30688.21959
147282,19,1,3,0,Married,7,624.1928257,0,30762.64129
122282,46,29,3,1,Married,3,624.1397563,0,30609.98782
165514,62,41,2,1,Single,1,624.0649187,0,30653.24594
174395,18,0,2,0,Single,15,623.9346676,0,30848.73338
171784,23,5,1,0,Married,7,623.8842385,0,30664.21193
188016,45,24,3,1,Single,0,623.8514651,0,30868.57326
120339,64,45,4,0,Single,2,623.8263682,0,30766.31841
186510,61,42,2,1,Married,3,623.7921552,0,30698.60776
189954,19,0,4,1,Single,9,623.740872,0,30949.0436
180832,35,15,1,0,Single,5,623.7337503,0,30983.68752
149929,48,28,2,1,Single,1,623.6019605,0,30678.09803
193990,49,32,2,1,Single,1,623.50911,0,30702.4555
129584,24,7,2,1,Single,14,623.1690893,0,30786.45447
108023,57,39,2,1,Married,4,623.0270061,0,30552.35031
196398,37,18,4,0,Married,0,622.9318655,0,30632.59328
177425,51,32,2,1,Single,1,622.8166758,0,30882.83379
163177,57,36,1,0,Married,1,622.801627,0,30577.08135
188660,21,3,3,0,Married,6,622.7737057,0,30700.68529
109586,19,0,2,0,Single,9,622.6425648,0,30680.12824
190277,19,1,2,0,Married,9,622.5991221,0,30846.95611
154684,44,25,3,1,Married,3,622.2847527,0,30627.23764
116001,20,2,3,1,Single,10,622.2253314,0,30640.26657
108415,22,5,3,1,Single,12,622.2000691,0,30787.00345
191111,22,5,4,0,Single,14,622.1645176,0,30588.22588
127036,16,0,1,1,Single,15,622.0789033,0,30534.94517
197161,20,1,2,0,Single,12,621.8172611,0,30577.86306
145307,66,47,3,0,Married,3,621.7868996,0,30529.34498
195906,29,10,2,1,Single,0,621.6363332,0,30738.81666
195553,69,51,2,1,Single,5,621.628598,0,30851.4299
115986,16,0,4,1,Single,9,621.2361814,0,30724.80907
184789,32,11,3,0,Married,5,620.9978398,0,30685.89199
148491,45,24,2,1,Married,3,620.9608287,0,30560.04144
100749,26,5,2,0,Single,3,620.9459254,0,30476.29627
106410,53,32,2,0,Married,1,620.8278988,0,30459.39494
170007,28,10,2,1,Married,5,620.6286596,0,30576.43298
190342,31,14,3,1,Single,5,620.6278901,0,30536.39451
154195,45,27,1,0,Married,4,620.4772924,0,30634.86462
150336,19,1,3,0,Single,14,620.410962,0,30451.5481
186642,45,25,3,0,Married,3,620.3858534,0,30521.29267
117579,24,7,4,0,Single,12,620.0075791,0,30482.37896
122612,17,0,3,0,Single,13,619.9914755,0,30651.57378
139817,21,4,2,0,Single,15,619.9056497,0,30790.28249
146398,40,23,4,0,Married,3,619.8452402,0,30745.26201
103329,17,0,4,1,Single,6,619.8070262,0,30571.35131
123096,52,35,1,1,Married,0,619.7939909,0,30553.69955
122060,37,20,2,0,Married,3,619.6989419,0,30718.9471
172196,25,6,1,0,Single,12,619.623702,0,30570.1851
192821,20,1,1,1,Single,10,619.4987572,0,30530.93786
116004,49,32,3,1,Single,3,619.4956953,0,30649.78477
189584,20,2,2,0,Single,13,619.4805931,0,30459.02966
117323,46,28,2,1,Married,2,619.4605895,0,30460.02948
197964,42,24,2,1,Married,0,619.4023233,0,30503.11617
124243,55,34,3,1,Single,2,619.389806,0,30662.4903
121456,21,3,1,0,Single,12,619.3364482,0,30576.82241
136781,19,1,2,0,Married,6,619.2836967,0,30372.18484
176702,42,23,2,1,Married,3,618.8939074,0,30688.69537
168487,17,0,4,0,Single,12,618.5573771,0,30528.86886
152794,30,10,4,1,Married,1,618.3818105,0,30364.09053
148067,19,1,3,0,Married,7,618.3688318,0,30583.44159
158813,43,25,3,1,Single,3,618.2242062,0,30495.21031
133942,69,49,4,0,Married,5,618.1234853,0,30410.17427
132161,24,6,3,0,Married,13,618.1098159,0,30417.4908
151674,23,4,3,1,Single,14,617.8746458,0,30347.73229
196341,26,6,3,0,Married,2,617.847154,0,30312.3577
166998,53,32,2,1,Married,5,617.772548,0,30455.6274
152308,54,35,3,1,Single,4,617.4427847,0,30650.13924
110969,16,0,4,0,Single,12,617.3432772,0,30419.16386
103254,35,18,3,0,Married,3,617.3324292,0,30345.62146
151167,18,0,2,0,Married,7,617.2558574,0,30504.79287
139039,55,38,1,0,Married,4,617.1669192,0,30274.34596
173245,38,20,3,1,Married,3,617.1545058,0,30466.72529
101627,31,12,2,1,Single,2,617.0991685,0,30511.95843
147247,44,25,4,0,Married,3,616.9569873,0,30484.84937
168212,23,4,3,1,Single,6,616.8932904,0,30470.66452
139639,27,10,3,0,Single,5,616.4575626,0,30528.87813
110581,58,37,2,1,Married,0,616.392507,0,30576.62535
122083,23,6,1,0,Married,15,616.3823065,0,30388.11533
195131,36,16,3,0,Married,0,616.2520273,0,30407.60137
104439,53,33,2,0,Married,5,616.0916256,0,30455.58128
122049,25,8,1,0,Single,11,616.045,0,30321.25
115630,32,13,1,0,Single,1,616.0345533,0,30221.72767
173358,44,23,3,0,Single,5,615.9580721,0,30360.90361
176932,57,39,2,1,Single,3,615.6461146,0,30530.30573
133354,22,5,2,0,Married,6,615.4151608,0,30319.75804
186952,23,4,1,0,Single,13,615.2990801,0,30539.95401
111509,45,27,3,0,Single,4,615.2764216,0,30353.82108
109940,16,0,2,0,Single,11,615.1075981,0,30435.37991
174649,24,7,1,1,Single,8,614.8820182,0,30299.10091
148835,21,2,2,1,Single,9,614.7846154,0,30538.23077
139524,20,2,3,1,Single,12,614.7809363,0,30404.04682
102046,25,8,2,0,Single,6,614.7198868,0,30142.99434
118706,36,15,4,0,Married,0,614.7065539,0,30221.3277
130623,21,3,3,1,Married,2,614.590703,0,30219.53515
175291,17,0,3,1,Single,11,614.3353233,0,30358.76617
185106,23,6,3,0,Married,8,614.285921,0,30241.29605
130894,61,41,3,1,Single,1,614.1358644,0,30135.79322
148884,39,20,3,1,Married,4,614.0226004,0,30118.13002
183710,56,36,4,1,Married,5,613.8447847,0,30163.23924
195044,25,8,2,0,Single,14,613.8435182,0,30412.17591
157189,19,1,3,0,Married,11,613.6257588,0,30435.28794
133975,51,31,2,1,Single,5,613.5126653,0,30241.63327
159681,46,27,2,0,Married,0,613.432478,0,30330.6239
143297,26,7,2,0,Single,3,613.3696547,0,30144.48274
134645,16,0,4,0,Married,11,613.3288072,0,30173.44036
184838,67,49,2,0,Single,3,613.2362078,0,30067.81039
191082,39,22,3,1,Married,4,613.1473579,0,30285.3679
141928,23,4,2,1,Single,13,613.0166806,0,30351.83403
173638,16,0,3,0,Single,14,612.9537999,0,30159.69
129555,57,40,2,1,Married,1,612.8607726,0,30053.03863
169737,27,7,4,1,Married,1,612.7830483,0,30093.15242
147705,23,5,3,0,Married,11,612.7802233,0,30212.01117
132475,38,19,3,0,Married,5,612.7651926,0,30124.25963
126054,16,0,3,1,Married,2,612.6985693,0,30285.92847
158515,24,5,4,0,Married,14,612.3938545,0,30404.69273
114999,59,41,2,1,Single,2,612.2546511,0,30024.73256
195298,55,37,2,1,Married,0,612.1433415,0,30114.16708
157932,58,39,3,0,Married,1,612.1191026,0,30358.95513
173790,31,11,2,1,Married,1,612.0679151,0,30046.39576
126786,48,28,4,1,Single,4,612.0164632,0,30364.82316
155862,43,25,1,1,Single,5,611.9370486,0,30330.85243
120967,51,32,3,1,Single,1,611.885022,0,30380.2511
147012,65,47,4,0,Single,1,611.5439852,0,29999.19926
178760,70,50,3,0,Single,5,611.5310237,0,30151.55119
192069,25,6,3,0,Married,6,611.5135423,0,30310.67712
126308,44,24,2,0,Married,3,611.3809676,0,29981.04838
142102,31,14,3,0,Married,3,611.2974017,0,30265.87009
148979,58,37,2,0,Single,0,611.2208659,0,29981.0433
191968,49,29,3,0,Single,1,611.2013614,0,30112.06807
154310,16,0,3,0,Single,12,611.1097232,0,30336.48616
170672,25,7,3,0,Single,11,610.8998125,0,30010.99063
154026,19,2,2,0,Married,10,610.6187313,0,30187.93657
178782,16,0,1,1,Single,12,610.5008673,0,30321.04337
176052,21,3,4,1,Single,10,610.4427025,0,29930.13513
158673,25,6,3,0,Married,4,610.3816365,0,30207.08183
119034,16,0,3,0,Married,10,610.3039207,0,30250.19604
140782,32,15,4,1,Married,3,610.1817019,0,30272.0851
154115,25,6,1,0,Single,15,610.1699363,0,30292.49682
110723,29,12,2,0,Married,1,610.0814772,0,30250.07386
133053,41,24,1,1,Single,1,609.7857707,0,29931.28854
195792,20,2,3,1,Single,11,609.7680208,0,30253.40104
173688,17,0,3,1,Single,8,609.7247591,0,30235.23796
122545,19,1,4,0,Single,12,609.613509,0,30257.67545
159422,28,7,2,0,Single,2,609.4739476,0,30111.69738
130607,34,15,2,0,Single,1,609.4384353,0,29936.92177
188698,47,27,3,1,Married,3,609.2892848,0,30264.46424
174909,31,12,2,0,Single,5,609.1627076,0,30017.13538
145495,20,2,2,0,Single,11,608.6501064,0,30212.50532
187711,47,28,2,1,Married,0,608.6368975,0,29936.84488
100369,23,4,2,0,Married,11,608.2682405,0,30009.41203
135829,18,0,4,0,Married,14,608.2213981,0,29973.06991
117332,20,1,4,0,Single,12,607.965726,0,29995.2863
164817,40,22,4,1,Single,0,607.8027059,0,29899.1353
136134,23,4,2,1,Single,12,607.7779773,0,30056.89887
155755,25,8,1,1,Single,10,607.6422879,0,30005.1144
184049,33,14,2,1,Married,4,607.6109504,0,30112.54752
108463,48,28,3,0,Married,4,607.5414533,0,30028.07267
142131,53,35,3,1,Single,5,607.3659448,0,30008.29724
156809,48,29,2,1,Married,2,607.126919,0,29976.34595
143932,30,12,3,0,Married,0,607.0810357,0,29803.05179
177328,16,0,4,0,Single,13,606.8939997,0,29954.69999
111448,26,7,1,1,Married,5,606.3573767,0,30090.86884
173137,17,0,3,0,Married,14,606.1284445,0,29803.42223
154898,63,43,3,0,Single,0,606.0708998,0,29874.54499
113032,20,3,1,0,Single,15,606.0372524,0,29926.86262
119913,30,12,2,1,Single,4,605.9771739,0,29851.8587
164982,18,1,3,0,Married,7,605.9417217,0,29773.08609
165095,36,16,4,1,Married,2,605.7083676,0,29938.41838
181182,22,3,2,1,Single,6,605.641365,0,29906.06825
191335,22,4,4,0,Married,14,605.6072165,0,29898.36083
116862,55,37,3,0,Single,1,605.5670847,0,29825.35424
102395,39,19,4,0,Married,3,605.5566529,0,29957.83265
179482,38,17,3,0,Married,4,605.5402007,0,29854.01004
186428,21,3,1,1,Single,13,605.5328369,0,29858.64185
146119,23,4,3,0,Married,11,605.1992338,0,29879.96169
126561,22,5,2,0,Single,11,605.1604348,0,29776.02174
148896,31,11,2,0,Married,5,605.1251804,0,30018.25902
164683,49,29,2,0,Single,2,605.113279,0,29929.66395
100744,29,9,3,0,Married,2,605.035559,0,29962.77795
130233,68,48,3,0,Married,0,604.9970689,0,29838.85345
134768,28,10,3,1,Married,2,604.9855479,0,29756.2774
100579,20,3,2,0,Married,7,604.9244768,0,29762.22384
100285,21,2,3,1,Single,15,604.877098,0,29828.8549
190637,16,0,2,0,Married,12,604.7130011,0,29838.65006
182161,24,5,1,0,Married,15,604.5968924,0,29980.84462
116294,17,0,2,1,Single,13,604.4759269,0,30006.79635
196553,16,0,2,1,Single,14,604.4276573,0,29832.38287
115084,42,24,2,1,Married,2,604.4075127,0,29826.37564
146679,31,11,2,0,Married,5,604.2948518,0,29797.74259
107300,19,1,2,1,Single,14,604.2520723,0,29643.60362
123070,23,4,3,0,Single,13,604.2383648,0,29780.91824
107110,56,36,1,1,Married,2,603.7338515,0,29973.69258
166561,44,24,3,1,Married,0,603.6616696,0,29863.08348
149627,24,6,3,0,Married,1,603.4243667,0,29580.21834
159319,29,8,1,1,Single,2,603.1089156,0,29668.44578
113556,62,45,3,1,Single,0,603.1041914,0,29918.20957
128931,58,37,2,0,Single,4,603.0673537,0,29906.36769
163319,20,3,3,1,Married,3,603.0666933,0,29697.33467
192126,52,34,2,0,Single,2,603.0119453,0,29833.59727
185760,26,5,4,0,Married,2,603.0047702,0,29665.23851
167093,32,13,2,1,Married,0,602.9203731,0,29884.01866
158972,55,34,2,1,Married,4,602.9165094,0,29570.82547
108478,28,8,3,0,Married,2,602.8252592,0,29793.26296
178508,58,41,3,0,Single,2,602.7224416,0,29781.12208
132461,34,15,2,0,Single,1,602.7205126,0,29932.02563
188998,25,7,3,1,Single,0,602.7133477,0,29934.66739
144736,18,0,4,1,Single,11,602.6899319,0,29869.4966
131109,16,0,3,0,Married,15,602.4458911,0,29525.29456
132825,27,10,2,1,Married,1,602.4381233,0,29830.90617
120767,16,0,4,1,Single,13,602.4129398,0,29864.64699
190767,66,46,2,1,Single,3,602.3493797,0,29531.46899
166328,56,37,2,0,Single,1,602.3317886,0,29786.58943
169199,33,14,3,1,Single,1,602.261663,0,29560.08315
131294,25,7,1,0,Married,14,602.2169971,0,29597.84986
150868,25,8,1,1,Single,12,602.1457063,0,29829.28532
163605,40,21,1,1,Single,2,602.0505692,0,29608.52846
168056,48,30,1,1,Married,0,601.8255275,0,29875.27638
157859,58,40,3,1,Married,4,601.7748475,0,29664.74238
180101,36,19,3,0,Married,5,601.6753911,0,29636.76956
103611,23,5,1,1,Single,15,601.5725579,0,29488.6279
121056,64,43,3,0,Single,5,601.4215068,0,29661.07534
132110,30,9,1,1,Single,1,601.3926631,0,29722.63316
141202,30,10,2,1,Single,5,601.052766,0,29742.6383
147567,65,47,4,0,Married,4,601.0411263,0,29801.05632
149939,59,38,4,1,Married,1,600.9593922,0,29547.96961
191628,37,16,2,0,Single,2,600.8962859,0,29795.8143
162129,18,0,3,0,Single,15,600.8702076,0,29659.51038
118385,39,18,1,0,Married,2,600.7874896,0,29519.37448
140016,45,27,2,1,Single,4,600.7819651,0,29467.09826
101008,24,7,4,1,Single,1,600.6729391,0,29613.64696
164588,35,14,4,0,Married,1,600.5539835,0,29700.69918
198581,21,2,1,1,Single,12,600.5185664,0,29706.92832
179803,43,26,2,1,Married,5,600.5002692,0,29464.01346
159778,47,26,3,1,Married,1,600.4432552,0,29741.16276
125564,51,31,1,0,Single,4,600.436007,0,29429.80035
132075,43,23,2,1,Married,1,600.3944111,0,29534.72056
160432,22,5,3,0,Married,11,600.3886553,0,29809.43277
121389,57,40,3,0,Single,3,600.294342,0,29744.7171
121313,31,13,1,0,Single,5,600.2850832,0,29621.25416
144534,16,0,4,1,Single,15,600.0017263,0,29418.08632
165488,62,43,1,1,Married,2,599.9778705,0,29406.89353
171330,52,31,3,1,Married,5,599.9480653,0,29522.40327
160235,25,7,2,0,Married,12,599.941101,0,29607.05505
191349,23,5,2,0,Married,8,599.9407541,0,29570.03771
190878,17,0,2,1,Single,11,599.7468055,0,29503.34028
199990,24,6,2,1,Single,12,599.6876001,0,29497.38001
170950,52,31,4,0,Single,0,599.6689883,0,29721.44942
165241,23,5,3,0,Married,4,599.5931126,0,29573.65563
139938,52,35,3,0,Married,1,599.5536436,0,29525.68218
174078,18,1,2,1,Single,14,599.5107367,0,29724.53684
178948,52,35,2,1,Single,1,599.4664433,0,29414.32217
124768,32,11,3,1,Married,3,599.4292132,0,29607.46066
171326,23,6,2,1,Single,10,599.3552457,0,29762.76229
176110,66,47,3,0,Single,3,599.339974,0,29697.9987
176805,22,5,3,1,Married,3,598.9419665,0,29551.09833
100651,38,18,1,0,Married,2,598.9350854,0,29519.75427
135264,20,2,2,0,Single,11,598.8943012,0,29491.71506
131388,36,19,3,1,Married,3,598.7167497,0,29709.83749
100408,33,15,1,0,Married,0,598.6892757,0,29389.46379
109689,20,3,2,1,Single,12,598.6210114,0,29549.05057
167577,37,19,2,1,Married,3,598.5651052,0,29500.25526
147301,44,25,2,0,Single,0,598.4781604,0,29333.90802
139621,62,45,3,1,Married,2,598.3740899,0,29398.7045
183636,22,4,4,0,Married,6,598.1417857,0,29609.08929
138353,24,7,2,1,Single,15,598.090822,0,29318.5411
160037,31,12,2,0,Single,0,598.0787648,0,29502.93824
195242,28,9,3,1,Single,2,598.04402,0,29625.201
187834,41,20,1,1,Single,0,597.7945105,0,29650.72553
117230,45,25,2,1,Single,4,597.4670941,0,29365.35471
162445,25,6,2,1,Single,12,597.4473589,0,29363.36795
196090,25,6,1,0,Married,9,597.4422021,0,29471.11011
199116,20,1,3,0,Married,6,597.2813134,0,29412.06567
131959,48,30,3,0,Married,0,597.1726794,0,29559.63397
133035,31,11,2,1,Married,0,596.7270258,0,29452.35129
118174,21,3,2,0,Married,3,596.6727517,0,29270.63759
101453,57,38,1,1,Married,4,596.5321778,0,29495.60889
157141,18,0,3,1,Single,12,596.5048263,0,29523.24132
189475,46,27,2,1,Single,4,596.4954903,0,29502.77452
171190,27,7,3,1,Single,5,596.4746962,0,29439.73481
115111,23,6,4,1,Married,4,596.4062735,0,29319.31368
189155,22,4,3,0,Married,11,596.4039365,0,29497.19683
163555,46,27,1,0,Single,10,596.3227132,0,29348.13566
177949,29,11,2,0,Single,2,596.1340755,0,29583.70378
169450,49,32,4,1,Single,4,595.7744477,0,29380.72239
169410,27,7,2,0,Single,0,595.6818148,0,29499.09074
194224,39,22,3,1,Married,3,595.5328823,0,29569.64412
126286,50,29,4,0,Single,0,595.5204232,0,29419.02116
173455,24,6,3,1,Married,1,595.3860883,0,29272.30442
131126,20,1,3,0,Married,14,595.2857875,0,29468.28938
111131,35,17,2,1,Married,1,595.2500438,0,29376.50219
187384,21,4,3,0,Married,0,595.2186944,0,29166.93472
129152,18,0,4,0,Married,4,595.0163864,0,29361.81932
170087,44,25,2,0,Single,2,594.8041445,0,29516.20723
197705,20,2,3,1,Single,14,594.6565616,0,29341.82808
190839,22,3,3,1,Single,4,594.6229127,0,29497.14564
163987,19,0,2,1,Single,11,594.4983221,0,29489.91611
178103,28,7,3,0,Single,1,594.2272725,0,29355.36363
185742,16,0,1,1,Married,5,594.1389262,0,29266.94631
110688,53,36,3,1,Married,5,594.1275756,0,29497.37878
111460,37,20,1,1,Married,3,594.0556495,0,29148.78248
135692,21,4,2,0,Single,11,593.9904687,0,29244.52344
102839,19,1,3,0,Married,9,593.9311115,0,29324.55558
138982,66,46,4,1,Single,4,593.9165336,0,29423.82668
115587,52,31,3,1,Married,2,593.7741147,0,29336.70574
120162,58,40,2,0,Single,3,593.7613643,0,29338.06822
156416,29,9,3,0,Married,3,593.7606112,0,29280.03056
179034,24,7,4,1,Single,14,593.7551538,0,29169.75769
133169,19,2,3,1,Single,10,593.7213625,0,29371.06813
151017,35,14,3,0,Married,3,593.5300039,0,29097.5002
122164,67,49,1,0,Single,3,593.4274113,0,29313.37057
108782,46,28,2,1,Single,1,593.3446961,0,29233.23481
184394,23,6,2,1,Single,13,593.3395827,0,29446.97914
132547,40,23,1,0,Married,1,593.2666445,0,29366.33223
152164,20,3,2,1,Single,13,593.249595,0,29146.47975
153317,20,1,2,0,Single,11,593.2050205,0,29287.25103
159302,46,27,3,0,Married,4,593.0466566,0,29344.33283
159655,42,25,3,1,Married,0,592.6050524,0,29361.25262
195594,43,26,1,0,Married,2,592.2871812,0,29181.35906
197475,62,44,2,0,Single,4,592.2771478,0,29022.85739
185318,21,3,3,1,Single,11,592.2506615,0,29295.53308
123888,54,35,3,0,Single,3,592.1765216,0,29285.82608
189905,39,20,3,1,Single,4,592.1448618,0,29014.24309
196808,21,4,3,1,Single,12,591.9987049,0,29012.93525
100580,21,4,2,0,Married,11,591.9903556,0,29160.51778
127270,44,26,1,0,Married,5,591.9070066,0,29032.35033
179843,18,0,2,0,Single,12,591.6967258,0,29109.83629
153992,21,3,2,0,Single,10,591.6375591,0,29081.87796
111907,47,26,3,1,Single,4,591.6038049,0,29264.19025
153057,24,6,3,0,Single,15,591.5682794,0,29214.41397
123636,52,32,2,1,Married,3,591.5503428,0,29353.51714
163736,33,14,2,1,Married,2,591.5242872,0,29300.21436
104525,62,42,2,0,Single,3,591.4482438,0,29228.41219
162809,24,5,3,1,Single,11,591.4297387,0,29082.48694
136137,62,45,4,1,Single,5,591.4174326,0,29028.87163
113602,49,31,2,1,Single,0,591.3840974,0,29198.20487
166095,23,5,2,1,Single,11,591.3458088,0,29012.29044
191243,25,7,3,0,Married,15,591.3379236,0,29024.89618
141619,58,41,2,1,Single,2,591.3276195,0,28969.38098
192917,42,22,1,0,Single,0,591.3249195,0,28993.24598
126964,43,26,3,0,Married,1,591.0435319,0,29176.1766
144221,26,9,1,1,Married,4,590.936418,0,29054.8209
199468,34,17,1,1,Single,4,590.8513496,0,29181.56748
108588,16,0,1,1,Single,6,590.7871142,0,29132.35571
171774,16,0,3,1,Single,11,590.7475099,0,28960.3755
135938,18,0,2,0,Married,10,590.7316221,0,29210.58111
144446,51,30,1,1,Single,5,590.6407796,0,29215.03898
168965,21,2,3,1,Married,2,590.5546604,0,29040.73302
147105,48,30,2,1,Married,0,590.5028673,0,29043.14337
133327,51,32,1,1,Single,0,590.2667386,0,29132.33693
178294,18,0,3,0,Married,10,590.1640123,0,29276.20062
124091,34,15,3,0,Single,4,590.1613276,0,28961.06638
130163,49,31,3,0,Single,2,590.0447627,0,28913.23814
127793,20,1,3,0,Single,11,589.8996087,0,29127.98044
134192,60,39,2,0,Single,3,589.8219306,0,29212.09653
114355,16,0,3,0,Single,13,589.7118387,0,29156.59194
135290,19,2,2,1,Single,15,589.7096364,0,29066.48182
158439,39,20,4,1,Married,4,589.6208365,0,29200.04183
129931,17,0,2,0,Single,12,589.5833291,0,29046.16646
100956,36,18,1,0,Single,5,589.4040255,0,29262.20128
116831,17,0,4,1,Single,6,589.1773458,0,29209.86729
135284,20,3,3,0,Single,14,589.1073039,0,29205.3652
168122,37,19,1,1,Single,3,589.0525916,0,29092.62958
174112,23,5,2,1,Single,9,588.9731914,0,29036.65957
146829,24,5,2,1,Single,3,588.8563028,0,29086.81514
130414,23,4,3,1,Single,15,588.7309849,0,29134.54925
170632,20,1,3,1,Single,12,588.5465292,0,29177.32646
147082,42,23,2,1,Married,2,588.4206179,0,28853.0309
124947,69,51,1,0,Married,2,588.3241783,0,28868.20892
128874,38,18,1,0,Single,3,588.097128,0,29044.8564
103612,24,6,4,0,Married,14,588.0650147,0,28948.25074
179821,20,3,3,1,Married,1,587.9843954,0,29082.21977
164931,50,31,3,0,Married,1,587.8995553,0,29065.97777
137648,50,29,1,1,Married,4,587.8910956,0,28923.55478
154100,36,16,4,0,Married,4,587.8348869,0,28934.74435
145789,20,2,1,0,Married,13,587.7639519,0,28896.1976
149561,35,14,1,0,Married,0,587.7592971,0,28795.96486
181584,30,12,3,1,Single,4,587.6479685,0,29087.39843
161785,59,40,4,1,Married,0,587.6122574,0,29106.61287
114480,19,0,3,1,Single,15,587.5860617,0,28858.30309
180627,54,35,3,1,Married,5,587.5284398,0,29116.42199
188014,28,10,3,1,Single,5,587.3366262,0,29151.83131
147410,58,39,3,1,Single,4,587.1796237,0,29099.98119
157495,36,18,3,0,Single,4,587.1620594,0,29109.10297
186838,57,38,1,1,Married,2,587.121065,0,29026.05325
136678,17,0,2,1,Married,5,587.0938381,0,29019.69191
147654,22,5,3,1,Single,0,587.0766576,0,28803.83288
159287,56,38,3,1,Single,4,586.9474364,0,28837.37182
122759,18,0,2,0,Married,10,586.9042931,0,28812.21466
160588,49,31,3,0,Married,4,586.8401022,0,29046.00511
168461,21,3,1,0,Single,15,586.595506,0,29030.7753
179271,23,5,1,0,Single,14,586.5806227,0,28864.03114
181003,36,16,2,1,Single,1,586.5780957,0,28761.90479
160796,29,10,2,1,Single,2,586.5237976,0,29084.18988
197185,51,33,3,1,Single,0,586.5051397,0,29112.25699
175997,25,8,2,0,Married,12,586.4575226,0,28884.87613
151974,34,15,2,1,Single,2,586.3877207,0,28984.38604
162214,21,2,4,1,Single,11,586.2712475,0,28731.56238
167003,25,6,3,0,Married,7,586.1389841,0,28736.94921
159246,39,19,2,1,Single,5,585.9087075,0,28805.43538
158637,22,4,1,1,Single,12,585.7777296,0,28959.88648
124690,25,7,4,1,Married,3,585.6720152,0,28983.60076
156139,34,17,3,0,Single,2,585.611415,0,28739.57075
119973,17,0,2,0,Married,2,585.4125944,0,28736.62972
138629,21,3,3,1,Single,7,585.3022678,0,28782.11339
115502,41,21,2,1,Married,5,585.2146,0,28765.73
182091,20,2,4,1,Single,13,585.1896968,0,28793.48484
196053,25,7,4,1,Single,9,585.0701177,0,28952.50589
116227,40,23,3,1,Married,0,585.0351549,0,28934.75775
135242,18,0,3,0,Married,14,584.9928807,0,28831.64404
157795,24,6,3,1,Single,14,584.9279896,0,28794.39948
194063,17,0,3,0,Married,8,584.7817657,0,28657.08829
152934,64,45,2,0,Single,4,584.73356,0,28799.678
191601,52,31,4,0,Married,3,584.728272,0,28786.4136
157336,36,17,3,0,Married,5,584.6503363,0,29000.51682
137741,23,5,4,0,Married,6,584.6084829,0,28705.42415
168033,39,20,4,0,Married,2,584.5988272,0,28829.94136
135354,17,0,2,0,Married,7,584.5653071,0,28757.26536
137692,17,0,3,1,Single,11,584.5555815,0,28807.77908
145233,35,14,2,1,Single,0,584.5321559,0,28694.6078
199301,21,3,1,1,Single,15,584.4584795,0,28972.92398
108937,47,30,2,1,Married,5,584.4515855,0,28892.57928
199511,21,4,1,0,Married,15,584.209495,0,28813.47475
124822,16,0,3,0,Single,7,584.1028246,0,29002.14123
152972,25,6,1,0,Married,11,583.9925703,0,28871.62852
143993,37,19,3,0,Single,0,583.618097,0,28923.90485
131030,39,20,2,0,Married,5,583.6112958,0,28811.56479
152414,45,27,2,0,Married,0,583.5034299,0,28827.1715
111176,50,33,2,0,Married,3,583.4451347,0,28822.25674
161526,26,6,3,1,Single,1,583.1973893,0,28563.86947
114172,28,9,4,1,Single,3,583.1287926,0,28800.43963
168371,46,26,2,0,Single,3,582.9595775,0,28936.97888
111995,29,12,2,0,Single,4,582.957999,0,28799.89995
147477,53,32,2,0,Married,2,582.7640352,0,28903.20176
180222,38,21,3,1,Married,3,582.7183001,0,28843.91501
149317,21,4,3,0,Married,10,582.6908244,0,28791.54122
113284,39,22,3,0,Married,0,582.6297549,0,28821.48775
160491,24,6,3,0,Single,11,582.4509006,0,28832.54503
123481,20,3,3,1,Single,6,582.4014834,0,28859.07417
158890,33,12,3,1,Married,2,582.334854,0,28527.7427
112970,24,5,3,0,Married,15,582.2666052,0,28735.33026
123183,40,20,3,0,Married,3,582.2109746,0,28797.54873
143864,49,32,2,0,Married,3,582.1363851,0,28745.81926
130340,18,0,3,1,Single,11,582.1070541,0,28848.35271
135581,35,15,3,0,Single,2,582.0355859,0,28699.7793
179023,25,7,4,0,Married,6,581.7991773,0,28535.95887
152084,25,6,3,0,Married,10,581.550003,0,28562.50015
160843,45,24,3,0,Single,10,581.4841711,0,28814.20856
124324,67,48,2,1,Single,2,581.4427893,0,28503.13947
158864,48,31,1,1,Single,4,581.3363152,0,28560.81576
167778,58,40,3,0,Single,2,581.3015799,0,28710.079
174806,16,0,3,0,Married,7,581.2386757,0,28852.93379
121659,60,39,3,0,Married,1,581.1578264,0,28835.89132
170443,56,38,3,0,Single,1,581.1573171,0,28833.86586
147704,63,42,1,0,Married,0,581.0593991,0,28564.96996
190595,61,44,3,0,Single,5,580.6063261,0,28625.31631
147453,21,4,3,1,Single,11,580.5289052,0,28737.44526
159762,51,34,2,1,Single,1,580.5239334,0,28607.19667
194504,37,20,2,0,Married,2,580.4834146,0,28520.17073
134636,66,49,3,1,Single,4,580.4705323,0,28664.52662
176960,17,0,4,0,Single,13,580.3361641,0,28526.80821
124134,24,7,1,0,Married,8,580.2514514,0,28591.57257
168025,18,0,2,1,Single,14,580.2211714,0,28439.05857
119280,35,14,3,0,Single,6,580.1694084,0,28686.47042
155526,16,0,1,0,Single,13,580.1143576,0,28645.71788
139891,43,26,4,0,Single,1,580.0371126,0,28616.85563
184196,63,45,2,1,Single,4,579.7994154,0,28413.97077
121349,21,2,2,1,Single,14,579.6378969,0,28639.89485
155426,23,6,2,1,Single,13,579.545733,0,28522.28665
152325,55,36,2,1,Single,3,579.5288127,0,28412.44064
124931,58,38,4,0,Married,1,579.3588041,0,28636.94021
180886,50,29,2,1,Married,1,579.3465404,0,28652.32702
141828,21,2,3,0,Single,11,579.306829,0,28719.34145
195735,66,45,2,0,Single,3,579.2662045,0,28709.31023
107987,60,42,2,0,Single,3,579.1298354,0,28419.49177
152066,23,5,3,0,Married,7,579.042782,0,28743.1391
145782,36,19,4,1,Married,4,578.9294915,0,28602.47458
157869,70,51,3,0,Single,0,578.8589413,0,28658.94707
114765,22,3,2,0,Single,11,578.6284008,0,28338.42004
174995,20,3,2,0,Married,10,578.3413716,0,28687.06858
141610,53,35,2,0,Single,7,578.285181,0,28684.25905
122447,29,12,1,0,Single,8,578.251118,0,28354.5559
195155,25,6,3,0,Married,0,578.231489,0,28666.57445
188360,25,8,2,0,Single,13,578.1605117,0,28658.02559
145204,16,0,3,1,Single,15,577.8884849,0,28573.42425
193579,70,52,2,0,Single,2,577.8240207,0,28317.20104
132121,61,42,3,0,Married,2,577.6616085,0,28452.08043
114889,21,2,4,0,Single,15,577.3659621,0,28438.29811
171414,63,43,3,0,Married,1,577.3169418,0,28378.84709
101160,61,44,1,0,Single,0,577.1904324,0,28340.52162
197278,27,10,4,1,Single,0,576.8273667,0,28593.36834
200435,24,6,4,0,Single,13,576.8021942,0,28334.10971
121784,20,2,1,1,Single,6,576.7216887,0,28608.08444
105221,17,0,1,0,Married,15,576.58074,0,28618.037
114447,24,7,2,1,Single,15,576.5547032,0,28627.73516
140895,45,28,1,1,Single,1,576.543884,0,28281.1942
192060,48,30,1,0,Single,5,576.4028819,0,28330.1441
197497,63,46,2,0,Single,5,576.3439788,0,28587.19894
151257,23,5,2,0,Married,9,576.2774754,0,28378.87377
129904,38,18,1,1,Married,4,576.2550331,0,28432.75166
126080,46,29,1,0,Single,4,576.0714176,0,28600.57088
183903,23,4,4,1,Single,11,576.0591251,0,28506.95626
109670,34,17,1,1,Single,1,575.9114126,0,28258.57063
112760,23,6,4,0,Single,13,575.8389248,0,28437.94624
158104,32,13,2,0,Married,3,575.7807875,0,28481.03938
195420,65,48,3,0,Single,1,575.7109753,0,28229.54877
154972,43,25,1,0,Single,1,575.6892982,0,28390.46491
110502,18,0,4,0,Married,15,575.6616867,0,28543.08434
169037,19,1,3,1,Single,9,575.6223618,0,28401.11809
104074,45,24,3,1,Married,0,575.5410986,0,28553.05493
125583,25,6,1,0,Married,14,575.4669131,0,28219.34566
191109,36,16,3,1,Married,5,575.4100503,0,28529.50252
151532,25,6,4,0,Single,13,575.2285438,0,28490.42719
122151,22,5,3,1,Single,13,575.2028406,0,28224.14203
128142,42,22,2,0,Married,3,575.1646979,0,28217.2349
111513,56,37,2,1,Married,4,575.1516927,0,28394.58464
104417,16,0,2,0,Married,7,575.1301833,0,28438.50917
167395,41,23,4,1,Married,4,575.1008818,0,28384.04409
112597,17,0,4,0,Single,15,574.9427892,0,28466.13946
169080,16,0,2,1,Married,4,574.9202244,0,28374.01122
111217,51,34,4,0,Single,0,574.9018743,0,28295.09372
105726,22,4,3,0,Single,7,574.8410883,0,28252.05442
183596,48,27,1,0,Single,0,574.8216925,0,28152.08463
185207,40,21,2,0,Married,2,574.7860207,0,28276.30104
105644,60,40,4,0,Married,1,574.7578672,0,28151.89336
173538,22,5,2,0,Married,13,574.688578,0,28170.4289
129628,54,33,2,1,Single,4,574.6820178,0,28195.10089
118391,18,0,2,0,Married,11,574.4828221,0,28165.14111
145141,55,34,2,1,Married,1,574.4400041,0,28484.00021
167757,52,31,4,0,Married,1,574.4374634,0,28412.87317
126673,43,25,3,0,Married,3,574.399332,0,28503.9666
124209,52,32,2,0,Single,6,574.3451601,0,28415.25801
170180,31,10,3,1,Single,4,574.2567511,0,28137.83756
169743,47,27,4,0,Single,4,574.137997,0,28417.89985
156506,61,40,4,1,Single,4,574.0934294,0,28244.67147
176149,53,34,2,0,Single,4,573.9830238,0,28382.15119
130703,24,5,3,0,Single,15,573.8657603,0,28352.28802
142072,29,9,1,1,Married,2,573.7730434,0,28278.65217
155203,24,6,3,0,Married,12,573.7401335,0,28143.00668
167938,42,23,3,0,Married,2,573.6219236,0,28143.09618
131625,41,23,4,0,Single,0,573.5862881,0,28449.31441
124841,17,0,1,1,Single,12,573.5534203,0,28159.67102
137197,20,1,3,1,Single,14,573.5512437,0,28357.56219
165547,31,10,2,0,Married,2,573.4375511,0,28360.87756
176112,41,24,2,1,Single,0,573.3508955,0,28077.54478
100015,35,15,3,1,Married,2,573.3058219,0,28209.2911
159328,17,0,3,0,Single,15,573.3040013,0,28463.20007
172355,59,42,4,1,Married,5,573.2609017,0,28123.04509
180152,22,4,2,1,Single,6,573.186881,0,28180.34405
116568,18,0,2,0,Married,9,573.1399134,0,28329.99567
116821,47,26,3,1,Married,2,573.1388359,0,28403.9418
139619,18,1,2,1,Single,12,573.0557924,0,28391.78962
163830,40,22,2,1,Single,4,572.9965536,0,28222.82768
104232,25,7,4,0,Single,8,572.8837682,0,28205.18841
154332,56,35,2,0,Married,2,572.8256397,0,28287.28199
120707,22,4,1,1,Single,15,572.8165632,0,28424.82816
136771,24,7,4,0,Married,11,572.5681386,0,28406.40693
152506,24,7,2,1,Single,9,572.3978778,0,28227.89389
169643,32,14,2,1,Married,5,572.0640955,0,28144.20478
107434,18,0,2,0,Married,13,571.9483062,0,28105.41531
100210,56,35,1,1,Married,5,571.9363703,0,28088.81852
147620,48,27,2,1,Married,1,571.8643026,0,28136.21513
110807,16,0,1,0,Married,6,571.822653,0,28233.13265
176267,47,29,4,0,Married,5,571.7911611,0,28293.55806
123853,16,0,2,1,Single,12,571.7196568,0,28149.98284
168114,27,7,3,0,Married,3,571.5633043,0,28094.16522
190923,26,5,2,0,Married,5,571.5603593,0,28246.01797
174123,66,48,2,1,Single,5,571.5239264,0,28315.19632
122452,26,7,3,1,Married,3,571.4763718,0,28296.81859
106289,50,31,2,1,Single,3,571.0809936,0,28125.04968
121994,17,0,3,0,Single,15,571.0311774,0,28195.55887
108934,25,7,3,0,Single,13,571.0066707,0,27968.33354
138788,26,5,1,0,Single,10,570.8218264,0,28175.09132
125754,25,6,1,0,Single,15,570.7708186,0,28284.54093
116861,40,19,1,0,Married,5,570.7365692,0,28268.82846
100110,24,5,2,1,Single,13,570.6371,0,28023.855
197770,17,0,1,0,Married,10,570.6075147,0,28021.37574
150352,33,12,4,0,Married,0,570.5972744,0,28229.86372
190062,26,9,4,1,Married,2,570.5741831,0,28058.70916
132375,30,11,1,1,Single,1,570.5547459,0,28294.7373
104953,17,0,2,0,Married,8,570.4028307,0,28076.14154
120894,21,2,2,0,Single,15,570.4021388,0,28047.10694
128216,28,9,1,0,Married,0,570.3726954,0,28291.63477
173014,65,47,1,0,Married,2,570.1182067,0,27948.91034
130549,41,21,3,1,Married,1,570.0005691,0,28014.02846
161680,17,0,2,0,Married,8,569.9857577,0,27914.28789
133262,52,33,2,0,Single,3,569.9136846,0,27926.68423
174991,17,0,3,0,Married,11,569.8105747,0,28077.52874
116462,17,0,3,0,Single,12,569.526355,0,28087.31775
166153,25,8,3,1,Single,12,569.2702851,0,28090.51426
118865,25,8,3,1,Married,1,569.2346293,0,27924.73147
176523,32,13,3,1,Single,5,569.2331006,0,28218.65503
126266,22,4,1,1,Single,4,569.1210983,0,28092.05492
132342,65,47,2,1,Married,2,568.9578977,0,28141.89489
119426,16,0,3,1,Single,15,568.7389461,0,28224.94731
199542,59,41,2,0,Single,2,568.5054218,0,27906.27109
129852,31,12,3,0,Married,0,568.4514326,0,27835.57163
112104,19,2,1,1,Single,7,568.2705563,0,27962.52782
167381,47,27,2,1,Single,1,568.1048662,0,28172.24331
156936,33,14,3,1,Married,3,568.1035644,0,27846.17822
151393,21,4,1,1,Single,12,568.0651961,0,27884.25981
145849,64,47,3,1,Single,4,568.0166811,0,28165.83406
116039,28,9,3,1,Married,4,567.9869082,0,27900.34541
190605,19,1,3,0,Single,15,567.8291037,0,28085.45519
136626,24,5,1,1,Single,10,567.8207249,0,28142.03625
111288,19,0,2,0,Single,13,567.8116732,0,27870.58366
182712,51,33,2,0,Married,2,567.6471059,0,27875.3553
160147,39,19,3,1,Married,5,567.6042812,0,28013.21406
191739,21,2,1,0,Married,11,567.532752,0,27864.6376
143279,23,4,4,0,Married,12,567.2643283,0,27830.21642
137678,17,0,1,0,Single,12,567.2103512,0,27943.51756
116735,32,11,4,0,Married,5,567.0899565,0,27754.49783
115444,22,3,2,0,Married,10,567.0899153,0,28001.49577
178402,22,3,1,1,Single,14,567.0537545,0,27963.68773
156288,24,6,4,0,Married,13,567.0280937,0,27991.40469
188975,68,48,3,1,Single,4,567.0247191,0,27819.23596
109316,52,33,4,1,Married,0,567.0127054,0,27866.63527
162032,17,0,4,1,Single,7,566.9732079,0,28062.6604
142017,24,6,2,1,Single,13,566.748022,0,27959.4011
141785,45,25,2,1,Married,5,566.3801492,0,27744.00746
117161,26,9,4,1,Married,1,566.3665086,0,27816.32543
118220,57,38,3,1,Single,3,566.2735358,0,27904.67679
195858,51,30,1,0,Married,4,566.1994377,0,27848.97189
150419,40,19,2,0,Married,0,566.1969736,0,27978.84868
175618,56,38,2,0,Single,2,565.9103367,0,27812.51684
166963,39,22,3,1,Married,4,565.9091424,0,27882.45712
114755,63,44,3,0,Married,1,565.874036,0,27693.7018
170128,20,1,2,0,Married,14,565.7705528,0,27773.52764
156935,23,4,1,0,Single,11,565.75928,0,27991.964
145864,40,21,2,0,Married,0,565.7537055,0,28067.68528
157952,41,21,3,1,Married,2,565.606013,0,27752.30065
167819,17,0,2,0,Single,15,565.5446324,0,27835.23162
103229,32,11,4,0,Married,1,565.3811755,0,27738.05878
187590,25,6,1,1,Single,15,565.3485155,0,28010.42578
150749,58,39,2,1,Married,2,565.2358083,0,27684.79042
105578,26,7,4,1,Single,3,565.0505548,0,27771.52774
171865,21,4,1,0,Married,0,565.0137498,0,27930.68749
112053,18,1,2,0,Single,14,564.8968135,0,27936.84068
188861,37,19,2,1,Single,5,564.7421804,0,27980.10902
156261,21,3,1,1,Single,14,564.7148465,0,27771.74232
195724,21,2,3,0,Married,15,564.6980013,0,27667.90007
142513,24,6,2,0,Married,10,564.5605181,0,27767.02591
136482,23,6,2,0,Single,14,564.5416455,0,27638.08228
129764,56,37,3,0,Single,7,564.435626,0,27672.7813
115569,38,21,4,1,Married,3,564.4140951,0,27735.70476
189714,28,10,1,0,Single,5,564.3810046,0,28015.05023
141125,16,0,3,0,Single,11,564.343477,0,28011.17385
131239,16,0,2,0,Single,13,564.3328789,0,27842.64395
167489,47,28,3,0,Married,0,564.3003014,0,27761.01507
107574,43,26,2,0,Married,4,563.9837574,0,27616.18787
113759,25,8,3,1,Single,9,563.9122609,0,27864.61305
182710,18,0,1,0,Single,13,563.8058374,0,27757.29187
139651,17,0,1,0,Married,12,563.7051521,0,27755.25761
176348,24,5,2,1,Single,11,563.6946496,0,27869.73248
176079,57,37,4,0,Married,3,563.6273605,0,27684.36803
123065,19,2,2,1,Married,10,563.5223583,0,27639.11792
181986,56,35,4,1,Single,1,563.4264779,0,27822.3239
144530,48,28,2,0,Single,2,563.4226944,0,27872.13472
182642,26,7,1,0,Single,9,563.268251,0,27767.41255
139777,18,0,3,1,Married,8,563.2612593,0,27568.06297
127071,19,1,3,0,Married,8,563.1467632,0,27949.33816
107733,37,20,4,1,Married,5,563.0655171,0,27894.27586
151512,31,12,4,0,Married,2,563.0420952,0,27862.10476
151315,22,4,3,0,Single,12,563.0406414,0,27581.03207
165479,17,0,3,1,Single,14,563.0131992,0,27715.65996
179671,42,22,3,0,Married,4,563.0070177,0,27893.35089
130125,47,27,2,0,Single,3,562.9853303,0,27685.26652
149897,68,50,2,0,Single,4,562.8376208,0,27587.88104
171900,52,32,3,0,Single,1,562.8152316,0,27558.76158
105859,25,7,4,0,Married,14,562.6906005,0,27927.53003
174489,25,8,4,0,Married,14,562.6395892,0,27661.97946
159112,56,38,2,1,Single,3,562.6314955,0,27803.57478
134722,18,0,2,1,Single,11,562.6110667,0,27639.55334
183136,25,8,3,1,Single,12,562.5552069,0,27823.76035
135139,25,8,3,0,Single,12,562.5482144,0,27789.41072
157731,23,4,4,0,Married,15,562.4550285,0,27917.75143
157451,26,7,1,0,Married,1,562.2875351,0,27572.37676
154284,20,1,2,0,Single,8,562.2078905,0,27606.39453
150169,32,14,2,1,Single,1,562.2045212,0,27619.22606
110799,25,7,2,1,Single,10,562.181514,0,27807.0757
196911,66,46,3,1,Married,5,561.9063626,0,27678.31813
139789,23,4,1,1,Single,11,561.8599305,0,27617.99653
124248,55,35,4,1,Single,4,561.8060967,0,27505.30484
117625,47,28,1,0,Married,1,561.796776,0,27780.8388
154356,52,33,1,0,Married,2,561.5039553,0,27766.19777
195753,18,0,4,1,Single,11,561.3795472,0,27709.97736
113196,65,46,1,1,Single,2,561.2968092,0,27750.84046
107878,26,5,2,1,Married,2,561.2020227,0,27683.10114
143309,23,5,2,0,Single,11,561.1817658,0,27584.08829
184024,35,14,2,1,Married,2,561.1540369,0,27690.70185
122484,25,8,3,1,Single,11,561.1434278,0,27528.17139
123781,16,0,2,1,Single,4,561.0042148,0,27511.21074
124761,22,4,2,1,Single,6,560.9708858,0,27605.54429
162367,70,50,3,0,Single,2,560.6357008,0,27655.78504
175028,16,0,3,1,Single,13,560.4544128,0,27800.72064
119082,57,36,2,0,Single,2,560.2708214,0,27465.54107
152681,55,38,2,1,Married,5,560.2024583,0,27526.12292
181358,47,26,2,0,Single,10,560.0044404,0,27598.22202
136668,24,5,3,1,Single,13,559.9182036,0,27404.91018
166615,17,0,3,0,Single,14,559.5988379,0,27630.9419
127415,58,40,2,1,Single,4,559.5844128,0,27632.22064
188005,64,44,2,0,Single,5,559.5341787,0,27544.70894
197254,61,41,2,1,Single,5,559.3882097,0,27397.41049
135678,19,2,1,1,Single,2,559.1728954,0,27525.64477
161277,25,6,1,1,Single,12,559.1451267,0,27539.25634
174811,24,7,3,1,Single,11,559.0372689,0,27628.86345
195811,19,0,4,0,Single,12,559.0337155,0,27569.68578
198694,17,0,4,0,Single,7,558.9765314,0,27423.82657
124612,36,19,3,1,Single,0,558.9101034,0,27581.50517
105842,29,9,3,1,Married,0,558.8847208,0,27431.23604
198806,34,14,2,0,Married,5,558.5397196,0,27664.98598
188394,40,23,2,0,Single,8,558.4147228,0,27586.73614
112251,42,21,4,1,Married,2,558.195991,0,27343.79955
195137,17,0,3,0,Married,14,558.1666236,0,27431.33118
117150,51,32,3,1,Married,1,558.1007803,0,27655.03902
128330,33,15,3,1,Single,4,558.0653071,0,27385.26536
192900,60,42,3,0,Single,3,557.9149076,0,27440.74538
148887,42,23,4,1,Married,5,557.8448289,0,27503.24145
198776,57,40,1,0,Single,5,557.8158927,0,27531.79464
119856,40,19,4,1,Single,0,557.8021951,0,27541.10976
116433,56,35,3,0,Single,3,557.7715797,0,27329.57899
192550,38,19,3,0,Married,1,557.7685397,0,27653.42699
189520,17,0,3,0,Single,12,557.7324632,0,27561.62316
148198,23,6,1,0,Single,11,557.6682868,0,27325.41434
103872,49,29,2,0,Single,6,557.6323479,0,27365.6174
110551,44,23,4,0,Married,0,557.5516972,0,27635.58486
105943,17,0,1,0,Single,11,557.4409401,0,27281.04701
196159,66,49,2,0,Married,3,557.369016,0,27554.4508
102437,19,1,3,1,Single,12,557.0871936,0,27375.35968
129534,18,0,3,0,Married,8,557.0582137,0,27376.91069
194429,24,6,2,0,Married,7,557.0278671,0,27349.39336
138686,54,35,3,0,Married,3,557.0193211,0,27566.96606
108638,53,32,1,1,Married,4,556.9123571,0,27342.61786
143239,21,2,2,0,Single,13,556.8865116,0,27587.32558
140327,22,4,2,0,Single,14,556.8383931,0,27308.91966
185347,21,3,4,0,Single,13,556.5350622,0,27519.75311
138717,22,3,2,0,Married,4,556.4903536,0,27352.51768
139998,22,3,3,0,Married,15,556.4574225,0,27593.87113
199104,29,11,2,1,Single,5,556.4168189,0,27457.84095
189659,44,24,1,1,Married,4,556.3332589,0,27373.66295
168951,23,4,3,0,Married,11,556.176094,0,27319.8047
145225,19,2,2,0,Married,15,555.6668504,0,27583.34252
196413,23,4,4,1,Single,14,555.6394366,0,27277.97183
106047,62,42,1,0,Married,2,555.6005312,0,27367.02656
144452,18,0,2,0,Married,10,555.4625578,0,27270.12789
175102,56,36,2,1,Single,1,555.4445444,0,27221.22722
121865,41,21,2,1,Married,1,555.4271146,0,27512.35573
143260,24,5,3,0,Married,11,555.2651296,0,27321.25648
194069,21,4,2,1,Married,6,555.2635092,0,27493.17546
169611,56,36,1,0,Single,7,555.0751738,0,27203.75869
158110,30,12,3,0,Single,3,555.0565883,0,27154.82942
125637,48,28,2,0,Married,1,555.037452,0,27532.8726
116468,59,39,3,0,Married,4,554.9753541,0,27326.76771
177648,21,4,4,1,Single,2,554.9500925,0,27308.50463
167171,41,20,3,1,Married,0,554.9068669,0,27269.34335
142932,60,40,1,1,Married,0,554.8941047,0,27298.70524
179066,51,34,4,1,Single,0,554.8745914,0,27191.72957
194123,57,38,2,0,Single,2,554.85812,0,27316.906
125215,50,32,3,1,Married,1,554.7138947,0,27208.69474
131971,19,1,2,0,Single,10,554.6362621,0,27352.81311
177419,56,36,3,1,Married,5,554.6144048,0,27143.72024
145845,69,49,1,0,Single,2,554.2172221,0,27442.86111
115864,25,7,1,0,Married,12,554.2015344,0,27387.07672
183489,39,20,3,0,Married,2,554.14553,0,27146.2765
160890,52,34,2,0,Married,4,554.0828553,0,27440.14277
123728,24,6,1,0,Married,11,553.9527603,0,27183.63802
138683,17,0,2,0,Married,6,553.8790435,0,27370.95218
173441,23,6,2,1,Single,9,553.862587,0,27320.12935
183812,34,17,2,1,Married,0,553.8533345,0,27339.66673
188283,18,0,3,1,Single,15,553.8470439,0,27422.3522
131214,34,15,4,0,Married,4,553.7771784,0,27191.85892
134503,19,1,1,1,Single,14,553.7473001,0,27105.36501
181030,37,20,2,0,Married,3,553.7000692,0,27252.00346
170576,63,42,4,0,Single,0,553.635973,0,27409.79865
170906,22,4,2,0,Married,9,553.6027214,0,27187.13607
137086,27,10,2,1,Married,2,553.5448463,0,27175.24232
166103,42,23,3,0,Married,4,553.52436,0,27246.218
112381,29,11,3,0,Single,12,553.4949769,0,27106.74885
119956,20,3,4,1,Single,12,553.4820444,0,27275.10222
112719,25,7,3,1,Married,9,553.462844,0,27241.1422
179608,52,33,4,0,Single,6,553.3991282,0,27139.95641
120427,60,43,3,0,Single,3,553.352897,0,27151.64485
109880,19,2,3,0,Married,9,553.266107,0,27078.30535
176414,56,38,3,1,Single,2,553.2323994,0,27443.61997
133742,56,37,1,0,Married,0,553.1320959,0,27328.6048
199197,34,17,2,0,Married,3,553.0695916,0,27406.47958
185335,20,3,1,1,Married,8,553.0150379,0,27374.7519
100019,51,31,3,1,Single,3,552.9365137,0,27167.82569
182033,20,2,3,0,Single,13,552.7515415,0,27074.57708
109807,31,11,4,0,Married,3,552.5747807,0,27418.73904
190513,60,40,4,1,Single,0,552.5655449,0,27420.27725
148948,64,45,2,0,Single,0,552.5432092,0,27416.16046
128834,52,31,2,0,Single,6,552.3598059,0,27302.9903
158372,33,13,3,0,Married,2,552.3288797,0,27144.44399
103014,50,29,1,0,Single,8,552.2467274,0,27313.33637
169382,23,5,3,0,Single,11,551.8394674,0,27378.97337
187693,23,4,2,0,Married,5,551.8162587,0,27333.81294
193623,52,31,3,1,Married,4,551.8101323,0,27096.50662
105215,23,4,2,0,Single,13,551.7209522,0,27385.04761
162281,39,22,2,0,Married,1,551.5416673,0,27271.08337
117546,27,9,1,1,Single,4,551.5028929,0,27069.14465
135735,24,7,2,0,Single,13,551.4259978,0,27011.29989
100925,57,39,2,0,Single,10,551.4158376,0,27174.79188
126988,21,2,2,1,Married,5,551.3165937,0,27289.82969
185739,45,26,2,0,Married,3,551.2472252,0,27260.36126
146111,19,1,3,1,Single,12,551.2024325,0,27037.12163
132371,24,7,4,0,Married,12,551.1643552,0,27324.21776
136059,18,0,4,1,Single,15,551.0234945,0,27198.17473
149397,66,46,1,0,Married,0,550.8999939,0,27302.9997
168792,18,0,3,1,Single,11,550.7666379,0,27299.3319
159474,18,0,3,1,Single,8,550.6338249,0,26971.69125
157387,66,47,1,0,Married,4,550.5213257,0,27226.06629
147143,47,28,3,0,Single,7,550.5173157,0,27184.86579
154960,29,8,3,1,Single,3,550.4771397,0,27204.85699
165329,16,0,3,1,Single,11,550.4194392,0,27148.97196
195258,43,22,4,1,Single,2,550.4181639,0,26974.9082
101713,41,24,4,0,Single,1,550.4005101,0,27270.02551
105356,30,10,2,1,Married,5,550.3847401,0,26920.23701
192734,70,53,2,0,Married,0,550.2256006,0,27053.28003
141027,25,6,3,1,Single,15,550.1674199,0,27292.371
132911,27,8,1,1,Married,0,550.1011897,0,26968.05949
160283,18,0,1,0,Single,12,550.0535565,0,26978.67783
177806,67,49,3,0,Married,5,549.9165017,0,27052.82509
146684,65,44,2,1,Married,2,549.8650833,0,27266.25417
160150,17,0,4,0,Married,14,549.8274165,0,26958.37083
131455,23,6,4,1,Single,13,549.7817678,0,27151.08839
166657,56,39,4,1,Single,3,549.6540629,0,27209.70315
132233,16,0,2,0,Single,14,549.6537494,0,27007.68747
173734,16,0,3,0,Single,13,549.6470562,0,27065.35281
197105,28,8,2,0,Single,3,549.5779838,0,27022.89919
186538,20,2,3,1,Single,5,549.4615996,0,26934.07998
107498,44,26,1,0,Single,7,549.1403768,0,27256.01884
172371,18,1,2,1,Single,6,549.0982587,0,27252.91294
134813,20,3,4,0,Married,9,548.6780026,0,27094.90013
169698,21,2,3,0,Single,12,548.579218,0,27208.9609
124469,51,32,3,0,Single,10,548.363274,0,26838.1637
136724,40,20,4,1,Married,4,548.1177189,0,26818.88595
112520,29,9,3,0,Married,1,548.0053385,0,26915.26693
168472,23,5,2,1,Single,9,547.9896053,0,27172.48027
118576,64,47,2,0,Married,2,547.8867472,0,26915.33736
124228,51,30,2,0,Married,4,547.7794054,0,27073.97027
198479,25,6,4,1,Single,14,547.6141399,0,26824.707
117086,16,0,1,0,Married,12,547.4455297,0,26826.27649
197190,31,14,4,1,Married,4,547.1145488,0,26934.72744
119599,18,1,2,0,Single,15,547.0661173,0,26927.30587
178028,16,0,1,1,Single,12,547.0137533,0,26783.68767
143539,26,6,2,0,Single,3,547.009841,0,27042.49205
133980,22,4,1,0,Married,12,546.9662639,0,26947.3132
111070,22,5,4,0,Married,0,546.8840089,0,26790.20045
164077,27,6,1,0,Single,11,546.8137954,0,27113.68977
168057,51,31,4,0,Married,1,546.7820913,0,27044.10457
188170,21,2,2,1,Married,9,546.7752557,0,26912.76279
182326,50,32,3,0,Single,9,546.7070054,0,27042.35027
163382,41,22,4,0,Married,5,546.6772185,0,27118.86093
145800,27,8,2,1,Single,1,546.6553508,0,26987.76754
198118,21,4,3,0,Married,15,546.6375649,0,26931.87825
195601,18,1,1,1,Single,12,546.5943051,0,26904.71526
172927,33,16,1,1,Married,1,546.5523699,0,26786.6185
178723,33,15,1,0,Married,1,546.5349024,0,26776.74512
183541,25,6,1,0,Married,3,546.4265523,0,27082.32762
110773,26,5,3,1,Married,4,546.3187204,0,26767.93602
148106,68,50,3,0,Single,1,546.2450947,0,26930.25474
139720,21,3,2,1,Married,8,546.2304531,0,27060.52266
171415,24,7,2,1,Single,10,545.9616166,0,26859.08083
121547,69,49,2,1,Married,0,545.8851835,0,26817.25918
180125,35,18,3,0,Single,3,545.8582689,0,26724.91345
191403,45,28,3,1,Married,5,545.857869,0,26700.89345
102087,41,22,3,0,Married,3,545.6882857,0,26798.41429
182092,56,38,2,1,Married,2,545.6744992,0,26694.72496
180764,17,0,1,0,Married,13,545.648712,0,26856.4356
185349,20,1,2,0,Single,15,545.648396,0,26791.4198
190826,25,8,2,0,Married,15,545.6301088,0,26714.50544
197985,27,10,2,1,Married,5,545.6230918,0,26720.15459
134863,55,36,2,0,Single,7,545.6146351,0,26933.73176
102361,46,27,4,1,Single,1,545.5296984,0,27007.48492
191229,47,26,3,0,Married,1,545.4377592,0,26927.88796
119693,40,23,4,0,Single,1,545.415933,0,26685.79665
139100,39,18,2,1,Married,1,545.1540724,0,26904.70362
139632,18,1,1,0,Married,12,545.1262388,0,26834.31194
133079,50,32,4,0,Married,0,545.097663,0,26752.88315
145717,19,1,2,0,Single,11,545.0539578,0,26905.69789
172312,62,44,1,1,Married,5,544.9158279,0,26804.7914
175175,20,3,2,1,Single,12,544.9146942,0,26951.73471
101167,30,11,4,0,Single,0,544.860822,0,27038.0411
144976,23,5,2,0,Single,12,544.8236918,0,26751.18459
184349,39,22,2,1,Single,4,544.7799528,0,26924.99764
121965,41,21,3,0,Single,10,544.5625563,0,26629.12782
114693,38,17,1,1,Single,1,544.5163493,0,26729.81747
175145,31,14,3,0,Single,0,544.4035359,0,26906.1768
191466,57,40,3,1,Married,2,544.3064563,0,26923.32282
101326,21,4,3,0,Married,11,544.2942127,0,26800.71064
184178,21,2,1,0,Married,0,544.2032325,0,26965.16163
104625,43,26,1,0,Married,3,544.0874689,0,26795.37345
166873,20,1,2,0,Married,7,543.9432608,0,26645.16304
188791,17,0,2,0,Single,12,543.916028,0,26772.8014
109793,31,14,2,0,Single,8,543.8830552,0,26683.15276
105757,70,49,4,1,Single,4,543.7224607,0,26866.12304
133959,17,0,2,0,Married,9,543.7158302,0,26827.79151
180038,58,41,2,0,Single,4,543.6620257,0,26901.10129
125998,19,0,3,1,Married,1,543.6092206,0,26714.46103
108323,30,10,4,1,Married,3,543.4510752,0,26826.55376
110944,23,5,3,0,Married,12,543.1251437,0,26819.25719
185142,58,38,2,0,Married,5,543.1069812,0,26709.34906
137923,39,22,2,1,Single,4,542.9901738,0,26906.50869
157998,31,13,4,0,Single,3,542.9156551,0,26618.78276
185665,57,40,4,0,Single,2,542.9116362,0,26708.58181
125850,34,13,2,1,Single,1,542.7039232,0,26913.19616
168801,30,10,2,1,Married,3,542.4600195,0,26559.00098
135621,63,45,4,1,Single,0,542.1535219,0,26714.6761
167968,19,2,3,0,Married,10,542.1328336,0,26849.64168
126530,41,24,3,0,Single,6,542.0815196,0,26767.07598
126732,39,20,2,1,Married,3,541.7833951,0,26508.16976
106113,21,4,3,1,Married,4,541.7544178,0,26746.72089
198834,48,27,4,0,Single,10,541.6842648,0,26783.21324
146633,44,27,4,0,Single,10,541.6637242,0,26624.18621
154231,20,3,3,0,Married,8,541.5791259,0,26798.9563
117008,25,6,4,1,Single,10,541.5292244,0,26491.46122
190735,32,13,3,1,Married,4,541.5014923,0,26604.07462
117299,44,25,3,0,Married,1,541.441289,0,26797.06445
170748,20,3,3,0,Married,7,541.3064267,0,26505.32134
127310,32,13,2,1,Single,3,541.3010963,0,26864.05482
113473,59,42,4,1,Married,0,541.2316688,0,26464.58344
191263,16,0,3,1,Single,6,541.1654019,0,26744.2701
139975,47,30,2,1,Married,3,541.0056262,0,26809.28131
197568,16,0,2,0,Married,9,540.9575032,0,26791.87516
172593,39,19,3,0,Single,7,540.9037321,0,26529.18661
144487,27,8,1,0,Single,4,540.7363438,0,26612.81719
146751,19,0,1,1,Married,12,540.6418989,0,26530.09495
128085,55,34,1,0,Married,0,540.5405296,0,26791.02648
157434,25,8,3,1,Married,3,540.4608782,0,26763.04391
101104,19,1,4,0,Married,0,540.408481,0,26495.42405
181462,43,24,2,1,Single,5,540.3638924,0,26559.19462
170337,51,31,4,1,Single,1,540.2230723,0,26443.15362
154586,45,26,3,1,Married,2,540.1990971,0,26586.95486
112263,21,2,2,1,Married,10,540.0722726,0,26573.61363
175107,54,37,3,0,Single,8,539.986628,0,26468.3314
123081,49,30,1,1,Married,0,539.8117592,0,26542.58796
166365,20,2,3,0,Married,8,539.7603865,0,26577.01933
112603,48,29,3,0,Single,2,539.7408509,0,26472.04255
144064,28,9,3,1,Married,2,539.693869,0,26597.69345
130631,52,32,2,1,Single,3,539.6302317,0,26538.51159
145494,39,20,3,0,Married,2,539.5619297,0,26662.09649
100606,44,27,4,0,Married,5,539.5374638,0,26754.87319
173771,23,6,3,1,Married,1,539.3637156,0,26758.18578
104761,38,18,3,0,Married,1,539.3382414,0,26565.91207
183121,16,0,2,0,Married,9,539.2670892,0,26501.35446
163579,20,1,3,0,Single,8,539.1182634,0,26589.91317
118826,16,0,3,0,Married,5,539.0109188,0,26484.54594
114557,40,19,2,0,Single,0,538.9219722,0,26513.09861
113182,51,34,2,0,Married,1,538.7582235,0,26624.91118
108691,41,20,3,0,Single,6,538.7307087,0,26385.53544
124536,17,0,3,0,Married,6,538.5786777,0,26695.93389
100596,28,11,1,0,Married,4,538.4955542,0,26325.77771
186068,25,6,3,0,Married,12,538.4290782,0,26655.45391
133032,45,26,4,0,Single,7,538.4001789,0,26648.00895
104456,43,23,2,0,Married,3,538.3375081,0,26446.87541
113142,39,22,4,1,Married,4,538.2692114,0,26676.46057
176541,33,12,4,1,Married,4,538.0784879,0,26671.9244
132179,37,19,2,0,Single,10,538.0641605,0,26510.20803
198526,70,53,3,0,Married,2,538.012156,0,26698.6078
127936,57,36,3,1,Married,5,538.0067996,0,26570.33998
175602,21,3,3,0,Single,12,537.6073349,0,26589.36675
100792,22,4,3,1,Single,10,537.3387366,0,26326.93683
180586,43,26,3,0,Single,0,537.3116014,0,26341.58007
157711,28,11,1,0,Married,2,537.3045231,0,26296.22616
123383,16,0,2,0,Single,15,537.2615494,0,26617.07747
125723,24,6,4,0,Married,5,537.2526154,0,26650.63077
188911,25,8,2,0,Married,10,537.1319403,0,26546.59702
137786,17,0,4,0,Married,10,537.1256702,0,26261.28351
152415,49,32,4,1,Single,3,537.0080753,0,26574.40377
189679,44,24,2,0,Single,5,536.9876034,0,26460.38017
158150,21,4,4,1,Married,2,536.9874292,0,26370.37146
144191,22,5,3,0,Single,14,536.8686847,0,26341.43424
198562,22,5,1,0,Married,11,536.8602461,0,26384.01231
100664,21,4,4,1,Single,12,536.8296313,0,26414.48157
142046,24,7,2,1,Married,4,536.7156439,0,26276.7822
152335,30,10,3,0,Married,3,536.6853095,0,26541.26548
146307,19,0,3,0,Single,15,536.6286554,0,26331.43277
181262,35,16,3,0,Single,0,536.617917,0,26473.89585
181754,23,5,2,0,Married,7,536.5691039,0,26371.4552
152439,69,48,4,1,Married,3,536.4160863,0,26300.80432
190221,23,6,1,1,Single,14,536.4084716,0,26443.42358
188573,18,0,1,1,Single,12,536.4047089,0,26505.23544
177151,67,47,3,0,Married,0,536.2712451,0,26445.56226
137024,17,0,1,0,Married,12,536.2573482,0,26434.86741
108856,55,34,1,0,Married,3,536.2569214,0,26446.84607
167978,24,5,3,1,Single,15,536.0817614,0,26504.08807
151822,70,51,4,0,Single,1,536.0607195,0,26304.03598
178790,24,7,4,0,Single,13,535.9953989,0,26590.76995
123743,51,31,2,1,Married,0,535.9249164,0,26423.24582
167442,61,42,4,0,Single,2,535.7703156,0,26396.51578
151449,28,11,1,0,Single,10,535.6739106,0,26389.69553
183020,25,8,4,0,Married,12,535.6306016,0,26359.53008
127015,22,5,2,1,Single,14,535.4361195,0,26293.80598
112483,16,0,2,1,Single,15,535.3980403,0,26484.90202
178353,29,11,4,1,Married,2,535.380846,0,26281.0423
155567,43,22,2,0,Single,7,535.3585014,0,26278.92507
148775,19,2,2,0,Married,12,535.3469827,0,26458.34914
157984,47,30,2,1,Single,0,535.32901,0,26493.4505
134676,46,29,2,0,Single,10,535.2924095,0,26459.62048
170474,61,42,2,0,Married,4,535.2367128,0,26216.83564
120879,24,6,2,1,Single,11,535.1427594,0,26533.13797
165626,70,50,4,0,Single,0,535.0623087,0,26486.11544
180726,19,1,1,0,Single,14,535.0124158,0,26450.62079
113068,22,5,1,0,Married,9,534.9538756,0,26272.69378
102276,24,7,2,1,Single,8,534.9424314,0,26246.12157
189489,52,33,3,0,Single,8,534.9380745,0,26305.90373
152873,63,46,2,0,Married,4,534.8545625,0,26413.72813
135239,43,25,2,1,Married,0,534.8194156,0,26283.97078
182307,25,6,3,0,Married,11,534.7978634,0,26464.89317
133099,30,12,3,1,Single,4,534.7241487,0,26302.20744
107293,29,9,3,0,Single,4,534.7082137,0,26461.41069
123564,16,0,2,0,Married,11,534.6710846,0,26473.55423
149411,16,0,1,0,Single,13,534.6193671,0,26151.96836
136257,22,5,1,1,Single,11,534.4777431,0,26187.88716
196912,31,12,2,1,Married,4,534.422954,0,26284.1477
166269,21,3,1,1,Single,9,534.3700065,0,26462.50033
182319,24,7,3,1,Single,10,534.2235319,0,26482.1766
167589,45,26,2,1,Single,1,534.0985077,0,26473.92539
106880,53,33,3,1,Single,0,533.8597606,0,26293.98803
191803,55,35,3,0,Married,4,533.8138112,0,26395.69056
138451,24,6,4,1,Single,11,533.7637776,0,26146.18888
112510,18,0,4,1,Single,8,533.701051,0,26391.05255
179657,64,46,4,0,Married,0,533.5487877,0,26162.43939
132014,65,45,2,0,Single,6,533.4980434,0,26428.90217
191533,29,8,2,1,Married,4,533.3330744,0,26415.65372
194260,24,7,4,0,Single,12,533.3063473,0,26098.31737
152587,36,17,3,0,Married,5,533.1888339,0,26381.4417
149325,23,5,2,0,Married,8,533.156235,0,26164.81175
190060,40,21,2,1,Married,1,533.0020736,0,26100.10368
150950,59,40,3,0,Married,2,532.7518908,0,26230.59454
127246,37,19,2,0,Single,1,532.6556978,0,26203.78489
136633,28,8,1,0,Single,5,532.6531171,0,26078.65586
132724,69,52,2,0,Single,0,532.616571,0,26074.82855
142599,45,27,4,0,Single,14,532.4487946,0,26182.43973
162958,45,26,4,1,Married,3,532.4444303,0,26141.22152
120275,17,0,2,1,Single,10,532.4260271,0,26087.30136
144173,61,40,4,1,Single,1,532.3122908,0,26087.61454
177244,21,3,4,1,Single,9,532.2788507,0,26094.94254
129393,48,29,2,1,Married,0,532.1842989,0,26374.21495
161497,47,27,3,0,Single,1,532.1506147,0,26198.53074
142362,54,33,4,1,Single,1,532.1328122,0,26147.64061
194414,58,37,3,1,Single,0,532.1056554,0,26117.28277
128963,18,0,2,1,Single,12,532.0880332,0,26182.40166
107219,39,18,2,1,Single,5,532.0812066,0,26036.06033
128401,22,3,2,0,Single,12,531.7350409,0,26283.75205
126224,58,39,1,0,Single,1,531.4576965,0,26052.88483
158546,36,18,3,1,Single,4,531.4476528,0,26021.38264
165067,52,33,3,0,Single,5,531.4447436,0,26353.23718
141544,65,46,2,0,Married,2,531.4259253,0,26351.29627
132478,25,6,2,0,Single,11,531.4220299,0,26092.1015
130757,21,3,2,1,Single,9,531.3488591,0,26240.44296
155644,22,3,2,1,Single,11,531.3287706,0,26166.43853
144105,18,1,3,0,Married,15,531.285276,0,26192.2638
158292,24,5,3,0,Married,11,531.217548,0,26126.8774
151171,58,39,4,1,Married,5,531.2165162,0,26229.82581
138311,23,5,2,0,Single,7,531.1712975,0,26127.56488
183957,39,22,2,1,Married,5,531.1566917,0,26209.83459
184185,24,7,4,0,Single,13,531.1071095,0,26145.35548
154525,18,0,3,0,Single,11,531.0883406,0,26274.41703
173862,49,29,1,0,Single,6,531.0568631,0,26068.84316
154398,59,41,3,1,Married,5,530.9394513,0,26109.97257
109678,40,22,4,0,Married,1,530.8373756,0,26300.86878
190925,43,24,3,0,Married,1,530.8330641,0,26015.65321
163088,23,6,2,1,Single,11,530.7044789,0,26196.22395
121210,22,3,4,1,Single,10,530.7000455,0,26161.00228
178968,39,22,2,0,Single,1,530.6876262,0,26161.38131
129036,64,44,1,0,Single,1,530.6500807,0,26301.50404
102169,56,38,4,0,Single,11,530.6103621,0,26254.51811
129570,22,4,3,0,Married,11,530.5909941,0,26181.54971
146676,17,0,2,1,Single,10,530.5900491,0,26233.50246
103977,23,6,1,0,Single,11,530.4131783,0,26222.65892
122168,22,4,4,1,Single,15,530.3134314,0,26019.67157
125027,43,23,2,0,Single,6,530.2998472,0,26004.99236
173785,21,4,2,1,Single,12,530.298609,0,26276.93045
133156,56,39,2,0,Single,10,530.0779528,0,26207.89764
135846,16,0,2,0,Married,15,530.021191,0,26293.05955
152880,44,25,3,1,Single,1,529.9217527,0,26085.08764
195581,16,0,3,0,Single,14,529.9111881,0,26280.55941
181771,52,32,2,0,Married,5,529.7386863,0,26085.93432
163258,57,37,3,0,Married,1,529.7027485,0,25924.13743
149197,44,23,2,1,Married,4,529.6163371,0,26258.81686
135679,67,47,3,1,Single,2,529.5924504,0,26250.62252
109545,32,13,2,0,Single,10,529.4510662,0,26111.55331
176191,56,38,3,0,Married,0,529.4150257,0,25988.75129
175723,19,2,3,0,Married,6,529.3968674,0,26004.84337
133632,56,38,4,0,Single,9,529.3321479,0,26093.6074
129048,29,9,2,1,Single,5,529.3161021,0,26261.80511
117402,33,16,3,0,Married,5,529.1670493,0,26099.35247
118466,21,3,3,0,Single,13,529.1282353,0,26253.41177
110296,22,4,4,0,Married,6,529.1230508,0,25997.15254
194646,44,24,4,1,Single,2,529.1194447,0,26065.97224
172367,20,3,3,1,Single,6,528.9670284,0,25904.35142
132719,58,41,2,1,Married,2,528.9243841,0,25898.21921
112521,34,17,3,0,Single,10,528.8520586,0,26215.60293
158096,42,24,3,0,Married,0,528.6530207,0,25999.65104
181351,47,28,2,0,Single,10,528.6512528,0,25936.56264
163456,50,30,3,0,Single,5,528.6100455,0,26098.50228
192213,22,5,2,0,Married,5,528.3099659,0,26089.4983
199417,49,28,2,0,Single,2,528.2853443,0,25886.26721
144363,20,1,3,0,Married,10,528.2847155,0,26089.23578
169292,22,5,2,1,Single,10,528.072594,0,26191.6297
118020,24,7,4,0,Single,14,528.0043687,0,25872.21844
182237,48,29,1,1,Married,5,527.9214511,0,26170.07256
124538,19,2,2,0,Married,13,527.8022838,0,25846.11419
117778,17,0,2,0,Married,12,527.7419482,0,25944.09741
104552,49,28,1,1,Married,3,527.5459518,0,26025.29759
150225,18,1,3,1,Married,14,527.4669017,0,26055.34509
124745,48,28,3,0,Single,9,527.253295,0,26008.66475
125727,17,0,3,0,Married,11,527.2441967,0,26063.20984
195987,19,2,2,0,Married,10,527.15865,0,25922.9325
112181,24,5,4,0,Married,13,527.1154549,0,25873.77275
143670,22,3,3,0,Single,12,527.0086334,0,25838.43167
158623,23,6,3,1,Single,8,526.9840446,0,26088.20223
127917,18,1,1,0,Single,13,526.7659998,0,26002.29999
127873,23,6,1,1,Single,14,526.5853106,0,25818.26553
155764,42,22,2,1,Married,5,526.5832167,0,25904.16084
178479,46,27,2,1,Married,5,526.5789001,0,25886.94501
133089,21,3,2,1,Single,3,526.4446386,0,25728.23193
182934,17,0,3,1,Single,8,526.4224252,0,26036.12126
198734,21,2,3,1,Single,13,526.4212626,0,25801.06313
147124,61,41,1,0,Married,4,526.2793522,0,25974.96761
114249,45,24,2,1,Single,1,526.2110395,0,25789.55198
147681,28,10,2,0,Single,3,526.0735068,0,25947.67534
198788,67,49,4,0,Married,5,526.0695468,0,25897.47734
129305,18,1,2,1,Married,0,526.0381785,0,25794.90893
169136,47,28,2,0,Married,3,526.0220163,0,25840.10082
158891,68,48,4,0,Single,10,525.898743,0,25844.93715
133254,59,42,2,0,Single,11,525.6856088,0,25709.28044
170024,20,2,2,1,Single,8,525.5917164,0,25807.58582
196564,49,31,1,1,Single,0,525.5303261,0,26050.51631
145235,58,38,2,1,Married,5,525.3887506,0,25707.43753
135895,16,0,2,1,Single,2,525.3045843,0,25956.22922
128446,23,4,3,1,Single,15,525.2903006,0,25860.51503
192402,51,33,2,0,Single,1,525.1884374,0,25682.42187
195481,36,15,1,0,Married,3,525.1836212,0,26059.18106
197807,58,38,1,1,Married,3,525.1432803,0,25707.16402
189931,25,8,2,1,Single,8,525.0689309,0,26001.44655
176584,44,23,1,1,Married,0,525.0527842,0,25756.63921
185458,25,8,1,0,Married,15,525.0439169,0,25814.19585
163315,31,12,4,0,Married,2,525.0244159,0,25828.2208
171570,40,22,3,1,Single,4,524.9501759,0,26026.5088
125172,34,13,3,1,Married,1,524.9369061,0,25666.84531
144666,25,8,4,0,Single,14,524.87801,0,25895.9005
191067,20,1,4,0,Married,8,524.7778553,0,25943.89277
103036,35,17,2,1,Single,2,524.7749293,0,25675.74647
194970,25,7,4,0,Single,11,524.729119,0,26032.45595
128316,44,27,3,1,Married,0,524.6173436,0,25918.86718
175313,23,4,2,1,Married,4,524.5472718,0,25818.36359
151701,19,2,1,0,Married,7,524.4730901,0,25659.65451
109235,20,2,3,0,Married,15,524.4369489,0,25811.84745
131082,19,0,3,1,Single,13,524.4048121,0,25992.24061
162062,64,45,2,0,Single,2,524.356007,0,25945.80035
172739,20,3,2,1,Single,10,524.2636061,0,25938.18031
155547,31,14,3,1,Married,4,524.2253273,0,25642.26637
113580,40,20,4,0,Single,5,524.2077031,0,25973.38516
131556,23,4,2,0,Single,11,524.1589774,0,25612.94887
131504,19,1,2,0,Married,12,524.1147808,0,25926.73904
142865,35,16,4,0,Married,1,524.1141209,0,25688.70605
131965,27,7,4,0,Single,9,524.0742274,0,25967.71137
125784,45,27,3,0,Single,1,524.0265757,0,25966.32879
119016,52,35,4,1,Single,5,524.0062141,0,25783.31071
169843,63,44,1,0,Married,4,523.9833142,0,25751.16571
166022,55,36,2,1,Single,1,523.9525796,0,25679.62898
198039,57,40,1,1,Married,1,523.9478985,0,25724.39493
195144,70,51,1,0,Single,6,523.5559353,0,25904.79677
158343,26,7,2,0,Single,2,523.5247446,0,25576.23723
105316,26,7,3,0,Married,3,523.4855829,0,25875.27915
118275,19,0,3,1,Single,10,523.4836397,0,25669.18199
148134,47,28,2,1,Married,1,523.3737571,0,25809.68786
124922,23,5,1,1,Single,11,523.3410437,0,25677.05219
187399,69,50,3,1,Married,2,523.299883,0,25843.99415
177150,23,6,4,1,Single,15,523.2093617,0,25577.46809
129841,16,0,2,1,Married,0,523.1735888,0,25950.67944
150961,48,28,3,1,Single,2,523.0894728,0,25707.47364
151091,21,2,2,0,Single,15,523.0865746,0,25607.32873
161951,28,7,3,0,Single,9,523.084682,0,25953.2341
178496,25,7,1,1,Married,15,523.0781246,0,25952.90623
183078,55,34,1,1,Married,2,522.9070466,0,25620.35233
196869,36,15,3,0,Single,1,522.8906031,0,25866.53016
120208,38,20,1,0,Married,1,522.8312209,0,25941.56105
119413,36,15,3,0,Single,5,522.8168614,0,25744.84307
100870,50,31,1,0,Single,4,522.7825524,0,25655.12762
110378,16,0,2,1,Married,9,522.6998746,0,25704.99373
132114,33,15,1,0,Single,2,522.6579568,0,25774.89784
160442,17,0,4,0,Married,7,522.5961596,0,25603.80798
150186,35,14,2,1,Single,5,522.577545,0,25922.87725
134545,24,5,4,0,Married,11,522.5298648,0,25686.49324
148057,35,16,1,0,Single,9,522.5136507,0,25617.68254
130647,36,17,1,0,Single,7,522.412567,0,25878.62835
193076,37,19,3,0,Married,3,522.3375633,0,25877.87817
102853,25,6,4,1,Married,11,522.2008189,0,25572.04095
125126,16,0,2,1,Married,9,522.0844338,0,25821.22169
103353,30,11,4,0,Single,5,521.9381434,0,25600.90717
143204,17,0,1,0,Married,10,521.923239,0,25718.16195
158306,23,6,2,0,Married,8,521.9025364,0,25877.12682
112861,61,40,2,0,Single,8,521.8490944,0,25589.45472
115009,26,9,2,1,Single,0,521.7207259,0,25583.0363
117917,19,0,3,1,Married,8,521.6204512,0,25593.02256
146889,26,6,2,1,Single,0,521.4251691,0,25748.25846
142066,45,27,2,0,Single,1,521.4135648,0,25549.67824
114219,43,23,2,0,Single,9,521.2527857,0,25706.63929
115152,57,36,1,0,Single,6,521.1869239,0,25780.3462
127403,48,27,3,1,Married,3,520.9510609,0,25706.55305
119319,16,0,2,1,Single,8,520.8966399,0,25473.832
196611,18,0,1,1,Married,11,520.6205018,0,25644.02509
185342,16,0,2,0,Married,11,520.5728419,0,25719.6421
146620,24,6,3,0,Married,8,520.4128953,0,25459.64477
136864,59,40,1,1,Single,1,520.1692747,0,25553.46373
155156,25,6,1,1,Single,15,520.1172483,0,25789.86242
121299,46,29,2,1,Married,3,519.9399947,0,25558.99974
139738,35,17,3,0,Single,2,519.9221783,0,25717.10892
159708,61,43,3,0,Married,3,519.7360058,0,25619.80029
159595,20,1,1,1,Single,12,519.6358893,0,25452.79447
147006,20,2,4,1,Single,14,519.3969085,0,25676.84543
101704,22,5,3,1,Single,14,519.3589465,0,25649.94733
187667,21,4,2,1,Single,10,519.3587409,0,25516.93705
139894,37,20,4,0,Single,9,519.2443029,0,25715.21515
122843,27,6,1,0,Single,2,519.1625522,0,25745.12761
151388,29,9,2,0,Single,10,519.1499283,0,25628.49642
178041,45,25,3,0,Single,6,519.1243411,0,25667.21706
103714,21,3,3,0,Single,14,519.0797905,0,25619.98953
171700,17,0,3,0,Married,12,519.0569181,0,25682.84591
100608,21,3,2,1,Married,8,518.8952396,0,25509.76198
139805,43,25,3,0,Single,1,518.8539379,0,25606.6969
152028,48,31,3,0,Single,6,518.8537848,0,25642.68924
122422,36,18,4,0,Single,4,518.8250552,0,25646.25276
120034,22,3,2,0,Married,8,518.8089458,0,25403.44729
178390,21,4,2,0,Single,14,518.7770254,0,25478.85127
188012,23,4,2,1,Single,14,518.588689,0,25400.43445
179804,40,20,2,0,Married,2,518.4711624,0,25661.55812
135362,18,1,2,0,Single,13,518.4613744,0,25629.06872
120976,35,18,2,1,Married,3,518.4082923,0,25625.41462
163324,22,5,1,1,Single,9,518.3756905,0,25612.78453
106788,47,28,2,0,Married,1,518.3633147,0,25642.16574
117762,60,43,4,1,Single,3,518.349276,0,25541.4638
111152,62,44,2,0,Single,8,518.2563145,0,25466.81573
126520,50,29,2,0,Single,11,518.1773009,0,25621.86505
164478,28,10,2,0,Single,9,518.1463703,0,25315.31852
125626,37,17,1,1,Married,3,518.1264247,0,25579.32124
122980,17,0,3,1,Single,3,518.0821154,0,25523.10577
158539,18,0,1,1,Single,13,518.0761387,0,25369.80694
121156,59,42,3,1,Married,0,518.0649303,0,25560.24652
145765,24,7,1,1,Single,6,518.0476289,0,25432.38145
113027,52,32,2,1,Married,1,517.9428444,0,25446.14222
186416,59,39,4,0,Single,7,517.8330859,0,25433.6543
111505,61,44,3,1,Married,2,517.8114409,0,25560.57205
173645,21,4,1,0,Single,14,517.8087863,0,25665.43932
161718,43,26,4,0,Married,4,517.8031515,0,25370.15758
155836,18,0,2,1,Single,15,517.8020413,0,25383.10207
156477,24,5,3,0,Married,8,517.7877526,0,25555.38763
158905,42,21,4,1,Married,4,517.7582409,0,25670.91205
188013,44,27,4,1,Single,4,517.716189,0,25358.80945
118001,46,26,1,1,Married,0,517.6533908,0,25425.66954
134471,35,17,1,1,Married,4,517.6154431,0,25379.77216
153906,32,12,3,1,Single,3,517.531517,0,25499.57585
103001,20,2,4,0,Married,6,517.381102,0,25653.0551
143561,38,20,1,0,Single,7,517.2833796,0,25334.16898
123419,56,36,2,1,Married,0,517.2785548,0,25315.92774
165592,22,5,3,1,Single,12,517.2495203,0,25593.47602
171213,44,23,1,1,Single,4,517.2484855,0,25642.42428
117962,19,0,1,1,Married,6,517.2311218,0,25606.55609
135039,48,29,1,1,Married,2,517.2279524,0,25485.39762
120373,53,32,1,1,Married,1,517.2034459,0,25319.1723
171444,57,39,3,0,Single,9,517.1821163,0,25377.10582
178888,64,43,3,0,Single,3,517.1569153,0,25595.84577
144467,30,12,3,0,Single,7,517.1248229,0,25283.24115
103606,41,22,1,0,Married,4,516.9890908,0,25560.45454
166433,37,16,2,0,Single,10,516.8995385,0,25626.97693
138096,44,24,3,1,Single,3,516.8704031,0,25636.52016
177475,16,0,1,1,Single,9,516.8054766,0,25454.27383
119389,54,36,1,1,Single,4,516.7950519,0,25270.7526
180169,35,18,3,0,Single,8,516.7643522,0,25629.21761
113517,23,6,3,1,Married,1,516.683386,0,25424.1693
196695,18,0,2,0,Single,12,516.6607639,0,25435.0382
189521,44,26,1,0,Single,2,516.6473229,0,25385.36615
109556,19,1,3,0,Married,15,516.6382649,0,25390.91325
168877,25,7,3,1,Single,14,516.6068537,0,25365.34269
135138,20,2,3,0,Married,14,516.6017866,0,25430.08933
152328,36,16,1,1,Married,1,516.5329848,0,25228.64924
148679,21,4,2,1,Single,7,516.4370055,0,25250.85028
118874,25,7,4,0,Married,7,516.216328,0,25384.8164
182396,36,15,2,0,Single,6,516.1114568,0,25535.57284
159663,24,6,1,1,Single,11,516.0790928,0,25208.95464
156028,27,6,3,0,Single,7,515.9986972,0,25216.93486
180780,51,32,4,0,Single,10,515.9404578,0,25394.02289
142073,16,0,3,0,Single,15,515.8698595,0,25511.49298
146273,30,13,4,1,Single,0,515.8469815,0,25308.34908
173181,42,22,4,0,Single,0,515.8336994,0,25372.68497
174466,61,42,4,0,Single,5,515.8096795,0,25402.48398
100308,38,21,3,0,Single,8,515.4679101,0,25262.39551
154480,17,0,3,0,Married,15,515.426578,0,25316.3289
171705,29,12,2,0,Married,3,515.4020847,0,25496.10424
126030,52,31,3,0,Married,5,515.0600561,0,25285.00281
126031,37,20,2,0,Single,6,515.0405076,0,25339.02538
186434,48,28,2,0,Single,8,514.9922298,0,25168.61149
177206,37,17,2,0,Single,9,514.8116374,0,25353.58187
148820,24,5,3,1,Single,14,514.7946934,0,25438.73467
171677,17,0,3,0,Married,15,514.7311087,0,25420.55544
155482,20,1,1,1,Single,12,514.7287257,0,25387.43629
138165,53,33,2,1,Single,1,514.7171097,0,25511.85549
196143,51,31,1,0,Married,0,514.6965606,0,25489.82803
100032,68,47,3,0,Married,2,514.643375,0,25417.16875
111170,57,39,3,0,Single,15,514.5581364,0,25400.90682
149102,19,0,1,1,Single,15,514.3464743,0,25365.32372
177191,33,13,3,0,Single,7,514.3160063,0,25200.80032
190100,16,0,3,0,Single,12,514.2703663,0,25229.51832
135112,51,32,4,0,Single,5,514.2654663,0,25433.27332
112951,53,33,4,0,Married,5,514.2234708,0,25158.17354
198747,41,24,1,0,Single,6,514.2098312,0,25369.49156
135988,16,0,2,0,Single,13,514.1504616,0,25192.52308
179439,67,49,4,0,Single,3,513.9254653,0,25149.27327
110555,26,5,1,0,Single,2,513.9091147,0,25103.45574
198408,22,3,2,0,Married,6,513.8630957,0,25322.15479
172391,30,11,1,0,Single,7,513.8387199,0,25420.936
142970,39,18,3,1,Married,0,513.8101821,0,25131.50911
131869,18,0,3,1,Single,13,513.7467041,0,25143.33521
166968,52,34,1,1,Single,6,513.7114819,0,25180.5741
121216,19,2,2,0,Married,13,513.4584495,0,25413.92248
158249,38,20,2,0,Married,3,513.4092655,0,25451.46328
113051,17,0,2,0,Married,14,513.3606865,0,25084.03433
137912,17,0,3,1,Single,9,513.3529081,0,25424.64541
166046,55,36,3,1,Single,8,513.3494544,0,25120.47272
123667,52,34,2,1,Married,4,513.308942,0,25395.4471
156872,32,12,2,0,Single,7,513.061039,0,25112.05195
147832,41,22,4,1,Single,3,513.0333792,0,25426.66896
120901,50,31,4,1,Single,10,513.03153,0,25357.5765
115142,25,6,1,0,Single,14,512.9049801,0,25317.24901
109084,55,36,2,1,Married,0,512.8732401,0,25383.66201
156019,51,34,4,0,Single,7,512.8579436,0,25275.89718
158875,23,4,1,0,Married,14,512.8447128,0,25172.23564
190848,19,0,3,0,Single,13,512.7732665,0,25299.66333
155514,47,27,4,1,Single,7,512.7147153,0,25271.73577
188991,28,8,3,0,Single,8,512.6005403,0,25291.02702
195376,43,23,2,1,Married,1,512.4255573,0,25225.27787
127905,23,4,3,1,Married,1,512.2381967,0,25200.90984
133108,39,21,3,0,Single,7,512.1963882,0,25295.81941
166014,19,0,3,0,Married,14,512.1920681,0,25311.60341
125364,20,1,1,0,Married,6,512.1821988,0,25262.10994
147414,52,31,1,1,Married,5,511.9886291,0,25021.43146
189634,47,26,3,0,Married,5,511.9737592,0,25119.68796
132480,56,39,3,0,Single,10,511.9265755,0,25059.32878
166232,31,14,1,0,Married,2,511.7580432,0,25187.90216
189773,16,0,1,0,Married,14,511.7329733,0,25035.64867
160621,46,28,1,0,Married,3,511.7252723,0,25373.26362
126059,19,0,2,0,Single,12,511.7228049,0,25166.14025
102183,51,31,2,0,Single,5,511.6676298,0,25267.38149
123415,38,21,3,0,Single,8,511.628823,0,25245.44115
199205,54,35,2,0,Single,10,511.6047185,0,25354.23593
196028,25,6,3,1,Single,13,511.5813257,0,25247.06629
145884,17,0,4,0,Married,7,511.5249382,0,25041.24691
175816,32,15,1,0,Single,0,511.4659889,0,25304.29945
195518,47,28,2,1,Married,5,511.4521387,0,25170.60694
140147,17,0,1,1,Single,12,511.3999351,0,25226.99676
110585,50,32,4,0,Single,10,511.3851929,0,25241.25965
166875,35,18,2,0,Single,7,511.3257622,0,24993.28811
151488,61,44,3,0,Single,2,511.3069738,0,25263.34869
136275,27,6,1,0,Married,5,511.235708,0,25350.7854
128335,23,4,3,0,Married,14,511.201805,0,24963.09025
127811,24,7,1,1,Single,15,511.193896,0,25260.6948
153983,67,47,2,1,Single,4,511.0969097,0,25032.84549
198827,25,6,3,1,Married,9,510.886715,0,25169.33575
165527,35,18,2,0,Single,9,510.6502619,0,25140.5131
101465,25,7,3,0,Single,12,510.5356987,0,25202.78494
179573,51,31,2,1,Married,3,510.472611,0,25270.63055
129990,56,36,2,0,Single,8,510.3591905,0,25006.95953
122527,19,1,1,0,Married,13,510.3590398,0,25093.95199
154645,68,48,3,0,Single,3,510.2800031,0,25298.00016
171930,48,31,1,0,Single,2,510.2678438,0,25110.39219
193224,28,9,2,0,Single,6,510.2597547,0,25204.98774
134841,59,38,4,0,Single,9,510.2355171,0,24933.77586
120156,58,41,2,0,Single,7,510.2294704,0,24950.47352
126568,19,0,2,1,Single,14,509.9011956,0,24897.05978
177375,68,50,2,0,Single,4,509.8965713,0,25163.82857
142384,16,0,3,0,Married,9,509.7677548,0,25125.38774
137959,25,6,2,0,Single,12,509.6212314,0,25261.06157
119658,33,16,2,0,Single,9,509.6045066,0,25148.22533
141569,57,37,4,0,Single,7,509.5510796,0,25258.55398
194623,55,35,2,0,Single,9,509.4627758,0,25123.13879
156485,31,14,2,1,Married,1,509.4617109,0,25101.08555
127222,30,12,2,1,Married,2,509.4519769,0,24887.59885
184025,63,46,2,0,Single,3,509.4245965,0,25233.22983
128416,20,2,2,0,Single,14,509.3897629,0,24906.48815
118796,25,7,4,1,Married,3,509.3772513,0,24986.86257
125089,48,31,2,0,Single,8,509.3666843,0,25218.33422
172870,56,38,3,0,Single,8,509.3638436,0,25138.19218
127117,18,0,1,0,Married,12,509.1766552,0,25118.83276
192220,55,34,1,0,Single,12,509.1345942,0,24995.72971
102912,23,5,2,0,Married,13,509.1251757,0,24924.25879
166836,29,11,1,1,Single,0,509.0594248,0,25084.97124
105045,58,41,3,0,Single,7,508.9858737,0,25083.29369
131219,49,29,2,1,Single,1,508.6189206,0,25142.94603
191073,35,16,2,1,Married,5,508.5575122,0,25218.87561
174772,16,0,2,0,Single,14,508.5325412,0,25222.62706
128625,32,13,4,0,Single,10,508.4851291,0,24829.25646
175646,43,22,3,0,Single,5,508.3812419,0,24884.0621
193193,49,31,1,0,Married,3,508.3393905,0,25075.96953
140197,19,0,4,0,Married,15,508.3261896,0,24976.30948
145461,48,29,4,1,Married,2,508.2982535,0,25012.91268
154426,21,3,3,0,Married,13,508.1753085,0,25090.76543
168914,22,3,3,0,Married,3,507.9072134,0,25035.36067
186623,52,33,2,0,Single,7,507.8694992,0,25111.47496
163791,25,8,2,0,Married,13,507.5708927,0,25171.54464
187137,34,17,3,0,Single,13,507.4618644,0,25167.09322
182918,44,24,1,0,Single,10,507.3039313,0,24813.19657
174405,18,0,3,0,Married,12,507.2981493,0,24891.90747
198462,25,8,2,1,Single,14,507.1576806,0,25089.88403
173426,28,8,2,0,Married,3,506.9298049,0,25114.49025
115395,38,19,2,0,Married,0,506.922688,0,24766.1344
161868,17,0,3,1,Married,2,506.9099905,0,24990.49953
188060,55,34,3,0,Single,8,506.8969568,0,25075.84784
191104,57,39,2,0,Single,2,506.7966527,0,24971.83264
175866,43,25,2,1,Married,3,506.7637966,0,24921.18983
181044,47,28,3,1,Married,5,506.6747288,0,24767.73644
102155,32,14,3,1,Married,1,506.6642713,0,24881.21357
176858,57,38,2,0,Married,5,506.5945122,0,24851.72561
187062,16,0,2,0,Married,2,506.5618893,0,24756.09447
131870,27,9,2,0,Single,11,506.5426469,0,24819.13235
113593,50,33,4,0,Single,11,506.5247959,0,24948.2398
194747,42,22,3,1,Single,5,506.4480532,0,24853.40266
157166,17,0,3,0,Married,7,506.4295787,0,25100.47894
165461,34,16,1,1,Married,5,506.4191291,0,24862.95646
121451,35,14,2,1,Married,3,506.3626082,0,24745.13041
182881,19,2,3,1,Single,11,506.3021724,0,25078.10862
109721,21,2,2,1,Single,15,506.2082003,0,24909.41002
174283,29,9,4,0,Single,7,505.8833102,0,24800.16551
110141,68,51,3,0,Married,2,505.8732548,0,24961.66274
176183,27,8,2,0,Single,10,505.7678196,0,25005.39098
155059,66,45,4,1,Single,1,505.5540679,0,25004.7034
180380,52,33,1,0,Single,7,505.4161096,0,24826.80548
171454,43,22,3,1,Married,1,505.324672,0,24689.2336
136851,70,49,3,1,Single,0,505.3069656,0,24962.34828
180062,44,25,3,0,Married,2,505.2710024,0,25021.55012
192290,25,8,2,0,Single,11,505.2605779,0,24704.0289
183441,34,13,3,0,Single,8,505.2486386,0,24697.43193
182805,46,26,4,0,Single,9,505.1093518,0,24743.46759
145342,21,2,3,1,Single,12,504.9550731,0,24921.75366
173255,46,27,4,1,Single,10,504.8856084,0,24721.28042
193102,19,2,3,0,Single,15,504.8639467,0,25036.19734
169030,21,2,2,0,Single,13,504.8449361,0,24651.24681
116427,32,15,3,0,Married,1,504.8083705,0,24723.41853
166434,53,34,2,0,Single,6,504.6687082,0,24676.43541
173463,52,35,2,1,Married,1,504.5877345,0,24979.38673
120443,38,19,3,0,Single,7,504.547965,0,24736.39825
160006,54,37,2,0,Single,14,504.3927277,0,24806.63639
146277,34,14,2,0,Single,10,504.3764517,0,25005.82259
177154,57,38,2,0,Single,10,504.3286737,0,24907.43369
165495,56,39,2,0,Single,6,504.3038973,0,24748.19487
118922,54,33,3,0,Single,7,504.28471,0,24915.2355
121810,37,20,4,1,Married,1,504.2440072,0,24715.20036
192994,52,34,2,1,Single,2,504.1945897,0,24630.72949
185260,26,6,4,0,Single,7,504.1454866,0,24782.27433
148617,44,25,2,1,Married,0,504.0314278,0,24925.57139
100949,38,19,2,1,Single,2,503.8319877,0,24729.59939
149570,48,28,2,0,Single,9,503.8264386,0,24645.32193
128399,64,46,3,1,Single,0,503.8090316,0,24848.45158
112313,24,5,3,1,Single,11,503.7813256,0,24841.06628
151556,21,2,4,0,Married,11,503.7421438,0,24951.10719
106265,45,26,4,1,Single,1,503.6657596,0,24796.28798
169892,23,4,3,0,Married,14,503.6588735,0,24967.94368
106602,24,6,3,1,Single,14,503.5861299,0,24783.3065
120439,32,13,2,0,Single,4,503.5517722,0,24845.58861
143333,21,2,4,1,Single,13,503.5175373,0,24599.87687
118078,34,15,3,0,Single,7,503.4558814,0,24914.79407
124862,43,23,3,1,Married,0,503.3990749,0,24591.95375
124401,40,19,1,0,Single,8,503.3720137,0,24678.60069
144549,19,1,3,1,Single,13,503.3603026,0,24945.01513
109715,33,15,4,0,Married,2,503.3392623,0,24786.96312
119219,35,15,3,1,Married,2,503.1992237,0,24770.96119
180117,16,0,3,1,Single,12,503.171863,0,24799.59315
183239,18,1,4,1,Single,13,503.1516614,0,24658.58307
164123,25,8,1,1,Married,6,503.1421534,0,24933.10767
141433,36,15,3,0,Single,10,503.1180735,0,24662.90368
103743,23,6,2,1,Single,8,503.0672471,0,24562.36236
183996,53,34,1,1,Married,1,503.0542024,0,24780.71012
136359,20,3,1,1,Married,3,503.0228692,0,24658.14346
164738,18,1,3,1,Single,12,502.9435603,0,24731.17802
124859,51,33,2,0,Single,0,502.9362785,0,24564.81393
177437,38,21,4,1,Single,5,502.9167088,0,24809.83544
153031,23,5,4,0,Married,14,502.8963159,0,24801.8158
144878,48,28,4,0,Married,2,502.8923299,0,24738.6165
175249,31,13,1,1,Single,5,502.7124322,0,24560.62161
131412,16,0,4,0,Married,11,502.6916232,0,24811.58116
121949,16,0,2,1,Single,11,502.645551,0,24781.27755
153029,64,47,2,1,Single,1,502.643824,0,24827.1912
104070,40,20,1,0,Married,3,502.6195503,0,24861.97752
104307,16,0,1,1,Married,6,502.6132944,0,24703.66472
183975,32,11,3,1,Single,0,502.5834979,0,24771.1749
141436,20,3,2,0,Married,6,502.4378501,0,24675.89251
198147,22,3,3,1,Single,7,502.4354966,0,24706.77483
183731,24,6,2,0,Married,11,502.3428123,0,24682.14062
191980,18,1,2,0,Married,7,502.3279723,0,24727.39862
173338,41,21,4,1,Married,1,502.2764181,0,24597.82091
148158,24,5,2,1,Married,6,502.240405,0,24795.02025
109487,19,2,1,0,Married,14,502.1504835,0,24612.52418
148649,20,1,3,1,Single,15,502.1421869,0,24822.10935
122093,54,37,1,1,Single,5,502.1356528,0,24859.78264
162680,32,12,1,0,Single,10,502.0650609,0,24603.25305
181140,25,8,1,1,Single,9,501.9949382,0,24654.74691
144536,25,7,3,0,Single,15,501.9788486,0,24776.94243
147971,41,23,3,0,Single,8,501.5088854,0,24782.44427
169063,63,46,4,1,Single,2,501.3705536,0,24547.52768
176266,18,0,1,1,Single,9,501.3537223,0,24547.68612
159876,34,15,2,0,Single,7,501.3198275,0,24593.99138
176246,39,18,3,0,Single,6,501.2964869,0,24810.82435
156144,20,1,3,0,Married,13,501.2273032,0,24580.36516
164720,53,35,3,0,Married,5,501.2102467,0,24847.51234
106220,30,12,2,1,Single,2,501.1905383,0,24696.52692
175647,16,0,2,1,Married,13,501.1084558,0,24514.42279
174727,37,18,4,0,Married,5,500.9940391,0,24503.70196
181623,21,4,2,0,Single,13,500.89131,0,24620.5655
186109,54,35,3,1,Single,3,500.8659766,0,24627.29883
182938,47,29,2,0,Single,8,500.8359073,0,24748.79537
195026,21,2,2,1,Married,9,500.8230195,0,24597.15098
143836,22,5,2,1,Single,15,500.6980582,0,24821.90291
163648,38,19,2,0,Single,7,500.6701502,0,24535.50751
170703,22,5,4,1,Married,9,500.5121407,0,24612.60704
162343,52,34,3,0,Single,7,500.4497483,0,24561.48742
148213,51,31,2,0,Single,10,500.4022292,0,24697.11146
195198,53,33,3,0,Single,5,500.3690353,0,24532.45177
165493,22,5,3,0,Married,2,500.339043,0,24705.95215
180162,57,40,4,1,Single,9,500.2550126,0,24698.75063
185378,18,0,1,1,Single,8,500.2238116,0,24727.19058
180410,44,24,4,1,Married,5,500.1520161,0,24698.60081
186614,69,52,3,0,Single,1,500.0884252,0,24539.42126
135690,53,32,3,0,Single,14,500.0798791,0,24796.99396
131180,23,6,4,0,Single,13,500.059073,0,24669.95365
180133,23,4,2,1,Married,8,500.0157464,0,24539.78732
123531,38,19,4,1,Single,4,499.9388563,0,24458.94282
134594,37,18,3,0,Married,1,499.8944309,0,24643.72155
125569,26,9,2,1,Single,6,499.890252,0,24705.5126
153049,17,0,3,0,Married,11,499.8436639,0,24488.1832
195610,17,0,2,0,Married,12,499.7639256,0,24610.19628
129054,34,14,4,0,Single,9,499.7077501,0,24411.38751
190507,60,39,4,0,Married,5,499.5409298,0,24582.04649
154000,21,4,4,0,Married,14,499.3441883,0,24578.20942
146987,24,5,2,1,Married,6,499.1907587,0,24554.53794
123961,16,0,3,1,Single,11,499.1825389,0,24472.12695
156049,25,8,3,1,Single,13,499.159061,0,24527.95305
148155,66,49,1,1,Married,5,499.0903996,0,24390.51998
199603,29,11,4,1,Single,6,499.0814058,0,24657.07029
122133,44,26,3,1,Married,4,499.0778914,0,24495.89457
177116,24,7,3,0,Married,6,499.0346116,0,24699.73058
177073,20,1,3,0,Single,14,499.032731,0,24718.63655
124558,67,49,2,0,Married,1,499.0143263,0,24703.71632
103861,35,17,1,0,Single,8,499.0115671,0,24583.57836
106081,18,1,4,1,Married,9,498.9931769,0,24479.65885
172624,45,28,1,0,Single,10,498.8552508,0,24362.76254
148144,38,21,3,0,Single,9,498.819096,0,24446.9548
163909,18,0,3,0,Married,9,498.7428318,0,24588.14159
174836,55,37,3,0,Single,9,498.7307055,0,24358.53528
185290,33,12,2,1,Single,7,498.6506277,0,24363.53139
132621,18,0,2,1,Married,9,498.6226712,0,24649.13356
158058,16,0,4,1,Single,7,498.5867323,0,24513.33662
104762,23,5,3,0,Married,14,498.5825402,0,24693.12701
109315,35,14,3,0,Married,0,498.4484834,0,24551.42417
107342,63,42,3,0,Single,3,498.3575125,0,24450.87563
105266,19,1,3,0,Single,14,498.2880731,0,24542.40366
116086,53,35,2,1,Single,7,498.2544249,0,24350.72125
163761,54,34,3,1,Married,5,498.1254997,0,24522.27499
168471,23,6,3,1,Married,9,498.0613531,0,24453.06766
121118,20,1,4,0,Married,14,497.9847456,0,24534.23728
125643,41,23,3,1,Married,2,497.9610354,0,24520.05177
119017,69,50,3,1,Single,2,497.8430728,0,24376.15364
139852,22,4,3,0,Married,13,497.7441376,0,24420.20688
126278,48,31,3,1,Single,2,497.7211086,0,24371.05543
116042,18,0,3,0,Single,14,497.6852173,0,24594.26087
150407,27,7,3,1,Single,5,497.6672225,0,24652.36113
146939,61,41,2,1,Single,4,497.5727228,0,24393.63614
134034,47,27,3,1,Single,4,497.5374821,0,24397.87411
145012,21,2,3,0,Married,15,497.4716531,0,24316.58266
155311,19,0,2,1,Married,12,497.3690476,0,24501.45238
122071,22,5,3,0,Married,11,497.3269336,0,24602.34668
122589,31,11,4,0,Single,6,497.3027208,0,24432.13604
183936,41,20,3,0,Single,7,497.2943847,0,24555.71924
138606,41,23,4,0,Single,9,497.2546463,0,24388.73232
150525,22,5,1,1,Married,14,497.2378044,0,24354.89022
167235,17,0,2,0,Married,13,497.1846427,0,24441.23214
140215,25,8,4,1,Married,5,497.1707541,0,24280.53771
126050,33,14,3,0,Single,8,497.0919139,0,24405.5957
130244,27,10,3,1,Single,3,496.9909239,0,24425.5462
111542,34,15,3,0,Single,10,496.9869339,0,24525.3467
172679,25,6,1,1,Married,1,496.9800239,0,24278.0012
142880,38,19,2,0,Single,10,496.9686527,0,24525.43264
157886,55,37,1,1,Single,5,496.9354782,0,24279.77391
144994,25,7,2,0,Married,6,496.8511596,0,24369.55798
135336,37,18,3,0,Single,10,496.808397,0,24353.41985
183785,20,1,3,0,Single,15,496.7315303,0,24360.57652
162109,24,6,1,0,Married,13,496.7009686,0,24434.04843
102337,41,20,1,0,Single,5,496.6251555,0,24603.25778
119743,20,3,2,1,Single,10,496.3034295,0,24553.17148
175141,56,35,3,0,Single,3,496.2508304,0,24411.54152
128569,26,8,4,0,Single,9,496.2432547,0,24415.16274
125994,38,19,2,1,Single,4,496.1258875,0,24568.29438
126387,47,28,3,0,Single,5,496.1250167,0,24308.25084
108976,20,2,2,1,Single,9,496.105301,0,24449.26505
101190,22,4,3,1,Single,15,496.1007866,0,24595.03933
136278,49,31,3,1,Single,1,496.0153048,0,24323.76524
144906,37,20,2,0,Single,12,496.0066722,0,24232.33361
131925,24,6,2,1,Single,14,495.9313333,0,24295.56667
108655,53,33,3,0,Single,0,495.8811039,0,24403.0552
175941,60,41,2,0,Single,7,495.8744707,0,24328.72354
139715,54,33,4,0,Single,7,495.8577099,0,24343.8855
122194,61,42,1,0,Single,12,495.8440724,0,24397.20362
157870,47,30,2,0,Married,1,495.8239471,0,24576.19736
109842,45,28,3,1,Single,8,495.786754,0,24247.3377
190425,67,49,3,1,Married,0,495.6786828,0,24271.93414
182273,40,20,2,0,Married,3,495.4931261,0,24214.65631
131251,48,29,3,1,Single,2,495.4474053,0,24414.37027
139932,39,19,3,0,Single,10,495.4356608,0,24298.78304
198863,41,23,3,0,Single,6,495.3473402,0,24425.36701
138741,23,4,3,0,Married,9,495.3208549,0,24286.04275
115130,37,18,2,1,Married,5,495.27612,0,24173.806
168185,16,0,2,1,Married,4,495.2585145,0,24365.92573
110599,22,5,1,0,Married,2,495.1954386,0,24202.77193
175429,21,2,4,1,Married,12,495.187766,0,24237.3883
121475,26,8,3,0,Married,3,495.1714825,0,24422.57413
128658,35,16,2,0,Married,5,495.1681692,0,24408.40846
123516,19,0,4,1,Married,6,495.0950612,0,24238.75306
126775,25,8,1,1,Single,10,495.0498383,0,24171.49192
181356,48,28,2,1,Single,2,494.9337793,0,24445.68897
189548,24,5,4,0,Married,11,494.8396278,0,24304.98139
130638,21,4,3,1,Single,14,494.7451375,0,24490.25688
187258,51,32,3,0,Married,4,494.7170351,0,24469.85176
195516,20,3,3,0,Single,11,494.7122485,0,24203.61243
100799,60,40,2,1,Married,1,494.682189,0,24469.10945
155044,20,1,3,1,Single,13,494.6590334,0,24315.95167
132427,43,23,2,1,Single,1,494.6508114,0,24516.54057
184343,44,23,1,0,Single,11,494.557812,0,24396.8906
149696,18,0,2,0,Single,11,494.5553381,0,24374.76691
104920,59,38,2,0,Single,12,494.465162,0,24321.2581
135709,67,48,3,0,Single,7,494.4420049,0,24202.10025
177793,31,11,3,0,Married,4,494.3033414,0,24189.16707
178418,43,25,4,1,Single,2,494.2415521,0,24203.0776
102683,40,20,2,1,Married,0,494.1563874,0,24135.81937
150113,38,20,1,0,Single,6,494.1024466,0,24503.12233
189640,62,44,3,1,Married,4,494.0244144,0,24481.22072
126728,68,48,2,0,Married,3,494.0108596,0,24252.54298
170272,24,6,1,1,Married,12,493.9555063,0,24330.77532
193964,20,1,3,1,Single,12,493.9541163,0,24250.70582
118084,50,29,3,1,Married,4,493.8233283,0,24298.16642
170986,20,2,1,0,Single,13,493.8180385,0,24445.90193
187317,22,5,2,1,Married,13,493.7547271,0,24331.73636
141248,22,5,2,1,Married,8,493.3039681,0,24177.19841
133005,24,5,1,1,Single,13,493.2976733,0,24454.88367
189365,45,26,1,0,Married,1,493.0585896,0,24363.92948
192465,55,37,2,1,Married,5,492.963983,0,24216.19915
147765,53,32,4,0,Single,12,492.9224979,0,24190.1249
152347,30,10,2,1,Single,8,492.7189607,0,24191.94804
117485,43,26,3,1,Single,2,492.5751319,0,24283.7566
198418,65,47,3,0,Married,5,492.5699465,0,24396.49733
165431,62,44,4,1,Married,2,492.556772,0,24305.8386
168366,21,4,3,1,Married,4,492.2762627,0,24278.81314
143895,39,19,3,1,Single,9,492.2734875,0,24312.67438
125080,18,0,3,1,Single,13,492.2584052,0,24124.92026
132925,49,30,4,0,Single,14,492.2416352,0,24305.08176
109070,36,19,3,0,Single,9,492.0101209,0,24293.50605
168567,32,14,2,0,Single,7,491.9689152,0,24121.44576
184812,49,30,2,0,Single,10,491.907308,0,24355.3654
137680,24,7,2,0,Married,15,491.8858119,0,24104.2906
194350,42,21,2,1,Single,1,491.8573176,0,24254.86588
112803,23,5,3,0,Single,14,491.6209559,0,24300.0478
190759,59,40,3,0,Married,5,491.5911903,0,24149.55952
128525,27,8,3,1,Single,7,491.5678067,0,24171.39034
184004,52,35,2,0,Single,9,491.5015654,0,24085.07827
133783,26,6,1,0,Single,6,491.4299422,0,24092.49711
106945,21,4,2,0,Married,8,491.2875129,0,24227.37565
102548,16,0,3,1,Single,6,491.1764037,0,24122.82019
120100,32,15,3,0,Single,10,491.1227067,0,24238.13533
195053,57,39,2,0,Single,9,491.049278,0,23953.4639
176062,41,23,2,0,Single,3,491.0277254,0,24329.38627
132881,25,8,3,1,Single,6,490.9960318,0,24349.80159
132756,49,32,2,0,Single,10,490.9612311,0,24127.06156
100693,47,27,1,1,Married,1,490.9507383,0,24263.53692
146726,44,26,4,0,Single,10,490.8547467,0,24150.73734
120151,23,6,3,1,Single,11,490.8473595,0,23988.36798
107065,69,52,2,1,Married,0,490.8277044,0,23964.38522
149865,56,39,3,1,Married,3,490.7600314,0,24241.00157
192820,18,0,2,0,Married,12,490.7504061,0,23989.52031
101681,21,2,4,1,Single,13,490.7404258,0,24122.02129
175890,59,40,4,0,Single,2,490.6784955,0,23972.92478
127747,53,33,2,0,Single,10,490.6586864,0,24279.93432
117291,57,39,3,0,Single,8,490.6286884,0,24157.43442
172823,31,10,3,0,Single,10,490.590609,0,24171.53045
132418,53,35,2,0,Single,8,490.554657,0,24044.73285
110024,24,6,3,1,Single,15,490.4523918,0,24161.61959
191384,19,0,2,1,Single,6,490.4280641,0,24229.40321
104885,33,12,2,0,Single,10,490.2322292,0,23948.61146
153850,36,18,2,0,Married,3,490.1887384,0,24274.43692
177448,22,4,3,1,Single,13,490.1057473,0,24214.28737
111578,45,28,2,1,Single,1,490.0793064,0,24030.96532
104411,56,36,2,0,Single,5,490.056154,0,23973.8077
199401,16,0,3,1,Single,14,490.0551278,0,24299.75639
128673,33,12,4,0,Married,0,490.0355162,0,24250.77581
142619,59,38,1,1,Single,15,490.0252621,0,24300.26311
139367,45,28,4,1,Married,5,490.0024758,0,24285.12379
186288,25,6,3,0,Single,14,489.9412642,0,24136.06321
133314,37,18,2,0,Single,15,489.9230992,0,23950.15496
191999,17,0,4,0,Married,12,489.8625991,0,24092.12996
106458,22,4,1,1,Married,6,489.859737,0,24239.98685
129757,24,5,1,0,Married,7,489.8566707,0,24252.83354
199853,19,1,2,1,Married,14,489.8100134,0,24015.50067
148317,26,8,1,0,Single,7,489.774795,0,24228.73975
180896,47,29,4,1,Single,5,489.70268,0,24133.134
143868,58,40,2,0,Single,8,489.6712881,0,23942.56441
164027,24,6,4,1,Single,12,489.5695559,0,24090.4778
161962,23,5,1,0,Married,9,489.5382292,0,23942.91146
168320,46,28,1,0,Single,8,489.5102333,0,24036.51167
179057,70,52,2,1,Married,4,489.4043716,0,24014.21858
157198,16,0,3,0,Single,15,489.3780602,0,24067.90301
111010,21,2,4,1,Married,4,489.3396942,0,24169.98471
128381,63,46,2,1,Single,1,489.1814974,0,24156.07487
160812,31,13,2,1,Married,1,489.1019963,0,23929.09982
141246,21,3,3,0,Married,12,489.0396797,0,23912.98399
113622,31,13,4,1,Married,3,489.0028192,0,23887.14096
108871,48,30,3,0,Single,0,488.9406356,0,23944.03178
101872,16,0,3,1,Married,10,488.9296432,0,24143.48216
131988,54,35,2,1,Married,0,488.9204235,0,23938.02118
114358,21,3,3,0,Single,15,488.8615895,0,23979.07948
188268,38,17,4,0,Single,2,488.8218131,0,24171.09066
102492,21,2,3,1,Married,7,488.7621336,0,24146.10668
109730,46,28,3,0,Single,8,488.755678,0,23965.7839
174141,27,7,1,1,Married,1,488.6956097,0,23977.78049
138819,43,22,3,0,Single,9,488.6824004,0,23950.12002
101197,35,16,3,1,Married,1,488.6605084,0,23928.02542
140249,18,0,2,1,Married,7,488.5919464,0,24177.59732
110053,24,5,2,1,Single,15,488.3864875,0,24130.32438
132739,32,11,2,0,Single,6,488.3738286,0,24164.69143
192481,43,22,2,0,Single,9,488.3588583,0,23926.94292
167511,55,38,1,0,Married,0,488.3571976,0,23960.85988
181808,37,18,2,1,Married,3,488.343685,0,24046.18425
186734,54,37,4,0,Single,7,488.2994065,0,23828.97033
158855,19,1,1,1,Married,2,488.2883074,0,23931.41537
117822,20,1,2,1,Single,12,488.2861253,0,24034.30627
100178,53,33,3,0,Single,1,488.2703453,0,24024.51727
177338,50,29,3,0,Single,15,488.2609925,0,24051.04963
157860,52,35,2,0,Married,1,488.2366164,0,24045.83082
121586,21,2,3,1,Married,9,488.227282,0,24147.3641
158744,17,0,2,0,Married,15,488.2250123,0,23953.25062
115787,28,8,4,0,Married,2,488.1996322,0,24208.98161
132908,17,0,3,0,Married,14,488.1049567,0,24123.24784
198848,30,11,3,0,Married,1,488.0595669,0,24163.97835
196420,30,12,2,1,Single,10,488.0137975,0,23868.68988
154889,16,0,2,0,Married,12,487.8985999,0,23866.93
148740,30,11,2,1,Single,0,487.8912641,0,24045.56321
143867,28,9,1,0,Married,2,487.8515553,0,23935.57777
132302,54,36,1,1,Single,8,487.8492932,0,23893.46466
195426,22,5,4,1,Single,11,487.8183517,0,23999.91759
115868,52,32,2,0,Married,4,487.7118325,0,23826.59163
144077,33,15,2,0,Married,1,487.6017329,0,24129.08665
140510,35,16,2,1,Married,1,487.5793938,0,24084.96969
185068,70,52,4,0,Single,10,487.4054935,0,23893.27468
177964,56,35,3,0,Single,10,487.3814349,0,24026.07175
170265,68,48,4,0,Married,3,487.3707488,0,23806.53744
160431,40,20,2,1,Single,9,487.3630135,0,23896.15068
155295,32,15,4,1,Married,3,487.361598,0,23956.0799
173205,58,39,3,1,Single,1,487.3560719,0,23852.8036
130001,28,11,3,1,Single,2,487.3538882,0,23767.69441
115154,23,6,1,0,Single,11,487.1763139,0,23780.8157
126611,41,20,1,0,Single,12,487.0448096,0,23870.24048
129101,24,7,2,1,Single,14,487.0326234,0,24029.63117
177919,29,9,4,1,Married,2,486.7930053,0,23784.65027
193275,38,18,4,0,Single,9,486.7611141,0,23870.05571
147945,58,39,2,0,Single,13,486.7217129,0,24054.08565
123079,25,8,1,0,Married,8,486.7162359,0,23989.8118
131296,37,16,3,1,Married,2,486.7029206,0,23967.14603
108447,25,6,2,1,Married,6,486.6687182,0,23800.43591
182490,49,28,4,1,Married,1,486.6595459,0,23915.9773
109977,52,31,4,1,Married,2,486.6339382,0,23731.69691
129783,16,0,3,1,Married,6,486.6290318,0,23982.45159
123111,45,25,3,0,Single,4,486.594638,0,24043.7319
120303,18,1,2,1,Single,12,486.5719053,0,23895.59527
194599,26,9,2,0,Single,6,486.5580131,0,23736.90066
131621,52,32,2,1,Married,0,486.4673105,0,23840.36553
165213,51,31,2,0,Single,10,486.428662,0,24009.4331
165567,48,30,2,0,Single,1,486.393953,0,23830.69765
144117,27,10,2,0,Single,6,486.329603,0,23773.48015
193752,52,34,3,0,Married,3,486.2978109,0,23826.89055
103279,52,33,1,0,Single,6,486.2943804,0,23988.71902
137040,47,26,3,0,Single,9,486.2389158,0,24036.94579
134035,43,23,3,0,Married,2,486.206212,0,23984.3106
191220,22,5,2,1,Married,15,486.1967145,0,23948.83573
143784,57,37,3,0,Single,9,486.1554713,0,23887.77357
172354,29,9,4,0,Married,2,486.1416042,0,23874.08021
198443,58,37,2,0,Married,5,486.1067787,0,23868.33894
128453,24,5,2,0,Single,14,486.0783301,0,23752.91651
153986,22,4,3,1,Single,12,486.0362198,0,23937.81099
139519,16,0,3,1,Single,7,485.9079225,0,24007.39613
190897,18,0,3,0,Married,11,485.739117,0,23848.95585
149615,22,3,4,1,Married,10,485.7020951,0,23717.10476
131564,46,26,2,1,Single,8,485.6433513,0,23802.16757
176186,50,30,3,1,Single,8,485.592403,0,23786.62015
163045,45,28,2,0,Single,8,485.4976689,0,23852.88345
174865,23,6,4,0,Single,13,485.4475021,0,24066.37511
165185,22,4,1,0,Single,12,485.4291054,0,23993.45527
144067,48,30,2,0,Single,9,485.3971298,0,23953.85649
151489,26,6,2,1,Single,1,485.3858243,0,23752.29122
136201,43,23,2,1,Married,1,485.269917,0,23875.49585
171392,25,6,2,1,Single,7,485.1853442,0,23781.26721
178213,63,42,3,0,Single,3,485.1799258,0,23849.99629
122051,56,39,3,1,Married,0,485.0029454,0,23873.14727
168315,63,44,2,1,Married,3,484.9096114,0,23791.48057
140220,17,0,1,0,Single,13,484.7934584,0,23748.67292
189674,23,4,3,1,Single,9,484.7094429,0,23684.47215
165949,19,0,1,1,Single,9,484.6961144,0,23641.80572
138689,23,5,3,1,Married,10,484.6273438,0,23998.36719
156841,21,3,2,0,Married,6,484.6149446,0,23839.74723
189276,38,18,1,1,Single,7,484.5883292,0,23687.41646
145027,24,7,3,1,Single,12,484.5543496,0,23741.71748
118085,25,6,2,1,Married,1,484.5439984,0,23833.19992
112606,41,20,3,0,Single,8,484.5409196,0,23887.04598
112431,27,8,1,0,Single,9,484.5144708,0,23679.72354
182192,18,0,2,0,Married,5,484.4999933,0,23723.99967
200208,21,2,2,0,Married,15,484.4608066,0,23944.04033
119029,18,0,3,1,Single,13,484.403153,0,23972.15765
185296,27,8,3,1,Single,6,484.3969299,0,23815.8465
139800,38,19,2,1,Single,9,484.3743228,0,23976.71614
125046,29,11,3,0,Married,1,484.3687625,0,24006.43813
114559,22,3,2,0,Single,11,484.3501358,0,23876.50679
179511,58,39,3,1,Married,0,484.3306049,0,23897.53025
142055,49,28,4,0,Married,3,484.2915255,0,23927.57628
152327,56,38,1,1,Single,6,484.2868664,0,23820.34332
190969,36,19,3,0,Single,9,484.2749778,0,23816.74889
184650,42,24,1,0,Single,6,484.2579932,0,23935.89966
111764,20,2,2,1,Single,12,484.2490831,0,23999.45416
152601,23,4,2,1,Single,14,484.2424026,0,23944.12013
117693,47,27,3,1,Married,5,484.2264568,0,23653.32284
186334,22,4,1,1,Single,10,484.2131818,0,23859.65909
165622,46,26,2,0,Single,8,484.2038417,0,23894.19209
118698,32,12,2,0,Single,3,484.183253,0,23956.16265
104415,20,3,4,0,Married,14,484.181639,0,23910.08195
103020,18,0,2,0,Married,7,484.1695927,0,23945.47964
106808,19,2,2,1,Single,13,484.1244503,0,23689.22252
194519,34,13,1,0,Single,9,484.1111346,0,23660.55673
118752,42,24,2,0,Single,6,483.9802141,0,23808.01071
199262,19,2,2,1,Married,9,483.7474678,0,23909.37339
190873,25,6,2,0,Married,6,483.6953914,0,23938.76957
183329,44,26,3,1,Single,7,483.656607,0,23945.83035
104758,56,39,2,0,Single,9,483.6039055,0,23635.19528
167948,65,45,3,0,Single,1,483.5479476,0,23913.39738
123194,46,26,4,0,Single,0,483.4326523,0,23869.63262
168704,50,32,2,1,Single,5,483.4007776,0,23840.03888
111812,22,4,4,1,Single,9,483.225785,0,23880.28925
161371,67,48,3,1,Married,5,483.1804634,0,23797.02317
148000,54,35,4,1,Single,5,482.9441626,0,23575.20813
119483,24,6,3,1,Single,12,482.918177,0,23776.90885
137286,25,8,1,0,Single,12,482.8592229,0,23902.96115
125984,24,7,1,1,Married,9,482.8298421,0,23784.49211
185320,39,21,4,1,Single,5,482.8221754,0,23804.10877
129117,22,3,2,1,Single,15,482.7932164,0,23822.66082
168569,21,4,3,1,Single,14,482.7765463,0,23704.82732
158933,18,1,4,0,Single,15,482.7386923,0,23930.93462
191062,66,47,4,1,Single,3,482.7107033,0,23654.53517
107080,50,30,3,0,Single,12,482.6817559,0,23861.0878
151793,45,27,2,1,Single,8,482.675552,0,23818.7776
196644,28,7,2,1,Single,10,482.6396475,0,23538.98238
131996,44,24,4,0,Single,8,482.6284386,0,23765.42193
179260,25,6,3,1,Single,11,482.6161107,0,23856.80554
104613,25,7,2,1,Single,14,482.6127533,0,23537.63767
121135,23,4,2,0,Single,12,482.5630144,0,23651.15072
187245,30,11,1,0,Single,10,482.5174629,0,23799.87315
133470,21,2,2,0,Single,10,482.433572,0,23712.6786
131442,64,45,3,1,Single,4,482.3919225,0,23584.59613
162225,19,2,3,0,Married,12,482.3618892,0,23763.09446
176964,19,0,3,0,Single,13,482.2764565,0,23790.82283
141655,31,12,1,0,Single,6,482.2712561,0,23519.56281
168301,61,42,1,0,Married,3,482.2699173,0,23850.49587
159198,20,1,4,1,Single,10,482.2676902,0,23715.38451
180166,19,2,3,0,Single,13,482.2491178,0,23553.45589
108598,25,8,3,1,Married,12,482.1907972,0,23728.53986
152351,25,6,1,1,Married,5,481.9361346,0,23601.80673
170974,25,8,1,0,Married,12,481.9321159,0,23726.6058
186358,46,26,4,0,Single,9,481.9254144,0,23584.27072
118207,35,18,2,1,Single,7,481.8868022,0,23742.34011
167845,54,36,2,0,Single,12,481.8815938,0,23837.07969
196724,28,9,1,1,Single,1,481.8169922,0,23609.84961
189720,16,0,3,0,Married,6,481.8037116,0,23600.18558
169883,47,26,4,0,Single,11,481.8013066,0,23867.06533
130311,23,4,2,1,Married,1,481.7940738,0,23830.70369
165197,37,20,2,0,Single,3,481.7089095,0,23739.44548
136860,17,0,3,1,Single,13,481.6609542,0,23798.04771
152722,17,0,1,0,Married,11,481.5460256,0,23570.30128
160356,34,14,3,0,Single,1,481.4673179,0,23551.3659
189904,17,0,2,1,Married,14,481.4394642,0,23855.97321
196262,22,3,2,0,Married,14,481.4381007,0,23694.90504
122207,26,8,1,0,Single,6,481.3734693,0,23756.67346
139437,58,40,2,1,Single,1,481.3603897,0,23757.01949
165045,58,41,3,0,Single,10,481.3387811,0,23864.93906
123166,16,0,2,1,Single,13,481.335332,0,23656.7666
171682,26,6,1,1,Single,1,481.2460799,0,23695.304
196594,22,4,1,0,Single,12,481.2308348,0,23614.54174
164469,25,8,3,0,Single,14,481.1921774,0,23654.60887
131627,24,5,1,1,Single,14,481.1598112,0,23797.99056
178913,47,28,1,0,Single,10,481.06576,0,23823.288
160670,30,13,2,1,Married,4,481.0608707,0,23647.04354
103284,16,0,1,1,Single,14,480.9872605,0,23718.36303
113307,28,8,2,0,Single,9,480.7857976,0,23458.28988
142478,59,42,3,0,Single,8,480.7611661,0,23510.05831
113437,68,48,3,0,Single,4,480.6950282,0,23561.75141
103588,43,24,1,1,Single,1,480.6784621,0,23516.92311
153748,38,19,3,0,Single,9,480.5787102,0,23649.93551
105181,46,26,4,1,Single,3,480.4791045,0,23541.95523
135494,19,2,3,0,Single,15,480.4408846,0,23717.04423
189757,67,50,1,1,Single,8,480.3987095,0,23531.93548
100454,43,23,2,0,Single,5,480.2553519,0,23613.7676
133689,70,53,1,0,Married,0,480.2444319,0,23564.2216
144868,19,1,3,1,Single,15,480.1608244,0,23585.04122
136948,24,5,3,0,Married,11,480.1355112,0,23619.77556
189856,36,17,1,0,Single,8,480.0813937,0,23603.06969
152444,20,1,2,1,Single,12,480.0008503,0,23510.04252
162637,50,31,2,1,Single,6,479.8953476,0,23701.76738
176790,65,44,1,0,Married,2,479.8133205,0,23790.66603
107134,19,2,2,0,Married,10,479.8115482,0,23662.57741
141110,21,4,1,0,Married,12,479.7951029,0,23669.75515
144571,40,19,3,1,Single,10,479.7233194,0,23762.16597
122863,59,39,3,1,Single,8,479.6351699,0,23762.7585
112711,40,21,2,1,Married,5,479.4438256,0,23597.19128
196931,17,0,1,1,Married,13,479.4391977,0,23660.95989
120992,30,11,2,0,Married,1,479.4186448,0,23499.93224
109596,16,0,2,1,Married,12,479.370976,0,23460.5488
185766,48,27,3,1,Single,9,479.3608092,0,23387.04046
141603,16,0,4,0,Single,15,479.3410737,0,23538.05369
173574,19,2,4,1,Married,9,479.3297971,0,23662.48986
184240,21,4,3,0,Married,9,479.3087251,0,23562.43626
113940,56,36,3,0,Married,2,479.2886062,0,23495.43031
163041,54,35,2,0,Single,7,479.2419774,0,23635.09887
140088,25,6,3,1,Single,7,479.2261433,0,23515.30717
120916,18,0,2,0,Single,14,479.1547418,0,23632.73709
167809,49,29,3,0,Single,7,479.1289101,0,23736.44551
144947,52,33,3,0,Married,3,479.0491104,0,23557.45552
123896,23,6,1,0,Married,6,479.0179823,0,23671.89912
103181,23,4,3,1,Single,2,478.9629212,0,23734.14606
139960,43,24,2,1,Single,2,478.956515,0,23372.82575
153449,65,45,4,0,Single,3,478.7439131,0,23627.19566
194182,19,2,4,0,Married,15,478.6726502,0,23528.63251
148796,47,28,1,1,Married,5,478.6587564,0,23359.93782
176483,32,11,3,1,Single,2,478.6572702,0,23510.86351
155734,20,1,2,1,Married,6,478.6484947,0,23701.42474
112625,17,0,3,1,Married,2,478.5992078,0,23521.96039
169908,53,36,3,0,Single,9,478.4915536,0,23332.57768
101018,63,45,3,1,Single,4,478.4706675,0,23410.53338
199343,57,38,1,1,Single,4,478.456794,0,23472.8397
174342,35,16,1,0,Single,6,478.423885,0,23660.19425
176878,18,1,3,0,Married,14,478.4200123,0,23559.00062
181488,25,7,2,0,Married,11,478.4084259,0,23520.4213
130299,42,25,3,1,Single,9,478.3853629,0,23581.26815
197034,17,0,2,0,Single,11,478.3743453,0,23385.71727
186036,16,0,1,0,Married,10,478.3520156,0,23450.60078
138462,28,9,3,0,Married,2,478.3088134,0,23369.44067
144856,22,4,2,0,Single,14,478.234253,0,23446.71265
146919,17,0,3,0,Married,14,478.2292338,0,23630.46169
173963,19,2,1,1,Single,11,478.2095857,0,23525.47929
169850,69,48,1,1,Married,3,478.2047046,0,23326.23523
120518,39,19,2,0,Single,10,478.1855179,0,23359.2759
123772,16,0,3,0,Married,14,478.0992495,0,23548.96248
163031,45,25,1,0,Single,5,478.0213205,0,23645.06603
193811,45,26,3,0,Single,1,478.0020698,0,23607.10349
128255,30,12,3,1,Single,9,477.9730572,0,23502.65286
136685,45,24,4,0,Married,1,477.9370871,0,23406.85436
139013,46,29,2,1,Single,3,477.8429567,0,23656.14784
102932,25,6,3,1,Married,12,477.7597317,0,23622.98659
141420,48,27,1,1,Single,8,477.7134309,0,23463.67155
113620,41,21,4,1,Single,3,477.5895786,0,23495.47893
167721,44,23,2,1,Single,1,477.4794187,0,23350.97094
190736,17,0,4,0,Married,8,477.4649244,0,23467.24622
159651,66,46,1,0,Single,3,477.3863378,0,23452.31689
120390,29,8,3,0,Single,9,477.317545,0,23476.87725
175008,32,12,3,1,Single,9,477.2962467,0,23416.81234
166336,36,18,2,1,Single,1,477.2749712,0,23622.74856
128035,38,19,4,0,Single,8,477.2124292,0,23356.62146
184068,53,34,1,1,Married,4,477.1837723,0,23513.18862
110949,49,31,2,0,Single,7,477.1025136,0,23301.12568
193501,62,45,2,1,Married,5,477.0693649,0,23620.46825
100377,17,0,2,0,Married,8,477.0562697,0,23464.81349
193500,54,35,2,1,Single,2,477.0516414,0,23406.58207
144231,42,23,3,0,Single,8,476.9330893,0,23257.65447
190114,63,45,3,1,Single,5,476.8854492,0,23292.27246
104980,17,0,1,0,Married,8,476.875928,0,23638.7964
140440,55,35,3,1,Single,10,476.8177299,0,23295.8865
183270,17,0,2,1,Married,6,476.8176623,0,23358.88312
166146,29,10,2,0,Married,4,476.6718857,0,23242.59429
183726,44,24,1,0,Single,9,476.6461403,0,23436.30702
188301,38,18,2,1,Single,9,476.5783822,0,23480.91911
176096,53,36,2,1,Married,1,476.5679194,0,23240.39597
125668,16,0,1,0,Married,12,476.5346165,0,23526.73083
189976,51,32,2,1,Married,1,476.4940151,0,23262.70076
182609,47,30,2,0,Single,10,476.4459709,0,23467.29855
174849,51,33,3,0,Single,8,476.4370649,0,23466.85325
144713,66,48,1,1,Single,3,476.4294007,0,23224.47004
140370,32,15,3,0,Married,4,476.3075253,0,23607.37627
104040,54,33,2,1,Single,9,476.2559945,0,23362.79973
157972,48,29,2,1,Single,8,476.174582,0,23497.7291
122075,20,2,2,1,Single,10,476.0955866,0,23460.77933
139263,24,7,3,1,Single,13,476.0368826,0,23550.84413
158016,28,10,2,0,Married,3,475.9737206,0,23329.68603
148072,23,6,4,0,Married,15,475.862397,0,23220.11985
148120,16,0,3,1,Single,10,475.8312058,0,23484.56029
178891,25,8,3,1,Single,6,475.7604088,0,23341.02044
158351,43,24,1,0,Single,10,475.7226569,0,23424.13285
127584,45,26,2,1,Single,10,475.6811129,0,23346.05565
110871,55,34,1,1,Single,9,475.6477521,0,23186.38761
188537,20,2,1,1,Single,12,475.6435642,0,23293.17821
155349,24,7,3,1,Married,10,475.6342245,0,23292.71123
138948,25,8,2,1,Married,13,475.6124439,0,23268.6222
114603,16,0,2,0,Single,14,475.5995397,0,23185.97699
149784,25,6,1,0,Married,15,475.4581044,0,23373.90522
182243,68,51,2,1,Married,3,475.4555198,0,23271.77599
187111,55,34,2,0,Single,7,475.419257,0,23469.96285
197069,31,10,2,0,Single,10,475.2954651,0,23258.77326
159776,36,15,3,0,Single,6,475.1906527,0,23448.53264
180497,55,34,3,1,Married,3,475.1007265,0,23456.03633
163806,46,26,2,1,Married,1,475.0563753,0,23272.81877
199466,57,39,3,0,Single,9,475.0068252,0,23535.34126
120285,53,35,2,0,Single,6,474.9232008,0,23266.16004
113585,29,12,2,1,Single,8,474.8403496,0,23538.01748
147780,31,12,3,1,Married,5,474.8034165,0,23378.17083
129251,57,39,4,0,Single,8,474.8023125,0,23449.11563
198453,41,21,3,0,Single,6,474.7189621,0,23390.94811
104458,58,39,3,0,Single,9,474.5904622,0,23279.52311
199860,20,3,3,0,Married,11,474.5805687,0,23300.02844
101265,50,33,3,0,Single,7,474.5715389,0,23242.57695
158325,36,16,2,1,Single,0,474.552357,0,23254.61785
164327,43,22,2,0,Single,10,474.4684224,0,23482.42112
117127,24,6,2,0,Single,15,474.4609259,0,23474.0463
160730,35,16,3,0,Single,6,474.3647466,0,23328.23733
125809,25,7,2,1,Married,6,474.2768161,0,23367.84081
188388,34,13,4,1,Single,3,474.2729374,0,23330.64687
140989,17,0,3,1,Single,13,474.1642879,0,23439.2144
185134,59,42,1,1,Single,1,474.1468687,0,23467.34344
149644,21,3,2,1,Single,13,474.1410582,0,23435.05291
188343,31,14,2,0,Married,0,474.1045373,0,23255.22687
176281,39,20,4,1,Single,9,474.0969887,0,23397.84944
170432,21,4,2,0,Married,8,473.9589177,0,23438.94589
175900,20,2,3,1,Married,10,473.9554607,0,23248.77304
102414,38,19,3,1,Married,3,473.9448117,0,23487.24059
168813,47,29,4,1,Single,4,473.8614676,0,23093.07338
111236,32,15,3,1,Married,1,473.7378405,0,23173.89203
152528,19,0,3,0,Married,11,473.6489793,0,23082.44897
125726,23,5,1,0,Married,12,473.6257225,0,23403.28613
146152,49,32,2,0,Single,6,473.6158634,0,23238.79317
191569,23,6,3,0,Married,14,473.6061402,0,23462.30701
134215,41,20,3,1,Single,6,473.4078969,0,23445.39485
110604,58,39,2,0,Single,6,473.3205102,0,23097.02551
149672,59,39,4,0,Married,5,473.3197121,0,23182.98561
198250,19,1,4,0,Married,13,473.3081657,0,23450.40829
138762,33,15,2,1,Married,3,473.2762357,0,23435.81179
131607,28,8,2,1,Single,7,473.2256212,0,23117.28106
105549,30,12,2,0,Single,7,473.1793042,0,23268.96521
141593,65,45,2,0,Single,13,473.1478957,0,23406.39479
140821,46,29,2,1,Married,0,473.0895948,0,23128.47974
113371,52,35,3,0,Married,0,473.0839452,0,23273.19726
125227,20,2,1,0,Married,10,473.0586347,0,23240.93174
145945,24,6,2,0,Single,13,473.0396868,0,23193.98434
121436,35,18,3,1,Single,7,473.0285608,0,23139.42804
122538,40,19,2,0,Single,6,473.0012563,0,23384.06282
197936,36,18,3,0,Married,1,472.9943625,0,23112.71813
181088,47,30,2,1,Single,10,472.9030689,0,23405.15345
101476,30,12,2,0,Single,10,472.8663498,0,23361.31749
191389,50,29,4,0,Single,6,472.8194975,0,23336.97488
190557,29,12,1,0,Single,7,472.8070804,0,23121.35402
125273,61,43,3,0,Single,8,472.7858557,0,23275.29279
155548,39,21,4,1,Married,0,472.7488777,0,23059.44389
123363,58,37,3,0,Single,10,472.6673133,0,23195.36567
177758,20,1,4,0,Married,11,472.6239431,0,23113.19716
165177,44,27,1,1,Single,10,472.5998112,0,23042.99056
129781,18,0,3,0,Single,14,472.598635,0,23268.93175
179284,34,17,3,1,Single,10,472.5141741,0,23389.70871
127847,46,26,3,0,Single,9,472.4845356,0,23424.22678
194850,43,26,1,1,Single,10,472.460003,0,23239.00015
190961,45,27,1,0,Single,7,472.4328688,0,23183.64344
175296,30,11,4,0,Single,7,472.252075,0,23224.60375
114111,23,5,4,0,Single,13,472.2269678,0,23373.34839
162438,59,42,4,0,Single,6,472.1804482,0,23283.02241
123968,19,0,2,1,Single,10,472.1382826,0,23169.91413
190347,27,9,2,0,Married,2,472.0646401,0,23178.23201
117800,52,33,3,1,Single,10,471.9688196,0,23136.44098
177787,57,40,3,1,Single,3,471.9466109,0,23300.33055
107360,54,36,3,1,Single,6,471.8267349,0,23209.33675
129185,42,25,1,1,Married,2,471.8190516,0,23351.95258
153121,28,10,2,0,Single,6,471.784096,0,23121.2048
156988,23,4,3,0,Married,12,471.764284,0,23237.2142
160935,19,0,2,1,Single,15,471.6957611,0,23376.78806
102156,32,11,3,1,Single,6,471.6810004,0,23357.05002
169405,55,38,3,1,Single,0,471.5194783,0,23266.97392
171178,53,35,1,1,Married,5,471.5150074,0,23374.75037
177859,33,13,2,1,Single,8,471.4780636,0,23034.90318
117435,55,37,3,0,Single,8,471.4751538,0,23051.75769
166669,36,16,2,1,Married,4,471.424518,0,23236.2259
158963,22,3,3,1,Married,13,471.4209351,0,23105.04676
181157,20,3,1,0,Married,8,471.4103848,0,23187.51924
111432,53,33,3,0,Married,5,471.378059,0,23000.90295
194116,47,27,3,1,Single,2,471.3673475,0,23173.36738
178567,57,40,3,0,Married,3,471.3671809,0,23155.35905
115124,40,19,1,1,Single,7,471.3562274,0,22974.81137
199615,61,43,2,0,Married,3,471.323236,0,23151.1618
195315,32,15,2,0,Single,9,471.2372417,0,23313.86209
114531,28,8,2,1,Married,3,471.2356391,0,23135.78196
166966,29,12,2,1,Single,15,471.1708052,0,23121.54026
188070,43,22,3,0,Single,10,471.1239413,0,23319.19707
138802,49,31,4,0,Single,8,471.018327,0,23088.91635
110353,45,26,2,1,Single,9,470.9851536,0,23154.25768
168414,31,13,3,0,Single,3,470.9101548,0,23263.50774
158902,35,14,2,1,Single,8,470.8810033,0,23240.05017
130624,19,0,4,1,Single,15,470.8143033,0,23229.71517
128068,46,26,4,0,Single,8,470.7920924,0,22940.60462
127745,18,1,1,0,Married,9,470.7737738,0,23019.68869
171899,44,23,2,1,Married,2,470.772339,0,23122.61695
124242,69,52,4,1,Single,3,470.723629,0,22998.18145
190805,51,30,2,0,Single,7,470.7177821,0,23127.88911
179235,23,6,3,0,Single,13,470.6802469,0,22961.01235
116472,38,18,2,1,Married,1,470.6478699,0,23228.3935
122508,59,42,4,1,Single,9,470.6478182,0,23066.39091
133065,37,16,3,1,Single,6,470.6396289,0,22931.98145
168263,29,9,3,0,Single,8,470.4042582,0,22941.21291
187002,21,2,3,0,Single,15,470.4019604,0,22990.09802
199755,35,14,2,0,Married,4,470.3827303,0,23264.13652
196340,67,46,3,0,Married,0,470.3734647,0,23094.67324
123616,19,0,2,0,Single,12,470.3204577,0,23264.02289
132839,27,9,3,1,Single,10,470.2704766,0,23291.52383
189461,22,4,1,1,Single,15,470.262049,0,23261.10245
101066,16,0,2,0,Married,6,470.2433745,0,22919.16873
115080,55,38,2,0,Single,10,470.179427,0,23282.97135
170617,37,20,1,0,Married,4,470.1173279,0,23010.8664
129116,27,6,4,0,Single,9,470.0810446,0,23063.05223
136198,65,46,4,0,Married,3,470.0706335,0,23130.53168
145251,23,6,2,1,Single,8,470.0655917,0,23218.27959
197537,51,34,2,1,Single,12,470.0477801,0,23130.38901
191545,52,34,1,0,Single,8,469.9768848,0,23070.84424
197342,66,48,3,0,Single,4,469.964291,0,23072.21455
173044,23,4,2,1,Married,10,469.9437156,0,23125.18578
100325,54,35,2,0,Single,10,469.9318977,0,23281.59489
142297,17,0,1,0,Married,15,469.8951722,0,23123.75861
151801,40,21,3,0,Single,6,469.8517485,0,22931.58743
114656,64,44,2,0,Single,9,469.8507048,0,22910.53524
118798,19,2,3,1,Single,8,469.8495943,0,23132.47972
192733,20,2,1,1,Single,14,469.8435041,0,23274.17521
133346,23,4,4,1,Single,12,469.5534854,0,23060.67427
176253,25,8,2,0,Single,15,469.5215724,0,23238.07862
111714,18,0,2,0,Married,9,469.4447224,0,23202.23612
110315,53,36,3,0,Single,6,469.4146909,0,23197.73455
114362,57,37,4,0,Single,10,469.4075879,0,23009.3794
102999,19,0,2,1,Married,6,469.2722573,0,22911.61287
164206,64,47,2,0,Single,5,469.211976,0,23092.5988
105763,18,0,2,0,Married,10,469.1917795,0,23232.58898
107598,18,1,3,1,Single,10,469.0238476,0,23094.19238
143641,17,0,1,1,Single,15,469.0068711,0,22942.34356
120778,37,16,3,0,Single,7,468.9861935,0,23032.30968
156278,40,22,2,0,Single,1,468.9365058,0,23105.82529
140743,33,12,2,1,Single,13,468.9008097,0,23209.04049
185817,17,0,4,1,Married,7,468.812173,0,23213.60865
130940,49,29,4,1,Married,5,468.7989474,0,23223.94737
112156,20,3,3,0,Single,11,468.6116157,0,22976.58079
194371,38,19,3,0,Single,8,468.6082087,0,22976.41044
125528,33,14,4,0,Single,10,468.5610259,0,23208.0513
141549,56,35,2,0,Single,10,468.5555107,0,23150.77554
116421,17,0,2,0,Single,13,468.546896,0,22828.3448
151606,45,24,3,0,Single,10,468.5291497,0,22911.45749
179546,25,7,4,0,Single,12,468.4496821,0,22865.48411
155683,62,44,1,0,Married,0,468.4475546,0,23001.37773
164081,17,0,2,1,Single,7,468.4346379,0,22954.7319
127206,58,41,3,1,Single,6,468.4270538,0,23128.35269
197995,51,34,3,1,Married,5,468.4252814,0,23140.26407
170399,40,23,4,0,Single,10,468.3255721,0,22823.27861
135648,47,26,4,0,Married,3,468.3063111,0,23093.31556
157639,38,21,1,0,Married,4,468.29548,0,23160.774
156582,39,19,2,0,Single,2,468.2881432,0,23152.40716
175108,49,32,2,1,Single,8,468.2818545,0,23087.09273
108135,51,34,4,0,Single,7,468.1876134,0,22851.38067
119590,52,32,2,1,Married,3,468.1645654,0,23022.22827
162919,51,33,1,0,Single,9,468.1149602,0,22949.74801
141369,43,23,3,1,Single,0,468.0946106,0,23054.73053
145768,46,27,2,1,Married,0,468.0371383,0,23155.85692
121307,17,0,2,1,Married,13,468.033494,0,22947.6747
101787,21,2,3,1,Single,15,467.9908112,0,22837.54056
118785,20,3,3,0,Married,13,467.9868215,0,23156.34108
137438,56,36,1,0,Single,6,467.848014,0,22938.4007
177722,36,19,4,0,Single,9,467.8385341,0,23130.92671
118991,34,14,4,0,Single,14,467.7716405,0,22864.58203
136113,37,17,2,0,Single,8,467.7524182,0,22930.62091
152982,39,21,2,0,Single,7,467.7115021,0,23151.57511
113900,38,20,2,0,Single,9,467.7065653,0,22833.32827
130815,21,2,3,1,Single,15,467.6318976,0,22920.59488
192798,40,20,2,0,Married,4,467.6091343,0,22969.45672
187871,34,14,2,1,Single,10,467.5399965,0,23107.99983
123426,22,5,4,0,Single,15,467.4324169,0,23002.62085
189577,44,26,3,0,Married,5,467.2539355,0,22785.69678
126569,35,17,3,1,Single,8,467.2536567,0,23122.68284
165328,25,7,3,1,Married,8,467.1826494,0,23131.13247
139552,22,4,2,1,Single,11,467.1744391,0,22957.72196
137416,21,2,4,1,Single,10,467.0681244,0,22905.40622
188413,50,32,2,0,Single,8,466.9493141,0,22822.46571
182624,20,1,1,0,Single,15,466.7879145,0,22994.39573
189372,62,43,3,1,Married,4,466.7494146,0,23109.47073
145729,47,29,1,0,Married,5,466.7029815,0,23013.14908
191289,55,36,2,0,Married,4,466.6843585,0,22766.21793
149155,40,23,2,0,Single,12,466.4680783,0,22979.40392
182686,67,47,1,1,Single,0,466.4515276,0,22945.57638
188018,36,17,3,0,Single,9,466.4344497,0,22980.72249
114729,69,50,4,0,Married,2,466.3893452,0,22731.46726
124963,16,0,2,0,Married,7,466.3200385,0,22731.00193
121153,24,7,3,0,Married,11,466.2623592,0,22892.11796
179794,46,26,2,0,Single,6,466.1547247,0,23073.73624
131441,38,20,3,1,Single,10,466.1189789,0,22845.94895
113082,27,6,1,0,Single,9,466.0522963,0,23100.61482
152967,27,10,3,0,Married,0,465.9948173,0,22810.74087
138980,40,20,3,0,Single,8,465.9272761,0,23022.36381
136481,16,0,2,1,Married,11,465.852799,0,22988.63995
192091,30,9,2,0,Single,9,465.8489466,0,22963.44733
101286,53,34,4,0,Single,9,465.8402699,0,23046.0135
189180,54,34,1,1,Single,8,465.8029002,0,22730.14501
128818,50,29,1,1,Single,8,465.7864789,0,22983.32395
147254,55,35,4,1,Single,0,465.783215,0,22868.16075
117605,69,50,2,0,Single,15,465.7644331,0,22981.22166
144790,29,8,1,1,Single,6,465.7509838,0,22720.54919
153677,43,23,2,0,Single,6,465.7509793,0,22938.54897
129597,22,3,4,1,Single,0,465.6766501,0,22902.83251
150327,63,44,1,1,Single,4,465.658248,0,22924.9124
136115,59,38,3,1,Married,3,465.6428112,0,22923.14056
124725,49,32,2,1,Married,1,465.6171367,0,22740.85684
110830,22,5,1,1,Single,10,465.6053779,0,22963.2689
103287,21,2,3,0,Married,12,465.5588706,0,22773.94353
167838,60,43,3,1,Single,2,465.5372033,0,22750.86017
174285,51,31,4,0,Single,9,465.5054531,0,23010.27266
183284,62,41,2,0,Single,0,465.4877164,0,23041.38582
146253,43,22,3,0,Single,10,465.4857511,0,22998.28756
120200,22,3,2,0,Married,9,465.4738686,0,22695.69343
181190,27,8,2,1,Single,5,465.4325412,0,22891.62706
161934,28,7,1,1,Single,8,465.3484514,0,22868.42257
165059,55,38,2,0,Single,7,465.282525,0,22722.12625
110390,19,2,2,1,Single,12,465.2822433,0,22705.11217
117796,42,24,4,0,Single,7,465.2539004,0,22742.69502
175217,64,43,3,0,Married,4,465.2157485,0,22735.78743
160413,49,30,3,1,Single,8,465.2037594,0,22937.18797
178704,18,0,4,1,Single,11,465.1659936,0,22804.29968
167992,68,49,1,0,Single,13,465.1176686,0,22921.88343
179621,51,30,3,0,Single,8,465.0455411,0,22822.27706
173340,18,0,3,0,Married,11,465.0447898,0,22881.23949
154080,61,41,2,0,Single,0,464.9369759,0,22720.84879
151345,25,8,4,1,Married,3,464.8812183,0,22869.06092
185968,28,10,4,0,Single,10,464.8304919,0,22992.5246
181025,34,17,4,1,Single,3,464.8205543,0,22711.02772
101242,47,28,3,0,Single,6,464.8176385,0,22699.88193
142841,43,24,2,1,Single,3,464.751069,0,22693.55345
134458,16,0,2,0,Married,15,464.6484311,0,22753.42156
187417,48,30,4,0,Single,6,464.6320627,0,22864.60314
185932,27,9,3,0,Single,10,464.6240989,0,22885.20495
112199,50,31,3,0,Single,13,464.6077712,0,22882.38856
105495,44,24,4,0,Married,2,464.5986547,0,22655.93274
174867,41,21,3,0,Single,1,464.5602035,0,22643.01018
134229,33,13,2,0,Single,6,464.5138413,0,22957.69207
169280,58,37,3,0,Single,10,464.475188,0,22912.7594
178684,52,34,3,1,Single,9,464.2904242,0,22629.52121
153448,57,40,1,0,Single,8,464.182696,0,22815.1348
145995,16,0,4,0,Single,13,464.1600324,0,22781.00162
160655,49,30,2,0,Single,12,464.0981718,0,22830.90859
132824,36,16,3,1,Single,7,464.082626,0,22605.1313
171508,37,20,2,0,Single,12,464.0577779,0,22934.8889
160371,22,4,2,1,Single,12,464.0568769,0,22928.84385
130522,62,41,1,1,Single,5,463.944586,0,22708.2293
168982,22,5,4,0,Married,12,463.934934,0,22761.7467
103192,24,5,2,1,Single,14,463.9210844,0,22664.05422
166553,54,37,3,0,Single,12,463.8873235,0,22959.36618
161819,24,5,2,1,Single,15,463.8862629,0,22901.31315
173269,26,7,2,0,Single,8,463.8537185,0,22908.68593
148055,57,38,3,0,Single,8,463.8369854,0,22630.84927
126999,58,38,3,0,Single,8,463.7408875,0,22959.04438
172510,68,49,2,0,Single,10,463.7267647,0,22880.33824
135767,40,23,2,0,Single,10,463.6257757,0,22614.28879
156699,34,14,1,0,Single,10,463.571379,0,22623.56895
144660,18,0,3,0,Married,15,463.5702261,0,22645.51131
142705,21,4,3,1,Married,12,463.5494703,0,22977.47352
191480,50,32,1,0,Single,7,463.4877059,0,22575.3853
108733,24,7,4,0,Married,15,463.4450523,0,22810.25262
153646,35,14,2,0,Single,7,463.3467959,0,22881.3398
101768,18,1,1,0,Single,13,463.3050457,0,22802.25229
142316,27,9,2,1,Single,2,463.2861116,0,22827.30558
190133,18,0,2,0,Single,15,463.2390525,0,22928.95263
193882,39,20,4,0,Single,7,463.1983216,0,22571.91608
167793,18,0,2,0,Single,13,463.1947126,0,22696.73563
108147,27,9,4,0,Single,6,463.1633708,0,22887.16854
197005,35,14,3,1,Married,3,463.1365903,0,22773.82952
148827,22,3,2,1,Married,15,463.1297887,0,22628.48944
178777,33,13,1,0,Single,10,463.1202594,0,22734.01297
133945,19,1,3,0,Married,14,463.1103208,0,22942.51604
182064,33,14,1,1,Single,6,463.0778287,0,22777.89144
103345,45,24,3,0,Single,7,463.0315385,0,22793.57693
180045,46,25,3,1,Single,7,463.0235278,0,22856.17639
186293,52,32,2,0,Single,8,462.983244,0,22857.1622
131071,17,0,2,1,Single,12,462.9619884,0,22889.09942
111031,18,1,1,0,Single,15,462.9537014,0,22606.68507
109106,21,3,2,1,Single,11,462.9136625,0,22921.68313
140849,26,9,4,0,Married,3,462.8754754,0,22657.77377
140892,45,28,2,0,Single,6,462.7939537,0,22710.69769
164505,63,44,3,1,Married,4,462.7926885,0,22680.63443
123863,53,34,2,1,Single,9,462.7846202,0,22665.23101
196396,59,38,3,0,Single,13,462.7667653,0,22706.33827
135079,63,43,4,1,Single,7,462.7438298,0,22755.19149
105117,52,35,4,0,Married,2,462.6800395,0,22907.00198
186081,67,50,3,1,Married,3,462.6231671,0,22531.15836
176721,41,20,4,1,Single,1,462.5024015,0,22670.12008
172687,61,40,2,0,Single,5,462.5010563,0,22796.05282
146592,49,32,2,1,Married,1,462.4542071,0,22530.71036
110093,50,30,3,0,Single,7,462.3508888,0,22694.54444
133081,22,3,2,0,Married,15,462.3067681,0,22579.33841
176667,44,26,1,0,Single,8,462.2550909,0,22797.75455
200181,51,32,3,0,Single,8,462.2527861,0,22767.63931
132995,51,33,1,1,Single,8,462.2505406,0,22766.52703
107940,25,6,3,1,Single,14,462.2409311,0,22905.04656
199376,49,32,2,0,Single,6,462.1949318,0,22841.74659
191483,25,7,3,1,Single,12,462.1866766,0,22854.33383
183396,36,19,1,1,Single,0,462.1588562,0,22541.94281
164002,45,26,4,0,Single,13,462.1234713,0,22890.17357
188739,46,25,3,1,Married,1,462.1202654,0,22884.01327
196429,43,22,1,1,Married,0,461.9718465,0,22665.59233
164723,57,40,4,0,Single,9,461.872203,0,22721.61015
139572,21,4,4,0,Single,15,461.8640445,0,22613.20223
157075,48,28,3,0,Single,7,461.818948,0,22759.9474
189575,48,30,1,0,Single,9,461.7150312,0,22571.75156
147383,58,39,2,0,Single,6,461.662769,0,22812.13845
157227,42,25,3,0,Married,5,461.6472663,0,22746.36332
136928,20,3,3,1,Married,13,461.6107734,0,22746.53867
122692,17,0,2,0,Married,12,461.5888607,0,22721.44304
163744,56,37,2,0,Single,7,461.5670823,0,22544.35412
125098,46,25,4,1,Single,5,461.5428561,0,22601.14281
181731,57,40,3,0,Single,6,461.4786744,0,22580.93372
116143,42,23,2,0,Single,8,461.4186021,0,22707.93011
195739,27,8,2,1,Single,0,461.2807466,0,22536.03733
131852,58,37,4,0,Single,7,461.0803128,0,22649.01564
126573,24,7,3,0,Single,13,460.9530036,0,22804.65018
167186,16,0,3,0,Single,13,460.9485643,0,22841.42822
132446,16,0,2,0,Single,15,460.8457793,0,22521.28897
182688,30,11,2,0,Single,13,460.7589211,0,22742.94606
189765,21,3,3,0,Married,14,460.7552001,0,22474.76001
132387,61,44,1,0,Single,3,460.7266415,0,22801.33208
139324,21,3,3,1,Married,11,460.6325193,0,22741.62597
135578,50,29,3,1,Single,9,460.5038004,0,22718.19002
152775,21,4,1,0,Single,13,460.4609532,0,22759.04766
116185,22,4,2,0,Married,7,460.4194493,0,22750.97247
160225,47,26,2,1,Single,7,460.3342515,0,22668.71258
199192,17,0,4,1,Married,8,460.3187601,0,22744.93801
104653,27,8,3,0,Married,3,460.2782849,0,22606.91425
137875,50,31,2,0,Single,9,460.0990221,0,22695.95111
190016,39,18,2,0,Married,2,460.048472,0,22624.4236
182356,52,31,3,1,Married,0,459.9969586,0,22782.84793
114383,20,3,1,0,Married,7,459.9911635,0,22556.55818
123059,36,18,1,0,Single,6,459.9861188,0,22424.30594
196969,63,45,2,0,Single,2,459.8051617,0,22679.25809
151706,49,31,2,1,Single,10,459.7258028,0,22611.29014
107859,69,51,2,0,Single,5,459.7043208,0,22581.21604
169731,16,0,3,0,Single,11,459.6553555,0,22498.76778
157864,52,34,3,1,Single,3,459.6339659,0,22520.6983
199592,54,33,3,1,Married,2,459.6079281,0,22690.39641
125217,45,25,2,0,Single,11,459.5727404,0,22561.63702
153740,58,38,3,1,Single,4,459.5673309,0,22387.36655
173755,50,30,3,1,Single,7,459.5537069,0,22694.68535
118695,20,2,1,1,Married,0,459.488058,0,22591.4029
190592,37,17,1,1,Single,9,459.278839,0,22636.94195
185811,63,46,4,1,Married,2,459.15844,0,22414.922
189352,70,50,2,0,Single,7,459.0836812,0,22678.18406
158694,47,28,3,0,Married,2,459.0836351,0,22482.18176
162308,61,41,3,0,Married,2,459.0270451,0,22636.35226
186507,22,5,2,1,Married,15,458.8803812,0,22520.01906
182386,39,20,3,1,Married,4,458.8186227,0,22345.93114
182906,54,34,3,1,Single,10,458.7848756,0,22389.24378
122728,55,36,3,0,Single,7,458.7115177,0,22536.57589
183024,69,52,2,1,Single,0,458.6602342,0,22412.01171
147703,34,16,1,1,Single,1,458.5579608,0,22465.89804
197931,17,0,1,1,Married,8,458.5196732,0,22494.98366
162503,18,0,2,1,Married,9,458.5047008,0,22716.23504
162930,47,26,3,1,Single,10,458.4858255,0,22536.29128
100874,53,36,2,0,Single,9,458.4681505,0,22437.40753
143053,31,12,3,0,Single,10,458.4249573,0,22493.24787
147154,58,41,1,0,Single,10,458.3461769,0,22501.30885
132443,21,4,4,1,Married,10,458.3320697,0,22604.60349
199387,39,20,1,1,Single,4,458.3103986,0,22461.51993
194620,45,27,4,1,Single,7,458.3080678,0,22326.40339
172677,36,18,3,1,Single,11,458.3073155,0,22403.36578
193044,42,23,1,0,Single,9,458.218025,0,22384.90125
105180,25,8,3,1,Single,14,458.2146143,0,22529.73072
188418,18,0,2,0,Married,9,458.1821405,0,22709.10703
102965,20,1,3,1,Married,9,458.1800391,0,22356.00196
167408,24,7,1,1,Married,1,458.1587158,0,22391.93579
101259,69,48,1,1,Single,0,458.0629055,0,22672.14528
195853,55,35,3,0,Single,9,457.9983437,0,22560.91719
141188,39,20,3,1,Single,7,457.9628483,0,22656.14242
156856,50,29,2,1,Single,8,457.8160532,0,22420.80266
150318,53,33,2,0,Single,7,457.7897754,0,22512.48877
120460,17,0,4,1,Single,10,457.7525064,0,22447.62532
153924,43,25,1,1,Single,6,457.7243384,0,22502.21692
147905,40,20,3,0,Single,7,457.7210762,0,22606.05381
109305,25,7,2,0,Married,14,457.672818,0,22542.6409
134484,27,10,1,1,Single,10,457.6599332,0,22452.99666
139857,57,36,3,0,Single,6,457.6225089,0,22526.12545
170981,20,2,3,1,Single,11,457.5992138,0,22668.96069
112296,36,18,4,0,Single,10,457.5852066,0,22444.26033
100599,16,0,3,1,Married,2,457.5623259,0,22666.1163
111120,22,3,4,1,Single,6,457.4523172,0,22603.61586
114281,34,16,1,1,Single,3,457.4466378,0,22340.33189
165834,52,33,2,0,Single,8,457.3598309,0,22525.99155
106133,30,13,2,0,Single,6,457.3564072,0,22373.82036
155271,37,20,4,1,Single,8,457.2530171,0,22475.65086
157568,43,23,3,0,Single,10,457.1755592,0,22329.77796
163120,35,17,1,0,Married,3,457.1550705,0,22512.75353
144657,16,0,1,0,Married,10,457.1441353,0,22270.20677
116613,22,4,3,0,Single,12,457.1419101,0,22578.09551
134668,22,4,3,0,Single,13,457.1156862,0,22419.78431
163040,19,1,4,1,Single,15,457.1144727,0,22490.72364
164065,43,23,2,1,Single,5,457.0761152,0,22310.80576
173604,33,16,2,0,Single,15,457.054282,0,22576.7141
121667,32,13,2,1,Single,9,457.0390777,0,22386.95389
103942,62,44,2,1,Single,0,456.9286682,0,22249.43341
162009,52,31,2,0,Single,9,456.8647913,0,22616.23957
135237,29,9,1,0,Single,14,456.8106223,0,22348.53112
168810,50,32,3,0,Married,1,456.8043889,0,22367.21945
136563,19,0,1,1,Married,12,456.7314758,0,22631.57379
102006,47,29,2,0,Single,10,456.6376307,0,22286.88154
113268,54,33,1,0,Single,7,456.6285444,0,22432.42722
169559,60,43,3,0,Single,3,456.5790617,0,22373.95309
193091,22,5,4,0,Married,1,456.5629329,0,22561.14665
157675,20,2,4,1,Single,13,456.5229362,0,22501.14681
102744,41,20,3,0,Single,10,456.5223966,0,22597.11983
162477,41,22,3,0,Married,2,456.4605943,0,22313.02972
122973,27,7,2,0,Married,1,456.406225,0,22443.31125
186902,39,22,4,0,Single,9,456.36196,0,22293.098
158121,26,8,3,0,Single,6,456.344638,0,22406.2319
177000,22,4,1,0,Married,8,456.2160705,0,22376.80353
182607,50,33,2,1,Married,2,456.1775467,0,22514.87734
132331,27,6,3,1,Single,6,456.0590024,0,22354.95012
175095,58,38,3,1,Single,13,456.0547896,0,22283.73948
160902,18,1,2,1,Single,2,456.0412087,0,22258.06044
115944,16,0,3,1,Single,15,455.7646207,0,22285.23104
144572,48,30,3,0,Single,13,455.7566006,0,22529.83003
157569,33,12,2,0,Married,3,455.7450818,0,22205.25409
178378,40,23,2,1,Single,6,455.7260968,0,22310.30484
129215,51,30,4,1,Married,1,455.6210424,0,22563.05212
115032,64,46,4,1,Married,2,455.6194336,0,22370.97168
159560,56,38,3,0,Married,5,455.6105209,0,22498.52605
196144,42,25,2,1,Single,8,455.5840948,0,22402.20474
100174,67,49,2,1,Single,0,455.5473483,0,22517.36742
183005,47,28,3,1,Married,5,455.4233876,0,22562.16938
132910,17,0,2,1,Married,11,455.3669137,0,22230.34569
141769,67,50,4,1,Married,0,455.3660093,0,22214.30047
181072,56,35,4,0,Single,7,455.3487464,0,22408.43732
163980,21,3,2,0,Single,11,455.2843485,0,22263.21743
119279,29,8,1,0,Single,1,455.2546475,0,22175.73238
189150,57,38,4,0,Single,13,455.1469933,0,22263.34967
155325,57,37,2,0,Married,5,455.0825979,0,22397.1299
188548,30,9,2,1,Single,9,455.0550828,0,22307.75414
185449,24,5,3,1,Single,10,455.0281209,0,22472.40605
129020,65,47,2,0,Married,2,455.0181933,0,22291.90967
154595,64,43,1,0,Single,3,455.0087752,0,22264.43876
116796,64,46,1,1,Single,7,454.9945793,0,22506.72897
175734,45,26,3,1,Single,6,454.9878371,0,22343.39186
143483,59,42,4,0,Single,8,454.9585533,0,22343.92767
189873,26,5,4,1,Single,1,454.9421894,0,22535.10947
172385,30,9,3,0,Single,9,454.8720816,0,22447.60408
152524,18,1,1,0,Single,13,454.8691711,0,22196.45856
171105,49,30,1,0,Single,10,454.8247336,0,22203.23668
136861,33,12,1,0,Single,10,454.822674,0,22167.1337
142474,48,28,3,0,Single,8,454.7726978,0,22170.63489
121749,23,6,2,0,Single,14,454.7390152,0,22351.95076
129667,69,52,2,0,Single,5,454.7246128,0,22366.23064
143190,16,0,2,1,Married,10,454.7101392,0,22183.50696
107853,53,35,2,0,Single,7,454.7100058,0,22303.50029
176862,47,28,2,0,Single,8,454.691061,0,22190.55305
138623,21,3,1,0,Married,15,454.6213775,0,22357.06888
104049,55,37,2,0,Single,9,454.6174136,0,22390.87068
174349,43,25,4,0,Single,9,454.5790551,0,22473.95276
146508,42,23,1,0,Single,1,454.5588872,0,22233.94436
119275,19,2,2,0,Single,12,454.4784827,0,22456.92414
190665,29,9,2,1,Single,10,454.4628239,0,22289.1412
116488,35,16,4,0,Single,9,454.4616625,0,22499.08313
190115,35,15,3,1,Single,1,454.455568,0,22146.7784
164033,23,5,4,1,Single,14,454.4548915,0,22340.74458
101294,16,0,2,0,Married,11,454.3752391,0,22281.76196
162321,56,39,2,0,Single,10,454.3229904,0,22207.14952
160279,68,50,2,0,Single,2,454.1709307,0,22208.54654
140237,68,47,4,0,Married,4,454.0613287,0,22424.06644
151087,46,25,3,1,Married,1,453.9322856,0,22359.61428
109498,40,23,2,1,Married,0,453.924572,0,22240.2286
188650,25,8,1,0,Married,12,453.8468682,0,22157.34341
114279,65,44,3,1,Married,4,453.8406352,0,22334.03176
129450,25,8,3,0,Married,15,453.7812962,0,22222.06481
160716,16,0,3,1,Single,9,453.7388068,0,22314.94034
135659,43,26,3,0,Single,10,453.6746323,0,22444.73162
160490,54,36,2,1,Single,10,453.621032,0,22356.0516
153318,20,3,4,1,Single,12,453.5753348,0,22431.76674
161937,16,0,2,0,Married,12,453.5616864,0,22303.08432
101843,54,36,3,0,Married,1,453.5452161,0,22417.26081
109004,47,28,3,1,Single,10,453.4353813,0,22203.76907
167185,22,5,1,1,Married,13,453.4195793,0,22142.97897
186995,34,16,4,0,Single,12,453.4172473,0,22431.86237
176105,49,32,4,0,Single,8,453.3110996,0,22391.55498
142856,28,8,3,1,Married,5,453.2883566,0,22242.41783
179726,54,35,2,0,Single,13,453.218362,0,22459.9181
107167,35,18,4,1,Single,9,453.1715685,0,22242.57843
166581,34,13,4,1,Married,1,453.1583048,0,22343.91524
147680,61,43,3,0,Single,7,453.0865876,0,22122.32938
192363,67,46,4,0,Single,4,453.0719535,0,22198.59768
138848,18,0,4,1,Married,7,453.060783,0,22429.03915
163787,44,24,3,1,Single,10,453.0593091,0,22314.96546
176178,43,23,3,0,Single,11,453.0564284,0,22108.82142
146817,47,30,1,1,Single,7,453.0462645,0,22375.31323
107009,33,12,3,0,Single,9,452.9667674,0,22188.33837
164414,47,28,4,0,Married,1,452.9421807,0,22205.10904
171660,28,11,3,0,Single,9,452.9193311,0,22084.96656
103382,54,33,2,0,Single,9,452.9083255,0,22128.41628
156050,49,32,3,1,Single,6,452.8799118,0,22088.99559
189511,27,8,3,0,Married,5,452.8524893,0,22413.62447
133195,59,41,2,0,Single,14,452.8011333,0,22052.05667
199826,56,36,2,0,Married,3,452.7747636,0,22370.73818
105618,50,31,2,0,Single,9,452.7632766,0,22290.16383
114658,44,27,1,1,Single,6,452.5516079,0,22419.5804
197812,31,14,4,1,Single,3,452.5507926,0,22336.53963
121685,66,48,2,1,Single,2,452.5183265,0,22237.91633
188093,51,34,4,1,Married,4,452.4733169,0,22287.66585
111933,50,30,1,1,Single,6,452.3457636,0,22258.28818
154763,30,11,2,0,Single,10,452.3283936,0,22030.41968
116385,59,41,4,1,Single,6,452.3030991,0,22019.15496
102373,63,45,3,1,Married,2,452.3014816,0,22154.07408
172733,63,45,3,0,Married,2,452.2534672,0,22175.67336
122660,20,1,2,0,Married,13,452.1823501,0,22268.11751
195955,21,4,3,0,Married,13,452.1745937,0,22026.72969
107630,25,6,2,0,Married,11,452.1485721,0,22134.42861
123579,39,18,4,1,Married,1,452.061256,0,22195.0628
152294,40,23,2,0,Single,9,452.0526951,0,22199.63476
131813,16,0,2,1,Single,13,451.9667081,0,22262.33541
169966,58,41,3,1,Married,4,451.8802037,0,22216.01019
182863,69,51,3,0,Single,6,451.8541201,0,22345.70601
172178,24,5,1,0,Married,14,451.8458055,0,22150.29028
168485,49,31,2,0,Single,11,451.8193288,0,22180.96644
111507,57,38,2,0,Single,9,451.7958416,0,22360.79208
178822,16,0,2,1,Single,13,451.591187,0,22026.55935
105780,23,5,3,0,Single,15,451.5880196,0,22000.40098
161274,23,5,2,0,Single,13,451.5804951,0,22267.02476
158326,16,0,3,1,Married,11,451.5678298,0,21986.39149
103267,59,42,4,0,Single,10,451.5560957,0,22019.80479
134424,35,14,4,0,Single,9,451.5186848,0,22289.93424
132198,22,4,3,1,Single,13,451.471187,0,22353.55935
158545,36,18,4,1,Single,14,451.4545471,0,22342.72736
102408,62,41,3,1,Single,3,451.4395443,0,22101.97722
175936,66,46,2,0,Single,2,451.3951911,0,22077.75956
164129,37,17,2,0,Single,13,451.39195,0,22083.5975
184027,56,36,3,0,Single,8,451.3842453,0,22002.21227
151750,67,49,4,0,Single,5,451.3594441,0,22110.97221
191520,22,4,3,1,Single,13,451.2899392,0,22226.49696
177641,27,8,3,1,Single,2,451.2241762,0,22337.20881
143953,57,38,2,0,Single,6,451.2019764,0,22193.09882
167465,28,11,1,0,Single,11,450.9763993,0,22209.81997
169169,34,17,4,0,Single,6,450.9590523,0,22229.95262
126167,47,28,3,1,Single,8,450.932951,0,22333.64755
137739,62,43,2,0,Married,1,450.9282755,0,22253.41378
172249,42,22,2,0,Single,10,450.9084701,0,22134.42351
142360,69,50,4,1,Married,0,450.8989218,0,22045.94609
190620,16,0,1,0,Single,15,450.8608341,0,22343.04171
193630,39,21,3,1,Single,13,450.7779733,0,22155.89867
151980,50,29,2,0,Single,10,450.7769182,0,21940.84591
164809,22,4,3,1,Married,8,450.753906,0,22143.6953
136602,41,21,2,1,Single,7,450.7127007,0,21944.63504
119996,55,37,4,0,Married,5,450.437702,0,22065.8851
101432,58,37,2,0,Married,1,450.3063337,0,21958.31669
102207,20,3,2,0,Single,11,450.2720017,0,22249.60009
146047,36,18,3,1,Single,8,450.2463441,0,22047.31721
162880,17,0,2,0,Married,7,450.1794072,0,21939.97036
197165,28,10,3,0,Single,7,450.1408952,0,22266.04476
163894,37,16,4,1,Single,10,450.0801777,0,22293.00889
116527,24,6,3,1,Single,15,450.0590681,0,22247.95341
190276,54,34,2,1,Single,1,450.0530373,0,22246.65187
170159,28,9,3,0,Married,1,449.9609808,0,22020.04904
189149,43,22,1,1,Single,10,449.7033597,0,22260.16799
102667,51,30,3,0,Single,13,449.6786853,0,22039.93427
115097,34,13,1,0,Single,10,449.6680037,0,22022.40019
112270,49,29,4,0,Single,9,449.5227665,0,22197.13833
120804,68,50,1,0,Married,2,449.4269416,0,22115.34708
172083,26,5,2,0,Single,8,449.4244708,0,21895.22354
174905,44,26,3,0,Single,6,449.3916101,0,22079.58051
118724,23,5,3,1,Single,13,449.3817899,0,22093.0895
142432,70,50,2,0,Married,0,449.3385602,0,22137.92801
178907,55,36,3,1,Single,10,449.3011334,0,22122.05667
157849,40,20,3,1,Married,2,449.2976957,0,22135.88479
151310,17,0,1,0,Married,8,449.274253,0,22263.71265
165859,25,6,2,1,Married,8,449.2381392,0,22072.90696
125131,58,37,3,1,Married,4,449.230989,0,21876.54945
172462,34,15,3,0,Married,1,449.2230127,0,21906.15064
134145,57,36,2,1,Single,6,449.1830842,0,22255.15421
133573,59,41,2,0,Single,7,449.1829468,0,22161.14734
166500,26,7,4,1,Single,10,449.1761852,0,21959.80926
151502,49,28,1,1,Single,4,449.0947929,0,21939.73965
182983,29,10,4,0,Single,9,449.0144241,0,22113.72121
138659,24,6,1,0,Married,15,448.9819859,0,22177.0993
198520,25,6,4,0,Married,11,448.9265704,0,22139.32852
136090,35,15,4,0,Single,9,448.7875087,0,22114.37544
144395,26,5,3,0,Single,7,448.7766873,0,21929.83437
137351,56,36,1,0,Single,9,448.7108596,0,21938.54298
118683,24,7,3,0,Married,8,448.6384964,0,21919.92482
136648,40,20,3,1,Single,6,448.6032097,0,22166.16049
124375,41,22,2,0,Married,0,448.5947005,0,22206.73503
200391,45,27,1,1,Married,3,448.5662485,0,22178.31243
198512,63,45,2,0,Married,2,448.5640905,0,21889.20453
186600,64,47,4,1,Single,2,448.5428067,0,22188.14034
190560,24,7,4,1,Single,10,448.5008404,0,22194.04202
137807,63,45,3,0,Single,8,448.4838259,0,22200.1913
159888,56,37,2,1,Married,3,448.4698573,0,22168.49287
199395,69,52,2,1,Single,6,448.4012457,0,22168.06229
192229,29,12,3,0,Single,8,448.2765204,0,22118.82602
126478,34,14,3,1,Single,9,448.2246201,0,22178.23101
167963,39,22,2,0,Married,2,448.20283,0,21947.1415
161213,30,11,2,0,Single,7,448.1780055,0,21937.90028
110579,16,0,2,1,Married,8,448.1563944,0,21983.81972
122387,21,4,2,0,Married,14,448.0424916,0,21959.12458
178778,20,2,3,1,Single,9,448.0370161,0,22173.85081
143777,39,19,2,0,Single,7,448.0344081,0,22056.72041
100687,49,31,3,0,Single,10,447.9592236,0,22197.96118
102684,51,33,2,0,Married,0,447.8181147,0,22152.90574
154935,44,27,3,0,Married,0,447.8064522,0,21977.32261
166290,24,5,2,1,Single,9,447.7371893,0,21860.85947
196363,57,40,1,0,Married,5,447.7210355,0,22027.05178
194771,43,23,2,0,Married,1,447.7196241,0,22026.98121
100175,21,4,3,1,Married,5,447.7142831,0,21981.71416
194535,41,23,1,0,Single,6,447.7009575,0,21983.04788
124065,62,41,4,1,Married,1,447.6461648,0,22024.30824
118927,56,39,1,0,Single,14,447.6154456,0,21993.77228
185058,44,24,2,0,Single,7,447.5415679,0,21789.0784
108266,69,49,2,1,Married,3,447.4350794,0,22127.75397
108007,22,5,1,1,Single,14,447.3969202,0,22011.84601
196852,60,40,2,1,Married,4,447.3291084,0,21875.45542
198511,44,25,1,1,Single,13,447.2328034,0,22107.64017
198567,36,15,2,0,Single,10,447.2300541,0,22048.50271
181545,24,7,4,0,Married,11,447.220707,0,22063.03535
127200,32,13,1,1,Single,6,447.2109481,0,22133.54741
154848,30,9,3,1,Single,2,447.189233,0,21791.46165
113370,21,4,2,1,Single,11,447.0450284,0,21847.25142
192416,49,28,3,0,Married,4,447.0101645,0,21800.50822
140484,24,7,3,1,Single,13,446.9873113,0,21878.36557
160675,37,17,4,1,Married,3,446.9680208,0,21912.40104
185251,69,48,3,0,Married,2,446.9364469,0,22137.82235
144576,61,44,2,1,Single,6,446.9026627,0,22095.13314
150005,59,41,4,1,Single,10,446.8831455,0,21836.15728
159733,50,32,3,0,Single,9,446.8177939,0,21771.8897
189882,37,18,4,1,Single,0,446.8091657,0,22071.45829
140583,64,43,1,1,Married,0,446.8073503,0,22119.36752
186749,34,15,2,0,Single,8,446.7676713,0,21892.38357
105052,30,13,1,0,Single,1,446.7508297,0,21886.54149
155863,50,33,4,1,Married,1,446.7192461,0,22099.96231
123757,22,3,3,1,Married,6,446.5981693,0,22034.90847
162467,16,0,4,0,Married,13,446.5852619,0,21743.2631
153668,39,18,3,1,Single,8,446.5734614,0,21955.67307
106282,48,31,3,1,Single,9,446.5730685,0,22022.65343
196078,38,20,3,0,Single,7,446.5640427,0,21807.20214
174624,57,36,3,1,Married,4,446.5017736,0,22067.08868
188681,67,50,2,0,Single,6,446.4710429,0,21988.55215
186465,30,11,2,0,Single,6,446.4560288,0,22062.80144
113310,25,8,4,0,Single,15,446.4383494,0,21878.91747
132984,37,19,3,1,Single,9,446.4184447,0,21765.92224
136023,17,0,2,1,Single,12,446.4040877,0,21757.20439
176756,30,10,3,1,Single,6,446.3542376,0,21991.71188
119790,46,28,3,0,Single,8,446.3412369,0,22086.06185
180183,51,32,4,1,Single,1,446.3276583,0,22075.38292
145651,51,33,1,1,Single,8,446.3231382,0,21834.15691
126196,22,5,2,1,Married,1,446.3145823,0,21751.72912
174730,43,26,3,1,Married,4,446.2320038,0,21828.60019
160884,18,1,3,0,Single,14,446.1958455,0,21809.79228
193575,54,36,2,0,Married,1,446.1902944,0,22058.51472
162820,43,26,3,0,Married,0,446.1783716,0,21755.91858
155842,28,11,3,0,Single,6,446.0887616,0,22000.43808
193942,47,27,4,1,Single,6,445.9317379,0,21760.5869
184246,43,26,3,0,Single,8,445.9301345,0,21765.50673
128059,64,44,3,0,Married,1,445.9037489,0,21829.18745
116634,34,17,2,1,Single,7,445.8645954,0,21865.22977
186998,22,4,2,0,Married,7,445.7715681,0,21699.57841
145617,36,18,3,0,Single,3,445.7698957,0,22022.49479
125303,47,29,3,1,Single,6,445.759839,0,21773.99195
147114,56,38,4,0,Single,11,445.7017775,0,21742.08888
108302,16,0,2,0,Married,8,445.6753701,0,22037.76851
131875,20,1,3,0,Married,14,445.6734078,0,21729.67039
149303,25,6,1,1,Married,9,445.6656637,0,21728.28319
172397,62,42,1,0,Married,5,445.5511262,0,21744.55631
132904,55,37,2,1,Single,8,445.5424259,0,21794.1213
108781,16,0,3,1,Single,14,445.5253649,0,21790.26825
188700,19,1,2,0,Married,9,445.4787861,0,21921.93931
156161,29,11,3,0,Single,6,445.3978588,0,21882.89294
153893,51,32,1,0,Single,7,445.3525652,0,22046.62826
130537,58,38,4,1,Single,8,445.3099623,0,21763.49812
133518,32,13,3,0,Single,7,445.2562954,0,21721.81477
166443,25,7,4,1,Single,14,445.1506552,0,22002.53276
140159,36,16,3,0,Single,9,445.1326713,0,22050.63357
174256,52,34,4,0,Single,3,445.1230469,0,21957.15235
116061,48,30,3,0,Single,10,445.1047033,0,21718.23517
138111,19,2,2,1,Single,15,445.0967115,0,21796.83558
196256,28,8,2,1,Single,9,445.0621135,0,21925.10567
142545,22,5,2,0,Single,15,444.9877384,0,22001.38692
119260,49,30,4,0,Single,6,444.9146431,0,21871.73216
160951,31,11,1,1,Single,6,444.9004968,0,21942.02484
163731,16,0,1,0,Married,15,444.7072951,0,21719.36476
185025,49,31,2,0,Married,10,444.6162479,0,21778.8124
109803,57,36,1,1,Married,1,444.5948574,0,21633.74287
151458,61,42,3,0,Single,7,444.5795763,0,21644.97882
128036,21,2,3,1,Single,6,444.5153126,0,21707.76563
127342,33,14,2,0,Single,10,444.4745895,0,21668.72948
108969,48,29,3,0,Single,9,444.4498458,0,21718.49229
150018,21,2,2,1,Married,6,444.4156203,0,21792.78102
130661,24,6,3,1,Married,8,444.4068961,0,21728.34481
186700,16,0,2,0,Married,13,444.4002378,0,21680.01189
114342,19,1,3,0,Married,11,444.3421521,0,21924.10761
126963,18,0,2,0,Married,15,444.3351534,0,21901.75767
170331,30,12,2,0,Married,8,444.3293777,0,21993.46889
102817,16,0,3,0,Married,9,444.268167,0,21650.40835
173549,35,18,1,0,Single,7,444.2241383,0,21877.20692
105048,42,22,2,1,Married,0,444.2194531,0,21926.97266
154541,48,28,4,0,Single,2,444.1549303,0,21822.74652
126826,36,16,4,1,Single,8,444.1166015,0,21925.83008
184395,36,19,1,0,Single,7,444.0920771,0,21751.60386
146565,31,11,2,1,Single,10,444.075263,0,21691.76315
184484,45,24,4,1,Single,9,444.0067488,0,21801.33744
186875,28,10,3,0,Single,13,443.9090315,0,21640.45158
158733,37,20,3,0,Single,14,443.8011419,0,21658.0571
138860,22,4,4,0,Married,13,443.8005421,0,21832.02711
101299,61,40,3,0,Single,2,443.7570469,0,21893.85235
103522,16,0,2,1,Married,7,443.7048512,0,21960.24256
143977,19,1,2,1,Married,9,443.6650907,0,21723.25454
111657,39,19,3,0,Single,6,443.629057,0,21590.45285
160831,61,41,2,1,Married,3,443.5355878,0,21619.77939
104181,57,37,3,0,Married,0,443.5297142,0,21891.48571
184719,34,16,2,0,Single,12,443.5268424,0,21730.34212
125989,39,22,2,1,Single,6,443.5045498,0,21619.22749
182982,30,11,3,1,Married,3,443.4162487,0,21793.81244
105778,57,40,3,1,Single,10,443.3882806,0,21729.41403
110330,16,0,1,0,Married,14,443.3265557,0,21598.32779
167995,66,47,3,1,Married,5,443.2798699,0,21823.9935
175808,49,30,2,1,Married,1,443.2641355,0,21866.20678
161027,31,11,2,0,Single,13,443.2015152,0,21758.07576
151636,58,37,1,0,Single,9,443.1218792,0,21599.09396
149722,44,23,2,1,Single,6,443.1036083,0,21841.18042
145404,40,22,4,1,Married,4,443.0579343,0,21577.89672
108986,57,36,3,1,Single,10,443.0355432,0,21707.77716
108550,22,4,1,0,Married,13,442.9296618,0,21751.48309
129558,57,38,4,0,Single,6,442.8722012,0,21613.61006
134092,55,38,1,0,Single,6,442.8394769,0,21749.97385
137204,37,16,4,1,Single,9,442.8171912,0,21848.85956
160664,20,1,4,1,Married,1,442.7993053,0,21725.96527
148293,19,0,3,1,Married,8,442.7890589,0,21556.45295
101350,18,0,3,1,Married,10,442.7886923,0,21762.43462
189012,22,5,4,1,Single,13,442.7727452,0,21906.63726
192355,16,0,3,1,Married,11,442.763682,0,21698.1841
174157,25,7,2,0,Married,8,442.7372903,0,21763.86452
181152,32,14,3,1,Single,6,442.700593,0,21679.02965
117870,67,50,2,1,Single,6,442.6722795,0,21639.61398
106926,46,29,3,0,Single,8,442.6504186,0,21823.52093
172726,25,7,3,0,Single,14,442.6206801,0,21763.03401
149597,43,23,4,0,Married,8,442.6114551,0,21887.57276
195234,22,4,4,1,Single,15,442.5756581,0,21754.78291
186287,70,49,4,1,Single,13,442.4073421,0,21724.36711
195470,58,40,4,1,Single,3,442.4068984,0,21692.34492
105053,66,49,4,0,Single,5,442.3922455,0,21573.61228
115873,66,46,3,1,Single,2,442.3541467,0,21784.70734
111019,59,38,2,1,Single,7,442.349611,0,21869.48055
105262,36,15,2,1,Single,6,442.328608,0,21707.4304
105130,19,0,2,1,Single,13,442.3044664,0,21872.22332
143164,57,36,2,1,Married,3,442.2950584,0,21647.75292
122697,56,38,2,1,Single,9,442.2016245,0,21511.08123
123980,54,34,3,1,Single,10,442.1869973,0,21797.34987
116445,61,43,3,0,Single,4,442.1473967,0,21547.36984
168041,17,0,3,0,Married,12,442.1200214,0,21708.00107
133002,41,24,3,1,Single,6,442.1138921,0,21869.69461
130937,51,33,4,1,Single,10,442.088138,0,21566.4069
111874,68,50,3,0,Married,5,442.0778924,0,21591.89462
166318,23,6,2,1,Married,4,442.0467628,0,21853.33814
114436,42,25,2,0,Single,8,442.0249542,0,21726.24771
188597,45,26,2,1,Single,6,442.0024723,0,21659.12362
114184,42,22,4,0,Single,7,441.994414,0,21629.7207
153058,65,47,2,0,Married,4,441.9746791,0,21710.73396
188645,44,26,3,1,Single,6,441.9692376,0,21738.46188
165686,24,5,1,1,Married,14,441.8763829,0,21841.81915
192456,44,23,3,0,Single,7,441.8691366,0,21814.45683
197570,45,28,4,1,Single,7,441.6952086,0,21884.76043
179584,61,44,3,1,Married,1,441.6845237,0,21801.22619
102131,29,9,2,0,Single,9,441.6635365,0,21627.17683
158884,49,30,2,0,Single,9,441.657568,0,21746.8784
151066,17,0,3,1,Married,7,441.6426591,0,21521.13296
140375,67,46,4,1,Single,0,441.6132031,0,21696.66016
115764,64,44,3,1,Married,4,441.5664395,0,21832.32198
148356,43,26,3,0,Single,2,441.4447936,0,21702.23968
185725,24,6,4,1,Single,12,441.3935364,0,21691.67682
123092,41,24,4,1,Single,1,441.3793803,0,21775.96902
182015,67,46,3,1,Single,5,441.3103863,0,21588.51932
193236,28,11,3,1,Single,9,441.2911224,0,21697.55612
148654,51,34,1,0,Single,9,441.2667676,0,21793.33838
148766,37,20,3,0,Single,6,441.2537341,0,21775.68671
119535,39,21,1,1,Single,7,441.2336954,0,21524.68477
174783,48,28,3,0,Married,4,441.2173704,0,21622.86852
102222,35,18,1,0,Single,10,441.1543119,0,21667.7156
138577,53,34,4,0,Single,6,441.1122255,0,21788.61128
119978,63,46,2,0,Married,0,441.0022517,0,21530.11259
116997,23,6,2,1,Single,7,440.9578964,0,21526.89482
176184,48,30,4,0,Single,6,440.947621,0,21802.38105
137050,53,36,4,0,Single,8,440.8848186,0,21567.24093
158420,45,24,1,0,Single,7,440.8602363,0,21692.01182
169473,23,6,3,0,Married,10,440.7947497,0,21523.73749
131336,38,19,1,1,Married,5,440.7741586,0,21505.70793
132706,31,10,2,1,Single,5,440.6938419,0,21483.6921
154603,65,46,4,1,Married,5,440.6706032,0,21643.53016
192847,26,8,3,0,Single,9,440.6492298,0,21657.46149
122518,37,17,2,0,Single,10,440.5955997,0,21608.77999
109043,25,8,1,0,Single,15,440.5874752,0,21668.37376
121279,22,5,2,1,Married,15,440.5414769,0,21813.07385
133723,16,0,3,1,Married,10,440.489178,0,21750.4589
123113,20,2,2,1,Single,14,440.4829608,0,21461.14804
132264,35,14,1,1,Single,8,440.4771322,0,21678.85661
119519,28,9,1,0,Single,9,440.4587335,0,21653.93668
101867,29,12,3,1,Single,6,440.450693,0,21442.53465
192333,62,43,3,1,Single,2,440.2501085,0,21512.50543
117910,50,30,3,0,Married,4,440.1817513,0,21506.08757
190618,27,7,2,0,Single,6,440.158269,0,21635.91345
161834,67,46,2,0,Married,1,440.1540283,0,21618.70142
116684,30,9,1,0,Single,8,440.0845453,0,21727.22727
193478,33,15,4,1,Single,8,440.0273323,0,21493.36662
191530,57,38,3,1,Single,9,440.0203128,0,21478.01564
109844,54,35,3,1,Single,10,439.9538341,0,21530.69171
117458,33,12,2,0,Single,10,439.9439695,0,21727.19848
185418,65,46,3,0,Married,1,439.8757049,0,21464.78525
197092,29,8,1,1,Single,10,439.8534506,0,21641.67253
191863,57,37,3,1,Single,7,439.7754556,0,21557.77278
105127,34,17,1,0,Married,8,439.7564936,0,21613.82468
162298,68,50,3,1,Single,0,439.728919,0,21662.44595
126034,18,0,2,1,Single,14,439.6913022,0,21682.56511
122673,18,0,2,1,Single,12,439.6833242,0,21699.16621
157654,23,4,4,1,Single,12,439.6785216,0,21569.92608
123424,55,35,3,0,Single,9,439.6770204,0,21673.85102
173555,60,39,4,1,Single,7,439.6718132,0,21413.59066
117599,59,42,4,0,Single,6,439.6628398,0,21661.14199
176180,43,25,1,1,Married,4,439.646889,0,21427.34445
177821,52,32,1,1,Married,3,439.5392875,0,21416.96438
184605,34,14,3,1,Single,12,439.4799764,0,21517.99882
186745,60,43,2,0,Married,5,439.4426207,0,21518.13104
134351,18,1,2,1,Single,11,439.4230095,0,21404.15048
119436,17,0,3,0,Married,15,439.401243,0,21596.06215
195494,37,17,3,0,Single,5,439.3880266,0,21584.40133
159204,42,22,3,0,Single,6,439.3203039,0,21562.0152
130131,25,8,4,1,Married,0,439.2923396,0,21643.61698
115228,32,12,1,0,Single,10,439.2919376,0,21407.59688
172926,23,6,3,0,Married,10,439.2788954,0,21466.94477
192216,39,22,3,0,Single,7,439.1429781,0,21396.14891
112616,55,36,2,1,Single,4,439.1418913,0,21503.09457
110295,18,0,3,0,Married,1,439.1286723,0,21716.43362
115610,24,5,1,1,Single,11,439.1176788,0,21541.88394
105227,59,40,3,1,Single,8,439.1101835,0,21590.50918
118179,59,38,4,0,Single,8,439.1049171,0,21429.24586
155716,57,37,4,0,Single,10,439.1014192,0,21376.07096
130255,66,46,4,0,Married,4,439.094311,0,21568.71555
124698,39,18,2,0,Married,4,438.9234708,0,21659.17354
168568,34,17,1,0,Single,9,438.8540277,0,21708.70139
150051,48,27,2,1,Single,10,438.8192687,0,21368.96344
108959,51,34,4,1,Single,10,438.8067065,0,21553.33533
101604,23,4,1,0,Married,10,438.7076785,0,21556.38393
140373,52,33,3,0,Single,8,438.665973,0,21530.29865
144500,29,11,4,1,Single,7,438.6081936,0,21496.40968
184955,22,3,4,1,Married,9,438.5915609,0,21483.57805
153488,54,34,1,1,Single,11,438.4493431,0,21491.46716
171470,44,27,3,1,Single,10,438.3824803,0,21442.12402
194579,31,10,2,1,Single,6,438.3801826,0,21433.00913
113829,38,21,4,0,Single,10,438.3532057,0,21417.66029
116924,27,9,2,1,Married,1,438.326246,0,21392.3123
158929,23,5,4,0,Single,14,438.2569961,0,21444.84981
155758,43,25,3,0,Single,2,438.1639396,0,21526.19698
108690,35,17,4,0,Married,6,438.1548553,0,21354.74277
148777,18,1,3,1,Single,14,438.128881,0,21447.44405
150038,49,31,2,0,Married,4,438.0736832,0,21574.68416
109389,56,35,4,1,Single,7,438.0690014,0,21520.45007
141599,56,35,2,0,Single,7,438.0103504,0,21681.51752
164812,48,28,3,0,Single,15,437.9751715,0,21393.75858
145580,48,27,4,1,Single,2,437.9594676,0,21324.97338
188556,30,12,4,0,Single,7,437.9343846,0,21325.71923
167788,53,35,4,1,Single,8,437.9194858,0,21549.97429
144787,33,15,2,1,Single,15,437.9020483,0,21464.10242
166717,26,9,4,1,Single,6,437.8661847,0,21423.30924
177844,36,15,2,0,Single,2,437.8476737,0,21691.38369
124019,44,23,2,1,Single,7,437.8435798,0,21482.17899
156221,48,29,1,1,Single,5,437.82145,0,21394.0725
158355,16,0,2,0,Single,14,437.8069624,0,21633.34812
123435,50,33,4,0,Single,6,437.7636042,0,21427.18021
155234,35,15,2,1,Single,10,437.6261485,0,21476.30743
172931,40,23,1,0,Single,6,437.5853059,0,21369.2653
103126,55,38,1,1,Single,7,437.5486569,0,21610.43285
111615,32,11,3,0,Single,8,437.5326073,0,21597.63037
126686,22,5,1,0,Single,11,437.3787493,0,21339.93747
113189,30,12,2,0,Married,1,437.3786203,0,21522.93102
125285,56,39,1,0,Married,5,437.3601045,0,21529.00523
188882,46,28,4,0,Single,6,437.3429689,0,21328.14845
144653,27,10,3,0,Single,10,437.302042,0,21607.1021
197638,57,39,2,0,Single,13,437.2575179,0,21651.8759
160743,22,5,4,0,Single,12,437.2300123,0,21535.50062
196295,22,3,4,1,Single,12,437.2083539,0,21542.4177
157185,65,47,1,0,Single,1,437.1775426,0,21436.87713
168348,48,30,3,0,Single,8,437.1661679,0,21574.3084
109637,51,33,1,0,Single,8,437.1309872,0,21334.54936
132638,51,33,3,1,Single,7,437.0706614,0,21376.53307
171180,22,5,4,0,Married,5,437.0523196,0,21526.61598
101674,17,0,3,1,Married,8,437.0023845,0,21391.11923
106089,23,5,2,0,Married,9,436.8418596,0,21315.09298
197114,53,32,3,0,Single,7,436.8340804,0,21512.70402
144314,18,1,2,0,Single,15,436.7715984,0,21254.57992
143897,55,34,3,0,Single,9,436.7674904,0,21281.37452
131150,18,0,1,0,Single,11,436.7417282,0,21575.08641
150283,25,7,2,1,Married,15,436.6690835,0,21549.45418
190565,43,24,1,1,Married,1,436.6028495,0,21340.14248
173116,64,46,3,1,Married,2,436.5779985,0,21572.89993
135903,20,3,2,1,Married,10,436.5643972,0,21437.21986
178170,41,22,3,1,Single,8,436.5459141,0,21315.29571
157326,64,43,3,0,Single,7,436.5286065,0,21246.43033
188178,19,2,4,1,Single,13,436.4979325,0,21489.89663
193469,58,37,2,0,Single,10,436.4935985,0,21528.67993
150597,59,39,2,1,Single,1,436.4761219,0,21462.8061
197553,59,40,2,0,Married,0,436.4017589,0,21610.08795
178501,26,8,1,0,Single,8,436.3278273,0,21585.39137
154338,70,49,3,1,Married,0,436.314571,0,21588.72855
183176,30,11,2,1,Single,6,436.3091977,0,21358.45989
192288,25,6,4,1,Married,7,436.2493995,0,21366.46998
121197,57,37,4,1,Married,3,436.2360182,0,21550.80091
198239,54,35,2,0,Single,1,436.2093066,0,21554.46533
160403,70,50,3,0,Married,5,436.1887136,0,21351.43568
106991,27,10,4,0,Single,10,436.186264,0,21241.3132
128728,26,9,1,0,Single,12,436.1834558,0,21254.17279
134777,31,13,2,0,Single,9,436.1708524,0,21376.54262
101165,34,16,2,1,Single,3,436.1101317,0,21399.50659
115010,25,8,1,1,Single,12,436.0393992,0,21564.96996
172958,41,23,2,0,Married,2,436.0292348,0,21514.46174
131727,24,6,4,1,Single,8,436.0129371,0,21317.64686
144306,57,40,3,0,Single,6,435.9986149,0,21262.93075
174932,32,12,2,1,Single,10,435.997452,0,21295.8726
112642,34,13,1,0,Single,7,435.9831882,0,21517.15941
179055,53,35,1,0,Single,7,435.959851,0,21451.99255
179659,18,0,4,0,Married,13,435.8457162,0,21428.28581
137332,29,11,3,1,Married,3,435.6879662,0,21561.39831
175948,19,1,2,0,Married,11,435.6867047,0,21301.33524
105243,26,7,3,1,Single,2,435.6844113,0,21196.22057
184517,18,0,4,1,Single,12,435.6660993,0,21505.30497
111260,35,18,3,1,Married,0,435.6444132,0,21321.22066
169509,65,48,2,0,Single,3,435.6436387,0,21480.18194
111979,65,47,3,0,Single,5,435.4396914,0,21261.98457
186170,18,1,2,1,Married,14,435.429934,0,21317.4967
127806,45,24,1,1,Single,6,435.42961,0,21263.4805
129073,45,28,2,0,Single,13,435.4216584,0,21417.08292
150090,20,3,3,0,Married,10,435.4007981,0,21311.03991
189680,53,34,3,0,Single,8,435.4005785,0,21521.02893
186078,47,30,2,0,Single,12,435.3769461,0,21412.84731
110878,16,0,4,0,Married,14,435.3721562,0,21327.60781
149344,44,25,2,1,Single,8,435.3669469,0,21482.34735
187285,43,24,3,1,Single,6,435.2699647,0,21405.49824
188589,16,0,3,0,Single,14,435.2293799,0,21414.469
188248,33,13,3,1,Married,5,435.1204689,0,21310.02345
174077,20,2,1,1,Married,9,435.088519,0,21211.42595
189797,54,33,2,0,Single,6,435.0601621,0,21259.00811
104390,26,6,3,0,Married,8,435.0296062,0,21456.48031
129744,23,5,2,1,Married,14,434.938752,0,21505.9376
149212,34,15,1,1,Single,11,434.8411458,0,21436.05729
193616,47,27,4,1,Married,5,434.8229191,0,21326.14596
192592,33,16,4,0,Single,9,434.8206346,0,21511.03173
153570,62,43,2,1,Single,0,434.7456671,0,21514.28336
110811,21,3,3,0,Single,12,434.7249998,0,21289.24999
193479,65,46,1,0,Married,2,434.7234839,0,21440.1742
116697,22,5,4,0,Single,14,434.7105998,0,21270.52999
121473,28,11,4,1,Married,1,434.7078232,0,21448.39116
118420,37,20,4,0,Single,9,434.701813,0,21455.09065
145776,26,6,1,0,Single,10,434.5793359,0,21259.9668
133247,66,45,4,1,Married,1,434.5548793,0,21503.74397
112921,54,35,1,1,Single,6,434.5258236,0,21156.29118
110459,57,39,2,1,Single,7,434.4769865,0,21493.84933
145730,18,1,2,0,Married,13,434.4512132,0,21258.56066
136228,25,8,3,0,Married,11,434.4101055,0,21336.50528
109261,19,0,2,0,Married,9,434.4062385,0,21137.31193
187674,47,27,1,0,Single,6,434.40134,0,21221.067
171324,16,0,2,1,Single,14,434.3302549,0,21460.51275
140938,58,37,2,1,Single,13,434.3252095,0,21429.26048
133866,43,26,2,0,Single,12,434.2490493,0,21503.45247
168214,54,34,1,0,Single,9,434.1198694,0,21361.99347
121238,33,12,2,0,Single,12,434.0823777,0,21268.11889
182495,52,32,2,0,Married,5,433.9690892,0,21307.45446
142525,65,45,1,1,Single,8,433.9037761,0,21312.18881
193194,61,41,3,0,Single,4,433.8956731,0,21360.78366
179381,22,4,3,1,Married,11,433.8649639,0,21489.2482
151864,41,20,2,0,Single,13,433.8180465,0,21263.90233
199233,30,10,2,0,Single,2,433.817628,0,21166.8814
150981,54,37,2,1,Married,4,433.7988397,0,21398.94199
188845,63,44,4,0,Single,3,433.7320858,0,21318.60429
166710,51,30,3,0,Single,10,433.7248915,0,21239.24458
155530,17,0,4,0,Single,11,433.6890578,0,21236.45289
165028,31,11,3,0,Single,7,433.6557018,0,21375.78509
118700,43,25,3,1,Single,4,433.6436742,0,21241.18371
181126,58,39,2,1,Single,7,433.5899259,0,21267.4963
150602,45,27,2,0,Single,10,433.4797677,0,21449.98839
140861,57,38,3,0,Single,10,433.4630009,0,21424.15005
157311,60,39,3,0,Married,2,433.4556152,0,21154.78076
115952,20,2,2,1,Single,13,433.3371921,0,21438.85961
190433,61,43,2,0,Single,1,433.3229127,0,21413.14564
123927,59,39,3,1,Single,7,433.2388438,0,21405.94219
128651,43,25,2,0,Single,6,433.2281911,0,21183.40956
123822,19,0,1,1,Single,10,433.2087995,0,21095.43998
124587,57,36,2,1,Single,8,433.1765586,0,21064.82793
197585,40,20,2,0,Single,15,433.1540449,0,21152.70225
186132,38,18,1,0,Single,9,433.1419791,0,21178.09896
143651,18,0,2,1,Single,11,433.0880457,0,21348.40229
114819,16,0,1,0,Single,11,433.080861,0,21378.04305
142249,25,7,4,1,Married,2,433.0065501,0,21133.32751
192359,43,23,2,1,Single,9,433.0022757,0,21352.11379
120349,51,32,4,0,Single,10,432.9486699,0,21343.4335
176476,17,0,2,1,Married,8,432.9213332,0,21226.06666
182186,58,40,2,0,Married,4,432.885157,0,21200.25785
137980,48,27,2,1,Married,2,432.8803242,0,21331.01621
137782,23,5,3,1,Married,13,432.8053738,0,21343.26869
104397,21,2,3,0,Married,11,432.7997392,0,21206.98696
169020,26,5,2,1,Single,6,432.7976128,0,21114.88064
178595,42,22,4,1,Single,9,432.7490525,0,21303.45263
133181,60,42,3,0,Single,3,432.707755,0,21264.38775
134522,37,17,1,0,Married,10,432.6751776,0,21354.75888
148015,53,35,2,0,Married,3,432.6201947,0,21209.00974
122573,51,31,4,0,Single,8,432.6065462,0,21261.32731
108104,58,39,4,1,Single,8,432.5799167,0,21188.99584
134754,64,44,3,0,Married,1,432.5580693,0,21110.90347
103882,25,6,3,1,Married,15,432.5538698,0,21233.69349
196392,66,46,4,0,Single,7,432.5229054,0,21382.14527
120974,34,17,2,0,Married,4,432.4997239,0,21200.9862
156370,18,0,3,0,Married,11,432.441388,0,21189.0694
162786,24,6,2,0,Married,7,432.4344208,0,21337.72104
146116,48,27,3,0,Single,9,432.352609,0,21344.63045
103000,44,26,3,0,Single,6,432.3500041,0,21408.50021
122666,37,20,2,1,Single,6,432.3490479,0,21176.4524
127930,46,27,3,0,Single,8,432.3452389,0,21161.26195
152204,21,3,4,1,Married,14,432.3332971,0,21051.66486
190535,20,3,3,0,Single,14,432.3149763,0,21157.74882
148336,29,10,4,0,Married,2,432.3005906,0,21235.02953
111588,40,21,2,0,Married,10,432.2681353,0,21262.40677
140083,25,8,2,0,Married,10,432.2537352,0,21148.68676
101711,21,4,1,1,Married,6,432.2491717,0,21141.45859
100314,33,16,4,1,Single,8,432.2238432,0,21332.19216
184180,28,8,4,0,Single,6,432.1829193,0,21099.14597
173508,23,6,2,1,Married,6,432.1793564,0,21108.96782
137964,59,41,2,1,Single,10,432.1720254,0,21043.60127
135721,17,0,2,0,Married,11,432.1548344,0,21089.74172
115819,50,32,3,1,Single,9,432.1347485,0,21213.73743
113134,16,0,1,1,Single,11,432.1205891,0,21312.02946
123916,33,16,4,1,Single,6,432.1164909,0,21190.82455
109664,45,27,1,1,Single,8,432.0837395,0,21091.18698
133350,44,25,3,0,Single,9,432.0578807,0,21168.89404
144257,38,17,1,0,Single,6,432.002112,0,21326.1056
171825,19,2,2,0,Single,14,431.9491025,0,21101.45513
113512,21,2,2,1,Married,4,431.9004518,0,21233.02259
100936,37,17,2,1,Single,7,431.8786997,0,21350.93499
191447,65,47,3,0,Single,7,431.8637009,0,21351.18505
159897,20,2,3,0,Married,12,431.8164641,0,21090.82321
184426,67,47,4,1,Single,4,431.625472,0,21104.2736
166154,42,25,1,0,Single,9,431.5898049,0,21379.49025
109078,40,21,3,0,Single,10,431.5806008,0,21326.03004
146143,21,4,1,0,Married,13,431.5361637,0,21033.80819
190585,55,34,3,1,Married,2,431.5115537,0,21053.57769
133581,32,13,3,0,Single,8,431.5077143,0,21368.38572
160839,39,19,2,0,Single,11,431.4657323,0,21329.28662
123482,27,7,2,1,Single,9,431.4405981,0,21258.02991
138119,41,22,1,0,Married,5,431.418055,0,21263.90275
197443,53,32,2,0,Single,6,431.3881701,0,21268.40851
162683,41,20,1,0,Single,9,431.375072,0,21190.7536
146137,23,5,3,0,Married,15,431.3719661,0,21274.59831
172531,26,5,3,0,Married,8,431.3532752,0,21358.66376
181383,57,37,4,1,Single,5,431.2847917,0,21247.23959
185606,30,9,3,1,Single,10,431.2678646,0,21023.39323
141945,22,3,2,0,Married,7,431.2372528,0,21163.86264
141926,59,42,4,0,Single,15,431.2316572,0,21125.58286
193035,50,33,1,1,Single,7,431.1665527,0,21222.32764
133136,45,25,3,0,Married,0,431.1181724,0,21323.90862
117198,56,38,3,0,Single,8,431.1060154,0,21341.30077
176403,46,29,4,1,Single,6,431.0993117,0,21149.96559
173050,44,24,1,0,Single,7,431.0471708,0,21286.35854
149898,31,11,2,1,Married,4,431.0282312,0,21123.41156
115013,16,0,3,0,Married,13,431.0177082,0,21125.88541
117722,19,0,1,0,Single,11,430.9655363,0,21294.27682
107934,22,5,3,0,Married,14,430.962319,0,20968.11595
145080,56,38,2,1,Single,8,430.9502505,0,21071.51253
159134,18,1,2,1,Single,10,430.9324283,0,21221.62142
183970,31,10,3,0,Single,10,430.929523,0,21004.47615
161859,20,2,3,1,Single,7,430.9069948,0,21267.34974
145974,49,30,3,1,Single,6,430.8618542,0,21145.09271
198390,27,8,1,0,Single,6,430.7272458,0,21209.36229
145959,31,12,3,1,Single,6,430.7245431,0,21030.22716
113718,18,0,4,0,Married,11,430.7125121,0,20992.62561
128735,31,13,3,0,Married,1,430.6421926,0,20978.10963
191789,40,19,2,1,Married,3,430.571871,0,21132.59355
179805,41,22,1,1,Single,8,430.5555209,0,21171.77605
138282,48,31,3,0,Single,10,430.5422419,0,21277.1121
105145,17,0,2,1,Married,9,430.4902428,0,20963.51214
111706,23,6,4,0,Married,8,430.4848221,0,21252.24111
152228,16,0,2,1,Married,14,430.4119517,0,21053.59759
172572,38,19,2,1,Single,10,430.3666938,0,20969.33469
171670,58,40,1,1,Single,6,430.3296192,0,21282.48096
179398,51,30,2,0,Married,3,430.3243254,0,21079.21627
125059,66,46,1,1,Single,4,430.2457695,0,20939.28848
130926,70,50,3,0,Married,1,430.2231153,0,20944.15577
198429,55,34,3,1,Single,13,430.2150373,0,21183.75187
139939,27,9,1,1,Single,7,430.2106048,0,21101.53024
186162,52,35,4,1,Single,7,430.1810861,0,21192.05431
169392,25,6,2,1,Single,6,430.1482025,0,21269.41013
148692,50,31,3,1,Single,0,430.1435472,0,21269.17736
168957,29,9,3,0,Married,1,430.122261,0,21006.11305
140768,27,10,1,0,Single,6,430.1199309,0,21001.99655
177394,29,11,2,0,Single,6,429.9656904,0,20931.28452
134701,52,34,3,0,Married,9,429.9422569,0,21166.11285
107616,24,5,2,1,Single,12,429.933187,0,21074.65935
140469,21,4,1,1,Single,15,429.8619791,0,21137.09896
140516,27,10,3,0,Single,8,429.840519,0,21026.02595
145276,36,16,1,0,Married,0,429.8250505,0,21042.25253
136478,28,8,2,1,Single,2,429.7236818,0,21149.18409
174791,49,30,3,1,Single,7,429.6544251,0,21005.72126
182370,31,14,2,1,Single,13,429.6450408,0,20920.25204
194267,32,11,3,1,Single,6,429.6147915,0,21220.73958
101567,60,41,3,1,Single,1,429.5967528,0,20921.83764
197277,38,18,4,0,Single,8,429.5579461,0,20988.89731
163020,58,40,1,0,Single,9,429.506863,0,21061.34315
161574,43,23,2,0,Single,6,429.4852152,0,21232.26076
112612,64,43,2,1,Married,0,429.4798613,0,21253.99307
114987,19,1,2,0,Single,12,429.4439787,0,21187.19894
160784,35,17,2,0,Married,7,429.4151524,0,21073.75762
177622,34,16,1,1,Married,1,429.4072773,0,20959.36387
165901,30,9,2,1,Single,10,429.406501,0,21144.32505
127375,26,9,4,0,Single,13,429.386194,0,21181.3097
198181,25,6,3,0,Married,11,429.3403008,0,21130.01504
127767,61,41,2,1,Single,5,429.3195189,0,21195.97595
195684,16,0,3,1,Single,12,429.2779309,0,21073.89655
170639,23,6,4,1,Single,12,429.2584884,0,21213.92442
123228,50,31,4,0,Single,9,429.2560964,0,21143.80482
197861,35,18,3,0,Single,6,429.2311462,0,21058.55731
148603,33,13,3,0,Single,13,429.151349,0,21217.56745
122069,18,0,1,1,Married,12,429.1487774,0,20936.43887
160708,40,21,2,0,Single,9,429.1482942,0,21209.41471
150239,21,3,3,0,Married,15,429.1453009,0,21200.26505
194460,43,24,3,1,Single,7,429.0042771,0,20887.21386
156341,62,44,3,0,Single,13,428.9311772,0,20962.55886
107897,47,29,4,0,Single,7,428.8987044,0,21088.93522
186870,45,26,3,1,Single,15,428.8885515,0,20904.42758
196073,35,18,1,1,Single,9,428.8765231,0,20953.82616
146923,58,39,2,1,Single,14,428.8592904,0,21030.96452
133135,19,2,3,0,Married,12,428.8329385,0,21012.64693
179835,24,7,1,1,Married,15,428.8144302,0,20911.72151
169490,27,9,3,1,Single,6,428.7863104,0,21093.31552
130984,25,8,3,1,Married,14,428.7713277,0,21002.56639
106357,26,9,3,1,Single,7,428.7595533,0,21069.97767
142609,40,21,2,0,Single,15,428.6185743,0,21120.92872
196893,25,6,3,1,Married,10,428.5948527,0,20955.74264
164860,64,46,3,0,Married,3,428.5855128,0,20884.27564
161769,45,25,3,0,Single,15,428.5556062,0,20924.78031
131590,46,25,2,1,Single,6,428.5202743,0,20924.01372
124647,45,28,3,1,Single,10,428.5178983,0,21111.89492
126842,41,24,1,1,Single,10,428.4593856,0,21117.96928
158776,17,0,2,1,Married,6,428.437278,0,21074.8639
190438,27,8,1,0,Single,9,428.3436991,0,21123.18496
192140,40,19,2,1,Married,5,428.3322982,0,20879.61491
107837,16,0,2,0,Married,11,428.3233658,0,20952.16829
108627,19,0,2,1,Single,15,428.2658156,0,21150.29078
162261,53,33,2,0,Single,7,428.262359,0,20916.11795
189071,26,8,4,0,Single,10,428.2598359,0,20855.9918
181984,42,24,3,1,Married,2,428.2211269,0,21052.05635
180260,22,3,4,1,Single,15,428.1849528,0,20939.24764
137017,22,5,3,1,Married,10,428.1788423,0,20994.94212
199993,65,48,3,1,Single,3,428.1292276,0,20851.46138
144777,50,31,1,1,Single,1,428.0443064,0,21114.21532
144224,23,6,4,1,Married,15,428.0332128,0,20925.66064
120469,17,0,3,1,Single,15,427.9976492,0,21013.88246
102401,19,2,4,1,Married,14,427.8977662,0,21066.88831
190999,66,48,2,0,Single,5,427.8914224,0,21163.57112
199606,58,39,1,0,Married,9,427.8410376,0,20899.05188
134992,62,42,2,0,Single,13,427.7748107,0,21027.74054
194642,50,29,2,1,Single,8,427.7290944,0,20931.45472
112671,40,22,4,0,Single,8,427.7195464,0,21083.97732
130679,21,3,1,1,Married,13,427.7138738,0,20930.69369
177512,33,16,4,1,Single,10,427.6512446,0,21094.56223
158084,52,32,3,1,Single,10,427.6283088,0,20928.41544
116665,57,40,4,1,Single,5,427.6201278,0,21044.00639
157150,57,37,2,0,Married,5,427.524697,0,20804.23485
150837,18,0,4,1,Married,7,427.4092113,0,20812.46057
114704,27,6,3,1,Single,6,427.4058448,0,21017.29224
161226,33,12,1,1,Married,4,427.3927161,0,20913.63581
140244,44,25,1,1,Single,3,427.346936,0,20871.3468
137232,27,9,3,0,Single,8,427.3192717,0,20975.96359
102641,59,40,1,0,Single,7,427.2013464,0,20860.06732
109196,46,25,2,0,Married,9,427.1871568,0,21129.35784
192660,52,34,2,0,Married,0,427.1674938,0,20840.37469
167349,44,23,1,1,Single,9,427.1032675,0,20924.16338
148509,64,45,3,0,Single,15,427.0756419,0,20834.7821
183580,24,5,4,1,Married,15,426.9693417,0,20799.46709
127476,42,22,3,0,Single,6,426.9031577,0,21013.15789
137377,44,26,1,0,Single,7,426.8421815,0,21120.10908
175309,19,0,4,1,Single,12,426.8408152,0,20894.04076
118408,23,5,3,0,Single,15,426.7706397,0,21042.53199
164194,43,25,3,0,Single,7,426.7671338,0,20789.35669
193048,24,5,2,1,Single,12,426.7631092,0,20806.15546
105899,70,53,1,0,Single,8,426.6658746,0,20750.29373
135309,26,7,3,1,Married,2,426.6371651,0,21093.85826
158450,38,21,2,1,Single,10,426.5349909,0,20828.74955
153306,22,3,3,1,Married,15,426.5257171,0,20885.28586
162960,69,49,3,0,Single,10,426.5091944,0,20747.45972
142490,30,12,1,1,Single,8,426.4783725,0,21118.91863
108002,55,37,3,1,Single,9,426.4624974,0,20795.12487
190463,58,39,2,1,Single,10,426.4276795,0,20933.38398
196160,70,50,1,0,Single,5,426.4032579,0,20770.1629
113291,39,18,3,1,Single,9,426.3535978,0,21023.67989
125358,24,5,2,0,Single,12,426.3458887,0,21110.29444
131762,29,11,3,1,Single,10,426.3416661,0,20845.08331
125826,17,0,3,0,Married,13,426.3072964,0,21068.36482
123025,24,5,1,1,Single,7,426.2893605,0,20997.46803
109507,22,4,3,1,Single,11,426.2884302,0,20823.42151
127163,44,25,2,0,Single,7,426.268402,0,21036.4201
140699,33,14,3,0,Single,11,426.2252706,0,20963.26353
145778,42,21,2,0,Single,10,426.2098135,0,20971.49068
159866,16,0,1,1,Married,7,426.1706197,0,20973.53099
171870,17,0,2,1,Married,14,426.1425635,0,21014.12818
138985,45,26,4,0,Married,8,426.1181883,0,20799.90942
142860,30,12,3,0,Single,9,426.0966284,0,20764.83142
147502,38,17,2,1,Single,6,426.0835848,0,20804.17924
139115,44,27,1,1,Single,8,426.0637585,0,20863.18793
180549,32,15,3,0,Single,8,426.0389212,0,20888.94606
171138,16,0,4,0,Married,15,426.0346014,0,20756.73007
161536,59,41,1,0,Single,8,426.0073629,0,20870.36815
134066,21,4,3,1,Married,7,426.0055263,0,20728.27632
115938,20,2,2,1,Married,15,425.9558647,0,20903.79324
105475,39,21,3,1,Single,7,425.9032742,0,20935.16371
190118,36,16,2,0,Married,7,425.862971,0,20867.14855
142113,62,42,3,1,Single,14,425.8344765,0,20816.72383
106467,45,27,2,0,Single,6,425.7957411,0,20733.78706
166753,33,14,2,1,Single,5,425.7547902,0,20883.73951
159697,42,24,3,1,Married,4,425.718669,0,21000.93345
135704,48,29,2,1,Single,9,425.7026245,0,20782.13123
100520,30,11,1,1,Single,2,425.6230015,0,20797.15008
112884,31,10,3,0,Single,6,425.5953663,0,20745.76832
200000,48,27,2,1,Single,7,425.5806709,0,20990.03355
106818,67,46,2,0,Married,1,425.5115144,0,20918.57572
140917,55,35,2,0,Single,8,425.4967109,0,20703.83555
165851,28,8,3,0,Single,8,425.4738051,0,20761.69026
164199,64,44,2,1,Single,5,425.4517567,0,20849.58784
141730,25,7,3,1,Single,11,425.4141837,0,20813.70919
107571,26,8,1,0,Married,9,425.3481104,0,20772.40552
166740,17,0,3,0,Single,13,425.2846908,0,20869.23454
168573,31,14,3,1,Married,3,425.262654,0,20832.1327
114754,23,6,4,0,Married,11,425.2231669,0,20749.15835
178842,66,45,1,1,Married,3,425.2158617,0,20885.79309
123972,32,12,1,1,Single,7,425.2124819,0,20920.6241
105960,38,17,1,1,Single,6,425.1927224,0,20977.63612
121331,43,23,3,1,Married,1,425.1740805,0,20902.70403
100137,26,6,3,1,Married,4,425.0964309,0,20829.82155
141815,32,12,3,0,Single,9,425.0750354,0,20978.75177
174126,29,9,3,1,Single,9,425.0698045,0,20987.49023
140549,32,13,4,0,Single,6,425.0154124,0,21043.77062
142931,64,47,1,1,Single,10,424.8797525,0,20668.98763
137641,28,9,3,0,Married,2,424.8302291,0,20649.51146
122221,17,0,1,1,Single,12,424.7723034,0,20973.61517
163838,23,5,2,0,Married,8,424.7652212,0,20804.26106
106858,54,34,4,0,Married,7,424.7402596,0,20642.01298
181704,21,3,4,1,Married,11,424.7213268,0,20884.06634
125557,61,44,4,0,Single,2,424.6948368,0,20690.74184
198124,29,12,3,1,Single,8,424.6311436,0,20950.55718
193281,49,29,4,1,Single,10,424.6187252,0,20979.93626
145491,62,42,2,1,Single,6,424.5097932,0,21017.48966
125290,34,14,3,0,Single,9,424.4260365,0,20920.30183
125152,54,33,3,1,Single,7,424.4244307,0,20851.22154
150918,65,47,3,0,Single,6,424.4213686,0,20635.06843
143677,63,44,2,1,Single,8,424.4147273,0,21011.73637
195840,39,22,1,0,Single,6,424.411475,0,20645.57375
139580,27,8,2,0,Married,7,424.3758894,0,20787.79447
199702,46,27,1,1,Married,5,424.3622371,0,20753.11186
180127,31,12,2,1,Single,7,424.2943953,0,20752.71977
168118,43,24,2,1,Single,10,424.2836355,0,20753.18178
170513,23,6,2,1,Single,12,424.2575249,0,20693.87625
116991,49,28,3,0,Married,9,424.2184833,0,20795.92417
176859,31,11,3,0,Married,8,424.0797025,0,20877.98513
185726,27,10,2,0,Single,7,423.9501643,0,20816.50822
109538,69,48,3,0,Married,5,423.923075,0,20634.15375
186671,28,11,3,1,Single,15,423.8902896,0,20717.51448
102098,46,25,3,1,Single,6,423.8582987,0,20987.91494
117652,67,46,1,1,Single,4,423.8349611,0,20944.74806
190278,36,15,4,0,Married,4,423.8233631,0,20871.16816
160305,21,3,4,0,Married,11,423.8093901,0,20916.46951
101192,68,51,2,0,Single,8,423.8090522,0,20678.45261
122371,45,25,2,1,Married,3,423.7712298,0,20868.56149
148117,24,5,3,1,Single,15,423.7363909,0,20916.81955
154621,41,24,2,0,Single,8,423.7351571,0,20602.75786
191061,40,20,1,0,Single,8,423.7222281,0,20590.11141
104352,39,20,3,0,Married,8,423.7212376,0,20875.06188
193020,58,38,3,0,Single,7,423.6973989,0,20799.86995
105398,53,35,4,1,Single,14,423.6948653,0,20758.74327
180218,17,0,1,1,Married,8,423.6904935,0,20715.52468
101145,22,5,3,0,Single,12,423.6899364,0,20938.49682
190303,29,11,3,0,Single,10,423.6515734,0,20909.57867
194333,19,1,2,1,Single,12,423.6379873,0,20605.89937
109006,47,30,3,0,Married,9,423.5838729,0,20739.19365
122440,50,33,3,0,Single,8,423.5739827,0,20787.69914
159893,19,2,3,1,Single,11,423.5684617,0,20868.42309
101602,45,26,1,1,Single,6,423.4963358,0,20946.81679
170960,27,9,3,1,Single,9,423.3146269,0,20824.73135
166201,32,12,3,0,Single,9,423.3057527,0,20626.28764
143444,68,49,2,0,Single,9,423.2962596,0,20626.81298
131562,18,0,2,1,Married,8,423.2506965,0,20798.53483
119624,64,46,2,0,Single,4,423.1791916,0,20721.95958
119015,21,2,3,1,Married,10,423.1654373,0,20686.27187
179617,31,11,1,1,Single,11,423.1563186,0,20581.81593
176966,23,4,2,0,Single,11,423.1087427,0,20683.43714
113390,47,28,4,1,Single,9,423.1020821,0,20619.10411
199358,37,20,2,0,Married,3,423.0475688,0,20790.37844
162826,47,29,2,1,Single,0,423.0057603,0,20826.28802
130806,39,18,1,0,Married,13,423.0039085,0,20946.19543
189122,63,43,1,1,Single,2,422.9930733,0,20887.65367
105407,53,33,4,1,Single,9,422.9142045,0,20595.71023
196484,36,19,2,1,Single,10,422.9126911,0,20834.63456
138859,57,37,3,1,Single,10,422.8974879,0,20671.8744
136916,62,42,4,0,Married,3,422.8597577,0,20672.98789
104324,48,27,3,0,Married,0,422.7622226,0,20735.11113
103583,53,32,4,1,Married,2,422.7035968,0,20838.17984
108827,25,7,1,1,Single,7,422.6834837,0,20659.17419
178477,48,28,4,1,Single,6,422.6596933,0,20720.98467
151816,55,38,2,0,Single,7,422.6259844,0,20856.29922
131193,49,30,4,0,Married,4,422.6198125,0,20746.99063
169479,49,30,2,1,Single,8,422.5942955,0,20878.71478
173060,20,1,2,1,Married,11,422.5476808,0,20689.38404
183751,16,0,4,0,Married,8,422.5263359,0,20724.3168
138712,37,16,2,0,Married,9,422.4744399,0,20768.722
156354,45,28,3,0,Single,8,422.4733447,0,20871.66724
172482,24,7,2,1,Married,10,422.4311103,0,20789.55552
156228,25,6,3,0,Married,9,422.4070511,0,20884.35256
111740,35,14,3,0,Single,11,422.3681748,0,20906.40874
155064,41,24,4,1,Single,9,422.2574058,0,20579.87029
185864,59,41,1,1,Single,8,422.246098,0,20821.3049
130393,53,33,1,1,Single,6,422.243912,0,20668.1956
110267,36,17,4,1,Married,0,422.2094254,0,20643.47127
153290,34,17,3,1,Single,10,422.1964652,0,20594.82326
113983,50,33,2,1,Married,0,422.1836566,0,20874.18283
150429,17,0,1,0,Single,15,422.1703601,0,20903.51801
196925,47,29,1,0,Married,1,422.1639047,0,20759.19524
173850,48,31,2,0,Single,9,422.157111,0,20810.85555
100647,51,33,1,0,Single,10,422.0508228,0,20873.54114
176233,18,0,2,0,Married,14,422.0503089,0,20708.51545
113063,51,33,2,1,Single,10,421.9898177,0,20707.49089
136760,57,39,2,1,Single,5,421.8803667,0,20883.01834
103993,45,26,3,0,Single,9,421.8538011,0,20638.69006
130082,25,7,3,0,Married,7,421.8518345,0,20637.59173
132671,17,0,4,1,Single,6,421.8215092,0,20646.07546
152323,19,2,2,0,Single,11,421.8094081,0,20656.47041
105845,59,39,3,0,Single,8,421.7638665,0,20585.19333
134798,33,13,1,0,Single,9,421.731451,0,20586.57255
146046,41,21,2,1,Single,6,421.7293981,0,20785.46991
115606,59,39,4,0,Single,8,421.6409867,0,20655.04934
140732,38,20,2,1,Married,4,421.6299652,0,20647.49826
190369,16,0,2,1,Married,15,421.608874,0,20525.4437
175012,31,14,4,1,Single,6,421.5918256,0,20858.59128
197209,17,0,2,0,Married,15,421.5276772,0,20805.38386
171095,51,30,3,1,Married,1,421.5093144,0,20593.46572
115825,30,10,1,0,Single,9,421.4794433,0,20821.97217
128448,45,28,3,0,Married,8,421.442929,0,20839.14645
153152,37,19,2,1,Single,9,421.4320573,0,20839.60287
142373,28,8,2,0,Single,10,421.3970439,0,20712.8522
151147,34,15,3,1,Single,11,421.3343631,0,20556.71816
195444,57,36,4,1,Single,15,421.3057238,0,20753.28619
129325,23,4,1,1,Single,7,421.2640345,0,20603.20173
192958,51,30,1,1,Single,8,421.1261475,0,20688.30738
108623,70,49,4,0,Married,0,421.1059883,0,20721.29942
121330,21,4,3,0,Single,12,421.0218123,0,20675.09062
145156,27,8,3,1,Single,8,421.0077464,0,20722.38732
122243,57,40,2,1,Single,9,420.9816334,0,20841.08167
178004,62,42,2,1,Single,3,420.9783021,0,20822.91511
126149,21,4,1,1,Single,12,420.898228,0,20810.9114
180741,36,15,4,1,Single,7,420.8842146,0,20743.21073
145841,39,22,4,0,Single,6,420.8534944,0,20690.67472
131669,55,36,2,1,Single,14,420.8526954,0,20566.63477
180852,23,6,2,0,Married,15,420.8510329,0,20445.55164
115194,35,15,3,0,Married,0,420.7846261,0,20771.23131
122891,23,6,1,1,Married,6,420.7564271,0,20559.82136
141562,48,29,2,0,Single,9,420.7400793,0,20721.00397
107695,42,21,2,1,Single,6,420.7152764,0,20448.76382
105998,38,18,1,1,Married,5,420.6612799,0,20646.064
183688,27,7,4,0,Married,7,420.6473521,0,20450.36761
188699,56,37,2,1,Single,10,420.6183775,0,20598.91888
107730,38,18,3,0,Married,8,420.5568513,0,20541.84257
138027,37,20,2,1,Single,8,420.5444009,0,20654.22005
112590,23,5,1,0,Married,9,420.4922713,0,20565.61357
194671,50,32,3,0,Single,11,420.3676325,0,20697.38163
191858,18,1,3,0,Single,13,420.3531826,0,20549.65913
134886,53,34,2,1,Single,13,420.2826154,0,20639.13077
181824,31,14,1,1,Single,8,420.2708203,0,20475.54102
123320,21,2,1,0,Married,6,420.1291134,0,20766.45567
185091,19,0,3,0,Married,11,420.1144297,0,20616.72149
183544,32,12,2,1,Married,5,420.1064878,0,20695.32439
159299,36,15,1,1,Single,6,420.0788009,0,20742.94005
170077,28,7,2,0,Married,7,420.0356419,0,20774.7821
179527,22,4,2,0,Married,12,420.0226607,0,20594.13304
168851,26,5,3,1,Single,10,420.0175843,0,20736.87922
195932,57,40,2,0,Single,12,419.9939116,0,20629.69558
122420,65,46,2,0,Single,6,419.9502626,0,20694.51313
189463,42,25,1,1,Single,9,419.8965691,0,20595.82846
110232,22,5,4,1,Married,9,419.8804724,0,20581.02362
151169,28,8,2,0,Single,7,419.8749439,0,20783.7472
189381,38,21,1,1,Single,15,419.8097249,0,20434.48625
169299,51,33,2,1,Single,6,419.7613,0,20731.065
126815,24,6,2,1,Single,14,419.7611847,0,20740.05924
155034,46,25,2,0,Married,4,419.7205648,0,20559.02824
150328,39,21,4,0,Single,6,419.6725481,0,20541.62741
197535,17,0,3,1,Single,14,419.6674977,0,20732.37489
161064,32,12,1,1,Married,0,419.5637371,0,20516.18686
198388,30,13,3,1,Single,10,419.5318237,0,20683.59119
118722,36,17,1,0,Single,8,419.4122176,0,20616.61088
104729,59,39,3,0,Single,9,419.4106329,0,20577.53165
184100,59,40,2,0,Single,8,419.3886548,0,20496.43274
165436,56,38,2,1,Single,8,419.3565651,0,20564.82826
184351,52,31,3,0,Married,9,419.3280614,0,20446.40307
115409,22,5,2,0,Married,13,419.2947294,0,20756.73647
105170,50,30,3,1,Single,9,419.2766608,0,20544.83304
182242,17,0,2,1,Married,14,419.2755042,0,20599.77521
185494,49,30,4,0,Single,8,419.2073391,0,20739.36696
160977,59,41,1,0,Single,10,419.1877776,0,20667.38888
146470,40,20,2,0,Married,10,419.155267,0,20636.76335
172053,34,17,4,1,Single,7,419.1292879,0,20453.4644
161656,56,36,2,1,Single,8,419.1132254,0,20411.66127
153719,22,4,4,1,Single,12,419.1085743,0,20626.42872
101662,16,0,1,1,Single,13,419.0931662,0,20553.65831
128961,38,18,3,1,Single,12,419.042823,0,20689.14115
101374,66,49,3,1,Married,3,419.020557,0,20369.02785
143838,17,0,3,1,Married,14,419.0158481,0,20575.79241
182547,30,10,2,0,Married,10,419.0082341,0,20719.41171
109143,54,34,4,1,Married,5,418.9882422,0,20558.41211
119703,23,5,4,0,Single,13,418.9797333,0,20723.98667
146548,45,25,2,1,Single,7,418.979255,0,20495.96275
151567,35,14,4,0,Single,10,418.9780571,0,20597.90286
132797,19,0,1,1,Married,8,418.9765851,0,20641.82926
126462,18,1,2,0,Married,7,418.9755266,0,20564.77633
188860,23,4,2,1,Single,15,418.9717831,0,20577.58916
125697,22,4,3,0,Married,14,418.8913073,0,20455.56537
190084,47,27,4,0,Single,15,418.8647344,0,20517.23672
176119,65,48,4,0,Single,7,418.7576737,0,20398.88369
184327,65,48,4,1,Single,5,418.7371198,0,20358.85599
167862,29,11,1,1,Married,5,418.6738227,0,20362.69114
102188,31,10,3,1,Single,9,418.6217458,0,20625.08729
134038,48,30,2,0,Single,7,418.6160845,0,20400.80423
132546,57,39,3,0,Single,6,418.5709616,0,20462.54808
173069,58,39,3,0,Married,7,418.5033569,0,20377.16785
171589,49,29,1,1,Single,6,418.456828,0,20712.8414
111001,42,21,3,0,Single,6,418.3983242,0,20385.91621
128021,27,10,2,1,Single,9,418.3414724,0,20510.07362
173615,62,44,2,1,Single,8,418.2324464,0,20376.62232
126242,25,8,4,1,Married,8,418.1871029,0,20487.35515
157523,50,31,2,1,Single,8,418.0718072,0,20420.59036
189903,18,0,4,0,Single,13,418.0118186,0,20619.59093
171123,66,47,4,1,Married,4,417.996476,0,20440.8238
147170,59,38,2,0,Single,8,417.9445843,0,20634.22922
198892,34,14,2,0,Single,7,417.9206263,0,20547.03132
126822,61,41,4,0,Single,6,417.9111891,0,20307.55946
168387,67,47,4,0,Single,10,417.8562987,0,20361.81494
130671,35,18,3,0,Single,8,417.8506231,0,20307.53116
109313,34,15,4,1,Single,7,417.824665,0,20499.23325
173900,68,51,3,1,Single,5,417.8022104,0,20664.11052
193336,69,49,2,1,Single,4,417.6620295,0,20606.10148
136216,44,25,2,1,Single,9,417.6475313,0,20294.37657
157465,54,33,2,1,Single,8,417.620922,0,20472.0461
185559,30,10,2,1,Single,3,417.6042699,0,20294.2135
187927,17,0,3,0,Married,13,417.5986667,0,20428.93334
106690,23,5,3,1,Married,7,417.5890389,0,20546.45195
115498,27,10,2,1,Single,10,417.5322802,0,20344.61401
164108,27,9,2,0,Married,12,417.445698,0,20665.2849
182645,54,35,2,0,Single,9,417.4354139,0,20466.7707
129268,45,26,3,1,Single,4,417.3547846,0,20267.73923
166911,33,13,3,1,Single,9,417.3440064,0,20603.20032
181735,46,29,3,0,Married,9,417.3312181,0,20650.56091
150948,51,33,2,0,Married,9,417.3171358,0,20469.85679
171035,17,0,3,1,Single,6,417.3126082,0,20396.63041
128988,31,10,2,1,Single,7,417.2227443,0,20522.13722
180220,51,34,1,0,Married,9,417.171405,0,20521.57025
172625,45,26,2,0,Single,13,417.1290961,0,20286.45481
157906,18,1,2,1,Single,12,417.0831495,0,20600.15748
183719,22,5,4,1,Single,13,417.0685146,0,20370.42573
185383,39,19,3,0,Single,10,417.0442126,0,20317.21063
154536,37,19,1,1,Single,10,416.9465298,0,20542.32649
121844,45,24,2,0,Single,9,416.9153157,0,20603.76579
131995,57,39,1,0,Married,8,416.8936624,0,20435.68312
115736,40,22,2,0,Married,1,416.8876782,0,20330.38391
121413,30,13,1,0,Married,1,416.8568223,0,20301.84112
119367,16,0,2,1,Married,11,416.8155548,0,20418.77774
199557,30,13,2,0,Married,9,416.784968,0,20618.2484
166926,58,40,3,0,Married,2,416.7597944,0,20623.98972
138211,45,25,3,0,Single,9,416.7552524,0,20282.76262
147194,44,27,4,1,Single,6,416.7455539,0,20531.2777
175308,16,0,3,1,Single,11,416.6699389,0,20246.49695
107276,43,25,2,0,Single,10,416.602557,0,20564.12785
129766,41,21,3,1,Single,7,416.4597065,0,20423.98533
147466,18,0,3,1,Married,8,416.4371054,0,20551.85527
109713,58,39,1,0,Single,8,416.3768884,0,20399.84442
141836,53,35,2,1,Married,5,416.3579204,0,20461.89602
119170,17,0,4,1,Single,15,416.3096147,0,20466.48074
139838,28,8,1,1,Single,6,416.2969813,0,20602.84907
148327,43,24,4,1,Married,4,416.2711873,0,20488.55937
155141,39,18,3,0,Single,15,416.2677296,0,20526.38648
177938,30,10,3,1,Single,6,416.235882,0,20399.7941
106337,24,5,4,0,Single,13,416.1952707,0,20331.76354
177395,21,4,4,0,Married,12,416.1769399,0,20552.847
144225,59,40,2,1,Single,8,416.0208561,0,20505.04281
168456,45,25,2,1,Single,14,416.0056676,0,20498.28338
119820,25,7,3,0,Married,14,415.8954178,0,20252.77089
175740,25,6,3,0,Married,12,415.8853382,0,20592.26691
163112,29,8,1,1,Single,9,415.8751673,0,20271.75837
187153,39,19,1,1,Single,7,415.8200933,0,20588.00467
198178,20,3,3,1,Single,13,415.7713599,0,20242.568
188721,60,43,1,0,Single,9,415.7502168,0,20559.51084
159728,32,11,2,0,Single,9,415.6980603,0,20385.90302
139507,41,23,2,1,Single,8,415.6893802,0,20237.46901
194942,50,30,2,1,Single,8,415.6838212,0,20217.19106
154978,27,6,3,1,Married,3,415.6381902,0,20543.90951
124226,24,5,1,0,Married,6,415.5792872,0,20465.96436
169334,25,8,1,1,Married,8,415.5747896,0,20453.73948
152552,20,3,4,1,Married,11,415.5023861,0,20546.11931
150029,18,1,1,1,Single,12,415.4689569,0,20261.44785
137586,43,23,2,1,Married,4,415.3391371,0,20277.95686
155942,56,37,2,1,Single,10,415.3206391,0,20448.03196
149558,23,4,4,1,Single,11,415.2982284,0,20541.91142
157969,39,18,4,1,Single,14,415.2957537,0,20324.78769
117322,41,24,3,0,Single,8,415.2278726,0,20233.39363
199887,16,0,2,1,Single,14,415.1010704,0,20319.05352
146847,22,4,3,1,Married,8,415.0884452,0,20186.42226
114779,36,15,4,1,Single,0,415.0757374,0,20499.78687
110472,39,22,2,0,Single,7,415.0274743,0,20285.37372
134171,47,29,4,0,Single,12,415.0184049,0,20408.92025
125873,22,3,2,0,Married,10,415.0134118,0,20538.67059
106368,55,37,2,1,Single,7,414.9357568,0,20504.78784
129398,26,6,2,0,Married,5,414.8684683,0,20353.42342
153249,30,9,3,0,Single,10,414.750865,0,20293.54325
168134,16,0,2,1,Single,11,414.7386962,0,20265.93481
150034,22,5,4,1,Single,13,414.7337103,0,20209.68552
151652,46,25,2,1,Married,3,414.6760995,0,20515.80498
184505,22,5,3,1,Married,7,414.6705578,0,20428.52789
128167,22,5,1,1,Single,15,414.6493457,0,20207.46729
132958,61,44,1,0,Single,9,414.6447652,0,20198.23826
149721,19,0,3,0,Married,14,414.609823,0,20186.49115
109897,22,5,3,1,Married,9,414.6055435,0,20408.27718
101017,53,35,3,0,Single,14,414.5835726,0,20507.17863
170426,16,0,2,0,Married,12,414.5709368,0,20361.54684
188725,25,7,3,1,Single,11,414.5234549,0,20329.17275
111139,24,7,3,0,Married,15,414.5094075,0,20364.47038
108069,53,35,3,0,Single,12,414.3733572,0,20469.66786
134760,23,4,3,0,Single,13,414.2637215,0,20220.18608
125623,67,49,1,0,Single,3,414.2560905,0,20119.80453
105320,48,28,2,0,Single,6,414.2553355,0,20350.76678
141824,39,21,4,0,Married,6,414.2517863,0,20491.58932
196234,53,35,2,0,Married,0,414.2298226,0,20359.49113
109561,21,2,3,0,Married,10,414.1606562,0,20404.03281
184428,43,24,3,0,Married,7,414.133462,0,20278.6731
174726,23,4,3,1,Married,4,414.1243237,0,20339.21619
192977,34,14,2,0,Married,4,414.1075733,0,20314.37867
100420,28,8,3,1,Single,6,414.0764024,0,20452.82012
141320,28,11,3,1,Single,10,414.0625038,0,20122.12519
142047,44,24,3,0,Married,1,414.0474787,0,20123.37394
178406,29,8,2,0,Married,2,414.0429425,0,20342.14713
123134,26,6,2,1,Married,4,414.0355088,0,20109.77544
199619,59,41,3,0,Married,9,414.0135982,0,20263.67991
179862,19,0,1,0,Married,11,413.9661157,0,20486.30579
127195,70,52,2,0,Single,2,413.9640896,0,20410.20448
159340,20,1,2,0,Single,14,413.9629325,0,20150.14663
181435,63,46,3,0,Married,2,413.961804,0,20370.0902
197130,55,37,1,1,Single,10,413.932018,0,20337.6009
170066,21,2,4,1,Married,12,413.9059865,0,20273.29933
134380,42,22,3,0,Married,8,413.9032242,0,20446.16121
124845,69,48,3,1,Married,1,413.889962,0,20311.4981
140871,24,6,1,0,Single,13,413.8328632,0,20231.64316
130297,60,40,1,0,Married,5,413.7740071,0,20239.70036
138505,52,32,2,0,Single,1,413.7544243,0,20249.72122
164488,23,6,2,0,Married,8,413.6891005,0,20433.45503
111377,70,53,4,1,Married,0,413.6092759,0,20181.4638
121480,20,3,1,1,Single,11,413.5916968,0,20140.58484
124703,19,2,3,1,Married,6,413.5590589,0,20274.95295
190048,66,48,3,1,Single,5,413.5438916,0,20413.19458
168240,48,29,3,1,Single,3,413.4234023,0,20275.17012
143798,39,19,2,1,Single,10,413.414678,0,20444.7339
155208,23,6,3,1,Married,11,413.4068052,0,20140.34026
127794,35,18,3,0,Married,7,413.3484585,0,20167.42293
165200,55,37,3,1,Single,7,413.3197159,0,20453.9858
103670,37,18,4,0,Married,9,413.2981039,0,20361.9052
134294,16,0,4,1,Single,9,413.2911066,0,20357.55533
153413,17,0,1,0,Married,14,413.2819072,0,20319.09536
192468,25,7,3,1,Single,15,413.2305429,0,20282.52715
111331,18,1,3,0,Married,13,413.0485498,0,20348.42749
155770,48,30,4,0,Single,10,412.9476089,0,20253.38045
173120,50,31,4,0,Single,10,412.8486244,0,20209.43122
191661,19,1,3,1,Single,13,412.8010801,0,20376.05401
117832,33,14,3,1,Single,8,412.7850437,0,20160.25219
148050,64,46,3,1,Single,10,412.7042813,0,20046.21407
186429,34,16,3,1,Single,6,412.6961419,0,20111.8071
197528,29,10,3,0,Single,10,412.6409725,0,20189.04863
119594,67,48,3,0,Single,14,412.6374818,0,20368.87409
166390,23,4,2,1,Married,15,412.5617816,0,20234.08908
125208,22,3,4,1,Married,15,412.5535681,0,20328.67841
105456,16,0,2,1,Single,15,412.5500186,0,20122.50093
115201,39,21,3,0,Single,9,412.50814,0,20066.407
136003,23,4,3,1,Married,15,412.4406058,0,20392.03029
183774,20,3,1,0,Married,13,412.3728753,0,20335.64377
107899,30,10,4,1,Single,7,412.3114852,0,20064.57426
154113,20,3,4,0,Married,11,412.2869822,0,20046.34911
190949,23,4,3,1,Single,11,412.2243603,0,20347.21802
170149,57,39,2,0,Single,12,412.1684969,0,20392.42485
179104,64,46,3,0,Single,0,412.164081,0,20200.20405
166277,53,35,2,1,Single,6,412.098784,0,20013.9392
116765,33,16,3,1,Single,7,412.0823159,0,20100.1158
117170,70,53,2,1,Single,5,412.0798292,0,20050.99146
167987,22,5,3,0,Married,13,412.02684,0,20399.342
127301,27,8,1,1,Single,10,412.0192066,0,20214.96033
130691,44,24,3,0,Single,11,412.0016654,0,20268.08327
110638,21,3,2,0,Married,11,411.9805899,0,20164.0295
136204,41,21,2,1,Single,10,411.9519695,0,20298.59848
197726,39,18,3,0,Married,10,411.933519,0,20230.67595
170153,59,41,2,1,Single,15,411.8978325,0,20021.89163
138164,31,14,3,1,Single,7,411.8840574,0,20047.20287
152479,45,27,2,1,Single,7,411.8218575,0,20262.09288
136849,64,46,4,0,Married,1,411.8180828,0,20319.90414
148910,54,36,2,1,Single,7,411.804291,0,20118.21455
139084,58,41,4,0,Single,15,411.7883391,0,20091.41696
199736,26,8,2,1,Single,11,411.7546684,0,20130.73342
154992,55,36,4,0,Single,2,411.7339244,0,20310.69622
132327,45,24,3,0,Married,7,411.7331993,0,20179.65997
131453,70,50,3,0,Married,3,411.6984136,0,20206.92068
166754,16,0,2,1,Married,13,411.69064,0,20369.532
194953,38,20,3,0,Single,6,411.634984,0,20039.7492
186462,18,0,4,1,Married,15,411.6126174,0,20039.63087
171617,25,7,4,1,Married,15,411.6037626,0,20255.18813
186648,59,39,1,1,Married,1,411.5706433,0,20351.53217
198278,21,2,1,1,Single,14,411.5067461,0,20255.33731
158172,63,46,1,0,Single,9,411.4934109,0,20012.67055
100094,51,30,4,0,Married,10,411.4866939,0,20360.3347
182390,19,1,3,0,Married,9,411.4666098,0,20344.33049
146678,58,41,4,0,Single,6,411.45302,0,20075.651
182387,34,15,4,0,Single,12,411.4021606,0,20062.10803
121786,28,7,1,0,Single,6,411.2520222,0,20196.60111
118394,45,25,2,1,Single,10,411.038857,0,20184.94285
136489,43,22,3,0,Married,5,411.0143991,0,20091.71996
196241,50,31,2,0,Married,2,411.0110495,0,20341.55248
140731,21,3,3,0,Married,12,410.9777887,0,20261.88944
183729,24,6,4,1,Single,11,410.966369,0,20075.31845
132675,29,10,3,1,Single,11,410.903145,0,20016.15725
159247,17,0,2,1,Single,12,410.8438061,0,19977.19031
134143,57,40,4,1,Single,6,410.8316476,0,20164.58238
144440,53,34,3,0,Single,11,410.8314787,0,20144.57394
168830,63,46,2,0,Single,7,410.8016095,0,20225.08048
153721,23,5,3,0,Married,10,410.7954896,0,19990.77448
114595,47,27,2,0,Single,6,410.7950507,0,19968.75254
114394,47,27,2,1,Single,10,410.7671135,0,20048.35567
180810,19,1,3,1,Married,12,410.760981,0,19977.04905
182629,39,21,3,1,Single,9,410.7412796,0,20148.06398
150059,47,29,2,0,Single,15,410.7083923,0,20035.41962
138688,24,6,3,0,Single,14,410.7031171,0,20135.15586
160458,44,24,2,0,Single,9,410.6672768,0,20074.36384
159270,53,36,3,0,Single,10,410.6653661,0,20019.26831
177629,31,14,3,1,Single,8,410.5703012,0,19937.51506
158865,21,3,3,1,Single,13,410.5488979,0,20278.4449
159427,50,29,2,0,Single,9,410.5228298,0,20165.14149
197213,35,17,2,1,Single,6,410.5218487,0,20225.09244
129417,20,3,4,0,Married,11,410.5109323,0,20031.54662
154652,50,29,2,1,Single,7,410.4866322,0,19947.33161
184288,17,0,4,0,Married,15,410.4657153,0,20151.28577
172506,35,16,3,0,Married,14,410.4520199,0,20095.601
124750,57,36,3,1,Single,13,410.398618,0,20268.9309
158356,40,23,3,0,Single,11,410.3906928,0,20201.53464
141819,43,23,3,0,Single,12,410.3537401,0,20208.68701
124480,56,39,2,1,Single,14,410.3294936,0,20227.47468
194236,55,38,3,0,Married,1,410.2967298,0,20236.83649
125093,43,26,1,1,Married,3,410.2660657,0,20035.30329
172205,48,28,3,0,Single,10,410.2600858,0,20205.00429
153564,16,0,4,1,Single,11,410.2387404,0,20242.93702
102638,46,27,4,1,Single,12,410.1741085,0,20250.70543
175053,22,3,2,1,Single,12,410.1476426,0,20157.38213
136178,18,1,4,1,Married,13,410.1421679,0,20065.1084
108252,46,27,2,0,Married,6,410.1188182,0,20148.94091
136361,38,19,2,1,Single,8,410.1154769,0,20197.77385
119687,20,3,1,0,Married,12,410.0568927,0,20137.84464
196361,53,33,2,0,Married,1,410.0488244,0,20008.44122
112151,33,16,3,1,Single,7,410.0049702,0,20104.24851
186180,19,1,1,0,Married,1,409.9745118,0,20279.72559
104566,18,0,3,1,Single,14,409.9402636,0,20035.01318
191415,42,24,1,0,Married,6,409.9283918,0,20184.41959
122683,48,29,3,1,Single,8,409.9217362,0,20100.08681
116644,28,10,4,0,Single,7,409.919158,0,20280.9579
164611,25,8,1,0,Married,14,409.9010323,0,20185.05162
146071,39,20,4,0,Single,8,409.8641991,0,20255.20996
184724,51,30,3,0,Married,0,409.8565591,0,19898.82796
176351,22,5,4,0,Married,13,409.7724218,0,19941.62109
138546,34,13,2,0,Single,11,409.7559707,0,20223.79854
164769,53,36,3,1,Married,5,409.7377822,0,19996.88911
152856,42,25,2,1,Single,9,409.6868628,0,19930.34314
139612,27,8,2,1,Single,7,409.6832917,0,19909.16459
121615,21,2,2,1,Married,3,409.6457061,0,20161.28531
169772,28,7,4,1,Single,7,409.6446156,0,20233.23078
105193,37,20,4,0,Married,3,409.6414329,0,20049.07165
175235,54,34,3,0,Single,12,409.5840825,0,19962.20413
154666,53,36,4,1,Single,6,409.5776595,0,20137.88298
169157,53,32,2,0,Single,10,409.5574213,0,20182.87107
159796,56,36,2,1,Single,8,409.5358825,0,19950.79413
138084,42,25,1,1,Single,0,409.5283292,0,20013.41646
115657,28,8,2,0,Married,9,409.5202285,0,19900.01143
124927,56,39,4,0,Married,5,409.4983924,0,20069.91962
186022,70,50,4,0,Single,7,409.4627203,0,20008.13602
152613,26,7,1,0,Single,7,409.4059691,0,19908.29846
141020,35,15,4,0,Single,9,409.3317426,0,20125.58713
151323,58,37,4,0,Married,13,409.1785866,0,19979.92933
184870,67,46,1,0,Single,15,409.1758254,0,19909.79127
171246,31,13,2,1,Single,11,409.157673,0,20182.88365
119640,42,25,3,1,Single,8,409.0904312,0,20244.52156
176973,32,13,2,0,Married,9,409.0031004,0,20019.15502
117040,22,3,4,0,Married,13,408.9283722,0,19955.41861
155457,40,19,2,1,Single,7,408.9222735,0,19956.11368
183321,44,24,4,0,Married,14,408.8994529,0,20221.97265
153135,57,40,1,0,Married,3,408.8901347,0,19989.50674
145312,36,18,3,1,Single,9,408.8804819,0,19966.0241
104461,54,37,4,0,Married,10,408.8604562,0,19954.02281
120503,48,27,2,0,Married,6,408.8104413,0,20236.52207
190832,58,41,1,1,Single,7,408.7395066,0,19903.97533
147543,50,33,1,0,Married,6,408.7089448,0,20052.44724
101117,32,12,3,1,Single,15,408.663755,0,19940.18775
178082,16,0,3,1,Married,15,408.6305456,0,20163.52728
131440,17,0,3,0,Married,15,408.5475155,0,19834.37578
120184,40,22,2,1,Single,7,408.5210693,0,20078.05347
147136,53,36,4,1,Single,13,408.5192601,0,20081.96301
139648,18,1,3,1,Married,14,408.4532423,0,19989.66212
166857,68,50,1,1,Single,6,408.4515225,0,20090.57613
102860,20,2,2,1,Married,5,408.4267315,0,19963.33658
131408,69,51,2,0,Single,9,408.3617678,0,19971.08839
108024,55,35,2,0,Single,13,408.31942,0,19999.971
143774,17,0,1,1,Married,13,408.2572079,0,19918.8604
171893,39,18,2,0,Single,15,408.2384347,0,19934.92174
165993,27,8,3,0,Married,6,408.2288248,0,20064.44124
112018,28,11,2,0,Married,8,408.1579098,0,19860.89549
152233,46,28,1,1,Single,8,408.0924936,0,19830.62468
151124,19,1,1,1,Married,14,408.0746893,0,19843.73447
111939,46,25,3,0,Single,14,408.0652122,0,19943.26061
131038,49,32,3,1,Single,9,408.0235502,0,20152.17751
152236,39,18,3,0,Married,7,408.018059,0,20121.90295
158519,37,16,3,1,Married,5,408.0070168,0,20120.35084
139622,49,30,1,0,Married,7,407.9761934,0,20093.80967
101168,19,0,3,1,Married,7,407.9717447,0,19927.58724
130570,40,21,2,0,Married,4,407.9658565,0,19941.29283
193355,42,23,3,1,Single,7,407.9470347,0,19866.35174
183614,62,45,3,0,Married,1,407.9024019,0,19845.1201
120179,61,44,3,1,Single,4,407.8221575,0,19881.10788
157329,47,26,2,1,Single,6,407.8200497,0,19846.00249
148118,69,52,1,1,Single,10,407.7675108,0,19890.37554
118827,46,25,1,1,Single,7,407.7608046,0,20173.04023
140714,67,47,2,0,Single,10,407.7598873,0,20146.99437
105308,17,0,2,1,Single,11,407.7316449,0,19922.58225
160886,16,0,2,1,Single,11,407.7216167,0,20150.08084
106416,28,11,2,1,Married,5,407.7212568,0,19908.06284
173747,29,8,2,0,Married,0,407.6990253,0,20036.95127
175044,30,10,4,0,Single,11,407.6963193,0,19987.81597
129000,43,25,3,0,Married,6,407.6469385,0,20071.34693
108351,59,38,3,1,Single,6,407.63027,0,20051.5135
140773,30,10,3,1,Married,1,407.5406097,0,20149.03049
137393,32,14,1,0,Married,11,407.4961917,0,20147.80959
179648,19,1,3,1,Married,0,407.478199,0,19956.90995
192425,45,24,3,1,Single,7,407.4733234,0,20042.66617
165982,56,36,2,1,Single,9,407.4627197,0,19812.13599
121021,29,10,4,0,Single,10,407.4383768,0,20109.91884
101955,48,27,3,1,Married,1,407.4288189,0,20032.44095
116606,34,15,3,1,Single,10,407.3931117,0,20131.65559
187293,29,8,1,1,Single,14,407.3893742,0,20071.46871
155167,24,6,3,1,Single,12,407.353849,0,20098.69245
189615,22,5,1,1,Single,7,407.3289004,0,20114.44502
120717,56,37,4,0,Single,13,407.2254318,0,20136.27159
199576,46,29,3,0,Married,10,407.2148587,0,19952.74294
145693,48,29,1,1,Single,10,407.1739205,0,20104.69603
179593,17,0,3,1,Single,11,407.1083508,0,20148.41754
157672,38,20,3,0,Single,9,407.0604016,0,19919.02008
172612,48,29,3,0,Married,6,407.0164062,0,19994.82031
153885,33,13,2,1,Single,7,407.0085068,0,20019.42534
104966,17,0,4,0,Married,9,406.9418929,0,20046.09465
182875,67,50,1,0,Single,12,406.9366871,0,20110.83436
157201,20,2,4,1,Married,2,406.9247402,0,20009.23701
173922,22,5,1,1,Single,8,406.8305601,0,20041.52801
164992,33,13,2,0,Single,12,406.8141711,0,19983.70856
120847,54,37,2,1,Single,15,406.7887894,0,20134.43947
184191,36,18,2,0,Married,2,406.7601439,0,19800.0072
172063,54,37,3,0,Married,1,406.7479402,0,20031.39701
100087,58,37,2,1,Single,5,406.7311102,0,19935.55551
139004,48,28,2,0,Single,11,406.6455748,0,19757.27874
116281,20,3,3,1,Married,5,406.6253826,0,19996.26913
182924,42,24,2,1,Single,7,406.6019613,0,20058.09807
189874,16,0,2,1,Married,10,406.556567,0,19969.82835
191138,26,5,1,1,Single,12,406.5523388,0,20108.61694
143874,33,15,1,1,Married,0,406.5515049,0,19821.57525
148563,26,7,3,0,Married,10,406.5399135,0,19769.99568
129163,67,50,1,1,Married,3,406.5367908,0,20016.83954
101446,44,24,4,1,Single,6,406.5101689,0,19749.50845
199465,61,40,3,0,Single,7,406.4730699,0,19723.6535
144507,38,21,2,1,Single,6,406.4648363,0,19778.24182
126954,42,25,3,0,Single,10,406.4183598,0,19803.91799
106894,40,20,2,1,Married,4,406.2615853,0,19985.07927
133894,48,30,2,1,Single,9,406.1601712,0,19818.00856
126335,42,23,3,0,Married,4,406.1436827,0,19931.18414
132372,64,45,2,1,Single,3,406.1312293,0,20073.56147
129226,33,16,2,0,Single,13,406.125247,0,20095.26235
140488,37,19,4,0,Single,7,406.0777897,0,19749.88949
171514,22,3,1,1,Single,15,405.999425,0,19886.97125
192686,17,0,3,1,Married,6,405.9392526,0,20088.96263
179702,16,0,3,1,Single,15,405.773486,0,19905.6743
106175,58,37,2,1,Married,2,405.7733647,0,19694.66824
183669,49,30,1,0,Single,10,405.7686642,0,19985.43321
143554,51,32,2,0,Single,10,405.7130849,0,20014.65425
118548,47,27,3,1,Married,3,405.6995836,0,20019.97918
193428,60,42,3,0,Single,2,405.6595645,0,19915.97823
140129,22,4,1,1,Single,13,405.6484005,0,19755.42003
153916,66,46,1,0,Married,1,405.6157569,0,19976.78785
190856,19,1,2,1,Single,15,405.5768507,0,19740.84254
107195,46,28,2,0,Single,8,405.5666572,0,19877.33286
174792,53,34,2,1,Single,8,405.5257902,0,19834.28951
192099,31,14,1,0,Single,8,405.4731829,0,19774.65915
179457,28,10,2,1,Single,9,405.4427644,0,20027.13822
142652,53,35,1,0,Single,15,405.4233286,0,20032.16643
194543,18,0,3,0,Single,14,405.4088517,0,19789.44259
190340,55,37,2,1,Single,14,405.3609024,0,19957.04512
112585,51,32,2,1,Single,9,405.3250515,0,19832.25258
155091,26,6,2,0,Married,8,405.298637,0,19961.93185
175272,57,36,3,0,Single,12,405.2483994,0,19839.41997
175302,49,31,3,1,Single,7,405.2084152,0,19672.42076
127043,51,32,2,0,Married,4,405.1841261,0,19661.2063
124603,34,14,1,1,Single,3,405.1765298,0,19944.82649
177828,44,27,2,1,Single,6,405.1558956,0,19794.79478
182107,49,31,2,0,Married,4,405.1173123,0,19741.86562
195316,20,3,3,1,Married,5,405.0870183,0,19983.35092
126073,68,47,4,0,Married,4,404.9320508,0,19919.60254
172527,31,11,4,0,Married,4,404.8704685,0,19677.52343
171097,52,31,1,0,Single,8,404.8567609,0,19728.83805
180890,55,38,1,1,Single,10,404.8388241,0,19883.94121
157577,27,7,2,0,Single,2,404.8344756,0,19806.72378
172127,28,9,2,0,Single,13,404.7934855,0,19787.67428
101205,31,10,3,1,Single,8,404.7788623,0,19761.94312
145111,49,31,3,1,Single,9,404.7783083,0,19736.91542
132374,40,23,4,1,Single,7,404.6852461,0,19983.26231
132312,57,40,2,0,Married,6,404.6666799,0,19848.334
116759,36,19,4,0,Single,15,404.640385,0,19997.01925
155786,63,43,3,0,Single,10,404.6254161,0,19995.27081
168682,27,10,4,0,Married,8,404.6099493,0,19799.49747
129940,22,5,4,1,Single,13,404.6017735,0,19928.08868
177910,35,18,1,1,Single,6,404.5501101,0,19824.50551
122374,60,42,2,1,Single,11,404.5349121,0,19732.74561
156851,33,15,3,0,Married,4,404.5052672,0,19659.26336
129207,35,15,2,0,Married,2,404.4756817,0,19820.78409
113255,41,23,1,0,Married,9,404.4662799,0,19959.314
145073,40,21,4,0,Single,10,404.4660495,0,19855.30248
133784,22,5,2,0,Married,15,404.4555197,0,19640.77599
143671,21,4,3,0,Single,14,404.4477972,0,19719.38986
110243,28,8,1,0,Married,7,404.4041105,0,19758.20553
193849,40,22,2,0,Single,8,404.3905315,0,19936.52658
137347,47,30,4,1,Single,6,404.3627944,0,19982.13972
190392,16,0,1,0,Married,13,404.3094024,0,19674.47012
199508,45,26,3,1,Married,0,404.2840185,0,19887.20093
118609,38,21,1,1,Single,10,404.2819242,0,19679.09621
118365,28,11,2,1,Married,0,404.2568287,0,19630.84144
164810,17,0,4,1,Single,10,404.2290392,0,19656.45196
152488,35,16,4,0,Married,15,404.2230478,0,19931.15239
157468,22,3,3,1,Married,6,404.1804576,0,19738.02288
157038,40,21,3,0,Married,4,404.1548976,0,19965.74488
157353,44,27,3,0,Single,11,404.1123008,0,19849.61504
154679,46,29,2,1,Single,10,404.0795519,0,19898.9776
112917,63,42,3,0,Single,0,404.0128634,0,19600.64317
176182,54,34,2,0,Single,10,403.9223957,0,19926.11979
111946,29,8,2,1,Single,10,403.9064009,0,19743.32005
148545,58,37,3,0,Married,9,403.8567074,0,19798.83537
118332,61,41,3,0,Single,7,403.8071551,0,19664.35776
136348,46,25,3,1,Single,7,403.7961705,0,19646.80853
198932,31,13,2,0,Single,12,403.7603394,0,19700.01697
140156,42,22,1,1,Single,7,403.70609,0,19925.3045
155222,16,0,2,0,Single,15,403.6831755,0,19941.15878
120604,38,20,2,1,Single,14,403.5861793,0,19873.30897
103932,16,0,3,1,Single,9,403.5725088,0,19934.62544
167839,50,32,4,0,Married,2,403.4675137,0,19938.37569
132669,20,3,3,0,Married,12,403.4462956,0,19838.31478
112487,62,43,3,0,Single,8,403.3975727,0,19721.87864
163642,19,0,4,0,Single,13,403.3128501,0,19796.64251
193318,29,9,1,1,Single,9,403.2553677,0,19716.76839
111210,55,36,4,0,Single,7,403.2331793,0,19940.65897
120482,51,33,4,0,Single,9,403.2248436,0,19706.24218
191519,23,6,3,0,Married,13,403.2019585,0,19790.09793
154239,21,3,4,1,Married,9,403.1786444,0,19788.93222
185217,43,25,2,1,Single,8,403.1651617,0,19576.25809
183977,66,49,3,1,Single,3,403.1558398,0,19763.79199
179740,50,32,3,0,Married,8,403.1170891,0,19877.85446
153939,23,4,2,0,Married,11,403.0817042,0,19590.08521
138018,61,41,3,0,Single,5,403.0233863,0,19553.16932
145898,34,16,2,0,Married,3,402.9783385,0,19578.91693
140733,35,17,1,0,Single,10,402.9137936,0,19599.68968
172529,21,4,3,0,Married,13,402.8665523,0,19590.32762
111512,66,46,3,1,Single,6,402.7977956,0,19879.88978
187197,40,21,2,1,Single,3,402.7403861,0,19857.01931
161054,24,6,2,1,Single,14,402.7228306,0,19546.14153
118894,21,3,3,1,Married,12,402.7099246,0,19683.49623
183394,27,9,2,1,Single,14,402.6720946,0,19835.60473
178657,46,29,3,0,Single,10,402.6710901,0,19903.55451
141726,31,12,2,0,Married,11,402.6617444,0,19815.08722
175386,16,0,3,1,Single,12,402.6410592,0,19639.05296
125014,61,43,2,0,Single,1,402.5741106,0,19543.70553
117425,32,15,1,0,Married,9,402.5737594,0,19818.68797
125336,60,42,2,1,Single,7,402.556931,0,19863.84655
129482,65,45,2,0,Single,0,402.5284304,0,19700.42152
126976,34,15,1,1,Single,6,402.5129906,0,19546.64953
119337,28,7,3,0,Single,15,402.4556459,0,19691.7823
114177,54,36,3,1,Single,7,402.4294053,0,19747.47027
198531,69,51,2,0,Married,3,402.4020588,0,19763.10294
178623,47,30,3,0,Single,10,402.3655839,0,19860.2792
104587,54,35,3,1,Single,9,402.3263418,0,19801.31709
114349,32,15,1,1,Single,10,402.3212675,0,19718.06338
171379,25,6,3,1,Married,10,402.2985092,0,19704.92546
143665,47,27,1,0,Single,6,402.2915712,0,19893.57856
164320,62,43,2,1,Married,2,402.2262839,0,19518.3142
144880,31,10,1,1,Single,6,402.1973024,0,19880.86512
127883,53,32,3,1,Single,10,402.1950357,0,19862.75179
169959,27,8,3,0,Married,4,402.1485789,0,19753.42895
184037,42,24,4,0,Married,6,402.0720708,0,19585.60354
167557,29,9,3,0,Single,9,402.0454408,0,19593.27204
148089,18,1,3,1,Married,10,402.0429849,0,19821.14925
162335,28,9,2,1,Single,9,402.0234652,0,19728.17326
181463,57,40,1,1,Single,15,402.0108039,0,19763.5402
152593,42,25,2,0,Married,1,402.0085932,0,19586.42966
142065,44,23,3,0,Single,8,402.0038533,0,19597.19267
171399,49,28,3,0,Single,10,402.0034132,0,19710.17066
133332,17,0,2,0,Single,15,401.972946,0,19570.6473
129981,70,49,1,0,Married,1,401.9377187,0,19772.88594
153086,54,36,3,0,Married,8,401.8401494,0,19874.00747
138708,35,18,2,0,Married,9,401.7803639,0,19799.0182
189722,22,4,3,1,Single,15,401.7802414,0,19845.01207
180076,64,45,3,0,Single,10,401.7687185,0,19759.43593
134916,38,21,4,1,Single,14,401.7646691,0,19834.23346
159494,27,9,4,0,Married,7,401.657943,0,19855.89715
158279,34,16,2,1,Single,6,401.6438973,0,19624.19487
134499,58,40,4,1,Single,9,401.6373251,0,19771.86626
119068,35,16,1,1,Single,10,401.6252338,0,19703.26169
123328,65,46,2,1,Single,5,401.5321373,0,19776.60687
193335,28,8,3,1,Single,10,401.5186952,0,19854.93476
180665,57,36,2,1,Married,4,401.5015592,0,19497.07796
136623,45,25,2,0,Single,10,401.4969817,0,19543.84909
118038,39,21,3,0,Single,14,401.4792942,0,19853.96471
100603,32,15,4,1,Single,8,401.4259936,0,19717.29968
147345,46,26,3,1,Single,6,401.3908712,0,19750.54356
110631,59,39,2,0,Married,6,401.3888133,0,19810.44067
147844,53,35,3,0,Married,8,401.3823926,0,19800.11963
124254,30,12,2,1,Married,4,401.3791781,0,19746.95891
101967,53,33,2,0,Married,8,401.3555902,0,19726.77951
144808,54,37,1,0,Single,13,401.34274,0,19852.137
177825,30,9,1,0,Single,9,401.3427019,0,19523.1351
163654,39,18,3,1,Married,3,401.3410448,0,19706.05224
116564,52,31,4,1,Married,4,401.321637,0,19498.08185
152171,50,30,2,1,Married,0,401.3072645,0,19737.36323
133837,54,36,3,0,Married,9,401.2842426,0,19515.21213
149757,41,21,3,0,Married,3,401.2398028,0,19563.99014
126741,56,37,4,0,Single,8,401.2190964,0,19777.95482
181968,60,43,1,0,Single,3,401.1388059,0,19674.9403
184859,24,5,3,0,Single,15,401.1068785,0,19604.34393
139108,59,42,4,1,Single,9,401.1018133,0,19778.09067
150595,47,29,4,0,Married,7,401.0545251,0,19693.72626
110879,35,18,3,0,Married,2,401.0148797,0,19667.74399
141793,16,0,2,0,Single,13,400.9965388,0,19538.82694
164909,41,24,3,0,Married,9,400.9269963,0,19772.34982
118669,45,26,2,0,Single,3,400.765589,0,19504.27945
166822,58,39,4,0,Married,2,400.762108,0,19746.1054
154623,68,48,3,1,Married,4,400.7613756,0,19611.06878
115299,23,4,3,0,Married,11,400.7494328,0,19517.47164
165303,28,11,3,0,Single,7,400.6639675,0,19658.19838
155602,23,6,3,1,Single,11,400.6522371,0,19488.61186
117438,63,45,3,1,Single,1,400.6438918,0,19814.19459
159465,19,1,3,1,Married,12,400.6414306,0,19454.07153
143138,54,36,2,1,Married,1,400.6379427,0,19826.89714
170850,16,0,4,0,Single,15,400.6040559,0,19816.2028
146458,39,20,3,0,Single,6,400.5657359,0,19500.2868
113784,54,36,1,1,Single,10,400.5331803,0,19648.65902
116037,22,4,2,1,Married,12,400.5274009,0,19529.37005
192062,28,9,2,0,Married,7,400.5056903,0,19633.28452
181227,65,44,4,1,Single,4,400.4735077,0,19467.67539
103906,52,34,3,0,Single,7,400.4432248,0,19567.16124
114822,43,25,2,0,Married,2,400.4156792,0,19521.78396
187161,40,22,3,0,Single,7,400.3701723,0,19608.50862
107060,22,5,3,1,Single,12,400.361137,0,19446.05685
198195,49,28,2,1,Single,10,400.3563336,0,19604.81668
129661,25,8,2,0,Married,12,400.3345821,0,19684.72911
141991,37,18,1,0,Married,6,400.3321226,0,19610.60613
165360,27,8,2,0,Single,9,400.293361,0,19664.66805
164601,60,40,3,1,Single,10,400.2435035,0,19782.17518
151611,29,9,3,0,Single,15,400.1784244,0,19740.92122
172850,20,2,3,1,Married,3,400.1357313,0,19747.78657
118804,20,2,1,0,Married,14,400.110449,0,19540.52245
126368,70,50,2,0,Single,9,400.0748994,0,19535.74497
131459,61,42,2,0,Single,6,400.0163768,0,19406.81884
118462,59,42,3,0,Married,10,400.0158111,0,19440.79056
150046,44,26,1,0,Single,6,399.9851595,0,19432.25798
144475,46,29,2,0,Single,10,399.9512129,0,19643.56065
190152,62,45,3,1,Single,5,399.9473789,0,19585.36895
140791,19,1,2,1,Single,13,399.9370831,0,19695.85416
141059,26,8,2,1,Single,9,399.7696099,0,19454.4805
107067,59,38,2,1,Single,6,399.7542272,0,19722.71136
105783,31,14,2,0,Single,5,399.6932001,0,19538.66001
184112,24,7,3,1,Single,13,399.6577432,0,19681.88716
169151,35,17,4,1,Single,9,399.644485,0,19477.22425
159653,61,43,4,0,Single,9,399.6381174,0,19386.90587
172696,16,0,3,1,Married,10,399.6369887,0,19625.84944
185121,36,15,2,0,Single,8,399.6292212,0,19621.46106
183007,39,20,2,1,Single,8,399.5714797,0,19466.57399
126982,21,3,2,1,Single,6,399.5469783,0,19685.34892
131652,60,40,1,1,Single,1,399.5053889,0,19426.26945
145450,48,29,4,0,Single,9,399.4958264,0,19642.79132
101274,31,12,4,1,Single,10,399.3725433,0,19486.62717
116605,52,32,2,1,Single,10,399.2326472,0,19410.63236
161949,22,3,1,1,Married,0,399.165851,0,19536.29255
133076,46,26,3,1,Single,12,399.1592176,0,19542.96088
125095,49,29,4,1,Married,8,399.1267942,0,19736.33971
196806,47,29,2,1,Married,3,399.0874425,0,19637.37213
131861,28,9,3,0,Single,7,399.0786122,0,19499.93061
120918,65,44,3,1,Single,9,399.0043001,0,19485.21501
163870,25,6,1,1,Single,15,398.9445904,0,19429.22952
120286,58,37,3,0,Married,7,398.9208892,0,19508.04446
152324,68,50,1,0,Single,7,398.8494738,0,19399.47369
178803,47,28,2,0,Married,15,398.8196598,0,19447.98299
133098,25,6,3,0,Married,14,398.8105861,0,19551.52931
139148,59,38,2,1,Single,14,398.7762381,0,19619.81191
100443,60,40,4,1,Single,7,398.7462537,0,19491.31269
159042,70,53,2,0,Single,6,398.7210364,0,19614.05182
138558,43,25,1,0,Married,7,398.6963317,0,19358.81659
107182,25,7,4,1,Married,14,398.6486033,0,19614.43017
151485,50,32,3,0,Married,10,398.6416823,0,19626.08412
163073,33,13,2,1,Single,9,398.6037432,0,19433.18716
173004,29,12,2,0,Married,7,398.5699075,0,19581.49538
113852,50,33,2,1,Single,4,398.5613849,0,19455.06925
141523,70,49,2,0,Single,2,398.4667681,0,19598.33841
169830,21,3,1,0,Married,14,398.4218218,0,19321.09109
188811,45,24,2,0,Married,13,398.4128657,0,19716.64328
192164,27,8,3,0,Married,4,398.4086021,0,19492.43011
173797,21,4,2,1,Single,7,398.3864132,0,19649.32066
173610,21,4,4,1,Married,12,398.3638732,0,19343.19366
172940,41,23,2,0,Married,3,398.3501109,0,19328.50555
151273,24,6,3,1,Single,14,398.3466837,0,19516.33419
196934,36,19,4,0,Married,6,398.3014514,0,19436.07257
192236,40,20,1,1,Single,10,398.2783895,0,19566.91948
162638,41,24,1,1,Single,9,398.2594462,0,19457.97231
178824,48,31,2,1,Married,1,398.2591455,0,19552.95728
154566,34,14,2,1,Single,8,398.2315021,0,19658.57511
145615,45,27,2,1,Single,12,398.1651222,0,19358.25611
177789,22,5,3,0,Single,11,398.161129,0,19612.05645
197954,22,3,3,1,Single,15,398.1277997,0,19420.38999
134065,64,47,2,0,Single,4,398.1255067,0,19572.27534
198396,21,4,3,1,Single,14,398.1246864,0,19485.23432
176454,69,52,4,0,Single,12,398.1235611,0,19497.17806
139119,37,18,2,0,Single,12,398.1223287,0,19539.11644
181285,40,19,2,0,Married,2,398.0874134,0,19593.37067
187312,42,25,1,0,Married,9,398.0518761,0,19687.59381
196862,18,1,2,1,Married,2,398.0487131,0,19594.43566
174583,27,6,4,1,Single,12,398.024417,0,19670.22085
116173,16,0,2,1,Married,15,397.9675903,0,19500.37952
102878,25,6,4,0,Married,10,397.9628824,0,19390.14412
108063,19,0,2,0,Married,11,397.9584215,0,19463.92108
128144,56,36,3,0,Married,9,397.9560458,0,19420.80229
139361,33,12,4,0,Single,12,397.9327008,0,19371.63504
125320,38,19,3,0,Single,6,397.9303009,0,19485.51505
182862,33,16,1,0,Single,14,397.8918445,0,19668.59223
160265,20,2,3,0,Single,12,397.8586159,0,19635.9308
109477,19,0,1,1,Single,14,397.852454,0,19631.6227
106882,52,32,3,1,Single,9,397.850668,0,19593.5334
111556,31,13,3,1,Single,8,397.8501902,0,19536.50951
181868,39,18,2,1,Single,10,397.8159816,0,19378.79908
162454,54,33,3,0,Married,6,397.8113762,0,19598.56881
100946,53,33,4,1,Single,10,397.806322,0,19312.3161
178668,37,17,2,1,Single,12,397.7075707,0,19301.37854
157273,31,11,3,1,Married,4,397.6946214,0,19527.73107
198807,45,27,1,1,Single,6,397.6475864,0,19473.37932
105239,18,0,3,0,Single,15,397.6290788,0,19469.45394
115966,39,21,3,0,Single,8,397.6257605,0,19361.28803
154704,54,33,2,1,Married,5,397.561892,0,19661.0946
121996,51,32,1,0,Married,9,397.5167043,0,19436.83522
141039,20,2,2,1,Married,7,397.4623988,0,19603.11994
174920,27,9,1,0,Married,3,397.4175426,0,19515.87713
154344,35,16,2,0,Single,12,397.4040398,0,19545.20199
135507,51,32,1,1,Single,9,397.3724159,0,19315.6208
126791,44,23,3,0,Married,8,397.3717136,0,19540.58568
155036,57,39,3,1,Married,3,397.3381248,0,19536.90624
184171,32,11,4,1,Married,5,397.3229693,0,19598.14847
177612,25,6,3,1,Single,13,397.3137631,0,19337.68816
137009,49,30,4,1,Single,6,397.2310449,0,19401.55225
178867,67,48,1,0,Single,0,397.2069605,0,19515.34803
111797,40,20,2,1,Single,6,397.1100067,0,19568.50034
182616,49,32,1,0,Single,7,397.0559669,0,19371.79835
149670,16,0,2,0,Married,8,396.9247136,0,19603.23568
143406,30,11,1,0,Single,12,396.9147809,0,19489.73905
117078,30,11,2,0,Single,9,396.8697556,0,19245.48778
166756,30,10,1,0,Married,0,396.8456932,0,19269.28466
166234,36,17,4,0,Single,13,396.7605129,0,19438.02565
117760,53,34,4,1,Single,9,396.7072453,0,19455.36227
185928,50,30,1,1,Single,0,396.6472629,0,19447.36315
107047,59,39,3,1,Single,15,396.6264869,0,19527.32435
143265,36,19,3,0,Married,10,396.6059963,0,19622.29982
145331,50,32,4,1,Single,2,396.5585999,0,19365.93
156172,49,30,1,0,Single,9,396.5517713,0,19389.58857
120413,49,30,3,1,Married,1,396.5168252,0,19228.84126
164309,18,0,2,1,Married,10,396.5146413,0,19527.73207
194848,52,33,4,1,Single,10,396.4863563,0,19372.31782
151565,46,26,2,1,Single,13,396.4820768,0,19239.10384
151755,52,35,1,1,Single,8,396.4647332,0,19516.23666
126428,30,9,1,1,Single,10,396.4566513,0,19247.83257
101440,37,18,3,0,Single,10,396.4468939,0,19236.3447
190540,30,13,2,1,Single,7,396.435747,0,19467.78735
172261,35,17,2,1,Single,10,396.4333495,0,19572.66748
198404,46,28,3,0,Married,7,396.4231609,0,19584.15805
180229,59,38,2,0,Married,8,396.3900374,0,19257.50187
194718,54,36,3,0,Single,9,396.3582085,0,19577.91043
108538,48,28,4,0,Single,14,396.29863,0,19335.9315
188176,68,50,2,1,Single,14,396.2453688,0,19574.26844
197169,46,28,2,1,Single,9,396.236596,0,19457.8298
126654,52,31,2,1,Single,12,396.1720549,0,19484.60275
143943,60,42,4,1,Single,15,396.1565253,0,19442.82627
127229,38,20,3,1,Single,9,396.1533774,0,19491.66887
101700,25,6,3,0,Married,14,396.1469344,0,19282.34672
137410,25,7,1,1,Married,15,396.1408555,0,19430.04278
134377,20,3,4,1,Single,15,396.1260471,0,19252.30236
174455,25,7,3,1,Single,15,396.0988181,0,19234.94091
192891,64,45,3,0,Single,9,396.0848547,0,19314.24274
160678,20,1,2,1,Married,12,396.0667996,0,19338.33998
132860,38,17,3,0,Single,11,396.0646839,0,19387.2342
177813,57,38,3,0,Married,9,396.0531492,0,19514.65746
105577,54,33,2,0,Married,7,396.0368097,0,19346.84049
152113,62,41,2,1,Single,6,396.007265,0,19223.36325
125820,54,34,1,0,Married,9,395.9018181,0,19449.09091
117038,59,38,4,1,Single,7,395.7263244,0,19320.31622
196550,45,27,3,1,Single,8,395.6871017,0,19315.35509
186937,68,51,1,1,Single,1,395.6797105,0,19319.98553
153345,49,31,3,0,Married,6,395.6705624,0,19473.52812
177195,25,7,3,1,Single,15,395.659444,0,19243.9722
120281,26,5,1,1,Single,6,395.6273655,0,19571.36828
167213,34,13,1,1,Married,2,395.6067606,0,19222.33803
132983,38,19,2,1,Single,8,395.5648042,0,19412.24021
164360,19,0,1,1,Married,12,395.5430993,0,19330.15497
107935,34,16,1,1,Single,6,395.503013,0,19289.15065
178849,26,9,4,0,Single,14,395.4993553,0,19443.96777
132345,40,23,2,1,Married,1,395.4783752,0,19497.91876
119004,19,2,3,1,Married,13,395.4681364,0,19205.40682
188315,43,23,1,0,Single,11,395.4510456,0,19438.55228
195150,46,28,1,0,Single,13,395.3882725,0,19206.41363
114104,34,17,2,0,Single,15,395.3699722,0,19375.49861
179303,16,0,2,0,Married,15,395.3114021,0,19476.57011
121853,52,31,3,1,Married,4,395.2845173,0,19311.22587
199148,65,48,2,1,Single,4,395.259523,0,19469.97615
149191,21,2,4,1,Married,6,395.228496,0,19300.4248
184066,39,18,2,1,Single,8,395.2145941,0,19203.72971
141808,56,37,2,0,Single,11,395.1916986,0,19551.58493
107555,20,1,1,0,Married,15,395.1330661,0,19474.65331
186389,40,23,3,0,Married,8,395.1167029,0,19442.83515
170895,63,45,3,1,Single,5,395.1008282,0,19280.04141
117263,45,27,1,0,Married,4,395.081989,0,19505.09945
123310,61,44,2,0,Single,12,395.0740598,0,19198.70299
167952,27,9,3,0,Married,9,395.030539,0,19545.52695
116127,39,20,2,0,Married,8,395.0082274,0,19201.41137
110208,35,16,1,1,Single,7,394.9982853,0,19487.91427
155508,45,26,2,1,Single,9,394.9737061,0,19316.68531
126952,28,10,4,0,Single,11,394.9000223,0,19235.00112
146311,39,20,2,0,Married,9,394.8217047,0,19366.08524
135975,16,0,3,1,Married,10,394.7197409,0,19412.98705
154580,57,40,3,0,Married,8,394.7196274,0,19221.98137
129633,44,27,1,0,Married,7,394.6713476,0,19295.56738
165140,54,37,3,1,Single,8,394.6427341,0,19440.13671
108621,68,47,2,1,Married,1,394.5895239,0,19155.4762
161490,54,35,3,1,Single,10,394.5028092,0,19365.14046
145071,18,0,3,0,Married,10,394.4975892,0,19388.87946
123037,34,14,1,0,Single,6,394.4787051,0,19417.93526
137538,25,6,3,1,Married,11,394.4263946,0,19468.31973
151344,59,40,2,0,Married,7,394.3574183,0,19335.87092
158667,42,24,4,1,Single,8,394.3551484,0,19362.75742
134629,20,3,2,0,Single,11,394.3168124,0,19380.84062
117077,16,0,1,0,Married,14,394.2908041,0,19357.54021
116276,25,8,1,0,Single,11,394.2728194,0,19502.64097
199063,16,0,2,1,Married,11,394.2596954,0,19412.98477
119064,55,37,1,0,Single,6,394.234324,0,19474.7162
144427,55,36,2,1,Single,6,394.2260359,0,19227.3018
177471,68,48,4,0,Married,4,394.213779,0,19382.68895
144219,52,31,1,0,Married,0,394.2066246,0,19307.33123
108303,19,0,2,0,Married,14,394.2036344,0,19407.18172
116479,54,37,3,0,Married,6,394.1426351,0,19281.13176
179256,47,26,3,1,Single,6,394.1176669,0,19334.88335
140500,58,38,2,0,Single,8,394.0867239,0,19113.3362
166197,35,17,1,1,Single,7,394.0789555,0,19265.94778
125399,28,11,3,0,Married,9,394.0374154,0,19185.87077
163990,58,38,3,0,Single,9,394.0148309,0,19361.74155
105772,44,25,2,1,Single,10,393.9951758,0,19438.75879
183600,16,0,4,0,Married,8,393.9599731,0,19278.99866
115748,42,25,4,1,Married,1,393.9097482,0,19162.48741
176367,55,36,2,1,Single,6,393.897053,0,19385.85265
162849,58,40,2,1,Single,13,393.8860297,0,19486.30149
122329,37,19,4,0,Married,10,393.883468,0,19146.1734
188416,23,5,1,0,Married,14,393.8349042,0,19222.74521
157978,18,0,4,1,Single,13,393.821824,0,19218.0912
168530,40,19,2,0,Married,7,393.7580921,0,19225.90461
123408,38,18,4,1,Single,10,393.7192587,0,19480.96294
129465,40,20,2,1,Single,10,393.7172618,0,19239.86309
189972,35,15,2,1,Single,9,393.6657261,0,19248.28631
117846,59,38,3,0,Married,10,393.5972642,0,19208.86321
113996,19,2,4,1,Single,12,393.5823167,0,19339.11584
109958,26,6,2,0,Married,6,393.4216658,0,19272.08329
108106,51,31,4,0,Married,7,393.3857401,0,19311.28701
118116,25,7,4,1,Married,8,393.3757944,0,19254.78972
114351,49,31,3,1,Single,8,393.3566814,0,19304.83407
160251,46,26,1,1,Single,9,393.3384205,0,19171.92103
173344,52,31,2,0,Single,10,393.3277765,0,19221.38883
160525,46,27,1,0,Married,10,393.3264183,0,19172.32092
180377,65,47,1,1,Single,7,393.3125586,0,19276.62793
127481,36,18,1,0,Married,9,393.2955575,0,19221.77788
145290,68,47,3,0,Single,10,393.2196126,0,19128.98063
157822,17,0,3,0,Single,15,393.2158172,0,19400.79086
108971,25,7,4,0,Married,11,393.198843,0,19076.94215
159350,43,26,2,1,Married,6,393.1848298,0,19411.24149
180849,40,19,2,1,Single,7,393.1373351,0,19062.86676
177987,43,26,3,0,Married,10,393.0837453,0,19301.18727
163950,31,14,4,0,Married,8,393.0277436,0,19228.38718
170693,59,41,2,0,Married,5,393.0023037,0,19419.11519
110486,45,24,2,0,Single,14,392.9888125,0,19131.44063
164403,42,22,3,0,Married,8,392.980047,0,19368.00235
177882,60,40,3,1,Married,2,392.9651895,0,19295.25948
110437,51,31,2,1,Single,7,392.9302356,0,19274.51178
178921,28,10,1,1,Single,10,392.8457469,0,19320.28735
145630,41,21,4,0,Married,10,392.7729478,0,19411.64739
160156,37,16,3,0,Married,5,392.7113111,0,19311.56556
171500,18,0,2,0,Married,7,392.6845652,0,19047.22826
139359,54,35,3,1,Married,2,392.6819742,0,19419.09871
136282,62,44,4,0,Single,10,392.6550953,0,19100.75477
163256,16,0,3,0,Married,13,392.6517604,0,19068.58802
198620,29,12,2,0,Married,15,392.6468725,0,19302.34363
200408,51,30,2,1,Single,6,392.6467754,0,19186.33877
176059,42,25,3,1,Single,7,392.6355064,0,19243.77532
198756,29,11,4,0,Married,7,392.5738636,0,19418.69318
121352,38,17,3,0,Married,6,392.5108202,0,19302.54101
153954,64,47,3,0,Married,10,392.4944029,0,19298.72015
138888,50,29,3,0,Married,5,392.4860907,0,19277.30454
120613,41,20,3,1,Single,6,392.482767,0,19105.13835
169184,69,49,3,1,Single,1,392.4644353,0,19208.22177
167980,33,14,2,1,Single,6,392.3951286,0,19096.75643
103266,40,21,3,0,Single,11,392.3423001,0,19414.11501
119773,57,37,4,0,Single,9,392.3417354,0,19286.08677
117494,46,26,3,0,Married,15,392.3400775,0,19045.00388
107127,22,5,3,1,Married,6,392.3148547,0,19395.74274
186085,16,0,3,0,Single,13,392.2991853,0,19119.95927
144222,19,2,2,0,Married,15,392.2846806,0,19304.23403
110036,70,50,1,1,Single,6,392.2734468,0,19212.67234
115817,18,0,3,1,Married,14,392.2099559,0,19047.4978
184116,27,6,1,1,Single,10,392.2091444,0,19101.45722
162310,49,29,4,1,Single,6,392.1790808,0,19248.95404
126506,58,41,1,1,Single,7,392.0911466,0,19182.55733
111384,49,28,3,1,Single,14,392.0745558,0,19213.72779
138970,22,4,3,1,Married,7,392.0740512,0,19125.70256
123723,30,9,3,0,Married,0,392.0647141,0,19310.23571
176336,54,35,3,1,Married,4,391.9832716,0,19356.16358
127143,69,50,1,1,Single,9,391.9819016,0,19187.09508
123835,58,41,4,0,Married,8,391.9557976,0,19328.78988
125802,70,53,4,0,Single,6,391.9501586,0,19125.50793
126776,40,22,2,0,Married,7,391.9146906,0,19227.73453
194634,66,46,2,1,Single,4,391.9022651,0,19371.11326
188328,40,19,2,0,Married,4,391.901918,0,19137.0959
137127,33,12,4,0,Married,10,391.8985761,0,19173.92881
139078,42,21,3,1,Single,9,391.8918127,0,19241.59064
158186,70,52,3,0,Single,8,391.838551,0,19254.92755
167025,55,37,2,1,Single,6,391.7506282,0,19112.53141
122140,49,31,3,0,Married,6,391.7092633,0,19140.46317
162230,38,21,2,1,Married,10,391.6733379,0,19091.6669
154305,51,34,3,0,Married,6,391.6723014,0,19104.61507
143015,25,7,3,0,Married,14,391.6632275,0,19350.16138
169897,39,19,3,1,Married,10,391.6516394,0,19119.58197
166138,31,11,3,1,Married,3,391.5921212,0,19364.60606
116966,46,28,1,0,Married,3,391.5392555,0,19284.96278
112893,36,17,3,1,Single,10,391.514442,0,19101.7221
141986,36,18,1,1,Single,9,391.5134925,0,19300.67463
122575,59,41,3,1,Single,8,391.5025839,0,19133.1292
158767,35,16,2,0,Single,15,391.4638186,0,19003.19093
129705,37,19,1,0,Married,5,391.4331666,0,19326.65833
190147,28,11,4,1,Single,9,391.3933857,0,19128.66929
118147,52,35,2,1,Married,8,391.3500926,0,19005.50463
163510,33,16,3,0,Single,6,391.3119538,0,19283.59769
186554,45,28,4,1,Single,8,391.2879304,0,19001.39652
191931,43,22,3,1,Married,2,391.2692388,0,18991.46194
113056,22,5,1,1,Married,7,391.2488532,0,19145.44266
193913,45,26,1,0,Married,9,391.2383998,0,19137.91999
109864,52,33,1,1,Single,6,391.2338229,0,19304.69115
143073,33,13,2,0,Single,13,391.2171737,0,19350.85869
112484,28,7,4,0,Single,9,391.1950655,0,19160.75328
192546,59,41,4,0,Married,8,391.0937662,0,19081.68831
127853,42,23,2,0,Single,9,390.9589293,0,18981.94647
134751,56,37,4,1,Married,5,390.9509227,0,19325.54614
141578,43,23,2,1,Married,2,390.9438171,0,19132.19086
186257,27,10,3,1,Single,9,390.9353302,0,19112.76651
147346,55,36,1,0,Married,6,390.9282046,0,19287.41023
101991,30,13,3,1,Single,13,390.9071442,0,19238.35721
103917,32,14,2,0,Married,6,390.9048704,0,19120.24352
140645,59,42,3,0,Married,12,390.902063,0,18983.10315
101383,48,27,2,1,Single,11,390.9019909,0,19309.09955
113127,38,18,3,0,Single,15,390.9018922,0,19223.09461
121875,70,49,2,1,Single,7,390.8637873,0,19321.18937
192731,49,29,3,1,Single,6,390.8618207,0,19136.09104
134585,28,11,2,0,Married,7,390.8413478,0,19341.06739
162392,23,6,4,1,Single,13,390.7991057,0,19102.95529
181554,21,4,2,0,Single,11,390.7943819,0,18981.7191
110533,44,26,4,0,Married,6,390.7695842,0,18973.47921
140470,61,41,3,0,Single,3,390.7584802,0,19330.92401
197751,31,14,4,0,Single,12,390.7308708,0,19145.54354
190768,22,5,2,1,Married,10,390.7031895,0,19278.15948
163501,19,1,1,1,Married,9,390.682358,0,19196.1179
191631,64,45,3,0,Single,9,390.6774964,0,18979.87482
158532,53,33,1,0,Single,10,390.6032919,0,18949.1646
139754,17,0,2,0,Married,12,390.5690694,0,19031.45347
164609,40,21,1,1,Single,7,390.5619161,0,19166.09581
141306,16,0,1,1,Single,11,390.521486,0,19010.0743
134203,22,5,2,0,Married,6,390.4995846,0,19167.97923
155067,39,21,3,0,Married,2,390.4654393,0,19175.27197
144656,19,2,2,0,Married,14,390.4570927,0,18997.85464
183797,64,46,1,0,Married,4,390.4062578,0,19003.31289
103187,53,34,1,1,Single,6,390.3979113,0,19113.89557
111323,61,43,2,1,Single,3,390.3536993,0,19307.68497
146771,51,30,2,0,Married,9,390.3393024,0,19279.96512
179399,38,19,4,0,Single,10,390.3124558,0,19267.62279
192745,62,44,2,0,Married,1,390.3009479,0,19151.0474
167930,40,23,2,0,Married,10,390.2854666,0,19193.27333
115929,42,25,3,0,Married,2,390.2772461,0,18997.86231
197702,50,33,4,0,Single,14,390.2230925,0,18978.15463
163824,45,25,2,0,Married,6,390.2215123,0,19038.07562
171093,44,26,2,0,Married,1,390.1296231,0,18956.48116
175149,70,50,2,1,Single,6,390.0849495,0,19020.24748
158330,20,2,3,1,Married,0,390.052406,0,18938.6203
160960,46,25,1,0,Married,2,390.0515833,0,19028.57917
111926,65,44,3,0,Single,9,390.0503247,0,19137.51624
149176,69,48,3,0,Single,3,390.0472123,0,18967.36062
126585,16,0,4,1,Married,15,390.0462135,0,19247.31068
106914,32,11,2,1,Married,5,390.0040602,0,19159.20301
137624,24,7,2,1,Married,12,389.967096,0,19109.3548
143149,52,32,2,1,Single,9,389.9110341,0,18955.55171
157193,20,1,2,1,Married,11,389.8397239,0,19257.9862
135757,50,31,2,1,Single,8,389.7354599,0,19089.773
187994,18,0,1,0,Single,12,389.7294061,0,19010.47031
160237,49,30,1,1,Single,7,389.6227552,0,19208.13776
181789,44,26,3,0,Married,9,389.5962654,0,19085.81327
153082,33,14,2,1,Single,8,389.5417014,0,18920.08507
144850,48,27,2,0,Married,10,389.5266353,0,18999.33177
127590,61,43,2,1,Single,13,389.4722314,0,18923.61157
117862,18,0,3,1,Married,14,389.422059,0,18998.10295
114715,50,31,4,0,Single,2,389.4203675,0,19035.01838
104732,22,3,3,0,Married,14,389.3521158,0,19076.60579
177720,43,22,1,0,Married,14,389.3255588,0,18873.27794
114263,55,35,1,0,Single,8,389.3103123,0,18875.51562
189493,26,6,3,1,Single,8,389.3048929,0,18892.24465
187013,59,40,3,0,Single,12,389.2357428,0,19248.78714
181558,65,48,3,0,Single,10,389.0980171,0,19108.90086
166839,21,4,4,0,Married,11,389.0623132,0,18929.11566
120933,53,34,2,1,Single,8,389.060504,0,19032.0252
197635,47,27,3,1,Single,9,389.0604522,0,18910.02261
141893,16,0,4,1,Married,11,389.0382552,0,18899.91276
185075,25,7,4,1,Single,13,389.0232199,0,18969.161
189429,20,1,1,1,Married,15,388.9897943,0,19232.48972
144160,23,5,2,0,Married,7,388.9859276,0,18970.29638
100678,51,30,3,0,Married,7,388.9625929,0,19223.12965
141130,19,1,4,1,Single,10,388.9394613,0,19128.97307
145159,59,41,4,0,Married,8,388.9232682,0,18972.16341
101930,26,8,3,0,Single,14,388.9023917,0,18979.11959
119887,48,30,1,0,Single,8,388.8524692,0,18970.62346
171125,38,17,3,1,Single,9,388.8350024,0,19238.75012
112569,24,7,4,1,Married,12,388.8024772,0,19010.12386
120476,48,31,3,1,Single,3,388.7980122,0,18884.90061
128174,53,36,4,1,Single,10,388.7396022,0,18852.98011
145329,20,3,4,0,Married,7,388.6624743,0,19103.12372
111279,33,16,4,1,Married,0,388.6614615,0,18866.07308
105058,22,3,2,1,Married,3,388.6563906,0,19034.81953
108996,61,40,1,0,Single,5,388.6544422,0,18991.72211
105817,43,26,3,0,Married,10,388.6325127,0,18966.62564
150460,62,44,1,0,Married,1,388.631076,0,19072.5538
143916,56,35,3,1,Single,15,388.5680362,0,18844.40181
124866,27,6,4,0,Married,10,388.4883752,0,19140.41876
184129,36,15,3,1,Married,2,388.4354977,0,18979.77489
166223,23,4,2,0,Married,11,388.4135299,0,18921.6765
131310,58,39,1,1,Married,5,388.37178,0,19076.589
127337,21,4,2,1,Married,14,388.349014,0,19050.4507
129528,22,4,4,1,Married,7,388.3373806,0,19148.86903
151981,32,12,2,1,Single,8,388.2892987,0,19161.46494
155509,56,36,3,0,Married,8,388.2396037,0,18865.98019
161127,41,22,3,0,Married,6,388.1827446,0,19201.13723
162174,46,28,1,1,Single,5,388.163314,0,18976.1657
126252,68,51,2,1,Single,4,388.1607412,0,19050.03706
132668,46,26,1,0,Single,15,388.1507245,0,19158.53623
178315,24,5,2,0,Single,13,388.0295722,0,18976.47861
144692,55,36,3,0,Married,7,388.0066414,0,19007.33207
190983,24,6,3,0,Married,12,387.9977776,0,18837.88888
160767,50,32,2,0,Married,6,387.9552071,0,19129.76036
105440,45,27,2,1,Single,12,387.9520813,0,19104.60407
123112,30,9,1,0,Married,6,387.9152486,0,18974.76243
143882,33,14,2,0,Single,10,387.8970548,0,19150.85274
183187,36,19,2,1,Married,7,387.8513132,0,18944.56566
167515,24,6,3,0,Single,12,387.8124404,0,18924.62202
161182,44,27,3,0,Married,9,387.7739202,0,18822.69601
159643,54,35,2,0,Married,8,387.7583701,0,18798.91851
169904,42,21,2,1,Married,6,387.7468473,0,19023.34237
137599,18,0,2,0,Married,13,387.745597,0,18893.27985
152487,20,2,4,1,Single,12,387.73725,0,18876.8625
177967,26,6,4,1,Married,1,387.7293836,0,18960.46918
129311,22,5,4,1,Married,12,387.7169808,0,19106.84904
196228,68,51,3,0,Single,1,387.7001867,0,18873.00934
138293,27,6,2,1,Single,7,387.6816898,0,18830.08449
185848,19,2,4,0,Married,6,387.6639715,0,18916.19858
151715,38,21,2,0,Married,6,387.567204,0,19047.3602
139865,28,11,2,1,Single,9,387.5613219,0,18856.0661
101665,40,19,1,1,Single,9,387.4851589,0,18795.25795
116079,53,32,4,1,Single,6,387.4840358,0,19090.20179
132674,47,29,2,0,Married,9,387.4795441,0,18833.97721
163935,30,12,2,1,Single,8,387.4681138,0,19168.40569
120834,30,9,1,0,Married,6,387.4435732,0,19099.17866
186295,50,30,1,1,Single,6,387.366763,0,18911.33815
156116,46,29,1,1,Single,12,387.3513371,0,19101.56686
166533,27,9,2,0,Married,3,387.3510246,0,19081.55123
131600,48,30,4,1,Single,8,387.2872741,0,18887.36371
164614,43,23,3,0,Married,8,387.2686296,0,18763.43148
103445,61,42,2,1,Single,10,387.259968,0,18895.9984
110085,62,42,1,0,Married,10,387.2442899,0,19064.2145
125899,32,14,1,0,Single,14,387.2409986,0,18813.04993
101592,19,1,1,1,Married,12,387.2362217,0,18984.81109
187420,22,4,2,1,Married,3,387.2199773,0,18947.99887
154443,28,7,2,1,Single,8,387.1835882,0,19076.17941
153763,29,11,1,1,Married,0,387.1689797,0,18882.44899
144289,24,6,3,0,Married,11,386.9850888,0,18810.25444
103175,49,30,1,1,Married,5,386.9751075,0,18808.75538
183226,34,13,4,1,Single,7,386.927101,0,18851.35505
166742,25,7,3,1,Married,7,386.9087291,0,18746.43646
137331,70,50,2,1,Married,2,386.8985782,0,19006.92891
145758,69,51,2,1,Married,4,386.8914346,0,19101.57173
155702,66,47,2,1,Married,1,386.8540181,0,19041.70091
177474,25,6,3,1,Married,7,386.8409149,0,18781.04575
110379,31,10,2,0,Single,10,386.8273102,0,19121.36551
129879,22,3,3,1,Married,7,386.7767284,0,18767.83642
106673,42,21,3,0,Married,8,386.7136229,0,18986.68115
133428,62,44,2,1,Single,10,386.6502847,0,19120.51424
150449,32,15,3,1,Single,10,386.5875011,0,18849.37506
138538,47,26,3,0,Married,0,386.5782655,0,19050.91328
123960,53,35,4,1,Single,10,386.473788,0,18789.6894
145847,51,30,2,0,Single,13,386.4362948,0,19108.81474
140998,30,13,3,1,Single,9,386.4149473,0,18916.74737
176413,50,30,3,0,Married,12,386.4064153,0,18867.32077
166143,46,27,2,0,Married,9,386.3281397,0,18735.40699
148011,35,17,2,0,Married,8,386.3073455,0,18816.36728
140697,66,47,4,0,Single,2,386.2912251,0,19023.56126
125489,30,13,2,0,Married,9,386.2883496,0,19024.41748
103590,47,26,3,0,Single,7,386.2420903,0,18998.10452
186108,50,30,2,0,Married,7,386.207255,0,18766.36275
128654,22,3,4,1,Married,5,386.1943007,0,18856.71504
173155,26,8,2,0,Married,7,386.1919932,0,18732.59966
191928,68,50,2,1,Single,1,386.1855164,0,18836.27582
136647,58,38,1,1,Married,2,386.1270382,0,18982.35191
171394,24,6,3,1,Married,11,386.0939344,0,18866.69672
196998,54,33,1,0,Single,10,386.0425259,0,18719.1263
126121,33,14,3,0,Married,10,386.0097307,0,18758.48654
116758,70,52,3,0,Single,6,385.9814073,0,18748.07037
151188,16,0,2,0,Single,11,385.9700392,0,18810.50196
141477,22,3,3,0,Married,14,385.9403212,0,18880.01606
135730,27,7,3,0,Married,6,385.928791,0,18788.43955
181345,23,6,1,1,Single,13,385.8799478,0,18900.99739
150647,53,36,2,0,Married,9,385.8222053,0,18955.11027
187144,28,7,3,1,Single,8,385.7587634,0,18923.93817
170804,20,2,3,1,Single,14,385.7584332,0,18978.92166
157069,61,40,3,0,Single,8,385.7580268,0,18816.90134
136848,38,21,1,1,Single,9,385.7485614,0,18852.42807
115175,59,38,1,1,Single,7,385.7248952,0,18778.24476
157058,31,11,2,0,Single,15,385.6705408,0,18685.52704
143394,51,32,4,0,Married,1,385.5920763,0,18705.60382
168645,34,15,3,0,Married,8,385.5605702,0,18757.02851
187729,67,46,2,0,Single,12,385.4475519,0,18782.3776
180355,30,10,4,0,Married,8,385.3979731,0,19024.89866
122481,58,37,4,0,Single,9,385.3793869,0,18996.96935
146712,24,6,2,1,Single,11,385.3673578,0,18991.36789
125776,58,39,1,0,Married,10,385.3239242,0,18956.19621
172292,34,15,2,0,Married,6,385.3054114,0,18864.27057
106426,37,18,3,0,Married,9,385.3041485,0,18792.20743
191778,65,46,1,0,Married,8,385.3041121,0,18712.20561
154406,57,36,2,1,Single,6,385.2545372,0,18921.72686
129431,42,22,2,0,Married,8,385.2484008,0,18855.42004
107847,51,31,2,0,Married,0,385.2405668,0,18888.02834
107873,24,7,2,1,Married,14,385.23888,0,18845.944
194952,49,32,3,1,Single,15,385.2239247,0,18961.19624
129315,63,45,3,0,Single,2,385.2021787,0,18835.10894
186978,40,21,3,0,Single,8,385.193288,0,18923.6644
161779,64,43,2,1,Married,4,385.1507846,0,18993.53923
193274,48,30,2,0,Single,11,385.0728079,0,19011.6404
192208,47,29,4,0,Single,11,385.0698403,0,18748.49202
118382,25,6,3,1,Single,14,385.0272885,0,18697.36443
178536,16,0,4,0,Single,15,385.0269627,0,18655.34814
132569,70,52,1,1,Single,1,385.0208974,0,18949.04487
145750,34,13,2,1,Single,15,384.9820021,0,19013.10011
148788,34,16,3,0,Married,15,384.903832,0,19038.1916
129176,23,4,2,1,Single,12,384.884809,0,18910.24045
108864,26,9,1,0,Single,8,384.8792499,0,18914.9625
144394,17,0,2,1,Single,14,384.8647472,0,18795.23736
131058,68,47,3,1,Single,7,384.8580779,0,18778.9039
170473,46,28,2,1,Single,7,384.8378553,0,18685.89277
185239,59,42,4,0,Single,14,384.7952953,0,18699.76477
119002,23,5,2,0,Married,13,384.7692913,0,18747.46457
194225,65,47,3,0,Single,9,384.7250407,0,18786.25204
199800,32,12,2,0,Married,10,384.7066176,0,18652.33088
174975,68,49,3,1,Single,4,384.7051283,0,18823.25642
171298,31,13,3,0,Married,8,384.679476,0,18905.9738
142548,29,8,1,0,Married,9,384.6763948,0,18949.81974
134360,49,28,4,0,Married,8,384.6726759,0,18684.6338
195809,59,39,2,0,Married,8,384.6369469,0,18922.84735
185781,25,8,4,0,Married,9,384.6180335,0,18813.90168
153857,35,14,2,0,Married,7,384.541988,0,18766.0994
113706,59,41,3,1,Single,6,384.5275042,0,18756.37521
130552,43,26,2,1,Single,10,384.506558,0,18911.3279
172588,21,4,3,1,Single,14,384.5008776,0,19006.04388
118901,58,40,3,0,Married,6,384.4978346,0,18635.89173
136033,47,29,2,1,Single,7,384.4920277,0,18945.60139
128282,64,43,4,0,Single,4,384.452464,0,18810.6232
174142,30,11,3,1,Single,4,384.3993253,0,18857.96627
170780,38,18,2,1,Single,9,384.3948008,0,18696.74004
174093,32,14,3,0,Single,15,384.3913073,0,18854.56537
180479,23,5,3,1,Married,12,384.38384,0,18668.192
114424,26,9,4,1,Single,7,384.3670391,0,18994.35196
192845,64,43,2,0,Single,11,384.3539154,0,18986.69577
197948,32,13,4,1,Single,7,384.2924002,0,18958.62001
179397,59,39,4,1,Single,7,384.2764664,0,18848.82332
171103,16,0,4,0,Married,12,384.2687037,0,18701.43519
113793,55,36,2,1,Single,9,384.2686902,0,18983.43451
152653,21,4,3,0,Married,13,384.1860668,0,18747.30334
189302,58,41,3,1,Single,10,384.1628919,0,18617.1446
168588,69,49,2,0,Single,6,384.149582,0,18613.4791
153613,22,3,2,1,Married,6,384.1372989,0,18650.86495
180828,63,42,3,0,Single,4,384.13711,0,18701.8555
180327,64,45,3,1,Single,9,384.0884018,0,18666.42009
170302,36,19,3,0,Married,8,384.0194223,0,18637.97112
186930,70,51,3,0,Single,10,384.006876,0,18701.3438
105909,27,8,4,0,Single,7,383.975515,0,18949.77575
104035,45,27,4,1,Single,14,383.9529906,0,18884.64953
146149,27,8,3,1,Single,10,383.9293078,0,18736.46539
130279,35,14,2,1,Single,10,383.899652,0,18715.9826
190490,69,51,3,0,Married,4,383.8839835,0,18691.19918
108894,19,0,3,1,Married,9,383.875196,0,18902.7598
134475,48,31,3,1,Single,7,383.8576238,0,18930.88119
110726,31,12,3,0,Married,9,383.8345056,0,18914.72528
111149,19,2,2,0,Married,6,383.7567704,0,18623.83852
139464,32,12,4,1,Single,9,383.6844203,0,18619.22102
114344,39,19,2,0,Single,10,383.6642098,0,18827.21049
117449,37,18,2,0,Married,9,383.5760292,0,18957.80146
138087,63,45,4,0,Single,7,383.5706642,0,18836.53321
181021,38,21,2,0,Married,10,383.5138009,0,18876.69005
129885,57,40,3,0,Single,15,383.4909815,0,18922.54908
155622,23,5,2,0,Married,14,383.4600835,0,18683.00418
153514,27,6,2,0,Married,10,383.4595893,0,18709.97947
174635,29,10,4,0,Married,9,383.4540805,0,18598.70403
185581,49,30,3,1,Single,14,383.4475114,0,18835.37557
176582,45,26,1,1,Single,10,383.44538,0,18780.269
187888,18,1,4,1,Single,11,383.4385754,0,18666.92877
104903,32,13,3,1,Single,7,383.3799544,0,18865.99772
123043,35,18,3,1,Married,1,383.3741104,0,18781.70552
197243,61,43,3,1,Married,3,383.3588336,0,18758.94168
197640,53,32,3,1,Married,0,383.3544135,0,18598.72068
124607,53,35,2,1,Single,8,383.309022,0,18905.4511
127717,18,0,1,1,Married,14,383.2031519,0,18757.1576
186708,49,32,3,1,Married,11,383.1808039,0,18640.0402
112720,24,6,1,0,Single,15,383.1709874,0,18742.54937
184441,42,24,4,1,Single,10,383.158681,0,18784.93405
115364,25,7,4,1,Single,14,383.1214308,0,18780.07154
187792,43,25,1,1,Single,11,383.111618,0,18792.5809
168948,21,2,2,1,Married,14,383.1051406,0,18761.25703
140775,43,22,1,0,Single,8,383.0935474,0,18909.67737
148145,69,50,1,1,Married,1,383.0844977,0,18856.22489
165265,68,50,2,0,Single,0,383.0202439,0,18717.0122
199074,16,0,3,1,Single,14,383.011526,0,18680.5763
120467,53,36,3,1,Single,7,382.9838409,0,18741.19205
151991,16,0,4,0,Married,12,382.9730687,0,18833.65344
192266,46,29,2,0,Married,10,382.9371189,0,18870.85595
174219,51,34,3,0,Married,9,382.9360523,0,18871.80262
124026,28,9,2,0,Married,6,382.9229789,0,18553.14895
190959,46,29,1,0,Married,7,382.8006063,0,18820.03032
162340,22,3,4,1,Married,7,382.7989886,0,18832.94943
153573,55,34,2,1,Single,8,382.7987489,0,18679.93745
110919,58,41,2,0,Married,7,382.7712477,0,18628.56239
161377,59,39,2,1,Married,7,382.7498593,0,18542.49297
102840,53,36,4,0,Married,6,382.7331096,0,18876.65548
100356,16,0,3,1,Married,14,382.6980639,0,18925.9032
146863,17,0,2,0,Married,8,382.6898135,0,18600.49068
155512,42,25,2,0,Married,9,382.6718535,0,18762.59268
164603,48,30,4,1,Single,6,382.6662277,0,18926.31139
192841,28,10,3,1,Single,11,382.6058803,0,18601.29402
108779,30,13,2,1,Single,12,382.5545497,0,18920.72749
174567,25,7,2,1,Single,14,382.5513738,0,18853.56869
106897,58,39,2,0,Married,7,382.5430192,0,18563.15096
121360,65,44,1,0,Single,12,382.5191511,0,18700.95756
188214,16,0,1,1,Married,8,382.4581608,0,18567.90804
128542,17,0,3,0,Married,6,382.4534923,0,18913.67462
118587,33,14,2,0,Single,8,382.3996542,0,18565.98271
144735,29,9,2,0,Married,9,382.3871743,0,18673.35872
181080,45,28,3,1,Single,10,382.325365,0,18751.26825
166748,26,9,2,1,Married,5,382.2884571,0,18752.42286
147616,56,39,3,1,Single,10,382.263524,0,18619.1762
161620,46,29,2,0,Married,6,382.230708,0,18794.5354
167451,43,24,2,1,Single,7,382.2184399,0,18810.922
184682,16,0,2,1,Married,3,382.2013599,0,18733.068
194153,16,0,3,1,Single,12,382.1958906,0,18664.79453
118280,25,6,3,0,Married,12,382.1901316,0,18638.50658
126307,53,35,1,0,Married,9,382.1779015,0,18619.89508
180455,18,0,1,0,Married,15,382.1035218,0,18728.17609
160518,26,6,1,1,Married,9,382.1026866,0,18878.13433
146706,59,41,2,0,Single,13,382.0857577,0,18549.28788
181009,44,23,3,0,Single,8,382.0787055,0,18737.93528
184210,36,15,3,0,Single,15,382.0609448,0,18505.04724
153484,37,19,4,0,Single,9,382.0433616,0,18693.16808
148071,55,35,2,0,Married,14,382.0276144,0,18630.38072
185030,67,48,1,1,Married,0,381.9884749,0,18783.42375
176012,35,16,2,0,Single,9,381.9784381,0,18508.92191
125737,34,16,4,0,Married,8,381.9658381,0,18687.29191
121539,43,25,3,1,Single,10,381.9142724,0,18643.71362
101407,68,49,3,0,Single,14,381.8479586,0,18566.39793
183184,31,12,2,1,Single,7,381.8474106,0,18646.37053
183849,17,0,1,0,Single,12,381.8262364,0,18875.31182
176362,26,7,3,1,Single,10,381.7875709,0,18742.37855
126188,17,0,1,1,Married,14,381.7838871,0,18665.19436
166042,64,45,3,0,Single,6,381.7761939,0,18709.8097
167843,52,31,4,1,Married,7,381.7601006,0,18877.00503
167746,58,39,1,1,Single,2,381.7477309,0,18671.38655
103343,34,17,3,1,Single,11,381.6900721,0,18860.50361
116581,43,22,1,0,Married,7,381.6731499,0,18500.6575
189437,68,48,2,1,Married,2,381.6604906,0,18702.02453
199927,49,29,3,0,Single,1,381.6267381,0,18714.33691
172740,31,13,1,1,Single,9,381.6263384,0,18774.31692
158057,43,22,2,1,Married,0,381.6187691,0,18703.93846
109560,32,11,2,1,Single,10,381.610363,0,18536.51815
102993,20,1,2,0,Single,13,381.605109,0,18689.25545
191049,36,15,3,0,Married,9,381.5654494,0,18705.27247
114136,44,27,2,1,Married,0,381.557682,0,18822.8841
186896,49,31,2,1,Single,8,381.5180503,0,18643.90252
132687,45,26,2,1,Single,6,381.4841626,0,18656.20813
107998,68,47,3,1,Single,1,381.4821507,0,18703.10754
180237,42,25,2,0,Married,7,381.4533716,0,18777.66858
180370,55,36,3,0,Married,9,381.4232944,0,18866.16472
107744,49,28,2,0,Married,14,381.410906,0,18711.5453
144196,18,0,4,0,Married,11,381.4094542,0,18550.47271
161232,49,32,2,0,Married,2,381.4077958,0,18554.38979
140751,18,1,2,0,Married,15,381.394809,0,18830.74045
167255,66,46,3,0,Single,7,381.3855125,0,18598.27563
190045,26,8,4,1,Single,8,381.3791983,0,18508.95992
113023,39,20,2,0,Single,2,381.372027,0,18597.60135
154387,20,1,3,1,Married,12,381.3569653,0,18583.84827
109528,70,51,2,0,Single,15,381.3348415,0,18487.74208
180708,26,8,3,0,Married,6,381.3059277,0,18614.29639
131012,51,31,3,1,Single,9,381.2959442,0,18717.79721
122834,42,25,2,1,Single,8,381.2696849,0,18844.48425
136951,19,2,2,1,Single,11,381.1952687,0,18816.76344
185443,27,9,2,0,Single,8,381.18968,0,18591.484
188935,49,31,3,1,Married,0,381.0714704,0,18597.57352
193819,42,24,2,1,Single,9,381.0444737,0,18555.22369
144911,41,22,1,1,Married,1,381.0411775,0,18643.05888
171710,29,8,3,0,Single,8,381.0256228,0,18603.28114
132712,56,36,3,0,Married,7,381.0230378,0,18816.15189
198326,27,6,2,1,Single,10,380.9761342,0,18681.80671
113171,35,18,4,0,Married,8,380.9647394,0,18513.23697
118652,59,40,3,0,Married,9,380.9262192,0,18571.31096
195634,36,15,1,1,Married,1,380.8848309,0,18549.24155
186816,43,26,4,0,Married,3,380.882934,0,18612.1467
141491,31,14,2,0,Married,13,380.8398167,0,18800.99084
163337,42,21,1,1,Single,10,380.8165241,0,18712.82621
189037,30,11,3,0,Married,15,380.7836487,0,18679.18244
102031,50,30,3,0,Single,9,380.7686488,0,18684.43244
181271,16,0,4,0,Married,6,380.723096,0,18585.1548
174759,29,8,3,0,Married,7,380.684214,0,18723.2107
168375,43,24,3,0,Married,6,380.6256736,0,18655.28368
195882,40,23,3,1,Single,6,380.6014831,0,18725.07416
191467,44,26,3,0,Married,9,380.5811035,0,18561.05518
106599,32,15,3,1,Single,6,380.5640053,0,18692.20027
138414,37,18,2,0,Married,8,380.5634077,0,18825.17039
136810,63,44,2,0,Single,7,380.4963738,0,18769.81869
145650,40,19,2,1,Married,6,380.4858605,0,18594.29303
118627,25,6,2,0,Single,13,380.4447772,0,18789.23886
187776,29,10,4,0,Married,7,380.4149657,0,18460.74829
181090,62,45,2,1,Single,2,380.4116361,0,18511.58181
174278,56,39,2,0,Married,10,380.4080074,0,18727.40037
195469,43,25,2,1,Single,9,380.3847115,0,18433.23558
158526,46,29,3,0,Married,6,380.3607881,0,18420.03941
180685,46,27,3,0,Married,8,380.3565842,0,18725.82921
198847,31,13,2,1,Single,7,380.3387109,0,18753.93555
188161,46,27,3,0,Single,11,380.3042296,0,18484.21148
153288,62,45,2,0,Single,6,380.2629278,0,18647.14639
175464,39,22,2,1,Single,14,380.2390255,0,18677.95128
113057,18,1,2,0,Married,14,380.1822674,0,18520.11337
129218,33,13,4,0,Single,11,380.1495652,0,18434.47826
101903,55,36,3,0,Single,13,380.1288578,0,18591.44289
171548,53,34,2,1,Single,7,380.0523299,0,18429.6165
125821,30,11,1,0,Married,10,380.0318156,0,18446.59078
135275,17,0,3,1,Married,11,380.029545,0,18709.47725
163103,35,17,4,1,Single,9,380.0241943,0,18525.20972
187545,61,42,1,1,Single,8,380.0193648,0,18413.96824
116980,22,3,1,1,Married,15,379.979143,0,18599.95715
184965,20,1,2,0,Married,8,379.9197983,0,18755.98992
120320,57,37,3,0,Married,10,379.8931437,0,18583.65719
117645,51,30,3,1,Single,9,379.8887744,0,18407.43872
148221,41,24,4,1,Single,15,379.8803505,0,18768.01753
108958,52,31,1,1,Single,12,379.8610274,0,18766.05137
125999,29,12,3,1,Single,6,379.8254789,0,18579.27395
116419,43,23,1,0,Married,10,379.8147306,0,18449.73653
105386,18,0,2,1,Single,13,379.7999706,0,18520.99853
108917,45,28,3,1,Single,8,379.6942352,0,18557.71176
127244,58,41,3,0,Married,0,379.6722838,0,18606.61419
181682,59,42,3,1,Single,10,379.6260286,0,18632.30143
141735,58,39,1,0,Married,8,379.5952303,0,18424.76152
185447,33,13,2,0,Married,7,379.5789069,0,18665.94535
194732,27,7,4,0,Married,3,379.5332113,0,18590.66057
120054,50,30,3,0,Married,8,379.5283124,0,18478.41562
110055,45,28,3,1,Single,6,379.5054224,0,18581.27112
130290,50,30,4,0,Married,7,379.4078932,0,18588.39466
151097,22,3,4,1,Single,7,379.3928765,0,18733.64383
131880,59,42,3,1,Single,9,379.3433643,0,18373.16822
199624,25,6,2,0,Single,13,379.2887008,0,18683.43504
197785,52,33,2,0,Married,6,379.2837929,0,18712.18965
132587,37,17,3,1,Single,10,379.273186,0,18574.6593
158387,57,40,1,1,Single,7,379.2612055,0,18755.06028
129418,55,34,3,0,Married,8,379.216129,0,18653.80645
188684,44,27,1,1,Single,7,379.1966162,0,18674.83081
148021,50,33,3,1,Single,15,379.1833775,0,18589.16888
110718,56,37,4,1,Single,15,379.1802773,0,18466.01387
161219,19,2,3,1,Married,9,379.1506144,0,18587.53072
118616,50,29,4,1,Married,3,379.1346056,0,18399.73028
177311,56,39,2,0,Married,8,379.0977842,0,18369.88921
145469,23,6,1,0,Married,12,379.0887054,0,18380.43527
185764,36,17,3,0,Married,9,379.0811443,0,18471.05722
149450,47,26,2,1,Married,5,379.0807708,0,18572.03854
135563,49,28,2,0,Married,6,379.0701103,0,18436.50552
198414,16,0,1,0,Married,9,379.0664688,0,18591.32344
182836,30,13,3,1,Married,3,379.0646862,0,18421.23431
149785,38,21,4,0,Married,14,379.0386202,0,18568.93101
127248,44,23,2,0,Married,8,379.0301602,0,18676.50801
163333,41,23,2,0,Married,11,378.9668781,0,18746.34391
122503,25,8,2,0,Married,14,378.9522982,0,18681.61491
131638,16,0,3,0,Single,14,378.9476811,0,18517.38406
171127,53,32,2,0,Single,8,378.9284189,0,18684.42095
177005,65,46,3,1,Single,9,378.8241333,0,18425.20667
144685,51,33,2,1,Single,7,378.7651634,0,18506.25817
194583,36,17,2,0,Married,6,378.7564551,0,18560.82276
170659,27,6,3,1,Single,6,378.7468718,0,18487.34359
183217,45,25,4,0,Single,10,378.6973128,0,18529.86564
181081,32,11,2,0,Married,14,378.6520106,0,18613.60053
121254,47,30,2,1,Single,7,378.6471116,0,18395.35558
126001,16,0,1,1,Married,3,378.5785956,0,18673.92978
155077,55,36,4,0,Married,8,378.5538202,0,18454.69101
124309,63,42,3,1,Single,10,378.4457888,0,18462.28944
182571,21,2,1,1,Married,12,378.441003,0,18653.05015
190916,17,0,4,0,Single,14,378.4220976,0,18327.10488
140698,53,35,3,1,Single,6,378.397866,0,18627.8933
154018,25,8,3,1,Married,11,378.368312,0,18420.4156
107601,50,32,1,1,Married,3,378.3183766,0,18623.91883
101505,54,34,2,0,Married,5,378.3054213,0,18342.27107
199651,27,9,4,0,Single,7,378.2870801,0,18334.35401
160933,16,0,4,1,Single,14,378.2856373,0,18331.28187
191205,44,25,2,1,Single,8,378.2642892,0,18508.21446
102951,39,19,3,0,Married,8,378.2457592,0,18456.28796
144482,57,38,1,0,Married,8,378.1551984,0,18329.75992
184801,16,0,1,1,Married,9,378.1407208,0,18321.03604
141174,67,50,3,1,Single,4,378.1294338,0,18475.47169
161229,38,18,2,0,Married,6,378.0871083,0,18541.35542
192865,23,6,2,1,Married,13,378.0683314,0,18444.41657
119211,34,13,2,0,Married,10,378.058951,0,18314.94755
171496,57,38,2,0,Married,8,378.0352916,0,18391.76458
143546,45,26,1,1,Single,7,377.9836825,0,18448.18413
151496,55,38,4,0,Married,2,377.9807964,0,18396.03982
133147,49,29,2,0,Married,6,377.9771534,0,18447.85767
183680,38,20,4,0,Married,9,377.9346584,0,18631.73292
124057,39,19,3,0,Married,12,377.8486606,0,18538.43303
143261,63,46,1,0,Single,0,377.8277597,0,18429.38799
181303,23,6,3,1,Married,12,377.7385891,0,18587.92946
169733,31,13,2,1,Single,0,377.7105445,0,18347.52723
135950,38,21,2,0,Married,7,377.7103177,0,18480.51589
189035,30,9,2,1,Single,3,377.6546915,0,18381.73458
186769,65,46,1,0,Single,8,377.6421689,0,18672.10845
100029,45,25,3,1,Single,12,377.4992925,0,18365.96463
103362,16,0,3,1,Married,7,377.4714749,0,18286.57375
134244,31,12,3,1,Single,9,377.4174984,0,18585.87492
163506,18,0,3,0,Married,12,377.3766415,0,18514.83208
167715,65,46,3,0,Married,4,377.3416426,0,18614.08213
145754,66,46,3,0,Married,1,377.3397888,0,18567.98944
150042,30,10,2,1,Single,5,377.3268283,0,18466.34142
104169,49,30,2,0,Married,1,377.2664157,0,18414.32079
149319,63,46,1,0,Married,0,377.2200274,0,18490.00137
145441,28,8,2,0,Married,9,377.2063695,0,18492.31848
112680,36,17,2,1,Single,12,377.2055573,0,18295.27787
189817,26,9,4,1,Single,9,377.1557203,0,18352.78602
169962,55,35,4,0,Single,8,377.1115577,0,18515.57789
174988,22,4,3,1,Married,12,377.0783636,0,18508.91818
184497,16,0,4,1,Married,6,377.0209798,0,18291.04899
129335,68,50,2,1,Single,10,377.0163973,0,18424.81987
140599,37,19,1,0,Married,9,377.0143389,0,18583.71695
109628,50,33,3,1,Single,9,377.0024966,0,18570.12483
120466,61,43,2,0,Single,10,376.9877925,0,18649.38963
163087,32,13,2,0,Married,6,376.9814566,0,18459.07283
178383,39,22,3,0,Married,10,376.9462415,0,18601.31208
153423,39,19,3,1,Single,10,376.9376247,0,18628.88124
170004,32,14,4,1,Single,9,376.9357628,0,18453.78814
167078,30,12,2,1,Single,13,376.9146316,0,18564.73158
140481,26,9,2,1,Single,7,376.9055765,0,18368.27883
128756,42,25,1,1,Married,6,376.9025625,0,18486.12813
145797,48,29,3,0,Married,8,376.8821343,0,18454.10672
131776,28,10,3,1,Married,1,376.851857,0,18469.59285
139638,18,0,1,1,Single,11,376.8459815,0,18387.29908
192512,29,12,4,0,Married,14,376.8424395,0,18634.12198
120964,36,17,3,1,Single,8,376.8405796,0,18315.02898
176091,26,6,2,0,Married,6,376.8354896,0,18389.77448
189647,38,19,3,0,Single,14,376.7908524,0,18381.54262
159386,39,22,3,0,Married,7,376.7639777,0,18405.19889
159861,41,22,2,0,Married,12,376.743896,0,18542.1948
144624,28,8,4,0,Married,7,376.705297,0,18605.26485
119105,46,28,2,0,Married,9,376.7052751,0,18437.26376
124105,64,43,4,0,Single,6,376.6981804,0,18315.90902
165134,41,24,3,0,Married,6,376.6615546,0,18332.07773
195573,37,19,3,0,Married,9,376.6539901,0,18268.69951
164701,55,37,3,1,Single,9,376.6515652,0,18562.57826
193948,54,33,2,0,Single,13,376.6252068,0,18322.26034
169811,18,0,2,1,Single,15,376.5990356,0,18483.95178
156774,47,28,1,1,Single,6,376.5497194,0,18270.48597
113344,47,29,2,1,Single,9,376.5415728,0,18310.07864
117234,42,25,3,0,Single,12,376.5347662,0,18261.73831
181188,36,18,1,0,Married,8,376.5126581,0,18371.63291
102171,61,44,4,1,Single,0,376.5063767,0,18443.31884
108361,35,17,1,1,Single,11,376.4717043,0,18335.58522
153353,49,32,3,0,Married,9,376.457237,0,18601.86185
179499,33,14,3,1,Married,2,376.4119155,0,18303.59578
139403,40,20,3,0,Married,14,376.384769,0,18535.23845
136757,65,46,3,0,Married,1,376.3318364,0,18239.59182
127128,36,15,1,1,Married,2,376.2971849,0,18410.85925
197336,26,7,2,1,Single,8,376.2800309,0,18545.00155
138516,20,3,2,0,Single,13,376.2787735,0,18247.93868
192032,23,6,1,0,Single,12,376.1669213,0,18456.34607
143894,24,6,2,0,Married,12,376.1645156,0,18523.22578
102285,17,0,4,1,Single,9,376.1581822,0,18566.90911
103414,49,28,2,1,Single,8,376.1090335,0,18218.45168
166559,33,16,2,0,Married,3,376.0741809,0,18247.70905
132644,58,41,3,1,Single,8,375.9411608,0,18283.05804
162548,39,21,2,1,Single,7,375.8618914,0,18520.09457
129921,59,38,2,0,Married,2,375.8090896,0,18373.45448
124430,60,40,3,0,Single,9,375.8030948,0,18549.15474
178767,47,27,3,1,Single,7,375.7721751,0,18348.60876
121306,45,28,3,1,Single,10,375.7681425,0,18235.40713
132417,26,5,4,0,Married,8,375.727011,0,18331.35055
162119,57,36,2,1,Single,10,375.6861167,0,18412.30584
171560,41,20,1,0,Married,6,375.6052838,0,18556.26419
126877,53,32,2,0,Single,13,375.5798676,0,18218.99338
136266,20,3,4,0,Married,14,375.565844,0,18293.2922
140422,37,17,3,0,Married,9,375.5569674,0,18501.84837
144999,64,47,2,0,Single,4,375.5132962,0,18340.66481
115851,53,34,2,1,Single,6,375.5090453,0,18484.45227
175968,27,6,1,1,Single,6,375.4289998,0,18294.44999
141073,52,33,1,0,Married,8,375.4213374,0,18265.06687
191767,48,28,3,1,Single,9,375.3919368,0,18427.59684
140410,41,23,3,0,Married,13,375.3914082,0,18531.57041
107436,45,26,3,0,Single,9,375.3770344,0,18433.85172
181496,34,16,3,0,Married,7,375.3365456,0,18177.82728
101150,24,7,3,1,Married,4,375.3185546,0,18482.92773
170881,60,40,1,0,Single,11,375.2794805,0,18438.97403
114508,23,4,1,1,Married,15,375.2405979,0,18532.0299
174654,49,31,3,0,Married,9,375.2196967,0,18353.98484
187305,57,36,1,0,Married,10,375.2039276,0,18296.19638
163539,68,47,4,0,Married,3,375.1919183,0,18431.59592
157802,64,43,1,1,Married,0,375.1662201,0,18396.31101
164470,24,7,2,1,Single,15,375.1448818,0,18473.24409
130663,32,13,3,0,Married,9,375.1443978,0,18315.21989
127639,41,23,3,1,Married,2,375.1356238,0,18424.78119
150804,36,17,1,0,Married,9,375.07503,0,18304.7515
130369,36,16,3,1,Single,13,375.0535035,0,18228.67518
191089,56,38,2,1,Single,7,374.9310319,0,18511.5516
174779,53,35,3,0,Married,6,374.9234889,0,18275.17445
135091,34,15,2,0,Married,9,374.8912283,0,18463.56142
133490,38,19,4,0,Married,7,374.8623946,0,18497.11973
105942,54,35,4,1,Single,10,374.8406799,0,18160.034
142244,53,36,1,1,Single,10,374.7773463,0,18339.86732
134674,28,11,1,1,Married,7,374.7148925,0,18228.74463
165765,36,17,2,0,Married,7,374.6881469,0,18352.40735
103240,56,35,2,0,Married,8,374.6621594,0,18204.10797
148630,37,20,3,0,Married,0,374.6334282,0,18437.67141
141158,48,29,4,1,Single,9,374.6222191,0,18454.11096
169763,20,1,4,1,Married,7,374.547514,0,18263.3757
166320,29,8,2,0,Married,8,374.5191657,0,18370.95829
107539,18,0,2,1,Single,15,374.4713792,0,18346.56896
145036,38,17,3,1,Single,7,374.4689304,0,18485.44652
116286,56,39,3,0,Married,8,374.4610928,0,18139.05464
100066,43,23,3,0,Married,7,374.440065,0,18333.00325
124034,47,26,3,1,Married,6,374.3454105,0,18218.27053
133655,16,0,2,0,Married,11,374.3379542,0,18501.89771
182054,37,20,3,0,Married,8,374.3331685,0,18512.65843
181772,29,12,1,1,Single,9,374.2554735,0,18239.77368
133533,44,24,3,0,Married,10,374.2402912,0,18349.01456
146238,50,30,1,0,Married,9,374.2330001,0,18246.65001
133093,25,7,1,1,Married,13,374.1729297,0,18126.64649
184764,35,14,3,1,Married,8,374.1665035,0,18297.32518
125783,55,35,2,0,Single,14,374.1490041,0,18343.45021
108815,58,41,3,0,Married,8,374.0012739,0,18168.0637
101166,30,12,3,1,Single,7,373.9815528,0,18463.07764
111559,20,3,2,0,Single,15,373.9408351,0,18274.04176
147523,48,31,3,1,Married,10,373.9080561,0,18190.40281
161882,25,7,2,1,Single,6,373.8939521,0,18301.69761
151038,33,14,4,1,Married,9,373.8715169,0,18378.57585
193245,61,40,3,1,Single,1,373.7628135,0,18403.14068
120261,36,15,3,1,Married,6,373.752712,0,18126.6356
166991,33,14,2,0,Married,7,373.7516396,0,18174.58198
173993,27,7,3,0,Married,6,373.7460352,0,18429.30176
169973,43,26,4,1,Single,7,373.7273369,0,18217.36685
183297,32,13,1,1,Single,10,373.6726323,0,18453.63162
192044,21,4,2,0,Single,15,373.6690031,0,18241.45016
193903,32,14,2,1,Single,2,373.6270097,0,18364.35049
119713,61,42,2,1,Single,8,373.6039557,0,18374.19779
198612,41,20,2,0,Married,10,373.5899538,0,18164.49769
162489,35,18,2,1,Married,7,373.5689412,0,18133.44706
193153,38,19,1,1,Single,10,373.5628167,0,18335.14084
179035,57,36,2,0,Married,6,373.5484093,0,18395.42047
150568,52,32,3,1,Single,11,373.460559,0,18304.02795
113387,39,21,2,0,Married,15,373.4394242,0,18140.97121
151384,20,2,1,0,Married,12,373.3754632,0,18363.77316
113962,23,5,4,0,Married,11,373.3622448,0,18151.11224
180594,68,47,4,0,Single,6,373.3520913,0,18068.60457
144349,50,31,3,1,Single,5,373.3074332,0,18134.37166
143646,46,27,1,1,Single,5,373.2717697,0,18447.58849
123680,57,38,4,0,Married,10,373.2540709,0,18402.70355
151827,35,15,2,0,Married,6,373.2377504,0,18443.88752
119399,25,6,3,1,Married,8,373.2290396,0,18252.45198
196404,64,47,4,0,Single,9,373.2124307,0,18322.62154
104251,40,22,1,1,Married,10,373.2009063,0,18366.04532
100998,42,25,2,0,Married,8,373.1624671,0,18164.12336
120905,59,42,2,0,Married,6,373.134439,0,18299.72195
151911,34,13,1,1,Married,7,373.1316241,0,18157.58121
121393,21,2,3,1,Married,10,373.1262647,0,18431.31324
118666,25,6,3,1,Single,12,373.0683159,0,18066.4158
116167,28,9,2,0,Married,9,373.0122733,0,18111.61367
121508,18,0,1,1,Single,14,372.963556,0,18296.1778
183169,32,15,1,0,Single,14,372.9603759,0,18428.0188
188629,32,14,1,1,Single,9,372.8808668,0,18205.04334
171818,21,3,3,1,Single,12,372.8354284,0,18081.77142
198334,67,46,3,1,Single,5,372.8116559,0,18334.5828
103219,57,38,4,1,Single,13,372.7775131,0,18214.87566
177548,67,47,1,0,Single,15,372.7635182,0,18397.17591
151397,19,1,2,0,Single,14,372.7307075,0,18244.53538
175415,35,14,3,1,Single,9,372.7246494,0,18137.23247
178788,25,8,4,0,Married,7,372.7214431,0,18288.07216
154548,22,3,2,1,Married,9,372.7072207,0,18276.36104
196227,24,6,4,1,Single,14,372.6953761,0,18337.7688
163934,44,27,1,1,Single,8,372.644576,0,18181.2288
177995,26,8,3,0,Married,7,372.6185169,0,18126.92585
154223,57,38,4,0,Married,6,372.55767,0,18082.8835
100612,43,26,2,0,Married,8,372.5381172,0,18107.90586
178295,35,16,3,0,Married,6,372.5237849,0,18202.18925
191272,41,20,3,1,Single,9,372.5168307,0,18346.84154
129164,41,23,3,1,Single,9,372.5076295,0,18383.38148
129906,70,53,3,0,Married,5,372.4882643,0,18070.41322
181832,23,4,2,1,Married,15,372.4864791,0,18313.32396
101618,70,49,2,0,Single,7,372.4552543,0,18063.76272
131892,69,51,3,1,Single,3,372.3735452,0,18327.67726
132805,69,51,2,1,Single,14,372.345364,0,18319.2682
111320,46,28,3,0,Married,8,372.3125049,0,18019.62525
127194,40,20,3,1,Married,6,372.2936525,0,18221.68263
192431,34,17,2,1,Married,10,372.2736021,0,18174.68011
176287,45,24,3,1,Married,9,372.1326209,0,18285.63105
175579,42,25,3,1,Married,0,372.1311092,0,18107.55546
103669,24,6,3,1,Married,12,372.0321396,0,18322.60698
103377,20,2,2,0,Married,12,372.0202933,0,18138.01467
155179,49,30,2,1,Married,9,371.9917979,0,18038.5899
135717,58,37,4,1,Single,10,371.9870649,0,18161.35325
118073,44,27,4,1,Married,7,371.9072315,0,18101.36158
182316,67,49,3,0,Single,7,371.8820887,0,18302.10444
190607,21,2,3,1,Married,12,371.8546013,0,18076.73007
178380,44,25,2,0,Married,6,371.8017855,0,18069.08928
114467,48,31,3,0,Married,7,371.7466035,0,18254.33018
153816,25,7,3,1,Single,8,371.7248492,0,18122.24246
173506,53,36,3,1,Married,10,371.6455345,0,18206.27673
142009,19,1,3,0,Married,7,371.6416025,0,18286.08013
103557,26,9,3,0,Married,9,371.6037639,0,18375.1882
198285,54,37,2,0,Married,7,371.5487931,0,18351.43966
151993,67,46,4,0,Single,14,371.4577215,0,18175.88608
105450,68,47,3,1,Single,6,371.3811134,0,18211.05567
180489,66,46,2,0,Single,13,371.3213023,0,18167.06512
179567,69,48,2,0,Single,7,371.2506915,0,17974.53458
115430,62,45,2,1,Single,7,371.1917185,0,18333.58593
108843,61,40,3,1,Single,4,371.1505459,0,18279.5273
165357,20,1,2,1,Single,13,371.0895625,0,18268.47813
126591,49,29,4,0,Married,6,371.0670046,0,18292.35023
152563,56,39,2,1,Married,9,371.0201511,0,18307.00756
126352,56,38,3,1,Single,6,370.9666912,0,18176.33456
102603,51,30,2,1,Married,8,370.9645791,0,17955.22896
107673,35,16,2,1,Married,8,370.9385613,0,18047.92807
139374,24,6,2,0,Married,15,370.8972654,0,18330.86327
187121,16,0,3,1,Married,12,370.8454307,0,18070.27154
178128,39,19,2,0,Married,10,370.8280203,0,18280.40102
139974,59,42,4,0,Married,12,370.8265951,0,18021.32976
133785,67,48,2,0,Single,7,370.8086612,0,18237.43306
124247,62,43,4,1,Single,10,370.8021618,0,18324.10809
140641,18,0,4,1,Single,12,370.7909449,0,18278.54725
193077,26,8,4,0,Married,6,370.7876311,0,18123.38156
136007,43,23,1,0,Single,8,370.703901,0,18126.19505
160863,49,28,1,0,Married,13,370.6966267,0,17974.83134
119660,24,6,1,1,Married,8,370.6644896,0,18165.22448
145513,24,7,2,1,Married,11,370.6578777,0,17938.89389
168063,18,0,2,1,Single,8,370.6460868,0,18113.30434
116480,38,21,3,0,Married,9,370.6445233,0,18239.22617
113429,49,28,3,0,Married,10,370.6164099,0,17953.8205
117984,57,40,2,0,Married,8,370.6151907,0,18213.75954
174843,31,11,3,1,Married,10,370.5898533,0,17936.49267
101555,47,26,2,1,Single,10,370.577407,0,18172.87035
124657,38,18,3,0,Married,10,370.5548054,0,18325.74027
192280,58,41,2,0,Married,10,370.5524575,0,17951.62288
155317,22,4,2,1,Married,8,370.5457414,0,18245.28707
164819,32,11,2,0,Married,7,370.4949476,0,18081.74738
148524,53,33,3,1,Single,13,370.4228264,0,18009.14132
107635,61,44,3,0,Single,4,370.4084796,0,18229.42398
156704,49,31,4,0,Married,9,370.397558,0,18170.8779
199290,37,17,1,0,Married,6,370.327551,0,17991.37755
129120,34,15,1,1,Single,4,370.307938,0,17955.3969
179479,28,9,3,1,Single,7,370.2506663,0,18200.53332
165122,21,2,2,1,Married,12,370.2364866,0,18102.82433
169485,22,3,2,0,Married,14,370.2018107,0,18035.09054
156226,55,35,3,0,Married,10,370.165043,0,18250.25215
178705,44,23,1,1,Single,8,370.0811782,0,18161.05891
138151,41,20,3,1,Single,11,370.0369949,0,18118.84975
178014,50,33,3,0,Married,10,370.0097979,0,18090.4899
188682,51,33,2,1,Single,6,369.9864994,0,18020.32497
120475,44,26,4,0,Married,7,369.9474902,0,18209.37451
102771,19,0,2,0,Single,14,369.9169511,0,17962.84756
161572,19,0,1,1,Married,14,369.8911603,0,18272.55802
156322,62,41,1,1,Single,11,369.8815672,0,18082.07836
178300,53,35,2,1,Married,10,369.8709847,0,18236.54924
139590,37,20,1,0,Married,9,369.8291176,0,18112.45588
120661,36,16,3,1,Married,9,369.8083673,0,18203.41837
107184,26,8,3,0,Married,6,369.7629582,0,18065.14791
107011,36,16,2,0,Married,8,369.7343028,0,18229.71514
104078,59,42,2,0,Married,4,369.7334225,0,17904.67113
161845,61,44,3,0,Single,10,369.7127844,0,18277.63922
127175,61,44,4,0,Married,6,369.7100602,0,18054.50301
136536,62,42,4,1,Single,6,369.6051975,0,18072.25988
140630,37,20,4,0,Married,10,369.5853726,0,18256.26863
146652,51,31,1,1,Married,7,369.549543,0,18123.47715
154495,69,48,4,1,Single,6,369.5479418,0,17897.39709
121712,43,25,4,1,Married,1,369.5478749,0,18133.39375
172073,34,14,2,1,Single,8,369.5443707,0,18060.21854
110703,22,4,1,0,Married,14,369.5317511,0,18073.58756
175674,20,1,2,0,Married,10,369.5311906,0,18037.55953
173740,59,42,1,1,Single,9,369.5012883,0,18154.06442
127971,21,3,3,1,Married,9,369.4871547,0,18112.35774
136627,28,10,1,0,Married,10,369.4802741,0,18097.01371
169975,52,31,3,0,Married,10,369.4695395,0,17948.47698
185071,18,0,3,1,Married,11,369.4561706,0,18189.80853
188417,27,9,3,0,Married,9,369.4463946,0,18262.31973
194154,54,37,1,1,Married,9,369.3789377,0,18146.94689
166113,53,32,1,1,Single,13,369.3748417,0,18197.74209
148970,60,40,2,1,Single,14,369.3615068,0,18110.07534
143057,52,35,4,0,Married,10,369.3236174,0,18145.18087
122925,29,11,2,1,Married,5,369.3109119,0,18087.5456
109988,54,37,3,0,Married,8,369.2756122,0,18116.78061
117311,47,26,1,0,Married,7,369.2694089,0,18121.47045
161557,16,0,2,0,Single,14,369.250598,0,18139.5299
128924,20,2,1,1,Single,12,369.2310887,0,17887.55444
178561,41,23,3,0,Married,6,369.2072341,0,18251.36171
196877,56,36,1,1,Married,0,369.2066993,0,17895.33497
145669,21,4,2,0,Married,15,369.1190717,0,18156.95359
175844,24,6,1,1,Married,7,369.0963317,0,17865.81659
146298,18,1,2,1,Single,14,369.0694765,0,18107.47383
150485,34,13,3,1,Married,8,369.048159,0,18079.40795
122776,19,1,2,1,Married,9,369.0471847,0,18168.35924
197916,19,0,2,0,Married,13,369.0411629,0,18209.05815
161418,49,28,3,0,Married,8,368.9977862,0,18060.88931
131602,36,19,3,1,Married,10,368.9880854,0,18240.40427
119576,42,21,1,0,Single,11,368.9411753,0,18004.05877
125858,24,7,3,1,Married,8,368.9330034,0,17873.65017
108957,40,19,3,0,Married,10,368.9068097,0,17858.34049
121972,33,16,3,0,Single,8,368.8617356,0,18179.08678
191057,17,0,3,0,Married,15,368.8470038,0,18143.35019
147209,25,7,3,1,Married,8,368.7636096,0,18189.18048
121355,61,42,1,0,Single,2,368.7234314,0,17995.17157
120406,69,50,3,0,Single,10,368.7215463,0,18220.07732
184934,36,17,2,1,Single,7,368.6566367,0,17950.83184
116917,41,20,3,1,Married,8,368.6544117,0,17946.72059
100374,40,21,2,0,Married,10,368.6516749,0,17937.58375
172541,52,32,3,1,Single,7,368.5971401,0,18187.85701
168054,33,12,3,1,Single,1,368.5908459,0,17947.5423
190810,69,49,3,0,Married,5,368.5459227,0,18083.29614
181441,18,0,4,0,Married,14,368.4986693,0,18154.93347
160997,69,52,3,0,Married,10,368.4782178,0,18114.91089
146256,70,51,3,0,Single,7,368.4560306,0,17935.80153
105474,62,45,3,0,Single,8,368.4536516,0,18114.68258
179586,52,34,2,0,Single,15,368.4395827,0,17867.97914
149604,38,18,2,1,Married,0,368.3743589,0,18141.71795
161441,57,36,3,0,Married,8,368.3710915,0,17921.55458
153487,17,0,4,0,Single,14,368.3671133,0,17973.35567
144259,27,10,3,1,Married,0,368.2570687,0,17920.85344
140181,31,12,3,0,Married,13,368.1900985,0,17939.50493
128006,29,8,2,0,Married,7,368.1771031,0,17858.85516
116891,32,13,3,0,Married,5,368.1514513,0,17984.57257
143433,66,45,2,0,Married,1,368.1462245,0,17933.31123
158472,33,12,3,0,Married,7,368.0982266,0,18032.91133
104300,28,7,2,1,Single,6,368.042135,0,17881.10675
173624,42,21,2,1,Married,3,368.0245804,0,18110.22902
180704,37,17,3,1,Married,6,367.9607088,0,17956.03544
175548,51,30,3,0,Married,6,367.9464285,0,18045.32143
153518,34,15,1,1,Married,7,367.9259859,0,18037.2993
123977,25,7,3,0,Single,12,367.9006858,0,17971.03429
118396,57,39,1,1,Single,11,367.8672027,0,18051.36014
166958,47,28,4,0,Single,15,367.8319775,0,18108.59888
134303,18,1,2,0,Married,14,367.7398645,0,18057.99323
170919,32,11,2,1,Single,9,367.69469,0,17799.7345
160218,27,10,3,1,Married,6,367.6932365,0,18002.66183
105297,62,44,3,0,Married,8,367.6187397,0,17928.93699
144244,27,7,3,1,Married,8,367.6105839,0,17994.5292
162208,30,11,1,0,Married,7,367.58451,0,18170.2255
112099,56,35,2,1,Married,6,367.5815434,0,18095.07717
184416,52,34,2,0,Married,9,367.5651389,0,18068.25695
182409,32,13,4,0,Married,7,367.5626878,0,18057.13439
154293,50,33,1,1,Single,8,367.5426684,0,18036.13342
175327,25,7,3,1,Married,12,367.5419089,0,18100.09545
184796,35,18,4,0,Single,13,367.5393468,0,17990.96734
192217,55,34,3,1,Single,6,367.5251252,0,18130.25626
165117,33,12,4,0,Married,8,367.5004938,0,18001.02469
120837,31,10,2,1,Single,8,367.4919132,0,17865.59566
159108,31,13,2,0,Married,7,367.4895123,0,17864.47562
160012,19,0,3,0,Married,11,367.4541349,0,18128.70675
104836,34,16,3,1,Single,11,367.4196531,0,17919.98266
157139,41,22,3,0,Married,10,367.407107,0,17770.35535
112790,55,36,3,1,Single,6,367.3384677,0,17962.92339
168444,37,19,2,1,Single,9,367.3292173,0,18053.46087
141613,53,33,2,0,Married,7,367.3270477,0,17826.35239
109052,27,6,3,1,Single,8,367.304245,0,18082.21225
190239,23,5,2,1,Single,12,367.2975155,0,18105.87578
158163,49,29,3,0,Married,9,367.2334641,0,17900.67321
199043,22,4,3,1,Single,15,367.2309591,0,18122.54796
171007,50,29,2,0,Married,10,367.2249058,0,17919.24529
124527,45,24,3,0,Married,9,367.2157317,0,17860.78659
155462,28,9,3,1,Married,10,367.2097593,0,17813.48797
151023,19,2,3,0,Married,11,367.19389,0,17995.6945
132158,57,37,4,0,Single,14,367.1787648,0,17903.93824
192595,64,44,1,0,Single,12,367.1782376,0,17895.91188
140934,27,7,1,1,Single,12,367.1256363,0,17909.28182
110376,62,44,3,1,Single,12,367.119913,0,18092.99565
160140,48,29,3,0,Married,10,367.1165347,0,17933.82674
143088,33,12,3,0,Married,13,367.1027674,0,17814.13837
171112,63,45,1,0,Married,5,367.0996594,0,17849.98297
175857,27,7,3,1,Married,8,367.0493584,0,17759.46792
184508,40,20,4,0,Married,9,366.9648887,0,17943.24444
134542,47,27,2,1,Single,15,366.9579826,0,17894.89913
111087,42,21,2,1,Single,7,366.9309369,0,17960.54685
126039,48,31,2,0,Married,7,366.9173952,0,17994.86976
112298,55,34,3,1,Married,2,366.86116,0,17911.058
161364,55,38,3,0,Married,8,366.8421072,0,18120.10536
172136,39,20,2,1,Single,14,366.7958778,0,17829.79389
192371,22,3,3,0,Married,11,366.7344384,0,17737.72192
188758,61,44,2,0,Single,6,366.7104914,0,17986.52457
118329,49,30,2,0,Single,11,366.6215454,0,18061.07727
150142,63,45,2,0,Married,4,366.5280661,0,17728.40331
150319,43,25,1,1,Married,4,366.5270882,0,17884.35441
199355,70,53,3,1,Single,4,366.5098281,0,17910.49141
196983,31,11,2,0,Married,10,366.4905058,0,18101.52529
115018,40,19,3,0,Married,6,366.487367,0,17926.36835
146372,20,3,1,0,Single,15,366.4842282,0,17729.21141
163481,62,44,3,1,Single,9,366.4794045,0,17725.97023
173932,67,46,2,0,Single,10,366.4552188,0,18046.76094
177630,53,32,2,0,Married,8,366.4283637,0,17866.41819
159958,26,9,3,1,Single,8,366.4181839,0,17888.9092
139160,22,5,1,0,Single,13,366.3782619,0,17930.9131
152243,32,12,3,0,Married,9,366.3153217,0,17970.76609
112474,39,19,4,1,Single,10,366.3004751,0,17792.02376
140627,53,35,2,0,Married,6,366.2923626,0,17730.61813
128670,54,37,2,1,Single,12,366.2778176,0,17768.89088
177467,59,40,3,1,Married,6,366.2593322,0,17821.96661
117926,26,7,4,1,Single,8,366.2268678,0,17715.34339
107087,56,38,2,0,Married,7,366.2171734,0,17904.85867
151902,51,31,2,1,Married,3,366.1772033,0,18005.86017
102481,24,7,3,1,Single,11,366.1291161,0,18064.45581
161693,49,28,3,1,Single,8,366.0757229,0,17839.78615
181279,39,22,2,1,Single,6,366.0566454,0,17863.83227
150607,25,7,3,1,Single,12,366.0362108,0,17873.81054
116768,35,15,2,0,Married,9,366.0017822,0,18081.08911
102898,18,0,4,0,Single,15,365.9026075,0,17924.13038
154404,63,42,1,1,Single,9,365.7762262,0,18030.81131
113737,53,32,3,1,Married,2,365.7678726,0,18011.39363
105086,39,20,1,1,Single,6,365.7417475,0,17817.08738
101257,70,49,1,0,Married,1,365.7364661,0,17731.82331
185957,34,14,2,0,Married,9,365.7027876,0,17855.13938
147439,70,53,2,0,Married,4,365.6882069,0,17962.41035
103755,42,25,2,1,Single,7,365.6788645,0,17769.94323
147745,37,16,3,0,Married,12,365.6708341,0,17860.54171
158825,68,47,3,0,Married,9,365.6197951,0,17898.98976
178136,30,9,3,0,Married,8,365.6165189,0,17739.82595
161429,23,4,1,0,Married,12,365.5849778,0,18021.24889
160980,41,21,3,1,Married,5,365.5685813,0,17762.42907
138192,54,34,3,1,Married,9,365.5224138,0,18055.12069
116935,29,11,2,0,Married,12,365.509736,0,17773.4868
173914,40,20,2,1,Single,13,365.4732879,0,17692.6644
168474,58,41,4,0,Married,9,365.4426038,0,17933.13019
106507,41,24,2,0,Married,10,365.3682534,0,17953.41267
103275,69,52,3,0,Single,12,365.3308447,0,17989.54224
197562,58,38,2,1,Single,10,365.3096842,0,17728.48421
153822,36,19,4,1,Married,8,365.2978239,0,17851.8912
175957,69,52,4,1,Single,6,365.2719284,0,17924.59642
100796,58,40,2,1,Single,8,365.2165078,0,18004.82539
108566,41,23,3,1,Married,8,365.1680329,0,17760.40165
186676,39,22,2,0,Married,15,365.1659112,0,17855.29556
152382,26,9,3,1,Married,6,365.0512636,0,17928.56318
152783,19,1,2,0,Married,13,364.9693307,0,17928.46654
109289,16,0,3,1,Single,11,364.9645903,0,17700.22952
149391,56,36,2,1,Single,6,364.958553,0,18035.92765
159250,36,17,2,1,Married,10,364.9209585,0,17898.04793
173729,34,16,4,0,Single,15,364.8843178,0,17814.21589
193915,46,28,4,1,Married,9,364.8709313,0,17796.54657
192514,36,18,2,1,Married,10,364.8268076,0,17919.34038
146695,70,49,1,1,Single,9,364.8126232,0,17949.63116
113398,41,22,4,1,Married,0,364.7726498,0,17993.63249
147242,24,5,3,1,Married,10,364.6049834,0,18018.24917
123110,68,51,3,0,Married,10,364.5807656,0,17947.03828
109418,38,17,3,0,Married,8,364.5436701,0,17736.18351
102648,25,7,1,0,Married,13,364.522933,0,17936.14665
168180,62,45,3,1,Single,8,364.4265849,0,17795.32925
103437,20,1,2,1,Single,14,364.4239491,0,17866.19746
195537,38,17,3,1,Married,1,364.3623789,0,17805.11895
113814,19,1,1,0,Married,15,364.3565246,0,17842.82623
110639,35,15,2,0,Single,13,364.3370129,0,17958.85065
184604,28,9,3,0,Married,6,364.2679575,0,17984.39788
140096,25,8,1,1,Married,13,364.2571062,0,17987.85531
102089,65,45,3,0,Single,8,364.2249733,0,17940.24867
185338,62,41,4,0,Single,7,364.1975184,0,17637.87592
141556,69,51,2,0,Married,10,364.1887136,0,17696.43568
192497,46,29,2,0,Married,6,364.1707624,0,17881.53812
167084,34,13,2,0,Married,6,364.1695599,0,17997.478
140454,40,19,1,1,Single,10,364.1423345,0,17744.11673
147179,26,6,4,1,Single,1,364.0440382,0,17956.20191
145834,46,26,1,0,Single,12,364.0410413,0,17644.05207
183606,70,49,2,0,Single,6,364.0376773,0,17922.88387
108349,66,45,2,0,Single,14,364.0053791,0,17913.26896
187628,60,39,1,0,Married,14,363.9823492,0,17848.11746
159787,29,12,1,1,Single,12,363.980308,0,17895.0154
190056,43,23,1,0,Married,10,363.9739969,0,17809.69985
177768,32,12,3,0,Married,6,363.9676756,0,17807.38378
122292,49,32,2,1,Married,9,363.882174,0,17848.1087
166361,31,11,2,1,Single,9,363.8792246,0,17835.96123
189604,39,21,3,1,Married,2,363.8399712,0,17912.99856
105393,17,0,3,1,Single,15,363.822959,0,17646.14795
104281,58,38,3,1,Single,10,363.8162131,0,17930.81066
120982,67,49,4,0,Married,9,363.7838029,0,17635.19015
192103,30,11,3,1,Single,8,363.7587341,0,17700.93671
194157,43,25,2,0,Married,8,363.758169,0,17782.90845
185713,27,8,1,0,Single,10,363.7386019,0,17941.9301
167077,42,21,1,0,Married,11,363.7094092,0,17835.47046
110601,64,44,4,0,Married,13,363.6650699,0,17781.2535
164893,44,25,1,1,Single,7,363.644134,0,17606.2067
113382,39,21,3,1,Single,8,363.5975285,0,17964.87643
120444,42,22,2,0,Married,12,363.5707997,0,17785.53999
121888,26,9,2,0,Married,7,363.5544426,0,17629.72213
157608,34,16,2,1,Single,9,363.5457416,0,17658.28708
133563,40,19,2,0,Married,6,363.5190657,0,17685.95329
173398,16,0,3,1,Single,11,363.4525696,0,17893.62848
145174,43,25,2,0,Married,7,363.4525324,0,17704.62662
159702,34,16,3,0,Married,6,363.4212453,0,17735.06227
159783,23,4,2,1,Married,10,363.3703599,0,17743.518
144813,58,41,4,1,Married,2,363.323362,0,17604.1681
116145,26,9,4,0,Married,8,363.2476661,0,17709.38331
197644,41,23,3,0,Married,12,363.2213957,0,17578.06979
115160,16,0,1,1,Married,9,363.1691719,0,17924.4586
107381,39,20,2,1,Single,10,363.1498046,0,17939.49023
177767,55,35,4,1,Married,7,363.1357921,0,17663.78961
133384,63,45,3,1,Single,10,363.134888,0,17700.7444
179539,21,3,3,0,Married,13,363.1280411,0,17811.40206
104473,37,17,1,1,Married,10,363.091096,0,17936.5548
147729,24,6,3,1,Married,12,363.089954,0,17642.4977
118470,27,10,1,1,Single,6,362.9964994,0,17639.82497
101398,54,35,4,1,Single,11,362.9638107,0,17770.19054
170673,22,3,3,1,Married,2,362.9413112,0,17795.06556
193852,22,5,1,1,Single,14,362.8274602,0,17674.37301
169287,19,2,2,1,Married,12,362.8225461,0,17571.12731
144816,33,16,2,1,Married,9,362.8138301,0,17910.69151
165119,34,14,4,1,Married,4,362.8075815,0,17711.37908
107811,37,20,3,1,Single,10,362.8027863,0,17576.13932
136920,55,37,4,0,Married,6,362.7935448,0,17546.67724
190943,57,38,3,1,Married,6,362.7180358,0,17808.90179
153976,22,5,2,0,Married,11,362.70455,0,17835.2275
141527,39,20,2,0,Married,8,362.6800565,0,17614.00283
103517,68,50,3,1,Single,6,362.580154,0,17718.0077
123163,43,22,2,1,Single,10,362.5033576,0,17882.16788
176396,25,6,2,1,Married,7,362.4831808,0,17526.15904
106579,64,45,3,1,Married,1,362.4479795,0,17805.39898
138812,59,40,3,0,Married,6,362.4078242,0,17802.39121
157157,24,5,2,1,Single,14,362.3409641,0,17665.04821
127784,57,37,2,0,Single,14,362.3350871,0,17557.75436
183901,36,15,2,0,Married,12,362.3329689,0,17732.64845
159584,53,36,3,1,Married,6,362.3113115,0,17772.56558
171831,70,52,1,0,Married,1,362.263205,0,17766.16025
187117,54,33,3,1,Married,7,362.2406906,0,17601.03453
101790,22,3,2,1,Married,10,362.2390293,0,17890.95147
139299,65,48,3,0,Single,8,362.2361244,0,17637.80622
117119,18,1,3,1,Married,14,362.2145736,0,17597.72868
111606,62,45,2,1,Single,10,362.1946304,0,17658.73152
150305,30,12,3,0,Married,6,362.174745,0,17820.73725
168626,30,10,3,1,Married,6,362.0945447,0,17674.72724
192503,54,34,3,1,Married,0,362.0445498,0,17889.22749
123948,65,47,4,0,Single,10,361.9707844,0,17682.53922
116208,19,0,3,0,Married,14,361.9234501,0,17763.17251
108301,38,19,1,1,Married,8,361.9043137,0,17787.21569
177493,62,41,1,1,Single,6,361.8315328,0,17565.57664
134604,31,10,3,1,Married,7,361.8274396,0,17884.37198
110047,53,34,3,0,Married,15,361.8216276,0,17780.08138
150876,52,32,3,1,Married,8,361.8206825,0,17606.03413
163339,56,39,2,0,Married,6,361.756353,0,17683.81765
150975,19,2,2,1,Married,5,361.7308025,0,17523.54013
143519,46,28,4,1,Single,7,361.7241882,0,17760.20941
178118,47,30,1,1,Single,14,361.7061298,0,17783.30649
120790,35,16,4,0,Married,9,361.6928031,0,17539.64016
132257,59,40,2,0,Single,13,361.6928003,0,17823.64002
154297,52,31,2,0,Married,6,361.688925,0,17580.44625
167792,21,4,2,0,Married,13,361.6632453,0,17863.16227
155157,26,9,4,0,Married,4,361.5741002,0,17609.70501
197597,61,42,4,0,Married,0,361.5514503,0,17672.57252
120204,41,20,4,0,Married,10,361.5262011,0,17844.31006
197379,16,0,4,1,Married,12,361.4998505,0,17690.99253
132847,36,19,2,1,Married,6,361.4984098,0,17719.92049
163550,42,23,3,0,Married,9,361.475884,0,17839.7942
162291,18,0,3,0,Married,15,361.4742935,0,17483.71468
199595,64,47,4,1,Married,4,361.4699987,0,17608.49994
102751,32,11,2,1,Married,10,361.4693117,0,17859.46559
151421,59,40,3,1,Married,8,361.4627714,0,17594.13857
116999,54,34,1,0,Married,10,361.4416937,0,17833.08469
169667,37,18,3,0,Married,0,361.4324692,0,17753.62346
156846,33,14,4,1,Single,6,361.4192118,0,17608.96059
177435,16,0,4,0,Married,7,361.3825928,0,17858.12964
149419,56,38,2,0,Married,10,361.2911271,0,17842.55636
171418,43,22,1,0,Married,6,361.1706404,0,17474.53202
153123,45,25,3,0,Married,0,361.1530109,0,17749.65055
118235,25,7,4,0,Married,14,361.0933392,0,17584.66696
118853,46,29,3,1,Single,10,361.0768625,0,17594.84313
177624,22,3,3,0,Married,13,361.040788,0,17500.0394
133531,17,0,2,1,Single,15,361.0162131,0,17801.81066
166422,28,8,1,1,Married,7,361.0075735,0,17843.37868
188047,28,7,3,0,Married,6,360.9738576,0,17450.69288
193916,24,6,2,0,Married,15,360.9731498,0,17694.65749
121840,32,14,2,1,Single,9,360.9385688,0,17552.92844
113688,49,31,3,1,Married,5,360.8784326,0,17823.92163
137799,50,31,1,0,Married,10,360.8718623,0,17509.59312
153194,54,37,3,0,Married,6,360.8491918,0,17639.45959
118612,22,4,2,1,Single,11,360.8423963,0,17760.11982
179628,22,5,1,0,Married,12,360.8051782,0,17793.25891
183376,26,7,2,1,Married,0,360.7666759,0,17747.3338
160586,50,33,1,1,Single,11,360.7660378,0,17777.30189
172968,62,41,3,0,Married,7,360.7618037,0,17808.09019
122212,37,19,1,1,Married,1,360.7519195,0,17770.59598
108330,22,3,3,0,Married,14,360.7239489,0,17765.19745
146709,16,0,1,0,Married,11,360.7064431,0,17781.32216
122274,19,1,3,0,Single,11,360.6821801,0,17569.10901
137079,18,0,3,1,Single,14,360.5706407,0,17610.53204
175191,28,8,2,0,Married,6,360.5535877,0,17653.67939
118321,21,2,2,1,Single,9,360.5355759,0,17591.7788
114441,44,24,4,1,Married,9,360.51379,0,17588.6895
125133,43,23,3,1,Single,9,360.5060028,0,17756.30014
108668,49,31,2,0,Single,10,360.4891664,0,17432.45832
164655,38,21,3,0,Married,10,360.4100235,0,17811.50118
140919,21,3,3,1,Single,15,360.4024528,0,17724.12264
197762,62,45,2,1,Single,3,360.3568235,0,17521.84118
136200,50,30,1,0,Married,8,360.3359183,0,17680.79592
176968,44,24,2,0,Married,8,360.3239825,0,17532.19913
193098,38,19,2,0,Married,4,360.2925308,0,17698.62654
106331,24,7,3,1,Married,13,360.2741388,0,17614.70694
107210,45,24,3,1,Married,9,360.2732696,0,17577.66348
143264,40,22,2,1,Married,10,360.2654461,0,17697.27231
150528,16,0,4,1,Married,6,360.2057426,0,17561.28713
191054,16,0,2,0,Married,12,360.1461124,0,17741.30562
167411,23,5,1,1,Single,11,360.1011196,0,17773.05598
196853,32,12,2,1,Married,7,360.0906291,0,17649.53146
145763,23,4,1,0,Married,14,360.0686388,0,17653.43194
168542,70,51,3,0,Single,6,360.066833,0,17659.34165
144597,47,26,1,1,Married,7,360.0556483,0,17490.78242
195042,39,20,4,1,Married,8,360.0393453,0,17641.96727
141167,35,18,3,1,Single,14,360.0315403,0,17561.57702
182024,24,7,2,0,Single,13,359.9859003,0,17453.29502
106292,64,44,4,1,Single,8,359.9608642,0,17403.04321
168051,52,34,4,1,Married,14,359.9510785,0,17505.55393
163690,41,24,3,1,Married,8,359.884876,0,17477.2438
146211,46,26,1,1,Married,6,359.8784498,0,17679.92249
154563,57,39,2,1,Single,14,359.8603833,0,17682.01917
130784,40,20,3,0,Married,13,359.8486765,0,17723.43383
114919,18,0,3,1,Single,13,359.8209941,0,17782.04971
132929,47,29,2,1,Single,10,359.8056854,0,17395.28427
104250,68,47,2,0,Single,9,359.7460851,0,17718.30426
118528,47,27,1,0,Married,15,359.6943071,0,17741.71536
119958,32,15,3,1,Married,11,359.6533563,0,17424.66782
107487,48,29,4,1,Married,8,359.6133215,0,17456.66608
107033,70,49,2,0,Married,5,359.5786357,0,17734.93179
145873,30,12,1,1,Single,7,359.5554052,0,17438.77026
154255,53,32,1,1,Married,9,359.4808954,0,17611.04477
188976,65,46,2,0,Married,14,359.4725616,0,17740.62808
100413,26,9,2,0,Married,8,359.4419992,0,17471.09996
108874,48,27,3,1,Single,7,359.3704443,0,17525.52222
102206,47,28,1,0,Married,10,359.3406363,0,17560.03182
146816,22,3,3,0,Married,12,359.3258172,0,17558.29086
117983,53,35,4,0,Married,8,359.3057307,0,17567.28654
161864,40,20,1,1,Married,10,359.3019561,0,17458.09781
133716,57,36,3,0,Married,6,359.2734733,0,17656.67367
115291,25,6,2,1,Single,14,359.1666871,0,17680.33436
133667,38,19,3,1,Single,9,359.1562356,0,17478.81178
131909,38,21,3,0,Married,12,359.1558898,0,17509.79449
152774,49,29,2,0,Married,10,359.134403,0,17381.72015
105584,63,44,2,1,Single,4,359.1195409,0,17696.97705
137714,36,19,1,0,Married,14,359.1130124,0,17471.65062
160244,16,0,3,1,Married,11,359.1024007,0,17440.12004
180369,67,48,3,0,Single,9,359.1002684,0,17635.01342
116002,41,24,1,0,Married,10,359.0747385,0,17519.73693
181120,57,39,2,0,Married,9,359.0615895,0,17747.07948
169359,24,7,2,1,Married,9,359.046212,0,17490.3106
195547,34,13,1,0,Married,10,358.9893059,0,17534.4653
157878,31,12,3,1,Single,10,358.9696898,0,17514.48449
165833,69,52,4,1,Single,5,358.8832187,0,17388.16094
191637,16,0,2,1,Single,14,358.8569449,0,17479.84725
192270,41,23,4,1,Married,9,358.8415152,0,17703.07576
162916,51,33,1,0,Married,12,358.8084011,0,17584.42006
167632,30,9,2,0,Single,13,358.7909877,0,17604.54939
180328,38,20,3,0,Married,10,358.7905874,0,17543.52937
184590,61,40,2,0,Single,0,358.7900449,0,17608.50225
137038,62,43,4,0,Single,6,358.6892509,0,17382.46255
165997,24,6,2,0,Married,12,358.6754086,0,17660.77043
187108,64,46,2,1,Single,9,358.6409834,0,17612.04917
160699,33,14,3,0,Married,4,358.6146054,0,17717.73027
188689,40,19,1,1,Married,8,358.5975643,0,17694.87822
105333,55,36,3,0,Married,4,358.5410346,0,17682.05173
110383,41,21,1,1,Single,6,358.5332434,0,17336.66217
157356,46,27,1,0,Married,7,358.521705,0,17512.08525
146187,50,32,2,1,Single,14,358.4916028,0,17550.58014
180452,35,15,3,0,Married,9,358.4898649,0,17397.49325
196191,35,18,2,1,Single,11,358.4436832,0,17665.18416
124063,16,0,2,1,Single,12,358.4103971,0,17323.51986
104372,59,38,1,0,Married,15,358.3923987,0,17593.61994
100544,62,44,2,0,Married,3,358.3767748,0,17505.83874
100346,56,38,2,1,Single,10,358.3455731,0,17414.27866
122791,53,34,2,1,Married,7,358.3340559,0,17526.7028
160704,64,45,2,0,Single,11,358.3010608,0,17606.05304
173129,52,33,3,0,Married,7,358.285605,0,17696.28025
155792,39,19,2,0,Married,7,358.2851037,0,17539.25519
134378,44,27,3,0,Single,14,358.2696999,0,17660.485
157905,28,11,2,1,Single,13,358.2237357,0,17696.18679
191438,17,0,2,1,Single,14,358.1998153,0,17585.99077
111573,24,6,3,0,Single,12,358.1857548,0,17680.28774
132370,39,20,3,1,Married,10,358.1750534,0,17309.75267
108846,34,14,3,1,Married,10,358.1689867,0,17404.44934
194622,29,11,4,1,Married,10,358.0808641,0,17483.04321
104753,21,2,3,0,Single,11,358.0658029,0,17356.29015
191015,50,30,1,1,Single,7,358.0521139,0,17478.6057
130536,43,23,3,1,Married,7,358.0178007,0,17425.89004
161816,50,31,1,0,Married,7,358.0054248,0,17357.27124
172782,60,43,1,0,Single,5,357.9918615,0,17609.59308
161449,19,1,3,1,Married,7,357.9498603,0,17372.49302
137893,69,51,3,1,Single,6,357.8914295,0,17637.57148
158991,16,0,1,0,Married,12,357.890352,0,17643.5176
117004,55,38,3,0,Married,7,357.8847715,0,17517.23858
120095,24,7,3,1,Single,15,357.8838995,0,17517.19498
170555,37,19,2,1,Married,8,357.7547807,0,17344.73904
181346,17,0,2,1,Married,8,357.7523531,0,17429.61766
143609,42,25,4,1,Single,12,357.7458818,0,17542.29409
119086,48,27,3,1,Married,15,357.7439843,0,17584.19922
149446,50,29,1,1,Single,10,357.7424259,0,17586.1213
103533,18,0,3,1,Single,13,357.722728,0,17400.1364
189651,21,4,2,0,Married,13,357.6916523,0,17521.58262
183120,20,3,2,0,Married,9,357.6863146,0,17659.31573
157312,69,51,3,1,Single,6,357.6622667,0,17523.11334
176955,20,2,3,1,Single,14,357.6618486,0,17581.09243
192362,50,29,3,1,Single,12,357.6420146,0,17423.10073
111464,17,0,3,1,Married,15,357.561568,0,17501.0784
129317,17,0,3,1,Single,12,357.5306666,0,17624.53333
188017,41,23,4,0,Single,10,357.4448225,0,17510.24113
103128,44,27,4,0,Married,6,357.4247898,0,17525.23949
133889,30,13,2,1,Married,11,357.4243453,0,17563.21727
127894,58,40,2,0,Married,15,357.4228043,0,17290.14022
171488,43,23,1,1,Married,7,357.4166068,0,17545.83034
161892,28,7,3,0,Married,7,357.4069156,0,17569.34578
120164,45,27,2,0,Married,5,357.3396368,0,17655.98184
181132,19,2,4,1,Single,14,357.3328031,0,17333.64016
130640,57,40,2,0,Married,13,357.3194131,0,17566.97066
178261,18,0,3,1,Single,14,357.2899993,0,17311.49997
152602,34,15,3,1,Married,9,357.2540341,0,17396.70171
197174,57,39,2,1,Single,13,357.2358453,0,17461.79227
126038,48,28,4,1,Married,4,357.2248106,0,17527.24053
161671,26,8,3,1,Married,11,357.1884355,0,17434.42178
119145,24,7,2,1,Married,1,357.1767153,0,17264.83577
165421,53,35,2,0,Married,8,357.1699618,0,17381.49809
163348,63,44,3,0,Married,8,357.0844063,0,17343.22032
171179,65,46,1,1,Single,6,357.0767817,0,17450.83909
131351,20,1,1,0,Married,11,357.0735933,0,17437.67967
154403,50,33,2,1,Married,6,357.0360115,0,17570.80058
152472,63,44,4,1,Single,6,357.0268647,0,17595.34324
153050,47,26,2,0,Married,9,357.0108591,0,17585.54296
114345,70,49,2,1,Single,8,356.9909506,0,17534.54753
195353,42,22,2,0,Married,9,356.950762,0,17417.5381
187602,35,15,2,1,Single,3,356.9179157,0,17568.89579
118100,31,11,3,1,Single,10,356.8588871,0,17374.94436
152452,29,8,2,1,Married,9,356.7637017,0,17550.18509
162825,42,23,3,0,Married,7,356.6924048,0,17543.62024
162269,45,27,4,0,Married,8,356.6566289,0,17384.83145
176291,43,23,1,1,Single,8,356.6430284,0,17612.15142
114359,21,2,4,1,Married,11,356.6248281,0,17549.24141
172376,18,0,3,0,Married,15,356.6074334,0,17629.37167
198494,47,28,1,0,Married,13,356.572264,0,17518.6132
148023,53,35,3,1,Single,10,356.5541485,0,17312.70743
151938,46,25,4,1,Married,9,356.5424441,0,17419.12221
149004,45,25,1,0,Married,12,356.5330417,0,17310.65209
134511,23,6,2,1,Single,15,356.4963621,0,17329.81811
118035,28,7,1,1,Married,6,356.4938337,0,17568.69169
140961,27,9,4,0,Married,10,356.4852108,0,17438.26054
160297,37,19,3,1,Single,13,356.4652078,0,17419.26039
136163,20,1,1,1,Married,11,356.4554345,0,17391.77173
156179,18,0,2,0,Married,9,356.413532,0,17228.6766
145985,52,32,3,0,Single,12,356.412001,0,17251.60005
110838,17,0,1,0,Single,11,356.3976584,0,17498.88292
179314,43,22,1,0,Married,8,356.3656226,0,17499.28113
121960,67,49,2,0,Single,7,356.3458968,0,17346.29484
154520,25,7,3,1,Single,13,356.2680382,0,17226.40191
137494,53,36,2,1,Single,9,356.2582565,0,17521.91283
180084,41,20,3,1,Married,4,356.2079068,0,17579.39534
196513,32,11,3,0,Married,7,356.1896072,0,17552.48036
179414,45,26,2,0,Married,11,356.1514895,0,17476.57448
148448,70,50,4,0,Single,10,356.1324328,0,17470.62164
187398,29,12,3,0,Married,7,356.1236164,0,17547.18082
138682,55,38,1,0,Married,6,356.072465,0,17270.62325
124520,22,4,3,1,Married,11,356.0651866,0,17580.25933
122506,43,25,3,0,Married,8,356.0433931,0,17575.16966
138422,17,0,3,0,Single,15,356.0351485,0,17343.75743
182020,32,14,3,0,Married,14,355.9797147,0,17485.98574
181395,38,20,3,1,Single,9,355.9446692,0,17271.23346
123546,22,3,2,1,Single,15,355.9348674,0,17494.74337
171990,61,43,1,0,Single,7,355.8451532,0,17553.25766
181657,22,3,3,0,Married,11,355.8316015,0,17305.58008
146076,41,23,3,0,Married,12,355.8288778,0,17369.44389
181260,27,8,1,1,Single,7,355.8158451,0,17559.79226
187547,59,40,2,0,Married,6,355.7809566,0,17474.04783
159600,46,27,1,0,Married,10,355.735993,0,17459.79965
141612,29,11,3,1,Married,2,355.727515,0,17567.37575
163437,26,9,4,1,Married,9,355.7123012,0,17205.61506
138093,16,0,3,0,Married,14,355.6553901,0,17335.76951
156211,49,31,4,0,Married,9,355.5963269,0,17184.81635
119888,34,14,1,0,Married,9,355.5928162,0,17309.64081
159002,25,8,3,1,Single,15,355.5696848,0,17568.48424
103760,20,3,4,1,Married,8,355.5594833,0,17192.97417
151530,33,14,1,1,Single,9,355.4608932,0,17200.04466
178330,66,46,3,0,Single,1,355.4420655,0,17219.10328
192851,21,2,3,1,Married,13,355.4298125,0,17352.49063
114377,40,21,3,0,Married,6,355.4171396,0,17233.85698
195171,43,22,4,0,Married,8,355.3785351,0,17416.92676
118010,30,9,4,0,Married,10,355.3730872,0,17496.65436
150077,53,34,3,0,Married,7,355.368379,0,17229.41895
158358,69,52,3,0,Single,8,355.337431,0,17375.87155
161579,68,51,3,0,Single,8,355.3273666,0,17275.36833
109945,35,14,1,1,Single,6,355.2515755,0,17180.57878
199314,49,30,2,0,Married,7,355.1656604,0,17423.28302
150678,57,36,4,1,Single,15,355.1653687,0,17505.26844
159336,26,5,3,0,Married,8,355.149888,0,17467.4944
195107,56,35,2,0,Married,9,355.1419086,0,17415.09543
156852,22,3,1,1,Married,7,355.0916144,0,17362.58072
155082,16,0,3,0,Married,14,355.016182,0,17449.8091
194917,18,0,3,0,Married,14,354.9811613,0,17224.05807
127158,55,38,1,0,Married,6,354.9675474,0,17229.37737
102033,40,20,4,1,Single,6,354.9667235,0,17442.33618
197856,32,13,1,0,Married,6,354.9459359,0,17174.2968
171375,19,0,3,0,Married,11,354.9298111,0,17252.49056
175368,66,46,3,1,Married,0,354.9292685,0,17352.46343
177466,52,34,2,1,Single,13,354.8917762,0,17244.58881
181743,30,11,2,1,Married,6,354.8746025,0,17385.73013
185354,33,15,4,0,Married,13,354.8681203,0,17374.40602
100633,67,49,1,0,Married,8,354.8619578,0,17206.09789
111419,45,24,1,0,Married,6,354.786418,0,17278.3209
145922,31,13,3,1,Married,7,354.7310573,0,17414.55287
107980,47,27,2,1,Married,4,354.7019674,0,17447.09837
149010,17,0,1,0,Married,14,354.6611912,0,17421.05956
134142,18,1,3,0,Single,13,354.615834,0,17457.7917
193598,57,40,2,0,Married,12,354.5973766,0,17183.86883
114343,28,8,3,1,Married,3,354.5941095,0,17449.70548
162468,29,11,3,0,Married,9,354.5562406,0,17503.81203
175202,44,23,2,0,Single,15,354.5241248,0,17375.20624
120872,40,21,3,1,Single,15,354.5028023,0,17219.14012
141659,35,15,1,1,Single,8,354.4909509,0,17227.54755
190731,54,36,4,1,Married,1,354.4719431,0,17132.59716
159043,59,41,4,1,Married,0,354.4576718,0,17303.88359
157176,65,48,4,1,Single,8,354.4444888,0,17360.22444
110193,43,22,3,0,Married,10,354.4434427,0,17286.17214
197124,19,1,3,1,Married,14,354.4080467,0,17477.40234
105534,48,30,3,1,Married,5,354.4078726,0,17484.39363
140689,19,1,2,0,Single,15,354.3986786,0,17227.93393
109005,25,7,3,1,Married,8,354.3809089,0,17320.04545
142498,35,17,3,1,Single,10,354.3611687,0,17196.05844
175753,28,7,2,1,Single,14,354.2739323,0,17334.69662
109388,28,10,2,0,Married,10,354.2663393,0,17325.31697
185366,41,24,2,0,Married,7,354.2510202,0,17339.55101
172970,48,30,3,1,Single,6,354.2319723,0,17404.59862
181125,65,47,2,0,Married,3,354.2202083,0,17241.01042
118810,54,34,3,0,Married,10,354.1951505,0,17303.75753
113214,49,29,4,1,Married,6,354.1825345,0,17175.12673
181457,55,37,1,1,Single,9,354.1750277,0,17495.75139
101288,70,50,3,0,Married,1,354.158526,0,17124.9263
102189,20,2,1,1,Married,8,354.1103301,0,17402.51651
196910,69,51,4,0,Married,5,354.0453347,0,17364.26674
147513,30,9,1,0,Married,6,353.9876552,0,17386.38276
146036,54,33,2,0,Single,10,353.9874896,0,17223.37448
145645,24,7,1,1,Single,14,353.9868016,0,17257.34008
188545,36,16,2,1,Married,8,353.951666,0,17373.5833
149107,18,0,2,1,Married,10,353.9067638,0,17310.33819
191562,56,39,3,0,Married,8,353.9065494,0,17314.32747
149006,70,49,3,1,Married,0,353.9014798,0,17379.07399
140578,50,31,2,1,Married,15,353.8972413,0,17473.86207
102507,42,23,2,1,Married,10,353.8887175,0,17466.43588
105426,25,8,2,1,Married,11,353.8385538,0,17297.92769
106813,33,14,1,1,Single,11,353.8293525,0,17195.46763
124584,32,11,4,1,Married,3,353.7627687,0,17196.13844
123957,61,41,4,0,Married,8,353.7379997,0,17171.89999
123231,46,26,3,0,Married,7,353.723634,0,17414.1817
119475,33,14,2,0,Married,6,353.7225604,0,17447.12802
100537,22,4,3,0,Married,14,353.6721522,0,17356.60761
171964,52,31,1,0,Married,10,353.6364025,0,17380.82013
117988,25,6,2,0,Single,11,353.6333985,0,17180.66993
132954,42,24,2,1,Married,9,353.6248492,0,17195.24246
143469,49,29,3,1,Single,6,353.6232199,0,17383.161
155225,37,19,3,1,Single,9,353.5884154,0,17448.42077
163874,19,2,2,1,Single,15,353.5400426,0,17322.00213
161887,64,44,3,0,Single,12,353.4922551,0,17322.61276
125506,33,13,2,0,Married,11,353.4707712,0,17172.53856
179587,50,30,1,0,Single,12,353.4644169,0,17387.22085
105979,16,0,2,0,Married,10,353.4630295,0,17360.15148
137576,38,19,3,0,Single,14,353.4471605,0,17400.35803
162666,53,33,1,0,Married,7,353.4243914,0,17322.21957
167409,49,29,4,1,Married,5,353.4226679,0,17185.1334
157560,45,26,3,1,Single,11,353.4119788,0,17123.59894
159795,26,7,3,0,Married,7,353.4056805,0,17107.28403
159829,33,12,2,1,Single,7,353.2495832,0,17106.47916
180505,56,39,1,0,Married,7,353.2470892,0,17202.35446
154475,51,34,2,0,Married,6,353.2327721,0,17116.63861
121497,39,21,2,1,Married,7,353.2253567,0,17204.26784
100312,34,14,2,0,Married,7,353.1886871,0,17191.43436
183209,26,6,3,1,Married,8,353.185245,0,17061.26225
186584,69,52,2,1,Single,9,353.1683465,0,17102.41733
133911,47,29,2,0,Married,10,353.1585726,0,17371.92863
188342,57,36,2,0,Married,4,353.1485605,0,17147.42803
180027,31,14,3,0,Married,8,353.0847916,0,17402.23958
108802,34,17,3,0,Single,14,353.0514545,0,17092.57273
105509,50,30,3,0,Married,10,353.0451445,0,17086.25723
146365,19,2,4,1,Married,8,353.0359293,0,17304.79647
170137,33,13,2,0,Married,8,353.0118007,0,17279.59004
125962,16,0,3,1,Married,8,352.9864805,0,17383.32403
178458,48,27,3,0,Married,6,352.9830599,0,17295.153
106000,35,17,2,1,Married,6,352.9391772,0,17235.95886
185046,22,3,3,1,Single,11,352.901898,0,17184.0949
192198,43,25,4,0,Married,8,352.8938943,0,17325.69472
190883,32,12,4,1,Single,7,352.8787669,0,17132.93835
113253,16,0,2,0,Married,12,352.871164,0,17226.5582
115037,47,26,3,1,Married,9,352.8150666,0,17327.75333
173126,36,19,2,1,Single,14,352.7988494,0,17253.94247
176585,47,29,2,0,Married,6,352.7070831,0,17263.35416
182494,69,52,3,0,Married,12,352.6882863,0,17134.41432
121925,50,31,3,1,Married,0,352.6876976,0,17170.38488
166139,17,0,4,1,Married,15,352.6766172,0,17110.83086
196621,26,9,2,0,Married,7,352.6742687,0,17270.71344
131325,58,37,2,1,Single,7,352.6099119,0,17337.4956
160642,17,0,1,1,Single,13,352.5326175,0,17154.63088
188907,38,19,2,0,Married,7,352.5256432,0,17168.28216
191525,38,20,2,1,Married,15,352.5064648,0,17256.32324
169519,24,6,2,1,Married,8,352.411265,0,17128.56325
134238,33,14,4,0,Married,8,352.3556656,0,17359.78328
174735,49,28,3,1,Married,0,352.3441305,0,17119.20653
131977,25,6,3,1,Single,12,352.3418282,0,17217.09141
101179,17,0,3,0,Married,12,352.2672316,0,17239.36158
140850,70,49,2,1,Married,1,352.2486719,0,17393.4336
184407,47,30,4,0,Married,8,352.2256809,0,17139.28405
125351,39,22,1,0,Married,7,352.218165,0,17340.90825
180475,39,21,1,0,Single,8,352.1776675,0,17010.88338
103901,30,13,2,1,Single,14,352.1600587,0,17180.00294
132329,31,13,2,0,Married,9,352.0486787,0,17043.43394
112191,22,4,2,1,Single,14,352.0244029,0,17026.22015
164991,50,33,2,0,Single,12,352.0222124,0,17134.11062
128643,42,23,3,1,Married,6,351.9808591,0,17052.04296
193323,46,27,3,0,Married,10,351.9557039,0,17018.7852
154044,17,0,1,1,Married,15,351.8847284,0,17197.23642
170816,59,38,2,1,Married,10,351.8203858,0,17256.01929
148095,36,18,3,0,Single,14,351.8171489,0,17326.85745
144098,34,15,2,1,Single,15,351.7873354,0,17223.36677
117379,55,36,2,1,Single,14,351.7866849,0,17203.33425
154110,25,8,4,0,Married,8,351.7191213,0,17026.95607
146649,20,2,1,0,Single,12,351.7077018,0,17004.38509
133691,48,29,4,1,Single,15,351.6855186,0,17128.27593
181502,68,49,3,1,Single,3,351.6841533,0,17031.20767
100512,54,34,2,1,Married,11,351.6777248,0,17140.88624
165097,34,14,2,1,Married,10,351.6458198,0,17164.29099
159469,63,46,2,1,Single,9,351.5957984,0,17098.78992
102488,55,38,2,1,Married,10,351.5892955,0,17366.46478
193424,32,15,4,0,Married,8,351.5053283,0,16993.26642
118161,62,44,1,0,Single,9,351.5052862,0,17265.26431
144261,31,14,2,0,Married,10,351.4895098,0,17323.47549
137905,70,49,4,0,Single,8,351.479994,0,17219.9997
168419,33,12,2,0,Married,10,351.3479905,0,16974.39953
191346,23,6,4,1,Married,12,351.3224314,0,17034.12157
154802,60,39,1,1,Single,6,351.3205902,0,17341.02951
174421,56,35,2,1,Single,9,351.3018979,0,17181.0949
159166,24,6,4,1,Single,12,351.2954732,0,16989.77366
194689,27,10,2,1,Married,8,351.2590205,0,17251.95103
149669,65,45,2,1,Married,9,351.1961222,0,17347.80611
149440,59,42,1,0,Single,11,351.1678295,0,17342.39148
106286,50,31,3,1,Single,12,351.1635587,0,17259.17794
131665,35,16,3,1,Married,7,351.1338432,0,17127.69216
183216,49,29,2,0,Married,6,351.0978894,0,16995.89447
130372,57,37,1,1,Married,1,351.0954403,0,17094.77202
167670,59,41,3,1,Married,15,351.0509605,0,17149.54803
122982,32,12,4,1,Married,0,351.0086366,0,16976.43183
117203,55,36,2,1,Married,8,350.9456921,0,17345.28461
126933,40,19,3,0,Married,7,350.8803961,0,16953.01981
135046,53,34,4,1,Single,7,350.837945,0,17073.89725
122145,58,41,3,0,Single,13,350.8363724,0,17163.81862
122611,50,33,4,0,Married,11,350.8148488,0,17198.74244
196325,32,13,2,0,Married,8,350.8046896,0,17095.23448
130529,17,0,1,1,Single,13,350.7660344,0,17040.30172
110031,20,2,3,1,Single,14,350.7393091,0,17107.96546
119122,23,4,2,1,Married,13,350.6884471,0,17147.42236
149152,36,15,4,0,Married,8,350.6783228,0,17305.91614
118114,53,33,2,0,Married,8,350.6464345,0,17054.32173
104521,64,45,1,0,Single,14,350.6281031,0,17116.40516
192373,41,21,1,0,Married,9,350.5732049,0,17275.66025
103008,52,32,1,1,Married,3,350.5612148,0,17060.06074
129758,25,8,2,1,Single,13,350.5601193,0,17325.00597
185743,28,9,2,0,Single,14,350.5269451,0,17092.34726
129798,50,30,3,1,Married,10,350.5188669,0,17056.94335
192390,45,25,4,0,Married,15,350.5132525,0,17205.66263
121560,43,23,2,1,Single,6,350.5014551,0,16955.07276
185829,46,27,3,1,Married,0,350.4766253,0,17183.83127
183795,51,33,3,0,Married,8,350.4480587,0,17099.40294
114414,57,36,3,0,Married,14,350.4226226,0,17172.13113
138213,41,23,3,0,Single,11,350.3983156,0,17276.91578
188884,17,0,1,1,Single,12,350.3614044,0,17184.07022
150501,63,43,4,1,Married,0,350.3483783,0,16995.41892
114625,61,44,3,0,Married,4,350.342034,0,17163.1017
165315,69,51,1,1,Married,2,350.3338126,0,17126.69063
190879,37,20,2,1,Single,15,350.2811076,0,17287.05538
118532,61,43,3,1,Married,10,350.277242,0,17129.8621
108278,60,41,3,0,Single,13,350.2495642,0,16925.47821
187350,38,18,2,1,Married,7,350.2369548,0,17173.84774
143187,45,26,3,0,Married,5,350.18656,0,16972.328
120528,44,27,3,0,Married,7,350.1702777,0,16969.51389
169563,28,10,4,0,Married,10,350.1622449,0,16939.11225
170217,49,31,3,1,Married,11,350.1605282,0,17058.02641
196584,25,7,2,1,Single,12,350.0970634,0,17013.85317
100442,70,52,2,0,Single,0,350.0212837,0,16950.06419
133639,35,16,2,0,Married,8,349.9969519,0,17164.8476
132987,69,51,4,0,Single,7,349.9869437,0,17232.34719
116099,54,35,2,1,Single,14,349.9708937,0,17185.54469
158139,63,42,4,0,Single,14,349.9552586,0,17069.76293
128584,55,36,1,0,Married,7,349.9393754,0,16964.96877
168237,49,28,1,0,Single,11,349.9370395,0,16989.85198
185934,37,16,2,1,Married,5,349.8526063,0,16999.63032
123452,55,37,2,0,Single,6,349.693678,0,17091.6839
176289,39,18,3,1,Single,14,349.6626186,0,16932.13093
102432,53,36,1,0,Married,9,349.6512041,0,17114.56021
195788,42,25,3,0,Married,8,349.6260907,0,17030.30454
167934,33,13,3,0,Married,6,349.5705684,0,17003.52842
142195,52,32,1,1,Single,10,349.501615,0,17160.08075
148882,58,38,2,1,Single,10,349.4907146,0,17029.53573
190026,21,2,2,0,Married,9,349.479746,0,17195.9873
101950,20,1,1,1,Single,11,349.4773628,0,17206.86814
117678,22,4,1,1,Married,13,349.4635858,0,17261.17929
147595,51,30,3,0,Married,11,349.455222,0,17124.7611
186736,25,8,4,1,Married,6,349.4449791,0,17053.24896
122572,31,10,3,0,Married,10,349.4433066,0,17146.16533
172368,24,7,2,1,Single,12,349.4374446,0,16919.87223
172954,26,5,1,1,Married,10,349.4050007,0,16974.25004
153576,44,23,3,1,Married,4,349.4022276,0,17014.11138
133601,68,49,2,1,Married,2,349.3780289,0,16901.90145
120585,47,26,3,0,Married,10,349.3442012,0,16926.21006
185721,22,5,2,0,Single,14,349.3144355,0,17025.72178
163227,54,33,2,0,Married,13,349.3069773,0,16985.34887
127397,23,5,3,1,Single,13,349.2956825,0,17034.78413
105716,20,1,3,1,Married,12,349.2639569,0,17083.19785
105061,58,39,2,0,Married,1,349.2612861,0,16937.06431
141230,42,22,2,0,Married,6,349.1696365,0,17063.48183
171216,30,12,2,0,Single,15,349.1487161,0,16933.43581
163327,35,17,1,0,Married,10,349.1377167,0,17229.88584
109062,40,20,3,1,Married,9,349.1010083,0,16867.05042
151723,51,32,2,1,Single,11,349.0996117,0,17203.98059
102867,70,53,2,0,Single,14,349.0545647,0,16865.72824
137770,63,44,2,1,Married,0,349.0543922,0,17031.71961
168563,28,10,3,0,Married,8,349.0195329,0,17186.97665
196430,51,32,2,1,Married,10,349.0100809,0,16896.50405
151511,20,2,3,0,Married,14,348.9557284,0,17201.78642
149808,34,14,3,0,Married,10,348.8530319,0,17143.6516
105779,64,45,2,0,Married,4,348.8134872,0,17025.67436
122469,68,49,1,0,Married,8,348.7748871,0,17058.74436
130322,25,8,3,0,Married,12,348.7321726,0,16924.60863
182556,24,6,3,1,Single,15,348.6990042,0,17203.95021
102852,44,24,1,1,Married,10,348.6916777,0,16904.58389
138868,61,43,1,0,Married,8,348.6501515,0,17194.50758
177911,39,18,2,1,Married,10,348.6496435,0,17130.48218
120599,29,9,2,1,Single,13,348.6479125,0,17199.39563
109082,19,0,4,0,Married,14,348.6048979,0,17045.2449
109980,51,31,2,0,Married,9,348.55901,0,16906.9505
117129,19,2,3,1,Married,7,348.5274268,0,17050.37134
168556,20,1,4,1,Married,9,348.4810314,0,17150.05157
140466,25,7,2,1,Married,7,348.4781211,0,16841.90606
148421,24,7,2,1,Married,7,348.4464703,0,16998.32352
124746,29,12,2,0,Single,11,348.4339922,0,17159.69961
100227,63,45,3,1,Single,3,348.3947227,0,16839.73614
156605,50,29,3,1,Single,14,348.3946724,0,17052.73362
193965,48,29,2,0,Married,6,348.3139311,0,17000.69656
142747,54,34,2,1,Married,10,348.1976648,0,17120.88324
178513,41,20,3,1,Married,9,348.1221337,0,17042.10669
172107,45,25,3,0,Married,8,348.1212095,0,16908.06048
143245,45,27,3,1,Married,9,348.0958406,0,17054.79203
133523,67,47,3,1,Single,9,348.092367,0,16942.61835
124111,26,5,4,1,Single,13,348.0904081,0,17008.52041
198428,18,0,3,1,Married,14,348.0681577,0,17175.40789
129497,39,22,4,1,Married,8,348.0553847,0,16902.76924
165944,36,15,4,0,Married,6,348.0303265,0,17130.51633
177289,49,30,4,0,Married,7,348.0281364,0,16930.40682
162840,40,23,2,0,Married,7,347.9572848,0,17107.86424
101771,56,36,2,1,Married,6,347.9234569,0,17087.17285
155682,41,22,2,1,Single,8,347.8741141,0,16882.70571
145809,19,0,2,0,Single,15,347.8682994,0,16899.41497
164706,22,4,2,0,Married,11,347.8397092,0,16868.98546
194380,38,18,2,1,Married,7,347.8272596,0,16871.36298
112870,45,24,3,1,Married,14,347.7710942,0,16976.55471
164342,31,10,2,0,Married,8,347.7058726,0,17007.29363
113334,40,19,3,0,Single,13,347.6765338,0,16956.82669
108853,44,26,2,1,Single,14,347.6735122,0,16932.67561
119238,31,11,3,1,Married,7,347.6324753,0,17104.62377
175486,63,46,2,0,Single,13,347.5310605,0,16780.55303
196651,35,14,3,0,Married,7,347.5060604,0,16820.30302
163200,32,11,2,0,Married,11,347.442301,0,16869.11505
119095,63,43,4,1,Single,6,347.4387209,0,17005.93605
102209,23,6,2,1,Single,11,347.3335975,0,16987.67988
134680,48,28,4,0,Married,7,347.283444,0,16991.1722
195939,31,10,2,0,Single,13,347.2765155,0,17113.82578
151007,68,47,2,0,Married,3,347.2530136,0,16992.65068
168685,31,11,4,0,Married,7,347.2452613,0,16856.26307
132774,24,7,2,1,Married,10,347.2159474,0,16924.79737
196210,17,0,3,1,Married,13,347.1903494,0,16974.51747
177527,35,17,1,0,Married,7,347.1522303,0,17035.61152
116373,63,45,4,1,Single,8,347.1100323,0,16974.50162
192545,40,20,2,0,Married,7,347.0980232,0,17098.90116
144803,66,47,1,0,Single,0,347.0821846,0,16764.10923
182189,27,8,3,0,Married,9,347.0485681,0,16912.42841
126504,58,41,2,1,Single,8,347.0430197,0,16865.15099
186576,47,27,2,1,Single,14,347.0346467,0,17137.73234
126838,20,3,2,0,Married,8,347.0277294,0,16905.38647
160998,55,38,3,1,Single,10,347.0080358,0,16931.40179
127866,33,15,1,0,Married,8,346.9535288,0,17107.67644
173492,60,39,4,0,Married,10,346.9386523,0,17137.93262
154346,69,50,2,0,Single,6,346.84849,0,17048.4245
162557,28,10,3,0,Married,7,346.8444477,0,16961.22239
124551,38,20,4,1,Married,15,346.8394825,0,17012.97413
119496,26,6,3,1,Married,7,346.81155,0,16767.5775
182167,58,38,3,0,Married,6,346.7603534,0,17061.01767
177508,55,37,3,0,Married,10,346.7413411,0,16760.06706
151694,21,4,3,1,Single,14,346.6996714,0,17013.98357
198440,53,32,3,0,Married,6,346.6276954,0,17126.38477
139691,26,6,2,1,Single,7,346.6259326,0,17034.29663
146438,30,9,4,1,Single,13,346.5663868,0,17012.31934
141729,27,8,4,0,Married,12,346.5660888,0,17124.30444
116736,40,19,4,1,Married,13,346.5575078,0,16861.87539
113690,19,1,2,0,Married,13,346.5476839,0,17095.3842
133464,16,0,2,1,Married,11,346.5111224,0,16901.55612
100122,59,39,2,0,Married,12,346.4663918,0,16919.31959
164259,42,23,2,0,Single,11,346.4584209,0,16948.92105
122307,56,37,1,0,Married,10,346.4563025,0,16848.81513
196040,41,22,1,1,Married,8,346.4537728,0,17032.68864
124424,45,26,2,1,Married,6,346.4500172,0,17066.50086
152632,53,35,4,1,Single,10,346.4233164,0,16917.16582
102165,23,5,1,1,Single,14,346.4052876,0,17107.26438
115664,61,41,3,0,Single,8,346.35015,0,16946.5075
160257,27,7,1,0,Married,9,346.3474987,0,16867.37494
173290,27,10,3,0,Married,6,346.313464,0,16759.6732
154758,27,9,1,1,Married,9,346.2778383,0,16860.89192
103195,25,7,2,1,Married,7,346.2476458,0,16730.38229
175685,51,31,3,1,Single,12,346.2398888,0,16873.99444
177835,55,35,1,0,Married,8,346.214594,0,16817.7297
144294,41,20,2,1,Married,6,346.2067461,0,16951.33731
184161,26,7,1,1,Married,6,346.2011049,0,16796.05525
129777,61,42,4,1,Single,15,346.1730892,0,16938.65446
151534,18,0,1,1,Married,3,346.1635562,0,17061.17781
160855,47,26,3,0,Married,10,346.1455862,0,16728.27931
112497,36,17,3,1,Married,8,346.1351912,0,16804.75956
128580,36,15,2,1,Married,9,346.1261774,0,16745.30887
151099,29,11,3,1,Single,6,346.1008642,0,17038.04321
158808,41,22,2,1,Single,10,346.0779642,0,16948.89821
156600,70,52,3,0,Single,7,346.0747865,0,16837.73933
181459,54,35,2,1,Married,6,346.0521767,0,16795.60884
123990,69,49,4,1,Single,10,346.0410754,0,16956.05377
110498,24,5,3,0,Single,11,345.9625425,0,16760.12713
155394,24,5,2,1,Single,11,345.91391,0,17042.6955
148988,19,0,4,1,Married,15,345.8881119,0,16923.4056
198793,34,17,2,1,Married,10,345.8832494,0,16763.16247
176776,45,25,3,1,Married,7,345.8649432,0,16911.24716
131387,49,31,2,0,Married,8,345.8435284,0,16913.17642
164405,45,28,3,1,Married,9,345.7872311,0,17054.36156
164250,42,23,2,1,Married,6,345.775639,0,16717.78195
100710,65,46,1,1,Single,4,345.7533158,0,16790.66579
165897,33,16,4,1,Single,9,345.7442022,0,16757.21011
160983,29,12,4,1,Married,10,345.7301215,0,16818.50608
120304,49,28,4,1,Single,10,345.7293045,0,17053.46523
180335,62,41,2,0,Single,10,345.7139435,0,16780.69718
174547,22,4,3,1,Single,13,345.617154,0,16772.8577
168527,50,30,3,0,Married,10,345.6061938,0,16910.30969
159830,48,28,2,1,Single,11,345.5637767,0,16860.18884
177284,21,4,4,0,Married,8,345.5634481,0,16864.17241
161876,42,24,3,0,Single,10,345.5479558,0,17076.39779
176380,35,14,4,1,Married,7,345.5422834,0,16862.11417
200081,20,3,3,1,Married,8,345.5420496,0,16966.10248
145396,60,42,4,1,Married,0,345.5389397,0,16955.94699
144697,59,42,3,1,Married,7,345.4612249,0,16697.06125
126383,52,31,2,1,Married,7,345.4512548,0,16892.56274
106114,20,1,4,1,Single,14,345.4076428,0,16690.38214
105472,39,20,2,1,Married,8,345.3943365,0,16885.71683
185379,27,6,3,1,Single,6,345.3647249,0,16907.23625
129428,36,15,3,1,Married,9,345.3383377,0,16749.91689
178976,42,21,4,1,Married,10,345.3225416,0,16842.12708
131335,47,27,2,0,Married,6,345.3103834,0,16851.51917
175274,70,51,1,0,Married,2,345.2723201,0,17055.61601
103919,58,37,1,0,Married,11,345.2673698,0,16702.36849
198952,60,39,4,1,Married,10,345.2054142,0,16726.27071
129289,48,31,4,0,Married,6,345.1297645,0,16989.48823
155695,43,25,3,0,Single,8,345.1220392,0,17054.10196
172251,44,26,4,0,Married,7,345.1190055,0,16776.95028
179614,33,16,4,0,Married,9,345.1048901,0,16999.24451
155148,33,13,1,0,Married,12,345.0914583,0,16941.57292
113443,28,9,3,0,Single,12,345.0866831,0,16852.33416
185622,16,0,4,1,Single,14,345.0605119,0,16695.0256
157188,19,0,1,1,Married,0,345.0465518,0,16865.32759
153802,51,30,1,0,Married,7,345.0123467,0,16708.61734
139182,27,7,1,1,Single,14,345.0019193,0,16726.09597
160522,47,26,3,0,Married,6,344.9483448,0,16913.41724
139321,40,22,1,1,Married,6,344.8873218,0,16956.36609
114187,40,23,3,1,Married,7,344.8632529,0,16796.16265
189990,70,49,4,0,Single,8,344.8395259,0,16975.9763
132143,58,39,1,0,Married,10,344.8105362,0,16981.52681
118451,30,10,3,0,Married,7,344.7161475,0,16781.80738
178669,49,28,3,1,Single,12,344.7069153,0,17026.34577
191140,18,0,2,1,Single,15,344.6924813,0,16998.62407
180326,61,43,2,1,Single,15,344.6696756,0,16656.48378
139567,52,31,4,0,Single,12,344.6429943,0,16680.14972
118368,21,3,3,1,Single,14,344.6144448,0,17018.72224
153251,47,30,4,1,Married,7,344.5877953,0,16910.38977
148159,38,18,2,1,Married,10,344.580356,0,16979.0178
120741,55,36,3,0,Single,13,344.5180581,0,16725.90291
121345,62,44,4,0,Married,4,344.5008914,0,16909.04457
193440,25,6,2,1,Married,0,344.3833581,0,16874.16791
125911,34,15,3,0,Married,10,344.3373564,0,16698.86782
189329,34,16,2,1,Single,9,344.2895924,0,16732.47962
150218,58,39,4,1,Married,5,344.2672179,0,16920.3609
174693,39,22,2,1,Married,9,344.244867,0,16658.24335
151362,46,28,1,1,Single,13,344.2283454,0,16972.41727
199689,59,41,3,1,Married,6,344.1885488,0,16807.42744
152793,51,32,2,1,Single,12,344.1671782,0,16988.35891
142665,22,3,4,1,Single,12,344.1224663,0,16696.12332
182957,16,0,2,0,Married,12,344.1137958,0,16888.68979
165148,59,40,1,0,Married,10,344.0693671,0,16606.46836
145311,22,4,4,1,Single,12,344.041623,0,16723.08115
152104,31,10,2,1,Single,11,344.0389435,0,16933.94718
161244,29,12,3,0,Married,8,343.9541429,0,16635.70715
114544,56,35,3,0,Married,7,343.942022,0,16988.1011
158579,38,21,3,0,Married,11,343.9203845,0,16777.01923
143884,54,34,3,0,Married,7,343.9026776,0,16661.13388
174819,19,2,2,0,Single,11,343.8728644,0,16791.64322
130129,51,34,3,1,Single,10,343.8319521,0,16722.59761
178649,39,18,2,0,Married,13,343.8099988,0,16864.49994
141124,49,31,3,0,Married,8,343.8024341,0,16912.12171
179239,48,31,3,0,Married,7,343.7715996,0,16670.57998
127059,34,16,2,0,Married,8,343.7377114,0,16733.88557
149574,16,0,4,0,Married,12,343.736168,0,16778.8084
181229,23,6,3,0,Married,13,343.7103014,0,16623.51507
151873,26,9,3,0,Married,9,343.6898797,0,16784.49399
180156,49,29,3,1,Married,9,343.6724293,0,16620.62147
181793,50,32,3,0,Married,12,343.6651084,0,16674.25542
143747,17,0,2,1,Single,13,343.6331572,0,16665.65786
124245,32,14,3,1,Single,14,343.5746726,0,16756.73363
143346,54,37,3,0,Married,6,343.5739111,0,16626.69556
188652,23,4,2,1,Married,14,343.544987,0,16755.24935
155005,28,11,2,0,Married,9,343.5043559,0,16692.2178
152927,49,28,2,0,Married,10,343.4731297,0,16645.65649
101613,21,2,1,1,Married,6,343.4349944,0,16952.74972
105158,23,6,1,1,Married,15,343.4044974,0,16934.22487
131275,47,30,3,0,Single,12,343.3677913,0,16806.38957
147912,58,40,2,1,Single,13,343.342176,0,16679.1088
111389,52,32,2,0,Married,7,343.3311881,0,16891.55941
195569,40,19,1,1,Married,6,343.294489,0,16865.72445
100861,22,4,2,1,Single,12,343.2845714,0,16632.22857
108172,54,36,2,1,Married,10,343.2754032,0,16786.77016
194825,70,51,1,1,Single,8,343.2685172,0,16616.42586
193643,20,1,4,1,Married,15,343.2462723,0,16579.31362
108388,53,36,3,0,Single,15,343.2347351,0,16805.73676
163057,44,27,3,1,Married,10,343.1851723,0,16876.25862
188454,31,14,1,1,Single,7,343.1756364,0,16777.78182
184771,22,3,2,0,Married,13,343.1106416,0,16889.53208
128515,23,4,1,1,Single,14,342.9983742,0,16636.91871
144980,60,43,2,1,Single,7,342.9824095,0,16581.12048
188098,48,27,3,1,Married,10,342.9730674,0,16731.65337
103168,25,8,3,0,Single,14,342.9189128,0,16665.94564
114142,55,37,1,1,Married,11,342.9086256,0,16642.43128
114961,36,15,3,1,Married,8,342.8995743,0,16790.97872
181719,23,5,2,1,Married,15,342.8880303,0,16632.40152
170515,22,4,3,1,Single,15,342.8773162,0,16938.86581
166667,61,44,2,0,Married,2,342.8013707,0,16677.06854
115439,67,46,2,0,Single,14,342.7898172,0,16665.49086
136032,59,40,3,0,Married,9,342.7872945,0,16919.36473
186656,49,28,4,0,Married,9,342.7815248,0,16735.07624
138549,38,17,3,0,Single,15,342.7787276,0,16763.93638
102874,64,44,1,1,Single,10,342.7594432,0,16560.97216
150733,37,16,2,0,Single,12,342.75108,0,16652.554
170019,63,45,4,0,Married,12,342.7440086,0,16595.20043
155329,56,38,3,1,Married,5,342.7053956,0,16750.26978
111222,69,48,2,1,Single,7,342.6985342,0,16810.92671
116521,38,17,1,1,Married,0,342.69694,0,16854.847
141044,17,0,4,1,Married,9,342.6813578,0,16721.06789
100868,25,8,1,1,Single,12,342.6650991,0,16695.25496
124868,34,13,4,0,Married,6,342.642589,0,16663.12945
199850,40,19,3,1,Married,9,342.635767,0,16787.78835
148130,47,28,1,0,Single,12,342.6143104,0,16770.71552
191134,69,49,3,0,Single,7,342.600086,0,16794.0043
133987,17,0,1,1,Married,1,342.5996055,0,16701.98028
103303,40,22,3,1,Married,7,342.5927481,0,16676.63741
195325,33,14,4,1,Married,7,342.5903152,0,16729.51576
164331,58,40,4,0,Married,12,342.5776694,0,16678.88347
197662,55,34,2,0,Married,6,342.5774884,0,16684.87442
102064,68,48,3,1,Single,8,342.5345692,0,16539.72846
111967,24,6,3,0,Single,15,342.5168582,0,16829.84291
135019,45,27,2,1,Single,13,342.4931823,0,16800.65912
179485,26,9,4,1,Single,6,342.4890431,0,16772.45216
191539,23,4,3,0,Single,12,342.4540929,0,16703.70465
181192,30,9,2,1,Married,7,342.4533934,0,16765.66967
155596,64,44,3,0,Single,5,342.4532548,0,16720.66274
115094,39,21,3,1,Married,8,342.4510042,0,16623.55021
127011,19,1,2,1,Married,15,342.4184957,0,16863.92479
154254,48,29,1,1,Married,10,342.4184844,0,16664.92422
140047,51,32,2,0,Married,9,342.4155532,0,16566.77766
123346,35,14,1,0,Married,10,342.3811594,0,16787.05797
141146,64,44,2,1,Married,3,342.3560723,0,16729.80362
107944,45,27,4,0,Married,10,342.268027,0,16746.40135
140188,62,43,1,1,Single,1,342.2447597,0,16889.23799
180947,35,15,2,0,Married,7,342.2152122,0,16790.76061
137595,49,29,2,1,Married,9,342.1803999,0,16768.02
156440,37,19,4,0,Married,9,342.0620114,0,16546.10057
121723,57,38,2,0,Married,15,342.0250702,0,16757.25351
111474,45,27,2,1,Married,9,341.9799628,0,16833.99814
159188,26,6,4,1,Married,5,341.9402652,0,16621.01326
196422,30,10,4,1,Single,10,341.9201456,0,16544.00728
168288,23,6,2,0,Married,15,341.9096485,0,16604.48243
185013,70,51,1,0,Single,7,341.9072784,0,16714.36392
187466,61,42,1,0,Married,12,341.8587982,0,16802.93991
131256,48,31,2,0,Married,9,341.8221344,0,16811.10672
172641,65,44,2,1,Married,0,341.8161348,0,16762.80674
111409,60,40,4,0,Single,10,341.7987715,0,16884.93858
128356,22,5,1,0,Single,12,341.7569511,0,16699.84756
153245,38,20,4,0,Married,10,341.7410458,0,16577.05229
161340,51,33,2,0,Married,12,341.7148314,0,16578.74157
115106,20,1,3,0,Married,12,341.7086908,0,16807.43454
142696,57,38,4,0,Married,8,341.6449796,0,16680.24898
184666,42,23,2,1,Single,10,341.5910745,0,16735.55373
172822,48,27,3,1,Married,10,341.5685446,0,16718.42723
172068,33,14,2,1,Married,6,341.567577,0,16781.37885
195871,24,5,3,1,Married,7,341.551092,0,16565.5546
127169,43,26,3,0,Married,9,341.468415,0,16611.42075
154832,22,5,4,1,Married,9,341.4633368,0,16548.16684
171075,69,51,3,0,Married,4,341.3515381,0,16865.57691
198759,26,9,4,1,Married,6,341.321384,0,16728.0692
160894,22,4,4,1,Single,14,341.2988319,0,16479.9416
118019,35,18,4,1,Single,6,341.2750996,0,16854.75498
148335,54,37,3,0,Married,6,341.2651472,0,16635.25736
165484,44,25,2,0,Married,10,341.1858165,0,16728.29083
122874,42,25,2,0,Married,8,341.1715541,0,16545.57771
159310,47,28,3,1,Married,6,341.1694065,0,16567.47033
126648,38,21,3,1,Single,12,341.1446327,0,16615.23164
105742,21,3,3,0,Married,14,341.1303247,0,16726.51624
116550,37,17,1,0,Married,12,341.0717472,0,16550.58736
150241,45,27,1,0,Married,7,341.0716297,0,16778.58149
157097,26,6,2,0,Single,15,341.0697115,0,16777.48558
105748,40,19,2,0,Married,11,341.0328439,0,16778.6422
199601,20,2,4,0,Married,11,341.0282104,0,16632.41052
101508,41,24,4,0,Married,10,340.9718103,0,16775.59052
140099,54,37,3,1,Married,10,340.9616183,0,16662.08092
116898,50,30,2,1,Married,3,340.9452969,0,16589.26485
184282,67,46,1,1,Single,10,340.8910535,0,16652.55268
107678,34,13,2,0,Married,9,340.8726681,0,16540.63341
168024,26,7,3,0,Married,7,340.8476184,0,16453.38092
114228,40,21,3,0,Married,10,340.8087636,0,16839.43818
121485,48,30,1,1,Single,8,340.8082596,0,16641.41298
120846,69,51,2,1,Single,2,340.7535528,0,16461.67764
150310,35,15,2,0,Married,10,340.7464028,0,16704.32014
170309,16,0,3,0,Single,15,340.6809661,0,16632.04831
133860,55,34,2,1,Single,15,340.6756615,0,16457.78308
122304,40,22,3,1,Single,6,340.6393441,0,16762.96721
188422,46,27,1,1,Married,0,340.607225,0,16585.36125
147501,20,3,1,1,Married,8,340.5856515,0,16525.28258
148101,19,2,4,1,Married,6,340.5823945,0,16618.11973
175930,38,17,3,1,Married,3,340.5794941,0,16671.97471
137276,55,37,3,1,Single,8,340.5382002,0,16817.91001
106938,25,6,4,1,Married,7,340.5355528,0,16745.77764
166936,68,51,2,1,Single,9,340.4844834,0,16444.22417
115668,62,44,2,0,Married,6,340.4622361,0,16794.11181
186367,48,31,2,0,Single,15,340.439031,0,16737.95155
119772,43,24,3,0,Married,11,340.3817789,0,16703.08895
102284,44,25,1,1,Single,11,340.3533667,0,16585.66834
138994,31,11,4,1,Married,8,340.3491803,0,16682.45902
174607,68,49,3,1,Single,9,340.3195615,0,16607.97808
140319,65,44,2,1,Married,0,340.2747107,0,16774.73554
130396,45,28,3,1,Single,8,340.2354144,0,16659.77072
178650,43,23,2,1,Single,12,340.2184013,0,16436.92007
137482,41,22,3,0,Married,11,340.180379,0,16744.01895
162391,61,41,3,0,Single,7,340.144875,0,16622.24375
167269,27,8,3,1,Married,14,340.095393,0,16765.76965
189179,46,26,2,0,Married,7,340.084002,0,16444.2001
153887,21,4,1,0,Married,13,340.0697223,0,16458.48612
193914,26,9,3,0,Married,9,340.0277129,0,16743.38565
119000,57,39,1,0,Married,8,340.0212605,0,16661.06303
156649,17,0,2,0,Married,11,339.9900303,0,16601.50152
173433,32,13,2,1,Married,9,339.9877417,0,16637.38709
116113,40,21,2,1,Single,15,339.9531281,0,16758.65641
149075,59,41,3,0,Married,10,339.9364265,0,16765.82133
108241,29,12,2,1,Married,8,339.9327006,0,16774.63503
158953,51,34,3,0,Single,13,339.9271734,0,16785.35867
148193,50,33,3,1,Married,8,339.9036379,0,16468.1819
132571,16,0,2,1,Married,11,339.903063,0,16434.15315
163150,48,29,2,1,Married,14,339.8958901,0,16487.79451
152047,45,26,2,0,Married,8,339.879647,0,16448.98235
103888,47,30,3,0,Married,10,339.872347,0,16754.61735
180042,33,15,3,1,Married,1,339.8535018,0,16706.67509
190293,70,53,2,0,Married,7,339.8415865,0,16545.07933
194715,53,32,3,0,Single,14,339.8202074,0,16584.01037
135335,36,19,1,1,Married,6,339.811243,0,16594.56215
122994,56,38,2,0,Single,15,339.7842178,0,16670.21089
101548,19,2,1,0,Married,6,339.775025,0,16562.75125
144763,61,40,3,0,Single,6,339.7156576,0,16551.78288
181679,32,13,2,1,Single,13,339.7074083,0,16392.37042
102500,29,12,4,0,Married,7,339.6659879,0,16541.2994
100630,37,17,3,1,Single,9,339.6570056,0,16472.85028
161133,25,7,4,1,Single,11,339.6139328,0,16640.69664
199433,44,27,2,1,Married,8,339.6077025,0,16568.38513
158931,39,19,1,1,Married,10,339.6011591,0,16393.05796
191299,50,29,2,1,Single,8,339.5974725,0,16655.87363
119721,49,32,3,0,Married,8,339.5879226,0,16408.39613
113389,37,18,4,0,Single,15,339.551513,0,16435.57565
197421,50,30,4,1,Single,11,339.4924146,0,16495.62073
176198,62,44,3,0,Married,4,339.4783345,0,16548.91673
180395,24,5,2,0,Single,13,339.441453,0,16461.07265
165062,42,24,1,1,Married,4,339.3914837,0,16556.57419
189283,49,31,2,0,Married,10,339.3651969,0,16655.25985
128804,46,28,2,1,Married,7,339.3509979,0,16610.5499
103071,36,17,3,1,Married,10,339.2887071,0,16407.43536
139123,33,15,4,1,Married,9,339.2867738,0,16569.33869
116562,66,46,3,0,Married,3,339.2291573,0,16520.45787
107019,54,34,2,1,Married,9,339.2091481,0,16644.45741
100798,30,13,3,0,Married,7,339.1913977,0,16758.56989
145631,58,40,4,0,Married,10,339.1586915,0,16610.93458
190417,61,40,4,0,Single,2,339.1459945,0,16612.29973
149520,54,33,2,0,Married,12,339.1419475,0,16468.09738
111723,37,20,2,1,Married,9,339.0855567,0,16599.27784
100081,16,0,4,1,Married,15,339.0782084,0,16592.91042
193325,30,10,3,1,Married,9,339.0187299,0,16352.9365
166252,63,43,3,1,Single,10,339.005342,0,16416.2671
112315,36,15,3,0,Married,10,339.0030757,0,16742.15379
112202,44,24,3,1,Single,7,338.9907365,0,16719.53683
109615,64,44,4,0,Single,9,338.9800943,0,16374.00472
154215,67,46,2,0,Married,7,338.9769113,0,16563.84557
155534,68,49,2,0,Single,3,338.9155024,0,16578.77512
152543,16,0,3,0,Married,12,338.8692443,0,16713.46222
102861,39,22,2,0,Married,6,338.865123,0,16705.25615
158516,21,3,1,1,Married,11,338.8357105,0,16359.78553
127543,25,7,2,1,Single,14,338.8142936,0,16416.71468
145402,47,28,3,1,Married,7,338.6870138,0,16699.35069
132503,63,43,1,1,Single,0,338.6799977,0,16422.99989
109823,23,6,2,1,Married,10,338.6778191,0,16584.89096
193094,43,26,3,1,Married,9,338.6659402,0,16719.29701
178557,54,37,2,1,Married,6,338.6143252,0,16402.71626
199224,54,34,4,0,Married,14,338.5725101,0,16605.62551
153634,51,30,4,1,Single,8,338.5637579,0,16716.1879
110540,33,16,4,1,Married,9,338.5552168,0,16652.76084
127607,57,36,3,1,Married,6,338.5430311,0,16512.15156
183604,33,16,3,0,Married,7,338.5373245,0,16492.86623
112660,26,6,1,1,Married,12,338.521163,0,16659.05815
105590,63,44,1,1,Married,5,338.5093334,0,16523.46667
108410,66,46,3,0,Single,1,338.4848283,0,16606.24142
171701,44,26,3,1,Single,13,338.4338689,0,16572.69345
138738,24,6,3,0,Married,10,338.417008,0,16677.8504
114275,40,19,3,1,Married,8,338.3952343,0,16485.76172
149712,20,1,2,1,Married,13,338.3642916,0,16375.21458
121883,60,40,3,1,Single,7,338.3607926,0,16697.03963
114244,44,23,3,1,Single,13,338.3345866,0,16392.72933
139947,40,20,3,1,Single,7,338.333264,0,16554.6632
151013,57,39,3,1,Married,8,338.3037737,0,16371.18869
147692,45,25,2,1,Married,6,338.295675,0,16493.78375
194874,59,42,2,1,Single,14,338.2666757,0,16382.33379
150639,69,52,3,0,Married,9,338.2597543,0,16517.98772
121255,50,31,1,1,Married,10,338.2484618,0,16361.42309
196424,19,0,2,1,Married,12,338.227528,0,16400.3764
168165,16,0,2,1,Married,14,338.1644742,0,16521.22371
186987,32,12,3,1,Married,9,338.1616198,0,16491.08099
132518,40,20,2,0,Married,10,338.1253431,0,16405.26716
156159,56,35,1,1,Married,6,338.0992415,0,16386.96208
128090,48,29,3,1,Single,8,338.0449029,0,16484.24515
113768,62,44,3,0,Married,9,337.990563,0,16578.52815
185061,44,27,2,0,Married,6,337.9666721,0,16676.33361
100950,19,0,3,1,Single,13,337.9628924,0,16616.14462
111880,48,30,2,1,Married,9,337.8656877,0,16362.28439
123749,50,31,3,1,Married,13,337.8178584,0,16457.89292
126531,69,51,2,1,Married,2,337.7953338,0,16299.76669
172363,64,45,2,0,Married,5,337.7526133,0,16406.63067
124458,41,24,1,1,Married,7,337.6700037,0,16294.50019
152931,40,22,1,0,Married,7,337.6245613,0,16406.22807
198365,41,22,3,1,Married,0,337.6231236,0,16302.15618
188873,49,32,1,0,Married,10,337.6150066,0,16641.75033
193740,20,1,3,0,Married,13,337.5997169,0,16644.98585
150702,24,5,1,1,Married,10,337.5616638,0,16390.08319
147831,54,35,2,0,Single,13,337.541232,0,16596.0616
138460,65,44,1,0,Married,1,337.486374,0,16612.3187
120883,37,18,3,0,Married,7,337.4521818,0,16554.60909
157575,41,21,4,1,Married,6,337.4332674,0,16382.66337
116160,56,36,2,1,Married,7,337.4271396,0,16288.35698
140875,34,17,3,1,Married,7,337.4105308,0,16498.52654
114705,50,31,3,1,Married,8,337.4037694,0,16330.18847
188777,43,25,2,1,Single,13,337.3980296,0,16470.90148
198860,22,5,4,0,Married,11,337.3874253,0,16491.37127
197244,55,34,4,1,Single,9,337.3539515,0,16628.69758
133197,68,50,2,1,Single,15,337.3376026,0,16509.88013
174513,51,31,3,1,Single,8,337.3336847,0,16294.68424
132645,43,23,2,1,Married,10,337.2904711,0,16362.52356
133972,23,6,4,1,Married,15,337.2515171,0,16394.57586
115145,31,12,4,0,Married,9,337.2214808,0,16654.07404
166331,26,9,3,0,Married,12,337.1969806,0,16351.84903
175687,40,20,4,0,Married,10,337.1846081,0,16289.23041
158234,17,0,1,0,Single,12,337.170483,0,16405.52415
110563,22,3,2,1,Married,9,337.168717,0,16449.43585
167677,63,45,2,0,Married,8,337.1433254,0,16405.16627
118881,55,35,3,1,Married,7,336.9801312,0,16556.00656
137861,33,12,3,1,Single,8,336.9777824,0,16395.88912
198454,29,10,3,0,Married,10,336.9577303,0,16397.88652
128538,39,18,3,1,Married,8,336.9570075,0,16248.85038
186711,19,2,1,0,Single,13,336.9348452,0,16553.74226
180192,49,32,3,1,Single,6,336.9093948,0,16541.46974
101443,21,4,2,1,Married,15,336.8241458,0,16450.20729
124294,70,53,4,1,Single,4,336.8198933,0,16324.99467
141447,66,47,2,0,Married,9,336.8045779,0,16293.2289
151303,60,41,2,1,Single,10,336.7476761,0,16374.38381
106773,44,27,2,1,Married,10,336.7475878,0,16398.37939
168964,47,30,4,0,Married,6,336.704949,0,16581.24745
125926,36,17,4,1,Married,4,336.6827739,0,16443.1387
135547,47,28,2,0,Married,9,336.6691002,0,16620.45501
127992,55,38,2,1,Married,4,336.6198194,0,16516.99097
164189,18,0,2,1,Single,12,336.609826,0,16278.4913
198185,37,17,3,1,Married,7,336.59998,0,16437.999
113832,17,0,3,1,Married,13,336.5965323,0,16292.82662
120580,41,20,3,1,Married,8,336.5769978,0,16259.84989
135388,46,25,2,0,Married,13,336.5725933,0,16610.62967
112089,49,29,1,1,Married,6,336.5527431,0,16543.63716
168702,19,2,3,0,Married,14,336.5321866,0,16464.60933
102758,61,43,3,0,Single,14,336.5303485,0,16564.51743
178563,67,50,2,1,Single,1,336.4989489,0,16473.94745
194457,55,36,3,1,Married,13,336.4670809,0,16524.35405
144033,22,5,4,1,Married,10,336.3712977,0,16606.56488
193151,23,6,2,0,Married,15,336.3698196,0,16353.49098
141558,17,0,4,1,Married,14,336.3312774,0,16499.56387
157994,58,41,1,0,Married,6,336.3238418,0,16612.19209
153700,69,49,4,0,Single,0,336.3024921,0,16468.12461
162404,34,14,2,1,Married,0,336.2421645,0,16501.10823
161015,57,39,3,0,Single,15,336.2414658,0,16453.07329
189835,32,13,2,1,Married,10,336.2135837,0,16269.67919
101207,41,21,2,1,Single,8,336.1982882,0,16227.91441
165539,59,38,2,0,Married,8,336.1798645,0,16369.99323
107460,47,28,3,0,Married,9,336.1773364,0,16545.86682
114735,19,1,4,1,Married,11,336.0323478,0,16260.61739
151432,20,3,2,0,Married,11,336.0320803,0,16585.60402
142927,59,39,4,0,Married,11,336.0241915,0,16575.20958
193311,31,14,3,1,Married,10,335.9916574,0,16311.58287
157482,20,1,1,1,Married,8,335.9492312,0,16431.46156
156043,62,42,1,0,Single,8,335.9417946,0,16391.08973
185404,34,15,4,1,Married,10,335.8831424,0,16440.15712
158191,40,21,3,1,Married,9,335.8491238,0,16238.45619
124753,67,50,3,0,Single,14,335.8424386,0,16495.12193
127401,63,42,3,0,Single,9,335.8138914,0,16198.69457
180963,48,27,3,1,Married,9,335.7293722,0,16505.46861
115076,35,14,2,1,Married,10,335.7239662,0,16317.19831
180587,27,8,2,1,Married,9,335.7146214,0,16321.73107
129126,53,35,2,0,Single,14,335.7056368,0,16307.28184
196071,24,5,1,0,Married,11,335.7048594,0,16218.24297
182720,41,24,3,1,Married,11,335.6455523,0,16195.27762
144368,26,8,3,0,Single,14,335.6246774,0,16577.23387
181019,16,0,1,0,Single,13,335.6097647,0,16258.48824
140842,40,21,2,1,Married,7,335.6067935,0,16233.33968
163512,44,27,3,1,Married,14,335.5799953,0,16462.99977
124024,18,0,4,0,Married,15,335.5532115,0,16508.66058
103444,56,38,1,0,Married,8,335.5032735,0,16338.16368
177037,49,28,2,1,Married,10,335.493889,0,16257.69445
170235,21,3,4,1,Married,7,335.4810806,0,16445.05403
129722,56,36,3,0,Married,12,335.4308506,0,16346.54253
113132,68,48,4,0,Single,5,335.4109417,0,16542.54709
178416,49,28,4,1,Married,14,335.4017196,0,16481.08598
169120,23,5,3,1,Single,14,335.3995789,0,16568.97895
198813,64,46,1,1,Married,2,335.3879955,0,16194.39978
175598,50,31,2,1,Married,7,335.3727761,0,16313.63881
145479,51,32,3,0,Married,7,335.3373837,0,16561.86919
155735,47,29,3,1,Married,9,335.3199272,0,16547.99636
133084,26,7,1,0,Married,8,335.2827816,0,16371.13908
145028,31,13,3,0,Married,8,335.2771126,0,16427.85563
187712,29,9,4,0,Married,10,335.2730384,0,16406.65192
170854,47,28,3,1,Married,7,335.2523398,0,16319.61699
118428,56,38,2,0,Married,10,335.2309377,0,16480.54689
144555,37,16,2,1,Married,8,335.2123869,0,16197.61935
181713,49,31,3,1,Married,6,335.1623482,0,16192.11741
186999,38,18,3,1,Married,10,335.13541,0,16489.7705
158925,35,15,2,0,Married,10,335.1009955,0,16555.04978
162994,40,23,4,1,Married,10,335.0197592,0,16474.98796
118523,24,7,2,1,Married,13,335.0177934,0,16444.88967
110388,49,28,1,0,Married,8,334.9851495,0,16276.25748
154251,43,26,2,0,Single,14,334.9675153,0,16242.37577
130127,55,34,3,1,Married,13,334.9283336,0,16151.41668
167353,18,0,2,1,Single,13,334.9006802,0,16170.03401
134164,22,4,1,0,Single,13,334.8882663,0,16217.41332
164783,36,17,1,0,Married,0,334.8874633,0,16388.37316
101733,24,6,2,0,Married,13,334.8767772,0,16189.83886
127328,48,28,1,1,Married,10,334.7612678,0,16298.06339
180989,39,22,1,1,Single,6,334.7391302,0,16305.95651
138881,38,18,4,0,Married,7,334.7287652,0,16190.43826
116929,61,40,3,0,Married,10,334.6916841,0,16464.58421
171769,43,26,1,1,Married,9,334.6358125,0,16320.79063
162938,28,7,2,1,Married,6,334.5390852,0,16429.95426
125603,42,21,2,1,Single,11,334.5252695,0,16172.26348
131853,16,0,2,1,Married,15,334.5228852,0,16422.14426
188103,62,41,3,0,Married,9,334.48129,0,16284.0645
120125,23,5,2,1,Married,12,334.4644319,0,16251.2216
155877,27,8,3,1,Married,10,334.4239027,0,16158.19514
188162,34,14,2,1,Married,10,334.4126622,0,16267.63311
143059,42,25,3,1,Married,8,334.4120546,0,16424.60273
175764,19,1,2,0,Married,15,334.3978505,0,16376.89253
129154,30,11,3,1,Married,7,334.384651,0,16243.23255
150611,51,32,4,1,Married,6,334.3324841,0,16363.62421
103788,22,3,3,1,Married,15,334.2904507,0,16506.52254
160497,21,4,3,1,Married,10,334.2721438,0,16313.60719
134904,56,38,3,0,Married,8,334.2720603,0,16242.60302
152969,59,38,3,0,Married,14,334.1823816,0,16234.11908
172101,38,21,3,0,Married,9,334.1721313,0,16312.60657
125466,47,28,2,1,Married,10,334.1708324,0,16167.54162
144323,49,31,4,1,Married,2,334.1259667,0,16181.29834
111247,20,2,2,0,Married,15,334.1035772,0,16116.17886
166099,57,36,3,0,Single,11,334.0226029,0,16115.13015
169276,18,0,4,1,Married,15,333.997535,0,16392.87675
140812,34,17,3,0,Married,7,333.9880075,0,16391.40038
141208,51,34,2,1,Married,9,333.9594722,0,16178.97361
155691,17,0,3,1,Married,11,333.9574544,0,16345.87272
178003,43,25,1,1,Married,8,333.9538906,0,16438.69453
186846,45,25,2,1,Married,10,333.8893909,0,16296.46954
191538,56,38,2,1,Married,1,333.8694586,0,16233.47293
124098,44,27,3,1,Married,7,333.8670745,0,16470.35373
177322,47,27,3,1,Single,6,333.8327365,0,16415.63683
163129,16,0,2,0,Married,11,333.81286,0,16179.643
130092,21,3,4,1,Single,12,333.8098672,0,16157.49336
166551,16,0,3,1,Married,11,333.7185875,0,16411.92938
166779,24,5,2,1,Single,13,333.7091493,0,16155.45747
154357,31,11,2,1,Married,9,333.7069214,0,16284.34607
193656,60,43,3,1,Single,10,333.6874196,0,16365.37098
154741,55,35,1,1,Married,10,333.633504,0,16391.6752
168790,63,45,2,0,Married,5,333.6016894,0,16122.08447
175449,53,36,1,0,Married,7,333.5916552,0,16410.58276
151890,20,3,3,1,Married,14,333.5899085,0,16396.49543
135549,27,10,2,0,Married,8,333.5663511,0,16177.31756
138591,59,41,4,1,Single,10,333.5661323,0,16436.30662
144686,17,0,4,1,Single,12,333.51709,0,16359.8545
172028,25,6,2,0,Married,12,333.4970862,0,16391.85431
137892,54,34,3,1,Single,9,333.4723997,0,16300.61998
106382,64,47,2,1,Married,5,333.4567358,0,16447.83679
181264,67,47,4,1,Married,3,333.4236021,0,16419.18011
145110,42,22,2,1,Married,14,333.3446093,0,16306.23047
193147,22,3,3,0,Married,11,333.3442393,0,16288.21197
145876,23,4,4,0,Married,11,333.2677765,0,16306.38883
133501,42,24,1,1,Married,8,333.2618886,0,16412.09443
109884,18,0,1,0,Married,15,333.2548377,0,16233.74189
164400,40,19,3,0,Single,13,333.2375338,0,16118.87669
188540,68,47,2,1,Single,6,333.1969856,0,16171.84928
163671,45,25,2,1,Single,6,333.1953864,0,16196.76932
136454,21,4,3,1,Married,15,333.1761337,0,16215.80669
105226,58,39,2,0,Married,6,333.1704666,0,16143.52333
162370,37,17,3,1,Married,7,333.1410952,0,16334.05476
133472,69,49,3,0,Married,6,333.1220539,0,16091.1027
125573,68,47,3,1,Married,9,333.1090304,0,16075.45152
117724,30,11,2,0,Single,11,333.0913628,0,16199.56814
191927,16,0,1,0,Married,6,333.0519897,0,16384.59949
184053,36,15,2,0,Married,8,333.0432102,0,16322.16051
106094,52,32,3,1,Married,6,333.0237772,0,16374.18886
196576,17,0,2,1,Married,11,333.0102117,0,16082.51059
198957,16,0,3,1,Married,8,333.0099508,0,16384.49754
190057,41,23,3,1,Single,11,332.9836387,0,16420.18194
120582,53,36,3,1,Married,7,332.9713842,0,16390.56921
107641,19,2,2,1,Married,15,332.9501822,0,16083.50911
198799,65,45,4,0,Married,9,332.9091596,0,16155.45798
178494,40,23,2,1,Married,9,332.8686315,0,16077.43158
142529,38,17,1,1,Married,6,332.8659118,0,16162.29559
196218,34,15,3,1,Married,10,332.7724649,0,16060.62325
130563,28,8,4,1,Married,9,332.7544088,0,16244.72044
126995,59,41,3,1,Married,10,332.6400036,0,16083.00018
100938,50,31,1,0,Married,6,332.5962019,0,16286.8101
145801,25,6,4,1,Married,12,332.5473656,0,16321.36828
142396,64,43,3,0,Married,9,332.5415902,0,16116.07951
154414,59,41,2,0,Married,7,332.5312112,0,16289.56056
178457,24,6,3,1,Married,0,332.5239841,0,16371.19921
181352,39,19,4,0,Married,6,332.5219251,0,16185.09626
173148,66,48,4,0,Married,8,332.5152365,0,16274.76183
120777,54,34,3,1,Married,6,332.4973352,0,16330.86676
179291,55,36,3,1,Married,8,332.4785656,0,16271.92828
180878,40,20,4,0,Married,3,332.4729909,0,16027.64955
130583,48,29,2,1,Married,9,332.4600167,0,16075.00084
182876,52,34,2,1,Married,7,332.3828307,0,16390.14154
110271,34,15,2,1,Married,8,332.3397089,0,16065.98544
108514,21,3,3,1,Married,12,332.2190755,0,16251.95378
162083,18,0,2,0,Married,15,332.2094764,0,16331.47382
128541,30,13,2,0,Married,7,332.1763765,0,16111.81883
191861,52,31,1,0,Married,7,332.1391406,0,16240.95703
116100,29,8,3,1,Married,10,332.1195223,0,16275.97612
174364,57,37,3,0,Married,8,331.9404469,0,16059.02235
145726,44,24,2,1,Married,7,331.9379418,0,16077.89709
145201,18,0,2,1,Single,12,331.890297,0,16318.51485
109376,70,51,4,0,Married,10,331.8877544,0,16279.38772
118693,58,39,3,1,Married,10,331.8867547,0,16029.33774
159114,56,35,4,1,Single,8,331.7787397,0,16294.93699
155719,70,51,4,0,Married,9,331.7728568,0,16288.64284
126656,26,7,1,0,Married,13,331.7301861,0,16234.50931
136312,47,28,2,1,Married,9,331.711632,0,16271.5816
110278,33,16,2,1,Single,7,331.6901581,0,16156.50791
135440,66,47,3,0,Single,6,331.6892866,0,16333.46433
178621,35,15,4,1,Single,9,331.6762154,0,16249.81077
169953,52,31,1,0,Married,6,331.6699986,0,16263.49993
144833,22,5,2,0,Married,15,331.6519931,0,16065.59966
109165,27,9,2,1,Single,13,331.6471059,0,15988.3553
106417,52,34,2,1,Married,9,331.644446,0,16034.2223
183463,27,9,3,1,Married,9,331.6333184,0,16268.66592
163543,31,13,2,1,Married,8,331.6192981,0,16254.96491
151470,22,5,4,1,Married,15,331.6047282,0,16140.23641
136999,44,23,1,0,Married,10,331.5437249,0,16086.18625
129258,20,1,2,0,Married,15,331.5114821,0,15991.57411
117658,16,0,3,1,Single,12,331.4292124,0,16354.46062
101794,19,1,4,1,Single,13,331.3962522,0,16367.81261
161546,29,9,3,1,Single,13,331.3621786,0,16031.10893
184798,17,0,4,0,Married,11,331.3457506,0,16256.28753
119471,18,0,2,1,Married,14,331.3291491,0,16251.45746
173088,57,36,1,1,Married,10,331.272518,0,16246.6259
114991,24,6,3,0,Single,11,331.2267942,0,16166.33971
167129,23,6,2,0,Married,12,331.2204708,0,16111.02354
112888,37,16,2,1,Married,10,331.1874007,0,16078.37004
125187,32,11,3,1,Married,7,331.1851862,0,16180.25931
162740,59,38,3,1,Married,8,331.1771662,0,15963.85831
123405,69,52,2,1,Single,10,331.1734981,0,16168.67491
181875,56,39,4,1,Single,9,331.1692166,0,16168.46083
104431,67,49,2,1,Single,5,331.1223796,0,16306.11898
195401,53,36,3,0,Single,13,331.0453792,0,16118.26896
120963,47,26,2,1,Married,9,331.0313176,0,15989.56588
129139,46,25,1,0,Single,13,331.0279696,0,16164.39848
145588,68,50,3,0,Single,6,331.0060826,0,15971.30413
155517,61,44,3,0,Married,9,331.0056722,0,15979.28361
114613,23,5,3,0,Single,13,331.0044826,0,15979.22413
164172,22,3,2,0,Married,15,330.9809756,0,16128.04878
118033,25,6,2,0,Married,12,330.9647165,0,15993.23583
176817,49,28,3,0,Married,11,330.9539262,0,15971.69631
167486,64,43,4,0,Married,2,330.9392944,0,16298.96472
166282,52,34,4,0,Married,8,330.9141175,0,15968.70588
151790,28,11,3,1,Married,9,330.9052159,0,15990.2608
190272,20,1,4,1,Single,14,330.8872262,0,16088.36131
118163,58,41,3,1,Married,9,330.8133013,0,16278.66507
166566,31,13,1,1,Single,15,330.805852,0,16081.2926
128711,69,50,2,1,Single,1,330.7499558,0,16103.49779
198956,53,33,1,1,Married,9,330.7438724,0,16283.19362
168660,67,47,2,0,Married,8,330.6806201,0,16278.03101
179402,49,32,4,0,Single,13,330.6725427,0,16098.62714
135956,17,0,4,0,Married,15,330.6429977,0,16244.14989
138209,70,53,3,0,Married,9,330.5704282,0,15998.52141
128751,61,42,2,0,Married,10,330.4881474,0,15967.40737
136158,67,46,3,0,Married,2,330.4842953,0,16322.21477
173527,17,0,2,1,Single,12,330.4692588,0,16180.46294
192894,36,16,3,1,Married,10,330.4307178,0,16082.53589
101840,37,17,3,0,Married,10,330.36069,0,16082.0345
123725,22,3,2,1,Married,15,330.295575,0,16041.77875
195435,22,5,2,1,Married,14,330.2577944,0,16118.88972
127185,68,47,3,0,Married,6,330.2103463,0,16179.51732
129254,46,26,3,0,Married,11,330.1933101,0,16233.66551
193219,48,29,1,0,Married,12,330.1933096,0,16208.66548
153728,58,37,1,0,Married,7,330.1871961,0,15927.35981
179605,52,35,2,1,Single,13,330.1739026,0,16100.69513
140894,23,5,2,1,Single,14,330.1514958,0,15965.57479
130073,61,44,1,0,Single,6,330.1496076,0,16178.48038
143348,24,5,4,1,Married,7,330.1472651,0,16189.36326
103406,24,7,3,1,Married,15,330.1407156,0,15929.03578
100901,43,23,2,0,Married,6,330.1191091,0,16258.95546
186911,41,24,1,1,Married,10,330.0584664,0,16056.92332
176788,35,16,2,1,Married,10,330.0194831,0,16185.97416
101099,19,0,4,1,Married,12,329.9957677,0,16169.78839
164953,60,43,1,1,Married,1,329.9864173,0,16103.32087
145799,27,6,3,0,Married,9,329.9286743,0,16236.43372
103890,50,31,2,0,Married,14,329.9110453,0,15917.55227
188553,41,23,2,0,Single,12,329.9058702,0,16046.29351
122330,67,47,2,1,Single,11,329.8879277,0,16009.39639
105080,49,28,2,1,Married,9,329.8876395,0,16022.38198
140721,56,35,2,0,Single,14,329.831764,0,15931.5882
173593,52,34,3,0,Married,7,329.8316829,0,16201.58415
137380,31,11,3,1,Married,9,329.8279846,0,16215.39923
178774,22,3,3,1,Married,15,329.8254579,0,16036.2729
118717,41,20,2,1,Married,10,329.7994814,0,16255.97407
125732,50,32,4,1,Married,10,329.7975764,0,16287.87882
199975,24,6,2,1,Married,14,329.7783553,0,16199.91777
190492,42,21,3,1,Married,7,329.7768065,0,16227.84033
102106,57,36,3,0,Single,12,329.742425,0,16120.12125
147951,67,48,1,0,Single,6,329.7247293,0,16015.23647
149456,52,35,1,1,Married,2,329.647359,0,16093.36795
147849,21,4,3,0,Married,11,329.6421981,0,15935.10991
102319,47,30,1,1,Married,6,329.6301926,0,16044.50963
111468,56,35,3,1,Married,8,329.5878524,0,16190.39262
140847,19,0,4,0,Single,14,329.5090874,0,16165.45437
192640,24,6,4,1,Married,10,329.5041121,0,16114.20561
132425,20,1,2,1,Single,15,329.4951415,0,15949.75708
143383,44,25,3,1,Married,12,329.4730403,0,16101.65202
126919,41,23,2,0,Single,13,329.4684763,0,16143.42382
146153,38,21,1,0,Married,11,329.4565402,0,16166.82701
196098,59,42,3,1,Married,8,329.4459971,0,16019.29986
131721,22,5,3,1,Single,12,329.439408,0,15886.9704
184554,61,44,3,1,Single,10,329.4015182,0,16049.07591
109809,59,40,3,0,Single,14,329.3882844,0,15875.41422
153760,51,31,3,0,Married,13,329.3468008,0,16003.34004
166347,18,0,3,1,Married,11,329.3307815,0,16125.53908
193780,23,5,2,0,Married,12,329.3240747,0,16079.20374
104313,47,27,1,1,Married,10,329.280977,0,16226.04885
139150,34,15,2,1,Married,8,329.2801738,0,15999.00869
197615,53,35,3,1,Married,11,329.2734723,0,16116.67362
195536,18,1,3,0,Married,15,329.2631006,0,16083.15503
194658,54,36,3,0,Married,7,329.2370531,0,16141.85266
109534,67,50,1,1,Married,8,329.23095,0,16142.5475
110180,45,24,3,1,Single,9,329.2219199,0,16220.096
129264,44,26,4,1,Married,10,329.220829,0,16149.04145
109675,31,10,3,0,Married,15,329.2102879,0,16250.5144
104036,22,5,2,1,Married,15,329.2043961,0,16098.21981
178142,48,28,4,0,Married,10,329.178784,0,16024.9392
176037,16,0,3,0,Single,13,329.155451,0,15859.77255
176016,22,3,1,1,Married,14,329.1457386,0,15990.28693
135610,34,13,3,0,Married,12,329.096529,0,16025.82645
179870,43,25,1,1,Married,7,329.0893262,0,16033.46631
182612,63,43,2,0,Married,2,329.0603972,0,15916.01986
142332,16,0,1,1,Married,14,329.0441386,0,15928.20693
138181,27,10,3,1,Married,0,329.0125562,0,15906.62781
121362,61,44,1,0,Single,5,328.9980029,0,16060.90015
185679,27,6,4,0,Married,9,328.956993,0,16066.84965
174600,18,1,2,1,Married,11,328.9220663,0,16040.10332
181936,33,12,2,1,Married,6,328.9200556,0,16135.00278
192924,56,35,3,1,Married,9,328.9069773,0,15986.34887
142275,50,29,2,1,Married,6,328.9045926,0,15856.22963
115865,46,29,1,1,Married,8,328.9045117,0,15977.22559
141403,25,8,1,1,Married,15,328.8843527,0,16002.21764
113393,23,4,3,1,Married,6,328.8671672,0,16187.35836
125907,22,3,4,0,Married,11,328.8657175,0,15950.28588
173912,16,0,3,0,Married,12,328.7653044,0,16203.26522
179926,31,14,3,1,Married,10,328.735011,0,16010.75055
107595,69,52,2,0,Married,9,328.6760988,0,15895.80494
131347,67,50,3,1,Single,9,328.6631954,0,16079.15977
152401,19,2,2,0,Married,12,328.6549633,0,16194.74817
135469,28,10,3,1,Single,12,328.5768603,0,15833.84302
133498,32,11,4,1,Married,8,328.5716508,0,16142.58254
196315,33,16,3,1,Single,10,328.569826,0,15965.4913
191510,28,8,3,1,Married,6,328.5205018,0,16212.02509
193862,60,41,2,1,Married,8,328.5159353,0,16181.79677
131801,67,46,3,1,Married,5,328.4842689,0,16160.21345
157001,20,1,2,1,Single,11,328.4752048,0,16025.76024
184088,25,6,2,1,Married,15,328.4621503,0,15915.10752
193212,19,0,1,0,Married,7,328.3589705,0,16198.94853
107387,37,17,3,1,Married,9,328.3563071,0,16165.81536
126540,52,32,4,0,Married,13,328.2961076,0,15935.80538
178899,53,34,2,1,Single,14,328.2917116,0,16116.58558
123566,25,7,1,1,Single,12,328.2082804,0,15941.41402
137451,26,9,2,0,Married,6,328.180122,0,15821.0061
119006,53,34,2,1,Married,10,328.0856883,0,16145.28442
173308,49,28,4,0,Married,10,328.0313668,0,15862.56834
133237,30,10,2,1,Married,10,328.0235113,0,15925.17557
198434,60,40,1,1,Married,11,327.9731107,0,15954.65554
156638,40,21,3,1,Married,7,327.9714218,0,15968.57109
101048,40,23,2,1,Married,3,327.9646537,0,15911.23269
161421,70,52,1,1,Single,7,327.935663,0,16087.78315
132126,29,10,4,1,Married,9,327.9181678,0,15976.90839
173480,48,28,4,1,Married,6,327.8850451,0,16015.25226
164174,51,30,2,0,Single,14,327.7978185,0,16022.89093
152720,25,8,2,1,Married,15,327.7078753,0,15902.39377
111196,29,9,2,1,Married,8,327.6833589,0,15784.16795
150286,28,8,3,1,Single,12,327.6406969,0,15946.03485
106814,58,37,3,1,Married,6,327.6239282,0,15871.19641
174265,58,40,2,1,Single,13,327.6220304,0,15797.10152
110255,49,32,2,1,Married,8,327.5916895,0,16054.58448
176160,42,24,3,1,Married,6,327.5799744,0,15914.99872
109370,32,11,2,1,Married,9,327.5293142,0,16148.46571
179986,45,26,2,1,Married,10,327.5076178,0,15874.38089
137146,52,35,3,1,Married,7,327.4735937,0,15809.67969
121036,23,4,2,0,Single,15,327.4650096,0,15812.25048
128683,19,1,4,1,Married,7,327.4433776,0,15802.16888
125808,55,34,4,1,Single,12,327.3929411,0,16094.64706
147979,32,12,4,1,Married,9,327.3299205,0,15793.49603
100360,24,7,3,1,Single,13,327.3226285,0,15982.13143
100778,65,47,2,0,Single,7,327.3019603,0,15939.09802
187464,39,20,1,1,Married,6,327.3002242,0,15797.01121
194531,53,33,1,1,Married,10,327.2980775,0,15933.90388
102995,40,22,4,1,Married,10,327.2693144,0,15908.46572
167095,17,0,2,1,Married,6,327.145844,0,16153.2922
185442,67,50,1,0,Single,8,327.1425875,0,15796.12938
185432,61,41,2,1,Single,11,327.1240961,0,15898.20481
179667,29,11,2,1,Married,7,327.1132464,0,16033.66232
152166,54,33,3,1,Married,10,327.0209958,0,16038.04979
186250,50,30,3,1,Single,7,327.0172654,0,16096.86327
169309,47,26,4,0,Married,10,327.0054935,0,15850.27468
176586,33,12,2,1,Married,6,326.9949846,0,15754.74923
108227,46,26,4,1,Married,6,326.9483064,0,15936.41532
119774,33,15,2,1,Married,7,326.9088065,0,15926.44033
135502,35,17,2,1,Single,6,326.8978365,0,15839.89183
186042,61,42,3,0,Married,8,326.8851808,0,16127.25904
186684,22,4,1,1,Married,14,326.841169,0,15967.05845
115634,61,43,3,1,Single,11,326.8117612,0,15935.58806
118811,40,22,3,0,Married,15,326.8109742,0,15859.54871
169626,28,9,3,1,Married,8,326.8085285,0,15960.42643
164973,64,47,3,1,Single,10,326.7835962,0,15981.17981
129188,43,23,2,1,Married,6,326.7579322,0,15996.89661
133737,21,3,2,1,Married,13,326.7531806,0,15848.65903
116477,44,24,1,1,Married,10,326.6636811,0,15933.18406
172086,43,24,3,0,Married,9,326.5984595,0,15821.92298
106720,28,9,4,1,Married,6,326.5485067,0,15867.42534
178472,38,19,4,0,Single,11,326.5461766,0,15970.30883
196908,43,26,1,0,Married,6,326.467776,0,15908.3888
188456,34,16,3,1,Single,11,326.44074,0,15778.037
149666,24,5,4,0,Married,13,326.3896388,0,16015.48194
192834,69,50,3,1,Single,12,326.3812098,0,16114.06049
158998,41,23,2,1,Married,14,326.3402496,0,15817.01248
176360,38,18,2,0,Married,7,326.3390318,0,15971.95159
161330,36,18,2,1,Married,9,326.3205155,0,15998.02578
161385,34,14,3,0,Married,7,326.3151271,0,15771.75636
167513,18,0,2,1,Single,13,326.2736703,0,16057.68352
126364,52,32,4,1,Married,6,326.2572924,0,16012.86462
183504,62,42,2,1,Single,9,326.240357,0,16002.01785
185576,25,8,2,1,Married,12,326.2332323,0,15833.66162
104120,19,1,3,0,Married,11,326.194937,0,15905.74685
170338,68,50,1,0,Married,7,326.1802691,0,15978.01346
108549,35,15,3,1,Married,6,326.1731789,0,16079.65895
112145,59,40,3,1,Married,10,326.165019,0,15790.25095
190098,59,42,3,1,Married,7,326.1645728,0,15999.22864
102157,21,3,1,1,Married,9,326.0461919,0,16000.3096
176252,26,9,3,1,Married,6,326.0453233,0,15822.26617
159583,61,42,2,0,Married,4,326.0285606,0,15895.42803
111842,17,0,3,0,Married,13,325.9654039,0,16025.2702
139570,61,42,2,1,Single,6,325.964527,0,15715.22635
111185,54,36,4,1,Married,9,325.9460374,0,15969.30187
132864,37,17,2,1,Married,8,325.9364228,0,15888.82114
186088,58,37,3,1,Married,10,325.9104854,0,15865.52427
188395,25,8,2,0,Married,15,325.8331662,0,16029.65831
172144,53,33,2,1,Single,12,325.810068,0,16070.5034
136737,59,40,1,1,Married,8,325.8069501,0,15932.34751
105417,66,48,1,0,Single,8,325.770291,0,15794.51455
192620,59,41,3,1,Married,6,325.749984,0,16024.4992
180350,43,26,2,1,Single,8,325.7362074,0,15880.81037
173558,65,48,3,0,Married,10,325.7298449,0,16021.49225
195847,45,25,1,1,Single,12,325.720255,0,16031.01275
173703,20,3,4,1,Married,8,325.5778853,0,16068.89427
117590,19,0,1,0,Single,13,325.562891,0,15997.14455
194493,61,43,4,1,Single,1,325.5620407,0,15750.10204
103826,35,16,2,1,Single,9,325.5331817,0,15883.65909
153250,45,27,3,1,Single,13,325.5182642,0,16005.91321
151804,54,35,3,0,Married,6,325.5070234,0,15995.35117
164530,17,0,3,0,Married,11,325.5061273,0,16047.30637
159894,19,1,2,1,Single,11,325.4653116,0,16048.26558
194542,48,28,2,1,Married,10,325.4433647,0,15845.16824
182479,30,11,3,1,Married,10,325.4345877,0,15790.72939
191676,53,34,3,1,Married,1,325.4272548,0,15930.36274
173721,40,19,1,1,Married,11,325.4188051,0,15886.94026
152608,23,6,2,1,Married,15,325.4021154,0,15836.10577
115554,60,41,3,0,Married,13,325.4016644,0,15949.08322
106051,34,14,3,1,Married,6,325.3502316,0,15717.51158
166512,28,9,3,1,Married,10,325.3412948,0,15816.06474
174311,67,49,3,1,Single,1,325.3397826,0,15959.98913
133149,23,6,4,0,Single,12,325.3316908,0,15802.58454
108088,53,32,3,1,Married,3,325.2040585,0,16048.20293
128433,40,22,2,1,Married,14,325.1315004,0,15666.57502
191668,17,0,4,1,Married,11,325.0980665,0,15850.90333
131450,55,38,3,1,Married,8,325.0693377,0,16023.46689
145934,36,16,3,1,Single,7,325.0640194,0,16045.20097
166605,67,46,2,0,Married,10,325.0409191,0,16046.04596
124407,67,46,3,0,Single,15,325.0100078,0,15703.50039
185180,57,37,2,0,Married,12,324.9856394,0,15780.28197
132245,54,35,2,0,Married,10,324.9784715,0,16032.92358
150584,66,47,1,0,Married,6,324.965049,0,15942.25245
174755,64,46,4,1,Married,10,324.9433376,0,15693.16688
191839,58,39,1,0,Married,11,324.9266555,0,15901.33278
181029,47,28,3,1,Married,13,324.8769661,0,15704.84831
109384,35,18,2,1,Married,8,324.8720717,0,15719.60359
197656,59,41,4,1,Married,11,324.8447893,0,15887.23947
163882,18,0,2,0,Single,11,324.8442166,0,15790.21083
156685,25,7,4,1,Single,11,324.8406026,0,15744.03013
194468,31,12,1,0,Married,11,324.8298627,0,15836.49314
149804,33,14,1,1,Married,8,324.7471949,0,15724.35975
189619,62,45,1,1,Married,2,324.736977,0,16033.84885
127041,62,41,4,1,Married,10,324.7090949,0,15690.45475
116905,49,30,2,1,Married,9,324.7023211,0,15829.11606
178505,54,36,3,1,Married,0,324.6919476,0,15810.59738
183250,50,33,2,1,Married,7,324.6840138,0,15802.20069
198229,27,8,3,1,Married,10,324.6483153,0,15821.41577
157276,55,37,3,1,Married,10,324.6420613,0,15754.10307
135783,47,29,2,1,Married,9,324.6189544,0,15788.94772
109527,60,40,3,0,Married,7,324.6184822,0,15992.92411
188603,28,8,2,1,Married,9,324.6162633,0,15802.81317
155175,59,38,2,1,Married,10,324.6048449,0,15813.24225
126513,17,0,1,0,Married,6,324.5930853,0,15940.65427
171452,42,24,1,0,Married,9,324.5803862,0,15759.01931
164136,35,17,3,0,Married,7,324.578445,0,15746.92225
189020,17,0,2,0,Single,11,324.5750725,0,15772.75363
197492,55,34,3,1,Married,6,324.5377282,0,15869.88641
173976,19,0,4,1,Single,12,324.4128197,0,15659.64099
146571,45,28,3,0,Married,9,324.4122407,0,15682.61203
178555,29,9,3,1,Married,9,324.3069675,0,15924.34838
197536,31,10,2,1,Married,12,324.1953274,0,15995.76637
102051,37,17,3,1,Single,15,324.1946037,0,15999.73019
199940,43,26,4,0,Married,13,324.1798889,0,15862.99445
122067,40,19,4,1,Married,1,324.1757887,0,15921.78944
148325,26,8,3,1,Married,7,324.1735647,0,15907.67823
199913,37,19,1,0,Married,10,324.1466938,0,15848.33469
150986,31,12,2,1,Single,12,324.1258041,0,15913.29021
119689,48,30,4,1,Married,0,324.0938241,0,15702.69121
194899,48,28,2,1,Married,10,324.0911072,0,15946.55536
155337,64,44,1,0,Married,7,324.076563,0,15790.82815
193842,30,11,4,1,Married,6,324.0256252,0,15842.28126
134381,45,27,3,1,Married,10,324.0121677,0,15753.60839
188306,20,1,4,1,Married,13,324.0092688,0,15998.46344
196507,67,46,2,1,Single,13,323.9683907,0,15603.41954
124081,67,48,3,0,Married,6,323.9677269,0,15720.38635
149145,28,9,2,0,Married,14,323.9466932,0,15771.33466
121991,20,1,3,0,Married,15,323.9178715,0,15819.89358
156408,66,47,4,0,Single,3,323.9017759,0,15931.0888
170043,52,32,2,1,Single,13,323.8725235,0,15793.62618
190124,36,18,2,1,Single,10,323.8457012,0,15601.28506
133928,19,0,4,1,Married,15,323.8297945,0,15735.48973
136896,35,17,3,1,Married,7,323.8245299,0,15714.2265
158280,21,4,4,0,Married,13,323.8147729,0,15911.73865
118352,64,43,2,1,Married,2,323.7629515,0,15726.14758
107969,49,30,2,0,Married,6,323.7231033,0,15616.15517
116926,65,44,3,1,Single,6,323.6883989,0,15859.41995
176050,34,13,4,0,Married,6,323.6055821,0,15895.27911
174936,61,43,2,0,Married,7,323.592949,0,15748.64745
116763,50,29,3,1,Married,9,323.5928472,0,15871.64236
150125,29,12,3,1,Married,7,323.5461564,0,15889.30782
130446,58,37,2,1,Married,9,323.5138689,0,15669.69345
105878,29,11,2,1,Married,8,323.4860547,0,15780.30274
165073,70,51,2,0,Single,7,323.4648917,0,15575.24459
165278,25,8,4,0,Married,11,323.4131916,0,15645.65958
140740,52,32,1,1,Single,14,323.3784995,0,15719.92498
177885,68,47,4,1,Married,4,323.3339868,0,15657.69934
199644,29,8,4,0,Married,10,323.3243513,0,15760.21757
117533,25,7,3,1,Married,8,323.3091998,0,15596.45999
125549,42,21,2,1,Single,9,323.249291,0,15568.46455
102726,55,35,4,0,Married,6,323.230056,0,15826.5028
104782,29,8,4,1,Married,8,323.1856434,0,15822.28217
195304,58,38,2,0,Married,12,323.152546,0,15878.6273
166646,27,7,4,0,Single,13,323.1131684,0,15773.65842
140529,17,0,3,1,Single,15,323.0925407,0,15673.62704
193040,60,43,4,0,Single,13,323.0459727,0,15918.29864
168868,66,47,4,0,Married,8,323.0443489,0,15775.21745
126078,45,24,3,1,Married,8,323.0151216,0,15820.75608
141568,65,48,2,1,Married,2,323.012883,0,15774.64415
170123,48,27,4,1,Single,14,322.9842187,0,15851.21094
196826,68,47,2,0,Married,6,322.9711803,0,15692.55902
155380,18,1,1,1,Married,15,322.9551348,0,15605.75674
142964,55,38,3,1,Married,1,322.9303903,0,15785.51952
196833,47,28,4,0,Married,8,322.9076919,0,15778.3846
193690,18,0,3,0,Single,13,322.8584503,0,15927.92252
144824,54,33,3,1,Married,10,322.7617005,0,15655.08503
187820,41,22,3,1,Married,7,322.7181033,0,15925.90517
115369,22,3,3,0,Single,11,322.6918838,0,15611.59419
119719,61,41,2,1,Single,9,322.6711304,0,15553.55652
166650,32,12,2,1,Married,9,322.654642,0,15541.7321
131576,32,13,2,1,Married,8,322.6367008,0,15626.83504
174389,68,50,2,0,Married,15,322.596226,0,15902.8113
152008,49,30,4,1,Married,10,322.5905582,0,15780.52791
109326,42,21,3,1,Married,7,322.5451135,0,15642.25568
130445,28,10,3,1,Married,4,322.5253972,0,15909.26986
145275,19,0,2,1,Married,10,322.5251261,0,15673.25631
152219,55,38,3,1,Married,8,322.5172508,0,15785.86254
110692,61,40,3,1,Single,7,322.4749899,0,15739.7495
114886,69,51,3,0,Married,8,322.4693076,0,15774.46538
116454,68,48,2,0,Single,7,322.4341027,0,15530.70514
145394,56,38,1,1,Married,15,322.4044828,0,15750.22414
180729,57,39,2,1,Married,9,322.3969738,0,15728.84869
171147,60,39,2,1,Single,3,322.3391134,0,15610.95567
155866,45,28,2,1,Married,7,322.3281216,0,15628.40608
164654,23,4,4,1,Single,12,322.2788795,0,15714.94398
157957,64,43,4,1,Single,12,322.2242639,0,15901.2132
151329,47,28,4,1,Married,9,322.213064,0,15554.6532
126204,44,25,4,1,Married,9,322.211742,0,15645.5871
163563,29,10,2,0,Married,7,322.1484453,0,15812.42227
128346,33,12,2,1,Married,9,322.1399486,0,15713.99743
133214,49,29,2,0,Married,10,322.1237309,0,15760.18655
157222,36,17,1,1,Married,7,322.0024982,0,15747.12491
104911,24,5,2,0,Married,14,321.9312326,0,15759.56163
107829,58,40,2,1,Married,7,321.8637795,0,15627.18898
127667,17,0,4,1,Married,2,321.8444221,0,15615.22111
125718,16,0,3,0,Married,15,321.7955306,0,15654.77653
169323,17,0,3,1,Single,12,321.7816892,0,15630.08446
150622,44,26,3,1,Married,6,321.7725148,0,15860.62574
162911,24,5,3,0,Married,13,321.6806677,0,15487.03339
112771,30,9,2,1,Married,9,321.6091082,0,15740.45541
142616,67,46,4,0,Single,5,321.5625934,0,15678.12967
190247,28,10,4,0,Married,13,321.5327656,0,15714.63828
142070,47,26,1,1,Married,7,321.5142862,0,15545.71431
131803,57,40,3,0,Single,11,321.5092889,0,15710.46445
131702,21,2,2,1,Married,6,321.5083476,0,15827.41738
189925,52,33,1,0,Married,9,321.4892996,0,15611.46498
122165,30,13,3,1,Married,13,321.455237,0,15627.76185
176644,65,46,3,1,Married,5,321.4443002,0,15794.21501
171466,36,19,2,0,Married,7,321.4183747,0,15750.91874
172581,42,23,2,0,Married,8,321.4161214,0,15855.80607
128278,59,41,2,1,Married,7,321.3799122,0,15751.99561
148363,20,3,2,1,Married,15,321.3449548,0,15855.24774
182223,34,13,3,1,Married,9,321.3345312,0,15570.72656
176998,25,7,4,1,Married,15,321.281734,0,15673.0867
179255,61,40,2,0,Single,1,321.2791476,0,15752.95738
154299,20,1,3,1,Married,13,321.2749071,0,15621.74536
196439,21,4,2,0,Single,14,321.2721691,0,15493.60846
115406,64,46,4,0,Single,8,321.2017016,0,15613.08508
140333,62,41,3,1,Married,5,321.1671345,0,15595.35673
142339,62,42,1,0,Single,5,321.1319105,0,15604.59553
104876,70,49,4,1,Married,7,321.1239141,0,15781.19571
193111,69,51,3,0,Single,14,321.0702282,0,15572.51141
140588,70,49,2,0,Married,2,321.0624448,0,15703.12224
198927,38,21,1,0,Married,9,321.0614316,0,15837.07158
192669,20,1,3,1,Married,15,321.0200474,0,15536.00237
144829,62,41,4,0,Married,8,321.0193108,0,15483.96554
136005,25,7,4,0,Married,13,321.0144185,0,15472.72093
100280,42,25,4,1,Married,8,320.9891287,0,15467.45644
188528,66,47,3,1,Single,9,320.9715915,0,15564.57958
171291,54,37,1,1,Married,6,320.885311,0,15677.26555
154086,70,52,1,1,Single,3,320.8710566,0,15652.55283
133897,36,17,2,1,Married,13,320.8155551,0,15762.77776
127719,28,7,3,1,Single,12,320.805101,0,15700.25505
154615,44,27,3,1,Married,12,320.7584761,0,15729.92381
165568,30,13,2,0,Married,15,320.6727996,0,15827.63998
170070,21,4,4,1,Single,11,320.6237384,0,15489.18692
195034,56,36,3,0,Married,11,320.5961914,0,15716.80957
139511,31,13,1,0,Married,6,320.5519106,0,15484.59553
149254,60,40,2,1,Single,5,320.4630531,0,15457.15266
109311,47,28,3,1,Single,14,320.4092254,0,15786.46127
116660,42,24,3,0,Married,8,320.3936649,0,15551.68325
136038,39,22,4,1,Married,3,320.3874686,0,15675.37343
162565,46,26,1,1,Married,13,320.3867101,0,15752.33551
173047,60,39,3,1,Married,0,320.3579811,0,15760.89906
120013,31,11,2,0,Married,9,320.3458509,0,15523.29255
159993,28,8,2,1,Married,2,320.3349091,0,15782.74546
194545,36,17,2,1,Married,7,320.3074776,0,15622.37388
157421,53,35,1,1,Married,10,320.3068082,0,15643.34041
112856,36,17,1,1,Married,13,320.2940291,0,15459.70146
150655,17,0,3,1,Married,11,320.28926,0,15670.463
120998,20,2,4,0,Married,14,320.2286573,0,15534.43287
132592,23,4,3,1,Married,10,320.2235935,0,15763.17968
147689,31,14,1,1,Married,8,320.2231634,0,15589.15817
186397,40,23,2,0,Married,9,320.1990221,0,15559.95111
176279,53,35,3,0,Married,7,320.1661934,0,15699.30967
140921,44,23,2,1,Married,1,320.1570071,0,15488.85036
115383,51,30,3,1,Single,15,320.1286042,0,15451.43021
117664,44,27,3,1,Married,9,320.1273103,0,15598.36552
104748,54,35,2,1,Married,8,320.1108191,0,15542.54096
112427,47,27,2,1,Married,9,320.1014266,0,15668.07133
158254,60,43,2,1,Single,8,320.0999341,0,15477.99671
168515,28,10,1,1,Married,8,320.0403218,0,15686.01609
168528,70,50,2,1,Married,9,320.0256227,0,15780.28114
130143,31,14,2,0,Married,8,320.0112652,0,15582.56326
102121,39,20,1,0,Married,10,320.0088665,0,15683.44333
116323,64,43,1,1,Single,8,319.998898,0,15792.9449
181298,65,47,2,1,Married,3,319.996219,0,15565.81095
100994,70,53,1,0,Single,12,319.9288788,0,15560.44394
190163,67,50,3,1,Single,8,319.9003843,0,15586.01922
141510,44,24,1,1,Married,8,319.8835702,0,15459.17851
195235,40,21,2,1,Married,7,319.8772113,0,15557.86057
173146,29,11,2,0,Married,7,319.8674797,0,15411.37399
115140,68,47,2,1,Single,6,319.8246911,0,15710.23456
159048,18,0,2,0,Married,14,319.8078097,0,15718.39049
190145,53,34,3,1,Married,8,319.79676,0,15562.838
101325,25,8,1,1,Married,15,319.7637959,0,15402.1898
196649,57,39,2,1,Married,6,319.7561592,0,15543.80796
133706,65,48,4,1,Married,7,319.7444939,0,15615.2247
114246,40,22,2,0,Married,15,319.7404011,0,15452.02006
112854,60,43,3,1,Single,6,319.7258089,0,15568.29045
163522,49,32,1,1,Married,7,319.7251844,0,15628.25922
156503,43,26,3,1,Single,8,319.7097846,0,15430.48923
150806,28,9,2,1,Married,13,319.5596127,0,15496.98064
165188,36,15,3,1,Married,8,319.5458609,0,15735.29305
122896,22,4,4,1,Married,6,319.535728,0,15582.7864
136386,64,46,2,0,Married,6,319.5355367,0,15598.77684
133123,19,0,1,1,Married,13,319.5264432,0,15620.32216
191806,62,44,4,0,Married,10,319.4895725,0,15481.47863
172595,38,17,3,0,Married,11,319.4842514,0,15398.21257
191603,70,50,3,1,Single,9,319.4656502,0,15714.28251
174872,59,42,2,0,Married,7,319.4358717,0,15683.79359
196985,27,7,1,1,Single,8,319.4147025,0,15744.73513
108849,20,1,3,1,Married,6,319.4140616,0,15661.70308
126802,20,1,3,0,Married,13,319.3916327,0,15403.58164
177247,27,6,2,1,Married,9,319.3783999,0,15731.92
149802,32,14,2,1,Married,12,319.3703577,0,15401.51789
155859,20,1,3,1,Single,14,319.3306004,0,15442.53002
108633,49,32,2,1,Married,10,319.2959212,0,15634.79606
108968,67,50,3,0,Married,13,319.2946602,0,15597.73301
182492,21,4,2,1,Married,15,319.286823,0,15417.34115
172989,21,2,3,1,Married,13,319.2840867,0,15406.20434
135462,56,37,2,1,Married,8,319.1857115,0,15592.28558
163160,32,13,4,1,Married,5,319.1793514,0,15511.96757
100899,58,40,2,0,Married,7,319.1722652,0,15375.61326
126334,16,0,3,0,Single,11,319.0940162,0,15656.70081
101606,34,15,3,1,Married,8,319.0562067,0,15672.81034
194569,64,44,2,0,Married,6,319.0044147,0,15532.22074
171333,55,35,3,1,Married,10,318.9899857,0,15637.49929
105380,18,0,1,0,Married,7,318.9302287,0,15474.51144
164009,50,29,3,1,Married,10,318.8629879,0,15593.1494
111357,21,4,3,0,Married,14,318.8481406,0,15374.40703
147888,19,0,2,1,Married,13,318.7951069,0,15362.75535
172317,40,23,3,0,Married,8,318.7812044,0,15518.06022
160813,36,19,2,0,Married,6,318.751178,0,15454.5589
146940,61,42,3,0,Single,8,318.7379138,0,15449.89569
141628,22,4,1,1,Married,8,318.7108563,0,15425.54282
184703,40,23,1,1,Married,6,318.7104773,0,15723.52387
143969,56,35,3,1,Married,9,318.6977928,0,15499.88964
192227,70,49,4,1,Married,7,318.638959,0,15418.94795
123804,51,34,4,1,Married,6,318.6337262,0,15352.68631
188142,65,45,2,1,Single,10,318.5919676,0,15659.59838
133665,45,25,2,0,Married,7,318.5733495,0,15329.66748
131415,27,10,3,0,Married,15,318.52301,0,15368.1505
180991,62,42,3,0,Single,14,318.5117924,0,15581.58962
106862,16,0,4,1,Married,7,318.5084006,0,15546.42003
171854,60,40,3,0,Married,1,318.4869933,0,15570.34967
145952,16,0,3,1,Married,7,318.459411,0,15460.97055
131575,37,17,3,1,Single,11,318.4334379,0,15605.6719
172043,16,0,3,0,Single,15,318.4263663,0,15694.31832
181622,26,8,3,0,Married,9,318.4066722,0,15478.33361
148897,65,46,2,0,Married,8,318.3560537,0,15344.80269
127371,64,45,1,1,Married,1,318.332047,0,15694.60235
127681,55,34,2,1,Married,7,318.3150965,0,15605.75483
181334,24,6,2,1,Married,12,318.311808,0,15702.5904
165487,29,10,3,1,Married,12,318.2893902,0,15485.46951
109185,37,20,2,0,Married,7,318.2712128,0,15547.56064
119842,50,32,4,1,Married,7,318.2711261,0,15382.55631
160851,42,25,3,0,Married,8,318.220063,0,15317.00315
199617,39,21,2,1,Married,10,318.2124455,0,15683.62228
126470,46,29,3,1,Married,10,318.1656947,0,15527.28474
148702,66,49,1,0,Married,7,318.1532303,0,15321.66152
117296,61,43,2,0,Single,11,318.0824409,0,15701.12205
174771,35,18,3,1,Married,10,318.0362048,0,15379.81024
156940,24,7,3,0,Married,13,318.0342662,0,15628.71331
155914,28,7,3,1,Married,8,317.9877778,0,15495.38889
106565,45,25,1,0,Married,10,317.9583714,0,15627.91857
137704,35,14,1,1,Married,8,317.9431993,0,15388.15997
163777,52,34,3,1,Married,9,317.9298964,0,15581.49482
173469,58,41,4,0,Married,11,317.8837177,0,15391.18589
156402,52,35,2,1,Married,7,317.8069357,0,15543.34679
155933,64,44,4,1,Married,13,317.7917793,0,15529.58897
163701,30,10,4,0,Married,12,317.7728698,0,15519.64349
128826,25,7,3,1,Single,13,317.7531429,0,15478.65715
140074,22,4,3,1,Married,13,317.7358252,0,15431.79126
116968,40,23,1,0,Single,12,317.7000496,0,15622.00248
104716,19,1,3,1,Married,11,317.6965932,0,15659.82966
199916,23,6,2,1,Married,14,317.6692411,0,15374.46206
160668,26,6,3,0,Single,12,317.6423176,0,15345.11588
186464,60,43,2,1,Single,5,317.6100662,0,15410.50331
180493,27,7,3,1,Married,0,317.5787319,0,15648.9366
161406,30,9,2,1,Married,9,317.5279985,0,15650.39993
169651,16,0,2,0,Married,11,317.5033725,0,15636.16863
105418,31,12,3,0,Married,10,317.4971253,0,15564.85627
185143,21,2,1,0,Married,11,317.4901628,0,15604.50814
183302,34,14,2,1,Married,15,317.4409122,0,15577.04561
187949,69,48,2,0,Married,2,317.3049656,0,15549.24828
121466,28,9,2,1,Married,15,317.3020942,0,15343.10471
154397,37,16,2,1,Married,11,317.2610036,0,15625.05018
129182,37,17,2,1,Married,9,317.2435822,0,15526.17911
111622,51,33,1,0,Married,7,317.2362189,0,15263.81095
121790,64,47,3,0,Married,6,317.2252761,0,15313.26381
130345,22,3,4,1,Single,14,317.1931767,0,15431.65884
134962,61,42,3,0,Married,10,317.1926179,0,15299.6309
196173,39,19,2,1,Married,10,317.1897689,0,15633.48845
165237,37,17,3,0,Married,13,317.1883999,0,15421.42
192647,34,13,3,1,Married,10,317.1596852,0,15304.98426
161029,52,35,3,1,Married,7,317.1256856,0,15552.28428
106782,20,3,4,1,Married,9,317.1153563,0,15567.76782
182889,67,46,4,0,Married,2,317.0264351,0,15331.32176
169358,22,4,1,0,Married,14,316.9985022,0,15414.92511
163172,49,30,2,0,Married,6,316.982423,0,15497.12115
134220,59,41,2,1,Single,13,316.9793543,0,15478.96772
102622,57,36,1,0,Single,11,316.9570048,0,15309.85024
195769,58,39,3,0,Single,15,316.9003365,0,15494.01683
101175,62,44,3,0,Single,13,316.8827303,0,15631.13652
158349,22,4,3,1,Single,14,316.8059894,0,15301.29947
198652,17,0,3,0,Married,14,316.7655233,0,15492.27617
196186,28,8,4,1,Single,13,316.7052401,0,15520.26201
148528,69,49,3,0,Single,8,316.6816588,0,15540.08294
119343,45,24,4,1,Married,15,316.6639021,0,15386.19511
185961,39,19,3,1,Married,14,316.6071358,0,15390.35679
120888,41,23,2,1,Single,10,316.6019221,0,15267.09611
175520,54,35,3,1,Single,13,316.5779989,0,15553.89995
179961,56,35,3,1,Married,6,316.5688739,0,15357.4437
114900,33,15,2,1,Married,4,316.562315,0,15547.11575
144860,22,5,2,0,Single,12,316.5447298,0,15291.23649
136845,61,44,1,0,Single,4,316.5254941,0,15422.27471
198111,69,50,2,0,Single,15,316.5217782,0,15616.08891
158690,47,28,3,1,Single,13,316.5179913,0,15507.89957
154742,38,21,2,1,Married,8,316.5019388,0,15498.09694
185535,35,15,3,0,Single,11,316.4754963,0,15383.77482
128644,58,41,4,1,Married,9,316.4531916,0,15394.65958
114220,27,8,2,1,Married,10,316.3652417,0,15512.26209
147999,60,43,3,1,Married,8,316.3342789,0,15464.71395
172836,46,28,4,1,Married,6,316.294927,0,15402.74635
164563,28,10,1,1,Married,10,316.2639336,0,15466.19668
103302,49,30,3,0,Single,14,316.0836756,0,15307.18378
184313,65,48,3,1,Married,5,315.9213453,0,15244.06727
189247,31,11,3,1,Married,7,315.9044514,0,15210.22257
148453,30,9,2,1,Married,6,315.8990068,0,15299.95034
147891,38,19,3,1,Married,7,315.8700753,0,15587.50377
156978,66,47,3,0,Married,6,315.7652576,0,15300.26288
123377,26,7,3,1,Married,8,315.7468499,0,15208.3425
110215,21,3,2,1,Single,14,315.7115233,0,15576.57617
162251,30,13,4,1,Married,10,315.6685673,0,15512.42837
173726,62,44,3,1,Married,8,315.6520505,0,15268.60253
121285,18,0,3,0,Married,11,315.6330931,0,15535.65466
101320,20,3,2,1,Single,15,315.5982887,0,15490.91444
115197,27,9,3,0,Married,13,315.5611003,0,15301.05502
133507,59,38,2,1,Married,6,315.5270639,0,15212.3532
147777,29,9,2,1,Married,9,315.51902,0,15348.951
150505,22,5,3,1,Single,11,315.5171703,0,15311.85852
116261,36,15,2,1,Married,9,315.4863007,0,15436.31504
173531,28,10,3,1,Married,12,315.4713089,0,15498.56545
102202,42,25,2,1,Married,7,315.3726493,0,15395.63247
120132,28,11,1,0,Married,11,315.3474326,0,15379.37163
174927,63,44,4,0,Married,13,315.2988023,0,15181.94012
125818,37,18,3,1,Married,15,315.291104,0,15423.5552
139637,34,15,2,0,Married,13,315.2849102,0,15463.24551
182988,29,8,2,1,Married,12,315.2526124,0,15345.63062
193015,69,51,4,1,Single,2,315.2465065,0,15423.32533
153002,54,33,3,1,Married,7,315.1998983,0,15402.99492
158937,28,7,2,1,Married,9,315.1065652,0,15447.32826
110767,21,4,2,1,Single,13,315.0421147,0,15392.10574
191697,55,37,3,1,Married,5,315.0349231,0,15465.74616
174134,55,38,3,1,Single,13,315.0257709,0,15447.28855
171724,55,38,3,1,Single,14,314.9887539,0,15472.4377
137954,58,38,3,1,Married,8,314.9882766,0,15384.41383
147298,21,4,3,0,Married,10,314.9871314,0,15395.35657
128199,42,22,2,1,Married,10,314.9728176,0,15530.64088
133468,19,1,2,1,Married,6,314.9461087,0,15365.30544
145084,47,29,3,0,Single,12,314.8957605,0,15485.78803
125775,39,21,4,1,Married,9,314.889906,0,15284.4953
171234,42,23,1,0,Married,9,314.8458289,0,15176.29145
111790,38,18,2,1,Married,7,314.8310797,0,15261.55399
106049,26,8,1,1,Married,6,314.7922365,0,15437.61183
157186,34,13,3,0,Married,14,314.7800995,0,15324.00498
128519,22,4,3,1,Married,13,314.748963,0,15421.44815
195978,31,10,4,1,Married,9,314.7164641,0,15206.82321
158063,21,4,2,1,Married,6,314.6901717,0,15311.50859
113778,27,10,4,1,Married,7,314.6883094,0,15467.41547
167022,58,38,1,1,Married,10,314.6819894,0,15419.09947
109614,37,19,1,1,Married,7,314.6814073,0,15135.07037
141606,40,20,2,1,Married,10,314.6769002,0,15482.84501
160387,57,40,1,0,Married,15,314.6620972,0,15457.10486
190257,51,31,1,1,Married,15,314.6486414,0,15368.43207
107732,16,0,3,0,Married,14,314.6315333,0,15418.57667
184374,43,23,3,1,Married,9,314.6065556,0,15291.32778
123352,24,5,3,1,Single,14,314.4685197,0,15319.42599
104974,43,22,2,1,Married,8,314.461356,0,15243.0678
129155,59,41,4,0,Married,13,314.4598733,0,15155.99367
121031,37,17,2,1,Married,7,314.4466173,0,15249.33087
165516,25,6,2,1,Single,15,314.4390289,0,15487.95145
193472,19,2,2,1,Married,7,314.3495085,0,15317.47543
159773,34,13,3,1,Married,7,314.3353919,0,15190.7696
111562,61,43,3,1,Single,8,314.3065782,0,15165.32891
143211,55,38,2,0,Married,7,314.2763929,0,15188.81965
163043,52,34,3,0,Married,14,314.2609786,0,15187.04893
181905,51,31,2,0,Married,13,314.2585416,0,15131.92708
195382,52,31,2,1,Married,8,314.2248501,0,15349.24251
171536,52,35,3,1,Married,9,314.2055786,0,15185.27893
193267,52,31,1,0,Married,10,314.1997614,0,15137.98807
175995,62,43,1,1,Married,13,314.1903441,0,15449.51721
193809,32,11,3,1,Single,12,314.1710238,0,15402.55119
197186,36,17,2,1,Married,6,314.1239865,0,15376.19933
130223,58,37,2,1,Married,6,314.0996953,0,15484.98477
162209,44,27,2,1,Married,7,314.095723,0,15404.78615
113151,20,1,4,0,Married,6,314.0949184,0,15264.74592
151417,51,33,4,1,Single,13,314.0866634,0,15374.33317
161398,54,34,1,0,Married,9,314.085069,0,15136.25345
136428,55,36,4,1,Married,7,314.0664581,0,15358.32291
176322,50,29,2,1,Married,8,314.0491544,0,15189.45772
122128,32,12,4,1,Married,6,314.0395774,0,15352.97887
182777,67,46,4,0,Single,8,314.0322396,0,15110.61198
152674,39,22,2,0,Married,14,313.9752158,0,15419.76079
180742,52,33,3,1,Married,6,313.9598192,0,15218.99096
199882,25,7,2,1,Single,12,313.958467,0,15391.92335
161194,35,18,2,0,Married,8,313.9556337,0,15361.78169
180292,21,3,3,1,Married,8,313.9542274,0,15163.71137
124522,29,8,3,0,Single,14,313.862297,0,15206.11485
101187,21,4,3,1,Married,12,313.8382522,0,15190.91261
184032,43,25,3,1,Married,8,313.8178437,0,15429.89219
149483,55,35,3,1,Married,7,313.7580238,0,15281.90119
198522,58,40,4,0,Single,14,313.6963702,0,15469.81851
163263,38,17,3,1,Married,6,313.5413947,0,15263.06974
127589,69,49,3,0,Single,3,313.5289808,0,15255.44904
175519,26,5,3,1,Married,9,313.5074495,0,15438.37248
164624,53,33,1,1,Married,7,313.4599585,0,15117.99793
114783,55,35,4,1,Married,7,313.4504471,0,15144.52236
155732,63,44,2,1,Single,5,313.4314506,0,15397.57253
160858,62,42,2,0,Single,7,313.4091523,0,15365.45762
116386,38,21,3,1,Married,9,313.334855,0,15361.74275
100471,58,40,2,1,Married,6,313.3115668,0,15432.57834
153627,22,3,3,1,Married,12,313.306241,0,15394.31205
142730,18,0,4,0,Married,11,313.2976091,0,15306.88046
123179,43,26,3,1,Married,7,313.2618716,0,15090.09358
187145,23,6,3,1,Married,8,313.251889,0,15136.59445
169656,18,0,2,0,Married,14,313.2257204,0,15421.28602
127296,29,8,2,1,Single,9,313.2083114,0,15153.41557
152641,38,19,3,1,Married,11,313.1903744,0,15059.51872
142477,43,25,3,1,Married,6,313.1865975,0,15091.32988
155451,20,1,2,0,Married,10,313.162049,0,15094.10245
106404,34,16,4,1,Married,15,313.1482523,0,15226.41262
128261,66,47,1,0,Married,5,313.1380724,0,15449.90362
168174,25,8,3,1,Married,15,313.1366249,0,15310.83125
111020,18,0,2,1,Married,15,313.1189401,0,15426.94701
157143,55,38,4,0,Single,15,313.0968221,0,15285.84111
163400,67,47,1,0,Single,9,313.0615814,0,15085.07907
173757,70,49,3,1,Single,6,313.0489022,0,15196.44511
120787,68,49,1,1,Single,8,313.0465443,0,15183.32722
135539,39,18,3,1,Married,8,313.0405547,0,15334.02774
153328,34,17,1,1,Married,10,313.033365,0,15430.66825
160133,43,22,1,1,Married,9,313.0289438,0,15375.44719
127280,65,45,2,0,Married,3,313.0059872,0,15336.29936
148479,20,1,4,0,Married,15,313.0024539,0,15336.1227
121496,33,16,1,0,Married,9,312.9428646,0,15103.14323
177083,36,15,3,1,Single,13,312.8123389,0,15216.61695
121408,41,22,1,1,Married,8,312.7213527,0,15056.06764
171489,37,20,3,1,Married,14,312.6920688,0,15118.60344
184932,21,4,2,0,Married,15,312.6715176,0,15102.57588
137402,35,18,4,1,Married,2,312.5287596,0,15186.43798
192905,61,43,4,1,Single,12,312.522305,0,15206.11525
188150,24,7,1,1,Married,13,312.4945546,0,15377.72773
127144,33,12,2,1,Married,11,312.4806557,0,15066.03279
108553,21,3,2,1,Single,11,312.4802515,0,15171.01258
166043,57,36,3,1,Married,9,312.458045,0,15235.90225
106273,31,10,2,1,Married,11,312.4465176,0,15290.32588
107717,62,42,4,0,Married,10,312.3915256,0,15088.57628
196562,18,0,2,1,Married,10,312.3798415,0,15363.99208
171962,55,34,2,0,Married,14,312.3318375,0,15191.59188
177817,32,12,1,1,Married,9,312.2857892,0,15328.28946
162655,24,7,3,1,Single,11,312.2006784,0,15361.03392
128836,67,46,4,0,Married,7,312.1924883,0,15016.62442
132178,23,4,2,1,Single,14,312.1130913,0,15340.65457
145524,30,13,3,1,Married,10,312.0818596,0,15075.09298
180190,51,31,1,0,Married,6,312.0653155,0,15132.26578
187532,38,19,3,0,Married,12,312.0402091,0,15360.01046
111042,29,10,3,1,Married,7,312.0352054,0,15048.76027
184512,46,28,2,1,Married,6,312.0185174,0,15181.92587
175998,52,35,2,1,Married,9,311.9594323,0,15360.97162
183546,25,7,4,0,Single,14,311.9501958,0,15350.50979
152361,52,31,1,1,Married,10,311.9191271,0,15059.95636
170226,26,8,3,1,Married,6,311.9054139,0,15357.2707
158449,32,12,4,1,Married,6,311.8931682,0,15062.65841
144598,33,14,2,0,Married,8,311.8560379,0,15385.8019
134726,40,20,1,1,Single,13,311.8538565,0,15261.69283
193045,63,44,3,1,Single,6,311.837761,0,15242.88805
155782,34,13,2,1,Married,0,311.7754509,0,14999.77255
124618,50,30,4,1,Married,6,311.735022,0,15378.7511
113796,24,7,4,0,Married,14,311.7199146,0,15182.99573
152501,23,5,3,1,Single,13,311.7155923,0,15044.77962
147890,60,41,3,0,Single,5,311.6503256,0,15038.51628
126248,23,6,3,1,Single,12,311.6227524,0,14992.13762
153785,21,2,4,1,Single,11,311.6125588,0,15021.62794
151767,48,28,2,1,Married,10,311.5554564,0,15302.77282
135713,49,28,3,1,Married,9,311.4554159,0,15096.7708
154381,60,41,2,1,Married,8,311.4301899,0,15141.5095
107714,54,36,3,1,Single,13,311.4123886,0,15123.61943
147658,27,9,1,1,Married,6,311.3847328,0,15351.23664
168689,22,4,4,1,Married,11,311.3831572,0,15307.15786
117284,33,15,3,1,Married,12,311.3746217,0,15034.73109
134437,22,5,2,0,Married,14,311.3666598,0,15089.33299
198272,69,50,3,0,Single,8,311.3562258,0,15197.81129
156505,21,3,1,0,Married,13,311.353198,0,15102.6599
129038,69,52,4,1,Married,6,311.3325728,0,15283.62864
162873,54,36,4,1,Married,1,311.3049652,0,15122.24826
180823,44,25,2,1,Married,9,311.2450717,0,15304.25359
143890,31,13,3,1,Single,11,311.2100189,0,15045.50095
159859,29,12,4,1,Married,8,311.1506004,0,15351.53002
154373,46,29,2,1,Married,10,311.0704893,0,15201.52447
170699,51,30,2,1,Single,14,311.0094363,0,15047.47182
120221,27,6,2,1,Married,7,311.0017988,0,15203.08994
181519,21,2,3,1,Married,11,310.9789476,0,15215.94738
100681,22,3,3,1,Married,8,310.9574139,0,15147.8707
106064,38,19,1,1,Married,7,310.9397719,0,15180.9886
151683,33,15,3,1,Married,10,310.9373969,0,15249.86985
135487,59,40,1,1,Married,10,310.9230046,0,14953.15023
169265,61,43,3,1,Single,4,310.9042823,0,14996.21412
172398,54,36,3,1,Married,7,310.8779976,0,15099.89988
135642,55,38,2,0,Married,12,310.8686668,0,15158.43334
162203,24,5,1,0,Single,15,310.8532989,0,15084.66495
151645,23,4,4,0,Married,14,310.7973575,0,14955.86788
162813,66,49,3,0,Married,1,310.7172356,0,15310.86178
197494,20,1,2,1,Married,13,310.703127,0,15079.15635
125221,51,30,2,1,Married,10,310.6833687,0,15025.16844
188024,60,41,3,1,Single,9,310.6458562,0,14956.29281
138702,34,16,1,0,Single,12,310.6430719,0,15069.1536
151678,45,26,2,1,Married,6,310.6077188,0,15145.38594
148258,59,41,3,1,Married,11,310.5947859,0,15293.7393
171657,66,48,3,1,Married,6,310.5503805,0,14962.51903
144419,37,19,2,1,Single,14,310.53615,0,15222.8075
104143,41,24,1,1,Married,9,310.4958386,0,15282.79193
107756,36,15,2,1,Married,8,310.4836679,0,15182.1834
132202,49,29,3,1,Single,13,310.4775272,0,15195.87636
161077,17,0,2,0,Married,11,310.4487937,0,15180.43969
160131,61,44,2,0,Married,6,310.4345511,0,15173.72756
118282,36,15,2,1,Single,11,310.4054126,0,15238.27063
159872,25,8,4,0,Married,13,310.3989646,0,14962.94823
147239,35,18,2,1,Married,7,310.3556518,0,15019.78259
126210,26,9,4,0,Married,12,310.3356728,0,15134.78364
180115,16,0,1,1,Married,15,310.3054332,0,15046.27166
198521,22,5,3,1,Single,13,310.2679868,0,15057.39934
130685,25,7,3,0,Married,13,310.2675403,0,15158.37702
121820,45,28,3,1,Married,9,310.257686,0,14962.8843
190698,51,30,3,1,Married,12,310.2260018,0,15018.30009
150208,38,17,1,1,Married,8,310.2184717,0,15057.92359
110844,23,6,2,1,Married,7,310.2142008,0,15310.71004
103591,68,51,3,1,Married,6,310.2132471,0,15046.66236
158242,23,5,2,1,Single,15,310.1527237,0,15229.63619
167166,52,31,3,1,Married,6,310.1040789,0,14961.20395
143538,56,38,4,1,Married,8,310.0303143,0,15024.51572
102304,53,32,2,0,Married,10,309.9887649,0,14941.43825
159999,57,38,2,1,Married,6,309.9708434,0,15249.54217
170879,52,35,1,1,Married,10,309.9621785,0,15219.10893
124188,19,0,3,1,Single,14,309.952845,0,14921.64225
188105,16,0,2,1,Married,14,309.9269399,0,15253.347
138287,68,47,3,0,Single,11,309.9202624,0,15135.01312
186381,23,5,2,0,Married,14,309.907631,0,15163.38155
138642,37,19,2,0,Married,9,309.8817858,0,15230.08929
190232,70,51,4,0,Single,11,309.8533226,0,15204.66613
155439,46,29,3,1,Married,10,309.8248095,0,14942.24048
173074,65,44,3,0,Married,8,309.8096523,0,15258.48262
166446,29,12,3,1,Married,8,309.7743793,0,14957.71897
135552,65,44,4,0,Married,9,309.7591136,0,15002.95568
134297,56,36,2,1,Married,6,309.7220679,0,15286.1034
169360,44,27,4,1,Married,7,309.7076764,0,14938.38382
172282,43,25,1,1,Married,6,309.6777545,0,15109.88773
105915,26,6,1,1,Married,7,309.6459659,0,15089.2983
128537,30,10,3,1,Married,6,309.6060813,0,14896.30407
143584,43,25,3,1,Single,8,309.590637,0,14969.53185
174983,20,2,3,0,Married,13,309.5882368,0,15001.41184
123563,49,31,3,1,Married,6,309.5785738,0,15193.92869
165672,17,0,3,1,Single,11,309.5589377,0,15263.94689
181605,33,15,3,0,Married,13,309.5275766,0,15124.37883
162297,23,6,3,1,Single,13,309.5079244,0,15039.39622
186962,49,31,3,1,Married,14,309.4576313,0,14982.88157
188867,37,20,3,1,Married,10,309.4043147,0,15029.21574
195001,23,5,4,0,Married,14,309.3622537,0,15172.11269
163282,51,30,2,0,Single,14,309.2919752,0,14903.59876
119203,45,27,4,1,Married,7,309.2550353,0,14960.75177
120108,55,37,1,1,Married,10,309.2396041,0,15029.98021
157592,29,9,2,1,Single,11,309.237974,0,14979.8987
129544,25,6,3,1,Married,14,309.2151888,0,15137.75944
194051,69,52,3,0,Single,7,309.1605204,0,15120.02602
192110,21,3,3,0,Married,15,309.1510858,0,15041.55429
194459,50,29,3,1,Married,6,309.1338776,0,14916.69388
129504,56,35,3,1,Married,6,309.1169012,0,15165.84506
169052,48,30,1,1,Married,9,309.1141925,0,15149.70963
110912,70,52,3,1,Single,9,309.0849575,0,15250.24788
134097,52,35,4,0,Single,13,309.0594526,0,15188.97263
147833,39,19,3,1,Married,7,309.0087753,0,14939.43877
106815,25,6,2,1,Married,8,308.9951899,0,14985.7595
141141,61,40,3,1,Married,9,308.9740656,0,15186.70328
179725,43,25,3,1,Married,7,308.9086909,0,15182.43455
172562,20,1,3,1,Married,14,308.9061638,0,15059.30819
104075,69,51,1,0,Single,3,308.9019548,0,15192.09774
189263,54,34,2,0,Married,12,308.8783677,0,14995.91839
190025,20,3,2,1,Married,12,308.8496566,0,14912.48283
175417,51,34,1,1,Married,8,308.8097112,0,14921.48556
192285,61,41,3,1,Married,2,308.8055458,0,14870.27729
153821,51,31,2,1,Single,11,308.7935312,0,15166.67656
174299,36,16,2,1,Single,12,308.7655925,0,14912.27963
145568,25,7,4,0,Married,15,308.7451306,0,14844.25653
194534,25,8,1,1,Single,14,308.6653638,0,14906.26819
173341,25,8,2,1,Married,13,308.6550336,0,15135.75168
167665,61,41,1,1,Married,9,308.6526154,0,15105.63077
199733,28,7,3,1,Married,11,308.6392806,0,14831.96403
137279,33,15,3,0,Married,11,308.619471,0,15003.97355
185162,30,12,1,1,Married,8,308.5713815,0,15060.56908
188341,27,6,2,1,Married,10,308.5647245,0,14895.23623
116900,40,20,4,1,Married,8,308.5336043,0,14918.68022
145566,50,32,1,1,Married,12,308.5023481,0,15150.11741
180717,31,11,3,1,Married,8,308.4657403,0,15096.28702
103823,65,48,2,1,Single,3,308.462944,0,14892.1472
158622,39,18,3,1,Single,13,308.4601596,0,15150.00798
148014,52,32,2,0,Married,12,308.4501787,0,15042.50894
101694,39,20,3,1,Married,7,308.4500336,0,15134.50168
103633,27,7,4,0,Married,13,308.436658,0,15054.8329
127083,45,26,2,1,Married,7,308.4056167,0,14882.28084
132743,22,5,3,1,Single,15,308.3745452,0,14855.72726
179686,46,26,3,1,Single,13,308.2932183,0,14997.66092
127738,60,43,2,0,Married,11,308.2903275,0,15184.51638
106957,25,7,2,1,Single,13,308.2719485,0,14942.59743
179454,25,6,2,1,Single,14,308.2629494,0,14975.14747
102292,65,48,2,0,Married,8,308.2452353,0,14989.26177
144566,32,12,1,1,Married,6,308.2346622,0,14905.73311
125452,58,38,1,1,Married,8,308.233244,0,15181.6622
153543,29,12,2,1,Married,8,308.1962701,0,15000.81351
116338,35,16,2,0,Single,11,308.1654928,0,15116.27464
166672,66,47,4,1,Single,8,308.136766,0,14919.8383
117422,27,8,2,1,Married,10,308.1027723,0,15203.13862
134800,23,6,2,0,Married,12,308.101933,0,14866.09665
142852,41,24,2,1,Married,12,308.0973519,0,15163.8676
103676,49,30,3,1,Married,14,308.0832138,0,15125.16069
172404,55,36,4,0,Married,6,308.0539618,0,14886.69809
194617,50,32,3,1,Married,8,308.0470493,0,14821.35247
119323,34,14,3,1,Married,10,308.0223421,0,14893.11711
196463,17,0,4,1,Married,12,308.0193194,0,14850.96597
192092,16,0,3,1,Single,11,308.0115413,0,15048.57707
151043,28,8,1,1,Married,10,308.0062911,0,15030.31456
120262,43,23,4,1,Married,7,307.9904309,0,15159.52155
180075,33,14,3,0,Married,15,307.983331,0,15125.16655
159016,46,29,3,1,Married,1,307.950249,0,14874.51245
155725,24,7,3,1,Single,12,307.9414794,0,15129.07397
169827,23,4,1,0,Married,13,307.9160669,0,15159.80335
194998,58,40,1,1,Married,7,307.8470487,0,15000.35244
131293,58,41,4,1,Single,12,307.8314635,0,14904.57318
112207,45,25,1,1,Married,10,307.8064177,0,15088.32089
194528,49,30,3,1,Married,8,307.6696858,0,15040.48429
151098,70,52,1,1,Single,10,307.6472449,0,15111.36225
135597,25,6,3,1,Married,13,307.6357729,0,15116.78865
157613,29,12,4,0,Single,12,307.6140337,0,14783.70169
133663,20,3,1,1,Married,7,307.6119306,0,15016.59653
171124,49,30,1,0,Married,10,307.6032853,0,14817.16427
186545,41,22,2,0,Married,15,307.5550597,0,14944.75299
104443,22,3,3,1,Single,11,307.5161346,0,14814.80673
102876,16,0,2,1,Married,8,307.503338,0,14895.1669
114127,64,43,2,0,Single,6,307.4466053,0,14843.33027
108141,52,32,1,1,Married,4,307.429253,0,15115.46265
161391,55,36,3,1,Married,10,307.4165301,0,14858.82651
174512,61,41,1,0,Married,12,307.4146315,0,15124.73158
188399,68,48,4,1,Single,8,307.4119981,0,15043.59991
191411,17,0,4,1,Married,15,307.386164,0,14879.3082
188397,21,3,2,0,Single,15,307.3796409,0,15110.98205
178996,41,21,2,1,Married,7,307.3442838,0,15060.21419
146070,49,29,4,1,Married,8,307.3441817,0,14903.20909
124623,54,35,1,1,Married,8,307.3220363,0,14959.10182
195890,33,14,2,1,Married,8,307.2958376,0,15101.79188
158957,43,23,2,1,Single,15,307.2698672,0,15023.49336
154636,43,23,1,1,Married,9,307.2503882,0,15131.51941
151503,32,13,1,0,Married,13,307.2191317,0,15143.95659
159365,62,45,2,0,Married,9,307.2162304,0,14831.81152
169988,53,35,1,1,Married,8,307.2012361,0,14821.06181
157196,63,42,2,0,Single,7,307.1713191,0,15110.56596
169880,64,47,3,1,Married,7,307.1403785,0,15048.01893
188049,62,45,2,0,Single,12,307.1214269,0,15037.07135
186260,69,51,2,1,Single,8,306.9941545,0,15120.70773
186477,32,15,1,1,Married,7,306.9422816,0,15035.11408
149890,70,51,4,1,Single,10,306.8943535,0,14901.71768
172200,17,0,2,0,Married,13,306.8533105,0,14796.66553
130067,22,3,1,0,Single,12,306.8513302,0,14985.56651
164494,67,47,3,0,Married,10,306.8342585,0,14822.71293
132009,23,4,3,1,Married,8,306.7922108,0,14869.61054
127360,65,48,1,0,Single,11,306.7395011,0,15035.97506
151357,46,25,1,1,Married,8,306.682273,0,15027.11365
138587,28,10,2,1,Married,8,306.676477,0,14941.82385
109605,32,12,2,1,Married,15,306.6404225,0,14957.02113
154226,26,8,1,1,Single,14,306.6197955,0,14747.98978
113797,31,12,2,1,Married,8,306.5892045,0,15020.46023
185098,20,1,2,1,Married,9,306.5802683,0,15079.01342
111259,68,51,1,1,Single,8,306.5707397,0,14736.53699
168909,50,33,1,1,Married,11,306.5442945,0,14962.21473
187381,27,6,4,0,Married,14,306.4509931,0,14761.54966
193265,32,15,2,1,Married,6,306.3533269,0,15059.66635
179754,68,51,2,0,Married,8,306.3404608,0,14872.02304
104006,22,5,4,0,Married,11,306.3150769,0,14788.75385
114423,29,12,2,1,Married,9,306.3115973,0,15112.57987
119499,18,0,3,0,Married,15,306.3038174,0,14800.19087
102779,67,47,1,0,Single,13,306.2881697,0,15031.40849
109979,18,0,3,1,Single,15,306.2860105,0,15069.30053
114435,25,6,4,1,Married,5,306.2268891,0,15094.34446
196311,40,23,3,1,Married,15,306.1517631,0,14782.58816
192722,28,10,2,1,Married,10,306.1515312,0,14904.57656
154460,24,6,4,1,Single,13,306.0167496,0,14899.83748
185844,53,34,2,1,Married,8,305.9895502,0,14934.47751
198387,50,30,3,1,Married,7,305.9487254,0,14769.43627
185490,54,34,3,1,Married,15,305.8906134,0,14912.53067
189957,16,0,1,1,Married,11,305.8610846,0,14930.05423
190669,50,33,3,0,Married,12,305.856311,0,14776.81555
151490,25,7,2,1,Single,14,305.8109081,0,15061.54541
167836,66,47,3,1,Married,3,305.8034818,0,14904.17409
116739,63,45,2,0,Married,7,305.7939811,0,14915.69906
173057,60,43,4,1,Single,7,305.7744547,0,15001.72274
190024,58,37,4,1,Married,10,305.7425777,0,14865.12889
152055,27,8,2,0,Single,12,305.7422196,0,14999.11098
189565,23,6,3,1,Single,14,305.7334947,0,14799.67474
198618,55,34,4,1,Married,14,305.7163394,0,14751.81697
107484,68,49,2,0,Single,4,305.6524736,0,15051.62368
110745,33,12,1,1,Married,10,305.6462484,0,15012.31242
147104,35,16,2,0,Single,13,305.599627,0,14681.98135
199841,21,3,4,1,Married,8,305.521177,0,14813.05885
112234,17,0,4,1,Married,15,305.5204447,0,14823.02224
125289,57,38,2,1,Married,8,305.4447304,0,14895.23652
146239,42,23,3,0,Single,12,305.4003966,0,15053.01983
132654,32,11,2,0,Single,13,305.3024049,0,14878.12025
111781,33,15,3,0,Married,15,305.2700497,0,14865.50249
147851,27,9,2,1,Single,14,305.2494871,0,15023.47436
185406,62,43,2,0,Married,7,305.2483756,0,14923.41878
155126,63,45,2,0,Married,11,305.1540949,0,14872.70475
171878,46,27,3,1,Married,13,305.0855007,0,14679.27504
133690,53,36,2,1,Married,7,305.0790016,0,14826.95008
188979,46,26,2,0,Single,13,305.075432,0,14672.7716
198008,25,7,2,0,Married,14,305.0281605,0,14782.40803
178342,69,48,3,1,Married,3,305.0065965,0,14766.32983
108161,18,0,2,1,Single,13,304.9882514,0,14760.41257
193282,65,48,3,1,Single,10,304.9781505,0,14689.90753
150335,19,1,2,0,Single,15,304.9770159,0,14937.8508
124317,50,31,1,1,Single,13,304.9612526,0,14836.06263
115516,54,37,2,1,Married,6,304.8658354,0,14998.29177
108396,69,48,3,1,Single,9,304.8243163,0,14707.21582
106548,60,42,2,1,Single,7,304.7559646,0,14728.79823
133266,69,52,4,1,Single,7,304.7423722,0,14788.11861
160727,60,40,2,1,Single,14,304.6880075,0,14679.40038
175481,46,28,3,1,Married,14,304.6095956,0,14730.47978
168584,29,10,3,1,Married,9,304.5682218,0,14654.41109
142111,69,48,4,1,Married,9,304.5191342,0,14934.95671
162923,29,11,4,1,Single,12,304.4703177,0,14732.51589
106836,63,45,2,1,Single,7,304.4479143,0,14698.39572
195717,62,43,2,1,Married,0,304.4294792,0,14699.47396
166193,59,40,3,1,Married,10,304.4177007,0,14909.88504
166127,16,0,2,1,Married,13,304.3596949,0,14700.98475
131367,56,35,2,1,Married,10,304.3149226,0,14898.74613
184879,19,1,2,0,Married,13,304.289917,0,14865.49585
168100,24,6,3,1,Married,12,304.2591454,0,14946.95727
150074,60,41,3,0,Single,2,304.2014304,0,14716.07152
127268,26,9,2,0,Single,11,304.1275375,0,14955.37688
198331,23,4,3,1,Married,7,304.0993493,0,14780.96747
121573,25,7,2,1,Single,12,304.0808254,0,14635.04127
108280,26,6,1,1,Married,10,304.0332799,0,14640.664
172914,25,8,3,1,Married,7,304.0324125,0,14746.62063
176651,54,36,2,0,Married,11,304.0070767,0,14917.35384
123573,20,2,3,1,Single,13,303.9224718,0,14941.12359
157900,70,50,1,1,Single,6,303.9037184,0,14988.18592
164097,69,50,2,0,Married,9,303.8524855,0,14938.62428
115157,64,43,3,0,Single,3,303.8452258,0,14705.26129
111146,42,23,3,1,Married,11,303.7557202,0,14831.78601
145739,60,39,2,0,Single,10,303.7258336,0,14666.29168
198066,40,21,3,1,Married,7,303.6954464,0,14789.77232
135849,69,50,3,0,Single,7,303.6715788,0,14774.57894
187152,49,29,3,0,Single,14,303.5159812,0,14821.79906
187863,47,29,3,1,Married,8,303.4466458,0,14583.33229
173236,17,0,2,0,Married,11,303.444704,0,14916.2352
111806,63,45,1,0,Single,4,303.426557,0,14890.32785
152494,22,3,4,0,Married,11,303.4177109,0,14693.88555
130779,52,31,2,1,Married,14,303.4051642,0,14782.25821
114690,16,0,1,1,Married,15,303.4043979,0,14642.2199
185733,16,0,2,1,Married,13,303.3959931,0,14944.79966
106865,27,6,4,0,Single,11,303.3951338,0,14609.75669
174978,27,6,3,1,Married,9,303.3747837,0,14632.73919
144210,63,45,2,0,Single,15,303.3502155,0,14619.51078
193260,45,24,2,1,Married,6,303.3457758,0,14817.28879
131079,16,0,4,1,Single,13,303.3291757,0,14761.45879
129481,67,47,2,1,Married,10,303.2492958,0,14954.46479
112852,31,11,2,1,Married,9,303.2293178,0,14945.46589
127718,65,45,4,1,Single,8,303.1838448,0,14712.19224
166104,32,11,1,1,Married,9,303.1333791,0,14595.66896
174661,38,19,1,1,Married,11,303.1201775,0,14913.00888
143315,19,2,4,0,Married,14,303.0820897,0,14645.10449
172701,21,3,2,1,Married,10,303.0263672,0,14789.31836
107265,22,3,1,0,Married,12,302.9412015,0,14831.06008
150040,21,3,4,0,Married,12,302.910467,0,14788.52335
164713,67,49,2,0,Single,8,302.8791172,0,14886.95586
100560,66,49,3,1,Married,2,302.877603,0,14568.88015
143153,25,8,1,0,Single,15,302.8673506,0,14891.36753
135883,62,41,3,0,Single,0,302.8527283,0,14759.63642
139438,66,46,1,1,Married,9,302.8231225,0,14822.15613
109986,28,9,3,1,Married,8,302.8204314,0,14808.02157
134352,64,43,2,1,Single,10,302.8175023,0,14676.87512
190788,25,8,3,1,Married,12,302.7966664,0,14556.83332
179441,40,22,3,1,Married,8,302.787052,0,14734.3526
149949,60,41,1,1,Single,5,302.7598559,0,14894.9928
121023,21,4,1,1,Married,9,302.7542004,0,14715.71002
169057,59,38,3,1,Married,7,302.7425401,0,14861.12701
127988,50,33,3,1,Married,9,302.6707065,0,14909.53533
196029,19,1,3,0,Single,13,302.6470886,0,14817.35443
183911,58,41,3,1,Married,8,302.6160105,0,14832.80053
176813,30,10,2,1,Married,8,302.5936644,0,14857.68322
114256,54,34,2,1,Married,9,302.5911553,0,14567.55777
143622,20,2,3,1,Married,6,302.5659292,0,14860.29646
115404,66,47,3,1,Married,5,302.5600291,0,14652.00146
164350,70,50,1,0,Married,2,302.4864881,0,14893.32441
110946,48,28,2,1,Single,12,302.4315301,0,14834.57651
174602,63,43,2,0,Married,0,302.4183973,0,14539.91987
164748,66,46,3,1,Married,3,302.3751056,0,14792.75528
120968,20,1,1,1,Single,12,302.3470413,0,14820.35207
131279,28,10,3,0,Married,14,302.2831964,0,14570.15982
175050,21,4,2,0,Single,14,302.257992,0,14858.8996
123145,53,34,3,1,Married,9,302.2555434,0,14528.77717
118684,41,20,3,0,Single,15,302.1979214,0,14640.89607
118614,17,0,3,1,Single,11,302.1749564,0,14906.74782
200235,50,32,1,1,Married,7,302.1415562,0,14768.07781
192238,69,49,2,1,Single,10,302.1412162,0,14673.06081
130319,39,22,4,1,Married,7,302.1335426,0,14639.67713
186476,20,2,3,1,Single,15,302.0814942,0,14684.07471
182999,64,43,3,1,Married,7,302.0582232,0,14773.91116
191051,70,51,1,0,Single,15,302.0260161,0,14899.30081
109562,54,37,3,0,Married,11,301.9860187,0,14699.30094
132620,52,33,2,1,Married,8,301.968685,0,14580.43425
144801,23,5,2,1,Single,13,301.8903378,0,14703.51689
165024,45,25,2,1,Married,6,301.8893886,0,14715.46943
189796,64,44,4,1,Single,7,301.8739204,0,14815.69602
164286,38,20,2,0,Single,15,301.8672605,0,14590.36303
191124,57,38,3,1,Married,7,301.8101255,0,14513.50628
196243,64,44,2,1,Single,7,301.7878038,0,14881.39019
112945,30,10,4,1,Married,8,301.6996352,0,14813.98176
180600,47,30,1,1,Married,8,301.6710047,0,14744.55024
119153,63,43,2,0,Single,11,301.6389251,0,14508.94626
121964,37,16,3,1,Married,6,301.6203459,0,14645.0173
191324,60,43,3,0,Married,2,301.6186751,0,14590.93376
101147,66,46,3,0,Married,0,301.5997948,0,14849.98974
152782,70,53,3,1,Married,10,301.5181844,0,14519.90922
196319,39,20,2,1,Married,7,301.4943436,0,14537.71718
105404,68,47,2,1,Single,14,301.4452901,0,14815.26451
131862,67,50,2,1,Single,10,301.4422157,0,14559.11079
166383,16,0,3,1,Married,15,301.2966589,0,14596.83295
168634,16,0,3,1,Single,14,301.2494851,0,14683.47426
172964,60,39,2,1,Single,6,301.1482711,0,14584.41356
137112,61,42,3,0,Single,7,301.1465293,0,14482.32647
165559,65,47,3,0,Single,6,301.0920538,0,14814.60269
178579,19,2,2,1,Married,13,301.062505,0,14774.12525
199618,23,5,2,1,Married,9,301.0322857,0,14517.61429
101071,18,1,4,1,Married,11,301.015681,0,14464.78405
166402,68,50,4,1,Married,3,300.9867581,0,14765.33791
112655,55,35,2,0,Married,14,300.9405869,0,14473.02935
188370,18,1,3,1,Married,7,300.9062125,0,14543.31063
192750,26,7,2,1,Married,8,300.8567133,0,14510.83567
175991,32,14,4,1,Married,7,300.8454963,0,14495.27482
171438,24,7,3,0,Married,13,300.826389,0,14713.31945
178940,53,33,1,1,Married,6,300.7818655,0,14768.09328
137600,39,19,2,1,Married,15,300.7346324,0,14815.73162
139101,18,0,2,1,Single,13,300.7249513,0,14560.24757
124181,16,0,4,1,Married,13,300.6688027,0,14502.44014
116246,21,2,2,1,Married,12,300.6285279,0,14679.4264
123806,65,44,3,0,Single,9,300.5896711,0,14724.48356
174989,23,4,3,1,Single,15,300.5757063,0,14572.78532
165934,48,31,3,1,Married,9,300.5180508,0,14603.90254
105242,32,13,4,0,Single,12,300.5163912,0,14626.81956
148840,61,42,4,1,Married,6,300.4728614,0,14783.64307
198858,33,15,4,1,Married,7,300.3903436,0,14595.51718
199254,62,43,4,1,Married,9,300.3703849,0,14708.51925
125421,50,33,1,1,Married,10,300.3399914,0,14479.99957
127564,68,48,2,0,Single,9,300.270217,0,14742.51085
116257,70,52,4,0,Single,2,300.2694754,0,14807.47377
196524,56,38,1,1,Married,15,300.2605517,0,14571.02759
128564,38,20,3,0,Married,15,300.211206,0,14723.5603
131890,70,51,1,0,Married,8,300.1399029,0,14776.99515
196490,68,49,3,1,Married,6,300.1130519,0,14425.6526
196991,58,37,2,1,Married,12,300.0849615,0,14655.24808
196730,25,8,1,1,Married,6,299.9629532,0,14657.14766
181186,17,0,1,1,Married,15,299.9273424,0,14759.36712
187229,64,43,1,1,Single,6,299.9230428,0,14736.15214
148399,30,10,3,1,Married,13,299.9212102,0,14726.06051
112751,40,19,3,1,Married,9,299.8475134,0,14679.37567
103946,24,5,2,1,Single,12,299.8042767,0,14539.21384
116624,55,35,3,1,Single,12,299.7912697,0,14602.56349
102501,68,51,3,0,Married,3,299.7789219,0,14576.9461
115611,67,46,1,1,Married,7,299.7655866,0,14408.27933
199435,26,9,3,1,Married,8,299.7132474,0,14583.66237
180419,40,20,3,0,Single,15,299.6729622,0,14692.64811
198843,38,21,4,1,Single,11,299.6722412,0,14690.61206
122599,67,50,1,0,Married,5,299.5960275,0,14494.80138
134780,21,3,2,1,Single,14,299.5568724,0,14576.84362
179022,52,31,4,1,Married,11,299.5390467,0,14562.95234
116177,17,0,4,1,Married,9,299.5350945,0,14675.75473
191401,54,36,1,1,Married,8,299.5314296,0,14437.57148
110883,16,0,3,1,Married,14,299.5276328,0,14378.38164
119928,62,41,1,0,Single,11,299.5254454,0,14738.27227
111974,24,7,3,1,Married,13,299.4952697,0,14696.76349
170763,22,3,3,1,Single,12,299.4129266,0,14547.64633
148703,19,0,2,0,Married,14,299.3878756,0,14710.39378
107100,28,8,2,0,Married,12,299.3629426,0,14442.14713
162876,65,47,2,1,Married,8,299.3307887,0,14637.53944
168587,34,16,1,1,Married,12,299.3079623,0,14566.39812
108677,23,5,2,1,Married,12,299.3018475,0,14679.09238
181715,68,51,3,0,Single,8,299.2517997,0,14604.58999
169456,44,27,1,1,Married,8,299.2104772,0,14707.52386
168611,16,0,2,1,Single,12,299.2080751,0,14681.40376
123920,28,8,3,0,Single,12,299.2028514,0,14673.14257
177367,18,0,3,1,Married,10,299.1970496,0,14427.85248
127475,19,2,3,1,Married,15,299.190366,0,14668.5183
145447,69,51,3,0,Single,6,299.1873834,0,14668.36917
182026,24,5,2,1,Married,10,299.1868257,0,14665.34129
125774,65,46,2,1,Single,9,299.1648586,0,14563.24293
147959,21,3,2,1,Married,12,299.1271076,0,14494.35538
156388,26,6,3,0,Single,12,299.0696088,0,14522.48044
132882,16,0,2,1,Married,11,299.0554411,0,14361.77206
196043,60,40,4,1,Married,7,299.0533046,0,14715.66523
190864,70,53,3,0,Single,6,299.026457,0,14696.32285
107467,65,45,2,1,Single,10,298.9834019,0,14684.1701
135632,30,13,2,0,Married,11,298.9794918,0,14647.97459
139249,29,8,3,1,Married,13,298.9395304,0,14360.97652
123554,23,4,4,1,Married,14,298.9305038,0,14497.52519
111501,26,8,1,0,Single,15,298.8722661,0,14591.61331
154817,22,3,3,1,Single,11,298.8265379,0,14565.3269
167588,40,22,3,0,Married,15,298.8156209,0,14529.78105
131051,33,16,3,1,Married,15,298.7625083,0,14612.12542
189476,56,39,2,1,Single,12,298.7437178,0,14444.18589
129371,37,18,1,1,Married,10,298.7168796,0,14446.84398
105919,63,45,4,0,Married,9,298.6952639,0,14621.7632
149864,24,5,1,1,Single,12,298.6945051,0,14542.72526
168434,19,1,3,1,Married,10,298.6623855,0,14733.11928
162743,70,53,2,0,Married,2,298.5889495,0,14630.44748
161487,41,24,4,1,Married,7,298.5527536,0,14332.63768
167940,43,24,1,1,Married,8,298.5223534,0,14503.11767
110817,59,42,2,1,Single,15,298.5194061,0,14473.97031
183133,61,44,2,1,Single,10,298.4618592,0,14386.09296
197349,52,34,4,1,Married,10,298.446885,0,14404.34425
144262,46,28,4,0,Married,12,298.3104064,0,14348.52032
113126,18,0,3,0,Married,14,298.2323025,0,14315.61513
197380,18,0,4,0,Married,13,298.1731808,0,14433.65904
120912,50,30,4,1,Single,12,298.1695747,0,14701.47874
147208,40,19,3,1,Married,7,298.1310198,0,14548.55099
188298,62,43,1,0,Married,3,298.1251488,0,14660.25744
144672,70,53,2,0,Single,1,298.1249602,0,14607.24801
126295,16,0,4,1,Married,11,298.0911559,0,14612.5578
106575,35,16,2,1,Married,12,298.0254257,0,14672.27129
124582,47,28,2,1,Married,7,297.9558558,0,14458.79279
123401,52,31,1,1,Married,8,297.9455922,0,14630.27961
181434,24,6,3,1,Single,15,297.8965927,0,14667.82964
198535,20,2,2,1,Single,11,297.7994598,0,14660.97299
114955,60,41,3,1,Single,8,297.7264273,0,14310.32137
159645,31,11,3,1,Married,9,297.6731318,0,14371.65659
113499,16,0,2,1,Married,9,297.6385347,0,14569.92674
198578,68,49,2,1,Married,10,297.618713,0,14431.93565
148852,60,43,3,0,Single,14,297.5778166,0,14346.89083
188951,47,26,2,1,Married,6,297.5640493,0,14366.20247
134970,69,52,3,1,Single,10,297.5445188,0,14284.22594
187630,32,11,3,1,Married,8,297.5352388,0,14400.76194
160027,53,36,1,1,Married,11,297.5190349,0,14556.95175
190855,19,0,2,0,Married,11,297.5026953,0,14438.13477
191814,52,33,2,1,Married,9,297.4858939,0,14448.2947
138726,40,23,3,1,Married,6,297.4827401,0,14375.13701
187775,48,31,4,1,Married,6,297.4743852,0,14464.71926
167649,19,0,4,0,Married,15,297.4673372,0,14582.36686
150800,28,11,1,0,Single,12,297.3944661,0,14299.72331
181109,26,7,3,0,Married,14,297.3700277,0,14272.50139
133913,31,13,3,1,Married,6,297.3618365,0,14298.09183
189606,57,40,2,0,Married,11,297.269335,0,14491.46675
181822,18,0,3,0,Married,15,297.265109,0,14357.25545
173841,22,4,1,0,Married,12,297.2343156,0,14482.71578
168167,52,31,3,1,Married,7,297.2306529,0,14353.53265
129014,55,37,2,1,Married,10,297.1989156,0,14547.94578
181349,30,11,3,0,Married,15,297.1587186,0,14430.93593
174967,64,44,2,1,Married,5,297.0548993,0,14392.74497
161378,61,41,3,0,Married,7,297.0394266,0,14639.97133
153236,57,38,2,1,Single,12,297.0075424,0,14385.37712
155171,53,34,3,1,Single,13,297.0060897,0,14514.30449
191455,35,17,3,1,Married,7,296.9827634,0,14619.13817
179248,37,19,3,1,Married,1,296.9675239,0,14539.3762
143664,41,21,3,1,Married,6,296.9633824,0,14468.16912
121161,70,50,1,0,Single,10,296.9626966,0,14606.13483
164236,29,10,2,1,Single,13,296.9345562,0,14491.72781
107955,22,5,2,0,Single,11,296.909497,0,14559.47485
149488,53,33,2,1,Married,15,296.877291,0,14346.86455
118250,64,43,3,0,Single,15,296.8730702,0,14620.65351
170061,16,0,1,1,Married,9,296.8130956,0,14615.65478
151476,17,0,1,1,Single,11,296.8081537,0,14346.40769
159308,66,49,1,0,Single,12,296.8001778,0,14624.00889
170542,63,45,4,1,Single,6,296.787742,0,14371.3871
140702,31,13,1,1,Single,12,296.7397873,0,14532.98937
122547,22,5,2,1,Married,11,296.7119032,0,14634.59516
106413,60,41,2,1,Single,2,296.6977344,0,14548.88672
199130,62,45,3,1,Married,9,296.6591367,0,14326.95684
144119,34,17,4,1,Married,7,296.6095431,0,14359.47716
155088,22,5,3,1,Married,13,296.5688334,0,14420.44167
177659,49,30,2,0,Single,11,296.4003825,0,14278.01913
119681,70,53,2,0,Married,10,296.3736051,0,14471.68026
117061,66,46,3,1,Single,11,296.3598008,0,14339.99004
153203,56,35,3,1,Married,12,296.3172017,0,14344.86009
129471,22,4,1,1,Married,15,296.220441,0,14469.02205
120331,38,17,3,1,Married,6,296.1898285,0,14417.49143
198583,49,31,2,1,Married,7,296.1796164,0,14338.98082
138535,22,5,2,0,Single,15,296.1718486,0,14295.59243
118516,22,4,2,0,Married,12,296.1653493,0,14598.26747
145803,60,40,1,1,Married,10,296.1591075,0,14427.95538
149005,16,0,1,1,Single,15,296.1514067,0,14398.57034
118562,36,16,2,1,Single,12,296.1230721,0,14268.15361
193505,56,39,4,0,Single,15,296.0830274,0,14589.15137
159509,62,44,4,0,Single,8,296.0247801,0,14297.23901
105539,21,4,1,1,Married,7,296.0244249,0,14599.22125
167717,63,42,1,0,Single,7,296.012821,0,14596.64105
173173,38,17,3,1,Married,7,295.9782119,0,14581.9106
108573,39,22,3,0,Married,14,295.9723067,0,14343.61534
103768,24,5,4,0,Married,15,295.9290993,0,14260.45497
168782,38,17,1,1,Married,13,295.925333,0,14512.26665
188954,23,4,3,1,Single,14,295.918131,0,14311.90655
124229,45,28,4,0,Single,15,295.8985641,0,14290.92821
178092,68,48,1,0,Single,12,295.8847896,0,14458.23948
196322,20,3,1,1,Married,14,295.8795577,0,14434.97789
109550,31,14,4,0,Married,14,295.8537538,0,14563.68769
176335,48,31,1,0,Married,13,295.8162876,0,14406.81438
178800,39,20,4,1,Married,13,295.7260312,0,14240.30156
180052,34,13,2,1,Married,14,295.7163865,0,14267.81933
129122,59,40,2,1,Single,11,295.693528,0,14390.6764
144776,40,21,1,1,Married,7,295.6511677,0,14250.55839
185021,70,52,4,1,Married,5,295.6484789,0,14568.42395
194011,70,51,2,0,Single,13,295.5957236,0,14301.78618
176764,69,51,2,0,Single,7,295.5783635,0,14236.91818
142663,70,49,1,0,Single,6,295.5723484,0,14282.61742
144185,65,46,1,0,Married,14,295.5631484,0,14451.15742
144031,25,8,3,1,Married,15,295.5071185,0,14287.35593
115671,27,10,3,1,Married,10,295.489609,0,14374.48045
145692,32,12,1,0,Married,12,295.4869352,0,14565.34676
100347,40,23,4,1,Married,10,295.4246166,0,14530.23083
111412,41,21,1,1,Married,11,295.4013748,0,14539.06874
133641,31,12,2,1,Married,8,295.3917924,0,14364.58962
100745,32,11,2,1,Married,8,295.3755699,0,14450.7785
169546,27,8,3,1,Married,6,295.3055244,0,14249.27622
197797,19,2,3,1,Married,15,295.1582098,0,14221.91049
118578,23,6,3,1,Married,15,295.1269042,0,14230.34521
111462,63,44,1,0,Married,7,295.0976342,0,14215.88171
170998,29,9,3,1,Married,14,295.0696489,0,14431.48245
108222,45,25,1,1,Married,7,295.0005078,0,14531.02539
193937,68,51,2,1,Married,7,294.9060223,0,14472.30112
141402,61,42,4,1,Married,8,294.8875844,0,14264.37922
130780,55,38,3,0,Single,12,294.8744195,0,14308.72098
191589,66,45,3,0,Married,9,294.8200829,0,14295.00415
178088,50,30,2,0,Single,15,294.8160539,0,14378.8027
138790,25,6,3,0,Married,14,294.7964371,0,14193.82186
146393,22,3,2,0,Married,15,294.7767246,0,14217.83623
196778,22,3,2,0,Married,12,294.7744672,0,14408.72336
184850,51,31,2,0,Single,13,294.7696212,0,14222.48106
192184,28,10,3,1,Married,11,294.7493408,0,14253.46704
152355,24,6,3,1,Single,11,294.6843038,0,14205.21519
116657,16,0,3,1,Single,11,294.6289145,0,14277.44573
132918,69,51,3,1,Married,10,294.6244362,0,14391.22181
198753,24,5,3,0,Single,15,294.6140497,0,14142.70249
199647,50,33,2,0,Married,15,294.5488726,0,14254.44363
149081,58,38,2,1,Married,11,294.5484416,0,14185.42208
198204,24,5,2,1,Single,13,294.5121624,0,14394.60812
127251,62,41,2,1,Married,9,294.4686241,0,14277.43121
110701,63,45,3,1,Married,5,294.436679,0,14288.83395
146319,24,7,3,1,Married,14,294.4125223,0,14397.62612
149020,52,31,4,1,Married,14,294.3852523,0,14415.26262
181344,63,42,3,0,Single,0,294.3530223,0,14393.65112
168971,21,4,3,1,Married,11,294.349948,0,14238.4974
135840,56,39,2,1,Married,8,294.1870698,0,14118.35349
165338,24,6,1,0,Married,15,294.1784171,0,14233.92086
162644,25,7,2,1,Single,11,294.1566306,0,14338.83153
148365,40,20,2,1,Married,12,294.1190275,0,14441.95138
152671,17,0,3,1,Single,11,294.0972002,0,14313.86001
163531,19,2,1,1,Married,15,294.094824,0,14397.7412
176827,29,9,2,1,Married,6,294.0604321,0,14303.02161
180037,51,33,1,1,Single,12,294.0259335,0,14232.29668
149930,59,38,2,1,Married,10,294.0112896,0,14237.56448
146421,48,28,3,0,Married,13,293.9512088,0,14113.56044
136691,23,4,3,0,Single,11,293.8245967,0,14216.22984
123350,33,14,2,1,Married,12,293.7842,0,14284.21
124164,66,46,1,0,Married,6,293.7614685,0,14170.07343
166927,31,11,3,1,Married,13,293.741185,0,14411.05925
119963,35,18,1,1,Married,7,293.7317934,0,14354.58967
195317,67,47,1,1,Single,10,293.6649609,0,14350.24805
146494,18,1,4,1,Married,12,293.6557987,0,14257.78994
188733,16,0,4,1,Married,12,293.5591049,0,14126.95525
173362,23,4,4,1,Single,11,293.4971294,0,14262.85647
188640,17,0,4,1,Single,14,293.4915586,0,14153.57793
108632,65,45,2,0,Married,10,293.4708894,0,14245.54447
108841,65,47,3,0,Married,8,293.4360274,0,14100.80137
111575,23,6,1,0,Married,14,293.4321973,0,14214.60987
111243,31,14,2,0,Single,12,293.3821636,0,14129.10818
122577,35,14,2,1,Married,9,293.3026507,0,14326.13254
102450,21,2,1,1,Single,11,293.2690619,0,14098.4531
141220,55,36,2,1,Married,14,293.2596459,0,14116.9823
179615,43,25,2,1,Single,15,293.0462488,0,14296.31244
186695,28,10,3,1,Married,7,292.9718501,0,14119.59251
116504,68,47,3,1,Married,0,292.9110316,0,14295.55158
112134,57,39,4,1,Single,13,292.909646,0,14366.4823
169548,41,23,4,0,Married,11,292.866565,0,14199.32825
145115,50,33,1,1,Married,10,292.8056952,0,14221.28476
176235,62,44,2,1,Single,1,292.7781058,0,14126.90529
107801,16,0,1,1,Married,12,292.7728955,0,14181.64478
138798,45,26,4,1,Single,14,292.768699,0,14431.43495
135723,68,49,3,1,Single,8,292.7373979,0,14251.8699
194678,62,44,2,0,Single,6,292.7272148,0,14216.36074
103109,22,4,4,1,Single,15,292.7197131,0,14232.98566
101974,34,15,2,1,Married,8,292.5934558,0,14032.67279
157636,25,6,2,1,Single,11,292.5574174,0,14357.87087
134795,21,2,2,1,Married,12,292.5262569,0,14060.31285
135117,61,40,2,0,Married,5,292.5180765,0,14220.90383
107372,66,47,2,1,Married,6,292.5163962,0,14276.81981
105765,30,9,1,1,Married,8,292.5163921,0,14153.81961
116481,68,47,2,0,Single,6,292.4898702,0,14169.49351
145687,65,48,3,0,Married,6,292.4787908,0,14379.93954
176608,21,3,3,1,Single,13,292.477184,0,14061.8592
122078,61,41,3,0,Married,8,292.4699695,0,14416.49848
153272,58,40,2,1,Married,14,292.4367434,0,14181.83717
151282,52,31,1,1,Married,15,292.4305792,0,14295.52896
176879,55,38,2,1,Married,7,292.3974533,0,14255.87267
123640,66,47,2,1,Single,12,292.3974265,0,14405.87133
196388,49,31,1,1,Married,7,292.3818226,0,14052.09113
198224,68,49,1,0,Single,3,292.3735058,0,14346.67529
130282,22,5,3,1,Married,11,292.367162,0,14368.3581
142100,22,3,4,1,Married,13,292.3054943,0,14359.27472
162322,63,46,3,1,Married,10,292.2918382,0,14375.59191
196964,38,18,1,0,Single,11,292.2905735,0,14284.52868
106454,65,47,2,1,Single,14,292.2713654,0,14359.56827
107916,19,0,3,1,Single,11,292.240232,0,14248.0116
173363,24,5,2,1,Married,12,292.2321227,0,14411.60614
123331,33,15,2,1,Married,15,292.2050753,0,14041.25377
144106,35,16,2,0,Married,12,292.0929944,0,14099.64972
199587,24,5,1,0,Married,11,292.0878142,0,14031.39071
188570,41,24,1,1,Married,7,292.0494433,0,14089.47217
195039,35,16,3,1,Married,6,292.0457519,0,14165.2876
174034,62,41,4,0,Married,7,292.0239269,0,14027.19635
139062,64,44,1,1,Single,9,292.0086982,0,14287.43491
199695,19,1,3,0,Married,12,291.9833811,0,14366.16906
190380,23,4,1,1,Married,12,291.9627918,0,14334.13959
174777,28,11,4,1,Married,8,291.9148536,0,14235.74268
176531,30,11,2,1,Single,15,291.7805945,0,14221.02973
147110,53,32,4,1,Married,6,291.7664483,0,14379.32242
112477,63,46,3,0,Single,7,291.7045386,0,14090.22693
174564,34,14,3,1,Married,9,291.7016507,0,14290.08254
159268,42,23,1,1,Married,7,291.6776414,0,14066.88207
113487,63,46,4,1,Married,6,291.6686666,0,14103.43333
150033,28,9,2,1,Married,9,291.6173826,0,14043.86913
126479,37,20,3,1,Married,9,291.6092093,0,14091.46047
136770,21,4,4,0,Married,12,291.570191,0,14167.50955
144616,55,38,4,1,Married,10,291.5165346,0,13991.82673
169635,38,17,3,1,Married,8,291.4424914,0,14083.12457
127220,68,47,3,1,Married,8,291.4284143,0,14171.42072
152002,66,49,2,0,Married,9,291.4064942,0,14332.32471
127409,61,44,3,0,Single,0,291.3963704,0,14127.81852
138193,70,53,3,0,Single,10,291.3569076,0,14252.84538
193234,68,49,2,0,Married,10,291.3346239,0,14084.7312
198995,31,10,2,1,Married,8,291.3184574,0,14137.92287
162956,40,19,1,0,Single,12,291.3088903,0,14028.44452
108246,17,0,1,1,Married,14,291.2674839,0,14123.3742
174487,55,34,4,1,Married,7,291.0763148,0,13967.81574
103750,18,0,1,1,Married,14,291.0696945,0,14015.48473
163497,52,31,3,0,Married,11,290.9601261,0,14168.00631
115459,50,30,3,0,Single,15,290.9001871,0,14031.00936
137028,58,40,3,1,Married,7,290.8758858,0,13960.79429
193097,58,41,2,0,Single,12,290.8615744,0,14070.07872
163451,25,8,2,1,Married,10,290.85714,0,14032.857
152138,69,50,2,0,Married,7,290.8309896,0,14317.54948
151690,35,18,2,1,Married,11,290.8274344,0,14172.37172
173665,68,49,2,0,Married,10,290.7900443,0,14132.50222
133658,67,50,3,1,Married,5,290.7665179,0,14233.3259
135733,65,44,1,0,Single,14,290.7360244,0,14264.80122
168625,62,43,3,0,Single,12,290.7196026,0,13977.98013
155432,63,42,4,0,Married,7,290.6463151,0,14137.31576
175097,70,49,2,1,Single,7,290.5468122,0,14025.34061
125295,23,4,4,1,Single,12,290.535341,0,14155.76705
147903,25,8,3,0,Single,12,290.4448807,0,13977.24404
177500,67,49,3,1,Married,9,290.3883404,0,13953.41702
153102,33,12,2,1,Single,11,290.3603949,0,13956.01975
133024,18,0,2,1,Married,13,290.262723,0,14202.13615
143246,16,0,3,0,Married,11,290.2619311,0,14132.09656
107052,41,21,2,0,Single,14,290.2313195,0,14241.56598
195119,19,0,2,1,Married,14,290.1292949,0,14050.46475
108721,20,1,4,1,Married,13,290.1191915,0,14246.95958
191183,24,6,4,1,Married,12,290.0682866,0,14131.41433
104725,64,46,3,0,Married,5,290.025022,0,14194.2511
190236,44,23,1,1,Married,10,289.9684397,0,13951.42199
151112,61,43,4,0,Single,8,289.9680308,0,14169.40154
120839,29,8,3,1,Married,6,289.9355464,0,14249.77732
116513,23,4,3,1,Married,13,289.8433582,0,14044.16791
125307,20,1,3,1,Married,8,289.8156293,0,13920.78147
196188,47,30,3,0,Single,15,289.8098663,0,13901.49332
161738,28,9,3,1,Married,12,289.7942597,0,14200.71299
140087,35,16,1,1,Married,10,289.7668906,0,14011.34453
142899,17,0,3,1,Married,6,289.7260717,0,14026.30359
196503,29,9,1,1,Married,6,289.6606846,0,14087.03423
189660,57,38,2,0,Single,13,289.5776329,0,14138.88165
159938,64,44,1,1,Single,14,289.5307529,0,14233.53765
151916,25,8,2,1,Single,15,289.5292201,0,14048.46101
151354,18,0,2,1,Married,14,289.5240989,0,14073.20495
148927,59,42,3,1,Married,10,289.381618,0,14200.0809
189721,53,34,1,1,Married,9,289.3514634,0,14032.57317
142350,18,0,2,1,Married,12,289.3441822,0,14222.20911
164381,19,2,1,0,Married,12,289.320228,0,14056.0114
138715,43,23,1,1,Married,11,289.315993,0,14243.79965
133600,34,13,2,1,Married,11,289.2435646,0,14120.17823
190814,20,3,2,1,Married,15,289.2416865,0,14054.08433
133678,64,47,2,0,Single,7,289.2225933,0,13908.12967
154235,58,38,3,1,Married,9,289.2175028,0,14095.87514
150679,57,38,3,1,Married,9,289.1721885,0,14138.60943
123078,64,46,2,1,Single,7,289.1676379,0,14188.3819
159115,30,10,4,1,Married,10,289.1312252,0,13944.56126
145037,18,0,3,1,Married,15,289.1148939,0,13895.7447
129656,41,22,3,1,Married,15,289.1040693,0,13961.20347
182749,25,7,2,1,Married,13,288.9798219,0,14164.9911
126846,39,19,2,1,Married,0,288.9363679,0,13849.8184
154208,60,40,2,1,Married,1,288.8449569,0,14072.24785
134512,56,38,3,0,Single,15,288.8223917,0,14074.11959
107981,25,8,2,1,Single,15,288.7365091,0,14087.82546
111550,63,45,3,1,Married,1,288.708758,0,14190.4379
100151,23,5,3,1,Married,13,288.6727542,0,13926.63771
128378,64,44,2,1,Single,4,288.6478358,0,14010.39179
158850,63,42,3,0,Married,0,288.539194,0,13842.9597
106872,34,17,3,1,Married,12,288.4836684,0,13844.18342
150940,24,7,2,1,Married,11,288.4618053,0,13861.09027
103716,33,15,2,1,Married,8,288.4368473,0,13947.84237
114880,46,27,3,0,Single,12,288.2849049,0,14127.24525
118395,18,0,4,1,Married,14,288.2335135,0,13826.67568
175003,56,37,2,1,Married,8,288.2242538,0,13861.21269
144296,17,0,4,1,Single,12,288.191003,0,13897.55015
174049,43,23,2,0,Married,14,288.172329,0,13869.61645
119090,25,6,2,1,Single,15,288.1330928,0,14206.65464
122645,61,43,3,0,Married,3,288.0919694,0,14165.59847
138167,70,51,2,1,Single,10,288.0876461,0,14008.38231
108711,65,48,1,0,Single,3,288.0778841,0,13804.89421
192739,61,43,2,1,Single,10,288.0075676,0,13948.37838
164643,29,9,1,1,Married,6,287.9462439,0,14185.3122
115954,21,2,2,1,Married,11,287.8762396,0,14097.81198
153406,23,4,3,0,Single,14,287.8380428,0,14171.90214
192933,24,7,2,1,Single,14,287.8092615,0,13967.46308
167985,68,50,2,0,Married,7,287.7884577,0,13926.42289
178158,61,40,1,1,Married,9,287.7680412,0,13902.40206
173586,61,43,3,0,Married,7,287.7556186,0,14148.78093
109789,19,1,2,1,Married,6,287.5581669,0,13990.90835
182742,43,25,1,1,Married,9,287.5238284,0,13829.19142
155769,32,13,4,1,Married,9,287.4991457,0,13856.95729
177280,35,14,1,0,Married,13,287.4881203,0,14128.40602
140254,21,4,4,1,Single,13,287.4566621,0,13927.83311
127568,51,33,2,1,Married,7,287.440725,0,14065.03625
141083,19,1,4,1,Married,15,287.4289745,0,13773.44873
182683,33,12,1,1,Married,8,287.4214409,0,13777.07205
127089,66,48,3,1,Married,8,287.4060986,0,13849.30493
196387,19,0,4,0,Single,12,287.3495349,0,14160.47675
114628,36,19,1,1,Married,6,287.3493637,0,14012.46819
146653,61,44,3,0,Single,8,287.3031558,0,13798.15779
191694,67,46,3,1,Single,9,287.2942512,0,13878.71256
138443,17,0,2,1,Married,14,287.2941473,0,14003.70737
165056,61,41,3,0,Single,15,287.231523,0,13902.57615
129698,23,5,2,0,Married,11,287.2007063,0,13897.03532
188282,38,17,2,1,Married,10,287.1500967,0,14008.50484
167132,63,44,4,1,Married,10,287.1088186,0,13825.44093
178658,44,23,4,1,Married,13,287.0759822,0,13936.79911
186705,45,25,4,1,Married,6,287.0757361,0,13905.78681
133272,70,53,2,0,Single,7,287.0716344,0,14096.58172
125677,46,29,3,1,Married,8,287.0311919,0,13890.5596
185771,50,29,2,1,Married,10,286.9764941,0,13749.82471
185715,26,9,2,1,Married,14,286.970849,0,14130.54245
125100,53,36,3,1,Married,9,286.9080924,0,13991.40462
147151,64,43,3,0,Married,10,286.8998103,0,13926.99052
198766,70,52,2,0,Married,4,286.8812378,0,13983.06189
144164,31,11,2,1,Single,13,286.8740409,0,14105.70205
165014,42,22,2,1,Single,11,286.818748,0,13955.9374
135976,42,21,2,0,Married,12,286.71261,0,14075.6305
150562,17,0,3,1,Single,12,286.6977228,0,14045.88614
103725,69,50,3,0,Married,6,286.6598643,0,14064.99322
167380,21,3,3,1,Married,15,286.6560119,0,13802.8006
109839,56,35,1,1,Single,13,286.5238132,0,13778.19066
103366,20,1,4,1,Married,11,286.4642331,0,13943.21166
110613,58,40,3,1,Single,15,286.4599021,0,13904.99511
192330,51,31,4,1,Married,12,286.3316671,0,13763.58336
162373,23,6,3,1,Married,11,286.2937389,0,13740.68695
112512,31,14,3,0,Single,12,286.2276689,0,13944.38345
115414,31,13,3,1,Married,10,286.1992473,0,14063.96237
200263,64,45,3,1,Single,12,286.1335231,0,13809.67616
135970,34,16,3,0,Married,11,286.085105,0,13935.25525
147615,65,48,2,0,Single,13,286.0536902,0,13816.68451
156529,19,1,2,0,Married,14,286.0007918,0,13981.03959
137135,62,41,2,1,Single,0,285.9760284,0,13707.80142
147882,63,43,4,0,Single,7,285.9694186,0,13929.47093
182801,19,0,2,0,Married,15,285.96304,0,13825.152
157666,66,49,2,1,Married,3,285.9494897,0,14000.47449
184363,24,6,2,1,Single,13,285.9482313,0,13727.41157
126129,24,7,1,0,Married,11,285.9332715,0,14057.66358
156361,32,14,3,0,Single,15,285.7805089,0,13788.02545
140198,25,6,2,1,Married,13,285.7204047,0,13892.02024
185003,60,41,1,1,Married,8,285.7046838,0,13913.23419
173443,20,2,2,1,Married,12,285.6902765,0,13942.51383
197903,49,28,3,1,Married,10,285.6855511,0,13897.27756
178304,62,43,2,1,Single,5,285.6495297,0,13909.47649
104094,55,38,2,1,Single,15,285.5379888,0,13836.89944
184086,43,23,3,0,Single,12,285.5213968,0,13712.06984
198838,67,50,3,1,Married,8,285.4702579,0,13759.5129
121273,64,44,3,1,Married,2,285.3769127,0,13898.84564
128094,30,13,2,0,Married,11,285.3522292,0,13836.61146
129355,28,11,1,1,Married,8,285.3417405,0,13984.08703
164784,28,11,2,1,Married,9,285.2523626,0,13672.61813
179038,30,10,3,0,Married,13,285.2478079,0,13751.3904
183666,63,42,3,0,Married,12,285.2398444,0,13735.99222
178873,60,40,4,0,Married,8,285.1611597,0,13684.05799
100979,50,29,3,0,Married,15,285.1148334,0,13895.74167
136520,60,40,1,0,Married,7,285.1038637,0,13805.19319
186647,41,23,1,1,Married,8,285.0448892,0,13675.24446
113861,25,6,1,1,Married,12,284.8746199,0,13696.731
195958,62,45,3,0,Married,9,284.8127977,0,13641.63989
187100,21,4,3,1,Married,12,284.7555892,0,13902.77946
182803,17,0,2,1,Married,15,284.7434642,0,13732.17321
105252,66,46,4,1,Married,9,284.7056429,0,13864.28215
104751,54,36,2,0,Single,14,284.6947686,0,13986.73843
160582,56,37,3,0,Married,14,284.6856048,0,13814.28024
100313,19,1,4,1,Single,12,284.6476935,0,13776.38468
192867,62,43,1,0,Married,6,284.6270671,0,13785.35336
139878,65,48,2,0,Married,12,284.5806468,0,13688.03234
163252,60,39,4,0,Married,6,284.5710122,0,14019.55061
114356,63,43,2,0,Married,7,284.5705762,0,13808.52881
187027,70,50,1,0,Single,9,284.5698704,0,13708.49352
145555,21,4,3,0,Single,11,284.5356716,0,13843.78358
131253,27,8,4,1,Married,7,284.5326999,0,13735.635
188171,69,50,4,1,Married,1,284.499931,0,13971.99655
107201,65,45,2,1,Single,10,284.4893908,0,13938.46954
134150,35,17,1,1,Married,6,284.4274153,0,13662.37077
135045,16,0,3,1,Married,8,284.3556184,0,13990.78092
173005,58,38,1,1,Married,14,284.2330566,0,13732.65283
195321,68,51,2,0,Married,10,284.1424955,0,13993.12478
181297,21,4,3,1,Married,14,284.1389726,0,13864.94863
102783,19,1,3,1,Married,14,284.0776759,0,13784.8838
196397,41,20,3,1,Married,7,284.0133087,0,13772.66544
138700,34,15,4,0,Single,14,284.0077688,0,13950.38844
160873,65,44,1,1,Single,12,283.9687422,0,13608.43711
191962,41,24,2,1,Single,12,283.9643354,0,13705.21677
151729,20,1,2,1,Married,12,283.9508851,0,13844.54426
123535,19,0,4,1,Single,13,283.9207249,0,13873.03625
126164,17,0,1,1,Married,12,283.9100974,0,13658.50487
129203,61,44,4,1,Married,6,283.7738187,0,13610.69094
120187,26,7,1,1,Married,6,283.7506272,0,13825.53136
193950,68,51,1,0,Married,14,283.7470142,0,13807.35071
138068,47,28,3,1,Married,13,283.7200076,0,13771.00038
166545,46,27,1,1,Married,15,283.6638328,0,13616.19164
151270,54,34,3,1,Single,15,283.6484753,0,13838.42377
101597,55,35,2,0,Single,11,283.6160347,0,13857.80174
142676,36,15,2,1,Married,7,283.6065702,0,13979.32851
127446,25,7,2,1,Married,7,283.5781437,0,13758.90719
126790,62,41,4,1,Married,1,283.5426922,0,13632.13461
149035,62,44,3,1,Married,7,283.4877653,0,13863.38827
175949,68,49,2,0,Single,13,283.4616443,0,13700.08222
183476,26,9,4,0,Married,13,283.425868,0,13749.2934
103113,69,49,2,0,Single,8,283.4059068,0,13689.29534
155465,31,10,1,1,Single,15,283.3677239,0,13955.3862
104294,21,2,2,0,Married,15,283.3502652,0,13737.51326
169529,56,37,3,0,Married,13,283.3282852,0,13862.41426
113117,34,13,4,1,Single,14,283.3004662,0,13794.02331
181759,19,0,2,1,Single,11,283.2421494,0,13853.10747
180138,18,1,4,1,Married,15,283.2271113,0,13893.35557
146690,24,7,3,1,Married,15,283.1980525,0,13592.90263
113960,69,52,3,0,Single,14,283.1627247,0,13583.13624
125031,21,3,2,0,Married,15,283.0848197,0,13842.24099
119020,20,2,2,1,Married,10,283.0697002,0,13910.48501
177299,62,41,2,1,Married,4,283.0600948,0,13752.00474
151554,62,44,3,1,Single,8,283.0433401,0,13575.16701
162559,22,3,1,1,Married,14,283.0236307,0,13742.18154
143343,41,20,2,0,Married,13,282.9530993,0,13777.65497
134071,43,23,3,0,Married,13,282.9321492,0,13908.60746
137466,25,6,1,1,Single,14,282.9126405,0,13759.63203
134400,63,44,4,1,Single,7,282.8681993,0,13874.40997
151308,17,0,3,1,Single,11,282.841367,0,13605.06835
191865,61,42,3,1,Married,7,282.8026485,0,13912.13243
128684,67,46,2,0,Single,3,282.699963,0,13885.99815
161320,32,15,3,1,Married,10,282.6876905,0,13837.38453
161195,21,2,4,1,Married,13,282.6123076,0,13758.61538
112334,69,51,3,0,Single,10,282.5751978,0,13587.75989
143837,65,44,3,0,Married,1,282.5451664,0,13565.25832
160180,39,19,3,1,Married,9,282.542256,0,13836.1128
158873,53,34,2,1,Married,9,282.4691551,0,13862.45776
144229,64,43,2,1,Married,1,282.4677621,0,13605.38811
111093,19,2,1,1,Married,14,282.4138477,0,13885.69239
198773,18,0,1,0,Married,11,282.3993159,0,13853.9658
146747,70,51,2,0,Married,10,282.3902591,0,13754.51296
161019,64,47,3,1,Married,13,282.3852994,0,13906.26497
120449,66,49,3,0,Married,8,282.3446176,0,13589.23088
104822,23,5,4,0,Single,15,282.3222806,0,13767.11403
135261,17,0,1,1,Married,10,282.2230386,0,13693.15193
121346,18,0,3,0,Married,15,282.1934552,0,13602.67276
156861,21,2,2,1,Married,4,282.1805458,0,13704.02729
176444,69,51,4,1,Married,13,282.1491477,0,13545.45739
152831,54,33,3,1,Married,15,282.1229567,0,13817.14784
137055,16,0,2,0,Married,12,282.1225824,0,13568.12912
157805,25,7,2,1,Single,13,282.1028252,0,13632.14126
160687,27,9,1,1,Married,12,281.9988866,0,13649.94433
111475,30,11,3,1,Married,10,281.9505713,0,13833.52857
121591,53,35,4,0,Married,14,281.9285033,0,13659.42517
145819,46,29,3,1,Single,13,281.9092425,0,13746.46213
192585,70,50,3,0,Married,11,281.8606979,0,13679.0349
145578,44,25,1,1,Married,11,281.8028525,0,13536.14263
194506,21,3,4,0,Married,14,281.7888965,0,13527.44483
130544,20,1,3,0,Married,13,281.7290259,0,13607.4513
117176,52,34,2,0,Married,13,281.6759718,0,13708.79859
171108,63,44,4,0,Married,7,281.6387779,0,13618.9389
190090,69,49,1,0,Married,11,281.6230067,0,13489.15034
180914,68,51,2,0,Single,10,281.57493,0,13506.7465
110464,63,45,4,0,Married,9,281.5223611,0,13601.11806
124779,67,46,1,1,Single,4,281.4939094,0,13725.69547
138066,39,19,4,1,Married,6,281.4826615,0,13678.13308
183958,60,39,4,1,Married,8,281.4717512,0,13738.58756
171107,32,14,1,1,Single,13,281.440499,0,13596.02495
136370,28,8,2,1,Married,9,281.4052085,0,13551.26043
183196,25,8,2,1,Married,11,281.3731044,0,13523.65522
104022,27,8,3,0,Single,15,281.3557517,0,13580.78759
109252,22,4,3,1,Single,11,281.2414019,0,13643.0701
145844,22,4,4,1,Married,14,281.1619271,0,13509.09636
196348,46,26,4,0,Single,13,281.1185963,0,13673.92982
180388,21,2,4,0,Married,14,281.1133604,0,13609.66802
112958,20,2,2,1,Married,12,281.0141047,0,13671.70524
131608,51,31,3,1,Single,14,280.9900694,0,13662.50347
150533,67,48,1,1,Single,2,280.9848214,0,13698.24107
172116,18,0,3,1,Single,12,280.97167,0,13848.5835
194615,35,14,3,0,Married,15,280.9519112,0,13829.59556
116608,66,46,3,0,Married,9,280.863753,0,13738.18765
169288,20,1,3,0,Married,14,280.8443506,0,13589.21753
169584,35,18,1,0,Married,11,280.7923877,0,13747.61939
172669,58,37,4,0,Married,11,280.7864789,0,13664.32395
176789,46,28,3,1,Married,11,280.781273,0,13727.06365
164070,49,28,2,0,Married,13,280.7433366,0,13545.16683
114940,61,43,3,1,Single,14,280.6973636,0,13742.86818
132682,65,45,2,0,Married,1,280.664265,0,13771.21325
174591,18,0,2,0,Married,14,280.6475126,0,13610.37563
118997,66,45,2,0,Married,6,280.6386077,0,13485.93039
122915,66,45,4,1,Single,8,280.6237231,0,13781.18616
195625,70,52,2,1,Single,5,280.5897624,0,13613.48812
136546,23,6,2,0,Single,11,280.5738406,0,13445.69203
136762,65,48,2,1,Married,3,280.5577716,0,13706.88858
142589,70,51,2,1,Single,5,280.5294298,0,13630.47149
163511,19,0,4,1,Single,13,280.4403218,0,13706.01609
123467,68,49,2,1,Single,8,280.1584591,0,13697.92296
117713,46,26,2,1,Single,12,280.1469219,0,13732.3461
113975,55,38,2,1,Married,9,280.135132,0,13700.7566
155474,21,2,3,0,Married,13,280.0843828,0,13576.21914
173632,16,0,2,1,Married,11,280.0804628,0,13596.02314
114629,70,53,4,0,Married,6,280.0771197,0,13549.85599
151573,24,6,3,1,Married,7,279.993291,0,13643.66455
191368,34,15,1,1,Married,8,279.9932715,0,13734.66358
163409,25,6,1,1,Married,8,279.9889923,0,13551.44962
102843,25,8,2,0,Married,15,279.9560585,0,13591.80293
126956,57,37,3,0,Married,13,279.9207188,0,13484.03594
179852,66,48,2,1,Married,9,279.9183728,0,13528.91864
118618,43,22,2,1,Married,6,279.8884006,0,13609.42003
134977,18,0,1,0,Married,14,279.8615512,0,13613.07756
191513,41,22,4,1,Married,15,279.8487014,0,13481.43507
106099,26,6,4,1,Single,14,279.8312708,0,13719.56354
174733,23,6,1,1,Married,15,279.7679188,0,13758.39594
118427,62,44,3,1,Single,6,279.7571562,0,13396.85781
116970,25,8,1,0,Married,12,279.6556808,0,13665.78404
128959,62,42,2,1,Married,7,279.6486554,0,13630.43277
110042,25,8,2,1,Married,14,279.5721564,0,13715.60782
122111,41,20,4,1,Single,11,279.5460828,0,13443.30414
163639,63,44,2,1,Married,11,279.4194643,0,13750.97322
139097,19,2,1,1,Single,14,279.4165022,0,13672.82511
169789,60,43,3,0,Single,8,279.4074456,0,13388.37228
180143,44,23,2,1,Married,13,279.4071319,0,13695.3566
198719,68,51,3,0,Married,10,279.3915976,0,13690.57988
109655,46,29,3,0,Single,15,279.3767853,0,13660.83927
139484,16,0,1,1,Married,14,279.3074554,0,13522.37277
172772,63,45,3,0,Married,1,279.3035661,0,13567.17831
176914,68,50,3,1,Married,4,279.2845698,0,13426.22849
198271,63,43,3,0,Married,10,279.283507,0,13429.17535
125550,56,35,3,1,Married,10,279.20235,0,13603.1175
199026,23,6,4,1,Single,11,279.1963188,0,13554.81594
124835,67,50,3,0,Married,10,279.1536358,0,13550.68179
105326,23,6,2,1,Married,9,279.0614405,0,13467.07203
195852,56,38,4,0,Married,14,279.0504705,0,13561.52353
186718,35,15,2,0,Single,13,278.9718352,0,13667.59176
166108,40,20,2,1,Married,12,278.9710642,0,13405.55321
168710,26,5,1,1,Married,7,278.9689585,0,13599.44793
137448,20,1,2,0,Married,13,278.9382034,0,13365.91017
196895,66,46,2,1,Married,7,278.9371994,0,13604.85997
196666,42,24,3,0,Single,15,278.9160901,0,13389.80451
157565,21,4,2,1,Single,12,278.9000884,0,13356.00442
134804,33,14,2,1,Married,14,278.7847833,0,13407.23917
183150,21,4,2,0,Married,11,278.7830603,0,13631.15302
187204,48,30,1,0,Single,15,278.7787348,0,13447.93674
165319,32,15,3,0,Married,11,278.7566055,0,13591.83028
126663,20,2,4,1,Married,6,278.7454211,0,13502.27106
126102,16,0,1,0,Married,13,278.6843827,0,13490.21914
126989,66,46,3,0,Single,14,278.672627,0,13605.63135
124463,63,46,3,0,Married,6,278.6703519,0,13578.5176
104777,61,44,2,1,Single,4,278.6510593,0,13365.55297
177809,22,5,2,1,Married,11,278.5209343,0,13571.04672
128851,61,43,1,0,Married,6,278.5029941,0,13684.14971
178277,28,8,3,1,Married,7,278.4953082,0,13360.76541
168299,18,0,3,0,Married,13,278.4743325,0,13574.71663
186726,20,2,1,1,Married,13,278.4734322,0,13597.67161
133989,23,6,3,0,Single,14,278.4217692,0,13517.08846
132123,46,28,4,0,Single,14,278.3763544,0,13644.81772
127878,25,8,3,0,Married,12,278.3718827,0,13682.59414
101880,23,6,2,0,Married,15,278.2510373,0,13318.55187
181121,62,43,3,0,Single,15,278.1608605,0,13669.04303
191371,70,52,2,1,Single,12,278.1082209,0,13543.41105
195314,21,4,4,1,Single,15,278.0756923,0,13491.78462
110629,23,4,1,1,Married,13,278.0711026,0,13490.55513
125508,22,3,3,1,Married,12,278.047453,0,13412.37265
132131,52,32,3,1,Single,15,278.0438613,0,13420.19306
144279,38,18,3,0,Single,13,278.028599,0,13467.42995
178228,68,49,3,1,Single,7,277.9042945,0,13593.21473
121620,25,8,3,0,Married,11,277.8978964,0,13329.89482
198736,60,42,2,0,Married,12,277.8842178,0,13378.21089
144993,20,1,3,1,Married,14,277.8466904,0,13496.33452
148218,18,0,3,1,Married,11,277.8208258,0,13672.04129
180007,67,48,4,0,Married,6,277.7798696,0,13427.99348
158716,66,49,2,0,Single,0,277.6940932,0,13465.70466
113498,31,12,4,0,Married,11,277.5898567,0,13306.49284
172849,24,5,2,0,Married,12,277.5433395,0,13396.16698
123887,64,45,3,0,Married,1,277.5412207,0,13628.06104
169888,67,47,3,1,Single,9,277.4594881,0,13393.97441
174769,41,21,1,0,Single,15,277.4546101,0,13291.73051
150141,34,14,2,1,Married,6,277.4110881,0,13365.55441
165210,67,49,4,1,Married,5,277.3865618,0,13557.32809
138496,68,49,2,0,Single,5,277.3031401,0,13637.15701
117473,17,0,1,1,Married,14,277.295218,0,13366.7609
149385,49,30,3,0,Single,11,277.2942155,0,13646.71078
115399,33,14,2,1,Married,12,277.1370095,0,13279.85048
147966,32,14,3,1,Single,14,277.0366197,0,13303.83099
177308,59,38,1,1,Single,12,277.0128261,0,13337.64131
189508,68,48,1,0,Married,2,276.9982494,0,13580.91247
103758,46,27,4,1,Single,15,276.9096614,0,13331.48307
195718,69,50,3,0,Single,2,276.8951398,0,13464.75699
139137,68,49,2,0,Single,0,276.8300778,0,13370.50389
110313,64,45,1,0,Married,10,276.7748308,0,13391.74154
138261,18,0,4,1,Married,14,276.7711105,0,13283.55553
177676,51,32,4,0,Married,12,276.6929005,0,13595.64503
100466,60,39,2,0,Married,2,276.6664704,0,13415.32352
118478,21,3,4,1,Married,15,276.6608014,0,13233.04007
163468,19,1,2,1,Married,14,276.603563,0,13393.17815
180841,19,0,1,1,Married,13,276.5807741,0,13374.03871
194538,35,16,3,1,Single,15,276.4816299,0,13413.0815
173354,39,18,1,1,Married,9,276.4724081,0,13521.62041
129443,66,49,4,1,Single,12,276.4526681,0,13329.63341
119311,22,3,2,1,Married,15,276.3948562,0,13297.74281
159816,66,47,2,0,Married,4,276.3640713,0,13605.20357
183621,68,48,4,0,Single,6,276.3244812,0,13392.22406
186337,43,22,3,1,Married,14,276.2756458,0,13410.78229
177520,70,50,2,0,Married,8,276.21984,0,13430.992
167941,63,45,2,0,Married,8,276.2114965,0,13322.57483
163961,17,0,2,0,Married,12,276.122448,0,13257.1224
188607,62,44,1,1,Married,3,276.1089391,0,13507.44696
146882,61,42,2,0,Married,6,276.0410019,0,13398.0501
155018,35,15,4,1,Married,12,275.9957072,0,13458.78536
137606,31,12,3,0,Married,13,275.9303159,0,13518.5158
100934,23,6,4,1,Single,13,275.9210759,0,13378.0538
156912,61,40,1,1,Married,8,275.8448446,0,13204.24223
169592,53,34,4,0,Single,12,275.8334439,0,13310.6722
135839,25,8,3,1,Married,13,275.7229211,0,13279.14606
137313,22,5,3,1,Married,15,275.7064957,0,13462.32479
193197,17,0,1,0,Married,15,275.7037214,0,13403.18607
148600,64,43,2,1,Married,2,275.6702897,0,13284.51449
111633,60,39,1,0,Married,11,275.6651835,0,13393.25918
101493,57,38,2,1,Married,10,275.5481922,0,13425.40961
137168,39,22,1,0,Single,15,275.4917981,0,13397.58991
162491,29,8,3,1,Married,7,275.3316651,0,13420.58326
165026,41,22,1,1,Single,13,275.294995,0,13561.74975
144955,68,50,3,1,Single,15,275.2849001,0,13361.24501
139569,41,20,4,1,Single,14,275.2239178,0,13241.19589
103555,62,45,1,0,Married,0,275.2144091,0,13203.72046
100406,63,44,2,0,Married,7,275.1142612,0,13156.71306
149842,17,0,1,0,Married,11,275.0695642,0,13449.47821
176762,56,39,2,1,Single,15,275.0537291,0,13467.68646
122042,57,38,3,1,Married,12,275.0114383,0,13173.57192
127836,19,1,3,0,Single,15,274.9943267,0,13149.71634
183817,64,46,1,1,Single,8,274.9807802,0,13394.03901
175335,46,25,1,0,Single,15,274.8857402,0,13517.28701
107949,61,40,3,0,Single,4,274.8692495,0,13503.46248
170905,62,41,2,0,Single,5,274.8170949,0,13344.85475
176967,21,4,4,1,Single,15,274.8065412,0,13333.32706
120169,68,47,3,0,Married,4,274.7739139,0,13464.6957
190955,65,47,1,1,Married,10,274.7667043,0,13366.33522
180566,33,13,3,1,Single,14,274.7218705,0,13207.09353
124141,61,43,2,1,Married,8,274.702862,0,13185.1431
143493,57,39,4,1,Married,10,274.6941979,0,13251.7099
138784,68,50,3,1,Married,0,274.6770646,0,13313.85323
154885,23,5,4,1,Married,14,274.6576474,0,13381.88237
160456,65,46,3,0,Married,1,274.6465865,0,13181.32933
137420,62,42,3,1,Married,7,274.644567,0,13239.22835
139937,68,51,4,0,Single,14,274.6316652,0,13283.58326
188513,67,49,4,0,Married,6,274.5961193,0,13240.80597
143177,50,30,1,1,Married,9,274.564189,0,13197.20945
196888,16,0,4,1,Married,13,274.4523472,0,13490.61736
102413,69,51,2,0,Single,3,274.4210471,0,13179.05236
171513,65,46,2,0,Married,10,274.408471,0,13315.42355
160746,62,45,2,0,Single,1,274.3831727,0,13271.15863
124684,55,38,2,0,Single,12,274.3548628,0,13199.74314
101557,23,4,2,1,Married,13,274.3517512,0,13133.58756
135696,70,49,1,0,Single,10,274.3217731,0,13416.08866
196222,50,32,1,0,Single,13,274.3142835,0,13133.71418
128112,24,5,3,1,Married,14,274.2246925,0,13416.23463
169046,19,0,3,0,Married,15,274.2005254,0,13302.02627
128050,60,39,1,0,Single,5,274.194428,0,13123.7214
161804,63,44,3,0,Married,3,274.189885,0,13223.49425
122582,66,45,3,0,Married,6,274.1892299,0,13182.4615
183777,19,1,4,0,Married,11,274.0396233,0,13304.98117
110896,16,0,2,1,Married,6,274.0220669,0,13376.10335
132857,66,45,4,1,Married,6,273.9834818,0,13155.17409
146346,17,0,3,1,Single,14,273.951206,0,13159.5603
151649,64,46,2,0,Single,2,273.8764314,0,13240.82157
158347,65,48,2,1,Married,7,273.7696758,0,13155.48379
117285,19,0,1,0,Married,11,273.7568806,0,13252.84403
168131,32,13,3,1,Married,12,273.6634165,0,13357.17083
139225,21,4,4,0,Married,13,273.640839,0,13368.04195
187168,64,43,2,0,Married,10,273.6257213,0,13401.28607
192434,65,44,1,0,Single,5,273.523717,0,13131.18585
176073,61,41,2,1,Single,9,273.5220197,0,13467.10099
117702,63,43,4,0,Married,10,273.4975821,0,13464.87911
108926,60,39,2,0,Single,3,273.4860076,0,13176.30038
123681,22,5,3,1,Single,13,273.4728774,0,13469.64387
147350,58,40,4,0,Married,12,273.4724339,0,13315.6217
132533,22,3,4,1,Married,9,273.4440173,0,13106.20087
137341,19,2,3,1,Married,9,273.4011244,0,13130.05622
146639,20,3,1,1,Single,13,273.2692258,0,13242.46129
143776,18,0,1,1,Married,14,273.2499754,0,13130.49877
148923,63,45,3,0,Single,8,273.2318752,0,13077.59376
160434,60,39,3,0,Single,4,273.1819312,0,13353.09656
198709,66,49,1,1,Single,6,273.1567349,0,13299.83675
125976,24,7,1,0,Married,15,273.1274702,0,13424.37351
158419,65,44,2,1,Single,14,272.9090816,0,13096.45408
184811,63,42,2,0,Single,9,272.8974502,0,13062.87251
175756,67,49,4,0,Single,10,272.8842105,0,13368.21053
195432,16,0,2,1,Single,11,272.757036,0,13392.8518
185357,54,36,2,0,Single,12,272.7405242,0,13241.02621
161813,33,15,3,1,Married,10,272.6997251,0,13187.98626
185675,23,4,1,1,Married,15,272.6984372,0,13197.92186
132731,63,43,3,1,Single,11,272.6765058,0,13121.82529
181837,21,4,1,1,Single,11,272.4924191,0,13345.62096
143097,65,47,3,0,Single,3,272.4736288,0,13305.68144
189795,68,51,4,1,Single,10,272.4608285,0,13088.04143
167417,21,4,2,1,Married,15,272.4149126,0,13183.74563
106253,30,9,2,1,Single,13,272.3988253,0,13328.94127
172744,39,22,2,1,Married,6,272.3351511,0,13082.75756
169944,17,0,4,1,Married,7,272.2854661,0,13067.27331
125780,48,31,3,0,Married,13,272.2801077,0,13109.00539
160770,39,22,1,1,Single,14,272.2594633,0,13403.97317
171325,70,52,3,0,Married,6,272.2354176,0,13158.77088
111756,30,9,2,0,Single,14,272.1104504,0,13007.52252
171067,23,5,3,1,Married,15,272.0489279,0,13287.4464
153044,19,2,3,1,Married,14,272.0471721,0,13177.35861
153534,16,0,1,1,Single,15,272.0139319,0,13364.6966
185859,64,45,3,0,Married,6,271.9578787,0,13242.89394
160694,19,2,1,1,Single,14,271.8929537,0,13064.64769
141386,20,3,3,1,Married,15,271.8783756,0,13243.91878
136877,19,2,4,0,Married,13,271.8448659,0,13248.2433
138491,62,42,2,1,Married,2,271.7521943,0,13334.60972
104480,70,52,3,1,Married,4,271.7119805,0,13036.59903
171848,18,0,2,1,Married,12,271.6989729,0,13291.94865
100535,18,0,1,1,Married,12,271.6985859,0,13380.9293
143090,21,2,3,0,Married,11,271.6780306,0,13292.90153
121512,35,14,4,1,Single,13,271.6713621,0,13139.56811
101080,65,45,4,1,Single,15,271.5216373,0,13375.08187
169744,25,7,2,1,Married,13,271.4999002,0,13357.99501
122242,26,9,2,1,Married,13,271.4612047,0,13072.06024
151662,64,45,3,0,Married,9,271.4513684,0,13195.56842
141392,70,51,1,1,Single,0,271.4200491,0,13371.00246
100067,20,1,3,1,Single,12,271.4028246,0,13292.14123
132107,62,41,4,1,Single,15,271.3816388,0,12975.08194
101708,31,12,2,1,Single,15,271.3698987,0,13210.49494
106987,17,0,3,1,Married,11,271.2911711,0,13118.55856
177343,48,30,3,0,Married,11,271.2293863,0,13229.46932
175324,23,5,4,0,Married,13,271.1201143,0,13162.00572
137242,60,40,2,1,Single,12,271.1036119,0,13203.1806
174908,68,47,3,0,Married,4,271.0117578,0,13237.58789
130427,69,48,2,1,Single,6,270.9878941,0,13040.39471
113175,20,2,4,1,Married,14,270.895184,0,12964.7592
185552,17,0,4,0,Married,13,270.8778565,0,12989.89283
171066,68,50,3,1,Single,9,270.8397673,0,12975.98837
136534,16,0,3,0,Married,11,270.8012465,0,13016.06233
199207,56,39,3,1,Single,14,270.8012223,0,13054.06112
188702,69,49,2,0,Married,9,270.7597922,0,12979.98961
147880,63,42,4,0,Married,11,270.7254849,0,12941.27425
168516,19,2,2,1,Married,10,270.6896074,0,13111.48037
156108,17,0,1,1,Married,12,270.6146732,0,13150.73366
111533,20,1,2,1,Married,15,270.5998219,0,12931.9911
192228,21,3,3,1,Married,15,270.560534,0,13081.0267
199696,63,43,2,1,Married,6,270.5565864,0,13235.82932
172040,16,0,2,1,Single,11,270.440841,0,13024.04205
104424,62,45,3,0,Single,0,270.399887,0,13162.99435
104536,54,36,3,1,Married,15,270.3934543,0,13189.67272
158773,33,15,2,0,Single,13,270.3458042,0,13032.29021
199226,24,5,3,0,Married,14,270.3439618,0,13231.19809
127032,64,47,1,1,Married,5,270.2730694,0,13058.65347
176502,70,53,3,1,Married,6,270.2556744,0,12974.78372
135195,25,7,2,0,Married,15,270.1715725,0,13145.57863
105707,24,5,2,1,Single,13,270.0766154,0,13274.83077
122173,37,20,4,1,Married,13,270.0050947,0,13105.25474
194131,70,52,1,1,Married,8,269.810358,0,12932.5179
113948,45,27,1,0,Married,14,269.786051,0,13058.30255
190402,55,36,3,1,Married,14,269.7842954,0,13151.21477
115433,24,6,4,1,Single,15,269.7292976,0,12955.46488
159552,62,43,3,0,Single,9,269.5615653,0,12931.07827
188175,16,0,3,1,Single,13,269.5369389,0,13174.84695
162148,25,8,2,0,Married,15,269.4863751,0,13047.31876
126261,68,51,1,1,Married,0,269.4692369,0,13127.46185
117644,23,5,3,1,Single,11,269.4477175,0,13103.38588
101576,25,6,3,0,Single,14,269.4409717,0,13044.04859
173165,61,43,3,1,Married,6,269.4229961,0,13143.14981
163597,18,1,1,1,Married,12,269.4121493,0,13072.60747
191765,47,29,3,1,Married,10,269.4113554,0,13022.56777
135979,62,44,1,1,Single,9,269.3517877,0,13176.58939
151137,68,50,3,0,Single,5,269.2805318,0,12963.02659
122797,61,40,2,0,Married,8,269.2613967,0,13060.06984
156482,18,0,3,0,Married,12,269.239046,0,13198.9523
109526,22,3,3,0,Married,14,269.235249,0,12995.76245
178344,17,0,2,1,Single,12,269.2318208,0,13031.59104
174887,41,22,2,1,Married,15,269.1793261,0,13137.96631
195493,52,34,3,1,Married,12,269.1665689,0,13060.32845
163523,66,49,3,0,Single,7,269.1243075,0,13051.21538
111160,18,1,4,1,Married,8,269.1023927,0,13045.11964
134746,61,43,4,0,Married,10,269.0218439,0,13084.0922
109865,70,53,4,0,Single,9,269.0197341,0,12902.98671
138395,24,5,1,0,Married,13,268.9314723,0,12851.57362
134554,22,4,3,1,Single,13,268.9297063,0,12936.48532
198150,27,9,3,1,Single,15,268.8387187,0,13117.93594
172375,68,47,3,0,Married,9,268.8206765,0,12847.03383
198313,68,48,4,1,Married,8,268.8178331,0,13203.89166
198000,59,39,3,0,Single,13,268.7853382,0,13142.26691
187957,31,10,1,0,Married,14,268.77102,0,13025.551
158103,69,51,4,0,Married,15,268.6879041,0,12885.39521
102022,18,1,4,1,Married,15,268.6742597,0,13050.71299
102921,22,4,1,1,Single,14,268.6718533,0,13053.59267
174087,35,14,3,1,Single,15,268.6189791,0,13215.94896
123344,69,52,3,0,Married,12,268.5815213,0,12913.07607
199427,19,2,2,0,Married,12,268.5775197,0,13065.87599
100393,61,44,2,1,Single,10,268.5666419,0,12899.3321
193957,63,45,4,1,Married,7,268.5457094,0,13164.28547
135252,52,35,1,0,Married,15,268.5415695,0,13099.07848
154333,31,10,1,1,Married,11,268.5320136,0,13064.60068
145777,68,47,2,1,Single,13,268.5031131,0,12999.15566
145757,50,29,3,1,Single,15,268.48299,0,12903.1495
143428,65,46,3,1,Married,10,268.4666895,0,13200.33448
139540,18,0,2,0,Married,15,268.461219,0,13066.06095
165002,67,47,3,1,Single,6,268.4580461,0,13111.90231
197447,50,30,4,0,Single,14,268.4336686,0,12843.68343
115707,31,13,2,0,Married,14,268.3762987,0,13168.81494
187898,39,20,4,0,Married,15,268.351566,0,12833.5783
181091,27,8,2,1,Single,12,268.3414507,0,13013.07254
151348,41,20,4,1,Single,11,268.3231338,0,12858.15669
133794,28,8,3,1,Single,12,268.2158673,0,13203.79337
106870,63,46,2,1,Married,12,268.2037864,0,12864.18932
181773,68,51,1,1,Married,12,268.1844154,0,12961.22077
140774,56,39,4,0,Single,12,268.177543,0,12964.87715
152203,18,0,3,0,Married,15,268.1598164,0,13180.99082
133848,48,28,3,0,Married,11,268.1174236,0,13113.87118
130763,36,16,4,0,Married,14,268.109113,0,13197.45565
196374,70,50,3,1,Single,12,267.9997867,0,12954.98934
111388,16,0,4,0,Married,14,267.9816964,0,12877.08482
120680,68,49,2,0,Single,11,267.943607,0,12824.18035
178152,63,43,4,1,Single,6,267.8670043,0,13030.35022
154939,66,47,2,1,Single,11,267.7806547,0,12924.03274
198810,57,36,3,1,Married,9,267.774759,0,13131.73795
113183,38,17,2,1,Single,13,267.597903,0,13170.89515
134103,54,33,2,1,Married,11,267.5866584,0,12813.33292
129907,26,8,1,0,Single,11,267.485859,0,12794.29295
113219,65,45,4,1,Married,6,267.3935689,0,13063.67845
157541,19,1,2,1,Married,12,267.3694438,0,12770.47219
112376,61,43,3,0,Married,6,267.3346815,0,12864.73408
141209,26,9,4,1,Single,13,267.2991108,0,13060.95554
198566,62,42,2,1,Single,6,267.284417,0,13120.22085
115302,65,47,2,1,Married,7,267.276004,0,12830.8002
131578,43,23,3,1,Married,13,267.265559,0,12924.27795
185901,67,50,2,1,Married,3,267.2641669,0,12904.20835
111659,65,44,2,0,Single,11,267.2544801,0,13098.72401
119461,39,22,2,0,Married,15,267.2414317,0,13117.07159
182744,66,45,4,1,Single,7,267.2255956,0,12832.27978
144413,68,51,2,0,Single,5,267.2097953,0,12972.48977
107171,25,6,2,1,Married,1,267.20863,0,13026.4315
129219,63,46,3,1,Single,9,267.059759,0,12910.98795
128685,34,16,1,1,Married,12,267.0481689,0,12919.40845
114155,25,6,2,1,Married,11,267.0447435,0,13138.23718
176575,62,44,2,1,Single,14,266.9167964,0,12957.83982
166534,38,17,4,1,Married,14,266.8677973,0,13021.38987
173542,20,3,3,0,Married,11,266.856939,0,12951.84695
196528,18,1,2,0,Married,12,266.8493854,0,13040.46927
176055,25,8,3,1,Married,7,266.8474848,0,13129.37424
179588,20,2,3,1,Married,11,266.7945785,0,13089.72893
194514,64,47,3,1,Single,14,266.761618,0,13052.0809
142398,62,45,3,1,Married,5,266.7128611,0,12901.64306
131913,64,43,3,1,Married,9,266.6488728,0,12862.44364
190609,24,6,2,0,Married,11,266.6348714,0,12836.74357
136349,70,53,3,0,Single,6,266.5498728,0,12870.49364
177515,59,41,3,0,Married,14,266.5304811,0,13016.52406
110939,38,18,2,0,Single,13,266.5227536,0,12822.13768
185012,54,33,3,1,Married,12,266.4995397,0,12929.97699
199634,62,45,1,0,Single,6,266.4859016,0,12859.29508
102623,21,4,2,0,Married,13,266.2417314,0,12903.08657
149219,61,41,2,1,Single,12,266.1838957,0,12948.19479
181590,70,49,4,1,Single,2,266.163006,0,12997.1503
137758,41,22,4,0,Married,15,266.1462605,0,13063.31303
180191,20,3,1,1,Single,14,266.0967514,0,12719.83757
196872,60,43,4,1,Single,5,266.0933319,0,12849.6666
198523,22,4,1,1,Married,11,266.0329124,0,12743.64562
136616,17,0,1,1,Married,13,266.0062224,0,12754.31112
118859,62,45,3,0,Married,9,265.9944631,0,13055.72316
111219,67,47,2,0,Married,5,265.9392975,0,13011.96488
168087,17,0,1,0,Married,15,265.8563455,0,12986.81728
123218,63,42,2,1,Married,10,265.7598887,0,12706.99444
134279,63,43,2,1,Married,7,265.6889909,0,12687.44955
159755,23,4,2,1,Married,15,265.6368544,0,12820.84272
185066,44,26,2,0,Single,13,265.634568,0,12969.7284
159384,65,44,2,0,Married,6,265.6083469,0,12739.41735
118526,23,4,2,1,Married,15,265.5556163,0,13011.78082
179318,23,5,2,1,Married,13,265.3761537,0,12819.80769
106561,18,0,2,1,Single,15,265.3680179,0,12834.4009
105391,23,6,1,0,Single,15,265.3413142,0,12702.06571
121850,43,23,3,1,Married,12,265.3348589,0,12706.74295
116290,56,36,2,0,Married,12,265.3119797,0,12774.59899
121215,67,48,3,1,Married,7,265.3039356,0,12803.19678
146914,20,2,3,1,Single,15,265.2991479,0,12967.9574
195252,24,5,3,0,Married,12,265.1746075,0,13053.73038
197502,20,1,2,1,Single,14,265.1163252,0,12921.81626
108773,38,20,3,1,Married,11,265.0697544,0,12654.48772
114418,54,33,4,1,Married,14,265.0341188,0,12848.70594
115199,42,24,3,1,Married,11,265.0117856,0,13042.58928
113850,32,13,3,0,Single,14,264.9566153,0,12726.83077
151268,66,47,3,0,Single,2,264.9169424,0,12880.84712
118167,66,46,3,1,Married,0,264.9156003,0,12964.78002
140976,52,33,1,0,Married,13,264.9045348,0,12726.22674
118439,69,52,1,1,Single,9,264.9025025,0,12649.12513
170772,28,8,2,0,Married,14,264.8209968,0,12827.04984
102706,17,0,3,1,Married,15,264.8026987,0,12876.13494
178969,69,51,2,0,Married,5,264.7913926,0,13009.56963
132104,16,0,1,0,Married,15,264.7891684,0,13026.45842
143621,32,11,2,1,Single,12,264.7846314,0,12838.23157
157848,51,31,3,0,Married,11,264.7459169,0,12678.29585
144286,37,19,2,1,Single,12,264.6726718,0,12802.63359
107169,25,8,1,1,Married,13,264.656479,0,12694.82395
168532,60,43,2,1,Married,9,264.655757,0,13019.78785
167698,55,36,1,0,Single,12,264.6489308,0,12803.44654
174628,26,9,3,1,Single,15,264.6129075,0,12886.64538
192237,37,19,2,1,Married,12,264.6079872,0,12856.39936
195762,61,44,4,0,Married,9,264.600332,0,12717.0166
103961,54,37,2,1,Married,12,264.5751093,0,12738.75547
187025,20,2,2,1,Married,13,264.5572383,0,12676.86192
193474,16,0,2,0,Married,14,264.5211779,0,12778.0589
109348,19,2,2,1,Married,11,264.520156,0,12957.0078
159347,16,0,3,0,Married,15,264.5165731,0,12823.82866
148947,63,44,1,0,Single,13,264.4793918,0,12734.96959
184695,61,41,3,0,Married,2,264.3370459,0,12824.8523
132167,70,53,1,0,Single,10,264.2712706,0,12622.56353
178748,21,4,4,1,Married,12,264.256172,0,12863.8086
142393,61,43,3,1,Single,8,264.2017463,0,12773.08732
114662,48,29,3,1,Married,14,264.1574614,0,12806.87307
145339,18,0,3,0,Married,12,264.1244449,0,12725.22225
130288,19,1,3,1,Married,14,264.0771175,0,12884.85588
104090,21,2,3,1,Married,13,264.056303,0,12921.81515
149881,24,5,3,0,Married,11,263.9893442,0,12985.46721
157357,23,4,2,1,Married,12,263.9431562,0,12647.15781
158380,39,19,2,0,Married,11,263.9384452,0,12938.92226
142107,21,3,3,1,Married,14,263.8780435,0,12747.90218
101075,64,45,3,1,Married,7,263.8373293,0,12924.86647
103363,65,47,2,1,Married,9,263.766748,0,12960.3374
118255,21,4,2,1,Married,13,263.7464543,0,12697.32272
167123,23,5,2,1,Single,14,263.7207796,0,12697.03898
165820,32,11,3,0,Single,13,263.7207697,0,12797.03849
132853,68,49,3,0,Married,6,263.6322305,0,12968.61153
197533,17,0,4,0,Married,13,263.5806493,0,12694.03247
154995,42,24,4,0,Single,14,263.4834406,0,12630.17203
140277,19,1,2,0,Married,12,263.4730577,0,12879.65289
180149,24,6,4,1,Single,15,263.4604824,0,12914.02412
165079,38,20,3,1,Married,14,263.4229657,0,12662.14829
140553,32,15,4,1,Married,14,263.3927649,0,12870.63825
186308,60,39,3,0,Married,10,263.2503566,0,12780.51783
107521,27,7,4,0,Single,15,263.1616943,0,12676.08472
116009,70,49,3,1,Married,6,263.1277167,0,12643.38584
148156,36,17,3,1,Married,11,263.0936688,0,12941.68344
109930,55,34,2,1,Married,13,263.0665303,0,12908.32652
141061,69,49,3,0,Married,9,262.981783,0,12774.08915
146920,42,25,1,0,Single,14,262.9411765,0,12772.05883
139723,70,51,2,1,Married,11,262.9003887,0,12742.01944
177363,21,2,1,1,Married,10,262.8439707,0,12809.19854
111048,49,30,2,0,Single,11,262.8293092,0,12629.46546
197636,61,44,3,1,Married,14,262.7851245,0,12691.25623
164056,42,22,3,0,Married,11,262.6984205,0,12868.92103
162350,39,19,3,0,Single,13,262.6699139,0,12568.4957
133918,22,4,2,0,Married,13,262.6320129,0,12652.60065
176542,28,11,2,1,Single,14,262.5192527,0,12564.96264
176553,67,49,2,0,Single,14,262.4713199,0,12917.566
197445,23,5,4,1,Married,15,262.3792553,0,12744.96277
179609,61,44,3,1,Married,9,262.291223,0,12814.56115
192598,50,32,3,1,Single,11,262.2647694,0,12723.23847
137745,21,2,3,0,Married,13,262.2304821,0,12713.52411
192012,63,44,1,0,Single,0,262.1983443,0,12662.91722
183715,58,39,4,0,Married,14,262.1914364,0,12732.57182
142159,22,4,3,1,Single,11,262.1890732,0,12680.45366
198979,69,51,4,1,Married,7,262.1851171,0,12806.25586
123019,48,27,3,1,Single,11,262.1489059,0,12727.4453
118563,16,0,3,1,Single,14,262.0928116,0,12902.64058
126213,68,50,4,0,Married,6,262.0895176,0,12657.47588
184669,60,41,2,0,Married,12,262.0311129,0,12830.55565
117417,55,36,3,1,Single,14,262.0110004,0,12555.55002
103547,17,0,4,1,Married,15,261.9462079,0,12839.3104
178226,70,52,3,0,Married,0,261.933688,0,12653.6844
147580,52,32,2,0,Single,14,261.9109882,0,12547.54941
126793,21,4,3,1,Single,13,261.7968569,0,12752.84285
185536,23,6,2,1,Married,12,261.7516407,0,12858.58204
199047,62,42,4,0,Single,12,261.7209216,0,12516.04608
178296,61,43,1,0,Single,4,261.6904151,0,12695.52076
128869,55,37,4,1,Married,13,261.6604347,0,12563.02174
129009,55,35,2,1,Married,11,261.6586678,0,12633.93339
148546,62,42,1,1,Single,2,261.6444496,0,12709.22248
137337,18,0,3,0,Married,13,261.5421899,0,12840.1095
115407,62,43,2,0,Married,10,261.5115246,0,12537.57623
185668,63,42,1,1,Married,5,261.5094393,0,12763.47197
111846,19,2,2,1,Married,10,261.4601079,0,12762.0054
164351,23,5,3,1,Married,7,261.3574567,0,12811.87284
178490,27,6,3,1,Single,14,261.3264249,0,12481.32125
189612,66,47,3,1,Single,10,261.2730634,0,12561.65317
138646,64,44,2,0,Married,8,261.2635133,0,12628.17567
156908,62,43,4,0,Married,1,261.1149887,0,12495.74944
122176,65,44,1,1,Married,7,261.0802834,0,12815.01417
184105,37,20,1,0,Married,14,261.0602312,0,12593.01156
115796,53,33,2,1,Married,12,260.947997,0,12827.39985
175229,33,14,3,1,Single,11,260.9161949,0,12748.80975
184022,67,48,2,1,Married,4,260.9071055,0,12799.35528
172044,65,46,3,1,Married,5,260.8163845,0,12649.81923
115759,23,4,4,1,Single,11,260.7504228,0,12539.52114
107532,60,41,1,0,Married,6,260.7493276,0,12757.46638
120176,23,4,3,1,Married,11,260.7172564,0,12789.86282
190962,25,7,1,0,Married,12,260.7111751,0,12802.55876
159763,24,5,4,1,Married,12,260.6415955,0,12797.07978
136502,25,6,2,1,Single,13,260.5924515,0,12511.62258
183896,18,1,3,1,Single,13,260.524676,0,12549.2338
122244,32,13,4,1,Married,15,260.3735854,0,12467.67927
115303,64,46,1,1,Married,11,260.2725911,0,12474.62956
179672,70,51,3,0,Married,3,260.2474794,0,12619.37397
172805,17,0,1,0,Married,12,260.2345014,0,12525.72507
113918,51,32,1,1,Married,15,260.2033477,0,12530.16739
196919,18,1,2,0,Married,12,260.0719096,0,12551.59548
133452,22,4,2,1,Married,15,260.0544308,0,12412.72154
169783,24,5,2,1,Married,12,260.016964,0,12611.8482
153008,55,37,3,1,Married,14,259.9785414,0,12508.92707
136960,44,27,1,0,Single,15,259.9723271,0,12761.61636
111072,53,36,1,0,Single,14,259.8909694,0,12446.54847
109960,67,50,4,0,Single,15,259.8633131,0,12614.16566
119986,60,40,2,0,Single,9,259.7822074,0,12783.11037
189455,24,5,2,1,Single,15,259.7794723,0,12444.97362
139746,35,17,4,0,Married,11,259.7741753,0,12475.70877
101138,34,16,2,0,Single,11,259.7734467,0,12416.67234
134318,26,6,4,0,Married,15,259.7499425,0,12643.49713
167322,63,44,1,0,Married,8,259.7498031,0,12561.49016
153192,17,0,2,1,Single,11,259.7446292,0,12622.23146
165703,70,50,2,0,Single,0,259.7310588,0,12594.55294
190171,16,0,2,1,Single,12,259.7144582,0,12495.72291
197817,32,11,1,0,Married,14,259.6195249,0,12438.97625
138952,66,47,2,0,Single,1,259.4806222,0,12516.03111
164581,60,40,4,1,Married,12,259.4478149,0,12436.39075
167802,20,2,2,1,Single,11,259.4363701,0,12474.81851
167739,22,4,1,0,Married,13,259.4013808,0,12626.06904
102379,61,40,3,1,Married,6,259.3614327,0,12572.07164
121935,37,19,2,0,Married,14,259.357527,0,12392.87635
191172,23,5,2,1,Married,14,259.3449509,0,12428.24755
140560,66,46,1,1,Married,7,259.3302137,0,12390.51069
126888,63,43,3,0,Married,2,259.2207378,0,12658.03689
150201,25,6,3,1,Married,14,259.2170314,0,12408.85157
101642,58,40,3,1,Single,12,259.0942332,0,12583.71166
166554,59,40,1,1,Single,11,258.9963475,0,12382.81738
134074,25,8,2,1,Married,13,258.95265,0,12426.6325
130412,17,0,2,1,Married,13,258.9133824,0,12472.66912
150547,21,2,2,0,Single,13,258.9005436,0,12422.02718
112640,63,42,4,1,Married,10,258.8828219,0,12473.1411
196294,63,42,2,1,Married,8,258.8729838,0,12464.64919
155597,68,51,3,1,Married,7,258.834171,0,12468.70855
181163,37,20,1,1,Married,14,258.8203437,0,12719.01719
110924,69,50,3,1,Single,9,258.8036676,0,12495.18338
133731,61,41,3,1,Single,0,258.794741,0,12347.73705
107287,37,19,2,0,Married,14,258.7126838,0,12526.63419
198044,18,0,3,1,Single,13,258.6985443,0,12558.92722
154361,23,6,3,1,Married,12,258.6640919,0,12575.2046
146550,27,7,3,0,Married,11,258.6368128,0,12725.84064
167898,50,29,3,1,Married,11,258.6340919,0,12549.7046
161523,18,0,1,1,Married,13,258.6207438,0,12469.03719
163030,19,1,3,1,Single,11,258.6034522,0,12711.17261
168719,20,3,2,0,Married,11,258.5981163,0,12431.90582
134837,42,23,2,0,Married,11,258.4579809,0,12430.89905
115732,20,3,3,1,Married,0,258.3707563,0,12709.53782
119492,16,0,1,1,Married,12,258.3328842,0,12442.64421
180051,65,46,4,1,Single,4,258.2303987,0,12667.51994
171604,28,9,2,1,Married,14,258.2119882,0,12682.59941
140761,33,15,3,0,Single,14,258.1998141,0,12508.99071
115174,69,48,4,0,Married,4,258.1710632,0,12440.55316
138945,17,0,1,1,Single,15,257.9940404,0,12435.70202
100458,22,5,4,1,Single,14,257.9823303,0,12311.11652
101583,16,0,1,1,Married,10,257.9819928,0,12579.09964
115853,19,1,4,1,Married,14,257.9728994,0,12439.64497
141072,19,0,2,0,Married,13,257.8478061,0,12608.39031
107834,54,33,3,1,Married,15,257.8225661,0,12324.12831
161586,39,20,2,1,Married,15,257.7862464,0,12625.31232
134696,41,20,3,0,Married,13,257.7639364,0,12290.19682
193331,53,34,1,0,Single,14,257.7397425,0,12613.98713
130823,33,16,4,1,Married,15,257.7345241,0,12427.72621
134162,19,1,2,1,Married,12,257.6636706,0,12428.18353
112905,19,2,2,1,Married,14,257.6394232,0,12650.97116
136173,21,2,3,1,Married,13,257.5727407,0,12513.63704
115950,60,40,1,1,Single,4,257.538506,0,12513.9253
145024,22,4,1,1,Married,7,257.4622527,0,12336.11264
158947,64,46,3,1,Married,3,257.3383251,0,12278.91626
139786,61,44,4,0,Married,10,257.3348853,0,12278.74427
106640,62,44,2,1,Married,8,257.3304459,0,12637.5223
190349,44,27,3,1,Married,11,257.320812,0,12375.0406
139049,22,5,4,1,Married,6,257.3038848,0,12495.19424
103924,62,45,2,1,Single,6,257.2819425,0,12651.09713
196247,20,2,2,1,Single,12,257.2422185,0,12422.11093
143574,29,9,4,1,Married,13,257.1925233,0,12468.62617
101116,60,43,1,0,Single,11,257.1813241,0,12513.06621
170404,67,50,2,1,Single,15,257.157292,0,12301.8646
170783,34,13,4,1,Single,15,257.1299028,0,12284.49514
166068,47,26,3,1,Single,11,257.1076017,0,12495.38009
111662,35,17,2,0,Married,13,257.0650988,0,12356.25494
196249,51,34,2,0,Single,14,257.0408231,0,12284.04116
154189,65,44,3,0,Married,15,256.9809464,0,12620.04732
126006,25,6,2,1,Married,14,256.9070413,0,12634.35207
148013,25,7,2,0,Married,15,256.904249,0,12335.21245
121046,62,42,1,1,Married,14,256.9014828,0,12620.07414
173346,18,1,1,1,Married,15,256.8966878,0,12266.83439
134914,63,42,3,0,Single,12,256.8653081,0,12246.26541
173285,33,16,2,1,Married,11,256.8330831,0,12404.65416
124911,23,5,2,1,Single,11,256.8265508,0,12351.32754
137312,63,45,4,0,Married,7,256.7659327,0,12360.29664
107271,16,0,2,1,Married,6,256.7210287,0,12370.05144
184172,70,53,4,1,Married,10,256.7184947,0,12612.92474
160682,69,48,3,1,Single,9,256.7040757,0,12304.20379
151161,32,15,2,1,Single,12,256.6548162,0,12453.74081
119381,67,47,3,1,Single,1,256.6426024,0,12516.13012
126690,17,0,3,1,Single,15,256.5646766,0,12628.23383
150686,60,40,2,1,Married,13,256.5214825,0,12618.07413
138487,62,41,2,1,Married,13,256.4519265,0,12359.59633
192823,70,50,1,0,Single,13,256.4290973,0,12617.45487
111481,19,0,4,1,Married,13,256.4216357,0,12329.08179
162664,68,49,3,0,Single,11,256.3954396,0,12411.77198
106931,24,7,2,1,Married,11,256.3592184,0,12401.96092
186245,17,0,1,0,Married,14,256.3104419,0,12405.5221
194043,29,8,3,1,Single,14,256.3007473,0,12458.03737
128032,19,0,2,0,Married,14,256.2949037,0,12297.74519
116082,67,50,2,1,Married,11,256.2403446,0,12544.01723
110905,19,0,2,1,Married,15,256.1425496,0,12229.12748
140922,63,43,2,1,Married,3,256.1034855,0,12346.17428
185310,62,43,3,0,Married,15,256.071587,0,12308.57935
103908,61,40,3,1,Married,13,256.0388721,0,12227.94361
125816,53,35,2,1,Married,15,255.9480053,0,12270.40027
176272,69,52,2,0,Married,5,255.8123577,0,12542.61789
166827,27,6,3,1,Married,13,255.7960805,0,12353.80403
131357,65,44,3,1,Married,2,255.5804782,0,12448.02391
128137,17,0,2,1,Married,11,255.5687642,0,12561.43821
137626,23,6,2,1,Married,15,255.527599,0,12206.37995
163325,61,44,3,0,Single,14,255.4191954,0,12336.95977
121398,21,3,2,1,Single,12,255.3929748,0,12345.64874
156406,62,45,3,1,Single,8,255.3001921,0,12394.00961
147650,24,7,2,1,Married,15,255.2058567,0,12272.29284
182837,20,1,3,1,Married,15,255.2054927,0,12239.27464
167861,63,45,1,0,Married,5,255.1907019,0,12442.5351
121157,23,5,1,1,Single,13,255.1761581,0,12359.80791
179728,21,3,2,1,Married,15,255.1657713,0,12506.28857
177948,66,47,2,0,Single,7,255.0436158,0,12333.18079
191429,18,0,3,1,Married,10,255.0223564,0,12152.11782
169726,60,40,2,1,Single,0,255.005754,0,12467.2877
103799,58,39,3,0,Married,12,255.0014446,0,12262.07223
150919,23,5,4,1,Single,11,254.9418043,0,12180.09022
151708,36,18,3,0,Single,13,254.8772833,0,12255.86417
136873,48,29,2,1,Married,15,254.8727625,0,12169.63813
163067,63,42,3,1,Single,5,254.8645781,0,12400.22891
105146,55,36,2,0,Married,12,254.8229555,0,12396.14778
197935,46,27,4,0,Married,12,254.7859673,0,12209.29837
148138,17,0,1,1,Married,11,254.7692296,0,12526.46148
140551,69,51,1,1,Single,5,254.7680456,0,12400.40228
111285,38,21,3,0,Married,13,254.7635616,0,12475.17808
161766,24,5,2,1,Married,14,254.5990556,0,12375.95278
149904,22,3,3,0,Married,11,254.5378949,0,12478.89475
154508,68,48,1,1,Married,8,254.3947227,0,12201.73614
156589,68,48,4,1,Single,6,254.3091107,0,12474.45554
119878,43,22,3,0,Married,12,254.2583337,0,12282.91669
101235,41,23,3,1,Married,15,254.212927,0,12286.64635
190583,49,28,3,1,Married,12,254.1878307,0,12124.39154
158233,23,6,3,1,Married,13,254.098712,0,12225.9356
178351,23,4,1,1,Married,12,254.0958405,0,12171.79203
117691,19,1,1,0,Married,11,254.0805596,0,12479.02798
101390,60,39,2,0,Married,6,253.978336,0,12413.9168
152317,67,46,1,1,Single,10,253.9768052,0,12172.84026
126766,67,49,3,1,Married,7,253.9659522,0,12296.29761
171552,25,7,2,1,Married,15,253.9624512,0,12101.12256
180597,23,6,3,1,Married,15,253.9558468,0,12405.79234
141018,70,49,2,0,Married,3,253.9444432,0,12456.22216
199495,68,47,4,1,Married,6,253.7716719,0,12202.5836
116989,53,34,2,0,Single,12,253.7332963,0,12324.66482
106326,41,20,4,1,Married,13,253.5777019,0,12121.8851
179231,60,40,3,0,Married,15,253.5710605,0,12475.55303
106493,60,41,4,1,Married,12,253.4675589,0,12252.37795
173295,63,46,1,1,Single,3,253.4663373,0,12327.31687
143131,43,24,2,1,Married,15,253.4649124,0,12386.24562
124718,17,0,4,1,Single,14,253.4289936,0,12372.44968
196980,66,47,3,0,Married,10,253.4225005,0,12289.12503
141368,62,42,3,1,Married,0,253.4212085,0,12358.06043
120932,61,42,2,0,Married,5,253.3873605,0,12274.36803
103819,67,49,2,1,Single,3,253.2816075,0,12444.08038
110888,67,49,3,1,Single,5,253.2197256,0,12380.98628
174835,25,8,4,0,Married,12,253.2165616,0,12160.82808
195194,16,0,2,1,Married,8,253.2009313,0,12062.04657
157213,50,31,4,1,Married,13,253.1904409,0,12217.52205
125442,66,49,3,0,Single,13,253.1358009,0,12118.79005
189113,20,3,3,1,Married,6,253.1215171,0,12193.07586
170963,61,44,2,0,Single,11,253.083236,0,12133.1618
162092,30,10,4,0,Married,11,253.0046386,0,12352.23193
155923,65,48,2,0,Single,12,252.9633203,0,12437.16602
174515,23,5,4,0,Married,13,252.8911414,0,12430.55707
183067,66,48,2,1,Married,5,252.7599084,0,12229.99542
100721,18,1,2,1,Single,13,252.6893344,0,12126.46672
154895,52,31,4,1,Married,15,252.6448131,0,12129.24066
185797,57,38,2,1,Married,13,252.6447592,0,12349.23796
142854,70,49,3,1,Married,10,252.5770707,0,12242.85354
184646,25,7,4,1,Married,12,252.5429156,0,12312.14578
132390,22,5,4,1,Married,11,252.520541,0,12151.02705
121660,61,43,2,1,Married,10,252.4533935,0,12266.66968
181265,54,34,2,0,Married,11,252.4345156,0,12166.72578
167556,49,29,3,1,Married,15,252.4183494,0,12120.91747
153902,21,4,1,0,Married,15,252.3853189,0,12363.26595
190043,23,5,3,1,Married,7,252.3327088,0,12129.63544
180898,54,33,2,0,Married,12,252.1813357,0,12017.06679
165164,19,0,3,1,Married,15,252.1111285,0,12246.55643
190769,28,8,3,0,Married,13,252.0558287,0,12036.79144
120064,65,48,3,0,Married,5,252.0520392,0,12119.60196
153707,44,25,3,0,Married,14,252.0360663,0,12266.80332
152628,20,1,3,0,Married,11,251.9960488,0,12168.80244
151220,23,5,1,1,Married,15,251.9734581,0,12384.67291
186207,36,19,4,0,Single,15,251.9122772,0,12236.61386
189126,29,12,2,1,Married,11,251.8850248,0,12183.25124
161476,49,29,3,1,Single,14,251.875814,0,12101.7907
177319,50,33,4,1,Single,13,251.8672765,0,12359.36383
130318,69,51,3,1,Single,8,251.7472081,0,12314.36041
124503,24,6,2,1,Married,15,251.7436492,0,12059.18246
190502,16,0,2,0,Married,13,251.7212245,0,12220.06123
145490,67,46,3,0,Single,0,251.5984868,0,12238.92434
168142,44,23,3,0,Married,15,251.5648271,0,12168.24136
155539,64,47,3,0,Single,3,251.5561758,0,12086.80879
131739,17,0,3,1,Married,15,251.4617866,0,11991.08933
100882,25,7,3,1,Single,15,251.3519536,0,12215.59768
104434,63,44,3,0,Single,13,251.2715857,0,12320.57929
195967,18,0,2,1,Married,15,251.2023753,0,12111.11877
186175,70,53,1,0,Single,15,251.1951899,0,12023.7595
191769,16,0,3,0,Married,13,251.1469014,0,12005.34507
127700,67,46,1,0,Single,7,251.1320166,0,12309.60083
174803,69,52,3,1,Married,0,251.1110221,0,12102.55111
160751,36,19,3,1,Married,14,251.0929693,0,12302.64847
150236,23,5,3,0,Married,15,251.0801107,0,11999.00554
145564,25,6,2,1,Married,14,251.0376094,0,12145.88047
138557,18,0,2,1,Married,15,250.9612776,0,12129.06388
122191,55,34,2,1,Single,13,250.9543018,0,12018.71509
102736,61,42,1,1,Married,9,250.8953606,0,12199.76803
116611,70,50,3,1,Single,12,250.8693099,0,12301.4655
192661,25,8,1,1,Married,14,250.8077066,0,12211.38533
123562,59,38,2,1,Married,14,250.5868113,0,11948.34057
154317,34,14,2,1,Married,11,250.5664433,0,12044.32217
194081,16,0,2,1,Married,14,250.5357625,0,12006.78813
191165,17,0,2,1,Married,11,250.4820965,0,12085.10483
138205,23,5,4,0,Married,13,250.3969718,0,12183.84859
148296,69,50,1,1,Married,9,250.2576733,0,12280.88367
149178,58,37,3,0,Single,11,250.244213,0,11972.21065
173662,18,0,1,1,Married,15,250.2204478,0,12274.02239
190389,29,12,1,1,Married,14,250.159522,0,12016.9761
109686,69,48,3,1,Single,8,250.1486479,0,12265.4324
168046,29,10,3,1,Married,11,250.1315134,0,12119.57567
106297,66,47,2,0,Married,12,250.1021146,0,12086.10573
114314,29,9,2,0,Married,15,250.0997358,0,12036.98679
128822,21,3,1,0,Married,13,250.099125,0,12017.95625
137629,20,2,1,0,Married,13,250.0483034,0,12212.41517
137795,31,14,2,0,Married,13,249.9909233,0,11910.54617
113169,36,15,1,1,Married,13,249.9467293,0,12058.33647
131795,36,17,1,1,Married,13,249.8851289,0,12226.25645
103553,19,0,3,1,Married,14,249.873609,0,12122.68045
170955,23,5,3,1,Married,14,249.867441,0,12264.37205
136046,68,51,4,1,Married,10,249.8264059,0,12210.3203
177609,61,43,3,0,Single,9,249.735638,0,12175.7819
186335,23,4,3,1,Married,15,249.68224,0,12168.112
196201,68,51,4,1,Married,8,249.6509133,0,11945.54567
164806,67,47,2,1,Single,10,249.5596089,0,12170.98045
115592,20,1,3,1,Married,7,249.5085311,0,11875.42656
165969,16,0,2,1,Married,12,249.4421413,0,12118.10707
152108,16,0,2,1,Married,13,249.4198391,0,12157.99196
106267,51,34,3,0,Married,13,249.4177861,0,12167.88931
111345,61,44,3,0,Single,14,249.3815901,0,12130.07951
153109,22,3,1,0,Married,12,249.3555374,0,12009.77687
130789,60,41,3,1,Single,7,249.3394928,0,12062.97464
103849,67,49,1,0,Married,3,249.1713156,0,12051.56578
100565,67,50,1,1,Married,8,249.1075462,0,11865.37731
135029,61,42,3,0,Married,0,248.8836026,0,12151.18013
147937,64,47,2,1,Single,7,248.8582016,0,11858.91008
199225,60,41,3,1,Single,15,248.7837207,0,11887.18604
141113,55,35,3,1,Single,11,248.7568834,0,12004.84417
179421,70,53,3,0,Married,10,248.7119133,0,12227.59567
163270,70,53,3,0,Married,12,248.6125482,0,11837.62741
140856,19,1,3,1,Single,13,248.6097919,0,12096.4896
161704,67,46,3,0,Married,9,248.5982222,0,11986.91111
111174,70,51,1,1,Single,9,248.5762478,0,12136.81239
145106,30,11,4,1,Married,11,248.5739382,0,12028.69691
112692,22,4,1,0,Married,15,248.5382876,0,12189.91438
154945,65,46,4,0,Single,6,248.5372686,0,11965.86343
163513,66,46,1,1,Married,12,248.5359904,0,11850.79952
193391,57,40,3,0,Single,12,248.332043,0,12063.60215
164457,70,49,3,0,Married,8,248.283635,0,12088.18175
197413,18,0,4,1,Married,7,248.2747272,0,12074.73636
191785,64,47,1,1,Married,9,248.2674932,0,12025.37466
183306,58,37,4,0,Married,14,248.2583182,0,12097.91591
184832,68,51,2,0,Married,13,248.1590625,0,11989.95313
106311,40,22,3,0,Single,12,248.0955132,0,11937.77566
161289,19,2,2,1,Married,9,248.0099392,0,11935.49696
126456,20,2,2,1,Married,12,248.0012105,0,11999.06053
133167,42,22,3,0,Married,11,247.9977949,0,12091.88975
196578,68,49,2,1,Single,8,247.9587716,0,12160.93858
134652,64,46,2,1,Married,10,247.9520139,0,11861.6007
175026,61,44,3,1,Single,13,247.6704538,0,11783.52269
124399,22,4,3,1,Married,13,247.666634,0,12105.3317
112411,43,25,1,1,Married,11,247.6518147,0,11929.59074
112090,22,4,2,1,Married,14,247.617549,0,11898.87745
155146,68,51,4,0,Single,15,247.5534711,0,11940.67356
117228,67,49,2,1,Married,5,247.5417378,0,11896.08689
184013,19,1,2,1,Married,15,247.4912291,0,11828.56146
159023,18,0,3,1,Single,15,247.3642922,0,12082.21461
125378,18,0,3,1,Single,14,247.3580636,0,11936.90318
181717,67,46,2,1,Single,14,247.3375139,0,11915.8757
194446,55,34,2,1,Married,15,247.3008553,0,11932.04277
149148,68,48,2,0,Single,4,247.28952,0,12065.476
161489,22,5,2,1,Married,14,247.2079108,0,11875.39554
104025,25,8,2,1,Married,12,247.1359451,0,11900.79726
121258,16,0,2,1,Married,14,247.1279121,0,11956.39561
172894,65,46,3,1,Married,14,247.0818669,0,11957.09335
148119,65,48,2,0,Married,12,247.0451395,0,11923.25698
194078,62,43,2,0,Married,10,246.9965913,0,11905.82957
165279,19,1,2,1,Single,11,246.9395067,0,11748.97534
138903,24,6,2,0,Married,14,246.9232925,0,12022.16463
156998,56,37,3,0,Single,11,246.873531,0,11750.67655
151462,66,45,2,0,Married,8,246.8388232,0,11765.94116
101774,29,10,2,1,Single,13,246.7790164,0,11806.95082
180480,24,6,1,1,Married,12,246.7782666,0,12010.91333
110225,68,51,2,0,Single,0,246.7727365,0,11957.63683
103054,36,16,2,0,Married,14,246.7423689,0,12001.11845
161068,70,53,2,1,Single,9,246.7127998,0,12047.63999
110357,17,0,1,1,Married,11,246.7103971,0,11739.51986
158528,66,47,2,1,Single,1,246.7029727,0,11987.14864
181437,31,10,1,0,Single,12,246.6193297,0,12121.96649
130344,65,46,1,1,Married,3,246.6135668,0,12036.67834
109285,44,25,1,0,Married,14,246.6122491,0,12109.61246
139131,67,50,3,0,Married,11,246.6048061,0,11951.24031
174407,20,3,1,0,Single,15,246.5405761,0,12054.02881
136793,16,0,1,1,Married,14,246.5317349,0,12038.58675
192662,20,1,4,1,Single,15,246.4211463,0,12045.05732
112568,60,43,3,0,Single,4,246.4036417,0,11975.18209
114963,63,45,2,1,Married,7,246.368989,0,11867.44945
105133,59,40,2,1,Single,12,246.3331978,0,12022.65989
187273,17,0,4,1,Single,15,246.2737827,0,11809.68914
120206,33,14,2,0,Single,13,246.2217902,0,12106.08951
101479,65,48,2,0,Single,11,246.1791269,0,11908.95635
190266,60,43,4,1,Married,6,246.1529271,0,11964.64636
132535,58,37,3,0,Married,13,246.0309156,0,12082.54578
173901,23,4,2,1,Single,13,245.9475767,0,11969.37884
126905,16,0,3,1,Single,14,245.9460385,0,11973.30193
147256,19,1,2,1,Married,9,245.8298788,0,11880.49394
125236,17,0,3,1,Married,12,245.8145155,0,12075.72578
183025,46,25,1,0,Single,14,245.7979046,0,11818.89523
194453,50,33,3,0,Single,15,245.7227991,0,11713.13996
120794,70,50,2,1,Single,2,245.6653967,0,11974.26984
150779,64,47,1,1,Married,2,245.6001242,0,11804.00621
197914,64,45,4,1,Married,8,245.557733,0,12016.88665
128660,16,0,2,1,Married,13,245.5514543,0,11998.57272
176854,67,48,2,1,Single,2,245.5495025,0,11806.47513
158747,70,49,3,1,Married,8,245.499107,0,11912.95535
128390,61,43,1,0,Married,11,245.4681424,0,12015.40712
120405,22,5,1,0,Married,11,245.4353808,0,12021.76904
124720,38,18,2,1,Single,15,245.3965721,0,11968.82861
166571,35,18,2,0,Married,14,245.3859265,0,11675.29633
129986,25,6,2,0,Married,11,245.3725447,0,11849.62724
182162,67,49,3,1,Married,7,245.3339849,0,11714.69925
131255,63,44,2,1,Married,15,245.3088282,0,11922.44141
190017,36,19,3,0,Single,11,245.3059375,0,12011.29688
163691,21,4,4,1,Married,12,245.2963139,0,12056.8157
137250,44,27,3,1,Married,11,245.2365078,0,11773.82539
118060,53,35,2,0,Single,11,245.1474822,0,11678.37411
153649,68,49,2,1,Single,8,245.0899707,0,11841.49854
100976,24,5,4,0,Single,11,244.9397385,0,11907.98693
171002,22,5,4,1,Single,14,244.891672,0,11644.5836
184504,18,0,2,0,Married,12,244.8853277,0,11837.26639
178880,24,5,2,1,Married,14,244.8453631,0,11975.26816
158437,20,3,4,1,Married,9,244.8310746,0,11917.55373
132742,19,0,1,1,Single,11,244.7718762,0,12038.59381
130218,42,25,3,1,Single,13,244.7535805,0,11860.67903
110194,19,2,2,0,Married,12,244.748224,0,11945.4112
134286,61,41,2,1,Single,9,244.7033745,0,12035.16873
101870,64,47,2,0,Married,4,244.6449144,0,11768.24572
133265,51,33,3,0,Single,15,244.6252522,0,11777.26261
163146,22,5,3,1,Married,14,244.5733694,0,11932.66847
102760,68,49,4,0,Married,6,244.5084598,0,11686.42299
175384,63,46,2,1,Married,10,244.4837712,0,11964.18856
176820,31,12,2,0,Single,15,244.4753204,0,12001.76602
107089,64,47,2,1,Single,0,244.4110213,0,11923.55107
143832,24,6,2,0,Married,14,244.2048053,0,11864.24027
117811,36,19,1,0,Married,11,244.1962448,0,11902.81224
143189,64,44,3,1,Married,15,244.1959166,0,11667.79583
184858,68,50,4,1,Single,3,244.1770033,0,11625.85017
128107,68,51,3,0,Single,13,244.1757467,0,11719.78734
174852,63,45,2,0,Married,9,244.1756341,0,11680.78171
157572,69,50,2,1,Married,6,244.1751053,0,11861.75527
122628,17,0,3,1,Married,8,244.1398636,0,11835.99318
195752,67,49,4,0,Married,8,244.1388984,0,11663.94492
110664,64,44,1,0,Single,10,244.1284514,0,11960.42257
100302,25,8,2,1,Married,13,244.1245194,0,11865.22597
161445,66,49,4,0,Single,13,244.1192847,0,11742.96424
131698,35,17,2,1,Single,14,243.9893016,0,11810.46508
124198,23,5,2,1,Married,13,243.9805999,0,11996.03
138394,20,2,1,1,Single,15,243.9793541,0,11936.96771
136789,25,6,2,1,Married,12,243.9594903,0,11814.97452
116677,43,23,3,1,Married,15,243.9526945,0,11721.63473
108504,61,40,2,0,Married,6,243.9249822,0,11847.24911
184120,48,28,2,1,Married,12,243.8562855,0,11598.81428
103655,18,1,3,0,Married,15,243.8487109,0,11664.43555
198920,65,46,4,1,Single,13,243.8226255,0,11827.13128
123629,44,27,2,0,Single,15,243.782371,0,11907.11855
111215,63,46,1,0,Married,7,243.7608577,0,11930.04289
178787,62,41,3,0,Single,7,243.7567173,0,11910.83587
168426,17,0,2,1,Married,7,243.6923338,0,11663.61669
153430,38,19,2,1,Single,14,243.6849608,0,11798.24804
191373,20,1,2,1,Married,15,243.6259786,0,11726.29893
181332,70,52,4,1,Married,2,243.6051987,0,11705.25994
166319,66,46,2,1,Married,3,243.5328737,0,11646.64369
196114,22,5,4,0,Married,15,243.5247613,0,11657.23807
114449,61,43,3,1,Single,4,243.4541214,0,11928.70607
185300,19,2,2,0,Married,13,243.4427274,0,11806.13637
168901,46,27,2,0,Married,11,243.3636502,0,11571.18251
175330,16,0,2,1,Married,15,243.3503986,0,11729.51993
177510,65,46,3,1,Single,1,243.3202757,0,11948.01379
196959,67,48,1,1,Married,8,243.3079366,0,11784.39683
141588,48,30,2,0,Single,14,243.2859844,0,11943.29922
181234,41,24,4,0,Married,11,243.2859125,0,11939.29563
110593,20,2,3,0,Married,12,243.2329213,0,11611.64607
128252,16,0,2,0,Married,11,243.1939735,0,11803.69868
187491,21,4,2,0,Married,11,243.1086631,0,11885.43316
176448,18,0,2,0,Married,14,243.0692844,0,11917.46422
132688,53,33,1,0,Married,15,242.9716237,0,11591.58119
129859,20,2,2,0,Married,14,242.9389306,0,11828.94653
190611,26,9,3,1,Married,15,242.9112158,0,11729.56079
159545,22,3,2,1,Single,15,242.903909,0,11744.19545
163905,67,49,1,0,Married,7,242.8887756,0,11889.43878
121735,63,45,4,1,Single,3,242.881342,0,11617.0671
135090,19,0,3,1,Married,9,242.878505,0,11876.92525
192628,16,0,1,1,Single,14,242.7725761,0,11902.62881
101773,32,14,2,0,Single,14,242.76391,0,11699.1955
178791,16,0,3,1,Single,15,242.7266242,0,11636.33121
155636,16,0,3,1,Married,13,242.684074,0,11624.2037
125838,31,14,1,1,Single,13,242.6541754,0,11712.70877
133166,63,42,2,0,Single,1,242.6317197,0,11762.58599
126025,39,20,1,0,Married,15,242.4366585,0,11807.83293
103790,27,9,3,0,Married,11,242.4077606,0,11791.38803
129902,19,2,2,1,Married,15,242.3291789,0,11690.45895
105088,45,24,1,1,Single,11,242.3262443,0,11844.31222
180267,27,10,1,1,Single,13,242.2763623,0,11514.81812
187007,63,43,3,0,Married,11,242.2761302,0,11646.80651
160702,68,48,3,0,Single,13,242.2497696,0,11679.48848
147188,20,1,2,0,Married,15,242.2109513,0,11776.54757
180381,63,44,3,0,Single,15,242.1154048,0,11584.77024
150799,20,1,3,0,Married,15,242.069893,0,11626.49465
122448,68,51,3,1,Married,1,242.0480509,0,11535.40255
115669,61,44,2,1,Married,11,241.8722224,0,11567.61112
194777,66,47,2,0,Married,10,241.7737035,0,11610.68518
169439,63,42,3,0,Married,5,241.7116028,0,11685.58014
141643,22,3,2,1,Married,13,241.6787662,0,11676.93831
178818,38,20,2,1,Married,14,241.6032239,0,11729.1612
198984,61,44,2,1,Single,7,241.5646908,0,11617.23454
146387,24,5,3,1,Married,13,241.4800399,0,11661.002
185211,30,12,1,0,Married,13,241.4614472,0,11629.07236
116524,22,3,3,1,Married,12,241.4249882,0,11634.24941
129161,63,42,3,1,Single,2,241.4038842,0,11656.19421
119194,36,18,1,1,Single,11,241.2948785,0,11743.74393
161775,63,46,4,0,Married,7,241.281305,0,11619.06525
121239,67,50,2,0,Married,1,241.2812083,0,11611.06042
127928,49,28,3,0,Single,12,241.2678611,0,11832.39306
191430,25,6,2,1,Married,14,241.2589403,0,11498.94702
139381,22,4,2,0,Married,11,241.235819,0,11556.79095
141649,64,45,3,0,Married,7,241.2137582,0,11782.68791
175892,61,43,2,0,Married,8,241.2089516,0,11553.44758
105871,41,24,3,1,Single,11,241.1847367,0,11491.23684
106960,53,36,1,0,Married,11,241.126041,0,11595.30205
151031,35,14,4,1,Married,13,241.0123441,0,11520.61721
141479,16,0,2,1,Single,15,240.8688041,0,11808.44021
163089,32,11,3,0,Single,12,240.8530538,0,11606.65269
141426,64,47,2,0,Married,10,240.6671701,0,11698.35851
172030,60,42,1,1,Single,8,240.6219433,0,11781.09717
178636,69,50,2,0,Married,3,240.5232703,0,11666.16352
181684,60,40,4,1,Married,9,240.4557744,0,11587.78872
141837,19,2,2,1,Married,14,240.4026167,0,11600.13084
141363,59,38,4,1,Single,15,240.3603785,0,11787.01893
148181,23,6,3,1,Married,14,240.3339434,0,11563.69717
194603,64,47,4,0,Married,9,240.3289721,0,11446.44861
175931,70,51,4,1,Single,4,240.288773,0,11647.43865
166450,38,19,2,1,Married,15,240.2642571,0,11749.21286
169437,70,49,3,1,Married,2,240.2148339,0,11739.7417
191981,20,2,3,1,Single,11,240.1517478,0,11609.58739
189749,60,42,2,0,Single,10,240.0642337,0,11529.21169
147839,69,50,2,1,Married,8,240.0257169,0,11603.28585
167642,66,45,2,0,Married,6,240.0206554,0,11432.03277
174940,22,3,1,1,Single,11,240.0029509,0,11468.14755
161852,21,4,2,1,Single,12,239.9623437,0,11461.11719
195120,68,47,4,1,Single,12,239.9516656,0,11657.58328
181122,64,45,3,1,Single,2,239.8447979,0,11681.2399
178054,42,24,4,0,Single,14,239.8069277,0,11595.34639
174143,19,0,3,1,Married,7,239.8043929,0,11585.21965
168401,61,43,1,1,Married,10,239.7626287,0,11787.13144
137955,63,44,4,0,Single,13,239.7316508,0,11411.58254
163951,17,0,2,1,Married,14,239.6490383,0,11538.45192
190312,60,42,3,1,Married,1,239.5380711,0,11557.90356
153183,65,44,2,0,Single,3,239.5372616,0,11597.86308
146562,61,40,1,0,Married,10,239.5030073,0,11490.15037
142257,39,18,2,1,Single,13,239.4395729,0,11436.97865
156959,69,52,3,0,Married,10,239.4214956,0,11435.07478
176393,18,1,3,0,Married,15,239.3866933,0,11374.33467
162163,48,27,3,1,Married,14,239.3755164,0,11700.77582
112761,69,51,3,0,Single,5,239.28812,0,11605.406
161436,36,18,3,0,Single,13,239.283694,0,11427.1847
163233,65,48,1,0,Married,7,239.2136809,0,11614.68405
127821,66,47,2,0,Married,9,239.1793387,0,11454.96694
139558,34,17,3,1,Single,13,239.1510886,0,11616.55443
193692,64,44,4,1,Single,13,239.1381641,0,11507.90821
184963,63,45,4,1,Married,9,239.1191156,0,11601.95578
180093,62,44,3,0,Married,2,239.05033,0,11643.5165
172570,62,41,3,1,Married,7,239.0465548,0,11719.32774
144121,61,43,2,0,Married,5,239.0118742,0,11562.59371
179886,64,45,3,0,Married,8,238.9792737,0,11632.96369
193214,65,47,2,1,Single,7,238.9766815,0,11353.83408
114711,39,22,4,1,Married,14,238.9538651,0,11390.69326
194382,31,11,2,0,Married,14,238.8860408,0,11588.30204
116783,36,15,2,1,Single,13,238.8627913,0,11684.13957
181797,24,7,2,1,Single,12,238.8361649,0,11417.80825
162164,25,8,2,0,Married,11,238.828835,0,11386.44175
131763,16,0,4,1,Married,15,238.747151,0,11573.35755
175022,47,27,3,0,Single,13,238.7447296,0,11546.23648
151405,60,41,4,0,Single,10,238.6813378,0,11485.06689
114098,70,51,3,1,Single,8,238.6651294,0,11394.25647
176115,62,42,2,0,Married,7,238.649198,0,11655.4599
190307,52,33,3,0,Single,12,238.6296586,0,11405.48293
104708,23,4,1,0,Married,14,238.6039585,0,11725.19793
180693,69,48,2,1,Single,10,238.5960917,0,11697.80459
154175,21,4,2,1,Married,14,238.4388989,0,11469.94495
108980,67,47,3,1,Married,6,238.3957425,0,11430.78713
196887,16,0,1,0,Married,13,238.350289,0,11642.51445
128942,19,1,1,0,Married,12,238.3462253,0,11415.31127
116465,19,1,4,1,Married,6,238.344623,0,11617.23115
157515,21,2,3,1,Married,15,238.3182885,0,11441.91443
103746,53,36,3,1,Single,15,238.2228723,0,11650.14362
180649,56,38,3,1,Single,15,238.1997447,0,11610.98724
107429,22,3,3,1,Married,15,238.1693797,0,11561.46899
150893,22,3,1,1,Single,14,238.1623425,0,11617.11713
139032,62,44,3,1,Single,7,238.0341751,0,11334.70876
135219,25,8,3,1,Married,12,238.0171747,0,11484.85874
100171,70,53,1,1,Married,14,237.9571792,0,11352.85896
194944,45,28,1,1,Single,15,237.9548251,0,11472.74126
146752,70,53,3,1,Single,7,237.8484673,0,11351.42337
153384,37,19,2,0,Single,13,237.8149266,0,11335.74633
169457,24,7,3,1,Married,15,237.8048754,0,11547.24377
111789,41,20,3,1,Married,13,237.7582284,0,11483.91142
156016,19,0,2,0,Single,12,237.7426178,0,11346.13089
136060,70,53,3,1,Married,6,237.6977683,0,11518.88842
142689,43,24,4,0,Married,11,237.6651953,0,11357.25977
199690,63,43,1,0,Single,6,237.6454656,0,11662.27328
197153,66,47,4,1,Single,12,237.6114904,0,11656.57452
129160,43,24,3,1,Single,11,237.6068066,0,11583.34033
191209,60,43,3,1,Single,9,237.4477042,0,11330.38521
139499,65,46,4,0,Single,4,237.4197455,0,11336.98728
165226,22,5,2,1,Married,15,237.2801998,0,11480.00999
118951,65,46,1,0,Married,1,237.2436329,0,11644.18165
154024,33,12,2,1,Single,12,237.228971,0,11499.44855
192278,23,6,1,0,Single,11,237.1568502,0,11321.84251
187822,48,30,2,1,Married,13,237.1254764,0,11528.27382
167494,22,4,2,1,Married,10,237.0846862,0,11445.23431
166349,58,37,3,1,Single,14,237.0564788,0,11552.82394
189563,20,1,3,1,Married,11,237.0407509,0,11552.03755
168264,48,30,1,0,Married,12,237.0227564,0,11422.13782
147625,68,47,1,0,Single,7,237.0065584,0,11302.32792
147906,44,25,2,0,Married,14,237.0021047,0,11341.10524
196489,22,4,2,0,Married,13,236.9671667,0,11463.35834
196516,19,0,2,1,Married,13,236.9340245,0,11310.70123
155238,60,43,2,1,Single,5,236.8506906,0,11586.53453
143474,70,53,3,0,Married,9,236.7952419,0,11506.7621
112009,33,12,2,1,Married,11,236.7238366,0,11319.19183
143224,61,42,1,1,Married,2,236.7089588,0,11372.44794
134796,61,41,1,0,Single,15,236.6978813,0,11414.89407
132916,17,0,2,0,Married,13,236.6929213,0,11439.64607
134752,54,37,4,1,Single,15,236.6637807,0,11376.18904
147312,51,34,2,1,Married,14,236.6097765,0,11502.48883
124158,23,5,1,1,Married,11,236.5676919,0,11368.3846
142228,16,0,1,1,Married,14,236.5629119,0,11342.1456
148131,30,9,3,0,Single,11,236.5273888,0,11334.36944
185159,68,50,1,0,Single,15,236.5013685,0,11446.06843
187105,41,23,2,1,Married,11,236.3888221,0,11548.44111
125801,45,26,1,0,Single,12,236.3433438,0,11288.16719
125908,65,47,1,0,Single,7,236.2200847,0,11540.00424
183612,66,45,3,0,Single,0,236.1916541,0,11383.58271
148128,24,7,3,1,Married,15,236.1908828,0,11425.54414
118998,66,46,2,0,Single,3,236.1840765,0,11239.20383
174724,65,48,3,1,Single,13,236.0982174,0,11454.91087
152118,23,6,3,1,Married,13,236.0737906,0,11585.68953
164407,18,0,2,0,Single,15,236.0549354,0,11351.74677
183760,63,45,2,0,Single,14,236.0096439,0,11581.4822
192386,55,35,1,1,Single,12,235.9757695,0,11513.78848
139548,69,48,1,0,Single,7,235.8586141,0,11421.93071
144818,23,5,3,0,Married,15,235.7885358,0,11235.42679
119546,36,18,2,1,Single,15,235.7482469,0,11566.41235
124482,51,32,1,0,Single,13,235.7394144,0,11415.97072
133207,21,2,1,1,Single,13,235.7333404,0,11566.66702
144335,64,44,1,1,Married,2,235.6730053,0,11444.65027
111841,17,0,3,0,Married,15,235.6602821,0,11216.01411
172035,70,51,4,0,Married,10,235.6577836,0,11308.88918
146345,55,38,3,0,Married,11,235.6211136,0,11413.05568
191625,20,1,1,1,Single,13,235.5727989,0,11249.63995
144840,22,5,2,1,Married,13,235.5643519,0,11312.2176
114668,69,52,3,0,Married,0,235.5001354,0,11539.00677
148855,45,24,4,0,Single,14,235.4909106,0,11317.54553
146019,67,47,3,0,Married,9,235.4645742,0,11450.22871
131281,28,7,4,1,Married,11,235.4002033,0,11359.01017
112764,17,0,3,1,Married,12,235.3708111,0,11237.54056
126220,19,1,3,1,Single,13,235.3451905,0,11364.25953
187141,58,38,3,1,Single,14,235.1946644,0,11218.73322
128057,64,47,4,0,Single,13,235.1618894,0,11311.09447
176699,68,49,1,1,Married,2,235.1357629,0,11324.78815
151577,70,53,3,1,Single,2,235.0819294,0,11384.09647
169347,23,4,1,0,Married,15,235.0776441,0,11313.88221
186696,63,43,2,1,Married,13,235.020936,0,11523.0468
176566,62,44,3,1,Married,11,234.9140033,0,11191.70017
130480,65,48,2,0,Single,15,234.8463574,0,11253.31787
155277,39,22,3,0,Single,11,234.8444199,0,11433.221
113034,17,0,1,1,Single,12,234.8382168,0,11458.91084
123788,69,52,1,1,Single,14,234.7357156,0,11184.78578
191921,65,45,2,1,Single,14,234.7325107,0,11335.62554
106026,38,20,3,1,Married,15,234.6940061,0,11413.70031
173364,70,49,1,1,Married,6,234.6474302,0,11526.37151
141692,63,45,3,0,Single,4,234.6412859,0,11504.0643
140497,65,46,2,1,Single,5,234.6325213,0,11182.62607
106686,68,51,3,0,Married,8,234.4712329,0,11298.56165
118355,44,26,1,1,Married,15,234.4652252,0,11387.26126
128930,61,40,3,0,Single,7,234.409396,0,11174.4698
182372,19,2,4,1,Single,12,234.3263463,0,11241.31732
175204,19,2,2,1,Married,12,234.2815341,0,11196.07671
155250,35,16,3,1,Married,15,234.2771423,0,11165.85712
173882,35,18,1,1,Married,12,234.2473182,0,11492.36591
137839,65,48,1,0,Single,6,234.2227558,0,11197.13779
170068,34,16,2,0,Married,11,234.1258388,0,11205.29194
113819,62,41,2,1,Single,3,234.0169203,0,11472.84602
170141,62,45,3,0,Married,6,234.0059424,0,11261.29712
150886,20,2,3,1,Married,14,233.8950101,0,11134.75051
145089,67,47,3,1,Married,7,233.8176362,0,11482.88181
139432,68,50,3,0,Single,14,233.8172637,0,11483.86319
161581,23,6,2,1,Married,12,233.7931559,0,11187.6578
152369,59,39,3,0,Married,14,233.7902735,0,11397.51368
127769,62,45,2,0,Married,13,233.7861279,0,11338.3064
130882,23,6,2,0,Married,14,233.7394111,0,11200.97056
138992,63,46,2,1,Single,14,233.7057014,0,11232.28507
119419,24,6,2,1,Married,8,233.6744687,0,11149.72344
181573,70,50,3,1,Single,9,233.6415368,0,11303.07684
128589,16,0,2,1,Married,11,233.632557,0,11245.62785
173187,37,17,4,0,Married,13,233.5515837,0,11234.57919
142178,22,5,2,1,Single,14,233.5014361,0,11448.07181
138062,20,1,2,0,Married,15,233.4894332,0,11349.47166
157328,19,0,4,1,Married,13,233.4680003,0,11419.40002
188822,36,18,1,0,Married,11,233.4536465,0,11361.68233
158748,68,50,4,1,Married,1,233.2926778,0,11091.63389
177263,36,16,2,0,Married,15,233.2750792,0,11073.75396
104333,18,0,3,1,Married,14,233.2470049,0,11212.35025
146423,19,0,2,1,Single,15,233.2412985,0,11216.06493
184637,21,2,1,1,Single,15,233.2261935,0,11303.30968
133916,45,24,1,1,Married,12,233.2052224,0,11452.26112
150901,68,48,3,1,Single,9,233.0871264,0,11270.35632
180651,66,49,2,0,Single,2,233.0582785,0,11186.91393
101188,19,2,1,1,Married,12,233.0418479,0,11102.0924
136373,58,38,3,1,Married,13,232.9513357,0,11310.56679
125222,65,46,1,1,Single,15,232.7596434,0,11260.98217
175197,70,49,3,0,Single,9,232.7517279,0,11218.5864
160666,16,0,2,1,Married,13,232.744495,0,11139.22475
199787,30,11,2,0,Married,15,232.6932893,0,11236.66447
192042,17,0,2,0,Married,13,232.6281293,0,11080.40647
113682,45,24,1,1,Married,15,232.5300927,0,11036.50464
186826,21,3,3,0,Married,12,232.4790034,0,11407.95017
161393,33,13,3,1,Married,12,232.4521862,0,11171.60931
137825,26,9,4,1,Single,13,232.2603309,0,11107.01655
179053,19,1,3,0,Married,13,232.254786,0,11409.7393
186379,20,3,3,1,Married,14,232.2478375,0,11274.39188
156162,40,22,2,0,Single,11,232.1852081,0,11302.26041
140664,22,3,4,0,Married,15,232.1748725,0,11379.74363
138643,27,9,2,0,Married,11,232.1488457,0,11277.44229
157355,62,45,3,1,Single,5,232.0335156,0,11137.67578
105703,50,33,1,1,Married,11,232.0329603,0,11286.64802
127224,66,49,2,1,Married,5,231.9684781,0,11381.42391
162389,24,6,3,1,Single,12,231.8729302,0,11171.64651
141965,58,37,4,1,Single,11,231.7936213,0,11313.68107
175752,66,49,2,1,Married,10,231.7778468,0,11007.89234
137174,23,5,3,0,Married,15,231.5194537,0,11002.97269
143814,69,51,2,1,Single,2,231.4608776,0,11309.04388
108015,70,51,2,1,Married,12,231.4313883,0,11148.56942
148471,33,13,4,1,Single,15,231.4087613,0,11232.43807
136933,61,42,4,1,Married,7,231.1797507,0,11008.98754
159110,62,42,3,1,Married,6,231.1658511,0,11174.29256
182367,63,44,3,1,Married,0,231.1091174,0,11085.45587
164401,25,8,4,0,Married,14,231.0028611,0,11237.14306
105576,66,46,2,1,Single,11,230.9981212,0,11141.90606
131521,61,44,2,0,Single,13,230.9838027,0,11266.19014
160313,59,39,4,0,Married,15,230.9526917,0,10993.63459
145466,68,47,1,0,Single,4,230.9517193,0,11175.58597
149337,31,12,3,1,Married,11,230.9309007,0,11267.54504
117861,60,39,3,0,Married,14,230.930367,0,11052.51835
177439,54,35,1,0,Married,15,230.9037288,0,11286.18644
199077,40,20,4,0,Married,14,230.8480023,0,10980.40012
106899,22,3,2,0,Married,12,230.831422,0,10954.5711
131981,62,45,2,0,Married,6,230.7990564,0,11322.95282
153575,49,30,3,0,Married,12,230.7690082,0,11290.45041
184033,60,40,2,0,Single,7,230.7395709,0,11027.97855
186176,26,5,1,0,Married,11,230.6920433,0,10949.60217
182958,65,44,4,0,Single,13,230.6718199,0,11138.591
164264,47,30,1,0,Single,15,230.6091431,0,11030.45716
120212,69,49,1,1,Married,10,230.589839,0,11187.49195
114825,69,48,1,0,Married,12,230.5859594,0,11315.29797
173397,70,51,3,0,Married,4,230.5496678,0,11028.48339
170332,25,7,3,1,Married,14,230.4548282,0,11081.74141
198082,54,33,1,1,Single,14,230.4496289,0,11068.48145
193389,22,3,2,0,Married,15,230.1703288,0,11037.51644
110057,19,2,1,0,Married,15,230.1150988,0,11080.75494
101379,17,0,2,0,Married,15,230.1025724,0,10924.12862
195897,25,7,3,0,Married,14,229.9060162,0,10998.30081
174394,66,48,2,1,Married,7,229.8680235,0,11231.40118
178506,39,20,3,1,Married,11,229.8630713,0,11190.15357
173396,60,43,4,1,Single,11,229.8413217,0,11187.06609
177673,69,49,4,1,Single,6,229.7905566,0,11166.52783
196535,66,47,1,0,Single,8,229.6906948,0,10945.53474
175701,34,16,1,1,Single,12,229.5774269,0,11124.87135
152638,63,44,1,1,Married,6,229.5443975,0,11186.21988
154072,63,42,2,1,Single,14,229.4940513,0,10981.70257
136603,67,47,1,1,Married,14,229.3791024,0,10978.95512
148785,60,41,2,1,Married,8,229.341363,0,11030.06815
108392,25,7,2,1,Married,13,229.2872644,0,11049.36322
183199,30,9,2,1,Married,15,229.269854,0,11202.4927
182468,61,44,3,1,Married,8,229.2590542,0,11097.95271
165744,47,28,4,1,Married,12,229.2315232,0,11066.57616
100960,16,0,2,1,Single,15,229.1935947,0,10986.67974
197203,48,29,3,1,Single,15,229.1652738,0,10869.26369
134743,69,49,2,0,Single,9,229.1536957,0,11088.68479
101923,60,42,4,1,Married,7,229.1425485,0,10907.12743
109539,24,5,2,0,Married,12,229.1217479,0,10923.0874
154597,17,0,2,1,Married,12,229.0497519,0,10958.4876
197274,22,3,3,0,Married,15,229.0209616,0,11074.04808
163232,61,41,4,0,Married,6,229.0131944,0,11192.65972
187323,43,24,2,1,Married,13,228.9655835,0,11227.27918
148301,54,35,2,0,Married,15,228.9247941,0,10975.23971
173206,25,8,4,1,Married,11,228.9181882,0,11082.90941
176317,69,52,1,1,Single,0,228.8331198,0,10924.65599
181023,67,49,2,0,Single,0,228.811037,0,11159.55185
173501,18,1,2,0,Married,13,228.8044552,0,11113.22276
164050,23,5,2,1,Married,12,228.7286679,0,11115.4334
160589,65,45,2,1,Single,8,228.7047373,0,11015.23687
124801,18,0,1,1,Married,12,228.6247326,0,10966.23663
156815,69,51,1,0,Single,4,228.5962068,0,11225.81034
118029,25,6,2,1,Married,13,228.5841335,0,10894.20668
113578,66,49,2,0,Married,8,228.5812898,0,10964.06449
188288,16,0,2,1,Single,13,228.5100351,0,11151.50176
165780,68,51,3,1,Single,10,228.4789443,0,11071.94722
191287,67,50,3,0,Single,7,228.4207208,0,11042.03604
165320,52,31,3,1,Single,12,228.4081003,0,10972.40502
132808,24,7,3,1,Married,8,228.3200078,0,10875.00039
137836,68,47,1,1,Single,8,228.2723787,0,10988.61894
179289,29,8,2,1,Married,15,228.2340701,0,10834.70351
171233,43,22,1,0,Single,11,228.2297609,0,11071.48805
187388,58,39,4,1,Married,11,228.2181154,0,10822.90577
177850,19,0,1,1,Married,15,228.2167881,0,10959.83941
188932,64,43,3,1,Married,8,228.1734686,0,11032.67343
190643,64,46,1,1,Married,8,228.1717927,0,10842.58964
160641,70,52,2,0,Single,15,228.1268705,0,10815.34353
114085,40,19,3,0,Married,12,228.0744586,0,11149.72293
135471,56,36,2,1,Married,13,227.967879,0,10930.39395
185959,25,7,1,0,Married,15,227.9545285,0,11186.72643
108909,61,41,3,1,Married,7,227.9537517,0,10882.68759
162831,20,1,2,1,Married,11,227.9283072,0,10852.41536
125055,61,42,3,1,Single,2,227.7540504,0,10895.70252
193742,68,50,3,1,Married,0,227.7406491,0,10833.03246
126647,64,44,4,0,Single,8,227.7282693,0,10844.41347
104800,22,5,4,1,Married,14,227.7061218,0,10876.30609
166202,32,13,4,1,Married,13,227.6942386,0,11179.71193
113124,25,7,4,1,Married,11,227.6795538,0,11039.97769
120268,60,41,1,1,Married,8,227.6644878,0,10915.22439
142771,66,48,3,1,Single,5,227.6529975,0,11168.64988
157115,68,47,2,0,Married,7,227.6438612,0,11096.19306
157705,62,45,4,0,Single,10,227.5972038,0,10892.86019
107917,53,36,2,0,Married,12,227.5880033,0,11045.40016
184834,60,39,1,1,Married,4,227.5730607,0,10778.65304
116688,22,3,1,1,Single,11,227.5204163,0,11066.02082
134577,62,41,3,1,Married,11,227.4959551,0,11140.79776
115133,61,42,2,1,Single,0,227.4842193,0,11102.21097
158441,16,0,1,1,Single,11,227.3926774,0,11131.63387
101617,25,6,1,1,Single,14,227.3336458,0,11096.68229
109088,20,2,3,0,Married,15,227.3249044,0,10931.24522
187892,67,50,1,1,Married,9,227.2721222,0,10859.60611
148266,60,42,3,1,Single,10,227.2208439,0,10800.0422
101732,51,34,3,0,Married,13,227.1612949,0,10835.06475
129298,38,18,3,1,Married,15,227.0946699,0,10835.7335
194044,61,44,1,0,Married,6,227.0893362,0,11127.46681
169518,35,14,2,0,Married,11,227.0030475,0,10954.15238
163618,17,0,4,1,Married,12,226.9991331,0,11044.95666
106758,64,43,3,0,Single,7,226.8358175,0,11105.79088
180655,49,30,2,0,Married,12,226.7195827,0,10856.97914
106374,20,3,4,1,Single,13,226.6735195,0,10837.67598
150285,67,47,4,1,Single,6,226.5989951,0,11092.94976
179749,23,6,3,1,Single,13,226.5918853,0,11089.59427
183999,21,3,3,1,Married,13,226.5756152,0,10746.78076
127859,60,43,4,0,Single,12,226.5006233,0,11060.03117
113624,26,6,3,0,Married,15,226.4835305,0,10905.17653
149313,67,46,4,0,Married,8,226.4673076,0,10891.36538
110283,50,31,2,1,Married,12,226.4231869,0,10902.15935
133527,69,51,3,1,Single,8,226.3846678,0,10976.23339
143075,57,39,4,1,Married,11,226.3594829,0,10747.97415
162778,16,0,1,0,Married,14,226.3113181,0,10812.56591
147285,63,42,3,1,Single,15,226.3008659,0,10920.0433
170008,48,29,4,0,Single,11,226.2995011,0,10892.97506
197226,64,43,2,1,Single,7,226.1814157,0,10775.07079
168168,45,28,4,0,Single,11,226.1603243,0,11025.01622
131645,70,52,4,0,Single,9,226.1021153,0,11087.10577
156290,42,25,4,1,Married,11,226.0877025,0,10748.38513
155661,63,45,2,1,Married,6,226.0781931,0,10799.90966
156570,65,45,2,0,Married,10,225.9537702,0,10895.68851
164197,60,40,4,1,Married,4,225.9448107,0,10871.24054
130135,22,3,4,1,Married,14,225.9193112,0,10890.96556
116019,23,5,3,1,Married,8,225.9165184,0,10762.82592
147072,65,46,3,1,Married,7,225.8507068,0,11064.53534
106028,69,49,4,1,Married,10,225.8124156,0,10897.62078
125275,61,41,3,1,Married,6,225.8028494,0,10692.14247
157960,64,47,4,1,Single,7,225.7661269,0,11079.30635
191440,32,13,3,0,Married,14,225.7347436,0,10989.73718
105414,60,42,2,0,Married,3,225.6248,0,11068.24
106607,24,6,3,1,Single,12,225.5672078,0,10840.36039
105593,33,16,3,0,Married,14,225.5206341,0,10829.03171
194925,66,48,3,0,Single,3,225.5144867,0,10912.72434
132030,55,36,2,0,Married,11,225.4633325,0,10984.16663
138172,69,49,2,1,Married,10,225.4572368,0,10674.86184
138744,64,45,3,0,Single,9,225.4229908,0,10829.14954
189771,23,5,1,0,Single,12,225.4107538,0,10752.53769
114514,25,8,2,0,Married,14,225.4093546,0,10951.46773
120983,20,2,4,1,Single,11,225.3755843,0,10674.77922
153673,19,0,2,1,Single,13,225.3479421,0,11045.39711
154904,28,8,2,0,Married,13,225.344401,0,10893.22005
197225,56,37,2,0,Married,15,225.1149734,0,10954.74867
118097,68,47,2,0,Married,14,225.102024,0,10821.1012
187424,70,50,3,0,Married,14,225.0972474,0,10802.86237
196942,64,45,2,0,Single,12,225.0781285,0,11018.90643
113500,70,50,2,1,Married,11,225.0333846,0,10990.66923
149532,37,20,3,0,Single,12,225.0169511,0,11022.84756
111336,61,41,2,1,Married,7,225.0140233,0,10941.70117
117488,63,44,3,0,Single,10,224.9680764,0,10947.40382
110532,67,48,1,0,Single,9,224.8441035,0,10744.20518
188144,25,6,1,1,Married,9,224.8163937,0,10874.81969
128372,40,19,3,0,Married,12,224.7668654,0,11005.34327
124619,64,43,2,0,Married,6,224.7382043,0,10867.91022
187257,43,26,3,1,Married,12,224.7339429,0,10839.69715
126141,43,22,2,0,Single,11,224.6955736,0,11010.77868
176379,59,38,2,0,Married,15,224.6655498,0,11033.27749
175033,69,52,3,0,Married,10,224.5342862,0,10650.71431
126764,18,0,3,0,Married,13,224.4649619,0,10628.2481
184220,41,23,4,1,Married,14,224.4487186,0,10891.43593
122587,63,43,3,1,Single,6,224.3856465,0,10874.28233
149154,67,49,2,1,Single,13,224.2958682,0,10628.79341
143825,65,45,4,1,Married,5,224.2590729,0,10975.95365
162989,56,37,4,0,Single,12,224.2033155,0,10787.16578
160864,39,19,1,1,Married,14,224.1903456,0,10882.51728
120267,63,44,3,0,Single,15,224.1336125,0,10741.68063
146129,53,36,3,0,Single,12,224.0934193,0,10810.67097
132298,62,45,4,0,Married,7,224.0740543,0,10869.70272
102352,70,51,3,0,Married,7,224.0523894,0,10718.61947
159840,20,3,2,0,Married,12,224.0309344,0,10870.54672
169015,67,49,3,1,Single,5,224.0212974,0,10900.06487
105689,53,35,3,1,Single,15,223.9912977,0,10784.56488
164763,68,49,2,0,Single,13,223.9625265,0,10686.12633
191627,66,46,2,1,Single,9,223.9489133,0,10971.44567
194317,47,27,4,0,Single,11,223.9143018,0,10993.71509
189288,22,4,3,1,Married,15,223.8614904,0,10756.07452
136917,16,0,1,1,Married,13,223.813659,0,10619.68295
160967,45,26,4,0,Married,15,223.8024695,0,10816.12348
199602,63,45,4,1,Single,9,223.7955877,0,10610.77939
189085,50,32,3,0,Married,13,223.7777919,0,10608.8896
176558,63,45,3,1,Married,6,223.7667542,0,10882.33771
128649,66,47,2,0,Married,7,223.7601178,0,10813.00589
162166,70,52,4,1,Single,7,223.7433521,0,10770.16761
119566,58,39,1,1,Single,15,223.6843185,0,10898.21593
137811,52,32,2,0,Single,14,223.683626,0,10832.1813
114015,21,4,2,1,Married,10,223.6134939,0,10959.6747
121318,22,4,4,1,Married,11,223.6058931,0,10690.29466
135371,66,46,1,0,Married,8,223.5564035,0,10739.82018
195261,69,49,4,0,Married,8,223.5485958,0,10855.42979
128557,70,51,3,0,Married,12,223.5059129,0,10719.29565
190521,21,4,4,1,Married,13,223.4801671,0,10795.00836
146770,34,16,3,1,Single,12,223.4797734,0,10814.98867
103657,35,14,3,0,Single,14,223.4491102,0,10901.45551
167507,42,23,3,0,Single,13,223.4481119,0,10943.4056
165218,23,6,3,0,Married,14,223.2843328,0,10893.21664
122039,20,3,3,1,Single,13,223.2821085,0,10650.10543
103785,62,44,3,1,Single,8,223.2655717,0,10840.27859
178382,47,28,3,0,Single,14,223.2012912,0,10955.06456
185884,43,25,4,1,Single,14,223.1991794,0,10762.95897
138357,27,7,2,0,Married,14,223.1738865,0,10646.69433
172961,32,11,4,1,Married,14,223.1211246,0,10840.05623
157563,64,43,4,1,Married,8,223.1133403,0,10811.66702
187872,58,37,1,1,Married,13,223.0815797,0,10628.07899
184270,60,40,2,1,Single,9,223.0726304,0,10922.63152
175234,19,1,3,1,Married,8,223.0245441,0,10838.22721
145674,26,8,4,0,Single,14,223.0231917,0,10821.15959
128007,40,20,2,0,Married,13,223.0110434,0,10679.55217
164306,69,52,1,0,Single,6,222.9949749,0,10594.74875
135076,55,37,2,1,Married,14,222.9448193,0,10817.24097
165481,17,0,4,0,Married,13,222.9376199,0,10590.881
127398,23,5,4,1,Married,14,222.9062998,0,10931.31499
139116,55,34,3,0,Single,15,222.8133474,0,10769.66737
183158,34,15,2,1,Married,14,222.8087464,0,10787.43732
185247,70,50,4,0,Married,3,222.7849424,0,10722.24712
154877,30,10,4,1,Single,12,222.776852,0,10697.8426
121437,64,47,2,1,Married,7,222.6386693,0,10819.93347
124943,46,26,2,1,Married,13,222.6129854,0,10810.64927
104101,21,3,3,1,Married,13,222.5284037,0,10664.42019
147708,70,53,3,1,Single,12,222.4809035,0,10630.04518
171542,67,46,1,1,Married,7,222.4130618,0,10567.65309
132224,28,8,2,1,Married,13,222.4028062,0,10640.14031
188944,16,0,3,1,Married,12,222.3914688,0,10596.57344
142980,23,6,4,0,Married,12,222.3326203,0,10735.63102
133506,65,47,1,1,Married,2,222.2934213,0,10702.67107
176701,67,46,2,1,Married,10,222.2513137,0,10615.56569
184755,68,51,2,1,Single,10,222.221463,0,10696.07315
135158,45,26,3,0,Single,14,222.1080969,0,10688.40485
187431,18,0,1,1,Married,11,222.1065657,0,10749.32829
143648,70,50,4,0,Single,1,222.091632,0,10563.5816
134288,54,34,4,0,Single,11,221.9293161,0,10509.46581
155334,51,34,3,0,Single,12,221.8571445,0,10706.85723
157373,25,6,1,1,Married,15,221.8503711,0,10659.51856
111655,23,4,1,1,Single,11,221.8433043,0,10548.16522
119055,61,41,3,1,Married,9,221.7109503,0,10486.54752
106484,40,22,3,1,Married,14,221.6829192,0,10769.14596
112035,67,47,2,0,Married,10,221.5763437,0,10504.81719
152263,22,3,2,1,Married,11,221.563228,0,10491.1614
184675,67,50,1,0,Married,14,221.5471761,0,10764.35881
101792,66,47,4,1,Single,14,221.525705,0,10554.28525
159005,68,51,3,0,Single,9,221.4922591,0,10642.61296
128115,46,28,2,1,Married,12,221.3601744,0,10795.00872
146976,62,43,3,1,Married,6,221.3121444,0,10658.60722
132693,67,47,4,1,Single,11,221.311805,0,10621.59025
128360,64,44,1,1,Married,9,221.2919568,0,10638.59784
126737,63,43,3,0,Single,12,221.2165216,0,10685.82608
145387,61,43,1,1,Single,14,221.2043504,0,10683.21752
135832,27,10,1,1,Married,13,221.1329875,0,10529.64938
139591,23,6,3,1,Married,14,221.052516,0,10784.6258
199612,64,43,2,1,Single,7,220.9166822,0,10470.83411
134929,17,0,4,1,Married,14,220.9105963,0,10677.52982
187983,42,24,4,0,Married,13,220.8931975,0,10785.65988
162140,21,3,3,1,Married,12,220.8900408,0,10568.50204
102668,68,50,4,1,Married,10,220.8653295,0,10571.26648
172410,53,33,2,1,Single,12,220.8136093,0,10585.68047
133148,69,51,1,1,Single,8,220.8134293,0,10839.67147
131676,18,0,3,0,Married,12,220.7307903,0,10525.53952
194923,60,40,2,1,Single,10,220.663507,0,10586.17535
155134,22,3,1,1,Single,11,220.6385329,0,10530.92664
188172,54,35,1,1,Single,13,220.615156,0,10496.7578
128853,65,47,2,0,Married,4,220.5537003,0,10673.68502
183143,67,48,2,1,Married,2,220.5360282,0,10543.80141
150903,70,52,2,1,Married,9,220.4840155,0,10612.20078
187071,22,4,1,1,Married,11,220.4425159,0,10554.12579
126002,23,4,2,1,Married,12,220.3915989,0,10808.57995
172574,67,46,2,0,Married,10,220.2491894,0,10534.45947
134669,42,22,3,0,Single,15,220.1787534,0,10534.93767
199527,53,36,2,1,Married,14,220.1477128,0,10807.38564
164115,60,39,3,1,Married,12,220.1351237,0,10745.75619
115248,69,51,1,1,Single,0,220.1163053,0,10671.81527
133893,64,45,3,1,Single,8,220.0928308,0,10627.64154
107562,69,49,3,0,Single,11,220.0696315,0,10617.48158
157310,27,10,2,0,Married,14,219.9800089,0,10702.00045
174479,69,48,1,1,Single,11,219.9773419,0,10572.8671
133406,70,53,2,0,Married,5,219.9071599,0,10459.358
162353,23,5,4,0,Married,12,219.8912405,0,10459.56203
130621,60,43,3,1,Single,4,219.8676949,0,10701.38475
186218,21,2,4,1,Single,11,219.7394077,0,10663.97039
115714,20,2,4,1,Married,11,219.7389538,0,10702.94769
176668,69,50,3,0,Married,7,219.661608,0,10742.0804
182750,31,11,2,0,Married,12,219.5942883,0,10616.71442
155407,64,44,3,0,Single,9,219.549482,0,10710.4741
127563,61,40,3,1,Married,5,219.3331213,0,10618.65607
170289,32,12,3,1,Single,12,219.2675302,0,10503.37651
191567,67,50,1,0,Single,9,219.2630685,0,10568.15343
131839,25,8,3,0,Married,13,219.2088215,0,10453.44108
156326,18,0,3,1,Single,13,219.1593044,0,10499.96522
131341,68,50,3,1,Single,9,219.1549893,0,10636.74947
119397,67,50,2,0,Married,7,219.1167835,0,10639.83918
179625,69,51,3,0,Married,1,219.0498408,0,10698.49204
113036,33,16,2,0,Single,12,219.0374119,0,10520.8706
111157,29,9,1,1,Married,14,218.9770126,0,10617.85063
157863,17,0,3,1,Married,11,218.8950009,0,10640.75005
103807,65,45,1,0,Married,4,218.8769746,0,10438.84873
195202,32,15,2,0,Married,14,218.8316923,0,10581.58462
132187,56,38,2,1,Married,12,218.8234575,0,10564.17288
138756,66,46,2,1,Single,6,218.8142042,0,10701.71021
116144,60,40,4,0,Single,9,218.8113795,0,10668.56898
177861,68,47,4,1,Single,6,218.7703356,0,10417.51678
161204,25,7,3,0,Single,15,218.7631965,0,10413.15983
181694,25,6,2,0,Married,11,218.7403587,0,10618.01794
184954,19,2,2,1,Married,13,218.7243513,0,10383.21757
108631,19,2,3,1,Married,11,218.7236106,0,10658.18053
112495,68,50,1,0,Single,8,218.6882265,0,10404.41133
107236,40,23,1,1,Single,11,218.583891,0,10359.19455
116625,67,47,1,1,Married,4,218.5837842,0,10584.18921
149064,25,6,4,1,Married,12,218.4827576,0,10478.13788
102613,24,6,4,0,Married,12,218.4733069,0,10700.66535
157748,63,45,2,1,Married,11,218.4228579,0,10547.1429
135049,43,23,1,0,Married,11,218.4008806,0,10358.04403
121057,68,47,2,1,Single,12,218.3978314,0,10359.89157
157918,66,49,3,1,Single,12,218.3564541,0,10467.82271
104669,67,48,3,1,Single,15,218.3320804,0,10697.60402
158043,16,0,4,1,Single,14,218.3148391,0,10600.74196
199969,68,48,2,0,Single,13,218.2954901,0,10661.77451
190594,19,0,4,1,Married,14,218.2815363,0,10690.07682
174814,68,49,2,1,Single,8,218.2808394,0,10595.04197
125207,65,48,4,0,Single,4,218.2755328,0,10319.77664
154559,31,11,4,1,Single,12,218.2384338,0,10549.92169
147700,62,41,3,0,Single,5,218.2211854,0,10518.05927
115700,23,5,3,1,Married,11,218.1995744,0,10610.97872
164082,25,8,2,1,Married,15,218.1612906,0,10703.06453
113216,70,52,4,1,Married,14,218.1447717,0,10320.23859
138601,59,39,3,0,Single,15,218.1396062,0,10641.98031
162290,65,48,2,1,Single,6,218.0505222,0,10423.52611
155284,65,46,2,1,Married,7,218.0438855,0,10607.19428
192584,56,37,3,1,Married,12,218.0230937,0,10647.15469
116233,70,52,3,0,Single,7,218.0029668,0,10502.14834
123763,67,47,1,0,Single,11,218.0002008,0,10409.01004
140190,25,8,4,1,Married,11,217.9798893,0,10367.99447
157539,37,16,1,1,Single,13,217.9595747,0,10315.97874
190397,66,49,3,0,Single,8,217.9451433,0,10592.25717
108783,37,16,2,0,Single,15,217.9004107,0,10630.02054
104998,18,0,3,1,Married,14,217.8683427,0,10638.41714
115242,68,50,1,0,Single,12,217.8427303,0,10510.13652
103087,22,5,3,0,Married,12,217.7758088,0,10469.79044
103927,68,47,4,1,Married,7,217.7755553,0,10483.77777
191400,61,41,3,0,Married,15,217.741155,0,10370.05775
189255,18,0,3,0,Married,12,217.6720939,0,10422.6047
101696,60,43,3,0,Married,13,217.6535603,0,10455.67802
147949,70,50,3,1,Married,6,217.63747,0,10511.8735
126702,61,40,3,0,Single,13,217.6126794,0,10380.63397
101468,46,26,2,1,Married,13,217.5709589,0,10519.54795
100461,60,39,2,1,Single,4,217.5407121,0,10355.03561
126857,25,8,3,1,Married,15,217.5385211,0,10570.92606
135191,63,43,4,1,Single,9,217.5262881,0,10495.31441
109822,59,42,2,1,Married,11,217.4953575,0,10553.76788
108116,69,52,2,1,Single,0,217.3825075,0,10447.12538
101204,22,5,4,1,Single,15,217.2248811,0,10270.24406
183203,70,52,3,0,Married,8,217.2116428,0,10646.58214
166343,24,7,3,1,Single,14,217.1717344,0,10493.58672
163180,64,46,1,0,Single,7,217.1632874,0,10421.16437
136743,63,43,1,1,Single,4,217.1205792,0,10389.02896
169826,18,0,2,1,Married,11,217.1088822,0,10357.44411
139980,40,21,3,0,Married,13,217.088508,0,10454.4254
144265,59,42,3,0,Married,14,217.0481069,0,10320.40535
100632,29,10,3,1,Married,12,216.998695,0,10466.93475
156424,66,46,2,0,Single,3,216.9776741,0,10307.88371
104349,65,48,2,1,Single,10,216.9588572,0,10565.94286
123722,41,21,3,1,Single,11,216.9474854,0,10463.37427
176568,61,44,4,1,Single,7,216.9420344,0,10618.10172
103561,61,43,4,1,Single,10,216.9190092,0,10499.95046
111581,18,0,3,1,Single,12,216.9013125,0,10616.06563
118961,63,46,1,0,Married,12,216.8897072,0,10543.48536
174357,61,42,2,1,Married,1,216.8792098,0,10363.96049
106317,20,2,4,1,Married,14,216.8379707,0,10463.89854
115346,63,46,3,1,Single,7,216.837958,0,10463.8979
190980,19,0,3,1,Married,11,216.7433355,0,10628.16678
114053,60,42,3,0,Single,14,216.7305363,0,10409.52682
102003,62,43,1,1,Married,8,216.683164,0,10534.1582
130130,18,0,1,0,Married,12,216.6488257,0,10604.44129
115799,61,42,3,1,Single,11,216.6225747,0,10246.12874
153886,18,0,4,1,Single,14,216.5953773,0,10530.76887
198976,70,53,2,1,Single,11,216.5357324,0,10499.78662
148030,68,51,3,1,Married,14,216.5320216,0,10447.60108
178221,68,50,3,1,Married,12,216.5277599,0,10302.388
198115,57,40,3,0,Single,15,216.5253736,0,10619.26868
143516,38,19,2,0,Single,11,216.4373517,0,10437.86759
191160,25,8,1,1,Married,12,216.4240688,0,10615.20344
169564,68,50,3,0,Single,7,216.2477443,0,10498.38722
197721,23,4,3,1,Married,9,216.229611,0,10211.48055
184658,70,50,1,1,Single,11,216.1725882,0,10361.62941
129908,66,46,3,0,Married,10,216.106487,0,10497.32435
169958,62,43,4,1,Single,13,216.1005343,0,10236.02672
161541,41,24,1,1,Married,12,216.0974478,0,10231.87239
115108,47,26,4,1,Married,15,216.0850503,0,10536.25252
106210,70,52,2,1,Married,5,216.0499587,0,10545.49794
159012,60,41,2,0,Married,11,216.0407596,0,10343.03798
177031,68,47,3,0,Married,15,215.998473,0,10525.92365
106529,55,35,3,1,Married,14,215.938831,0,10197.94155
154576,34,17,3,1,Married,11,215.9218628,0,10479.09314
128342,31,12,3,1,Married,13,215.8363076,0,10462.81538
151605,48,27,2,0,Single,11,215.8307792,0,10565.53896
180980,23,4,3,1,Married,12,215.7950525,0,10331.75263
137771,20,3,2,1,Single,15,215.7948489,0,10461.74245
192942,57,40,2,0,Married,15,215.7074338,0,10567.37169
147857,19,2,2,0,Married,11,215.5797122,0,10342.98561
127656,63,44,2,1,Single,0,215.5351089,0,10483.75545
166435,67,48,3,1,Married,15,215.4968285,0,10345.84143
135330,24,7,3,1,Married,15,215.4890519,0,10340.4526
157889,64,43,2,0,Single,9,215.4143946,0,10357.71973
169128,70,52,2,0,Married,15,215.3720019,0,10314.6001
119404,70,53,3,0,Single,6,215.3692714,0,10173.46357
140043,20,2,2,0,Married,11,215.3327311,0,10418.63656
101829,67,46,2,0,Single,14,215.2318603,0,10447.59302
184533,17,0,2,1,Married,15,215.2266118,0,10554.33059
196978,20,3,4,1,Married,11,215.224046,0,10397.2023
162588,47,28,3,0,Married,12,215.2018204,0,10343.09102
173101,61,43,2,1,Single,10,215.1046013,0,10193.23007
195408,51,34,1,0,Single,13,214.9897118,0,10427.48559
129569,67,47,3,0,Married,2,214.8925247,0,10213.62624
142016,64,45,3,0,Single,9,214.7957362,0,10456.78681
190811,69,52,3,1,Single,8,214.7374509,0,10525.87255
104505,67,50,4,1,Single,9,214.6759856,0,10487.79928
181662,67,50,2,1,Single,13,214.5655106,0,10334.27553
140904,63,43,2,1,Married,8,214.5320694,0,10297.60347
153771,16,0,3,0,Married,13,214.5116593,0,10246.58297
155472,21,3,3,0,Married,13,214.4838318,0,10129.19159
145475,63,43,4,1,Single,10,214.4701092,0,10194.50546
104955,63,44,2,1,Single,3,214.4203495,0,10382.01748
170899,61,44,2,0,Married,12,214.3767328,0,10450.83664
189580,66,46,4,0,Single,7,214.342541,0,10288.12705
181099,68,51,2,1,Married,9,214.3347694,0,10153.73847
197791,62,44,2,0,Married,9,214.3062182,0,10456.31091
112904,40,21,4,1,Married,13,214.3016717,0,10471.08359
163097,69,52,4,1,Single,13,214.2633765,0,10281.16883
101445,66,45,4,0,Single,3,214.1919435,0,10388.59718
168844,65,48,3,1,Married,7,214.1776836,0,10361.88418
182141,29,10,3,1,Married,12,214.1620153,0,10475.10077
179210,18,0,2,1,Married,14,214.155848,0,10422.7924
116416,58,38,1,0,Single,15,214.1358992,0,10133.79496
168558,32,13,1,1,Single,13,214.1314498,0,10485.57249
128104,43,26,2,0,Married,15,214.126933,0,10377.34665
192086,63,42,4,0,Married,13,214.0595924,0,10203.97962
170071,47,28,4,1,Single,12,214.0519855,0,10330.59928
148243,70,52,4,0,Single,0,213.9903772,0,10326.51886
166770,22,5,4,0,Married,14,213.8804476,0,10353.02238
166546,17,0,4,0,Married,13,213.8382363,0,10386.91181
133292,34,14,3,0,Single,15,213.8203957,0,10478.01979
196558,65,46,2,0,Married,10,213.8189643,0,10485.94822
126539,29,9,3,0,Married,14,213.8024002,0,10330.12001
160917,44,23,3,0,Single,13,213.6662321,0,10153.31161
157511,21,3,3,1,Married,15,213.634549,0,10422.72745
154990,34,13,2,0,Married,11,213.6119766,0,10244.59883
178009,70,53,2,0,Married,8,213.597578,0,10236.8789
149769,19,0,3,1,Married,15,213.5661255,0,10408.30628
169357,27,7,2,1,Married,13,213.5544649,0,10405.72325
140119,64,43,2,0,Married,2,213.5175971,0,10294.87986
142010,45,28,2,0,Single,15,213.4488362,0,10206.44181
181230,16,0,2,1,Married,13,213.396302,0,10258.8151
145426,61,44,3,0,Single,1,213.3879924,0,10338.39962
161841,69,52,2,0,Married,15,213.322691,0,10235.13455
128200,64,47,1,0,Married,11,213.2790506,0,10141.95253
180561,17,0,2,0,Single,14,213.2513253,0,10210.56627
191584,62,41,3,0,Single,7,213.2164678,0,10358.82339
130514,38,21,3,1,Married,14,213.2050793,0,10215.25397
113378,63,43,2,1,Married,7,213.1897429,0,10272.48715
103844,20,1,4,1,Married,12,213.1578344,0,10359.89172
110823,48,27,3,1,Married,11,213.1440843,0,10090.20422
155246,23,5,4,0,Single,12,213.1314131,0,10077.57066
146342,60,40,3,1,Married,0,213.1146395,0,10083.73198
122977,66,45,3,1,Married,7,213.0406668,0,10274.03334
157610,37,18,4,1,Single,13,213.0291996,0,10145.45998
129066,55,38,2,0,Married,14,212.9556896,0,10250.78448
166178,37,18,3,1,Married,13,212.7409458,0,10167.04729
132615,63,44,2,1,Married,5,212.7331935,0,10054.65968
133473,34,15,3,1,Married,11,212.6998602,0,10163.99301
106073,63,46,3,0,Married,7,212.6638004,0,10196.19002
193108,21,4,2,0,Married,12,212.53249,0,10229.6245
144873,70,52,1,0,Single,12,212.5038196,0,10168.19098
140269,47,29,3,1,Single,12,212.481478,0,10376.0739
188655,60,41,3,0,Married,1,212.4583193,0,10212.91597
144340,70,49,2,0,Married,14,212.3940304,0,10340.70152
101441,23,4,2,0,Married,12,212.3423871,0,10117.11936
103717,64,47,1,0,Single,13,212.3076172,0,10153.38086
116897,65,47,2,1,Single,13,212.2956194,0,10390.78097
126714,44,25,2,0,Single,13,212.2804581,0,10356.02291
196637,39,18,2,0,Single,12,212.2239094,0,10341.19547
184243,57,38,2,0,Single,12,212.2227816,0,10025.13908
193857,59,42,4,0,Married,15,212.2194107,0,10365.97054
134269,17,0,3,0,Married,11,212.1423656,0,10365.11828
109843,21,3,1,0,Married,15,212.0839926,0,10211.19963
134141,37,18,3,0,Single,12,212.0380752,0,10037.90376
113730,17,0,2,0,Married,12,211.9856302,0,10295.28151
172427,60,41,3,0,Married,12,211.7846228,0,10011.23114
198970,25,6,2,0,Married,11,211.756897,0,10133.84485
155564,65,44,2,0,Married,6,211.6373126,0,10318.86563
189896,17,0,4,0,Married,14,211.4928315,0,10041.64158
112987,45,24,4,1,Single,14,211.3252361,0,10161.26181
190415,66,48,2,0,Married,7,211.3174496,0,10287.87248
136795,16,0,4,1,Married,12,211.2528407,0,10181.64204
127510,23,5,1,1,Married,14,211.2160579,0,10162.8029
126522,23,6,3,0,Married,12,211.1797329,0,10162.98665
190491,61,44,4,0,Single,4,211.163816,0,10156.1908
148984,69,49,3,1,Married,15,211.0603833,0,10316.01917
193621,65,45,3,0,Married,13,211.0550607,0,10312.75304
143835,62,45,4,1,Single,12,210.8979095,0,10246.89548
194688,60,43,4,1,Single,13,210.8383868,0,10188.91934
121530,64,45,3,1,Married,15,210.829491,0,10210.47455
158298,60,39,2,0,Single,9,210.8084136,0,10196.42068
101245,23,4,2,1,Married,11,210.6583084,0,10119.91542
134355,16,0,4,0,Married,13,210.492293,0,10225.61465
125118,61,40,4,0,Married,5,210.4615775,0,10079.07888
132218,38,19,4,0,Single,14,210.3512655,0,9978.563275
147573,69,51,3,0,Single,7,210.3325329,0,10242.62665
137463,29,12,3,0,Married,12,210.3156052,0,10014.78026
138988,41,22,1,1,Married,13,210.312302,0,10023.6151
156364,58,41,3,1,Single,11,210.2356894,0,9989.78447
101533,40,21,2,0,Married,15,210.2209069,0,10051.04535
162004,68,51,3,0,Married,6,210.1623656,0,10051.11828
173970,49,30,2,0,Married,11,210.0725182,0,10053.62591
134233,67,50,2,0,Single,9,210.0136448,0,9996.68224
193277,39,22,4,0,Married,12,209.9582639,0,10228.9132
180625,53,32,3,1,Married,14,209.7641275,0,10087.20638
143119,66,46,4,1,Single,14,209.7177944,0,10133.88972
159076,26,6,2,0,Married,12,209.6780374,0,10279.90187
177807,67,50,2,0,Single,15,209.6606122,0,10071.03061
132991,39,19,2,1,Single,12,209.6514751,0,9891.573755
186922,26,6,2,0,Single,11,209.4683176,0,10073.41588
148906,22,5,2,1,Single,13,209.4161645,0,10258.80823
186301,66,48,1,1,Single,7,209.3377482,0,10128.88741
145558,32,12,1,0,Married,15,209.2601387,0,10007.00694
115652,51,34,1,1,Married,14,209.161744,0,10202.0872
183201,46,25,4,0,Married,14,209.1297056,0,10159.48528
171408,61,42,2,1,Single,13,209.1193573,0,9886.967865
107896,60,39,3,1,Single,0,209.0993733,0,10185.96867
165432,67,47,2,0,Married,12,209.0977182,0,10080.88591
141121,63,46,1,0,Single,13,209.0394555,0,10010.97278
187051,65,44,4,0,Married,11,209.0290624,0,9980.45312
160761,53,36,3,1,Single,12,208.9619344,0,9986.09672
151105,67,48,1,1,Married,7,208.9477017,0,9871.385085
171613,70,51,3,1,Married,9,208.9475058,0,10003.37529
126023,48,28,2,1,Single,11,208.9160835,0,10235.80418
167210,70,50,2,0,Single,15,208.7989506,0,9841.94753
182825,50,33,2,1,Single,12,208.6607525,0,9930.037625
102723,67,46,3,0,Single,9,208.6007724,0,10182.03862
193018,17,0,2,0,Married,12,208.5861351,0,9910.306755
129451,30,10,2,0,Married,11,208.5272352,0,9923.36176
120634,25,8,2,1,Single,14,208.4650989,0,10175.25495
151058,31,11,4,0,Single,13,208.4000403,0,10068.00202
115530,39,18,3,0,Single,15,208.3850664,0,10080.25332
169427,52,32,1,0,Married,13,208.3574065,0,9827.870325
114137,68,48,3,0,Married,15,208.3529943,0,10134.64972
118242,65,46,4,0,Married,10,208.1482744,0,10012.41372
139207,64,45,3,0,Single,12,208.090373,0,9920.51865
142587,21,2,3,1,Single,11,208.0440912,0,10151.20456
137909,68,48,3,1,Single,4,208.0358699,0,10082.7935
144698,67,48,2,0,Married,9,208.0302662,0,10093.51331
129733,69,51,4,1,Married,8,207.9999461,0,9910.997305
129823,60,41,3,0,Married,15,207.9826322,0,9847.13161
153045,36,18,2,1,Single,14,207.8974194,0,9794.87097
135042,56,37,1,1,Single,12,207.8627204,0,9989.13602
171056,45,24,2,1,Married,13,207.803368,0,9998.1684
167211,49,31,2,1,Single,15,207.7780178,0,9933.90089
188827,48,28,4,1,Single,11,207.7646245,0,9836.231225
183397,67,49,2,1,Married,8,207.7536776,0,10169.68388
138104,60,39,3,0,Married,0,207.6820103,0,9914.100515
112370,22,5,3,1,Single,14,207.658368,0,9872.9184
154532,23,5,3,1,Married,14,207.6335866,0,9848.67933
185233,67,46,1,1,Married,6,207.5631629,0,9908.158145
119843,25,8,2,1,Married,12,207.5516057,0,9856.580285
110355,67,50,3,0,Married,11,207.5480439,0,10020.4022
124315,43,24,3,1,Married,13,207.5409761,0,9943.048805
143557,21,4,3,1,Single,14,207.5386901,0,9880.934505
114872,70,53,2,0,Married,7,207.503429,0,9986.17145
151482,19,1,3,0,Married,11,207.4152565,0,10028.76283
167737,40,19,1,0,Married,15,207.3153847,0,9817.769235
148268,19,2,2,1,Single,11,207.188738,0,9826.4369
161025,38,17,2,1,Married,14,207.145874,0,9773.2937
130452,22,3,3,0,Married,15,207.1125283,0,9757.626415
166876,21,2,1,0,Married,12,207.011744,0,10147.5872
164160,51,32,2,1,Single,15,206.9912785,0,9896.563925
131704,22,3,2,1,Married,14,206.9257402,0,10142.28701
178660,16,0,4,1,Married,15,206.8778665,0,10040.89333
197733,42,22,1,1,Married,15,206.8762564,0,10059.81282
103527,61,43,2,0,Single,10,206.8668828,0,10129.34414
120347,62,44,4,1,Married,13,206.7535913,0,9861.679565
128634,64,46,1,0,Married,8,206.7379713,0,10091.89857
117839,26,9,2,1,Married,15,206.6178357,0,9902.891785
152617,21,4,2,1,Married,11,206.5551444,0,10101.75722
151353,70,49,2,0,Married,11,206.4216834,0,9786.08417
117745,17,0,2,1,Married,12,206.3304124,0,9850.52062
118601,23,4,3,1,Married,12,206.3104833,0,10102.52417
151447,35,15,3,0,Married,15,206.3023011,0,10042.11506
169513,60,39,3,1,Single,13,206.2727154,0,10046.63577
199529,26,5,3,0,Single,13,206.2133582,0,9799.66791
164727,66,49,2,0,Single,3,206.2083952,0,10061.41976
121839,51,30,2,0,Single,13,206.0172124,0,9776.86062
163467,65,46,3,1,Single,13,206.0140439,0,9771.702195
164823,17,0,2,0,Married,11,205.9999473,0,9804.997365
119901,46,27,1,1,Married,12,205.9106439,0,9882.532195
176406,56,38,1,1,Single,12,205.9058749,0,9730.293745
184612,70,52,3,1,Single,4,205.8680136,0,9923.40068
154712,18,0,1,1,Married,12,205.8550367,0,9911.751835
110805,31,12,1,1,Single,14,205.8293962,0,9948.46981
185882,58,39,3,1,Single,14,205.7879229,0,10088.39615
112180,66,49,3,1,Single,15,205.7500451,0,10083.50226
151216,37,17,3,0,Single,11,205.7312224,0,9936.56112
193305,25,8,4,1,Married,12,205.7232741,0,9834.163705
160240,25,7,4,1,Married,11,205.7178632,0,9717.89316
198045,22,5,2,1,Married,14,205.7011349,0,9827.056745
157423,38,21,1,1,Married,12,205.6650527,0,9920.252635
177671,17,0,2,1,Married,12,205.6203524,0,9819.01762
162642,22,5,4,1,Married,14,205.5534585,0,9905.672925
167551,54,36,2,1,Single,14,205.5315763,0,9725.578815
153220,60,40,1,0,Married,13,205.4665587,0,9875.327935
154017,61,40,3,0,Married,15,205.4507752,0,9831.53876
173603,36,17,2,0,Married,12,205.3902518,0,9960.51259
155818,63,46,1,1,Married,3,205.3675635,0,9798.378175
163886,28,9,4,1,Married,11,205.3600391,0,9935.001955
180877,60,43,2,0,Married,3,205.2984168,0,9862.92084
161516,67,46,3,1,Married,12,205.2756075,0,10027.78038
129112,60,40,4,1,Single,15,205.2117461,0,9779.587305
183339,64,43,2,1,Married,3,205.2059665,0,10045.29833
199302,67,48,2,0,Single,9,205.1513061,0,9949.565305
118353,20,3,1,1,Married,15,204.9823703,0,9702.118515
138552,25,6,3,1,Married,13,204.95908,0,9935.954
157762,16,0,2,0,Married,13,204.9570603,0,10041.85302
100488,21,3,3,1,Married,9,204.8841599,0,9814.207995
137187,25,7,2,1,Married,12,204.869826,0,9733.4913
113460,69,51,1,0,Married,8,204.8013733,0,9850.068665
132770,68,51,4,1,Single,7,204.7419495,0,10030.09748
159464,68,48,3,1,Married,8,204.7237459,0,9725.187295
153270,30,12,1,0,Married,14,204.6908311,0,9946.541555
188520,70,50,3,0,Married,13,204.6746092,0,9811.73046
116367,26,7,3,0,Married,12,204.6722834,0,9880.61417
138131,70,49,4,0,Married,0,204.6221308,0,9674.10654
123026,68,47,1,0,Single,6,204.5985099,0,9880.925495
176376,36,19,4,0,Single,12,204.462118,0,9726.1059
161900,69,52,3,1,Single,10,204.455335,0,9644.76675
126181,54,35,4,1,Married,14,204.4297688,0,9979.48844
113283,20,2,1,1,Married,11,204.4293461,0,9942.467305
131238,62,44,3,0,Single,10,204.401161,0,9807.05805
110907,45,24,1,0,Single,15,204.3980122,0,9764.90061
110130,66,47,2,1,Married,10,204.3919559,0,9983.597795
139812,24,5,2,1,Married,14,204.3831549,0,9956.157745
178573,64,43,2,1,Single,3,204.2793002,0,9774.96501
182659,68,47,4,0,Married,5,204.2376448,0,9972.88224
168708,16,0,3,1,Single,15,204.2349164,0,9735.74582
154009,41,22,2,0,Married,11,204.2078793,0,9725.393965
173239,64,47,4,1,Married,12,204.1908375,0,9828.541875
132729,16,0,2,1,Married,13,204.122513,0,9967.12565
118549,60,42,2,0,Single,7,203.9939529,0,9813.697645
116566,67,46,2,0,Single,12,203.9843161,0,9897.215805
144431,53,33,1,0,Married,15,203.9808667,0,9856.043335
157380,25,7,4,1,Married,15,203.7830455,0,9913.152275
197138,49,31,3,0,Married,14,203.7829559,0,9962.147795
184701,33,12,2,1,Married,14,203.7546187,0,9697.730935
183593,21,3,3,1,Married,12,203.6651875,0,9946.259375
189163,67,47,3,1,Married,8,203.6594537,0,9774.972685
129202,67,50,3,1,Married,2,203.6277956,0,9785.38978
119555,24,7,4,1,Married,13,203.6245304,0,9744.22652
171582,70,53,4,1,Married,15,203.5985154,0,9697.92577
153157,27,10,4,1,Married,11,203.481941,0,9848.09705
176005,51,31,1,0,Single,13,203.4147846,0,9681.73923
143116,40,22,3,1,Married,15,203.3406576,0,9941.03288
161923,57,40,2,0,Married,15,203.3260782,0,9705.30391
169722,45,24,3,0,Married,13,203.321005,0,9924.05025
150024,24,5,1,0,Married,11,203.3195946,0,9852.97973
170014,61,42,1,1,Single,6,203.2806148,0,9610.03074
157481,67,48,2,0,Married,12,203.244197,0,9774.20985
102029,18,0,4,1,Married,15,203.1868301,0,9904.341505
106521,30,12,4,0,Single,11,203.0570334,0,9909.85167
185602,64,46,4,0,Single,10,203.0555186,0,9565.77593
136484,66,45,2,1,Married,8,203.0543293,0,9656.716465
129730,60,39,1,0,Married,1,203.0429805,0,9860.149025
197045,19,1,3,1,Married,13,203.0366103,0,9826.830515
140991,66,48,2,0,Single,10,203.034266,0,9910.7133
175923,64,44,4,1,Single,7,202.9383455,0,9853.917275
187634,61,40,4,1,Married,13,202.9013457,0,9830.067285
154004,33,15,3,0,Married,12,202.8790638,0,9860.95319
174937,63,46,2,1,Single,11,202.8723728,0,9864.61864
124138,24,7,4,0,Married,13,202.8604871,0,9770.024355
164211,24,6,4,0,Married,13,202.7844046,0,9813.22023
132357,65,45,3,0,Single,9,202.7412844,0,9675.06422
153722,25,6,2,1,Married,14,202.6975915,0,9724.879575
157205,42,23,3,1,Married,15,202.6206629,0,9678.033145
148430,55,38,4,0,Single,15,202.5981806,0,9836.90903
121593,64,45,1,1,Married,5,202.4989254,0,9728.94627
197499,66,45,3,1,Married,4,202.4608462,0,9790.04231
128663,46,26,1,1,Married,12,202.4587138,0,9607.93569
167712,63,42,3,1,Single,7,202.4144153,0,9581.720765
180916,63,46,2,1,Married,15,202.3255141,0,9707.275705
145289,17,0,2,0,Married,12,202.2061182,0,9515.30591
133226,17,0,3,0,Married,14,202.1994123,0,9774.970615
156581,64,44,3,1,Married,11,202.1669107,0,9876.345535
190534,21,2,4,1,Married,15,202.1352795,0,9675.763975
189409,65,46,3,1,Married,15,202.1033221,0,9854.166105
163121,28,7,2,1,Single,15,202.0789605,0,9830.948025
154695,69,50,2,1,Married,8,202.0447247,0,9565.236235
127647,68,49,3,0,Married,10,202.0056174,0,9886.28087
178057,52,34,2,0,Married,11,201.989253,0,9548.46265
106650,30,11,3,0,Single,11,201.945056,0,9808.2528
101826,34,15,1,0,Married,14,201.8179313,0,9659.896565
127948,51,32,3,0,Married,12,201.7821036,0,9788.10518
103314,54,36,3,1,Married,13,201.7431279,0,9714.156395
115402,68,49,2,1,Married,13,201.7299071,0,9572.495355
189192,46,29,1,0,Married,12,201.6782523,0,9575.912615
182086,48,30,4,1,Married,15,201.6354415,0,9825.772075
173451,41,22,3,0,Married,13,201.6146841,0,9788.734205
175052,65,44,1,0,Married,0,201.5901302,0,9764.50651
160087,34,17,2,0,Single,13,201.5720329,0,9711.601645
150245,68,48,3,1,Married,14,201.5175786,0,9589.87893
114832,61,42,4,1,Single,6,201.5009295,0,9769.046475
103992,63,46,3,1,Single,14,201.4868881,0,9728.344405
128601,26,6,2,0,Married,11,201.4621834,0,9537.10917
147340,60,40,2,0,Married,13,201.4466206,0,9844.33103
157431,68,49,2,1,Married,12,201.4450651,0,9787.253255
160728,69,48,3,1,Single,7,201.3937545,0,9683.687725
116505,61,44,2,1,Married,9,201.3620415,0,9725.102075
192726,66,45,4,0,Single,0,201.3255307,0,9754.276535
131124,68,48,2,0,Married,10,201.3112188,0,9842.56094
139396,31,11,2,1,Single,15,201.3067102,0,9859.33551
115989,21,3,3,1,Single,12,201.296301,0,9580.81505
124277,23,6,4,1,Married,12,201.2941482,0,9750.70741
128867,16,0,1,0,Married,12,201.2702657,0,9653.513285
163061,62,42,2,0,Married,0,201.0919381,0,9520.596905
179564,26,6,3,0,Married,11,201.0865597,0,9757.327985
151051,44,27,2,0,Married,14,201.0248122,0,9611.24061
130351,45,28,2,1,Single,14,201.0110126,0,9574.55063
171857,38,17,4,0,Married,13,200.981019,0,9653.05095
119284,27,7,3,1,Married,12,200.850912,0,9489.5456
112976,30,9,1,0,Single,13,200.7912195,0,9477.560975
182618,66,45,2,0,Single,12,200.7840003,0,9723.200015
125611,67,48,2,1,Married,14,200.7611311,0,9813.056555
170383,65,46,2,1,Married,6,200.7420917,0,9728.104585
157014,63,45,3,1,Married,0,200.6700106,0,9461.50053
123100,66,45,3,1,Married,10,200.6430165,0,9744.150825
178601,70,52,2,1,Married,13,200.6360384,0,9520.80192
141507,62,43,2,1,Married,3,200.5838031,0,9812.190155
156478,29,10,3,0,Married,12,200.5554985,0,9672.774925
114288,33,15,2,0,Married,11,200.5543237,0,9497.716185
122190,63,46,2,0,Married,5,200.5488273,0,9668.441365
110481,61,41,3,1,Married,12,200.5344542,0,9775.72271
199870,26,5,2,1,Married,13,200.4837906,0,9662.18953
185813,65,44,4,1,Single,3,200.4547619,0,9531.738095
131687,69,50,3,0,Married,6,200.4243184,0,9670.21592
163331,42,21,4,0,Married,13,200.3970746,0,9698.85373
136159,51,33,3,0,Single,14,200.3709466,0,9558.54733
190820,43,22,3,1,Married,13,200.3668453,0,9580.342265
162946,16,0,4,0,Married,15,200.3590918,0,9650.95459
102107,67,49,2,0,Married,2,200.3566489,0,9692.832445
157657,22,5,4,0,Married,11,200.3516389,0,9787.581945
162020,63,43,3,0,Single,14,200.3485313,0,9518.426565
156187,17,0,1,1,Married,11,200.343212,0,9756.1606
106658,43,22,3,1,Single,12,200.3362514,0,9708.81257
182648,20,1,2,1,Married,13,200.3239374,0,9578.19687
194434,17,0,2,1,Married,11,200.3149848,0,9727.74924
197221,25,8,2,1,Married,12,200.3094091,0,9787.470455
144639,49,32,4,1,Single,11,200.1818598,0,9687.09299
170564,33,16,4,0,Married,11,200.1626019,0,9494.130095
169240,68,49,3,1,Single,15,200.1491698,0,9806.45849
120939,36,17,4,1,Married,15,200.0321643,0,9684.608215
138224,18,0,2,0,Married,11,200.0279099,0,9537.395495
146753,24,7,2,1,Married,15,200.0278653,0,9437.393265
182730,69,52,2,0,Married,10,199.9784707,0,9680.923535
169685,65,45,3,0,Single,6,199.8544749,0,9524.723745
143988,66,46,4,1,Married,3,199.8308238,0,9791.54119
127614,22,4,2,1,Married,11,199.7041546,0,9779.20773
130907,60,39,3,1,Married,9,199.6621481,0,9638.107405
170518,48,28,2,0,Single,12,199.5676093,0,9711.380465
165885,19,2,1,1,Married,11,199.5115967,0,9690.579835
134039,23,4,4,1,Married,13,199.4459329,0,9736.296645
126442,60,43,4,1,Single,4,199.4456744,0,9650.28372
195249,62,42,2,0,Single,10,199.4172719,0,9413.863595
111459,41,21,2,1,Married,11,199.3998978,0,9447.99489
116528,26,6,2,0,Married,11,199.3608972,0,9424.04486
190229,60,41,2,0,Married,8,199.3588822,0,9553.94411
165302,18,0,3,1,Married,11,199.3309369,0,9539.546845
142959,25,7,3,0,Married,11,199.3297949,0,9606.489745
183615,68,47,4,0,Single,7,199.271303,0,9390.56515
194751,69,52,3,1,Single,11,199.2010198,0,9414.05099
135957,67,47,3,1,Married,8,199.2006577,0,9693.032885
105222,40,20,4,1,Married,12,199.1938556,0,9480.69278
162071,45,28,1,1,Married,15,199.0159526,0,9647.79763
190784,67,48,2,1,Married,5,199.0056741,0,9657.283705
191922,68,50,2,0,Single,11,198.9769978,0,9446.84989
171043,61,42,2,1,Married,7,198.9666197,0,9372.330985
139550,67,46,3,1,Single,11,198.965951,0,9707.29755
154762,64,45,2,0,Married,8,198.961681,0,9509.08405
136868,19,0,4,1,Married,12,198.9362937,0,9543.814685
131883,68,51,3,0,Single,6,198.9315602,0,9477.57801
109484,66,49,3,1,Single,0,198.9229634,0,9710.14817
140170,54,34,1,0,Married,13,198.8889181,0,9713.445905
162707,60,43,4,1,Single,12,198.8351675,0,9663.758375
116512,21,4,1,1,Married,11,198.8228959,0,9374.144795
122223,66,47,3,0,Married,3,198.8215393,0,9518.076965
175472,47,26,2,1,Married,11,198.7961508,0,9494.80754
131048,67,48,3,0,Single,10,198.7510108,0,9665.55054
178696,67,50,3,0,Single,12,198.6812895,0,9544.064475
175886,61,43,2,1,Married,8,198.6730444,0,9400.65222
145325,61,40,2,0,Single,11,198.66967,0,9557.4835
141929,62,45,1,0,Single,8,198.6312063,0,9345.560315
115238,60,40,4,0,Single,5,198.4374006,0,9536.87003
172117,53,36,3,0,Married,11,198.4209969,0,9580.049845
143791,62,44,3,0,Married,11,198.4064948,0,9670.32474
138789,66,49,3,1,Single,0,198.3613213,0,9612.066065
121570,66,46,2,1,Single,4,198.3467374,0,9623.33687
162913,17,0,2,1,Married,14,198.3257158,0,9448.28579
176728,22,4,3,1,Single,13,198.2290041,0,9711.450205
124452,19,2,2,1,Married,11,198.1871239,0,9326.356195
174165,70,53,3,0,Married,13,198.1738019,0,9525.690095
104141,18,0,3,1,Married,15,198.0938563,0,9458.692815
198006,23,4,1,1,Married,13,198.011932,0,9395.5966
169238,59,42,2,0,Married,11,197.9518324,0,9508.59162
173780,24,6,1,1,Married,15,197.9280454,0,9316.40227
187351,22,4,1,0,Married,14,197.9124841,0,9363.624205
144068,18,0,3,1,Married,13,197.8385671,0,9635.928355
149333,47,27,2,1,Single,11,197.830166,0,9304.5083
182512,21,2,4,1,Married,11,197.7820461,0,9639.102305
102115,42,23,3,1,Single,15,197.7668115,0,9505.340575
108870,40,23,1,1,Married,11,197.7098654,0,9560.49327
119884,70,49,2,0,Married,6,197.6420414,0,9526.10207
198829,61,41,2,1,Married,9,197.565334,0,9637.2667
137975,17,0,4,0,Married,12,197.5427324,0,9551.13662
141789,16,0,4,0,Married,14,197.4519735,0,9425.598675
148111,61,44,2,0,Married,1,197.3907116,0,9424.53558
137134,25,7,2,1,Single,12,197.3667253,0,9282.336265
133868,25,6,4,0,Married,13,197.3016678,0,9472.08339
174859,69,49,1,1,Married,6,197.272486,0,9563.6243
131916,21,4,2,1,Married,12,197.2533859,0,9655.669295
129321,23,5,3,1,Married,11,197.2419763,0,9363.098815
172953,51,33,4,1,Married,11,197.1474312,0,9530.37156
105730,69,48,2,0,Single,9,197.0761174,0,9653.80587
100273,68,50,3,1,Single,10,197.0621961,0,9466.109805
143243,29,9,3,1,Married,11,197.0374183,0,9508.870915
197699,16,0,3,1,Single,12,196.9921736,0,9556.60868
145854,60,43,2,1,Single,3,196.9877085,0,9496.385425
108576,28,10,4,1,Single,11,196.9823997,0,9448.119985
140955,70,53,3,0,Single,2,196.9077858,0,9284.38929
124003,20,1,2,1,Married,11,196.8994328,0,9621.97164
186431,25,8,3,1,Married,15,196.8324535,0,9512.622675
176937,70,49,2,0,Married,10,196.8275736,0,9371.37868
160066,67,48,2,0,Single,14,196.8111414,0,9434.55707
108545,53,36,2,0,Married,12,196.7456397,0,9545.281985
121248,21,4,4,1,Married,11,196.7071274,0,9364.35637
147496,21,2,4,1,Married,13,196.6010967,0,9239.054835
149954,63,45,4,0,Single,14,196.5375197,0,9290.875985
178574,34,14,1,1,Married,12,196.5073839,0,9580.369195
181725,66,48,3,1,Single,10,196.504217,0,9232.21085
139864,68,50,4,1,Single,12,196.4903654,0,9253.51827
182806,69,51,3,0,Single,15,196.4844495,0,9565.222475
197913,61,41,3,0,Single,0,196.4831555,0,9264.157775
131127,21,4,3,1,Married,13,196.4292229,0,9504.461145
101263,18,0,4,1,Single,12,196.4010079,0,9372.050395
179907,67,48,2,1,Married,7,196.3942623,0,9250.713115
174318,24,6,3,0,Married,14,196.3814834,0,9463.07417
144488,64,46,2,1,Single,2,196.2813898,0,9601.06949
100786,51,31,4,0,Married,12,196.2019206,0,9591.09603
141788,50,32,4,1,Married,15,196.1852896,0,9518.26448
171811,37,16,3,1,Single,12,196.1466007,0,9317.330035
185950,16,0,3,1,Married,12,196.0456832,0,9274.28416
173982,64,44,4,1,Married,8,196.0038918,0,9322.19459
180774,55,36,4,0,Single,12,195.891037,0,9389.55185
108019,67,50,3,0,Single,7,195.7851854,0,9407.25927
125369,27,10,3,1,Married,13,195.7336016,0,9228.68008
197780,21,3,2,1,Married,13,195.7200478,0,9352.00239
158137,41,22,3,0,Single,12,195.7114003,0,9402.570015
101944,60,43,3,1,Married,6,195.6874702,0,9575.37351
109590,22,4,2,1,Single,13,195.6228447,0,9415.142235
154436,66,49,2,1,Single,3,195.606033,0,9544.30165
178047,60,39,3,0,Single,7,195.5953374,0,9465.76687
119783,69,48,3,1,Single,4,195.5864596,0,9525.32298
163300,22,3,1,1,Married,14,195.4700505,0,9431.502525
132737,70,50,3,0,Married,7,195.396991,0,9510.84955
121769,68,50,3,1,Single,7,195.2557508,0,9448.78754
103512,38,18,3,1,Single,11,195.2299201,0,9453.496005
118175,33,14,2,1,Married,14,195.2139227,0,9533.696135
145981,70,50,1,0,Single,9,195.1892798,0,9259.46399
102717,39,19,3,1,Married,11,195.163177,0,9495.15885
131728,65,44,2,1,Single,9,195.119902,0,9466.9951
187663,43,24,3,0,Married,12,195.034878,0,9344.7439
193059,16,0,1,0,Married,14,194.9484606,0,9396.42303
179760,68,47,3,0,Married,5,194.9321915,0,9190.609575
132732,20,3,4,0,Married,13,194.8801724,0,9282.00862
172842,22,5,2,1,Married,13,194.8327799,0,9386.638995
105519,17,0,3,1,Married,14,194.7675783,0,9494.378915
184302,31,11,1,1,Single,12,194.7580825,0,9233.904125
148478,69,52,3,1,Married,11,194.7009916,0,9337.04958
187274,53,33,2,1,Single,12,194.5732908,0,9422.66454
175337,67,47,4,1,Married,4,194.5460108,0,9482.30054
177349,70,49,1,1,Single,5,194.4767543,0,9262.837715
151129,22,4,2,1,Single,11,194.43627,0,9495.8135
170774,66,48,3,1,Married,5,194.4116368,0,9154.58184
170667,18,0,3,1,Single,15,194.3768974,0,9349.84487
170285,63,45,1,0,Married,8,194.3165171,0,9413.825855
196038,42,24,3,0,Single,14,194.2705281,0,9265.526405
147202,18,0,1,1,Married,12,194.2417501,0,9403.087505
146265,64,45,2,1,Single,13,194.23594,0,9199.797
189710,66,48,3,0,Married,6,194.2179076,0,9454.89538
138583,62,41,2,1,Single,0,194.188239,0,9200.41195
117028,22,3,2,0,Married,11,194.0060904,0,9102.30452
163827,64,45,3,1,Married,13,193.9876647,0,9429.383235
138933,39,18,2,0,Married,13,193.9831886,0,9194.15943
198152,64,47,3,1,Single,6,193.9608062,0,9187.04031
186894,42,23,1,0,Single,13,193.9378831,0,9444.894155
184259,65,46,1,0,Married,15,193.9029859,0,9325.149295
157414,70,50,4,1,Married,15,193.7024626,0,9330.12313
180498,68,48,3,1,Single,4,193.6240177,0,9416.200885
117731,65,44,2,0,Married,12,193.5785778,0,9475.92889
191285,17,0,2,1,Married,14,193.4207041,0,9384.035205
120085,65,46,3,1,Married,9,193.3740988,0,9448.70494
169682,26,9,2,1,Married,12,193.3558559,0,9251.792795
148873,61,40,4,0,Married,9,193.2447361,0,9188.236805
135923,16,0,4,1,Married,11,193.096925,0,9198.84625
140942,21,4,2,1,Married,15,193.0892992,0,9442.46496
180357,47,28,2,1,Married,12,193.0646773,0,9157.233865
127339,17,0,2,0,Married,14,193.0424347,0,9222.121735
128605,56,36,1,0,Single,14,193.0311549,0,9413.557745
178993,61,42,3,1,Single,9,193.0191417,0,9080.957085
139624,66,49,3,0,Single,1,192.9668642,0,9186.34321
120884,16,0,3,0,Married,13,192.9318487,0,9194.592435
113441,39,22,2,0,Married,11,192.9130479,0,9236.652395
118192,66,48,4,1,Married,14,192.8927852,0,9265.63926
100684,67,46,2,0,Single,9,192.8122051,0,9127.610255
143528,60,41,4,0,Married,8,192.6938225,0,9082.691125
177032,56,36,1,0,Married,12,192.6799087,0,9151.995435
111626,66,49,2,1,Married,1,192.6596779,0,9233.983895
190813,63,42,1,1,Single,13,192.5154704,0,9395.77352
111304,59,38,4,1,Single,11,192.4641554,0,9257.20777
164802,53,32,2,0,Single,12,192.3880419,0,9067.402095
141700,33,13,3,1,Married,11,192.2837667,0,9312.188335
111486,63,42,2,0,Single,14,192.1257344,0,9375.28672
104548,23,5,3,1,Married,11,192.0896525,0,9201.482625
134473,25,7,3,1,Married,14,191.9748218,0,9346.74109
115117,21,4,2,1,Married,14,191.8774987,0,9123.874935
101692,67,49,3,0,Married,12,191.85171,0,9212.5855
133183,64,47,1,0,Married,13,191.8395784,0,9317.97892
136631,20,2,1,1,Married,12,191.8077892,0,9032.38946
125091,22,3,1,1,Single,12,191.7527247,0,9322.636235
127123,55,37,1,0,Single,15,191.6213777,0,8992.068885
161814,62,43,2,1,Married,11,191.6071081,0,8994.355405
119868,19,0,2,0,Married,14,191.6044838,0,9201.22419
106621,16,0,3,1,Married,13,191.5885188,0,9160.42594
189566,69,48,2,0,Single,3,191.5872763,0,9204.363815
165262,49,32,2,1,Single,15,191.5855411,0,9099.277055
184163,66,45,4,0,Married,12,191.5424114,0,9088.12057
192797,58,38,1,1,Married,15,191.5121155,0,9218.605775
101760,20,1,1,1,Married,14,191.4841522,0,9200.20761
147647,50,31,2,1,Married,11,191.4664255,0,9029.321275
193510,60,42,4,1,Single,8,191.4111201,0,9350.556005
163589,50,31,2,0,Married,11,191.4045912,0,9166.22956
165452,70,51,1,1,Married,6,191.2679535,0,9059.397675
108879,62,43,3,0,Married,14,191.2545419,0,9092.727095
113081,65,46,2,1,Married,10,191.1909845,0,9212.549225
101552,20,3,3,0,Married,12,191.1613149,0,9186.065745
160734,22,3,2,1,Married,13,191.0921132,0,9204.60566
121698,37,19,4,0,Single,11,191.0554736,0,8990.77368
191744,55,38,1,1,Single,14,191.0414252,0,9073.07126
175829,69,51,4,1,Married,10,191.019486,0,9094.9743
100568,67,48,1,0,Single,8,190.9903208,0,9165.51604
196346,32,13,4,1,Single,15,190.9859457,0,9229.297285
126710,70,52,4,0,Single,10,190.9236188,0,9341.18094
119755,18,1,2,1,Married,13,190.9235119,0,9206.175595
151402,16,0,3,0,Married,15,190.9106916,0,9213.53458
116088,69,52,3,1,Single,8,190.859132,0,9043.9566
103536,68,47,3,1,Married,3,190.8082711,0,9138.413555
123402,22,3,2,1,Married,12,190.7129418,0,9005.64709
159085,18,0,3,1,Married,13,190.6478915,0,9255.394575
144054,22,4,1,1,Married,12,190.6377645,0,9313.888225
104795,60,40,2,1,Married,13,190.6288924,0,9114.44462
175847,70,51,2,0,Married,13,190.5913758,0,9028.56879
153110,45,25,3,0,Single,13,190.5893545,0,9267.467725
163469,64,43,2,0,Married,7,190.5879139,0,8959.395695
123074,61,43,4,0,Married,0,190.573219,0,9183.66095
164642,67,48,3,1,Married,7,190.5703824,0,9254.51912
173257,58,38,2,0,Married,12,190.4604887,0,9034.024435
108517,43,25,3,0,Married,14,190.4142592,0,9126.71296
187524,64,47,3,0,Married,14,190.3031871,0,9207.159355
144062,24,6,4,1,Married,14,190.2866048,0,9274.33024
109581,28,8,3,1,Single,13,190.2790812,0,9157.95406
169495,33,12,2,1,Single,12,190.2790068,0,9260.95034
155382,58,41,3,0,Single,11,190.219481,0,9011.97405
194310,66,45,2,0,Married,14,190.206769,0,9000.33845
158427,46,27,2,0,Married,15,190.1974887,0,9292.874435
177928,70,52,1,0,Married,8,190.0997216,0,9107.98608
164167,67,50,2,0,Single,9,190.0838598,0,8928.19299
119429,17,0,3,1,Married,13,190.0467519,0,9248.337595
109402,41,21,2,0,Married,12,190.0323311,0,9154.616555
124892,69,51,2,0,Single,2,189.9808457,0,8950.042285
198533,61,42,2,0,Married,15,189.9784108,0,9224.92054
155793,65,46,2,1,Single,10,189.9302495,0,8907.512475
183782,38,18,2,1,Single,15,189.9247473,0,9099.237365
135590,69,51,3,0,Single,8,189.9229735,0,9282.148675
119398,28,9,2,0,Married,14,189.8927235,0,8919.636175
134774,18,1,2,1,Married,12,189.8845127,0,9166.225635
138565,19,2,2,1,Married,12,189.8172756,0,8923.86378
171534,65,47,2,1,Married,8,189.801804,0,9239.0902
158468,69,48,3,1,Married,7,189.7614984,0,9027.07492
174797,47,26,4,0,Single,12,189.7427782,0,9078.13891
134044,40,22,2,0,Married,13,189.723144,0,8983.1572
132002,18,0,2,1,Married,15,189.7144759,0,8949.723795
133285,24,6,3,1,Married,13,189.6737391,0,9205.686955
132699,24,5,2,0,Married,11,189.6282815,0,9193.414075
164771,68,48,2,0,Married,1,189.547546,0,9215.3773
196740,50,33,3,1,Married,12,189.5383761,0,9002.918805
193699,16,0,3,1,Married,15,189.5305195,0,9148.525975
163997,57,39,2,0,Single,13,189.3379564,0,8874.89782
124490,53,35,3,1,Married,12,189.2697406,0,9238.48703
157466,24,7,3,1,Married,13,189.2308268,0,9205.54134
141229,22,3,3,1,Married,15,189.1872376,0,9046.36188
132905,65,46,2,0,Married,2,189.1190181,0,9080.950905
174839,36,15,2,0,Married,15,189.1055937,0,9106.279685
109891,51,30,3,0,Married,15,189.0375584,0,9112.87792
102360,62,43,2,1,Married,10,189.0358649,0,9236.793245
114307,69,49,2,1,Married,4,189.0142535,0,9234.712675
126972,63,44,4,1,Married,14,189.0105214,0,8916.52607
145098,62,44,3,1,Single,15,188.9333637,0,9014.668185
117920,57,36,2,1,Single,13,188.9153008,0,9045.76504
113980,63,44,2,1,Single,2,188.8993416,0,9243.96708
139734,22,5,3,0,Married,11,188.8720271,0,8878.601355
190604,68,48,1,1,Married,13,188.8429635,0,9210.148175
158802,44,24,1,1,Married,14,188.8398295,0,9017.991475
106476,67,48,3,1,Married,6,188.8283073,0,8955.415365
174043,61,43,3,0,Married,9,188.826862,0,8863.3431
159443,55,36,1,1,Married,14,188.8132454,0,8998.66227
159605,64,43,4,0,Married,7,188.6404174,0,9187.02087
196332,70,52,2,1,Single,13,188.6227569,0,9193.137845
146401,51,34,2,0,Single,13,188.5864851,0,8888.324255
178173,64,47,3,1,Single,7,188.5203952,0,9094.01976
119233,56,37,4,1,Married,13,188.5005846,0,9171.02923
146315,66,45,2,1,Married,11,188.4897452,0,9176.48726
183878,50,29,2,0,Single,15,188.4853743,0,8934.268715
144754,24,6,1,0,Married,11,188.4712932,0,9083.56466
130161,64,47,2,1,Single,15,188.4492252,0,9184.46126
172798,70,51,3,1,Single,8,188.4347158,0,9045.73579
117343,19,2,3,1,Single,13,188.395066,0,8832.7533
165375,69,52,1,0,Single,10,188.3179804,0,8844.89902
118031,16,0,2,1,Married,13,188.3173905,0,9068.869525
107183,70,49,4,0,Married,12,188.3055871,0,8895.279355
188061,67,48,2,0,Single,9,188.2925171,0,8832.625855
180779,61,43,2,0,Single,7,188.2874401,0,8989.372005
128296,18,0,4,1,Single,12,188.2252113,0,8841.260565
170704,53,36,2,0,Married,14,188.2102556,0,9016.51278
147514,66,46,3,1,Single,0,188.2023997,0,8833.119985
157566,60,39,1,1,Single,10,188.1206318,0,8992.03159
174965,33,12,4,1,Single,15,188.0890053,0,9030.450265
191616,21,2,2,1,Married,14,188.0668631,0,8878.343155
153364,16,0,3,0,Married,12,188.002546,0,9036.1273
129663,23,6,1,1,Married,15,187.9585249,0,9007.926245
142037,60,41,2,1,Married,8,187.9258968,0,8992.29484
188674,26,6,3,1,Single,12,187.9088322,0,8890.44161
188210,45,24,4,1,Single,15,187.8711782,0,8996.55891
187042,61,41,4,0,Married,1,187.6599767,0,9135.998835
154896,70,50,3,1,Single,7,187.6522711,0,9013.613555
157990,63,46,3,0,Married,7,187.5932977,0,9015.664885
146056,43,22,3,0,Married,15,187.5931491,0,8848.657455
139882,18,0,1,1,Married,14,187.5632107,0,9017.160535
156002,46,28,3,1,Married,11,187.5576346,0,9130.88173
177506,61,42,2,1,Single,0,187.5365645,0,9086.828225
195135,60,39,1,0,Single,12,187.5124909,0,9151.624545
112670,64,46,4,0,Married,7,187.4899744,0,9076.49872
123908,65,44,3,0,Single,6,187.4859603,0,8931.298015
118830,44,26,2,1,Single,11,187.420251,0,8844.01255
178312,49,29,2,1,Single,14,187.4045156,0,8955.22578
153814,70,49,3,0,Married,14,187.4043212,0,8844.21606
105247,69,52,3,1,Married,13,187.4042145,0,8863.210725
164071,58,37,1,0,Married,14,187.4003044,0,8804.01522
110500,64,46,2,1,Married,13,187.3943975,0,9139.719875
138680,25,8,4,1,Married,14,187.3454786,0,8841.27393
132591,19,1,3,1,Single,13,187.3398595,0,9003.992975
153210,19,2,4,1,Married,15,187.3141493,0,8795.707465
187343,32,15,2,0,Married,13,187.264194,0,8936.2097
142699,61,44,4,1,Married,9,187.2638955,0,8886.194775
111897,17,0,3,0,Married,14,187.2352379,0,8936.761895
102197,63,44,2,0,Single,13,187.1632252,0,8935.16126
144638,66,47,3,0,Married,9,187.0689746,0,9034.44873
130933,67,48,4,0,Married,7,186.993559,0,8893.67795
162881,56,37,4,0,Married,12,186.985364,0,9123.2682
115843,70,50,2,0,Married,15,186.9770685,0,9100.853425
107660,67,50,1,1,Single,0,186.951001,0,8868.55005
133281,62,45,4,1,Single,4,186.8713381,0,8894.566905
103307,61,44,3,1,Married,2,186.8503764,0,8804.51882
145489,62,42,3,1,Married,13,186.7640373,0,8788.201865
165274,66,48,4,0,Single,4,186.667605,0,8743.38025
113494,62,42,1,1,Single,9,186.6485083,0,9106.425415
183026,70,52,4,1,Married,9,186.5952713,0,9125.763565
191210,23,5,2,0,Married,15,186.5815867,0,8912.079335
166472,67,47,1,1,Married,2,186.5116392,0,9086.58196
112707,19,0,2,1,Single,14,186.4975253,0,8945.876265
131000,23,6,4,1,Married,14,186.3419152,0,8913.09576
117972,69,49,2,0,Married,7,186.3223813,0,8922.119065
170916,62,45,3,1,Single,1,186.2644712,0,8968.22356
141243,31,12,4,1,Married,14,186.2549065,0,8766.745325
189968,66,49,1,0,Single,5,186.2522302,0,8971.61151
130063,64,45,3,0,Married,7,186.2505434,0,8853.52717
120594,38,19,3,0,Single,14,186.1176727,0,8745.883635
164168,23,4,3,1,Single,15,186.0547352,0,8785.73676
179266,69,51,3,0,Married,13,185.944511,0,9070.22555
165561,18,0,3,1,Married,14,185.8723642,0,8904.61821
115949,61,41,3,1,Single,4,185.8614547,0,8740.072735
128471,62,44,2,1,Married,13,185.8265531,0,8860.327655
134161,65,44,2,1,Married,15,185.8110244,0,8903.55122
131283,18,0,3,0,Married,15,185.7922048,0,8861.61024
192053,49,30,2,1,Single,15,185.759338,0,8984.9669
142617,66,45,3,1,Married,8,185.723881,0,8999.19405
170646,41,21,1,1,Married,15,185.6857913,0,8997.289565
191944,21,3,3,1,Married,14,185.6792609,0,8700.963045
147491,29,11,4,1,Married,14,185.6099405,0,8984.497025
171573,62,45,4,0,Married,10,185.4994852,0,8782.97426
122107,59,40,2,1,Single,15,185.4654981,0,9060.274905
101039,22,5,2,0,Married,13,185.4327464,0,8845.63732
176278,34,16,3,1,Married,13,185.3279228,0,8848.39614
129345,67,48,3,0,Married,9,185.2837875,0,8978.189375
148944,39,22,2,0,Single,14,185.2458451,0,8773.292255
170073,61,43,3,1,Single,4,185.2092509,0,8788.462545
104444,47,27,1,1,Married,13,185.1463607,0,8946.318035
134638,62,43,2,1,Married,1,185.1302781,0,8784.513905
159752,67,50,2,0,Married,3,185.123639,0,8664.18195
107444,60,39,2,0,Married,6,185.0751905,0,8901.759525
128479,46,25,3,0,Single,11,185.0649027,0,8924.245135
146169,67,49,2,0,Single,10,185.0628056,0,8979.14028
100935,63,43,3,0,Single,15,185.0165371,0,8779.826855
129110,62,43,3,0,Single,8,184.9488906,0,8691.44453
103695,63,42,2,1,Single,11,184.9471341,0,8670.356705
118231,68,48,3,0,Married,10,184.9239301,0,9040.196505
139883,61,42,4,0,Single,10,184.9221483,0,8908.107415
197283,66,48,3,0,Single,7,184.8876343,0,9004.381715
143885,49,31,3,1,Single,12,184.8374195,0,8850.870975
134476,23,4,2,1,Married,12,184.7622965,0,8709.114825
122535,68,47,3,1,Married,4,184.7557897,0,8693.789485
122442,19,1,2,1,Married,13,184.7519116,0,8763.59558
156093,64,45,1,0,Married,14,184.7351579,0,8677.757895
134724,62,43,3,1,Single,10,184.7169557,0,8909.847785
138318,45,24,4,1,Single,11,184.693913,0,8959.69565
113553,61,42,3,1,Single,12,184.6753971,0,8886.769855
116241,70,52,3,1,Married,10,184.5360255,0,8860.801275
159440,67,50,3,1,Single,1,184.5250104,0,8956.25052
132046,70,50,3,1,Married,9,184.4794014,0,8911.97007
164558,70,52,1,0,Married,7,184.317085,0,8891.85425
159342,64,43,3,1,Married,8,184.2904129,0,8837.520645
182604,18,0,2,1,Married,15,184.2619675,0,8995.098375
138990,67,49,3,0,Married,13,184.1834476,0,8837.17238
159694,61,40,3,0,Married,6,184.1530611,0,8843.653055
148634,64,47,2,0,Married,9,184.1390066,0,8975.95033
155351,61,41,2,0,Single,11,184.1277861,0,8753.389305
181130,48,29,3,1,Married,12,184.127155,0,8830.35775
146192,68,47,2,1,Married,6,184.1173057,0,8929.865285
181147,20,3,4,1,Married,14,184.0785484,0,8708.92742
119747,28,7,2,0,Single,11,183.9996698,0,8786.98349
132026,61,41,2,0,Married,8,183.9851,0,8815.255
162797,19,1,2,1,Married,14,183.9637908,0,8749.18954
170823,62,44,2,1,Single,8,183.9014883,0,8801.074415
161333,29,11,3,1,Married,15,183.8856392,0,8654.28196
119447,65,44,1,0,Single,8,183.8554703,0,8714.773515
111725,18,0,4,0,Married,12,183.7710119,0,8870.550595
125451,25,8,3,0,Married,12,183.728461,0,8749.42305
172126,28,9,1,0,Married,15,183.7265292,0,8725.32646
129276,21,3,4,1,Married,13,183.7059455,0,8663.297275
190853,25,6,1,1,Married,13,183.6143818,0,8658.71909
138616,51,33,4,1,Married,15,183.6142249,0,8688.711245
137238,60,41,3,1,Married,6,183.5908763,0,8829.543815
145997,31,13,3,0,Married,11,183.5154636,0,8635.77318
173567,18,0,3,0,Married,12,183.4939003,0,8896.695015
125996,33,15,1,1,Married,15,183.4843533,0,8689.217665
146327,69,48,3,1,Single,10,183.3820513,0,8858.102565
117289,25,8,3,1,Married,12,183.3627786,0,8627.13893
108011,67,49,2,1,Married,8,183.2906958,0,8777.53479
109726,63,46,3,1,Single,11,183.2439798,0,8835.19899
115581,18,0,1,1,Married,13,183.2307779,0,8845.538895
103938,18,0,4,0,Married,11,183.2227266,0,8749.13633
141646,68,48,3,1,Single,6,183.1017348,0,8893.08674
135312,70,49,3,0,Single,14,183.0651561,0,8628.257805
187253,21,2,1,1,Married,13,183.0600479,0,8938.002395
102110,62,44,4,0,Single,15,183.0472726,0,8830.36363
178046,67,50,2,0,Single,12,183.0391836,0,8796.95918
161733,58,39,2,0,Single,13,182.9690787,0,8927.453935
124116,65,45,4,0,Single,11,182.8816086,0,8565.08043
148672,61,44,2,0,Single,9,182.8579739,0,8808.898695
136641,20,3,3,1,Married,11,182.8532659,0,8905.663295
180762,27,6,4,0,Married,11,182.8311436,0,8779.55718
190391,53,35,2,0,Single,12,182.8276235,0,8780.381175
103030,24,6,1,1,Married,12,182.8039805,0,8902.199025
182981,68,50,4,0,Single,7,182.6635195,0,8666.175975
107621,59,42,3,1,Married,15,182.6407921,0,8763.039605
187738,42,21,1,0,Married,11,182.4160381,0,8887.801905
196691,64,44,3,1,Married,13,182.3851447,0,8818.257235
125475,53,32,4,1,Married,11,182.3320966,0,8566.60483
147211,48,28,4,1,Married,13,182.3174409,0,8709.872045
164014,38,20,2,1,Married,14,182.2446094,0,8593.23047
115043,67,50,4,0,Married,0,182.2331212,0,8704.65606
185977,69,51,3,1,Single,3,182.1959742,0,8665.79871
134228,66,47,2,0,Married,14,182.1895769,0,8726.478845
123108,37,19,4,0,Married,11,182.180109,0,8593.00545
196097,64,43,2,0,Single,15,182.1604591,0,8509.022955
148352,33,16,2,1,Single,12,182.0703443,0,8671.517215
159633,60,40,3,0,Married,3,182.021842,0,8842.0921
183366,52,33,1,1,Married,11,181.8773342,0,8507.86671
193481,68,49,3,0,Single,7,181.7617628,0,8806.08814
190939,18,1,1,1,Married,14,181.7398301,0,8650.991505
180588,69,49,2,1,Single,0,181.6719283,0,8759.596415
142328,23,4,4,0,Married,11,181.6667072,0,8819.33536
172038,35,14,4,1,Married,14,181.6477538,0,8566.38769
127183,67,48,3,1,Married,2,181.6005884,0,8493.02942
119752,68,47,3,1,Single,10,181.5693741,0,8720.468705
173587,23,4,3,0,Married,12,181.556529,0,8524.82645
100073,51,30,4,0,Married,12,181.5434522,0,8575.17261
104695,57,38,4,0,Single,12,181.4862783,0,8633.313915
113798,63,45,2,0,Married,0,181.4507002,0,8672.53501
181071,16,0,4,1,Single,14,181.4341208,0,8492.70604
108745,70,49,2,1,Married,15,181.4338152,0,8521.69076
155731,69,52,3,0,Single,15,181.3884095,0,8667.420475
106539,31,10,3,0,Married,13,181.3666447,0,8705.332235
198110,69,49,2,0,Married,15,181.2225401,0,8571.127005
168954,24,7,3,1,Married,13,181.1828705,0,8661.143525
170144,20,1,2,1,Married,11,181.1133935,0,8675.669675
142508,62,41,2,1,Married,9,181.0643817,0,8520.219085
119614,62,44,2,1,Single,10,180.8560733,0,8745.803665
104766,16,0,2,1,Married,11,180.7581336,0,8793.90668
165470,32,12,2,0,Married,14,180.7486757,0,8532.433785
135635,70,52,2,0,Married,11,180.7449177,0,8626.245885
101748,62,45,2,0,Married,9,180.6887643,0,8675.438215
101937,40,19,1,1,Single,13,180.5799829,0,8786.999145
107464,31,12,2,1,Married,14,180.4446841,0,8756.234205
173551,70,53,1,1,Married,11,180.3930764,0,8423.65382
183076,62,42,2,0,Single,15,180.3825103,0,8749.125515
177790,66,46,2,0,Married,10,180.3764038,0,8782.82019
150326,63,45,4,0,Married,10,180.3697143,0,8758.485715
155233,36,19,3,0,Single,11,180.3267926,0,8425.33963
112858,16,0,2,1,Married,11,180.3062395,0,8510.311975
169171,25,7,1,0,Married,14,180.2839243,0,8506.196215
152003,56,37,2,0,Married,13,180.2726827,0,8785.634135
193413,20,2,3,1,Single,14,180.211505,0,8680.57525
125542,24,7,4,1,Married,15,180.1656024,0,8499.28012
166908,39,19,4,1,Married,14,180.1626966,0,8501.13483
146463,30,10,3,0,Single,12,180.1317101,0,8737.585505
163356,63,45,2,1,Married,12,180.0690882,0,8444.45441
149654,21,4,1,1,Married,11,180.0366847,0,8685.834235
112045,30,12,2,0,Married,11,180.0011804,0,8541.05902
125497,70,52,3,1,Married,11,179.9030368,0,8551.15184
190275,61,44,3,0,Single,3,179.8658272,0,8500.29136
187857,70,49,1,0,Single,3,179.8226162,0,8690.13081
117338,68,47,4,1,Married,10,179.8111318,0,8788.55659
183039,61,41,2,1,Married,4,179.7751336,0,8694.75668
169793,18,0,2,1,Married,12,179.7314757,0,8429.573785
104256,64,43,3,0,Married,8,179.7186989,0,8532.934945
180044,25,7,4,1,Married,13,179.6525692,0,8527.62846
103401,29,9,3,1,Single,12,179.6450386,0,8442.25193
147462,67,48,2,0,Single,7,179.5737097,0,8460.685485
177175,45,28,3,0,Single,14,179.5417467,0,8565.087335
145644,53,36,2,0,Single,15,179.5108887,0,8557.544435
175460,41,22,4,1,Married,13,179.50327,0,8644.1635
178552,69,50,4,0,Single,14,179.4037183,0,8506.185915
191023,31,13,3,1,Married,15,179.3982397,0,8372.911985
112351,20,2,2,1,Single,13,179.3510569,0,8739.552845
163841,69,52,2,1,Married,4,179.2152555,0,8378.762775
114224,33,14,3,0,Married,12,179.1852303,0,8501.261515
131422,49,32,4,0,Married,12,179.1359401,0,8392.797005
106485,64,45,2,0,Single,7,179.1332926,0,8586.66463
170878,20,1,1,1,Married,12,179.1249378,0,8707.24689
148374,28,10,1,1,Married,15,179.0536986,0,8448.68493
198754,61,43,1,0,Married,1,178.9945855,0,8383.729275
194309,49,29,2,0,Married,12,178.9903899,0,8536.519495
144854,46,25,4,0,Single,14,178.9685786,0,8684.42893
133804,70,53,4,1,Married,11,178.943418,0,8531.1709
189068,69,52,3,0,Single,11,178.9169669,0,8632.848345
134521,69,48,2,0,Single,9,178.9047923,0,8673.239615
149404,40,22,2,0,Single,13,178.8576714,0,8342.88357
190427,46,25,4,1,Single,13,178.8078918,0,8575.39459
195142,62,41,2,1,Single,1,178.7833904,0,8459.16952
134240,63,46,3,1,Married,6,178.7704606,0,8485.52303
143550,61,43,2,0,Single,0,178.7270613,0,8662.353065
104617,67,50,4,1,Married,8,178.7059053,0,8562.295265
175351,24,5,3,1,Married,13,178.7058171,0,8457.290855
103876,61,42,3,0,Married,12,178.6744353,0,8482.721765
195418,19,1,2,0,Married,11,178.5321406,0,8390.60703
109548,41,23,2,0,Married,11,178.4860222,0,8539.30111
120642,68,47,4,1,Single,9,178.4785918,0,8551.92959
176774,69,48,3,1,Married,7,178.4709609,0,8552.548045
181720,32,12,1,0,Married,15,178.4623223,0,8334.116115
178471,62,44,2,0,Single,8,178.3216863,0,8520.084315
176918,20,1,3,0,Married,11,178.2315351,0,8336.576755
117770,20,2,1,1,Married,12,178.225309,0,8420.26545
145636,18,1,2,1,Married,14,178.1916351,0,8520.581755
145616,27,10,1,0,Single,15,178.1544544,0,8535.72272
136575,61,41,3,1,Married,3,178.1435445,0,8674.177225
145560,60,41,1,1,Single,4,178.1144493,0,8435.722465
115335,26,6,2,1,Married,15,178.0410823,0,8305.054115
108401,65,48,3,0,Single,8,178.0224268,0,8504.12134
129821,46,26,1,1,Single,13,178.0214425,0,8426.072125
187138,22,5,3,0,Married,14,177.998881,0,8468.94405
100467,68,51,2,1,Single,8,177.9324401,0,8496.622005
157713,49,28,3,0,Single,13,177.9025636,0,8438.12818
196112,47,26,4,1,Single,12,177.900394,0,8665.0197
151780,61,41,2,1,Single,6,177.7606627,0,8370.033135
163114,63,42,3,0,Married,13,177.7387364,0,8436.93682
118544,35,16,3,1,Married,11,177.7262584,0,8685.31292
109757,22,3,4,1,Married,15,177.6937993,0,8516.689965
195169,64,47,3,1,Married,12,177.6837241,0,8641.186205
158741,63,46,4,1,Single,6,177.6663666,0,8554.31833
119361,16,0,3,1,Married,11,177.5911607,0,8319.558035
116963,29,8,2,0,Married,13,177.4783708,0,8470.91854
162599,19,1,4,1,Married,14,177.4671892,0,8593.35946
171030,65,44,1,1,Married,0,177.3780284,0,8393.90142
119248,22,3,1,1,Married,15,177.3409102,0,8607.04551
133882,18,0,2,1,Married,14,177.2984363,0,8584.921815
167117,62,44,2,1,Single,8,177.0450044,0,8594.25022
192380,23,6,4,1,Married,15,176.9938099,0,8567.690495
152244,42,24,2,1,Single,13,176.9334845,0,8483.674225
168465,25,8,1,1,Married,12,176.8627735,0,8616.138675
127482,61,41,2,0,Single,11,176.6996786,0,8320.98393
149164,64,44,2,1,Married,9,176.5731112,0,8297.65556
160307,25,8,3,1,Married,15,176.5082561,0,8385.412805
126247,64,46,3,0,Married,6,176.4612428,0,8276.06214
139239,18,1,3,1,Married,12,176.4416255,0,8274.081275
150652,18,1,3,1,Married,14,176.3763535,0,8523.817675
158840,67,48,2,0,Married,10,176.3653434,0,8326.26717
178339,62,45,3,0,Married,8,176.3626753,0,8509.133765
183801,61,44,2,0,Single,15,176.3209485,0,8458.047425
195208,69,48,4,1,Married,15,176.2240353,0,8416.201765
194702,57,36,2,1,Married,15,176.084205,0,8458.21025
132193,46,25,3,0,Single,11,176.0687113,0,8235.435565
178927,62,43,2,0,Single,12,176.0482467,0,8484.412335
131605,16,0,3,0,Married,11,176.0440184,0,8592.20092
159056,67,50,3,1,Married,2,175.9963419,0,8412.817095
122932,64,43,2,1,Married,6,175.9839897,0,8299.199485
178247,61,43,2,0,Single,14,175.983661,0,8210.18305
128269,54,36,2,0,Married,15,175.9420376,0,8309.10188
191825,66,49,2,0,Married,13,175.8884718,0,8390.42359
108833,29,12,4,1,Married,11,175.8800322,0,8233.00161
188945,59,38,1,0,Single,15,175.7300736,0,8191.50368
133092,70,51,1,1,Married,7,175.6159266,0,8477.79633
114444,47,28,1,1,Married,14,175.4767629,0,8335.838145
115960,69,49,2,1,Single,3,175.4666117,0,8372.330585
157800,19,0,3,0,Married,14,175.4484132,0,8196.42066
158932,31,13,2,1,Single,15,175.4208202,0,8214.04101
159462,61,40,4,1,Married,11,175.3317811,0,8402.589055
144857,61,44,3,0,Married,7,175.2508525,0,8279.542625
195412,53,34,1,1,Married,12,175.2074655,0,8452.373275
133704,21,4,3,0,Married,12,175.1169078,0,8540.84539
198506,64,47,3,1,Married,12,175.0617314,0,8334.08657
184448,43,26,3,1,Married,12,175.0369842,0,8527.84921
139509,65,47,4,0,Married,1,175.020681,0,8223.03405
171625,67,49,2,1,Married,14,174.9818267,0,8498.091335
182942,19,2,2,1,Married,13,174.9719929,0,8418.599645
183300,65,48,1,1,Married,13,174.8904686,0,8365.52343
155153,28,8,3,1,Married,13,174.7393553,0,8300.967765
136995,65,45,2,1,Married,12,174.7134006,0,8434.67003
176415,68,49,2,1,Married,2,174.6473393,0,8312.366965
181893,59,42,2,1,Single,13,174.6297877,0,8436.489385
192011,28,9,3,0,Married,13,174.6051107,0,8466.255535
119306,66,49,2,0,Single,15,174.5920961,0,8276.604805
196428,66,45,3,1,Married,15,174.5840572,0,8141.20286
103121,70,49,2,0,Single,7,174.5438715,0,8162.193575
170695,63,43,1,0,Married,9,174.5264045,0,8251.320225
161149,70,50,3,0,Single,13,174.5025078,0,8398.12539
151160,68,49,2,1,Married,12,174.5014413,0,8458.072065
167264,66,45,3,0,Single,8,174.4246564,0,8206.23282
170723,49,31,3,1,Single,11,174.3786518,0,8238.93259
140959,68,51,4,1,Married,7,174.324187,0,8474.20935
176831,56,35,1,0,Single,15,174.2884032,0,8376.42016
195559,18,0,2,1,Married,13,173.9147251,0,8144.736255
177077,20,2,4,1,Married,13,173.9120919,0,8225.604595
150766,57,38,2,1,Single,11,173.9017406,0,8143.08703
115225,60,41,3,0,Single,10,173.8695025,0,8309.475125
127578,50,32,2,0,Married,14,173.8582284,0,8200.91142
115905,66,49,2,0,Single,8,173.812546,0,8461.6273
162697,24,6,2,1,Married,13,173.7551777,0,8341.758885
109702,68,51,3,1,Married,10,173.74814,0,8295.407
194539,64,44,2,1,Single,10,173.7304605,0,8229.523025
147120,64,44,4,0,Married,9,173.7223101,0,8147.115505
120277,16,0,3,0,Married,12,173.687322,0,8140.3661
163503,37,20,1,1,Married,13,173.5817214,0,8316.08607
108367,54,33,3,0,Single,14,173.5506182,0,8146.53091
156399,52,33,3,0,Single,15,173.524937,0,8473.24685
136589,62,44,2,1,Single,11,173.4938588,0,8449.69294
149600,61,44,4,0,Single,11,173.467736,0,8134.3868
106922,61,40,3,1,Married,11,173.464338,0,8375.2169
109139,65,44,2,1,Married,7,173.4234717,0,8361.173585
159368,61,40,1,0,Single,9,173.3577025,0,8352.885125
153842,65,44,3,1,Married,9,173.3381687,0,8394.908435
142612,67,46,1,0,Single,2,173.2162688,0,8356.81344
180435,17,0,4,1,Married,13,173.1951407,0,8231.757035
159782,23,4,2,1,Married,15,172.9344638,0,8211.72319
119625,70,53,3,1,Married,5,172.8805566,0,8061.02783
110636,69,50,1,0,Single,6,172.8367227,0,8170.836135
142590,68,50,3,1,Married,3,172.8255983,0,8426.279915
153316,60,42,3,1,Married,12,172.6227493,0,8340.137465
145550,70,52,3,1,Married,7,172.5748663,0,8144.743315
109455,60,39,3,1,Married,5,172.5732732,0,8245.66366
177062,64,45,1,0,Single,11,172.5265908,0,8405.32954
126901,29,9,1,0,Married,15,172.5128998,0,8059.64499
166825,36,16,3,1,Married,11,172.393355,0,8067.66775
156845,63,45,2,0,Single,13,172.3885303,0,8336.426515
190311,69,49,2,1,Single,6,172.3857854,0,8175.28927
127474,61,40,2,1,Married,1,172.3572205,0,8151.861025
182152,52,32,3,1,Married,15,172.3553627,0,8202.768135
163746,43,23,3,0,Married,12,172.3442437,0,8143.212185
165297,57,40,2,0,Single,14,172.2985294,0,8405.92647
152304,68,48,2,0,Married,10,172.1532142,0,8288.66071
173842,66,45,2,1,Married,5,172.1497979,0,8157.489895
151805,62,44,4,1,Married,10,172.0243517,0,8017.217585
187115,60,39,3,0,Married,12,172.0000079,0,8178.000395
117475,67,49,2,0,Single,11,171.9947345,0,8246.736725
181640,65,44,1,1,Married,0,171.9613068,0,8076.06534
161595,66,48,1,0,Married,12,171.9308623,0,8260.543115
174987,63,46,3,0,Single,12,171.8706404,0,8245.53202
196176,64,47,3,0,Married,13,171.7661605,0,8215.308025
122746,45,26,1,1,Married,13,171.7162674,0,8237.81337
161014,58,41,4,0,Married,15,171.6787836,0,8246.93918
172179,40,20,2,1,Married,14,171.6549449,0,8229.747245
172018,70,51,3,0,Married,1,171.6544023,0,7992.720115
198435,69,52,4,1,Single,5,171.6213587,0,8291.067935
193055,52,35,2,0,Single,12,171.6202607,0,7998.013035
107832,67,48,3,1,Married,6,171.4715648,0,8090.57824
161519,16,0,1,1,Single,12,171.3772645,0,8183.863225
161439,67,50,2,1,Married,4,171.2743369,0,8279.716845
182077,26,6,2,0,Single,12,171.2690932,0,8259.45466
110665,55,37,3,0,Married,15,171.2677656,0,7996.38828
174525,69,52,4,1,Married,1,171.1947519,0,8123.737595
155733,69,49,2,1,Married,5,171.1449136,0,8236.24568
109762,25,6,1,1,Married,15,171.1116362,0,8241.58181
112065,24,6,2,1,Married,14,171.0885338,0,8279.42669
190382,65,45,1,0,Single,6,171.0101059,0,8064.505295
162461,66,45,3,1,Single,15,170.9627664,0,8277.13832
111249,22,4,1,1,Single,13,170.9577581,0,8188.887905
152550,67,50,1,0,Married,11,170.9532587,0,7953.662935
152612,66,47,3,1,Married,2,170.914122,0,8002.7061
117470,70,49,2,1,Married,4,170.8973382,0,8314.86691
115060,29,12,4,0,Single,14,170.865496,0,8139.2748
111541,21,3,1,0,Married,13,170.8080013,0,8193.400065
184656,63,46,3,1,Married,15,170.6766187,0,8213.830935
101550,63,46,2,0,Single,11,170.6091203,0,8033.456015
187619,63,45,2,1,Single,6,170.6039685,0,8180.198425
139096,21,4,2,1,Married,12,170.4990327,0,8320.951635
174333,69,49,2,0,Married,14,170.4699518,0,8004.49759
198245,59,42,4,0,Married,15,170.3543122,0,7992.71561
192464,66,47,3,0,Married,10,170.2748571,0,8107.742855
147601,67,47,3,1,Single,14,170.1828617,0,7918.143085
183887,64,46,2,0,Married,5,170.0679613,0,8276.398065
153809,66,48,2,0,Married,7,170.0476499,0,8083.382495
162229,69,52,4,1,Single,6,170.0445035,0,8001.225175
139409,34,16,3,0,Single,14,170.003943,0,7965.19715
115945,40,19,2,1,Married,11,169.938668,0,7909.9334
170018,41,22,2,1,Single,14,169.9292973,0,8196.464865
194420,44,27,2,1,Married,13,169.9274906,0,8011.37453
111978,68,47,4,1,Married,2,169.9171958,0,8082.85979
196800,16,0,1,0,Married,15,169.8362524,0,8270.81262
127009,60,39,2,1,Single,1,169.8270482,0,8111.35241
103922,41,21,1,0,Single,15,169.8042272,0,7914.21136
111270,16,0,2,1,Married,12,169.6975258,0,8141.87629
153115,56,37,4,1,Married,15,169.6628918,0,8025.14459
142200,67,47,3,0,Married,2,169.6031021,0,8221.155105
163998,39,19,3,1,Married,11,169.5110743,0,8077.553715
153977,21,2,2,1,Married,13,169.5087022,0,8113.43511
196401,69,49,1,0,Married,0,169.4418198,0,7913.09099
104183,33,16,1,0,Single,13,169.3764585,0,8005.822925
127716,21,2,2,0,Married,14,169.3389215,0,7910.946075
147554,18,0,2,1,Married,12,169.3143782,0,8159.71891
125272,17,0,1,1,Married,14,169.3142533,0,8243.712665
105150,61,42,3,1,Married,8,169.295741,0,8255.78705
127574,22,3,4,0,Married,14,169.2278782,0,8180.39391
189808,62,44,1,0,Single,15,169.0810077,0,7948.050385
117158,66,46,1,0,Single,13,169.0761351,0,8156.806755
178218,39,21,3,1,Married,15,168.9975863,0,8196.879315
102871,61,42,4,1,Married,7,168.9545003,0,8219.725015
106838,67,49,1,1,Married,15,168.9340361,0,8067.701805
144417,62,45,3,1,Single,15,168.9206484,0,8111.03242
171632,70,51,2,0,Single,7,168.8770652,0,8124.85326
134101,25,8,3,1,Married,12,168.8458668,0,8097.29334
148338,43,25,4,1,Single,14,168.7608246,0,7998.04123
194966,50,30,3,0,Single,11,168.7528974,0,8184.64487
188771,64,46,2,0,Married,3,168.7403015,0,7899.015075
116743,43,24,3,0,Married,14,168.7395333,0,7844.976665
173600,70,50,2,1,Single,12,168.6027365,0,8041.136825
104383,24,5,3,1,Married,14,168.5848448,0,8095.24224
146163,23,4,3,1,Married,11,168.507276,0,7956.3638
117941,45,27,2,0,Single,14,168.4427657,0,8073.138285
110113,45,26,4,1,Single,13,168.3363531,0,8159.817655
142812,23,5,2,1,Married,11,168.1472026,0,8193.36013
188043,68,47,2,1,Married,0,168.1164938,0,7821.82469
156509,64,46,2,0,Married,12,168.0988202,0,7836.94101
173994,22,5,2,1,Married,13,168.0063452,0,8164.31726
157712,17,0,1,0,Married,11,167.9065824,0,8143.32912
192742,35,17,1,0,Married,12,167.8386883,0,8058.934415
155813,65,45,2,1,Married,7,167.7461765,0,7885.308825
178345,63,42,2,0,Married,6,167.7456981,0,8173.284905
102581,50,32,3,0,Single,14,167.7291226,0,7788.45613
121909,66,47,2,1,Married,6,167.7100834,0,7852.50417
149172,65,46,3,1,Single,14,167.6625428,0,7817.12714
181062,17,0,4,1,Married,11,167.6206261,0,7815.031305
173200,35,18,2,0,Single,15,167.6070249,0,8104.351245
107066,49,30,3,1,Single,13,167.5979735,0,7929.898675
104652,55,38,2,1,Single,14,167.5431667,0,8048.158335
127828,62,45,1,1,Married,4,167.5425068,0,8016.12534
153469,61,40,2,1,Married,11,167.4991317,0,7918.956585
186321,36,18,2,0,Married,12,167.4946277,0,7949.731385
163062,48,29,2,0,Married,12,167.3977343,0,7775.886715
174210,62,44,4,1,Single,1,167.3491294,0,7960.45647
148886,67,47,1,1,Married,12,167.3008064,0,8070.04032
147243,63,43,3,0,Married,5,167.2330632,0,7906.65316
108673,62,45,1,0,Married,9,167.2173799,0,7995.868995
102493,65,48,1,1,Married,1,167.1251828,0,8016.25914
122529,65,44,3,1,Married,9,167.106551,0,8024.32755
106595,68,50,2,1,Married,10,167.0878234,0,7963.39117
105601,67,50,1,0,Single,10,167.0764612,0,7874.82306
115497,69,48,4,0,Married,11,167.0242795,0,8075.213975
152397,39,18,2,1,Single,13,166.9814039,0,7786.070195
189812,64,43,3,1,Married,6,166.9618452,0,8095.09226
118599,61,40,2,1,Married,11,166.9536987,0,7895.684935
195891,23,6,2,1,Single,12,166.9503447,0,7885.517235
132734,42,25,3,0,Single,15,166.9423064,0,8046.11532
100281,20,3,1,0,Married,11,166.9365543,0,7930.827715
168274,37,18,1,0,Single,12,166.9310997,0,7837.554985
134189,24,7,1,1,Married,12,166.8433273,0,7927.166365
110684,26,9,3,1,Single,12,166.793992,0,7950.6996
182034,19,2,3,1,Married,11,166.7179783,0,7834.898915
163002,41,21,2,0,Single,11,166.691093,0,7758.55465
187328,53,35,1,1,Single,13,166.6465849,0,7754.329245
174204,67,46,4,1,Single,3,166.635409,0,8120.77045
112397,63,45,3,1,Married,8,166.6288805,0,8069.444025
134274,29,8,2,1,Married,14,166.6050216,0,7947.25108
194673,70,49,4,0,Single,12,166.5562894,0,7815.81447
151108,20,2,1,1,Married,11,166.5373225,0,7985.866125
133003,62,44,4,0,Single,13,166.501947,0,7770.09735
184684,45,25,3,1,Married,12,166.4743347,0,7934.716735
112823,31,11,4,0,Married,12,166.4256636,0,8053.28318
172056,33,16,3,1,Married,15,166.4127054,0,7983.63527
171373,67,50,1,0,Married,10,166.3963464,0,7803.81732
193445,47,27,3,0,Married,14,166.3781897,0,8016.909485
168060,25,6,2,1,Married,13,166.2965953,0,7725.829765
122621,68,50,2,1,Married,12,166.2718467,0,7722.592335
109898,16,0,3,1,Married,11,166.2082968,0,7973.41484
170345,41,22,4,1,Single,12,166.1953416,0,7847.76708
136140,16,0,3,1,Married,11,166.1846197,0,7886.230985
142345,22,3,2,1,Married,15,166.1769046,0,8028.84523
193822,53,33,3,1,Married,14,166.1118462,0,7940.59231
175839,55,36,3,0,Single,11,166.1093996,0,7818.46998
171410,17,0,2,1,Married,13,166.057532,0,7938.8766
104692,42,24,4,1,Married,12,165.9889146,0,7934.44573
124781,60,39,1,1,Single,10,165.9840948,0,7940.20474
175670,51,33,2,1,Single,14,165.9665931,0,7723.329655
124286,25,6,3,1,Married,13,165.8405147,0,7943.025735
158126,37,18,3,1,Single,14,165.7243499,0,7832.217495
186511,22,5,3,0,Married,14,165.7234505,0,7971.172525
135624,47,27,3,0,Married,11,165.7161892,0,8057.80946
163581,69,51,4,0,Single,9,165.7078246,0,7754.39123
105910,43,23,4,1,Single,15,165.679963,0,7929.99815
179121,67,48,1,1,Married,14,165.6764223,0,7854.821115
160393,62,43,3,1,Single,5,165.6664059,0,7733.320295
189401,61,43,2,1,Single,12,165.6591786,0,7736.95893
188208,21,2,2,1,Married,15,165.62476,0,7985.238
130239,49,32,4,0,Married,11,165.6106937,0,7894.534685
180015,49,31,1,0,Single,12,165.6043454,0,7958.21727
106044,28,8,4,0,Single,11,165.5921294,0,7789.60647
136102,38,18,3,0,Married,11,165.5710814,0,7946.55407
177585,18,1,1,1,Married,12,165.552408,0,8037.6204
142074,19,1,3,1,Single,12,165.5362405,0,7773.812025
113880,43,24,3,1,Married,14,165.5358994,0,7863.79497
186539,23,6,2,1,Married,14,165.4759298,0,8001.79649
167773,68,47,3,0,Single,7,165.4354244,0,7929.77122
113506,61,44,2,1,Married,5,165.3805781,0,7698.028905
194881,17,0,4,1,Married,15,165.3738542,0,7713.69271
124306,63,44,3,1,Married,10,165.2589847,0,7729.949235
187965,49,30,3,1,Single,14,165.2570255,0,7821.851275
183153,62,44,4,0,Single,2,165.1976018,0,7877.88009
136908,57,39,3,0,Married,12,165.1423676,0,7932.11838
173656,60,39,2,1,Single,14,165.0434016,0,7873.17008
113400,64,43,1,0,Single,0,164.9924266,0,7941.62133
163352,65,44,2,0,Single,2,164.9241647,0,8023.208235
153400,69,52,4,1,Single,8,164.9225449,0,7769.127245
135472,41,22,2,1,Married,15,164.8625721,0,7662.128605
160639,68,50,2,0,Married,11,164.8240283,0,7799.201415
199766,19,2,2,1,Married,11,164.8102033,0,7824.510165
136723,69,50,3,1,Single,1,164.7885931,0,7759.429655
168535,50,32,2,1,Married,11,164.7838719,0,7722.193595
195834,66,45,4,0,Married,9,164.7761872,0,7773.80936
157379,51,31,4,0,Single,13,164.7506686,0,7723.53343
101747,27,10,3,1,Single,13,164.717481,0,7820.87405
124281,46,28,2,1,Single,12,164.6822683,0,7732.113415
187457,26,9,3,0,Married,12,164.6698998,0,7703.49499
116936,24,6,3,1,Single,13,164.5682304,0,7772.41152
193993,18,0,2,0,Married,15,164.5270118,0,8009.35059
116031,24,5,3,1,Married,11,164.4273179,0,7633.365895
123461,20,2,1,1,Married,12,164.3226813,0,8007.134065
194448,60,43,4,1,Married,10,164.2509223,0,7929.546115
180713,70,51,3,1,Single,10,164.1767484,0,7960.83742
136674,41,22,2,1,Married,14,164.1363746,0,7951.81873
143596,68,51,1,0,Married,3,164.0909204,0,7691.54602
139692,68,49,3,1,Married,9,163.9798518,0,7716.99259
197334,51,34,3,1,Married,11,163.9311136,0,7639.55568
133359,54,33,2,0,Single,12,163.907041,0,7907.35205
133010,43,22,3,0,Married,11,163.9059855,0,7721.299275
163137,67,48,3,0,Married,10,163.8980662,0,7690.90331
121740,60,43,3,0,Married,0,163.8877602,0,7881.38801
160369,67,48,4,0,Married,9,163.8689268,0,7760.44634
187338,19,1,3,0,Married,12,163.8360284,0,7749.80142
102772,23,6,4,1,Married,14,163.7824754,0,7876.12377
179195,18,0,3,1,Married,12,163.7183516,0,7591.91758
150600,61,43,2,0,Single,12,163.6062869,0,7756.314345
106322,17,0,2,1,Married,14,163.4888262,0,7888.44131
195165,67,47,4,1,Single,7,163.1749036,0,7834.74518
117465,56,36,3,1,Single,14,163.0612602,0,7863.06301
119505,69,49,3,1,Married,11,162.9468688,0,7926.34344
192817,44,27,3,1,Single,11,162.9210878,0,7793.05439
151779,70,52,3,0,Single,12,162.8552724,0,7836.76362
169103,69,49,4,1,Married,7,162.843711,0,7772.18555
109752,45,27,2,1,Married,11,162.6375943,0,7679.879715
146052,68,49,3,1,Single,9,162.6360826,0,7770.80413
181811,60,41,2,0,Single,7,162.5575411,0,7673.877055
175469,70,52,1,0,Married,15,162.5111147,0,7910.555735
164569,37,18,3,1,Single,15,162.4271008,0,7792.35504
163118,48,30,2,1,Married,13,162.4063403,0,7743.317015
188455,54,34,3,1,Single,14,162.3829021,0,7723.145105
138553,60,42,1,0,Married,13,162.3570971,0,7588.854855
107405,35,15,1,0,Single,14,162.1912987,0,7893.564935
187079,23,4,4,1,Married,15,162.1769788,0,7643.84894
109954,48,29,2,0,Married,14,162.1766053,0,7616.830265
132545,67,46,1,1,Married,5,162.0948232,0,7798.74116
140592,21,4,3,0,Married,13,162.0895478,0,7575.47739
103809,61,41,3,1,Married,10,162.04085,0,7525.0425
124865,67,48,1,1,Married,0,162.021823,0,7748.09115
129655,69,50,1,0,Single,15,161.9476996,0,7527.38498
179665,63,44,3,0,Married,7,161.9351747,0,7781.758735
111646,60,42,1,0,Married,13,161.9238698,0,7685.19349
174642,60,42,3,1,Married,1,161.7843451,0,7886.217255
168648,42,21,2,1,Single,11,161.718722,0,7549.9361
198392,67,47,4,1,Married,11,161.6531642,0,7595.65821
120056,61,40,4,1,Married,13,161.6488695,0,7793.443475
197835,69,48,2,1,Married,2,161.6239766,0,7671.19883
170242,41,23,3,0,Married,11,161.5843569,0,7744.217845
173214,20,1,1,1,Married,13,161.5795211,0,7831.976055
102591,69,51,2,0,Married,11,161.4997804,0,7852.98902
115899,69,51,1,0,Single,12,161.4972836,0,7850.86418
148492,16,0,3,0,Married,11,161.229285,0,7668.46425
132989,18,0,2,1,Married,12,161.2216649,0,7580.083245
176775,27,8,1,1,Married,15,161.1440266,0,7503.20133
140483,62,42,2,1,Single,12,161.0879016,0,7680.39508
144420,43,26,2,1,Single,15,161.068729,0,7664.43645
138566,67,49,1,0,Single,13,161.0657087,0,7495.285435
185770,53,32,2,0,Married,11,161.021328,0,7512.0664
176917,49,29,2,1,Married,15,161.0206519,0,7582.032595
166214,65,47,4,0,Single,2,161.0202216,0,7836.01108
162185,64,44,3,1,Married,9,160.9837229,0,7836.186145
110177,23,5,3,1,Married,11,160.8956892,0,7695.78446
127004,64,45,2,1,Single,10,160.6994065,0,7530.970325
161005,61,43,2,0,Single,7,160.655388,0,7504.7694
155004,65,46,2,1,Married,0,160.5910585,0,7675.552925
113260,67,50,2,0,Married,1,160.5817889,0,7453.089445
198197,25,6,4,1,Married,15,160.5397728,0,7478.98864
174916,69,50,2,1,Single,6,160.4764846,0,7681.82423
116978,64,44,4,0,Married,3,160.4725396,0,7636.62698
153590,63,46,2,1,Single,8,160.393469,0,7707.67345
136609,63,42,1,1,Married,7,160.2682984,0,7483.41492
114029,68,51,2,0,Married,15,160.226286,0,7565.3143
194432,56,38,3,0,Married,12,160.0331819,0,7663.659095
117382,47,27,2,1,Married,11,159.9424657,0,7742.123285
177864,16,0,2,0,Married,11,159.8900168,0,7791.50084
148403,70,49,3,1,Married,11,159.7829766,0,7527.14883
148442,46,26,3,0,Married,13,159.7145116,0,7405.72558
171130,69,49,2,1,Single,15,159.6179518,0,7530.89759
107640,55,36,4,1,Married,14,159.5991093,0,7657.955465
121722,70,49,3,1,Single,8,159.5136816,0,7460.68408
170934,40,22,1,0,Married,13,159.4855752,0,7418.27876
107023,61,41,1,0,Married,9,159.4451332,0,7713.25666
123001,16,0,3,1,Married,13,159.4404343,0,7529.021715
184138,48,29,2,1,Single,11,159.3969869,0,7644.849345
160508,28,11,1,1,Married,14,159.3673423,0,7393.367115
197104,56,39,3,1,Married,14,159.3118041,0,7552.590205
114170,18,0,1,1,Married,12,159.3039355,0,7668.196775
171559,68,50,1,0,Married,0,159.269216,0,7646.4608
125396,68,48,1,0,Single,10,159.1670464,0,7584.35232
170150,53,33,1,0,Married,11,159.1531007,0,7417.655035
155572,65,44,3,0,Single,12,159.0713667,0,7476.568335
169561,18,0,4,1,Married,12,158.9961809,0,7374.809045
122380,61,43,3,1,Married,2,158.9756726,0,7510.78363
182797,66,47,2,0,Married,15,158.9727499,0,7400.637495
130070,18,1,4,1,Married,14,158.9726986,0,7370.63493
154849,70,53,1,1,Single,9,158.83813,0,7475.9065
155676,66,46,3,1,Single,6,158.835418,0,7520.7709
174985,69,48,3,0,Single,7,158.6079375,0,7614.396875
131588,61,43,2,1,Married,6,158.5958011,0,7520.790055
178435,35,17,3,1,Married,13,158.5750712,0,7573.75356
187544,46,26,2,0,Married,14,158.5565828,0,7723.82914
118015,65,47,3,0,Single,10,158.4413402,0,7670.06701
141262,18,0,2,1,Married,14,158.3725387,0,7613.626935
122584,59,38,1,1,Married,15,158.3517998,0,7483.58999
102619,69,48,2,1,Single,12,158.2984678,0,7666.92339
150080,63,42,3,1,Single,0,158.1009031,0,7365.045155
152876,18,0,3,1,Married,14,158.0440421,0,7354.202105
139879,22,5,1,0,Married,14,157.9683334,0,7397.41667
132978,60,40,3,0,Single,11,157.9560016,0,7534.80008
165149,70,53,2,0,Married,8,157.8844554,0,7458.22277
137161,61,44,3,0,Married,9,157.8614352,0,7369.07176
100526,69,48,4,1,Married,9,157.8040424,0,7662.20212
125441,66,48,2,0,Single,7,157.7540557,0,7459.702785
194939,70,50,2,1,Single,7,157.7522036,0,7586.61018
148945,64,43,2,0,Single,13,157.6960164,0,7644.80082
145208,64,46,3,0,Single,9,157.4779452,0,7633.89726
163645,30,13,3,1,Married,11,157.4228629,0,7398.143145
188199,60,41,4,1,Single,6,157.3960444,0,7318.80222
189185,23,6,3,1,Married,15,157.395913,0,7607.79565
155201,67,48,2,0,Single,10,157.375481,0,7570.77405
121376,70,52,1,0,Married,15,157.3571743,0,7527.858715
156780,67,49,1,1,Single,14,157.3049769,0,7589.248845
174611,64,43,2,0,Single,14,157.256396,0,7304.8198
198468,32,15,3,0,Married,13,157.1967172,0,7418.83586
199708,61,40,3,1,Single,8,157.1944975,0,7592.724875
132412,65,48,3,0,Single,3,157.119858,0,7397.9929
161838,49,28,1,0,Married,15,157.064147,0,7536.20735
126128,66,48,2,1,Married,13,157.0376403,0,7554.882015
124776,46,28,3,0,Single,11,157.0372217,0,7380.861085
100462,60,39,3,0,Single,14,157.0194721,0,7251.973605
125744,28,9,2,1,Single,12,156.9877317,0,7300.386585
152105,63,45,3,1,Married,6,156.9771093,0,7471.855465
171431,39,20,2,1,Married,12,156.9707169,0,7577.535845
148214,70,51,3,1,Married,7,156.8181207,0,7327.906035
186340,63,42,3,0,Single,8,156.799638,0,7422.9819
154125,59,42,2,1,Married,12,156.7735468,0,7603.67734
165214,70,53,1,0,Married,0,156.7486616,0,7271.43308
106829,54,35,3,1,Single,14,156.7126802,0,7342.63401
112321,69,51,2,1,Married,4,156.703677,0,7289.18385
135522,62,43,1,1,Single,9,156.6812854,0,7329.06427
142500,20,3,1,1,Single,14,156.6693023,0,7475.465115
198926,20,1,3,1,Married,14,156.6293566,0,7232.46783
191302,68,48,2,0,Single,9,156.5890344,0,7443.45172
180041,68,51,3,0,Married,1,156.5496923,0,7464.484615
177486,29,10,2,0,Married,12,156.545439,0,7411.27195
165076,68,49,3,1,Single,13,156.5367839,0,7623.839195
116469,61,41,2,1,Married,7,156.5355798,0,7444.77899
185584,39,21,1,1,Married,13,156.5256279,0,7370.281395
161753,17,0,3,1,Married,12,156.3794748,0,7481.97374
119548,63,46,3,0,Single,4,156.3483341,0,7591.416705
111879,41,24,2,0,Married,11,156.1142015,0,7533.710075
101976,70,53,1,1,Single,10,156.106545,0,7225.32725
146898,62,42,3,0,Single,8,156.103407,0,7254.17035
187832,65,44,2,1,Married,11,156.075533,0,7270.77665
159482,57,39,3,1,Married,14,156.0504168,0,7541.52084
137119,21,3,3,0,Married,15,156.0473375,0,7283.366875
109661,46,27,3,0,Married,14,156.0179153,0,7501.895765
112786,47,30,1,0,Single,15,155.9301797,0,7420.508985
126485,65,45,1,1,Married,6,155.8039596,0,7402.19798
112285,54,36,2,1,Married,12,155.7420185,0,7250.100925
128861,68,49,1,1,Married,9,155.7208447,0,7585.042235
193125,63,46,4,1,Single,12,155.7034451,0,7496.172255
109204,20,2,3,1,Married,11,155.7032293,0,7370.161465
191823,44,25,3,1,Married,14,155.6652916,0,7465.26458
148935,65,47,3,0,Single,6,155.5908324,0,7549.54162
199250,69,51,3,0,Single,13,155.5790104,0,7314.95052
133864,24,6,2,0,Married,15,155.5642248,0,7216.21124
137592,66,47,1,1,Single,12,155.5223241,0,7302.116205
102090,66,49,2,1,Single,6,155.4935516,0,7252.67758
110303,23,6,3,1,Married,14,155.4213447,0,7524.067235
169329,68,51,3,0,Single,3,155.4189571,0,7474.947855
189286,18,0,2,1,Married,13,155.371894,0,7523.5947
145072,30,9,2,1,Married,11,155.2510028,0,7522.55014
194050,60,40,2,1,Married,13,155.1971736,0,7510.85868
125738,64,47,2,0,Married,1,155.1176981,0,7476.884905
152542,68,50,2,0,Married,12,154.8628105,0,7247.140525
135972,21,4,3,1,Married,14,154.8437996,0,7538.18998
117381,70,53,2,1,Married,12,154.7307542,0,7360.53771
177053,70,53,4,0,Single,5,154.5567899,0,7286.839495
140940,18,0,3,1,Married,15,154.4916574,0,7432.58287
194922,68,51,3,1,Married,6,154.4412287,0,7287.061435
168400,44,27,2,0,Married,11,154.3197842,0,7233.98921
189683,62,43,2,0,Married,0,154.3180839,0,7125.904195
101316,17,0,2,1,Married,15,154.2538357,0,7252.691785
190055,16,0,3,1,Married,11,154.2308605,0,7333.543025
108988,19,0,3,1,Married,13,154.199217,0,7447.96085
132573,68,50,4,0,Single,8,154.1986373,0,7151.931865
143462,28,8,1,0,Married,14,154.1620196,0,7470.10098
104303,70,52,2,0,Married,7,154.1280993,0,7455.404965
141310,34,16,3,1,Married,14,153.9872357,0,7331.361785
183081,68,50,1,1,Married,6,153.9380961,0,7399.904805
177535,22,5,2,1,Married,12,153.8491847,0,7151.459235
198193,63,44,1,1,Single,0,153.8388035,0,7115.940175
194121,66,45,2,1,Married,8,153.7567657,0,7348.838285
150790,70,50,1,0,Married,5,153.752197,0,7088.60985
198363,21,3,2,1,Married,14,153.7438074,0,7340.19037
196394,27,7,2,1,Married,14,153.7026157,0,7451.130785
157760,26,9,2,0,Married,15,153.6713242,0,7339.56621
145440,39,22,1,0,Married,14,153.61885,0,7153.9425
141582,62,41,3,0,Single,12,153.5149829,0,7285.749145
119952,41,20,3,1,Married,13,153.4046273,0,7377.231365
182664,61,42,3,1,Single,7,153.2983699,0,7178.918495
158152,63,43,1,0,Married,13,153.2763754,0,7211.81877
134444,67,49,4,0,Married,14,153.2302241,0,7414.511205
192469,64,44,4,1,Married,2,153.1984942,0,7379.92471
107757,69,48,2,1,Married,12,153.1905641,0,7104.528205
108903,70,51,2,0,Married,2,153.182301,0,7201.11505
131121,70,53,1,1,Single,14,153.1615919,0,7431.079595
181341,64,46,2,1,Married,9,153.1456789,0,7246.283945
106999,65,45,1,1,Married,4,153.1137653,0,7336.688265
191288,68,48,3,0,Single,7,153.110499,0,7111.52495
186249,66,47,2,0,Single,14,153.0128534,0,7156.64267
185663,42,23,4,1,Single,13,152.877192,0,7153.8596
159832,57,40,3,1,Married,12,152.8377247,0,7285.886235
189047,68,51,1,0,Married,2,152.8376727,0,7246.883635
153308,66,47,2,0,Married,4,152.8192257,0,7052.961285
101331,37,16,3,1,Married,12,152.6981784,0,7160.90892
109540,32,11,3,0,Married,11,152.6720974,0,7308.60487
173154,69,52,1,0,Single,8,152.6024359,0,7360.121795
182783,66,45,4,1,Single,11,152.4554771,0,7319.773855
113320,66,46,2,1,Single,4,152.4283968,0,7413.41984
161908,22,5,4,0,Married,13,152.4110337,0,7313.551685
115139,69,52,1,0,Single,9,152.3553777,0,7042.768885
180770,25,8,3,0,Married,13,152.287053,0,7114.35265
106246,69,48,1,0,Single,13,152.279689,0,7380.98445
140706,66,49,1,0,Married,9,152.1838825,0,7243.194125
133592,64,45,4,1,Married,7,152.1742871,0,7264.714355
107827,64,46,3,0,Married,13,152.1678633,0,7115.393165
134137,61,40,2,1,Single,5,152.1417303,0,7280.086515
159297,60,40,1,0,Married,13,152.0660029,0,7401.300145
164279,57,37,2,0,Single,12,152.0623207,0,7008.116035
142662,69,48,2,0,Married,7,152.0255922,0,7029.27961
125382,64,46,2,1,Married,14,152.0243906,0,7080.21953
107057,60,42,4,1,Single,5,151.9905867,0,7011.529335
103619,69,50,4,1,Single,1,151.9653298,0,7087.26649
174138,59,41,3,0,Married,15,151.8233735,0,7151.168675
182169,63,45,3,1,Single,15,151.7419694,0,7279.09847
174933,66,48,3,1,Married,7,151.6493063,0,6989.465315
195442,42,23,4,1,Married,11,151.6055874,0,7028.27937
187652,29,8,3,1,Single,15,151.5827388,0,7022.13694
104122,25,8,3,0,Married,13,151.5064785,0,7312.323925
106384,37,16,3,1,Married,15,151.5045773,0,7012.228865
126985,69,52,1,1,Married,3,151.4867587,0,7232.337935
158709,56,37,3,0,Married,14,151.4752513,0,7268.762565
155723,51,31,2,1,Married,14,151.429648,0,7046.4824
187281,21,2,3,1,Married,14,151.4183442,0,7343.91721
138897,67,50,3,0,Married,15,151.3785083,0,7053.925415
113399,60,42,1,0,Married,15,151.2964501,0,7216.822505
178596,65,44,1,1,Married,8,151.2848344,0,7229.24172
168283,70,52,2,0,Married,10,151.2817751,0,7241.088755
141654,34,13,1,1,Married,12,151.2591629,0,7143.958145
111950,65,47,2,1,Married,0,151.1795166,0,7049.97583
198489,24,6,3,1,Married,14,151.1045358,0,7234.22679
144963,60,39,3,0,Married,10,151.0873866,0,6955.36933
170930,60,42,2,1,Married,10,150.919353,0,7169.96765
134417,68,50,2,0,Married,3,150.864178,0,7023.2089
113022,64,47,3,0,Single,8,150.8323571,0,7270.617855
124160,64,47,3,0,Single,11,150.7902049,0,6994.510245
158610,69,49,1,1,Married,2,150.6347241,0,6948.736205
182302,25,8,3,1,Married,14,150.6143353,0,7236.716765
109212,61,42,4,1,Single,15,150.5963101,0,7151.815505
173817,41,20,1,1,Single,15,150.5320913,0,7299.604565
156219,66,49,1,1,Married,7,150.5195128,0,7033.97564
187364,46,25,1,1,Married,14,150.4301012,0,7318.50506
109512,41,21,2,1,Single,13,150.4155806,0,6929.77903
152241,62,43,2,1,Married,12,150.3941983,0,7022.709915
104175,25,8,4,1,Single,12,150.236184,0,7184.8092
179962,41,20,2,1,Married,15,150.1916779,0,7125.583895
171301,38,17,1,1,Married,12,150.128075,0,7171.40375
169461,60,43,4,1,Married,13,150.1278153,0,7050.390765
162415,24,5,3,0,Married,11,150.1191536,0,7218.95768
147475,70,50,2,0,Married,12,150.0765561,0,7211.827805
140900,66,47,3,1,Single,14,149.8102519,0,7218.512595
116693,65,47,2,1,Single,13,149.7798516,0,6921.99258
163281,68,51,4,1,Married,11,149.7620467,0,7224.102335
134579,63,44,3,0,Single,12,149.5495403,0,7022.477015
161587,62,43,3,0,Married,2,149.4906992,0,6941.53496
112844,20,1,3,1,Married,14,149.4781184,0,7262.90592
140873,66,48,3,1,Married,15,149.4642395,0,7046.211975
147174,70,50,4,1,Married,11,149.458186,0,7083.9093
153807,61,44,3,1,Single,2,149.4016837,0,7176.084185
123293,26,6,2,0,Single,14,149.3552416,0,7165.76208
173974,29,10,2,1,Single,14,149.3536713,0,7153.683565
167986,61,42,4,1,Married,6,149.3488157,0,7083.440785
142807,67,46,4,0,Married,9,149.3350989,0,7203.754945
128113,65,48,3,0,Married,8,149.3061342,0,7077.30671
102583,64,45,2,1,Single,11,149.2965848,0,7241.82924
112865,67,50,3,1,Married,6,149.2953303,0,6892.766515
119335,53,33,4,1,Single,12,149.2581064,0,6926.90532
111821,64,47,2,1,Married,15,149.1605053,0,7117.025265
146928,22,3,4,1,Single,11,149.0841365,0,7190.206825
178085,69,52,2,0,Married,10,149.0625524,0,6989.12762
166914,65,47,2,1,Married,12,149.0452224,0,6868.26112
141586,66,46,3,1,Married,3,149.0157461,0,7200.787305
119092,44,27,4,0,Single,15,148.9778909,0,7135.894545
145661,23,6,2,1,Married,14,148.9163613,0,6916.818065
110178,65,48,2,1,Married,7,148.8802465,0,6866.012325
110870,64,45,2,0,Married,3,148.8621741,0,7049.108705
112944,61,40,3,1,Married,9,148.8466413,0,7119.332065
150675,61,40,3,0,Married,8,148.7518968,0,7181.59484
104745,68,49,3,0,Single,13,148.6890676,0,7036.45338
136088,63,44,1,1,Married,3,148.6616823,0,7023.084115
148646,24,7,3,1,Married,12,148.6353199,0,6973.765995
110397,70,49,1,1,Married,8,148.5966606,0,6868.83303
100120,60,42,1,0,Single,6,148.5037972,0,6984.18986
160302,67,48,4,0,Married,7,148.4713062,0,7064.56531
123416,27,8,2,1,Married,11,148.4701987,0,7132.509935
172451,51,33,2,1,Married,13,148.3842226,0,7194.21113
123355,20,2,1,1,Married,11,148.3836732,0,7206.18366
108070,17,0,3,1,Married,13,148.3585953,0,6885.929765
124926,57,36,1,0,Married,14,148.2439976,0,6947.19988
158997,67,46,2,1,Married,11,148.2309601,0,7096.548005
103598,60,39,1,1,Married,12,148.1725629,0,7115.628145
137090,64,44,2,0,Single,15,148.1388958,0,7150.94479
129376,60,40,3,1,Single,8,148.1313416,0,7182.56708
159549,65,48,2,0,Single,6,148.1187688,0,6957.93844
194834,66,48,2,1,Married,11,148.0625438,0,6910.12719
181938,68,47,2,0,Married,3,148.0620456,0,7120.10228
170477,19,0,2,1,Married,12,148.0307842,0,7191.53921
145421,50,31,2,1,Single,12,147.9677119,0,6869.385595
121934,62,45,3,1,Married,8,147.9488486,0,7032.44243
110970,60,40,3,1,Single,9,147.9479363,0,6941.396815
191566,69,52,2,0,Married,2,147.9464928,0,7057.32464
153190,63,46,4,1,Single,15,147.9328623,0,7159.643115
147197,24,7,4,1,Married,12,147.8908582,0,7090.54291
114500,65,46,2,0,Single,4,147.8135462,0,6856.67731
168785,26,9,1,1,Single,11,147.7489935,0,6917.449675
165410,65,48,1,1,Married,14,147.7444072,0,6821.22036
103549,16,0,3,1,Married,12,147.6278424,0,6974.39212
153614,68,48,3,1,Married,4,147.4983244,0,7131.91622
162314,70,49,3,0,Married,11,147.4118266,0,6937.59133
182446,52,32,1,0,Married,11,147.3761696,0,7035.80848
128507,34,16,1,0,Married,11,147.2741212,0,6826.70606
112726,27,7,4,1,Single,14,147.2167572,0,6958.83786
152041,20,3,3,1,Single,12,147.2161158,0,6807.80579
180972,65,46,1,1,Single,5,147.1745904,0,7086.72952
126138,27,6,2,0,Married,15,147.0449807,0,7138.249035
126613,61,41,2,0,Married,11,147.0430071,0,6955.150355
200406,57,39,3,0,Married,14,146.9718123,0,7012.590615
154899,66,46,2,1,Single,6,146.9028197,0,6910.140985
136058,63,44,4,0,Single,2,146.8841318,0,7115.20659
182567,47,30,1,0,Married,15,146.8817882,0,6925.08941
180608,53,36,3,1,Married,14,146.8312685,0,6787.563425
147161,66,49,4,0,Single,10,146.8139734,0,7095.69867
163095,60,43,4,1,Married,6,146.7581689,0,6906.908445
121123,66,48,3,0,Married,6,146.6469132,0,7027.34566
195187,65,45,3,1,Single,10,146.6299982,0,7063.49991
178290,66,49,1,0,Single,8,146.5715444,0,6743.57722
154547,67,46,2,1,Single,4,146.3974235,0,6982.871175
129524,68,47,2,1,Single,3,146.3867019,0,6823.335095
126854,25,7,4,1,Married,12,146.3538524,0,6739.69262
182908,18,0,2,1,Married,13,146.1876575,0,7076.382875
120483,38,17,3,0,Married,12,146.1065191,0,6839.325955
123153,61,40,1,1,Married,1,146.0636593,0,6741.182965
190213,55,35,4,1,Married,11,146.0157924,0,7056.78962
129599,69,48,3,1,Married,10,145.9433377,0,7070.166885
167074,43,24,3,1,Single,12,145.9076735,0,6853.383675
106334,35,16,2,1,Single,14,145.8210783,0,6813.053915
132681,62,41,3,0,Single,11,145.8210181,0,7081.050905
159058,70,52,3,1,Married,7,145.6857607,0,7082.288035
108753,36,16,1,1,Married,12,145.6264409,0,6806.322045
104224,18,0,3,1,Married,15,145.4764498,0,6932.82249
136991,17,0,3,0,Married,12,145.3968279,0,6990.841395
170267,68,47,3,1,Single,6,145.3831304,0,6869.15652
176791,20,3,2,1,Married,14,145.380928,0,6877.0464
160041,17,0,3,1,Married,15,145.3607974,0,6803.03987
184449,62,41,3,0,Single,13,145.2461263,0,6755.306315
149269,25,6,3,1,Married,14,145.2109314,0,6771.54657
191504,39,18,3,1,Married,12,145.1942492,0,6729.71246
178830,63,44,3,1,Single,12,145.1676168,0,7006.38084
102484,62,41,4,0,Single,11,145.1097016,0,6754.48508
119148,65,45,2,0,Married,14,145.1000834,0,6672.00417
135391,18,0,3,1,Married,12,145.0555247,0,6897.776235
110371,22,4,3,0,Married,11,145.0265333,0,6886.326665
134510,61,40,4,1,Married,6,145.003963,0,6806.19815
116432,36,17,3,1,Married,15,144.8309384,0,6660.54692
183178,29,11,3,0,Single,14,144.8065927,0,6807.329635
122400,26,5,1,1,Married,13,144.6856321,0,6867.281605
109210,37,16,2,0,Married,13,144.6547725,0,6787.738625
163775,68,50,2,1,Married,7,144.6236185,0,6643.180925
165713,68,49,3,1,Single,10,144.562484,0,6680.1242
126348,49,32,2,1,Married,14,144.5372572,0,6734.86286
195517,69,51,1,1,Married,6,144.507744,0,7009.3872
134422,70,51,2,1,Married,12,144.5022633,0,6831.113165
151757,60,40,2,0,Married,0,144.495086,0,6924.7543
173811,39,20,1,0,Married,15,144.4791732,0,6766.95866
180777,29,10,3,1,Married,11,144.4621015,0,6764.105075
159292,18,0,3,1,Married,12,144.454508,0,6857.7254
112220,19,2,1,1,Married,11,144.4525566,0,6941.62783
137257,66,46,2,1,Single,6,144.4047705,0,6642.238525
190671,27,8,2,1,Single,12,144.3156563,0,6716.782815
149113,18,0,2,1,Married,15,144.2135895,0,6740.679475
142870,36,16,4,1,Single,14,144.1626216,0,6697.13108
196789,70,51,1,1,Married,12,144.1533344,0,6781.66672
120287,62,42,4,1,Married,9,144.1073474,0,6717.36737
137505,65,48,4,0,Single,9,144.0930224,0,6816.65112
133635,38,18,2,1,Married,13,144.07381,0,6776.6905
167479,62,44,2,1,Single,15,143.9763704,0,6931.81852
181189,69,51,2,0,Married,9,143.9417697,0,6762.088485
133968,22,5,3,1,Married,14,143.8981868,0,6833.90934
111443,18,0,2,1,Married,15,143.8874645,0,6768.373225
162790,62,44,3,1,Single,0,143.7954583,0,6922.772915
153623,47,29,2,1,Married,13,143.7807696,0,6610.03848
151572,62,41,3,0,Married,8,143.6831371,0,6714.156855
192953,66,45,4,1,Single,9,143.6529969,0,6711.649845
149463,69,50,2,0,Married,7,143.6121635,0,6672.608175
111386,20,1,2,1,Married,11,143.6071362,0,6588.35681
123117,50,30,3,0,Married,11,143.5985795,0,6917.928975
157530,62,44,2,0,Single,7,143.5748367,0,6851.741835
123583,68,51,2,0,Married,6,143.5538933,0,6848.694665
180397,66,48,1,0,Married,7,143.4630701,0,6848.153505
188409,37,20,1,0,Single,15,143.4336905,0,6948.684525
123164,22,3,2,1,Married,12,143.4011115,0,6924.055575
157558,29,8,3,1,Married,12,143.3820035,0,6944.100175
157692,39,22,4,1,Married,11,143.2968421,0,6830.842105
155587,19,2,2,1,Married,14,143.2132108,0,6825.66054
152422,35,18,2,0,Married,14,143.0972717,0,6866.863585
131523,60,42,4,1,Married,3,143.0938411,0,6898.692055
192687,16,0,2,1,Married,12,143.0806463,0,6744.032315
139086,24,5,3,1,Married,11,143.0501778,0,6849.50889
109770,28,8,3,1,Married,12,143.0281647,0,6553.408235
192232,68,51,3,1,Single,10,142.9617837,0,6666.089185
157804,64,43,3,0,Single,12,142.9188975,0,6663.944875
171367,24,6,3,1,Married,11,142.8980831,0,6722.904155
146069,45,27,1,1,Married,12,142.8929684,0,6549.64842
123384,65,47,3,0,Married,6,142.8313511,0,6824.567555
109411,46,26,3,0,Married,14,142.801774,0,6707.0887
168812,60,40,2,1,Married,9,142.7739137,0,6876.695685
186723,57,38,1,1,Married,15,142.7516263,0,6818.581315
117277,62,43,2,1,Married,10,142.728526,0,6673.4263
160472,18,0,2,1,Married,13,142.6550883,0,6926.754415
181619,25,7,2,1,Married,14,142.6101548,0,6758.50774
186813,70,50,3,0,Married,10,142.6065572,0,6818.32786
131461,65,46,4,1,Married,0,142.5694647,0,6920.473235
196406,50,33,1,0,Married,11,142.5319857,0,6535.599285
151866,46,25,3,0,Married,11,142.5220495,0,6816.102475
125294,63,46,1,1,Single,1,142.4987316,0,6605.93658
124321,62,42,3,1,Married,12,142.4978964,0,6553.89482
176605,70,51,2,1,Married,10,142.4823203,0,6807.116015
143752,66,48,4,0,Single,0,142.4643846,0,6837.21923
104918,68,49,3,1,Single,11,142.4226098,0,6913.13049
143068,61,41,3,1,Single,11,142.4210784,0,6805.05392
197757,18,0,2,1,Married,11,142.3504531,0,6747.522655
143466,61,43,3,0,Married,11,142.3332636,0,6769.66318
177443,35,16,1,1,Married,15,142.2592456,0,6730.96228
160040,47,26,1,0,Married,12,142.2443975,0,6731.219875
129017,57,37,2,1,Married,15,142.2245697,0,6579.228485
149873,62,42,2,0,Married,13,142.1607176,0,6565.03588
174974,64,47,2,1,Married,4,141.9896861,0,6638.484305
104843,31,13,2,0,Married,15,141.979313,0,6810.96565
137580,17,0,2,1,Married,12,141.9759881,0,6858.799405
123642,62,41,3,1,Single,0,141.9405766,0,6634.02883
177272,56,37,3,0,Married,11,141.8416532,0,6739.08266
179694,69,50,2,1,Married,3,141.8314831,0,6577.574155
138153,60,43,2,1,Married,9,141.8266068,0,6516.33034
193872,65,47,2,0,Married,9,141.6715803,0,6712.579015
196152,43,23,2,1,Single,15,141.6558586,0,6562.79293
138061,47,26,3,1,Married,12,141.5556972,0,6649.78486
157229,64,47,4,1,Married,8,141.4576799,0,6631.883995
118245,67,46,2,1,Married,15,141.440566,0,6517.0283
132775,64,44,3,0,Married,2,141.4184224,0,6683.92112
124334,63,43,3,0,Single,7,141.3235751,0,6807.178755
196573,20,3,2,1,Married,11,141.3183162,0,6635.91581
101926,62,43,2,1,Married,8,141.3103188,0,6714.51594
168787,64,44,3,1,Married,15,141.2192271,0,6690.961355
134402,68,51,2,0,Single,6,141.1640445,0,6514.202225
153617,62,42,3,0,Married,13,141.1557738,0,6786.78869
161155,46,29,2,1,Married,11,141.1461203,0,6792.306015
148202,67,49,3,1,Married,15,141.0942506,0,6646.71253
182636,63,43,4,1,Single,9,141.050927,0,6747.54635
186451,61,40,2,1,Single,6,141.0243744,0,6459.21872
118745,60,42,3,1,Single,7,141.0194911,0,6812.974555
176953,56,35,2,0,Married,14,140.9900455,0,6649.502275
148922,62,44,4,1,Married,8,140.9300026,0,6492.50013
146082,69,48,4,1,Single,12,140.8914396,0,6724.57198
155619,69,49,2,1,Married,7,140.8741022,0,6738.70511
166436,54,35,3,0,Married,13,140.869927,0,6568.49635
100018,29,10,1,0,Married,12,140.7946555,0,6705.732775
161242,35,15,2,1,Married,11,140.7193293,0,6585.966465
159099,70,52,2,0,Married,5,140.6283107,0,6711.415535
122620,68,51,2,1,Single,2,140.6200114,0,6744.00057
190001,63,45,3,0,Single,12,140.4538959,0,6541.694795
178923,24,6,4,1,Married,13,140.4341427,0,6758.707135
174179,25,8,3,1,Married,11,140.2345358,0,6622.72679
183180,70,52,1,1,Single,7,140.2225367,0,6713.126835
107472,67,49,2,0,Married,13,140.175988,0,6500.7994
111554,45,25,3,1,Married,12,140.1734136,0,6521.67068
121938,63,44,3,0,Married,7,140.1000249,0,6452.001245
191912,39,22,3,0,Married,12,140.0731916,0,6797.65958
133591,66,48,2,0,Married,1,140.0369644,0,6429.84822
165964,66,46,2,0,Single,5,140.0041326,0,6584.20663
165843,62,41,2,1,Single,8,139.9632449,0,6560.162245
123051,67,49,4,0,Single,11,139.8835245,0,6470.176225
163390,62,42,1,1,Married,13,139.802146,0,6559.1073
176995,53,36,1,0,Married,12,139.7488865,0,6546.444325
146794,69,49,2,0,Married,13,139.7236893,0,6660.184465
118965,28,7,3,0,Married,12,139.7018171,0,6687.090855
165307,61,40,3,0,Married,6,139.6749114,0,6678.74557
126915,40,21,3,0,Married,11,139.6440318,0,6540.20159
185641,32,12,4,1,Married,13,139.6396144,0,6655.98072
173228,62,42,2,0,Married,10,139.5981739,0,6529.908695
164238,35,16,3,0,Married,12,139.4895176,0,6537.47588
134497,37,17,3,0,Married,15,139.4538857,0,6659.694285
110849,64,46,2,1,Married,6,139.3373677,0,6372.868385
200361,21,3,3,1,Married,11,139.1990263,0,6507.951315
130363,63,42,4,0,Married,10,139.1636665,0,6635.183325
185096,61,44,4,1,Single,1,139.1003056,0,6488.01528
187489,69,49,2,0,Single,0,139.045733,0,6654.28665
170647,30,12,3,0,Single,13,139.0305861,0,6523.529305
175539,63,46,3,1,Married,12,138.959039,0,6698.95195
105949,67,50,3,1,Single,14,138.9115574,0,6424.57787
123886,62,41,4,0,Married,2,138.9066927,0,6608.334635
142235,66,46,2,1,Married,10,138.8448941,0,6516.244705
198791,67,48,2,0,Single,5,138.816053,0,6666.80265
130006,62,42,3,1,Married,14,138.6775023,0,6619.875115
109874,62,45,3,0,Married,11,138.6518271,0,6619.591355
117551,58,37,3,0,Married,11,138.6467824,0,6633.33912
185775,60,41,3,0,Married,1,138.5991513,0,6340.957565
122238,59,42,4,0,Married,15,138.4931379,0,6363.656895
113174,61,41,3,1,Married,3,138.4420834,0,6515.10417
179575,63,45,1,0,Married,12,138.3989724,0,6332.94862
113891,21,2,3,1,Married,11,138.3497971,0,6522.489855
100002,53,32,3,1,Single,13,138.2480792,0,6525.40396
131006,58,40,3,0,Married,15,138.2319906,0,6328.59953
199525,23,6,1,1,Married,12,138.2124308,0,6700.62154
106735,20,3,4,1,Married,13,138.1916119,0,6634.580595
150761,64,46,4,1,Married,5,138.0756959,0,6510.784795
163463,61,43,4,1,Married,0,138.0732638,0,6356.66319
122754,34,14,1,0,Married,12,138.0157484,0,6379.78742
181412,67,48,4,1,Single,14,138.0062197,0,6312.310985
146672,19,1,2,1,Married,13,137.9957896,0,6314.78948
160816,32,15,3,0,Married,13,137.9323284,0,6675.61642
136625,65,46,3,1,Single,15,137.9224414,0,6323.12207
124235,32,12,4,1,Married,15,137.7164092,0,6527.82046
105813,67,48,3,0,Married,7,137.6797819,0,6596.989095
115473,24,5,3,0,Married,14,137.6009186,0,6403.04593
184818,40,21,4,0,Married,11,137.5946148,0,6615.73074
122013,41,23,1,1,Single,15,137.5378909,0,6536.894545
174841,19,1,2,1,Married,15,137.4678068,0,6301.39034
165118,70,50,4,1,Single,2,137.4275111,0,6488.375555
109129,31,10,2,1,Single,14,137.3592826,0,6523.96413
172286,70,52,4,1,Married,7,137.3271349,0,6383.356745
141887,65,48,2,0,Married,12,137.2012265,0,6340.061325
173041,19,2,3,1,Married,11,137.17354,0,6525.677
191657,50,32,2,1,Married,15,137.1415013,0,6284.075065
194687,65,48,4,0,Single,8,137.106691,0,6506.33455
108440,23,5,1,0,Married,14,137.0620299,0,6648.101495
159388,67,49,2,0,Married,2,137.0327478,0,6316.63739
137063,52,32,2,1,Single,12,137.021926,0,6550.0963
100144,55,37,3,1,Married,14,137.0154322,0,6277.77161
166476,68,50,4,0,Married,15,136.9524561,0,6345.622805
166509,67,48,3,1,Married,14,136.9499219,0,6639.496095
128222,66,45,3,0,Single,8,136.8656011,0,6398.280055
165809,60,40,3,1,Married,4,136.8294005,0,6284.470025
116097,63,46,1,0,Married,6,136.7283326,0,6572.41663
181851,67,48,1,1,Single,4,136.6655437,0,6440.277185
130909,65,46,2,1,Single,14,136.6277081,0,6420.385405
155173,61,42,4,0,Married,8,136.6014107,0,6391.070535
151172,69,52,2,0,Single,7,136.5511694,0,6566.55847
142551,69,50,4,0,Married,7,136.5326408,0,6316.63204
100128,41,20,1,1,Married,14,136.4526367,0,6491.631835
120046,70,52,2,1,Married,9,136.3567233,0,6378.836165
119818,69,49,4,1,Single,12,136.3448607,0,6437.243035
186487,67,47,4,1,Married,13,136.2829968,0,6393.14984
185601,62,43,2,0,Single,7,136.2646741,0,6270.233705
176572,61,44,2,0,Single,7,136.1851032,0,6332.25516
134905,66,45,3,0,Single,8,136.1581248,0,6266.90624
120710,61,40,3,0,Married,9,136.0925644,0,6254.62822
142094,70,51,3,0,Single,13,136.073391,0,6491.66955
136279,67,46,4,1,Single,12,136.0358513,0,6407.792565
187246,69,51,2,1,Married,13,135.9787403,0,6211.937015
179453,68,47,4,1,Single,11,135.9413147,0,6483.065735
119200,68,51,2,1,Single,11,135.8270226,0,6381.35113
161076,58,37,2,1,Married,11,135.8263113,0,6584.315565
147166,39,22,2,1,Married,14,135.8245917,0,6512.229585
148079,66,45,2,1,Single,15,135.7872789,0,6560.363945
193963,30,13,4,1,Married,14,135.7651654,0,6487.25827
103713,61,42,2,0,Single,8,135.7425789,0,6498.128945
114902,32,13,3,0,Married,12,135.4948734,0,6295.74367
169764,60,41,3,0,Single,9,135.4107831,0,6208.539155
104541,67,49,3,1,Married,6,135.3421608,0,6249.10804
124139,25,8,2,1,Married,12,135.2900127,0,6452.500635
184589,69,52,1,0,Married,13,135.2854399,0,6342.271995
154096,62,43,1,1,Married,13,135.2248829,0,6413.244145
194786,69,51,2,1,Married,7,135.1706324,0,6253.53162
166007,16,0,4,1,Married,13,135.1657465,0,6519.287325
133235,50,31,3,0,Married,11,135.1341608,0,6256.70804
127962,65,47,3,1,Single,12,135.1319574,0,6271.59787
122755,57,38,1,0,Married,11,135.0784212,0,6285.92106
117534,70,53,4,0,Married,11,135.0308263,0,6187.541315
174786,60,40,3,0,Married,14,134.9997371,0,6389.986855
150937,67,48,3,0,Married,6,134.6595529,0,6322.977645
134779,70,50,4,1,Single,9,134.5427115,0,6438.135575
153428,22,3,1,1,Married,11,134.5213178,0,6406.06589
162258,64,47,3,0,Single,7,134.4716114,0,6253.58057
159409,63,45,4,0,Single,13,134.4660797,0,6225.303985
167085,68,48,4,0,Single,15,134.349538,0,6229.4769
122909,65,44,2,0,Married,9,134.3009248,0,6152.04624
161894,45,27,2,1,Married,11,134.267431,0,6132.37155
168963,18,1,2,1,Married,14,133.9794244,0,6496.97122
184418,69,49,2,1,Single,1,133.9532326,0,6248.66163
113770,68,47,4,1,Single,2,133.9413601,0,6343.068005
116255,68,50,2,0,Single,15,133.9050267,0,6308.251335
137192,22,5,2,1,Married,11,133.8298896,0,6209.49448
193972,56,36,2,1,Single,14,133.8254731,0,6325.273655
128099,63,43,2,0,Married,11,133.8021934,0,6455.10967
199871,64,45,3,1,Single,10,133.7874856,0,6401.37428
104985,55,35,3,1,Married,13,133.7700683,0,6419.503415
117452,51,30,3,0,Married,11,133.7543921,0,6126.719605
173500,64,43,3,1,Married,2,133.7400289,0,6281.001445
140426,66,45,3,0,Married,15,133.7394263,0,6279.971315
116313,64,46,2,1,Single,13,133.7223801,0,6242.119005
106721,62,45,2,0,Married,10,133.6531928,0,6128.65964
109697,51,31,2,1,Married,13,133.6082773,0,6084.413865
182711,68,48,3,1,Married,2,133.5505717,0,6173.528585
197576,65,47,1,0,Single,9,133.5021321,0,6438.106605
142607,69,49,2,0,Married,14,133.4962693,0,6345.813465
139647,64,46,1,1,Single,15,133.2849044,0,6314.24522
175676,60,39,1,1,Single,9,133.2658411,0,6126.292055
142531,67,47,2,1,Married,11,133.2366318,0,6097.83159
164317,63,45,3,1,Single,13,133.2352704,0,6219.76352
182514,64,47,4,1,Single,14,133.2210934,0,6242.05467
113436,63,46,3,1,Married,10,133.1117657,0,6379.588285
177610,68,50,3,1,Single,7,133.0597403,0,6165.987015
169364,69,51,3,0,Single,14,133.0207591,0,6434.037955
194192,68,50,4,1,Single,12,133.0078784,0,6218.39392
169735,69,49,3,0,Married,8,132.9184966,0,6183.92483
180738,35,17,2,1,Single,12,132.9111475,0,6168.557375
153922,57,38,3,1,Married,12,132.8949097,0,6294.745485
168038,70,50,3,0,Married,0,132.866686,0,6408.3343
128917,34,14,3,1,Married,11,132.8603678,0,6051.01839
102924,67,47,2,1,Married,12,132.6892237,0,6196.461185
165555,63,46,4,0,Married,9,132.6763307,0,6272.816535
113303,67,50,2,1,Married,8,132.6744557,0,6194.722785
164835,54,33,3,1,Married,11,132.5911568,0,6217.55784
141317,70,53,3,0,Married,8,132.5846809,0,6221.234045
124794,61,43,3,1,Married,5,132.3852185,0,6039.260925
144030,17,0,2,1,Married,15,132.3798915,0,6342.994575
114617,63,44,2,0,Married,12,132.314349,0,6255.71745
107202,24,5,3,1,Married,13,132.3113654,0,6246.56827
128995,16,0,4,1,Married,12,132.2946781,0,6336.733905
142363,61,43,3,1,Married,9,132.2840456,0,6121.20228
162473,54,37,1,1,Married,12,132.1925258,0,6186.62629
126625,65,48,4,0,Single,11,132.1886584,0,6218.43292
130749,69,51,4,0,Married,6,132.1610204,0,6381.05102
138231,60,42,2,1,Married,8,131.9921409,0,6143.607045
162260,16,0,3,1,Married,12,131.9561189,0,6368.805945
163100,63,45,4,1,Single,10,131.9174632,0,6225.87316
152333,61,44,3,1,Married,14,131.7915451,0,6039.577255
198809,69,51,3,0,Married,10,131.7787154,0,6139.93577
168337,60,43,2,0,Married,11,131.7476077,0,6120.380385
108420,69,50,4,1,Married,7,131.6252861,0,6345.264305
174585,33,12,1,1,Married,14,131.6187607,0,6103.938035
168184,63,44,3,1,Single,9,131.59412,0,5997.706
193825,63,46,4,0,Married,0,131.5856775,0,6195.283875
192463,65,46,3,1,Single,14,131.5417155,0,6187.085775
105422,68,48,4,0,Married,6,131.529243,0,6117.46215
149780,60,40,3,1,Married,4,131.413806,0,6118.6903
135206,63,44,3,1,Single,9,131.3811093,0,6099.055465
131113,63,44,3,1,Married,12,131.3389266,0,6081.94633
157533,68,47,4,1,Single,13,131.3252517,0,6118.262585
144719,35,16,3,1,Married,13,131.2594534,0,6246.97267
155835,68,47,3,1,Single,14,131.2580644,0,6023.90322
124575,59,38,4,0,Married,12,131.2553139,0,6092.765695
162527,68,49,3,0,Single,14,131.2298579,0,6304.492895
172378,28,10,3,1,Single,13,131.1657194,0,6045.28597
171270,61,40,3,1,Married,8,131.1560146,0,6310.80073
118484,69,52,3,0,Single,15,131.122573,0,6201.12865
133791,29,9,2,1,Married,11,131.1032891,0,6148.164455
142969,64,45,3,0,Married,12,131.1006337,0,6284.031685
138510,63,44,1,0,Single,3,131.0544977,0,6194.724885
102315,57,40,1,1,Married,11,131.0465368,0,6181.32684
147909,60,43,2,0,Single,11,131.0345546,0,6061.72773
198379,61,40,4,0,Married,1,131.0132821,0,6229.664105
137468,54,34,2,1,Married,13,131.0101625,0,6100.508125
117445,61,41,1,1,Married,15,130.9914388,0,6106.57194
119033,63,45,4,0,Married,15,130.9616216,0,5960.08108
105944,42,22,4,1,Married,12,130.8957786,0,6032.78893
197842,31,10,2,1,Single,11,130.8381263,0,6280.906315
108477,61,41,3,1,Single,13,130.7757095,0,6085.785475
109523,62,41,2,0,Married,8,130.7330803,0,6157.654015
101097,69,52,2,1,Married,10,130.7103753,0,6127.518765
155937,67,47,4,1,Married,9,130.6352895,0,6111.764475
163750,60,42,3,1,Single,9,130.5836234,0,6209.18117
142416,67,50,1,1,Single,1,130.5786189,0,6022.930945
167148,63,45,2,0,Married,10,130.4559443,0,6021.797215
107428,63,46,4,0,Single,13,130.2295943,0,5978.479715
128224,67,47,3,0,Single,12,130.1853094,0,6247.26547
196751,37,20,1,1,Married,13,130.1393257,0,6241.966285
163134,68,49,2,1,Single,12,130.1047303,0,6096.236515
173661,70,53,3,0,Married,0,130.0411373,0,6175.056865
174729,60,40,3,0,Single,12,129.991384,0,6289.5692
159533,63,42,3,1,Single,10,129.9683434,0,5974.41717
171672,67,47,3,0,Married,15,129.8813839,0,5943.069195
101061,28,7,2,0,Married,12,129.8275184,0,5938.37592
179908,63,42,1,1,Married,7,129.7356,0,6033.78
158708,24,6,3,1,Married,15,129.674854,0,6149.7427
126896,63,42,2,1,Single,12,129.5973895,0,6057.869475
118878,24,5,1,1,Married,12,129.5130703,0,6244.653515
174189,28,10,3,1,Married,12,129.3972831,0,5969.864155
161826,62,44,2,0,Married,6,129.3390843,0,6057.954215
186780,68,48,2,1,Married,7,129.1156282,0,6088.78141
103739,49,32,3,0,Married,15,129.0761649,0,6195.808245
196147,64,45,4,0,Single,2,128.9756382,0,5906.78191
174309,26,5,1,1,Married,11,128.8692649,0,6196.463245
179116,68,49,3,1,Married,5,128.7233246,0,5921.16623
116809,25,7,2,0,Married,14,128.6345383,0,5834.726915
194376,66,49,3,1,Single,11,128.4923283,0,5900.616415
194418,65,46,1,1,Married,11,128.3992407,0,5863.962035
106329,20,2,1,1,Married,14,128.3109544,0,6089.54772
106563,45,25,2,1,Married,14,128.3079288,0,6133.39644
197929,24,6,3,1,Married,14,128.2515031,0,6087.575155
114520,69,52,3,1,Married,3,128.1344342,0,5884.72171
116440,64,45,2,0,Married,6,127.9956872,0,6030.78436
178340,61,42,3,0,Married,13,127.970664,0,5925.5332
155038,49,31,1,1,Married,14,127.9555841,0,6126.779205
107254,65,46,2,0,Married,7,127.9495891,0,6088.479455
165636,66,47,2,1,Single,8,127.9202249,0,6087.011245
136133,63,46,2,1,Married,12,127.8806096,0,5827.03048
147782,67,47,2,1,Married,1,127.7929693,0,5856.648465
112132,62,41,3,1,Married,8,127.7680796,0,6174.40398
151281,60,39,1,0,Single,7,127.5767945,0,5813.839725
116996,62,41,1,1,Married,5,127.554803,0,6106.74015
177763,70,51,4,0,Married,6,127.4992474,0,6092.96237
155399,66,47,3,1,Single,14,127.4626527,0,5780.132635
126567,62,42,1,1,Single,9,127.4005318,0,6038.02659
146457,62,44,2,0,Married,14,127.3617176,0,6080.08588
190750,66,47,3,1,Single,14,127.2663704,0,5929.31852
105750,67,48,2,1,Married,14,127.1520722,0,5863.60361
193379,70,53,3,1,Married,8,127.1246684,0,5773.23342
175408,22,3,2,1,Married,15,127.0787797,0,6094.938985
166018,32,14,3,1,Married,11,127.0057963,0,5842.289815
135024,64,44,1,0,Single,9,126.9300629,0,5776.503145
160677,60,43,2,1,Single,3,126.8795277,0,5948.976385
186681,66,46,2,1,Single,6,126.8610648,0,6016.05324
120571,61,44,3,0,Single,9,126.798749,0,6039.93745
155724,23,6,1,1,Married,14,126.7299892,0,6098.49946
114767,64,45,2,1,Married,13,126.7240801,0,6118.204005
177742,46,25,2,0,Married,11,126.4813408,0,5987.06704
150149,53,36,2,1,Married,11,126.4379963,0,5827.899815
156296,66,48,2,0,Single,3,126.4213959,0,5763.069795
133205,57,37,3,0,Married,12,126.3580808,0,5861.90404
156788,65,47,3,1,Married,12,126.3508732,0,6070.54366
107793,66,45,2,0,Married,12,126.3438308,0,6033.19154
156119,33,16,3,1,Married,12,126.1962248,0,5859.81124
138141,31,10,3,0,Married,13,126.1228945,0,5999.144725
177909,60,40,4,1,Single,8,126.1104143,0,5890.520715
158346,35,18,1,1,Married,11,126.0782536,0,5755.91268
155322,69,48,3,0,Single,14,126.0497697,0,5777.488485
192294,69,50,4,1,Single,14,125.9758822,0,6088.79411
142091,26,8,1,1,Married,12,125.8771424,0,5843.85712
159604,69,51,1,1,Married,7,125.8350503,0,5716.752515
143790,64,43,2,0,Married,13,125.7636573,0,5964.182865
165957,69,52,1,1,Single,12,125.7505907,0,5745.529535
155797,70,52,3,0,Married,12,125.7227704,0,5962.13852
155521,67,46,2,0,Married,11,125.7203764,0,5952.01882
191011,25,7,2,1,Married,14,125.7112886,0,5903.56443
146095,17,0,3,1,Married,13,125.6791073,0,6060.955365
191995,67,50,2,1,Married,15,125.642613,0,5837.13065
118755,68,51,3,1,Single,13,125.6315413,0,5870.577065
199431,70,53,2,1,Single,14,125.4541326,0,5998.70663
179660,61,43,1,1,Single,6,125.3379165,0,5900.895825
104154,64,44,3,0,Married,12,125.2914201,0,5963.571005
195802,62,45,3,1,Single,10,125.1904244,0,5743.52122
193738,65,45,2,1,Single,13,125.0625266,0,5660.12633
131246,63,44,3,0,Married,15,125.061786,0,5788.0893
123147,19,1,2,1,Married,15,125.004172,0,6027.2086
102067,65,47,2,0,Married,9,124.9805569,0,6012.027845
117725,68,47,2,1,Married,8,124.974486,0,5923.7243
128873,62,44,3,0,Single,4,124.9309238,0,5763.54619
170464,61,42,3,1,Married,3,124.9121363,0,5766.606815
195819,67,46,2,0,Single,7,124.8982061,0,5768.910305
105002,30,9,2,1,Single,15,124.8572518,0,5911.86259
110682,55,36,2,1,Married,11,124.8469363,0,5740.346815
167755,52,32,4,0,Married,14,124.7120567,0,6022.602835
113311,66,47,4,1,Married,14,124.7072547,0,5661.362735
152196,49,30,3,1,Married,11,124.661857,0,5702.09285
172347,48,30,1,1,Married,11,124.6556691,0,5994.783455
185332,60,40,3,1,Single,2,124.6380942,0,5643.90471
164157,38,19,2,1,Married,12,124.6151363,0,5800.756815
150922,61,44,4,1,Married,11,124.5851478,0,5869.25739
173294,24,7,3,1,Married,13,124.5419691,0,5732.098455
155642,62,45,1,0,Married,14,124.498519,0,5907.92595
180306,20,3,2,1,Married,12,124.4894147,0,5721.470735
138991,67,49,3,0,Married,4,124.4855245,0,5861.276225
104906,66,46,3,1,Single,13,124.4374167,0,5879.870835
161611,63,42,2,1,Single,2,124.4352468,0,5725.76234
171517,47,28,1,0,Married,15,124.3721637,0,5857.608185
120364,26,6,2,1,Married,11,124.348319,0,5965.41595
150329,65,47,1,1,Married,3,124.2207011,0,5965.035055
108414,70,53,4,0,Married,10,124.1461659,0,5800.308295
134782,68,49,3,1,Single,14,124.1052986,0,6004.26493
184541,61,40,3,1,Single,7,124.0761077,0,5660.805385
110482,63,42,3,1,Married,14,124.0040164,0,5943.20082
153843,25,8,2,0,Married,12,123.9473868,0,5851.36934
105550,62,43,2,1,Married,13,123.8890856,0,5867.45428
165710,38,17,2,0,Married,12,123.8587097,0,5937.935485
189806,70,51,4,1,Married,12,123.7757506,0,5898.78753
158451,69,51,4,1,Married,7,123.7747029,0,5713.735145
175567,64,47,3,1,Married,11,123.7441614,0,5828.20807
180865,65,48,2,1,Married,13,123.6650647,0,5730.253235
186772,69,48,3,1,Married,14,123.6269694,0,5760.34847
143079,62,44,3,0,Single,13,123.6078003,0,5892.390015
191094,17,0,3,1,Married,12,123.5602154,0,5672.01077
121838,63,45,2,0,Single,8,123.4575718,0,5707.87859
162575,49,29,3,0,Married,11,123.3073712,0,5654.36856
181890,68,50,4,1,Married,12,123.2312106,0,5568.56053
122889,66,45,1,1,Single,9,123.1604341,0,5797.021705
116000,69,48,3,1,Married,15,123.1253857,0,5649.269285
158175,61,44,2,1,Married,12,123.1137754,0,5602.68877
108914,65,47,2,1,Married,15,123.027051,0,5623.35255
170444,60,43,2,0,Married,0,122.9757014,0,5935.78507
134959,45,26,3,0,Married,12,122.9422353,0,5714.111765
105602,70,50,4,1,Married,7,122.9259417,0,5748.297085
197918,61,41,1,1,Married,7,122.8351382,0,5867.75691
104135,68,48,1,1,Married,9,122.8164329,0,5794.821645
141114,70,51,2,0,Married,6,122.6835746,0,5748.17873
150724,70,53,3,0,Married,8,122.6117209,0,5812.586045
165496,62,45,3,1,Single,1,122.5853378,0,5641.26689
113888,60,43,3,1,Married,1,122.5669928,0,5570.34964
151963,63,44,2,0,Single,10,122.490126,0,5906.5063
197435,24,5,3,1,Married,13,122.4885697,0,5623.428485
180670,64,45,3,1,Single,15,122.4597923,0,5564.989615
146711,62,45,3,0,Married,9,122.4011314,0,5724.05657
175391,65,47,1,1,Single,12,122.3285063,0,5751.425315
143608,66,49,3,1,Single,12,122.2920575,0,5814.602875
103762,69,52,1,1,Married,12,122.2172897,0,5826.864485
183354,62,43,3,0,Single,10,122.1896664,0,5894.48332
183488,66,48,3,1,Married,4,122.0725058,0,5505.62529
195893,68,50,2,1,Single,15,122.0274734,0,5502.37367
166997,69,50,2,1,Single,10,121.9628389,0,5831.141945
130134,18,0,4,1,Married,11,121.9517404,0,5799.58702
134712,66,45,4,1,Married,8,121.7890849,0,5542.454245
160928,19,1,3,1,Married,14,121.7661902,0,5836.30951
101053,65,47,4,1,Married,9,121.5888263,0,5794.441315
134307,65,45,3,0,Single,11,121.3480129,0,5860.400645
179188,66,46,3,0,Married,6,121.3427129,0,5813.135645
190896,23,4,2,1,Married,11,121.329313,0,5710.46565
157740,60,42,1,1,Married,6,121.2896348,0,5625.48174
179440,63,46,3,0,Married,9,121.2747787,0,5491.738935
117215,63,43,2,1,Single,9,121.2528717,0,5512.643585
138437,60,40,3,1,Single,15,121.2487356,0,5554.43678
197874,39,18,3,1,Married,11,121.1913592,0,5848.56796
194661,60,42,2,1,Married,11,121.1234462,0,5808.17231
150132,19,1,2,1,Married,13,121.0765599,0,5576.827995
177592,60,41,3,1,Married,14,121.0487195,0,5830.435975
114199,70,50,3,0,Single,5,120.8152534,0,5577.76267
148222,66,46,2,0,Single,10,120.7814513,0,5680.072565
199650,64,46,1,0,Married,14,120.6584421,0,5708.922105
116190,61,43,2,1,Single,13,120.6554309,0,5459.771545
171448,62,42,3,1,Married,11,120.5440216,0,5622.20108
140079,70,50,2,1,Single,6,120.4937763,0,5618.688815
109058,66,47,2,1,Married,10,120.3573508,0,5469.86754
133995,68,50,4,1,Single,7,120.26212,0,5501.106
108946,70,49,3,0,Married,8,120.1212386,0,5628.06193
132798,65,45,3,1,Married,0,120.0809037,0,5417.045185
127480,64,46,2,0,Single,10,120.0611593,0,5567.057965
131774,70,52,1,1,Married,10,120.0064291,0,5690.321455
173634,62,45,3,0,Single,10,119.9968984,0,5614.84492
129830,60,42,3,1,Married,6,119.9810556,0,5413.05278
137785,33,12,1,1,Married,14,119.974399,0,5575.71995
158970,67,50,3,1,Married,0,119.8880277,0,5677.401385
164421,60,40,4,1,Single,9,119.7794738,0,5573.97369
171608,68,47,1,1,Married,7,119.7330443,0,5534.652215
170979,69,48,1,1,Married,6,119.6500676,0,5741.50338
165660,64,45,2,0,Single,9,119.6491594,0,5413.45797
188299,64,45,2,1,Married,11,119.5106847,0,5497.534235
198415,65,48,1,0,Married,9,119.5070203,0,5637.351015
195087,62,43,1,1,Single,11,119.3495526,0,5663.47763
173210,20,1,2,1,Married,13,119.3118833,0,5441.594165
188084,60,43,3,0,Married,9,119.1686919,0,5360.434595
189099,63,45,3,1,Married,1,118.8829905,0,5717.149525
104634,24,5,3,1,Married,13,118.8773941,0,5410.869705
161409,68,50,3,1,Single,15,118.8089687,0,5508.448435
172065,70,50,4,1,Single,9,118.8025487,0,5612.127435
199563,65,47,2,1,Married,6,118.7481336,0,5611.40668
174375,68,48,1,0,Single,6,118.5946454,0,5499.73227
191941,69,51,2,0,Married,11,118.5701308,0,5585.50654
146283,70,53,3,1,Married,14,118.567419,0,5576.37095
178298,60,43,2,1,Single,7,118.4561493,0,5351.807465
125647,63,43,1,0,Married,3,118.1535711,0,5356.678555
155070,22,5,1,1,Married,15,117.9850178,0,5399.25089
100782,69,52,3,0,Single,13,117.9697311,0,5468.486555
128204,60,42,2,0,Married,6,117.8851828,0,5397.25914
191072,61,41,3,1,Single,7,117.8687744,0,5470.43872
171938,61,44,2,0,Single,12,117.5956818,0,5564.78409
118753,58,41,1,1,Married,12,117.5921567,0,5508.607835
191795,69,52,2,1,Married,6,117.5886176,0,5489.43088
122734,67,49,1,1,Married,6,117.5752762,0,5599.76381
189685,63,43,2,1,Married,8,117.5557386,0,5340.78693
137418,44,26,2,0,Married,15,117.5247355,0,5592.236775
105776,63,43,2,1,Married,7,117.5225301,0,5349.126505
172754,60,39,1,0,Married,14,117.4743023,0,5579.715115
144978,69,52,2,0,Married,14,117.261906,0,5414.0953
181167,64,45,1,1,Married,8,117.1345839,0,5644.729195
111755,69,52,1,0,Single,15,117.101737,0,5391.08685
147728,21,4,1,1,Married,14,117.0669225,0,5466.346125
154198,45,28,1,1,Married,11,117.0495979,0,5317.479895
114239,18,1,2,1,Married,14,117.0109629,0,5346.548145
140999,67,47,3,1,Married,9,116.9269408,0,5413.34704
162111,65,44,2,0,Single,2,116.8334234,0,5536.67117
151703,70,52,2,1,Married,10,116.8177979,0,5607.889895
180126,60,43,1,0,Single,3,116.7747096,0,5304.73548
175206,69,48,2,1,Married,3,116.7150602,0,5264.75301
168622,70,52,1,0,Married,3,116.6878815,0,5261.394075
119985,52,31,4,1,Married,12,116.6594267,0,5353.971335
153371,66,47,3,0,Married,6,116.6539191,0,5523.695955
104344,60,39,3,0,Married,6,116.601308,0,5397.0654
132861,62,41,3,0,Single,9,116.5855886,0,5537.27943
107329,68,51,1,1,Married,13,116.4833382,0,5309.16691
104155,60,41,3,0,Married,10,116.4824895,0,5375.124475
106807,62,43,2,0,Married,9,116.4320793,0,5603.603965
165550,20,1,2,1,Married,15,116.4235412,0,5502.17706
104616,39,20,2,1,Married,12,116.3270713,0,5219.353565
132753,63,42,3,0,Married,3,116.2992582,0,5593.96291
171425,65,47,3,1,Married,9,116.146832,0,5532.3416
138478,62,41,4,1,Single,2,116.0909781,0,5415.548905
170182,62,45,2,1,Married,2,116.0408986,0,5286.04493
180525,70,51,2,0,Single,7,115.9904612,0,5484.52306
179669,60,43,2,0,Married,1,115.9275168,0,5479.37584
197144,64,43,3,1,Married,8,115.8444065,0,5330.220325
127095,61,44,4,0,Single,6,115.7827809,0,5443.139045
185785,68,47,2,0,Married,15,115.7755309,0,5545.776545
153419,63,43,1,1,Married,7,115.7704846,0,5588.52423
187890,60,39,2,0,Married,14,115.7533082,0,5561.66541
161210,63,44,2,0,Single,3,115.7203446,0,5566.01723
176097,21,2,2,0,Married,14,115.6370943,0,5306.854715
176193,68,48,3,0,Single,7,115.4222858,0,5534.11429
170000,67,50,2,0,Married,9,115.3953038,0,5485.76519
143585,17,0,2,1,Married,11,115.307812,0,5313.3906
149292,69,51,2,0,Single,10,115.2835278,0,5314.17639
127752,70,53,2,0,Single,6,115.2820125,0,5369.100625
116156,67,47,3,1,Single,13,115.2146289,0,5259.731445
161806,47,30,4,1,Married,11,115.1744809,0,5382.724045
166050,64,44,1,1,Single,10,115.134362,0,5418.7181
184381,25,6,2,1,Married,15,115.0959938,0,5370.79969
115594,61,42,2,0,Single,8,114.9834089,0,5540.170445
123547,62,41,1,1,Married,15,114.9781753,0,5313.908765
137173,69,51,2,1,Married,10,114.976627,0,5420.83135
146542,68,50,4,1,Married,12,114.7936974,0,5303.68487
181593,64,44,1,1,Married,13,114.6509439,0,5270.547195
141988,67,49,3,1,Single,6,114.6169331,0,5241.846655
113806,64,47,2,1,Married,10,114.6104389,0,5513.521945
114393,28,7,3,1,Married,14,114.51536,0,5358.768
146456,62,41,2,0,Single,11,114.4745013,0,5318.725065
181746,66,47,3,1,Single,13,114.4684687,0,5423.423435
169087,67,50,4,0,Married,2,114.3150713,0,5317.753565
125551,61,44,4,1,Married,10,114.302366,0,5323.1183
179563,62,43,2,0,Single,6,114.2518974,0,5245.59487
184072,67,49,2,0,Married,10,114.2397792,0,5143.98896
131592,65,48,3,0,Single,4,114.0439503,0,5420.197515
146355,16,0,2,1,Married,15,113.9895561,0,5205.477805
149094,65,44,1,1,Single,11,113.9687533,0,5401.437665
191318,60,41,4,0,Single,4,113.9536153,0,5202.680765
165395,21,2,3,1,Married,13,113.9149167,0,5304.745835
123715,61,44,4,0,Married,6,113.8351057,0,5248.755285
125856,68,49,2,0,Married,12,113.8333117,0,5323.665585
104422,62,41,4,0,Married,13,113.7164516,0,5224.82258
175648,70,53,3,1,Married,12,113.592863,0,5293.64315
105900,68,50,3,1,Married,15,113.2388088,0,5371.94044
108645,69,49,3,1,Married,10,113.149769,0,5302.48845
180986,61,42,4,1,Single,4,113.0978462,0,5203.89231
174721,65,45,4,1,Single,9,113.0630237,0,5313.151185
143002,65,46,4,1,Married,7,113.0374475,0,5434.872375
112036,67,47,3,0,Single,15,112.8720049,0,5082.600245
111647,70,49,4,0,Married,15,112.7982958,0,5164.91479
126646,70,50,2,0,Single,3,112.6224518,0,5396.12259
178149,63,42,3,1,Single,15,112.5311831,0,5386.559155
169134,68,50,1,1,Married,0,112.2473507,0,5169.367535
143703,69,49,3,1,Single,15,112.1463546,0,5350.31773
124043,65,44,3,0,Married,10,112.1420079,0,5180.100395
141421,46,26,4,1,Married,14,111.9844381,0,5243.221905
174683,70,49,4,1,Single,7,111.9323709,0,5182.618545
194536,55,38,1,1,Married,11,111.8798755,0,5295.993775
118821,62,45,3,1,Single,11,111.8627906,0,5093.13953
117868,67,50,1,0,Single,10,111.8604052,0,5309.02026
197655,63,45,2,0,Married,6,111.811625,0,5236.58125
195404,64,47,3,1,Single,10,111.7814172,0,5122.07086
191742,67,50,3,1,Married,9,111.7540737,0,5157.703685
174558,60,43,4,0,Married,1,111.6783922,0,5329.91961
138073,25,8,2,1,Married,11,111.6679556,0,5230.39778
107070,61,40,3,1,Single,10,111.6639866,0,5055.19933
176986,65,47,2,0,Single,1,111.6358758,0,5110.79379
189016,63,44,3,0,Married,5,111.6084269,0,5173.421345
132897,62,43,4,0,Single,12,111.4821338,0,5262.10669
183467,34,13,2,1,Married,14,111.3992454,0,5359.96227
156747,65,44,4,0,Single,9,111.3796145,0,5196.980725
197495,67,48,3,0,Married,15,111.3633951,0,5072.169755
148191,65,48,2,1,Single,1,111.3605932,0,4975.02966
136050,19,2,4,1,Married,14,111.3219211,0,5146.096055
193687,62,41,3,1,Single,11,111.3062088,0,5154.31044
198383,68,49,2,1,Single,13,111.2906968,0,5031.53484
178591,18,0,2,1,Married,13,111.2766466,0,5270.83233
164972,64,43,3,1,Single,14,111.2749942,0,5233.74971
129252,66,45,2,1,Single,14,111.1771647,0,4966.858235
110850,69,49,3,1,Single,10,111.0382749,0,5137.913745
104316,68,49,3,0,Married,0,110.9761282,0,5197.80641
128382,60,42,3,0,Single,2,110.973556,0,5123.6778
123041,63,44,2,0,Married,15,110.9730614,0,5313.65307
156889,61,40,3,0,Married,13,110.9295321,0,5210.476605
117071,67,48,3,1,Single,9,110.8961699,0,5295.808495
111976,70,53,2,1,Single,0,110.8403382,0,5019.01691
159506,68,47,4,0,Married,6,110.7679747,0,5295.398735
198258,70,52,4,1,Married,9,110.764513,0,5086.22565
125963,63,43,4,1,Married,10,110.7175798,0,5056.87899
139753,64,47,1,1,Single,11,110.6599318,0,5017.99659
106217,62,44,3,1,Single,7,110.6444159,0,5069.220795
164639,60,43,3,1,Married,6,110.6284884,0,5096.42442
145064,69,48,3,1,Single,12,110.6013722,0,4940.06861
196704,69,51,2,0,Married,12,110.534983,0,5133.74915
159455,65,45,4,1,Single,15,110.4387776,0,5093.93888
190765,69,52,2,1,Married,1,110.4164762,0,5228.82381
180338,67,50,2,1,Married,4,110.4044529,0,5250.222645
114563,63,44,4,0,Single,10,110.3872037,0,4955.360185
110157,60,43,1,1,Married,6,110.3300495,0,5143.502475
109794,19,0,3,1,Married,15,110.3025653,0,5166.128265
191725,64,47,4,1,Single,13,110.2807929,0,5257.039645
104097,63,46,2,0,Married,6,110.2380746,0,5260.90373
136954,65,45,2,1,Married,13,110.1622715,0,4972.113575
123062,64,47,3,0,Single,6,109.9055963,0,5189.279815
131622,61,43,2,1,Married,1,109.5964897,0,4902.824485
157904,60,39,2,1,Married,7,109.5108721,0,5106.543605
165523,65,46,4,1,Single,10,109.4723436,0,5088.61718
119964,65,46,2,1,Single,5,109.4517523,0,5127.587615
136921,70,52,1,1,Married,12,109.4343565,0,4878.717825
102159,62,42,3,0,Single,8,109.4005125,0,5228.025625
173094,63,44,3,0,Single,9,109.3752692,0,5024.76346
171925,63,43,4,0,Married,14,109.36009,0,5096.0045
131641,64,43,2,0,Married,6,109.3481556,0,5032.40778
185542,69,51,2,1,Married,3,109.3434997,0,5245.174985
175563,67,50,3,0,Single,9,109.2622168,0,5115.11084
128544,60,43,2,1,Married,13,109.2549184,0,5217.74592
126631,65,47,2,0,Single,7,109.1803572,0,5081.01786
153715,70,51,3,0,Married,4,109.1431196,0,5092.15598
196177,65,46,1,1,Married,3,109.1127004,0,5223.63502
139099,70,49,2,1,Single,13,109.0816165,0,4894.080825
116579,64,47,1,0,Married,5,109.0263426,0,5133.31713
149001,64,46,3,1,Married,13,108.8598924,0,5041.99462
102417,25,7,3,1,Married,12,108.7571924,0,4970.85962
162907,69,49,2,0,Single,13,108.4678671,0,4885.393355
124790,63,43,2,1,Single,15,108.4033084,0,4880.16542
163604,68,49,2,0,Married,8,108.3790936,0,4888.95468
128784,24,6,2,1,Married,12,108.3392623,0,5048.963115
181101,68,47,3,1,Single,0,108.1947388,0,4859.73694
139778,62,43,3,1,Married,12,108.1910087,0,4931.550435
134063,61,41,4,0,Single,0,108.1750946,0,4866.75473
151297,61,44,4,0,Married,12,108.0378484,0,4912.89242
153258,67,48,4,1,Single,12,107.9707894,0,5174.53947
147389,67,49,1,1,Married,12,107.968914,0,5109.4457
189048,69,50,2,0,Single,13,107.9637789,0,5174.188945
139897,65,48,2,0,Married,6,107.9557709,0,4888.788545
183570,22,4,1,1,Married,15,107.8775639,0,5072.878195
175164,60,43,3,1,Single,7,107.8733525,0,5179.667625
177017,62,41,3,1,Married,11,107.8515639,0,4827.578195
151563,60,43,4,0,Single,11,107.8114166,0,5048.57083
116919,69,49,4,1,Married,7,107.7943184,0,5165.71592
106666,68,47,2,0,Single,5,107.7776996,0,4998.88498
125020,60,41,2,1,Single,1,107.6786636,0,5049.93318
179940,63,42,2,0,Married,6,107.6584512,0,5090.92256
116588,20,2,2,1,Married,13,107.500208,0,5052.0104
172921,29,10,1,1,Married,11,107.4184383,0,5120.921915
196534,69,52,2,1,Married,4,107.3456572,0,5156.28286
119817,65,46,3,1,Single,3,107.2822308,0,5082.11154
177755,64,44,3,1,Married,2,107.2227766,0,5074.13883
110662,63,46,2,1,Married,14,107.1457327,0,4994.286635
162791,19,1,3,1,Married,13,107.0444896,0,5071.22448
188100,67,46,2,0,Married,2,106.9917583,0,4983.587915
113853,67,46,2,1,Married,13,106.9446878,0,5090.23439
110816,64,45,2,1,Married,2,106.9182747,0,5064.913735
152767,64,45,3,0,Married,6,106.8391346,0,5003.95673
153275,67,50,2,1,Married,11,106.756089,0,4745.80445
149691,68,51,4,1,Married,12,106.744504,0,5011.2252
118032,64,44,3,0,Single,9,106.4613638,0,4904.06819
155078,61,43,3,1,Married,8,106.4436373,0,4830.181865
195830,62,41,2,0,Married,14,106.3385177,0,5043.925885
188497,63,45,3,0,Married,15,106.3036463,0,4989.182315
141294,67,46,1,0,Married,4,106.2451549,0,4884.257745
176467,63,44,2,0,Single,12,106.1697165,0,5096.485825
177736,66,46,2,1,Single,0,106.127757,0,4739.38785
197625,69,48,3,0,Single,2,106.0529282,0,5053.64641
116297,61,42,2,1,Married,10,105.9940436,0,4929.70218
188885,62,42,2,0,Single,15,105.953581,0,5064.67905
109520,68,48,1,1,Single,4,105.8026452,0,5035.13226
178919,68,47,2,1,Married,15,105.7788908,0,4785.94454
147736,62,45,2,0,Married,10,105.6599415,0,4943.997075
171629,16,0,3,1,Married,11,105.5919565,0,5076.597825
184217,62,43,1,0,Married,15,105.5850539,0,5008.252695
179705,17,0,2,1,Married,14,105.5195049,0,4836.975245
161679,64,45,1,0,Single,0,105.4432074,0,4725.16037
116585,21,2,1,1,Married,11,105.2846005,0,5022.230025
139159,67,46,3,0,Married,12,105.2584553,0,5012.922765
170491,22,4,1,1,Married,11,105.2078607,0,4845.393035
149551,62,41,2,1,Married,13,105.1956516,0,4761.78258
126867,67,50,4,0,Married,3,105.1953427,0,4745.767135
191575,65,47,3,0,Married,11,105.0798029,0,4660.990145
110506,63,45,3,1,Married,9,104.7313367,0,4995.566835
160216,70,50,2,0,Single,11,104.418739,0,4996.93695
127414,67,47,4,1,Single,5,104.3685546,0,4988.42773
184680,65,45,2,0,Single,15,104.3684605,0,4944.423025
133818,64,47,1,1,Married,4,104.2926078,0,4991.63039
121390,66,46,3,1,Married,9,104.2912674,0,4700.56337
152238,24,7,3,1,Married,13,104.2250878,0,4815.25439
154285,61,42,2,0,Single,7,104.1123598,0,4992.61799
132511,62,45,2,0,Single,13,104.0642762,0,4961.21381
108895,68,49,3,0,Single,14,104.0232158,0,4976.16079
172082,62,44,3,1,Single,6,103.9977882,0,4631.88941
195698,64,47,2,0,Married,10,103.9783831,0,4828.919155
185083,61,41,3,0,Married,12,103.8177576,0,4979.88788
178706,65,44,4,0,Married,13,103.759105,0,4636.95525
141030,63,43,2,0,Single,11,103.607787,0,4929.38935
146908,68,49,2,1,Single,0,103.5921231,0,4968.606155
128749,16,0,3,1,Married,13,103.5901994,0,4667.50997
149592,64,44,3,0,Married,5,103.5818012,0,4929.09006
165882,69,48,3,1,Married,5,103.5107252,0,4643.53626
145434,65,46,2,0,Single,12,103.3600888,0,4904.00444
136940,60,41,1,0,Married,15,103.2561255,0,4841.806275
194509,64,44,2,0,Married,9,103.2539105,0,4795.695525
154012,70,53,1,1,Single,8,103.2503942,0,4599.51971
168668,70,50,4,1,Single,14,103.23583,0,4923.7915
151048,61,44,2,0,Single,11,103.2168077,0,4847.840385
183778,67,46,2,1,Single,13,103.1529002,0,4948.64501
182144,66,47,3,1,Married,7,103.0465771,0,4747.328855
153165,61,44,1,0,Single,11,103.0438125,0,4676.190625
144515,67,50,2,0,Married,7,103.0383765,0,4707.918825
126404,68,47,2,0,Married,11,102.9205046,0,4881.02523
124897,68,48,4,0,Married,9,102.8559119,0,4932.795595
157377,62,42,4,1,Married,14,102.7519463,0,4643.597315
184008,68,49,4,1,Married,11,102.7367941,0,4671.839705
131410,62,41,2,1,Single,13,102.6944895,0,4826.724475
152227,69,51,1,1,Single,12,102.5836151,0,4554.180755
140007,61,44,3,1,Single,8,102.5411698,0,4832.05849
115919,25,7,3,1,Married,13,102.3794161,0,4548.970805
156828,24,5,1,1,Married,12,102.363644,0,4824.1822
149779,60,39,3,0,Single,6,102.3460608,0,4866.30304
130384,61,42,1,1,Married,13,102.3352065,0,4686.760325
144398,60,39,2,1,Single,15,102.1883069,0,4698.415345
126084,60,43,3,0,Married,10,102.1563167,0,4900.815835
164359,69,50,3,1,Married,7,102.1084932,0,4649.42466
164997,63,46,2,1,Married,5,102.0848918,0,4783.24459
128550,67,50,1,0,Single,6,102.0728666,0,4842.64333
190230,61,43,3,1,Single,15,102.0576231,0,4528.881155
156610,63,42,2,0,Single,7,102.0442511,0,4835.212555
150881,66,45,2,1,Single,8,101.9951459,0,4613.757295
145953,68,48,3,1,Married,6,101.9932844,0,4867.66422
110789,67,46,3,1,Single,13,101.8740628,0,4816.70314
135944,66,46,3,0,Married,0,101.8565895,0,4773.829475
171182,67,46,2,0,Single,4,101.7339118,0,4790.69559
196657,66,45,1,0,Single,1,101.6847135,0,4604.235675
171031,68,51,3,0,Single,10,101.6246447,0,4670.232235
177434,63,45,1,0,Married,12,101.5269237,0,4533.346185
153473,63,43,3,1,Single,10,101.5033229,0,4836.166145
167394,70,50,3,1,Married,3,101.285223,0,4488.26115
187212,67,46,4,1,Single,14,101.2815455,0,4540.077275
110958,70,49,4,1,Single,15,101.2669787,0,4713.348935
158910,68,47,1,1,Single,15,101.120868,0,4781.0434
147781,23,6,2,1,Married,15,101.0070034,0,4517.35017
186771,68,51,3,1,Single,10,100.9401647,0,4717.008235
177389,63,42,2,0,Single,7,100.8304387,0,4643.521935
184942,67,48,3,1,Single,12,100.5125468,0,4585.62734
181946,70,49,3,1,Single,9,100.4205333,0,4789.026665
149607,60,42,3,1,Single,10,100.3853562,0,4463.26781
116854,68,48,4,0,Married,8,100.228615,0,4460.43075
129391,51,31,2,1,Married,12,100.2061501,0,4766.307505
141866,60,40,2,0,Married,2,100.1397243,0,4672.986215
196610,62,41,2,0,Single,13,99.98969209,0,4561.484605
110501,70,50,2,0,Married,12,99.98183865,0,4474.091933
193446,70,51,4,1,Married,12,99.97282631,0,4744.641316
105121,63,43,2,1,Married,11,99.96692428,0,4459.346214
135686,70,49,1,1,Married,14,99.83090559,0,4557.54528
189981,23,6,3,1,Married,12,99.81541486,0,4562.770743
145181,63,46,1,0,Married,10,99.59866543,0,4771.933272
176019,64,45,2,1,Married,4,99.51138783,0,4595.569392
148963,64,45,4,1,Married,13,99.41027091,0,4434.513546
129024,67,49,3,0,Single,11,99.39182437,0,4529.591219
187736,70,53,1,0,Single,11,99.29601274,0,4624.800637
182401,70,49,3,0,Single,9,99.22745932,0,4757.372966
173663,67,46,3,1,Single,8,99.15114648,0,4357.557324
149451,66,45,3,1,Single,10,98.98947239,0,4463.47362
158679,63,43,3,0,Married,7,98.92988566,0,4485.494283
134650,67,49,3,1,Married,1,98.87404547,0,4364.702274
109423,64,46,2,0,Single,8,98.82646966,0,4548.323483
169141,67,50,2,0,Married,7,98.78782384,0,4680.391192
146420,64,44,3,0,Single,12,98.58804303,0,4580.402152
150426,63,42,2,1,Married,8,98.55136527,0,4411.568264
146994,67,46,1,1,Single,12,98.54406755,0,4420.203378
190808,70,53,2,0,Married,10,98.40162872,0,4408.081436
147015,63,43,3,0,Married,10,98.36057673,0,4684.028837
182191,70,53,3,0,Single,5,98.35032902,0,4429.516451
146724,62,43,2,1,Married,8,98.25327356,0,4507.663678
163832,70,53,1,1,Married,13,98.22413289,0,4452.206645
145906,70,49,3,1,Married,0,98.13246618,0,4345.623309
176137,66,46,2,1,Single,7,98.13020042,0,4651.510021
113860,67,47,1,0,Single,10,98.11645572,0,4577.822786
131927,69,48,1,1,Married,0,98.07355841,0,4459.677921
169338,66,48,2,1,Married,15,98.03562763,0,4683.781382
193982,17,0,2,1,Married,15,98.00493411,0,4446.246706
186797,70,50,1,0,Single,1,97.8872245,0,4603.361225
170777,60,42,1,0,Single,0,97.82479138,0,4292.239569
125885,68,51,4,1,Married,6,97.82043236,0,4687.021618
163695,60,43,4,0,Single,7,97.80988885,0,4367.494443
156696,70,53,1,0,Married,8,97.80945261,0,4635.472631
194164,68,48,2,1,Married,6,97.79617698,0,4504.808849
195711,65,45,2,0,Married,7,97.54387268,0,4585.193634
186402,70,52,1,0,Married,15,97.49609506,0,4546.804753
120578,68,51,3,0,Single,9,97.47832546,0,4670.916273
100660,63,46,3,0,Married,14,97.4740293,0,4391.701465
182472,67,49,3,0,Single,13,97.36663877,0,4575.331939
150270,16,0,2,1,Married,15,97.35629417,0,4532.814709
101678,68,49,2,0,Married,8,97.17633569,0,4332.816785
133715,69,49,2,1,Single,8,97.16610705,0,4458.305353
107666,62,45,1,1,Married,6,97.0052298,0,4648.26149
131438,60,43,4,0,Married,9,96.96107447,0,4302.053724
153727,69,49,3,0,Married,8,96.94203234,0,4638.101617
103987,68,50,1,0,Married,7,96.75876211,0,4379.938106
174510,61,40,2,0,Single,6,96.69153093,0,4402.576547
180795,61,42,3,1,Married,1,96.66775288,0,4270.387644
174549,69,50,1,1,Single,7,96.57177764,0,4429.588882
113899,66,47,3,1,Married,4,96.51896574,0,4227.948287
160360,68,49,2,1,Single,12,96.42275023,0,4587.137512
175891,68,47,2,1,Married,7,96.39583307,0,4526.791654
124942,65,48,1,1,Married,10,96.36032976,0,4371.016488
110744,69,51,2,1,Married,7,96.32803426,0,4243.401713
160866,19,1,3,1,Married,14,96.32654401,0,4408.327201
154515,67,48,3,1,Married,7,96.31551372,0,4285.775686
188200,64,46,3,1,Married,7,96.20616382,0,4295.308191
126559,64,43,2,0,Single,2,96.06678677,0,4243.339339
100459,63,45,3,0,Single,10,95.78214073,0,4487.107037
118053,63,43,4,0,Married,9,95.74811004,0,4211.405502
169127,64,46,3,0,Single,9,95.65189276,0,4407.594638
161043,63,42,1,0,Single,13,95.64112278,0,4372.056139
193859,68,48,2,1,Single,5,95.47112785,0,4473.556393
189378,61,44,2,1,Married,4,95.41219208,0,4188.609604
124754,69,50,1,0,Married,14,95.24364537,0,4431.182269
139395,70,53,2,1,Single,6,95.23895266,0,4223.947633
139640,66,46,2,0,Married,4,94.93996425,0,4279.998213
184537,66,45,2,1,Married,6,94.8925662,0,4506.62831
162681,25,6,3,1,Married,13,94.57991524,0,4518.995762
138412,67,50,2,0,Married,13,94.42754236,0,4189.377118
160000,64,43,2,0,Single,6,94.35787495,0,4336.893748
193533,61,40,2,0,Married,10,94.32144577,0,4194.072289
127333,63,45,1,0,Married,7,94.16102473,0,4401.051237
187870,65,46,2,0,Single,4,94.13262662,0,4311.631331
169076,63,42,1,0,Married,5,94.0483353,0,4200.416765
127217,66,45,2,0,Married,15,93.98528936,0,4216.264468
168894,68,50,2,0,Married,6,93.95976672,0,4138.988336
138509,69,52,2,0,Single,7,93.95705645,0,4325.852823
172799,63,46,2,0,Married,13,93.90273659,0,4224.13683
142739,21,2,2,1,Married,15,93.8245816,0,4482.22908
176798,70,52,3,1,Single,15,93.7226595,0,4168.132975
109023,67,46,3,0,Single,6,93.64557058,0,4457.278529
184410,66,45,2,1,Single,13,93.63595187,0,4092.797594
180278,70,52,2,1,Married,1,93.57814333,0,4447.907167
178997,68,50,3,0,Single,1,93.56574673,0,4335.287337
101158,70,52,2,1,Single,3,93.54095493,0,4215.047747
191041,67,50,1,0,Married,7,93.51562689,0,4116.781345
172191,70,52,2,0,Married,11,93.4308714,0,4250.54357
162595,20,2,3,1,Married,14,93.08350236,0,4188.175118
188140,68,48,2,1,Single,14,93.06167252,0,4420.083626
153214,63,43,3,1,Single,7,92.98932417,0,4312.466209
165243,61,40,1,1,Married,15,92.94404984,0,4121.202492
135081,62,45,2,1,Single,8,92.90626476,0,4202.313238
181131,61,40,4,1,Single,1,92.87239312,0,4292.619656
128464,62,42,1,1,Single,10,92.7900941,0,4194.504705
113221,61,40,2,1,Married,4,92.74831323,0,4186.415662
193723,64,45,4,0,Single,7,92.6382434,0,4215.91217
104092,69,48,2,1,Married,8,92.497508,0,4235.8754
104816,66,46,3,0,Single,0,92.48026347,0,4125.013174
161878,61,41,3,0,Married,5,92.47682971,0,4050.841486
188085,69,49,2,0,Married,15,92.40841781,0,4390.420891
136479,64,43,2,0,Single,7,92.36532405,0,4168.266203
144460,63,45,3,1,Single,7,92.17754646,0,4402.877323
162398,69,51,4,0,Single,9,91.45075257,0,3981.537629
192993,66,49,4,0,Married,6,91.43353114,0,4297.676557
139695,62,41,1,0,Single,7,91.22254626,0,4029.127313
118893,68,51,2,0,Single,3,91.19959014,0,4085.979507
191518,67,47,3,1,Married,9,90.96473248,0,4110.236624
139304,61,40,4,0,Single,5,90.9177374,0,3965.88687
107413,69,48,4,1,Single,10,90.61895816,0,4162.947908
155220,66,45,2,1,Married,14,90.59799692,0,4045.899846
191987,62,44,2,0,Married,7,90.58388932,0,4199.194466
158296,60,41,3,0,Married,9,90.3835635,0,4226.178175
116492,60,42,2,1,Married,12,90.34735176,0,4065.367588
128860,62,42,4,1,Single,8,90.32206754,0,4223.103377
178033,65,46,3,1,Single,4,90.2890851,0,4282.454255
157345,65,47,4,1,Married,4,90.23492874,0,4092.746437
169574,68,47,4,1,Married,10,90.18956327,0,4088.478164
153980,70,52,2,0,Married,15,90.11877425,0,4164.938713
149742,65,48,4,1,Married,2,90.11203101,0,4202.601551
144545,61,44,1,0,Married,10,90.01448314,0,3925.724157
155679,66,47,2,0,Single,0,89.95110132,0,4237.555066
165940,69,51,2,1,Married,13,89.86262965,0,3920.131483
107719,63,46,2,0,Married,9,89.85484669,0,4135.742335
124048,65,44,1,1,Single,6,89.85327017,0,4076.663509
166195,60,42,1,0,Single,10,89.69023459,0,3894.51173
104567,64,43,3,1,Married,6,89.6601082,0,4026.00541
193900,60,39,4,0,Married,7,89.57334772,0,4263.667386
166666,65,48,2,0,Single,3,89.56574204,0,3901.287102
130906,63,43,2,1,Married,3,89.54089032,0,4185.044516
111368,64,45,2,1,Single,5,89.47124329,0,4129.562165
136071,61,40,1,0,Married,13,89.43264583,0,4113.632292
114920,62,42,3,1,Married,13,89.30299607,0,4168.149804
135934,61,43,1,0,Single,5,89.22649655,0,4083.324828
113156,62,42,2,1,Married,9,89.11187614,0,4114.593807
192657,65,46,4,0,Single,6,89.07133084,0,4182.566542
110911,68,47,4,0,Single,7,88.93732999,0,4203.8665
152042,70,51,2,1,Married,4,88.89470277,0,4088.735139
143537,61,42,1,1,Married,14,88.79955695,0,4167.977848
171139,62,43,3,1,Single,14,88.62556914,0,4117.278457
112627,64,45,4,0,Single,7,88.61306666,0,3940.653333
111264,64,43,1,0,Married,4,88.54856139,0,4050.42807
197258,67,50,2,1,Single,13,88.36932498,0,4193.466249
142908,68,49,3,0,Married,3,88.22713977,0,3956.356989
107709,69,52,1,0,Married,6,88.21345418,0,3950.672709
192779,60,40,4,1,Married,9,88.15150547,0,4056.575274
114225,70,52,2,1,Single,12,88.01923871,0,3936.961936
193119,67,46,2,0,Single,5,88.0141516,0,4145.70758
123127,62,43,1,1,Married,11,87.76790241,0,3846.395121
179475,62,43,4,1,Single,7,87.75144136,0,4094.572068
119216,66,49,2,0,Married,12,87.4444744,0,4011.22372
115648,64,47,1,1,Married,1,87.44162875,0,3863.081438
141609,67,46,1,1,Married,4,87.42638215,0,3944.319108
117139,67,47,3,1,Married,3,87.39936221,0,4044.968111
114988,66,49,2,1,Single,15,87.34698788,0,4016.349394
111483,63,43,3,1,Married,11,87.29106215,0,4020.553108
109334,66,48,2,1,Married,11,87.21915326,0,3774.957663
167780,62,41,4,0,Married,12,87.16046168,0,4075.023084
157072,63,44,4,1,Married,1,87.14737152,0,4059.368576
123996,70,51,3,1,Single,12,87.09666633,0,3955.833317
146664,64,44,2,0,Married,2,87.04182469,0,4138.091235
169983,69,50,4,1,Single,5,86.98662692,0,3879.331346
114196,69,50,3,0,Married,12,86.94278092,0,4057.139046
165353,69,52,4,0,Married,14,86.88355432,0,3875.177716
193810,61,42,3,1,Married,9,86.88180627,0,4140.090314
135461,65,47,4,1,Single,11,86.84018565,0,3922.009283
148473,61,41,3,1,Married,13,86.80751319,0,4076.37566
181084,61,42,2,0,Single,0,86.80603022,0,4054.301511
128457,66,46,3,0,Single,11,86.74451832,0,3865.225916
174821,67,47,1,0,Married,5,86.7404906,0,3937.02453
199294,68,49,2,1,Married,13,86.71810146,0,3945.905073
152631,68,47,3,1,Single,0,86.65837786,0,4077.918893
123332,67,48,3,0,Married,11,86.59232756,0,3861.616378
134670,64,45,4,1,Married,9,86.49540823,0,3985.770412
109392,68,50,2,0,Single,10,86.40253407,0,3946.126704
171217,67,47,3,0,Married,12,86.3034625,0,3963.173125
139113,67,50,3,0,Married,13,86.18806373,0,3994.403187
110686,62,42,3,1,Married,13,86.11922175,0,3885.961088
194809,60,43,3,0,Single,2,86.02090789,0,4017.045395
191144,16,0,3,1,Married,11,85.8812279,0,3841.061395
161728,65,47,3,1,Married,14,85.80766767,0,3735.383384
137052,68,48,2,1,Married,14,85.80168233,0,3812.084117
116131,69,49,2,0,Married,3,85.7968658,0,3933.84329
172672,68,51,2,1,Single,7,85.73583983,0,3896.791992
193520,70,50,2,0,Single,6,85.61036078,0,4051.518039
189507,66,48,1,1,Single,6,85.54529253,0,3809.264627
156939,61,41,2,1,Single,12,85.48223065,0,3851.111533
147445,61,40,3,1,Married,13,85.44059006,0,4041.029503
198681,64,46,2,1,Single,10,85.41548414,0,3908.774207
133096,61,40,2,0,Single,0,85.40313363,0,3854.156682
106181,64,43,4,1,Married,14,85.27095726,0,3936.547863
102109,67,48,4,1,Single,12,85.12375805,0,4032.187903
100366,69,48,2,0,Married,8,85.12163185,0,3845.081593
129735,65,47,4,0,Married,12,84.94993314,0,3792.496657
133990,70,50,1,1,Married,12,84.88651418,0,3788.325709
170381,65,47,2,1,Married,1,84.86618483,0,3866.309242
118460,66,48,4,0,Married,1,84.7957942,0,3882.78971
130589,62,44,1,1,Single,2,84.75366158,0,3649.683079
187678,63,42,2,0,Single,2,84.57802091,0,3871.901046
134836,68,47,2,1,Married,11,84.54595559,0,3885.29778
187201,61,41,2,0,Married,8,84.50606754,0,3629.303377
150203,69,48,3,1,Married,15,84.41786459,0,3640.89323
170282,68,51,3,0,Single,8,84.41738979,0,3631.86949
191325,67,50,2,1,Married,15,84.28116303,0,3921.058152
176392,67,50,3,1,Married,0,84.05013733,0,3951.506867
119896,66,46,3,1,Married,12,84.04800903,0,3707.400452
130840,65,45,2,1,Single,6,84.04450836,0,3848.225418
151843,68,50,2,0,Single,9,83.85243398,0,3709.621699
105359,61,42,4,0,Single,13,83.7684502,0,3593.42251
195497,66,45,4,1,Single,9,83.72879492,0,3679.439746
144356,69,51,1,1,Single,4,83.64543896,0,3951.271948
160155,64,44,3,1,Married,14,83.60785695,0,3856.392848
141347,61,44,2,0,Single,13,83.60707739,0,3667.35387
142309,68,48,3,1,Single,6,83.52161667,0,3872.080834
194142,70,53,3,1,Single,4,83.38302321,0,3792.151161
141567,69,49,1,0,Married,9,83.36179822,0,3954.089911
142376,69,49,4,1,Single,4,83.35991013,0,3677.995507
160652,70,53,1,0,Married,9,83.09996407,0,3582.998204
171140,67,46,3,0,Single,13,82.80010174,0,3591.005087
181651,63,42,2,0,Married,1,82.69181053,0,3656.590527
110623,66,47,3,1,Single,3,82.63991354,0,3561.995677
170556,64,45,2,1,Single,1,82.57091774,0,3707.545887
130758,66,47,2,0,Married,1,82.53339398,0,3720.669699
136475,62,45,3,1,Single,6,82.45299676,0,3547.649838
172308,68,49,1,1,Single,13,82.41769821,0,3643.884911
144048,70,49,1,1,Married,5,82.40758602,0,3607.379301
150292,65,47,3,0,Married,3,82.26778019,0,3704.38901
168016,65,48,3,1,Single,12,82.21585193,0,3862.792597
126035,68,49,3,1,Single,8,82.14186529,0,3745.093265
161046,63,44,2,0,Married,7,82.07796289,0,3886.898145
133986,66,46,3,0,Married,7,82.05360918,0,3875.680459
179135,67,50,2,0,Single,2,81.92175004,0,3712.087502
177381,60,43,3,1,Married,14,81.87050588,0,3751.525294
167884,70,51,3,1,Single,13,81.85843404,0,3551.921702
131340,63,43,3,1,Married,11,81.7970462,0,3650.85231
148972,66,47,2,1,Married,7,81.75370667,0,3829.685334
141976,67,49,4,0,Single,0,81.71550137,0,3527.775069
120744,70,50,1,1,Married,5,81.55524621,0,3702.762311
161974,70,52,3,1,Married,14,81.52838355,0,3594.419178
116349,61,43,3,1,Single,7,81.44744839,0,3490.37242
115007,66,47,3,0,Single,3,81.4208887,0,3680.044435
109788,70,51,3,1,Married,8,81.34570916,0,3766.285458
134049,69,51,2,0,Single,5,81.31264814,0,3697.632407
140050,63,43,4,1,Married,12,81.22172776,0,3583.086388
110246,60,43,2,1,Married,9,81.06218672,0,3599.109336
119204,63,44,3,0,Married,9,80.99372304,0,3833.686152
137261,67,48,3,0,Single,1,80.8727244,0,3808.63622
124058,62,45,2,1,Married,7,80.70727476,0,3566.363738
156544,62,42,2,1,Single,8,80.70565909,0,3800.282955
102755,68,51,3,1,Married,15,80.59141132,0,3574.570566
159850,65,48,3,0,Single,11,80.56296598,0,3815.148299
190934,62,41,2,0,Married,14,80.35244519,0,3599.62226
151333,64,43,1,0,Married,8,80.34581115,0,3691.290558
161345,62,44,4,1,Single,8,80.29749149,0,3793.874575
183413,61,40,4,1,Married,12,80.2596089,0,3639.980445
121073,70,49,2,0,Married,7,80.24221083,0,3484.110542
192027,66,45,2,0,Single,8,80.11881426,0,3727.940713
100927,68,50,3,0,Single,9,80.09244893,0,3768.622447
148016,68,50,2,1,Married,12,80.03609073,0,3546.804537
184686,63,43,3,0,Single,9,79.93536521,0,3555.768261
112676,63,45,4,0,Married,11,79.87546674,0,3764.773337
128103,66,49,3,0,Married,12,79.82822354,0,3564.411177
189682,67,49,2,1,Single,13,79.72859359,0,3493.42968
112620,64,45,2,0,Married,14,79.71157625,0,3615.578813
101395,63,43,3,0,Single,12,79.57325501,0,3732.662751
120396,65,47,2,1,Married,12,79.38704147,0,3650.352074
117630,67,50,3,0,Single,4,79.30935632,0,3565.467816
154249,70,53,3,0,Married,9,79.30528645,0,3417.264323
188524,67,50,3,0,Single,6,79.30068479,0,3693.03424
103047,67,47,2,1,Married,9,79.29913049,0,3681.956525
174658,69,49,3,1,Single,15,79.29765712,0,3733.882856
113818,67,47,4,0,Single,7,79.27369925,0,3626.684963
192799,64,44,1,0,Married,0,79.02764007,0,3729.382004
107371,66,46,2,1,Single,12,78.95193381,0,3573.596691
111536,67,49,2,0,Married,11,78.8749044,0,3635.74522
111213,69,52,4,1,Single,15,78.78334759,0,3510.16738
199665,66,49,1,1,Single,9,78.76982783,0,3594.491392
169500,61,43,2,0,Married,14,78.58096229,0,3638.048115
145869,64,45,2,1,Married,10,78.39925553,0,3488.962777
188352,67,50,1,0,Single,6,78.38607932,0,3321.303966
131572,68,48,2,1,Married,4,78.37664648,0,3713.832324
190332,60,41,2,1,Married,13,78.35393177,0,3599.696589
168140,67,48,2,1,Single,6,78.31542177,0,3383.771089
163130,60,42,3,0,Married,14,78.04625637,0,3693.312819
109749,66,47,3,1,Married,6,78.03983088,0,3514.991544
120448,69,52,2,1,Married,7,78.00479255,0,3679.239628
115121,70,49,4,1,Married,1,77.99627349,0,3669.813675
174548,60,39,3,1,Married,14,77.93429821,0,3341.714911
163370,66,47,3,0,Married,0,77.83166638,0,3466.583319
128560,69,50,4,0,Married,12,77.80208494,0,3324.104247
193960,67,46,4,1,Single,11,77.75878053,0,3297.939027
125980,65,44,4,1,Married,9,77.58551518,0,3428.275759
114670,61,40,2,1,Single,9,77.56680706,0,3672.340353
185414,64,46,3,0,Single,14,77.55603969,0,3483.801985
155999,60,43,2,0,Married,2,77.42575389,0,3567.287695
187987,68,50,2,0,Single,6,77.22867015,0,3603.433508
106840,66,45,2,1,Single,1,77.19025279,0,3557.51264
176039,65,46,4,1,Single,12,77.12915746,0,3361.457873
167689,67,49,4,0,Married,10,77.10876788,0,3313.438394
179730,68,48,2,1,Single,0,77.05782973,0,3626.891487
196457,70,53,4,1,Married,5,76.89883449,0,3262.941725
186192,62,44,3,1,Single,3,76.88300323,0,3383.150162
179298,69,48,2,0,Married,5,76.74981055,0,3300.490528
163334,67,48,3,1,Single,11,76.70863982,0,3475.431991
121672,65,48,2,0,Married,4,76.61165366,0,3306.582683
134273,67,48,3,0,Single,10,76.52820295,0,3467.410148
100754,64,47,2,0,Married,14,76.32943276,0,3309.471638
149664,63,46,4,1,Married,15,76.30185594,0,3430.092797
198167,67,46,1,0,Married,11,75.84119327,0,3276.059664
169496,63,46,1,0,Married,2,75.81174432,0,3574.587216
194250,61,42,4,1,Married,3,75.79561871,0,3312.780936
142918,61,41,2,0,Married,5,75.7535758,0,3496.67879
171960,63,43,4,0,Married,9,75.74933284,0,3527.466642
127599,68,50,1,0,Married,6,75.70366892,0,3448.183446
141782,68,49,1,1,Single,6,75.69629415,0,3484.814708
159432,67,47,2,1,Married,6,75.60464898,0,3188.232449
171691,62,43,2,1,Single,10,75.38855302,0,3300.427651
195756,63,43,3,1,Married,8,75.20657542,0,3441.328771
107141,61,40,3,1,Single,11,75.17008379,0,3450.50419
187708,68,47,4,1,Single,11,75.14913897,0,3554.456949
119400,70,50,3,1,Single,5,75.14196651,0,3176.098326
199598,60,39,3,0,Single,14,75.09425088,0,3223.712544
191176,63,44,2,0,Married,8,75.03338088,0,3323.669044
140462,61,41,3,0,Married,13,75.00200112,0,3410.100056
181569,63,45,2,0,Married,13,74.96116919,0,3460.05846
145978,64,44,4,1,Married,13,74.89958368,0,3369.979184
175822,67,48,3,1,Single,10,74.85602416,0,3231.801208
106544,65,46,4,0,Single,2,74.7869296,0,3184.34648
124052,69,50,3,1,Married,13,74.67838008,0,3141.919004
196681,65,46,1,1,Single,10,74.64014392,0,3346.007196
100648,63,42,4,0,Single,13,74.5877807,0,3368.389035
160188,69,48,3,1,Single,9,74.58336239,0,3499.16812
180933,63,44,3,0,Married,0,74.48355032,0,3522.177516
104190,60,43,2,1,Single,5,74.39969556,0,3251.984778
193426,64,44,2,0,Married,12,74.34267212,0,3445.133606
167663,70,53,2,1,Single,6,74.26939143,0,3405.469572
116786,60,42,2,1,Married,6,74.08359006,0,3161.179503
106648,60,42,4,0,Married,14,74.03704187,0,3413.852094
177832,61,44,3,1,Married,12,73.97578093,0,3151.789047
147922,68,51,2,1,Single,15,73.92056559,0,3280.02828
168534,65,46,3,1,Married,12,73.8504676,0,3360.52338
121180,67,48,2,0,Married,12,73.79096185,0,3142.548093
135198,63,43,3,0,Single,3,73.77395873,0,3216.697937
151406,66,47,1,0,Single,3,73.7182254,0,3195.91127
128305,65,47,4,0,Married,0,73.66004219,0,3216.00211
163098,63,45,3,0,Married,15,73.64012637,0,3084.006319
153716,62,41,2,1,Single,10,73.50576102,0,3436.288051
199252,62,45,3,0,Single,5,73.44526457,0,3096.263229
151614,64,46,3,0,Married,3,73.38371864,0,3140.185932
190520,61,41,3,1,Single,3,73.35201539,0,3341.60077
187718,63,42,2,1,Married,4,73.30751023,0,3210.375512
151742,61,43,3,0,Single,6,73.30396425,0,3088.198213
142835,69,50,3,0,Married,11,73.20865642,0,3363.432821
191821,68,51,3,1,Married,10,73.17241539,0,3315.62077
117609,68,48,3,0,Single,2,73.11737859,0,3269.86893
116319,65,48,3,0,Single,5,73.06398872,0,3161.199436
160191,65,44,1,1,Single,11,72.71566223,0,3284.783112
127131,70,50,3,1,Single,9,72.66213331,0,3095.106666
192549,61,42,4,1,Married,4,72.46236019,0,3037.11801
100902,66,47,1,0,Married,10,72.31203068,0,3293.601534
128450,68,48,3,0,Married,1,72.21033584,0,3199.516792
176688,62,42,2,0,Married,7,71.88830779,0,3153.41539
105295,68,50,4,0,Married,13,71.86205108,0,3094.102554
128414,69,48,3,1,Married,6,71.80712459,0,3078.35623
120429,66,49,1,1,Single,0,71.76435204,0,3353.217602
164764,67,46,2,1,Married,6,71.75674071,0,3087.837036
106324,69,52,3,0,Married,5,71.75612159,0,3283.80608
133569,69,50,4,0,Married,1,71.72666395,0,3209.333198
186300,65,47,3,1,Single,3,71.59925919,0,3263.96296
125455,62,45,4,1,Single,5,71.59746867,0,3352.873434
160562,61,43,2,1,Single,13,71.53949632,0,3272.974816
159500,60,39,2,1,Single,9,71.51716314,0,3254.858157
110213,68,50,3,1,Single,6,71.49379042,0,3074.689521
138740,70,53,2,0,Single,15,71.48274719,0,3170.13736
155258,68,48,1,1,Married,11,71.4770177,0,3128.850885
197772,61,40,2,1,Single,0,71.45820678,0,3031.910339
198587,64,43,2,1,Married,3,71.34780307,0,3234.390154
121933,62,42,3,1,Single,10,71.28362096,0,3363.181048
166539,70,52,2,0,Single,15,71.14057848,0,3317.028924
107985,66,48,2,0,Single,1,71.04885364,0,3129.442682
120341,65,44,2,1,Single,13,71.02245508,0,3140.122754
138378,70,53,2,1,Married,15,70.90479127,0,2956.239564
164184,67,50,3,0,Married,6,70.819124,0,3247.9562
157122,66,48,4,0,Married,6,70.78413813,0,3173.206907
137269,66,48,2,1,Married,7,70.7391574,0,3162.95787
187812,70,51,2,1,Single,8,70.5900955,0,3033.504775
130079,70,50,3,1,Single,6,70.44161206,0,3055.080603
192105,69,50,2,0,Married,11,70.4133616,0,3187.66808
118672,69,48,4,1,Single,5,70.36978543,0,3297.489272
121846,68,51,1,1,Married,13,70.30320012,0,3134.160006
149617,61,42,2,1,Married,6,70.23548331,0,3233.774166
103770,68,47,3,0,Married,11,70.1697747,0,3307.488735
143156,60,42,3,1,Single,7,70.05365,0,3276.6825
155009,63,42,2,0,Single,10,70.02348197,0,3261.174099
130710,67,47,3,0,Married,11,70.00565285,0,2915.282643
164299,61,44,2,1,Single,6,70.00176368,0,3228.088184
137434,69,48,1,0,Single,5,69.89304091,0,2902.652046
110617,68,51,2,0,Single,13,69.87144406,0,3211.572203
118556,62,42,4,1,Married,14,69.6618494,0,3142.09247
122696,70,53,3,1,Single,3,69.57448047,0,2967.724024
158147,69,48,3,0,Married,1,69.48465591,0,2981.232796
191679,67,50,2,0,Single,9,69.48225196,0,2920.112598
115217,70,50,2,1,Single,13,69.30784152,0,3050.392076
184810,64,47,1,0,Single,1,69.22817764,0,2931.408882
163488,64,44,2,0,Married,0,69.10484034,0,3001.242017
148468,69,49,3,1,Single,5,68.99066105,0,3132.533053
177816,63,44,4,0,Married,14,68.98139104,0,2943.069552
123902,61,43,4,0,Married,6,68.89962569,0,2977.981285
199093,60,41,2,0,Married,2,68.87011296,0,2928.505648
146654,67,50,3,1,Single,7,68.86972204,0,3130.486102
127053,63,45,3,0,Married,8,68.79881215,0,3146.940608
180077,63,43,1,0,Single,8,68.70466955,0,3127.233478
104358,65,46,4,0,Married,3,68.65999655,0,3043.999828
193955,69,51,2,1,Married,9,68.62782962,0,3068.391481
148638,65,46,2,1,Married,4,68.62394417,0,2874.197209
111066,67,46,4,0,Single,15,68.45241461,0,2908.620731
126236,64,44,3,0,Single,13,68.35890723,0,3053.945362
164213,61,40,1,0,Single,5,68.35529078,0,2821.764539
122192,69,48,4,1,Married,8,68.28344329,0,3131.172165
145004,60,43,1,0,Single,15,68.2247322,0,3077.23661
133177,66,46,4,0,Single,8,68.2039752,0,3033.19876
144903,70,49,2,1,Married,5,68.16716855,0,2906.358428
185136,67,50,4,0,Single,12,68.15882577,0,3148.941289
111341,63,46,2,0,Married,11,68.13913798,0,3196.956899
149278,68,49,3,1,Single,14,67.88157272,0,2933.078636
100163,65,48,2,1,Married,12,67.60303585,0,2787.151793
195786,65,48,1,0,Single,7,67.57033377,0,3016.516689
113866,69,51,2,0,Married,14,67.45867564,0,2978.933782
197322,60,41,2,0,Single,11,67.44728236,0,3148.364118
187608,64,44,1,0,Married,15,67.44164804,0,3079.082402
135853,65,44,4,1,Married,3,67.40853771,0,3151.426886
140830,70,53,2,1,Married,7,67.1244936,0,3040.22468
114149,68,51,2,0,Married,7,67.09859785,0,2808.929893
169432,64,44,1,0,Married,6,67.08095282,0,3120.047641
197211,69,50,1,0,Single,12,66.92891767,0,2995.445884
179974,63,42,3,0,Single,7,66.89798915,0,3012.899458
105976,64,44,3,1,Married,14,66.89404456,0,3068.702228
133817,69,50,2,1,Married,15,66.89326391,0,2955.663196
110298,63,44,3,1,Single,3,66.80455665,0,2899.227833
143980,67,46,2,1,Single,14,66.7865407,0,2881.327035
162289,64,45,3,1,Married,12,66.60388249,0,2795.194125
156633,67,48,4,1,Married,6,66.53102098,0,2921.551049
169308,70,52,4,0,Married,1,66.52075201,0,2820.037601
120763,63,43,3,1,Married,11,66.48518655,0,3003.259328
166296,61,44,4,1,Married,10,66.45717733,0,2953.858867
163773,70,53,3,0,Married,15,66.37737968,0,2850.868984
164942,63,43,2,1,Married,12,66.30321234,0,2912.160617
157046,70,51,2,1,Married,7,66.24725714,0,2769.362857
138241,63,46,2,0,Married,11,66.23357793,0,3077.678897
199154,65,45,2,1,Married,8,66.10940926,0,2938.470463
123016,65,44,2,1,Single,5,66.10569402,0,2751.284701
155631,69,48,3,1,Married,3,65.82613541,0,2958.306771
189632,64,47,1,0,Single,14,65.68992357,0,2945.496179
124299,62,42,2,0,Single,8,65.68530084,0,2764.265042
131225,65,46,1,0,Married,6,65.67824927,0,2896.912464
199994,70,50,3,0,Married,8,65.66019059,0,2717.00953
180139,66,47,4,0,Single,7,65.60367336,0,2839.183668
197831,66,48,4,1,Married,5,65.57457103,0,2690.728552
142541,62,45,3,0,Married,13,65.53624329,0,2857.812165
136504,60,39,1,0,Married,2,65.4394601,0,2942.973005
130817,68,48,4,0,Married,4,65.36114567,0,2848.057284
192038,65,45,3,0,Single,11,65.3378324,0,2697.89162
163029,69,50,2,0,Married,12,65.30246234,0,2965.123117
165865,66,48,2,0,Married,9,65.19885583,0,3040.942792
185510,64,44,1,1,Married,14,65.10147637,0,2688.073819
144658,69,52,2,1,Married,6,65.05792786,0,2726.896393
197424,70,50,3,0,Single,13,64.74551084,0,2886.275542
125251,69,49,3,1,Married,3,64.74424852,0,2916.212426
114490,69,48,2,0,Married,2,64.72794527,0,3029.397264
111826,60,39,2,0,Single,1,64.58064165,0,2978.032083
160739,67,49,2,1,Married,7,64.56557844,0,2726.278922
101482,70,52,4,1,Single,8,64.50148927,0,2810.074464
119169,60,40,3,1,Single,4,64.49974894,0,2752.987447
105151,70,49,2,0,Married,9,64.38351041,0,2955.175521
160973,64,47,2,1,Married,1,64.2533819,0,2634.669095
108325,67,50,3,1,Single,0,64.23003046,0,2709.501523
144791,67,49,3,0,Married,11,64.04509929,0,2904.254965
118465,60,39,2,1,Married,0,63.94251473,0,2920.125737
162974,63,45,3,0,Married,11,63.91577391,0,2657.788696
135099,60,43,1,0,Married,11,63.83158513,0,2793.579257
181589,70,49,2,0,Single,0,63.81299519,0,2948.64976
177563,64,43,3,1,Married,3,63.76613471,0,2723.306736
125006,68,50,3,0,Married,11,63.75359974,0,2925.679987
144036,63,45,3,0,Married,2,63.73827093,0,2663.913547
169935,69,49,3,0,Single,14,63.63810066,0,2733.905033
194704,70,49,3,1,Single,7,63.63507594,0,2686.753797
158663,63,44,2,0,Single,9,63.61993252,0,2867.996626
115206,62,42,3,1,Married,13,63.52049237,0,2818.024619
105781,65,47,4,1,Single,11,63.51792436,0,2746.896218
103114,70,53,2,0,Married,15,63.50212418,0,2727.106209
169129,70,53,3,0,Single,1,63.48688115,0,2620.344058
132073,64,47,4,0,Single,9,63.36369571,0,2918.184786
110395,70,53,1,1,Married,8,63.34173415,0,2636.086708
115370,60,41,3,0,Married,11,63.32357383,0,2693.178692
130633,60,42,3,1,Single,14,63.23567462,0,2876.783731
137617,61,41,2,0,Married,5,63.21606807,0,2695.803404
191471,61,40,3,1,Married,5,63.17475212,0,2681.737606
141191,69,51,2,1,Married,13,63.16716826,0,2883.358413
186616,64,43,3,0,Single,11,63.08515924,0,2680.257962
109600,69,51,3,1,Married,9,62.97042879,0,2636.52144
152990,62,43,1,1,Married,13,62.94165861,0,2823.082931
100607,62,43,2,1,Married,8,62.914229,0,2556.71145
169243,63,43,3,1,Married,14,62.87299302,0,2607.649651
190579,66,48,1,0,Married,12,62.86831105,0,2884.415553
164151,68,51,3,0,Married,7,62.85517407,0,2810.758704
170524,63,44,3,0,Married,10,62.69957597,0,2771.978799
135756,70,51,2,0,Married,2,62.6291482,0,2789.45741
132128,63,44,3,0,Single,15,62.61394395,0,2547.697198
147644,61,43,2,1,Single,3,62.52807026,0,2924.403513
128193,60,43,2,1,Single,15,62.45029758,0,2789.514879
109419,70,50,3,1,Single,14,62.40754107,0,2732.377054
134513,64,45,4,1,Married,13,62.38825937,0,2719.412969
164859,68,51,4,1,Married,1,62.36394588,0,2756.197294
146910,67,50,2,1,Single,5,62.35947994,0,2899.973997
148048,61,41,3,0,Married,15,62.31399058,0,2892.699529
166179,64,45,2,1,Married,7,62.21862184,0,2597.931092
140859,61,43,3,0,Single,15,62.21317163,0,2587.658582
105327,70,49,2,1,Married,14,62.20126455,0,2856.063228
134587,69,52,2,0,Married,13,62.13109269,0,2666.554635
130097,63,43,3,0,Single,11,61.95281383,0,2503.640692
191198,60,40,3,1,Single,3,61.86675766,0,2727.337883
198857,60,41,3,0,Married,10,61.70621631,0,2633.310816
145700,70,53,3,1,Single,9,61.69388936,0,2859.694468
178397,68,49,2,0,Single,8,61.63125261,0,2557.562631
148262,61,44,2,0,Single,0,61.60442174,0,2750.221087
176431,64,44,2,1,Single,3,61.42014606,0,2489.007303
179714,70,50,2,0,Single,7,61.39327511,0,2709.663756
178671,69,48,3,0,Married,6,61.37332962,0,2836.666481
192747,64,44,1,1,Married,5,61.3380877,0,2739.904385
100713,63,45,3,1,Married,1,61.27975132,0,2564.987566
162323,69,50,2,0,Married,3,61.21346914,0,2548.673457
113558,62,41,3,1,Single,10,61.17420029,0,2842.710015
152338,64,45,2,0,Married,15,61.02612944,0,2846.306472
157408,63,45,1,1,Married,3,60.82862632,0,2539.431316
134040,63,45,2,1,Single,2,60.64871447,0,2777.435724
104841,64,44,2,0,Married,4,60.55368144,0,2486.684072
112447,63,42,2,0,Single,10,60.43868173,0,2681.934087
127499,69,52,3,0,Single,2,60.43479361,0,2524.739681
169581,68,48,2,1,Married,15,60.43473783,0,2603.736892
190541,62,42,2,0,Married,12,60.42093258,0,2724.046629
183655,64,46,1,1,Married,1,60.38892485,0,2591.446243
195940,66,47,4,1,Married,13,60.36703842,0,2757.351921
180458,66,45,3,0,Married,9,60.30212247,0,2425.106124
193315,61,43,4,1,Married,12,60.11621977,0,2731.810989
107895,70,51,2,1,Married,1,59.95154582,0,2423.577291
138045,66,45,4,0,Single,2,59.74835755,0,2721.417878
153788,61,41,1,0,Single,7,59.64387603,0,2595.193802
105710,64,47,3,0,Married,10,59.63046307,0,2621.523154
120726,60,43,2,0,Single,8,59.48996052,0,2418.498026
143871,60,43,1,0,Single,2,59.46503446,0,2484.251723
106944,67,48,4,0,Married,8,59.42698007,0,2718.349004
110153,68,51,3,1,Married,2,59.35788412,0,2628.894206
104707,66,48,2,1,Single,3,59.24822739,0,2652.41137
131753,60,42,4,0,Married,1,59.24040111,0,2703.020056
160895,61,44,2,0,Married,4,59.08454763,0,2460.227382
161703,66,48,3,0,Married,13,59.06325549,0,2520.162775
148533,68,51,1,0,Married,15,59.01522562,0,2490.761281
160827,70,53,1,1,Married,8,59.00575916,0,2585.287958
102954,63,44,3,0,Married,7,58.88540421,0,2724.270211
167759,61,43,2,0,Single,3,58.82742683,0,2351.371342
144815,66,46,2,1,Single,14,58.82300028,0,2360.150014
140111,63,43,3,1,Married,11,58.74829096,0,2632.414548
158718,67,50,3,0,Married,6,58.74199899,0,2340.09995
150150,69,48,3,1,Single,13,58.73057119,0,2519.52856
177461,64,43,4,1,Married,14,58.70979023,0,2502.489512
167049,63,46,2,0,Married,14,58.6555409,0,2529.777045
138354,61,44,2,1,Married,4,58.570891,0,2478.54455
182956,62,43,1,1,Married,6,58.49819066,0,2466.909533
189499,61,43,4,1,Single,11,58.42642905,0,2535.321453
198231,63,43,2,0,Married,11,58.4202252,0,2387.01126
144351,67,46,3,1,Married,9,58.33931515,0,2496.965758
133610,69,48,3,0,Single,9,58.17556382,0,2332.778191
193638,63,44,2,0,Married,14,58.11371062,0,2307.685531
170301,66,45,4,0,Married,12,57.98909532,0,2508.454766
196280,70,49,1,1,Single,13,57.96961012,0,2379.480506
113734,65,47,2,0,Single,5,57.96417136,0,2646.208568
187210,70,49,2,1,Single,3,57.94600572,0,2620.300286
122823,70,49,2,0,Single,2,57.82588923,0,2682.294462
143203,70,50,3,0,Single,14,57.68439327,0,2361.219664
100764,66,49,2,1,Married,15,57.5769928,0,2515.84964
177621,62,42,1,0,Single,1,57.5163719,0,2572.818595
122934,62,44,1,0,Married,15,57.34341336,0,2404.170668
143523,68,50,2,1,Single,14,57.32931261,0,2605.465631
142322,63,42,1,0,Married,11,57.22333519,0,2386.16676
158782,60,40,2,1,Married,1,57.19166287,0,2350.583144
161095,68,51,2,0,Married,5,57.11119396,0,2291.559698
161909,62,42,4,1,Single,4,57.02113835,0,2466.056918
143839,60,40,2,0,Single,1,57.02071414,0,2624.035707
168127,69,52,2,1,Married,12,56.98060462,0,2498.030231
119670,60,39,3,0,Married,1,56.94671486,0,2555.335743
135694,67,48,2,1,Married,15,56.76405585,0,2434.202793
146798,66,48,2,1,Married,4,56.64461393,0,2399.230697
111391,69,48,2,1,Single,10,56.59454021,0,2598.727011
167021,63,44,1,0,Married,14,56.572462,0,2330.6231
128850,62,41,1,0,Married,5,56.4476765,0,2223.383825
139933,70,51,1,0,Married,3,56.35271511,0,2401.635756
152054,61,43,2,1,Single,6,56.28654154,0,2567.327077
117036,62,42,3,1,Single,10,56.28531315,0,2538.265658
106285,62,43,1,0,Single,6,56.27149032,0,2247.574516
146673,68,48,1,1,Married,13,56.19741974,0,2255.870987
159468,64,46,3,0,Single,1,56.18666438,0,2250.333219
164170,67,47,3,0,Married,15,56.16608104,0,2245.304052
162354,60,41,1,1,Single,7,56.0717095,0,2328.585475
155801,63,45,3,0,Single,4,55.93426459,0,2237.71323
161762,65,45,2,0,Married,2,55.87164916,0,2518.582458
182238,61,44,3,0,Single,9,55.86250372,0,2505.125186
167135,64,45,3,0,Single,6,55.67112525,0,2264.556263
126277,67,47,4,0,Married,14,55.63394174,0,2326.697087
146295,68,47,2,1,Single,1,55.59478521,0,2241.739261
137338,70,51,3,0,Married,10,55.57032626,0,2260.516313
168017,68,51,4,0,Single,12,55.36811306,0,2197.405653
146367,69,50,4,1,Single,1,55.20901516,0,2352.450758
144355,66,46,2,1,Married,9,55.19671094,0,2371.835547
130138,62,42,2,0,Single,15,55.15740612,0,2427.870306
101028,61,40,4,1,Married,14,55.15691526,0,2222.845763
144165,64,46,2,1,Married,6,55.11078038,0,2413.539019
164101,64,47,2,1,Single,7,55.04694292,0,2406.347146
178465,68,51,2,1,Married,15,55.03064026,0,2467.532013
190791,61,44,2,1,Single,12,55.0100417,0,2535.502085
122591,62,44,1,1,Single,5,54.94484401,0,2416.242201
125021,61,41,2,1,Married,5,54.84257125,0,2374.128563
101495,65,44,4,1,Single,2,54.82996678,0,2508.498339
146185,68,48,1,1,Single,5,54.77794095,0,2188.897048
148231,60,39,2,0,Married,5,54.76870422,0,2146.435211
173563,70,49,3,0,Married,11,54.74148115,0,2424.074058
183569,69,51,2,1,Single,11,54.54551587,0,2396.275794
184160,63,46,4,1,Married,9,54.38544048,0,2314.272024
140556,63,46,4,1,Single,13,54.31716805,0,2438.858403
184298,68,48,4,0,Married,12,54.27492999,0,2118.7465
113033,69,48,2,0,Married,12,54.20270961,0,2416.135481
169991,60,39,3,0,Married,1,54.10787889,0,2464.393945
140672,67,50,3,0,Single,7,54.08364527,0,2436.182264
186152,66,46,4,0,Married,6,53.98897059,0,2243.44853
102576,67,49,2,0,Single,9,53.98103733,0,2380.051867
134080,61,44,3,1,Single,6,53.97127125,0,2316.563563
197629,69,51,3,0,Married,6,53.92353365,0,2136.176683
180488,68,47,3,0,Married,10,53.891042,0,2371.5521
121348,60,43,1,1,Single,7,53.84875132,0,2429.437566
196620,69,49,4,1,Single,10,53.80049352,0,2229.024676
187052,61,44,2,0,Married,10,53.73608936,0,2268.804468
122945,65,44,3,1,Married,6,53.71707444,0,2331.853722
114241,61,44,2,0,Married,6,53.6449905,0,2265.249525
127147,60,41,1,0,Single,6,53.64272115,0,2242.136058
129132,66,47,2,0,Single,9,53.59342313,0,2284.671157
195078,65,45,1,0,Married,9,53.55900503,0,2117.950252
156919,68,48,3,0,Married,4,53.49667949,0,2102.833975
127304,62,42,4,0,Single,14,53.1797087,0,2280.985435
153155,60,40,4,1,Married,4,53.14456848,0,2262.228424
183670,64,47,1,1,Married,6,53.08119666,0,2367.059833
118324,66,48,1,0,Married,6,52.94552614,0,2181.276307
163420,70,50,2,1,Married,7,52.89583767,0,2313.791884
157241,63,45,3,0,Married,2,52.84267257,0,2252.133629
137085,64,43,3,0,Single,8,52.81746327,0,2192.873164
197284,69,48,3,0,Single,10,52.80196713,0,2157.098357
132624,60,41,2,0,Single,13,52.79849916,0,2193.924958
105446,62,42,2,1,Married,1,52.64347366,0,2308.173683
199698,64,43,1,1,Married,10,52.51168524,0,2395.584262
186755,67,47,2,0,Single,15,52.49891636,0,2033.945818
147473,64,43,1,1,Single,3,52.41921348,0,2181.960674
174427,69,48,3,1,Single,0,52.31631384,0,2332.815692
148054,65,46,3,0,Single,14,52.25793464,0,2347.896732
195099,67,48,2,1,Single,9,52.23572795,0,2332.786398
110367,63,44,3,1,Single,10,52.01138969,0,2210.569485
114164,60,40,3,1,Single,5,51.84614092,0,2325.307046
195513,63,44,2,1,Single,12,51.84031551,0,2290.015776
128659,64,46,3,0,Single,6,51.63782265,0,2379.891133
117553,63,43,2,1,Married,6,51.5165782,0,2050.82891
193617,62,42,1,0,Single,9,51.49368116,0,2337.684058
158396,61,42,3,0,Single,13,51.46807921,0,2002.403961
187862,65,48,2,0,Single,1,51.42612216,0,2170.306108
165309,70,49,2,0,Married,11,51.35095439,0,2114.54772
170210,60,41,3,0,Single,11,51.29370383,0,2060.685192
191030,63,43,3,1,Single,2,51.27537893,0,2003.768947
161734,67,46,3,1,Married,6,51.18918863,0,2174.459432
186767,61,44,2,1,Single,6,51.12059722,0,2279.029861
199711,63,44,3,0,Single,7,50.93564413,0,2183.782207
118883,60,39,3,0,Married,8,50.92346602,0,1953.173301
194621,69,48,4,1,Single,8,50.86308542,0,2118.154271
140270,66,49,1,1,Single,5,50.71157626,0,2138.578813
115058,61,40,4,1,Married,9,50.68790597,0,2291.395299
171269,70,53,2,0,Married,10,50.65198172,0,2136.599086
118145,70,50,4,0,Single,9,50.63007245,0,2091.503623
114742,69,51,3,0,Single,13,50.62058206,0,2330.029103
155361,66,48,3,0,Single,9,50.6039284,0,2181.19642
115472,68,47,3,1,Married,3,50.60141328,0,2174.070664
118090,63,42,3,0,Single,9,50.5939685,0,2066.698425
196693,64,43,2,0,Single,15,50.57339885,0,2250.669943
147750,64,46,1,1,Married,6,50.40620435,0,2087.310218
168780,70,52,4,1,Married,10,50.34487695,0,2128.243848
191721,66,48,2,1,Single,13,50.2255099,0,1938.275495
105905,68,49,3,0,Married,15,50.16883194,0,1943.441597
171675,68,47,3,0,Single,13,50.13035055,0,2003.517528
192507,66,45,2,0,Single,7,50.05503781,0,2183.751891
170833,65,45,2,0,Married,7,49.96863373,0,1947.431687
107448,67,48,3,1,Married,8,49.94751491,0,2270.375746
144156,67,48,3,1,Single,13,49.77959251,0,2281.979626
162564,69,50,4,1,Single,15,49.66980511,0,2247.490256
136904,65,48,3,1,Married,14,49.61236035,0,2261.618018
116260,65,47,1,1,Married,1,49.57582016,0,1954.791008
155541,70,49,2,0,Single,13,49.57439811,0,2039.719906
196539,65,44,3,0,Single,15,49.5115222,0,1882.57611
198778,64,47,3,0,Single,4,49.49748794,0,2198.874397
102364,64,45,4,1,Married,1,49.48939436,0,1919.469718
179112,60,41,3,1,Married,7,49.43295102,0,2068.647551
144506,63,43,3,0,Married,13,49.41264077,0,1927.632039
171611,67,46,2,1,Single,11,49.41061922,0,2005.530961
171597,66,45,1,0,Married,9,49.35555095,0,1946.777548
123294,69,52,2,0,Single,6,49.25237813,0,2054.618907
151440,63,46,4,1,Single,8,49.08683959,0,2211.34198
185203,68,47,4,1,Single,2,49.06175555,0,1871.087778
171271,60,40,2,0,Married,0,48.97986108,0,2071.993054
100866,63,42,2,0,Married,10,48.95881303,0,2118.940652
150235,66,48,3,0,Single,10,48.87574286,0,1935.787143
163542,65,44,3,1,Married,9,48.75805138,0,1892.902569
131444,70,49,2,0,Married,7,48.7363347,0,2137.816735
146590,65,48,4,1,Married,2,48.71728852,0,1891.864426
188506,63,42,2,1,Married,15,48.66470637,0,2203.235319
152749,64,47,2,1,Single,10,48.64040499,0,1848.02025
147067,65,47,2,0,Single,9,48.42269189,0,2177.134595
128734,66,46,4,1,Single,10,48.41311884,0,1930.655942
131290,61,40,4,0,Single,4,48.38472563,0,2036.236282
195016,66,48,3,0,Single,11,48.3477202,0,2109.38601
193036,61,43,3,1,Married,11,48.30701346,0,1898.350673
168637,66,46,3,1,Married,8,48.2986823,0,2107.934115
116273,64,47,2,0,Single,11,48.20137783,0,1920.068892
131942,60,42,2,0,Single,9,48.19660683,0,2096.830342
161597,68,51,1,0,Single,6,48.10845199,0,2091.4226
124693,70,52,4,0,Married,8,48.1058628,0,2088.29314
149465,60,39,3,1,Single,9,48.08326373,0,2141.163187
168459,60,40,3,1,Married,5,48.07241107,0,2130.620554
127729,64,47,1,0,Married,11,48.01445411,0,2167.722706
113141,67,48,2,1,Single,0,47.99292722,0,1834.646361
176565,65,46,2,0,Single,11,47.95475079,0,1975.73754
101936,62,43,1,1,Married,8,47.90406835,0,2164.203418
190485,68,50,4,1,Married,4,47.900954,0,2101.0477
190465,65,44,4,1,Married,14,47.86517271,0,1959.258636
181896,65,47,2,0,Married,9,47.80091757,0,1951.045879
100873,63,46,3,1,Single,13,47.77115772,0,2181.557886
136786,70,52,4,1,Single,0,47.67779644,0,2035.889822
148589,65,44,4,1,Single,4,47.60971016,0,2048.485508
166782,60,41,3,0,Single,14,47.37650767,0,1777.825384
189009,65,47,1,1,Single,6,47.27603743,0,2148.801872
106461,61,43,1,1,Married,13,47.21609296,0,2015.804648
145603,62,45,4,0,Single,9,47.17617264,0,1950.808632
104522,69,49,1,0,Single,9,47.16275981,0,1887.137991
137175,61,43,2,1,Single,9,47.1281509,0,1985.407545
109329,67,46,2,1,Married,14,47.05114417,0,2093.557209
164088,68,51,4,0,Single,7,47.04962724,0,1893.481362
105678,70,49,3,1,Married,11,46.85303594,0,2086.651797
161677,68,51,1,0,Single,7,46.76212563,0,1920.106282
156707,67,47,2,0,Single,6,46.74914103,0,1967.457052
116168,65,45,3,1,Married,1,46.67613691,0,2019.806846
131350,65,46,4,0,Single,8,46.63437622,0,1833.718811
157066,62,44,4,1,Single,14,46.58064516,0,2127.032258
177277,64,44,2,1,Single,6,46.53849164,0,1927.924582
112113,66,47,2,1,Single,11,46.50032692,0,1975.016346
196064,66,45,2,1,Single,7,46.47222042,0,2036.611021
184130,68,51,4,0,Married,4,46.46185302,0,2033.092651
193904,60,41,1,0,Married,12,46.42128042,0,1785.064021
198046,60,42,1,1,Single,12,46.01271223,0,1967.635612
136606,68,50,2,0,Single,6,45.97570117,0,1766.785059
114614,64,47,2,1,Married,8,45.8953299,0,1924.766495
133862,61,41,1,1,Single,8,45.83002147,0,1925.501074
196432,62,41,2,1,Single,11,45.68742013,0,1699.371007
132745,62,41,3,1,Married,3,45.67505452,0,2062.752726
133877,64,45,2,0,Single,9,45.5612943,0,1884.064715
105890,68,50,2,0,Single,10,45.39186178,0,2030.593089
152911,60,41,4,0,Single,9,45.34530098,0,1698.265049
176463,69,51,1,1,Single,8,45.27574714,0,1732.787357
178209,68,47,3,1,Married,4,45.2596903,0,1936.984515
134527,65,45,3,0,Single,10,45.1664325,0,1878.321625
194840,64,47,3,1,Single,8,45.15699617,0,1838.849809
115708,68,50,2,1,Single,15,45.13376464,0,1905.688232
146625,66,47,2,0,Single,8,45.03924025,0,1791.962013
100775,60,40,3,1,Married,5,45.03801412,0,2001.900706
153914,66,47,2,0,Single,10,45.008951,0,1789.44755
155152,63,46,2,0,Single,13,44.94946668,0,1971.473334
176401,66,49,4,1,Single,2,44.949165,0,1683.45825
161573,68,47,1,0,Single,15,44.93262419,0,2042.63121
174935,66,46,3,0,Single,9,44.88695383,0,1978.347692
169304,66,46,2,1,Single,10,44.8549712,0,1787.74856
118109,69,49,1,1,Married,14,44.85076261,0,1679.538131
145231,69,50,2,1,Married,11,44.81424683,0,1995.712342
196710,60,39,1,1,Single,6,44.73706716,0,1728.853358
176168,70,49,2,0,Single,12,44.64055267,0,1784.027634
140519,65,45,4,0,Single,10,44.61956432,0,1894.978216
199820,69,48,3,1,Single,7,44.52318265,0,1810.159133
186725,64,45,3,0,Single,1,44.46068165,0,1791.034083
124322,63,44,2,1,Married,7,44.41811572,0,1761.905786
104852,68,49,3,1,Single,12,44.414606,0,1989.7303
153355,69,49,3,0,Single,8,44.39369101,0,1795.684551
108887,68,51,2,0,Single,8,44.3808888,0,1979.04444
150019,64,47,3,1,Single,10,44.37077418,0,2010.538709
129370,61,42,3,1,Single,11,44.34005249,0,1805.002625
160768,64,46,3,0,Married,8,44.2977249,0,1619.886245
115463,69,48,4,1,Married,1,44.2414336,0,1703.07168
172844,70,52,2,0,Single,12,44.22971624,0,1662.485812
118009,64,47,2,1,Single,9,44.15410129,0,1992.705065
139453,66,47,3,0,Married,6,44.06714137,0,1640.357069
173582,69,51,2,1,Married,13,43.91984176,0,1694.992088
180934,65,47,2,0,Single,10,43.90765359,0,1815.38268
137314,65,44,1,0,Single,15,43.80902637,0,1729.451319
118463,60,41,3,0,Single,7,43.7963994,0,1662.81997
154316,62,43,4,1,Single,2,43.7908123,0,1679.540615
137415,61,42,3,0,Single,7,43.74710322,0,1764.355161
142238,60,39,4,0,Single,9,43.74282845,0,1686.141423
126303,69,50,2,0,Single,1,43.69150749,0,1780.575375
183757,70,53,4,1,Single,12,43.4980825,0,1722.904125
194240,60,39,3,1,Married,14,43.48535521,0,1706.267761
175284,68,49,2,1,Single,1,43.40882502,0,1949.441251
157158,64,45,4,0,Married,10,43.37500608,0,1780.750304
122926,61,40,4,1,Single,12,43.25624195,0,1908.812098
183237,61,43,4,0,Married,1,43.22105671,0,1698.052836
192544,63,46,2,1,Married,6,43.1104786,0,1763.52393
142555,64,45,4,0,Single,10,43.04892652,0,1739.446326
156225,60,43,4,0,Single,13,43.03610311,0,1740.805156
151850,68,50,2,0,Single,9,42.99304197,0,1642.652099
195879,64,47,1,1,Single,9,42.93175722,0,1596.587861
130896,68,51,2,1,Single,9,42.80763176,0,1689.381588
140673,69,51,3,0,Single,10,42.80605723,0,1621.302862
177497,61,42,3,1,Married,5,42.79730356,0,1891.865178
115723,67,50,3,0,Single,7,42.57792575,0,1602.896288
144626,62,43,4,1,Single,12,42.54588433,0,1578.294217
163959,66,49,2,0,Married,10,42.54560578,0,1858.280289
147230,60,42,3,0,Single,9,42.37361933,0,1774.680967
126382,69,52,2,1,Single,8,42.35932941,0,1891.966471
149131,67,49,3,1,Single,9,42.04285729,0,1624.142865
160081,70,51,3,0,Married,12,41.98062044,0,1594.031022
152726,63,46,4,0,Single,14,41.95861116,0,1565.930558
161558,66,48,3,0,Married,1,41.87528879,0,1710.76444
121194,66,47,4,1,Single,12,41.84070321,0,1646.035161
124808,67,49,2,0,Married,9,41.79881552,0,1592.940776
112037,67,46,3,1,Single,3,41.70424779,0,1863.21239
122822,65,46,1,0,Married,9,41.68772747,0,1487.386374
156024,67,50,4,1,Single,12,41.58975302,0,1716.487651
118246,65,47,1,1,Single,8,41.57656736,0,1640.828368
150504,61,41,3,0,Single,12,41.55716101,0,1785.858051
151474,66,46,1,1,Single,15,41.48382202,0,1727.191101
196750,65,46,4,1,Single,11,41.47417968,0,1542.708984
145853,60,42,1,0,Married,4,41.44745886,0,1845.372943
137612,65,46,3,1,Single,13,41.38235702,0,1567.117851
126809,67,47,3,1,Married,15,41.21404742,0,1758.702371
183997,67,50,4,1,Single,10,41.08219521,0,1699.109761
122809,69,51,2,1,Married,12,41.06894118,0,1846.447059
103769,66,46,2,0,Single,7,40.97559422,0,1748.779711
198482,60,43,2,0,Single,12,40.97117488,0,1681.558744
108791,62,44,4,0,Married,6,40.96079291,0,1604.039646
106615,67,47,2,1,Single,8,40.95171614,0,1561.585807
172758,63,43,1,1,Married,13,40.93404521,0,1602.702261
190537,65,45,2,1,Married,15,40.86242464,0,1591.121232
199869,68,47,3,0,Single,6,40.80796448,0,1563.398224
124726,70,49,4,0,Married,10,40.76967803,0,1720.483902
118239,66,48,3,1,Single,6,40.73239872,0,1537.619936
120501,66,48,3,1,Married,10,40.62843822,0,1460.421911
148925,66,45,1,0,Married,7,40.62820414,0,1508.410207
177908,67,49,4,1,Single,11,40.59822642,0,1586.911321
157794,60,42,2,1,Single,8,40.57228227,0,1573.614114
134515,63,43,4,1,Single,10,40.5347543,0,1601.737715
192393,62,43,2,1,Single,11,40.44687431,0,1447.343716
165558,64,45,2,0,Single,9,40.36425713,0,1464.212857
194352,67,47,2,1,Single,8,40.3245526,0,1711.22763
120823,60,40,2,0,Single,11,40.235724,0,1441.7862
196608,66,48,3,0,Married,10,40.1800701,0,1488.003505
176766,69,52,4,0,Married,8,40.09194914,0,1627.597457
117356,70,49,1,1,Married,7,40.08895078,0,1669.447539
187609,62,44,1,1,Married,4,39.95489011,0,1668.744506
133290,63,46,2,1,Married,7,39.9461635,0,1504.308175
164436,64,47,4,0,Married,6,39.93803147,0,1589.901574
122519,70,49,3,1,Married,8,39.90354642,0,1713.177321
113549,70,52,1,1,Married,13,39.86574338,0,1792.287169
157837,63,45,2,0,Married,10,39.85376081,0,1630.688041
174207,69,48,2,1,Married,14,39.79180144,0,1587.590072
155919,66,45,4,1,Married,5,39.76589882,0,1642.294941
144967,69,51,4,0,Married,6,39.74574801,0,1587.287401
140659,62,41,1,1,Single,9,39.72257576,0,1555.128788
168889,60,43,2,1,Single,13,39.69274047,0,1430.637024
150589,69,51,3,0,Married,7,39.62666635,0,1698.333318
130258,62,44,2,0,Married,10,39.60422258,0,1422.211129
188425,62,42,1,1,Married,8,39.59872587,0,1624.936294
140562,70,49,2,1,Married,5,39.57800425,0,1768.900213
153696,63,44,4,0,Married,9,39.55406514,0,1420.703257
113920,62,45,3,0,Married,4,39.5477811,0,1476.389055
112456,60,40,3,0,Married,15,39.53856189,0,1512.928095
138263,61,40,4,0,Married,15,39.41953671,0,1485.976836
132804,62,45,1,0,Single,10,39.37000756,0,1466.500378
185252,62,44,3,0,Married,10,39.31633036,0,1599.816518
193489,62,45,2,0,Single,9,39.28796041,0,1688.398021
126637,60,39,2,1,Single,7,39.218085,0,1496.90425
195900,68,48,1,0,Married,2,39.12363354,0,1647.181677
186893,63,42,4,1,Married,2,39.08095218,0,1623.047609
153042,64,46,3,0,Married,6,39.07322505,0,1571.661253
194863,61,40,1,0,Married,10,39.06923279,0,1530.46164
137342,66,48,4,0,Single,11,38.99781338,0,1638.890669
122916,60,41,2,1,Single,14,38.87372586,0,1371.686293
171118,69,52,2,0,Single,12,38.86346018,0,1424.173009
169295,66,49,4,1,Single,9,38.81184155,0,1408.592078
133072,69,52,4,0,Married,8,38.7881759,0,1611.408795
138215,62,42,2,1,Single,13,38.75278934,0,1351.639467
120178,61,42,4,1,Single,12,38.69770761,0,1450.885381
165986,64,45,3,0,Married,7,38.69400015,0,1383.700008
135457,62,41,1,1,Married,6,38.63812819,0,1616.90641
150233,61,43,2,1,Married,10,38.61900356,0,1550.950178
163758,64,45,4,1,Married,15,38.5875034,0,1535.37517
125823,70,50,3,1,Single,7,38.57444556,0,1709.722278
184311,66,49,1,1,Single,11,38.46999158,0,1660.499579
175045,65,48,1,0,Married,11,38.46779326,0,1554.389663
161689,64,44,4,0,Single,14,38.39959509,0,1372.979755
154569,60,41,2,1,Single,7,38.35399028,0,1664.699514
150685,68,48,2,1,Single,7,38.31712094,0,1325.856047
101140,64,44,2,1,Single,6,38.29982989,0,1403.991495
153901,64,47,2,0,Single,15,38.10976558,0,1539.488279
137244,66,48,4,1,Single,12,38.02391143,0,1641.195572
181598,70,50,1,0,Married,15,38.00033731,0,1648.016866
197601,61,43,2,0,Married,11,37.96323838,0,1434.161919
115567,60,43,3,0,Married,13,37.90618249,0,1491.309125
101981,62,44,2,0,Married,8,37.8728243,0,1567.641215
122045,67,47,2,0,Married,6,37.85146275,0,1341.573138
142829,63,46,3,1,Single,7,37.79738698,0,1545.869349
129224,67,48,3,1,Married,5,37.76313019,0,1655.15651
118908,70,51,2,1,Single,9,37.74299444,0,1485.149722
133077,62,44,4,0,Single,10,37.67848475,0,1317.924238
171247,61,43,1,0,Married,7,37.58319669,0,1666.159835
133469,67,50,3,0,Married,1,37.55027467,0,1336.513734
146703,69,51,2,1,Single,7,37.54144893,0,1657.072447
109372,67,50,4,1,Single,8,37.5169384,0,1573.84692
162011,67,48,2,0,Married,13,37.44283273,0,1391.141637
131083,69,50,3,0,Married,6,37.17637449,0,1305.818725
141639,66,45,1,1,Single,11,37.17363736,0,1570.681868
104871,66,49,2,0,Single,14,37.11762558,0,1473.881279
162049,70,53,4,0,Single,11,37.08766322,0,1382.383161
155650,70,49,1,0,Married,6,37.08168294,0,1356.084147
166634,64,47,3,0,Married,8,37.0624991,0,1511.124955
199783,62,44,2,0,Married,14,37.02154202,0,1465.077101
152364,64,47,3,0,Single,13,36.87360679,0,1421.68034
190457,64,45,3,1,Single,14,36.85347931,0,1570.673966
111528,63,42,1,1,Single,10,36.83406316,0,1316.703158
137044,66,49,3,0,Married,10,36.77684301,0,1353.842151
108405,70,50,1,1,Single,14,36.73113056,0,1628.556528
181894,69,50,2,1,Single,7,36.64815659,0,1465.40783
157704,66,45,1,0,Married,9,36.63901131,0,1423.950566
112937,64,47,3,1,Single,11,36.6118076,0,1589.59038
104824,60,42,1,1,Single,12,36.43842415,0,1551.921208
171275,70,53,2,1,Married,4,36.43109953,0,1479.554977
122816,61,44,2,1,Married,14,36.41245947,0,1239.622974
100642,63,44,4,0,Single,13,36.41125918,0,1606.562959
166606,60,41,3,0,Married,10,36.38635058,0,1574.317529
187556,67,47,1,1,Single,0,36.34557314,0,1429.278657
121304,68,47,4,0,Married,9,36.23159236,0,1602.579618
103257,68,51,4,1,Married,8,36.17224095,0,1254.612048
115318,60,40,2,1,Single,7,36.16669187,0,1357.334594
130180,65,48,3,1,Married,10,36.120624,0,1442.0312
186226,64,43,3,0,Married,9,36.06111866,0,1429.055933
104017,65,44,2,0,Married,6,35.99278088,0,1369.639044
150632,63,46,1,0,Married,6,35.92061417,0,1300.030709
111451,65,44,2,1,Single,7,35.9044908,0,1454.22454
147429,63,45,2,1,Single,15,35.87820782,0,1334.910391
191683,60,39,4,0,Married,10,35.85821979,0,1434.91099
109416,61,43,3,0,Married,6,35.84183739,0,1204.09187
187326,60,43,3,0,Married,7,35.84046263,0,1275.023132
159996,60,40,2,1,Married,6,35.8098218,0,1551.49109
188615,67,47,2,0,Married,6,35.7787252,0,1296.93626
162151,68,50,4,1,Married,10,35.77154323,0,1279.577162
101429,61,41,1,0,Single,11,35.74744486,0,1362.372243
191667,65,45,3,1,Married,8,35.65714861,0,1275.857431
133376,70,52,2,0,Married,12,35.64217047,0,1502.108524
191541,64,43,3,0,Married,9,35.62884287,0,1546.442144
177686,67,49,2,0,Single,14,35.55235284,0,1488.617642
122609,64,45,1,1,Married,4,35.48597259,0,1363.29863
110675,69,49,3,1,Married,12,35.481002,0,1478.0501
153172,66,47,1,0,Married,9,35.42012774,0,1349.006387
146057,69,48,2,0,Married,6,35.389962,0,1195.4981
148841,64,44,2,1,Married,6,35.28752129,0,1209.376065
154638,64,47,3,0,Married,6,35.28494815,0,1428.247408
160671,64,46,4,1,Single,9,35.28147697,0,1312.073849
149037,67,50,3,1,Married,6,35.27560523,0,1555.780262
197071,62,44,4,1,Married,10,35.2579192,0,1244.89596
165088,66,45,3,0,Married,7,35.00934622,0,1179.467311
148560,66,49,1,1,Married,11,34.99649779,0,1364.82489
168776,64,44,2,1,Single,11,34.92223883,0,1151.111942
199905,65,44,3,0,Married,8,34.81620613,0,1355.810307
168872,60,43,2,1,Married,13,34.80998953,0,1404.499477
155079,69,50,3,0,Married,12,34.78307855,0,1369.153928
146073,64,44,2,1,Married,15,34.68738518,0,1304.369259
126554,63,46,1,1,Married,6,34.62634009,0,1395.317005
114456,64,43,2,1,Married,6,34.56669768,0,1245.334884
150354,66,49,2,1,Married,6,34.55899311,0,1138.949656
145976,68,47,4,1,Single,14,34.55719926,0,1482.859963
112727,66,47,4,1,Married,13,34.54813739,0,1397.40687
163858,70,49,3,1,Married,15,34.5158146,0,1364.79073
131643,63,44,2,0,Married,6,34.46317069,0,1482.158535
157732,66,46,2,0,Single,12,34.41105044,0,1234.552522
109065,67,48,2,0,Married,7,34.37740054,0,1201.870027
191167,61,40,2,0,Married,14,34.36715649,0,1150.357825
118829,63,46,2,1,Married,9,34.36052183,0,1324.026092
151022,70,51,1,1,Married,10,34.33625065,0,1361.812533
126801,62,41,2,0,Married,13,34.18558791,0,1114.279396
160828,69,49,2,1,Married,6,34.17131932,0,1311.565966
198649,65,45,2,1,Married,9,34.16824007,0,1465.412004
110207,63,42,1,1,Single,9,34.08131785,0,1387.065893
188953,68,50,2,1,Married,1,34.07808788,0,1198.904394
150093,65,48,1,0,Married,10,34.05129958,0,1185.564979
119255,60,41,3,1,Single,13,33.96946247,0,1275.473124
188354,70,50,2,1,Married,6,33.92207189,0,1215.103595
175072,70,53,3,0,Married,7,33.87448799,0,1297.7244
140708,61,41,2,0,Married,7,33.80039913,0,1313.019957
166918,60,41,3,0,Married,12,33.7281892,0,1164.40946
153112,69,51,3,0,Married,9,33.68713333,0,1420.356667
183084,65,47,4,0,Married,9,33.56408507,0,1176.204254
194451,62,41,3,1,Married,10,33.47560981,0,1379.780491
186589,69,48,3,0,Married,10,33.46665127,0,1283.332564
161522,61,40,2,0,Married,13,33.45428635,0,1392.714318
123795,60,41,1,0,Married,14,33.45093763,0,1408.546882
185682,68,49,1,1,Married,7,33.34637246,0,1293.318623
129589,68,47,3,1,Married,5,33.2907655,0,1232.538275
152883,60,40,4,1,Married,8,33.23964927,0,1260.982464
138693,66,48,2,0,Married,10,33.19193201,0,1338.596601
118945,62,43,4,1,Married,0,33.1677643,0,1151.388215
174231,67,48,1,1,Married,5,33.15465722,0,1240.732861
162909,68,50,4,1,Married,9,33.10928734,0,1347.464367
180425,61,42,1,0,Single,12,33.06159082,0,1296.079541
107267,65,45,4,1,Married,7,33.04468917,0,1216.234459
182266,62,42,3,1,Married,4,33.00902796,0,1165.451398
134528,70,51,1,1,Married,11,32.84523715,0,1177.261858
125914,68,48,2,0,Married,13,32.80356503,0,1429.178252
154060,64,43,1,1,Married,14,32.72555801,0,1228.277901
157085,61,40,4,1,Married,12,32.66640367,0,1414.320184
113800,69,51,2,1,Single,11,32.66384596,0,1360.192298
109042,66,46,4,1,Married,6,32.65583535,0,1120.791768
188066,65,46,4,1,Married,9,32.58045006,0,1074.022503
165251,60,43,1,0,Married,13,32.51585346,0,1195.792673
116914,69,48,4,1,Single,14,32.46081575,0,1104.040788
161975,60,41,4,1,Single,13,32.4096623,0,1028.483115
180363,70,51,2,1,Married,12,32.376349,0,1341.81745
101827,60,39,2,1,Married,10,32.3286988,0,1042.43494
132129,66,45,1,1,Married,14,32.26493105,0,1148.246553
172145,61,44,2,0,Married,15,32.13850999,0,1117.9255
161533,62,45,3,1,Single,11,32.05340234,0,1388.670117
146369,61,42,1,1,Married,9,31.96379288,0,1243.189644
180437,70,52,3,0,Married,12,31.95768318,0,1379.884159
163289,65,46,2,1,Married,7,31.93228812,0,1221.614406
176466,68,49,4,1,Married,11,31.91330615,0,1071.665308
162106,65,47,2,0,Married,9,31.86598582,0,1186.299291
185807,62,44,1,0,Married,14,31.83119043,0,1060.559522
171352,63,42,3,1,Married,8,31.78189277,0,1334.094639
112718,67,49,1,0,Married,13,31.77827713,0,1331.913857
163760,61,43,3,1,Married,9,31.70000207,0,1286.000104
154855,66,49,3,1,Married,13,31.59694774,0,993.847387
116077,70,51,4,1,Single,11,31.52674399,0,1116.3372
105925,69,49,4,1,Married,6,31.48278112,0,1127.139056
198492,67,49,4,1,Married,10,31.29387843,0,1054.693922
117881,60,40,1,1,Married,8,31.26927851,0,1198.463926
101083,66,48,3,1,Married,6,31.26546602,0,1206.273301
147408,60,41,4,1,Married,11,31.24657297,0,1191.328649
129952,68,51,2,0,Married,12,31.12221416,0,1319.110708
160033,63,45,3,0,Married,15,31.09246645,0,1241.623323
198225,68,50,2,0,Married,12,31.07107483,0,1109.553742
103308,68,50,3,1,Married,6,31.03608979,0,994.8044895
172647,62,45,1,1,Married,10,30.93276225,0,964.6381125
120525,69,48,1,1,Single,14,30.84570505,0,1042.285253
112610,64,44,2,0,Married,13,30.7732617,0,945.663085
192201,66,46,3,0,Married,15,30.71424992,0,1004.712496
174068,63,44,2,0,Married,12,30.56477072,0,1028.238536
178108,67,47,2,1,Married,6,30.4361475,0,1063.807375
102573,61,42,3,0,Single,14,30.31565715,0,1231.782858
192305,67,50,3,1,Married,10,30.22512832,0,1285.256416
140321,65,46,2,0,Single,11,30.18541363,0,1214.270682
178693,62,43,2,1,Married,15,30.17443424,0,1076.721712
101209,69,52,3,0,Married,15,30.01958668,0,1195.979334
161857,67,48,2,1,Married,7,29.94505399,0,1081.2527
183986,68,49,3,0,Single,12,29.91010708,0,1176.505354
125904,68,47,1,0,Married,11,29.78216176,0,1181.108088
179533,61,41,3,0,Married,13,29.64896138,0,1050.448069
120340,66,47,2,1,Married,9,29.48296987,0,977.1484935
116022,70,52,3,1,Married,8,29.44775017,0,904.3875085
121288,61,44,2,0,Single,13,29.1256495,0,979.282475
150869,62,43,4,1,Married,10,29.1124105,0,1205.620525
135433,61,44,3,1,Married,15,29.05900014,0,902.950007
106644,60,43,3,1,Single,14,29.03167189,0,946.5835945
119438,63,45,3,0,Married,14,29.01610379,0,977.8051895
161544,65,46,3,1,Married,7,28.86286952,0,907.143476
113449,70,50,2,1,Single,15,28.82071718,0,868.035859
185353,69,51,2,1,Single,14,28.69860484,0,1071.930242
103771,70,52,1,1,Married,12,28.52569444,0,999.284722
185826,67,48,4,1,Single,13,28.51860588,0,925.930294
160947,65,46,2,0,Married,13,28.27238083,0,1124.619042
188632,60,39,2,1,Married,14,28.26507244,0,1077.253622
158184,66,46,2,1,Married,11,28.05839824,0,1077.919912
184866,69,52,3,1,Married,13,28.03244612,0,972.622306
190381,60,39,2,0,Single,13,28.01903018,0,825.951509
186134,64,47,2,0,Married,15,27.9400468,0,1023.00234
139843,63,44,2,1,Married,13,27.93650816,0,984.825408
130198,60,40,3,0,Single,15,27.84268659,0,881.1343295
143427,64,46,2,1,Married,13,27.82519769,0,798.2598845
180081,68,51,3,1,Married,12,27.64545617,0,788.2728085
134902,65,48,3,0,Single,12,27.6123893,0,802.619465
129973,64,44,3,0,Married,15,27.43700116,0,860.850058
117684,67,48,2,0,Married,11,27.29098745,0,1130.549373
155389,67,50,3,1,Single,15,27.26619006,0,964.309503
122762,62,45,1,1,Married,14,27.13439185,0,1117.719593
166268,64,45,2,1,Married,15,26.84437074,0,905.218537
125788,69,48,4,1,Single,13,26.80173728,0,940.086864
197637,70,49,4,0,Single,14,26.67952108,0,950.976054
177251,61,40,3,0,Single,12,26.54142028,0,1068.071014
118323,66,47,1,0,Married,12,26.44116868,0,968.058434
193973,60,43,3,1,Married,15,26.29789532,0,943.894766
172278,70,51,3,1,Single,12,25.91082086,0,825.541043
146306,65,48,1,0,Single,15,25.88680711,0,855.3403555
105434,61,42,2,0,Single,14,25.78176113,0,948.0880565
122166,66,49,2,0,Single,15,25.64671403,0,1034.335702
145034,60,43,3,0,Married,12,25.42452485,0,1017.226243
122017,62,43,1,1,Single,12,25.39965998,0,913.982999
100604,70,50,3,1,Married,14,25.37564972,0,698.782486
135837,65,45,2,1,Single,13,25.2281853,0,971.409265
181524,62,41,2,1,Single,13,24.67729816,0,817.864908
130444,62,44,4,1,Married,14,24.66727168,0,935.363584
114977,60,42,2,1,Married,13,24.50552264,0,646.276132
196093,68,47,2,1,Single,13,24.4505445,0,830.527225
150300,62,43,1,1,Single,11,24.39857653,0,918.9288265
194753,64,43,3,1,Married,13,24.31025624,0,626.512812
128215,66,49,1,1,Married,11,24.28756977,0,921.3784885
123394,61,42,2,1,Single,14,24.21601283,0,698.8006415
115046,60,43,2,0,Single,11,24.14757387,0,882.3786935
146446,70,51,4,1,Single,12,24.13380504,0,935.690252
193979,63,43,3,0,Married,15,24.11464048,0,842.732024
178098,61,41,3,1,Married,13,23.91195282,0,810.597641
184697,61,41,1,1,Married,11,23.87112656,0,630.556328
151459,60,43,4,0,Married,15,23.82191985,0,685.0959925
118320,67,48,2,1,Single,14,23.17656269,0,702.8281345
142532,70,49,2,0,Married,11,23.15897362,0,643.948681
126180,64,43,3,1,Married,15,22.66043856,0,567.021928
143880,63,42,2,1,Married,13,22.47577446,0,733.788723
157943,67,46,2,0,Married,12,22.4023338,0,520.11669
139329,67,47,2,0,Married,14,22.38307969,0,780.1539845
100531,64,45,4,0,Married,11,22.17549513,0,601.7747565
167485,64,44,4,1,Married,13,22.14732403,0,840.3662015
183198,61,41,3,1,Married,11,22.1340816,0,820.70408
143349,61,41,2,1,Married,14,21.89517467,0,495.7587335
189979,62,43,2,0,Single,12,21.7772422,0,705.86211
114287,60,43,3,1,Married,13,21.6923393,0,824.616965
126746,69,49,3,0,Married,12,21.44102999,0,715.0514995
186299,65,47,4,0,Married,11,21.31311833,0,785.6559165
135153,64,45,3,0,Single,14,21.13027274,0,656.513637
165032,67,50,2,1,Single,15,21.04379396,0,608.189698
171070,62,45,3,1,Married,15,20.85022796,0,540.511398
129262,63,46,4,1,Single,15,20.71180013,0,735.5900065
198335,65,47,1,1,Married,15,20.18344798,0,590.172399
154574,70,52,2,1,Single,14,20.06833059,0,628.4165295
163778,60,41,3,1,Married,13,19.90344326,0,792.172163
138334,60,39,4,0,Married,15,19.85951362,0,415.975681
127778,62,43,2,0,Single,12,19.84465222,0,566.232611
144950,64,45,1,0,Married,14,19.82277821,0,609.1389105
122946,60,41,3,1,Married,13,19.7637924,0,447.18962
132767,65,45,4,1,Married,15,19.74528225,0,649.2641125
133630,69,49,2,1,Single,11,19.72645556,0,745.322778
182394,64,47,4,0,Single,15,19.35304501,0,727.6522505
140518,60,43,2,1,Single,15,19.3055825,0,727.279125
164586,69,52,2,1,Single,15,19.1986151,0,744.930755
131247,61,41,3,1,Married,13,18.95850938,0,511.925469
110052,60,42,4,1,Married,14,18.92269289,0,629.1346445
103005,60,41,2,1,Married,12,18.87214094,0,701.607047
167576,70,52,3,1,Single,11,18.79262699,0,359.6313495
173913,68,49,2,1,Single,12,18.79131042,0,402.565521
105197,70,53,2,0,Single,15,18.61774917,0,640.8874585
139104,63,43,2,1,Single,11,18.39388561,0,615.6942805
182308,66,45,3,1,Single,12,18.26548029,0,398.2740145
107394,64,45,4,1,Married,11,18.10363382,0,540.181691
115338,60,43,2,0,Single,14,18.07329299,0,428.6646495
153800,63,45,3,1,Single,15,17.63866433,0,613.9332165
159207,62,43,2,1,Married,12,17.62387915,0,431.1939575
177941,65,44,3,0,Married,13,17.59097452,0,473.548726
186860,67,46,3,1,Married,14,17.41813348,0,577.906674
158651,61,41,3,1,Married,13,17.28856652,0,295.428326
169190,65,47,1,1,Single,11,17.14401051,0,280.2005255
137145,65,47,3,0,Married,13,16.7670884,0,332.35442
147362,61,43,1,1,Married,14,16.73906244,0,558.953122
169100,64,47,2,1,Married,11,16.61784175,0,458.8920875
171816,70,50,2,0,Single,13,16.55863596,0,524.931798
195434,67,48,1,1,Single,11,16.53853704,0,367.926852
138348,67,47,2,1,Single,14,16.47542036,0,357.771018
107252,68,49,3,1,Single,13,16.47449989,0,297.7249945
104103,68,48,3,0,Married,14,16.09391973,0,530.6959865
101640,70,50,2,1,Married,11,15.95334825,0,357.6674125
185269,67,50,3,0,Single,11,15.87568511,0,345.7842555
154107,69,52,4,0,Married,13,15.78501164,0,404.250582
182583,66,45,1,1,Married,12,15.76108943,0,354.0544715
122855,66,45,1,1,Married,12,15.5892684,0,360.46342
117196,70,52,2,1,Single,13,15.35319666,0,460.659833
148660,66,46,3,1,Married,11,15.24467979,0,298.2339895
177232,67,49,4,1,Married,12,14.86060602,0,179.030301
183044,63,45,2,1,Married,12,13.90110971,0,126.0554855
168101,65,45,4,1,Married,15,13.29062799,0,261.5313995
168897,67,49,4,0,Married,12,12.6140927,0,316.704635
144717,64,46,3,1,Married,11,12.53452056,0,365.726028


================================================
FILE: 87-FA/Packages Pre-requisites_v03.R
================================================
install.packages("car")
library("car")

install.packages("glmnet")
library("glmnet")

install.packages("caTools")
library(caTools)
install.packages("dplyr")
library(dplyr)




install.packages("rpart")
library(rpart)

install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)


install.packages("rpart.plot")
library(rpart.plot)

install.packages("forecast")
library(forecast)

install.packages("party")
library(party)

install.packages("partykit")
library(partykit)

install.packages("arules")
library(arules)

install.packages("amap")
library(amap)

install.packages("rpart")
library(rpart)

install.packages("https://cran.r-project.org/bin/windows/contrib/3.3/RGtk2_2.20.31.zip", repos=NULL)
#or
install.packages("RGtk2")

#Install GTK+
library(RGtk2)

install.packages("rattle")
library(rattle)


================================================
FILE: 87-FA/aapl.csv
================================================
Date,Open,High,Low,Close,Volume,Adj Close
2008-10-14,116.26,116.40,103.14,104.08,70749800,104.08
2008-10-13,104.55,110.53,101.02,110.26,54967000,110.26
2008-10-10,85.70,100.00,85.00,96.80,79260700,96.80
2008-10-09,93.35,95.80,86.60,88.74,57763700,88.74
2008-10-08,85.91,96.33,85.68,89.79,78847900,89.79
2008-10-07,100.48,101.50,88.95,89.16,67099000,89.16
2008-10-06,91.96,98.78,87.54,98.14,75264900,98.14
2008-10-03,104.00,106.50,94.65,97.07,81942800,97.07
2008-10-02,108.01,108.79,100.00,100.10,57477300,100.10
2008-10-01,111.92,112.36,107.39,109.12,46303000,109.12
2008-09-30,108.25,115.00,106.30,113.66,58095800,113.66
2008-09-29,119.62,119.68,100.59,105.26,93581400,105.26
2008-09-26,124.91,129.80,123.00,128.24,40208700,128.24
2008-09-25,129.80,134.79,128.52,131.93,35865600,131.93
2008-09-24,127.27,130.95,125.15,128.71,37393400,128.71
2008-09-23,131.85,135.80,126.66,126.84,45727300,126.84
2008-09-22,139.94,140.25,130.66,131.05,30577300,131.05
2008-09-19,142.60,144.20,136.31,140.91,51102700,140.91
2008-09-18,130.57,135.43,120.68,134.09,59819300,134.09
2008-09-17,138.49,138.51,127.83,127.83,42847200,127.83
2008-09-16,133.86,142.50,132.15,139.88,42804800,139.88
2008-09-15,142.03,147.69,140.36,140.36,32852600,140.36
2008-09-12,150.91,150.91,146.50,148.94,28322400,148.94
2008-09-11,148.18,152.99,146.00,152.65,34666800,152.65
2008-09-10,152.32,154.99,148.80,151.61,34755100,151.61
2008-09-09,156.86,159.96,149.79,151.68,44442500,151.68
2008-09-08,164.57,164.89,151.46,157.92,37356400,157.92
2008-09-05,158.59,162.40,157.65,160.18,28083800,160.18
2008-09-04,165.86,167.91,160.81,161.22,26549500,161.22
2008-09-03,166.84,168.68,164.00,166.96,26244100,166.96
2008-09-02,172.40,173.50,165.00,166.19,27884400,166.19
2008-08-29,172.96,173.50,169.04,169.53,21403200,169.53
2008-08-28,175.28,176.25,172.75,173.74,15394500,173.74
2008-08-27,173.31,175.76,172.19,174.67,17045900,174.67
2008-08-26,172.76,174.88,172.61,173.64,15912500,173.64
2008-08-25,176.15,176.23,171.66,172.55,17300900,172.55
2008-08-22,175.82,177.50,175.57,176.79,15700400,176.79
2008-08-21,174.47,175.45,171.89,174.29,19276600,174.29
2008-08-20,174.77,176.94,173.61,175.84,18105400,175.84
2008-08-19,174.54,177.07,171.81,173.53,21997000,173.53
2008-08-18,175.57,177.81,173.82,175.39,19691200,175.39
2008-08-15,179.04,179.75,175.05,175.74,25294700,175.74
2008-08-14,178.33,180.45,177.84,179.32,25393200,179.32
2008-08-13,177.98,180.00,175.90,179.30,30083800,179.30
2008-08-12,173.52,179.29,173.51,176.73,29867100,176.73
2008-08-11,170.07,176.50,169.67,173.56,31821100,173.56
2008-08-08,163.86,169.65,163.75,169.55,25499900,169.55
2008-08-07,162.71,166.15,161.50,163.57,24013300,163.57
2008-08-06,159.97,167.40,158.00,164.19,28264600,164.19
2008-08-05,155.42,160.80,154.82,160.64,24584700,160.64
2008-08-04,156.60,157.90,152.91,153.23,21161700,153.23
2008-08-01,159.90,159.99,155.75,156.66,19451400,156.66
2008-07-31,157.54,162.20,156.98,158.95,22767800,158.95
2008-07-30,157.78,160.49,156.08,159.88,25899400,159.88
2008-07-29,155.41,159.45,153.65,157.08,24431100,157.08
2008-07-28,162.34,162.47,154.02,154.40,27882600,154.40
2008-07-25,160.40,163.00,158.65,162.12,22629900,162.12
2008-07-24,164.32,165.26,158.45,159.03,29986400,159.03
2008-07-23,164.99,168.37,161.56,166.26,37920300,166.26
2008-07-22,149.00,162.76,146.53,162.02,67128300,162.02
2008-07-21,166.90,167.50,161.12,166.29,48588200,166.29
2008-07-18,168.52,169.65,165.00,165.15,31014800,165.15
2008-07-17,174.10,174.98,171.39,171.81,27054500,171.81
2008-07-16,170.20,172.93,168.60,172.81,26706800,172.81
2008-07-15,172.48,173.74,166.39,169.64,37144400,169.64
2008-07-14,179.24,179.30,173.08,173.88,31644800,173.88
2008-07-11,175.47,177.11,171.00,172.58,33214700,172.58
2008-07-10,174.92,177.34,171.37,176.63,30024600,176.63
2008-07-09,180.20,180.91,174.14,174.25,31992000,174.25
2008-07-08,175.40,179.70,172.74,179.55,31726800,179.55
2008-07-07,173.16,177.13,171.90,175.16,29299700,175.16
2008-07-03,169.59,172.17,165.75,170.12,18691500,170.12
2008-07-02,175.20,177.45,168.18,168.18,29911400,168.18
2008-07-01,164.23,174.72,164.00,174.68,39688600,174.68
2008-06-30,170.19,172.00,166.62,167.44,24435600,167.44
2008-06-27,166.51,170.57,164.15,170.09,37223200,170.09
2008-06-26,174.07,174.84,168.01,168.26,31057500,168.26
2008-06-25,174.61,178.83,173.88,177.39,23016100,177.39
2008-06-24,172.37,175.78,171.63,173.25,22212400,173.25
2008-06-23,174.74,175.88,171.56,173.16,23063600,173.16
2008-06-20,179.35,181.00,175.00,175.27,31727400,175.27
2008-06-19,178.55,182.34,176.80,180.90,28283900,180.90
2008-06-18,181.12,182.20,177.35,178.75,28981000,178.75
2008-06-17,178.10,181.99,177.41,181.43,32130600,181.43
2008-06-16,171.30,177.90,169.07,176.84,37561800,176.84
2008-06-13,171.64,174.16,165.31,172.37,48069900,172.37
2008-06-12,181.49,182.60,171.20,173.26,46726200,173.26
2008-06-11,184.34,186.00,179.59,180.81,34341100,180.81
2008-06-10,180.51,186.78,179.02,185.64,40728600,185.64
2008-06-09,184.79,184.94,175.75,181.61,67442600,181.61
2008-06-06,188.00,189.95,185.55,185.64,34438700,185.64
2008-06-05,186.34,189.84,185.70,189.43,26980200,189.43
2008-06-04,184.02,187.09,183.23,185.19,25963700,185.19
2008-06-03,186.86,188.20,182.34,185.37,26804300,185.37
2008-06-02,188.60,189.65,184.53,186.10,24280000,186.10
2008-05-30,187.45,189.54,187.38,188.75,21792300,188.75
2008-05-29,186.76,188.20,185.50,186.69,23113800,186.69
2008-05-28,187.41,187.95,183.72,187.01,26570700,187.01
2008-05-27,182.75,186.43,181.84,186.43,28210900,186.43
2008-05-23,180.77,181.99,177.80,181.17,32389900,181.17
2008-05-22,179.26,181.33,172.00,177.05,43097700,177.05
2008-05-21,185.67,187.95,176.25,178.19,41344900,178.19
2008-05-20,181.82,186.16,180.12,185.90,34637500,185.90
2008-05-19,187.86,188.69,181.30,183.60,33779300,183.60
2008-05-16,190.11,190.30,187.00,187.62,27348900,187.62
2008-05-15,186.81,189.90,184.20,189.73,31186000,189.73
2008-05-14,191.23,192.24,185.57,186.26,32743700,186.26
2008-05-13,188.61,191.45,187.86,189.96,29401300,189.96
2008-05-12,185.21,188.87,182.85,188.16,29234400,188.16
2008-05-09,183.16,184.25,181.37,183.45,24038300,183.45
2008-05-08,183.77,186.50,183.07,185.06,32110200,185.06
2008-05-07,186.05,188.20,180.54,182.59,41326200,182.59
2008-05-06,184.66,187.12,182.18,186.66,32816800,186.66
2008-05-05,181.92,185.31,181.05,184.73,30519900,184.73
2008-05-02,180.19,181.92,178.55,180.94,35931500,180.94
2008-05-01,174.96,180.00,174.86,180.00,32270600,180.00
2008-04-30,176.19,180.00,172.92,173.95,40697300,173.95
2008-04-29,171.11,175.66,170.25,175.05,32981300,175.05
2008-04-28,169.75,173.75,169.13,172.24,28114800,172.24
2008-04-25,170.70,171.10,166.42,169.73,35445500,169.73
2008-04-24,165.34,169.98,159.19,168.94,60573800,168.94
2008-04-23,164.05,164.84,161.08,162.89,53721100,162.89
2008-04-22,167.40,168.00,158.09,160.20,51413300,160.20
2008-04-21,162.21,168.50,161.76,168.16,37112600,168.16
2008-04-18,159.12,162.26,158.38,161.04,36670200,161.04
2008-04-17,154.17,156.00,153.35,154.49,25152400,154.49
2008-04-16,151.72,154.10,150.62,153.70,28420500,153.70
2008-04-15,149.40,149.72,145.72,148.38,24929900,148.38
2008-04-14,146.77,149.25,144.54,147.78,30181700,147.78
2008-04-11,152.72,153.30,146.40,147.14,43217000,147.14
2008-04-10,151.13,155.42,150.60,154.55,34134400,154.55
2008-04-09,153.31,153.89,150.46,151.44,31192800,151.44
2008-04-08,153.55,156.45,152.32,152.84,36224800,152.84
2008-04-07,156.13,159.69,155.11,155.89,41368800,155.89
2008-04-04,152.19,154.71,150.75,153.08,30514900,153.08
2008-04-03,147.06,153.63,147.00,151.61,37556000,151.61
2008-04-02,148.78,151.20,145.85,147.49,37320300,147.49
2008-04-01,146.30,149.66,143.61,149.53,36877400,149.53
2008-03-31,143.27,145.71,142.52,143.50,27430900,143.50
2008-03-28,141.80,144.65,141.60,143.01,25521800,143.01
2008-03-27,144.95,145.31,139.99,140.25,35708200,140.25
2008-03-26,140.87,145.74,140.64,145.06,42217300,145.06
2008-03-25,139.96,143.10,137.33,140.98,37585400,140.98
2008-03-24,134.01,140.85,133.64,139.53,38104300,139.53
2008-03-20,131.12,133.29,129.18,133.27,32456700,133.27
2008-03-19,133.12,134.29,129.67,129.67,36090600,129.67
2008-03-18,129.18,133.00,128.67,132.82,43040000,132.82
2008-03-17,122.55,128.59,122.55,126.73,38307100,126.73
2008-03-14,129.88,130.30,124.20,126.61,41308600,126.61
2008-03-13,124.10,129.50,123.00,127.94,45075100,127.94
2008-03-12,127.04,128.68,125.17,126.03,37843900,126.03
2008-03-11,124.10,127.48,122.00,127.35,41569400,127.35
2008-03-10,121.98,123.46,119.37,119.69,35699600,119.69
2008-03-07,120.41,122.98,119.05,122.25,43945100,122.25
2008-03-06,124.61,127.50,120.81,120.93,52632100,120.93
2008-03-05,123.58,125.14,122.25,124.49,43637000,124.49
2008-03-04,121.99,124.88,120.40,124.62,63763700,124.62
2008-03-03,124.44,125.98,118.00,121.73,56894400,121.73
2008-02-29,129.29,130.21,124.80,125.02,44838600,125.02
2008-02-28,127.20,132.20,125.77,129.91,57794800,129.91
2008-02-27,118.23,123.05,118.09,122.96,52683500,122.96
2008-02-26,117.64,121.09,115.44,119.15,53746000,119.15
2008-02-25,118.59,120.17,116.66,119.74,44884800,119.74
2008-02-22,122.48,122.51,115.87,119.46,54638500,119.46
2008-02-21,126.05,126.47,120.86,121.54,33504100,121.54
2008-02-20,122.20,124.60,121.68,123.82,34551400,123.82
2008-02-19,125.99,126.75,121.44,122.18,35894500,122.18
2008-02-15,126.27,127.08,124.06,124.63,32189300,124.63
2008-02-14,129.40,130.80,127.01,127.46,34074900,127.46
2008-02-13,126.68,129.78,125.63,129.40,34590500,129.40
2008-02-12,130.70,131.00,123.62,124.86,43785000,124.86
2008-02-11,128.01,129.98,127.20,129.45,42908300,129.45
2008-02-08,122.08,125.70,121.60,125.48,48427600,125.48
2008-02-07,119.97,124.78,117.27,121.24,74404700,121.24
2008-02-06,130.83,131.92,121.77,122.00,56188300,122.00
2008-02-05,130.43,134.00,128.90,129.36,40751500,129.36
2008-02-04,134.21,135.90,131.42,131.65,32115500,131.65
2008-02-01,136.24,136.59,132.18,133.75,36098000,133.75
2008-01-31,129.45,136.65,129.40,135.36,48059800,135.36
2008-01-30,131.37,135.45,130.00,132.18,44394700,132.18
2008-01-29,131.15,132.79,129.05,131.54,39285100,131.54
2008-01-28,128.16,133.20,126.45,130.01,52673000,130.01
2008-01-25,138.99,139.09,129.61,130.01,55526400,130.01
2008-01-24,139.99,140.70,132.01,135.60,71638100,135.60
2008-01-23,136.19,140.00,126.14,139.07,120463200,139.07
2008-01-22,148.06,159.98,146.00,155.64,86955500,155.64
2008-01-18,161.71,165.75,159.61,161.36,61583700,161.36
2008-01-17,161.51,165.36,158.42,160.89,62780700,160.89
2008-01-16,165.23,169.01,156.70,159.64,79065900,159.64
2008-01-15,177.72,179.22,164.66,169.04,83688500,169.04
2008-01-14,177.52,179.42,175.17,178.78,39301800,178.78
2008-01-11,176.00,177.85,170.00,172.69,44010200,172.69
2008-01-10,177.58,181.00,175.41,178.02,52963400,178.02
2008-01-09,171.30,179.50,168.30,179.40,64781500,179.40
2008-01-08,180.14,182.46,170.80,171.25,54422000,171.25
2008-01-07,181.25,183.60,170.23,177.64,74006900,177.64
2008-01-04,191.45,193.00,178.89,180.05,51994000,180.05
2008-01-03,195.41,197.39,192.69,194.93,30073800,194.93
2008-01-02,199.27,200.26,192.55,194.84,38542100,194.84
2007-12-31,199.50,200.50,197.75,198.08,19261900,198.08
2007-12-28,200.59,201.56,196.88,199.83,24987400,199.83
2007-12-27,198.95,202.96,197.80,198.57,28411700,198.57
2007-12-26,199.01,200.96,196.82,198.95,25133300,198.95
2007-12-24,195.03,199.33,194.79,198.80,17150100,198.80
2007-12-21,190.12,193.91,189.89,193.91,35498600,193.91
2007-12-20,185.43,187.83,183.33,187.21,27644900,187.21
2007-12-19,182.98,184.64,180.90,183.12,29552800,183.12
2007-12-18,186.52,187.33,178.60,182.98,43664400,182.98
2007-12-17,190.72,192.65,182.98,184.40,36596200,184.40
2007-12-14,190.37,193.20,189.54,190.39,24082600,190.39
2007-12-13,190.19,192.12,187.82,191.83,30879200,191.83
2007-12-12,193.44,194.48,185.76,190.86,43773600,190.86
2007-12-11,194.75,196.83,187.39,188.54,39675900,188.54
2007-12-10,193.59,195.66,192.69,194.21,25799200,194.21
2007-12-07,190.54,194.99,188.04,194.30,38073800,194.30
2007-12-06,186.19,190.10,186.12,189.95,32136100,189.95
2007-12-05,182.89,186.00,182.41,185.50,31871500,185.50
2007-12-04,177.15,180.90,176.99,179.81,27635700,179.81
2007-12-03,181.86,184.14,177.70,178.86,34338200,178.86
2007-11-30,187.34,187.70,179.70,182.22,42421500,182.22
2007-11-29,179.43,185.17,179.15,184.29,37533100,184.29
2007-11-28,176.82,180.60,175.35,180.22,41104000,180.22
2007-11-27,175.22,175.79,170.01,174.81,47036800,174.81
2007-11-26,173.59,177.27,172.35,172.54,46634100,172.54
2007-11-23,172.00,172.05,169.75,171.54,16634200,171.54
2007-11-21,165.84,172.35,164.67,168.46,43493200,168.46
2007-11-20,165.67,171.79,163.53,168.85,55130100,168.85
2007-11-19,166.10,168.20,162.10,163.95,41196800,163.95
2007-11-16,165.30,167.02,159.33,166.39,49391300,166.39
2007-11-15,166.39,169.59,160.30,164.30,53095600,164.30
2007-11-14,177.16,177.57,163.74,166.11,51695400,166.11
2007-11-13,160.85,170.98,153.76,169.96,62034100,169.96
2007-11-12,165.28,167.70,150.63,153.76,63057700,153.76
2007-11-09,171.15,175.12,165.21,165.37,54458700,165.37
2007-11-08,186.67,186.90,167.77,175.47,67458500,175.47
2007-11-07,190.61,192.68,186.13,186.30,35473400,186.30
2007-11-06,187.05,192.00,185.27,191.79,34097400,191.79
2007-11-05,185.29,188.96,184.24,186.18,28720600,186.18
2007-11-02,189.21,189.44,183.49,187.87,35789800,187.87
2007-11-01,188.60,190.10,180.00,187.44,28751300,187.44
2007-10-31,187.63,190.12,184.95,189.95,29761100,189.95
2007-10-30,186.18,189.37,184.73,187.00,33550500,187.00
2007-10-29,185.45,186.59,184.70,185.09,19305500,185.09
2007-10-26,185.29,185.37,182.88,184.70,25219800,184.70
2007-10-25,184.87,185.90,181.66,182.78,34771500,182.78
2007-10-24,185.81,187.21,179.24,185.93,46017200,185.93
2007-10-23,188.56,188.60,182.76,186.16,64113000,186.16
2007-10-22,170.35,174.90,169.96,174.36,58910700,174.36
2007-10-19,174.24,174.63,170.00,170.42,46135000,170.42
2007-10-18,171.50,174.19,171.05,173.50,29417000,173.50
2007-10-17,172.69,173.04,169.18,172.75,40271900,172.75
2007-10-16,165.54,170.18,165.15,169.58,38136800,169.58
2007-10-15,167.98,169.57,163.50,166.98,38497500,166.98
2007-10-12,163.01,167.28,161.80,167.25,35292000,167.25
2007-10-11,169.49,171.88,153.21,162.23,58714000,162.23
2007-10-10,167.55,167.88,165.60,166.79,23842500,166.79
2007-10-09,170.20,171.11,166.68,167.86,39438800,167.86
2007-10-08,163.49,167.91,162.97,167.91,29854600,167.91
2007-10-05,158.37,161.58,157.70,161.45,33695400,161.45
2007-10-04,158.00,158.08,153.50,156.24,23462800,156.24
2007-10-03,157.78,159.18,157.01,157.92,24732800,157.92
2007-10-02,156.55,158.59,155.89,158.45,28288200,158.45
2007-10-01,154.63,157.41,152.93,156.34,29895300,156.34
2007-09-28,153.44,154.60,152.75,153.47,21967900,153.47
2007-09-27,153.77,154.52,152.32,154.50,23507100,154.50
2007-09-26,154.47,155.00,151.25,152.77,34831000,152.77
2007-09-25,146.84,153.22,146.82,153.18,42591100,153.18
2007-09-24,146.73,149.85,146.65,148.28,37577200,148.28
2007-09-21,141.14,144.65,140.31,144.15,40674300,144.15
2007-09-20,140.15,141.79,139.32,140.31,24708600,140.31
2007-09-19,143.02,143.16,139.40,140.77,36674300,140.77
2007-09-18,139.06,142.85,137.83,140.92,38003200,140.92
2007-09-17,138.99,140.59,137.60,138.41,28334700,138.41
2007-09-14,136.57,138.98,136.20,138.81,21690000,138.81
2007-09-13,138.83,139.00,136.65,137.20,23434400,137.20
2007-09-12,135.99,139.40,135.75,136.85,36527500,136.85
2007-09-11,137.90,138.30,133.75,135.49,34710200,135.49
2007-09-10,136.99,138.04,133.95,136.71,53137100,136.71
2007-09-07,132.01,132.30,130.00,131.77,51092000,131.77
2007-09-06,135.56,137.57,132.71,135.01,67902200,135.01
2007-09-05,144.97,145.84,136.10,136.76,83150800,136.76
2007-09-04,139.94,145.73,139.84,144.16,47030100,144.16
2007-08-31,139.49,139.65,137.41,138.48,31317400,138.48
2007-08-30,132.67,138.25,132.30,136.25,51270800,136.25
2007-08-29,129.88,134.18,129.54,134.08,41673600,134.08
2007-08-28,130.99,132.41,126.63,126.82,42120200,126.82
2007-08-27,133.39,134.66,132.10,132.25,25265700,132.25
2007-08-24,130.53,135.37,129.81,135.30,32565500,135.30
2007-08-23,133.09,133.34,129.76,131.07,30958500,131.07
2007-08-22,131.22,132.75,130.33,132.51,37920200,132.51
2007-08-21,122.21,128.96,121.00,127.57,46537400,127.57
2007-08-20,123.96,124.50,120.50,122.22,28689900,122.22
2007-08-17,122.01,123.50,119.82,122.06,42680800,122.06
2007-08-16,117.01,118.50,111.62,117.05,66667500,117.05
2007-08-15,122.74,124.86,119.65,119.90,35459000,119.90
2007-08-14,128.29,128.30,123.71,124.03,26393100,124.03
2007-08-13,128.32,129.35,126.50,127.79,26889700,127.79
2007-08-10,123.12,127.75,120.30,125.00,50383900,125.00
2007-08-09,131.11,133.00,125.09,126.39,40192700,126.39
2007-08-08,136.76,136.86,132.00,134.01,28860600,134.01
2007-08-07,134.94,137.24,132.63,135.03,33926300,135.03
2007-08-06,132.90,135.27,128.30,135.25,33041800,135.25
2007-08-03,135.26,135.95,131.50,131.85,24256700,131.85
2007-08-02,136.65,136.96,134.15,136.49,30451600,136.49
2007-08-01,133.64,135.38,127.77,135.00,62505600,135.00
2007-07-31,142.97,143.48,131.52,131.76,62942600,131.76
2007-07-30,144.33,145.45,139.57,141.43,39535300,141.43
2007-07-27,146.19,148.92,143.78,143.85,41467800,143.85
2007-07-26,145.91,148.50,136.96,146.00,78093900,146.00
2007-07-25,137.35,138.36,135.00,137.26,53435100,137.26
2007-07-24,138.88,141.00,134.15,134.89,64117600,134.89
2007-07-23,143.31,145.22,140.93,143.70,37017500,143.70
2007-07-20,141.65,144.18,140.00,143.75,41706200,143.75
2007-07-19,140.30,140.81,139.65,140.00,26174700,140.00
2007-07-18,138.19,138.44,136.04,138.12,27030600,138.12
2007-07-17,138.30,139.60,137.50,138.91,25355700,138.91
2007-07-16,138.39,139.98,137.50,138.10,33432600,138.10
2007-07-13,135.03,137.85,134.52,137.73,32414500,137.73
2007-07-12,133.85,134.24,132.39,134.07,25164600,134.07
2007-07-11,132.07,133.70,131.31,132.39,29349000,132.39
2007-07-10,128.88,134.50,128.81,132.35,44821700,132.35
2007-07-09,132.38,132.90,129.18,130.33,35565000,130.33
2007-07-06,133.13,133.34,130.40,132.30,31239100,132.30
2007-07-05,128.80,132.97,128.69,132.75,51894700,132.75
2007-07-03,122.00,127.40,121.50,127.17,41517200,127.17
2007-07-02,121.05,122.09,119.30,121.26,35530800,121.26
2007-06-29,121.97,124.00,121.09,122.04,40637200,122.04
2007-06-28,122.36,122.49,120.00,120.56,29933700,120.56
2007-06-27,120.61,122.04,119.26,121.89,34810600,121.89
2007-06-26,123.98,124.00,118.72,119.65,48035900,119.65
2007-06-25,124.19,125.09,121.06,122.34,34478700,122.34
2007-06-22,123.85,124.45,122.38,123.00,22567000,123.00
2007-06-21,121.70,124.29,120.72,123.90,30965900,123.90
2007-06-20,123.87,124.66,121.50,121.55,32054000,121.55
2007-06-19,124.69,125.01,122.91,123.66,33679500,123.66
2007-06-18,123.28,125.18,122.54,125.09,32521600,125.09
2007-06-15,120.62,120.67,119.86,120.50,28972100,120.50
2007-06-14,117.20,119.45,116.42,118.75,34759500,118.75
2007-06-13,121.15,121.19,115.40,117.50,61476900,117.50
2007-06-12,119.35,121.71,118.31,120.38,50948800,120.38
2007-06-11,126.00,126.15,119.54,120.19,66937800,120.19
2007-06-08,125.82,125.83,122.29,124.49,44345800,124.49
2007-06-07,124.99,127.61,123.19,124.07,68395700,124.07
2007-06-06,122.30,124.05,121.95,123.64,39722900,123.64
2007-06-05,121.41,122.69,120.50,122.67,32885200,122.67
2007-06-04,118.63,121.73,117.90,121.33,31666900,121.33
2007-06-01,121.10,121.19,118.29,118.40,31616500,118.40
2007-05-31,120.07,122.17,119.54,121.19,46323800,121.19
2007-05-30,114.30,118.88,113.53,118.77,52801600,118.77
2007-05-29,114.45,114.86,112.69,114.35,23060500,114.35
2007-05-25,112.00,113.78,111.50,113.62,22605700,113.62
2007-05-24,112.81,114.46,110.37,110.69,31691500,110.69
2007-05-23,114.02,115.00,112.59,112.89,32549100,112.89
2007-05-22,112.49,113.75,112.01,113.54,20443200,113.54
2007-05-21,110.31,112.45,110.05,111.98,22853300,111.98
2007-05-18,110.23,110.64,109.77,110.02,22190900,110.02
2007-05-17,107.15,109.87,107.15,109.44,26260400,109.44
2007-05-16,108.53,108.83,103.42,107.34,40241700,107.34
2007-05-15,109.57,110.20,106.48,107.52,34089800,107.52
2007-05-14,109.62,110.00,108.25,109.36,23283800,109.36
2007-05-11,107.74,109.13,106.78,108.74,23346300,108.74
2007-05-10,106.63,108.84,105.92,107.34,42759200,107.34
2007-05-09,104.91,106.96,104.89,106.88,25634200,106.88
2007-05-08,103.47,105.15,103.42,105.06,27999900,105.06
2007-05-07,101.08,104.35,101.01,103.92,30769900,103.92
2007-05-04,100.80,101.60,100.50,100.81,13642400,100.81
2007-05-03,100.73,101.45,100.01,100.40,20574200,100.40
2007-05-02,99.65,100.54,99.47,100.39,18040900,100.39
2007-05-01,99.59,100.35,98.55,99.47,19018700,99.47
2007-04-30,100.09,101.00,99.67,99.80,22018200,99.80
2007-04-27,98.18,99.95,97.69,99.92,24978700,99.92
2007-04-26,101.58,102.50,98.30,98.84,62063500,98.84
2007-04-25,94.23,95.40,93.80,95.35,42398000,95.35
2007-04-24,93.96,96.39,91.30,93.24,37687600,93.24
2007-04-23,91.59,93.80,91.42,93.51,27867500,93.51
2007-04-20,90.89,91.18,90.55,90.97,18670700,90.97
2007-04-19,90.19,91.25,89.83,90.27,15211200,90.27
2007-04-18,90.16,90.85,89.60,90.40,16573000,90.40
2007-04-17,92.00,92.30,89.70,90.35,26854300,90.35
2007-04-16,90.57,91.50,90.25,91.43,21751200,91.43
2007-04-13,90.90,91.40,90.06,90.24,25712200,90.24
2007-04-12,92.04,92.31,90.72,92.19,23452700,92.19
2007-04-11,93.90,93.95,92.33,92.59,19607800,92.59
2007-04-10,93.67,94.26,93.41,94.25,12588100,94.25
2007-04-09,95.21,95.30,93.04,93.65,14762200,93.65
2007-04-05,94.12,94.68,93.52,94.68,12697000,94.68
2007-04-04,94.94,95.14,94.13,94.27,17028000,94.27
2007-04-03,94.14,95.23,93.76,94.50,20854800,94.50
2007-04-02,94.14,94.25,93.02,93.65,17928300,93.65
2007-03-30,94.28,94.68,92.75,92.91,21448500,92.91
2007-03-29,94.19,94.19,92.23,93.75,25918700,93.75
2007-03-28,94.88,95.40,93.15,93.24,33654900,93.24
2007-03-27,95.71,96.83,95.00,95.46,33287600,95.46
2007-03-26,93.99,95.90,93.30,95.85,30892400,95.85
2007-03-23,93.35,94.07,93.30,93.52,16103000,93.52
2007-03-22,93.73,94.36,93.00,93.96,20053300,93.96
2007-03-21,91.99,94.00,91.65,93.87,24532000,93.87
2007-03-20,91.35,91.84,91.06,91.48,17461300,91.48
2007-03-19,90.24,91.55,89.59,91.13,25462900,91.13
2007-03-16,89.54,89.99,89.32,89.59,20418000,89.59
2007-03-15,89.96,90.36,89.31,89.57,19982100,89.57
2007-03-14,88.60,90.00,87.92,90.00,28449500,90.00
2007-03-13,89.41,90.60,88.40,88.40,30996100,88.40
2007-03-12,88.07,89.99,87.99,89.87,26050300,89.87
2007-03-09,88.80,88.85,87.40,87.97,16137000,87.97
2007-03-08,88.59,88.72,87.46,88.00,18250400,88.00
2007-03-07,88.05,88.97,87.45,87.72,22367300,87.72
2007-03-06,87.80,88.31,87.40,88.19,25828100,88.19
2007-03-05,85.89,88.65,85.76,86.32,29960700,86.32
2007-03-02,86.77,87.54,85.21,85.41,30714300,85.41
2007-03-01,84.03,88.31,83.75,87.06,50554600,87.06
2007-02-28,83.00,85.60,83.00,84.61,32838400,84.61
2007-02-27,86.30,87.08,83.41,83.93,40921900,83.93
2007-02-26,89.84,90.00,87.61,88.51,21994600,88.51
2007-02-23,89.16,90.34,88.85,89.07,18496200,89.07
2007-02-22,90.80,90.81,88.53,89.51,29936600,89.51
2007-02-21,85.98,89.49,85.96,89.20,41261200,89.20
2007-02-20,84.65,86.16,84.16,85.90,22060800,85.90
2007-02-16,85.25,85.41,84.66,84.83,14281000,84.83
2007-02-15,85.44,85.62,84.78,85.21,12987900,85.21
2007-02-14,84.63,85.64,84.57,85.30,18142200,85.30
2007-02-13,85.16,85.29,84.30,84.70,20749500,84.70
2007-02-12,84.43,85.18,83.63,84.88,25859700,84.88
2007-02-09,85.88,86.20,83.21,83.27,30733600,83.27
2007-02-08,85.43,86.51,85.41,86.18,24251100,86.18
2007-02-07,84.48,86.38,83.55,86.15,38100900,86.15
2007-02-06,84.45,84.47,82.86,84.15,30871200,84.15
2007-02-05,84.30,85.23,83.94,83.94,20673300,83.94
2007-02-02,84.12,85.25,83.70,84.75,22197500,84.75
2007-02-01,86.23,86.27,84.74,84.74,23726500,84.74
2007-01-31,84.86,86.00,84.35,85.73,30573900,85.73
2007-01-30,86.43,86.49,85.25,85.55,20641800,85.55
2007-01-29,86.30,86.65,85.53,85.94,32202300,85.94
2007-01-26,87.11,87.37,84.99,85.38,35245500,85.38
2007-01-25,87.11,88.50,86.03,86.25,32356200,86.25
2007-01-24,86.68,87.15,86.08,86.70,33136200,86.70
2007-01-23,85.73,87.51,85.51,85.70,43122300,85.70
2007-01-22,89.14,89.16,85.65,86.79,51929500,86.79
2007-01-19,88.63,89.65,88.12,88.50,48731200,88.50
2007-01-18,92.10,92.11,89.05,89.07,84450200,89.07
2007-01-17,97.56,97.60,94.82,94.95,58795000,94.95
2007-01-16,95.68,97.25,95.45,97.10,44431300,97.10
2007-01-12,94.59,95.06,93.23,94.62,46881800,94.62
2007-01-11,95.94,96.78,95.10,95.80,51437600,95.80
2007-01-10,94.75,97.80,93.45,97.00,105460000,97.00
2007-01-09,86.45,92.98,85.15,92.57,119617800,92.57
2007-01-08,85.96,86.53,85.28,85.47,28468100,85.47
2007-01-05,85.77,86.20,84.40,85.05,29812200,85.05
2007-01-04,84.05,85.95,83.82,85.66,30259300,85.66
2007-01-03,86.29,86.58,81.90,83.80,44225700,83.80
2006-12-29,83.95,85.40,83.36,84.84,38443900,84.84
2006-12-28,80.22,81.25,79.65,80.87,39995600,80.87
2006-12-27,78.15,82.00,76.77,81.52,69134100,81.52
2006-12-26,82.15,82.57,80.89,81.51,17524600,81.51
2006-12-22,83.46,84.04,81.60,82.20,21903700,82.20
2006-12-21,84.70,85.48,82.20,82.90,32271400,82.90
2006-12-20,86.47,86.67,84.74,84.76,20274700,84.76
2006-12-19,84.73,86.68,83.62,86.31,32550200,86.31
2006-12-18,87.63,88.00,84.59,85.47,25770600,85.47
2006-12-15,89.02,89.22,87.33,87.72,26426400,87.72
2006-12-14,89.05,90.00,88.26,88.55,29726100,88.55
2006-12-13,87.95,89.07,87.15,89.05,30609000,89.05
2006-12-12,88.61,88.84,85.53,86.14,36665000,86.14
2006-12-11,88.90,89.30,88.05,88.75,17849300,88.75
2006-12-08,87.23,89.39,87.00,88.26,28009900,88.26
2006-12-07,90.03,90.50,86.90,87.04,35886700,87.04
2006-12-06,90.64,91.39,89.67,89.83,22792300,89.83
2006-12-05,91.65,92.33,90.87,91.27,23672800,91.27
2006-12-04,91.88,92.05,90.50,91.12,25340600,91.12
2006-12-01,91.80,92.33,90.10,91.32,28395700,91.32
2006-11-30,92.21,92.68,91.06,91.66,31088800,91.66
2006-11-29,93.00,93.15,90.25,91.80,41324400,91.80
2006-11-28,90.36,91.97,89.91,91.81,37006200,91.81
2006-11-27,92.51,93.16,89.50,89.54,38387000,89.54
2006-11-24,89.53,93.08,89.50,91.63,18524200,91.63
2006-11-22,88.99,90.75,87.85,90.31,23997900,90.31
2006-11-21,87.42,88.60,87.11,88.60,22238100,88.60
2006-11-20,85.40,87.00,85.20,86.47,20385500,86.47
2006-11-17,85.14,85.94,85.00,85.85,16658000,85.85
2006-11-16,84.87,86.30,84.62,85.61,24783600,85.61
2006-11-15,85.05,85.90,84.00,84.05,23404400,84.05
2006-11-14,84.80,85.00,83.90,85.00,21034100,85.00
2006-11-13,83.22,84.45,82.64,84.35,16095500,84.35
2006-11-10,83.55,83.60,82.50,83.12,13352300,83.12
2006-11-09,82.90,84.69,82.12,83.34,32966200,83.34
2006-11-08,80.02,82.69,79.89,82.45,24675600,82.45
2006-11-07,80.45,81.00,80.13,80.51,18783300,80.51
2006-11-06,78.95,80.06,78.43,79.71,15520600,79.71
2006-11-03,79.36,79.53,77.79,78.29,15424600,78.29
2006-11-02,78.92,79.32,78.50,78.98,16624400,78.98
2006-11-01,81.10,81.38,78.36,79.16,21828300,79.16
2006-10-31,81.45,81.68,80.23,81.08,17909800,81.08
2006-10-30,79.99,80.90,79.50,80.42,17854200,80.42
2006-10-27,81.75,82.45,80.01,80.41,21248800,80.41
2006-10-26,81.90,82.60,81.13,82.19,15455600,82.19
2006-10-25,81.35,82.00,81.01,81.68,17329100,81.68
2006-10-24,81.21,81.68,80.20,81.05,16543300,81.05
2006-10-23,79.99,81.90,79.75,81.46,29732400,81.46
2006-10-20,78.97,79.99,78.67,79.95,22836200,79.95
2006-10-19,79.26,79.95,78.16,78.99,54034900,78.99
2006-10-18,74.75,75.37,73.91,74.53,40496700,74.53
2006-10-17,75.04,75.27,74.04,74.29,17175900,74.29
2006-10-16,75.19,75.88,74.79,75.40,18167600,75.40
2006-10-13,75.63,76.88,74.74,75.02,24435600,75.02
2006-10-12,73.61,75.39,73.60,75.26,21173400,75.26
2006-10-11,73.42,73.98,72.60,73.23,20423400,73.23
2006-10-10,74.54,74.58,73.08,73.81,18985300,73.81
2006-10-09,73.80,75.08,73.53,74.63,15650800,74.63
2006-10-06,74.42,75.04,73.81,74.22,16677100,74.22
2006-10-05,74.53,76.16,74.13,74.83,24424400,74.83
2006-10-04,74.10,75.46,73.16,75.38,29610100,75.38
2006-10-03,74.45,74.95,73.19,74.08,28239600,74.08
2006-10-02,75.10,75.87,74.30,74.86,25451400,74.86
2006-09-29,77.11,77.52,76.68,76.98,14493300,76.98
2006-09-28,77.02,77.48,75.95,77.01,25843200,77.01
2006-09-27,77.17,77.47,75.82,76.41,28941900,76.41
2006-09-26,76.18,77.78,76.10,77.61,39391000,77.61
2006-09-25,73.81,75.86,73.72,75.75,30678300,75.75
2006-09-22,74.30,74.34,72.58,73.00,23754000,73.00
2006-09-21,75.25,76.06,74.02,74.65,28361600,74.65
2006-09-20,74.38,75.68,74.22,75.26,29385400,75.26
2006-09-19,74.10,74.36,72.80,73.77,25358900,73.77
2006-09-18,73.80,74.86,73.30,73.89,25188500,73.89
2006-09-15,74.60,74.98,73.29,74.10,35066200,74.10
2006-09-14,73.72,74.67,73.46,74.17,28633200,74.17
2006-09-13,72.85,74.32,72.30,74.20,40933500,74.20
2006-09-12,72.81,73.45,71.45,72.63,60167400,72.63
2006-09-11,72.43,73.73,71.42,72.50,33897300,72.50
2006-09-08,73.37,73.57,71.91,72.52,31997200,72.52
2006-09-07,70.60,73.48,70.25,72.80,45284200,72.80
2006-09-06,71.08,71.69,69.70,70.03,34789400,70.03
2006-09-05,68.97,71.50,68.55,71.48,36159200,71.48
2006-09-01,68.48,68.65,67.82,68.38,14589100,68.38
2006-08-31,67.28,68.30,66.66,67.85,20524900,67.85
2006-08-30,67.34,67.82,66.68,66.96,24290800,66.96
2006-08-29,66.99,67.26,65.12,66.48,33833300,66.48
2006-08-28,68.50,68.61,66.68,66.98,26362900,66.98
2006-08-25,67.34,69.05,67.31,68.75,19427100,68.75
2006-08-24,67.89,68.19,66.27,67.81,23399700,67.81
2006-08-23,68.00,68.65,66.94,67.31,19152100,67.31
2006-08-22,66.68,68.32,66.50,67.62,20606000,67.62
2006-08-21,67.30,67.31,66.15,66.56,18793800,66.56
2006-08-18,67.71,68.40,67.26,67.91,19155500,67.91
2006-08-17,68.00,68.66,67.18,67.59,20755300,67.59
2006-08-16,67.10,68.07,66.33,67.98,27903000,67.98
2006-08-15,65.34,66.50,64.80,66.45,30762600,66.45
2006-08-14,64.05,65.22,63.60,63.94,25629300,63.94
2006-08-11,63.23,64.13,62.58,63.65,27768900,63.65
2006-08-10,63.25,64.81,62.70,64.07,24920000,64.07
2006-08-09,65.43,65.60,63.40,63.59,34137100,63.59
2006-08-08,67.09,67.11,64.51,64.78,35638000,64.78
2006-08-07,67.72,69.60,66.31,67.21,44482600,67.21
2006-08-04,67.05,68.61,64.96,68.30,66173800,68.30
2006-08-03,67.91,70.00,67.81,69.59,30037300,69.59
2006-08-02,67.65,68.68,67.51,68.16,19670300,68.16
2006-08-01,67.22,67.93,65.94,67.18,25420200,67.18
2006-07-31,66.83,68.63,66.28,67.96,31887200,67.96
2006-07-28,63.94,65.68,63.50,65.59,24696700,65.59
2006-07-27,64.50,65.02,62.86,63.40,26251600,63.40
2006-07-26,62.00,64.64,61.68,63.87,32086700,63.87
2006-07-25,61.78,62.09,60.78,61.93,21038200,61.93
2006-07-24,61.26,62.10,60.43,61.42,25816300,61.42
2006-07-21,59.82,61.15,59.64,60.72,31853300,60.72
2006-07-20,60.96,61.59,59.72,60.50,70433800,60.50
2006-07-19,52.96,55.08,52.36,54.10,49669400,54.10
2006-07-18,53.16,53.85,51.85,52.90,35730300,52.90
2006-07-17,51.73,53.11,51.65,52.37,36590800,52.37
2006-07-14,52.50,52.89,50.16,50.67,35465600,50.67
2006-07-13,52.03,54.12,51.41,52.25,44639500,52.25
2006-07-12,55.17,55.24,52.92,52.96,33118900,52.96
2006-07-11,55.11,55.99,54.53,55.65,29465100,55.65
2006-07-10,55.70,56.49,54.50,55.00,18905200,55.00
2006-07-07,55.48,56.55,54.67,55.40,28548600,55.40
2006-07-06,57.09,57.40,55.61,55.77,22614600,55.77
2006-07-05,57.15,57.60,56.56,57.00,18508600,57.00
2006-07-03,57.52,58.18,57.34,57.95,6956100,57.95
2006-06-30,57.59,57.75,56.50,57.27,26417700,57.27
2006-06-29,56.76,59.09,56.39,58.97,31192800,58.97
2006-06-28,57.29,57.30,55.41,56.02,30382300,56.02
2006-06-27,59.09,59.22,57.40,57.43,19664700,57.43
2006-06-26,59.17,59.20,58.37,58.99,16662000,58.99
2006-06-23,59.72,60.17,58.73,58.83,23578700,58.83
2006-06-22,58.20,59.75,58.07,59.58,34486900,59.58
2006-06-21,57.74,58.71,57.30,57.86,30832000,57.86
2006-06-20,57.61,58.35,57.29,57.47,24034800,57.47
2006-06-19,57.83,58.18,57.00,57.20,25163400,57.20
2006-06-16,58.96,59.19,57.52,57.56,29932200,57.56
2006-06-15,57.30,59.74,56.75,59.38,42513700,59.38
2006-06-14,58.28,58.78,56.69,57.61,31362000,57.61
2006-06-13,57.61,59.10,57.36,58.33,38594400,58.33
2006-06-12,59.40,59.73,56.96,57.00,25635200,57.00
2006-06-09,61.18,61.56,59.10,59.24,27708500,59.24
2006-06-08,58.44,60.93,57.15,60.76,49910100,60.76
2006-06-07,60.10,60.40,58.35,58.56,26803800,58.56
2006-06-06,60.22,60.63,58.91,59.72,25929900,59.72
2006-06-05,61.15,61.15,59.97,60.00,21635200,60.00
2006-06-02,62.99,63.10,60.88,61.66,24492400,61.66
2006-06-01,59.85,62.28,59.52,62.17,33661000,62.17
2006-05-31,61.76,61.79,58.69,59.77,45749200,59.77
2006-05-30,63.29,63.30,61.22,61.22,20121500,61.22
2006-05-26,64.31,64.56,63.14,63.55,15462500,63.55
2006-05-25,64.26,64.45,63.29,64.33,16549000,64.33
2006-05-24,62.99,63.65,61.56,63.34,32715400,63.34
2006-05-23,64.86,65.19,63.00,63.15,24800500,63.15
2006-05-22,63.87,63.99,62.77,63.38,25677700,63.38
2006-05-19,63.26,64.88,62.82,64.51,35209500,64.51
2006-05-18,65.68,66.26,63.12,63.18,23515800,63.18
2006-05-17,64.71,65.70,64.07,65.26,26935500,65.26
2006-05-16,68.10,68.25,64.75,64.98,33455000,64.98
2006-05-15,67.37,68.38,67.12,67.79,18899200,67.79
2006-05-12,67.85,68.69,66.86,67.70,22920500,67.70
2006-05-11,70.79,70.84,67.55,68.15,29024600,68.15
2006-05-10,71.29,71.33,69.61,70.60,16424600,70.60
2006-05-09,71.82,72.56,70.62,71.03,18988100,71.03
2006-05-08,72.99,73.80,71.72,71.89,21244700,71.89
2006-05-05,71.86,72.25,71.15,71.89,20139700,71.89
2006-05-04,71.22,72.89,70.46,71.13,30729300,71.13
2006-05-03,71.83,71.95,70.18,71.14,24535400,71.14
2006-05-02,70.15,71.98,70.11,71.62,27559400,71.62
2006-05-01,70.77,71.54,69.16,69.60,26799300,69.60
2006-04-28,69.38,71.30,69.20,70.39,27144200,70.39
2006-04-27,67.73,69.86,67.35,69.36,30212400,69.36
2006-04-26,66.65,68.28,66.40,68.15,25388800,68.15
2006-04-25,65.96,66.59,65.56,66.17,18895100,66.17
2006-04-24,66.85,66.92,65.50,65.75,25251000,65.75
2006-04-21,68.19,68.64,66.47,67.04,28178100,67.04
2006-04-20,69.51,70.00,66.20,67.63,59535100,67.63
2006-04-19,66.82,67.00,65.47,65.65,38786900,65.65
2006-04-18,65.04,66.47,64.79,66.22,28387300,66.22
2006-04-17,66.51,66.84,64.35,64.81,25783500,64.81
2006-04-13,66.34,67.44,65.81,66.47,26238500,66.47
2006-04-12,68.01,68.17,66.30,66.71,26424800,66.71
2006-04-11,68.99,69.30,67.07,67.99,33547000,67.99
2006-04-10,70.29,70.93,68.45,68.67,32268400,68.67
2006-04-07,70.93,71.21,68.47,69.79,55187100,69.79
2006-04-06,68.30,72.05,68.20,71.24,95134600,71.24
2006-04-05,64.71,67.21,64.15,67.21,79764600,67.21
2006-04-04,62.10,62.22,61.05,61.17,33283000,61.17
2006-04-03,63.67,64.12,62.61,62.65,29135400,62.65
2006-03-31,63.25,63.61,62.24,62.72,29119900,62.72
2006-03-30,62.82,63.30,61.53,62.75,49666100,62.75
2006-03-29,59.13,62.52,57.67,62.33,83815500,62.33
2006-03-28,59.63,60.14,58.25,58.71,48940100,58.71
2006-03-27,60.35,61.38,59.40,59.51,39574000,59.51
2006-03-24,60.25,60.94,59.03,59.96,38285000,59.96
2006-03-23,61.82,61.90,59.61,60.16,50993800,60.16
2006-03-22,62.16,63.25,61.27,61.67,48067700,61.67
2006-03-21,64.29,64.34,61.39,61.81,47991700,61.81
2006-03-20,65.22,65.46,63.87,63.99,21622900,63.99
2006-03-17,64.75,65.54,64.11,64.66,29001500,64.66
2006-03-16,66.85,66.90,64.30,64.31,26772800,64.31
2006-03-15,67.71,68.04,65.52,66.23,31857000,66.23
2006-03-14,65.77,67.32,65.50,67.32,22929300,67.32
2006-03-13,65.05,66.28,64.79,65.68,30756700,65.68
2006-03-10,64.05,64.49,62.45,63.19,37255100,63.19
2006-03-09,65.98,66.47,63.81,63.93,28546600,63.93
2006-03-08,66.29,67.20,65.35,65.66,23330400,65.66
2006-03-07,65.76,66.90,65.08,66.31,31174200,66.31
2006-03-06,67.69,67.72,64.94,65.48,32595200,65.48
2006-03-03,69.40,69.91,67.53,67.72,26345300,67.72
2006-03-02,68.99,69.99,68.67,69.61,22331200,69.61
2006-03-01,68.84,69.49,68.02,69.10,27279200,69.10
2006-02-28,71.58,72.40,68.10,68.49,45249300,68.49
2006-02-27,71.99,72.12,70.65,70.99,28258600,70.99
2006-02-24,72.14,72.89,71.20,71.46,19098000,71.46
2006-02-23,71.79,73.00,71.43,71.75,30604200,71.75
2006-02-22,69.00,71.67,68.00,71.32,34937100,71.32
2006-02-21,70.59,70.80,68.68,69.08,27843100,69.08
2006-02-17,70.30,70.89,69.61,70.29,20571400,70.29
2006-02-16,69.91,71.01,69.48,70.57,33863400,70.57
2006-02-15,67.16,69.62,66.75,69.22,41420400,69.22
2006-02-14,65.10,68.10,65.00,67.64,41462100,67.64
2006-02-13,66.63,66.75,64.64,64.71,31553500,64.71
2006-02-10,65.18,67.67,62.90,67.31,62874200,67.31
2006-02-09,69.10,69.23,64.53,64.95,41063000,64.95
2006-02-08,68.49,69.08,66.00,68.81,34039800,68.81
2006-02-07,68.27,69.48,66.68,67.60,49601100,67.60
2006-02-06,72.02,72.51,66.74,67.30,58991700,67.30
2006-02-03,72.24,72.79,71.04,71.85,24718700,71.85
2006-02-02,75.10,75.36,72.05,72.10,25261500,72.10
2006-02-01,74.95,76.46,74.64,75.42,18613800,75.42
2006-01-31,75.50,76.34,73.75,75.51,32626500,75.51
2006-01-30,71.17,76.60,70.87,75.00,49942900,75.00
2006-01-27,72.95,73.60,71.10,72.03,34066600,72.03
2006-01-26,74.53,75.43,71.93,72.33,42192400,72.33
2006-01-25,77.39,77.50,73.25,74.20,45563800,74.20
2006-01-24,78.76,79.42,75.77,76.04,40794800,76.04
2006-01-23,76.10,79.56,76.00,77.67,37847500,77.67
2006-01-20,79.28,80.04,75.83,76.09,40527100,76.09
2006-01-19,81.25,81.66,78.74,79.04,60566000,79.04
2006-01-18,83.08,84.05,81.85,82.49,42879900,82.49
2006-01-17,85.70,86.38,83.87,84.71,29843700,84.71
2006-01-13,84.99,86.01,84.60,85.59,27725200,85.59
2006-01-12,84.97,86.40,83.62,84.29,45743200,84.29
2006-01-11,83.84,84.80,82.59,83.90,53349800,83.90
2006-01-10,76.25,81.89,75.83,80.86,81423900,80.86
2006-01-09,76.73,77.20,75.74,76.05,24108600,76.05
2006-01-06,75.25,76.70,74.55,76.30,25159200,76.30
2006-01-05,74.83,74.90,73.75,74.38,16050800,74.38
2006-01-04,75.13,75.98,74.50,74.97,22128700,74.97
2006-01-03,72.38,74.75,72.25,74.75,28829800,74.75
2005-12-30,70.91,72.43,70.34,71.89,22295100,71.89
2005-12-29,73.78,73.82,71.42,71.45,17500900,71.45
2005-12-28,74.47,74.76,73.32,73.57,14218400,73.57
2005-12-27,74.00,75.18,73.95,74.23,21092500,74.23
2005-12-23,74.17,74.26,73.30,73.35,8209200,73.35
2005-12-22,73.91,74.49,73.60,74.02,13236100,74.02
2005-12-21,72.60,73.61,72.54,73.50,16990600,73.50
2005-12-20,71.63,72.38,71.12,72.11,17111000,72.11
2005-12-19,71.11,72.60,71.04,71.38,18903400,71.38
2005-12-16,72.14,72.30,71.06,71.11,23970400,71.11
2005-12-15,72.68,72.86,71.35,72.18,20041500,72.18
2005-12-14,72.53,73.30,70.27,72.01,51811300,72.01
2005-12-13,74.85,75.46,74.21,74.98,17636300,74.98
2005-12-12,74.87,75.35,74.56,74.91,18749800,74.91
2005-12-09,74.21,74.59,73.35,74.33,19835800,74.33
2005-12-08,73.20,74.17,72.60,74.08,28231500,74.08
2005-12-07,74.23,74.46,73.12,73.95,24266600,73.95
2005-12-06,73.93,74.83,73.35,74.05,30608200,74.05
2005-12-05,71.95,72.53,71.49,71.82,20845400,71.82
2005-12-02,72.27,72.74,70.70,72.63,31991500,72.63
2005-12-01,68.95,71.73,68.81,71.60,29031900,71.60
2005-11-30,68.43,68.85,67.52,67.82,21274100,67.82
2005-11-29,69.99,70.30,67.35,68.10,31836900,68.10
2005-11-28,70.72,71.07,69.07,69.66,36375700,69.66
2005-11-25,67.66,69.54,67.50,69.34,14107600,69.34
2005-11-23,66.88,67.98,66.69,67.11,17351900,67.11
2005-11-22,64.84,66.76,64.52,66.52,19295800,66.52
2005-11-21,64.82,65.19,63.72,64.96,18275400,64.96
2005-11-18,65.31,65.43,64.37,64.56,18748700,64.56
2005-11-17,65.59,65.88,64.25,64.52,24150200,64.52
2005-11-16,63.15,65.06,63.09,64.95,28018400,64.95
2005-11-15,61.60,63.08,61.46,62.28,19172900,62.28
2005-11-14,61.54,61.98,60.91,61.45,13211900,61.45
2005-11-11,61.54,62.11,61.34,61.54,15194600,61.54
2005-11-10,60.64,61.20,59.01,61.18,23762300,61.18
2005-11-09,60.00,61.21,60.00,60.11,19747500,60.11
2005-11-08,59.95,60.38,59.10,59.90,16920200,59.90
2005-11-07,60.85,61.67,60.14,60.23,22815400,60.23
2005-11-04,60.35,61.24,59.62,61.15,31358400,61.15
2005-11-03,60.26,62.32,60.07,61.85,31585100,61.85
2005-11-02,57.72,60.00,57.60,59.95,30609300,59.95
2005-11-01,57.24,58.14,56.87,57.50,26774500,57.50
2005-10-31,55.20,57.98,54.75,57.59,33601600,57.59
2005-10-28,56.04,56.43,54.17,54.47,27492400,54.47
2005-10-27,56.99,57.01,55.41,55.41,14697900,55.41
2005-10-26,56.28,57.56,55.92,57.03,22556900,57.03
2005-10-25,56.40,56.85,55.69,56.10,16611700,56.10
2005-10-24,55.25,56.79,55.09,56.79,21776900,56.79
2005-10-21,56.84,56.98,55.36,55.66,28454500,55.66
2005-10-20,54.47,56.50,54.35,56.14,48491500,56.14
2005-10-19,52.07,54.96,51.21,54.94,36024400,54.94
2005-10-18,53.25,53.95,52.20,52.21,21771000,52.21
2005-10-17,53.98,54.23,52.68,53.44,22029800,53.44
2005-10-14,54.03,54.35,52.79,54.00,36984000,54.00
2005-10-13,49.44,53.95,49.27,53.74,66627700,53.74
2005-10-12,48.65,50.30,47.87,49.25,96338800,49.25
2005-10-11,51.23,51.87,50.40,51.59,43781600,51.59
2005-10-10,51.76,51.91,50.28,50.37,18125200,50.37
2005-10-07,51.72,51.93,50.55,51.30,24210100,51.30
2005-10-06,53.20,53.49,50.87,51.70,27054900,51.70
2005-10-05,54.33,54.36,52.75,52.78,21813200,52.78
2005-10-04,54.95,55.35,53.64,53.75,19266400,53.75
2005-10-03,54.16,54.54,53.68,54.44,18126900,54.44
2005-09-30,52.33,53.65,51.88,53.61,18986900,53.61
2005-09-29,51.23,52.59,50.81,52.34,22744500,52.34
2005-09-28,53.07,53.11,50.59,51.08,40198000,51.08
2005-09-27,53.92,54.24,53.43,53.44,12203700,53.44
2005-09-26,54.03,54.56,53.32,53.84,19520100,53.84
2005-09-23,52.10,53.50,51.84,53.20,19944900,53.20
2005-09-22,51.88,52.47,51.32,51.90,16561700,51.90
2005-09-21,52.96,53.05,51.86,52.11,15526700,52.11
2005-09-20,52.99,53.81,52.92,53.19,29279600,53.19
2005-09-19,51.05,52.89,51.05,52.64,27990400,52.64
2005-09-16,50.23,51.21,49.95,51.21,21107300,51.21
2005-09-15,50.00,50.18,49.33,49.87,14827000,49.87
2005-09-14,51.06,51.19,49.46,49.61,16943800,49.61
2005-09-13,51.02,51.29,50.32,50.82,17603000,50.82
2005-09-12,51.10,51.63,50.58,51.40,16171300,51.40
2005-09-09,50.07,51.35,49.79,51.31,21987200,51.31
2005-09-08,49.35,50.12,49.14,49.78,25094300,49.78
2005-09-07,49.05,49.40,47.92,48.68,34395500,48.68
2005-09-06,46.70,48.88,46.55,48.80,29236400,48.80
2005-09-02,46.30,46.80,46.12,46.22,7942100,46.22
2005-09-01,47.00,47.17,46.09,46.26,12727400,46.26
2005-08-31,46.86,47.03,46.27,46.89,14391300,46.89
2005-08-30,45.99,46.79,45.92,46.57,18527200,46.57
2005-08-29,45.27,46.03,45.26,45.84,9153400,45.84
2005-08-26,46.12,46.34,45.36,45.74,9323500,45.74
2005-08-25,46.12,46.49,45.81,46.06,9866200,46.06
2005-08-24,45.60,47.12,45.59,45.77,20431100,45.77
2005-08-23,45.85,46.10,45.32,45.74,10557300,45.74
2005-08-22,46.15,46.75,45.26,45.87,13847600,45.87
2005-08-19,46.28,46.70,45.77,45.83,13448900,45.83
2005-08-18,46.91,47.00,45.75,46.30,15805700,46.30
2005-08-17,46.40,47.44,46.37,47.15,17847300,47.15
2005-08-16,47.39,47.50,46.21,46.25,19200800,46.25
2005-08-15,46.48,48.33,46.45,47.68,38811700,47.68
2005-08-12,43.46,46.22,43.36,46.10,32715600,46.10
2005-08-11,43.39,44.12,43.25,44.00,9713700,44.00
2005-08-10,44.00,44.39,43.31,43.38,12890900,43.38
2005-08-09,42.93,43.89,42.91,43.82,13601400,43.82
2005-08-08,43.00,43.25,42.61,42.65,6299400,42.65
2005-08-05,42.49,43.36,42.02,42.99,8640400,42.99
2005-08-04,42.89,43.00,42.29,42.71,9618000,42.71
2005-08-03,43.19,43.31,42.77,43.22,9225800,43.22
2005-08-02,42.89,43.50,42.61,43.19,10602700,43.19
2005-08-01,42.57,43.08,42.08,42.75,11223200,42.75
2005-07-29,43.56,44.38,42.26,42.65,20074300,42.65
2005-07-28,43.85,44.00,43.30,43.80,8975400,43.80
2005-07-27,43.83,44.07,42.67,43.99,10133900,43.99
2005-07-26,44.01,44.11,43.36,43.63,9592600,43.63
2005-07-25,43.99,44.28,43.73,43.81,10522400,43.81
2005-07-22,43.44,44.00,43.39,44.00,10753800,44.00
2005-07-21,43.70,44.04,42.90,43.29,14438000,43.29
2005-07-20,42.86,43.80,42.65,43.63,16192700,43.63
2005-07-19,41.52,43.23,41.07,43.19,23966500,43.19
2005-07-18,41.41,42.10,41.37,41.49,20939200,41.49
2005-07-15,40.97,41.57,40.46,41.55,24560100,41.55
2005-07-14,40.79,42.01,40.23,40.75,74859300,40.75
2005-07-13,38.29,38.50,37.90,38.35,24458400,38.35
2005-07-12,38.23,38.40,37.91,38.24,13822800,38.24
2005-07-11,38.37,38.65,37.78,38.10,13885300,38.10
2005-07-08,37.87,38.28,37.47,38.25,10383400,38.25
2005-07-07,36.81,37.76,36.80,37.63,13704400,37.63
2005-07-06,37.71,38.16,37.20,37.39,14093800,37.39
2005-07-05,36.55,38.15,36.50,37.98,16223900,37.98
2005-07-01,36.83,36.97,36.29,36.50,8928600,36.50
2005-06-30,36.61,37.16,36.31,36.81,14942500,36.81
2005-06-29,37.23,37.29,36.12,36.37,16012800,36.37
2005-06-28,37.49,37.59,37.17,37.31,12510700,37.31
2005-06-27,36.84,38.10,36.68,37.10,21434700,37.10
2005-06-24,39.09,39.12,37.68,37.76,14668200,37.76
2005-06-23,38.83,39.78,38.65,38.89,24080500,38.89
2005-06-22,38.26,38.60,38.14,38.55,15175900,38.55
2005-06-21,37.72,38.19,37.38,37.86,13233100,37.86
2005-06-20,37.85,38.09,37.45,37.61,11561300,37.61
2005-06-17,38.47,38.54,37.83,38.31,21290200,38.31
2005-06-16,37.19,38.08,36.82,37.98,19559800,37.98
2005-06-15,36.87,37.30,36.30,37.13,20119400,37.13
2005-06-14,35.92,36.15,35.75,36.00,12423100,36.00
2005-06-13,35.89,36.61,35.82,35.90,15563300,35.90
2005-06-10,37.40,37.40,35.52,35.81,24247600,35.81
2005-06-09,37.00,37.94,36.82,37.65,13937700,37.65
2005-06-08,36.63,37.25,36.57,36.92,14428800,36.92
2005-06-07,37.60,37.73,36.45,36.54,26616600,36.54
2005-06-06,38.33,38.63,37.56,37.92,28998800,37.92
2005-06-03,38.16,38.58,37.77,38.24,34173900,38.24
2005-06-02,40.05,40.32,39.60,40.04,13356200,40.04
2005-06-01,39.89,40.76,39.86,40.30,16207600,40.30
2005-05-31,40.66,40.74,39.58,39.76,14435900,39.76
2005-05-27,40.64,40.79,40.01,40.56,11286000,40.56
2005-05-26,39.94,40.94,39.94,40.74,18768600,40.74
2005-05-25,39.50,39.95,39.32,39.78,14143100,39.78
2005-05-24,39.45,39.99,39.03,39.70,21195000,39.70
2005-05-23,37.85,39.90,37.85,39.76,37234800,39.76
2005-05-20,37.25,37.65,37.19,37.55,16166100,37.55
2005-05-19,35.78,37.68,35.78,37.55,28327200,37.55
2005-05-18,35.45,37.56,34.99,35.84,22740100,35.84
2005-05-17,35.14,35.46,34.54,35.36,21012300,35.36
2005-05-16,34.56,35.70,34.53,35.55,16939100,35.55
2005-05-13,34.20,35.23,34.07,34.77,25096900,34.77
2005-05-12,35.42,35.59,34.00,34.13,34651500,34.13
2005-05-11,35.20,35.67,33.11,35.61,72927900,35.61
2005-05-10,36.75,37.25,36.33,36.42,15723700,36.42
2005-05-09,37.28,37.45,36.75,36.97,12703400,36.97
2005-05-06,36.89,37.33,36.79,37.24,11651700,37.24
2005-05-05,37.25,37.27,36.47,36.68,13834500,36.68
2005-05-04,36.11,37.20,36.10,37.15,16006300,37.15
2005-05-03,36.40,36.74,36.03,36.21,17740700,36.21
2005-05-02,36.21,36.65,36.02,36.43,16640000,36.43
2005-04-29,36.15,36.23,35.22,36.06,23986800,36.06
2005-04-28,36.29,36.34,35.24,35.54,20539500,35.54
2005-04-27,35.89,36.36,35.51,35.95,21924600,35.95
2005-04-26,36.78,37.51,36.12,36.19,28946700,36.19
2005-04-25,36.49,37.02,36.11,36.98,26659300,36.98
2005-04-22,36.84,37.00,34.90,35.50,29968900,35.50
2005-04-21,36.40,37.21,35.90,37.18,27128300,37.18
2005-04-20,37.66,37.74,35.44,35.51,33754700,35.51
2005-04-19,36.60,37.44,35.87,37.09,38630100,37.09
2005-04-18,35.00,36.30,34.00,35.62,47399200,35.62
2005-04-15,36.62,37.25,35.28,35.35,61717400,35.35
2005-04-14,38.81,39.56,36.84,37.26,98328300,37.26
2005-04-13,42.95,42.99,40.39,41.04,48998100,41.04
2005-04-12,42.49,43.19,42.01,42.66,35037900,42.66
2005-04-11,44.15,44.25,41.91,41.92,29345100,41.92
2005-04-08,43.70,44.45,43.54,43.74,23212500,43.74
2005-04-07,42.33,43.75,42.25,43.56,18106700,43.56
2005-04-06,42.40,42.81,42.15,42.33,14815200,42.33
2005-04-05,41.22,42.24,41.09,41.89,19865700,41.89
2005-04-04,40.99,41.31,40.16,41.09,20714800,41.09
2005-04-01,42.09,42.18,40.57,40.89,22903000,40.89
2005-03-31,42.45,42.52,41.59,41.67,22719100,41.67
2005-03-30,42.07,42.80,41.82,42.80,14105700,42.80
2005-03-29,42.56,42.83,41.50,41.75,16477000,41.75
2005-03-28,42.75,42.96,42.47,42.53,9836100,42.53
2005-03-24,42.91,43.00,42.50,42.50,12596600,42.50
2005-03-23,42.45,43.40,42.02,42.55,21779400,42.55
2005-03-22,43.71,43.96,42.68,42.83,19693400,42.83
2005-03-21,43.29,43.97,42.86,43.70,19326000,43.70
2005-03-18,43.33,43.44,42.50,42.96,33576800,42.96
2005-03-17,41.53,42.88,41.32,42.25,28640000,42.25
2005-03-16,41.21,42.31,40.78,41.18,24921900,41.18
2005-03-15,40.64,41.14,40.25,40.96,18164600,40.96
2005-03-14,40.52,40.79,39.52,40.32,21620900,40.32
2005-03-11,40.21,40.59,39.80,40.27,22601100,40.27
2005-03-10,39.53,40.26,39.10,39.83,27753900,39.83
2005-03-09,39.64,40.28,38.83,39.35,47230900,39.35
2005-03-08,41.90,42.16,40.10,40.53,36480400,40.53
2005-03-07,42.80,43.25,42.35,42.75,16094000,42.75
2005-03-04,42.76,43.01,41.85,42.81,27022100,42.81
2005-03-03,44.37,44.41,41.22,41.79,50416200,41.79
2005-03-02,44.25,44.89,44.08,44.12,16362900,44.12
2005-03-01,44.99,45.11,44.16,44.50,16721000,44.50
2005-02-28,44.68,45.14,43.96,44.86,23271800,44.86
2005-02-25,89.62,89.91,88.19,88.99,32696800,44.49
2005-02-24,88.48,89.31,87.73,88.93,54251000,44.47
2005-02-23,86.72,88.45,85.55,88.23,48042200,44.12
2005-02-22,86.30,88.30,85.29,85.29,43546200,42.65
2005-02-18,87.74,87.86,86.25,86.81,41544800,43.40
2005-02-17,90.65,90.88,87.45,87.81,54231200,43.90
2005-02-16,88.15,90.20,87.35,90.13,58544400,45.06
2005-02-15,86.66,89.08,86.00,88.41,82579200,44.21
2005-02-14,82.73,84.79,82.05,84.63,45409400,42.31
2005-02-11,79.86,81.76,78.94,81.21,42894800,40.60
2005-02-10,78.72,79.28,76.66,78.36,39036400,39.18
2005-02-09,81.04,81.99,78.10,78.74,42552000,39.37
2005-02-08,79.07,81.38,78.79,80.90,31786400,40.45
2005-02-07,78.93,79.35,77.50,78.94,18730600,39.47
2005-02-04,77.87,78.93,77.53,78.84,20127000,39.42
2005-02-03,79.10,79.43,77.33,77.81,26130400,38.90
2005-02-02,77.95,79.91,77.69,79.63,36430800,39.81
2005-02-01,77.05,77.77,76.58,77.53,24228400,38.76
2005-01-31,74.58,77.89,74.51,76.90,60039200,38.45
2005-01-28,72.62,73.98,72.44,73.98,28629000,36.99
2005-01-27,72.16,72.92,71.55,72.64,17722400,36.32
2005-01-26,72.66,72.75,71.22,72.25,26410600,36.12
2005-01-25,71.37,72.84,70.94,72.05,34615400,36.03
2005-01-24,70.98,71.78,70.55,70.76,30058200,35.38
2005-01-21,71.31,71.60,70.00,70.49,32547600,35.24
2005-01-20,69.65,71.27,69.47,70.46,32675800,35.23
2005-01-19,70.49,71.46,69.75,69.88,26853400,34.94
2005-01-18,69.85,70.70,67.75,70.65,35945000,35.33
2005-01-14,70.25,71.72,69.19,70.20,63240800,35.10
2005-01-13,73.71,74.42,69.73,69.80,113025600,34.90
2005-01-12,65.45,65.90,63.30,65.46,68560800,32.73
2005-01-11,68.25,69.15,64.14,64.56,93272400,32.28
2005-01-10,69.83,70.70,67.88,68.96,61618200,34.48
2005-01-07,65.00,69.63,64.75,69.25,79551800,34.62
2005-01-06,64.67,64.91,63.33,64.55,25198400,32.28
2005-01-05,64.46,65.25,64.05,64.50,24301200,32.25
2005-01-04,63.79,65.47,62.97,63.94,39171800,31.97
2005-01-03,64.78,65.11,62.60,63.29,24714000,31.65
2004-12-31,64.89,65.00,64.03,64.40,9949600,32.20
2004-12-30,64.81,65.03,64.22,64.80,12333600,32.40
2004-12-29,63.81,64.98,63.57,64.44,16055800,32.22
2004-12-28,63.30,64.25,62.05,64.18,21848400,32.09
2004-12-27,64.80,65.15,62.88,63.16,19981800,31.58
2004-12-23,63.75,64.25,63.60,64.01,8783200,32.01
2004-12-22,63.66,64.36,63.40,63.75,20208200,31.88
2004-12-21,63.56,63.77,61.60,63.69,38014800,31.84
2004-12-20,65.47,66.00,61.76,62.72,41718800,31.36
2004-12-17,66.84,67.04,64.90,64.99,27982000,32.49
2004-12-16,66.15,67.50,66.05,66.60,40218400,33.30
2004-12-15,65.24,65.46,64.66,65.26,14227200,32.63
2004-12-14,65.40,65.88,65.02,65.29,14847200,32.65
2004-12-13,65.62,65.90,64.60,64.91,14108600,32.46
2004-12-10,65.03,66.05,64.70,65.15,27706200,32.58
2004-12-09,62.81,64.40,62.07,63.99,26482200,32.00
2004-12-08,63.08,64.43,62.05,63.28,24710800,31.64
2004-12-07,65.93,66.73,62.56,62.89,37746400,31.44
2004-12-06,64.25,66.24,62.95,65.78,44568600,32.89
2004-12-03,64.53,65.00,61.75,62.68,44244600,31.34
2004-12-02,66.13,66.90,64.66,65.21,35265800,32.60
2004-12-01,67.79,67.95,66.27,67.79,28591200,33.90
2004-11-30,68.79,68.79,67.05,67.05,36732800,33.53
2004-11-29,68.95,69.57,67.41,68.44,61175600,34.22
2004-11-26,65.35,65.76,64.34,64.55,19648000,32.28
2004-11-24,61.69,65.20,61.55,64.05,49671000,32.03
2004-11-23,62.30,62.45,61.05,61.27,32551800,30.64
2004-11-22,61.80,64.00,57.90,61.35,91721800,30.67
2004-11-19,55.49,56.91,54.50,55.17,27331400,27.58
2004-11-18,54.30,55.45,54.29,55.39,16398200,27.69
2004-11-17,55.19,55.45,54.22,54.90,14205400,27.45
2004-11-16,55.16,55.20,54.48,54.94,10539400,27.47
2004-11-15,55.20,55.46,54.34,55.24,13430200,27.62
2004-11-12,55.01,55.69,54.84,55.50,14132200,27.75
2004-11-11,54.95,55.43,54.23,55.30,14546400,27.65
2004-11-10,53.95,55.39,53.91,54.75,18167000,27.38
2004-11-09,54.23,54.55,53.38,54.05,16991600,27.02
2004-11-08,54.27,55.45,53.86,54.38,18818600,27.19
2004-11-05,54.86,55.00,52.04,54.72,43037400,27.36
2004-11-04,55.03,55.55,54.37,54.45,33165200,27.23
2004-11-03,54.37,56.11,53.99,55.31,43006200,27.66
2004-11-02,52.40,54.08,52.40,53.50,26071000,26.75
2004-11-01,52.50,53.26,52.04,52.45,21501800,26.23
2004-10-29,51.84,53.20,51.80,52.40,28936400,26.20
2004-10-28,49.98,52.22,49.50,52.19,30866600,26.09
2004-10-27,48.51,50.62,48.17,50.30,42624800,25.15
2004-10-26,47.45,48.05,46.97,47.97,21227200,23.99
2004-10-25,47.20,47.84,47.07,47.55,14023000,23.77
2004-10-22,47.54,47.67,47.02,47.41,17252400,23.70
2004-10-21,47.48,48.13,47.36,47.94,25875200,23.97
2004-10-20,47.18,47.60,46.65,47.47,21611000,23.74
2004-10-19,48.10,48.35,47.31,47.42,28642600,23.71
2004-10-18,44.70,47.75,44.70,47.75,42884000,23.88
2004-10-15,44.88,45.61,44.19,45.50,36826000,22.75
2004-10-14,43.19,45.75,42.55,44.98,98872400,22.49
2004-10-13,38.87,39.76,38.74,39.75,41536000,19.88
2004-10-12,38.50,38.58,37.65,38.29,16435400,19.15
2004-10-11,38.80,39.06,38.20,38.59,11566800,19.30
2004-10-08,39.56,39.77,38.84,39.06,12829600,19.53
2004-10-07,40.54,40.93,39.46,39.62,15219600,19.81
2004-10-06,39.50,40.76,39.47,40.64,15939400,20.32
2004-10-05,38.56,39.67,38.40,39.37,14505800,19.68
2004-10-04,39.18,39.18,38.75,38.79,20503000,19.40
2004-10-01,39.12,39.19,38.58,38.67,16621600,19.33
2004-09-30,39.00,39.27,38.45,38.75,15179000,19.38
2004-09-29,37.93,38.86,37.82,38.68,9768200,19.34
2004-09-28,37.46,38.29,37.45,38.04,12613800,19.02
2004-09-27,36.95,37.98,36.83,37.53,14197000,18.76
2004-09-24,37.45,38.00,37.15,37.29,13196000,18.65
2004-09-23,37.04,37.50,36.93,37.27,14193000,18.64
2004-09-22,38.10,38.14,36.81,36.92,14346000,18.46
2004-09-21,37.75,38.87,37.46,38.01,13809000,19.00
2004-09-20,36.88,37.98,36.87,37.71,8750000,18.85
2004-09-17,36.55,37.38,36.40,37.14,17939600,18.57
2004-09-16,35.20,36.76,35.08,36.35,17925600,18.17
2004-09-15,35.36,35.48,34.80,35.20,8309600,17.60
2004-09-14,35.24,35.55,34.78,35.49,9100800,17.75
2004-09-13,35.88,36.07,35.32,35.59,10070600,17.80
2004-09-10,35.66,36.23,35.46,35.87,11714800,17.93
2004-09-09,36.10,36.30,35.28,35.70,16476400,17.85
2004-09-08,35.70,36.57,35.68,36.35,12268800,18.17
2004-09-07,35.40,36.19,35.23,35.76,10784200,17.88
2004-09-03,35.01,35.92,35.01,35.23,10481000,17.61
2004-09-02,35.50,35.81,34.83,35.66,14511600,17.83
2004-09-01,34.30,35.99,34.19,35.86,18418800,17.93
2004-08-31,34.07,34.95,34.00,34.49,13448600,17.25
2004-08-30,34.00,34.72,33.96,34.12,7790800,17.06
2004-08-27,34.68,34.76,34.00,34.35,13886200,17.17
2004-08-26,33.04,35.18,32.74,34.66,34137800,17.33
2004-08-25,31.87,33.15,31.73,33.05,18057800,16.52
2004-08-24,31.26,31.95,31.19,31.95,13362000,15.98
2004-08-23,30.86,31.27,30.60,31.08,9095000,15.54
2004-08-20,30.71,30.99,30.49,30.80,11313600,15.40
2004-08-19,31.51,31.86,30.36,30.71,13890000,15.35
2004-08-18,30.51,31.85,30.49,31.74,13023400,15.87
2004-08-17,30.58,31.13,30.35,30.87,11536400,15.44
2004-08-16,31.00,31.72,30.64,30.78,15559800,15.39
2004-08-13,30.60,31.28,30.40,30.84,11716000,15.42
2004-08-12,30.45,30.85,30.28,30.37,8078600,15.19
2004-08-11,31.10,31.13,30.26,31.01,11514000,15.51
2004-08-10,30.39,31.54,30.35,31.52,12537000,15.76
2004-08-09,29.85,30.45,29.81,30.30,10387400,15.15
2004-08-06,30.90,31.10,29.70,29.78,17581800,14.89
2004-08-05,31.81,32.30,31.25,31.39,8732200,15.69
2004-08-04,31.19,32.12,31.17,31.79,9874600,15.90
2004-08-03,31.45,31.72,31.15,31.29,7558200,15.65
2004-08-02,31.18,32.20,31.13,31.58,13039000,15.79
2004-07-30,32.65,33.00,32.00,32.34,8679400,16.17
2004-07-29,32.47,32.82,32.13,32.64,7934200,16.32
2004-07-28,32.31,32.41,31.16,32.27,10180400,16.14
2004-07-27,31.80,32.75,31.57,32.43,15178800,16.22
2004-07-26,30.85,31.45,30.78,31.26,14069000,15.63
2004-07-23,31.53,31.75,30.48,30.70,9770400,15.35
2004-07-22,31.25,31.73,31.06,31.68,11932800,15.84
2004-07-21,32.42,32.71,31.34,31.62,10759200,15.81
2004-07-20,31.95,32.20,31.55,32.20,11562400,16.10
2004-07-19,32.01,32.22,31.66,31.97,19041800,15.98
2004-07-16,32.80,32.92,32.12,32.20,17442200,16.10
2004-07-15,32.66,33.63,32.11,32.93,63133000,16.47
2004-07-14,28.86,29.97,28.74,29.58,29850000,14.79
2004-07-13,29.25,29.60,29.02,29.22,11292000,14.61
2004-07-12,30.02,30.04,28.93,29.14,18272200,14.57
2004-07-09,30.27,30.50,30.03,30.03,7459400,15.02
2004-07-08,30.13,30.68,29.95,30.14,8335000,15.07
2004-07-07,30.85,31.36,30.13,30.39,14214000,15.19
2004-07-06,31.27,31.42,30.80,30.95,12463600,15.48
2004-07-02,30.48,31.18,29.73,31.08,32524400,15.54
2004-07-01,32.10,32.48,31.90,32.30,12212200,16.15
2004-06-30,32.56,32.97,31.89,32.54,13323000,16.27
2004-06-29,32.07,32.99,31.41,32.50,21091200,16.25
2004-06-28,34.18,34.19,32.21,32.49,18610600,16.25
2004-06-25,33.07,33.70,33.00,33.70,11551000,16.85
2004-06-24,33.51,33.70,32.98,33.18,9018400,16.59
2004-06-23,33.00,33.83,32.89,33.70,13959600,16.85
2004-06-22,32.30,33.09,32.29,33.00,12875400,16.50
2004-06-21,33.12,33.50,32.12,32.33,13936200,16.17
2004-06-18,32.66,33.41,32.43,32.91,14509000,16.45
2004-06-17,32.56,33.13,32.21,32.81,19690000,16.41
2004-06-16,30.66,33.32,30.53,32.74,32487200,16.37
2004-06-15,30.54,31.14,30.26,30.69,15879800,15.35
2004-06-14,30.65,30.68,29.50,30.12,8713800,15.06
2004-06-10,30.20,30.97,30.20,30.74,9199200,15.37
2004-06-09,30.09,30.71,30.00,30.20,12471600,15.10
2004-06-08,29.99,30.44,29.83,30.35,14843600,15.18
2004-06-07,29.04,29.98,28.81,29.81,10567000,14.90
2004-06-04,28.56,29.25,28.51,28.78,14254000,14.39
2004-06-03,28.72,28.99,28.29,28.40,8961800,14.20
2004-06-02,28.03,29.17,27.80,28.92,11382600,14.46
2004-06-01,27.79,28.20,27.61,28.06,6504800,14.03
2004-05-28,28.08,28.27,27.80,28.06,5204200,14.03
2004-05-27,28.46,28.60,27.82,28.17,8427600,14.09
2004-05-26,28.33,28.78,28.00,28.51,11506000,14.26
2004-05-25,27.50,28.51,27.29,28.41,11427800,14.20
2004-05-24,27.29,27.90,27.11,27.34,8414400,13.67
2004-05-21,26.90,27.20,26.73,27.11,6424800,13.56
2004-05-20,26.63,27.00,26.47,26.71,7010600,13.35
2004-05-19,27.40,27.50,26.42,26.47,13414000,13.23
2004-05-18,26.97,27.29,26.80,27.06,7359400,13.53
2004-05-17,26.70,27.06,26.36,26.64,10730200,13.32
2004-05-14,27.25,27.32,26.45,27.06,9207200,13.53
2004-05-13,27.10,27.72,26.90,27.19,8209000,13.60
2004-05-12,26.79,27.34,26.24,27.30,8765000,13.65
2004-05-11,26.40,27.19,26.40,27.14,10899000,13.57
2004-05-10,26.27,26.60,25.94,26.28,8927800,13.14
2004-05-07,26.55,27.57,26.55,26.67,14965600,13.34
2004-05-06,26.40,26.75,25.90,26.58,9412800,13.29
2004-05-05,26.20,26.75,25.96,26.65,8503800,13.32
2004-05-04,25.97,26.55,25.50,26.14,9999400,13.07
2004-05-03,26.00,26.33,25.74,26.07,10629800,13.03
2004-04-30,26.71,26.96,25.49,25.78,16660800,12.89
2004-04-29,26.45,27.00,25.98,26.77,16456800,13.39
2004-04-28,26.82,27.01,26.34,26.45,8256000,13.23
2004-04-27,27.24,27.44,26.69,26.94,10138000,13.47
2004-04-26,27.58,27.64,27.00,27.13,8254600,13.56
2004-04-23,27.70,28.00,27.05,27.70,11279600,13.85
2004-04-22,27.56,28.18,27.11,27.78,12306600,13.89
2004-04-21,27.60,28.12,27.37,27.73,11638400,13.86
2004-04-20,28.21,28.41,27.56,27.73,12661400,13.86
2004-04-19,28.12,28.75,27.83,28.35,25441200,14.18
2004-04-16,29.15,29.31,28.50,29.18,14390400,14.59
2004-04-15,28.82,29.58,28.16,29.30,62908800,14.65
2004-04-14,26.74,27.07,26.31,26.64,22847600,13.32
2004-04-13,27.98,28.03,26.84,26.93,15585600,13.47
2004-04-12,27.50,28.10,27.49,28.04,8233600,14.02
2004-04-08,27.88,28.00,27.20,27.53,8604200,13.77
2004-04-07,27.61,27.70,26.92,27.31,9111400,13.65
2004-04-06,27.71,28.15,27.43,27.83,9214000,13.91
2004-04-05,27.48,28.37,27.44,28.32,13774000,14.16
2004-04-02,27.75,27.93,27.23,27.50,9802800,13.75
2004-04-01,26.89,27.27,26.62,27.11,11369000,13.56
2004-03-31,27.92,27.98,26.95,27.04,13956200,13.52
2004-03-30,27.74,27.95,27.34,27.92,12845600,13.96
2004-03-29,27.37,27.99,27.20,27.91,12526000,13.95
2004-03-26,27.00,27.36,26.91,27.04,14996200,13.52
2004-03-25,26.14,26.91,25.89,26.87,20230200,13.44
2004-03-24,25.27,25.75,25.27,25.50,15293400,12.75
2004-03-23,25.88,26.00,25.22,25.29,13768400,12.65
2004-03-22,25.37,26.17,25.25,25.86,14965400,12.93
2004-03-19,25.56,26.94,25.54,25.86,14592000,12.93
2004-03-18,25.94,26.06,25.59,25.67,11467200,12.84
2004-03-17,25.96,26.38,25.78,26.19,14694000,13.10
2004-03-16,26.55,26.61,25.39,25.82,21622600,12.91
2004-03-15,27.03,27.35,26.26,26.45,17204200,13.23
2004-03-12,27.32,27.78,27.17,27.56,11758000,13.78
2004-03-11,27.27,28.04,27.09,27.15,21280400,13.57
2004-03-10,27.04,28.14,26.94,27.68,35963000,13.84
2004-03-09,25.90,27.23,25.75,27.10,22084400,13.55
2004-03-08,26.62,26.79,25.80,26.00,18674000,13.00
2004-03-05,24.95,27.49,24.90,26.74,55021400,13.37
2004-03-04,23.93,25.22,23.91,25.16,23579400,12.58
2004-03-03,23.60,24.19,23.60,23.92,8040400,11.96
2004-03-02,24.00,24.10,23.77,23.81,9167400,11.90
2004-03-01,24.10,24.30,23.87,24.02,11488600,12.01
2004-02-27,22.96,24.02,22.95,23.92,16744200,11.96
2004-02-26,22.88,23.18,22.80,23.04,7086000,11.52
2004-02-25,22.28,22.90,22.21,22.81,9867000,11.40
2004-02-24,22.14,22.74,22.00,22.36,9252000,11.18
2004-02-23,22.34,22.46,21.89,22.19,6931400,11.10
2004-02-20,22.50,22.51,22.21,22.40,9914400,11.20
2004-02-19,23.33,23.64,22.41,22.47,11538600,11.23
2004-02-18,23.18,23.44,23.05,23.26,5058400,11.63
2004-02-17,23.10,23.49,23.10,23.16,6105600,11.58
2004-02-13,23.85,24.10,22.83,23.00,11285000,11.50
2004-02-12,23.61,23.99,23.60,23.73,6571000,11.86
2004-02-11,23.09,23.87,23.05,23.80,12448000,11.90
2004-02-10,22.62,23.12,22.44,22.98,9119400,11.49
2004-02-09,22.62,22.86,22.50,22.67,6723600,11.34
2004-02-06,22.45,22.89,22.40,22.71,6905000,11.35
2004-02-05,21.82,22.91,21.81,22.42,12601600,11.21
2004-02-04,22.00,22.09,21.70,21.79,10912600,10.90
2004-02-03,22.30,22.40,22.00,22.26,6457600,11.13
2004-02-02,22.46,22.81,22.08,22.32,10265400,11.16
2004-01-30,22.65,22.87,22.42,22.56,6617800,11.28
2004-01-29,22.63,22.80,22.19,22.68,7596400,11.34
2004-01-28,22.84,23.38,22.41,22.52,9835800,11.26
2004-01-27,23.04,23.25,22.80,23.07,10966800,11.53
2004-01-26,22.46,23.06,22.43,23.01,9688200,11.51
2004-01-23,22.42,22.74,22.25,22.56,8113200,11.28
2004-01-22,22.56,22.83,22.18,22.18,7321600,11.09
2004-01-21,22.70,22.97,22.43,22.61,8095000,11.31
2004-01-20,22.67,22.80,22.25,22.73,11283800,11.36
2004-01-16,22.89,23.04,22.61,22.72,13315000,11.36
2004-01-15,22.91,23.40,22.50,22.85,36364600,11.43
2004-01-14,24.40,24.54,23.78,24.20,22144400,12.10
2004-01-13,24.70,24.84,23.86,24.12,24250600,12.06
2004-01-12,23.25,24.00,23.10,23.73,17412400,11.86
2004-01-09,23.23,24.13,22.79,23.00,15266400,11.50
2004-01-08,22.84,23.73,22.65,23.36,16439400,11.68
2004-01-07,22.10,22.83,21.93,22.59,20959800,11.30
2004-01-06,22.25,22.42,21.71,22.09,18191000,11.05
2004-01-05,21.42,22.39,21.42,22.17,14107800,11.09
2004-01-02,21.55,21.75,21.18,21.28,5165800,10.64
2003-12-31,21.35,21.53,21.18,21.37,6230400,10.69
2003-12-30,21.18,21.50,21.15,21.28,7316200,10.64
2003-12-29,20.91,21.16,20.86,21.15,8337800,10.57
2003-12-26,20.35,20.91,20.34,20.78,3703400,10.39
2003-12-24,19.72,20.59,19.65,20.41,6338400,10.20
2003-12-23,19.92,19.95,19.60,19.81,11017800,9.90
2003-12-22,19.65,19.89,19.25,19.85,13466600,9.93
2003-12-19,20.19,20.42,19.62,19.70,16198600,9.85
2003-12-18,19.90,20.18,19.90,20.04,11818400,10.02
2003-12-17,20.08,20.13,19.79,19.88,9795000,9.94
2003-12-16,20.19,20.49,20.01,20.12,13355600,10.06
2003-12-15,21.49,21.49,20.07,20.17,13889600,10.09
2003-12-12,21.32,21.32,20.70,20.89,6881200,10.44
2003-12-11,20.25,21.34,20.21,21.21,6540600,10.60
2003-12-10,20.45,20.61,19.96,20.38,9690600,10.19
2003-12-09,21.17,21.25,20.40,20.45,4826600,10.23
2003-12-08,20.78,21.08,20.41,21.05,5294200,10.52
2003-12-05,20.90,21.15,20.73,20.85,6649200,10.43
2003-12-04,20.94,21.17,20.77,21.15,6355000,10.57
2003-12-03,21.54,21.84,20.96,21.03,6832000,10.52
2003-12-02,21.60,21.90,21.41,21.54,7332000,10.77
2003-12-01,21.04,21.85,21.00,21.71,12912000,10.85
2003-11-28,20.78,21.07,20.52,20.91,2717800,10.45
2003-11-26,20.89,21.15,20.25,20.72,8754600,10.36
2003-11-25,21.23,21.25,20.61,20.68,9594800,10.34
2003-11-24,20.50,21.27,20.45,21.15,13636600,10.57
2003-11-21,20.34,20.58,19.85,20.28,8637000,10.14
2003-11-20,20.10,21.08,20.10,20.38,8556800,10.19
2003-11-19,20.56,20.65,20.26,20.42,12306600,10.21
2003-11-18,21.21,21.34,20.35,20.41,9542600,10.20
2003-11-17,21.35,21.37,20.95,21.13,8152000,10.56
2003-11-14,22.48,22.61,21.28,21.46,8466000,10.73
2003-11-13,22.07,22.56,21.92,22.42,7599000,11.21
2003-11-12,21.48,22.72,21.48,22.33,10714400,11.16
2003-11-11,21.90,22.02,21.48,21.54,7681200,10.77
2003-11-10,22.45,22.65,21.84,21.90,8367000,10.95
2003-11-07,23.19,23.24,22.45,22.50,7505200,11.25
2003-11-06,22.91,23.15,22.65,23.12,14181200,11.56
2003-11-05,22.82,23.13,22.47,23.03,11516800,11.52
2003-11-04,23.07,23.10,22.59,22.91,8901200,11.45
2003-11-03,22.83,23.30,22.78,23.15,10815800,11.57
2003-10-31,23.30,23.35,22.78,22.89,7791200,11.44
2003-10-30,23.99,24.00,22.87,23.09,9305600,11.55
2003-10-29,23.51,23.90,23.34,23.69,9538600,11.85
2003-10-28,22.56,23.77,22.40,23.72,8989800,11.86
2003-10-27,22.75,22.89,22.49,22.60,5786200,11.30
2003-10-24,22.56,22.85,22.23,22.60,7852000,11.30
2003-10-23,22.73,23.15,22.59,22.99,5900400,11.49
2003-10-22,22.94,23.20,22.68,22.76,5771400,11.38
2003-10-21,23.31,23.40,22.75,23.18,6302200,11.59
2003-10-20,22.60,23.34,22.38,23.22,9969000,11.61
2003-10-17,23.38,23.49,22.43,22.75,12850400,11.38
2003-10-16,23.80,23.84,22.41,23.25,34845800,11.62
2003-10-15,24.85,25.01,24.58,24.82,21789400,12.41
2003-10-14,24.32,24.74,24.19,24.55,9836400,12.27
2003-10-13,23.73,24.41,23.72,24.35,9995200,12.18
2003-10-10,23.50,23.81,23.37,23.68,6244200,11.84
2003-10-09,23.30,23.67,22.79,23.45,12419600,11.73
2003-10-08,23.25,23.54,22.73,23.06,15309600,11.53
2003-10-07,22.05,23.41,21.91,23.22,14934800,11.61
2003-10-06,21.67,22.33,21.58,22.29,9583200,11.15
2003-10-03,20.99,21.86,20.88,21.69,10700000,10.85
2003-10-02,20.80,20.80,20.28,20.57,7287800,10.28
2003-10-01,20.71,21.10,20.19,20.79,8432600,10.40
2003-09-30,21.09,21.22,20.44,20.72,10193800,10.36
2003-09-29,21.49,21.67,20.65,21.30,13060800,10.65
2003-09-26,20.30,21.70,20.15,20.69,12401800,10.35
2003-09-25,21.34,21.37,20.25,20.43,20513600,10.22
2003-09-24,22.21,22.31,21.08,21.32,10760200,10.66
2003-09-23,22.02,22.46,21.88,22.43,4730400,11.22
2003-09-22,22.18,22.50,21.92,22.08,6422200,11.04
2003-09-19,22.88,23.05,22.43,22.58,7355600,11.29
2003-09-18,22.10,22.99,21.95,22.88,9032400,11.44
2003-09-17,22.37,22.38,21.85,22.12,10335600,11.06
2003-09-16,22.21,22.69,22.20,22.36,9607400,11.18
2003-09-15,22.81,22.90,22.12,22.21,8101600,11.10
2003-09-12,22.51,23.14,22.31,23.10,6428200,11.55
2003-09-11,22.25,22.79,22.10,22.56,7631600,11.28
2003-09-10,22.25,22.61,22.11,22.18,8031800,11.09
2003-09-09,22.53,22.67,22.12,22.37,6441800,11.19
2003-09-08,22.48,22.79,22.47,22.74,5973000,11.37
2003-09-05,22.73,23.15,22.41,22.50,8576200,11.25
2003-09-04,23.16,23.25,22.77,22.83,7135000,11.41
2003-09-03,22.80,23.32,22.76,22.95,9601000,11.48
2003-09-02,22.66,22.90,22.40,22.85,8647600,11.43
2003-08-29,22.20,22.85,22.05,22.61,9398400,11.31
2003-08-28,21.33,22.22,21.33,22.19,11415200,11.10
2003-08-27,20.91,21.48,20.66,21.48,8060800,10.74
2003-08-26,20.75,21.07,20.35,21.05,5891400,10.52
2003-08-25,20.78,20.91,20.49,20.86,4920800,10.43
2003-08-22,21.81,22.00,20.64,20.88,8938000,10.44
2003-08-21,21.03,21.71,20.95,21.68,9118800,10.84
2003-08-20,20.18,21.27,20.14,21.01,9757600,10.51
2003-08-19,20.37,20.45,20.00,20.32,4774600,10.16
2003-08-18,19.86,20.41,19.72,20.34,6884800,10.17
2003-08-15,20.02,20.07,19.66,19.71,4495200,9.85
2003-08-14,20.21,20.33,19.94,19.97,6885000,9.98
2003-08-13,19.86,20.34,19.58,20.18,10146400,10.09
2003-08-12,19.76,19.80,19.46,19.70,5872800,9.85
2003-08-11,19.82,19.93,19.51,19.66,4901000,9.83
2003-08-08,20.11,20.13,19.60,19.64,4916400,9.82
2003-08-07,19.73,20.09,19.42,19.93,6227800,9.97
2003-08-06,20.06,20.17,19.50,19.63,8766600,9.81
2003-08-05,21.35,21.40,20.10,20.38,8908600,10.19
2003-08-04,20.53,21.50,20.28,21.21,8218400,10.60
2003-08-01,21.00,21.27,20.64,20.73,5343000,10.36
2003-07-31,20.74,21.35,20.57,21.08,10766600,10.54
2003-07-30,20.77,20.90,20.17,20.28,6199800,10.14
2003-07-29,20.99,21.08,20.52,20.72,7040000,10.36
2003-07-28,21.50,21.50,20.86,20.99,6084200,10.49
2003-07-25,20.41,21.57,20.40,21.54,7738800,10.77
2003-07-24,21.04,21.50,20.38,20.51,8187000,10.26
2003-07-23,20.95,20.96,20.46,20.79,5108400,10.40
2003-07-22,20.87,20.96,20.50,20.80,7086600,10.40
2003-07-21,20.69,20.80,20.30,20.61,6564600,10.31
2003-07-18,20.90,21.18,20.40,20.86,10672800,10.43
2003-07-17,20.19,20.95,20.13,20.90,26829000,10.45
2003-07-16,19.97,20.00,19.38,19.87,8961800,9.94
2003-07-15,20.02,20.24,19.43,19.61,7380200,9.81
2003-07-14,20.01,20.40,19.87,19.90,6728800,9.95
2003-07-11,19.66,20.00,19.53,19.85,4887800,9.93
2003-07-10,19.88,19.94,19.37,19.58,6104800,9.79
2003-07-09,20.21,20.45,19.89,19.89,7630200,9.94
2003-07-08,19.52,20.50,19.49,20.40,9169200,10.20
2003-07-07,19.27,20.18,19.13,19.87,10224000,9.94
2003-07-03,19.00,19.55,18.98,19.13,4920400,9.56
2003-07-02,19.03,19.40,19.02,19.27,11617800,9.64
2003-07-01,18.87,19.18,18.51,19.09,6464000,9.55
2003-06-30,18.68,19.21,18.59,19.06,7934000,9.53
2003-06-27,19.30,19.31,18.48,18.73,13064000,9.36
2003-06-26,18.70,19.32,18.70,19.29,5775200,9.65
2003-06-25,18.86,19.40,18.71,19.09,11779000,9.55
2003-06-24,19.47,19.67,18.72,18.78,18370800,9.39
2003-06-23,19.30,19.69,18.75,19.06,10977200,9.53
2003-06-20,19.35,19.58,18.90,19.20,12733800,9.60
2003-06-19,19.36,19.61,18.77,19.14,13626000,9.57
2003-06-18,18.45,19.48,18.31,19.12,16249400,9.56
2003-06-17,18.41,18.50,17.99,18.19,6338000,9.10
2003-06-16,17.60,18.27,17.45,18.27,8518800,9.14
2003-06-13,17.75,17.95,17.13,17.42,6830200,8.71
2003-06-12,17.55,17.88,17.45,17.77,9021000,8.89
2003-06-11,17.15,17.51,16.81,17.45,8039800,8.73
2003-06-10,16.89,17.29,16.75,17.18,6308800,8.59
2003-06-09,16.94,17.04,16.63,16.79,9284000,8.40
2003-06-06,17.74,18.04,17.14,17.15,8621000,8.57
2003-06-05,17.45,17.74,17.33,17.64,7339200,8.82
2003-06-04,17.30,17.79,17.14,17.60,9685800,8.80
2003-06-03,17.44,17.67,17.02,17.31,12887800,8.65
2003-06-02,18.10,18.29,17.27,17.45,14949600,8.73
2003-05-30,18.12,18.18,17.53,17.95,13669600,8.98
2003-05-29,18.29,18.50,17.90,18.10,11920200,9.05
2003-05-28,18.50,18.66,18.15,18.28,12131400,9.14
2003-05-27,17.96,18.90,17.91,18.88,10361800,9.44
2003-05-23,18.21,18.46,17.96,18.32,7382800,9.16
2003-05-22,17.89,18.40,17.74,18.24,6373600,9.12
2003-05-21,17.79,18.09,17.67,17.85,10893200,8.93
2003-05-20,18.10,18.16,17.60,17.79,14865000,8.90
2003-05-19,18.53,18.65,18.06,18.10,15924600,9.05
2003-05-16,18.59,19.01,18.28,18.80,12201000,9.40
2003-05-15,18.60,18.85,18.47,18.73,10178400,9.36
2003-05-14,18.83,18.84,18.43,18.55,12696000,9.27
2003-05-13,18.43,18.97,17.95,18.67,15957000,9.34
2003-05-12,18.15,18.74,18.13,18.56,14977600,9.28
2003-05-09,18.33,18.40,17.88,18.30,21013800,9.15
2003-05-08,17.70,18.07,17.29,18.00,24562000,9.00
2003-05-07,17.33,18.24,17.11,17.65,37656400,8.82
2003-05-06,16.12,17.90,16.10,17.50,54089000,8.75
2003-05-05,14.77,16.88,14.75,16.09,55561000,8.05
2003-05-02,14.46,14.59,14.34,14.45,11470800,7.22
2003-05-01,14.25,14.39,14.00,14.36,12241400,7.18
2003-04-30,13.93,14.35,13.85,14.22,16363400,7.11
2003-04-29,13.98,14.16,13.58,14.06,16365600,7.03
2003-04-28,13.48,13.96,13.43,13.86,22742800,6.93
2003-04-25,13.46,13.58,13.23,13.35,7332800,6.68
2003-04-24,13.52,13.61,13.00,13.44,11611000,6.72
2003-04-23,13.53,13.63,13.36,13.58,7488600,6.79
2003-04-22,13.18,13.62,13.09,13.51,10734600,6.76
2003-04-21,13.13,13.19,12.98,13.14,5440000,6.57
2003-04-17,13.20,13.25,12.72,13.12,22009200,6.56
2003-04-16,12.99,13.67,12.92,13.24,36292000,6.62
2003-04-15,13.59,13.60,13.30,13.39,10856000,6.70
2003-04-14,13.71,13.75,13.50,13.58,17962800,6.79
2003-04-11,14.05,14.44,12.93,13.20,49739600,6.60
2003-04-10,14.20,14.39,14.20,14.37,3825000,7.18
2003-04-09,14.52,14.62,14.14,14.19,5240200,7.09
2003-04-08,14.51,14.65,14.36,14.45,4604800,7.22
2003-04-07,14.85,14.95,14.41,14.49,7030800,7.24
2003-04-04,14.52,14.67,14.39,14.41,5215000,7.20
2003-04-03,14.56,14.70,14.35,14.46,5204000,7.23
2003-04-02,14.36,14.69,14.27,14.60,6120400,7.30
2003-04-01,14.20,14.31,14.07,14.16,5512200,7.08
2003-03-31,14.33,14.53,14.04,14.14,9166400,7.07
2003-03-28,14.40,14.62,14.37,14.57,5189400,7.28
2003-03-27,14.32,14.70,14.32,14.49,4371200,7.24
2003-03-26,14.55,14.56,14.30,14.41,6369400,7.20
2003-03-25,14.41,14.83,14.37,14.55,5989200,7.28
2003-03-24,14.67,14.80,14.35,14.37,5753600,7.18
2003-03-21,15.09,15.15,14.82,15.00,10641000,7.50
2003-03-20,14.93,14.99,14.60,14.91,5827800,7.45
2003-03-19,15.07,15.15,14.79,14.95,5047000,7.47
2003-03-18,15.00,15.09,14.82,15.00,8213600,7.50
2003-03-17,14.89,15.07,14.71,15.01,14282600,7.51
2003-03-14,14.68,15.01,14.64,14.78,5467800,7.39
2003-03-13,14.47,14.80,14.17,14.72,11980200,7.36
2003-03-12,14.17,14.39,14.06,14.22,7948600,7.11
2003-03-11,14.36,14.49,14.12,14.23,5756800,7.11
2003-03-10,14.51,14.67,14.30,14.37,4806200,7.18
2003-03-07,14.47,14.71,14.31,14.53,7178000,7.26
2003-03-06,14.58,14.60,14.40,14.56,3566400,7.28
2003-03-05,14.61,14.80,14.52,14.62,4524400,7.31
2003-03-04,14.74,14.81,14.44,14.56,4514800,7.28
2003-03-03,15.01,15.16,14.55,14.65,7277200,7.32
2003-02-28,14.86,15.09,14.77,15.01,6967800,7.51
2003-02-27,14.57,15.00,14.51,14.86,5512200,7.43
2003-02-26,14.99,15.02,14.48,14.50,7753400,7.25
2003-02-25,14.68,15.08,14.58,15.02,6737200,7.51
2003-02-24,14.86,15.03,13.80,14.74,6437600,7.37
2003-02-21,14.82,15.06,14.65,15.00,5623000,7.50
2003-02-20,14.85,14.96,14.71,14.77,8012600,7.39
2003-02-19,15.07,15.15,14.68,14.85,8584600,7.43
2003-02-18,14.75,15.30,14.72,15.27,10389200,7.64
2003-02-14,14.61,14.72,14.35,14.67,8689200,7.34
2003-02-13,14.41,14.64,14.24,14.54,7446200,7.27
2003-02-12,14.27,14.60,14.27,14.39,8167400,7.20
2003-02-11,14.50,14.63,14.20,14.35,5885000,7.18
2003-02-10,14.26,14.57,14.06,14.35,5996000,7.18
2003-02-07,14.55,14.60,14.07,14.15,9632200,7.07
2003-02-06,14.36,14.59,14.22,14.43,6398200,7.22
2003-02-05,14.71,14.93,14.44,14.45,7914800,7.22
2003-02-04,14.45,14.65,14.31,14.60,11336200,7.30
2003-02-03,14.41,14.91,14.35,14.66,9456600,7.33
2003-01-31,14.19,14.55,14.05,14.36,12186600,7.18
2003-01-30,14.98,15.07,14.29,14.32,14537800,7.16
2003-01-29,14.55,15.10,14.30,14.93,13323000,7.47
2003-01-28,14.24,14.69,14.16,14.58,10223400,7.29
2003-01-27,13.68,14.50,13.65,14.13,13978800,7.07
2003-01-24,14.24,14.24,13.56,13.80,10909600,6.90
2003-01-23,14.05,14.36,13.95,14.17,8152000,7.09
2003-01-22,13.98,14.15,13.80,13.88,7683600,6.94
2003-01-21,14.21,14.41,14.00,14.02,9052000,7.01
2003-01-17,14.56,14.56,14.08,14.10,9527200,7.05
2003-01-16,14.21,14.76,14.21,14.62,19966800,7.31
2003-01-15,14.59,14.70,14.26,14.43,13254600,7.22
2003-01-14,14.69,14.82,14.49,14.61,6673600,7.30
2003-01-13,14.90,14.90,14.36,14.63,6390800,7.32
2003-01-10,14.58,14.82,14.49,14.72,6253600,7.36
2003-01-09,14.62,14.92,14.50,14.68,7687600,7.34
2003-01-08,14.58,14.71,14.44,14.55,8201600,7.28
2003-01-07,14.79,15.00,14.47,14.85,12226600,7.43
2003-01-06,15.03,15.38,14.88,14.90,13947600,7.45
2003-01-03,14.80,14.93,14.59,14.90,5266200,7.45
2003-01-02,14.36,14.92,14.35,14.80,6479600,7.40
2002-12-31,14.00,14.36,13.95,14.33,7168800,7.16
2002-12-30,14.08,14.15,13.84,14.07,5537200,7.03
2002-12-27,14.31,14.38,14.01,14.06,2858400,7.03
2002-12-26,14.42,14.81,14.28,14.40,3050800,7.20
2002-12-24,14.44,14.47,14.30,14.36,1405000,7.18
2002-12-23,14.16,14.55,14.12,14.49,4493800,7.24
2002-12-20,14.29,14.56,13.78,14.14,11360600,7.07
2002-12-19,14.53,14.92,14.10,14.20,12411400,7.10
2002-12-18,14.80,14.86,14.50,14.57,5382200,7.28
2002-12-17,14.85,15.19,14.66,15.08,7952200,7.54
2002-12-16,14.81,15.10,14.61,14.85,8986600,7.43
2002-12-13,15.14,15.15,14.65,14.79,5885000,7.39
2002-12-12,15.51,15.55,15.01,15.19,5333600,7.59
2002-12-11,15.30,15.49,15.08,15.49,9053600,7.74
2002-12-10,14.75,15.45,14.73,15.28,11021800,7.64
2002-12-09,14.94,14.95,14.67,14.75,8431600,7.38
2002-12-06,14.65,15.19,14.52,14.95,8762800,7.47
2002-12-05,15.03,15.08,14.53,14.63,8692800,7.32
2002-12-04,15.18,15.19,14.50,14.97,11634200,7.49
2002-12-03,15.20,15.34,15.10,15.16,8138200,7.58
2002-12-02,15.90,16.10,15.01,15.18,14240800,7.59
2002-11-29,15.79,15.88,15.41,15.50,5122600,7.75
2002-11-27,15.60,15.86,15.45,15.72,10242800,7.86
2002-11-26,15.85,15.90,15.27,15.41,8580800,7.70
2002-11-25,16.03,16.14,15.71,15.97,7122400,7.99
2002-11-22,16.09,16.30,15.90,16.01,8137800,8.01
2002-11-21,15.90,16.44,15.75,16.35,14945800,8.18
2002-11-20,15.30,15.70,15.25,15.53,7455000,7.76
2002-11-19,15.55,15.75,15.01,15.27,7534000,7.64
2002-11-18,16.19,16.20,15.52,15.65,5877800,7.82
2002-11-15,16.23,16.24,15.76,15.95,5749800,7.97
2002-11-14,15.90,16.41,15.78,16.30,5061200,8.15
2002-11-13,15.50,16.07,15.28,15.59,8276400,7.80
2002-11-12,15.32,16.04,15.28,15.64,7992600,7.82
2002-11-11,15.74,15.89,15.12,15.16,5463400,7.58
2002-11-08,16.01,16.20,15.52,15.84,6788000,7.92
2002-11-07,16.94,17.10,15.81,16.00,12006400,8.00
2002-11-06,17.08,17.32,16.70,17.22,7728200,8.61
2002-11-05,16.75,16.96,16.35,16.90,7524800,8.45
2002-11-04,16.50,17.38,16.35,16.89,13457800,8.44
2002-11-01,15.94,16.50,15.89,16.36,6779600,8.18
2002-10-31,15.99,16.44,15.92,16.07,10565600,8.03
2002-10-30,15.49,16.37,15.48,15.98,9667000,7.99
2002-10-29,15.57,15.88,14.96,15.44,9256400,7.72
2002-10-28,15.55,15.95,15.25,15.61,12475000,7.80
2002-10-25,14.69,15.45,14.59,15.42,9966800,7.71
2002-10-24,15.02,15.21,14.55,14.69,6241000,7.34
2002-10-23,14.63,14.98,14.50,14.88,7465600,7.44
2002-10-22,14.47,14.88,14.26,14.70,7791000,7.35
2002-10-21,14.26,14.63,14.00,14.56,8518600,7.28
2002-10-18,14.00,14.35,13.93,14.34,10296400,7.17
2002-10-17,14.21,14.38,13.98,14.11,16760600,7.05
2002-10-16,14.86,15.13,13.90,14.56,10986600,7.28
2002-10-15,15.22,15.25,14.78,15.16,14482800,7.58
2002-10-14,14.55,14.98,14.44,14.77,6943000,7.39
2002-10-11,14.25,14.78,14.10,14.51,10524200,7.26
2002-10-10,13.63,14.22,13.58,14.11,11484800,7.05
2002-10-09,13.54,13.85,13.41,13.59,12738800,6.80
2002-10-08,13.90,13.96,13.36,13.68,16201600,6.84
2002-10-07,13.97,14.21,13.76,13.77,8739200,6.89
2002-10-04,14.36,14.40,13.99,14.03,6815200,7.01
2002-10-03,14.18,14.60,14.06,14.30,7782000,7.15
2002-10-02,14.33,14.63,14.10,14.17,8191000,7.09
2002-10-01,14.59,14.60,14.00,14.51,12229400,7.26
2002-09-30,14.40,14.57,14.14,14.50,8489200,7.25
2002-09-27,14.49,14.85,14.48,14.72,7362600,7.36
2002-09-26,15.10,15.19,14.55,14.70,7451600,7.35
2002-09-25,14.69,15.17,14.65,14.93,9095800,7.47
2002-09-24,14.40,14.82,14.40,14.64,8952200,7.32
2002-09-23,14.76,14.96,14.45,14.85,9418200,7.43
2002-09-20,14.62,14.94,14.52,14.87,12599600,7.43
2002-09-19,14.75,14.80,14.48,14.58,7355200,7.29
2002-09-18,14.69,15.09,14.52,15.02,11737200,7.51
2002-09-17,14.57,15.03,14.57,14.80,15285600,7.40
2002-09-16,14.14,14.61,14.12,14.50,10237200,7.25
2002-09-13,14.13,14.34,14.05,14.17,10105400,7.09
2002-09-12,14.20,14.51,14.12,14.14,9636800,7.07
2002-09-11,14.34,14.60,14.15,14.29,7229000,7.14
2002-09-10,14.41,14.49,14.12,14.33,8909600,7.16
2002-09-09,14.28,14.53,14.15,14.37,5651600,7.18
2002-09-06,14.51,14.65,14.23,14.38,6485400,7.19
2002-09-05,14.22,14.36,14.05,14.18,8077800,7.09
2002-09-04,14.20,14.78,14.17,14.48,15023600,7.24
2002-09-03,14.49,14.55,14.05,14.05,9890600,7.03
2002-08-30,14.73,15.14,14.58,14.75,6911400,7.38
2002-08-29,14.65,15.08,14.51,14.70,5863200,7.35
2002-08-28,14.80,15.12,14.65,14.70,8856200,7.35
2002-08-27,15.71,15.74,14.71,14.85,9365400,7.43
2002-08-26,15.95,15.95,15.16,15.53,6784600,7.76
2002-08-23,15.90,15.93,15.45,15.73,5830200,7.86
2002-08-22,16.20,16.25,15.66,15.97,9225400,7.99
2002-08-21,16.01,16.24,15.45,16.12,7229600,8.06
2002-08-20,15.97,16.09,15.53,15.91,6665200,7.95
2002-08-19,15.78,16.25,15.72,15.98,7734200,7.99
2002-08-16,15.45,16.10,15.28,15.81,8758000,7.91
2002-08-15,15.25,15.75,15.01,15.61,11502800,7.80
2002-08-14,14.67,15.35,14.54,15.17,14253000,7.59
2002-08-13,14.90,15.21,14.55,14.59,9638200,7.30
2002-08-12,14.90,15.02,14.69,14.99,6420200,7.49
2002-08-09,15.25,15.25,14.75,15.00,7347000,7.50
2002-08-08,14.77,15.38,14.77,15.30,8119600,7.65
2002-08-07,15.09,15.36,14.35,15.03,11909800,7.51
2002-08-06,14.21,15.23,14.08,14.74,9716200,7.37
2002-08-05,14.51,14.70,13.97,13.99,7286600,6.99
2002-08-02,14.74,15.00,14.25,14.45,6395000,7.22
2002-08-01,15.11,15.42,14.73,14.80,8177000,7.40
2002-07-31,15.40,15.42,14.90,15.26,11096400,7.63
2002-07-30,14.85,15.51,14.56,15.43,12672800,7.72
2002-07-29,14.48,15.10,14.37,15.02,9820000,7.51
2002-07-26,14.46,14.53,13.80,14.34,7418000,7.17
2002-07-25,14.93,14.95,14.01,14.36,17119800,7.18
2002-07-24,14.33,15.22,14.25,15.20,14521200,7.60
2002-07-23,14.90,15.13,14.44,14.47,14281800,7.24
2002-07-22,14.75,15.19,14.61,14.92,15389200,7.46
2002-07-19,14.70,15.17,14.53,14.96,13757400,7.48
2002-07-18,15.50,15.56,14.75,14.99,19980800,7.49
2002-07-17,16.13,16.20,15.19,15.63,43410200,7.82
2002-07-16,18.15,18.57,17.61,17.86,15956000,8.93
2002-07-15,17.43,18.60,16.81,18.23,10571200,9.11
2002-07-12,18.55,18.79,17.26,17.51,15839000,8.76
2002-07-11,17.26,18.35,16.97,18.30,13345600,9.15
2002-07-10,17.71,18.17,17.25,17.32,7388600,8.66
2002-07-09,18.09,18.29,17.46,17.53,8098200,8.77
2002-07-08,18.52,18.61,17.68,18.01,7543000,9.01
2002-07-05,17.71,18.75,17.71,18.74,5773200,9.37
2002-07-03,16.81,17.68,16.75,17.55,7108200,8.77
2002-07-02,17.03,17.16,16.83,16.94,10899600,8.47
2002-07-01,17.71,17.88,17.05,17.06,7953200,8.53
2002-06-28,17.10,17.82,17.00,17.72,9637800,8.86
2002-06-27,16.79,17.27,16.42,17.06,8987800,8.53
2002-06-26,16.80,17.29,15.98,16.55,19962600,8.27
2002-06-25,17.40,17.68,16.86,17.14,10757200,8.57
2002-06-24,16.77,17.73,16.70,17.27,15426200,8.64
2002-06-21,16.97,17.49,16.79,16.85,15899200,8.43
2002-06-20,17.17,17.60,16.85,17.11,14165600,8.56
2002-06-19,17.37,17.60,16.88,17.12,61052400,8.56
2002-06-18,20.42,20.59,19.98,20.15,12620000,10.07
2002-06-17,20.24,20.63,19.85,20.54,11593200,10.27
2002-06-14,19.24,20.36,18.11,20.10,15175000,10.05
2002-06-13,20.02,20.05,19.38,19.54,12574400,9.77
2002-06-12,20.41,20.75,19.94,20.09,18882800,10.05
2002-06-11,21.64,21.70,20.41,20.46,12482000,10.23
2002-06-10,21.48,21.84,21.34,21.48,9913400,10.74
2002-06-07,21.76,21.94,20.93,21.40,21870600,10.70
2002-06-06,22.96,23.23,22.04,22.16,9285600,11.08
2002-06-05,22.83,22.83,22.35,22.72,9895800,11.36
2002-06-04,22.88,23.04,22.18,22.78,12422200,11.39
2002-06-03,23.39,23.45,22.58,22.91,8396800,11.45
2002-05-31,24.09,24.25,23.28,23.30,13053400,11.65
2002-05-30,23.77,24.38,23.51,24.20,7013400,12.10
2002-05-29,23.92,24.44,23.45,23.98,7921200,11.99
2002-05-28,23.69,24.20,23.43,23.98,5347000,11.99
2002-05-24,24.99,24.99,23.96,24.15,5934800,12.07
2002-05-23,24.45,25.24,24.07,25.18,13192800,12.59
2002-05-22,23.37,24.37,23.32,24.32,10388400,12.16
2002-05-21,24.83,25.00,23.40,23.46,10035400,11.73
2002-05-20,24.57,24.93,24.53,24.74,9639800,12.37
2002-05-17,25.49,25.78,24.61,25.01,8446200,12.51
2002-05-16,25.06,25.45,24.75,25.21,8109000,12.60
2002-05-15,25.37,25.98,24.84,25.28,11993800,12.64
2002-05-14,24.45,25.68,24.22,25.61,18803800,12.81
2002-05-13,23.52,24.09,22.94,23.94,9486000,11.97
2002-05-10,24.29,24.29,22.98,23.32,8407000,11.66
2002-05-09,24.25,24.35,23.80,24.19,8022000,12.10
2002-05-08,23.20,24.52,23.04,24.37,15595800,12.19
2002-05-07,22.94,22.95,22.14,22.47,8669600,11.23
2002-05-06,23.35,23.50,22.46,22.65,8916600,11.32
2002-05-03,23.57,24.02,23.43,23.51,8242200,11.76
2002-05-02,23.81,24.34,23.60,23.69,8548000,11.85
2002-05-01,24.29,24.29,23.36,23.98,7668000,11.99
2002-04-30,23.89,24.38,23.75,24.27,10034400,12.14
2002-04-29,23.16,24.06,23.09,23.96,9724600,11.98
2002-04-26,24.28,24.37,23.00,23.01,10892200,11.51
2002-04-25,23.56,24.34,23.55,24.12,6935800,12.06
2002-04-24,24.30,24.50,23.68,23.77,5016000,11.89
2002-04-23,24.54,24.78,24.09,24.25,8338200,12.12
2002-04-22,24.84,24.93,24.23,24.53,9622400,12.27
2002-04-19,25.49,25.49,24.93,24.98,13407400,12.49
2002-04-18,25.50,25.52,24.88,25.41,14346800,12.70
2002-04-17,25.93,26.17,25.38,26.11,14151800,13.06
2002-04-16,25.15,25.99,25.12,25.74,21949200,12.87
2002-04-15,25.06,25.15,24.80,25.00,10691800,12.50
2002-04-12,25.01,25.17,24.57,25.06,11437200,12.53
2002-04-11,25.03,25.20,24.75,24.86,14544800,12.43
2002-04-10,24.21,24.95,24.01,24.66,8035000,12.33
2002-04-09,24.59,25.00,24.01,24.10,6840400,12.05
2002-04-08,24.16,24.68,23.78,24.56,9339800,12.28
2002-04-05,24.95,25.19,24.10,24.74,9941000,12.37
2002-04-04,23.67,25.05,23.67,24.90,12089200,12.45
2002-04-03,24.05,24.49,23.60,23.75,7661800,11.88
2002-04-02,24.00,24.30,23.87,24.07,7278400,12.03
2002-04-01,23.38,24.70,23.28,24.46,7108800,12.23
2002-03-28,23.70,23.88,23.46,23.67,3873400,11.84
2002-03-27,23.35,23.72,23.26,23.47,4560800,11.73
2002-03-26,23.20,23.64,23.00,23.46,9208600,11.73
2002-03-25,24.07,24.09,23.24,23.35,9386800,11.68
2002-03-22,24.22,24.56,23.87,24.09,7221200,12.05
2002-03-21,23.86,24.30,23.26,24.27,22012600,12.14
2002-03-20,24.66,25.14,24.50,24.92,10511400,12.46
2002-03-19,24.69,25.30,24.30,24.85,8655200,12.43
2002-03-18,24.95,25.05,24.32,24.74,10877000,12.37
2002-03-15,24.46,24.96,24.25,24.95,8603600,12.48
2002-03-14,24.30,24.60,23.87,24.43,7760600,12.22
2002-03-13,24.37,24.85,24.15,24.49,7170200,12.24
2002-03-12,24.51,24.74,24.10,24.72,9073400,12.36
2002-03-11,24.60,25.14,24.10,25.06,9385200,12.53
2002-03-08,24.74,25.09,24.30,24.66,9634800,12.33
2002-03-07,24.06,24.53,23.61,24.38,9223200,12.19
2002-03-06,23.48,24.34,22.93,24.07,8078800,12.03
2002-03-05,24.15,24.43,23.40,23.53,9810800,11.77
2002-03-04,23.26,24.58,22.76,24.29,12437800,12.15
2002-03-01,21.93,23.50,21.82,23.45,12464000,11.73
2002-02-28,22.15,22.59,21.35,21.70,16319200,10.85
2002-02-27,23.94,24.25,20.94,21.96,36791400,10.98
2002-02-26,23.91,24.37,23.25,23.67,9290400,11.84
2002-02-25,22.85,24.72,22.36,23.81,15244600,11.90
2002-02-22,21.66,22.95,21.50,22.74,14517000,11.37
2002-02-21,22.92,23.00,21.45,21.50,15955400,10.75
2002-02-20,22.77,23.20,22.35,23.13,10194400,11.56
2002-02-19,23.76,23.87,22.48,22.62,13937800,11.31
2002-02-15,24.53,24.98,23.85,23.90,9292400,11.95
2002-02-14,25.05,25.23,24.38,24.60,9291800,12.30
2002-02-13,24.73,25.24,24.65,25.01,11174000,12.51
2002-02-12,24.66,25.04,24.45,24.71,8010000,12.35
2002-02-11,23.93,25.00,23.74,24.98,14235800,12.49
2002-02-08,24.40,24.64,23.37,24.03,12690400,12.02
2002-02-07,24.65,25.29,24.08,24.30,12422600,12.15
2002-02-06,25.60,25.98,24.15,24.67,21342000,12.34
2002-02-05,25.09,25.98,25.08,25.45,16317400,12.73
2002-02-04,24.32,25.52,24.20,25.35,18656200,12.68
2002-02-01,24.34,24.96,24.34,24.41,14225200,12.20
2002-01-31,24.16,24.73,24.11,24.72,16730200,12.36
2002-01-30,23.07,24.14,22.94,24.09,16842000,12.05
2002-01-29,23.22,23.54,22.85,23.07,8583000,11.53
2002-01-28,23.40,23.55,22.72,23.27,6658800,11.64
2002-01-25,22.89,23.42,22.66,23.25,6639800,11.62
2002-01-24,22.91,23.51,22.90,23.21,12285800,11.60
2002-01-23,21.80,23.04,21.59,23.02,15831400,11.51
2002-01-22,22.27,22.37,21.82,21.82,11689800,10.91
2002-01-18,22.00,22.60,21.96,22.17,12100400,11.09
2002-01-17,21.96,22.74,21.87,22.48,23592000,11.24
2002-01-16,21.41,21.41,20.50,20.78,20246200,10.39
2002-01-15,21.32,21.76,21.21,21.70,10368600,10.85
2002-01-14,21.01,21.40,20.90,21.15,14857000,10.57
2002-01-11,21.39,21.84,20.60,21.05,12457200,10.52
2002-01-10,21.22,21.46,20.25,21.23,16169200,10.61
2002-01-09,22.80,22.93,21.28,21.65,11708400,10.82
2002-01-08,22.75,23.05,22.46,22.61,16072800,11.31
2002-01-07,23.72,24.00,22.75,22.90,15878000,11.45
2002-01-04,23.34,23.95,22.99,23.69,14642000,11.85
2002-01-03,23.00,23.75,22.77,23.58,21857400,11.79
2002-01-02,22.05,23.30,21.96,23.30,18910600,11.65
2001-12-31,22.51,22.66,21.83,21.90,4920800,10.95
2001-12-28,21.97,23.00,21.96,22.43,10683000,11.22
2001-12-27,21.58,22.25,21.58,22.07,6839600,11.03
2001-12-26,21.35,22.30,21.14,21.49,5228600,10.74
2001-12-24,20.90,21.45,20.90,21.36,1808200,10.68
2001-12-21,21.01,21.54,20.80,21.00,9154800,10.50
2001-12-20,21.40,21.47,20.62,20.67,7888000,10.34
2001-12-19,20.58,21.68,20.47,21.62,10355600,10.81
2001-12-18,20.89,21.33,20.22,21.01,8401400,10.51
2001-12-17,20.40,21.00,20.19,20.62,6204000,10.31
2001-12-14,20.73,20.83,20.09,20.39,6781600,10.19
2001-12-13,21.49,21.55,20.50,21.00,7065800,10.50
2001-12-12,21.87,21.92,21.25,21.49,6873600,10.74
2001-12-11,22.67,22.85,21.65,21.78,7338400,10.89
2001-12-10,22.29,22.99,22.23,22.54,6071800,11.27
2001-12-07,22.46,22.71,22.00,22.54,7268400,11.27
2001-12-06,23.48,23.50,22.14,22.78,12104800,11.39
2001-12-05,22.36,24.03,22.17,23.76,20306400,11.88
2001-12-04,21.05,22.56,20.72,22.40,13586400,11.20
2001-12-03,21.06,21.28,20.60,21.05,6470200,10.52
2001-11-30,20.47,21.44,20.25,21.30,10854000,10.65
2001-11-29,20.60,20.70,20.19,20.42,7241600,10.21
2001-11-28,20.85,21.21,20.41,20.53,8950400,10.27
2001-11-27,21.20,21.52,20.50,21.00,9591200,10.50
2001-11-26,19.94,21.55,19.88,21.37,16453200,10.69
2001-11-23,19.71,19.95,19.57,19.84,2143000,9.92
2001-11-21,19.61,19.80,19.26,19.68,7199400,9.84
2001-11-20,19.82,20.20,19.50,19.53,9878000,9.77
2001-11-19,19.00,20.05,18.96,20.00,11878200,10.00
2001-11-16,19.27,19.29,18.40,18.97,8238000,9.48
2001-11-15,19.45,19.90,19.23,19.45,7608200,9.73
2001-11-14,19.59,19.90,19.15,19.61,7898200,9.81
2001-11-13,19.08,19.39,18.71,19.37,8024000,9.69
2001-11-12,18.66,19.17,17.96,18.75,7196400,9.38
2001-11-09,18.60,19.25,18.55,18.71,4796200,9.35
2001-11-08,19.63,19.89,18.57,18.71,12219400,9.35
2001-11-07,19.46,20.13,19.33,19.59,13678200,9.80
2001-11-06,18.96,19.62,18.53,19.57,11286400,9.78
2001-11-05,18.84,19.25,18.61,19.07,8421200,9.53
2001-11-02,18.52,18.86,18.16,18.57,7043000,9.28
2001-11-01,17.65,18.78,17.25,18.59,11178400,9.30
2001-10-31,17.73,18.40,17.44,17.56,9776800,8.78
2001-10-30,17.38,18.00,17.06,17.60,9884400,8.80
2001-10-29,18.57,18.67,17.60,17.63,8542200,8.81
2001-10-26,18.86,19.25,18.62,18.67,9963000,9.34
2001-10-25,18.44,19.25,18.16,19.19,9105400,9.60
2001-10-24,18.06,19.09,17.75,18.95,13372400,9.48
2001-10-23,19.12,19.42,17.87,18.14,24463600,9.07
2001-10-22,18.21,19.07,18.09,19.02,13997800,9.51
2001-10-19,17.94,18.40,17.88,18.30,5956800,9.15
2001-10-18,17.29,18.23,17.29,18.00,21877600,9.00
2001-10-17,18.34,18.41,16.96,16.99,10197800,8.49
2001-10-16,18.09,18.20,17.77,18.01,7248200,9.01
2001-10-15,17.95,18.38,17.95,17.99,11384000,8.99
2001-10-12,17.31,18.08,16.86,18.01,10279000,9.01
2001-10-11,16.92,17.74,16.85,17.74,11934400,8.87
2001-10-10,16.10,16.85,15.95,16.82,10991400,8.41
2001-10-09,16.05,16.20,15.63,16.00,6215200,8.00
2001-10-08,15.57,16.35,15.50,16.20,7428000,8.10
2001-10-05,15.40,16.15,14.99,16.14,12238800,8.07
2001-10-04,15.35,16.25,14.99,15.88,14325800,7.94
2001-10-03,14.95,15.36,14.83,14.98,24394400,7.49
2001-10-02,15.43,15.83,14.88,15.05,8424400,7.53
2001-10-01,15.49,15.99,15.23,15.54,7436000,7.77
2001-09-28,15.71,15.91,15.39,15.51,13039600,7.76
2001-09-27,15.25,15.75,15.20,15.51,11508600,7.76
2001-09-26,15.81,15.89,14.93,15.15,17635600,7.57
2001-09-25,16.14,16.22,15.35,15.54,13371600,7.77
2001-09-24,16.11,16.84,15.95,16.45,10519200,8.23
2001-09-21,14.80,16.25,14.68,15.73,20375600,7.86
2001-09-20,16.29,16.95,15.50,15.68,14684800,7.84
2001-09-19,16.50,17.10,15.60,17.02,13332800,8.51
2001-09-18,16.90,17.72,16.17,16.28,11682200,8.14
2001-09-17,16.00,17.07,15.73,16.99,16357400,8.49
2001-09-10,17.00,17.50,16.92,17.37,11030200,8.69
2001-09-07,17.50,18.10,17.20,17.28,8636800,8.64
2001-09-06,18.40,18.93,17.65,17.72,10084600,8.86
2001-09-05,18.24,18.95,18.12,18.55,12859200,9.27
2001-09-04,18.50,19.08,18.18,18.25,12436200,9.12
2001-08-31,17.73,18.60,17.65,18.55,7746600,9.27
2001-08-30,17.74,18.18,17.28,17.83,13167600,8.91
2001-08-29,18.44,18.83,17.83,17.83,8570400,8.91
2001-08-28,18.90,19.14,18.40,18.40,6133400,9.20
2001-08-27,18.60,19.30,18.16,18.92,6273000,9.46
2001-08-24,18.00,18.62,17.65,18.57,10369000,9.28
2001-08-23,18.20,18.34,17.58,17.81,7752800,8.90
2001-08-22,17.94,18.25,17.61,18.21,6213400,9.10
2001-08-21,18.14,18.14,17.70,17.92,6632200,8.96
2001-08-20,18.14,18.23,17.81,18.12,9010800,9.06
2001-08-17,18.00,18.45,17.99,18.07,7443800,9.03
2001-08-16,18.27,18.75,17.97,18.65,10289000,9.32
2001-08-15,18.76,18.94,18.20,18.44,10331400,9.22
2001-08-14,19.20,19.36,18.67,18.73,8176800,9.36
2001-08-13,19.10,19.33,18.76,19.09,5285600,9.55
2001-08-10,19.04,19.32,18.59,19.02,6677200,9.51
2001-08-09,18.96,19.15,18.72,19.05,7166600,9.52
2001-08-08,19.26,19.70,18.54,18.90,9863200,9.45
2001-08-07,19.33,19.67,18.98,19.25,6019600,9.62
2001-08-06,19.04,19.66,19.00,19.13,3559000,9.56
2001-08-03,19.89,19.90,19.00,19.50,6644800,9.75
2001-08-02,19.65,19.87,19.26,19.82,9003200,9.91
2001-08-01,19.01,19.78,18.95,19.06,10862000,9.53
2001-07-31,19.27,19.42,18.51,18.79,8393800,9.40
2001-07-30,19.12,19.36,18.51,18.93,8691400,9.47
2001-07-27,18.75,19.25,18.50,18.96,11933400,9.48
2001-07-26,18.48,18.80,17.85,18.59,13183600,9.30
2001-07-25,19.12,19.30,17.97,18.47,15852800,9.23
2001-07-24,19.39,19.92,18.73,19.09,12442000,9.55
2001-07-23,20.09,20.50,19.51,19.54,8620000,9.77
2001-07-20,19.70,20.06,19.49,19.98,15878000,9.99
2001-07-19,21.23,21.42,19.75,19.96,30755000,9.98
2001-07-18,21.78,22.78,20.42,20.79,40607600,10.40
2001-07-17,23.98,25.22,23.01,25.10,23136800,12.55
2001-07-16,24.88,25.10,23.91,23.96,9952400,11.98
2001-07-13,24.13,25.01,23.84,24.85,16240800,12.43
2001-07-12,23.30,24.81,23.30,24.36,21957200,12.18
2001-07-11,21.03,22.55,21.00,22.54,16803800,11.27
2001-07-10,22.95,23.07,20.84,21.14,14116800,10.57
2001-07-09,22.09,23.00,21.68,22.70,12052400,11.35
2001-07-06,22.76,22.96,21.72,22.03,10818600,11.02
2001-07-05,23.60,23.77,23.01,23.19,5439000,11.60
2001-07-03,23.51,24.18,23.50,23.84,4019400,11.92
2001-07-02,23.64,24.23,23.14,23.90,8216000,11.95
2001-06-29,23.66,25.10,23.20,23.25,18406800,11.62
2001-06-28,23.05,23.91,22.94,23.54,12443200,11.77
2001-06-27,23.83,24.00,22.50,23.34,13361800,11.67
2001-06-26,23.34,23.77,23.01,23.75,9742200,11.88
2001-06-25,22.50,24.00,22.45,23.99,15698200,11.99
2001-06-22,22.48,23.00,21.76,22.26,10215200,11.13
2001-06-21,21.55,23.00,21.10,22.49,12190400,11.24
2001-06-20,20.00,21.85,19.98,21.67,15415000,10.84
2001-06-19,20.85,21.40,20.01,20.19,11467400,10.10
2001-06-18,20.41,20.85,20.00,20.33,12354000,10.16
2001-06-15,20.10,20.75,19.35,20.44,16236600,10.22
2001-06-14,20.04,20.45,19.77,19.88,10619600,9.94
2001-06-13,21.42,21.73,20.06,20.47,18267400,10.23
2001-06-12,19.77,20.69,19.76,20.31,10849800,10.15
2001-06-11,21.05,21.07,19.95,20.04,10500000,10.02
2001-06-08,21.65,21.65,20.71,21.32,12236600,10.66
2001-06-07,20.71,21.70,20.45,21.66,11613600,10.83
2001-06-06,20.93,20.93,20.33,20.73,7970600,10.36
2001-06-05,20.80,21.10,20.35,20.94,16849800,10.47
2001-06-04,21.08,21.11,20.46,20.66,10068600,10.33
2001-06-01,20.13,21.09,19.98,20.89,16288400,10.44
2001-05-31,19.80,20.24,19.49,19.95,15817600,9.98
2001-05-30,20.76,20.76,19.30,19.78,27752800,9.89
2001-05-29,22.32,22.50,20.81,21.47,18428200,10.73
2001-05-25,23.20,23.29,22.50,22.76,5669400,11.38
2001-05-24,23.29,23.30,22.62,23.20,9705600,11.60
2001-05-23,23.75,23.75,22.86,23.23,10037200,11.61
2001-05-22,24.00,24.13,23.40,23.50,14747000,11.75
2001-05-21,23.63,23.91,23.05,23.56,16464200,11.78
2001-05-18,23.36,23.64,23.12,23.53,5680400,11.77
2001-05-17,24.23,24.33,23.25,23.55,11861400,11.77
2001-05-16,23.26,24.50,22.85,24.10,11511800,12.05
2001-05-15,23.37,25.50,23.04,23.18,8465200,11.59
2001-05-14,22.89,23.68,22.75,23.29,11043600,11.65
2001-05-11,23.01,23.49,22.76,22.85,7251600,11.43
2001-05-10,24.21,24.50,22.95,23.00,10320600,11.50
2001-05-09,24.14,24.55,23.67,23.98,11603200,11.99
2001-05-08,25.35,25.45,23.95,24.57,11265600,12.28
2001-05-07,25.62,25.76,24.84,24.96,9876800,12.48
2001-05-04,24.24,25.85,23.96,25.75,10037600,12.88
2001-05-03,25.97,26.25,24.73,24.96,10769400,12.48
2001-05-02,26.34,26.70,25.76,26.59,13161600,13.30
2001-05-01,25.41,26.50,25.20,25.93,15259000,12.97
2001-04-30,26.70,27.12,24.87,25.49,17670600,12.74
2001-04-27,25.20,26.29,24.75,26.20,16179000,13.10
2001-04-26,25.17,26.10,24.68,24.69,28560600,12.35
2001-04-25,24.21,24.86,23.57,24.72,11813600,12.36
2001-04-24,24.33,24.75,23.51,24.03,13469200,12.02
2001-04-23,24.34,25.00,24.00,24.25,19340200,12.12
2001-04-20,24.93,25.63,24.60,25.04,24764400,12.52
2001-04-19,25.55,25.75,23.60,25.72,66916800,12.86
2001-04-18,21.57,24.08,21.08,22.79,39315800,11.40
2001-04-17,21.20,21.21,19.60,20.40,24471400,10.20
2001-04-16,22.09,22.40,20.86,21.44,10186600,10.72
2001-04-12,21.42,23.02,21.15,22.42,10676200,11.21
2001-04-11,22.98,23.00,21.28,21.80,11932000,10.90
2001-04-10,20.90,22.70,20.78,22.04,16334800,11.02
2001-04-09,20.69,21.34,20.06,20.54,9520800,10.27
2001-04-06,20.80,21.04,19.90,20.59,11603200,10.30
2001-04-05,20.60,22.50,20.00,20.87,15955800,10.44
2001-04-04,19.76,20.25,18.75,19.50,24481600,9.75
2001-04-03,21.36,21.40,20.13,20.24,13167400,10.12
2001-04-02,22.09,22.66,21.40,21.59,12175400,10.80
2001-03-30,22.55,22.72,21.34,22.07,14298200,11.03
2001-03-29,21.77,23.45,21.50,22.53,21895200,11.27
2001-03-28,22.08,22.50,21.50,22.17,20880800,11.09
2001-03-27,21.94,23.05,21.90,22.87,19422200,11.44
2001-03-26,23.13,23.75,21.13,21.78,26230400,10.89
2001-03-23,22.06,23.56,22.00,23.00,33749400,11.50
2001-03-22,20.37,21.75,20.19,21.62,25839000,10.81
2001-03-21,19.78,20.87,19.37,20.12,13265400,10.06
2001-03-20,20.72,20.94,19.69,19.69,17833800,9.85
2001-03-19,19.75,20.62,19.50,20.56,12722800,10.28
2001-03-16,19.00,20.31,18.87,19.62,16806600,9.81
2001-03-15,20.87,21.37,19.69,19.69,18906600,9.85
2001-03-14,18.50,20.50,18.44,20.44,17065400,10.22
2001-03-13,18.87,19.56,18.19,19.56,15840600,9.78
2001-03-12,19.69,19.87,18.12,18.62,13967800,9.31
2001-03-09,20.62,20.69,20.00,20.25,10685400,10.12
2001-03-08,20.69,21.12,20.44,20.81,7325600,10.40
2001-03-07,21.31,21.62,20.75,21.25,14985600,10.62
2001-03-06,20.72,22.06,20.69,21.50,26144600,10.75
2001-03-05,19.37,20.50,19.25,20.37,11587600,10.19
2001-03-02,18.31,20.44,18.25,19.25,14511200,9.62
2001-03-01,17.81,18.75,17.19,18.75,11803400,9.38
2001-02-28,19.37,19.44,18.12,18.25,18157600,9.12
2001-02-27,19.28,19.44,18.69,19.37,12451000,9.69
2001-02-26,19.06,19.69,18.56,19.50,7380000,9.75
2001-02-23,18.62,18.87,18.25,18.81,10503800,9.40
2001-02-22,19.06,19.37,18.00,18.81,15431200,9.40
2001-02-21,18.25,19.94,18.25,18.87,13947800,9.44
2001-02-20,19.19,19.44,18.19,18.31,11249600,9.15
2001-02-16,19.00,19.50,18.75,19.00,9428400,9.50
2001-02-15,19.69,20.56,19.69,20.06,11123200,10.03
2001-02-14,19.19,19.62,18.50,19.50,11040000,9.75
2001-02-13,19.94,20.44,19.00,19.12,8470600,9.56
2001-02-12,19.06,20.00,18.81,19.69,9795600,9.85
2001-02-09,20.50,20.81,18.69,19.12,21082600,9.56
2001-02-08,20.56,21.06,20.19,20.75,21585000,10.38
2001-02-07,20.66,20.87,19.81,20.75,14071600,10.38
2001-02-06,20.16,21.39,20.00,21.12,16528400,10.56
2001-02-05,20.50,20.56,19.75,20.19,10228800,10.10
2001-02-02,21.12,21.94,20.50,20.62,15263400,10.31
2001-02-01,20.69,21.50,20.50,21.12,13205400,10.56
2001-01-31,21.50,22.50,21.44,21.62,26106000,10.81
2001-01-30,21.56,22.00,20.87,21.75,24734600,10.88
2001-01-29,19.56,21.75,19.56,21.69,30562800,10.85
2001-01-26,19.50,19.81,19.06,19.56,17245600,9.78
2001-01-25,20.56,20.56,19.75,19.94,17495000,9.97
2001-01-24,20.62,20.69,19.56,20.50,25616200,10.25
2001-01-23,19.31,20.94,19.06,20.50,31418400,10.25
2001-01-22,19.06,19.62,18.44,19.25,18551600,9.62
2001-01-19,19.44,19.56,18.69,19.50,27748200,9.75
2001-01-18,17.81,18.75,17.62,18.69,43822800,9.35
2001-01-17,17.56,17.56,16.50,16.81,30037600,8.40
2001-01-16,17.44,18.25,17.00,17.12,10940000,8.56
2001-01-12,17.87,18.00,17.06,17.19,15121000,8.60
2001-01-11,16.25,18.50,16.25,18.00,28707600,9.00
2001-01-10,16.69,17.00,16.06,16.56,20743400,8.28
2001-01-09,16.81,17.64,16.56,17.19,21040600,8.60
2001-01-08,16.94,16.98,15.94,16.56,13350000,8.28
2001-01-05,16.94,17.37,16.06,16.37,14731000,8.19
2001-01-04,18.14,18.50,16.81,17.06,26411000,8.53
2001-01-03,14.50,16.69,14.44,16.37,29181800,8.19
2001-01-02,14.88,15.25,14.56,14.88,16161800,7.44
2000-12-29,14.69,15.00,14.50,14.88,22518800,7.44
2000-12-28,14.38,14.94,14.31,14.81,10910000,7.41
2000-12-27,14.34,14.81,14.19,14.81,11626000,7.41
2000-12-26,14.88,15.00,14.25,14.69,7745400,7.34
2000-12-22,14.13,15.00,14.13,15.00,11369600,7.50
2000-12-21,14.25,15.00,13.88,14.06,13102600,7.03
2000-12-20,13.78,14.63,13.63,14.38,20196200,7.19
2000-12-19,14.38,15.25,14.00,14.00,13367200,7.00
2000-12-18,14.56,14.63,13.94,14.25,11645000,7.12
2000-12-15,14.56,14.69,14.00,14.06,18363800,7.03
2000-12-14,15.03,15.25,14.44,14.44,9406600,7.22
2000-12-13,15.56,15.56,14.88,15.00,12327200,7.50
2000-12-12,15.25,16.00,15.00,15.38,13803400,7.69
2000-12-11,15.19,15.38,14.88,15.19,11884000,7.59
2000-12-08,14.81,15.31,14.44,15.06,15568200,7.53
2000-12-07,14.44,14.88,14.00,14.31,14606600,7.16
2000-12-06,14.63,15.00,14.00,14.31,49092400,7.16
2000-12-05,16.94,17.44,16.37,17.00,21932200,8.50
2000-12-04,17.19,17.19,16.44,16.69,13273400,8.35
2000-12-01,17.00,17.50,16.81,17.06,13783800,8.53
2000-11-30,16.69,17.00,16.12,16.50,28922200,8.25
2000-11-29,18.09,18.31,17.25,17.56,17586200,8.78
2000-11-28,18.69,19.00,17.94,18.03,9618200,9.02
2000-11-27,19.87,19.94,18.50,18.69,9244000,9.35
2000-11-24,18.86,19.50,18.81,19.31,5751800,9.65
2000-11-22,18.81,19.12,18.37,18.50,10029600,9.25
2000-11-21,19.19,19.50,18.75,18.81,10786200,9.40
2000-11-20,18.59,19.50,18.25,18.94,14581600,9.47
2000-11-17,19.19,19.25,18.25,18.50,15943400,9.25
2000-11-16,19.50,19.81,18.87,19.00,8554000,9.50
2000-11-15,20.03,20.19,19.25,19.87,10086600,9.94
2000-11-14,19.94,20.50,19.56,20.25,14611200,10.12
2000-11-13,18.75,20.00,18.25,19.37,15423200,9.69
2000-11-10,19.36,19.87,19.06,19.06,15080600,9.53
2000-11-09,19.87,20.50,19.06,20.19,17035400,10.10
2000-11-08,21.37,21.44,19.81,20.06,15082800,10.03
2000-11-07,21.50,21.81,20.81,21.31,10786800,10.65
2000-11-06,22.44,22.62,20.87,21.44,14060000,10.72
2000-11-03,23.00,23.00,21.94,22.25,18423400,11.12
2000-11-02,21.12,22.44,21.06,22.31,21105400,11.15
2000-11-01,19.44,20.87,19.44,20.50,20553800,10.25
2000-10-31,19.75,20.25,19.25,19.56,31649000,9.78
2000-10-30,19.12,19.94,18.75,19.31,22832800,9.65
2000-10-27,18.87,19.19,17.87,18.56,26594600,9.28
2000-10-26,18.81,18.87,17.50,18.50,25780600,9.25
2000-10-25,19.06,19.19,18.44,18.50,23720600,9.25
2000-10-24,20.69,20.87,18.81,18.87,28736200,9.44
2000-10-23,20.27,20.56,19.44,20.37,19694000,10.19
2000-10-20,19.06,20.37,18.94,19.50,28270400,9.75
2000-10-19,19.16,19.81,18.31,18.94,53818200,9.47
2000-10-18,19.44,21.06,18.75,20.12,29803800,10.06
2000-10-17,21.69,21.94,19.69,20.12,21495600,10.06
2000-10-16,22.31,23.25,21.37,21.50,29298800,10.75
2000-10-13,20.25,22.12,20.00,22.06,44564000,11.03
2000-10-12,20.31,20.81,19.50,20.00,42548200,10.00
2000-10-11,20.12,21.00,19.12,19.62,42801200,9.81
2000-10-10,21.62,22.44,20.50,20.87,24683400,10.44
2000-10-09,22.62,22.87,21.12,21.75,21342600,10.88
2000-10-06,22.69,22.94,21.00,22.19,21881000,11.10
2000-10-05,23.50,24.50,22.00,22.06,31189400,11.03
2000-10-04,22.37,23.75,21.87,23.62,52368200,11.81
2000-10-03,24.94,25.00,22.19,22.31,72795600,11.15
2000-10-02,26.69,26.75,23.50,24.25,86610600,12.12
2000-09-29,28.19,29.00,25.37,25.75,265069000,12.88
2000-09-28,49.31,53.81,48.13,53.50,34988200,26.75
2000-09-27,51.75,52.75,48.25,48.94,14370000,24.47
2000-09-26,53.31,54.75,51.38,51.44,10396600,25.72
2000-09-25,52.75,55.50,52.06,53.50,15564000,26.75
2000-09-22,50.31,52.44,50.00,52.19,25961200,26.09
2000-09-21,58.50,59.63,55.25,56.69,18238400,28.34
2000-09-20,59.41,61.44,58.56,61.05,8121600,30.52
2000-09-19,59.75,60.50,58.56,59.94,9706200,29.97
2000-09-18,55.25,60.75,55.06,60.66,15163200,30.33
2000-09-15,57.75,58.19,54.25,55.23,14095400,27.61
2000-09-14,58.56,59.63,56.81,56.86,15241800,28.43
2000-09-13,56.75,59.50,56.75,58.00,10932600,29.00
2000-09-12,57.34,60.06,57.00,57.75,6722200,28.88
2000-09-11,58.69,60.38,58.13,58.44,6699000,29.22
2000-09-08,61.63,61.63,58.50,58.88,6984400,29.44
2000-09-07,59.13,62.56,58.25,62.00,7770400,31.00
2000-09-06,61.38,62.38,57.75,58.44,12700400,29.22
2000-09-05,62.66,64.12,62.25,62.44,10669000,31.22
2000-09-01,61.31,63.63,61.13,63.44,9181800,31.72
2000-08-31,58.97,61.50,58.94,60.94,14988800,30.47
2000-08-30,59.00,60.00,58.70,59.50,10199600,29.75
2000-08-29,57.88,59.44,57.69,59.19,9546200,29.59
2000-08-28,57.25,59.00,57.06,58.06,12822600,29.03
2000-08-25,56.50,57.50,56.38,56.81,11947800,28.41
2000-08-24,54.67,56.63,53.38,56.11,11109400,28.06
2000-08-23,51.47,54.75,51.06,54.31,8470400,27.16
2000-08-22,50.63,52.81,50.38,51.69,9889000,25.84
2000-08-21,50.25,51.56,49.63,50.50,4803800,25.25
2000-08-18,51.38,51.81,49.88,50.00,6798800,25.00
2000-08-17,48.38,52.44,48.31,51.44,9683400,25.72
2000-08-16,46.88,49.00,46.81,48.50,5137600,24.25
2000-08-15,47.25,47.94,46.50,46.69,4089000,23.34
2000-08-14,47.59,47.69,46.31,47.06,5603400,23.53
2000-08-11,46.84,48.00,45.56,47.69,8503200,23.84
2000-08-10,48.00,48.44,47.38,47.56,8995400,23.78
2000-08-09,48.13,48.44,47.25,47.50,13569000,23.75
2000-08-08,47.94,48.00,46.31,46.75,6315400,23.38
2000-08-07,47.88,49.06,47.19,47.94,6697200,23.97
2000-08-04,49.47,51.25,46.31,47.38,9406800,23.69
2000-08-03,45.56,48.06,44.25,48.00,12150000,24.00
2000-08-02,49.00,49.94,47.19,47.25,5808800,23.62
2000-08-01,50.31,51.16,49.25,49.31,4904600,24.66
2000-07-31,49.16,51.63,48.75,50.81,5550000,25.41
2000-07-28,52.28,52.50,46.88,48.31,8505400,24.16
2000-07-27,50.00,53.25,49.88,52.00,10543800,26.00
2000-07-26,49.84,51.25,49.25,50.06,7526200,25.03
2000-07-25,50.31,50.63,49.06,50.06,7567200,25.03
2000-07-24,52.56,52.88,47.50,48.69,14720600,24.34
2000-07-21,54.36,55.63,52.94,53.56,7013200,26.78
2000-07-20,55.00,57.06,54.13,55.13,16631800,27.57
2000-07-19,55.19,56.81,51.75,52.69,16359600,26.34
2000-07-18,58.50,58.88,56.88,57.25,11378200,28.62
2000-07-17,58.25,58.81,57.13,58.31,9289000,29.16
2000-07-14,57.13,59.00,56.88,57.69,6804400,28.84
2000-07-13,58.50,60.63,54.75,56.50,15925600,28.25
2000-07-12,58.13,58.94,56.38,58.88,8057600,29.44
2000-07-11,57.00,59.25,55.44,56.94,12783200,28.47
2000-07-10,54.09,58.25,53.75,57.13,14211000,28.57
2000-07-07,52.59,54.81,52.13,54.44,9422600,27.22
2000-07-06,52.50,52.94,49.63,51.81,11063800,25.91
2000-07-05,53.25,55.19,50.75,51.63,9478800,25.82
2000-07-03,52.13,54.31,52.13,53.31,2535000,26.66
2000-06-30,52.81,54.94,51.69,52.38,11550000,26.19
2000-06-29,53.06,53.94,51.06,51.25,7281200,25.62
2000-06-28,53.31,55.38,51.50,54.44,10235000,27.22
2000-06-27,53.78,55.50,51.63,51.75,7270600,25.88
2000-06-26,52.50,54.75,52.13,54.13,6631000,27.07
2000-06-23,53.78,54.63,50.81,51.69,7320400,25.84
2000-06-22,55.75,57.63,53.56,53.75,16706200,26.88
2000-06-21,50.50,56.94,50.31,55.63,17500000,27.82
2000-06-20,98.50,103.94,98.37,101.25,17922000,25.31
2000-06-19,90.56,97.87,89.81,96.62,14089200,24.16
2000-06-16,93.50,93.75,89.06,91.19,10842400,22.80
2000-06-15,91.25,93.37,89.00,92.37,8898800,23.09
2000-06-14,94.69,96.25,90.12,90.44,9925200,22.61
2000-06-13,91.19,94.69,88.19,94.50,12570000,23.62
2000-06-12,96.37,96.44,90.87,91.19,10374400,22.80
2000-06-09,96.75,97.94,94.37,95.75,9020000,23.94
2000-06-08,97.62,98.50,93.12,94.81,8540800,23.70
2000-06-07,93.62,97.00,91.62,96.56,12056800,24.14
2000-06-06,91.97,96.75,90.31,92.87,18771200,23.22
2000-06-05,93.31,95.25,89.69,91.31,11582000,22.83
2000-06-02,93.75,99.75,89.00,92.56,28336400,23.14
2000-06-01,81.75,89.56,80.37,89.12,32280000,22.28
2000-05-31,86.87,91.25,83.81,84.00,15483600,21.00
2000-05-30,87.62,88.12,81.75,87.56,25481200,21.89
2000-05-26,88.00,89.87,85.25,86.37,6486400,21.59
2000-05-25,88.50,92.66,86.00,87.27,14530800,21.82
2000-05-24,86.19,89.75,83.00,87.69,24248000,21.92
2000-05-23,90.50,93.37,85.62,85.81,18488000,21.45
2000-05-22,93.75,93.75,86.00,89.94,26995200,22.49
2000-05-19,99.25,99.25,93.37,94.00,26459200,23.50
2000-05-18,103.00,104.94,100.62,100.75,13365600,25.19
2000-05-17,103.62,103.69,100.37,101.37,14227600,25.34
2000-05-16,104.52,109.06,102.75,105.69,15736400,26.42
2000-05-15,108.06,108.06,100.12,101.00,24252000,25.25
2000-05-12,106.00,110.50,104.77,107.62,10962000,26.91
2000-05-11,101.37,104.25,99.00,102.81,17852400,25.70
2000-05-10,104.06,105.00,98.75,99.31,19127600,24.83
2000-05-09,110.31,111.25,104.87,105.44,11685600,26.36
2000-05-08,112.09,113.69,110.00,110.12,6605600,27.53
2000-05-05,110.81,114.75,110.72,113.12,10160000,28.28
2000-05-04,115.12,115.25,110.56,110.69,14284400,27.67
2000-05-03,118.94,121.25,111.62,115.06,17500000,28.76
2000-05-02,123.25,126.25,117.50,117.87,8446400,29.47
2000-05-01,124.87,125.12,121.87,124.31,8100000,31.08
2000-04-28,127.12,127.50,121.31,124.06,8932400,31.01
2000-04-27,117.19,127.00,116.58,126.75,11678000,31.69
2000-04-26,126.62,128.00,120.00,121.31,13117600,30.33
2000-04-25,122.12,128.75,122.06,128.31,14002400,32.08
2000-04-24,115.00,120.50,114.75,120.50,15845600,30.12
2000-04-20,123.69,124.75,117.06,118.87,25806800,29.72
2000-04-19,126.19,130.25,119.75,121.12,18586400,30.28
2000-04-18,123.50,126.87,119.37,126.87,13962400,31.72
2000-04-17,109.50,123.94,109.06,123.87,14642400,30.97
2000-04-14,109.31,118.00,109.00,111.87,23845600,27.97
2000-04-13,111.50,120.00,108.50,113.81,18923600,28.45
2000-04-12,119.00,119.00,104.87,109.25,33618800,27.31
2000-04-11,123.50,124.87,118.06,119.44,19368000,29.86
2000-04-10,131.69,132.75,124.75,125.00,7592400,31.25
2000-04-07,127.25,131.88,125.50,131.75,8668800,32.94
2000-04-06,130.63,134.50,123.25,125.19,9290800,31.30
2000-04-05,126.47,132.88,124.00,130.38,16359200,32.60
2000-04-04,132.63,133.00,116.75,127.31,23596400,31.83
2000-04-03,135.50,139.50,129.44,133.31,11742400,33.33
2000-03-31,127.44,137.25,126.00,135.81,14457600,33.95
2000-03-30,133.56,137.69,125.44,125.75,14800000,31.44
2000-03-29,139.38,139.44,133.83,135.94,8568800,33.99
2000-03-28,137.25,142.00,137.13,139.13,7253600,34.78
2000-03-27,137.63,144.75,136.88,139.56,9976800,34.89
2000-03-24,142.44,143.94,135.50,138.69,15962000,34.67
2000-03-23,142.00,150.38,140.00,141.31,20098000,35.33
2000-03-22,132.78,144.38,131.56,144.19,20288800,36.05
2000-03-21,122.56,136.75,121.62,134.94,18729200,33.74
2000-03-20,123.50,126.25,122.37,123.00,7316400,30.75
2000-03-17,120.12,125.00,119.62,125.00,10902400,31.25
2000-03-16,117.31,122.00,114.50,121.56,13516800,30.39
2000-03-15,115.62,120.25,114.12,116.25,15845200,29.06
2000-03-14,121.22,124.25,114.00,114.25,15321200,28.56
2000-03-13,122.12,126.50,119.50,121.31,10864400,30.33
2000-03-10,121.69,127.94,121.00,125.75,8900800,31.44
2000-03-09,120.87,125.00,118.25,122.25,9884400,30.56
2000-03-08,122.87,123.94,118.56,122.00,9690800,30.50
2000-03-07,126.44,127.44,121.12,122.87,9767600,30.72
2000-03-06,126.00,129.13,125.00,125.69,7520000,31.42
2000-03-03,124.87,128.23,120.00,128.00,11565200,32.00
2000-03-02,127.00,127.94,120.69,122.00,11136800,30.50
2000-03-01,118.56,132.06,118.50,130.31,38478000,32.58
2000-02-29,113.56,117.25,112.56,114.62,13186800,28.66
2000-02-28,110.12,115.00,108.37,113.25,11729200,28.31
2000-02-25,114.81,117.00,110.12,110.37,8908000,27.59
2000-02-24,117.31,119.12,111.75,115.20,13446400,28.80
2000-02-23,113.23,119.00,111.00,116.25,16905600,29.06
2000-02-22,110.12,116.94,106.69,113.81,15083200,28.45
2000-02-18,114.62,115.37,110.87,111.25,8346800,27.81
2000-02-17,115.19,115.50,113.12,114.87,10350000,28.72
2000-02-16,117.75,118.12,112.12,114.12,13525200,28.53
2000-02-15,115.25,119.94,115.19,119.00,17363600,29.75
2000-02-14,109.31,115.87,108.62,115.81,13130000,28.95
2000-02-11,113.62,114.12,108.25,108.75,7592000,27.19
2000-02-10,112.87,113.87,110.00,113.50,10832400,28.38
2000-02-09,114.12,117.12,112.44,112.62,10698000,28.16
2000-02-08,114.00,116.12,111.25,114.87,14613600,28.72
2000-02-07,108.00,114.25,105.94,114.06,15770800,28.51
2000-02-04,103.94,110.00,103.62,108.00,15206800,27.00
2000-02-03,100.31,104.25,100.25,103.31,16977600,25.83
2000-02-02,100.75,102.12,97.00,98.81,16588800,24.70
2000-02-01,104.00,105.00,100.00,100.25,11380000,25.06
2000-01-31,101.00,103.87,94.50,103.75,25071200,25.94
2000-01-28,108.19,110.87,100.62,101.62,15142000,25.41
2000-01-27,108.81,113.00,107.00,110.00,12163600,27.50
2000-01-26,110.00,114.19,109.75,110.19,13131200,27.55
2000-01-25,105.00,113.12,102.37,112.25,17775200,28.06
2000-01-24,108.44,112.75,105.12,106.25,15760000,26.56
2000-01-21,114.25,114.25,110.19,111.31,17729200,27.83
2000-01-20,115.50,121.50,113.50,113.50,65418800,28.38
2000-01-19,105.62,108.75,103.37,106.56,21358000,26.64
2000-01-18,101.00,106.00,100.44,103.94,16421200,25.99
2000-01-14,100.00,102.25,99.37,100.44,13954400,25.11
2000-01-13,94.48,98.75,92.50,96.75,36882400,24.19
2000-01-12,95.00,95.50,86.50,87.19,34870800,21.80
2000-01-11,95.94,99.37,90.50,92.75,15775200,23.19
2000-01-10,102.00,102.25,94.75,97.75,18059200,24.44
2000-01-07,96.50,101.00,95.50,99.50,16463200,24.88
2000-01-06,106.12,107.00,95.00,95.00,27443200,23.75
2000-01-05,103.75,110.56,103.00,104.00,27818000,26.00
2000-01-04,108.25,110.62,101.19,102.50,18310000,25.62
2000-01-03,104.87,112.50,101.69,111.94,19144400,27.99
1999-12-31,100.94,102.87,99.50,102.81,5856400,25.70
1999-12-30,102.19,104.12,99.62,100.31,7419200,25.08
1999-12-29,96.81,102.19,95.50,100.69,10161200,25.17
1999-12-28,99.12,99.62,95.00,98.19,8843200,24.55
1999-12-27,104.37,104.44,99.25,99.31,6022000,24.83
1999-12-23,101.81,104.25,101.06,103.50,8218800,25.88
1999-12-22,102.87,104.56,98.75,99.94,11682000,24.99
1999-12-21,98.19,103.06,97.94,102.50,11000000,25.62
1999-12-20,99.56,99.62,96.62,98.00,10155200,24.50
1999-12-17,100.87,102.00,98.50,100.00,17700800,25.00
1999-12-16,98.00,98.37,94.00,98.31,16568000,24.58
1999-12-15,93.25,97.25,91.06,97.00,22254400,24.25
1999-12-14,98.37,99.75,94.75,94.87,15570800,23.72
1999-12-13,102.39,102.50,98.94,99.00,18931200,24.75
1999-12-10,105.31,109.25,99.00,103.00,22786800,25.75
1999-12-09,111.00,111.00,100.87,105.25,30555600,26.31
1999-12-08,116.25,117.87,109.50,110.06,14730800,27.51
1999-12-07,116.56,118.00,114.00,117.81,15901200,29.45
1999-12-06,114.56,117.31,111.44,116.00,16688000,29.00
1999-12-03,112.19,115.56,111.87,115.00,23151200,28.75
1999-12-02,103.12,110.62,101.75,110.19,20275600,27.55
1999-12-01,101.00,104.50,100.06,103.06,22098000,25.76
1999-11-30,98.12,103.75,97.37,97.87,30132400,24.47
1999-11-29,94.25,99.75,93.25,94.56,16586800,23.64
1999-11-26,94.75,95.50,94.12,95.06,4737600,23.76
1999-11-24,93.00,95.00,91.69,94.69,7683600,23.67
1999-11-23,91.75,95.25,88.50,92.81,19406400,23.20
1999-11-22,91.75,91.75,89.25,90.62,7242400,22.66
1999-11-19,89.50,92.87,88.06,92.44,11162000,23.11
1999-11-18,91.06,91.12,88.44,89.62,13043600,22.41
1999-11-17,90.69,94.75,90.00,90.25,13032000,22.56
1999-11-16,90.00,91.75,88.50,91.19,8370000,22.80
1999-11-15,89.62,92.87,88.50,89.44,9283600,22.36
1999-11-12,91.94,92.00,87.37,90.62,9970000,22.66
1999-11-11,91.59,92.62,89.87,92.25,9660000,23.06
1999-11-10,88.25,93.25,88.12,91.44,20661200,22.86
1999-11-09,94.37,94.50,88.00,89.62,28910000,22.41
1999-11-08,87.75,97.73,86.75,96.37,33962400,24.09
1999-11-05,84.62,88.37,84.00,88.31,14889200,22.08
1999-11-04,82.06,85.37,80.62,83.62,13549200,20.91
1999-11-03,81.62,83.25,81.00,81.50,11736800,20.38
1999-11-02,78.00,81.69,77.31,80.25,14268800,20.06
1999-11-01,80.00,80.69,77.37,77.62,9965600,19.41
1999-10-29,78.81,81.06,78.81,80.12,18680800,20.03
1999-10-28,77.06,79.00,76.06,77.87,18005200,19.47
1999-10-27,74.37,76.62,73.44,76.37,15837600,19.09
1999-10-26,74.94,75.50,73.31,75.06,12924400,18.76
1999-10-25,74.25,76.12,73.75,74.50,11677600,18.62
1999-10-22,77.12,77.25,73.37,73.94,14995200,18.49
1999-10-21,72.56,77.06,72.37,76.12,28347600,19.03
1999-10-20,70.00,75.25,70.00,75.12,38633600,18.78
1999-10-19,71.62,75.00,68.44,68.50,36521200,17.12
1999-10-18,73.87,74.25,71.12,73.25,27733600,18.31
1999-10-15,71.12,75.81,70.19,74.56,41910000,18.64
1999-10-14,69.25,73.31,69.00,73.19,67822400,18.30
1999-10-13,66.62,69.50,63.75,64.03,22752000,16.01
1999-10-12,67.87,69.62,67.00,67.69,20142000,16.92
1999-10-11,66.00,68.25,66.00,66.69,9418000,16.67
1999-10-08,66.19,66.31,63.50,65.56,13689200,16.39
1999-10-07,68.44,68.62,64.87,66.37,21660800,16.59
1999-10-06,69.37,69.62,67.00,67.19,28726400,16.80
1999-10-05,65.62,68.12,64.75,67.94,29100800,16.99
1999-10-04,62.38,64.87,62.38,64.56,16408800,16.14
1999-10-01,62.13,62.44,59.50,61.72,21977600,15.43
1999-09-30,59.56,64.19,59.25,63.31,32449200,15.83
1999-09-29,60.25,61.25,58.00,59.06,23493600,14.77
1999-09-28,61.50,62.00,57.44,59.63,50542400,14.91
1999-09-27,66.37,66.75,61.19,61.31,33877600,15.33
1999-09-24,63.38,67.02,63.00,64.94,42148800,16.24
1999-09-23,71.12,71.25,63.00,63.31,40853200,15.83
1999-09-22,69.75,71.62,69.02,70.31,40132000,17.58
1999-09-21,73.19,73.25,69.00,69.25,119931200,17.31
1999-09-20,77.00,80.12,76.87,79.06,16326400,19.76
1999-09-17,77.31,77.75,76.25,76.94,9915600,19.24
1999-09-16,76.06,78.06,73.87,76.81,15793600,19.20
1999-09-15,78.87,79.12,75.25,75.37,12843200,18.84
1999-09-14,74.72,78.50,74.69,77.81,13883200,19.45
1999-09-13,77.06,77.06,74.81,75.00,9000000,18.75
1999-09-10,76.00,77.69,74.69,77.44,16398000,19.36
1999-09-09,75.50,75.94,73.87,75.56,19093600,18.89
1999-09-08,76.19,77.69,74.50,74.50,27233600,18.62
1999-09-07,73.75,77.94,73.50,76.37,35177600,19.09
1999-09-03,71.94,75.25,70.50,73.50,58403600,18.38
1999-09-02,67.62,71.44,66.87,70.56,31975200,17.64
1999-09-01,67.00,68.81,66.00,68.62,28168000,17.16
1999-08-31,62.59,65.87,62.06,65.25,22675200,16.31
1999-08-30,65.00,65.00,62.00,62.06,12033200,15.52
1999-08-27,62.75,65.00,62.69,64.75,15980000,16.19
1999-08-26,61.13,63.13,61.13,62.13,14449200,15.53
1999-08-25,60.69,61.50,60.13,61.38,10553600,15.35
1999-08-24,60.38,60.75,59.94,60.38,17948000,15.10
1999-08-23,59.38,61.38,59.31,60.75,12709200,15.19
1999-08-20,59.25,59.38,58.19,59.19,11730800,14.80
1999-08-19,59.81,60.50,58.56,58.75,19645600,14.69
1999-08-18,60.06,62.00,59.63,60.13,16743200,15.03
1999-08-17,60.31,60.38,58.94,60.31,11474400,15.08
1999-08-16,59.81,60.69,59.50,60.50,9896400,15.12
1999-08-13,60.63,62.00,59.88,60.06,10668800,15.02
1999-08-12,59.06,61.38,58.63,60.00,23806400,15.00
1999-08-11,56.00,59.75,55.94,59.69,30374400,14.92
1999-08-10,54.00,56.00,53.63,55.38,14879200,13.85
1999-08-09,54.34,55.19,54.25,54.44,8338000,13.61
1999-08-06,54.06,55.31,53.50,54.13,15575600,13.53
1999-08-05,53.50,54.88,52.13,54.75,11541200,13.69
1999-08-04,55.19,55.88,53.25,53.81,13279200,13.45
1999-08-03,56.75,57.44,53.63,55.25,13176800,13.81
1999-08-02,55.63,58.00,55.50,55.75,12958000,13.94
1999-07-30,54.50,56.13,54.50,55.69,13685600,13.92
1999-07-29,53.38,55.25,53.13,53.88,9860000,13.47
1999-07-28,53.88,55.38,53.00,54.38,11762000,13.60
1999-07-27,52.63,53.94,52.50,53.69,14150800,13.42
1999-07-26,52.88,53.00,50.88,50.94,12555200,12.73
1999-07-23,52.81,53.75,52.69,53.31,8192000,13.33
1999-07-22,53.63,53.88,51.13,52.38,14529200,13.10
1999-07-21,54.06,55.44,52.88,54.06,25653600,13.52
1999-07-20,54.56,55.50,52.75,52.88,15804400,13.22
1999-07-19,53.94,55.81,52.31,54.44,20050000,13.61
1999-07-16,53.63,54.50,53.00,53.06,14705600,13.27
1999-07-15,55.88,55.94,51.31,53.25,60433600,13.31
1999-07-14,54.50,56.63,54.50,55.94,22320000,13.98
1999-07-13,53.50,54.19,52.88,53.69,10136800,13.42
1999-07-12,55.50,55.63,54.19,54.50,10862000,13.62
1999-07-09,54.50,55.63,53.00,55.63,21750000,13.91
1999-07-08,51.13,55.06,50.88,54.50,58058000,13.62
1999-07-07,47.38,50.75,47.00,49.88,39264400,12.47
1999-07-06,45.94,47.63,45.81,47.38,16212000,11.85
1999-07-02,45.53,46.88,45.19,46.31,4426800,11.58
1999-07-01,46.31,46.56,45.25,45.31,5334400,11.33
1999-06-30,45.69,46.94,44.94,46.31,12270800,11.58
1999-06-29,42.72,45.56,42.63,45.38,13599200,11.35
1999-06-28,42.44,42.94,42.38,42.56,9938800,10.64
1999-06-25,42.50,42.69,42.06,42.19,10518800,10.55
1999-06-24,43.63,43.63,42.25,42.31,15498000,10.58
1999-06-23,45.06,45.09,43.56,43.69,18994400,10.92
1999-06-22,46.31,46.94,45.38,45.38,5415600,11.35
1999-06-21,47.00,47.25,46.00,46.50,4842000,11.62
1999-06-18,45.38,47.25,45.19,47.13,7448000,11.78
1999-06-17,47.63,48.00,45.75,46.38,8022400,11.60
1999-06-16,46.38,48.06,46.38,47.94,8056800,11.98
1999-06-15,45.19,46.75,45.13,46.06,4666400,11.52
1999-06-14,46.50,46.63,45.13,45.44,5615600,11.36
1999-06-11,48.13,48.50,46.25,46.44,6613600,11.61
1999-06-10,47.88,48.25,47.31,48.13,11325200,12.03
1999-06-09,47.44,48.50,47.44,48.44,12655200,12.11
1999-06-08,48.75,48.81,47.56,47.69,11203200,11.92
1999-06-07,48.13,49.00,47.50,48.94,14949200,12.23
1999-06-04,47.63,48.19,47.25,48.13,13171200,12.03
1999-06-03,46.88,48.00,46.81,47.44,17450800,11.86
1999-06-02,44.50,47.94,44.00,46.56,18614400,11.64
1999-06-01,45.00,45.31,44.38,44.81,16479200,11.20
1999-05-28,43.31,44.31,43.13,44.06,7196400,11.02
1999-05-27,43.19,43.75,42.69,43.50,12042400,10.88
1999-05-26,41.75,44.38,41.25,44.06,15642000,11.02
1999-05-25,41.56,42.44,40.94,41.50,13095200,10.38
1999-05-24,43.63,44.31,41.88,41.94,9340800,10.48
1999-05-21,43.00,44.31,42.56,43.94,16555200,10.98
1999-05-20,45.44,45.75,42.50,42.50,14940000,10.62
1999-05-19,45.50,45.75,43.50,45.19,10660000,11.30
1999-05-18,44.81,46.00,44.38,45.25,14954400,11.31
1999-05-17,43.75,44.69,43.00,44.38,7531200,11.10
1999-05-14,45.13,45.81,44.38,44.38,8102000,11.10
1999-05-13,46.44,46.81,45.50,46.19,10573600,11.55
1999-05-12,44.88,46.50,44.13,46.50,14129200,11.62
1999-05-11,44.88,46.19,43.56,44.75,16388800,11.19
1999-05-10,46.75,46.94,44.63,45.25,14055600,11.31
1999-05-07,44.63,45.88,42.75,45.88,15528800,11.47
1999-05-06,46.56,46.88,44.00,44.50,15486400,11.12
1999-05-05,46.31,47.00,44.63,47.00,20694400,11.75
1999-05-04,48.25,48.63,46.19,46.50,28980000,11.62
1999-05-03,46.06,50.00,45.75,49.56,52535600,12.39
1999-04-30,44.00,47.13,44.00,46.00,52596400,11.50
1999-04-29,43.25,44.38,41.78,43.00,28206400,10.75
1999-04-28,44.63,45.69,43.63,44.06,34122000,11.02
1999-04-27,43.00,45.81,43.00,45.75,75225200,11.44
1999-04-26,39.50,41.25,39.25,40.94,33152000,10.23
1999-04-23,36.25,39.44,36.25,39.19,37402400,9.80
1999-04-22,35.06,36.63,35.06,36.38,26454400,9.10
1999-04-21,34.00,34.38,33.50,34.38,12566800,8.60
1999-04-20,33.88,34.75,33.50,34.06,18725600,8.52
1999-04-19,35.69,36.00,33.50,33.88,32923200,8.47
1999-04-16,35.88,36.06,35.25,35.44,17945600,8.86
1999-04-15,35.38,36.19,34.31,35.75,61960000,8.94
1999-04-14,35.25,37.06,35.00,35.53,24323600,8.88
1999-04-13,36.31,36.81,34.50,34.63,14732400,8.66
1999-04-12,35.00,36.88,34.88,36.25,14145600,9.06
1999-04-09,36.25,37.25,35.94,36.75,9608000,9.19
1999-04-08,36.88,37.06,36.00,36.88,10600800,9.22
1999-04-07,38.06,38.25,36.38,37.13,14723200,9.28
1999-04-06,36.81,38.31,36.81,38.00,22455200,9.50
1999-04-05,36.00,37.88,36.00,37.06,16474400,9.27
1999-04-01,36.06,36.69,35.75,36.06,9381200,9.02
1999-03-31,36.38,37.13,35.88,35.94,15086400,8.98
1999-03-30,35.00,36.38,35.00,35.88,19806800,8.97
1999-03-29,33.50,35.44,33.44,35.38,20337600,8.85
1999-03-26,33.75,33.81,33.00,33.25,9080000,8.31
1999-03-25,34.38,34.88,33.38,33.81,14286800,8.45
1999-03-24,33.25,33.75,32.50,33.69,14297600,8.42
1999-03-23,34.44,34.44,32.75,33.00,14842000,8.25
1999-03-22,34.00,35.19,32.94,35.06,21200800,8.77
1999-03-19,35.94,36.00,32.88,33.50,19161200,8.38
1999-03-18,34.38,35.63,34.25,35.50,8126800,8.88
1999-03-17,35.94,36.06,33.94,34.06,13084400,8.52
1999-03-16,35.00,35.56,34.94,35.50,14302000,8.88
1999-03-15,33.31,35.00,33.25,34.06,12586800,8.52
1999-03-12,32.31,33.50,32.31,33.19,9700000,8.30
1999-03-11,32.25,33.88,32.00,32.19,16936800,8.05
1999-03-10,34.19,34.19,32.44,32.56,19526800,8.14
1999-03-09,34.31,34.38,33.50,34.13,11427600,8.53
1999-03-08,33.25,34.69,33.19,34.38,19682000,8.60
1999-03-05,34.31,34.31,32.38,33.19,16735600,8.30
1999-03-04,34.50,34.50,32.38,33.44,13137600,8.36
1999-03-03,34.75,35.13,33.50,34.19,10497600,8.55
1999-03-02,34.13,35.31,33.75,34.63,24414400,8.66
1999-03-01,34.81,34.81,33.63,33.75,17435200,8.44
1999-02-26,36.50,37.00,34.50,34.81,23847600,8.70
1999-02-25,37.31,37.69,36.50,36.94,9455600,9.23
1999-02-24,38.81,39.00,37.38,37.44,7620000,9.36
1999-02-23,38.56,39.56,37.94,38.44,11521200,9.61
1999-02-22,37.38,38.88,37.25,38.44,10682000,9.61
1999-02-19,36.25,37.69,36.19,37.19,12938800,9.30
1999-02-18,37.56,37.88,35.56,36.00,17876400,9.00
1999-02-17,38.13,38.69,36.94,37.00,10581200,9.25
1999-02-16,38.88,38.88,37.88,38.31,10723600,9.58
1999-02-12,39.13,39.13,37.00,37.69,15339200,9.42
1999-02-11,38.75,39.75,38.56,39.63,20200000,9.91
1999-02-10,36.88,38.69,36.00,38.31,20135200,9.58
1999-02-09,37.94,39.06,37.06,37.19,25042000,9.30
1999-02-08,36.69,37.94,36.25,37.75,16723600,9.44
1999-02-05,38.25,38.38,35.50,36.31,27778000,9.08
1999-02-04,40.19,40.25,37.75,37.88,16565600,9.47
1999-02-03,39.00,40.56,38.75,40.19,12108000,10.05
1999-02-02,40.38,40.75,39.00,39.19,10975600,9.80
1999-02-01,41.69,41.94,40.31,40.94,9962000,10.23
1999-01-29,41.19,41.56,40.00,41.19,8684400,10.30
1999-01-28,40.88,41.25,40.31,40.88,12015600,10.22
1999-01-27,41.00,41.38,39.94,40.13,13053200,10.03
1999-01-26,39.94,40.88,39.63,40.50,20002400,10.12
1999-01-25,39.25,39.56,38.81,39.38,13763200,9.85
1999-01-22,37.69,39.50,37.06,38.75,12365200,9.69
1999-01-21,40.44,40.56,37.50,38.81,21449200,9.70
1999-01-20,41.06,42.00,40.50,40.56,27806800,10.14
1999-01-19,41.94,42.31,40.38,40.88,19116400,10.22
1999-01-15,41.81,42.13,40.00,41.31,35933600,10.33
1999-01-14,45.50,46.00,41.06,41.38,61570000,10.35
1999-01-13,42.88,47.31,42.25,46.50,37434400,11.62
1999-01-12,46.31,46.63,44.13,46.13,29330000,11.53
1999-01-11,45.75,46.06,44.88,45.88,20054400,11.47
1999-01-08,46.56,46.88,44.00,45.00,24246400,11.25
1999-01-07,42.25,45.06,42.13,45.00,51056800,11.25
1999-01-06,44.13,44.13,41.00,41.75,48165200,10.44
1999-01-05,41.94,43.94,41.50,43.31,50362000,10.83
1999-01-04,42.13,42.25,40.00,41.25,34049200,10.31
1998-12-31,40.50,41.38,39.50,40.94,9716400,10.23
1998-12-30,40.13,41.13,40.00,40.06,8498000,10.02
1998-12-29,41.13,41.50,40.25,40.81,13853200,10.20
1998-12-28,39.00,41.13,39.00,40.88,25917600,10.22
1998-12-24,39.88,40.00,39.19,39.25,7155200,9.81
1998-12-23,38.63,40.50,38.38,39.81,44124400,9.95
1998-12-22,36.38,38.13,36.00,38.00,41111200,9.50
1998-12-21,35.38,35.63,34.25,35.06,12769200,8.77
1998-12-18,33.38,35.38,33.25,35.19,28283200,8.80
1998-12-17,32.94,33.75,32.75,33.44,11812000,8.36
1998-12-16,33.75,34.19,32.63,32.81,13375200,8.20
1998-12-15,32.75,33.63,32.75,33.56,9462000,8.39
1998-12-14,32.88,33.31,32.25,32.50,17925200,8.12
1998-12-11,32.25,34.00,32.00,33.75,24644400,8.44
1998-12-10,32.69,32.94,31.87,32.00,13980800,8.00
1998-12-09,32.69,32.88,31.62,32.00,21184400,8.00
1998-12-08,33.94,33.94,32.00,32.06,24295200,8.02
1998-12-07,33.38,33.75,32.75,33.75,20255600,8.44
1998-12-04,34.31,34.44,32.00,32.75,25765200,8.19
1998-12-03,36.31,36.50,33.63,33.69,22380800,8.42
1998-12-02,34.13,36.88,33.50,36.00,34382400,9.00
1998-12-01,32.00,34.81,31.62,34.13,30941200,8.53
1998-11-30,34.56,34.81,31.75,31.94,20060800,7.99
1998-11-27,35.06,35.13,34.75,35.06,5483600,8.77
1998-11-25,35.88,36.06,34.94,35.13,10855600,8.78
1998-11-24,36.13,36.75,35.75,35.94,11430800,8.98
1998-11-23,35.56,36.81,35.19,36.25,20642000,9.06
1998-11-20,36.44,36.75,34.75,35.31,14268000,8.83
1998-11-19,35.50,37.19,35.44,35.75,12385200,8.94
1998-11-18,35.19,36.00,34.88,35.44,11781200,8.86
1998-11-17,35.75,35.81,34.75,34.81,7529200,8.70
1998-11-16,35.94,36.75,35.44,36.00,13740800,9.00
1998-11-13,34.94,36.06,34.69,35.69,28301200,8.92
1998-11-12,33.13,34.44,32.88,34.00,21261200,8.50
1998-11-11,35.75,35.81,32.75,33.56,33895200,8.39
1998-11-10,36.19,36.25,35.00,35.13,31576800,8.78
1998-11-09,37.69,38.13,35.50,36.63,23622000,9.16
1998-11-06,37.88,38.25,37.25,38.06,28496800,9.52
1998-11-05,38.38,39.38,38.06,38.19,21684400,9.55
1998-11-04,38.56,39.13,38.13,38.69,22438000,9.67
1998-11-03,37.38,38.25,37.31,37.81,13247600,9.45
1998-11-02,37.50,37.75,37.25,37.63,9076400,9.41
1998-10-30,36.81,37.50,36.25,37.13,11358000,9.28
1998-10-29,36.44,37.44,35.81,36.44,12321200,9.11
1998-10-28,35.25,37.00,35.13,36.81,13006400,9.20
1998-10-27,38.00,38.94,35.06,35.25,19233200,8.81
1998-10-26,36.06,37.75,35.50,37.44,17013600,9.36
1998-10-23,36.75,36.88,35.13,35.50,12732400,8.88
1998-10-22,36.88,37.63,36.25,36.75,11343200,9.19
1998-10-21,36.75,37.44,35.75,37.13,15390000,9.28
1998-10-20,37.94,38.19,36.00,36.06,13649200,9.02
1998-10-19,36.69,38.06,35.88,37.50,17010000,9.38
1998-10-16,37.13,38.06,36.50,36.69,21998000,9.17
1998-10-15,36.25,37.25,35.50,36.63,30037600,9.16
1998-10-14,39.75,41.31,36.81,37.38,81445600,9.35
1998-10-13,38.06,39.19,36.00,38.75,33646400,9.69
1998-10-12,37.50,38.44,36.56,37.44,22250000,9.36
1998-10-09,31.75,35.25,30.75,35.13,23880000,8.78
1998-10-08,31.00,31.19,28.50,30.81,24623200,7.70
1998-10-07,32.38,33.31,31.87,31.94,16920000,7.99
1998-10-06,33.69,34.31,32.50,32.56,14281200,8.14
1998-10-05,34.00,34.56,31.50,32.19,19726800,8.05
1998-10-02,35.50,36.25,34.13,35.06,16998800,8.77
1998-10-01,36.75,38.00,35.38,35.69,13234400,8.92
1998-09-30,38.75,39.25,38.00,38.13,5976800,9.53
1998-09-29,39.06,40.00,38.13,39.50,10907600,9.88
1998-09-28,39.75,40.19,38.00,39.06,14501200,9.77
1998-09-25,38.19,39.19,37.63,38.75,8172000,9.69
1998-09-24,37.88,39.56,37.75,38.50,17246800,9.62
1998-09-23,37.25,38.38,36.56,38.31,10284400,9.58
1998-09-22,37.13,37.63,36.38,37.00,9218800,9.25
1998-09-21,35.69,36.94,35.31,36.94,10570800,9.23
1998-09-18,36.06,36.75,35.56,36.75,10904400,9.19
1998-09-17,36.06,37.13,35.88,36.00,9627600,9.00
1998-09-16,38.63,38.75,37.00,37.31,9248800,9.33
1998-09-15,36.75,38.56,36.50,38.19,15492000,9.55
1998-09-14,38.25,38.81,37.13,37.19,8837600,9.30
1998-09-11,38.50,39.63,36.88,37.63,12593600,9.41
1998-09-10,36.25,38.25,35.75,38.13,18826800,9.53
1998-09-09,38.06,38.13,37.00,37.38,12683200,9.35
1998-09-08,38.00,38.25,36.75,38.25,14400000,9.56
1998-09-04,35.50,36.44,33.75,35.13,13493200,8.78
1998-09-03,35.00,35.13,34.00,34.63,14653200,8.66
1998-09-02,35.50,37.38,35.25,35.56,30122400,8.89
1998-09-01,31.37,35.38,30.62,34.13,31060000,8.53
1998-08-31,34.75,34.88,31.00,31.19,31012400,7.80
1998-08-28,37.13,38.50,34.13,34.19,33303200,8.55
1998-08-27,39.25,39.25,35.63,37.50,39813600,9.38
1998-08-26,39.88,41.13,39.50,40.38,14538000,10.10
1998-08-25,42.38,42.38,40.31,40.81,17709200,10.20
1998-08-24,43.44,43.50,40.13,41.19,21810000,10.30
1998-08-21,40.00,43.56,39.00,43.00,29054400,10.75
1998-08-20,41.00,41.13,40.25,40.63,14018000,10.16
1998-08-19,43.50,43.75,41.00,41.00,17377600,10.25
1998-08-18,42.44,43.38,42.25,42.56,21642000,10.64
1998-08-17,41.00,42.81,39.88,41.94,33248800,10.48
1998-08-14,40.69,40.75,39.50,40.50,16110000,10.12
1998-08-13,39.94,40.75,39.38,39.44,13976800,9.86
1998-08-12,39.75,40.94,39.48,40.06,24654400,10.02
1998-08-11,37.75,41.00,37.38,39.00,62860000,9.75
1998-08-10,36.31,38.06,36.25,37.94,17455600,9.48
1998-08-07,37.19,37.38,36.00,36.50,10645600,9.12
1998-08-06,35.06,36.88,34.88,36.88,15678800,9.22
1998-08-05,33.75,36.00,33.50,36.00,16226800,9.00
1998-08-04,35.50,36.00,34.00,34.19,10506800,8.55
1998-08-03,34.25,35.56,33.25,35.13,10786800,8.78
1998-07-31,36.63,36.75,34.50,34.63,6550800,8.66
1998-07-30,35.81,36.75,35.50,36.50,12950000,9.12
1998-07-29,33.75,35.88,33.69,35.13,16006800,8.78
1998-07-28,34.06,34.63,33.00,33.63,8054400,8.41
1998-07-27,34.25,34.88,33.25,34.44,7657600,8.61
1998-07-24,35.38,35.50,33.81,34.69,9693600,8.67
1998-07-23,34.81,35.63,34.75,34.94,9040800,8.73
1998-07-22,34.94,35.63,34.25,35.00,10040800,8.75
1998-07-21,36.13,37.00,35.56,35.63,11772400,8.91
1998-07-20,36.56,36.63,35.50,36.25,13727600,9.06
1998-07-17,37.25,37.25,36.19,36.88,22486400,9.22
1998-07-16,37.88,38.13,35.75,37.50,91497600,9.38
1998-07-15,33.69,34.69,33.50,34.44,21253600,8.61
1998-07-14,33.94,34.00,33.13,33.44,19607600,8.36
1998-07-13,31.94,34.13,31.87,33.94,25566400,8.48
1998-07-10,32.19,32.63,31.75,32.06,10806800,8.02
1998-07-09,32.94,33.63,31.44,31.69,20256400,7.92
1998-07-08,30.75,32.94,30.69,32.56,33334400,8.14
1998-07-07,30.37,30.87,30.00,30.50,8637600,7.62
1998-07-06,29.50,30.37,29.12,30.37,9697600,7.59
1998-07-02,29.69,30.06,29.00,29.00,10650800,7.25
1998-07-01,28.87,30.00,28.50,29.94,11228800,7.49
1998-06-30,28.62,28.81,28.12,28.69,4681200,7.17
1998-06-29,28.25,28.81,28.06,28.69,5943600,7.17
1998-06-26,28.50,28.62,27.75,28.19,3973200,7.05
1998-06-25,28.56,28.81,28.31,28.56,6856400,7.14
1998-06-24,27.75,28.62,27.31,28.25,9788800,7.06
1998-06-23,27.44,28.12,27.25,27.81,8258800,6.95
1998-06-22,27.00,27.56,26.75,27.37,4809200,6.84
1998-06-19,27.37,27.44,26.75,27.06,4931200,6.76
1998-06-18,27.75,28.06,27.19,27.31,4288800,6.83
1998-06-17,28.00,28.56,27.94,28.12,6687600,7.03
1998-06-16,27.69,28.12,27.31,28.00,4649200,7.00
1998-06-15,27.25,28.25,27.25,27.50,4881200,6.88
1998-06-12,27.62,28.25,27.37,28.12,8014400,7.03
1998-06-11,28.19,28.62,27.81,27.81,6451200,6.95
1998-06-10,28.00,29.00,27.62,28.06,8202000,7.01
1998-06-09,27.37,28.50,27.37,28.25,9852400,7.06
1998-06-08,27.00,27.69,26.81,27.25,4523600,6.81
1998-06-05,26.87,27.25,26.37,26.87,4406800,6.72
1998-06-04,26.62,26.87,25.81,26.81,5585600,6.70
1998-06-03,27.12,27.25,26.19,26.31,5196800,6.58
1998-06-02,26.44,27.31,26.00,26.87,6405600,6.72
1998-06-01,26.50,27.62,25.62,26.25,11427600,6.56
1998-05-29,27.50,27.56,26.44,26.62,7751200,6.66
1998-05-28,26.75,27.87,26.75,27.44,10672000,6.86
1998-05-27,25.69,26.81,25.62,26.75,13233200,6.69
1998-05-26,28.06,28.25,26.62,26.69,11143200,6.67
1998-05-22,28.75,28.75,27.31,27.87,9522000,6.97
1998-05-21,29.56,29.69,28.62,28.87,4700000,7.22
1998-05-20,29.62,29.87,28.75,29.56,6810000,7.39
1998-05-19,28.94,29.44,28.81,29.37,7815200,7.34
1998-05-18,29.37,29.56,28.37,28.50,8310800,7.12
1998-05-15,30.06,30.37,29.25,29.56,9743600,7.39
1998-05-14,30.37,30.44,29.75,30.06,5815600,7.51
1998-05-13,30.06,30.81,29.62,30.44,11245600,7.61
1998-05-12,30.56,30.75,29.94,30.12,9212000,7.53
1998-05-11,30.87,31.62,30.75,30.94,23768000,7.74
1998-05-08,30.06,30.50,29.94,30.44,9690000,7.61
1998-05-07,30.56,30.62,29.87,30.19,19761200,7.55
1998-05-06,29.87,30.44,29.25,30.31,32056400,7.58
1998-05-05,29.25,29.87,29.12,29.69,14982400,7.42
1998-05-04,28.87,29.50,28.87,29.06,20419200,7.26
1998-05-01,27.50,28.25,26.87,28.00,6582000,7.00
1998-04-30,27.37,27.62,27.06,27.37,6442000,6.84
1998-04-29,26.94,27.44,26.75,27.00,6774400,6.75
1998-04-28,27.87,28.00,26.25,26.94,8487600,6.74
1998-04-27,26.75,27.75,26.75,27.75,14655600,6.94
1998-04-24,27.75,28.25,27.50,27.94,7708000,6.99
1998-04-23,27.44,29.00,27.19,27.69,16983200,6.92
1998-04-22,28.75,29.00,27.50,27.50,10186400,6.88
1998-04-21,29.06,29.12,28.50,29.00,12446400,7.25
1998-04-20,27.62,29.50,27.56,29.00,18498800,7.25
1998-04-17,28.56,28.62,27.69,27.94,21165200,6.99
1998-04-16,29.25,29.62,28.19,28.62,65642000,7.16
1998-04-15,27.19,27.50,26.62,27.44,19928800,6.86
1998-04-14,26.37,27.25,26.37,26.94,11725200,6.74
1998-04-13,25.62,26.69,25.00,26.44,10305600,6.61
1998-04-09,25.06,25.87,25.00,25.62,6083600,6.41
1998-04-08,25.25,25.37,24.69,25.00,8044400,6.25
1998-04-07,25.81,26.00,24.87,25.50,10461200,6.38
1998-04-06,27.00,27.00,26.19,26.25,12422000,6.56
1998-04-03,27.12,27.25,26.81,27.06,7259200,6.76
1998-04-02,27.31,27.44,26.94,27.31,6950800,6.83
1998-04-01,27.44,27.81,27.06,27.50,6693600,6.88
1998-03-31,27.44,27.81,27.25,27.50,9538800,6.88
1998-03-30,26.75,27.50,26.75,27.44,8972400,6.86
1998-03-27,26.62,27.31,26.37,26.94,9133200,6.74
1998-03-26,26.75,27.00,26.44,26.56,7253600,6.64
1998-03-25,27.62,27.75,26.37,27.16,13854400,6.79
1998-03-24,26.37,28.00,26.25,28.00,24152000,7.00
1998-03-23,25.94,26.25,24.62,26.12,14818800,6.53
1998-03-20,26.69,26.87,26.00,26.37,7704400,6.59
1998-03-19,26.87,26.94,26.56,26.75,5736800,6.69
1998-03-18,26.00,26.94,26.00,26.94,9900000,6.74
1998-03-17,26.50,26.69,25.87,26.34,14658800,6.59
1998-03-16,27.12,27.25,26.19,26.69,14375600,6.67
1998-03-13,27.25,27.25,26.25,27.12,20231200,6.78
1998-03-12,26.12,27.00,25.56,27.00,26598000,6.75
1998-03-11,25.12,26.19,24.56,26.12,43374400,6.53
1998-03-10,23.00,24.50,22.94,24.06,25472400,6.01
1998-03-09,23.75,24.31,22.50,22.75,20540800,5.69
1998-03-06,23.87,24.50,23.37,24.44,23803600,6.11
1998-03-05,23.25,24.25,23.12,24.06,24129200,6.01
1998-03-04,22.87,24.75,22.87,24.44,29212400,6.11
1998-03-03,21.87,23.19,21.62,23.12,11937600,5.78
1998-03-02,23.56,23.56,22.25,22.75,14313600,5.69
1998-02-27,23.31,23.87,22.56,23.62,18578000,5.91
1998-02-26,22.31,23.56,21.87,23.50,21263200,5.88
1998-02-25,21.31,22.75,20.94,22.31,25459200,5.58
1998-02-24,21.31,21.37,20.75,21.31,16322000,5.33
1998-02-23,20.12,21.62,20.00,21.25,17060800,5.31
1998-02-20,20.50,20.56,19.81,20.00,11634400,5.00
1998-02-19,20.87,20.94,20.00,20.44,14292400,5.11
1998-02-18,19.56,20.75,19.56,20.56,17677600,5.14
1998-02-17,19.50,19.75,19.50,19.62,6530800,4.91
1998-02-13,19.19,19.87,19.00,19.50,7444400,4.88
1998-02-12,19.12,19.44,19.06,19.37,7297600,4.84
1998-02-11,19.50,19.50,18.87,19.00,7582000,4.75
1998-02-10,19.12,19.56,19.06,19.44,15090000,4.86
1998-02-09,18.37,19.50,18.37,19.19,17682000,4.80
1998-02-06,18.37,18.69,18.25,18.50,7241200,4.62
1998-02-05,18.25,18.50,18.00,18.31,8526400,4.58
1998-02-04,18.06,18.50,18.00,18.25,6100000,4.56
1998-02-03,17.69,18.62,17.69,18.31,14390000,4.58
1998-02-02,18.50,18.50,17.37,17.69,22752400,4.42
1998-01-30,18.31,18.87,18.25,18.31,5802400,4.58
1998-01-29,18.94,19.12,18.50,18.50,7571200,4.62
1998-01-28,19.19,19.37,18.62,19.19,5418000,4.80
1998-01-27,19.19,19.69,19.00,19.12,4013200,4.78
1998-01-26,19.44,19.56,18.81,19.44,5246800,4.86
1998-01-23,19.37,19.69,19.25,19.50,8331200,4.88
1998-01-22,18.69,19.75,18.62,19.25,11785200,4.81
1998-01-21,18.75,19.06,18.56,18.91,6812000,4.73
1998-01-20,19.06,19.31,18.62,19.06,8642400,4.76
1998-01-16,19.44,19.44,18.69,18.81,8820000,4.70
1998-01-15,19.19,19.75,18.62,19.19,19982000,4.80
1998-01-14,19.87,19.94,19.25,19.75,21048000,4.94
1998-01-13,18.62,19.62,18.50,19.50,22758800,4.88
1998-01-12,17.44,18.62,17.12,18.25,18444400,4.56
1998-01-09,18.12,19.37,17.50,18.19,31675200,4.55
1998-01-08,17.44,18.62,16.94,18.19,27645600,4.55
1998-01-07,18.81,19.00,17.31,17.50,37201200,4.38
1998-01-06,15.94,20.00,14.75,18.94,64737600,4.74
1998-01-05,16.50,16.56,15.19,15.88,23282000,3.97
1998-01-02,13.63,16.25,13.50,16.25,25650800,4.06
1997-12-31,13.13,13.63,12.94,13.13,14531200,3.28
1997-12-30,13.00,13.44,12.75,13.19,12250800,3.30
1997-12-29,13.31,13.44,12.88,13.13,9944400,3.28
1997-12-26,13.06,13.38,13.00,13.31,3860000,3.33
1997-12-24,13.00,13.25,13.00,13.13,3502000,3.28
1997-12-23,13.13,13.31,12.94,12.94,16402000,3.23
1997-12-22,13.88,14.00,13.19,13.31,5704400,3.33
1997-12-19,13.56,13.88,13.25,13.69,6812000,3.42
1997-12-18,14.00,14.00,13.75,13.81,7225200,3.45
1997-12-17,14.31,14.56,13.94,13.94,9494400,3.48
1997-12-16,14.00,14.38,14.00,14.31,6646400,3.58
1997-12-15,14.13,14.25,13.75,13.94,5927600,3.48
1997-12-12,14.75,14.88,14.00,14.13,5742400,3.53
1997-12-11,14.44,14.56,13.88,14.56,9185600,3.64
1997-12-10,15.06,15.06,14.50,14.75,6960000,3.69
1997-12-09,15.50,15.69,15.00,15.25,8680800,3.81
1997-12-08,15.56,15.75,15.38,15.56,4776800,3.89
1997-12-05,15.56,16.00,15.56,15.81,7926400,3.95
1997-12-04,16.00,16.00,15.63,15.63,7135600,3.91
1997-12-03,16.06,16.12,15.69,15.75,12258800,3.94
1997-12-02,17.37,17.50,15.88,15.88,14178800,3.97
1997-12-01,17.69,17.94,17.25,17.75,3135600,4.44
1997-11-28,17.62,17.87,17.44,17.75,1495600,4.44
1997-11-26,17.37,17.69,17.25,17.50,2178800,4.38
1997-11-25,17.69,17.87,16.87,17.37,7346400,4.34
1997-11-24,17.56,18.00,17.50,17.62,5630800,4.41
1997-11-21,18.62,18.69,18.00,18.19,3498800,4.55
1997-11-20,18.19,18.62,18.12,18.50,4587600,4.62
1997-11-19,17.87,18.31,17.87,18.25,2843600,4.56
1997-11-18,18.50,18.50,18.06,18.06,5258000,4.51
1997-11-17,18.87,18.94,18.33,18.50,7323600,4.62
1997-11-14,18.25,18.50,18.00,18.44,4835600,4.61
1997-11-13,18.00,18.06,17.50,18.00,9218000,4.50
1997-11-12,18.06,18.50,17.56,17.62,7448000,4.41
1997-11-11,19.00,19.00,18.12,18.37,11893600,4.59
1997-11-10,21.00,21.50,18.50,18.69,49946800,4.67
1997-11-07,18.87,20.00,18.75,19.75,28423200,4.94
1997-11-06,18.87,19.50,18.87,19.00,22060800,4.75
1997-11-05,18.25,18.62,18.06,18.37,13840000,4.59
1997-11-04,17.75,18.12,17.50,17.94,6033200,4.49
1997-11-03,17.56,17.75,17.06,17.37,4512000,4.34
1997-10-31,17.37,17.37,16.62,17.03,9549200,4.26
1997-10-30,17.06,17.56,16.50,16.50,6764400,4.12
1997-10-29,18.44,18.50,17.25,17.50,6355200,4.38
1997-10-28,16.00,18.50,15.88,18.12,12273200,4.53
1997-10-27,16.75,18.12,16.75,16.75,11764400,4.19
1997-10-24,18.12,18.37,16.50,16.56,13880000,4.14
1997-10-23,18.00,18.19,17.75,17.75,6688000,4.44
1997-10-22,19.06,19.25,18.50,18.56,5421200,4.64
1997-10-21,18.87,19.31,18.69,19.06,16982000,4.76
1997-10-20,20.12,20.19,18.62,18.69,14724400,4.67
1997-10-17,21.12,21.12,19.87,20.12,15682000,5.03
1997-10-16,21.12,22.06,20.87,21.50,26422000,5.38
1997-10-15,22.12,24.75,22.12,23.81,28982000,5.95
1997-10-14,22.69,22.75,22.19,22.69,5923200,5.67
1997-10-13,22.75,22.87,22.19,22.69,5679200,5.67
1997-10-10,21.50,22.75,21.50,22.69,9666800,5.67
1997-10-09,21.25,22.50,21.19,21.75,6696400,5.44
1997-10-08,21.75,21.81,21.31,21.50,3891200,5.38
1997-10-07,21.87,22.00,21.81,21.81,3916400,5.45
1997-10-06,22.19,22.25,21.69,21.94,3338800,5.49
1997-10-03,22.00,22.25,21.69,22.12,5813200,5.53
1997-10-02,21.44,22.00,21.37,21.94,4856400,5.49
1997-10-01,21.69,21.75,21.37,21.53,4670800,5.38
1997-09-30,22.00,22.31,21.69,21.69,5032000,5.42
1997-09-29,21.69,22.25,21.56,22.06,5980000,5.51
1997-09-26,21.50,21.94,21.12,21.31,7440000,5.33
1997-09-25,21.31,21.75,21.00,21.12,7988000,5.28
1997-09-24,21.69,21.75,21.37,21.50,7957600,5.38
1997-09-23,22.25,22.25,21.69,21.75,7163200,5.44
1997-09-22,22.12,23.06,22.00,22.81,7176400,5.70
1997-09-19,22.19,22.19,21.75,21.94,3407600,5.49
1997-09-18,21.50,22.50,21.50,22.31,6042400,5.58
1997-09-17,22.00,22.00,21.69,21.81,3109200,5.45
1997-09-16,22.06,22.14,21.75,21.94,4812400,5.49
1997-09-15,21.87,22.12,21.50,21.50,3473200,5.38
1997-09-12,22.19,22.25,21.44,22.06,4071200,5.51
1997-09-11,22.87,23.00,22.06,22.37,7504400,5.59
1997-09-10,21.75,23.12,21.69,22.94,9803600,5.74
1997-09-09,21.31,21.87,21.25,21.81,5702000,5.45
1997-09-08,22.25,22.25,21.44,21.50,6264400,5.38
1997-09-05,22.62,22.87,22.00,22.19,4883600,5.55
1997-09-04,22.56,22.87,22.25,22.50,4385600,5.62
1997-09-03,22.37,23.25,22.31,22.50,10163200,5.62
1997-09-02,22.00,22.56,21.94,22.37,6646800,5.59
1997-08-29,21.81,22.00,21.50,21.75,3937600,5.44
1997-08-28,22.12,22.50,22.00,22.00,3426400,5.50
1997-08-27,22.37,22.75,21.87,22.69,6813200,5.67
1997-08-26,22.62,23.00,22.12,22.25,8100800,5.56
1997-08-25,23.62,23.69,22.94,23.06,4968800,5.76
1997-08-22,23.44,24.00,23.37,23.62,8135200,5.91
1997-08-21,24.50,24.69,23.87,24.00,9271200,6.00
1997-08-20,24.44,25.12,24.19,24.62,11595200,6.16
1997-08-19,23.69,24.50,23.31,24.44,10331200,6.11
1997-08-18,23.31,23.75,22.75,23.62,7791200,5.91
1997-08-15,23.12,23.44,22.81,23.25,9320000,5.81
1997-08-14,23.62,24.25,22.69,23.00,15536400,5.75
1997-08-13,22.25,23.87,20.44,23.62,42923600,5.91
1997-08-12,24.06,24.25,21.87,22.06,37444400,5.51
1997-08-11,26.31,26.44,23.50,24.56,55411200,6.14
1997-08-08,27.81,28.37,26.12,26.81,64809200,6.70
1997-08-07,28.75,29.56,28.37,29.19,134124400,7.30
1997-08-06,25.25,27.75,25.00,26.31,149671200,6.58
1997-08-05,19.94,20.00,19.48,19.75,8840800,4.94
1997-08-04,19.19,19.81,19.19,19.75,21851200,4.94
1997-08-01,17.62,19.19,17.56,19.19,17217600,4.80
1997-07-31,17.37,17.75,17.25,17.50,9434400,4.38
1997-07-30,16.94,17.69,16.75,17.37,13372400,4.34
1997-07-29,16.44,16.62,16.37,16.50,2558000,4.12
1997-07-28,16.44,16.50,16.25,16.44,3962000,4.11
1997-07-25,15.88,16.56,15.75,16.25,7798000,4.06
1997-07-24,16.12,16.12,15.63,15.81,4772000,3.95
1997-07-23,16.75,16.87,16.00,16.12,5049200,4.03
1997-07-22,16.37,16.69,16.31,16.56,8274400,4.14
1997-07-21,17.56,17.69,16.00,16.16,12695600,4.04
1997-07-18,17.87,17.94,17.06,17.34,11353600,4.34
1997-07-17,17.00,18.12,16.44,17.50,26659200,4.38
1997-07-16,15.81,16.50,15.63,16.44,15947600,4.11
1997-07-15,15.75,16.00,15.63,15.94,14953200,3.98
1997-07-14,15.25,15.63,14.88,15.63,14700800,3.91
1997-07-11,13.38,15.50,13.31,15.19,26252400,3.80
1997-07-10,12.88,13.38,12.75,13.25,17606400,3.31
1997-07-09,13.81,13.88,13.63,13.69,5090000,3.42
1997-07-08,13.88,14.00,13.69,13.75,3427600,3.44
1997-07-07,13.94,14.25,13.75,13.81,6860000,3.45
1997-07-03,13.13,13.88,13.00,13.69,6688000,3.42
1997-07-02,13.25,13.38,13.00,13.06,8931200,3.27
1997-07-01,13.94,14.00,13.13,13.19,16104400,3.30
1997-06-30,14.75,14.75,14.00,14.25,6132400,3.56
1997-06-27,14.69,14.81,14.63,14.69,5642000,3.67
1997-06-26,15.13,15.13,14.63,14.69,13643600,3.67
1997-06-25,15.31,15.38,15.00,15.13,7102000,3.78
1997-06-24,15.44,15.56,15.25,15.31,3974800,3.83
1997-06-23,15.50,15.63,15.38,15.38,3574800,3.85
1997-06-20,15.69,15.75,15.50,15.56,3943600,3.89
1997-06-19,16.00,16.00,15.69,15.75,4323600,3.94
1997-06-18,16.12,16.25,15.75,15.94,3936400,3.98
1997-06-17,15.56,16.50,15.50,16.34,5080800,4.09
1997-06-16,15.88,15.88,15.38,15.50,4800800,3.88
1997-06-13,16.06,16.12,15.75,15.81,4737600,3.95
1997-06-12,16.37,16.37,16.00,16.06,2816400,4.01
1997-06-11,16.31,16.44,16.25,16.31,3766800,4.08
1997-06-10,16.75,16.75,16.06,16.25,4969200,4.06
1997-06-09,16.69,16.94,16.62,16.62,2689200,4.16
1997-06-06,16.62,16.75,16.50,16.75,1893200,4.19
1997-06-05,16.62,17.12,16.56,16.69,2323200,4.17
1997-06-04,16.62,16.75,16.50,16.62,2889200,4.16
1997-06-03,16.75,16.94,16.62,16.69,2335600,4.17
1997-06-02,17.00,17.00,16.75,16.94,1488000,4.24
1997-05-30,16.50,17.00,16.37,16.62,6340800,4.16
1997-05-29,17.12,17.12,16.62,16.62,3976800,4.16
1997-05-28,17.37,17.50,17.00,17.00,3130000,4.25
1997-05-27,16.75,17.37,16.75,17.25,2938000,4.31
1997-05-23,16.62,17.00,16.62,16.87,2413200,4.22
1997-05-22,16.75,16.87,16.50,16.62,2753600,4.16
1997-05-21,17.12,17.12,16.50,16.87,4369200,4.22
1997-05-20,17.00,17.44,16.75,17.25,3046400,4.31
1997-05-19,17.50,17.62,17.00,17.00,1881200,4.25
1997-05-16,17.50,17.62,17.25,17.25,3338800,4.31
1997-05-15,17.75,18.00,17.50,17.75,3544800,4.44
1997-05-14,17.87,18.00,17.50,17.69,4846800,4.42
1997-05-13,17.50,17.87,17.00,17.56,7056800,4.39
1997-05-12,17.25,17.62,17.00,17.56,5898800,4.39
1997-05-09,17.00,17.50,17.00,17.06,6732000,4.26
1997-05-08,16.62,17.12,16.50,17.00,2963200,4.25
1997-05-07,16.87,17.00,16.37,16.50,4101200,4.12
1997-05-06,17.00,17.12,16.75,16.87,2974800,4.22
1997-05-05,17.00,17.12,16.75,17.00,3538800,4.25
1997-05-02,17.00,17.12,16.75,17.00,3643600,4.25
1997-05-01,16.87,17.12,16.75,17.00,2596800,4.25
1997-04-30,17.00,17.25,16.75,17.00,9202000,4.25
1997-04-29,18.00,18.00,17.50,17.69,1853200,4.42
1997-04-28,17.75,17.87,17.50,17.62,1687600,4.41
1997-04-25,17.62,17.87,17.37,17.50,3121200,4.38
1997-04-24,18.50,18.50,17.75,17.87,2696800,4.47
1997-04-23,18.37,18.50,18.12,18.12,1960800,4.53
1997-04-22,18.12,18.50,17.87,18.50,3392000,4.62
1997-04-21,18.62,18.62,18.00,18.00,3197600,4.50
1997-04-18,19.12,19.12,18.37,18.37,5058000,4.59
1997-04-17,18.25,19.12,18.12,19.00,7859200,4.75
1997-04-16,18.62,19.00,18.37,18.56,3101200,4.64
1997-04-15,19.12,19.25,18.12,18.44,4869200,4.61
1997-04-14,18.37,18.87,18.00,18.75,4020000,4.69
1997-04-11,18.87,18.87,18.12,18.25,2842400,4.56
1997-04-10,19.00,19.12,18.50,18.87,4188000,4.72
1997-04-09,19.25,19.25,18.87,19.00,8766400,4.75
1997-04-08,19.62,19.62,18.62,19.12,6923600,4.78
1997-04-07,19.75,19.87,19.25,19.50,9136800,4.88
1997-04-04,19.12,19.62,19.00,19.25,16980800,4.81
1997-04-03,18.50,19.12,18.25,18.87,19603200,4.72
1997-04-02,17.87,18.06,17.62,18.00,7957600,4.50
1997-04-01,17.62,17.81,17.37,17.50,7881200,4.38
1997-03-31,18.62,19.37,17.25,18.25,34658000,4.56
1997-03-27,17.50,19.25,17.25,18.62,40695200,4.66
1997-03-26,16.37,16.87,16.25,16.75,3824400,4.19
1997-03-25,16.62,16.62,16.08,16.50,4031200,4.12
1997-03-24,16.50,16.62,16.25,16.50,2556800,4.12
1997-03-21,17.50,17.50,16.37,16.62,4892400,4.16
1997-03-20,16.00,17.50,15.88,17.25,11324400,4.31
1997-03-19,16.37,16.37,15.88,16.12,7457600,4.03
1997-03-18,16.37,16.50,16.12,16.25,4548800,4.06
1997-03-17,16.25,16.50,16.00,16.50,6886400,4.12
1997-03-14,16.37,16.75,16.25,16.56,8245600,4.14
1997-03-13,16.37,16.37,16.12,16.37,3772000,4.09
1997-03-12,16.25,16.75,16.12,16.25,2544400,4.06
1997-03-11,16.62,16.62,16.00,16.37,3539200,4.09
1997-03-10,16.62,16.75,16.44,16.62,3554800,4.16
1997-03-07,16.75,16.75,16.37,16.50,2523200,4.12
1997-03-06,17.00,17.00,16.50,16.62,4172000,4.16
1997-03-05,16.62,17.00,16.50,17.00,3453600,4.25
1997-03-04,16.25,16.50,16.00,16.50,3688800,4.12
1997-03-03,16.50,16.50,16.00,16.12,4670000,4.03
1997-02-28,16.87,16.87,16.25,16.25,4371200,4.06
1997-02-27,17.00,17.12,16.75,17.00,3700000,4.25
1997-02-26,17.00,17.12,16.75,17.12,3687600,4.28
1997-02-25,17.00,17.37,16.87,16.87,4938000,4.22
1997-02-24,16.25,16.87,16.25,16.62,4222000,4.16
1997-02-21,16.87,17.00,16.00,16.37,7549200,4.09
1997-02-20,17.62,17.62,17.00,17.00,4474800,4.25
1997-02-19,17.87,17.87,17.12,17.62,8627600,4.41
1997-02-18,16.62,17.87,16.25,17.87,13171200,4.47
1997-02-14,16.25,16.37,16.00,16.31,8492000,4.08
1997-02-13,15.75,16.12,15.50,16.12,7013200,4.03
1997-02-12,15.75,15.88,15.50,15.75,6303600,3.94
1997-02-11,15.88,16.00,15.50,15.69,5004400,3.92
1997-02-10,16.12,16.12,15.63,15.63,6633600,3.91
1997-02-07,16.50,16.50,15.75,15.81,8403600,3.95
1997-02-06,15.25,16.12,15.25,16.00,14283600,4.00
1997-02-05,15.25,15.63,15.25,15.25,14093600,3.81
1997-02-04,16.25,16.37,15.13,15.38,25458000,3.85
1997-02-03,16.87,17.00,16.25,16.31,13162000,4.08
1997-01-31,16.62,16.62,16.50,16.62,7135200,4.16
1997-01-30,16.75,16.75,16.50,16.75,5018800,4.19
1997-01-29,16.62,16.75,16.50,16.62,5428000,4.16
1997-01-28,17.00,17.00,16.50,16.62,7520000,4.16
1997-01-27,17.12,17.25,16.62,16.62,7646800,4.16
1997-01-24,17.25,17.25,16.87,16.87,6726800,4.22
1997-01-23,17.25,17.37,17.12,17.25,6175200,4.31
1997-01-22,17.37,17.50,17.00,17.19,7356800,4.30
1997-01-21,17.00,17.25,16.87,17.25,10179200,4.31
1997-01-20,16.87,17.12,16.75,16.94,10423600,4.24
1997-01-17,16.75,17.12,16.62,16.75,11619200,4.19
1997-01-16,17.12,17.12,16.62,16.75,23983600,4.19
1997-01-15,18.00,18.00,17.12,17.25,15483200,4.31
1997-01-14,18.37,18.37,17.75,17.87,9143200,4.47
1997-01-13,18.50,18.50,18.12,18.12,10942000,4.53
1997-01-10,17.62,18.25,17.62,18.25,12651200,4.56
1997-01-09,17.75,17.87,17.50,17.75,15970000,4.44
1997-01-08,18.25,18.37,17.37,17.62,39296400,4.41
1997-01-07,18.12,18.25,17.50,17.50,34896400,4.38
1997-01-06,17.62,18.34,17.25,17.87,67246400,4.47
1997-01-03,21.12,22.25,21.00,21.75,4295600,5.44
1997-01-02,21.12,21.25,20.75,21.00,5128800,5.25
1996-12-31,21.37,21.50,20.75,20.87,13719200,5.22
1996-12-30,23.12,23.25,21.75,21.75,9366800,5.44
1996-12-27,22.87,23.75,22.87,23.12,4900000,5.78
1996-12-26,23.25,23.25,22.87,23.00,3049200,5.75
1996-12-24,23.25,23.37,22.87,23.12,2067600,5.78
1996-12-23,24.00,24.25,23.25,23.25,11883600,5.81
1996-12-20,22.50,23.62,21.37,23.50,19535600,5.88
1996-12-19,23.00,23.25,22.25,22.25,4893600,5.56
1996-12-18,22.75,23.12,22.62,23.12,7326400,5.78
1996-12-17,22.37,22.50,22.25,22.50,5625200,5.62
1996-12-16,23.50,23.50,22.50,22.62,5335600,5.66
1996-12-13,23.75,23.87,23.25,23.25,3194400,5.81
1996-12-12,24.12,24.25,23.87,23.87,3122400,5.97
1996-12-11,23.75,24.25,23.75,24.00,5853600,6.00
1996-12-10,24.87,25.00,24.25,24.50,6593600,6.12
1996-12-09,25.25,25.37,24.81,25.00,5680800,6.25
1996-12-06,24.37,25.37,24.00,25.12,8210800,6.28
1996-12-05,25.00,25.25,25.00,25.00,5096800,6.25
1996-12-04,25.12,25.37,24.87,25.00,6823600,6.25
1996-12-03,25.25,25.50,25.00,25.12,9840800,6.28
1996-12-02,24.12,25.12,23.87,25.12,6254400,6.28
1996-11-29,24.50,24.62,24.00,24.12,1527600,6.03
1996-11-27,24.12,24.62,24.12,24.50,3191200,6.12
1996-11-26,24.87,25.00,24.00,24.25,4054800,6.06
1996-11-25,25.37,25.50,25.00,25.00,2830800,6.25
1996-11-22,24.50,25.25,24.50,25.25,3732400,6.31
1996-11-21,24.87,25.00,24.37,24.50,2522400,6.12
1996-11-20,24.87,25.37,24.87,25.00,3683200,6.25
1996-11-19,24.87,25.12,24.62,24.87,4446400,6.22
1996-11-18,25.00,25.12,24.50,24.75,5468800,6.19
1996-11-15,25.87,26.00,25.00,25.00,4684400,6.25
1996-11-14,25.50,25.75,25.37,25.62,1740800,6.41
1996-11-13,25.37,25.87,25.00,25.56,3000800,6.39
1996-11-12,26.12,26.25,25.12,25.25,5120000,6.31
1996-11-11,26.37,26.37,25.87,26.00,3318800,6.50
1996-11-08,25.87,26.25,25.75,26.25,6750800,6.56
1996-11-07,25.37,26.00,25.25,25.87,5548800,6.47
1996-11-06,25.62,25.75,24.87,25.50,6462000,6.38
1996-11-05,24.50,25.87,24.50,25.50,13517600,6.38
1996-11-04,24.37,24.50,23.75,24.37,3270800,6.09
1996-11-01,23.37,24.25,23.12,24.25,7563200,6.06
1996-10-31,23.25,23.37,22.25,23.00,6945600,5.75
1996-10-30,23.50,24.00,22.87,22.87,9192000,5.72
1996-10-29,24.62,24.75,23.12,23.25,7135200,5.81
1996-10-28,25.12,25.12,24.50,24.50,4288800,6.12
1996-10-25,24.87,25.00,24.50,24.50,2775600,6.12
1996-10-24,25.00,25.00,24.50,24.75,3020800,6.19
1996-10-23,24.75,25.25,24.37,24.75,5736800,6.19
1996-10-22,25.62,25.62,24.25,24.87,7651200,6.22
1996-10-21,26.50,26.62,25.50,25.62,6712000,6.41
1996-10-18,26.50,26.62,26.00,26.56,13681200,6.64
1996-10-17,27.50,27.75,26.37,26.37,36679200,6.59
1996-10-16,25.25,26.12,24.62,25.75,11975200,6.44
1996-10-15,25.75,25.87,25.00,25.25,12970000,6.31
1996-10-14,24.50,25.37,24.25,25.25,9649200,6.31
1996-10-11,24.37,24.62,24.00,24.25,4327600,6.06
1996-10-10,23.87,24.50,23.75,24.19,9883200,6.05
1996-10-09,23.37,23.62,22.87,23.00,3044800,5.75
1996-10-08,23.50,24.25,23.25,23.25,6802000,5.81
1996-10-07,23.00,23.37,22.87,23.12,3428800,5.78
1996-10-04,22.87,23.12,22.12,22.81,4770000,5.70
1996-10-03,23.62,23.75,22.37,22.37,8140000,5.59
1996-10-02,23.62,24.62,23.12,23.62,9890000,5.91
1996-10-01,22.00,24.75,22.00,24.62,19269200,6.16
1996-09-30,22.12,22.37,22.12,22.19,3058000,5.55
1996-09-27,22.25,22.37,22.12,22.31,2932000,5.58
1996-09-26,22.37,22.50,22.25,22.37,3693600,5.59
1996-09-25,22.50,22.62,22.00,22.37,3902400,5.59
1996-09-24,22.37,22.87,22.37,22.50,5143600,5.62
1996-09-23,22.87,22.87,22.37,22.37,1653600,5.59
1996-09-20,23.37,23.50,22.75,22.87,5330800,5.72
1996-09-19,23.62,23.62,23.37,23.37,4282000,5.84
1996-09-18,23.00,24.12,22.87,23.50,12631200,5.88
1996-09-17,22.87,23.12,22.50,23.00,7487600,5.75
1996-09-16,21.50,23.00,21.37,22.37,8747600,5.59
1996-09-13,20.37,21.25,20.37,21.00,5967600,5.25
1996-09-12,21.00,21.12,20.25,20.37,9340000,5.09
1996-09-11,21.50,21.75,21.00,21.12,5266800,5.28
1996-09-10,22.12,22.12,21.50,21.50,5562000,5.38
1996-09-09,22.62,22.75,21.87,22.00,5302400,5.50
1996-09-06,23.12,23.25,22.62,23.00,8602000,5.75
1996-09-05,23.50,23.75,22.87,22.87,9999200,5.72
1996-09-04,23.87,24.62,23.87,24.12,3636400,6.03
1996-09-03,24.12,24.37,23.87,24.12,2461200,6.03
1996-08-30,24.75,24.75,24.25,24.25,3784800,6.06
1996-08-29,24.87,24.87,24.37,24.50,3829200,6.12
1996-08-28,24.87,25.00,24.50,24.87,5844400,6.22
1996-08-27,24.12,25.00,24.00,24.86,10339200,6.22
1996-08-26,23.87,24.12,23.50,24.12,3204400,6.03
1996-08-23,23.00,24.00,23.00,23.87,7281200,5.97
1996-08-22,23.00,23.25,22.87,23.25,3138000,5.81
1996-08-21,23.50,23.62,22.87,23.00,4052400,5.75
1996-08-20,23.87,23.87,23.37,23.50,7564400,5.88
1996-08-19,22.37,23.62,22.37,23.62,8084400,5.91
1996-08-16,22.62,22.62,22.12,22.50,5075600,5.62
1996-08-15,22.62,22.75,22.25,22.25,3845600,5.56
1996-08-14,22.62,23.00,22.62,22.75,2570000,5.69
1996-08-13,22.87,23.12,22.37,22.50,3706400,5.62
1996-08-12,23.37,23.62,22.37,23.00,5408000,5.75
1996-08-09,22.25,23.37,22.12,23.12,8243600,5.78
1996-08-08,22.37,22.37,21.87,22.12,3640000,5.53
1996-08-07,21.75,22.62,21.62,22.37,8892400,5.59
1996-08-06,21.00,21.50,20.75,21.50,3354800,5.38
1996-08-05,21.62,21.87,20.87,21.00,3612000,5.25
1996-08-02,21.62,22.00,21.25,21.62,4574800,5.41
1996-08-01,22.00,22.00,21.12,21.25,3942400,5.31
1996-07-31,21.25,22.00,21.25,22.00,3332400,5.50
1996-07-30,22.62,22.75,21.25,21.37,6766800,5.34
1996-07-29,22.00,22.50,21.75,22.25,7005600,5.56
1996-07-26,21.50,22.00,21.12,22.00,4426800,5.50
1996-07-25,21.12,21.37,20.75,21.00,4090800,5.25
1996-07-24,20.00,21.00,19.87,20.81,9448800,5.20
1996-07-23,20.50,20.62,20.25,20.50,4651200,5.12
1996-07-22,20.87,20.87,20.00,20.25,5456400,5.06
1996-07-19,20.87,21.00,20.75,20.75,9510000,5.19
1996-07-18,21.50,21.75,20.36,20.87,32058800,5.22
1996-07-17,17.37,17.50,16.62,16.87,8355600,4.22
1996-07-16,17.37,17.37,16.00,16.87,10334400,4.22
1996-07-15,18.12,18.12,17.12,17.19,4779200,4.30
1996-07-12,18.37,18.37,17.25,18.06,9610800,4.51
1996-07-11,18.75,18.87,17.37,17.87,10420000,4.47
1996-07-10,19.12,19.50,18.75,18.75,6055200,4.69
1996-07-09,19.50,19.62,19.00,19.00,6723600,4.75
1996-07-08,19.62,19.87,19.00,19.12,6762000,4.78
1996-07-05,19.37,19.75,19.25,19.50,3808800,4.88
1996-07-03,20.37,20.37,19.37,19.37,10323200,4.84
1996-07-02,21.37,21.50,21.00,21.00,3189200,5.25
1996-07-01,21.12,21.50,21.00,21.50,4732400,5.38
1996-06-28,20.87,21.00,20.62,21.00,4138000,5.25
1996-06-27,20.00,21.00,19.75,20.62,8202400,5.16
1996-06-26,20.62,20.75,19.62,19.87,14440800,4.97
1996-06-25,22.12,22.25,20.37,20.62,8831200,5.16
1996-06-24,22.62,22.62,22.12,22.25,4398000,5.56
1996-06-21,22.87,22.87,22.37,22.62,5792000,5.66
1996-06-20,23.37,23.37,22.50,22.75,5260800,5.69
1996-06-19,23.12,23.37,22.62,23.12,4803600,5.78
1996-06-18,23.62,23.75,22.62,22.75,7979200,5.69
1996-06-17,24.12,24.12,23.62,23.62,4052000,5.91
1996-06-14,24.75,24.75,23.87,23.94,5186800,5.99
1996-06-13,24.37,24.92,24.00,24.62,6856800,6.16
1996-06-12,24.50,24.50,24.00,24.25,5440000,6.06
1996-06-11,24.25,24.25,24.00,24.00,5481200,6.00
1996-06-10,24.37,24.50,24.00,24.12,3820800,6.03
1996-06-07,24.00,24.37,23.50,24.37,9565200,6.09
1996-06-06,25.00,25.25,24.12,24.25,12938800,6.06
1996-06-05,25.37,25.50,24.25,25.12,18228000,6.28
1996-06-04,24.00,24.37,23.87,24.19,27235600,6.05
1996-06-03,25.87,26.00,24.75,24.75,4481200,6.19
1996-05-31,25.62,26.62,25.50,26.12,5813600,6.53
1996-05-30,24.87,25.75,24.75,25.50,3703600,6.38
1996-05-29,26.25,26.25,24.75,24.87,7840000,6.22
1996-05-28,26.75,27.25,26.37,26.37,3658800,6.59
1996-05-24,26.25,26.87,26.12,26.75,4046800,6.69
1996-05-23,26.12,26.62,25.75,26.25,4447600,6.56
1996-05-22,27.37,27.37,25.75,26.06,7215600,6.51
1996-05-21,28.00,28.12,27.12,27.12,4088000,6.78
1996-05-20,27.87,28.12,27.62,27.94,3028800,6.99
1996-05-17,28.37,28.37,27.50,27.62,4405600,6.91
1996-05-16,28.25,28.62,27.87,28.37,4648800,7.09
1996-05-15,27.87,28.87,27.75,28.50,10442400,7.12
1996-05-14,27.75,28.00,27.50,27.50,7068000,6.88
1996-05-13,27.12,27.62,26.62,27.06,6701200,6.76
1996-05-10,26.25,27.37,26.00,27.25,3966400,6.81
1996-05-09,26.37,26.50,25.75,26.12,3515600,6.53
1996-05-08,27.25,27.25,25.62,26.75,6688800,6.69
1996-05-07,26.37,27.37,26.25,26.87,12641200,6.72
1996-05-06,24.87,25.87,24.75,25.62,10349200,6.41
1996-05-03,24.12,24.12,23.50,23.87,3892400,5.97
1996-05-02,24.50,24.50,23.50,23.75,6728000,5.94
1996-05-01,24.37,24.75,24.12,24.37,4039200,6.09
1996-04-30,24.87,24.87,24.12,24.37,4881200,6.09
1996-04-29,25.00,25.00,24.50,24.75,4324800,6.19
1996-04-26,25.00,25.12,24.62,24.75,6759200,6.19
1996-04-25,24.37,24.87,24.12,24.87,6245200,6.22
1996-04-24,24.62,24.75,24.19,24.25,4596800,6.06
1996-04-23,25.12,25.25,24.62,24.75,6086400,6.19
1996-04-22,25.25,25.50,24.87,25.12,3973200,6.28
1996-04-19,24.62,25.12,24.62,25.06,3655600,6.26
1996-04-18,25.37,25.39,24.25,24.75,7780800,6.19
1996-04-17,25.87,26.00,25.12,25.25,3056400,6.31
1996-04-16,25.87,26.00,25.62,25.87,3634400,6.47
1996-04-15,25.50,25.75,25.00,25.75,5515600,6.44
1996-04-12,25.87,25.87,25.37,25.50,2924400,6.38
1996-04-11,26.12,26.25,25.50,25.75,3526400,6.44
1996-04-10,26.12,26.50,25.87,26.00,6242400,6.50
1996-04-09,24.87,26.50,24.37,26.00,8415600,6.50
1996-04-08,23.87,24.50,23.75,24.37,6046400,6.09
1996-04-04,24.62,24.62,24.00,24.12,3092000,6.03
1996-04-03,25.12,25.12,24.33,24.56,2591200,6.14
1996-04-02,25.62,25.62,24.87,25.00,3635600,6.25
1996-04-01,25.12,25.87,24.52,25.50,5680000,6.38
1996-03-29,24.25,24.75,23.75,24.56,5962400,6.14
1996-03-28,24.75,25.62,24.12,24.19,10572000,6.05
1996-03-27,23.25,25.25,23.00,25.25,15338800,6.31
1996-03-26,24.00,24.50,23.62,23.87,5755600,5.97
1996-03-25,25.50,25.75,24.00,24.00,5887600,6.00
1996-03-22,25.25,25.37,24.87,25.37,3842400,6.34
1996-03-21,25.50,25.50,25.00,25.12,3932400,6.28
1996-03-20,25.75,25.75,25.12,25.25,4154800,6.31
1996-03-19,26.37,26.50,25.62,25.75,4442400,6.44
1996-03-18,25.94,26.12,25.75,26.12,3907600,6.53
1996-03-15,26.00,26.00,25.50,25.87,3632400,6.47
1996-03-14,25.87,25.87,25.50,25.62,3342400,6.41
1996-03-13,25.87,26.12,25.62,25.75,3560000,6.44
1996-03-12,26.00,26.37,25.62,25.81,3453200,6.45
1996-03-11,26.25,26.37,25.75,25.87,4544800,6.47
1996-03-08,25.75,26.25,25.00,26.00,5322400,6.50
1996-03-07,26.25,26.37,25.37,25.81,9292400,6.45
1996-03-06,26.75,26.87,26.12,26.19,3547600,6.55
1996-03-05,26.50,26.75,26.25,26.62,4246800,6.66
1996-03-04,27.25,27.37,26.25,26.25,6708800,6.56
1996-03-01,27.62,27.62,26.62,26.87,8263200,6.72
1996-02-29,27.50,27.75,27.25,27.50,4049200,6.88
1996-02-28,28.87,28.87,27.62,27.75,6728800,6.94
1996-02-27,29.87,29.87,28.50,28.62,5331200,7.16
1996-02-26,30.00,30.12,29.50,29.50,4238000,7.38
1996-02-23,29.87,30.25,29.62,29.87,6205200,7.47
1996-02-22,30.00,30.12,29.62,29.87,6588000,7.47
1996-02-21,29.37,29.75,29.12,29.62,7924400,7.41
1996-02-20,28.00,29.50,28.00,29.00,13473200,7.25
1996-02-16,28.12,28.37,27.50,27.50,5602400,6.88
1996-02-15,27.62,28.12,27.37,28.00,4360000,7.00
1996-02-14,28.25,28.25,27.44,27.62,5843600,6.91
1996-02-13,28.00,28.87,27.87,28.12,8161200,7.03
1996-02-12,28.12,28.50,28.00,28.37,6948800,7.09
1996-02-09,27.87,28.50,27.62,27.75,7360800,6.94
1996-02-08,27.50,28.12,27.50,27.87,9420800,6.97
1996-02-07,29.75,29.75,27.75,28.25,12885200,7.06
1996-02-06,29.25,30.00,29.25,29.62,8101200,7.41
1996-02-05,29.69,29.75,29.00,29.25,11396400,7.31
1996-02-02,28.87,29.62,28.75,29.25,19865600,7.31
1996-02-01,27.50,28.37,27.50,28.37,11902400,7.09
1996-01-31,27.75,28.00,27.37,27.62,11736800,6.91
1996-01-30,27.00,28.12,26.86,27.31,22246800,6.83
1996-01-29,29.00,29.75,28.75,29.12,11900000,7.28
1996-01-26,30.37,31.25,28.62,30.62,26297600,7.66
1996-01-25,31.75,32.00,30.12,30.25,15911200,7.56
1996-01-24,32.13,32.25,31.75,32.25,23438800,8.06
1996-01-23,33.75,34.00,31.00,31.62,35305200,7.91
1996-01-22,29.75,31.00,29.25,30.50,17852400,7.62
1996-01-19,31.00,31.75,29.37,29.87,29623600,7.47
1996-01-18,32.88,33.38,30.37,31.94,24955200,7.99
1996-01-17,34.38,34.38,33.75,34.00,8445200,8.50
1996-01-16,34.38,34.75,33.63,34.56,12606400,8.64
1996-01-15,33.75,34.50,33.38,34.13,12971200,8.53
1996-01-12,34.75,34.75,33.25,33.88,14370000,8.47
1996-01-11,32.63,35.00,32.38,35.00,27041200,8.75
1996-01-10,32.50,34.75,32.25,34.25,13057600,8.56
1996-01-09,34.63,34.63,32.75,32.75,8978800,8.19
1996-01-08,34.50,35.50,34.00,34.63,4341200,8.66
1996-01-05,31.62,34.25,31.37,34.25,15929200,8.56
1996-01-04,32.38,32.38,31.37,31.56,10721200,7.89
1996-01-03,32.00,32.88,31.87,32.13,15368800,8.03
1996-01-02,32.25,32.25,31.75,32.13,4983200,8.03
1995-12-29,32.00,32.38,31.62,31.87,10874400,7.97
1995-12-28,32.13,32.75,31.87,32.00,8933200,8.00
1995-12-27,32.13,33.38,31.87,32.38,9609200,8.10
1995-12-26,32.50,32.50,31.75,32.06,4994800,8.02
1995-12-22,32.63,32.88,32.13,32.25,8392400,8.06
1995-12-21,32.75,32.75,31.62,32.50,11893200,8.12
1995-12-20,33.50,33.63,32.50,32.63,13074400,8.16
1995-12-19,32.75,33.25,32.25,32.75,15403600,8.19
1995-12-18,35.13,35.25,31.87,32.25,23807600,8.06
1995-12-15,35.50,36.63,34.38,35.25,25960000,8.81
1995-12-14,38.88,39.38,38.00,38.25,11928000,9.56
1995-12-13,38.25,39.00,36.75,38.38,24472400,9.60
1995-12-12,38.63,38.63,38.00,38.00,6353200,9.50
1995-12-11,39.50,39.63,38.38,38.63,4003200,9.66
1995-12-08,38.75,39.38,37.88,39.38,5053200,9.85
1995-12-07,38.75,38.75,37.88,38.56,5084800,9.64
1995-12-06,39.75,39.88,38.38,38.75,7195200,9.69
1995-12-05,38.50,39.88,38.25,39.50,13000000,9.88
1995-12-04,40.13,40.13,39.00,39.50,17171200,9.88
1995-12-01,38.00,38.25,37.13,37.63,7300800,9.41
1995-11-30,38.88,39.00,38.00,38.13,6247600,9.53
1995-11-29,40.13,40.13,39.00,39.25,3782000,9.81
1995-11-28,39.38,40.13,39.25,40.00,6305200,10.00
1995-11-27,40.63,40.63,39.38,39.38,4148800,9.85
1995-11-24,38.88,40.38,38.75,40.19,3930800,10.05
1995-11-22,38.63,39.25,38.50,38.63,3533600,9.66
1995-11-21,38.75,38.75,37.88,38.63,6845200,9.66
1995-11-20,40.25,40.25,38.50,38.63,5314400,9.63
1995-11-17,40.00,40.38,39.75,40.13,4607600,10.00
1995-11-16,40.88,41.50,39.50,39.94,8102000,9.95
1995-11-15,42.00,42.00,40.13,41.00,8874400,10.22
1995-11-14,41.00,42.50,41.00,41.50,14560000,10.34
1995-11-13,40.25,41.25,40.00,40.88,11343200,10.19
1995-11-10,39.38,40.25,38.88,39.75,7973200,9.91
1995-11-09,39.75,40.00,38.88,39.38,9295200,9.81
1995-11-08,39.75,41.00,38.75,38.88,12823600,9.69
1995-11-07,37.75,40.50,37.50,39.63,26310800,9.88
1995-11-06,36.50,38.75,36.38,38.13,11143200,9.50
1995-11-03,36.75,36.88,35.88,36.50,6413200,9.10
1995-11-02,36.88,36.88,36.25,36.63,5464400,9.13
1995-11-01,36.63,37.13,35.50,36.63,6913200,9.13
1995-10-31,35.25,36.63,35.13,36.31,10334400,9.05
1995-10-30,34.88,35.25,34.63,35.25,6291200,8.79
1995-10-27,34.88,34.88,34.13,34.75,5523200,8.66
1995-10-26,34.88,35.00,34.50,34.88,4503600,8.69
1995-10-25,35.25,35.38,34.75,34.75,4761200,8.66
1995-10-24,35.50,35.50,34.88,35.13,7638800,8.76
1995-10-23,35.13,35.13,34.75,35.13,7078000,8.76
1995-10-20,35.25,35.25,34.63,35.13,13818800,8.76
1995-10-19,35.88,36.13,34.75,34.75,33761200,8.66
1995-10-18,37.00,39.56,36.75,37.38,18311200,9.32
1995-10-17,36.50,36.88,35.88,36.63,6390000,9.13
1995-10-16,36.25,37.00,35.88,36.13,6515200,9.00
1995-10-13,35.75,36.88,35.50,36.00,8422000,8.97
1995-10-12,35.00,35.38,34.75,35.31,5803200,8.80
1995-10-11,35.25,35.63,34.13,34.88,11893200,8.69
1995-10-10,34.38,35.00,33.63,34.69,14303600,8.65
1995-10-09,35.38,35.75,34.38,34.81,13320800,8.68
1995-10-06,36.75,37.00,35.63,35.69,11058000,8.89
1995-10-05,36.25,36.63,35.88,36.50,8737600,9.10
1995-10-04,36.63,37.00,36.00,36.38,9532000,9.07
1995-10-03,38.13,38.50,37.13,37.63,10368000,9.38
1995-10-02,37.75,38.50,37.50,37.63,14000000,9.38
1995-09-29,38.00,38.25,36.88,37.25,10123200,9.28
1995-09-28,36.50,37.88,36.50,37.75,11843600,9.41
1995-09-27,37.50,37.50,34.75,36.25,16135600,9.03
1995-09-26,37.75,37.88,37.13,37.38,8961200,9.32
1995-09-25,38.25,38.27,37.38,37.52,11267600,9.35
1995-09-22,36.88,37.25,36.38,37.06,14258000,9.24
1995-09-21,36.50,37.50,36.38,37.00,12407600,9.22
1995-09-20,37.25,37.38,36.50,36.63,11500800,9.13
1995-09-19,36.75,37.13,36.13,36.75,17512400,9.16
1995-09-18,36.38,36.81,35.88,36.69,22216400,9.14
1995-09-15,37.38,39.88,35.50,35.88,43286800,8.94
1995-09-14,41.38,41.63,39.75,40.00,19675600,9.97
1995-09-13,42.88,43.38,42.00,42.38,11530800,10.56
1995-09-12,44.50,44.88,42.63,42.94,11658800,10.70
1995-09-11,44.88,45.50,44.25,44.25,6160800,11.03
1995-09-08,44.75,44.88,44.50,44.75,6243200,11.15
1995-09-07,44.00,45.31,43.75,44.75,9373600,11.15
1995-09-06,43.88,44.17,43.50,43.75,7175600,10.90
1995-09-05,43.50,43.50,42.75,43.50,6443200,10.84
1995-09-01,43.00,43.50,42.88,42.94,3532400,10.70
1995-08-31,43.38,43.50,43.00,43.00,3148000,10.72
1995-08-30,43.25,43.75,43.13,43.38,5482000,10.81
1995-08-29,43.00,43.25,42.50,43.13,11325600,10.75
1995-08-28,44.88,45.00,43.00,43.00,8680000,10.72
1995-08-25,45.88,45.88,44.63,44.75,4819200,11.15
1995-08-24,45.63,46.25,45.50,45.75,10285200,11.40
1995-08-23,44.88,45.88,44.63,45.50,9078000,11.34
1995-08-22,44.38,45.13,44.13,44.75,7769200,11.15
1995-08-21,44.88,45.38,44.13,44.13,9721200,11.00
1995-08-18,44.88,45.13,43.75,44.88,8620000,11.19
1995-08-17,44.63,45.50,44.13,44.63,8827600,11.12
1995-08-16,44.00,44.50,43.63,44.50,10457600,11.09
1995-08-15,43.88,44.13,43.13,44.06,11370800,10.95
1995-08-14,43.00,43.75,42.88,43.38,5989200,10.78
1995-08-11,42.88,43.13,41.88,43.06,7407600,10.70
1995-08-10,43.13,43.25,42.63,42.75,5868000,10.63
1995-08-09,42.63,43.75,42.50,43.13,13190000,10.72
1995-08-08,43.63,43.75,42.38,42.50,8388800,10.56
1995-08-07,44.13,44.63,43.13,43.38,6920000,10.78
1995-08-04,45.00,45.13,43.75,44.25,6884400,11.00
1995-08-03,44.13,45.63,43.88,45.00,7640800,11.18
1995-08-02,43.88,45.00,43.75,44.38,9840800,11.03
1995-08-01,44.88,44.88,43.50,43.50,7540000,10.81
1995-07-31,45.50,45.63,44.75,45.00,5673600,11.18
1995-07-28,46.75,47.25,45.00,45.50,9341200,11.31
1995-07-27,45.50,47.50,45.50,46.81,11621200,11.63
1995-07-26,46.25,46.25,45.38,45.38,6125200,11.28
1995-07-25,46.00,46.38,45.63,45.75,9418000,11.37
1995-07-24,44.00,45.50,43.75,45.38,7679200,11.28
1995-07-21,43.00,44.88,43.00,43.75,27082400,10.87
1995-07-20,46.00,47.38,45.00,47.06,11848800,11.70
1995-07-19,47.00,48.00,45.00,45.50,18613200,11.31
1995-07-18,49.00,49.56,47.75,48.13,9102000,11.96
1995-07-17,48.88,49.75,48.63,49.00,8098000,12.18
1995-07-14,47.38,49.00,47.00,48.75,9929200,12.12
1995-07-13,47.38,48.75,47.13,47.63,12596400,11.84
1995-07-12,47.25,48.00,46.13,47.00,10145200,11.68
1995-07-11,47.75,48.63,47.06,47.13,7683200,11.71
1995-07-10,48.63,49.88,48.13,48.63,10640800,12.09
1995-07-07,46.88,49.25,46.75,48.63,13840000,12.09
1995-07-06,46.50,47.00,45.75,47.00,6583200,11.68
1995-07-05,46.88,47.88,46.50,46.50,6325600,11.56
1995-07-03,46.50,47.13,46.25,46.94,1410800,11.67
1995-06-30,47.25,47.88,46.13,46.44,5927600,11.54
1995-06-29,46.38,48.13,46.00,47.25,8320000,11.74
1995-06-28,46.00,47.50,45.38,46.63,9531200,11.59
1995-06-27,47.38,48.25,46.38,46.38,7772400,11.53
1995-06-26,48.25,48.50,47.63,48.13,5465600,11.96
1995-06-23,48.75,49.00,47.75,48.75,8286800,12.12
1995-06-22,49.00,49.63,48.63,49.13,16928800,12.21
1995-06-21,47.63,50.13,46.75,49.38,22378800,12.27
1995-06-20,46.00,47.75,46.00,47.38,26385200,11.78
1995-06-19,43.88,45.25,43.50,44.38,16774400,11.03
1995-06-16,43.88,44.00,43.50,43.88,3200800,10.91
1995-06-15,43.63,43.75,43.38,43.63,3331200,10.84
1995-06-14,43.88,43.88,43.38,43.63,4224800,10.84
1995-06-13,44.50,44.63,43.88,44.00,4508000,10.94
1995-06-12,44.00,44.50,43.88,44.17,7584400,10.98
1995-06-09,43.63,43.75,43.13,43.50,6679200,10.81
1995-06-08,43.38,43.38,42.13,42.94,4874400,10.67
1995-06-07,44.13,44.13,43.13,43.13,4451200,10.72
1995-06-06,43.63,44.38,43.50,44.00,11270800,10.94
1995-06-05,42.38,43.50,42.13,43.50,9103200,10.81
1995-06-02,41.88,42.38,41.50,42.13,3783200,10.47
1995-06-01,41.88,42.50,41.75,42.19,6685200,10.49
1995-05-31,42.13,42.13,41.00,41.56,5707600,10.33
1995-05-30,42.63,42.88,41.50,42.00,7021200,10.44
1995-05-26,43.00,43.13,42.25,42.69,4097600,10.61
1995-05-25,43.25,44.00,43.00,43.38,6536800,10.75
1995-05-24,43.75,44.25,42.88,43.50,9459200,10.78
1995-05-23,44.13,44.38,43.50,43.88,9881200,10.88
1995-05-22,42.50,44.13,42.25,44.13,13282400,10.94
1995-05-19,42.88,43.75,42.63,42.75,11522000,10.60
1995-05-18,44.13,44.13,43.25,43.38,13287600,10.75
1995-05-17,43.75,44.38,43.50,44.00,9419200,10.91
1995-05-16,43.13,44.38,42.50,43.75,11895600,10.84
1995-05-15,43.13,43.75,42.50,43.63,14053200,10.81
1995-05-12,40.88,43.69,40.50,43.63,23153200,10.81
1995-05-11,41.63,41.63,40.38,41.00,18712400,10.16
1995-05-10,41.50,41.88,40.75,41.44,9837600,10.27
1995-05-09,40.63,41.38,40.00,41.25,11540800,10.22
1995-05-08,39.88,41.00,39.75,40.50,13832000,10.04
1995-05-05,38.75,39.13,38.13,38.88,7445200,9.64
1995-05-04,38.25,39.88,38.00,38.50,10846800,9.54
1995-05-03,38.25,38.63,38.00,38.13,6043600,9.45
1995-05-02,38.25,38.38,37.50,38.13,4289200,9.45
1995-05-01,38.25,38.75,38.00,38.25,6375600,9.48
1995-04-28,38.00,38.38,37.50,38.25,6984400,9.48
1995-04-27,38.50,38.50,37.75,37.88,5014800,9.39
1995-04-26,37.63,38.75,37.38,38.25,8246800,9.48
1995-04-25,39.13,39.38,37.25,37.75,9780000,9.36
1995-04-24,39.00,39.63,38.50,39.00,9724400,9.67
1995-04-21,37.25,39.50,37.13,39.13,23812400,9.70
1995-04-20,37.13,38.50,36.63,37.63,11772400,9.33
1995-04-19,37.50,37.50,35.63,36.38,9990800,9.02
1995-04-18,38.50,38.63,37.50,37.50,8263200,9.29
1995-04-17,38.13,39.38,37.88,38.38,7467600,9.51
1995-04-13,39.25,39.25,37.88,38.25,6242400,9.48
1995-04-12,38.25,39.63,37.38,39.00,16973200,9.67
1995-04-11,36.75,37.88,36.63,37.75,7673200,9.36
1995-04-10,36.88,37.00,36.13,36.63,4211200,9.08
1995-04-07,37.00,37.13,36.25,36.75,10562400,9.11
1995-04-06,37.25,38.00,35.53,36.75,25823600,9.11
1995-04-05,34.13,34.75,33.75,34.75,9470000,8.61
1995-04-04,35.75,35.88,33.63,33.88,15300000,8.40
1995-04-03,35.50,35.75,35.13,35.50,5528000,8.80
1995-03-31,35.13,35.63,34.75,35.25,6558000,8.74
1995-03-30,34.63,35.50,34.50,35.38,9767600,8.77
1995-03-29,34.00,34.88,33.88,34.38,17760000,8.52
1995-03-28,36.25,36.34,34.13,34.38,24655600,8.52
1995-03-27,37.63,37.63,36.63,37.19,5111200,9.22
1995-03-24,37.38,37.88,37.25,37.75,4584400,9.36
1995-03-23,37.88,38.00,36.98,37.13,6094400,9.20
1995-03-22,36.25,39.50,36.25,38.06,17130800,9.43
1995-03-21,35.50,36.75,35.25,36.25,10920800,8.98
1995-03-20,35.13,35.63,35.00,35.25,6793600,8.74
1995-03-17,35.50,35.50,34.88,35.13,7713600,8.71
1995-03-16,35.25,36.00,35.00,35.25,11330000,8.74
1995-03-15,35.50,36.25,34.88,35.00,26120800,8.67
1995-03-14,38.25,38.25,34.50,35.00,26015200,8.67
1995-03-13,39.63,39.63,38.00,38.13,11653200,9.45
1995-03-10,39.63,40.38,39.38,39.50,4923200,9.79
1995-03-09,39.88,40.38,39.38,39.75,7038000,9.85
1995-03-08,38.75,40.13,37.75,39.56,13048800,9.81
1995-03-07,39.88,39.88,38.25,38.31,5399200,9.50
1995-03-06,39.75,40.00,39.50,39.75,4751200,9.85
1995-03-03,39.75,40.69,39.50,40.25,5209200,9.98
1995-03-02,40.13,40.75,39.75,40.00,9619200,9.91
1995-03-01,39.75,40.13,39.42,40.00,8025200,9.91
1995-02-28,38.50,39.88,38.00,39.50,7965200,9.79
1995-02-27,38.25,39.00,38.11,38.25,9600800,9.48
1995-02-24,40.13,40.38,38.50,39.00,20334400,9.67
1995-02-23,41.13,41.88,40.00,40.19,11262000,9.96
1995-02-22,40.63,41.00,40.13,40.81,10501200,10.12
1995-02-21,42.63,42.75,40.88,41.00,10776800,10.16
1995-02-17,42.88,43.00,42.50,42.50,4366400,10.53
1995-02-16,43.13,43.25,42.63,43.19,7821200,10.70
1995-02-15,43.25,43.50,42.50,42.56,6604400,10.55
1995-02-14,43.75,44.13,42.63,42.94,5934400,10.64
1995-02-13,43.50,44.50,43.25,43.75,10120800,10.84
1995-02-10,43.63,44.19,43.38,43.75,12542400,10.81
1995-02-09,42.13,43.88,42.13,43.63,16988800,10.78
1995-02-08,41.00,42.38,40.88,42.31,14403600,10.46
1995-02-07,40.38,41.00,40.00,40.81,7200000,10.09
1995-02-06,40.75,40.75,39.50,40.50,8702000,10.01
1995-02-03,42.00,42.13,40.38,40.50,11400800,10.01
1995-02-02,40.13,41.88,40.13,41.63,7288000,10.29
1995-02-01,40.75,40.75,39.88,40.13,5665200,9.92
1995-01-31,40.50,40.88,40.00,40.38,7621200,9.98
1995-01-30,40.13,40.50,39.88,40.13,8255200,9.92
1995-01-27,39.88,40.38,39.00,39.88,10676400,9.86
1995-01-26,40.88,41.50,39.25,39.50,8822000,9.76
1995-01-25,39.50,42.00,39.50,40.98,18482000,10.13
1995-01-24,42.25,42.38,41.38,41.63,7805600,10.29
1995-01-23,41.88,42.63,41.00,42.25,14252400,10.44
1995-01-20,47.00,47.00,42.50,42.63,35731200,10.54
1995-01-19,45.50,46.00,45.00,45.88,11238800,11.34
1995-01-18,45.00,45.63,44.75,45.63,4581200,11.28
1995-01-17,44.50,45.50,44.13,45.00,11806400,11.12
1995-01-16,44.88,45.25,44.25,44.50,6765600,11.00
1995-01-13,46.13,46.13,44.38,44.88,12565600,11.09
1995-01-12,46.13,46.38,44.75,45.38,19721200,11.22
1995-01-11,43.75,48.06,42.69,46.75,31212400,11.56
1995-01-10,41.25,44.00,41.25,43.69,21977600,10.80
1995-01-09,41.63,41.88,41.00,41.20,9805200,10.18
1995-01-06,41.63,43.13,41.13,42.00,38456800,10.38
1995-01-05,39.25,39.38,38.75,38.88,2646800,9.61
1995-01-04,38.63,39.63,38.63,39.38,5682400,9.73
1995-01-03,38.88,38.88,37.88,38.38,3726400,9.49
1994-12-30,39.38,39.88,38.75,39.00,2616400,9.64
1994-12-29,39.25,39.88,39.13,39.50,4341200,9.76
1994-12-28,39.13,39.25,38.25,39.13,3198000,9.67
1994-12-27,39.25,39.75,38.88,39.13,2928800,9.67
1994-12-23,38.50,39.38,38.50,38.88,3372000,9.61
1994-12-22,38.50,38.88,38.25,38.63,4771200,9.55
1994-12-21,37.88,38.50,37.50,38.38,5635600,9.49
1994-12-20,39.13,39.25,38.38,38.50,6263600,9.52
1994-12-19,37.25,39.38,37.25,39.13,11890000,9.67
1994-12-16,37.25,37.75,36.75,37.25,6432400,9.21
1994-12-15,38.00,38.38,36.88,37.13,8133200,9.18
1994-12-14,36.50,38.13,36.50,37.88,11123600,9.36
1994-12-13,36.63,36.94,36.25,36.38,4266800,8.99
1994-12-12,36.38,36.75,35.50,36.50,8004400,9.02
1994-12-09,35.88,36.38,34.75,36.25,9329200,8.96
1994-12-08,36.88,37.00,35.75,35.88,6081200,8.87
1994-12-07,37.50,37.81,36.06,36.63,4916800,9.05
1994-12-06,37.00,38.38,36.88,37.56,8516400,9.28
1994-12-05,36.50,37.38,36.13,37.19,6460000,9.19
1994-12-02,36.50,36.75,35.63,36.56,6170000,9.04
1994-12-01,37.00,37.63,36.00,36.19,11051200,8.95
1994-11-30,38.38,39.38,37.00,37.25,11157600,9.21
1994-11-29,38.00,38.50,37.75,38.25,5163200,9.45
1994-11-28,37.63,38.25,37.31,37.81,4971200,9.35
1994-11-25,36.88,37.75,36.75,37.75,3012400,9.33
1994-11-23,37.00,37.88,36.38,36.88,11723200,9.12
1994-11-22,37.75,39.13,37.25,37.38,8018800,9.24
1994-11-21,40.00,40.25,38.00,38.13,7255600,9.42
1994-11-18,40.00,40.50,39.63,40.00,5257600,9.89
1994-11-17,40.88,41.00,39.88,40.00,5380000,9.86
1994-11-16,40.75,41.56,40.63,40.94,6700000,10.09
1994-11-15,42.50,43.00,41.25,41.38,6001200,10.20
1994-11-14,41.25,42.75,41.25,42.50,5002000,10.47
1994-11-11,41.25,41.50,41.00,41.13,2237600,10.14
1994-11-10,41.75,41.88,41.00,41.31,5476800,10.18
1994-11-09,42.75,43.00,41.00,41.63,14530000,10.26
1994-11-08,40.63,42.63,40.25,42.25,12476400,10.41
1994-11-07,40.38,41.25,40.13,40.75,4058000,10.04
1994-11-04,41.50,41.63,40.00,40.38,6869200,9.95
1994-11-03,41.75,42.00,41.00,41.50,3962400,10.23
1994-11-02,43.13,43.25,41.38,41.38,7819200,10.20
1994-11-01,42.88,43.48,42.38,43.13,7805600,10.63
1994-10-31,42.00,43.38,41.50,43.19,12728000,10.64
1994-10-28,42.38,42.88,41.75,42.13,9762400,10.38
1994-10-27,43.25,43.75,42.50,42.75,5700800,10.54
1994-10-26,42.63,43.27,42.63,43.25,7043200,10.66
1994-10-25,41.63,42.63,41.50,42.63,10771200,10.51
1994-10-24,42.75,43.13,41.88,42.25,7316800,10.41
1994-10-21,40.75,42.75,40.75,42.63,11528000,10.51
1994-10-20,41.25,41.81,40.50,41.00,7808000,10.10
1994-10-19,41.00,42.13,41.00,41.25,12549200,10.17
1994-10-18,40.63,41.63,40.50,41.25,16749200,10.17
1994-10-17,40.88,41.50,38.88,39.75,10866400,9.80
1994-10-14,41.50,42.00,40.88,41.13,6292000,10.14
1994-10-13,42.63,42.88,40.63,41.13,18761200,10.14
1994-10-12,39.63,42.63,39.13,42.13,21340000,10.38
1994-10-11,41.38,41.88,39.38,39.63,30083600,9.77
1994-10-10,37.13,39.63,37.00,38.88,18700800,9.58
1994-10-07,36.13,37.06,35.50,37.00,13022000,9.12
1994-10-06,37.38,37.48,36.00,36.25,18828800,8.93
1994-10-05,33.63,38.13,33.38,37.88,25366800,9.33
1994-10-04,33.25,34.00,33.00,33.75,5822000,8.32
1994-10-03,33.63,33.75,32.50,33.13,4644400,8.16
1994-09-30,34.13,34.50,33.63,33.69,2561200,8.30
1994-09-29,33.75,34.38,33.38,34.13,3921200,8.41
1994-09-28,34.00,34.38,33.63,33.88,2914800,8.35
1994-09-27,33.75,34.13,33.38,33.88,3904800,8.35
1994-09-26,33.88,34.50,33.63,33.94,5072400,8.36
1994-09-23,33.88,34.50,33.88,33.94,4760000,8.36
1994-09-22,34.25,34.25,33.63,33.88,5235600,8.35
1994-09-21,34.50,34.63,33.75,34.13,8402400,8.41
1994-09-20,35.13,35.38,34.38,34.56,7047600,8.52
1994-09-19,36.38,36.75,35.50,35.50,6242000,8.75
1994-09-16,35.88,37.25,35.50,36.38,13008000,8.97
1994-09-15,35.13,36.13,35.13,36.00,9253200,8.87
1994-09-14,35.63,35.75,35.00,35.13,3549200,8.66
1994-09-13,35.75,36.25,35.63,35.81,3723600,8.82
1994-09-12,35.63,35.75,35.38,35.75,3252400,8.81
1994-09-09,35.75,36.00,35.38,35.75,5624400,8.81
1994-09-08,36.00,36.25,35.63,36.13,5691200,8.90
1994-09-07,35.63,36.63,35.38,36.13,7283200,8.90
1994-09-06,35.25,35.63,35.00,35.56,3279200,8.76
1994-09-02,35.25,35.50,35.00,35.38,3628000,8.72
1994-09-01,35.38,35.75,34.63,35.00,7305200,8.63
1994-08-31,36.00,37.38,35.75,36.19,12568800,8.92
1994-08-30,35.25,36.38,35.13,36.25,6515600,8.93
1994-08-29,35.75,36.13,35.25,35.38,5450800,8.72
1994-08-26,35.25,36.13,35.25,35.75,7300000,8.81
1994-08-25,34.25,36.38,34.25,35.06,10688800,8.64
1994-08-24,34.75,35.00,34.38,34.88,6132400,8.60
1994-08-23,34.88,35.88,34.75,35.00,7669200,8.63
1994-08-22,34.75,35.00,34.63,34.88,5445600,8.60
1994-08-19,34.75,35.00,34.25,34.88,4674800,8.60
1994-08-18,34.75,35.25,34.50,34.63,7370000,8.53
1994-08-17,34.88,35.38,34.63,35.00,10232400,8.63
1994-08-16,34.38,34.75,34.00,34.75,5563200,8.56
1994-08-15,34.75,35.00,34.25,34.63,4293200,8.53
1994-08-12,34.38,35.13,33.88,34.75,6425200,8.53
1994-08-11,34.25,35.13,33.88,34.31,10649200,8.43
1994-08-10,33.63,34.88,33.25,34.63,9065200,8.50
1994-08-09,33.50,33.88,33.13,33.63,2811200,8.26
1994-08-08,33.13,34.00,33.00,33.75,5048800,8.29
1994-08-05,32.88,33.38,32.88,33.25,3123200,8.17
1994-08-04,33.13,33.75,33.13,33.25,6620000,8.17
1994-08-03,32.75,33.25,32.13,33.13,8113600,8.14
1994-08-02,33.50,33.63,32.38,32.56,9642400,8.00
1994-08-01,33.63,33.75,32.75,33.38,8204400,8.20
1994-07-29,31.87,34.00,31.87,33.69,19853600,8.27
1994-07-28,31.00,32.13,30.87,31.87,8762000,7.83
1994-07-27,31.25,31.37,30.62,31.06,4788000,7.63
1994-07-26,31.75,32.00,31.12,31.37,6756400,7.70
1994-07-25,31.12,31.87,30.75,31.69,15103200,7.78
1994-07-22,31.62,31.97,30.00,31.00,28098800,7.61
1994-07-21,26.62,28.50,26.50,28.00,10348800,6.88
1994-07-20,27.37,27.62,26.37,26.62,7765200,6.54
1994-07-19,28.62,28.75,27.37,27.69,4176400,6.80
1994-07-18,28.12,29.00,28.00,28.37,2734800,6.97
1994-07-15,28.23,28.62,27.50,28.25,3409200,6.94
1994-07-14,29.62,29.75,28.25,28.62,6459200,7.03
1994-07-13,28.50,30.25,28.50,29.69,16081200,7.29
1994-07-12,27.00,28.44,26.37,28.37,8662000,6.97
1994-07-11,27.12,27.37,26.62,27.00,3801200,6.63
1994-07-08,26.50,27.62,26.50,27.06,7457600,6.65
1994-07-07,25.87,27.00,25.50,26.81,6097600,6.58
1994-07-06,26.25,26.50,26.00,26.12,3499200,6.41
1994-07-05,25.62,26.75,25.62,26.50,3080800,6.51
1994-07-01,26.37,26.50,25.37,25.75,6404400,6.32
1994-06-30,26.25,26.87,26.25,26.50,3652000,6.51
1994-06-29,26.75,27.12,25.87,26.12,4842400,6.41
1994-06-28,26.25,27.12,25.62,26.75,6235200,6.57
1994-06-27,25.25,26.25,24.62,26.25,9153200,6.45
1994-06-24,25.12,26.12,24.75,25.61,10470000,6.29
1994-06-23,26.25,26.25,24.87,25.12,7283200,6.17
1994-06-22,26.25,26.75,26.00,26.25,4081200,6.45
1994-06-21,26.87,27.25,25.75,26.00,8693200,6.39
1994-06-20,26.25,27.25,26.00,27.12,7150000,6.66
1994-06-17,26.00,26.75,25.87,26.50,8027600,6.51
1994-06-16,27.75,27.75,26.12,26.37,7812400,6.48
1994-06-15,27.00,28.00,26.87,27.81,5704400,6.83
1994-06-14,27.25,27.37,26.62,27.06,5531200,6.65
1994-06-13,26.37,27.19,26.37,27.00,3339200,6.63
1994-06-10,27.12,27.37,26.37,26.50,5107600,6.51
1994-06-09,25.62,27.00,25.50,27.00,10485200,6.63
1994-06-08,27.50,27.62,26.00,26.12,9809200,6.41
1994-06-07,27.25,27.75,27.25,27.50,5013600,6.75
1994-06-06,27.50,27.75,27.00,27.37,4513200,6.72
1994-06-03,27.12,28.00,26.75,27.62,12649200,6.78
1994-06-02,28.37,28.50,27.12,27.37,13762400,6.72
1994-06-01,28.50,28.62,27.87,28.25,13786800,6.94
1994-05-31,29.50,29.50,28.50,29.25,9211200,7.18
1994-05-27,30.25,30.75,29.50,29.94,3882400,7.35
1994-05-26,31.50,31.50,30.25,30.50,2613200,7.46
1994-05-25,30.25,31.75,30.00,31.25,4873200,7.64
1994-05-24,31.00,31.25,30.25,30.75,4536400,7.52
1994-05-23,31.00,31.25,30.00,30.50,4286400,7.46
1994-05-20,31.75,32.25,31.00,31.06,3519200,7.60
1994-05-19,30.75,32.50,30.50,32.13,9776800,7.86
1994-05-18,29.75,30.75,29.25,30.62,4436800,7.49
1994-05-17,29.75,29.75,28.75,29.37,6450800,7.18
1994-05-16,30.00,30.50,29.50,29.50,4854800,7.22
1994-05-13,29.75,30.50,29.25,30.00,3323200,7.34
1994-05-12,30.50,30.75,29.50,29.69,3839200,7.26
1994-05-11,31.00,31.50,29.75,30.25,5218000,7.40
1994-05-10,31.75,32.00,31.00,31.00,5246800,7.58
1994-05-09,32.25,32.50,30.75,31.25,5026400,7.64
1994-05-06,32.25,32.75,31.25,32.31,6721200,7.90
1994-05-05,33.25,33.75,32.25,32.88,10307600,8.04
1994-05-04,31.00,33.25,30.50,33.00,13008800,8.07
1994-05-03,31.00,31.25,29.50,30.25,4761200,7.40
1994-05-02,30.00,31.25,30.00,31.00,4401200,7.58
1994-04-29,30.00,30.50,29.75,30.00,3399200,7.34
1994-04-28,31.00,31.25,29.75,30.25,3604400,7.40
1994-04-26,31.50,31.50,31.00,31.25,5879200,7.64
1994-04-25,29.75,31.00,29.50,31.00,12846800,7.58
1994-04-22,31.25,32.00,28.50,29.75,24923600,7.28
1994-04-21,28.50,30.50,27.00,29.62,14674400,7.25
1994-04-20,29.25,30.00,28.00,28.25,10080800,6.91
1994-04-19,29.75,30.00,28.50,29.00,5947600,7.09
1994-04-18,30.50,30.50,29.25,29.62,8238800,7.25
1994-04-15,31.25,31.50,30.00,30.25,6730800,7.40
1994-04-14,30.50,31.75,30.00,31.50,7933200,7.71
1994-04-13,32.25,32.50,31.25,31.75,8330000,7.77
1994-04-12,33.38,33.38,31.75,32.00,4890800,7.83
1994-04-11,33.50,33.50,32.50,33.50,3823600,8.19
1994-04-08,33.75,34.00,33.25,33.50,6336400,8.19
1994-04-07,33.50,33.75,32.75,33.38,2764800,8.17
1994-04-06,34.00,34.00,32.75,33.50,4616400,8.19
1994-04-05,33.75,34.25,33.50,33.50,3505600,8.19
1994-04-04,32.25,33.25,31.75,33.25,6016800,8.13
1994-03-31,32.50,33.50,31.50,33.25,7481200,8.13
1994-03-30,32.50,33.25,31.75,32.50,6079200,7.95
1994-03-29,33.25,33.75,32.25,32.75,7640000,8.01
1994-03-28,33.00,34.00,32.75,33.25,10098800,8.13
1994-03-25,34.75,34.75,32.75,32.75,12291200,8.01
1994-03-24,35.13,35.25,34.00,34.63,6738800,8.47
1994-03-23,35.25,35.50,34.25,35.13,7749200,8.59
1994-03-22,35.25,35.50,34.50,35.00,8690800,8.56
1994-03-21,36.38,36.50,35.25,35.50,8806400,8.68
1994-03-18,36.75,36.75,35.75,36.38,8004400,8.90
1994-03-17,36.75,37.00,36.25,36.50,5590800,8.93
1994-03-16,37.50,37.75,36.50,36.75,5265200,8.99
1994-03-15,38.25,38.25,37.25,37.63,7319200,9.20
1994-03-14,38.50,38.50,37.75,38.13,15783600,9.33
1994-03-11,37.00,37.75,36.75,37.25,5791200,9.11
1994-03-10,37.25,37.63,36.75,37.25,5142400,9.11
1994-03-09,36.63,37.50,36.00,37.50,8896800,9.17
1994-03-08,38.00,38.00,36.75,37.00,6647600,9.05
1994-03-07,37.00,38.13,36.75,37.88,11088800,9.27
1994-03-04,36.00,37.50,35.75,36.75,8113600,8.99
1994-03-03,35.75,36.25,35.50,35.75,6737600,8.75
1994-03-02,35.25,36.25,34.75,35.63,10519200,8.72
1994-03-01,36.75,36.75,35.75,36.25,7570800,8.87
1994-02-28,36.25,37.00,36.00,36.50,4434800,8.93
1994-02-25,37.00,37.25,35.50,36.00,8468000,8.81
1994-02-24,37.00,37.25,36.25,36.63,7081200,8.96
1994-02-23,37.25,38.25,37.00,37.25,9318800,9.11
1994-02-22,36.25,37.50,35.75,37.25,7676400,9.11
1994-02-18,36.50,37.00,36.25,36.25,5326400,8.87
1994-02-17,37.25,37.88,36.25,37.00,5197600,9.05
1994-02-16,37.50,37.50,36.75,36.75,4379200,8.99
1994-02-15,36.75,37.50,36.25,37.13,4654400,9.08
1994-02-14,37.00,38.00,36.75,37.00,8775200,9.05
1994-02-11,36.25,37.50,36.25,37.00,5880800,9.05
1994-02-10,36.25,37.50,36.00,36.50,10802000,8.93
1994-02-09,35.75,36.50,35.25,36.25,6699200,8.87
1994-02-08,36.00,36.50,35.25,35.75,10210800,8.75
1994-02-07,33.50,37.13,33.50,36.50,25925200,8.93
1994-02-04,33.50,35.00,33.25,33.50,12645200,8.17
1994-02-03,33.00,33.63,32.50,33.50,4933200,8.17
1994-02-02,33.25,33.25,32.50,33.00,5247600,8.04
1994-02-01,33.00,33.50,32.25,33.25,5618000,8.10
1994-01-31,33.50,33.75,32.75,32.75,8532400,7.98
1994-01-28,34.25,34.75,33.75,34.00,4891200,8.29
1994-01-27,33.50,34.25,33.00,34.13,4724800,8.32
1994-01-26,33.75,34.00,33.25,33.50,5922400,8.17
1994-01-25,34.75,35.00,33.25,33.88,15818800,8.26
1994-01-24,33.25,35.25,33.25,35.00,24742000,8.53
1994-01-21,33.25,33.50,32.25,33.38,35007600,8.14
1994-01-20,29.50,30.75,29.50,29.87,9582400,7.28
1994-01-19,29.25,29.75,28.75,29.25,10066400,7.13
1994-01-18,30.25,30.25,29.00,29.37,12978000,7.16
1994-01-17,31.00,31.50,30.00,30.37,5206400,7.40
1994-01-14,30.75,31.75,30.50,31.00,7673200,7.56
1994-01-13,30.00,30.75,29.75,30.62,19000000,7.46
1994-01-12,32.25,32.25,30.50,30.50,15684400,7.43
1994-01-11,33.50,33.75,31.75,31.87,12700000,7.77
1994-01-10,33.00,33.88,32.75,33.63,7222000,8.20
1994-01-07,32.00,33.25,31.25,33.13,10688800,8.08
1994-01-06,33.75,34.00,32.50,32.75,13095200,7.98
1994-01-05,31.75,33.88,31.75,33.75,21874400,8.23
1994-01-04,30.25,31.50,30.00,31.50,10198800,7.68
1994-01-03,29.50,30.00,29.00,29.87,6485200,7.28
1993-12-31,29.75,30.25,29.25,29.25,5765200,7.13
1993-12-30,28.50,30.25,28.50,29.75,11253200,7.25
1993-12-29,29.25,29.25,28.50,28.50,3853200,6.95
1993-12-28,28.75,29.50,28.50,29.12,5705600,7.10
1993-12-27,27.75,28.75,27.25,28.50,5730000,6.95
1993-12-23,27.25,27.25,26.50,27.25,8120000,6.64
1993-12-22,27.25,28.50,27.00,28.00,6498800,6.82
1993-12-21,28.50,28.75,27.25,27.50,8973600,6.70
1993-12-20,29.25,29.75,28.25,28.50,6768800,6.95
1993-12-17,29.50,29.75,29.12,29.50,5197600,7.19
1993-12-16,29.50,29.75,29.00,29.37,4532000,7.16
1993-12-15,29.00,29.75,29.00,29.75,4438000,7.25
1993-12-14,29.25,29.75,29.00,29.12,10492400,7.10
1993-12-13,28.25,29.50,27.75,29.50,8729200,7.19
1993-12-10,30.25,30.50,27.75,28.25,17781200,6.89
1993-12-09,31.75,32.00,29.75,30.00,6531200,7.31
1993-12-08,32.00,32.25,31.50,31.87,1422000,7.77
1993-12-07,32.00,32.25,31.50,32.25,2280800,7.86
1993-12-06,31.50,32.50,31.25,32.25,5610000,7.86
1993-12-03,31.75,32.00,31.00,31.50,4314800,7.68
1993-12-02,31.75,32.00,31.00,31.75,3614400,7.74
1993-12-01,32.00,32.25,31.25,31.50,3978800,7.68
1993-11-30,31.75,32.63,31.50,31.50,4036800,7.68
1993-11-29,32.25,32.50,31.50,31.75,3462000,7.74
1993-11-26,32.75,33.00,32.25,32.63,1569200,7.95
1993-11-24,32.75,33.50,32.63,33.00,3246800,8.04
1993-11-23,32.50,33.00,31.25,33.00,6653600,8.04
1993-11-22,32.75,33.00,32.25,32.50,5389200,7.92
1993-11-19,33.00,33.50,32.50,33.00,4409200,8.04
1993-11-18,33.50,33.75,33.00,33.50,4089200,8.14
1993-11-17,34.00,35.00,32.75,33.50,10812400,8.14
1993-11-16,32.00,34.25,31.75,34.00,10838000,8.26
1993-11-15,31.50,32.75,31.50,32.00,5616800,7.77
1993-11-12,31.50,32.00,30.50,31.75,5136800,7.71
1993-11-11,30.75,32.00,30.50,31.37,5090800,7.62
1993-11-10,30.25,30.75,30.00,30.75,2765600,7.47
1993-11-09,31.00,31.25,29.75,30.12,6136400,7.32
1993-11-08,32.00,32.13,30.50,30.75,5966400,7.47
1993-11-05,31.87,32.25,30.75,31.87,13513200,7.74
1993-11-04,31.50,32.25,30.75,32.25,6632000,7.83
1993-11-03,33.00,33.00,31.00,31.62,6320000,7.68
1993-11-02,31.25,33.00,31.00,32.75,8013600,7.95
1993-11-01,30.75,31.50,30.25,31.50,3798800,7.65
1993-10-29,31.00,31.75,30.50,30.75,4892400,7.47
1993-10-28,31.75,32.25,31.00,31.00,8736800,7.53
1993-10-27,30.00,32.25,29.75,31.75,16415200,7.71
1993-10-26,29.75,30.00,29.00,29.75,7960000,7.23
1993-10-25,30.25,30.50,29.62,30.00,7840800,7.29
1993-10-22,30.50,31.50,29.75,30.25,14160000,7.35
1993-10-21,27.50,31.25,27.25,30.25,22417600,7.35
1993-10-20,28.00,28.25,27.25,27.75,4956400,6.74
1993-10-19,28.25,28.50,27.25,27.75,7643200,6.74
1993-10-18,28.00,28.75,27.75,28.37,11900000,6.89
1993-10-15,27.75,28.50,26.75,28.25,34136400,6.86
1993-10-14,24.00,24.50,23.50,23.75,5749200,5.77
1993-10-13,24.25,24.25,23.50,24.00,6322400,5.83
1993-10-12,24.00,25.00,23.75,24.00,10952400,5.83
1993-10-11,22.75,24.00,22.75,23.75,5775200,5.77
1993-10-08,23.25,23.25,22.25,22.62,4989200,5.49
1993-10-07,23.50,23.75,22.75,23.00,4828000,5.59
1993-10-06,23.75,24.00,23.37,23.62,6271200,5.74
1993-10-05,23.00,24.00,23.00,23.50,6306400,5.71
1993-10-04,22.62,23.00,22.00,22.75,6891200,5.53
1993-10-01,22.75,23.00,22.50,22.75,12022000,5.53
1993-09-30,24.00,24.00,23.00,23.37,9828000,5.68
1993-09-29,24.25,24.87,23.75,23.87,8463600,5.80
1993-09-28,24.75,25.00,24.25,24.75,3386400,6.01
1993-09-27,25.00,25.25,24.25,24.75,4043200,6.01
1993-09-24,25.00,25.25,24.50,25.00,2743200,6.07
1993-09-23,25.50,25.50,24.50,24.75,4697600,6.01
1993-09-22,24.25,25.50,24.25,25.50,3960800,6.19
1993-09-21,24.75,25.25,23.87,24.50,5250000,5.95
1993-09-20,25.25,25.50,24.75,24.87,3968800,6.04
1993-09-17,24.37,25.50,24.25,25.25,6157600,6.13
1993-09-16,24.25,25.00,24.25,24.75,3086800,6.01
1993-09-15,24.50,25.00,23.50,24.50,9206800,5.95
1993-09-14,24.25,25.00,24.00,24.25,9880000,5.89
1993-09-13,26.25,26.50,24.75,25.25,9143600,6.13
1993-09-10,26.25,26.25,25.37,26.25,4804800,6.38
1993-09-09,26.75,27.00,26.00,26.00,5352000,6.31
1993-09-08,26.25,27.00,26.00,26.75,8102000,6.50
1993-09-07,26.00,27.00,25.75,26.25,5130000,6.38
1993-09-03,26.00,26.00,25.25,25.75,5830000,6.25
1993-09-02,26.00,26.25,25.25,25.75,10081200,6.25
1993-09-01,26.50,26.75,25.75,26.12,8065200,6.34
1993-08-31,26.50,26.75,26.00,26.50,4570800,6.44
1993-08-30,26.50,26.50,25.87,26.00,9785600,6.31
1993-08-27,27.00,27.00,26.25,26.50,6676400,6.44
1993-08-26,27.25,27.25,26.50,26.87,6296800,6.53
1993-08-25,28.00,28.25,26.75,27.25,5209200,6.62
1993-08-24,28.25,28.75,27.75,28.00,3625600,6.80
1993-08-23,28.00,28.75,27.50,28.37,3265600,6.89
1993-08-20,27.75,28.00,27.00,28.00,3574400,6.80
1993-08-19,28.75,28.75,27.50,27.50,5452000,6.68
1993-08-18,29.00,29.75,28.25,28.50,6751200,6.92
1993-08-17,27.75,28.50,27.25,28.37,3876800,6.89
1993-08-16,27.50,28.00,27.25,27.50,3669200,6.68
1993-08-13,26.50,27.75,26.25,27.37,4978800,6.62
1993-08-12,27.50,27.75,26.00,26.50,12098800,6.41
1993-08-11,28.50,28.50,27.00,27.50,5965200,6.65
1993-08-10,29.50,29.75,28.25,28.50,5465600,6.89
1993-08-09,29.25,30.25,29.00,29.75,5767600,7.19
1993-08-06,29.25,30.25,29.25,29.25,4506800,7.07
1993-08-05,30.75,30.75,29.00,29.50,7498800,7.13
1993-08-04,29.25,30.50,29.00,30.25,8700000,7.31
1993-08-03,29.00,29.25,28.75,29.00,6315600,7.01
1993-08-02,28.25,29.25,28.00,28.50,7728000,6.89
1993-07-30,27.50,28.25,27.00,27.75,7669200,6.71
1993-07-29,27.00,27.50,26.75,27.25,4343200,6.59
1993-07-28,26.25,27.00,26.25,26.87,3300000,6.50
1993-07-27,26.75,27.50,26.25,26.50,7100800,6.41
1993-07-26,26.75,27.50,26.00,26.87,5468000,6.50
1993-07-23,27.00,27.50,26.00,26.25,8365600,6.35
1993-07-22,26.00,27.00,25.75,26.50,7554400,6.41
1993-07-21,26.00,26.75,25.50,26.25,16283600,6.35
1993-07-20,26.25,27.75,25.75,26.87,19017600,6.50
1993-07-19,28.00,28.75,25.50,25.62,28813200,6.20
1993-07-16,28.50,29.62,26.50,27.50,75744400,6.65
1993-07-15,37.25,37.75,35.25,35.75,12091200,8.64
1993-07-14,36.75,37.50,35.75,37.25,8816800,9.01
1993-07-13,38.75,38.75,37.00,37.25,5650800,9.01
1993-07-12,36.75,38.13,36.25,38.00,6215600,9.19
1993-07-09,37.00,37.25,36.50,36.75,5604400,8.89
1993-07-08,36.50,37.50,36.25,36.50,4964800,8.83
1993-07-07,37.50,37.88,36.25,36.50,8124400,8.83
1993-07-06,38.25,39.00,37.50,37.75,5558800,9.13
1993-07-02,38.25,38.75,37.75,38.50,6846400,9.31
1993-07-01,39.00,39.75,38.00,38.00,7809200,9.19
1993-06-30,38.75,39.75,38.50,39.50,7170000,9.55
1993-06-29,40.25,40.25,38.50,39.00,10526400,9.43
1993-06-28,40.50,40.50,38.75,40.13,12645600,9.70
1993-06-25,40.38,40.75,39.50,40.00,9198000,9.67
1993-06-24,40.50,41.75,40.00,41.75,7980000,10.10
1993-06-23,41.75,41.75,40.00,40.50,6462400,9.79
1993-06-22,40.88,42.00,39.75,41.38,12021200,10.01
1993-06-21,40.50,40.50,39.50,39.63,9776800,9.58
1993-06-18,41.63,42.13,39.75,41.00,11138800,9.91
1993-06-17,42.50,42.50,40.50,41.25,14635600,9.97
1993-06-16,42.25,43.25,41.50,42.25,12615600,10.22
1993-06-15,45.25,45.25,41.88,42.00,16018000,10.16
1993-06-14,44.00,44.75,43.50,44.63,8927600,10.79
1993-06-11,45.00,45.25,43.38,43.75,8662400,10.58
1993-06-10,43.50,44.75,42.75,44.50,19783600,10.76
1993-06-09,45.00,45.63,44.00,44.25,42090000,10.70
1993-06-08,48.75,50.00,48.00,49.50,22194400,11.97
1993-06-07,54.50,54.75,50.38,50.75,17239200,12.27
1993-06-04,55.75,56.25,54.50,54.88,7649200,13.27
1993-06-03,57.00,57.25,56.00,56.38,5603200,13.63
1993-06-02,56.75,58.25,56.00,57.00,7160000,13.78
1993-06-01,56.50,57.75,56.50,57.00,4837600,13.78
1993-05-28,57.00,57.50,56.25,56.63,6575200,13.69
1993-05-27,57.75,58.50,57.25,57.50,7049200,13.87
1993-05-26,56.00,57.75,55.38,57.75,4353600,13.94
1993-05-25,56.75,57.50,55.75,56.38,6462400,13.60
1993-05-24,56.75,58.75,56.75,57.63,5373200,13.91
1993-05-21,58.75,59.13,56.75,57.50,5300000,13.87
1993-05-20,57.25,59.00,57.25,58.75,10385200,14.18
1993-05-19,54.75,57.50,54.50,57.25,6176400,13.81
1993-05-18,55.50,56.25,55.00,55.50,5860000,13.39
1993-05-17,55.50,56.00,55.00,55.75,2491200,13.45
1993-05-14,55.25,56.00,55.00,55.50,4212000,13.39
1993-05-13,53.50,55.75,53.50,55.50,12940800,13.39
1993-05-12,54.25,54.75,53.00,53.25,3779200,12.85
1993-05-11,55.00,55.25,54.00,54.50,5665600,13.15
1993-05-10,55.00,55.88,55.00,55.00,4929200,13.27
1993-05-07,53.50,54.75,53.50,54.75,2927600,13.21
1993-05-06,54.50,54.75,53.50,53.75,2536800,12.97
1993-05-05,53.00,55.50,53.00,54.50,9059200,13.15
1993-05-04,52.25,54.25,52.00,53.38,6112400,12.88
1993-05-03,51.25,52.00,51.00,51.88,2332400,12.52
1993-04-30,50.75,52.50,50.75,51.25,4730000,12.37
1993-04-29,51.50,51.75,50.13,50.75,2958000,12.25
1993-04-28,49.75,52.00,49.75,51.38,5846800,12.40
1993-04-27,48.75,50.25,48.75,50.25,4648800,12.13
1993-04-26,49.25,49.75,48.50,49.00,3689200,11.82
1993-04-23,49.75,50.25,48.75,49.25,4808000,11.88
1993-04-22,49.25,50.50,49.00,50.00,5648800,12.06
1993-04-21,50.25,50.75,49.25,49.63,7337600,11.98
1993-04-20,48.75,50.25,48.25,50.00,8580800,12.06
1993-04-19,48.50,49.50,48.25,48.50,8148000,11.70
1993-04-16,48.25,48.75,47.38,48.13,24533200,11.61
1993-04-15,48.25,48.25,46.75,47.25,7816800,11.40
1993-04-14,48.25,48.75,47.63,48.75,6092400,11.76
1993-04-13,50.50,51.25,48.25,48.50,5893600,11.70
1993-04-12,49.50,51.00,49.50,50.00,3324800,12.06
1993-04-08,50.00,50.50,49.00,49.75,5857600,12.00
1993-04-07,49.00,50.75,48.50,50.50,5825200,12.19
1993-04-06,50.00,50.25,48.75,48.75,6020800,11.76
1993-04-05,50.00,50.50,49.50,50.00,5332000,12.06
1993-04-02,50.50,51.25,49.50,50.13,9077600,12.10
1993-04-01,51.25,52.00,51.00,51.75,3878000,12.49
1993-03-31,52.50,52.75,51.25,51.50,7968800,12.43
1993-03-30,51.13,52.25,50.25,52.25,9447600,12.61
1993-03-29,52.25,52.50,50.75,51.00,9362000,12.31
1993-03-26,54.75,54.75,52.50,53.25,5431200,12.85
1993-03-25,53.75,54.75,53.50,54.75,6125200,13.21
1993-03-24,52.75,54.25,52.50,53.75,5126400,12.97
1993-03-23,53.25,54.00,52.63,52.75,3674400,12.73
1993-03-22,53.50,53.88,52.75,53.25,5911200,12.85
1993-03-19,55.00,55.25,53.50,53.75,5516800,12.97
1993-03-18,55.00,55.63,54.50,54.50,3810800,13.15
1993-03-17,56.50,57.00,55.00,55.13,6301200,13.30
1993-03-16,57.25,57.75,56.50,56.50,3626800,13.63
1993-03-15,56.00,57.25,55.38,57.00,4868800,13.75
1993-03-12,56.75,56.75,55.50,56.25,4527600,13.57
1993-03-11,57.00,57.25,56.25,56.88,5167600,13.73
1993-03-10,56.75,57.25,56.00,56.75,4738800,13.69
1993-03-09,56.50,57.50,56.50,56.75,5535200,13.69
1993-03-08,55.00,56.75,55.00,56.50,6322400,13.63
1993-03-05,54.75,55.75,54.75,55.00,4001200,13.27
1993-03-04,54.50,55.25,53.50,55.00,6730000,13.27
1993-03-03,54.00,55.00,53.25,54.63,7261200,13.18
1993-03-02,53.00,54.50,53.00,54.25,5294400,13.09
1993-03-01,53.00,53.50,52.75,53.25,4272400,12.85
1993-02-26,54.25,54.25,52.25,53.00,10538000,12.79
1993-02-25,53.25,54.75,53.25,54.75,5979200,13.21
1993-02-24,52.13,53.88,52.13,53.63,10253600,12.94
1993-02-23,55.00,55.25,54.00,54.25,6937600,13.09
1993-02-22,55.00,56.00,54.75,55.13,3531200,13.30
1993-02-19,55.25,55.50,54.75,55.00,6366800,13.27
1993-02-18,55.00,55.25,53.50,55.00,10006800,13.27
1993-02-17,53.25,54.00,52.00,53.88,8932400,13.00
1993-02-16,53.50,53.50,51.50,53.00,14563200,12.79
1993-02-12,55.00,55.50,53.75,53.88,9855600,13.00
1993-02-11,55.75,56.25,55.00,55.13,6015200,13.27
1993-02-10,57.00,57.25,55.00,55.75,9593600,13.42
1993-02-09,57.00,57.38,56.50,56.88,8525600,13.70
1993-02-08,57.00,57.50,55.50,56.50,10060000,13.60
1993-02-05,59.25,59.50,56.25,57.25,13134400,13.78
1993-02-04,60.00,60.25,59.00,59.50,7453200,14.33
1993-02-03,61.00,61.00,58.50,60.00,9455200,14.45
1993-02-02,60.75,61.50,60.25,60.25,6530000,14.51
1993-02-01,59.25,61.25,59.25,61.25,8608800,14.75
1993-01-29,60.25,61.25,59.00,59.50,9516800,14.33
1993-01-28,60.00,60.25,59.25,59.88,6580000,14.42
1993-01-27,61.00,61.75,58.75,60.25,8101200,14.51
1993-01-26,60.50,62.00,60.50,60.75,10201200,14.63
1993-01-25,59.25,60.50,59.25,60.00,7237600,14.45
1993-01-22,60.25,60.25,59.00,59.50,5252400,14.33
1993-01-21,59.75,60.25,58.75,60.00,6601200,14.45
1993-01-20,59.75,60.25,59.50,60.00,5685600,14.45
1993-01-19,59.75,60.50,59.25,59.75,9802400,14.39
1993-01-18,59.50,60.00,58.00,59.50,11935600,14.33
1993-01-15,61.00,62.25,60.00,60.25,32257600,14.51
1993-01-14,64.00,65.25,63.75,65.00,13145200,15.65
1993-01-13,61.50,64.00,61.25,63.50,7135600,15.29
1993-01-12,62.75,63.75,61.50,61.50,12364400,14.81
1993-01-11,62.00,64.37,61.75,64.12,9785200,15.44
1993-01-08,60.75,63.00,59.75,62.25,11474400,14.99
1993-01-07,61.75,62.50,60.63,61.00,9741200,14.69
1993-01-06,60.75,62.00,60.50,61.75,10055600,14.87
1993-01-05,58.00,59.25,57.25,59.25,6658800,14.27
1993-01-04,59.50,60.00,57.75,58.25,4618800,14.03
1992-12-31,58.75,60.00,58.75,59.75,3302000,14.39
1992-12-30,59.75,59.75,58.75,58.75,3610800,14.15
1992-12-29,59.50,60.75,59.50,59.63,4171200,14.36
1992-12-28,59.25,59.75,59.25,59.50,2536400,14.33
1992-12-24,60.00,60.00,59.00,59.00,1642400,14.21
1992-12-23,60.25,60.50,59.25,59.75,4018800,14.39
1992-12-22,59.75,61.25,59.75,60.63,10009200,14.60
1992-12-21,58.25,60.00,58.00,59.63,9159200,14.36
1992-12-18,57.50,59.25,57.25,58.25,8414400,14.03
1992-12-17,55.25,57.50,55.25,56.88,8370800,13.70
1992-12-16,56.25,57.00,54.50,55.00,8085200,13.24
1992-12-15,56.75,57.00,55.50,56.38,6541200,13.57
1992-12-14,57.50,57.75,56.75,57.25,3962000,13.78
1992-12-11,57.25,58.25,57.25,57.50,4299200,13.84
1992-12-10,57.25,57.63,56.50,57.25,5010800,13.78
1992-12-09,57.75,58.00,57.25,57.63,5700800,13.88
1992-12-08,57.75,58.75,57.75,58.13,7035600,14.00
1992-12-07,56.75,57.75,56.75,57.75,5168000,13.90
1992-12-04,57.25,57.50,56.50,56.88,3432400,13.70
1992-12-03,56.50,57.63,56.13,57.50,6710800,13.84
1992-12-02,58.25,58.50,57.00,57.25,3498800,13.78
1992-12-01,57.25,59.00,56.75,58.25,4652400,14.03
1992-11-30,56.25,57.50,55.63,57.50,5739200,13.84
1992-11-27,56.50,57.25,56.25,56.50,1688800,13.57
1992-11-25,57.00,57.25,56.00,56.50,4208000,13.57
1992-11-24,57.00,57.50,56.50,57.50,5601200,13.82
1992-11-23,56.50,57.00,56.25,56.75,5462400,13.63
1992-11-20,58.50,58.75,57.00,57.50,5572000,13.82
1992-11-19,57.75,59.50,57.75,58.25,8608000,14.00
1992-11-18,56.00,58.25,55.50,57.75,10889200,13.88
1992-11-17,57.25,57.50,54.88,55.25,6045200,13.27
1992-11-16,56.25,57.75,56.00,57.38,2419200,13.79
1992-11-13,57.00,57.25,56.00,56.25,3042000,13.51
1992-11-12,57.00,57.50,56.38,56.88,3844400,13.67
1992-11-11,56.50,58.25,56.25,56.75,5023600,13.63
1992-11-10,55.00,56.50,54.75,56.25,4368000,13.51
1992-11-09,56.00,56.00,54.75,55.25,4052000,13.27
1992-11-06,54.75,56.50,54.75,55.75,9443200,13.39
1992-11-05,52.50,55.00,52.50,55.00,10647600,13.21
1992-11-04,52.00,52.75,52.00,52.50,5086800,12.61
1992-11-03,52.50,52.50,51.50,52.00,4042000,12.49
1992-11-02,52.50,52.75,51.75,52.25,6094400,12.55
1992-10-30,53.50,53.50,52.00,52.50,4657600,12.61
1992-10-29,52.25,54.00,51.50,53.25,7661200,12.79
1992-10-28,51.25,52.75,50.75,52.25,7033200,12.55
1992-10-27,51.50,52.50,51.00,51.50,7575600,12.37
1992-10-26,48.75,51.50,48.50,51.50,8972000,12.37
1992-10-23,49.25,49.50,48.25,48.75,3279200,11.71
1992-10-22,48.50,49.25,48.25,48.75,3026400,11.71
1992-10-21,49.25,49.50,48.00,48.50,4080800,11.65
1992-10-20,49.00,50.00,48.50,49.13,10269200,11.80
1992-10-19,49.00,49.25,48.50,49.00,7002400,11.77
1992-10-16,46.75,49.50,46.50,49.00,16142000,11.77
1992-10-15,45.75,46.00,45.25,45.50,2701200,10.93
1992-10-14,45.25,46.25,45.00,46.00,3429200,11.05
1992-10-13,44.75,46.00,44.00,45.38,5265600,10.90
1992-10-12,43.25,44.25,43.25,44.00,2580000,10.57
1992-10-09,43.50,44.00,43.00,43.38,2108000,10.42
1992-10-08,44.00,44.25,43.00,43.50,4543200,10.45
1992-10-07,45.00,45.25,43.50,43.75,4050800,10.51
1992-10-06,43.75,45.00,42.75,44.75,4058000,10.75
1992-10-05,43.25,43.75,41.50,43.50,9475600,10.45
1992-10-02,44.50,44.75,43.00,43.75,4063600,10.51
1992-10-01,44.75,45.13,44.25,44.25,4396400,10.63
1992-09-30,45.00,45.50,44.50,45.13,3580800,10.84
1992-09-29,44.50,45.50,44.00,44.88,5626400,10.78
1992-09-28,45.00,45.00,43.75,44.75,5351200,10.75
1992-09-25,46.25,46.50,45.25,45.50,4926400,10.93
1992-09-24,47.25,47.75,46.25,46.25,4492000,11.11
1992-09-23,46.00,47.50,45.50,47.50,4443200,11.41
1992-09-22,46.75,46.75,45.25,45.75,3996800,10.99
1992-09-21,46.75,47.75,46.25,46.50,3204400,11.17
1992-09-18,45.75,46.88,45.25,46.50,4133600,11.17
1992-09-17,47.25,47.25,45.38,46.00,6180000,11.05
1992-09-16,47.75,48.25,46.50,47.00,6395600,11.29
1992-09-15,49.25,49.25,47.75,48.25,7806800,11.59
1992-09-14,49.00,50.00,48.50,49.50,7682400,11.89
1992-09-11,49.00,49.25,47.50,47.63,6438000,11.44
1992-09-10,48.00,49.50,47.50,49.25,8165600,11.83
1992-09-09,48.00,49.25,47.75,49.00,5622400,11.77
1992-09-08,46.75,48.00,46.50,47.75,2511200,11.47
1992-09-04,48.25,48.25,46.75,47.25,2268800,11.35
1992-09-03,49.00,49.25,47.75,47.75,7570000,11.47
1992-09-02,46.50,48.75,46.50,48.50,6794400,11.65
1992-09-01,46.25,46.50,45.75,46.50,2172000,11.17
1992-08-31,45.00,46.25,44.75,46.00,4328800,11.05
1992-08-28,44.25,45.25,44.00,45.00,2202400,10.81
1992-08-27,44.75,45.13,44.25,44.50,2974800,10.69
1992-08-26,44.25,44.50,43.25,44.25,4325600,10.63
1992-08-25,43.25,44.50,43.25,44.38,4731200,10.66
1992-08-24,44.25,44.75,43.25,43.25,5454400,10.39
1992-08-21,44.75,45.25,44.00,44.63,3926400,10.72
1992-08-20,44.75,45.00,44.25,44.75,3894800,10.75
1992-08-19,44.63,45.25,44.50,44.50,6096800,10.69
1992-08-18,44.50,45.25,44.50,44.75,4017600,10.75
1992-08-17,44.25,44.75,43.75,44.75,4617600,10.75
1992-08-14,45.00,45.25,44.50,44.75,4872400,10.72
1992-08-13,44.50,45.50,44.25,44.75,6122000,10.72
1992-08-12,43.75,44.25,43.25,44.13,4343600,10.57
1992-08-11,44.50,44.50,43.00,43.50,4339200,10.42
1992-08-10,43.25,44.50,43.00,44.13,3280800,10.57
1992-08-07,42.00,43.75,41.50,43.38,7842400,10.39
1992-08-06,44.25,44.50,42.75,44.00,9220800,10.54
1992-08-05,45.50,45.50,44.50,44.75,4981200,10.72
1992-08-04,45.00,45.75,44.75,45.50,4295600,10.90
1992-08-03,46.75,47.25,45.50,45.75,2452400,10.96
1992-07-31,47.25,47.50,46.75,46.75,3262000,11.20
1992-07-30,47.25,47.50,46.75,47.25,4927600,11.32
1992-07-29,46.63,47.75,46.50,47.25,8976400,11.32
1992-07-28,45.50,46.50,45.25,46.50,4813600,11.14
1992-07-27,45.75,46.50,45.25,45.25,88800,10.84
1992-07-24,44.50,46.25,44.00,45.88,4832000,10.99
1992-07-23,44.50,44.75,43.75,44.75,6128800,10.72
1992-07-22,45.25,45.50,44.00,44.25,5798800,10.60
1992-07-21,45.50,46.25,45.00,45.75,4730800,10.96
1992-07-20,44.75,45.25,44.00,44.75,6873600,10.72
1992-07-17,45.00,46.00,44.63,45.00,15135600,10.78
1992-07-16,47.75,49.00,47.25,48.75,5011200,11.68
1992-07-15,47.50,49.00,47.25,48.00,6248000,11.50
1992-07-14,47.00,48.00,47.00,47.50,4510800,11.38
1992-07-13,45.75,47.13,45.25,47.00,4486800,11.26
1992-07-10,46.00,46.25,44.88,45.75,5144400,10.96
1992-07-09,46.00,46.50,45.75,45.88,5922000,10.99
1992-07-08,44.00,45.75,44.00,45.75,7020000,10.96
1992-07-07,46.25,46.25,43.50,44.25,7416400,10.60
1992-07-06,46.50,46.75,45.50,46.25,4378000,11.08
1992-07-02,49.00,49.00,45.75,46.25,9169200,11.08
1992-07-01,48.00,49.50,47.75,49.00,5129200,11.74
1992-06-30,46.75,48.25,46.50,48.00,6919200,11.50
1992-06-29,45.75,47.13,45.25,46.75,6735200,11.20
1992-06-26,45.75,46.00,44.50,45.25,3953600,10.84
1992-06-25,46.50,46.50,45.25,45.63,5745200,10.93
1992-06-24,45.50,46.00,45.25,46.00,7548000,11.02
1992-06-23,45.00,45.50,44.50,45.25,11130800,10.84
1992-06-22,44.00,44.75,42.75,44.25,13930000,10.60
1992-06-19,46.00,46.00,43.75,44.75,15280000,10.72
1992-06-18,47.50,49.00,44.75,45.25,15495600,10.84
1992-06-17,49.00,49.25,47.00,47.50,10880800,11.38
1992-06-16,51.75,52.00,48.75,49.25,13053200,11.80
1992-06-15,54.00,54.00,52.50,52.63,6777600,12.61
1992-06-12,54.50,55.00,54.25,54.63,3450800,13.09
1992-06-11,53.75,54.25,53.50,53.88,5028800,12.91
1992-06-10,54.00,54.75,53.50,53.75,4522400,12.88
1992-06-09,54.25,54.25,53.50,54.00,3626800,12.94
1992-06-08,55.00,55.00,54.00,54.25,3730000,13.00
1992-06-05,54.75,55.25,54.25,54.88,4040800,13.15
1992-06-04,54.25,54.75,53.50,54.50,6453200,13.06
1992-06-03,56.50,56.50,54.00,54.13,10743200,12.97
1992-06-02,57.50,57.50,56.25,56.50,5560000,13.54
1992-06-01,57.25,59.50,56.00,57.50,8869200,13.78
1992-05-29,59.75,60.63,59.50,59.75,6369200,14.29
1992-05-28,60.00,60.25,59.00,59.50,4558000,14.23
1992-05-27,59.25,60.25,59.00,60.25,5516400,14.41
1992-05-26,59.50,59.75,58.75,59.25,3423200,14.17
1992-05-22,59.00,59.75,59.00,59.50,1670800,14.23
1992-05-21,60.25,60.25,58.75,59.13,4938800,14.14
1992-05-20,59.75,60.25,59.25,60.00,6200800,14.35
1992-05-19,60.75,60.75,59.00,59.38,4715600,14.20
1992-05-18,61.50,61.50,60.00,60.38,4616400,14.44
1992-05-15,61.00,61.25,60.50,60.63,4339200,14.50
1992-05-14,62.75,63.00,60.25,61.38,5606800,14.68
1992-05-13,62.50,63.25,62.25,62.75,3482000,15.01
1992-05-12,62.25,63.00,61.75,62.25,2769200,14.89
1992-05-11,62.00,62.75,61.50,62.25,3250000,14.89
1992-05-08,61.50,62.88,61.00,62.00,7105600,14.83
1992-05-07,61.50,62.25,60.50,60.75,6175600,14.53
1992-05-06,60.75,62.13,60.50,61.75,6377600,14.77
1992-05-05,60.50,60.63,59.50,60.50,6449200,14.47
1992-05-04,59.50,61.25,59.25,60.50,4402000,14.47
1992-05-01,60.00,60.75,58.25,59.25,4821200,14.17
1992-04-30,57.25,60.25,56.50,60.13,9303600,14.38
1992-04-29,54.25,57.00,54.25,57.00,7116800,13.63
1992-04-28,55.25,55.75,53.00,54.25,6229200,12.97
1992-04-27,56.00,56.25,55.00,55.75,5014800,13.33
1992-04-24,57.00,58.25,56.00,56.50,3526800,13.51
1992-04-23,57.50,58.25,56.00,57.00,6534400,13.63
1992-04-22,56.25,58.00,56.25,57.63,6129200,13.78
1992-04-21,57.00,57.25,56.00,56.25,6442400,13.45
1992-04-20,59.00,59.00,56.00,56.75,7380800,13.57
1992-04-16,60.25,60.75,58.50,59.00,9260800,14.11
1992-04-15,58.00,60.88,57.50,60.50,7764400,14.47
1992-04-14,57.75,59.25,57.25,58.75,5178000,14.05
1992-04-13,55.50,56.75,55.25,56.50,4402000,13.51
1992-04-10,57.25,57.50,55.00,55.50,9803600,13.27
1992-04-09,56.00,58.25,55.25,57.25,6874400,13.69
1992-04-08,57.00,57.00,54.75,55.88,13123600,13.36
1992-04-07,61.00,61.25,57.25,57.25,8234400,13.69
1992-04-06,59.00,61.00,59.00,60.75,3643600,14.53
1992-04-03,58.75,59.25,58.50,59.00,4181200,14.11
1992-04-02,59.00,59.50,58.38,58.75,4798800,14.05
1992-04-01,57.25,59.25,57.25,59.00,5714400,14.11
1992-03-31,58.25,59.75,58.00,58.25,7613200,13.93
1992-03-30,61.25,61.25,57.75,58.13,12124400,13.90
1992-03-27,63.88,64.00,60.50,61.00,9452000,14.59
1992-03-26,64.75,65.25,63.75,64.00,4412400,15.30
1992-03-25,65.00,65.00,64.25,64.50,4353200,15.42
1992-03-24,63.50,65.00,63.25,65.00,7501200,15.54
1992-03-23,63.00,63.75,63.00,63.00,1804400,15.07
1992-03-20,63.00,63.25,63.00,63.25,1942400,15.13
1992-03-19,63.75,63.75,62.75,63.00,4251200,15.07
1992-03-18,63.25,64.00,63.00,63.75,2902000,15.25
1992-03-17,63.50,63.75,62.75,62.88,3061200,15.04
1992-03-16,62.75,63.50,61.75,63.38,2016400,15.16
1992-03-13,63.25,63.75,62.00,63.13,2843600,15.10
1992-03-12,63.25,63.75,61.50,62.75,5472400,15.01
1992-03-11,63.75,64.25,63.00,63.25,4714400,15.13
1992-03-10,64.00,64.75,63.75,63.75,4394400,15.25
1992-03-09,63.75,64.25,63.50,63.75,3896800,15.25
1992-03-06,63.50,64.00,63.00,64.00,4816400,15.30
1992-03-05,64.50,65.50,63.00,63.50,8462400,15.19
1992-03-04,66.25,66.75,64.75,65.00,4120800,15.54
1992-03-03,67.75,68.00,66.25,66.37,3560000,15.87
1992-03-02,67.75,68.50,67.25,67.25,3203200,16.08
1992-02-28,68.50,69.00,67.00,67.50,3244400,16.14
1992-02-27,70.00,70.00,68.00,68.50,4364800,16.38
1992-02-26,68.25,70.00,68.25,69.87,8193600,16.71
1992-02-25,66.25,68.50,65.25,68.50,8134400,16.38
1992-02-24,66.25,66.50,65.75,66.12,6122400,15.81
1992-02-21,64.75,65.50,64.50,65.00,5421200,15.54
1992-02-20,62.50,64.75,62.25,64.62,4692400,15.45
1992-02-19,62.75,63.00,61.75,62.00,3426400,14.83
1992-02-18,64.25,64.50,62.75,62.75,2442000,15.01
1992-02-14,63.75,64.25,63.25,64.12,2610800,15.33
1992-02-13,65.25,65.25,63.75,64.25,2734400,15.34
1992-02-12,63.75,65.50,63.00,65.25,4931200,15.57
1992-02-11,63.00,63.75,62.25,62.88,4378800,15.01
1992-02-10,64.00,64.25,63.00,63.13,3091200,15.07
1992-02-07,64.25,64.75,62.75,64.00,5285600,15.28
1992-02-06,65.75,66.00,64.00,64.12,3330000,15.30
1992-02-05,66.25,66.75,65.12,66.12,5772400,15.78
1992-02-04,65.75,66.25,65.00,65.75,6896400,15.69
1992-02-03,64.75,66.25,64.50,65.75,5652000,15.69
1992-01-31,64.00,65.25,63.50,64.75,5164400,15.46
1992-01-30,63.50,63.75,62.75,63.75,3128800,15.22
1992-01-29,64.75,65.75,63.25,63.25,5164400,15.10
1992-01-28,64.75,65.37,63.00,65.25,6206800,15.57
1992-01-27,64.75,65.25,64.25,64.50,2992000,15.40
1992-01-24,64.50,65.75,64.00,64.62,6356400,15.42
1992-01-23,64.25,64.75,63.00,64.50,4953200,15.40
1992-01-22,61.50,63.75,61.25,63.50,6560000,15.16
1992-01-21,64.25,64.25,61.00,61.13,6938000,14.59
1992-01-20,64.50,65.25,64.00,64.00,7492400,15.28
1992-01-17,67.75,69.00,64.75,64.75,30308800,15.46
1992-01-16,63.75,64.25,62.50,62.75,10485200,14.98
1992-01-15,64.50,65.00,63.00,63.50,11652400,15.16
1992-01-14,62.25,64.75,62.25,64.50,9789200,15.40
1992-01-13,62.25,62.75,61.50,62.00,3858800,14.80
1992-01-10,61.50,62.50,61.00,62.25,7012400,14.86
1992-01-09,60.50,62.25,60.25,62.25,7450800,14.86
1992-01-08,58.50,61.25,58.50,60.50,8330800,14.44
1992-01-07,57.50,59.50,57.50,59.13,5059200,14.11
1992-01-06,58.75,59.00,57.75,58.00,4080000,13.84
1992-01-03,60.00,60.25,58.25,59.00,6814400,14.08
1992-01-02,55.75,59.75,55.50,59.50,8357600,14.20
1991-12-31,57.38,58.00,56.00,56.38,4802000,13.46
1991-12-30,55.00,57.25,55.00,56.75,6580800,13.55
1991-12-27,54.75,55.75,54.50,55.00,6008000,13.13
1991-12-26,52.75,55.00,52.25,54.88,4805600,13.10
1991-12-24,52.00,53.75,51.75,52.25,6742400,12.47
1991-12-23,50.50,51.75,50.00,51.50,3686800,12.29
1991-12-20,51.25,51.50,50.25,50.25,4588000,11.99
1991-12-19,51.25,51.75,50.75,50.75,4140800,12.11
1991-12-18,50.25,52.00,50.00,51.75,6678000,12.35
1991-12-17,50.50,51.00,50.25,50.50,3502400,12.05
1991-12-16,50.38,50.75,50.00,50.50,2777600,12.05
1991-12-13,49.75,50.75,49.75,50.38,3418000,12.03
1991-12-12,49.38,49.75,49.00,49.38,3297600,11.79
1991-12-11,49.25,49.75,48.50,49.00,3031200,11.70
1991-12-10,49.00,49.50,48.50,49.13,4390000,11.73
1991-12-09,49.00,50.00,48.75,49.13,3502000,11.73
1991-12-06,49.50,49.75,48.50,48.75,7055200,11.64
1991-12-05,50.50,51.00,49.25,50.00,3555600,11.93
1991-12-04,50.75,50.75,50.00,50.50,2897600,12.05
1991-12-03,52.00,52.00,50.25,50.50,3692400,12.05
1991-12-02,50.75,52.00,50.00,51.75,4250000,12.35
1991-11-29,50.50,51.50,50.50,50.75,1227600,12.11
1991-11-27,51.25,51.50,50.50,51.00,2268800,12.17
1991-11-26,51.50,52.00,50.00,51.50,4982000,12.29
1991-11-25,51.00,52.25,51.00,51.25,2802000,12.23
1991-11-22,51.00,51.75,50.25,51.25,3502400,12.23
1991-11-21,50.50,51.75,50.50,51.00,3823200,12.17
1991-11-20,51.25,52.00,50.25,50.50,6005600,12.05
1991-11-19,51.75,51.75,49.75,51.25,10216400,12.23
1991-11-18,50.00,52.50,50.00,52.13,8530000,12.44
1991-11-15,54.50,54.75,49.75,50.00,9186400,11.91
1991-11-14,54.25,55.25,54.00,54.75,6733600,13.04
1991-11-13,54.00,54.50,53.50,54.13,6640000,12.89
1991-11-12,54.25,54.75,53.75,54.50,5972000,12.98
1991-11-11,53.50,54.50,53.25,53.75,5896800,12.80
1991-11-08,51.25,53.75,51.00,53.25,13435200,12.68
1991-11-07,48.50,50.50,48.25,49.75,10618800,11.85
1991-11-06,49.00,49.25,47.50,48.00,8466400,11.43
1991-11-05,49.75,50.50,48.75,48.75,7711200,11.61
1991-11-04,50.75,50.75,48.50,49.75,6983200,11.85
1991-11-01,51.25,52.00,50.50,51.00,7203600,12.14
1991-10-31,50.75,51.75,50.00,51.50,8300800,12.26
1991-10-30,52.00,52.75,49.50,49.75,5302400,11.85
1991-10-29,51.50,52.00,50.75,51.75,3624400,12.32
1991-10-28,51.50,51.75,50.75,51.50,2792400,12.26
1991-10-25,51.75,52.25,50.75,51.25,3832000,12.20
1991-10-24,53.00,53.25,51.50,52.13,6372400,12.41
1991-10-23,55.00,55.25,52.75,53.13,6046400,12.65
1991-10-22,55.50,56.25,54.50,54.50,7456400,12.98
1991-10-21,55.25,55.88,54.25,54.75,4172000,13.04
1991-10-18,55.13,55.50,54.50,55.00,15964400,13.10
1991-10-17,53.00,53.25,51.50,52.38,5423200,12.47
1991-10-16,52.50,54.00,52.25,53.50,7182000,12.74
1991-10-15,50.50,52.50,50.00,52.50,10300800,12.50
1991-10-14,49.00,50.25,48.75,49.88,4015600,11.88
1991-10-11,48.13,48.88,46.50,48.50,4292000,11.55
1991-10-10,48.75,49.00,46.75,47.75,5623200,11.37
1991-10-09,48.25,48.75,47.75,48.00,4752400,11.43
1991-10-08,48.13,48.50,46.50,48.25,6170000,11.49
1991-10-07,48.00,48.75,47.50,48.13,2328000,11.46
1991-10-04,48.00,48.75,47.50,48.25,2854400,11.49
1991-10-03,50.00,50.00,47.50,47.75,6478000,11.37
1991-10-02,51.75,51.75,49.50,49.75,643600,11.85
1991-10-01,49.25,51.25,49.00,50.75,4698800,12.08
1991-09-30,49.25,49.75,49.00,49.50,2266800,11.79
1991-09-27,50.00,50.75,48.75,49.00,2245200,11.67
1991-09-26,50.25,50.25,49.00,50.00,2556800,11.91
1991-09-25,50.25,50.50,49.25,50.50,1959200,12.02
1991-09-24,49.50,50.38,48.25,50.25,3805600,11.97
1991-09-23,50.00,50.75,49.25,49.50,3136800,11.79
1991-09-20,49.75,51.00,49.50,50.63,6742000,12.06
1991-09-19,50.25,50.50,49.50,49.75,6374400,11.85
1991-09-18,48.75,50.50,48.50,50.13,4342000,11.94
1991-09-17,47.00,49.00,46.75,49.00,4856400,11.67
1991-09-16,49.25,49.25,46.50,47.25,7365600,11.25
1991-09-13,50.00,50.25,48.50,48.63,5974400,11.58
1991-09-12,51.25,51.25,49.75,50.63,4267600,12.06
1991-09-11,50.75,51.00,49.50,50.50,6378000,12.02
1991-09-10,52.75,53.38,49.75,50.13,6535600,11.94
1991-09-09,51.75,53.50,51.50,53.25,4538000,12.68
1991-09-06,51.00,51.75,50.50,51.50,2848800,12.26
1991-09-05,51.50,51.75,50.75,51.00,2793600,12.14
1991-09-04,52.75,52.75,51.38,51.50,4299200,12.26
1991-09-03,52.75,53.25,52.00,52.50,2443200,12.50
1991-08-30,53.00,53.25,52.25,53.00,2363200,12.62
1991-08-29,53.25,53.88,52.50,53.00,4053200,12.62
1991-08-28,54.00,54.25,53.13,53.25,3843600,12.68
1991-08-27,53.00,54.00,52.75,54.00,3597600,12.86
1991-08-26,53.00,53.50,52.50,53.00,3644400,12.62
1991-08-23,54.00,55.50,52.75,53.00,8601200,12.62
1991-08-22,54.00,54.75,53.75,54.25,5936400,12.92
1991-08-21,52.50,54.13,52.00,53.75,7987600,12.80
1991-08-20,51.50,51.75,50.50,51.00,7123600,12.14
1991-08-19,49.50,51.63,48.50,50.50,11538000,12.02
1991-08-16,52.75,54.25,52.25,53.25,5689200,12.65
1991-08-15,55.00,55.00,53.00,53.25,5219200,12.65
1991-08-14,54.75,55.00,53.88,54.88,7173200,13.04
1991-08-13,52.00,54.00,52.00,53.50,10255200,12.71
1991-08-12,50.75,52.25,50.50,51.75,5096400,12.29
1991-08-09,50.50,51.00,49.75,50.75,5533600,12.06
1991-08-08,50.75,51.75,50.00,50.50,6769200,12.00
1991-08-07,49.50,51.00,49.38,50.38,7578800,11.97
1991-08-06,48.75,50.25,47.75,49.50,7890800,11.76
1991-08-05,49.75,49.75,48.25,48.50,3620800,11.52
1991-08-02,49.75,50.25,49.00,50.00,9767600,11.88
1991-08-01,46.00,49.25,45.75,49.13,16023600,11.67
1991-07-31,46.50,46.88,45.00,46.25,3689200,10.99
1991-07-30,45.50,46.75,45.50,46.50,3281200,11.05
1991-07-29,45.25,45.50,44.50,45.50,1916800,10.81
1991-07-26,45.75,45.75,44.75,44.88,2657600,10.66
1991-07-25,45.25,45.75,45.00,45.25,2366800,10.75
1991-07-24,45.25,45.75,44.50,45.00,4703200,10.69
1991-07-23,46.25,46.50,44.50,45.00,4770000,10.69
1991-07-22,45.75,46.25,45.50,46.00,3882000,10.93
1991-07-19,45.25,46.25,45.00,46.00,4601200,10.93
1991-07-18,44.00,45.13,43.00,44.88,14240000,10.66
1991-07-17,43.50,44.50,42.25,42.50,7474400,10.10
1991-07-16,45.50,45.75,43.50,43.75,7966400,10.39
1991-07-15,46.75,46.75,45.50,45.50,4932400,10.81
1991-07-12,47.25,47.25,46.25,46.75,4753200,11.11
1991-07-11,47.00,47.25,46.00,46.75,5217600,11.11
1991-07-10,47.50,48.25,46.75,47.25,5610000,11.23
1991-07-09,47.25,48.25,46.50,46.88,8091200,11.14
1991-07-08,45.25,47.25,45.00,46.75,10971200,11.11
1991-07-05,43.00,46.00,42.75,45.63,11842000,10.84
1991-07-03,42.25,43.50,41.75,43.13,11087600,10.25
1991-07-02,42.25,42.75,41.75,42.25,4296800,10.04
1991-07-01,42.25,43.00,41.75,42.50,6979200,10.10
1991-06-28,42.25,42.50,40.25,41.50,8102400,9.86
1991-06-27,42.50,42.75,41.75,42.50,5400000,10.10
1991-06-26,42.75,43.50,42.25,43.00,8958000,10.22
1991-06-25,42.00,43.00,41.75,42.38,8151200,10.07
1991-06-24,41.75,42.25,41.25,41.75,7443600,9.92
1991-06-21,42.00,42.50,41.75,42.00,7378800,9.98
1991-06-20,41.25,42.00,40.75,42.00,5158000,9.98
1991-06-19,41.75,42.25,41.25,41.75,6408000,9.92
1991-06-18,42.25,43.25,41.50,42.13,8749200,10.01
1991-06-17,41.00,42.25,41.00,42.00,5966800,9.98
1991-06-14,42.75,42.75,40.75,41.13,8049200,9.77
1991-06-13,42.50,43.00,41.75,42.13,7565200,10.01
1991-06-12,44.00,44.75,41.25,42.38,15580000,10.07
1991-06-11,45.00,45.50,44.25,44.63,6742400,10.60
1991-06-10,46.00,47.13,45.75,46.00,5991200,10.93
1991-06-07,46.25,47.00,45.63,46.13,5463600,10.96
1991-06-06,48.25,48.25,46.50,46.63,6028000,11.08
1991-06-05,49.25,49.25,47.75,48.00,4760800,11.40
1991-06-04,49.50,49.50,48.50,49.13,6593600,11.67
1991-06-03,47.00,49.50,46.75,49.25,7870800,11.70
1991-05-31,47.50,47.75,46.25,47.00,7792400,11.17
1991-05-30,47.00,47.75,46.50,47.63,5663600,11.32
1991-05-29,46.25,47.75,45.88,47.00,13733600,11.17
1991-05-28,46.00,46.25,45.25,46.00,6124400,10.93
1991-05-24,45.50,46.00,45.00,45.88,3484800,10.90
1991-05-23,46.50,46.75,44.75,45.13,7458800,10.72
1991-05-22,45.75,46.50,45.50,46.25,8137600,10.99
1991-05-21,45.25,46.50,44.75,45.25,12500000,10.75
1991-05-20,47.25,47.50,44.00,44.25,9365200,10.51
1991-05-17,48.75,48.75,46.50,47.00,16836800,11.14
1991-05-16,51.00,51.25,48.50,49.00,13652000,11.61
1991-05-15,51.50,52.00,49.00,50.50,18530800,11.97
1991-05-14,52.75,53.75,52.50,53.50,7763600,12.68
1991-05-13,52.25,53.50,51.50,52.75,8763600,12.50
1991-05-10,51.50,53.25,50.75,51.25,8652000,12.14
1991-05-09,50.00,51.50,49.75,50.75,8523200,12.03
1991-05-08,50.75,50.75,49.25,49.75,6332400,11.79
1991-05-07,51.00,51.25,50.50,50.63,9671200,12.00
1991-05-06,48.50,50.50,48.25,50.25,7596400,11.91
1991-05-03,49.00,49.50,48.25,49.00,8717600,11.61
1991-05-02,47.75,49.75,47.50,49.00,28973600,11.61
1991-05-01,48.00,49.00,47.00,47.25,66732000,11.20
1991-04-30,57.75,58.25,54.50,55.00,25413600,13.03
1991-04-29,58.50,60.25,58.25,58.25,7395200,13.80
1991-04-26,58.50,59.00,57.75,58.63,4481200,13.89
1991-04-25,59.75,59.75,58.50,58.50,11276800,13.86
1991-04-24,61.75,62.00,60.50,60.75,3769200,14.40
1991-04-23,62.25,63.00,60.25,61.50,8494400,14.57
1991-04-22,59.50,62.00,58.75,61.50,9190000,14.57
1991-04-19,61.00,61.50,59.50,59.63,10272400,14.13
1991-04-18,62.75,63.00,60.75,61.00,8853600,14.46
1991-04-17,65.00,65.00,62.00,63.25,11533600,14.99
1991-04-16,63.25,64.50,62.50,64.25,22176800,15.23
1991-04-15,61.75,64.50,60.00,62.25,60732400,14.75
1991-04-12,71.50,73.25,69.75,71.75,13140000,17.00
1991-04-11,67.75,71.37,67.50,71.00,12710800,16.83
1991-04-10,68.50,69.25,66.75,66.87,7733600,15.85
1991-04-09,69.75,70.00,68.25,68.75,4280800,16.29
1991-04-08,69.25,70.00,68.75,70.00,2604400,16.59
1991-04-05,71.75,71.75,68.75,69.37,5567600,16.44
1991-04-04,70.00,72.00,69.50,71.50,6024400,16.94
1991-04-03,72.50,72.75,70.00,70.00,8585200,16.59
1991-04-02,69.00,72.75,68.50,72.75,10473600,17.24
1991-04-01,68.00,69.50,67.50,68.50,4218000,16.23
1991-03-28,69.25,70.00,67.75,68.00,2816800,16.11
1991-03-27,70.00,70.25,68.50,69.25,6812400,16.41
1991-03-26,64.75,70.25,64.75,70.00,11935200,16.59
1991-03-25,63.50,65.00,63.25,64.50,4858800,15.28
1991-03-22,64.00,64.75,62.25,63.25,12096400,14.99
1991-03-21,68.25,68.75,63.75,64.75,10600000,15.34
1991-03-20,69.25,69.50,66.87,67.75,12939200,16.06
1991-03-19,66.50,70.25,65.75,69.50,15100000,16.47
1991-03-18,65.75,68.25,65.75,67.75,7645200,16.06
1991-03-15,65.75,66.50,65.25,66.25,7335600,15.70
1991-03-14,66.75,67.50,64.50,65.25,8126400,15.46
1991-03-13,62.75,66.50,62.75,66.25,6253200,15.70
1991-03-12,63.00,63.75,62.50,62.88,8360000,14.90
1991-03-11,64.50,64.75,62.25,63.50,6276400,15.05
1991-03-08,67.75,68.25,65.00,65.00,11522400,15.40
1991-03-07,63.50,67.50,63.25,67.25,11497600,15.94
1991-03-06,64.00,65.62,62.88,63.00,18731200,14.93
1991-03-05,59.00,63.25,59.00,63.13,15769200,14.96
1991-03-04,58.00,58.75,57.00,58.38,3175600,13.83
1991-03-01,57.00,59.00,57.00,57.75,4518800,13.69
1991-02-28,58.25,58.50,56.25,57.25,8120000,13.57
1991-02-27,58.25,58.50,57.50,58.25,6243200,13.80
1991-02-26,57.50,58.75,56.50,58.25,8934400,13.80
1991-02-25,60.25,60.50,57.50,58.00,12848800,13.74
1991-02-22,59.00,61.75,58.50,59.75,8320800,14.16
1991-02-21,61.25,62.25,58.75,59.00,6826400,13.98
1991-02-20,59.50,61.75,59.25,61.00,7646800,14.46
1991-02-19,57.50,60.25,57.38,60.00,8080800,14.22
1991-02-15,57.25,58.50,57.25,57.63,13067600,13.66
1991-02-14,60.00,60.00,56.75,57.13,13493200,13.51
1991-02-13,60.00,60.25,58.00,60.00,9130800,14.19
1991-02-12,61.00,61.25,59.38,60.00,8042000,14.19
1991-02-11,60.00,61.50,59.75,61.38,11546400,14.51
1991-02-08,57.50,60.25,57.50,59.88,11220000,14.16
1991-02-07,57.00,58.75,55.75,57.75,18587600,13.66
1991-02-06,57.75,58.25,56.50,56.88,7965200,13.45
1991-02-05,55.25,58.00,54.75,57.75,12740000,13.66
1991-02-04,55.75,56.00,55.00,55.25,9569200,13.07
1991-02-01,55.50,57.88,55.50,55.75,15897600,13.18
1991-01-31,55.50,56.00,54.75,55.50,8677600,13.12
1991-01-30,53.25,55.75,53.25,55.50,12043200,13.12
1991-01-29,54.25,54.50,52.25,53.75,7708800,12.71
1991-01-28,53.25,55.25,53.25,54.50,9771200,12.89
1991-01-25,52.00,53.63,52.00,53.50,8012000,12.65
1991-01-24,51.50,52.75,51.50,52.13,8374400,12.33
1991-01-23,51.25,52.25,51.00,51.75,8725600,12.24
1991-01-22,51.00,52.50,50.50,51.25,15296400,12.12
1991-01-21,49.75,51.50,49.75,50.75,11595200,12.00
1991-01-18,48.75,50.75,48.50,50.25,33691200,11.88
1991-01-17,52.50,52.75,49.00,51.25,21137600,12.12
1991-01-16,47.00,50.00,46.75,49.75,13968800,11.76
1991-01-15,46.50,46.75,46.00,46.75,6870000,11.06
1991-01-14,46.00,46.75,46.00,46.25,7535600,10.94
1991-01-11,47.00,47.25,46.00,47.00,11003200,11.11
1991-01-10,45.75,47.25,45.75,47.13,15562400,11.15
1991-01-09,44.25,46.00,43.75,45.25,16692400,10.70
1991-01-08,43.75,43.88,42.50,43.25,7816400,10.23
1991-01-07,43.00,45.25,43.00,43.25,11111200,10.23
1991-01-04,43.00,44.25,43.00,43.25,5062400,10.23
1991-01-03,43.50,44.25,43.00,43.00,5365600,10.17
1991-01-02,42.75,44.00,42.00,43.50,5543600,10.29
1990-12-31,43.00,43.25,42.75,43.00,1593200,10.17
1990-12-28,43.25,43.50,42.75,43.00,2285200,10.17
1990-12-27,43.25,44.00,43.25,43.50,3492000,10.29
1990-12-26,44.00,44.25,43.00,43.75,3682000,10.35
1990-12-24,44.75,45.00,44.00,44.00,2106800,10.40
1990-12-21,44.25,45.25,43.50,45.00,12363200,10.64
1990-12-20,41.25,44.50,41.25,44.00,14326400,10.40
1990-12-19,42.50,42.50,41.13,41.88,5036800,9.90
1990-12-18,41.00,42.50,40.75,42.25,7899200,9.99
1990-12-17,39.00,40.50,39.00,40.13,4683600,9.49
1990-12-14,40.25,40.50,39.50,39.88,3126400,9.43
1990-12-13,39.50,41.00,39.50,40.75,5752000,9.64
1990-12-12,39.75,40.00,39.00,39.63,8664400,9.37
1990-12-11,41.25,41.50,40.00,40.00,12438000,9.46
1990-12-10,42.25,42.50,41.50,41.75,8966400,9.87
1990-12-07,41.00,42.75,41.00,42.50,11781200,10.05
1990-12-06,41.25,41.75,40.50,41.25,19013600,9.75
1990-12-05,38.50,40.25,37.88,40.13,7822000,9.49
1990-12-04,37.50,38.75,37.50,38.50,5453200,9.10
1990-12-03,37.25,38.25,37.00,38.13,5922400,9.02
1990-11-30,36.25,37.25,36.25,36.75,4350800,8.69
1990-11-29,37.00,37.00,36.25,36.75,4528000,8.69
1990-11-28,37.75,38.50,36.75,36.75,6250800,8.69
1990-11-27,37.00,38.25,36.75,37.50,5899200,8.87
1990-11-26,36.00,37.00,36.00,36.75,2925600,8.69
1990-11-23,36.25,37.00,36.00,36.38,1911200,8.60
1990-11-21,35.25,36.25,34.75,36.13,4400800,8.54
1990-11-20,36.50,36.75,35.25,35.50,5490800,8.39
1990-11-19,35.50,36.38,35.25,36.38,8017600,8.60
1990-11-16,35.75,36.00,34.75,35.13,6545200,8.31
1990-11-15,36.75,37.00,35.50,36.00,5787600,8.48
1990-11-14,35.75,37.25,35.75,37.00,6819200,8.72
1990-11-13,36.25,36.50,35.75,36.00,5086400,8.48
1990-11-12,35.50,36.75,35.25,36.25,5192000,8.54
1990-11-09,35.00,35.75,34.50,35.50,7102000,8.37
1990-11-08,33.00,35.00,33.00,34.50,7136400,8.13
1990-11-07,33.50,33.75,32.63,33.25,7254400,7.84
1990-11-06,33.50,34.50,33.25,33.50,6620800,7.90
1990-11-05,32.25,33.50,32.00,33.25,6604400,7.84
1990-11-02,30.50,32.38,30.50,31.75,5323200,7.48
1990-11-01,30.50,31.00,29.75,30.50,3258800,7.19
1990-10-31,30.50,31.87,30.25,30.75,5331200,7.25
1990-10-30,29.75,30.75,28.87,30.37,3513600,7.16
1990-10-29,30.25,30.50,29.75,29.87,4415600,7.04
1990-10-26,29.75,31.25,29.75,30.00,4811200,7.07
1990-10-25,30.25,31.25,29.62,30.00,5481200,7.07
1990-10-24,30.75,31.00,30.00,30.50,5079200,7.19
1990-10-23,31.00,31.50,30.25,31.00,5969200,7.31
1990-10-22,31.50,31.50,30.50,31.12,9041200,7.33
1990-10-19,31.25,31.75,30.25,31.37,33363200,7.39
1990-10-18,26.50,28.75,26.50,28.50,11255600,6.72
1990-10-17,25.25,26.50,25.00,26.50,11059200,6.25
1990-10-16,27.50,27.50,24.25,25.00,10913200,5.89
1990-10-15,28.50,28.75,26.62,27.75,7190000,6.54
1990-10-12,28.25,28.50,27.00,28.25,8169200,6.66
1990-10-11,26.75,27.87,25.50,27.75,7376800,6.54
1990-10-10,27.25,28.00,26.00,26.50,5283600,6.25
1990-10-09,28.50,29.00,27.75,28.00,4321200,6.60
1990-10-08,28.75,29.25,28.25,29.12,2218800,6.86
1990-10-05,27.00,28.75,27.00,28.00,3572000,6.60
1990-10-04,26.75,28.00,26.25,28.00,7638800,6.60
1990-10-03,29.75,29.75,26.75,27.00,9591200,6.36
1990-10-02,31.00,32.00,29.50,29.62,9699200,6.98
1990-10-01,29.50,31.00,29.25,30.50,5581200,7.19
1990-09-28,28.50,29.00,27.25,29.00,6291200,6.83
1990-09-27,30.00,30.50,28.00,28.25,5085600,6.66
1990-09-26,30.00,30.50,29.75,29.75,3363200,7.01
1990-09-25,30.50,30.75,29.25,30.00,5642000,7.07
1990-09-24,31.50,31.50,29.75,30.25,4961200,7.13
1990-09-21,32.00,32.50,31.00,31.50,5503600,7.42
1990-09-20,32.25,32.25,31.25,31.62,3607600,7.45
1990-09-19,33.25,33.75,32.00,32.50,6536800,7.66
1990-09-18,33.75,33.75,33.00,33.38,4456400,7.87
1990-09-17,34.00,35.25,33.50,33.75,2782000,7.95
1990-09-14,33.50,34.25,33.25,34.00,4084400,8.01
1990-09-13,34.50,34.75,33.00,33.75,3492400,7.95
1990-09-12,34.50,34.50,33.50,34.00,3600800,8.01
1990-09-11,36.00,36.13,33.75,34.00,6370800,8.01
1990-09-10,37.00,37.00,35.75,35.75,2732400,8.43
1990-09-07,35.50,36.75,35.13,36.38,2098800,8.57
1990-09-06,35.50,36.00,35.25,35.75,3134800,8.43
1990-09-05,37.25,37.25,35.75,36.00,2292000,8.48
1990-09-04,36.50,37.50,36.50,37.00,2974800,8.72
1990-08-31,36.00,37.25,36.00,37.00,3570000,8.72
1990-08-30,37.25,37.50,36.00,36.25,4388800,8.54
1990-08-29,38.00,38.13,36.75,37.25,5407600,8.78
1990-08-28,37.50,38.38,37.25,38.13,2877600,8.99
1990-08-27,36.75,38.00,36.25,37.75,4214800,8.90
1990-08-24,35.25,36.00,34.75,35.50,2634400,8.37
1990-08-23,34.25,35.00,33.50,34.50,5138800,8.13
1990-08-22,37.00,37.00,34.88,35.13,4395600,8.28
1990-08-21,35.75,36.75,35.25,36.25,5769200,8.54
1990-08-20,36.50,37.50,36.25,36.75,2681200,8.66
1990-08-17,38.50,38.50,35.75,36.50,8806400,8.58
1990-08-16,39.00,39.63,38.50,38.50,4438800,9.05
1990-08-15,40.00,40.25,39.25,39.25,3292000,9.22
1990-08-14,40.00,40.00,39.25,39.75,3520800,9.34
1990-08-13,38.00,40.00,37.88,39.88,5584400,9.37
1990-08-10,38.75,39.25,38.25,38.75,3683600,9.11
1990-08-09,40.25,40.50,39.25,39.50,3443600,9.28
1990-08-08,39.50,40.75,39.50,40.13,3674400,9.43
1990-08-07,40.25,40.63,38.75,39.50,7096400,9.28
1990-08-06,39.00,40.50,38.50,39.50,6425600,9.28
1990-08-03,43.50,43.75,39.75,41.25,9609200,9.69
1990-08-02,41.25,43.75,41.25,43.50,7973600,10.22
1990-08-01,42.00,42.75,41.50,42.38,3350800,9.96
1990-07-31,42.50,42.75,41.50,42.00,3444800,9.87
1990-07-30,40.75,42.50,40.75,42.38,3058800,9.96
1990-07-27,41.25,41.75,40.50,41.38,2240000,9.72
1990-07-26,42.25,42.50,41.00,41.38,2885600,9.72
1990-07-25,42.00,43.25,41.75,42.25,3762400,9.93
1990-07-24,42.00,42.25,41.00,42.13,6928800,9.90
1990-07-23,41.00,41.75,40.00,41.50,9655200,9.75
1990-07-20,42.00,42.50,40.75,41.00,6858000,9.63
1990-07-19,40.75,42.50,40.00,41.75,20932400,9.81
1990-07-18,44.50,45.00,43.00,44.63,10309200,10.49
1990-07-17,45.75,46.00,44.00,44.25,4892000,10.40
1990-07-16,46.75,47.13,45.25,45.63,6428000,10.72
1990-07-13,47.50,47.75,46.75,46.75,8254400,10.99
1990-07-12,46.75,47.50,46.50,47.38,6537600,11.13
1990-07-11,46.75,47.00,45.75,47.00,8808800,11.04
1990-07-10,47.00,47.50,46.75,47.00,12923600,11.04
1990-07-09,45.00,47.00,44.75,46.63,11281200,10.96
1990-07-06,43.50,45.00,43.25,44.75,7481200,10.52
1990-07-05,43.75,44.25,43.25,43.50,3859200,10.22
1990-07-03,43.88,44.50,43.75,44.00,3572400,10.34
1990-07-02,44.50,44.50,43.75,44.00,4856400,10.34
1990-06-29,43.00,44.88,42.75,44.75,11622000,10.52
1990-06-28,42.75,43.25,41.75,43.00,8930000,10.10
1990-06-27,40.75,42.00,40.25,41.50,3490800,9.75
1990-06-26,41.75,42.00,40.38,40.63,4558800,9.55
1990-06-25,41.50,41.75,40.25,41.25,4378000,9.69
1990-06-22,42.00,42.63,41.25,41.50,10154400,9.75
1990-06-21,40.00,42.00,40.00,41.88,7455600,9.84
1990-06-20,39.88,40.25,39.75,40.00,5530000,9.40
1990-06-19,39.00,39.75,38.38,39.63,5623600,9.31
1990-06-18,39.25,39.50,39.00,39.25,3988800,9.22
1990-06-15,39.75,40.00,39.13,39.50,5163600,9.28
1990-06-14,40.00,40.25,39.25,39.75,5018000,9.34
1990-06-13,40.38,40.75,39.75,39.75,4963600,9.34
1990-06-12,39.13,40.50,38.75,40.50,5902000,9.52
1990-06-11,37.75,39.00,37.75,39.00,5661200,9.16
1990-06-08,38.50,38.50,37.50,38.25,11926800,8.99
1990-06-07,39.50,39.75,38.50,39.00,6668800,9.16
1990-06-06,39.00,39.50,38.75,39.50,7563600,9.28
1990-06-05,41.00,41.00,39.00,39.50,10702000,9.28
1990-06-04,40.75,41.00,39.75,40.75,6412400,9.58
1990-06-01,41.38,42.00,40.75,40.75,5624400,9.58
1990-05-31,41.50,41.50,41.00,41.25,3682400,9.69
1990-05-30,41.63,41.75,41.25,41.38,9890000,9.72
1990-05-29,40.00,41.25,39.25,41.00,8689200,9.63
1990-05-25,39.50,40.75,39.00,40.00,11562400,9.40
1990-05-24,42.25,42.25,41.50,42.00,5296400,9.87
1990-05-23,41.25,42.50,41.25,42.00,7417600,9.87
1990-05-22,40.13,41.50,40.00,41.38,10772000,9.72
1990-05-21,39.50,40.00,38.75,39.50,9382400,9.28
1990-05-18,41.25,41.50,39.50,39.75,9248000,9.31
1990-05-17,41.75,42.25,41.00,41.50,5488000,9.72
1990-05-16,41.75,41.75,41.00,41.63,3139200,9.76
1990-05-15,41.38,42.00,41.00,41.75,5343600,9.78
1990-05-14,42.75,42.75,41.25,41.75,8088000,9.78
1990-05-11,41.38,42.75,40.75,42.63,7691200,9.99
1990-05-10,41.75,41.75,40.50,41.38,6413600,9.70
1990-05-09,41.63,42.00,41.25,41.88,3491200,9.81
1990-05-08,41.00,42.00,41.00,41.75,4025600,9.78
1990-05-07,39.75,41.75,39.75,41.50,4866400,9.72
1990-05-04,40.00,40.75,39.25,40.00,6063200,9.37
1990-05-03,39.75,40.25,39.75,40.00,5950800,9.37
1990-05-02,39.75,40.00,39.25,39.75,4857600,9.31
1990-05-01,39.75,40.00,39.38,39.63,5845200,9.29
1990-04-30,39.25,39.75,39.00,39.38,4888800,9.23
1990-04-27,39.00,39.50,38.75,39.13,4178800,9.17
1990-04-26,39.00,39.50,38.13,38.88,5098000,9.11
1990-04-25,38.75,39.00,38.25,38.75,4743200,9.08
1990-04-24,40.00,40.50,38.50,38.75,10852000,9.08
1990-04-23,40.25,40.50,39.50,39.75,4597600,9.31
1990-04-20,40.88,41.50,39.75,40.25,11573600,9.43
1990-04-19,41.75,43.13,40.00,40.25,17215600,9.43
1990-04-18,43.25,43.75,42.50,43.25,6925200,10.13
1990-04-17,43.25,43.50,42.75,43.25,4683600,10.13
1990-04-16,43.50,44.25,43.25,43.75,8116400,10.25
1990-04-12,43.00,44.00,42.50,43.25,7566800,10.13
1990-04-11,41.50,43.00,41.50,42.50,7620000,9.96
1990-04-10,41.25,42.00,41.00,41.25,4695600,9.67
1990-04-09,39.75,41.50,39.50,41.13,3771200,9.64
1990-04-06,40.25,41.25,39.75,39.88,4235600,9.35
1990-04-05,41.00,41.25,40.00,40.25,3877600,9.43
1990-04-04,41.50,42.00,40.75,41.25,5363200,9.67
1990-04-03,40.50,41.75,40.50,41.75,5006400,9.78
1990-04-02,40.00,40.63,39.50,40.25,5332000,9.43
1990-03-30,40.00,41.00,40.00,40.25,7986400,9.43
1990-03-29,41.00,41.50,40.75,41.13,3472000,9.64
1990-03-28,42.00,42.13,41.00,41.25,3696800,9.67
1990-03-27,42.00,42.25,41.25,42.00,3033600,9.84
1990-03-26,42.50,43.38,42.00,42.25,4581200,9.90
1990-03-23,41.25,43.00,41.00,42.25,8155200,9.90
1990-03-22,41.75,42.25,40.75,40.75,8292400,9.55
1990-03-21,41.25,42.25,41.25,41.63,5463200,9.76
1990-03-20,42.25,43.00,40.75,41.38,13984400,9.70
1990-03-19,40.50,42.50,40.00,42.38,15433200,9.93
1990-03-16,40.00,40.75,39.13,40.25,23042400,9.43
1990-03-15,36.50,38.00,36.50,36.75,4302000,8.61
1990-03-14,36.75,37.25,36.50,37.00,3654800,8.67
1990-03-13,36.50,37.25,36.25,36.88,5321200,8.64
1990-03-12,37.25,37.50,36.25,36.63,5864400,8.58
1990-03-09,36.75,37.50,36.25,36.88,8248800,8.64
1990-03-08,35.75,37.00,35.00,36.75,8013600,8.61
1990-03-07,35.00,36.00,35.00,35.38,7301200,8.29
1990-03-06,35.00,35.25,34.50,35.25,5578800,8.26
1990-03-05,33.50,34.75,33.50,34.50,6537600,8.08
1990-03-02,33.50,34.75,33.25,33.75,3761200,7.91
1990-03-01,33.50,34.75,33.25,34.25,7283200,8.03
1990-02-28,33.50,34.00,33.25,34.00,3918800,7.97
1990-02-27,34.00,34.25,33.50,33.50,2642000,7.85
1990-02-26,33.00,34.25,33.00,34.00,2844800,7.97
1990-02-23,32.75,33.50,32.75,33.25,5375600,7.79
1990-02-22,34.00,34.50,33.00,33.00,6976800,7.73
1990-02-21,32.75,34.25,32.50,34.00,6283600,7.97
1990-02-20,33.50,33.75,33.00,33.50,4402400,7.85
1990-02-16,34.25,34.50,33.75,33.75,4556400,7.91
1990-02-15,33.75,34.25,33.50,34.25,3509200,8.00
1990-02-14,34.50,34.75,33.75,34.25,3448000,8.00
1990-02-13,34.00,35.00,33.75,34.50,3653600,8.06
1990-02-12,34.25,34.50,33.75,34.00,2695600,7.94
1990-02-09,33.50,34.50,33.25,34.25,6004400,8.00
1990-02-08,33.25,33.50,32.25,33.00,6680000,7.71
1990-02-07,33.00,34.00,32.50,33.25,11180800,7.77
1990-02-06,34.75,35.00,34.00,34.75,2640000,8.12
1990-02-05,34.25,35.25,34.00,35.00,3653200,8.18
1990-02-02,33.25,34.75,33.25,34.25,4248800,8.00
1990-02-01,34.50,34.63,33.50,33.63,4193200,7.86
1990-01-31,34.50,34.75,33.00,34.00,5152400,7.94
1990-01-30,33.25,34.50,33.00,34.00,4180800,7.94
1990-01-29,33.00,33.50,32.13,33.25,4284800,7.77
1990-01-26,34.00,34.00,32.25,32.75,6492000,7.65
1990-01-25,34.25,34.75,34.00,34.13,3996800,7.97
1990-01-24,32.50,34.25,32.25,34.00,6077600,7.94
1990-01-23,33.75,34.25,33.00,33.75,5048800,7.88
1990-01-22,34.00,34.50,33.25,33.25,5200800,7.77
1990-01-19,33.75,34.50,33.50,34.25,9485600,8.00
1990-01-18,33.00,33.50,32.25,32.38,9760800,7.56
1990-01-17,34.75,34.75,33.00,33.25,7050000,7.77
1990-01-16,33.50,35.00,32.75,34.88,7658000,8.15
1990-01-15,34.50,35.75,34.25,34.25,5785600,8.00
1990-01-12,34.25,34.75,33.75,34.50,6150000,8.06
1990-01-11,36.25,36.25,34.50,34.50,7547600,8.06
1990-01-10,37.63,37.63,35.75,36.00,7140000,8.41
1990-01-09,38.00,38.00,37.00,37.63,3096800,8.79
1990-01-08,37.50,38.00,37.00,38.00,3643200,8.88
1990-01-05,37.75,38.25,37.00,37.75,4406400,8.82
1990-01-04,38.25,38.75,37.25,37.63,7928800,8.79
1990-01-03,38.00,38.00,37.50,37.50,7444400,8.76
1990-01-02,35.25,37.50,35.00,37.25,6555600,8.70
1989-12-29,34.75,35.75,34.38,35.25,5445200,8.23
1989-12-28,35.00,35.25,34.25,34.63,5403200,8.09
1989-12-27,35.50,35.75,35.00,35.13,9189200,8.21
1989-12-26,36.75,36.75,35.25,35.50,4849200,8.29
1989-12-22,36.25,37.25,36.00,36.50,6610800,8.53
1989-12-21,35.75,36.25,35.50,36.25,10889200,8.47
1989-12-20,35.75,36.25,35.25,35.75,6377600,8.35
1989-12-19,34.50,35.50,34.50,35.00,8977600,8.18
1989-12-18,33.75,35.00,33.75,34.75,10978000,8.12
1989-12-15,34.75,35.00,32.50,33.75,18520800,7.88
1989-12-14,35.75,36.13,34.50,34.88,10886400,8.15
1989-12-13,36.00,36.50,35.50,36.00,13920000,8.41
1989-12-12,39.25,39.50,35.00,36.00,36634400,8.41
1989-12-11,41.00,41.50,38.38,39.25,23223200,9.17
1989-12-08,42.50,43.00,41.25,41.75,9032400,9.75
1989-12-07,42.25,43.25,42.00,42.75,6378800,9.99
1989-12-06,45.00,45.25,41.00,42.75,11965600,9.99
1989-12-05,45.25,45.75,44.50,45.00,4364800,10.51
1989-12-04,43.75,45.50,43.75,45.25,3498000,10.57
1989-12-01,44.50,45.00,43.63,44.00,5235200,10.28
1989-11-30,43.75,44.50,43.50,44.25,2280800,10.34
1989-11-29,43.50,44.25,42.50,44.00,5475200,10.28
1989-11-28,43.75,44.25,42.75,44.13,4854400,10.31
1989-11-27,44.75,45.25,43.75,44.00,3774800,10.28
1989-11-24,44.75,45.00,44.75,44.75,1014400,10.45
1989-11-22,45.50,45.75,44.50,44.75,3508000,10.45
1989-11-21,45.25,46.50,45.25,45.25,5013600,10.57
1989-11-20,45.00,45.50,44.50,45.25,3870800,10.57
1989-11-17,44.50,45.25,44.50,44.75,3164400,10.45
1989-11-16,44.50,44.75,43.75,44.75,3453600,10.43
1989-11-15,45.00,45.25,44.00,44.25,3499200,10.31
1989-11-14,46.50,46.75,44.50,44.75,3021200,10.43
1989-11-13,46.50,47.25,46.50,46.50,2445600,10.83
1989-11-10,45.75,47.00,45.75,46.75,2336800,10.89
1989-11-09,45.00,46.00,44.50,46.00,3166400,10.72
1989-11-08,44.25,45.25,44.25,45.00,5102000,10.49
1989-11-07,43.25,44.50,43.25,44.00,5406800,10.25
1989-11-06,43.50,44.00,43.00,43.25,4416400,10.08
1989-11-03,44.00,44.50,43.25,43.25,6258800,10.08
1989-11-02,45.00,45.00,43.00,44.00,16170800,10.25
1989-11-01,46.25,46.75,45.75,46.13,2199200,10.75
1989-10-31,45.75,46.50,45.50,46.50,3288800,10.83
1989-10-30,45.50,46.00,45.00,45.75,3121200,10.66
1989-10-27,45.25,45.75,44.50,45.25,4634400,10.54
1989-10-26,45.50,46.50,45.00,45.25,6048000,10.54
1989-10-25,47.75,47.75,46.25,46.50,4263600,10.83
1989-10-24,46.25,48.50,45.25,47.63,7735600,11.10
1989-10-23,48.00,48.25,46.25,46.75,4375600,10.89
1989-10-20,47.75,49.25,47.50,48.00,9350800,11.18
1989-10-19,48.25,49.50,48.25,48.75,4016800,11.36
1989-10-18,46.50,48.25,46.00,48.25,5157600,11.24
1989-10-17,46.00,48.75,45.00,47.25,8935600,11.01
1989-10-16,44.75,46.75,42.50,46.75,15184400,10.89
1989-10-13,48.75,49.50,45.00,45.75,7195600,10.66
1989-10-12,49.00,49.25,48.50,48.75,2969200,11.36
1989-10-11,48.75,49.25,48.00,48.88,5608800,11.39
1989-10-10,49.75,50.38,48.50,49.50,10262400,11.53
1989-10-09,48.00,49.75,47.50,49.50,6997600,11.53
1989-10-06,46.25,48.25,46.00,48.13,12939200,11.21
1989-10-05,44.50,46.50,44.25,45.50,8760000,10.60
1989-10-04,43.75,44.63,43.50,44.25,5687600,10.31
1989-10-03,44.25,44.50,43.13,43.63,6094400,10.17
1989-10-02,44.50,44.75,43.75,44.38,4922400,10.34
1989-09-29,45.25,45.50,44.50,44.50,2500800,10.37
1989-09-28,45.00,45.75,45.00,45.50,2856800,10.60
1989-09-27,44.25,45.13,44.00,44.75,3229200,10.43
1989-09-26,45.00,45.50,44.75,45.25,2762400,10.54
1989-09-25,44.75,45.75,44.75,45.25,4875600,10.54
1989-09-22,44.75,45.25,44.25,44.88,2605600,10.46
1989-09-21,45.00,46.00,44.25,44.75,7186800,10.43
1989-09-20,44.00,45.00,43.75,44.63,4230800,10.40
1989-09-19,44.25,44.50,43.00,43.25,2888800,10.08
1989-09-18,44.50,45.00,44.00,44.00,2264400,10.25
1989-09-15,45.00,45.25,44.25,45.00,4470800,10.49
1989-09-14,45.00,45.25,44.50,44.75,4693600,10.43
1989-09-13,46.25,46.63,45.00,45.00,4616400,10.49
1989-09-12,45.50,46.75,45.00,46.00,3710800,10.72
1989-09-11,44.75,46.00,44.50,45.75,3522000,10.66
1989-09-08,44.75,45.25,44.50,45.00,2013200,10.49
1989-09-07,44.75,45.50,44.75,44.75,4083200,10.43
1989-09-06,44.75,44.88,44.00,44.75,3108800,10.43
1989-09-05,44.50,45.38,44.50,44.75,4112400,10.43
1989-09-01,44.50,44.75,44.25,44.63,2651200,10.40
1989-08-31,44.50,45.00,44.25,44.50,2016400,10.37
1989-08-30,44.00,44.75,44.00,44.50,4161200,10.37
1989-08-29,44.75,45.00,43.75,44.13,6339200,10.28
1989-08-28,44.50,45.00,44.00,44.75,2936800,10.43
1989-08-25,44.00,45.00,44.00,44.75,5766400,10.43
1989-08-24,43.75,44.50,43.50,44.13,5829200,10.28
1989-08-23,43.00,44.25,42.50,43.75,6202400,10.19
1989-08-22,42.00,43.00,42.00,42.88,4013200,9.99
1989-08-21,42.25,43.25,42.00,42.25,4923600,9.84
1989-08-18,41.75,42.50,41.50,42.25,3003600,9.82
1989-08-17,40.25,41.25,40.00,41.00,5495600,9.53
1989-08-16,41.50,41.75,40.00,40.38,4318800,9.39
1989-08-15,40.75,41.50,40.75,41.38,5852000,9.62
1989-08-14,41.50,42.00,40.50,40.75,3690800,9.47
1989-08-11,44.00,44.00,41.25,41.88,8226800,9.74
1989-08-10,44.00,44.00,42.75,43.25,5442400,10.05
1989-08-09,44.00,45.75,43.88,44.00,6975600,10.23
1989-08-08,43.50,44.75,43.50,44.13,7366400,10.26
1989-08-07,43.00,44.00,42.63,43.75,6012000,10.17
1989-08-04,41.25,42.75,41.13,42.75,6564400,9.94
1989-08-03,40.50,41.50,40.50,41.25,6185600,9.59
1989-08-02,39.75,40.50,39.50,40.50,3633600,9.41
1989-08-01,39.75,40.25,39.25,39.88,4996800,9.27
1989-07-31,39.25,40.00,39.00,39.75,4014800,9.24
1989-07-28,39.25,39.75,39.00,39.38,4274400,9.15
1989-07-27,38.25,39.50,38.00,39.25,6193200,9.12
1989-07-26,38.25,38.50,37.75,38.25,8363600,8.89
1989-07-25,39.25,39.75,38.00,38.75,7502400,9.01
1989-07-24,39.75,39.75,39.25,39.25,4154800,9.12
1989-07-21,39.75,40.00,39.00,40.00,4993600,9.30
1989-07-20,40.75,41.25,39.75,40.00,8448800,9.30
1989-07-19,39.50,40.75,39.00,40.50,8543200,9.41
1989-07-18,40.75,40.75,38.75,39.25,17050800,9.12
1989-07-17,40.75,41.25,39.75,40.75,4694400,9.47
1989-07-14,40.75,41.00,39.75,40.75,9206800,9.47
1989-07-13,40.00,41.00,39.50,40.63,8057600,9.44
1989-07-12,39.75,40.25,39.50,40.00,4452000,9.30
1989-07-11,40.75,41.00,39.75,39.75,8729200,9.24
1989-07-10,41.00,41.25,40.00,40.50,7294400,9.41
1989-07-07,41.25,42.00,40.50,41.25,3806400,9.59
1989-07-06,40.75,41.75,40.25,41.25,6218000,9.59
1989-07-05,40.50,40.75,40.00,40.50,4264400,9.41
1989-07-03,41.75,41.75,40.75,40.75,1730800,9.47
1989-06-30,40.50,41.75,39.50,41.25,5885600,9.59
1989-06-29,41.00,41.25,40.00,40.63,8351200,9.44
1989-06-28,42.25,42.25,41.00,41.75,9190800,9.70
1989-06-27,43.75,44.25,42.50,42.63,3788000,9.91
1989-06-26,44.00,44.00,43.25,43.50,6568800,10.11
1989-06-23,43.25,44.25,43.25,43.88,4438800,10.20
1989-06-22,42.50,43.75,42.00,43.25,4911200,10.05
1989-06-21,43.00,43.50,42.25,42.50,4659200,9.88
1989-06-20,44.00,44.00,42.25,43.00,4807600,10.00
1989-06-19,44.50,44.75,43.50,44.00,6551200,10.23
1989-06-16,44.75,45.50,43.50,44.50,19378000,10.34
1989-06-15,49.50,49.75,47.50,47.50,5766800,11.04
1989-06-14,49.00,50.25,48.25,49.63,8983600,11.54
1989-06-13,47.50,48.75,47.00,48.50,8254400,11.27
1989-06-12,46.75,47.75,46.25,47.50,2892400,11.04
1989-06-09,47.25,47.75,46.50,47.00,3378800,10.93
1989-06-08,48.50,49.00,47.25,47.63,6378800,11.07
1989-06-07,46.75,48.50,46.75,48.25,6293200,11.22
1989-06-06,46.75,47.00,46.25,46.75,5189200,10.87
1989-06-05,48.75,49.00,46.50,47.00,4451200,10.93
1989-06-02,48.50,49.50,48.50,49.00,4448800,11.39
1989-06-01,47.75,49.25,47.50,48.75,6416800,11.33
1989-05-31,47.50,48.13,47.00,47.75,4134400,11.10
1989-05-30,48.25,49.00,47.38,47.50,4018000,11.04
1989-05-26,48.25,49.00,48.00,48.50,4028800,11.27
1989-05-25,47.25,49.00,47.25,48.25,8309200,11.22
1989-05-24,45.25,47.75,45.25,47.75,10645200,11.10
1989-05-23,46.00,46.00,45.25,45.50,4803600,10.58
1989-05-22,45.75,46.25,45.25,46.00,6800000,10.69
1989-05-19,44.75,46.25,44.75,45.75,11820800,10.61
1989-05-18,45.25,45.50,44.75,44.75,7558800,10.38
1989-05-17,45.25,45.50,45.00,45.25,8892400,10.50
1989-05-16,46.00,46.25,45.00,45.38,8170800,10.53
1989-05-15,44.75,46.25,44.75,46.00,11372400,10.67
1989-05-12,44.50,45.00,44.00,45.00,16685600,10.44
1989-05-11,43.25,44.25,43.00,43.88,10763600,10.18
1989-05-10,43.00,43.50,42.50,43.25,8380000,10.03
1989-05-09,42.00,43.00,42.00,42.50,12398800,9.86
1989-05-08,41.50,42.25,41.50,42.25,7373600,9.80
1989-05-05,42.50,42.75,41.50,41.50,16464400,9.63
1989-05-04,40.25,41.25,40.00,41.00,6762000,9.51
1989-05-03,39.75,40.75,39.75,40.25,7896800,9.34
1989-05-02,39.00,40.25,39.00,39.88,7719200,9.25
1989-05-01,38.50,39.25,38.50,39.00,2881200,9.05
1989-04-28,39.25,39.50,38.50,39.00,3725600,9.05
1989-04-27,39.50,40.00,39.00,39.38,4988000,9.13
1989-04-26,40.00,40.25,39.13,39.75,6652000,9.22
1989-04-25,40.00,40.50,39.75,40.00,4165600,9.28
1989-04-24,40.00,40.25,39.50,40.13,3977600,9.31
1989-04-21,40.50,40.88,39.75,40.13,4132000,9.31
1989-04-20,40.75,41.50,40.25,40.75,6434400,9.45
1989-04-19,40.00,41.63,39.75,40.88,15215600,9.48
1989-04-18,39.50,40.50,39.25,40.13,20055200,9.31
1989-04-17,38.50,39.25,38.00,39.25,5008000,9.10
1989-04-14,39.00,39.25,38.25,38.75,4408800,8.99
1989-04-13,38.75,39.50,38.25,38.50,6493200,8.93
1989-04-12,38.25,39.25,37.88,38.50,13862000,8.93
1989-04-11,37.50,38.00,37.00,37.75,5252400,8.76
1989-04-10,37.25,38.00,36.75,37.00,4854400,8.58
1989-04-07,36.00,37.50,36.00,37.38,12699200,8.67
1989-04-06,34.75,36.13,34.50,36.00,5598800,8.35
1989-04-05,34.50,35.25,34.25,35.00,4303200,8.12
1989-04-04,34.50,34.88,33.88,34.50,4140800,8.00
1989-04-03,35.50,36.25,34.75,35.00,5949200,8.12
1989-03-31,35.00,35.75,34.75,35.63,6630800,8.26
1989-03-30,34.25,35.00,34.00,34.75,3780800,8.06
1989-03-29,34.00,34.50,34.00,34.25,2666800,7.94
1989-03-28,34.00,34.50,34.00,34.00,5047600,7.89
1989-03-27,34.25,34.50,33.50,33.75,5425600,7.83
1989-03-23,34.00,34.50,33.75,34.38,4250800,7.97
1989-03-22,34.25,34.75,33.75,33.88,5180800,7.86
1989-03-21,35.50,35.50,34.75,34.88,4588800,8.09
1989-03-20,35.00,35.25,34.50,34.88,6480800,8.09
1989-03-17,34.50,35.75,34.00,34.88,8485200,8.09
1989-03-16,35.00,35.50,34.50,35.25,6880000,8.18
1989-03-15,35.25,35.50,34.75,35.00,3225600,8.12
1989-03-14,35.00,35.50,34.88,35.25,5796800,8.18
1989-03-13,35.00,35.50,34.75,35.00,4683600,8.12
1989-03-10,34.50,35.00,34.25,35.00,3684400,8.12
1989-03-09,35.25,35.75,34.50,34.50,4768800,8.00
1989-03-08,35.63,36.25,35.25,35.25,7727600,8.18
1989-03-07,35.50,36.00,35.00,35.75,9327600,8.29
1989-03-06,35.00,35.88,34.50,35.50,6028800,8.23
1989-03-03,35.25,35.25,34.00,34.75,13854400,8.06
1989-03-02,35.75,36.25,34.75,35.00,13440800,8.12
1989-03-01,36.25,36.50,35.50,36.00,6096400,8.35
1989-02-28,36.50,36.75,36.00,36.25,6290000,8.41
1989-02-27,36.00,36.50,35.75,36.50,4151200,8.47
1989-02-24,37.00,37.00,36.00,36.00,5452000,8.35
1989-02-23,36.50,37.00,36.25,36.75,3409200,8.52
1989-02-22,37.25,37.50,36.50,36.75,8529200,8.52
1989-02-21,36.88,37.75,36.75,37.50,6808800,8.70
1989-02-17,36.25,37.00,36.25,36.75,4180800,8.52
1989-02-16,36.25,37.25,36.00,36.38,9138800,8.41
1989-02-15,35.75,36.25,35.50,36.25,11812400,8.38
1989-02-14,36.88,37.00,35.25,35.75,31843200,8.27
1989-02-13,36.75,37.25,36.75,37.00,8422000,8.56
1989-02-10,38.25,38.25,37.00,37.25,12441200,8.62
1989-02-09,38.25,39.00,38.00,38.25,5756400,8.85
1989-02-08,39.00,39.50,38.00,38.25,5612000,8.85
1989-02-07,38.25,39.25,38.25,39.00,5908800,9.02
1989-02-06,39.50,39.50,38.25,38.50,4174400,8.91
1989-02-03,40.00,40.25,39.00,39.25,6406400,9.08
1989-02-02,39.50,40.25,39.25,39.75,16927600,9.19
1989-02-01,37.75,39.63,37.38,39.25,17420000,9.08
1989-01-31,37.25,37.75,36.75,37.75,16442000,8.73
1989-01-30,37.63,38.00,37.25,37.38,20961200,8.65
1989-01-27,38.25,39.25,36.25,37.63,75976400,8.70
1989-01-26,40.75,42.13,40.63,41.75,10203600,9.66
1989-01-25,41.75,42.00,41.00,41.50,3963200,9.60
1989-01-24,41.00,41.75,40.75,41.63,7983200,9.63
1989-01-23,40.75,41.25,40.75,41.00,6452000,9.48
1989-01-20,40.50,41.50,40.25,41.00,6207600,9.48
1989-01-19,40.50,41.00,40.00,40.50,9155200,9.37
1989-01-18,40.75,41.13,39.50,39.75,17440800,9.19
1989-01-17,43.25,43.50,40.00,40.38,27033600,9.34
1989-01-16,43.25,44.00,43.00,43.75,6033200,10.12
1989-01-13,42.75,43.50,42.38,43.25,6928000,10.00
1989-01-12,42.25,43.00,42.00,42.75,5373200,9.89
1989-01-11,42.25,42.50,41.25,42.13,5585200,9.74
1989-01-10,42.50,42.88,41.50,42.63,3695600,9.86
1989-01-09,43.00,43.13,42.25,43.00,2850800,9.95
1989-01-06,42.25,43.50,42.25,42.63,7103600,9.86
1989-01-05,42.00,43.25,41.25,42.25,10985200,9.77
1989-01-04,40.75,42.13,40.50,42.00,8575200,9.71
1989-01-03,40.25,40.50,40.00,40.38,3578800,9.34
1988-12-30,40.50,41.25,40.25,40.25,2938800,9.31
1988-12-29,40.25,40.75,40.25,40.50,4212000,9.37
1988-12-28,40.50,40.75,39.75,40.25,1841200,9.31
1988-12-27,41.00,41.50,40.50,40.50,2155200,9.37
1988-12-23,41.00,41.38,41.00,41.13,1475600,9.51
1988-12-22,41.75,42.00,40.75,41.00,3802000,9.48
1988-12-21,41.00,42.00,41.00,41.75,8642400,9.66
1988-12-20,41.00,41.50,40.63,41.00,9810800,9.48
1988-12-19,40.25,41.00,40.00,40.75,8373600,9.43
1988-12-16,39.50,40.50,39.25,40.13,6572000,9.28
1988-12-15,40.00,40.50,39.25,39.50,4032000,9.14
1988-12-14,38.50,40.00,38.50,39.75,6916800,9.19
1988-12-13,38.50,38.75,38.25,38.75,4386400,8.96
1988-12-12,39.25,39.50,38.50,38.50,4215600,8.91
1988-12-09,39.25,39.50,38.75,39.13,1608800,9.05
1988-12-08,39.25,39.25,38.75,39.13,2125600,9.05
1988-12-07,39.00,39.50,38.75,39.38,3518800,9.11
1988-12-06,39.25,39.75,39.00,39.50,3763200,9.14
1988-12-05,39.50,40.00,38.75,39.50,5534400,9.14
1988-12-02,38.25,39.88,38.00,39.25,11940000,9.08
1988-12-01,37.75,39.00,37.50,38.75,7586800,8.96
1988-11-30,36.75,38.00,36.75,37.63,6013600,8.70
1988-11-29,36.50,36.75,36.00,36.75,3326400,8.50
1988-11-28,36.50,36.75,36.00,36.50,4986800,8.44
1988-11-25,36.25,36.75,36.00,36.50,1727600,8.44
1988-11-23,35.75,37.00,35.50,36.88,6733200,8.53
1988-11-22,36.50,36.88,36.00,36.13,5299200,8.36
1988-11-21,37.50,37.75,36.25,36.63,7928000,8.47
1988-11-18,38.50,38.50,38.00,38.00,2066400,8.77
1988-11-17,38.00,38.50,38.00,38.25,2841200,8.82
1988-11-16,39.00,39.25,37.75,38.00,5280000,8.77
1988-11-15,39.00,39.25,38.75,39.00,2866800,9.00
1988-11-14,38.75,39.00,38.25,38.88,3046400,8.97
1988-11-11,39.00,39.63,38.50,38.50,3882400,8.88
1988-11-10,39.50,39.75,39.00,39.50,3573200,9.11
1988-11-09,38.25,39.38,38.00,39.25,7206800,9.05
1988-11-08,37.50,38.75,37.38,38.50,5540800,8.88
1988-11-07,37.25,37.75,37.00,37.50,6093600,8.65
1988-11-04,36.75,38.00,36.75,37.75,5500000,8.71
1988-11-03,37.25,37.50,36.75,37.13,8670000,8.57
1988-11-02,38.25,38.25,36.75,37.25,7451200,8.59
1988-11-01,38.50,38.75,37.75,38.00,5138800,8.77
1988-10-31,38.75,38.75,37.50,38.63,8695200,8.91
1988-10-28,39.00,39.50,38.50,38.50,3026800,8.88
1988-10-27,38.75,39.25,38.25,39.00,5138000,9.00
1988-10-26,40.00,40.00,38.50,39.25,6751200,9.05
1988-10-25,40.25,40.25,39.75,39.88,3043600,9.20
1988-10-24,41.25,41.25,39.63,40.00,4842400,9.23
1988-10-21,41.25,41.75,40.75,41.00,4422400,9.46
1988-10-20,40.00,41.63,40.00,41.50,6215200,9.57
1988-10-19,39.75,40.75,39.50,40.00,9918000,9.23
1988-10-18,39.00,39.50,38.25,39.38,5100000,9.08
1988-10-17,38.50,39.00,38.25,38.50,3360800,8.88
1988-10-14,39.50,39.50,38.13,38.75,5625200,8.94
1988-10-13,38.50,39.75,38.50,39.00,5892400,9.00
1988-10-12,38.50,39.00,38.00,38.75,4763600,8.94
1988-10-11,38.25,39.50,38.25,39.00,6964400,9.00
1988-10-10,39.50,39.75,37.50,38.50,11880000,8.88
1988-10-07,39.00,39.75,38.38,39.75,16355200,9.17
1988-10-06,40.50,40.88,39.25,39.75,6009200,9.17
1988-10-05,41.25,41.75,40.50,40.88,4400000,9.43
1988-10-04,42.25,42.75,41.13,41.50,1847600,9.57
1988-10-03,43.00,43.25,42.00,42.50,3243200,9.80
1988-09-30,44.00,44.00,43.25,43.25,3338800,9.98
1988-09-29,43.75,44.25,43.50,44.00,3804400,10.15
1988-09-28,43.50,44.13,43.25,43.50,3038800,10.04
1988-09-27,42.50,43.50,42.50,43.38,5832400,10.01
1988-09-26,43.75,44.00,42.50,42.75,3124400,9.86
1988-09-23,43.50,44.25,43.50,43.75,3638000,10.09
1988-09-22,43.00,44.00,42.75,44.00,5203600,10.15
1988-09-21,41.75,43.00,41.50,42.75,3274800,9.86
1988-09-20,41.75,42.25,41.38,41.50,3682400,9.57
1988-09-19,42.00,42.25,41.25,41.75,3296400,9.63
1988-09-16,41.50,42.75,41.38,42.25,4431200,9.75
1988-09-15,42.00,42.75,41.50,41.63,5920000,9.60
1988-09-14,41.75,42.38,41.50,42.00,8520800,9.69
1988-09-13,40.25,41.25,40.00,41.00,4293600,9.46
1988-09-12,41.00,41.75,40.13,41.00,5290800,9.46
1988-09-09,38.75,41.00,37.75,40.50,8393200,9.34
1988-09-08,38.25,39.50,37.75,38.75,7403200,8.94
1988-09-07,39.00,39.50,37.75,38.25,6417600,8.82
1988-09-06,40.00,40.00,38.75,38.88,5125200,8.97
1988-09-02,39.50,40.00,39.00,39.75,6661200,9.17
1988-09-01,39.75,39.75,38.50,38.88,8818800,8.97
1988-08-31,41.00,41.13,39.50,39.88,8493600,9.20
1988-08-30,40.75,41.00,40.00,40.88,1809200,9.43
1988-08-29,40.75,41.00,40.50,40.88,2046400,9.43
1988-08-26,40.00,40.75,40.00,40.25,1453200,9.29
1988-08-25,40.25,40.50,39.25,40.13,4560000,9.26
1988-08-24,39.75,40.75,39.50,40.75,4482000,9.40
1988-08-23,39.75,40.25,39.25,39.50,5843200,9.11
1988-08-22,40.25,40.75,39.50,39.75,6100000,9.17
1988-08-19,42.50,42.75,40.50,40.75,8120000,9.40
1988-08-18,42.00,43.00,41.75,42.50,2648000,9.80
1988-08-17,42.50,42.75,41.75,42.00,4252400,9.69
1988-08-16,41.00,43.25,40.75,42.50,4397600,9.80
1988-08-15,42.25,42.25,40.50,41.25,5971200,9.52
1988-08-12,43.00,43.00,42.25,42.50,2771200,9.79
1988-08-11,42.25,43.25,42.00,43.25,3803200,9.96
1988-08-10,43.75,43.75,41.75,41.88,5300800,9.64
1988-08-09,44.00,44.25,43.00,43.50,6090800,10.02
1988-08-08,44.50,44.75,44.00,44.00,1085600,10.13
1988-08-05,44.50,45.00,44.25,44.25,1881200,10.19
1988-08-04,44.75,45.25,44.50,44.63,2473200,10.28
1988-08-03,44.75,44.75,44.00,44.75,3980800,10.30
1988-08-02,45.00,45.50,44.50,44.63,4338000,10.28
1988-08-01,44.50,45.75,44.25,45.00,3085600,10.36
1988-07-29,43.25,44.50,43.00,44.38,5697600,10.22
1988-07-28,42.50,43.00,42.25,42.63,3326800,9.82
1988-07-27,42.75,43.25,42.50,42.75,4162400,9.84
1988-07-26,42.75,43.25,42.25,42.75,3640800,9.84
1988-07-25,42.75,43.25,42.25,42.75,3794400,9.84
1988-07-22,43.00,43.25,42.50,42.50,3724800,9.79
1988-07-21,43.75,44.00,42.75,43.00,5323600,9.90
1988-07-20,44.75,45.00,44.00,44.25,4293600,10.19
1988-07-19,45.00,45.50,43.88,44.75,4372400,10.30
1988-07-18,45.38,46.00,45.25,45.50,4061200,10.48
1988-07-15,45.00,45.50,44.75,45.00,2968000,10.36
1988-07-14,44.75,45.25,44.50,45.00,2245200,10.36
1988-07-13,44.75,45.00,44.25,44.75,4132000,10.30
1988-07-12,45.00,45.25,44.50,44.75,3605600,10.30
1988-07-11,45.50,45.50,44.88,45.13,2646400,10.39
1988-07-08,45.50,46.00,45.00,45.25,3766400,10.42
1988-07-07,46.50,46.50,45.25,45.88,3778000,10.56
1988-07-06,47.13,47.50,46.13,46.50,5608800,10.71
1988-07-05,46.50,47.25,46.13,47.25,3736400,10.88
1988-07-01,46.50,46.88,46.25,46.50,3385600,10.71
1988-06-30,46.25,46.75,46.00,46.25,4104800,10.65
1988-06-29,46.00,46.75,45.75,46.38,5125200,10.68
1988-06-28,44.75,46.25,44.50,46.25,5809200,10.65
1988-06-27,44.50,45.38,44.50,44.50,3001200,10.25
1988-06-24,45.00,45.50,44.50,45.00,2684400,10.36
1988-06-23,45.75,45.75,45.00,45.00,2566400,10.36
1988-06-22,45.50,45.88,45.00,45.63,6998000,10.51
1988-06-21,44.00,45.00,43.88,44.88,4422000,10.33
1988-06-20,44.38,44.75,44.00,44.13,2811200,10.16
1988-06-17,44.75,44.75,44.25,44.75,3410800,10.30
1988-06-16,45.00,45.25,44.25,44.50,3854400,10.25
1988-06-15,45.25,45.75,45.00,45.75,4360000,10.53
1988-06-14,45.25,46.00,45.00,45.25,10445600,10.42
1988-06-13,45.00,45.25,44.25,45.00,5320000,10.36
1988-06-10,43.50,44.75,43.00,44.50,6320000,10.25
1988-06-09,45.00,45.25,43.25,43.50,9640000,10.02
1988-06-08,44.25,45.50,44.00,45.00,9240000,10.36
1988-06-07,43.75,45.25,43.50,44.00,11120000,10.13
1988-06-06,42.75,44.00,42.75,44.00,5880000,10.13
1988-06-03,41.75,43.25,41.75,43.00,6280000,9.90
1988-06-02,42.00,42.50,41.50,41.75,4760000,9.61
1988-06-01,41.50,42.50,41.25,42.50,8200000,9.79
1988-05-31,40.00,41.50,39.75,41.50,4400000,9.56
1988-05-27,39.25,40.00,39.00,39.75,3020000,9.15
1988-05-26,38.50,39.50,38.50,39.38,3076800,9.07
1988-05-25,39.00,39.75,38.50,38.50,4840000,8.87
1988-05-24,38.00,39.00,37.75,38.88,5080000,8.95
1988-05-23,38.50,38.88,37.38,38.00,6560000,8.75
1988-05-20,39.25,39.50,38.75,38.75,2941200,8.92
1988-05-19,39.50,39.75,38.50,39.00,8920000,8.98
1988-05-18,40.50,40.75,39.50,39.75,6240000,9.15
1988-05-17,41.50,42.00,40.25,40.50,6920000,9.33
1988-05-16,40.50,41.38,40.00,41.25,2686800,9.50
1988-05-13,40.25,40.50,40.00,40.50,2566800,9.31
1988-05-12,39.50,40.25,39.50,39.75,2965600,9.13
1988-05-11,40.25,40.75,39.50,39.50,6240000,9.08
1988-05-10,40.50,41.00,40.25,40.88,3439200,9.39
1988-05-09,41.25,41.25,40.50,40.75,2732000,9.36
1988-05-06,41.63,41.75,41.25,41.25,3835600,9.48
1988-05-05,42.00,42.25,41.50,41.75,2536800,9.59
1988-05-04,41.88,43.13,41.75,42.00,8000000,9.65
1988-05-03,41.00,42.25,40.75,41.75,4440000,9.59
1988-05-02,40.75,41.25,40.50,41.00,2944400,9.42
1988-04-29,41.25,41.50,40.50,41.00,3222000,9.42
1988-04-28,41.75,42.00,41.25,41.38,3553600,9.51
1988-04-27,41.75,42.00,41.50,41.75,4520000,9.59
1988-04-26,41.00,41.75,40.75,41.50,6280000,9.54
1988-04-25,40.25,41.00,40.00,40.88,5360000,9.39
1988-04-22,39.75,40.25,39.50,40.13,3846800,9.22
1988-04-21,40.38,40.50,39.00,39.50,6360000,9.08
1988-04-20,40.25,40.50,39.25,39.75,7680000,9.13
1988-04-19,40.13,41.50,40.13,40.25,7596400,9.25
1988-04-18,39.75,40.75,39.25,40.00,6080000,9.19
1988-04-15,39.75,40.00,38.50,39.50,8320000,9.08
1988-04-14,40.50,41.50,39.00,39.50,6720000,9.08
1988-04-13,41.75,42.00,41.00,41.25,5120000,9.48
1988-04-12,41.75,42.25,41.25,41.75,6200000,9.59
1988-04-11,41.75,42.00,41.00,41.50,5320000,9.54
1988-04-08,40.75,41.75,39.75,41.00,7240000,9.42
1988-04-07,41.75,42.38,40.75,40.75,5840000,9.36
1988-04-06,39.50,41.75,39.00,41.75,6800000,9.59
1988-04-05,39.25,39.50,38.50,39.25,5280000,9.02
1988-04-04,39.75,40.50,38.50,38.75,6480000,8.91
1988-03-31,39.75,40.50,39.25,40.00,7760000,9.19
1988-03-30,40.75,41.25,38.75,39.50,13280000,9.08
1988-03-29,41.50,42.00,40.63,41.00,7640000,9.42
1988-03-28,40.00,41.75,39.50,41.50,6160000,9.54
1988-03-25,40.75,41.25,40.00,40.13,4680000,9.22
1988-03-24,41.75,42.50,40.00,40.88,11440000,9.39
1988-03-23,44.00,44.00,41.88,42.50,7480000,9.77
1988-03-22,44.00,44.50,43.25,44.00,4265600,10.11
1988-03-21,44.38,44.63,43.00,43.88,8120000,10.08
1988-03-18,45.00,45.50,44.25,44.75,9720000,10.28
1988-03-17,46.25,46.50,44.75,45.00,9320000,10.34
1988-03-16,44.88,46.38,44.50,46.13,4240000,10.60
1988-03-15,46.00,46.25,44.75,45.00,6480000,10.34
1988-03-14,45.75,46.50,45.50,46.25,3518000,10.63
1988-03-11,45.50,45.75,44.50,45.75,5640000,10.51
1988-03-10,47.00,47.25,45.25,45.25,6320000,10.40
1988-03-09,46.25,47.25,46.25,46.75,4800000,10.74
1988-03-08,46.75,47.00,46.00,46.25,5160000,10.63
1988-03-07,46.75,47.75,46.50,46.88,7400000,10.77
1988-03-04,46.00,47.00,45.50,46.88,7480000,10.77
1988-03-03,44.50,47.00,44.50,46.50,16920000,10.69
1988-03-02,43.75,45.00,43.50,44.75,10440000,10.28
1988-03-01,43.25,43.50,42.50,43.25,6120000,9.94
1988-02-29,41.75,43.25,41.50,43.00,4000000,9.88
1988-02-26,42.00,42.25,41.25,41.75,2952400,9.59
1988-02-25,42.00,43.00,41.75,41.75,6400000,9.59
1988-02-24,42.75,43.00,42.00,42.25,5200000,9.71
1988-02-23,43.25,43.75,42.25,42.75,7880000,9.82
1988-02-22,41.50,43.63,41.50,43.25,7160000,9.94
1988-02-19,41.75,42.00,41.50,41.75,3242400,9.59
1988-02-18,41.63,42.75,41.50,41.75,5120000,9.59
1988-02-17,41.25,42.50,41.25,41.88,9160000,9.62
1988-02-16,41.00,41.25,40.00,41.25,5520000,9.48
1988-02-12,40.63,41.50,40.50,41.00,4920000,9.42
1988-02-11,41.00,41.25,40.25,40.63,5280000,9.32
1988-02-10,39.75,41.50,39.75,41.00,8160000,9.40
1988-02-09,39.00,39.88,38.75,39.75,4160000,9.12
1988-02-08,38.50,39.25,37.75,38.75,7280000,8.89
1988-02-05,40.00,40.38,38.50,38.63,4720000,8.86
1988-02-04,39.50,40.13,39.00,39.75,7120000,9.12
1988-02-03,41.00,41.25,39.25,39.50,8080000,9.06
1988-02-02,41.50,41.88,40.50,41.25,6840000,9.46
1988-02-01,41.75,42.50,41.38,41.75,7120000,9.58
1988-01-29,41.50,41.75,40.25,41.50,9480000,9.52
1988-01-28,40.00,41.50,39.75,41.25,8320000,9.46
1988-01-27,40.25,40.50,38.75,39.75,9240000,9.12
1988-01-26,40.75,41.00,39.25,39.75,5120000,9.12
1988-01-25,39.50,41.50,39.50,40.88,7160000,9.38
1988-01-22,40.50,40.75,38.25,39.25,15920000,9.00
1988-01-21,40.50,40.75,39.38,40.13,17640000,9.20
1988-01-20,43.00,43.00,38.25,39.75,24320000,9.12
1988-01-19,42.25,43.25,41.38,42.75,9800000,9.80
1988-01-18,43.00,43.00,42.00,42.75,4480000,9.80
1988-01-15,43.50,45.00,42.50,42.88,12280000,9.83
1988-01-14,42.75,42.88,42.00,42.25,4720000,9.69
1988-01-13,42.00,43.25,41.13,42.25,7560000,9.69
1988-01-12,43.00,43.50,39.75,42.00,14320000,9.63
1988-01-11,40.00,42.75,39.75,42.50,14440000,9.75
1988-01-08,44.50,45.25,39.50,40.00,17360000,9.17
1988-01-07,43.50,44.75,42.50,44.50,7600000,10.21
1988-01-06,45.00,45.00,43.75,43.75,9600000,10.03
1988-01-05,46.00,46.25,44.25,44.63,11040000,10.24
1988-01-04,42.75,44.75,42.25,44.75,11800000,10.26
1987-12-31,42.50,43.00,41.88,42.00,4200000,9.63
1987-12-30,42.50,43.75,42.50,43.38,5560000,9.95
1987-12-29,40.50,42.25,40.25,42.13,4240000,9.66
1987-12-28,42.25,42.50,39.50,40.25,8200000,9.23
1987-12-24,42.00,43.00,41.75,42.63,2508000,9.78
1987-12-23,41.75,42.75,41.25,42.25,6120000,9.69
1987-12-22,41.75,41.75,40.50,41.50,4600000,9.52
1987-12-21,40.50,41.75,40.25,41.75,6720000,9.58
1987-12-18,39.50,41.25,39.25,40.50,10800000,9.29
1987-12-17,40.50,40.75,39.25,39.25,11640000,9.00
1987-12-16,37.75,39.75,37.25,39.25,11800000,9.00
1987-12-15,37.75,38.25,37.00,37.50,10680000,8.60
1987-12-14,34.50,37.50,34.25,37.25,12200000,8.54
1987-12-11,34.75,34.75,33.50,34.00,4360000,7.80
1987-12-10,33.75,36.00,33.25,34.75,9880000,7.97
1987-12-09,34.50,36.25,33.88,35.00,6400000,8.03
1987-12-08,33.50,34.88,33.25,34.50,9080000,7.91
1987-12-07,31.00,33.25,31.00,33.00,7280000,7.57
1987-12-04,30.25,31.25,29.75,30.75,8720000,7.05
1987-12-03,33.00,33.38,29.75,30.50,11400000,7.00
1987-12-02,33.25,33.50,32.50,32.50,5080000,7.45
1987-12-01,33.50,34.00,32.75,33.25,6480000,7.63
1987-11-30,33.75,34.50,30.50,33.00,14880000,7.57
1987-11-27,36.25,36.50,34.75,35.00,2526800,8.03
1987-11-25,37.00,37.00,36.00,36.50,3311200,8.37
1987-11-24,36.75,37.75,36.13,37.00,7040000,8.49
1987-11-23,35.50,36.25,34.75,36.25,3500000,8.31
1987-11-20,34.00,36.00,33.25,35.50,8960000,8.14
1987-11-19,36.50,36.50,34.00,34.50,6520000,7.91
1987-11-18,35.75,36.50,34.50,36.25,9480000,8.31
1987-11-17,36.75,37.00,35.00,35.00,9600000,8.03
1987-11-16,37.75,38.50,36.50,36.75,6600000,8.41
1987-11-13,39.25,39.50,37.00,37.25,5520000,8.52
1987-11-12,38.50,40.00,38.38,38.75,8800000,8.87
1987-11-11,37.25,38.25,36.75,37.25,6640000,8.52
1987-11-10,36.50,37.50,36.00,36.25,8280000,8.30
1987-11-09,37.00,37.50,36.25,37.25,7520000,8.52
1987-11-06,38.25,39.50,37.00,37.75,6680000,8.64
1987-11-05,36.25,38.75,36.25,38.00,9120000,8.70
1987-11-04,35.50,37.25,34.75,36.00,8360000,8.24
1987-11-03,38.00,38.50,34.25,36.25,11200000,8.30
1987-11-02,38.75,39.50,37.50,38.75,6720000,8.87
1987-10-30,40.00,43.00,38.50,38.63,15040000,8.84
1987-10-29,34.25,40.00,32.25,39.50,11840000,9.04
1987-10-28,30.75,33.75,29.25,33.50,14960000,7.67
1987-10-27,29.50,32.25,29.00,30.25,16280000,6.92
1987-10-26,34.50,35.00,27.62,28.00,11200000,6.41
1987-10-23,35.75,36.50,34.25,35.50,7080000,8.12
1987-10-22,39.25,40.50,36.00,36.75,13760000,8.41
1987-10-21,38.50,42.00,38.00,40.50,19080000,9.27
1987-10-20,38.50,42.00,32.63,34.50,20320000,7.90
1987-10-19,48.25,48.25,35.50,36.50,17000000,8.35
1987-10-16,52.25,53.00,47.50,48.25,15000000,11.04
1987-10-15,53.25,54.50,51.75,52.00,12440000,11.90
1987-10-14,53.75,54.00,52.00,53.25,9240000,12.19
1987-10-13,54.50,54.75,53.25,54.50,5800000,12.47
1987-10-12,54.25,54.38,51.75,53.25,7120000,12.19
1987-10-09,54.25,55.50,54.00,54.13,5200000,12.39
1987-10-08,55.50,56.00,53.25,54.25,5880000,12.42
1987-10-07,55.50,55.75,54.25,55.50,8000000,12.70
1987-10-06,59.50,59.50,55.50,55.75,7200000,12.76
1987-10-05,58.50,59.75,57.75,59.25,4800000,13.56
1987-10-02,58.25,58.75,57.50,58.50,3450000,13.39
1987-10-01,56.75,58.75,56.50,58.25,4160000,13.33
1987-09-30,54.25,57.00,54.25,56.50,4360000,12.93
1987-09-29,56.00,56.00,54.25,54.50,6120000,12.47
1987-09-28,57.50,58.75,55.50,55.75,7280000,12.76
1987-09-25,56.75,58.00,56.50,57.50,3806800,13.16
1987-09-24,55.25,57.88,55.25,56.50,6520000,12.93
1987-09-23,54.13,56.00,53.75,55.25,9098800,12.64
1987-09-22,50.50,54.25,50.25,54.13,5480000,12.39
1987-09-21,51.75,52.75,50.25,50.25,4600000,11.50
1987-09-18,52.00,52.25,51.38,51.75,2555600,11.84
1987-09-17,52.00,52.25,51.00,52.00,2400000,11.90
1987-09-16,51.75,52.63,51.25,51.75,6000000,11.84
1987-09-15,53.00,53.00,51.50,51.75,3744800,11.84
1987-09-14,54.75,55.25,52.75,53.00,2928000,12.13
1987-09-11,54.00,55.50,52.75,54.50,4440000,12.47
1987-09-10,53.25,54.50,53.13,53.75,5000000,12.30
1987-09-09,50.25,53.00,49.50,52.75,5640000,12.07
1987-09-08,50.25,50.50,48.50,49.88,6280000,11.42
1987-09-04,51.25,51.75,50.00,50.50,3891200,11.56
1987-09-03,52.50,52.75,50.25,51.25,6600000,11.73
1987-09-02,52.00,53.25,50.75,52.00,8200000,11.90
1987-09-01,54.75,55.25,52.50,52.50,4960000,12.01
1987-08-31,52.25,54.25,51.75,54.00,5360000,12.36
1987-08-28,52.00,52.50,51.50,52.00,3434400,11.90
1987-08-27,52.25,52.75,51.50,52.00,4440000,11.90
1987-08-26,53.00,53.50,52.00,52.00,7000000,11.90
1987-08-25,52.75,53.25,52.00,52.00,4880000,11.90
1987-08-24,53.00,53.50,52.25,52.25,4320000,11.96
1987-08-21,51.75,53.75,51.50,53.00,5000000,12.13
1987-08-20,50.25,52.50,49.75,51.75,6280000,11.84
1987-08-19,49.50,50.00,49.00,50.00,2404400,11.44
1987-08-18,49.25,49.50,48.25,48.75,8480000,11.16
1987-08-17,49.50,50.00,48.75,49.50,5200000,11.33
1987-08-14,48.50,50.00,48.00,49.00,3758800,11.21
1987-08-13,48.75,50.25,48.50,49.00,7000000,11.21
1987-08-12,49.50,49.75,48.25,48.75,5760000,11.16
1987-08-11,49.50,50.25,48.75,49.50,9680000,11.33
1987-08-10,48.25,48.25,45.75,48.25,2800000,11.04
1987-08-07,46.25,47.25,46.00,46.50,5440000,10.63
1987-08-06,43.25,46.75,42.75,46.25,9000000,10.57
1987-08-05,42.25,43.50,42.00,43.25,4640000,9.89
1987-08-04,40.50,42.25,40.00,42.25,4320000,9.66
1987-08-03,41.00,41.50,40.25,40.25,2275600,9.20
1987-07-31,41.25,42.00,41.25,41.25,2613600,9.43
1987-07-30,41.00,41.50,40.75,41.50,3727600,9.49
1987-07-29,42.00,42.00,40.50,41.00,3534800,9.37
1987-07-28,42.50,42.75,41.75,41.88,2660800,9.57
1987-07-27,42.50,43.00,42.00,42.25,2035600,9.66
1987-07-24,41.50,42.75,41.50,42.50,4200000,9.71
1987-07-23,43.00,43.50,40.50,41.75,2685600,9.54
1987-07-22,41.50,42.75,41.25,42.50,2185200,9.71
1987-07-21,42.00,42.50,41.25,41.38,3966400,9.46
1987-07-20,43.00,43.25,41.50,41.75,4440000,9.54
1987-07-17,44.25,44.75,42.75,43.25,3300000,9.89
1987-07-16,44.00,44.00,43.25,44.00,3388000,10.06
1987-07-15,43.00,44.75,42.25,44.00,9680000,10.06
1987-07-14,41.00,43.00,41.00,43.00,9200000,9.83
1987-07-13,39.00,40.75,38.75,40.50,9120000,9.26
1987-07-10,38.00,39.25,37.75,38.00,5600000,8.69
1987-07-09,37.25,38.75,37.25,37.75,8560000,8.63
1987-07-08,39.25,39.25,36.50,37.25,12200000,8.51
1987-07-07,40.50,41.00,38.75,39.25,7280000,8.97
1987-07-06,40.75,41.75,40.50,40.75,3060800,9.31
1987-07-02,40.00,41.00,39.75,40.63,2931200,9.29
1987-07-01,40.75,40.75,39.75,40.00,3402000,9.14
1987-06-30,40.50,41.00,39.75,40.50,5160000,9.26
1987-06-29,40.50,40.75,40.00,40.75,3628000,9.31
1987-06-26,40.75,41.50,40.00,40.50,4560000,9.26
1987-06-25,42.00,42.50,40.50,40.50,4320000,9.26
1987-06-24,41.50,43.25,40.50,42.00,4240000,9.60
1987-06-23,42.00,42.13,40.75,41.25,2892000,9.43
1987-06-22,41.25,42.25,40.88,42.00,6040000,9.60
1987-06-19,41.50,41.75,40.38,41.00,4480000,9.37
1987-06-18,40.25,41.75,39.50,41.50,8200000,9.49
1987-06-17,41.50,42.50,40.00,40.50,10640000,9.26
1987-06-16,41.50,41.75,38.00,41.50,12240000,9.49
1987-06-15,79.00,79.50,77.50,78.50,9280000,8.97
1987-06-12,79.00,79.75,78.75,79.00,3653600,9.03
1987-06-11,78.50,80.00,78.00,79.00,4521600,9.03
1987-06-10,78.75,80.25,78.00,78.50,5235200,8.97
1987-06-09,77.50,79.50,77.50,78.50,4570400,8.97
1987-06-08,77.75,78.00,76.75,77.75,7213600,8.89
1987-06-05,78.75,78.75,77.75,77.75,4696000,8.89
1987-06-04,78.00,78.75,77.00,78.50,5511200,8.97
1987-06-03,77.25,79.50,77.25,77.75,6140000,8.89
1987-06-02,77.50,78.00,77.00,77.25,4927200,8.83
1987-06-01,79.50,79.50,77.50,77.75,2984000,8.89
1987-05-29,80.25,80.50,79.00,79.00,3322400,9.03
1987-05-28,79.50,80.25,78.50,80.00,5424000,9.14
1987-05-27,78.00,80.25,77.50,79.50,6484000,9.09
1987-05-26,74.50,78.00,74.00,78.00,5481600,8.91
1987-05-22,75.00,75.50,73.75,74.12,3484000,8.47
1987-05-21,74.75,75.75,74.50,74.50,6233600,8.51
1987-05-20,73.00,75.00,72.50,74.50,10320000,8.51
1987-05-19,75.75,75.75,72.62,73.25,8560000,8.37
1987-05-18,78.25,78.50,75.50,75.75,8640000,8.66
1987-05-15,79.25,79.25,78.00,78.25,5220000,8.94
1987-05-14,78.25,79.50,78.25,79.25,5316000,9.06
1987-05-13,75.75,78.62,75.50,78.50,11120000,8.97
1987-05-12,76.00,76.50,75.00,75.50,9280000,8.63
1987-05-11,77.00,79.50,76.75,77.00,7048800,8.80
1987-05-08,80.50,81.00,79.00,79.00,6618400,9.01
1987-05-07,79.75,81.00,79.75,80.25,6488800,9.16
1987-05-06,80.50,82.25,79.25,80.00,10240000,9.13
1987-05-05,80.00,80.75,78.00,80.25,8240000,9.16
1987-05-04,79.50,80.25,79.00,79.75,5095200,9.10
1987-05-01,79.50,80.00,78.75,80.00,4751200,9.13
1987-04-30,78.00,80.00,77.75,79.25,9040000,9.04
1987-04-29,77.25,79.75,77.00,77.75,10400000,8.87
1987-04-28,75.75,77.87,75.50,77.00,11600000,8.79
1987-04-27,74.25,75.25,73.25,75.00,13680000,8.56
1987-04-24,75.75,76.50,74.50,74.75,9120000,8.53
1987-04-23,74.25,77.25,74.25,76.00,10880000,8.67
1987-04-22,76.62,77.00,74.00,74.25,14400000,8.47
1987-04-21,70.25,75.00,69.50,74.75,15440000,8.53
1987-04-20,71.50,72.75,70.75,71.12,5353600,8.12
1987-04-16,71.25,73.25,71.00,71.50,12400000,8.16
1987-04-15,69.50,71.00,68.75,71.00,12480000,8.10
1987-04-14,66.75,69.75,66.50,68.00,14560000,7.76
1987-04-13,70.00,70.25,67.50,67.50,5101600,7.70
1987-04-10,71.25,71.50,69.75,70.25,7791200,8.02
1987-04-09,68.75,71.50,67.75,71.00,8480000,8.10
1987-04-08,67.75,70.25,67.50,69.00,8240000,7.87
1987-04-07,69.75,70.25,67.75,67.75,9280000,7.73
1987-04-06,71.50,72.75,69.25,70.00,10320000,7.99
1987-04-03,71.50,71.87,70.25,71.75,19280000,8.19
1987-04-02,68.25,71.75,67.00,71.75,27760000,8.19
1987-04-01,63.00,67.00,62.38,66.75,7792800,7.62
1987-03-31,62.25,64.75,62.25,64.50,9760000,7.36
1987-03-30,63.50,64.25,62.25,62.50,9280000,7.13
1987-03-27,67.25,67.50,64.75,65.00,4817600,7.42
1987-03-26,66.75,67.75,66.50,67.25,5146400,7.67
1987-03-25,66.50,67.00,65.25,66.75,9760000,7.62
1987-03-24,67.75,68.50,66.25,66.25,9600000,7.56
1987-03-23,68.00,68.25,66.25,67.50,8800000,7.70
1987-03-20,68.25,69.75,68.25,68.25,12400000,7.79
1987-03-19,65.75,68.50,65.50,68.37,7396000,7.80
1987-03-18,67.25,67.50,64.75,66.00,10800000,7.53
1987-03-17,65.50,68.00,65.00,67.00,8720000,7.65
1987-03-16,63.50,65.25,62.50,65.25,8800000,7.45
1987-03-13,65.25,66.00,63.50,63.50,7067200,7.25
1987-03-12,66.00,66.25,63.63,65.25,10800000,7.45
1987-03-11,67.25,68.00,66.25,66.25,7826400,7.56
1987-03-10,64.50,66.87,64.50,66.75,8720000,7.62
1987-03-09,66.50,66.75,64.50,64.62,9120000,7.37
1987-03-06,67.25,68.37,66.75,67.25,6332800,7.67
1987-03-05,67.50,69.00,67.25,68.50,12080000,7.82
1987-03-04,65.75,68.25,65.37,67.62,16000000,7.72
1987-03-03,67.50,68.12,64.75,65.00,15600000,7.42
1987-03-02,70.25,70.50,67.00,67.50,14160000,7.70
1987-02-27,69.12,71.00,67.75,70.00,14480000,7.99
1987-02-26,69.50,71.37,68.00,69.12,17840000,7.89
1987-02-25,65.50,69.50,64.62,69.12,16240000,7.89
1987-02-24,63.25,66.00,63.13,65.50,12720000,7.47
1987-02-23,60.88,64.25,59.63,63.13,12560000,7.20
1987-02-20,62.38,62.50,60.63,61.25,6813600,6.99
1987-02-19,63.50,63.50,61.75,62.38,11200000,7.12
1987-02-18,66.62,67.37,63.38,63.50,16800000,7.25
1987-02-17,62.13,66.50,61.88,66.37,14640000,7.57
1987-02-13,58.63,62.50,58.00,62.13,18240000,7.09
1987-02-12,57.00,59.88,57.00,58.63,25360000,6.69
1987-02-11,53.00,56.75,52.75,56.50,12240000,6.45
1987-02-10,52.50,52.75,51.63,52.75,5977600,6.02
1987-02-09,52.88,53.38,52.25,52.63,5611200,6.01
1987-02-06,54.00,54.00,52.88,54.00,10480000,6.16
1987-02-05,55.00,55.13,53.13,53.88,12160000,6.15
1987-02-04,55.50,55.50,54.38,55.00,7791200,6.28
1987-02-03,56.00,56.13,54.75,55.50,6412800,6.33
1987-02-02,55.50,56.00,54.25,55.88,8800000,6.38
1987-01-30,54.00,55.88,52.63,55.50,14640000,6.33
1987-01-29,55.88,57.25,53.38,54.13,19920000,6.18
1987-01-28,53.00,55.75,52.13,55.38,14800000,6.32
1987-01-27,50.00,53.13,49.88,52.75,13520000,6.02
1987-01-26,50.00,50.50,49.50,49.75,12560000,5.68
1987-01-23,52.50,53.00,50.25,50.25,16400000,5.73
1987-01-22,48.88,52.63,48.50,52.50,16880000,5.99
1987-01-21,50.88,51.13,49.00,49.00,19040000,5.59
1987-01-20,55.00,55.75,51.50,51.63,27680000,5.89
1987-01-19,48.75,53.13,47.88,53.13,12960000,6.06
1987-01-16,50.00,50.00,47.75,48.75,14560000,5.56
1987-01-15,48.25,51.38,48.00,49.88,19520000,5.69
1987-01-14,44.63,48.25,44.50,48.13,18000000,5.49
1987-01-13,45.13,45.38,44.63,44.63,7584800,5.09
1987-01-12,45.50,45.75,44.75,45.50,8320000,5.19
1987-01-09,44.75,45.75,44.38,45.38,8560000,5.18
1987-01-08,44.75,45.13,44.50,44.75,10400000,5.11
1987-01-07,43.88,44.88,43.63,44.75,15520000,5.11
1987-01-06,43.13,44.00,42.63,43.75,11600000,4.99
1987-01-05,41.25,43.25,41.00,43.00,8560000,4.91
1987-01-02,40.38,41.13,40.13,40.88,4360000,4.66
1986-12-31,41.00,41.38,40.38,40.50,4742400,4.62
1986-12-30,40.50,41.50,40.38,41.00,5297600,4.68
1986-12-29,41.00,41.13,40.25,40.50,4224800,4.62
1986-12-26,41.88,41.88,41.00,41.00,3215200,4.68
1986-12-24,42.00,42.13,41.63,41.88,3453600,4.78
1986-12-23,42.25,42.38,41.88,42.13,8720000,4.81
1986-12-22,42.00,42.50,41.75,42.13,5887200,4.81
1986-12-19,41.38,42.50,41.38,42.13,7149600,4.81
1986-12-18,41.13,41.88,40.75,41.38,6258400,4.72
1986-12-17,42.38,42.50,40.88,41.25,5417600,4.71
1986-12-16,41.63,42.50,41.63,42.50,5464000,4.85
1986-12-15,41.00,41.75,40.38,41.75,7481600,4.76
1986-12-12,42.88,43.00,41.25,41.25,6451200,4.71
1986-12-11,43.63,43.88,42.63,42.88,8080000,4.89
1986-12-10,42.38,43.75,42.00,43.50,8720000,4.96
1986-12-09,42.38,42.63,41.13,42.38,10800000,4.84
1986-12-08,43.63,43.88,42.38,42.50,12400000,4.85
1986-12-05,42.63,43.75,42.50,43.75,9360000,4.99
1986-12-04,42.63,42.75,42.00,42.50,9600000,4.85
1986-12-03,41.63,43.00,41.50,42.75,12000000,4.88
1986-12-02,40.50,41.75,40.00,41.50,13200000,4.74
1986-12-01,40.00,40.13,39.13,40.13,12400000,4.58
1986-11-28,40.50,40.63,39.63,40.00,7897600,4.56
1986-11-26,40.13,41.25,40.00,40.50,18080000,4.62
1986-11-25,38.00,40.38,38.00,40.25,30320000,4.59
1986-11-24,36.25,38.13,36.00,38.00,13440000,4.34
1986-11-21,35.25,36.25,35.13,36.00,10240000,4.11
1986-11-20,34.88,35.38,34.88,35.25,10560000,4.02
1986-11-19,35.13,35.25,34.50,35.00,10800000,3.99
1986-11-18,36.38,36.75,35.13,35.38,6115200,4.04
1986-11-17,35.25,37.00,35.00,36.38,5071200,4.15
1986-11-14,35.50,35.50,34.88,35.25,4840000,4.02
1986-11-13,36.50,36.50,35.50,35.50,4928800,4.05
1986-11-12,35.75,36.63,35.63,36.63,4700000,4.18
1986-11-11,35.50,35.75,35.25,35.50,1809600,4.05
1986-11-10,35.88,35.88,35.13,35.38,3793600,4.04
1986-11-07,36.00,36.13,34.88,35.75,5153600,4.08
1986-11-06,36.63,36.88,35.75,36.13,11840000,4.12
1986-11-05,35.75,37.13,35.50,37.00,22320000,4.22
1986-11-04,34.88,35.88,33.88,35.75,8800000,4.08
1986-11-03,34.75,35.13,34.63,35.00,5457600,3.99
1986-10-31,34.25,34.88,34.25,34.63,4338400,3.95
1986-10-30,33.50,34.75,33.38,34.25,10480000,3.91
1986-10-29,33.50,33.50,33.13,33.38,3057600,3.81
1986-10-28,34.00,34.13,33.00,33.38,5102400,3.81
1986-10-27,33.50,34.00,33.25,34.00,5422400,3.88
1986-10-24,33.13,33.25,32.75,33.00,2718400,3.77
1986-10-23,32.50,33.13,32.50,33.13,4441600,3.78
1986-10-22,32.75,32.88,32.25,32.50,3382400,3.71
1986-10-21,33.00,33.00,32.63,32.75,4096000,3.74
1986-10-20,33.50,33.63,32.88,32.88,5344000,3.75
1986-10-17,33.75,34.00,33.38,33.63,5460000,3.84
1986-10-16,33.38,33.88,33.25,33.63,4876000,3.84
1986-10-15,33.50,33.50,32.75,33.38,7367200,3.81
1986-10-14,34.63,35.25,33.75,34.00,7164000,3.88
1986-10-13,33.13,34.63,33.00,34.63,3582400,3.95
1986-10-10,32.88,33.38,32.38,33.25,2096000,3.79
1986-10-09,32.75,33.25,32.63,33.00,2820000,3.77
1986-10-08,32.88,33.00,32.25,32.75,4021600,3.74
1986-10-07,34.00,34.13,32.88,33.00,4577600,3.77
1986-10-06,33.75,34.25,33.63,34.13,3384000,3.89
1986-10-03,34.38,34.75,33.38,33.75,4997600,3.85
1986-10-02,33.75,34.38,33.50,34.13,3401600,3.89
1986-10-01,33.38,34.50,33.38,34.13,4988800,3.89
1986-09-30,32.88,33.88,32.63,33.50,6488800,3.82
1986-09-29,33.63,33.88,31.62,32.50,7475200,3.71
1986-09-26,34.13,34.38,33.88,34.25,2512800,3.91
1986-09-25,35.13,35.25,33.63,34.50,6744800,3.94
1986-09-24,36.13,36.38,34.00,35.13,6360000,4.01
1986-09-23,35.25,36.25,35.13,36.13,12080000,4.12
1986-09-22,33.50,35.38,33.50,35.25,8560000,4.02
1986-09-19,33.75,33.88,33.25,33.63,4601600,3.84
1986-09-18,34.25,34.50,33.75,34.00,3546400,3.88
1986-09-17,34.88,35.00,34.25,34.25,4181600,3.91
1986-09-16,33.13,35.13,32.50,34.88,8800000,3.98
1986-09-15,32.25,33.13,32.00,33.13,7973600,3.78
1986-09-12,32.50,32.75,31.75,31.75,8160000,3.62
1986-09-11,34.63,34.75,32.50,32.63,4842400,3.72
1986-09-10,35.63,35.88,34.75,35.00,2737600,3.99
1986-09-09,34.63,36.00,34.63,35.75,5398400,4.08
1986-09-08,35.00,35.00,33.63,34.75,4522400,3.97
1986-09-05,35.63,35.88,35.00,35.13,3561600,4.01
1986-09-04,35.00,35.50,34.75,35.50,7133600,4.05
1986-09-03,34.75,34.88,34.13,34.75,4216000,3.97
1986-09-02,37.13,37.13,34.75,34.75,8320000,3.97
1986-08-29,37.63,38.00,36.88,37.00,4846400,4.22
1986-08-28,37.00,38.00,36.88,37.75,7849600,4.31
1986-08-27,36.63,37.00,36.25,37.00,5280000,4.22
1986-08-26,36.38,36.88,36.38,36.63,4713600,4.18
1986-08-25,36.50,36.88,36.38,36.38,4533600,4.15
1986-08-22,35.88,36.63,35.88,36.25,4162400,4.14
1986-08-21,36.13,36.38,35.75,35.75,6992800,4.08
1986-08-20,35.25,36.50,35.25,36.25,6140000,4.14
1986-08-19,35.13,35.50,34.63,35.38,4944000,4.04
1986-08-18,35.75,35.88,35.00,35.38,5297600,4.04
1986-08-15,36.13,36.50,35.63,35.75,4910400,4.08
1986-08-14,36.00,37.00,36.00,36.00,8240000,4.11
1986-08-13,34.25,36.25,34.25,36.00,16240000,4.11
1986-08-12,33.38,34.38,33.38,34.25,8720000,3.91
1986-08-11,31.87,33.50,31.75,33.50,6591200,3.82
1986-08-08,31.87,32.38,31.62,31.62,3941600,3.61
1986-08-07,31.12,32.63,31.12,31.75,6211200,3.62
1986-08-06,32.13,32.13,31.00,31.12,6644800,3.55
1986-08-05,31.62,32.38,31.50,32.13,4238400,3.67
1986-08-04,31.37,31.50,30.62,31.50,4653600,3.59
1986-08-01,31.12,31.75,31.12,31.37,5360000,3.58
1986-07-31,30.50,31.50,30.50,31.25,10080000,3.57
1986-07-30,31.25,31.50,30.00,30.50,9120000,3.48
1986-07-29,32.25,32.25,30.75,31.25,21280000,3.57
1986-07-28,33.88,34.00,32.25,32.38,8800000,3.69
1986-07-25,33.13,34.00,33.00,34.00,7769600,3.88
1986-07-24,34.25,34.38,33.00,33.13,5187200,3.78
1986-07-23,34.63,34.63,34.13,34.13,6416000,3.89
1986-07-22,33.50,34.63,33.25,34.63,8560000,3.95
1986-07-21,33.00,33.75,32.75,33.50,8160000,3.82
1986-07-18,32.25,32.50,31.25,31.75,11040000,3.62
1986-07-17,33.50,33.75,32.13,32.25,8960000,3.68
1986-07-16,35.50,35.63,32.75,33.50,19280000,3.82
1986-07-15,35.00,35.00,34.25,34.88,10640000,3.98
1986-07-14,37.13,37.38,36.25,36.25,8480000,4.14
1986-07-11,35.38,37.75,35.25,37.13,8000000,4.24
1986-07-10,34.75,35.38,34.63,35.38,7453600,4.04
1986-07-09,34.25,34.75,34.00,34.63,13040000,3.95
1986-07-08,35.25,35.25,34.13,34.25,9782400,3.91
1986-07-07,37.63,37.75,35.38,35.63,6501600,4.07
1986-07-03,36.13,37.75,35.63,37.63,6509600,4.29
1986-07-02,35.38,36.25,35.38,36.13,5202400,4.12
1986-07-01,35.88,36.13,34.75,35.38,3140000,4.04
1986-06-30,35.88,36.25,35.75,35.88,2553600,4.09
1986-06-27,36.25,36.75,35.50,35.88,1811200,4.09
1986-06-26,35.88,36.38,35.50,36.25,4184800,4.14
1986-06-25,35.00,36.00,35.00,35.88,4755200,4.09
1986-06-24,34.75,35.13,34.38,34.88,5088800,3.98
1986-06-23,36.00,36.25,34.63,34.75,4196000,3.97
1986-06-20,35.00,36.13,35.00,36.00,5761600,4.11
1986-06-19,34.25,35.75,33.88,35.00,12347200,3.99
1986-06-18,34.25,34.75,32.50,34.25,15381600,3.91
1986-06-17,35.88,36.00,34.00,34.25,7936000,3.91
1986-06-16,36.38,36.88,35.63,35.88,6222400,4.09
1986-06-13,36.00,36.38,35.25,36.38,5144800,4.15
1986-06-12,36.13,36.38,36.00,36.00,4638400,4.11
1986-06-11,36.00,36.25,35.50,36.13,6692800,4.12
1986-06-10,36.00,36.00,35.13,36.00,8827200,4.11
1986-06-09,37.75,37.88,35.88,36.00,8835200,4.11
1986-06-06,38.88,38.88,37.50,37.75,6342400,4.31
1986-06-05,38.75,39.13,38.50,38.88,5282400,4.44
1986-06-04,37.88,38.88,37.75,38.75,10747200,4.42
1986-06-03,37.13,38.13,37.13,37.88,11661600,4.32
1986-06-02,37.00,37.38,36.75,37.13,7158400,4.24
1986-05-30,37.00,37.25,36.50,37.00,4591200,4.22
1986-05-29,37.25,37.25,36.50,37.00,3635200,4.22
1986-05-28,36.88,37.50,36.75,37.25,7418400,4.25
1986-05-27,37.00,37.00,36.38,36.88,3058400,4.21
1986-05-23,36.75,37.13,36.38,37.00,5013600,4.22
1986-05-22,37.00,37.50,35.75,36.75,7895200,4.19
1986-05-21,35.38,37.25,35.00,37.00,12418400,4.22
1986-05-20,35.63,35.63,34.25,35.38,8811200,4.04
1986-05-19,36.00,36.50,35.50,35.63,7506400,4.07
1986-05-16,36.00,36.25,35.13,36.00,11424800,4.11
1986-05-15,36.88,37.00,35.63,36.00,7964000,4.11
1986-05-14,36.00,37.38,36.00,36.88,17277600,4.21
1986-05-13,36.38,36.50,35.25,36.00,16876000,4.11
1986-05-12,33.38,36.63,33.25,36.38,14335200,4.15
1986-05-09,33.00,33.63,32.75,33.38,7961600,3.81
1986-05-08,31.50,33.13,31.50,33.00,8342400,3.77
1986-05-07,32.63,32.88,31.25,31.50,7133600,3.59
1986-05-06,32.25,33.25,32.25,32.63,7829600,3.72
1986-05-05,30.50,32.50,30.50,32.13,5364000,3.67
1986-05-02,30.25,31.00,30.12,30.50,3377600,3.48
1986-05-01,30.25,30.25,29.75,30.25,9218400,3.45
1986-04-30,31.25,31.62,30.25,30.25,4944000,3.45
1986-04-29,32.00,32.25,26.87,31.25,4750400,3.57
1986-04-28,32.25,32.75,31.75,32.00,5241600,3.65
1986-04-25,31.37,32.63,31.37,32.25,9348800,3.68
1986-04-24,29.62,31.50,29.50,31.37,16398400,3.58
1986-04-23,29.87,30.37,29.37,29.62,9371200,3.38
1986-04-22,30.37,31.25,29.62,29.87,11726400,3.41
1986-04-21,29.87,30.75,29.87,30.37,9775200,3.47
1986-04-18,29.00,29.87,28.75,29.75,8871200,3.39
1986-04-17,28.25,29.12,28.00,29.00,9672800,3.31
1986-04-16,27.37,28.50,27.37,28.25,7535200,3.22
1986-04-15,26.87,27.50,26.87,27.37,4722400,3.12
1986-04-14,27.00,27.25,26.75,26.87,3076000,3.07
1986-04-11,27.25,27.50,27.00,27.00,2737600,3.08
1986-04-10,27.12,27.37,26.87,27.25,3932800,3.11
1986-04-09,27.62,27.75,26.87,27.12,4851200,3.09
1986-04-08,27.25,27.75,27.25,27.62,6912800,3.15
1986-04-07,26.75,27.50,26.25,27.25,4318400,3.11
1986-04-04,27.00,27.00,26.62,26.75,4508800,3.05
1986-04-03,27.25,27.62,26.87,27.00,7548800,3.08
1986-04-02,27.25,27.37,26.25,27.25,11627200,3.11
1986-04-01,28.25,28.25,27.00,27.25,7973600,3.11
1986-03-31,28.25,28.50,28.00,28.25,6744800,3.22
1986-03-27,28.25,29.00,28.25,28.25,7856000,3.22
1986-03-26,27.87,28.75,27.87,28.25,7941600,3.22
1986-03-25,26.75,27.87,26.75,27.87,10060000,3.18
1986-03-24,27.62,27.62,26.37,26.75,10528800,3.05
1986-03-21,28.25,28.75,27.50,27.62,9309600,3.15
1986-03-20,28.00,29.62,28.00,28.25,32318400,3.22
1986-03-19,26.87,27.25,26.37,26.50,6816000,3.02
1986-03-18,26.00,27.25,25.87,26.87,8920000,3.07
1986-03-17,26.00,26.00,25.37,26.00,4240000,2.97
1986-03-14,24.75,26.25,24.75,26.12,13781600,2.98
1986-03-13,24.75,25.00,24.37,24.75,4176000,2.82
1986-03-12,24.87,25.12,24.75,24.75,3071200,2.82
1986-03-11,24.62,24.87,24.50,24.87,3681600,2.84
1986-03-10,24.75,24.87,24.62,24.62,2727200,2.81
1986-03-07,25.37,25.37,24.75,24.75,3477600,2.82
1986-03-06,25.25,25.75,25.12,25.37,3630400,2.89
1986-03-05,24.62,25.50,24.25,25.25,6324000,2.88
1986-03-04,24.62,25.00,24.50,24.62,3217600,2.81
1986-03-03,25.00,25.12,24.50,24.62,3912800,2.81
1986-02-28,25.62,25.87,24.87,25.00,4507200,2.85
1986-02-27,26.00,26.12,25.50,25.62,3873600,2.92
1986-02-26,26.37,26.75,26.00,26.00,5907200,2.97
1986-02-25,25.75,26.37,25.12,26.37,8041600,3.01
1986-02-24,25.25,25.75,25.00,25.75,8840000,2.94
1986-02-21,25.12,25.75,25.12,25.25,6771200,2.88
1986-02-20,25.00,25.37,24.87,25.12,4951200,2.87
1986-02-19,23.87,25.50,23.87,25.00,12871200,2.85
1986-02-18,23.75,24.00,23.25,23.87,5295200,2.72
1986-02-14,23.87,24.12,23.75,23.75,4928800,2.71
1986-02-13,24.00,24.00,23.75,23.87,3944000,2.72
1986-02-12,23.87,24.00,23.75,24.00,4770400,2.74
1986-02-11,23.87,24.00,23.50,23.87,5504000,2.72
1986-02-10,24.00,24.50,23.75,23.87,4036000,2.72
1986-02-07,24.12,24.12,23.50,24.00,4656000,2.74
1986-02-06,23.75,24.25,23.62,24.12,4835200,2.75
1986-02-05,23.75,23.87,23.50,23.75,7042400,2.71
1986-02-04,23.87,24.37,23.75,23.75,9298400,2.71
1986-02-03,23.12,24.00,22.87,23.87,12512800,2.72
1986-01-31,23.00,23.25,22.87,23.12,5317600,2.64
1986-01-30,23.50,23.50,22.87,23.00,8493600,2.62
1986-01-29,22.25,24.37,22.00,23.62,21064800,2.70
1986-01-28,22.12,22.37,22.00,22.25,7949600,2.54
1986-01-27,22.62,22.75,22.00,22.12,13955200,2.52
1986-01-24,23.00,23.37,22.62,22.62,4044000,2.58
1986-01-23,23.37,23.50,22.75,23.00,5624000,2.62
1986-01-22,24.00,24.12,22.37,23.37,5144800,2.67
1986-01-21,23.87,24.12,23.75,24.00,5464800,2.74
1986-01-20,24.00,24.00,23.37,23.87,4590400,2.72
1986-01-17,24.50,24.75,23.87,24.00,12344000,2.74
1986-01-16,23.87,24.75,23.87,24.50,19132800,2.80
1986-01-15,23.25,24.00,23.12,23.87,15126400,2.72
1986-01-14,23.00,23.75,22.50,23.25,9772800,2.65
1986-01-13,22.75,23.12,22.50,23.00,7701600,2.62
1986-01-10,22.62,23.12,22.62,22.75,5491200,2.60
1986-01-09,22.87,23.00,21.87,22.62,16002400,2.58
1986-01-08,23.00,23.50,22.75,22.87,21711200,2.61
1986-01-07,22.25,23.00,22.12,23.00,16807200,2.62
1986-01-06,22.37,22.37,21.87,22.25,6636000,2.54
1986-01-03,22.25,22.37,22.12,22.37,8653600,2.55
1986-01-02,22.00,22.25,21.75,22.25,4212800,2.54
1985-12-31,22.25,22.37,22.00,22.00,3158400,2.51
1985-12-30,22.37,22.62,22.12,22.25,3848800,2.54
1985-12-27,21.75,22.62,21.75,22.37,4427200,2.55
1985-12-26,21.75,22.00,21.62,21.75,1658400,2.48
1985-12-24,21.87,22.00,21.62,21.75,2344800,2.48
1985-12-23,22.37,22.50,21.62,21.87,5157600,2.50
1985-12-20,22.50,22.75,22.25,22.37,7402400,2.55
1985-12-19,22.25,22.75,22.12,22.50,9673600,2.57
1985-12-18,21.37,22.87,21.37,22.25,20033600,2.54
1985-12-17,20.87,21.00,20.37,20.62,3926400,2.35
1985-12-16,20.00,21.25,20.00,20.87,10362400,2.38
1985-12-13,20.00,20.25,19.75,20.00,8975200,2.28
1985-12-12,19.87,20.25,19.87,20.00,4515200,2.28
1985-12-11,19.50,20.12,19.50,19.75,8489600,2.25
1985-12-10,19.37,19.62,19.25,19.50,7206400,2.23
1985-12-09,19.75,20.00,19.25,19.37,5015200,2.21
1985-12-06,20.12,20.12,19.62,19.75,2347200,2.25
1985-12-05,20.50,20.75,20.00,20.12,4508800,2.30
1985-12-04,20.12,20.62,20.12,20.50,5928800,2.34
1985-12-03,20.25,20.37,20.00,20.12,5548800,2.30
1985-12-02,20.12,20.25,20.00,20.25,3611200,2.31
1985-11-29,20.00,20.12,19.87,20.12,3546400,2.30
1985-11-27,19.37,20.12,19.25,20.00,6873600,2.28
1985-11-26,19.12,19.50,19.00,19.37,5892800,2.21
1985-11-25,19.00,19.25,19.00,19.12,3488800,2.18
1985-11-22,19.00,19.25,18.87,19.00,4620000,2.17
1985-11-21,19.00,19.25,19.00,19.00,3720000,2.17
1985-11-20,19.25,19.37,19.00,19.00,3548800,2.17
1985-11-19,19.87,20.00,19.25,19.25,3373600,2.20
1985-11-18,19.87,20.00,19.87,19.87,2342400,2.27
1985-11-15,20.00,20.25,19.87,19.87,2932800,2.27
1985-11-14,20.00,20.12,20.00,20.00,4995200,2.28
1985-11-13,19.87,19.87,19.37,19.37,3642400,2.21
1985-11-12,20.00,20.25,19.87,19.87,6224800,2.27
1985-11-11,20.50,20.75,20.00,20.00,6421600,2.28
1985-11-08,20.50,20.75,20.50,20.50,10517600,2.34
1985-11-07,19.62,19.87,19.62,19.62,11352800,2.24
1985-11-06,19.25,19.37,19.25,19.25,7181600,2.20
1985-11-05,18.75,19.12,18.62,18.62,3841600,2.12
1985-11-04,18.75,19.12,18.75,18.75,5584800,2.14
1985-11-01,18.62,19.00,18.62,18.62,3320000,2.12
1985-10-31,19.00,19.25,18.62,18.62,5548800,2.12
1985-10-30,19.00,19.00,19.00,19.00,8098400,2.17
1985-10-29,18.00,18.00,17.87,17.87,4693600,2.04
1985-10-28,18.00,18.12,18.00,18.00,2148800,2.05
1985-10-25,18.37,18.37,18.00,18.00,2271200,2.05
1985-10-24,18.37,18.87,18.37,18.37,9768800,2.10
1985-10-23,18.00,18.50,18.00,18.00,5309600,2.05
1985-10-22,18.00,18.25,18.00,18.00,15186400,2.05
1985-10-21,17.75,17.75,17.25,17.25,4248800,1.97
1985-10-18,18.25,18.37,17.75,17.75,8268800,2.03
1985-10-17,18.25,19.12,18.25,18.25,12455200,2.08
1985-10-16,18.00,18.12,18.00,18.00,10336000,2.05
1985-10-15,17.00,17.12,17.00,17.00,10504800,1.94
1985-10-14,16.62,16.62,16.62,16.62,5555200,1.90
1985-10-11,16.00,16.25,16.00,16.00,4261600,1.83
1985-10-10,15.88,16.00,15.88,15.88,9386400,1.81
1985-10-09,15.13,15.25,15.00,15.00,3001600,1.71
1985-10-08,15.13,15.13,15.13,15.13,3144000,1.73
1985-10-07,15.00,15.25,15.00,15.00,3284800,1.71
1985-10-04,15.50,15.50,15.00,15.00,2484800,1.71
1985-10-03,15.63,15.63,15.50,15.50,1784800,1.77
1985-10-02,15.75,15.88,15.63,15.63,795200,1.78
1985-10-01,15.75,15.88,15.75,15.75,3175200,1.80
1985-09-30,15.88,16.00,15.75,15.75,1324800,1.80
1985-09-27,15.88,16.00,15.88,15.88,250400,1.81
1985-09-26,15.88,16.00,15.88,15.88,1949600,1.81
1985-09-25,16.50,16.50,15.88,15.88,3761600,1.81
1985-09-24,16.87,17.25,16.50,16.50,3161600,1.88
1985-09-23,16.87,17.12,16.87,16.87,4277600,1.92
1985-09-20,17.00,17.12,16.75,16.75,4846400,1.91
1985-09-19,17.00,17.00,17.00,17.00,6662400,1.94
1985-09-18,16.25,16.25,16.25,16.25,4316000,1.85
1985-09-17,15.25,15.25,15.25,15.25,6564000,1.74
1985-09-16,15.75,15.75,15.25,15.25,1344000,1.74
1985-09-13,16.12,16.12,15.75,15.75,2541600,1.80
1985-09-12,16.12,16.12,16.12,16.12,3998400,1.84
1985-09-11,15.50,15.63,15.50,15.50,3150400,1.77
1985-09-10,15.38,15.63,15.38,15.38,4364800,1.75
1985-09-09,15.25,15.38,15.25,15.25,4728800,1.74
1985-09-06,15.00,15.00,15.00,15.00,3333600,1.71
1985-09-05,14.88,15.00,14.88,14.88,1201600,1.70
1985-09-04,14.88,15.13,14.88,14.88,1708800,1.70
1985-09-03,15.00,15.00,14.75,14.75,1369600,1.68
1985-08-30,15.00,15.00,15.00,15.00,1537600,1.71
1985-08-29,15.25,15.25,14.88,14.88,2006400,1.70
1985-08-28,15.25,15.38,15.25,15.25,1475200,1.74
1985-08-27,15.25,15.25,15.25,15.25,1540000,1.74
1985-08-26,15.13,15.13,15.13,15.13,1315200,1.73
1985-08-23,14.88,15.00,14.75,14.75,1601600,1.68
1985-08-22,15.25,15.25,14.88,14.88,4406400,1.70
1985-08-21,15.25,15.25,15.25,15.25,2767200,1.74
1985-08-20,15.25,15.25,15.25,15.25,2431200,1.74
1985-08-19,15.00,15.25,15.00,15.00,1726400,1.71
1985-08-16,14.63,14.88,14.63,14.63,3008800,1.67
1985-08-15,14.63,14.75,14.50,14.50,3800000,1.65
1985-08-14,15.25,15.25,14.63,14.63,10372800,1.67
1985-08-13,15.25,15.50,15.25,15.25,1555200,1.74
1985-08-12,15.25,15.25,15.00,15.00,1988800,1.71
1985-08-09,15.25,15.25,15.25,15.25,2186400,1.74
1985-08-08,15.13,15.25,15.13,15.13,5321600,1.73
1985-08-07,15.25,16.00,14.88,14.88,5452800,1.70
1985-08-06,15.38,15.75,15.25,15.25,2260000,1.74
1985-08-05,15.75,15.88,15.38,15.38,3307200,1.75
1985-08-02,15.88,15.88,15.75,15.75,3501600,1.80
1985-08-01,15.88,16.12,15.88,15.88,1842400,1.81
1985-07-31,16.25,16.37,15.88,15.88,2917600,1.81
1985-07-30,16.25,16.37,16.25,16.25,3237600,1.85
1985-07-29,16.62,16.62,16.00,16.00,2808800,1.83
1985-07-26,16.62,16.75,16.62,16.62,4673600,1.90
1985-07-25,16.62,16.75,16.62,16.62,11282400,1.90
1985-07-24,16.50,16.75,16.25,16.25,6040000,1.85
1985-07-23,16.87,17.12,16.50,16.50,6038400,1.88
1985-07-22,17.37,17.37,16.87,16.87,6906400,1.92
1985-07-19,17.37,17.37,17.37,17.37,4117600,1.98
1985-07-18,17.62,17.62,17.25,17.25,6437600,1.97
1985-07-17,17.62,17.87,17.62,17.62,4255200,2.01
1985-07-16,17.75,17.87,17.50,17.50,5120000,2.00
1985-07-15,17.87,18.25,17.75,17.75,2804800,2.03
1985-07-12,18.00,18.00,17.87,17.87,1680000,2.04
1985-07-11,18.00,18.12,18.00,18.00,2361600,2.05
1985-07-10,18.00,18.00,18.00,18.00,3802400,2.05
1985-07-09,17.62,17.75,17.62,17.62,5284000,2.01
1985-07-08,17.62,17.75,17.62,17.62,3301600,2.01
1985-07-05,17.62,17.75,17.62,17.62,1321600,2.01
1985-07-03,17.50,17.50,17.50,17.50,2472800,2.00
1985-07-02,18.12,18.25,17.25,17.25,2807200,1.97
1985-07-01,18.12,18.25,18.12,18.12,3702400,2.07
1985-06-28,18.37,18.50,18.00,18.00,4875200,2.05
1985-06-27,18.37,18.50,18.37,18.37,6915200,2.10
1985-06-26,18.12,18.12,18.12,18.12,4722400,2.07
1985-06-25,17.50,17.87,17.50,17.50,10506400,2.00
1985-06-24,17.25,17.50,17.25,17.25,7387200,1.97
1985-06-21,16.12,16.50,16.12,16.12,5941600,1.84
1985-06-20,15.75,15.75,15.75,15.75,6822400,1.80
1985-06-19,15.63,15.88,15.63,15.63,6177600,1.78
1985-06-18,15.25,15.50,15.25,15.25,9489600,1.74
1985-06-17,14.88,15.00,14.88,14.88,8464000,1.70
1985-06-14,14.88,15.75,14.75,14.75,20226400,1.68
1985-06-13,15.75,15.88,14.88,14.88,13573600,1.70
1985-06-12,16.12,16.25,15.75,15.75,8888800,1.80
1985-06-11,16.12,16.50,16.12,16.12,10751200,1.84
1985-06-10,16.37,16.50,16.12,16.12,11296000,1.84
1985-06-07,17.00,17.00,16.37,16.37,16980000,1.87
1985-06-06,17.00,17.00,17.00,17.00,9688800,1.94
1985-06-05,17.25,17.75,16.87,16.87,10267200,1.92
1985-06-04,17.25,17.37,17.25,17.25,14373600,1.97
1985-06-03,17.00,17.00,16.00,16.00,20578400,1.83
1985-05-31,17.62,18.00,17.37,17.37,13235200,1.98
1985-05-30,17.62,17.87,17.62,17.62,11273600,2.01
1985-05-29,17.12,17.25,17.12,17.12,8808800,1.95
1985-05-28,17.87,17.87,16.87,16.87,18253600,1.92
1985-05-24,19.75,19.75,18.12,18.12,21060000,2.07
1985-05-23,20.50,20.50,19.75,19.75,8576000,2.25
1985-05-22,20.75,20.87,20.62,20.62,4342400,2.35
1985-05-21,21.25,21.25,20.75,20.75,5452800,2.37
1985-05-20,21.75,22.25,21.37,21.37,7044000,2.44
1985-05-17,21.37,22.12,21.25,21.75,7592800,2.48
1985-05-16,21.37,22.00,21.37,21.37,8275200,2.44
1985-05-15,20.00,20.37,20.00,20.00,4668800,2.28
1985-05-14,20.00,20.12,19.75,19.75,4364000,2.25
1985-05-13,20.25,20.37,20.00,20.00,3157600,2.28
1985-05-10,20.00,20.50,20.00,20.25,4893600,2.31
1985-05-09,20.00,20.12,20.00,20.00,4571200,2.28
1985-05-08,19.87,19.87,19.87,19.87,5177600,2.27
1985-05-07,20.00,20.00,20.00,20.00,3844800,2.28
1985-05-06,20.00,20.25,19.75,19.75,2007200,2.25
1985-05-03,19.25,20.12,19.25,20.00,5673600,2.28
1985-05-02,20.62,20.62,19.25,19.25,11787200,2.20
1985-05-01,21.25,21.37,20.87,20.87,2075200,2.38
1985-04-30,21.25,21.37,21.25,21.25,3396000,2.42
1985-04-29,21.87,22.00,21.12,21.12,2256000,2.41
1985-04-26,22.00,22.62,21.87,21.87,4295200,2.50
1985-04-25,22.00,22.12,22.00,22.00,3135200,2.51
1985-04-24,22.12,22.50,22.00,22.00,2830400,2.51
1985-04-23,22.12,22.25,22.12,22.12,4261600,2.52
1985-04-22,22.50,22.50,21.62,21.62,3700000,2.47
1985-04-19,22.87,22.87,22.37,22.50,3468800,2.57
1985-04-18,22.87,23.00,22.87,22.87,7246400,2.61
1985-04-17,22.62,22.87,22.62,22.62,4402400,2.58
1985-04-16,21.62,21.75,21.62,21.62,2424800,2.47
1985-04-15,21.37,21.62,21.37,21.37,2168800,2.44
1985-04-12,21.37,21.37,20.75,20.87,2607200,2.38
1985-04-11,21.37,22.00,21.37,21.37,5260000,2.44
1985-04-10,21.00,21.25,21.00,21.00,8117600,2.40
1985-04-09,19.62,19.75,19.62,19.62,9461600,2.24
1985-04-08,20.87,21.00,19.62,19.62,7129600,2.24
1985-04-04,21.00,21.12,20.62,20.87,5792800,2.38
1985-04-03,21.00,21.12,21.00,21.00,8681600,2.40
1985-04-02,21.62,21.75,21.00,21.00,8146400,2.40
1985-04-01,22.12,22.62,21.62,21.62,4115200,2.47
1985-03-29,21.87,22.25,21.87,22.12,3155200,2.52
1985-03-28,21.87,22.25,21.87,21.87,4667200,2.50
1985-03-27,22.50,22.75,21.87,21.87,4008800,2.50
1985-03-26,22.50,22.50,22.50,22.50,4346400,2.57
1985-03-25,22.25,22.25,21.62,21.62,3931200,2.47
1985-03-22,22.62,23.00,22.25,22.25,2910400,2.54
1985-03-21,22.62,23.00,22.62,22.62,5826400,2.58
1985-03-20,22.25,22.62,22.25,22.25,14498400,2.54
1985-03-19,22.87,23.12,22.00,22.00,6147200,2.51
1985-03-18,22.87,23.12,22.87,22.87,4487200,2.61
1985-03-15,21.75,23.12,21.62,22.62,6524000,2.58
1985-03-14,21.75,21.87,21.75,21.75,8667200,2.48
1985-03-13,23.00,23.00,21.75,21.75,8973600,2.48
1985-03-12,23.00,23.25,23.00,23.00,7880000,2.62
1985-03-11,22.25,22.37,22.25,22.25,10244800,2.54
1985-03-08,22.12,22.12,20.75,21.50,16931200,2.45
1985-03-07,24.62,24.75,22.12,22.12,26244000,2.52
1985-03-06,25.87,25.87,24.62,24.62,6933600,2.81
1985-03-05,25.87,25.87,25.87,25.87,4687200,2.95
1985-03-04,25.25,26.00,25.25,25.25,5484000,2.88
1985-03-01,24.75,24.87,24.00,24.87,8857600,2.84
1985-02-28,25.12,25.12,24.75,24.75,11415200,2.82
1985-02-27,26.75,26.75,25.12,25.12,14421600,2.87
1985-02-26,27.25,27.37,26.75,26.75,6764800,3.05
1985-02-25,27.62,27.75,27.25,27.25,3564000,3.11
1985-02-22,26.87,27.87,26.87,27.62,8096000,3.15
1985-02-21,26.87,27.00,26.87,26.87,11035200,3.07
1985-02-20,27.62,27.75,26.37,26.37,7864800,3.01
1985-02-19,27.87,27.87,27.62,27.62,5391200,3.15
1985-02-15,27.62,28.12,27.37,28.00,6224000,3.19
1985-02-14,28.37,28.62,27.62,27.62,15268800,3.15
1985-02-13,29.75,29.75,28.37,28.37,18835200,3.24
1985-02-12,30.50,30.62,29.75,29.75,8095200,3.39
1985-02-11,30.50,30.75,30.50,30.50,12431200,3.48
1985-02-08,29.87,30.00,29.50,29.87,4757600,3.41
1985-02-07,30.00,30.37,29.87,29.87,8793600,3.41
1985-02-06,30.00,30.00,30.00,30.00,6980000,3.42
1985-02-05,29.50,30.00,29.50,29.50,6824800,3.37
1985-02-04,29.25,29.37,29.25,29.25,7801600,3.34
1985-02-01,29.00,29.12,28.37,28.62,4941600,3.27
1985-01-31,29.87,30.00,29.00,29.00,9880000,3.31
1985-01-30,29.87,30.50,29.87,29.87,17624800,3.41
1985-01-29,30.25,30.50,29.87,29.87,8029600,3.41
1985-01-28,30.25,30.62,30.25,30.25,14721600,3.45
1985-01-25,29.00,29.62,28.37,29.62,11381600,3.38
1985-01-24,29.62,29.62,29.00,29.00,14192800,3.31
1985-01-23,30.12,30.25,29.62,29.62,15384000,3.38
1985-01-22,30.12,30.25,30.12,30.12,15202400,3.44
1985-01-21,29.25,29.50,29.25,29.25,11635200,3.34
1985-01-18,28.12,29.25,28.00,28.62,12615200,3.27
1985-01-17,30.25,30.75,28.12,28.12,19573600,3.21
1985-01-16,30.25,30.75,30.25,30.25,6816000,3.45
1985-01-15,30.62,31.12,30.00,30.00,9476000,3.42
1985-01-14,30.62,30.87,30.62,30.62,9691200,3.49
1985-01-11,30.00,30.25,29.50,29.75,7347200,3.39
1985-01-10,30.00,30.12,30.00,30.00,9926400,3.42
1985-01-09,28.75,29.12,28.75,28.75,5973600,3.28
1985-01-08,28.25,28.50,28.00,28.00,5040000,3.19
1985-01-07,28.37,28.50,28.25,28.25,6117600,3.22
1985-01-04,28.37,28.50,28.00,28.37,4915200,3.24
1985-01-03,28.37,29.12,28.37,28.37,5967200,3.24
1985-01-02,29.12,29.12,27.87,27.87,6272800,3.18
1984-12-31,29.12,29.25,29.12,29.12,7453600,3.32
1984-12-28,27.75,28.87,27.62,28.75,5941600,3.28
1984-12-27,27.75,27.87,27.75,27.75,3531200,3.17
1984-12-26,27.62,27.87,27.62,27.62,2444000,3.15
1984-12-24,27.50,27.62,27.50,27.50,2418400,3.14
1984-12-21,27.37,27.50,26.75,27.00,4438400,3.08
1984-12-20,27.50,28.00,27.37,27.37,5013600,3.12
1984-12-19,28.62,28.75,27.50,27.50,11372800,3.14
1984-12-18,28.62,28.75,28.62,28.62,12164800,3.27
1984-12-17,27.00,27.25,27.00,27.00,4513600,3.08
1984-12-14,25.75,26.62,25.75,26.37,3475200,3.01
1984-12-13,25.75,26.25,25.75,25.75,2424800,2.94
1984-12-12,26.37,26.37,25.50,25.50,3937600,2.91
1984-12-11,26.75,27.12,26.37,26.37,4432800,3.01
1984-12-10,27.25,27.25,26.75,26.75,4016000,3.05
1984-12-07,27.37,28.37,27.12,27.25,17696000,3.11
1984-12-06,27.37,27.50,27.37,27.37,11360000,3.12
1984-12-05,26.12,26.12,26.12,26.12,9406400,2.98
1984-12-04,24.87,25.37,24.87,24.87,4332800,2.84
1984-12-03,24.75,24.87,24.37,24.37,3533600,2.78
1984-11-30,25.37,25.62,24.62,24.75,3906400,2.82
1984-11-29,25.87,25.87,25.37,25.37,6248800,2.89
1984-11-28,25.87,26.50,25.87,25.87,14673600,2.95
1984-11-27,24.62,24.87,24.62,24.62,4590400,2.81
1984-11-26,24.00,24.00,24.00,24.00,3636000,2.74
1984-11-23,23.37,24.12,23.37,23.75,4904800,2.71
1984-11-21,23.12,23.25,23.12,23.12,6418400,2.64
1984-11-20,22.62,22.75,22.62,22.62,9424800,2.58
1984-11-19,23.25,23.37,21.87,21.87,8321600,2.50
1984-11-16,23.75,24.12,23.12,23.25,5920000,2.65
1984-11-15,23.75,24.00,23.75,23.75,3833600,2.71
1984-11-14,23.75,24.00,23.75,23.75,3752800,2.71
1984-11-13,24.12,24.62,23.50,23.50,4548800,2.68
1984-11-12,24.12,24.25,24.12,24.12,4070400,2.75
1984-11-09,24.75,24.87,23.00,23.25,10518400,2.65
1984-11-08,25.75,25.75,24.75,24.75,3162400,2.82
1984-11-07,26.25,26.37,25.75,25.75,8286400,2.94
1984-11-06,26.25,26.37,26.25,26.25,8073600,3.00
1984-11-05,24.87,25.37,24.75,24.75,3764800,2.82
1984-11-02,25.00,25.12,24.75,24.87,1004800,2.84
1984-11-01,25.00,25.25,25.00,25.00,1680000,2.85
1984-10-31,25.00,25.25,24.87,24.87,2191200,2.84
1984-10-30,25.00,25.25,25.00,25.00,2677600,2.85
1984-10-29,24.75,24.87,24.75,24.75,1836000,2.82
1984-10-26,25.25,25.25,24.50,24.62,4113600,2.81
1984-10-25,26.25,26.25,25.25,25.25,5676000,2.88
1984-10-24,26.25,26.50,26.25,26.25,5989600,3.00
1984-10-23,26.00,26.25,26.00,26.00,6668800,2.97
1984-10-22,25.62,26.00,25.37,25.37,4108800,2.89
1984-10-19,25.62,27.37,25.50,25.62,11673600,2.92
1984-10-18,25.62,25.75,25.62,25.62,8842400,2.92
1984-10-17,24.87,25.00,24.87,24.87,5636000,2.84
1984-10-16,24.00,24.12,23.87,23.87,4246400,2.72
1984-10-15,24.00,24.25,24.00,24.00,8715200,2.74
1984-10-12,23.75,23.87,22.50,22.75,9522400,2.60
1984-10-11,23.87,24.50,23.75,23.75,6553600,2.71
1984-10-10,24.62,24.62,23.87,23.87,13070400,2.72
1984-10-09,24.87,25.00,24.62,24.62,4515200,2.81
1984-10-08,24.87,25.00,24.87,24.87,1721600,2.84
1984-10-05,25.37,25.37,24.75,24.87,3510400,2.84
1984-10-04,25.37,25.62,25.37,25.37,4482400,2.89
1984-10-03,25.12,25.50,25.12,25.12,4335200,2.87
1984-10-02,24.75,25.62,24.75,24.75,4258400,2.82
1984-10-01,25.00,25.00,24.50,24.50,3521600,2.80
1984-09-28,25.75,25.75,24.62,25.12,8344800,2.87
1984-09-27,25.75,25.87,25.75,25.75,3796000,2.94
1984-09-26,26.12,27.25,25.75,25.75,3987200,2.94
1984-09-25,26.50,26.50,26.12,26.12,5977600,2.98
1984-09-24,26.87,27.00,26.62,26.62,2833600,3.04
1984-09-21,27.12,27.87,26.50,26.87,3591200,3.07
1984-09-20,27.12,27.37,27.12,27.12,2387200,3.09
1984-09-19,27.62,27.87,27.00,27.00,3816000,3.08
1984-09-18,28.62,28.87,27.62,27.62,3495200,3.15
1984-09-17,28.62,29.00,28.62,28.62,6886400,3.27
1984-09-14,27.62,28.50,27.62,27.87,8826400,3.18
1984-09-13,27.50,27.62,27.50,27.50,7429600,3.14
1984-09-12,26.87,27.00,26.12,26.12,4773600,2.98
1984-09-11,26.62,27.37,26.62,26.87,5444000,3.07
1984-09-10,26.50,26.62,25.87,26.37,2346400,3.01
1984-09-07,26.50,26.87,26.25,26.50,2981600,3.02


================================================
FILE: 87-FA/fa-iitg-dataanalysis.R
================================================
# Finance Stock Analysis
#Stock Download

library(Quandl)
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
ICICI
HDFC = Quandl("NSE/HDFCBANK",collapse="daily",start_date="2017-09-01",type="raw")
HDFC
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI

allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
dim(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)
#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#Library to store data in xlsx files
Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre1.8.0_191")
library(xlsx) #needs rJava

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
(df1 <- allstocks %>% select(Stock, Open, Close))
df1 = as.data.frame(df1)
write.xlsx(df1, './data/iitgfa.xlsx', sheetName = "IITG12", append = T)


#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
#store this data into DF
(df2 <- allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean) )
write.csv(df2, './data/iitgfa.csv')

#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx#using-xlsx-package


#write.xlsx(USArrests, file = "myworkbook.xlsx", #check with this file sheetName = "USA-ARRESTS", append = FALSE)
df2 = as.data.frame(df2)
write.xlsx(df2, './data/iitgfa.xlsx', sheetName = "IITG2", append = T)


options(dplyr.print_max = 1e9)  #print all rows
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)

allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 87-FA/finTS1.R
================================================
#create TS Data
#https://www.r-bloggers.com/r-financial-time-series-tips-everyone-should-know-about/
#
library(xts)
myTs <- xts(floor(runif(400,40,100)),order.by=seq(Sys.Date()-399,Sys.Date(),"days"))
head(myTs)
summary(myTs)
str(myTs)

#head & tail together
ht <- function(d, m=5, n=m){
  # print the head and tail together
  list(HEAD = head(d,m), TAIL = tail(d,n))
}


#Joining 2 TS
myOtherTs <- xts(floor(runif(200, 60,100)),order.by=seq(Sys.Date()-199,Sys.Date(),"days"))
myOtherTs
ht(myOtherTs)
head

# dates intersection
tsInter <- merge.xts(myTs,myOtherTs,join="inner")
ht(tsInter)
dim(tsInter)

# dates union and blanks filled with NAs
tsUnion <- merge.xts(myTs,myOtherTs,join="outer")
dim(tsUnion)
tsUnion


monthlyRtn = apply.monthly(tsInter[,2], sum)
ht(monthlyRtn)
yearlyRtn = apply.yearly(tsInter[,2], sum)
yearlyRtn


#End Points
(newTs.m = endpoints(tsInter, on='months'))
tsInter[1:30]
(newTs.y = endpoints(tsInter, on='years'))
#days, weeks, months, quarters, years
ht(tsInter)
?endpoints


#Missing Values
n = 100; k = 5; N = k *n
prices = cumsum(floor(runif(N, 5,10)))
ht(prices)
theSample = sample(c(1:length(prices)), 10)
prices[theSample] = NA
cumsum(prices)  # NA after first NA value
prices2 = na.locf(prices)
cumsum(prices2)
df1=(cbind(prices, prices2))
df1[!complete.cases(df1),]
df1

library(PerformanceAnalytics)
# Graphs
prices3 = xts(prices2, order.by = seq(Sys.Date()- (length(prices)-1), Sys.Date(),'days'))
charts.PerformanceSummary(Return.calculate(prices3, method='discrete'))
?charts.

================================================
FILE: 87-FA/findata1.R
================================================
#Finance
#https://msperlin.github.io/pafdR/Financial-data.html#data-from-financial-markets

# set project cashflow
CF <- c(-8000, 2500, 3000, 3000, 5000)
names(CF) <- paste('Year', 0:(length(CF)-1))

# print result
print(CF)

#first element is negative. This means that, at time zero, cash will be spent. Now, we can use package FinCalc (Fan 2016) to evaluate the project using NPV and IRR.


library(FinCal)

# set discount rate
r <- 0.1

# calculate npv
my.NPV <- npv(r = r, cf = CF)
my.NPV

# calculate irr
my.IRR <- irr(cf = CF)
my.IRR

# calculate payback
my.payback <- min(which(cumsum(CF) > 0)) - 1
my.payback

# print results
cat('NPV = ', my.NPV )
## NPV =  2421.078
cat('IRR = ', my.IRR)
## IRR =  0.2183874
cat('Payback = ', my.payback, 'years')
## Payback =  3 years

#The initial investment is paid back in 3 years. From the financial side, this fictional project creates value and should be accepted.




#2----

#set seed for reproducibility
set.seed(10)

# set number of time periods
nT <- 5

# simulate prices
P.1 <- 10*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))
P.2 <- 20*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))
P.3 <- 30*(cumprod(1+rnorm(nT,mean = 0, sd = 0.1)))
P.1; P.2; P.3
# gather info in df
my.df <- data.frame(ref.date = Sys.Date()+1:nT,  prices = c(P.1,P.2,P.3),
ticker = paste('Stock ',c(rep('A',nT),rep('B',nT),rep('C',nT))))

# print result  
print(my.df)

# set price matrix
my.price.mat <- matrix(my.df$prices, nrow = nT)
my.price.mat

# set row and col names
colnames(my.price.mat) <- unique(my.df$ticker)
rownames(my.price.mat) <- as.character(unique(my.df$ref.date))

# print result
print(my.price.mat)

9.12/10.08-1; 8.94/9.12 -1
# apply return formula for each column
my.ret.mat <- my.price.mat[2: nrow(my.price.mat)   , ]/
  my.price.mat[1:(nrow(my.price.mat)-1), ] -1 
my.ret.mat


# set weight of portfolio (evenly weighted)
w <- matrix(rep(1/3,3), nrow = 3)
w
# calculate return of portfolio over time
ret.port <- my.ret.mat %*% w
ret.port
# print result
print(ret.port)

#Vector ret.port show the return an investor would receive if the total value of the portfolio is evenly divided between the stocks.

================================================
FILE: 87-FA/finstmts1.R
================================================
#Financial Statements
#https://github.com/bergant/finstr
library(finstr)

library(devtools)
install_github("bergant/finstr")

library(XBRL)
# parse XBRL (Apple 10-K report)
xbrl_url2014 <- "https://www.sec.gov/Archives/edgar/data/320193/000119312514383437/aapl-20140927.xml"
xbrl_url2013 <- "https://www.sec.gov/Archives/edgar/data/320193/000119312513416534/aapl-20130928.xml"
old_o <- options(stringsAsFactors = FALSE)
xbrl_data_aapl2014 <- xbrlDoAll(xbrl_url2014)
xbrl_data_aapl2013 <- xbrlDoAll(xbrl_url2013)
options(old_o)


library(finstr)

st2013 <- xbrl_get_statements(xbrl_data_aapl2013)
st2014 <- xbrl_get_statements(xbrl_data_aapl2014)
st2014
#> Financial statements repository
#>                                              From         To Rows Columns
#> StatementOfIncome                      2012-09-29 2014-09-27    3      15
#> StatementOfOtherComprehensiveIncome    2012-09-29 2014-09-27    3      14
#> StatementOfFinancialPositionClassified 2013-09-28 2014-09-27    2      33
#> StatementOfCashFlowsIndirect           2012-09-29 2014-09-27    3      33
#
balance_sheet2013 <- st2013$StatementOfFinancialPositionClassified
balance_sheet2014 <- st2014$StatementOfFinancialPositionClassified
income2013 <- st2013$StatementOfIncome
income2014 <- st2014$StatementOfIncome
balance_sheet2014

check <- check_statement(balance_sheet2014)
check
#> Number of errors:  0 
#> Number of elements in errors:  0

check_statement(
  within(balance_sheet2014, InventoryNet <- InventoryNet * 2)
)


check <- check_statement(income2014, element_id = "OperatingIncomeLoss")
check
#> Number of errors:  0 
#> Number of elements in errors:  0
check$expression[1]
#> [1] "+ GrossProfit - OperatingExpenses"
check$calculated / 10^6
#> [1] 55241 48999 52503
balance_sheet <- merge( balance_sheet2013, balance_sheet2014 )
balance_sheet
# merge all statements
st_all <- merge( st2013, st2014 )
# check if balance sheets are merged:
balance_sheet <- st_all$StatementOfFinancialPositionClassified
balance_sheet$endDate
#> [1] "2012-09-29" "2013-09-28" "2014-09-27"
merge.statement(
  st_all$StatementOfFinancialPositionClassified, 
  st_all$StatementOfIncome )


library(dplyr)

balance_sheet %>% transmute(
  date = endDate, 
  CurrentRatio = AssetsCurrent / LiabilitiesCurrent
)
balance_sheet %>% calculate( digits = 2,
                             
                             Current_Ratio = AssetsCurrent / LiabilitiesCurrent,
                             
                             Quick_Ratio =  
                               ( CashAndCashEquivalentsAtCarryingValue + 
                                   AvailableForSaleSecuritiesCurrent +
                                   AccountsReceivableNetCurrent
                               ) / LiabilitiesCurrent
                             
)

merge(balance_sheet, st_all$StatementOfIncome ) %>% calculate( digits = 2,
                                                               
                                                               .AccountReceivableLast = lag(AccountsReceivableNetCurrent),
                                                               .AccountReceivableAvg = (.AccountReceivableLast + AccountsReceivableNetCurrent)/2,
                                                               
                                                               DaysSalesOutstanding = .AccountReceivableAvg / SalesRevenueNet * 365 
)



================================================
FILE: 87-FA/finstmts2.R
================================================
#Computational Finance
#http://computationalfinance.lsi.upc.edu/?page_id=104

##Technical analysis with quantmod
library(quantmod)
##Chart a stock with MA(50), Bollinger, Volume and RSI
getSymbols("AAPL")
chartSeries(AAPL, subset='2008-06::2009-04',theme=chartTheme('white',up.col='green',dn.col='red'),TA=c(addBBands(n=20,sd=2,),addSMA(n=50,col="blue"),addSMA(n=10,col="black"),addRSI(n=14)))
#up.col, dn.col= color of up (down) candle,
#
library(quantmod)
##getFinancials retrives financials from src (default google)
##returns six individual matrices organized in a list of class ‘financials’:
# IS a list containing (Q)uarterly and (A)nnual Income Statements
# BS a list containing (Q)uarterly and (A)nnual Balance Sheets
# CF a list containing (Q)uarterly and (A)nnual Cash Flow Statements
getFinancials('AAPL',src="yahoo")  #returns AAPL.f to "env"
viewFin(AAPL.f, "CF", "A") #view Annual Cash Flows 
#above code NW
viewFin(AAPL.f, "BS", "A", "2010/2011")

applBS = viewFin(AAPL.f, "BS","A")
colnames(applBS) ##give four columns labeled by the date (yyyy-mm-dd) the statement was issued
rownames(applBS) ##give 42 rows labelled with financial indicators

applBS["Total Current Assets","2012-09-29"] ##returns the value of Total Current Assets in that year

##Compute some liquidity measures:
CurrentRat=applBS["Total Current Assets","2012-09-29"]/applBS["Total Current Liabilities","2012-09-29"]
CurrentRat

================================================
FILE: 87-FA/finstmts3.R
================================================
# Financial Statements


library(quantmod)
chooseCRANmirror(0)
getFin("LNKD")
getFin("SBIN.NS")

head(viewFin(LNKD.f, type= 'BS', period = 'Q' ))
fin <- c('GOOGL','AAPL','AMZN','EBAY','CRM','FB','LNKD','ALBIY')

getFin_HANDLED <- function (symb) {
  return(tryCatch(getFin(symb, auto.assign = "FALSE"), error=function(e) NULL)) 
}

fin.f <- lapply(fin, getFin_HANDLED) 
head(fin.f)


myData <- new.env()
class(myData)
ls(myData)
NVS <- getFinancials('NVS',  env = myData, src = "yahoo", auto.assign=TRUE)
getFinancials('APPL', env = .GlobalEnv, src = "yahoo", 
              auto.assign = TRUE)


viewFinancials(x, type=c('BS','IS','CF'), period=c('A','Q'),
               subset = NULL)

JAVA <- getFinancials('JAVA')
AAPL <- getFin('AAPL')

JAVA$IS$Q   # Quarterly Income Statement
AAPL$CF$A   # Annual Cash Flows

str(AAPL)

================================================
FILE: 87-FA/gtrends1.R
================================================
# Google Trends
# https://datascienceplus.com/analyzing-google-trends-data-in-r/
# 
library(gtrendsR)
library(reshape2)

google.trends = gtrends(c("pnb"), gprop = "web", time = "all")[[1]]
google.trends
google.trends = dcast(google.trends, date ~ keyword + geo, value.var = "hits")
rownames(google.trends) = google.trends$date
google.trends$date = NULL

Sys.setenv(TZ = "GMT")

google.trends = gtrends(c("skiing"), geo = c("CA", "NZ"), gprop = "web", time = "2010-06-30 2017-06-30")[[1]]
google.trends

library(gtrendsR)
geo.codes = sort(unique(countries[substr(countries$sub_code, 1, 2) == "IT", ]$sub_code))
geo.codes


================================================
FILE: 87-FA/intrino1.R
================================================

library(quantmod)
getFinancials('GE')
viewFinancials(GE.f)
To get only the income statement, reported anually, as a data frame use this:
  
viewFinancials(GE.f, "IS", "A")


#Create variables for your usename and password, get those at intrinio.com/login
username <- "62125b9aa13fe147cd0f3477be69d602"
password <- "b00a3fc5c70b0a5c20a0049b298adfc9"

#Making an api call for roic. This puts together the different parts of the API call

base <- "https://api.intrinio.com/"
endpoint <- "financials/"
type <- "standardized"
stock <- "YUM"
statement <- "income_statement"
fiscal_period <- "Q2"
fiscal_year <- "2015"

#Pasting them together to make the API call
call1 <- paste(base,endpoint,type,"?","identifier","=", stock, "&","statement","=",statement,"&","fiscal_period",
               "=", fiscal_period, "&", "fiscal_year", "=", fiscal_year, sep="")

# call1 Looks like this "https://api.intrinio.com/financials/standardized?identifier=YUM&statement=income_statement&fiscal_period=Q2&fiscal_year=2015"

#Now we use the API call to request the data from Intrinio's database

YUM_Income <- GET(call1, authenticate(username,password, type = "basic"))

#That gives us the ROIC value, but it isn't in a good format so we parse it

test1 <- unlist(content(YUM_Income, "text"))

#Convert from JSON to flattened list

parsed_statement <- fromJSON(test1)

#Then make your data frame:

df1 <- data.frame(parsed_statement)



================================================
FILE: 87-FA/intrino2.R
================================================


#rm(list=ls(())
#install.packages("httr")
require("httr")






username <- "62125b9aa13fe147cd0f3477be69d602"
password <- "b00a3fc5c70b0a5c20a0049b298adfc9"
base <- "https://api.intrinio.com/"
endpoint <- "data_point"
stock <- "AAPL"
item1 <- "close_price"
item2 <- "pricetoearnings"
call1 <- paste(base,endpoint,"?","ticker","=", stock, "&","item","=",item1, sep="")
call2 <- paste(base,endpoint,"?","ticker","=", stock, "&","item","=",item2, sep="")


apple_price <- get(call1, authenticate(username,password, type = "basic"))
apple_pricetoearnings <- get(call2, authenticate(username,password, type = "basic"))

test1 <- unlist(content(apple_price,"parsed"))
test2 <- unlist(content(apple_pricetoearnings,"parsed"))

df <- data.frame(test1,test2)






#1----2
prices <- function(ticker){
  price_base <- "https://api.intrinio.com/prices?identifier=" 
  uusername <- "62125b9aa13fe147cd0f3477be69d602"
  password <- "b00a3fc5c70b0a5c20a0049b298adfc9"
  
  price <- paste(price_base,ticker,sep="") 
  tp <- GET(price, authenticate(username, password, type = "basic")) 
  z <- unlist(content(tp,"parsed"))
  
  n=length(z)
  b=as.data.frame(matrix(z[1:(n-5)],(n-5)/13, byrow = T))
  names(b)=names(z)[1:13]
  
  return(b)
}

#If you call the function, creating an object, and using your own API keys for the "username" and "password" objects within the function, you will have your data frame:
  
t <- prices("AAPL")
t
#The really nice part about this function is that you can now create data frames quickly by swapping out AAPL for any tickers you are interested in analyzing. Graphing prices becomes very easy, but my goal is quant modeling.

#For that, I need financial metrics that I expect to be correlated with price. This second function returns daily historical data over the period of your choice for the metric of your choice.

history <- function(ticker, item, start_date, end_date){
  history_base <- "https://api.intrinio.com/historical_data?ticker=" 
  username <- "a543b029ec930ab0c7add95bfa1ea3ac" 
  password <- "991d8ca925d74ecfbe7f78b4784d88b0"
  historical <- paste(history_base, ticker, "&item=", item, "&start_date=", start_date, "&end_date=", end_date, sep="") 
  tp <- GET(historical, authenticate(username, password, type = "basic")) 
  z <- unlist(content(tp,"parsed"))
  n=length(z)
  b=as.data.frame(matrix(z[1:(n-5)],(n-5)/2, byrow = T))
  names(b)=names(z)[1:2]
  return(b)
}

#This time, the function needs a couple of inputs because we are looking at a certain period for a specific financial metric. You could, for example, call the function with these inputs to get the daily market cap for AAPL ranging from October 1, 2016 back to January 1, 2010.

app_hist <- history("AAPL","marketcap", "2010-01-01","2016-10-01")


================================================
FILE: 87-FA/lag1.R
================================================
#Lag

x <- sample(c(1:9), 10, replace = T)
y <- lag(x, 1)
(ds <- cbind(x, y))
ds

y <- lag(x, 2)
(ds <- cbind(x, y))


require(zoo)
set.seed(123)
x <- zoo(sample(c(1:9), 10, replace = T))
y <- lag(x, -1, na.pad = TRUE)  #shift down
cbind(x, y)

y <- lag(x, 1, na.pad = TRUE)  #from bottom
cbind(x, y)


#diff

x
diff(x)
lag(x, k=-1)
diff(x) / lag(x, k=-1)
log(x)
diff(log(x))  #x2-x1



================================================
FILE: 87-FA/logistic_regression.R
================================================
# Logistic Regression
# Import the dataset

setwd("E:\\HHges - Mkt Anyts\\BITS Pilani Bussiness Anallytics\\04.Logistic Regression")

dataset = read.csv('Customer Churn.csv')
summary(dataset)
# Split the dataset into the Training set and Test set

#install.packages('caTools')
library(caTools)


set.seed(2000)
#Split data from vector Y into two sets in predefined ratio 
#while preserving relative ratios of different labels in Y. 
#Used to split the data used during classification into train and test subsets
split = sample.split(dataset$Churn, SplitRatio = 0.75)
tail(dataset)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)





names(training_set)

# Fitting Logistic Regression to the Training set
classifier = glm(formula = Churn ~
                   as.factor(Gender)+
                   Age+
                   EstimatedSalary,
                 family = binomial,
                 data = training_set)

summary(classifier)

# Fitting Logistic Regression to the Training set - Gender is being insignificant is dropped here
classifier = glm(formula = Churn ~ Age+EstimatedSalary,
                 family = binomial,
                 data = training_set)

summary(classifier)$coefficient

# Predicting the Test set results 
# type=reponse is used to give predicted probibilites
prob_pred = predict(classifier, type = 'response', 
                    newdata = test_set)

df_prob_pred = as.data.frame(prob_pred)
head(df_prob_pred)

y_pred = ifelse(prob_pred > 0.5, 1, 0)
y_pred

# Making the Confusion Matrix
cm = table(test_set[,5], y_pred)
cm

forecast::accuracy(test_set[,5], y_pred)
forecast::con

================================================
FILE: 87-FA/qf1.R
================================================
# Quantitative Finance in R
#Chap-1
#https://github.com/matplotlib/sample_data/blob/master/aapl.csv
aapl = read.zoo('aapl.csv', sep=',', header=T, format="%Y-%m-%d")

plot(aapl, main='Apple Closing Prices on NASDAQ', ylab='Price USD', xlab='Date')

ht <- function(d, m=5, n=m){
  list(HEAD = head(d,m), TAIL = tail(d,n))
}
#First & Last Part of TS
ht(aapl)
#all time high on open
aapl[which.max(aapl$Open)]

#simple returns
x = c(1,5,3,7)
diff(x)
lag(x,1)
lag(x, k=-1)
ret_simple = diff(aapl)/ lag(aapl, k=-1) * 100
ret_simple
#compound returns
ret_cont = diff(log(aapl)) * 100
ret_cont

#summary stats about simple returns
head(ret_simple)
summary(coredata(ret_simple))  # biggest loss -51
?coredata

hist(ret_simple, breaks=100, main='Histogram of Simple Returns', xlab='%')
tail(aapl)
aapl_2007 = window(aapl, start = '2007-01-01', end='2007-12-31')
head(aapl_2007)
aapl_2007[which.max(aapl_2007$Close)]

quantile(ret_simple, probs=0.01)


================================================
FILE: 87-FA/sentianalysistrading1.R
================================================
# Sentimental Analysis in Trading
# https://www.quantinsti.com/blog/sentiment-analysis-in-trading/
# 
# 

================================================
FILE: 87-FA/shares1.R
================================================
#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/
#Stock Analysis  

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2017-01-01")
end <- as.Date("2018-04-01")

# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the
# quantmod function getSymbols, and pass a string as a first argument to
# identify the desired ticker symbol, pass 'yahoo' to src for Yahoo!
# Finance, and from and to specify date ranges

# The default behavior for getSymbols is to load data directly into the
# global environment, with the object being named after the loaded ticker
# symbol. This feature may become deprecated in the future, but we exploit
# it now.

getSymbols("AAPL", src = "yahoo", from = start, to = end)

# What is AAPL?
class(AAPL)
head(AAPL)

plot(AAPL[, "AAPL.Close"], main = "AAPL")
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")


# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google is
# held by a holding company called Alphabet, Inc., which is the company
# traded on the exchange and uses the ticker symbol GOOG).
getSymbols(c("MSFT", "GOOG"), src = "yahoo", from = start, to = end)

# Create an xts object (xts is loaded with quantmod) that contains closing
# prices for AAPL, MSFT, and GOOG
stocks = as.xts(data.frame(AAPL = AAPL[, "AAPL.Close"], MSFT = MSFT[, "MSFT.Close"], GOOG = GOOG[, "GOOG.Close"]))
head(stocks)


# Create a plot showing all series as lines; must use as.zoo to use the zoo
# method for plot, which allows for multiple series to be plotted on same
# plot
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

plot(as.zoo(stocks[, c("AAPL.Close", "MSFT.Close")]), screens = 1, lty = 1:2,  xlab = "Date", ylab = "Price")
par(new = TRUE)
plot(as.zoo(stocks[, "GOOG.Close"]), screens = 1, lty = 3, xaxt = "n", yaxt = "n", xlab = "", ylab = "")
axis(4)
mtext("Price", side = 4, line = 3)
legend("topleft", c("AAPL (left)", "MSFT (left)", "GOOG"), lty = 1:3, cex = 0.5)


# Get pipe operator!
if (!require("magrittr")) {
  install.packages("magrittr")
  library(magrittr)
}
stock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% 
  t %>% as.xts

head(stock_return)

plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Return")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

stock_change = stocks %>% log %>% diff
head(stock_change)

plot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = "Date", ylab = "Log Difference")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)  


candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addSMA(n = 20)



start = as.Date("2010-01-01")
getSymbols(c("AAPL", "MSFT", "GOOG"), src = "yahoo", from = start, to = end)
# The subset argument allows specifying the date range to view in the chart.
# This uses xts style subsetting. Here, I'm using the idiom
# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is
# the start date, and the date on the right-hand side is the end date. If
# either is left blank, either the earliest date or latest date in the
# series is used (as appropriate). This method can be used for any xts
# object, say, AAPL
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = 20)

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = c(20, 50, 200))


================================================
FILE: 87-FA/shares2.R
================================================
# Indian Stocks
#stocks2

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2017-01-01")
end <- as.Date("2018-04-01")
getSymbols("SBIN.NS", src = "yahoo", from = start, to = end)
# What is SBI?
class(SBIN.NS)
head(SBIN.NS)

plot(SBIN.NS[, "SBIN.NS.Close"], main = "SBIN.NS")
candleChart(SBIN.NS, up.col = "black", dn.col = "red", theme = "white")


#ICICIBANK.NS
#TATAMOTORS.NS
getSymbols(c("ICICIBANK.NS", "TATAMOTORS.NS"), src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"]))
stocks
stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"], ICICI = ICICIBANK.NS[, "ICICIBANK.NS.Close"], TATAMOTORS = TATAMOTORS.NS[, "TATAMOTORS.NS.Close"]))
head(stocks)

plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("SBIN", "ICICI", "TATATMOTORS"), lty = 1:3, cex = 0.5)



================================================
FILE: 87-FA/stock3.R
================================================
# Getting stock Details

library(quantmod)

mdate = '2017-01-01'
amzPrices.y = getSymbols('AMZN', from=mdate, auto.assign = 0)
amzPrices.y

amzRets = ROC(amzPrices.y, type='discrete')
amzRets

amzRets2 = periodReturn(amzPrices.y, period='monthly', type='log', subset='2018')
amzRets2
amzPrices.g = getSymbols.google('AMZN', from=mdate, auto.assign = 0)
amzPrices.g

#----
getSymbols('SBIN.NS', src='yahoo', from=mdate)
SBIN.NS
getSymbols('SBIN', src='google', from=mdate)
SBIN.NS

x <- getSymbols('^GSPC', auto.assign = F)
x
from <- c("USD", "USD", "EUR", "GBP", "EUR", "EUR")
to <- c("CAD", "JPY", "USD", "CAD", "JPY", "AUD")
df <- getQuote(paste0(from, to, "=X"))
df

getSymbols(Symbols = "LIQUIDBEES", src = "google") 
getSymbols.google(Symbols = "LIQUIDBEES")

#-----
from       <- "2016-04-01"
to         <- Sys.Date()
period     <- "daily"
envir      <- globalenv()
crumb      <- "yourCrumb"
DLdir      <- "~/shares/"
assetList  <- c("AAPL", "ADBE", "AMAT")
lapply(assetList, getSymbols.yahoo, from, to, envir = globalenv(), crumb = crumb, DLdir)


================================================
FILE: 87-FA/stockanalysis1.R
================================================
# Finance Stock Analysis
# https://www.analyticsvidhya.com/blog/2017/09/comparative-stock-analysis/
#Install Packages 
#pckgs<-c("Quandl","Sweep","tidyverse","tidyquant","ggplot","forcats","stringr")
#install.packages(pckgs,dependencies = TRUE)

library(Quandl)
library(tidyverse)
library(ggplot2)
library(tidyquant)
library(timetk)
library(forcats)
library(stringr)
library(gganimate)  #install as mentioned below
#devtools::install_github("dgrtwo/gganimate")
library(plyr)
library(stringr)
library(gridExtra)

#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)
#ICICI BOB CANARA AXIS SBI PNB  price band of Rs 200 to Rs 500. 
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2016-09-01",type="raw")
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2016-09-01",type="raw")
Axis=Quandl("NSE/AXISBANK",collapse="daily",start_date="2016-09-01",type="raw")
Canara=Quandl("NSE/CANBK",collapse="daily",start_date="2016-09-01",type="raw")
BOB=Quandl("NSE/BANKBARODA",collapse="daily",start_date="2016-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2016-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
ICICI<-cbind(ICICI,Stock="")
PNB<-cbind(PNB,Stock="")
Axis<-cbind(Axis,Stock="")
SBI<-cbind(SBI,Stock="")
Canara<-cbind(Canara,Stock="")
BOB<-cbind(BOB,Stock="")

## Paste the stock name in stock column

ICICI$Stock<-paste(ICICI$Stock,"ICICI",sep="")
PNB$Stock<-paste(PNB$Stock,"PNB",sep="")
Axis$Stock<-paste(Axis$Stock,"Axis",sep="")
SBI$Stock<-paste(SBI$Stock,"SBI",sep="")
Canara$Stock<-paste(Canara$Stock,"Canara",sep="")
BOB$Stock<-paste(BOB$Stock,"BOB",sep="")

## Consolidate under one dataset
Master_Data<-rbind(ICICI,PNB,Axis,SBI,Canara,BOB)

## Visualisation in ggplot2 ("Comparative Visulisation of Close Price listed on NSE")

## Convert the dates into character in order to split the coloumn into "Y" "m" "dd"" columns
Master_Data$Date<-as.character(Master_Data$Date)

## Split the date and create a list for the same

list<-strsplit(Master_Data$Date,"-")

## Convert the list into dataframe
library(plyr)
Master_Date1<-ldply(list)
colnames(Master_Date1)<-c("Year","Month","Day")

## Column bind with the main dataframe
Master_Data<-cbind(Master_Data,Master_Date1)
names(Master_Data)

## Change the scale for Traded Quantity

Master_Data$`Total Trade Quantity`<-Master_Data$`Total Trade Quantity`/100000

## Convert the Date to as.Date()

Master_Data$Date<-as.Date(Master_Data$Date)

## Visualisation with Bubble Plot
P<- ggplot(Master_Data,aes(factor(Stock),Close,color=Stock,frame=Month)) +
  geom_jitter(aes(size = Close, colour=Stock, alpha=.02)) +
  ylim(0,1000)+
  labs(title = "Bank Stock Monthly Prices", x = "Banks", y= "Close Price") +
  theme(panel.border = element_blank(),
        panel.grid.major = element_line(colour = "grey61", size = 0.5, linetype = "dotted"),
        panel.grid.minor = element_blank(),
        axis.line=element_line(colour="black"),
        plot.title = element_text(hjust = 0.5,size=18,colour="indianred4"))+
  theme(legend.position="none")

P1<-gganimate(P,'Price_Range.gif',ani.width=600,ani.height=400,interval=1)

## Group By Stock

Master_Data<-Master_Data%>%
  tibble::as.tibble()%>%
  group_by(Stock)

## Visualisation for Daily Stock Prices

Master_Data %>%  ggplot(aes(x = Date, y = Close, color = Stock)) +  geom_point() +  labs(title = "Daily Close Price", x = "Month",y="Close Price") +  facet_wrap(~ Stock, ncol = 3, scale = "free_y") + scale_fill_tq(fill="green4",theme="light") +   theme_tq() + theme(panel.border = element_blank(), panel.grid.major = element_line(colour = "grey61", size = 0.5, linetype = "dotted"),    panel.grid.minor = element_blank(), axis.line=element_line(colour="black"), plot.title = element_text(hjust = 0.5,size=18,colour="indianred4"))+  theme(legend.position="none")


## Traded Quantity vs Price
names(Master_Data)

z<-Master_Data %>%  ggplot(aes(x = `Total Trade Quantity`, y = Close, color = Stock, frame=Month)) +  geom_smooth(method='loess') +  xlim(0,400)+ labs(title = "Monthly Traded Quantity vs Price", x = "Traded Quantity (Lacs)",y="Close Price") + facet_wrap(~ Stock, ncol = 3, scale = "free_y") +  scale_fill_tq(fill="green4",theme="light") +  theme_tq() + theme(panel.border = element_blank(),   panel.grid.major = element_blank(),   panel.grid.minor = element_blank(),    plot.title = element_text(hjust = 0.5,size=18,colour="indianred4"), axis.line = element_line(colour = "black"))+  theme(legend.position="none")

#Error
z1<-gganimate(z,'Quantity_Price.gif',ani.width=600,ani.height=400,interval=0.7)

## Deviation from High & Low Price
Master_Data_High<-Master_Data%>%mutate(Dev_High=High-Open)
Master_Data_Low<-Master_Data%>%mutate(Dev_Low=Open-Low)

## Computation of weekly average for high Price

Master_Data_High_Week <- Master_Data_High %>%
  tq_transmute(
    select     = Dev_High,
    mutate_fun = apply.weekly, 
    FUN        = mean,
    na.rm      = TRUE,
    col_rename = "Dev_High_Mean"
  )

## Computation weekly average for Low Price

Master_Data_Low_Week<-Master_Data_Low%>%
  tq_transmute(
    select  = Dev_Low,
    mutate_fun = apply.weekly,
    FUN = mean,
    na.rm = TRUE,
    col_rename = "Dev_Low_Mean"
  )

## Visualisation of density distribution of High Price

High<-Master_Data_High_Week%>%ggplot(aes(x=Dev_High_Mean,color=Stock))+
  geom_dotplot(binwidth=0.50,aes(fill=Stock))+
  xlim(0,10)+
  scale_fill_manual(values=c("#999999", "#E69F00","#CC9933","#99FF00","#CC3399","#FF9933"))+
  labs(title="Distribution of High Price Deviation from Open Price",x="Weekly Mean Deviation")+
  facet_wrap(~Stock,ncol=3,scale="free_y")+
  scale_color_tq(values=c("#999999"))+
  theme_tq()+
  theme(panel.border = element_blank(),
        panel.grid.major = element_line(colour = "grey61", size = 0.5, linetype = "dotted"),
        panel.grid.minor = element_blank(),
        axis.line=element_line(colour="black"),
        plot.title = element_text(hjust = 0.5,size=16,colour="indianred4"))+
  theme(legend.position="none")

## Visualisation of density distribution of Low Price
Low<-Master_Data_Low_Week%>%ggplot(aes(x=Dev_Low_Mean,color=Stock))+
  geom_dotplot(binwidth=0.50,aes(fill=Stock))+
  xlim(0,10)+
  scale_fill_manual(values=c("#999999", "#E69F00","#CC9933","#99FF00","#CC3399","#FF9933"))+
  labs(title="Distribution of Weekly Low Price Deviation from Open Price",x="Weekly Mean Deviation")+
  facet_wrap(~Stock,ncol=3,scale="free_y")+
  scale_color_tq(values=c("#999999"))+
  theme_tq()+
  theme(panel.border = element_blank(),
        panel.grid.major = element_line(colour = "grey61", size = 0.5, linetype = "dotted"),
        panel.grid.minor = element_blank(),
        axis.line=element_line(colour="black"),
        plot.title = element_text(hjust = 0.5,size=16,colour="indianred4"))+
  theme(legend.position="none")
## Arrange
grid.arrange(High,Low,ncol = 2, nrow = 1)


k <- 1:180
col_names <- paste0("lag_", k)

## Only Select Columns "Date" and "Close" from hte master data frame.

Master_Data_lags<-Master_Data%>%
  tibble::as_tibble() %>%
  group_by(Stock)

Master_Data_lags<-Master_Data_lags%>%select(Date,Close)
# Apply lag.xts function using tq_mutate

Master_Data_lags<-Master_Data_lags%>%
  tq_mutate(
    select = Close,
    mutate_fun = lag.xts,
    k=1:180,
    col_rename=col_names
  )

# Calculate the autocorrelations and 95% cutoffs

Master_Data_AutoCorrelations<-Master_Data_lags %>%
  gather(key = "lag", value = "lag_value", -c(Stock,Date, Close)) %>%
  mutate(lag = str_sub(lag, start = 5) %>% as.numeric) %>%
  group_by(Stock, lag) %>%
  summarize(
    cor = cor(x = Close, y = lag_value, use = "pairwise.complete.obs"),
    cutoff_upper = 2/(n())^0.5,
    cutoff_lower = -2/(n())^0.5
  )

## Visualisation of Autocorrelation: ACF Plot

Master_Data_AutoCorrelations %>%
  ggplot(aes(x = lag, y = cor, color = Stock, group = Stock)) +
  
  # Add horizontal line a y=0
  geom_hline(yintercept = 0) +
  
  # Plot autocorrelations
  geom_point(size = 2) +
  geom_segment(aes(xend = lag, yend = 0), size = 1) +
  
  # Add cutoffs
  geom_line(aes(y = cutoff_upper), color = "blue", linetype = 2) +
  geom_line(aes(y = cutoff_lower), color = "blue", linetype = 2) +
  
  # Add facets
  facet_wrap(~ Stock, ncol = 3) +
  
  # Aesthetics
  expand_limits(y = c(-1, 1)) +
  scale_color_tq() +
  theme_tq() +
  labs(
    title = paste0("Tidyverse ACF Plot: Lags ", rlang::expr_text(k)),
    x = "Lags"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.major = element_line(colour = "grey61", size = 0.5, linetype = "dotted"),
    plot.title = element_text(hjust = 0.5,size=18,colour="indianred4")
  )


================================================
FILE: 87-FA/stocks5.R
================================================
# Financial Statements
#Not working through Google Financee

library(quantmod)
getFin("LNKD")
head(viewFin(LNKD.f, type= 'BS', period = 'Q' ))
fin <- c('GOOGL','AAPL','AMZN','EBAY','CRM','FB','LNKD','ALBIY')


================================================
FILE: 87-FA/stocksanalysis3.R
================================================
# Finance Stock Analysis

#Install Packages 
#pckgs<-c("Quandl","Sweep","tidyverse")
#install.packages(pckgs,dependencies = TRUE)

library(Quandl)
# library(tidyverse)
# library(ggplot2)
# library(stringr)
# library(plyr)
# library(stringr)
# library(gridExtra)

#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)
#ICICI SBI PNB  price band of Rs 200 to Rs 500. 
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI

allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)
#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
allstocks %>% select(Stock, Open, Close)

#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)
options(dplyr.print_max = 1e9)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)
allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 87-FA/stocksanalysis4.R
================================================
# Stock Prices Analysis
#https://www.r-bloggers.com/stock-analysis-using-r/
library('quantmod')


getSymbols('AAPL')
chartSeries(AAPL, subset='last 3 months')
addBBands()

#getSymbols function is used to retrieve stock data.  Data can originate in a number of locations.  In the example above, we are obtaining a single stock, Apple.  If you wanted to download several different stock quotes, you can do so in a single command.

getSymbols(c('ORCL','IBM'))

#Once you have retrieved stock data, you can focus on subsets of dates quickly.
AAPL['2010-06-01::2010-06-26']

#also merge data to view comparisons.

head(as.xts(merge(ORCL,IBM)))

#The chartSeries command creates the plot pictured above.  It captures a large amount of information, the date, open and close price, and volume of trading for each day.  Finally, the addBBands() call adds Bollinger Bands to the chart.  Informally, this amounts to a line indicating moving average and two lines a standard deviation above and below this moving average. For the uninitiated, technical indicators (and overlays) can be broken up into four categories – Trend, Volatility, Momentum, and Volume. 


================================================
FILE: 87-FA/volatity1.R
================================================
#Asset Volatity

#https://rviews.rstudio.com/2017/07/12/introduction-to-volatility/

library(quantmod)
#A vector of symbol for our ETFs.
symbols <- sort(c("SPY","VGT","EFA","DBC","AGG"))

# Pipe them to getSymbols, extract the closing prices, and merge to one xts object. 
# Take a look at result before moving on to calculate the returns.
# Notice that we are only grabbing prices from 2013 to present, but that is 
# only to keep the loading time shorter for the post. 
prices <- 
  getSymbols(symbols, src = 'yahoo', from = "2016-01-01", 
             auto.assign = TRUE, warnings = FALSE)    
head(SPY)
head(VGT)

library(ti)

# We are going to make heavy use of the tidyquant package to get monthly returns.
portfolio_component_monthly_returns_xts <- 
  prices %>%  
  # Convert to tibble so can stay in the tidyquant/verse.
  as_tibble(preserve_row_names = TRUE) %>%
  # Add a date column. 
  mutate(date = ymd(row.names)) %>% 
  # Remove the row.names column; it's not needed anymore.
  select(-row.names) %>% 
  # I like to have the date column as the first column. 
  select(date, everything()) %>% 
  # We need to gather into long format in order to use tq_transmute().
  gather(asset, return, -date) %>%
  group_by(asset) %>% 
  # Use the function from tidyquant; note how easily we could change to
  # a different time period like weekly or yearly.
  tq_transmute(mutate_fun = periodReturn, period = "monthly") %>%
  # Put the results back to wide format.
  spread(asset, monthly.returns) %>% 
  # Convert back to an xts, so we can use the cov() and StdDev() functions.
  as_xts(date_col = date)

head(portfolio_component_monthly_returns_xts)


================================================
FILE: 88-Network/NetSciX 2016 Workshop.R
================================================

##======================================================##
##                                                      ##
##    NetSciX School of Code Workshop                   ##
##    Network analysis with R and igraph                ##
##    Wroclaw, Poland, January 10 2016                  ##
##                                                      ##
##    Katya Ognyanova, katya@ognyanova.net              ##
##    www.kateto.net/netscix2016                        ##
##                                                      ##
##======================================================##


# Handouts and example data: bitly.com/netscix2016  
# Online version of the tutoiral: kateto.net/netscix2016

# CONTENTS
# 1. A quick R introduction/refresher
# 2. Networks in igraph
# 3. Reading network data from files
# 4. Turning networks into igraph objects
# 5. Plotting networks with igraph
# 6. Network and node descriptives
# 7. Distances and paths
# 8. Subgroups and communities
# 9. Assortativity and Homophily


# Install the package "igraph" if you don't have its latest version (1.0.1) 
# The package (www.igraph.org) is maintained by Gabor Csardi and Tamas Nepusz.

 install.packages("igraph") 


# ================ 1. A quick R introduction/reminder ================


# You can assign a value to an object using assign(),  "<-", or "=".

x <- 3         # Assignment
x              # Evaluate the expression and print result

y <- 4         # Assignment
y + 5          # Evaluation, y remains 4

z <- x + 17*y  # Assignment
z              # Evaluation

rm(z)          # Remove z: deletes the object.
z              # Error!


#  ------->> Value comparisons: --------

# Comparisons return boolean values: TRUE or FALSE (often abbreviated to T and F)
 
2==2  # Equality
2!=2  # Inequality
x <= y # less than or equal: "<", ">", and ">=" also work


#  ------->> Special constants -------- 

# NA, NULL, Inf, -Inf, NaN

# NA - missing or undefined data
5 + NA      # When used in an expression, the result is generally NA
is.na(5+NA) # Check if missing

# NULL - an empty object, e.g. a null/empty list
10 + NULL     # use returns an empty object (length zero)
is.null(NULL) # check if NULL

# Inf and -Inf represent positive and negative infinity
# They can be returned by  mathematical operations like division of a number by zero:

5/0
is.finite(5/0) # Check if a number is finite

# NaN (Not a Number) - the result of an operation that cannot be reasonably defined 
0/0
is.nan(0/0)


#  ------->> Vectors --------  

v1 <- c(1, 5, 11, 33)       # Numeric vector, length 4
v2 <- c("hello","world")    # Character vector, length 2 (a vector of strings)
v3 <- c(TRUE, TRUE, FALSE)  # Logical vector, same as c(T, T, F)

# Combining different types of elements in one vector will coerce the elements 
# to the least restrictive type:

v4 <- c(v1,v2,v3,"boo") 	# All elements turn into strings

# Other ways to create vectors:
v <- 1:7         # same as c(1,2,3,4,5,6,7)  
v <- rep(0, 77)  # repeat zero 77 times: v is a vector of 77 zeroes
v <- rep(1:3, times=2) # Repeat 1,2,3 twice  
v <- rep(1:10, each=2) # Repeat each element twice  
v <- seq(10,20,2) # sequence: numbers between 10 and 20, in jumps of 2  

length(v)        # check the length of the vector

v1 <- 1:5         # 1,2,3,4,5
v2 <- rep(1,5)    # 1,1,1,1,1 

# Element-wise operations:
v1 + v2      # Element-wise addition
v1 + 1       # Add 1 to each element
v1 * 2       # Multiply each element by 2
v1 + c(1,7)  # This doesn't work: (1,7) is a vector of different length

# Mathematical operations:
sum(v1)      # The sum of all elements
mean(v1)     # The average of all elements
sd(v1)       # The standard deviation
cor(v1,v1*5) # Correlation between v1 and v1*5 

# Logical operations:
v1 > 2       # Each element is compared to 2, returns logical vector
v1==v2       # Are corresponding elements equivalent, returns logical vector.
v1!=v2       # Are corresponding elements *not* equivalent? Same as !(v1==v2)
(v1>2) | (v2>0)   # | is the boolean OR, returns a vector.
(v1>2) & (v2>0)   # & is the boolean AND, returns a vector.
(v1>2) || (v2>0)  # || is the boolean OR, returns a single value
(v1>2) && (v2>0)  # && is the boolean AND, ditto

# Vector elements
v1[3]             # third element of v1
v1[2:4]           # elements 2, 3, 4 of v1
v1[c(1,3)]        # elements 1 and 3 - note that your indexes are a vector
v1[c(T,T,F,F,F)]  # elements 1 and 2 - only the ones that are TRUE
v1[v1>3]          # v1>3 is a logical vector TRUE for elements >3

# NOTE: If you are used to languages indexing from 0, R will surprise you by indexing from 1.

# To add more elements to a vector, simply assign them values.
v1[6:10] <- 6:10

# We can also directly assign the vector a length:
length(v1) <- 15 # the last 5 elements are added as missing data: NA


#  ------->> Factors --------

# Factors are used to store categorical data.

eye.col.v <- c("brown", "green", "brown", "blue", "blue", "blue")         #vector
eye.col.f <- factor(c("brown", "green", "brown", "blue", "blue", "blue")) #factor
eye.col.v
eye.col.f

# R will identify the different levels of the factor - e.g. all distinct values. 
# The data is stored internally as integers - each number corresponding to a factor level.

levels(eye.col.f)  # The levels (distinct values) of the factor (categorical variable)

as.numeric(eye.col.f)  # The factor as numeric values: 1 is  blue, 2 is brown, 3 is green
as.numeric(eye.col.v)  # The character vector, however, can not be coerced to numeric

as.character(eye.col.f)  
as.character(eye.col.v) 

#  ------->> Matrces & Arrays --------  

# A matrix is a vector with dimensions:
m <- rep(1, 20)   # A vector of 20 elements, all 1
dim(m) <- c(5,4)  # Dimensions set to 5 & 4, so m is now a 5x4 matrix

# Create a matrix using matrix():
m <- matrix(data=1, nrow=5, ncol=4)  # same matrix as above, 5x4, full of 1s
m <- matrix(1,5,4) 			             # same matrix as above
dim(m)                               # What are the dimensions of m?

# Create a matrix by combining vectors:
m <- cbind(1:5, 5:1, 5:9)  # Bind 3 vectors as columns, 5x3 matrix
m <- rbind(1:5, 5:1, 5:9)  # Bind 3 vectors as rows, 3x5 matrix

m <- matrix(1:10,10,10)

# Select matrix elements: 
m[2,3]  # Matrix m, row 2, column 3 - a single cell
m[2,]   # The whole second row of m as a vector
m[,2]   # The whole second column of m as a vector
m[1:2,4:6] # submatrix: rows 1 and 2, columns 4, 5 and 6
m[-1,]     # all rows *except* the first one

m[1,]==m[,1]  # Are elements in row 1 equivalent to corresponding elements from column 1? 
m>3           # A logical matrix: TRUE for m elements >3, FALSE otherwise
m[m>3]        # Selects only TRUE elements - that is ones greater than 3


t(m)          # Transpose m     
m <- t(m)     # Assign m the transposed m
m %*% t(m)    # %*% does matrix multiplication
m * m         # * does element-wise multiplication

# Arrays: more than 2 dimensions
# Created with the array() function:

a <- array(data=1:18,dim=c(3,3,2)) # 3d with dimensions 3x3x2
a <- array(1:18,c(3,3,2))          # the same array


#  ------->> Lists --------  

# Lists are collections of objects (e.g. of strings, vectors, matrices, other lists, etc.)

l1 <- list(boo=v1,foo=v2,moo=v3,zoo="Animals!")  # A list with four components
l2 <- list(v1,v2,v3,"Animals!")

l3 <- list()
l4 <- NULL

l1["boo"]      # Access boo: this returns a list.
l1[["boo"]]    # Access boo: this returns the numeric vector
l1[[1]]        # Returns the first component of the list, equivalent to above.
l1$boo         # Named elements can be accessed using the $ operator - equivalent to [[]]

# Add more elements to a list:
l3[[1]] <- 11 # add an element to the empty list l3
l4[[3]] <- c(22, 23) # add a vector as element 3 in the empty list l4. 
                     # Since we added element 3, elements 1 & 2 will be generated and empty (NULL)
l1[[5]] <- "More elements!" # The list l1 had 4 elements, we're adding a 5th here.
l1[[8]] <- 1:11 # We added an 8th element, but not 6th or 7th. Those will be created empty (NULL)
l1$Something <- "A thing"  # Adds a ninth element - "A thing", named "Something"


#  ------->> Data Frames --------  

# The data frame is a special kind of list used for storing dataset tables.
# Think of rows as cases, columns as variables. Each column is a vector or factor.

# Creating a dataframe:

dfr1 <- data.frame( ID=1:4,
                    FirstName=c("John","Jim","Jane","Jill"),
                    Female=c(F,F,T,T), 
                    Age=c(22,33,44,55) )

dfr1$FirstName   # Access the second column of dfr1. 
# Notice that R thinks this is a categorical variable 
# and so it's treating it like a factor, not a character vector.

# Let's get rid of the factor by telling R to treat FirstName as a vector:
dfr1$FirstName <- as.vector(dfr1$FirstName)

# Alternatively, you can tell R you don't like factors from the start using stringsAsFactors=FALSE
dfr2 <- data.frame(FirstName=c("John","Jim","Jane","Jill"), stringsAsFactors=FALSE)
dfr2$FirstName   # Success: not a factor.

# Access elements of the data frame
dfr1[1,]   # First row, all columns
dfr1[,1]   # First column, all rows
dfr1$Age   # Age column, all rows
dfr1[1:2,3:4] # Rows 1 and 2, columns 3 and 4 - the gender and age of John & Jim
dfr1[c(1,3),] # Rows 1 and 3, all columns

# Find the names of everyone over the age of 30 in the data
dfr1[dfr1$Age>30,2]

# Find the average age of all females in the data:
mean ( dfr1[dfr1$Female==TRUE,4] )



#  ------->> Flow Control --------

# if (condition) expr1 else expr2
x <- 5; y <- 10
if (x==0) y <- 0 else y <- y/x #  
y

# for (variable in sequence) expr
ASum <- 0; AProd <- 1
for (i in 1:x)  
{
  ASum <- ASum + i
  AProd <- AProd * i
}
ASum  # equivalent to sum(1:x)
AProd # equivalemt to prod(1:x)

# while (condintion) expr
while (x > 0) {print(x); x <- x-1;}

# repeat expr, use break to exit the loop
repeat { print(x); x <- x+1; if (x>10) break}



#  ------->> R plots and colors --------

# In most R functions, you can use named colors, hex, or rgb values:
# (In the simple base plot chart below x and y are point coordiantes, pch 
# is the point symbol shape, cex is the point size, and col is the color.
# to see the parameters for ploting in base R, check out ?par
plot(x=1:10, y=rep(5,10), pch=19, cex=5, col="dark red")
points(x=1:10, y=rep(6, 10), pch=19, cex=5, col="#557799")
points(x=1:10, y=rep(4, 10), pch=19, cex=5, col=rgb(.25, .5, .3))

# You may notice that rgb here ranges from 0 to 1. While this is the R default,
# you can also set it for the 0-255 range: 
rgb(10, 100, 100, maxColorValue=255) 

# We can also set the opacity/transparency using the parameter 'alpha' (range 0-1):
plot(x=1:5, y=rep(5,5), pch=19, cex=16, col=rgb(.25, .5, .3, alpha=.5), xlim=c(0,6))  

# If we have a hex color representation, we can set the transparency alpha 
# using 'adjustcolor' from package 'grDevices'. For fun, let's also set the
# the plot background to gray using the par() function for graphical parameters.
par(bg="black")
col.tr <- grDevices::adjustcolor("#557799", alpha=0.7)
plot(x=1:5, y=rep(5,5), pch=19, cex=20, col=col.tr, xlim=c(0,6)) 
par(bg="white")

# If you plan on using the built-in color names, here's what they are: 
colors()
grep("blue", colors(), value=T)

# In many cases, we need a number of contrasting colors, or multiple shades of a color.
# R comes with some predefined palette function that can generate those for us.
pal1 <- heat.colors(5, alpha=1)   # generate 5 colors from the heat palette, opaque
pal2 <- rainbow(5, alpha=.5)      # generate 5 colors from the heat palette, semi-transparent
plot(x=1:10, y=1:10, pch=19, cex=10, col=pal1)
plot(x=10:1, y=1:10, pch=19, cex=10, col=pal2)

# We can also generate our own gradients using colorRampPalette.
# Note that colorRampPalette returns a *function* that we can use 
# to generate as many colors from that palette as we need.

palf <- colorRampPalette(c("gray70", "red")) 
plot(x=10:1, y=1:10, pch=19, cex=10, col=palf(100)) 

# To add transparency to colorRampPalette, you need to add a parameter `alpha=TRUE`:
palf <- colorRampPalette(c(rgb(1,1,1, .2),rgb(.8,0,0, .7)), alpha=TRUE)
plot(x=10:1, y=1:10, pch=19, cex=10, col=palf(10)) 


#  ------->> R troubleshooting --------

# While I generate many (and often very creative) errors in R, there are three
# simple things that will most often go wrong for me. Those include: 

# 1) Capitalization. R is case sensitive - a graph vertex named "Jack" is not the same
# as one named "jack". The function "rowSums" won't work as "rowsums" or "RowSums".
#
# 2) Object class. While many functions are willing to take anything you throw
# at them, some will still surprisingly require character vector or a factor instead of
# a numeric vector, or a matrix instead of a data frame. Functions will also occasionally
# return results in an unexpected formats.
#
# 3) Package namespaces. Occasionally problems will arise when different packages
# contain functions with the same name. R may warn you about this by saying something
# like "The following object(s) are masked from 'package:igraph'" as you load a package.
# One way to deal with this is to call functions from a package explicitly using '::'.
# For instance, if function 'blah' is present in packages A and B, you can call
# A::blah and B::blah. In other cases the problem is more complicated, and you may
# have to load packages in certain order, or not use them together at all.
# For example (and pertinent to this workshop), igraph and statnet packages cause
# some problems when loaded at the same time. It is best to detach one before loading
# the other.

 library(igraph)          # load a package
 detach(package:igraph)   # detach a package

# For more advanced troubleshooting, check out try(), tryCatch(), and debug().
?tryCatch



# ================ 2. Networks in igraph ================

rm(list = ls()) # Remove all the objects we created so far.

library(igraph) # Load the igraph package


#  ------->> Create networks --------

g1 <- graph( edges=c(1,2, 2,3, 3,1), n=3, directed=F ) # an undirected graph with 3 edges
# The numbers are interpreted as vertex IDs, so the edges are 1-->2, 2-->3, 3-->1
plot(g1) # A simple plot of the network - we'll talk more about plots later
class(g1)
g1

g2 <- graph( edges=c(1,2, 2,3, 3,1), n=10 ) # now with 10 vertices, and directed by default
plot(g2)   
g2

g3 <- graph( c("John", "Jim", "Jim", "Jill", "Jill", "John")) # named vertices
# When the edge list has vertex names, the number of nodes is not needed
plot(g3)
g3

g4 <- graph( c("John", "Jim", "Jim", "Jack", "Jim", "Jack", "John", "John"), 
             isolates=c("Jesse", "Janis", "Jennifer", "Justin") )  
# In named graphs we can specify isolates by providing a list of their names.

plot(g4, edge.arrow.size=.5, vertex.color="gold", vertex.size=15, 
     vertex.frame.color="gray", vertex.label.color="black", 
     vertex.label.cex=1.5, vertex.label.dist=2, edge.curved=0.2) 

# Small graphs can also be generated with a description of this kind:
# '-' for undirected tie, "+-' or "-+" for directed ties pointing left & right, 
# "++" for a symmetric tie, and ":" for sets of vertices

plot(graph_from_literal(a---b, b---c)) # the number of dashes doesn't matter
plot(graph_from_literal(a--+b, b+--c))
plot(graph_from_literal(a+-+b, b+-+c)) 
plot(graph_from_literal(a:b:c---c:d:e))

gl <- graph_from_literal(a-b-c-d-e-f, a-g-h-b, h-e:f:i, j)
plot(gl)



#  ------->> Edge, vertex, and network attributes --------

# Access vertices and edges:
E(g4) # The edges of the object
V(g4) # The vertices of the object


# You can also manipulate the network matrix directly:
g4[]
g4[1,]
g4[3,3] <- 10
g4[5,7] <- 10

# Add attributes to the network, vertices, or edges:
V(g4)$name # automatically generated when we created the network.
V(g4)$gender <- c("male", "male", "male", "male", "female", "female", "male")
E(g4)$type <- "email" # Edge attribute, assign "email" to all edges
E(g4)$weight <- 10    # Edge weight, setting all existing edges to 10

# Examine attributes
edge_attr(g4)
vertex_attr(g4)
graph_attr(g4)

# Another way to set attributes
# (you can similarly use set_edge_attr(), set_vertex_attr(), etc.)
g4 <- set_graph_attr(g4, "name", "Email Network")
g4 <- set_graph_attr(g4, "something", "A thing")

graph_attr_names(g4)
graph_attr(g4, "name")
graph_attr(g4)

g4 <- delete_graph_attr(g4, "something")
graph_attr(g4)

plot(g4, edge.arrow.size=.5, vertex.label.color="black", vertex.label.dist=1.5,
     vertex.color=c( "pink", "skyblue")[1+(V(g4)$gender=="male")] ) 

# g4 has two edges going from Jim to Jack, and a loop from John to himself.
# We can simplify our graph to remove loops & multiple edges between the same nodes.
# Use 'edge.attr.comb' to indicate how edge attributes are to be combined - possible 
# options include "sum", "mean", "prod" (product), min, max, first/last (selects 
# the first/last edge's attribute). Option "ignore" says the attribute should be 
# disregarded and dropped.

g4s <- simplify( g4, remove.multiple = T, remove.loops = F, 
                 edge.attr.comb=list(weight="sum", type="ignore") )
plot(g4s, vertex.label.dist=1.5)
g4s

# Let's take a look at the description of the igraph object.
# Those will typically start with up to four letters:
# 1. D or U, for a directed or undirected graph
# 2. N for a named graph (where nodes have a name attribute)
# 3. W for a weighted graph (where edges have a weight attribute)
# 4. B for a bipartite (two-mode) graph (where nodes have a type attribute)
#
# The two numbers that follow refer to the number of nodes and edges in the graph. 
# The description also lists graph, node & edge attributes, for example:
# (g/c) - graph-level character attribute
# (v/c) - vertex-level character attribute
# (e/n) - edge-level numeric attribute


# ------->> Specific graphs and graph models --------

# Empty graph
eg <- make_empty_graph(40)
plot(eg, vertex.size=10, vertex.label=NA)

# Full graph
fg <- make_full_graph(40)
plot(fg, vertex.size=10, vertex.label=NA)

# Star graph 
st <- make_star(40)
plot(st, vertex.size=10, vertex.label=NA) 

# Tree graph
tr <- make_tree(40, children = 3, mode = "undirected")
plot(tr, vertex.size=10, vertex.label=NA) 

# Ring graph
rn <- make_ring(40)
plot(rn, vertex.size=10, vertex.label=NA)

# Erdos-Renyi random graph 
# ('n' is number of nodes, 'm' is the number of edges)
er <- sample_gnm(n=100, m=40) 
plot(er, vertex.size=6, vertex.label=NA)  

# Watts-Strogatz small-world graph
# Creates a lattice with 'dim' dimensions of 'size' nodes each, and rewires edges 
# randomly with probability 'p'. You can allow 'loops' and 'multiple' edges.
# The neighborhood in which edges are connected is 'nei'.
sw <- sample_smallworld(dim=2, size=10, nei=1, p=0.1)
plot(sw, vertex.size=6, vertex.label=NA, layout=layout_in_circle)
 
# Barabasi-Albert preferential attachment model for scale-free graphs
# 'n' is number of nodes, 'power' is the power of attachment (1 is linear)
# 'm' is the number of edges added on each time step 
 ba <-  sample_pa(n=100, power=1, m=1,  directed=F)
 plot(ba, vertex.size=6, vertex.label=NA)
 
#igraph can also give you some notable historical graphs. For instance:
 zach <- graph("Zachary") # the Zachary carate club
 plot(zach, vertex.size=10, vertex.label=NA)
 
  # Rewiring a graph
 # 'each_edge()' is a rewiring method that changes the edge endpoints
 # uniformly randomly with a probability 'prob'.
 rn.rewired <- rewire(rn, each_edge(prob=0.1))
 plot(rn.rewired, vertex.size=10, vertex.label=NA)
 
 # Rewire to connect vertices to other vertices at a certain distance. 
 rn.neigh = connect.neighborhood(rn, 5)
 plot(rn.neigh, vertex.size=8, vertex.label=NA) 
 
 
 # Combine graphs (disjoint union, assuming separate vertex sets): %du%
 plot(rn, vertex.size=10, vertex.label=NA) 
 plot(tr, vertex.size=10, vertex.label=NA) 
 plot(rn %du% tr, vertex.size=10, vertex.label=NA) 

  
 
# ================ 3. Reading network data from files ================

 
rm(list = ls()) # clear the workspace again

# Download the archive with the data files from http://bitly.com/netscix2016 
 
# Set the working directory to the folder containing the workshop files:
setwd("C:/DOCS/Conferences/2016-NetSciX/NetSciX Workshop")  
 
# DATASET 1: edgelist 

nodes <- read.csv("Dataset1-Media-Example-NODES.csv", header=T, as.is=T)
links <- read.csv("Dataset1-Media-Example-EDGES.csv", header=T, as.is=T)

# Examine the data:
head(nodes)
head(links)
nrow(nodes); length(unique(nodes$id))
nrow(links); nrow(unique(links[,c("from", "to")]))

# Collapse multiple links of the same type between the same two nodes
# by summing their weights, using aggregate() by "from", "to", & "type":
# (we don't use "simplify()" here so as not to collapse different link types)
links <- aggregate(links[,3], links[,-3], sum)
links <- links[order(links$from, links$to),]
colnames(links)[4] <- "weight"
rownames(links) <- NULL


# DATASET 2: matrix 

nodes2 <- read.csv("Dataset2-Media-User-Example-NODES.csv", header=T, as.is=T)
links2 <- read.csv("Dataset2-Media-User-Example-EDGES.csv", header=T, row.names=1)

# Examine the data:
head(nodes2)
head(links2)

# links2 is an adjacency matrix for a two-mode network:
links2 <- as.matrix(links2)
dim(links2)
dim(nodes2)


# ================ 4. Turning networks into igraph objects ================ 
 
library(igraph)

#  ------->> DATASET 1 -------- 

# Converting the data to an igraph object:
# The graph.data.frame function, which takes two data frames: 'd' and 'vertices'.
# 'd' describes the edges of the network - it should start with two columns 
# containing the source and target node IDs for each network tie.
# 'vertices' should start with a column of node IDs.
# Any additional columns in either data frame are interpreted as attributes.

net <- graph_from_data_frame(d=links, vertices=nodes, directed=T) 

# Examine the resulting object:
class(net)
net 

# We can look at the nodes, edges, and their attributes:
E(net)
V(net)
E(net)$type
V(net)$media

plot(net, edge.arrow.size=.4,vertex.label=NA)

# Removing loops from the graph:
net <- simplify(net, remove.multiple = F, remove.loops = T) 

# If you need them, you can extract an edge list or a matrix from igraph networks.
as_edgelist(net, names=T)
as_adjacency_matrix(net, attr="weight")

# Or data frames describing nodes and edges:
as_data_frame(net, what="edges")
as_data_frame(net, what="vertices")


#  ------->> DATASET 2 --------

head(nodes2)
head(links2)

net2 <- graph_from_incidence_matrix(links2)

# A built-in vertex attribute 'type' shows which mode vertices belong to.
table(V(net2)$type)

plot(net2,vertex.label=NA)

# To transform a one-mode network matrix into an igraph object,
# use graph_from_adjacency_matrix()

# We can also easily generate bipartite projections for the two-mode network:
# (co-memberships are easy to calculate by multiplying the network matrix by
# its transposed matrix, or using igraph's bipartite.projection function)

net2.bp <- bipartite.projection(net2)

# We can calculate the projections manually as well:
#   as_incidence_matrix(net2)  %*% t(as_incidence_matrix(net2))
# t(as_incidence_matrix(net2)) %*%   as_incidence_matrix(net2)

plot(net2.bp$proj1, vertex.label.color="black", vertex.label.dist=1,
     vertex.label=nodes2$media[!is.na(nodes2$media.type)])

plot(net2.bp$proj2, vertex.label.color="black", vertex.label.dist=1,
     vertex.label=nodes2$media[ is.na(nodes2$media.type)])


# ================ 5. Plotting networks with igraph ================

 
#  ------->> Plot parameters in igraph --------

# Plotting with igraph: node options (starting with 'vertex.') and edge options
# (starting with 'edge.'). A list of options is included in your handout.
?igraph.plotting

# We can set the node & edge options in two ways - one is to specify
# them in the plot() function, as we are doing below.

# Plot with curved edges (edge.curved=.1) and reduce arrow size:
plot(net, edge.arrow.size=.4, edge.curved=.1)

# Set node color to orange and the border color to hex #555555
# Replace the vertex label with the node names stored in "media"
plot(net, edge.arrow.size=.2, edge.curved=0,
     vertex.color="orange", vertex.frame.color="#555555",
     vertex.label=V(net)$media, vertex.label.color="black",
     vertex.label.cex=.7) 

# The second way to set attributes is to add them to the igraph object.

# Generate colors based on media type:
colrs <- c("gray50", "tomato", "gold")
V(net)$color <- colrs[V(net)$media.type]

# Set node size based on audience size:
V(net)$size <- V(net)$audience.size*0.7

# The labels are currently node IDs.
# Setting them to NA will render no labels:
V(net)$label.color <- "black"
V(net)$label <- NA

# Set edge width based on weight:
E(net)$width <- E(net)$weight/6

#change arrow size and edge color:
E(net)$arrow.size <- .2
E(net)$edge.color <- "gray80"

plot(net) 

# We can also override the attributes explicitly in the plot:
plot(net, edge.color="orange", vertex.color="gray50") 


# We can also add a legend explaining the meaning of the colors we used:
plot(net) 
legend(x=-1.1, y=-1.1, c("Newspaper","Television", "Online News"), pch=21,
       col="#777777", pt.bg=colrs, pt.cex=2.5, bty="n", ncol=1)


# Sometimes, especially with semantic networks, we may be interested in 
# plotting only the labels of the nodes:

plot(net, vertex.shape="none", vertex.label=V(net)$media, 
     vertex.label.font=2, vertex.label.color="gray40",
     vertex.label.cex=.7, edge.color="gray85")


# Let's color the edges of the graph based on their source node color.
# We'll get the starting node for each edge with "ends()".
edge.start <- ends(net, es=E(net), names=F)[,1]
edge.col <- V(net)$color[edge.start]

plot(net, edge.color=edge.col, edge.curved=.1)


#  ------->> Network Layouts --------

# Network layouts are algorithms that return coordinates for each
# node in a network.

# Let's generate a slightly larger 80-node graph.

net.bg <- sample_pa(80, 1.2) 
V(net.bg)$size <- 8
V(net.bg)$frame.color <- "white"
V(net.bg)$color <- "orange"
V(net.bg)$label <- "" 
E(net.bg)$arrow.mode <- 0
plot(net.bg)

# You can set the layout in the plot function:
plot(net.bg, layout=layout_randomly)

# Or calculate the vertex coordinates in advance:
l <- layout_in_circle(net.bg)
plot(net.bg, layout=l)

# l is simply a matrix of x,y coordinates (N x 2) for the N nodes in the graph. 
# You can generate your own:
l
l <- cbind(1:vcount(net.bg), c(1, vcount(net.bg):2))
plot(net.bg, layout=l)

# This layout is just an example and not very helpful - thankfully
# igraph has a number of built-in layouts, including:

# Randomly placed vertices
l <- layout_randomly(net.bg)
plot(net.bg, layout=l)

# Circle layout
l <- layout_in_circle(net.bg)
plot(net.bg, layout=l)

# 3D sphere layout
l <- layout_on_sphere(net.bg)
plot(net.bg, layout=l)

# The Fruchterman-Reingold force-directed algorithm 
# Nice but slow, most often used in graphs smaller than ~1000 vertices. 
l <- layout_with_fr(net.bg)
plot(net.bg, layout=l)

# You will also notice that the layout is not deterministic - different runs 
# will result in slightly different configurations. Saving the layout in l
# allows us to get the exact same result multiple times.
par(mfrow=c(2,2), mar=c(1,1,1,1))
plot(net.bg, layout=layout_with_fr)
plot(net.bg, layout=layout_with_fr)
plot(net.bg, layout=l)
plot(net.bg, layout=l)
dev.off()

# By default, the coordinates of the plots are rescaled to the [-1,1] interval
# for both x and y. You can change that with the parameter "rescale=FALSE"
# and rescale your plot manually by multiplying the coordinates by a scalar.
# You can use norm_coords to normalize the plot with the boundaries you want.

# Get the layout coordinates:
l <- layout_with_fr(net.bg)
# Normalize them so that they are in the -1, 1 interval:
l <- norm_coords(l, ymin=-1, ymax=1, xmin=-1, xmax=1)

par(mfrow=c(2,2), mar=c(0,0,0,0))
plot(net.bg, rescale=F, layout=l*0.4)
plot(net.bg, rescale=F, layout=l*0.8)
plot(net.bg, rescale=F, layout=l*1.2)
plot(net.bg, rescale=F, layout=l*1.6)
dev.off()

# Another popular force-directed algorithm that produces nice results for
# connected graphs is Kamada Kawai. Like Fruchterman Reingold, it attempts to 
# minimize the energy in a spring system.

l <- layout_with_kk(net.bg)
plot(net.bg, layout=l)

# The LGL algorithm is for large connected graphs. Here you can specify a root - 
# the node that will be placed in the middle of the layout.
plot(net.bg, layout=layout_with_lgl)

# By default, igraph uses a layout called layout_nicely which selects
# an appropriate layout algorithm based on the properties of the graph. 

# Check out all available layouts in igraph:
?igraph::layout_

layouts <- grep("^layout_", ls("package:igraph"), value=TRUE)[-1] 
# Remove layouts that do not apply to our graph.
layouts <- layouts[!grepl("bipartite|merge|norm|sugiyama|tree", layouts)]

par(mfrow=c(3,3), mar=c(1,1,1,1))

for (layout in layouts) {
  print(layout)
  l <- do.call(layout, list(net)) 
  plot(net, edge.arrow.mode=0, layout=l, main=layout) }

dev.off()


-----------------------------------
# * TASK FOR WORKSHOP PARTICIPANTS:

# Plot the Zachary karate club network with four different layouts of your choice.

-----------------------------------

  

# ------->> Improving network plots --------

plot(net)

# Notice that this network plot is still not too helpful.
# We can identify the type and size of nodes, but cannot see
# much about the structure since the links we're examining are so dense.
# One way to approach this is to see if we can sparsify the network.

hist(links$weight)
mean(links$weight)
sd(links$weight)

# There are more sophisticated ways to extract the key edges,
# but for the purposes of this excercise we'll only keep ones
# that have weight higher than the mean for the network.

# We can delete edges using delete_edges(net, edges)
cut.off <- mean(links$weight) 
net.sp <- delete_edges(net, E(net)[weight<cut.off])
plot(net.sp) 

# Another way to think about this is to plot the two tie types 
# (hyperlik & mention) separately:

E(net)$width <- 2
plot(net, edge.color=c("dark red", "slategrey")[(E(net)$type=="hyperlink")+1],
      vertex.color="gray40", layout=layout_in_circle)

# Another way to delete edges:  
net.m <- net - E(net)[E(net)$type=="hyperlink"]
net.h <- net - E(net)[E(net)$type=="mention"]

# Plot the two links separately:
par(mfrow=c(1,2))

plot(net.h, vertex.color="orange", main="Tie: Hyperlink")
plot(net.m, vertex.color="lightsteelblue2", main="Tie: Mention")

dev.off()

# Make sure the nodes stay in place in both plots:
par(mfrow=c(1,2),mar=c(1,1,4,1))

l <- layout_with_fr(net)
plot(net.h, vertex.color="orange", layout=l, main="Tie: Hyperlink")
plot(net.m, vertex.color="lightsteelblue2", layout=l, main="Tie: Mention")

dev.off()


# ------->> Interactive plotting with tkplot -------- 

# R and igraph offer interactive plotting capabilities
# (mostly helpful for small networks)

tkid <- tkplot(net) #tkid is the id of the tkplot
l <- tkplot.getcoords(tkid) # grab the coordinates from tkplot
tk_close(tkid, window.close = T)
plot(net, layout=l)


# ------->> Heatmaps as a way to represent networks -------- 

# A quick reminder that there are other ways to represent a network:

# Heatmap of the network matrix:
netm <- get.adjacency(net, attr="weight", sparse=F)
colnames(netm) <- V(net)$media
rownames(netm) <- V(net)$media

palf <- colorRampPalette(c("gold", "dark orange")) 
heatmap(netm[,17:1], Rowv = NA, Colv = NA, col = palf(20), 
        scale="none", margins=c(10,10) )


# ------->> Plotting two-mode networks with igraph --------  

head(nodes2)
head(links2)

net2
plot(net2)

# This time we will make nodes look different based on their type.
V(net2)$color <- c("steel blue", "orange")[V(net2)$type+1]
V(net2)$shape <- c("square", "circle")[V(net2)$type+1]
V(net2)$label <- ""
V(net2)$label[V(net2)$type==F] <- nodes2$media[V(net2)$type==F] 
V(net2)$label.cex=.6
V(net2)$label.font=2

plot(net2, vertex.label.color="white", vertex.size=(2-V(net2)$type)*8) 

plot(net2, vertex.label=NA, vertex.size=7, layout=layout_as_bipartite) 

 
# Using text as nodes:
par(mar=c(0,0,0,0))
plot(net2, vertex.shape="none", vertex.label=nodes2$media,
     vertex.label.color=V(net2)$color, vertex.label.font=2, 
     vertex.label.cex=.95, edge.color="gray70",  edge.width=2)

dev.off()


# ================ 6. Network and node descriptives ================


# Density
# The proportion of present edges from all possible ties.
edge_density(net, loops=F)
ecount(net)/(vcount(net)*(vcount(net)-1)) #for a directed network

# Reciprocity
# The proportion of reciprocated ties (for a directed network).
reciprocity(net)
dyad_census(net) # Mutual, asymmetric, and null node pairs
2*dyad_census(net)$mut/ecount(net) # Calculating reciprocity

# Transitivity
# global - ratio of triangles (direction disregarded) to connected triples
# local - ratio of triangles to connected triples each vertex is part of
transitivity(net, type="global")  # net is treated as an undirected network
transitivity(as.undirected(net, mode="collapse")) # same as above
transitivity(net, type="local")
triad_census(net) # for directed networks

# Triad types (per Davis & Leinhardt):
# 
# 003  A, B, C, empty triad.
# 012  A->B, C 
# 102  A<->B, C  
# 021D A<-B->C 
# 021U A->B<-C 
# 021C A->B->C
# 111D A<->B<-C
# 111U A<->B->C
# 030T A->B<-C, A->C
# 030C A<-B<-C, A->C.
# 201  A<->B<->C.
# 120D A<-B->C, A<->C.
# 120U A->B<-C, A<->C.
# 120C A->B->C, A<->C.
# 210  A->B<->C, A<->C.
# 300  A<->B<->C, A<->C, completely connected.


# Diameter (longest geodesic distance)
# Note that edge weights are used by default, unless set to NA.
diameter(net, directed=F, weights=NA)
diameter(net, directed=F)
diam <- get_diameter(net, directed=T)
diam

# Note: vertex sequences asked to behave as a vector produce numeric index of nodes
class(diam)
as.vector(diam)

# Color nodes along the diameter:
vcol <- rep("gray40", vcount(net))
vcol[diam] <- "gold"
ecol <- rep("gray80", ecount(net))
ecol[E(net, path=diam)] <- "orange" 
# E(net, path=diam) finds edges along a path, here 'diam'
plot(net, vertex.color=vcol, edge.color=ecol, edge.arrow.mode=0)

# Node degrees
# 'degree' has a mode of 'in' for in-degree, 'out' for out-degree,
# and 'all' or 'total' for total degree. 
deg <- degree(net, mode="all")
plot(net, vertex.size=deg*3)
hist(deg, breaks=1:vcount(net)-1, main="Histogram of node degree")

# Degree distribution
deg.dist <- degree_distribution(net, cumulative=T, mode="all")
plot( x=0:max(deg), y=1-deg.dist, pch=19, cex=1.2, col="orange", 
      xlab="Degree", ylab="Cumulative Frequency")


# Centrality & centralization

# Centrality functions (vertex level) and centralization functions (graph level).
# The centralization functions return "res" - vertex centrality, "centralization", 
# and "theoretical_max" - maximum centralization score for a graph of that size.
# The centrality functions can run on a subset of nodes (set with the "vids" parameter)

# Degree (number of ties)
degree(net, mode="in")
centr_degree(net, mode="in", normalized=T)

# Closeness (centrality based on distance to others in the graph)
# Inverse of the node's average geodesic distance to others in the network
closeness(net, mode="all", weights=NA) 
centr_clo(net, mode="all", normalized=T) 

# Eigenvector (centrality proportional to the sum of connection centralities)
# Values of the first eigenvector of the graph adjacency matrix
eigen_centrality(net, directed=T, weights=NA)
centr_eigen(net, directed=T, normalized=T) 

# Betweenness (centrality based on a broker position connecting others)
# (Number of geodesics that pass through the node or the edge)
betweenness(net, directed=T, weights=NA)
edge_betweenness(net, directed=T, weights=NA)
centr_betw(net, directed=T, normalized=T)



-----------------------------------
# * TASK FOR WORKSHOP PARTICIPANTS:

# Compute the degree, closeness, eigenvector, and betweenness centrality of
# the actors in the Zachary karate club network. Plot the network, sizing the
# nodes based on the different centrality types.

-----------------------------------

  

# Hubs and authorities

# The hubs and authorities algorithm developed by Jon Kleinberg was initially used 
# to examine web pages. Hubs were expected to contain catalogues with a large number 
# of outgoing links; while authorities would get many incoming links from hubs, 
# presumably because of their high-quality relevant information. 

hs <- hub_score(net, weights=NA)$vector
as <- authority_score(net, weights=NA)$vector

par(mfrow=c(1,2))
 plot(net, vertex.size=hs*50, main="Hubs")
 plot(net, vertex.size=as*30, main="Authorities")
dev.off()


# ================ 7. Distances and paths ================


# Average path length 
# The mean of the shortest distance between each pair of nodes in the network 
# (in both directions for directed graphs). 
mean_distance(net, directed=F)
mean_distance(net, directed=T)

# We can also find the length of all shortest paths in the graph:
distances(net) # with edge weights
distances(net, weights=NA) # ignore weights

# We can extract the distances to a node or set of nodes we are interested in.
# Here we will get the distance of every media from the New York Times.
dist.from.NYT <- distances(net, v=V(net)[media=="NY Times"], to=V(net), weights=NA)

# Set colors to plot the distances:
oranges <- colorRampPalette(c("dark red", "gold"))
col <- oranges(max(dist.from.NYT)+1)
col <- col[dist.from.NYT+1]

plot(net, vertex.color=col, vertex.label=dist.from.NYT, edge.arrow.size=.6, 
     vertex.label.color="white")

# We can also find the shortest path between specific nodes.
# Say here between MSNBC and the New York Post:
news.path <- shortest_paths(net, 
                            from = V(net)[media=="MSNBC"], 
                             to  = V(net)[media=="New York Post"],
                             output = "both") # both path nodes and edges

# Generate edge color variable to plot the path:
ecol <- rep("gray80", ecount(net))
ecol[unlist(news.path$epath)] <- "orange"
# Generate edge width variable to plot the path:
ew <- rep(2, ecount(net))
ew[unlist(news.path$epath)] <- 4
# Generate node color variable to plot the path:
vcol <- rep("gray40", vcount(net))
vcol[unlist(news.path$vpath)] <- "gold"

plot(net, vertex.color=vcol, edge.color=ecol, 
     edge.width=ew, edge.arrow.mode=0)


# Identify the edges going into or out of a vertex, for instance the WSJ.
# For a single node, use 'incident()', for multiple nodes use 'incident_edges()'
inc.edges <- incident(net, V(net)[media=="Wall Street Journal"], mode="all")

# Set colors to plot the selected edges.
ecol <- rep("gray80", ecount(net))
ecol[inc.edges] <- "orange"
vcol <- rep("grey40", vcount(net))
vcol[V(net)$media=="Wall Street Journal"] <- "gold"
plot(net, vertex.color=vcol, edge.color=ecol)


# We can also easily identify the immediate neighbors of a vertex, say WSJ.
# The 'neighbors' function finds all nodes one step out from the focal actor.
# To find the neighbors for multiple nodes, use 'adjacent_vertices()'.
# To find node neighborhoods going more than one step out, use function 'ego()'
# with parameter 'order' set to the number of steps out to go from the focal node(s).

neigh.nodes <- neighbors(net, V(net)[media=="Wall Street Journal"], mode="out")

# Set colors to plot the neighbors:
vcol[neigh.nodes] <- "#ff9d00"
plot(net, vertex.color=vcol)

# Special operators for the indexing of edge sequences: %--%, %->%, %<-%
# E(network)[X %--% Y] selects edges between vertex sets X and Y, ignoring direction
# E(network)[X %->% Y] selects edges from vertex sets X to vertex set Y
# E(network)[X %->% Y] selects edges from vertex sets Y to vertex set X

# For example, select edges from newspapers to online sources:
E(net)[ V(net)[type.label=="Newspaper"] %->% V(net)[type.label=="Online"] ]

# Cocitation (for a couple of nodes, how many shared nominations they have)
cocitation(net)



# ================ 8. Subgroups and communities ================

# Converting 'net' to an undirected network.
# There are several ways to do that: we can create an undirected link between any pair
# of connected nodes (mode="collapse), or create an undirected link for each directed
# one (mode="each"), or create an undirected link for each symmetric link (mode="mutual").
# In cases when A -> B and B -> A are collapsed into a single undirected link, we
# need to specify what to do with the edge attributes. Here we have said that
# the 'weight' of links should be summed, and all other edge attributes ignored.

net.sym <- as.undirected(net, mode="collapse", edge.attr.comb=list(weight="sum", "ignore"))


#  ------->> Cliques --------

 # Find cliques (complete subgraphs of an undirected graph)
cliques(net.sym) # list of cliques       
sapply(cliques(net.sym), length) # clique sizes
largest_cliques(net.sym) # cliques with max number of nodes

vcol <- rep("grey80", vcount(net.sym))
vcol[unlist(largest_cliques(net.sym))] <- "gold"
plot(net.sym, vertex.label=V(net.sym)$name, vertex.color=vcol)



#  ------->> Communities --------

# A number of algorithms aim to detect groups that consist of densely connected nodes
# with fewer connections across groups. 

# Community detection based on edge betweenness (Newman-Girvan)
# High-betweenness edges are removed sequentially (recalculating at each step)
# and the best partitioning of the network is selected.
ceb <- cluster_edge_betweenness(net) 
dendPlot(ceb, mode="hclust")
plot(ceb, net) 

# Let's examine the community detection igraph object:
class(ceb)
length(ceb)     # number of communities
membership(ceb) # community membership for each node
crossing(ceb, net)   # boolean vector: TRUE for edges across communities
modularity(ceb) # how modular the graph partitioning is

# High modularity for a partitioning reflects dense connections within communities 
# and sparse connections across communities.


# Community detection based on propagating labels
# Assigns node labels, randomizes, and replaces each vertex's label with
# the label that appears most frequently among neighbors. Repeated until
# each vertex has the most common label of its neighbors.
clp <- cluster_label_prop(net)
plot(clp, net)

# Community detection based on greedy optimization of modularity
cfg <- cluster_fast_greedy(as.undirected(net))
plot(cfg, as.undirected(net))
 
# We can also plot the communities without relying on their built-in plot:
V(net)$community <- cfg$membership
colrs <- adjustcolor( c("gray50", "tomato", "gold", "yellowgreen"), alpha=.6)
plot(net, vertex.color=colrs[V(net)$community])


-----------------------------------
# * TASK FOR WORKSHOP PARTICIPANTS:

# Plot the results of the three different community detection algorithms 
#  applied to  the Zachary karate club network. 
  
-----------------------------------


# K-core decomposition
# The k-core is the maximal subgraph in which every node has degree of at least k
# This also means that the (k+1)-core will be a subgraph of the k-core.
# The result here gives the coreness of each vertex in the network.
kc <- coreness(net, mode="all")
plot(net, vertex.size=kc*6, vertex.label=kc, vertex.color=colrs[kc])


# ================ 9. Assortativity and Homophily ================

# Assortativity (homophily)
# The tendency of nodes to connect to others who are similar on some variable.
# assortativity_nominal() is for categorical variables (labels)
# assortativity() is for ordinal and above variables
# assortativity_degree() checks assortativity in node degrees

V(net)$type.label
V(net)$media.type

assortativity_nominal(net, V(net)$media.type, directed=F)

assortativity(net, V(net)$audience.size, directed=F)

assortativity_degree(net, directed=F)


# ================ The End ================






================================================
FILE: 88-Network/network1.R
================================================
# Network i graph
#http://kateto.net/networks-r-igraph
library(igraph) # Load the igraph package
g1 <- graph( edges=c(1,2, 2,3, 3, 1), n=3, directed=F ) 
plot(g1)
class(g1)
g1
# Now with 10 vertices, and directed by default:
g2 <- graph( edges=c(1,2, 2,3, 3, 1), n=10 )
plot(g2)  

g3 <- graph( c("John", "Jim", "Jim", "Jill", "Jill", "John")) # named vertices
# When the edge list has vertex names, the number of nodes is not needed
plot(g3)

g4 <- graph( c("John", "Jim", "Jim", "Jack", "Jim", "Jack", "John", "John"), 
             isolates=c("Jesse", "Janis", "Jennifer", "Justin") )  
# In named graphs we can specify isolates by providing a list of their names.
plot(g4, edge.arrow.size=.5, vertex.color="gold", vertex.size=15,     
     vertex.frame.color="gray", vertex.label.color="black", 
     vertex.label.cex=0.8, vertex.label.dist=2, edge.curved=0.2) 
plot(graph_from_literal(a---b, b---c)) # the number of dashes doesn't matter
plot(graph_from_literal(a--+b, b+--c))
plot(graph_from_literal(a+-+b, b+-+c)) 
plot(graph_from_literal(a:b:c---c:d:e))
gl <- graph_from_literal(a-b-c-d-e-f, a-g-h-b, h-e:f:i, j)
plot(gl)

#Edge, vertex, and network attributes -------------------------
E(g4) # The edges of the object
## + 4/4 edges (vertex names):
## [1] John->Jim  Jim ->Jack Jim ->Jack John->John
V(g4) # The vertices of the object
## + 7/7 vertices, named:
## [1] John     Jim      Jack     Jesse    Janis    Jennifer Justin
g4[]
g4[1,] 

V(g4)$name # automatically generated when we created the network.
## [1] "John"     "Jim"      "Jack"     "Jesse"    "Janis"    "Jennifer"

## [7] "Justin"
V(g4)$gender <- c("male", "male", "male", "male", "female", "female", "male")
E(g4)$type <- "email" # Edge attribute, assign "email" to all edges
E(g4)$weight <- 10    # Edge weight, setting all existing edges to 10

#Examine attributes:
edge_attr(g4)
## $type
## [1] "email" "email" "email" "email"
## 
## $weight
## [1] 10 10 10 10
vertex_attr(g4)

graph_attr(g4)
## named list()
#Another way to set attributes (you can similarly use set_edge_attr(), set_vertex_attr(), etc.):
g4 <- set_graph_attr(g4, "name", "Email Network")
g4 <- set_graph_attr(g4, "something", "A thing")
graph_attr_names(g4)

graph_attr(g4, "name")
## [1] "Email Network"
graph_attr(g4)
## $name
## [1] "Email Network"
## $something
## [1] "A thing"
g4 <- delete_graph_attr(g4, "something")
graph_attr(g4)

## $name
## [1] "Email Network"
plot(g4, edge.arrow.size=.5, vertex.label.color="black", vertex.label.dist=1.5,
     vertex.color=c( "pink", "skyblue")[1+(V(g4)$gender=="male")] ) 

g4s <- simplify( g4, remove.multiple = T, remove.loops = F, 
                 edge.attr.comb=c(weight="sum", type="ignore") )
plot(g4s, vertex.label.dist=1.5)
g4s

#2.3 Specific graphs and graph models ----------------
eg <- make_empty_graph(40)
plot(eg, vertex.size=10, vertex.label=NA)
eg <- make_empty_graph(100)
plot(eg, vertex.size=6, vertex.label=1:100)

# Full Graph
fg <- make_full_graph(40)
plot(fg, vertex.size=10, vertex.label=NA)

#Simple star graph
st <- make_star(40)
plot(st, vertex.size=10, vertex.label=NA) 

#Tree graph
tr <- make_tree(40, children = 4, mode = "undirected")
plot(tr, vertex.size=10, vertex.label=NA) 

#Ring graph
rn <- make_ring(40)
plot(rn, vertex.size=10, vertex.label=NA)

#Erdos-Renyi random graph model (‘n’ is number of nodes, ‘m’ is the number of edges).
er <- sample_gnm(n=100, m=40) 
plot(er, vertex.size=6, vertex.label=NA)  


================================================
FILE: 88-Network/network2.R
================================================
#Network2

nodes <- read.csv("Dataset1-Media-Example-NODES.csv", header=T, as.is=T)
links <- read.csv("Dataset1-Media-Example-EDGES.csv", header=T, as.is=T)
head(nodes)
head(links)
nrow(nodes); length(unique(nodes$id))
nrow(links); nrow(unique(links[,c("from", "to")]))
links <- aggregate(links[,3], links[,-3], sum)
links <- links[order(links$from, links$to),]
colnames(links)[4] <- "weight"
rownames(links) <- NULL

#DATASET 2: matrix
nodes2 <- read.csv("Dataset2-Media-User-Example-NODES.csv", header=T, as.is=T)
links2 <- read.csv("Dataset2-Media-User-Example-EDGES.csv", header=T, row.names=1)
head(nodes2)
head(links2)
#We can see that links2 is an adjacency matrix for a two-mode network:
links2 <- as.matrix(links2)
dim(links2)
dim(nodes2)

#Turning networks into igraph objects
library(igraph)
net <- graph_from_data_frame(d=links, vertices=nodes, directed=T) 
class(net)
## [1] "igraph"
net

E(net)       # The edges of the "net" object
V(net)       # The vertices of the "net" object
E(net)$type  # Edge attribute "type"
V(net)$media # Vertex attribute "media"
#Now that we have our igraph network object, let’s make a first attempt to plot it.
plot(net, edge.arrow.size=.4,vertex.label=NA)
plot(net, edge.arrow.size=.4,vertex.label=links$from, vertex.label.cex=0.5)

net <- simplify(net, remove.multiple = F, remove.loops = T) 
as_edgelist(net, names=T)

as_adjacency_matrix(net, attr="weight")
#Or data frames describing nodes and edges:
as_data_frame(net, what="edges")
as_data_frame(net, what="vertices")

# DataSet2
head(nodes2)
head(links2)
net2 <- graph_from_incidence_matrix(links2)

table(V(net2)$type)
net2.bp <- bipartite.projection(net2)
as_incidence_matrix(net2)  %*% t(as_incidence_matrix(net2)) 

t(as_incidence_matrix(net2)) %*%   as_incidence_matrix(net2)
plot(net2.bp$proj1, vertex.label.color="black", vertex.label.dist=1,
     
vertex.size=7, vertex.label=nodes2$media[!is.na(nodes2$media.type)])
plot(net2.bp$proj2, vertex.label.color="black", vertex.label.dist=1,
     
     vertex.size=7, vertex.label=nodes2$media[ is.na(nodes2$media.type)])


================================================
FILE: 88-Network/traveltime1.R
================================================
# Timing Calculations
#install.packages("ggmap")
#http://aakarner.github.io/blog/2015/03/17/comparing-gtfs-results-from-two-different-sources/

library(ggmap)

#input Origin Destination (it accepts Lat Long info)
from <- c("houston", "houston", "dallas")
to <- c("waco, texas", "san antonio", "houston")

mapdist(from, to, mode = c("transit"))
mapdist(from, to, mode = c("driving"))

from <- c("delhi")
to <- c("noida")
mapdist(from, to, mode = c('walking'))
from <- c("haldwani")
to <- c("kathgodam")
mapdist(from, to, mode = c('walking'))



================================================
FILE: 89-rVideos/clustering-rV.R
================================================
#clustering1  : kmeans & heirachical 

pacman::p_load(dplyr, ggplot2)
#kmeans - number of clusters to be provided before. Numeric data

#marks of one subject : group them
marks1 = c(65, 60, 70, 75, 67)  #5 students  #2 groups
(exam1 = data.frame(marks1))
cluster2  = kmeans(exam1, centers=2)
cluster2
cluster2$cluster
cbind(exam1, group=cluster2$cluster)

#how does it happen
#for every point distance with other points are calculated
#eclidean :  sqrt((x1-x2)^2)

marks1 = c(65, 60, 70, 75, 67)  #5 students  #2 groups
marks2 = c(95, 80, 52, 55, 57)
(exam2 = data.frame(marks1, marks2))
cluster2B  = kmeans(exam2, centers=2)
cluster2B
cluster2B$cluster
cbind(exam2, group=cluster2B$cluster)
cluster2B$centers
#eclidean :  sqrt[ (x1-x2)^2 + (y1-y2)^2 ] 
#https://www.youtube.com/watch?v=5I3Ei69I40s

marks1 = c(65, 60, 70, 75, 67)  #5 students  #2 groups
marks2 = c(95, 80, 52, 55, 57)
project = c(120, 100, 90, 80,110)
(exam3 = data.frame(marks1, marks2, project))
#project marks will be more dominating
exam3scaled = scale(exam3)
exam3scaled #-3 to 3 : Z score scaling
cluster2C  = kmeans(exam3scaled, centers=2)
cluster2C
cluster2C$cluster
cbind(exam3, group=cluster2C$cluster)
cluster2C$centers
exam3 %>% group_by(group=cluster2C$cluster) %>% summarise(mean1 = mean(marks1), mean2= mean(marks2), P= mean(project))

#-----
#numeric, cluster Nos, scaling
df = mtcars
sapply(df, class)  #all are numeric

table(df[,c('cyl','gear','am','vs')])  #different categories and their count

kmModel1 = kmeans(df, centers=2)
kmModel1  #attributes of this model
kmModel1$size #how many cars in each cluster
kmModel1$cluster  #which rownumber of car goes into which cluster
kmModel1$centers  #average values of data of each cluster

df %>% dplyr::group_by(cluster=kmModel1$cluster) %>% summarise(meanMPG =mean(mpg), meanWt = mean(wt, na.rm=T)) #average mpg & wt in each cluster

plot(x=df$mpg, y=df$wt, col=kmModel1$cluster) #mpg vs wt with color as per cluster No

#how to choose no of cluster mathematically (business reqmt can be seperate)
#cohesion within the cluster, separation with cluster
pacman::p_load(NbClust, factoextra)
factoextra::fviz_cluster(kmModel1, data = df)

#how to choose best cluster
set.seed(123)
factoextra::fviz_nbclust(df, kmeans, method = "wss")
factoextra::fviz_nbclust(df, kmeans, method = "silhouette")

head(df)
km1 = kmeans(df, centers=1)
km1
km1$withinss
km1$totss
km1$tot.withinss
km2 = kmeans(df, centers=2)
km2
  km2$withinss  #2 cluster: within each cluster
km2$totss
km2$tot.withinss
km2$withinss[1] + km2$withinss[2]




#NbClust method----
set.seed(1234)
NbClust::NbClust(df, min.nc=2, max.nc=8, method='kmeans')
nc <- NbClust::NbClust(df, distance = "euclidean", min.nc=2, max.nc=8, method = "kmeans", index='all')
nc
nc$All.index
nc$Best.nc
nc$Best.partition

table(nc$Best.partition)
df %>% group_by(cluster = nc$Best.partition) %>% summarise(meanMPG= mean(mpg), meanWt = mean(wt, na.rm=T))

#now construct the Kmean Model again with k=3
set.seed(1234)
kmModel2 = kmeans(x=df, centers=3)
kmModel2$centers
#means from nc above and this way can be different because of randomness
df$cluster3 = kmModel2$cluster
head(df)
df %>% arrange(cluster)
df %>% filter(cluster == 1)
table(df$cyl, df$cluster3)
#Cluster 1- 6 Cylinder; Cluster2 - 8 Cylinder; Cluster3- Cylinder4
#when divide these cars into 3 groups, there is correlation between cluster & cylinder type

#if we divide it into 2 groups : Is there relation between am & cluster No
cbind(df, cluster2=kmModel1$cluster) %>% arrange(cluster2)
cbind(df, cluster2=kmModel1$cluster) %>% arrange(cluster2) %>% filter(cluster2==1)
#almost car with am=0 fall into 1 cluster; and cars with am=1, go into other group.

#can be used for market segmentation
#values have to be numeric 

#scaling ---------------------
#when values of different column have different ranges, we scale the values.
#most common scale is z scores, which scales values of column between -3 to 0 to 3
dfScale = scale(mtcars)
head(mtcars)
head(dfScale)

kmModelS1 <- kmeans(dfScale, centers=3)
kmModelS1

mtcars %>% group_by(cluster3 = kmModelS1$cluster) %>% summarise(meanMPG = mean(mpg, na.rm=T), meanWt = mean(wt, na.rm=T))

mtcars %>% group_by(cluster3 = kmModel2$cluster) %>% summarise(meanMPG = mean(mpg, na.rm=T), meanWt = mean(wt, na.rm=T))

factoextra::fviz_cluster(kmModel1, data = df)
factoextra::fviz_cluster(kmModel2, data = df)
factoextra::fviz_cluster(kmModelS1, data = df)
kmModel1$totss
kmModel2$totss
kmModelS1$totss #lesser the better, but here we have used scaled values

#good cohesions inside a group; good seperation between the groups.. that is the aim


#heirachical clustering
head(df)

dfScaled = scale(df)
data = dist(dfScaled)
head(data)
as.matrix(data)
fit.average = hclust(data, method = 'average')
plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering : Cluster Nos (k value)')
rect.hclust(fit.average, k=5)

plot(fit.average, hang=-1, cex=.8, main='Average Linkage Clustering : height (hvalue')
rect.hclust(fit.average, h=1)
NbClust::NbClust(data, min.nc=2, max.nc=8, method = "ward.D2", index='all')

#https://uc-r.github.io/hc_clustering


#data

# Compute distance matrix
marks1 = c(65, 60, 70, 75, 67)  #5 students  #2 groups
marks2 = c(95, 80, 52, 55, 57)
project = c(120, 100, 90, 80,110)
(exam3 = data.frame(marks1, marks2, project))
#project marks will be more dominating
exam3scaled = scale(exam3)
res.dist <- dist(exam3scaled, method = "euclidean")

# Compute 2 hierarchical clusterings
hc1 <- hclust(res.dist, method = "complete")
hc2 <- hclust(res.dist, method = "ward.D2")

# Create two dendrograms
dend1 <- as.dendrogram (hc1)
dend2 <- as.dendrogram (hc2)

plot(dend1)
plot(dend2)
dendextend::tanglegram(dend1, dend2)  
  
fviz_nbclust(exam3scaled, FUN = hcut, method = "silhouette")
#https://www.statsandr.com/blog/clustering-analysis-k-means-and-hierarchical-clustering-by-hand-and-in-r/
    

================================================
FILE: 92-wksp2/1a1-start.R
================================================
#initial commands

# assign
x1 = 3 #press control + enter to run the line
x2 <- 3 # same 
#which is better
x1
x2
y
ls() #variables in env
women

?AirPassengers
data() # datasets available for use

library()  # libraries currently loaded

?mean  #help
help(mean)
??mean  #search through other sources
x=0:10
x
x <- c(0:10, 50)
x
xm <- mean(x)
xm
mean(x, trim = 0.10)
x=c(1,1,1,1,5,5,5,5,7,7)
mean(x)
mean(x, trim=.3)
x=c(1,5,5)
mean(x)
c(mean(x), mean(x, trim = 0.10))

1:10
1:10000000
x=c(1,34,5)
x
?c
version #version of R

Sys.Date() # todays date

getwd()  # working directory

methods(class='matrix')  #methods available for a class of object

plot(10:100) #basic command to plot
plot(women)


================================================
FILE: 92-wksp2/1a3-packages1.R
================================================
# Packages installation

#List avl packages
library()


#Total Avl Packages
nrow(available.packages())

#Install Package amap
install.packages('amap')

#Load package
library(amap)

#Find functions in package
library(help=amap)

#Help wrt a package
help(package='amap')  #see on right side pane


#Unload---
install.packages('tm')

library(tm)
library(VIM)
search()
detach('package:tm', unload=TRUE)
detach(package:VIM, unload = T) 
search()


#----------------- Part I Over
#Detach Multiple Packages
(detpkg = c('plyr','tm'))
library('plyr') #load lib
library('tm')  #load lib
search()  #check if loaded
Vectorize(detach)(name=paste0("package:", detpkg), unload=TRUE, character.only=TRUE)  #code to detach
search()  # confirm if removed
#specify the argument unload=TRUE; otherwise, R removes the package from the search path but doesn’t unload it.

#Detach from memory all packages
rm(list = ls(all = TRUE))
sessionInfo()


#Remove Packages ----- uninstall 
remove.packages("tm")
require('tm')# check if unistalled



#multiple packages

#Function
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}
packagelist <- c('NLP','tm', 'lubridate')
ipak(packagelist)

#  package lists
pkg1 = c('dplyr','plyr', 'data.table','xlsx', 'Hmisc','rJava','ggplot2','lattice','gsheet','curl','stringr', 'syuzhet','e10171', 'catools','caret', 'olsrr' ,'swirl','sqldf','XML','VIM', 'outliers','car','MASS','DMwR','rvest')
pkg2 = c('forecast','rpart', 'rpart.plot', 'partykit','strucchange', 'didrooRFM')
pkg3 = c('zoo', 'astsa','lubridate','timeSeries','tseries','xts')
pkg4 = c('arules' , 'arulesViz')
pkg5 = c('twitterR','ROAuth','RGtk2','RTextTools','wordcloud')
install.packages("CHAID", repos="http://R-Forge.R-project.org")


library(lubridate)

#multiple load 
easypackages::libraries(pkg1)


#easypackages
#install.packages('easypackages')
library(easypackages)

#Install Multiple Packages
packages("plyr", "psych", "tm")
libraries("plyr", "psych", "tm")



#lubripack
install.packages('lubripack')  #NA for some versions
#install older version of R packages
#https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages
#https://rdrr.io/github/Espanta/lubripack/

require(devtools)
install_github("Espanta/lubripack")
library(lubripack)
lubripack("plyr", "psych", "tm", "quantmod")



pack <- available.packages()
pack["ggplot2","Depends"]
pack["ggplot2","Imports"]
pack["data.table","Depends"]

packrat:::recursivePackageDependencies("ggplot2",lib.loc = .libPaths()[1])
tools::dependsOnPkgs('ggplot2')
tools::dependsOnPkgs('dplyr')
tools::dependsOnPkgs("ggplot2",installed=available.packages())
library(rusk)
#Remove Package
remove.packages('quantmod')
library(quantmod)



#This will remove all/Detach all  packages
library(mise)
search()
#mise(vars = TRUE, figs = TRUE, console = TRUE, pkgs = FALSE)
mise(pkgs=T)
search()



# list all packages where an update is available
old.packages()

# update all available packages
update.packages()

# update, without prompts for permission/clarification
update.packages(ask = FALSE)


================================================
FILE: 92-wksp2/1b2-ds.R
================================================
# Data Structures in R

#control+enter when you are in the line to execute
# Vectors-----
c(2,4,6)
seq(2,3,.5)
seq(by=.5, from=2,to=3)
rep(1:3,times=4)
rep(c(3,6,7,2),each=4)
rep(c(3,6,7,2), times=4)

?rep

x=1:10   #create seq of nos from 1 to 10
x
(x1 <- 1:20)

(x1=1:30)
(x2=c(1,2,13,4,5))
class(x2)

(x3=c('a',"ABC"))
class(x3)
(x3=letters[1:10])
class(x3)
LETTERS[1:26]
(x3b = c('a',"Henry",4))
class(x3b)

(x4=c(T,FALSE,TRUE,T,F))
class(x4)
class(c(3,5))
x5a = c(3,5.5)
class(x5a)
as.integer(x5a)

x5=c(3L,5L)
class(x5)
x5a = c(3,5)
class(x5a)
(x5b = c(1, 'a',T, 4L))
class(x5b)
#blank variable ?

#access elements
?seq
(x6 = seq(0,100,by=3))
seq(0,100,3)
seq(to=100,from=0,by=3)
seq(1,5,2)
?seq
#[1]  0  2  4  6  8 10
ls()  #variables in my environment
x6
length(x6)
x6[1:5]
x6[10:20]
x6[ seq(1,length(x6), 2)]
x6[3]  # access 3rd element
#[1] 4
x6[c(2, 4)]     # access 2nd and 4th element
x6[-1]          # access all but 1st element
x6[-c(1:10, 15:20)]
x6[c(2, -4)]    # cannot mix positive and negative integers
#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts
x6[c(2.4, 3.54)]    # real numbers are truncated to integers
x6[c(2,3)]

x6[-c(1,5,20)]
x6
x6[x6 > 30]

x6[x6 > 30 & x6 < 40]  # 31-39
#or |
length(x6)
x6[-(length(x6)-1)]
(x7 = c(x6, x2))


#modify
x6
set.seed(1234)
(x6 = sample(1:50))
(x6b = sort(sample(1:50)))
sort(x6)
sort(x6[-c(1,2)])
sort(x6, decreasing=T)
x6
rev(x6)

seq(-3, 10, by=.2)
x6[-c(1:12)]
x6
x6[x6> 30 & x6 < 40]
(x = -3:2)
x6
x6[2:10] <- 99; x6        # modify 2nd element
x6[x6 > 30 & x6 < 40] = 999
x6


x6
x7 = x6[1:4]; x7      # truncate x to first 4 elements

1:5
#equal partitions within a range
(x = seq(1,5, length.out = 15))
x
x = NULL
x
#NULL
x[4]
#NULL
?distribution
?rnorm
(x = rnorm(100))
plot(density(x))
abline(v=c(-3,0,3))
mean(x)
(x1 = rnorm(100, mean=50, sd=5))
plot(density(x1))
abline(v=mean(x1),h=0.04)
hist(x1, breaks=7)
hist(x1)
hist(x1, freq=F)
lines(density(x1), col=2)
summary(x1)
quantile(x1)
quantile(x1, seq(0,1,.25))
quantile(x1,c(.1, .5, .8))
quantile(x1,seq(0,1,.01))
stem(x1)

#Matrix-----
100:111
length(100:111)
matrix(1,ncol=3, nrow=4)
(m1 = matrix(100:111, nrow=4))
(m2 = matrix(100:111, ncol=3, byrow=T))

x=101:124
length(x)
matrix(x, ncol=6)
class(m1)
attributes(m1)
dim(m1)
m1

# access elements of matrix
m1[1,]
m1[,1]
m1[,1, drop=F]
m1[,-1]  #remove 1st column
m1[1,2:3]
m1[c(1,3),]
m1[,-c(1,3), drop=F]
m1[m1> 105 & m1 < 108]

#names of cols and rows
m1

paste("C","D",sep="-")
paste("C",1:100,sep="-")
paste("C",1:3,sep='')
(colnames(m1) = paste('C',1:3, sep=''))
m1
(rownames(m1) = paste("R",1:4, sep=''))
m1
attributes(m1)
m1[,c('C1','C3')]
m1[,c(1,3)]
#Vector to Matrix
(m3 = 1:24)
m3
dim(m3)= c(6,4)
m3

#access elements
m2
m2[1,]  #first row
m2[c(1,3,4),]  #1st,3rd,4th row

m2[,1]  #first col
m2[,2:3] # 2nd to 3rd coln

m2[c(1,2),c(2,3)]
m2[,]
m2[-2,] # exclude 2nd row
m2
m2[1:5] # matrix is like vector
m2
m2[c(TRUE,F,T,F),c(F, T, T)] #logical indexing
m2[m2 > 5 & m2 < 10]

m1
m1[1:2,1:2]
m1[c('R1','R2'),c('C1','C2')]
m1[1:2,]
m1[c(T,T,F,F),]
m1

#modify Vector
m2
m2[2,2]
m2[2,2] = 10
m2
m2[,2] = 10
m2
m2[m2> 107] = 9999
m2
rbind(m2, c(50,60,70))
rbind(m2,m2)
m2
cbind(m2, c(55,65,75,85))
m2m2= cbind(m2,m2)
m2m2
m2
cbind(m2,m2)
rbind(m2,m2)
#row and col wise summary

m1
colSums(m1)
rowSums(m1)
colMeans(m1)
rowMeans(m1)

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN="*" ) #colwise

#addmargins
m1
?addmargins
addmargins(m1,margin=1,sum) #colwise function
addmargins(m1,1,sd) #colwise function

addmargins(m1,2,mean) #rowwise function
addmargins(m1,c(1,2),mean) #row & col wise function
?addmargins
(M1sum= addmargins(m1,c(1,2),list(list(mean,sum,max, min), list(var,sd, max, min)))) #row & col wise function
round(M1sum,0)

#Array-----
length(100:123)
4*3*2
#2 coys, 3 products, 4 locations sold qty
(a1 = array(100:123, dim=c(4,3,2)))
(loc = paste('loc', 1:4,sep='-'))
(product = paste('p', 1:3,sep='@'))
(coy = paste('coy', 1:2,sep='%'))
dimnames(a1) = list(loc, product, coy)
a1
apply(a1,1, sum) #locationwise
apply(a1,2, sum) #productwise
apply(a1,c(1,2), sum) #product-location wise
apply(a1,c(2,3), sum) #product-coy wise
apply(a1,c(1,3), sum) #coy-location
apply(a1,3, sum) #coywise
sum(a1) #total


#DataFrame----
#create Vectors to be combined into DF
(rollno = 1:30)
(sname = paste('student',1:30,sep=''))
(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
(marks1 = floor(rnorm(30,mean= 50,sd=10)))
(marks2 = ceiling(rnorm(30,40,5)))
(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))
rollno; sname; gender
marks1 ; marks2; course

#create DF
df1= data.frame(rollno, sname, gender, marks1, marks2, course, stringsAsFactors = F)
str(df1) #structure of DF
head(df1) #top 6 rows
head(df1,n=3) #top 3 rows
tail(df1) #last 6 rows
class(df1) # DF
summary(df1) #summary
nrow(df1) 
dim(df1)
length(df1)
df1$course
df1$gender = factor(df1$gender)
df1$course = factor(df1$course)
#df1$sname = as.character(df1$sname)
str(df1)
summary(df1)
boxplot(marks1 ~ gender + course, data=df1)

df1  #full data
df1$gender  # one column
head(df1[ , c(2,4)]) #multiple columns
df1[1:10 ,] #select rows, all columns
df1[1:5,1:4]
#as per conditionis
df1[ marks1 > 50 & gender=='F', c('rollno', 'sname','gender', 'marks1')]
df1[ marks1 > 50 & gender=='F', c(1,2)]
df1[ marks1 > 50 | gender=='F', ]

names(df1)  # names of columns
dim(df1)  #Dimensions

aggregate(df1$marks1, by=list(df1$gender), FUN=sum)
aggregate(marks1 ~ gender, data=df1, FUN=max)
aggregate(cbind(marks1, marks2) ~ gender, data=df1, FUN=max)


(df2 = aggregate(cbind(marks1,marks2) ~ gender + course, data=df1, FUN=mean))
df2

df1





#List -----
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages





#Factor -----

(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))
summary(grades)
table(grades)
(gradesFactor = factor(grades))
summary(gradesFactor)

(gradesFactorOrdered = factor(grades, ordered=T))
summary(gradesFactorOrdered)

(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))
summary(gradesFactorOrderedLevels)
gradesFactor
gradesFactorOrdered
gradesFactorOrderedLevels
pie(c(10,15,17))
pie(summary(gradesFactorOrderedLevels))
barplot(summary(gradesFactorOrderedLevels), col=1:4)

class(grades)
class(gradesFactorOrdered)
class(gradesFactorOrderedLevels)



# Object Properties
#vector
v1= 1:100
class(v1) ; typeof(v1)
v2=letters[1:10]
class(v2) ; typeof(v2)
length(v2)
summary(v1)

#matrix
m1= matrix(1:24,nrow=6)
class(m1)
summary(m1)
dim(m1)
str(m1)

#Array
a1 =array(1:24, dim=c(4,3,2))
class(a1)
str(a1)
dim(a1)
summary(a1)



#DF
#data() #built in datasets
df1= iris 
str(df1)
summary(df1)
class(df1); dim(df1)
nrow(df1) ; names(df1) ;NROW(df1)
colnames(df1)
rownames(df1)

#list
list1 = list(v1,m1,a1,df1)
str(list1)

#Statistical Description
library(Hmisc)
describe(df1)


#Next Topics
x= c(123.2234, 33333.544, 43243.8442)
floor(x)
ceiling(x)
trunc(x)
round(x,-2)
round(x, digits = 5)


================================================
FILE: 92-wksp2/1b3-factor.R
================================================


(grades = sample(c(LETTERS[1:4]), size=30, replace=T, prob=c(.4,.2,.3,.1 )))
summary(grades)
(gradesF = factor(grades))
summary(gradesF)
table(grades)
table(gradesF)
class(gradesF)
(gradesFO = factor(grades, ordered=T))
(gradesFO1 = factor(grades, ordered=T, levels=c('B','C','A','D')))
summary(gradesFO1)

(marks = ceiling(rnorm(30, mean=60, sd=5)))
(gender = factor(sample(c('M', 'F'), size=30, replace=T)))
(student1 = data.frame(marks, gender, gradesFO1))
boxplot( marks ~ gradesFO1, data=student1)      
boxplot( marks ~ gradesFO1 + gender, data=student1)      

boxplot(marks)
summary(marks)
abline(h = summary(marks))
quantile(marks)


================================================
FILE: 92-wksp2/1d2-basicstats.R
================================================
# Basic Stats
x = ceiling(rnorm(10000, mean=60, sd=20))
mean(x)
median(x)
#there is no mode function for mode stats
table(x)
sort(table(x), decreasing=T)

#mode
library(modeest)
mlv(x,method='shorth')

#quantile
quantile(x)
quantile(x,seq(.1,1,by=.1)) #decile
quantile(x,seq(.01,1,by=.01)) #percentile

library(e1071)                    # load e1071 

plot(density(x))    #density plot
e1071::skewness(x)                # apply the skewness 
kurtosis(x)

sd(x); var(x)
cov(women$weight, women$height)
cor(women$height, women$height)

stem(x)

#Freq Table
library(fdth)  #fast way of creating FT
ftable1 = fdt(x)
ftable1


================================================
FILE: 92-wksp2/1d2-dm-student1.R
================================================
# Data Manipulation : Academic Data

#Method1 : gsheet
library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1qLHa5qFTyWacta8F-IGo6J3Zpf-BVR9OrlqONuJDqYc/edit#gid=2051155174"
student1 = as.data.frame(gsheet2tbl(url))

#Method2 : read.csv
student2 = read.csv('./data/student1.csv')
names(student2)

#save the imported data object into a new object
student=student1  # or student2
str(student)
names(student)
df1= student
lm(df1$btechmarks ~ df1$attnd, data=df1)

(colnames = names(df1))
#attributes(df1) = NULL
#df1 = as.data.frame(df1)
attributes(df1)
attr(df1, which='spec') = NULL
#names(df1) = colnames
str(df1)
#class of each column
class(df1$gender)
sapply(df1, class)

# convert character to factor
factorcols = c('gender', 'cat', 'class12', 'batch','batchyr', 'br', 'city', 'finalgrade', 'btechfinal')

df1[factorcols] = lapply(df1[factorcols] ,factor)
sapply(df1, class)
str(df1)


# Now do summarisation
#attach(df1)
names(df1)
table(df1$gender)
#combine it in single command 
(l1= lapply(df1[factorcols],factor,ordered=TRUE)) #ordering is not necessary for all
sapply(l1,table)

table(df1$gender)
table(df1$batchyr)
sapply(df1[factorcols],table)


sapply(lapply(df1[factorcols],factor,ordered=TRUE), table)  #Method1


sapply(df1[factorcols], table)   #Method2
sapply(df1[c('gender', 'cat', 'class12')], table)
?lapply
str(df1)
# Numeric Cols
sapply(df1, is.numeric)
(numcols = sapply(df1, is.numeric))
class(numcols)
(numcols = names(df1[numcols]))
#remove rollno
(numcols = numcols[-1])
head(df1[numcols])
colSums(df1[numcols])
colSums(df1[numcols],dims=1)
colMeans(df1[numcols],dims=1)
numcols[c(1,3)]
colMeans(df1[c('age','java', 'cpp')])
names(df1)
colMeans(df1[numcols[c(1:3)]])

names(df1)
#Look for other summarisation and grouping
aggregate(df1[numcols], by=list(df1$br), FUN=mean)
aggregate(cbind(sem1, sem2) ~ br + gender, data=student, FUN=mean)

aggregate(java + cbnst ~ br, data=df1, FUN=mean)  #sum of java & cbnst wrt branch
aggregate( cbnst ~ gender, data=df1, FUN=mean)
aggregate( cbnst ~ gender + br, data=df1, FUN=mean)



#using dplyr package
library(dplyr)

#Top 2 students from each Branch---- 
df1 %>% select(br, sname, btechmarks) %>% group_by(br) %>% arrange(desc(btechmarks)) %>% top_n(n=2)

# Average Fees Paid by Batch Yr----
df1 %>% group_by(batchyr) %>% select(batchyr, feepaid) %>% summarize(mean_fees = mean(feepaid, na.rm = TRUE))

names(df1)
# Avg BTech Marks and Min Marks in Java : Group by Gender, Granch and Final Grade ----
df1 %>% filter(finalgrade == 'A') %>% group_by(gender, br, finalgrade) %>% summarize(btechmks = mean(btechmarks, na.rm = TRUE), javamin = min(java, na.rm = TRUE))

#Count by Gender ----
df1 %>%   group_by(gender) %>%  tally()

#Filter by Final Grade = B : select only few columns ----
df1 %>% filter(finalgrade == 'B') %>% select(rollno, sname, finalgrade)

#sample : select few rows on randowm basis ----
df1 %>% sample_frac(0.2, replace = TRUE)  %>% select(rollno, sname)  # % of Total
df1 %>% sample_frac(0.2, replace = F)  %>% select(rollno, sname) # % of total : replace should be False to have non repeated rows
df1 %>% sample_n(3, replace = F)  %>% select(rollno, sname) # select only 3 rows

#few rows : 10 to 15
slice(df1, 10:15)

library(tidyr) #Reshape
df1long <- df1 %>% select(sname, java, cbnst)
df1long <- df1 %>% select(sname, java, cbnst) %>% tidyr::gather(key = subject, value = marks, java, cbnst) 

head(df1long)
distinct(df1long)

tidyr::unite(df1, 'rollnoname', c(rollno, sname), sep="-") %>% select(1:5)%>% head


# data
str(df1$dob)
as.Date("5-Apr-91", '%d-%b-%y')
df1$dob = as.Date(df1$dob, '%d-%b-%y')
df1 %>% select(sname, dob) %>% tidyr::separate(dob, c("y", "m", "d"))


# Rows
df1 %>% slice(1:n():5)
slice(df1, 1:5)

df1 %>% slice(1:n())
df1 %>% slice(1:10)
slice(df1, n()-10: n())  #different way

names(df1)
#defaults to the last variable in the tbl
df1 %>% group_by(gender) %>% top_n(2)  #Rank not data on last column
# top_n(n = 5, wt = x)
df1 %>% group_by(gender) %>% top_n(2,wt=class10)  #Rank not data wrt class10 marks

df1 %>% top_n(-2, wt=class10) %>% select(rollno, sname, class10, cgpa) #bottom 2 ranks - see 3 values
df1 %>% group_by(gender) %>% tally(java)  #total count
df1 %>% select(finalgrade, btechmarks, sname) %>% top_n(1, btechmarks)

#Selecting Columns
df1 %>% select(1:5)
df1 %>% select(contains('java'))
df1 %>% select(starts_with('btech'))
df1 %>% select(ends_with('s'))
df1 %>% select(everything())
df1 %>% select(finalgrade, everything()) %>% select(1:5) %>% select(-2)
names(df1)


# Summarise
df1 %>% summarise(avgjava = mean(java))
df1 %>% group_by(gender) %>% summarise(avgjava = mean(java),sumcbnst = sum(cbnst))
df1 %>% group_by(br, batch) %>% select(numcols) %>% summarise_each( funs(mean))

df1 %>% group_by(cat, gender) %>% count(class12, finalgrade)




#Summary Functions
v1 = df1$btechmarks
v1
v1 %>% first
v1 %>% last
v1 %>% sd
v1 %>% mean
v1 %>% n_distinct()


# Combine Data Sets

(a=data.frame(x1=c('A','B','C'), x2=c(1,2,3)))
(b=data.frame(x1=c('A','B','D'), x2=c('T','F','T')))

dplyr::left_join(a, b, by = "x1")
#Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")
#Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")
#Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")
#Join data. Retain all values, all rows.

dplyr::semi_join(a, b, by = "x1")
#All rows in a that have a match in b.

cbind(a,b)

dplyr::anti_join(a, b, by = "x1")
#All rows in a that do not have a match in b.

#-----
y = data.frame(x1=c('A','B','C'), x2=c(1,2,3))
z = data.frame(x1=c('B','C','D'), x2=c(2,3,4))
cbind(y,z)

dplyr::intersect(y, z)  # B&C
#Rows that appear in both y and z.

dplyr::union(y, z)
#Rows that appear in either or both y and z.

dplyr::setdiff(y, z)
#Rows that appear in y but not z.

#Bind
dplyr::bind_rows(y, z)
#Append z to y as new rows.
dplyr::bind_cols(y, z)
#Append z to y as new columns.
#Caution: matches rows by position




================================================
FILE: 92-wksp2/1d3-dencoCase.R
================================================
# Case Study - Denco  
#Manufacturing Firm with sales data of partnum and customer with region wise sales

# Should know - import, table, dplyr, aggregate etc

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1) #see if data is loaded and check its structure
head(sales1)
#read file : Method2 : when location is not in project folder
sales2 = read.csv(file.choose())
str(sales2)
head(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)
head(sales3)


# whichever you read the data, store it in sales object
sales = sales1  # keeping a backuph
head(sales)  #top 6 rows
str(sales)  # structure
class(sales) # class type - Df
dim(sales)  # rows & columns
summary(sales) # summary- colname, type
names(sales) # col names
head(sales) # first 6 values
tail(sales) # last 6 values

# Some other functions
unique(sales$custname)  # unique customer names
length(unique(sales$custname)) # no of unique customers
length(unique(sales$region )) # no of distinct regions

#Information Required----
# 1. Who are the most loyal Customers - What to do : Improve repeated sales, Target customers with low sales Volumes
# 2. Which customers contribute the most to their revenue : How do I retain these customers & target incentives
# 3a. What part numbers bring in to significant portion of revenue : Maximise revenue from high value parts
# 3b. What parts have the highest profit margin : What parts are driving profits & what parts need to build further


#dplyr
names(sales)
library(dplyr)  #use this library to do summarisation

#Case-1 : Loyal Customers----
# Finding Frequency and sort them in descending order
names(sales)
(t1=table(sales$custname))  # freq table for all customers
class(t1); length(t1)
head(t1) # top 6 but not sorted
t2= sort(t1,decreasing=T )
head(t2)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

#other methods through dplyr
library(dplyr)
sales %>% count(custname, sort=TRUE) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n)) %>% head(n=5)
#Ans: CHIZ BROS INC most loyal with 253 times purchase


#Case-2 : Customer- Highest Revenue---
sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue)) %>% head(n=5)
#Ans2: Triumph Insulation gave max revenue

# save this object and then analyse
salesrevenue = sales %>% group_by(custname) %>%  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))

#dply uses tibble format and does not show all rows/cols by default
options(tibble.width = Inf) # displays all columns.
options(tibble.print_max = Inf) # to show all the rows.
salesrevenue[1:5,] # first 5 rows
#Ans2: here also Triump insulation gives max revenue = 35592K

#Case3 : Most Profitable PartsNums ----
# Look for freq, revenue and/ or profit margin
head(sales)
# Summarise by Part Num for frequency
sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))  %>% head(n=5)
#Ans3a: Part Num- 764821000 was purchased max times -   122

# Summarise Partnum for Profit(margin) : sum(profit)
names(sales)
sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()
#Ans 3b: Part Num - 733648000 gave max margin profit - 11003367

# Summarise Partnum for revenue
#(practise it )


#Case : Extra ----
#Find Regions wise sales

(salesregionrevenue = sales %>% group_by(region) %>% summarise(Revenue = sum(revenue)) %>% arrange(desc(Revenue)))

#Some Graphs related to Data Summarised
pie(x = salesregionrevenue$Revenue, labels=unique(sales$region))
barplot(salesregionrevenue$Revenue, col=1:4)

# Filter Data----
#Rows where revenue > 700000 : show only custname, revenue
sales %>% filter(revenue >= 700000 ) %>% select(custname, revenue)

#select 1% rows on random basis
sales %>% sample_frac(.01)  %>% select(custname, revenue)

#select 10 rows on random basis and sort them regionwise
sales %>% sample_n(10)  %>% select(custname, region) %>% arrange(region)
sample_n(sales, 10) # similar method

#End of session for Denco Case - Manufacturing Firm
#Descriptive Analysis
#Loyal Customers, Customer giving max revenue
#Profitable Partnums - Freq, Revenue and Margin



================================================
FILE: 92-wksp2/1d4-DA-dencoCase.R
================================================
# Case Study - Denco  

#read file : Method1
sales1 = read.csv("./data/denco.csv")
str(sales1)

#read file : Method2
sales2 = read.csv(file.choose())
str(sales2)

#read file: Method3
#install.packages('gsheet')
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=216113907"
sales3 = as.data.frame(gsheet2tbl(url))
str(sales3)


#using gsheet
library(gsheet)
denco2 = as.data.frame(gsheet2tbl(url))
str(denco2)

#head(sales1,n=7)
#names(sales1)

# whichever you read the data, store it in sales object
sales = sales1  # keeping a backup
str(sales)
class(sales)
str(sales)
?summary
summary(sales)

str(sales)
dim(sales) #dimensios of DF rows & colnum
unique(sales$custname)
length(unique(sales$custname))
length(unique(sales$region ))

# aggregation
aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
df1 = aggregate(sales$revenue , by=list(sales$custname), FUN=sum)
head(df1)
str(df1)

df1=df1[order(df1$x, decreasing=TRUE),]
head(df1,10)

head(df1[order(df1$x, decreasing=TRUE),], 5)

aggregate(sales$revenue, by=list(sales$region), FUN=mean)
df2= aggregate(formula=revenue ~ region, data=sales, FUN=sum)
df2[order(df2$revenue, decreasing=F),]


#Aggregate Formula
(df2 = aggregate(revenue ~ custname + region, data=sales, FUN=sum))
head(df2[order(df2$revenue,decreasing=T),],10)

#List
list1= tapply(sales$revenue, sales$custname, FUN=sum)
head(list1)
list1
head(sort(list1, decreasing=T))
summary(df3)
str(df3)

#dplyr
names(sales)

library(dplyr)

sales %>% filter(margin > 10000) %>% arrange(region, desc(revenue))
filter(sales, margin > 1000000)

sales %>% filter(region == '01-East' & revenue > 400000) %>% select(partnum, region, revenue)

names(sales)
sales %>% group_by(custname) %>% 
  summarize(Revenue = sum(revenue)) %>% arrange(desc(Revenue))


library(data.table)
dt1 = as.data.table(sales)
dt2 = dt1[, sum(revenue), by=custname]
names(dt2)
dt1[, dt1[, sum(revenue), by=custname]]
#dt1[, order( decreasing = T)]

# Select
library(sqldf)
df5 =sqldf("Select custname, sum(revenue) from sales Group By custname order by sum(revenue) desc ")
head(df5)

# Frequency --------
names(sales)
t1=table(sales$custname)
class(t1)
length(t1)
head(t1)
t2= sort(t1,decreasing=T )
head(t2)
head(sort(table(sales$custname), decreasing=T), n=10)
tail(sort(table(sales$custname), decreasing=T), n=10)

#xtab
head(sort(xtabs(~ custname, sales), decreasing=T))

#
library(dplyr)
sales %>% dplyr::count(custname, sort=TRUE)

sales %>% dplyr::group_by(custname) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))



#plyr
df2a= plyr::count(sales, c('custname'))
str(df2a); names(df2a)
head(df2a[order(-df2a$freq),])

# Summarise by Part Num

df3a= aggregate(sales$revenue, by=list(sales$partnum), FUN=sum)
head(df3a)
str(df3a)
df3a[order(-df3a$x),][1:6,]
head(df3a[order(-df3a$x),])

df3b = aggregate(revenue ~ partnum, data=sales, FUN=sum)
head(df3b)
head(df3b[order(df3b$revenue, decreasing=T),])

sales %>% dplyr::group_by(partnum) %>% dplyr::summarise(n = n()) %>% dplyr::arrange(desc(n))


# which parts have highest Profit : partno - sum(profit)
names(sales)
df4a = aggregate(margin ~ partnum, data=sales, FUN=sum)
aggregate(margin ~ partnum, data=sales, FUN=sum)
head(df4a[order(df4a$margin, decreasing = T),])

sales %>% group_by (partnum) %>% summarise(TotalMargin= sum(margin)) %>% arrange(desc(TotalMargin)) %>% head()



================================================
FILE: 92-wksp2/1e-graphs-basic.R
================================================
# Combined Plots
#plot, histogram, pie, boxplot, linechart, correlation plot

#plot
women
plot(women)
?plot
plot(women, type='p', pch=16, col='red')
plot(women, type='l')
plot(women, type='b')
plot(women, type='b', pch=18, lty=1, col=2, lwd=4)
plot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)

#more features with plot
plot(x=women$weight, y=women$height, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title', sub='Sub Title')
#see cheat sheet on base graphs
mtcars$cyl
#plot(x=mtcars$wt, y=mtcars$mpg, col=mtcars$gear, pch=c(4,6,8), cex=c(1,2))
#as.numeric(levels(as.factor(mtcars$cyl)))
plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)


#boxplot
boxplot(women$height)
abline(h=c(58, 62,65,68,72))

#draw lines on plot for number summary
summary(women)
quantile(women$height)
quantile(women$height, seq(0,1,.1))
quantile(women$height, seq(0,1,.01))
stem(women$height)
boxplot(women$height, col='green')
abline(h=quantile(women$height))
text(1, quantile(women$height), labels=c('min','1Q','median','3Q','max'))

#histogram
hist(women$height)
hist(women$height, breaks=10)
hist(women$height, breaks=5, col=1:5)

#histogram2
(x = rnorm(100,50,10))
hist(x)

hist(x, freq=F, col=1:10)
lines(density(x))

#density plot : shape of data
plot(density(x), col='red')

#pie
gender= sample(c('M','F'), size=100, replace=T)
table(gender)
pie(table(gender))
x = c(10,20,40,50)
pie(x)
xlabels = c('A ','B ','C ','D ')
x/sum(x)
(labels2 = paste(xlabels, round(x/sum(x),2) * 100 , sep='-'))
(labels3 = paste0(labels2,"%%"))
(labels2 = paste0(xlabels, round(x/sum(x),2) * 100, '%'))

pie(x, labels=labels2)
x
#barplot
barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

#correlation plot
pairs(women)
cor(women$height,women$weight)
cov(women$height, women$weight)
head(mtcars)
?mtcars

cor(mtcars)
names(mtcars)
pairs(mtcars)
pairs(mtcars[1:4])
options(digits=4)
pairs(mtcars[c('mpg', 'wt','hp')])


================================================
FILE: 92-wksp2/1e2-graphs.R
================================================
# Combined Plots

#plot, histogram, pie, boxplot, linechart, correlation plot


#plot
women
?women
str(women)
plot(women)
plot(x=women$height, y=women$weight)
?plot
plot(women, type='p', pch=17)
plot(women, type='l')
plot(women, type='b', pch=18, lty=2, col=2)
plot(women, xlim=c(30,100), ylim=c(min(women$weight)-10, 200), pch=10)
data()

#more features with plot
plot(y=women$height, x=women$weight, pch=15, xlab='Weight', ylab='Height', col='red', cex=2, type='b')
title(main='Main Title- PDU', sub='Sub Title')
#see cheat sheet on base graphs

plot(women)
abline(lm(women$weight ~ women$height), col='red', lty=2, lwd=4)
abline(h = c(130, 150), col='green') 
abline(v=c(62, 66, 70), col='blue')
abline(v=women$height, col='purple')

#boxplot
boxplot(women$height)
boxplot(df$marks1)

abline(h=c(58, 62,65,68,72))

#draw lines on plot for number summary
summary(women)
quantile(women$height)
boxplot(women$height, col='green')
abline(h=quantile(women$height))


#histogram
hist(women$height)
hist(women$height, breaks=10)
hist(women$height, breaks=5, col=1:5)
hist(df$marks2, breaks=3)
#histogram2
?rnorm
x = rnorm(n=100000,mean=50,sd=10)
hist(x)

hist(x, freq=F, col=1:5)
lines(density(x))

#density plot : shape of data
plot(density(x), col='red')


#pie
x = c(10,20,40,50)
pie(x)
xlabels = c('A','B','C','D')
pie(x, labels=xlabels)
pie(x, labels=paste(round(x/sum(x) * 100,0),'%') )

x
#barplot
barplot(x,col=1:4)
barplot(x,col=1:4, horiz = T)

#correlation plot
pairs(women)
cor(women$height,women$weight)

names(mtcars)
cor(mtcars)
pairs(mtcars)
options(digits=4)
pairs(mtcars[1:4])


================================================
FILE: 92-wksp2/1e3-advgraphs.R
================================================
#Advanced Graphs


library(corrgram)
cor(mtcars[1:4])
corrgram(mtcars[1:4], order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Car Milage Data in PC2/PC1 Order")


#alternative of box plot

boxplot(mpg ~ cyl, data=mtcars)


library(corrplot)
relationship=cor(mtcars)
relationship
corrplot(relationship)
corrplot(relationship, type="upper")

#-----

library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
x1; x2; x3
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), 
        col="gold")
title("Violin Plots of Miles Per Gallon")
abline(h=c(15,20))


================================================
FILE: 92-wksp2/1f-SLR-women.R
================================================
#topics ----
#factors, env, import/export. package install
#rep, recode, split, partition, subset, loops, cast & melt
#missing values. duplicates, apply
#graphs - bar, multiple line, pie, box, corrgram
# predict weight for certain height

head(women)
dim(women)
fit = lm(weight ~ height,data = women)
summary(fit)
range(women$height)
(ndata = data.frame(height = c(60.5, 70.5, 71.5)))
(predictedwt = predict(fit, newdata = ndata))
cbind(ndata, predictedwt)

resid(fit)
fitted(fit)
cbind(women, fitted(fit), resid(fit))
#assumptions

plot(fit)




================================================
FILE: 92-wksp2/1h1-dplyr.R
================================================
#dplyr - mtcars
library(dplyr)
#library(tidyverse)
#Filter----

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()-2))



#mutate----
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col
transmute(mtcars, displ_l = disp / 61.0237) #removes other cols
mutate(mtcars, cyl = NULL) #do not display cyl


#slice-----
slice(mtcars, 1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

(by_cyl <- group_by(mtcars, cyl)) # ???
slice(by_cyl, 1:2)

#structure----
tbl_df(mtcars) # convert to tbl class
glimpse(mtcars)  # dense summary of tbl data
View(mtcars) # spreasheet like form base pacakge

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt), min(wt))


#summarise----
summarise(mtcars, mean(disp))  
summarise(group_by(mtcars, cyl), mean(disp)) 
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))
mtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))



#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))



#summarise if : 
mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
str(iris)  #Species is a factor
iris %>% summarise_all(mean)

iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)


#------------------------------------
#unsorted----
dplyr::tbl_df(iris)  #all rows not displayed
print(dplyr::tbl_df(mtcars), n=20)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=11)
tbl_df(mtcars) %>% print(n = Inf)  #all rows
tbl_df(mtcars) %>% print(width = Inf)
tbl_df(mtcars) %>% as.data.frame(mtcars)

glimpse(mtcars)
df = mtcars
row.names(df) = NULL  #remove rownames
df %>% select(mpg)
#head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)

#distinct rows
distinct(mtcars)
(df3  = data.frame(a=c(2,2,3),b=c(2,2,1)))
distinct(df3)

#sampling
sample_frac(mtcars, 0.2, replace=F)
sample_n(mtcars, 2, replace=F)
#%>% select(mpg)
slice(mtcars,10:14)
sort(mtcars$mpg, decreasing = T)
top_n(mtcars,-2, mpg)  #least 2 mpg

select(mtcars, mpg) %>% arrange(desc(mpg))

#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

df= mtcars
df$names = rownames(mtcars)
head(df)
df %>% select(1:5,12) %>% arrange(mpg)

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

(df4 = data.frame(marks=c(1,2,2,3,7,1,100)))
cbind(df4, dplyr::mutate_all(df4, funs(min_rank)))

#shift the columns
mtcars %>% lead() 
mtcars %>% lag()
mtcars %>% summarise(n())

select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df

df = women
rename(df, HeightWomen = height)
df %>% mutate(height2 = height + 2, weight2 = weight + 4)
#does not show orginal columns
df %>% transmute(height2 = height + 2, weight2 = weight + 4)

library(nycflights13)
data(flights)
head(flights)
destinations <- group_by(flights, dest)
destinations
summarise(destinations, planes = n_distinct(tailnum), flights = n())


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter

#error ???????
# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)






================================================
FILE: 92-wksp2/1h2-freqtable.R
================================================
# Frequency Distribution

#Discrete Cat Data
(attend = c('A','P','P','A','P','A'))
table(attend)
cbind(table(attend))  # A & P on left side

#Discrete Numeric Data

color=c('Blue','Green','Magenta','Green','Black','Blue','Black')
x2 = table(color)
x2
x2a = cbind(x2)
x2a
hist(x2a)  # not relevant 
barplot(x2a)  # not good
barplot(x2a, beside=T)  # Better
unique(color) ; length(unique(color))
barplot(x2a, beside=T, col=rainbow(length(unique(color))))
pie(x2a)

#Continuous Data 
set.seed(1234)
x3 = runif(100,0,150)  # 0 to 150 marks range, 100 values 
x3
x3 = ceiling(x3)  #round to higher value
x3
range(x3)
# Divide range into step of 15 ie 10 levels
breaks = seq(0,150,by=15)
breaks
length(breaks)
x3
#x3[1] = 60; x3[2] = 75
x3.cut = cut(x3, breaks)
x3.cut
table(x3.cut)
cbind(table(x3.cut))  #see it vertically

#give intervals a character values a, b..
(x3.cut = cut(x3, breaks, labels=letters[1:10]))
#(x3.cut = cut(x3, breaks, labels=letters[1:length(breaks)-1]))
x3.cut

(x3a = table(x3.cut))
(x3b = cbind(x3a))

#plot these freq Table: which is better
hist(x3b)
pie(x3b)
barplot(x3b, beside=T)
barplot(x3b, beside=T, names.arg =rownames(x3b))
plot(x3b)


# and so..on like previous eg




================================================
FILE: 92-wksp2/2a-importExport.R
================================================
# Read Data into R Environment

#CSV Files----
#Read from CSV file in PC
head(iris)
write.csv(iris, "./data/iris.csv", row.names=F)
read1 = read.csv(file="./data/iris.csv", header = TRUE,sep = ",")
read1 = read.csv(file="./data/dhiraj.csv", header = TRUE,sep = ",")

str(read1)
class(read1)
head(read1)
read2 = read.table(file="./data/iris.csv", header = TRUE,sep = ",")
str(read2); class(read2)
head(read2)
read3 = read.delim(file="./data/iris.csv", header = TRUE,sep = ",")
str(read3) ; class(read3)
head(read3)
#difference is use of specify delimeter(read.csv takes default as comma)
#or location is different from Project Folders, or want to search for the file
read4 = read.csv(file=file.choose())
str(read4)
head(read4)


# From URL : Read CSV from Web----
read_web1 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')
head(read_web1)
library(data.table)
read_web2 = fread("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv")
head(read_web2)
class(read_web2)

#Text file from Web-----
read_txt = read.table("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/test.txt", header = FALSE)
head(read_txt)

#Google Sheets-----
library(gsheet) #install it
install.packages('gsheet')
library(gsheet)
url_gsheet = "https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534"
df_gsheet = as.data.frame(gsheet2tbl(url_gsheet))
head(df_gsheet)

#Excel----
#Create a excel file with data in 2 sheets
# first row contains variable names
#C:\Program Files\Java\jre1.8.0_191
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_191')
#library(rjava)
library(xlsx)
df_excel1 = read.xlsx( "./data/myexcel.xlsx", 1)
df_excel1
# read in the worksheet named mysheet
df_excel2a = read.xlsx("./data/myexcel.xlsx", sheetName = "bowlers")
df_excel2a
df_excel2b = read.xlsx( "./data/myexcel.xlsx", sheetIndex = 2)
df_excel2b


================================================
FILE: 92-wksp2/2b-SLR-salesarea.R
================================================
#Simple Linear Regression - Case Study
# Regression : Areas vs Sales
#Given data of area and sales, predict value for sales for specific areas eg : (1.5,2,3,4,5)
#manual way of doing regression see online URL

#Create/ Import Data-----
#Method1 : creating data from Vectors
#X -is area sqft Y-sales in 1000s units; Find relationship betn X & Y
X = c(1.7,1.6,2.8,5.6,1.3,2.2,1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )
df1 = data.frame(X,Y)
head(df1)

#2nd method of importing data
#import from ggsheet  #pickup the correct url
library(gsheet)
area1 = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
df2 = as.data.frame(gsheet::gsheet2tbl(area1))
str(df2)
head(df2)

#Third method of importing data from csv
df3 = read.csv('./data/slr1.csv')
str(df3)

#Method4 for importing from CSV file with choose location
df4 = read.csv(file.choose())
str(df4)

# Use Vector Data or method used to import data
#make one of the DF active
df = df1
df
#simple stats
mean(df$X); mean(df$Y)
sum(df$X); sum(df$Y)
sd(df$X) ; var(df$Y)
cov(df$X,df$Y); cov(df$Y,df$X)
cor(df$X,df$Y) ; cor(df$Y,df$X)
#cor.test(df$X,df$Y)

#some plots to understand pattern
plot(df$X, df$Y)  #simple command to plot : Next with features
plot(y=df$Y, x=df$X,xlab='Area in sqft', ylab='Sales Amount', type='p', ylim=c(0, max(df$Y)+1), main='Plot of Area Vs Sales', xlim=c(0,max(df$X)+ 1), col='red',pch=17)
?plot
abline(lm(Y ~ X,data=df1), lty=1, lwd=2, col='green') # with regression line
abline(v=c(3,5, min(df$X), max(df$X)),h=c(6,10, min(df$Y), max(df$Y)), col=c('red','blue','green','yellow')) # few straight lines at x & y axis
range(df$X)
#Model
fit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X
fit1
summary(fit1)


#few extras
names(fit1) # attributes(fit1)
system.time(lm(Y ~ X, data=df)) #time taken to compute linear regression
coef(fit1)  # Coefficients of Equation Y = mX + C
fitted(fit1) # predicted values for all X in orginal data
predict(fit1, newdata=data.frame(X))
residuals(fit1) # diff between actual and predicted values - residuals
(R= df$Y - fitted(fit1))
plot(density(residuals(fit1)))
#residuals should be less :Diff of Y actual - Y predicted
#abline(h=coef(fit1)[1])

summary(fit1)  # summary statistics of Linear Model(LM)
#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

names(fit1)  #output variables names of LM
names(summary(fit1))
#u can select them to see output

summary(fit1)$r.squared
coef(fit1)[2]  # slope or beta
fitted(fit1)
#combine the data with Ypredicted, errors
cbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))

#Mathematical Equation and predictions
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4
#predict for area = 4
#using equation
summary(fit1)
coef(fit1)
(Y = coef(fit1)[1] + coef(fit1)[2] * 4)
#using model
range(df$X)
(new1 = data.frame(X=c(4,5,3,2)))
predicted2= predict(fit1, newdata= new1)
cbind(new1, predicted2)
#prediction can only be interpolated not extrapolated
range(df$X)  #min to max value of X: area

#select new data given in the case
(new1 = data.frame(X=c(1.5,2,3,4,5)))
#sample data for X for prediction, should be between the range of X values
predict(fit1, newdata= new1) # Predict Function for 4 values of X
#columnbind with input and predicted values
cbind(new1, predictedY = predict(fit1, newdata= new1) )

library(forecast) #install it first
?accuracy(fit1)
#RMSE is generally used and should be least when selecting models

anova(fit1)
summary(fit1)$r.squared  # R2 from Model
summary(fit1)$sigma  #Residual Std Error SD along the LM Line

#---------------------------------------#Assumptions--------
#Assumption : Graphical Analysis : IMP STEP
plot(fit1)
par(mfrow=c(2,2))
plot(fit1)
par(mfrow=c(1,1))

plot(fit1, which=1)
# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y
plot(df)
abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
?car::durbinWatsonTest
#pvalue > 0 : Do not reject Ho. that means there is no autocorrelation

#Normality of residuals
resid(fit1)
#qqplot(fitted(fit1),resid(fit1) )
plot(fit1, which=2)
#points to be around the straight line

#Equal Variance : 4th Assumption : homoscedasticity
plot(fit1, which=1)
#no funnel shape to show hetero-cedasticity

#Outlier Analysis
plot(fit1, which=4)
#abline(h=c(.5))
#no value of cooks distance > .5 : no data to be removed
#outlier values can affect the model

#see all diagnostic plots together
par(mfrow=c(2,2))
#multiple frames per row : rowwise filling, 2 rows, 2 columns
plot(fit1)
par(mfrow=c(1,1))

#SUMMARY ------
summary(fit1)$r.squared  # 90% variation in Y explained by X
summary(fit1)
#F Stats pvalue < 0.05: Model exists : At least 1 indep variable has strong relationship with Dependent variable (Y)
#pvalue of Coef (X) < 0.05 : Significant X


#End of Simple Linear Regression
#Do different SLR on different data sets
#Learn what to do if there are violations of assumptions




df # dataset being used for LM
fit= lm(Y ~ X, data=df) #model creation
summary(fit)  #summary of linear model
plot(fit)  #diagnostic plots
predict(fit, newdata=data.frame(X=mean(df$X)))
#Multiple R2 explains the variation, model fitness



================================================
FILE: 92-wksp2/2b-allmodels.R
================================================
# All models
library(dplyr)

#Linear Regression
head(women)
head(mtcars)
#predict weight for new height
plot(women)
plot(women, ylim=c(0, 160), xlim=c(0,90))
fit1 = lm(weight ~ height, data=women)
summary(fit1)
range(women$height)
(ndata1=data.frame(height=c(59.5, 62.5)))
(predicted1 = predict(fit1, newdata= ndata1))
cbind(ndata1, predicted1)
plot(fit1) #check for assumptions
par(mfrow=c(2,2))
plot(fit1)
par(mfrow=c(1,1))


#Logistic Regression
data2 = read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
head(data2)
str(data2)
summary(data2)
data2$rank = factor(data2$rank)
data2$admit = factor(data2$admit)
table(data2$rank, data2$admit) #2 way table
xtabs(~admit + rank, data = data2)
#create Logistic Model
fit2 <- glm(admit ~ gre + gpa + rank,data=data2,family="binomial")
summary(fit2)
(ndata2 = sample_n(data2, 3))  #pick up sample rows
#Predict admit for input data
(predicted2=predict(fit2,newdata=ndata2, type="response"))
(predictedclass2=ifelse(predicted2 > .5, 1,0))
cbind(ndata2, predicted2, predictedclass2)

#Data Partition




#Decision Tree - Classification 
url3 = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'
data3a = read.csv(url3)
head(data3a)
names(data3a)
data3 = data3a[,c(2,3,5,6,7)]  #select few columns only
head(data3)

#install & load libraries
library(rpart)
library(rpart.plot)

#Decision Tree
fit3 = rpart(survived ~ ., data = data3, method = 'class')
fit3
rpart.plot(fit3, extra = 106, cex=.8,nn=T)  #plot
printcp(fit3) #select complexity parameter
prunetree3 = prune(fit3, cp=.014) #make tree smaller
rpart.plot(prunetree3, cex=.8,nn=T)
prunetree3
table(data3$survived)
#Predict class category or probabilities
(ndata3 = sample_n(data3,4))
predicted3 = predict(prunetree3, newdata=ndata3, type='class')
predicted3b= predict(prunetree3, newdata=ndata3, type='prob')
cbind(ndata3, predicted3, predicted3b)

#similarly Regression Tree can be made



#Clustering----
#install.packages("amap")
##Read the data in the file
url4 = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data4 = as.data.frame(gsheet2tbl(url4))
head(data4)
summary(data4)
str(data4)
nrow(data4)
###Verify the data
colnames(data4)
apply(data4, 2, FUN= class)  #are all numeric
fit4 = kmeans(data4[,-1],centers=3)
fit4$centers  # group means
fit4$size  #how rows in which cluster
fit4$withinss  #math difference within each cluster; Which is more cohesive gp?
fit4$cluster
table(fit4$cluster)
cluster2 = data4[ fit4$cluster == 2,]
head(cluster2)
cluster2[-1] %>% summarise_all(mean)
write.csv(cluster2, file = "./data/data4cluster2.csv")


## Association Rules - Groceries data set----
library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries')
Groceries
arules::LIST(Groceries[1:6]) #different format
#Find Frequent Itemset
frequentItems = eclat(Groceries,parameter=list(supp = 0.01,minlen=3, maxlen=5)) 
inspect(frequentItems[1:10])
frequentItems
inspect(sort(frequentItems,by="count",decreasing=TRUE)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = apriori(Groceries, parameter = list(supp = 0.001, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
#similary it can be done for lift and support
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf

#subset -----
#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.
#Find what factors influenced an event ‘X’ :
#Find out what events were influenced by a given event
subset1a = subset(rules, subset=rhs %in% "whole milk")
inspect(subset1a[1:10])
subset1b = subset(rules, subset=rhs %in% 'bottled beer' )
inspect(subset1b)  #no such rule with beer on rhs, change some parameters to such rules 
#inspect(rules)
#Items in : all or any
subset2a = subset(rules, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2a) #all items in 1 rule
subset2b = subset(rules, subset=lhs %in% c('baking powder','soda') )
inspect(subset2b[1:5]) #any of the items in the rule
#rhs- beer, confidence , sort by lift
subset3a = subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3a)
subset4 = subset(rules, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' ) # lhs- beer, rhs- milk
inspect(subset4)

#Visualizing The Rules -----
plot(subset1[1:10]) 
plot(subset1[1:10], measure=c("support", "lift"), shading="confidence")

#

#install.packages('wordcloud2')
library(wordcloud2)

df = data.frame(word=c('cbap','cmap','iim','imt','calcutta'),freq=c(20,23,15,10,13))
df
wordcloud2(df)


head(demoFreq)
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")
names(demoFreq)


================================================
FILE: 92-wksp2/2b2-SLM-women.R
================================================
# Regression Analysis
# Simple Linear with 1 IV and 1 DV

data(women)
women
names(women)
str(women)

cov(women$height, women$weight)
#69 : which show positive relationship between height and weight

cor(women$height, women$weight)
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight, type='b')
abline(lm(weight ~ height, data=women), col='red')
names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)  # creating a model
summary(fit1)  #summary of the Model

#Ho: (F Test) : No relationship between Y and any X
#Ha:  There is relationship between Y and at least one X
# p < 0.05 Reject Ho in favour of Ha

attributes(fit1)  #output of model

#coefficients
coef(fit1)
#p values for b0 & b1 are significant as it is < 0.05
#pvalue1 = 1.71e-09 < 0.05
#pvalue2 = 1.09e-14 < 0.05

coef(fit1)
#Y = -87 + 3.4 * X  # no extrapolations
(Y = -87 + 3.4 * 20)  # weight cannot be negative
range(women$height)
(Y = -87 + 3.4 * 58)
(Y = -87 + 3.4 * 61.5)
(Y = -87 + 3.4 * 72)
range(women$height)
women


#R^2
(summary(fit1))$r.squared  #0.991
(summary(fit1))$adj.r.squared  #0.9903
#99% of variation in weight is explained by Height
#Good Linear Model for Prediction


#FStats : p value
(summary(fit1))$fstatistic 
#pvalue : 1.09e-14  < 0.05 : Model exists
#There is at least 1 IV which explains variation in Y (DV)


#Prediction : for height = 65, 66

(new1 = data.frame(height=c(65,66,66.5)))
new1
(p1=predict(fit1, newdata = new1))
cbind(new1, p1)
#136.7333 140.1833

(new2 = data.frame(height=c(60,69)))
(p2=predict(fit1, newdata = new2))
cbind(new2, p2)

?predict
predict(fit1, newdata = new1, interval='confidence')
predict(fit1, newdata = new1, interval='prediction')

women
women$weight
fitted(fit1) # predicted values of all original Xs
predict(fit1, newdata = data.frame(women$height)) # same as above for single X

new3 = data.frame(women$height)  # DF using height of original data
cbind(women, fitted(fit1), residuals(fit1))  # compare women, predicted, errors
residuals(fit1)  # diff between actual and predicted values of weight
summary(fit1)
summary(residuals(fit1))

plot(fit1)
cor(fitted(fit1), women$weight)
summary(fit1)


================================================
FILE: 92-wksp2/2b3-SLM-women-A.R
================================================
# Simple Linear Regression : Built in Data Set Women
# Check for assumptions of Regression in the data Set
women
?women
str(women)
fit = lm(weight ~ height, data=women)
?plot
#Initial Checks
cor(women$height, women$weight)
#there is Strong and Positive Relationship between height and weight
plot(women$height, women$weight)
par(mfrow=c(1,1))
plot(fit, which=2)
plot(women)

str(women)
head(women)
women[,2]
fitted(fit)
cbind(women, fitted(fit), predicted=3.45 * women$height - 87)
cbind(women, fitted(fit), residue=fitted(fit)-women$weight, resid(fit))
y = 3.4 * height - 87
plot(residuals(fit))
hist(women$height, breaks=4)
hist(residuals(fit))

hist(residuals(fit), freq=F)
lines(density(residuals(fit)))

#Prediction
ndata = data.frame(height=52.5)
predict(fit, newdata=ndata, type='response')


#Assumptions Regression
#Linearity----
#Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.
# component + residual plot 
plot(women$height, fitted(fit))
plot(residuals(fit) ~ fitted(fit))
plot(fit, which=1)
#residuals should be randomly distributed and not increase or decrease 


#Normality----
#Normality of residuals. The residual errors are assumed to be normally distributed.
plot(density(resid(fit)))
plot(fit, which=2)

#Homoscedasticity----
#Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity - opposite of heteroscedasticity)
plot(fit, which=3)
#No funnel shape, random distribution of residuals
plot(fit, which=4)

#Auto-Correlation----
#Independence of residuals error terms. (Not dependent on previous values)
durbinWatsonTest(fit)
#pvalue < 0; Ho that there is no correlation (r2=0) is accepted

#Outliers
plot(fit, which=4)
#potential outliers are highlighted 1, 14, 15 row
women[c(1,14,15),]

#Lets remove these values and then find R2
fit2 = lm(weight ~ height, data=women[-c(1,14,15),])
summary(fit2)
(summary(fit))$r.squared
(summary(fit2))$r.squared
AIC(fit, fit2) #lower value of AIC is better


#Potential Problems
#Non-linearity of the outcome - predictor relationships
#Heteroscedasticity: Non-constant variance of error terms.
#Auto Collinearity, Multi-collinearity
#Presence of influential values in the data that can be:
#Outliers: extreme values in the outcome (y) variable
#High-leverage points: extreme values in the predictors (x) variable

#All these assumptions and potential problems can be checked by producing some diagnostic plots visualizing the residual errors.

plot(fit1)


================================================
FILE: 92-wksp2/2b4-LM-cars.R
================================================

#http://r-statistics.co/Linear-Regression.html
head(cars) 
fit2=lm(dist ~ speed, data=cars)
summary(fit2)
predict(fit2, newdata=data.frame(speed=c(5,8)))
plot(fit2)
nrow(cars)

index = sample(1:nrow(cars), size=0.8 * nrow(cars))
index
length(index)
length(unique(index))
traindata = cars[index,]
traindata
testdata = cars[-index,]
testdata
nrow(traindata) + nrow(testdata)

model1 = lm(dist ~ speed, data = traindata)
coef(model1)
(P1 = predict(model1, newdata = testdata))
df_test = cbind(testdata, P1)
cor(df_test$dist, P1)
(error = testdata$dist - P1)

AIC(model1)
BIC(model1)
(MAPE_error = mean(abs(error)/testdata$dist))

library(forecast)
accuracy(model1)
accuracy(error)

#https://www.ritchieng.com/machine-learning-evaluate-linear-regression-model/
#https://www.guru99.com/r-decision-trees.html
#http://www.sthda.com/english/articles/35-statistical-machine-learning-essentials/141-cart-model-decision-tree-essentials/
  

================================================
FILE: 92-wksp2/2b4-SLR-women.R
================================================

fit = lm(weight ~ height, data=women)
summary(fit)
range(women$height)
(ndata = data.frame(height= c(58.5, 60.7)))
(p = predict(fit, newdata = ndata))
cbind(ndata, p)
plot(fit)

sum((fitted(fit) - women$weight)^2)


================================================
FILE: 92-wksp2/2c3-MLM-salespromotion.R
================================================
#Multiple Linear Regression : DV vs more than 1 IVs
#sales Qty vs price & promotion
#Predict Sales Qty from Price and Promotion of the Product


#Omni Store
#creating data using Vector
sales= c(4141,3842,3056,3519,4226, 4630,3507,3754, 5000,5120,4011, 5015,1916,675, 3636,3224,2295, 2730,2618,4421, 4113,3746, 3532, 3825,1096, 761,2088,820,2114, 1882,2159,1602,3354,2927)
price = c(59,59,59,59,59,59,59,59,59,59,59,59, 79,79,79,79,79,79,79,79,79, 79,79,79,99,99, 99,99,99,99,99,99,99,99)
promotion= c(200,200,200,200,400,400,400,400, 600,600,600,600,200,200,200,200, 400,400,400,400,600,600,600,600, 200,200,200,200,400,400,400,400,600,600)
#Create a DF from 3 variables
omni1 = data.frame(sales,price,promotion)
head(omni1)

#2nd Method : from CSV file
#omni2 = read.csv(file.choose())

#3rd Method : from gsheet 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
omni3 = as.data.frame(gsheet::gsheet2tbl(url))
head(omni3)
#Make one of data frames active
omni = omni1
head(omni)
str(omni)
nrow(omni)
dim(omni)
#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
fit2 = lm(sales ~ price + promotion, data=omni)

# summary statistics of model IMP STEP
summary(fit2)
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals
#F Stats pvalue = 2.86e-10 < 0.05 : Model Exists
#At least 1 IV can be used to predict sales
names(summary(fit2))
summary(fit2)$adj.r.squared  # Adjt R2 here > .6 
#>74% of variation in sales is explained by price and promotion

#coefficients b1, b2
coef(fit2)
summary(fit2)
#price  : -53 , pvalue = 9.2e-09 < 0.05 *** : Significant
#keeping promotion constant, if price is increased by 1 unit, salesqty decreases by 53 units
#promotion  : +3.6 , pvalue = 9.82e-06 < 0.05 ***: Significant
#keeping price constant, if promotion is increased by 1 unit, salesqty increases by 53 units

fitted(fit2)
omni$sales
residuals(fit2)
summary(residuals(fit2))
summary(fit2)
#Predict SalesQty for new combination of Values----

#create a dataframe of new sample values
range(omni$price) ; range(omni$promotion)
(ndata2 = data.frame(price=c(60,70), promotion=c(300,400)))
p2sales = predict(fit2, newdata=ndata2)
cbind(ndata2, p2sales)

#Assumptions
par(mfrow=c(2,2))
plot(fit2)
par(mfrow=c(1,1))

plot(fit2,which=1)  # no pattern, equal variance
plot(fit2,2)  # Residuals are normally distributed
plot(fit2,3)  # No hetero-scedascity
plot(fit2,4)  # tells outliers which affect model
omni[c(11,14,15),]

fit3 = lm(sales ~ price + promotion, data=omni[-c(11,14,15),])
plot(fit3,4)
summary(fit3)

#End of Multiple Linear Regression

#when variables are large, select only significant variables
#Model with higher R2 to be selected
#other measures of model selection : AIC, BIC, RMSE
#Dataset can be divided into train(70%) and test(30%) set to check the accuracy

#create model with t





#questions
fit2
summary(fit2)
head(omni)
cbind(omni, predict(fit2, newdata = data.frame(omni$price, omni$promotion)))
cbind(omni, fitted(fit2))
cbind(omni, fitted(fit2), omni$sales - fitted(fit2), residuals(fit2))


================================================
FILE: 92-wksp2/2c4-MLM-mtcars1.R
================================================
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr) #install it first

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
summary(model)
#model <- lm(mpg ~ hp + wt , data = mtcars)
#summary(model)
k <- ols_step_all_possible(model)
#plot(k)
k
summary(lm(mpg ~ wt, data=mtcars))
summary(lm(mpg ~ wt+ hp, data=mtcars))
summary(lm(mpg ~ hp + wt + qsec, data=mtcars))


#library(olsrr)
fit = lm(mpg ~ disp + hp + wt + qsec, data = train)
k = ols_step_all_possible(fit)
plot(k)
k
summary(lm(mpg ~ wt, data= train))
summary(lm(mpg ~ wt + hp, data= train))
finalmodel = lm(mpg ~ wt + hp, data= train)
library(gvlma)
gvmodel = gvlma(finalmodel)
gvmodel

finalmodel = lm(mpg ~ wt + hp, data= train)
(predictedvalues = predict(finalmodel, ndata=test))
cbind(test$mpg, predictedvalues)



================================================
FILE: 92-wksp2/2d1-missingvalues.R
================================================
# Missing values

x = c(NA, 1, NA, 2,3, NA)
is.na(x)
sum(is.na(x))
sum(c(T,F,T,F,F))
mean(x)
?mean
mean(x, na.rm=T)
x
x[is.na(x)]
x[is.na(x)] = mean(x, na.rm=T)
x
x1 = c(4,6,8,9)
length(x1[x1 >= 6])
sum(x1 >= 6)
x1 >= 6

x2 = rnorm(100000, mean=50, sd=5)
length(x2)
posn=sample(100000, size=30)
x2[posn] = NA
sum(is.na(x2))

#install this library
library(VIM)
?sleep

head(sleep) #first few rows of sleep
dim(sleep)  #dimensions of sleep data
complete.cases(sleep)  # which row have complete data in T/ F
sum(complete.cases(sleep))  # no of rows have which no missing data
sum(!complete.cases(sleep))  # no of rows which have missing data
sleep[complete.cases(sleep),]  #rows which are complete
sleep[!complete.cases(sleep),] #rows which have missing values
colSums(is.na(sleep))  #which column how many data missing
rowSums(is.na(sleep))  #which row how many data missing


#use mice package
library(mice)


================================================
FILE: 92-wksp2/2d3-datapartition.R
================================================
#partition the data into train and test set
mtcars
nrow(mtcars)
#train-70%, test-30%
sample(x=1:32, size=.7 * 32)
index = sample(x=1:nrow(mtcars), size=.7 * nrow(mtcars), replace=F)
index
train= mtcars[index,]
test= mtcars[-index,]
nrow(train)
nrow(test)
nrow(train) + nrow(test)





#-------
data(mtcars)

## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)

train <- mtcars[train_ind, ]
test <- mtcars[-train_ind, ]

# -----
require(caTools)
set.seed(101) 
sample = sample.split(mtcars$am, SplitRatio = .75)
sample
train = subset(mtcars, sample == TRUE)
test  = subset(mtcars, sample == FALSE)
train; test
table(train$am); table(test$am)

mtcars$id <- 1:nrow(mtcars)
train <- mtcars %>% dplyr::sample_frac(.75)
test  <- dplyr::anti_join(mtcars, train, by = 'id')

library(caret)
intrain<-createDataPartition(y=factor(mtcars$am),p=0.7,list=FALSE)
intrain
training<-mtcars[intrain,]
testing<-mtcars[-intrain,]
training  
testing
table(training$am)
table(testing$am)















================================================
FILE: 92-wksp2/2e1-logR-purchase.R
================================================
# Logistic Regression : Predict Purchase


# Import the dataset
df1 = read.csv('./data/logr2.csv')
head(df1)

url="https://docs.google.com/spreadsheets/d/1Md_ro2t3M7nA9JMH1DsE12jfeX7qq-UPw6p8WQd6A2Y/edit#gid=120271978"
library(gsheet)
df2 = as.data.frame(gsheet2tbl(url))
head(df2)

dataset=df2  #or df2 if data is imported from google sheets
head(dataset)
str(dataset)
summary(dataset)
dim(dataset)
View(dataset)
dataset$gender = factor(dataset$gender)


# Split the dataset into the Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(2000)
split = sample.split(dataset$purchased, SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

dim(dataset); dim(training_set); dim(test_set)
names(dataset)


# Logisitic Model on Training Set
logitmodel1 = glm(purchased ~ gender + age + salary, family = binomial,  data = training_set)
summary(logitmodel1)

# gender not insignificant dropped here
logitmodel2 = glm(purchased ~ age + salary, family = binomial, data = training_set)
summary(logitmodel2)

#summary(logitmodel2)$coefficient  # they are in log terms
head(training_set)
#predict on sample data
test_set2 = data.frame(age=c(40,65), gender=c('Male', 'Female'), salary=c(40000, 50000))
test_set2
(prob_pred2 = predict(logitmodel1, type = 'response', newdata = test_set2))
cbind(test_set2, prob_pred2)
#age=65 person likely to purchase

# Predicting the Test set results from testset
head(test_set)
prob_pred = predict(logitmodel1, type = 'response', newdata = test_set)
summary(prob_pred)
head(cbind(test_set,prob_pred ),10)

#if prob > 0.5 make it 1, else 0
y_pred = ifelse(prob_pred > 0.5, 1, 0)
head(cbind(test_set$purchased, y_pred),100)

# Making the Confusion Matrix
cm = table(test_set[,5], y_pred)
cm
library(caret)
caret::confusionMatrix(cm)

names(dataset)




================================================
FILE: 92-wksp2/2e2-LOGR-adult.R
================================================
#Logistic Regression : Binary Cls : 0 or 1

#Case Study :  predict if an individual will earn more than $50K using logistic regression based on demographic variables available in the adult data.

#Steps
# Import the data
# Check for class bias
# Create training and test samples
# Compute information value to find out important variables
# Build logit models and predict on test data
# Do model diagnostics

#Data Import ----
#from URL
inputData <- read.csv("http://rstatistics.net/wp-content/uploads/2015/09/adult.csv")
head(inputData)
str(inputData)
names(inputData)
removeColumns = c('FNLWGT','EDUCATION')

data= inputData[,-which(names(inputData) %in% removeColumns)]
names(data)

data$ABOVE50K = factor(data$ABOVE50K)

#train and test sets
library(caret)
Index <- createDataPartition(y=data$ABOVE50K, p=0.70, list=FALSE)
head(Index)
nrow(data)
trainData = data[Index ,]
testData = data[-Index, ]
table(data$ABOVE50K); prop.table(table(data$ABOVE50K))
summary(trainData$ABOVE50K); summary(testData$ABOVE50K)
nrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)
prop.table(table(trainData$ABOVE50K))
prop.table(table(testData$ABOVE50K))
str(testData)


#Logistic Regression on selected columns
names(data)
logitMod <- glm(ABOVE50K ~ RELATIONSHIP + AGE + CAPITALGAIN + OCCUPATION , data=trainData, family='binomial')
summary(logitMod)
AIC(logitMod)

#Check the probabilities predicted for test data - 2 methods
predicted <- plogis(predict(logitMod, testData))  
head(predicted)
predicted <- predict(logitMod, testData, type="response")
head(predicted)

#what should be the cutoff value between 0 and 1 to categorise them into 0 or 1, so that accuracy is high (correct splitting)

#cutoff value
library(InformationValue)
(optCutOff <- optimalCutoff(testData$ABOVE50K, predicted)[1] )

#Confusion Matrix
(cm1= confusionMatrix(testData$ABOVE50K, predicted, threshold = optCutOff))
#0 classified as 0, 1 classified as 1



#diagnostics

car::vif(logitMod)
#all X variables in the model to have VIF below 4.

misClassError(testData$ABOVE50K, predicted, threshold = optCutOff)
#Misclassification error is the percentage mismatch of predcited vs actuals, irrespective of 1’s or 0’s. The lower the misclassification error, the better is your model.




ROC
#Receiver Operating Characteristics Curve traces the percentage of true positives accurately predicted by a given logit model as the prediction probability cutoff is lowered from 1 to 0. For a good model, as the cutoff is lowered, it should mark more of actual 1’s as positives and lesser of actual 0’s as 1’s. So for a good model, the curve should rise steeply, indicating that the TPR (Y-Axis) increases faster than the FPR (X-Axis) as the cutoff score decreases. Greater the area under the ROC curve, better the predictive ability of the model.

plotROC(testData$ABOVE50K, predicted)



















#http://r-statistics.co/Logistic-Regression-With-R.html
#https://rpubs.com/H_Zhu/235617




================================================
FILE: 92-wksp2/2e3-LOGR-gre.R
================================================
#Logistic Regresion : GRE
#https://stats.idre.ucla.edu/r/dae/logit-regression/
#A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable


inputData <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(inputData)
dim(inputData)
inputData
summary(inputData)
sapply(inputData, sd)
str(inputData)
data= inputData  # make a copy for futher analysis

data$rank = factor(data$rank)
data$admit = factor(data$admit)
str(data)
## 2way contingency table of cat outcome and predictors we want
## to make sure there are not 0 cells
table(data$rank, data$admit)
xtabs(~admit + rank, data = data)
xtabs(~ gear + cyl + am , data=mtcars)
#create Logistic Model
mylogit <- glm(admit ~ gre + gpa + rank, data = data, family = "binomial")

summary(mylogit)
#gre,gpa, rank are statistically significant, 
#For every one unit change in gre, the log odds of admission (versus non-admission) increases by 0.002.
#For a one unit increase in gpa, the log odds of being admitted to graduate school increases by 0.804.
#The indicator variables for rank have a slightly different interpretation. For example, having attended an undergraduate institution with rank of 2, versus an institution with a rank of 1, changes the log odds of admission by -0.675.

## odds ratios only
exp(coef(mylogit))
library(dplyr)
(ndata = sample_n(data, 3))
#Predict admit for input data
(prob=predict(mylogit,newdata=ndata, type=c("response")))
prob
cbind(ndata, prob)


#cutoff value
library(InformationValue)
(optCutOff <- optimalCutoff(data$admit, prob)[1] ) #.46
confusionMatrix(data$admit, prob, threshold = optCutOff)
(accuracy = (247+38)/ (sum(247+38+89+26))) # .715
confusionMatrix(data$admit, prob, threshold = .7)
(accuracy = (272+2)/ (sum(272+2+125+1))) #.685

confusionMatrix(data$admit, prob, threshold = .2)


## view data frame
library(dplyr)
sample_n(data,size=1)
(newdata1 = data.frame(gre=450, gpa=3.7, rank=factor(3) ))
(newdata1$admitPredicted <- predict(mylogit, newdata = newdata1, type = "response"))
(newdata1$admitClass = ifelse(newdata1$admitPredicted > .46,1,0))
newdata1  #b=not admitted to institute

#End of Logistic Regression
#also check for assumptions of residues, VIF, Multi-collinearity
#Parition the data into train and test



library(caret)
Index <- createDataPartition(y=data$admit, p=0.70, list=FALSE)
head(Index)
nrow(data)
trainData = data[Index ,]
testData = data[-Index, ]
table(data$admit); prop.table(table(data$admit))
summary(trainData$admit); summary(testData$admit)
nrow(trainData) ; nrow(testData); nrow(trainData) + nrow(testData)
prop.table(table(trainData$admit))
prop.table(table(testData$admit))
#same promotion of admit in test and train
str(testData)
#now construct a model with train and then test on testdata



================================================
FILE: 92-wksp2/3b1-DT-CART-carseats.R
================================================
# Decision Trees : - regression tree
#install and load this library
library(ISLR)
data(Carseats)
?Carseats
data = Carseats
head(data)

#Libraries for Decision Tree
library(rpart)
library(rpart.plot)

#Model
tree1 = rpart(Sales ~ . , data=data, method='anova' )
tree1
rpart.plot(tree1, cex=.8,nn=T)

#this is large tree, so prune it: check cp
printcp(tree1)
#cp value should be chosen such that xerror is least
prunetree = prune(tree1, cp=0.03)
prunetree
#here we have selected a different value to simplify the tree
mean(data$Sales)
prunetree
rpart.plot(prunetree, nn=T)
#Interpretation
#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2

#improve the plot
rpart.plot(prunetree, nn=T, cex=.8, type=4)
#read this document to improve the plot
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
#http://www.milbo.org/rpart-plot/prp.pdf
library(dplyr)
#Predict for test value
(testdata = sample_n(data,2))
(predictedSales=predict(prunetree, newdata=testdata, type='vector'))
cbind(testdata, predictedSales)
#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class
(predict(prunetree, newdata=testdata, type='class'))
#ERROR - use vector

#see online help here
#https://www.datacamp.com/community/tutorials/decision-trees-R



================================================
FILE: 92-wksp2/3b2-DT-CART-R-sales.R
================================================
# CART Models - HH Case Study - Regression
library(rpart)
library(rpart.plot)
library(forecast)

library(gsheet)
url='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'
data = as.data.frame(gsheet2tbl(url))
str(data)

# Summarize the dataset
summary(data)
names(data)
# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(data), size = 0.7*nrow(data))
Index

# Create Train dataset
train= data[Index, ]
nrow(train)

# Create Test dataset
test = data[-Index, ]
nrow(test)
nrow(test) + nrow(train)

########################### Modeling #################################

trainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = "anova")
trainModel
mean(train$Annual_Sales)

# Plot the Regression Tree
rpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)

#cp selection
printcp(trainModel)
trainModel_prune = prune(trainModel, cp=0.01)
rpart.plot(trainModel_prune)

#Predict and check accuracy
predictSales_test = predict(trainModel_prune, newdata = test, type = "vector")
predictSales_test  #vector to print values of sales predicted

library(forecast)
# Validate RMSE and MAPE calculation with a function in R
ModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)
ModelAccuarcy
#RMSE should be as less as possible



================================================
FILE: 92-wksp2/3b3-DT-CART-titanic.R
================================================
# Decision Tree - Classification
#we want predict for combination of input variables, is a person likely to servive or not

#import data from online site
path = 'https://raw.githubusercontent.com/thomaspernet/data_csv_r/master/data/titanic_csv.csv'
titanic <- read.csv(path)
head(titanic)
names(titanic)
data = titanic[,c(2,3,5,6,7)]  #select few columns only
head(data)
dim(data)
#load libraries
library(rpart)
library(rpart.plot)
str(data)
#Decision Tree
names(data)
fit <- rpart(survived ~ ., data = data, method = 'class')
fit
rpart.plot(fit, extra = 104, cex=.8,nn=T)  #plot

printcp(fit) #select complexity parameter
prunetree2 = prune(fit, cp=.014)
rpart.plot(prunetree2, cex=.8,nn=T, extra=104)
prunetree2
nrow(data)
table(data$survived)
#Predict class category or probabilities
(testdata = sample_n(data,2))
predict(prunetree2, newdata=testdata, type='class')
predict(prunetree2, newdata=testdata, type='prob')

#Use decision trees for predicting
#customer is likely to buy a product or not with probabilities
#customer is likely to default on payment or not with probabilities
#Student is likely to get selected, cricket team likely to win etc

#Imp steps
#select columns for prediction
#load libraries, create model
#prune the tree with cp value
#plot the graph
#predict for new cases

================================================
FILE: 92-wksp2/3b4-DT-CART-R-loan.R
================================================


#Decision Tree
#(https://rpubs.com/fabiorocha5150/decisiontreemodel)

url='https://raw.githubusercontent.com/fabiorcampos/Bank-Marketing/master/data/bank-full.csv'
data= read.table(url,sep=';', header = T )
head(data)
str(data)
names(data)
df1 = data
df1
str(df1)
hist(df1$age)
hist(df1$balance)
table(df1$marital)
barplot(table(df1$marital), col=1:3, beside=T)
barplot(table(df1$loan), col=1:3, beside=T)


================================================
FILE: 92-wksp2/3b5-DT-loanapproved1.R
================================================
# Decision Tree # loanapproved = age + job + house + credit
loanapproved = sample(x=c('Yes','No'), size=50, replace=T)
age = runif(50,30,60)
house = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.4,.6))
job = sample(x=c('Yes','No'), size=50, replace=T, prob=c(.6,.4))
credit = ceiling(rnorm(50,100, 10))
loan = data.frame(loanapproved, age,job,house, credit)
head(loan)
ctree = rpart(loanapproved ~ ., data=loan)
ctree
rpart.plot(ctree, cex=1)
ndata = data.frame(age=c(45,55), house=c("No",'Yes'), job=c("Yes",'No'), credit=c(90,100))
ndata
(p1=predict(ctree, newdata=ndata,type='class'))

(p2=predict(ctree, newdata=ndata,type='prob'))
(p3=predict(ctree, newdata=ndata,type='matrix'))
cbind(ndata, p1, p2, p3)




loan_tree = rpart(loanapproved ~ ., data=loan, method='class', control=rpart.control(minsplit=5, cp=-1))
loan_tree
rpart.plot(loan_tree)
rpart.plot(loan_tree,  type = 4,fallen.leaves = T, cex = 0.6)
rpart.plot(loan_tree,type=2, extra=104, cex=1, tweak=1, under=T, shadow=c('brown', 'green','red'), nn=T)

printcp(loan_tree)





plot(loan_tree)
text(loan_tree)

================================================
FILE: 92-wksp2/3b5-DT-rpart-iris.R
================================================
#CART Regression Tree

#Load Libraries
library(rpart)  #does only binary splits; CART
library(rpart.plot)

#DataSet
str(iris)
head(iris)

table(iris$Species)

library(dplyr)
sample_n(iris, 3)
sample_frac(iris, .1)
# Classification Tree
summary(iris)
set.seed(1234)
#Predict Species
ctree = rpart(Species ~ ., method='class', data=iris)
ctree
rpart.plot(ctree, main='Classification Tree', nn=T, type=4, cex=1,extra=104)
printcp(ctree)
ctreeprune = prune(ctree, cp=0.44)
ctreeprune
rpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)
# Regression Tree - Predict Continuous Value Length
#Predict Sepal.Length
rtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method="anova", data=iris )
rtree
rpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)

#another way to plot
plot(rtree, uniform=TRUE, main="Regression Tree for Sepal Length")
text(rtree, use.n=TRUE, cex = 1)
rtree

#CP
printcp(rtree)
rsq.rpart(rtree)  #r2 on anova only

rtreeprune = prune(rtree, cp=0.023)
rpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)



#https://analytics4all.org/2016/11/23/r-decision-trees-regression/


#Predict : Class  : Category of Flower
str(iris)
testData1  = data.frame ( Sepal.Length = 5, Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(ctree, testData1, method = "class")  #setosa


#Predict : Continous Value : Length

testData2  = data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2, Petal.Width=0.3)
predict(rtree, testData2, method = "anova") #5.17



================================================
FILE: 92-wksp2/3d1-DT-CHAID-usvote.R
================================================
#CHAID - dataset USvote #multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)  #library for performing CHAID decision tree

#Dataset
data(USvote)  #from lib CHAID
?USvote

#Quick CHAID analysis
set.seed(101)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
head(sample1)
str(sample1)
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 10, minsplit=20, minprob=0))

?chaid
print(chaidModel)
plot(chaidModel)
sample1 %>% filter(marstat=='married') %>% tally()
#sample1 %>% filter(ager=='18-24') %>% tally
dim(sample1)
chaidModel2 <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=20, minprob=0, maxheight=2))
plot(chaidModel2) 

#Summary
#More than 2 splits
#used in Marketing / Business 
#Net Promoter Score, Feedback, Where all variables are categories

================================================
FILE: 92-wksp2/3e1-clust-customer.R
================================================
# HH MA example  - customer

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)

names(data)
summary(data)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200,nstart = 1,  method = c("euclidean"))

k1$centers  # group means
###Fetch size/n of obs for the groups
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")


================================================
FILE: 92-wksp2/3e1-clustering.R
================================================
#Clustering
#sample data, iris, no of clusters
library(cluster)
library(fpc)
library(dplyr)

#sample Data
marks = data.frame(sub1=c(0,1,2,4,5,4,6,7),sub2=c(0,1,2,3,3,4,5,5))
marks
#with 1 column
marks[,1]
km12 = kmeans(marks[,1], center=3)
#analyse output
km12

km12$cluster  #which row is assigned to which cluster No
km12$size  #size of each cluster
km12$centers  #average of each variable for each cluster
km12$iter  #no cycles, optimal cluster was found

marks %>% mutate(cluster = km12$cluster) %>% group_by(cluster) %>%  summarise(mean(sub1))

#Distances
km12$withinss
km12$tot.withinss  #sum of withinss
km12$betweenss
km12$tot.withinss + km12$betweenss
km12$totss
km12$iter

#now do it for both the columns
km23 = kmeans(marks, center=3)
km23
km23$centers

km23$betweenss
km23$tot.withinss
km23$withinss
km23$betweenss/ (km23$betweenss + km23$tot.withinss)
km23$betweenss; km23$betweenss/ (km23$totss) #same as below
km23  #within cluster sum of sq by cluster
# should be high for good clustering

#plots
plot(marks,col=km23$cluster,cex=1.5)
points(km23$center,col=1:2,pch=8,cex=2)  #center point
#see the change in columns : use PCA
plotcluster(marks, km23$cluster)
clusplot(marks, clus=km23$cluster, color=TRUE, shade=TRUE, labels=2, lines=1, plotchar=T, span=T,main="Cluster Plot")
?clusplot

#iris dataset

#Plot1----
library(cluster)
library(fpc)

data(iris)
head(iris)
data = iris[, -5] # without known classification 
# Kmeans cluster analysis
iriskm1 =  kmeans(data, centers=3)
plotcluster(data, iriskm1$cluster)

# More complex : PCA method: 2 dim
clusplot(data, iriskm1$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
iris[c(16,19),]

# another method
with(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) 
with(iris, pairs(data, col=c(1:3)[iriskm1$cluster])) 


#Finding optimal No of Clusters
iris
table(iris$Species)
data = iris[-5]  #remove target column
head(data)

km1= kmeans(data,centers=1)
km1$tot.withinss; km1$withinss

km2= kmeans(data,centers=2)
km2$tot.withinss; km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss ; km3$withinss


km4= kmeans(data,centers=4)
km4$tot.withinss; km4$withinss

km5= kmeans(data,centers=5)
km5$tot.withinss;km5$withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km5$tot.withinss ; km5$tot.withinss
data[km5$cluster==4,]

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")  
#avg -  distance Dij between two clusters Ci and Cj is the mean of the distances between the pair of points x and y

kiris = kmeans(data, centers=3)
kiris$centers
irisclusters = cbind(iris$Species, data, kiris$cluster)
head(irisclusters)

library(dplyr)
iris %>% group_by(Species) %>% summarise_all(mean)
kiris$centers
#setsoa - Cluster1, etc
#grouping may not be perfect, but close to orginal classification
#this way we can do Customer, Product Segmentation
#Other Clustering Techniques - Hierarchical etc





#Scaling in Clusters: marks of different max
set.seed(1234); marks50 = ceiling(runif(100, 1,50)) 
set.seed(1234); marks500 = ceiling(runif(100, 250,500)) 

students1= data.frame(marks50, marks500)
head(students1)

skm1 = kmeans(students1, centers=3)
skm1$centers
skm1$withinss

#scaling gives equal importance to all variables
#avoid obtaining clusters that are dominated by variables having the largest amount of variation

students2 = scale(students1)
head(students2)
skm2 = kmeans(students2, centers=3)
skm2$centers
skm2$withinss

par(mfrow=c(1,2))
plot(students1$marks50, students1$marks500, col=skm1$cluster)
plot(students1$marks50, students1$marks500, col=skm2$cluster)
#better cluster in 2nd case
par(mfrow=c(1,1))


================================================
FILE: 92-wksp2/3e2-clust-samplecase.R
================================================
# Clustering

set.seed(1234)
subject1 = trunc(rnorm(30, mean=60, sd=15))
range(subject1)
subject1
marks = data.frame(subject1)
head(marks)

k2 = kmeans(marks, centers=2)
k2
k2$size
length(marks[k2$cluster==1,])
marks[k2$cluster==2,]
mean(marks[k2$cluster==1,])
mean(marks[k2$cluster==2,])
k2$centers



k2a = kmeans(marks, centers=c(50,70))
k2a
k2a$centers


================================================
FILE: 92-wksp2/3e3-clust-segmentation.R
================================================
# HH MA example  - customer

#install.packages("amap")
library(amap)
##Read the data in the file
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=2073914016'
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)
dim(data)
names(data)
summary(data)
str(data)
###Verify the data
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)  #are all numeric
dim(data)
head(data)
summary(data)
###Run the kmeans algorithm to generate the clusters
#?amap::Kmeans
names(data)

k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200)
?Kmeans
k1$centers  # group means
###Fetch size/n of obs for the groups
attributes(k1)
k1$size
###Fetch sum of squared  for the groups
k1$withinss
###Fetch the cluster for each obs
#k1$cluster
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id

# Write CSV
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv")


================================================
FILE: 92-wksp2/3e4-clust-noOfclusters.R
================================================
#Optimal Number of Clusters in data
#Reduce total within ss
iris
head(iris)
table(iris$Species)

data = iris[-5]
head(data)

km1= kmeans(data,centers=1)
km1$withinss
km1$tot.withinss

km2= kmeans(data,centers=2)
km2$tot.withinss
km2$withinss

km3= kmeans(data,centers=3)
km3$tot.withinss

km4= kmeans(data,centers=4)
km4$tot.withinss

km5= kmeans(data,centers=5)
km5$tot.withinss

km1$tot.withinss; km2$tot.withinss ; km3$tot.withinss ; km4$tot.withinss ; km5$tot.withinss

#Selecting the number of clusters
library(NbClust)
nc = NbClust(data, distance="euclidean",min.nc=2, max.nc=15, method="average")
nc

?NbClust
kiris = kmeans(data, centers=3)
kiris$centers

cbind(iris$Species, data, kiris$cluster)
aggregate(cbind(Sepal.Length, Sepal.Width) ~ Species, data=iris, mean)

names(mtcars)
data2 = mtcars[c('mpg','disp','hp','wt')]
head(data2)
nc = NbClust(data2, distance="euclidean",min.nc=2, max.nc=15, method="average")
det(as.matrix(mtcars))
?na.action
km3= kmeans(data,centers=3)
km3$tot.withinss

cbind(km1$tot.withinss, km2$tot.withinss, km3$tot.withinss, km4$tot.withinss,km5$tot.withinss)

#we select no clusters at elbow point
#adding more clusters does not significantly reduce total withinss


================================================
FILE: 92-wksp2/4b1-AR-groceries.R
================================================
# Association Rules - Groceries data set ####

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries')
Groceries

#Structure of Groceries
str(Groceries)
Groceries
arules::LIST(Groceries[1:6])  #another view
arules::inspect(Groceries[1:5])

#Find Frequent Itemset
frequentItems = eclat (Groceries, parameter = list(supp = 0.006, minlen= 4, maxlen = 5)) 
inspect(frequentItems[1:4])
frequentItems
inspect(frequentItems[10:200])
#inspect(frequentItems[100:122])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.15)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=TRUE)
inspect (rulesl[1:5])
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) 
inspect(rules2[1:15])

#Find what factors influenced an event ‘X’
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
inspect(rules3[1:5])
inspect(rules3)

#Find out what events were influenced by a given event
subset1 = subset(rules2, subset=rhs %in% "whole milk")
inspect(subset1)
subset1 = subset(rules2, subset=rhs %in% 'bottled beer' )
inspect(subset1)
inspect(rules2)
subset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2)
subset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )
inspect(subset2a)


subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3)
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4)

#Visualizing The Rules -----
plot(subset1) 
plot(subset1, measure=c("support", "lift"), shading="confidence")

#


rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)



================================================
FILE: 92-wksp2/4b2-AR-samplecase.R
================================================
# Association Rule - Simple Example Case
# read this pdf for help
#https://cran.r-project.org/web/packages/arules/arules.pdf

#libraries
library(arules)
library(arulesViz)

#Create Data

#Method3 Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
itemlist
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
summary(tdata3)

tdata=tdata3

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
itemlist
image(tdata)

#Analysis
freqitems = eclat(tdata) #default support=.1
freqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))

freqitems
inspect(freqitems)

support(items(freqitems[1:2]), transactions=tdata)
inspect(freqitems[1])
inspect(items(freqitems[1]))

itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)
write.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')


#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules[1:5])
inspect(rules)
write.csv(as.data.frame(inspect(rules)),'rules1.csv')
#sort rules by support
rules_s = sort(rules, by="support", decreasing=TRUE )
inspect(rules_s)
inspect(rules_s[1:5])  #itemsset having high support

#sort rules by confidence
rules_c = sort(rules, by="confidence", decreasing=TRUE )
inspect(rules_c)
inspect(rules_c[1:5])  #itemsset having high confidence

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

#Quality Data of Rules
quality(rules_c) 

#Redundant Rules
inspect(rules)
(redundant = which(is.redundant(rules)))
inspect(rules[c(8,9,10,11,12,14,14)])

inspect(rules[redundant])
inspect(rules)
write.csv(as(rules,"data.frame"), file='./data/rulesR.csv')

#Remove Redundant Rules
rulesNR <- rules[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now
inspect(rulesNR)

#Rules with LHS and RHS: single or combination
rules2= rulesNR
inspect(rules2)
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)



# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './data/myrules1.csv')

#Visualisation
plot(rules)


================================================
FILE: 92-wksp2/4b3-AR-groceries-subset.R
================================================
#AR - Groceries - Subset

#Subsetting rules and itemsets
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7,   maxlen=5, target='rules' ))
rules
#target='rules' # to mine for rules)

inspect(sort(rules, by="confidence", decreasing = T)[1:5])

#subset conditions
#rhs should be ‘bottled beer’
#confidence should be above .7
#results should be sorted by lift

#Subset1----
inspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7),   by = 'lift', decreasing = T))

#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”

# Another rule with different parameters
#subset2----
canned_rules <- apriori(Groceries,parameter = list(support=.001,  confidence=.01, maxlen=5, target='rules' ))

inspect(subset(canned_rules, subset=lhs %ain% c("liquor", "red/blush wine") & rhs %in% 'canned beer' ))
#no rule found or it could be significant low % less than 1 in 100 people would do

#legend to condition commands 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

#either side lhs or rhs
#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95
#subset3----
inspect(subset(rules, subset=items %ain% c("whole milk","yogurt") & confidence >.95))

#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9
inspect(subset(rules, subset=lhs %ain% c("whole milk","yogurt") & confidence >.9))

#Subset4----
#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9

inspect(subset(rules, subset= lhs %pin% "bread" & rhs %in% "whole milk" & confidence > .9))


#Subset5----
#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs
inspect(subset(rules, subset= lhs %ain% c("flour","whole milk") & confidence>.7))



#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.
rules <- apriori(Groceries,  parameter = list(support=.001, conf = .01, minlen=2, maxlen=2, target='rules'))

#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:

inspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))

crossTable(Groceries)['canned beer','bottled beer']

#the probability of a consecutive purchase (confidence) is pretty small: ~3%
#this is despite both bottled beer and canned beer being pretty popular purchases
eclat(Groceries, maxlen=1)
singleitems = eclat (Groceries, parameter = list(supp = 0.05, maxlen = 2)) 
inspect(singleitems)
inspect(sort (singleitems, by="count", decreasing=TRUE)[1:25])

crossTable(Groceries)['canned beer','canned beer']
crossTable(Groceries)['bottled beer','bottled beer']

crossTable(Groceries)['bottled beer','canned beer']
crossTable(Groceries)['canned beer','bottled beer']

quality(rules)$chi  <- interestMeasure(rules, measure='chi', significance=T, Groceries)
quality(rules)$chi[4785:4786]
#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes.



================================================
FILE: 92-wksp2/4b5-AR-finproducts.R
================================================
# AR data for Finance

library("arules")

set.seed(101)
transactionID = sample(1:500, 1000, replace=T)
transactionID

finproducts = c('Mutual Funds', 'NPS', 'Savings Account', 'PPF', 'FD', 'Bonds', 'Stocks', 'General Insurance', 'NRI Banking', 'Car Insurance', 'Debit Card', 'Credit Card', 'Mobile Banking')
length(finproducts)
item = sample(finproducts,1000, replace=T)
item
orders = data.frame(transactionID, item)
head(orders)

write.csv(orders, "./data/fintransactions.csv")

ordertrans <- arules::read.transactions(
  file = "./data/fintransactions.csv",  format = "single",
  sep = ",",  cols=c("transactionID","item"),  rm.duplicates = T
)
ordertrans
inspect(ordertrans[1:5])
ordertrans
summary(ordertrans)
str(ordertrans)
head(ordertrans)


#find frequent item set
frequentItems <- eclat (ordertrans, parameter = list(supp = 0.005, minlen= 2, maxlen = 5)) 
inspect(frequentItems[1:10])
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:15])
#support(A&B) = n(A&B)/ N

frequentItems

itemFrequencyPlot (ordertrans,topN = 15,type="absolute")
itemFrequencyPlot(ordertrans, topN = 10, type='relative')
abline(h=0.15)

#Create Rules
rules1 <- arules::apriori(ordertrans, parameter = list(supp = 0.005, conf = 0.5))
rules1
write.csv(inspect(rules1, './data/rules.csv'))
inspect(rules1[1:5])

rules1L = sort (rules1, by="lift", decreasing=TRUE)
inspect (rules1L[1:5])


#How To Control The Number Of Rules in Output ?
rules2 = apriori (ordertrans, parameter = list (supp = 0.005, conf = 0.5, minlen=3, maxlen=5)) # minlen = 3 & maxlen limits the elements in a rule to 3 & 5
inspect(rules2)

rules3 = apriori(ordertrans)  #use default values  no rules


#Find what factors influenced an event ‘X’


rules4 <- apriori(data=ordertrans, parameter=list (supp=0.001,conf = 0.08), appearance = list (default="lhs",rhs="Credit Card"), control = list (verbose=F))  #verbose=F no log
inspect(rules4[1:15])

#Find out what events were influenced by a given event
rules5 <- apriori (data=ordertrans, parameter=list (supp=0.001,conf = 0.05,minlen=2), appearance = list (default="rhs",lhs="General Insurance"), control = list (verbose=F)) 
inspect(rules5)

inspect( subset( rules5, subset = rhs %pin% c("Bonds") ))
inspect( subset( rules5, subset = rhs %pin% c("FD") ))


#Control lhs and rhs
rules6 <- apriori(ordertrans, parameter=list(support =0.00001, confidence =0.05, minlen=2, maxlen=5), appearance = list(lhs=c("Debit Card"), rhs=c("Credit Card"), default="none"))
inspect(rules6)

# commands for subset 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

inspect(subset(rules1, subset=items %ain% c("Mobile Banking") & confidence >.95))
inspect(subset(rules1, subset=lhs %ain% c("Mobile Banking") & confidence >.9))

inspect(subset(rules1, subset= lhs %pin% "Debit Card" & rhs %in% "Savings Account" & confidence > .7))

#Export Rules into a table
library(data.table)
rules_dt <- data.table( lhs = labels( lhs(rules2) ),      rhs = labels( rhs(rules2) ), 
                        quality(rules2) )[ order(-lift), ]
rules_dt
DT::datatable(rules_dt)  # wrapper for datatables
write.csv(rules_dt, './data/arulesfin.csv')


#Visualizing The Rules -----
library(arulesViz)
inspect(rules1)
plot(rules1, measure=c("support", "lift"), shading="confidence")
plot(rules1, measure=c("support", "confidence"), shading="lift")

plot(rules1,method="graph",engine='interactive', shading="confidence") 



================================================
FILE: 92-wksp2/4e1-twitter1.R
================================================
#Twitter 1 - Configure Tweets and Download them
#@dupadhyaya  #Working using my Keys
#Load libraries
library("curl")
library("twitteR")
library("ROAuth")
library("syuzhet") #library for sentiment analysis - comparison

download.file(url="http://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")

#https://apps.twitter.com/
#different for each account
consumerKey="uRDuync3BziwQnor1MZFBKp0x"
consumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"
AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"

#Common for all accounts except the keys

cred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')

cred$handshake(cainfo="cacert.pem") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt
save(cred, file="twitter authentication.Rdata") # store this to avoid asking again
#Load saved authentication cert
load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)

#type 1 : Yes 
search.string <- "#businessanalytics"
#search.string <- "#marketinganalytics"

no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets
tweets[1:10]
#Another Topics
search.string <- "#indvsaus"
#search.string <- "#asiacup"

no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets[1:5]

#My Tweets : will change if you use your own account
homeTimeline(n=15)  #my tweets
mentions(n=15)   # my tags
mentions(n=5)

#for user - 
(tweets = userTimeline("riddheishad", n=10))
userTimeline("PrabhanshRai", n=5)

#------------------------------------
?userTimeline
tweets = userTimeline("realDonaldTrump", n=100)
#english
tweets[1:5]
n.tweet <- length(tweets)
n.tweet
tweets.df = twListToDF(tweets) 
head(tweets.df)
summary(tweets.df)

#Remove hashtags & unnecessary characters
tweets.df2 <- gsub("http.*","",tweets.df$text)
tweets.df2 <- gsub("https.*","",tweets.df2)
tweets.df2 <- gsub("#.*","",tweets.df2)
tweets.df2 <- gsub("@.*","",tweets.df2)

head(tweets.df2)
#-----
library("syuzhet") #library for sentiment analysis - comparison
word.df <- as.vector(tweets.df2)
emotion.df <- get_nrc_sentiment(word.df)
emotion.df2 <- cbind(tweets.df2, emotion.df) 
head(emotion.df2)

#-----
sent.value <- get_sentiment(word.df)
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(tweets,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)
tweets[13]


================================================
FILE: 92-wksp2/4e2-wordcloud.R
================================================
# Word Cloud

##http://dni-institute.in/blogs/colorful-word-cloud-using-r/
# tm for text mining
# SnowballC for text stemming
# wordcloud for generating word cloud images
# RCurl and XML packages to download and parse web pages
# RColorBrewer for color palettes

library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

source('http://www.sthda.com/upload/rquery_wordcloud.r')
#or
#source('./TM/rquery_wordcloud.R')
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
#filePath2 <- "./data/martin-luther-king-i-have-a-dream-speech.txt"

res<-rquery.wordcloud(filePath, type ="file", lang = "english")
#res<-rquery.wordcloud(filePath2, type ="file", lang = "english")



================================================
FILE: 92-wksp2/4e3-worldcloud2.R
================================================
# World Cloud 2
library(wordcloud)
library(RColorBrewer)
library(SnowballC)
library(RCurl)
library(XML)
library(tm)

# Read the text file from file
#text = readLines(file.choose())
text = readLines(con= file("ximb.txt"))
text
text[1]
# Load the data as a corpus
docs = Corpus(VectorSource(text))
docs

#Text transformation
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
docs
#Cleaning Text
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
docs
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
docs <- tm_map(docs, stemDocument)


#Document Matrix
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Generate Word Cloud
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,    max.words=200, random.order=FALSE, rot.per=0.35,        colors=brewer.pal(8, "Dark2"))

findFreqTerms(dtm, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
head(d, 10)

#Plot Freq
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
       col ="lightblue", main ="Most frequent words",
       ylab = "Word frequencies")



================================================
FILE: 92-wksp2/4e5-wordcloud3.R
================================================
# World Cloud
#http://stat.ethz.ch/R-manual/R-devel/library/base/html/strsplit.html
#https://stackoverflow.com/questions/4350440/split-a-column-of-a-data-frame-to-multiple-columns

library(stringr)
library(wordcloud)
library(RColorBrewer)
library(tm)
library(SnowballC)
library(RCurl)
library(XML)



#How to use strsplit
strsplit('IIT-Gawahati', "-")
strsplit('IIT Gawahati', " ")

library(gsheet)
url = 'https://docs.google.com/spreadsheets/d/1_GQ-h4bgdNlIxcAanwRp_ak1u3JoWI-Vx2HKYj4FstA/edit#gid=0'
#check for dashtype, they are not always same
df1 = as.data.frame(gsheet2tbl(url))
df1
head(df1)
df1$wordexplanation
base::strsplit(df1$wordexplanation,'-')  #output as list need in DF

#Different Methods split and put in dataframe objects

#Stringr - str_split
stringr::str_split('IIT-Guwahati', "-")

#Method
(out1 = strsplit(as.character('IIT - Guwahati'),'-')) #if the word was not character
head(df1)
out = strsplit(df1$wordexplanation,'-') 
head(out)

t(sapply(out[1:5], '['))
df2=data.frame(t(sapply(out[1:200], '[')))

head(df2)
df= cbind(df1,df2)
head(df)
names(df)[c(2,3)] = c('word','explanation') #rename columns
head(df)

# create random frequencies for all words in a col
df$freq = floor(runif(100, 1,100))
head(df)

#Method for word cloud
library(wordcloud)
wc1 = wordcloud(df$word, df$freq, random.order=T)
wc2 = wordcloud(df$word, df$freq, random.order=F, colors = topo.colors(10))
wc3 = wordcloud(df$word, df$freq, scale=c(4,.1), min.freq=2, max.words=50,  random.order=F)
wc4 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F)


#------

library(RColorBrewer)
library(tm)
pal <- brewer.pal(8,"Dark2")
wc5 = wordcloud(df$word, df$freq, scale=c(4,.1), random.order=F, colors=pal)


#load the function rquery.wordcloud
source('http://www.sthda.com/upload/rquery_wordcloud.r')

res <- rquery.wordcloud(filePath, type ="file", lang = "english",min.freq = 1,  max.words = 200)

# Reds color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english",colorPalette = "Reds")
# RdBu color palette
res <- rquery.wordcloud(filePath, type ="file", lang = "english", colorPalette = "RdBu")
# use unique color
res<-rquery.wordcloud(filePath, type ="file", lang = "english",  colorPalette = "black")


tdm <- res$tdm
freqTable <- res$freqTable
# Show the top10 words and their frequency
head(freqTable, 10)

# Bar plot of the frequency for the top10
barplot(freqTable[1:10,]$freq, las = 2, 
        names.arg = freqTable[1:10,]$word,
        col ="lightblue", main ="Most frequent words",
        ylab = "Word frequencies")

findFreqTerms(tdm, lowfreq = 4)

findAssocs(tdm, terms = "freedom", corlimit = 0.3)

url = "http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-software"
rquery.wordcloud(x=url, type="url")



================================================
FILE: 92-wksp2/4f2-quantmod1.R
================================================
#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/
#Stock Analysis  

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2017-01-01")
end <- as.Date("2018-10-01")

# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the quantmod function getSymbols, and pass a string as a first argument to identify the desired ticker symbol, pass 'yahoo' to src for Yahoo! Finance, and from and to specify date ranges
# The default behavior for getSymbols is to load data directly into the global environment, with the object being named after the loaded ticker symbol. This feature may become deprecated in the future, but we exploit it now.

getSymbols("AAPL", src = "yahoo", from = start, to = end)

# What is AAPL?
class(AAPL)
head(AAPL)
tail(AAPL)
plot(AAPL[, "AAPL.Close"], main = "AAPL")
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")


# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google isheld by a holding company called Alphabet, Inc., which is the company traded on the exchange and uses the ticker symbol GOOG).
getSymbols(c("MSFT", "GOOG"), src = "yahoo", from = start, to = end)

# Create an xts object (xts is loaded with quantmod) that contains closing prices for AAPL, MSFT, and GOOG
stocks = as.xts(data.frame(AAPL = AAPL[, "AAPL.Close"], MSFT = MSFT[, "MSFT.Close"], GOOG = GOOG[, "GOOG.Close"]))
head(stocks)
tail(stocks)

# Create a plot showing all series as lines; must use as.zoo to use the zoo method for plot, which allows for multiple series to be plotted on same plot
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

plot(as.zoo(stocks[, c("AAPL.Close", "MSFT.Close")]), screens = 1, lty = 1:2,  xlab = "Date", ylab = "Price")
par(new = TRUE)
plot(as.zoo(stocks[, "GOOG.Close"]), screens = 1, lty = 3, xaxt = "n", yaxt = "n", xlab = "", ylab = "")
axis(4)
mtext("Price", side = 4, line = 3)
legend("topleft", c("AAPL (left)", "MSFT (left)", "GOOG"), lty = 1:3, cex = 0.5)


# Get pipe operator!
if (!require("magrittr")) {
  install.packages("magrittr")
  library(magrittr)
}
stock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>%  t %>% as.xts

head(stock_return)

plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Return")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

stock_change = stocks %>% log %>% diff
head(stock_change)

plot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = "Date", ylab = "Log Difference")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)  


candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addSMA(n = 20)


#------
start = as.Date("2010-01-01")
getSymbols(c("AAPL", "MSFT", "GOOG"), src = "yahoo", from = start, to = end)
# The subset argument allows specifying the date range to view in the chart.
# This uses xts style subsetting. Here, we r using the idiom
# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is
# the start date, and the date on the right-hand side is the end date. If
# either is left blank, either the earliest date or latest date in the series is used (as appropriate). This method can be used for any xts object, say, AAPL

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = 20)

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = c(20, 50, 200))



================================================
FILE: 92-wksp2/4f3-indianstocks.R
================================================
# Indian Stocks

library(quantmod)
start <- as.Date("2017-01-01")
end <- as.Date("2018-10-01")
getSymbols("SBIN.NS", src = "yahoo", from = start, to = end)

# What is SBI?
class(SBIN.NS)
head(SBIN.NS)
tail(SBIN.NS)
plot(SBIN.NS[, "SBIN.NS.Close"], main = "SBIN.NS")
candleChart(SBIN.NS, up.col = "black", dn.col = "red", theme = "white")


#ICICIBANK.NS
#TATAMOTORS.NS
getSymbols(c("ICICIBANK.NS", "TATAMOTORS.NS"), src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"]))
stocks
stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"], ICICI = ICICIBANK.NS[, "ICICIBANK.NS.Close"], TATAMOTORS = TATAMOTORS.NS[, "TATAMOTORS.NS.Close"]))
head(stocks)

plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("SBIN", "ICICI", "TATATMOTORS"), lty = 1:3, cex = 0.5)



================================================
FILE: 92-wksp2/5-wordcloud2-New.R
================================================
#wordcloud2

#install.packages('wordcloud2')
library(wordcloud2)

?wordcloud2

df = data.frame(word=c('mdi','iim','imt'),freq=c(20,23,15))
df
wordcloud2(df)


head(demoFreq)
wordcloud2(demoFreq, size = 2, color = "random-light", backgroundColor = "grey")
names(demoFreq)
wordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6,   rotateRatio = 1)
wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = pi/6,    rotateRatio = 0.9)

wordcloud2(demoFreqC, size = 2,  color = "random-light", backgroundColor = "grey")
wordcloud2(demoFreqC, size = 2, minRotation = -pi/6, maxRotation = -pi/6,  rotateRatio = 1)

# Color Vector

colorVec = rep(c('red', 'skyblue'), length.out=nrow(demoFreq))
wordcloud2(demoFreq, color = colorVec, fontWeight = "bold")

wordcloud2(demoFreq,
           color = ifelse(demoFreq[, 2] > 20, 'red', 'skyblue'))


================================================
FILE: 92-wksp2/5b-LP-marketingspend.R
================================================
#LP in R : Marketing Spend
#https://analyticsprofile.com/business-analytics/how-to-optimise-digital-marketing-spend-using-linear-programming-in-r/
  

================================================
FILE: 92-wksp2/5c2-LP-marketingspend-case.R
================================================
# LP - Marketing Spend
## Code to solve LP

#install.packages("linprog")
library(linprog)

Max_ROI = c(0.07, 0.03, 0.15, 0.12, 0.05) #Objective Function

Contraint_Vector = c(5000, -500, 0, -200, 0, 0, -300, 900, -100, 2500) #Constraints

#Decision variables under constraints 
Decision_Var <- rbind(
  c(1,1,1,1,1), 
  c(-1, 0, 0, 0, 0), 
  c(-0.05, 0.95, -0.05, -0.05, -0.05), 
  c(-0, -1, 0, 0, 0), 
  c(0.5, 0.5, -0.5, -0.5, 0.5), 
  c(0, 0, 1, -2.5, 0), 
  c(0, 0, 0, -1, 0), 
  c(0, 0, 0, 1, 0), 
  c(0, 0, 0, 0, -1),
  c(2, 0.3, 1.8, 0.9, 2)
)
Decision_Var
solveLP(Max_ROI, Contraint_Vector, Decision_Var, TRUE)



================================================
FILE: 92-wksp2/5d-wordcloud2.R
================================================
##http://www.sthda.com/english/wiki/text-mining-and-word-cloud-fundamentals-in-r-5-simple-steps-you-should-know

#https://www.r-graph-gallery.com/196-the-wordcloud2-library/

# library #install this
library(wordcloud2) 

# have a look to the example dataset
head(demoFreq)
wordcloud2(demoFreq, size=1)
?wordcloud2
#create your set of words and freq
df = data.frame(word=c('cbap','cmap','iim','imt','calcutta'),freq=c(20,23,15,10,13))
df
wordcloud2(df)



# Gives a proposed palette
wordcloud2(demoFreq, size=1.6, color='random-dark')

# or a vector of colors. vector must be same length than input data
wordcloud2(demoFreq, size=1.6, color=rep_len( c("green","blue"), nrow(demoFreq) ) )

# Change the background color
wordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor="black")

# Change the shape:
wordcloud2(demoFreq, size = 0.7, shape = 'star')
head(demoFreq)
?wordcloud2
# Change the shape using your image
wordcloud2(demoFreq, figPath = "india.jpg", size = 1.5, color = "skyblue", backgroundColor="black")


================================================
FILE: 92-wksp2/5d2-LP-tpt.R
================================================
# LP - Transportation Problem 
#https://docs.google.com/spreadsheets/d/1G6-iPDoD_i4THQAHwBeOLeiTfuqn7a6Q7MrOg9v1C5U/edit#gid=166724984
#https://cran.r-project.org/web/packages/lpSolve/lpSolve.pdf
library(lpSolve)

(costs <- matrix (c(3,1,5,2,5,4),ncol=2))
row.signs <- c("<", "<", "<")
row.rhs <- c(45, 60, 35)
col.signs <- rep (">", 2)
col.rhs <- c(50, 60)
#edit(costs)
# Run
tptproblem = lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)
## Not run: Success: the objective function is 7790
tptproblem$solution
#lp.transport (costs, "min", row.signs, row.rhs, col.signs, col.rhs)$solution

#Eg2
(cost <- matrix(c(3,1,5,2,5,4),ncol=2))
lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )$solution
lp.transport(cost, "min" , rep("<",3) , c(45, 60, 35) , rep(">=",2) , c(50, 60) )
?lp.transport

library(lpSolve)
x <- matrix(c(.91,.32,.86,.14,.59,.36,.67,.34,.87,.56,.10,.09),ncol=3,byrow=T)
x
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3)) 
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3) )$solution
lp.transport(x, "min" , rep("==",4) , rep(1,4) , rep(">=",3) , rep(1,3) )$object


================================================
FILE: 92-wksp2/5e2-LP-machassign.R
================================================
#----------------------------------------------#
#Another Method
#https://cran.r-project.org/web/packages/lpSolveAPI/lpSolveAPI.pdf
#https://ecreee.wikischolars.columbia.edu/file/view/lpSolveAPI+Tutorial.Rmd

library(lpSolveAPI)

#First we create an empty model x.
?make.lp
#max: x + y - 50
lprec <- make.lp(0, 2)
lprec
set.objfn(lprec, c(1, 1))
lprec
#maximise it
lp.control(lprec, sense="max")
lprec

#set.type(lprec, c(1,2), type = c("integer"))
lprec
#50x + 24y <= 2400
add.constraint(lprec, c(50,24), "<=", 2400)
lprec
#30x + 33y <= 2100
add.constraint(lprec, c(30,33), "<=", 2100)
lprec
#x >= 45
#add.constraint(lprec, c(1,0), ">=", 45)
#lprec
#y >= 5
#add.constraint(lprec, c(0,1), ">=", 5)
#lprec
#50 >= x + y
add.constraint(lprec, c(1,1), ">=", 50)
lprec

set.bounds(lprec, lower = c(45, 5), columns = c(1, 2))
lprec

#set.bounds(lprec, upper = 48.98, columns = 4)
#RowNames <- c("MachineA", "MachineB","InitalA", "InitalB","TotalInitial")
RowNames <- c("MachineA", "MachineB","TotalInitial")
ColNames <- c("ProductX", "ProductY")
dimnames(lprec) <- list(RowNames, ColNames)
lprec
solve(lprec)   #[1] 0  ok
#get.dual.solution(lprec)
get.objective(lprec)
get.variables(lprec)
get.constraints(lprec)
plot(lprec)
print(lprec)


================================================
FILE: 92-wksp2/5e5-LP-farmer1.R
================================================
#Farmer Problem in LP
#A farmer plans to plant two crops, A and B. The cost of cultivating crop A is $40/acre, whereas the cost of crop B is $60/acre. The farmer has a maximum of $7400 available for land cultivation. Each acre of crop A requires 20 labor-hours and each acre of crop B requires 25 labor-hours. The farmer has a maximum of 3300 labor-hours available. If she expects to make a profit of $150/acre on crop A and $200/acre on crop B, how many acres of each crop should she plant in order to maximize her profit?
  
library(lpSolveAPI)

#First we create an empty model x.
?make.lp
#two variables ie. crops A & B: find which crop to be grown how many acres to max profit
lprecF1 <- make.lp(0, 2)
lprecF1
#Profit :: 150A + 200B
set.objfn(lprecF1, c(150, 200))
lprecF1
#Change from min to max problem
lp.control(lprecF1, sense="max")
lprecF1

#answer required in integer or real no for A & B: default Real
lprecF1
#1st Constraint : Budget Avl
#40x + 60y <= 7400
add.constraint(lprecF1, c(40,60), "<=", 7400)
lprecF1
#2nd constraint : Labour Hours Avl
#20x + 25y <= 3300
add.constraint(lprecF1, c(20,25), "<=", 3300)
lprecF1
#set lower limits : A & B > 0
set.bounds(lprecF1, lower = c(0, 0), columns = c(1, 2))
lprecF1
#upper bounds can also be set only for 1 or more columns
#set.bounds(lprec, upper = c(200), columns = 2)
ColNames <- c("CropA", "CropB")
RowNames <- c("Budget", "Labor")
dimnames(lprecF1) <- list(RowNames, ColNames)
lprecF1
solve(lprecF1)   #if 0  then solution found
#get.dual.solution(lprec)
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B
150* 65 + 200 * 80
get.constraints(lprecF1) #constraints of budget & labor used
plot(lprecF1)  # print graphical output : only when type is real
#if type is integer, the plot will not work
print(lprecF1)  #see the model


#add more constraints like water
#35x + 40y <= 10000
add.constraint(lprecF1, c(5,10), "<=", 1000)
lprecF1
delete.constraint(lprecF1, 3)
solve(lprecF1)   #if 0  then solution found
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B

#setting integer value
set.type(lprecF1, c(1,2), type = c("integer"))
lprecF1
solve(lprecF1)   #if 0  then solution found
get.objective(lprecF1) # profit achieved
get.variables(lprecF1)  #how much of each crop A & B


#http://lpsolve.sourceforge.net/5.5/R.htm
?lp
?lp.assign
?lp.object

?lp.transport
?print.lp


================================================
FILE: 92-wksp2/6b1-dates.R
================================================
#Data Format in R 
#When we import data into R, dates and times are usually stored as character or factor by default due to symbols such as “-”, “:” and “/”

cdate1='15/August/1947'
cdate1
cdate2 = c('15-Aug-1947', "26-Jan-1950", "01-Oct-2018")
cdate2

class(cdate1) ; class(cdate2)
#convert to Dates
#Default Format of Date in R
?as.Date()

#See/ Convert Date format
as.Date('2018-12-01')
#Default Format : Year-Month-Date : %Y-%m-%d
(date3= as.Date('2018-12-01',format='%Y-%m-%d'))
class(date3)

#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan) 
#%d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

#convert cdate1 cdate2 to date formats
cdate1 #"15/August/1947
date1 = as.Date(cdate1, format='%d/%B/%Y')
date1
class(date1)
date1 - 1:15
#
cdate2 #"15-Aug-1947" "26-Jan-1950"
date2 = as.Date(cdate2, format='%d-%b-%Y')
date2
#

#one more practise on date format
date3 =as.Date('30Apr18',format("%d%b%y"))
class(date3)

#Date to Characters
class(date3)
(cdate2 = as.character(date3))
class(cdate2)

#excel date columns
#01 Jan 2018, 01/Jan/18, 01-Jan 2018, 2018-Jan-01, 2018 01 jan
#this is wrong
#The “Date” class means dates are stored as the number of days since January 1, 1970, with negative values for earlier dates. We can use the as.numeric function to view the raw values.

date1
class(date1)
as.numeric(date1)
date2
as.numeric(date2)
as.Date('1970-01-01') 
as.numeric(Sys.Date())
Sys.Date() - as.Date('1970-01-01') 

#Sequence of Dates
#Create Sequence of Dates

#Next day after certain date
as.Date('2018-12-01') + 1
#Sequence of Dates
as.Date('2018-12-01') + 0:14
as.Date('2018-12-01') + 0:33

#alternative Dates
seq(1,30,2)
as.Date('2018-12-09') + seq(0,29,7)


#system date
Sys.Date()
#next 15 days after todays date
Sys.Date() + 1:15

##increment/ decrement dates 
(startdt = as.Date("2018-12-01", format="%Y-%m-%d"))
(next20days = startdt + 1:20) # start : 20 days course
startdt - 1
(prev20days = startdt - c(0:19)) # before : 20 days course
rev(prev20days)



#Data Arithmetic
# date Arithmetic and subsets

#start course on YYYY-MM-DD for 30 days
(course1 = as.Date("2018-12-01") + 0:29)

#Difference in dates
course1
length(course1)  #no of days
min(course1)  # first date
max(course1)  # last date
range(course1) # start to end
mean(course1)  # center of the course period
median(course1) # middle date

#sd(course1) #no meaning

#subset / select dates
course1
#1st and 5th dates
course1[c(1,5)] # 1st & 5th date 
#1st and 5th dates
course1[1] ; course1[5]

#duration from 8th date to first date
(duration1 = course1[8] - course1[1])
#total duration
(duration2 = max(course1) - min(course1)+ 1)

#duration since independence
(independencedays = Sys.Date() - as.Date('15-08-1947', '%d-%m-%Y'))
independencedays/365  #years

as.character(Sys.Date(), format="%Y--%m--/%d %A")
course1
as.character(course1, format="%A")
paste(course1 , as.character(course1, format="%A"), sep=":: ")

#day on which you were born
dob = "07/03/1996"
dob_date = as.Date(dob,format='%d/%m/%Y')
dob_date
as.character(dob_date, format="%A")



================================================
FILE: 92-wksp2/6b1-ts-data.R
================================================
# create a time series data

#first create a vector of numerical values
# 36 observations 
set.seed(1234)
(sales = round(runif(36, 0,100)))
length(sales)

#This data can be daily, weekly, monthly, quarter, yearly data
#create yearly time series : start year 1980

#Yearly----
(ysales = ts(sales, frequency = 1))

(yearlysales = ts(sales, start=c(1980), frequency=1))
plot(yearlysales)
(yearlysales1 = ts(sales, start=c(1980,3), frequency=1)) 
# 3rd yr from 1980
plot(yearlysales)

#find the year when sales was > 50
yearlysales1[ yearlysales1 > 50]
class(yearlysales1)
methods(class=ts)
yearlysales1
(w1= window(yearlysales1, start=1983, end=1990))
plot(w1)



#Quarterly -----
12/4  # freq=4
(qtrsales = ts(sales, start=c(1980), frequency=4))
plot(qtrsales)
#list data from Qtr3 1980 to  1985
window(qtrsales, start=c(1980, 3), end=c(1985, 2))


#Monthly -----
12/12  # freq=12 start month=Apr/ 1990
(monsales = ts(sales, start=c(1990,4), frequency=12))
plot(monsales)
window(monsales, start=c(1991, 3))

#create data from Feb 2000 to Nov 2002
(monsales1 = ts(sales, start=c(2000,2), end=c(2003,3), frequency=12)) #recycling of elements beyond given sales value
monsales1
str(monsales1)
length(monsales1)

#see subset of sales data : May 2000 to Aug 2001
window(monsales1, start=c(2000, 5), end=c(2001, 8))

#Monthly TS
sales2 = ceiling(rnorm(365, mean=100, sd=10))
sales2
#YYYY,day
(dailysales = ts(sales2, start=c(2017,10), frequency=365))
window(dailysales, start=c(2017,50), end=c(2017,100))
mean(window(dailysales, start=c(2017,50), end=c(2017,100)))
head(sales2)
plot(dailysales)
class(dailysales)

#quarterly
sales3 = floor(rnorm(16, mean=200, sd = 12))
(qtrsales = ts(sales3, start = c(2018,1), frequency = 4))
plot(qtrsales)

#weekly



================================================
FILE: 92-wksp2/6c2-dates-lubridate.R
================================================
#Package Lubridate https://data.library.virginia.edu/working-with-dates-and-time-in-r-using-the-lubridate-package/
# Date Functions - Deal with Date & Time

#lubridate provides a series of functions that are a permutation of the letters “m”, “d” and “y” to represent the ordering of month, day and year. For example, if our data has a column of dates such as May 11, 1996, our dates are ordered month-day-year. Therefore we would use the mdy function to transform the column to a date object. If our dates were in the order of, say, year-month-day, we would use the ymd function. lubridate provides functions for every permutation of “m”, “d”, “y”.
  
  
#Eg1
library(lubridate)
(date8a = lubridate::ymd("20110604"))
(date8 = ymd("20110604"))
class(date8)
date8 + 1:10
#date in different format seq
mdy("06-04-2011") #mon-date-year
dmy("04/06/2011") #date-mon-year

#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

begin = c("May 11, 1996", "September 12, 2001", "July 1, 1988")
end = c("7/8/97","10/23/02","1/4/91")
class(begin)  ## [1] "character"
class(end) ## [1] "character"

(begin = mdy(begin))
## [1] "1996-05-11" "2001-09-12" "1988-07-01"
class(begin)

(end = mdy(end))
## [1] "1997-07-08" "2002-10-23" "1991-01-04"

class(begin) ; class(end)## [1] "Date"


#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.

begin1 = c("May 11, 1996 12:05", "September 12, 2001 1:00", "July 1, 1988 3:32")
end1 = c("7/8/97 8:00","10/23/02: 12:00","1/4/91 2:05")
(begin1a = mdy_hm(begin1))
(end1a = mdy_hm(end1))

class(begin1a) ; class(end1a) ## [1] "POSIXct" "POSIXt"
begin1a ; as.numeric(begin1a)
end1a ; as.numeric(end1a)

#class is now “POSIXct”. “POSIXct” represents the number of seconds since the beginning of 1970. If a date is before 1970, the number of seconds is negative. 

#Notice also the the letters “UTC” have been appended to the date-times. UTC is short for Universal Coordinated Time. it’s basically the time standard by which the world regulates clocks. If we prefer we can specify a time zone when formatting dates by using the tz argument. Here’s how we can specify the Eastern Time Zone in the United States when formatting our dates.

(begin1b = mdy_hm(begin1, tz = "US/Eastern"))
(begin1c = mdy_hm(begin1, tz = "Asia/Calcutta"))

#use the OlsonNames function to see a character vector of all time zone names
OlsonNames()
Sys.timezone()

#read in times without dates using the functions ms, hm, or hms, where again “h”, “m”, and “s” stand for “hours”, “minutes”, and “seconds”

time1 = c("1:13", "0:58", "1:01")
time2 = c("12:23:11", "09:45:31", "12:05:22")
time3 = c("2:14", "2:16", "3:35")

(time1a <- ms(time1)) ## [1] "1M 13S" "58S"    "1M 1S"
(time2a <- hms(time2)) ## [1] "12H 23M 11S" "9H 45M 31S"  "12H 5M 22S"
(time3a <- hm(time3)) ## [1] "2H 14M 0S" "2H 16M 0S" "3H 35M 0S"



arrive <- ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
arrive
leave <- ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
leave

leave - arrive  # Time Difference

#Setting and Extracting information
#Functions

#Eg
second(arrive)
second(arrive) = 25  #change
second(arrive)
arrive
minute(arrive)
hour(arrive)
day(arrive)
wday(arrive)
wday(arrive)
wday(arrive, label = TRUE)

week(arrive)

month(arrive)
year(arrive)
tz(arrive)

#Time Intervals
#save an interval of time as an Interval class object
(student1 <- interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                     ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                         ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))



================================================
FILE: 92-wksp2/6d-TS-airpassengers.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=3)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object

plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregular
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years
plot(AirPassengers)
(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change

#STL
?stl
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
AirPassengers
stl1 = stl(AirPassengers, s.window = 'periodic')
monthplot(AirPassengers) #max traffic in Jun/ Jul across years
monthplot(LogAirPassengers)
monthplot(stl1, choice='seasonal') # less in winters, more in summers
monthplot(stl1, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(stl1, choice='remainder') # irregular components
?monthplot

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(stl1, choice='seasonal')
monthplot(stl1, choice='trend')
monthplot(stl1, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast) #install the library
# Forecast # adjust for multiplicative model
?ets
fit2b = ets(AirPassengers, model='MAM')
fit2b
tail(AirPassengers)
(f2b=forecast(fit2b, 12))
head(f2b)$mean

plot(f2b)


================================================
FILE: 92-wksp2/6d-ts-components-airp.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=2)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object

plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregual
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years

(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change


#STL
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
monthplot(AirPassengers) #max traffic in Jun/ Jul across years
monthplot(fit, choice='seasonal') # less in winters, more in summers
monthplot(fit, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(fit, choice='remainder') # irregular components

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(fit, choice='seasonal')
monthplot(fit, choice='trend')
monthplot(fit, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast)
# Forecast # adjust for multiplicative model
fit2b = ets(AirPassengers, model='MAM')
fit2b
(f2b=forecast(fit2b, 12))
head(f2b)$mean



================================================
FILE: 92-wksp2/6d-ts-johnson.R
================================================
#Johnson Case - TS
#time series analysis
#plot, decompose, forecast, 
JohnsonJohnson
?JohnsonJohnson

monthplot(JohnsonJohnson)
quarters.Date(Sys.Date())

stl_jj = stl(JohnsonJohnson, s.window = 'periodic')
plot(stl_jj)
plot(JohnsonJohnson)
monthplot(stl_jj, choice='seasonal')
monthplot(stl_jj, choice='trend')
monthplot(stl_jj, choice='remainder')

library(forecast)
ets_jj = ets(JohnsonJohnson, model = "MAM")
forecast(ets_jj,h=3)
plot(forecast(ets_jj,h=3))


#---
require(stats); require(graphics)
JJ <- log10(JohnsonJohnson)
plot(JohnsonJohnson)
plot(JJ)

## This example gives a possible-non-convergence warning on some
## platforms, but does seem to converge on x86 Linux and Windows.
(fit <- StructTS(JJ, type = "BSM"))
tsdiag(fit)
sm <- tsSmooth(fit)
plot(cbind(JJ, sm[, 1], sm[, 3]-0.5), plot.type = "single",
     col = c("black", "green", "blue"))
abline(h = -0.5, col = "grey60")

monthplot(fit)

================================================
FILE: 92-wksp2/6d-ts-xts-data.R
================================================
# xts  - create object and export data

library(xts)

#create matrix : 1 col for 1 share
(stockprices = matrix(c(100,103, 105, 205, 210, 207, 530, 500, 535), ncol=3, byrow = F))
stockprices

(cdtindex = c('25-09-2018', '27-09-2018', '28-09-2018'))

#convert to date format
(dtindex = as.Date(cdtindex, format="%d-%m-%Y"))
#now we have index + matrix for xts object

(ts_xts = xts(x=stockprices, order.by=dtindex))
#this series is not continuous can have missing dates

ts_xts
colnames(ts_xts) = c("SBI", "ICICI", 'HDFC')
ts_xts
#-----
#matrix data extract
coredata(ts_xts)
#extract dates
index(ts_xts)


#write to csv file
write.zoo(ts_xts, "./data/zoodata.csv")

================================================
FILE: 92-wksp2/6e-TS-auto-arima-johnson.R
================================================
#Times Series Analysis 
# is the price of Johnson and Johnson shares change over time
# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year
# Can you forecast what future share prices will be and to what degree of accuracy

#dataset - Johnson
#Quarterly earnings per Johnson Shares
#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA

JohnsonJohnson

library(forecast)

#ets auto select best predicton model
?ets
#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time

fit1 = ets(JohnsonJohnson)
fit1
#alpha - trend
#beta  = seasonal
#gamma  - irregular

JohnsonJohnson
head(JohnsonJohnson)
tail(JohnsonJohnson)

(f1= forecast(fit1,h=10))  # 
?forecast.ets
plot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3)  # linetype for forecast area
#shaded portion is confidence intervals area

par(mfrow=c(1,1))

# ARIMA Forecasting : 
#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.

#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg

f2 = auto.arima(JohnsonJohnson)
summary(f2)
tail(JohnsonJohnson)  # last few values
forecast(f2,h=5)
plot(forecast(f2,h=5))

#ARIMA Forecasting  : compare two datasets
library(tseries)
plot(JohnsonJohnson)
ndiffs(JohnsonJohnson)
plot(diff(JohnsonJohnson))

plot(Nile)
plot(diff(Nile))
ndiffs(Nile)

#-----
djj = diff(JohnsonJohnson)
plot(djj)

dnile = diff(Nile)
plot(dnile)

#----
adf.test(djj)
#if pv < 0.05 accept Alt Hypothesis that series is stationary

#Model Selection 
#parameters p, d , q
# d = no of diffs applied to make the series stationary
#https://people.duke.edu/~rnau/arimrule.htm
Acf(dnile)
#Trail off to zero : Zero after lag ; 0,1(p)
#Zero after lag q : Trails off to zero ; 1(q), 0 
#Trails off to zero : Trial off to zero : 0,0
#Nile - 1 large auto correlation at lag 1 : 
#Nile - pacf trails off to zero as the lags gets bigger
?arima
Pacf(dnile)

fit3 = arima(Nile, order=c(0,1,1)) # p,d,q
fit3

(fit3b = arima(Nile, order=c(1,1,1)))


#Model Test
qqnorm(fit3$residuals) # residuals ND
qqline(fit3$residuals)

#auto correl = 0 : check
Box.test(fit3$residuals, type='Ljung-Box')
#Test auto corr : H0= r=0 (True)

#Forecast
forecast(fit3,4)

Nile
#Auto ARIMA
plot(Nile)

library(forecast)
#forecast::auto.arima()
fit4 = auto.arima(Nile)
fit4

forecast(fit4,5)
plot(forecast(fit4,5))



================================================
FILE: 92-wksp2/6g-ts-TTR-ma.R
================================================
#Time Series - SMA
library(TTR)

library(forecast)
#MA
#https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/ma
head(wineind)
?wineind
class(wineind)
str(wineind)
plot(wineind)
sm <- ma(wineind,order=3)
?ma
sm
head(wineind,n=3)
head(sm, n=3)
sum(head(wineind,n=3)) / 3

lines(sm,col="red")
smF <- ma(wineind,order=3, centre=F)
head(smF)


#centered
x = ts(runif(25,50,80))
x
forecast::ma(x, order=2, centre=T)

plot(wineind)
csm <- ma(wineind,order=12, centre=T)
lines(csm,col="red")



#method1
#https://rpubs.com/ajaydecis/ts5
kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
kings
kingstimeseries <- ts(kings)
kingstimeseries
plot.ts(kingstimeseries)
#install.packages("TTR")
library("TTR")
(kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8))
?SMA
plot.ts(kingstimeseriesSMA8)

library(forecast)
fit <- HoltWinters(kingstimeseries, beta=FALSE, gamma=FALSE)
fit1<- ets(kingstimeseries)
?ets
forecast(fit, 3)
plot(forecast(fit, 3)) 

forecast(fit1, 3)
plot(forecast(fit1, 3)) 




#Method2
#https://cran.r-project.org/web/packages/smooth/vignettes/sma.html
require(smooth)
require(Mcomp)
sma(M3$N2457$x, h=18, silent=FALSE)
sma(M3$N2568$x, h=18)


================================================
FILE: 92-wksp2/8-fa-quandl.R
================================================
# Finance Stock Analysis
#Stock Download

library(Quandl)
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
ICICI
HDFC = Quandl("NSE/HDFCBANK",collapse="daily",start_date="2017-09-01",type="raw")
HDFC
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI

allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
dim(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)
#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#Library to store data in xlsx files
Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre1.8.0_191")
library(xlsx) #needs rJava

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
(df1 <- allstocks %>% select(Stock, Open, Close))
df1 = as.data.frame(df1)
write.xlsx(df1, './data/iitgfa.xlsx', sheetName = "IITG12", append = T)


#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
#store this data into DF
(df2 <- allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean) )
write.csv(df2, './data/iitgfa.csv')

#http://www.sthda.com/english/wiki/writing-data-from-r-to-excel-files-xls-xlsx#using-xlsx-package


#write.xlsx(USArrests, file = "myworkbook.xlsx", #check with this file sheetName = "USA-ARRESTS", append = FALSE)
df2 = as.data.frame(df2)
write.xlsx(df2, './data/iitgfa.xlsx', sheetName = "IITG2", append = T)


options(dplyr.print_max = 1e9)  #print all rows
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)

allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 92-wksp2/8-fa-quandl2.R
================================================
# Finance Stock Analysis

#Install Packages 
#pckgs<-c("Quandl","Sweep","tidyverse")
#install.packages(pckgs,dependencies = TRUE)

library(Quandl)
# library(tidyverse)
# library(ggplot2)
# library(stringr)
# library(plyr)
# library(stringr)
# library(gridExtra)

#Quandl(Code=“NSE/—”,collapse=“—”,start_date=“—-”,type=“…”)
#ICICI SBI PNB  price band of Rs 200 to Rs 500. 
#https://www.quandl.com/account/api 4D8hkYAV4WEkcTmD9LMW

Quandl.api_key("4D8hkYAV4WEkcTmD9LMW")

## Download the data Set
ICICI = Quandl("NSE/ICICIBANK",collapse="daily",start_date="2017-09-01",type="raw")
PNB= Quandl("NSE/PNB",collapse="daily",start_date="2017-09-01",type="raw")
SBI=Quandl("NSE/SBIN",collapse="daily",start_date="2017-09-01",type="raw")

## Add another ("Stock") column in Datasets using cbind command
head(ICICI)
## Paste the stock name in stock column

ICICI$Stock = "ICICI"
PNB$Stock = "PNB"
SBI$Stock = "SBI"
SBI
#combine them
allstocks = rbind(ICICI, PNB, SBI)
str(allstocks)
allstocks$Stock = factor(allstocks$Stock)
str(allstocks)
names(allstocks)
names(allstocks)[c(7,8)] = c('Qty','Turnover')
names(allstocks)


#Using Aggregations
names(allstocks)
aggregate(allstocks$Close, by=list(allstocks$Stock), mean)
aggregate(cbind(Close,  Open) ~ Stock , data= allstocks, mean)

#https://www.statmethods.net/input/dates.html
aggregate(allstocks['Close'], by=list(allstocks$Date), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%d")), mean)
aggregate(allstocks['Close'], by=list(format(allstocks$Date,"%b")), mean)
aggregate(allstocks[c(3,7)], by=list(format(allstocks$Date,"%m")), mean)
aggregate(allstocks[c('Close','Open')], by=list(format(allstocks$Date,"%Y")), mean)

#using Dplyr Package to do Data Manipulation
library(dplyr)
names(allstocks)
#select Columns
allstocks %>% select(Stock, Open, Close)

#Groupby
allstocks %>% group_by(Stock)  #nothing summarised

#summarise
allstocks %>% summarise(mean(Open), max(High))

allstocks %>% group_by(Stock) %>% summarise_all(mean)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)
options(dplyr.print_max = 1e9)
allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(mean)

allstocks %>% group_by(Stock, format(Date,'%b')) %>% summarise_all(funs(mean, max))

#sample using dplyr
allstocks %>% sample_n(10)
allstocks %>% sample_frac(.05)
allstocks %>% group_by(Stock) %>%   sample_n(2)

allstocks %>% group_by(Stock) %>% tally(Qty) %>%  top_n(1)
allstocks %>% group_by(Stock, format(Date,'%Y')) %>% top_n(2, Turnover)


# Linear Modeling
names(ICICI)
df= cbind(ICICI[2],SBI[2] )
names(df) = c('icici','sbi')
head(df)
plot(df)
fit = lm(icici ~ sbi, data=df)
summary(fit)
new1 = data.frame(sbi=200)
(p1=predict(fit,new=new1, interval='confidence' ))
cbind(new1, p1)
plot(x=df$sbi, y=residuals(fit))  #Linearity
qqnorm(residuals(fit))
qqline(residuals(fit))

library(car)
#Multiple LM
df1 = cbind(ICICI[2],SBI[2],PNB[2] )
names(df1) = c('icici','sbi','pnb')
head(df1)
#No Plots
pairs(df1)
fit1 = lm(icici ~ sbi + pnb, data=df1)
summary(fit1)
summary(fit1)$r.squared
summary(fit1)$adj.r.squared
new2= data.frame(sbi=c(200,300), pnb=c(250,350))
p2=predict(fit1, new=new2, interval='confidence')
cbind(new2, p2)

crPlots(fit1)
vif(fit1)# variance inflation factors 
sqrt(vif(fit1)) > 2 # problem? Use only 1 variable
durbinWatsonTest(fit1)
car::outlierTest(fit1) # Bonferonni p-value for most extreme obs
car::qqPlot(fit1, main="QQ Plot")
library(gvlma)
gvmodel <- gvlma(fit1) 
summary(gvmodel)

av.Plots(fit1)
?car::av.Plots
car::influencePlot(fit1,	id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
car::ncvTest(fit1)
car::spreadLevelPlot(fit1)


================================================
FILE: 92-wksp2/8-fa-quantmod.R
================================================
#https://ntguardian.wordpress.com/2017/03/27/introduction-stock-market-data-r-1/
#Stock Analysis  

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2018-01-01")
end <- as.Date("2018-12-09")

# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the
# quantmod function getSymbols, and pass a string as a first argument to
# identify the desired ticker symbol, pass 'yahoo' to src for Yahoo!
# Finance, and from and to specify date ranges

# The default behavior for getSymbols is to load data directly into the
# global environment, with the object being named after the loaded ticker
# symbol. This feature may become deprecated in the future, but we exploit
# it now.

getSymbols("AAPL", src = "yahoo", from = start, to = end)

# What is AAPL?
class(AAPL)
head(AAPL)
tail(AAPL)
plot(AAPL[, "AAPL.Close"], main = "AAPL")
candleChart(AAPL[1:10,], up.col = "black", dn.col = "red", theme = "white")
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
#30-Nov-2018	180.29	180.33	177.03	178.58	178.58	3,94,83,800

# Let's get data for Microsoft (MSFT) and Google (GOOG) (actually, Google is
# held by a holding company called Alphabet, Inc., which is the company
# traded on the exchange and uses the ticker symbol GOOG).
getSymbols(c("MSFT", "GOOG"), src = "yahoo", from = start, to = end)
MSFT
# Create an xts object (xts is loaded with quantmod) that contains closing
# prices for AAPL, MSFT, and GOOG
stocks = as.xts(data.frame(AAPL = AAPL[, "AAPL.Close"], MSFT = MSFT[, "MSFT.Close"], GOOG = GOOG[, "GOOG.Close"]))
head(stocks)
class(stocks)

# Create a plot showing all series as lines; must use as.zoo to use the zoo
# method for plot, which allows for multiple series to be plotted on same
# plot
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

plot(as.zoo(stocks[, c("AAPL.Close", "MSFT.Close")]), screens = 1, lty = 1:2,  xlab = "Date", ylab = "Price")
par(new = TRUE)
plot(as.zoo(stocks[, "GOOG.Close"]), screens = 1, lty = 3, xaxt = "n", yaxt = "n", xlab = "", ylab = "")
axis(4)
mtext("Price", side = 4, line = 3)
legend("topleft", c("AAPL (left)", "MSFT (left)", "GOOG"), lty = 1:3, cex = 0.5)


# Get pipe operator!
if (!require("magrittr")) {
  install.packages("magrittr")
  library(magrittr)
}
stock_return = apply(stocks, 1, function(x) {x / stocks[1,]}) %>% 
  t %>% as.xts

head(stock_return)

plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Return")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)

stock_change = stocks %>% log %>% diff
head(stock_change)

plot(as.zoo(stock_change), screens = 1, lty = 1:3, xlab = "Date", ylab = "Log Difference")
legend("topleft", c("AAPL", "MSFT", "GOOG"), lty = 1:3, cex = 0.5)  


candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addSMA(n = 20)



start = as.Date("2010-01-01")
getSymbols(c("AAPL", "MSFT", "GOOG"), src = "yahoo", from = start, to = end)
# The subset argument allows specifying the date range to view in the chart.
# This uses xts style subsetting. Here, I'm using the idiom
# 'YYYY-MM-DD/YYYY-MM-DD', where the date on the left-hand side of the / is
# the start date, and the date on the right-hand side is the end date. If
# either is left blank, either the earliest date or latest date in the
# series is used (as appropriate). This method can be used for any xts
# object, say, AAPL
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = 20)

candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addSMA(n = c(20, 50, 200))


================================================
FILE: 92-wksp2/8-quantmod-I-stocks.R
================================================
#Indian Stocks

# Indian Stocks
#stocks2

# Get quantmod
if (!require("quantmod")) {
  install.packages("quantmod")
  library(quantmod)
}

start <- as.Date("2018-01-01")
end <- as.Date("2018-12-09")
getSymbols("SBIN.NS", src = "yahoo", from = start, to = end)
# What is SBI?
class(SBIN.NS)
head(SBIN.NS)
tail(SBIN.NS)
plot(SBIN.NS[, "SBIN.NS.Close"], main = "SBIN.NS")
candleChart(SBIN.NS, up.col = "black", dn.col = "red", theme = "white")


#ICICIBANK.NS
#TATAMOTORS.NS
getSymbols(c("ICICIBANK.NS", "TATAMOTORS.NS"), src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"]))
stocks
stocks = as.xts(data.frame(SBIN = SBIN.NS[, "SBIN.NS.Close"], ICICI = ICICIBANK.NS[, "ICICIBANK.NS.Close"], TATAMOTORS = TATAMOTORS.NS[, "TATAMOTORS.NS.Close"]))
head(stocks)

plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("SBIN", "ICICI", "TATATMOTORS"), lty = 1:3, cex = 0.5)



================================================
FILE: 92-wksp2/zz-practise.R
================================================
# Practise Exercise - XIMB

#Create a 100 row DF with following Variables
#gender, spl, age, experience, grade, placement
head(students)
str(students)
#


(gender = sample(c('M','F'), size=100, replace=T, prob=c(.6,.4)))
(spl = sample(c('Marketing','Finance','HR'), size=100, replace=T, prob=c(.3,.4,.3)))
(age = round(runif(100, 21, 30),2))
(experience = round(rnorm(100, 4, 1),2))
(grade = sample(c('A','B','C','D'), size=100, replace=T, prob=c(.4,.3,.2,.1)))
(placement = sample(c('yes','no'), size=100, replace=T, prob=c(.7,.3)))

students = data.frame(gender, spl, age, experience, grade, placement)
str(students)
summary(students)

#summarise the data in various ways using dplyr
library(dplyr)
names(students)
students %>% group_by(placement, gender) %>% summarise(mean(experience), max(experience), mean(age))

students %>% filter(spl=='Marketing') %>% summarise(mean(age), mean(experience))
students %>% filter(spl=='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))
students %>% filter(spl!='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))
students %>% filter(spl=='Marketing'& gender=='F' & grade=='A') %>% group_by(spl) %>% summarise(mean(age), mean(experience))


#draw few graphs to understand the distribution of data

hist(students$age)
t1=table(students$placement)
barplot(t1, col=1:2)
boxplot(students$age)
pairs(students[,c('age','experience')])
pie(t1)

par(mfrow=c(2,2))
pie(table(students$gender))
pie(table(students$placement))
pie(table(students$grade))
pie(table(students$spl))
par(mfrow=c(1,1))
table(students$gender, students$placement, students$spl)



#find students having largest experience in each spl for each gender

students %>% group_by(spl,gender) %>% arrange(spl, gender,experience) %>% top_n(1, experience) 



#how many were placed : draw pie plot



write.csv(students, './data/ximb.csv')

students2 = read.csv('./data/ximb.csv')
head(students2)


#clustering
km3 = kmeans(students[,c('age','experience')], centers=3)
km3
km3$centers
plot(students[,c('age','experience')], col=km3$cluster)

#decision tree
library(rpart)
library(rpart.plot)

tree = rpart(placement ~ . , data=students)
tree
rpart.plot(tree, nn=T, cex=1)
printcp(tree)
prune(tree, cp=.03)

ndata = sample_n(students, 5)
ndata
predict(tree, newdata= ndata, type='class')
predict(tree, newdata= ndata, type='prob')


#logistic Regression
logitmodel1 = glm(placement ~ . , data =students, family='binomial')
summary(logitmodel1)
logitmodel1a = glm(placement ~ age , data =students, family='binomial')
summary(logitmodel1a)
logitmodel2 = glm(placement ~ age + gender , data =students, family='binomial')
summary(logitmodel2)

#linear regression
linear1 = lm(age ~ . , data=students)
summary(linear1)


#iimkpv

#vectors
x = 1:100
class(x)
x = c(1,3,5.5)
class(x)
x = LETTERS[1:10]
class(x)
x = c(T,F,T,F,F)
class(x)
#matrix
m = matrix(1:24, ncol=6)
m
class(m)
#dataframes
head(mtcars)
class(mtcars)

#datastructures
#filter, sort, delete, missingvalues, stats operations
colMeans(mtcars)
colMeans(m)
?colMeans

#manipulation / summarisation
library(dplyr)
names(mtcars)
mtcars %>% group_by(cyl) %>% summarise(mean(mpg), max(wt), n())

plot(y=mtcars$mpg, x=mtcars$wt)
abline(lm(mpg ~ wt, data=mtcars))
#visualisation

#Modeling
fit = lm(mpg ~ wt + hp, data=mtcars)
summary(fit)
#linear regression, logistic regression, decision tree, clustering, market basket analysis

#Time Series 
#stock data, sales data
#Financial Analytics, Business 

#Dates are handled in the R
#create TS objects in R
#manipulate, Plot, Forecasting
#decomp

# Decision Trees

#types - Regression  & Classification
iris
head(iris)

library(rpart)
library(rpart.plot)
#regression tree
#DV - Sepal.Length
rtree = rpart(Sepal.Length ~ . , data= iris, method='anova')
rtree
rpart.plot(rtree,  nn=T, cex=.8, fallen.leaves = T)
#classification tree
#DV - Species
ctree = rpart(Species ~ . ,data = iris, method='class')
ctree
rpart.plot(ctree, cex=.8, nn=T, extra=104)
printcp(ctree)
ctreeprune1 = prune(ctree, cp=.44)
ctreeprune1
rpart.plot(ctreeprune1, cex=.8, nn=T, extra=104)
(ndata = sample_n(iris,3))
predict(ctree,newdata=ndata, type='prob')


library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA"
df = as.data.frame(gsheet2tbl(url))
head(df)

url2= "https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA/edit#gid=1123010023"
df2 = as.data.frame(gsheet2tbl(url2))
head(df2)


df3 = read.csv('./data/iimc1.csv')
head(df3)



================================================
FILE: 93-wksp3/Graph-matrixplots.R
================================================
# Matrix Plots
# Multiple Series on one Graph

table(iris$Species) # is data.frame with 'Species' factor
iS <- iris$Species == "setosa"
iV <- iris$Species == "versicolor"
iS;iV
op <- par(bg = "bisque")
matplot(c(1, 8), c(0, 4.5), type =  "n", xlab = "Length", 
        ylab = "Width",  main = "Petal and Sepal Dimensions in Iris Blossoms")
matpoints(iris[iS,c(1,3)], iris[iS,c(2,4)], pch = "sS", 
          col = c(2,4))
matpoints(iris[iV,c(1,3)], iris[iV,c(2,4)], pch = "vV", 
          col = c(2,4))
legend(1, 4, c("    Setosa Petals", "    Setosa Sepals",
               "Versicolor Petals", "Versicolor Sepals"),
       pch = "sSvV", col = rep(c(2,4), 2))

# Example 2

a <- rnorm(100)
b <- 2*a + 3
c <- 3*a + 2
a;b;c
matplot(a, cbind(b,c), pch=1:2, col=c(2,4))
legend("bottomright", inset=.05, legend=c("b", "c"), 
       pch=1:2, col=c(2,4), horiz=F)



# Example 3
x = 1:10
A = c(15, 36, 54, 60, 68, 71, 73, 75, 78, 78)
B = c(20, 49, 58, 69, 75, 80, 83, 86, 88, 89)
C = c(24, 58, 68, 75, 83, 90, 93, 93, 95, 96)
Performance = data.frame(A,B,C)
matplot(x,Performance, type="o", pch=c(1,2,3), col=c("red","green","blue"))
legend("bottomright", inset=.05, legend=c("A", "B",'C'),
      pch=1:3, col=c("red","green","blue"), horiz=F)


================================================
FILE: 93-wksp3/LMtrainTest.R
================================================
#partition the data into train and test set
mtcars
library(caret)
nrow(mtcars)
index = sample(x=1:nrow(mtcars), size=.7 * nrow(mtcars))
index
train= mtcars[index,]
test= mtcars[-index,]
nrow(train)
nrow(test)
nrow(train) + nrow(test)

library(olsrr)
fit = lm(mpg ~ disp + hp + wt + qsec, data = train)
k = ols_step_all_possible(fit)
plot(k)
k
summary(lm(mpg ~ wt, data= train))
summary(lm(mpg ~ wt + hp, data= train))

library(gvlma)
gvmodel = gvlma(finalmodel)
gvmodel

finalmodel = lm(mpg ~ wt + hp, data= train)
(predictedvalues = predict(finalmodel, ndata=test))
cbind(test$mpg, predictedvalues)


================================================
FILE: 93-wksp3/Links_DAR
================================================

Classification
http://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/

#Clustering
https://www.statmethods.net/advstats/cluster.html

http://www.sthda.com/english/articles/25-cluster-analysis-in-r-practical-guide/111-types-of-clustering-methods-overview-and-quick-start-r-code/
  
https://rstudio-pubs-static.s3.amazonaws.com/33876_1d7794d9a86647ca90c4f182df93f0e8.html


================================================
FILE: 93-wksp3/Links_DAR.R
================================================


================================================
FILE: 93-wksp3/TS-arima-johnson.R
================================================
#Times Series Analysis 
# is the price of Johnson and Johnson shares change over time
# are there quarterly effects with share prices rising & falling in a regular fashion throughtout the year
# Can you forecast what future share prices will be and to what degree of accuracy

#dataset - Johnson
#Quarterly earnings per Johnson Shares
#Steps - Plot, Describe, Decompose, Forecast - Simple MA, Exp, ARIMA

JohnsonJohnson

library(forecast)

#ets auto select best predicton model
?ets
#smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time

fit1 = ets(JohnsonJohnson)
fit1
#alpha - trend
#beta  = seasonal
#gamma  - irregular

JohnsonJohnson
head(JohnsonJohnson)
tail(JohnsonJohnson)

(f1= forecast(fit1,h=10))  # 
?forecast.ets
plot(f1, main='Johnson Shares', ylab='Quartery Earnings', xlab='Time', flty = 3)  # linetype for forecast area
#shaded portion is confidence intervals area

par(mfrow=c(1,1))

# ARIMA Forecasting : 
#popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.

#http://slideplayer.com/5259056/16/images/98/Seasonal+Components--Model+Selection.jpg

f2 = auto.arima(JohnsonJohnson)
summary(f2)
tail(JohnsonJohnson)  # last few values
forecast(f2,h=5)


#ARIMA Forecasting  : compare two datasets
library(tseries)
plot(JohnsonJohnson)
ndiffs(JohnsonJohnson)
plot(diff(JohnsonJohnson))

plot(Nile)
plot(diff(Nile))
ndiffs(Nile)

#-----
djj = diff(JohnsonJohnson)
plot(djj)

dnile = diff(Nile)
plot(dnile)

#----
adf.test(djj)
#if pv < 0.05 accept Alt Hypothesis that series is stationary

#Model Selection 
#parameters p, d , q
# d = no of diffs applied to make the series stationary
#https://people.duke.edu/~rnau/arimrule.htm
Acf(dnile)
#Trail off to zero : Zero after lag ; 0,1(p)
#Zero after lag q : Trails off to zero ; 1(q), 0 
#Trails off to zero : Trial off to zero : 0,0
#Nile - 1 large auto correlation at lag 1 : 
#Nile - pacf trails off to zero as the lags gets bigger
?arima
Pacf(dnile)

fit3 = arima(Nile, order=c(0,1,1)) # p,d,q
fit3

(fit3b = arima(Nile, order=c(1,1,1)))


#Model Test
qqnorm(fit3$residuals) # residuals ND
qqline(fit3$residuals)

#auto correl = 0 : check
Box.test(fit3$residuals, type='Ljung-Box')
#Test auto corr : H0= r=0 (True)

#Forecast
forecast(fit3,4)

Nile
#Auto ARIMA
library(forecast)
#forecast::auto.arima()
fit4 = auto.arima(Nile)
fit4

forecast(fit4,5)
plot(forecast(fit4,5))



================================================
FILE: 93-wksp3/TS-components-airpassengers.R
================================================
# Time Series Case Study - Decomposition

#https://rpubs.com/emb90/137525
# Data Set - AirPassengers
x=c(9.23221232,5.3430000)
x
options(digits=2)
x

?AirPassengers
head(AirPassengers)
AirPassengers
str(AirPassengers)
class(AirPassengers)

#The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns
# TS data components : Level + Irregular + Seasonal

#stl(x, s.window, t.window = ) # command to do decomp
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
# save it in an object
AirPassengers[1]
plot(AirPassengers) # Pattern of data : see increasing seasonal values suggesting multiplicative Model
#no cyclic here - only seasonal, trend, irregual
#s.window - specifies seasonal effects to be identical across years
#can handle on additive models

stl1 = stl(AirPassengers, s.window = 'periodic')
plot(stl1) # actual data, seasonal, long term trends, remainder/ irregular

class(stl1)

stl1$time.series
#(df = stl1$time.series)
#df = as.data.frame(df)
#write.csv(df, './data/airpsng.csv')


#Additive Model Y = Trend + Seasonal + Irregular
#sales increase by 300 qty in month of Nov
#Multiplicative Model Y = Trend * Seasonal * Irregular
#sales increase by 10% in month of Nov


#dataset
AirPassengers
class(AirPassengers)

# Plot
plot(AirPassengers)
#variability increases with level. at low values of passengers variations are less, at later years seasonal variations seem to be more -> Multiplicative model suggested

#stabilise the plot
LogAirPassengers = log(AirPassengers)  # make it additive because stl handles only additive models

# YA = T + S + I  : 
#YM= T * S * I  : take log of this
# log(YM) = log(T) + log(S) +log(I)

plot(LogAirPassengers)  #stabilises variation due to multiplication
#looks like additive : no increase of seasonsal component now over years

(m1 = matrix(1:2, nrow=1, byrow = F))
layout(m1)
plot(AirPassengers); plot(LogAirPassengers)  # see again the change


#STL
fit = stl(LogAirPassengers, s.window = 'periodic' )
#Seasonal components constrainted to be same across years : periodic

plot(fit)
fit$time.series  #decompose the data into S, T, R/I 

#december of all months same value for seasonal
#this was after taking log : so take antilog
#toprow = actual data with all series
exp(fit$time.series)

head(exp(fit$time.series),n=20)  # first 20 values see them

# df= exp(fit$time.series)
# names(df) = c('S','T','I')
# head(AirPassengers)
# head(cbind(AirPassengers, df))

#Various Plots - Monthwise, quarter, 
layout(matrix(1,nrow=1))
#Avg of each month
monthplot(AirPassengers) #max traffic in Jun/ Jul across years

monthplot(fit, choice='seasonal') # less in winters, more in summers
monthplot(fit, choice='trend')  #slight increase from Jan to Dec
#trend increasing for each month, highest passengers in Jul
monthplot(fit, choice='remainder') # irregular components

# see combined plots
(m2 = matrix(1:3, nrow=3, byrow = T))
layout(m2)  # change layout of plots
monthplot(fit, choice='seasonal')
monthplot(fit, choice='trend')
monthplot(fit, choice='remainder')


# Practise with different methods - Self Practise

#Decompose another way
AP.decompM = decompose(AirPassengers, type = "multiplicative")
plot(AP.decompM)

library(forecast)
# Forecast # adjust for multiplicative model
fit2b = ets(AirPassengers, model='MAM')
fit2b
(f2b=forecast(fit2b, 12))
head(f2b)$mean



================================================
FILE: 93-wksp3/TS-data.R
================================================
#ts9
# save a numeric vector containing 72 monthly observations # from Jan 2009 to Dec 2014 as a time series object
myvector = round(runif(6*12)*100,0)
myvector
myts <- ts(myvector, start=c(2009, 1), end=c(2014, 12), frequency=12)
myts
# subset the time series (June 2014 to December 2014)
myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))
myts2
# plot series
plot(myts)


#Eg2
sales= c(18,33,41,7,34,35,24,25,24,21,25,20, 22,31,40,29,25,21,22,54,31,25,26,35)
tsales = ts(sales, start=c(2003,1), frequency=12)
tsales
plot(tsales)
plot(tsales, type='o', pch=19)
start(tsales)
end(tsales)
frequency(tsales)
tsales.subset = window(tsales, start=c(2003,5), end=c(2004,6))
tsales.subset

#Yearly Series

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,2), end=c(2014,11), frequency=3)
myts
myts2 <- window(myts, start=c(2010), end=c(2013))
myts2


# quarterly 

myvector = round(runif(50) * 100,0)
myvector
myts <- ts(myvector, start=c(2009,1), end=c(2014,11), frequency=3)
myts
str(myts)
plot(myts, type='o', pch=19)
text()

plot.ts(myts, type='o', pch=19, xy.labels=F)

axis(1, myts$Date, format(myts$Date, "%b %d"), cex.axis = .7)


myts <- ts(myvector, start=c(2009,2), end=c(2014,9), frequency=3)
myts
str(myts)


myts2 <- window(myts, start=c(2010), end=c(2013))
myts2
plot(myts, type='o', pch=19)
str(myts2)



myts2 <- window(myts, start=c(2014, 6), end=c(2014, 12))
plot(myts,type='o', pch=19)
text()
plot(myts2,type='o', pch=19)


library(zoo)
x.Date <- as.Date(paste(rep(2003:2004, each = 12), rep(1:12, 2), 1, sep = "-"))
x.Date
?zoo
x <- zoo(rnorm(24), x.Date)
x
plot(x)



#If we want different tick locations, we can suppress the default axis plotting and add our own:
  
plot(x, xaxt = "n")
axis(1, at = time(x), labels = FALSE)
#Or combine them:
  
plot(x)
axis(1, at = time(x), labels = FALSE)

## weekly ticks
plot(x)
times <- time(x)
ticks <- seq(times[1], times[length(times)], by = "weeks")
axis(1, at = ticks, labels = FALSE, tcl = -0.3)


library(zoo)
library(lattice)

# create test data
z <- zooreg(1:83, start = as.Date("2009-04-01"), deltat = 7)

xyplot(z)




# Time series plots with good X axis labels
library(zoo)
# data
today = Sys.Date()
dates = as.Date((today-500):today)
dates
z = zoo (100+cumsum(rnorm(501)), dates)
z
# method1 : default X axis labels do not look good
?plot.zoo
plot(z)
?plot.ts
plot(ts(z))
time(z)
# method 2 : Lattice
library(lattice)
?xyplot.zoo
xyplot(z)
xyplot(z, lwd=2, col="tomato")

# method 3 : XTS
library(xts)
?plot.xts
plot(as.xts(z))
plot(as.xts(z), auto.grid=F, major.format="%b %y", las=2)

# method 4 : Base graph
timeline = time(z)
summary(timeline)
index = seq(from=1, to=length(timeline), 90) # put ticks every 90 days
plot(z, xaxt="n")
axis(side=1, at=timeline[index], label=format(timeline[index], "%b %y"), cex.axis=0.8)

# method 5 : ggplot
library(ggplot2)
library(scales)
?date_breaks
df = data.frame(date=as.POSIXct(time(z)), value=as.numeric(z))
head(df)
# default plot
ggplot(df, aes(x=date, y=value)) + geom_line()
# formatted
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"))
# custom breaks
ggplot(df, aes(x=date, y=value)) + geom_line() + 
  scale_x_datetime(labels=date_format("%b '%y"), breaks=date_breaks("3 months"))


================================================
FILE: 93-wksp3/TS-dates.R
================================================
#Handling and creating Dates in R

#date in yyyy-mm-dd
dates1a = c('2018-21-4', '2018-29-4')
class(dates1a)
?as.Date
as.Date(dates1a)  # error
dates1a
dates1b = as.Date(dates1a, format='%Y-%d-%m')
class(dates1b)
dates1b
dates1b + 1
as.Date('2018-04-29') + 1

#default format %Y-%m-%d
dates2a = c('2018-4-21')
class(dates2a)  # character so far
(dates2b = as.Date(dates2a))  # convert to date
class(dates2b)  # date now
format(dates2b, format=('%d-%m-%Y')) # print in different format
format(dates2b, format=('%d / %B - %Y')) # print in different format
dates2b


#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan)
#, %d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

format(dates2b, format="%A  %d  %m  %y") # another format

seminardates = c('09-07-1993', '08-08-1994', '04-11-1994')
class(seminardates)
seminardates1 = as.Date(seminardates, '%d-%m-%Y')
format(seminardates1, '%A')

format(as.Date('18-04-1994', '%d-%m-%Y'), '%A')

#System Date
Sys.Date()
format(Sys.Date(), format="%A: %d %B")

#increment/ decrement dates 
dates2b
(course = dates2b + 0:11) # start : 11 days course

cat(format(course, format="%B-%d"))
months(course)
weekdays(course)
quarters(course)
paste(quarters(course),'2018',sep='-')


#Date to character
dates2b; class(dates2b)
(dtoc = as.character(dates2b))
class(dtoc)

#one more practise on date format
dates3a =as.Date('30Apr18',format("%d%b%y"))
class(dates3a)

#Difference in dates
course
min(course)  # first date
max(course)  # last date
range(course) # start to end
mean(course)  # avg date ?
median(course) # middle date
course
course[c(1,5)] # 1st & 5th date 
course[1] ; course[9]
(duration = course[8] - course[1])

#Date of Birth
dob = as.Date('14-08-1994', '%d-%m-%Y')
dob
dob1= dob + seq(1,1000,30)  # create 1000 dates
length(dob1)
Sys.Date()
as.numeric((Sys.Date() - dob)/365)  #: Years
#par(mfrow=c(1,1))
boxplot(as.numeric((Sys.Date() - dob1)/365))# : Years
as.numeric((Sys.Date() - dob1)/365)# : Years
mean(as.numeric((Sys.Date() - dob1)/365))# : Years
(duration2 = max(course) - min(course))

#Sequence of Dates----
seq(1,10,2)
#seq(from, to, by, length.out = NULL, along.with = NULL, ...)
?seq
#by years : from start date to end date
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), "years")
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), "months")
seq(as.Date("2017/1/1"), by = "days", length.out = 6)

(students = c('ashish','chirag'))

## by month : 6 months
seq(as.Date("2017/1/1"), by = "month", length.out = 6)
seq(as.Date("2017/1/1"), by = "month", along.with = c(3,2,43,5,6,7))  # 
seq(as.Date("2017/1/1"), by = "month", along.with = students)  # 
seq(as.Date("2017/1/1"), by = "quarter", along.with = students)  # 

seq(as.Date("2017/1/1"), by = "month", length.out = length(c(3,2,43))) 


## quarters
seq(as.Date("2017/1/1"), as.Date("2018/1/1"), by = "quarter")

#Find 7th of all months between two dates
(dateseqF = seq(as.Date("2017/1/7"), as.Date("2018/1/7"), by = "1 month"))  # order
# dates in opposite order
(dateseq = seq(as.Date("2018/1/7"), as.Date("2017/1/7"), by = "-2 month"))  # reverse order
# then reverse them
rev(dateseq)# correct the order
format(rev(dateseq), "%A : %d-%b")

#another Sequence : using another package
chron::seq.dates("01/01/2017", "12/31/2017", by = "months")
chron::seq.dates("01/01/2017", "12/31/2017", by = "weeks")



================================================
FILE: 93-wksp3/TS-lubridate.R
================================================
#Package Lubridate

#functions which allow you to specify the order in which year, month and day components appear in date


library(lubridate)
ymd("20170601")
mdy("06-01-2017")
dmy("01/06/2017")
dmy("01-06-2017")

Sys.timezone(location=T)
#282- Asia/Kolkata
('Asia/Kolkatta')

(arrive <- ymd_hms("2018-04-09 14:00:00", tz = "Asia/Calcutta"))
(leave <- ymd_hms("2018-04-14 13:00:00", tz = "Asia/Calcutta"))
(leave2 <- ymd_hms("2018-04-14 13:00:00", tz = "GMT"))
leave - arrive
leave2- arrive
#Check the seconds for arrival
second(arrive)
#change seconds in arrival data
second(arrive) <- 25
arrive

#Check which date/day/month I arrived
mday(arrive) # arrived on 9 of the month
wday(arrive) ##  in no start from Sun-1, Mon-2 ..
wday(arrive, label = TRUE)  # arrived on Mon
month(arrive)
month(arrive, label=T, abbr=T)
month(arrive, label=T, abbr=F)
year(arrive)


#practise upto here  
#Students to look up other practise exercises for Data below


meeting <- ymd_hms("2011-07-01 09:00:00", tz = "Pacific/Auckland")
## "2011-07-01 09:00:00 NZST"
with_tz(meeting, "America/Chicago")
## "2011-06-30 16:00:00 CDT"

mistake <- force_tz(meeting, "America/Chicago")
## "2011-07-01 09:00:00 CDT"
with_tz(mistake, "Pacific/Auckland")
## "2011-07-02 02:00:00 NZST"
## 
auckland <- interval(arrive, leave)
auckland
## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST
auckland <- arrive %--% leave
## 2011-06-04 12:00:00 NZST--2011-08-10 14:00:00 NZST
auckland
tdiff = arrive - leave
tdiff

jsm <- interval(ymd(20110720, tz = "Pacific/Auckland"), ymd(20110831, tz = "Pacific/Auckland"))
jsm
int_overlaps(jsm, auckland) ## TRUE
setdiff(auckland, jsm) ## 2011-06-04 12:00:00 NZST–2011-07-20 NZST

minutes(2) # period
## 2 minutes
dminutes(2) # duration
## 120s (~2 minutes)
dhours(2)
seconds(2)
dyears(2)
leap_year(2011)
## FALSE
ymd(20110101) + dyears(1)
## "2012-01-01 UTC"
ymd(20110101) + years(1)
## "2012-01-01 UTC"

leap_year(2012)
## TRUE
ymd(20120101) + dyears(1)
## "2012-12-31 UTC"
ymd(20120101) + years(1)
## "2013-01-01 UTC"

meetings <- meeting + weeks(0:5)
meetings
## [1] "2011-07-01 09:00:00 NZST" "2011-07-08 09:00:00 NZST"
## [3] "2011-07-15 09:00:00 NZST" "2011-07-22 09:00:00 NZST"
## [5] "2011-07-29 09:00:00 NZST" "2011-08-05 09:00:00 NZST"

periods = meeting + days(1:5)
periods


second(arrive)
## 0
second(arrive) <- 25
arrive
## "2017-06-01 12:00:25 IST"
second(arrive) <- 0
wday(arrive)
## 5
wday(arrive, label = TRUE)
## Thurs



meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meeting

with_tz(meeting, "America/Chicago")
mistake <- force_tz(meeting, "America/Chicago")
with_tz(mistake, "Asia/Calcutta")
mistake


udit <- interval(arrive, leave)
udit
nakul <- arrive %--% leave
nakul

dhiraj <- interval(ymd(20170702, tz = "Asia/Calcutta"),
                   ymd(20170704, tz = "Asia/Calcutta"))
dhiraj
udit
#Will  visit overlap with and his travels
int_overlaps(udit, dhiraj) ## TRUE

#For what part of Dhiraj's' visit will Udit will be there?
setdiff(udit, dhiraj)
setdiff(dhiraj, udit)

minutes(2) # period
## 2 minutes
dminutes(2) # duration
## 120s (~2 minutes)
eminutes(2)


leap_year(2011)
## FALSE
ymd(20110101) + dyears(1)
## "2012-01-01 UTC"
ymd(20110101) + years(1)
## "2012-01-01 UTC"

leap_year(c(2012, 2017))
## TRUE FALSE
ymd(20170101) + dyears(1)
## "2018-01-01"
ymd(20170101) + years(1)
## "2018-01-01"
## 

meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meetings <- meeting + weeks(0:5)
meetings

friend = interval(ymd(20170705, tz = "Asia/Calcutta"),
                ymd(20170715, tz = "Asia/Calcutta"))
friend
mypd = interval(ymd(20170702, tz = "Asia/Calcutta"),
                          ymd(20170803, tz = "Asia/Calcutta"))
mypd
int_overlaps(friend, mypd)

#For what part of Dhiraj's' visit will Udit will be there?
setdiff(friend, mypd)


?interval
mypd
meetings %within% mypd

meeting <- ymd_hms("2017-07-01 09:00:00", tz = "Asia/Calcutta")
meetings <- meeting + weeks(0:5)
meetings
meetings %within% mypd

mypd / edays(1)
mypd / ddays(1)
mypd / ddays(2)
mypd / dminutes(1)
mypd / dseconds(1)
mypd / months(1)
mypd / years(1)


as.period(mypd %% months(1))
as.period(mypd)

last_day <- function(date) {
  ceiling_date(date, "month") - days(1)
}
# try last_day(ymd(20000101) + months(0:11))

last_day(ymd(20000101))
last_day(ymd(20000101) + months(0:11))


================================================
FILE: 93-wksp3/TS-movavg-Nile.R
================================================
# TS - Simple Moving Average
#SMA
# technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends


Nile  # river flow from year 1871 to 1970

plot(Nile)

library(forecast)  # for forecasting
opar = par(no.readonly = T)  # save current parameters
par(mfrow=c(2,2))
(ylim = range(Nile))
plot(Nile, main='Original TS')
head(Nile)
ma(Nile,3)
head(Nile,4)
head(ma(Nile,3))
(1120+1160+963)/3
mean(Nile[1:3]);mean(Nile[2:4]); mean(Nile[3:5]); mean(Nile[4:6])
ma(Nile,3)[1:10]

plot(ma(Nile,3), main='SMA k=3', ylim=ylim)
plot(ma(Nile,7), main='SMA k=7', ylim=ylim)
plot(ma(Nile,15),main='SMA k=15', ylim=ylim)
par(opar)
plot(ma(Nile,3), main='SMA k=3', ylim=ylim)




#mfrow mfcol
mtcars
names(mtcars)
plot(mtcars$mpg)
par(mfrow=c(1,2))
plot(mtcars$mpg)
plot(mtcars$disp)

par(mfrow=c(2,1))
plot(mtcars$mpg)
plot(mtcars$disp)

par(mfrow=c(2,2))
plot(mtcars$mpg)
barplot(table(mtcars$cyl))
pie(table(mtcars$am))
hist(mtcars$disp)


================================================
FILE: 93-wksp3/TS-movavg.R
================================================
# Mov Average
#https://www.otexts.org/fpp/6/2

admission = c(100,110,115,130,150,145,160,125,123,134)
length(admission)
admyr = ts(admission, start=2001)
admyr
(100+110+115)/3
forecast::ma(admyr, order=3)
plot(admyr, main="Yearly Admissions", ylab="Admissions", xlab="Year")
lines(ma(admyr,3),col="red")

admyr1 <- window(admyr,start=2005)
admyr1

ma4 <- ma(admyr1, order=4, centre=FALSE)
ma4
ma2x4 <- ma(admyr1, order=4, centre=TRUE)
ma2x4




abbshares <- scan(file="")
#type the data in console windows
library(pracma)
plot(abbshares, type = "l", col = 1, ylim = c(15, 30),
     main = "Types of moving averages", sub = "Mid 2011--Mid 2012",
     xlab = "Days", ylab = "ABB Shares Price (in USD)")
y <-  pracma::movavg(abbshares, 50, "s"); lines(y, col = 2)
y <- pracma::movavg(abbshares, 50, "t"); lines(y, col = 3)
y <- pracma::movavg(abbshares, 50, "w"); lines(y, col = 4)
y <- pracma::movavg(abbshares, 50, "m"); lines(y, col = 5)
y <- pracma::movavg(abbshares, 50, "e"); lines(y, col = 6)
y <- pracma::movavg(abbshares, 50, "r"); lines(y, col = 7)
grid()
legend(120, 29, c("original data", "simple", "triangular", "weighted",
     "modified", "exponential", "running"),
     col = 1:7, lty = 1, lwd = 1, box.col = "gray", bg = "white")

#x
elecs = read.csv(file='./data/elect.csv')
elecs1 = elecs$Sales
electsales = ts(elecs1, start= 1989)
electsales

forecast::ma(elecsales, order=3)
forecast::ma(elecsales, order=5)


================================================
FILE: 93-wksp3/assocrule1.R
================================================
# Association Rules - Groceries data set ####

library(arules)  #install first
library(arulesViz) #install first
library(datasets)  # no need to install, just load it reqd for Groceries
data('Groceries')
Groceries

#Structure of Groceries
str(Groceries)
Groceries
inspect(Groceries[1:5])  #view
LIST(Groceries[1:6])  #another view

#Find Frequent Itemset
frequentItems = eclat (Groceries)
inspect(frequentItems)

frequentItems = eclat (Groceries, parameter = list(supp = 0.01, minlen= 3, maxlen = 5)) 
inspect(frequentItems[1:6])
frequentItems
inspect(frequentItems[10:32])
#inspect(frequentItems[100:122])
#Descending Sort frequent items by count : 1 to 25 itemsets
inspect(sort (frequentItems, by="count", decreasing=TRUE)[1:25])
inspect(sort (frequentItems, by="count", decreasing=F)[1:25])

#Support is : support(A&B) = n(A&B)/ N
#Plot the Frequency Plot
itemFrequencyPlot(Groceries,topN = 15,type="absolute")
itemFrequencyPlot(Groceries, topN = 10, type='relative')
abline(h=0.2)

# Create rules and the relationship between items
#parameters are min filter conditions 
rules = apriori(Groceries, parameter = list(supp = 0.005, conf = 0.5, minlen=2))
rules
inspect (rules[1:5])
#Sort Rules by confidence, lift and see the data
rulesc <- sort (rules, by="confidence", decreasing=TRUE)
inspect(rulesc[1:5])
rulesl <- sort (rules, by="lift", decreasing=TRUE)
inspect (rulesl[1:5])
#which items have strong confidence and lift 

#How To Control The Number Of Rules in Output ?
#maxlen, minlen, supp, conf
rules2 = apriori (Groceries, parameter = list (supp = 0.001, conf = 0.5, minlen=2, maxlen=3)) 
inspect(rules2[1:5])

# Are there any duplicate/ Redundant Rules 
#https://rdrr.io/cran/arules/man/is.redundant.html

sum(is.redundant(rules2))
(redundant = which(is.redundant(rules2)))
inspect(rules2[redundant])
#inspect(subset(rules2, subset=lhs %ain% c('citrus fruit','rice') & rhs %in% 'whole milk' ))
#remove it
rulesNR = rules2[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now



#Find what factors influenced an event ‘X’
rules3 = apriori (data=Groceries, parameter=list (supp=0.002,conf = 0.8), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F))
#verbose - output display T or F
inspect(rules3[1:5])
inspect(rules3)

#Find out what events were influenced by a given event
rules3b = apriori (data=Groceries, parameter=list (supp=0.002, conf = 0.7), appearance = list (default="lhs",rhs="whole milk"))
inspect(rules3b[1:15])

#using subset
subset1 = subset(rules2, subset=rhs %in% 'bottled beer' )
inspect(subset1)
inspect(rules2)
subset2 = subset(rules2, subset=lhs %ain% c('baking powder','soda') )
inspect(subset2)
subset2a = subset(rules2, subset=lhs %in% c('baking powder','soda') )
inspect(subset2a)
subset2b = subset(rules2, subset=lhs %ain% c('bottled beer','liquor') )
inspect(subset2b)
subset2c = subset(rules2, subset=rhs %ain% c('whole milk') )
inspect(subset2c)


subset3 = subset(rules2, subset=rhs %in% 'bottled beer' & confidence > .7, by = 'lift', decreasing = T)
inspect(subset3)
subset4 = subset(rules2, subset=lhs %in% 'bottled beer' & rhs %in% 'whole milk' )
inspect(subset4)

#Visualizing The Rules -----
plot(subset1[1:10]) 
plot(subset1[1:10], measure=c("support", "lift"), shading="confidence")

#

rules4 = apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.4), appearance = list (default="rhs",lhs=c('tropical fruit','herbs')), control = list (verbose=F))
inspect(rules4[1:5])
inspect(rules4)



================================================
FILE: 93-wksp3/assocrule2.R
================================================
#AR - Groceries - Subset

#Subsetting rules and itemsets
rules <- apriori(Groceries, parameter = list(support=.001, confidence=.7, maxlen=5, target='rules' ))
#target='rules' # to mine for rules)

inspect(sort(rules, by="confidence", decreasing = T)[1:5])

#subset conditions
#rhs should be ‘bottled beer’
#confidence should be above .7
#results should be sorted by lift

#Subset1----
inspect(sort(subset(rules, subset=rhs %in% 'bottled beer' & confidence > .7),   by = 'lift', decreasing = T))
#people buying “liquor” and “red wine” are almost certain to buy “bottled beer” (9 times out of 10), but not “canned beer”


# Another rule with different parameters
#subset2----
canned_rules <- apriori(Groceries,parameter = list(support=.001,
  confidence=.01, maxlen=5, target='rules' ))
inspect(subset(canned_rules, subset=lhs %ain% c("liquor", "red/blush wine") & rhs %in% 'canned beer' ))
#no rule found or it could be significant low % less than 1 in 100 people would do

# 
# lhs - means left hand side, or antecendent
# rhs - mean right hand side, or consequent
# items - items, that make up itemsets
# %in% - matches any
# %ain% - matches all
# %pin% - matches partially
# default - no restrictions applied
# & - additional restrictions on lift, confidence etc.

#either side lhs or rhs
#“whole milk” and “yogurt” must be present and rule’s confidence must be higher than .95
#subset3----
inspect(subset(rules, subset=items %ain% c("whole milk","yogurt") & confidence >.95))

#whole milk” and “yogurt” must be present in lhs and rule’s confidence must be higher than .9
inspect(subset(rules, subset=lhs %ain% c("whole milk","yogurt") & confidence >.9))

#Subset4----
#“Bread” must be present in lhs: any type of “bread” – “white bread”, “brown bread” – both qualify. “Whole milk” must be present in rhs “as is”. confidence of the rule must be higher than .9

inspect(subset(rules, subset= lhs %pin% "bread" & rhs %in% "whole milk" & confidence > .9))


#Subset5----
#what we can expect at rhs with confidence higher than .7 if we have both “flour” and “whole milk” on the lhs
inspect(subset(rules, subset= lhs %ain% c("flour","whole milk") & confidence>.7))



#Let’s consider case “Bottled beer Vs. Canned beer” and prove that people tend to buy either one or the other, and rarely do they buy both, qualifying these two as substitute products.
rules <- apriori(Groceries,  parameter = list(support=.001,
    conf = .01, minlen=2, maxlen=2, target='rules'))

#Let’s only look at the rules where “beer” is present at both left- and right-hand-side of the rule and add chiSquared p-value to prove statistical significance of our findings:

inspect(subset(rules, lhs %pin% 'beer' & rhs %pin% 'beer'))
crossTable(Groceries)['canned beer','bottled beer']

#the probability of a consecutive purchase (confidence) is pretty small: ~3%
#this is despite both bottled beer and canned beer being pretty popular purchases

crossTable(Groceries)['canned beer','canned beer']
crossTable(Groceries)['bottled beer','bottled beer']

quality(rules)$chi  <- interestMeasure(rules, measure='chi', significance=T, Groceries)
quality(rules)$chi[4785:4786]
#All these figures, combined with statistically significant lift below 1 (chi ~ 1e-6) tells us that “bottled beer” and “canned beer” do behave as substitutes.

================================================
FILE: 93-wksp3/assocrule3.R
================================================
# Association Rule - Simple Example Case
# read this pdf for help
#https://cran.r-project.org/web/packages/arules/arules.pdf
#libraries
library(arules)
library(arulesViz)
#Create Data

#Method3 Use: ----
#Data in the form of list
itemlist = list(c('I1','I2','I5'), c('I2','I4'), c('I2','I3'),c('I1','I2','I4'),c('I1','I3'),c('I2','I3'),c('I1','I3'),c('I1','I2','I3','I5'),c('I1','I2','I3'))
itemlist
length(itemlist)
## set transaction names
names(itemlist) <- paste("Tr",c(1:9), sep = "")
itemlist
## coerce into transactions
tdata3 <- as(itemlist, "transactions")
tdata3
Groceries
summary(tdata3)

tdata=tdata3

#Data ready - Perform AR ----
## analyze transactions
summary(tdata)
itemlist
image(tdata)

#Analysis
freqitems = eclat(tdata) #default support=.1
inspect(freqitems)
freqitems = eclat(tdata, parameter = list(minlen=1, supp=.1, maxlen=2 ))
inspect(freqitems)

freqitems
inspect(freqitems)

support(items(freqitems[1:2]), transactions=tdata)
inspect(freqitems[1])
inspect(items(freqitems[1]))

itemFrequencyPlot(tdata,topN = 5,type="absolute")
itemFrequencyPlot(tdata,topN = 5,type="relative", horiz=T)
write.csv(as.data.frame(inspect(freqitems)),'freqitems1.csv')


#Construct the Rules
rules = apriori(tdata, parameter = list(supp = 0.2, conf = 0.5, minlen=2))
itemFrequencyPlot(items(rules))

inspect(rules[1:5])
inspect(rules)
write.csv(as.data.frame(inspect(rules)),'rules1.csv')
#sort rules by support
rules_s = sort(rules, by="support", decreasing=TRUE )
inspect(rules_s)
inspect(rules_s[1:5])  #itemsset having high support

#sort rules by confidence
rules_c = sort(rules, by="confidence", decreasing=TRUE )
inspect(rules_c)
inspect(rules_c[1:5])  #itemsset having high confidence

#sort rules by lift
inspect(head(rules, n = 3, by ="lift"))
rules_l = sort(rules, by="lift", decreasing=TRUE )
inspect(rules_l)
inspect(rules_l[1:5])  #itemsset having high confidence

#Quality Data of Rules
quality(rules_c) 

#Redundant Rules
inspect(rules)
(redundant = which(is.redundant(rules)))
inspect(rules[c(8,9,10,11,12,14,14)])
inspect(rules[redundant])
inspect(rules)
write.csv(as(rules,"data.frame"), file='./data/rulesR.csv')

#Remove Redundant Rules
rulesNR <- rules[-redundant] 
is.redundant(rulesNR)
sum(is.redundant(rulesNR))  #ok now
inspect(rulesNR)

#Rules with LHS and RHS: single or combination
rules2= rulesNR
inspect(rules2)
rules2.lhs1 <- subset(rules2, lhs %in% c("I1", "I5"))
inspect(rules2.lhs1)

rules2.rhs1 <- subset(rules2, rhs %in% c("I3"))
inspect(rules2.rhs1)

rules2.lhsrhs1 = subset(rules2, lhs %in% c("I1") & rhs %in% c("I3"))
inspect(rules2.lhsrhs1)

rules2.lhsrhs2 = subset(rules2, lhs %in% c("I1") | rhs %in% c("I3"))
inspect(rules2.lhsrhs2)



# Rules as DF: original rules
rules_DF <- as(rules,"data.frame")
rules_DF
str(rules_DF)
write.csv(rules_DF, './data/myrules1.csv')

#Visualisation
plot(rules)


================================================
FILE: 93-wksp3/decisiontree1.R
================================================
#Decision Tree

library(rpart)
library(rpart.plot)

education=factor(c(3,2,2,3,2,2,3,2,2,2))
married=factor(c('S','M','S','M','M','M','S','S','M','S'))
income=c(125,100,70, 120, 95, 60, 220, 85, 75, 90)
buy=factor(c(0,0,0,0,1,0,0,1,0,1))
(data= data.frame(buy, education, married, income))
head(data)

xtabs( ~ buy + education, data=data)

table(data$buy, data$education)

dtree1 = rpart(buy ~ education + married + income, data=data)
dtree1
dtree1 = rpart(buy ~ education + married , data=data,   minsplit=4, minbucket=2,cp=-1)
table(data$buy)
dtree1
rpart.plot(dtree1, nn=T)
printcp(dtree1)

head(data)
(ndata1 = data.frame(education=factor(c(2,3)), married=factor(c('S','M')), income=c(110,120)))
(p1=predict(dtree1, newdata=ndata1, type='class'))

(p2=predict(dtree1, newdata=ndata1, type='prob'))
cbind(ndata1, p1,p2)



================================================
FILE: 93-wksp3/decisiontree2.R
================================================

marry=sample(c('Yes',"No"), size=100, replace=T)
selfGender=sample(c('M',"F"), size=100, replace=T)
selfAge=ceiling(runif(100, 23,30))
selfEdn=sample(c(1,2,3,4), size=100, replace=T, prob=c(.1,.3,.4,.2))
spouseAge=ceiling(rnorm(100, mean=26,sd=5))
spouseEdn=sample(c(1,2,3,4), size=100, replace=T, prob=c(.1,.3,.4,.2))
spouseSiblings=sample(c(0,1,2,3), size=100, replace=T, prob=c(.2,.3,.3,.2))
spouseCity=sample(c('Rural',"Urban"), size=100, replace=T)
spouseSalary= ceiling(runif(100, 0,100000))

df = data.frame(marry, selfGender, selfAge, selfEdn, spouseAge, spouseEdn, spouseSiblings, spouseCity, spouseSalary )

head(df)

library(rpart)

dtree1 = rpart(marry ~ . , data=df)
dtree1
library(rpart.plot)
rpart.plot(dtree1, cex=.8, nn=T)
printcp(dtree1)
rpart.plot(prune(dtree1, cp=.03))
df1=df[spouseAge>= 25 & spouseSalary >= 90e+3, c('spouseSalary','spouseAge', 'marry')]
table(df1$marry)

dtree2 = rpart(marry ~ . , data=df, cp=-1)
dtree2
rpart.plot(dtree2)

printcp(dtree2)
ptree2 = prune(dtree2, cp=.027)
rpart.plot(ptree2)

#PRedict
ndata1 = data.frame(selfGender="M", selfAge=27, selfEdn=3, spouseAge=29, spouseEdn=4, spouseSiblings=4, spouseCity='Rural', spouseSalary=45000 )
head(df)
ndata1
predict(dtree1, newdata= ndata1, type="prob")


================================================
FILE: 93-wksp3/decisiontree3.R
================================================
# Decision Trees : - regression tree

library(ISLR)  #download this
data(Carseats)
data = Carseats
head(data)
?Carseats

#Libraries for Decision Tree
library(rpart)
library(rpart.plot)

#Model
tree1 = rpart(Sales ~ . , data=data, method='anova' )
tree1
rpart.plot(tree1, cex=.8)

#this is large tree, so prune it: check cp
printcp(tree1)
#cp value should be chosen such that xerror is least
prunetree = prune(tree1, cp=0.024)
#here we have selected a different value to simplify the tree

prunetree
mean(data$Sales)
length(data[data$ShelveLoc=="Bad" | data$ShelveLoc=="Medium", c("Sales")])
rpart.plot(prunetree, nn=T)
#Interpretation
#if ShelveLoc=Good, and Price >= 109.5, sales predicted is 9.2

#improve the plot
rpart.plot(prunetree, nn=T, cex=.8, type=4)
#read this document to improve the plot
#https://cran.r-project.org/web/packages/rpart.plot/rpart.plot.pdf
#http://www.milbo.org/rpart-plot/prp.pdf

#Predict for test value
(testdata = sample_n(data,2))
(testdata2 = sample_frac(data,.2))

(predictedSales=predict(prunetree, newdata=testdata, type='vector'))
cbind(testdata, predictedSales)
#next line will show error because we have to predict numerical value instead of class/ category, so type of response reqd is vector not class
(predict(prunetree, newdata=testdata, type='class'))


#see online help here
#https://www.datacamp.com/community/tutorials/decision-trees-R

rpart.plot(prunetree)
prp(prunetree, digits=4)
rpart.plot(prunetree, digits=5,cex=.8)


================================================
FILE: 93-wksp3/decisiontree4.R
================================================
# CART Models - HH Case Study - Regression
library(rpart)
library(rpart.plot)
library(forecast)

library(gsheet)
url='https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit#gid=1941519952'
data = as.data.frame(gsheet2tbl(url))
str(data)

# Summarize the dataset
summary(data)
names(data)
# Random Sampling
set.seed(777) # To ensure reproducibility
Index = sample(x = 1:nrow(data), size = 0.7*nrow(data))
Index

# Create Train dataset
train= data[Index, ]
nrow(train)

# Create Test dataset
test = data[-Index, ]
nrow(test)
nrow(test) + nrow(train)

########################### Modeling #################################

trainModel = rpart(Annual_Sales ~ . , data = train[,-1], method = "anova")
trainModel
mean(train$Annual_Sales)

# Plot the Regression Tree
rpart.plot(trainModel, type = 4,fallen.leaves = T, cex = 1.0, nn=T)

#cp selection
printcp(trainModel)
trainModel_prune = prune(trainModel, cp=0.01)
rpart.plot(trainModel_prune)

#Predict and check accuracy
predictSales_test = predict(trainModel_prune, newdata = test, type = "vector")
predictSales_test  #vector to print values of sales predicted

library(forecast)
# Validate RMSE and MAPE calculation with a function in R
ModelAccuarcy = accuracy(predictSales_test, test$Annual_Sales)
ModelAccuarcy
#RMSE should be as less as possible



================================================
FILE: 93-wksp3/decisiontree5.R
================================================
#CHAID - dataset USvote #multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)  #library for performing CHAID decision tree

#Dataset
data(USvote)  #from lib CHAID
?USvote

#Quick CHAID analysis
set.seed(101)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))
print(chaidModel)
plot(chaidModel)



================================================
FILE: 93-wksp3/decisiontree5CHAID.R
================================================
#CHAID - dataset USvote #multisplit
# require(rsample) # for dataset and splitting also loads broom and tidyr
#install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID)  #library for performing CHAID decision tree

#Dataset
data(USvote)  #from lib CHAID
?USvote

#Quick CHAID analysis
set.seed(101)
sample1 = USvote[sample(1:nrow(USvote), 1000),]
chaidModel <- chaid(vote3 ~ ., data = sample1, control=chaid_control(minbucket = 20, minsplit=7, minprob=0))
print(chaidModel)
plot(chaidModel)



================================================
FILE: 93-wksp3/df.R
================================================
#data frame

#rollno, name, batch, gender, marks1, marks2

(rollno = 1:30)
(name = paste('student',1:30,sep='-'))
(batch = sample(c(2016, 2017,2018), size=30, replace=T))
table(batch)
(gender = sample(c('M', 'F'), size=30, replace=T))
table(gender)
(marks1 = rnorm(30, mean=65, sd=7))
(marks2 = rnorm(30, mean=60, sd=10))

df = data.frame(rollno, name, batch, gender, marks1, marks2, stringsAsFactors = F)
df
summary(df)
str(df)
df$gender = factor(df$gender)
str(df)
df$batch = factor(df$batch, ordered=T, levels=c('2016','2017','2018'))
str(df)
df

summary(df)

df$rollno = as.character(df$rollno)
summary(df)

str(df)  #structure
head(df)  #first 6 rows
tail(df)
head(df, n=2)
dim(df)
nrow(df)  #no of rows
ncol(df)  #no of cols
names(df)  #names of cols
df[1:2,1:4]
df[ c(1,3), c(1,4,5,6)]
df[ df$gender=='M', 3:5 ]  # Males ; Display 3 to 5 th col
df[ df$gender=='M' & df$marks1 > 70, 3:5 ]  # Males ; Display 3 to 5 th col

length(df)
nrow(df[df$gender=='M',])

#sort
sort(df$marks1)
order(df$marks1)
df$marks1
df[ order(df$marks1), ]
sort(df$marks1)
rev(sort(df$marks1))
df[order(gender, batch), ]
df[order(gender, - batch), ]


#summary/ aggregating
aggregate(df$marks1, by=list(df$batch), FUN=mean)
aggregate(marks1 ~ batch, data=df, FUN=mean)
aggregate(cbind(marks1, marks2) ~ batch, data=df, FUN=mean)
aggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=mean)
df2=aggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=mean)
df2

aggregate(cbind(marks1, marks2) ~ batch + gender, data=df, FUN=max)


df
write.csv(df, "./data/bitsgoa.csv")

df3 = read.csv("./data/bitsgoa.csv")
head(df3)
df3 = df3[,-1]
head(df3)


================================================
FILE: 93-wksp3/environ.R
================================================
# Environment

#objects in memory
ls()

#create an object
x = 1:5
ls()  #check
y=100:200
ls()  #check

#remove one
rm(x)  #remove x from environ

ls() #check
#remove all
rm(list = ls())

ls()  #check

   

================================================
FILE: 93-wksp3/freqtable.R
================================================
# Frequency Distribution

duration = faithful$eruptions 
range(duration)
breaks = seq(1.5, 5.5, by=0.5)# half-integer sequence 
breaks 
duration.cut = cut(duration, breaks, right=FALSE)
#left side not closed [1.5-2.0) ie 1.5 to 1.9999
#value of 2 will go into [2-2.5) interval
duration.cut
duration.freq = table(duration.cut)
duration.freq
cbind(duration.freq) 


# Relative Freq
duration.relfreq = duration.freq / nrow(faithful)
duration.relfreq

old = options(digits=1) 
duration.relfreq 
options(old) # restore
duration.relfreq 
cbind(duration.freq, duration.relfreq) 

# Cumulative freq
duration.cumfreq = cumsum(duration.freq)

#graphically-----
#using histogram
h = hist(faithful$eruptions)
h$breaks
h$counts
#compare with
cbind(duration.freq) 
h$mids


#right=T
duration.cut.R = cut(duration, breaks, right=T)
##value of 2 will go into (1.5-2] interval
duration.cut.R
duration.freq.R = table(duration.cut.R)
duration.freq.R
cbind(duration.freq.R) 
h$counts  #same



================================================
FILE: 93-wksp3/lm-salesarea.R
================================================
#Simple Linear - Sales vs Area

#import from ggsheet  #pickup the correct url
library(gsheet)
area = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=2023826519"
df = as.data.frame(gsheet::gsheet2tbl(area1))
str(df)
head(df)

#some stats
mean(df$X); mean(df$Y)
sd(df$X) ; var(df$Y)
cor(df$X,df$Y) 
#cor.test(df$X,df$Y)

plot(df$X, df$Y)  #simple command to plot : Next with features
abline(lm(df$Y ~ df$X), lty=3, lwd=4, col='green') # with regression line

#Model
fit1 = lm(Y ~ X, data=df) # create Simple Linear Model Y wrt X

names(fit1)
system.time(lm(Y ~ X, data=df)) #time taken for LM
coef(fit1)  # Coefficients of Equation Y = mX + C
residuals(fit1)  # Diff of Y actual - Y predicted

summary(fit1)  # summary statistics of Linear Model(LM)
#understand the model values - R2, AdjR2, FStats, Residuals, Coeff p values - IMP STEP

#combine the data with Ypredicted, errors
cbind(df, fitted(fit1), fitted(fit1)- df$Y, residuals(fit1))

# Predictions----
(Y = 0.9645 + 1.6699 * 4)  # Predict Y for X=4

#Predict for X=4
new1 = data.frame(X=c(4))
predict(fit1, newdata= new1)

#Predict for more than 1 input
new1 = data.frame(X=c(1.5,2,3,4,5)) #sample data for X for prediction, should be between the range of X values
new1 #prediction data should be in form of Data Frame

predict(fit1, newdata= new1) # Predict Function for 4 values of X

#fitted(fit1) is equal to predict function using original X values
cbind(new1, predictedY = predict(fit1, newdata= new1) )

#Accuracy of Model
library(forecast)
accuracy(fit1)  #RMSE should be least


#Assumption : Graphical Analysis : IMP STEP
plot(fit1, which=1)
plot(fit1)

# Linearity plot of residuals & X # No pattern for assumption that there is linearity betw X & Y

abline(h=0)
plot(residuals(fit1))

#Auto Collinearity : relation between successive values of Y
car::durbinWatsonTest(fit1)
#pvalue > 0 : Do not reject Ho that there is no correlation


#Normality of residuals
hist(residuals(fit1)) #distribution of Residuals

hist(residuals(fit1), freq=F)
lines(density(residuals(fit1)))

#histogram values to show how hist rectangle are created
(h=hist(residuals(fit1)))
names(h)
cbind(h$breaks, h$counts)

#Normality Plot to check assumptions of LM
qqnorm(residuals(fit1))
qqline(residuals(fit1))

#Normality Plot can also be used with Std Values also
residuals(fit1)
rstandard(fit1)
sales.stdres = rstandard(fit1)
qqnorm(sales.stdres, ylab="Standardized Residuals", xlab="Normal Scores", main="Checking normality of Residuals") 
qqline(sales.stdres)

stem(residuals(fit1))

#Equal Variance : 4th Assumption : homoscedasticity
plot(y=residuals(fit1), x=df$X)
abline(h=0)  #Variance across all x values is almost constant


#Outlier Analysis
df
df[14,]
df$Y
residuals(fit1)
boxplot( df$Y)
boxplot( fitted(fit1))

boxplot( df$Y, fitted(fit1), residuals(fit1), names=c('Y','predictY', 'Residuals'))
abline(h=c(4.1, 5.97, -1.87))

boxplot(residuals(fit1), names=c('Residuals'))
#identify(rep(1, length(residuals(fit1))), residuals(fit1), labels = seq_along(residuals(fit1)))

car::outlierTest(fit1)
df[14,]

car::outlierTest(lm(Y ~ X, data=df[-c(14,12),]))

fit1


================================================
FILE: 93-wksp3/lm-salesqty.R
================================================
#Multiple Linear Regression 
#Linear Modeling : DV vs more than 1 IVs
#sales Qty vs price & promotion

#Omni Store

#Method : gsheet or else download the sheet as csv and then import 
library(gsheet)
url = "https://docs.google.com/spreadsheets/d/1h7HU0X_Q4T5h5D1Q36qoK40Tplz94x_HZYHOJJC_edU/edit#gid=1595306231"
df = as.data.frame(gsheet::gsheet2tbl(url))
head(df)

#MLR  Create Multiple Linear Regression
# we want to see how Sales Qty depend on Price and Promotion Values
fit2 = lm(sqty ~ price + promotion, data=df)
summary(fit2)

# summary statistics of model IMP STEP
#understand values : R2, AdjR2, Fstats pvalue, Coeff, ***, Residuals
#coefficients b1, b2
#Predicted Values----
#create a dataframe of new sample values
(ndata1 = data.frame(price=c(60,70), promotion=c(300,400)))

#cbind the values
predict(fit2, newdata=ndata1)
cbind(ndata1, predictedSqty = predict(fit2, newdata=ndata1))

#end of LM
#check for assumptions of Linear Regression
plot(fit2)



================================================
FILE: 93-wksp3/lm-women-simple.R
================================================
# Simple Linear Regression : Built in Data Set Women
# Predit Weight of heights of women - 60.5, 75.5

#Check the Data set
women 
str(women)

cov(women$height, women$weight)  #direction of relationship
#69 : which show positive relationship between height and weight

cor(women$height, women$weight)  #strength and direction of relationship
#0.995 : which shows Strong and Positive relationship betw height & weight
0.995^2 # also equal R^2 value in this case (Simple Linear Regression)

plot(x=women$height, y=women$weight)
abline(lm(weight ~ height, data=women), col='red')

names(women)  #x- IV, y -DV
fit1 = lm(weight ~ height, data=women)  # creating a model
summary(fit1)  #summary of the Model


#Predict
range(women$height)  #values to be within this range
(ndata = data.frame(height = c(60.5, 75.5)))
predictedwt = predict(fit1, newdata = ndata)
cbind(ndata, predictedwt)

#End : Simple way to predict in SLM

#Next task if check for assumptions
plot(fit1)


================================================
FILE: 93-wksp3/lm.R
================================================
#BITS LM

#simple linear regression
#how Y depends on X
#women
women
str(women)
cov(women$height, women$weight)
cor(women$height, women$weight)
plot(women$height, women$weight)
#create Linear Model
fit1 = lm(weight~ height, data=women)
summary(fit1)
#weight = -87 + 3.45 * height
fitted(fit1)
cbind(women, fitted(fit1), residuals(fit1))
names(fit1)
(ndata1 = data.frame(height=c(62.5,63.5)))
predict(fit1, newdata=ndata1)
cbind(ndata1, predictWt=predict(fit1, newdata=ndata1))

#



#area vs sales


#Multiple Linear Regression
#mtcars : mpg vs wt, hp
names(mtcars)

fit2 = lm(mpg ~ wt + hp, data=mtcars)
summary(fit2)
range(mtcars$wt) ; range(mtcars$hp)
ndata2 = data.frame(wt=c(2,2,3), hp=c(57, 250,300))
predict(fit2, newdata=ndata2)
cbind(ndata2, predictMPG=predict(fit2, newdata=ndata2))
#
names(mtcars)
fit2a = lm(mpg ~ wt + hp + vs + am + gear, data=mtcars)
summary(fit2a)




#sales vs price & promotion




================================================
FILE: 93-wksp3/logR.R
================================================
#Logistic Regression

#probability of Y based on single or multiple variables
#chance of default based on X1, X2



================================================
FILE: 93-wksp3/logr-gre.R
================================================
#Logistic Regression : Predict Probability of Selection 

df =  read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")

## view the first few rows of the data
head(df)
sum(is.na(df))

## two-way contingency table of categorical outcome and predictors we want to make sure there are no empty cells
xtabs(~admit + rank, data = df)

str(df)
#convert rank into factors
df$rank = factor(df$rank)
df$admit = factor(df$admit)

fit3 = glm(admit ~ gre + gpa + rank, data=df,family="binomial")
summary(fit3)

#predict probabilities of original values
(prob=predict(fit3,type=c("response")))
head(cbind(df, prob))

#Test with new data
(newdata1 = data.frame(gre = mean(df$gre), gpa = mean(df$gpa), rank = factor(1)))
cbind(newdata1, predictProb=predict(fit3, newdata = newdata1, type="response"))
#if prob > 0.5, we say select = 1

#another set of data for prediction
range(df$gre); range(df$gpa);levels(df$rank)
(newdata2 = data.frame(gre = c(200, 300, 400, 500), gpa = c(2.5, 3, 3.3, 3.75), rank = factor(c(1,2,3,4))))
str(newdata2)
newdata2b = cbind(newdata2, predictProb2=predict(fit3, newdata = newdata2, type = "response"))
newdata2b


#this way you predict Probabilites 


================================================
FILE: 93-wksp3/matrix.R
================================================
#matrices
?matrix

(m1 = matrix(1:12, nrow=4))
marks3 = floor(runif(30, 50,90))
marks3
m2 = matrix(marks3, nrow=6)
m2

(m3 = matrix(marks3, nrow=6, byrow = T))
(m4 = matrix(marks3, ncol=5, byrow = T))
#summary
colSums(m4)
rowSums(m4)
colMeans(m4)
rowMeans(m4)
sum(m4)
m4 [ m4 > 70]
m4
m4[1,]
m4[,2]
m4[3,5]
m4[1:2, 3:5]
m4


================================================
FILE: 93-wksp3/missingvalues.R
================================================
# Missing Values
# Create, Detect, Replace, Remove, Impute, Visualise
library(VIM)

(v1 = c(1,2,NA,NA,5))
is.na(v1)
v1=NULL
v1  # remove all values
(v1 = c(1,2,NA,NA,5,6,7,NA))
sum(is.na(v1))  # sum TRUE values ie missing values
mean(v1)  # cannot calculate if NA values in vector
mean(v1, na.rm=T)  # remove and calc
(v1a = na.omit(v1))  # omit missing values and put in new vector
v1a
mean(v1a)
sum(v1a) # now you can sum on non NA values
?na.omit

anyNA(v1) # is there any NA value
anyNA(v1a)
?anyNA

#impute missing values of vector with mean value of non NA values
v1
is.na(v1)
v1[c(F,F,T,T,F,F,F,T)]
v1[is.na(v1)]
v1[is.na(v1)] = mean(v1, na.rm=T)
v1
is.na(v1)

#Use VIM package to load sleep dataset with NA values in rows & col
library(VIM)
data(sleep, package='VIM')
summary(sleep)# see NA values in cols
str(sleep)
head(sleep)
dim(sleep)
complete.cases(sleep) #all complete sirows without any missing values
sum(complete.cases(sleep)) # count non NA rows/ complete 42
sum(!complete.cases(sleep))  #count NA rows 20
mean(complete.cases(sleep))  #42/62
# total cases = 62, complete=42  
df2 = sleep[complete.cases(sleep),] # list rows which are complete
df2
sleep[!complete.cases(sleep),] # list rows which have NA values


# Particular Column
is.na(sleep$Dream)
sum(is.na(sleep$Dream))
mean(is.na(sleep$Dream))  # 12/62

# How many missing values
sum(is.na(sleep))  #total in data frame
names(sleep)# check columname
colSums(is.na(sleep))  #NA values in each column
v2 = colSums(is.na(sleep))
v2
v2[v2> 10]
rowSums(is.na(sleep))  #NA values in each row
head(sleep)
as.data.table(sleep)  #data.table format
is.na(sleep[1:3,]) # first 3 rows
head(sleep,n=3)

#Tabulate
library(mice)
mice::md.pattern(sleep)
#42 rows without any missing values # 2 rows with 1 NA NonD

#Visualisation
VIM::aggr(sleep, prop=F, numbers=T)
#NonD max NA values 
VIM::aggr(sleep, prop=T)  # Proportion
VIM::matrixplot(sleep) # rowno and missing values in red
VIM::marginmatrix(sleep)
VIM::marginplot(sleep[c('Gest','Dream')], pch=c(20), col=c("darkgray", "red", "blue"))  # finding relationship between NA value


#Exploration
x = as.data.frame(abs(is.na(sleep)))
head(sleep, na=5)
head(x, n=5)
y = x[which(apply(x, 2, sum) > 0)]
cor(y)  #Dream - NonD 0.9
cor(sleep, y, use="pairwise.complete.obs")
#ignore NA, 
#

#Listwise Deletion
options(digits=3)
na.omit(sleep)
cor(na.omit(sleep))
cor(sleep, use="complete.obs")

fit = lm(Dream ~ Span + Gest, data= na.omit(sleep))
summary(fit)


#imputations Packages - mice, Amelia, mi : mice, with, pool

imp = mice::mice(sleep, seed=1234)
?mice

# explore more


================================================
FILE: 93-wksp3/packages1.R
================================================
#Packages

#List avl packages
library()

#Total Avl Packages - CRAN Site
nrow(available.packages())

#Install Package amap
install.packages('amap')

#Load package
library(amap)
library(dplyr)

#check if loaded 
search()

#Find functions in package
library(help=amap)

#Help wrt a package
help(package='amap')  #see on right side pane

#Packages avl for loading
.packages(all.available = T)   #avl for loading


#Unload---
library(tm)  #load tm package
library(VIM)  #load VIM package
search()   #check if loaded
detach('package:tm', unload=TRUE)

search.packages("package:tm")
?search
detach(package:VIM, unload = T) 
search()



#Detach from memory all packages
rm(list = ls(all = TRUE))
search()
sessionInfo()

#Remove Packages ----- uninstall 
remove.packages("tm")
require('tm')# check if unistalled

find.package("tm")


================================================
FILE: 93-wksp3/packages2.R
================================================
# Install multiple Packages

#Install packages for Data Analytics Course


packages1 = c('rJava','xlsx','dplyr')
list.of.packages <- packages1
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
new.packages
if(length(new.packages)) install.packages(new.packages)
#if length is 0,all are installed, else missing packages will be installed

#another list
packages2 = c('plyr', 'psych', 'tm', 'quantmod')

#another list
packages3 = c('lubridate')

#another list
packages4 = c('dplyr','plyr','data.table','tidyr','gsheet','car','ggplot2','ISLR','amap','arules','arulesViz','forecast','rattle','CHAID','rpart','rpart.plot','ts','zoo','twitterR','ROAuth','RGtk2','partykit','strucchange','didrooRFM','curl','syuzhet','stringr','RTextTools','e10171','wordcloud','caret','catools','olsrr','xlsr','rJava')					

#amend below scripts to use packages list1 to list4
list.of.packages <- packages4
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]

new.packages

if(length(new.packages)) install.packages(new.packages)



================================================
FILE: 93-wksp3/stats2.R
================================================
#Stats 

#Package fBasics

library(fBasics)
#skewness	returns value of skewness,
skewness(mtcars)
plot(density(mtcars$cyl))
plot(density(mtcars$mpg))

#kurtosis	returns value of kurtosis,
kurtosis(mtcars)
plot(density(mtcars$qsec))

#basicStats	computes an overview of basic statistical values,
basicStats(mtcars)

#rowStats	calculates row statistics,
rowStats(mtcars,FUN=mean)

#colStats	calculates column statistics,
colStats(mtcars, FUN=median)

#rowAvgs	calculates row means,
rowAvgs(mtcars)

#colAvgs	calculates column means,
colAvgs(mtcars)

#rowVars	calculates row variances,
rowVars(mtcars)

#colVars	calculates column variances,
colVars(mtcars)

#rowStdevs	calculates row standard deviations,
rowStdevs(mtcars)

#colStdevs	calculates column standard deviations,
colStdevs(mtcars)

#rowSkewness	calculates row skewness,

#colSkewness	calculates column skewness,

#rowKurtosis	calculates row kurtosis,

#colKurtosis	calculates column kurtosis,
rowCumsums	calculates row cumulated Sums,
colCumsums	calculates column cumulated Sums.

================================================
FILE: 93-wksp3/twitter.R
================================================
#Twitter 1 - Configure Tweets and Download them
#@dupadhyaya  #Working using my Keys
#Load libraries
library("curl")
library("twitteR")
library("ROAuth")
library("syuzhet") #library for sentiment analysis - comparison

download.file(url="http://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")

#https://apps.twitter.com/
#different for each account
consumerKey="uRDuync3BziwQnor1MZFBKp0x"
consumerSecret="t8QPLr7RKpAg4qa7vth1SBsDvoPKawwwdEhNRjdpY0mfMMdRnV"
AccessToken="14366551-Fga25zWM1YefkTb2TZYxsrx2LVVSsK0uSpF08sugW"
AccessTokenSecret="3ap8BZNVoBhE2GaMGLfuvuPF2OrHzM3MhGuPm96p3k6Cz"

#Common for all accounts except the keys

cred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize')

cred$handshake(cainfo="cacert.pem") # it will take you to browser: authorise, copy key and paste in R Studio at Console. Once it stores, it move to R prompt
save(cred, file="twitter authentication.Rdata") # store this to avoid asking again
#Load saved authentication cert
load("twitter authentication.Rdata")
#registerTwitterOAuth(cred)

setup_twitter_oauth(consumerKey, consumerSecret, AccessToken, AccessTokenSecret)
#type 1 : Yes 
search.string <- "#businessanalytics"
no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets
tweets[1:10]
#Another Topics
search.string <- "#indvseng"
no.of.tweets <- 100

tweets <- searchTwitter(search.string, n=no.of.tweets,lang="en")
tweets[1:5]

#My Tweets : will change if you use your own account
homeTimeline(n=30)  #my tweets
mentions(n=30)   # my tags

tweets <- userTimeline("sarthak231297", n=10)
tweets

#------------------------------------
?userTimeline
tweets <- userTimeline("realDonaldTrump", n=100)
#english
tweets[1:5]
n.tweet <- length(tweets)
n.tweet
tweets.df <- twListToDF(tweets) 
head(tweets.df)
summary(tweets.df)

#Remove hashtags & unnecessary characters
tweets.df2 <- gsub("http.*","",tweets.df$text)
tweets.df2 <- gsub("https.*","",tweets.df2)
tweets.df2 <- gsub("#.*","",tweets.df2)
tweets.df2 <- gsub("@.*","",tweets.df2)

head(tweets.df2)
#-----
library("syuzhet") #library for sentiment analysis - comparison
word.df <- as.vector(tweets.df2)
emotion.df <- get_nrc_sentiment(word.df)
emotion.df2 <- cbind(tweets.df2, emotion.df) 
head(emotion.df2)

#-----
sent.value <- get_sentiment(word.df)
most.positive <- word.df[sent.value == max(sent.value)]
most.positive
most.negative<- word.df[sent.value <= min(sent.value)] 
most.negative
sent.value

#-----
positive.tweets <- word.df[sent.value > 0]
head(positive.tweets)
negative.tweets <- word.df[sent.value < 0] 
head(negative.tweets)
neutral.tweets <- word.df[sent.value == 0]
head(neutral.tweets)
#----
# Alternate way to classify as Positive, Negative or Neutral tweets
category_senti <- ifelse(sent.value < 0, "Negative", ifelse(sent.value > 0, "Positive", "Neutral"))
head(category_senti)
category_senti2 <- cbind(tweets,category_senti,sent.value) 
head(category_senti2)

#----
table(category_senti)



================================================
FILE: 93-wksp3/vectors.R
================================================
#Vector

x = c(1,2,3)
x1 = 1:10000000
length(x1)
x
x1

x2 = seq(10, 100, 2)
seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),
    length.out = NULL, along.with = NULL, ...)
x2
x3 = seq(from=10, to=100, by=3)
x3
?seq


#numeric vector----

(marks = rnorm(n=30, mean=60, sd=10))
mean(marks)
median(marks)
mode(marks)  #no mode
sd(marks)
var(marks)
summary(marks)
range(marks)

#Properties of DS
length(marks)
str(marks)
class(marks)
#
hist(marks)
plot(density(marks))

#character Vector ----

(names = c('Ram','Shyam', 'Robin'))
names
length(names)
mean(names)
class(names)
summary(names)
#
gender = c('M','F','M')
summary(gender)
genderF = factor(gender)
summary(genderF)

(grades = c('A','B','C','D','A','D','A'))
gradesF = factor(grades, ordered=T)
summary(gradesF)
gradesF

gradesF1 = factor(grades, ordered=T, levels=c('D','B','A','C'))
summary(gradesF1)
gradesF1
table(gradesF1)
table(gender)

barplot(table(gradesF1))
pie(table(gradesF1))
mean(gradesF1)


#logical Vector -----

(married = c(TRUE, FALSE, T, F, T, F,T,T))
married
sum(married)
table(married)
class(married)
summary(married)
#

#subset of marks
marks
trunc(marks); round(marks,1); floor(marks); ceiling(marks)
marks1 = trunc(marks)
marks1
marks1[1]
marks1[18]
marks1[1:5]
marks1[-c(10:15)]
marks1[c(1,5,10,30)]
mean(marks1[c(1,5,10,30)])
marks1[marks1 > 60]
marks1 > 60
marks1[marks1 > 60 & marks1 < 80]

#
set.seed(1234)
gender2 = sample(c('M', 'F'), size=1000000, replace=T, prob=c(.6,.4))
gender2
table(gender2)#M-600087
prop.table(table(gender2))


================================================
FILE: 93-wksp3/wordcloud1.R
================================================
# World Cloud 2

# Read the text file from file
text <- readLines(file.choose())
text

# Load the data as a corpus
docs = Corpus(VectorSource(text))
docs

#Text transformation
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

#Cleaning Text
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
docs <- tm_map(docs, stemDocument)


#Document Matrix
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

#Generate Word Cloud
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

findFreqTerms(dtm, lowfreq = 4)
findAssocs(dtm, terms = "freedom", corlimit = 0.3)
head(d, 10)

#Plot Freq
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
       col ="lightblue", main ="Most frequent words",
       ylab = "Word frequencies")



================================================
FILE: 93-wksp3/wordcloud2.R
================================================
#word cloud

# Load
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")


text <- readLines(file.choose())
text

# Read the text file from internet
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text2 <- readLines(filePath)
text2

text=text2

# Load the data as a corpus
docs <- Corpus(VectorSource(text))
inspect(docs)

toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")

# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2")) 
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10)

set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,  max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))


================================================
FILE: 95-studqueries/Sapient_Big Data.R
================================================
library(dplyr)
library(TTR)
library(tidyr)
library(stringr)
train = read.csv('./data/Analytics_Vidhya/household.csv', header = T, stringsAsFactors = F)
test1 = read.csv('./data/Analytics_Vidhya/alert_type_1.csv', header = T, stringsAsFactors = F)
test2 = read.csv('./data/Analytics_Vidhya/alert_type_2.csv', header = T, stringsAsFactors = F)
str(train)
str(test1)
str(test2)
head(train)
head(test1)
head(test2)
summary(train)
range(train$timestamp)
names(train)
nrow(train)
#check for duplicates, select only non-duplicates
train[duplicated(train[c(1,2,3)]),]
subset(train, duplicated(subset(train, select=c(1:3))))
#No duplicates proceed with data
anyNA(train)
df1 = train
test1 = separate(data = test1, col = "id", into = c("hid","hhid","date","hr"),sep = "_")
str(test1)
View(test1)
test2 = separate(data = test2, col = "id", into = c("hid","hhid","date","hr"),sep = "_")
View(test2)
str(test2)
as.data.frame(colSums(is.na(test1)))
as.data.frame(colSums(is.na(test2)))
test1$date = as.Date(test1$date, "%d-%m-%Y"); test1$date
str(test1)
View(test1)
test2$date = as.Date(test2$date, "%d-%m-%Y"); test2$date
str(test2)
test1$date = format(as.Date(test1$date),"%d-%m-%Y"); test1$date
test2$date = format(as.Date(test2$date),"%d-%m-%Y"); test2$date



================================================
FILE: 95-studqueries/achal1.R
================================================
# Extract Strings in R : Achal
#https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html
#https://github.com/siskavera/r4ds_exercises/blob/master/strings.R

data = read.csv(file.choose())
data
str(data)

#Output
User	Group			
A	Cat_Svc_Users			
A	Cat_UXSu_Users			
B	Cat_27_Users			
B	Cat_29_Users			
B	Cat_58_Replay			
B	Cat_58_Users			
B	Cat_UXS_Users			

A	Cat_Svc_Users	Cat_UXSu_Users		
B	Cat_27_Users	Cat_29_Users	Cat_58_Replay	Cat_UXS_Users

#----
User	Group
A	Cat_Svc_Users
A	Cat_UXSu_Users
B	Cat_27_Users
B	Cat_29_Users
B	Cat_58_Replay
B	Cat_58_Users
B	Cat_UXS_Users

library(stringr)

#Extract Character Strings
matchstr1 = "Cat"
matchstr2 = "Cat[^a-zA-Z0-9]"

str_detect()
str_subset()
#(similar to grep() with value = TRUE)`.

# Which strings contain phone numbers?
str_detect(data$Group, matchstr2)
str_subset(data$Group, matchstr2)
str_count(data$Group, matchstr2)
(loc <- str_locate(data$Group, matchstr2))
(locall <- str_locate_all(data$Group, matchstr2))
data


str_extract(data$Group, matchstr2)
str_extract_all(data$Group, matchstr2)
str_extract_all(data$Group, matchstr2, simplify = TRUE)

word(data$Group,1)




#grep
# some text
text=data$Group
# pattern
pat = "Cat"
# default usage
grep(pat, text)
regexec(pat,text)
strsplit(text, ' ')
strsplit('Dhiraj Upadhyaya', ' ')
text
str_extract_all(text, 'Cat_[A-Za-z0-9]{1}')
words = c('Cat_27_Users', 'Cat_29_Users')
grep('Cat', words)
grepl('Cat', words)
grepl("\\<Cat", words)    
matchstr3 = "\\<Cat"
str_extract_all(data$Group, matchstr3)

matchstr3 = "\bCat"
str_extract_all(data$Group, matchstr3)
matchstr4 = "Cat_[a-zA-Z0-9_]{8}"

str_match_all(data$Group, matchstr4)


st = list(words)
st = text
st = data$Group
t = str_match_all(st, "\\bCat_[a-zA-Z0-9_]+\\b")
t


as.data.frame(str(t))


================================================
FILE: 95-studqueries/achal1.csv
================================================
User,Group
A,"CN=Cat_Svc_Users,OU=Groups,DC=comp,DC=com|CN=Cat_UXSu_Users,OU=Groups,,DC=comp,DC=com"
B,"CN=Cat_27_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_29_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_58_Replay,OU=Groups,,DC=comp,DC=com|CN=Cat_58_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_UXS_Users,OU=Groups,,DC=comp,DC=com"
C,"CN=Cat_27_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_29_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_58_Replay,OU=Groups,,DC=comp,DC=com|CN=Cat_58_Users,OU=Groups,,DC=comp,DC=com|CN=Cat_UXS_Users,OU=Groups,,DC=comp,DC=com"


================================================
FILE: 95-studqueries/achal2.R
================================================
#Achal 2 : String Extract


data = read.csv(file.choose())
data = read.csv('./students/achal1.csv')

data
str(data)
library(stringr)
t = str_match_all(data$Group, "\\bCat_[a-zA-Z0-9_]+\\b")

for (i in 1:nrow(data))
  print(cbind(as.vector(data$User[i]),unlist(t[i]) ))


y  <- NULL
for (i in 1:nrow(data))
{ 
  tmp <- cbind(as.vector(data$User[i]),unlist(t[i]) )
  y <- rbind(y, tmp)
}
y
output = as.data.frame(y)
names(output) = c('User','Group')
output

write.csv(y, './students/achal2.csv', row.names = F)

head(output)
library(dplyr)
output %>% group_by(User) %>% paste(Group)


#2nd part
smatch=  "\\bCat_[a-zA-Z0-9_]+\\b"

data$Group
str_split(data$Group, boundary("word"))
str_split(data$Group, "CN")
x1= str_extract_all(data$Group, boundary("word"))
class(x1)
df2= data.frame(unlist(x1))
class(df2)

# to list
x1= str_extract_all(data$Group, boundary("word"))
x1

#List to DF
library(qdapTools)
list2df(x1)


================================================
FILE: 95-studqueries/achal2.csv
================================================
"V1","V2"
"A","Cat_Svc_Users"
"A","Cat_UXSu_Users"
"B","Cat_27_Users"
"B","Cat_29_Users"
"B","Cat_58_Replay"
"B","Cat_58_Users"
"B","Cat_UXS_Users"
"C","Cat_27_Users"
"C","Cat_29_Users"
"C","Cat_58_Replay"
"C","Cat_58_Users"
"C","Cat_UXS_Users"


================================================
FILE: 95-studqueries/deepak.R
================================================
#####################################
#####Author Deepak Sharma###########
#####Date 15 Sep 2018###############
##### The File inlcude 
#######Data Processing (Outlier and Missing Values analysis)#####
#######Build Decison Tree, Random Forest, Logistic Regression, K-Nearest Neighbour and KMeans Clustering

#Clearing the memory
rm(list=ls(all=T))

#Setting the working directory
setwd("E:/CodeSample/MarketingCampaign")

#Load Libraries
x = c("ggplot2", "corrgram", "DMwR", "caret", "randomForest", "unbalanced", "C50", "dummies", "e1071", "Information",
      "MASS", "rpart", "gbm", "ROSE", 'sampling', 'DataCombine', 'inTrees')

#install.packages(x)
lapply(x, require, character.only = TRUE)
rm(x)

## Read the data
marketing_train = read.csv("marketing.csv", header = T, na.strings = c(" ", "", "NA"))

###########################################Explore the data##########################################
str(marketing_train)

## Univariate Analysis and Variable Consolidation
marketing_train$schooling[marketing_train$schooling %in% "illiterate"] = "unknown"
marketing_train$schooling[marketing_train$schooling %in% c("basic.4y","basic.6y","basic.9y","high.school","professional.course")] = "high.school"
marketing_train$default[marketing_train$default %in% "yes"] = "unknown"
marketing_train$default = as.factor(as.character(marketing_train$default))
marketing_train$marital[marketing_train$marital %in% "unknown"] = "married"
marketing_train$marital = as.factor(as.character(marketing_train$marital))
marketing_train$month[marketing_train$month %in% c("sep","oct","mar","dec")] = "dec"
marketing_train$month[marketing_train$month %in% c("aug","jul","jun","may","nov")] = "jun"
marketing_train$month = as.factor(as.character(marketing_train$month))
marketing_train$loan[marketing_train$loan %in% "unknown"] = "no"
marketing_train$loan = as.factor(as.character(marketing_train$loan))
marketing_train$schooling = as.factor(as.character(marketing_train$schooling))
marketing_train$profession[marketing_train$profession %in% c("management","unknown","unemployed","admin.")] = "admin."
marketing_train$profession[marketing_train$profession %in% c("blue-collar","housemaid","services","self-employed","entrepreneur","technician")] = "blue-collar"
marketing_train$profession = as.factor(as.character(marketing_train$profession))

##################################Missing Values Analysis###############################################
missing_val = data.frame(apply(marketing_train,2,function(x){sum(is.na(x))}))
missing_val$Columns = row.names(missing_val)
names(missing_val)[1] =  "Missing_percentage"
missing_val$Missing_percentage = (missing_val$Missing_percentage/nrow(marketing_train)) * 100
missing_val = missing_val[order(-missing_val$Missing_percentage),]
row.names(missing_val) = NULL
missing_val = missing_val[,c(2,1)]

#Writing the csv file at local machine
write.csv(missing_val, "Missing_perc.csv", row.names = F)

# ggplot(data = missing_val[1:3,], aes(x=reorder(Columns, -Missing_percentage),y = Missing_percentage))+
#   geom_bar(stat = "identity",fill = "grey")+xlab("Parameter")+
#   ggtitle("Missing data percentage (Train)") + theme_bw()

#Mean Method
#marketing_train$custAge[is.na(marketing_train$custAge)] = mean(marketing_train$custAge, na.rm = T)

#Median Method
#marketing_train$custAge[is.na(marketing_train$custAge)] = median(marketing_train$custAge, na.rm = T)

# Using kNN to impute missing Values 
marketing_train = knnImputation(marketing_train, k = 3)
#sum(is.na(marketing_train))

#write.csv(marketing_train, 'marketing_train_missing.csv', row.names = F)

##Data Manupulation; convert string categories into factor numeric
for(i in 1:ncol(marketing_train)){
  
  if(class(marketing_train[,i]) == 'factor'){
    
    marketing_train[,i] = factor(marketing_train[,i], labels=(1:length(levels(factor(marketing_train[,i])))))
    
  }
}


############################################Outlier Analysis#############################################
# ## BoxPlots - Distribution and Outlier Check
numeric_index = sapply(marketing_train,is.numeric) #selecting only numeric

numeric_data = marketing_train[,numeric_index]

cnames = colnames(numeric_data)
# 
# for (i in 1:length(cnames))
# {
#   assign(paste0("gn",i), ggplot(aes_string(y = (cnames[i]), x = "responded"), data = subset(marketing_train))+ 
#            stat_boxplot(geom = "errorbar", width = 0.5) +
#            geom_boxplot(outlier.colour="red", fill = "grey" ,outlier.shape=18,
#                         outlier.size=1, notch=FALSE) +
#            theme(legend.position="bottom")+
#            labs(y=cnames[i],x="responded")+
#            ggtitle(paste("Box plot of responded for",cnames[i])))
# }
# 
# ## Plotting plots together
# gridExtra::grid.arrange(gn1,gn5,gn2,ncol=3)
# gridExtra::grid.arrange(gn6,gn7,ncol=2)
# gridExtra::grid.arrange(gn8,gn9,ncol=2)
# 
# # #Remove outliers using boxplot method
# df = marketing_train
# marketing_train = df
# 
# val = marketing_train$previous[marketing_train$previous %in% boxplot.stats(marketing_train$previous)$out]
# 
# marketing_train = marketing_train[which(!marketing_train$previous %in% val),]
#                                   
# # #loop to remove from all variables
# for(i in cnames){
#   print(i)
#   val = marketing_train[,i][marketing_train[,i] %in% boxplot.stats(marketing_train[,i])$out]
#   #print(length(val))
#   marketing_train = marketing_train[which(!marketing_train[,i] %in% val),]
# }
# 
# #Replace all outliers with NA and impute
# #create NA on "custAge
# for(i in cnames){
#   val = marketing_train[,i][marketing_train[,i] %in% boxplot.stats(marketing_train[,i])$out]
#   #print(length(val))
#   marketing_train[,i][marketing_train[,i] %in% val] = NA
# }
# 
# marketing_train = knnImputation(marketing_train, k = 3)

##################################Feature Selection################################################
## Correlation Plot 
corrgram(marketing_train[,numeric_index], order = F,
         upper.panel=panel.pie, text.panel=panel.txt, main = "Correlation Plot")

## Chi-squared Test of Independence
factor_index = sapply(marketing_train,is.factor)
factor_data = marketing_train[,factor_index]

for (i in 1:10)
{
  print(names(factor_data)[i])
  print(chisq.test(table(factor_data$responded,factor_data[,i])))
}

## Dimension Reduction
marketing_train = subset(marketing_train, 
                         select = -c(pdays,emp.var.rate,day_of_week, loan, housing))

##################################Feature Scaling################################################
#Normality check
qqnorm(marketing_train$custAge)
hist(marketing_train$campaign)

#Normalisation - Bringing the scale between 0 and 1
cnames = c("custAge","campaign","previous","cons.price.idx","cons.conf.idx","euribor3m","nr.employed",
           "pmonths","pastEmail")

for(i in cnames){
  print(i)
  marketing_train[,i] = (marketing_train[,i] - min(marketing_train[,i]))/
    (max(marketing_train[,i] - min(marketing_train[,i])))
}

# #Standardisation
# for(i in cnames){
#   print(i)
#   marketing_train[,i] = (marketing_train[,i] - mean(marketing_train[,i]))/
#                                  sd(marketing_train[,i])
# }

#############################################Sampling#############################################
# ##Simple Random Sampling
# data_sample = marketing_train[sample(nrow(marketing_train), 4000, replace = F), ]
# 
# ##Stratified Sampling
# stratas = strata(marketing_train, c("profession"), size = c(100, 199, 10, 5), method = "srswor")
# stratified_data = getdata(marketing_train, stratas)
# 
# ##Systematic sampling
# #Function to generate Kth index
# sys.sample = function(N,n){
#   k = ceiling(N/n)
#   r = sample(1:k, 1)
#   sys.samp = seq(r, r + k*(n-1), k)
# }
# 
# lis = sys.sample(7414, 400) #select the repective rows
# 
# #Create index variable in the data
# marketing_train$index = 1:7414
# 
# #Extract subset from whole data
# systematic_data = marketing_train[which(marketing_train$index %in% lis),]

###################################Model Development#######################################
#Clean the environment
rmExcept("marketing_train")

#Divide data into train and test using stratified sampling method
set.seed(1234)
train.index = createDataPartition(marketing_train$responded, p = .80, list = FALSE)
train = marketing_train[ train.index,]
test  = marketing_train[-train.index,]

##Decision tree for classification
#Develop Model on training data
C50_model = C5.0(responded ~., train, trials = 100, rules = TRUE)

#Summary of DT model
summary(C50_model)

#write rules into disk
write(capture.output(summary(C50_model)), "c50Rules.txt")

#Lets predict for test cases
C50_Predictions = predict(C50_model, test[,-17], type = "class")

##Evaluate the performance of classification model
ConfMatrix_C50 = table(test$responded, C50_Predictions)
confusionMatrix(ConfMatrix_C50)

#False Negative rate
FNR = FN/FN+TP 



###Random Forest
RF_model = randomForest(responded ~ ., train, importance = TRUE, ntree = 500)

#Extract rules fromn random forest
#transform rf object to an inTrees' format
# treeList = RF2List(RF_model)  
# 
# #Extract rules
# exec = extractRules(treeList, train[,-17])  # R-executable conditions
# 
# #Visualize some rules
# exec[1:2,]
# 
# #Make rules more readable:
# readableRules = presentRules(exec, colnames(train))
# readableRules[1:2,]
# 
# #Get rule metrics
# ruleMetric = getRuleMetric(exec, train[,-17], train$responded)  # get rule metrics
# 
# #evaulate few rules
# ruleMetric[1:2,]

#Presdict test data using random forest model
RF_Predictions = predict(RF_model, test[,-17])

##Evaluate the performance of classification model
ConfMatrix_RF = table(test$responded, RF_Predictions)
confusionMatrix(ConfMatrix_RF)

#False Negative rate
FNR = FN/FN+TP 


#Logistic Regression
logit_model = glm(responded ~ ., data = train, family = "binomial")

#summary of the model
summary(logit_model)

#predict using logistic regression
logit_Predictions = predict(logit_model, newdata = test, type = "response")

#convert prob
logit_Predictions = ifelse(logit_Predictions > 0.5, 1, 0)


##Evaluate the performance of classification model
ConfMatrix_RF = table(test$responded, logit_Predictions)

#False Negative rate
FNR = FN/FN+TP 


##KNN Implementation
library(class)

#Predict test data
KNN_Predictions = knn(train[, 1:16], test[, 1:16], train$responded, k = 7)

#Confusion matrix
Conf_matrix = table(KNN_Predictions, test$responded)

#Accuracy
sum(diag(Conf_matrix))/nrow(test)

#False Negative rate
FNR = FN/FN+TP 

#naive Bayes
library(e1071)

#Develop model
NB_model = naiveBayes(responded ~ ., data = train)

#predict on test cases #raw
NB_Predictions = predict(NB_model, test[,1:16], type = 'class')

#Look at confusion matrix
Conf_matrix = table(observed = test[,17], predicted = NB_Predictions)
confusionMatrix(Conf_matrix)


#statical way
mean(NB_Predictions == test$responded)

#Kmeans implementation
irisCluster <- kmeans(train[,1:16], 2, nstart = 20)

table(irisCluster$cluster, train$responded)




================================================
FILE: 95-studqueries/hitesh-dec18.R
================================================
#Hitesh - Dec 18

data = read.csv(file.choose())
head(data)
summary(data)
head(data,100)
# Data is samples at 1 minute interval and for 2 months
# Here, below are the definition of each data point:
#   voltage >  0 means   good power,
# Voltage == 0 means  Power failure but UPS giving backup
# Voltage data missing means UPS is fully discharged 
library(dplyr)
names(data)
data2<- data %>% mutate(site1 = ifelse(is.na(Site.1), 'UPS-Discharged',(ifelse(Site.1 > 0, 'Good', (ifelse(Site.1 == 0, 'UPS', ''))))))

data3 <- data2 %>% mutate(site2 = ifelse(is.na(Site.2), 'UPS-Discharged',(ifelse(Site.2 > 0, 'Good', (ifelse(Site.2 == 0, 'UPS', ''))))))

head(data3)

# Problem Statement:
#   Summarise the instances of Power Failure on the following grounds:
#count of Power Failure and when UPS-Discharged
data3 %>% filter(site1=='UPS' | site1=='UPS-Discharged' ) %>% tally
data3 %>% filter(site2=='UPS' | site2=='UPS-Discharged') %>% tally


#   Number & Duration of Power Failures.
#Power failure is when voltage shifts from > 0 to 0 and remains in 0 till voltage comes ie > 0
lag(data3$Site.1)

# Number of times & Duration for which UPS did not provide any backup.

# Number of times & Duration for which UPS provided backup.

# Any Trends/Patterns in the Power Failure.


# Compare the UPS performance at 2 sites


# Also compare the two sites on the basis of above analysis.



# Try to summarise your observations using some self-explanatory plots.

================================================
FILE: 95-studqueries/hitesh1.R
================================================
#hitesh TS
library(ts)
library(forecast)
tsdata = read.csv(file.choose())
str(tsdata)
tsdata$date = as.Date(tsdata$date)
tsdata$views = as.integer(tsdata$views)
str(tsdata)
summary(tsdata)
dim(tsdata)
aggregate(tsdata$chid, by=list(tsdata$chid), FUN=NROW)

library(dplyr)

table(tsdata$chid)
# for each Channel Predict Views for next 3 months

chn1 = tsdata$chid[1]
#subset data
names(tsdata)

tsdata.subset <- subset(tsdata, chid == chn1, select = c(views, subscriber, videoscount, date))
tsdata.subset

names(tsdata.subset)
library(xts)
subscriber = xts(tsdata.subset[,2], order.by=tsdata.subset$date)
str(subscriber)
head(subscriber)
plot(subscriber)

#Manual Method NOT Working : use autoarima
library(tseries)
adf.test(subscriber)
ndiffs(subscriber)
dsub = diff(subscriber)
dsub  = na.omit(dsub)
plot(dsub)
adf.test(dsub)
?adf.test
acf(dsub)
Pacf(dsub)
fit1a = arima(subscriber, order= c(0,1,1))
fit1a
forecast(fit1a, 90)


library(forecast)
fit1b = auto.arima(subscriber)
fit1b
forecast(fit1b, 90)
accuracy(fit1b)



# Views
head(tsdata.subset)
library(xts)
views = xts(tsdata.subset[,1], order.by=tsdata.subset$date)
head(views)
fit2b = auto.arima(views)
fit2b
forecast(fit2b, 90)
accuracy(fit2b)

## Videos Cpimt
head(tsdata.subset)

videocount = xts(tsdata.subset[,3], order.by=tsdata.subset$date)
head(videocount)
fit3b = auto.arima(videocount)
fit3b
forecast(fit3b, 90)
accuracy(fit3b)


================================================
FILE: 95-studqueries/hitesh2.R
================================================
# Ad Views - Hitesh - May 18

dfTrain = read.csv(file.choose())
dfTest = read.csv(file.choose())

names(dfTrain)
names(dfTest)

#predict adview 
summary(dfTrain)
unique(dfTrain$adview)
table(dfTrain$adview)

table(dfTrain$duration)
#Extract Time duration in seconds
s1 = c("PT1H57M3S", "PT1H3M46S","PT1H33S","PT1H2M", "PT18S","PT18M9S", "PT1H39M22S")

library(tidyverse)
library(stringi)
out <- map2_df(stri_extract_all_regex(s1, "\\d+"), 
               stri_extract_all_regex(s1, "[HMS]"), ~ .x %>%
                 as.integer %>%
                 as.list %>% 
                 set_names(.y) ) 
out

out2 <- map2_df(stri_extract_all_regex(dfTrain$duration, "\\d+"), 
               stri_extract_all_regex(dfTrain$duration, "[HMS]"), ~ .x %>%
                 as.integer %>%
                 as.list %>% 
                 set_names(.y) ) 
out2
out2[is.na(out2)] <- 0
dfTrain1 = cbind(dfTrain, out2)
names(dfTrain1)
dfTrain1[,c('duration','H','M','S') ]
dfTrain1$totalTime = dfTrain1$H*60*60 + dfTrain1$M*60 + dfTrain1$S
dfTrain1[,c('duration','H','M','S', 'totalTime') ]

names(dfTrain1)
head(dfTrain1)
summary(dfTrain1)
apply(dfTrain,2, class)
# convert numeric columns 
dfTrain1$adview = as.numeric(dfTrain1$adview)
dfTrain1$views = as.numeric(dfTrain1$views)
dfTrain1$dislikes = as.numeric(dfTrain1$dislikes)

dfTrain1$likes = as.numeric(dfTrain1$likes)
dfTrain1$comment = as.numeric(dfTrain1$comment)
dfTrain1$published = as.Date(dfTrain1$published)
dfTrain1$dayspub = as.numeric(Sys.Date() - dfTrain1$published)
head(dfTrain1$dayspub)

head(dfTrain1$published)
head(dfTrain1)
summary(dfTrain1)
names(dfTrain1)
plot(y=dfTrain1$adview, x= dfTrain1[,3])
#pairs(c('adview','views','likes','dislikes','comment','totalTime','dayspub'), data=dfTrain1)
#car::scatterplot(c('adview','views','likes','dislikes','comment','totalTime','dayspub'), data=dfTrain1)
#pairs(~adview + views + likes + dislikes + comment + totalTime + dayspub,data=dfTrain1) 

library(rpart)
fit1 = rpart(adview ~ views + likes + dislikes + comment + category + totalTime + dayspub, data= dfTrain1, method='anova')
fit1
library(rpart.plot)

rpart.plot(fit1)
summary(dfTrain1$adview)
boxplot(dfTrain1$adview)
boxplot(dfTrain1$dayspub)


#ctree
library(party)
fit2 = ctree(adview ~ views + likes + dislikes + comment + category + totalTime + dayspub, data= dfTrain1)
fit2

print(fit2)
plot(fit2)


================================================
FILE: 95-studqueries/hitesh3.R
================================================
#hitesh - 11 Jun 18

df1 = read.csv(file.choose())
df2 = read.csv(file.choose())
#Test and filter outliers.
#Understand price fluctuations accounting the seasonal effect
#Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities
#De-seasonalise prices for each commodity and APMC according to the detected seasonality type
#Compare prices in APMC/Mandi with MSP(Minimum Support Price)- raw and deseasonalised
#Flag set of APMC/mandis and commodities with highest price fluctuation across different commodities in each relevant season, and year.
#Data: https://drive.google.com/drive/u/0/folders/0B-zoMsiXW40gZlNtNnlINEszRTg
# Variable description:
# msprice- Minimum Support Price
# arrivals_in_qtl- Quantity arrival in market (in quintal)
# min_price- Minimum price charged per quintal
# max_price- Maximum price charged per quintal
# modal_price- Mode (Average) price charged per quintal

# Submissions:Final cleaned file(s). 
# if the files are shared using GitHub with well versioned log)
# Documentation around the methodology, analysis, and final results that you want to share with the
# Government of Maharashtra. Do use graphs and charts to substantiate your analysis


#Outliers
#(https://stackoverflow.com/questions/16089572/how-to-detect-outliers-in-the-columns-of-a-dataframe-in-r)
  
#(https://eurekastatistics.com/using-mahalanobis-distance-to-find-outliers/)
#(http://www.questionflow.org/2017/12/26/combined-outlier-detection-with-dplyr-and-ruler/)
#(http://rischanlab.github.io/Outlier.html#Outlier_Detection_in_R)
#(https://www.math.csi.cuny.edu/Statistics/R/simpleR/printable/simpleR.pdf)
#(https://cran.r-project.org/web/packages/mvoutlier/mvoutlier.pdf)

#Modify this code
quantiles<-tapply(var1,names,quantile)
minq <- sapply(names, function(x) quantiles[[x]]["25%"])
maxq <- sapply(names, function(x) quantiles[[x]]["75%"])
var1[var1<minq | var1>maxq] <- NA
#----


str(df1)
head(df1)
outlier_values <- boxplot.stats(df1$msprice)$out  
outlier_values
boxplot(df1$msprice)
#no outliers

# for df2 - you will have split DF based in factor columns and then find outliers
# see links above




#Seasonality
names(df2)
#Detect seasonality type (multiplicative or additive) for each cluster of APMC and commodities
summary(df2)
df2$APMC = factor(df2$APMC)
df2$Commodity = factor(df2$Commodity)
summary(df2)
length(unique(df2$APMC))
length(unique(df2$Commodity))
head(df2)

dim(df2)
#Create a Time Series Format data from Year and Month

splt.by <- c('APMC','Commodity')
X1= split(df2, df2[,splt.by] )
X1[[1]]
lapply(X1, length)



X2 = split(df2, df2[,c('Commodity')])
X2[[1]]
lapply(X2, length)

X3 = split(df2, df2[,c('APMC')])
X3[[1]]
head(lapply(X3, length))


#(https://stackoverflow.com/questions/29046311/how-to-convert-data-frame-into-time-series-in-r)
#Convert to time series
X2[[1]]


head(df2, n=20)
names(df2)
df2a = df2[,c('date','Commodity','modal_price')]
head(df2a,n=20)
df2a[order(df2a$Commodity),][1:25,]
class(df2a$date)  #character

library(xts)
stocks <- xts(df[,-1], order.by=as.Date(df[,1], "%m/%d/%Y"))
as.Date('2015-04-01',format='%Y-%m-%d')

#(https://www.displayr.com/r-date-conversion/)
library(lubridate)
parse_date_time("July/1998", orders = c("bdy", "bY"))
parse_date_time('2015-04',orders=c('Ym'))

#(https://www.earthdatascience.org/courses/earth-analytics/time-series-data/summarize-time-series-by-month-in-r/)

library(zoo)

df2a$ym <- as.yearmon(df2a$date)
head(df2a$ym) # look at it
names(df2a)
plot(modal_price ~ ym, df2a) # plot it

#or you could create a zoo object:
  
dfz <- zoo(df2a$modal_price, df2a$ym)  #error since index values are not unique
#head(z) # look at it
#plot(z) # plot it

# convert your data to xts object
library(xts)
data.xts <- xts(df2a$modal_price,
                     as.POSIXct(df2a$ym))
library(rts)
# aggregate by ..
ep <- endpoints(data.xts,'years') # extract the end index on each year period
ep
ndvi.y <- period.apply(data.xts,ep,mean) # apply the mean function on each year
ndvi.y

epm <- endpoints(data.xts,'month') # month period
epm
ndvi.m <- period.apply(data.xts,epm,mean) # apply the mean function on each month
ndvi.m
dataTS = ndvi.m

library(ts)
#now use this for TS Analysis
stl(AirPassengers, s.window = 'periodic') # seasons to be considered periodic ie not changing over time
stl(dataTS, s.window = 'periodic')  #less period
dataTS
plot(dataTS)
#(https://stackoverflow.com/questions/12330581/too-few-periods-for-decompose) read this
#data less for extraction of seasonal componenet
monthplot(dataTS)
?stl
stl(dataTS, s.window = 4)  #less period


================================================
FILE: 95-studqueries/hitesh4.R
================================================
#Hitesh Time Series Jul 18
#Hitesh 06 Jul 18

data = read.csv(file.choose())
str(data)
summary(data)
head(data)
range(data$timestamp)
names(data)
nrow(data)
#check for duplicates, select only non-duplicates
data[duplicated(data[c(1,2,3)]),]
subset(data, duplicated(subset(data, select=c(1:3))))  #another method
#No duplicates proceed with data

#Missing Values
anyNA(data)

#make a copy
df= data
#str from time eg
help(strftime)
t = Sys.time()
cat(t,"\n") #1390506463
strftime(t,"%Y-%m-%d %H:%M:%S")
(timeStamp <-  strftime(t,"%Y-%m-%d %H:%M"))
typeof(timeStamp)

# Time from Timestamp
as.POSIXct(1377986400,origin="1970-01-01")
df$timestamp2 = as.POSIXct(df$timestamp ,origin="1970-01-01")

library(lubridate)
library(xts)
names(df)
df.xts = xts(df[1:4], order.by=df$timestamp2)
head(df.xts)

#subset(ts, ts$household_id==1)  #working
apply.daily(df.xts$value,mean)  #daily mean across all household & houseid

#use dplyr
library(dplyr)
hour <- as.POSIXlt(as.POSIXct(df$timestamp ,origin="1970-01-01"))$hour
head(hour,n=100)
head(df);names(df)


hourdf <- df %>%
  group_by(hour=floor_date(timestamp2, "1 hour"), house_id, household_id) %>%
  summarize(sumV=sum(value), meanV = mean(value), sdV = sd(value))

print(tbl_df(hourdf), n=30)
summary(df)
hourdf %>% arrange(hour, house_id, household_id) %>% select(hour, house_id, household_id, sumV,meanV, sdV)  %>% filter(house_id==0) %>% print(n=100)


# Need more ways of grouping and rollmean, rollsd 

ends <- endpoints(df.xts,'hours',2) 
period.apply(df.xts,ends ,sum)


library(TTR)
TTR::rollsd


================================================
FILE: 95-studqueries/hiteshJul18.R
================================================
#Hitesh 06 Jul 18

data = read.csv(file.choose())
str(data)
summary(data)
head(data)
range(data$timestamp)
names(data)
nrow(data)
#check for duplicates, select only non-duplicates
data[duplicated(data[c(1,2,3)]),]
data2= subset(data, !duplicated(subset(data, select=c(1:3))))
nrow(data2)
#No duplicates proceed with data
aggregate(data$value, by=list(data$house_id), FUN=mean)
aggregate(data$value, by=list(data$house_id, data$household_id), FUN=mean)
# this is what we dont need

#make a copy
df= data2
#str from time eg
help(strftime)
t = Sys.time()
cat(t,"\n") #1390506463
strftime(t,"%Y-%m-%d %H:%M:%S")
(timeStamp <-  strftime(t,"%Y-%m-%d %H:%M"))
typeof(timeStamp)

# Time from Timstamp
as.POSIXct(1377986400,origin="1970-01-01")
df$timestamp2 = as.POSIXct(df$timestamp ,origin="1970-01-01")
head(df)

library(lubridate)
library(xts)
names(df)
df.xts = xts(df[1:4], order.by=df$timestamp2)
head(df.xts)
#subset(ts, ts$household_id==1)  #working
str(ts)
aggregate(ts$value, by=list(ts$house_id,ts$household_id), FUN=mean)
apply.daily(ts,mean)  #daily mean across all household & houseid
?apply.daily

library(highfrequency)
aggregatets(ts, FUN="previoustick", on="minutes", k=1, weights=NULL,
            dropna=FALSE)

names(ts)
aggregate(ts, by=list(ts$house_id), FUN = sum, sd,
          regular = NULL, frequency = NULL, coredata = TRUE)



# Cumulative average for last 60 min for each household and house id.
# then compare


================================================
FILE: 95-studqueries/lalit1.R
================================================
# Lalit
# 

#Missing Values
item = c('item1', 'item2', 'item1', 'item1', 'item2')
rate = c(59, 60, NA, NA, NA)
class = c('fmcg','type2', 'type2', 'type1', NA)
df = data.frame(item,rate,class)
df

sum(is.na(df$rate))

aggregate(.~ item, df, FUN=NROW, na.rm=T, na.action=NULL)

aggregate(. ~ item, df, function(x) sum(x, na.rm=NULL), na.action = na.pass)

complete.cases(df)

sapply(df, function(x) sum(is.na(x)))

library(dplyr)
df %>%   group_by(item) %>% 
  summarise_each(funs(sum(., na.rm = NULL)))

library(tidyverse)
map(df, ~sum(is.na(.)))

#dplyr -----
library(dplyr)
df %>%
  select(everything()) %>%  # replace to your needs
  summarise_all(funs(sum(is.na(.))))

apply(df, MARGIN = 1, function(x) sum(is.na(x)))
apply(df, MARGIN = 2, function(x) sum(is.na(x)))

df %>% rowwise %>% summarise(NA_per_row = sum(is.na(.)))

#zoo ------
library(zoo)
na.aggregate(df, by=1,FUN=mean,na.rm = FALSE,maxgap = Inf)
df

(df2=na.aggregate(df[,1:2], by=list(item), FUN=first))
(df2=na.aggregate(df[,c('item','rate')], by=list(item), FUN=first))

df2
(df3=na.aggregate(df, by=list(item), FUN=first))
str(df3)
df3$rate = as.numeric(df3$rate)
str(df3)
na.aggregate(df, item)

#dplyr ------ for filter
library(dplyr)
df %>% tally()
df %>% count(item)
df %>% add_tally()
df %>% count(item, sort = TRUE)
df
df %>%  add_count(item) %>% filter(n >= 3)
df %>%  add_tally(class) %>% filter(n >= 1)


#dplyr & zoo -----
library("dplyr")
library("zoo")

df %>% group_by(item) %>% transmute(date=na.locf(rate, na.rm=FALSE))
df %>% group_by(item) %>% transmute(date=na.locf(class, na.rm=FALSE))

#data- table -----
require(data.table)
(dfT= setDT(df))   ## change it to data.table in place
setkey(dfT, item)     ## needed for fast join
dfT.woNA <- dfT[!is.na(rate)]  ## version without the NA rows
dfT.woNA
# rolling self-join will return what you want
dfT.woNA[dfT$item, roll=TRUE]  ## will match previous if not found

#Plyr ---------------

impute <- function(x, fun) {
  missing <- is.na(x)
  replace(x, missing, fun(x[!missing]))
}
library(plyr)
plyr::ddply(df, ~ item, transform, rate = impute(rate, first))
plyr::ddply(df, ~ item, transform, class = impute(class, first))


# aggreage ----
count_zero<-function(x){
  sum(x==60,na.rm=TRUE)
}
aggregate(df[,-1],list(df$item),count_zero)

df
count_NA<-function(x){
  sum(is.na(x),na.rm=F)
}
aggregate(df[,-1],list(df$item),count_NA)
df


df %>% group_by(item) %>% summarise(rateNA=sum(is.na(rate)),  classNA=sum(is.na(class)))
#%>% arrange(desc(rateNA))


library(magrittr)
aggregate(df,list(df$item), . %>% is.NA %>% sum(na.rm=F))



#filter - dplyr
filter(df, item == "item1")
select(df, item, class)
df %>% filter(item == "item1"  | "item2") %>%  select(item, rate)

df %>%  mutate(rate = first(rate)) %>%  filter(is.na(class)) %>%  head
detach("package:plyr", unload=TRUE)
library(dplyr)
df %>% group_by(rate) %>%  summarize(n=n())          

aggregate(df$rate, by=list(item, rate), FUN=length, na.action='keep')


library(visdat)
vis_miss(df)

library(Amelia)
missmap(df)

row.has.na <- apply(df, 1, function(x){any(is.na(x))})
row.has.na

df
df %>% mutate(condition = complete.cases(select(., item:class)))
df %>% mutate(condition = complete.cases(select(., class)))







cat = c('small', 'medium',NA, NA)
type = c('Grocery', NA,'SM1','SM1')
tier = c('T1','T2',NA,'T2')
df5 = data.frame(cat, type, tier)
df5
is.na(df5)
vis_miss(df5)


df
df$gender = c(rep('NA',5))
df
df[item=='item1' & class=='type1',]
df[item=='item1' & class=='type1',]
df$gender[ item=='item1' | class=='type1'] = 'Male'
df
df %>% filter(item=='item1')
df[1:2,3:4]
df


================================================
FILE: 95-studqueries/meena1
================================================
Please find your assignment. Submission Deadline : ASAP 

Assignment:

Hope you are aware of Chit Funds available in market, where a group of people pool in some money and the person who win the lottery every month takes the lump some that particular month and pay back monthly a pre-fixed amount .

So for the complex cash flows & returns, build a R program to get the IRR(Internal Rate of Return) for the investment.

Sample chit to work on :

Duration : 40 Months

Instalment : 5000/- Per month till he lift the chit from group. After he lift - he start paying 7000/-

Case 1: Customer lift the chit in 4th Month : He paid 3X5000 + 37X7000 = 274000/- But Customer offered an yield of 203000/-

Case 2: Customer lift the chit in the 40th Month : He paid 39X5000 + 1X7000 = 202000/- Customer offered an yield of 239000/-

Attaching the Chit Fund details for reference- Reach me @ 8800446604 for any queries


================================================
FILE: 95-studqueries/meena2.R
================================================
#Meena IRR
#https://www.rdocumentation.org/packages/FinancialMath/versions/0.1.1/topics/IRR
library(FinancialMath)

#IRR(cf0,cf,times,plot=FALSE)

IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))
IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6), plot=T)

#Case - DU
library(FinCal)
irr(cf=c(-5, 1.6, 2.4, 2.8))






#Case 1: Customer lift the chit in 4th Month : He paid 3X5000 + 37X7000 = 274000/- But Customer offered an yield of 203000/-
#Method1
cf1=c(rep(5000,3),rep(7000,37))
length(cf1)
times1=c(1:3,4:40)
length(times1)
cf01=203000
#IRR(cf0=203000,cf=c(rep(5000,3),rep(7000,37)), times=c(1:3,4:40))
IRR(cf0=cf01,cf=cf1, times=times1)

#Method2
cf1=c(5000, 7000)
length(cf1)
times1=c(3,37)
length(times1)
cf01=203000
IRR(cf0=cf01,cf=cf1, times=times1)

  
#Case 2: Customer lift the chit in the 40th Month : He paid 39X5000 + 1X7000 = 202000/- Customer offered an yield of 239000/-

#Method1
cf2=c(rep(5000,39),rep(7000,1))
length(cf2)
times2=c(1:39,40)
length(times2)
cf02=239000
IRR(cf0=cf02,cf=cf2, times=times2,plot=T)


#Method2
cf2=c(5000, 7000)
length(cf2)
times2=c(39,1)
length(times2)
cf02=239000
IRR(cf0=cf02,cf=cf2, times=times2)



?IRR
#https://cran.r-project.org/web/packages/FinCal/FinCal.pdf
#https://cran.r-project.org/web/packages/irr/irr.pdf
#https://stackoverflow.com/questions/11660187/any-r-package-available-to-calculate-irr-from-uneven-payments-on-specific-dates
#
#Meena2
library(financial)
?discount.rate
FinCal::discount.rate(n, pv, fv, pmt, type = 0)

?irr
FinCal::irr(cf=c(-5, 1.6, 2.4, 2.8))
?irr2
FinCal::irr2(cf=c(-5, 1.6, 2.4, 2.8))
FinCal::irr2(cf=c(-200, 50, 60, -70, 30, 20))

?IRR
FinancialMath::IRR(cf0=1,cf=c(1,2,1),times=c(1,3,4))
FinancialMath::IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))
FinancialMath::IRR(cf0=203000,cf=c(5000, 7000),times=c(3,37))


FinCal::irr2(cf=c(-203000, rep(5000,3), rep(7000,37))) #high processing time

#Case1:203000, 5000 x 3, 7000 x 37
s1= sum(rep(5000,3), rep(7000,37))
s1-203000
rep(5000,3)
IRR.Project1 <- IRR(-203000, c(5000,5000,5000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000,7000), times=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15))
IRR.Project1
?IRR


#Case2: 239000, 5000 x 29, 7000 x 1
s2= sum(rep(5000,39), rep(7000,1))
s2-239000


#Case

IRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5))
IRR.Project2 <- IRR(-1000, c(-10, 0, 10, 20, 2000), c(1:5))

================================================
FILE: 95-studqueries/meena3.R
================================================
#Meena2
#https://www.free-online-calculator-use.com/xirr-calculator.html

library(financial)
?discount.rate
FinCal::discount.rate(n, pv, fv, pmt, type = 0)

?irr
FinCal::irr(cf=c(-5, 1.6, 2.4, 2.8))
?irr2
FinCal::irr2(cf=c(-5, 1.6, 2.4, 2.8))
FinCal::irr2(cf=c(-200, 50, 60, -70, 30, 20))

?IRR
FinancialMath::IRR(cf0=1,cf=c(1,2,1),times=c(1,3,4))
FinancialMath::IRR(cf0=100,cf=c(1,1,30,40,50,1),times=c(1,1,3,4,5,6))
FinancialMath::IRR(cf0=203000,cf=c(5000, 7000),times=c(3,37))


FinCal::irr2(cf=c(-203000, rep(5000,3), rep(7000,37))) #high processing time
FinCal::irr2(cf=c(-100, rep(5,3), rep(7,37))) #this worked #0.059908


#Case1:203000, 5000 x 3, 7000 x 37
s1= sum(rep(5000,3), rep(7000,37))
s1-203000
rep(5000,3)
(IRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5)))
(IRR.Project1 <- IRR(-203000, c(rep(5000,3), rep(7000,37)), c(1:40)))


#Case2: 239000, 5000 x 29, 7000 x 1
s2= sum(rep(5000,39), rep(7000,1))
s2-239000


#Case

IRR.Project1 <- IRR(-1000, c(1250, 10, 10, 20, 20), c(1:5))
IRR.Project2 <- IRR(-1000, c(-10, 0, 10, 20, 2000), c(1:5))

================================================
FILE: 95-studqueries/missingValue.R
================================================
#missing Value Imputation

pacman::p_load(mice, VIM, ggplot2, tidyverse)

df= sleep
df

missing_by_column <- df %>% 
  is.na %>% # check if each cell is na
  as_data_frame %>% # convert to data-frame
  mutate(row_number = 1:nrow(.)) %>% # add a column with the row number
  gather(variable, is_missing, -row_number) # turn wide data into narrow 
data

ggplot(missing_by_column, aes(x = variable, y = row_number, fill = is_missing)) +  geom_tile() +    theme_minimal() +   scale_fill_grey(name = "",labels = c("Present","Missing")) +  theme(axis.text.x  = element_text(angle=45, vjust=0.5, size = 8)) +   labs(x = "Variables in Dataset", y = "Rows / observations")


# initialize an empty model to take the parameters from
empty_model <- mice(df, maxit=0) 
empty_model
method <- empty_model$method
predictorMatrix <- empty_model$predictorMatrix

# first make a bunch of guesses...
imputed_data <- mice(df, method, predictorMatrix, m=5)
# then pick one for each variable
imputed_data <- complete(imputed_data)


================================================
FILE: 95-studqueries/sidana2.R
================================================
#sidhana

bank = read.csv(file.choose())
head(bank)
bank$y = factor(bank$y)
library(rpart)
library(rpart.plot)

fit = rpart(y ~. , data= bank)
fit
library(rattle)
fancyRpartPlot(fit)
rpart.plot::prp(fit, extra=104, type=4, fallen.leaves = T)


================================================
FILE: 95-studqueries/tanviTS1.R
================================================
#tanvi tiwari - TS assignment

#store dataset in E:/data/tanviTS.xlsx

pacman::p_load('dplyr', 'directlabels', 'googlesheets4', 'readxl', 'ggplot2', 'reshape2', 'expss', 'gridExtra', 'grid', 'xts', 'lubridate')

dir1 <- "E:/data/"
dir(dir1)
file1 <- c("tanviTS.xlsx") 

(loc1 <- paste(dir1, file1, sep=""))
(mysheets <- readxl::excel_sheets(loc1))

head(readxl::read_excel(loc1, sheet=mysheets[1], skip=0))
head(readxl::read_excel(loc1, sheet=mysheets[2], skip=0))
head(readxl::read_excel(loc1, sheet=mysheets[3], skip=0))
head(readxl::read_excel(loc1, sheet=mysheets[4], skip=0))
head(readxl::read_excel(loc1, sheet=mysheets[5], skip=0))

#RGDP_Q------
(RGDP <- readxl::read_excel(loc1, sheet=mysheets[3], skip=0))
str(RGDP)
names(RGDP)
head(RGDP[,1:6])  #1995 - Q1
tail(RGDP[,1:6])  #2019 - Q4
nrow(RGDP)
#convert to xts time series
?yearqtr
as.yearqtr('1995 Q1'); as.yearqtr('2019 Q4')
seq(as.Date("1995-01-01"), length=100, by="quarter")
xtsIndex <- as.yearqtr(seq(as.Date("1995-01-01"), length=100, by="quarter"))

# Use xts() to create TS
(RGDP1 <- xts(x = RGDP[,-c(1:5)], order.by = xtsIndex))
plot(RGDP1, legend.loc = 'top')
addLegend("right")
head(RGDP1)
head(RGDP)
RGDP1A <- data.frame(period = xtsIndex, coredata(RGDP1))
RGDP1B <- reshape2::melt(data=RGDP1A, id.vars='period', variable.name ='country')
head(RGDP1B)
str(RGDP1B)

options(scipen = F)
RGDP1B %>% ggplot(., aes(x=period, y=value, group=country)) + geom_line(aes(colour=country)) + guides(colour=guide_legend(ncol=1)) 

#create Quarterly Time Series for Austria only
?ts
austria1 <- ts(RGDP1[,c('Austria')], start=c(1995,1), end=c(2019,4), frequency = 4)
austria1

austria1TS <- decompose(austria1)
austria1TS
austria1TS$seasonal
austria1TS$random
austria1TS$trend

plot(austria1TS)

install.packages("remotes")
remotes::install_github("RamiKrispin/TSstudio", force=T)
library(RamiKrispin/TSstudio)
ts_decompose(austria1, type = "additive", showline = TRUE)



3
RDGP1 <- reshape2::melt()

status1 <- readxl::read_excel(loc1, sheet='status1', skip=1, col_types = c('date','text', rep('numeric',16), 'date', 'text') )

================================================
FILE: 95-studqueries/vivekIIMLN.R
================================================
#Vivek Ranjan - IIMLN

pacman::p_load(readxl, dplyr)

#Importing the dataset in R

dataset = readxl::read_excel(path='E:/data/students/Anand Vihar_CPCB Data.xlsx', sheet='Sheet1')
colSums(is.na(dataset))
#Getting independent variables in the training set as below:
training_data   = na.omit(dataset)
colSums(is.na(training_data))

#Creating testing data for blank PM2.5 variables:
testing_data <- dataset %>% select(PM2.5, PM10, NO2, NOx, NH3, CO, Benzene) %>% filter(is.na(dataset$PM2.5))

#Fitting Multiple Linear Regression to the Training Set
regressor = lm(PM2.5 ~ ., data = training_data)

#Finally predicting the data:
P = predict(regressor, testing_data)

#Through this approach, I am able to get imputed values for PM2.5 but how to put them at exact places in my excel sheet.
#Also, I need to impute for other missing variables also.

#Imputing missing values

#simulated case--------------------
#create Vectors to be combined into DF

(name = paste('student',1:5, sep=''))
(marks1 = c(70, 50, NA, 50, NA))
(marks2 = c(NA, 40, 30, NA, 60))
(marks3 = c(NA, NA, 40, NA, 70))
#add missing values
df <- data.frame(name, marks1, marks2, marks3, stringsAsFactors = F)
df
row.names(df) = df$name
df
df$name = NULL
df
colSums(is.na(df))
is.na(df)
which(is.na(df))
sum(is.na(df))  #7 missing values

#list of missing values
#predict values
(MVpredict = c(100,200,300,400, 600, 700,800))
(VectorDF <- unlist(df))  #make it into a vector
VectorDF[which(is.na(df))]
(VectorDF[which(is.na(df))] <- MVpredict)
VectorDF
newDF <- matrix(VectorDF, nrow=5)
colnames(newDF) = names(df)
row.names(newDF) = rownames(df)
newDF
df
cbind(df, newDF)
#see if the values have been imputed..

#convert DF into a vector to impute.
#predicted values also have to be in sequence of missing values.
#recombine into matrix then DF

#this is approach not a solution.. similar to the problem u have described.

# 
# https://medium.com/coinmonks/dealing-with-missing-data-using-r-3ae428da2d17
# http://r-statistics.co/Missing-Value-Treatment-With-R.html
# https://towardsdatascience.com/data-cleaning-and-detecting-missing-values-from-australian-open-tennis-data-in-r-part-2-b4f6f32beffd
# https://datascienceplus.com/imputing-missing-data-with-r-mice-package/
# http://www.stat.columbia.edu/~gelman/arm/missing.pdf
# https://www.kdnuggets.com/2017/09/missing-data-imputation-using-r.html
# https://www.kaggle.com/rtatman/data-cleaning-challenge-imputing-missing-values


#------- 24 May 20------
dataset = read.csv('E:/DUdata/students/ranjan2.csv')
head(dataset)
str(dataset)
dataset$From.Date
summary(dataset$From.Date)  #444 NAs
dataset$From.Date = as.POSIXct(dataset$From.Date, '%d-%m-%Y %H:%M', tz='Asia/Calcutta')
(dataset$wwkdays1 = weekdays(dataset$From.Date, abbreviate = T))
(dataset$wwkdays2 = as.character(dataset$From.Date, '%w'))
(dataset$wwkdays3 = as.character(dataset$From.Date, '%W'))
(dataset$wwkdays4 = lubridate::wday(dataset$From.Date, label = TRUE))
(dataset$wwkdays5 = lubridate::wday(dataset$From.Date))
dataset[,c('From.Date','wwkdays1', 'wwkdays2', 'wwkdays3', 'wwkdays4', 'wwkdays5')]

#https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/weekdays
#https://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html
#https://stat.ethz.ch/R-manual/R-devel/library/base/html/strptime.html


================================================
FILE: 96-cancer/data-cancer.R
================================================
# Data for Classfication

# dataset for Classification
#https://github.com/kabacoff/RiA2/blob/master/Ch17%20Classification.R

breast = read.table('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', sep=',', header=F, na.strings='?')
names(breast)

names(breast) <- c("ID", "clumpThickness", "sizeUniformity",
                   "shapeUniformity", "maginalAdhesion", 
                   "singleEpithelialCellSize", "bareNuclei", 
                   "blandChromatin", "normalNucleoli", "mitosis", "class")
names(breast)

df <- breast[-1]
df$class <- factor(df$class, levels=c(2,4), labels=c("benign", "malignant"))



set.seed(1234)
train <- sample(nrow(df), 0.7*nrow(df))
df.train <- df[train,]
df.validate <- df[-train,]
table(df.train$class)
table(df.validate$class)

================================================
FILE: 96-cancer/rf-cancer.R
================================================
# Random Forests
#http://www.instituteofanalytics.com/forum/uploads/editor/ls/4kivialj5lvj.pdf

library(randomForest)
set.seed(1234)

fit.forest = randomForest(class ~ . , data=df.train, na.action = na.roughfix, importance=T)
fit.forest

importance(fit.forest, type=2)

forest.pred = predict(fit.forest, df.validate)
forest.perf = table(df.validate$class, forest.pred, dnn=c('Actual', 'Predicted'))
forest.perf

caret::confusionMatrix(forest.perf)


#Plot Random Forests
library(reprtree)
getTree(fit.forest, 1, labelVar=TRUE)
  
library(reprtree)

cforest(Species ~ ., data=iris, controls=cforest_control(mtry=2, mincriterion=0))



#----
library(devtools)
if(!('reprtree' %in% installed.packages())){
  install_github('araastat/reprtree')
}


================================================
FILE: 96-cancer/svm-cancer1.R
================================================
# Support Vector Machine
#https://rpubs.com/ricardosc/breast-cancer
#https://github.com/kabacoff/RiA2/blob/master/Ch17%20Classification.R

#first setup the data from data-breast.R
source('./cls/case-cancer/data-cancer.R')
library(e1071)
set.seed(1234)

names(df.train)
head(df.train)
summary(df.train)
fit.svm = svm(class ~ . , df.train)
fit.svm
plot(fit.svm, data=df.train)  # will not work
plot(fit.svm, df.train, clumpThickness ~ sizeUniformity)
#    slice = list(Sepal.Width = 3, Sepal.Length = 4))
#http://ugrad.stat.ubc.ca/R/library/e1071/html/plot.svm.html



svm.pred = predict(fit.svm, na.omit(df.validate))
svm.pred
plot(svm.pred)
plot(svm.pred, na.omit(df.validate))


na.omit(df.validate)$class

svm.perf = table(na.omit(df.validate)$class, svm.pred, dnn=c('Actual','Predicted'))
svm.perf                 


#Cannot handle missing values in IV
#scaling done before classification model
#better accuracy but < random forests
# Predicted
# Actual      benign malignant
# benign       116         4
# malignant      3        77

#Tuning
set.see(1234)
#tuned = tune.svm(class ~ ., data=df.train, gamma=10 ^ (-6:1), cost=10^(-10:10))
#tuned

fit.svm = svm(class ~. , df.train, gamma=0.2, cost=1)
fit.svm
svm.pred = predict(fit.svm, na.omit(df.validate))
svm.pred
svm.perf = table(na.omit(df.validate)$class, svm.pred, dnn=c('Actual', 'Predicted'))
svm.perf
# Predicted
# Actual      benign malignant
# benign       116         4
# malignant      3        77

dim(df.validate)
library(caret) 
confusionMatrix(svm.perf)
(116+77) ; 193/220


================================================
FILE: 96-cancer/svm-examples.R
================================================
# SVM

# Solved Eg
## a simple example
library(MASS)
data(cats)
m <- svm(Sex~., data = cats)
head(cats) # 2dim : fix for higher dime
plot(m, cats)

## more than two variables: fix 2 dimensions
data(iris)
m2 <- svm(Species~., data = iris)
plot(m2, iris, Petal.Width ~ Petal.Length,
     slice = list(Sepal.Width = 3, Sepal.Length = 4))

#small examples on SVM
library(e1071)

day = c(0,1,2,3,4,5,6)
weather = c(1,0,0,0,0,0,0)
happy = factor(c(T,F,F,F,F,F,F))

d = data.frame(day=day, weather=weather, happy=happy)
head(d)
model = svm(happy ~ day + weather, data = d)

plot(x=d$weather, y=d$day, xlim=c(0,2))
plot(model, d,add=T, yaxt=n, xaxt=n, xlim=c(0,2))

#add another row
d1a = data.frame(day=c(7), weather=c(1), happy=c(T))
d2 = rbind(d, d1a)
d2
model2 = svm(happy ~ day + weather, data = d2)
plot(x=d2$weather, y=d2$day, xlim=c(0,2))
plot(model2, d2,add=T, yaxt=n, xaxt=n, xlim=c(0,2))

#add another row
d1b = data.frame(day=c(8), weather=c(0), happy=c(T))
d3 = rbind(d2, d1b)
d3
model3 = svm(happy ~ day + weather, data = d3)
plot(x=d3$weather, y=d3$day, xlim=c(0,2))
plot(model3, d3,add=T, yaxt=n, xaxt=n, xlim=c(0,2))



================================================
FILE: 96-iris/sumgraph1.R
================================================
# Descriptive Stats and Graphs
# http://www.sthda.com/english/wiki/descriptive-statistics-and-graphics


#Import your data into R----

# Store the data in the variable my_data
my_data <- iris

#Check your data----
# Print the first 6 rows
head(my_data, 6)

#R functions for computing descriptive statistics----

#Descriptive statistics for a single group----

#Measure of central tendency: mean, median, mode----
# Compute the mean value
mean(my_data$Sepal.Length)

# Compute the median value
median(my_data$Sepal.Length)

# Compute the mode
#install.packages("modeest")
require(modeest)
mfv(my_data$Sepal.Length)


#Measure of variablity----

#Range: minimum & maximum----
# Compute the minimum value
min(my_data$Sepal.Length)

# Compute the maximum value
max(my_data$Sepal.Length)

# Range
range(my_data$Sepal.Length)

#Interquartile range---
sort(my_data$Sepal.Length)
quantile(my_data$Sepal.Length)

#By default, the function returns the minimum, the maximum and three quartiles (the 0.25, 0.50 and 0.75 quartiles).
quantile(my_data$Sepal.Length, seq(0, 1, 0.1))
IQR(my_data$Sepal.Length)

#Variance and standard deviation----

# Compute the variance
var(my_data$Sepal.Length)
# Compute the standard deviation =
# square root of th variance
sqrt(var(my_data$Sepal.Length))
sd(my_data$Sepal.Length)

#Median absolute deviation----
# Compute the median
median(my_data$Sepal.Length)
# Compute the median absolute deviation : Dev wrt median value
mad(my_data$Sepal.Length)

#Which measure to use?----

# Range. It’s not often used because it’s very sensitive to outliers.
# Interquartile range. It’s pretty robust to outliers. It’s used a lot in combination with the median.
# Variance. It’s completely uninterpretable because it doesn’t use the same units as the data. It’s almost never used except as a mathematical tool
# Standard deviation. This is the square root of the variance. It’s expressed in the same units as the data. The standard deviation is often used in the situation where the mean is the measure of central tendency.
# Median absolute deviation. It’s a robust way to estimate the standard deviation, for data with outliers. It’s not used very often.
# In summary, the IQR and the standard deviation are the two most common measures used to report the variability of the data.




#Computing an overall summary of a variable and an entire data frame----

#summary() function----
summary(my_data$Sepal.Length)
summary(my_data, digits = 1)

# summary() is automatically applied to each column. The format of the result depends on the type of the data contained in the column. For example:
#   If the column is a numeric variable, mean, median, min, max and quartiles are returned.
# If the column is a factor variable, the number of observations in each group is returned.



#sapply() function----

# Compute the mean of each column
sapply(my_data[, -5], mean)

# Compute quartiles
sapply(my_data[, -5], quantile)


#stat.desc() function----
#install.packages("pastecs")

# Compute descriptive statistics
library(pastecs)
res <- stat.desc(my_data[, -5])
round(res, 2)

#Case of missing values----
mean(my_data$Sepal.Length, na.rm = TRUE)

#Graphical display of distributions----

#Installation and loading ggpubr----
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
#or install.packages("ggpubr")

library(ggpubr)

#Box plots----
ggboxplot(my_data, y = "Sepal.Length", width = 0.5)

#Histogram----
#Histograms show the number of observations that fall within specified divisions (i.e., bins).
gghistogram(my_data, x = "Sepal.Length", bins = 9, add = "mean")


#Empirical cumulative distribution function (ECDF)----
#ECDF is the fraction of data smaller than or equal to x.
ggecdf(my_data, x = "Sepal.Length")


#Q-Q plots----
ggqqplot(my_data, x = "Sepal.Length")


#Descriptive statistics by groups----

#Frequency tables----
library(dplyr)
group_by(my_data, Species) %>% 
  summarise(count = n(),   mean = mean(Sepal.Length, na.rm = TRUE),
    sd = sd(Sepal.Length, na.rm = TRUE)
  )

library("ggpubr")
# Box plot colored by groups: Species
ggboxplot(my_data, x = "Species", y = "Sepal.Length",
    color = "Species",  palette = c("#00AFBB", "#E7B800", "#FC4E07"))

# Stripchart colored by groups: Species
ggstripchart(my_data, x = "Species", y = "Sepal.Length",
             color = "Species",
             palette = c("#00AFBB", "#E7B800", "#FC4E07"),
             add = "mean_sd")

#Create some data----

# Hair/eye color data
str(HairEyeColor)
HairEyeColor
apply(HairEyeColor, c(1,2), sum)

df <- as.data.frame(HairEyeColor)
hair_eye_col <- df[rep(row.names(df), df$Freq), 1:3]
rownames(hair_eye_col) <- 1:nrow(hair_eye_col)
head(hair_eye_col)

#Simple frequency distribution: one categorical variable----
# hair/eye variables
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Frequency distribution of hair color
table(Hair)
table(Eye)

#Two-way contingency table: Two categorical variables----
# Compute table and convert as data frame
df <- as.data.frame(table(Hair))
df

# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq")

#Multiway tables: More than two categorical variables----
tbl2 <- table(Hair , Eye)
tbl2
#function xtabs(), which will create cross tabulation of data frames with a formula interface.
xtabs(~ Hair + Eye, data = hair_eye_col)

#Compute table margins and relative frequency----

df <- as.data.frame(tbl2)
head(df)


# Visualize using bar plot
library(ggpubr)
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", 
          palette = c("brown", "blue", "gold", "green"))

# position dodge
ggbarplot(df, x = "Hair", y = "Freq",
          color = "Eye", position = position_dodge(),
          palette = c("brown", "blue", "gold", "green"))


#Multiway tables: More than two categorical variables
#Hair and Eye color distributions by sex using xtabs():
xtabs(~Hair + Eye + Sex, data = hair_eye_col)

#ftable when you have more than two variables:
ftable(Sex + Hair ~ Eye, data = hair_eye_col)


#Compute table margins and relative frequency
#The function margin.table() and prop.table() can be used to compute table margins and relative frequencies, respectively.
#Format of the functions: margin.table(x, margin = NULL), prop.table(x, margin = NULL)
#x: table; margin: index number (1 for rows and 2 for columns)

#compute table margins:
Hair <- hair_eye_col$Hair
Eye <- hair_eye_col$Eye
# Hair/Eye color table
he.tbl <- table(Hair, Eye)
he.tbl

# Margin of rows
margin.table(he.tbl, 1)

# Margin of columns
margin.table(he.tbl, 2)

# Frequencies relative to row total
prop.table(he.tbl, 1)

# Table of percentages
round(prop.table(he.tbl, 1), 2)*100

#express the frequencies relative to the grand total, use this:
he.tbl/sum(he.tbl)


================================================
FILE: 96-mtcars/10b-datastructures.R
================================================
# Data Structures in Slides

?seq
#control+enter when you are in the line to execute
#
# Vectors-----
(x=1:10)
(x=c(1,2,3,4,5,10))
(x=letters[1:10])
(x=c(T,F,T,T))

#access elements
(x = seq(0,10,by=2))
#[1]  0  2  4  6  8 10
x[3]  # access 3rd element
#[1] 4
x[c(2, 4)]     # access 2nd and 4th element
#[1] 2 6
x[-1]          # access all but 1st element
#[1]  2  4  6  8 10
x[c(2, -4)]    # cannot mix positive and negative integers
#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts
x[c(2.4, 3.54)]    # real numbers are truncated to integers
#[1] 2 4

#modify
(x = -3:2)
#[1] -3 -2 -1  0  1  2
x[2] <- 0; x        # modify 2nd element
#[1] -3  0 -1  0  1  2
x[x<0] = 5; x   # modify elements less than 0
#[1] 5 0 5 0 1 2
x = x[1:4]; x      # truncate x to first 4 elements
#[1] 5 0 5 0

#delete vector
(x = seq(1,5, length.out = 10))
#[1] 1.000 1.444 1.889 2.333 2.778 3.222 3.667 4.111 4.556 5.000
x = NULL
x
#NULL
x[4]
#NULL




#Matrix-----
(m1 = matrix(1:12, nrow=4))
(m2 = matrix(1:12, ncol=3, byrow=T))

class(m1)
#[1] "matrix"
attributes(m1)
#$dim
#[1] 4 3
dim(m1)
#[1] 4 3

#names of cols and rows
m1
(colnames(m1) = paste('C',1:3, sep=''))
(rownames(m1) = paste('R',1:4, sep=''))
m1

#Vector to Matrix
(m3 = 1:24)
dim(m3)= c(6,4)
m3

#access elements
m2
m2[1,]  #first row
m2[c(1,3,4),]  #1st,3rd,4th row

m2[,1]  #first col
m2[,2:3] # 2nd to 3rd coln

m2[c(1,2),c(2,3)]
m2[,]
m2[-2,] # exclude 2nd row
m2[1:5] # matrix is like vector
m2[c(TRUE,FALSE),c(2,3)] #logical indexing
m2[m2 > 5]

#modify Vector
m2[2,2]
m2[2,2] = 10
m2
m2[m2> 10] = 99
m2
rbind(m2, c(50,60,70))
cbind(m2, c(55,65,75,85))

#row and col wise summary
m1
colSums(m1); rowSums(m1)
colMeans(m1); rowMeans(m1)

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4), FUN="*" ) #colwise

#addmargins
m1
addmargins(m1,1,sum) #colwise function
addmargins(m1,2,mean) #rowwise function
addmargins(m1,c(1,2),mean) #row & col wise function
addmargins(m1,c(1,2),list(list(mean,sum), list(var,sd))) #row & col wise function


#Array-----
length(100:123)
4*3*2
#2 coys, 3 products, 4 locations sold qty
(a1 = array(100:123, dim=c(4,3,2)))
(loc = paste('loc', 1:4,sep='-'))
(product = paste('p', 1:3,sep='@'))
(coy = paste('coy', 1:2,sep='%'))
dimnames(a1) = list(loc, product, coy)
a1
apply(a1,1, sum) #locationwise
apply(a1,2, sum) #productwise
apply(a1,c(1,2), sum) #product-location wise
apply(a1,c(2,3), sum) #product-coy wise
apply(a1,c(1,3), sum) #coy-location
apply(a1,3, sum) #coywise
sum(a1) #total


#DataFrame----
#create Vectors to be combined into DF
(rollno = 1:30)
(sname = paste('student',1:30,sep=''))
(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
(marks = floor(rnorm(30, mean=50,sd=10)))
plot(density(marks)); abline(v=50)
(marks2 = ceiling(rnorm(30,40,5)))
plot(density(marks2)); abline(v=c(40,50))
(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))
#create DF
df1= data.frame(rollno, sname, gender, marks, marks2, course)
str(df1) #structure of DF
head(df1) #top 6 rows
head(df1,n=3) #top 3 rows
tail(df1) #last 6 rows
class(df1) # DF
summary(df1) #summary

df1  #full data
df1[,c('course')]
df1$course
df1$gender  # one column
df1[ , c(2,4)] #multiple columns
df1[1:10 ,] #select rows, all columns
#as per conditionis
names(df1)
df1[ marks > 50 & gender=='F', c('rollno', 'sname','marks')]
df1[ marks < 50 & gender=='F', c(1,2,3,4)]
df1[ marks > 50 & gender=='F', ]

names(df1)  # names of columns
dim(df1)  #Dimensions

aggregate(df1$marks, by=list(df1$gender), FUN=min)

(df2 = aggregate(cbind(marks,marks2) ~ gender + course, data=df1, FUN=max))




#List -----
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
mylist = list(title=g, ages=h, j, h)
mylist
mylist[2]
mylist[[2]]
mylist[['ages']]
mylist$ages





#Factor -----

(grades = sample(c('A','B','C','D'), size=30, replace=T, prob=c(.3,.2,.4,.1)))
table(grades)
(gradesFactor = factor(grades))
(gradesFactorOrdered = factor(grades, ordered=T))
(gradesFactorOrderedLevels = factor(grades, ordered=T, levels=c('D','C','B','A')))
class(grades)
class(gradesFactorOrdered)
class(gradesFactorOrderedLevels)



# Object Properties
#vector
v1= 1:100
class(v1) ; typeof(v1)
v2=letters[1:10]
class(v2) ; typeof(v2)
length(v2)
summary(v1)

#matrix
m1= matrix(1:24,nrow=6)
class(m1)
summary(m1)
dim(m1)
str(m1)

#Array
a1 =array(1:24, dim=c(4,3,2))
class(a1)
str(a1)
dim(a1)
summary(a1)



#DF
#data() #built in datasets
df1= iris 
str(df1)
summary(df1)
class(df1); dim(df1)
nrow(df1) ; names(df1) ;NROW(df1)
colnames(df1)
rownames(df1)

#list
list1 = list(v1,m1,a1,df1)
str(list1)

#Statistical Description
library(Hmisc)

describe(df1)


================================================
FILE: 96-mtcars/11b-mtcars.R
================================================
#mtcars data set - base R

?mtcars #help on dataset

mtcars #print dataset
str(mtcars) #structure of dataset
#can be used a matrix
dim(mtcars)#dimensions of dataset
class(mtcars)#class
typeof(mtcars) #internal storage mode - list
mode(mtcars) #Get/set the type or storage mode of object- list
attributes(mtcars) #col and rownames
names(mtcars) #colnames
colnames(mtcars) #colnames
rownames(mtcars) #rownames

head(mtcars) #top 6 rows 
tail(mtcars) #bottom 6 rows
head(mtcars,n=3) #top 3 rows
dim(mtcars)  #dimensions of DF 32 rows, 11 columns
NROW(mtcars)
length(mtcars) #variables nos
mtcars$mpg
length(mtcars$mpg)  #length of the vector
summary(mtcars)
ncol(mtcars) #matrix
nrow(mtcars)
View(mtcars)  #View DF in seperate tab like excel
mtcars[1]  #print 1st column
mtcars$mpg[1] #1 in vectors print 1st position value in the vector

x = c(1,4,2, 20:300)
x
x[274:280]


================================================
FILE: 96-mtcars/11c-mtcars-filter.R
================================================
#mtcars - filtering data, missing values, split data

mtcars # print dataset
head(mtcars)
tail(mtcars)
head(mtcars,n=3)
tail(mtcars,n=3)

#filter data----
#selected columns
#mtcars$mpg[1:10]  #vector
head(mtcars[1:3]) #DF
head(mtcars[,1:3]) #DF
(mtcars[1:6,1:3])
names(mtcars)[c(1,5,6,7)]
head(mtcars[c('mpg','cyl','am')])
head(mtcars[,c(1,5,6,7)])
head(mtcars[,-1])  #mpg excluded
head(mtcars[,-c(1:5)]) #excluded 1st to 5th

#selected rows----
head(mtcars)
mtcars[1:5,]
mtcars['Mazda RX4',]
mtcars[mtcars$mpg > 25,]
mtcars[mtcars$mpg <= 25,]
nrow(mtcars[mtcars$mpg > 25,]) ;nrow(mtcars[mtcars$mpg <= 25,])
dim(mtcars)
mtcars[mtcars$mpg > 25 & mtcars$gear==4, 1:5]
mtcars[grep("Mazda", rownames(mtcars)),] #match name in rownames or a column
head(mtcars)
?grep
rownames(mtcars)
?toupper
grep("^Merc",rownames(mtcars))
mtcars[c(8,9,10,11,12,13,14,15),1:5]
#rows with names starting with Merc
rownames(mtcars)
mtcars[grep("^Merc",rownames(mtcars)),]
mtcars[grep("0$",rownames(mtcars)),]

mtcars[-1,1:5]
mtcars[-c(1:20),-c(1:5)]

#combine rows and columns-----
mtcars[1:5, 1:3]
mtcars[mtcars$mpg > 25 & mtcars$gear==4,1:3]


#subset----
#	 am	 Transmission (0 = automatic, 1 = manual)
mtcars[mtcars$am == 1,]
subset(mtcars, am==1)
subset(mtcars, am==1 & hp > 66, select=c(4,8:11))
names(mtcars)
subset(mtcars, cyl==4 | cyl==8, select=-c(mpg:am))


#filter----
vars1= c('mpg', 'disp', 'hp','drat','wt','qsec')
vars1
mtcars[vars1]


#which----
mtcars[which(mtcars$cyl==4),1:5]

#unique----
x=c(1,3,4,6,6)
unique(x)
length(unique(x))
duplicated(x)
x[duplicated(x)]

length(unique(mtcars$mpg)) #unique value of mpg
length(mtcars$mpg)
unique(mtcars$mpg)
mtcars$mpg
unique(mtcars$cyl)
duplicated(mtcars[,c('cyl')])
mtcars$cyl[duplicated(mtcars[,c('cyl')])]

#missing values----
#complete cases- rows which are complete

(x1=c(1,2,3,4,5,6,1,2,3,4,5))
duplicated(x1)

#missing values in a vector
(xna=c(1,NA,5,NA,10,5,NA))
xna
mean(xna) #mean not calculated
mean(xna, na.rm=T)
is.na(xna)
sum(is.na(xna))  #count missing values

#missing values in a df
df_mtcars = mtcars
head(df_mtcars)
dim(df_mtcars)
sum(is.na(df_mtcars))
df_mtcars$disp
#put missing values in DF for a condition
df_mtcars[df_mtcars$mpg > 25, c('disp') ] = NA
df_mtcars[df_mtcars$mpg > 25, c('mpg','disp') ]
df_mtcars$disp

anyNA(df_mtcars)

colSums(is.na(df_mtcars)) #disp has 6
complete.cases(df_mtcars)
sum(complete.cases(df_mtcars)) # 26 row complete
sum(!complete.cases(df_mtcars)) # 6 row incomplete


#replace missing values with mean of the column
mean(df_mtcars$disp, na.rm=T) #mean of disp column
df_mtcars[is.na(df_mtcars$disp),]  #display rows with missing values in disp column       
df_mtcars[is.na(df_mtcars$disp),c('disp')] = mean(df_mtcars$disp, na.rm=T)       
anyNA(df_mtcars)
sum(is.na(df_mtcars))

#split data----
head(mtcars)
table(mtcars$am)
(vars1=c('mpg','cyl','gear','wt','disp','am'))
(names(mtcars))
mtcars[vars1]
split(mtcars[vars1], mtcars$am) #split on am 0 or 1
split(mtcars, mtcars$am) #split on am 0 or 1


(df_split_am= split(mtcars[vars1], mtcars$am)) #split on am

df_split_am[[1]]  #am=0
df_split_am[[2]]  #am=1

table(mtcars$cyl)
(df_split_cyl= split(mtcars[vars1], mtcars$cyl)) #split on cyl
unique(mtcars$cyl)

df_split_cyl[[1]]
df_split_cyl[[2]]
df_split_cyl[[3]]
dfs22 = as.data.frame(df_split_am[[2]])
str(dfs22)
dfs22



df2 = mtcars[vars1]
head(df2)
df2$am = as.character(df2$am)
head(df2)
str(df2)
split(df2, df2$am)

split(mtcars[vars1], list(mtcars$am,mtcars$cyl))
split(mtcars[vars1], list(mtcars$am,mtcars$wt))

str(mtcars)
range(df_mtcars$mpg)
(mpgcode <- car::recode(df_mtcars$mpg, "lo:15=1; 15.1:25=2;else = 3"))


================================================
FILE: 96-mtcars/11d-mtcars-descp.R
================================================
#mtcars - descriptive values

head(mtcars)

#check for data type
str(mtcars)
lapply(mtcars, class)
unlist(lapply(mtcars, class)) 
#all numeric, can also be used as matrix

#sums & means of columns
head(mtcars)
colSums(mtcars)
rowSums(mtcars)
colMeans(mtcars)
rowMeans(mtcars)

#groupwise
aggregate(mtcars$mpg, by=list(mtcars$cyl), FUN=mean)
aggregate(mtcars$mpg, by=list(mtcars$cyl), FUN=min)
?aggregate
aggregate(mpg ~ cyl, data=mtcars, FUN=mean)
aggregate(mpg ~ cyl + am, data=mtcars, FUN=mean)
aggregate(cbind(mpg,wt) ~ cyl + am, data=mtcars, FUN=sum)
aggregate(. ~ am, data=mtcars, FUN=length)

aggregate(cbind(mpg,wt) ~ cyl + am, data=mtcars, FUN=function(x) c(meann = mean(x), medn = median(x) ) )


#another way
x.mean <- aggregate(cbind(mpg,wt) ~ cyl+am, mtcars, FUN=mean)
x.mean
x.max  <- aggregate(cbind(mpg,wt) ~ cyl+am, mtcars, FUN=max)
x.max
merge(x.mean, x.max, by = c("cyl", "am"))


================================================
FILE: 96-mtcars/11f-mtcars-loops.R
================================================
# mtcars - loops and apply

names(mtcars)
vars1= c('mpg', 'disp', 'hp','drat','wt','qsec')
vars2 = c('gear','cyl', 'vs', 'am','gear','carb')

paste('mtcars','mpg',sep='$')
noquote(paste('mtcars','mpg',sep='$'))

length(mtcars)
mean(mtcars[,1]); mean(mtcars[,2]); mean(mtcars[,3]) ;mean(mtcars[,4]); mean(mtcars[,5])

#using indices
for(i in 1:5) {
  print(mean(mtcars[,i]))
}

vars1
#using vector of names
for(i in vars1) {
  print(mean(mtcars[,i]))
}

?lapply
lapply(mtcars[vars1], mean)
lapply(mtcars[vars1],quantile)
sapply(mtcars[vars1], mean)
sapply(mtcars[vars1], quantile)
quantile(mtcars$mpg, c(.1,.3,.4,.8))

apply(mtcars,1,FUN=mean) #rowsise mean
apply(mtcars,2,FUN=mean) #colsise mean

#ifelse
ifelse(mtcars$mpg > 25,'High', 'Low')
cbind(mtcars$mpg, ifelse(mtcars$mpg > 30,'High', 'Low'))

#frequency Table
table(mtcars$cyl)
table(mtcars$gear, mtcars$cyl)
(t1= table(mtcars$gear, mtcars$cyl, dnn=list('gears','cylinder') ))
t1

#add.margin----
addmargins(t1) #sum all sides rows and columns
addmargins(t1,FUN=mean)
addmargins(t1,FUN=list(list(mean,sum), list(sd, median)))

#margin.table----
t1
margin.table(t1)
?margin.table
margin.table(t1,margin=1)
margin.table(t1,margin=2)
#prop.table
t1
prop.table(t1)
1/32
addmargins(prop.table(t1, 1))
addmargins(prop.table(t1, 2))

#freq distribution
mtcars$mpg
range(mtcars$mpg)
breaks = seq(from=floor(min(mtcars$mpg))-5,to=ceiling(max(mtcars$mpg))+ 5,by=5) #seq(5,35,5)
breaks
class(breaks)
mtcars$mpg
(mpg.cut = cut(mtcars$mpg, breaks))  #cut/place into interval
?cut
(t2a =table(mpg.cut))
mtcars$mpg
(t2b =cbind(table(mpg.cut))) #transpose
#(t2b_new = t2b[-3])

hist(mtcars$mpg)
breaks
(breaks1 = c(5,10,20,22,30,40, max(mtcars$mpg)))
hist(mtcars$mpg,breaks=breaks)
hist(mtcars$mpg,breaks=breaks1)

barplot(t2b, beside = T,col=1:6, names.arg = rownames(t2b))


#----

(mpg.cut2 = cut(mtcars$mpg, breaks, labels=LETTERS[1:6]))

(mpg.cut2 = cut(mtcars$mpg, breaks, labels=LETTERS[1:6], levels=LETTERS[1:6], ordered=T))
table(mpg.cut2)
(freqdist2= cbind(table(mpg.cut2)))  

#Creating discontinous intervals using cut command
#mridula



================================================
FILE: 96-mtcars/11g-mtcars-sort.R
================================================
#sort, rank, order, sample

head(mtcars)

#sort by mpg
sort(mtcars$mpg) # one column
sort(mtcars$mpg, decreasing = T) # one column

mtcars[order(mtcars$mpg),][1:5] #Sort DF by mpg

mtcars[order(mtcars$cyl, mtcars$mpg), c('cyl','mpg','wt')] #Sort DF by cyl, mpg

mtcars[order(mtcars$cyl, -mtcars$mpg), c('cyl','mpg','wt')] # desc mpg

rank(mtcars$mpg, ties.method = 'min')
x=c(1,2,36,3)
rank(x)

rorder =cbind(mtcars$mpg, rank(mtcars$mpg, ties.method = 'min'))

rorder[order(rorder[,2]),1:2]


================================================
FILE: 96-mtcars/11h-mtcars-dplyr.R
================================================
#dplyr - mtcars
library(dplyr)
mtcars
head(mtcars)

#Filter----

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

# Multiple criteria
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)

# Multiple arguments are equivalent to and
filter(mtcars, cyl < 6, vs == 1)


filter(mtcars, row_number() == 1L)
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()-2))



#mutate----
mutate(mtcars, displ_l = disp / 61.0237) #keeps other col
transmute(mtcars, displ_l = disp / 61.0237) #removes other cols
mutate(mtcars, cyl = NULL) #do not display cyl


#slice-----
slice(mtcars, 1L)
slice(mtcars, n())
slice(mtcars, 5:n())
slice(mtcars, c(2,4,5,10))

(by_cyl <- group_by(mtcars, cyl)) # ???
slice(by_cyl, 1:2)

#structure----
tbl_df(mtcars) # convert to tbl class
glimpse(mtcars)  # dense summary of tbl data
View(mtcars) # spreasheet like form base pacakge



#rownames----
df = tibble::rownames_to_column(df, var='cars')
df2 = mtcars
names(df)

#has
tibble::has_rownames(mtcars)
tibble::has_rownames(df)

#remove rownames  
#see without rownames
head(df2[1:5])
tibble::remove_rownames(df2)

#rowid as column
tibble::rowid_to_column(df, var = "rowid")

#column to rownames
head(df[1:5])
tibble::column_to_rownames(df, var = "cars")

mtcars %>% group_by(am) 
#nothing - just separation

mtcars %>% group_by(am) %>% summarise(mean(mpg), max(wt))


#summarise----
summarise(mtcars, mean(disp))  
summarise(group_by(mtcars, cyl), mean(disp)) 
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))


#summarise_all
mtcars %>% group_by(am, gear) %>% summarise_all(mean)
mtcars %>% group_by(am, gear)%>% summarise_all(c("min", "max"))
mtcars %>% group_by(am, gear)%>% summarise_all(funs(med = median))



#without Group
mtcars %>% summarise(mean(mpg), max(wt))
mtcars %>% summarise_all(mean)
mtcars %>% select(wt, gear)%>% summarise_all(c("min", "max"))
mtcars %>% summarise_all(funs(med = median))





mtcars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
iris %>% summarise_if(is.numeric, mean, na.rm = TRUE)

#specific columns
mtcars %>% summarise_at(c("mpg", "wt"), mean, na.rm = TRUE)


#------------------------------------
#unsortd----
dplyr::tbl_df(iris)
print(dplyr::tbl_df(mtcars), n=20)  #display more columns and rows
#print(dplyr::tbl_df(mtcars), width=11)
tbl_df(mtcars) %>% print(n = Inf)
tbl_df(mtcars) %>% print(width = Inf)
tbl_df(mtcars) %>% as.data.frame(mtcars)

glimpse(mtcars)
df = mtcars
row.names(df) = NULL
df %>% select(mpg)
#head(mtcars)
select(mtcars, mpg, vs)
mtcars %>% dplyr::select(vs, mpg, wt)
mtcars %>% group_by(cyl) %>% summarise(avgwt = mean(wt), meanhp = mean(hp)) %>% arrange( desc(meanhp), avgwt)
mtcars

names(mtcars)
filter(mtcars, mpg > 23 | wt < 2)
mtcars %>% filter(mpg > 23 & wt > 2)
mtcars %>% select(mpg, wt) %>% filter(mpg > 23) 
mtcars %>% 
  
  filter(iris, Sepal.Length > 7)
filter(mtcars, cyl == 4)
distinct(mtcars)
df  = data.frame(a=c(2,2),b=c(2,2))
df
distinct(df)

sample_frac(mtcars, 0.2, replace=T)
sample_n(mtcars, 60, replace=T) %>% select(mpg)
slice(mtcars,10:14)
top_n(mtcars,-2, mpg)

select(mtcars, mpg) %>% arrange(desc(mpg))
#Columns
select(mtcars, mpg, wt)
select(mtcars, contains('a'))
names(mtcars)
select(mtcars, contains ='vs')
select(mtcars, everything())

mtcars %>% group_by(cyl, am) %>% summarise_all(mean)

df = data.frame(marks=c(1,2,3,7,1))
cbind(df, dplyr::mutate_each(df, funs(min_rank)))

mtcars %>% lead() %>% lag()
dplyr::n(mtcars)
select(mtcars, mpg2 = mpg)

df = mtcars[1:4]
names(df) = c('MPG','C1','C2','C3')
df= rename(df, C5=C1)
names(df)
df
rename(df, marks2 = marks)
df %>% mutate(marks2 = marks + 2, marks3 = marks + 4)
df %>% transmute(marks2 = marks + 2, marks3 = marks + 4)

library(nycflights13)
data(flights)


destinations <- group_by(flights, dest)
destinations
summarise(destinations,
          planes = n_distinct(tailnum),
          flights = n()
)


select(iris, -ends_with("Width")) %>% head
vars <- c("Petal.Length", "Petal.Width1")
select(iris, from=1, to=n()) 
filter(mtcars, row_number() == n())
filter(mtcars, between(row_number(), 5, n()))

mtcars %>% group_by(cyl) %>% filter(1:3)
> mtcars %.% group_by(cyl) %.% filter(sample(n(), 10))
group_by( mtcars, cyl ) %>% integer_filter(1:2)
?integer_filter


# Select odd
mtcars %>% slice(from = 1, to = n(), by = 2)
# Select even
slice(mtcars, from = 2, to = n(), by = 2)
# Select first 10
slice(mtcars, from = 1, to = 10)
# Select last 10
slice(mtcars, n()-10: n())
slice(mtcars, 1:4)
mtcars

df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
)
df
df %>%  slice(n()-2:n())

var1 <- quo(letters[1:5])
var1
quo(toupper(!!var1))

# Here we capture `letters[1:5]` as an expression:
quo(toupper(letters[1:5]))
#> ~toupper(letters[1:5])

# Here we capture the value of `letters[1:5]`
quo(toupper(!!letters[1:5]))
#> ~toupper(c("a", "b", "c", "d", "e"))
quo(toupper(UQ(letters[1:5])))
#> ~toupper(c("a", "b", "c", "d", "e"))
#
toupper(letters[1:5])
quote(toupper(letters[1:5]))

head(mtcars)
slice(mtcars, 1:5)
slice(mtcars, 1)  #rowno missing, first row
slice(mtcars, 1L)

tail(mtcars,n=5)
slice(mtcars, n()-5:n())
slice(mtcars, n()) #last row
slice(mtcars, n() - 1) #2nd last row

mtcars %>% top_n(2)
mtcars %>% top_n(-2)
mtcars %>% group_by(cyl) %>% tally(cyl) %>% top_n(1, cyl)
dim(mtcars)
bind_rows(mtcars, mtcars)
bind_cols(mtcars,mtcars)
gtable_combine(list(mtcars, mtcars))
dim_desc(mtcars)

# combine applies the same coercion rules
f1 <- factor("a")
f2 <- factor("b")
c(f1, f2)
unlist(list(f1, f2))

gtable_combine(f1, f2)
gtable_combine(list(f1, f2))

slice( mtcars, c(1L,3L,2L,7L)) 

by_cyl <- mtcars %>% group_by(cyl)
# Select first row in each group
mtcars %>% slice(1)
by_cyl %>% slice(1)
# Select last row in each group
mtcars %>% slice(n())
by_cyl %>% slice(n())
# Rows not present in group silently ignored
mtcars %>% slice(10)
by_cyl %>% slice(10)

# Select arbitrary rows
mtcars %>% slice(1:9)
by_cyl %>% slice(1:3)

mtcars %>% slice(c(1, 3, 9))
by_cyl %>% slice(c(1, 3, 5))

# Select even rows
mtcars %>% slice(seq(2, n(), by = 2))
by_cyl %>% slice(seq(2, n(), by = 2)) %>% select(cyl, everything())

# Drop first row in each group
mtcars %>% group_by(cyl, am) %>% slice(1)
by_cyl %>% slice(1)

# Returns all values
by_cyl %>% slice()




df <- data.frame(x = c(10, 4, 1, 6, 3, 1, 1))
df %>% top_n(2)

# Negative values select bottom from group. Note that we get more
# than 2 values here because there's a tie: top_n() either takes
# all rows with a value, or none.
df %>% top_n(-2)





================================================
FILE: 96-mtcars/12d-mtcars-graph1.R
================================================
#mtcars graphs

head(mtcars)

#plot - scatter
plot(mtcars$mpg)
plot(x=mtcars$wt,y=mtcars$mpg,pch=17,cex=2,col='red' )

#histogram
hist(mtcars$mpg)
hist(mtcars$mpg, breaks=5, col=1:5)

#----
cars_auto = subset(mtcars, am == 0)
cars_manu = subset(mtcars, am == 1)
par(mfrow = c(2, 1)) #2 row,1 col
hist(cars_auto$mpg, main = "Distribution mpg - automatic transmission", xlab = "mpg")

abline(v = mean(cars_auto$mpg), col = "red")
hist(cars_manu$mpg, main = "Distribution mpg - manual transmission", xlab = "mpg")
abline(v = mean(cars_manu$mpg), col = "red")
par(mfrow=c(1,1))


#pie----
t1=table(mtcars$cyl)
pie(t1)

#barplot
(count1 = table(mtcars$cyl))
barplot(count1, col=2:4)

# Grouped Bar Plot
(counts <- table(mtcars$vs, mtcars$gear))
barplot(counts)
barplot(counts, beside=T,col=1:2)

barplot(counts, main="Car Distribution by Gears and VS",
        xlab="Number of Gears", col=c("darkblue","red"),
        legend = rownames(counts), beside=TRUE)

#boxplot
boxplot(mtcars$mpg)

boxplot(mpg ~ cyl, data=mtcars, col=5:7)
boxplot(mpg~cyl*am, data=mtcars, notch=TRUE, 
        col=(c("gold","darkgreen")),
        main="MT Cars", xlab="cyl & transmission")
#4.1 = cyl=4, am=1



#line chart
plot(mtcars$wt, mtcars$mpg, type='b')
df=mtcars[order(mtcars$wt),]
plot(df$wt, df$mpg[cyl==4], type='b', col='red', lwd=2)
lines(df$wt, df$hp, type='b', col='green')



# Basic Scatterplot Matrix
pairs(~mpg+disp+drat+wt,data=mtcars, 
      main="Simple Scatterplot Matrix")


# 3D Scatterplot
library(scatterplot3d)
scatterplot3d(mtcars$wt,mtcars$disp,mtcars$mpg, main="3D Scatterplot")


library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), 
        col="gold")
title("Violin Plots of Miles Per Gallon")


#dotplot----
dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,
         main="Gas Milage for Car Models", 
         xlab="Miles Per Gallon")

#correllogram----
library(corrplot)
res=cor(mtcars)
res
corrplot(res, type = "upper", order = "hclust", 
         tl.col = "black", tl.srt = 45)

library(corrgram)
cor(mtcars[1:5])
corrgram(mtcars[1:5], order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt)



library("PerformanceAnalytics")
my_data <- mtcars[, c(1,3,4,5,6,7)]
chart.Correlation(my_data, histogram=TRUE, pch=19)


#heatmap-----
# Get some colors
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(x = res, col = col, symm = TRUE)

#level plot

z <- cor(mtcars)
require(lattice)  #install first
levelplot(z)





#https://rstudio-pubs-static.s3.amazonaws.com/45786_b5e54c2d3c824f51abd2d559506cf81f.html



================================================
FILE: 96-mtcars/12e-mtcars-graph2.R
================================================
#Advance plot with ggplot2

library(ggplot2)
#use color brewer as default discrete colors
scale_colour_discrete <- function(...) scale_color_brewer(palette="Set1", ...)
scale_fill_discrete <- function(...) scale_fill_brewer(palette="Set1", ...)

data('mtcars')
df=mtcars
# create factors with value labels
df$gear <- factor(df$gear,levels=c(3,4,5),
                      labels=c("3gears","4gears","5gears"))
df$am <- factor(df$am,levels=c(0,1),
                    labels=c("Automatic","Manual"))
df$cyl <- factor(df$cyl,levels=c(4,6,8),
                     labels=c("4cyl","6cyl","8cyl"))
head(df)
str(df)


#density Plot
library(ggplot2)
qplot(mpg, data=df, geom="density", fill=gear, alpha=I(.5),
      main="Distribution of Gas Milage", xlab="Miles Per Gallon",    ylab="Density")


qplot(hp, mpg, data=df, shape=am, color=am,
      facets=gear~cyl, size=I(3),
      xlab="Horsepower", ylab="Miles per Gallon")

ggplot(df, aes(wt, mpg, color = cyl)) +
  geom_point() +  geom_smooth(method = "lm", formula = y~x) +
  labs(title = "Regression of MPG on Weight", x = "Weight", y = "Miles per Gallon")


qplot(gear, mpg, data=df, geom=c("boxplot", "jitter"),
      fill=gear, main="Mileage by Gear Number",
      xlab="", ylab="Miles per Gallon")


================================================
FILE: 96-mtcars/12e-mtcars-summarise-dplyr.R
================================================
#tidyverse

library(dplyr)
#Options to print increased no of rows and columns
#options(dplyr.print_max = 1e9)
#options(tibble.print_max = n, tibble.print_min = 3)
#: if there are more than n rows, print only the first m rows. Use 
options(tibble.print_max = Inf) #to always show all rows.
options(tibble.width = Inf)

#group by cyl, calculate avg hp and wt
mtcars %>% group_by(cyl) %>% summarise(mean(hp), mean(wt))
#group by cyl, am
mtcars %>% group_by(cyl,am) %>% summarise(mean(hp), mean(wt))

#mean and count of all columns when gp by cyl, am
mtcars %>%group_by(cyl, am) %>% summarise_all(funs(mean, n()))

#selected columns
mtcars %>% select(cyl, am, mpg, wt, hp, qsec) %>% group_by(cyl, am) %>% summarise_all(funs(mean, max, min))

#sample 
sample_n(mtcars, 2)
sample_frac(mtcars, .2)
mtcars %>% sample_frac(.2)

#


================================================
FILE: 96-mtcars/12f-diag-ggplot2-mtcars.R
================================================
#Diagnostics plots using ggplot2 for mtcars
#(https://rpubs.com/therimalaya/43190)


#-----
#Using the mtcars datasets, a linear model is fitted with mpg as response and cyl, disp, hp, drat and wt has predictor variable

lm.model = lm(mpg~cyl+disp+hp+drat+wt, data=mtcars)
diagPlts = diagPlot(lm.model)
#To display the plots in a grid, some packages mentioned above should be installed.
lbry = c("grid", "gridExtra")
lapply(lbry, require, character.only=TRUE, warn.conflicts = FALSE, quietly = TRUE)

#Thus the plot obtained is,
do.call(grid.arrange, c(diagPlts, main="Diagnostic Plots", ncol=1))

plot(lm.model) #one by one
par(mfrow=c(2,2)) # 4 plots in 1 space
plot(lm.model)
par(mfrow=c(1,1)) # Reset

#--------------------
model=lm.model
p1<-ggplot(model, aes(.fitted, .resid))+geom_point()
p1<-p1+stat_smooth(method="loess")+geom_hline(yintercept=0, col="red", linetype="dashed")
p1<-p1+xlab("Fitted values")+ylab("Residuals")
p1<-p1+ggtitle("Residual vs Fitted Plot")+theme_bw()
p1
#-----
#error in this code

p2<-ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)
p2<-p2+geom_abline(aes(qqline(.stdresid)))+xlab("Theoretical Quantiles")+ylab("Standardized Residuals")
p2<-p2+ggtitle("Normal Q-Q")+theme_bw()
p2

#------

p3<-ggplot(model, aes(.fitted, sqrt(abs(.stdresid))))+geom_point(na.rm=TRUE)
p3<-p3+stat_smooth(method="loess", na.rm = TRUE)+xlab("Fitted Value")
p3<-p3+ylab(expression(sqrt("|Standardized residuals|")))
p3<-p3+ggtitle("Scale-Location")+theme_bw()
p3
#------

p4<-ggplot(model, aes(seq_along(.cooksd), .cooksd))+geom_bar(stat="identity", position="identity")
p4<-p4+xlab("Obs. Number")+ylab("Cook's distance")
p4<-p4+ggtitle("Cook's distance")+theme_bw()
p4

#-----

p5<-ggplot(model, aes(.hat, .stdresid))+geom_point(aes(size=.cooksd), na.rm=TRUE)
p5<-p5+stat_smooth(method="loess", na.rm=TRUE)
p5<-p5+xlab("Leverage")+ylab("Standardized Residuals")
p5<-p5+ggtitle("Residual vs Leverage Plot")
p5<-p5+scale_size_continuous("Cook's Distance", range=c(1,5))
p5<-p5+theme_bw()+theme(legend.position="bottom")
p5

#-----

p6<-ggplot(model, aes(.hat, .cooksd))+geom_point(na.rm=TRUE)+stat_smooth(method="loess", na.rm=TRUE)
p6<-p6+xlab("Leverage hii")+ylab("Cook's Distance")
p6<-p6+ggtitle("Cook's dist vs Leverage hii/(1-hii)")
p6<-p6+geom_abline(slope=seq(0,3,0.5), color="gray", linetype="dashed")
p6<-p6+theme_bw()
p6


#------------------------

require(ggplot2)
diagPlot<-function(model){
  p1<-ggplot(model, aes(.fitted, .resid))+geom_point()
  p1<-p1+stat_smooth(method="loess")+geom_hline(yintercept=0, col="red", linetype="dashed")
  p1<-p1+xlab("Fitted values")+ylab("Residuals")
  p1<-p1+ggtitle("Residual vs Fitted Plot")+theme_bw()
  
  p2<-ggplot(model, aes(qqnorm(.stdresid)[[1]], .stdresid))+geom_point(na.rm = TRUE)
  p2<-p2+geom_abline(aes(qqline(.stdresid)))+xlab("Theoretical Quantiles")+ylab("Standardized Residuals")
  p2<-p2+ggtitle("Normal Q-Q")+theme_bw()
  
  p3<-ggplot(model, aes(.fitted, sqrt(abs(.stdresid))))+geom_point(na.rm=TRUE)
  p3<-p3+stat_smooth(method="loess", na.rm = TRUE)+xlab("Fitted Value")
  p3<-p3+ylab(expression(sqrt("|Standardized residuals|")))
  p3<-p3+ggtitle("Scale-Location")+theme_bw()
  
  p4<-ggplot(model, aes(seq_along(.cooksd), .cooksd))+geom_bar(stat="identity", position="identity")
  p4<-p4+xlab("Obs. Number")+ylab("Cook's distance")
  p4<-p4+ggtitle("Cook's distance")+theme_bw()
  
  p5<-ggplot(model, aes(.hat, .stdresid))+geom_point(aes(size=.cooksd), na.rm=TRUE)
  p5<-p5+stat_smooth(method="loess", na.rm=TRUE)
  p5<-p5+xlab("Leverage")+ylab("Standardized Residuals")
  p5<-p5+ggtitle("Residual vs Leverage Plot")
  p5<-p5+scale_size_continuous("Cook's Distance", range=c(1,5))
  p5<-p5+theme_bw()+theme(legend.position="bottom")
  
  p6<-ggplot(model, aes(.hat, .cooksd))+geom_point(na.rm=TRUE)+stat_smooth(method="loess", na.rm=TRUE)
  p6<-p6+xlab("Leverage hii")+ylab("Cook's Distance")
  p6<-p6+ggtitle("Cook's dist vs Leverage hii/(1-hii)")
  p6<-p6+geom_abline(slope=seq(0,3,0.5), color="gray", linetype="dashed")
  p6<-p6+theme_bw()
  
  return(list(rvfPlot=p1, qqPlot=p2, sclLocPlot=p3, cdPlot=p4, rvlevPlot=p5, cvlPlot=p6))
}


================================================
FILE: 96-mtcars/12f-ggplot2-mtcars.R
================================================
#ggplot2 with mtcars
#(https://rpubs.com/BillB/217355)

library(ggplot2)
ggplot(mtcars, aes(mpg)) + geom_histogram(binwidth = 4) + xlab('Miles per Gallon') + ylab('Number of Cars') +   ggtitle('Distribution of Cars by Mileage')

ggplot(mtcars, aes(cyl)) +  geom_histogram(binwidth=1) + xlab('Cylinders') + ylab('Number of Cars') +   ggtitle('Distribution of Cars by Cylinders')

ggplot(mtcars, aes(hp)) +  geom_histogram(binwidth=20) + xlab('horsepower') + ylab('Number of Cars') +  ggtitle('Distribution of Cars by Horsepower')
#HP vs MPG
ggplot(mtcars, aes(hp, mpg)) + geom_point() 
#with prediction line  
ggplot(mtcars, aes(hp, mpg)) + geom_point() +
  geom_smooth(method = "lm", se = T) +
  ylab("Miles per Gallon") +
  xlab("No. of Horsepower") +
  ggtitle("Impact of Number of Horsepower on MPG")

#apply smoothing since mpg unlikely to hit zero
ggplot(mtcars, aes(hp, mpg)) +
  stat_smooth() + geom_point() +
  ylab("Miles per Gallon") +
  xlab ("No. of Horsepower") +
  ggtitle("Impact of Number of Horsepower on MPG")

qplot(cyl, mpg, data = mtcars, colour = cyl, geom = "point",     
      ylab = "Miles per Gallon", xlab = "No. of Cylinders",
      main = "Impact of Number of Cylinders on MPG") 

#lm----
#linear regression to fit the data to a line. We use geom_smooth with the linear model method.
ggplot(mtcars, aes(cyl, mpg)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  ylab("Miles per Gallon") + xlab("No. of Cylinders") +
  ggtitle("Impact of Number of Cylinders on MPG")


================================================
FILE: 96-mtcars/13b-mtcars-lm1.R
================================================
#Multiple Linear Regression - mtcars

mtcars # mtcars data set
dim(mtcars); names(mtcars)

#predict mpg for following data wt=c(2.5,3.5),cyl=c(4,6)
(ndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6)))
(cbind(ndata, data.frame(mpg=c('?','?'))))

#Linear Model
lmfit1 = lm(mpg ~ wt + cyl, data=mtcars)

summary(lmfit1)

#if p value of F Stats < .05 : Linear Model exists with at least one X

#both are significant variables
attributes(summary(lmfit1)) # to use output values

#adjusted R2
summary(lmfit1)$adj.r.squared
#adjs R2 = .819
#82% of variation in Y are explained by two X's in the model


#head(mtcars[c('wt','cyl')])
(ndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6)))

#predict for above wt and cyl combination
predict(lmfit1,newdata=ndata, type='response')
#for wt = 2.5 and cyl =4, mpg predicted is 25.67

cbind(ndata, predicted=predict(lmfit1,newdata=ndata, type='response'))

#how to select from large number of variables
#how to select the best model - from variable combination



================================================
FILE: 96-mtcars/13c-mtcars-lm2.R
================================================
#Multiple Linear Regression - mtcars

mtcars # mtcars data set
dim(mtcars); names(mtcars)

mtcarsfit1 = lm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am +gear + carb , data = mtcars)
summary(mtcarsfit1)
#only wt seems to be significant

#select all variables with .
mtcarsfit2 = lm(mpg ~ . , data = mtcars)
summary(mtcarsfit2)
#still the same

#select random variables and try to test the model
mtcarsfit3 = lm(mpg ~ wt + hp + cyl, data=mtcars)
summary(mtcarsfit3)
#hp not significant
mtcarsfit3b = lm(mpg ~ wt + cyl, data=mtcars)
summary(mtcarsfit3b)
#adjs R2 = .819
head(mtcars[c('wt','cyl')])
ndata=data.frame(wt=c(2.5,3.5),cyl=c(4,6))
predict(mtcarsfit3b,newdata=ndata, type='response')

#Faster method for model selection
#there are large variables, we need a method to select variables, not possible to do one by one elimination

library(MASS)
(model1a = lm(mpg ~ ., data=mtcars)) #full model
#(model1b = lm(mpg ~ 1, data=mtcars)) #wihout any variables

summary(model1a)
#summary(model1b)

#step method to select best model
#works on AIC which should be less
stepboth = stepAIC(model1a,   direction='both')

# select combination of variable where AIC is minimum
#least AIC with wt, qsec, am
fit1= lm(mpg ~ wt + qsec + am, data=mtcars)
summary(fit1)


#Step AIC helps is select the variables from large number of vars
#variable combination which gives least AIC is selected



================================================
FILE: 96-mtcars/13e-mtcars-lm3.R
================================================
#mtcars linear regression
#https://cran.r-project.org/web/packages/olsrr/olsrr.pdf
#install.packages('olsrr')
library(olsrr)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_all_possible(model)
plot(k)
k

#create model and check which model is better
(m1 = lm(mpg ~ wt, data=mtcars))
summary(m1)
(m2 = lm(mpg ~ hp + wt + qsec , data=mtcars))
summary(m2)

anova(m1, m2)  #if p value is < 0.05 reject Ho
#Ho : Model m1, is better : m1 should be subset of m2

AIC(m1, m2)
AIC(m1)
#m2 AIC < m2 AIC

#what is AIC
#alkaline information criteria : way to check which model is better


================================================
FILE: 96-mtcars/14b-mtcars-logr.R
================================================
#Logistic Regression : mtcars
#http://www.r-tutor.com/elementary-statistics/logistic-regression/significance-test-logistic-regression
head(mtcars)
table(mtcars$am)
df = mtcars
df$am = factor(df$am)
#requirement : estimate the probability of a vehicle being fitted with a manual transmission if it has a 120hp engine and weights 2800 lbs.
(ndata = data.frame(hp=120, wt=2.8))
cbind(ndata, data.frame(probTx=c('?')))  #0 or 1
#create Model
am.glm = glm(am ~ hp + wt, data=df, family=binomial)

summary(am.glm)
#hp & wt are significant as pvalues < 0.05

#test data
(ndata = data.frame(hp=120, wt=2.8))

#predict
predict(am.glm, newdata=ndata, type="response") 

cbind(ndata, prob=predict(am.glm, newdata=ndata, type="response") )
?predict
#For an automobile with 120hp engine and 2800 lbs weight, the probability of it being fitted with a manual transmission is about 64%.



================================================
FILE: 96-mtcars/15b-mtcars-DT-class.R
================================================
#Classification - mtcars

library(rpart)
library(rpart.plot)

df = mtcars
df$am = factor(df$am)
#predict likelihood of car being auto or manual using DT

fit.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, minsplit=7, cp=-1)
#fit.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, cp=-1)

#minsplit - no of observation to create a split
fit.class
printcp(fit.class)

rpart.plot(fit.class, cex=1, nn=T)

(prune1 = prune(fit.class, cp=0))
rpart.plot(prune1)

library(dplyr)
(ndata = sample_n(df,5))
(ndata = ndata[c('am','wt','cyl','mpg','hp','gear')])

predict(fit.class, newdata=ndata, type='prob')
predict(fit.class, newdata=ndata, type='class')

cbind(ndata, predicted=predict(fit.class, newdata=ndata, type='class') )

fit2.class = rpart(am ~ wt + cyl + mpg + hp + gear, data=df, minsplit=7)
fit2.class
rpart.plot(fit2.class)




================================================
FILE: 96-mtcars/15c-mtcars-DT-anova.R
================================================
#Classification - Regression Tree- mtcars

library(rpart)
library(rpart.plot)

df = mtcars
mean(df$mpg)

#predict likelihood of car mileage using DT

fit.anova = rpart(mpg ~ wt + cyl + am + hp + gear, data=df, minsplit=7, cp=-1, method='anova')
fit.anova


rpart.plot(fit.anova, cex=1, nn=T)

library(dplyr)
ndata = sample_n(df,5)
(ndata = ndata[c('am','wt','cyl','mpg','hp','gear')])
ndata

predict(fit.anova, newdata=ndata, type='vector')

cbind(ndata, predicted=predict(fit.anova, newdata=ndata, type='vector') )

# Prune
printcp(fit.anova)
ptree2 = prune(fit.anova, cp=0.01)
#ideally .0051, but for testing select 
ptree2
rpart.plot(ptree2,nn=T, cex=.8, roundint = F)


================================================
FILE: 96-mtcars/16b-mtcars-cluster1.R
================================================
#Clustering on mtcars

head(mtcars)

#kmeans - select only few column to group them
df = mtcars[, c('mpg', 'cyl', 'wt')]
head(df)
km3 = kmeans(df, centers=3)
km3
km3$centers  #average of each cluster
km3$size  #how many into each cluster


#Plotting Clusters
library(cluster)
library(fpc)

plotcluster(df, km3$cluster)

#Plot2:  PCA
clusplot(df, km3$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)


#2nd Set
#kmeans - select only few column to group them

df = mtcars[, c('mpg', 'cyl', 'wt')]
km2 <- kmeans(df, centers=2)
km2
km2$centers  #average of each cluster
km2$size  #how many into each cluster

cbind(df, mtcars$am, km2$cluster)



df = mtcars[, c('mpg', 'cyl', 'wt')]
km4 <- kmeans(df, centers=4)
km4
km4$centers  #average of each cluster
km4$size  #how many into each cluster
cbind(df, mtcars$gear, km4$cluster)
table(mtcars$gear)
table(mtcars$cyl)
table(mtcars$carb)

summary(mtcars)





================================================
FILE: 96-mtcars/16c-mtcars-cluster2.R
================================================
#Clustering on mtcars

#https://rstudio-pubs-static.s3.amazonaws.com/111803_f6ec030f9a544fb2a058170f8047eb67.html#/4

#kmeans - select only few column to group them
df = mtcars[, c('mpg', 'cyl', 'wt')]
km_fit1 <- kmeans(df, centers=3)
km_fit1
km_fit1$centers  #average of each cluster
km_fit1$size  #how many into each cluster

#
kmeanPlot <- par(mar = c(5.1, 4.1, 0, 1))
plot(df,   col = km_fit1$cluster,
     pch = 20, cex = 3)
points(km_fit1$centers, pch = 15, cex = 4, lwd = 4, col='pink')

#Interactive Clustering at
#https://mikedamour.shinyapps.io/ShinyProject/

var1='cyl'
var2='gear'
(vars=sample(x=c('cyl','gear','vs','am','mpg','disp'), replace=F, size=2))
data1 = mtcars[c(vars)]
cluster1 = kmeans(data1,3)
plot(data1,col=cluster1$cluster, cex=cluster1$cluster)
points(cluster1$centers, pch = 15, cex = 2, col='violet')

cluster1 = kmeans(data1,4)
plot(data1,col=cluster1$cluster, cex=cluster1$cluster)
points(cluster1$centers, pch = 12, cex = 2, col='black')






================================================
FILE: 96-mtcars/22f-tidyr-mtcars.R
================================================
#tidyr - mtcars
#https://datascienceplus.com/data-manipulation-with-tidyr/

# gather – converts wide data to longer format. It is analogous to the melt function from reshape2.
# spread – converts long data to wider format. It is analogous to the cast function from reshape2.
# unite – combines two or more columns into a single column.
# separate – splits one column into two or more columns.


library(tidyr)
library(dplyr)
head(mtcars)

#include the names of the cars in a column called car for easier manipulation.
mtcars$car <- rownames(mtcars)
mtcars <- mtcars[, c(12, 1:11)]
head(mtcars)
names(mtcars)

#gather
mtcarsNew <- mtcars %>% gather(attribute, value, -car)
dim(mtcarsNew)
head(mtcarsNew)
tail(mtcarsNew)


mtcarsNew <- mtcars %>% gather(attribute, value, mpg:gear)
head(mtcarsNew)
tail(mtcarsNew)


#spread
head(mtcarsNew)
mtcarsSpread <- mtcarsNew %>% spread(attribute, value)
head(mtcarsSpread)

#unite -----
#test data
set.seed(1)
date<- as.Date('2016-01-01') + 0:14
hour <- sample(1:24, 15)
min <- sample(1:60, 15)
second <- sample(1:60, 15)
event <- sample(letters, 15)
data <- data.frame(date, hour, min, second, event)
data
head(data)
#
dataNew <- data %>%
  unite(datehour, date, hour, sep = ' ') %>%
  unite(datetime, datehour, min, second, sep = ':')
dataNew
head(dataNew)

#separate-----
data1 <- dataNew %>% 
  separate(datetime, c('date', 'time'), sep = ' ') %>% 
  separate(time, c('hour', 'min', 'second'), sep = ':')
head(data1)


================================================
FILE: 96-mtcars/mtcars-clust1.R
================================================
#Clustering on mtcars

#https://rstudio-pubs-static.s3.amazonaws.com/111803_f6ec030f9a544fb2a058170f8047eb67.html#/4

selectedData <- mtcars[, c('mpg', 'cyl', 'wt')]
clusters <- kmeans(selectedData, 3)
kmeanPlot <- par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData,
     col = clusters$cluster,
     pch = 20, cex = 3)
points(clusters$centers, pch = 15, cex = 4, lwd = 4, col='pink')

#Interactive Clustering at
#https://mikedamour.shinyapps.io/ShinyProject/

var1=.'cyl'
var2='gear'
(vars=sample(x=c('cyl','gear','vs','am','mpg','disp'), replace=F, size=2))
data1 = mtcars[c(vars)]
par(mfrow=c(1,2))
cluster1 = kmeans(data1,3)
plot(data1,col=cluster1$cluster, cex=cluster1$cluster)
points(cluster1$centers, pch = 15, cex = 2, col='violet')

cluster1 = kmeans(data1,4)
plot(data1,col=cluster1$cluster, cex=cluster1$cluster)
points(cluster1$centers, pch = 12, cex = 2, col='black')





================================================
FILE: 96-mtcars/mtcars-hclust.R
================================================
#Clustering - mtcars


# scale data to mean=0, sd=1 and convert to matrix
mtscaled <- as.matrix(scale(mtcars))

# create heatmap and don't reorder columns
heatmap(mtscaled, Colv=F, scale='none')

# cluster rows
hc.rows <- hclust(dist(mtscaled))
plot(hc.rows)

# transpose the matrix and cluster columns
hc.cols <- hclust(dist(t(mtscaled)))

# draw heatmap for first cluster
heatmap(mtscaled[cutree(hc.rows,k=2)==1,], Colv=as.dendrogram(hc.cols), scale='none')

# draw heatmap for second cluster
heatmap(mtscaled[cutree(hc.rows,k=2)==2,], Colv=as.dendrogram(hc.cols), scale='none')

palette <- colorRampPalette(c('#f0f3ff','#0033BB'))(256)
heatmap(mtscaled, Colv=F, scale='none', col=palette)



#
ClustMTC = kmeans(mtcars[, c('wt', 'mpg')], center=3)
ClustMTC = kmeans(scale(mtcars), center=3)
ClustMTC$centers
plot(x=mtcars$wt, y=mtcars$mpg, col=ClustMTC$cluster)
mtcars %>% group_by(ClustMTC$cluster) %>% summarise_all(mean)


================================================
FILE: 96-mtcars/s1.R
================================================
mtcars[1:2]
mtcars$cyl2 <- with(mtcars, cyl +1)
mtcars$variable <- with(mtcars, disp * 2)
cor(mtcars[,c("am","carb","cyl")], use="complete")
normalityTest(~mpg, test="shapiro.test", data=mtcars)
RegModel.1 <- lm(mpg~am+carb, data=mtcars)
summary(RegModel.1)
RegModel.2 <- lm(mpg~am+carb+cyl+disp+drat, data=mtcars)
summary(RegModel.2)
Boxplot( ~ mpg, data=mtcars, id.method="y")



================================================
FILE: 97-artwork/AuctionsData - artwork.csv
================================================
recid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus
2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1,6998013,Untitled,PAINTINGS,Christopher Wool,"600,000","400,000","729,000",Sold
2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10,6998014,Angel Of The North,SCULPTURE,Antony Gormley,"2,000,000","1,500,000","2,860,600",Sold
2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,"800,000","600,000","1,029,000",Sold
2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,"18,000,000","14,000,000","14,376,400",Sold
2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,"1,500,000","1,000,000","1,209,000",Sold
2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,"3,000,000","2,000,000","2,169,000",Sold
2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,"2,000,000","1,500,000","1,689,000",Sold
2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,"350,000","250,000","513,000",Sold
2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17,6998021,Untitled,SCULPTURE,Richard Prince,"1,500,000","1,000,000","2,049,000",Sold
2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18,6998022,Nov. 8,PAINTINGS,On Kawara,"800,000","600,000","789,000",Sold
2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19,6998023,Untitled,PAINTINGS,Rudolf Stingel,"6,000,000","4,000,000","4,667,000",Sold
2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2,6998024,Untitled,PAINTINGS,Laura Owens,"350,000","250,000","435,000",Sold
2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,"3,000,000","2,300,000","2,634,800",Sold
2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,"600,000","400,000","621,000",Sold
2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,"2,000,000","1,500,000","1,809,000",Sold
2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23,6998028,"Oiks, Tarts, Wierdoes And Contemporary Art",SCULPTURE,Grayson Perry,"120,000","80,000","175,000",Sold
2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24,6998029,Border Country,PAINTINGS,Peter Doig,"700,000","500,000","609,000",Sold
2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25,6998030,In Central Park,PAINTINGS,Howard Hodgkin,"800,000","600,000",,Unsold
2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26,6998031,"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica",PAINTINGS,David Hockney,"8,000,000","6,000,000",,Unsold
2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27,6998032,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"3,000,000","2,000,000","5,005,700",Sold
2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,"10,000,000","7,000,000","10,876,500",Sold
2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,"900,000","700,000","849,000",Sold
2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3,6998035,Untitled,PAINTINGS,Albert Oehlen,"1,500,000","1,000,000","1,929,000",Sold
2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,"450,000","350,000","405,000",Sold
2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31,6998037,Alphabet (U),SCULPTURE,Danh Vo,"200,000","150,000",,Unsold
2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,"200,000","150,000","200,000",Sold
2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33,6998039,Untitled,SCULPTURE,Francesco Lo Savio,"350,000","250,000","309,000",Sold
2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,"700,000","500,000","609,000",Sold
2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,"800,000","600,000","753,000",Sold
2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,"900,000","700,000",,Unsold
2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,"1,200,000","800,000","1,269,000",Sold
2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38,6998044,Untitled,PAINTINGS,Rudolf Stingel,"2,000,000","1,500,000","1,869,000",Sold
2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,"600,000","400,000","489,000",Sold
2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4,6998046,Untitled,PAINTINGS,Christopher Wool,"6,200,000","4,200,000","10,424,900",Sold
2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,"1,800,000","1,200,000","1,269,000",Sold
2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,"600,000","400,000","465,000",Sold
2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42,6998049,Down Under,SCULPTURE,Jason Rhoades,"550,000","350,000","345,000",Sold
2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43,6998050,Untitled,PAINTINGS,Albert Oehlen,"450,000","350,000","429,000",Sold
2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,"600,000","400,000","753,000",Sold
2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","297,000",Sold
2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,"900,000","700,000","1,029,000",Sold
2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,"1,200,000","800,000",,Unsold
2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,"3,500,000","2,500,000","3,538,000",Sold
2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49,6998056,Two Guys Twice,PAINTINGS,Richard Prince,"800,000","600,000","693,000",Sold
2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,"2,800,000","2,400,000","2,634,800",Sold
2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50,6998058,African Elephant,PAINTINGS,Andy Warhol,"1,800,000","1,400,000","1,569,000",Sold
2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,"2,000,000","1,500,000","2,409,000",Sold
2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,"800,000","600,000","849,000",Sold
2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,"1,500,000","1,000,000","2,349,000",Sold
2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,"350,000","250,000","669,000",Sold
2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55,6998063,"Sometimes Life Is Really, Really, Really Dark",PAINTINGS,Damien Hirst,"500,000","300,000","309,000",Sold
2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,"650,000","450,000","429,000",Sold
2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57,6998065,"Untitled (July 23, 1988)",TEXTILES,Alighiero e Boetti,"350,000","250,000","381,000",Sold
2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,"300,000","200,000","345,000",Sold
2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59,6998067,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"1,200,000","800,000","1,329,000",Sold
2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6,6998068,Wind,PAINTINGS,Gerhard Richter,"3,500,000","2,500,000","3,086,400",Sold
2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,"650,000","450,000",,Unsold
2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,"300,000","200,000","225,000",Sold
2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,"2,000,000","1,500,000","2,169,000",Sold
2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8,6998072,1025 Farben,PAINTINGS,Gerhard Richter,"7,000,000","5,000,000","7,376,600",Sold
2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9,6998073,Loving You,PAINTINGS,Damien Hirst,"1,200,000","800,000","993,000",Sold
2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,"300,000","200,000","333,000",Sold
2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,"90,000","70,000","273,000",Sold
2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","465,000",Sold
2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4,6998820,The Man,PHOTOGRAPHS,Gilbert and George,"500,000","300,000","321,000",Sold
2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5,6998821,T-riffic,PAINTINGS,Allen Jones,"350,000","250,000","705,000",Sold
2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6,6998822,Family Group,SCULPTURE,Henry Moore,"350,000","250,000","93,000",Sold
2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,"600,000","400,000","429,000",Sold
2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,"600,000","400,000","729,000",Sold
2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,"7,000,000","5,000,000","14,859,000",Sold
2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,"18,000,000","12,000,000","41,859,000",Sold
2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,"600,000","400,000","789,000",Sold
2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12,6998828,Untitled,WORKS ON PAPER,Cy Twombly,"700,000","500,000","609,000",Sold
2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,"2,000,000","1,500,000","1,749,000",Sold
2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,"8,000,000","6,000,000","8,671,500",Sold
2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,"700,000","500,000","729,000",Sold
2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16,6998832,Untitled,PAINTINGS,Albert Oehlen,"350,000","250,000","345,000",Sold
2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,"2,000,000","1,500,000","1,929,000",Sold
2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,"2,000,000","1,500,000","1,809,000",Sold
2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19,6998835,Untitled,PAINTINGS,Sigmar Polke,"1,200,000","800,000","969,000",Sold
2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,"600,000","400,000","465,000",Sold
2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21,6998837,Untitled,SCULPTURE,Rudolf Stingel,"6,000,000","4,000,000","5,746,500",Sold
2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,"1,200,000","800,000","1,029,000",Sold
2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23,6998839,Bridlington Violets,PAINTINGS,David Hockney,"700,000","500,000","609,000",Sold
2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24,6998840,Woman & Man,PAINTINGS,George Condo,"800,000","600,000","1,329,000",Sold
2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,"200,000","150,000","175,000",Sold
2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,"1,500,000","1,000,000","1,089,000",Sold
2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,"1,800,000","1,200,000","1,449,000",Sold
2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28,6998844,Colombe blanche,PAINTINGS,Max Ernst,"500,000","350,000","393,000",Sold
2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,"450,000","350,000","429,000",Sold
2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,"550,000","350,000",,Unsold
2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,"400,000","300,000","501,000",Sold
2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34,6998850,"Beautiful, Half a Red Headless Centipede (Painting)",PAINTINGS,Damien Hirst,"350,000","250,000","273,000",Sold
2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35,6998851,Grey Red,PAINTINGS,Sean Scully,"800,000","600,000","1,149,000",Sold
2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36,6998852,"Peinture 130 x 162 cm, 12 mai 1965",PAINTINGS,Pierre Soulages,"1,200,000","800,000","933,000",Sold
2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37,6998853,Cuadro 128,PAINTINGS,Manolo Millares,"150,000","100,000","206,250",Sold
2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38,6998854,Tôle,WORKS ON PAPER,Raymond Hains,"200,000","150,000",,Unsold
2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,"600,000","400,000",,Unsold
2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,"350,000","250,000","267,000",Sold
2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41,6998857,"Concetto spaziale, Attese",PAINTINGS,Lucio Fontana,"850,000","650,000","669,000",Sold
2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,"250,000","180,000","321,000",Sold
2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,"350,000","250,000","249,000",Sold
2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,"350,000","250,000","441,000",Sold
2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,"700,000","500,000",,Unsold
2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46,6998862,Homage to the Square,PAINTINGS,Josef Albers,"500,000","300,000","381,000",Sold
2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47,6998863,Found,PAINTINGS,Adrian Ghenie,"500,000","300,000","249,000",Sold
2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48,6998864,Without You,SCULPTURE,Damien Hirst,"700,000","500,000","585,000",Sold
2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49,6998865,Apparition,PAINTINGS,Damien Hirst,"700,000","500,000","609,000",Sold
2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50,6998866,Skulldiver II,PAINTINGS,Cecily Brown,"600,000","400,000","513,000",Sold

================================================
FILE: 97-artwork/AuctionsData - set1.csv
================================================
RecID,AuctionCurrency,AuctionDate,AuctionID,Indicator-Day-Afternoon,Auction Title,Auction House,AuctionLocation,lot LotNo,artwork ID,Artwork Title,ArtworkCategory,Artist_Full_Name,lot_EstimateHigh,lot_EstimateLow,Sale Price,Lot Status
288822,,,,,,,,0,,,,,191810000,137080000,207017950,
numeric,character,character,numeric,character,character,character,character,numeric,numeric,character,factor,character,numeric,numeric,numeric,factor
recid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus
2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1,6998013,Untitled,PAINTINGS,Christopher Wool,"600,000","400,000","729,000",Sold
2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10,6998014,Angel Of The North,SCULPTURE,Antony Gormley,"2,000,000","1,500,000","2,860,600",Sold
2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,"800,000","600,000","1,029,000",Sold
2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,"18,000,000","14,000,000","14,376,400",Sold
2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,"1,500,000","1,000,000","1,209,000",Sold
2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,"3,000,000","2,000,000","2,169,000",Sold
2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,"2,000,000","1,500,000","1,689,000",Sold
2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,"350,000","250,000","513,000",Sold
2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17,6998021,Untitled,SCULPTURE,Richard Prince,"1,500,000","1,000,000","2,049,000",Sold
2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18,6998022,Nov. 8,PAINTINGS,On Kawara,"800,000","600,000","789,000",Sold
2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19,6998023,Untitled,PAINTINGS,Rudolf Stingel,"6,000,000","4,000,000","4,667,000",Sold
2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2,6998024,Untitled,PAINTINGS,Laura Owens,"350,000","250,000","435,000",Sold
2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,"3,000,000","2,300,000","2,634,800",Sold
2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,"600,000","400,000","621,000",Sold
2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,"2,000,000","1,500,000","1,809,000",Sold
2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23,6998028,"Oiks, Tarts, Wierdoes And Contemporary Art",SCULPTURE,Grayson Perry,"120,000","80,000","175,000",Sold
2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24,6998029,Border Country,PAINTINGS,Peter Doig,"700,000","500,000","609,000",Sold
2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25,6998030,In Central Park,PAINTINGS,Howard Hodgkin,"800,000","600,000",,Unsold
2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26,6998031,"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica",PAINTINGS,David Hockney,"8,000,000","6,000,000",,Unsold
2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27,6998032,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"3,000,000","2,000,000","5,005,700",Sold
2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,"10,000,000","7,000,000","10,876,500",Sold
2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,"900,000","700,000","849,000",Sold
2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3,6998035,Untitled,PAINTINGS,Albert Oehlen,"1,500,000","1,000,000","1,929,000",Sold
2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,"450,000","350,000","405,000",Sold
2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31,6998037,Alphabet (U),SCULPTURE,Danh Vo,"200,000","150,000",,Unsold
2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,"200,000","150,000","200,000",Sold
2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33,6998039,Untitled,SCULPTURE,Francesco Lo Savio,"350,000","250,000","309,000",Sold
2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,"700,000","500,000","609,000",Sold
2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,"800,000","600,000","753,000",Sold
2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,"900,000","700,000",,Unsold
2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,"1,200,000","800,000","1,269,000",Sold
2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38,6998044,Untitled,PAINTINGS,Rudolf Stingel,"2,000,000","1,500,000","1,869,000",Sold
2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,"600,000","400,000","489,000",Sold
2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4,6998046,Untitled,PAINTINGS,Christopher Wool,"6,200,000","4,200,000","10,424,900",Sold
2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,"1,800,000","1,200,000","1,269,000",Sold
2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,"600,000","400,000","465,000",Sold
2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42,6998049,Down Under,SCULPTURE,Jason Rhoades,"550,000","350,000","345,000",Sold
2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43,6998050,Untitled,PAINTINGS,Albert Oehlen,"450,000","350,000","429,000",Sold
2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,"600,000","400,000","753,000",Sold
2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","297,000",Sold
2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,"900,000","700,000","1,029,000",Sold
2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,"1,200,000","800,000",,Unsold
2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,"3,500,000","2,500,000","3,538,000",Sold
2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49,6998056,Two Guys Twice,PAINTINGS,Richard Prince,"800,000","600,000","693,000",Sold
2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,"2,800,000","2,400,000","2,634,800",Sold
2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50,6998058,African Elephant,PAINTINGS,Andy Warhol,"1,800,000","1,400,000","1,569,000",Sold
2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,"2,000,000","1,500,000","2,409,000",Sold
2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,"800,000","600,000","849,000",Sold
2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,"1,500,000","1,000,000","2,349,000",Sold
2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,"350,000","250,000","669,000",Sold
2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55,6998063,"Sometimes Life Is Really, Really, Really Dark",PAINTINGS,Damien Hirst,"500,000","300,000","309,000",Sold
2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,"650,000","450,000","429,000",Sold
2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57,6998065,"Untitled (July 23, 1988)",TEXTILES,Alighiero e Boetti,"350,000","250,000","381,000",Sold
2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,"300,000","200,000","345,000",Sold
2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59,6998067,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"1,200,000","800,000","1,329,000",Sold
2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6,6998068,Wind,PAINTINGS,Gerhard Richter,"3,500,000","2,500,000","3,086,400",Sold
2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,"650,000","450,000",,Unsold
2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,"300,000","200,000","225,000",Sold
2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,"2,000,000","1,500,000","2,169,000",Sold
2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8,6998072,1025 Farben,PAINTINGS,Gerhard Richter,"7,000,000","5,000,000","7,376,600",Sold
2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9,6998073,Loving You,PAINTINGS,Damien Hirst,"1,200,000","800,000","993,000",Sold
2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,"300,000","200,000","333,000",Sold
2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,"90,000","70,000","273,000",Sold
2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","465,000",Sold
2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4,6998820,The Man,PHOTOGRAPHS,Gilbert and George,"500,000","300,000","321,000",Sold
2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5,6998821,T-riffic,PAINTINGS,Allen Jones,"350,000","250,000","705,000",Sold
2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6,6998822,Family Group,SCULPTURE,Henry Moore,"350,000","250,000","93,000",Sold
2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,"600,000","400,000","429,000",Sold
2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,"600,000","400,000","729,000",Sold
2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,"7,000,000","5,000,000","14,859,000",Sold
2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,"18,000,000","12,000,000","41,859,000",Sold
2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,"600,000","400,000","789,000",Sold
2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12,6998828,Untitled,WORKS ON PAPER,Cy Twombly,"700,000","500,000","609,000",Sold
2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,"2,000,000","1,500,000","1,749,000",Sold
2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,"8,000,000","6,000,000","8,671,500",Sold
2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,"700,000","500,000","729,000",Sold
2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16,6998832,Untitled,PAINTINGS,Albert Oehlen,"350,000","250,000","345,000",Sold
2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,"2,000,000","1,500,000","1,929,000",Sold
2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,"2,000,000","1,500,000","1,809,000",Sold
2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19,6998835,Untitled,PAINTINGS,Sigmar Polke,"1,200,000","800,000","969,000",Sold
2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,"600,000","400,000","465,000",Sold
2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21,6998837,Untitled,SCULPTURE,Rudolf Stingel,"6,000,000","4,000,000","5,746,500",Sold
2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,"1,200,000","800,000","1,029,000",Sold
2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23,6998839,Bridlington Violets,PAINTINGS,David Hockney,"700,000","500,000","609,000",Sold
2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24,6998840,Woman & Man,PAINTINGS,George Condo,"800,000","600,000","1,329,000",Sold
2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,"200,000","150,000","175,000",Sold
2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,"1,500,000","1,000,000","1,089,000",Sold
2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,"1,800,000","1,200,000","1,449,000",Sold
2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28,6998844,Colombe blanche,PAINTINGS,Max Ernst,"500,000","350,000","393,000",Sold
2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,"450,000","350,000","429,000",Sold
2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,"550,000","350,000",,Unsold
2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,"400,000","300,000","501,000",Sold
2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34,6998850,"Beautiful, Half a Red Headless Centipede (Painting)",PAINTINGS,Damien Hirst,"350,000","250,000","273,000",Sold
2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35,6998851,Grey Red,PAINTINGS,Sean Scully,"800,000","600,000","1,149,000",Sold
2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36,6998852,"Peinture 130 x 162 cm, 12 mai 1965",PAINTINGS,Pierre Soulages,"1,200,000","800,000","933,000",Sold
2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37,6998853,Cuadro 128,PAINTINGS,Manolo Millares,"150,000","100,000","206,250",Sold
2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38,6998854,Tôle,WORKS ON PAPER,Raymond Hains,"200,000","150,000",,Unsold
2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,"600,000","400,000",,Unsold
2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,"350,000","250,000","267,000",Sold
2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41,6998857,"Concetto spaziale, Attese",PAINTINGS,Lucio Fontana,"850,000","650,000","669,000",Sold
2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,"250,000","180,000","321,000",Sold
2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,"350,000","250,000","249,000",Sold
2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,"350,000","250,000","441,000",Sold
2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,"700,000","500,000",,Unsold
2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46,6998862,Homage to the Square,PAINTINGS,Josef Albers,"500,000","300,000","381,000",Sold
2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47,6998863,Found,PAINTINGS,Adrian Ghenie,"500,000","300,000","249,000",Sold
2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48,6998864,Without You,SCULPTURE,Damien Hirst,"700,000","500,000","585,000",Sold
2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49,6998865,Apparition,PAINTINGS,Damien Hirst,"700,000","500,000","609,000",Sold
2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50,6998866,Skulldiver II,PAINTINGS,Cecily Brown,"600,000","400,000","513,000",Sold

================================================
FILE: 97-artwork/artwork-cls1.R
================================================
#Artwork - Classification
# Build Decision Tree for predicting and Classification how sale happened


auctiondata2
names(auctiondata2)
keepcols = c('idafn', 'auchouse', 'aucloc', 'awcat', 'lotesthigh', 'lotestlow', 'saleprice','lotstatus') 
df_cls = auctiondata2[keepcols]
summary(df_cls)

library(rpart)
library(rpart.plot)
#model1 Anova
clstree1 = rpart(saleprice ~ awcat + lotesthigh + lotestlow + auchouse, minbucket=5, method='anova', data=df_cls)
#plot(clstree1)
clstree1
rpart.plot(clstree1)
prp(clstree1, type=4, ni=T, fallen.leaves = T, left=T, xflip=F, varlen = 0, faclen=0)


#model2 Anova
clstree2 = rpart(lotstatus ~ awcat + lotesthigh , method='class', data=df_cls, control=rpart(control(cp=.2)))
#plot(clstree)
clstree2
rpart.plot(clstree2)
prp(clstree2, type=1, ni=T, fallen.leaves = T, left=T, xflip=F, varlen = 0, faclen=0)
printcp(clstree2)


================================================
FILE: 97-artwork/artwork-descp.R
================================================
#Artwork 2 : Descriptive Analysis

library(dplyr)
library(data.table)
options(dplyr.width = Inf)

#Numeric Data
numdata = auctiondata2[sapply(auctiondata2,is.numeric)]
numdata
(summarise_all(numdata, funs(n(),mean,median, max, min)))

?transpose
#Factor Data
facdata = auctiondata2[sapply(auctiondata2,is.factor)]
facdata
summarise_all(auctiondata2[names(facdata)], funs(nlevels(.), nmiss=sum(is.na(.))))


#Arrange Data
arrange(auctiondata2, lotstatus, desc(saleprice), awcat) %>% select(lotstatus, saleprice, awcat)
auctiondata2 %>% select(lotstatus, saleprice, awcat) %>% arrange(lotstatus, desc(saleprice), awcat)



#Select Random Rows
(dt = sample_n(select(auctiondata2, awtitle, awcat),10))
(dt = auctiondata2 %>% select(awtitle, awcat) %>% sample_n(10))

#Group By
group_by(auctiondata2, awcat)
auctiondata2 %>% group_by(awcat)  

#Summaries by Groupby
(t = summarise_at(group_by(auctiondata2, awcat), vars(lotesthigh, lotestlow, saleprice), funs(n(), max, min, mean(., na.rm = TRUE))))

#The above code can also be written like
(t = na.omit(auctiondata2) %>% group_by(awcat) %>%
  summarise_at(vars(lotesthigh, lotestlow, saleprice), funs(n(), mean(., na.rm = TRUE))))
write.csv(t, 'output2.csv')

#Compute within group : Top2 rows from each gp
(t = na.omit(auctiondata2) %>% select(awcat, auchouse, arname, saleprice, lotesthigh, lotestlow) %>% filter(awcat %in% c("PAINTINGS", "PHOTOGRAPHS")) %>% group_by(awcat, auchouse) %>%  do(head( . , 2)))
write.csv(t, 'output3.csv')

# 3rd & 4th Max Value of each cat
(t = auctiondata2 %>% select(awcat, auchouse, saleprice) %>%
filter(awcat %in% c("PAINTINGS", "PHOTOGRAPHS")) %>%
  group_by(awcat) %>%
  do(arrange(.,desc(saleprice))) %>%  slice(3:4))


# Rank for variables

mutate_at(auctiondata2, vars(lotesthigh:saleprice), funs(Rank=min_rank(.))) %>% select(recid, lotesthigh_Rank, lotestlow_Rank, saleprice, saleprice_Rank ) %>% arrange(desc(saleprice))
#min_rank() assigns 1 to the smallest value and high number to the largest value

mutate_at(auctiondata2, vars(lotesthigh:saleprice), funs(Rank=min_rank(desc(.)))) %>% select(recid, lotesthigh_Rank, lotestlow_Rank, saleprice, saleprice_Rank ) %>% arrange(desc(saleprice))

#Category which generated max saleprice and its artist name
auctiondata2 %>% group_by(awcat) %>% filter(min_rank(desc(saleprice)) == 1) %>%  select(recid, awcat, arname, saleprice)



#Select Varaibles
select(auctiondata2, starts_with("l"))
select(auctiondata2, -starts_with("a"), -starts_with("l"))
names(auctiondata2)
select(auctiondata2, contains("lot"))

select(auctiondata2, saleprice, everything()) %>% select(saleprice, awcat)

filter(auctiondata2, awcat == "PAINTINGS")  %>%  select(saleprice, awcat)
filter(auctiondata2, !awcat == "PAINTINGS")  %>%  select(saleprice, awcat)
filter(auctiondata2, awcat %in% c("PAINTINGS", "PHOTOGRAPHS") & saleprice >= 10000000 ) %>%  select(saleprice, awcat, awtitle)
filter(auctiondata2, awcat %in% c("PAINTINGS", "PHOTOGRAPHS") |saleprice >= 10000000 ) %>%  select(saleprice, awcat, awtitle)

filter(auctiondata2, grepl("Art", auctitle)) %>% select(awcat, auctitle)


auctiondata2 %>% if_else(saleprice < lotesthigh, "SP > Est High", "SP < Est High", missing = "missing value")

#if else
df <- c(-10,2, NA)
if_else(df < 0, "negative", "positive", missing = "missing value")


auctiondata2 %>% group_by(auchouse) %>%
  summarise(Pecentile_25=quantile(saleprice, probs=0.25, na.rm = TRUE),
            Pecentile_50=quantile(saleprice, probs=0.5, na.rm = TRUE),
            Pecentile_75=quantile(saleprice, probs=0.75, na.rm = TRUE),
            Pecentile_99=quantile(saleprice, probs=0.99, na.rm = TRUE))

auctiondata2 %>% filter(!is.na(saleprice)) %>% summarise(Pecentile_25=quantile(saleprice, probs=0.25))



#Max in Lot High, Low Low, SP
auctiondata2 %>%
  rowwise() %>% mutate(Max= max(lotesthigh:lotestlow)) %>%
  select(aucid, awcat, lotesthigh:lotestlow,Max)



================================================
FILE: 97-artwork/artwork-eda1.R
================================================
# Artwork EDA1
options(scipen = 999999999)
head(auctiondata2)

#Check the data
head(auctiondata2)

#Descriptive Analysis

#Missing values
sum(is.na(auctiondata2))
colSums(is.na(auctiondata2))
rowSums(is.na(auctiondata2))
auctiondata2[complete.cases(auctiondata2),][c('recid','awid','lotstatus')]


#Set Numeric Columns
numcols = c('lotesthigh','lotestlow', 'saleprice') 

#Describe the data set
library(Hmisc)
Hmisc::describe(auctiondata2)

Hmisc::describe(auctiondata2[,c(numcols)])

library(pastecs)
pastecs::stat.desc(auctiondata2) 

library(psych)  #describe by group
psych::describe.by(auctiondata2, auctiondata2$auchouse)

names(auctiondata2)
boxplot(auctiondata2[,c('saleprice')])
#outliers / Extreme values
boxplot(auctiondata2[,numcols], col=1:3)
ylim= range(auctiondata2$saleprice, na.rm=T)
boxplot(auctiondata2[,numcols], col=8:5, ylim=c(90000,4000000))
abline(h=median(auctiondata2$lotesthigh))


# Table
# Count Artwork Cat vs Auction House
(t1= table( auctiondata2$awcat, auctiondata2$auchouse))
(mt1= addmargins(t1,c(1,2), FUN= list(sum)))
prop.table(table( auctiondata2$awcat, auctiondata2$auchouse))

#Table Summary/ Count - Status - Category - Auction House
table( auctiondata2$awcat, auctiondata2$auchouse, auctiondata2$lotstatus )



#Artists
length(unique(auctiondata2$arname))


================================================
FILE: 97-artwork/artwork-eda2.R
================================================
# Artwork - EDA2

#Aggregations
names(auctiondata2)
auctiondata2[c('recid','lotno')]
length(unique(auctiondata2$lotno))
aggregate( recid ~ lotno, data=auctiondata2, FUN=length)

#One lot could have more than 1 recid

names(auctiondata2)
#Average Sale on each day
(ag1a = aggregate( saleprice ~ aucdate, data= auctiondata2, FUN=sum))
(ag1b = aggregate( saleprice ~ aucdate, data = auctiondata2, FUN = function(x) c(mean = mean(x), (sum= sum(x)), (max=max(x)), (min=min(x)), (numbers = length(x)) ) ))
       
#Artwork Category Total Sales in 2 days

(ag2a = aggregate(saleprice ~ awcat , data=auctiondata2, FUN=sum))
ag2a = ag2a[ order(ag2a$saleprice),]
(sp = round(ag2a$saleprice/ sum(ag2a$saleprice)*100,1))
pie(ag2a$saleprice, labels=paste0(ag2a$awcat,"\n", sp, " %"), radius=1.1) 


(ag2b = aggregate(saleprice ~ awcat + auchouse, data=auctiondata2, FUN=sum))
(ag2b = ag2b[ order(ag2a$saleprice),])


names(ag2b)


(g1  = ggplot(ag2b, aes(x=1,y=saleprice,fill=awcat)) + 
  geom_bar(stat="identity",width=2) + coord_polar(theta='y')+
  theme(axis.ticks=element_blank(), axis.title=element_blank(),axis.text.y = element_blank(),axis.text.x = element_blank(), panel.grid  = element_blank())) + facet_wrap(~ auchouse) 



#Compare via Barplots
(ag3 = aggregate(saleprice ~ awcat + auchouse, data=auctiondata2, FUN=sum))

# Stacked barplot with multiple groups
ggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +
  geom_bar(stat="identity")
# Use position=position_dodge()
ggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +
  geom_bar(stat="identity", position=position_dodge())


ggplot(data=ag3, aes(x=awcat, fill=auchouse, y=saleprice)) +
  geom_bar(stat="identity", position=position_dodge()) + coord_flip()

#Bubble Chart
range(auctiondata2$lotesthigh)
#ggplot(data=na.omit(auctiondata2), aes(x=lotestlow, y=lotesthigh)) + geom_point(aes(size=saleprice)) +
  scale_size_continuous(range=c(80000,20000000)) +
  theme(legend.position = "none")

na.omit(auctiondata2) %>% ggplot(aes(x=lotestlow, y=lotesthigh, size=saleprice, colour = awcat, shape = auchouse)) + geom_point(alpha = 0.7, position = position_jitter()) 

g1a = stat_smooth(method = "lm")



#Box Plot
ggplot(subset(na.omit(auctiondata2), lotstatus=='Sold'), aes(x = awcat, y = lotestlow , group=auchouse)) + geom_boxplot()

ggplot(subset(auctiondata2, !is.na(saleprice)), aes(x = awcat, y = saleprice)) +  geom_boxplot()

ggplot(subset(auctiondata2, !is.na(saleprice)), aes(x = awcat, y = saleprice)) +  geom_point(alpha=0.3, color="tomato", position = "jitter") +   geom_boxplot(alpha=0) + coord_flip()


ggplot(subset(auctiondata2, !is.na(saleprice)) , aes(x=auchouse, y=saleprice)) +   geom_point(alpha=0.3, color="tomato", position = "jitter") +   geom_boxplot(alpha=0) + coord_flip() + facet_wrap( ~ awcat, scales = "free")

# expand_limits(pred=range_act, value=range_act) 
scales = "free_x"



names(auctiondata2)


#Artists whose art fetch more value : mean
library(dplyr)  
auctiondata2 %>%
  group_by(arname) %>%
  summarize(mean_sale = mean(saleprice, na.rm = TRUE),
        max_price = max(saleprice, na.rm = TRUE), 
        nos= length(awid)) %>%
        arrange(desc(mean_sale), desc(nos), arname)


#Difference with Max and Min Lot estimate of SalePrice
auctiondata2 %>%
   filter(lotstatus == 'Sold') %>% 
   select(recid, awid, awcat, lotesthigh, lotestlow, saleprice, lotstatus ) 
diffwithSP = auctiondata2[auctiondata2$lotstatus =='Sold', c('awid', 'awcat', 'lotesthigh', 'lotestlow', 'saleprice', 'lotstatus')]
diffwithSP$diffmax = diffwithSP$saleprice - diffwithSP$lotesthigh   
diffwithSP$diffmin = diffwithSP$saleprice  - diffwithSP$lotestlow  #should b +ve
head(diffwithSP)
diffwithSP

#Items sold below LowEst
diffwithSP[ diffwithSP$diffmin < 0,][c(1,3:9)]
diffwithSP %>% select(c(1,3:9)) %>% filter(diffmin < 0) %>% arrange(diffmin)

#Items sold above High
diffwithSP[ diffwithSP$diffmax > 0,][c(1,3:9)]
diffwithSP %>% select(c(1,3:9)) %>% filter(diffmax > 0) %>% arrange(desc(diffmax))



================================================
FILE: 97-artwork/artwork-rought.R
================================================
# rough work -

docurl = "https://docs.google.com/spreadsheets/d/"
sheeturl = paste0("1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k","/edit#gid=")
sheetname = "560796239"
fullurl = paste0(docurl, sheeturl, sheetname)
fullurl
df = as.data.frame(gsheet::gsheet2tbl(fullurl, skip=2))
summary(df)
str(df)


colcs1 = c('numeric','character', NA,NA,'character', 'character',	'character',	'character'	,'numeric',	'numeric',	'character','factor',	'character',	'numeric','numeric'	,'numeric',	'factor')
length(colcs1)
auctiondata = read.csv('./Data/AuctionsData - set1.csv', skip=2)
dim(auctiondata)
str(auctiondata)


#(sp = round(ag2b$saleprice/ sum(ag2b$saleprice)*100,1))
names(ag2b)
gb1 = ggplot(data = ag2b, aes(x = "", y = saleprice, fill = auchouse ))
gb2 = geom_bar(stat = "identity", position = position_fill()) 
gb3 = geom_text(aes(label = awcat), position = position_fill(vjust = 0.5))

gb4 = coord_polar(theta = "y")

gb5 = facet_wrap(~ auchouse)
gb6 = theme(axis.title.x = element_blank(),  axis.title.y = element_blank())
gb7 = theme(legend.position='bottom')
gb8 = guides(fill=guide_legend(nrow=2,byrow=TRUE))
gb1+gb2+gb3 + gb4 + gb5 + gb6 + gb7 + gb8 
+ gb4 + ga5

================================================
FILE: 97-artwork/artwork1.R
================================================
# Auction Data Analysis
# Data Set

docurl = "https://docs.google.com/spreadsheets/d/"
sheeturl = paste0("1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k","/edit#gid=")
sheetname = "560796239"
fullurl = paste0(docurl, sheeturl, sheetname)
fullurl
df = as.data.frame(gsheet::gsheet2tbl(fullurl, skip=2))
summary(df)
str(df)




auctiondata = read.csv('data/AuctionsData - set1.csv', skip=1)
colclass = c('character'. )
#auctiondata = read.csv(file.choose())
str(auctiondata)
names(auctiondata)
auctiondata$lotesthigh = as.numeric(auctiondata$lotesthigh)


================================================
FILE: 97-artwork/artwork2.R
================================================
# Auction Data Analysis
# Data Set

auctiondata = read.csv('./Data/AuctionsData - set1.csv', skip=3, stringsAsFactors = F, na.strings=c(""))
names(auctiondata)
dim(auctiondata)

#auctiondata = read.csv(file.choose())
str(auctiondata)
summary(auctiondata)
colSums(is.na(auctiondata))

auctiondata$recid = as.numeric(auctiondata$recid)
summary(auctiondata$recid)
auctiondata$aucdate = as.Date(auctiondata$aucdate, format="%m/%d/%Y") 
summary(auctiondata$aucdate)

auctiondata$auchouse = as.factor(auctiondata$auchouse)
auctiondata$auchouse

auctiondata$aucloc = as.factor(auctiondata$aucloc)
auctiondata$aucloc
auctiondata$lotno = as.numeric(auctiondata$lotno)
auctiondata$lotno

auctiondata$awcat = as.factor(auctiondata$awcat)
auctiondata$awcat
auctiondata$lotesthigh = as.double(auctiondata$lotesthigh)
summary(auctiondata$lotesthigh)
summary(x)
as.numeric(x)
as.character(auctiondata$lotesthigh)

auctiondata$lotestlow = as.numeric(auctiondata$lotestlow)

auctiondata$saleprice = as.numeric(auctiondata$saleprice)
auctiondata$lotstatus = as.factor(auctiondata$lotstatus)

summary(auctiondata)



================================================
FILE: 97-artwork/artwork4.R
================================================
# Artwork 4

library(readr)
AuctionsData_set1 <- read_csv("Data/AuctionsData - set1.csv", skip = 3)
#View(AuctionsData_set1)

summary(AuctionsData_set1)
auctiondata2 = as.data.frame(AuctionsData_set1)
str(auctiondata2)

names(auctiondata2)
summary(auctiondata2)
numcols = c('lotesthigh','lotestlow', 'saleprice') 
auctiondata2$auchouse = as.factor(auctiondata2$auchouse)
auctiondata2$awcat = as.factor(auctiondata2$awcat)
auctiondata2$lotstatus= as.factor(auctiondata2$lotstatus)

auctiondata2$recid = as.character(auctiondata2$recid)
auctiondata2$awid = as.character(auctiondata2$awid)
auctiondata2$lotno = as.character(auctiondata2$lotno)

auctiondata2$aucur = as.factor(auctiondata2$auccur)
auctiondata2$aucid = as.factor(auctiondata2$aucid)


summary(auctiondata2)





================================================
FILE: 97-artwork/awdata1.R
================================================
# Artwork Data 
library(readr)
AuctionsData_set1 <- read_csv("Data/AuctionsData - set1.csv", skip = 3)
#View(AuctionsData_set1)

summary(AuctionsData_set1)
auctiondata2 = as.data.frame(AuctionsData_set1)
str(auctiondata2)

names(auctiondata2)
summary(auctiondata2)
auctiondata2$recid = as.character(auctiondata2$recid)
auctiondata2$auccur = as.factor(auctiondata2$auccur)
auctiondata2$aucdate= as.Date(auctiondata2$aucdate,'%m/%d/%Y')
auctiondata2$aucid = as.factor(auctiondata2$aucid)
auctiondata2$auchouse = as.factor(auctiondata2$auchouse)
auctiondata2$awid = as.numeric(auctiondata2$awid)
auctiondata2$awcat = as.factor(auctiondata2$awcat)
auctiondata2$aucloc = as.factor(auctiondata2$aucloc)

auctiondata2$aucid= as.character(auctiondata2$aucid)
auctiondata2$awid = as.character(auctiondata2$awid)
auctiondata2$lotno = as.character(auctiondata2$lotno)
auctiondata2$lotstatus= as.factor(auctiondata2$lotstatus)

summary(auctiondata2)


#Structure Columnswise

for (i in 1:length(names(auctiondata2)))
  print(summary(auctiondata2[i]))

saveRDS('auctiondata2','./data/artwork.rds' )



================================================
FILE: 97-artwork/density.R
================================================

density_standard_norm <- function(x)
{
  1/sqrt(2*pi)*exp(-0.5*x^2)
}

dnorm(1, mean = 0, sd = 1)
#[1] 0.2419707
density_standard_norm(1)
#[1] 0.2419707

dnorm(2, mean = 0, sd = 1)

#[1] 0.05399097

density_standard_norm(2) 
#[1] 0.0539909


dnorm(0) #0.3989423
dnorm(0)*sqrt(2*pi) #[1] 1
dnorm(0,mean=4) #[1] 0.0001338302
dnorm(0,mean=4,sd=10) # 0.03682701
v <- c(0,1,2) 
dnorm(v) #0.39894228 0.24197072 0.05399097
x <- seq(-20,20,by=.1)
y <- dnorm(x)
plot(x,y)
y <- dnorm(x,mean=2.5,sd=0.1)
plot(x,y)
#----------
#

set.seed(3000)
xseq<-seq(-4,4,.01)
densities<-dnorm(xseq, 0,1)
cumulative<-pnorm(xseq, 0, 1)
randomdeviates<-rnorm(1000,0,1)

par(mfrow=c(1,3), mar=c(3,4,4,2))

plot(xseq, densities, col="darkgreen",xlab="", ylab="Density", type="l",lwd=2, cex=2, main="PDF of Standard Normal", cex.axis=.8)

plot(xseq, cumulative, col="darkorange", xlab="", ylab="Cumulative Probability",type="l",lwd=2, cex=2, main="CDF of Standard Normal", cex.axis=.8)

hist(randomdeviates, main="Random draws from Std Normal", cex.axis=.8, xlim=c(-4,4))

# Histogram
xseq<-seq(-4,4,.01)
y<-2*xseq + rnorm(length(xseq),0,5.5)
range(y)
par(mfrow=c(1,1))
hist(y, prob=TRUE, ylim=c(0,.06), breaks=20)
curve(dnorm(x, mean(y), sd(y)), add=TRUE, col="darkblue", lwd=2)

y1 = c(2,3,4,4,5)
hist(y1, prob=T)


================================================
FILE: 97-artwork/file2.R
================================================
par(mfrow=c(2, 2))
par(mar=c(7, 0, 3, 1))
par(mex=0.7)
dotchart(t(VADeaths[1:3,]), xlim = c(0,40), cex=0.6,
         labels=c("RM", "RF", "UM", "UF"))

par(mex=1)
par(mar=c(5, 3, 2, 1))
sunflowerplot(x=sort(round(rnorm(100))), y= round(rnorm(100),0),
              xlab="", ylab="", xlim=c(-3, 3), ylim=c(-3, 3), cex=0.5,
              size=1/12, seg.lwd=1, seg.col="grey",
              axes=FALSE)
axis(1, at=seq(-3, 3, 3))
axis(2, at=seq(-3, 3, 3))


hc <- hclust(dist(USArrests), "ave")
dend1 <- as.dendrogram(hc)
dend2 <- cut(dend1, h=70)
par(cex=0.7)
par(mar=c(1, 0, 2, 5.5))
#  dend2$lower is *NOT* a dendrogram, but a list of .. :
plot(dend2$lower[[3]], 
     horiz = TRUE, type = "tr", axes=FALSE, cex=0.8)
par(mar=c(6, 0, 2, 0))
# "inner" and "leaf" edges in different type & color :
plot(dend2$lower[[2]], 
     edgePar = list(col=c("black", "grey")), edge.root=TRUE, 
     axes=FALSE, cex=0.8)

library(lattice)
trellis.par.set(theme = canonical.theme("postscript", col=FALSE))
trellis.par.set(list(dot.symbol=list(pch=4)))
print(
  xyplot(lat ~ long, data=quakes, pch="*")
  
)
tplot <- xyplot(lat ~ long, data=quakes, pch=".")



trellis.par.set(theme = canonical.theme("postscript", col=FALSE))
trellis.par.set(list(dot.symbol=list(pch=1)))
print(
  update(tplot, 
         main="Earthquakes in the Pacific Ocean\n(since 1964)")
  
)

trellis.par.set(theme = canonical.theme("postscript", col=FALSE))
trellis.par.set(list(fontsize=list(text=9, points=8)))
show.settings()
library(grid)
pushViewport(plotViewport(c(5, 4, 2, 2)))
pushViewport(dataViewport(pressure$temperature,
                          pressure$pressure,
                          name="plotRegion"))
pushViewport(plotViewport(c(3,34,2,3)))
?pushViewport
# push the same viewport several times
grid.newpage()
vp <- viewport(width=0.5, height=0.5)
pushViewport(vp)
grid.rect(gp=gpar(col="blue"))
grid.text("Quarter of the device",
          y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="blue"))
pushViewport(vp)
grid.rect(gp=gpar(col="red"))
grid.text("Quarter of the parent viewport",
          y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="red"))
popViewport(2)
# push several viewports then navigate amongst them
grid.newpage()
grid.rect(gp=gpar(col="grey"))


================================================
FILE: 99-GD/aboutSL
================================================
# Supervised Learning
Algo consists of a target/ outcome variable (DV) which is to be predicted from a given set of predictors (IV). Using tese set of variables, we generate a function that map inputs to desired outputs. 
The training process continues until the model achieves a desired level of accuracy on the training data. 
Egs - Regression, Decision Treee, Random Forest, KNN, Logistic Regression


================================================
FILE: 99-GD/gradientdescent1.R
================================================
# Gradient Descent
# http://econometricsense.blogspot.in/2011/06/gradient-descent.html
# Read above gradient concepts
#http://econometricsense.blogspot.in/2011/11/gradient-descent-in-r.html
#f(x) = x^4 - 3*x^3 + 2
# set up a stepsize
alpha = 0.003

# set up a number of iteration
iter = 500

# define the gradient of f(x) = x^4 - 3*x^3 + 2
gradient = function(x) return((4*x^3) - (9*x^2))

# randomly initialize a value to x
set.seed(100)
x = floor(runif(1)*10)
x
# create a vector to contain all xs for all steps
x.All = vector("numeric",iter) # iterate 500 times
#vector('integer',10)
?vector
x.All
# gradient descent method to find the minimum
# Stepwise 
#x ;gradient(x); alpha*gradient(x);  x - alpha*gradient(x); x = x - alpha*gradient(x)
#x ;gradient(x); alpha*gradient(x);  x - alpha*gradient(x); x = x - alpha*gradient(x)
#...
# these value of x are stored in blank vector
for(i in 1:iter){
  x = x - alpha*gradient(x)
  x.All[i] = x
  print(x) # print last value of x
}
head(x.All); x
# print result and plot all xs for every iteration
print(paste("The minimum of f(x) is ", x, sep = ""))
#layout(matrix(c(1,2,3,4),2,2))
layout(1,1)
plot(x.All, type = "l")



================================================
FILE: 99-GD/gradientdescent2.R
================================================
# Multivariate Gradient Descent 
# https://www.r-bloggers.com/multivariable-gradient-descent/

x <- seq(-5,5,length=100)
x
y <- x  # similar values of x and y

fun <- function(x,y)
{
  return(x**2+y**2-2*x*y)
}
# Outer Product
#x=1:9 ; x%o%x
#outer(x,x,'*')

z <- outer(x,y,fun) # Outer Product of arrays
z

?persp  # Perspective Plots of surface over x-y plane


persp(x,y,z,theta=30,phi=30,expand=0.5,col="lightgreen",
      ltheta=100,xlab="x",ticktype="detailed",ylab="y",zlab="z")
#
#Well in this case, we need to calculate two thetas in order 
#to find the point (theta,theta1) such that f(theta,theta1) = minimum.
#

================================================
FILE: 99-GD/gradientdescent3.R
================================================
# Gradient Descent in R
# https://www.r-bloggers.com/gradient-descent-in-r/

xs <- seq(0,4,len=20) # create some values
xs
# define the function we want to optimize

f <-  function(x) {
  1.2 * (x-2)^2 + 3.2
}

# plot the function 
plot(xs , f (xs), type="l",xlab="x",
     ylab=expression(1.2(x-2)^2 +3.2))

# calculate the gradeint df/dx
# derivative of the function
grad <- function(x){
  1.2*2*(x-2)
}


# df/dx = 2.4(x-2), if x = 2 then 2.4(2-2) = 0
# The actual solution we will approximate with gradeint descent
# is  x = 2 as depicted in the plot below

lines (c (2,2), c (3,8), col="red",lty=2)
text (2.1,7, "Closedform solution",col="red",pos=4)
#http://mathworld.wolfram.com/Closed-FormSolution.html
#

# gradient descent implementation
x <- 0.1 # initialize the first guess for x-value
xtrace <- x # store x -values for graphing purposes (initial)
ftrace <- f(x) # store y-values (function evaluated at x) for graphing purposes (initial)
f(x)
stepFactor <- 0.6 # learning rate 'alpha'
for (step in 1:100) {
  x <- x - stepFactor*grad(x) # gradient descent update
  xtrace <- c(xtrace,x) # update for graph
  ftrace <- c(ftrace,f(x)) # update for graph
  print(x)
  
  }

lines ( xtrace , ftrace , type="b",col="blue")
text (0.5,6, "Gradient Descent",col="blue",pos= 4)

# print final value of x
print(x) # x converges to 2.0


================================================
FILE: 99-GD/gradientdescent4.R
================================================
# gradient descent4
#https://rstudio-pubs-static.s3.amazonaws.com/159940_4a7d620cb4e0460486c364281cdf5780.html

#First, let’s generate some data to work with, let’s define our training example with 4 features:
  
set.seed(11)
x <- matrix(rnorm(400), ncol = 4)
x
y <- rnorm(100)
y
m <- length(y)
m
X<-cbind(rep(1, 100), x)
X
theta<-rep(0,5)
theta
#Second, set up the cost function for least square linear regression:
length(y)  
compCost<-function(X, y, theta){
    m <- length(y)
    J <- sum((X%*%theta - y)^2)/(2*m)
    return(J)
  }
#Next, set up the gradient descent function, running for iterations:
  
gradDescent<-function(X, y, theta, alpha, num_iters){
    m <- length(y)
    J_hist <- rep(0, num_iters)
    for(i in 1:num_iters){
      
      # this is a vectorized form for the gradient of the cost function
      # X is a 100x5 matrix, theta is a 5x1 column vector, y is a 100x1 column vector
      # X transpose is a 5x100 matrix. So t(X)%*%(X%*%theta - y) is a 5x1 column vector
      theta <- theta - alpha*(1/m)*(t(X)%*%(X%*%theta - y))
      
      # this for-loop records the cost history for every iterative move of the gradient descent,
      # and it is obtained for plotting number of iterations against cost history.
      J_hist[i]  <- compCost(X, y, theta)
    }
    # for a R function to return two values, we need to use a list to store them:
    results<-list(theta, J_hist)
    return(results)
  }
#Then, let’s set a training rate alpha and number of iterations to perform gradient descent:
  
alpha <- .1
num_iters <- 150
results <- gradDescent(X, y, theta, alpha, num_iters)
theta <- results[[1]]
cost_hist <- results[[2]]
print(theta)
##             [,1]
## [1,]  0.04636521
## [2,]  0.09681053
## [3,]  0.10089729
## [4,] -0.11856147
## [5,] -0.20665900
#Finally let’s plot the cost history:
  
plot(1:num_iters, cost_hist, type = 'l')


================================================
FILE: 99-GD/regr1.R
================================================
# Regression

#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
#x_train <- input_variables_values_training_datasets
#y_train <- target_variables_values_training_datasets
#x_test <- input_variables_values_test_datasets

x <- data.frame(cbind(x_train,y_train))
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
layout(matrix(c(1,2,3,4),2,2))
plot(linear)
head(x_test)

#Predict Output
#newdata = data.frame(waiting=80) # wrap the parameter
#predict(eruption.lm, newdata)    # apply predict 
newdate = data.frame()
predicted= predict(linear,x_test) 
?predict


================================================
FILE: 99-HTML/aboutUSL
================================================
# Unsupervised Learning
We do not have any target or outcome variable to predict / estimate
It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.
Egs - Apriori Algo, K-means


================================================
FILE: 99-Misc/dhmethods.R
================================================
#Various Functions on Data
#Handle objects ----
x = 1:5

#Functions/ Methods applicable to Objects
methods(class=class(x))
#helps you know which function is applicable for which type of object
methods(class=class(mtcars))

#seq
1:5
rev(x)
match(5, x)


#all combinations---
(df=expand.grid(x=c('A','B','C','D'), y=1:3))

choose(6,4)

weighted.mean(c(1,2), c(3,4))
(1*3 + 2*4)/(3+4)

df2 = data.frame(x=c(1,2), y=c(3,4), z=c(5,6))
stars(df2[,1:2])
stars(df2[,1:3])

filled.contour((df2))
?image
image(df2)

df2 = mtcars[1:5,1:8]


================================================
FILE: 99-Misc/funcpgm1.R
================================================
# Functional Programming
#http://adv-r.had.co.nz/Functional-programming.html

?Map
Map(sum, 1:5, 1:5, 1:5)
Map(sum, 1:5, 1:5, 1:5, simplify2array=F)
Map(sum, 1:5, 11:15, 21:25, simplify=T)

Reduce(f, x, init, right = FALSE, accumulate = FALSE)
Filter(f, x)
Find(f, x, right = FALSE, nomatch = NULL)
Map(f, ...)
Negate(f)
Position(f, x, right = FALSE, nomatch = NA_integer_)

x = 1:12
Position(f=Find, x)


#http://www.rmanchester.org/presentations/2013/05/ManchesterR_-_FP_in_R_-_David_Springate_-_20130502.pdf

'+'(1,2)
':'(1,10)


================================================
FILE: 99-Misc/h2o.R
================================================
#Package H20
#https://cran.r-project.org/web/packages/h2o/h2o.pdf

library(h2o)
h2o.init()
iris.hex <- as.h2o(iris)
dim(iris.hex

================================================
FILE: 99-Misc/skimr-package.R
================================================
#Data Understanding
#https://cran.r-project.org/web/packages/skimr/vignettes/Using_skimr.html
#https://cran.r-project.org/web/packages/skimr/skimr.pdf

library(skimr)
#skimr, a package for quickly skimming data by data type

skim(iris)
skim(iris) %>%  dplyr::filter(stat == "mean")
skim(mtcars) %>%  dplyr::filter(stat == "mean")

mtcars %>% dplyr::group_by(gear) %>% skim()

skim(iris, Sepal.Length, Species)

skim(datasets::lynx)

iris_setosa <- iris %>%  skim_tee(iris) %>% dplyr::filter(Species == "setosa")

iris %>% skim_to_list()

n_missing(mtcars)


================================================
FILE: 99-json/21b-json-format.R
================================================

#https://data.metinyazici.org/2017/10/working-with-web-data-in-r.html

library(jsonlite)

chocolate_brands <- c('
[ { "name" : "Dove Bar","founded" : 1956},
  { "name" : "Toblerone", "founded" : 1908}
]')
chocolate_brands

choco_json <- fromJSON(chocolate_brands, simplifyVector = F) # parse that with fromJSON()
print(choco_json)

library(dplyr)
name <- choco_json %>%  bind_rows %>% select(name, founded)
print(name)




================================================
FILE: 99-json/23b-xml-import.R
================================================


library(xml2)
url = "https://en.wikipedia.org/wiki/List_of_chocolate_bar_brands" #Repeat again here
choco_xml <- read_xml(url)
print(choco_xml)

write_xml(choco_xml,'chocolates.xml',options="as_xml")
write_html(choco_xml,file='chocolates.html',options="as_html")

r <- xml_find_all(choco_xml, "//table[@class='wikitable sortable']")
head(r)
a <- xml_children(r)
head(a)
x <- xml_text(a)
head(x)


z01 <- strsplit(x, '\n', fixed = T)
#set column names by the first element of list `z01[[1]]`
z01
df <- setNames(data.frame(matrix(unlist(z01), nrow = 285, byrow = T)), z01[[1]])
#not need the first row that became the column names
df <- df[-1,]
#not need "Image" column so remove
df$Image <- NULL

================================================
FILE: 99-json/25a-httr1.R
================================================
#httr package to import data from webpages

#Load the httr package
library("httr")
get_http <- GET("http://httpbin.org/get")
str(get_http, list.len = 3)


arg_pars <- list(country = "UK", manufacturer = "Mars")
arg_resp <- GET("https://httpbin.org/get", query = arg_pars)
print(arg_resp)
#Status: 200, means successful request

#POST request
post_http <- POST(url="http://httpbin.org/post", body="This message is from strboul")
print(post_http)


#You are expected to be reasonable and respect the API service1, for sure. APIs have access tokens to control the misuse of their services, which usually limit the number of requests coming by unique users or sessions making the servers very busy or locked. Giving an example, Twitter controls the number of requests to the server by rate-limiting. That makes available GET requests in two ways: “15 calls every 15 minutes, and 180 calls every 15 minutes.” To correspond this request time, you can use Sys.sleep() function in a for loop in order to send expressions in a specified time interval.


================================================
FILE: 99-json/25c-httr2.R
================================================
#httr2

library(httr)
page_source <- function(url_name, parse_format){
  url <- modify_url(
    "https://en.wikipedia.org/w/api.php?",
    query = list(
      action = "query",
      titles = url_name,
      prop = "revisions",
      rvprop = "content",
      format = parse_format)
  )
  response <- GET(url)
  if(http_error(response)){
    stop("The request failed")}
  else {
    result <- content(response)
    return(result)
  }
}


a <- page_source("List_of_chocolate_bar_brands", "json")
str(a)

a01 <- page_source("List_of_chocolate_bar_brands", "jsonfm")
print(a01)


================================================
FILE: 99-json/json-1.R
================================================


install.packages("rjson")
install.packages("RJSONIO")
install.packages("(RCurl")


{ 
  "ID":["1","2","3","4","5","6","7","8" ],
  "Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
  "Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
  "StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013", "7/30/2013","6/17/2014"],
  "Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}

# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")


library(RJSONIO)
library(RCurl)
json_file = getURL("https://raw.githubusercontent.com/isrini/SI_IS607/master/books.json")
json_file2 = RJSONIO::fromJSON(json_file)
head(json_file2)
# Print the result.
print(json_file2)
df <- as.data.frame(json_file2)
df
str(df)

#--------------------------

Token <- "245432532532"
source <- "http://......."
header_type <- "applcation/json"
full_token <- paste0("Bearer ", Token)
response <- GET(n_source, add_headers(Authorization = full_token, Accept = h_type), timeout(120), verbose())
text_json <- content(response, type = 'text', encoding = "UTF-8")
jfile <- fromJSON(text_json)
df <- as.data.frame(jfile)


data(Adult)
Adult
## select first 10 transactions
Adult[1:10]
## select first 10 items for first 100 transactions
Adult[1:100, 1:10]
## select the first 100 transactions for the items containing
## "income" or "age=Young" in their labels
Adult[1:100, c("income=small", "income=large" ,"age=Young")]


library("rjson")

{
  "TemperatureRegionID" : 419,
  "StartDate" : "07/23/2018 01:55:45 PM",
  "StopDate" : "07/23/2018 09:55:45 PM",
  "Interval" : "Minute",
}


#http://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/

library(jsonlite)
library(httr)

req <- POST("http://api.scb.se/OV0104/v1/doris/sv/ssd/START/PR/PR0101/PR0101A/KPIFastM2", body = '{ "query": [], "response": { "format": "json" } }')
http://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/
  req <- POST("http://adminapi.fat.test.fleetmode.io/temperature-regions/get-history/", body = '{ "query": [], "response": { "format": "json" } }')

req  

stop_for_status(req)
json <- content(req, "text")


# JSON starts with an invalid character:
validate(json)
json <- substring(json, 2)
validate(json)

# Now we can parse
object <- jsonlite::fromJSON(json)
print(objects)


library(jsonlite)

library(httr)

pc_json <- list{(
  'TemperatureRegionID' : 419,
  'StartDate' : '07/23/2018 01:55:45 PM',
  'StopDate' : '07/23/2018 09:55:45 PM',
  'Interval' : 'Minute',
)}


res <- POST("https://api.postcodes.io/postcodes"
            , body = pc_json
            , encode = "json")
appData <- content(res)

library(jsonlite)

# A JSON array of primitives
json <- '["Mario", "Peach", null, "Bowser"]'
json

# Simplifies into an atomic vector
fromJSON(json)

# No simplification:
fromJSON(json, simplifyVector = FALSE)

# to Df
# No simplification:
json <-
  '[
{"Name" : "Mario", "Age" : 32, "Occupation" : "Plumber"}, 
{"Name" : "Peach", "Age" : 21, "Occupation" : "Princess"},
{},
{"Name" : "Bowser", "Occupation" : "Koopa"}
]'
mydf <- fromJSON(json, simplifyDataFrame = TRUE)
mydf
str(mydf)

#----

json <-
  '[
{"Name" : "Mario", "Age" : 32, "Occupation" : "Plumber"}, 
{"Name" : "Peach", "Age" : 21, "Occupation" : "Princess"},
{},
{"Name" : "Bowser", "Occupation" : "Koopa"}
]'

{
  "TemperatureRegionID" : 419,
  "StartDate" : "07/23/2018 01:55:45 PM",
  "StopDate" : "07/23/2018 09:55:45 PM",
  "Interval" : "Minute",
}


================================================
FILE: 99-json/json2.R
================================================
# read json file

library(jsonlite)
zips <- stream_in(url("http://media.mongodb.org/zips.json"))
#If the server uses https, you can use the curl package:
str(zips)
  
library(jsonlite)
library(curl)
zips <- stream_in(curl("http://media.mongodb.org/zips.json"))

library(dplyr)
myJSON <- . %>% file(open="r") %>% stream_in()
myJSON('test.json')

df <- filename %>% file %>% stream_in

datasets <- lapply(res.clean[1:2, ], jsonlite::fromJSON)

#If each element is a data frame you can subsequently use
alldata <- jsonlite:: rbind_pages(datasets)




require(jsonlite)
id = "dup1966"
url = paste0("https://gdata.youtube.com/feeds/api/videos/", id, "?v=2&alt=json")
raw.data <- readLines(url, warn = "F")
rd <- fromJSON(raw.data)
term <- rd$entry$category$term[2]
label <- rd$entry$category$label[2]
title <- rd$entry$title
author <- rd$entry$author[1]
duration <- rd$entry$`media$group`$`media$content`$duration[1]

================================================
FILE: 99-phd/attendance2.R
================================================
attendance1 = read.csv('./attendance/attendance1.csv')
attendance1
str(attendance1)

attendance2 = read.csv('./attendance/attendance2.csv')
head(names(attendance2))
str(attendance2)
attendance2a = attendance2[,-1]
str(attendance2a)
attendance2a$Student   = as.character(attendance2a$Student)
str(attendance2a)

#head(names(mutate(attendance2, RollNo=NULL))) 

library(tidyr)
tidyr_attend = gather(attendance2a, Item, Score, 2:41)
str(tidyr_attend)



================================================
FILE: 99-phd/grades.R
================================================
library(readr)
maths = read_csv('./grades/grades.csv')
maths
str(maths)
names(maths)
head(maths)
dim(maths)
library(knitr)
kable(head(maths[,5:9]))


#subsetting & Cleaning
dim(maths)      # 100 rows, 13 colns
#general info like comments can be dropped
#dplyr - select, contains
library(dplyr)
names(maths)
maths2 = select(maths, `student`, contains('Internal ')
    , contains('Viva'), contains('Assignment'),  contains('Attendance')
    , contains('Final'), contains('Assessment')
    , contains('Grade'), - contains('Comment'))
maths2
names(maths2)
head(names(maths2))

# removing long names from anems
# stringr - str_sub, str_trim

library(stringr) 
dim(maths2)
names(maths2)[1:8]
str_sub(names(maths2)[2:3], start=1, end=-8) # only 2 & 3 have long names
names(maths2)[2:3] = str_sub(names(maths2)[2:3], start=1, end=-8)
names(maths2)

names(maths2) = str_trim(names(maths2)) # remove blank spaces
names(maths2)
dim(maths2)

# multiple colns - collapse - key value pairs
library(tidyr)
tidy_maths = gather(maths2, item,score,2:6)
head(tidy_maths)
dim(tidy_maths)

kable(tidy_maths)

#add another column based on certain values
tidy_maths2 = mutate(tidy_maths, type = ifelse(grepl('Internal', item), 'Internals',
    ifelse(grepl('Viva', item), 'Viva',
    ifelse(grepl('Attendance', item),'Attendance',
    ifelse(grepl('Assignment', item),'Assignment','Other' )))),
    result=ifelse(`Calculated Grade` > 60,'Passed', 'Failed'))

head(tidy_maths2)
table(tidy_maths2$type)

str(tidy_maths2)

library(magrittr)
str(tidy_maths2)
names(maths)
maths3 = maths[c('student', 'course', 'batch', 'gender')]
rich_maths = left_join(tidy_maths2, maths3, by='student')
rich_maths

rich_maths %>% filter(score > 0, type=='Viva') %>% group_by(student) %>%
  summarize(Viva_Mean = round(mean(score),2)) %>% arrange(desc(Viva_Mean)) %>% head(n=10) %>% kable()  

rich_maths %>% filter(score > 0, type=='Internals') %>% group_by(item) %>%
  summarize(Mean = mean(score), Median=median(score), Std_dev = sd(score)) %>% kable()  

table(rich_maths$type)
rich_maths %>% filter(score > 0, type=='Attendance' | type== 'Viva') %>%  
  group_by(gender, type) %>% summarize(Mean_Score= round(mean(score),2))

cor(rich_maths$score, rich_maths$Final)

# Correlation
rich_maths %>% group_by(item) %>% summarize(corr_to_final = round(cor(score,Final),2)) %>%
  arrange(corr_to_final) %>% head() %>% kable()
x1 = rich_maths %>% group_by(item)
names(x1)

library(ggplot2)
ggplot(rich_maths, aes(Final)) + geom_histogram(binwidth=5, fill='lightgreen', 
   color='black') + ggtitle('Histogram') + theme_minimal()

ggplot(rich_maths, aes(`Calculated Grade`)) + geom_histogram(binwidth=5, fill='lightgreen', 
      color='black') + ggtitle('Histogram') + theme_minimal()

ggplot(rich_maths, aes(`score`)) + geom_histogram(binwidth=2, fill='lightgreen', 
       color='black') + ggtitle('Histogram') + facet_wrap(~item) +
       theme_minimal()
  

================================================
FILE: 99-sports/cricket1.R
================================================
# Sports Analytics
#https://gigadom.wordpress.com/2016/05/14/re-introducing-cricketr-an-r-package-to-analyze-performances-of-cricketers/
#http://www.espncricinfo.com/india/content/player/35320.html


#install.packages('cricketr') 

library(cricketr)

# Retrieve the file path of a data file installed with cricketr
pathToFile ,"Sachin Tendulkar")

# sachin - 35320

#stores players data in csv file
tendulkar <- getPlayerData(35320,dir="./sports",file="tendulkar.csv",type="batting",homeOrAway=c(1,2), result=c(1,2,4))
tendulkar <- getPlayerData(35320,dir="./sports",file="tendulkar.csv",type="batting",homeOrAway=c(1,2), result=c(1,2,4))
Sachin Tendulkar 
#Jacques Kallis http://www.espncricinfo.com/southafrica/content/player/45789.html
Ricky Ponting 
Kumara Sangakarra



?getPlayerData
tendulkar
?tendulkar

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf(tendulkar,"Sachin Tendulkar")

batsmanRunsFreqPerf("./sports/tendulkar.csv","Sachin Tendulkar")
batsmanMeanStrikeRate("./sports/tendulkar.csv","Sachin Tendulkar")
batsmanRunsRanges("./sports/tendulkar.csv","Sachin Tendulkar")


par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./sports/tendulkar.csv","Tendulkar")
batsman6s("./sports/tendulkar.csv","Tendulkar")
batsmanDismissals("./sports/tendulkar.csv","Tendulkar")

battingPerf3d("./sports/tendulkar.csv","Sachin Tendulkar")
batsmanAvgRunsGround("./sports/tendulkar.csv","Sachin Tendulkar")
batsmanAvgRunsOpposition("./sports/tendulkar.csv","Tendulkar")
batsmanRunsLikelihood("./sports/tendulkar.csv","Sachin Tendulkar")
batsmanPerfBoxHist("./sports/tendulkar.csv","Sachin Tendulkar")


batsmanPerfBoxHist("./sports/kallis.csv","Jacques Kallis")

================================================
FILE: 99-sports/cricket2-york.R
================================================
# Cricket Analysis
#https://gigadom.wordpress.com/2016/04/02/introducing-cricket-package-yorkr-part-1-beaten-by-sheer-pace/

install.packages('yorkr')
library(yorkr)

convertYaml2RDataframe("225171.yaml","./source","./sports")  


================================================
FILE: 99-weka/cls-ID3.R
================================================
# Classification - ID3 
#https://cis.temple.edu/~giorgio/cis587/readings/id3-c45.html

# basic ideas behind ID3 are that:
# In the decision tree each node corresponds to a non-categorical attribute and each arc to a possible value of that attribute. A leaf of the tree specifies the expected value of the categorical attribute for the records described by the path from the root to that leaf. [This defines what is a Decision Tree.]
# In the decision tree at each node should be associated the non-categorical attribute which is most informative among the attributes not yet considered in the path from the root. [This establishes what is a "Good" decision tree.]
# Entropy is used to measure how informative is a node. [This defines what we mean by "Good". By the way, this notion was introduced by Claude Shannon in Information Theory.]
#C4.5 is an extension of ID3 that accounts for unavailable values, continuous attribute value ranges, pruning of decision trees, rule derivation, and so on.

library(data.tree)

# http://r.789695.n4.nabble.com/Is-there-an-ID3-implementation-in-R-td4696375.html

## load RWeka 

Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_111\\bin') # for 64-bit version

system("java -version")
# This is important.. Use rJava Home instead of Windows Home
if(Sys.getenv("JAVA_HOME")!=""){
  Sys.setenv(JAVA_HOME="")
}
library(rJava)
library("RWekajars")
library(RWeka)  # needs rJava
## look for a package providing id3 
WPM("refresh-cache") 
WPM("list-packages", "available") ## look for id3 
## install package providing id3 
WPM("install-package", "simpleEducationalLearningSchemes") 
## load the package 
WPM("load-package", "simpleEducationalLearningSchemes") 
## make classifier 
ID3 <- make_Weka_classifier("weka/classifiers/trees/Id3") 
## test it out. 
DF2 <- read.arff(system.file("arff", "contact-lenses.arff", package = "RWeka")) 
.jinit(".")
str(DF2)
ID3(`contact-lenses` ~ ., data = DF2) 

WPM("load-package", "Decorate") 
make_Weka_classifier("Decorate") 
WPM("load-package", "naiveBayesTree")

print(.jclassPath())


================================================
FILE: 99-weka/cls-c45weka.R
================================================
# C4.5 Algo  https://en.wikipedia.org/wiki/C4.5_algorithm
# C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan.[1] C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, 
# and for this reason, C4.5 is often referred to as a statistical classifier.
# C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy.
# At each node of the tree, C4.5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy). The attribute with the highest normalized information gain is chosen to make the decision. 
# The C4.5 algorithm then recurs on the smaller sublists.
# C5.0 offers a number of improvements on C4.5. 

#https://www.r-bloggers.com/r-talks-to-weka-about-data-mining/
summary(iris)
library(RWeka)
iris_j48 <- J48(Species ~ ., data = iris)
iris_j48
summary(iris_j48)
plot(iris_j48)

# Evaluation
eval_j48 <- evaluate_Weka_classifier(iris_j48, numFolds = 10, complexity = FALSE, 
    seed = 1, class = TRUE)
eval_j48

# More Functions
WOW("J48")

j48_control <- J48(Species ~ ., data = iris, control = Weka_control(M = 10))
j48_control

# Cost Sensitive Cls
csc <- CostSensitiveClassifier(Species ~ ., data = iris, control = Weka_control(`cost-matrix` = matrix(c(0, 
    10, 0, 0, 0, 0, 0, 10, 0), ncol = 3), W = "weka.classifiers.trees.J48", 
M = TRUE))
csc

matrix(c(0, 1, 0, 0, 0, 0, 0, 1, 0), ncol = 3)
eval_csc <- evaluate_Weka_classifier(csc, numFolds = 10, complexity = FALSE, 
    seed = 1, class = TRUE)
eval_csc


================================================
FILE: 99-weka/clsW-iris.R
================================================
R Cls4
Sys.getenv("JAVA_HOME")

Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre1.8.0_111")


#Eg1
library(RWeka)
data(iris)
fit <- J48(Species~., data=iris)
summary(fit)
predictions <- predict(fit, iris[,1:4])
table(predictions, iris$Species)
#Eg2
library(RWeka)
data(iris)
fit <- PART(Species~., data=iris)
summary(fit)
predictions <- predict(fit, iris[,1:4])
table(predictions, iris$Species)





================================================
FILE: 99-weka/clsW-iris2.R
================================================
R Cls4
#Eg1
library(RWeka)
data(iris)
fit <- J48(Species~., data=iris)
summary(fit)
predictions <- predict(fit, iris[,1:4])
table(predictions, iris$Species)
#Eg2
library(RWeka)
data(iris)
fit <- PART(Species~., data=iris)
summary(fit)
predictions <- predict(fit, iris[,1:4])
table(predictions, iris$Species)


================================================
FILE: MBArules.csv
================================================
"rules","support","confidence","coverage","lift","count"
"1","{Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"2","{Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"3","{Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"4","{Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"5","{Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"6","{Product D} => {Product G}",0.2,1,0.2,3,3
"7","{Product D} => {Product E}",0.2,1,0.2,2.5,3
"8","{Product D} => {Product H}",0.2,1,0.2,2.14285714285714,3
"9","{Product D} => {Product F}",0.2,1,0.2,2.14285714285714,3
"10","{Product K} => {Product E}",0.2,1,0.2,2.5,3
"11","{Product K} => {Product J}",0.2,1,0.2,2.5,3
"12","{Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"13","{Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"14","{Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"15","{Product B} => {Product A}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"16","{Product B} => {Product C}",0.4,1,0.4,2.14285714285714,6
"17","{Product C} => {Product B}",0.4,0.857142857142857,0.466666666666667,2.14285714285714,6
"18","{Product B} => {Product I}",0.333333333333333,0.833333333333333,0.4,1.13636363636364,5
"19","{Product G} => {Product E}",0.266666666666667,0.8,0.333333333333333,2,4
"20","{Product G} => {Product H}",0.266666666666667,0.8,0.333333333333333,1.71428571428571,4
"21","{Product G} => {Product F}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"22","{Product A} => {Product I}",0.466666666666667,1,0.466666666666667,1.36363636363636,7
"23","{Product C} => {Product I}",0.4,0.857142857142857,0.466666666666667,1.16883116883117,6
"24","{Product E} => {Product H}",0.4,1,0.4,2.14285714285714,6
"25","{Product H} => {Product E}",0.4,0.857142857142857,0.466666666666667,2.14285714285714,6
"26","{Product E} => {Product F}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"27","{Product J} => {Product F}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"28","{Product J} => {Product I}",0.4,1,0.4,1.36363636363636,6
"29","{Product H} => {Product F}",0.4,0.857142857142857,0.466666666666667,1.83673469387755,6
"30","{Product F} => {Product H}",0.4,0.857142857142857,0.466666666666667,1.83673469387755,6
"31","{Product D,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"32","{Product G,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"33","{Product D,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"34","{Product E,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"35","{Product D,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"36","{Product H,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"37","{Product D,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"38","{Product F,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"39","{Product G,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"40","{Product E,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"41","{Product G,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"42","{Product H,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"43","{Product G,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"44","{Product F,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"45","{Product E,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"46","{Product H,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"47","{Product E,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"48","{Product F,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"49","{Product H,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"50","{Product F,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"51","{Product D,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"52","{Product D,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"53","{Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"54","{Product D,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"55","{Product D,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"56","{Product D,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"57","{Product D,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"58","{Product D,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"59","{Product D,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"60","{Product D,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"61","{Product D,Product I} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"62","{Product D,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"63","{Product G,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"64","{Product D,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"65","{Product E,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"66","{Product D,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"67","{Product D,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"68","{Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"69","{Product D,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"70","{Product H,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"71","{Product D,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"72","{Product F,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"73","{Product D,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"74","{Product D,Product I} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"75","{Product D,Product G} => {Product E}",0.2,1,0.2,2.5,3
"76","{Product D,Product E} => {Product G}",0.2,1,0.2,3,3
"77","{Product D,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"78","{Product D,Product G} => {Product H}",0.2,1,0.2,2.14285714285714,3
"79","{Product D,Product H} => {Product G}",0.2,1,0.2,3,3
"80","{Product D,Product G} => {Product F}",0.2,1,0.2,2.14285714285714,3
"81","{Product D,Product F} => {Product G}",0.2,1,0.2,3,3
"82","{Product D,Product I} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"83","{Product D,Product J} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"84","{Product D,Product E} => {Product H}",0.2,1,0.2,2.14285714285714,3
"85","{Product D,Product H} => {Product E}",0.2,1,0.2,2.5,3
"86","{Product D,Product E} => {Product F}",0.2,1,0.2,2.14285714285714,3
"87","{Product D,Product F} => {Product E}",0.2,1,0.2,2.5,3
"88","{Product D,Product I} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"89","{Product D,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"90","{Product D,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"91","{Product D,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"92","{Product D,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"93","{Product D,Product H} => {Product F}",0.2,1,0.2,2.14285714285714,3
"94","{Product D,Product F} => {Product H}",0.2,1,0.2,2.14285714285714,3
"95","{Product D,Product I} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"96","{Product D,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"97","{Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"98","{Product G,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"99","{Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"100","{Product E,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"101","{Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"102","{Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"103","{Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"104","{Product H,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"105","{Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"106","{Product F,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"107","{Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"108","{Product G,Product K} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"109","{Product G,Product K} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"110","{Product G,Product K} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"111","{Product G,Product K} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"112","{Product G,Product K} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"113","{Product E,Product K} => {Product J}",0.2,1,0.2,2.5,3
"114","{Product J,Product K} => {Product E}",0.2,1,0.2,2.5,3
"115","{Product E,Product J} => {Product K}",0.2,1,0.2,5,3
"116","{Product E,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"117","{Product H,Product K} => {Product E}",0.2,1,0.2,2.5,3
"118","{Product E,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"119","{Product F,Product K} => {Product E}",0.2,1,0.2,2.5,3
"120","{Product E,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"121","{Product I,Product K} => {Product E}",0.2,1,0.2,2.5,3
"122","{Product E,Product I} => {Product K}",0.2,1,0.2,5,3
"123","{Product J,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"124","{Product H,Product K} => {Product J}",0.2,1,0.2,2.5,3
"125","{Product J,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"126","{Product F,Product K} => {Product J}",0.2,1,0.2,2.5,3
"127","{Product J,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"128","{Product I,Product K} => {Product J}",0.2,1,0.2,2.5,3
"129","{Product H,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"130","{Product F,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"131","{Product H,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"132","{Product I,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"133","{Product F,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"134","{Product I,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"135","{Product M,Product O} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"136","{Product L,Product O} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"137","{Product M,Product O} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"138","{Product M,Product O} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"139","{Product B,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"140","{Product C,Product L} => {Product M}",0.133333333333333,1,0.133333333333333,3.75,2
"141","{Product J,Product M} => {Product L}",0.133333333333333,1,0.133333333333333,3,2
"142","{Product H,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"143","{Product F,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"144","{Product B,Product M} => {Product C}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"145","{Product A,Product M} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"146","{Product C,Product J} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"147","{Product H,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"148","{Product C,Product H} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"149","{Product F,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"150","{Product C,Product F} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"151","{Product H,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"152","{Product F,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"153","{Product J,Product M} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"154","{Product H,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"155","{Product F,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"156","{Product H,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"157","{Product F,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"158","{Product L,Product O} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"159","{Product B,Product L} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"160","{Product L,Product O} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"161","{Product A,Product O} => {Product B}",0.133333333333333,1,0.133333333333333,2.5,2
"162","{Product B,Product O} => {Product C}",0.2,1,0.2,2.14285714285714,3
"163","{Product C,Product O} => {Product B}",0.2,1,0.2,2.5,3
"164","{Product G,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"165","{Product E,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"166","{Product G,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"167","{Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"168","{Product G,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"169","{Product H,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"170","{Product G,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"171","{Product F,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"172","{Product G,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"173","{Product A,Product O} => {Product C}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"174","{Product A,Product O} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"175","{Product E,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"176","{Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"177","{Product E,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"178","{Product H,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"179","{Product E,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"180","{Product F,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"181","{Product E,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"182","{Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"183","{Product H,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"184","{Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"185","{Product F,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"186","{Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"187","{Product H,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"188","{Product F,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"189","{Product H,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"190","{Product F,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"191","{Product B,Product L} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"192","{Product G,Product L} => {Product A}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"193","{Product A,Product G} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"194","{Product G,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"195","{Product G,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"196","{Product G,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"197","{Product A,Product L} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"198","{Product A,Product J} => {Product L}",0.133333333333333,1,0.133333333333333,3,2
"199","{Product A,Product F} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"200","{Product A,Product L} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"201","{Product C,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"202","{Product C,Product H} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"203","{Product C,Product F} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"204","{Product E,Product L} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"205","{Product F,Product L} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"206","{Product J,Product L} => {Product I}",0.2,1,0.2,1.36363636363636,3
"207","{Product I,Product L} => {Product J}",0.2,1,0.2,2.5,3
"208","{Product F,Product L} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"209","{Product A,Product B} => {Product C}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"210","{Product B,Product C} => {Product A}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"211","{Product A,Product C} => {Product B}",0.333333333333333,1,0.333333333333333,2.5,5
"212","{Product A,Product B} => {Product I}",0.333333333333333,1,0.333333333333333,1.36363636363636,5
"213","{Product B,Product I} => {Product A}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"214","{Product B,Product C} => {Product I}",0.333333333333333,0.833333333333333,0.4,1.13636363636364,5
"215","{Product B,Product I} => {Product C}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"216","{Product C,Product I} => {Product B}",0.333333333333333,0.833333333333333,0.4,2.08333333333333,5
"217","{Product A,Product G} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"218","{Product A,Product G} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"219","{Product A,Product F} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"220","{Product A,Product G} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"221","{Product E,Product G} => {Product H}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"222","{Product G,Product H} => {Product E}",0.266666666666667,1,0.266666666666667,2.5,4
"223","{Product E,Product G} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"224","{Product F,Product G} => {Product E}",0.266666666666667,0.8,0.333333333333333,2,4
"225","{Product E,Product F} => {Product G}",0.266666666666667,0.8,0.333333333333333,2.4,4
"226","{Product G,Product J} => {Product F}",0.2,1,0.2,2.14285714285714,3
"227","{Product G,Product J} => {Product I}",0.2,1,0.2,1.36363636363636,3
"228","{Product G,Product I} => {Product J}",0.2,1,0.2,2.5,3
"229","{Product G,Product H} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"230","{Product F,Product G} => {Product H}",0.266666666666667,0.8,0.333333333333333,1.71428571428571,4
"231","{Product G,Product I} => {Product F}",0.2,1,0.2,2.14285714285714,3
"232","{Product A,Product C} => {Product I}",0.333333333333333,1,0.333333333333333,1.36363636363636,5
"233","{Product C,Product I} => {Product A}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"234","{Product A,Product F} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"235","{Product A,Product J} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"236","{Product A,Product F} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"237","{Product C,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"238","{Product C,Product H} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"239","{Product C,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"240","{Product C,Product F} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"241","{Product C,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"242","{Product C,Product H} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"243","{Product C,Product F} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"244","{Product C,Product H} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"245","{Product C,Product F} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"246","{Product E,Product J} => {Product H}",0.2,1,0.2,2.14285714285714,3
"247","{Product E,Product J} => {Product F}",0.2,1,0.2,2.14285714285714,3
"248","{Product E,Product J} => {Product I}",0.2,1,0.2,1.36363636363636,3
"249","{Product E,Product I} => {Product J}",0.2,1,0.2,2.5,3
"250","{Product E,Product H} => {Product F}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"251","{Product E,Product F} => {Product H}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"252","{Product F,Product H} => {Product E}",0.333333333333333,0.833333333333333,0.4,2.08333333333333,5
"253","{Product E,Product I} => {Product H}",0.2,1,0.2,2.14285714285714,3
"254","{Product E,Product I} => {Product F}",0.2,1,0.2,2.14285714285714,3
"255","{Product H,Product J} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"256","{Product F,Product J} => {Product H}",0.266666666666667,0.8,0.333333333333333,1.71428571428571,4
"257","{Product H,Product J} => {Product I}",0.266666666666667,1,0.266666666666667,1.36363636363636,4
"258","{Product H,Product I} => {Product J}",0.266666666666667,1,0.266666666666667,2.5,4
"259","{Product F,Product J} => {Product I}",0.333333333333333,1,0.333333333333333,1.36363636363636,5
"260","{Product I,Product J} => {Product F}",0.333333333333333,0.833333333333333,0.4,1.78571428571429,5
"261","{Product F,Product I} => {Product J}",0.333333333333333,1,0.333333333333333,2.5,5
"262","{Product H,Product I} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"263","{Product F,Product I} => {Product H}",0.266666666666667,0.8,0.333333333333333,1.71428571428571,4
"264","{Product D,Product G,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"265","{Product D,Product E,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"266","{Product E,Product G,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"267","{Product D,Product G,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"268","{Product D,Product H,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"269","{Product G,Product H,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"270","{Product D,Product G,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"271","{Product D,Product F,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"272","{Product F,Product G,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"273","{Product D,Product E,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"274","{Product D,Product H,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"275","{Product E,Product H,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"276","{Product D,Product E,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"277","{Product D,Product F,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"278","{Product E,Product F,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"279","{Product D,Product H,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"280","{Product D,Product F,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"281","{Product F,Product H,Product N} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"282","{Product E,Product G,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"283","{Product G,Product H,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"284","{Product E,Product H,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"285","{Product E,Product G,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"286","{Product F,Product G,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"287","{Product E,Product F,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"288","{Product G,Product H,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"289","{Product F,Product G,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"290","{Product F,Product H,Product N} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"291","{Product E,Product H,Product N} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"292","{Product E,Product F,Product N} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"293","{Product F,Product H,Product N} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"294","{Product D,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"295","{Product D,Product G,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"296","{Product D,Product G,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"297","{Product G,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"298","{Product D,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"299","{Product D,Product E,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"300","{Product D,Product E,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"301","{Product E,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"302","{Product D,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"303","{Product D,Product J,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"304","{Product D,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"305","{Product J,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"306","{Product D,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"307","{Product D,Product H,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"308","{Product D,Product H,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"309","{Product H,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"310","{Product D,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"311","{Product D,Product F,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"312","{Product D,Product F,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"313","{Product F,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"314","{Product D,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"315","{Product D,Product I,Product K} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"316","{Product D,Product I,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"317","{Product I,Product K,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"318","{Product D,Product G,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"319","{Product D,Product E,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"320","{Product D,Product G,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"321","{Product D,Product J,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"322","{Product D,Product G,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"323","{Product D,Product G,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"324","{Product D,Product H,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"325","{Product D,Product G,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"326","{Product D,Product F,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"327","{Product D,Product G,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"328","{Product D,Product I,Product K} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"329","{Product D,Product G,Product I} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"330","{Product D,Product E,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"331","{Product D,Product J,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"332","{Product D,Product E,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"333","{Product D,Product E,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"334","{Product D,Product H,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"335","{Product D,Product E,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"336","{Product D,Product F,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"337","{Product D,Product E,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"338","{Product D,Product I,Product K} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"339","{Product D,Product E,Product I} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"340","{Product D,Product J,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"341","{Product D,Product H,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"342","{Product D,Product H,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"343","{Product D,Product J,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"344","{Product D,Product F,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"345","{Product D,Product F,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"346","{Product D,Product J,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"347","{Product D,Product I,Product K} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"348","{Product D,Product I,Product J} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"349","{Product D,Product H,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"350","{Product D,Product F,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"351","{Product D,Product H,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"352","{Product D,Product I,Product K} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"353","{Product D,Product H,Product I} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"354","{Product D,Product F,Product K} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"355","{Product D,Product I,Product K} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"356","{Product D,Product F,Product I} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"357","{Product D,Product G,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"358","{Product D,Product E,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"359","{Product E,Product G,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"360","{Product D,Product G,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"361","{Product D,Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"362","{Product D,Product G,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"363","{Product G,Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"364","{Product D,Product G,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"365","{Product D,Product H,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"366","{Product G,Product H,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"367","{Product D,Product G,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"368","{Product D,Product F,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"369","{Product F,Product G,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"370","{Product D,Product G,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"371","{Product D,Product I,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"372","{Product D,Product G,Product I} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"373","{Product G,Product I,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"374","{Product D,Product E,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"375","{Product D,Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"376","{Product D,Product E,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"377","{Product E,Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"378","{Product D,Product E,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"379","{Product D,Product H,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"380","{Product E,Product H,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"381","{Product D,Product E,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"382","{Product D,Product F,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"383","{Product E,Product F,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"384","{Product D,Product E,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"385","{Product D,Product I,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"386","{Product D,Product E,Product I} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"387","{Product E,Product I,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"388","{Product D,Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"389","{Product D,Product H,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"390","{Product D,Product H,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"391","{Product H,Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"392","{Product D,Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"393","{Product D,Product F,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"394","{Product D,Product F,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"395","{Product F,Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"396","{Product D,Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"397","{Product D,Product I,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"398","{Product D,Product I,Product J} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"399","{Product I,Product J,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"400","{Product D,Product H,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"401","{Product D,Product F,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"402","{Product F,Product H,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"403","{Product D,Product H,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"404","{Product D,Product I,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"405","{Product D,Product H,Product I} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"406","{Product H,Product I,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"407","{Product D,Product F,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"408","{Product D,Product I,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"409","{Product D,Product F,Product I} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"410","{Product F,Product I,Product O} => {Product D}",0.0666666666666667,1,0.0666666666666667,5,1
"411","{Product D,Product G,Product J} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"412","{Product D,Product E,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"413","{Product D,Product E,Product G} => {Product H}",0.2,1,0.2,2.14285714285714,3
"414","{Product D,Product G,Product H} => {Product E}",0.2,1,0.2,2.5,3
"415","{Product D,Product E,Product H} => {Product G}",0.2,1,0.2,3,3
"416","{Product D,Product E,Product G} => {Product F}",0.2,1,0.2,2.14285714285714,3
"417","{Product D,Product F,Product G} => {Product E}",0.2,1,0.2,2.5,3
"418","{Product D,Product E,Product F} => {Product G}",0.2,1,0.2,3,3
"419","{Product D,Product G,Product I} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"420","{Product D,Product E,Product I} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"421","{Product D,Product G,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"422","{Product D,Product H,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"423","{Product D,Product G,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"424","{Product D,Product F,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"425","{Product D,Product G,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"426","{Product D,Product G,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"427","{Product D,Product I,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"428","{Product D,Product G,Product H} => {Product F}",0.2,1,0.2,2.14285714285714,3
"429","{Product D,Product F,Product G} => {Product H}",0.2,1,0.2,2.14285714285714,3
"430","{Product D,Product F,Product H} => {Product G}",0.2,1,0.2,3,3
"431","{Product D,Product G,Product I} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"432","{Product D,Product H,Product I} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"433","{Product D,Product G,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"434","{Product D,Product F,Product I} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"435","{Product D,Product E,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"436","{Product D,Product H,Product J} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"437","{Product D,Product E,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"438","{Product D,Product F,Product J} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"439","{Product D,Product E,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"440","{Product D,Product E,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"441","{Product D,Product I,Product J} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"442","{Product D,Product E,Product H} => {Product F}",0.2,1,0.2,2.14285714285714,3
"443","{Product D,Product E,Product F} => {Product H}",0.2,1,0.2,2.14285714285714,3
"444","{Product D,Product F,Product H} => {Product E}",0.2,1,0.2,2.5,3
"445","{Product D,Product E,Product I} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"446","{Product D,Product H,Product I} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"447","{Product D,Product E,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"448","{Product D,Product F,Product I} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"449","{Product D,Product H,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"450","{Product D,Product F,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"451","{Product D,Product H,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"452","{Product D,Product I,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"453","{Product D,Product H,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"454","{Product D,Product F,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"455","{Product D,Product I,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"456","{Product D,Product F,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"457","{Product D,Product H,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"458","{Product D,Product F,Product I} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"459","{Product G,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"460","{Product E,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"461","{Product E,Product G,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"462","{Product G,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"463","{Product J,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"464","{Product G,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"465","{Product G,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"466","{Product H,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"467","{Product G,Product H,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"468","{Product G,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"469","{Product F,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"470","{Product F,Product G,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"471","{Product G,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"472","{Product I,Product K,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"473","{Product G,Product I,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"474","{Product E,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"475","{Product J,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"476","{Product E,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"477","{Product E,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"478","{Product H,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"479","{Product E,Product H,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"480","{Product E,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"481","{Product F,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"482","{Product E,Product F,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"483","{Product E,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"484","{Product I,Product K,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"485","{Product E,Product I,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"486","{Product J,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"487","{Product H,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"488","{Product H,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"489","{Product J,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"490","{Product F,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"491","{Product F,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"492","{Product J,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"493","{Product I,Product K,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"494","{Product I,Product J,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"495","{Product H,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"496","{Product F,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"497","{Product F,Product H,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"498","{Product H,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"499","{Product I,Product K,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"500","{Product H,Product I,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"501","{Product F,Product K,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"502","{Product I,Product K,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"503","{Product F,Product I,Product O} => {Product K}",0.0666666666666667,1,0.0666666666666667,5,1
"504","{Product E,Product G,Product K} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"505","{Product G,Product J,Product K} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"506","{Product E,Product G,Product J} => {Product K}",0.133333333333333,1,0.133333333333333,5,2
"507","{Product E,Product G,Product K} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"508","{Product G,Product H,Product K} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"509","{Product E,Product G,Product K} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"510","{Product F,Product G,Product K} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"511","{Product E,Product G,Product K} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"512","{Product G,Product I,Product K} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"513","{Product E,Product G,Product I} => {Product K}",0.133333333333333,1,0.133333333333333,5,2
"514","{Product G,Product J,Product K} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"515","{Product G,Product H,Product K} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"516","{Product G,Product H,Product J} => {Product K}",0.133333333333333,1,0.133333333333333,5,2
"517","{Product G,Product J,Product K} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"518","{Product F,Product G,Product K} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"519","{Product G,Product J,Product K} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"520","{Product G,Product I,Product K} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"521","{Product G,Product H,Product K} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"522","{Product F,Product G,Product K} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"523","{Product G,Product H,Product K} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"524","{Product G,Product I,Product K} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"525","{Product G,Product H,Product I} => {Product K}",0.133333333333333,1,0.133333333333333,5,2
"526","{Product F,Product G,Product K} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"527","{Product G,Product I,Product K} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"528","{Product E,Product J,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"529","{Product E,Product H,Product K} => {Product J}",0.2,1,0.2,2.5,3
"530","{Product H,Product J,Product K} => {Product E}",0.2,1,0.2,2.5,3
"531","{Product E,Product H,Product J} => {Product K}",0.2,1,0.2,5,3
"532","{Product E,Product J,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"533","{Product E,Product F,Product K} => {Product J}",0.2,1,0.2,2.5,3
"534","{Product F,Product J,Product K} => {Product E}",0.2,1,0.2,2.5,3
"535","{Product E,Product F,Product J} => {Product K}",0.2,1,0.2,5,3
"536","{Product E,Product J,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"537","{Product E,Product I,Product K} => {Product J}",0.2,1,0.2,2.5,3
"538","{Product I,Product J,Product K} => {Product E}",0.2,1,0.2,2.5,3
"539","{Product E,Product I,Product J} => {Product K}",0.2,1,0.2,5,3
"540","{Product E,Product H,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"541","{Product E,Product F,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"542","{Product F,Product H,Product K} => {Product E}",0.2,1,0.2,2.5,3
"543","{Product E,Product H,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"544","{Product E,Product I,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"545","{Product H,Product I,Product K} => {Product E}",0.2,1,0.2,2.5,3
"546","{Product E,Product H,Product I} => {Product K}",0.2,1,0.2,5,3
"547","{Product E,Product F,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"548","{Product E,Product I,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"549","{Product F,Product I,Product K} => {Product E}",0.2,1,0.2,2.5,3
"550","{Product E,Product F,Product I} => {Product K}",0.2,1,0.2,5,3
"551","{Product H,Product J,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"552","{Product F,Product J,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"553","{Product F,Product H,Product K} => {Product J}",0.2,1,0.2,2.5,3
"554","{Product H,Product J,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"555","{Product I,Product J,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"556","{Product H,Product I,Product K} => {Product J}",0.2,1,0.2,2.5,3
"557","{Product F,Product J,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"558","{Product I,Product J,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"559","{Product F,Product I,Product K} => {Product J}",0.2,1,0.2,2.5,3
"560","{Product F,Product H,Product K} => {Product I}",0.2,1,0.2,1.36363636363636,3
"561","{Product H,Product I,Product K} => {Product F}",0.2,1,0.2,2.14285714285714,3
"562","{Product F,Product I,Product K} => {Product H}",0.2,1,0.2,2.14285714285714,3
"563","{Product L,Product M,Product O} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"564","{Product B,Product M,Product O} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"565","{Product B,Product L,Product M} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"566","{Product B,Product L,Product O} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"567","{Product L,Product M,Product O} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"568","{Product C,Product M,Product O} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"569","{Product C,Product L,Product O} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"570","{Product B,Product M,Product O} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"571","{Product C,Product M,Product O} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"572","{Product B,Product L,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"573","{Product B,Product C,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"574","{Product A,Product L,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"575","{Product A,Product J,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"576","{Product A,Product L,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"577","{Product C,Product J,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"578","{Product C,Product J,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"579","{Product H,Product L,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"580","{Product C,Product H,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"581","{Product C,Product H,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"582","{Product F,Product L,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"583","{Product C,Product F,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"584","{Product C,Product F,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"585","{Product C,Product I,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"586","{Product H,Product L,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"587","{Product H,Product J,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"588","{Product H,Product J,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"589","{Product F,Product L,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"590","{Product F,Product J,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"591","{Product J,Product L,Product M} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"592","{Product I,Product L,Product M} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"593","{Product I,Product J,Product M} => {Product L}",0.133333333333333,1,0.133333333333333,3,2
"594","{Product H,Product L,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"595","{Product F,Product L,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"596","{Product F,Product H,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"597","{Product F,Product H,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"598","{Product H,Product L,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"599","{Product H,Product I,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"600","{Product H,Product I,Product L} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"601","{Product F,Product L,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"602","{Product F,Product I,Product M} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"603","{Product A,Product B,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"604","{Product A,Product C,Product M} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"605","{Product A,Product B,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"606","{Product B,Product I,Product M} => {Product A}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"607","{Product B,Product I,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"608","{Product A,Product C,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"609","{Product A,Product J,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"610","{Product C,Product J,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"611","{Product C,Product H,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"612","{Product H,Product J,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"613","{Product C,Product H,Product J} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"614","{Product C,Product J,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"615","{Product C,Product F,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"616","{Product F,Product J,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"617","{Product C,Product F,Product J} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"618","{Product C,Product J,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"619","{Product C,Product I,Product J} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"620","{Product C,Product H,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"621","{Product C,Product F,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"622","{Product F,Product H,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"623","{Product C,Product F,Product H} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"624","{Product C,Product H,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"625","{Product H,Product I,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"626","{Product C,Product H,Product I} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"627","{Product C,Product F,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"628","{Product F,Product I,Product M} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"629","{Product C,Product F,Product I} => {Product M}",0.0666666666666667,1,0.0666666666666667,3.75,1
"630","{Product H,Product J,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"631","{Product F,Product J,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"632","{Product F,Product H,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"633","{Product H,Product J,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"634","{Product H,Product I,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"635","{Product F,Product J,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"636","{Product F,Product I,Product M} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"637","{Product F,Product H,Product M} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"638","{Product H,Product I,Product M} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"639","{Product F,Product I,Product M} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"640","{Product B,Product L,Product O} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"641","{Product C,Product L,Product O} => {Product B}",0.0666666666666667,1,0.0666666666666667,2.5,1
"642","{Product B,Product C,Product L} => {Product O}",0.0666666666666667,1,0.0666666666666667,3.75,1
"643","{Product A,Product B,Product O} => {Product C}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"644","{Product A,Product C,Product O} => {Product B}",0.133333333333333,1,0.133333333333333,2.5,2
"645","{Product A,Product B,Product O} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"646","{Product B,Product I,Product O} => {Product A}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"647","{Product A,Product I,Product O} => {Product B}",0.133333333333333,1,0.133333333333333,2.5,2
"648","{Product B,Product I,Product O} => {Product C}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"649","{Product C,Product I,Product O} => {Product B}",0.133333333333333,1,0.133333333333333,2.5,2
"650","{Product E,Product G,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"651","{Product G,Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"652","{Product E,Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"653","{Product E,Product G,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"654","{Product G,Product H,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"655","{Product E,Product H,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"656","{Product E,Product G,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"657","{Product F,Product G,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"658","{Product E,Product F,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"659","{Product E,Product G,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"660","{Product G,Product I,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"661","{Product E,Product I,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"662","{Product G,Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"663","{Product G,Product H,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"664","{Product H,Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"665","{Product G,Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"666","{Product F,Product G,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"667","{Product F,Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"668","{Product G,Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"669","{Product G,Product I,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"670","{Product I,Product J,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"671","{Product G,Product H,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"672","{Product F,Product G,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"673","{Product F,Product H,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"674","{Product G,Product H,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"675","{Product G,Product I,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"676","{Product H,Product I,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"677","{Product F,Product G,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"678","{Product G,Product I,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"679","{Product F,Product I,Product O} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"680","{Product A,Product C,Product O} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"681","{Product A,Product I,Product O} => {Product C}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"682","{Product C,Product I,Product O} => {Product A}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"683","{Product E,Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"684","{Product E,Product H,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"685","{Product H,Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"686","{Product E,Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"687","{Product E,Product F,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"688","{Product F,Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"689","{Product E,Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"690","{Product E,Product I,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"691","{Product I,Product J,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"692","{Product E,Product H,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"693","{Product E,Product F,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"694","{Product F,Product H,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"695","{Product E,Product H,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"696","{Product E,Product I,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"697","{Product H,Product I,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"698","{Product E,Product F,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"699","{Product E,Product I,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"700","{Product F,Product I,Product O} => {Product E}",0.0666666666666667,1,0.0666666666666667,2.5,1
"701","{Product H,Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"702","{Product F,Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"703","{Product F,Product H,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"704","{Product H,Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"705","{Product I,Product J,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"706","{Product H,Product I,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"707","{Product F,Product J,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"708","{Product I,Product J,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"709","{Product F,Product I,Product O} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"710","{Product F,Product H,Product O} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"711","{Product H,Product I,Product O} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"712","{Product F,Product I,Product O} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"713","{Product A,Product G,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"714","{Product G,Product J,Product L} => {Product A}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"715","{Product A,Product G,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"716","{Product A,Product G,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"717","{Product F,Product G,Product L} => {Product A}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"718","{Product A,Product F,Product L} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"719","{Product A,Product F,Product G} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"720","{Product A,Product G,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"721","{Product G,Product I,Product L} => {Product A}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"722","{Product A,Product G,Product I} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"723","{Product G,Product J,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"724","{Product F,Product G,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"725","{Product G,Product J,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"726","{Product G,Product I,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"727","{Product F,Product G,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"728","{Product G,Product I,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"729","{Product A,Product F,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"730","{Product A,Product F,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"731","{Product A,Product J,Product L} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"732","{Product A,Product I,Product L} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"733","{Product A,Product I,Product J} => {Product L}",0.133333333333333,1,0.133333333333333,3,2
"734","{Product A,Product F,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"735","{Product A,Product F,Product I} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"736","{Product C,Product J,Product L} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"737","{Product C,Product H,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"738","{Product H,Product J,Product L} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"739","{Product C,Product H,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"740","{Product C,Product J,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"741","{Product C,Product F,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"742","{Product C,Product F,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"743","{Product C,Product J,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"744","{Product C,Product I,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"745","{Product C,Product I,Product J} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"746","{Product C,Product H,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"747","{Product C,Product F,Product L} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"748","{Product F,Product H,Product L} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"749","{Product C,Product F,Product H} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"750","{Product C,Product H,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"751","{Product C,Product I,Product L} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"752","{Product H,Product I,Product L} => {Product C}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"753","{Product C,Product H,Product I} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"754","{Product C,Product F,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"755","{Product C,Product I,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"756","{Product C,Product F,Product I} => {Product L}",0.0666666666666667,1,0.0666666666666667,3,1
"757","{Product H,Product J,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"758","{Product F,Product H,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"759","{Product H,Product J,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"760","{Product H,Product I,Product L} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"761","{Product F,Product J,Product L} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"762","{Product F,Product I,Product L} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"763","{Product F,Product H,Product L} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"764","{Product H,Product I,Product L} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"765","{Product A,Product B,Product C} => {Product I}",0.333333333333333,1,0.333333333333333,1.36363636363636,5
"766","{Product A,Product B,Product I} => {Product C}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"767","{Product B,Product C,Product I} => {Product A}",0.333333333333333,1,0.333333333333333,2.14285714285714,5
"768","{Product A,Product C,Product I} => {Product B}",0.333333333333333,1,0.333333333333333,2.5,5
"769","{Product A,Product G,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"770","{Product A,Product F,Product G} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"771","{Product A,Product F,Product J} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"772","{Product A,Product G,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"773","{Product A,Product G,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"774","{Product A,Product F,Product G} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"775","{Product A,Product G,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"776","{Product A,Product F,Product I} => {Product G}",0.0666666666666667,1,0.0666666666666667,3,1
"777","{Product E,Product G,Product J} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"778","{Product G,Product H,Product J} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"779","{Product E,Product G,Product J} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"780","{Product E,Product G,Product J} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"781","{Product E,Product G,Product I} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"782","{Product E,Product G,Product H} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"783","{Product E,Product F,Product G} => {Product H}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"784","{Product F,Product G,Product H} => {Product E}",0.266666666666667,1,0.266666666666667,2.5,4
"785","{Product E,Product F,Product H} => {Product G}",0.266666666666667,0.8,0.333333333333333,2.4,4
"786","{Product E,Product G,Product I} => {Product H}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"787","{Product G,Product H,Product I} => {Product E}",0.133333333333333,1,0.133333333333333,2.5,2
"788","{Product E,Product G,Product I} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"789","{Product G,Product H,Product J} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"790","{Product G,Product H,Product J} => {Product I}",0.133333333333333,1,0.133333333333333,1.36363636363636,2
"791","{Product G,Product H,Product I} => {Product J}",0.133333333333333,1,0.133333333333333,2.5,2
"792","{Product F,Product G,Product J} => {Product I}",0.2,1,0.2,1.36363636363636,3
"793","{Product G,Product I,Product J} => {Product F}",0.2,1,0.2,2.14285714285714,3
"794","{Product F,Product G,Product I} => {Product J}",0.2,1,0.2,2.5,3
"795","{Product G,Product H,Product I} => {Product F}",0.133333333333333,1,0.133333333333333,2.14285714285714,2
"796","{Product A,Product F,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"797","{Product A,Product F,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"798","{Product C,Product H,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"799","{Product C,Product F,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"800","{Product C,Product F,Product H} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"801","{Product C,Product H,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"802","{Product C,Product I,Product J} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"803","{Product C,Product H,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"804","{Product C,Product F,Product J} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"805","{Product C,Product I,Product J} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"806","{Product C,Product F,Product I} => {Product J}",0.0666666666666667,1,0.0666666666666667,2.5,1
"807","{Product C,Product F,Product H} => {Product I}",0.0666666666666667,1,0.0666666666666667,1.36363636363636,1
"808","{Product C,Product H,Product I} => {Product F}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"809","{Product C,Product F,Product I} => {Product H}",0.0666666666666667,1,0.0666666666666667,2.14285714285714,1
"810","{Product E,Product H,Product J} => {Product F}",0.2,1,0.2,2.14285714285714,3
"811","{Product E,Product F,Product J} => {Product H}",0.2,1,0.2,2.14285714285714,3
"812","{Product E,Product H,Product J} => {Product I}",0.2,1,0.2,1.36363636363636,3
"813","{Product E,Product I,Product J} => {Product H}",0.2,1,0.2,2.14285714285714,3
"814","{Product E,Product H,Product I} => {Product J}",0.2,1,0.2,2.5,3
"815","{Product E,Product F,Product J} => {Product I}",0.2,1,0.2,1.36363636363636,3
"816","{Product E,Product I,Product J} => {Product F}",0.2,1,0.2,2.14285714285714,3
"817","{Product E,Product F,Product I} => {Product J}",0.2,1,0.2,2.5,3
"818","{Product E,Product H,Product I} => {Product F}",0.2,1,0.2,2.14285714285714,3
"819","{Product E,Product F,Product I} => {Product H}",0.2,1,0.2,2.14285714285714,3
"820","{Product F,Product H,Product J} => {Product I}",0.266666666666667,1,0.266666666666667,1.36363636363636,4
"821","{Product H,Product I,Product J} => {Product F}",0.266666666666667,1,0.266666666666667,2.14285714285714,4
"822","{Product F,Product I,Product J} => {Product H}",0.266666666666667,0.8,0.333333333333333,1.71428571428571,4
"823","{Product F,Product H,Product I} => {Product J}",0.266666666666667,1,0.266666666666667,2.5,4


================================================
FILE: README.md
================================================
# rAnalytics
Analytics in R 
This is repository for Learning Analytics using R programming


================================================
FILE: Unsorted/CLT.R
================================================
#CLT
sdm.sim <- function(n,src.dist=NULL,param1=NULL,param2=NULL) {
  r <- 10000  # Number of replications/samples - DO NOT ADJUST
  # This produces a matrix of observations with  
  # n columns and r rows. Each row is one sample:
  my.samples <- switch(src.dist,
                       "E" = matrix(rexp(n*r,param1),r),
                       "N" = matrix(rnorm(n*r,param1,param2),r),
                       "U" = matrix(runif(n*r,param1,param2),r),
                       "P" = matrix(rpois(n*r,param1),r),
                       "C" = matrix(rcauchy(n*r,param1,param2),r),
                       "B" = matrix(rbinom(n*r,param1,param2),r),
                       "G" = matrix(rgamma(n*r,param1,param2),r),
                       "X" = matrix(rchisq(n*r,param1),r),
                       "T" = matrix(rt(n*r,param1),r))
  all.sample.sums <- apply(my.samples,1,sum)
  all.sample.means <- apply(my.samples,1,mean)   
  all.sample.vars <- apply(my.samples,1,var) 
  par(mfrow=c(2,2))
  hist(my.samples[1,],col="gray",main="Distribution of One Sample")
  hist(all.sample.sums,col="gray",main="Sampling Distributionnof
       the Sum")
  hist(all.sample.means,col="gray",main="Sampling Distributionnof the Mean")
  hist(all.sample.vars,col="gray",main="Sampling Distributionnof
       the Variance")
}
sdm.sim(50,src.dist="E",param1=1)
set.seed(13)
fg <- sample(x=c(0,1),size=8*10,replace=TRUE,prob=c(.20,.80))
fgm <- matrix(fg,nrow=10)
fgmd <- cbind(fgm,apply(fgm,1,mean)*100)
fgmd



================================================
FILE: Unsorted/R-Exercise.R
================================================
#..............................Exercise on R..............................#

# Q.1
sessionInfo()
# Clear environment
rm(list = ls())
# Q.2
abc<- 10

# Q.3
a<-c(1,2,3)
is.numeric(a)
b<-c("aa","bb","cc")
is.character(b)
c<- c(TRUE, FALSE, TRUE)
is.logical(c)

# Q.4
ls()

# Q.5
x<-c(4,6,5,4,7,2,9,3)
# Q.5.a
length(x)
mean(x)
sum(x)
max(x)
min(x)
var(x)
# Q.5.b
x[3]
x[c(1, 3, 5, 7)]
x[2:6]  

# Q.6
y<-matrix(1:24,6,4)

# Q.7
df<-data.frame(StoreID=c(111,208,113,408),
                  Tenure=c(25,34,28,52),
                  StoreType=c("Type1","Type2","Type3","Type4"),
                  Status=c("Poor","Improved","Good","Excellent"))

# Q.8.a
df[,c(1,2)]
# Q.8.b
df[,c(3,4)]
# Q.8.c
storedata$Tenure

# Q.9
category<- c("A", "B", "C", "D")
category <- factor(category)

status <- c(1, 2, 3, 4)
status <- factor(status, levels=c(1,2,3,4), labels=c("Poor", "Improved", "Excellent", "Super"))
status

outcome <- c(1, 3, 2, 4, 3, 1, 1)
outcome <- factor(outcome, ordered=TRUE,
                  levels=c(1,2,3,4), 
                  labels=c("Poor", "Average", "Good", "Excellent"))
outcome

# Q.10
h<- c(5,26,19,88)
j<- matrix(c(1:15),5,3)
k<- c("one","two","three","four")
mylist<- list(title="My First List", ages=h,j,k) 
print(mylist)

mylist[[2]]  
mylist[["ages"]]
mylist[[2]][2]
mylist[[3]][2,2]
mylist[[3]][3,2]



================================================
FILE: Unsorted/RCommander.R.R
================================================

library(abind, pos=18)
library(e1071, pos=19)
numSummary(df2[,"a", drop=FALSE], statistics=c("mean", "sd", "IQR",   "quantiles", "skewness"), quantiles=c(0,.25,.5,.75,1), type="2")

set.seed(52410)
qnorm(c(.4), mean=0, sd=1, lower.tail=TRUE)
summary(df)
with(df2, Hist(excel, groups=course, 
  scale="frequency", breaks="Sturges", col="darkgray", 
  xlab="X", ylab="Y", main="Title"))
local({
  .Table <- xtabs(~city+course, data=df2)
  cat("\nFrequency table:\n")
  print(.Table)
  .Test <- chisq.test(.Table, correct=FALSE)
  print(.Test)
})
local({
  .Table <- xtabs(~gender+hostel+city, data=df2)
  cat("\nFrequency table:\n")
  print(.Table) 
})
Dataset <- 
  read.table("E:/rWork/rdata/Student Attnd Marks.csv", 
  header=TRUE, sep="", na.strings="NA", dec=".", 
  strip.white=TRUE)
Dataset <- read.table("E:/rWork/rdata/dbacs4.csv", 
  header=TRUE, sep="", na.strings="NA", dec=".", 
  strip.white=TRUE)
Dataset <- 
  read.table("E:/rWork/rProjects/dspgmsc2017/data/student.csv",
   header=TRUE, sep="", na.strings="NA", dec=".", 
  strip.white=TRUE)
Dataset <- 
  read.table("E:/rWork/rProjects/dspgmsc2017/data/student.csv",
   header=TRUE, sep="", na.strings="NA", dec=".", 
  strip.white=TRUE)
summary(student)
local({
  .Table <- xtabs(~batch+city, data=student)
  cat("\nFrequency table:\n")
  print(.Table)
  .Test <- chisq.test(.Table, correct=FALSE)
  print(.Test)
})
local({
  .Table <- with(student, table(gender))
  cat("\ncounts:\n")
  print(.Table)
  cat("\npercentages:\n")
  print(round(100*.Table/sum(.Table), 2))
})
sapply(student, function(x)(sum(is.na(x)))) 
  # NA counts
cor(student[,c("cbnst","cgpa")], use="complete")
normalityTest(~java, test="shapiro.test", 
  data=student)
with(student, (t.test(age, alternative='less', mu=30, 
  conf.level=.95)))



================================================
FILE: Unsorted/basiclm1.R
================================================
#Linear Regression 
#https://msperlin.github.io/pafdR/models.html

set.seed(10)

# number of time periods
nT <- 1000

# set parameters
my.intercept <- 0.5
my.beta <- 1.5

# simulate
x <- rnorm(nT)
y <- my.intercept + my.beta*x + rnorm(nT)

# set df
df <- data.frame(y, x)

# estimate model----
my.lm <- lm(data = df, 
            formula = y ~ x )

library(car)

# set test matrix ----
test.matrix <- matrix(c(my.intercept,  # alpha test value
                        my.beta))  # beta test value

# hypothesis matrix ----
hyp.mat <- matrix(c(1,0,
                    0,1),nrow = 2)
summary(lm(y ~ x))

# do test ----
my.waldtest <- linearHypothesis(my.lm, 
                                hypothesis.matrix = hyp.mat, 
                                rhs = test.matrix)

# print result
print(my.waldtest)

#Hypothesis Tests ----
library(lmtest)

# Breush Pagan test 1 - Serial correlation
# Null Hypothesis: No serial correlation in residual
print(bgtest(my.lm, order = 5))

# Breush Pagan test 2 - Homocesdasticity of residuals
# Null Hypothesis: homocesdasticity  (constant variance of residuals)
print(ncvTest(my.lm))

# Durbin Watson test - Serial correlation
# Null Hypothesis: No serial correlation in residual
print(dwtest(my.lm))

# Shapiro test  - Normality
# Null Hypothesis: Data is normally distributed
print(shapiro.test(my.lm$residuals))


library(gvlma)

# global validation of model----
gvmodel <- gvlma(my.lm) 

# print result
summary(gvmodel)


================================================
FILE: Unsorted/binomial.R
================================================
# Binomial
choose(5,2)
choose(5, 0:5)
for (n in 0:10) print(choose(n, 0:n))
dbinom(2, size=5, prob=1/6) 
choose(5,2) * (1/6)^2 * (5/6)^3 
probs = dbinom(0:5, size=5, prob=1/6) 
data.frame(0:5, probs)
sum(probs) 
plot(0:5, probs, type="h", xlim=c(0,5), ylim=c(0,.5))
points(0:5, probs, pch=16, cex=2) 
probs2 = dbinom(0:100, size=100, prob=1/2) 
data.frame(0:100, probs2) 
round(data.frame(0:100, probs2), digits=5) 
plot(0:100, probs2, type="h", xlim=c(0,100), ylim=c(0,.1))
points(0:100, probs2, pch=16, cex=.5) 
sum(dbinom(59:100, size=100, prob=1/2)) 


================================================
FILE: Unsorted/boxplot.R
================================================
# Box Plot 
# http://www.statmethods.net/graphs/boxplot.html

# Boxplot of MPG by Car Cylinders 
# Reset Frame/ Plot area
par(mfrow=c(1,1))
dev.off()

boxplot(mpg~cyl,data=mtcars, main="Car Mileage Data", xlab="Number of Cylinders", ylab="Miles Per Gallon")

# Notched Boxplot of Tooth Growth Against 2 Crossed Factors
# boxes colored for ease of interpretation 
boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, 
        col=(c("gold","darkgreen")),
        main="Tooth Growth", xlab="Suppliment and Dose")

# Violin Plots
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"), 
        col="gold")
title("Violin Plots of Miles Per Gallon")

# Example of a Bagplot
library(aplpack)
attach(mtcars)
bagplot(wt,mpg, xlab="Car Weight", ylab="Miles Per Gallon",
        main="Bagplot Example")


================================================
FILE: Unsorted/c.R
================================================
library(foreign)
db = file.choose()
dataset = read.spss(db,to.data.frame=TRUE)

x=c(1,3,4,6,8,9,11,14)
y=c(1,2,2,4,6,8,8,9)
fit1 = lm(x~y)
summary(fit1)


================================================
FILE: Unsorted/central1.R
================================================
# Central Tendencies

x = ceiling(rnorm(100, 50, 10))  # 100 random nos, mean=50,sd=10
x
hist(x)
density(x)
plot(density(x), xlab='Marks', ylab='Relative %', 
     main='Distribution of Marks')
abline(v=mean(x), col='red', lty=1)
abline(v=median(x), col='green', lty=2)
abline(v=mode(x))  # error
library(modeest)
m =mlv(x,  method = "mfv")
m$M
abline(v=m$M, col='blue' , lty=3)
?abline
text(c(mean(x), median(x), m$M ),c(0.01,0.02,0.03), 
     col=c('red','green','blue'), cex=.5, labels= c('Mean', 'Median', 'Mode'))
?text
text=c(mean(x), median(x), m$M)
text
mtext(text=c(mean(x), median(x), m$M), side=1,col=c('red','green','blue')
      ,cex=0.5,at= c(mean(x), median(x), m$M ))
?mtext



================================================
FILE: Unsorted/colstats1.R
================================================
#Column Stats

# colStats {timeSeries}
# 
# colStats	 calculates column statistics,
# colSums	 calculates column sums,
# colMeans	 calculates column means,
# colSds	 calculates column standard deviations,
# colVars	 calculates column variances,
# colSkewness	 calculates column skewness,
# colKurtosis	 calculates column kurtosis,
# colMaxs	 calculates maximum values in each column,
# colMins	 calculates minimum values in each column,
# colProds	 computes product of all values in each column,
# colQuantiles	 computes quantiles of each column.

library(timeSeries)
mtcars
colQuantiles(mtcars)
colSkewness(mtcars)
?colQuantiles
colStats(mtcars,mean)


================================================
FILE: Unsorted/complextables.R
================================================
# Complex Tables
# http://r-exercises.com/2016/04/26/complex-tables-exercises/
# http://r-exercises.com/2016/04/26/complex-tables-solutions/

# Exercise 1 : In order to demonstrate the ftable() function’s capabilities, input the Titanic data from R:
data(Titanic)
Titanic
#For the first exercise, create a basic flat contingency table from the Titanic data, using the ftable() function.
ftable(Titanic)

#Exercise 2 : The row.vars argument specifies the table variables that will format as table rows. row.vars= is definable with variable numbers, or the variable names.
# Therefore, use row.vars= to specify the variable, Class, as the row variables.
ftable(Titanic, row.vars = 1)
ftable(Titanic, row.vars = "Class")
# Exercise 3 : Combine the row.vars and col.vars arguments to specify Class & Sex as the row variables, and Survived as the column variable.
#row.vars and col.vars are definable with variable numbers, or the variable names.
ftable(Titanic, row.vars = 1:2, col.vars = 4)
ftable(Titanic, row.vars = c("Class", "Sex"), col.vars = "Survived")

#Exercise 4
#With the parameters from the ftable used in Exercise 3, reverse the order of the Class and Sex columns.
ftable(Titanic, row.vars = 2:1, col.vars = 4)
ftable(Titanic, row.vars = c("Sex", "Class"), col.vars = "Survived")


#Exercise 5
#Next, using the ftable() code from Exercise 4, specify Age as the column variable.
ftable(Titanic, row.vars = 1:2, col.vars = 3)
#As you can see from this exercise, ftable() allows for the formatting of data for different areas of inquiry.
ftable(Titanic, row.vars = c("Sex", "Class"),col.vars = "Age")

#Exercise 6 : Using the data.frame() function will coerce ftable columns into rows. To demonstrate this, place the ftable() from Exercise 5, within the data.frame() function.
data.frame(ftable(Titanic, row.vars = 1:2, col.vars = 3))
data.frame(ftable(Titanic, row.vars = c("Sex", "Class"),col.vars = "Age"))

#Exercise 7 :Function ftable.formula provides a formula interface, (a data = . argument), for creating flat contingency tables.
#For example:  ftable(Survived ~ ., data = Titanic)
# Use the formula interface for ftable() to display the quantities in the Titanic data for Male/Female passengers, by Class and Age.
ftable(Sex ~ Class + Age, data = Titanic)

# Exercise 8 : The ftable() function creates an object of class ftable. In order to demonstrate this, save the results of the ftable formula from Exercise 5 as an ftable variable called titanicStats.
titanicStats <- ftable(Sex ~ Class + Age, data = Titanic)
titanicStats

# Exercise 9 : Using the write.table() function, write the ftable, titanicStats, to a file. Make sure your working directory is set to a folder where you can find the resulting file. Name the file, “table1”.
write.ftable(titanicStats, "table1")

# Exercise 10 : read.ftable() reads in a flat-like contingency table from a file.
# Using read.ftable(), read the file, “table1”, into an R language environment variable called data1
data1 <- read.ftable("table1")
data1
df = data.frame(data1)
df


================================================
FILE: Unsorted/cor1.R
================================================
# Covariance  and Correlation
# 
#
#Covariance----

#Correlation----
 
library(Hmisc)
splom(Orange)  # Correlation Plot
with(Orange, cor(age,circumference))
#0.91
#
#Correlation Test ----

cor.test(Orange$age, Orange$circumference)

Hmisc::rcorr(Orange$age, Orange$circumference)
rcorr(as.matrix( mtcars))


================================================
FILE: Unsorted/crossfold1.R
================================================
#Steps
library(MASS, quietly=T)
library(caret)
set.seed(123)
df = iris
help(iris)
str(df)
dim(df)
head(df)
summary(df)
apply(df, 2, function(x) length(unique(x)))

#Create train & test data
library(caTools)
library(caret)
(ind = createDataPartition(df$Species, p = 2/3, list=F))
(trainDF = df[ind,])  
(testDF = df[-ind,])  

ControlParameters = trainControl(method='cv', number=5,
    savePredictions =T, classProbs=T)
ControlParameters

(parameterGrid = expand.grid(mtry= c(2,3,4)))
method='cv'
number=5
classProbs=T

modelRandom = train(Species ~ . , data = trainDF, 
      method='rf', trControl = ControlParameters, 
      tuneGrid = parameterGrid)  
#preProcess = c('center', 'scale')
modelRandom

names(getModelInfo())

(predictions = predict(modelRandom, testDF))

(t = table(predictions= predictions, actual=testDF$Species))

modelRandom = train(Species ~ . , data = trainDF, 
      method='rf', trControl = ControlParameters, 
      tuneGrid = parameterGrid) 



================================================
FILE: Unsorted/cut1.R
================================================
#cut
?cut  # divides the range of x into intervals & codes the values in x 
# according to which interval they fall
cut(x, breaks, labels = NULL,
    include.lowest = FALSE, right = TRUE, dig.lab = 3,
    ordered_result = FALSE, ...)


set.seed(1234)
marks = ceiling(rnorm(100,60,15))
marks
mean(marks)
range(marks)
(bks = seq(20,80,1))
(bks = seq(20,80,5))  # 12 interval
# Slabs : 20-25, 25-30 ....

(bks = seq(20,80,10))  # 6 intervals
# Slabs : 20-30, 30-40 ....

sort(marks)
classint = cut(marks, breaks=bks)
head(classint)
head(marks)
# 60 is enclosed in (50-60]
table(classint)  # Freq Table
cbind(table(classint)) # column format view 


# without giving manual breaks
cut(marks, breaks=6)
#(23.9-32.5] - not a clean break
#right=TRUE : interval close on right & open on left : Default
#right (0-10] : value of 10 included in this instead of (10-20]

# include.lowest =FALSE (default)
# x[i] = lowest when right=FALSE
# x[i] = highest when right=TRUE
# 0 2 5 5.1 10  : x[i] = 1 when right=F [0,5)
# 
# 0 2 5 5.1 10  : x[i] = 10 when right=T (5-10]
x1 = c(0,2,5.1,3,10,14.5, 15,16,20)
x1
cut(x1,breaks=4, include.lowest=T )  # not good
#Levels: [-0.02,5] (5,10] (10,15] (15,20]

cut(x1,breaks=4, include.lowest=T)  # not good
#Levels: [-0.02,5] (5,10] (10,15] (15,20]


(bks2 = seq(0,20,5))

cut(x1,breaks=bks2, include.lowest=T )  # not correct
#Levels: sq[0,5] (5,10] (10,15] (15,20]
x1 
# 0.0  2.0  5.1  3.0 10.0 14.5 15.0 16.0 20.0

cut(x1,breaks=bks2, include.lowest=T, right=F )  #  correct
#Levels: s[0,5) [5,10) [10,15) s[15,20]s
#s[0,5)[0,5)[5,10) [0,5)[10,15)[10,15)[15,20][15,20] s[15,20]s

#understand these steps

#when u do right=TRUE, intervals closed on right. 
#max value included in last interval, but min value may be lost
#therefore include.lowest=T required to have it in first interval

x1
(xc1= cut(x1,breaks=bks2, include.lowest=T, right=T ) ) #  correct
#s[0,5] (5,10] (10,15] (15,20]s
#[0,5] [0,5](5,10][0,5] (5,10](10,15](10,15](15,20] (15,20]

# vice-versa works as include.highest now
(xc2= cut(x1,breaks=bks2, include.lowest=F, right=F ))  # not  correct
# NA value for max -> correct it
(xc3= cut(x1,breaks=bks2, include.lowest=T, right=F ))  #  correct
#Levels: (0,5]s (5,10] (10,15] s[15,20]s 
#[0,5)[0,5)[5,10)[0,5)[10,15)[10,15)[15,20][15,20][15,20]

table(xc1,useNA='ifany')
table(xc2, useNA='ifany')
table(xc3, useNA='ifany')
?table


# labels to classes
marks
(bks = seq(20,80,10))  # 6 intervals
classint = cut(marks, breaks=bks)
bks
length(bks)
(classint = cut(marks, breaks=bks))  # intervals
table(classint)
(classint = cut(marks, breaks=bks, labels=F))  # integers
table(classint)
(classint = cut(marks, breaks=bks, labels=LETTERS[1:length(bks)-1]))  # integers
table(classint)
?cut
(classint = cut(marks, breaks=bks, ordered_result = T,
        labels=LETTERS[1:length(bks)-1]))  # integers
table(classint)
plot(classint)

#dig.lab
## sometimes the default dig.lab is not enough to be avoid confusion:
aaa <- c(1,2,3,4,5,2,3,4,5,6,7)
cut(aaa, 3)
cut(aaa, 3, dig.lab = 4, ordered = TRUE)

## Label construction:
y <- stats::rnorm(100)
table(cut(y, breaks = pi/3*(-3:3)))
table(cut(y, breaks = pi/3*(-3:3), dig.lab = 1))
table(cut(y, breaks = pi/3*(-3:3), dig.lab = 2))
table(cut(y, breaks = pi/3*(-3:3), dig.lab = 3))
table(cut(y, breaks = pi/3*(-3:3), dig.lab = 4))


## sometimes the default dig.lab is not enough to be avoid confusion:
aaa <- c(1,2,3,4,5,2,3,4,5,6,7)
cut(aaa, 3)
cut(aaa, 3, dig.lab = 4, ordered = TRUE)



## generate data for clinical trial example----------
clinical.trial <-
    data.frame(patient = 1:100,              
               age = rnorm(100, mean = 60, sd = 8),
               year.enroll = sample(paste("19", 85:99, sep = ""),
                                    100, replace = TRUE))
summary(clinical.trial)
## basic usage of cut with a numeric variable
c1 <- cut(clinical.trial$age, breaks = 4)
table(c1)

c1

## year.enroll is a factor, so must convert to numeric first!
c2 <- cut(as.numeric(as.character(clinical.trial$year.enroll)),
              breaks = 3)
table(c2)
c2



# Missing values due to close on Right or Left
set.seed(1234)

marks = ceiling(rnorm(100,60,15))
marks
mean(marks)
range(marks)
table(marks)
marks[c(1,2,3,4,5)] = c(0,10,11,99,100)
head(marks,n=5)
(bks = seq(0,100,10))  # 10 intervals

# Slabs : 0-10,20-30 ....
classint1 = cut(marks, breaks=bks)  # default right=T
head(classint1,n=5)
head(marks,n=5)
table(classint1)
table(classint1, useNA='ifany')

classint2 = cut(marks, breaks=bks,right=F)  # default right=T
head(classint2,n=5)
head(marks,n=5)
table(classint2, useNA='ifany')

# correct the problem of NA
classint1a = cut(marks, breaks=bks, include.lowest = T)  # default right=T
as.character(head(classint1a,n=5))
head(marks,n=5)
table(classint1a,useNA='ifany')


classint2a = cut(marks, breaks=bks, include.lowest=T, right=F)  # default right=T
as.character(head(classint2a,n=5))
head(marks,n=5)
table(classint2a,useNA='ifany')

LETTERS[1:5]; letters[1:8]; month.abb[1:5]
#Giving Labels
length(bks)
#levels(classint)
LETTERS[1:length(bks)-1]
classint1b = cut(marks, breaks=bks, include.lowest = T, 
                 labels=LETTERS[1:length(bks)-1])  # default right=T
as.character(head(classint1b,n=5))
head(marks,n=5)
table(classint1b,useNA='ifany')




# Ordered_labels
classint1c = cut(marks, breaks=bks, include.lowest = T,
                 labels=c('B','C','F','A','G','P','L','H','K','O'))  # default right=T
as.character(head(classint1b,n=5))
head(marks,n=5)
table(classint1c,useNA='ifany')






# classint1d = cut(marks, breaks=bks, include.lowest = T, ordered_result = T,
#                  labels=c('1','3','5','8','4','6','7','10','9','2'))  # default right=T
# as.character(head(classint1b,n=5))
# head(marks,n=5)
# table(classint1d,useNA='ifany')





# try for dig.lab =3
classint5 = cut(marks, breaks=10, dig.lab=2)
classint5
table(classint5)


================================================
FILE: Unsorted/cutprety1.R
================================================
# Equally spaced values - pretty

set.seed(1234)
marks = ceiling(rnorm(100,60,15))

?pretty
pretty(marks,10)

marks2 = ceiling(runif(100,20,90))
pretty(marks2,4) #4+1 Interval
pretty(marks2,5) #5+1 Interval

marks2 = ceiling(runif(100,25,90))
pretty4 = pretty(marks2,4) #4+1 Interval
pretty(marks2,5) #5+1 Interval

cut4 = cut(marks2, pretty4 )
table(cut4)
cbind(table(cut4))

cut4L = cut(marks2, pretty4, labels=c("L2040", "L4060","L6080", "L80100") )
table(cut4L)
cbind(table(cut4L))
pie(cbind(table(cut4L)), labels=c("L2040", "L4060","L6080", "L80100"))
barplot(cbind(table(cut4L)), names.arg = c("L2040", "L4060","L6080", "L80100"), beside = T)


================================================
FILE: Unsorted/datalevels.R
================================================
#Types of Data

# Create the function.
getmode <- function(v) {
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}

# Nominal
tshirt = c('red', 'blue', rep('green',3), rep('black',3))
tshirt
mean(tshirt)
median(tshirt)
tshirt = c(tshirt, rep('yellow',2))
tshirt
median(tshirt)
table(tshirt)
getmode(tshirt) # first color in entry

# Ordinal
grades = c(rep('Good',2), rep('Poor',4), rep('Excellent'))
grades
mean(grades)
median(grades)
getmode(grades)

f1 = factor(grades, levels=c( 'Good','Poor','Excellent'), ordered=F)
f1
f2 = factor(grades, levels=c('Poor', 'Good', 'Excellent'), ordered=T)
f2

mean(f2)
f2
median(f2)# not working - it is Good
getmode(f2)
summary(f2)

# Interval



# Ratio



================================================
FILE: Unsorted/dbconnection.R
================================================
library(DBI)
con <- dbConnect(odbc::odbc(), "ds")
data <- sqlQuery( con , paste ("select * from student"))
dbListTables(con)

# List tables beginning with f
dbListTables(con, table_name = "f%")

# List all fields in the 'flights' database
dbListFields(con, "Students")

Students <- dbReadTable(con, "Students")
Students

library(tibble)
as_tibble(Students)


dbWriteTable(con, "Studentnew", as.data.frame(Students))


================================================
FILE: Unsorted/dec17.R
================================================
paste("a", "b", se = ":")
x= 1:5
y= NULL
is.null(x)
f <- function(a, b) a^2
f(2)
paste("a", "b", sep = ":")
paste("a", "b", se = ":")
Sys.Date()
class(as.Date('1970-01-02'))

x = 1:5
apply(x, FUN=sqrt)
?apply
data(package = .packages(all.available = TRUE))
paste('Data', 'Science', 'from', 'MUIT', sep='-')

month.abb[1:12]
(sd = paste('01',month.abb[1:6],2016,sep='-'))
sd1 = as.Date(sd,'%d-%b-%Y')
saledate = rep(sd1,times=4)
saledate
dept = rep(c('Dept1','Dept2'), each=12)
(dept = factor(dept, ordered=T, levels=c('Dept2', 'Dept1')))
(city = rep(c('Delhi','Noida'), times=2, each=6))
set.seed(1234)
saleamt = ceiling(runif(24, 100, 200))
set.seed(1234)
advamt = ceiling(runif(24, 25,40))
df = data.frame(saledate, dept=as.factor(dept), city=as.factor(city), saleamt, advamt)
df
with(df, boxplot(saleamt ~ dept))
with(df, boxplot(saleamt ~ dept + city))

agdeptcity = aggregate(df$saleamt, by=list(dept, city), mean)
pie(agdeptcity$x, labels=paste(agdeptcity$Group.1, agdeptcity$Group.2, sep='-'))


(t1 = xtabs(saleamt ~ dept + city, data=df))
margin.table(t1,c(2))
addmargins(t1,c(1,2), FUN=list(list(sd,mean), sum))
prop.table(t1)*100

(df2= subset(df, city='noida', select=c(dept, saleamt,advamt)))

plot(y=df2$saleamt, x=df2$advamt)
abline(lm(df2$saleamt ~ df2$advamt))
cor(df2$saleamt, df2$advamt)
df2
df2[order(saleamt, -advamt),]
with(df2,plot(x=dept, y=saleamt))
within(df2, profit <- saleamt - advamt)
df2
(mat2 = as.matrix(df2[2:3]))
sweep(df2[2:3],2,1,'+')

reshape2::melt(df2, id='dept')



matrix1 = as.matrix(airquality)

matrix1
colSums(is.na(matrix1))
mean(matrix1[,1],na.rm=T)

colSums(is.na(matrix1))

sapply(matrix1, function(x)all(is.na(x)))

which(is.na(matrix1))
matrix1[which(is.na(matrix1[,1])),1]
length(matrix1[which(is.na(matrix1[,1])),1])
matrix1[which(is.na(matrix1[,2])),2]
length(matrix1[which(is.na(matrix1[,2])),2])

length(matrix1[which(is.na(matrix1[,1]))])
length(matrix1[which(is.na(matrix1[,2]))])
matrix1[which(is.na(matrix1[,2]))]
which(is.na(matrix1[,2]))
which(is.na(matrix1[,2]))


ceiling(mean(matrix1[,1],na.rm=T))
ceiling(mean(matrix1[,2],na.rm=T))
matrix1[which(is.na(matrix1[,1])),1] = ceiling(mean(matrix1[,1],na.rm=T))
matrix1[which(is.na(matrix1[,2])),2] = ceiling(mean(matrix1[,2],na.rm=T))
colSums(is.na(matrix1))

matrix1[which(is.na(matrix1[,2])),2]
matrix1[c(5,6,11,27,96,98)]
which(is.na(matrix1[,2]))

apply(matrix1, 2, mean)
sapply(matrix1[1:10], log)
matrix1
apply(matrix1[1:10], 2, log)
?lapply



# List
(myList = list(nostudents = 1:10, school = 'Data Science', 
              course=c('PG','MSc')))
length(myList$course)

#myArray
set.seed(1234)
(x=ceiling(rnorm(2*3*5*4,50,10)))

depts=c('D1','D2')
courses=c('C1','C2','C3')
students=c('S1','S2','S3','S4','S5')
subjects=c('Sb1', 'Sb2', 'Sb3', 'Sb4')
myArray = array(x, dim = c(5,4,3,2), dimnames = list(students, subjects, courses,depts))


myArray

sum(myArray)
lapply(myArray,c(4), mean)
?apply
apply(myArray, c(2), function(x) max(x))
apply(myArray, c(4), function(x) mean(x))
apply(myArray, c(1,3,4), function(x) sum(x))
apply(myArray, c(2), function(x) sd(x))


(myList = list(rollno = 100:120, school = 'Data Science', 
               course=c('PG','MSc')))

length(myList$rollno)

x = 11:20
for (i in c(5,2,7)) {
  print(x[i]^2)
}

func1 = function(x) sqrt(x)
func1(x)


================================================
FILE: Unsorted/dec17b.R
================================================

array1 = array(1:12, dim=c(2,3,2) )
dimnames1 = list(letters[1:2],
                 LETTERS[1:3], c('One','Two') )
dimnames(array1) = dimnames1
array1

mat1 = matrix(1:24, nrow=4)
dimnames2 = list(letters[1:4], LETTERS[1:6])
dimnames(mat1) = dimnames2
mat1
mat1[4]= NA

apply(array1, 1, FUN=sum)
apply(array1, c(2,3), FUN=sum)

apply(mat1, 1, FUN=mean)
apply(mat1, MARGIN=2, FUN=median, na.rm=T)

rowSums(array1, dims=3)
colSums(array1, dims=1)
colSums(array1, dims=2)

colSums(mat1,dims=1, na.rm=T)
colSums(mat1, na.rm=T)

rowSums(mat1,dims=1, na.rm=T)


a <- c(1,1,1,1,2,2,2,2,2)
b <- c(10,12,15,12,NA,30,42,38,40)
(s=split(b,a))
lapply(s,mean, na.rm=T)


================================================
FILE: Unsorted/demo1.R
================================================
# Demos
# http://r-pkgs.had.co.nz/demo.html

#Show all available demos: demo()
demo()

library(graphics)
demo()

library(base)
demo()

library(grDevices)
demo()

library(stats)
demo()

demo(package = .packages(all.available = TRUE))

#Show all demos in a package: demo(package = "httr")

demo(package = "httr")

#Run a specific demo: demo("oauth1-twitter", package = "httr").
demo("oauth1-twitter", package = "httr")

#Find a demo: system.file("demo", "oauth1-twitter.R", package = "httr")
system.file("demo", "oauth1-twitter.R", package = "httr").

#https://cran.r-project.org/web/packages/TeachingDemos/TeachingDemos.pdf
library(TeachingDemos)
ci.examp()
plot.dice( expand.grid(1:6,1:6), layout=c(6,6) )


cal(2011)


================================================
FILE: Unsorted/density2.R
================================================
# Density

set.seed(1234)
x = ceiling(rnorm(100,50,15))
summary(x)
head(x)
hist(x)

write(x, file='./file/sampledata.txt', ncolumns = 1)
# write to a file to copy to execl
# 
hist(x, freq=F)
lines(density(x), lwd=2)


plot(density(x))
rug(x)  # jitters at points
abline(v=x) # vertical lines

#another method
plot(density(x)$x, density(x)$y)
rug(density(x)$x)
density(x)$x


================================================
FILE: Unsorted/descstatsgraphs1.R
================================================

?pastecs::stat.desc(mtcars)
?fivenum
library(DescTools)
data("iris")
Desc(iris, plotit = T)

tabplot::tableplot(iris, sortCol=Species)
tabplot::tableplot(mtcars, sortCol=am)


f_summary <- function(data_to_plot)
{
  ## univariate data summary
  require(nortest)
  #data <- as.numeric(scan ("data.txt")) #commenting out by mike
  data <- na.omit(as.numeric(as.character(data_to_plot))) #added by mike
  dataFull <- as.numeric(as.character(data_to_plot))
  
  # first job is to save the graphics parameters currently used
  def.par <- par(no.readonly = TRUE)
  par("plt" = c(.2,.95,.2,.8))
  layout( matrix(c(1,1,2,2,1,1,2,2,4,5,8,8,6,7,9,10,3,3,9,10), 5, 4, byrow = TRUE))
  
  #histogram on the top left
  h <- hist(data, breaks = "Sturges", plot = FALSE)
  xfit<-seq(min(data),max(data),length=100)
  yfit<-yfit<-dnorm(xfit,mean=mean(data),sd=sd(data))
  yfit <- yfit*diff(h$mids[1:2])*length(data)
  plot (h, axes = TRUE, main = paste(deparse(substitute(data_to_plot))), cex.main=2, xlab=NA)
  lines(xfit, yfit, col="blue", lwd=2)
  leg1 <- paste("mean = ", round(mean(data), digits = 4))
  leg2 <- paste("sd = ", round(sd(data),digits = 4))
  count <- paste("count = ", sum(!is.na(dataFull)))
  missing <- paste("missing = ", sum(is.na(dataFull)))
  legend(x = "topright", c(leg1,leg2,count,missing), bty = "n")
  
  ## normal qq plot
  qqnorm(data, bty = "n", pch = 20)
  qqline(data)
  p <- ad.test(data)
  leg <- paste("Anderson-Darling p = ", round(as.numeric(p[2]), digits = 4))
  legend(x = "topleft", leg, bty = "n")
  
  ## boxplot (bottom left)
  boxplot(data, horizontal = TRUE)
  leg1 <- paste("median = ", round(median(data), digits = 4))
  lq <- quantile(data, 0.25)
  leg2 <- paste("25th percentile =  ", round(lq,digits = 4))
  uq <- quantile(data, 0.75)
  leg3 <- paste("75th percentile = ", round(uq,digits = 4))
  legend(x = "top", leg1, bty = "n")
  legend(x = "bottom", paste(leg2, leg3, sep = "; "), bty = "n")
  
  ## the various histograms with different bins
  h2 <- hist(data,  breaks = (0:20 * (max(data) - min (data))/20)+min(data), plot = FALSE)
  plot (h2, axes = TRUE, main = "20 bins")
  
  h3 <- hist(data,  breaks = (0:10 * (max(data) - min (data))/10)+min(data), plot = FALSE)
  plot (h3, axes = TRUE, main = "10 bins")
  
  h4 <- hist(data,  breaks = (0:8 * (max(data) - min (data))/8)+min(data), plot = FALSE)
  plot (h4, axes = TRUE, main = "8 bins")
  
  h5 <- hist(data,  breaks = (0:6 * (max(data) - min (data))/6)+min(data), plot = FALSE)
  plot (h5, axes = TRUE,main = "6 bins")
  
  ## the time series, ACF and PACF
  plot (data, main = "Time series", pch = 20, ylab = paste(deparse(substitute(data_to_plot))))
  acf(data, lag.max = 20)
  pacf(data, lag.max = 20)
  
  ## reset the graphics display to default
  par(def.par)
  
  #original code for f_summary by respiratoryclub
  
}

f_summary(mtcars$mpg)

library(PerformanceAnalytics)
chart.Correlation(iris[,1:4],col=iris$Species)

library(Hmisc)



================================================
FILE: Unsorted/dplyr1.R
================================================
#dplyr

library(dplyr)

# set rnd df
set.seed(10)
N <- 5
my.df <- data.frame(COL1 = runif(N), 
                    COL2 = runif(N), 
                    G = runif(N),
                    B = runif(N))

# select columns with dplyr::select
my.temp.df <- select(my.df, COL1, G)
print(my.temp.df)


================================================
FILE: Unsorted/ds1.R
================================================
# Data Structures
# exploring data structures, using data entry, importing data, annotating dataset
# data types - numeric, character, logical, complex, raw
# Vectors
a = c(1,2,5,6,-2,4)
b = c("one", "two", 'three')
c <- c(TRUE, FALSE, T,F,TRUE,FALSE)
a ; b; c
a[3] ; a[c(1,3,5)] ; a[c(2:6)]

# Matrices
# 2 D arrray where each element as same datatype
y = matrix(1:20,nrow=5,ncol=4)
y
cells = c(1,26,24,68)
rnames = c('R1','R2')
cnames = c('C1','C2')
# filled by coln first
mymatrix = matrix(cells,nrow=2,ncol=2,byrow=F,dimnames=list(rnames,cnames))
mymatrix
# filled by row first
mymatrix = matrix(cells,nrow=2,ncol=2,byrow=T,dimnames=list(rnames,cnames))
mymatrix
mymatrix[1,2]  # first row, second coln
x = matrix(1:10,nrow=2)
x
x[2,] ; x[,2];x[1,4] ;x[1,c(4,5)]

#Arrays
# similar to matrices but can have more than 2 dim
# myarray= array(vector,dim,dimnames)
dim1 = c('A1','A2')
dim2 = c('B1','B2','B3')
dim3 = c('C1','C2','C3','C4')
z = array(1:24,c(2,3,4))
z
z = array(1:24,c(2,3,4),dimnames=list(dim1,dim2,dim3))
z

# data frame
# different colns contain different modes of data (numeric, character)
# mydata = data.frame(col1,col2,col3)
patientid = c(1,2,3,4)
age = c(25,34,28,52)
diabetes = c('Type1','Type2','Type1','Type1')
status = c('Poor','Improved','Excellent','Poor')
patientdata = data.frame(patientid,age,diabetes,status)
patientdata
patientdata[1:2]
patientdata[c('diabetes','status')]
patientdata$age
table(patientdata$diabetes,patientdata$status)
table(patientdata$diabetes,patientdata$status,patientdata$age)

#attach, detach, with
summary(mtcars$mpg)
summary(patientdata)
plot(mtcars$mpg, mtcars$disp)
plot(mtcars$mpg, mtcars$wt)
# another way of writing
attach(mtcars)
summary(mpg)
plot(mpg, disp)
plot(mpg,wt)
detach(mtcars)
# with
with(mtcars, {
  summary(mpg,disp,wt)
  plot(mpg,disp)
  plot(mpg,wt)
} )
# special assignment : with recommended over attach
with(mtcars, {
  nokeepstats = summary(mpg)
  keepstats <<- summary(mpg)
} )
nokeepstats  # no output
keepstats

# case identifiers : identify each row with rowname option
patientdata = data.frame(patientid,age,diabetes,status,row.names = patientid)
patientdata
summary(patientdata)

# factors
diabetes = c('Type1','Type2','Type1','Type1')
diabetes = factor(diabetes)
diabetes
status = c('Poor','Improved','Excellent','Poor')
status = factor(status, ordered=T)
status
patientdata = data.frame(patientid,age,diabetes,status)

summary(patientdata)
status = factor(status, ordered=T,levels=c('Poor','Improved','Excellent'))
status

patientdata = data.frame(patientid,age,diabetes,status)
patientdata
str(patientdata)

#Lists : most complex - ordered collection of objects
# combinatino of matrices, data frames, other lists
#mylist = list(object1, object2)

g = 'My first list'
h = c(25,26,18,39)
j = matrix(1:10,nrow=5)
k = c("one",'two','Three')
mylist = list(title=g,ages=h,j,k)
mylist
mylist[[2]]
mylist[['ages']]


================================================
FILE: Unsorted/env1.R
================================================
# Session Info


sessionInfo()
toLatex(sessionInfo(), locale = FALSE)



#http://adv-r.had.co.nz/memory.html

library(pryr)
mem_used()


library(pryr)
object_size(1:10)
## 88 B
object_size(mean)
## 832 B
object_size(mtcars)
## 6.74 kB

rm(list=ls())
sizes <- sapply(0:50, function(n) object_size(seq_len(n)))
plot(0:50, sizes, xlab = "Length", ylab = "Size (bytes)", 
     type = "s")


object_size(numeric())
## 40 B
object_size(logical())
## 40 B
object_size(raw())
## 40 B
object_size(list())
## 40 B

plot(0:50, sizes - 40, xlab = "Length", 
     ylab = "Bytes excluding overhead", type = "n")
abline(h = 0, col = "grey80")
abline(h = c(8, 16, 32, 48, 64, 128), col = "grey80")
abline(a = 0, b = 4, col = "grey90", lwd = 4)
lines(sizes - 40, type = "s")

x <- 1:1e6
object_size(x)
## 4 MB
y <- list(x, x, x)
object_size(y)
## 4 MB


sort( sapply(ls(),function(x){object.size(get(x))})) 


#https://stackoverflow.com/questions/1395270/determining-memory-usage-of-objects

memory.profile()
lsos()
gc
memory.size()
object.size(1)
#32 bytes
object.size("Hello world!")
#72 bytes
#object.size(audioscrobbler)
#39374504 bytes

================================================
FILE: Unsorted/examB.R
================================================
#Array -------
set.seed(1234)
(x=round(runif(2*3*5,2,5)))
(elections = array(x, dim=c(5,3,2)))
(state = c('gujrat','hp'))
(party = c('bjp','congress', 'aap'))
(district = paste('D',1:5,sep=''))
(dimnames(elections)= list(district, party, state))
elections

#summarise the elections
#statewise
apply(elections,3, sum)
#partywise
apply(elections,2, sum)
#state and partywise
(t1 = apply(elections,c(2,3), sum))
addmargins(t1,c(1,2),FUN=list(mean, list(sd,sum)))
#prop in each state
prop.table(t1,2)


# Built in Data Set
data("iris")
str(iris)           
dim(iris)
df2 = iris

ml = sample(50:100,4,replace=F) 
df2[ml,2] = NA
#list out the column 1 & 3 of complete cases
colSums(is.na(df2[1:4]))

sum(complete.cases(df2))
sum(!complete.cases(df2))

df2[complete.cases(df2),][c(1:5),c(1,3)]
#replace missing values in column 2 with mean of column1
df2[is.na(df2[2]),2]
mean(df2$Sepal.Length)
df2[is.na(df2[2]),2] = mean(df2$Sepal.Length)
#check if values have been changed
colSums(is.na(df2[1:4]))

si = function(principal, time, rate=.10) 
  return(principal * time * rate )
si(1000, 2)
si(1000, 2,.20)

#Count Even Nos
count=0
for (i in c(2,5,3,9,8,11,6)) {
  if (i%%2 == 0) {
    count <<- count + 1
#    print(count)
  }
}
print(count)

x = c(2,5,3,9,8,11,6)
sum(x%%2==0)


data(Arthritis, package='vcd')
summary(Arthritis)
str(Arthritis)
table(Arthritis$Treatment, Arthritis$Improved, Arthritis$Sex)
ftable(Arthritis$Treatment ~ Arthritis$Improved + Arthritis$Sex )
table(Arthritis$Improved)


================================================
FILE: Unsorted/extra.R
================================================


#1
sales = c(10000, 13000, 12000, 15000)
names(sales) = paste('Q',1:4,sep='')
sales
barplot(sales)
barplot(sales, horiz=T)
pie(sales)
sales[sales > 11000]
sales[ names(sales) == 'Q3']

# 1
# Attendance
attnd05Sep17 = sample(c('A','P'), size=30, replace=T, prob = c(0.3,0.7)) 
table(attnd05Sep17)

# Cricket Players
set.seed(1000)
(p1 = ceiling(runif(10,50,100)))
set.seed(1100)
(p2 = ceiling(runif(10,50,100)))
set.seed(1200)
(p3 = ceiling(runif(10,50,100)))
set.seed(1300)
(p4 = ceiling(runif(10,50,100)))
set.seed(1400)
(p5 = ceiling(runif(10,50,100)))
scores = matrix(c(p1,p2,p3,p4,p5), byrow=F,ncol=5,
                dimnames=list(paste('M',1:10,sep=''),paste('P',1:5,sep='')))
scores
colMeans(scores)
rowMeans(scores)


#2 


# 
guests =c('Achal','Apoorva', 'Lalit', 'Goldie')
hosts = data.frame(course=c('BSc','LLB','MSc'), 
                   school=c('Animation', 'Law','DS'),
                   nos =c(50, 20, 24))
hosts$male = round(hosts$nos * .8)
hosts$female = hosts$nos - hosts$male
hosts
bills = matrix(c(100,300, 400, 500, 230,
                 500,200,150, 400, 700),
               nrow=2, byrow=T,
               dimnames=list(c('decoration','food'),
                             paste('B',1:5,sep='')))
bills               
freshers=list(guests=guests, hosts=hosts, bills=bills)
freshers
sum(freshers$bills[1,])
sum(freshers$bills[2,])
freshers$hosts$male
freshers$hosts$female
length(freshers$guests)


================================================
FILE: Unsorted/fd1.R
================================================
# Frequency Distribution

#Discrete Cat Data
attend = c('A','P','P','A','P','A')
table(attend)
cbind(table(attend))  # A & P on left side

#Discrete Numeric Data

color=c('Blue','Green','Magenta','Green','Black','Blue','Black')
x2 = table(color)
x2
x2a = cbind(x2)
x2a
hist(x2a)  # not relevant 
barplot(x2a)  # not good
barplot(x2a, beside=T)  # Better
unique(color) ; length(unique(color))
barplot(x2a, beside=T, col=rainbow(length(unique(color))))
pie(x2a)

#Continuous Data 
set.seed(1234)
x3 = runif(100,0,150)  # 0 to 150 marks range, 100 values 
x3
x3 = ceiling(x3)
x3
range(x3)
# Divide range into step of 15 ie 10 levels
breaks = seq(0,150,by=15)
breaks
x3[1] = 60
x3[2] = 75
x3.cut = cut(x3, breaks)
x3.cut = cut(x3, breaks, labels=letters[1:10])
x3.cut = cut(x3, breaks, labels=letters[1:length(breaks)])

length(breaks)
x3.cut
?cut
x3a = table(x3.cut)
x3a
x3b = cbind(x3a)
x3b
hist(x3b)
pie(x3b)
barplot(x3b, beside=T)
barplot(x3b, beside=T, names.arg =rownames(x3b))
plot(x3b)


================================================
FILE: Unsorted/fd2.R
================================================
# Frequency 2
# Relative Frequency
#http://www.r-tutor.com/elementary-statistics/qualitative-data/relative-frequency-distribution-qualitative-data

#Eg1
library(MASS)                 # load the MASS package 
school = painters$School      # the painter schools 
school.freq = table(school)   # apply the table function
school
school.freq
head(painters)
nrow(painters) # no of rows/ records
school.relfreq = school.freq / nrow(painters)
school.relfreq
cbind(school.relfreq) 
round(cbind(school.relfreq),2)
school2 = data.frame(round(cbind(school.relfreq),2))
hist(school2$school.relfreq)

#Cumulative Relative Frequency
school2$cum = cumsum(as.numeric(school2$school.relfreq))
school2
plot(school2$cum, type='l')



#Eg2 https://cnx.org/contents/yTlmgmzh@4/Frequency-Relative-Frequency-a
# Entering the data : 20 records
hours.worked = c(5, 6, 3, 3, 2, 4, 7, 5, 2, 3, 5, 6, 
                 5, 4, 4, 3, 5, 2, 5, 3)

# A general frequency table
table(hours.worked)
## hours.worked
## 2 3 4 5 6 7 
## 3 5 3 6 2 1

# Relative frequency table
length(hours.worked)
table(hours.worked)/length(hours.worked)
## hours.worked
##    2    3    4    5    6    7 
## 0.15 0.25 0.15 0.30 0.10 0.05

# To get cumulative frequencies, we need to put
# the hours into different intervals
x = table(cut(hours.worked, breaks = c(1:7)))
x
# Cumulative frequencies
cumsum(x)
## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] 
##     3     8    11    17    19    20

# Cumulative relative frequencies
cumsum(x)/length(hours.worked)
## (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] 
##  0.15  0.40  0.55  0.85  0.95  1.00


================================================
FILE: Unsorted/fd3.R
================================================
#FD3





# cum relative Freq Graph
duration = faithful$eruptions 
breaks = seq(1.5, 5.5, by=0.5) 
duration.cut = cut(duration, breaks, right=FALSE) #closed on left
duration.freq = table(duration.cut) 
duration.cumfreq = cumsum(duration.freq) 
duration.cumfreq
duration.cumrelfreq = duration.cumfreq / nrow(faithful)

cumrelfreq0 = c(0, duration.cumrelfreq) 
cumrelfreq0
plot(breaks, cumrelfreq0, 
   main="Old Faithful Eruptions",  # main title 
   xlab="Duration minutes", 
   ylab="Cumulative eruption proportion") 
lines(breaks, cumrelfreq0)        # join the points


#Eg3
Fn = ecdf(duration) 
?ecdf
plot(Fn, 
   main="Old Faithful Eruptions", 
   xlab="Duration minutes", 
   ylab="Cumulative eruption proportion")


================================================
FILE: Unsorted/fd4.R
================================================
# Freq Distribution


================================================
FILE: Unsorted/fd5-means.R
================================================
# Table of Value - Mean Median Mode

# Observation Data  -------------
marks = c(43, 60, 37, 48, 65, 48, 57, 78, 31, 59, 50)
marks1 = scan() 
# copy column of values from Excel 
#and paste & enter after the last value
marks1

mean(marks) # 52.36
median(marks) # 50

estimate_mode <- function(x) {
  d <- density(x)
  d$x[which.max(d$y)]
}
estimate_mode(marks)
#50.89

# Quantile, Percentile, --------
quantile(marks)
sort(marks)
quantile(marks, c(0,.1,.2,.25, .4, .5, .75, .9, 1))
quantile(marks, c(0,.09,.18,.27, .36, .45, .55, .64, .73, .82, .91, 1))
IQR(marks)
quantile(marks, c(.25,.75))
(iqr = quantile(marks, .75) - quantile(marks,.25))

summary(marks)


# Discrete Data

#marks2 = scan()

marks2a =  c(3.5, 4.5, 5.5, 6.5, 7.5,8.5, 9.5)
freq2 = c(3,7,22,60,85,32,8)
cbind(marks2a, freq)  # see how it looks - excel
df2 = data.frame(marks2a, freq2)
df2
df2$marksfreq = df2$marks2a * df2$freq2
df2
sum(df2$marksfreq)
meanmarks2a = sum(df2$marksfreq)/ sum(df2$freq2)
meanmarks2a
midfreq = sum(freq2)/2
df2$cumfreq = cumsum(df2$freq2)
df2
# Median
df2$marks2a[df2$cumfreq > midfreq]  # X which are more than mid value
# use min to select least value
median2 = min(df2$marks2a[df2$cumfreq > midfreq]) # 
median2

#mode
max(df2$freq2)
which(df2$freq2 == max(df2$freq2))  # index No
df2$marks2[5]

mode2 = df2$marks2[ which(df2$freq2 == max(df2$freq2))]
mode2

# real data is not like this - it is observations
#create data from the table given
x2a = rep(marks2a, freq2)
x2a  # total obsverations
table(x2a)
cbind(table(x2a)) # Looks like the orginal table
mean(x2a)
median(x2a)
estimate_mode(x2a)


# Discrete Data - Cat Labels -------------
?scan
#marks2b = scan(what = character())
marks2b =c ("A", "B", "C", "D", "E", "F", "G") 
freq2
x2b = rep(marks2b, freq2)
x2b
table(x2b)
cbind(table(x2b))  # looks similar

mean(x2b) # will not work becoz data is not numeric
# Median  - E
median(x2b) # mid value if arranged in series
length(x2b)
mid = ceiling(length(x2b)/2) 
mid
x2b[mid]
order(x2b)[mid]
x2b[order(x2b)[mid]]

# Mode - max freq

estimate_mode(x2b)  # does not work
density(x2b)  # because u cannot calc density 

#simples 
table(x2b)
rownames(table(x2b))
colnames(table(x2b))  # does not work

max(table(x2b))
mode2b1 = rownames(table(x2b))[which(table(x2b) == max(table(x2b)))]
mode2b1

#Method3
freq <- tapply(x2b,x2b,length)
table(x2b)
names(freq)[which.max(freq)]


# continuous Data

#class = scan(what='character')
class3 =c("0-10","10-20","20-30","30-40","40-50","50-60", "60-70")
class3
#freq3 = scan()
freq3 = c(3,  7, 22, 60, 85, 32,  8)
as.numeric(unlist(strsplit(class3,"-")))  # long method
midx3 = c(5,15,25,35,45,55,65)
cbind(class3, midx3, freq3)  #midx3 is also character
df3 = data.frame(class3, midx3, freq3)
df3
df3$midxfreq = df3$midx3 * df3$freq3
df3

# and so..on like previous eg

# create observation data from contiuous series
x3 = rep(midx3, freq3)
x3
sum(x3)
table(x3)
cbind(table(x3))  # looks like original table

mean(x3)
median(x3)
estimate_mode(x3)


================================================
FILE: Unsorted/googleS.R
================================================
# Google Places
#https://stackoverflow.com/questions/28026897/google-place-with-r


library(googleway)

# NOT RUN {
## query restaurants in Melbourne (will return 20 results)
api_key <- 'your_api_key'

res <- google_places(search_string = "Restaurants in Melbourne, Australia",  key = api_key)


## melbourne, AU  
df_places <- google_places(search_string = "cafe",    location = c(-37.81827, 144.9671),   key = key)

df_places$results$name
## use the 'next_page_token' from the previous search to get the next 20 results
res_next <- google_places(search_string = "Restaurants in Melbourne, Australia",  page_token = res$next_page_token, key = api_key)

## search for a specific place type
google_places(location = c(-37.817839,144.9673254),place_type = "bicycle_store", radius = 20000, key = api_key)

## search for places that are open at the time of query
google_places(search_string = "Bicycle shop, Melbourne, Australia", open_now = TRUE, key = api_key)

# }

================================================
FILE: Unsorted/knitr.R
================================================
# Code and Output

library("knitr")
knit2html("./a1/as1.R")


================================================
FILE: Unsorted/kurtosis.R
================================================
#Kurtosis
#describes the tail shape of the data distribution
# The normal distribution has zero kurtosis and 
# thus the standard tail shape. 
# It is said to be mesokurtic. 
# Negative kurtosis would indicate a thin-tailed data distribution, 
# and is said to be platykurtic. 
# Positive kurtosis would indicate a fat-tailed distribution, 
# and is said to be leptokurtic.

set.seed(1234)
(marks = ceiling(rnorm(100,50,20)))
x=marks
library(e1071)                    # load e1071 
hist(x)
plot(density(x))
e1071::kurtosis(x)  # -0.38  -vely Thin Tail




x1= c(2, 5, -1, 3, 4, 5, 0, 2)
kurt(x1)



library(e1071)                    # load e1071 
duration = faithful$eruptions     # eruption durations
plot(density(duration))
kurtosis(duration)                # apply the kurtosis function 
#[1] -1.5116 -ve : thin Tail

================================================
FILE: Unsorted/lm-sales.R
================================================
# Linear Regression 
# Area(X) - Sales(Y)
# Create Data
X = c(1.7,1.6,2.8,5.6,1.3,2.2,1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7,2.7,5.5,2.9,10.7,7.6,11.8,4.1 )

# Summary
length(X)
sum(X); mean(X)
sum(Y); mean(Y)

par(mfrow=c(1,1))
# Covariance
cov(X,Y)

# Correlation
cor(X,Y)
plot(X,Y, main=' Plot between X and Y')
abline(lm(Y ~ X), col='red')

# Correlation Test
cor.test(X,Y)


(df  = data.frame(X,Y))
(xm = mean(X))
(ym = mean(Y))
df
# Calculate diff between X and xm and similarly for Y
(df$x = round(X - xm,2))
transform(df, y = round(Y - ym,2))
df  # only x

df= transform(df, y = round(Y - ym,2))  # save into df

(df$xy = round(df$x * df$y,2))
df
# temporarily display - does not add column permanently
plyr::mutate(df, x2 = x ^ 2)  
df
df = plyr::mutate(df, x2 = x ^ 2)  
df

colSums(df)
(SSXY = with(df, sum(xy)))
(SSX2 = sum(df$x2))

(b1 = round(SSXY/ SSX2,2))
(b0 = ym - b1 * xm)
# Predicted Values based on coefficients
(Yp = round(b0 + b1 * X,2))
(df$Yp = Yp)
df

# Errors : SST = SSE (predict ~ actual) + SSR (predict ~ meany) + 
(df$SSe = round((Y - Yp)^2,2))
df
(df$SSr = round((Yp - ym)^2,2))
df
(SSE = sum(df$SSe))  # Unexplained Variation
(SSR = sum(df$SSr)) # Regression Sum of Squares
(SST = SSE + SSR)  # Total Sum of Squares should be min

#Coeff of Determination
(r2 = round(SSR/ SST,3))

#Variability due to other factors 
(1 - r2)  # unexplained Variation

#Std Error of Estimate : Residual Std Error
n = length(X)
(Syx = round(sqrt(SSE/(n - 2)),3))
#Variability of Actual Values of Y (Yi) from Yp

#Residuals
residue = Y-Yp
summary(residue)

fit=lm(formula = Y ~ X)
summary(fit)
par(mar=c(5,4,4,2))
plot(Y, X,col = "blue",main = "Area & Sales", abline(lm(x~y)),cex = 1.3, pch =
       16,xlab = "Area in sq ft",ylab = "Sales in 1000 Rs")

layout(matrix(c(1,2,3,4),2,2))
plot(fit)

plot(fit, which=1:4)

lmtest::dwtest(fit)
car::durbinWatsonTest(fit)

aov(fit)


================================================
FILE: Unsorted/lm1-sales.R
================================================
# Linear Modeling : Sales
# 
# 
X = c(1.7,1.6,2.8,5.6,1.3,2.2, 1.3,1.1,3.2,1.5,5.2,4.6,5.8,3 )
Y = c(3.7,3.9,6.7,9.5,3.4,5.6,3.7, 2.7,5.5,2.9,10.7,7.6,11.8,4.1 )

cor.test(X,Y)
#Plot
plot(x=X,y=Y, main=' Plot between X and Y')
abline(lm(Y ~ X), col='blue')
text(x=X,y=Y,labels=paste0(X,Y),cex=.7, col='red')
?text
fit =lm(Y ~ X)
summary(fit)

df =data.frame(X,Y)
df
df$x = round(X - mean(X),2)
df$y = round(Y - mean(Y),2)
df
colMeans(df)
df$xy = round(df$x * df$y,2)
df$x2 = round(df$x^2,2)
SSxy = sum(df$xy)
SSx2 = sum(df$x2)
(b1 = SSxy / SSx2)
(b0 = mean(Y) - b1 * mean(X))
(df$Yh = round(fitted(fit),2))
df
plot(df$Yh, X)
(df$residuals = round(df$Y - df$Yh, 2))
summary(df$residuals)
summary(fit)
df$SSr = round((df$Yh - mean(Y))^2, 2)
df$SSe = round((df$Y - df$Yh) ^ 2, 2)
df
colSums(df)
(SSR = round(sum(df$SSr),2))
(SSE = round(sum(df$SSe),2))
(R2 = round(sum(df$SSr)/ (sum(df$SSe) + sum(df$SSr)),4))
df
n=length(X)
(stderror = sqrt(sum(df$SSe)/(n-2)))

p = 1 # No of IV Variables
(MSR = SSR/p)
(MSE = SSE/(n-p-1))
(Fstats = MSR/MSE)
summary(fit)








================================================
FILE: Unsorted/lm1.R
================================================
# Regression - Car Data Set
# http://r-statistics.co/Linear-Regression.html
# 
par(mfrow=c(1, 1))
?cars
plot(x=cars$speed, y=cars$dist)
scatter.smooth(x=cars$speed, y=cars$dist, main="Dist ~ Speed")  # scatterplot

par(mfrow=c(1, 2))  # divide graph area in 2 columns
boxplot.stats(cars$dist)
boxplot(cars$speed, main="Speed", sub=paste("Outlier rows: ", 
        boxplot.stats(cars$speed)$out))  # box plot for 'speed'
boxplot(cars$dist, main="Distance", sub=paste("Outlier rows: ", 
            boxplot.stats(cars$dist)$out))  # box plot for 'distance'


# Check for normality of x 
library(e1071)
par(mfrow=c(1, 2))  # divide graph area in 2 columns
plot(density(cars$speed), main="Density Plot: Speed", 
     ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$speed), 2))) 
# density plot for 'speed'
polygon(density(cars$speed), col="red")
plot(density(cars$dist), main="Density Plot: Distance", 
     ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$dist), 2))) 
# density plot for 'dist'
polygon(density(cars$dist), col="red")


# correlation
cor(cars$speed, cars$dist)  # calculate correlation between speed and distance


#LM
linearMod <- lm(dist ~ speed, data=cars)  # build linear regression model on full data
print(linearMod)
#dist = Intercept + (β ∗ speed) #=> dist = −17.579 + 3.932∗speed
summary(linearMod)

#AIC and BIC :For model comparison, the model with the lowest AIC and BIC score is preferred.
AIC(linearMod)  # AIC => 419.1569
BIC(linearMod)  # BIC => 424.8929
?AIC



# Predicting LM
# Create Training and Test data -
set.seed(100)  # setting seed to reproduce results of random sampling
trainingRowIndex <- sample(1:nrow(cars), 0.8*nrow(cars))  # row indices for training data
trainingData <- cars[trainingRowIndex, ]  # model training data
testData  <- cars[-trainingRowIndex, ]   # test data

# Build the model on training data -
lmMod <- lm(dist ~ speed, data=trainingData)  # build the model
distPred <- predict(lmMod, testData)  # predict distance

summary (lmMod)  # model summary

AIC (lmMod)

actuals_preds <- data.frame(cbind(actuals=testData$dist, predicteds=distPred))  # make actuals_predicteds dataframe.
(correlation_accuracy <- cor(actuals_preds))  # 82.7%
head(actuals_preds)

actuals_preds
apply(actuals_preds, 1, min)
apply(actuals_preds, 1, max)
(min_max_accuracy <- mean(apply(actuals_preds, 1, min) / apply(actuals_preds, 1, max))  )
# => (38%) 58.42%, min_max accuracy
(mape <- mean(abs((actuals_preds$predicteds - actuals_preds$actuals))/actuals_preds$actuals)  )
# => 69% (48.38%), mean absolute percentage deviation
# 

#K Fold
library(DAAG)
cvResults <- suppressWarnings(CVlm(data=cars, form.lm=dist ~ speed, m=5, dots=FALSE, seed=29, legend.pos="topleft",  printit=FALSE, main="Small symbols are predicted values while bigger ones are actuals."));  # performs the CV
attr(cvResults, 'ms')  # => 251.2783 mean squared error


# Assumptions
mod <- lm(dist ~ speed, data=cars)
mean(mod$residuals)

par(mfrow=c(2,2))  # set 2 rows and 2 column plot layout
mod_1 <- lm(mpg ~ disp, data=mtcars)  # linear model
plot(mod_1)

mod <- lm(dist ~ speed, data=cars[1:20, ])  #  linear model
plot(mod)

par(mfrow=c(1,1))
#Auto Correlation
# Method 1: Visualise with acf plot
library(ggplot2)
data(economics)
lmMod <- lm(pce ~ pop, data=economics)
acf(lmMod$residuals)  # highly autocorrelated from the picture.


#Assumption 3
#Homoscedasticity of residuals or equal variance
par(mfrow=c(2,2))  # set 2 rows and 2 column plot layout
mod_1 <- lm(mpg ~ disp, data=mtcars)  # linear model
plot(mod_1)



#Assumption 4 :No autocorrelation of residuals
par(mfrow=c(1,1))
# Method 1: Visualise with acf plot
library(ggplot2)
data(economics)
lmMod <- lm(pce ~ pop, data=economics)
acf(lmMod$residuals)  # highly autocorrelated from the picture.

# Method 2: Runs test to test for randomness
lawstat::runs.test(lmMod$residuals)
#=>   Runs Test - Two sided
#=> data:  lmMod$residuals
#=> Standardized Runs Statistic = -23.812, p-value < 2.2e-16

# Method 3: Durbin-Watson test
lmtest::dwtest(lmMod)
#=>   Durbin-Watson test

#=> data:  lmMod
#=> DW = 0.0021559, p-value < 2.2e-16
#=> alternative hypothesis: true autocorrelation is greater than 0
#
# Rectify
library(DataCombine)
econ_data <- data.frame(economics, resid_mod1=lmMod$residuals)
econ_data_1 <- slide(econ_data, Var="resid_mod1", NewVar = "lag1", slideBy = -1)
econ_data_2 <- na.omit(econ_data_1)
lmMod2 <- lm(pce ~ pop + lag1, data=econ_data_2)

#Check 1

acf(lmMod2$residuals)

#Check 2
library(lawstat)
lawstat::runs.test(lmMod2$residuals)  # runs test

#Check 3

lmtest::dwtest(lmMod2)


#Assumption 5
mod.lm <- lm(dist ~ speed, data=cars)
cor.test(cars$speed, mod.lm$residuals)  # do correlation test 

#Assumption 6
dim(cars)

#Assumption 7 
var(cars$speed)


#Assumptions 9
library(car)
mod2 <- lm(mpg ~ ., data=mtcars)
vif(mod2)

library(corrplot)
corrplot(cor(mtcars[, -1]))

mod <- lm(mpg ~ cyl + gear, data=mtcars)
vif(mod)


#Assumption 10 
par(mfrow=c(2,2))
mod <- lm(dist ~ speed, data=cars)
plot(mod)


#Check Auto
par(mfrow=c(2,2))  # draw 4 plots in same window
mod <- lm(dist ~ speed, data=cars)
library(gvlma)
gvlma::gvlma(mod)

summary(gvlma::gvlma(mod))

plot(mod)

mod <- lm(dist ~ speed, data=cars[-c(23, 35, 49), ])
gvlma::gvlma(mod)
plot(mod)

influence.measures(mod)


================================================
FILE: Unsorted/miscscripts.R
================================================
.libPaths("E:/rWork/rLibrary")
.First = function() cat("\n Welcome Dhiraj to R ")
.Last = function() cat("\n Thanks for working in R")
install.packages("mosaic")
library(mosaic)
#require(mosaic)
Sys.setenv(https_proxy="")
Sys.setenv(http_proxy="")
Sys.getenv("http_proxy")
options(digits=3)


================================================
FILE: Unsorted/nd1.R
================================================
# Stats Questions
# Normal Distribution

# Q1: 95% of students at school weigh between 62 kg and 90 kg.
# Assuming this data is normally distributed,
# what are the mean and standard deviation?
#pnorm, qnorm
#(z1 = for p = .025); z2 for p2 =(.95+.025)) 
x1=62; y1=90
#Linear Equations
#z1 * sd + m = x1
#z2 * sd + m = x2
library(matlib) 
#https://cran.r-project.org/web/packages/matlib/vignettes/linear-equations.html
#
#Eg1a
(z1 = qnorm(p=.025)); (z2=qnorm(p=.975))
(A <- matrix(c(z1,1, z2, 1), ncol=2, byrow=T))
(b <- c(62,95))
cbind(A,b)
showEqn(A, b)
plotEqn(A,b)
plotEqn(A,b, xlim=c(-10, 10))
abline(v=8, h=78)
(x <- MASS::ginv(A) %*% b)
points(x[1], x[2], pch=15)   # approx Solution
solve(A, b)
#8.418522 78.500000
# Ans mean-76, mean-7
# Proba values from Table equivalent to this command Z -> P
round(pnorm(c(-3,0,3)),4)
#0.0013 0.5000 0.9987

# Probability to Z :P -> Z
round(qnorm(c(0.0013, 0.5000, 0.9987)),3)
#[1] -3.011  0.000  3.011

# 95% students betw 62 and 90 : area lies in center : 2.5% on each side




# Function to calculate p values (area)
funcZ <- function(x1,mean1=0, sd1=1) {
  (exp(-(1/2)* ((x1- mean1)/sd1)^2) / (sd1 * (2 * pi)^.5))
}
funcZ(x1=-3)
#Table of Z
(zvalues = seq(-3,2.99,by=.01))
(pvalues = pnorm(zvalues))
(mztable = matrix(round(pvalues,3), ncol=10, byrow=T)  )
rownames(mztable) = c(-3,3,by=.1)
dim(mztable)

round(pnorm(zvalues),4)

#Q2: 99.7% of the components have lengths between 1.176 cm and 1.224 cm.
#Assuming this data is normally distributed, what are the mean and standard deviation?
(z2a=qnorm(p=(1-.997)/2))
(z2b=qnorm(p=.997+(1-.997)/2))
x2a=1.176; x2b=1.224
(A <- matrix(c(z2a,1, z2b, 1), ncol=2, byrow=T))
(b <- c(x2a,x2b))
cbind(A,b)
showEqn(A, b)
plotEqn(A,b)
plotEqn(A,b, xlim=c(-3, 3))
#abline(v=8, h=78)
(x <- MASS::ginv(A) %*% b)
points(x[1], x[2], pch=15)   # approx Solution
solve(A, b)
#[1] 0.008086967 1.200000000

#Q3: 68% of the marks in a test are between 51 and 64
perc=.68
(z3a=qnorm(p=(1-perc)/2))
(z3b=qnorm(p=perc+(1-perc)/2))
x3a=51; x3b=64
(A <- matrix(c(z3a,1, z3b, 1), ncol=2, byrow=T))
(b <- c(x3a,x3b))
cbind(A,b)
showEqn(A, b)
plotEqn(A,b)
plotEqn(A,b, xlim=c(-5, 10), ylim=c(0,100))
#abline(v=8, h=78)
(x <- MASS::ginv(A) %*% b)
points(x[1], x[2], pch=15)   # approx Solution
solve(A, b)
#[1]  6.536225 57.500000

#Q5 : x1= 250 ; mean=255; sd=2.5 Tea packets
x1= 250 ; mean=255; sd=2.5
(pnorm(x1, mean, sd)) * 100
# Perc 2.275013 % are underweight ie less than 250

#Q6 : x1 = 50 ; mean= 42 ; sd=8 : Scores what percentage of students pass the test
x1 = 50 ; mean= 42 ; sd=8
1-(p1=(pnorm(q=x1, mean, sd)) )
(p1=(pnorm(q=50, mean, sd,lower.tail = F)) ) * 100
# 10% of students pass the exam
# # alternative
((50-42)/8)
#1sd -> 68%; #bal - 32% ; #one side= 32%/2= 16%
#

#Q6: mean=3.1 ; sd=0.005 ; perc=99.7 : Machine Parts: Limits ?
mean=3.1 ; sd=0.005 ; perc=.997
# q values ?
(z1 = qnorm(p=(1-perc)/2, mean, sd))
(z2 = qnorm(p=perc+ (1-perc)/2, mean, sd))
#limits are 3.08, 3.11

#Q7: Temp : x1=39; x2=42; mean=36; sd= 3 ; find perc between
x1=39; x2=42; mean=36; sd= 3
perc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd))  # 14%
(days = perc * 30) # jun is of 30 days- 4

#Q8 : Height: x1=1.4; x2=1.6; mean=1.7; sd= 0.2 ; n= 400
x1=1.4; x2=1.6; mean=1.7; sd= 0.2 ; n = 400
(perc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd)))  # 24%
(nos = perc * n) # jun is of 30 days- 4
#96

#Q9 : Rainfall : x1=8.5 ; mean=10; sd=1.5; n=31
x1=8.5 ; mean=10; sd=1.5;  n =31
(perc = (pnorm(q=x1,mean,sd, lower.tail = T)))
(days = round(perc * n))
# 5 days

#Q10 :  : x1=22 ; x2=57 ; mean= 43; sd=14; n=5000 ; Population - Age
x1=22 ; x2=57 ; mean=43; sd=14;  n =5000
(perc = abs(pnorm(q=x1,mean,sd) - pnorm(q=x2,mean,sd)))  # .77
(nos = perc * n) # 3872



#Quiz2
#Q1: find P(0 < Z ≤ 1)
pnorm(q=0)- pnorm(q=1)

#Q2 : P(Z ≤ 2)
pnorm(q=2,lower.tail = T)

#Q3: P(-1.65 < Z ≤ 1.93)
pnorm(q=-1.65)- pnorm(q=1.93)

#Q4 : P(0.85 < Z ≤ 2.23)
pnorm(q=0.85)- pnorm(q=2.23)

#Q5: P(Z > 1.75)
1- pnorm(q=1.75, lower.tail = T)
pnorm(q=1.75, lower.tail = F)

#Q6: P(Z ≤ -0.69)
pnorm(q=-0.69, lower.tail = T)


#Q7 : P(-1.27 < Z ≤ 0)
(perc = pnorm(q=-1.27) - pnorm(q=0))

#Q8: P(Z  > -2.64)
1- pnorm(q=-2.64, lower.tail = T)
pnorm(q=-2.64, lower.tail = F)

#Q9: P(Z  ≤ 0.96)
pnorm(q=0.96, lower.tail = T)

#Q10 : P(-2.31 < Z  ≤ 0.82)
(perc = pnorm(q=-2.31) - pnorm(q=0.82))


#Quiz 3

#Q1 : mean=200 ; sd=40; x1>=250 ; P(x>250)
mean=200 ; sd=40; x1=250
1-pnorm(q=x1, mean, sd)
pnorm(q=x1, mean, sd, lower.tail = F)


================================================
FILE: Unsorted/normal.R
================================================
#normal Distribution  
curve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = "z", ylab="f(z)")

#Change this value if you’d like to do a graph for another value of z, where3 < z < 3.
z = -1.24
x = c(-3, seq(-3, z, by=.001), z)
y = c(0, dnorm(seq(-3, z, by=.001)), 0)
polygon(x, y, col="red")
pnorm(1.24) - pnorm(0)

curve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = "z", ylab="f(z)")
plot.new()
#• Change these values if you’d like to do a graph for other values of zleft or
#zright, where both values are between 
 3 and 3.
zleft = 0
zright = 1.24
x = c(zleft, seq(zleft, zright, by=.001), zright)
y = c(0, dnorm(seq(zleft, zright, by=.001)), 0)
polygon(x, y, col="red")
#--------------

#Find P Z( ) > 
1.24 #• Graph and shade for the above problem.
1 - pnorm(-1.24)
plot.new()
curve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = "z", ylab="f(z)")
z = -1.24
x = c(z, seq(z, 3, by=.001), 3)
y = c(0, dnorm(seq(z, 3, by=.001)), 0)
polygon(x, y, col="red")

qnorm(0.85) 
plot.new()
curve(dnorm, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = "z", ylab="f(z)")
#• Change this value if you’d like to do another quantile.

prob = 0.85

x = c(-3, seq(-3, qnorm(prob), by=.001), qnorm(prob))
y = c(0, dnorm(seq(-3, qnorm(prob), by=.001)), 0)
polygon(x, y, col="red")
#• 85% of the total area under the curve is shaded red. (This includes the tiny
                                                        piece to the left of z = 
 3; our figure cuts it off.) 
pnorm(95, mean=100, sd=15) - pnorm(80, mean=100, sd=15)
1 - pnorm(125, mean=100, sd=15)

qnorm(0.75, mean=100, sd=15) 


================================================
FILE: Unsorted/paneldata1.R
================================================
#Panel Data

set.seed(25)

# number of obs for each case
(nT <- 5)

# set number of groups
(N <- 12)

# set possible cases
(possible.cases <- LETTERS[1:N])

# set parameters
(my.alphas <- seq(-10,10,length.out = N))
(my.beta <- 1.5)

# set indep var (x) and dates
rep(nT,N)
sapply(5,rnorm)

(indep.var <- sapply(rep(nT,N), rnorm))
(my.dates <- Sys.Date() + 1:nT)

# create response matrix (y)
(response.matrix <- matrix(rep(my.alphas,nT), nrow = nT, 
  byrow = TRUE) + indep.var*my.beta + sapply(rep(nT,N),rnorm, sd = 0.25) )

# set df
(sim.df <- data.frame(G = as.character(sapply(possible.cases, 
        rep, times=nT )), dates = rep(my.dates, times=N),
        y = as.numeric(response.matrix),   x = as.numeric(indep.var), 
        stringsAsFactors = FALSE))

# print result
print(str(sim.df))

#Plot
library(ggplot2)
#one Plot
(dataA = sim.df[sim.df$G=='A',])
plot(x ~ y, dataA)

#Multiple Plots
p <- ggplot(sim.df, aes(x=x, y=y))
p <- p + geom_point()
p <- p + facet_wrap(~G)
print(p)


library(plm)

# estimate panel data model with fixed effects
my.pdm <- plm(data = sim.df, 
              formula = y ~ x, 
              model = 'within',
              index = c('G','dates'))

# print result
print(summary(my.pdm,))
print(fixef(my.pdm))



#Case Study
library(plm)

# data from Grunfeld
data("Grunfeld")

# print it
print(str(Grunfeld))
head(Grunfeld)



my.fct <- function(df) {
  # Estimates a linear model from Grunfeld data
  # Args: #   df - dataframe from Grunfeld
  #Returns: #   lm object
  my.model <- lm(data = df,  formula = inv ~  value + capital)
  return(my.model)
}
summary(Grunfeld)
head(Grunfeld)
Grunfeld$firm
# estimate model for each firm
my.l <- by(Grunfeld, INDICES = Grunfeld$firm, FUN = my.fct)

# print result
my.coefs <- sapply(my.l, coef)
print(my.coefs)

# test if all coef are the same across firms
my.pooltest <- pooltest(inv~value+capital, 
                        data = Grunfeld, 
                        model = "pooling")

# print result
print(my.pooltest)


# set options for Hausman test
my.formula <- inv ~ value + capital
my.index <- c('firm','year')

# do Hausman test
my.hausman.test <- phtest(x = my.formula, 
                          data = Grunfeld,
                          model = c('within', 'random'),
                          index = my.index)

# print result
print(my.hausman.test)


# set panel data model with random effects
my.model <- 'random'
my.formula <- inv ~ value + capital
my.index <- c('firm','year')

# estimate it
my.pdm.random <- plm(data = Grunfeld, 
                     formula = my.formula, 
                     model = my.model,
                     index = my.index)

# print result
print(summary(my.pdm.random))


library(systemfit)

# set pdataframe
(p.Grunfeld <- pdata.frame(Grunfeld, c( "firm", "year" )))

# estimate sur
my.SUR <- systemfit(formula = inv ~value + capital,
            method =  "SUR",  data = p.Grunfeld)
print(my.SUR)


================================================
FILE: Unsorted/plot1.R
================================================


# collect the values together, and assign them to a variable called y
c(6,10,10,17,7,12,7,11,6,16,3,8,13,8,7,12,6,5,10,9) -> y
hist( y ) -> h # do a histogram of y and assign its info to h
h$counts <- cumsum(h$counts) # replace the cell freq.s by cumulative freq.s
plot( h ) # plot a cumulative histogram of y


plot(sort(y), 1:length(y)) # plot rank on value of y

plot(y, rank(y))

n <- length(y)
p <- (.5:n)/n
plot(sort(y),p)
points(sort(y),1-p)
abline(v=median(y))


================================================
FILE: Unsorted/practise-dec17c.R
================================================
# Practise
#create random 10 dates between 01 and 30
#month - jan 
#with random years 1990 and 1994
# save in date of birth column
set.seed(1234)
(day = round(runif(10, 1, 30),0)) 
(month = 'Jan')
(year = sample(x=1990:2000,10,replace=T))  
(dob1= paste(year,month,day,sep='-'))
(dob = as.Date(dob1,'%Y-%b-%d'))
class(dob)  
#vector of student1 to student2 and give sname
(sname = paste('student',1:10, sep=''))
(marks = ceiling(runif(10,50,100)))
#put student1 to student5 to Dept1, rest in Dept2
(dept = rep(c('dept1','dept2'),each=5))
#Every alternate student gender as M or F
(gender = rep(c('M','F'),5))
(df1=data.frame(sname, dept, gender, dob,marks))

#calculate age column as on date; round of the age
(df1$agedays =  Sys.Date() - df1$dob)
(df1$age = round(as.numeric(df1$agedays)/365,0))
#create a column specify Grades A to C as follows
#A  > 80, B : 60-79 and C < 60

df1$grade = ifelse(df1$marks >= 80, 'Pass', 'Fail')
df1
#aggregate data 
aggregate(df1$age, by=list(dept, gender), mean)
#draw a pie graph for grades
pie(table(df1$grade))
#plot age vs marks 
plot(x=age , y=marks, data=df1)
#draw fit line for the plot above
abline(lm(marks ~ age, data=df1))
df1

#Array -------
# Two states hold elections gujrat, hp
# They have 3 parties - bjp, congress, aap
# Each state has 5 districts
# put values between 2 to 5 to each district of state for each party
# on random basis. use set.seed(1234)
(x=round(runif(2*3*5,2,5)))
(elections = array(x, dim=c(5,3,2)))
(state = c('gujrat','hp'))
(party = c('bjp','congress', 'aap'))
(district = paste('D',1:5,sep=''))
(dimnames(elections)= list(district, party, state))
elections

#summarise the elections
#statewise
apply(elections,3, sum)
#partywise
apply(elections,2, sum)
#state and partywise
(t1 = apply(elections,c(2,3), sum))
addmargins(t1,c(1,2),FUN=list(mean, list(sd,sum)))
#prop in each state
prop.table(t1,2)


# Built in Data Set
data("iris")
str(iris)           
dim(iris)
df2 = iris
#introduce NA values at (R,C) - (1,2), (2,4),(2,3),(1,5)
#select 4 random locations between 50 & 100 in Coln 2 using
#sample command and put NA missing values
ml = sample(50:100,4,replace=F) 
df2[ml,2] = NA
#list out the column 1 & 3 of complete cases
colSums(is.na(df2[1:4]))

sum(complete.cases(df2))
sum(!complete.cases(df2))

df2[complete.cases(df2),][c(1:5),c(1,3)]
#replace missing values in column 2 with mean of column1
df2[is.na(df2[2]),2]
mean(df2$Sepal.Length)
df2[is.na(df2[2]),2] = mean(df2$Sepal.Length)
#check if values have been changed
colSums(is.na(df2[1:4]))


#use data set mtcars 
#Find properties of dataset : structure, dimensions
#
data(mtcars)
str(mtcars)
dim(mtcars)
#plot mpg vs wt 
plot(mpg ~ wt, data=mtcars)
#draw abline for above graph
abline(lm(mpg ~ wt, data=mtcars))
#draw histogram of mpg with default settings
hist(mtcars$mpg)
#list out variables names of mtcars
names(mtcars)
#copy mtcars into another data frame df3 and use this for further calc
df3=mtcars
#convert am to factors
df3$am =factor(df3$am)
#aggregate DF by am with mean values of cols 1 to 5
aggregate(df3[1:5], by=list(df3$am), mean)
#list Df rows 25 to 30, and colns 5:8
df3[25:30,5:8]
#list DF with am=0 & cyl=4
df3[df3$am==0 & df3$cyl==4,1:4]
#List the DF in order cyl (increasing) & mpg(decreasing) - only 1st 2 column
df3[order(df3$cyl, -df3$mpg),1:2]


#Eg1: Freq Distribution #faithful - durations
duration = faithful$eruptions
range(duration)
#1.6 5.1
breaks = seq(1.5, 5.5, by=0.5)
breaks
duration.cut = cut(duration, breaks, right=FALSE)
duration.freq = table(duration.cut)
cbind(duration.freq)
#duration.freq  [1.5,2)            51  [2,2.5)            41
hist(duration,labels=T)

#Descriptive 
# Quantile Values fo Temp variable of airqualty Data Set
# 
quantile(airquality$Temp)


================================================
FILE: Unsorted/practise.R
================================================
student = read.csv("student.csv",stringsAsFactors = F)
student
library("dplyr")
mutate(student, avgmarks = JAVA/2)
str(student)
my_data <- as_data_frame(student)
str(my_data)
student = read.csv("student.csv", header=T, na.strings=c("","NA"), stringsAsFactors = F)
student$ser = NULL
head(student)
row.names(student) = student$rollno
#------------
x <- c(a = 1, b = 2,c=3,d=4)
class(x)
str(x)
x
y=c(1,2,3)
seq(5,11,along.with = y)

# first code --------------------------------------------------------------

# second code ------------------------------------------

# abc ---------------------------------------------------------------------



# 19 Nov 16 Split and combine

cyl=c(4,4,8,6,8,4,6,4)
mpg=c(36,38,21,28,19,35,25,32)
hp=c(100,93,340,258,365,97,240,103)
brand=c("Ford","Honda","Nissan","Nissan","Honda","Honda","Ford","Ford")

myCars = data.frame(cyl,mpg,hp,brand)
myCars$cyl = factor(myCars$cyl)
# str(myCars)
myCars
# split on factors
splitCars = split(myCars, myCars$cyl)
splitCars

#Linear Problems
res <- c(2,2,3,3)
mat <- matrix (c(14, 8, 16, 6, 5, 3, 7, 6, 5, 4, 3, 1, 2, 4, 7, 9), ncol=4)
solve (mat)

# 20 Nov 16
our.data = scan()


================================================
FILE: Unsorted/rattle1.R
================================================
# rattle
library(rattle)
#data(weather) 
#http://rattle.togaware.com/weatherAUS.csv
#
rattle()
#http://archive.ics.uci.edu/ml/datasets/Heart+Disease
#https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/heart_for_rattle.txt
#above data link
heart = read.table('./data/heart_for_rattle.txt', header=T)
summary(heart)


fname <- "file:///E:/rWork/rDataSets/nyc.csv" 
crs$dataset <- read.csv(fname,
                        na.strings=c(".", "NA", "", "?"),
                        strip.white=TRUE, encoding="UTF-8")
str(crs$dataset)
crs$dataset$X
crs$y = data.frame(a=1:10, b=10:19)
crs$y
rattle(system.file("csv", "weather.csv", package = "rattle"))
eapply(crs,FUN=summary)


# Using Rattle
#  Generate the table data for plotting.
ds <- table(crs$dataset[crs$sample,]$sex, crs$dataset[crs$sample,]$disease)

# Sort the entries.
ord <- order(apply(ds, 1, sum), decreasing=TRUE)

mosaicplot(ds[ord,], main="Mosaic of sex (sample)
by disease", sub="Rattle 2017-Oct-16 06:51:21 dupad", color=colorspace::rainbow_hcl(3)[-1], cex=0.7, xlab="sex", ylab="disease")


================================================
FILE: Unsorted/rattle2.R
================================================

rattle(system.file("csv", "weather.csv", package = "rattle"))


================================================
FILE: Unsorted/rcdr1.R
================================================
# using R Commander
library(Rcmdr)

# Entering Data
attach(mtcars)
load('./data/du3.Rdata')
with(df2, Hist(excel, groups=course, 
      scale="frequency", breaks="Sturges", col="darkgray"))

with(df2, Hist(age, groups=course, 
               scale="frequency", breaks="Sturges", col="darkgray"))
xtabs(~gender+hostel+city, data=df2)

t.test(student$age, alternative='less', mu=30, 
      conf.level=.95)

summary(women[,'height'])
boxplot(age ~ gender, data=student)
Rcmdr> Boxplot(age ~ gender, data = student)


================================================
FILE: Unsorted/rjava.R
================================================
# Java in R
# 

Sys.getenv("PATH")
Sys.getenv("JAVA_HOME")
#JAVA_HOME path to the jre folder inside "jdk1.8.0_121"
#

install.packages('rJava')
library(rJava)

#https://support.microsoft.com/en-us/help/3103813/qa-when-i-try-to-load-the-rjava-package-using-the-library-command--i-g
# Linux
# https://stackoverflow.com/questions/3311940/r-rjava-package-install-failing
# http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2133155.html
# 
# 
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144\\bin')

remove.packages('rJava')
?remove
.rs.restartR()

#https://stackoverflow.com/questions/43788788/another-rjava-library-install-error-rjava-rdb-is-corrupt
#

================================================
FILE: Unsorted/rle1.R
================================================
?rle
rle(x=1:10)
c( 22, 0, 0, 20, 0, 50, 23, 1, 2, 21, 0) -> y
y
rle(y)
dist=rle(sort(y))
m=dist$lengths==max(dist$lengths) # note the most common value
m
dist$values[which(m)] # give the modal value in variable y 
dist$lengths[which(m)] # give the frequency of that value

x <- rev(rep(6:10, 1:5))
x
rle(x)


================================================
FILE: Unsorted/sample1.R
================================================
#Sampling
x = 1:3
sample(x)

sample(1:3, size=3, replace=FALSE)  # same as previous line
sample(1:3, size=3, replace=TRUE)  # no may get repeated

# 4 Val
sample(c(1,2,3,4,5,6), size=4, replace=TRUE)

# 10 Values  with ratio of probabilities
sample(1:2, size=10, prob=c(1,3), replace=TRUE)


sample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10))
table(sample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10)))

barplot(table(sample(1:3, size=1000, replace=TRUE, prob=c(.30,.60,.10))))



================================================
FILE: Unsorted/sample2.R
================================================
#Sample
?sample

#Simple
x = c('A', 'B', 'C', 'D', 'E')
sample(x, 2)
sample(x, 2)
sample(x, 4, replace=TRUE) # repeats samples

# with Probability

probability = c(0.1, 0.15, 0.2, 0.25, 0.3  )
sum(probability)
#They need not sum to one, but they should be nonnegative and not all zero.
#probability of choosing the next item is proportional to the probabilities amongst the remaining items. The number of nonzero weights must be at least size in this case.

sample(x, 3, prob=probability)
x

# Bernoillis Trial
sample(c(0,1), 100, replace = TRUE)

# Data as Data Frame
mtcars
(mtcars1 = mtcars[c(1:4)]) # reduce the no of cols
str(mtcars1)
(mtcars2 = sample(mtcars1, 1, replace=FALSE)) # Select either colns
(mtcars3 = mtcars1[sample(1:nrow(mtcars1), 3, replace=FALSE),])  #select any 3 rows

# 
(m = matrix(1:24, nrow=6))
sample(m, 3)  # any value from the matrix cell not row
nrow(m)
m[sample(1:nrow(m), 3),]   # select any row of matrix

# Give probability to each row
pm = c(0.1, 0.15, 0.2, 0.25, 0.3, 0.2  )
sum(pm)  # incorrect but still worked
m[sample(1:nrow(m), 3, prob= pm),]
m



#Multiple Samples stored in a file
idorg = c(1,2,3,4,5)
x = c(14,20,21,16,17)
y = c(31,21,20,50,13)
dataset = cbind (idorg,x,y)
dataset

for (i in 1:4) {
  attempt = dataset[sample(1:nrow(dataset), 3, replace=FALSE),]
  print(attempt)
  }

# print to a file
for (i in 1:4)  {
  attempt <- dataset[sample(1:nrow(dataset), 3, replace=FALSE),]
  write.table(attempt, sprintf( "./data/dataset[%d].csv", i ), sep='\t')
}



# List
g ="My First List"
h = c(25, 26,18,39)
j = matrix(1:10,nrow=2)
k = c('one','two','three')
l = data.frame(formno = c(17010, 17045, 17012), 
               name=c('Achal', 'Apoorva', 'Goldie'), 
               gender=c('M','F','M'), stringsAsFactors = F)
g;h;j;k;l
mylist = list(title=g, ages=h, j, h,students=l)
mylist   # full list
sample(mylist,2)  # anytwo objects from the list
sample(mylist$students ,2 )  # any two cols of DF
mylist$students[sample(1:nrow(mylist$students),2), 1] # from col1
mylist$students[sample(1:nrow(mylist$students),2), 2] # from col2
mylist$students[sample(1:nrow(mylist$students),2), 3] # from col3

sample(mylist$ages ,2 )  # any two values of numeric vector age




# Sample based on conditional value
x = 1:10
x
sample(x[x >  8]) # length 2
sample(x[x >  9]) # oops -- length 10!  # incorrect value
sample(x[x > 10]) # error!

try(sample(x[x > 10])) #no output

?try
## This is safer:
resample <- function(x) x[sample.int(length(x))]
resample(x[x >  8]) # length 2
resample(x[x >  9]) # length 1
resample(x[x > 10]) # length 0

?sample.int
#sample.int is a bare interface in which both n and size must be supplied as integers.


sample.int(1e5, 12, replace = TRUE)
sample.int(1e5, 12) # not that there is much chance of duplicates

# Extras

library(data.table)
set.seed(10)

mtcars <- data.table(mtcars)
mtcars[sample(.N, 6)]


library(dplyr)
df= mtcars
sample_n(df, 10)



#install.packages('kimisc')

library(kimisc)
example(sample.rows)

set.seed(42)

sample.rows(data.frame(a=c(1,2,3), b=c(4,5,6),
                row.names=c('a', 'b', 'c')), 10, replace=TRUE)
sample.rows(mtcars,5)


# 
library("tibble")    
a = mtcars[sample(1:nrow(mtcars), 3),]
a


# Library kimisc
library(kimisc)
example(sample.rows)
set.seed(12)
df3= data.frame(a=c(1,2,3), b=c(4,5,6), 
      row.names = c('a','b','c'))
sample.rows(df3, 10, replace=TRUE)  # show how many times repeated
  

================================================
FILE: Unsorted/scripting1.R
================================================
# Looping
#ex1

i <- 0
repeat{
  i <- i + 2
  print(i)
  if(i == 10) {
    break
  }
}

#ex2
msg <- c("Hello")
i <- 1
repeat {
  i <- i + 1
  print(c(msg,i))
  if(i > 5) {
    break
  }
}

# Ex3
i <- 1
while(i < 8) {
  print(i)
  i <- i + 2
}

# Ex4
msg <- c("Hello")
i <- 1

while (i < 7) {
  print(msg)
  i = i + 1
}

#ex5
x <- c(7, 4, 3, 8, 9, 25)
for(i in 1:4) {
  print(x[i])
}

#ex6
y <- c("q", "w", "e", "r", "z", "c")
for(letter in y) {
  print(letter)
}

#ex7
i <- 1
while(i < 5) {
  i <- i + 1
  if (i == 3) break
  print(i)
}

#ex8
for (a in 1:3)
{
  for (b in 1:3)
  {
    print(c(a, b))
    if (b == 2) break
  }
}

#ex9
i <- 1
while(i < 5) {
  i <- i + 1
  if (i == 3) {
    next
  }
  print(i)
}

#ex10
i <- 1:5
for (val in i) {
  if (val == 3){
    next
  }
  print(val)
}

================================================
FILE: Unsorted/skewness1.R
================================================
# Skewness
#measure of symmetry. As a rule, negative skewness indicates 
#that the mean of the data values is less than the median, and the data distribution is left-skewed. Positive skewness would indicate that the mean of the data values is larger than the median, 
#and the data distribution is right-skewed.



set.seed(1234)
(marks = ceiling(rnorm(100,50,20)))
x=marks
library(e1071)                    # load e1071 
hist(x)
plot(density(x))
e1071::skewness(x)  #0.5884632  +vely 

# Mean Median Mode
(m1 = mean(x))
(m2 = median(x))
(m3 = modeest::mlv(x,  method = "mfv"))  # this is not atomic value
#str(m3)
centraltendency= c(MEAN= m1,MEDIAN= m2,MODE= m3$M)
sort(centraltendency)

# Other Packages
library(moments)
moments::skewness(x) #0.5974018



library(e1071)                    # load e1071 
duration = faithful$eruptions     # eruption durations 
skewness(duration)                # apply the skewness function 

library(fBasics) # skewness part of timeDate package
timeDate::skewness(x)
?skewness # see where all help is available
timeDate::skewness(x, na.rm = FALSE, method = c("moment", "fisher"))

# Psych Package
library(psych)
psych::describe(x)
psych::describeBy(mtcars,group= mtcars$am)


# Skewness of multiple Columns
?colSkewness
timeSeries::colSkewness(mtcars, na.rm = FALSE)



# Generate Various Directions
N <- 10000
x <- rnbinom(N, 10, .5)
hist(x, 
     xlim=c(min(x),max(x)), probability=T, nclass=max(x)-min(x)+1, 
     col='lightblue', xlab=' ', ylab=' ', axes=F,
     main='Positive Skewed')
lines(density(x,bw=1), col='red', lwd=3)
skewness(x)


ndata = rbeta(10000,5,2)
pdata =  rbeta(10000,2,5)
data = rbeta(10000,5,5)
head(ndata); head(data) ; head(ndata)
hist(pdata)
hist(data)
hist(ndata)

skewness(ndata)
skewness(data)
skewness(pdata)

# all 3 together
timeSeries::colSkewness(cbind(ndata,data,pdata), na.rm = FALSE)

library(asbio)
asbio::skew(x)

exp<-rexp(10000)
exp
skew(exp)
kurt(exp)


================================================
FILE: Unsorted/skewness2.R
================================================
#skewness2


library(fGarch)
# dsnorm(x, mean = 0, sd = 1, xi = 1.5, log = FALSE)
# psnorm(q, mean = 0, sd = 1, xi = 1.5)
# qsnorm(p, mean = 0, sd = 1, xi = 1.5)
# rsnorm(n, mean = 0, sd = 1, xi = 1.5)

par(mfrow = c(2, 2))
set.seed(1953)
r = rsnorm(n = 1000)
plot(r, type = "l", main = "snorm", col = "steelblue")

# Plot empirical density and compare with true density:
hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue")
box()
x = seq(min(r), max(r), length = 201)
lines(x, dsnorm(x), lwd = 2)

# Plot df and compare with true df:
plot(sort(r), (1:1000/1000), main = "Probability", col = "steelblue",
     ylab = "Probability")
lines(x, psnorm(x), lwd = 2)

# Compute quantiles:
round(qsnorm(psnorm(q = seq(-1, 5, by = 1))), digits = 6)



================================================
FILE: Unsorted/skewness3.R
================================================
#Skewness 3
library(e1071)
data(airquality)
ozone <- airquality$Ozone
ozone <- ozone[!is.na(ozone)]
hist(ozone, col = "tomato")
skewness(ozone)


================================================
FILE: Unsorted/smpdist1.R
================================================
# Sampling Distribution ####
plot.new()

# Generate Random Nos from ND ####
rnorm(100)
rnorm(100)*10
round(rnorm(100)*10)
x=round(rnorm(100)*10)
meanx = mean(x)
sdx = sd(x)
# plot ####
plot(density(x))

# dnorm() returns the probability density function or pdf.####
dnorm(1)
dnorm(-2)
dnorm(3)
#height of the normal curve at some value along the x-axis. ####
dnorm(1,mean=meanx,sd=sdx)
dnorm(10,mean=meanx,sd=sdx)
dnorm(10,mean=meanx,sd=sdx)
abline(h=dnorm(-10,mean=meanx,sd=sdx))
abline(h=dnorm(20,mean=meanx,sd=sdx))

# Cumulative Freq / Area under the curve
# default - lower tail. area from left side
pnorm(0)
pnorm(0,mean=meanx, sd=sdx)
pnorm(10,mean=meanx, sd=sdx)
pnorm(100,mean=meanx, sd=sdx)
pnorm(30,mean=meanx, sd=sdx)

abline(v=30,col=4,lwd=2)
abline(v=-25,col=4,lwd=2)
abline(v=meanx - 1 * sdx,col=4,lwd=3)
# Upper Tail
pnorm(15,mean=meanx, sd=sdx,lower.tail = F)
abline(v=15,col=6,lwd=2)  # area to the right of 15

plot.new()
plot(density(x))

#Quantiles
qnorm(.95)
qnorm(.10) # 10% values
abline(v=qnorm(.95,mean=meanx,sd=sdx),col="red",lwd=4)
# 95% values upto this point from left
# 60% values from right
qnorm(.60,mean=meanx,sd=sdx,lower.tail = F)
abline(v=qnorm(.60,mean=meanx,sd=sdx,lower.tail = F),col="red",lwd=5)
qnorm(c(.025,.975),mean=meanx,sd=sdx)
abline(v=qnorm(c(.025,.975),mean=meanx,sd=sdx),col="blue",lwd=3)


================================================
FILE: Unsorted/summary1.R
================================================
# Summary ----------


set.seed(1234)
x = ceiling(rnorm(100, 50, 10))  # 100 random nos mean-50, Sd-10
x

summary(x)

# Five Number Summary ---------
#lies in the lack of universal agreement on how the 1st and 3rd quartiles should be calculated.
#https://www.r-bloggers.com/exploratory-data-analysis-the-5-number-summary-two-different-methods-in-r/
  
fivenum(x)

x = seq(1, 9, by = 2)
x # Odd Nos
fivenum(x)
summary(x)  # same output

y = seq(1, 11, by = 2)
y  # Even Nos
fivenum(y)  # no control for digits
summary(y)  #Different for 1Q, 3Q
summary(x, digit=2)
y
(Q1 = y[1] + (length(y) - 1 ) * .25)
# This position is not a integer, so cannot extract it

#*fivenum() does not have an argument for controlling the number of decimal places in its output, while summary() has the “digits” option for doing so.  You may need to invoke this option in summary() to get more decimal places to when comparing its output with fivenum()’s output.

?fivenum
fivenum(c(rnorm(100), -1:1/0))


================================================
FILE: Unsorted/sumstats1.R
================================================
# Summary Stats
#http://sphweb.bumc.bu.edu/otlt/mph-modules/bs/r/r2_summarystats-graphs/R2_SummaryStats-Graphs_print.html

airquality[1:5,]
mean(airquality$Temp)
median(airquality$Temp)
var(airquality$Wind)
attach(airquality)
var(Wind)

summary(airquality)


par(mar=c(3,3,2,2), mfrow=c(1,1))
#Fit normal curve over data (use dnorm & curve instead of curve)
hist(airquality$Temp, prob=T, main='Temperature')
curve(dnorm(x, mean= mean(airquality$Temp), sd = sqrt(var(airquality$Temp))), col='darkblue', lwd=2, add=TRUE)
?dnorm


qqnorm(airquality$Temp)
qqline(airquality$Temp)
boxplot(airquality[, 1:4])



x <- rnorm(1000)
hist(x, freq = FALSE, col = "grey")
curve(dnorm, col = 2, add = TRUE)

library(lessR)
# generate 100 random normal data values
y <- rnorm(100)
# normal curve and general density curves superimposed over histogram
# all defaults
hist(y)
color.density(y)
Histogram(y)



library(e1071) # For the "skewness" and "kurtosis" functions
n <- 1000
x <- rnorm(n)
qqnorm(x, main=paste("kurtosis =", round(kurtosis(x), digits=2),
                     "(gaussian)"))
qqline(x, col="red")
op <- par(fig=c(.02,.5,.5,.98), new=TRUE)
hist(x, probability=T,
     col="light blue", xlab="", ylab="", main="", axes=F)
lines(density(x), col="red", lwd=2)
box()
par(op)


================================================
FILE: Unsorted/ttest1.R
================================================
# Distributions

# t-test
?t.test
attach(CO2)
t.test(CO2$uptake, mu=30)
tstats = t.test(uptake, mu=30)
tstats$statistic

CO2$uptake
(m=mean(CO2$uptake))
(s= sd(CO2$uptake))
(N = length(CO2$uptake))
mu=30
(tcalc = (m - mu)/(s * sqrt(N)))

?qt
qt(p=.975, df=N-1)


a = c(65, 78, 88, 55, 48, 95, 66, 57, 79, 81)

t.test (a, mu=75)


================================================
FILE: Unsorted/vaibhavi.R
================================================
# vaibhav
# 
library(ggplot2)
uniDataCateg<- read.csv("bic.csv",header = TRUE)
ggplot(uniDataCateg, aes(x = values, y = perc)) + geom_bar(stat = "identity")+ facet_wrap(~ variable, scales = "free")
str(uniDataCateg)
barplot(uniDataCateg$perc)



dat <- read.table(text = "    ONE TWO THREE
1   23  234 324
                  2   34  534 12
                  3   56  324 124
                  4   34  234 124
                  5   123 534 654",sep = "",header = TRUE)
dat
library(reshape2)
#Add an id variable for the filled regions
datm <- melt(cbind(dat, ind = rownames(dat)), id.vars = c('ind'))
datm
library(scales)
ggplot(datm,aes(x = variable, y = value,fill = ind)) + 
  geom_bar(position = "fill",stat = "identity") + 
  scale_y_continuous(labels = percent_format())



your_matrix<-( 
  rbind(
    c(10,20), 
    c(20,30), 
    c(40,50)
    )
)
your_matrix
barplot(prop.table(your_matrix, 2) )

str(uniDataCateg)
df1 = as.matrix(uniDataCateg[,c('Perc_churn','diff')])
head(df1)
barplot(prop.table(df1,1))


library(cowplot)
# Bar plot
bp <- ggplot(diamonds, aes(clarity, fill = cut)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=70, vjust=0.5))
bp
diamonds
bp2 = ggplot(diamonds, aes(clarity, fill = cut)) + geom_bar()
bp2

str(Students)

library(Lock5Data)
data(SleepStudy)
data(student)
heart = read.table("heart-rate", header = TRUE)
students = read.csv("students.csv")


https://rpubs.com/escott8908/RGC_Ch3_Gar_Graphs
library(gcookbook)
library(ggplot2)
library(plyr) #Is this automatically loaded with ggplot2?
library(dplyr)

cabbage_exp
ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) + 
  geom_bar(stat='identity')


================================================
FILE: cacert.pem
================================================
##
## Bundle of CA Root Certificates
##
## Certificate data from Mozilla as of: Tue Oct 26 03:12:05 2021 GMT
##
## This is a bundle of X.509 certificates of public Certificate Authorities
## (CA). These were automatically extracted from Mozilla's root certificates
## file (certdata.txt).  This file can be found in the mozilla source tree:
## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt
##
## It contains the certificates in PEM format and therefore
## can be directly used with curl / libcurl / php_curl, or with
## an Apache+mod_ssl webserver for SSL client authentication.
## Just configure this file as the SSLCACertificateFile.
##
## Conversion done with mk-ca-bundle.pl version 1.28.
## SHA256: bb36818a81feaa4cca61101e6d6276cd09e972efcb08112dfed846918ca41d7f
##


GlobalSign Root CA
==================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign Root CA - R2
=======================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

Entrust.net Premium 2048 Secure Server CA
=========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Baltimore CyberTrust Root
=========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Entrust Root Certification Authority
====================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Comodo AAA Services root
========================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

QuoVadis Root CA 2
==================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

QuoVadis Root CA 3
==================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Security Communication Root CA
==============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

XRamp Global CA Root
====================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

Go Daddy Class 2 CA
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Starfield Class 2 CA
====================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Assured ID Root CA
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Global Root CA
=======================
-----BEGIN CERTIFICATE-----
MIIDrzCCApegAwIBAgIQCDvgVpBCRrGhdWrJWZHHSjANBgkqhkiG9w0BAQUFADBhMQswCQYDVQQG
EwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSAw
HgYDVQQDExdEaWdpQ2VydCBHbG9iYWwgUm9vdCBDQTAeFw0wNjExMTAwMDAwMDBaFw0zMTExMTAw
MDAwMDBaMGExCzAJBgNVBAYTAlVTMRUwEwYDVQQKEwxEaWdpQ2VydCBJbmMxGTAXBgNVBAsTEHd3
dy5kaWdpY2VydC5jb20xIDAeBgNVBAMTF0RpZ2lDZXJ0IEdsb2JhbCBSb290IENBMIIBIjANBgkq
hkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA4jvhEXLeqKTTo1eqUKKPC3eQyaKl7hLOllsBCSDMAZOn
TjC3U/dDxGkAV53ijSLdhwZAAIEJzs4bg7/fzTtxRuLWZscFs3YnFo97nh6Vfe63SKMI2tavegw5
BmV/Sl0fvBf4q77uKNd0f3p4mVmFaG5cIzJLv07A6Fpt43C/dxC//AH2hdmoRBBYMql1GNXRor5H
4idq9Joz+EkIYIvUX7Q6hL+hqkpMfT7PT19sdl6gSzeRntwi5m3OFBqOasv+zbMUZBfHWymeMr/y
7vrTC0LUq7dBMtoM1O/4gdW7jVg/tRvoSSiicNoxBN33shbyTApOB6jtSj1etX+jkMOvJwIDAQAB
o2MwYTAOBgNVHQ8BAf8EBAMCAYYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQUA95QNVbRTLtm
8KPiGxvDl7I90VUwHwYDVR0jBBgwFoAUA95QNVbRTLtm8KPiGxvDl7I90VUwDQYJKoZIhvcNAQEF
BQADggEBAMucN6pIExIK+t1EnE9SsPTfrgT1eXkIoyQY/EsrhMAtudXH/vTBH1jLuG2cenTnmCmr
EbXjcKChzUyImZOMkXDiqw8cvpOp/2PV5Adg06O/nVsJ8dWO41P0jmP6P6fbtGbfYmbW0W5BjfIt
tep3Sp+dWOIrWcBAI+0tKIJFPnlUkiaY4IBIqDfv8NZ5YBberOgOzW6sRBc4L0na4UU+Krk2U886
UAb3LujEV0lsYSEY1QSteDwsOoBrp+uvFRTp2InBuThs4pFsiv9kuXclVzDAGySj4dzp30d8tbQk
CAUw7C29C79Fv1C5qfPrmAESrciIxpg0X40KPMbp1ZWVbd4=
-----END CERTIFICATE-----

DigiCert High Assurance EV Root CA
==================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SwissSign Gold CA - G2
======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SwissSign Silver CA - G2
========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SecureTrust CA
==============
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Secure Global CA
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

COMODO Certification Authority
==============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Network Solutions Certificate Authority
=======================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

COMODO ECC Certification Authority
==================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Certigna
========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Cybertrust Global Root
======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

ePKI Root Certification Authority
=================================
-----BEGIN CERTIFICATE-----
MIIFsDCCA5igAwIBAgIQFci9ZUdcr7iXAF7kBtK8nTANBgkqhkiG9w0BAQUFADBeMQswCQYDVQQG
EwJUVzEjMCEGA1UECgwaQ2h1bmdod2EgVGVsZWNvbSBDby4sIEx0ZC4xKjAoBgNVBAsMIWVQS0kg
Um9vdCBDZXJ0aWZpY2F0aW9uIEF1dGhvcml0eTAeFw0wNDEyMjAwMjMxMjdaFw0zNDEyMjAwMjMx
MjdaMF4xCzAJBgNVBAYTAlRXMSMwIQYDVQQKDBpDaHVuZ2h3YSBUZWxlY29tIENvLiwgTHRkLjEq
MCgGA1UECwwhZVBLSSBSb290IENlcnRpZmljYXRpb24gQXV0aG9yaXR5MIICIjANBgkqhkiG9w0B
AQEFAAOCAg8AMIICCgKCAgEA4SUP7o3biDN1Z82tH306Tm2d0y8U82N0ywEhajfqhFAHSyZbCUNs
IZ5qyNUD9WBpj8zwIuQf5/dqIjG3LBXy4P4AakP/h2XGtRrBp0xtInAhijHyl3SJCRImHJ7K2RKi
lTza6We/CKBk49ZCt0Xvl/T29de1ShUCWH2YWEtgvM3XDZoTM1PRYfl61dd4s5oz9wCGzh1NlDiv
qOx4UXCKXBCDUSH3ET00hl7lSM2XgYI1TBnsZfZrxQWh7kcT1rMhJ5QQCtkkO7q+RBNGMD+XPNjX
12ruOzjjK9SXDrkb5wdJfzcq+Xd4z1TtW0ado4AOkUPB1ltfFLqfpo0kR0BZv3I4sjZsN/+Z0V0O
WQqraffAsgRFelQArr5T9rXn4fg8ozHSqf4hUmTFpmfwdQcGlBSBVcYn5AGPF8Fqcde+S/uUWH1+
ETOxQvdibBjWzwloPn9s9h6PYq2lY9sJpx8iQkEeb5mKPtf5P0B6ebClAZLSnT0IFaUQAS2zMnao
lQ2zepr7BxB4EW/hj8e6DyUadCrlHJhBmd8hh+iVBmoKs2pHdmX2Os+PYhcZewoozRrSgx4hxyy/
vv9haLdnG7t4TY3OZ+XkwY63I2binZB1NJipNiuKmpS5nezMirH4JYlcWrYvjB9teSSnUmjDhDXi
Zo1jDiVN1Rmy5nk3pyKdVDECAwEAAaNqMGgwHQYDVR0OBBYEFB4M97Zn8uGSJglFwFU5Lnc/Qkqi
MAwGA1UdEwQFMAMBAf8wOQYEZyoHAAQxMC8wLQIBADAJBgUrDgMCGgUAMAcGBWcqAwAABBRFsMLH
ClZ87lt4DJX5GFPBphzYEDANBgkqhkiG9w0BAQUFAAOCAgEACbODU1kBPpVJufGBuvl2ICO1J2B0
1GqZNF5sAFPZn/KmsSQHRGoqxqWOeBLoR9lYGxMqXnmbnwoqZ6YlPwZpVnPDimZI+ymBV3QGypzq
KOg4ZyYr8dW1P2WT+DZdjo2NQCCHGervJ8A9tDkPJXtoUHRVnAxZfVo9QZQlUgjgRywVMRnVvwdV
xrsStZf0X4OFunHB2WyBEXYKCrC/gpf36j36+uwtqSiUO1bd0lEursC9CBWMd1I0ltabrNMdjmEP
NXubrjlpC2JgQCA2j6/7Nu4tCEoduL+bXPjqpRugc6bY+G7gMwRfaKonh+3ZwZCc7b3jajWvY9+r
GNm65ulK6lCKD2GTHuItGeIwlDWSXQ62B68ZgI9HkFFLLk3dheLSClIKF5r8GrBQAuUBo2M3IUxE
xJtRmREOc5wGj1QupyheRDmHVi03vYVElOEMSyycw5KFNGHLD7ibSkNS/jQ6fbjpKdx2qcgw+BRx
gMYeNkh0IkFch4LoGHGLQYlE535YW6i4jRPpp2zDR+2zGp1iro2C6pSe3VkQw63d4k3jMdXH7Ojy
sP6SHhYKGvzZ8/gntsm+HbRsZJB/9OTEW9c3rkIO3aQab3yIVMUWbuF6aC74Or8NpDyJO3inTmOD
BCEIZ43ygknQW/2xzQ+DhNQ+IIX3Sj0rnP0qCglN6oH4EZw=
-----END CERTIFICATE-----

certSIGN ROOT CA
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

NetLock Arany (Class Gold) Főtanúsítvány
========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Hongkong Post Root CA 1
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SecureSign RootCA11
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Microsec e-Szigno Root CA 2009
==============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign Root CA - R3
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Autoridad de Certificacion Firmaprofesional CIF A62634068
=========================================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Izenpe.com
==========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Go Daddy Root Certificate Authority - G2
========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Starfield Root Certificate Authority - G2
=========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Starfield Services Root Certificate Authority - G2
==================================================
-----BEGIN CERTIFICATE-----
MIID7zCCAtegAwIBAgIBADANBgkqhkiG9w0BAQsFADCBmDELMAkGA1UEBhMCVVMxEDAOBgNVBAgT
B0FyaXpvbmExEzARBgNVBAcTClNjb3R0c2RhbGUxJTAjBgNVBAoTHFN0YXJmaWVsZCBUZWNobm9s
b2dpZXMsIEluYy4xOzA5BgNVBAMTMlN0YXJmaWVsZCBTZXJ2aWNlcyBSb290IENlcnRpZmljYXRl
IEF1dGhvcml0eSAtIEcyMB4XDTA5MDkwMTAwMDAwMFoXDTM3MTIzMTIzNTk1OVowgZgxCzAJBgNV
BAYTAlVTMRAwDgYDVQQIEwdBcml6b25hMRMwEQYDVQQHEwpTY290dHNkYWxlMSUwIwYDVQQKExxT
dGFyZmllbGQgVGVjaG5vbG9naWVzLCBJbmMuMTswOQYDVQQDEzJTdGFyZmllbGQgU2VydmljZXMg
Um9vdCBDZXJ0aWZpY2F0ZSBBdXRob3JpdHkgLSBHMjCCASIwDQYJKoZIhvcNAQEBBQADggEPADCC
AQoCggEBANUMOsQq+U7i9b4Zl1+OiFOxHz/Lz58gE20pOsgPfTz3a3Y4Y9k2YKibXlwAgLIvWX/2
h/klQ4bnaRtSmpDhcePYLQ1Ob/bISdm28xpWriu2dBTrz/sm4xq6HZYuajtYlIlHVv8loJNwU4Pa
hHQUw2eeBGg6345AWh1KTs9DkTvnVtYAcMtS7nt9rjrnvDH5RfbCYM8TWQIrgMw0R9+53pBlbQLP
LJGmpufehRhJfGZOozptqbXuNC66DQO4M99H67FrjSXZm86B0UVGMpZwh94CDklDhbZsc7tk6mFB
rMnUVN+HL8cisibMn1lUaJ/8viovxFUcdUBgF4UCVTmLfwUCAwEAAaNCMEAwDwYDVR0TAQH/BAUw
AwEB/zAOBgNVHQ8BAf8EBAMCAQYwHQYDVR0OBBYEFJxfAN+qAdcwKziIorhtSpzyEZGDMA0GCSqG
SIb3DQEBCwUAA4IBAQBLNqaEd2ndOxmfZyMIbw5hyf2E3F/YNoHN2BtBLZ9g3ccaaNnRbobhiCPP
E95Dz+I0swSdHynVv/heyNXBve6SbzJ08pGCL72CQnqtKrcgfU28elUSwhXqvfdqlS5sdJ/PHLTy
xQGjhdByPq1zqwubdQxtRbeOlKyWN7Wg0I8VRw7j6IPdj/3vQQF3zCepYoUz8jcI73HPdwbeyBkd
iEDPfUYd/x7H4c7/I9vG+o1VTqkC50cRRj70/b17KSa7qWFiNyi2LSr2EIZkyXCn0q23KXB56jza
YyWf/Wi3MOxw+3WKt21gZ7IeyLnp2KhvAotnDU0mV3HaIPzBSlCNsSi6
-----END CERTIFICATE-----

AffirmTrust Commercial
======================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

AffirmTrust Networking
======================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

AffirmTrust Premium
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

AffirmTrust Premium ECC
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Certum Trusted Network CA
=========================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

TWCA Root Certification Authority
=================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Security Communication RootCA2
==============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

EC-ACC
======
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Hellenic Academic and Research Institutions RootCA 2011
=======================================================
-----BEGIN CERTIFICATE-----
MIIEMTCCAxmgAwIBAgIBADANBgkqhkiG9w0BAQUFADCBlTELMAkGA1UEBhMCR1IxRDBCBgNVBAoT
O0hlbGxlbmljIEFjYWRlbWljIGFuZCBSZXNlYXJjaCBJbnN0aXR1dGlvbnMgQ2VydC4gQXV0aG9y
aXR5MUAwPgYDVQQDEzdIZWxsZW5pYyBBY2FkZW1pYyBhbmQgUmVzZWFyY2ggSW5zdGl0dXRpb25z
IFJvb3RDQSAyMDExMB4XDTExMTIwNjEzNDk1MloXDTMxMTIwMTEzNDk1MlowgZUxCzAJBgNVBAYT
AkdSMUQwQgYDVQQKEztIZWxsZW5pYyBBY2FkZW1pYyBhbmQgUmVzZWFyY2ggSW5zdGl0dXRpb25z
IENlcnQuIEF1dGhvcml0eTFAMD4GA1UEAxM3SGVsbGVuaWMgQWNhZGVtaWMgYW5kIFJlc2VhcmNo
IEluc3RpdHV0aW9ucyBSb290Q0EgMjAxMTCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEB
AKlTAOMupvaO+mDYLZU++CwqVE7NuYRhlFhPjz2L5EPzdYmNUeTDN9KKiE15HrcS3UN4SoqS5tdI
1Q+kOilENbgH9mgdVc04UfCMJDGFr4PJfel3r+0ae50X+bOdOFAPplp5kYCvN66m0zH7tSYJnTxa
71HFK9+WXesyHgLacEnsbgzImjeN9/E2YEsmLIKe0HjzDQ9jpFEw4fkrJxIH2Oq9GGKYsFk3fb7u
8yBRQlqD75O6aRXxYp2fmTmCobd0LovUxQt7L/DICto9eQqakxylKHJzkUOap9FNhYS5qXSPFEDH
3N6sQWRstBmbAmNtJGSPRLIl6s5ddAxjMlyNh+UCAwEAAaOBiTCBhjAPBgNVHRMBAf8EBTADAQH/
MAsGA1UdDwQEAwIBBjAdBgNVHQ4EFgQUppFC/RNhSiOeCKQp5dgTBCPuQSUwRwYDVR0eBEAwPqA8
MAWCAy5ncjAFggMuZXUwBoIELmVkdTAGggQub3JnMAWBAy5ncjAFgQMuZXUwBoEELmVkdTAGgQQu
b3JnMA0GCSqGSIb3DQEBBQUAA4IBAQAf73lB4XtuP7KMhjdCSk4cNx6NZrokgclPEg8hwAOXhiVt
XdMiKahsog2p6z0GW5k6x8zDmjR/qw7IThzh+uTczQ2+vyT+bOdrwg3IBp5OjWEopmr95fZi6hg8
TqBTnbI6nOulnJEWtk2C4AwFSKls9cz4y51JtPACpf1wA+2KIaWuE4ZJwzNzvoc7dIsXRSZMFpGD
/md9zU1jZ/rzAxKWeAaNsWftjj++n08C9bMJL/NMh98qy5V8AcysNnq/onN694/BtZqhFLKPM58N
7yLcZnuEvUUXBj08yrl3NI/K6s8/MT7jiOOASSXIl7WdmplNsDz4SgCbZN2fOUvRJ9e4
-----END CERTIFICATE-----

Actalis Authentication Root CA
==============================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

Buypass Class 2 Root CA
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Buypass Class 3 Root CA
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

T-TeleSec GlobalRoot Class 3
============================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

D-TRUST Root Class 3 CA 2 2009
==============================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

D-TRUST Root Class 3 CA 2 EV 2009
=================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

CA Disig Root R2
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

ACCVRAIZ1
=========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

TWCA Global Root CA
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

TeliaSonera Root CA v1
======================
-----BEGIN CERTIFICATE-----
MIIFODCCAyCgAwIBAgIRAJW+FqD3LkbxezmCcvqLzZYwDQYJKoZIhvcNAQEFBQAwNzEUMBIGA1UE
CgwLVGVsaWFTb25lcmExHzAdBgNVBAMMFlRlbGlhU29uZXJhIFJvb3QgQ0EgdjEwHhcNMDcxMDE4
MTIwMDUwWhcNMzIxMDE4MTIwMDUwWjA3MRQwEgYDVQQKDAtUZWxpYVNvbmVyYTEfMB0GA1UEAwwW
VGVsaWFTb25lcmEgUm9vdCBDQSB2MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAMK+
6yfwIaPzaSZVfp3FVRaRXP3vIb9TgHot0pGMYzHw7CTww6XScnwQbfQ3t+XmfHnqjLWCi65ItqwA
3GV17CpNX8GH9SBlK4GoRz6JI5UwFpB/6FcHSOcZrr9FZ7E3GwYq/t75rH2D+1665I+XZ75Ljo1k
B1c4VWk0Nj0TSO9P4tNmHqTPGrdeNjPUtAa9GAH9d4RQAEX1jF3oI7x+/jXh7VB7qTCNGdMJjmhn
Xb88lxhTuylixcpecsHHltTbLaC0H2kD7OriUPEMPPCs81Mt8Bz17Ww5OXOAFshSsCPN4D7c3TxH
oLs1iuKYaIu+5b9y7tL6pe0S7fyYGKkmdtwoSxAgHNN/Fnct7W+A90m7UwW7XWjH1Mh1Fj+JWov3
F0fUTPHSiXk+TT2YqGHeOh7S+F4D4MHJHIzTjU3TlTazN19jY5szFPAtJmtTfImMMsJu7D0hADnJ
oWjiUIMusDor8zagrC/kb2HCUQk5PotTubtn2txTuXZZNp1D5SDgPTJghSJRt8czu90VL6R4pgd7
gUY2BIbdeTXHlSw7sKMXNeVzH7RcWe/a6hBle3rQf5+ztCo3O3CLm1u5K7fsslESl1MpWtTwEhDc
TwK7EpIvYtQ/aUN8Ddb8WHUBiJ1YFkveupD/RwGJBmr2X7KQarMCpgKIv7NHfirZ1fpoeDVNAgMB
AAGjPzA9MA8GA1UdEwEB/wQFMAMBAf8wCwYDVR0PBAQDAgEGMB0GA1UdDgQWBBTwj1k4ALP1j5qW
DNXr+nuqF+gTEjANBgkqhkiG9w0BAQUFAAOCAgEAvuRcYk4k9AwI//DTDGjkk0kiP0Qnb7tt3oNm
zqjMDfz1mgbldxSR651Be5kqhOX//CHBXfDkH1e3damhXwIm/9fH907eT/j3HEbAek9ALCI18Bmx
0GtnLLCo4MBANzX2hFxc469CeP6nyQ1Q6g2EdvZR74NTxnr/DlZJLo961gzmJ1TjTQpgcmLNkQfW
pb/ImWvtxBnmq0wROMVvMeJuScg/doAmAyYp4Db29iBT4xdwNBedY2gea+zDTYa4EzAvXUYNR0PV
G6pZDrlcjQZIrXSHX8f8MVRBE+LHIQ6e4B4N4cB7Q4WQxYpYxmUKeFfyxiMPAdkgS94P+5KFdSpc
c41teyWRyu5FrgZLAMzTsVlQ2jqIOylDRl6XK1TOU2+NSueW+r9xDkKLfP0ooNBIytrEgUy7onOT
JsjrDNYmiLbAJM+7vVvrdX3pCI6GMyx5dwlppYn8s3CQh3aP0yK7Qs69cwsgJirQmz1wHiRszYd2
qReWt88NkvuOGKmYSdGe/mBEciG5Ge3C9THxOUiIkCR1VBatzvT4aRRkOfujuLpwQMcnHL/EVlP6
Y2XQ8xwOFvVrhlhNGNTkDY6lnVuR3HYkUD/GKvvZt5y11ubQ2egZixVxSK236thZiNSQvxaz2ems
WWFUyBy6ysHK4bkgTI86k4mloMy/0/Z1pHWWbVY=
-----END CERTIFICATE-----

E-Tugra Certification Authority
===============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

T-TeleSec GlobalRoot Class 2
============================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

Atos TrustedRoot 2011
=====================
-----BEGIN CERTIFICATE-----
MIIDdzCCAl+gAwIBAgIIXDPLYixfszIwDQYJKoZIhvcNAQELBQAwPDEeMBwGA1UEAwwVQXRvcyBU
cnVzdGVkUm9vdCAyMDExMQ0wCwYDVQQKDARBdG9zMQswCQYDVQQGEwJERTAeFw0xMTA3MDcxNDU4
MzBaFw0zMDEyMzEyMzU5NTlaMDwxHjAcBgNVBAMMFUF0b3MgVHJ1c3RlZFJvb3QgMjAxMTENMAsG
A1UECgwEQXRvczELMAkGA1UEBhMCREUwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCV
hTuXbyo7LjvPpvMpNb7PGKw+qtn4TaA+Gke5vJrf8v7MPkfoepbCJI419KkM/IL9bcFyYie96mvr
54rMVD6QUM+A1JX76LWC1BTFtqlVJVfbsVD2sGBkWXppzwO3bw2+yj5vdHLqqjAqc2K+SZFhyBH+
DgMq92og3AIVDV4VavzjgsG1xZ1kCWyjWZgHJ8cblithdHFsQ/H3NYkQ4J7sVaE3IqKHBAUsR320
HLliKWYoyrfhk/WklAOZuXCFteZI6o1Q/NnezG8HDt0Lcp2AMBYHlT8oDv3FdU9T1nSatCQujgKR
z3bFmx5VdJx4IbHwLfELn8LVlhgf8FQieowHAgMBAAGjfTB7MB0GA1UdDgQWBBSnpQaxLKYJYO7R
l+lwrrw7GWzbITAPBgNVHRMBAf8EBTADAQH/MB8GA1UdIwQYMBaAFKelBrEspglg7tGX6XCuvDsZ
bNshMBgGA1UdIAQRMA8wDQYLKwYBBAGwLQMEAQEwDgYDVR0PAQH/BAQDAgGGMA0GCSqGSIb3DQEB
CwUAA4IBAQAmdzTblEiGKkGdLD4GkGDEjKwLVLgfuXvTBznk+j57sj1O7Z8jvZfza1zv7v1Apt+h
k6EKhqzvINB5Ab149xnYJDE0BAGmuhWawyfc2E8PzBhj/5kPDpFrdRbhIfzYJsdHt6bPWHJxfrrh
TZVHO8mvbaG0weyJ9rQPOLXiZNwlz6bb65pcmaHFCN795trV1lpFDMS3wrUU77QR/w4VtfX128a9
61qn8FYiqTxlVMYVqL2Gns2Dlmh6cYGJ4Qvh6hEbaAjMaZ7snkGeRDImeuKHCnE96+RapNLbxc3G
3mB/ufNPRJLvKrcYPqcZ2Qt9sTdBQrC6YB3y/gkRsPCHe6ed
-----END CERTIFICATE-----

QuoVadis Root CA 1 G3
=====================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

QuoVadis Root CA 2 G3
=====================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

QuoVadis Root CA 3 G3
=====================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Assured ID Root G2
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Assured ID Root G3
===========================
-----BEGIN CERTIFICATE-----
MIICRjCCAc2gAwIBAgIQC6Fa+h3foLVJRK/NJKBs7DAKBggqhkjOPQQDAzBlMQswCQYDVQQGEwJV
UzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSQwIgYD
VQQDExtEaWdpQ2VydCBBc3N1cmVkIElEIFJvb3QgRzMwHhcNMTMwODAxMTIwMDAwWhcNMzgwMTE1
MTIwMDAwWjBlMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3
d3cuZGlnaWNlcnQuY29tMSQwIgYDVQQDExtEaWdpQ2VydCBBc3N1cmVkIElEIFJvb3QgRzMwdjAQ
BgcqhkjOPQIBBgUrgQQAIgNiAAQZ57ysRGXtzbg/WPuNsVepRC0FFfLvC/8QdJ+1YlJfZn4f5dwb
RXkLzMZTCp2NXQLZqVneAlr2lSoOjThKiknGvMYDOAdfVdp+CW7if17QRSAPWXYQ1qAk8C3eNvJs
KTmjQjBAMA8GA1UdEwEB/wQFMAMBAf8wDgYDVR0PAQH/BAQDAgGGMB0GA1UdDgQWBBTL0L2p4ZgF
UaFNN6KDec6NHSrkhDAKBggqhkjOPQQDAwNnADBkAjAlpIFFAmsSS3V0T8gj43DydXLefInwz5Fy
YZ5eEJJZVrmDxxDnOOlYJjZ91eQ0hjkCMHw2U/Aw5WJjOpnitqM7mzT6HtoQknFekROn3aRukswy
1vUhZscv6pZjamVFkpUBtA==
-----END CERTIFICATE-----

DigiCert Global Root G2
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Global Root G3
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

DigiCert Trusted Root G4
========================
-----BEGIN CERTIFICATE-----
MIIFkDCCA3igAwIBAgIQBZsbV56OITLiOQe9p3d1XDANBgkqhkiG9w0BAQwFADBiMQswCQYDVQQG
EwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3d3cuZGlnaWNlcnQuY29tMSEw
HwYDVQQDExhEaWdpQ2VydCBUcnVzdGVkIFJvb3QgRzQwHhcNMTMwODAxMTIwMDAwWhcNMzgwMTE1
MTIwMDAwWjBiMQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3
d3cuZGlnaWNlcnQuY29tMSEwHwYDVQQDExhEaWdpQ2VydCBUcnVzdGVkIFJvb3QgRzQwggIiMA0G
CSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQC/5pBzaN675F1KPDAiMGkz7MKnJS7JIT3yithZwuEp
pz1Yq3aaza57G4QNxDAf8xukOBbrVsaXbR2rsnnyyhHS5F/WBTxSD1Ifxp4VpX6+n6lXFllVcq9o
k3DCsrp1mWpzMpTREEQQLt+C8weE5nQ7bXHiLQwb7iDVySAdYyktzuxeTsiT+CFhmzTrBcZe7Fsa
vOvJz82sNEBfsXpm7nfISKhmV1efVFiODCu3T6cw2Vbuyntd463JT17lNecxy9qTXtyOj4DatpGY
QJB5w3jHtrHEtWoYOAMQjdjUN6QuBX2I9YI+EJFwq1WCQTLX2wRzKm6RAXwhTNS8rhsDdV14Ztk6
MUSaM0C/CNdaSaTC5qmgZ92kJ7yhTzm1EVgX9yRcRo9k98FpiHaYdj1ZXUJ2h4mXaXpI8OCiEhtm
mnTK3kse5w5jrubU75KSOp493ADkRSWJtppEGSt+wJS00mFt6zPZxd9LBADMfRyVw4/3IbKyEbe7
f/LVjHAsQWCqsWMYRJUadmJ+9oCw++hkpjPRiQfhvbfmQ6QYuKZ3AeEPlAwhHbJUKSWJbOUOUlFH
dL4mrLZBdd56rF+NP8m800ERElvlEFDrMcXKchYiCd98THU/Y+whX8QgUWtvsauGi0/C1kVfnSD8
oR7FwI+isX4KJpn15GkvmB0t9dmpsh3lGwIDAQABo0IwQDAPBgNVHRMBAf8EBTADAQH/MA4GA1Ud
DwEB/wQEAwIBhjAdBgNVHQ4EFgQU7NfjgtJxXWRM3y5nP+e6mK4cD08wDQYJKoZIhvcNAQEMBQAD
ggIBALth2X2pbL4XxJEbw6GiAI3jZGgPVs93rnD5/ZpKmbnJeFwMDF/k5hQpVgs2SV1EY+CtnJYY
ZhsjDT156W1r1lT40jzBQ0CuHVD1UvyQO7uYmWlrx8GnqGikJ9yd+SeuMIW59mdNOj6PWTkiU0Tr
yF0Dyu1Qen1iIQqAyHNm0aAFYF/opbSnr6j3bTWcfFqK1qI4mfN4i/RN0iAL3gTujJtHgXINwBQy
7zBZLq7gcfJW5GqXb5JQbZaNaHqasjYUegbyJLkJEVDXCLG4iXqEI2FCKeWjzaIgQdfRnGTZ6iah
ixTXTBmyUEFxPT9NcCOGDErcgdLMMpSEDQgJlxxPwO5rIHQw0uA5NBCFIRUBCOhVMt5xSdkoF1BN
5r5N0XWs0Mr7QbhDparTwwVETyw2m+L64kW4I1NsBm9nVX9GtUw/bihaeSbSpKhil9Ie4u1Ki7wb
/UdKDd9nZn6yW0HQO+T0O/QEY+nvwlQAUaCKKsnOeMzV6ocEGLPOr0mIr/OSmbaz5mEP0oUA51Aa
5BuVnRmhuZyxm7EAHu/QD09CbMkKvO5D+jpxpchNJqU1/YldvIViHTLSoCtU7ZpXwdv6EM8Zt4tK
G48BtieVU+i2iW1bvGjUI+iLUaJW+fCmgKDWHrO8Dw9TdSmq6hN35N6MgSGtBxBHEa2HPQfRdbzP
82Z+
-----END CERTIFICATE-----

COMODO RSA Certification Authority
==================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

USERTrust RSA Certification Authority
=====================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

USERTrust ECC Certification Authority
=====================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign ECC Root CA - R4
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign ECC Root CA - R5
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Staat der Nederlanden EV Root CA
================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

IdenTrust Commercial Root CA 1
==============================
-----BEGIN CERTIFICATE-----
MIIFYDCCA0igAwIBAgIQCgFCgAAAAUUjyES1AAAAAjANBgkqhkiG9w0BAQsFADBKMQswCQYDVQQG
EwJVUzESMBAGA1UEChMJSWRlblRydXN0MScwJQYDVQQDEx5JZGVuVHJ1c3QgQ29tbWVyY2lhbCBS
b290IENBIDEwHhcNMTQwMTE2MTgxMjIzWhcNMzQwMTE2MTgxMjIzWjBKMQswCQYDVQQGEwJVUzES
MBAGA1UEChMJSWRlblRydXN0MScwJQYDVQQDEx5JZGVuVHJ1c3QgQ29tbWVyY2lhbCBSb290IENB
IDEwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQCnUBneP5k91DNG8W9RYYKyqU+PZ4ld
hNlT3Qwo2dfw/66VQ3KZ+bVdfIrBQuExUHTRgQ18zZshq0PirK1ehm7zCYofWjK9ouuU+ehcCuz/
mNKvcbO0U59Oh++SvL3sTzIwiEsXXlfEU8L2ApeN2WIrvyQfYo3fw7gpS0l4PJNgiCL8mdo2yMKi
1CxUAGc1bnO/AljwpN3lsKImesrgNqUZFvX9t++uP0D1bVoE/c40yiTcdCMbXTMTEl3EASX2MN0C
XZ/g1Ue9tOsbobtJSdifWwLziuQkkORiT0/Br4sOdBeo0XKIanoBScy0RnnGF7HamB4HWfp1IYVl
3ZBWzvurpWCdxJ35UrCLvYf5jysjCiN2O/cz4ckA82n5S6LgTrx+kzmEB/dEcH7+B1rlsazRGMzy
NeVJSQjKVsk9+w8YfYs7wRPCTY/JTw436R+hDmrfYi7LNQZReSzIJTj0+kuniVyc0uMNOYZKdHzV
WYfCP04MXFL0PfdSgvHqo6z9STQaKPNBiDoT7uje/5kdX7rL6B7yuVBgwDHTc+XvvqDtMwt0viAg
xGds8AgDelWAf0ZOlqf0Hj7h9tgJ4TNkK2PXMl6f+cB7D3hvl7yTmvmcEpB4eoCHFddydJxVdHix
uuFucAS6T6C6aMN7/zHwcz09lCqxC0EOoP5NiGVreTO01wIDAQABo0IwQDAOBgNVHQ8BAf8EBAMC
AQYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQU7UQZwNPwBovupHu+QucmVMiONnYwDQYJKoZI
hvcNAQELBQADggIBAA2ukDL2pkt8RHYZYR4nKM1eVO8lvOMIkPkp165oCOGUAFjvLi5+U1KMtlwH
6oi6mYtQlNeCgN9hCQCTrQ0U5s7B8jeUeLBfnLOic7iPBZM4zY0+sLj7wM+x8uwtLRvM7Kqas6pg
ghstO8OEPVeKlh6cdbjTMM1gCIOQ045U8U1mwF10A0Cj7oV+wh93nAbowacYXVKV7cndJZ5t+qnt
ozo00Fl72u1Q8zW/7esUTTHHYPTa8Yec4kjixsU3+wYQ+nVZZjFHKdp2mhzpgq7vmrlR94gjmmmV
YjzlVYA211QC//G5Xc7UI2/YRYRKW2XviQzdFKcgyxilJbQN+QHwotL0AMh0jqEqSI5l2xPE4iUX
feu+h1sXIFRRk0pTAwvsXcoz7WL9RccvW9xYoIA55vrX/hMUpu09lEpCdNTDd1lzzY9GvlU47/ro
kTLql1gEIt44w8y8bckzOmoKaT+gyOpyj4xjhiO9bTyWnpXgSUyqorkqG5w2gXjtw+hG4iZZRHUe
2XWJUc0QhJ1hYMtd+ZciTY6Y5uN/9lu7rs3KSoFrXgvzUeF0K+l+J6fZmUlO+KWA2yUPHGNiiskz
Z2s8EIPGrd6ozRaOjfAHN3Gf8qv8QfXBi+wAN10J5U6A7/qxXDgGpRtK4dw4LTzcqx+QGtVKnO7R
cGzM7vRX+Bi6hG6H
-----END CERTIFICATE-----

IdenTrust Public Sector Root CA 1
=================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Entrust Root Certification Authority - G2
=========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Entrust Root Certification Authority - EC1
==========================================
-----BEGIN CERTIFICATE-----
MIIC+TCCAoCgAwIBAgINAKaLeSkAAAAAUNCR+TAKBggqhkjOPQQDAzCBvzELMAkGA1UEBhMCVVMx
FjAUBgNVBAoTDUVudHJ1c3QsIEluYy4xKDAmBgNVBAsTH1NlZSB3d3cuZW50cnVzdC5uZXQvbGVn
YWwtdGVybXMxOTA3BgNVBAsTMChjKSAyMDEyIEVudHJ1c3QsIEluYy4gLSBmb3IgYXV0aG9yaXpl
ZCB1c2Ugb25seTEzMDEGA1UEAxMqRW50cnVzdCBSb290IENlcnRpZmljYXRpb24gQXV0aG9yaXR5
IC0gRUMxMB4XDTEyMTIxODE1MjUzNloXDTM3MTIxODE1NTUzNlowgb8xCzAJBgNVBAYTAlVTMRYw
FAYDVQQKEw1FbnRydXN0LCBJbmMuMSgwJgYDVQQLEx9TZWUgd3d3LmVudHJ1c3QubmV0L2xlZ2Fs
LXRlcm1zMTkwNwYDVQQLEzAoYykgMjAxMiBFbnRydXN0LCBJbmMuIC0gZm9yIGF1dGhvcml6ZWQg
dXNlIG9ubHkxMzAxBgNVBAMTKkVudHJ1c3QgUm9vdCBDZXJ0aWZpY2F0aW9uIEF1dGhvcml0eSAt
IEVDMTB2MBAGByqGSM49AgEGBSuBBAAiA2IABIQTydC6bUF74mzQ61VfZgIaJPRbiWlH47jCffHy
AsWfoPZb1YsGGYZPUxBtByQnoaD41UcZYUx9ypMn6nQM72+WCf5j7HBdNq1nd67JnXxVRDqiY1Ef
9eNi1KlHBz7MIKNCMEAwDgYDVR0PAQH/BAQDAgEGMA8GA1UdEwEB/wQFMAMBAf8wHQYDVR0OBBYE
FLdj5xrdjekIplWDpOBqUEFlEUJJMAoGCCqGSM49BAMDA2cAMGQCMGF52OVCR98crlOZF7ZvHH3h
vxGU0QOIdeSNiaSKd0bebWHvAvX7td/M/k7//qnmpwIwW5nXhTcGtXsI/esni0qU+eH6p44mCOh8
kmhtc9hvJqwhAriZtyZBWyVgrtBIGu4G
-----END CERTIFICATE-----

CFCA EV ROOT
============
-----BEGIN CERTIFICATE-----
MIIFjTCCA3WgAwIBAgIEGErM1jANBgkqhkiG9w0BAQsFADBWMQswCQYDVQQGEwJDTjEwMC4GA1UE
CgwnQ2hpbmEgRmluYW5jaWFsIENlcnRpZmljYXRpb24gQXV0aG9yaXR5MRUwEwYDVQQDDAxDRkNB
IEVWIFJPT1QwHhcNMTIwODA4MDMwNzAxWhcNMjkxMjMxMDMwNzAxWjBWMQswCQYDVQQGEwJDTjEw
MC4GA1UECgwnQ2hpbmEgRmluYW5jaWFsIENlcnRpZmljYXRpb24gQXV0aG9yaXR5MRUwEwYDVQQD
DAxDRkNBIEVWIFJPT1QwggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDXXWvNED8fBVnV
BU03sQ7smCuOFR36k0sXgiFxEFLXUWRwFsJVaU2OFW2fvwwbwuCjZ9YMrM8irq93VCpLTIpTUnrD
7i7es3ElweldPe6hL6P3KjzJIx1qqx2hp/Hz7KDVRM8Vz3IvHWOX6Jn5/ZOkVIBMUtRSqy5J35DN
uF++P96hyk0g1CXohClTt7GIH//62pCfCqktQT+x8Rgp7hZZLDRJGqgG16iI0gNyejLi6mhNbiyW
ZXvKWfry4t3uMCz7zEasxGPrb382KzRzEpR/38wmnvFyXVBlWY9ps4deMm/DGIq1lY+wejfeWkU7
xzbh72fROdOXW3NiGUgthxwG+3SYIElz8AXSG7Ggo7cbcNOIabla1jj0Ytwli3i/+Oh+uFzJlU9f
py25IGvPa931DfSCt/SyZi4QKPaXWnuWFo8BGS1sbn85WAZkgwGDg8NNkt0yxoekN+kWzqotaK8K
gWU6cMGbrU1tVMoqLUuFG7OA5nBFDWteNfB/O7ic5ARwiRIlk9oKmSJgamNgTnYGmE69g60dWIol
hdLHZR4tjsbftsbhf4oEIRUpdPA+nJCdDC7xij5aqgwJHsfVPKPtl8MeNPo4+QgO48BdK4PRVmrJ
tqhUUy54Mmc9gn900PvhtgVguXDbjgv5E1hvcWAQUhC5wUEJ73IfZzF4/5YFjQIDAQABo2MwYTAf
BgNVHSMEGDAWgBTj/i39KNALtbq2osS/BqoFjJP7LzAPBgNVHRMBAf8EBTADAQH/MA4GA1UdDwEB
/wQEAwIBBjAdBgNVHQ4EFgQU4/4t/SjQC7W6tqLEvwaqBYyT+y8wDQYJKoZIhvcNAQELBQADggIB
ACXGumvrh8vegjmWPfBEp2uEcwPenStPuiB/vHiyz5ewG5zz13ku9Ui20vsXiObTej/tUxPQ4i9q
ecsAIyjmHjdXNYmEwnZPNDatZ8POQQaIxffu2Bq41gt/UP+TqhdLjOztUmCypAbqTuv0axn96/Ua
4CUqmtzHQTb3yHQFhDmVOdYLO6Qn+gjYXB74BGBSESgoA//vU2YApUo0FmZ8/Qmkrp5nGm9BC2sG
E5uPhnEFtC+NiWYzKXZUmhH4J/qyP5Hgzg0b8zAarb8iXRvTvyUFTeGSGn+ZnzxEk8rUQElsgIfX
BDrDMlI1Dlb4pd19xIsNER9Tyx6yF7Zod1rg1MvIB671Oi6ON7fQAUtDKXeMOZePglr4UeWJoBjn
aH9dCi77o0cOPaYjesYBx4/IXr9tgFa+iiS6M+qf4TIRnvHST4D2G0CvOJ4RUHlzEhLN5mydLIhy
PDCBBpEi6lmt2hkuIsKNuYyH4Ga8cyNfIWRjgEj1oDwYPZTISEEdQLpe/v5WOaHIz16eGWRGENoX
kbcFgKyLmZJ956LYBws2J+dIeWCKw9cTXPhyQN9Ky8+ZAAoACxGV2lZFA4gKn2fQ1XmxqI1AbQ3C
ekD6819kR5LLU7m7Wc5P/dAVUwHY3+vZ5nbv0CO7O6l5s9UCKc2Jo5YPSjXnTkLAdc0Hz+Ys63su
-----END CERTIFICATE-----

OISTE WISeKey Global Root GB CA
===============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SZAFIR ROOT CA2
===============
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Certum Trusted Network CA 2
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Hellenic Academic and Research Institutions RootCA 2015
=======================================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Hellenic Academic and Research Institutions ECC RootCA 2015
===========================================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

ISRG Root X1
============
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

AC RAIZ FNMT-RCM
================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

Amazon Root CA 1
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Amazon Root CA 2
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Amazon Root CA 3
================
-----BEGIN CERTIFICATE-----
MIIBtjCCAVugAwIBAgITBmyf1XSXNmY/Owua2eiedgPySjAKBggqhkjOPQQDAjA5MQswCQYDVQQG
EwJVUzEPMA0GA1UEChMGQW1hem9uMRkwFwYDVQQDExBBbWF6b24gUm9vdCBDQSAzMB4XDTE1MDUy
NjAwMDAwMFoXDTQwMDUyNjAwMDAwMFowOTELMAkGA1UEBhMCVVMxDzANBgNVBAoTBkFtYXpvbjEZ
MBcGA1UEAxMQQW1hem9uIFJvb3QgQ0EgMzBZMBMGByqGSM49AgEGCCqGSM49AwEHA0IABCmXp8ZB
f8ANm+gBG1bG8lKlui2yEujSLtf6ycXYqm0fc4E7O5hrOXwzpcVOho6AF2hiRVd9RFgdszflZwjr
Zt6jQjBAMA8GA1UdEwEB/wQFMAMBAf8wDgYDVR0PAQH/BAQDAgGGMB0GA1UdDgQWBBSrttvXBp43
rDCGB5Fwx5zEGbF4wDAKBggqhkjOPQQDAgNJADBGAiEA4IWSoxe3jfkrBqWTrBqYaGFy+uGh0Psc
eGCmQ5nFuMQCIQCcAu/xlJyzlvnrxir4tiz+OpAUFteMYyRIHN8wfdVoOw==
-----END CERTIFICATE-----

Amazon Root CA 4
================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

TUBITAK Kamu SM SSL Kok Sertifikasi - Surum 1
=============================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GDCA TrustAUTH R5 ROOT
======================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

TrustCor RootCert CA-1
======================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

TrustCor RootCert CA-2
======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

TrustCor ECA-1
==============
-----BEGIN CERTIFICATE-----
MIIEIDCCAwigAwIBAgIJAISCLF8cYtBAMA0GCSqGSIb3DQEBCwUAMIGcMQswCQYDVQQGEwJQQTEP
MA0GA1UECAwGUGFuYW1hMRQwEgYDVQQHDAtQYW5hbWEgQ2l0eTEkMCIGA1UECgwbVHJ1c3RDb3Ig
U3lzdGVtcyBTLiBkZSBSLkwuMScwJQYDVQQLDB5UcnVzdENvciBDZXJ0aWZpY2F0ZSBBdXRob3Jp
dHkxFzAVBgNVBAMMDlRydXN0Q29yIEVDQS0xMB4XDTE2MDIwNDEyMzIzM1oXDTI5MTIzMTE3Mjgw
N1owgZwxCzAJBgNVBAYTAlBBMQ8wDQYDVQQIDAZQYW5hbWExFDASBgNVBAcMC1BhbmFtYSBDaXR5
MSQwIgYDVQQKDBtUcnVzdENvciBTeXN0ZW1zIFMuIGRlIFIuTC4xJzAlBgNVBAsMHlRydXN0Q29y
IENlcnRpZmljYXRlIEF1dGhvcml0eTEXMBUGA1UEAwwOVHJ1c3RDb3IgRUNBLTEwggEiMA0GCSqG
SIb3DQEBAQUAA4IBDwAwggEKAoIBAQDPj+ARtZ+odnbb3w9U73NjKYKtR8aja+3+XzP4Q1HpGjOR
MRegdMTUpwHmspI+ap3tDvl0mEDTPwOABoJA6LHip1GnHYMma6ve+heRK9jGrB6xnhkB1Zem6g23
xFUfJ3zSCNV2HykVh0A53ThFEXXQmqc04L/NyFIduUd+Dbi7xgz2c1cWWn5DkR9VOsZtRASqnKmc
p0yJF4OuowReUoCLHhIlERnXDH19MURB6tuvsBzvgdAsxZohmz3tQjtQJvLsznFhBmIhVE5/wZ0+
fyCMgMsq2JdiyIMzkX2woloPV+g7zPIlstR8L+xNxqE6FXrntl019fZISjZFZtS6mFjBAgMBAAGj
YzBhMB0GA1UdDgQWBBREnkj1zG1I1KBLf/5ZJC+Dl5mahjAfBgNVHSMEGDAWgBREnkj1zG1I1KBL
f/5ZJC+Dl5mahjAPBgNVHRMBAf8EBTADAQH/MA4GA1UdDwEB/wQEAwIBhjANBgkqhkiG9w0BAQsF
AAOCAQEABT41XBVwm8nHc2FvcivUwo/yQ10CzsSUuZQRg2dd4mdsdXa/uwyqNsatR5Nj3B5+1t4u
/ukZMjgDfxT2AHMsWbEhBuH7rBiVDKP/mZb3Kyeb1STMHd3BOuCYRLDE5D53sXOpZCz2HAF8P11F
hcCF5yWPldwX8zyfGm6wyuMdKulMY/okYWLW2n62HGz1Ah3UKt1VkOsqEUc8Ll50soIipX1TH0Xs
J5F95yIW6MBoNtjG8U+ARDL54dHRHareqKucBK+tIA5kmE2la8BIWJZpTdwHjFGTot+fDz2LYLSC
jaoITmJF4PkL0uDgPFveXHEnJcLmA4GLEFPjx1WitJ/X5g==
-----END CERTIFICATE-----

SSL.com Root Certification Authority RSA
========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SSL.com Root Certification Authority ECC
========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SSL.com EV Root Certification Authority RSA R2
==============================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

SSL.com EV Root Certification Authority ECC
===========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign Root CA - R6
=======================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

OISTE WISeKey Global Root GC CA
===============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GTS Root R1
===========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GTS Root R2
===========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GTS Root R3
===========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GTS Root R4
===========
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

UCA Global G2 Root
==================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

UCA Extended Validation Root
============================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Certigna Root CA
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

emSign Root CA - G1
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

emSign ECC Root CA - G3
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

emSign Root CA - C1
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

emSign ECC Root CA - C3
=======================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

Hongkong Post Root CA 3
=======================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Entrust Root Certification Authority - G4
=========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Microsoft ECC Root Certificate Authority 2017
=============================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Microsoft RSA Root Certificate Authority 2017
=============================================
-----BEGIN CERTIFICATE-----
MIIFqDCCA5CgAwIBAgIQHtOXCV/YtLNHcB6qvn9FszANBgkqhkiG9w0BAQwFADBlMQswCQYDVQQG
EwJVUzEeMBwGA1UEChMVTWljcm9zb2Z0IENvcnBvcmF0aW9uMTYwNAYDVQQDEy1NaWNyb3NvZnQg
UlNBIFJvb3QgQ2VydGlmaWNhdGUgQXV0aG9yaXR5IDIwMTcwHhcNMTkxMjE4MjI1MTIyWhcNNDIw
NzE4MjMwMDIzWjBlMQswCQYDVQQGEwJVUzEeMBwGA1UEChMVTWljcm9zb2Z0IENvcnBvcmF0aW9u
MTYwNAYDVQQDEy1NaWNyb3NvZnQgUlNBIFJvb3QgQ2VydGlmaWNhdGUgQXV0aG9yaXR5IDIwMTcw
ggIiMA0GCSqGSIb3DQEBAQUAA4ICDwAwggIKAoICAQDKW76UM4wplZEWCpW9R2LBifOZNt9GkMml
7Xhqb0eRaPgnZ1AzHaGm++DlQ6OEAlcBXZxIQIJTELy/xztokLaCLeX0ZdDMbRnMlfl7rEqUrQ7e
S0MdhweSE5CAg2Q1OQT85elss7YfUJQ4ZVBcF0a5toW1HLUX6NZFndiyJrDKxHBKrmCk3bPZ7Pw7
1VdyvD/IybLeS2v4I2wDwAW9lcfNcztmgGTjGqwu+UcF8ga2m3P1eDNbx6H7JyqhtJqRjJHTOoI+
dkC0zVJhUXAoP8XFWvLJjEm7FFtNyP9nTUwSlq31/niol4fX/V4ggNyhSyL71Imtus5Hl0dVe49F
yGcohJUcaDDv70ngNXtk55iwlNpNhTs+VcQor1fznhPbRiefHqJeRIOkpcrVE7NLP8TjwuaGYaRS
MLl6IE9vDzhTyzMMEyuP1pq9KsgtsRx9S1HKR9FIJ3Jdh+vVReZIZZ2vUpC6W6IYZVcSn2i51BVr
lMRpIpj0M+Dt+VGOQVDJNE92kKz8OMHY4Xu54+OU4UZpyw4KUGsTuqwPN1q3ErWQgR5WrlcihtnJ
0tHXUeOrO8ZV/R4O03QK0dqq6mm4lyiPSMQH+FJDOvTKVTUssKZqwJz58oHhEmrARdlns87/I6KJ
ClTUFLkqqNfs+avNJVgyeY+QW5g5xAgGwax/Dj0ApQIDAQABo1QwUjAOBgNVHQ8BAf8EBAMCAYYw
DwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4EFgQUCctZf4aycI8awznjwNnpv7tNsiMwEAYJKwYBBAGC
NxUBBAMCAQAwDQYJKoZIhvcNAQEMBQADggIBAKyvPl3CEZaJjqPnktaXFbgToqZCLgLNFgVZJ8og
6Lq46BrsTaiXVq5lQ7GPAJtSzVXNUzltYkyLDVt8LkS/gxCP81OCgMNPOsduET/m4xaRhPtthH80
dK2Jp86519efhGSSvpWhrQlTM93uCupKUY5vVau6tZRGrox/2KJQJWVggEbbMwSubLWYdFQl3JPk
+ONVFT24bcMKpBLBaYVu32TxU5nhSnUgnZUP5NbcA/FZGOhHibJXWpS2qdgXKxdJ5XbLwVaZOjex
/2kskZGT4d9Mozd2TaGf+G0eHdP67Pv0RR0Tbc/3WeUiJ3IrhvNXuzDtJE3cfVa7o7P4NHmJweDy
AmH3pvwPuxwXC65B2Xy9J6P9LjrRk5Sxcx0ki69bIImtt2dmefU6xqaWM/5TkshGsRGRxpl/j8nW
ZjEgQRCHLQzWwa80mMpkg/sTV9HB8Dx6jKXB/ZUhoHHBk2dxEuqPiAppGWSZI1b7rCoucL5mxAyE
7+WL85MB+GqQk2dLsmijtWKP6T+MejteD+eMuMZ87zf9dOLITzNy4ZQ5bb0Sr74MTnB8G2+NszKT
c0QWbej09+CVgI+WXTik9KveCjCHk9hNAHFiRSdLOkKEW39lt2c0Ui2cFmuqqNh7o0JMcccMyj6D
5KbvtwEwXlGjefVwaaZBRA+GsCyRxj3qrg+E
-----END CERTIFICATE-----

e-Szigno Root CA 2017
=====================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

certSIGN Root CA G2
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Trustwave Global Certification Authority
========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Trustwave Global ECC P256 Certification Authority
=================================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Trustwave Global ECC P384 Certification Authority
=================================================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

NAVER Global Root Certification Authority
=========================================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

AC RAIZ FNMT-RCM SERVIDORES SEGUROS
===================================
-----BEGIN CERTIFICATE-----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=
-----END CERTIFICATE-----

GlobalSign Root R46
===================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

GlobalSign Root E46
===================
-----BEGIN CERTIFICATE-----
MIICCzCCAZGgAwIBAgISEdK7ujNu1LzmJGjFDYQdmOhDMAoGCCqGSM49BAMDMEYxCzAJBgNVBAYT
AkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRwwGgYDVQQDExNHbG9iYWxTaWduIFJvb3Qg
RTQ2MB4XDTE5MDMyMDAwMDAwMFoXDTQ2MDMyMDAwMDAwMFowRjELMAkGA1UEBhMCQkUxGTAXBgNV
BAoTEEdsb2JhbFNpZ24gbnYtc2ExHDAaBgNVBAMTE0dsb2JhbFNpZ24gUm9vdCBFNDYwdjAQBgcq
hkjOPQIBBgUrgQQAIgNiAAScDrHPt+ieUnd1NPqlRqetMhkytAepJ8qUuwzSChDH2omwlwxwEwkB
jtjqR+q+soArzfwoDdusvKSGN+1wCAB16pMLey5SnCNoIwZD7JIvU4Tb+0cUB+hflGddyXqBPCCj
QjBAMA4GA1UdDwEB/wQEAwIBhjAPBgNVHRMBAf8EBTADAQH/MB0GA1UdDgQWBBQxCpCPtsad0kRL
gLWi5h+xEk8blTAKBggqhkjOPQQDAwNoADBlAjEA31SQ7Zvvi5QCkxeCmb6zniz2C5GMn0oUsfZk
vLtoURMMA/cVi4RguYv/Uo7njLwcAjA8+RHUjE7AwWHCFUyqqx0LMV87HOIAl0Qx5v5zli/altP+
CAezNIm8BZ/3Hobui3A=
-----END CERTIFICATE-----

GLOBALTRUST 2020
================
-----BEGIN CERTIFICATE-----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==
-----END CERTIFICATE-----

ANF Secure Server Root CA
=========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

Certum EC-384 CA
================
-----BEGIN CERTIFICATE-----
MIICZTCCAeugAwIBAgIQeI8nXIESUiClBNAt3bpz9DAKBggqhkjOPQQDAzB0MQswCQYDVQQGEwJQ
TDEhMB8GA1UEChMYQXNzZWNvIERhdGEgU3lzdGVtcyBTLkEuMScwJQYDVQQLEx5DZXJ0dW0gQ2Vy
dGlmaWNhdGlvbiBBdXRob3JpdHkxGTAXBgNVBAMTEENlcnR1bSBFQy0zODQgQ0EwHhcNMTgwMzI2
MDcyNDU0WhcNNDMwMzI2MDcyNDU0WjB0MQswCQYDVQQGEwJQTDEhMB8GA1UEChMYQXNzZWNvIERh
dGEgU3lzdGVtcyBTLkEuMScwJQYDVQQLEx5DZXJ0dW0gQ2VydGlmaWNhdGlvbiBBdXRob3JpdHkx
GTAXBgNVBAMTEENlcnR1bSBFQy0zODQgQ0EwdjAQBgcqhkjOPQIBBgUrgQQAIgNiAATEKI6rGFtq
vm5kN2PkzeyrOvfMobgOgknXhimfoZTy42B4mIF4Bk3y7JoOV2CDn7TmFy8as10CW4kjPMIRBSqn
iBMY81CE1700LCeJVf/OTOffph8oxPBUw7l8t1Ot68KjQjBAMA8GA1UdEwEB/wQFMAMBAf8wHQYD
VR0OBBYEFI0GZnQkdjrzife81r1HfS+8EF9LMA4GA1UdDwEB/wQEAwIBBjAKBggqhkjOPQQDAwNo
ADBlAjADVS2m5hjEfO/JUG7BJw+ch69u1RsIGL2SKcHvlJF40jocVYli5RsJHrpka/F2tNQCMQC0
QoSZ/6vnnvuRlydd3LBbMHHOXjgaatkl5+r3YZJW+OraNsKHZZYuciUvf9/DE8k=
-----END CERTIFICATE-----

Certum Trusted Root CA
======================
-----BEGIN CERTIFICATE-----
MIIFwDCCA6igAwIBAgIQHr9ZULjJgDdMBvfrVU+17TANBgkqhkiG9w0BAQ0FADB6MQswCQYDVQQG
EwJQTDEhMB8GA1UEChMYQXNzZWNvIERhdGEgU3lzdGVtcyBTLkEuMScwJQYDVQQLEx5DZXJ0dW0g
Q2VydGlmaWNhdGlvbiBBdXRob3JpdHkxHzAdBgNVBAMTFkNlcnR1bSBUcnVzdGVkIFJvb3QgQ0Ew
HhcNMTgwMzE2MTIxMDEzWhcNNDMwMzE2MTIxMDEzWjB6MQswCQYDVQQGEwJQTDEhMB8GA1UEChMY
QXNzZWNvIERhdGEgU3lzdGVtcyBTLkEuMScwJQYDVQQLEx5DZXJ0dW0gQ2VydGlmaWNhdGlvbiBB
dXRob3JpdHkxHzAdBgNVBAMTFkNlcnR1bSBUcnVzdGVkIFJvb3QgQ0EwggIiMA0GCSqGSIb3DQEB
AQUAA4ICDwAwggIKAoICAQDRLY67tzbqbTeRn06TpwXkKQMlzhyC93yZn0EGze2jusDbCSzBfN8p
fktlL5On1AFrAygYo9idBcEq2EXxkd7fO9CAAozPOA/qp1x4EaTByIVcJdPTsuclzxFUl6s1wB52
HO8AU5853BSlLCIls3Jy/I2z5T4IHhQqNwuIPMqw9MjCoa68wb4pZ1Xi/K1ZXP69VyywkI3C7Te2
fJmItdUDmj0VDT06qKhF8JVOJVkdzZhpu9PMMsmN74H+rX2Ju7pgE8pllWeg8xn2A1bUatMn4qGt
g/BKEiJ3HAVz4hlxQsDsdUaakFjgao4rpUYwBI4Zshfjvqm6f1bxJAPXsiEodg42MEx51UGamqi4
NboMOvJEGyCI98Ul1z3G4z5D3Yf+xOr1Uz5MZf87Sst4WmsXXw3Hw09Omiqi7VdNIuJGmj8PkTQk
fVXjjJU30xrwCSss0smNtA0Aq2cpKNgB9RkEth2+dv5yXMSFytKAQd8FqKPVhJBPC/PgP5sZ0jeJ
P/J7UhyM9uH3PAeXjA6iWYEMspA90+NZRu0PqafegGtaqge2Gcu8V/OXIXoMsSt0Puvap2ctTMSY
njYJdmZm/Bo/6khUHL4wvYBQv3y1zgD2DGHZ5yQD4OMBgQ692IU0iL2yNqh7XAjlRICMb/gv1SHK
HRzQ+8S1h9E6Tsd2tTVItQIDAQABo0IwQDAPBgNVHRMBAf8EBTADAQH/MB0GA1UdDgQWBBSM+xx1
vALTn04uSNn5YFSqxLNP+jAOBgNVHQ8BAf8EBAMCAQYwDQYJKoZIhvcNAQENBQADggIBAEii1QAL
LtA/vBzVtVRJHlpr9OTy4EA34MwUe7nJ+jW1dReTagVphZzNTxl4WxmB82M+w85bj/UvXgF2Ez8s
ALnNllI5SW0ETsXpD4YN4fqzX4IS8TrOZgYkNCvozMrnadyHncI013nR03e4qllY/p0m+jiGPp2K
h2RX5Rc64vmNueMzeMGQ2Ljdt4NR5MTMI9UGfOZR0800McD2RrsLrfw9EAUqO0qRJe6M1ISHgCq8
CYyqOhNf6DR5UMEQGfnTKB7U0VEwKbOukGfWHwpjscWpxkIxYxeU72nLL/qMFH3EQxiJ2fAyQOaA
4kZf5ePBAFmo+eggvIksDkc0C+pXwlM2/KfUrzHN/gLldfq5Jwn58/U7yn2fqSLLiMmq0Uc9Nneo
WWRrJ8/vJ8HjJLWG965+Mk2weWjROeiQWMODvA8s1pfrzgzhIMfatz7DP78v3DSk+yshzWePS/Tj
6tQ/50+6uaWTRRxmHyH6ZF5v4HaUMst19W7l9o/HuKTMqJZ9ZPskWkoDbGs4xugDQ5r3V7mzKWmT
OPQD8rv7gmsHINFSH5pkAnuYZttcTVoP0ISVoDwUQwbKytu4QTbaakRnh6+v40URFWkIsr4WOZck
bxJF0WddCajJFdr60qZfE2Efv4WstK2tBZQIgx51F9NxO5NQI1mg7TyRVJ12AMXDuDjb
-----END CERTIFICATE-----

TunTrust Root CA
================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

HARICA TLS RSA Root CA 2021
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----

HARICA TLS ECC Root CA 2021
===========================
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----


================================================
FILE: caseStudies/allCases.R
================================================
#All Case Studies

#linear Regression - Marketing

#https://medium.com/x8-the-ai-community/linear-regression-in-r-example-in-code-a84af29222fb
#data --------
data('marketing', package='datarium')
?marketing
head(marketing)
write.csv(marketing, 'E:/8-datasets/marketing.csv', row.names = F)
#https://github.com/kassambara/datarium
#it is a dataset containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. The first three columns are the advertising budget in thousands of dollars along with the fourth column as sales. The advertising experiment has been repeated 200 times. Hence, it has 200 rows.

#Loading and understanding the dataset
#Preparing the data
#Building the model
#Model accuracy analysis

pacman::p_load(datarium, caTools, ggplot2, GGally)

#dataunderstanding 
summary(marketing)
dim(marketing)
names(marketing)

##Pairwise plotting technique 1
plot(marketing, col="purple", main="Plotting Pairs Against Each Other")
#Pairwise plotting technique 2
GGally::ggpairs(marketing)

# preparation----

splitRatio = 0.75
#Splitting The Data
set.seed(101)# Set Seed so that same sample can be reproduced in future also
#Now Selecting 75% of data as sample from total 'n' rows of the data
sample = sample.split(marketing$youtube, SplitRatio = splitRatio)
train = subset(marketing, sample == TRUE)
test = subset(marketing, sample == FALSE)
train_size = dim(train)
test_size = dim(test)

#creating the model
Model <- lm(sales ~ youtube + facebook + newspaper, data = marketing)
summary(Model)

summary(Model)$r.squared
attributes(summary(Model))
summary(Model)$adj.r.squared
summary(Model)$fstatistic
summary(Model)$coefficients

#Predicting
pred <- predict(Model, test)
#compare
cbind(test$sales, pred) 

#rmse
original = test$sales; predicted=pred
d = original-predicted
(mse = mean((d)^2))
(mae = mean(abs(d)))
(rmse = sqrt(mse))
R2 = 1-(sum((d)^2)/sum((original-mean(original))^2))

#---
pacman::p_load(Metrics, DescTools)
Metrics::mse(actual=original, predicted=predicted)
Metrics::rmse(actual=original, predicted=predicted)
Metrics::mape(actual=original, predicted=predicted)

DescTools::RMSE(x=predicted, ref=original)
DescTools::MAPE(x=predicted, ref=original)

#Plotting the predicted values against the actual values
(seqNos = seq(1, nrow(test)))
plotValues = data.frame(seqNos, actual=test$sales, predicted=pred, diff=pred - test$sales)
head(plotValues)

colors = c("actual" = "blue", "predicted" = "red")

ggplot(data=plotValues, aes(x=seqNos)) + geom_point(aes(y=actual, color='actual')) + geom_line(aes(y=actual, color= 'actual')) + geom_point(aes(y=predicted, color='predicted')) + geom_line(aes(y=predicted, color='predicted')) + labs(title ='Compare Actual and Predicted Values with differences',  x='Serial No', y='Actual(Blue), Predicted(Red)') + geom_text(aes(label=round(diff,1) , y=predicted))  + scale_color_manual(values = colors)


#predict
newdata = data.frame(youtube =c(50,60,70), facebook=c(20, 30, 40), newspaper = c(70,75,80))
newdata
predict(Model, newdata=newdata)
head(marketing)




#logistic Regression
#https://www.rdocumentation.org/packages/ISLR/versions/1.2/topics/Smarket
pacman::p_load(Amelia,mlbench, ISLR)
data(Smarket)
missmap(Smarket, col=c("blue", "red"), legend=FALSE)

#data description
?Smarket
head(Smarket)
dim(Smarket) # 2001 and 2005.
summary(Smarket)
sum(is.na(Smarket))  #no missing values

#visualise
head(Smarket)
par(mfrow=c(1,1))
hist(Smarket[,2])
boxplot(Smarket[,2])

round(cor(Smarket[,1:8]),2)
library(corrplot)
correlations <- cor(Smarket[,1:8])
corrplot(correlations, method="circle")
#no correlation

pairs(Smarket[, 1:8])

library(caret)
x <- Smarket[,1:8]
y <- Smarket[,9]
scales <- list(x=list(relation="free"), y=list(relation="free"))
featurePlot(x=x, y=y, plot="density", scales=scales)


#model
# Logistics Regression
glm.fit <- glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = Smarket, family = binomial)

glm.fit
summary(glm.fit) #none are significant
glm.probs <- predict(glm.fit,type = "response")
glm.probs[1:5]
glm.pred <- ifelse(glm.probs > 0.5, "Up", "Down")
table(glm.pred,Smarket$Direction)


================================================
FILE: data/AuctionsData - set1.csv
================================================
RecID,AuctionCurrency,AuctionDate,AuctionID,Indicator-Day-Afternoon,Auction Title,Auction House,AuctionLocation,lot LotNo,artwork ID,Artwork Title,ArtworkCategory,Artist_Full_Name,lot_EstimateHigh,lot_EstimateLow,Sale Price,Lot Status
288822,,,,,,,,3166,,,,,191810000,137080000,207017950,
numeric,character,character,numeric,character,character,character,character,numeric,numeric,character,factor,character,numeric,numeric,numeric,factor
recid,auccur,aucdate,aucid,idafn,auctitle,auchouse,aucloc,lotno,awid,awtitle,awcat,arname,lotesthigh,lotestlow,saleprice,lotstatus
2547,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,1.00,6998013,Untitled,PAINTINGS,Christopher Wool,"600,000","400,000","729,000",Sold
2548,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,10.00,6998014,Angel Of The North,SCULPTURE,Antony Gormley,"2,000,000","1,500,000","2,860,600",Sold
2549,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,11.00,6998015,Some People (Welcome Series),PAINTINGS,Hurvin Anderson,"800,000","600,000","1,029,000",Sold
2550,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,12.00,6998016,The Architect's Home In The Ravine,PAINTINGS,Peter Doig,"18,000,000","14,000,000","14,376,400",Sold
2551,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,13.00,6998017,A Swan Comforting A Snake,PAINTINGS,Cecily Brown,"1,500,000","1,000,000","1,209,000",Sold
2552,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,14.00,6998018,Eifrau Die Man Nicht Schubladieren Kann (Egg Lady Who Can’t Be Piegeonholed),PAINTINGS,Martin Kippenberger,"3,000,000","2,000,000","2,169,000",Sold
2553,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,15.00,6998019,Memory Ware Flat #29,PAINTINGS,Mike Kelley,"2,000,000","1,500,000","1,689,000",Sold
2554,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,16.00,6998020,Yellow Cell With Triple Conduit,PAINTINGS,Peter Halley,"350,000","250,000","513,000",Sold
2555,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,17.00,6998021,Untitled,SCULPTURE,Richard Prince,"1,500,000","1,000,000","2,049,000",Sold
2556,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,18.00,6998022,Nov. 8,PAINTINGS,On Kawara,"800,000","600,000","789,000",Sold
2557,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,19.00,6998023,Untitled,PAINTINGS,Rudolf Stingel,"6,000,000","4,000,000","4,667,000",Sold
2558,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,2.00,6998024,Untitled,PAINTINGS,Laura Owens,"350,000","250,000","435,000",Sold
2559,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,20.00,6998025,Amanti (Lovers),PAINTINGS,Michelangelo Pistoletto,"3,000,000","2,300,000","2,634,800",Sold
2560,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,21.00,6998026,Tenda Blu (Blue Curtain),PAINTINGS,Michelangelo Pistoletto,"600,000","400,000","621,000",Sold
2561,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,22.00,6998027,Figura Femminile Con Fiori (Female Figure With Flowers),SCULPTURE,Lucio Fontana,"2,000,000","1,500,000","1,809,000",Sold
2562,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,23.00,6998028,"Oiks, Tarts, Wierdoes And Contemporary Art",SCULPTURE,Grayson Perry,"120,000","80,000","175,000",Sold
2563,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,24.00,6998029,Border Country,PAINTINGS,Peter Doig,"700,000","500,000","609,000",Sold
2564,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,25.00,6998030,In Central Park,PAINTINGS,Howard Hodgkin,"800,000","600,000",,Unsold
2565,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,26.00,6998031,"Different Kinds Of Water Pouring Into A Swimming Pool, Santa Monica",PAINTINGS,David Hockney,"8,000,000","6,000,000",,Unsold
2566,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,27.00,6998032,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"3,000,000","2,000,000","5,005,700",Sold
2567,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,28.00,6998033,Gelbgrun (Yellow-green),PAINTINGS,Gerhard Richter,"10,000,000","7,000,000","10,876,500",Sold
2568,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,29.00,6998034,Untitled (19.3.86),PAINTINGS,Gerhard Richter,"900,000","700,000","849,000",Sold
2569,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,3.00,6998035,Untitled,PAINTINGS,Albert Oehlen,"1,500,000","1,000,000","1,929,000",Sold
2570,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,30.00,6998036,Kleine Geister (Little Ghosts),SCULPTURE,Thomas Schutte,"450,000","350,000","405,000",Sold
2571,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,31.00,6998037,Alphabet (U),SCULPTURE,Danh Vo,"200,000","150,000",,Unsold
2572,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,32.00,6998038,After Micro W #2,PAINTINGS,Sam Gilliam,"200,000","150,000","200,000",Sold
2573,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,33.00,6998039,Untitled,SCULPTURE,Francesco Lo Savio,"350,000","250,000","309,000",Sold
2574,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,34.00,6998040,Edera (Ivy),SCULPTURE,Luciano Fabro,"700,000","500,000","609,000",Sold
2575,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,35.00,6998041,Sandinsel (Sand Island),SCULPTURE,Gunther Uecker,"800,000","600,000","753,000",Sold
2576,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,36.00,6998042,Untitled (Relief No. 348),SCULPTURE,Sergio de Camargo,"900,000","700,000",,Unsold
2577,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,37.00,6998043,Infinity Nets (Oq4),PAINTINGS,Yayoi Kusama,"1,200,000","800,000","1,269,000",Sold
2578,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,38.00,6998044,Untitled,PAINTINGS,Rudolf Stingel,"2,000,000","1,500,000","1,869,000",Sold
2579,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,39.00,6998045,Caribou Skull,SCULPTURE,Sherrie Levine,"600,000","400,000","489,000",Sold
2580,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,4.00,6998046,Untitled,PAINTINGS,Christopher Wool,"6,200,000","4,200,000","10,424,900",Sold
2581,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,40.00,6998047,The Housewife And The Grocer,PAINTINGS,Richard Prince,"1,800,000","1,200,000","1,269,000",Sold
2582,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,41.00,6998048,O.t. (Death’s Heads),SCULPTURE,Rosemarie Trockel,"600,000","400,000","465,000",Sold
2583,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,42.00,6998049,Down Under,SCULPTURE,Jason Rhoades,"550,000","350,000","345,000",Sold
2584,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,43.00,6998050,Untitled,PAINTINGS,Albert Oehlen,"450,000","350,000","429,000",Sold
2585,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,44.00,6998051,Different From Case To Case,PAINTINGS,Sigmar Polke,"600,000","400,000","753,000",Sold
2586,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,45.00,6998052,Greifbar 40,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","297,000",Sold
2587,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,46.00,6998053,Untitled (Cowboy),PHOTOGRAPHS,Richard Prince,"900,000","700,000","1,029,000",Sold
2588,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,47.00,6998054,Mount Something Or Other,PAINTINGS,Ed Ruscha,"1,200,000","800,000",,Unsold
2589,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,48.00,6998055,Hammer And Sickle,PAINTINGS,Andy Warhol,"3,500,000","2,500,000","3,538,000",Sold
2590,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,49.00,6998056,Two Guys Twice,PAINTINGS,Richard Prince,"800,000","600,000","693,000",Sold
2591,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,5.00,6998057,Untitled (Figure Jmb #1),PAINTINGS,Jean-Michel Basquiat,"2,800,000","2,400,000","2,634,800",Sold
2592,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,50.00,6998058,African Elephant,PAINTINGS,Andy Warhol,"1,800,000","1,400,000","1,569,000",Sold
2593,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,51.00,6998059,Ero,PAINTINGS,Jean-Michel Basquiat,"2,000,000","1,500,000","2,409,000",Sold
2594,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,52.00,6998060,Pink Circus: Embrace Peace And Darkness Within Thy Heart,PAINTINGS,Takashi Murakami,"800,000","600,000","849,000",Sold
2595,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,53.00,6998061,Marilyn Monroe (Marilyn),PRINTS,Andy Warhol,"1,500,000","1,000,000","2,349,000",Sold
2596,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,54.00,6998062,Bacchus At The Seaside,PAINTINGS,Banksy,"350,000","250,000","669,000",Sold
2597,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,55.00,6998063,"Sometimes Life Is Really, Really, Really Dark",PAINTINGS,Damien Hirst,"500,000","300,000","309,000",Sold
2598,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,56.00,6998064,Study To Homage To The Square: Evident,PAINTINGS,Josef Albers,"650,000","450,000","429,000",Sold
2599,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,57.00,6998065,"Untitled (July 23, 1988)",TEXTILES,Alighiero e Boetti,"350,000","250,000","381,000",Sold
2600,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,58.00,6998066,Incontri E Scontri (Meetings And Collisions),WORKS ON PAPER,Alighiero e Boetti,"300,000","200,000","345,000",Sold
2601,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,59.00,6998067,"Concetto Spaziale, Attese",PAINTINGS,Lucio Fontana,"1,200,000","800,000","1,329,000",Sold
2602,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,6.00,6998068,Wind,PAINTINGS,Gerhard Richter,"3,500,000","2,500,000","3,086,400",Sold
2603,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,60.00,6998069,Three White Dots On Orange Stack,SCULPTURE,Alexander Calder,"650,000","450,000",,Unsold
2604,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,61.00,6998070,Untitled Film Still #57,PHOTOGRAPHS,Cindy Sherman,"300,000","200,000","225,000",Sold
2605,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,7.00,6998071,Autounfall (Car Crash) Or The Three Sisters,PAINTINGS,Sigmar Polke,"2,000,000","1,500,000","2,169,000",Sold
2606,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,8.00,6998072,1025 Farben,PAINTINGS,Gerhard Richter,"7,000,000","5,000,000","7,376,600",Sold
2607,GBP,3/7/2018,22,Contemporary Art Evening Auction,Contemporary Art Evening Auction,Sotheby's,London,9.00,6998073,Loving You,PAINTINGS,Damien Hirst,"1,200,000","800,000","993,000",Sold
2608,GBP,3/8/2018,23,20th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,1.00,6998817,Bright Moments: For R.R. Kirk,PAINTINGS,Jack Whitten,"300,000","200,000","333,000",Sold
2609,GBP,3/8/2018,23,21st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,2.00,6998818,Politics,PAINTINGS,Lynette Yiadom Boakye,"90,000","70,000","273,000",Sold
2610,GBP,3/8/2018,23,22nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,3.00,6998819,Greifbar 27,PHOTOGRAPHS,Wolfgang Tillmans,"300,000","200,000","465,000",Sold
2611,GBP,3/8/2018,23,23rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,4.00,6998820,The Man,PHOTOGRAPHS,Gilbert and George,"500,000","300,000","321,000",Sold
2612,GBP,3/8/2018,23,24th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,5.00,6998821,T-riffic,PAINTINGS,Allen Jones,"350,000","250,000","705,000",Sold
2613,GBP,3/8/2018,23,25th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,6.00,6998822,Family Group,SCULPTURE,Henry Moore,"350,000","250,000","93,000",Sold
2614,GBP,3/8/2018,23,26th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,7.00,6998823,Piccolo Cavaliere,SCULPTURE,Marino Marini,"600,000","400,000","429,000",Sold
2615,GBP,3/8/2018,23,27th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,8.00,6998824,Two Three-Quarter Figures on Base,SCULPTURE,Henry Moore,"600,000","400,000","729,000",Sold
2616,GBP,3/8/2018,23,28th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,9.00,6998825,Nu allongé I (Aurore),SCULPTURE,Henri Matisse,"7,000,000","5,000,000","14,859,000",Sold
2617,GBP,3/8/2018,23,29th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,10.00,6998826,La Dormeuse,PAINTINGS,Pablo Picasso,"18,000,000","12,000,000","41,859,000",Sold
2618,GBP,3/8/2018,23,30th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,11.00,6998827,Small Naked Portrait,PAINTINGS,Lucian Freud,"600,000","400,000","789,000",Sold
2619,GBP,3/8/2018,23,31st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,12.00,6998828,Untitled,WORKS ON PAPER,Cy Twombly,"700,000","500,000","609,000",Sold
2620,GBP,3/8/2018,23,32nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,13.00,6998829,The Pilgrim,PAINTINGS,Marlene Dumas,"2,000,000","1,500,000","1,749,000",Sold
2621,GBP,3/8/2018,23,33rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,14.00,6998830,Helter Skelter I,PAINTINGS,Mark Bradford,"8,000,000","6,000,000","8,671,500",Sold
2622,GBP,3/8/2018,23,34th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,15.00,6998831,Singing in the Rain,PAINTINGS,Luc Tuymans,"700,000","500,000","729,000",Sold
2623,GBP,3/8/2018,23,35th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,16.00,6998832,Untitled,PAINTINGS,Albert Oehlen,"350,000","250,000","345,000",Sold
2624,GBP,3/8/2018,23,36th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,17.00,6998833,P.D. Idol,PAINTINGS,Georg Baselitz,"2,000,000","1,500,000","1,929,000",Sold
2625,GBP,3/8/2018,23,37th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,18.00,6998834,Die Meistersinger,PAINTINGS,Anselm Kiefer,"2,000,000","1,500,000","1,809,000",Sold
2626,GBP,3/8/2018,23,38th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,19.00,6998835,Untitled,PAINTINGS,Sigmar Polke,"1,200,000","800,000","969,000",Sold
2627,GBP,3/8/2018,23,39th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,20.00,6998836,Omegasachen-35,PAINTINGS,Albert Oehlen,"600,000","400,000","465,000",Sold
2628,GBP,3/8/2018,23,40th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,21.00,6998837,Untitled,SCULPTURE,Rudolf Stingel,"6,000,000","4,000,000","5,746,500",Sold
2629,GBP,3/8/2018,23,41st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,22.00,6998838,Monochrome rose sans titre (MP 27),WORKS ON PAPER,Yves Klein,"1,200,000","800,000","1,029,000",Sold
2630,GBP,3/8/2018,23,42nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,23.00,6998839,Bridlington Violets,PAINTINGS,David Hockney,"700,000","500,000","609,000",Sold
2631,GBP,3/8/2018,23,43rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,24.00,6998840,Woman & Man,PAINTINGS,George Condo,"800,000","600,000","1,329,000",Sold
2632,GBP,3/8/2018,23,44th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,25.00,6998841,Untitled #220,PHOTOGRAPHS,Cindy Sherman,"200,000","150,000","175,000",Sold
2633,GBP,3/8/2018,23,45th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,26.00,6998842,Untitled,PAINTINGS,Jean-Michel Basquiat,"1,500,000","1,000,000","1,089,000",Sold
2634,GBP,3/8/2018,23,46th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,27.00,6998843,Profil Genre Aztèque,PAINTINGS,Jean Dubuffet,"1,800,000","1,200,000","1,449,000",Sold
2635,GBP,3/8/2018,23,47th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,28.00,6998844,Colombe blanche,PAINTINGS,Max Ernst,"500,000","350,000","393,000",Sold
2636,GBP,3/8/2018,23,48th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,29.00,6998845,Le Surréalisme et la peinture,WORKS ON PAPER,Max Ernst,"450,000","350,000","429,000",Sold
2637,GBP,3/8/2018,23,49th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,30.00,6998846,Ohne Titel,WORKS ON PAPER,Wassily Kandinsky,"550,000","350,000",,Unsold
2638,GBP,3/8/2018,23,50th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,31.00,6998847,Feuille de vigne femelle,SCULPTURE,Marcel Duchamp,"400,000","300,000","501,000",Sold
2639,GBP,3/8/2018,23,51st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,32.00,6998848,Diamond Dust Shoes (Pink),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2640,GBP,3/8/2018,23,52nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,33.00,6998849,Diamond Dust Shoes (Green),PAINTINGS,Andy Warhol,"1,800,000","1,400,000",,Unsold
2641,GBP,3/8/2018,23,53rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,34.00,6998850,"Beautiful, Half a Red Headless Centipede (Painting)",PAINTINGS,Damien Hirst,"350,000","250,000","273,000",Sold
2642,GBP,3/8/2018,23,54th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,35.00,6998851,Grey Red,PAINTINGS,Sean Scully,"800,000","600,000","1,149,000",Sold
2643,GBP,3/8/2018,23,55th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,36.00,6998852,"Peinture 130 x 162 cm, 12 mai 1965",PAINTINGS,Pierre Soulages,"1,200,000","800,000","933,000",Sold
2644,GBP,3/8/2018,23,56th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,37.00,6998853,Cuadro 128,PAINTINGS,Manolo Millares,"150,000","100,000","206,250",Sold
2645,GBP,3/8/2018,23,57th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,38.00,6998854,Tôle,WORKS ON PAPER,Raymond Hains,"200,000","150,000",,Unsold
2646,GBP,3/8/2018,23,58th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,39.00,6998855,Opalka 1965/1 – ∞ Detail 5210331 - 5226270,PAINTINGS,Roman Opalka,"600,000","400,000",,Unsold
2647,GBP,3/8/2018,23,59th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,40.00,6998856,Superficie bianca,PAINTINGS,Enrico Castellani,"350,000","250,000","267,000",Sold
2648,GBP,3/8/2018,23,60th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,41.00,6998857,"Concetto spaziale, Attese",PAINTINGS,Lucio Fontana,"850,000","650,000","669,000",Sold
2649,GBP,3/8/2018,23,61st Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,42.00,6998858,Métal sur le Losange,SCULPTURE,Jesus Rafael Soto,"250,000","180,000","321,000",Sold
2650,GBP,3/8/2018,23,62nd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,43.00,6998859,Reihung,WORKS ON PAPER,Gunther Uecker,"350,000","250,000","249,000",Sold
2651,GBP,3/8/2018,23,63rd Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,44.00,6998860,R72-73-M-14,PAINTINGS,Jan Schoonhoven,"350,000","250,000","441,000",Sold
2652,GBP,3/8/2018,23,64th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,45.00,6998861,Concetto spaziale,PAINTINGS,Lucio Fontana,"700,000","500,000",,Unsold
2653,GBP,3/8/2018,23,65th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,46.00,6998862,Homage to the Square,PAINTINGS,Josef Albers,"500,000","300,000","381,000",Sold
2654,GBP,3/8/2018,23,66th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,47.00,6998863,Found,PAINTINGS,Adrian Ghenie,"500,000","300,000","249,000",Sold
2655,GBP,3/8/2018,23,67th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,48.00,6998864,Without You,SCULPTURE,Damien Hirst,"700,000","500,000","585,000",Sold
2656,GBP,3/8/2018,23,68th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,49.00,6998865,Apparition,PAINTINGS,Damien Hirst,"700,000","500,000","609,000",Sold
2657,GBP,3/8/2018,23,69th Century & Contemporary Art Evening Sale,20th Century & Contemporary Art Evening Sale,Phillips,London,50.00,6998866,Skulldiver II,PAINTINGS,Cecily Brown,"600,000","400,000","513,000",Sold

================================================
FILE: data/Churn.csv
================================================
customerID,gender,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Churn
7590-VHVEG,Female,0,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.85,29.85,No
5575-GNVDE,Male,0,No,No,34,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,56.95,1889.5,No
3668-QPYBK,Male,0,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.85,108.15,Yes
7795-CFOCW,Male,0,No,No,45,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),42.3,1840.75,No
9237-HQITU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,151.65,Yes
9305-CDSKC,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,820.5,Yes
1452-KIOVK,Male,0,No,Yes,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.1,1949.4,No
6713-OKOMC,Female,0,No,No,10,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.75,301.9,No
7892-POOKP,Female,0,Yes,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.8,3046.05,Yes
6388-TABGU,Male,0,No,Yes,62,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),56.15,3487.95,No
9763-GRSKD,Male,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.95,587.45,No
7469-LKBCI,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.95,326.8,No
8091-TTVAX,Male,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),100.35,5681.1,No
0280-XJGEX,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.7,5036.3,Yes
5129-JLPIS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,2686.05,No
3655-SNQYZ,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.25,7895.15,No
8191-XWSZG,Female,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,1022.95,No
9959-WOFKT,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),106.7,7382.25,No
4190-MFLUW,Female,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,528.35,Yes
4183-MYFRB,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,1862.9,No
8779-QRDMV,Male,1,No,No,1,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,39.65,39.65,Yes
1680-VDCWW,Male,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,202.25,No
1066-JKSGK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes
3638-WEABW,Female,0,Yes,No,58,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),59.9,3505.1,No
6322-HRPFA,Male,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.6,2970.3,No
6865-JZNKO,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1530.6,No
6467-CHFZW,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.35,4749.15,Yes
8665-UTDHZ,Male,0,Yes,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.2,30.2,Yes
5248-YGIJN,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.25,6369.45,No
8773-HHUOZ,Female,0,No,Yes,17,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,64.7,1093.1,Yes
3841-NFECX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),96.35,6766.95,No
4929-XIHVW,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.5,181.65,No
6827-IEAUQ,Female,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,66.15,1874.45,No
7310-EGVHZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,20.2,No
3413-BMNZE,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.25,45.25,No
6234-RAAPL,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),99.9,7251.7,No
6047-YHPVI,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,316.9,Yes
6572-ADKRS,Female,0,No,No,46,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),74.8,3548.3,No
5380-WJKOV,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,3549.25,Yes
8168-UQWWF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.85,1105.4,Yes
8865-TNMNX,Male,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,49.55,475.7,No
9489-DEDVP,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),69.2,4872.35,No
9867-JCZSP,Female,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,418.25,No
4671-VJLCL,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.85,4861.45,No
4080-IIARD,Female,0,Yes,No,13,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,76.2,981.45,No
3714-NTNFO,Female,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.5,3906.7,No
5948-UJZLF,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,97,No
7760-OYPDY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.65,144.15,Yes
7639-LIAYI,Male,0,No,No,52,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.75,4217.8,No
2954-PIBKO,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.15,4254.1,No
8012-SOUDQ,Female,1,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.25,3838.75,No
9420-LOJKX,Female,0,No,No,15,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.1,1426.4,Yes
6575-SUVOI,Female,1,Yes,No,25,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1752.65,No
7495-OOKFY,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.65,633.3,Yes
4667-QONEA,Female,1,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),74.85,4456.35,No
1658-BYGOY,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,1752.55,Yes
8769-KKTPH,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),99.65,6311.2,No
5067-XJQFU,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.45,7076.35,No
3957-SQXML,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.95,894.3,No
5954-BDFSG,Female,0,No,No,72,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.5,7853.7,No
0434-CSFON,Female,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,4707.1,No
1215-FIGMP,Male,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.9,5450.7,No
0526-SXDJP,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),42.1,2962,No
0557-ASKVU,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),54.4,957.1,No
5698-BQJOH,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,857.25,Yes
5122-CYFXA,Female,0,No,No,3,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.3,244.1,No
8627-ZYGSZ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Electronic check,78.9,3650.35,No
3410-YOQBQ,Female,0,No,No,31,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,79.2,2497.2,No
3170-NMYVV,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,930.9,No
7410-OIEDU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.85,887.35,No
2273-QCKXA,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.05,49.05,No
0731-EBJQB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,1090.65,No
1891-QRQSA,Male,1,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,7099,No
8028-PNXHQ,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.25,1424.6,No
5630-AHZIL,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.5,177.4,No
2673-CXQEU,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,110.5,6139.5,No
6416-JNVRK,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),55.65,2688.85,No
5590-ZSKRV,Female,0,Yes,Yes,8,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.65,482.25,No
0191-ZHSKZ,Male,1,No,No,30,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.75,2111.3,No
3887-PBQAO,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.9,1216.6,No
5919-TMRGD,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.35,79.35,Yes
8108-UXRQN,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,50.55,565.35,No
9191-MYQKX,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.15,496.9,Yes
9919-YLNNG,Female,0,No,No,42,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.8,4327.5,No
0318-ZOPWS,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.15,973.35,No
4445-ZJNMU,Male,0,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,918.75,No
4808-YNLEU,Female,0,Yes,No,35,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),62.15,2215.45,No
1862-QRWPE,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1057,No
2796-NNUFI,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,927.1,No
3016-KSVCP,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,33.75,1009.25,No
4767-HZZHQ,Male,0,Yes,Yes,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),82.05,2570.2,No
2424-WVHPL,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74.7,74.7,No
7233-PAHHL,Male,0,Yes,Yes,66,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84,5714.25,No
6067-NGCEU,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),111.05,7107,No
9848-JQJTX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),100.9,7459.05,No
8637-XJIVR,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,927.35,Yes
9803-FTJCG,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.85,4748.7,No
0278-YXOOG,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.05,113.85,Yes
3212-KXOCR,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1107.2,No
4598-XLKNJ,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,2514.5,Yes
6380-ARCEH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
3679-XASPY,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No
7123-WQUHX,Male,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),95,3605.6,No
5386-THSLQ,Female,1,Yes,No,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),45.55,3027.25,No
3192-NQECA,Male,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,7611.85,Yes
6180-YBIQI,Male,0,No,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,100.2,No
6728-DKUCO,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.15,7303.05,No
9750-BOOHV,Female,0,No,No,32,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,30.15,927.65,No
8597-CWYHH,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Mailed check,94.35,3921.3,No
2848-YXSMW,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1363.25,No
0486-HECZI,Male,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.75,5238.9,Yes
4549-ZDQYY,Female,0,No,No,52,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),57.95,3042.25,No
5712-AHQNN,Female,0,No,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,3954.1,No
4846-WHAFZ,Female,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,2868.15,Yes
5256-SKJGO,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,54.6,3423.5,No
3071-VBYPO,Male,0,Yes,Yes,3,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,89.85,248.4,No
9560-BBZXK,Female,0,No,No,36,No,No phone service,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),31.05,1126.35,No
5299-RULOA,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,1064.65,Yes
8402-OOOHJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.65,835.15,No
9445-ZUEQE,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.2,2151.6,No
1091-SOZGA,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.8,5515.45,No
2928-HLDBA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,112.75,No
0404-SWRVG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,229.55,Yes
6497-TILVL,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.7,350.35,No
7219-TLZHO,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,62.9,No
4622-YNKIJ,Male,0,No,No,33,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Two year,Yes,Electronic check,88.95,3027.65,No
4412-YLTKF,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.05,2135.5,Yes
6734-PSBAW,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),23.55,1723.95,No
3930-ZGWVE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No
2639-UGMAZ,Male,1,No,No,71,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,56.45,3985.35,No
2876-GZYZC,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,1215.65,No
6207-WIOLX,Female,0,Yes,Yes,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),58.6,1502.65,Yes
8587-XYZSF,Male,0,No,No,67,Yes,No,DSL,No,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),50.55,3260.1,No
3091-FYHKI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.45,35.45,Yes
2372-HWUHI,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.35,81.25,Yes
7799-LGRDP,Female,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1188.2,No
7850-VWJUU,Female,0,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75,1778.5,No
3774-VBNXY,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,1277.75,No
6217-KDYWC,Male,0,No,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,1170.55,No
0390-DCFDQ,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes
3146-MSEGF,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.05,6425.65,No
4080-OGPJL,Female,0,No,No,8,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.15,563.65,Yes
1095-WGNGG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.05,5971.25,No
2636-SJDOU,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),84.3,5289.05,No
1131-QQZEB,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1756.2,No
5716-EZXZN,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),99.05,6416.7,No
6837-BJYDQ,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,61.35,No
2135-RXIHG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes
6440-DKQGE,Male,0,No,Yes,30,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),64.5,1929.95,No
3466-BYAVD,Male,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.5,1071.4,No
3780-YVMFA,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,68.55,564.35,No
3874-EQOEP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95,655.5,Yes
1679-JRFBR,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),108.15,7930.55,No
9073-ZZIAY,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,86.1,5215.25,No
3077-RSNTJ,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,113.5,No
6551-GNYDG,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.9,1152.8,No
9167-APMXZ,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.15,1821.95,No
2749-CTKAJ,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,419.9,No
6371-NZYEG,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Mailed check,64.25,1024,No
7554-NEWDD,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,251.6,No
8992-VONJD,Female,0,No,Yes,13,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,56,764.55,No
0867-MKZVY,Female,0,Yes,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,82.4,1592.35,Yes
4482-EWFMI,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,135.2,No
4648-YPBTM,Male,0,No,No,53,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),73.9,3958.25,No
2907-ILJBN,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,233.9,No
6345-FZOQH,Male,0,Yes,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1363.45,No
3376-BMGFE,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.9,273,Yes
5997-OPVFA,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6254.45,No
3445-HXXGF,Male,1,Yes,No,58,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45.3,2651.2,Yes
1159-WFSGR,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.4,321.4,No
7654-YWJUF,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),84.25,3539.25,No
1875-QIVME,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,242.8,Yes
6727-IOTLZ,Male,0,Yes,No,14,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81.95,1181.75,No
0691-JVSYA,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.85,5000.2,Yes
5918-VUKWP,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.55,654.55,No
1744-JHKYS,Female,0,Yes,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,24.7,780.2,No
2656-FMOKZ,Female,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,1145.7,Yes
2070-FNEXE,Female,1,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),76.45,503.6,Yes
5947-SGKCL,Female,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,1559.25,No
3712-PKXZA,Male,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1252,No
6317-YPKDH,Female,0,No,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.95,29.95,Yes
6582-OIVSP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.3,45.3,No
9367-WXLCH,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.5,662.65,Yes
5524-KHNJP,Male,0,Yes,Yes,33,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),74.75,2453.3,No
1918-ZBFQJ,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.25,1111.65,Yes
1024-GUALD,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes
4827-USJHP,Male,0,No,No,20,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.8,1023.85,No
8167-GJLRN,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.4,82.15,No
0956-SYCWG,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.65,244.8,No
8017-UVSZU,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.6,2379.1,No
7100-FQPRV,Male,0,Yes,Yes,43,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),71.9,3173.35,No
2472-OVKUP,Male,0,Yes,No,6,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,91,531,Yes
2984-RGEYA,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1375.4,No
9680-NIAUV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.7,8129.3,No
2146-EGVDT,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1192.7,No
2604-IJPDU,Female,0,Yes,No,20,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.55,1901.65,No
9178-JHUVJ,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,587.4,No
6168-YBYNP,Male,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.35,6519.75,No
7255-SSFBC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.25,8041.65,No
3645-DEYGF,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,20.75,No
9323-HGFWY,Female,0,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),101.9,2681.15,No
8544-GOQSH,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),80.05,1112.3,No
3363-DTIVD,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,7405.5,No
7018-WBJNK,Male,0,No,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),78.3,1033.95,No
9142-KZXOP,Male,0,No,No,44,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),68.85,2958.95,No
7674-YTAFD,Female,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),79.95,2684.85,No
6348-SNFUS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),55.45,4179.2,No
1285-OKIPP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.9,79.9,Yes
7825-ECJRF,Female,0,No,No,19,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.6,1934.45,Yes
1347-KTTTA,Male,0,Yes,No,64,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6654.1,No
7841-TZDMQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),46,84.5,Yes
4195-NZGTA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,No
7157-SMCFK,Male,0,No,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,1124.2,No
4709-LKHYG,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20,540.05,No
2504-DSHIH,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.8,1975.85,No
0699-NDKJM,Female,0,Yes,No,57,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),58.75,3437.45,No
9286-BHDQG,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),45.25,3139.8,No
0230-WEQUW,Male,0,Yes,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),56.6,3789.2,No
2040-LDIWQ,Male,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5324.5,No
6496-JDSSB,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80,624.6,No
9408-SSNVZ,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,268.35,Yes
4443-EMBNA,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1836.9,No
6469-MRVET,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.2,20.2,No
0742-MOABM,Male,0,Yes,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,179.35,Yes
5961-VUSRV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.35,219.35,No
6778-JFCMK,Male,0,No,No,24,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Mailed check,50.6,1288.75,No
6341-JVQGF,Female,0,Yes,Yes,31,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,81.15,2545.75,No
2232-DMLXU,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.2,55.2,Yes
4811-JBUVU,Male,0,No,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.9,2723.15,No
0945-TSONX,Female,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.3,4107.25,No
2651-ZCBXV,Male,0,No,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108,5760.65,No
3316-UWXUY,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),93.5,4747.5,No
8937-RDTHP,Male,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.6,84.6,Yes
7083-MIOPC,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1566.9,No
1984-GPTEH,Female,0,No,No,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.15,702,No
1251-KRREG,Male,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,54.4,114.1,Yes
0621-JFHOL,Female,0,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Two year,Yes,Mailed check,29.6,299.05,No
9903-LYSAB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.15,1305.95,No
0094-OIFMO,Female,1,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,1120.3,Yes
9227-UAQFT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,284.35,No
7301-ABVAD,Female,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.6,6350.5,No
6614-FHDBO,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.2,7878.3,No
7576-ASEJU,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),74.7,3187.65,No
9058-HRZSV,Female,1,Yes,No,65,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,94.4,6126.15,No
4522-AKYLR,Female,1,No,No,13,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,54.8,731.3,No
0221-WMXNQ,Male,1,No,No,4,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,273.4,No
0303-UNCIP,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,65,2531.8,No
9947-OTFQU,Male,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,1074.3,Yes
0322-YINQP,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.55,48.55,Yes
0959-WHOKV,Male,0,No,No,42,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,99,4298.45,No
4075-JFPGR,Female,0,Yes,No,51,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.5,4619.55,No
4629-NRXKX,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,147.15,Yes
9514-JDSKI,Male,1,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,40.2,40.2,Yes
3282-ZISZV,Male,0,No,Yes,32,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),83.7,2633.3,No
3675-YDUPJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,193.05,No
4111-BNXIF,Female,0,Yes,Yes,67,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,59.55,4103.9,No
7017-VFHAY,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.1,7008.15,No
6655-LHBYW,Male,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),114.35,5791.1,No
4959-JOSRX,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.6,80.55,Yes
5046-NUHWD,Female,1,Yes,No,29,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45,1228.65,No
7273-TEFQD,Male,1,No,No,3,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,41.15,132.2,Yes
3606-TWKGI,Male,1,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,1364.3,Yes
7529-ZDFXI,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.85,4925.35,No
7605-BDWDC,Female,0,No,No,31,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,1520.1,No
1950-KSVVJ,Female,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,113.3,5032.25,No
0123-CRBRT,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,88.1,5526.75,No
6292-TOSSS,Male,0,No,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.9,1195.25,No
3197-ARFOY,Female,1,No,No,19,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105,2007.25,No
6323-AYBRX,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1099.6,Yes
7014-ZZXAW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.25,1732.95,No
4385-GZQXV,Female,1,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,1511.2,Yes
7633-MVPUY,Male,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Electronic check,59.75,3450.15,No
6366-ZGQGL,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),24.8,24.8,Yes
4716-HHKQH,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,107.05,2172.05,No
5940-AHUHD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.6,70.6,Yes
6432-TWQLB,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.4,401.1,Yes
4484-GLZOU,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,5624.85,Yes
3179-GBRWV,Male,1,Yes,No,21,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.95,1339.8,No
8645-KWHJO,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,55,771.95,No
4130-MZLCC,Female,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.55,244.75,No
0314-TKOSI,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,55.15,322.9,No
8229-MYEJZ,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.2,498.25,No
2080-SRCDE,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.4,25.4,No
9577-WJVCQ,Female,0,No,No,68,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,54.45,3687.75,No
9512-UIBFX,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,95.15,1779.95,Yes
6202-DYYFX,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Credit card (automatic),76,1783.6,No
3808-HFKDE,Female,0,No,No,20,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.35,927.15,No
5583-SXDAG,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70,70,Yes
3488-PGMQJ,Male,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,606.55,Yes
3580-REOAC,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.85,435.4,Yes
7534-BFESC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,1712.7,Yes
3727-OWVYD,Male,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,61.2,2021.2,No
2294-SALNE,Male,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,No,Mailed check,86.8,1940.8,No
4847-TAJYI,Female,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,567.8,No
1563-IWQEX,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,220.35,No
8203-XJZRC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,No
6556-DBKZF,Female,0,Yes,Yes,71,Yes,No,Fiber optic,No,No,Yes,No,No,No,Two year,No,Electronic check,76.05,5436.45,No
6851-WEFYX,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,3437.5,No
2985-JUUBZ,Male,0,Yes,Yes,40,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.55,3015.75,No
6390-DSAZX,Female,0,No,Yes,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.6,73.6,Yes
0895-LMRSF,Male,0,No,No,23,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.9,1509.8,No
8098-LLAZX,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,396.1,Yes
8266-VBFQL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.4,356.65,No
8181-YHCMF,Female,0,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.3,4109,No
2240-HSJQD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,81.85,3141.7,No
1248-DYXUB,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.8,1229.1,No
0265-EDXBD,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,2303.35,Yes
4115-BNPJY,Male,0,Yes,Yes,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,75.55,2054.4,No
3167-SNQPL,Male,1,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,3741.85,No
4091-TVOCN,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),78.75,3682.45,No
1098-TDVUQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.25,19.25,No
7277-OZCGZ,Female,0,No,No,22,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.05,1886.25,No
1557-EMYVT,Female,0,No,No,43,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,4895.1,No
2799-ARNLO,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,341.6,No
7563-BIUPC,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.6,415.55,Yes
5027-YOCXN,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),110.05,5686.4,No
3973-SKMLN,Male,0,No,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1355.1,No
2321-OMBXY,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.3,3058.65,Yes
2840-XANRC,Male,1,Yes,No,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.15,2231.05,Yes
6745-JEFZB,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),91.5,3236.35,No
5020-ZSTTY,Female,1,No,No,54,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),82.45,4350.1,Yes
9880-TDQAC,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,60,4264,No
8705-WZCYL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.8,44.8,No
7102-JJVTX,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,48.6,422.3,No
8626-PTQGE,Male,0,No,No,69,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,4176.7,No
4983-CLMLV,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.7,5138.1,No
5701-YVSVF,Female,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,82.9,880.05,No
5804-LEPIM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,139.05,Yes
5697-GOMBF,Female,1,Yes,Yes,28,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.9,973.65,No
2739-CACDQ,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),82.65,1470.05,No
9385-EHGDO,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,739.35,No
9498-FIMXL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,161.95,No
2379-GYFLQ,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),94.9,4422.95,No
0122-OAHPZ,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,511.25,Yes
2868-SNELZ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,80.6,155.8,Yes
4322-RCYMT,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.8,5293.95,Yes
6680-NENYN,Female,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,104.6,4759.85,Yes
2088-IEBAU,Female,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,88.15,6148.45,No
7982-VCELR,Female,0,No,No,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.8,3565.65,No
1343-EHPYB,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.4,6603,Yes
6035-BXTTY,Female,1,No,No,32,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1830.1,No
6885-PKOAM,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.75,6223.8,No
7520-HQWJU,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),67.45,4508.65,No
9639-BUJXT,Male,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.5,1328.15,No
5924-SNGKP,Female,0,No,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,865,No
0021-IKXGC,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,72.1,72.1,No
2034-GDRCN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.4,168.2,Yes
8966-SNIZF,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.45,1303.5,No
6243-OZGFH,Female,0,No,No,23,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.95,996.85,No
4654-DLAMQ,Female,1,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),97,6430.9,No
0513-RBGPE,Male,0,Yes,Yes,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),62.8,2278.75,No
5160-UXJED,Male,0,No,Yes,17,Yes,No,DSL,No,No,No,No,No,No,One year,No,Mailed check,44.6,681.4,No
4115-NZRKS,Female,1,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.15,574.35,No
0219-YTZUE,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,371.9,Yes
0623-IIHUG,Female,1,No,No,21,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,41.9,840.1,Yes
4572-DVCGN,Female,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.25,846,Yes
3351-NGXYI,Female,1,No,No,16,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.1,889,No
8984-EYLLL,Male,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,6823.4,No
9057-MSWCO,Male,1,Yes,No,27,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),30.75,805.1,Yes
9833-TGFHX,Male,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Electronic check,97.1,4016.75,No
9294-TDIPC,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,83.75,No
5229-DTFYB,Female,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),98.8,3959.15,No
0104-PPXDV,Male,0,Yes,No,58,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Credit card (automatic),50.3,2878.55,No
5176-LMJXE,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.55,945.7,No
3583-KRKMD,Male,0,No,No,18,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,1373.05,No
1010-DIAUQ,Male,0,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.5,492.55,Yes
9069-LGEUL,Male,0,Yes,No,23,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.95,1406,No
7302-ZHMHP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,19.15,No
9571-EDEBV,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),98.65,6962.85,No
3520-FJGCV,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.6,8126.65,No
6563-VRERX,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,690.25,No
0259-GBZSH,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,181.5,Yes
6122-EFVKN,Male,0,No,Yes,24,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.75,830.8,No
2805-EDJPQ,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),99.75,5608.4,No
6862-CQUMB,Male,0,No,No,37,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.1,3646.8,No
7156-MXBJE,Female,0,No,No,43,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),85.1,3662.25,No
6158-HDPXZ,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.35,25.35,No
9601-BRXPO,Female,0,Yes,No,25,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,2566.5,Yes
2863-IMQDR,Female,0,No,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,89.65,5308.7,No
5686-CMAWK,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,86.75,1410.25,No
5651-CRHKQ,Female,0,Yes,No,41,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),86.2,3339.05,No
6905-NIQIN,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.65,50.65,Yes
8204-YJCLA,Male,1,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),64.8,4732.35,No
5167-ZFFMM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,90.85,Yes
6583-SZVGP,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,5067.45,No
4895-TMWIR,Male,1,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,214.75,Yes
0533-BNWKF,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.45,4874.7,Yes
1708-PBBOA,Female,0,No,No,42,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,54.75,2348.45,No
8782-LKFPK,Male,0,No,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.4,4063,No
5522-JBWMO,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
3597-MVHJT,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,2595.25,No
9774-NRNAU,Male,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),84.8,2309.55,No
0224-RLWWD,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.3,89.3,No
9967-ATRFS,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,367.55,No
3951-NJCVI,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,3944.5,No
2977-CEBSX,Female,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.05,5965.95,No
0177-PXBAT,Male,1,Yes,No,33,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.9,3694.7,No
6599-CEBNN,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.95,2524.45,Yes
2519-ERQOJ,Male,1,No,No,33,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.6,1803.7,No
5876-QMYLD,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,415.1,No
2277-AXSDC,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,624.15,No
9442-JTWDL,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,237.7,No
0979-PHULV,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,7007.6,Yes
3067-SVMTC,Female,0,Yes,No,68,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),55.9,3848.8,No
5495-GPSRW,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,419.4,No
7606-BPHHN,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1468.75,No
4742-DRORA,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95.4,5812,No
0111-KLBQG,Male,1,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.95,2861.45,No
4800-VHZKI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,Yes
7989-CHGTL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes
0334-GDDSO,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.35,233.7,Yes
4163-NCJAK,Female,0,Yes,No,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,1066.15,No
5233-AOZUF,Female,0,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,2149.05,No
5973-EJGDP,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.35,4473,No
1996-DBMUS,Female,1,Yes,No,48,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.65,3545.05,No
7916-VCCPB,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),73.25,1195.75,No
4686-GEFRM,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.7,6858.9,No
5249-QYHEX,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1024.7,No
0578-SKVMF,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,83.3,1845.9,Yes
5564-NEMQO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.3,75.3,Yes
2233-FAGXV,Female,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.3,132.25,No
5605-IYGFG,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.85,515.45,No
7663-ZTEGJ,Male,0,No,Yes,29,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),100.55,2830.45,No
3935-TBRZZ,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.7,1110.5,No
8111-BKVDS,Female,0,No,No,10,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.7,449.3,No
2055-SIFSS,Female,1,Yes,No,55,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51.65,2838.55,No
2806-MLNTI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.1,5376.4,No
8734-DKSTZ,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.95,858.6,No
4360-PNRQB,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.6,1395.05,No
6152-ONASV,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),58.25,3975.7,No
9063-ZGTUY,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,1182.55,Yes
7781-HVGMK,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.2,4784.45,No
2181-UAESM,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.45,119.5,No
2957-LOLHO,Male,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.4,518.9,Yes
6048-NJXHX,Male,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,19.75,899.45,No
2320-SLKMB,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),44.45,1183.8,No
4980-URKXC,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,720.05,No
4376-KFVRS,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8468.2,No
5886-VLQVU,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.85,3161.2,No
3577-AMVUX,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,55.05,55.05,No
0771-WLCLA,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,112.95,1882.55,No
5628-RKIFK,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.55,5070.4,No
0206-TBWLC,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,114.65,6049.5,No
2937-FTHUR,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,64.8,1166.7,No
1910-FMXJM,Female,0,Yes,No,36,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.4,2937.65,No
7752-XUSCI,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6396.45,Yes
4110-PFEUZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.55,69.55,Yes
0732-OCQOC,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.05,1270.25,No
5168-MSWXT,Male,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,759.55,No
1090-ESELR,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.5,7611.55,No
8592-PLTMQ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.7,1642.75,No
5760-WRAHC,Female,1,No,No,22,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.75,1545.4,No
8847-GEOOQ,Male,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),60.2,3582.4,No
0256-LTHVJ,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,2227.1,Yes
4785-FCIFB,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.4,1417.9,No
8313-NDOIA,Female,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.15,2494.65,No
5149-CUZUJ,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),92.9,2768.35,No
0942-KOWSM,Female,0,Yes,Yes,30,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.8,2369.3,No
4237-CLSMM,Male,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,38,No
1452-VOQCH,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,No
4719-UMSIY,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,100.9,No
6614-VBEGU,Female,0,Yes,No,24,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),69.45,1614.05,No
0880-TKATG,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,385.9,Yes
3811-VBYBZ,Male,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,673.25,Yes
1480-BKXGA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8404.9,No
2996-XAUVF,Male,0,No,No,70,No,No phone service,DSL,No,No,No,Yes,No,Yes,Two year,Yes,Mailed check,40.05,2799.75,No
9076-AXYIK,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,102.1,6538.45,No
5968-XQIVE,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,89.7,6588.95,No
8896-RAZCR,Female,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,868.1,No
4640-UHDOS,Female,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,55.95,734.35,Yes
4933-IKULF,Female,1,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,330.6,No
3583-EKAPL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,55,Yes
1304-BCCFO,Male,0,Yes,No,9,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,70.05,564.4,No
4104-PVRPS,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.6,1315.35,No
9399-APLBT,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
2359-KMGLI,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,1861.5,Yes
3780-DDGSE,Male,1,Yes,Yes,35,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,76.05,2747.2,No
4431-EDMIQ,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,75.7,554.05,No
0306-JAELE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,453.4,Yes
6227-HWPWX,Female,0,No,Yes,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69,994.8,Yes
0486-LGCCH,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,225.75,No
0447-BEMNG,Female,0,Yes,No,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),45.3,2145,Yes
4612-SSVHJ,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.45,1671.6,No
5168-MQQCA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.5,8003.8,No
5949-XIKAE,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,680.05,Yes
7971-HLVXI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Two year,Yes,Credit card (automatic),84.5,6130.85,No
9094-AZPHK,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,1415,No
3649-JPUGY,Male,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),88.6,6201.95,No
4472-LVYGI,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),52.55, ,No
8372-JUXUI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes
3552-CTCYF,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,6597.25,No
6778-YSNIH,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59,114.15,No
0388-EOPEX,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,139.4,Yes
5756-OZRIO,Male,1,Yes,No,61,Yes,Yes,DSL,No,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),64.05,3902.6,No
6579-JPICP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No
8205-OTCHB,Male,0,No,No,22,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),43.75,903.6,Yes
4134-BSXLX,Male,0,Yes,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.9,1785.65,No
0505-SPOOW,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1397.65,No
6235-VDHOM,Female,1,No,No,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,28.45,131.05,Yes
7783-YKGDV,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.7,1238.45,Yes
4374-YMUSQ,Male,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),116.25,3899.05,No
4513-CXYIX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,Yes,Credit card (automatic),80.7,5676,No
3957-HHLMR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,No,No,Yes,One year,No,Bank transfer (automatic),65.2,4543.15,No
7803-XOCCZ,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.05,4326.8,No
5736-YEJAX,Male,0,No,Yes,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.45,5502.55,No
5609-CEBID,Female,1,No,No,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,1782.4,Yes
8981-FJGLA,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,78,851.8,No
7218-HKQFK,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,167.5,Yes
4636-QRJKY,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,502.85,Yes
1135-LMECX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No
4332-MUOEZ,Male,1,Yes,Yes,20,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),94.3,1818.3,No
8535-SFUTN,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.45,6300.15,No
5956-VKDTT,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,334.8,Yes
8677-HDZEE,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.45,5916.95,No
2475-MROZF,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95,2852.4,No
9412-GHEEC,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),104.8,4131.95,Yes
3482-ABPKK,Female,0,No,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.3,1546.3,No
6705-LXORM,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.05,302.6,No
0257-ZESQC,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.2,1929.35,Yes
7531-GQHME,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,265.45,No
5174-ITUMV,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.4,6989.45,No
4109-CYRBD,Male,1,Yes,No,29,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.6,1442,No
0913-XWSCN,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),85.5,4713.4,No
6825-UYPFK,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.6,1758.6,Yes
8397-MVTAZ,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3480,Yes
0750-EBAIU,Male,0,No,No,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Electronic check,91.25,4738.3,No
8606-CIQUL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8399.15,No
3571-DPYUH,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),94.7,5430.35,No
7601-GNDYK,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,686.95,No
0356-OBMAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),99.9,5706.3,No
8067-NIOYM,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),21.1,490.65,No
1403-GYAFU,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1360.25,No
4234-XTNEA,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,174.45,No
1297-VQDRP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),107.15,7379.8,No
9282-IZGQK,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes
5348-CAGXB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.55,1021.75,No
0621-HJWXJ,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),81.55,5029.05,No
5844-QVTAT,Female,0,Yes,Yes,33,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,58.45,1955.4,No
8905-IAZPF,Female,0,Yes,No,69,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.65,6744.2,No
5394-MEITZ,Female,0,Yes,Yes,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,4946.7,No
6859-QNXIQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.1,8248.5,No
2782-LFZVW,Female,0,No,No,11,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,58.95,601.6,No
2866-IKBTM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
1342-JPNKI,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),86.05,834.1,Yes
2817-NTQDO,Male,0,No,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),45.55,597,Yes
7129-AZJDE,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),78.95,2647.2,No
6986-IJDHX,Male,0,Yes,Yes,39,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.3,3266,Yes
2560-PPCHE,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6744.25,No
4676-MQUEA,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),101.9,5265.5,No
8138-EALND,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,311.6,No
3580-HYCSP,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.3,7966.9,No
1352-HNSAW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.6,8220.4,No
2075-PUEPR,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1153.25,No
1982-FEBTD,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.6,514.75,No
5301-GAUUY,Male,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,2596.15,Yes
5791-KAJFD,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),68.75,3808,No
2654-VBVPB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,19.9,No
1154-HYWWO,Male,0,No,No,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,70.6,2708.2,No
2501-XWWTZ,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,760.05,No
3716-UVSPD,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.3,49.3,No
6815-ABQFQ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,107.25,6033.3,No
7343-EOBEU,Male,0,Yes,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,23.6,89.05,No
3701-SFMUH,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69.7,516.15,No
6103-LIANB,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),99.5,5861.75,No
7319-VENRZ,Male,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),64.3,445.95,No
5846-NEQVZ,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),70.85,4973.4,No
6967-QIQRV,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,101.9,1667.25,No
5781-RFZRP,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.5,5357.75,No
0939-YAPAF,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,3527.6,Yes
0308-IVGOK,Female,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),40.4,422.6,No
7293-LSCDV,Female,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.25,1103.25,No
7025-WCBNE,Male,1,No,No,47,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.6,2754,No
5756-JYOJT,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),64.9,697.25,No
4710-FDUIZ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),100.3,5614.45,Yes
6030-REHUX,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.85,3204.4,No
9548-LIGTA,Male,0,Yes,No,61,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,81.05,4747.65,No
5150-LJNSR,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Bank transfer (automatic),98.05,3082.1,No
8270-RKSAP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.5,597.9,No
6522-YRBXD,Male,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.55,3365.4,No
2640-LYMOV,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,38.8,No
1218-VKFPE,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,233.55,Yes
3627-FHKBK,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,75.3,Yes
2865-TCHJW,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,346.2,Yes
1423-BMPBQ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19,19,No
2393-DIVAI,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,61.7,No
5192-EBGOV,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,85.7,Yes
4568-KNYWR,Male,0,No,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),63.25,3342.45,No
8752-IMQOS,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,85.1,No
0742-LAFQK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,99.15,7422.1,No
0795-LAFGP,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.4,6668.05,No
0619-OLYUR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.9,8071.05,No
5512-IDZEI,Male,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1174.8,No
0459-SPZHJ,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),83.5,5435,No
0215-BQKGS,Male,0,No,No,30,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.3,2438.6,No
9244-ZVAPM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.6,45.6,No
0719-SYFRB,Female,0,No,No,12,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.65,713.75,Yes
8208-EUMTE,Male,0,No,No,16,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,54.85,916.15,No
5172-MIGPM,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,65.55,237.2,No
1710-RCXUS,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.35,4614.55,No
0374-FIUCA,Male,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,1414.45,No
5839-SUYVZ,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),74.55,1170.5,No
5173-ZXXXL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,47.7,No
1096-ADRUX,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),74.25,4859.25,No
2001-MCUUW,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.65,4903.2,No
2731-GJRDG,Female,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.55,3608,No
4723-BEGSG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.65,6094.25,No
6516-NKQBO,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),81,3084.9,No
8672-OAUPW,Male,0,No,Yes,51,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),47.85,2356.75,No
8207-DMRVL,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.55,8306.05,No
3419-SNJJD,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),105.25,6786.4,Yes
6543-CPZMK,Male,0,Yes,Yes,9,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.95,248.95,Yes
4765-OXPPD,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Mailed check,65,663.05,Yes
2804-ETQDK,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1357.1,No
6689-VRRTK,Female,1,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.8,4860.35,No
7138-GIRSH,Male,0,No,No,50,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.5,3418.2,No
9396-ZSFLL,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.85,631.4,No
6464-KEXXH,Male,0,No,No,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.25,186.3,No
7134-MJPDY,Female,1,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.85,6976.75,No
5240-CAOYT,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),87.55,4884.85,No
4059-IIEBK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),78.55,522.95,No
4881-JVQOD,Male,1,Yes,Yes,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.55,362.6,No
0516-UXRMT,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,92.05,5755.8,No
4851-BQDNX,Male,0,Yes,Yes,40,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.05,3355.65,No
5148-HKFIR,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,406.95,No
1009-IRMNA,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,137.6,Yes
3003-CMDUU,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,2395.7,No
5016-IBERQ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,84.25,1968.1,No
6797-UCJHZ,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),104.6,6819.45,No
2469-DTSGX,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,111.65,7943.45,No
4554-YGZIH,Male,1,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),90.05,4547.25,Yes
5099-BAILX,Male,1,Yes,Yes,43,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),110.75,4687.9,Yes
9931-KGHOA,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),55,2473.95,No
1775-KWJKQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.85,6562.9,No
7665-VIGUD,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,176.3,No
9411-TPQQV,Female,0,No,No,40,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.55,2236.2,No
7207-RMRDB,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6985.65,Yes
7954-MLBUN,Male,0,No,No,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.45,3109.9,No
2077-DDHJK,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4911.35,No
4913-EHYUI,Male,1,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.55,5794.65,Yes
0195-IESCP,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,855.3,Yes
9574-BOSMD,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1620.2,No
4580-TMHJU,Female,0,Yes,Yes,43,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,56.15,2499.3,Yes
0970-ETWGE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.55,89.55,Yes
4908-XAXAY,Female,1,No,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),89.85,4287.2,No
8404-VLQFB,Female,0,Yes,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.25,394.85,No
1626-ERCMM,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,1899.65,Yes
0887-HJGAR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes
2391-IPLOP,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,69.65,3442.15,No
5644-PDMZC,Female,1,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.5,161.5,Yes
3509-GWQGF,Male,1,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70,1732.6,No
9576-ANLXO,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.55,222.3,Yes
2024-BASKD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.6,74.6,Yes
5845-BZZIB,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,655.3,No
1140-UKVZG,Female,0,No,No,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.8,475.25,No
4160-AMJTL,Female,1,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.65,164.3,Yes
5183-SNMJQ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.1,865.1,No
8100-PNJMH,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.85,6132.7,No
7838-LAZFO,Male,0,Yes,No,45,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),78.8,3597.5,No
4464-JCOLN,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,35.9,Yes
2085-JVGAD,Male,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.35,697.65,No
5650-VDUDS,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.25,96.05,Yes
8095-WANWK,Female,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,428.7,No
3030-ZKIWL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
9565-FLVCG,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Mailed check,69.55,4459.15,No
8755-OGKNA,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,1167.6,No
2800-VEQXM,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.75,238.1,No
7538-GWHML,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,145.15,Yes
5533-RJFTJ,Male,0,No,No,49,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,1453.1,No
3859-CVCET,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,191.05,Yes
0214-JHPFW,Female,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),57.8,4039.3,No
5642-MHDQT,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,1039.45,No
3088-FVYWK,Male,0,Yes,Yes,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1336.1,No
3276-HDUEG,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.05,75.05,Yes
9092-GDZKO,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.85,493.4,No
0823-HSCDJ,Male,1,No,No,52,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.15,2550.9,Yes
3729-OWRVL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.35,7246.15,No
2324-AALNO,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.55,1203.95,No
0822-GAVAP,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,62.25,Yes
5760-IFJOZ,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,107.95,313.6,No
2826-UWHIS,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81.4,3775.85,No
1448-PWKYE,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80,80,Yes
7501-IWUNG,Female,0,Yes,Yes,61,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),73.8,4616.05,No
4957-TREIR,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.4,195.65,No
7251-LJBQN,Female,1,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),103.75,4188.4,No
8040-MNRTF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,No
1536-HBSWP,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No
5313-FPXWG,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.6,1266.4,No
0067-DKWBL,Male,1,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.25,91.1,Yes
0946-FKYTX,Male,0,No,No,52,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,30.1,1623.4,No
5076-YVXCM,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.4,4149.45,No
8262-COGGB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No
6663-JOCQO,Male,0,Yes,Yes,31,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.25,2344.5,No
9620-QJREV,Male,0,No,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.55,1013.05,No
2276-YDAVZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.1,270.7,Yes
2682-KEVRP,Female,1,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.05,417,No
2480-JZOSN,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,20.65,No
0078-XZMHT,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.15,6316.2,No
5896-NPFWW,Male,0,Yes,Yes,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.15,168.15,Yes
9978-HYCIN,Male,1,Yes,Yes,47,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),84.95,4018.05,No
8338-QIUNR,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),66.5,4811.6,No
1525-LNLOJ,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),63.3,4189.7,No
9450-TRJUU,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,83.15,2848.45,No
1766-GKNMI,Male,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,2516.2,No
6942-LBFDP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,33.6,No
1456-TWCGB,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,208.45,No
7133-VBDCG,Female,0,No,No,25,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,2015.35,Yes
7596-ZYWBB,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,59.6,3739.8,No
8329-UTMVM,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,2964,No
3014-WJKSM,Male,0,Yes,No,29,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,2263.4,No
3347-YJZZE,Male,0,Yes,Yes,29,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),80.1,2211.8,No
1029-QFBEN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,Yes
7929-DMBCV,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,81,1683.7,No
9661-JALZV,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.7,1519,No
5433-KYGHE,Female,0,No,Yes,14,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,86,1164.05,No
4312-KFRXN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1710.9,No
5575-TPIZQ,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.15,4245.55,No
0114-IGABW,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),58.25,4145.9,No
9944-AEXBM,Male,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,2664.3,No
1853-ARAAQ,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.35,1277.5,No
6952-OMNWB,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.35,5589.3,No
4697-LUPSU,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,34.75,No
8434-VGEQQ,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1305.95,No
4952-YSOGZ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,381.3,Yes
1589-AGTLK,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.35,141.5,Yes
5244-IRFIH,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,3105.55,Yes
6549-YMFAW,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.25,204.55,No
4950-HKQTE,Female,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.25,605.9,No
6786-OBWQR,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,356.1,No
2684-EIWEO,Female,1,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),91.7,2758.15,Yes
2753-JMMCV,Male,0,No,No,65,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.2,4805.65,No
6439-GTPCA,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.25,3941.7,Yes
6621-YOBKI,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,92.75,No
1216-JWVUX,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1901.05,No
7564-GHCVB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),79.05,5730.7,No
1173-NOEYG,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),90.15,2423.4,No
7595-EHCDL,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),50.6,1653.45,No
6647-ZEDXT,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.45,3327.05,No
2521-NPUZR,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101,7085.5,No
1307-TVUFB,Male,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,3344.1,No
7503-MIOGA,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.85,6697.35,No
4381-MHQDC,Female,0,No,No,47,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Mailed check,65,2879.9,No
6923-JHPMP,Female,0,No,No,2,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,137.1,No
5138-WVKYJ,Male,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.55,1008.55,Yes
4018-PPNDW,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.1,1551.6,No
1635-FJFCC,Female,0,No,No,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.05,202.15,No
2499-AJYUA,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7882.25,No
6919-ELBGL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,8196.4,No
3966-HRMZA,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,75.05,202.9,No
6425-JWTDV,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,855.1,No
8405-IGQFX,Female,0,No,No,63,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),90.05,5817,No
8224-IVVPA,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,56.7,1652.95,No
9477-LGWQI,Male,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),80.15,5600.15,No
1410-RSCMR,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),71.35,515.75,No
3115-CZMZD,Male,0,No,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25, ,No
0139-IVFJG,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,90.35,190.5,No
6683-VLCTZ,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.55,1842.8,Yes
5730-DBDSI,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1253.8,No
0030-FNXPP,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,57.2,No
2189-WWOEW,Female,0,No,Yes,15,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.9,1269.55,Yes
5684-FJVYR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),90.35,6563.4,No
4013-GUXND,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,20.8,No
1894-IGFSG,Female,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.25,1907.85,Yes
7379-POKDZ,Male,0,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,208.85,Yes
1266-NZYUI,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),66.85,4758.8,No
7969-FFOWG,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1292.6,No
4718-DHSMV,Female,0,No,No,11,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,35.8,363.15,No
5175-WLYXL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,78.85,1600.25,No
7817-OMJNA,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.4,275.7,No
8728-SKJLR,Male,0,No,No,41,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,74.25,3089.1,No
3137-NYQQI,Male,0,Yes,No,17,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Mailed check,64.8,1175.6,No
7706-DZNKK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,237.3,No
0236-HFWSV,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.35,1444.65,Yes
3900-AQPHZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes
5842-POCOP,Female,0,Yes,No,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,88.9,454.15,Yes
2037-XJFUP,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,95.8,3036.75,Yes
8823-RLPWL,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.65,8065.65,No
9505-SQFSW,Female,0,Yes,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,40.3,92.5,No
7314-OXENN,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82,184.65,Yes
3758-CKOQL,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107,6152.3,No
0322-CHQRU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.35,89.5,Yes
5676-CFLYY,Male,0,Yes,Yes,71,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),73.35,5154.5,No
7521-AFHAB,Female,0,Yes,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.8,220.45,No
0285-INHLN,Male,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1510.3,No
4678-DVQEO,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,52.2,52.2,Yes
5125-CNDSP,Male,0,No,Yes,63,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.6,2588.95,No
0691-IFBQW,Female,1,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110,4874.8,Yes
4992-LTJNE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),55.3,3983.6,No
2202-OUTMO,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),60.85,2003.6,No
0810-BDHAW,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,78.4,1832.4,No
0229-LFJAF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.65,4908.25,No
7131-ZQZNK,Female,0,Yes,Yes,60,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.85,3590.2,No
3442-ZHHCC,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),76.9,5023,No
5726-CVNYA,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,146.6,No
9871-ELEYA,Female,0,No,Yes,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),67.65,2339.3,No
4257-GAESD,Female,0,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),45,298.7,No
5173-WXOQV,Male,0,Yes,No,2,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,64.2,143.65,No
2040-OBMLJ,Male,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,No,Credit card (automatic),81.7,2548.65,No
6286-ZHAOK,Female,0,Yes,Yes,20,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.55,507.4,No
3807-XHCJH,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,20,No
3009-JWMPU,Male,0,No,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,96.75,6125.4,Yes
5671-RQRLP,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),75.65,5411.4,No
1450-GALXR,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1058.25,Yes
8859-AXJZP,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,23.8,903.8,No
3174-AKMAS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),64.2,3009.5,No
3138-BKYAV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,489.45,Yes
9926-PJHDQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),76.8,5468.45,No
7382-DFJTU,Male,0,No,No,18,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,1058.1,No
2798-NYLMZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.55,7616,No
4289-DTDKW,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,4113.1,Yes
1820-TQVEV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes
2239-JALAW,Male,0,No,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.25,6017.65,Yes
4853-RULSV,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104,7250.15,Yes
8098-TDCBU,Female,0,Yes,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,25.25,1108.2,No
3551-GAEGL,Male,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),30.4,938.65,No
4785-NKHCX,Male,1,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,94.15,No
3196-NVXLZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.6,2088.05,No
6275-YDUVO,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,86.2,178.7,Yes
0036-IHMOT,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.7,5656.75,No
0115-TFERT,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.2,2317.1,Yes
4178-EGMON,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),88,5986.45,No
4220-TINQT,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,6751.35,No
5318-YKDPV,Male,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.15,3566.6,No
7975-TZMLR,Male,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.1,4889.3,No
0295-QVKPB,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),63.95,318.1,No
4335-BSMJS,Female,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.8,1563.95,No
2311-QYMUQ,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.45,1430.25,Yes
3643-AHCFP,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,644.35,Yes
9146-JRIOX,Female,0,Yes,Yes,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.55,372.45,No
3104-OWCGK,Male,0,Yes,Yes,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Electronic check,90.95,5453.4,Yes
5337-IIWKZ,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,44.85,1442.6,No
9101-BWFSS,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Electronic check,108.55,5610.7,Yes
9650-VBUOG,Male,0,Yes,Yes,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,963.95,No
3487-EARAT,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),74.1,5222.3,No
2672-TGEFF,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,3340.55,No
9231-ZJYAM,Female,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.85,292.8,Yes
4250-WAROZ,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.25,5774.55,No
8184-WMOFI,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),71.4,4487.3,No
6982-SSHFK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes
6092-QZVPP,Male,0,No,No,36,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.2,2854.95,No
4625-LAMOB,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,905.55,No
0727-BMPLR,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100,5509.3,Yes
0392-BZIUW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105,7589.8,No
1038-ZAGBI,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,229.6,Yes
6549-NNDYT,Female,0,No,No,13,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.85,394.1,No
3027-ZTDHO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.9,89.9,Yes
0422-OHQHQ,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.55,295.95,No
6916-HIJSE,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.85,5459.2,No
2316-ESMLS,Female,0,Yes,Yes,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),33.15,444.75,No
9778-OGKQZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92,6782.15,No
7408-OFWXJ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.8,6510.45,No
6007-TCTST,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8476.5,No
2252-NKNSI,Male,0,No,Yes,52,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,85.15,4461.85,No
8713-IGZSO,Male,0,No,No,2,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.85,62,No
7905-TVXTA,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.35,352.65,No
7695-PKLCZ,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1424.9,No
2382-BCKQJ,Female,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,6413.65,Yes
8374-UULRV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.05,6309.65,No
2207-NHRJK,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.8,50.8,Yes
3224-DFQNQ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89,5898.6,No
5275-PMFUT,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.8,4719.75,No
4795-UXVCJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,457.3,No
9777-IQHWP,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),93.4,5822.3,No
0947-MUGVO,Male,1,Yes,No,20,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,1463.5,Yes
9944-HKVVB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,307.4,Yes
4124-MMETB,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,2104.55,Yes
3671-SHRSP,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,319.15,Yes
0979-MOZQI,Male,0,Yes,No,62,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),39,2337.45,No
2732-ISEZX,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,104.3,No
3313-QKNKB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,No,Electronic check,85.55,5084.65,Yes
0323-XWWTN,Male,0,No,Yes,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.4,121.25,No
1937-OTUKY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),98.2,7015.9,No
1573-LGXBA,Male,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),97.55,5598,No
1764-VUUMT,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1269.1,No
5073-WXOYN,Female,0,No,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.8,3027.4,Yes
4713-ZBURT,Male,0,No,Yes,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.7,4634.35,No
3050-GBUSH,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.8,113.95,No
0207-MDKNV,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.1,1582.75,Yes
7876-AEHIG,Female,0,No,Yes,51,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),60.15,3077,No
7945-HLKEA,Female,0,No,No,60,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,64.75,4039.5,No
9342-VNIMQ,Male,0,No,No,33,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.65,1665.2,No
9851-KIELU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.1,1043.3,Yes
3523-BRGUW,Male,1,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,504.2,No
3908-BLSYF,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,83.9,497.55,Yes
3199-NPKCN,Female,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.25,7511.65,No
5170-PTRKA,Female,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),35.8,1782,No
4661-NJEUX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,20.05,No
2123-AGEEN,Female,1,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,609.65,No
1258-YMZNM,Female,1,No,No,27,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),110.5,2857.6,No
0048-LUMLS,Male,0,Yes,Yes,37,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),91.2,3247.55,No
7549-MYGPK,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.55,6215.35,Yes
5898-IGSLP,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.3,2823,No
3804-RVTGV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.85,5017.9,Yes
6259-WJQLC,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),81.1,2619.25,No
9227-LUNBG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.6,24.6,Yes
7997-EASSD,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),81.2,4965.1,No
0730-KOAVE,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.3,2679.7,No
8975-SKGRX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.1,8310.55,No
0678-RLHVP,Female,0,No,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,5682.25,No
4315-MURBD,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.9,1120.95,Yes
2267-FPIMA,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.4,4914.9,No
1051-GEJLJ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,27.55,No
9734-YWGEX,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),98.3,923.5,Yes
2719-BDAQO,Male,0,No,No,17,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,93.85,1625.65,Yes
5285-MVEHD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.6,6068.65,No
0379-DJQHR,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),81.35,5398.6,No
0781-LKXBR,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,918.6,Yes
5543-QDCRY,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,56.4,234.85,No
0297-RBCSG,Male,0,No,No,19,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),65.35,1231.85,No
4694-PHWFW,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,170.9,No
0835-JKADZ,Female,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.25,7984.15,No
1907-UBQFC,Male,1,No,No,10,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,72.85,688.65,Yes
7508-SMHXL,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Credit card (automatic),89,1288.3,No
3865-YIOTT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,7848.5,No
5993-BQHEA,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,267,No
6024-RUGGH,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.2,1798.9,No
6513-EECDB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,73.55,Yes
3956-CJUST,Female,1,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.4,1643.55,No
4079-WWQQQ,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.55,4807.45,No
6103-BOCOU,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.7,2193,No
5149-TGWDZ,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,104.55,2239.4,No
7471-WNSUF,Male,0,Yes,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.15,1505.9,No
8942-DBMHZ,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,255.35,No
4301-VVZKA,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1189.4,No
9199-PWQVC,Female,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),79.7,4786.15,No
4824-GUCBY,Female,1,No,No,22,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.7,1820.9,No
5393-HJZSM,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),76.3,2404.15,No
7074-IEVOJ,Female,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.4,205.05,Yes
9625-QSTYE,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.15,952.3,Yes
0862-PRCBS,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),103.75,7039.45,No
8812-ZRHFP,Female,0,Yes,Yes,30,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Electronic check,86.45,2538.05,No
5146-CBVOE,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.1,1212.85,No
0454-OKRCT,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),80.6,2651.1,No
5787-KXGIY,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1304.8,No
4750-ZRXIU,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,360.1,Yes
4198-VFOEA,Female,0,No,No,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,33.6,435.45,No
6630-UJZMY,Female,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.25,308.05,No
5709-LVOEQ,Female,0,Yes,Yes,0,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,80.85, ,No
6400-BWQKW,Female,0,No,No,6,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.05,434.5,Yes
2692-AQCPF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.05,7118.9,No
0347-UBKUZ,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,320.45,No
0835-DUUIQ,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),21.05,531.55,No
0811-GSDTP,Female,0,No,Yes,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.15,382.2,No
7567-ECMCM,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.85,2001,No
6115-ZTBFQ,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.5,4919.7,No
6353-BRMMA,Female,0,Yes,Yes,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.1,5645.8,No
6680-WKXRZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.4,215.8,Yes
6231-WFGFH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,77.6,No
9904-EHEVJ,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,91.35,2896.55,No
7028-DVOIQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3395.8,Yes
6169-PPETC,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,759.35,No
4208-UFFGW,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,85.15,Yes
8584-KMVXD,Female,0,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.6,535.55,No
8467-WYNSR,Male,0,No,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,55.1,1253.15,No
0851-DFJKB,Female,0,No,No,15,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,58.95,955.15,No
5382-SOYZL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2162.6,No
9448-REEVD,Male,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,44.7,Yes
3261-CQXOL,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1813.35,No
8388-FYNPZ,Male,0,No,No,4,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,56.75,245.15,No
4002-BQWPQ,Male,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),81.75,2028.8,No
5651-YLPRD,Female,0,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,2723.75,No
2826-DXLQO,Male,1,Yes,No,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.8,220.45,No
4378-BZYFP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.5,365.8,No
9489-UTFKA,Male,1,Yes,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),60.9,551.95,No
4849-PYRLQ,Female,1,No,No,56,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),73.25,4054.2,No
9117-SHLZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes
9889-TMAHG,Male,1,No,No,8,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,832.35,Yes
4541-RMRLG,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,112.3,Yes
7764-BDPEE,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,60.65,No
3429-IFLEM,Female,0,No,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5550.1,No
3158-MOERK,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,174.8,Yes
7294-TMAOP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.55,90.55,Yes
5002-GCQFH,Male,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.85,4733.1,No
0556-FJEGU,Male,0,No,No,58,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),70.1,4048.95,No
8919-FYFQZ,Male,1,Yes,No,44,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.35,1359.7,Yes
0604-THJFP,Female,0,Yes,Yes,59,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),75.95,4542.35,No
2834-JRTUA,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.05,7532.15,Yes
5875-YPQFJ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes
5879-SESNB,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,888.65,No
6646-QVXLR,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.75,6383.35,Yes
6461-PPAXN,Female,0,Yes,Yes,35,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),54.95,1916,No
3318-ISQFQ,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.5,413,No
1106-HRLKZ,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,808.95,No
2483-XSSMZ,Female,0,No,No,39,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,47.85,1886.4,No
8603-IJWDN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.6,86.6,Yes
8165-ZJRNM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.75,1679.25,No
9369-XFEHK,Female,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.6,2656.5,Yes
2604-XVDAM,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),43.8,540.95,No
3717-OFRTN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,19.75,No
9046-JBFWA,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,537.35,No
3280-NMUVX,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,678.8,No
1206-EHBDD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),80.3,4513.65,No
8361-LBRDI,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.35,1423.85,No
4883-KCPZJ,Female,0,Yes,Yes,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,555.4,No
9108-EQPNQ,Female,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.1,225.55,No
7277-KAMWT,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,268.45,No
3842-IYKUE,Female,0,No,No,35,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),85.3,2917.5,Yes
6641-XRPSU,Female,0,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70,2416.1,Yes
1374-DMZUI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,424.45,Yes
2545-LXYVJ,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.7,1492.1,No
3234-VKACU,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,70.3,132.4,No
8357-EQXFO,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,660.9,Yes
1989-PRJHP,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1893.95,Yes
8120-JDCAM,Male,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.55,284.9,No
8917-FAEMR,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,784.25,No
7047-YXDMZ,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,417.7,No
2858-EIMXH,Female,1,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.85,5016.25,No
9524-EGPJC,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.1,1612.75,Yes
6993-OHLXR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.95,119.75,Yes
8818-XYFCQ,Male,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.55,3204.65,Yes
6419-ZTTLE,Male,1,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.75,485.2,No
0929-HYQEW,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,160.85,No
6614-YOLAC,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Mailed check,58.65,4145.25,No
7426-RHZGU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.9,827.45,No
4065-JJAVA,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.5,49.5,No
4695-VADHF,Male,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,57.45,990.85,Yes
3863-IUBJR,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),53.65,696.35,Yes
7649-SIJJF,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,80.1,5585.4,No
9361-YNQWJ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.4,1601.2,No
3748-FVMZZ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.05,162.45,No
9391-TTOYH,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,470.2,No
1452-XRSJV,Female,0,Yes,Yes,39,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,2066,No
3422-WJOYD,Male,0,Yes,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.35,1426.45,No
8242-SOQUO,Female,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.7,392.5,No
7460-ITWWP,Female,1,Yes,No,45,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,86.1,3861.45,No
7147-AYBAA,Male,0,No,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.35,2552.9,No
7868-TMWMZ,Female,1,Yes,No,60,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,6668.35,No
4822-RVYBB,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,819.4,Yes
6732-FZUGP,Female,0,No,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),94.9,4615.25,No
8436-BJUMM,Male,0,Yes,Yes,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,2070.6,Yes
4184-TJFAN,Female,1,Yes,Yes,3,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.3,273.75,Yes
8329-GWVPJ,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,3557.7,No
1352-VHKAJ,Male,0,Yes,Yes,27,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),71.6,1957.1,No
4145-UQXUQ,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.1,729.95,Yes
2632-TACXW,Female,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.65,1416.75,No
8146-QQKZH,Female,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.85,5924.4,No
1767-CJKBA,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,1697.7,No
6445-TNRXS,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.7,7849.85,No
4581-LNWUM,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.15,649.4,No
4869-EPIUS,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Electronic check,80.9,4557.5,No
9948-YPTDG,Male,0,Yes,No,38,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,3013.05,Yes
1236-WFCDV,Male,1,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),90.45,1266.1,Yes
1915-OAKWD,Female,0,No,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.3,360.35,No
7296-PIXQY,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,1046.5,No
4883-QICIH,Male,0,Yes,Yes,32,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,2347.9,Yes
3354-OADJP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.25,447.75,No
3524-WQDSG,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.3,4209.95,No
0810-DHDBD,Female,0,No,No,52,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74,3877.65,No
4026-SKKHW,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.25,152.3,No
2829-HYVZP,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,572.2,No
8329-IBCTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes
1271-SJBGZ,Male,1,No,No,12,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,43.65,526.95,Yes
3845-JHAMY,Female,0,Yes,Yes,16,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),35.5,552.7,No
7013-PSXHK,Female,0,No,No,40,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,80.75,3208.65,No
5669-SRAIP,Female,0,No,No,5,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,39.5,210.75,Yes
5981-ITEMU,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.1,3706.95,Yes
3486-NPGST,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,620.75,No
6941-PMGEP,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80,412.5,Yes
1624-WOIWJ,Female,0,No,No,10,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.7,832.05,Yes
2074-GKOWZ,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,185.55,Yes
0376-YMCJC,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90.6,1943.2,Yes
6100-FJZDG,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,505.9,No
4829-ZLJTK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,112.4,8046.85,No
1730-VFMWO,Female,0,Yes,No,34,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.2,1815.3,No
7143-BQIBA,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),62.25,612.95,No
3800-LYTRK,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.7,795.15,No
0634-SZPQA,Female,0,No,No,23,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.05,2169.8,Yes
9646-NMHXE,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,973.1,No
7030-NJVDP,Male,0,Yes,No,24,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,2210.2,No
5536-RTPWK,Male,0,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,4853.75,Yes
8883-GRDWQ,Male,1,No,No,20,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54,1055.9,No
6166-ILMNY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,144.55,Yes
3097-NNSPB,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.05,91.1,Yes
7771-ZONAT,Male,0,No,No,22,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),56.75,1304.85,No
0655-RBDUG,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.05,713,Yes
2111-DWYHN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.1,21.1,No
4194-WHFCB,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),96.65,5580.8,No
4121-AGSIN,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.5,1497.9,No
4361-BKAXE,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),114.5,4527.45,Yes
9845-PEEKO,Female,1,No,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Two year,Yes,Credit card (automatic),79.2,4590.35,No
0455-XFASS,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,200.2,No
0301-KOBTQ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,614.45,No
1751-NCDLI,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,4564.9,No
4367-NUYAO,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75, ,No
9878-TNQGW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.95,171.15,Yes
9170-ARBTB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,1012.4,No
4441-NIHPT,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.3,940.35,Yes
8999-BOHSE,Female,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),89.7,1047.7,Yes
7241-AJHFS,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),87.65,2766.4,No
7029-RPUAV,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.45,1622.45,Yes
4546-FOKWR,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,1129.35,No
9036-CSKBW,Female,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),107.45,5680.9,No
5832-TRLPB,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.35,2243.9,No
8590-YFFQO,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.95,4523.25,No
8659-IOOPU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,100.45,7159.7,No
1338-CECEE,Male,0,Yes,Yes,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),68.5,2839.95,No
7439-DKZTW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,No
4646-QZXTF,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.25,580.1,No
4607-CHPCA,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,90.4,2178.6,Yes
9742-XOKTS,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89.55,6038.55,No
6921-OZMFH,Male,0,Yes,Yes,5,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.7,259.4,No
9578-FOMUK,Male,0,No,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24.8,324.15,No
4712-UYOOI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,417.65,No
8824-RWFXJ,Male,0,Yes,Yes,3,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.15,168.15,Yes
7722-CVFXN,Male,0,Yes,Yes,54,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.2,5637.85,No
8717-VCTXJ,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.55,839.4,No
7363-QTBIW,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.75,769.1,No
4159-NAAIX,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),97.45,6253,No
0971-QIFJK,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1641.8,No
9397-TZSHA,Female,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.6,1678.05,No
3391-JSQEW,Male,0,Yes,No,40,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2058.5,No
0343-QLUZP,Male,0,No,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),39.6,2424.5,No
9763-PDTKK,Female,0,No,No,4,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.4,387.2,Yes
2176-LVPNX,Female,1,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.85,6293.45,No
7627-JKIAZ,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.95,2839.65,Yes
3312-UUMZW,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.85,3145.9,No
1271-UODNO,Male,0,No,No,39,Yes,Yes,DSL,No,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),53.85,2200.7,No
8461-EFQYM,Female,0,No,No,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,24.25,914.4,No
6900-RBKER,Male,0,No,No,52,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),89.45,4577.75,No
6891-JPYFF,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.25,4997.5,No
1459-QNFQT,Male,0,Yes,Yes,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.5,4144.8,No
1047-NNCBF,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,1493.55,No
3696-XRIEN,Female,0,No,No,50,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82.5,4179.1,No
4081-DYXAV,Female,0,No,No,19,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),44.85,893.55,Yes
0074-HDKDG,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),61.6,1611,No
8791-GFXLZ,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.05,593.05,No
8111-SLLHI,Male,1,Yes,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.65,4284.8,Yes
0927-LCSMG,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,74.65,544.55,Yes
9330-DHBFL,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,66.25,1533.8,No
0098-BOWSO,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,529.8,No
3452-ABWRL,Male,1,No,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.05,3865.6,No
5859-HZYLF,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.15,515.75,No
8257-RZAHR,Female,0,Yes,No,14,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),64.7,941,Yes
5293-WXJAK,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.05,1133.65,Yes
3156-QLHBO,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,48.35,No
2208-NQBCT,Female,0,Yes,Yes,26,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.95,2070.05,No
1779-PWPMG,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.65,8333.95,No
6621-NRZAK,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1209.25,No
0831-JNISG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,1396.25,No
0774-IFUVM,Male,0,Yes,Yes,11,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),65.15,723.35,No
3082-WQRVY,Male,1,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,228.65,No
9553-DLCLU,Female,0,No,Yes,13,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.95,1161.75,No
1641-BYBTK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,98.35,No
2460-NGXBJ,Male,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,775.3,No
2446-ZKVAF,Male,0,Yes,No,18,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.8,1074.65,No
0841-NULXI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.55,35.55,Yes
3522-CDKHF,Female,0,Yes,No,32,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.5,2324.7,No
1430-SFQSA,Male,0,No,No,29,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,35.6,1072.6,No
0411-EZJZE,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60.25,170.5,No
7851-WZEKY,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,196.9,Yes
8844-TONUD,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,96.65,1162.85,Yes
8807-ARQET,Female,0,No,No,41,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,40.35,1677.85,No
8992-CEUEN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,18.85,18.85,No
4320-QMLLA,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,54.85,370.4,No
8777-PVYGU,Female,0,Yes,No,52,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Mailed check,64.3,3410.6,No
8292-ITGYJ,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1138.8,No
6870-ZWMNX,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),76.1,5264.25,No
0621-CXBKL,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.7,1005.7,No
5268-DSMNQ,Female,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),97.95,5936.55,No
5334-JLAXU,Female,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.1,5475.9,No
4086-YQSNZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.4,224.05,Yes
6242-MBHPK,Female,1,Yes,No,23,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2326.05,No
5868-CZJDR,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.35,31.35,Yes
9359-UGBTK,Female,0,No,No,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.35,4991.5,No
0135-NMXAP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.75,1052.4,Yes
4782-OSFXZ,Female,1,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.7,5831.2,No
6479-OAUSD,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,510.8,No
7129-ACFOG,Female,0,No,No,5,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.8,283.95,No
4189-NAKJS,Male,0,No,No,26,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),51.55,1295.4,No
5562-BETPV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,19.65,No
1282-IHQAC,Male,1,No,No,70,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),44.05,3011.65,No
9127-FHJBZ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114,8093.15,No
6270-OMFIW,Male,0,Yes,No,60,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,94.4,5610.25,Yes
1641-RQDAY,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,3217.65,No
0107-WESLM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,19.85,Yes
6994-ORCWG,Female,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Mailed check,54.25,773.2,No
1346-UFHAX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80,1029.35,No
3992-YWPKO,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.9,669.45,Yes
2933-XEUJM,Female,0,No,No,46,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Mailed check,79.2,3593.8,No
0125-LZQXK,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1553.95,Yes
5461-QKNTN,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,94.3,3953.15,No
4835-YSJMR,Male,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),49.8,1971.15,No
8399-YNDCH,Male,1,No,No,21,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),60.05,1236.15,Yes
3164-YAXFY,Male,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.75,3196,No
0887-WBJVH,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,93.45,4872.2,No
4660-IRIBM,Male,0,Yes,Yes,18,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,87.9,1500.5,No
5673-FSSMF,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,60.15,60.15,Yes
7670-ZBPOQ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),61.05,3478.75,No
8089-UZWLX,Female,1,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),104.05,7413.55,No
0080-OROZO,Female,0,No,No,35,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.25,3532,No
3916-NRPAP,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),85.7,256.75,No
6807-SIWJI,Male,0,No,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.85,3887.25,No
8221-HVAYI,Male,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),69.15,2490.15,No
1579-KLYDT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.45,593.45,Yes
5232-NXPAY,Female,0,No,No,47,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,74.45,3510.3,No
8967-SZQAS,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.45,765.45,No
4468-KAZHE,Female,1,Yes,No,20,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,60,1259.35,No
0455-ENTCR,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,85.25,5538.35,No
8944-AILEF,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,340.85,No
5542-NKVRU,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.75,844.45,No
7126-RBHSD,Female,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,78.9,1348.95,No
5370-IIVVL,Male,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.5,3778,No
6789-HJBWG,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,611.65,No
3927-NLNRY,Male,0,Yes,No,53,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.25,4867.95,Yes
9087-EYCPR,Female,0,No,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1505.05,No
6791-YBNAK,Male,0,Yes,Yes,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.55,467.85,No
6358-LYNGM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes
6077-BDPXA,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,194.2,No
0013-MHZWF,Female,0,No,Yes,9,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.4,571.45,No
5494-HECPR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,80.25,Yes
8268-YDIXR,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,93.15,5253.95,No
9824-BEMCV,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,1149.65,Yes
1373-ORVIZ,Female,0,Yes,Yes,11,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.35,740.8,Yes
4291-SHSBH,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,521.35,No
6980-IMXXE,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1412.65,No
9866-QEVEE,Male,0,No,No,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),86,1532.45,Yes
9897-KXHCM,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.3,250.05,Yes
0040-HALCW,Male,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,1090.6,No
0784-GTUUK,Male,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),23.75,1446.8,No
7979-CORPM,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.55,2282.55,Yes
2294-DMMUS,Female,0,Yes,Yes,62,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.45,4300.45,No
0872-JCPIB,Male,0,No,No,17,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),65.75,1111.2,No
3055-MJDSB,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.6,190.1,No
9091-WTUUY,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,69.25,4447.75,No
1618-CFHME,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.9,143.35,Yes
3165-HDOEW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes
6581-NQCBA,Female,0,Yes,No,16,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,49.95,810.2,Yes
7115-IRDHS,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1830.05,No
8496-DMZUK,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),90.4,2820.65,No
2040-VZIKE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,4847.35,No
9068-VPWQQ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.35,4729.3,No
0178-SZBHO,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,87.2,4017.45,No
0384-RVBPI,Male,0,No,No,20,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),64.4,1398.6,No
1689-MRZQR,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.3,2564.3,Yes
1299-AURJA,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1685.9,No
4525-VZCZG,Male,0,No,Yes,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.85,5826.65,No
1543-LLLFT,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,98.3,6066.55,No
5835-BEQEU,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.95,228.4,Yes
2788-CJQAQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,270.2,No
5565-FILXA,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.15,1529.2,Yes
0319-QZTCO,Female,0,Yes,Yes,3,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.7,168.6,No
2120-SMPEX,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,536.35,No
0096-FCPUF,Male,0,No,No,30,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,64.5,1888.45,No
0668-OGMHD,Female,0,Yes,No,21,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,28.5,629.35,No
5552-ZNFSJ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes
2223-KAGMX,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,289.3,Yes
6507-ZJSUR,Male,1,Yes,No,23,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.45,2117.25,No
9408-HRXRK,Female,0,Yes,Yes,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.15,4730.9,No
5593-SUAOO,Female,0,Yes,Yes,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),83.15,2033.05,No
7321-PKUYW,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,90.15,987.95,Yes
2833-SLKDQ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.05,45.05,Yes
6766-HFKLA,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.2,5744.35,No
7595-EUIVN,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.8,75.8,Yes
7617-EYGLW,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No
2026-TGDHM,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.3,523.15,Yes
9220-ZNKJI,Female,1,Yes,No,55,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),88.8,4805.3,No
4030-VPZBD,Female,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,30.9,59.05,Yes
2226-ICFDO,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.9,6110.75,No
0723-VSOBE,Female,1,No,No,45,No,No phone service,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,34.2,1596.6,No
5529-GIBVH,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,1046.2,No
4187-CINZD,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),95.25,4424.2,Yes
9992-UJOEL,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,92.75,No
4741-WWJQZ,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.15,194.55,No
6625-UTXEW,Female,0,Yes,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.25,612.1,No
6818-WOBHJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,6127.6,Yes
6244-BESBM,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,6671.7,No
1004-NOZNR,Male,1,No,Yes,56,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Credit card (automatic),94.8,5264.3,No
1251-STYSZ,Female,1,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,303.7,No
2612-PHGOX,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),76.1,4818.8,No
2408-TZMJL,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110.15,6448.05,Yes
8480-PPONV,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.55,7159.05,No
8780-IHCRN,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.65,1574.5,No
4598-ZADCK,Female,0,No,No,53,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,53.6,2879.2,No
1257-SXUXQ,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,86.05,No
9681-KYGYB,Male,1,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,88.2,4159.45,No
7182-OVLBJ,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.15,6638.35,No
5095-ETBRJ,Female,0,Yes,No,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,56.8,3112.05,No
7005-CYUIL,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Electronic check,99.4,7168.25,No
4821-WQOYN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.1,1326.25,No
4730-AWNAU,Male,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),60.7,2234.55,No
3452-FLHYD,Male,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.95,495.15,No
2388-LAESQ,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.85,8317.95,No
9531-NSBMR,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.25,679.8,No
6260-ONULR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,62.8,62.8,No
4389-UEFCZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105.5,7544,No
8711-LOBKY,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1188.25,No
9134-CEQMF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,676.7,Yes
8985-OOPOS,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),74.1,74.1,No
8800-ZKRFW,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,3242.5,No
2616-FLVQC,Male,0,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1240.15,No
9968-FFVVH,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),68.8,4111.35,No
3108-PCCGG,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),84.45,5899.85,No
7993-PYKOF,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75,632.95,Yes
8390-FESFV,Female,0,No,No,62,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),84.5,5193.2,No
3022-BEXHZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.2,7530.8,No
5027-XWQHA,Male,0,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,270.95,Yes
6248-TKCQV,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,5460.2,No
6729-GDNGC,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),80.7,1614.2,No
6198-ZFIOJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,402.5,No
5989-OMNJE,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,57.6,1367.75,No
4566-QVRRW,Female,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.05,483.7,Yes
1291-CUOCY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.6,7962.2,No
9795-SHUHB,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),58.2,3810.8,No
3230-IUALN,Female,0,Yes,Yes,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,81,3533.6,No
0042-RLHYP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1396.9,No
8519-IMDHU,Male,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.6,1345.55,Yes
4945-RVMTE,Female,0,No,No,28,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.55,1646.45,No
0201-OAMXR,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.55,8127.6,Yes
1866-NXPSP,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Mailed check,75.55,2680.15,No
3372-KWFBM,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.6,1281,Yes
7831-QGOXH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,1553.9,Yes
6393-WRYZE,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.65,3207.55,Yes
3941-XTSKM,Male,0,Yes,Yes,42,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.1,2049.05,No
1661-CZBAU,Male,0,No,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),70.95,3629.2,No
6599-RCLCJ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.55,5124.55,Yes
9831-BPFRI,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,89.55,3474.45,Yes
5158-RIVOP,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,202.3,No
9788-YTFGE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,147.5,No
9277-JOOMO,Female,0,No,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.6,86.35,No
1907-YLNYW,Male,0,No,No,8,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,66.7,579,No
1725-MIMXW,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.45,19.45,Yes
8947-YRTDV,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,94.8,3131.55,No
3161-ONRWK,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),65.85,3928.3,No
0114-RSRRW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.95,187.75,No
4565-NLZBV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1710.15,No
0031-PVLZI,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,76.35,Yes
7206-GZCDC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes
6682-VCIXC,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),51.25,2151.6,No
4791-QRGMF,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.5,5961.1,Yes
6475-VHUIZ,Female,0,Yes,No,23,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,1221.55,No
3910-MRQOY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.4,1496.45,No
0661-WCQNQ,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),56.25,1292.2,No
7537-RBWEA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.15,25.15,No
4656-CAURT,Male,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1713.1,No
0121-SNYRK,Male,0,No,No,50,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,35.4,1748.9,No
1768-ZAIFU,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.2,25.2,Yes
4671-LXRDQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45,96.45,Yes
3733-LSYCE,Female,0,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.35,1114.55,No
6265-FRMTQ,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,609.1,No
1934-SJVJK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes
3838-OZURD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105,7133.25,Yes
1371-DWPAZ,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),56.05, ,No
9269-CQOOL,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),54.7,169.45,Yes
2017-CCBLH,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,141.6,No
7690-KPNCU,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),73.05,4688.65,No
0536-BGFMZ,Female,0,Yes,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,563.05,No
2293-IJWPS,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.75,5985,No
2845-HSJCY,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,87.25,1258.6,Yes
5469-NUJUR,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,373.5,No
1184-PJVDB,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,857.2,Yes
2625-TRCZQ,Female,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.65,2553.35,No
4102-HLENU,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,65.65,4322.85,No
7266-GSSJX,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.45,250.8,No
7722-VJRQD,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.95,4549.05,No
7073-QETQY,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.35,1359.5,No
9415-DPEWS,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.35,1639.3,Yes
5624-RYAMH,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.5,178.85,No
0196-JTUQI,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,633.85,No
7130-YXBRO,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),111.45,5315.1,No
9272-LSVYH,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.15,735.5,No
7943-RQCHR,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,889.9,Yes
3793-MMFUH,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,1290,Yes
3249-ZPQRG,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.45,330.05,Yes
2568-BRGYX,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,237.95,Yes
3084-DOWLE,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6474.4,No
1084-MNSMJ,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.5,4421.95,No
7721-JXEAW,Male,0,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),41.05,2452.7,Yes
7249-WBIYX,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.6,813.85,Yes
4238-HFHSN,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.15,4904.85,No
6250-CGGUN,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,84.4,4484.05,No
5478-JJVZK,Female,0,No,No,33,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,60.9,2033.85,No
7596-IIWYC,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.25,538.2,No
6567-HOOPW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.2,79.2,Yes
9793-WECQC,Male,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.3,2192.9,No
4291-HPAXL,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No
8999-YPYBV,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.35,3858.05,No
1839-FBNFR,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.85,854.8,No
3164-AALRN,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,70,347.4,Yes
3071-MVJCD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),82.3,5815.15,No
1697-BCSHV,Female,0,Yes,Yes,58,Yes,Yes,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),66.8,3970.4,No
0562-KBDVM,Female,0,No,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),44.6,3058.15,No
1131-SUEKT,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),98.45,6145.2,No
3717-OEAUQ,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.7,129.2,No
4538-WNTMJ,Female,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.95,1165.9,No
3334-CTHOL,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.95,49.95,Yes
4704-ERYFC,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,1554,Yes
9432-RUVSL,Female,0,No,No,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),102.5,4904.25,No
8060-HIWJJ,Male,0,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.55,5632.55,No
7684-XSZIY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1643.25,No
9089-UOWJG,Female,0,Yes,Yes,12,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.35,740.55,No
8621-MNIHH,Female,1,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,3217.55,Yes
8039-ACLPL,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),68.75,4888.2,No
9885-AIBVB,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.8,2440.25,No
1934-MKPXS,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,620.55,No
2592-YKDIF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No
2272-JKMSI,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.8,6840.95,No
0471-LVHGK,Male,0,Yes,No,41,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,73,3001.2,Yes
9518-RWHZL,Female,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,6254.2,Yes
8714-CTZJW,Female,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),82.85,319.6,No
3569-EDBPQ,Female,0,No,No,24,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,84.35,1938.05,No
3131-NWVFJ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,294.5,No
7521-YXVZY,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58.3,No
5419-CONWX,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.8,442.85,Yes
6240-EURKS,Female,0,No,Yes,18,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35,553,Yes
2373-NTKOD,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.25,546.45,No
1970-KKFWL,Female,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.3,797.1,No
6960-HVYXR,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76,76,Yes
9337-SRRNI,Male,0,No,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1673.8,No
0895-UADGO,Male,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,44.55,343.45,No
5678-VFNEQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.1,7412.25,No
5977-CKHON,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),92.55,4039,No
7024-OHCCK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,170.85,Yes
2692-BUCFV,Male,1,No,No,29,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.45,2948.6,No
7861-UVUFT,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.3,1308.4,Yes
1830-GGFNM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),94.55,6078.75,No
5302-BDJNT,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,95.5,3418.2,No
5223-UZAVK,Male,0,No,No,64,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.3,6603.8,No
4859-ZSRDZ,Female,0,Yes,Yes,58,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),55.5,3166.9,No
5651-WYIPH,Female,1,No,No,18,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.85,865.75,No
9350-VLHMB,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,6373.1,No
3498-LZGQZ,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.15,1177.05,No
8785-CJSHH,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,5985.75,No
5357-TZHPP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.4,732.5,Yes
3870-SPZSI,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.05,7869.05,No
0680-DFNNY,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,1504.05,Yes
7560-QRBXH,Female,0,No,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,936.7,No
7077-XJMET,Male,0,Yes,No,12,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.15,741.4,No
8752-GHJFU,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,92,6585.2,No
6896-SRVYQ,Male,1,No,No,44,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,3122.4,No
7767-UXAGJ,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.45,50.45,Yes
4652-ODEVH,Male,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.45,1088.25,No
6510-UPNKS,Female,0,No,No,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,615.35,No
6718-BDGHG,Female,0,Yes,No,43,Yes,Yes,DSL,No,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),64.85,2908.2,No
9046-DQMTP,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.75,739.9,No
6439-LAJXL,Male,0,Yes,No,9,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,68.95,593.85,No
1571-SAVHK,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,1132.75,Yes
9052-VKDUW,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.4,7227.45,No
9546-CQJSU,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.4,193.6,Yes
1666-JZPZT,Male,0,No,No,27,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49,1291.35,No
5777-KJIRB,Female,0,No,No,40,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,50.25,2023.55,No
0506-LVNGN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.55,349.65,Yes
7677-SJJJK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,153.95,No
2480-EJWYP,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97.8,5458.8,No
3253-HKOKL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,100.3,5244.45,No
7055-HNEOJ,Male,0,Yes,No,3,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,55.8,154.55,No
5514-YQENT,Male,0,No,Yes,41,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.15,4507.15,No
3211-AAPKX,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,98.55,2031.95,No
8445-DNBAE,Male,0,No,Yes,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.05,50.05,No
2951-QOQTK,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.8,332.45,Yes
2958-NHPPS,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.85,473.9,No
6806-YDEUL,Female,1,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,106.8,No
1735-XMJVH,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.35,152.6,No
6890-PFRQX,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,69.5,1199.4,No
0222-CNVPT,Male,1,No,No,52,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),48.8,2555.05,No
5899-OUVKV,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,2979.2,No
8681-ICONS,Male,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,654.85,No
1621-YNCJH,Female,0,Yes,No,36,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),106.05,3834.4,No
3473-XIIIT,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,1534.75,Yes
6362-QHAFM,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.3,4586.15,No
7893-IXHRQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.55,20.55,Yes
3070-BDOQC,Female,0,No,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.65,5941.05,No
2952-QAYZF,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.3,424.15,Yes
6234-PFPXL,Male,0,Yes,No,22,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),95.9,2234.95,No
9824-QCJPK,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,666.75,No
4763-PGDPO,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,281,Yes
4283-IVYCI,Male,0,No,No,9,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,64.95,547.8,No
1866-OBPNR,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.6,74.6,No
8205-MQUGY,Male,0,Yes,Yes,12,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.2,571.15,No
8970-ANWXO,Female,0,No,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,73.75,1756.6,No
9480-BQJEI,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),92.3,5731.45,No
5394-SVGJV,Male,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.8,3475.55,Yes
6979-TNDEU,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,156.85,No
9777-WJJPR,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),88.65,2683.2,No
9283-LZQOH,Male,0,Yes,Yes,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.4,896.75,Yes
7079-QRCBC,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,2407.3,Yes
9495-SKLKD,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.95,4456.65,No
6048-UWKAL,Female,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),105.4,6998.95,No
5067-DGXLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,36.8,No
5469-CTCWN,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,106,6547.7,Yes
9851-QXEEQ,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.7,4346.4,Yes
6281-FKEWS,Female,0,No,No,44,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,49.05,2265,No
8898-KASCD,Male,0,No,No,39,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Mailed check,35.55,1309.15,No
9242-TKFSV,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.1,4754.3,No
9290-SHCMB,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.85,1235.55,No
0743-HNPFG,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),69.75,3562.5,No
2277-BKJKN,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.2,7213.75,No
9809-IMGCQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.7,2082.95,Yes
5208-HFSBT,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.05,102.75,Yes
5035-PGZXH,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.8,5914.4,No
8695-WDYEA,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.25,51.25,No
6543-JXSOO,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,57.75,1282.85,No
8016-ZMGMO,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.85,4738.85,No
8605-ITULD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
3254-YRILK,Male,1,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.2,1775.8,Yes
6416-YJTTB,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.5,795.65,No
2667-WYLWJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,145,Yes
4472-VESGY,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),98.15,4993.4,No
3195-TQDZX,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.25,61.45,No
3128-YOVTD,Female,0,Yes,Yes,51,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.15,4018.55,No
0529-ONKER,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.65,1146.65,Yes
1728-CXQBE,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,6081.4,No
7041-TXQJH,Female,0,No,No,37,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.2,1478.85,No
5014-GSOUQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,243.65,No
5724-BIDBU,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,55.35,2633.95,No
0481-SUMCB,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.15,4735.35,No
1769-GRUIK,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.1,1247.75,No
5240-IJOQT,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
8819-WFGGJ,Male,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),54.1,3794.5,No
7427-AUFPY,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1008.7,No
2811-POVEX,Female,1,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),88.45,2130.55,No
1092-GANHU,Male,0,No,No,17,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,76.65,1313.55,Yes
7898-PDWQE,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),80.4,5727.15,No
9972-EWRJS,Female,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.25,1372.9,No
9314-IJWSQ,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,1203.9,No
0661-XEYAN,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.8,25.8,Yes
5799-JRCZO,Female,0,No,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1215.1,No
1921-KYSAY,Female,0,No,No,41,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,68.6,2877.05,No
6198-RTPMF,Female,0,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,92.6,1579.7,No
2924-KHUVI,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.55,5514.95,No
1925-GMVBW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,96.1,No
7881-EVUAD,Female,0,No,No,2,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,42.6,72.4,Yes
6184-DYUOB,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,55.25,No
9207-ZPANB,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,67.45,2443.3,No
5766-XQXMQ,Female,0,No,No,29,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.85,1970.5,Yes
9327-QSDED,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.55,335.4,No
1656-DRSMG,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.85,7002.95,No
3012-VFFMN,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,158.95,No
2984-AFWNC,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.4,293.15,No
0640-YJTPY,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1493.75,No
8096-LOIST,Female,0,No,No,19,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.2,1093.4,No
9764-REAFF,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),18.4,1057.85,No
3703-VAVCL,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),90,190.05,Yes
7107-UBYKY,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.75,882.55,No
4881-GQJTW,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.6,300.4,No
8519-QJGJD,Female,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.35,1025.95,Yes
7876-DNYAP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1436.95,No
7905-NJMXS,Male,1,Yes,No,7,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,64.2,475,No
2882-WDTBA,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.75,5388.15,No
2091-GPPIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,78.95,5730.15,No
6326-MTTXK,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,819.55,Yes
5071-FBJFS,Female,0,Yes,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.3,217.1,No
2796-UUZZO,Male,0,Yes,Yes,63,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.3,4896.35,No
2429-AYKKO,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1434.1,No
9798-OPFEM,Female,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,21.1,937.1,No
0330-IVZHA,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,330.15,Yes
3794-NFNCH,Male,0,Yes,No,30,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,1474.9,No
5193-QLVZB,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.75,6536.5,No
7114-AEOZE,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1128.1,No
2886-KEFUM,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.5,6873.75,Yes
5522-NYKPB,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.9,2199.05,No
4237-RLAQD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes
9957-YODKZ,Male,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.8,457.1,No
6518-KZXCB,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,566.5,No
2245-ADZFJ,Female,0,Yes,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.55,2471.6,No
7776-QGYJC,Female,0,Yes,Yes,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),81.5,3107.3,No
9313-QOLTZ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,518.3,No
9651-GTSAQ,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,5769.75,Yes
3186-BAXNB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.7,91.7,Yes
4672-FOTSD,Male,0,No,No,12,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,67.25,832.3,No
0637-YLETY,Female,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.6,1555.65,Yes
9818-XQCUV,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,45.3,No
7338-ERIVA,Male,0,No,Yes,39,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),45.05,1790.6,No
1157-BQCUW,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,74.95,Yes
8259-NFJTV,Female,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.65,246.6,No
3223-DWFIO,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.35,261.65,No
2660-EMUBI,Male,1,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,898.35,Yes
6968-GMKPR,Female,0,No,No,55,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.55,4509.5,No
4751-ERMAN,Male,0,Yes,Yes,72,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.4,5480.25,No
1436-ZMJAN,Female,0,Yes,No,10,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),67.8,653.15,No
3292-PBZEJ,Male,1,No,No,11,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.4,1183.05,No
0799-DDIHE,Female,0,Yes,Yes,15,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,639.45,No
3070-FNFZQ,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,478.75,No
2812-SFXMJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
7675-OZCZG,Female,1,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45,127.1,Yes
5014-WUQMG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,96.1,4391.45,No
5312-TSZVC,Female,0,No,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,270.6,No
2003-CKLOR,Male,0,No,No,66,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.5,6710.5,Yes
0993-OSGPT,Female,1,Yes,No,68,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),60.65,3975.9,No
9254-RBFON,Female,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),98.6,1704.95,Yes
1205-WNWPJ,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,59.5,415.95,Yes
9391-EOYLI,Male,1,Yes,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.45,950.2,Yes
7108-DGVUU,Female,1,Yes,No,21,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),71.7,1497.05,No
2782-JEEBU,Male,0,No,No,21,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36,780.15,No
5127-BZENZ,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),65.2,3512.15,No
2720-FVBQP,Female,0,Yes,Yes,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.95,273.25,No
9906-NHHVC,Female,1,No,No,65,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),53.5,3517.9,No
4522-XRWWI,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.45,3375.9,No
3766-EJLFL,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.05,7508.55,No
5939-SXWHM,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.3,1245.05,No
8152-UOBNY,Female,1,No,No,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,106.8,5347.95,No
7351-KYHQH,Female,1,No,No,7,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,64.95,493.65,No
7643-RCHXS,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.35,1263.85,No
8246-SHFGA,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.1,385.55,No
8387-MOJJT,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),77.95,3384,Yes
0620-XEFWH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,84.2,No
6485-QXWWE,Female,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26,1638.7,No
2761-OCIAX,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,165.4,Yes
7321-VGNKU,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,120.25,No
5327-CNLUQ,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),96.9,4473.45,No
7552-KEYGT,Male,0,Yes,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,520.55,No
5816-JMLGY,Female,0,Yes,Yes,70,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.4,5717.85,No
3068-OMWZA,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,88.8,Yes
2927-QRRQV,Male,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,94.65,4312.5,No
6032-KRXXO,Male,0,No,No,30,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.25,2755.35,Yes
7459-RRWQZ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.65,994.55,Yes
6265-SXWBU,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),95.75,6511.25,No
7941-RCJOW,Male,0,No,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1218.65,No
6374-NTQLP,Male,1,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.1,7447.7,No
4154-AQUGT,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.05,1169.35,Yes
2387-KDZQY,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.1,279.5,No
3584-WKTTW,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.55,5720.35,No
3399-BMLVW,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),60.5,3121.45,No
1971-DTCZB,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.95,6468.6,No
3092-IGHWF,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,87.4,5918.8,Yes
3374-PZLXD,Male,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,675.6,No
3813-DHBBB,Male,0,Yes,No,67,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),50.95,3521.7,No
2812-REYAT,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,923.1,No
6518-PPLMZ,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.4,1110.35,No
4939-KYYPY,Male,0,No,No,27,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.45,1611.65,No
8017-LXHFA,Female,1,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,2293.6,Yes
5930-GBIWP,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),81.5,5553.25,No
6022-KOUQO,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.05,44.75,No
6352-TWCAU,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.45,3029.1,Yes
2361-UPSND,Female,0,Yes,No,46,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.6,3231.05,No
6035-RIIOM,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),97.2,5129.45,No
2929-QNSRW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,5508.35,Yes
1262-OPMFY,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,655.9,Yes
9504-DSHWM,Male,0,No,No,20,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.2,1191.2,No
5035-BVCXS,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,75.9,866.4,No
6267-DCFFZ,Female,1,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.05,2627.2,No
3533-UVMOM,Male,0,Yes,No,68,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),70.95,4741.45,No
2439-LYPMQ,Male,1,Yes,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.6,4009.2,No
4248-QPAVC,Female,1,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.35,1463.45,Yes
1899-VXWXM,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.1,5082.8,Yes
1478-VPOAD,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.8,43.8,No
9995-HOTOH,Male,0,Yes,Yes,63,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,59,3707.6,No
2988-PLAHS,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,69.95,220.45,No
1371-OJCEK,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.35,1133.7,No
4999-IEZLT,Male,0,No,No,66,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),29.45,1983.15,No
8883-ANODQ,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),84.4,5746.75,No
4690-LLKUA,Male,1,No,No,17,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,770.6,Yes
2351-RRBUE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,134.05,No
5980-BDHPY,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.1,6230.1,No
1498-DQNRX,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,573.05,No
9469-WEJBT,Male,0,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.35,3419.3,No
3331-HQDTW,Female,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,109.8,3587.25,Yes
9490-DFPMD,Female,1,No,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.65,3541.35,Yes
2581-VKIRT,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,65.5,3801.3,No
5442-XSDCW,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.5,868.5,Yes
7426-WEIJX,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.95,4859.1,No
2851-MMUTZ,Female,0,No,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,56.15,1439.35,No
3049-NDXFL,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.8,85.8,Yes
8580-AECUZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.1,79.1,Yes
3307-TLCUD,Male,0,Yes,No,17,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,34.4,592.75,No
6625-FLENO,Male,0,Yes,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1185.95,No
2967-MXRAV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,18.8,No
7963-GQRMY,Female,0,Yes,Yes,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.3,134.5,Yes
8189-HBVRW,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.8,4921.2,No
4163-KIUHY,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.6,901.25,No
1228-FZFRV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),105.95,5341.8,Yes
3500-NSDOA,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Credit card (automatic),70.8,4859.95,No
1171-TYKUR,Male,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.4,1139.2,No
3761-FLYZI,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.8,7082.45,No
2058-DCJBE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,324.6,Yes
5364-XYIRR,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,4812.75,Yes
4829-AUOAX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.05,4399.5,Yes
1219-NNDDO,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.85,663.55,No
8388-DMKAE,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,174.65,No
4403-BWPAY,Male,0,No,No,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.8,321.7,No
9659-QEQSY,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,115.65,5125.5,No
5405-ZMYXQ,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.6,548.9,No
5047-LHVLY,Male,1,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,Yes
1442-OKRJE,Male,0,Yes,Yes,66,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,7031.3,No
4737-AQCPU,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),72.1,5016.65,No
9158-VCTQB,Female,0,Yes,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),113.6,4594.95,Yes
2808-CHTDM,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,611.45,No
6311-UEUME,Female,0,No,No,29,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.9,2384.15,Yes
0793-TWELN,Female,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,319.85,No
3283-WCWXT,Male,0,Yes,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,153.3,No
1060-ENTOF,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),105.4,7035.6,No
0999-QXNSA,Male,1,No,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.75,344.2,No
5451-MHQOF,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.45,1431.65,No
4836-WNFNO,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1849.2,No
9225-BZLNZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,85.25,6083.1,No
0354-VXMJC,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,426.65,No
4422-QVIJA,Female,0,No,Yes,35,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.15,1655.35,No
9365-SRSZE,Male,1,Yes,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,1943.9,No
6839-ITVZJ,Female,0,Yes,Yes,26,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60.05,1616.15,Yes
8332-OSJDW,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26.4,314.95,No
4735-BJKOU,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,804.85,No
0274-JKUJR,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,58.85,465.7,No
5740-YHGTW,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),97.55,6669.05,No
8917-SZTTJ,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1161.75,No
1696-MZVAU,Male,0,Yes,Yes,39,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),25.25,947.75,No
7359-WWYJV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.45,8375.05,No
0375-HVGXO,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,34.7,34.7,Yes
4906-ZHGPK,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,70.7,3770,No
8593-WHYHV,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,264.8,Yes
3795-GWTRD,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),75.55,4707.85,No
1298-PHBTI,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Electronic check,84.8,6152.4,No
6223-DHJGV,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.65,958.1,No
6961-MJKBO,Male,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,943,No
6097-EQISJ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6615.15,Yes
4423-YLHDV,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.4,2200.25,Yes
8158-WPEZG,Male,0,No,No,11,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,35.65,425.1,No
0107-YHINA,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,99.75,Yes
4918-FYJNT,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.45,5044.8,No
0727-BNRLG,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),97.65,6743.55,No
4854-CIDCF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,196.4,No
8640-SDGKB,Male,0,No,No,4,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.4,299.7,Yes
3280-MRDOF,Male,1,No,No,30,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),69.1,2093.9,No
6435-SRWBJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,82.75,417.75,No
9964-WBQDJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.4,1725.4,No
6303-KFWSL,Female,0,No,No,29,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Electronic check,55.25,1620.2,No
1702-CCFNJ,Male,0,Yes,No,52,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.35,3169.55,No
8932-CZHRQ,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),76.75,5233.25,No
0386-CWRGM,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,967.85,No
5515-RUGKN,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),54.75,438.05,No
0404-AHASP,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1421.9,No
7279-NMVJC,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,323.15,No
2081-VEYEH,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.95,318.6,No
6407-UTSLV,Female,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.8,163.7,No
4116-TZAQJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.25,639.65,Yes
9060-HJJRW,Female,0,No,No,51,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),56.4,2928.5,No
2587-YNLES,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,100.35,No
7398-SKNQZ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.9,273.2,No
5935-FCCNB,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1608.15,No
1958-RNRKS,Male,0,Yes,No,30,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,49.9,1441.95,No
5136-RGMZO,Male,0,No,No,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,71.05,2168.15,No
8345-MVDYC,Female,0,No,No,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.65,3618.7,No
8226-BXGES,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),89.45,5692.65,No
3877-JRJIP,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.85,59.85,Yes
8375-DKEBR,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
9705-IOVQQ,Male,1,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,99,5969.3,No
1015-OWJKI,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,19.05,No
7511-YMXVQ,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.4,418.8,Yes
2040-XBAVJ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.45,8100.55,No
7551-JOHTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,Yes
8887-IPQNC,Female,0,Yes,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.25,313.45,No
8646-JCOMS,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.55,6130.95,No
9804-ICWBG,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes
1222-KJNZD,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.4,745.3,No
0106-GHRQR,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),71.4,1212.1,No
5318-IXUZF,Female,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.15,183.75,Yes
3768-VHXQO,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1583.5,No
8952-WCVCD,Female,0,Yes,No,41,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,4162.05,No
2418-TPEUN,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1119.9,No
3963-RYFNS,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.45,8013.55,No
3198-VELRD,Female,0,Yes,Yes,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),84.75,264.85,Yes
8540-ZQGEA,Female,0,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1102.4,No
1320-REHCS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.75,5832,No
4137-JOPHL,Female,0,No,No,50,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),89.7,4304.5,No
9436-ZBZCT,Male,0,No,No,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.95,1178.4,Yes
7801-CEDNV,Male,0,Yes,No,27,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),48.7,1421.75,No
2057-BOYKM,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),96.6,6827.5,No
3658-QQJYD,Male,0,No,No,62,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),74.3,4698.05,No
1803-BGNBD,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.3,654.5,No
0134-XWXCE,Female,1,No,No,44,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.85,3268.05,No
6950-TWMYB,Male,0,Yes,Yes,54,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),79.95,4362.05,No
5848-FHRFC,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1386.9,No
2243-FNMMI,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,415.4,No
2511-MORQY,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Bank transfer (automatic),54.9,2614.1,No
5356-KZCKT,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1513.6,No
9470-XCCEM,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.65,3161.6,No
6519-CFDBX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.4,80.95,No
3902-MIVLE,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,75.7,4676.7,No
0409-WTMPL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),110.65,6526.65,No
8763-KIAFH,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,583.3,No
3669-WHAFY,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8078.1,No
3055-VTCGS,Female,0,No,No,63,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),58.55,3503.5,No
3144-KMTWZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,6669.45,No
7279-BUYWN,Female,1,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,113.2,4689.5,Yes
7156-MHUGY,Male,1,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.5,1201.15,Yes
7198-GLXTC,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79,143.65,Yes
2007-QVGAW,Female,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.35,1292.65,No
5207-PLSTK,Male,0,Yes,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.75,48.75,No
2307-FYNNL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.05,7108.2,No
5605-XNWEN,Male,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,1802.55,No
2155-AMQRX,Female,0,No,No,28,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.9,1505.15,No
6181-AXXYF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1859.1,No
5091-HFAZW,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,168.5,No
0516-VRYBW,Female,0,No,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,390.85,Yes
2519-LBNQL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.35,6339.45,No
8623-ULFNQ,Female,1,No,No,26,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.05,1652.4,No
8380-PEFPE,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes
5687-DKDTV,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,77.5,Yes
1568-LJSZU,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.2,6392.85,No
7530-HDYDS,Female,0,No,No,38,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),84.25,3264.5,Yes
7789-HKSBS,Female,1,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4599.15,No
7416-CKTEP,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1134.25,No
2586-CWXVV,Male,0,Yes,No,54,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.4,1621.35,No
3096-IZETN,Female,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),78.1,947.3,Yes
2348-KCJLT,Female,0,Yes,No,44,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,61.5,2722.2,No
8401-EMUWF,Male,0,Yes,Yes,42,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.4,3058.3,No
4193-IBKSW,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1769.6,No
5377-NDTOU,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,91.05,6293.75,No
5922-ABDVO,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.65,1761.05,Yes
2474-LCNUE,Female,0,Yes,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),73.65,1642.75,No
0839-QNXME,Female,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,578.5,No
3506-OVLKD,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),26.2,954.9,No
9172-ANCRX,Female,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,973.25,Yes
6650-VJONK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),43.85,43.85,No
2178-PMGCJ,Male,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.7,1490.4,No
7492-TAFJD,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,38.55,280,No
2773-MADBQ,Female,0,No,No,36,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,53.1,1901.25,No
6016-LVTJQ,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,716.4,No
7860-KSUGX,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),64.45,4720,No
8966-KZXXA,Male,0,Yes,Yes,36,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,930.95,No
6910-HADCM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.35,76.35,Yes
4816-LXZYW,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1676.95,Yes
9606-PBKBQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2642.05,Yes
5149-QYTTU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),95.15,6770.85,No
2070-XYMFH,Female,1,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.35,1835.3,No
2085-BOJKI,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.65,1588.25,No
0817-HSUSE,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.5,75.5,No
5442-PPTJY,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,258.35,No
1927-QEWMY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1502.25,No
1663-MHLHE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No
5663-QBGIS,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.35,6929.4,No
4450-MDZFX,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.35,4453.3,No
6701-DHKWQ,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),51.35,3244.4,No
7554-AKDQF,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,323.45,No
3536-IQCTX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.3,2661.1,Yes
4911-BANWH,Female,0,No,Yes,31,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.55,2697.4,Yes
8496-EJAUI,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.85,1424.5,Yes
0794-YVSGE,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1401.15,No
5423-BHIXO,Female,0,No,No,32,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.2,1739.6,No
6908-VVYHM,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,5931,No
2959-EEXWB,Female,0,Yes,Yes,45,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),50.9,2333.85,No
1839-UMACK,Male,0,No,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.05,949.85,No
3030-YDNRM,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,572.45,No
7321-KKSDU,Male,0,No,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,696.8,No
3402-XRIUO,Female,1,Yes,No,22,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,63.55,1381.8,No
3132-TVFDZ,Male,1,Yes,No,57,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.85,2572.95,Yes
8286-AFUYI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,47.95,47.95,No
8080-DDEMJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.1,45.1,Yes
8356-WUAOJ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,45,No
6365-MTGZX,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96,2122.45,Yes
1349-WXNGG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,20.05,Yes
8058-DMYRU,Male,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,4931.8,No
9350-ZXYJC,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,116.95,No
6990-YNRIO,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),108.65,6937.95,Yes
8958-JPTRR,Female,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.3,1261.7,No
6959-GQEGV,Male,0,No,No,45,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,75.95,3273.8,No
3173-WSSUE,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1415.85,No
1265-HVPZB,Male,0,Yes,No,59,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),66.4,3958.2,No
4115-UMJFQ,Male,0,No,No,69,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),35.75,2492.25,No
7369-TRPFD,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,279.2,No
1098-KFQEC,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1083,No
7190-XHTWJ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,755.5,No
0621-TWIEM,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.55,402.6,Yes
3537-RYBHH,Female,1,Yes,No,47,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),67.45,3252,No
2485-ITVKB,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,68.75,Yes
3669-OYSJI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),46.2,46.2,Yes
1612-EOHDH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,45.15,Yes
6702-OHFWR,Male,1,No,No,1,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,43.3,43.3,Yes
5296-BFCYD,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.1,936.85,No
4510-PYUSH,Female,1,No,No,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),57.15,2250.65,Yes
9359-JANWS,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),58.9,3857.1,No
7517-SAWMO,Female,0,Yes,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.2,1441.1,Yes
4143-HHPMK,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.35,4338.6,Yes
3279-DYZQM,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,1378.45,No
7054-LGEQW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes
0523-VNGTF,Female,1,No,No,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50.5,2566.3,No
9575-IWCAZ,Male,0,Yes,No,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.1,171,No
7105-MXJLL,Female,1,Yes,No,26,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,60.7,1597.4,No
7064-FRRSW,Male,1,No,No,48,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,99,4744.35,No
7940-UQQUG,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.4,6721.6,No
0923-PNFUB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,83.75,247.25,Yes
3961-SXAXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.05,44.05,No
7010-BRBUU,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.1,1734.65,No
3566-HJGPK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.55,45.55,No
3062-ICYZQ,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.8,4539.35,No
9938-PRCVK,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,804.25,No
0973-KYVNF,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),70.65,5011.15,No
5129-HHMZC,Female,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,3574.5,No
9637-CDTKZ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.1,8086.4,No
3946-JEWRQ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.2,4563,No
7873-CVMAW,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.55,6362.35,No
0463-WZZKO,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.75,67.1,No
3494-JCHRQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,Yes
6474-FVJLC,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,165.45,Yes
4524-QCSSM,Male,0,No,No,26,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),44.65,1156.55,No
5832-EXGTT,Male,0,Yes,Yes,29,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,60.2,1834.15,No
8840-DQLGN,Female,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),100.5,3653.35,Yes
2039-JONDJ,Male,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),55.45,1477.65,No
7217-JYHOQ,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),70.3,1706.45,No
6695-FRVEC,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),60.4,3953.7,No
4547-LYTDD,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,72.65,1194.3,Yes
9894-QMIMJ,Female,0,No,No,23,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),55.8,1327.85,No
8069-YQQAJ,Male,0,No,No,14,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.1,419.7,No
6770-XUAGN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21,21,Yes
4193-ORFCL,Female,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.1,45.1,Yes
1636-NTNCO,Male,1,No,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,207.35,No
3466-WAESX,Male,0,No,Yes,16,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,69.1,1083.7,No
9281-PKKZE,Female,0,Yes,No,46,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,43.95,2007.85,No
3638-VBZTA,Male,0,No,Yes,68,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.5,5882.75,No
7459-IMVYU,Male,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,69.95,2657.55,No
7776-QWNFX,Male,1,Yes,No,30,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.4,1527.5,Yes
6689-TCZHQ,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.95,378.4,Yes
8563-OYMQY,Male,0,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.95,1612.2,No
0754-EEBDC,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,76.65,No
5777-ZPQNC,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.15,260.7,No
1951-IEYXM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.6,6441.85,No
3318-NMQXL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92,266.8,No
3143-ILDAL,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.45,5124.6,Yes
1022-RKXDR,Female,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.85,962.25,No
2361-FJWNO,Male,0,No,No,40,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Credit card (automatic),36,1382.9,No
2272-UOINI,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,78.5,571.05,No
8232-UTFOZ,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1399.35,No
3750-YHRYO,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,150,No
6637-KYRCV,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.5,167.2,No
5668-MEISB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),106.1,7657.4,No
0129-QMPDR,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.5,865.05,No
7188-CBBBA,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,95.5,6153.85,No
5356-CSVSQ,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,64.6,174.2,No
3221-CJMSG,Male,0,No,No,24,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),51.1,1269.6,No
4720-VSTSI,Female,0,No,No,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3862.55,Yes
3219-JQRSL,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.1,6352.4,No
2801-NISEI,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.95,1348.5,No
3946-MHCZW,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.9,50.9,Yes
4623-ZKHLY,Male,0,Yes,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.45,471.55,No
6732-VAILE,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.95,5931.75,No
8201-AAXCB,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,60.35,1404.65,No
7696-CFTAT,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,726.1,No
1845-CSBRZ,Female,1,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.35,1961.6,No
2123-VSCOT,Female,0,Yes,Yes,59,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),72.1,4194.85,No
6651-AZVTJ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4872.45,Yes
4566-GOLUK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.35,5118.95,Yes
2484-DGXPZ,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,658.95,Yes
2018-QKYGT,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,81.05,No
2792-VPPET,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.5,76.95,No
7409-JURKQ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),111.8,5809.75,No
3247-MHJKM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
1964-SVLEA,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7,415.9,No
4587-NUKOX,Female,0,No,No,3,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.1,246.5,Yes
7297-DVYGA,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,996.95,No
2239-CGBUZ,Female,0,Yes,No,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.5,3145.15,No
0854-UYHZD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,265.3,No
7243-LCGGZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,No
8267-KFGYD,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.05,21.05,No
4890-VMUAV,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,71.5,4576.3,No
9261-WDCAF,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,54.65,189.1,No
3764-MNMOI,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.2,908.15,No
7442-YGZFK,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No
0420-BWTPW,Male,0,No,Yes,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.5,215.2,Yes
8229-BUJHX,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1500.95,No
7449-HVPIV,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,5099.15,No
5504-WSIUR,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.25,6385.95,No
8183-ONMXC,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.75,159.45,Yes
8466-PZBLH,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),104.6,6885.75,No
9614-RMGHA,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),91.85,5940.85,Yes
8735-IJJEG,Male,0,Yes,No,14,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),50.2,668.85,No
0564-MUUQK,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.5,1916.2,No
5054-IEXZT,Male,0,No,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes
5834-ASPWA,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.45,75.45,Yes
0701-RFGFI,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,4613.95,No
0019-EFAEP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),101.3,7261.25,No
5619-PTMIK,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,53.1,2459.8,No
3737-XBQDD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.85,2048.8,No
5882-CMAZQ,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.25,163.55,No
5846-QFDFI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),88.6,2888.7,No
4445-KWOKW,Female,0,No,No,42,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),60.15,2421.6,No
3511-APPBJ,Male,0,No,No,23,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.95,2292.75,No
7967-HYCDE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.7,553.4,No
2430-RRYUW,Male,0,No,No,66,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,54.8,3465.7,No
3948-XHGNA,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,1210.4,Yes
3723-BFBGR,Male,1,No,No,24,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),54.8,1291.3,No
0565-IYCGT,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),78.6,5356.45,Yes
5447-WZAFP,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.3,5200.8,No
5110-CHOPY,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,53.6,3237.05,No
5445-UTODQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.1,576.65,Yes
4425-OWHWB,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.35,433.75,No
7892-QVYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.6,1868.4,No
9675-ICXCT,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,80.8,5728.55,No
1024-VRZHF,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,825.7,Yes
4703-MQYKT,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,390.4,No
9497-QCMMS,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,93.55,Yes
5692-ICXLW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.7,2845.15,No
0602-DDUML,Female,0,No,No,57,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,69.75,3894.4,No
2208-MPXIO,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20,886.4,No
1960-UYCNN,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.25,1021.55,No
0348-SDKOL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,5885.4,Yes
3190-FZATL,Male,0,No,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,268.4,No
7336-RLLRH,Male,0,Yes,No,27,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Mailed check,80.85,2204.35,No
4373-MAVJG,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),90.9,1259,Yes
8901-HJXTF,Female,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,309.1,Yes
7710-JSYOA,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),93.3,6398.05,No
5419-KLXBN,Female,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.15,2257.75,Yes
3424-NMNBO,Male,1,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.85,6287.25,Yes
9885-MFVSU,Female,0,Yes,Yes,35,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),46.35,1662.05,No
4514-GFCFI,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.75,1350.15,Yes
0607-MVMGC,Male,0,Yes,Yes,45,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.75,3600.65,No
3365-SAIGS,Female,0,No,No,17,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,1329.15,No
9828-AOQLM,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.7,45.7,Yes
8022-BECSI,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,422.5,No
8000-REIQB,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.95,69.95,Yes
9993-LHIEB,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,67.85,4627.65,No
0266-CLZKZ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6717.9,No
7615-ESMYF,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,44.6,97.1,Yes
3858-VOBET,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1710.45,Yes
7020-OZKXZ,Female,1,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.5,637.4,No
3977-QCRSL,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,117.95,No
0017-DINOC,Male,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Credit card (automatic),45.2,2460.55,No
1447-PJGGA,Female,0,No,No,57,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,95.25,5464.65,Yes
8565-CLBZW,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,89.85,2165.05,Yes
9139-WQQDY,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Mailed check,100.45,4941.8,Yes
0224-HJAPT,Male,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,47.15,223.15,Yes
8086-OVPWV,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.2,181.1,Yes
9430-FRQOC,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,87.1,341.45,Yes
7639-OPLNG,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),79.25,5731.85,No
3074-GQWYX,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,357.75,Yes
1492-QGCLU,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,85.7,4616.1,No
6845-RGTYS,Female,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.75,4533.7,Yes
7328-OWMOM,Female,0,No,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,589.25,No
4418-LZMSV,Male,0,Yes,Yes,13,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.8,750.1,No
5155-AZQPB,Female,0,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,1410.25,No
8861-HGGKB,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.45,830.85,Yes
1087-GRUYI,Male,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,743.5,No
7065-YUNRY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,45.3,Yes
7694-VLBWQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,104.1,7040.85,Yes
2546-KZAAT,Male,0,Yes,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,75.4,3865.45,No
0181-RITDD,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,108.15,6825.65,No
5989-PGKJB,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.25,1340.1,No
4795-KTRTH,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81,371.65,Yes
8272-ONJLV,Male,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,95.7,1184,No
1488-PBLJN,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.85,8477.7,No
0308-GIQJT,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),105.75,7382.85,No
3778-FOAQW,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,456.85,No
4452-ROHMO,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,331.6,No
6481-OGDOO,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.6,6056.15,Yes
3090-LETTY,Male,0,No,Yes,2,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.95,134.6,No
5349-AZPEW,Female,0,Yes,Yes,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,125.5,No
3753-TSEMP,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.15,1390.6,Yes
8305-VHZBZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
9720-JJJOR,Male,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),60.3,2511.3,No
8100-HZZLJ,Female,0,No,Yes,43,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,63.95,2737.05,No
8775-ERLNB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.3,74.3,No
8309-IEYJD,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No
9172-JITSM,Female,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.8,2361.8,Yes
6298-QDFNH,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.35,1730.35,Yes
7398-HPYZQ,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),90.55,6404,No
3546-GHEAE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,165.35,No
7361-YPXFS,Female,1,No,No,28,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),64.45,1867.6,No
6557-BZXLQ,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,1043.3,No
2550-QHZGP,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,128.6,No
7519-JTWQH,Female,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.5,7455.45,No
2538-OIMXF,Female,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.7,24.7,No
8543-MSDMF,Male,0,No,No,3,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,77.4,206.15,No
9961-JBNMK,Male,1,No,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),96.8,2030.3,Yes
1170-SASML,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.4,5869.4,No
4872-JCVCA,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),47.6,3377.8,No
5346-BZCHP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,1346.2,No
2038-LLMLM,Female,0,No,No,48,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,4946.05,No
6173-ITPWD,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.35,4065,Yes
9734-UYXQI,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,106.55,Yes
1216-BGTSP,Male,0,No,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.45,4964.7,No
4138-NAXED,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81,4085.75,No
2189-UXTKY,Female,0,Yes,No,22,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,79.2,1742.75,Yes
0744-BIKKF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.65,6224.8,No
7483-IQWIB,Male,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.95,3415.25,No
5248-KWLAR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.35,6325.25,No
4958-GZWIY,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.7,340.25,Yes
7996-MHXLW,Female,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1683.6,No
7833-PKIHD,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),76.4,3966.3,No
7061-OVMIM,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,608.5,No
5153-RTHKF,Female,0,No,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.35,2896.6,No
1852-QSWCD,Male,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1514.85,No
4832-VRBMR,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,6792.45,No
9079-LWTFD,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,100.75,4669.2,No
6356-ELRKD,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,95.6,Yes
8624-GIOUT,Female,0,No,No,49,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.75,2934.3,Yes
3392-EHMNK,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),94.1,6302.8,No
5986-WWXDV,Male,0,No,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.35,779.2,No
3061-BCKYI,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,283.75,No
6179-GJPSO,Female,1,No,No,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,4600.7,Yes
7901-TBKJX,Male,1,No,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,5594,No
7228-PAQPD,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Credit card (automatic),59.1,772.85,No
3177-LASXD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71.35,71.35,Yes
7746-QYVCO,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,55.85,857.8,No
5804-HYIEZ,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.05,7554.05,No
9919-FZDED,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,5981.65,No
5934-TSSAU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1702.9,No
3486-KHMLI,Male,0,No,Yes,21,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.7,467.15,No
4897-QSUYC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,20.15,Yes
1084-UQCHV,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,69.75,Yes
8290-YWKHZ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.2,2931,Yes
2955-BJZHG,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.85,1400.85,Yes
3806-DXQOM,Female,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,33.65,137.85,Yes
6784-XYJAE,Female,0,No,No,36,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Electronic check,55.8,1941.5,No
3933-DQPWX,Female,0,No,No,50,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),39.7,1932.75,No
6661-EIPZC,Female,0,Yes,Yes,48,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),29.5,1423.05,No
8957-THMOA,Female,0,No,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,970.85,No
2251-PYLPB,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),79.55,5810.9,No
5555-RNPGT,Male,0,No,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),24.8,223.9,No
1057-FOGLZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,391.7,No
9300-RENDD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,79.95,Yes
0761-AETCS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.3,19.3,Yes
8087-LGYHQ,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.05,811.65,No
4137-BTIKL,Male,0,No,No,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,90.75,174.75,No
2190-BCXEC,Female,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),78.85,3126.85,No
6227-FBDXH,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),99.5,6841.45,No
2153-MREFK,Female,0,Yes,No,37,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,99.2,3754.6,Yes
2911-WDXMV,Male,0,No,Yes,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1406.65,No
7206-PQBBZ,Male,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,834.7,No
3106-ULWFW,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.2,627.4,Yes
0925-VYDLG,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,242,Yes
4547-FZJWE,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,No,Credit card (automatic),59.45,3157,No
7422-WNBTY,Male,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.35,3092,No
0842-IWYCP,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.95,2168.9,No
3521-HTQTV,Male,0,No,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.1,980.35,No
3744-ZBHON,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.2,65.95,No
3373-DIUUN,Male,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21.25,711.9,No
8383-SGHJU,Female,0,No,No,33,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Electronic check,59.4,1952.8,No
7607-QKKTJ,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),95,4368.85,No
7707-PYBBH,Male,0,No,No,40,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2647.1,No
8984-HPEMB,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.65,8477.6,No
4349-GFQHK,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,54.35,54.35,Yes
4139-DETXS,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.45,4528,No
9779-DPNEJ,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.15,1790.65,No
9805-FILKB,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,845.6,No
5793-YOLJN,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,1210.3,No
0673-IGUQO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No
4123-FCVCB,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,75.85,854.45,No
8819-IMISP,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.45,2429.1,No
7802-EFKNY,Male,0,Yes,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,100.4,Yes
8311-UEUAB,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,75.5,5229.45,No
5858-EAFCZ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,44.45,No
8035-BUYVG,Male,0,Yes,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,42.35,1716.45,Yes
1163-ONYEY,Male,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),74.55,3023.55,No
9787-XVQIU,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.3,75.3,Yes
8945-MUQUF,Male,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,94.8,4837.6,Yes
5656-MJEFC,Male,0,Yes,Yes,42,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),48.15,2032.3,No
6082-OQFBA,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,436.9,No
8051-HJRLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,70.55,Yes
8974-OVACP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,No
4010-YLMVT,Female,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),106.6,5893.95,No
1379-FRVEB,Male,0,No,Yes,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91,1430.05,No
8612-GXIDD,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.4,313,No
6288-CHQJB,Female,0,Yes,Yes,54,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.95,3871.85,No
8160-HOWOX,Female,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.85,458.1,No
3023-GFLBR,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),86.15,2745.7,Yes
6648-INWPS,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,341.35,No
4223-BKEOR,Female,0,No,Yes,21,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,1336.8,No
4079-VTGLK,Male,1,Yes,No,30,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,74.85,2181.75,No
1763-WQFUK,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.5,147.75,No
1391-UBDAR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,72.9,818.45,No
8894-JVDCV,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),115.05,7133.45,No
2023-VQFDL,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19,348.8,No
1345-GKDZZ,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,128.6,No
2014-MKGMH,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.1,4674.4,No
5628-FCGYG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,1737.45,No
2560-WBWXF,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.15,1498.85,No
0248-IPDFW,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,50.1,No
7978-DKUQH,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.6,1797.75,No
4335-UPJSI,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,498.1,No
0524-IAVZO,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2624.25,Yes
2737-YNGYW,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.55,184.1,Yes
1784-EZDKJ,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.8,5498.8,No
9297-FVVDH,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.5,4845.4,No
8007-YYPWD,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.05,369.1,No
7101-HRBLJ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.7,6096.9,No
5159-YFPKQ,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.8,160.75,Yes
6635-CPNUN,Male,0,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),96.6,2684.35,No
4021-RQSNY,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,3004.15,Yes
5453-YBTWV,Male,0,Yes,Yes,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),101.1,6994.8,No
5039-LZRQT,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,273.25,No
2931-VUVJN,Female,1,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,94.05,5483.9,No
9061-TIHDA,Male,1,Yes,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,1233.65,Yes
8699-ASUFO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,527.9,Yes
6418-PIQSP,Female,0,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,4985.9,No
8220-OCUFY,Female,0,No,No,21,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,60.25,1258.35,No
3995-WFCSM,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,60.85,111.4,No
1895-QTKDO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),43.95,43.95,No
2038-OEQZH,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,86.05,308.1,No
1178-PZGAB,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.25,383.65,No
7927-AUXBZ,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.15,2555.9,Yes
2626-VEEWG,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.4,1284.2,No
2878-RMWXY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),102.65,7550.3,No
1657-DYMBM,Male,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1110.05,No
7311-MQJCH,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.55,99.6,No
7375-WMVMT,Male,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),95.5,6707.15,No
1136-XGEQU,Female,0,Yes,Yes,50,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),84.15,4164.4,No
2530-FMFXO,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,103.2,5873.75,No
6844-DZKRF,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),50.2,109.25,No
4695-WJZUE,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.55,179.25,Yes
7619-ODSGN,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.75,1338.15,Yes
5970-GHJAW,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,862.4,No
8879-XUAHX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116.25,8564.75,No
3689-MOZGR,Female,0,No,No,29,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,31.2,926.2,No
4195-PNGZS,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1718.2,No
5003-XZWWO,Male,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.2,5956.85,No
3988-RQIXO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.3,91.3,Yes
7622-FWGEW,Male,1,Yes,No,56,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.65,4824.45,No
6922-NCEDI,Male,0,No,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,21.2,1238.65,No
2514-GINMM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.5,79.5,Yes
9891-NQDBD,Female,0,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.55,672.2,No
6131-IUNXN,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,382.2,No
8548-AWOFC,Male,0,Yes,No,66,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,63.85,4264.6,No
9798-DRYDS,Female,0,Yes,Yes,17,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,61.95,1070.7,No
8532-UEFWH,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1345.85,No
6296-DDOOR,Female,0,No,No,19,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,No,Electronic check,58.2,1045.25,No
7951-VRDVK,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.85,3003.55,No
5931-FLJJF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,467.55,Yes
4815-YOSUK,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),104.9,7537.5,No
2659-VXMWZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,111.3,7482.1,Yes
0380-NEAVX,Male,1,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.85,3343.15,No
3207-OYBWH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.25,5427.05,Yes
4285-GYRQC,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.25,587.1,Yes
7216-EWTRS,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,100.8,Yes
4365-MSDYN,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,161.15,No
7036-TYDEC,Female,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,104,7028.5,No
5802-ADBRC,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,104.4,5232.9,No
8076-FEZKJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,225.85,No
5197-YPYBZ,Female,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,274.7,No
8337-MSSXB,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.3,1180.95,Yes
4312-GVYNH,Female,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,3370.2,No
8495-LJDFO,Female,1,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.95,7111.3,No
0839-JTCUD,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,5958.85,No
5494-WOZRZ,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82,5999.85,No
1302-UHBDD,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.95,1648.45,No
2234-EOFPT,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.35,5753.25,No
8619-IJNDK,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.05,4492.9,No
8378-LKJAF,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.15,3956.7,No
8182-BJDSI,Female,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.95,2625.55,Yes
2528-HFYZX,Male,1,Yes,No,17,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,1233.4,Yes
1153-GNOLC,Male,0,No,No,33,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,39.1,1309,No
3298-QEICA,Female,0,Yes,Yes,23,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,34.6,813.45,No
0788-DXBFY,Male,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.55,1108.8,No
3597-YASZG,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.45,7349.35,No
3496-LFSZU,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,294.2,No
5242-UOWHD,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,929.2,No
2482-CZGBB,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,740,Yes
6479-SZPLM,Male,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,754.5,No
8097-VBQTZ,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.9,3883.3,No
4500-HKANN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Mailed check,59.7,1414.2,No
9917-KWRBE,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),78.35,3211.2,No
3420-ZDBMA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.45,371.6,No
2212-LYASK,Male,0,Yes,Yes,27,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.85,1246.4,No
1393-IMKZG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,95.85,No
8069-RHUXK,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),35.7,2545.7,No
3398-GCPMU,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.55,6448.85,No
2908-WGAXL,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1468.9,No
3378-AJRAO,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.85,1013.6,No
1013-QCWAM,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,6690.75,No
0866-QLSIR,Female,0,No,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,64.4,2088.75,Yes
6050-FFXES,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.35,7240.65,No
7181-BQYBV,Female,0,Yes,Yes,1,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.45,102.45,Yes
0362-RAOQO,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,830.25,No
9554-DFKIC,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.45,1588.7,No
5527-ACHSO,Female,0,No,No,11,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.5,829.3,No
0829-DDVLK,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,302.45,No
1399-UBQIU,Male,0,No,No,11,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,712.25,No
1813-JLKWR,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,1336.65,No
0336-PIKEI,Male,1,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),74.4,5360.75,No
7322-OCWHC,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),93.05,6735.05,No
9537-VHDTA,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.2,51.2,No
4957-TIALW,Female,0,No,Yes,15,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),65.6,1010,No
2054-PJOCK,Female,0,No,No,60,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80.55,4847.05,No
9150-HEPMB,Male,0,No,No,56,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,52.7,3019.7,No
9030-QGZNL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,161.65,No
6204-IEUXJ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),80.1,217.55,Yes
3126-WQMGH,Female,0,Yes,No,49,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.15,2583.75,No
4529-CKBCL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.2,146.05,Yes
2506-CLAKW,Female,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),98.15,567.45,Yes
7176-WRTNX,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,7711.25,No
1583-IHQZE,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,112.95,1384.75,Yes
5732-IKGQH,Male,1,Yes,No,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,5481.25,No
9239-GZHZE,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.65,8124.2,No
7205-BAIAD,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.6,827.3,No
0151-ONTOV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.9,70.9,Yes
4140-MUHUG,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,220.95,Yes
0093-EXYQL,Female,1,No,No,40,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.55,3673.6,No
8064-RAVOH,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.85,49.85,No
0219-QAERP,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,576.65,No
0320-JDNQG,Male,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,2331.3,Yes
7180-PISOG,Male,0,Yes,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes
9168-INPSZ,Female,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,104.15,4495.65,No
3571-RFHAR,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.15,6941.2,Yes
0015-UOCOJ,Female,1,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,48.2,340.35,No
5334-AFQJB,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1789.9,No
2754-SDJRD,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.15,908.55,No
9578-VRMNM,Female,0,No,No,16,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),65.2,1043.35,Yes
1587-FKLZB,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.5,6822.15,Yes
6140-QNRQQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.55,71.55,Yes
8963-JLGJT,Male,0,No,Yes,3,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.9,157.55,No
4307-KTUMW,Male,0,Yes,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,93.9,5029.2,Yes
1465-LNTLJ,Male,1,Yes,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),64.4,581.7,No
5440-FLBQG,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.4,7318.2,Yes
2135-DQWAQ,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,85.3,420.45,No
4056-QHXHZ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,107.45,7576.7,No
7470-MCQTK,Female,0,Yes,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),48.75,633.4,Yes
7488-MXJIV,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,321.65,Yes
7401-JIXNM,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),91.3,4965,No
9339-FIIJL,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.95,6151.9,No
2027-FECZV,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.7,1253.9,Yes
2672-DZUOY,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,25.15,Yes
4706-DGAHW,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.2,45.2,No
3870-MQAMG,Female,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.35,5893.15,Yes
6670-MFRPK,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Two year,Yes,Credit card (automatic),79.2,5420.65,No
6177-PEVRA,Female,0,No,No,48,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Credit card (automatic),55.5,2627.35,No
4800-CZMPC,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),103.25,5037.55,Yes
4813-HQMGZ,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.25,743.75,No
7579-KKLOE,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,91.25,6589.6,No
7377-DMMRI,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,47.8,92.45,Yes
8402-EIVQS,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.9,6733.15,No
4947-DSMXK,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.7,3410,No
7245-NIIWQ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,199.85,No
0002-ORFBO,Female,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,65.6,593.3,No
3324-OIRTO,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,7288.4,No
5414-OFQCB,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),90.45,5229.8,No
4967-WPNCF,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.7,4464.8,No
8552-OBVRU,Female,1,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.5,5068.05,No
8499-BRXTD,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,401.85,No
9154-QDGTH,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.3,4451.85,No
8197-BFWVU,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.25,6688.95,No
2577-GVSIL,Male,0,Yes,Yes,35,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),73.45,2661.1,No
9367-OIUXP,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,73.05,No
6770-UAYGJ,Female,0,Yes,Yes,49,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1211.65,No
6463-HHXJR,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.5,7030.65,No
7928-VJYAB,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,1020.2,No
1187-WILMM,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.4,5597.65,No
9776-OJUZI,Female,1,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.45,6223.3,No
1306-RPWXZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.45,1024.65,No
8949-JTMAY,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.6,2933.2,Yes
2774-LVQUS,Female,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,83.05,1258.3,Yes
3097-PYWXL,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.95,82.9,No
2266-SJNAT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),109.15,7789.6,No
0869-PAPRP,Female,1,Yes,No,26,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.7,2067,No
4238-JSSWH,Female,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),102.05,3452.55,No
2972-YDYUW,Female,0,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Electronic check,94.7,5468.95,No
1104-FEJAM,Male,0,Yes,Yes,28,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,64.4,1802.15,No
2809-ILCYT,Female,0,Yes,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,26.8,733.55,No
5499-ECUTN,Female,0,Yes,No,47,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),66.05,3021.45,No
4981-FLTMF,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),65.2,3687.85,No
9121-PHQSR,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.05,1391.15,No
3113-IWHLC,Male,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.8,274.35,No
3211-ILJTT,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,1214.05,Yes
4612-THJBS,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.75,5510.65,Yes
4277-BWBML,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1322.85,No
4094-NSEDU,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1973.75,Yes
0234-TEVTT,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),45,2196.3,No
4304-TSPVK,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.9,7843.55,No
1552-AAGRX,Female,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.4,3211.9,No
2637-FKFSY,Female,0,Yes,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46.1,130.15,No
9796-MVYXX,Female,1,No,No,14,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.7,692.35,No
7874-ECPQJ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.05,85.5,No
0020-INWCK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),95.75,6849.4,No
7089-RKVSZ,Male,0,Yes,Yes,8,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.4,203.95,No
2683-JXWQQ,Male,0,Yes,Yes,61,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),33.6,2117.2,No
9548-ZMVTX,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),90.45,6565.85,No
8739-XNIKG,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84,424.75,No
9755-JHNMN,Female,0,No,No,49,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),67.4,3306.85,No
3981-QSVQI,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.7,168.9,No
2789-HQBOU,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.35,253.8,No
9424-CMPOG,Male,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,197.4,No
5067-WJEUN,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),54.2,3838.2,No
3450-WXOAT,Male,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.2,2065.15,No
9251-WNSOD,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,75.1,5064.45,No
6974-DAFLI,Female,0,Yes,No,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.7,1140.05,No
2616-UUTFK,Male,0,Yes,No,33,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.75,2447.45,No
7064-JHXCE,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1263.9,No
5103-MHMHY,Female,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes
7989-AWGEH,Male,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.2,1838.15,No
4373-VVHQL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.75,44.75,No
4559-UWIHT,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.65,1185,No
7268-IGMFD,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),93.9,1743.9,No
1846-XWOQN,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,70.15,Yes
0235-KGSLC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.55,85.55,Yes
6650-BWFRT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.15,8529.5,No
9570-KYEUA,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),99.25,6549.45,No
6993-YGFJV,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.55,7806.5,No
2712-SYWAY,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.7,25.7,No
0730-BGQGF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,6287.3,No
5498-IBWPI,Female,0,Yes,Yes,66,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),49.4,3251.85,No
9101-NTIXF,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,50.6,No
0013-SMEOE,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.7,7904.25,No
9314-QDMDW,Male,0,No,No,11,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,61.25,729.95,No
9308-ANMVE,Male,0,No,Yes,47,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,55.3,2654.05,No
2884-GBPFB,Female,0,Yes,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,2416.55,Yes
3757-NJYBX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.35,3520.75,Yes
3413-DHLPB,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.75,5969.95,No
7649-PHJVR,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,226.8,No
6114-TCFID,Female,0,No,No,29,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.5,1082.75,No
3787-TRIAL,Male,0,Yes,Yes,21,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,26.05,565.75,No
2573-GYRUU,Male,1,Yes,No,48,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.05,4370.75,No
5156-UMKOW,Female,0,No,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.65,90.05,No
1247-QBVSH,Female,0,Yes,Yes,43,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.2,2169.4,No
6734-GMPVK,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.3,550.6,No
9822-OAOVB,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.45,55.45,No
6161-ERDGD,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,85.45,6300.85,No
1226-JZNKR,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.8,160.05,No
7318-EIVKO,Male,0,No,No,8,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.25,436.6,No
3771-PZOBW,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.7,1781.35,No
5136-KCKGI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,103.7,3467,Yes
8231-BSWXX,Male,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),79.05,5552.5,No
6486-LHTMA,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.7,2835.5,No
4083-BFNYK,Female,1,Yes,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),95,3591.25,No
3722-WPXTK,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.35,88.35,Yes
7389-KBFIT,Female,0,Yes,Yes,2,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,30.25,63.75,No
7176-WIONM,Female,0,Yes,No,12,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.85,617.15,No
5141-ZUVBH,Female,0,No,Yes,9,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),93,870.25,No
1089-HDMKP,Male,1,No,Yes,11,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.55,601.25,No
7623-HKYRK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,111.65,No
0310-SUCIN,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84.8,6046.1,No
5197-PYEPU,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.45,3923.8,No
2929-ERCFZ,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,777.3,Yes
7548-SEPYI,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.25,512.45,Yes
8835-VSDSE,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,141.45,Yes
6619-RPLQZ,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,892.15,No
3275-RHRNE,Male,0,Yes,Yes,28,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60,1682.05,No
3503-TYDAY,Female,0,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.45,3398.9,No
6901-GOGZG,Male,0,No,Yes,60,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,4984.85,No
1623-NLDOT,Female,0,Yes,No,42,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,33.55,1445.3,Yes
7021-XSNYE,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),49.65,305.55,No
9621-OUPYD,Female,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,507.9,No
9898-KZQDZ,Female,1,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.55,3640.45,Yes
8982-NHAVY,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.5,2673.45,No
4307-KWMXE,Male,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,35.75,389.8,No
0141-YEAYS,Female,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),86.45,2401.05,No
2450-ZKEED,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),53.8,651.55,No
3694-DELSO,Male,0,Yes,Yes,4,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),38.55,156.1,No
3893-JRNFS,Male,0,No,No,68,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),39.9,2796.35,No
9603-OAIHC,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,No
1133-KXCGE,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,407.05,No
5236-PERKL,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.95,6465,Yes
0142-GVYSN,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.3,511.25,No
1049-FYSYG,Female,0,Yes,No,17,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),35.65,646.05,No
7854-FOKSF,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35.9,35.9,Yes
2519-FAKOD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.25,3777.15,Yes
8406-LNMHF,Male,1,Yes,No,59,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),82.95,4903.15,No
1821-BUCWY,Male,0,No,No,30,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,Yes,Mailed check,55.65,1653.85,No
8263-JQAIK,Male,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,47.5,Yes
0023-UYUPN,Female,1,Yes,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,1306.3,No
8314-DPQHL,Male,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Mailed check,50.8,463.6,No
1465-WCZVT,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,60.65,No
9481-SFCQY,Female,0,No,Yes,14,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.8,824.85,No
6360-SVNWV,Female,1,No,No,31,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.55,2094.65,No
0567-GGCAC,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,61.4,438.9,No
7089-IVVAZ,Female,0,No,No,8,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.35,847.3,Yes
8884-MRNSU,Male,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,329.75,No
2171-UDMFD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,674.55,No
9050-IKDZA,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,81.5,162.55,No
2205-YMZZJ,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,546.95,Yes
9802-CAQUT,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.55,7887.25,No
1254-IZEYF,Female,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,3186.65,Yes
0187-WZNAB,Female,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.4,1972.35,No
9492-TOKRI,Female,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90,1527.35,Yes
1475-VWVDO,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,490.55,No
9221-OTIVJ,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.85,1531.4,Yes
1357-MVDOZ,Male,0,Yes,Yes,11,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.65,683.25,No
7602-MVRMB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,8058.85,No
3325-FUYCG,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,106.1,2847.4,Yes
5908-QMGOE,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.2,1133.9,Yes
1197-BVMVG,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,294.45,No
5406-KGRMX,Female,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1719.15,No
8481-YYXWG,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.35,461.7,Yes
5968-HYJRZ,Male,0,Yes,Yes,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1160.45,No
5198-EFNBM,Male,1,Yes,No,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,90.65,5199.8,No
7516-GMHUV,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105.05,5163.3,No
7140-ADSMJ,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,162.3,No
2230-XTUWL,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,883.35,No
7706-YLMQA,Female,0,No,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,1341.5,No
2585-KTFRE,Male,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),70.45,70.45,No
7994-UYIVZ,Male,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,659.45,No
2609-IAICY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.15,77.15,Yes
1740-CSDJP,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),35.25,35.25,Yes
7717-BICXI,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1205.05,No
6559-RAKOZ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,97.95,4917.9,No
2636-OHFMN,Male,0,Yes,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.55,201,Yes
4716-MRVEN,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,599.3,No
2143-LJULT,Female,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.25,1733.15,No
1323-OOEPC,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.4,5149.5,Yes
3200-MNQTF,Male,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4677.1,No
6164-HXUGH,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,119.3,No
5630-IXDXV,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,4849.1,No
0320-DWVTU,Female,0,No,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,99.5,5424.25,No
9135-MGVPY,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.7,5878.9,No
1212-GLHMD,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,86.05,244.85,No
7878-JGDKK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.55,220.75,No
1088-AUUZZ,Male,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),75.85,4261.2,No
0397-GZBBC,Male,1,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.85,5574.75,Yes
6614-YWYSC,Male,1,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1501.75,No
9588-OZDMQ,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,89.75,No
7641-EUYET,Male,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.7,4541.2,Yes
6476-EPYZR,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,255.5,No
9921-ZVRHG,Female,0,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,1072,Yes
1174-FGIFN,Female,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,90.45,2509.25,No
6620-HVDUJ,Male,0,No,No,24,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),60.45,1440.75,No
4701-MLJPN,Male,0,No,No,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,55.25,1715.65,Yes
1032-MAELW,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,78.45,5333.35,No
7641-TQFHN,Male,0,No,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,No,Mailed check,100.55,3895.35,No
1552-TKMXS,Female,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,869.9,No
9206-GVPEQ,Male,0,Yes,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,706.85,Yes
8622-ZLFKO,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.75,512.25,No
1596-BBVTG,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.35,2636.05,Yes
6188-UXBBR,Female,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,814.75,No
2333-KWEWW,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,388.6,No
5702-SKUOB,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,93.45,No
1134-YWTYF,Male,0,Yes,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.8,1389.85,No
6061-GWWAV,Male,0,No,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,70.2,2894.55,No
0679-TDGAK,Male,0,Yes,Yes,50,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,75.5,4025.6,No
6585-WCEWR,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.35,1354.4,No
9067-YGSCA,Female,0,No,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),26.05,1856.4,No
9067-SQTNS,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,926,No
8433-WXGNA,Male,0,No,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,189.2,Yes
1776-SPBWV,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.1,682.1,No
8735-SDUFN,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1778.7,No
9668-PUGNU,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,24.5,1816.2,No
9405-GPBBG,Female,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.5,7069.25,No
1926-QUZNN,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.25,1841.2,No
3707-GNWHM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.25,74.25,Yes
7016-NVRIC,Male,1,Yes,No,29,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),90.1,2656.7,No
5829-NVSQN,Female,0,Yes,No,23,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),68.75,1689.45,No
9565-AXSMR,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.2,1054.75,No
8922-LIEGH,Female,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.7,2187.55,Yes
8869-LIHMK,Female,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.1,7334.05,No
8245-UMPYT,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.4,1581.2,Yes
5186-SAMNZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.5,69.5,Yes
0447-RXSGD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.65,2404.85,No
4927-WWOOZ,Male,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.45,171.45,No
5788-YPOEG,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.75,2839.45,No
5373-SFODM,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.25,3132.75,Yes
0661-KBKPA,Male,0,Yes,Yes,53,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.75,3942.45,No
9081-WWXKP,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,873.4,No
0784-ZQJZX,Male,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1529.65,No
3133-PZNSR,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),97.75,6991.6,No
5766-ZJYBB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,19.4,Yes
8931-GJJIQ,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.3,803.3,Yes
4277-PVRAN,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80.1,679.3,Yes
0022-TCJCI,Male,1,No,No,45,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),62.7,2791.5,Yes
0722-SVSFK,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,715,No
3612-YVGSJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1681.6,No
9825-YCXWZ,Female,1,No,No,41,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),101.1,4016.2,No
5397-NSKQG,Male,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Two year,No,Credit card (automatic),50.9,3281.65,No
8565-HBFNN,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107.2,7317.1,No
2000-DHJUY,Female,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),92.2,6474.45,No
0203-HHYIJ,Male,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.3,676.35,Yes
8670-ERCJH,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.4,8164.1,No
7758-UJWYS,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Electronic check,40.55,1325.85,No
2050-ONYDQ,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26,1654.85,No
7055-JCGNI,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.95,7795.95,No
0739-UUAJR,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),53.8,3952.45,No
4826-DXMUP,Male,0,No,Yes,35,Yes,No,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,72.1,2495.15,No
0952-KMEEH,Male,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,98.15,1230.25,Yes
7285-KLOTR,Female,0,Yes,No,12,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,78.85,876.75,No
0654-PQKDW,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),70.75,4263.45,No
7175-NTIXE,Female,0,No,No,25,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),76.15,1992.95,No
7963-SHNDT,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.1,1982.1,No
9796-BPKIW,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,562.7,No
0188-GWFLE,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,33.7,No
8129-GMVGI,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,1090.1,No
2882-DDZPG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,227.45,No
3547-LQRIK,Female,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,26.9,1250.85,No
9137-NOQKA,Male,1,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,37.2,No
5843-TTHGI,Female,0,No,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,892.7,No
1849-RJYIG,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60,487.75,No
8868-GAGIO,Male,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,84.55,3713.95,No
4061-UKJWL,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.45,141.7,No
5380-XPJNZ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,678.2,No
8263-QMNTJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.55,8425.3,No
8178-EYZUO,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.7,4154.8,Yes
3230-JCNZS,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99,7061.65,No
0277-ORXQS,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),50.55,3364.55,No
5130-IEKQT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,105.95,2655.25,Yes
3230-WYKIR,Male,0,No,No,18,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,1425.45,Yes
7996-BPXHY,Female,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,332.5,No
3227-WLKLI,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.55,5963.95,No
0407-BDJKB,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.75,5742.9,Yes
2266-FUBDZ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,278.85,No
8237-ULIXL,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1871.85,No
9600-UDOPK,Male,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.05,2866.45,Yes
8216-AZUUZ,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,100.4,4303.65,No
9153-BTBVV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1753,No
4074-SJFFA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,54.75,54.75,Yes
3269-ATYWD,Male,1,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,3759.05,Yes
5186-PEIZU,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,617.65,No
1074-AMIOH,Female,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.25,5935.1,No
4910-GMJOT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,94.6,Yes
0354-WYROK,Female,1,Yes,Yes,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,2911.3,Yes
4361-FEBGN,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,982.95,No
7748-UMTRK,Female,1,No,Yes,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,2974.5,No
0380-ZCSBI,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,198.7,No
7145-FEJWU,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,1275.65,No
6463-MVYRY,Female,1,No,No,57,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),69.85,4003,No
3969-JQABI,Female,0,Yes,No,58,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),65.25,3791.6,No
9624-EGDEQ,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,813.3,No
1051-EQPZR,Female,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,780.25,No
5849-ASHZJ,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,552.9,No
5780-INQIK,Female,0,No,No,8,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,49.4,408.25,No
7576-OYWBN,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,231.8,Yes
4526-ZJJTM,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),88.4,2191.15,No
8384-FZBJK,Female,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,5611.7,No
3750-RNQKR,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,246.25,No
0962-CQPWQ,Female,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1296.15,No
3096-YXENJ,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.65,7082.85,No
1265-BCFEO,Female,0,Yes,No,71,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5662.25,No
5837-LXSDN,Female,0,Yes,Yes,21,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.85,1215.45,No
5945-AZYHT,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,109.6,7854.15,No
8325-QRPZR,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.15,525,No
6384-VMJHP,Female,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),73,5265.2,No
2262-SLNVK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,70.1,No
7730-CLDSV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.65,7129.45,No
1135-HIORI,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.45,7266.95,No
0164-APGRB,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.9,8496.7,No
6481-ESCNL,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.55,2878.75,No
1790-NESIO,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.4,261.3,No
1550-EENBN,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,104.35,3205.6,No
5539-TMZLF,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,69.75,Yes
4323-SADQS,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,34.5,279.25,Yes
4446-BZKHU,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.55,6281.45,Yes
5202-IVJNU,Female,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),30.1,1131.3,Yes
5976-JCJRH,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,738.2,Yes
8198-RKSZG,Female,0,Yes,Yes,14,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),80.45,1137.05,No
0137-OCGAB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.2,80.2,Yes
3351-NQLDI,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),94.35,6341.45,Yes
9297-EONCV,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.35,6697.2,No
7593-JNWRU,Male,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.6,260.8,Yes
4588-YBNIB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes
1069-QJOEE,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,505.45,No
3336-JORSO,Female,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.45,3655.45,Yes
7799-DSEWS,Male,0,No,No,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,68.35,1299.8,No
8766-PAFNE,Male,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.1,5564.85,No
5315-CKEQK,Male,1,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Electronic check,51,1381.8,No
3130-ICDUP,Female,0,No,Yes,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),80.55,188.1,No
0820-FNRNX,Male,0,Yes,Yes,17,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,66.7,1077.05,No
0880-FVFWF,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,86.4,4922.4,No
4611-ANLQC,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,50.05,2911.5,No
4213-HKBJO,Female,0,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.7,826.1,No
2792-LSHWX,Female,0,No,No,1,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,83.4,83.4,No
5028-GZLDO,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.7,140.7,Yes
0014-BMAQU,Male,0,Yes,No,63,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.65,5377.8,No
6861-XWTWQ,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.25,665.45,Yes
9018-PCIOK,Female,0,No,No,55,Yes,No,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Mailed check,64.75,3617.1,No
4837-QUSFT,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),100.15,6643.5,No
6877-TJMBR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,84.8,84.8,Yes
9953-ZMKSM,Male,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1559.3,No
0907-HQNTS,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113,7987.6,No
7665-NKLAV,Female,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.65,1547.35,No
6769-DCQLI,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105,5426.85,Yes
2433-KMEAS,Male,0,No,No,22,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.45,1127.35,Yes
4391-LNRXK,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.95,2142.8,No
8250-ZNGGW,Female,1,No,No,5,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.9,287.85,No
2195-ZRVAX,Female,0,Yes,No,47,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,4045.65,Yes
3550-SAHFP,Female,0,No,No,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.35,2757.85,Yes
2011-TRQYE,Male,0,No,No,18,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),33.5,600,Yes
8562-GHPPI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.8,19.8,No
5893-PYOLZ,Male,0,No,No,56,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),81.8,4534.45,No
4986-MXSFP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,40.9,No
6131-FOYAS,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,59.6,2094.9,No
3027-YNWZU,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1584.8,No
5609-IMCGG,Female,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.35,1302.65,No
4873-ILOLJ,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.35,2238.5,Yes
4727-MCYZG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,55.55,55.55,No
9481-WHGWY,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),75.35,5437.75,No
2725-KXXWT,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.75,90.75,Yes
9565-DJPIB,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,89.6,365.65,Yes
4328-VUFWD,Female,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Electronic check,59.3,2209.15,No
3301-LSLWQ,Female,0,No,No,29,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,66.1,1912.15,No
7473-ZBDSN,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.8,255.55,No
3166-PNEOF,Female,0,No,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5175.3,No
5639-NTUPK,Male,0,No,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.1,670.65,No
8780-YRMTT,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Mailed check,47.4,3177.25,No
8348-HFYIV,Male,0,No,No,2,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.25,90.35,Yes
5140-FOMCQ,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.15,6557.75,No
6242-SGYTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.95,5791.85,No
8166-ORCHU,Male,1,Yes,No,33,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,93.55,3055.5,No
8414-OOEEL,Male,0,No,Yes,66,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.5,5196.1,No
8454-AATJP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,8405,No
6849-WLEYG,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes
4659-NZRUF,Female,0,No,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,1789.25,Yes
4531-AUZNK,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,One year,Yes,Mailed check,95.15,5000.05,No
4191-XOVOM,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.4,6713.2,No
2150-OEGBV,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,562.6,No
8429-XIBUM,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.35,2317.1,Yes
1855-CFULU,Female,1,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,91.45,No
4878-BUNFV,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.7,828.85,No
1872-EBWSC,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,617.35,No
2608-BHKFN,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.05,266.9,Yes
7026-YMSBE,Male,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,625.05,No
7341-LXCAF,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,301.4,Yes
6997-UVGOX,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.45,6029.9,No
9674-EHPPG,Male,0,Yes,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.4,1842.7,No
9462-MJUAW,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.4,206.6,Yes
8128-YVJRG,Female,0,No,No,7,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,79.65,604.7,Yes
5440-VHLUL,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,7386.05,No
5781-BKHOP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.65,7334.05,No
4283-FUTGF,Male,1,No,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.85,1471.75,Yes
5213-TWWJU,Male,0,No,No,28,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Electronic check,91,2626.15,No
1569-TTNYJ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.75,412.1,Yes
8628-MFKAX,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.75,8277.05,No
2397-BRLOM,Male,1,Yes,Yes,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.45,583.45,Yes
7629-WIXZF,Female,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,59.1,369.25,No
5445-GLVOT,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1049.05,No
3976-HXHCE,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,1414.8,No
2466-NEJOJ,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.65,169.75,No
0254-KCJGT,Male,0,Yes,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),81.4,4354.45,No
5472-CVMDX,Female,0,No,No,68,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.9,2719.2,No
6461-SZMCV,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.95,6365.35,No
8150-QUDFX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,51.6,No
9508-ILZDG,Female,1,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.35,3190.25,No
2346-DJQTB,Female,0,No,No,35,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.15,812.5,No
1697-LYYYX,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.1,1143.8,No
1942-OQFRW,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
4749-VFKVB,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.1,50.1,Yes
9640-ZSLDC,Female,0,Yes,Yes,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),60.6,3297,No
4231-LZUYM,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.65,1887,No
7598-UAASY,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.4,151.8,Yes
7938-OUHIO,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,293.65,Yes
8510-AWCXC,Female,1,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,1308.1,Yes
6128-AQBMT,Male,1,Yes,No,41,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.95,2215.4,No
3594-BDSOA,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.4,482.8,No
0431-APWVY,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.1,2598.95,Yes
5133-VRSAB,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.35,216.45,No
5996-DAOQL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No
6838-YAUVY,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),95.1,5064.85,No
0484-JPBRU,Male,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.25,996.45,No
7883-ROJOC,Female,0,Yes,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.9,839.65,No
0244-LGNFY,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92.65,6733,No
7274-CGTOD,Male,0,No,No,62,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),43.7,2618.3,No
4295-YURET,Female,1,Yes,Yes,56,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),72.6,4084.35,No
3426-NIYYL,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,51.55,765.5,Yes
8225-BTJAU,Male,1,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,793.55,Yes
4635-EJYPD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.95,613.95,No
1866-ZSLJM,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.5,402.85,No
4636-TVXVG,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1244.8,No
4236-UJPWO,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.5,46.4,No
9392-XBGTD,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,581.85,No
3387-VATUS,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.85,462.8,Yes
6402-SSEJG,Female,0,No,No,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,61.05,1540.2,No
1143-NMNQJ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.7,169.8,Yes
1169-SAOCL,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.65,5168.1,No
1110-KYLGQ,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.25,6780.1,No
9929-PLVPA,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.4,94.5,No
3518-PZXZQ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,55.3,55.3,No
2371-JUNGC,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,208,No
7693-QPEFS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.95,3829.75,No
0924-BJCRC,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.45,5294.6,No
5074-FBGHB,Male,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.65,6889.8,No
2351-BKRZW,Female,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),75.2,3254.35,No
4455-BFSPD,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),101.15,6383.9,No
0415-MOSGF,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes
1724-BQUHA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,477.7,Yes
3948-KXDUF,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),68.75,4447.55,No
2323-ARSVR,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.05,7321.05,No
0815-MFZGM,Female,0,Yes,No,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99,4135,No
4826-XTSOH,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.05,86.05,Yes
5480-XTFFL,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,697.7,No
8295-FHIVV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,168.65,No
2495-INZWQ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.55,174.3,Yes
9086-YJYXS,Male,0,Yes,Yes,34,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),77.2,2753.8,No
1179-INLAT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,69.25,No
7909-FIOIY,Female,0,Yes,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.85,434.8,No
4139-SUGLD,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),35.4,1077.5,Yes
6857-VWJDT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.65,95.65,Yes
6351-SCJKT,Male,0,No,No,3,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,41.35,107.25,No
5468-BPMMO,Male,0,Yes,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,851.2,No
5624-BQSSA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.95,20.95,Yes
0197-PNKNK,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Bank transfer (automatic),84.45,5848.6,No
2439-QKJUL,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,109.8,No
1194-SPVSP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,19.65,No
9534-NSXEM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.65,595.5,No
2408-WITXK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,329.8,Yes
8929-KSWIH,Male,0,No,No,25,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.3,2513.5,No
2250-IVBWA,Male,0,Yes,Yes,64,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.05,5135.35,No
1810-MVMAI,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,67.6,2000.2,No
9506-UXUSK,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,931.75,No
1229-RCALF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,115,7396.15,No
3572-UOLYZ,Female,0,No,Yes,46,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3958.85,No
1429-UYJSV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,260.9,No
5577-OTWWW,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,297.3,Yes
6100-QQHEB,Male,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1515.1,Yes
6366-XIVKZ,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,63.15,816.8,No
3470-OBUET,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),74,4868.4,No
2770-NSVDG,Male,0,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,29.1,688,No
9375-MHRRS,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,288.35,No
2634-HCZGT,Male,1,Yes,No,53,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,60.05,3229.65,Yes
7503-QQRVF,Male,1,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,1178.25,Yes
4860-YZGZM,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,185.4,No
6599-GZWCM,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,74.65,966.25,No
2691-NZETQ,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,758.6,Yes
4404-HIBDJ,Female,0,No,No,25,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Mailed check,74.3,1863.8,Yes
5533-NHFRF,Male,1,No,No,7,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,44.4,265.8,No
7037-MTYVW,Male,0,Yes,Yes,38,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.4,3297,No
4760-THGOT,Female,0,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,4107.3,No
7295-JOMMD,Female,0,No,Yes,4,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,396.3,Yes
4016-BJKTZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.9,2809.05,No
6584-VQMYT,Male,0,No,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.2,1567.55,No
9838-BFCQT,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,1851.45,No
2790-XUYMV,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),85.45,6028.95,No
0581-MDMPW,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.95,2072.75,No
5013-SBUIH,Female,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,109.65,5551.15,Yes
1023-BQXZE,Male,0,No,No,57,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.35,4317.35,No
9163-GHAYE,Female,0,No,No,15,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,48.85,736.8,No
3904-UKFRE,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.1,336.15,No
5353-WILCI,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.05,1522.65,No
0709-TVGUR,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,622.9,Yes
0058-EVZWM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.8,4959.6,No
6023-YEBUP,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.95,329.95,Yes
7209-JCUDS,Male,0,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.9,1048.85,Yes
7009-PCARS,Male,0,No,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.1,1001.5,No
0519-DRGTI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,442.6,No
1017-FBQMM,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.05,6703.5,No
3001-CBHLQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.9,3351.55,Yes
1985-MBRYP,Female,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.65,779.25,No
6158-DWPZT,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),24.1,259.8,Yes
9372-TXXPS,Female,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.85,3483.45,No
3259-QMXUN,Male,0,Yes,No,58,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,86.1,4890.5,No
1015-JPFYW,Male,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,136.75,No
6645-MXQJT,Male,0,Yes,Yes,2,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,97.1,184.15,No
4360-QRAVE,Male,1,No,No,37,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,36.65,1315,No
8433-WPJTV,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.9,6767.1,No
5804-JMYIO,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,757.95,No
3763-GCZHZ,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.05,6890,Yes
6484-LATFU,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1657.4,No
7599-NTMDP,Female,0,Yes,Yes,62,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),48.7,3008.55,No
0772-GYEQQ,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.35,262.05,Yes
0536-ACXIP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.55,8165.1,No
0936-NQLJU,Female,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,875.55,No
4831-EOBFE,Male,0,Yes,Yes,29,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),94.65,2649.15,Yes
1575-KRZZE,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.2,220.65,No
3251-YMVWZ,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.05,1301.9,No
4102-OQUPX,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes
9170-GYZJC,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),79.9,3326.2,Yes
4884-LEVMQ,Male,0,Yes,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.45,790,No
8857-CUPFQ,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,1237.65,No
7610-TVOPG,Male,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.35,378.6,No
3638-DIMPH,Female,0,Yes,No,13,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,43.8,592.65,No
1845-ZLLIG,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,No
8559-WNQZS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,20.45,No
3999-QGRJH,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,560.85,Yes
1699-UOTXU,Male,0,No,No,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,61.4,3638.25,No
2454-RPBRZ,Female,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.1,1060.2,Yes
0635-WKOLD,Male,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.75,2921.75,No
5993-JSUWV,Female,0,No,No,66,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),61.15,4017.45,No
4518-FZBSX,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,854.9,No
5387-ASZNZ,Female,1,No,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),63.85,4174.35,No
6988-CJEYV,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.7,4920.55,No
2002-MZHWP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes
3097-FQTVJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,810.3,No
5465-BUBFA,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,772.4,No
7299-GNVPL,Female,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,84.4,1936.85,No
9743-DQKQW,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.1,79.8,No
1215-VFYVK,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,48.25,202.25,No
9371-BITHB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1070.5,No
2265-CYWIV,Female,1,Yes,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.6,347.65,Yes
9093-FPDLG,Female,0,No,No,11,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.2,999.9,No
0541-FITGH,Female,0,Yes,No,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,62.15,113.1,No
6985-HAYWX,Female,0,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),79.3,2015.8,No
7508-KBIMB,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),56.25,1454.25,No
6838-HVLXG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,246.7,No
2277-DJJDL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99,6017.9,No
1897-OKVMW,Female,0,Yes,Yes,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.6,5817.45,No
5485-ITNPC,Male,0,Yes,No,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.9,5595.3,No
0233-FTHAV,Female,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),79.2,4765,No
4644-PIZRT,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.35,1201.65,No
8922-NPKBJ,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,867.3,No
2740-TVLFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,50.15,No
7771-CFQRQ,Female,0,Yes,Yes,47,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),63.8,3007.25,No
9512-PHSMG,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,252.75,No
6963-EZQEE,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.55,6306.5,No
2452-KDRRH,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.4,6841.05,No
2004-OCQXK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.95,81.95,Yes
5027-QPKTE,Male,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,451.1,Yes
4146-SVFUD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.6,44.6,No
1564-HJUVY,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),63.75,226.2,No
3617-XLSGQ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),109.25,7082.5,No
7517-LDMPS,Female,0,No,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1017.35,No
7244-KXYZN,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.45,527.35,No
5226-NOZFC,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.75,2146.5,No
2672-HUYVI,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,455.3,Yes
1400-WIVLL,Male,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,107.95,5969.85,No
7508-MYBOG,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,1235.55,Yes
8148-NLEGT,Female,0,Yes,Yes,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,22.95,1014.25,No
0148-DCDOS,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.7,2362.1,Yes
8347-GDTMP,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1225.65,No
9992-RRAMN,Male,0,Yes,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.1,1873.7,Yes
6746-WAUWT,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.7,386.5,No
0310-MVLET,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,6010.05,Yes
0428-IKYCP,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87,1850.65,No
4550-VBOFE,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.95,7101.5,Yes
2187-PKZAY,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.95,1043.4,No
4003-OCTMP,Female,0,Yes,No,31,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,64,1910.75,No
6652-YFFJO,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.9,716.1,No
7225-IILWY,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1686.15,No
5248-RPYWW,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.15,6716.45,No
7923-IYJWY,Male,1,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.1,7839.85,No
3170-GWYKC,Female,0,No,No,60,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,6236.75,No
5696-QURRL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.05,45.05,Yes
7409-KIUTL,Female,1,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes
9830-ECLEN,Male,0,Yes,Yes,58,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,50,2919.85,No
1732-VHUBQ,Female,1,Yes,Yes,47,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.55,3309.25,Yes
0495-RVCBF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.7,79.7,Yes
2668-TZSPS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No
7854-EDSSA,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59,1254.7,Yes
8869-TORSS,Female,0,No,No,48,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.35,2896.4,Yes
7446-SFAOA,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,717.5,No
9522-ZSINC,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.95,253.8,No
8234-GSZYK,Male,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.45,1110.05,No
6505-OZNPG,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,63.4,348.8,No
6164-HAQTX,Male,0,No,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),53.95,3888.65,No
0679-IDSTG,Female,1,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes
7765-LWVVH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Electronic check,95.1,6843.15,No
1658-TJVOA,Female,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.1,450.9,No
0953-LGOVU,Male,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,35.5,432.25,No
2115-BFTIW,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.95,1767.35,Yes
4692-NNQRU,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,79.2,1742.45,No
7742-MYPGI,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,48.8,297.35,No
0611-DFXKO,Male,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89,1820.45,Yes
7780-OTDSO,Male,0,Yes,No,18,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.4,1742.95,Yes
9619-GSATL,Female,0,No,No,43,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,No,Electronic check,55.45,2444.25,No
5622-UEJFI,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.4,949.8,No
9747-DDZOS,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),73.5,73.5,Yes
0674-DGMAQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.5,2970.8,No
6203-HBZPA,Male,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),63.9,3334.95,No
0484-FFVBJ,Male,0,No,No,32,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.85,2010.95,No
0301-FIDRB,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.8,4684.3,No
4139-JPIAM,Male,0,No,No,51,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),44.45,2181.55,No
2181-TIDSV,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1303.25,No
2761-XECQW,Male,1,Yes,No,8,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,43.35,371.4,No
1936-CZAKF,Male,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),49.65,2409.9,No
9453-PATOS,Female,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,85.1,6155.4,No
0564-JJHGS,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.5,829.1,Yes
3348-CFRNX,Female,0,Yes,No,28,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,92.35,2602.9,Yes
4402-FTBXC,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,89.8,4667,No
1428-GTBJJ,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,824.75,Yes
2931-XIQBR,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,103.05,5153.5,No
0106-UGRDO,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116,8182.85,No
5542-TBBWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,No
1930-QPBVZ,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),95.1,6683.4,No
2606-PKWJB,Male,0,No,Yes,40,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,1564.05,No
5322-ZSMZY,Male,0,Yes,Yes,31,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,755.6,No
8059-UDZFY,Female,1,No,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3465.05,No
7740-BTPUX,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.6,6292.7,No
5220-AGAAX,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,1664.3,No
0208-BPQEJ,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,198.1,No
9435-JMLSX,Male,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.1,6045.9,No
3352-ALMCK,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.65,4108.15,No
5312-IRCFR,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,92.85,5980.75,No
5294-IMHHT,Male,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),97.75,5043.2,No
8802-UNOJF,Male,1,No,No,12,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,83.8,1029.75,Yes
6313-GIDIT,Male,1,No,No,53,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,2854.55,Yes
6176-YJWAS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),97.95,7114.25,No
5310-NOOVA,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.95,907.05,No
4526-EXKKN,Male,0,No,No,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.6,973.95,No
5311-IHLEI,Male,0,No,No,12,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),50.95,605.75,No
3987-KQDDU,Male,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,661.55,No
3404-JNXAX,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.75,4116.9,No
5131-PONJI,Male,0,Yes,Yes,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,4494.65,No
2845-AFFTX,Male,1,Yes,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4259.3,Yes
3489-VSFRD,Female,0,No,No,56,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.25,3282.75,No
3345-JHUEO,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.2,55.7,No
5815-HGGHV,Male,0,Yes,No,20,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,64.15,1274.45,No
5260-UMPWX,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.25,493.95,No
2249-YPRNG,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.85,2239.65,Yes
7410-YTJIK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.45,480.75,Yes
4626-GYCZP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,635.6,Yes
5649-RXQTV,Male,0,No,No,51,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99,5038.15,No
2674-MIAHT,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.3,324.2,No
2576-HXMPA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.55,19.55,No
7587-AOVVU,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,100.75,2793.55,No
5590-BYNII,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,2095,Yes
6898-MDLZW,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.75,648.65,No
3237-AJGEH,Female,0,Yes,Yes,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,31,95.05,Yes
4707-YNOQA,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.6,917.15,No
9357-UJRUN,Male,0,Yes,No,24,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,58.35,1346.9,No
1591-NFNLQ,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),80,4242.35,Yes
8648-PFRMP,Female,1,No,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,46.35,672.7,No
7663-CUXZB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,113.75,6561.25,No
0258-NOKBL,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.4,268.45,No
1163-VIPRI,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.3,7337.55,No
2815-CPTUL,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.25,331.9,Yes
9348-ROUAI,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,5194.05,No
9443-JUBUO,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.25,4478.85,No
0596-BQCEQ,Female,0,Yes,Yes,62,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,6283.3,Yes
1481-ZUWZA,Male,0,No,No,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.5,2659.4,Yes
3043-TYBNO,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,60.65,196.9,No
4830-FAXFM,Male,0,No,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.1,439.2,No
5906-BFOZT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No
2960-NKRSO,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,85.95,2107.15,No
8996-ZROXE,Male,1,No,No,57,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Electronic check,53.5,3035.8,No
5598-IKHQQ,Female,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1866.45,No
0397-ZXWQF,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1430.95,No
5266-PFRQK,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.85,1071.6,No
4674-HGNUA,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,6457.15,No
6765-MBQNU,Female,0,Yes,No,26,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26,684.05,No
9786-IJYDL,Female,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,113.2,3914.05,No
1303-SRDOK,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),69.05,3842.6,No
3769-MHZNV,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,670.35,No
6295-OSINB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.65,7880.25,No
3308-MHOOC,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.2,19.2,No
2309-OSFEU,Male,0,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,33.9,298.45,Yes
2207-QPJED,Female,1,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,90,3371.75,No
4177-JPDFU,Male,0,No,No,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,34,442.45,No
2239-CFOUJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,20.4,No
8046-DNVTL,Male,0,Yes,No,62,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),38.6,2345.55,No
2209-XADXF,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.25,25.25,No
6620-JDYNW,Female,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,60.6,1156.35,No
1891-FZYSA,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.95,6143.15,Yes
4770-UEZOX,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,144.8,No
1038-RQOST,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,414.95,No
7613-LLQFO,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.45,1059.55,Yes
4568-TTZRT,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,181.8,No
9513-DXHDA,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,81.7,2212.55,No
2640-PMGFL,Male,0,No,Yes,27,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.5,2180.55,Yes
3801-HMYNL,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.15,89.15,Yes
0516-QREYC,Female,1,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,459.95,No
9685-WKZGT,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1036.75,Yes
6022-UGGSO,Female,1,No,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,2276.95,Yes
8084-OIVBS,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,211.95,No
8896-BQTTI,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25,25,No
3865-QBWSJ,Male,1,No,Yes,38,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,80.45,3162.65,No
3352-RICWQ,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,210.65,No
2160-GPFXD,Male,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),65.65,3566.7,No
2065-MMKGR,Female,0,No,No,29,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),71,2080.1,No
5857-TYBCJ,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,4040.2,No
1498-NHTLT,Male,0,Yes,Yes,59,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,86.75,5186,No
4484-CGXFK,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,196.15,Yes
1402-PTHGN,Female,0,Yes,Yes,18,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,61.5,1087.45,No
4176-RELJR,Male,1,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1672.15,No
6214-EDAKZ,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1206.05,Yes
5199-FPUSP,Male,0,No,Yes,33,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),34.05,1113.95,No
6377-KSLXC,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,107.05,No
1796-JANOW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,38.15,No
0238-WHBIQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.7,6339.3,No
8735-NBLWT,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,184.1,No
6651-RLGGM,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.3,1688.9,No
6127-ISGTU,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.95,1378.25,Yes
1614-JBEBI,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.7,137.6,No
8740-XLHDR,Male,0,No,No,5,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,43.25,219,Yes
1208-DNHLN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),48.35,1067.15,Yes
1761-AEZZR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.55,79.55,Yes
3923-CSIHK,Female,1,Yes,No,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,3444.85,Yes
5696-EXCYS,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,369.05,No
5795-KTGUD,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.8,7553.6,No
7120-RFMVS,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,84.5,Yes
7924-GJZFI,Female,1,Yes,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,1813.1,No
4702-HDRKD,Male,0,No,No,67,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),49.35,3321.35,No
8512-WIWYV,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,707.5,No
5897-ZYEKH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,105.6,7112.15,No
5456-ITGIC,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.45,4641.1,No
7317-GGVPB,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.6,7690.9,Yes
1406-PUQVY,Male,0,No,Yes,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No
1322-AGOQM,Male,0,No,No,46,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.3,1380.1,Yes
3677-IYRBF,Female,1,No,No,2,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,30.4,78.65,Yes
5692-FPTAH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,Yes
9114-AAFQH,Female,0,Yes,No,48,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,65.65,3094.65,No
8715-KKTFG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.3,6518.35,No
1550-LOAHA,Female,0,Yes,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.15,2585.95,Yes
1728-BQDMA,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.45,82.7,No
0268-QKIWO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,58.85,Yes
0876-WDUUZ,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),85.4,425.9,Yes
5117-ZSMHQ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),89.9,6342.7,No
5151-HQRDG,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.05,2030.75,No
0960-HUWBM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.1,6700.05,No
6465-GSRCL,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.6,7244.7,No
0617-FHSGK,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.2,3678.3,Yes
0263-FJTQO,Male,0,Yes,Yes,50,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.5,3486.65,No
7319-ZNRTR,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.6,411.15,No
2858-MOFSQ,Female,0,No,Yes,17,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,55.85,937.5,Yes
7503-EPSZW,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.05,1559.15,No
7089-XXAYG,Male,0,Yes,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),38.1,970.4,No
8118-LSUEL,Male,1,No,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,2483.5,Yes
8070-AAWZP,Male,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,34.25,828.2,No
1666-JXLKU,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3810.55,No
7855-DIWPO,Female,0,No,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.65,1493.2,No
5133-POWUA,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.8,45.8,No
5652-MSDEY,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.75,777.3,No
7005-CCBKV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.4,556.35,Yes
1810-BOHSY,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.4,4911.05,No
1784-BXEFA,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,187.35,No
7351-MHQVU,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),50.95,307.6,No
9224-VTYID,Male,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.5,4318.35,No
9500-IWPXQ,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,79.4,4820.55,No
5762-TJXGK,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.75,3038.55,No
4504-YOULA,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.45,2136.9,No
5569-IDSEY,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,7472.15,No
4250-FDVOU,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.25,389.1,Yes
7284-ZZLOH,Male,0,Yes,No,45,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),53.3,2296.25,No
5277-ZLOOR,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.55,187.45,Yes
2141-RRYGO,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),68.65,261.25,Yes
1777-JYQPJ,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,38.45,No
6376-GAHQE,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,77.85,299.2,Yes
3401-URHDA,Male,0,No,No,51,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),59.9,3043.6,No
1599-EAHXY,Male,0,Yes,Yes,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),23.95,1506.4,No
8631-XVRZL,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,163.7,No
5052-PNLOS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,323.25,Yes
3853-LYGAM,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,1640,No
6979-ZNSFF,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.05,762.1,Yes
5751-USDBL,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,3846.35,No
5680-LQOGP,Female,0,No,No,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5646.6,No
2386-LAHRK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.5,53.5,Yes
8189-DUKMV,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,79.05,No
6032-IGALN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.1,25.1,Yes
9931-DCEZH,Male,0,No,Yes,28,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.4,1516.6,No
1898-JSNDC,Female,0,No,No,39,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.6,2224.5,No
0315-LVCRK,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,84.8,888.75,No
3911-RSNHI,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.4,4310.35,No
2410-CIYFZ,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,42.9,No
4248-HCETZ,Male,1,Yes,No,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,2365.15,Yes
5505-OVWQW,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,353.65,No
7271-AJDTL,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),94.45,5073.1,No
1867-TJHTS,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.8,4526.85,No
0516-WJVXC,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.2,308.25,Yes
9174-FKWZE,Female,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.45,19.45,Yes
6860-YRJZP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.05,678.45,No
4429-WYGFR,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),49.15,1237.3,No
2817-LVCPP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1023.95,No
5038-ETMLM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,8182.75,No
5056-FIMPT,Female,0,No,Yes,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106,4532.3,No
4521-WFJAI,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.95,1444.05,No
6569-KTMDU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No
8809-RIHDD,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.4,7372.65,Yes
8809-XKHMD,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),100.55,7325.1,No
0396-YCHWO,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.4,3474.2,No
0867-LDTTC,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),75.15,392.65,No
4822-NGOCH,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.45,1058.6,Yes
9391-DXGGG,Female,1,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),89.15,3990.75,No
9844-FELAJ,Female,1,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.9,7475.85,No
2122-SZZZD,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.5,835.5,No
5307-DZCVC,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.95,2628.6,Yes
3148-AOIQT,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1718.35,No
8679-JOEVF,Female,1,No,No,16,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,59.4,1023.9,Yes
8395-ETZKQ,Male,1,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,1193.55,No
6692-YQHXC,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,1205.5,No
3889-VWBID,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),82.85,5776.45,No
5222-JCXZT,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,78.9,No
9754-CLVZW,Female,0,Yes,Yes,26,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),38.85,1025.15,No
1432-FPAXX,Female,0,No,No,29,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.6,856.35,Yes
2739-CCZMB,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,122,No
7080-TNUWP,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95,6602.9,No
0496-AHOOK,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.4,1712.9,No
8336-TAVKX,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.45,5682.25,No
2468-SJFLM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,74.3,74.3,No
3181-VTHOE,Male,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),51.05,3635.15,No
7168-HDQHG,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.2,702.9,No
4803-AXVYP,Female,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,3734.25,Yes
3884-HCSWG,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,1144.5,Yes
4567-AKPIA,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.1,4454.25,No
5077-DXTCG,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.3,45.3,Yes
1142-WACZW,Male,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.85,75.6,Yes
7901-HXJVA,Male,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.45,1117.55,No
5649-ANRML,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,180.25,Yes
4892-VLANZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,19.8,Yes
5924-IFQTT,Male,0,Yes,Yes,13,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,72.8,930.05,No
7968-QUXNS,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.95,110.15,No
2919-HBCJO,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),76.65,333.6,Yes
4236-XPXAV,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.15,465.05,Yes
8903-WMRNW,Female,0,Yes,No,15,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.75,1669.4,No
2452-SNHFZ,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.45,3545.1,No
3629-WEAAM,Female,0,No,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,64.1,504.05,No
6029-CSMJE,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.65,440.2,No
7993-NQLJE,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.1,1151.55,No
9909-DFRJA,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),95.85,2475.35,No
9099-FTUHS,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.4,1249.25,No
0581-BXBUB,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,72.75,317.75,No
4962-CHQPW,Male,0,No,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,535.05,No
9467-ROOLM,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,461.3,No
3030-YZADT,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,431,Yes
7410-KTVFV,Male,0,Yes,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.55,878.35,Yes
5150-ITWWB,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.85,335.75,No
2253-KPMNB,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),46.25,3121.4,No
1345-ZUKID,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,324.8,No
6429-SHBCB,Male,0,No,No,19,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,69.6,1394.55,No
9281-OFDMF,Male,1,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,90.7,3413.25,No
2603-HVKCG,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3143.65,No
1834-WULEG,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,439.75,No
9097-ZUBYC,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),48.8,664.4,No
5148-ORICT,Female,0,Yes,No,64,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,74.35,4759.55,No
4893-GYUJU,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.35,1033,No
4578-PHJYZ,Male,0,Yes,Yes,52,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,68.75,3482.85,No
7272-QDCKA,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,100.2,2688.45,No
8908-SLFCJ,Female,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.85,435.25,No
8393-DLHGA,Male,0,No,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.9,2448.75,Yes
9766-HGEDE,Female,0,Yes,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,307,No
6968-MHOMU,Male,0,Yes,No,58,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),45,2689.35,No
7395-IGJOS,Male,1,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),81.5,1329.2,Yes
5044-XDPYX,Female,0,Yes,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1281.25,No
1814-WFGVS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,48.9,3527,No
2834-SPCJV,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.1,4348.65,Yes
3721-WKIIL,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,561.15,No
6734-CKRSM,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,63.6,No
1265-ZFOSD,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,81.3,5129.3,No
6568-POCUI,Female,0,Yes,No,45,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,4285.8,No
7890-VYYWG,Male,1,Yes,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,36.45,93.7,Yes
5197-LQXXH,Female,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.3,5894.5,No
9907-SWKKF,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,25.05,Yes
3457-PQBYH,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1160.75,No
7682-AZNDK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.85,3091.75,No
0587-DMGBH,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.85,365.55,Yes
5384-ZTTWP,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,272.95,No
3745-HRPHI,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),54.65,3632,No
7636-OWBPG,Male,1,No,No,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.35,381.2,No
1231-YNDEK,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.15,1035.5,No
1407-DIGZV,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,52,No
6397-JNZZG,Female,1,Yes,No,43,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),55.55,2342.2,Yes
0570-BFQHT,Female,0,No,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,80.55,653.9,No
4393-OBCRR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,71.2,No
3523-QRQLL,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),69.5,1498.2,Yes
8564-LDKFL,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106,4178.65,No
3696-DFHHB,Female,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.5,1821.8,No
0895-DQHEW,Male,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.3,5278.15,Yes
4717-GHADL,Female,0,No,No,50,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,79.6,4024.2,No
5501-TVMGM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.25,55.25,No
5879-HMFFH,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),88.05,6520.8,No
6772-WFQRD,Male,0,No,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.4,854.9,No
3810-DVDQQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),117.6,8308.9,No
6972-SNKKW,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,109.2,No
3694-GLTJM,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.65,92.05,No
8550-XSXUQ,Male,0,Yes,No,48,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),70.55,3420.5,No
8149-RSOUN,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,93.85,Yes
9055-MOJJJ,Female,0,Yes,Yes,64,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,65.8,4068,No
4359-INNWN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,337.9,No
0585-EGDDA,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,80,3168.75,No
4032-RMHCI,Female,0,Yes,Yes,41,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,1412.4,No
0549-CYCQN,Male,1,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),79.6,3974.7,No
3481-JHUZH,Male,0,Yes,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,3439,No
7250-EQKIY,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.45,50.45,Yes
3594-IVHJZ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,42.45,No
6869-FGJJC,Male,0,No,No,68,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),79.6,5461.45,No
3896-ZVNET,Female,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.7,571.75,No
8205-VSLRB,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),77.3,5498.2,No
5960-MVTUK,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.75,96.85,No
6817-WTYHE,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.9,111.05,No
3082-VQXNH,Male,0,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.8,94.4,No
4013-UBXWQ,Female,0,No,No,7,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,521.1,Yes
4931-TRZWN,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,71.95,923.85,No
0750-EKNGL,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,141.1,No
7669-LCRSD,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,56.3,628.65,No
3567-PQTSO,Male,0,Yes,Yes,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.25,5576.3,No
5519-NPHVG,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1046.1,Yes
8043-PNYSD,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1245.6,No
9938-EKRGF,Female,0,No,No,15,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,84.45,1287.85,No
2703-AMTUL,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,53.65,1939.35,No
0928-JMXNP,Male,1,Yes,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.9,118.25,No
8173-RXAYP,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,452.55,No
4825-XJGDM,Female,0,No,No,61,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),43.7,2696.55,No
5402-HTOTQ,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.3,875.35,No
6734-JDTTV,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.85,1267.05,No
7850-THJMU,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,494.9,No
3890-RTCMS,Male,0,No,No,16,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.45,799,No
8849-GYOKR,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),106.55,5763.3,Yes
3148-BLQJT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,20.1,Yes
3717-FDJFU,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,106.9,No
3665-JATSN,Female,0,No,No,19,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.7,710.05,No
7966-YOTQW,Male,0,No,No,10,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.5,568.2,No
0461-CVKMU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,83.8,1900.25,Yes
8806-EAGWC,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.15,159.15,Yes
8853-TZDGH,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,8012.75,No
7779-LGOVN,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.65,856.65,Yes
4324-BZCKL,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.55,551.3,No
6924-TDGMT,Male,0,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,184.95,No
9710-ZUSHQ,Female,1,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.75,4056.75,Yes
1536-YHDOE,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,62.1,1096.65,No
8123-QBNAZ,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,3684.95,No
3629-ZNKXA,Male,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.8,1752.45,No
4827-LTQRJ,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.6,7210.85,No
7711-YIJWC,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),84.9,5264.5,No
5482-VXSXJ,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.2,2157.3,No
7365-BVCJH,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.4,24.4,No
9620-ENEJV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,433.95,No
4183-WCSEP,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,78.45,2435.15,Yes
2378-YIZKA,Female,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5607.75,No
5498-TXHLF,Female,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,87.45,2874.15,Yes
0689-DSXGL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),85.8,4433.3,No
6818-DJXAA,Female,0,No,Yes,10,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.1,964.35,No
9722-UJOJR,Male,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.75,1974.8,Yes
0464-WJTKO,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1460.85,No
8902-ZEOVF,Male,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,951.55,No
6778-EICRF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.8,1821.2,No
2662-NNTDK,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,1600.95,No
4132-KALRO,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,399.25,No
3902-FOIGH,Male,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1218.55,Yes
6772-KSATR,Male,0,No,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.7,81.7,Yes
7112-OPOTK,Male,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.25,2171.15,Yes
2874-YXVVA,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.1,3634.8,No
1245-HARPS,Female,0,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,292.4,No
4210-QFJMF,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.15,317.25,Yes
4323-ELYYB,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,218.55,No
4293-ETKAP,Female,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.4,5071.9,No
6064-PUPMC,Male,0,Yes,Yes,23,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),57.2,1423.35,No
6504-VBLFL,Male,0,Yes,No,55,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,58.6,3068.6,No
6322-PJJDJ,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,94.8,4690.65,No
0330-BGYZE,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),102.5,6157.6,No
1085-LDWAM,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,1442.65,No
7586-ZATGZ,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.9,3369.05,No
7197-VOJMM,Male,0,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),69.2,4671.65,No
3318-OSATS,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,3474.05,Yes
5828-AVIPD,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.95,1875.55,Yes
1843-TLSGD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,272.35,No
9626-VFRGG,Female,0,No,Yes,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),88.5,3645.05,No
7075-BNDVQ,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,35,135.75,No
9143-CANJF,Female,0,Yes,Yes,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1319.85,No
7284-BUYEC,Female,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,229.4,No
2041-JIJCI,Female,0,No,No,5,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,64,370.25,No
1086-LXKFY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.1,69.1,Yes
4900-MSOMT,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),80.2,5714.2,No
2229-VWQJH,Female,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),49.3,1233.25,No
9194-GFVOI,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.35,3571.6,No
1336-EZFZY,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,83.3,No
4282-MSACW,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),117.2,8035.95,No
1403-LKLIK,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,579.4,No
2636-ALXXZ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
7774-OJSXI,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.45,3066.45,Yes
7786-WBJYI,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),77.95,305.55,Yes
0136-IFMYD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.95,7634.25,No
5144-TVGLP,Male,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.75,3653,No
9643-AVVWI,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,80,241.3,No
0253-ZTEOB,Female,0,Yes,Yes,48,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,79.65,3870.3,No
2706-QZIHY,Female,0,Yes,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,387.9,No
6061-PQHMK,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,527.5,No
9885-CSMWE,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.45,78.45,Yes
6137-MFAJN,Female,0,No,No,48,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.8,2104.55,No
9122-UMROB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No
4232-JGKIY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,19.2,No
2402-TAIRZ,Female,0,No,No,37,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.05,3019.1,No
9659-ZTWSM,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),107.35,7051.95,No
9139-TWBAS,Female,0,Yes,No,26,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),47.85,1190.5,No
6685-GBWJZ,Male,0,Yes,No,63,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.8,4448.8,No
5016-ETTFF,Male,0,No,No,10,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,29.5,255.25,Yes
3866-MDTUB,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.75,146.9,Yes
2195-VVRJF,Male,1,Yes,No,18,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),59.1,1011.05,No
3913-RDSJZ,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.55,1714.95,No
8058-JMEQO,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.45,762.5,Yes
4203-QGNZA,Female,0,No,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,535.35,No
5043-TRZWM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.55,75.55,No
0697-ZMSWS,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.65,338.9,Yes
7657-DYEPJ,Male,1,No,No,38,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,2497.35,Yes
9494-BDNNC,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.3,6273.4,No
1640-PLFMP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,No
5366-OBVMR,Female,0,Yes,No,18,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.3,908.75,No
8276-MQBYC,Male,1,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.8,4913.3,Yes
7644-OMVMY,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85, ,No
7593-XFKDI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46.3,46.3,Yes
4573-JKNAE,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,212.3,No
0337-CNPZE,Female,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.3,4443.45,Yes
9817-APLHW,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25,316.2,No
8380-MQINP,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,1079.05,No
0840-DFEZH,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,564.65,No
1513-XNPPH,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.4,1095.65,Yes
8690-ZVLCL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88,6161.9,No
6015-VVHHE,Female,1,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),83.15,446.05,Yes
1125-SNVCK,Female,1,No,No,49,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.8,2106.05,No
0384-LPITE,Male,0,No,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),62.05,2511.55,No
4616-EWBNJ,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,318.6,No
6347-DCUIK,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.15,811.8,Yes
1335-HQMKX,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.35,7323.15,No
2545-EBUPK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.05,186.05,No
6923-AQONU,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,454,No
2172-EJXVF,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.9,7521.95,No
0897-FEGMU,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,1056.95,Yes
7663-RGWBC,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.15,44.15,Yes
1120-BMWUB,Female,0,No,No,16,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.9,834.15,Yes
9124-LHCJQ,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.45,85.45,Yes
4536-PLEQY,Male,0,Yes,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.05,999.8,No
7029-IJEJK,Female,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),44.1,2369.7,No
8871-JLMHM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),90.2,6297.65,No
2235-ZGKPT,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.85,239.55,Yes
3891-PUQOD,Female,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,59.2,59.2,Yes
5447-VYTKW,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.45,1461.45,No
3623-FQBOX,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,416.4,No
0689-NKYLF,Male,0,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,83.2,1060.6,Yes
8659-HDIYE,Female,1,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),74.65,4869.35,No
3658-KIBGF,Female,0,No,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.9,54.9,Yes
3474-BAFSJ,Male,0,Yes,No,57,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),57.5,3265.95,No
5519-YLDGW,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.9,2254.2,Yes
3865-ZFZIB,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,358.15,No
1855-AGAWH,Male,0,Yes,No,31,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,2939.8,No
7109-CQYUZ,Male,0,No,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.25,4652.4,No
1370-GGAWX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.15,4408.45,No
4680-KUTAJ,Female,1,No,No,11,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,55.6,580.8,No
5307-UVGNB,Female,0,Yes,Yes,53,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),48.7,2495.2,No
4946-EDSEW,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,180.3,Yes
2883-ILGWO,Male,1,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.9,5913.95,No
2516-VQRRV,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.45,158.4,Yes
7580-UGXNC,Female,1,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.85,104.2,Yes
3642-BYHDO,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1389.35,No
9629-NHXFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No
2696-RZVZW,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1629.2,No
5766-FTRTS,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.45,6033.1,No
0396-HUJBP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,44.4,No
5178-LMXOP,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,95.1,Yes
8879-ZKJOF,Female,0,No,No,41,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.85,3320.75,No
6285-FTQBF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1867.7,No
8185-UPYBR,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,438,Yes
4585-HETAI,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.75,325.45,Yes
7526-BEZQB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1148.1,Yes
1474-JUWSM,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,68.4,3972.25,No
3530-CRZSB,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,155.9,No
8498-XXGWA,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.15,3673.15,No
9617-INGJY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No
0621-TSSMU,Male,0,Yes,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.95,1126.75,No
7234-KMNRQ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,73.45,No
7636-PEPNS,Female,0,Yes,Yes,58,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,44.1,2413.05,No
4683-WYDOU,Male,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.6,6912.7,No
9052-DHNKM,Male,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,61.55,1581.95,No
6794-HKIAJ,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),90.7,5586.45,No
5578-NKCXI,Female,0,Yes,Yes,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),99.25,5846.65,No
1642-HMARX,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,91.7,6424.7,No
3096-WPXBT,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),100.7,6018.65,No
8434-PNQZX,Female,0,No,No,42,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),78.45,3373.4,No
5950-AAAGJ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),84.3,1537.9,No
4299-OPXEJ,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,1080.55,No
4951-UKAAQ,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.95,355.2,Yes
9618-LFJRU,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,82.85,No
6693-DJWTY,Female,0,No,Yes,35,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),55.6,2016.45,No
0744-GKNGE,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),86.8,5327.25,No
6447-EGDIV,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.95,683.25,No
1167-OYZJF,Female,1,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1263.05,No
2108-YKQTY,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.7,151.3,Yes
4806-DXQCE,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.65,7714.65,No
4918-QLLIW,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),53.4,188.7,Yes
7056-IMHCC,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.9,5549.4,Yes
4854-SSLTN,Male,0,Yes,Yes,2,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,59.5,130.5,No
5294-DMSFH,Female,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),87.8,2621.75,No
4837-PZTIC,Female,0,No,No,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,41.9,1875.25,No
1625-JAIIY,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,83,5685.8,Yes
0603-OLQDC,Male,0,No,Yes,12,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,69.85,837.5,No
3272-VUHPV,Female,0,Yes,Yes,8,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.3,401.5,No
4176-FXYBO,Male,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,109.55,6118.95,No
1063-DHQJF,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.15,6480.9,No
8663-UPDGF,Female,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.5,1800.05,No
6719-FGEDO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),97,7104.2,No
1837-YQUCE,Female,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.35,4214.25,No
2947-DOMLJ,Male,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.6,50.6,Yes
4112-LUEIZ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,863.1,Yes
0369-ZGOVK,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.4,1992.2,No
4510-HIMLV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes
9919-KNPOO,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,1948.35,No
8749-JMNKX,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.8,4750.95,Yes
7872-BAAZR,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,1007.9,No
9391-LMANN,Male,0,No,Yes,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.95,5036.9,No
2430-USGXP,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,2391.8,Yes
8174-TBVCF,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),94.8,6859.05,No
1698-XFZCI,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6521.9,No
1877-HKBQX,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,54.6,617.85,No
4450-DLLMH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71.3,157.75,No
0428-AXXLJ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.5,516.3,No
2746-DIJLO,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),56.3,2364,No
5955-ERIHD,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,1687.95,Yes
0917-EZOLA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.15,7689.95,Yes
3508-VLHCZ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.55,6239.05,No
4086-ATNFV,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,60.8,2042.05,No
0468-YRPXN,Male,0,No,No,29,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.8,2807.1,No
5996-NRVXR,Male,1,Yes,No,40,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),98.15,4116.8,No
3739-YBWAB,Male,0,Yes,No,36,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.35,1317.95,No
7047-FWEYA,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,103.15,4594.65,No
2000-MPKCA,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6332.75,No
9762-YAQAA,Male,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,No,Credit card (automatic),81.4,3213.75,No
5949-EBSQK,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),61.45,229.55,Yes
5473-KHBPS,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.7,4976.15,No
0100-DUVFC,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.8,7308.95,No
5397-TUPSH,Male,1,Yes,No,65,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.95,4555.2,No
8950-MTZNV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.95,44.95,No
0326-VDYXE,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.65,6982.5,No
6773-LQTVT,Female,1,Yes,Yes,29,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,35.65,1025.15,No
7274-RTAPZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.55,90.55,Yes
0436-TWFFZ,Female,0,No,No,67,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,85.25,5714.2,No
8566-YPRGL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No
0311-UNPFF,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),88.8,2274.35,Yes
6609-MXJHJ,Female,0,Yes,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.1,789.55,No
2669-OIDSD,Female,0,Yes,No,48,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,4834,No
8400-WZICQ,Female,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.7,3131.8,No
3834-XUIFC,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.2,602.55,Yes
9576-SYUHJ,Male,0,No,No,37,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,3369.25,No
0410-IPFTY,Female,0,Yes,No,31,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),83.85,2674.15,No
2831-EBWRN,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.9,199.75,No
9430-NKQLY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1790.8,No
8414-MYSHR,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.4,449.75,Yes
0247-SLUJI,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,No
9402-ORRAH,Female,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.5,1400.3,No
6483-OATDN,Male,0,Yes,Yes,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),51.3,411.6,No
1293-HHSHJ,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.1,741,No
4840-ORQXB,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.75,5841.35,No
7599-FKVXZ,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.15,4512.7,Yes
3982-DQLUS,Male,1,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.75,5688.45,No
8610-ZIKJJ,Female,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,31.9,No
3756-VNWDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.75,6674.65,No
7801-KICAO,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.15,1345.75,No
7673-LPRNY,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,78.55,1843.05,No
8229-TNIQA,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,196.95,Yes
6060-QBMGV,Male,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1433.8,No
7339-POGZN,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.7,214.55,No
2828-SLQPF,Male,0,No,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,865.85,No
4465-VDKIQ,Female,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,77.8,1358.6,No
2921-XWDJH,Female,1,Yes,No,38,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.45,3147.15,No
6221-AVQYL,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.25,131.05,Yes
2558-BUOZZ,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.8,4535.85,No
9257-AZMTZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1078.75,No
0003-MKNFE,Male,0,No,No,9,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,59.9,542.4,No
0975-UYDTX,Female,0,Yes,No,26,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),90.1,2312.55,No
7743-EXURX,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,51.05,415.05,Yes
4682-BLBUC,Male,0,Yes,No,44,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,70.95,3250.45,No
0975-VOOVL,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,98.5,No
5968-VXZLG,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),46.6,87.9,No
5569-KGJHX,Female,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.35,754.65,Yes
4988-IQIGL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes
5201-FRKKS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.3,1952.25,No
9799-CAYJJ,Female,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,153.8,No
7730-IUTDZ,Male,0,No,No,43,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,3198.6,Yes
0426-TIRNE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes
5443-SCMKX,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.3,5610.15,No
8295-KMENE,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Mailed check,76.45,4519.5,No
6738-ISCBM,Male,0,No,No,44,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54,2440.25,No
9821-BESNZ,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.25,6860.6,No
3678-MNGZX,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1377.7,No
1335-NTIUC,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,190.25,No
2916-BQZLN,Male,0,No,No,19,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.75,1651.95,No
4558-CGYCZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,78.3,No
2368-GAKKQ,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,7939.25,No
8710-YGLWG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.9,44.9,No
3199-XGZCY,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.25,576.7,No
3785-KTYSH,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.6,1279,No
6814-ZPWFQ,Male,1,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25,1260.7,No
4063-EIKNQ,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,267.35,No
6993-YCOBK,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.6,6586.85,No
5206-HPJKM,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,934.15,No
7587-RZNME,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.3,123.65,Yes
0748-RDGGM,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.5,7534.65,Yes
8393-JMVMB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,No
5019-GQVCR,Male,1,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,3645.6,No
6036-TTFYU,Female,0,Yes,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,314.45,No
2550-AEVRU,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,53.45,3053,No
0969-RGKCU,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,677.05,No
0378-CJKPV,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),112.1,7965.95,No
4706-AXVKM,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.8,906.85,Yes
5889-JTMUL,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.05,4888.7,Yes
9026-RNUJS,Male,1,No,No,5,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,50.35,237.25,Yes
3746-EUBYR,Male,0,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,74.6,Yes
2332-EFBJY,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.7,342.4,Yes
2880-FPNAE,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.2,140.1,No
1703-MGIAB,Female,0,No,No,17,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69,1108,No
4311-QTTAI,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.35,295.55,No
9474-PHLYD,Female,0,No,No,15,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,892.65,Yes
4318-RAJVY,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,198.25,No
2165-VOEGB,Female,0,No,Yes,46,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4822.85,Yes
3612-YUNGG,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.2,6741.15,No
2254-DLXRI,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.15,79.15,No
5062-CJJKH,Male,0,Yes,Yes,25,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,53.65,1355.45,No
9028-LIHRP,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),100.2,7209,No
2219-MVUSO,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.15,438.4,Yes
1053-MXTTK,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.65,7726.35,No
1530-ZTDOZ,Female,0,Yes,No,49,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),40.65,2070.75,No
1301-LOPVR,Male,0,Yes,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),55.35,1636.95,No
0853-TWRVK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),105.6,7581.5,No
8914-RBTSB,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,93.8,3019.5,Yes
6212-ATMLK,Female,0,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4729.75,No
8200-LGKSR,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,83.2,6126.1,No
1568-BEKZM,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.05,6333.4,No
0670-ANMUU,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),97.65,6687.85,No
6897-RWMUB,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),68.05,4158.25,No
0963-ZBDRN,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,3183.4,Yes
7594-RQHXR,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,79.6,Yes
6999-CHVCF,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),102.1,7149.35,No
6134-KWTBV,Male,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.4,1429.65,No
1077-HUUJM,Female,0,No,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,472.65,No
6331-EWIEB,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.25,1734.5,Yes
0895-LNKRC,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,113.5,No
2045-BMBTJ,Female,1,No,No,33,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),59.45,1884.65,No
8417-GSODA,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),92.2,2568.15,No
5171-EPLKN,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,470,No
4452-QIIEB,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.9,278.4,No
5998-VVEJY,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,2088.8,Yes
1624-NALOJ,Male,1,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,502.6,Yes
6741-EGCBI,Male,1,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.5,2595.85,No
7820-ZYGNY,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Credit card (automatic),90.45,5825.5,No
4317-VTEOA,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.75,50.75,Yes
9677-AVKED,Female,0,No,Yes,53,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,84.6,4449.75,No
1670-SVOWZ,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.65,1208.35,Yes
2227-JRSJX,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.15,1956.4,No
9847-HNVGP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,310.6,No
6696-YDAYZ,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,290.55,No
8980-WQFWL,Female,0,No,No,35,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,62.1,2096.1,No
1730-ZMAME,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.5,2665,No
6506-EYCNH,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,543.8,No
4634-JLRJT,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.35,20.35,No
9501-UKKNL,Male,0,No,No,59,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,51.7,3005.8,No
2560-QTSBS,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),23.3,1623.15,No
7541-YLXCL,Male,0,No,No,36,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,65.4,2498.4,Yes
4795-WRNVT,Female,0,No,No,40,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,65.1,2586,No
6080-TCMYC,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.2,3292.3,No
0260-ZDLGK,Female,0,No,Yes,9,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,72.9,651.4,Yes
7860-UXCRM,Male,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),74.5,4674.55,No
6357-JJPQT,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.5,232.35,No
2292-XQWSV,Male,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,60.3,2448.5,No
9552-TGUZV,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,658.1,No
5913-INRQV,Male,1,Yes,No,34,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,90.15,3128.8,No
8722-NGNBH,Male,0,No,No,5,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,40,223.45,Yes
6210-KBBPI,Male,1,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,919.4,Yes
9643-YBLUR,Male,0,Yes,No,9,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),69.05,653.95,No
8734-FNWVH,Male,0,Yes,Yes,31,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),59.7,1825.5,No
9447-YPTBX,Female,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,943.1,No
0133-BMFZO,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,86.25,181.65,Yes
6128-CZOMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes
9540-JYROE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,551.35,Yes
9026-LHEVG,Female,0,No,No,9,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,40.75,359.4,No
2260-USTRB,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.2,115.95,Yes
3656-TKRVZ,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.35,165.2,Yes
7011-CVEUC,Male,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.7,2338.35,No
6185-TASNN,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,46.3,No
7925-PNRGI,Female,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,81.3,3541.1,No
8623-TMRBY,Male,1,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.2,4146.05,Yes
1552-CZCLL,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,1087.25,No
3038-PQIUY,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),66.15,2522.4,No
1501-SGHBW,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.85,81,Yes
9313-CDOGY,Male,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,717.3,No
8148-BPLZQ,Male,0,No,No,34,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1734.2,No
2623-DRYAM,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.75,7069.3,No
9987-LUTYD,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.15,742.9,No
3208-YPIOE,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,3017.65,Yes
4488-KQFDT,Female,0,No,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,6423,No
2612-RANWT,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.15,1164.3,Yes
5693-PIPCS,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99.65,4220.35,No
0491-KAPQG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.7,1558.7,No
1925-LFCZZ,Male,1,No,No,55,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),50.05,2743.45,No
8039-EQPIM,Male,0,Yes,No,69,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.25,4055.5,No
6900-PXRMS,Male,1,Yes,Yes,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.75,2710.25,Yes
8707-RMEZH,Female,1,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),87.3,6055.55,No
3346-BRMIS,Female,1,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.35,810.7,Yes
3209-ZPKFI,Male,0,Yes,Yes,47,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.25,2538.2,No
6479-VDGRK,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,6129.2,No
9373-WSLOY,Male,1,Yes,No,33,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50,1750.85,No
2933-FILNV,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.4,36.55,Yes
3569-JFODW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.95,6652.45,No
9819-FBNSV,Male,1,Yes,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.25,2575.45,No
9544-PYPSJ,Female,1,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),96.1,6019.35,No
1591-XWLGB,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1379.6,No
0396-UKGAI,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1284.2,No
3243-ZHOHY,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,296.15,No
5186-EJEGL,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.5,653.25,No
7527-QNRUS,Male,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.15,412.6,No
9986-BONCE,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,85.5,Yes
2722-JMONI,Female,1,Yes,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,49.55,Yes
2203-GHNWN,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,1928.7,No
3878-AVSOQ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.25,71.25,No
3258-SYSWS,Male,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.8,7845.8,No
5296-PSYVW,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1750.7,No
6319-QSUSR,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,216.2,No
4083-EUGRJ,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,178.5,Yes
7579-OOPEC,Female,1,Yes,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.15,115.1,Yes
0512-FLFDW,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.5,6029,No
1771-OADNZ,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,2745.2,Yes
0487-CRLZF,Female,0,No,No,49,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.45,3721.9,No
2107-FBPTK,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,3121.1,No
9451-WLYRI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.05,990.45,No
8219-VYBVI,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1004.35,No
1991-VOPLL,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,157.65,No
9738-QLWTP,Male,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,81.9,3219.75,No
1016-DJTSV,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,572.85,No
9419-IPPBE,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,90.15,4554.85,No
0174-QRVVY,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.35,1847.55,No
5699-BNCAS,Male,1,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.65,1766.75,No
2900-PHPLN,Female,1,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1462.05,No
0612-RTZZA,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,Yes
8106-GWQOK,Male,0,Yes,No,38,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60,2193.2,No
8751-EDEKA,Female,0,Yes,No,28,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.9,2433.5,No
6878-GGDWG,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,641.15,No
4039-PIMHX,Male,1,Yes,No,49,Yes,No,DSL,No,Yes,No,No,No,No,Two year,No,Mailed check,49.8,2398.4,No
8450-LUGUK,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,861.85,No
3604-WLABM,Female,0,No,No,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,583,No
0795-GMVQO,Male,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),109.9,7332.4,No
2259-OUUSZ,Male,0,No,No,7,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),35.5,249.55,No
1142-IHLOO,Female,0,No,No,51,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),87.55,4475.9,No
6410-LEFEN,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,416.45,Yes
4633-MKHYU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,88.4,788.6,No
6257-RJOHI,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.8,202.3,No
1545-JFUML,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99,6994.6,No
3194-ORPIK,Female,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.4,4116.15,Yes
7826-VVKWT,Female,1,Yes,Yes,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,96.55,2263.45,No
1353-GHZOS,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),59.75,1374.35,No
2587-EKXTS,Male,0,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,111.5,4915.15,No
5442-BXVND,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.25,838.5,No
8816-VXNZD,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,Yes
9378-FXTIZ,Female,0,Yes,No,54,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),70.15,3715.65,Yes
1723-HKXJQ,Male,0,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.75,4273.45,Yes
7825-GKXMW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.8,45.8,Yes
9753-OYLBX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,No
2364-UFROM,Male,0,No,No,30,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Electronic check,70.4,2044.75,No
6656-JWRQX,Female,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,30.55,30.55,No
6473-ULUHT,Male,0,Yes,Yes,16,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,84.9,1398.25,No
1240-KNSEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes
0836-SEYLU,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,40.65,328.95,Yes
4433-JCGCG,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4680.05,Yes
3716-BDVDB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,69.1,Yes
7688-AWMDX,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.5,3778.2,No
2842-BCQGE,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.35,3161.4,No
7244-QWYHG,Male,0,Yes,No,50,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),44.45,2188.45,No
5899-MQZZL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,999.45,Yes
6849-OYAMU,Male,0,Yes,Yes,19,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),100,1888.65,Yes
5312-UXESG,Female,0,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.05,3990.6,No
9488-HGMJH,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.15,71.15,Yes
6038-GCYEC,Female,0,No,No,24,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),54.15,1240.25,Yes
2150-WLKUW,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),63.9,2635,No
7159-FVYPK,Female,0,Yes,Yes,3,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.15,235,No
7032-LMBHI,Female,0,No,No,37,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),64.65,2347.85,No
1150-WFARN,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.75,7156.2,Yes
6088-BXMRG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,3089.6,No
3144-AUDBS,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.15,270.8,Yes
4821-SJHJV,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.6,2901.8,No
7346-MEDWM,Female,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,83.25,4949.1,No
1137-DGOWI,Female,0,Yes,No,30,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),70.25,2198.9,No
5616-PRTNT,Male,0,No,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,374.5,Yes
3812-LRZIR,Female,0,Yes,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.5,761.95,No
4818-QIUFN,Female,1,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1520.9,Yes
9483-GCPWE,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,190.25,No
4227-OJHAL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),73,5163,No
9220-CXRSC,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),61.4,4059.85,No
4993-JCRGJ,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,84.3,2281.6,No
9537-JALFH,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1356.7,No
6698-OXETB,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,231.45,No
2103-ZRXFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.75,50.75,No
3724-BSCVH,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,242.4,No
4877-TSOFF,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.75,4264.25,No
9209-NWPGU,Male,0,No,No,44,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Electronic check,65.4,2774.55,No
6961-VCPMC,Male,1,Yes,No,46,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.4,3605.2,Yes
7306-YDSOI,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.75,4069.9,No
5288-AHOUP,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.5,874.2,No
6688-UZPWD,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,102,1145.35,Yes
0655-YDGFJ,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),48.95,1323.7,No
8468-EHYJA,Female,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,5497.05,No
6823-SIDFQ,Male,0,No,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),18.25,534.7,No
1097-FSPVW,Female,0,No,No,42,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),54.55,2455.05,No
2839-RFSQE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,38.7,Yes
8328-SKJNO,Male,0,No,Yes,23,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),40.65,947.4,No
7010-ZMVBF,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,357,No
5201-CBWYG,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.8,1476.25,No
0968-GSIKN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.8,70.8,Yes
1965-DDBWU,Male,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.05,1448.6,Yes
9057-SIHCH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.6,291.9,Yes
6352-GIGGQ,Male,0,No,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),88.8,5903.15,No
3635-QQRQD,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.05,1201.65,No
6771-XWBDM,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,5921.35,Yes
2897-DOVND,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,146.65,No
3082-YVEKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),77.15,1759.4,No
0191-EQUUH,Female,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),35.05,844.45,No
7134-HBPBS,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,7774.05,No
3489-HHPFY,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,134.05,No
2926-JEJJC,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,140.95,No
8601-QACRS,Female,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.6,249.95,Yes
4950-BDEUX,Male,0,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.2,1701.65,No
5789-LDFXO,Male,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.6,592.65,No
0508-OOLTO,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,135.75,No
2984-TBYKU,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),104.9,7732.65,No
9822-WMWVG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.5,4282.4,No
2391-SOORI,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.35,219.65,Yes
9040-KZVWO,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),75.5,2018.1,No
2645-QTLMB,Male,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,669,Yes
4806-HIPDW,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,68.95,Yes
1548-ARAGG,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,58.5,224.85,No
6339-RZCBJ,Male,0,No,No,48,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),78.9,3771.5,No
4424-TKOPW,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,196.75,Yes
4415-WNGVR,Female,1,Yes,No,12,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.2,943.85,No
2522-AHJXR,Male,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.45,6572.85,No
0412-UCCNP,Male,0,No,No,55,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Electronic check,59.2,3175.85,No
1816-FLZDK,Male,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,29.15,29.15,No
7096-UCLNH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
8443-ZRDBZ,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,318.9,Yes
9136-ALYBR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,24.45,Yes
4615-PIVVU,Female,0,No,No,42,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),66.5,2762.75,No
8885-QSQBX,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),49.55,49.55,No
7479-NITWS,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.35,631.85,Yes
8617-ENBDS,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),73.6,232.5,No
9385-NXKDA,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),82.65,5919.35,No
0430-IHCDJ,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49,749.25,No
2959-MJHIC,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.35,307.4,Yes
6674-KVJHG,Female,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,245.15,No
1814-DKOLC,Female,0,No,No,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.45,134.75,No
4201-JMNGR,Female,1,No,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,55.8,55.8,Yes
9351-HXDMR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.9,8240.85,No
3537-HPKQT,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),77.75,4266.4,No
2129-ALKBS,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.2,1077.5,No
5821-MMEIL,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1115.6,No
5960-WPXQM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.05,79.05,Yes
0684-AOSIH,Male,0,Yes,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,95,Yes
8260-NGFNY,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.2,25.2,Yes
1309-XGFSN,Male,1,Yes,Yes,52,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.85,4079.55,No
1866-RZZQS,Male,1,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.4,4187.75,Yes
6968-URWQU,Male,0,Yes,No,43,Yes,No,DSL,No,No,No,No,Yes,No,One year,Yes,Mailed check,56.35,2391.15,No
4742-TXUEX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,890.5,No
9631-XEYKE,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.4,137.25,No
8631-NBHFZ,Male,1,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.4,5154.6,Yes
1335-MXCSE,Male,0,Yes,Yes,55,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),55.25,3119.9,No
8873-TMKGR,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.1,529.5,No
8800-JOOCF,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,966.55,No
1469-LBJQJ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,6936.85,No
4958-XCBDQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3496.3,Yes
8337-UPOAQ,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,914.3,Yes
0064-YIJGF,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,1929,No
2386-OWURY,Female,0,No,No,58,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.3,5817.7,No
1725-IQNIY,Male,0,Yes,No,54,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.75,6110.2,Yes
0310-VQXAM,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,178.8,No
6598-RFFVI,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,28.3,Yes
3453-RTHJQ,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,435,No
6712-OAWRH,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.25,2351.8,Yes
6278-FEPBZ,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,186.15,No
4280-DLSHD,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.75,445.85,No
1508-DFXCU,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.45,912,No
8614-VGMMV,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.1,679.55,Yes
7868-BGSZA,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.2,3581.6,No
2800-QQUSO,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.3,4222.95,No
2930-UOTMB,Female,0,No,No,31,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),65.25,1994.3,Yes
7973-DZRKH,Female,0,No,Yes,66,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),90.95,5930.05,No
5914-DVBWJ,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.45,1505.85,Yes
7698-YFGEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,No
2533-QVMSK,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Electronic check,94.1,5638.3,Yes
9773-PEQBZ,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.85,797.25,No
8644-XLFBW,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes
0650-BWOZN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,1359.45,No
8625-AZYZY,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.65,2542.45,Yes
7785-RDVIG,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,54.7,No
9602-WCXPI,Male,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,989.05,No
2903-YYTBW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.55,44.55,No
3620-MWJNE,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.45,87.3,No
8573-JGCZW,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.65,351.55,No
6837-HAEVO,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105,7297.75,No
6701-YVNQG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.7,6301.7,No
9306-CPCBC,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.25,210.3,Yes
4304-XUMGI,Male,1,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),75.15,3822.45,No
9504-YAZWB,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,1048.45,No
8819-ZBYNA,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.1,6393.65,No
6174-NRBTZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),30.75,1489.3,No
9481-IEBZY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),112.9,8061.5,No
1833-VGRUM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,74.2,Yes
6137-NICCO,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,94.05,518.75,No
9065-ZCPQX,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.85,5763.15,No
6402-EJMWF,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.3,238.5,No
3620-EHIMZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1031.7,No
3213-VVOLG,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35, ,No
6870-ECSHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,34.8,No
8747-UDCOI,Female,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1319.95,No
8374-XGEJJ,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4388.4,Yes
2656-TABEH,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,420.2,Yes
6946-LMSQS,Male,1,Yes,No,25,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,89.05,2177.45,Yes
4626-OZDTJ,Female,0,Yes,No,51,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),78.65,3950.85,No
9169-BSVIN,Male,0,No,No,12,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),74.75,827.05,No
2325-ZUSFD,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),70.1,3913.3,Yes
8194-PEEBY,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,533.5,No
6872-HXFNF,Female,0,Yes,No,64,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),58.35,3756.45,No
3932-CMDTD,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,105.65,443.9,Yes
7714-YXSMB,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.5,2599.95,No
8387-UGUSU,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,284.3,No
1080-BWSYE,Male,1,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.65,1740.8,No
4039-HEUNW,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.5,3436.1,Yes
1194-HVAIF,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),95,2462.55,No
0812-WUPTB,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.85,70.85,Yes
3594-UVONA,Female,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),85.95,3110.1,Yes
0004-TLHLJ,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.9,280.85,Yes
1767-TGTKO,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.45,411.75,No
8439-LTUGF,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20,198.6,No
8805-JNRAZ,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.2,103.7,No
5089-IFSDP,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.45,6144.55,Yes
6418-HNFED,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.25,4089.45,No
0206-OYVOC,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.25,864.2,No
7291-CDTMJ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.65,19.65,No
7128-GGCNO,Male,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),72.8,3249.4,No
0237-YFUTL,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.65,5405.8,No
7691-KGKGP,Male,0,Yes,Yes,53,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),65,3363.8,No
6710-HSJRD,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,114.1,7132.15,No
3675-EQOZA,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.65,93.55,No
0840-DCNZE,Male,0,No,No,47,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),86.95,4138.9,No
1732-FEKLD,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),94.75,5121.75,No
9862-KJTYK,Male,0,No,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.35,566.1,No
0479-HMSWA,Female,0,No,Yes,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.45,2715.3,No
5854-KSRBJ,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1782.05,No
0365-BZUWY,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.55,1742.5,No
4817-VYYWS,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,2983.8,Yes
5701-SVCWR,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24,24,No
3129-AAQOU,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.6,485.9,No
4922-CVPDX,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.5,1905.7,No
1396-QWFBJ,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,1565.7,Yes
1357-BIJKI,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,4858.7,No
1099-BTKWT,Female,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54.4,3723.65,No
5299-SJCZT,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.55,298.35,Yes
2018-PZKMU,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.1,970.45,No
7340-KEFQE,Female,0,Yes,Yes,51,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),34.2,1782,No
4570-QHXHL,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.75,405.7,No
2403-BCASL,Male,1,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,4534.9,Yes
7392-YYPYJ,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.65,2415.95,No
2898-LSJGD,Female,0,Yes,Yes,21,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.95,1157.05,Yes
2223-GDSHL,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8297.5,No
7359-PTSXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.75,45.75,Yes
9470-RTWDV,Male,0,Yes,Yes,26,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,2083.1,No
2176-OSJUV,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.15,4681.75,No
9348-YVOMK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.8,176.2,No
8104-OSKWT,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.8,1001.2,No
4521-YEEHE,Female,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.85,1594.75,No
4090-KPJIP,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,212.4,No
3786-WOVKF,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.85,7677.4,No
4503-BDXBD,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,815.5,Yes
6086-ESGRL,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),80.15,80.15,Yes
2592-HODOV,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,259.65,No
3886-CERTZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.25,8109.8,Yes
7995-ZHHNZ,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),56.1,2386.85,No
3824-RHKVR,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,340.35,No
5816-SCGFC,Female,1,No,No,7,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.3,419.35,No
5989-AXPUC,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,118.6,7990.05,No
4979-HPRFL,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.15,1402.25,No
8590-OHDIW,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,749.35,No
8015-IHCGW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.5,8425.15,No
2332-TODQS,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),25.05,1171.5,No
9278-VZKCD,Female,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,109.1,5647.95,No
8008-OTEZX,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,708.8,No
2773-OVBPK,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.3,7567.2,No
5999-LCXAO,Female,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.9,29.9,No
5206-XZZQI,Male,0,No,No,53,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,4348.1,No
4803-LBYPN,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,635.9,No
5759-RCVCB,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),35.2,108.95,Yes
6372-RFVNS,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,78.8,Yes
2139-FQHLM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,89.95,1682.4,No
8261-GWDBQ,Female,1,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,6925.9,No
1093-YSWCA,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,223.15,No
8938-UMKPI,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,5127.95,Yes
0991-BRRFB,Male,0,No,No,18,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.4,874.8,Yes
3882-IYOIJ,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.25,6758.45,No
8749-TZYEC,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.8,1874.3,No
1755-FZQEC,Male,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,791.15,No
3115-JPJDD,Female,0,Yes,No,59,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.25,4639.45,No
5600-KTXFM,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.95,143.9,No
3871-IKPYH,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.1,69.1,Yes
6319-IEJWJ,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.2,1776.55,Yes
9025-AOMKI,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.55,536.4,Yes
6339-TBELP,Male,0,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),86.4,6172,No
7964-YESJC,Female,0,Yes,No,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,66.3,1559.45,No
6173-GOLSU,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,6079,No
5275-SQEIZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,80.85,80.85,Yes
7716-YTYHG,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Mailed check,82.05,4029.95,Yes
9938-ZREHM,Female,0,Yes,No,37,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,72.1,2658.4,No
7950-XWOVN,Male,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,34.7,383.55,No
4390-KYULV,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.55,51.15,Yes
4647-MUZON,Female,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.95,1745.5,No
9702-AIUJO,Male,0,Yes,Yes,50,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),44.8,2230.85,No
8849-PRIQJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.4,7281.6,No
8851-RAGOV,Female,0,Yes,No,25,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.05,1837.7,No
1304-NECVQ,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.55,149.55,Yes
5396-IZEPB,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,180.7,No
1729-VLAZJ,Female,0,No,Yes,10,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,411.45,No
8285-ABVLB,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1328.35,No
0701-TJSEF,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.25,576.95,No
5712-VBOXD,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,68.45,No
6629-LADHQ,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.95,123.05,No
8945-GRKHX,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.65,78.65,Yes
1559-DTODC,Male,0,No,No,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,468.35,No
4797-MIWUM,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.25,174.7,No
6959-UWKHF,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,42.9,42.9,Yes
8720-RQSBJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
4537-CIBHB,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,172.35,No
3815-SLMEF,Female,0,No,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.25,139.35,Yes
5154-VEKBL,Female,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,58.5,539.85,Yes
4324-AHJKS,Female,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.8,300.4,No
4355-CVPVS,Female,0,Yes,Yes,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),88.9,4968,No
4495-LHSSK,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,57.65,992.7,No
5655-JSMZM,Male,1,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,4718.25,Yes
5915-ANOEI,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,79.15,5536.5,No
9861-PDSZP,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.05,7806.6,No
4505-EXZHB,Female,1,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,74.4,434.1,Yes
7225-CBZPL,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.8,1563.9,No
6704-UTUKK,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.9,1332.4,No
4587-VVTOX,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,545.2,Yes
2019-HDCZY,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,102.6,6296.75,No
4652-NNHNY,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.85,1284.2,Yes
8788-DOXSU,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),61.35,3645.5,No
7404-JLKQG,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,57.55,161.45,No
7421-ZLUPA,Female,0,No,No,8,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,29.25,226.95,No
8972-HJWNV,Female,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.55,646.85,Yes
3274-NSDWE,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,1441.65,No
2632-IVXVF,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.75,7511.3,No
3692-JHONH,Female,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,5621.85,No
8915-NNTRC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.7,7919.8,No
5914-GXMDA,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,593.2,No
7463-IFMQU,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1423.65,No
2920-RNCEZ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,69.95,No
2541-YGPKE,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),63.7,2763.35,No
8515-OCTJS,Female,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,692.1,Yes
5382-TEMLV,Male,0,No,No,45,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.9,2298.55,No
3441-CGZJH,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.4,2640.55,No
9592-ERDKV,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Mailed check,79.25,2911.8,No
2860-RANUS,Female,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.8,1727.5,Yes
5261-QSHQM,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,86.6,Yes
4778-IZARL,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.1,6705.7,No
0432-CAJZV,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,90.7,237.65,No
7008-LZVOZ,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1672.35,No
8868-WOZGU,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.7,2979.5,Yes
1200-TUZHR,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.2,695.75,No
9365-CSLBQ,Male,0,No,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.35,1654.6,No
1334-FJSVR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.25,24.25,Yes
5884-FBCTL,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.1,1857.85,No
7130-CTCUS,Male,1,Yes,No,16,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.55,825.1,No
6242-FEGFD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,96.6,6424.25,No
7625-XCQRH,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,837.95,Yes
6194-HBGQN,Male,0,No,No,51,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),81.15,4126.2,No
7634-WSWDB,Female,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,38.5,330.8,No
6986-IXNDM,Male,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.9,1337.45,No
1731-TVIUK,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,93.5,362.2,Yes
2987-BJXIK,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,84.7,5991.05,No
9769-TSBZE,Female,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Electronic check,66,4891.5,No
0406-BPDVR,Female,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.5,5373.1,Yes
0618-XWMSS,Male,0,No,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2068.55,Yes
1395-WSWXR,Male,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,487.05,No
6023-GSSXW,Female,0,Yes,No,69,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),61.45,4131.2,No
6752-APNJL,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.5,2301.15,No
5276-KQWHG,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,131.65,Yes
0420-HLGXF,Female,1,No,No,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,4036,No
7446-KQISO,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,4900.65,No
9823-EALYC,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),80.85,5727.45,No
0582-AVCLN,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,743.05,No
5803-NQJZO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),67.8,4804.65,No
2565-JSLRY,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.05,24.05,Yes
2607-DHDAK,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1414.65,No
1073-XXCZD,Male,0,Yes,No,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,1443.65,No
0743-HRVFF,Female,0,Yes,Yes,51,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,56.15,2898.95,No
4006-HKYHO,Male,0,No,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.7,5309.5,No
3727-JEZTU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No
8143-ETQTI,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.65,451.55,No
6689-KXGBO,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.55,50.55,Yes
9667-TKTVZ,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.35,117.05,No
3657-COGMW,Female,1,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.1,5839.3,No
8570-KLJYJ,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,54.45,1893.5,No
7754-IXRMC,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.35,45.35,No
9473-CBZOP,Female,0,No,No,28,Yes,Yes,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,59,1654.45,No
2969-WGHQO,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Electronic check,69.45,477.05,No
6615-NGGZJ,Male,0,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.55,1415.55,Yes
4334-HOWRP,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.95,4546,No
4255-DDUOU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,20.5,Yes
5863-OOKCL,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,163.2,No
1686-STUHN,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,849.9,No
1329-VHWNP,Female,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.05,189.95,No
2984-MIIZL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.8,321.9,Yes
0266-GMEAO,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.3,8058.55,No
5590-YRFJT,Female,0,Yes,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,482.8,Yes
5574-NXZIU,Male,0,No,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.2,7049.75,No
0019-GFNTW,Female,0,No,No,56,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),45.05,2560.1,No
4256-ZWTZI,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,51,286.8,No
8309-PPCED,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,7982.5,No
4098-NAUKP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.65,5683.6,No
5196-WPYOW,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,60.05,3994.05,No
4608-LCIMN,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.65,369.15,No
1485-YDHMM,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,4631.7,No
4054-CUMIA,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,401.95,No
0603-TPMIB,Female,0,Yes,Yes,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.45,1534.05,No
2525-GVKQU,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1093,No
8161-QYMTT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,701.3,No
9581-GVBXT,Male,0,Yes,Yes,59,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,34.8,1980.3,No
5862-BRIXZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.75,2893.4,No
5404-GGUKR,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.35,262.3,No
3308-JSGML,Male,1,Yes,No,59,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.05,3886.85,No
2126-GSEGL,Female,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.8,5917.55,No
3677-TNKIO,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71,914,Yes
0440-QEXBZ,Female,0,No,No,44,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2139.1,No
2434-EEVDB,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),94.6,5948.7,No
6762-QVYJO,Female,0,Yes,Yes,58,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,59.75,3624.35,No
6199-IWKGC,Female,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,100.25,4753.85,No
2675-DHUTR,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,5780.7,No
8152-VETUR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),97.7,6869.7,No
9667-EQRXU,Female,1,No,No,30,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,40.3,1172.95,Yes
7446-YPODE,Male,1,No,No,11,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.25,662.95,No
9522-BNTHX,Female,1,No,No,34,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,56.25,1765.95,No
4676-WLUHT,Male,0,No,No,54,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),46.2,2431.95,No
3329-WDIOK,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,155.35,Yes
7980-MHFLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.9,1859.2,No
2873-ZLIWT,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.85,3303.05,Yes
3415-TAILE,Female,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,65.7,134.35,Yes
0757-WCUUZ,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Two year,No,Credit card (automatic),63.35,3409.1,No
1629-DQQVB,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.1,709.5,No
1915-IOFGU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,70.5,Yes
3045-XETSH,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.85,953.45,Yes
0374-AACSZ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.15,50.15,No
7239-HZZCX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes
4872-VXRIL,Male,0,No,No,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),64.65,3665.55,No
9140-CZQZZ,Female,0,Yes,No,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.6,5515.8,No
3423-HHXAO,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,272,No
9938-TKDGL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.55,6668,No
0531-ZZJWQ,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74,4052.4,No
6537-QLGEX,Female,0,No,No,16,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,38.9,664.4,No
2688-BHGOG,Male,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,718.55,No
7683-CBDKJ,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,65.45,937.6,Yes
0946-CLJTI,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5812.6,Yes
3160-TYXLT,Male,0,No,No,53,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),46.3,2546.85,No
1325-USMEC,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99.35,6944.5,No
0916-QOFDP,Female,1,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.8,1346.3,No
0628-CNQRM,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),67.5,1544.05,Yes
5606-AMZBO,Female,0,Yes,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),78.15,765.15,No
6199-IPCAO,Female,0,Yes,Yes,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,692.55,No
7665-TOALD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
0112-QWPNC,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,84.35,4059.35,Yes
0324-BRPCJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,6851.65,Yes
2777-PHDEI,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.05,78.05,Yes
5640-CAXOA,Female,0,No,No,30,No,No phone service,DSL,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),40.35,1187.05,No
2235-EZAIK,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.2,5401.9,No
3847-BAERP,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.9,247,No
1196-AMORA,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,73.6,520,Yes
4282-YMKNA,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,706.6,Yes
1453-RZFON,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,49.9,No
8263-OKETD,Female,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),68.9,1370.35,No
0670-KDOMA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
2476-YGEFM,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),76,2215.25,No
8687-BAFGU,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74,74,No
0641-EVBOJ,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,82.3,214.4,No
0829-XXPLX,Female,0,No,No,20,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.4,1871.15,No
9974-JFBHQ,Male,0,No,Yes,64,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.15,6171.2,No
5356-RHIPP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
6624-JDRDS,Female,0,No,No,6,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.45,161.45,No
0608-JDVEC,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.8,1013.2,No
9780-FKVVF,Male,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.15,336.7,No
1919-RTPQD,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,333.65,No
5214-NLTIT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),90.8,6511.8,No
3345-PBBFH,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,393.45,No
5055-BRMNE,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),106.7,7009.5,No
9189-JWSHV,Female,1,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,2264.05,Yes
2190-PHBHR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),94.45,6921.7,No
2650-GYRYL,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.45,600.25,No
0746-JTRFU,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.05,56.35,Yes
5208-FVQKB,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.95,4664.15,No
7184-LRUUR,Female,0,No,No,22,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),65.25,1441.8,No
6627-CFOSN,Female,0,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.45,5623.7,No
3982-JGSFD,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,695.85,No
1574-DYCWE,Female,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1028.75,No
7247-XOZPB,Male,0,Yes,No,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),77.4,4155.95,No
2466-FCCPT,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,395.6,No
6211-WWLTF,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),99.7,6330.4,No
4826-TZEVA,Female,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.8,2971.7,No
6016-NXBNJ,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.15,638,No
9138-EFSMO,Male,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,No,Bank transfer (automatic),78.95,2034.25,No
0576-WNXXC,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.55,2510.2,Yes
4632-XJMEX,Male,0,No,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),62.85,3419.5,No
8910-LEDAG,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.55,2427.35,No
3452-GWUIN,Female,1,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.95,1760.25,No
2716-GFZOR,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),86.1,3551.65,No
3724-UCSHY,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,122.9,No
8626-XHBIE,Male,0,No,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1424.2,No
4628-CTTLA,Male,0,No,No,57,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,2111.45,Yes
3192-LNKRK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.05,2909.95,No
4439-YRNVD,Female,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,36.25,374,No
2876-VBBBL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,Yes
6834-NXDCA,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.9,300.8,Yes
2208-UGTGR,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.6,5581.05,No
3005-TYFRD,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.65,5676.65,No
8670-MEFCP,Female,0,Yes,Yes,36,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.9,3379.25,No
3079-BCHLN,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,942.95,No
7777-UNYHB,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.1,232.4,No
5597-GLBUC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,85.45,85.45,Yes
7278-CKDNC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.5,2088.45,No
2748-MYRVK,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.9,6137,Yes
8450-UYIBU,Female,1,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.85,1434.6,No
2969-VAPYH,Female,0,No,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),60.5,3870,No
4822-YCXMX,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.8,2043.45,Yes
7181-OQCUT,Male,0,No,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,103.85,2215,No
8474-UMLNT,Female,0,No,No,13,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),67.8,842.25,No
9821-POOTN,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,2576.2,Yes
3836-FZSDJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.85,1901,No
9142-XMYJH,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.35,601.6,No
6559-ILWKJ,Male,0,Yes,No,71,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Electronic check,49.35,3515.25,Yes
0187-QSXOE,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89,605.45,No
7733-UDMTP,Female,1,No,No,57,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,55,3094.05,No
2649-HWLYB,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.15,4929.55,No
5214-CHIWJ,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,595.05,No
4229-CZMLL,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,469.8,Yes
6904-JLBGY,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.35,8436.25,No
2465-BLLEU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.75,19.75,No
1685-VAYJF,Male,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),45.2,492,No
8874-EJNSR,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,987.95,No
6917-YACBP,Female,1,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.75,5496.9,No
5049-MUBWG,Male,0,No,No,26,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),75,1908.35,No
4223-WOZCM,Male,1,No,No,2,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,49.95,107.1,No
0769-MURVM,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.7,4575.35,No
9253-VIFJQ,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),67.05,4309.55,No
7030-FZTFM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.9,7922.75,No
9741-YLNTD,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),87.95,522.35,No
5917-RYRMG,Male,1,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,587.7,No
5120-ZBLAI,Female,0,Yes,No,50,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),75.7,3876.2,No
1194-BHJYC,Male,0,Yes,No,61,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,62.15,3778.85,No
3663-MITLP,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,1457.25,Yes
0906-QVPMS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.15,8349.45,No
9025-ZRPVR,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,185.6,Yes
1905-OEILC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,No
7858-GTZSP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,1066.9,No
3285-UCQVC,Female,0,No,No,37,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),28.6,973.55,Yes
6248-BSHKG,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,1226.45,No
4685-TFLLS,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,342.3,No
3470-BTGQO,Male,0,No,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,985.05,No
1209-VFFOC,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,56.4,3948.45,No
8224-DWCKX,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.3,828.05,No
5482-PLVPE,Female,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.35,41.85,Yes
4553-DVPZG,Female,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,6164.7,No
4902-OHLSK,Female,1,No,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,98.65,2862.75,Yes
6917-IAYHD,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,33.6,33.6,No
0495-ZBNGW,Male,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.9,343.95,Yes
8620-RJPZN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.7,20.7,No
7572-KPVKK,Male,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,104.05,6590.5,No
2642-MAWLJ,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,717.95,No
2357-COQEK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.3,2890.65,Yes
7103-ZGVNT,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),73.7,4885.85,No
3737-GCSPV,Female,0,Yes,No,11,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.2,1222.05,Yes
2027-WKXMW,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.75,6871.7,No
7137-NAXML,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,405.6,No
2428-ZMCTB,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,208.25,No
2961-VNFKL,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.95,1801.9,No
1768-HNVGJ,Female,1,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,70.05,3062.45,No
6963-KQYQB,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.05,1764.75,No
5934-RMPOV,Female,0,No,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,1816.75,No
1207-BLKSA,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.05,1255.1,No
4088-YLDSU,Male,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.15,5743.05,Yes
8316-BBQAY,Female,0,No,No,65,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.95,3921.1,No
1166-PQLGG,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,1463.45,No
3146-JTQHR,Male,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.6,189.45,No
4291-YZODP,Female,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,96.8,No
4395-PZMSN,Male,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.55,408.5,No
6427-FEFIG,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),78.6,1846.65,No
0017-IUDMW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.8,8456.75,No
8706-HRADD,Male,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,43.55,1011.5,No
9955-QOPOY,Male,0,Yes,No,69,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.8,4263.4,No
4385-ZKVNW,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),54.9,2549.1,No
5446-DKWYW,Female,1,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,65.2,3965.05,No
2034-CGRHZ,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.95,2496.7,Yes
5797-APWZC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,90.6,Yes
3683-QKIUE,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),50.8,288.05,Yes
4228-ZGYUW,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,368.1,Yes
9031-ZVQPT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.2,7840.6,No
2990-IAJSV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6632.75,No
8107-KNCIM,Male,1,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,1013.35,No
9027-YFHQJ,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.05,152.95,No
5176-LDKUH,Female,0,No,No,48,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Electronic check,75.15,3772.65,No
4644-OBGFZ,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,1026.35,No
7926-IJOOU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.3,19.3,No
6480-YAGIY,Male,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),112.2,5031.85,No
4029-HPFVY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.3,220.4,No
7602-DBTOU,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,1416.5,No
5345-BMKWB,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,158.35,No
3519-ZKXGG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,256.6,Yes
5457-COLHT,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.65,5542.55,No
6416-TVAIH,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.5,68.5,Yes
5451-YHYPW,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8443.7,No
5108-ADXWO,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,791.75,Yes
7998-WNZEM,Male,0,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.6,5708.2,No
3066-RRJIO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.95,69.95,Yes
4664-NJCMS,Female,0,Yes,No,33,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.55,2016.3,No
0307-BCOPK,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.05,326.65,No
7629-WFGLW,Female,1,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,95.65,5471.75,No
2542-HYGIQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.95,19.95,No
6715-OFDBP,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.05,346.4,Yes
5016-LIPDW,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,1061.6,No
6481-LXPWL,Male,0,Yes,Yes,56,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),36.1,1971.5,No
1567-DSCIC,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94,773.65,Yes
2150-UWTFY,Female,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61.15,1422.05,Yes
6124-ACRHJ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No
9362-MWODR,Female,0,No,Yes,40,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.1,2460.35,No
9975-GPKZU,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,856.5,No
9625-RZFUK,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,1275.85,No
5153-LXKDT,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.2,7467.5,No
5161-UBZXI,Male,0,Yes,Yes,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),106.35,7261.75,No
1020-JPQOW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.55,5116.6,No
7919-ZODZZ,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Mailed check,65.9,660.05,No
0565-JUPYD,Male,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),104.5,6590.8,No
6867-ACCZI,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,52.5,1208.15,No
5939-XAIXZ,Female,0,No,No,19,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.1,1033.9,No
9054-FOWNV,Male,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.75,1885.15,No
2683-BPJSO,Male,0,Yes,No,29,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,84.45,2467.1,Yes
2157-MXBJS,Male,0,Yes,No,13,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,75.3,989.45,Yes
8207-VVMYB,Female,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,2006.95,No
9732-EQMWY,Female,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.4,5025,No
9360-AHGNL,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,109.55,4830.25,Yes
1087-UDSIH,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,59.75,Yes
5269-NRGDP,Male,0,Yes,Yes,42,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,73.15,3088.25,No
6195-MELTI,Male,0,No,No,57,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Mailed check,54.65,3134.7,No
5485-WUYWF,Male,1,No,No,2,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,94.55,Yes
2121-JAFOM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.55,8312.4,No
8818-DOPVL,Female,1,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,4863.85,No
2632-UCGVD,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,6871.9,Yes
5445-PZWGX,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Electronic check,102,6529.25,Yes
1227-UDMZR,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.15,6637.9,No
5198-HQAEN,Male,0,Yes,Yes,35,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.7,3165.6,No
9170-CCKOU,Male,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),90.2,1454.15,Yes
2167-FQSTQ,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,92.4,6786.1,No
2819-GWENI,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,543,No
1043-YCUTE,Male,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1327.15,Yes
3814-MLAXC,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.85,2404.15,Yes
3572-UUHRS,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),18.85,867.3,No
2692-PFYTJ,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.75,25.75,No
1226-UDFZR,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.6,114.7,Yes
5955-EPOAZ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.95,109.5,No
6821-JPCDC,Female,0,Yes,No,48,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.05,4692.95,No
8815-LMFLX,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.4,546.85,No
9135-HSWOC,Male,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.7,1274.05,No
8582-KRHPJ,Male,0,No,No,50,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35,1782.4,No
5811-IWXYM,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),101.25,5301.1,No
7044-YAACC,Male,1,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,280.35,No
8189-XRIKE,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.95,2897.95,No
1506-YJTYT,Male,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.85,3371,No
4123-DVHPH,Male,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.05,801.3,No
6425-YQLLO,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,6975.25,Yes
5442-UTCVD,Male,0,No,Yes,3,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,91.85,257.05,Yes
2228-BZDEE,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,1079.45,No
7734-DBOAI,Female,0,Yes,Yes,1,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,40.1,40.1,Yes
2789-CZANW,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),110.3,6997.3,No
2091-RFFBA,Female,1,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.9,2217.15,Yes
2114-MGINA,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.8,1129.1,Yes
7596-LDUXP,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),85.15,979.05,No
3740-RLMVT,Male,1,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.95,4119.4,No
4489-SNOJF,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,72.25,2568.55,Yes
2192-OZITF,Female,0,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,73.55,3349.1,No
5651-CPDND,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,492.1,No
2186-QZEYA,Female,1,No,No,29,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),58.55,1718.95,No
1131-ALZWV,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.6,605.25,No
7729-XBTWX,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1344.5,No
9134-WYRVP,Male,0,No,No,51,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,No,Mailed check,86.35,4267.15,No
3284-SVCRO,Female,0,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1121.05,No
9732-OUYRN,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,918.7,No
0559-CKHUS,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.55,521.8,No
2931-SVLTV,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),110.1,4469.1,No
6899-PPEEA,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.55,3580.3,Yes
7504-UWHNB,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.75,1729.35,No
2582-FFFZR,Female,0,No,No,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,1073.3,No
8908-NMQTX,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.6,4566.5,No
2187-LZGPL,Female,0,No,No,7,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.1,293.3,Yes
1431-AIDJQ,Male,0,Yes,Yes,33,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),82.1,2603.1,No
6288-LBEAR,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,79.1,1783.75,No
4597-NUCQV,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2440.15,Yes
9019-QVLZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.55,79.55,Yes
8413-YNHNV,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,90.65,6322.1,No
5808-TOTXO,Female,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,57.4,No
4369-NYSCF,Male,0,No,No,56,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.75,4284.65,No
6333-YDVLT,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110,7138.65,No
5324-KTGCG,Male,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.85,1539.75,No
5599-HVLTW,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,1058.1,No
7394-LWLYN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,123.8,No
3707-LRWZD,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.05,2781.85,Yes
6873-UDNLD,Male,0,No,No,40,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,67.45,2731,No
2700-LUEVA,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.75,20.75,No
1455-ESIQH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.1,89.1,Yes
0958-YHXGP,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,497.3,No
1101-SSWAG,Female,0,Yes,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.1,711.15,No
2979-SXESE,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.4,1607.2,Yes
4013-TLDHQ,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.25,1490.95,Yes
5743-KHMNA,Male,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.55,1898.1,No
4194-FJARJ,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),60,3273.95,No
5325-UWTWJ,Male,0,Yes,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),90.55,2929.75,No
3969-GYXEL,Female,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,76.4,838.7,No
9208-OLGAQ,Female,1,No,No,18,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,84.95,1443.65,No
3244-CQPHU,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.1,7746.7,No
2674-MLXMN,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.65,6951.15,No
9708-HPXWZ,Male,1,No,No,5,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Credit card (automatic),45.4,214.75,No
6992-TKNYO,Male,0,Yes,No,38,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),69,2669.45,No
4468-YDOVK,Male,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),48.65,235.2,No
3754-DXMRT,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.15,92.65,Yes
5792-JALQC,Female,1,No,No,52,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),59.85,3103.25,No
7130-VTEWQ,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,606.25,Yes
2200-DSAAL,Female,0,No,No,68,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,80.65,5330.2,No
4302-ZYFEL,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1403.1,No
9351-LZYGF,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),66.4,2727.8,No
4366-CTOUZ,Female,0,No,No,50,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.2,5038.45,No
6121-VZNQB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,19.1,Yes
7903-CMPEY,Male,1,Yes,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.3,80.3,Yes
1518-OMDIK,Male,0,Yes,No,33,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,1462.6,No
6671-NGWON,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,150.6,No
0595-ITUDF,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,91.8,5960.5,No
6996-KNSML,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes
1955-IBMMB,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.2,1192.3,No
0096-BXERS,Female,0,Yes,No,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.35,314.55,No
3806-YAZOV,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,56,No
7998-ZLXWN,Female,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,330.8,No
6253-WRFHY,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,64.75,877.35,No
9133-AYJZG,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.7,2249.1,No
2675-OTVVJ,Male,1,Yes,No,31,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.45,2807.65,No
8680-CGLTP,Male,0,No,No,29,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,58.75,1696.2,No
8430-TWCBX,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,1032.05,No
5018-GWURO,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.6,4902.8,No
6741-QRLUP,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),80.3,4995.35,No
4737-HOBAX,Male,0,Yes,No,63,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),79.8,5034.05,No
0537-QYZZN,Male,1,Yes,Yes,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.85,1857.75,No
3340-QBBFM,Male,1,Yes,No,36,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),54.1,1992.85,No
1704-NRWYE,Female,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,751.65,Yes
2592-SEIFQ,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,66.95,Yes
8200-KLNYW,Female,0,Yes,No,21,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,80.9,1714.95,No
3372-CDXFJ,Male,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.5,343.6,No
4781-ZXYGU,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.15,20.15,No
7632-YUTXB,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,520.1,No
2718-YSKCS,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1387.45,No
9896-UYMIE,Male,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),114.3,7383.7,No
0853-NWIFK,Female,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,100.3,4483.95,No
8212-CRQXP,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80,1706.45,No
6980-CDGFC,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.85,1327.4,No
7691-XVTZH,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),89.95,5974.3,No
2520-SGTTA,Female,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20, ,No
7481-ATQQS,Female,1,No,No,49,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,4515.85,Yes
2277-VWCNI,Female,1,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.75,179.85,No
1088-CNNKB,Male,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),80,5040.2,No
8642-GVWRF,Female,0,Yes,No,2,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),79.7,165,Yes
1930-BZLHI,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,422.7,No
3400-ESFUW,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),57.55,3046.4,Yes
5868-YTYKS,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
3525-DVKFN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,358.05,No
1482-OXZSY,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,2936.25,No
0377-JBKKT,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,57.95,1271.8,No
5778-BVOFB,Female,0,No,No,9,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.5,530.05,No
3373-YZZYM,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No
2057-ZBLPD,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.5,1808.7,Yes
0228-MAUWC,Male,0,No,No,19,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.55,1144.6,No
5502-RLUYV,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,7446.9,Yes
0023-HGHWL,Male,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.1,25.1,Yes
7663-YJHSN,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),103.95,7556.9,No
4915-BFSXL,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Credit card (automatic),68.95,4858.7,No
3086-RUCRN,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),103.1,6595,No
5215-LNLDJ,Female,0,Yes,Yes,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,149.05,No
7576-JMYWV,Female,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.2,4972.1,No
9039-RBEEE,Male,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),48.95,1880.85,Yes
0257-KXZGU,Female,0,Yes,Yes,32,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),62.45,2045.55,No
1307-ATKGB,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.55,2187.15,No
8417-FMLZI,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.55,477.55,Yes
3714-XPXBW,Female,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),78.9,2976.95,No
1850-AKQEP,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,178.7,No
2824-DXNKN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),71.45,5025.85,No
5227-JSCFE,Male,1,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),46.35,3353.4,No
0848-ZGQIJ,Female,0,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.65,1461.15,No
3621-CHYVB,Female,0,Yes,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),49.9,2782.4,No
8042-RNLKO,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,1699.15,No
1792-UXAFY,Female,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,1496.9,Yes
5372-FBKBN,Female,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,452.2,No
0420-TXVSG,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),66.1,4428.45,No
6217-TOWGS,Female,0,Yes,No,17,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),75.4,1322.55,No
0515-YPMCW,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes
0378-XSZPU,Male,0,Yes,No,58,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),60.3,3563.8,Yes
1045-LTCYT,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,139.75,No
1544-JJMYL,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),69.35,1927.3,No
4636-JGAAI,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,88.85,3000.25,No
6874-SGLHU,Male,0,No,No,30,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97,3021.3,No
5951-AOFIH,Male,0,No,No,33,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),66.4,2245.4,No
4729-XKASR,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,24.75,Yes
3059-NGMXB,Male,0,Yes,Yes,14,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,69.2,944.65,No
8652-YHIYU,Female,0,No,Yes,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),79.5,1264.2,No
5278-PNYOX,Female,0,No,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),100.65,4917.75,No
5449-FIBXJ,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.3,2012.7,Yes
3486-HOOGQ,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),79.7,5743.3,No
5061-PBXFW,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.4,1864.65,No
8630-FJLIB,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,1221.65,No
3891-NLXJB,Male,0,No,No,37,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,40.55,1390.85,No
4749-OJKQU,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.65,302.35,No
0577-WHMEV,Female,0,Yes,No,16,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.7,1374.9,No
5453-AXEPF,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,1336.9,No
3639-XJHKQ,Female,0,No,Yes,19,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,60.6,1297.8,No
3716-LRGXK,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),101.15,6067.4,No
2263-SFSQZ,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.95,1222.25,No
9950-MTGYX,Male,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.3,487.95,No
3397-AVTKU,Male,0,No,No,43,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Electronic check,60,2548.55,No
4825-FUREZ,Male,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.25,835.5,No
9837-BMCLM,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.5,242.05,Yes
2672-OJQZP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,44.75,No
0137-UDEUO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,63.75,Yes
3134-DSHVC,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Credit card (automatic),98,6218.45,No
6161-UUUTA,Male,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.9,260.9,Yes
7821-DPRQE,Male,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.7,7320.9,No
6543-XRMYR,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,2967.35,Yes
2001-EWBQU,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,104.7,6333.8,No
4925-LMHOK,Male,0,No,No,15,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),58.6,939.7,Yes
1608-GMEWB,Male,1,No,No,45,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.9,4200.25,No
6695-AMZUF,Female,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5950.2,No
1455-UGQVH,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1037.75,Yes
8410-BGQXN,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,93.4,No
8280-MQRQN,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.45,50.45,Yes
1619-YWUBB,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1614.9,No
7611-YKYTC,Male,0,Yes,Yes,22,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75,1573.95,No
2037-SGXHH,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94.65,3624.3,Yes
3178-FESZO,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.25,100.25,Yes
0365-GXEZS,Male,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,78.2,1468.75,No
0082-OQIQY,Male,0,No,No,29,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,2607.6,No
6087-MVHJH,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.45,1422.1,Yes
4603-JANFB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes
3018-TFTSU,Male,0,No,No,12,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),81.7,858.6,Yes
3606-SBKRY,Male,0,No,No,31,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,50.05,1523.4,No
4806-KEXQR,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.9,324.3,Yes
0667-NSRGI,Female,0,Yes,No,48,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,69.55,3435.6,No
7083-YNSKY,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),25.4,399.6,Yes
6893-ODYYE,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.1,4549.45,No
8242-JSVBO,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.65,322.5,No
2479-BRAMR,Male,1,Yes,No,41,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.75,3273.55,Yes
9541-ZPSEA,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),80.35,5375.15,No
2665-NPTGL,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,2510.7,No
1139-WUOAH,Male,0,No,No,57,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.35,3090.05,No
4693-VWVBO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.55,61.05,No
7434-SHXLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes
6937-GCDGQ,Male,0,Yes,Yes,19,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),48.95,955.6,No
0988-JRWWP,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.2,140.4,No
5075-JSDKI,Female,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.45,1493.1,No
7908-QCBCA,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.4,69.4,Yes
8644-XYTSV,Male,0,Yes,No,42,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),40.15,1626.05,No
6711-FLDFB,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,541.15,Yes
3873-WOSBC,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1784.9,No
7465-ZZRVX,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,70.35,No
7975-JMZNT,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.7,6075.9,No
7251-XFOIL,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.2,5500.6,No
4116-IQRFR,Male,0,Yes,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.1,73.1,No
3714-JTVOV,Female,1,Yes,No,42,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.15,3229.4,Yes
8259-DZLIZ,Female,0,Yes,Yes,64,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),53.85,3399.85,No
0442-ZXKVS,Female,1,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),115.6,6431.05,No
7853-WNZSY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.75,19.75,No
3120-FAZKD,Male,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1230.9,No
8606-OEGQZ,Female,0,No,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.3,454.65,No
0225-ZORZP,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.3,235.05,No
4702-IOQDC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes
9489-JMTTN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6595.9,No
0575-CUQOV,Male,1,Yes,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.95,5867,No
0967-BMLBD,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,196.35,No
6178-KFNHS,Female,0,No,Yes,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.3,909.25,Yes
2830-LEWOA,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),103.9,6449.15,No
5006-MXVRN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.7,762.45,No
1264-BYWMS,Male,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,5283.95,Yes
9658-WYUFB,Female,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,1617.5,Yes
8327-WKMIE,Male,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,785.75,No
6917-FIJHC,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26,1776,No
5329-KRDTM,Male,1,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5396.25,No
7797-EJMDP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.05,574.5,No
3530-VWVGU,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,400.3,No
2013-SGDXK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,84.3,84.3,Yes
6368-NWMCE,Female,0,No,No,38,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),68.15,2656.3,No
3633-CDBUW,Male,0,No,Yes,17,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,80.85,1445.95,No
0707-HOVVN,Female,1,No,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.5,5212.65,No
8580-QVLOC,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.45,6440.25,Yes
3956-MGXOG,Female,0,No,No,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.6,2244.95,No
9274-UARKJ,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.2,1130,No
4077-HWUYD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.55,6463.15,No
2012-NWRPA,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,1131.2,Yes
8808-ELEHO,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.25,585.95,Yes
0103-CSITQ,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.4,6252.7,No
3506-LCJDC,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.95,19.95,Yes
8671-KKKOS,Female,0,Yes,No,46,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,45.55,2062.15,No
7305-ZWMAJ,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,587.1,No
9518-XXBXE,Male,1,Yes,No,10,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.3,720.45,No
3934-HXCFZ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,99.25,2186.4,Yes
6578-KRMAW,Male,0,No,No,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.4,2979.3,No
4860-IJUDE,Male,0,No,No,13,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,956.65,No
7666-WKRON,Female,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Electronic check,80.45,3201.55,Yes
9688-YGXVR,Female,0,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.15,3973.2,No
0423-UDIJQ,Male,1,No,No,9,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.2,447.9,No
1945-XISKS,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1335.2,No
4910-AQFFX,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,661.25,Yes
2154-KVJFF,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.75,1111.85,Yes
5360-LJCNJ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.15,7555,No
2607-FBDFF,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),49,49,No
9647-ERGBE,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3046.15,Yes
3428-XZMAZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes
1100-DDVRV,Male,0,Yes,No,17,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,49.8,836.35,No
0378-TOVMS,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,85.8,272.2,Yes
4355-HBJHH,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,79.7,5293.4,Yes
6728-WYQBC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,20.95,No
9058-CBREO,Female,1,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.55,50.55,Yes
9029-FEGVJ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,79.3,2570,No
7216-KAOID,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,798.2,No
0318-QUUOB,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,Yes
6145-NNPNO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.15,44.15,No
5520-FVEWJ,Female,0,Yes,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,916.9,Yes
5339-TJFEK,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6487.2,No
5572-ZDXHY,Female,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.3,1855.65,Yes
2074-GUHPQ,Female,0,No,Yes,17,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),92.7,1556.85,No
4625-XMOYM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26.25,1988.05,No
5827-MWCZK,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.95,5432.2,No
1385-TQOZW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,147.55,No
5914-XRFQB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8424.9,No
4329-YPDDQ,Male,0,No,No,20,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.2,2203.7,No
4804-NCPET,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,387.4,No
3750-CKVKH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,67.75,116.65,Yes
5944-UGLLK,Male,0,No,No,53,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),54.9,3045.75,No
8063-GBATB,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,2287.25,Yes
7787-BNTZM,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,130.5,No
2252-ISRNH,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.35,767.9,No
9415-TPKRV,Female,0,Yes,Yes,8,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.75,446.8,No
5322-TEUJK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.6,8100.25,No
4547-KQRTM,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.05,830.7,Yes
4877-EVATK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,Yes
1866-DIOQZ,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),66.8,4689.15,No
8375-KVTHK,Male,0,Yes,No,68,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.3,6754.35,No
2697-NQBPF,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.35,3540.65,No
3709-OIJEA,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,85.2,2184.6,No
7639-SUPCW,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.8,1054.6,Yes
1386-ZIKUV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,130.55,No
0599-XNYDO,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.8,1540.35,No
6377-WHAOX,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,6411.25,No
6855-VLGOS,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.55,1432.55,No
8999-XXGNS,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.75,7629.85,No
8746-OQQRW,Male,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.25,101.9,No
3181-MIZBN,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,313.4,No
0471-ARVMX,Female,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.85,6312.9,No
7766-CLTIC,Female,0,No,No,10,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,60.95,629.55,No
5650-YLIBA,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,2640.55,No
0825-CPPQH,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.1,1372.45,No
9084-OAYKL,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.8,1212.25,No
6122-LJADA,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.15,6237.05,No
2400-XIWIO,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.1,6310.9,No
7524-VRLPL,Male,1,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),74.1,5031,No
1069-XAIEM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,85.05,Yes
7569-NMZYQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),118.75,8672.45,No
5201-USSQZ,Female,0,Yes,No,26,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),85.9,2196.45,No
2105-PHWON,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95,3008.15,No
1494-EJZDW,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,220.8,No
4884-TVUQF,Female,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101.3,5779.6,No
8003-EWNDZ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.2,222.65,No
1897-RCFUM,Female,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.2,914.6,No
9256-JTBNZ,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.3,246.3,No
4658-HCOHW,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,102.8,2110.15,Yes
2211-RMNHO,Female,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,5560,No
2432-TFSMK,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),89.6,1633,No
3440-JPSCL,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,547.65,Yes
4929-ROART,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.25,969.85,No
1834-ABKHQ,Female,0,Yes,Yes,52,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),50.95,2610.65,No
1741-WTPON,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,115.85,6567.9,No
3932-IJWDZ,Male,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.65,4747.85,No
1240-HCBOH,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.1,1759.55,No
3594-KADLU,Male,0,Yes,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,35.1,101.1,No
8065-BVEPF,Male,1,No,No,65,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.1,6496.15,No
3796-ENZGF,Male,0,Yes,No,63,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.25,4234.15,No
1734-ZMNTZ,Female,0,Yes,Yes,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,300.7,No
2853-CWQFQ,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,59.55,59.55,No
0813-TAXXS,Male,0,No,No,55,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),77.8,4323.35,No
2519-TWKFS,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,55.1,1466.1,No
2889-FPWRM,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),117.8,8684.8,Yes
0626-QXNGV,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.15,1776.45,No
6723-CEGQI,Female,0,No,Yes,65,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Mailed check,45.25,2933.95,No
6987-XQSJT,Female,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.5,4370.25,Yes
4732-RRJZC,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,144.35,No
9499-XPZXM,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),64.75,4804.75,No
4338-EYCER,Male,0,Yes,No,21,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),54.6,1125.2,No
3007-FDPEA,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.7,39.85,No
6350-XFYGW,Male,1,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,422.4,No
4290-BSXUX,Male,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),79.65,251.75,Yes
8513-OLYGY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.8,8332.15,No
0311-QYWSS,Female,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.45,314.6,No
8705-DWKTI,Male,0,No,No,52,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.8,4331.4,No
8755-IWJHN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.35,6382,No
0325-XBFAC,Male,0,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,740.3,Yes
4480-QQRHC,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,600.15,No
0929-PECLO,Female,1,No,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,5538.8,No
3680-CTHUH,Male,0,No,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),116.6,7049.5,No
1202-KKGFU,Female,0,Yes,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.2,690.5,No
6112-KTHFQ,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,279.3,No
1852-XEMDW,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,65.05,1427.55,No
2462-XIIJB,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.5,452.7,Yes
4760-XOHVN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,Yes
1820-DJFPH,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1709.15,No
9426-SXNHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),18.75,53.15,No
9068-FHQHD,Female,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,777.35,No
1269-FOYWN,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20,860.85,No
9470-YFUYI,Male,1,Yes,No,71,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),71,5012.1,No
7817-BOQPW,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,166.3,Yes
7402-EYFXX,Male,1,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,2404.1,No
1853-UDXBW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,70,Yes
9895-VFOXH,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No
5458-CQJTA,Male,0,Yes,Yes,65,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.8,4820.15,Yes
1230-QAJDW,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,65.25,209.9,No
5701-ZIKJE,Male,0,No,No,13,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.55,610.75,No
5219-YIPTK,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,3409.6,Yes
5032-MIYKT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.7,70.7,No
7396-VJUZB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,155.35,No
9717-QEBGU,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,144,No
5172-RKOCB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.95,7875,No
6509-TSGWN,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.45,911.6,No
3540-RZJYU,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,86.2,1270.2,Yes
3178-CIFOT,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.65,478.1,No
2091-MJTFX,Female,0,Yes,Yes,30,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),51.2,1561.5,Yes
4530-NDRKU,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.05,761.85,No
5985-TBABQ,Female,0,No,No,32,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.75,2282.95,No
9800-ONTFE,Female,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.8,1615.1,No
6743-HHQPF,Male,0,Yes,No,42,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,1097.15,No
8455-HIRAQ,Female,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,369.3,No
6616-AALSR,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),104.3,6725.3,No
0883-EIBTI,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,31.55,Yes
9415-ZNBSX,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89,6293.2,No
4018-KJYUY,Male,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.15,432.5,No
0722-TROQR,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,321.75,Yes
7571-YXDAD,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),74.9,147.15,Yes
3884-UEBXB,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),36.15,2434.45,No
8780-RSYYU,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.2,532.1,No
5537-UXXVS,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,375.25,No
1791-PQHBB,Female,0,No,Yes,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),61.2,125.95,No
8701-DGLVH,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,1042.65,No
8741-LQOBK,Female,0,Yes,Yes,46,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),35.05,1620.25,No
3393-FMZPV,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,2387.75,Yes
8082-GHXOP,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44,659.35,No
6402-ZFPPI,Female,1,No,No,25,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.8,2660.2,Yes
0980-FEXWF,Male,0,Yes,Yes,26,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Mailed check,50.35,1285.8,No
7486-KSRVI,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100,4211.55,Yes
3011-WQKSZ,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,377.55,No
8443-WVPSS,Male,0,Yes,No,10,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,990.9,Yes
3006-XIMLN,Female,0,No,Yes,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.2,193.8,Yes
8218-FFJDS,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.4,6058.95,No
1986-PHGZF,Male,1,No,No,18,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),58.4,964.9,No
9965-YOKZB,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.85,790.15,Yes
0504-HHAPI,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),88.3,2467.75,Yes
9360-OMDZZ,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,2322.85,No
6305-YLBMM,Male,0,No,No,69,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.05,7262,Yes
0345-XMMUG,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.9,4854.3,No
8024-XNAFQ,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),107.4,7748.75,No
3647-GMGDH,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.7,1914.9,Yes
2988-GBIVW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.85,6470.1,No
2832-SCUCO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.9,57.4,No
7036-ZZKBD,Male,0,Yes,No,31,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),66.4,2019.8,No
3791-LGQCY,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.65,5688.05,Yes
0265-PSUAE,Female,0,Yes,Yes,16,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.7,1522.7,No
0463-TXOAK,Male,0,No,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1334.5,No
0594-UFTUL,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,252,No
9509-MPYOD,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,700.45,No
9128-CPXKI,Female,0,No,No,59,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,95.8,5655.45,No
0129-KPTWJ,Male,0,Yes,No,72,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,6747.35,No
8166-ZZTFS,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.55,5265.1,Yes
5855-EIBDE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),106.65,5174.35,No
5365-LLFYV,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,105.6,No
5956-YHHRX,Male,1,No,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,2271.85,No
6008-NAIXK,Male,1,No,No,54,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.45,2966.95,No
2956-GGUCQ,Male,1,Yes,No,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.85,1772.25,Yes
1928-BXYIV,Male,0,No,No,1,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,61.15,61.15,No
5760-FXFVO,Male,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,494.95,No
6595-COKXZ,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.45,44.45,Yes
0961-ZWLVI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.2,5290.45,No
5181-OABFK,Female,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),61.3,3346.8,No
4007-NHVHI,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,96.85,5219.65,No
7816-VGHTO,Female,0,Yes,Yes,6,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Mailed check,40.55,217.5,No
5871-DGTXZ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,607.7,No
7550-WIQVA,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.25,5431.4,No
7544-ZVIKX,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6004.85,No
0016-QLJIS,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.45,5957.9,No
7508-DQAKK,Female,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),86.4,5442.05,No
5176-OLSKT,Female,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),66.9,4370.25,No
9356-AXGMP,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.7,4428.6,No
3556-BVQGL,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,416.45,No
0362-ZBZWJ,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,3067.2,Yes
8979-CAMGB,Male,1,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,102.1,6688.1,No
4211-MMAZN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,Yes
7581-EBBOU,Female,0,No,No,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,4224.7,No
4274-OWWYO,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes
5073-RZGBK,Female,0,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),80.05,4042.2,No
1541-ETJZO,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,62.05,62.05,Yes
2192-CKRLV,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,49.2,3580.95,No
3154-HMWUU,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.5,1198.8,No
6119-SPUDB,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.25,1755.35,No
6522-OIQSX,Female,0,Yes,Yes,69,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),54.95,3772.5,No
7813-ZGGAW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),96.6,2877.95,No
8748-HFWBO,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,357.7,No
1052-QJIBV,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1397.3,No
1319-YLZJG,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.6,959.9,No
4106-HADHQ,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80,3182.95,Yes
0723-FDLAY,Male,0,No,No,44,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),85.25,3704.15,No
3050-RLLXC,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),81.25,4620.4,No
9298-WGMRW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.5,8312.75,No
2369-UAPKZ,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,104.1,541.9,Yes
3088-LHEFH,Female,0,No,No,11,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79,929.3,No
2778-OCLGR,Male,1,Yes,No,24,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),39.1,971.3,Yes
6583-KQJLK,Female,1,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,1285.05,No
8544-JNBOX,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1521.2,No
6681-ZSEXG,Male,0,Yes,No,56,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),59.5,3389.25,No
6139-ZZRBQ,Male,1,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1198.05,No
6116-RFVHN,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.45,3414.65,No
0637-KVDLV,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,162.45,Yes
8884-FEEWR,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,754,No
3125-RAHBV,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.3,467.15,No
6633-MPWBS,Male,0,No,No,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.2,216.9,Yes
0373-AIVNJ,Male,0,No,No,9,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,39.55,373,No
8785-EPNCG,Male,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),23.15,245.2,Yes
1682-VCOIO,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,481.1,No
5729-KLZAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.85,302.75,Yes
2072-ZVJJX,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.25,1728.2,No
8849-AYPTR,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.25,2964.05,No
9518-IMLHK,Male,0,No,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.45,2156.25,No
1582-RAFML,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.1,60.1,Yes
3646-ITDGM,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1051.9,No
8740-CRYFY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.95,78.95,Yes
3569-VLDHH,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,75.1,5013,No
8224-KDLKN,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1738.9,No
7594-LZNWR,Male,1,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.15,2275.1,No
4001-TSBTV,Female,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.55,5511.65,No
2962-XPMCQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.15,98.5,Yes
8727-XDPUD,Male,0,No,No,37,No,No phone service,DSL,No,No,No,No,Yes,No,Two year,No,Credit card (automatic),35.8,1316.9,No
1043-UXOVO,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.15,7993.3,No
1064-FBXNK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,19.85,No
3996-ZNWYK,Male,1,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1388.45,No
2878-DHMIN,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,666,No
7762-ONLJY,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,94.45,No
1678-FYZOW,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.4,244.65,Yes
5795-BKOYE,Female,0,Yes,Yes,69,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),59.1,4134.7,No
8627-EHGIP,Male,0,No,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,53.95,2375.4,Yes
7402-PWYJJ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,91.15,4862.5,No
5480-TBGPH,Female,0,Yes,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,2431.35,Yes
0018-NYROU,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,351.5,No
7501-VTYLJ,Female,0,No,Yes,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,51.55,106.2,No
7608-RGIRO,Male,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.4,1413,No
0480-BIXDE,Female,0,Yes,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,1743.05,No
5895-QSXOD,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.05,657.5,No
9814-AOUDH,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1050.5,No
6175-IRFIT,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,78.75,426.35,No
8760-ZRHKE,Female,1,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,69.2,4982.5,No
1622-HSHSF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,19.55,No
8854-CCVSQ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.65,1451.9,Yes
6749-UTDVX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.65,7634.8,No
5542-DHSXL,Female,0,Yes,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.7,235.05,No
9102-OXKFY,Male,0,No,No,59,Yes,Yes,DSL,No,No,Yes,No,No,No,Two year,No,Credit card (automatic),54.15,3116.15,No
0511-JTEOY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,Yes
5216-WASFJ,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.85,2633.4,No
7808-DVWEP,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,49.65,No
7067-KSAZT,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.25,6979.8,Yes
2284-VFLKH,Male,0,Yes,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.25,4920.8,No
0366-NQSHS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,46.35,No
6532-YLWSI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.8,1021.8,Yes
3807-BPOMJ,Female,0,Yes,No,55,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,94.75,5276.1,No
3892-NXAZG,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8289.2,No
4948-WBBKL,Female,1,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2659.45,No
4182-BGSIQ,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.8,196.75,No
9300-AGZNL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,94,Yes
0988-AADSA,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.85,5824.75,No
9972-NKTFD,Female,0,No,No,28,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1517.5,No
3717-LNXKW,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),91.7,3479.05,No
5734-EJKXG,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,118.6,7365.7,No
2207-RYYRL,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1331.05,No
6729-FZWSY,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,1195.95,No
9695-IDRZR,Female,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.15,3946.9,No
8144-DGHXP,Female,0,No,No,54,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,80.6,4299.95,No
7814-LEEVE,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,20.3,No
1112-CUNAO,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.85,1424.95,Yes
5175-AOBHI,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46,193.6,Yes
5174-RNGBH,Female,0,No,No,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,66.25,620.55,Yes
8631-WUXGY,Female,0,No,Yes,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4391.25,No
7270-BDIOA,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90,1993.8,Yes
9565-JSNFM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.45,2597.6,Yes
5906-DVAPM,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),75,4213.9,No
6654-QGBZZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No
8348-JLBUG,Male,1,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),80.3,5017.7,No
0607-DAAHE,Male,0,No,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1052.35,Yes
5641-DMBFJ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.3,4916.4,No
9200-NLNPD,Male,0,Yes,No,56,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.05,2959.8,No
2664-XJZNO,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.9,7559.55,No
9732-KPKBW,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.95,53.95,Yes
3339-EAQNV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),97.25,7133.1,No
0921-OHLVP,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,83.05,1799.3,No
3389-YGYAI,Female,1,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,829.55,Yes
9560-ARGQJ,Female,0,No,Yes,16,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,1312.15,Yes
8350-NYMVI,Female,0,No,No,39,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),41.1,1597.05,No
1600-DILPE,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,524.35,No
0536-ESJEP,Male,0,Yes,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.55,4191.45,No
4654-GGUII,Female,0,No,No,18,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,40.2,711.95,No
6478-HRRCZ,Male,0,Yes,No,32,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.5,2201.75,No
8510-BBWMU,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,806.95,No
6857-TKDJV,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1620.45,No
2360-RDGRO,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,6812.95,No
0584-BJQGZ,Female,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.35,1837.9,No
5134-IKDAY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes
1360-XFJMR,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),109.7,7344.45,No
3070-DVEYC,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,73.75,545.15,Yes
5730-RIITO,Female,1,No,No,43,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),33.45,1500.25,No
9058-MJLZC,Female,0,No,No,24,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,2283.15,No
5707-ORNDZ,Male,1,No,No,9,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.55,494.05,Yes
0902-XKXPN,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1376.5,No
5177-RVZNU,Female,0,No,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.3,755.4,No
0056-EPFBG,Male,0,Yes,Yes,20,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.4,825.4,No
8993-IZEUX,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.15,488.65,No
8696-JKZNU,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,2841.55,Yes
5380-AFSSK,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.9,486.85,Yes
8190-ZTQFB,Male,0,No,No,41,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,51.35,2075.1,No
0931-MHTEM,Female,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,5299.65,No
2055-PDADH,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.4,204.7,Yes
7515-LODFU,Male,1,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1356.3,No
0440-UEDAI,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),94.45,5042.75,No
1337-BOZWO,Male,0,Yes,Yes,18,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),46.4,812.4,No
0868-VJRDR,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.05,6605.55,No
8714-EUHJO,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,91.15,2995.45,Yes
6344-SFJVH,Female,0,No,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),24.9,505.95,No
3950-VPYJB,Male,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,59.6,3509.4,No
8041-TMEID,Male,1,Yes,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),108.5,6991.9,No
7321-ZNSLA,Male,0,Yes,Yes,13,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,40.55,590.35,No
6941-KXRRV,Female,1,Yes,No,48,Yes,No,DSL,No,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),58.95,2789.7,No
3721-CNEYS,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.95,137.95,Yes
8727-JQFHV,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1123.15,No
9475-NNDGC,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.15,7953.25,No
1355-KUSBG,Female,0,Yes,Yes,7,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,48.8,349.8,No
3688-FTHLT,Female,0,No,No,16,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),63.05,1067.05,No
0899-LIIBW,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,3527.3,No
2568-OIADY,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,3762,Yes
1384-RCUXW,Male,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.55,1248.9,No
5825-XJOCM,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),64.4,3035.35,No
6848-YLDFR,Male,0,Yes,Yes,58,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),75.2,4300.8,No
8125-QPFJD,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.9,6065.3,No
2320-YKQBO,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,144.95,No
1193-RTSLK,Female,0,No,No,38,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.9,3233.6,Yes
2302-ANTDP,Female,1,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,117.45,5438.9,Yes
5923-GXUOC,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,1081.45,Yes
4973-RLZVI,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74.65,2308.6,No
7869-ZYDST,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.05,1882.8,No
4501-UYKBC,Female,1,No,No,46,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),69.1,3255.35,No
1215-EXRMO,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1067.65,No
2305-MRGLV,Male,0,Yes,No,28,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),76.55,2065.4,No
8404-VIOMB,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),62.5,4136.4,No
5233-GEEAX,Male,1,No,No,8,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.4,221.9,Yes
2359-KLTEK,Female,0,Yes,Yes,41,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.9,3848,No
5304-EFJLP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.65,8022.85,No
2673-ZALNP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,173.15,No
9184-GALIL,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,781.25,No
4393-RYCRE,Male,0,No,No,44,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.05,4510.8,No
9746-MDMBK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.45,5317.8,No
3162-ZJZFU,Male,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,92.55,4779.45,No
8404-GFGCZ,Male,0,Yes,No,4,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,65.6,250.1,No
8875-AKBYH,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,1745.2,No
4432-ADRLB,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.65,74.9,Yes
0533-UCAAU,Male,1,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),71.1,4140.1,No
1394-SUIUH,Female,1,Yes,No,44,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.15,3670.5,No
3521-MNKLV,Male,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.7,1167.8,No
2533-TIBIX,Male,0,Yes,Yes,15,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,469.65,No
8993-PHFWD,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,58.9,Yes
1565-RHDJD,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),84.05,333.55,Yes
7137-RYLPP,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,3171.15,Yes
3765-JXVKY,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
5092-STPKP,Female,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),56.35,1381.2,No
7330-WZLNC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.8,455.5,Yes
0114-PEGZZ,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,107.55,3645.5,No
3359-DSRKA,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,1158.85,No
8639-NHQEI,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),95.9,6954.15,No
7161-DFHUF,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,1672.1,No
3957-LXOLK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,3152.5,Yes
3720-DBRWL,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.85,4307.1,No
6635-MYYYZ,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.35,2530.4,Yes
8565-WUXZU,Male,1,Yes,No,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.8,6141.65,No
5281-BUZGT,Male,1,No,No,36,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.85,3186.7,Yes
4994-OBRSZ,Male,0,No,Yes,14,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.1,1054.8,No
0562-FGDCR,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Mailed check,74.55,5430.65,No
2436-QBZFP,Female,0,Yes,Yes,22,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,39.2,849.9,No
3420-YJLQT,Female,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.55,151.75,No
6040-CGACY,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,299.4,No
6582-PLFUU,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1086.75,No
8242-PDSGJ,Male,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.15,2692.75,No
0264-CNITK,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1389.6,No
0089-IIQKO,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.95,3767.4,No
7839-NUIAA,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59.8,3641.5,No
6075-QMNRR,Female,0,No,No,52,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.75,2535.55,No
5378-IKEEG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.75,35.75,Yes
5966-EMAZU,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,108.5,6880.85,No
0440-EKDCF,Male,0,Yes,No,62,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),60.15,3753.2,No
4774-HHGGS,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,637.55,No
2718-GAXQD,Female,1,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,181.6,Yes
2055-BFOCC,Male,1,Yes,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84,5329.55,No
8180-AKMJV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,44.55,No
4298-OYIFC,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,1539.8,No
5566-SOEZD,Male,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),80.65,2209.75,No
9842-EFSYY,Female,0,No,Yes,4,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,57.2,223.75,No
2272-WUSPA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.75,7751.7,No
4584-LBNMK,Male,1,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.7,1174.35,No
3898-GUYTS,Female,1,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,97.05,4385.05,No
0930-EHUZA,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,76.35,2606.35,No
6413-XKKPU,Male,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,1539.45,Yes
9975-SKRNR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.9,18.9,No
3703-KBKZP,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.45,1261.35,No
1449-XQEMT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,58.15,Yes
2626-URJFX,Male,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.9,225.6,Yes
4973-MGTON,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,5969.3,No
3682-YEUWS,Male,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.4,253.9,No
1223-UNPKS,Male,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,400,No
2612-RRIDN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,340.85,Yes
4735-ASGMA,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,2515.3,Yes
3446-QDSZF,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),55.5,227.35,No
3669-LVWZB,Male,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,51,305.95,Yes
6892-EZDTG,Female,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,365.4,Yes
5117-IFGPS,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.3,2357.75,No
7379-FNIUJ,Male,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.2,198.5,No
1627-AFWVJ,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,554.25,No
9725-SCPZG,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.85,90.85,Yes
5884-GCYMI,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.4,69.4,Yes
3217-FZDMN,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.45,742.95,Yes
4486-EFAEB,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.4,251.65,No
0060-FUALY,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.75,5597.65,No
7853-GVUDZ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,20.15,Yes
7480-QNVZJ,Male,1,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4816.7,Yes
6954-OOYZZ,Male,0,Yes,No,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,768.05,No
7088-FBAWU,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,74.55,1215.8,No
8313-AFGBW,Male,0,Yes,No,47,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,73.6,3522.65,No
3943-KDREE,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,1834.95,Yes
9239-ZBZZV,Female,0,No,No,6,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,47.95,305.1,Yes
3097-IDVPU,Male,0,Yes,Yes,19,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),50.1,910.45,No
4398-HSCJH,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,63.6,155.65,Yes
2197-OMWGI,Female,1,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,53,3656.25,No
2303-PJYHN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.85,52,No
9795-VOWON,Male,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),24.35,150.85,No
1237-WIYYZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,389.25,No
1987-AUELQ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1873.7,No
7852-LECYP,Male,1,Yes,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),93.8,1261,No
4430-UZIPO,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36.85,108.7,Yes
4822-LPTYJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),103.75,7346.2,No
8165-CBKXO,Male,0,Yes,Yes,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),56.75,3708.4,No
6527-PZFPV,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.8,469.65,No
4855-SNKMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.1,44.1,Yes
9593-CVZKR,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.45,1385.85,No
8595-SIZNC,Female,1,Yes,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.6,548.8,No
8008-HAWED,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,712.75,Yes
7124-UGSUR,Female,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.4,6405,Yes
1862-SKORY,Female,1,Yes,No,40,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,1637.4,Yes
5236-XMZJY,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),59.65,2536.55,No
4827-DPADN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.3,6042.7,No
2694-CIUMO,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.55,958.25,No
5846-ABOBJ,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1730.65,No
1439-LCGVL,Female,0,Yes,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.2,459.6,No
0909-SDHNU,Female,0,No,Yes,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.8,201.95,No
4647-XXZAM,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.5,285.2,No
1502-XFCVR,Female,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.45,6145.85,Yes
7740-KKCXF,Male,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.05,1529.45,No
5360-XGYAZ,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.65,4664.5,No
9692-TUSXH,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1740.7,Yes
7912-SYRQT,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.1,552.95,Yes
3557-HTYWR,Female,0,No,No,47,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,74.05,3496.3,No
4816-JBHOV,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.7,93.7,Yes
8920-NAVAY,Male,1,No,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.75,7053.35,No
1699-TLDLZ,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,301.55,No
5600-PDUJF,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.5,312.7,No
8292-TYSPY,Male,0,No,No,19,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55,1046.5,Yes
0567-XRHCU,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.95,2960.1,No
1867-BDVFH,Male,0,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,834.2,Yes
2067-QYTCF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.15,6953.4,No
2359-QWQUL,Female,0,Yes,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.7,4134.85,Yes
9103-TCIHJ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.7,899.8,Yes
7407-SUJIZ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,541.5,No
9150-KPBJQ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,116.85,No
0052-DCKON,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),115.8,7942.15,No
3654-ARMGP,Female,0,No,No,61,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.65,5321.25,No
9699-UBQFS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.5,4156.8,No
9367-TCUYN,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,223.6,No
1261-FWTTE,Male,1,No,No,23,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.65,768.45,No
3528-HFRIQ,Male,1,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),52.3,3765.05,No
0708-SJDIS,Female,0,No,No,34,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,65,2157.5,No
4140-WJAWW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,108.05,Yes
2073-QBVBI,Female,0,Yes,No,41,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,35.45,1391.65,No
6928-ONTRW,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1379.8,No
3320-VEOYC,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,1273.3,No
5231-FIQPA,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,810.45,No
6617-WLBQC,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Credit card (automatic),81.85,1810.85,No
2599-CIPQE,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.3,7782.85,No
6653-CBBOM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
8774-GSBUN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1797.1,No
7326-RIGQZ,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,377.85,No
1401-FTHFQ,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20,445.3,No
3247-ZVOUO,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.55,851.75,Yes
0254-FNMCI,Female,0,No,No,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.9,7624.2,No
1848-LBZHY,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.3,355.1,No
6101-IMRMM,Male,0,No,Yes,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,94.5,575.45,Yes
8118-TJAFG,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,906.85,No
5429-LWCMV,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,1057.55,No
7298-IZWLY,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No
9758-MFWGD,Female,1,Yes,Yes,48,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),29.9,1388.75,No
3955-JBZZM,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,1641.3,No
1268-ASBGA,Female,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.35,1375.15,Yes
8943-URTMR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,79.65,152.7,Yes
4815-TUMEQ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.3,185.2,No
4713-LZDRV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,195.05,Yes
9909-IDLEK,Male,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,96.8,1826.7,No
4092-OFQZS,Male,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,417.5,No
1561-BWHIN,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,344.5,No
4325-NFSKC,Male,1,No,No,19,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,1660,Yes
9927-DSWDF,Male,0,Yes,No,22,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.6,2180.55,No
9500-LTVBP,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.05,2835.9,No
7252-NTGSS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.15,45.15,No
8149-AIQCG,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.15,2730.85,No
1360-JYXKQ,Female,1,Yes,No,54,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.1,5437.1,No
2955-PSXOE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes
7762-URZQH,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.05,6981.35,Yes
0899-WZRSD,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105.35,5794.45,No
3255-GRXMG,Male,0,No,Yes,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.65,747.2,No
4828-FAZPK,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.95,1267.95,No
6094-ZIVKX,Female,0,No,No,68,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),54.45,3674.95,No
6925-BAYGL,Female,1,Yes,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1275.6,No
3262-EIDHV,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.7,5893.9,No
7354-OIJLX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,724.65,No
1376-HHBDV,Female,0,No,No,30,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,1536.75,No
6907-NZZIJ,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,3615.6,Yes
6133-OZILE,Female,0,No,No,18,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,35.2,607.3,No
4135-FRWKJ,Female,1,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Electronic check,76.25,4154.55,No
2911-UREFD,Female,0,Yes,No,39,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,55.9,2184.35,Yes
3744-ZRRDZ,Male,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.35,1852.85,Yes
5673-TIYIB,Male,0,No,No,2,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),40.4,77.15,Yes
6551-ZCOTS,Male,1,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.9,847.8,No
7191-ADRGF,Male,0,Yes,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),54.3,2390.45,No
5018-LXQQG,Female,0,Yes,Yes,30,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.3,1923.5,No
6892-BOGQE,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.9,1493.2,No
1602-IJQQE,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,338.1,Yes
4628-WQCQQ,Male,0,No,Yes,35,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.15,3030.6,Yes
1746-TGTWV,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.35,75.35,No
5995-SNNEW,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,2184.85,No
8050-WYBND,Female,0,No,Yes,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),49.45,1031.4,No
2821-WARNZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.45,921.3,No
4879-GZLFH,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.15,3875.4,No
1644-IRKSF,Female,0,Yes,Yes,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,3124.5,Yes
8314-HTWVE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,144.15,No
9402-ROUMJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),100.25,6689,No
2507-QZPQS,Male,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,95.7,1451.1,No
7159-NOKYQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,93.15,6368.2,No
5707-ZMDJP,Male,0,Yes,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,69.7,3729.6,No
8779-YIQQA,Male,0,Yes,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,350.1,No
7136-IHZJA,Female,0,Yes,Yes,40,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.35,2847.2,No
8966-OIQHG,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.75,452.35,No
7074-STDCN,Male,0,No,No,39,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.6,1494.5,No
3705-PSNGL,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,930.45,Yes
8739-QOTTN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,41.85,No
1399-OUPJN,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1272.05,No
0277-BKSQP,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.4,475.1,No
6502-HCJTI,Male,1,Yes,No,7,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.7,673.1,Yes
2606-RMDHZ,Male,0,No,No,6,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.5,208.7,Yes
5774-XZTQC,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,150.75,No
2676-SSLTO,Male,0,No,No,49,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),66.15,3199,No
6266-QHOJZ,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,89.85,5844.65,No
7269-JISCY,Female,0,No,No,55,Yes,No,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),45.05,2462.6,No
0363-SVHYR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.85,6263.8,No
8547-NSBBO,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,96.75,3403.4,No
8258-GSTJK,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77,237.75,Yes
6861-OKBCE,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.1,221.35,Yes
9940-RHLFB,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.3,75.3,Yes
6591-QGOYB,Male,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),106.65,1672.1,No
9070-BCKQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.15,7881.2,No
6421-SZVEM,Female,0,Yes,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),82.85,2320.8,No
1328-EUZHC,Female,0,Yes,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,370.5,No
2995-UPRYS,Female,1,Yes,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.2,4062.2,Yes
8878-RYUKI,Female,0,No,No,52,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,3043.7,No
9633-DENPU,Female,0,Yes,No,47,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),58.6,2723.4,No
7811-JIVPF,Female,0,No,No,23,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),49.7,1081.25,No
7113-HIPFI,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.85,4097.05,No
8541-QVFKM,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,632.2,No
6686-YPGHK,Male,1,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.5,4042.3,Yes
1383-EZRWL,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,164.85,Yes
9258-CNWAC,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.65,8166.8,No
5371-VYLSX,Female,1,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,83.4,4113.7,No
6374-AFWOX,Male,0,Yes,No,46,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.65,3047.15,No
4759-TRPLW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,70.4,Yes
5317-FLPJF,Female,0,No,No,66,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.35,4193.4,No
7621-VPNET,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.9,3729.75,No
6034-YMTOB,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,75.65,399.45,No
8563-IIOXK,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,49.75,331.3,Yes
4903-CNOZC,Male,0,No,No,29,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),70.9,1964.6,No
4353-HYOJD,Female,0,Yes,Yes,27,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.85,1336.15,No
8020-BWHYL,Female,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1147.45,Yes
7267-FRMJW,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,486.05,No
2982-VPSGI,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,1078.9,Yes
4188-FRABG,Male,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,103.05,5925.75,No
8199-ZLLSA,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),118.35,7804.15,Yes
4128-ETESU,Female,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.7,4747.2,No
0620-DLSLK,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,81.9,1028.9,No
4625-EWPTF,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,30.45,226.45,Yes
5980-NOPLP,Female,0,Yes,No,44,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,4364.1,Yes
3850-OKINF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Electronic check,66.2,4692.55,No
6892-XPFPU,Male,1,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,2433.9,Yes
8010-EZLOU,Male,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.2,1217.25,Yes
1156-ZFYDO,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No
3295-YVUSR,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,72.6,154.3,No
8016-NCFVO,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,116.5,6382.55,No
4119-ZYPZY,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,106.8,7623.2,No
5549-ZGHFB,Male,0,Yes,Yes,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.95,1261.45,No
7577-SWIFR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.25,89.25,No
0303-WMMRN,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,86.05,No
6408-OTUBZ,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),104.55,6779.05,No
5204-HMGYF,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,87.2,4345,No
1078-TDCRN,Female,1,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.75,82.85,No
3727-OVPRY,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.7,1714.55,No
3797-FKOGQ,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.2,893.2,No
7622-NXQZR,Male,0,No,No,28,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),30.1,810.85,No
6196-HBOBZ,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99.35,6347.55,No
3970-XGJDU,Female,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.2,1123.65,No
7017-VFULY,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.1,43.15,No
5562-YJQGT,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,35.1,No
8807-OPMBM,Female,0,Yes,Yes,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.65,1388,No
5439-WIKXB,Male,1,Yes,No,41,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,3851.45,No
9874-QLCLH,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,1743.5,Yes
8294-UIMBA,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),94.4,2638.1,No
8109-YUOHE,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.1,946.95,No
5840-NVDCG,Female,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),68.25,1114.85,No
8092-NLTGF,Male,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1777.6,No
5928-QLDHB,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,684.85,No
9840-EFJQB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.35,74.35,No
0696-UKTOX,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,54.15,1312.45,No
4801-KFYKL,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,159.2,No
3472-OAOOR,Male,0,Yes,Yes,19,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,34.95,610.2,No
6135-OZQVA,Female,0,No,No,7,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.65,404.35,No
8062-YBDOE,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
2252-JHJGE,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,104,6363.45,No
4188-PCPIG,Female,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.35,4124.65,No
6000-APYLU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,713.1,Yes
7105-BENQF,Male,0,No,Yes,15,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,950.75,No
7721-DVEKZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
3566-VVORZ,Female,0,Yes,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.9,505.95,No
9507-HSMMZ,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,1149.65,No
2480-SQIOB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.8,169.65,No
0947-IDHRQ,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.3,526.7,Yes
7813-TKCVO,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,393.15,No
0128-MKWSG,Female,0,No,Yes,26,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,45.8,1147,No
3672-YITQD,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,3021.6,Yes
4350-ZTLPI,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.95,5718.2,No
9048-JVYVF,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,191.35,Yes
0361-HJRDX,Female,0,No,No,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),64.35,4539.6,No
5727-MYATE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6397.6,No
3823-KYNQY,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.95,280.4,No
8988-ECPJR,Female,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.6,2718.3,Yes
7570-WELNY,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.7,5711.05,No
3021-VLNRJ,Female,0,No,Yes,50,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.8,3478.15,No
3776-EKTKM,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,36.45,36.45,Yes
2080-CAZNM,Female,1,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,4133.95,No
1028-FFNJK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,2917.65,No
6907-FLBER,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.3,54.3,No
3001-UNBTL,Male,1,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,2964.8,No
5982-PSMKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.1,2198.3,No
3507-GASNP,Male,0,No,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1189.9,No
7096-ZNBZI,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.45,1914.5,No
1902-XBTFB,Male,0,No,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,2001.5,Yes
1676-MQAOA,Male,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.1,5336.35,No
0786-IVLAW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),108.1,7238.6,No
7566-DSRLQ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.15,7998.8,No
0643-OKLRP,Female,1,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,3825.85,Yes
7245-JMTTQ,Female,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.5,5703.25,No
6050-IJRHS,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,7397,No
6202-JVYEU,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.9,164.6,No
8591-TKMZH,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,111.1,6555.2,No
0734-OXWBT,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.7,225.65,No
4282-ACRXS,Male,1,Yes,No,38,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.85,955.75,No
0365-TRTPY,Female,0,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.2,3382.3,No
7349-ALMUX,Male,0,No,No,37,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),65.6,2313.8,No
9381-NDKME,Female,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.65,933.3,Yes
6502-KUGLL,Female,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.45,780.85,No
1841-YSJGV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.95,7852.4,No
2794-XIMMO,Male,0,Yes,No,53,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,60.45,3184.25,Yes
8382-SHQEH,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,764.95,Yes
3511-BFTJW,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),38.5,2763,No
7668-XCFYV,Female,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1614.7,No
2983-ZANRP,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.55,145.4,Yes
7845-URHJN,Female,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,156.25,Yes
3034-ZBEQN,Female,0,Yes,No,48,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,34.7,1604.5,Yes
5018-HEKFO,Female,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.5,270.15,No
2923-ARZLG,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7, ,No
3976-NLDEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,No
2282-YGNOR,Female,0,No,No,29,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),58,1734.5,No
5336-UFNZP,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.45,7047.5,No
6854-EXGSF,Female,0,No,No,8,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),65.5,573.15,No
1241-EZFMJ,Male,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1538.6,No
9233-PSYHO,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.15,4459.8,No
5376-PCKNB,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.45,7459,No
8044-BGWPI,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.15,306.05,No
4060-LDNLU,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,96.2,639.7,No
3589-PPVKW,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.4,348.15,No
8327-LZKAS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.55,4533.9,Yes
5887-IKKYO,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),94.35,5563.65,No
1075-BGWOH,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,1587.55,Yes
1755-RMCXH,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,40.25,No
0302-JOIVN,Female,0,Yes,No,8,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,842.9,Yes
3858-XHYJO,Female,0,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),105.75,4228.55,No
4299-SIMNS,Male,0,No,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,784.45,No
7025-IWFHT,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.55,3729.75,No
6261-LHRTG,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1406.9,No
7841-FCRQD,Female,0,Yes,No,33,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),53.75,1857.3,No
2056-EVGZL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),105.75,7322.5,No
8777-MBMTS,Female,1,Yes,No,65,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),105.85,6725.5,No
7753-USQYQ,Male,0,No,No,55,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,64.2,3627.3,No
5366-IJEQJ,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.7,1761.45,Yes
7661-CPURM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),87.7,1725.95,No
6233-HXJMX,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),89.3,4192.15,No
5902-WBLSE,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.15,1411.2,No
1981-INRFU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.75,164.5,Yes
4971-PUYQO,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,94.55,2724.6,Yes
3239-TPHPZ,Female,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,20.05,264.55,No
5115-GZDEL,Male,0,No,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),67.2,4671.7,No
3338-CVVEH,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.55,1173.55,No
8485-GJCDN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,318.5,Yes
2615-YVMYX,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,7713.55,No
2851-STERV,Male,1,No,No,35,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,73,2471.25,No
4393-GEADV,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.75,7842.3,No
8486-AYEQH,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,2227.8,No
1527-SXDPN,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),96.25,4990.25,Yes
6029-WTIPC,Male,1,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,3744.05,Yes
8634-CILSZ,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.7,7220.35,Yes
5419-JKZNQ,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,77.9,2351.45,No
2495-KZNFB,Female,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,2989.6,No
1409-PHXTF,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.45,6077.75,No
4560-WQAQW,Female,0,No,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),68.7,4070.95,No
9591-YVTEB,Male,1,No,No,55,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.85,2479.05,No
9309-BZGNT,Male,1,Yes,No,69,No,No phone service,DSL,No,No,Yes,No,No,No,One year,Yes,Credit card (automatic),29.8,2134.3,No
4274-DRSQT,Female,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.9,6000.1,No
2027-DNKIV,Male,0,Yes,Yes,37,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,58.75,2203.1,No
8075-GXIUB,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,183.15,No
2885-HIJDH,Male,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.9,6194.1,No
5424-RLQLC,Male,0,No,No,10,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,59.65,638.95,No
6682-QJDGB,Male,0,No,Yes,40,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.25,2139.2,Yes
6507-DTJZV,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),66.4,831.75,No
8780-IXSTS,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,521.3,Yes
7673-BQGKU,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,1337.5,No
6723-WSNTY,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.1,7181.95,No
9530-EHPOH,Male,0,No,No,11,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.75,608,Yes
2725-IWWBA,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Mailed check,56.9,2560.1,No
0345-HKJVM,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.3,577.6,Yes
1061-PNTHC,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,109.6,5953,No
0394-YONDK,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1790.15,No
6574-MCOEH,Female,0,Yes,Yes,33,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.15,2531.4,No
7399-QHBJS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),66.75,4760.3,No
3049-SOLAY,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.2,292.85,Yes
0997-YTLNY,Female,0,No,Yes,19,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,48.8,953.65,No
3317-HRTNN,Female,1,No,No,5,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.7,198,Yes
9479-HYNYL,Female,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),80.7,5705.05,No
3235-ETOOB,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,609.9,Yes
9708-KFDBY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,20.55,No
8058-INTPH,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.65,79.65,Yes
3642-GKTCT,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,115.1,6993.65,No
0774-RMNUW,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.7,4122.65,No
1334-PDUKM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),86.45,5762.95,No
0756-MPZRL,Male,0,No,No,46,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),33.7,1537.85,No
2242-MFOTG,Male,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),80.1,2603.3,No
2927-CVULT,Female,0,Yes,Yes,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),104.05,5566.4,No
2144-BFDSO,Female,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),108.75,5431.9,No
8745-PVESG,Female,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),41.1,2258.25,No
7647-GYYKX,Female,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.35,1092.35,No
5647-FXOTP,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6401.25,No
5569-OUICF,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.3,2812.2,Yes
8821-XNHVZ,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.05,80.05,Yes
2082-OJVTK,Male,0,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.2,2698.35,Yes
9700-ISPUP,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,65.5,616.9,No
9839-ETQOE,Male,0,No,Yes,43,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,40.45,1912.85,No
6078-VESFR,Male,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.45,849.1,No
9027-TMATR,Female,0,Yes,No,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,78.8,3460.3,No
5940-NFXKV,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.65,1465.75,Yes
1941-HOSAM,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.1,90.1,No
9110-HSGTV,Female,0,No,No,69,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5555.3,No
0704-VCUMB,Female,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1278.8,No
6171-ZTVYB,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),66.25,2907.35,No
8053-WWDRO,Female,0,Yes,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,146.3,Yes
9564-KCLHR,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.25,51.25,Yes
1935-IMVBB,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.7,4952.95,No
2535-PBCGC,Female,0,Yes,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.55,4504.9,No
2082-CEFLT,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.6,45.6,Yes
1470-PSXNM,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.65,4520.15,No
1213-NGCUN,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),49.65,267.35,Yes
2498-XLDZR,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,73.6,2316.85,No
9866-OCCKE,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,109.75,8075.35,No
5338-YHWYT,Male,0,No,Yes,37,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),61.45,2302.35,No
7718-UPSKJ,Female,0,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.4,7251.9,No
8731-WBBMB,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.9,2078.55,No
1448-CYWKC,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.2,6225.4,No
7901-IIDQV,Male,0,No,No,24,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.6,1242.25,No
2690-DVRVK,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,99.45,No
5688-KZTSN,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,288.05,Yes
1270-XKUCC,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,599.25,No
0334-ZFJSR,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.05,3462.1,No
2894-QOJRX,Female,0,Yes,No,25,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),34,853,Yes
5747-PMBSQ,Male,1,Yes,No,10,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,92.5,934.1,Yes
5583-EJXRD,Male,0,Yes,Yes,44,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),54.05,2375.2,No
4565-EVZMJ,Female,0,No,No,47,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,58.9,2813.05,No
3143-JQEGI,Female,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.35,1222.8,Yes
6386-SZZKH,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.95,5293.2,Yes
3327-YBAKM,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,96.9,6314.35,No
9441-QHEVC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No
6705-LNMDD,Male,0,No,No,20,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50,1003.05,No
2580-ASVVY,Female,0,Yes,No,37,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,45.4,1593.1,No
3370-GQEAL,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.45,2509.95,No
5032-USPKF,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),84.1,3187.65,No
2982-IHMFT,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,74.45,Yes
3521-SYVOR,Female,0,No,No,37,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.75,2345.2,Yes
4254-QPEDE,Female,0,Yes,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),66.25,3330.1,No
6283-GITPX,Male,0,No,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),76.9,5522.7,No
9526-JAWYF,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,89.8,2335.3,Yes
0771-CHWSK,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),74.6,4798.4,No
9788-HNGUT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.95,8594.4,No
9495-REDIY,Male,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),40.65,970.55,No
5375-XLDOF,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.35,7665.8,No
1172-VIYBP,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),69.7,3686.05,No
5649-VUKMC,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Mailed check,95.5,1115.15,Yes
6559-PDZLR,Male,0,No,No,26,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.65,2537,No
8992-OBVDG,Male,0,No,No,21,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,61.65,1393.6,No
4273-MBHYA,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,89.35,No
4724-WXVWF,Male,0,No,No,48,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,95.4,4445.3,No
5701-GUXDC,Female,0,Yes,No,26,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,978.6,No
1460-UZPRJ,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1258.15,No
6082-GLJIX,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,331.35,No
5143-EGQFK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.65,291.4,Yes
4919-IKATY,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,453.75,Yes
2495-TTHBQ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.4,84.75,No
0485-ZBSLN,Male,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1715.1,No
3810-PJUHR,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35,1715.15,No
9050-QLROH,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.7,1597.25,Yes
0847-HGRML,Male,0,No,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20,1250.1,No
8232-CTLKO,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,No,No,No,Yes,No,Two year,Yes,Electronic check,59.75,3996.8,No
9227-YBAXE,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),82.5,5215.1,No
6168-WFVVF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,235.5,Yes
9860-LISIZ,Female,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,673.2,No
0112-QAWRZ,Male,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.8,1442.2,No
0877-SDMBN,Female,0,No,No,54,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.95,5639.05,Yes
2786-GCDPI,Female,1,No,No,50,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.95,5222.35,No
4043-MKDTV,Male,0,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,7291.75,No
8065-YKXKD,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,799.65,Yes
9637-EIHEQ,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,50.8,50.8,Yes
9229-RQABD,Male,0,No,No,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,23.75,424.5,No
8313-KTIHG,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,61.3,249.4,No
5320-BRKGK,Female,0,Yes,Yes,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,75.8,4415.75,No
6284-KMNUF,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,98,5270.6,No
9689-PTNPG,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,144.55,Yes
9465-RWMXL,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.9,2447.95,Yes
6229-UOLQL,Male,0,Yes,Yes,56,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,52,2884.9,No
2362-IBOOY,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,3050.15,Yes
3137-LUPIX,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,64.4,253,No
0843-WTBXE,Male,0,No,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.45,4517.25,Yes
6143-JQKEA,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.8,436.2,No
8676-OOQEJ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.5,118.4,No
5515-IDEJJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes
9701-CDXHR,Female,0,Yes,No,51,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.15,3649.6,No
7450-NWRTR,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.45,1200.15,Yes
8124-NZVGJ,Female,0,No,No,6,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,49.25,255.6,No
2162-FRZAA,Male,0,Yes,Yes,63,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),39.35,2395.05,No
5376-DEQCP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.6,70.6,Yes
5118-MUEYH,Female,0,Yes,No,48,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,5083.55,No
9095-HFAFX,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),81,389.6,Yes
5627-TVBPP,Female,0,No,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.1,644.5,No
2379-ENZGV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,523.5,Yes
3936-QQFLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,39.3,No
2589-AYCRP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,989.05,No
4067-HLYQI,Female,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,2406.1,No
5124-EOGYE,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,638.55,No
5057-RKGLH,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,191.1,No
0292-WEGCH,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,86.2,4524.05,No
8910-ICHIU,Female,0,No,No,46,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.65,4664.2,No
4097-YODCF,Male,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,103.8,3470.8,No
9715-WZCLW,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,97.2,6910.3,No
9786-YWNHU,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,63.55,4014.2,No
6407-GSJNL,Female,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.95,1288,No
6005-OBZPH,Female,1,No,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,2277.65,Yes
4049-ZPALD,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),99,6375.8,No
7932-WPTDS,Female,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes
3733-UOCWF,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),85.55,5251.75,No
6833-JMZYP,Female,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94,1505.45,No
2722-VOJQL,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.65,6903.1,Yes
1310-QRITU,Female,0,No,No,18,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.3,913.3,No
8961-QDZZJ,Female,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,95,5535.8,No
9715-SBVSU,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Bank transfer (automatic),61.4,815.55,No
8490-BXHEO,Male,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1411.65,No
7173-TETGO,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),78.5,5602.25,No
7929-SKFGK,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.3,8244.3,No
2300-RQGOI,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,741.5,No
3563-SVYLG,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),62.65,4375.8,No
5228-EXCET,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.85,1008.7,Yes
6435-VWCCY,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),92.7,5968.4,No
4998-IKFSE,Female,0,No,No,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.45,3096.9,No
8630-QSGXK,Male,0,Yes,No,51,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.2,3901.25,No
2455-USLMV,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.75,2613.4,No
7394-FKDNK,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.45,853.1,Yes
9488-FVZCC,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),79.5,5661.7,No
6331-LWDTQ,Male,0,No,No,10,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),72.15,794.25,Yes
5995-WWKKG,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.8,695.05,No
1597-FZREH,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,76.4,160.8,Yes
7879-CGSFV,Male,0,No,No,55,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,100.9,5552.05,No
9921-EZKBY,Male,0,No,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.3,3275.15,No
7432-FFVAR,Female,0,Yes,Yes,46,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),90.95,4236.6,No
7246-ZGQDF,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.5,54.5,No
7000-WCEVQ,Female,1,No,No,20,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,61.6,1174.35,Yes
0727-IWKVK,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.9,741.7,Yes
5959-BELXA,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.15,3019.25,Yes
4456-RHSNB,Female,0,Yes,Yes,19,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.6,962.9,No
3512-IZIKN,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),65.3,4759.75,Yes
2481-SBOYW,Female,0,No,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25,1498.35,No
7109-MFBYV,Male,0,No,No,26,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.45,1233.15,No
1833-TCXKK,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.75,4882.8,No
1832-PEUTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),89.1,5411.65,No
6141-OOXUQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes
8660-BUETV,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,148.05,No
0580-PIQHM,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),101.6,3930.55,No
5696-CEIQJ,Male,0,Yes,Yes,67,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.15,6895.5,No
7503-ZGUZJ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.65,84.65,Yes
1696-HXOWK,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,95.65,6856.95,No
3026-ATZYV,Female,0,Yes,Yes,37,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),75.1,2658.8,No
4012-YCFAI,Male,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.35,3766.2,No
2506-TNFCO,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes
9970-QBCDA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,129.55,No
8718-PTMEZ,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.05,347.25,Yes
9496-IVVRP,Female,0,Yes,Yes,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51,581.7,No
2207-OBZNX,Male,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51,354.05,Yes
2657-VPXTA,Female,0,Yes,Yes,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.85,929.45,No
6551-VLJMV,Male,0,Yes,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,679,No
1221-GHZEP,Female,0,No,No,62,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,65.1,3846.75,No
9289-LBQVU,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.15,4480.7,No
6142-VSJQO,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,44.35,Yes
3458-IDMFK,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.75,499.4,No
6933-FHBZC,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Mailed check,56.05,1553.2,No
0221-NAUXK,Male,0,No,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.95,219.5,No
9770-KXGQU,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,98.6,5311.85,No
6437-UKHMV,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,586.05,Yes
6538-POCHL,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),79,2576.8,No
6726-NNFWD,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),89.45,6435.25,No
3002-WQZWT,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,1993.25,No
4277-UDIEF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81,1923.85,No
1208-NBVFH,Male,0,Yes,Yes,20,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.6,939.8,No
4912-PIGUY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.6,84.6,No
1114-CENIM,Male,0,No,Yes,54,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55,3092.65,Yes
6060-DRTNL,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.85,415.55,Yes
2725-TTRIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5986.55,No
3374-TTZTK,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.3,5487,No
8990-ZXLSU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.05,651.5,No
2275-RBYQS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,No
8473-VUVJN,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,73.65,Yes
6289-CPNLD,Male,0,Yes,Yes,33,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,73.9,2405.05,Yes
5536-SLHPM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,77.75,4458.15,Yes
4419-UJMUS,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.35,6856.45,No
7794-JASDG,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.75,50.75,No
7609-YBPXG,Male,0,No,No,54,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.1,4735.2,No
5519-TEEUH,Male,0,No,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,682.15,No
7856-GANIL,Male,1,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),98.7,4525.8,No
0804-XBFBV,Female,0,No,Yes,11,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.2,321.05,No
2676-OXPPQ,Male,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,335.65,No
3703-TTEPD,Male,0,No,No,21,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),65.35,1424.4,No
2799-TSLAG,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.3,1748.55,No
0196-VULGZ,Female,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.35,474.9,Yes
8837-VVWLQ,Female,0,No,No,8,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.95,668.4,Yes
2696-NARTR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,73.85,926.25,Yes
2208-NKVVH,Male,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.25,1077.95,No
7614-QVWQL,Male,0,Yes,Yes,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),51.8,2541.25,Yes
1976-CFOCS,Female,1,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,46,Yes
4631-OACRM,Male,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.4,1156.1,Yes
7139-JZFVG,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),60.5,3694.45,No
4987-GQWPO,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.1,382.8,No
3755-JBMNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),71.8,1167.8,Yes
1757-TCATG,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,746.75,No
6345-ULYRW,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Mailed check,88.4,3912.9,Yes
4776-XSKYQ,Female,0,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),30.25,368.85,No
8048-DSDFQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
5753-QQWPW,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,59.9,1654.7,No
6010-DDPPW,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.15,1940.85,No
1809-DMJHQ,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,221.7,Yes
6693-FRIRW,Male,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.3,1794.65,No
6586-MYGKD,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),76.95,5289.8,No
3173-NVMPX,Female,0,Yes,Yes,9,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.3,501.2,No
0248-PGHBZ,Female,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),92.45,6140.85,No
0623-GDISB,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),48.45,48.45,No
2892-GESUL,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,309.25,No
6923-EFPNL,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.75,201.1,Yes
7472-EQOAV,Male,1,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),86.7,6179.35,No
9574-RKJIF,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,2838.7,Yes
2043-WVTQJ,Male,0,Yes,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.7,55.7,No
8034-RYTVV,Female,0,No,No,55,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),84.25,4589.85,No
3863-QSTYI,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.65,3735.45,No
2619-WFQWU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes
5044-LRQAQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.2,477.55,No
1716-LSAMB,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.65,2553.7,No
1333-PBMXB,Female,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.75,1342.15,No
6546-OPBBH,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,23.95,1216.35,No
5985-BEHZK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),105,7578.05,No
9127-QRZMH,Male,0,Yes,No,44,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),59.85,2603.95,No
5919-VCZYM,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,42.7,No
9644-KVCNC,Female,0,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),92.15,6056.9,No
2137-DQMEV,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,44.8,2983.65,No
8174-LNWMW,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,689.35,No
9279-CJEOJ,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,2025.1,No
7964-VEXDG,Male,0,No,Yes,21,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,1747.2,No
2404-JIBFC,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.1,4657.95,No
0778-NELLA,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.7,296.1,Yes
8029-XYPWT,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.05,8016.6,No
4614-NUVZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.1,81.1,Yes
4632-PAOYU,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,433.5,No
3803-KMQFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.55,20.55,Yes
6804-GDMOI,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.6,6428.4,Yes
2990-OGYTD,Female,0,Yes,No,67,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.15,5883.85,No
6646-JPPHA,Female,1,No,No,14,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.85,1043.8,No
9572-MTILT,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.75,6252.9,Yes
7658-UYUQS,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),86.55,1857.25,No
7880-XSOJX,Male,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,42.4,146.4,No
9611-CTWIH,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.45,240.45,Yes
4589-IUAJB,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1724.15,No
0329-GTIAJ,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,97.9,315.3,Yes
8746-BFOAJ,Male,1,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,429.55,No
8457-XIGKN,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.6,356.15,No
6072-NUQCB,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.25,488.25,No
6629-CZTTH,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.7,55.7,Yes
8838-GPHZP,Female,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.6,1298.7,No
8750-QWZAJ,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1378.75,No
5364-EVNIB,Male,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.8,973.45,Yes
6918-UMQCG,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.2,384.25,No
0675-NCDYU,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.4,8543.25,No
6339-DKLMK,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.65,389.95,No
1346-PJWTK,Male,0,Yes,No,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),94.15,5731.85,No
5088-QZLRL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.65,20.65,No
8023-QHAIO,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),76.85,4275.75,No
4397-FRLTA,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,84.5,No
3057-VJJQE,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Mailed check,55.25,1924.1,No
5087-SUURX,Female,0,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.05,669.85,Yes
2302-OUZXB,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),82.15,5784.3,No
8133-ANHHJ,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),103,5166.2,No
2270-CHBFN,Female,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95.1,4060.55,No
0013-EXCHZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,83.9,267.4,Yes
5982-FPVQN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,3532.85,No
9107-UKCKY,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,79.8,4914.8,No
4654-ULTTN,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.8,5315.8,No
5550-VFRLC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes
5546-BYZSM,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.45,775.6,No
1492-KGETH,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),78.35,5445.95,No
4929-BSTRX,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.55,53.55,Yes
4163-HFTUK,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.1,1007.8,No
8215-NGSPE,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,833.55,No
2225-ZRGSG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),93.9,6579.05,Yes
8859-YSTWS,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1004.5,No
5271-YNWVR,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.15,7856,Yes
7601-DHFWZ,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24,1183.05,No
2657-ALMWY,Female,1,Yes,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.95,2169.75,Yes
6330-JKLPC,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,896.9,Yes
4667-OHGKG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,Yes
1998-VHJHK,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,501.35,No
1707-HABPF,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),91.3,4126.35,No
1660-HSOOQ,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.65,49.65,Yes
7253-UVNDW,Female,0,No,No,46,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),54.35,2460.15,Yes
0487-VVUVK,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.15,477.6,No
7228-OMTPN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.45,370.65,Yes
5063-IUOKK,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,265.75,No
4508-OEBEY,Male,0,Yes,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),75.5,2424.45,No
0027-KWYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,1849.95,No
2308-STERM,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,61.05,No
5982-XMDEX,Female,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.5,1698.55,No
6284-AHOOQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.5,1910.6,Yes
6051-PTVNS,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.15,998.1,No
2344-JMOGN,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,94.85,890.6,Yes
3799-ISUZQ,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.95,529.5,Yes
2877-VDUER,Female,0,Yes,Yes,35,No,No phone service,DSL,No,No,No,Yes,Yes,No,One year,No,Mailed check,40.9,1383.6,No
9152-AMKAK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,80.25,493.4,No
1794-HBQTJ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.6,48.6,Yes
9432-VOFYX,Male,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.8,1207,No
8049-WJCLQ,Male,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.2,563.5,No
6586-PSJOX,Male,0,No,Yes,15,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),55.2,864.55,No
2460-FPSYH,Female,1,No,No,40,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,55.8,2109.35,Yes
9705-ZJBCG,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.15,701.05,No
4818-DRBQT,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.15,2265.25,Yes
1320-HTRDR,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,220.6,Yes
6847-KJLTS,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,5749.8,No
9670-BPNXF,Female,0,No,No,45,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),62.55,2796.45,No
3913-FCUUW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),70.45,5165.7,No
3301-VKTGC,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),85.5,5696.6,No
1493-AMTIE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes
9555-SAHUZ,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.5,2076.05,No
0816-TSPHQ,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,44.2,No
9932-WBWIK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,215.25,No
4619-EVPHY,Female,1,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),91,1859.5,No
5286-YHCVC,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,7470.1,No
6424-ELEYH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,229.5,Yes
4391-RESHN,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,104.05,2470.1,Yes
9115-YQHGA,Male,0,No,No,40,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,2092.9,No
4462-CYWMH,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.8,5629.55,No
8963-MQVYN,Female,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.55,469.85,No
2458-EOMRE,Female,0,No,No,11,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),64.05,733.95,No
9334-GWGOW,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,485.25,No
6821-BUXUX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.65,1244.5,Yes
5028-HTLJB,Male,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,Yes
9801-GDWGV,Female,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,3994.45,Yes
6542-LWGXJ,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25,78.25,No
5567-GZKQY,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1131.5,No
1222-LRYKO,Male,0,No,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.35,184.05,No
2320-JRSDE,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.9,19.9,Yes
2087-QAREY,Female,0,Yes,No,22,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.7,1178.75,No
0601-WZHJF,Male,0,Yes,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,46.35,667.7,Yes
4423-JWZJN,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),90.25,5629.15,No
5143-WMWOG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.95,19.95,Yes
6490-FGZAT,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,109.3,No
5393-RXQSZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.6,79.6,Yes
7452-FOLON,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,958.45,No
2320-TZRRH,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,403.15,No
0231-LXVAP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.9,75.9,Yes
9444-JTXHZ,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.2,76.2,Yes
4942-VZZOM,Male,0,Yes,No,64,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),66.15,4392.5,No
5510-BOIUJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,19.25,Yes
7502-BNYGS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.1,3168,No
4291-HYEBC,Female,1,Yes,Yes,28,No,No phone service,DSL,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,39.1,1096.6,No
6147-CBCRA,Female,0,Yes,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,669.45,No
6047-SUHPR,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,59.8,2343.85,No
4471-KXAUH,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.3,3588.4,Yes
9752-ZNQUT,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,48.6,48.6,No
7638-QVMVY,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,522.95,Yes
1576-PFZIW,Male,1,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.35,7511.9,No
5666-MBJPT,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.1,1725,No
7312-XSBAT,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.75,49.75,No
3096-GKWEB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,1691.9,No
2371-JQHZZ,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93,2248.05,No
0674-GCDXG,Male,0,No,No,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),71.9,4479.2,No
1121-QSIVB,Female,0,No,Yes,44,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,77.55,3471.1,No
4396-KLSEH,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,63,No
3244-DCJWY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,Yes
2824-MYYBN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,3314.15,No
0875-CABNR,Female,1,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.6,865.55,Yes
6345-HOVES,Male,0,No,No,34,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.05,852.7,No
8318-LCNBW,Male,0,Yes,No,35,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),53.15,1930.9,No
6469-QJKZW,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,91.4,No
0147-ESWWR,Female,1,Yes,No,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,3949.15,No
1217-VASWC,Male,1,Yes,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.55,4304,No
7812-FZHPE,Female,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.1,409.9,Yes
6370-ZVHDV,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.3,1554.9,No
5915-DGNVC,Female,0,Yes,No,49,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,71.8,3472.05,No
6260-XLACS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,117.8,No
3566-CAAYU,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,Two year,No,Electronic check,49.85,3210.35,No
4983-CCWMC,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.6,207.4,No
9103-CXVOK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No
2896-TBNBE,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.8,3132.75,No
4797-AXPXK,Female,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60,60,Yes
5229-PRWKT,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,649.65,Yes
6982-UQZLY,Female,1,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.85,20.85,Yes
2522-WLNSF,Female,1,Yes,No,34,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),64.2,2106.3,No
3841-CONLJ,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35,35,No
5057-LCOUI,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,2011.4,Yes
0193-ESZXP,Female,1,Yes,No,58,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.5,6205.5,Yes
4475-NVTLU,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.2,903.7,No
4369-HTUIF,Male,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.15,503.6,Yes
9386-LDCZR,Male,0,No,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.65,3882.3,No
9585-KKMFD,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20,879.8,No
5399-ZIMKF,Male,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,383.65,No
0336-KXKFK,Male,0,No,No,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.2,4390.25,No
5619-XZZKR,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,68.2,No
3948-FVVRP,Male,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,54.8,452.8,No
5327-XOKKY,Male,1,Yes,No,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.45,5329,No
0983-TATYJ,Female,0,Yes,No,33,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,51.45,1758.9,No
1813-JYWTO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),80.45,5737.6,No
0156-FVPTA,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.2,1152.7,Yes
1984-FCOWB,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.5,7674.55,Yes
0654-HMSHN,Male,0,Yes,Yes,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.4,2157.95,Yes
6719-OXYBR,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.3,1219.85,No
3312-ZWLGF,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.3,2414.55,No
6476-YHMGA,Female,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),76.5,1155.6,No
5287-QWLKY,Male,1,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,7548.1,Yes
0795-XCCTE,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1809.35,No
0168-XZKBB,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,1564.4,No
3785-NRHYR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
5196-SGOAK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,75.7,Yes
4537-DKTAL,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.55,84.4,No
7072-MBHEV,Female,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.1,864.85,No
5602-BVFMK,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,228.75,No
6297-NOOPG,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.5,7752.05,No
1891-UAWWU,Female,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,90.8,1951,Yes
8204-TIFGJ,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,470.6,No
8050-DVOJX,Male,1,No,No,49,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81.35,4060.9,No
6108-OQZDQ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.95,384.5,Yes
0363-QJVFX,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,3432.9,Yes
8903-XEBGX,Male,0,No,Yes,2,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,108.65,No
8169-SAEJD,Male,1,Yes,No,69,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),56.55,3952.65,No
9840-DVNDC,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.5,463.05,Yes
1089-XZWHH,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,494.05,No
2325-WINES,Female,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.05,3416.85,No
6064-ZATLR,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),52.85,1498.65,No
3096-JRDSO,Female,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.3,2867.75,Yes
7824-PANSQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.65,4807.35,No
8042-JVNFH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.35,71.35,Yes
9648-BCHKM,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,471.35,No
2888-ADFAO,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.3,1041.8,No
8707-HOEDG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.2,7689.8,No
3374-LXDEV,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.4,1132.35,Yes
4072-IPYLT,Female,0,Yes,Yes,36,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,1815,No
7167-PCEYD,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.8,1311.3,No
4936-YPJNK,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,199.45,No
1580-BMCMR,Male,1,No,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,87.3,1637.3,No
4817-KEQSP,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1326.35,No
6408-WHTEF,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.4,6376.55,No
9251-AWQGT,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,935.9,No
8749-CLJXC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
7989-VCQOH,Male,0,Yes,Yes,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.25,1611.15,No
5049-GLYVG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,Yes
4199-QHJNM,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),102.9,6989.7,No
8882-TLVRW,Male,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.1,2779.5,No
5883-GTGVD,Male,0,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,1931.75,Yes
5135-GRQJV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,114.5,8331.95,No
2384-OVPSA,Female,1,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,735.9,No
1371-WEPDS,Male,1,Yes,No,40,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,55.8,2283.3,No
3580-GICBM,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.2,1445.2,No
4817-QRJSX,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81,818.05,Yes
9730-DRTMJ,Male,0,Yes,No,32,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Credit card (automatic),72.8,2333.05,No
4686-UXDML,Female,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.85,1992.55,No
3349-ANQNH,Female,1,No,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.5,5890,No
2398-YPMUR,Female,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,916.75,Yes
0932-YIXYU,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1029.8,No
7480-SPLEF,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,1796.55,No
7636-XUHWW,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.9,33.7,No
1431-CYWMH,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.05,454.05,No
5937-EORGB,Male,1,Yes,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,96.5,1392.25,No
6349-JDHQP,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1049.6,No
5539-HIVAK,Female,1,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.7,734.6,No
5847-MXBEO,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,475.1,No
9985-MWVIX,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes
4583-PARNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,91.55,1540.05,No
1116-FRYVH,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.4,1978.65,No
1421-HCERK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),105.7,3181.8,No
9633-XQABV,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,229.7,No
0716-BQNDX,Male,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Two year,No,Electronic check,93.75,5625.55,No
1265-XTECC,Female,1,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.55,6581.9,Yes
1183-CANVH,Female,0,Yes,No,23,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),60,1347.15,No
1972-XMUWV,Female,0,Yes,No,65,Yes,No,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),59.8,3808.2,No
8679-LZBMD,Male,0,Yes,No,44,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.65,3974.15,No
3407-JMJQQ,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109,7661.8,No
6252-DFGTK,Female,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),68.1,2479.25,No
1539-LNKHM,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,266.6,No
8645-KOMJQ,Male,0,Yes,Yes,69,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81.95,5601.4,No
0289-IVARM,Female,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,60.55,1982.6,No
9761-XUJWD,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.6,339.9,No
4057-FKCZK,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Two year,No,Bank transfer (automatic),82.5,4828.05,No
4291-TPNFG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.3,5980.55,No
6087-YPWHO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,68.15,4808.7,No
3090-QFUVD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.3,20.3,No
2237-ZFSMY,Female,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,95.55,3692.85,Yes
1722-LDZJS,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.2,1068.15,No
1818-ESQMW,Female,0,No,No,27,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.2,2383.6,No
4871-JTKJF,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
1415-YFWLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.3,89.3,Yes
7401-RUBNK,Female,0,Yes,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,74.8,1438.05,No
0506-YLVKJ,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,917.45,No
8661-BOYNW,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,6096.45,No
6711-VTNRE,Female,0,No,No,36,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,87.55,3078.1,Yes
4815-GBTCD,Female,0,Yes,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,99.95,No
2805-AUFQN,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,475.2,No
0980-PVMRC,Female,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.85,2036.55,No
7233-DRTRF,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.4,6444.05,No
3500-RMZLT,Female,1,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,96.3,1426.75,Yes
3873-NFTGI,Male,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,767.55,No
1162-ECVII,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,7932.5,No
7048-GXDAY,Male,0,No,No,39,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,4040.65,No
8571-ZCMCX,Female,0,Yes,Yes,47,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),60,2768.65,No
1169-WCVAK,Male,0,Yes,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),88.8,1672.35,No
4548-SDBKE,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,474.8,Yes
8066-POXGX,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,446.1,Yes
7129-CAKJW,Female,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),80.05,1345.65,No
8695-ARGXZ,Male,1,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,2425.4,No
3570-YUEKJ,Female,0,No,No,42,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,2077.95,No
4143-OOBWZ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.3,416.3,Yes
1555-HAPSU,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1663.5,No
1697-NVVGY,Male,1,Yes,No,19,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,1286.05,No
7854-EKTJL,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.6,35.85,Yes
8464-EETCQ,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.8,1094.35,No
2419-FSORS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.4,7719.5,No
8132-YPVBX,Female,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.95,514.6,No
9102-IAYHT,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.45,1451.6,Yes
6754-LZUKA,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),80.9,4932.5,No
3422-LYEPQ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes
8099-MZPUJ,Male,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.8,5443.65,No
0787-LHDYT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.6,330.25,No
2642-DTVCO,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,746.5,Yes
0374-IOEGQ,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.6,122.7,No
4361-JEIVL,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.4,44.4,Yes
3197-NNYNB,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.1,6631.85,No
3396-DKDEL,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8250,No
1752-OZXFY,Male,0,Yes,No,60,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Mailed check,59.8,3561.15,No
9066-QRSDU,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,26.3,1763.55,No
9112-WSNPU,Female,1,No,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.55,2419,No
9867-NNXLC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,470.2,No
5321-NTRKC,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),79.85,5234.95,No
1363-TXLSL,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
9507-EXLTT,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.35,79.35,Yes
3161-GETRM,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.05,3097,No
0402-OAMEN,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1709.1,No
6933-VLYFX,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,59.95,1848.8,No
6754-WKSHP,Male,0,No,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.35,723.3,No
9846-GKXAS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),90.8,809.75,Yes
6017-PPLPX,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.45,1470.95,Yes
4976-LNFVV,Male,1,Yes,No,19,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),34.3,577.15,No
5055-MGMGF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,6914.95,No
4641-FROLU,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.3,602.9,No
2862-JVEOY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,124.4,No
2969-QWUBZ,Female,0,No,No,2,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,51.4,96.8,No
9822-BIIGN,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.85,3827.9,No
2077-MPJQO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,533.05,No
1534-OULXE,Female,0,Yes,Yes,61,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),49.7,2961.4,No
7136-RVDTZ,Male,1,No,No,70,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,45.25,3264.45,Yes
2971-SGAFL,Female,0,No,No,13,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,78.75,995.35,No
1480-IVEVR,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),81.6,2815.25,No
2905-KFQUV,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.4,154.8,No
4581-SSPWD,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.8,246.3,Yes
3370-HXOPH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76.1,257.6,No
9391-YZEJW,Female,0,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,5757.2,No
9958-MEKUC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),103.95,7517.7,No
0281-CNTZX,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1234.8,No
6927-WTFIV,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.3,1389.2,Yes
4118-CEVPF,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.8,3836.3,No
3398-ZOUAA,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,1474.75,Yes
9114-VEPUF,Male,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,96.1,6001.45,No
7876-BEUTG,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,720.1,No
2338-BQEZT,Female,0,No,No,55,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),50.55,2832.75,No
9873-MNDKV,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.65,472.25,No
0378-NHQXU,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,88.25,1460.65,Yes
8241-JUIQO,Female,0,No,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.45,1336.35,No
2194-IIQOF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.3,6388.65,No
4512-ZUIYL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70,153.05,Yes
9631-RXVJM,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,677.9,No
9584-EXCDZ,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.5,1165.6,No
7969-AULMZ,Female,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.35,2119.5,Yes
5093-FEGLU,Female,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,921.55,No
2621-UDNLU,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,72,No
7526-IVLYU,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,68.35,No
2428-HYUNX,Male,1,Yes,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.35,847.25,No
9214-EKVXR,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44,44,No
3410-MHHUM,Female,0,Yes,Yes,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,94.4,4295.35,No
0568-ONFPC,Male,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.9,135,Yes
0733-VUNUW,Male,0,No,No,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.65,1400.55,Yes
4550-EVXNY,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
2122-YWVYA,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1380.4,No
2968-SSGAA,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,1060.2,Yes
2296-DKZFP,Female,0,Yes,No,65,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),71,4386.2,No
4844-JJWUY,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,86,Yes
6777-TGHTM,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.95,5785.5,Yes
8909-BOLNL,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.2,52.05,No
6603-YRDCJ,Male,0,Yes,No,33,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.05,2018.4,No
3538-WZPHD,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,79.45,Yes
3229-USWAR,Female,0,No,No,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.95,2727.3,No
2138-VFAPZ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,263.65,No
6131-JLWZM,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,275.4,No
4785-QRJHC,Male,1,Yes,No,46,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),59.9,2816.65,Yes
0269-XFESX,Male,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.35,538.5,No
1709-EJDOX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,948.9,No
4316-XCSLJ,Male,0,No,Yes,17,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,846.8,No
8610-WFCJF,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,4783.5,Yes
9693-XMUOB,Male,1,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.25,2997.45,No
0383-CLDDA,Female,0,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.35,5897.4,No
4905-JEFDW,Male,0,No,No,11,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,41.6,470.6,Yes
2709-UQGNP,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51.65,524.5,No
3549-ZTMNH,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,269.65,No
7881-INRLC,Male,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.85,4740,Yes
8033-ATFAS,Female,0,Yes,Yes,39,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,59.85,2341.5,No
1635-NZATJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1789.65,No
0562-HKHML,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1626.4,No
8277-RVRSV,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.15,800.3,No
9943-VSZUV,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.7,5060.85,No
0117-LFRMW,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.2,1448.8,Yes
6172-FECYY,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,4254.85,Yes
7054-ENNGU,Female,0,Yes,No,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.85,466.6,No
1455-HFBXA,Male,0,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),91.6,4627.8,No
9402-CXWPL,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,98.9,6838.6,No
1628-BIZYP,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,No
1965-AKTSX,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,1101.85,Yes
0082-LDZUE,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.3,44.3,No
4786-UKSNZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,Yes
5343-SGUBI,Female,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,80.2,4297.6,No
0856-NAOES,Male,0,No,No,6,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.9,414.1,No
1976-AZZPJ,Male,0,Yes,No,7,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,34.2,256.6,No
7248-VZQLC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.2,3969.35,Yes
6156-UZDLF,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),87.15,2274.1,No
3099-OONVS,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.3,1296.8,No
9500-WBGRP,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.1,1268.85,No
5183-KLYEM,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.75,8192.6,No
5144-PQCDZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,59.25,No
8514-VZHEB,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,1147.85,No
6625-IUTTT,Male,0,No,No,67,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),65.55,4361.55,No
9355-NPPFS,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.8,2006.1,No
0068-FIGTF,Female,0,No,No,27,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,78.2,2078.95,No
5965-GGPRW,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),105.25,7609.75,No
8035-PWSEV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,487.05,No
2236-HILPA,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,1218.45,No
1840-BIUOG,Male,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,68.7,1416.2,No
1356-MKYSK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),78.65,483.3,No
6080-LNESI,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.75,1234.6,No
1830-IPXVJ,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,1311.6,No
5828-DWPIL,Male,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.1,5618.3,No
8398-TBIYD,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),84.7,6185.15,No
0011-IGKFF,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98,1237.85,Yes
4192-GORJT,Male,0,Yes,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,498.1,Yes
6496-SLWHQ,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105,294.45,Yes
9912-GVSEQ,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.85,2381.55,Yes
2931-FSOHN,Male,1,No,No,13,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.9,788.35,No
9418-RUKPH,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,756.4,No
5383-MMTWC,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84,613.4,Yes
9971-ZWPBF,Male,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.9,3625.2,No
4712-AUQZO,Male,0,No,No,18,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,33.6,550.35,No
9711-FJTBX,Male,0,Yes,Yes,56,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,85.85,4793.8,No
2908-ZTPNF,Female,0,No,No,36,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),34.85,1267.2,No
7240-ETPTR,Female,0,Yes,No,9,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.75,442.2,Yes
2202-CUYXZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,84.85,Yes
5995-OIGLP,Male,0,No,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,56.65,654.85,Yes
8421-WZOOW,Female,1,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),95.3,5567.45,No
6261-RCVNS,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.9,3160.55,Yes
7951-QKZPL,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.5,740.3,Yes
1414-YADCW,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),84.6,5706.2,No
8756-RDDLT,Female,0,No,No,68,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,44.95,3085.35,No
2862-PFNIK,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,24.7,No
5816-QVHRX,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,3541.4,No
4430-YHXGG,Female,0,No,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.45,84.2,No
6888-SBYAI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.7,50.7,No
7395-XWZOY,Male,0,No,No,20,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),55,1165.55,No
7169-YWAMK,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),68.4,4855.35,No
3640-PHQXK,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.9,2806.9,Yes
6877-LGWXO,Male,1,Yes,No,18,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.55,1422.65,Yes
6859-RKMZJ,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),55.05,608.15,No
2729-VNVAP,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,641.35,No
2957-JIRMN,Female,1,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.45,4959.15,No
2049-BAFNW,Female,0,No,No,1,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,35.9,35.9,No
3842-QTGDL,Male,0,Yes,No,16,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),80.75,1321.3,No
7163-OCEQI,Male,0,Yes,Yes,22,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.65,1663.75,No
9782-LGXMC,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.75,3024.15,No
5975-BAICR,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Credit card (automatic),63.7,2188.5,No
8019-ENHXU,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,99.45,4138.05,Yes
5167-GBFRE,Male,1,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.2,102.5,Yes
9033-EOXWV,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,No,Mailed check,74.05,872.65,Yes
4673-KKSLS,Female,0,No,No,31,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,87.6,2724.25,No
4853-OITSN,Male,0,Yes,No,5,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,413.25,No
9800-OUIGR,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1374.2,No
1324-NLTJE,Female,1,No,No,15,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),55,757.1,Yes
2324-EFHVG,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,6692.65,No
7094-MSZAO,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.05,218.5,No
1522-VVDMG,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.75,608.8,Yes
6907-CQGPN,Male,0,No,No,29,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.3,1004.75,No
0780-XNZFN,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1125.6,No
0239-OXEXL,Female,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,84.25,3847.6,No
2675-IJRGJ,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,978,No
7049-GKVZY,Female,0,No,No,17,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),79.85,1387.35,No
8577-QSOCG,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,746.05,No
3721-CNZHX,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,304.6,No
0212-ISBBF,Female,0,No,No,22,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,1098.85,No
9185-TQCVP,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.15,1139.2,Yes
3585-YNADK,Female,0,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),74.6,4368.95,No
5271-DBYSJ,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.15,827.7,No
1099-GODLO,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,20.35,No
6828-HMKWP,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.05,262.05,No
5567-WSELE,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,279.55,Yes
1472-TNCWL,Male,0,No,Yes,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,3512.5,No
8063-RJYNF,Male,0,No,No,16,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1483.25,Yes
0687-ZVTHB,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),72.45,4653.85,Yes
2080-GKCWQ,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,151.75,No
9661-ACXBS,Female,0,No,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,4400.75,Yes
2193-SFWQW,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),111.95,8033.1,No
5656-JAMLX,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1253.65,No
3462-BJQQA,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.75,552.65,No
0442-TDYUO,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.05,1036,No
6733-LRIZX,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.95,4025.5,No
9503-XJUME,Male,0,No,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.65,928.4,No
4367-NHWMM,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.9,24.9,No
3727-RJMEO,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,82.85,460.25,Yes
3779-OSWCF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),93.2,6506.15,No
6736-DHUQI,Female,0,Yes,Yes,67,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),84.8,5598.3,No
3915-ODIYG,Male,1,No,No,60,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,71.75,4374.55,No
1360-RCYRT,Male,0,Yes,Yes,23,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),30.35,678.75,No
2724-FJDYW,Male,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),54.85,2191.7,No
4451-RWASJ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,239.75,No
6646-VRFOL,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,5485.5,Yes
3460-TJBWI,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,609.05,No
5917-HBSDW,Female,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.35,683.75,No
5685-IIXLY,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.6,404.2,Yes
5671-UUNXD,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.65,5189.75,No
0956-ACVZC,Female,0,No,No,54,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),94.1,5060.9,No
2325-NBPZG,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,233.65,No
4250-ZBWLV,Male,0,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,108.45,7176.55,Yes
4482-FTFFX,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,56.15,291.45,No
8859-DZTGQ,Male,0,No,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,689.75,No
0440-MOGPM,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.55,3263.9,No
0020-JDNXP,Female,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,61.25,1993.2,No
3752-CQSJI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,254.5,No
5025-GOOKI,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),18.9,347.65,No
4698-KVLLG,Female,1,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,967.9,No
5095-AESKG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.5,242.95,Yes
2887-JPYLU,Female,0,No,Yes,41,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.2,1841.9,No
4770-QAZXN,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.45,232.1,No
4896-CPRPF,Male,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,809.25,No
1871-MOWRM,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.85,866.45,Yes
9714-EDSUC,Male,0,No,No,4,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,94.9,360.55,No
2027-OAQQC,Female,0,No,No,43,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.05,2076.2,Yes
0282-NVSJS,Female,1,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,29.3,355.9,No
9090-SGQXL,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,7299.65,Yes
6595-YGXIT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.95,2291.2,Yes
7353-YOWFP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,129.15,No
9835-ZIITK,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.85,7491.75,Yes
8008-ESFLK,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.5,5835.5,No
7537-CBQUZ,Male,1,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.4,7031.45,No
1555-DJEQW,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.2,7723.9,Yes
5649-TJHOV,Male,1,Yes,No,27,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),36.5,1032,Yes
0519-XUZJU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.75,70.75,Yes
3363-EWLGO,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,109.6,No
4750-UKWJK,Female,1,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,727.8,No
6338-AVWCY,Male,0,No,No,3,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,40.15,130.75,Yes
1689-YQBYY,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.6,893,No
4487-ZYJZK,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,763.1,No
2959-FENLU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.3,781.4,No
0708-LGSMF,Male,0,Yes,No,13,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,65.85,902.25,No
9253-QXKBE,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,2653.65,Yes
7634-HLQJR,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1016.7,No
0487-RPVUM,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),99.4,5943.65,No
4079-ULGFR,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,275.7,No
2516-XSJKX,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,78.45,3126.45,No
0057-QBUQH,Female,0,No,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,25.1,1070.15,No
9445-SZLCH,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),97.35,3457.9,Yes
6599-SFQVE,Female,0,No,No,6,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,340.4,No
8331-ZXFOE,Female,0,No,No,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),71.1,4299.2,No
4003-FUSHP,Male,0,No,No,19,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),61.55,1093.2,No
0356-ERHVT,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.9,521.9,No
7325-ENZFI,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.3,1630.4,No
4884-ZTHVF,Female,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.1,713.6,No
3920-HIHMQ,Female,0,No,Yes,26,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.5,1265.65,No
3055-OYMSE,Female,1,No,No,53,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.8,4003.85,No
0613-WUXUM,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.2,1401.4,No
7568-PODML,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,45.3,45.3,Yes
4458-KVRBJ,Male,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1510.5,No
5349-IECLD,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.95,178.1,Yes
1397-XKKWR,Male,0,No,No,7,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.3,264.8,No
3945-GFWQL,Female,0,No,Yes,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.55,480.6,Yes
8097-OMULG,Male,0,Yes,Yes,59,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),76.75,4541.9,No
6013-BHCAW,Male,0,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81,4976.15,No
0401-WDBXM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.55,7542.25,No
3387-PLKUI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,251.25,No
6096-EGVTU,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.9,1595.5,No
3797-VTIDR,Male,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.45,23.45,Yes
5081-NWSUP,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,64.9,685.55,No
2580-ATZSQ,Female,0,Yes,Yes,65,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),61.35,3874.1,No
8085-MSNLK,Female,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,113.95,6891.4,No
9691-HKOVS,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,90.15,4916.95,No
9881-VCZEP,Female,0,Yes,No,25,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.1,1373,No
9526-BIHHD,Male,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.7,29.7,Yes
8757-TFHHJ,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No
4523-WXCEF,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.1,6039.9,Yes
1468-DEFNC,Male,1,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.4,1548.65,No
5119-KEPFY,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95,3440.25,No
8364-TRMMK,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.65,151.3,Yes
0916-KNFAJ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Mailed check,69.9,4226.7,No
8319-QBEHW,Male,0,No,Yes,26,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),39.95,1023.75,No
3063-QFSZL,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.4,55.4,Yes
8775-LHDJH,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,90.6,Yes
9625-QNLUX,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),103.25,7074.4,No
3097-NQYSN,Male,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.85,156.35,Yes
4024-CSNBY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),94.25,6849.75,No
7110-BDTWG,Female,0,Yes,No,71,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Electronic check,47.05,3263.6,No
7493-GVFIO,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1252.85,No
0690-SRQID,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,67.55,No
9605-WGJVW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,70.2,No
9114-DPSIA,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81,5750,No
4700-UBQMV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),75.9,1549.75,No
7711-GQBZC,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1810.55,No
3565-UNOCC,Female,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,2952.85,Yes
4627-MIHJH,Female,1,No,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7467.55,No
9795-NREXC,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5484.4,No
8573-CGOCC,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,294.9,No
7356-AYNJP,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,23.9,97.5,No
9249-FXSCK,Female,0,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),111.25,5916.45,Yes
5493-SDRDQ,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.1,113.35,Yes
6286-SUUWT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,19.95,No
4819-HJPIW,Male,0,No,No,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.15,476.8,No
8654-DHAOW,Female,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.15,101.65,No
5438-QMDDL,Female,0,Yes,No,19,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,59.8,1130.85,No
9812-GHVRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.85,3532.25,No
5553-AOINX,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.9,6891.45,No
1228-ZLNBX,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,1570.7,No
4226-KKDON,Male,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,66.65,520.95,No
3831-YCPUO,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.5,7854.9,No
7445-WMRBW,Female,0,No,No,48,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),73.85,3581.4,No
3308-DGHKL,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.3,1447.9,No
9924-JPRMC,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.2,8547.15,No
0080-EMYVY,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),51.45,727.85,No
6218-KNUBD,Male,0,No,No,6,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,59.45,357.6,No
7337-CINUD,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,159.35,No
7609-NRNCA,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,280.85,No
0623-EJQEG,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Electronic check,93.55,6069.25,No
7153-CHRBV,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,59.3,3274.35,No
0871-URUWO,Male,0,Yes,No,13,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.25,1359,Yes
9190-MFJLN,Male,1,No,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.9,1777.9,Yes
6198-PNNSZ,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),109.8,6109.65,No
3317-VLGQT,Female,0,Yes,No,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.1,1122.4,No
4132-POCZS,Male,0,Yes,No,52,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.9,2020.9,No
5614-DNZCE,Female,0,No,No,58,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.9,3795.45,No
1095-JUDTC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,95.05,4504.55,Yes
3896-RCYYE,Female,0,No,No,67,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),53.4,3579.15,No
9638-JIQYA,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),24.9,49.7,No
3258-SANFR,Male,1,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,276.5,No
3726-TBHQT,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114,8175.9,No
3190-ITQXP,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,890.35,No
0870-VEMYL,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),53.85,259.8,Yes
4833-QTJNO,Male,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),83.85,5588.8,No
3039-MJSLN,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,50.6,No
0178-CIIKR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58,No
5385-SUIRI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,5568.35,Yes
3982-XWFZQ,Female,0,Yes,No,42,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.25,2203.65,Yes
5774-QPLTF,Male,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,938.95,No
2322-VCZHZ,Male,1,Yes,No,23,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90,2024.1,No
5010-IPEAQ,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),54.2,3623.95,No
4009-ALQFH,Female,0,No,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,2369.05,Yes
6383-ZTSIW,Female,1,Yes,No,39,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Mailed check,99.1,3877.95,No
8990-YOZLV,Female,0,No,No,69,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,66.9,4577.9,No
3069-SSVSN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Mailed check,25.85,25.85,No
5222-IMUKT,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.05,2871.5,No
3627-FCRDW,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71,672.55,Yes
7562-GSUHK,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.2,1573.7,Yes
6685-XSHHU,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.95,1270.55,No
8901-UPRHR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.2,7711.45,No
4903-UYAVB,Male,0,Yes,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,126.05,Yes
0118-JPNOY,Female,1,No,No,26,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.8,2193.65,No
6776-TLWOI,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,64.55,Yes
3845-FXCYS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.65,31.2,No
5857-XRECV,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,38.25,No
0725-CXOTM,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),89.65,3348.1,No
4343-EJVQB,Male,0,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.35,533.6,No
9000-PLFUZ,Female,1,Yes,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),49.45,2907.55,No
3427-GGZZI,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,1620.8,No
7779-ORAEL,Male,1,Yes,No,45,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.15,3480.35,No
0644-OQMDK,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,293.85,No
4077-CROMM,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,3243.45,Yes
3154-CFSZG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.05,4137.2,No
2868-MZAGQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,79.25,Yes
4847-QNOKA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.9,44.9,Yes
2220-IAHLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.4,19.4,No
1658-XUHBX,Female,1,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),88.75,5348.65,No
6379-RXJRQ,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,659.65,Yes
2378-HTWFW,Male,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),91,3180.5,No
8650-RHRKE,Male,0,No,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.65,118.5,Yes
4377-VDHYI,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,90.8,3023.85,No
0475-RIJEP,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,77.85,3365.85,Yes
1260-TTRXI,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,54.3,195.3,Yes
3719-TDVQB,Female,1,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.95,1031.1,No
6328-ZPBGN,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.15,997.65,Yes
0201-MIBOL,Female,1,No,No,66,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),102.4,6471.85,No
4937-QPZPO,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.9,6241.35,No
2925-VDZHY,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.7,6501.35,No
6981-TDRFT,Male,0,Yes,Yes,44,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,54.3,2317.1,No
3413-CSSTH,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,2237.55,No
8033-VCZGH,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,5231.3,No
4789-KWMXN,Male,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.85,5275.8,Yes
0224-NIJLP,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,165.5,No
7542-CYDDM,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,358.5,No
4718-WXBGI,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.95,6614.9,No
2867-UIMSS,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.5,80.5,Yes
8495-PRWFH,Female,1,No,No,42,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,55.65,2421.75,No
0439-IFYUN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,1294.6,No
5716-LIBJC,Female,0,No,Yes,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.15,1299.1,No
2868-LLSKM,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),83.65,5733.4,No
8111-RKSPX,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.2,305.55,Yes
2988-QRAJY,Male,0,No,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.05,7430.75,No
1585-MQSSU,Male,0,No,No,17,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.5,900.5,Yes
0071-NDAFP,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.5,630.6,No
2856-NNASM,Male,1,No,No,43,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.55,3856.75,Yes
7328-ZJAJO,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1222.65,No
6458-CYIDZ,Female,1,No,No,5,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.7,374.8,No
8559-CIZFV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,77.5,1625,Yes
1090-PYKCI,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),105.1,7234.8,No
3058-WQDRE,Male,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,331.85,No
7547-EKNFS,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.25,3959.35,Yes
2279-AXJJK,Male,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.65,5088.4,No
6769-DYBQN,Male,1,No,No,46,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,85,3969.4,Yes
0909-SELIE,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),80.8,4860.85,No
3417-TSCIC,Male,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,24.85,788.05,No
1042-HFUCW,Female,0,No,Yes,25,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.75,1266.35,No
4439-JMPMT,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,85.75,470.95,Yes
6537-OTKMY,Male,0,No,No,15,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,50.75,688.2,No
8999-EXMNO,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,387.7,No
6725-TPKJO,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,845.25,No
2446-BEGGB,Female,1,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.25,560.6,Yes
7162-WPHPM,Male,0,Yes,Yes,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),71.6,4230.25,No
1599-MMYRQ,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),81.45,4983.05,No
8821-KVZKQ,Female,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),58.4,4113.15,No
1496-GGSUK,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.7,25.7,Yes
6434-TTGJP,Male,0,Yes,Yes,10,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),53.7,521,No
0042-JVWOJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.6,471.85,No
0130-SXOUN,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,5976.9,No
2575-GFSOE,Female,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,506.9,Yes
2330-PQGDQ,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),84.2,4299.75,No
1452-UZOSF,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),106.1,7548.6,No
2097-YVPKN,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1654.75,No
2842-JTCCU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),46.05,80.35,Yes
8597-CTXVJ,Male,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.95,4551.5,No
6631-HMANX,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.45,6227.5,No
9962-BFPDU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
3296-SILRA,Female,1,Yes,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,76.4,76.4,Yes
9681-OXGVC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,514,Yes
6394-HHHZM,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,57.5,No
5995-LFTLE,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1474.35,No
5180-UCIIQ,Male,1,Yes,Yes,22,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,40.05,880.2,Yes
1932-UEDCX,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.6,3270.25,No
7153-OANIO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,69.95,Yes
5188-HGMLP,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74,3919.15,No
0665-XHDJU,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,99.4,7285.7,No
6521-YYTYI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.3,93.3,Yes
8878-HMWBV,Male,0,No,No,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.15,169.05,Yes
8265-HKSOW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),107.45,7658.3,No
3544-FBCAS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),83.6,5959.3,No
5331-RGMTT,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.05,5295.7,No
4759-PXTAN,Female,0,Yes,No,59,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,80.1,4693.2,No
7054-DMVAS,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),65.3,3512.9,No
3428-MMGUB,Male,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Electronic check,89.55,5231.2,No
6549-BTYPG,Female,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.8,3603.45,No
7823-JSOAG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.5,217.45,No
3705-RHRFR,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.15,6875.35,No
9374-YOLBJ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,19.25,No
9074-KGVOX,Male,0,Yes,Yes,50,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),39.45,2021.35,No
6128-DAFVY,Female,0,No,No,56,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),44.85,2564.95,No
9933-QRGTX,Female,0,Yes,No,60,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,97.2,5611.75,No
4476-OSWTN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,110.55,7610.1,No
3751-KTZEL,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,35.05,35.05,Yes
1977-STDKI,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73,73,Yes
5066-GFJMM,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,45.75,No
2661-GKBTK,Male,0,Yes,Yes,60,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),76.95,4543.95,No
0458-HEUZG,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,Yes,No,No,Two year,No,Mailed check,35.4,450.4,No
7268-WNTCP,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,1297.35,No
0824-VWDPO,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),96.75,4442.75,No
8409-WQJUX,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.2,1423.15,No
2578-JQPHZ,Male,0,No,No,44,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),100.1,4378.35,No
3278-FSIXX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.25,74.2,No
5813-UECBU,Male,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.85,2716.3,Yes
0328-GRPMV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes
9746-UGFAC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,450.65,No
2946-KIQSP,Female,0,No,No,35,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,33.45,1175.85,No
0625-AFOHS,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,558.8,No
6726-WEXXK,Male,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Electronic check,85.9,2220.1,No
4636-OLWOE,Male,0,No,Yes,54,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,61,3283.05,No
4342-HENTK,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,142.35,Yes
4685-ERGHK,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,86.9,4939.25,No
0885-HMGPY,Male,0,No,No,62,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.4,4237.5,No
5003-OKNNK,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,335.95,No
4195-SMMNX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,33.2,Yes
7208-PSIHR,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.3,7188.5,No
4012-ZTHBR,Female,0,Yes,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.95,926.25,No
0489-WMEMG,Female,0,No,Yes,23,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Electronic check,49.45,1119.35,No
7435-ZNUYY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,116.6,No
1354-YZFNB,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,68.8,No
6956-SMUCM,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99,287.4,Yes
2985-FMWYF,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.5,2341.55,No
2812-ENYMO,Male,0,No,No,26,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),54.55,1362.85,No
2717-HVIZY,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,163.6,No
3223-WZWJM,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.95,2254.2,Yes
3422-GALYP,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,145.15,No
9053-JZFKV,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),116.2,7752.3,Yes
2530-ENDWQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.7,6585.35,Yes
2890-WFBHU,Female,0,No,No,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),79.85,4786.1,No
0327-WFZSY,Male,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,100,3835.55,No
7977-HXJKU,Male,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,397,No
6615-ZGEDR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,Yes
7033-CLAMM,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1027.25,No
4023-RTIQM,Female,1,Yes,No,31,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),50.4,1580.1,No
0864-FVJNJ,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,113.35,7222.75,No
7356-IWLFW,Male,0,Yes,Yes,46,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80,3769.7,No
9541-PWTWO,Female,0,No,No,52,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.95,4233.95,No
3967-VQOGC,Female,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.9,1680.25,No
7872-RDDLZ,Female,1,No,No,67,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.9,3725.5,No
9250-WYPLL,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.55,413.65,Yes
6308-CQRBU,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.25,7707.7,No
2754-XBHTB,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),77.65,714.15,Yes
1597-LHYNC,Female,1,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95,2497.2,Yes
0186-CAERR,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),116.3,8309.55,No
3043-SUDUA,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,601.55,No
5442-BHQNG,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.35,139.25,No
2169-RRLFW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1888.25,No
5872-OEQNH,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Electronic check,44.45,2773.9,No
3162-KKZXO,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,5409.75,No
7055-VKGDA,Male,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.4,5643.4,Yes
0754-UKWQP,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,197.7,Yes
8873-GLDMH,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.85,401.3,No
5696-JVVQY,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),70.1,3238.4,No
8946-BFWSG,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1573.05,No
7493-TPUWZ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,79.15,Yes
0547-HURJB,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,235.8,No
8904-OPDCK,Male,1,Yes,No,54,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),24.95,1364.75,No
8845-LWKGE,Female,0,Yes,Yes,30,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,64.5,1985.15,No
1577-HKTFG,Female,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,655.85,Yes
8752-STIVR,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,303.15,Yes
0691-NIKRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,4335.2,No
5022-JNQEQ,Female,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.85,647.5,No
4558-FANTW,Female,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.85,1574.45,Yes
3249-VHRIP,Female,0,No,No,62,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.6,2748.7,No
3736-BLEPA,Male,0,No,No,28,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),91.25,2483.65,No
4701-LKOZD,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6367.2,No
6603-QWSPR,Female,0,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,4904.2,No
9921-QFQUL,Female,0,Yes,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,90.15,2044.95,No
1929-ZCBHE,Male,0,Yes,Yes,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,40.3,1794.8,No
4378-MYPGO,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.25,7173.15,No
8651-ENBZX,Female,1,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106,6441.4,Yes
4129-LYCOI,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,104,7039.05,No
7798-JVXYM,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.65,921.4,No
8647-SDTWQ,Male,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,4018.35,No
2696-ECXKC,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,100.9,5448.6,No
2081-KJSQF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
7623-TRNQN,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.9,49.9,Yes
3323-CPBWR,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.9,2085.45,No
9330-IJWIO,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.35,1358.85,Yes
3384-CTMSF,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,5135.15,No
0902-RFHOF,Male,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,730.1,No
9286-DOJGF,Female,1,Yes,No,38,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,2869.85,Yes
6048-QBXKL,Female,1,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),56.55,118.25,No
9661-MHUMO,Male,1,Yes,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.25,49.25,Yes
7718-RXDGG,Male,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),68.6,1108.6,No
1025-FALIX,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.05,1815.65,No
3451-VAWLI,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,730.4,No
3407-QGWLG,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,75.45,No
1842-EZJMK,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.7,5071.05,Yes
2347-WKKAE,Male,0,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.4,4014.6,No
8735-DCXNF,Male,0,Yes,No,10,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.95,568.85,No
3214-IYUUQ,Female,0,Yes,No,61,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),93.7,5860.7,No
1936-UAFEH,Female,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7279.35,No
3587-PMCOY,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.9,1064.95,No
8079-XRJRS,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,89.75,5769.6,Yes
1027-LKKQQ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5886.85,No
7148-XZPHA,Male,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,79.4,4238.45,No
8073-IJDCM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,20.3,Yes
5309-TAIKL,Female,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),62.8,418.3,No
6856-RAURS,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.9,136.05,No
8778-LMWTJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,708.2,No
9717-IOAAF,Male,0,No,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.85,788.55,No
8884-ADFVN,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.95,700.85,Yes
2845-KDHVX,Female,0,Yes,No,64,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,68.3,4378.8,No
0848-SOMKO,Male,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),48.4,3442.8,No
2720-WGKHP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,181.7,Yes
6734-FQAJX,Male,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.05,7171.7,No
2869-ADAWR,Female,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.3,4016.85,Yes
1535-VTJOQ,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,553,No
6368-TZZDT,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,96.85,No
0943-ZQPXH,Male,0,Yes,Yes,44,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),92.95,4122.9,No
1293-BSEUN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.7,1482.3,No
9894-EZEWG,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,74.3,Yes
1302-TPUBN,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1240.8,No
7851-FLGGQ,Male,0,No,No,1,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,44.65,44.65,Yes
0637-UBJRP,Male,0,Yes,Yes,13,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,84.05,1095.3,Yes
0940-OUQEC,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.7,788.8,Yes
2378-VTKDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,6578.55,No
0927-CNGRH,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
8608-OZTLB,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,74.05,2802.3,No
2335-GSODA,Male,0,No,Yes,23,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,40.1,857.75,No
1952-DVVSW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,184.4,No
0702-PGIBZ,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.7,364.55,Yes
6656-GULJQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.55,6093.3,No
9853-JFZDU,Female,0,Yes,No,35,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,56.85,1861.1,No
1963-VAUKV,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,Yes
3777-XROBG,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1079.65,No
6994-FGRHH,Male,0,Yes,Yes,70,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106.15,7475.1,No
4000-VGMQP,Male,0,Yes,Yes,38,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.95,2862.55,No
3999-WRNGR,Female,0,Yes,Yes,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.75,3069.45,No
3466-RITXD,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),92.4,2349.8,No
2680-XKKNJ,Female,0,No,No,8,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),58.2,469.25,No
3058-HJCUY,Male,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,102.6,4213.35,Yes
2974-GGUXS,Female,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),91.95,3301.05,No
7994-XIRTR,Male,1,No,No,54,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),65.25,3529.95,No
3259-FDWOY,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106,7723.7,Yes
2101-RANCD,Female,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.1,4144.9,No
7921-BEPCI,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),59.75,4265,No
0807-ZABDG,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.1,154.65,Yes
8510-TMWYB,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3246.45,No
3258-ZKPAI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.6,8337.45,No
1428-IEDPR,Male,0,No,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.3,5731.4,No
5298-GSTLM,Female,1,No,No,60,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),101.4,6176.6,No
4450-YOOHP,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.65,1905.4,No
6728-CZFEI,Female,0,No,No,15,Yes,No,DSL,No,No,No,No,Yes,No,One year,No,Mailed check,56.15,931.9,No
5748-RNCJT,Male,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.5,7348.8,Yes
3653-NCRDJ,Male,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,776.25,No
6121-TNHBO,Female,1,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),83,5243.05,No
5119-NZPTV,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,141.65,No
6519-ZHPXP,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.3,7679.65,No
7739-LAXOG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),91.05,2954.5,Yes
3472-QPRCH,Male,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,1006.9,No
8224-UAXBZ,Female,0,Yes,No,58,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,45.35,2540.1,No
9610-WCESF,Male,0,No,No,67,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,43.9,3097.2,No
9776-CLUJA,Female,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.5,3807.35,Yes
2486-WYVVE,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,79.3,2484,No
3865-ZYKAD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),84.9,5785.65,No
1422-DGUBX,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Electronic check,79.25,2619.15,No
2999-AANRQ,Female,0,No,No,21,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),71.05,1524.85,No
8668-KNZTI,Male,0,No,No,52,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Electronic check,53.75,2790.65,No
0480-KYJVA,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.25,1784.5,No
6034-ZRYCV,Female,0,Yes,No,72,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,54.2,3937.45,Yes
9746-YKGXB,Male,0,Yes,Yes,52,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),44.25,2276.1,No
3926-YZVVX,Female,0,No,No,41,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),50.05,2029.05,No
6000-UKLWI,Male,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,802.35,No
2079-FBMZK,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.25,418.4,Yes
6332-FBZRI,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),69.35,4653.25,No
2096-XOTMO,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,275.9,No
3266-FTKHB,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.15,343.45,No
8221-EQDGL,Male,0,Yes,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61,2130.45,No
2346-LOCWC,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1191.4,No
6608-QQLVK,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.5,50.5,Yes
0298-XACET,Male,0,Yes,Yes,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,50.2,2554,No
7560-QJAVJ,Female,0,No,No,70,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.6,5589.45,No
2995-YWTCD,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.9,467.7,No
3551-HUAZH,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes
9137-UIYPG,Female,0,Yes,Yes,35,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,3756.45,No
2809-ZMYOQ,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,3334.9,No
5084-OOVCJ,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.35,920.5,No
0254-WWRKD,Female,0,Yes,Yes,67,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),50.55,3431.75,No
7727-SHVZV,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,150.35,No
6302-JGYRJ,Male,0,No,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Mailed check,79.45,2587.7,Yes
5339-PXDVH,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,90.65,367.95,No
2205-LPVGL,Male,1,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),89.85,5125.75,No
8782-NUUOL,Male,0,No,No,60,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,79,4801.1,No
3685-YLCMQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.65,6219.6,Yes
7601-WFVZV,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
4609-KNNWG,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,550.1,No
4868-AADLV,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,116.25,7862.25,No
9529-OFXHY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.75,1242.2,No
8634-MPHTR,Male,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,4871.05,Yes
2669-QVCRG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81.3,3190.65,No
2478-EEWWM,Male,0,Yes,No,59,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.3,2666.75,No
9174-IHETN,Female,0,No,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,No,Credit card (automatic),70.35,3533.6,No
2233-TXSIU,Male,0,Yes,Yes,17,No,No phone service,DSL,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),44.45,792.15,No
9644-UMGQA,Male,0,Yes,Yes,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.15,295.65,No
3256-EZDBI,Male,1,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.45,1459.35,No
8761-NSOBC,Male,0,No,No,44,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.55,4398.15,Yes
0419-YAAPX,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.3,4297.95,No
8413-VONUO,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.65,167.3,Yes
0872-CASZJ,Male,0,Yes,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,69.1,4096.9,No
4726-DLWQN,Male,1,No,No,50,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),70.35,3454.6,No
8164-OCKUJ,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1286,No
3094-JOJAI,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,74.15,1387,No
9494-MRNYX,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.05,786.3,No
2599-CZABP,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,44.6,641.25,No
7945-PRBVF,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.45,705.45,No
4544-RXFMG,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,43.45,345.5,No
0859-YGKFW,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,345.9,No
1150-FTQGN,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.15,5811.8,No
9223-UCPVT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,94.4,Yes
7196-LIWRH,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,124.45,No
5448-VWNAM,Female,0,No,Yes,19,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,75.9,1375.6,No
1177-XZBJL,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.15,3491.55,No
3518-FSTWG,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.55,7920.7,No
9330-VOFSZ,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,6640.7,No
4351-QLCSU,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),55,55,Yes
0939-EREMR,Female,0,No,Yes,13,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,53.45,718.1,No
9389-ACWBI,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.95,320.4,No
5419-JPRRN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.45,101.45,Yes
3644-QXEHN,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,97,1334.45,No
2911-IJORQ,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),90.6,3358.65,No
5921-NGYRH,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.55,4764,No
2100-BDNSN,Female,0,Yes,No,5,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),67.95,350.3,Yes
5998-DZLYR,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),94.35,5703,No
0488-GSLFR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.5,69.5,Yes
9318-NKNFC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.85,18.85,Yes
3985-HOYPM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,525.55,No
9728-FTTVZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes
9548-LERKT,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,483.15,No
6576-FBXOJ,Male,0,Yes,No,17,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.6,934.8,No
4310-KEDTB,Female,0,No,Yes,26,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.8,786.5,No
7254-IQWOZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
2474-BRUCM,Male,1,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.85,4086.3,Yes
4062-HBMOS,Male,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.05,5364.8,No
0742-NXBGR,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.3,82.3,Yes
2676-ISHSF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No
9236-NDUCW,Female,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.1,770.4,No
4753-PADAS,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,6816.95,No
6103-QCKFX,Female,0,Yes,Yes,44,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.25,2419.55,No
7781-EWARA,Male,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.35,4138.7,No
6110-OHIHY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.25,267.6,Yes
7018-FPXHH,Male,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3457.45,No
5889-LFOLL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1115.2,Yes
5708-EVONK,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.4,5435.6,Yes
8708-XPXHZ,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,4186.3,Yes
0616-ATFGB,Male,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.05,25.05,Yes
0572-ZJKLT,Female,0,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,99.65,4630.2,No
5135-RDDQL,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,No,No,No,No,No,Two year,Yes,Bank transfer (automatic),50.65,3221.25,No
1353-LJWEM,Male,0,No,No,11,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,60.9,688.5,No
1794-SWWKL,Male,0,Yes,Yes,15,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),59.65,867.1,No
6166-YIPFO,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,64.7,4746.05,No
7016-BPGEU,Female,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,712.85,Yes
6876-ADESB,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.95,48.95,Yes
9332-GYWLO,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,54.85,355.1,No
1963-SVUCV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes
4184-VODJZ,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,91.35,5764.7,No
0220-EBGCE,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.85,167.3,Yes
1092-WPIVQ,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,428.45,No
7233-IOQNP,Female,0,Yes,No,43,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),34,1505.35,No
2834-JKOOW,Female,0,No,No,15,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,45.9,693.45,No
2754-VDLTR,Male,0,No,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.2,930.4,Yes
2400-FEQME,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,1177.95,No
3190-XFANI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),100.6,5069.65,Yes
7551-DACSP,Male,0,Yes,Yes,6,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.3,324.25,Yes
4957-SREEC,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,1458.1,No
7235-NXZCP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.85,156.4,Yes
0230-UBYPQ,Male,1,Yes,No,63,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),36.1,2298.9,No
4923-ADWXJ,Female,0,No,No,25,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.8,1679.65,No
1763-KUAAW,Female,1,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.35,369.6,No
1104-TNLZA,Male,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.8,2998,No
5195-KPUNQ,Female,1,No,No,53,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,96.75,5206.55,No
0520-FDVVT,Male,0,No,No,35,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.35,3626.1,Yes
3439-GVUSX,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No
1444-VVSGW,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.65,7968.85,Yes
5712-PTIWW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,79.85,152.45,Yes
5949-HGVJL,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,73.05,1959.5,No
5203-XEHAX,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,64.35,2053.05,No
1635-HDGFT,Female,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,398.55,No
3315-TOTBP,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,76,1130.85,Yes
8050-XGRVL,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),54.75,3425.35,No
6661-HBGWL,Female,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.75,4323.45,Yes
1518-VOWAV,Female,0,No,No,9,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,703.55,Yes
9828-QHFBK,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.15,1275.7,No
3908-MKIMJ,Male,1,Yes,No,68,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Electronic check,41.95,2965.75,No
5012-YSPJJ,Male,0,Yes,Yes,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,54.35,1647,No
5909-ECHUI,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.25,56.25,Yes
1309-BXVOQ,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.1,2249.95,Yes
6728-VOIFY,Female,0,Yes,No,63,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,96,6109.75,No
9317-WZPGV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.75,159.4,Yes
4891-NLUBA,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.45,3751.15,No
2856-HYAPG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.65,68.65,Yes
8969-PRHFK,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,411.25,No
0661-KQHNK,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,105.5,No
8709-KRDVL,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,3320.6,No
4488-PSYCG,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.25,327.45,Yes
1427-VERSM,Female,0,Yes,No,56,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5669.5,No
9801-NOSHQ,Male,0,No,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,465.45,No
6339-YPSAH,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.8,704.3,No
3621-CEOVK,Female,1,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,1369.8,Yes
6906-ANDWJ,Male,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,1107.25,Yes
2159-TURXX,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,95.55,No
1548-FEHVL,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.2,6375.2,No
3795-CAWEX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.55,8152.3,No
6215-NQCPY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,1566.75,No
5647-URDKA,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,130.25,No
2229-DPMBI,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,162.15,No
9626-WEQRM,Female,0,No,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,29.15,110.05,No
2188-SXWVT,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,55,1885.15,No
6258-PVZWJ,Male,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6302.85,No
1094-BKOSX,Female,0,Yes,Yes,45,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),51,2264.5,No
6969-MVBAI,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,816.8,No
2978-XXSOG,Female,0,No,No,22,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,59.05,1253.5,No
2342-CKIAO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,41.2,No
6953-PBDIN,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),72.95,5265.55,No
3898-BSJYF,Female,0,No,Yes,10,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.55,693.3,No
3938-YFPXD,Male,0,No,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.3,5997.1,No
7696-AMHOD,Female,0,Yes,Yes,49,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78,3824.2,No
2453-SAFNS,Female,1,No,No,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Mailed check,72.1,3886.05,No
9558-IHEZX,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.75,7283.25,No
9617-UDPEU,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.25,412.55,No
6340-DACFT,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1070.25,No
4955-VCWBI,Female,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,817.95,No
6813-GZQCG,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,1171.3,No
7426-GSWPO,Male,1,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.5,6548.65,No
3389-KTRXV,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,625.65,No
3118-UHVVQ,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.8,1911.5,No
1031-IIDEO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.85,70.85,Yes
5832-XKAES,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.8,134.7,Yes
3205-MXZRA,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Credit card (automatic),59.45,1507,No
4701-AHWMW,Male,0,Yes,No,55,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),54.55,2978.3,Yes
8317-BVKSO,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.05,299.3,No
5702-KVQRD,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,82.55,5832.65,No
0083-PIVIK,Male,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,81.25,5567.55,No
9210-IAHGH,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.75,450.8,Yes
6169-PGNCD,Female,0,No,No,57,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),74.3,4166.35,No
0023-XUOPT,Female,0,Yes,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.1,1215.6,Yes
4592-IWTJI,Female,0,Yes,Yes,3,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),29.7,91.7,Yes
4909-JOUPP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.7,7898.45,No
4657-FWVFY,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),96.35,3915.4,No
2257-BOVXD,Male,0,Yes,No,14,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),66.6,979.5,No
7729-JTEEC,Male,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.5,90.05,No
7632-MNYOY,Male,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.9,7432.05,Yes
6518-LGAOV,Female,0,Yes,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105,4026.4,Yes
7242-QZLXF,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.3,25.3,Yes
4576-CSAJH,Male,0,No,No,22,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),55.15,1193.05,Yes
1000-AJSLD,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes
9696-RMYBA,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.1,398.55,No
8690-UPCZI,Male,0,Yes,Yes,29,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),69.05,1958.45,No
1062-LHZOD,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,69.9,Yes
9770-LXDBK,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,63.15,No
4086-WITJG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1301.1,No
8344-WFMFH,Male,0,No,Yes,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,484.05,No
9600-NAXZN,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,4528,Yes
8822-KNBHV,Female,0,No,No,48,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),83.45,3887.85,No
8404-FYDIB,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),86.65,2208.75,No
2791-SFVEW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,238.15,No
6457-USBER,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,80.8,Yes
6762-NSODU,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,958.15,No
4662-EKDPQ,Male,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),62.05,118.3,Yes
9248-OJYKK,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,76.45,Yes
9888-ZCUMM,Male,0,Yes,Yes,64,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,3845.45,No
3398-FSHON,Female,1,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.3,1094.5,Yes
3319-DWOEP,Male,1,Yes,No,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.75,573.75,Yes
1074-WVEVG,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,1267,No
9979-RGMZT,Female,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,94.05,633.45,No
6437-UDQJM,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,6129.65,No
1226-IENZN,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.75,1218.25,No
2108-GLPQB,Male,0,Yes,No,25,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.55,1405.3,No
9259-PACGQ,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,62.65,2274.9,Yes
4415-IJZTP,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes
1465-VINDH,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,1068.85,Yes
5474-LAMUQ,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,533.9,No
3468-DRVQJ,Female,0,Yes,Yes,10,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,70.3,676.15,No
1122-YJBCS,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),53.65,3804.4,No
4990-ALDGW,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1118.8,No
4816-OKWNX,Male,0,Yes,Yes,50,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.4,5236.4,No
3315-IKYZQ,Male,0,Yes,Yes,28,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Mailed check,50.8,1386.8,No
2810-FTLEM,Female,0,No,No,16,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,762.25,Yes
4747-LCAQL,Male,0,No,No,25,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,79,1902,No
3411-WLRSQ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,239.05,No
0898-XCGTF,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.5,5673.7,No
8006-PYCSW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,39.8,No
8249-THVEC,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,997.75,No
8722-PRFDV,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),77.55,5574.35,No
2164-SOQXL,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,406.05,No
4020-KIUDI,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,138.85,No
5850-BDWCY,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,123.65,No
2038-YSEZE,Female,0,No,No,29,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,67.45,1801.1,No
0827-ITJPH,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),18.55,689,No
8799-OXZMD,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.75,790.7,No
1925-TIBLE,Female,0,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.5,582.5,Yes
1705-GUHPV,Female,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,1618.2,No
4779-ZGICK,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.55,1173.35,No
0048-PIHNL,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.45,900.9,No
0818-OCPZO,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.45,2122.05,Yes
3967-KXAPS,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.3,6719.9,No
1447-GIQMR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.15,69.15,Yes
3704-IEAXF,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.65,3784,No
6810-VCAEX,Female,0,No,No,47,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.65,1798.65,No
0674-EYYZV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.65,54.65,No
4480-MBMLB,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.8,3886.45,No
1395-OFUWC,Male,0,Yes,Yes,43,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.3,1224.05,No
0822-QGCXA,Female,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.85,2310.2,No
0872-NXJYS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,723.4,Yes
2832-KJCRD,Female,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.65,3988.5,No
3588-WSTTJ,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3554.6,No
4075-WKNIU,Female,0,Yes,Yes,0,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,73.35, ,No
5090-EMGTC,Female,0,Yes,No,59,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.05,6034.85,No
2346-CZYIL,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,531.6,No
3798-EPWRR,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.95,85.1,No
6258-NGCNG,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,23.5,173,No
4186-ZBUEW,Female,0,No,No,36,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.7,2511.95,No
5274-XHAKY,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),94.3,3893.6,No
1442-BQPVU,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,29.15,357.15,No
3978-YNKDD,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.85,467.5,No
4940-KHCWD,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),37.7,2288.7,No
9412-ARGBX,Female,0,No,Yes,48,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Mailed check,95.5,4627.85,Yes
1389-CXMLU,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,91.05,289.1,Yes
9589-ABEPT,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.45,6460.55,No
9388-ZEYVT,Male,0,No,No,43,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,One year,No,Electronic check,44.15,1931.3,No
0305-SQECB,Female,0,No,Yes,11,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,36.05,402.6,No
7605-SNLQG,Female,0,Yes,No,45,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),50.25,2221.55,No
4670-TABXH,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.75,7758.9,No
5561-NWEVX,Female,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.2,172.85,Yes
0064-SUDOG,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,224.5,No
8561-NMTBD,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),112.35,7388.45,No
5161-XEUVX,Male,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Mailed check,94.3,3460.95,No
8292-FRFZQ,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),41.15,1700.9,No
8591-NXRCV,Female,0,No,No,41,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),74.65,3090.65,No
7895-VONWT,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.25,1293.8,No
4526-RMTLL,Male,0,Yes,Yes,8,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),76.15,645.8,No
6253-GNHWH,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,71.1,5224.95,No
4566-NECEV,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,96.55,500.1,No
8080-POTJR,Female,0,No,No,30,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),79.3,2427.1,No
6906-MPARY,Male,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),89.6,3488.15,No
3662-FXJFO,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.5,1035.7,No
8107-RZLNV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.3,7565.35,No
0516-OOHAR,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),100.35,2799,No
2027-CWDNU,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,85.6,1601.5,No
2737-WFVYW,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.25,85.5,Yes
9912-OMZDS,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.15,6256.2,No
3733-ZEECP,Male,0,Yes,Yes,22,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,1232.9,No
7878-RTCZG,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No
2452-MRMZF,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1937.4,No
2272-QAGFO,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,1096.25,Yes
7103-IPXPJ,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,99.4,5059.75,No
4342-HFXWS,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),69.7,3023.65,No
3545-CNWRG,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,4889.2,No
1785-BPHTP,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.45,2289.9,No
4989-LIXVT,Male,1,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,6503.2,No
7315-WYOAW,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.75,1313.25,No
1173-XZPYF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,990.3,No
9850-OWRHQ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.1,228,Yes
3768-NLUBH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.6,5746.15,Yes
8676-TRMJS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75,209.1,Yes
2509-TFPJU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.75,1864.2,No
9530-GRMJG,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.1,5979.7,No
6898-RBTLU,Female,0,Yes,Yes,49,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.3,3902.45,No
5481-NTDOH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),107.05,7142.5,No
2068-WWXQZ,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,902,No
7359-SSBJK,Female,1,No,No,64,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.2,4481,Yes
2061-VVFST,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,805.2,No
0685-MLYYM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.75,154.85,Yes
0749-IRGQE,Female,1,Yes,No,13,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.3,528.45,No
2380-DAMQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,115.15,8349.7,No
2256-YLYLP,Male,0,Yes,Yes,68,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),72.95,4953.25,No
1272-ILHFG,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,332.65,No
0164-XAIRP,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,470.2,No
5666-CYCYZ,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.55,2259.35,No
4011-ARPHK,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.35,1411.35,No
8182-PNAGI,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.25,593.75,Yes
6365-HITVU,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),87.25,6328.7,No
7558-IMLMT,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1411.9,No
9488-FYQAU,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.25,6841.4,No
3590-TCXTB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No
6994-KERXL,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.9,238.5,No
7957-RYHQD,Female,1,No,No,40,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.2,3233.85,Yes
2180-DXNEG,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,1062.1,Yes
1960-UOTYM,Male,0,Yes,Yes,52,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,79.2,4016.3,No
2740-JFBOK,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24,226.55,No
6500-JVEGC,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),101.35,7110.75,No
5515-AKOAJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.1,5440.9,Yes
8976-OQHGT,Female,0,Yes,Yes,4,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,56.5,235.1,Yes
5245-VDBUR,Female,0,Yes,No,52,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.45,1958.95,No
6230-BSUXY,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes
8469-SNFFH,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),79.4,5528.9,No
2144-ESWKO,Male,0,No,No,43,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),35.2,1463.7,No
1241-FPMOF,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1025.05,No
9928-BZVLZ,Female,0,No,No,12,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,49.85,552.1,No
4114-QMKVN,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),68.75,3815.4,No
2775-SEFEE,Male,0,No,Yes,0,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),61.9, ,No
9003-CPATH,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.9,3313.4,No
1754-GKYPY,Male,1,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.75,1938.9,No
5294-CDGWY,Male,0,Yes,Yes,51,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,59.3,3014.65,Yes
7956-XQWGU,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.4,460.25,No
1591-MQJTP,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),93.65,4839.15,No
5295-PCJOO,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,184.4,Yes
2369-FEVNO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,No
9863-JZAIC,Male,0,No,No,35,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55,2010.55,Yes
7471-MQPOS,Male,1,Yes,Yes,71,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.9,5139.65,No
7660-HDPJV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes
5115-SQAAU,Female,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1673.4,No
9845-QOMAD,Male,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.75,309.35,No
7672-VFMXZ,Male,0,Yes,No,57,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.7,3171.6,No
9739-JLPQJ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),117.5,8670.1,No
9916-AYHTC,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,916,No
3855-ONCAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,78.9,299.75,No
1488-SYSFC,Male,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.65,702.05,No
7931-PXHFC,Male,0,No,No,38,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,62.3,2354.8,Yes
3990-QYKBE,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.5,3473.4,Yes
0970-QXPXW,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
3259-KNMRR,Male,1,No,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.75,4438.2,No
6120-RJKLU,Female,1,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.95,4819.75,No
9572-WUKSB,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.9,92.25,No
5893-KCLGT,Female,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1567,No
0076-LVEPS,Male,0,No,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,45,1242.45,No
3640-JQGJG,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.8,559.2,No
4919-MOAVT,Male,0,No,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,220.1,Yes
1596-OQSPS,Female,0,No,No,11,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.1,531.15,No
9867-XOBQA,Female,0,No,Yes,21,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,53.15,1183.2,No
9546-KDTRB,Female,0,No,No,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.7,465.85,No
3090-HAWSU,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.6,6876.05,Yes
9820-RMCQV,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),48.55,501,Yes
3174-RKMOW,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.95,3782.4,No
1760-CAZHT,Male,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,460.2,No
7839-QRKXN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
8441-SHIPE,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1790.35,No
5204-QZXPU,Male,0,No,No,19,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.65,733.35,Yes
4597-ELFTS,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1334,Yes
1320-GVNHT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),108.4,7767.25,No
1047-RNXZV,Male,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,876.15,No
3541-ZNUHK,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.1,4600.95,No
8450-JOVAH,Male,0,Yes,Yes,2,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.7,113.55,Yes
8725-JEDFD,Male,0,No,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,1793.25,No
7562-UXTPG,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,886.7,No
8071-SBTRN,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Mailed check,111.15,7737.55,No
1113-IUJYX,Female,0,Yes,No,14,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Mailed check,105.95,1348.9,Yes
6668-CNMFP,Female,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),89.35,1686.85,No
5626-MGTUK,Female,0,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.1,1879.25,No
5681-LLOEI,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),91.25,4013.8,No
8212-DJRCH,Male,0,Yes,Yes,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.35,434.5,No
4323-OHFOW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.55,7195.35,No
4933-BSAIP,Female,0,Yes,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,780.1,No
2030-BTZRO,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,107.6,No
1116-DXXDF,Male,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,100.45,3801.7,No
9274-CNFMO,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.95,308.7,Yes
7758-XKCBS,Male,0,No,No,15,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.7,438.25,Yes
8992-JQYUN,Male,0,Yes,No,1,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,50.35,Yes
6563-VNPMN,Female,1,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),85.7,3778.1,No
0617-AQNWT,Female,0,Yes,No,64,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,47.85,3147.5,Yes
2267-WTPYD,Female,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),94,5438.95,No
0270-THENM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.85,5102.35,No
2446-PLQVO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
4707-MAXGU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.85,1872.2,No
2710-WYVXG,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,71.1,213.35,No
3005-NFMTA,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.8,5617.75,Yes
6300-BWMJX,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,93.35,5386.5,No
8784-CGILN,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.85,1776.95,Yes
5389-FFVKB,Male,1,Yes,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),80.3,2483.05,Yes
7009-LGECI,Female,0,No,No,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.55,235.65,No
8444-WRIDW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.45,5224.35,Yes
5022-KVDQT,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.3,2272.8,No
7976-CICYS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.7,83.75,No
1036-GUDCL,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.05,4663.4,No
2005-DWQZJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,201.7,No
8148-WOCMK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,125,No
7815-PDTHL,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,684.4,No
9451-LPGOO,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.8,620.35,Yes
9173-IVZVP,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,1146.05,No
9129-UXERG,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.6,1806.35,No
3635-JBPSG,Female,0,No,No,15,No,No phone service,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Mailed check,38.8,603,No
7964-ZRKKG,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.4,5798.3,No
5868-YWPDW,Male,1,Yes,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.2,519.15,Yes
6229-LSCKB,Male,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,497.6,No
2025-JKFWI,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99,1301.7,Yes
4078-SAYYN,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,1129.75,Yes
1724-IQWNM,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,No
8217-QYOHV,Male,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.75,266.95,No
5546-QUERU,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.95,257,No
9940-HPQPG,Female,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.75,865.8,Yes
6897-UUBNU,Male,0,No,No,29,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,89.65,2623.65,No
6127-IYJOZ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,No
6618-RYATB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.55,79.55,Yes
8930-XOTDP,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.95,1082.8,No
5916-QEWPT,Female,0,Yes,No,2,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69,147.8,No
5060-TQUQN,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),83.55,2570.2,No
0531-XBKMM,Male,0,No,Yes,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.7,4378.9,No
8465-SBRXP,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),94.9,3616.25,No
2408-PSJVE,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2924.05,No
9079-YEXQJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,111.1,6014.85,Yes
9700-ZCLOT,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,32.7,No
8738-JOKAR,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,67.7,2882.25,No
4534-WGCIR,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1509.9,No
1930-WNXSB,Male,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.85,5305.05,No
2685-SREOM,Female,0,Yes,Yes,25,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,2368.4,Yes
3508-CFVZL,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,111.3,7985.9,No
0402-CQAJN,Female,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.9,3545.35,Yes
6692-UDPJC,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.65,1301,Yes
1273-MTETI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.85,372.45,Yes
0657-DOGUM,Female,0,Yes,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.6,2985.25,No
5480-HPRRX,Female,1,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.3,77.75,Yes
8792-AOROI,Female,0,Yes,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,65.5,564.35,No
0295-PPHDO,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,95.45,Yes
5146-YYFRZ,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1311.75,No
1195-OIYEJ,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,1135.7,Yes
5906-CVLHP,Female,0,Yes,Yes,45,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.15,2319.8,Yes
3452-SRFEG,Male,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,3720.35,No
4070-OKWVH,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),94.6,5025.8,No
5297-MDOIR,Female,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.15,5224.5,No
6369-MCAKO,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6185.8,No
4926-UMJZD,Female,0,Yes,No,31,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.2,1498.55,No
6848-HJTXY,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1208.6,No
6341-AEVKX,Female,0,Yes,No,62,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.3,6613.65,No
4501-EQDRN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.7,69.7,Yes
2990-HWIML,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,573.3,Yes
0682-USIXD,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,86.05,1818.9,No
6976-BWGLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.2,1787.35,No
7078-NVFAM,Female,0,Yes,Yes,32,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,35.15,1051.05,No
8065-QBYTO,Female,1,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),99.65,7181.25,No
2133-TSRRM,Female,0,No,Yes,34,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3688.6,No
7384-GHBPI,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.15,99.75,Yes
7603-USHJS,Male,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,871.4,Yes
7234-FECYN,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.35,780.5,Yes
2511-ALLCS,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,821.6,No
3191-CSNMG,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.7,239.45,No
7952-OBOYL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,89.85,244.45,No
7470-DYNOE,Male,0,No,No,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,61.1,3357.9,No
7853-OETYL,Female,0,Yes,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.05,129.6,No
1545-ACTAS,Female,0,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.7,4977.2,No
5876-HZVZM,Female,0,Yes,Yes,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),55.9,365.35,Yes
1400-MMYXY,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,334.65,Yes
5126-RCXYW,Male,0,Yes,Yes,54,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),46,2424.05,No
3085-QUOZK,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.95,43.95,Yes
2363-BJLSL,Male,0,No,No,62,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),80.4,4981.15,No
1956-YIFGE,Male,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.05,2090.25,No
3926-CUQZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.1,45.1,Yes
7921-LMDFQ,Male,1,No,No,51,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,4905.75,No
2925-MXLSX,Female,0,No,No,30,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),68.95,2038.7,No
5820-PTRYM,Female,1,Yes,No,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),68.45,4014,No
9609-BENEA,Male,0,Yes,No,35,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,69,2441.7,No
6211-WHMYA,Female,1,No,No,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,43.85,2751,No
4459-BBGHE,Male,0,No,Yes,30,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.5,1307.8,No
9945-PSVIP,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.7,383.65,No
1041-RXHRA,Female,0,No,No,41,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.25,2868.05,Yes
1750-CSKKM,Male,0,No,Yes,9,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,55.35,449.75,Yes
9108-EJFJP,Female,0,Yes,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.55,53.55,No
0530-IJVDB,Male,0,No,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.6,7882.5,No
0508-SQWPL,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.1,1087.7,No
2215-ZAFGX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.5,791.7,No
8213-TAZPM,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.75,7493.05,No
7142-HVGBG,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103,4414.3,Yes
1304-SEGFY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,97.85,6841.3,No
7242-EDTYC,Male,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,819.95,No
2904-GGUAZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.05,6052.25,No
8267-ZNYVZ,Female,0,Yes,No,33,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.75,3361.05,Yes
5136-GFPMB,Male,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,4869.5,No
2595-KIWPV,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,509.3,No
5243-SAOTC,Male,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,4308.25,No
9398-MMQTO,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.45,221.1,No
7619-PLRLP,Female,0,Yes,No,53,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),74.1,3833.95,No
6457-GIRWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes
6508-NJYRO,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,294.95,No
1450-SKCVI,Female,0,No,No,56,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,4092.85,Yes
4710-NKCAW,Male,0,Yes,Yes,5,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),64.4,316.9,No
5306-BVTKJ,Male,0,Yes,Yes,48,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),55.8,2651.2,No
0357-NVCRI,Female,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,471.7,No
5570-PTWEH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.15,216.75,Yes
2371-KFUOG,Male,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.15,5720.95,No
0463-ZSDNT,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),56.75,503.25,No
6502-MJQAE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
6257-DTAYD,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.15,7365.3,No
4616-ULAOA,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7245.9,No
7693-LCKZL,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,385,Yes
7746-AWNQW,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,35.75,961.4,No
5996-EBTKM,Female,0,Yes,Yes,67,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),69.9,4615.9,No
2758-RNWXS,Male,0,No,No,35,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,89.2,3251.3,No
2314-TNDJQ,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),55.65,3880.05,No
2405-LBMUW,Female,0,Yes,Yes,61,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),50.7,3088.75,No
3454-JFUBC,Male,1,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20,1396,No
0032-PGELS,Female,0,Yes,Yes,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.5,30.5,Yes
9039-ZVJDC,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,53.05,No
6797-LNAQX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.3,6859.5,Yes
9013-AQORL,Female,0,No,Yes,48,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),45.55,2108.35,No
2898-MRKPI,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.05,6770.5,No
2750-BJLSB,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.7,4730.6,No
3648-GZPHF,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,36.25,1151.05,No
2075-RMJIK,Female,0,Yes,Yes,5,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.4,232.55,No
9588-YRFHY,Male,0,No,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1022.6,No
6394-MFYNG,Female,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.4,5121.3,Yes
1564-NTYXF,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,82,1127.2,Yes
9364-YKUVW,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,309.4,No
5392-AKEMH,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,523.1,No
2451-YMUXS,Male,1,No,No,67,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),64.55,4250.1,No
3914-FDRHP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,86.25,770.5,No
3078-ZKNTS,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.75,246.7,No
1024-KPRBB,Female,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Mailed check,89.1,3342,No
4690-PKDQG,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.55,3930.6,No
8155-IBNHG,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.4,1747.85,Yes
0886-QGENL,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2754.45,Yes
9547-ITEFG,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.6,897.75,No
1264-FUHCX,Female,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),56.3,2780.6,No
7789-CRUVC,Female,1,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),94.2,5895.45,No
6598-KELSS,Male,0,No,Yes,50,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),43.05,2208.05,No
8739-WWKDU,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.5,2196.15,Yes
8685-WHQPW,Female,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.4,1692.6,Yes
4745-LSPLO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes
8083-YTZES,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,265.35,Yes
7240-FQLHE,Female,1,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),99.75,1836.25,Yes
6664-FPDAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,6418.9,Yes
9972-VAFJJ,Female,1,Yes,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,94,4871.45,No
0422-UXFAP,Female,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.85,4947.55,No
1904-WAJAA,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,64.35,1558.65,No
5130-YPIRV,Female,0,Yes,No,62,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),72,4284.2,No
2843-CQMEG,Male,0,No,No,24,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.7,1218.25,No
6439-PKTRR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Electronic check,80.7,5617.95,No
5351-QESIO,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.2,24.2,No
0786-VSSUD,Male,1,No,No,16,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,39,679.85,Yes
5568-DMXZS,Female,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,65.45,554.45,No
8468-FZTOE,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Electronic check,74.35,5237.4,No
6633-SYEUS,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),83.2,2032.3,No
6447-GORXK,Male,0,No,Yes,31,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25,789.2,No
6967-PEJLL,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,40.2,1525.35,No
3976-BWUCK,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),94.1,2804.45,Yes
5981-ZVXOT,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.35,3726.15,No
1684-FLBGS,Female,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1652.1,No
1389-WNUIB,Female,0,Yes,Yes,20,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),76,1588.75,No
0376-OIWME,Male,0,Yes,No,36,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,3366.05,No
3585-ISXZP,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),95.65,778.1,Yes
0218-QNVAS,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),100.55,7113.75,No
6583-QGCSI,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.05,4367.35,Yes
0804-YGEQV,Female,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,993.15,No
7164-BPTUT,Male,0,No,Yes,57,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.55,5012.35,No
4174-LPGTI,Female,0,Yes,Yes,41,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),66.5,2728.6,Yes
2523-EWWZL,Female,0,Yes,No,27,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,2093.4,No
0928-XUTSN,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.5,1011.8,No
2108-XWMPY,Male,0,No,No,3,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35.45,106.85,Yes
0052-YNYOT,Female,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,1343.4,No
6304-IJFSQ,Male,0,No,No,3,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.9,130.1,Yes
9586-JGQKH,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.4,6794.75,No
4501-VCPFK,Male,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,35.75,1022.5,No
6075-SLNIL,Male,0,No,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,3691.2,No
9347-AERRL,Male,0,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,486.2,No
0093-XWZFY,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,4036.85,Yes
2274-XUATA,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.1,4685.55,No
1980-KXVPM,Female,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.05,256.25,Yes
7703-ZEKEF,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81,1917.1,Yes
0723-DRCLG,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,74.45,Yes
5482-NUPNA,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,60.4,272.15,Yes
6691-CCIHA,Female,0,Yes,No,62,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,5150.55,No
1685-BQULA,Female,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.4,3756.4,No
9053-EJUNL,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,3645.75,No
0666-UXTJO,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.2,2874.45,No
1471-GIQKQ,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.95,49.95,No
4807-IZYOZ,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1020.75,No
1122-JWTJW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.65,70.65,Yes
9710-NJERN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,826,No
9837-FWLCH,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.2,239,No
1699-HPSBG,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,Yes,Electronic check,59.8,727.8,Yes
7203-OYKCT,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.95,7544.3,No
1035-IPQPU,Female,1,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.5,6479.4,No
7398-LXGYX,Male,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.8,3626.35,No
2823-LKABH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.05,1679.4,No
8775-CEBBJ,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.2,403.35,Yes
0550-DCXLH,Male,0,No,No,13,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,73.35,931.55,No
9281-CEDRU,Female,0,Yes,No,68,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),64.1,4326.25,No
2235-DWLJU,Female,1,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.4,263.05,No
0871-OPBXW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,39.25,No
3605-JISKB,Male,1,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),60,3316.1,No
6894-LFHLY,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,75.75,Yes
9767-FFLEM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.5,2625.25,No
0639-TSIQW,Female,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),102.95,6886.25,Yes
8456-QDAVC,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.7,1495.1,No
7750-EYXWZ,Female,0,No,No,12,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.65,743.3,No
2569-WGERO,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.15,1419.4,No
6840-RESVB,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,84.8,1990.5,No
2234-XADUH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),103.2,7362.9,No
4801-JZAZL,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,346.45,No
8361-LTMKD,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,306.6,Yes
3186-AJIEK,Male,0,No,No,66,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6844.5,No


================================================
FILE: data/Computers.csv
================================================
"","price","speed","hd","ram","screen","cd","multi","premium","ads","trend"
"1",1499,25,80,4,14,"no","no","yes",94,1
"2",1795,33,85,2,14,"no","no","yes",94,1
"3",1595,25,170,4,15,"no","no","yes",94,1
"4",1849,25,170,8,14,"no","no","no",94,1
"5",3295,33,340,16,14,"no","no","yes",94,1
"6",3695,66,340,16,14,"no","no","yes",94,1
"7",1720,25,170,4,14,"yes","no","yes",94,1
"8",1995,50,85,2,14,"no","no","yes",94,1
"9",2225,50,210,8,14,"no","no","yes",94,1
"10",2575,50,210,4,15,"no","no","yes",94,1
"11",2195,33,170,8,15,"no","no","yes",94,1
"12",2605,66,210,8,14,"no","no","yes",94,1
"13",2045,50,130,4,14,"no","no","yes",94,1
"14",2295,25,245,8,14,"no","no","yes",94,1
"15",2699,50,212,8,14,"no","no","yes",94,1
"16",2225,50,130,4,14,"no","no","yes",94,1
"17",1595,33,85,2,14,"no","no","yes",94,1
"18",2325,33,210,4,15,"no","no","yes",94,1
"19",2095,33,250,4,15,"no","no","yes",94,1
"20",4395,66,452,8,14,"no","no","yes",94,1
"21",1695,33,130,4,14,"no","no","yes",94,1
"22",2795,66,130,4,14,"no","no","yes",94,1
"23",2895,25,340,16,14,"no","no","yes",94,1
"24",2875,50,210,4,17,"no","no","yes",94,1
"25",4195,50,452,8,14,"no","no","yes",94,1
"26",1290,33,80,2,14,"no","no","yes",94,1
"27",1975,33,130,4,14,"no","no","yes",94,1
"28",3995,33,452,8,14,"no","no","yes",94,1
"29",3095,33,340,16,14,"no","no","yes",94,1
"30",3244,66,245,8,14,"no","no","yes",94,1
"31",1920,33,170,4,14,"yes","no","yes",94,1
"32",1995,25,130,4,14,"no","no","yes",94,1
"33",2595,33,210,8,17,"no","no","yes",94,1
"34",2475,50,210,4,15,"no","no","yes",94,1
"35",1999,33,170,4,14,"no","no","yes",94,1
"36",2675,66,210,4,15,"no","no","yes",94,1
"37",2325,66,210,8,14,"no","no","yes",94,1
"38",3795,66,500,8,14,"no","no","yes",94,1
"39",2405,50,210,8,14,"no","no","yes",94,1
"40",2425,50,210,8,14,"no","no","yes",94,1
"41",2895,50,245,8,14,"no","no","yes",94,1
"42",3895,66,500,8,15,"no","no","yes",94,1
"43",2499,33,212,8,14,"no","no","yes",94,1
"44",2255,33,210,8,14,"no","no","yes",94,1
"45",3495,50,340,16,14,"no","no","yes",94,1
"46",2695,33,245,8,14,"no","no","yes",94,1
"47",2195,33,130,4,14,"no","no","yes",94,1
"48",1749,25,120,4,14,"no","no","yes",94,1
"49",2399,50,212,4,14,"no","no","yes",94,1
"50",1995,33,250,4,14,"no","no","yes",94,1
"51",2499,33,170,4,14,"no","no","yes",94,1
"52",2395,33,130,4,14,"no","no","yes",94,1
"53",2995,66,340,8,15,"no","no","yes",94,1
"54",2190,33,210,4,14,"no","no","yes",94,1
"55",2199,33,212,8,14,"no","no","no",94,1
"56",2125,50,130,4,14,"no","no","yes",94,1
"57",2045,66,130,4,14,"no","no","yes",94,1
"58",3075,66,210,4,17,"no","no","yes",94,1
"59",2945,66,210,8,17,"no","no","yes",94,1
"60",1945,50,130,4,14,"no","no","yes",94,1
"61",3990,66,330,8,15,"no","no","yes",94,1
"62",1795,33,170,4,15,"no","no","yes",94,1
"63",2495,33,250,8,15,"no","no","yes",94,1
"64",2220,33,250,4,14,"yes","no","yes",94,1
"65",2195,66,85,2,14,"no","no","yes",94,1
"66",1495,25,170,4,14,"no","no","yes",94,1
"67",2325,66,130,4,14,"no","no","yes",94,1
"68",1499,25,120,4,14,"no","no","yes",94,1
"69",2199,33,212,4,14,"no","no","yes",94,1
"70",3095,66,245,8,14,"no","no","yes",94,1
"71",4020,66,500,8,14,"yes","no","yes",94,1
"72",2725,33,210,4,17,"no","no","yes",94,1
"73",1695,33,170,4,14,"no","no","yes",94,1
"74",3044,50,245,8,14,"no","no","yes",94,1
"75",2999,50,170,4,14,"no","no","yes",94,1
"76",2844,33,245,8,14,"no","no","yes",94,1
"77",1975,33,210,8,14,"no","no","yes",94,1
"78",3220,66,340,8,15,"yes","no","yes",94,1
"79",2595,50,130,4,14,"no","no","yes",94,1
"80",1629,25,80,8,14,"no","no","no",94,1
"81",3399,66,230,4,15,"no","no","yes",94,1
"82",2495,33,245,8,14,"no","no","yes",94,1
"83",2990,66,210,8,15,"no","no","yes",94,1
"84",2720,33,250,8,15,"yes","no","yes",94,1
"85",2595,25,245,8,14,"no","no","yes",94,1
"86",1999,33,120,8,14,"no","no","no",94,1
"87",2075,33,210,8,14,"no","no","yes",94,1
"88",1795,33,130,4,14,"no","no","yes",94,1
"89",1395,25,85,2,14,"no","no","yes",94,1
"90",3490,50,330,8,14,"no","no","yes",94,1
"91",2975,50,210,4,17,"no","no","yes",94,1
"92",2145,66,130,4,14,"no","no","yes",94,1
"93",2420,33,170,8,15,"yes","no","yes",94,1
"94",2505,50,210,8,14,"no","no","yes",94,1
"95",2995,66,340,16,14,"no","no","yes",95,2
"96",1495,25,170,4,14,"no","no","yes",95,2
"97",1999,33,170,4,14,"no","no","yes",95,2
"98",1920,33,170,4,14,"yes","no","yes",95,2
"99",2595,25,340,16,14,"no","no","yes",95,2
"100",1695,33,170,4,14,"no","no","yes",95,2
"101",1995,33,250,4,14,"no","no","yes",95,2
"102",2195,25,245,8,14,"no","no","yes",95,2
"103",1795,66,85,2,14,"no","no","yes",95,2
"104",2999,66,330,4,15,"no","no","yes",95,2
"105",1395,25,85,2,14,"no","no","yes",95,2
"106",2995,66,250,8,17,"no","no","yes",95,2
"107",2699,50,212,8,14,"no","no","yes",95,2
"108",1720,25,170,4,14,"yes","no","yes",95,2
"109",2045,66,170,4,14,"no","no","yes",95,2
"110",2145,66,170,4,14,"no","no","yes",95,2
"111",2995,66,340,8,15,"no","no","yes",95,2
"112",2095,33,130,4,14,"no","no","yes",95,2
"113",3895,50,452,8,14,"no","no","yes",95,2
"114",2895,50,340,16,14,"no","no","yes",95,2
"115",1499,25,170,4,14,"no","no","yes",95,2
"116",3165,66,250,4,17,"no","no","yes",95,2
"117",2525,50,250,4,15,"no","no","yes",95,2
"118",2325,66,250,8,14,"no","no","yes",95,2
"119",2499,33,170,4,14,"no","no","yes",95,2
"120",2799,33,230,4,14,"no","no","yes",95,2
"121",1795,33,170,4,14,"no","no","yes",95,2
"122",2199,33,210,4,14,"no","no","yes",95,2
"123",2355,50,250,8,14,"no","no","yes",95,2
"124",3220,66,340,8,15,"yes","no","yes",95,2
"125",2420,33,170,8,15,"yes","no","yes",95,2
"126",2544,33,245,8,14,"no","no","yes",95,2
"127",2945,50,250,4,17,"no","no","yes",95,2
"128",2295,33,245,8,14,"no","no","yes",95,2
"129",1495,33,85,2,14,"no","no","yes",95,2
"130",2065,50,170,4,14,"no","no","yes",95,2
"131",2295,66,130,4,14,"no","no","yes",95,2
"132",1799,25,170,4,14,"no","no","yes",95,2
"133",2220,33,250,4,14,"yes","no","yes",95,2
"134",2075,50,170,4,14,"no","no","yes",95,2
"135",1749,25,120,4,14,"no","no","yes",95,2
"136",2195,25,245,8,14,"no","no","yes",95,2
"137",2255,50,250,8,14,"no","no","yes",95,2
"138",2395,33,250,4,15,"no","no","yes",95,2
"139",2720,33,250,8,15,"yes","no","yes",95,2
"140",2195,50,130,4,14,"no","no","yes",95,2
"141",2225,33,250,8,14,"no","no","yes",95,2
"142",4020,66,500,8,14,"yes","no","yes",95,2
"143",2475,50,250,8,14,"no","no","yes",95,2
"144",1975,50,170,4,14,"no","no","yes",95,2
"145",3995,66,452,8,14,"no","no","yes",95,2
"146",2595,66,245,8,14,"no","no","yes",95,2
"147",2295,66,170,4,14,"no","no","yes",95,2
"148",2199,33,212,4,14,"no","no","yes",95,2
"149",1975,33,250,8,14,"no","no","yes",95,2
"150",3895,66,500,8,15,"no","no","yes",95,2
"151",2095,33,250,4,15,"no","no","yes",95,2
"152",2225,33,250,8,15,"no","no","yes",95,2
"153",3599,66,330,8,14,"no","no","yes",95,2
"154",2075,33,250,8,14,"no","no","yes",95,2
"155",2499,33,212,8,14,"no","no","yes",95,2
"156",2575,66,250,8,15,"no","no","yes",95,2
"157",2999,50,170,4,14,"no","no","yes",95,2
"158",2195,33,170,8,15,"no","no","yes",95,2
"159",3065,50,250,4,17,"no","no","yes",95,2
"160",3399,66,230,4,15,"no","no","yes",95,2
"161",1995,33,130,4,14,"no","no","yes",95,2
"162",1595,25,170,4,15,"no","no","yes",95,2
"163",2645,50,250,4,15,"no","no","yes",95,2
"164",2345,50,250,8,14,"no","no","yes",95,2
"165",2795,33,340,16,14,"no","no","yes",95,2
"166",1895,25,130,4,14,"no","no","yes",95,2
"167",2495,50,245,8,14,"no","no","yes",95,2
"168",2599,50,210,4,14,"no","no","yes",95,2
"169",1595,33,85,2,14,"no","no","yes",95,2
"170",1999,33,120,8,14,"no","no","no",95,2
"171",2425,66,250,8,14,"no","no","yes",95,2
"172",2495,33,250,8,15,"no","no","yes",95,2
"173",1795,33,170,4,15,"no","no","yes",95,2
"174",2744,66,245,8,14,"no","no","yes",95,2
"175",3795,33,452,8,14,"no","no","yes",95,2
"176",2745,66,250,4,15,"no","no","yes",95,2
"177",2399,50,212,4,14,"no","no","yes",95,2
"178",2645,33,250,8,17,"no","no","yes",95,2
"179",1945,33,170,4,14,"no","no","yes",95,2
"180",1695,50,85,2,14,"no","no","yes",95,2
"181",2195,50,170,4,14,"no","no","yes",95,2
"182",2815,33,250,4,17,"no","no","yes",95,2
"183",1899,25,120,4,14,"no","no","yes",95,2
"184",2644,50,245,8,14,"no","no","yes",95,2
"185",3795,66,500,8,14,"no","no","yes",95,2
"186",2395,33,245,8,14,"no","no","yes",95,2
"187",1695,33,170,4,14,"no","no","yes",95,2
"188",2575,66,250,8,14,"no","no","yes",95,2
"189",2695,33,340,16,14,"no","no","yes",95,2
"190",2999,66,245,16,15,"no","no","yes",100,3
"191",1899,33,170,4,14,"no","no","yes",100,3
"192",2499,50,210,4,14,"no","no","yes",100,3
"193",1999,33,213,8,14,"no","no","yes",100,3
"194",2055,50,170,4,14,"no","no","yes",100,3
"195",2065,50,170,4,14,"no","no","yes",100,3
"196",2055,33,250,8,14,"no","no","yes",100,3
"197",2099,33,212,4,14,"no","no","yes",100,3
"198",1995,33,130,4,14,"no","no","yes",100,3
"199",2220,33,250,4,14,"yes","no","yes",100,3
"200",3220,66,340,8,15,"yes","no","yes",100,3
"201",3995,66,452,8,14,"no","no","yes",100,3
"202",2785,33,250,8,17,"no","no","yes",100,3
"203",2475,50,250,8,15,"no","no","yes",100,3
"204",1499,25,170,4,14,"no","no","yes",100,3
"205",1595,33,85,2,14,"no","no","yes",100,3
"206",2099,66,120,4,14,"no","no","yes",100,3
"207",2885,66,170,8,15,"no","no","yes",100,3
"208",3795,33,452,8,14,"no","no","yes",100,3
"209",2295,66,170,4,14,"no","no","yes",100,3
"210",3895,66,500,8,15,"no","no","yes",100,3
"211",2395,33,245,8,14,"no","no","yes",100,3
"212",3135,66,250,8,17,"no","no","yes",100,3
"213",2420,33,170,8,15,"yes","no","yes",100,3
"214",2125,66,170,4,14,"no","no","yes",100,3
"215",2985,66,250,8,17,"no","no","yes",100,3
"216",1395,25,85,2,14,"no","no","yes",100,3
"217",2295,33,245,8,14,"no","no","yes",100,3
"218",2095,33,130,4,14,"no","no","yes",100,3
"219",2795,33,340,16,14,"no","no","yes",100,3
"220",1945,33,170,4,14,"no","no","yes",100,3
"221",2635,33,250,8,17,"no","no","yes",100,3
"222",2695,66,250,8,15,"no","no","yes",100,3
"223",2199,50,213,8,14,"no","no","yes",100,3
"224",3895,50,452,8,14,"no","no","yes",100,3
"225",2599,50,212,8,14,"no","no","yes",100,3
"226",2745,66,170,8,14,"no","no","yes",100,3
"227",1920,33,170,4,14,"yes","no","yes",100,3
"228",2895,50,340,16,14,"no","no","yes",100,3
"229",2199,33,210,4,14,"no","no","yes",100,3
"230",2915,50,250,8,17,"no","no","yes",100,3
"231",1999,33,120,8,14,"no","no","no",100,3
"232",2720,33,250,8,15,"yes","no","yes",100,3
"233",1999,33,170,4,14,"no","no","yes",100,3
"234",2195,33,170,8,15,"no","no","yes",100,3
"235",2195,25,245,8,14,"no","no","yes",100,3
"236",2295,66,130,4,14,"no","no","yes",100,3
"237",1595,25,170,4,15,"no","no","yes",100,3
"238",1695,33,170,4,14,"no","no","yes",100,3
"239",1495,25,170,4,14,"no","no","yes",100,3
"240",3795,66,500,8,14,"no","no","yes",100,3
"241",1699,33,120,4,14,"no","no","yes",100,3
"242",1895,25,130,4,14,"no","no","yes",100,3
"243",2335,50,250,8,14,"no","no","yes",100,3
"244",2495,50,245,8,14,"no","no","yes",100,3
"245",2575,66,250,8,14,"no","no","yes",100,3
"246",2495,33,250,8,15,"no","no","yes",100,3
"247",3299,66,245,16,15,"no","no","yes",100,3
"248",2699,66,213,8,14,"no","no","yes",100,3
"249",1695,50,85,2,14,"no","no","yes",100,3
"250",2999,66,170,4,14,"no","no","yes",100,3
"251",2399,66,213,8,14,"no","no","yes",100,3
"252",1499,25,170,4,14,"no","no","yes",100,3
"253",2395,33,170,8,14,"no","no","yes",100,3
"254",1720,25,170,4,14,"yes","no","yes",100,3
"255",1495,33,85,2,14,"no","no","yes",100,3
"256",2399,66,120,4,14,"no","no","yes",100,3
"257",3035,50,250,8,17,"no","no","yes",100,3
"258",2449,33,230,4,14,"no","no","yes",100,3
"259",1775,33,170,4,14,"no","no","yes",100,3
"260",2599,33,245,16,15,"no","no","yes",100,3
"261",2995,66,340,8,15,"no","no","yes",100,3
"262",2345,33,250,8,15,"no","no","yes",100,3
"263",2535,33,170,8,15,"no","no","yes",100,3
"264",2345,50,250,8,14,"no","no","yes",100,3
"265",1899,50,120,4,14,"no","no","yes",100,3
"266",2195,50,130,4,14,"no","no","yes",100,3
"267",2645,50,170,8,14,"no","no","yes",100,3
"268",2785,50,170,8,15,"no","no","yes",100,3
"269",2799,50,245,16,15,"no","no","yes",100,3
"270",2999,66,340,4,15,"no","no","yes",100,3
"271",2095,33,250,4,15,"no","no","yes",100,3
"272",4020,66,500,8,14,"yes","no","yes",100,3
"273",2595,50,250,8,15,"no","no","yes",100,3
"274",2695,33,340,16,14,"no","no","yes",100,3
"275",2195,33,250,8,15,"no","no","yes",100,3
"276",2995,66,340,16,14,"no","no","yes",100,3
"277",2195,50,170,4,14,"no","no","yes",100,3
"278",2399,33,212,8,14,"no","no","yes",100,3
"279",2405,66,250,8,14,"no","no","yes",100,3
"280",1795,66,85,2,14,"no","no","yes",100,3
"281",2225,33,250,8,14,"no","no","yes",100,3
"282",1995,33,250,4,14,"no","no","yes",100,3
"283",2299,50,212,4,14,"no","no","yes",100,3
"284",1795,33,170,4,15,"no","no","yes",100,3
"285",2545,66,250,8,15,"no","no","yes",100,3
"286",2595,66,245,8,14,"no","no","yes",100,3
"287",1399,25,170,4,14,"no","no","yes",100,3
"288",2475,50,250,8,14,"no","no","yes",100,3
"289",2595,25,340,16,14,"no","no","yes",100,3
"290",1695,50,85,2,14,"no","no","yes",108,4
"291",3995,66,452,8,14,"no","no","yes",108,4
"292",2999,66,240,4,15,"no","no","yes",108,4
"293",2495,50,245,8,14,"no","no","yes",108,4
"294",2455,66,250,8,14,"no","no","yes",108,4
"295",2299,50,212,4,14,"no","no","yes",108,4
"296",2690,25,340,16,14,"no","no","yes",108,4
"297",2155,33,250,8,14,"no","no","yes",108,4
"298",2890,33,340,16,14,"no","no","yes",108,4
"299",1899,33,170,4,14,"no","no","yes",108,4
"300",1595,25,170,4,14,"no","no","yes",108,4
"301",1990,25,130,4,14,"no","no","yes",108,4
"302",1895,25,130,4,14,"no","no","yes",108,4
"303",1895,33,170,4,14,"no","no","yes",108,4
"304",2799,50,245,16,15,"no","no","yes",108,4
"305",2590,50,245,8,14,"no","no","yes",108,4
"306",1499,25,170,4,14,"no","no","yes",108,4
"307",2955,50,250,8,17,"no","no","yes",108,4
"308",2285,50,250,8,14,"no","no","yes",108,4
"309",2475,50,250,8,15,"no","no","yes",108,4
"310",3795,33,452,8,14,"no","no","yes",108,4
"311",1999,33,213,8,14,"no","no","yes",108,4
"312",2599,50,212,8,14,"no","no","yes",108,4
"313",2895,50,340,16,14,"no","no","yes",108,4
"314",3895,50,452,8,14,"no","no","yes",108,4
"315",2695,66,250,8,14,"no","no","yes",108,4
"316",1995,50,170,4,14,"no","no","yes",108,4
"317",2399,66,120,4,14,"no","no","yes",108,4
"318",2605,33,250,8,17,"no","no","yes",108,4
"319",2335,66,250,8,14,"no","no","yes",108,4
"320",2195,66,170,4,14,"no","no","yes",108,4
"321",2425,66,250,8,15,"no","no","yes",108,4
"322",2995,66,340,16,14,"no","no","yes",108,4
"323",2599,33,245,16,15,"no","no","yes",108,4
"324",2390,33,245,8,14,"no","no","yes",108,4
"325",1690,33,85,2,14,"no","no","yes",108,4
"326",1495,33,85,2,14,"no","no","yes",108,4
"327",2395,33,250,8,14,"no","no","yes",108,4
"328",2099,33,212,4,14,"no","no","yes",108,4
"329",3090,66,340,16,14,"no","no","yes",108,4
"330",2599,33,240,4,15,"no","no","yes",108,4
"331",1999,33,170,4,14,"no","no","yes",108,4
"332",2195,50,130,4,14,"no","no","yes",108,4
"333",2190,33,130,4,14,"no","no","yes",108,4
"334",2025,50,170,4,14,"no","no","yes",108,4
"335",1699,33,120,4,14,"no","no","yes",108,4
"336",2790,33,340,16,14,"no","no","yes",108,4
"337",1795,33,170,4,14,"no","no","yes",108,4
"338",2990,50,340,16,14,"no","no","yes",108,4
"339",2905,66,250,8,17,"no","no","yes",108,4
"340",2825,33,250,8,17,"no","no","yes",108,4
"341",2390,66,130,4,14,"no","no","yes",108,4
"342",2699,66,213,8,14,"no","no","yes",108,4
"343",2995,66,340,8,15,"no","no","yes",108,4
"344",1590,33,85,2,14,"no","no","yes",108,4
"345",2345,33,250,8,15,"no","no","yes",108,4
"346",2199,50,213,8,14,"no","no","yes",108,4
"347",2290,50,130,4,14,"no","no","yes",108,4
"348",2595,50,250,8,15,"no","no","yes",108,4
"349",2515,33,250,8,15,"no","no","yes",108,4
"350",2145,50,170,4,14,"no","no","yes",108,4
"351",2405,50,250,8,14,"no","no","yes",108,4
"352",2525,50,250,8,14,"no","no","yes",108,4
"353",2595,50,250,8,15,"no","no","yes",108,4
"354",2090,33,130,4,14,"no","no","yes",108,4
"355",2075,66,170,4,14,"no","no","yes",108,4
"356",2799,50,240,4,15,"no","no","yes",108,4
"357",3299,66,245,16,15,"no","no","yes",108,4
"358",2645,66,250,8,15,"no","no","yes",108,4
"359",3125,66,250,8,17,"no","no","yes",108,4
"360",2295,33,245,8,14,"no","no","yes",108,4
"361",1775,33,170,4,14,"no","no","yes",108,4
"362",2095,33,130,4,14,"no","no","yes",108,4
"363",2155,50,250,8,14,"no","no","yes",108,4
"364",3075,50,250,8,17,"no","no","yes",108,4
"365",2815,66,250,8,15,"no","no","yes",108,4
"366",1599,25,170,4,14,"no","no","yes",108,4
"367",2099,33,120,4,14,"no","no","no",108,4
"368",2645,50,250,8,15,"no","no","yes",108,4
"369",1795,66,85,2,14,"no","no","yes",108,4
"370",2035,33,250,8,14,"no","no","yes",108,4
"371",2490,33,245,8,14,"no","no","yes",108,4
"372",3225,66,212,4,15,"no","no","no",108,4
"373",2690,66,245,8,14,"no","no","yes",108,4
"374",2195,25,245,8,14,"no","no","yes",108,4
"375",1890,66,85,2,14,"no","no","yes",108,4
"376",1995,33,130,4,14,"no","no","yes",108,4
"377",2695,33,340,16,14,"no","no","yes",108,4
"378",2595,25,340,16,14,"no","no","yes",108,4
"379",1595,33,85,2,14,"no","no","yes",108,4
"380",1490,25,85,2,14,"no","no","yes",108,4
"381",2125,33,250,8,15,"no","no","yes",108,4
"382",2399,66,213,8,14,"no","no","yes",108,4
"383",2795,33,340,16,14,"no","no","yes",108,4
"384",2290,25,245,8,14,"no","no","yes",108,4
"385",2395,33,245,8,14,"no","no","yes",108,4
"386",2399,33,212,8,14,"no","no","yes",108,4
"387",1790,50,85,2,14,"no","no","yes",108,4
"388",2099,66,120,4,14,"no","no","yes",108,4
"389",1395,25,85,2,14,"no","no","yes",108,4
"390",1895,50,170,4,14,"no","no","yes",108,4
"391",2295,66,130,4,14,"no","no","yes",108,4
"392",2595,66,245,8,14,"no","no","yes",108,4
"393",1899,50,120,4,14,"no","no","yes",108,4
"394",1399,25,170,4,14,"no","no","yes",108,4
"395",2999,66,170,4,14,"no","no","yes",108,4
"396",2999,66,245,16,15,"no","no","yes",108,4
"397",2449,33,230,4,14,"no","no","yes",108,4
"398",1795,33,170,4,14,"no","no","yes",139,5
"399",1899,33,212,4,14,"no","no","yes",139,5
"400",2395,33,340,8,14,"no","no","yes",139,5
"401",2399,50,320,8,14,"no","no","yes",139,5
"402",1595,33,170,4,14,"no","no","yes",139,5
"403",2490,33,340,8,14,"no","no","yes",139,5
"404",2995,66,452,16,14,"no","no","yes",139,5
"405",2890,33,452,16,14,"no","no","yes",139,5
"406",2190,33,214,4,15,"no","no","yes",139,5
"407",2399,66,213,8,14,"no","no","yes",139,5
"408",2285,50,250,8,14,"no","no","yes",139,5
"409",2590,33,340,8,15,"no","no","yes",139,5
"410",2795,33,452,16,14,"no","no","yes",139,5
"411",1495,33,170,4,14,"no","no","yes",139,5
"412",2075,66,170,4,14,"no","no","yes",139,5
"413",2090,33,214,4,15,"no","no","yes",139,5
"414",2195,33,250,8,15,"no","no","yes",139,5
"415",3999,66,340,16,17,"no","no","yes",139,5
"416",1699,33,120,4,14,"no","no","yes",139,5
"417",1995,33,250,8,14,"no","no","yes",139,5
"418",1690,33,107,2,14,"no","no","yes",139,5
"419",3495,33,452,8,14,"no","no","yes",139,5
"420",2195,66,170,4,14,"no","no","yes",139,5
"421",3090,66,452,16,14,"no","no","yes",139,5
"422",2690,25,452,16,14,"no","no","yes",139,5
"423",1895,25,214,4,14,"no","no","yes",139,5
"424",2495,50,250,8,15,"no","no","yes",139,5
"425",2405,50,250,8,14,"no","no","yes",139,5
"426",1595,33,107,2,14,"no","no","yes",139,5
"427",2890,33,452,16,15,"no","no","yes",139,5
"428",2390,25,340,8,14,"no","no","yes",139,5
"429",1690,33,107,2,15,"no","no","yes",139,5
"430",2295,25,340,8,14,"no","no","yes",139,5
"431",2790,33,452,16,14,"no","no","yes",139,5
"432",2695,33,452,16,14,"no","no","yes",139,5
"433",2595,25,452,16,14,"no","no","yes",139,5
"434",1995,33,214,4,14,"no","no","yes",139,5
"435",2049,33,405,4,14,"no","no","yes",139,5
"436",1399,25,170,4,14,"no","no","yes",139,5
"437",2805,33,250,8,17,"no","no","yes",139,5
"438",2019,33,120,4,14,"no","no","no",139,5
"439",3055,50,250,8,17,"no","no","yes",139,5
"440",2990,50,452,16,14,"no","no","yes",139,5
"441",2605,33,250,8,17,"no","no","yes",139,5
"442",2125,33,250,8,15,"no","no","yes",139,5
"443",1490,25,107,2,14,"no","no","yes",139,5
"444",1699,33,170,4,14,"no","no","yes",139,5
"445",2495,33,340,8,14,"no","no","yes",139,5
"446",2599,33,245,16,15,"no","no","yes",139,5
"447",2220,33,250,8,14,"yes","no","yes",139,5
"448",1590,33,107,2,15,"no","no","yes",139,5
"449",1499,33,120,4,14,"no","no","yes",139,5
"450",2625,66,250,8,15,"no","no","yes",139,5
"451",1395,25,107,2,14,"no","no","yes",139,5
"452",2395,33,250,8,14,"no","no","yes",139,5
"453",2420,33,250,8,15,"yes","no","yes",139,5
"454",2425,66,250,8,15,"no","no","yes",139,5
"455",2515,33,250,8,15,"no","no","yes",139,5
"456",2375,50,250,8,15,"no","no","yes",139,5
"457",3105,66,250,8,17,"no","no","yes",139,5
"458",2390,66,214,4,15,"no","no","yes",139,5
"459",2905,66,250,8,17,"no","no","yes",139,5
"460",2690,25,452,16,15,"no","no","yes",139,5
"461",2695,66,250,8,14,"no","no","yes",139,5
"462",1790,50,107,2,14,"no","no","yes",139,5
"463",3495,66,500,8,14,"no","no","yes",139,5
"464",2790,33,452,16,15,"no","no","yes",139,5
"465",1790,50,107,2,15,"no","no","yes",139,5
"466",1795,66,107,2,14,"no","no","yes",139,5
"467",2995,66,340,8,15,"yes","no","yes",139,5
"468",1995,50,170,4,14,"no","no","yes",139,5
"469",2599,50,405,8,14,"no","no","yes",139,5
"470",3720,66,500,8,14,"yes","no","yes",139,5
"471",2155,33,250,8,14,"no","no","yes",139,5
"472",1599,25,170,4,14,"no","no","yes",139,5
"473",2525,50,250,8,14,"no","no","yes",139,5
"474",2299,33,405,8,14,"no","no","yes",139,5
"475",1895,33,170,4,14,"no","no","yes",139,5
"476",1490,25,107,2,15,"no","no","yes",139,5
"477",2035,33,250,8,14,"no","no","yes",139,5
"478",1720,33,170,4,14,"yes","no","yes",139,5
"479",2595,50,250,8,15,"no","no","yes",139,5
"480",2290,50,214,4,15,"no","no","yes",139,5
"481",2455,50,250,8,15,"no","no","yes",139,5
"482",1695,50,107,2,14,"no","no","yes",139,5
"483",2249,50,230,4,14,"no","no","yes",139,5
"484",1999,50,212,4,14,"no","no","yes",139,5
"485",2455,66,250,8,14,"no","no","yes",139,5
"486",2595,50,340,8,14,"no","no","yes",139,5
"487",2859,66,212,4,15,"no","no","no",139,5
"488",2599,50,405,4,15,"no","no","yes",139,5
"489",2855,50,250,8,17,"no","no","yes",139,5
"490",2790,66,340,8,14,"no","no","yes",139,5
"491",3990,66,1000,16,14,"no","no","yes",139,5
"492",3595,50,452,8,14,"no","no","yes",139,5
"493",2195,50,214,4,14,"no","no","yes",139,5
"494",3599,66,405,8,14,"no","no","yes",139,5
"495",2690,50,340,8,15,"no","no","yes",139,5
"496",1999,33,213,8,14,"no","no","yes",139,5
"497",2990,50,452,16,15,"no","no","yes",139,5
"498",1890,66,107,2,14,"no","no","yes",139,5
"499",2190,33,214,4,14,"no","no","yes",139,5
"500",2720,50,250,8,15,"yes","no","yes",139,5
"501",2145,50,170,4,14,"no","no","yes",139,5
"502",2295,66,214,4,14,"no","no","yes",139,5
"503",1990,25,214,4,15,"no","no","yes",139,5
"504",2695,66,340,8,14,"no","no","yes",139,5
"505",3149,66,230,8,14,"no","no","yes",139,5
"506",1775,33,170,4,14,"no","no","yes",139,5
"507",2490,33,340,8,15,"no","no","yes",139,5
"508",3599,33,340,16,17,"no","no","yes",139,5
"509",2690,50,340,8,14,"no","no","yes",139,5
"510",3195,66,540,8,15,"no","no","yes",139,5
"511",3695,66,452,8,14,"no","no","yes",139,5
"512",2645,50,250,8,15,"no","no","yes",139,5
"513",3090,66,452,16,15,"no","no","yes",139,5
"514",1890,66,107,2,15,"no","no","yes",139,5
"515",1999,33,170,4,14,"no","no","yes",139,5
"516",2935,50,250,8,17,"no","no","yes",139,5
"517",1990,25,214,4,14,"no","no","yes",139,5
"518",2290,50,214,4,14,"no","no","yes",139,5
"519",2390,66,214,4,14,"no","no","yes",139,5
"520",2025,50,170,4,14,"no","no","yes",139,5
"521",2095,33,214,4,14,"no","no","yes",139,5
"522",2590,33,340,8,14,"no","no","yes",139,5
"523",1499,25,170,4,14,"no","no","yes",139,5
"524",2325,33,250,8,15,"no","no","yes",139,5
"525",2099,66,120,4,14,"no","no","yes",139,5
"526",2999,66,245,16,15,"no","no","yes",139,5
"527",2790,66,340,8,15,"no","no","yes",139,5
"528",1590,33,107,2,14,"no","no","yes",139,5
"529",2499,50,170,4,14,"no","no","yes",139,5
"530",2575,50,250,8,15,"no","no","yes",139,5
"531",2390,25,340,8,15,"no","no","yes",139,5
"532",1495,33,107,2,14,"no","no","yes",139,5
"533",2895,50,452,16,14,"no","no","yes",139,5
"534",2335,66,250,8,14,"no","no","yes",139,5
"535",2090,33,214,4,14,"no","no","yes",139,5
"536",2815,66,250,8,15,"no","no","yes",139,5
"537",2195,50,250,4,14,"no","no","yes",176,6
"538",1899,33,170,4,14,"no","no","yes",176,6
"539",3599,66,450,8,14,"no","no","yes",176,6
"540",2095,33,250,4,14,"no","no","yes",176,6
"541",2690,33,452,16,15,"no","no","yes",176,6
"542",2795,66,250,8,15,"no","no","yes",176,6
"543",2799,33,240,4,14,"no","no","no",176,6
"544",2698,66,245,8,14,"yes","no","yes",176,6
"545",2390,33,340,8,14,"no","no","yes",176,6
"546",2695,66,340,8,14,"no","no","yes",176,6
"547",2095,33,214,4,14,"no","no","yes",176,6
"548",2599,50,450,8,15,"no","no","yes",176,6
"549",1825,50,170,4,14,"no","no","yes",176,6
"550",1790,50,107,2,15,"no","no","yes",176,6
"551",2720,66,340,8,14,"yes","no","yes",176,6
"552",1295,25,107,2,14,"no","no","yes",176,6
"553",2990,50,452,16,14,"no","no","yes",176,6
"554",1890,66,107,2,15,"no","no","yes",176,6
"555",2595,33,452,16,14,"no","no","yes",176,6
"556",1490,33,107,2,14,"no","no","yes",176,6
"557",2075,50,250,8,14,"no","no","yes",176,6
"558",3699,33,345,16,17,"no","no","yes",176,6
"559",2690,33,452,16,14,"no","no","yes",176,6
"560",2245,66,250,4,15,"no","no","yes",176,6
"561",3999,66,345,16,17,"no","no","yes",176,6
"562",2195,50,214,4,14,"no","no","yes",176,6
"563",2999,50,240,4,14,"no","no","no",176,6
"564",1895,33,212,8,14,"no","no","yes",176,6
"565",2295,50,212,8,15,"no","no","yes",176,6
"566",2190,33,214,4,14,"no","no","yes",176,6
"567",1690,33,107,2,14,"no","no","yes",176,6
"568",1390,25,107,2,15,"no","no","yes",176,6
"569",1890,25,214,4,14,"no","no","yes",176,6
"570",2290,25,340,8,14,"no","no","yes",176,6
"571",1720,33,170,4,14,"yes","no","yes",176,6
"572",2325,66,250,8,15,"no","no","yes",176,6
"573",1699,33,170,4,14,"no","no","yes",176,6
"574",2045,50,250,4,15,"no","no","yes",176,6
"575",2299,66,120,4,14,"no","no","no",176,6
"576",2755,66,250,8,17,"no","no","yes",176,6
"577",2145,66,250,4,14,"no","no","yes",176,6
"578",3090,66,452,16,14,"no","no","yes",176,6
"579",2790,66,340,8,14,"no","no","yes",176,6
"580",2099,33,120,4,14,"no","no","no",176,6
"581",1998,66,130,4,14,"yes","no","yes",176,6
"582",2590,33,340,8,14,"no","no","yes",176,6
"583",3365,50,540,8,17,"no","no","yes",176,6
"584",2545,66,250,8,15,"no","no","yes",176,6
"585",3265,33,540,8,17,"no","no","yes",176,6
"586",2250,66,170,4,14,"yes","no","yes",176,6
"587",2890,33,452,16,14,"no","no","yes",176,6
"588",1599,25,170,4,14,"no","no","yes",176,6
"589",1825,33,170,4,15,"no","no","yes",176,6
"590",2125,66,170,4,15,"no","no","yes",176,6
"591",2245,33,250,8,15,"no","no","yes",176,6
"592",1945,33,250,4,15,"no","no","yes",176,6
"593",2890,33,452,16,15,"no","no","yes",176,6
"594",1599,66,130,4,14,"no","no","yes",176,6
"595",2295,66,214,4,14,"no","no","yes",176,6
"596",1520,25,80,4,14,"yes","no","yes",176,6
"597",2745,33,540,8,14,"no","no","yes",176,6
"598",2899,66,340,8,15,"no","no","yes",176,6
"599",2390,66,214,4,14,"no","no","yes",176,6
"600",1675,25,120,4,14,"no","no","no",176,6
"601",3149,66,230,8,14,"no","no","yes",176,6
"602",2590,25,452,16,14,"no","no","yes",176,6
"603",1999,50,212,4,14,"no","no","yes",176,6
"604",2590,25,452,16,15,"no","no","yes",176,6
"605",1449,25,120,4,14,"no","no","no",176,6
"606",1490,33,107,2,15,"no","no","yes",176,6
"607",2299,66,245,8,14,"no","no","yes",176,6
"608",1595,33,107,2,14,"no","no","yes",176,6
"609",2495,33,250,8,15,"no","no","yes",176,6
"610",1890,66,107,2,14,"no","no","yes",176,6
"611",2995,66,452,16,14,"no","no","yes",176,6
"612",3609,66,527,4,15,"no","no","no",176,6
"613",2790,66,340,8,15,"no","no","yes",176,6
"614",2295,33,340,8,14,"no","no","yes",176,6
"615",1799,33,120,4,14,"no","no","no",176,6
"616",3990,66,1000,16,14,"no","no","yes",176,6
"617",2999,66,240,4,15,"no","no","yes",176,6
"618",2275,33,250,8,15,"no","no","yes",176,6
"619",2290,50,214,4,15,"no","no","yes",176,6
"620",1499,33,130,4,14,"no","no","yes",176,6
"621",2555,50,250,8,17,"no","no","yes",176,6
"622",2025,66,170,4,14,"no","no","yes",176,6
"623",2299,33,450,8,14,"no","no","yes",176,6
"624",1975,33,250,8,14,"no","no","yes",176,6
"625",2109,33,120,4,14,"no","no","no",176,6
"626",3595,66,500,8,15,"no","no","yes",176,6
"627",2595,66,340,8,15,"no","no","yes",176,6
"628",2395,66,250,4,14,"no","no","yes",176,6
"629",1899,33,250,4,14,"no","no","yes",176,6
"630",2120,33,212,8,14,"yes","no","yes",176,6
"631",1790,50,107,2,14,"no","no","yes",176,6
"632",1899,33,212,4,14,"no","no","yes",176,6
"633",1990,33,214,4,14,"no","no","yes",176,6
"634",1399,25,170,4,14,"no","no","yes",176,6
"635",1898,33,130,4,14,"yes","no","yes",176,6
"636",1725,33,170,4,14,"no","no","yes",176,6
"637",2595,50,250,8,15,"no","no","yes",176,6
"638",1499,25,170,4,14,"no","no","yes",176,6
"639",1990,33,214,4,15,"no","no","yes",176,6
"640",2595,50,340,8,14,"no","no","yes",176,6
"641",1395,25,80,4,15,"no","no","yes",176,6
"642",2675,33,250,8,17,"no","no","yes",176,6
"643",1899,25,120,4,14,"no","no","no",176,6
"644",2990,50,452,16,15,"no","no","yes",176,6
"645",1925,50,170,4,15,"no","no","yes",176,6
"646",4098,33,345,16,17,"yes","no","yes",176,6
"647",2895,50,452,16,14,"no","no","yes",176,6
"648",2495,66,340,8,14,"no","no","yes",176,6
"649",1995,33,212,8,15,"no","no","yes",176,6
"650",1595,33,170,4,15,"no","no","yes",176,6
"651",2195,25,340,8,14,"no","no","yes",176,6
"652",2399,50,320,8,14,"no","no","yes",176,6
"653",2390,33,340,8,15,"no","no","yes",176,6
"654",2690,50,340,8,14,"no","no","yes",176,6
"655",1690,33,107,2,15,"no","no","yes",176,6
"656",2398,33,245,8,14,"yes","no","yes",176,6
"657",2775,50,250,8,17,"no","no","yes",176,6
"658",1395,33,107,2,14,"no","no","yes",176,6
"659",2520,50,212,8,15,"yes","no","yes",176,6
"660",2125,50,250,8,15,"no","no","yes",176,6
"661",4398,66,345,16,17,"yes","no","yes",176,6
"662",2290,50,214,4,14,"no","no","yes",176,6
"663",2489,50,250,8,15,"no","no","yes",176,6
"664",2190,33,214,4,15,"no","no","yes",176,6
"665",2599,33,240,4,15,"no","no","yes",176,6
"666",3789,66,527,8,15,"no","no","no",176,6
"667",2699,33,345,16,15,"no","no","yes",176,6
"668",2799,50,240,4,15,"no","no","yes",176,6
"669",2499,50,170,4,14,"no","no","yes",176,6
"670",2049,33,450,4,14,"no","no","yes",176,6
"671",2575,66,250,8,15,"no","no","yes",176,6
"672",2999,66,345,16,15,"no","no","yes",176,6
"673",3495,66,500,8,14,"no","no","yes",176,6
"674",2995,66,340,16,15,"yes","no","yes",176,6
"675",2275,66,250,8,14,"no","no","yes",176,6
"676",2590,33,340,8,15,"no","no","yes",176,6
"677",2975,66,250,8,17,"no","no","yes",176,6
"678",1950,33,170,4,14,"yes","no","yes",176,6
"679",2599,50,450,8,14,"no","no","yes",176,6
"680",1695,50,107,2,14,"no","no","yes",176,6
"681",2455,33,250,8,17,"no","no","yes",176,6
"682",1495,33,170,4,14,"no","no","yes",176,6
"683",2290,25,340,8,15,"no","no","yes",176,6
"684",3720,66,500,8,14,"yes","no","yes",176,6
"685",1795,66,107,2,14,"no","no","yes",176,6
"686",2495,33,340,8,14,"no","no","yes",176,6
"687",3565,66,540,8,17,"no","no","yes",176,6
"688",2845,50,540,8,14,"no","no","yes",176,6
"689",2025,33,250,8,15,"no","no","yes",176,6
"690",3045,66,540,8,14,"no","no","yes",176,6
"691",2299,50,230,4,14,"no","no","yes",176,6
"692",2345,50,250,8,15,"no","no","yes",176,6
"693",2390,66,214,4,15,"no","no","yes",176,6
"694",2795,33,452,16,14,"no","no","yes",176,6
"695",1390,25,107,2,14,"no","no","yes",176,6
"696",1749,33,250,4,14,"no","no","yes",176,6
"697",2690,50,340,8,15,"no","no","yes",176,6
"698",1579,25,120,4,14,"no","no","no",176,6
"699",3098,33,345,16,15,"yes","no","yes",176,6
"700",1295,25,80,4,14,"no","no","yes",176,6
"701",1945,50,250,4,14,"no","no","yes",176,6
"702",2099,33,120,4,14,"no","no","no",176,6
"703",1999,33,245,8,14,"no","no","yes",176,6
"704",3090,66,452,16,15,"no","no","yes",176,6
"705",1890,25,214,4,15,"no","no","yes",176,6
"706",1895,33,214,4,14,"no","no","yes",176,6
"707",3398,66,345,16,15,"yes","no","yes",176,6
"708",1845,33,250,4,14,"no","no","yes",176,6
"709",1795,25,214,4,14,"no","no","yes",176,6
"710",2050,50,170,4,14,"yes","no","yes",176,6
"711",2495,25,452,16,14,"no","no","yes",176,6
"712",2375,50,250,8,15,"no","no","yes",176,6
"713",1690,33,107,2,15,"no","no","yes",249,7
"714",3299,50,450,8,15,"no","no","yes",249,7
"715",1995,66,107,2,14,"no","no","yes",249,7
"716",1495,25,107,2,14,"no","no","yes",249,7
"717",2999,66,345,16,15,"no","no","yes",249,7
"718",3599,33,345,16,17,"no","no","yes",249,7
"719",3295,66,545,8,14,"no","no","yes",249,7
"720",3999,66,450,8,17,"no","no","yes",249,7
"721",3025,33,1370,8,14,"no","no","yes",249,7
"722",1890,25,214,4,15,"no","no","yes",249,7
"723",2648,66,245,8,14,"yes","no","yes",249,7
"724",2295,66,214,4,14,"no","no","yes",249,7
"725",2199,66,340,4,14,"no","no","yes",249,7
"726",2090,66,107,2,14,"no","no","yes",249,7
"727",2075,33,250,8,15,"no","no","yes",249,7
"728",2744,33,452,16,14,"no","no","yes",249,7
"729",2425,33,545,8,14,"no","no","yes",249,7
"730",1749,33,230,4,14,"no","no","yes",249,7
"731",1899,33,230,4,14,"no","no","yes",249,7
"732",1520,25,170,4,14,"yes","no","yes",249,7
"733",1795,33,214,8,14,"no","no","yes",249,7
"734",1879,50,210,4,15,"no","no","yes",249,7
"735",1590,25,107,2,15,"no","no","yes",249,7
"736",1890,33,107,2,14,"no","no","yes",249,7
"737",1720,33,212,4,14,"yes","no","yes",249,7
"738",1845,33,250,8,15,"no","no","yes",249,7
"739",2495,25,452,16,14,"no","no","yes",249,7
"740",3099,66,450,16,15,"yes","no","yes",249,7
"741",2990,50,452,16,14,"no","no","yes",249,7
"742",2144,66,107,2,14,"no","no","yes",249,7
"743",1890,33,107,2,15,"no","no","yes",249,7
"744",1975,33,250,8,14,"no","no","yes",249,7
"745",1945,33,250,4,14,"yes","yes","yes",249,7
"746",4248,66,345,16,17,"yes","no","yes",249,7
"747",2244,25,340,8,14,"no","no","yes",249,7
"748",2044,33,107,2,14,"no","no","yes",249,7
"749",1690,33,107,2,14,"no","no","yes",249,7
"750",2844,66,340,8,14,"no","no","yes",249,7
"751",1749,33,170,4,14,"no","no","yes",249,7
"752",2395,33,340,8,17,"no","no","yes",249,7
"753",1890,25,214,4,14,"no","no","yes",249,7
"754",2694,25,452,16,14,"yes","no","yes",249,7
"755",2465,33,250,8,17,"no","no","yes",249,7
"756",1999,33,245,8,14,"no","no","yes",249,7
"757",1599,25,120,4,14,"no","no","no",249,7
"758",2745,66,250,8,17,"no","no","yes",249,7
"759",1595,33,107,2,14,"no","no","yes",249,7
"760",1999,33,120,4,14,"no","no","no",249,7
"761",1939,50,170,4,14,"yes","yes","yes",249,7
"762",1590,25,107,2,14,"no","no","yes",249,7
"763",1695,33,250,4,14,"no","no","yes",249,7
"764",3048,33,345,16,15,"yes","no","yes",249,7
"765",1599,25,170,4,14,"no","no","yes",249,7
"766",2144,50,107,2,14,"no","no","yes",249,7
"767",2744,25,452,16,14,"no","no","yes",249,7
"768",1399,25,170,4,14,"no","no","yes",249,7
"769",2015,66,210,4,14,"yes","no","yes",249,7
"770",2799,66,450,8,15,"yes","no","yes",249,7
"771",2095,25,340,8,14,"no","no","yes",249,7
"772",2544,66,214,4,14,"no","no","yes",249,7
"773",3948,33,345,16,17,"yes","no","yes",249,7
"774",2690,33,452,16,15,"no","no","yes",249,7
"775",2699,33,345,16,15,"no","no","yes",249,7
"776",2490,33,340,8,14,"no","no","yes",249,7
"777",2244,33,214,4,14,"no","no","yes",249,7
"778",1399,33,130,4,14,"no","no","yes",249,7
"779",2795,33,452,16,14,"no","no","yes",249,7
"780",3595,50,452,8,14,"no","no","yes",249,7
"781",2045,33,250,4,15,"yes","yes","yes",249,7
"782",2290,50,214,4,15,"no","no","yes",249,7
"783",3090,66,452,16,15,"no","no","yes",249,7
"784",2644,33,340,8,14,"no","no","yes",249,7
"785",2348,33,245,8,14,"yes","no","yes",249,7
"786",2120,33,212,8,14,"yes","no","yes",249,7
"787",2494,66,214,4,14,"yes","no","yes",249,7
"788",2394,33,340,8,14,"yes","no","yes",249,7
"789",1845,66,210,4,14,"no","no","yes",249,7
"790",3044,50,452,16,14,"no","no","yes",249,7
"791",2195,50,214,4,14,"no","no","yes",249,7
"792",2995,66,340,16,15,"yes","no","yes",249,7
"793",1829,33,170,4,14,"yes","yes","yes",249,7
"794",2845,66,340,8,17,"yes","no","yes",249,7
"795",1395,25,107,4,14,"no","no","yes",249,7
"796",3495,33,452,8,14,"no","no","yes",249,7
"797",2890,33,452,16,14,"no","no","yes",249,7
"798",2344,33,214,4,14,"no","no","yes",249,7
"799",2944,33,452,16,14,"no","no","yes",249,7
"800",2290,33,340,8,15,"no","no","yes",249,7
"801",2799,33,240,4,14,"no","no","no",249,7
"802",2895,50,452,16,14,"no","no","yes",249,7
"803",2599,50,230,4,14,"yes","yes","yes",249,7
"804",3499,66,450,8,17,"no","no","yes",249,7
"805",1509,50,130,4,14,"no","no","yes",249,7
"806",2399,50,340,8,14,"yes","no","yes",249,7
"807",2290,50,214,4,14,"no","no","yes",249,7
"808",2544,33,340,8,14,"no","no","yes",249,7
"809",2295,66,250,8,15,"yes","no","yes",249,7
"810",1579,25,120,4,14,"no","no","no",249,7
"811",2495,50,340,8,14,"no","no","yes",249,7
"812",1944,33,107,2,14,"no","no","yes",249,7
"813",1769,66,170,4,14,"no","no","yes",249,7
"814",3094,50,452,16,14,"yes","no","yes",249,7
"815",1849,33,170,4,14,"no","no","yes",249,7
"816",1744,25,107,2,14,"no","no","yes",249,7
"817",2744,50,340,8,14,"no","no","yes",249,7
"818",1848,33,130,4,14,"yes","no","yes",249,7
"819",1644,25,107,2,14,"no","no","yes",249,7
"820",2344,50,214,4,14,"no","no","yes",249,7
"821",2694,50,340,8,14,"yes","no","yes",249,7
"822",2495,66,340,8,14,"yes","no","yes",249,7
"823",2945,66,250,8,15,"no","no","yes",249,7
"824",2395,50,340,8,15,"no","no","yes",249,7
"825",2590,25,452,16,15,"no","no","yes",249,7
"826",1795,33,107,2,14,"no","no","yes",249,7
"827",1559,50,130,4,14,"no","no","yes",249,7
"828",2444,50,214,4,14,"no","no","yes",249,7
"829",1794,33,107,2,14,"yes","no","yes",249,7
"830",3348,66,345,16,15,"yes","no","yes",249,7
"831",2999,66,540,8,15,"no","no","yes",249,7
"832",2294,33,214,4,14,"yes","no","yes",249,7
"833",2095,33,214,4,14,"no","no","yes",249,7
"834",1995,33,212,8,15,"no","no","yes",249,7
"835",1769,33,210,4,15,"no","no","yes",249,7
"836",2059,66,170,4,14,"yes","yes","yes",249,7
"837",2990,50,452,16,15,"no","no","yes",249,7
"838",2290,33,340,8,14,"no","no","yes",249,7
"839",1299,33,130,4,14,"no","no","yes",249,7
"840",2399,50,250,8,15,"no","no","yes",249,7
"841",3789,66,527,8,15,"no","no","no",249,7
"842",2325,66,250,8,15,"no","no","yes",249,7
"843",3495,66,500,8,14,"no","no","yes",249,7
"844",2644,25,452,16,14,"no","no","yes",249,7
"845",2644,50,340,8,14,"no","no","yes",249,7
"846",1694,25,107,2,14,"yes","no","yes",249,7
"847",1990,33,214,4,14,"no","no","yes",249,7
"848",2225,66,250,4,14,"yes","yes","yes",249,7
"849",1295,25,170,4,14,"no","no","yes",249,7
"850",2144,33,214,4,14,"no","no","yes",249,7
"851",1499,25,170,4,14,"no","no","yes",249,7
"852",2294,25,340,8,14,"yes","no","yes",249,7
"853",3695,66,452,8,14,"no","no","yes",249,7
"854",2099,33,120,4,14,"no","no","no",249,7
"855",2794,33,452,16,14,"yes","no","yes",249,7
"856",2590,50,340,8,15,"no","no","yes",249,7
"857",1795,25,214,4,14,"no","no","yes",249,7
"858",1539,33,170,4,14,"no","no","yes",249,7
"859",2090,66,107,2,15,"no","no","yes",249,7
"860",2344,25,340,8,14,"no","no","yes",249,7
"861",2195,33,340,8,14,"no","no","yes",249,7
"862",1879,50,170,4,14,"no","no","yes",249,7
"863",1975,66,250,4,14,"no","no","yes",249,7
"864",2390,66,214,4,15,"no","no","yes",249,7
"865",3720,66,500,8,14,"yes","no","yes",249,7
"866",1895,50,107,2,14,"no","no","yes",249,7
"867",1999,66,210,4,15,"no","no","yes",249,7
"868",2595,66,340,8,14,"no","no","yes",249,7
"869",2620,50,340,8,15,"yes","no","yes",249,7
"870",2344,33,340,8,14,"no","no","yes",249,7
"871",1990,50,107,2,14,"no","no","yes",249,7
"872",2094,50,107,2,14,"yes","no","yes",249,7
"873",1945,33,340,8,15,"no","no","yes",249,7
"874",2190,33,214,4,15,"no","no","yes",249,7
"875",2299,66,120,4,14,"no","no","no",249,7
"876",2444,33,340,8,14,"no","no","yes",249,7
"877",2175,66,250,8,15,"no","no","yes",249,7
"878",2044,33,214,4,14,"no","no","yes",249,7
"879",2244,66,107,2,14,"no","no","yes",249,7
"880",2125,66,250,8,15,"no","no","yes",249,7
"881",2845,66,250,8,14,"no","no","yes",249,7
"882",3899,66,345,16,17,"no","no","yes",249,7
"883",1649,50,170,4,14,"no","no","yes",249,7
"884",2595,66,340,8,15,"yes","no","yes",249,7
"885",3144,66,452,16,14,"no","no","yes",249,7
"886",1999,33,230,4,14,"no","no","yes",249,7
"887",2899,50,240,4,14,"no","no","no",249,7
"888",3090,66,452,16,14,"no","no","yes",249,7
"889",2094,33,214,4,14,"yes","no","yes",249,7
"890",1895,33,214,4,14,"no","no","yes",249,7
"891",2999,66,450,8,15,"no","no","yes",249,7
"892",2994,33,452,16,14,"yes","no","yes",249,7
"893",1495,33,107,4,14,"no","no","yes",249,7
"894",1395,25,170,4,15,"no","no","yes",249,7
"895",2675,66,340,8,17,"no","no","yes",249,7
"896",2394,50,214,4,14,"yes","no","yes",249,7
"897",2590,25,452,16,14,"no","no","yes",249,7
"898",2690,33,452,16,14,"no","no","yes",249,7
"899",1990,33,214,4,15,"no","no","yes",249,7
"900",2190,25,340,8,15,"no","no","yes",249,7
"901",3895,66,1370,8,14,"no","no","yes",249,7
"902",2999,66,320,8,15,"yes","yes","yes",249,7
"903",2890,33,452,16,15,"no","no","yes",249,7
"904",1799,33,120,4,14,"no","no","no",249,7
"905",2794,66,340,8,14,"yes","no","yes",249,7
"906",1899,25,120,4,14,"no","no","no",249,7
"907",2190,33,214,4,14,"no","no","yes",249,7
"908",1399,25,170,4,14,"no","no","yes",249,7
"909",2844,33,452,16,14,"no","no","yes",249,7
"910",1559,33,170,4,14,"no","no","yes",249,7
"911",2995,66,452,16,14,"no","no","yes",249,7
"912",2325,66,250,4,15,"yes","yes","yes",249,7
"913",1599,66,130,4,14,"no","no","yes",249,7
"914",2299,66,245,8,14,"no","no","yes",249,7
"915",1865,33,250,4,14,"yes","no","yes",249,7
"916",1990,50,107,2,15,"no","no","yes",249,7
"917",1994,33,107,2,14,"yes","no","yes",249,7
"918",2109,33,120,4,14,"no","no","no",249,7
"919",1948,66,130,4,14,"yes","no","yes",249,7
"920",1575,33,210,4,14,"no","no","yes",249,7
"921",2690,66,340,8,14,"no","no","yes",249,7
"922",1449,25,120,4,14,"no","no","no",249,7
"923",2690,66,340,8,15,"no","no","yes",249,7
"924",1695,25,214,8,14,"no","no","yes",249,7
"925",2145,66,250,4,14,"yes","no","yes",249,7
"926",2044,25,214,4,14,"no","no","yes",249,7
"927",1895,33,250,8,15,"no","no","yes",249,7
"928",2015,33,250,8,15,"yes","no","yes",249,7
"929",2490,33,340,8,15,"no","no","yes",249,7
"930",1844,33,107,2,14,"no","no","yes",249,7
"931",1049,33,80,2,14,"no","no","yes",249,7
"932",2595,33,452,16,14,"no","no","yes",249,7
"933",2390,66,214,4,14,"no","no","yes",249,7
"934",1799,33,250,4,14,"no","no","yes",249,7
"935",1744,33,107,2,14,"no","no","yes",249,7
"936",2565,33,340,8,17,"yes","no","yes",249,7
"937",2744,66,340,8,14,"no","no","yes",249,7
"938",2799,50,320,8,15,"no","no","yes",249,7
"939",1994,25,214,4,14,"yes","no","yes",249,7
"940",1495,33,212,4,14,"no","no","yes",249,7
"941",2045,33,250,8,15,"no","no","yes",249,7
"942",3194,66,452,16,14,"yes","no","yes",249,7
"943",1499,33,130,4,14,"no","no","yes",249,7
"944",2194,66,107,2,14,"yes","no","yes",249,7
"945",3609,66,527,4,15,"no","no","no",249,7
"946",2190,25,340,8,14,"no","no","yes",249,7
"947",1745,33,210,4,14,"yes","no","yes",249,7
"948",2590,50,340,8,14,"no","no","yes",249,7
"949",1639,66,130,4,14,"no","no","yes",249,7
"950",2044,50,107,2,14,"no","no","yes",249,7
"951",3244,66,452,16,14,"no","no","yes",249,7
"952",3595,66,500,8,15,"no","no","yes",249,7
"953",1595,33,212,4,15,"no","no","yes",249,7
"954",2444,66,214,4,14,"no","no","yes",249,7
"955",3144,50,452,16,14,"no","no","yes",249,7
"956",2395,33,340,8,14,"no","no","yes",249,7
"957",1944,25,214,4,14,"no","no","yes",249,7
"958",2594,33,340,8,14,"yes","no","yes",249,7
"959",1895,33,212,8,14,"no","no","yes",249,7
"960",3044,33,452,16,14,"no","no","yes",249,7
"961",2225,66,340,8,15,"no","no","yes",249,7
"962",1495,33,212,4,14,"no","no","yes",298,8
"963",2799,50,320,8,15,"no","no","yes",298,8
"964",1544,25,107,2,14,"no","no","yes",298,8
"965",2344,33,426,8,14,"no","no","yes",298,8
"966",2794,33,540,16,14,"yes","no","yes",298,8
"967",1399,25,170,4,14,"no","no","yes",298,8
"968",2295,50,107,4,14,"no","no","yes",298,8
"969",2195,66,214,4,14,"no","no","yes",298,8
"970",2594,50,426,8,14,"yes","no","yes",298,8
"971",3299,50,450,8,15,"no","no","yes",298,8
"972",2194,25,426,8,14,"yes","no","yes",298,8
"973",3499,66,450,8,17,"no","no","yes",298,8
"974",2090,33,214,4,14,"no","no","yes",298,8
"975",2695,66,340,4,14,"no","no","yes",298,8
"976",2644,50,426,8,14,"no","no","yes",298,8
"977",1695,25,214,4,14,"no","no","yes",298,8
"978",2095,50,214,4,14,"no","no","yes",298,8
"979",1594,25,107,2,14,"yes","no","yes",298,8
"980",1845,66,210,4,14,"no","no","yes",298,8
"981",2044,50,107,2,14,"no","no","yes",298,8
"982",4295,33,1100,16,14,"no","no","yes",298,8
"983",2795,33,540,16,14,"no","no","yes",298,8
"984",2045,33,250,8,15,"no","no","yes",298,8
"985",2544,33,426,8,14,"no","no","yes",298,8
"986",2399,50,340,8,14,"yes","no","yes",298,8
"987",2225,66,250,4,14,"yes","yes","yes",298,8
"988",1470,25,214,4,14,"no","no","yes",298,8
"989",1575,33,210,4,14,"no","no","yes",298,8
"990",2394,66,214,4,14,"yes","no","yes",298,8
"991",1795,50,107,2,14,"no","no","yes",298,8
"992",1599,50,170,4,14,"no","no","yes",298,8
"993",1299,33,130,4,14,"no","no","yes",298,8
"994",4494,33,1100,16,14,"yes","no","yes",298,8
"995",2744,33,540,16,14,"no","no","yes",298,8
"996",2444,33,426,8,14,"no","no","yes",298,8
"997",2090,25,426,8,15,"no","no","yes",298,8
"998",2290,66,214,4,14,"no","no","yes",298,8
"999",2469,50,340,8,17,"no","no","yes",298,8
"1000",1495,33,107,4,14,"no","no","yes",298,8
"1001",1994,33,214,4,14,"yes","no","yes",298,8
"1002",1644,33,107,2,14,"no","no","yes",298,8
"1003",2325,66,250,8,15,"no","no","yes",298,8
"1004",3515,66,424,16,17,"yes","no","yes",298,8
"1005",1544,33,107,4,14,"no","no","yes",298,8
"1006",1579,25,120,4,14,"no","no","no",298,8
"1007",1509,50,130,4,14,"no","no","yes",298,8
"1008",2589,66,340,8,17,"no","no","yes",298,8
"1009",2090,25,426,8,14,"no","no","yes",298,8
"1010",2565,33,340,8,17,"yes","no","yes",298,8
"1011",2494,50,107,4,14,"yes","no","yes",298,8
"1012",1865,25,170,4,17,"no","no","yes",298,8
"1013",2175,66,250,8,15,"no","no","yes",298,8
"1014",2190,50,214,4,14,"no","no","yes",298,8
"1015",1444,25,107,4,14,"no","no","yes",298,8
"1016",2109,33,120,4,14,"no","no","no",298,8
"1017",2395,50,426,8,14,"no","no","yes",298,8
"1018",1790,33,107,2,15,"no","no","yes",298,8
"1019",1795,25,107,4,14,"yes","yes","yes",298,8
"1020",1695,33,250,4,14,"no","no","yes",298,8
"1021",3144,66,540,16,14,"no","no","yes",298,8
"1022",1999,33,230,4,14,"no","no","yes",298,8
"1023",2494,33,426,8,14,"yes","no","yes",298,8
"1024",1894,25,214,4,14,"yes","no","yes",298,8
"1025",2894,66,340,4,14,"yes","no","yes",298,8
"1026",2399,50,250,8,15,"no","no","yes",298,8
"1027",1590,33,107,2,15,"no","no","yes",298,8
"1028",1799,33,120,4,14,"no","no","no",298,8
"1029",1999,33,245,8,14,"no","no","yes",298,8
"1030",3094,50,540,16,14,"yes","no","yes",298,8
"1031",2899,50,240,4,14,"no","no","no",298,8
"1032",1879,50,170,4,14,"no","no","yes",298,8
"1033",2744,25,540,16,14,"no","no","yes",298,8
"1034",2495,33,340,4,14,"no","no","yes",298,8
"1035",2594,66,107,4,14,"yes","no","yes",298,8
"1036",2845,66,340,8,17,"yes","no","yes",298,8
"1037",1799,33,170,4,14,"yes","yes","yes",298,8
"1038",2999,66,540,8,15,"no","no","yes",298,8
"1039",3495,50,452,8,14,"no","no","yes",298,8
"1040",3295,66,545,8,14,"no","no","yes",298,8
"1041",3099,66,450,16,15,"yes","no","yes",298,8
"1042",1890,50,107,2,14,"no","no","yes",298,8
"1043",4594,50,1100,16,14,"yes","no","yes",298,8
"1044",1819,50,210,4,15,"no","no","yes",298,8
"1045",1590,33,107,2,14,"no","no","yes",298,8
"1046",1899,25,120,4,14,"no","no","no",298,8
"1047",2090,33,214,4,15,"no","no","yes",298,8
"1048",3395,33,452,8,14,"no","no","yes",298,8
"1049",3025,33,1370,8,14,"no","no","yes",298,8
"1050",1795,33,214,4,14,"no","no","yes",298,8
"1051",2590,25,540,16,14,"no","no","yes",298,8
"1052",2295,66,250,8,15,"yes","no","yes",298,8
"1053",1999,33,120,4,14,"no","no","no",298,8
"1054",1945,33,250,4,14,"yes","yes","yes",298,8
"1055",1844,25,214,4,14,"no","no","yes",298,8
"1056",2845,66,250,8,14,"no","no","yes",298,8
"1057",1599,25,120,4,14,"no","no","no",298,8
"1058",1890,33,214,4,14,"no","no","yes",298,8
"1059",1295,25,170,4,14,"no","no","yes",298,8
"1060",2844,33,540,16,14,"no","no","yes",298,8
"1061",1944,33,214,4,14,"no","no","yes",298,8
"1062",1849,33,170,4,14,"no","no","yes",298,8
"1063",1994,33,214,8,14,"yes","no","yes",298,8
"1064",2045,33,250,4,15,"yes","yes","yes",298,8
"1065",2390,33,426,8,15,"no","no","yes",298,8
"1066",1995,33,340,8,14,"no","no","yes",298,8
"1067",1490,25,107,2,15,"no","no","yes",298,8
"1068",3599,33,345,16,17,"no","no","yes",298,8
"1069",2344,50,214,4,14,"no","no","yes",298,8
"1070",2644,66,426,8,14,"no","no","yes",298,8
"1071",2544,50,426,8,14,"no","no","yes",298,8
"1072",1990,66,107,2,15,"no","no","yes",298,8
"1073",1695,25,214,8,14,"no","no","yes",298,8
"1074",2195,33,214,8,14,"yes","yes","yes",298,8
"1075",2190,50,214,4,15,"no","no","yes",298,8
"1076",2994,33,540,16,14,"yes","no","yes",298,8
"1077",1544,50,130,4,14,"no","no","yes",298,8
"1078",2890,33,540,16,15,"no","no","yes",298,8
"1079",2995,66,424,16,15,"yes","no","yes",298,8
"1080",2294,50,214,4,14,"yes","no","yes",298,8
"1081",1559,33,170,4,14,"no","no","yes",298,8
"1082",3789,66,527,8,15,"no","no","no",298,8
"1083",1895,33,107,4,14,"yes","yes","yes",298,8
"1084",1919,50,170,4,14,"yes","yes","yes",298,8
"1085",2144,25,426,8,14,"no","no","yes",298,8
"1086",2295,33,426,8,14,"no","no","yes",298,8
"1087",2595,50,340,4,14,"no","no","yes",298,8
"1088",2648,66,245,8,14,"yes","no","yes",298,8
"1089",2590,66,426,8,15,"no","no","yes",298,8
"1090",1049,33,80,2,14,"no","no","yes",298,8
"1091",3495,66,500,16,14,"no","no","yes",298,8
"1092",1667,33,107,8,14,"no","no","yes",298,8
"1093",1744,33,107,2,14,"no","no","yes",298,8
"1094",2244,33,426,8,14,"no","no","yes",298,8
"1095",2295,50,340,8,15,"no","no","yes",298,8
"1096",1490,25,107,2,14,"no","no","yes",298,8
"1097",2595,33,540,16,14,"no","no","yes",298,8
"1098",2144,66,107,2,14,"no","no","yes",298,8
"1099",4495,66,1100,16,14,"no","no","yes",298,8
"1100",2444,66,214,4,14,"no","no","yes",298,8
"1101",2325,66,250,4,15,"yes","yes","yes",298,8
"1102",3895,66,1370,8,14,"no","no","yes",298,8
"1103",1975,33,250,8,14,"no","no","yes",298,8
"1104",1895,66,107,2,14,"no","no","yes",298,8
"1105",2299,66,245,8,14,"no","no","yes",298,8
"1106",2344,66,214,4,14,"no","no","yes",298,8
"1107",2999,66,320,8,15,"yes","yes","yes",298,8
"1108",2170,33,340,8,14,"yes","no","yes",298,8
"1109",2190,33,426,8,15,"no","no","yes",298,8
"1110",2834,66,426,8,15,"no","no","yes",298,8
"1111",1944,25,214,4,14,"no","no","yes",298,8
"1112",1894,33,107,2,14,"yes","no","yes",298,8
"1113",2099,33,120,4,14,"no","no","no",298,8
"1114",2225,66,340,8,15,"no","no","yes",298,8
"1115",2299,66,120,4,14,"no","no","no",298,8
"1116",2095,25,214,8,14,"yes","yes","yes",298,8
"1117",2644,25,540,16,14,"no","no","yes",298,8
"1118",3594,33,452,8,14,"yes","no","yes",298,8
"1119",2995,66,540,16,14,"no","no","yes",298,8
"1120",2244,33,214,4,14,"no","no","yes",298,8
"1121",2990,50,540,16,15,"no","no","yes",298,8
"1122",1939,66,210,4,15,"no","no","yes",298,8
"1123",4694,66,1100,16,14,"yes","no","yes",298,8
"1124",2039,66,170,4,14,"yes","yes","yes",298,8
"1125",1495,33,107,2,14,"no","no","yes",298,8
"1126",3334,66,540,16,15,"no","no","yes",298,8
"1127",3794,66,452,8,14,"yes","no","yes",298,8
"1128",2999,66,345,16,15,"no","no","yes",298,8
"1129",2094,66,107,2,14,"yes","no","yes",298,8
"1130",2694,25,540,16,14,"yes","no","yes",298,8
"1131",1395,25,170,4,15,"no","no","yes",298,8
"1132",1948,66,130,4,14,"yes","no","yes",298,8
"1133",3694,50,452,8,14,"yes","no","yes",298,8
"1134",1567,25,107,8,14,"no","no","yes",298,8
"1135",2125,66,250,8,15,"no","no","yes",298,8
"1136",2745,66,250,8,17,"no","no","yes",298,8
"1137",1944,33,107,2,14,"no","no","yes",298,8
"1138",1499,33,130,4,14,"no","no","yes",298,8
"1139",2999,66,450,8,15,"no","no","yes",298,8
"1140",1594,25,107,4,14,"yes","no","yes",298,8
"1141",1995,25,426,8,14,"no","no","yes",298,8
"1142",2095,33,212,4,17,"no","no","yes",298,8
"1143",2815,50,340,8,17,"no","no","yes",298,8
"1144",2495,66,426,8,14,"no","no","yes",298,8
"1145",3999,66,450,8,17,"no","no","yes",298,8
"1146",2690,33,540,16,15,"no","no","yes",298,8
"1147",3144,50,540,16,14,"no","no","yes",298,8
"1148",1449,25,120,4,14,"no","no","no",298,8
"1149",1994,50,107,2,14,"yes","no","yes",298,8
"1150",2495,25,540,16,14,"no","no","yes",298,8
"1151",3670,66,500,16,14,"yes","no","yes",298,8
"1152",2794,50,340,4,14,"yes","no","yes",298,8
"1153",1729,66,170,4,14,"no","no","yes",298,8
"1154",2690,33,540,16,14,"no","no","yes",298,8
"1155",4095,66,500,16,17,"no","no","yes",298,8
"1156",2590,25,540,16,15,"no","no","yes",298,8
"1157",2244,25,426,8,14,"no","no","yes",298,8
"1158",1799,33,250,4,14,"no","no","yes",298,8
"1159",2595,33,340,8,17,"no","no","yes",298,8
"1160",1599,66,130,4,14,"no","no","yes",298,8
"1161",2390,33,426,8,14,"no","no","yes",298,8
"1162",2675,66,340,8,17,"no","no","yes",298,8
"1163",2890,33,540,16,14,"no","no","yes",298,8
"1164",3090,66,540,16,15,"no","no","yes",298,8
"1165",1975,66,250,4,14,"no","no","yes",298,8
"1166",2944,33,540,16,14,"no","no","yes",298,8
"1167",1990,66,107,2,14,"no","no","yes",298,8
"1168",1895,33,250,8,15,"no","no","yes",298,8
"1169",2145,66,250,4,14,"yes","no","yes",298,8
"1170",2694,33,340,4,14,"yes","no","yes",298,8
"1171",1844,33,107,2,14,"no","no","yes",298,8
"1172",2990,50,540,16,14,"no","no","yes",298,8
"1173",2590,66,426,8,14,"no","no","yes",298,8
"1174",2015,66,210,4,14,"yes","no","yes",298,8
"1175",1694,33,107,2,14,"yes","no","yes",298,8
"1176",2395,33,212,8,17,"no","no","yes",298,8
"1177",4395,50,1100,16,14,"no","no","yes",298,8
"1178",3244,66,540,16,14,"no","no","yes",298,8
"1179",2945,66,250,8,15,"no","no","yes",298,8
"1180",2199,66,340,4,14,"no","no","yes",298,8
"1181",1890,50,107,2,15,"no","no","yes",298,8
"1182",2294,33,426,8,14,"yes","no","yes",298,8
"1183",1745,33,210,4,14,"yes","no","yes",298,8
"1184",2395,33,340,8,17,"no","no","yes",298,8
"1185",2395,66,107,4,14,"no","no","yes",298,8
"1186",1570,33,214,4,14,"no","no","yes",298,8
"1187",2699,33,345,16,15,"no","no","yes",298,8
"1188",1895,33,212,8,15,"no","no","yes",298,8
"1189",1709,33,210,4,15,"no","no","yes",298,8
"1190",2194,33,214,4,14,"yes","no","yes",298,8
"1191",1395,25,107,4,14,"no","no","yes",298,8
"1192",1639,66,130,4,14,"no","no","yes",298,8
"1193",2095,33,426,8,14,"no","no","yes",298,8
"1194",2195,33,107,4,14,"no","no","yes",298,8
"1195",1790,25,214,4,14,"no","no","yes",298,8
"1196",3044,33,540,16,14,"no","no","yes",298,8
"1197",2599,50,230,4,14,"yes","yes","yes",298,8
"1198",1890,33,214,4,15,"no","no","yes",298,8
"1199",2490,50,426,8,15,"no","no","yes",298,8
"1200",1749,33,230,4,14,"no","no","yes",298,8
"1201",1499,33,170,4,14,"no","no","yes",298,8
"1202",3899,66,345,16,17,"no","no","yes",298,8
"1203",2470,50,340,8,15,"yes","no","yes",298,8
"1204",3948,33,345,16,17,"yes","no","yes",298,8
"1205",1499,25,170,4,14,"no","no","yes",298,8
"1206",1894,25,214,8,14,"yes","no","yes",298,8
"1207",4248,66,345,16,17,"yes","no","yes",298,8
"1208",3048,33,345,16,15,"yes","no","yes",298,8
"1209",2044,33,214,4,14,"no","no","yes",298,8
"1210",3595,66,500,16,15,"no","no","yes",298,8
"1211",1644,25,107,2,14,"no","no","yes",298,8
"1212",1865,33,250,4,14,"yes","no","yes",298,8
"1213",1795,33,212,8,14,"no","no","yes",298,8
"1214",2095,33,340,8,15,"no","no","yes",298,8
"1215",2694,66,426,8,14,"yes","no","yes",298,8
"1216",1899,33,230,4,14,"no","no","yes",298,8
"1217",2425,33,545,8,14,"no","no","yes",298,8
"1218",2799,66,450,8,15,"yes","no","yes",298,8
"1219",1795,33,214,8,14,"no","no","yes",298,8
"1220",2015,33,250,8,15,"yes","no","yes",298,8
"1221",2895,50,540,16,14,"no","no","yes",298,8
"1222",1790,25,214,4,15,"no","no","yes",298,8
"1223",1694,33,107,4,14,"yes","no","yes",298,8
"1224",1728,25,120,4,14,"no","no","yes",298,8
"1225",3609,66,527,4,15,"no","no","no",298,8
"1226",2490,50,426,8,14,"no","no","yes",298,8
"1227",2075,33,250,8,15,"no","no","yes",298,8
"1228",2290,66,214,4,15,"no","no","yes",298,8
"1229",2799,33,240,4,14,"no","no","no",298,8
"1230",2465,33,250,8,17,"no","no","yes",298,8
"1231",1790,33,107,2,14,"no","no","yes",298,8
"1232",3348,66,345,16,15,"yes","no","yes",298,8
"1233",1399,33,130,4,14,"no","no","yes",298,8
"1234",1399,25,170,4,14,"no","no","yes",298,8
"1235",2044,66,107,2,14,"no","no","yes",298,8
"1236",3044,50,540,16,14,"no","no","yes",298,8
"1237",1595,33,212,4,15,"no","no","yes",298,8
"1238",1395,25,107,2,14,"no","no","yes",298,8
"1239",2348,33,245,8,14,"yes","no","yes",298,8
"1240",1845,33,250,8,15,"no","no","yes",298,8
"1241",2394,33,107,4,14,"yes","no","yes",298,8
"1242",1995,33,214,4,14,"no","no","yes",298,8
"1243",1945,33,340,8,15,"no","no","yes",298,8
"1244",1670,33,212,4,14,"yes","no","yes",298,8
"1245",1470,25,170,4,14,"yes","no","yes",298,8
"1246",1970,33,212,8,14,"yes","no","yes",298,8
"1247",3595,66,452,8,14,"no","no","yes",298,8
"1248",2244,50,214,4,14,"no","no","yes",298,8
"1249",1599,25,170,4,14,"no","no","yes",298,8
"1250",3090,66,540,16,14,"no","no","yes",298,8
"1251",1749,33,170,4,14,"no","no","yes",298,8
"1252",2144,33,214,4,14,"no","no","yes",298,8
"1253",1944,50,107,2,14,"no","no","yes",298,8
"1254",2359,33,340,8,17,"no","no","yes",298,8
"1255",1848,33,130,4,14,"yes","no","yes",298,8
"1256",2190,33,426,8,14,"no","no","yes",298,8
"1257",3194,66,540,16,14,"yes","no","yes",298,8
"1258",1695,33,107,2,14,"no","no","yes",298,8
"1259",2744,66,426,8,14,"no","no","yes",298,8
"1260",2990,50,528,16,14,"no","no","yes",246,9
"1261",1895,66,107,2,14,"no","no","yes",246,9
"1262",2395,50,426,8,14,"no","no","yes",246,9
"1263",2294,33,426,8,14,"yes","no","yes",246,9
"1264",2199,66,340,4,14,"no","no","yes",246,9
"1265",1890,33,212,4,14,"yes","yes","yes",246,9
"1266",3499,66,527,4,14,"no","no","no",246,9
"1267",2399,33,340,8,15,"no","no","yes",246,9
"1268",3348,66,345,16,15,"yes","no","yes",246,9
"1269",2295,50,340,8,15,"no","no","yes",246,9
"1270",2099,33,120,4,14,"no","no","no",246,9
"1271",1944,33,107,2,14,"no","no","yes",246,9
"1272",2590,25,528,16,15,"no","no","yes",246,9
"1273",1699,33,212,4,14,"no","no","yes",246,9
"1274",2644,25,528,16,14,"no","no","yes",246,9
"1275",1995,33,214,4,14,"no","no","yes",246,9
"1276",3699,66,345,16,17,"no","no","yes",246,9
"1277",2295,33,426,8,14,"no","no","yes",246,9
"1278",1579,25,120,4,14,"no","no","no",246,9
"1279",1795,33,214,4,14,"no","no","yes",246,9
"1280",2795,33,528,16,14,"no","no","yes",246,9
"1281",2090,33,214,4,15,"no","no","yes",246,9
"1282",1490,25,107,2,14,"no","no","yes",246,9
"1283",2499,50,340,8,15,"no","no","yes",246,9
"1284",2599,66,340,8,15,"yes","no","yes",246,9
"1285",1999,66,170,4,14,"yes","yes","yes",246,9
"1286",1579,50,170,4,14,"no","no","yes",246,9
"1287",2594,50,426,8,14,"yes","no","yes",246,9
"1288",1749,50,212,4,14,"no","no","yes",246,9
"1289",1499,33,170,4,14,"no","no","yes",246,9
"1290",1990,66,107,2,15,"no","no","yes",246,9
"1291",1894,25,120,4,14,"yes","no","no",246,9
"1292",1790,33,107,2,14,"no","no","yes",246,9
"1293",3599,66,527,4,15,"no","no","no",246,9
"1294",2294,50,214,4,14,"yes","no","yes",246,9
"1295",1899,25,120,4,14,"no","no","no",246,9
"1296",1944,33,214,4,14,"no","no","yes",246,9
"1297",2590,66,426,8,14,"no","no","yes",246,9
"1298",1929,66,210,4,15,"no","no","yes",246,9
"1299",2995,66,540,16,14,"no","no","yes",246,9
"1300",2544,50,426,8,14,"no","no","yes",246,9
"1301",1495,33,212,4,14,"no","no","yes",246,9
"1302",1974,25,120,4,14,"yes","no","no",246,9
"1303",1809,50,210,4,15,"no","no","yes",246,9
"1304",1895,33,212,8,15,"no","no","yes",246,9
"1305",2244,33,426,8,14,"no","no","yes",246,9
"1306",2890,33,528,16,14,"no","no","yes",246,9
"1307",2690,33,528,16,14,"no","no","yes",246,9
"1308",2194,25,426,8,14,"yes","no","yes",246,9
"1309",2395,33,212,8,17,"no","no","yes",246,9
"1310",1395,25,170,4,15,"no","no","yes",246,9
"1311",2199,50,230,8,15,"no","no","yes",246,9
"1312",1779,33,120,4,14,"no","no","no",246,9
"1313",2190,33,426,8,14,"no","no","yes",246,9
"1314",2095,33,212,4,17,"no","no","yes",246,9
"1315",1490,25,107,2,15,"no","no","yes",246,9
"1316",1859,33,120,4,14,"no","no","no",246,9
"1317",1994,50,107,2,14,"yes","no","yes",246,9
"1318",1395,25,107,4,14,"no","no","yes",246,9
"1319",3094,50,528,16,14,"yes","no","yes",246,9
"1320",1695,33,107,2,14,"no","no","yes",246,9
"1321",1469,33,170,4,14,"no","no","yes",246,9
"1322",2144,33,214,4,14,"no","no","yes",246,9
"1323",3789,66,527,8,15,"no","no","no",246,9
"1324",2044,50,107,2,14,"no","no","yes",246,9
"1325",2798,66,340,8,15,"yes","no","yes",246,9
"1326",2299,66,120,4,14,"no","no","no",246,9
"1327",3094,66,245,4,14,"yes","no","no",246,9
"1328",1944,25,214,4,14,"no","no","yes",246,9
"1329",1844,25,214,4,14,"no","no","yes",246,9
"1330",1999,66,245,8,14,"no","no","yes",246,9
"1331",1379,25,212,4,14,"no","no","yes",246,9
"1332",1799,33,170,4,14,"no","no","yes",246,9
"1333",2190,33,212,8,14,"yes","yes","yes",246,9
"1334",1890,33,214,4,15,"no","no","yes",246,9
"1335",2495,66,426,8,14,"no","no","yes",246,9
"1336",4095,66,500,16,17,"no","no","yes",246,9
"1337",2490,50,426,8,15,"no","no","yes",246,9
"1338",1659,25,120,4,14,"no","no","no",246,9
"1339",1899,33,120,4,14,"no","no","no",246,9
"1340",3899,66,345,16,17,"no","no","yes",246,9
"1341",2344,33,426,8,14,"no","no","yes",246,9
"1342",2744,25,528,16,14,"no","no","yes",246,9
"1343",2190,33,426,8,15,"no","no","yes",246,9
"1344",2248,33,245,8,14,"yes","no","yes",246,9
"1345",1594,25,107,2,14,"yes","no","yes",246,9
"1346",2744,33,528,16,14,"no","no","yes",246,9
"1347",2784,33,245,8,14,"yes","no","no",246,9
"1348",1695,25,214,4,14,"no","no","yes",246,9
"1349",2090,25,426,8,14,"no","no","yes",246,9
"1350",2144,66,107,2,14,"no","no","yes",246,9
"1351",2844,33,528,16,14,"no","no","yes",246,9
"1352",1865,25,170,4,17,"no","no","yes",246,9
"1353",2544,33,426,8,14,"no","no","yes",246,9
"1354",1549,25,212,4,14,"no","no","yes",246,9
"1355",2590,66,426,8,15,"no","no","yes",246,9
"1356",2290,66,214,4,15,"no","no","yes",246,9
"1357",2994,33,528,16,14,"yes","no","yes",246,9
"1358",1790,25,214,4,14,"no","no","yes",246,9
"1359",2444,66,214,4,14,"no","no","yes",246,9
"1360",2144,25,426,8,14,"no","no","yes",246,9
"1361",1399,33,130,4,14,"no","no","yes",246,9
"1362",2090,25,426,8,15,"no","no","yes",246,9
"1363",2099,33,120,4,14,"no","no","no",246,9
"1364",3334,66,540,16,15,"no","no","yes",246,9
"1365",3849,66,450,16,15,"no","no","yes",246,9
"1366",1449,25,120,4,14,"no","no","no",246,9
"1367",2469,33,245,8,14,"no","no","no",246,9
"1368",1644,25,107,2,14,"no","no","yes",246,9
"1369",2095,33,426,8,14,"no","no","yes",246,9
"1370",2899,50,240,4,14,"no","no","no",246,9
"1371",1499,66,130,4,14,"no","no","yes",246,9
"1372",1644,33,107,2,14,"no","no","yes",246,9
"1373",1279,33,130,4,14,"no","no","yes",246,9
"1374",2390,33,426,8,15,"no","no","yes",246,9
"1375",2044,33,214,4,14,"no","no","yes",246,9
"1376",2694,25,528,16,14,"yes","no","yes",246,9
"1377",2244,50,214,4,14,"no","no","yes",246,9
"1378",1890,50,107,2,15,"no","no","yes",246,9
"1379",2044,66,107,2,14,"no","no","yes",246,9
"1380",2699,66,340,8,15,"no","no","yes",246,9
"1381",1479,50,130,4,14,"no","no","yes",246,9
"1382",2579,33,340,4,14,"no","no","no",246,9
"1383",2344,33,120,4,14,"yes","no","no",246,9
"1384",2344,50,214,4,14,"no","no","yes",246,9
"1385",1990,66,107,2,14,"no","no","yes",246,9
"1386",2439,50,340,8,17,"no","no","yes",246,9
"1387",3194,66,424,16,15,"yes","yes","yes",246,9
"1388",2329,33,340,8,17,"no","no","yes",246,9
"1389",1544,25,107,2,14,"no","no","yes",246,9
"1390",1590,33,107,2,15,"no","no","yes",246,9
"1391",1894,25,214,4,14,"yes","no","yes",246,9
"1392",1899,33,245,8,14,"no","no","yes",246,9
"1393",2498,50,230,4,14,"yes","no","yes",246,9
"1394",2244,33,214,4,14,"no","no","yes",246,9
"1395",3515,66,424,16,17,"yes","no","yes",246,9
"1396",1599,25,120,4,14,"no","no","no",246,9
"1397",1369,33,130,4,14,"no","no","yes",246,9
"1398",2998,66,450,8,15,"yes","no","yes",246,9
"1399",1879,50,170,4,14,"yes","yes","yes",246,9
"1400",1995,25,426,8,14,"no","no","yes",246,9
"1401",2394,66,214,4,14,"yes","no","yes",246,9
"1402",1790,25,214,4,15,"no","no","yes",246,9
"1403",2190,50,214,4,14,"no","no","yes",246,9
"1404",3244,66,528,16,14,"no","no","yes",246,9
"1405",2390,33,340,8,14,"yes","yes","yes",246,9
"1406",2694,66,426,8,14,"yes","no","yes",246,9
"1407",2644,66,426,8,14,"no","no","yes",246,9
"1408",2744,66,426,8,14,"no","no","yes",246,9
"1409",3499,50,450,8,15,"no","no","yes",246,9
"1410",1395,25,107,2,14,"no","no","yes",246,9
"1411",2779,66,245,4,14,"no","no","no",246,9
"1412",2815,50,340,8,17,"no","no","yes",246,9
"1413",1699,66,170,4,14,"no","no","yes",246,9
"1414",999,33,80,2,14,"no","no","yes",246,9
"1415",2244,25,426,8,14,"no","no","yes",246,9
"1416",1599,66,130,4,14,"no","no","yes",246,9
"1417",2348,66,245,8,14,"yes","no","yes",246,9
"1418",2290,66,214,4,14,"no","no","yes",246,9
"1419",2794,33,528,16,14,"yes","no","yes",246,9
"1420",3144,66,528,16,14,"no","no","yes",246,9
"1421",1969,50,340,4,14,"no","no","yes",246,9
"1422",1744,33,107,2,14,"no","no","yes",246,9
"1423",2094,33,120,4,14,"yes","no","no",246,9
"1424",1999,33,170,4,14,"no","no","yes",246,9
"1425",1519,50,130,4,14,"no","no","yes",246,9
"1426",1799,33,120,4,14,"no","no","no",246,9
"1427",1795,50,107,2,14,"no","no","yes",246,9
"1428",1694,33,107,2,14,"yes","no","yes",246,9
"1429",1495,33,107,2,14,"no","no","yes",246,9
"1430",2195,66,214,4,14,"no","no","yes",246,9
"1431",1789,33,212,4,14,"no","no","yes",246,9
"1432",2999,33,450,16,15,"no","no","yes",246,9
"1433",3399,33,450,8,15,"no","no","yes",246,9
"1434",1844,33,107,2,14,"no","no","yes",246,9
"1435",3044,33,528,16,14,"no","no","yes",246,9
"1436",1994,33,214,4,14,"yes","no","yes",246,9
"1437",1295,25,170,4,14,"no","no","yes",246,9
"1438",2090,33,214,4,14,"no","no","yes",246,9
"1439",3495,66,500,16,14,"no","no","yes",246,9
"1440",2490,50,426,8,14,"no","no","yes",246,9
"1441",4999,66,525,8,17,"no","no","yes",246,9
"1442",1748,33,130,4,14,"yes","no","yes",246,9
"1443",2990,50,528,16,15,"no","no","yes",246,9
"1444",1590,33,107,2,14,"no","no","yes",246,9
"1445",3199,66,450,8,15,"no","no","yes",246,9
"1446",4248,66,345,16,17,"yes","no","yes",246,9
"1447",2190,50,214,4,15,"no","no","yes",246,9
"1448",2595,33,340,8,17,"no","no","yes",246,9
"1449",2174,33,120,4,14,"yes","no","no",246,9
"1450",2834,66,426,8,15,"no","no","yes",246,9
"1451",2944,33,528,16,14,"no","no","yes",246,9
"1452",1599,33,212,4,14,"no","no","yes",246,9
"1453",2590,25,528,16,14,"no","no","yes",246,9
"1454",1769,33,170,4,14,"yes","yes","yes",246,9
"1455",2995,66,424,16,15,"yes","no","yes",246,9
"1456",1848,66,130,4,14,"yes","no","yes",246,9
"1457",3148,66,345,16,15,"yes","no","yes",246,9
"1458",3595,66,500,16,15,"no","no","yes",246,9
"1459",2690,50,340,8,15,"yes","yes","yes",246,9
"1460",2495,25,528,16,14,"no","no","yes",246,9
"1461",2899,50,320,8,15,"no","no","yes",246,9
"1462",1999,33,230,8,15,"no","no","yes",246,9
"1463",2194,33,214,4,14,"yes","no","yes",246,9
"1464",3890,66,500,16,14,"yes","yes","yes",246,9
"1465",1690,25,170,4,14,"yes","yes","yes",246,9
"1466",2444,33,426,8,14,"no","no","yes",246,9
"1467",4104,66,527,8,15,"yes","no","no",246,9
"1468",2894,33,340,4,14,"yes","no","no",246,9
"1469",1790,33,107,2,15,"no","no","yes",246,9
"1470",1995,33,340,8,14,"no","no","yes",246,9
"1471",3194,66,528,16,14,"yes","no","yes",246,9
"1472",2414,33,120,4,14,"yes","no","no",246,9
"1473",2699,33,240,4,14,"no","no","no",246,9
"1474",2344,66,214,4,14,"no","no","yes",246,9
"1475",3814,66,527,4,14,"yes","no","no",246,9
"1476",2494,33,426,8,14,"yes","no","yes",246,9
"1477",2890,33,528,16,15,"no","no","yes",246,9
"1478",4048,66,345,16,17,"yes","no","yes",246,9
"1479",1499,25,120,4,14,"no","no","yes",246,9
"1480",3144,50,528,16,14,"no","no","yes",246,9
"1481",1795,33,212,8,14,"no","no","yes",246,9
"1482",3044,50,528,16,14,"no","no","yes",246,9
"1483",2995,66,528,16,14,"no","no","yes",246,9
"1484",2390,33,426,8,14,"no","no","yes",246,9
"1485",2799,66,345,16,15,"no","no","yes",246,9
"1486",2094,66,107,2,14,"yes","no","yes",246,9
"1487",1890,50,107,2,14,"no","no","yes",246,9
"1488",2999,66,345,16,15,"no","no","yes",246,9
"1489",2644,50,426,8,14,"no","no","yes",246,9
"1490",2095,50,214,4,14,"no","no","yes",246,9
"1491",3090,66,528,16,15,"no","no","yes",246,9
"1492",3090,66,528,16,14,"no","no","yes",246,9
"1493",3914,66,527,4,15,"yes","no","no",246,9
"1494",1595,33,212,4,15,"no","no","yes",246,9
"1495",1944,50,107,2,14,"no","no","yes",246,9
"1496",2690,33,528,16,15,"no","no","yes",246,9
"1497",2895,50,528,16,14,"no","no","yes",246,9
"1498",2029,33,120,4,14,"no","no","no",246,9
"1499",1699,33,210,4,15,"no","no","yes",246,9
"1500",1890,33,214,4,14,"no","no","yes",246,9
"1501",1894,33,107,2,14,"yes","no","yes",246,9
"1502",2595,33,528,16,14,"no","no","yes",246,9
"1503",2559,66,340,8,17,"no","no","yes",246,9
"1504",2095,33,340,8,15,"no","no","yes",246,9
"1505",1939,66,212,4,14,"no","no","yes",246,9
"1506",2544,33,426,8,14,"no","no","yes",283,10
"1507",5399,66,1200,32,17,"yes","no","yes",283,10
"1508",2090,33,214,4,15,"no","no","yes",283,10
"1509",2025,66,340,4,15,"no","no","yes",283,10
"1510",2195,33,212,4,17,"no","no","yes",283,10
"1511",2590,66,426,8,15,"no","no","yes",283,10
"1512",2190,50,214,4,15,"no","no","yes",283,10
"1513",2014,25,212,4,14,"yes","no","no",283,10
"1514",1709,25,120,4,14,"no","no","no",283,10
"1515",2344,50,340,8,15,"no","no","yes",283,10
"1516",2395,50,426,8,14,"no","no","yes",283,10
"1517",3904,66,527,8,15,"yes","no","no",283,10
"1518",1644,33,107,2,14,"no","no","yes",283,10
"1519",2190,33,426,8,14,"no","no","yes",283,10
"1520",2744,66,426,8,14,"no","no","yes",283,10
"1521",1790,33,212,4,14,"yes","yes","yes",283,10
"1522",3634,66,527,4,15,"yes","no","no",283,10
"1523",2095,33,426,8,14,"no","no","yes",283,10
"1524",1299,25,120,4,14,"yes","no","yes",283,10
"1525",3385,66,1370,8,14,"no","no","yes",283,10
"1526",1844,33,107,2,14,"no","no","yes",283,10
"1527",1395,33,212,4,14,"no","no","yes",283,10
"1528",2744,25,528,16,14,"no","no","yes",283,10
"1529",2144,25,426,8,14,"no","no","yes",283,10
"1530",2295,33,426,8,14,"no","no","yes",283,10
"1531",1995,33,214,4,14,"no","no","yes",283,10
"1532",1539,25,120,4,14,"no","no","no",283,10
"1533",2645,66,340,8,17,"no","no","yes",283,10
"1534",2090,25,426,8,14,"no","no","yes",283,10
"1535",2999,66,340,4,15,"no","no","yes",283,10
"1536",1899,25,120,4,14,"no","no","no",283,10
"1537",1649,25,120,4,14,"no","no","no",283,10
"1538",1699,66,170,4,14,"no","no","yes",283,10
"1539",1395,25,107,2,14,"no","no","yes",283,10
"1540",1769,33,170,4,14,"yes","yes","yes",283,10
"1541",1894,25,214,4,14,"yes","no","yes",283,10
"1542",1599,33,212,4,14,"no","no","yes",283,10
"1543",2565,33,340,8,17,"no","no","yes",283,10
"1544",2899,50,240,4,14,"no","no","no",283,10
"1545",1694,33,107,2,14,"yes","no","yes",283,10
"1546",2375,66,340,8,15,"no","no","yes",283,10
"1547",2344,66,214,4,14,"no","no","yes",283,10
"1548",1594,25,107,2,14,"yes","no","yes",283,10
"1549",1995,25,426,8,14,"no","no","yes",283,10
"1550",2749,33,340,4,15,"no","no","yes",283,10
"1551",2225,66,340,8,15,"no","no","yes",283,10
"1552",1999,33,340,8,14,"yes","no","yes",283,10
"1553",1994,50,240,4,15,"no","no","yes",283,10
"1554",1994,33,214,4,14,"yes","no","yes",283,10
"1555",2364,33,212,4,14,"yes","no","no",283,10
"1556",2795,33,528,16,14,"no","no","yes",283,10
"1557",1595,33,212,4,14,"no","no","yes",283,10
"1558",2590,25,528,16,14,"no","no","yes",283,10
"1559",2165,33,340,8,15,"yes","no","yes",283,10
"1560",2495,66,426,8,14,"no","no","yes",283,10
"1561",2390,33,340,8,14,"yes","yes","yes",283,10
"1562",2090,25,426,8,15,"no","no","yes",283,10
"1563",2374,33,120,4,14,"yes","no","no",283,10
"1564",2244,33,214,4,14,"no","no","yes",283,10
"1565",2498,50,340,8,15,"yes","no","yes",283,10
"1566",2329,33,340,8,17,"no","no","yes",283,10
"1567",2595,33,528,16,14,"no","no","yes",283,10
"1568",2290,66,214,4,15,"no","no","yes",283,10
"1569",2495,66,340,8,15,"yes","no","yes",283,10
"1570",2190,50,214,4,14,"no","no","yes",283,10
"1571",2459,33,340,4,14,"no","no","no",283,10
"1572",1395,25,107,4,14,"no","no","yes",283,10
"1573",2390,33,426,8,15,"no","no","yes",283,10
"1574",1599,25,120,4,14,"no","no","no",283,10
"1575",2644,50,426,8,14,"no","no","yes",283,10
"1576",1695,25,214,4,14,"no","no","yes",283,10
"1577",2095,66,340,4,15,"no","no","yes",283,10
"1578",2144,66,107,2,14,"no","no","yes",283,10
"1579",2699,66,245,8,15,"yes","no","yes",283,10
"1580",2595,33,340,8,17,"no","no","yes",283,10
"1581",1990,66,107,2,14,"no","no","yes",283,10
"1582",1499,25,120,4,14,"no","no","yes",283,10
"1583",1944,25,214,4,14,"no","no","yes",283,10
"1584",1999,66,170,4,14,"yes","yes","yes",283,10
"1585",2244,50,214,4,14,"no","no","yes",283,10
"1586",2294,33,426,8,14,"yes","no","yes",283,10
"1587",3015,66,340,8,17,"yes","no","yes",283,10
"1588",2699,66,245,8,15,"yes","no","yes",283,10
"1589",2499,33,250,8,15,"yes","no","yes",283,10
"1590",3144,66,528,16,14,"no","no","yes",283,10
"1591",1799,33,230,4,14,"no","no","yes",283,10
"1592",2290,66,214,4,14,"no","no","yes",283,10
"1593",1949,50,340,4,14,"no","no","yes",283,10
"1594",2395,50,340,4,17,"no","no","yes",283,10
"1595",1929,66,210,4,15,"no","no","yes",283,10
"1596",1799,33,120,4,14,"no","no","no",283,10
"1597",3899,66,540,16,17,"yes","no","yes",283,10
"1598",3334,66,528,16,15,"no","no","yes",283,10
"1599",2795,66,545,8,14,"no","no","yes",283,10
"1600",1469,33,170,4,14,"no","no","yes",283,10
"1601",2439,50,340,8,17,"no","no","yes",283,10
"1602",1644,25,107,2,14,"no","no","yes",283,10
"1603",2444,33,426,8,14,"no","no","yes",283,10
"1604",2249,33,230,4,15,"no","no","yes",283,10
"1605",3044,50,528,16,14,"no","no","yes",283,10
"1606",2494,33,426,8,14,"yes","no","yes",283,10
"1607",1789,33,120,4,14,"no","no","no",283,10
"1608",2990,50,528,16,15,"no","no","yes",283,10
"1609",1944,33,107,2,14,"no","no","yes",283,10
"1610",2090,33,214,4,14,"no","no","yes",283,10
"1611",3135,33,1370,8,14,"no","no","yes",283,10
"1612",1995,33,340,8,15,"no","no","yes",283,10
"1613",1790,25,214,4,15,"no","no","yes",283,10
"1614",3315,66,545,8,17,"no","no","yes",283,10
"1615",1790,25,214,4,14,"no","no","yes",283,10
"1616",1925,33,250,4,15,"no","no","yes",283,10
"1617",2705,66,545,8,15,"no","no","yes",283,10
"1618",3144,50,528,16,14,"no","no","yes",283,10
"1619",1890,50,107,2,15,"no","no","yes",283,10
"1620",2694,66,426,8,14,"yes","no","yes",283,10
"1621",2044,33,170,4,15,"yes","yes","yes",283,10
"1622",1925,66,340,4,14,"no","no","yes",283,10
"1623",2559,66,340,8,17,"no","no","yes",283,10
"1624",1295,25,170,4,14,"no","no","yes",283,10
"1625",2590,25,528,16,15,"no","no","yes",283,10
"1626",2095,50,214,4,14,"no","no","yes",283,10
"1627",2104,33,120,4,14,"yes","no","no",283,10
"1628",2644,66,426,8,14,"no","no","yes",283,10
"1629",2644,25,528,16,14,"no","no","yes",283,10
"1630",1990,66,107,2,15,"no","no","yes",283,10
"1631",2149,66,340,4,14,"no","no","yes",283,10
"1632",2449,50,230,4,15,"no","no","yes",283,10
"1633",2294,50,214,4,14,"yes","no","yes",283,10
"1634",1944,33,214,4,14,"no","no","yes",283,10
"1635",1544,25,170,4,15,"no","no","yes",283,10
"1636",1699,33,210,4,15,"no","no","yes",283,10
"1637",1995,33,340,8,14,"no","no","yes",283,10
"1638",3319,66,527,4,15,"no","no","no",283,10
"1639",3244,66,528,16,14,"no","no","yes",283,10
"1640",2299,66,120,4,14,"no","no","no",283,10
"1641",1590,33,107,2,15,"no","no","yes",283,10
"1642",2774,33,340,4,14,"yes","no","no",283,10
"1643",3589,66,527,8,15,"no","no","no",283,10
"1644",2495,25,528,16,14,"no","no","yes",283,10
"1645",1809,50,210,4,15,"no","no","yes",283,10
"1646",1519,50,130,4,14,"no","no","yes",283,10
"1647",1299,25,170,4,14,"no","no","yes",283,10
"1648",1395,25,170,4,14,"no","no","yes",283,10
"1649",1785,33,340,4,15,"no","no","yes",283,10
"1650",2194,33,214,4,14,"yes","no","yes",283,10
"1651",999,33,80,2,14,"no","no","yes",283,10
"1652",3065,33,545,8,17,"no","no","yes",283,10
"1653",1845,50,240,4,14,"no","no","yes",283,10
"1654",3044,33,528,16,14,"no","no","yes",283,10
"1655",2690,33,528,16,14,"no","no","yes",283,10
"1656",2099,33,120,4,14,"no","no","no",283,10
"1657",1699,25,212,4,14,"no","no","no",283,10
"1658",1964,25,120,4,14,"yes","no","no",283,10
"1659",3194,66,528,16,14,"yes","no","yes",283,10
"1660",1490,25,107,2,15,"no","no","yes",283,10
"1661",2244,25,426,8,14,"no","no","yes",283,10
"1662",2059,33,120,4,14,"no","no","no",283,10
"1663",3094,50,528,16,14,"yes","no","yes",283,10
"1664",1795,50,107,2,14,"no","no","yes",283,10
"1665",2844,33,528,16,14,"no","no","yes",283,10
"1666",2994,33,528,16,14,"yes","no","yes",283,10
"1667",2475,33,545,8,15,"no","no","yes",283,10
"1668",2049,33,212,4,14,"no","no","no",283,10
"1669",3090,66,528,16,14,"no","no","yes",283,10
"1670",2744,33,528,16,14,"no","no","yes",283,10
"1671",2729,66,245,4,15,"no","no","no",283,10
"1672",1944,50,107,2,14,"no","no","yes",283,10
"1673",2344,33,426,8,14,"no","no","yes",283,10
"1674",1685,33,340,4,14,"no","no","yes",283,10
"1675",1695,33,107,2,14,"no","no","yes",283,10
"1676",1495,33,107,2,14,"no","no","yes",283,10
"1677",2195,50,340,8,14,"no","no","yes",283,10
"1678",2195,66,214,4,14,"no","no","yes",283,10
"1679",2890,33,528,16,15,"no","no","yes",283,10
"1680",2199,50,230,8,15,"no","no","yes",283,10
"1681",2395,66,340,8,15,"yes","no","yes",283,10
"1682",2295,50,340,8,15,"yes","no","yes",283,10
"1683",1490,25,107,2,14,"no","no","yes",283,10
"1684",1895,33,170,4,14,"yes","yes","yes",283,10
"1685",1890,33,214,4,14,"no","no","yes",283,10
"1686",1795,33,214,4,14,"no","no","yes",283,10
"1687",1799,33,170,4,14,"no","no","yes",283,10
"1688",1899,50,340,4,14,"no","no","yes",283,10
"1689",2875,33,1370,4,15,"no","no","yes",283,10
"1690",2044,33,214,4,14,"no","no","yes",283,10
"1691",2394,66,214,4,14,"yes","no","yes",283,10
"1692",2244,33,426,8,14,"no","no","yes",283,10
"1693",2094,66,107,2,14,"yes","no","yes",283,10
"1694",2344,50,214,4,14,"no","no","yes",283,10
"1695",1895,66,107,2,14,"no","no","yes",283,10
"1696",2690,66,340,8,15,"yes","yes","yes",283,10
"1697",1895,25,170,4,17,"no","no","yes",283,10
"1698",1844,25,214,4,14,"no","no","yes",283,10
"1699",2799,66,340,16,15,"yes","no","yes",283,10
"1700",1894,33,107,2,14,"yes","no","yes",283,10
"1701",4999,66,525,8,17,"yes","no","yes",283,10
"1702",1544,25,107,2,14,"no","no","yes",283,10
"1703",1690,25,170,4,14,"yes","yes","yes",283,10
"1704",3649,66,450,8,15,"no","no","yes",283,10
"1705",2545,33,545,8,14,"no","no","yes",283,10
"1706",1890,50,107,2,14,"no","no","yes",283,10
"1707",2834,66,426,8,15,"no","no","yes",283,10
"1708",2490,50,340,8,15,"yes","yes","yes",283,10
"1709",2044,66,107,2,14,"no","no","yes",283,10
"1710",2794,33,528,16,14,"yes","no","yes",283,10
"1711",1279,33,130,4,14,"no","no","yes",283,10
"1712",2815,50,340,8,17,"yes","no","yes",283,10
"1713",3595,66,340,16,17,"yes","no","yes",283,10
"1714",2315,33,545,4,15,"no","no","yes",283,10
"1715",1449,25,120,4,14,"no","no","no",283,10
"1716",1369,33,130,4,14,"no","no","yes",283,10
"1717",1479,50,130,4,14,"no","no","yes",283,10
"1718",2649,50,340,8,15,"yes","no","yes",283,10
"1719",2095,33,250,4,15,"yes","no","yes",283,10
"1720",1999,33,170,4,14,"no","no","yes",283,10
"1721",2390,33,426,8,14,"no","no","yes",283,10
"1722",1599,66,130,4,14,"no","no","yes",283,10
"1723",2595,66,340,4,17,"no","no","yes",283,10
"1724",1790,33,107,2,14,"no","no","yes",283,10
"1725",2190,50,340,4,14,"yes","yes","yes",283,10
"1726",1879,50,170,4,14,"yes","yes","yes",283,10
"1727",2194,25,426,8,14,"yes","no","yes",283,10
"1728",1399,33,212,4,14,"no","no","yes",283,10
"1729",1995,66,340,4,14,"no","no","yes",283,10
"1730",2095,33,340,8,15,"no","no","yes",283,10
"1731",2674,33,245,8,14,"yes","no","no",283,10
"1732",2044,50,107,2,14,"no","no","yes",283,10
"1733",2995,66,528,16,14,"no","no","yes",283,10
"1734",1995,33,212,4,17,"no","no","yes",283,10
"1735",2390,66,340,4,14,"yes","yes","yes",283,10
"1736",1395,25,170,4,15,"no","no","yes",283,10
"1737",2995,66,424,16,15,"yes","no","yes",283,10
"1738",2999,66,450,16,15,"yes","no","yes",283,10
"1739",2944,33,528,16,14,"no","no","yes",283,10
"1740",3515,66,424,16,17,"yes","no","yes",283,10
"1741",2690,33,528,16,15,"no","no","yes",283,10
"1742",1990,33,212,4,14,"yes","yes","yes",283,10
"1743",1994,50,107,2,14,"yes","no","yes",283,10
"1744",2799,66,450,8,15,"yes","no","yes",283,10
"1745",2990,50,528,16,14,"no","no","yes",283,10
"1746",2359,33,245,8,14,"no","no","no",283,10
"1747",2345,66,250,4,15,"yes","no","yes",283,10
"1748",2175,66,250,4,15,"no","no","yes",283,10
"1749",2544,50,426,8,14,"no","no","yes",283,10
"1750",1899,33,120,4,14,"no","no","no",283,10
"1751",2490,50,426,8,15,"no","no","yes",283,10
"1752",3190,66,424,16,15,"yes","yes","yes",283,10
"1753",1590,33,107,2,14,"no","no","yes",283,10
"1754",2565,66,545,4,15,"no","no","yes",283,10
"1755",2895,50,528,16,14,"no","no","yes",283,10
"1756",3198,66,450,16,15,"yes","no","yes",283,10
"1757",3199,66,340,16,15,"yes","no","yes",283,10
"1758",2024,25,120,4,14,"yes","no","no",283,10
"1759",1789,33,250,4,14,"no","no","yes",283,10
"1760",2145,33,340,8,15,"no","no","yes",283,10
"1761",1854,25,120,4,14,"yes","no","no",283,10
"1762",1790,33,107,2,15,"no","no","yes",283,10
"1763",2415,33,340,8,17,"no","no","yes",283,10
"1764",3249,66,450,4,15,"no","no","yes",283,10
"1765",1895,50,340,4,15,"no","no","yes",283,10
"1766",2590,66,426,8,14,"no","no","yes",283,10
"1767",2190,33,426,8,15,"no","no","yes",283,10
"1768",2795,66,340,8,17,"no","no","yes",283,10
"1769",2890,33,528,16,14,"no","no","yes",283,10
"1770",2490,50,426,8,14,"no","no","yes",283,10
"1771",2144,33,214,4,14,"no","no","yes",283,10
"1772",1539,25,212,4,14,"no","no","yes",283,10
"1773",2694,25,528,16,14,"yes","no","yes",283,10
"1774",2699,33,240,4,14,"no","no","no",283,10
"1775",2594,50,426,8,14,"yes","no","yes",283,10
"1776",3044,66,245,4,15,"yes","no","no",283,10
"1777",1579,50,170,4,14,"no","no","yes",283,10
"1778",2999,66,424,8,15,"yes","no","yes",283,10
"1779",1695,33,212,4,15,"no","no","yes",283,10
"1780",1890,33,214,4,15,"no","no","yes",283,10
"1781",2444,66,214,4,14,"no","no","yes",283,10
"1782",1795,50,340,4,14,"no","no","yes",283,10
"1783",1744,33,107,2,14,"no","no","yes",283,10
"1784",1449,25,212,4,14,"no","no","yes",283,10
"1785",3125,66,1370,4,15,"no","no","yes",283,10
"1786",1495,33,212,4,15,"no","no","yes",283,10
"1787",3090,66,528,16,15,"no","no","yes",283,10
"1788",1499,33,212,4,14,"no","no","yes",283,10
"1789",2995,66,340,16,15,"yes","no","yes",259,11
"1790",3199,66,340,16,15,"yes","no","yes",259,11
"1791",3334,66,528,16,15,"no","no","yes",259,11
"1792",3595,66,452,8,14,"no","no","yes",259,11
"1793",1899,33,120,4,14,"no","no","no",259,11
"1794",2090,33,214,4,15,"no","no","yes",259,11
"1795",3244,66,528,16,14,"no","no","yes",259,11
"1796",1644,25,107,2,14,"no","no","yes",259,11
"1797",3044,33,528,16,14,"no","no","yes",259,11
"1798",1295,25,212,4,14,"no","no","yes",259,11
"1799",1798,25,120,4,14,"no","no","no",259,11
"1800",1690,25,212,4,14,"yes","yes","yes",259,11
"1801",3708,66,527,8,15,"no","no","no",259,11
"1802",2495,66,424,8,15,"yes","no","yes",259,11
"1803",2790,50,426,8,14,"yes","yes","yes",259,11
"1804",1890,33,214,4,15,"no","no","yes",259,11
"1805",1795,33,214,8,14,"no","no","yes",259,11
"1806",3395,33,452,8,14,"no","no","yes",259,11
"1807",2095,33,212,4,17,"no","no","yes",259,11
"1808",2890,33,528,16,15,"no","no","yes",259,11
"1809",2344,50,214,4,14,"no","no","yes",259,11
"1810",2390,25,426,8,14,"yes","yes","yes",259,11
"1811",1964,25,120,4,14,"yes","no","no",259,11
"1812",3015,66,424,8,17,"yes","no","yes",259,11
"1813",2190,50,107,2,14,"yes","yes","yes",259,11
"1814",3515,66,424,16,17,"yes","no","yes",259,11
"1815",1899,25,120,4,14,"no","no","no",259,11
"1816",2690,33,528,16,15,"no","no","yes",259,11
"1817",2490,50,426,8,15,"no","no","yes",259,11
"1818",3544,66,340,16,17,"yes","no","yes",259,11
"1819",1925,66,340,4,15,"no","no","yes",259,11
"1820",2545,66,340,8,17,"no","no","yes",259,11
"1821",2995,66,528,8,15,"no","no","yes",259,11
"1822",2125,66,340,8,15,"no","no","yes",259,11
"1823",2395,50,426,8,14,"no","no","yes",259,11
"1824",1825,66,340,4,14,"no","no","yes",259,11
"1825",2390,33,214,4,14,"yes","yes","yes",259,11
"1826",2244,50,214,4,14,"no","no","yes",259,11
"1827",2475,33,340,8,17,"no","no","yes",259,11
"1828",2699,66,245,8,15,"yes","no","yes",259,11
"1829",2144,25,426,8,14,"no","no","yes",259,11
"1830",2644,50,426,8,14,"no","no","yes",259,11
"1831",2799,50,230,8,15,"yes","yes","yes",259,11
"1832",2099,33,120,4,14,"no","no","no",259,11
"1833",1995,33,424,8,14,"no","no","yes",259,11
"1834",2705,66,340,8,17,"no","no","yes",259,11
"1835",2799,66,450,8,15,"yes","no","yes",259,11
"1836",3205,66,1370,8,14,"no","no","yes",259,11
"1837",3899,66,527,16,17,"yes","no","yes",259,11
"1838",2729,66,245,4,15,"no","no","no",259,11
"1839",2444,66,214,4,14,"no","no","yes",259,11
"1840",2395,50,424,4,17,"no","no","yes",259,11
"1841",2095,33,212,4,17,"no","no","yes",259,11
"1842",2995,66,424,16,15,"yes","no","yes",259,11
"1843",2329,33,340,8,17,"no","no","yes",259,11
"1844",2390,33,424,8,14,"yes","yes","yes",259,11
"1845",1649,25,120,4,14,"no","no","no",259,11
"1846",2290,66,107,2,14,"yes","yes","yes",259,11
"1847",1828,25,120,4,14,"no","no","no",259,11
"1848",2395,66,340,8,15,"no","no","yes",259,11
"1849",2944,33,528,16,14,"no","no","yes",259,11
"1850",1890,33,212,4,14,"yes","yes","yes",259,11
"1851",2605,66,545,8,15,"no","no","yes",259,11
"1852",2095,33,426,8,14,"no","no","yes",259,11
"1853",2024,25,120,4,14,"yes","no","no",259,11
"1854",3090,66,528,16,15,"no","no","yes",259,11
"1855",2590,66,426,8,15,"no","no","yes",259,11
"1856",1790,33,107,2,15,"no","no","yes",259,11
"1857",3714,66,527,4,15,"yes","no","no",259,11
"1858",2499,66,424,8,15,"no","no","yes",259,11
"1859",3144,66,528,16,14,"no","no","yes",259,11
"1860",2899,50,240,4,14,"no","no","no",259,11
"1861",2390,66,424,4,14,"yes","yes","yes",259,11
"1862",2375,33,545,8,15,"no","no","yes",259,11
"1863",1945,33,340,4,15,"no","no","yes",259,11
"1864",2090,25,214,4,14,"yes","yes","yes",259,11
"1865",2168,33,212,4,14,"no","no","no",259,11
"1866",2890,66,426,8,14,"yes","yes","yes",259,11
"1867",2595,33,424,8,17,"no","no","yes",259,11
"1868",3198,66,450,16,15,"yes","no","yes",259,11
"1869",2890,25,528,16,14,"yes","yes","yes",259,11
"1870",1744,33,107,2,14,"no","no","yes",259,11
"1871",2685,66,545,8,14,"no","no","yes",259,11
"1872",2044,66,107,2,14,"no","no","yes",259,11
"1873",1999,50,245,4,15,"yes","yes","yes",259,11
"1874",2095,50,214,4,14,"no","no","yes",259,11
"1875",2285,66,340,8,15,"no","no","yes",259,11
"1876",2364,33,212,4,14,"yes","no","no",259,11
"1877",1844,25,214,4,14,"no","no","yes",259,11
"1878",1349,33,212,4,14,"no","no","yes",259,11
"1879",3044,50,528,16,14,"no","no","yes",259,11
"1880",2149,66,245,4,15,"yes","yes","yes",259,11
"1881",1890,33,107,2,14,"yes","yes","yes",259,11
"1882",1818,25,212,4,14,"no","no","no",259,11
"1883",2999,66,340,8,15,"yes","yes","yes",259,11
"1884",1990,66,107,2,15,"no","no","yes",259,11
"1885",2990,50,528,16,15,"no","no","yes",259,11
"1886",1529,33,245,4,14,"no","no","yes",259,11
"1887",2144,33,214,4,14,"no","no","yes",259,11
"1888",1799,33,120,4,14,"no","no","no",259,11
"1889",1799,33,230,4,14,"no","no","yes",259,11
"1890",2439,50,340,8,17,"no","no","yes",259,11
"1891",2055,33,340,8,15,"no","no","yes",259,11
"1892",1695,25,214,4,14,"no","no","yes",259,11
"1893",1644,33,107,2,14,"no","no","yes",259,11
"1894",2065,33,340,8,15,"yes","no","yes",259,11
"1895",2295,50,424,8,15,"yes","no","yes",259,11
"1896",2490,33,426,8,14,"yes","yes","yes",259,11
"1897",3144,50,528,16,14,"no","no","yes",259,11
"1898",2844,33,528,16,14,"no","no","yes",259,11
"1899",1944,33,107,2,14,"no","no","yes",259,11
"1900",1899,66,245,4,15,"no","no","yes",259,11
"1901",2185,25,528,8,14,"no","no","yes",259,11
"1902",1795,33,214,4,14,"no","no","yes",259,11
"1903",1844,33,107,2,14,"no","no","yes",259,11
"1904",2190,50,214,4,15,"no","no","yes",259,11
"1905",2595,33,528,16,14,"no","no","yes",259,11
"1906",1595,33,340,4,14,"no","no","yes",259,11
"1907",1495,33,107,4,14,"no","no","yes",259,11
"1908",1795,50,424,4,14,"no","no","yes",259,11
"1909",2374,33,120,4,14,"yes","no","no",259,11
"1910",2498,50,320,8,15,"yes","no","yes",259,11
"1911",2175,66,340,4,15,"no","no","yes",259,11
"1912",1908,33,120,4,14,"no","no","no",259,11
"1913",1999,33,340,8,14,"yes","no","yes",259,11
"1914",1599,25,120,4,14,"no","no","no",259,11
"1915",2090,25,426,8,15,"no","no","yes",259,11
"1916",1670,25,212,4,14,"yes","yes","yes",259,11
"1917",2478,33,245,8,14,"no","no","no",259,11
"1918",2590,66,214,4,14,"yes","yes","yes",259,11
"1919",1749,50,245,4,15,"no","no","yes",259,11
"1920",1995,33,214,4,14,"no","no","yes",259,11
"1921",1929,33,245,4,15,"yes","yes","yes",259,11
"1922",3390,66,528,16,14,"yes","yes","yes",259,11
"1923",2044,50,107,2,14,"no","no","yes",259,11
"1924",3290,50,528,16,14,"yes","yes","yes",259,11
"1925",2175,33,425,8,15,"no","no","yes",259,11
"1926",1449,25,120,4,14,"no","no","no",259,11
"1927",2795,33,528,16,14,"no","no","yes",259,11
"1928",1795,50,107,2,14,"no","no","yes",259,11
"1929",2359,33,245,8,14,"no","no","no",259,11
"1930",2049,33,212,4,14,"no","no","no",259,11
"1931",2895,50,528,16,14,"no","no","yes",259,11
"1932",2608,33,340,4,14,"no","no","no",259,11
"1933",1944,25,214,4,14,"no","no","yes",259,11
"1934",2495,66,426,8,14,"no","no","yes",259,11
"1935",2405,66,425,8,15,"no","no","yes",259,11
"1936",2104,33,120,4,14,"yes","no","no",259,11
"1937",2190,33,214,4,14,"yes","yes","yes",259,11
"1938",2195,66,214,4,14,"no","no","yes",259,11
"1939",3589,66,527,8,15,"no","no","no",259,11
"1940",2344,66,214,4,14,"no","no","yes",259,11
"1941",1695,33,340,4,15,"no","no","yes",259,11
"1942",1890,33,212,4,14,"yes","yes","yes",259,11
"1943",1854,25,120,4,14,"yes","no","no",259,11
"1944",2299,66,120,4,14,"no","no","no",259,11
"1945",3044,66,245,4,15,"yes","no","no",259,11
"1946",2944,66,340,8,17,"no","no","yes",259,11
"1947",2145,33,340,4,15,"yes","no","yes",259,11
"1948",1944,50,107,2,14,"no","no","yes",259,11
"1949",2144,66,107,2,14,"no","no","yes",259,11
"1950",1395,25,107,4,14,"no","no","yes",259,11
"1951",2244,33,214,4,14,"no","no","yes",259,11
"1952",2744,33,528,16,14,"no","no","yes",259,11
"1953",2674,33,245,8,14,"yes","no","no",259,11
"1954",1495,33,107,2,14,"no","no","yes",259,11
"1955",3205,66,545,8,17,"no","no","yes",259,11
"1956",2390,33,426,8,15,"no","no","yes",259,11
"1957",1395,25,107,2,14,"no","no","yes",259,11
"1958",2815,50,424,8,17,"yes","no","yes",259,11
"1959",1599,50,245,4,14,"no","no","yes",259,11
"1960",2190,50,424,4,14,"yes","yes","yes",259,11
"1961",3678,66,527,8,15,"no","no","no",259,11
"1962",1695,33,107,2,14,"no","no","yes",259,11
"1963",1895,25,212,4,17,"no","no","yes",259,11
"1964",1275,25,212,4,14,"no","no","yes",259,11
"1965",3399,66,527,4,15,"no","no","no",259,11
"1966",1789,33,120,4,14,"no","no","no",259,11
"1967",2699,33,240,4,14,"no","no","no",259,11
"1968",2544,33,426,8,14,"no","no","yes",259,11
"1969",2744,66,426,8,14,"no","no","yes",259,11
"1970",1790,25,214,4,15,"no","no","yes",259,11
"1971",1490,25,107,2,15,"no","no","yes",259,11
"1972",2315,33,340,8,17,"no","no","yes",259,11
"1973",2285,33,528,8,14,"no","no","yes",259,11
"1974",3904,66,527,8,15,"yes","no","no",259,11
"1975",1699,25,212,4,14,"no","no","no",259,11
"1976",1695,25,214,8,14,"no","no","yes",259,11
"1977",2138,33,212,4,14,"no","no","no",259,11
"1978",1895,66,107,2,14,"no","no","yes",259,11
"1979",2490,50,214,4,14,"yes","yes","yes",259,11
"1980",1995,66,424,4,14,"no","no","yes",259,11
"1981",2644,25,528,16,14,"no","no","yes",259,11
"1982",1699,33,245,4,15,"no","no","yes",259,11
"1983",1544,25,107,2,14,"no","no","yes",259,11
"1984",2744,50,340,8,17,"no","no","yes",259,11
"1985",2495,25,528,16,14,"no","no","yes",259,11
"1986",1995,25,426,8,14,"no","no","yes",259,11
"1987",1539,25,120,4,14,"no","no","no",259,11
"1988",1878,33,120,4,14,"no","no","no",259,11
"1989",2090,33,107,2,14,"yes","yes","yes",259,11
"1990",1299,25,120,4,14,"yes","no","yes",259,11
"1991",2044,33,214,4,14,"no","no","yes",259,11
"1992",5399,66,1200,32,17,"yes","no","yes",259,11
"1993",1395,25,107,4,14,"no","no","yes",259,11
"1994",3365,66,450,16,15,"yes","yes","yes",259,11
"1995",1395,25,240,4,14,"no","no","yes",259,11
"1996",2014,25,212,4,14,"yes","no","no",259,11
"1997",2590,25,528,16,15,"no","no","yes",259,11
"1998",2295,66,340,8,15,"yes","no","yes",259,11
"1999",1788,25,212,4,14,"no","no","no",259,11
"2000",2975,33,545,8,17,"no","no","yes",259,11
"2001",3495,50,452,8,14,"no","no","yes",259,11
"2002",2578,33,340,4,14,"no","no","no",259,11
"2003",1494,25,240,4,15,"no","no","yes",259,11
"2004",2999,66,450,16,15,"yes","no","yes",259,11
"2005",2190,33,426,8,15,"no","no","yes",259,11
"2006",1590,33,107,2,15,"no","no","yes",259,11
"2007",2690,33,426,8,14,"yes","yes","yes",259,11
"2008",2489,33,340,4,14,"no","no","no",259,11
"2009",2544,50,426,8,14,"no","no","yes",259,11
"2010",2848,66,245,4,15,"no","no","no",259,11
"2011",2799,66,340,16,15,"yes","no","yes",259,11
"2012",2059,33,120,4,14,"no","no","no",259,11
"2013",1749,33,212,4,14,"yes","yes","yes",259,11
"2014",1495,33,212,4,14,"no","no","yes",259,11
"2015",2744,25,528,16,14,"no","no","yes",259,11
"2016",2699,66,245,8,15,"yes","no","yes",259,11
"2017",1749,66,245,4,14,"no","no","yes",259,11
"2018",2818,66,245,4,15,"no","no","no",259,11
"2019",1495,33,212,4,14,"no","no","yes",259,11
"2020",1790,25,107,2,14,"yes","yes","yes",259,11
"2021",1944,33,214,4,14,"no","no","yes",259,11
"2022",2344,33,426,8,14,"no","no","yes",259,11
"2023",1890,50,107,2,15,"no","no","yes",259,11
"2024",2644,66,426,8,14,"no","no","yes",259,11
"2025",1709,25,120,4,14,"no","no","no",259,11
"2026",2244,25,426,8,14,"no","no","yes",259,11
"2027",2804,33,340,4,14,"yes","no","no",259,11
"2028",1795,50,340,4,14,"no","no","yes",259,11
"2029",2244,33,426,8,14,"no","no","yes",259,11
"2030",2995,66,528,16,14,"no","no","yes",259,11
"2031",2585,50,528,8,14,"no","no","yes",259,11
"2032",2595,66,424,4,17,"no","no","yes",259,11
"2033",2559,66,340,8,17,"no","no","yes",259,11
"2034",2375,66,340,4,15,"yes","no","yes",259,11
"2035",2290,66,214,4,15,"no","no","yes",259,11
"2036",1895,33,340,8,15,"no","no","yes",259,11
"2037",2195,50,340,8,15,"no","no","yes",259,11
"2038",2295,33,426,8,14,"no","no","yes",259,11
"2039",2990,33,528,16,14,"yes","yes","yes",259,11
"2040",2455,33,545,8,14,"no","no","yes",259,11
"2041",1894,50,340,4,15,"no","no","yes",259,11
"2042",2448,33,245,8,14,"no","no","no",259,11
"2043",2975,33,1370,8,14,"no","no","yes",259,11
"2044",2485,33,528,8,14,"no","no","yes",259,11
"2045",2685,66,528,8,14,"no","no","yes",259,11
"2046",3190,33,528,16,14,"yes","yes","yes",259,11
"2047",2444,33,426,8,14,"no","no","yes",259,11
"2048",2069,33,340,4,14,"no","no","no",275,12
"2049",1709,25,170,4,14,"no","no","no",275,12
"2050",1994,33,120,4,14,"yes","no","no",275,12
"2051",3090,66,528,16,15,"no","no","yes",275,12
"2052",1425,25,120,4,14,"no","no","no",275,12
"2053",2995,66,528,16,14,"no","no","yes",275,12
"2054",1804,25,120,4,14,"yes","no","no",275,12
"2055",3015,66,424,8,17,"yes","no","yes",275,12
"2056",1574,25,212,4,14,"yes","yes","yes",275,12
"2057",1535,33,340,4,14,"no","no","yes",275,12
"2058",1914,25,212,4,14,"yes","no","no",275,12
"2059",2214,25,120,4,14,"yes","no","no",275,12
"2060",2299,66,120,4,14,"no","no","no",275,12
"2061",1999,33,250,4,14,"yes","yes","yes",275,12
"2062",3795,33,1000,16,14,"yes","no","yes",275,12
"2063",1895,33,340,8,14,"yes","no","yes",275,12
"2064",1795,50,424,4,14,"no","no","yes",275,12
"2065",1794,33,212,4,14,"yes","yes","yes",275,12
"2066",3899,66,527,16,17,"yes","no","yes",275,12
"2067",1299,25,212,4,14,"no","no","yes",275,12
"2068",3144,66,528,16,14,"no","no","yes",275,12
"2069",1595,33,107,2,14,"no","no","yes",275,12
"2070",2890,66,426,8,14,"yes","yes","yes",275,12
"2071",2199,50,340,8,15,"no","no","yes",275,12
"2072",2449,25,245,4,14,"yes","no","no",275,12
"2073",3127,66,424,16,15,"yes","yes","yes",275,12
"2074",2995,66,528,8,15,"no","no","yes",275,12
"2075",2943,33,340,8,14,"no","no","no",275,12
"2076",3190,50,528,16,14,"yes","yes","yes",275,12
"2077",2444,33,426,8,14,"no","no","yes",275,12
"2078",1995,33,424,8,14,"no","no","yes",275,12
"2079",1844,50,107,2,14,"no","no","yes",275,12
"2080",1494,33,240,4,15,"no","no","yes",275,12
"2081",2544,50,426,8,14,"no","no","yes",275,12
"2082",2290,66,214,4,15,"no","no","yes",275,12
"2083",1349,33,212,4,14,"no","no","yes",275,12
"2084",2095,33,426,8,14,"no","no","yes",275,12
"2085",2743,33,340,4,14,"no","no","no",275,12
"2086",2135,33,540,8,15,"no","no","yes",275,12
"2087",2999,66,450,16,15,"yes","no","yes",275,12
"2088",2495,25,528,16,14,"no","no","yes",275,12
"2089",1489,25,120,4,14,"no","no","no",275,12
"2090",2644,66,426,8,14,"no","no","yes",275,12
"2091",2374,33,212,4,14,"yes","no","no",275,12
"2092",1590,33,107,2,15,"no","no","yes",275,12
"2093",1944,25,214,4,14,"no","no","yes",275,12
"2094",2654,33,340,4,14,"no","no","no",275,12
"2095",1999,33,230,8,15,"no","no","yes",275,12
"2096",1990,33,214,4,15,"no","no","yes",275,12
"2097",5399,66,1200,32,17,"yes","no","yes",275,12
"2098",2244,33,426,8,14,"no","no","yes",275,12
"2099",2134,25,245,4,14,"no","no","no",275,12
"2100",1995,66,340,8,15,"no","no","yes",275,12
"2101",1499,33,170,4,14,"no","no","yes",275,12
"2102",2944,50,528,16,14,"no","no","yes",275,12
"2103",2785,33,1060,8,14,"no","no","yes",275,12
"2104",2854,33,340,8,14,"no","no","no",275,12
"2105",2344,33,426,8,14,"no","no","yes",275,12
"2106",2320,33,528,8,14,"no","no","yes",275,12
"2107",3099,66,450,16,15,"yes","no","yes",275,12
"2108",1644,33,107,2,14,"no","no","yes",275,12
"2109",2090,50,214,4,15,"no","no","yes",275,12
"2110",2744,25,528,16,14,"no","no","yes",275,12
"2111",3334,66,528,16,15,"no","no","yes",275,12
"2112",2844,33,528,16,14,"no","no","yes",275,12
"2113",2024,25,170,4,14,"yes","no","no",275,12
"2114",1799,25,424,4,14,"yes","yes","yes",275,12
"2115",2099,66,245,4,15,"yes","yes","yes",275,12
"2116",1890,33,214,4,15,"no","no","yes",275,12
"2117",1520,33,424,4,14,"no","no","yes",275,12
"2118",2290,33,214,4,14,"yes","yes","yes",275,12
"2119",2290,33,426,8,15,"no","no","yes",275,12
"2120",2969,33,340,4,14,"yes","no","no",275,12
"2121",2195,66,214,4,14,"no","no","yes",275,12
"2122",2944,66,340,8,17,"no","no","yes",275,12
"2123",2244,25,426,8,14,"no","no","yes",275,12
"2124",2190,33,214,4,14,"yes","yes","yes",275,12
"2125",1995,66,424,4,14,"no","no","yes",275,12
"2126",2490,33,426,8,14,"yes","yes","yes",275,12
"2127",2944,33,528,16,14,"no","no","yes",275,12
"2128",2185,33,340,8,17,"no","no","yes",275,12
"2129",1899,33,245,4,15,"yes","yes","yes",275,12
"2130",1790,25,107,2,14,"yes","yes","yes",275,12
"2131",1895,33,340,8,15,"no","no","yes",275,12
"2132",2444,66,214,4,14,"no","no","yes",275,12
"2133",2627,66,424,8,15,"yes","yes","yes",275,12
"2134",2995,66,340,16,15,"yes","no","yes",275,12
"2135",2179,33,340,4,14,"no","no","no",275,12
"2136",2444,50,426,8,14,"no","no","yes",275,12
"2137",3544,66,340,16,17,"yes","no","yes",275,12
"2138",1649,33,245,4,15,"no","no","yes",275,12
"2139",1499,25,120,4,14,"no","no","no",275,12
"2140",1798,25,170,4,14,"no","no","no",275,12
"2141",2223,25,245,4,14,"no","no","no",275,12
"2142",2999,66,340,8,15,"yes","yes","yes",275,12
"2143",3169,33,340,8,14,"yes","no","no",275,12
"2144",1999,33,340,8,14,"yes","no","yes",275,12
"2145",1949,50,245,4,15,"yes","yes","yes",275,12
"2146",2059,33,212,4,14,"no","no","no",275,12
"2147",2595,33,528,16,14,"no","no","yes",275,12
"2148",1275,25,212,4,14,"no","no","yes",275,12
"2149",3995,66,1000,16,14,"yes","no","yes",275,12
"2150",2595,66,424,4,17,"no","no","yes",275,12
"2151",1695,25,214,4,14,"no","no","yes",275,12
"2152",2785,33,540,8,17,"no","no","yes",275,12
"2153",2295,50,426,8,14,"no","no","yes",275,12
"2154",1569,50,245,4,14,"no","no","yes",275,12
"2155",2559,66,340,8,17,"no","no","yes",275,12
"2156",2495,66,424,8,15,"yes","no","yes",275,12
"2157",2268,33,340,4,14,"no","no","no",275,12
"2158",1495,33,212,4,14,"no","no","yes",275,12
"2159",2895,66,520,8,14,"no","no","yes",275,12
"2160",2595,33,424,8,17,"no","no","yes",275,12
"2161",2349,66,340,4,14,"yes","yes","yes",275,12
"2162",2795,66,340,8,15,"no","no","yes",275,12
"2163",1988,25,120,4,14,"no","no","no",275,12
"2164",1490,25,107,2,15,"no","no","yes",275,12
"2165",2320,33,424,4,17,"no","no","yes",275,12
"2166",1495,33,212,4,14,"no","no","yes",275,12
"2167",2390,50,426,8,15,"no","no","yes",275,12
"2168",1295,33,212,4,14,"no","no","yes",275,12
"2169",2144,25,426,8,14,"no","no","yes",275,12
"2170",1944,33,214,4,14,"no","no","yes",275,12
"2171",2495,66,540,8,14,"no","no","yes",275,12
"2172",2329,33,340,8,17,"no","no","yes",275,12
"2173",1995,25,426,8,14,"no","no","yes",275,12
"2174",1544,25,107,2,14,"no","no","yes",275,12
"2175",1790,50,107,2,15,"no","no","yes",275,12
"2176",1594,33,212,4,14,"yes","yes","yes",275,12
"2177",2344,33,426,8,14,"no","no","yes",275,12
"2178",1795,33,340,8,15,"no","no","yes",275,12
"2179",2985,66,540,8,17,"no","no","yes",275,12
"2180",2590,66,214,4,14,"yes","yes","yes",275,12
"2181",2335,66,540,8,15,"no","no","yes",275,12
"2182",2044,66,107,2,14,"no","no","yes",275,12
"2183",2195,33,426,8,14,"no","no","yes",275,12
"2184",2244,50,214,4,14,"no","no","yes",275,12
"2185",1575,33,120,4,14,"no","no","no",275,12
"2186",2494,33,340,4,14,"yes","no","no",275,12
"2187",2844,33,528,16,14,"no","no","yes",275,12
"2188",1975,66,340,4,15,"no","no","yes",275,12
"2189",3515,66,424,16,17,"yes","no","yes",275,12
"2190",1890,33,107,2,14,"yes","yes","yes",275,12
"2191",1844,25,214,4,14,"no","no","yes",275,12
"2192",1695,50,107,2,14,"no","no","yes",275,12
"2193",2427,50,424,8,15,"yes","yes","yes",275,12
"2194",2144,66,107,2,14,"no","no","yes",275,12
"2195",1894,50,340,4,15,"no","no","yes",275,12
"2196",2420,50,528,8,14,"no","no","yes",275,12
"2197",3399,66,527,8,15,"no","no","no",275,12
"2198",2395,50,424,4,17,"no","no","yes",275,12
"2199",1795,50,340,4,14,"no","no","yes",275,12
"2200",1699,50,245,4,15,"no","no","yes",275,12
"2201",2170,66,340,4,15,"yes","no","yes",275,12
"2202",2120,33,424,4,17,"no","no","yes",275,12
"2203",2690,50,426,8,14,"yes","yes","yes",275,12
"2204",1735,66,340,4,14,"no","no","yes",275,12
"2205",3895,50,1000,16,14,"yes","no","yes",275,12
"2206",1475,33,250,4,14,"no","no","yes",275,12
"2207",2890,50,528,16,15,"no","no","yes",275,12
"2208",2384,33,340,4,14,"yes","no","no",275,12
"2209",1495,33,107,2,14,"no","no","yes",275,12
"2210",2399,50,340,8,17,"no","no","yes",275,12
"2211",2294,66,424,4,14,"yes","yes","yes",275,12
"2212",2749,50,340,8,15,"yes","yes","yes",275,12
"2213",1744,33,107,2,14,"no","no","yes",275,12
"2214",3044,50,528,16,14,"no","no","yes",275,12
"2215",1699,66,245,4,14,"no","no","yes",275,12
"2216",2699,66,245,8,15,"yes","no","yes",275,12
"2217",1679,33,120,4,14,"no","no","no",275,12
"2218",1599,25,212,4,14,"no","no","no",275,12
"2219",2495,66,426,8,14,"no","no","yes",275,12
"2220",1895,33,212,4,17,"no","no","yes",275,12
"2221",1849,66,245,4,15,"no","no","yes",275,12
"2222",2620,66,528,8,14,"no","no","yes",275,12
"2223",2094,50,424,4,14,"yes","yes","yes",275,12
"2224",1875,25,212,4,17,"no","no","yes",275,12
"2225",2744,66,426,8,14,"no","no","yes",275,12
"2226",2449,66,425,8,15,"no","no","yes",275,12
"2227",2744,50,340,8,17,"no","no","yes",275,12
"2228",1895,33,214,4,14,"no","no","yes",275,12
"2229",2395,66,340,8,15,"no","no","yes",275,12
"2230",1295,25,214,4,14,"no","no","yes",275,12
"2231",1990,33,107,2,14,"yes","yes","yes",275,12
"2232",2044,33,214,4,14,"no","no","yes",275,12
"2233",2299,25,120,4,14,"yes","yes","no",275,12
"2234",1895,66,107,2,14,"no","no","yes",275,12
"2235",1375,25,120,4,14,"no","no","no",275,12
"2236",2090,50,107,2,14,"yes","yes","yes",275,12
"2237",1744,33,107,2,14,"no","no","yes",275,12
"2238",1995,50,214,4,14,"no","no","yes",275,12
"2239",2590,66,426,8,15,"no","no","yes",275,12
"2240",1794,33,212,4,14,"yes","yes","yes",275,12
"2241",2220,33,528,8,14,"no","no","yes",275,12
"2242",1749,33,212,4,14,"yes","yes","yes",275,12
"2243",2990,33,528,16,14,"yes","yes","yes",275,12
"2244",2344,66,214,4,14,"no","no","yes",275,12
"2245",2644,25,528,16,14,"no","no","yes",275,12
"2246",2225,66,340,8,15,"yes","no","yes",275,12
"2247",2095,66,340,8,15,"no","no","yes",275,12
"2248",1499,33,245,4,14,"no","no","yes",275,12
"2249",3090,33,528,16,14,"yes","yes","yes",275,12
"2250",2385,66,340,8,17,"no","no","yes",275,12
"2251",3244,66,528,16,14,"no","no","yes",275,12
"2252",1899,25,120,4,14,"no","no","no",275,12
"2253",2890,25,528,16,14,"yes","yes","yes",275,12
"2254",3714,66,527,8,15,"yes","no","no",275,12
"2255",2294,33,424,8,14,"yes","yes","yes",275,12
"2256",1795,33,240,4,14,"yes","yes","yes",275,12
"2257",2388,25,120,4,14,"yes","yes","no",275,12
"2258",2044,33,214,4,14,"no","no","yes",275,12
"2259",1799,33,120,4,14,"no","no","no",275,12
"2260",1893,25,120,4,14,"no","no","no",275,12
"2261",2295,50,424,8,15,"yes","no","yes",275,12
"2262",1790,25,214,4,15,"no","no","yes",275,12
"2263",1688,25,212,4,14,"no","no","no",275,12
"2264",1695,33,340,8,14,"yes","no","yes",275,12
"2265",1775,33,340,4,15,"no","no","yes",275,12
"2266",1970,33,340,4,15,"yes","no","yes",275,12
"2267",2695,33,528,16,14,"no","no","yes",275,12
"2268",2195,50,340,8,15,"no","no","yes",275,12
"2269",2290,66,107,2,14,"yes","yes","yes",275,12
"2270",2815,50,424,8,17,"yes","no","yes",275,12
"2271",1844,33,107,2,14,"no","no","yes",275,12
"2272",3390,66,528,16,14,"yes","yes","yes",275,12
"2273",2019,33,424,4,14,"yes","yes","yes",275,12
"2274",1395,33,214,4,14,"no","no","yes",275,12
"2275",1395,25,107,4,14,"no","no","yes",275,12
"2276",2119,25,120,4,14,"yes","no","no",275,12
"2277",1675,66,250,4,14,"no","no","yes",275,12
"2278",2590,25,528,16,15,"no","no","yes",275,12
"2279",2690,33,528,16,15,"no","no","yes",275,12
"2280",2590,33,426,8,14,"yes","yes","yes",275,12
"2281",2158,33,340,4,14,"no","no","no",275,12
"2282",3994,66,424,32,15,"yes","no","yes",275,12
"2283",2985,66,1060,8,14,"no","no","yes",275,12
"2284",2790,33,528,16,15,"no","no","yes",275,12
"2285",1819,33,424,4,14,"yes","yes","yes",275,12
"2286",2025,33,340,8,15,"yes","no","yes",275,12
"2287",1595,33,214,4,14,"yes","no","yes",275,12
"2288",2120,25,528,8,14,"no","no","yes",275,12
"2289",2100,25,424,4,17,"no","no","yes",275,12
"2290",2795,50,528,16,14,"no","no","yes",275,12
"2291",1690,33,107,2,15,"no","no","yes",275,12
"2292",1395,25,107,2,14,"no","no","yes",275,12
"2293",1990,66,107,2,15,"no","no","yes",275,12
"2294",1395,33,240,4,14,"no","no","yes",275,12
"2295",1299,25,120,4,14,"yes","no","yes",275,12
"2296",3199,66,340,16,15,"yes","no","yes",275,12
"2297",1595,25,340,8,14,"yes","no","yes",275,12
"2298",2095,33,212,4,17,"no","no","yes",275,12
"2299",2190,33,426,8,15,"no","no","yes",275,12
"2300",1944,50,107,2,14,"no","no","yes",275,12
"2301",1804,25,120,4,14,"no","no","no",275,12
"2302",2095,33,212,4,17,"no","no","yes",275,12
"2303",2744,33,528,16,14,"no","no","yes",275,12
"2304",2295,33,540,8,14,"no","no","yes",275,12
"2305",1578,25,120,4,14,"no","no","no",275,12
"2306",2995,66,424,16,15,"yes","no","yes",275,12
"2307",2699,66,245,8,15,"yes","no","yes",275,12
"2308",2144,50,214,4,14,"no","no","yes",275,12
"2309",2799,66,340,16,15,"yes","no","yes",275,12
"2310",2390,50,214,4,14,"yes","yes","yes",275,12
"2311",1500,25,424,4,14,"no","no","yes",275,12
"2312",1749,33,120,4,14,"no","no","no",275,12
"2313",2090,25,426,8,15,"no","no","yes",275,12
"2314",2390,25,426,8,14,"yes","yes","yes",275,12
"2315",1720,33,424,4,14,"no","no","yes",275,12
"2316",1768,33,120,4,14,"no","no","no",275,12
"2317",1795,33,214,4,14,"no","no","yes",275,12
"2318",1644,25,107,2,14,"no","no","yes",275,12
"2319",2090,25,214,4,14,"yes","yes","yes",275,12
"2320",1299,25,170,4,14,"no","no","yes",275,12
"2321",2144,33,214,4,14,"no","no","yes",275,12
"2322",1799,33,230,4,14,"no","no","yes",275,12
"2323",2290,66,214,4,15,"no","no","yes",216,13
"2324",1299,33,212,4,14,"no","no","yes",216,13
"2325",1744,33,107,2,14,"no","no","yes",216,13
"2326",1644,25,107,2,14,"no","no","yes",216,13
"2327",2644,66,426,8,14,"no","no","yes",216,13
"2328",2420,50,528,8,14,"no","no","yes",216,13
"2329",1790,50,107,2,15,"no","no","yes",216,13
"2330",1644,33,107,2,14,"no","no","yes",216,13
"2331",3094,66,340,4,14,"yes","no","no",216,13
"2332",2590,33,426,8,14,"yes","yes","yes",216,13
"2333",1844,50,107,2,14,"no","no","yes",216,13
"2334",2790,33,528,16,15,"no","no","yes",216,13
"2335",2844,33,528,16,14,"no","no","yes",216,13
"2336",3699,66,527,16,17,"yes","no","yes",216,13
"2337",2779,66,340,4,14,"no","no","no",216,13
"2338",1689,25,212,4,14,"no","no","no",216,13
"2339",2769,33,340,8,15,"yes","yes","yes",216,13
"2340",1590,33,107,2,15,"no","no","yes",216,13
"2341",2890,50,528,16,15,"no","no","yes",216,13
"2342",2190,33,426,8,15,"no","no","yes",216,13
"2343",2185,33,420,8,17,"no","no","yes",216,13
"2344",1819,33,120,4,14,"no","no","no",216,13
"2345",1795,33,420,8,15,"no","no","yes",216,13
"2346",2944,33,528,16,14,"no","no","yes",216,13
"2347",2134,33,120,4,14,"yes","no","no",216,13
"2348",1695,33,420,4,15,"no","no","yes",216,13
"2349",2144,50,214,4,14,"no","no","yes",216,13
"2350",2244,33,426,8,14,"no","no","yes",216,13
"2351",2385,66,420,8,17,"no","no","yes",216,13
"2352",1799,66,245,4,15,"no","no","yes",216,13
"2353",1139,33,125,2,14,"no","no","yes",216,13
"2354",1975,66,420,4,14,"yes","no","yes",216,13
"2355",1558,25,120,4,14,"no","no","no",216,13
"2356",2698,50,320,8,15,"yes","yes","yes",216,13
"2357",2044,66,107,2,14,"no","no","yes",216,13
"2358",2844,33,528,16,14,"no","no","yes",216,13
"2359",1744,33,107,2,14,"no","no","yes",216,13
"2360",1999,33,340,8,14,"yes","no","yes",216,13
"2361",1749,33,120,4,14,"no","no","no",216,13
"2362",2690,50,426,8,14,"yes","yes","yes",216,13
"2363",2744,25,528,16,14,"no","no","yes",216,13
"2364",2390,50,214,4,14,"yes","yes","yes",216,13
"2365",3090,33,528,16,14,"yes","yes","yes",216,13
"2366",1990,66,107,2,15,"no","no","yes",216,13
"2367",1795,33,240,4,14,"yes","yes","yes",216,13
"2368",1895,33,214,4,14,"no","no","yes",216,13
"2369",2044,33,214,4,14,"no","no","yes",216,13
"2370",2195,50,340,8,15,"no","no","yes",216,13
"2371",2849,50,340,8,15,"yes","yes","yes",216,13
"2372",1799,33,270,4,14,"no","no","yes",216,13
"2373",2495,25,528,16,14,"no","no","yes",216,13
"2374",2744,50,340,8,17,"no","no","yes",216,13
"2375",2090,50,214,4,15,"no","no","yes",216,13
"2376",2144,33,214,4,14,"no","no","yes",216,13
"2377",2590,66,426,8,15,"no","no","yes",216,13
"2378",2328,66,212,4,14,"no","no","no",216,13
"2379",1494,33,240,4,15,"no","no","yes",216,13
"2380",2397,66,270,4,14,"yes","yes","yes",216,13
"2381",2999,66,340,8,15,"yes","yes","yes",216,13
"2382",2290,33,426,8,15,"no","no","yes",216,13
"2383",2490,33,426,8,14,"yes","yes","yes",216,13
"2384",2290,33,214,4,14,"yes","yes","yes",216,13
"2385",2799,50,320,8,15,"yes","yes","yes",216,13
"2386",999,33,125,2,14,"no","no","yes",216,13
"2387",3090,66,528,16,15,"no","no","yes",216,13
"2388",2695,33,528,16,14,"no","no","yes",216,13
"2389",1899,33,245,4,15,"yes","yes","yes",216,13
"2390",1795,33,214,4,14,"no","no","yes",216,13
"2391",1995,66,420,8,15,"no","no","yes",216,13
"2392",2354,33,212,4,14,"yes","no","no",216,13
"2393",1579,33,245,4,15,"no","no","yes",216,13
"2394",2944,50,528,16,14,"no","no","yes",216,13
"2395",1797,25,270,4,14,"yes","yes","yes",216,13
"2396",2199,33,340,8,17,"no","no","yes",216,13
"2397",1844,33,107,2,14,"no","no","yes",216,13
"2398",2499,50,320,8,15,"yes","no","yes",216,13
"2399",2090,50,107,2,14,"yes","yes","yes",216,13
"2400",2195,66,214,4,14,"no","no","yes",216,13
"2401",1990,33,107,2,14,"yes","yes","yes",216,13
"2402",2259,33,245,4,14,"no","no","no",216,13
"2403",2690,33,528,16,15,"no","no","yes",216,13
"2404",2723,33,340,4,14,"yes","no","no",216,13
"2405",3354,66,420,4,15,"yes","no","no",216,13
"2406",2099,66,245,4,15,"yes","yes","yes",216,13
"2407",2320,33,528,8,14,"no","no","yes",216,13
"2408",2744,33,528,16,14,"no","no","yes",216,13
"2409",1295,25,107,4,14,"no","no","yes",216,13
"2410",2593,50,212,4,14,"yes","no","no",216,13
"2411",2390,25,426,8,14,"yes","yes","yes",216,13
"2412",2495,66,426,8,14,"no","no","yes",216,13
"2413",1395,25,107,2,14,"no","no","yes",216,13
"2414",2095,33,426,8,14,"no","no","yes",216,13
"2415",2444,66,214,4,14,"no","no","yes",216,13
"2416",2643,66,212,4,14,"yes","no","no",216,13
"2417",2620,66,528,8,14,"no","no","yes",216,13
"2418",1475,33,120,4,14,"no","no","no",216,13
"2419",2699,66,528,8,15,"no","no","yes",216,13
"2420",1908,33,120,4,14,"no","no","no",216,13
"2421",2590,25,528,16,15,"no","no","yes",216,13
"2422",1795,50,340,4,14,"no","no","yes",216,13
"2423",3190,50,528,16,14,"yes","yes","yes",216,13
"2424",3544,66,340,16,17,"yes","no","yes",216,13
"2425",2090,25,426,8,15,"no","no","yes",216,13
"2426",1495,33,107,2,14,"no","no","yes",216,13
"2427",2417,66,212,4,14,"no","no","no",216,13
"2428",2890,25,528,16,14,"yes","yes","yes",216,13
"2429",1629,50,245,4,15,"no","no","yes",216,13
"2430",2295,50,426,8,14,"no","no","yes",216,13
"2431",1995,25,426,8,14,"no","no","yes",216,13
"2432",1895,66,420,4,15,"no","no","yes",216,13
"2433",2595,33,528,16,14,"no","no","yes",216,13
"2434",1795,66,420,4,14,"no","no","yes",216,13
"2435",1690,33,107,2,15,"no","no","yes",216,13
"2436",2244,25,426,8,14,"no","no","yes",216,13
"2437",1990,33,214,4,15,"no","no","yes",216,13
"2438",2995,66,528,16,14,"no","no","yes",216,13
"2439",2744,66,426,8,14,"no","no","yes",216,13
"2440",1894,50,340,4,15,"no","no","yes",216,13
"2441",1890,33,107,2,14,"yes","yes","yes",216,13
"2442",2868,66,340,4,14,"no","no","no",216,13
"2443",1699,33,120,4,14,"no","no","no",216,13
"2444",2144,25,426,8,14,"no","no","yes",216,13
"2445",1499,50,245,4,14,"no","no","yes",216,13
"2446",1490,25,107,2,15,"no","no","yes",216,13
"2447",2990,33,528,16,14,"yes","yes","yes",216,13
"2448",1890,33,214,4,15,"no","no","yes",216,13
"2449",2995,66,528,8,15,"no","no","yes",216,13
"2450",1299,25,120,4,14,"yes","no","yes",216,13
"2451",1949,50,245,4,15,"yes","yes","yes",216,13
"2452",2995,66,528,16,14,"no","no","yes",216,13
"2453",1944,33,214,4,14,"no","no","yes",216,13
"2454",1595,33,420,4,14,"no","no","yes",216,13
"2455",1399,25,270,4,14,"no","no","yes",216,13
"2456",2999,66,450,16,15,"yes","no","yes",216,13
"2457",1982,25,212,4,14,"yes","no","no",216,13
"2458",2544,50,426,8,14,"no","no","yes",216,13
"2459",2890,66,426,8,14,"yes","yes","yes",216,13
"2460",2395,66,340,8,14,"no","no","yes",216,13
"2461",2609,66,245,4,14,"no","no","no",216,13
"2462",3097,66,528,8,15,"yes","yes","yes",216,13
"2463",2799,50,230,8,15,"yes","yes","yes",216,13
"2464",2590,66,214,4,14,"yes","yes","yes",216,13
"2465",1778,25,212,4,14,"no","no","no",216,13
"2466",3198,66,450,16,15,"yes","yes","yes",216,13
"2467",1784,25,120,4,14,"yes","no","no",216,13
"2468",2090,25,214,4,14,"yes","yes","yes",216,13
"2469",4799,66,1000,16,17,"yes","no","yes",216,13
"2470",2795,66,340,8,15,"no","no","yes",216,13
"2471",1449,33,245,4,14,"no","no","yes",216,13
"2472",2799,66,340,16,15,"yes","no","yes",216,13
"2473",3334,66,528,16,15,"no","no","yes",216,13
"2474",2290,66,107,2,14,"yes","yes","yes",216,13
"2475",1375,25,120,4,14,"no","no","no",216,13
"2476",1799,33,170,4,14,"yes","yes","yes",216,13
"2477",2429,66,340,8,17,"no","no","yes",216,13
"2478",1895,66,107,2,14,"no","no","yes",216,13
"2479",1844,25,214,4,14,"no","no","yes",216,13
"2480",3397,66,450,16,15,"yes","yes","yes",216,13
"2481",2995,66,340,16,15,"yes","no","yes",216,13
"2482",2120,25,528,8,14,"no","no","yes",216,13
"2483",3485,66,540,16,17,"no","no","yes",216,13
"2484",1944,25,214,4,14,"no","no","yes",216,13
"2485",1790,25,214,4,15,"no","no","yes",216,13
"2486",2644,25,528,16,14,"no","no","yes",216,13
"2487",1599,33,120,4,14,"no","no","no",216,13
"2488",2348,33,245,4,14,"no","no","no",216,13
"2489",2899,66,340,16,15,"yes","no","yes",216,13
"2490",2144,66,107,2,14,"no","no","yes",216,13
"2491",1695,25,214,4,14,"no","no","yes",216,13
"2492",2504,50,212,4,14,"yes","no","no",216,13
"2493",1799,33,120,4,14,"no","no","no",216,13
"2494",1425,25,120,4,14,"no","no","no",216,13
"2495",1544,25,107,2,14,"no","no","yes",216,13
"2496",2344,33,426,8,14,"no","no","yes",216,13
"2497",2004,25,212,4,14,"yes","no","no",216,13
"2498",2279,50,340,8,17,"no","no","yes",216,13
"2499",1799,50,340,4,15,"no","no","yes",216,13
"2500",2190,33,214,4,14,"yes","yes","yes",216,13
"2501",2924,66,245,4,14,"yes","no","no",216,13
"2502",2039,33,212,4,14,"no","no","no",216,13
"2503",2244,50,214,4,14,"no","no","yes",216,13
"2504",2220,33,528,8,14,"no","no","yes",216,13
"2505",1595,33,107,2,14,"no","no","yes",216,13
"2506",1695,50,107,2,14,"no","no","yes",216,13
"2507",1669,66,245,4,14,"no","no","yes",216,13
"2508",2698,66,245,4,14,"no","no","no",216,13
"2509",3275,66,540,16,15,"yes","no","yes",216,13
"2510",2195,33,426,8,14,"no","no","yes",216,13
"2511",2197,33,270,4,14,"yes","yes","yes",216,13
"2512",3244,66,528,16,14,"no","no","yes",216,13
"2513",2444,50,426,8,14,"no","no","yes",216,13
"2514",2795,50,528,16,14,"no","no","yes",216,13
"2515",1995,50,214,4,14,"no","no","yes",216,13
"2516",2944,66,340,8,17,"no","no","yes",216,13
"2517",2344,66,214,4,14,"no","no","yes",216,13
"2518",2395,66,340,8,15,"no","no","yes",216,13
"2519",2444,33,426,8,14,"no","no","yes",216,13
"2520",3390,66,528,16,14,"yes","yes","yes",216,13
"2521",3044,50,528,16,14,"no","no","yes",216,13
"2522",3039,66,420,4,15,"no","no","no",216,13
"2523",1944,50,107,2,14,"no","no","yes",216,13
"2524",1893,25,212,4,14,"yes","no","no",216,13
"2525",3095,66,540,16,15,"no","no","yes",216,13
"2526",2099,66,120,4,14,"no","no","no",216,13
"2527",1999,66,270,4,14,"no","no","yes",216,13
"2528",2044,33,214,4,14,"no","no","yes",216,13
"2529",2499,66,425,8,15,"no","no","yes",216,13
"2530",3144,66,528,16,14,"no","no","yes",216,13
"2531",2390,50,426,8,15,"no","no","yes",216,13
"2532",2344,33,426,8,14,"no","no","yes",216,13
"2533",1395,33,240,4,14,"no","no","yes",216,13
"2534",1790,25,107,2,14,"yes","yes","yes",216,13
"2535",2634,33,340,4,14,"yes","no","no",216,13
"2536",2574,33,245,4,14,"yes","no","no",216,13
"2537",1775,33,420,4,14,"yes","no","yes",216,13
"2538",1469,25,120,4,14,"no","no","no",216,13
"2539",1790,33,214,4,14,"no","no","yes",292,14
"2540",2329,66,212,4,14,"no","no","no",292,14
"2541",1795,33,420,8,15,"no","no","yes",292,14
"2542",2627,66,424,8,15,"yes","yes","yes",292,14
"2543",2344,33,426,8,14,"yes","no","yes",292,14
"2544",2994,33,528,16,14,"yes","yes","yes",292,14
"2545",1444,25,107,2,14,"no","no","yes",292,14
"2546",2395,66,340,8,14,"no","no","yes",292,14
"2547",2405,33,424,8,14,"no","no","yes",292,14
"2548",3239,50,340,8,17,"no","no","no",292,14
"2549",3090,66,528,16,15,"yes","no","yes",292,14
"2550",1995,50,212,8,14,"yes","yes","yes",292,14
"2551",2799,50,230,8,15,"yes","yes","yes",292,14
"2552",2295,50,426,8,14,"yes","no","yes",292,14
"2553",1999,33,340,8,14,"yes","no","yes",292,14
"2554",1799,33,170,4,14,"yes","yes","yes",292,14
"2555",3090,66,528,16,14,"yes","no","yes",292,14
"2556",1844,66,107,2,14,"no","no","yes",292,14
"2557",1995,66,214,4,14,"no","no","yes",292,14
"2558",2244,66,214,4,14,"no","no","yes",292,14
"2559",1644,50,107,2,14,"no","no","yes",292,14
"2560",1395,33,107,2,14,"no","no","yes",292,14
"2561",2144,25,426,8,14,"yes","no","yes",292,14
"2562",1795,33,240,4,14,"yes","yes","yes",292,14
"2563",1744,33,214,4,14,"no","no","yes",292,14
"2564",1794,25,214,4,14,"yes","yes","yes",292,14
"2565",2094,50,424,4,14,"yes","yes","yes",292,14
"2566",1399,66,270,4,14,"no","no","yes",292,14
"2567",1825,50,420,4,14,"yes","no","yes",292,14
"2568",2889,33,340,4,17,"no","no","no",292,14
"2569",1544,33,107,2,14,"no","no","yes",292,14
"2570",1794,33,212,4,14,"yes","yes","yes",292,14
"2571",2294,66,424,4,14,"yes","yes","yes",292,14
"2572",2444,50,426,8,14,"yes","no","yes",292,14
"2573",2395,66,340,8,15,"no","no","yes",292,14
"2574",3244,66,528,16,14,"yes","no","yes",292,14
"2575",1299,33,212,4,14,"no","no","yes",292,14
"2576",2044,50,214,4,14,"no","no","yes",292,14
"2577",2699,66,528,8,15,"no","no","yes",292,14
"2578",2744,50,340,8,17,"no","no","yes",292,14
"2579",2259,50,340,4,14,"no","no","no",292,14
"2580",2929,50,340,4,17,"no","no","no",292,14
"2581",1890,50,214,4,14,"no","no","yes",292,14
"2582",2344,33,426,8,14,"yes","no","yes",292,14
"2583",2795,50,528,16,14,"yes","no","yes",292,14
"2584",1925,33,212,4,14,"no","no","yes",292,14
"2585",1290,25,107,2,15,"no","no","yes",292,14
"2586",2094,50,214,4,14,"yes","yes","yes",292,14
"2587",2195,33,426,8,14,"yes","no","yes",292,14
"2588",2594,50,426,8,14,"yes","yes","yes",292,14
"2589",1890,50,214,4,15,"no","no","yes",292,14
"2590",1559,66,245,4,14,"no","no","yes",292,14
"2591",1795,50,214,4,14,"no","no","yes",292,14
"2592",2049,50,340,8,14,"yes","no","yes",292,14
"2593",2995,66,540,16,15,"no","no","yes",292,14
"2594",1689,25,212,4,14,"no","no","no",292,14
"2595",2294,50,424,8,14,"yes","yes","yes",292,14
"2596",1994,66,107,2,14,"yes","yes","yes",292,14
"2597",1695,33,214,4,14,"no","no","yes",292,14
"2598",1390,33,107,2,15,"no","no","yes",292,14
"2599",1295,33,214,4,14,"no","no","yes",292,14
"2600",3229,66,527,8,14,"no","no","no",292,14
"2601",1369,25,120,4,14,"no","no","no",292,14
"2602",2199,50,340,8,17,"no","no","yes",292,14
"2603",2425,50,212,8,14,"yes","yes","yes",292,14
"2604",1494,33,240,4,15,"no","no","yes",292,14
"2605",1745,50,420,4,15,"no","no","yes",292,14
"2606",2695,33,528,16,14,"yes","no","yes",292,14
"2607",2509,50,340,4,15,"no","no","no",292,14
"2608",3439,66,340,8,17,"no","no","no",292,14
"2609",3144,66,528,16,14,"yes","no","yes",292,14
"2610",2294,25,426,8,14,"yes","yes","yes",292,14
"2611",2495,25,528,16,14,"yes","no","yes",292,14
"2612",1590,25,214,4,14,"no","no","yes",292,14
"2613",2844,33,528,16,14,"yes","no","yes",292,14
"2614",2259,25,212,4,17,"no","no","no",292,14
"2615",2845,66,424,8,17,"yes","no","yes",292,14
"2616",2890,50,528,16,15,"yes","no","yes",292,14
"2617",1590,50,107,2,15,"no","no","yes",292,14
"2618",1894,33,214,4,14,"yes","yes","yes",292,14
"2619",3379,66,527,8,15,"no","no","no",292,14
"2620",2320,33,528,8,14,"yes","no","yes",292,14
"2621",2989,66,424,8,14,"no","no","no",292,14
"2622",2199,50,425,8,15,"yes","no","yes",292,14
"2623",1495,25,214,4,14,"no","no","yes",292,14
"2624",1875,66,420,4,15,"no","no","yes",292,14
"2625",2669,25,170,4,17,"yes","no","no",292,14
"2626",3799,66,527,8,17,"no","no","no",292,14
"2627",2369,66,340,8,17,"no","no","yes",292,14
"2628",2144,66,214,4,14,"no","no","yes",292,14
"2629",2095,33,426,8,14,"yes","no","yes",292,14
"2630",2790,33,528,16,15,"yes","no","yes",292,14
"2631",1729,66,245,4,15,"no","no","yes",292,14
"2632",1995,25,426,8,14,"yes","no","yes",292,14
"2633",2644,66,426,8,14,"yes","yes","yes",292,14
"2634",2229,66,212,4,14,"no","no","no",292,14
"2635",1995,66,424,4,14,"no","no","yes",292,14
"2636",1449,50,245,4,14,"no","no","yes",292,14
"2637",3294,66,528,16,14,"yes","yes","yes",292,14
"2638",2420,50,528,8,14,"yes","no","yes",292,14
"2639",2090,66,214,4,15,"no","no","yes",292,14
"2640",2249,25,170,4,15,"yes","no","no",292,14
"2641",1975,66,420,8,15,"no","no","yes",292,14
"2642",1999,25,170,4,14,"yes","no","no",292,14
"2643",2359,50,340,4,14,"no","no","no",292,14
"2644",2944,66,340,8,17,"no","no","yes",292,14
"2645",1790,66,107,2,15,"no","no","yes",292,14
"2646",2995,66,256,8,15,"yes","no","yes",292,14
"2647",2495,66,424,8,15,"yes","no","yes",292,14
"2648",1955,66,420,4,14,"yes","no","yes",292,14
"2649",2090,25,426,8,14,"yes","no","yes",292,14
"2650",1894,50,340,4,15,"no","no","yes",292,14
"2651",2644,25,528,16,14,"yes","no","yes",292,14
"2652",3089,66,424,8,14,"no","no","no",292,14
"2653",1744,25,214,4,14,"no","no","yes",292,14
"2654",2744,25,528,16,14,"yes","no","yes",292,14
"2655",2479,66,212,4,15,"no","no","no",292,14
"2656",2244,25,426,8,14,"yes","no","yes",292,14
"2657",1469,25,120,4,14,"no","no","no",292,14
"2658",1195,25,107,2,14,"no","no","yes",292,14
"2659",1619,25,120,4,15,"no","no","no",292,14
"2660",2394,33,426,8,14,"yes","yes","yes",292,14
"2661",1590,50,107,2,14,"no","no","yes",292,14
"2662",1939,33,212,4,14,"no","no","no",292,14
"2663",2090,25,426,8,15,"yes","no","yes",292,14
"2664",1490,33,107,2,15,"no","no","yes",292,14
"2665",1495,25,214,4,14,"no","no","yes",292,14
"2666",1694,33,107,2,14,"yes","yes","yes",292,14
"2667",2590,66,426,8,14,"yes","no","yes",292,14
"2668",2590,25,528,16,15,"yes","no","yes",292,14
"2669",2425,50,424,8,14,"no","no","yes",292,14
"2670",1849,50,245,4,15,"yes","yes","yes",292,14
"2671",2275,66,530,8,15,"yes","no","yes",292,14
"2672",3397,66,450,16,15,"yes","yes","yes",292,14
"2673",3044,50,528,16,14,"yes","no","yes",292,14
"2674",1495,33,212,4,14,"no","no","yes",292,14
"2675",2099,25,170,4,14,"yes","no","no",292,14
"2676",2769,66,340,8,14,"no","no","no",292,14
"2677",1695,66,107,2,14,"no","no","yes",292,14
"2678",2995,66,528,16,14,"yes","no","yes",292,14
"2679",2590,25,528,16,14,"yes","no","yes",292,14
"2680",3659,66,424,8,17,"no","no","no",292,14
"2681",2644,66,426,8,14,"yes","no","yes",292,14
"2682",1119,33,125,2,14,"no","no","yes",292,14
"2683",2159,33,340,8,17,"no","no","yes",292,14
"2684",2645,66,424,8,17,"yes","no","yes",292,14
"2685",2195,50,340,8,15,"no","no","yes",292,14
"2686",3544,66,340,16,17,"yes","no","yes",292,14
"2687",1195,25,214,4,14,"no","no","yes",292,14
"2688",3019,66,340,8,15,"no","no","no",292,14
"2689",1719,33,120,4,14,"no","no","no",292,14
"2690",1799,33,245,4,15,"yes","yes","yes",292,14
"2691",2390,50,426,8,14,"yes","no","yes",292,14
"2692",2039,33,212,4,14,"no","no","no",292,14
"2693",2079,33,212,4,15,"no","no","no",292,14
"2694",2819,50,340,8,15,"no","no","no",292,14
"2695",2609,33,212,4,17,"no","no","no",292,14
"2696",2039,25,120,4,17,"no","no","no",292,14
"2697",1590,25,214,4,15,"no","no","yes",292,14
"2698",2944,33,528,16,14,"yes","no","yes",292,14
"2699",2744,33,528,16,14,"yes","no","yes",292,14
"2700",1399,25,245,4,14,"yes","no","yes",292,14
"2701",2799,66,340,16,15,"yes","no","yes",292,14
"2702",1544,33,107,2,14,"no","no","yes",292,14
"2703",2290,33,426,8,15,"yes","no","yes",292,14
"2704",2290,33,426,8,14,"yes","no","yes",292,14
"2705",3094,50,528,16,14,"yes","yes","yes",292,14
"2706",1795,50,424,4,14,"no","no","yes",292,14
"2707",1390,33,107,2,14,"no","no","yes",292,14
"2708",2495,66,426,8,14,"yes","no","yes",292,14
"2709",1944,33,214,4,14,"no","no","yes",292,14
"2710",2220,33,528,8,14,"yes","no","yes",292,14
"2711",1495,25,340,8,14,"no","no","yes",292,14
"2712",1790,66,107,2,14,"no","no","yes",292,14
"2713",1595,33,340,8,14,"no","no","yes",292,14
"2714",2849,66,340,8,15,"yes","yes","yes",292,14
"2715",2769,50,340,8,15,"yes","yes","yes",292,14
"2716",2365,66,420,8,17,"no","no","yes",292,14
"2717",1744,50,107,2,14,"no","no","yes",292,14
"2718",949,33,125,2,14,"no","no","yes",292,14
"2719",1844,33,214,4,14,"no","no","yes",292,14
"2720",1594,33,212,4,14,"yes","yes","yes",292,14
"2721",1589,25,212,4,14,"no","no","no",292,14
"2722",1529,33,245,4,15,"no","no","yes",292,14
"2723",1839,25,212,4,15,"no","no","no",292,14
"2724",2894,33,528,16,14,"yes","yes","yes",292,14
"2725",2427,66,424,8,15,"yes","yes","yes",292,14
"2726",1644,25,214,4,14,"no","no","yes",292,14
"2727",2690,33,528,16,15,"yes","no","yes",292,14
"2728",2390,50,426,8,15,"yes","no","yes",292,14
"2729",2795,66,424,16,15,"yes","no","yes",292,14
"2730",1794,33,212,4,14,"yes","yes","yes",292,14
"2731",2569,50,340,8,14,"no","no","no",292,14
"2732",1344,25,107,2,14,"no","no","yes",292,14
"2733",2225,50,424,4,14,"no","no","yes",292,14
"2734",2690,33,528,16,14,"yes","no","yes",292,14
"2735",1695,33,340,8,14,"no","no","yes",292,14
"2736",1929,33,212,4,14,"no","no","no",292,14
"2737",2899,66,212,4,17,"no","no","no",292,14
"2738",1844,33,214,4,14,"no","no","yes",292,14
"2739",2295,66,424,8,15,"yes","no","yes",292,14
"2740",3175,66,540,16,15,"yes","no","yes",292,14
"2741",1975,33,424,8,14,"no","no","yes",292,14
"2742",1999,33,170,4,14,"yes","yes","yes",292,14
"2743",2425,66,424,4,14,"no","no","yes",292,14
"2744",1794,50,107,2,14,"yes","yes","yes",292,14
"2745",1595,33,214,4,14,"no","no","yes",292,14
"2746",1595,33,420,4,14,"no","no","yes",292,14
"2747",2389,33,120,4,17,"no","no","no",292,14
"2748",1399,33,245,4,14,"no","no","yes",292,14
"2749",1395,33,214,4,14,"no","no","yes",292,14
"2750",2590,66,426,8,15,"yes","no","yes",292,14
"2751",1969,33,120,4,15,"no","no","no",292,14
"2752",2999,33,340,8,15,"yes","yes","yes",292,14
"2753",1644,33,107,2,14,"no","no","yes",292,14
"2754",3145,66,424,16,17,"yes","no","yes",292,14
"2755",2499,33,212,4,17,"no","no","no",292,14
"2756",1690,33,214,4,14,"no","no","yes",292,14
"2757",1495,50,107,2,14,"no","no","yes",292,14
"2758",1695,33,420,4,15,"no","no","yes",292,14
"2759",2899,66,340,16,15,"yes","no","yes",292,14
"2760",1775,66,420,4,14,"no","no","yes",292,14
"2761",1295,33,107,2,14,"no","no","yes",292,14
"2762",2090,66,214,4,14,"no","no","yes",292,14
"2763",2190,33,426,8,15,"yes","no","yes",292,14
"2764",1490,33,107,2,14,"no","no","yes",292,14
"2765",2869,66,340,8,14,"no","no","no",292,14
"2766",2795,66,340,8,15,"no","no","yes",292,14
"2767",2444,33,426,8,14,"yes","no","yes",292,14
"2768",2495,66,426,8,14,"yes","no","yes",292,14
"2769",1790,33,214,4,15,"no","no","yes",292,14
"2770",1295,33,212,4,14,"no","no","yes",292,14
"2771",1795,50,340,4,14,"no","no","yes",292,14
"2772",1559,50,245,4,15,"no","no","yes",292,14
"2773",1994,33,214,4,14,"yes","yes","yes",292,14
"2774",1829,33,212,4,14,"no","no","no",292,14
"2775",1494,25,107,2,14,"yes","yes","yes",292,14
"2776",2999,66,450,16,15,"yes","no","yes",292,14
"2777",2195,33,426,8,14,"yes","no","yes",292,14
"2778",2199,66,340,8,14,"yes","no","yes",292,14
"2779",2595,33,528,16,14,"yes","no","yes",292,14
"2780",1395,33,240,4,14,"no","no","yes",292,14
"2781",1444,33,107,2,14,"no","no","yes",292,14
"2782",2544,50,426,8,14,"yes","no","yes",292,14
"2783",1699,33,120,4,14,"no","no","no",292,14
"2784",2219,33,340,4,14,"no","no","no",292,14
"2785",2927,66,424,16,15,"yes","yes","yes",292,14
"2786",2794,66,426,8,14,"yes","yes","yes",292,14
"2787",1944,50,214,4,14,"no","no","yes",292,14
"2788",2144,25,426,8,14,"yes","yes","yes",292,14
"2789",1999,66,245,4,15,"yes","yes","yes",292,14
"2790",2620,66,528,8,14,"yes","no","yes",292,14
"2791",2944,50,528,16,14,"yes","no","yes",292,14
"2792",2190,33,426,8,14,"yes","no","yes",292,14
"2793",3129,66,527,8,14,"no","no","no",292,14
"2794",2319,33,340,4,14,"no","no","no",292,14
"2795",2890,50,528,16,14,"yes","no","yes",292,14
"2796",2744,66,426,8,14,"yes","no","yes",292,14
"2797",2185,33,420,8,17,"no","no","yes",292,14
"2798",1495,33,212,4,14,"no","no","yes",292,14
"2799",2494,33,426,8,14,"yes","yes","yes",292,14
"2800",1819,33,120,4,14,"no","no","no",292,14
"2801",2294,66,214,4,14,"yes","yes","yes",292,14
"2802",3397,66,450,16,15,"yes","yes","yes",292,14
"2803",1690,33,214,4,15,"no","no","yes",292,14
"2804",1594,33,107,2,14,"yes","yes","yes",292,14
"2805",2469,33,340,4,15,"no","no","no",292,14
"2806",1775,33,420,4,14,"yes","no","yes",292,14
"2807",1925,33,212,4,14,"no","no","yes",292,14
"2808",2274,33,424,8,14,"yes","yes","yes",292,14
"2809",1195,25,107,4,14,"no","no","yes",292,14
"2810",1944,66,107,2,14,"no","no","yes",292,14
"2811",2120,25,528,8,14,"yes","no","yes",292,14
"2812",1290,25,107,2,14,"no","no","yes",292,14
"2813",1559,33,212,4,14,"no","no","yes",292,14
"2814",2669,50,340,8,14,"no","no","no",292,14
"2815",2794,25,528,16,14,"yes","yes","yes",292,14
"2816",2995,66,528,16,14,"yes","no","yes",292,14
"2817",2189,33,212,4,15,"no","no","no",292,14
"2818",2344,33,426,8,14,"yes","yes","yes",292,14
"2819",3334,66,528,16,15,"yes","no","yes",292,14
"2820",3385,66,540,16,17,"no","no","yes",292,14
"2821",2995,66,340,16,15,"yes","no","yes",292,14
"2822",3239,66,424,8,15,"no","no","no",292,14
"2823",2244,33,426,8,14,"yes","no","yes",292,14
"2824",1725,33,212,4,14,"no","no","yes",292,14
"2825",2790,33,528,16,14,"yes","no","yes",292,14
"2826",1995,50,424,8,14,"no","no","yes",292,14
"2827",2844,33,528,16,14,"yes","no","yes",292,14
"2828",1645,50,420,4,14,"no","no","yes",292,14
"2829",2799,50,320,8,15,"yes","yes","yes",292,14
"2830",1695,33,214,4,14,"no","no","yes",292,14
"2831",1655,50,340,4,15,"no","no","yes",267,15
"2832",2425,50,212,8,15,"yes","yes","yes",267,15
"2833",3239,50,340,8,17,"no","no","no",267,15
"2834",1944,66,107,2,14,"no","no","yes",267,15
"2835",2495,66,424,8,15,"yes","no","yes",267,15
"2836",1999,33,340,8,14,"yes","no","yes",267,15
"2837",2644,33,528,16,14,"yes","no","yes",267,15
"2838",2090,33,426,8,14,"yes","no","yes",267,15
"2839",1895,66,425,8,15,"no","no","yes",267,15
"2840",2799,50,340,8,14,"yes","yes","yes",267,15
"2841",1444,25,107,2,14,"no","no","yes",267,15
"2842",2390,25,528,16,14,"yes","no","yes",267,15
"2843",1695,66,107,2,14,"no","no","yes",267,15
"2844",2609,33,212,4,17,"no","no","no",267,15
"2845",2895,66,528,8,15,"yes","no","yes",267,15
"2846",2049,50,340,8,14,"yes","no","yes",267,15
"2847",2499,33,212,4,17,"no","no","no",267,15
"2848",1999,66,340,8,15,"yes","no","yes",267,15
"2849",1395,33,214,4,14,"no","no","yes",267,15
"2850",2099,25,170,4,14,"yes","no","no",267,15
"2851",1719,33,120,4,14,"no","no","no",267,15
"2852",1799,33,170,4,14,"yes","yes","yes",267,15
"2853",2359,50,340,4,14,"no","no","no",267,15
"2854",1490,33,107,2,14,"no","no","yes",267,15
"2855",1595,33,340,8,14,"no","no","yes",267,15
"2856",2274,33,424,8,14,"yes","yes","yes",267,15
"2857",1495,33,212,4,14,"no","no","yes",267,15
"2858",1995,66,212,8,14,"no","no","yes",267,15
"2859",2744,50,528,16,14,"yes","no","yes",267,15
"2860",2490,66,426,8,15,"yes","no","yes",267,15
"2861",2190,33,426,8,15,"yes","no","yes",267,15
"2862",1990,50,420,4,14,"yes","yes","yes",267,15
"2863",2265,66,425,8,17,"no","no","yes",267,15
"2864",2699,66,245,8,15,"yes","no","yes",267,15
"2865",1590,25,214,4,15,"no","no","yes",267,15
"2866",2627,66,424,8,15,"yes","yes","yes",267,15
"2867",2039,33,212,4,14,"no","no","no",267,15
"2868",1999,25,170,4,14,"yes","no","no",267,15
"2869",2294,50,424,8,14,"yes","yes","yes",267,15
"2870",1490,33,107,2,15,"no","no","yes",267,15
"2871",2490,33,528,16,15,"yes","no","yes",267,15
"2872",2329,66,212,4,14,"no","no","no",267,15
"2873",2090,66,214,4,14,"no","no","yes",267,15
"2874",1525,33,340,4,14,"no","no","yes",267,15
"2875",2799,50,340,8,15,"yes","yes","yes",267,15
"2876",1835,66,340,4,14,"yes","no","yes",267,15
"2877",2590,33,528,16,15,"yes","no","yes",267,15
"2878",1999,66,450,4,14,"no","no","yes",267,15
"2879",1589,25,212,4,14,"no","no","no",267,15
"2880",1590,25,214,4,14,"no","no","yes",267,15
"2881",3129,66,527,8,14,"no","no","no",267,15
"2882",2144,66,214,4,14,"no","no","yes",267,15
"2883",2499,66,545,8,15,"yes","no","yes",267,15
"2884",1695,33,214,4,14,"no","no","yes",267,15
"2885",1590,50,107,2,14,"no","no","yes",267,15
"2886",1890,50,214,4,15,"no","no","yes",267,15
"2887",2427,66,424,8,15,"yes","yes","yes",267,15
"2888",2989,66,424,8,14,"no","no","no",267,15
"2889",2079,33,212,4,15,"no","no","no",267,15
"2890",2899,66,212,4,17,"no","no","no",267,15
"2891",1939,33,212,4,14,"no","no","no",267,15
"2892",1995,50,212,8,14,"yes","yes","yes",267,15
"2893",2295,25,528,16,14,"yes","no","yes",267,15
"2894",2795,66,528,16,14,"yes","no","yes",267,15
"2895",1595,33,214,4,14,"no","no","yes",267,15
"2896",2299,50,450,8,15,"no","no","yes",267,15
"2897",1915,33,425,8,15,"no","no","yes",267,15
"2898",1290,25,107,2,15,"no","no","yes",267,15
"2899",3379,66,527,8,15,"no","no","no",267,15
"2900",2525,66,530,16,15,"no","no","yes",267,15
"2901",1594,33,107,2,14,"yes","yes","yes",267,15
"2902",2389,33,120,4,17,"no","no","no",267,15
"2903",1925,33,212,4,15,"no","no","yes",267,15
"2904",1944,33,214,4,14,"no","no","yes",267,15
"2905",1555,50,340,4,14,"no","no","yes",267,15
"2906",1619,25,120,4,15,"no","no","no",267,15
"2907",2243,33,426,8,14,"yes","yes","yes",267,15
"2908",2135,33,425,8,17,"no","no","yes",267,15
"2909",2509,50,340,4,15,"no","no","no",267,15
"2910",2595,50,528,16,14,"yes","no","yes",267,15
"2911",2690,50,528,16,14,"yes","no","yes",267,15
"2912",2550,66,340,8,15,"yes","yes","yes",267,15
"2913",2259,25,212,4,17,"no","no","no",267,15
"2914",2344,50,426,8,14,"yes","no","yes",267,15
"2915",2970,66,420,16,15,"yes","yes","yes",267,15
"2916",2690,50,528,16,15,"yes","no","yes",267,15
"2917",1744,50,107,2,14,"no","no","yes",267,15
"2918",1944,50,214,4,14,"no","no","yes",267,15
"2919",1644,50,107,2,14,"no","no","yes",267,15
"2920",1795,50,420,4,14,"yes","no","yes",267,15
"2921",1735,50,340,4,14,"yes","no","yes",267,15
"2922",1344,25,107,2,14,"no","no","yes",267,15
"2923",2944,66,528,16,14,"yes","no","yes",267,15
"2924",3799,66,527,8,17,"no","no","no",267,15
"2925",2095,33,426,8,14,"yes","no","yes",267,15
"2926",1690,33,340,4,14,"yes","yes","yes",267,15
"2927",2395,33,528,16,14,"yes","no","yes",267,15
"2928",2590,33,528,16,14,"yes","no","yes",267,15
"2929",1625,33,340,4,15,"no","no","yes",267,15
"2930",2890,66,528,16,15,"yes","no","yes",267,15
"2931",2294,66,214,4,14,"yes","yes","yes",267,15
"2932",3044,66,528,16,14,"yes","no","yes",267,15
"2933",2895,66,530,16,17,"no","no","yes",267,15
"2934",1969,33,120,4,15,"no","no","no",267,15
"2935",2294,66,212,8,14,"yes","yes","yes",267,15
"2936",1469,25,120,4,14,"no","no","no",267,15
"2937",2390,25,528,16,15,"yes","no","yes",267,15
"2938",1790,66,107,2,15,"no","no","yes",267,15
"2939",3439,66,340,8,17,"no","no","no",267,15
"2940",1794,33,212,4,14,"yes","yes","yes",267,15
"2941",2890,66,528,16,14,"yes","no","yes",267,15
"2942",1765,33,425,8,15,"no","no","yes",267,15
"2943",2189,33,212,4,15,"no","no","no",267,15
"2944",2220,33,528,8,14,"yes","no","yes",267,15
"2945",2645,66,424,8,17,"yes","no","yes",267,15
"2946",2405,33,424,8,15,"no","no","yes",267,15
"2947",2769,66,340,8,14,"no","no","no",267,15
"2948",2929,50,340,4,17,"no","no","no",267,15
"2949",1839,25,212,4,15,"no","no","no",267,15
"2950",1369,25,120,4,14,"no","no","no",267,15
"2951",2927,66,424,16,15,"yes","yes","yes",267,15
"2952",1699,33,340,4,14,"no","no","yes",267,15
"2953",2229,66,212,4,14,"no","no","no",267,15
"2954",1790,33,214,4,15,"no","no","yes",267,15
"2955",1844,66,107,2,14,"no","no","yes",267,15
"2956",2090,66,214,4,15,"no","no","yes",267,15
"2957",2644,66,426,8,14,"yes","no","yes",267,15
"2958",1655,66,340,4,14,"no","no","yes",267,15
"2959",1994,33,214,4,14,"yes","yes","yes",267,15
"2960",2320,50,528,8,14,"yes","no","yes",267,15
"2961",1755,66,340,4,15,"no","no","yes",267,15
"2962",3089,66,424,8,14,"no","no","no",267,15
"2963",2520,66,528,8,14,"yes","no","yes",267,15
"2964",1699,33,120,4,14,"no","no","no",267,15
"2965",1790,66,107,2,14,"no","no","yes",267,15
"2966",2344,33,426,8,14,"yes","no","yes",267,15
"2967",2395,66,426,8,14,"yes","no","yes",267,15
"2968",1844,33,214,4,14,"no","no","yes",267,15
"2969",2669,50,340,8,14,"no","no","no",267,15
"2970",1559,33,212,4,14,"no","no","yes",267,15
"2971",1929,33,212,4,14,"no","no","no",267,15
"2972",2444,25,528,16,14,"yes","no","yes",267,15
"2973",2039,25,120,4,17,"no","no","no",267,15
"2974",1444,33,107,2,14,"no","no","yes",267,15
"2975",1594,33,212,4,14,"yes","yes","yes",267,15
"2976",2125,66,425,8,15,"yes","no","yes",267,15
"2977",2669,25,170,4,17,"yes","no","no",267,15
"2978",2244,33,426,8,14,"yes","no","yes",267,15
"2979",1544,33,107,2,14,"no","no","yes",267,15
"2980",3659,66,424,8,17,"no","no","no",267,15
"2981",2543,66,426,8,14,"yes","yes","yes",267,15
"2982",1705,33,340,4,14,"yes","no","yes",267,15
"2983",1390,33,107,2,15,"no","no","yes",267,15
"2984",2090,33,426,8,15,"yes","no","yes",267,15
"2985",1995,33,426,8,14,"yes","no","yes",267,15
"2986",2490,33,528,16,14,"yes","no","yes",267,15
"2987",1399,50,260,4,14,"no","no","yes",267,15
"2988",1819,33,120,4,14,"no","no","no",267,15
"2989",1894,33,214,4,14,"yes","yes","yes",267,15
"2990",1390,33,107,2,14,"no","no","yes",267,15
"2991",1795,50,214,4,14,"no","no","yes",267,15
"2992",3397,66,450,16,15,"yes","yes","yes",267,15
"2993",2249,25,170,4,15,"yes","no","no",267,15
"2994",1544,33,107,2,14,"no","no","yes",267,15
"2995",2644,33,528,16,14,"yes","no","yes",267,15
"2996",2744,33,528,16,14,"yes","no","yes",267,15
"2997",2244,66,214,4,14,"no","no","yes",267,15
"2998",2495,33,528,16,14,"yes","no","yes",267,15
"2999",1395,33,107,2,14,"no","no","yes",267,15
"3000",2290,50,426,8,15,"yes","no","yes",267,15
"3001",1895,25,426,8,14,"yes","no","yes",267,15
"3002",1990,25,426,8,15,"yes","no","yes",267,15
"3003",2744,50,420,8,17,"yes","no","yes",267,15
"3004",2259,50,340,4,14,"no","no","no",267,15
"3005",2195,50,420,8,15,"yes","no","yes",267,15
"3006",2144,25,426,8,14,"yes","no","yes",267,15
"3007",1494,25,107,2,14,"yes","yes","yes",267,15
"3008",1290,25,107,2,14,"no","no","yes",267,15
"3009",1495,33,212,4,14,"no","no","yes",267,15
"3010",1495,25,214,4,14,"no","no","yes",267,15
"3011",2425,50,424,8,15,"no","no","yes",267,15
"3012",3749,66,450,16,17,"yes","yes","yes",267,15
"3013",2844,50,528,16,14,"yes","no","yes",267,15
"3014",2199,50,425,8,15,"yes","no","yes",267,15
"3015",2295,66,424,8,15,"yes","no","yes",267,15
"3016",2044,50,214,4,14,"no","no","yes",267,15
"3017",2745,66,530,16,15,"yes","no","yes",267,15
"3018",2469,33,340,4,15,"no","no","no",267,15
"3019",3304,66,420,16,17,"yes","no","yes",267,15
"3020",1594,33,340,4,15,"yes","no","yes",267,15
"3021",1695,33,214,4,14,"no","no","yes",267,15
"3022",2869,66,340,8,14,"no","no","no",267,15
"3023",1590,50,107,2,15,"no","no","yes",267,15
"3024",3239,66,424,8,15,"no","no","no",267,15
"3025",2544,66,426,8,14,"yes","no","yes",267,15
"3026",2225,50,212,8,15,"no","no","yes",267,15
"3027",2190,33,426,8,14,"yes","no","yes",267,15
"3028",2044,25,426,8,14,"yes","no","yes",267,15
"3029",2095,33,426,8,14,"yes","no","yes",267,15
"3030",1295,33,214,4,14,"no","no","yes",267,15
"3031",2425,66,212,8,15,"no","no","yes",267,15
"3032",2045,66,425,8,15,"no","no","yes",267,15
"3033",1495,33,340,4,14,"yes","no","yes",267,15
"3034",1695,33,340,8,14,"no","no","yes",267,15
"3035",3229,66,527,8,14,"no","no","no",267,15
"3036",1399,25,245,4,14,"yes","no","yes",267,15
"3037",2199,66,340,8,14,"yes","no","yes",267,15
"3038",2319,33,340,4,14,"no","no","no",267,15
"3039",1495,50,107,2,14,"no","no","yes",267,15
"3040",1994,66,107,2,14,"yes","yes","yes",267,15
"3041",1925,33,212,4,15,"no","no","yes",267,15
"3042",1399,33,270,4,14,"no","no","yes",267,15
"3043",2799,66,340,16,15,"yes","no","yes",267,15
"3044",1694,33,107,2,14,"yes","yes","yes",267,15
"3045",2490,66,426,8,14,"yes","no","yes",267,15
"3046",2444,50,426,8,14,"yes","no","yes",267,15
"3047",1995,50,424,8,14,"no","no","yes",267,15
"3048",1794,25,214,4,14,"yes","yes","yes",267,15
"3049",1690,33,214,4,15,"no","no","yes",267,15
"3050",1644,25,214,4,14,"no","no","yes",267,15
"3051",1689,25,212,4,14,"no","no","no",267,15
"3052",1975,33,424,8,14,"no","no","yes",267,15
"3053",1890,50,214,4,14,"no","no","yes",267,15
"3054",1990,25,426,8,14,"yes","no","yes",267,15
"3055",1295,33,212,4,14,"no","no","yes",267,15
"3056",2479,66,212,4,15,"no","no","no",267,15
"3057",1644,33,107,2,14,"no","no","yes",267,15
"3058",2845,66,424,8,17,"yes","no","yes",267,15
"3059",2094,50,212,8,14,"yes","yes","yes",267,15
"3060",2889,33,340,4,17,"no","no","no",267,15
"3061",2020,25,528,8,14,"yes","no","yes",267,15
"3062",1995,66,214,4,14,"no","no","yes",267,15
"3063",2355,66,340,8,15,"yes","no","yes",267,15
"3064",2219,33,340,4,14,"no","no","no",267,15
"3065",3145,66,424,16,17,"yes","no","yes",267,15
"3066",2775,66,420,16,15,"yes","no","yes",267,15
"3067",2120,33,528,8,14,"yes","no","yes",267,15
"3068",1790,33,214,4,14,"no","no","yes",267,15
"3069",1829,33,212,4,14,"no","no","no",267,15
"3070",1495,25,340,8,14,"no","no","yes",267,15
"3071",2290,50,426,8,14,"yes","no","yes",267,15
"3072",1794,33,212,4,14,"yes","yes","yes",267,15
"3073",3019,66,340,8,15,"no","no","no",267,15
"3074",1894,50,420,4,15,"yes","no","yes",267,15
"3075",1299,33,212,4,14,"no","no","yes",267,15
"3076",2544,25,528,16,14,"yes","no","yes",267,15
"3077",2699,66,470,8,15,"yes","no","yes",267,15
"3078",2244,33,426,8,14,"yes","no","yes",267,15
"3079",1725,33,212,4,15,"no","no","yes",267,15
"3080",2094,50,214,4,14,"yes","yes","yes",267,15
"3081",1195,25,107,2,14,"no","no","yes",267,15
"3082",1744,25,214,4,14,"no","no","yes",267,15
"3083",2390,50,420,8,15,"yes","yes","yes",267,15
"3084",1295,33,107,2,14,"no","no","yes",267,15
"3085",1690,33,214,4,14,"no","no","yes",267,15
"3086",1744,33,214,4,14,"no","no","yes",267,15
"3087",2195,50,426,8,14,"yes","no","yes",267,15
"3088",1795,50,212,8,14,"no","no","yes",267,15
"3089",1195,25,214,4,14,"no","no","yes",267,15
"3090",1844,33,214,4,14,"no","no","yes",267,15
"3091",2904,66,340,8,17,"yes","no","yes",267,15
"3092",2144,33,426,8,14,"yes","no","yes",267,15
"3093",2544,33,528,16,14,"yes","no","yes",267,15
"3094",2569,50,340,8,14,"no","no","no",267,15
"3095",1794,50,107,2,14,"yes","yes","yes",267,15
"3096",2819,50,340,8,15,"no","no","no",267,15
"3097",2795,66,424,16,15,"yes","no","yes",267,15
"3098",2849,66,527,16,15,"yes","no","yes",307,16
"3099",1644,33,107,2,14,"no","no","yes",307,16
"3100",1799,33,170,4,14,"yes","yes","yes",307,16
"3101",1944,66,214,4,14,"no","no","yes",307,16
"3102",2690,100,426,8,14,"yes","no","yes",307,16
"3103",2399,66,212,4,15,"no","no","no",307,16
"3104",3090,100,528,16,15,"yes","no","yes",307,16
"3105",2989,66,424,8,14,"no","no","no",307,16
"3106",1944,50,214,4,14,"no","no","yes",307,16
"3107",2890,75,528,16,15,"yes","no","yes",307,16
"3108",2144,33,426,8,14,"yes","no","yes",307,16
"3109",2869,66,340,8,14,"no","no","no",307,16
"3110",1395,33,107,2,14,"no","no","yes",307,16
"3111",1299,33,212,4,14,"no","no","yes",307,16
"3112",2094,75,107,2,14,"yes","yes","yes",307,16
"3113",3089,66,424,8,14,"no","no","no",307,16
"3114",1995,75,214,4,14,"no","no","yes",307,16
"3115",2970,66,420,16,15,"yes","yes","yes",307,16
"3116",1999,33,527,8,14,"yes","no","yes",307,16
"3117",1890,75,107,2,14,"no","no","yes",307,16
"3118",1299,25,100,4,14,"no","no","no",307,16
"3119",1818,33,212,4,14,"yes","yes","yes",307,16
"3120",2295,66,424,8,15,"yes","no","yes",307,16
"3121",2544,75,426,8,14,"yes","no","yes",307,16
"3122",2929,66,527,16,15,"yes","yes","yes",307,16
"3123",3149,66,527,16,17,"yes","no","yes",307,16
"3124",2425,33,424,8,17,"yes","no","yes",307,16
"3125",1999,66,450,4,14,"no","no","yes",307,16
"3126",3090,100,528,16,14,"yes","no","yes",307,16
"3127",1894,33,214,4,14,"yes","yes","yes",307,16
"3128",1195,25,107,2,14,"no","no","yes",307,16
"3129",2124,33,240,4,14,"yes","yes","no",307,16
"3130",1794,33,212,4,14,"yes","yes","yes",307,16
"3131",2690,66,528,16,14,"yes","no","yes",307,16
"3132",2644,33,528,16,14,"yes","no","yes",307,16
"3133",2499,66,340,4,14,"no","no","no",307,16
"3134",1995,33,426,8,14,"yes","no","yes",307,16
"3135",2195,100,214,4,14,"no","no","yes",307,16
"3136",2904,66,340,8,17,"yes","no","yes",307,16
"3137",2320,66,528,8,14,"yes","no","yes",307,16
"3138",2219,33,340,4,14,"no","no","no",307,16
"3139",2344,25,528,16,14,"yes","no","yes",307,16
"3140",1744,50,107,2,14,"no","no","yes",307,16
"3141",1939,33,212,4,14,"no","no","no",307,16
"3142",1599,33,170,4,14,"yes","no","yes",307,16
"3143",3099,100,527,16,15,"yes","no","yes",307,16
"3144",1509,25,120,4,15,"no","no","no",307,16
"3145",2690,66,528,16,15,"yes","no","yes",307,16
"3146",1744,33,214,4,14,"no","no","yes",307,16
"3147",3299,66,1000,16,15,"yes","no","yes",307,16
"3148",2355,66,340,8,15,"yes","no","yes",307,16
"3149",1589,25,212,4,14,"no","no","no",307,16
"3150",3019,66,340,8,15,"no","no","no",307,16
"3151",1595,66,214,4,14,"no","no","yes",307,16
"3152",2819,66,212,4,17,"no","no","no",307,16
"3153",1694,33,107,2,14,"yes","yes","yes",307,16
"3154",2499,100,527,8,14,"yes","no","yes",307,16
"3155",2090,33,426,8,14,"yes","no","yes",307,16
"3156",1690,33,214,4,14,"no","no","yes",307,16
"3157",3799,66,527,8,17,"no","no","no",307,16
"3158",2195,66,426,8,14,"yes","no","yes",307,16
"3159",1290,25,107,2,15,"no","no","yes",307,16
"3160",2844,66,528,16,14,"yes","no","yes",307,16
"3161",2290,100,214,4,15,"no","no","yes",307,16
"3162",1690,66,107,2,14,"no","no","yes",307,16
"3163",1844,50,214,4,14,"no","no","yes",307,16
"3164",1544,25,214,4,14,"no","no","yes",307,16
"3165",2090,33,426,8,15,"yes","no","yes",307,16
"3166",2195,50,420,8,15,"yes","no","yes",307,16
"3167",1739,33,120,4,14,"no","no","no",307,16
"3168",2144,100,107,2,14,"no","no","yes",307,16
"3169",2999,100,527,16,15,"yes","no","yes",307,16
"3170",2044,75,107,2,14,"no","no","yes",307,16
"3171",2845,66,540,8,17,"yes","no","yes",307,16
"3172",2844,100,426,8,14,"yes","no","yes",307,16
"3173",2520,75,528,8,14,"yes","no","yes",307,16
"3174",1995,33,424,8,14,"yes","no","yes",307,16
"3175",1825,66,425,8,15,"no","no","yes",307,16
"3176",2929,50,340,4,17,"no","no","no",307,16
"3177",2744,50,528,16,14,"yes","no","yes",307,16
"3178",3399,100,1000,16,15,"yes","no","yes",307,16
"3179",1699,33,340,4,14,"no","no","yes",307,16
"3180",1794,50,107,2,14,"yes","yes","yes",307,16
"3181",1795,50,420,4,14,"yes","no","yes",307,16
"3182",2244,100,107,2,14,"no","no","yes",307,16
"3183",1790,50,214,4,14,"no","no","yes",307,16
"3184",2294,100,107,2,14,"yes","yes","yes",307,16
"3185",3049,100,527,16,15,"yes","no","yes",307,16
"3186",1594,33,340,4,15,"yes","no","yes",307,16
"3187",1590,50,107,2,14,"no","no","yes",307,16
"3188",2029,33,340,8,14,"yes","yes","yes",307,16
"3189",2499,100,340,8,15,"yes","no","yes",307,16
"3190",1495,50,107,2,14,"no","no","yes",307,16
"3191",2090,100,107,2,15,"no","no","yes",307,16
"3192",1795,66,214,4,14,"no","no","yes",307,16
"3193",3304,66,420,16,17,"yes","no","yes",307,16
"3194",2799,50,340,8,15,"yes","yes","yes",307,16
"3195",1490,33,107,2,15,"no","no","yes",307,16
"3196",2795,75,528,16,14,"yes","no","yes",307,16
"3197",3239,50,340,8,17,"no","no","no",307,16
"3198",1690,33,340,4,14,"yes","yes","yes",307,16
"3199",1899,33,340,8,14,"yes","no","yes",307,16
"3200",2343,66,426,8,14,"yes","yes","yes",307,16
"3201",1744,66,107,2,14,"no","no","yes",307,16
"3202",1999,33,340,8,15,"yes","no","yes",307,16
"3203",1359,25,120,4,14,"no","no","no",307,16
"3204",3659,66,424,8,17,"no","no","no",307,16
"3205",3129,100,527,16,15,"yes","yes","yes",307,16
"3206",1889,33,120,4,15,"no","no","no",307,16
"3207",2359,50,340,4,14,"no","no","no",307,16
"3208",2419,33,212,4,17,"no","no","no",307,16
"3209",2290,66,426,8,15,"yes","no","yes",307,16
"3210",1999,33,340,8,14,"yes","no","yes",307,16
"3211",1890,25,426,8,15,"yes","no","yes",307,16
"3212",2290,100,214,4,14,"no","no","yes",307,16
"3213",1849,33,212,4,14,"no","no","no",307,16
"3214",1844,66,107,2,14,"no","no","yes",307,16
"3215",2494,100,214,4,14,"yes","yes","yes",307,16
"3216",1929,25,120,4,17,"no","no","no",307,16
"3217",3249,100,527,16,17,"yes","no","yes",307,16
"3218",1475,33,340,4,14,"no","no","yes",307,16
"3219",1295,33,212,4,14,"no","no","yes",307,16
"3220",1259,25,120,4,14,"no","no","no",307,16
"3221",1444,25,107,2,14,"no","no","yes",307,16
"3222",2799,66,527,16,15,"yes","no","yes",307,16
"3223",1749,33,212,4,14,"no","no","no",307,16
"3224",1499,33,170,4,14,"yes","no","yes",307,16
"3225",2344,50,426,8,14,"yes","no","yes",307,16
"3226",2495,66,540,8,15,"yes","no","yes",307,16
"3227",2590,50,528,16,15,"yes","no","yes",307,16
"3228",1344,25,107,2,14,"no","no","yes",307,16
"3229",1595,66,107,2,14,"no","no","yes",307,16
"3230",1920,25,528,8,14,"yes","no","yes",307,16
"3231",3499,100,1000,16,15,"yes","no","yes",307,16
"3232",2595,100,426,8,14,"yes","no","yes",307,16
"3233",1399,33,170,4,14,"yes","no","yes",307,16
"3234",2143,33,426,8,14,"yes","yes","yes",307,16
"3235",1395,33,214,4,14,"no","no","yes",307,16
"3236",1595,33,214,4,14,"no","no","yes",307,16
"3237",1690,66,107,2,15,"no","no","yes",307,16
"3238",1795,25,426,8,14,"yes","no","yes",307,16
"3239",2944,75,528,16,14,"yes","no","yes",307,16
"3240",1849,33,340,4,14,"no","no","no",307,16
"3241",1469,33,340,4,14,"no","no","yes",307,16
"3242",1594,33,212,4,14,"yes","yes","yes",307,16
"3243",1865,33,425,8,15,"yes","no","yes",307,16
"3244",1395,25,214,4,14,"no","no","yes",307,16
"3245",1290,25,107,2,14,"no","no","yes",307,16
"3246",2644,75,426,8,14,"yes","no","yes",307,16
"3247",1999,66,340,8,15,"yes","no","yes",307,16
"3248",2395,33,528,16,14,"yes","no","yes",307,16
"3249",2645,66,424,8,17,"yes","no","yes",307,16
"3250",3349,100,527,16,17,"yes","no","yes",307,16
"3251",2120,33,528,8,14,"yes","no","yes",307,16
"3252",1994,50,214,4,14,"yes","yes","yes",307,16
"3253",3044,75,528,16,14,"yes","no","yes",307,16
"3254",1890,66,214,4,15,"no","no","yes",307,16
"3255",1595,33,214,4,14,"no","no","yes",307,16
"3256",1890,66,214,4,14,"no","no","yes",307,16
"3257",1944,75,107,2,14,"no","no","yes",307,16
"3258",2319,33,340,4,14,"no","no","no",307,16
"3259",1995,66,425,8,15,"yes","no","yes",307,16
"3260",1689,25,212,4,14,"no","no","no",307,16
"3261",2609,33,212,4,17,"no","no","no",307,16
"3262",2819,50,340,8,15,"no","no","no",307,16
"3263",1195,25,214,4,14,"no","no","yes",307,16
"3264",2039,33,212,4,14,"no","no","no",307,16
"3265",2425,50,212,8,17,"yes","yes","yes",307,16
"3266",1544,33,107,2,14,"no","no","yes",307,16
"3267",1990,50,420,4,14,"yes","yes","yes",307,16
"3268",2995,100,528,16,14,"yes","no","yes",307,16
"3269",2344,100,214,4,14,"no","no","yes",307,16
"3270",3439,66,340,8,17,"no","no","no",307,16
"3271",1895,100,340,4,14,"no","no","yes",307,16
"3272",2444,66,426,8,14,"yes","no","yes",307,16
"3273",3129,66,527,8,14,"no","no","no",307,16
"3274",1999,33,212,4,15,"no","no","no",307,16
"3275",2249,66,212,4,14,"no","no","no",307,16
"3276",2490,33,528,16,14,"yes","no","yes",307,16
"3277",2899,100,527,16,15,"yes","no","yes",307,16
"3278",2744,50,420,8,17,"yes","no","yes",307,16
"3279",2195,25,528,16,14,"yes","no","yes",307,16
"3280",2344,66,426,8,14,"yes","no","yes",307,16
"3281",2294,75,214,4,14,"yes","yes","yes",307,16
"3282",2094,66,214,4,14,"yes","yes","yes",307,16
"3283",1495,33,340,4,14,"yes","no","yes",307,16
"3284",2190,50,426,8,15,"yes","no","yes",307,16
"3285",3379,66,527,8,15,"no","no","no",307,16
"3286",2469,33,340,4,15,"no","no","no",307,16
"3287",1645,33,340,4,14,"yes","no","yes",307,16
"3288",2244,75,214,4,14,"no","no","yes",307,16
"3289",2290,66,426,8,14,"yes","no","yes",307,16
"3290",2090,100,107,2,14,"no","no","yes",307,16
"3291",2343,66,426,8,14,"yes","yes","yes",307,16
"3292",1644,50,107,2,14,"no","no","yes",307,16
"3293",2220,50,528,8,14,"yes","no","yes",307,16
"3294",2690,100,426,8,15,"yes","no","yes",307,16
"3295",1839,25,212,4,15,"no","no","no",307,16
"3296",1990,100,340,4,15,"no","no","yes",307,16
"3297",1490,33,107,2,14,"no","no","yes",307,16
"3298",2544,33,528,16,14,"yes","no","yes",307,16
"3299",2509,50,340,4,15,"no","no","no",307,16
"3300",2144,75,214,4,14,"no","no","yes",307,16
"3301",2095,50,426,8,14,"yes","no","yes",307,16
"3302",1695,50,214,4,14,"no","no","yes",307,16
"3303",3144,100,528,16,14,"yes","no","yes",307,16
"3304",1490,25,214,4,15,"no","no","yes",307,16
"3305",1499,33,170,4,15,"yes","no","yes",307,16
"3306",2099,33,340,8,14,"yes","no","yes",307,16
"3307",1595,66,340,4,14,"no","no","yes",307,16
"3308",1644,25,214,4,14,"no","no","yes",307,16
"3309",1995,33,426,8,14,"yes","no","yes",307,16
"3310",2769,66,340,8,14,"no","no","no",307,16
"3311",2669,50,340,8,14,"no","no","no",307,16
"3312",2744,100,426,8,14,"yes","no","yes",307,16
"3313",1490,25,214,4,14,"no","no","yes",307,16
"3314",2889,33,340,4,17,"no","no","no",307,16
"3315",1925,33,212,4,17,"no","no","yes",307,16
"3316",2444,25,528,16,14,"yes","no","yes",307,16
"3317",2195,66,426,8,14,"yes","no","yes",307,16
"3318",2775,66,420,16,15,"yes","no","yes",307,16
"3319",2499,66,528,16,15,"yes","no","yes",307,16
"3320",2199,50,450,8,15,"no","no","yes",307,16
"3321",1399,33,270,4,14,"no","no","yes",307,16
"3322",2290,25,528,16,15,"yes","no","yes",307,16
"3323",2490,75,426,8,15,"yes","no","yes",307,16
"3324",2899,66,527,16,15,"yes","no","yes",307,16
"3325",2090,75,214,4,15,"no","no","yes",307,16
"3326",1499,50,340,4,14,"no","no","yes",307,16
"3327",2695,66,528,8,15,"yes","no","yes",307,16
"3328",1690,66,340,4,15,"no","no","yes",307,16
"3329",2390,50,420,8,15,"yes","yes","yes",307,16
"3330",2399,100,340,8,14,"yes","no","yes",307,16
"3331",1590,50,107,2,15,"no","no","yes",307,16
"3332",2244,33,426,8,14,"yes","no","yes",307,16
"3333",2744,66,528,16,14,"yes","no","yes",307,16
"3334",2190,50,426,8,14,"yes","no","yes",307,16
"3335",2065,100,340,4,14,"yes","no","yes",307,16
"3336",1495,33,212,4,14,"no","no","yes",307,16
"3337",2529,100,340,8,14,"yes","yes","yes",307,16
"3338",1690,33,214,4,15,"no","no","yes",307,16
"3339",2569,50,340,8,14,"no","no","no",307,16
"3340",3239,66,424,8,15,"no","no","no",307,16
"3341",2044,66,214,4,14,"no","no","yes",307,16
"3342",1299,33,340,4,14,"no","no","yes",307,16
"3343",2199,50,425,8,15,"yes","no","yes",307,16
"3344",1495,50,214,4,14,"no","no","yes",307,16
"3345",2775,66,530,16,17,"no","no","yes",307,16
"3346",2444,100,214,4,14,"no","no","yes",307,16
"3347",1894,66,107,2,14,"yes","yes","yes",307,16
"3348",2890,75,528,16,14,"yes","no","yes",307,16
"3349",1795,66,340,8,14,"no","no","yes",307,16
"3350",2644,50,528,16,14,"yes","no","yes",307,16
"3351",1725,33,212,4,17,"no","no","yes",307,16
"3352",1795,75,107,2,14,"no","no","yes",307,16
"3353",3244,100,528,16,14,"yes","no","yes",307,16
"3354",2395,75,426,8,14,"yes","no","yes",307,16
"3355",2259,25,212,4,17,"no","no","no",307,16
"3356",2599,66,450,8,15,"yes","no","yes",307,16
"3357",1695,50,340,8,14,"no","no","yes",307,16
"3358",3029,100,527,16,15,"yes","yes","yes",307,16
"3359",1639,33,120,4,14,"no","no","no",307,16
"3360",1694,25,214,4,14,"yes","yes","yes",307,16
"3361",2499,66,528,8,15,"yes","no","yes",307,16
"3362",1559,33,212,4,14,"no","no","yes",307,16
"3363",2590,50,528,16,14,"yes","no","yes",307,16
"3364",2720,100,528,8,14,"yes","no","yes",307,16
"3365",1570,33,340,4,15,"no","no","yes",307,16
"3366",2743,100,426,8,14,"yes","yes","yes",307,16
"3367",2189,33,212,4,15,"no","no","no",307,16
"3368",2090,75,214,4,14,"no","no","yes",307,16
"3369",1944,25,426,8,14,"yes","no","yes",307,16
"3370",1529,33,170,4,14,"yes","yes","yes",307,16
"3371",2494,25,528,16,14,"yes","yes","yes",307,16
"3372",2499,100,340,8,14,"yes","no","yes",307,16
"3373",2595,66,530,16,15,"yes","no","yes",307,16
"3374",1558,33,212,4,14,"yes","yes","yes",307,16
"3375",1395,25,340,8,14,"no","no","yes",307,16
"3376",1625,33,240,4,14,"no","no","no",307,16
"3377",2309,33,120,4,17,"no","no","no",307,16
"3378",1695,33,425,8,15,"no","no","yes",307,16
"3379",1844,33,214,4,14,"no","no","yes",307,16
"3380",2425,66,530,16,15,"no","no","yes",307,16
"3381",2550,66,340,8,15,"yes","yes","yes",307,16
"3382",1995,50,212,8,14,"yes","yes","yes",307,16
"3383",1765,66,340,4,14,"yes","no","yes",307,16
"3384",1890,75,107,2,15,"no","no","yes",307,16
"3385",1595,33,340,8,14,"no","no","yes",307,16
"3386",1995,100,107,2,14,"no","no","yes",307,16
"3387",2949,100,527,16,15,"yes","no","yes",307,16
"3388",1494,25,107,2,14,"yes","yes","yes",307,16
"3389",2490,75,426,8,14,"yes","no","yes",307,16
"3390",1894,50,420,4,15,"yes","no","yes",307,16
"3391",2495,50,528,16,14,"yes","no","yes",307,16
"3392",2149,66,212,4,14,"no","no","no",307,16
"3393",2244,50,426,8,14,"yes","no","yes",307,16
"3394",3229,66,527,8,14,"no","no","no",307,16
"3395",2490,33,528,16,15,"yes","no","yes",307,16
"3396",2425,100,425,8,15,"yes","no","yes",307,16
"3397",1890,25,426,8,14,"yes","no","yes",307,16
"3398",2044,25,426,8,14,"yes","no","yes",307,16
"3399",2290,25,528,16,14,"yes","no","yes",307,16
"3400",1790,50,214,4,15,"no","no","yes",307,16
"3401",3397,66,450,16,15,"yes","yes","yes",307,16
"3402",2595,66,528,16,14,"yes","no","yes",307,16
"3403",2259,50,340,4,14,"no","no","no",307,16
"3404",2075,66,425,8,15,"yes","no","yes",307,16
"3405",1195,25,107,2,14,"no","no","yes",339,17
"3406",2999,100,527,16,15,"yes","no","yes",339,17
"3407",1795,25,426,8,14,"yes","no","yes",339,17
"3408",1599,33,210,4,14,"yes","no","yes",339,17
"3409",1644,25,214,4,14,"no","no","yes",339,17
"3410",1639,33,120,4,14,"no","no","no",339,17
"3411",2690,100,426,8,14,"yes","no","yes",339,17
"3412",1644,50,107,2,14,"no","no","yes",339,17
"3413",2694,75,426,8,14,"yes","yes","yes",339,17
"3414",1690,66,107,2,15,"no","no","yes",339,17
"3415",1465,66,210,4,14,"no","no","yes",339,17
"3416",1790,50,214,4,14,"no","no","yes",339,17
"3417",3129,66,527,8,14,"no","no","no",339,17
"3418",1690,33,340,4,14,"yes","yes","yes",339,17
"3419",2195,25,528,16,14,"yes","no","yes",339,17
"3420",2344,66,426,8,14,"yes","no","yes",339,17
"3421",1999,33,420,8,14,"yes","no","yes",339,17
"3422",1995,33,340,8,15,"yes","no","yes",339,17
"3423",2299,100,452,8,15,"yes","no","yes",339,17
"3424",3244,100,528,16,14,"yes","no","yes",339,17
"3425",2249,100,420,8,14,"yes","no","yes",339,17
"3426",1995,33,540,8,14,"yes","no","yes",339,17
"3427",2499,66,340,4,14,"no","no","no",339,17
"3428",2625,66,530,8,17,"yes","no","yes",339,17
"3429",2095,50,426,8,14,"yes","no","yes",339,17
"3430",1795,75,107,2,14,"no","no","yes",339,17
"3431",1799,66,212,4,14,"no","no","yes",339,17
"3432",1844,66,107,2,14,"no","no","yes",339,17
"3433",2365,66,540,8,15,"yes","no","yes",339,17
"3434",1689,25,212,4,14,"no","no","no",339,17
"3435",1995,66,340,8,14,"yes","yes","yes",339,17
"3436",2044,75,107,2,14,"no","no","yes",339,17
"3437",1999,33,212,4,15,"no","no","no",339,17
"3438",1795,66,340,8,14,"no","no","yes",339,17
"3439",2394,50,426,8,14,"yes","yes","yes",339,17
"3440",1295,33,340,4,14,"no","no","yes",339,17
"3441",2609,33,212,4,17,"no","no","no",339,17
"3442",1944,66,214,4,14,"no","no","yes",339,17
"3443",1890,25,426,8,15,"yes","no","yes",339,17
"3444",2469,33,340,4,15,"no","no","no",339,17
"3445",2144,75,214,4,14,"no","no","yes",339,17
"3446",1495,50,107,2,14,"no","no","yes",339,17
"3447",1644,33,107,2,14,"no","no","yes",339,17
"3448",2669,50,340,8,14,"no","no","no",339,17
"3449",1899,50,340,8,15,"yes","no","yes",339,17
"3450",2090,33,528,8,14,"yes","no","yes",339,17
"3451",1879,33,340,8,15,"yes","no","yes",339,17
"3452",2429,100,420,8,15,"yes","no","yes",339,17
"3453",1695,50,340,8,14,"no","no","yes",339,17
"3454",2995,100,528,16,14,"yes","no","yes",339,17
"3455",2265,100,340,8,15,"no","no","yes",339,17
"3456",1559,33,212,4,14,"no","no","yes",339,17
"3457",1495,33,340,4,14,"yes","no","yes",339,17
"3458",1589,25,212,4,14,"no","no","no",339,17
"3459",2259,50,340,4,14,"no","no","no",339,17
"3460",2215,100,425,8,15,"no","no","yes",339,17
"3461",1795,100,210,4,14,"no","no","yes",339,17
"3462",1849,33,212,4,14,"no","no","no",339,17
"3463",2190,50,426,8,14,"yes","no","yes",339,17
"3464",1794,33,340,4,14,"yes","yes","yes",339,17
"3465",1690,33,214,4,14,"no","no","yes",339,17
"3466",1844,50,214,4,14,"no","no","yes",339,17
"3467",2090,75,214,4,15,"no","no","yes",339,17
"3468",2344,100,214,4,14,"no","no","yes",339,17
"3469",1260,33,210,4,15,"no","no","yes",339,17
"3470",2144,100,107,2,14,"no","no","yes",339,17
"3471",2199,100,340,8,14,"no","no","yes",339,17
"3472",2029,33,420,8,14,"yes","yes","yes",339,17
"3473",2509,50,340,4,15,"no","no","no",339,17
"3474",1490,25,214,4,15,"no","no","yes",339,17
"3475",2065,66,540,8,15,"no","no","yes",339,17
"3476",1899,33,420,8,14,"yes","no","yes",339,17
"3477",1495,33,340,4,14,"no","no","yes",339,17
"3478",1890,25,426,8,14,"yes","no","yes",339,17
"3479",2215,66,425,8,15,"yes","yes","yes",339,17
"3480",2090,100,107,2,14,"no","no","yes",339,17
"3481",2299,100,425,8,14,"no","no","yes",339,17
"3482",2904,66,340,8,17,"yes","no","yes",339,17
"3483",2425,66,340,8,17,"yes","yes","yes",339,17
"3484",1999,66,340,8,15,"yes","no","yes",339,17
"3485",2115,66,340,8,15,"yes","no","yes",339,17
"3486",2744,66,528,16,14,"yes","no","yes",339,17
"3487",1999,33,420,8,15,"yes","no","yes",339,17
"3488",1795,33,425,8,15,"no","no","yes",339,17
"3489",2290,66,426,8,14,"yes","no","yes",339,17
"3490",1890,66,214,4,15,"no","no","yes",339,17
"3491",2090,75,214,4,14,"no","no","yes",339,17
"3492",1550,66,210,4,15,"no","no","yes",339,17
"3493",1890,75,107,2,15,"no","no","yes",339,17
"3494",2894,100,426,8,14,"yes","yes","yes",339,17
"3495",1490,33,107,2,14,"no","no","yes",339,17
"3496",2244,50,426,8,14,"yes","no","yes",339,17
"3497",2544,33,528,16,14,"yes","no","yes",339,17
"3498",2695,66,528,8,15,"yes","no","yes",339,17
"3499",1915,66,425,8,15,"no","no","yes",339,17
"3500",2890,75,528,16,14,"yes","no","yes",339,17
"3501",2244,75,214,4,14,"no","no","yes",339,17
"3502",1399,33,210,4,14,"yes","no","yes",339,17
"3503",2399,66,212,4,15,"no","no","no",339,17
"3504",2444,25,528,16,14,"yes","no","yes",339,17
"3505",3799,66,527,8,17,"no","no","no",339,17
"3506",1395,33,107,2,14,"no","no","yes",339,17
"3507",2944,75,528,16,14,"yes","no","yes",339,17
"3508",1495,50,214,4,14,"no","no","yes",339,17
"3509",3144,100,528,16,14,"yes","no","yes",339,17
"3510",1595,66,107,2,14,"no","no","yes",339,17
"3511",2590,50,528,16,14,"yes","no","yes",339,17
"3512",2644,75,426,8,14,"yes","no","yes",339,17
"3513",1590,50,107,2,14,"no","no","yes",339,17
"3514",1945,66,340,8,15,"no","no","yes",339,17
"3515",1995,33,426,8,14,"yes","no","yes",339,17
"3516",1529,33,210,4,14,"yes","yes","yes",339,17
"3517",2425,100,425,8,15,"yes","yes","yes",339,17
"3518",2349,100,420,8,15,"yes","no","yes",339,17
"3519",2144,33,426,8,14,"yes","no","yes",339,17
"3520",2925,100,530,8,17,"yes","no","yes",339,17
"3521",2249,66,212,4,14,"no","no","no",339,17
"3522",2095,100,425,8,15,"no","no","yes",339,17
"3523",2094,25,426,8,14,"yes","yes","yes",339,17
"3524",2344,50,426,8,14,"yes","no","yes",339,17
"3525",1744,33,214,4,14,"no","no","yes",339,17
"3526",2195,100,214,4,14,"no","no","yes",339,17
"3527",1890,66,214,4,14,"no","no","yes",339,17
"3528",2794,50,528,16,14,"yes","yes","yes",339,17
"3529",2309,33,120,4,17,"no","no","no",339,17
"3530",1825,33,340,8,15,"no","no","yes",339,17
"3531",2845,100,540,8,17,"yes","no","yes",339,17
"3532",3499,100,1000,16,15,"yes","no","yes",339,17
"3533",1515,33,210,4,14,"yes","no","yes",339,17
"3534",2558,100,425,8,14,"yes","yes","yes",339,17
"3535",2690,100,528,8,14,"yes","no","yes",339,17
"3536",2244,100,107,2,14,"no","no","yes",339,17
"3537",2694,33,528,16,14,"yes","yes","yes",339,17
"3538",3239,50,340,8,17,"no","no","no",339,17
"3539",1849,33,340,4,14,"no","no","no",339,17
"3540",2644,33,528,16,14,"yes","no","yes",339,17
"3541",2195,66,426,8,14,"yes","no","yes",339,17
"3542",2419,33,212,4,17,"no","no","no",339,17
"3543",2569,50,340,8,14,"no","no","no",339,17
"3544",3019,66,340,8,15,"no","no","no",339,17
"3545",3149,66,527,16,17,"yes","no","yes",339,17
"3546",2090,33,426,8,14,"yes","no","yes",339,17
"3547",2545,100,530,8,15,"yes","yes","yes",339,17
"3548",1345,33,210,4,14,"yes","no","yes",339,17
"3549",2259,25,212,4,17,"no","no","no",339,17
"3550",2590,50,528,16,15,"yes","no","yes",339,17
"3551",2345,33,425,8,17,"yes","yes","yes",339,17
"3552",3049,100,527,16,15,"yes","no","yes",339,17
"3553",2744,50,528,16,14,"yes","no","yes",339,17
"3554",2395,33,528,16,14,"yes","no","yes",339,17
"3555",2379,100,420,8,14,"yes","yes","yes",339,17
"3556",2645,66,540,8,17,"yes","no","yes",339,17
"3557",1594,33,340,4,15,"yes","no","yes",339,17
"3558",2445,66,425,8,17,"yes","yes","yes",339,17
"3559",2099,33,420,8,14,"yes","no","yes",339,17
"3560",3090,100,528,16,14,"yes","no","yes",339,17
"3561",2575,100,530,8,15,"yes","no","yes",339,17
"3562",1739,33,120,4,14,"no","no","no",339,17
"3563",2044,25,426,8,14,"yes","no","yes",339,17
"3564",2425,33,540,8,17,"yes","no","yes",339,17
"3565",2290,100,214,4,14,"no","no","yes",339,17
"3566",1839,25,212,4,15,"no","no","no",339,17
"3567",1890,25,528,8,14,"yes","no","yes",339,17
"3568",1290,25,107,2,15,"no","no","yes",339,17
"3569",2290,25,528,16,14,"yes","no","yes",339,17
"3570",2399,100,420,8,14,"yes","no","yes",339,17
"3571",2595,66,528,16,14,"yes","no","yes",339,17
"3572",2494,25,528,16,14,"yes","yes","yes",339,17
"3573",2435,100,340,8,15,"yes","no","yes",339,17
"3574",2844,100,426,8,14,"yes","no","yes",339,17
"3575",2989,66,424,8,14,"no","no","no",339,17
"3576",2545,100,425,8,15,"yes","yes","yes",339,17
"3577",2343,66,426,8,14,"yes","yes","yes",339,17
"3578",1249,25,100,4,14,"no","no","no",339,17
"3579",1890,75,107,2,14,"no","no","yes",339,17
"3580",2179,66,425,8,14,"no","no","yes",339,17
"3581",1939,33,212,4,14,"no","no","no",339,17
"3582",2390,50,420,8,15,"yes","yes","yes",339,17
"3583",1595,33,214,4,14,"no","no","yes",339,17
"3584",2929,50,340,4,17,"no","no","no",339,17
"3585",1945,33,540,8,15,"no","no","yes",339,17
"3586",2175,33,340,8,17,"no","no","yes",339,17
"3587",3129,100,527,16,15,"yes","yes","yes",339,17
"3588",1625,33,240,4,14,"no","no","no",339,17
"3589",2355,66,340,8,15,"yes","no","yes",339,17
"3590",1925,33,340,4,17,"no","no","yes",339,17
"3591",3090,100,528,16,15,"yes","no","yes",339,17
"3592",2299,66,340,16,15,"yes","no","yes",339,17
"3593",3099,100,527,16,15,"yes","no","yes",339,17
"3594",2490,33,528,16,15,"yes","no","yes",339,17
"3595",2615,100,530,8,15,"yes","no","yes",339,17
"3596",2189,33,212,4,15,"no","no","no",339,17
"3597",2743,100,426,8,14,"yes","yes","yes",339,17
"3598",1399,33,210,4,14,"no","no","yes",339,17
"3599",2699,100,450,8,15,"yes","no","yes",339,17
"3600",1599,50,340,4,14,"no","no","yes",339,17
"3601",2149,66,212,4,14,"no","no","no",339,17
"3602",2499,100,420,8,14,"yes","no","yes",339,17
"3603",2495,50,528,16,14,"yes","no","yes",339,17
"3604",2343,66,426,8,14,"yes","yes","yes",339,17
"3605",2190,50,426,8,15,"yes","no","yes",339,17
"3606",1844,33,214,4,14,"no","no","yes",339,17
"3607",2690,100,426,8,15,"yes","no","yes",339,17
"3608",1344,25,107,2,14,"no","no","yes",339,17
"3609",1695,50,214,4,14,"no","no","yes",339,17
"3610",1175,33,210,4,14,"no","no","yes",339,17
"3611",1995,75,214,4,14,"no","no","yes",339,17
"3612",1944,75,107,2,14,"no","no","yes",339,17
"3613",2849,66,527,16,15,"yes","no","yes",339,17
"3614",2544,75,426,8,14,"yes","no","yes",339,17
"3615",3659,66,424,8,17,"no","no","no",339,17
"3616",1749,33,212,4,14,"no","no","no",339,17
"3617",3439,66,340,8,17,"no","no","no",339,17
"3618",1944,50,214,4,14,"no","no","yes",339,17
"3619",1990,50,420,4,14,"yes","yes","yes",339,17
"3620",3349,100,527,16,17,"yes","no","yes",339,17
"3621",2395,75,426,8,14,"yes","no","yes",339,17
"3622",2495,100,540,8,15,"yes","no","yes",339,17
"3623",2615,100,340,8,17,"no","no","yes",339,17
"3624",3089,66,424,8,14,"no","no","no",339,17
"3625",3294,100,528,16,14,"yes","yes","yes",339,17
"3626",2127,33,540,8,14,"yes","yes","yes",339,17
"3627",1444,25,107,2,14,"no","no","yes",339,17
"3628",2244,33,426,8,14,"yes","no","yes",339,17
"3629",2644,50,528,16,14,"yes","no","yes",339,17
"3630",2315,66,530,8,15,"yes","no","yes",339,17
"3631",2665,100,540,8,15,"yes","no","yes",339,17
"3632",3239,66,424,8,15,"no","no","no",339,17
"3633",2275,66,530,8,15,"yes","no","yes",339,17
"3634",2490,75,426,8,14,"yes","no","yes",339,17
"3635",2427,66,540,8,15,"yes","yes","yes",339,17
"3636",2690,66,528,16,14,"yes","no","yes",339,17
"3637",1509,25,120,4,15,"no","no","no",339,17
"3638",2899,66,527,16,15,"yes","no","yes",339,17
"3639",2195,66,426,8,14,"yes","no","yes",339,17
"3640",2044,66,214,4,14,"no","no","yes",339,17
"3641",1490,33,107,2,15,"no","no","yes",339,17
"3642",2344,25,528,16,14,"yes","no","yes",339,17
"3643",2894,66,528,16,14,"yes","yes","yes",339,17
"3644",2819,50,340,8,15,"no","no","no",339,17
"3645",1944,25,426,8,14,"yes","no","yes",339,17
"3646",3094,75,528,16,14,"yes","yes","yes",339,17
"3647",2095,66,425,8,15,"yes","yes","yes",339,17
"3648",1965,100,210,4,14,"yes","no","yes",339,17
"3649",3299,66,1000,16,15,"yes","no","yes",339,17
"3650",2090,100,107,2,15,"no","no","yes",339,17
"3651",2190,50,528,8,14,"yes","no","yes",339,17
"3652",1345,33,210,4,14,"no","no","yes",339,17
"3653",3304,66,420,16,17,"yes","no","yes",339,17
"3654",1499,33,210,4,14,"yes","no","yes",339,17
"3655",1725,33,340,4,17,"no","no","yes",339,17
"3656",2290,66,426,8,15,"yes","no","yes",339,17
"3657",2115,33,530,8,15,"yes","yes","yes",339,17
"3658",1690,33,214,4,15,"no","no","yes",339,17
"3659",1595,33,214,4,14,"no","no","yes",339,17
"3660",1595,33,340,8,14,"no","no","yes",339,17
"3661",1675,33,425,8,15,"no","no","yes",339,17
"3662",1744,66,107,2,14,"no","no","yes",339,17
"3663",3379,66,527,8,15,"no","no","no",339,17
"3664",2744,100,426,8,14,"yes","no","yes",339,17
"3665",1259,25,120,4,14,"no","no","no",339,17
"3666",2449,100,420,8,14,"yes","no","yes",339,17
"3667",2889,33,340,4,17,"no","no","no",339,17
"3668",1744,50,107,2,14,"no","no","yes",339,17
"3669",1635,66,210,4,14,"yes","no","yes",339,17
"3670",1929,25,120,4,17,"no","no","no",339,17
"3671",2627,100,540,8,15,"yes","yes","yes",339,17
"3672",2744,50,420,8,17,"yes","no","yes",339,17
"3673",1999,50,340,4,14,"yes","yes","yes",339,17
"3674",1795,66,425,8,15,"no","no","yes",339,17
"3675",2819,66,212,4,17,"no","no","no",339,17
"3676",2890,75,528,16,15,"yes","no","yes",339,17
"3677",1499,33,210,4,15,"yes","no","yes",339,17
"3678",2295,66,540,8,15,"yes","no","yes",339,17
"3679",2290,66,528,8,14,"yes","no","yes",339,17
"3680",2690,66,528,16,15,"yes","no","yes",339,17
"3681",1595,66,214,4,14,"no","no","yes",339,17
"3682",1359,25,120,4,14,"no","no","no",339,17
"3683",2490,75,426,8,15,"yes","no","yes",339,17
"3684",2490,75,528,8,14,"yes","no","yes",339,17
"3685",1995,33,426,8,14,"yes","no","yes",339,17
"3686",2115,33,425,8,15,"yes","yes","yes",339,17
"3687",2124,33,240,4,14,"yes","yes","no",339,17
"3688",1795,50,420,4,14,"yes","no","yes",339,17
"3689",3229,66,527,8,14,"no","no","no",339,17
"3690",2775,100,425,8,17,"yes","yes","yes",339,17
"3691",1395,25,340,8,14,"no","no","yes",339,17
"3692",1899,66,340,8,15,"no","no","yes",339,17
"3693",1490,25,214,4,14,"no","no","yes",339,17
"3694",2775,66,420,16,15,"yes","no","yes",339,17
"3695",2359,50,340,4,14,"no","no","no",339,17
"3696",1544,25,214,4,14,"no","no","yes",339,17
"3697",2795,75,528,16,14,"yes","no","yes",339,17
"3698",1995,33,425,8,15,"yes","yes","yes",339,17
"3699",2494,66,426,8,14,"yes","yes","yes",339,17
"3700",2970,66,420,16,15,"yes","yes","yes",339,17
"3701",2444,100,214,4,14,"no","no","yes",339,17
"3702",2529,100,420,8,14,"yes","yes","yes",339,17
"3703",2769,66,340,8,14,"no","no","no",339,17
"3704",1544,33,107,2,14,"no","no","yes",339,17
"3705",1995,100,107,2,14,"no","no","yes",339,17
"3706",2595,100,426,8,14,"yes","no","yes",339,17
"3707",3044,75,528,16,14,"yes","no","yes",339,17
"3708",1395,33,214,4,14,"no","no","yes",339,17
"3709",2444,66,426,8,14,"yes","no","yes",339,17
"3710",2294,33,426,8,14,"yes","yes","yes",339,17
"3711",1795,66,214,4,14,"no","no","yes",339,17
"3712",1430,33,210,4,15,"no","no","yes",339,17
"3713",1889,33,120,4,15,"no","no","no",339,17
"3714",2290,100,214,4,15,"no","no","yes",339,17
"3715",2215,66,530,8,15,"yes","yes","yes",339,17
"3716",2799,66,527,16,15,"yes","no","yes",339,17
"3717",2295,66,340,8,17,"no","no","yes",339,17
"3718",2195,50,420,8,15,"yes","no","yes",339,17
"3719",1594,33,340,4,14,"yes","yes","yes",339,17
"3720",1290,25,107,2,14,"no","no","yes",339,17
"3721",2319,33,340,4,14,"no","no","no",339,17
"3722",2039,33,212,4,14,"no","no","no",339,17
"3723",1395,25,214,4,14,"no","no","yes",339,17
"3724",2929,66,527,16,15,"yes","yes","yes",339,17
"3725",1690,66,107,2,14,"no","no","yes",339,17
"3726",1590,50,107,2,15,"no","no","yes",339,17
"3727",2844,66,528,16,14,"yes","no","yes",339,17
"3728",2490,33,528,16,14,"yes","no","yes",339,17
"3729",1195,25,214,4,14,"no","no","yes",339,17
"3730",2869,66,340,8,14,"no","no","no",339,17
"3731",2385,100,540,8,15,"no","no","yes",339,17
"3732",1880,100,210,4,15,"no","no","yes",339,17
"3733",2349,100,420,8,14,"yes","no","yes",339,17
"3734",2550,66,340,8,15,"yes","yes","yes",339,17
"3735",1495,33,340,4,14,"yes","yes","yes",339,17
"3736",1790,50,214,4,15,"no","no","yes",339,17
"3737",2090,33,426,8,15,"yes","no","yes",339,17
"3738",1894,50,420,4,15,"yes","no","yes",339,17
"3739",2143,33,426,8,14,"yes","yes","yes",339,17
"3740",2219,33,340,4,14,"no","no","no",339,17
"3741",2499,75,340,16,15,"yes","no","yes",339,17
"3742",1925,33,340,4,17,"yes","yes","yes",339,17
"3743",2290,25,528,16,15,"yes","no","yes",339,17
"3744",2599,100,527,8,15,"yes","no","yes",273,18
"3745",2190,100,214,4,14,"no","no","yes",273,18
"3746",1359,25,120,4,14,"no","no","no",273,18
"3747",1999,33,527,4,14,"yes","no","yes",273,18
"3748",1740,33,214,4,14,"no","no","yes",273,18
"3749",2493,33,528,16,14,"yes","yes","yes",273,18
"3750",2127,33,540,8,14,"yes","yes","yes",273,18
"3751",2599,100,420,8,15,"yes","no","yes",273,18
"3752",3095,66,1080,16,15,"yes","no","yes",273,18
"3753",2349,100,420,8,15,"yes","no","yes",273,18
"3754",1379,33,340,4,14,"no","no","yes",273,18
"3755",2775,66,540,16,15,"yes","yes","yes",273,18
"3756",2718,75,428,16,17,"yes","no","yes",273,18
"3757",2243,66,426,8,14,"yes","yes","yes",273,18
"3758",2854,100,528,16,14,"yes","no","yes",273,18
"3759",2093,33,426,8,14,"yes","yes","yes",273,18
"3760",1625,33,240,4,14,"no","no","no",273,18
"3761",2943,100,528,16,14,"yes","yes","yes",273,18
"3762",2499,100,450,8,15,"yes","no","yes",273,18
"3763",2343,33,528,16,14,"yes","yes","yes",273,18
"3764",1694,33,214,4,14,"yes","yes","yes",273,18
"3765",1618,33,340,4,17,"no","no","yes",273,18
"3766",2345,33,528,16,14,"yes","no","yes",273,18
"3767",1399,33,210,4,14,"yes","no","yes",273,18
"3768",2758,33,170,4,17,"no","no","no",273,18
"3769",2044,33,426,8,14,"yes","no","yes",273,18
"3770",2045,33,540,8,15,"yes","no","yes",273,18
"3771",1995,66,540,8,15,"no","no","yes",273,18
"3772",2795,33,1000,24,14,"yes","no","yes",273,18
"3773",1599,50,340,4,14,"no","no","yes",273,18
"3774",2099,66,420,8,15,"yes","no","yes",273,18
"3775",2199,100,340,8,14,"no","no","yes",273,18
"3776",2695,66,540,16,15,"yes","no","yes",273,18
"3777",2818,100,428,16,17,"yes","no","yes",273,18
"3778",2395,100,426,8,14,"yes","no","yes",273,18
"3779",2065,33,540,8,15,"yes","no","yes",273,18
"3780",1740,33,214,4,15,"no","no","yes",273,18
"3781",2029,33,420,4,14,"yes","yes","yes",273,18
"3782",2093,33,426,8,14,"yes","yes","yes",273,18
"3783",3243,66,1000,24,14,"yes","yes","yes",273,18
"3784",3745,66,2100,16,15,"yes","no","yes",273,18
"3785",2189,50,212,4,14,"no","no","no",273,18
"3786",2399,75,428,16,15,"yes","no","yes",273,18
"3787",2338,33,170,4,15,"no","no","no",273,18
"3788",2799,66,527,16,15,"yes","no","yes",273,18
"3789",1895,50,420,4,14,"yes","yes","yes",273,18
"3790",1554,33,214,4,14,"no","no","yes",273,18
"3791",3095,66,1000,24,14,"yes","no","yes",273,18
"3792",2499,66,340,4,14,"no","no","no",273,18
"3793",2040,33,426,8,14,"yes","no","yes",273,18
"3794",1995,33,540,8,14,"yes","no","yes",273,18
"3795",2999,100,528,16,17,"yes","no","yes",273,18
"3796",3093,33,1000,24,14,"yes","yes","yes",273,18
"3797",3543,100,1000,24,14,"yes","yes","yes",273,18
"3798",2454,100,426,8,14,"yes","no","yes",273,18
"3799",1854,33,426,8,14,"yes","no","yes",273,18
"3800",1690,33,340,4,17,"no","no","yes",273,18
"3801",1865,66,425,8,15,"no","no","yes",273,18
"3802",1995,66,340,8,14,"yes","yes","yes",273,18
"3803",2854,33,1000,24,14,"yes","no","yes",273,18
"3804",1494,25,214,4,14,"yes","yes","yes",273,18
"3805",2590,66,528,16,14,"yes","no","yes",273,18
"3806",1899,33,420,4,14,"yes","no","yes",273,18
"3807",2543,100,426,8,14,"yes","yes","yes",273,18
"3808",1645,33,214,4,14,"no","no","yes",273,18
"3809",1689,33,212,4,14,"no","no","no",273,18
"3810",3415,66,1080,16,17,"yes","no","yes",273,18
"3811",1699,50,340,8,15,"yes","no","yes",273,18
"3812",1890,33,528,8,14,"yes","no","yes",273,18
"3813",2749,100,420,8,17,"yes","no","yes",273,18
"3814",2185,33,425,8,15,"yes","yes","yes",273,18
"3815",2018,50,340,8,17,"yes","no","yes",273,18
"3816",2938,50,340,4,15,"no","no","no",273,18
"3817",1599,33,210,4,14,"yes","no","yes",273,18
"3818",2254,33,528,16,14,"yes","no","yes",273,18
"3819",1299,33,210,4,14,"no","no","yes",273,18
"3820",2040,33,528,8,14,"yes","no","yes",273,18
"3821",2465,66,540,8,17,"yes","no","yes",273,18
"3822",2558,25,212,4,17,"no","no","no",273,18
"3823",1529,33,210,4,14,"yes","yes","yes",273,18
"3824",2145,66,540,8,15,"yes","no","yes",273,18
"3825",1795,66,214,4,14,"no","no","yes",273,18
"3826",2945,33,1000,24,14,"yes","no","yes",273,18
"3827",2440,33,528,16,15,"yes","no","yes",273,18
"3828",2385,33,1080,8,15,"no","no","yes",273,18
"3829",3358,50,340,4,17,"no","no","no",273,18
"3830",1999,33,420,4,14,"yes","no","yes",273,18
"3831",1945,33,426,8,14,"yes","no","yes",273,18
"3832",2555,66,540,8,15,"yes","yes","yes",273,18
"3833",1595,33,340,8,14,"no","no","yes",273,18
"3834",3004,33,1000,24,14,"yes","no","yes",273,18
"3835",2390,33,540,8,17,"yes","no","yes",273,18
"3836",2305,66,425,8,15,"yes","yes","yes",273,18
"3837",2095,66,540,8,15,"no","no","yes",273,18
"3838",2427,66,540,8,15,"yes","yes","yes",273,18
"3839",2744,66,528,16,14,"yes","no","yes",273,18
"3840",1594,33,340,4,14,"yes","yes","yes",273,18
"3841",2129,66,420,8,14,"yes","yes","yes",273,18
"3842",1899,66,250,4,15,"yes","yes","yes",273,18
"3843",2194,33,426,8,14,"yes","no","yes",273,18
"3844",1798,33,340,4,17,"no","no","yes",273,18
"3845",2004,33,426,8,14,"yes","no","yes",273,18
"3846",1999,33,420,4,15,"yes","no","yes",273,18
"3847",2658,50,212,4,15,"no","no","no",273,18
"3848",1288,33,340,4,15,"no","no","yes",273,18
"3849",1495,33,214,4,14,"no","no","yes",273,18
"3850",2499,100,428,16,15,"yes","no","yes",273,18
"3851",2040,33,426,8,15,"yes","no","yes",273,18
"3852",2795,100,528,16,14,"yes","no","yes",273,18
"3853",2485,66,1080,8,15,"no","no","yes",273,18
"3854",2890,33,1000,24,15,"yes","no","yes",273,18
"3855",2455,33,540,8,15,"yes","yes","yes",273,18
"3856",1679,33,340,8,15,"yes","no","yes",273,18
"3857",1468,33,340,4,15,"no","no","yes",273,18
"3858",1998,33,340,8,17,"yes","no","yes",273,18
"3859",1499,33,210,4,14,"yes","no","yes",273,18
"3860",2199,66,420,8,14,"yes","no","yes",273,18
"3861",1495,33,340,4,14,"no","no","yes",273,18
"3862",1999,66,420,8,14,"yes","no","yes",273,18
"3863",3190,66,1000,24,14,"yes","no","yes",273,18
"3864",1899,66,340,8,15,"no","no","yes",273,18
"3865",2118,66,340,8,17,"yes","no","yes",273,18
"3866",2099,33,420,4,14,"yes","no","yes",273,18
"3867",1645,33,214,4,14,"no","no","yes",273,18
"3868",1745,66,425,8,15,"no","no","yes",273,18
"3869",2290,33,528,16,15,"yes","no","yes",273,18
"3870",2688,33,212,4,17,"no","no","no",273,18
"3871",2440,33,528,16,14,"yes","no","yes",273,18
"3872",1704,33,214,4,14,"no","no","yes",273,18
"3873",2830,100,540,8,17,"yes","no","yes",273,18
"3874",2943,33,1000,24,14,"yes","yes","yes",273,18
"3875",2299,66,428,16,15,"yes","no","yes",273,18
"3876",2265,33,540,8,15,"yes","no","yes",273,18
"3877",1249,25,100,4,14,"no","no","no",273,18
"3878",2190,66,528,8,14,"yes","no","yes",273,18
"3879",3015,66,1080,16,15,"no","no","yes",273,18
"3880",2635,66,1080,8,15,"yes","no","yes",273,18
"3881",2190,66,426,8,15,"yes","no","yes",273,18
"3882",1895,33,540,8,15,"no","no","yes",273,18
"3883",1849,33,340,4,14,"no","no","no",273,18
"3884",2449,100,527,8,15,"yes","no","yes",273,18
"3885",2629,100,420,8,15,"yes","yes","yes",273,18
"3886",1595,66,214,4,14,"no","no","yes",273,18
"3887",1795,66,340,8,14,"no","no","yes",273,18
"3888",1938,25,120,4,15,"no","no","no",273,18
"3889",2365,33,540,8,17,"yes","no","yes",273,18
"3890",1495,33,340,4,14,"yes","yes","yes",273,18
"3891",3040,33,1000,24,15,"yes","no","yes",273,18
"3892",1890,66,214,4,14,"no","no","yes",273,18
"3893",2643,66,528,16,14,"yes","yes","yes",273,18
"3894",3044,33,1000,24,14,"yes","no","yes",273,18
"3895",2490,100,528,8,14,"yes","no","yes",273,18
"3896",1945,33,426,8,14,"yes","no","yes",273,18
"3897",2218,66,250,4,17,"yes","yes","yes",273,18
"3898",1995,33,540,8,15,"no","no","yes",273,18
"3899",2138,25,212,4,15,"no","no","no",273,18
"3900",2554,66,528,16,14,"yes","no","yes",273,18
"3901",3344,66,1000,24,14,"yes","no","yes",273,18
"3902",1794,33,340,4,14,"yes","yes","yes",273,18
"3903",2159,66,170,4,14,"no","no","no",273,18
"3904",2890,33,1000,24,14,"yes","no","yes",273,18
"3905",3454,100,1000,24,14,"yes","no","yes",273,18
"3906",2243,66,426,8,14,"yes","yes","yes",273,18
"3907",2495,66,528,16,14,"yes","no","yes",273,18
"3908",3154,66,1000,24,14,"yes","no","yes",273,18
"3909",1559,25,212,4,14,"no","no","no",273,18
"3910",2768,50,212,4,15,"no","no","no",273,18
"3911",3085,66,540,16,15,"yes","yes","yes",273,18
"3912",2779,66,340,4,14,"no","no","no",273,18
"3913",1499,33,210,4,15,"yes","no","yes",273,18
"3914",2245,66,540,8,15,"yes","no","yes",273,18
"3915",2618,66,428,16,17,"yes","no","yes",273,18
"3916",1488,50,340,4,15,"no","no","yes",273,18
"3917",2079,50,212,4,14,"no","no","no",273,18
"3918",3644,100,1000,24,14,"yes","no","yes",273,18
"3919",1894,33,214,4,14,"no","no","yes",273,18
"3920",2790,100,540,8,14,"yes","yes","yes",273,18
"3921",2535,33,1080,8,15,"yes","no","yes",273,18
"3922",3194,33,1000,24,14,"yes","no","yes",273,18
"3923",2268,33,212,4,15,"no","no","no",273,18
"3924",2490,100,426,8,15,"yes","no","yes",273,18
"3925",1199,33,340,4,14,"no","no","yes",273,18
"3926",1995,66,425,8,15,"yes","yes","yes",273,18
"3927",2595,66,528,8,15,"yes","no","yes",273,18
"3928",2099,66,420,8,14,"yes","no","yes",273,18
"3929",2490,100,426,8,14,"yes","no","yes",273,18
"3930",1399,50,340,4,14,"no","no","yes",273,18
"3931",2044,66,214,4,14,"no","no","yes",273,18
"3932",1890,66,214,4,15,"no","no","yes",273,18
"3933",2625,66,540,16,15,"no","no","yes",273,18
"3934",2190,66,426,8,14,"yes","no","yes",273,18
"3935",1590,33,214,4,15,"no","no","yes",273,18
"3936",2358,25,120,4,17,"no","no","no",273,18
"3937",2390,75,420,4,14,"yes","yes","yes",273,18
"3938",2590,66,528,16,15,"yes","no","yes",273,18
"3939",3358,66,340,4,15,"no","no","no",273,18
"3940",2890,100,528,16,14,"yes","no","yes",273,18
"3941",2165,66,540,8,15,"yes","no","yes",273,18
"3942",2738,66,170,4,15,"no","no","no",273,18
"3943",1943,33,426,8,14,"yes","yes","yes",273,18
"3944",2099,66,527,8,14,"yes","no","yes",273,18
"3945",2290,33,528,16,14,"yes","no","yes",273,18
"3946",2365,66,540,8,15,"yes","no","yes",273,18
"3947",2118,50,250,4,17,"yes","yes","yes",273,18
"3948",1395,33,214,4,14,"no","no","yes",273,18
"3949",2449,100,420,8,15,"yes","no","yes",273,18
"3950",2627,100,540,8,15,"yes","yes","yes",273,18
"3951",1854,66,214,4,14,"no","no","yes",273,18
"3952",2899,100,420,8,17,"yes","no","yes",273,18
"3953",2399,66,450,8,15,"yes","yes","yes",273,18
"3954",2594,33,528,16,14,"yes","no","yes",273,18
"3955",2245,66,540,8,15,"yes","yes","yes",273,18
"3956",2095,100,214,4,14,"no","no","yes",273,18
"3957",2479,100,420,8,15,"yes","yes","yes",273,18
"3958",3490,100,1000,24,15,"yes","no","yes",273,18
"3959",1495,33,214,4,14,"no","no","yes",273,18
"3960",2145,33,540,8,15,"yes","no","yes",273,18
"3961",2404,33,528,16,14,"yes","no","yes",273,18
"3962",2525,33,540,8,15,"yes","no","yes",273,18
"3963",1195,25,214,4,14,"no","no","yes",273,18
"3964",2625,66,540,8,15,"yes","no","yes",273,18
"3965",3188,50,212,4,17,"no","no","no",273,18
"3966",1695,50,340,8,14,"no","no","yes",273,18
"3967",1625,33,425,8,15,"no","no","yes",273,18
"3968",3190,66,1000,24,15,"yes","no","yes",273,18
"3969",1745,33,425,8,15,"no","no","yes",273,18
"3970",2929,66,527,16,15,"yes","yes","yes",273,18
"3971",2344,100,214,4,14,"no","no","yes",273,18
"3972",2344,66,426,8,14,"yes","no","yes",273,18
"3973",1590,33,214,4,14,"no","no","yes",273,18
"3974",1890,33,426,8,14,"yes","no","yes",273,18
"3975",1795,33,426,8,14,"yes","no","yes",273,18
"3976",3040,33,1000,24,14,"yes","no","yes",273,18
"3977",3044,100,528,16,14,"yes","no","yes",273,18
"3978",2095,66,426,8,14,"yes","no","yes",273,18
"3979",2495,100,540,8,15,"yes","no","yes",273,18
"3980",3490,100,1000,24,14,"yes","no","yes",273,18
"3981",2095,66,426,8,14,"yes","no","yes",273,18
"3982",1595,33,210,4,14,"yes","yes","yes",273,18
"3983",1799,66,340,8,15,"yes","no","yes",273,18
"3984",3199,66,527,16,17,"yes","no","yes",273,18
"3985",1295,33,340,4,14,"no","no","yes",273,18
"3986",1890,33,426,8,15,"yes","no","yes",273,18
"3987",1395,25,340,8,14,"no","no","yes",273,18
"3988",2444,33,528,16,14,"yes","no","yes",273,18
"3989",1818,50,340,4,17,"no","no","yes",273,18
"3990",1799,50,250,4,15,"yes","yes","yes",273,18
"3991",3778,66,340,4,17,"no","no","no",273,18
"3992",1744,33,214,4,14,"no","no","yes",273,18
"3993",2499,100,420,8,15,"yes","no","yes",273,18
"3994",2899,66,527,16,15,"yes","no","yes",273,18
"3995",2195,33,528,16,14,"yes","no","yes",273,18
"3996",3299,66,1000,16,15,"yes","no","yes",273,18
"3997",2154,100,214,4,14,"no","no","yes",273,18
"3998",2295,66,540,8,15,"yes","no","yes",273,18
"3999",2644,100,426,8,14,"yes","no","yes",273,18
"4000",2190,100,214,4,15,"no","no","yes",273,18
"4001",2359,50,340,4,14,"no","no","no",273,18
"4002",2630,66,540,8,17,"yes","no","yes",273,18
"4003",2525,66,540,16,15,"no","no","yes",273,18
"4004",3158,66,170,4,17,"no","no","no",273,18
"4005",3395,100,1000,24,14,"yes","no","yes",273,18
"4006",3078,50,212,4,17,"no","no","no",273,18
"4007",1890,33,340,4,17,"no","no","yes",273,18
"4008",1875,33,425,8,15,"yes","yes","yes",273,18
"4009",2595,100,540,8,14,"yes","no","yes",273,18
"4010",2154,66,426,8,14,"yes","no","yes",273,18
"4011",2549,100,420,8,15,"yes","no","yes",273,18
"4012",2195,75,420,4,14,"yes","no","yes",273,18
"4013",1759,33,170,4,14,"no","no","no",273,18
"4014",2890,100,528,16,15,"yes","no","yes",273,18
"4015",2145,33,540,8,15,"yes","yes","yes",273,18
"4016",2845,66,540,16,17,"no","no","yes",273,18
"4017",1995,66,340,4,14,"yes","no","yes",225,19
"4018",2154,100,426,4,14,"no","no","yes",225,19
"4019",1795,66,420,4,14,"yes","no","yes",225,19
"4020",2290,33,528,16,14,"yes","no","yes",225,19
"4021",1895,66,425,8,14,"yes","no","yes",225,19
"4022",3194,33,1000,24,14,"yes","no","yes",225,19
"4023",2099,33,212,8,14,"yes","yes","yes",225,19
"4024",1590,33,426,4,15,"no","no","yes",225,19
"4025",3928,100,728,8,15,"no","no","no",225,19
"4026",2854,33,1000,24,14,"yes","no","yes",225,19
"4027",2493,33,528,16,14,"yes","yes","yes",225,19
"4028",2095,66,426,8,14,"yes","no","yes",225,19
"4029",2118,66,250,4,17,"yes","yes","yes",225,19
"4030",1899,66,340,8,15,"no","no","yes",225,19
"4031",1795,66,426,4,14,"no","no","yes",225,19
"4032",2099,66,420,8,14,"yes","no","yes",225,19
"4033",1395,33,214,4,14,"no","no","yes",225,19
"4034",1943,33,426,8,14,"yes","yes","yes",225,19
"4035",1890,66,426,4,15,"no","no","yes",225,19
"4036",2929,66,527,16,15,"yes","yes","yes",225,19
"4037",1299,33,340,4,14,"yes","no","yes",225,19
"4038",2795,100,528,16,14,"yes","no","yes",225,19
"4039",2499,100,420,8,15,"yes","no","yes",225,19
"4040",3543,100,1000,24,14,"yes","yes","yes",225,19
"4041",1195,25,214,4,14,"no","no","yes",225,19
"4042",3044,100,528,16,14,"yes","no","yes",225,19
"4043",2554,66,528,16,14,"yes","no","yes",225,19
"4044",1590,33,426,4,14,"no","no","yes",225,19
"4045",2499,100,428,16,15,"yes","no","yes",225,19
"4046",2629,100,420,8,15,"yes","yes","yes",225,19
"4047",2295,66,420,8,15,"yes","no","yes",225,19
"4048",1925,66,425,8,15,"yes","yes","yes",225,19
"4049",1805,33,425,8,15,"yes","no","yes",225,19
"4050",2599,100,527,8,15,"yes","no","yes",225,19
"4051",1495,33,426,4,14,"no","no","yes",225,19
"4052",1799,66,250,4,15,"yes","yes","yes",225,19
"4053",2643,66,528,16,14,"yes","yes","yes",225,19
"4054",2595,66,528,8,15,"yes","no","yes",225,19
"4055",2290,33,528,16,15,"yes","no","yes",225,19
"4056",1554,33,426,4,14,"no","no","yes",225,19
"4057",2543,100,426,8,14,"yes","yes","yes",225,19
"4058",2629,66,270,4,14,"no","no","no",225,19
"4059",2943,100,528,16,14,"yes","yes","yes",225,19
"4060",2644,100,426,8,14,"yes","no","yes",225,19
"4061",1645,33,214,4,14,"no","no","yes",225,19
"4062",2075,66,540,8,15,"yes","no","yes",225,19
"4063",1645,33,425,8,15,"no","no","yes",225,19
"4064",2190,100,426,4,15,"no","no","yes",225,19
"4065",3564,66,340,8,15,"no","no","no",225,19
"4066",3040,33,1000,24,15,"yes","no","yes",225,19
"4067",3108,33,270,4,17,"no","no","no",225,19
"4068",2243,66,426,8,14,"yes","yes","yes",225,19
"4069",2449,100,420,8,15,"yes","no","yes",225,19
"4070",2568,33,270,4,15,"no","no","no",225,19
"4071",1488,50,340,4,15,"no","no","yes",225,19
"4072",1970,66,420,4,14,"yes","yes","yes",225,19
"4073",3243,66,1000,24,14,"yes","yes","yes",225,19
"4074",3984,66,340,8,17,"no","no","no",225,19
"4075",2190,66,426,8,15,"yes","no","yes",225,19
"4076",2195,33,528,16,14,"yes","no","yes",225,19
"4077",1618,33,340,4,17,"no","no","yes",225,19
"4078",2149,66,420,8,15,"yes","no","yes",225,19
"4079",2795,33,1000,24,14,"yes","no","yes",225,19
"4080",1699,50,340,8,15,"yes","no","yes",225,19
"4081",3344,66,1000,24,14,"yes","no","yes",225,19
"4082",2190,100,426,4,14,"no","no","yes",225,19
"4083",2495,66,528,16,14,"yes","no","yes",225,19
"4084",1608,33,340,4,14,"yes","yes","yes",225,19
"4085",2099,66,527,8,14,"yes","no","yes",225,19
"4086",1288,33,340,4,15,"no","no","yes",225,19
"4087",1599,33,210,4,14,"yes","yes","yes",225,19
"4088",2634,66,540,8,17,"yes","no","yes",225,19
"4089",3349,100,728,8,14,"no","no","no",225,19
"4090",3093,33,1000,24,14,"yes","yes","yes",225,19
"4091",1399,33,210,4,14,"yes","yes","yes",225,19
"4092",3628,66,270,4,17,"no","no","no",225,19
"4093",3190,66,1000,24,14,"yes","no","yes",225,19
"4094",2194,33,426,8,14,"yes","no","yes",225,19
"4095",2899,66,527,16,15,"yes","no","yes",225,19
"4096",1549,33,210,4,15,"yes","yes","yes",225,19
"4097",2590,66,528,16,15,"yes","no","yes",225,19
"4098",2599,100,420,8,15,"yes","no","yes",225,19
"4099",2004,33,426,8,14,"yes","no","yes",225,19
"4100",2943,33,1000,24,14,"yes","yes","yes",225,19
"4101",2040,33,426,8,14,"yes","no","yes",225,19
"4102",2594,33,528,16,14,"yes","no","yes",225,19
"4103",1740,33,426,4,15,"no","no","yes",225,19
"4104",1595,33,340,8,14,"no","no","yes",225,19
"4105",2093,33,426,8,14,"yes","yes","yes",225,19
"4106",2109,33,270,4,14,"no","no","no",225,19
"4107",1499,33,340,4,14,"yes","yes","yes",225,19
"4108",1408,33,340,4,14,"yes","yes","yes",225,19
"4109",2344,100,426,4,14,"no","no","yes",225,19
"4110",1379,33,340,4,14,"no","no","yes",225,19
"4111",2590,66,528,16,14,"yes","no","yes",225,19
"4112",1475,33,200,4,14,"no","no","no",225,19
"4113",1704,33,426,4,14,"no","no","yes",225,19
"4114",2890,33,1000,24,15,"yes","no","yes",225,19
"4115",1468,33,340,4,15,"no","no","yes",225,19
"4116",1499,33,340,4,14,"yes","no","yes",225,19
"4117",2299,66,540,8,15,"yes","no","yes",225,19
"4118",2049,33,270,4,14,"no","no","no",225,19
"4119",1854,33,426,8,14,"yes","no","yes",225,19
"4120",2618,66,428,16,17,"yes","no","yes",225,19
"4121",1974,33,200,4,14,"yes","no","no",225,19
"4122",2890,66,1000,16,14,"yes","no","yes",225,19
"4123",1945,33,426,8,14,"yes","no","yes",225,19
"4124",1890,66,426,4,14,"no","no","yes",225,19
"4125",2190,66,426,8,14,"yes","no","yes",225,19
"4126",4348,100,728,8,17,"no","no","no",225,19
"4127",2749,100,420,8,17,"yes","no","yes",225,19
"4128",1679,33,340,8,15,"yes","no","yes",225,19
"4129",2590,33,1000,16,14,"yes","no","yes",225,19
"4130",2899,100,420,8,17,"yes","no","yes",225,19
"4131",2399,66,528,8,15,"yes","yes","yes",225,19
"4132",3040,33,1000,24,14,"yes","no","yes",225,19
"4133",2129,66,420,8,14,"yes","yes","yes",225,19
"4134",1899,66,420,8,14,"yes","yes","yes",225,19
"4135",1890,33,426,8,15,"yes","no","yes",225,19
"4136",2799,66,527,16,15,"yes","no","yes",225,19
"4137",2440,33,528,16,14,"yes","no","yes",225,19
"4138",1249,25,100,4,14,"no","no","no",225,19
"4139",2254,33,528,16,14,"yes","no","yes",225,19
"4140",2718,75,428,16,17,"yes","no","yes",225,19
"4141",2404,33,528,16,14,"yes","no","yes",225,19
"4142",1725,33,340,4,14,"no","no","no",225,19
"4143",2985,66,340,8,14,"no","no","no",225,19
"4144",1998,33,340,8,17,"yes","no","yes",225,19
"4145",1818,50,340,4,17,"no","no","yes",225,19
"4146",2049,66,420,8,15,"yes","yes","yes",225,19
"4147",1969,66,425,8,14,"no","no","yes",225,19
"4148",1199,33,210,4,14,"no","no","yes",225,19
"4149",1799,66,340,8,15,"yes","no","yes",225,19
"4150",1559,66,212,4,14,"no","no","yes",225,19
"4151",2479,100,420,8,15,"yes","yes","yes",225,19
"4152",2740,33,1000,16,14,"yes","no","yes",225,19
"4153",2345,33,528,16,14,"yes","no","yes",225,19
"4154",1395,25,340,8,14,"no","no","yes",225,19
"4155",1499,33,210,4,14,"yes","yes","yes",225,19
"4156",2199,66,420,8,14,"yes","no","yes",225,19
"4157",2344,66,426,8,14,"yes","no","yes",225,19
"4158",2095,100,426,4,14,"no","no","yes",225,19
"4159",2818,100,428,16,17,"yes","no","yes",225,19
"4160",3190,100,1000,16,14,"yes","no","yes",225,19
"4161",3208,66,270,4,15,"no","no","no",225,19
"4162",1379,25,212,4,14,"no","no","no",225,19
"4163",2890,100,528,16,15,"yes","no","yes",225,19
"4164",2199,100,340,8,14,"no","no","yes",225,19
"4165",2444,33,528,16,14,"yes","no","yes",225,19
"4166",2408,66,540,8,15,"yes","yes","yes",225,19
"4167",2549,100,420,8,15,"yes","no","yes",225,19
"4168",2999,100,528,16,17,"yes","no","yes",225,19
"4169",1445,50,214,4,14,"no","no","yes",225,19
"4170",3454,100,1000,24,14,"yes","no","yes",225,19
"4171",2349,100,420,8,15,"yes","no","yes",225,19
"4172",2170,66,340,4,14,"yes","yes","yes",225,19
"4173",2490,100,426,8,14,"yes","no","yes",225,19
"4174",2399,75,428,16,15,"yes","no","yes",225,19
"4175",2854,100,528,16,14,"yes","no","yes",225,19
"4176",1745,66,340,8,14,"no","no","yes",225,19
"4177",3199,66,527,16,17,"yes","no","yes",225,19
"4178",1999,66,340,8,14,"yes","yes","yes",225,19
"4179",1855,66,425,8,15,"yes","no","yes",225,19
"4180",3490,100,1000,24,14,"yes","no","yes",225,19
"4181",2470,66,420,8,15,"yes","yes","yes",225,19
"4182",2040,33,426,8,15,"yes","no","yes",225,19
"4183",1894,33,426,4,14,"no","no","yes",225,19
"4184",1999,66,527,8,14,"yes","yes","yes",225,19
"4185",1744,33,426,4,14,"no","no","yes",225,19
"4186",2890,33,1000,24,14,"yes","no","yes",225,19
"4187",1854,66,426,4,14,"no","no","yes",225,19
"4188",1645,33,426,4,14,"no","no","yes",225,19
"4189",2299,66,340,4,14,"no","no","no",225,19
"4190",1999,66,420,8,14,"yes","no","yes",225,19
"4191",2454,100,426,8,14,"yes","no","yes",225,19
"4192",2095,66,426,8,14,"yes","no","yes",225,19
"4193",3190,66,1000,24,15,"yes","no","yes",225,19
"4194",1999,66,420,8,14,"yes","yes","yes",225,19
"4195",2118,66,340,8,17,"yes","no","yes",225,19
"4196",2875,66,1080,16,15,"yes","no","yes",225,19
"4197",2449,100,527,8,15,"yes","no","yes",225,19
"4198",1199,33,340,4,14,"no","no","yes",225,19
"4199",3395,100,1000,24,14,"yes","no","yes",225,19
"4200",2154,66,426,8,14,"yes","no","yes",225,19
"4201",2945,33,1000,24,14,"yes","no","yes",225,19
"4202",2744,66,528,16,14,"yes","no","yes",225,19
"4203",3095,66,1000,24,14,"yes","no","yes",225,19
"4204",1399,50,340,4,14,"no","no","yes",225,19
"4205",1645,50,340,8,14,"no","no","yes",225,19
"4206",2099,66,420,8,14,"yes","yes","yes",225,19
"4207",2895,100,528,8,15,"yes","no","yes",225,19
"4208",3490,100,1000,24,15,"yes","no","yes",225,19
"4209",2628,33,270,4,15,"no","no","no",225,19
"4210",2988,33,270,4,17,"no","no","no",225,19
"4211",1399,50,210,4,14,"no","no","yes",225,19
"4212",2044,66,426,4,14,"no","no","yes",225,19
"4213",2378,25,212,4,17,"no","no","no",225,19
"4214",1945,33,426,8,14,"yes","no","yes",225,19
"4215",3195,66,1080,16,17,"yes","no","yes",225,19
"4216",1890,33,426,8,14,"yes","no","yes",225,19
"4217",2299,66,540,8,15,"yes","yes","yes",225,19
"4218",1958,25,212,4,15,"no","no","no",225,19
"4219",3299,66,1000,16,15,"yes","no","yes",225,19
"4220",2044,33,426,8,14,"yes","no","yes",225,19
"4221",1695,66,425,8,15,"no","no","yes",225,19
"4222",3644,100,1000,24,14,"yes","no","yes",225,19
"4223",1545,66,214,4,14,"no","no","yes",225,19
"4224",1740,33,426,4,14,"no","no","yes",225,19
"4225",2343,33,528,16,14,"yes","yes","yes",225,19
"4226",2018,50,340,8,17,"yes","no","yes",225,19
"4227",3044,33,1000,24,14,"yes","no","yes",225,19
"4228",3048,33,270,4,17,"no","no","no",225,19
"4229",2015,66,425,8,14,"yes","yes","yes",225,19
"4230",1989,33,270,4,14,"no","no","no",225,19
"4231",2299,66,428,16,15,"yes","no","yes",225,19
"4232",2890,100,528,16,14,"yes","no","yes",225,19
"4233",1469,33,212,4,14,"no","no","yes",225,19
"4234",3154,66,1000,24,14,"yes","no","yes",225,19
"4235",2688,33,270,4,15,"no","no","no",225,19
"4236",2440,33,528,16,15,"yes","no","yes",225,19
"4237",2490,100,426,8,15,"yes","no","yes",225,19
"4238",1798,33,340,4,17,"no","no","yes",225,19
"4239",1795,33,426,8,14,"yes","no","yes",225,19
"4240",3004,33,1000,24,14,"yes","no","yes",225,19
"4241",2395,100,426,8,14,"yes","no","yes",225,19
"4242",2059,66,540,8,14,"no","no","yes",248,20
"4243",1940,33,528,8,15,"yes","no","yes",248,20
"4244",1495,33,214,4,14,"no","no","yes",248,20
"4245",2890,33,1000,24,14,"yes","no","yes",248,20
"4246",2189,33,212,8,15,"yes","yes","yes",248,20
"4247",2494,33,720,16,14,"yes","no","yes",248,20
"4248",2895,100,528,16,14,"yes","no","yes",248,20
"4249",2644,33,720,16,14,"yes","no","yes",248,20
"4250",1445,50,214,4,14,"no","no","yes",248,20
"4251",2094,33,528,8,14,"yes","no","yes",248,20
"4252",1488,66,340,4,15,"no","no","yes",248,20
"4253",1649,66,212,4,15,"no","no","yes",248,20
"4254",2345,100,528,8,14,"yes","no","yes",248,20
"4255",3443,100,1000,24,14,"yes","yes","yes",248,20
"4256",2145,66,730,8,15,"yes","no","yes",248,20
"4257",2299,66,340,4,14,"no","no","no",248,20
"4258",1995,33,528,8,14,"yes","no","yes",248,20
"4259",1379,50,340,4,14,"no","no","yes",248,20
"4260",2988,33,270,4,17,"no","no","no",248,20
"4261",3093,33,1000,24,14,"yes","yes","yes",248,20
"4262",1798,50,340,4,17,"no","no","yes",248,20
"4263",1590,33,214,4,14,"no","no","yes",248,20
"4264",2015,66,540,8,15,"yes","yes","yes",248,20
"4265",1645,50,340,8,14,"no","no","yes",248,20
"4266",1299,50,340,4,14,"no","no","yes",248,20
"4267",1418,33,250,2,17,"no","no","yes",248,20
"4268",3390,100,1000,24,14,"yes","no","yes",248,20
"4269",3108,33,270,4,17,"no","no","no",248,20
"4270",2240,33,1000,8,14,"yes","no","yes",248,20
"4271",2190,66,528,8,15,"yes","no","yes",248,20
"4272",2643,66,720,16,14,"yes","yes","yes",248,20
"4273",1149,33,250,2,14,"no","no","yes",248,20
"4274",1965,66,540,8,14,"yes","no","yes",248,20
"4275",1698,50,340,4,14,"yes","yes","yes",248,20
"4276",1840,66,214,4,14,"no","no","yes",248,20
"4277",1999,66,528,8,15,"yes","no","yes",248,20
"4278",1548,33,340,4,14,"yes","yes","yes",248,20
"4279",2304,33,720,16,14,"yes","no","yes",248,20
"4280",3193,66,1000,24,14,"yes","yes","yes",248,20
"4281",2495,66,720,16,14,"yes","no","yes",248,20
"4282",2299,66,540,8,15,"yes","no","yes",248,20
"4283",3984,66,364,8,17,"no","no","no",248,20
"4284",1599,66,340,4,14,"yes","no","yes",248,20
"4285",2699,66,527,16,15,"yes","yes","yes",248,20
"4286",2985,66,364,8,14,"no","no","no",248,20
"4287",1595,33,340,8,14,"no","no","yes",248,20
"4288",1728,33,212,4,14,"yes","yes","yes",248,20
"4289",1995,66,340,4,14,"yes","no","yes",248,20
"4290",2298,33,528,8,17,"yes","no","yes",248,20
"4291",1974,33,200,4,14,"yes","yes","no",248,20
"4292",3564,66,364,8,15,"no","no","no",248,20
"4293",2915,66,1080,16,15,"yes","yes","yes",248,20
"4294",999,33,250,2,14,"no","no","yes",248,20
"4295",2098,50,340,4,17,"no","no","yes",248,20
"4296",1925,66,425,8,15,"yes","yes","yes",248,20
"4297",1799,66,340,4,15,"no","no","yes",248,20
"4298",3104,66,1000,24,14,"yes","no","yes",248,20
"4299",2045,66,730,8,15,"yes","no","yes",248,20
"4300",1918,33,340,4,17,"yes","yes","yes",248,20
"4301",1989,33,270,4,14,"no","no","no",248,20
"4302",2143,33,528,8,14,"yes","yes","yes",248,20
"4303",2095,66,528,8,14,"yes","no","yes",248,20
"4304",2099,66,420,8,14,"yes","yes","yes",248,20
"4305",1499,33,340,4,14,"yes","yes","yes",248,20
"4306",1999,66,420,8,14,"yes","yes","yes",248,20
"4307",2095,66,528,8,14,"yes","no","yes",248,20
"4308",2170,66,340,4,14,"yes","yes","yes",248,20
"4309",2265,66,730,8,15,"yes","no","yes",248,20
"4310",3045,66,1000,24,14,"yes","no","yes",248,20
"4311",2128,66,425,8,14,"yes","no","yes",248,20
"4312",2090,100,214,4,14,"no","no","yes",248,20
"4313",1199,33,340,4,14,"no","no","yes",248,20
"4314",2318,66,428,8,17,"yes","yes","yes",248,20
"4315",1399,66,340,4,14,"no","no","yes",248,20
"4316",2340,33,720,16,15,"yes","no","yes",248,20
"4317",3208,66,270,4,15,"no","no","no",248,20
"4318",1695,66,340,8,14,"no","no","yes",248,20
"4319",2628,33,270,4,15,"no","no","no",248,20
"4320",1088,33,250,2,15,"no","no","yes",248,20
"4321",2190,66,528,8,14,"yes","no","yes",248,20
"4322",3140,66,1000,24,14,"yes","no","yes",248,20
"4323",1149,33,340,4,14,"no","no","yes",248,20
"4324",2744,66,720,16,14,"yes","no","yes",248,20
"4325",2490,33,720,16,15,"yes","no","yes",248,20
"4326",2165,66,730,8,15,"yes","yes","yes",248,20
"4327",1998,66,340,8,15,"yes","yes","yes",248,20
"4328",1195,33,528,8,14,"yes","no","yes",248,20
"4329",2745,100,720,16,14,"yes","no","yes",248,20
"4330",1499,33,340,4,14,"yes","yes","yes",248,20
"4331",3040,33,1000,24,15,"yes","no","yes",248,20
"4332",2090,33,528,8,15,"yes","no","yes",248,20
"4333",1299,33,340,4,14,"yes","no","yes",248,20
"4334",1299,33,210,4,14,"no","no","yes",248,20
"4335",1399,33,210,4,14,"no","no","yes",248,20
"4336",1559,33,212,4,15,"no","no","yes",248,20
"4337",2945,33,1000,24,14,"yes","no","yes",248,20
"4338",1469,33,212,4,14,"no","no","yes",248,20
"4339",1725,33,340,4,14,"no","no","no",248,20
"4340",1568,33,250,2,17,"no","no","yes",248,20
"4341",2795,33,1000,24,14,"yes","no","yes",248,20
"4342",1395,33,214,4,14,"no","no","yes",248,20
"4343",1588,33,340,4,15,"yes","yes","yes",248,20
"4344",1740,33,214,4,15,"no","no","yes",248,20
"4345",2568,33,270,4,15,"no","no","no",248,20
"4346",1798,33,340,4,17,"no","no","yes",248,20
"4347",1899,66,420,8,14,"yes","yes","yes",248,20
"4348",1499,33,210,4,14,"yes","yes","yes",248,20
"4349",2543,33,720,16,14,"yes","yes","yes",248,20
"4350",1904,33,528,8,14,"yes","no","yes",248,20
"4351",2554,66,720,16,14,"yes","no","yes",248,20
"4352",3194,33,1000,24,14,"yes","no","yes",248,20
"4353",2099,66,420,8,14,"yes","yes","yes",248,20
"4354",2590,66,720,16,15,"yes","no","yes",248,20
"4355",4292,100,728,8,17,"no","no","no",248,20
"4356",1549,33,210,4,15,"yes","yes","yes",248,20
"4357",2295,66,420,8,15,"yes","no","yes",248,20
"4358",1840,66,214,4,15,"no","no","yes",248,20
"4359",2943,33,1000,24,14,"yes","yes","yes",248,20
"4360",1993,33,528,8,14,"yes","yes","yes",248,20
"4361",1599,66,340,8,15,"no","no","yes",248,20
"4362",2099,66,420,8,14,"yes","no","yes",248,20
"4363",1960,66,425,8,15,"yes","no","yes",248,20
"4364",3048,33,270,4,17,"no","no","no",248,20
"4365",1199,33,210,4,14,"no","no","yes",248,20
"4366",3872,100,728,8,15,"no","no","no",248,20
"4367",1408,33,340,4,14,"yes","yes","yes",248,20
"4368",1999,66,527,8,14,"yes","no","yes",248,20
"4369",1779,50,340,4,15,"no","no","yes",248,20
"4370",1195,25,214,4,14,"no","no","yes",248,20
"4371",1399,33,210,4,14,"yes","yes","yes",248,20
"4372",2590,66,720,16,14,"yes","no","yes",248,20
"4373",2098,33,340,4,17,"no","no","yes",248,20
"4374",1994,66,214,4,14,"no","no","yes",248,20
"4375",2840,100,720,16,14,"yes","no","yes",248,20
"4376",2105,66,730,8,15,"yes","yes","yes",248,20
"4377",1795,66,420,4,14,"yes","no","yes",248,20
"4378",1779,33,340,4,15,"no","no","yes",248,20
"4379",2740,100,1000,8,14,"yes","no","yes",248,20
"4380",2049,66,420,8,15,"yes","no","yes",248,20
"4381",2198,66,540,8,15,"yes","yes","yes",248,20
"4382",1818,66,340,4,17,"no","no","yes",248,20
"4383",2594,100,528,8,14,"yes","no","yes",248,20
"4384",1745,66,214,4,14,"no","no","yes",248,20
"4385",2390,33,1000,8,14,"yes","no","yes",248,20
"4386",3115,66,1080,16,17,"yes","no","yes",248,20
"4387",2499,66,527,16,15,"yes","no","yes",248,20
"4388",1888,66,340,8,15,"yes","no","yes",248,20
"4389",1379,33,340,4,14,"no","no","yes",248,20
"4390",1970,66,420,4,14,"yes","yes","yes",248,20
"4391",1999,66,540,8,15,"yes","no","yes",248,20
"4392",2059,66,425,8,15,"no","no","yes",248,20
"4393",2228,66,425,8,14,"yes","yes","yes",248,20
"4394",2440,100,528,8,15,"yes","no","yes",248,20
"4395",1554,33,214,4,14,"no","no","yes",248,20
"4396",1645,33,214,4,14,"no","no","yes",248,20
"4397",1818,66,212,4,14,"yes","yes","yes",248,20
"4398",1895,66,730,8,15,"no","no","yes",248,20
"4399",2799,66,527,16,17,"yes","no","yes",248,20
"4400",3294,66,1000,24,14,"yes","no","yes",248,20
"4401",1999,66,527,8,14,"yes","yes","yes",248,20
"4402",1618,33,340,4,17,"no","no","yes",248,20
"4403",1238,33,250,2,15,"no","no","yes",248,20
"4404",1395,25,340,8,14,"no","no","yes",248,20
"4405",2090,33,528,8,14,"yes","no","yes",248,20
"4406",2545,66,528,8,15,"yes","no","yes",248,20
"4407",2840,100,720,16,15,"yes","no","yes",248,20
"4408",1940,33,528,8,14,"yes","no","yes",248,20
"4409",2599,66,527,16,15,"yes","no","yes",248,20
"4410",3628,66,270,4,17,"no","no","no",248,20
"4411",3004,33,1000,24,14,"yes","no","yes",248,20
"4412",3295,100,1000,24,14,"yes","no","yes",248,20
"4413",2244,100,214,4,14,"no","no","yes",248,20
"4414",2634,66,540,8,17,"yes","no","yes",248,20
"4415",2154,66,528,8,14,"yes","no","yes",248,20
"4416",2109,33,270,4,14,"no","no","no",248,20
"4417",1628,33,212,4,14,"yes","no","yes",248,20
"4418",1704,33,214,4,14,"no","no","yes",248,20
"4419",1468,33,340,4,15,"no","no","yes",248,20
"4420",1599,33,210,4,14,"yes","yes","yes",248,20
"4421",1894,33,214,4,14,"no","no","yes",248,20
"4422",3354,100,1000,24,14,"yes","no","yes",248,20
"4423",1249,25,100,4,14,"no","no","no",248,20
"4424",2490,33,720,16,14,"yes","no","yes",248,20
"4425",2493,100,528,8,14,"yes","yes","yes",248,20
"4426",1740,33,214,4,14,"no","no","yes",248,20
"4427",3390,100,1000,24,15,"yes","no","yes",248,20
"4428",3293,100,728,8,14,"no","no","no",248,20
"4429",2804,100,720,16,14,"yes","no","yes",248,20
"4430",1995,100,214,4,14,"no","no","yes",248,20
"4431",1969,66,425,8,14,"no","no","yes",248,20
"4432",2393,33,720,16,14,"yes","yes","yes",248,20
"4433",3140,66,1000,24,15,"yes","no","yes",248,20
"4434",1744,33,214,4,14,"no","no","yes",248,20
"4435",2404,100,528,8,14,"yes","no","yes",248,20
"4436",1999,66,420,8,14,"yes","no","yes",248,20
"4437",2118,66,340,4,17,"no","no","yes",248,20
"4438",1718,66,212,4,14,"yes","no","yes",248,20
"4439",2415,66,730,8,17,"yes","no","yes",248,20
"4440",2795,66,1080,16,15,"yes","no","yes",248,20
"4441",2994,100,720,16,14,"yes","no","yes",248,20
"4442",1590,33,214,4,15,"no","no","yes",248,20
"4443",3544,100,1000,24,14,"yes","no","yes",248,20
"4444",2049,33,270,4,14,"no","no","no",248,20
"4445",1559,66,212,4,14,"no","no","yes",248,20
"4446",2688,33,270,4,15,"no","no","no",248,20
"4447",2090,100,214,4,15,"no","no","yes",248,20
"4448",2055,66,730,8,14,"yes","no","yes",248,20
"4449",3044,33,1000,24,14,"yes","no","yes",248,20
"4450",2054,100,214,4,14,"no","no","yes",248,20
"4451",2408,66,540,8,15,"yes","yes","yes",248,20
"4452",2454,33,720,16,14,"yes","no","yes",248,20
"4453",2245,33,720,16,14,"yes","no","yes",248,20
"4454",2049,66,420,8,15,"yes","yes","yes",248,20
"4455",1899,66,420,8,14,"yes","no","yes",248,20
"4456",2318,66,528,8,17,"yes","no","yes",248,20
"4457",2490,66,1000,8,14,"yes","no","yes",248,20
"4458",3099,66,1000,16,15,"yes","no","yes",248,20
"4459",1799,66,340,8,14,"yes","no","yes",248,20
"4460",2890,33,1000,24,15,"yes","no","yes",248,20
"4461",2218,66,340,8,17,"yes","no","yes",248,20
"4462",2244,33,528,8,14,"yes","no","yes",248,20
"4463",2344,66,528,8,14,"yes","no","yes",248,20
"4464",2054,33,528,8,14,"yes","no","yes",248,20
"4465",1995,66,425,8,14,"yes","yes","yes",248,20
"4466",1845,33,528,8,14,"yes","no","yes",248,20
"4467",1875,66,425,8,14,"yes","no","yes",248,20
"4468",2095,66,730,8,15,"yes","no","yes",248,20
"4469",2629,66,270,4,14,"no","no","no",248,20
"4470",1495,66,214,4,14,"no","no","yes",248,20
"4471",2395,33,720,16,14,"yes","no","yes",248,20
"4472",2099,33,212,8,14,"yes","yes","yes",248,20
"4473",2854,33,1000,24,14,"yes","no","yes",248,20
"4474",1804,66,214,4,14,"no","no","yes",248,20
"4475",2340,33,720,16,14,"yes","no","yes",248,20
"4476",3040,33,1000,24,14,"yes","no","yes",248,20
"4477",1468,50,340,4,15,"no","no","yes",248,20
"4478",3485,66,2100,16,15,"yes","no","yes",248,20
"4479",2470,66,420,8,15,"yes","yes","yes",248,20
"4480",1999,66,340,8,14,"yes","yes","yes",248,20
"4481",2440,100,528,8,14,"yes","no","yes",248,20
"4482",1288,33,340,4,15,"no","no","yes",248,20
"4483",1708,66,340,4,14,"yes","yes","yes",248,20
"4484",1349,33,210,4,15,"no","no","yes",248,20
"4485",2243,66,528,8,14,"yes","yes","yes",248,20
"4486",1979,33,528,8,15,"yes","no","yes",248,20
"4487",1475,33,200,4,14,"no","no","no",248,20
"4488",2893,100,720,16,14,"yes","yes","yes",248,20
"4489",1999,66,428,8,15,"yes","yes","yes",248,20
"4490",2604,50,720,16,15,"yes","no","yes",205,21
"4491",2799,66,527,16,17,"yes","no","yes",205,21
"4492",2354,33,720,16,15,"yes","no","yes",205,21
"4493",1740,33,214,4,14,"no","no","yes",205,21
"4494",3095,50,1000,24,15,"yes","no","yes",205,21
"4495",1999,66,428,8,15,"yes","yes","yes",205,21
"4496",2504,33,720,16,15,"yes","no","yes",205,21
"4497",1999,66,528,8,15,"yes","no","yes",205,21
"4498",3044,66,720,16,15,"yes","no","yes",205,21
"4499",1778,33,340,4,17,"no","no","yes",205,21
"4500",2090,66,214,4,14,"no","no","yes",205,21
"4501",1499,33,340,4,15,"yes","yes","yes",205,21
"4502",1489,66,212,4,14,"no","no","yes",205,21
"4503",2995,33,1000,24,15,"yes","no","yes",205,21
"4504",1759,66,540,8,14,"no","no","yes",205,21
"4505",1395,33,214,4,14,"no","no","yes",205,21
"4506",1890,33,528,8,15,"yes","no","yes",205,21
"4507",1299,33,210,4,14,"no","no","yes",205,21
"4508",1748,66,212,4,14,"yes","yes","yes",205,21
"4509",1545,33,340,4,14,"yes","yes","yes",205,21
"4510",2593,33,720,16,15,"yes","yes","yes",205,21
"4511",1840,50,214,4,14,"no","no","yes",205,21
"4512",1945,33,528,8,14,"yes","no","yes",205,21
"4513",1704,33,214,4,14,"no","no","yes",205,21
"4514",3294,33,1000,24,15,"yes","no","yes",205,21
"4515",1359,50,340,4,14,"no","no","yes",205,21
"4516",1778,50,340,4,17,"no","no","yes",205,21
"4517",2390,33,720,16,15,"yes","no","yes",205,21
"4518",2244,66,214,4,14,"no","no","yes",205,21
"4519",2854,66,720,16,15,"yes","no","yes",205,21
"4520",2218,66,340,8,17,"yes","no","yes",205,21
"4521",1759,33,340,4,15,"no","no","yes",205,21
"4522",3440,66,1000,24,15,"yes","no","yes",205,21
"4523",1795,33,528,8,14,"yes","no","yes",205,21
"4524",1745,50,340,4,14,"yes","yes","yes",205,21
"4525",1804,50,214,4,14,"no","no","yes",205,21
"4526",2443,33,528,8,14,"yes","yes","yes",205,21
"4527",2090,66,214,4,15,"no","no","yes",205,21
"4528",1899,66,420,8,14,"yes","yes","yes",205,21
"4529",1395,50,214,4,14,"no","no","yes",205,21
"4530",3204,50,1000,24,15,"yes","no","yes",205,21
"4531",2495,66,528,8,14,"yes","no","yes",205,21
"4532",1799,66,340,4,15,"no","no","yes",205,21
"4533",2940,50,720,16,17,"yes","no","yes",205,21
"4534",3090,33,1000,24,15,"yes","no","yes",205,21
"4535",3193,33,1000,24,15,"yes","yes","yes",205,21
"4536",1648,66,212,4,14,"yes","no","yes",205,21
"4537",2240,33,1000,8,14,"yes","no","yes",205,21
"4538",2694,33,720,16,15,"yes","no","yes",205,21
"4539",2054,66,214,4,14,"no","no","yes",205,21
"4540",1495,66,214,4,14,"no","no","yes",205,21
"4541",2340,50,1000,8,14,"yes","no","yes",205,21
"4542",2390,66,528,8,14,"yes","no","yes",205,21
"4543",1828,66,425,8,14,"yes","no","yes",205,21
"4544",1499,33,340,4,14,"yes","yes","yes",205,21
"4545",2040,33,528,8,14,"yes","no","yes",205,21
"4546",1999,66,527,8,14,"yes","yes","yes",205,21
"4547",1759,66,425,8,15,"no","no","yes",205,21
"4548",1349,33,210,4,15,"no","no","yes",205,21
"4549",2544,33,720,16,15,"yes","no","yes",205,21
"4550",3345,66,1000,24,15,"yes","no","yes",205,21
"4551",3144,33,1000,24,15,"yes","no","yes",205,21
"4552",2568,33,270,4,15,"no","no","no",205,21
"4553",3543,66,1000,24,15,"yes","yes","yes",205,21
"4554",1999,66,420,8,14,"yes","yes","yes",205,21
"4555",1448,33,340,4,15,"no","no","yes",205,21
"4556",2295,66,528,8,14,"yes","no","yes",205,21
"4557",1395,33,340,4,14,"yes","no","yes",205,21
"4558",2099,66,420,8,14,"yes","no","yes",205,21
"4559",3540,50,1000,24,15,"yes","no","yes",205,21
"4560",2943,66,528,8,14,"yes","yes","yes",205,21
"4561",1999,66,420,8,14,"yes","no","yes",205,21
"4562",3043,33,1000,24,15,"yes","yes","yes",205,21
"4563",1899,66,420,8,14,"yes","no","yes",205,21
"4564",3293,50,1000,24,15,"yes","yes","yes",205,21
"4565",1979,33,528,8,15,"yes","no","yes",205,21
"4566",2443,33,720,16,15,"yes","yes","yes",205,21
"4567",1495,33,214,4,14,"no","no","yes",205,21
"4568",1945,33,528,8,14,"yes","no","yes",205,21
"4569",2954,33,1000,24,15,"yes","no","yes",205,21
"4570",2189,33,212,8,15,"yes","yes","yes",205,21
"4571",2118,66,340,4,17,"no","no","yes",205,21
"4572",2298,33,528,8,17,"yes","no","yes",205,21
"4573",2045,66,528,8,14,"yes","no","yes",205,21
"4574",1599,66,340,8,15,"no","no","yes",205,21
"4575",2194,33,528,8,14,"yes","no","yes",205,21
"4576",2590,66,1000,8,14,"yes","no","yes",205,21
"4577",1568,33,250,2,17,"no","no","yes",205,21
"4578",1918,33,340,4,17,"yes","yes","yes",205,21
"4579",1288,33,340,4,15,"no","no","yes",205,21
"4580",2499,66,527,16,15,"yes","no","yes",205,21
"4581",2693,50,720,16,15,"yes","yes","yes",205,21
"4582",2445,33,720,16,15,"yes","no","yes",205,21
"4583",2593,33,528,8,14,"yes","yes","yes",205,21
"4584",2640,50,720,16,15,"yes","no","yes",205,21
"4585",2308,66,730,8,15,"yes","yes","yes",205,21
"4586",1799,66,340,8,14,"yes","no","yes",205,21
"4587",1669,66,425,8,14,"no","no","yes",205,21
"4588",2795,66,720,16,15,"yes","no","yes",205,21
"4589",1588,33,340,4,15,"yes","yes","yes",205,21
"4590",1695,66,340,8,14,"no","no","yes",205,21
"4591",1999,66,420,8,14,"yes","yes","yes",205,21
"4592",2104,50,528,8,14,"yes","no","yes",205,21
"4593",2140,50,528,8,14,"yes","no","yes",205,21
"4594",1199,33,210,4,14,"no","no","yes",205,21
"4595",2299,66,340,4,14,"no","no","no",205,21
"4596",2045,50,528,8,14,"yes","no","yes",205,21
"4597",1399,66,340,4,14,"no","no","yes",205,21
"4598",1999,66,527,8,14,"yes","no","yes",205,21
"4599",2049,66,420,8,15,"yes","yes","yes",205,21
"4600",1549,33,210,4,15,"yes","yes","yes",205,21
"4601",1599,33,210,4,14,"yes","yes","yes",205,21
"4602",1740,33,214,4,15,"no","no","yes",205,21
"4603",2294,50,528,8,14,"yes","no","yes",205,21
"4604",1854,33,528,8,14,"yes","no","yes",205,21
"4605",1599,66,540,4,14,"yes","no","yes",205,21
"4606",2890,66,720,16,15,"yes","no","yes",205,21
"4607",1759,50,340,4,15,"no","no","yes",205,21
"4608",1408,33,340,4,14,"yes","yes","yes",205,21
"4609",1928,66,425,8,14,"yes","yes","yes",205,21
"4610",2295,33,720,16,15,"yes","no","yes",205,21
"4611",1595,33,340,8,14,"no","no","yes",205,21
"4612",3790,66,1000,24,15,"yes","no","yes",205,21
"4613",1299,33,340,4,14,"yes","no","yes",205,21
"4614",2544,66,528,8,14,"yes","no","yes",205,21
"4615",2140,50,528,8,15,"yes","no","yes",205,21
"4616",2199,66,528,8,15,"yes","yes","yes",205,21
"4617",3099,66,1000,16,15,"yes","no","yes",205,21
"4618",1590,33,214,4,14,"no","no","yes",205,21
"4619",3190,66,720,16,17,"yes","no","yes",205,21
"4620",1890,33,528,8,14,"yes","no","yes",205,21
"4621",3190,50,1000,24,15,"yes","no","yes",205,21
"4622",1399,33,210,4,14,"no","no","yes",205,21
"4623",1888,66,340,8,15,"yes","no","yes",205,21
"4624",1590,33,214,4,15,"no","no","yes",205,21
"4625",2693,50,528,8,14,"yes","yes","yes",205,21
"4626",1448,50,340,4,15,"no","no","yes",205,21
"4627",1195,25,214,4,14,"no","no","yes",205,21
"4628",2078,50,340,4,17,"no","no","yes",205,21
"4629",3394,50,1000,24,15,"yes","no","yes",205,21
"4630",2199,66,730,8,15,"yes","no","yes",205,21
"4631",1199,33,340,4,14,"no","no","yes",205,21
"4632",3454,66,1000,24,15,"yes","no","yes",205,21
"4633",2318,66,528,8,17,"yes","no","yes",205,21
"4634",1795,66,340,4,14,"yes","no","yes",205,21
"4635",2599,66,527,16,15,"yes","no","yes",205,21
"4636",2794,50,720,16,15,"yes","no","yes",205,21
"4637",3349,100,728,8,14,"no","no","no",205,21
"4638",1745,50,214,4,14,"no","no","yes",205,21
"4639",2390,66,528,8,15,"yes","no","yes",205,21
"4640",1818,66,340,4,17,"no","no","yes",205,21
"4641",2049,66,420,8,15,"yes","no","yes",205,21
"4642",3104,33,1000,24,15,"yes","no","yes",205,21
"4643",2545,50,720,16,15,"yes","no","yes",205,21
"4644",1249,25,100,4,14,"no","no","no",205,21
"4645",1725,33,340,4,14,"no","no","no",205,21
"4646",3928,100,728,8,15,"no","no","no",205,21
"4647",2354,66,528,8,14,"yes","no","yes",205,21
"4648",1995,66,214,4,14,"no","no","yes",205,21
"4649",2988,33,270,4,17,"no","no","no",205,21
"4650",2895,100,528,16,14,"yes","no","yes",205,21
"4651",2940,33,1000,24,15,"yes","no","yes",205,21
"4652",1399,33,210,4,14,"yes","yes","yes",205,21
"4653",1595,50,340,4,14,"yes","no","yes",205,21
"4654",2699,66,527,16,15,"yes","yes","yes",205,21
"4655",1994,50,214,4,14,"no","no","yes",205,21
"4656",2840,33,720,16,17,"yes","no","yes",205,21
"4657",1488,66,340,4,15,"no","no","yes",205,21
"4658",2318,66,428,8,17,"yes","yes","yes",205,21
"4659",1840,50,214,4,15,"no","no","yes",205,21
"4660",1099,33,340,4,14,"no","no","yes",205,21
"4661",1238,33,250,2,15,"no","no","yes",205,21
"4662",3644,66,1000,24,15,"yes","no","yes",205,21
"4663",3290,33,1000,24,15,"yes","no","yes",205,21
"4664",1989,33,270,4,14,"no","no","no",205,21
"4665",1499,33,210,4,14,"yes","yes","yes",205,21
"4666",1579,66,212,4,15,"no","no","yes",205,21
"4667",1475,33,200,4,14,"no","no","no",205,21
"4668",1708,66,540,4,14,"yes","yes","yes",205,21
"4669",2845,33,1000,24,15,"yes","no","yes",205,21
"4670",1744,33,214,4,14,"no","no","yes",205,21
"4671",1195,25,128,4,14,"no","no","yes",205,21
"4672",2540,33,720,16,15,"yes","no","yes",205,21
"4673",1595,50,340,8,14,"no","no","yes",205,21
"4674",1149,33,250,2,14,"no","no","yes",205,21
"4675",2099,66,420,8,14,"yes","yes","yes",205,21
"4676",2943,66,720,16,15,"yes","yes","yes",205,21
"4677",2078,33,340,4,17,"no","no","yes",205,21
"4678",2534,66,730,8,17,"yes","no","yes",205,21
"4679",1645,33,214,4,14,"no","no","yes",205,21
"4680",2090,33,1000,8,14,"yes","no","yes",205,21
"4681",1395,25,340,8,14,"no","no","yes",205,21
"4682",2099,66,420,8,14,"yes","yes","yes",205,21
"4683",1618,33,340,4,17,"no","no","yes",205,21
"4684",2099,33,212,8,14,"yes","yes","yes",205,21
"4685",2044,33,528,8,14,"yes","no","yes",205,21
"4686",4348,100,728,8,17,"no","no","no",205,21
"4687",2004,33,528,8,14,"yes","no","yes",205,21
"4688",1554,33,214,4,14,"no","no","yes",205,21
"4689",1945,66,340,4,14,"yes","yes","yes",205,21
"4690",3440,33,1000,24,15,"yes","no","yes",205,21
"4691",1894,33,214,4,14,"no","no","yes",205,21
"4692",1359,33,340,4,14,"no","no","yes",205,21
"4693",2690,33,720,16,17,"yes","no","yes",205,21
"4694",2040,33,528,8,15,"yes","no","yes",205,21
"4695",3768,100,728,8,15,"no","no","no",162,22
"4696",3244,33,1000,24,15,"yes","no","yes",162,22
"4697",1899,66,420,8,14,"yes","no","yes",162,22
"4698",1740,50,214,4,15,"no","no","yes",162,22
"4699",1590,33,214,4,14,"no","no","yes",162,22
"4700",2940,33,1000,24,15,"yes","no","yes",162,22
"4701",3189,100,728,8,14,"no","no","no",162,22
"4702",1899,66,425,8,14,"yes","no","yes",162,22
"4703",2495,66,720,16,15,"yes","no","yes",162,22
"4704",2295,33,720,16,15,"yes","no","yes",162,22
"4705",2690,33,720,16,17,"yes","no","yes",162,22
"4706",1945,33,528,8,14,"yes","no","yes",162,22
"4707",1890,33,528,8,14,"yes","no","yes",162,22
"4708",2845,100,528,16,14,"yes","no","yes",162,22
"4709",2445,50,720,16,15,"yes","no","yes",162,22
"4710",2340,33,1000,24,17,"yes","no","yes",162,22
"4711",1819,33,270,4,14,"no","no","no",162,22
"4712",2445,33,720,16,15,"yes","no","yes",162,22
"4713",2840,33,720,16,17,"yes","no","yes",162,22
"4714",2398,33,270,4,15,"no","no","no",162,22
"4715",2699,66,527,16,17,"yes","no","yes",162,22
"4716",2040,50,528,8,15,"yes","no","yes",162,22
"4717",2240,33,1000,8,14,"yes","no","yes",162,22
"4718",2499,66,527,16,15,"yes","no","yes",162,22
"4719",2194,50,528,8,14,"yes","no","yes",162,22
"4720",2643,66,720,16,15,"yes","yes","yes",162,22
"4721",2504,50,720,16,15,"yes","no","yes",162,22
"4722",1999,66,540,8,15,"yes","no","yes",162,22
"4723",2199,66,528,8,15,"yes","yes","yes",162,22
"4724",2554,66,720,16,15,"yes","no","yes",162,22
"4725",1395,33,214,4,14,"no","no","yes",162,22
"4726",2240,50,1000,8,14,"yes","no","yes",162,22
"4727",1554,33,214,4,14,"no","no","yes",162,22
"4728",1695,66,214,4,14,"no","no","yes",162,22
"4729",3143,50,1000,24,15,"yes","yes","yes",162,22
"4730",3244,50,1000,24,15,"yes","no","yes",162,22
"4731",2694,50,720,16,15,"yes","no","yes",162,22
"4732",2593,50,720,16,15,"yes","yes","yes",162,22
"4733",1740,33,214,4,14,"no","no","yes",162,22
"4734",2004,33,528,8,14,"yes","no","yes",162,22
"4735",1590,33,214,4,15,"no","no","yes",162,22
"4736",1595,33,340,8,14,"no","no","yes",162,22
"4737",1099,33,340,4,14,"no","no","yes",162,22
"4738",2443,33,528,8,14,"yes","yes","yes",162,22
"4739",3090,33,1000,24,15,"yes","no","yes",162,22
"4740",2904,33,1000,24,15,"yes","no","yes",162,22
"4741",1595,50,340,8,14,"no","no","yes",162,22
"4742",1944,66,214,4,14,"no","no","yes",162,22
"4743",1995,66,420,8,15,"yes","no","yes",162,22
"4744",2993,33,1000,24,15,"yes","yes","yes",162,22
"4745",2593,33,720,16,15,"yes","yes","yes",162,22
"4746",2049,66,420,8,15,"yes","yes","yes",162,22
"4747",1999,66,527,8,14,"yes","yes","yes",162,22
"4748",1349,33,270,4,14,"no","no","no",162,22
"4749",2399,66,527,16,15,"yes","no","yes",162,22
"4750",2593,50,528,8,14,"yes","yes","yes",162,22
"4751",1299,33,340,4,14,"yes","no","yes",162,22
"4752",1799,66,420,8,14,"yes","no","yes",162,22
"4753",2599,66,527,16,15,"yes","yes","yes",162,22
"4754",2845,33,1000,24,15,"yes","no","yes",162,22
"4755",1999,66,420,8,14,"yes","yes","yes",162,22
"4756",4188,100,728,8,17,"no","no","no",162,22
"4757",1499,50,545,4,15,"yes","no","yes",162,22
"4758",3143,33,1000,24,15,"yes","yes","yes",162,22
"4759",3394,66,1000,24,15,"yes","no","yes",162,22
"4760",1195,25,214,4,14,"no","no","yes",162,22
"4761",2004,50,528,8,14,"yes","no","yes",162,22
"4762",1395,50,214,4,14,"no","no","yes",162,22
"4763",1495,33,214,4,14,"no","no","yes",162,22
"4764",1599,66,340,8,15,"no","no","yes",162,22
"4765",1549,33,210,4,15,"yes","yes","yes",162,22
"4766",1945,50,528,8,14,"yes","no","yes",162,22
"4767",2040,33,528,8,15,"yes","no","yes",162,22
"4768",3104,66,1000,24,15,"yes","no","yes",162,22
"4769",2090,66,528,8,14,"yes","no","yes",162,22
"4770",3193,66,1000,24,15,"yes","yes","yes",162,22
"4771",2054,66,528,8,14,"yes","no","yes",162,22
"4772",2445,66,528,8,14,"yes","no","yes",162,22
"4773",1704,50,214,4,14,"no","no","yes",162,22
"4774",2890,66,720,16,17,"yes","no","yes",162,22
"4775",2199,66,730,8,15,"yes","no","yes",162,22
"4776",2995,50,1000,24,15,"yes","no","yes",162,22
"4777",1999,66,720,8,15,"yes","no","yes",162,22
"4778",1795,33,528,8,14,"yes","no","yes",162,22
"4779",3240,33,1000,24,17,"yes","no","yes",162,22
"4780",1395,25,340,8,14,"no","no","yes",162,22
"4781",1945,50,528,8,14,"yes","no","yes",162,22
"4782",1704,33,214,4,14,"no","no","yes",162,22
"4783",1445,66,214,4,14,"no","no","yes",162,22
"4784",2195,66,420,8,15,"yes","yes","yes",162,22
"4785",2244,66,528,8,14,"yes","no","yes",162,22
"4786",3440,66,1000,24,17,"yes","no","yes",162,22
"4787",2040,33,528,8,14,"yes","no","yes",162,22
"4788",1999,66,420,8,14,"yes","yes","yes",162,22
"4789",2590,66,720,16,15,"yes","no","yes",162,22
"4790",1949,66,420,8,15,"yes","no","yes",162,22
"4791",1899,66,420,8,14,"yes","yes","yes",162,22
"4792",1599,33,540,4,14,"yes","no","yes",162,22
"4793",2290,66,1000,8,14,"yes","no","yes",162,22
"4794",2694,33,720,16,15,"yes","no","yes",162,22
"4795",1199,33,428,4,15,"no","no","yes",162,22
"4796",3045,66,1000,24,15,"yes","no","yes",162,22
"4797",1299,33,428,4,15,"no","no","yes",162,22
"4798",2744,66,720,16,15,"yes","no","yes",162,22
"4799",1529,33,420,4,14,"no","no","no",162,22
"4800",1799,66,545,8,15,"yes","no","yes",162,22
"4801",2308,66,730,8,15,"yes","yes","yes",162,22
"4802",2643,66,528,8,14,"yes","yes","yes",162,22
"4803",1645,33,214,4,14,"no","no","yes",162,22
"4804",1499,33,340,4,15,"yes","yes","yes",162,22
"4805",1854,33,528,8,14,"yes","no","yes",162,22
"4806",1408,33,340,4,14,"yes","yes","yes",162,22
"4807",2840,50,720,16,17,"yes","no","yes",162,22
"4808",2995,33,1000,24,15,"yes","no","yes",162,22
"4809",1744,33,214,4,14,"no","no","yes",162,22
"4810",1799,66,212,8,14,"yes","no","yes",162,22
"4811",1740,33,214,4,15,"no","no","yes",162,22
"4812",2249,66,270,8,14,"no","no","no",162,22
"4813",1299,33,210,4,14,"no","no","yes",162,22
"4814",1790,66,214,4,15,"no","no","yes",162,22
"4815",1199,33,210,4,14,"no","no","yes",162,22
"4816",1645,50,214,4,14,"no","no","yes",162,22
"4817",2593,33,528,8,14,"yes","yes","yes",162,22
"4818",1499,33,428,4,14,"yes","yes","yes",162,22
"4819",1740,50,214,4,14,"no","no","yes",162,22
"4820",2495,66,810,16,15,"yes","yes","yes",162,22
"4821",1995,66,528,8,14,"yes","no","yes",162,22
"4822",2544,33,720,16,15,"yes","no","yes",162,22
"4823",1899,66,527,8,14,"yes","no","yes",162,22
"4824",1999,66,545,8,15,"yes","yes","yes",162,22
"4825",1890,33,528,8,15,"yes","no","yes",162,22
"4826",2040,50,528,8,14,"yes","no","yes",162,22
"4827",1708,66,540,4,14,"yes","yes","yes",162,22
"4828",1499,33,210,4,14,"yes","yes","yes",162,22
"4829",3094,33,1000,24,15,"yes","no","yes",162,22
"4830",2044,33,528,8,14,"yes","no","yes",162,22
"4831",1790,66,214,4,14,"no","no","yes",162,22
"4832",2145,66,420,8,15,"yes","yes","yes",162,22
"4833",1894,50,214,4,14,"no","no","yes",162,22
"4834",3054,50,1000,24,15,"yes","no","yes",162,22
"4835",3054,33,1000,24,15,"yes","no","yes",162,22
"4836",2899,66,1000,16,15,"yes","no","yes",162,22
"4837",1995,66,528,8,14,"yes","no","yes",162,22
"4838",3090,50,1000,24,15,"yes","no","yes",162,22
"4839",2354,33,720,16,15,"yes","no","yes",162,22
"4840",1894,33,214,4,14,"no","no","yes",162,22
"4841",2194,33,528,8,14,"yes","no","yes",162,22
"4842",2390,33,720,16,15,"yes","no","yes",162,22
"4843",1349,33,210,4,15,"no","no","yes",162,22
"4844",1399,33,210,4,14,"yes","yes","yes",162,22
"4845",2504,33,720,16,15,"yes","no","yes",162,22
"4846",1645,66,340,8,14,"no","no","yes",162,22
"4847",2390,50,1000,24,17,"yes","no","yes",162,22
"4848",2534,66,730,8,17,"yes","no","yes",162,22
"4849",3140,66,1000,24,15,"yes","no","yes",162,22
"4850",2090,33,1000,8,14,"yes","no","yes",162,22
"4851",2443,33,720,16,15,"yes","yes","yes",162,22
"4852",2540,50,720,16,15,"yes","no","yes",162,22
"4853",1754,66,214,4,14,"no","no","yes",162,22
"4854",2818,33,270,4,17,"no","no","no",162,22
"4855",2540,33,720,16,15,"yes","no","yes",162,22
"4856",2090,66,528,8,15,"yes","no","yes",162,22
"4857",2940,33,1000,24,15,"yes","no","yes",191,23
"4858",2304,33,720,16,15,"yes","no","yes",191,23
"4859",2440,33,720,16,15,"yes","no","yes",191,23
"4860",1769,66,540,8,14,"no","no","yes",191,23
"4861",2494,33,720,16,15,"yes","no","yes",191,23
"4862",3043,66,1000,24,15,"yes","yes","yes",191,23
"4863",1195,33,214,4,14,"no","no","yes",191,23
"4864",1545,33,214,4,14,"no","no","yes",191,23
"4865",1840,33,528,8,15,"yes","no","yes",191,23
"4866",1940,33,528,8,14,"yes","no","yes",191,23
"4867",1299,33,428,4,15,"no","no","yes",191,23
"4868",1709,50,540,8,15,"no","no","yes",191,23
"4869",2340,33,720,16,15,"yes","no","yes",191,23
"4870",2245,33,720,16,15,"yes","no","yes",191,23
"4871",1299,33,340,4,14,"yes","no","yes",191,23
"4872",1868,66,425,8,14,"yes","yes","yes",191,23
"4873",3094,33,1000,24,15,"yes","no","yes",191,23
"4874",1859,66,540,8,15,"no","no","yes",191,23
"4875",1299,33,212,4,14,"no","no","yes",191,23
"4876",2399,66,730,16,15,"yes","no","yes",191,23
"4877",1999,66,720,8,15,"yes","no","yes",191,23
"4878",1669,66,425,8,14,"no","no","yes",191,23
"4879",1999,66,545,8,15,"yes","yes","yes",191,23
"4880",2598,66,730,16,15,"yes","yes","yes",191,23
"4881",1690,66,214,4,15,"no","no","yes",191,23
"4882",1990,66,528,8,14,"yes","no","yes",191,23
"4883",2093,33,528,8,14,"yes","yes","yes",191,23
"4884",1940,33,528,8,15,"yes","no","yes",191,23
"4885",1999,66,730,8,15,"yes","no","yes",191,23
"4886",2334,66,730,8,17,"yes","no","yes",191,23
"4887",2543,66,720,16,15,"yes","yes","yes",191,23
"4888",2790,66,720,16,17,"yes","no","yes",191,23
"4889",1818,66,425,8,14,"yes","no","yes",191,23
"4890",1599,66,340,8,15,"no","no","yes",191,23
"4891",3240,50,1000,24,17,"yes","no","yes",191,23
"4892",2004,33,528,8,14,"yes","no","yes",191,23
"4893",2140,33,1000,8,14,"yes","no","yes",191,23
"4894",1739,33,212,4,17,"no","no","yes",191,23
"4895",1840,33,528,8,14,"yes","no","yes",191,23
"4896",1199,33,210,4,14,"no","no","yes",191,23
"4897",2094,33,528,8,14,"yes","no","yes",191,23
"4898",1548,33,424,4,14,"yes","no","yes",191,23
"4899",1504,33,214,4,14,"no","no","yes",191,23
"4900",3149,100,728,8,14,"no","no","no",191,23
"4901",1839,33,424,4,17,"no","no","yes",191,23
"4902",2940,50,1000,24,15,"yes","no","yes",191,23
"4903",1845,50,528,8,14,"yes","no","yes",191,23
"4904",1899,66,420,8,14,"yes","yes","yes",191,23
"4905",1445,33,214,4,14,"no","no","yes",191,23
"4906",2108,50,1000,8,15,"yes","yes","yes",191,23
"4907",1540,33,214,4,14,"no","no","yes",191,23
"4908",2644,66,720,16,15,"yes","no","yes",191,23
"4909",1499,50,545,4,15,"yes","no","yes",191,23
"4910",1999,100,420,8,14,"yes","no","yes",191,23
"4911",1845,33,528,8,14,"yes","no","yes",191,23
"4912",2994,33,1000,24,15,"yes","no","yes",191,23
"4913",1389,33,212,4,15,"no","no","yes",191,23
"4914",1799,66,545,8,15,"yes","no","yes",191,23
"4915",2118,66,730,8,15,"yes","yes","yes",191,23
"4916",1640,33,214,4,14,"no","no","yes",191,23
"4917",1973,33,528,8,14,"yes","yes","yes",191,23
"4918",1399,33,210,4,14,"yes","yes","yes",191,23
"4919",1895,66,528,8,14,"yes","no","yes",191,23
"4920",1499,33,340,4,14,"yes","yes","yes",191,23
"4921",1759,66,425,8,15,"no","no","yes",191,23
"4922",1745,33,528,8,14,"yes","no","yes",191,23
"4923",1788,66,425,4,14,"yes","yes","yes",191,23
"4924",2190,66,1000,8,14,"yes","no","yes",191,23
"4925",2058,50,1000,8,15,"yes","no","yes",191,23
"4926",2409,66,1000,8,17,"no","no","yes",191,23
"4927",1758,50,425,8,15,"yes","no","yes",191,23
"4928",1918,66,540,8,14,"yes","no","yes",191,23
"4929",1345,66,214,4,14,"no","no","yes",191,23
"4930",2740,50,720,16,17,"yes","no","yes",191,23
"4931",2143,66,528,8,14,"yes","yes","yes",191,23
"4932",3240,33,1000,24,17,"yes","no","yes",191,23
"4933",1968,66,540,8,14,"yes","yes","yes",191,23
"4934",1595,66,214,4,14,"no","no","yes",191,23
"4935",2493,50,720,16,15,"yes","yes","yes",191,23
"4936",2395,66,720,16,15,"yes","no","yes",191,23
"4937",1694,33,214,4,14,"no","no","yes",191,23
"4938",2059,66,1000,8,15,"no","no","yes",191,23
"4939",2345,33,720,16,15,"yes","no","yes",191,23
"4940",2539,100,270,4,14,"no","no","no",191,23
"4941",3290,66,1000,24,17,"yes","no","yes",191,23
"4942",1779,50,270,4,14,"no","no","no",191,23
"4943",1589,66,425,4,14,"no","no","yes",191,23
"4944",1848,66,270,4,14,"no","no","no",191,23
"4945",2093,50,528,8,14,"yes","yes","yes",191,23
"4946",1690,66,214,4,14,"no","no","yes",191,23
"4947",1858,50,540,8,15,"yes","no","yes",191,23
"4948",2895,66,1000,24,15,"yes","no","yes",191,23
"4949",2140,50,1000,8,14,"yes","no","yes",191,23
"4950",1640,33,214,4,15,"no","no","yes",191,23
"4951",1929,66,212,4,17,"no","no","yes",191,23
"4952",1904,33,528,8,14,"yes","no","yes",191,23
"4953",1844,66,214,4,14,"no","no","yes",191,23
"4954",1895,66,528,8,14,"yes","no","yes",191,23
"4955",1819,33,270,4,14,"no","no","no",191,23
"4956",1638,66,212,4,14,"yes","no","yes",191,23
"4957",2199,66,528,8,15,"yes","yes","yes",191,23
"4958",1640,50,214,4,14,"no","no","yes",191,23
"4959",2393,33,720,16,15,"yes","yes","yes",191,23
"4960",1395,33,340,8,14,"no","no","yes",191,23
"4961",2054,66,528,8,14,"yes","no","yes",191,23
"4962",3144,66,1000,24,15,"yes","no","yes",191,23
"4963",2594,50,720,16,15,"yes","no","yes",191,23
"4964",2249,66,270,8,14,"no","no","no",191,23
"4965",1679,66,425,4,15,"no","no","yes",191,23
"4966",2345,66,528,8,15,"yes","no","yes",191,23
"4967",1349,33,270,4,14,"no","no","no",191,23
"4968",1199,33,428,4,15,"no","no","yes",191,23
"4969",2040,33,1000,8,14,"yes","no","yes",191,23
"4970",2804,33,1000,24,15,"yes","no","yes",191,23
"4971",1545,50,214,4,14,"no","no","yes",191,23
"4972",1579,66,212,4,15,"no","no","yes",191,23
"4973",1609,50,425,8,15,"no","no","yes",191,23
"4974",2893,33,1000,24,15,"yes","yes","yes",191,23
"4975",1295,50,214,4,14,"no","no","yes",191,23
"4976",2009,66,270,4,14,"no","no","no",191,23
"4977",2845,50,1000,24,15,"yes","no","yes",191,23
"4978",1495,33,340,8,14,"no","no","yes",191,23
"4979",2840,33,1000,24,15,"yes","no","yes",191,23
"4980",1940,50,528,8,14,"yes","no","yes",191,23
"4981",1418,33,340,4,14,"yes","yes","yes",191,23
"4982",1794,33,214,4,14,"no","no","yes",191,23
"4983",1499,33,428,4,14,"yes","yes","yes",191,23
"4984",2954,66,1000,24,15,"yes","no","yes",191,23
"4985",1909,50,1000,8,15,"no","no","yes",191,23
"4986",2904,33,1000,24,15,"yes","no","yes",191,23
"4987",2209,66,540,8,17,"no","no","yes",191,23
"4988",2594,33,720,16,15,"yes","no","yes",191,23
"4989",1908,50,540,8,15,"yes","yes","yes",191,23
"4990",2198,100,420,8,14,"yes","yes","yes",191,23
"4991",2004,50,528,8,14,"yes","no","yes",191,23
"4992",2898,100,730,16,15,"yes","yes","yes",191,23
"4993",3140,33,1000,24,17,"yes","no","yes",191,23
"4994",1699,66,420,8,14,"yes","no","yes",191,23
"4995",1640,50,214,4,15,"no","no","yes",191,23
"4996",1498,33,212,4,14,"yes","yes","yes",191,23
"4997",1898,66,420,8,14,"yes","yes","yes",191,23
"4998",2029,66,425,4,17,"no","no","yes",191,23
"4999",2168,66,1000,8,14,"yes","yes","yes",191,23
"5000",2118,66,1000,8,14,"yes","no","yes",191,23
"5001",2194,50,528,8,14,"yes","no","yes",191,23
"5002",1495,50,340,8,14,"no","no","yes",191,23
"5003",1738,66,425,4,14,"yes","no","yes",191,23
"5004",2640,33,720,16,17,"yes","no","yes",191,23
"5005",1448,33,212,4,14,"yes","no","yes",191,23
"5006",1489,33,424,4,15,"no","no","yes",191,23
"5007",2404,50,720,16,15,"yes","no","yes",191,23
"5008",1540,33,214,4,15,"no","no","yes",191,23
"5009",3094,50,1000,24,15,"yes","no","yes",191,23
"5010",1794,50,214,4,14,"no","no","yes",191,23
"5011",2408,100,270,4,14,"no","no","no",191,23
"5012",2454,66,720,16,15,"yes","no","yes",191,23
"5013",1969,66,1000,8,14,"no","no","yes",191,23
"5014",2904,50,1000,24,15,"yes","no","yes",191,23
"5015",1545,66,340,8,14,"no","no","yes",191,23
"5016",1718,66,340,4,14,"yes","yes","yes",191,23
"5017",1604,33,214,4,14,"no","no","yes",191,23
"5018",2993,33,1000,24,15,"yes","yes","yes",191,23
"5019",2194,33,528,8,14,"yes","no","yes",191,23
"5020",2345,50,720,16,15,"yes","no","yes",191,23
"5021",2699,100,730,16,15,"yes","no","yes",191,23
"5022",1940,50,528,8,15,"yes","no","yes",191,23
"5023",2440,50,720,16,15,"yes","no","yes",191,23
"5024",1604,50,214,4,14,"no","no","yes",191,23
"5025",1808,50,425,8,15,"yes","yes","yes",191,23
"5026",1574,50,270,4,14,"no","no","no",191,23
"5027",2990,66,1000,24,15,"yes","no","yes",191,23
"5028",1599,66,340,4,14,"yes","no","yes",191,23
"5029",1598,33,424,4,14,"yes","yes","yes",191,23
"5030",2493,33,720,16,15,"yes","yes","yes",191,23
"5031",1654,66,214,4,14,"no","no","yes",191,23
"5032",2795,100,528,16,14,"yes","no","yes",191,23
"5033",1399,33,424,4,14,"no","no","yes",191,23
"5034",1295,33,214,4,14,"no","no","yes",191,23
"5035",2244,66,528,8,14,"yes","no","yes",191,23
"5036",2404,33,720,16,15,"yes","no","yes",191,23
"5037",2993,50,1000,24,15,"yes","yes","yes",191,23
"5038",1489,66,212,4,14,"no","no","yes",191,23
"5039",1688,66,212,4,14,"yes","yes","yes",191,23
"5040",1199,33,340,4,14,"no","no","yes",191,23
"5041",2845,33,1000,24,15,"yes","no","yes",191,23
"5042",2745,33,1000,24,15,"yes","no","yes",191,23
"5043",1990,66,528,8,15,"yes","no","yes",191,23
"5044",2109,66,425,8,17,"no","no","yes",191,23
"5045",2740,33,720,16,17,"yes","no","yes",191,23
"5046",1899,66,730,8,14,"yes","yes","yes",191,23
"5047",2490,66,720,16,15,"yes","no","yes",191,23
"5048",1590,33,214,4,14,"no","no","yes",182,24
"5049",1195,33,214,4,14,"no","no","yes",182,24
"5050",1989,66,540,8,15,"no","no","yes",182,24
"5051",2404,66,720,16,15,"yes","no","yes",182,24
"5052",3140,66,1000,24,15,"yes","yes","yes",182,24
"5053",1854,33,528,8,14,"yes","no","yes",182,24
"5054",1490,33,214,4,14,"no","no","yes",182,24
"5055",1890,50,528,8,14,"yes","no","yes",182,24
"5056",1858,66,545,4,17,"yes","no","yes",182,24
"5057",1890,33,528,8,14,"yes","no","yes",182,24
"5058",1940,66,528,8,14,"yes","no","yes",182,24
"5059",2944,33,1000,24,15,"yes","no","yes",182,24
"5060",2423,50,720,16,15,"yes","yes","yes",182,24
"5061",1399,66,428,4,14,"no","no","yes",182,24
"5062",2354,50,720,16,15,"yes","no","yes",182,24
"5063",1854,50,528,8,14,"yes","no","yes",182,24
"5064",2295,50,720,16,15,"yes","no","yes",182,24
"5065",2248,66,1000,8,14,"yes","no","yes",182,24
"5066",1799,66,425,8,14,"no","no","yes",182,24
"5067",2804,66,1000,24,15,"yes","no","yes",182,24
"5068",1545,66,214,4,14,"no","no","yes",182,24
"5069",1644,33,214,4,14,"no","no","yes",182,24
"5070",1744,33,214,4,14,"no","no","yes",182,24
"5071",2590,33,720,16,17,"yes","no","yes",182,24
"5072",1454,33,214,4,14,"no","no","yes",182,24
"5073",1779,33,424,4,17,"no","no","yes",182,24
"5074",2040,50,1000,8,14,"yes","no","yes",182,24
"5075",2740,66,720,16,17,"yes","no","yes",182,24
"5076",2335,33,212,8,17,"yes","yes","yes",182,24
"5077",2058,66,545,8,17,"yes","no","yes",182,24
"5078",1568,33,428,4,15,"yes","yes","yes",182,24
"5079",2345,66,720,16,15,"yes","no","yes",182,24
"5080",1899,66,730,8,15,"yes","no","yes",182,24
"5081",1794,66,214,4,14,"no","no","yes",182,24
"5082",2745,66,1000,24,15,"yes","no","yes",182,24
"5083",1499,33,428,4,14,"yes","yes","yes",182,24
"5084",2254,33,720,16,15,"yes","no","yes",182,24
"5085",2095,66,528,8,15,"yes","no","yes",182,24
"5086",1823,33,528,8,14,"yes","yes","yes",182,24
"5087",2339,66,540,8,17,"no","no","yes",182,24
"5088",1889,66,425,8,15,"no","no","yes",182,24
"5089",2944,50,1000,24,15,"yes","no","yes",182,24
"5090",1999,66,730,8,15,"yes","no","yes",182,24
"5091",1345,66,214,4,14,"no","no","yes",182,24
"5092",1890,50,528,8,15,"yes","no","yes",182,24
"5093",2754,50,1000,24,15,"yes","no","yes",182,24
"5094",2699,100,730,16,15,"yes","no","yes",182,24
"5095",2098,66,540,8,14,"yes","yes","yes",182,24
"5096",2195,33,720,16,15,"yes","no","yes",182,24
"5097",1599,66,340,8,15,"no","no","yes",182,24
"5098",2258,66,730,8,17,"yes","yes","yes",182,24
"5099",2295,33,720,16,15,"yes","no","yes",182,24
"5100",1999,100,420,8,14,"yes","no","yes",182,24
"5101",2323,33,720,16,15,"yes","yes","yes",182,24
"5102",1754,33,528,8,14,"yes","no","yes",182,24
"5103",2390,33,720,16,15,"yes","no","yes",182,24
"5104",1923,50,528,8,14,"yes","yes","yes",182,24
"5105",1295,33,214,4,14,"no","no","yes",182,24
"5106",1973,66,528,8,14,"yes","yes","yes",182,24
"5107",1368,50,428,4,15,"no","no","yes",182,24
"5108",2790,33,1000,24,15,"yes","no","yes",182,24
"5109",2390,50,720,16,15,"yes","no","yes",182,24
"5110",1339,33,424,4,14,"no","no","yes",182,24
"5111",1495,33,214,4,14,"no","no","yes",182,24
"5112",2044,33,528,8,14,"yes","no","yes",182,24
"5113",1890,33,528,8,15,"yes","no","yes",182,24
"5114",1899,66,540,8,14,"no","no","yes",182,24
"5115",1944,33,528,8,14,"yes","no","yes",182,24
"5116",1640,66,214,4,14,"no","no","yes",182,24
"5117",1989,66,425,8,15,"yes","yes","yes",182,24
"5118",2840,66,1000,24,15,"yes","no","yes",182,24
"5119",2094,66,528,8,14,"yes","no","yes",182,24
"5120",1490,33,214,4,15,"no","no","yes",182,24
"5121",1199,33,210,4,14,"no","no","yes",182,24
"5122",1795,33,528,8,14,"yes","no","yes",182,24
"5123",2690,33,720,16,17,"yes","no","yes",182,24
"5124",2259,66,730,8,17,"yes","yes","yes",182,24
"5125",1828,33,428,4,17,"yes","yes","yes",182,24
"5126",2994,66,1000,24,15,"yes","no","yes",182,24
"5127",2099,66,1000,8,14,"no","no","yes",182,24
"5128",2399,66,730,16,15,"yes","no","yes",182,24
"5129",1628,50,428,4,17,"no","no","yes",182,24
"5130",1604,66,214,4,14,"no","no","yes",182,24
"5131",1640,66,214,4,15,"no","no","yes",182,24
"5132",1398,50,340,4,14,"yes","yes","yes",182,24
"5133",2440,66,720,16,15,"yes","no","yes",182,24
"5134",1590,50,214,4,14,"no","no","yes",182,24
"5135",1845,66,528,8,14,"yes","no","yes",182,24
"5136",2258,66,730,8,17,"yes","no","yes",182,24
"5137",1985,33,212,8,15,"yes","yes","yes",182,24
"5138",2354,33,720,16,15,"yes","no","yes",182,24
"5139",1998,66,425,8,14,"yes","yes","yes",182,24
"5140",2199,66,540,8,15,"yes","yes","yes",182,24
"5141",1795,50,528,8,14,"yes","no","yes",182,24
"5142",1698,66,540,8,14,"yes","yes","yes",182,24
"5143",2544,33,720,16,15,"yes","no","yes",182,24
"5144",2040,33,1000,8,14,"yes","no","yes",182,24
"5145",1438,33,212,4,14,"yes","yes","yes",182,24
"5146",2249,66,270,8,14,"no","no","no",182,24
"5147",1495,66,528,8,14,"no","no","yes",182,24
"5148",1590,33,214,4,15,"no","no","yes",182,24
"5149",1999,66,730,8,15,"yes","yes","yes",182,24
"5150",1388,33,212,4,14,"yes","no","yes",182,24
"5151",2690,50,720,16,17,"yes","no","yes",182,24
"5152",2754,33,1000,24,15,"yes","no","yes",182,24
"5153",2695,33,1000,24,15,"yes","no","yes",182,24
"5154",2189,66,1000,8,15,"no","no","yes",182,24
"5155",2690,33,1000,24,15,"yes","no","yes",182,24
"5156",2339,66,425,8,17,"yes","yes","yes",182,24
"5157",1239,33,212,4,14,"no","no","yes",182,24
"5158",1758,50,545,4,17,"yes","no","yes",182,24
"5159",1899,66,730,8,14,"yes","yes","yes",182,24
"5160",2290,33,720,16,15,"yes","no","yes",182,24
"5161",1799,66,545,8,15,"yes","no","yes",182,24
"5162",2594,66,720,16,15,"yes","no","yes",182,24
"5163",1948,66,425,8,14,"yes","no","yes",182,24
"5164",1468,66,428,4,15,"no","no","yes",182,24
"5165",1299,50,428,4,14,"no","no","yes",182,24
"5166",2695,50,1000,24,15,"yes","no","yes",182,24
"5167",1679,33,212,4,17,"no","no","yes",182,24
"5168",1199,33,340,4,14,"no","no","yes",182,24
"5169",1790,33,528,8,15,"yes","no","yes",182,24
"5170",2844,33,1000,24,15,"yes","no","yes",182,24
"5171",1789,33,212,8,15,"yes","yes","yes",182,24
"5172",1699,66,420,8,14,"yes","no","yes",182,24
"5173",2159,66,730,8,17,"yes","no","yes",182,24
"5174",1299,50,340,4,14,"yes","no","yes",182,24
"5175",2239,66,425,8,17,"no","no","yes",182,24
"5176",2298,66,1000,8,14,"yes","yes","yes",182,24
"5177",1744,50,214,4,14,"no","no","yes",182,24
"5178",1499,50,545,4,15,"yes","no","yes",182,24
"5179",2473,66,720,16,15,"yes","yes","yes",182,24
"5180",1395,66,528,8,14,"no","no","yes",182,24
"5181",2654,33,1000,24,15,"yes","no","yes",182,24
"5182",1599,66,540,8,14,"yes","no","yes",182,24
"5183",2790,50,1000,24,15,"yes","no","yes",182,24
"5184",2423,33,720,16,15,"yes","yes","yes",182,24
"5185",1395,33,214,4,14,"no","no","yes",182,24
"5186",1895,33,212,8,14,"yes","yes","yes",182,24
"5187",1399,33,210,4,14,"yes","yes","yes",182,24
"5188",1429,33,424,4,15,"no","no","yes",182,24
"5189",1923,33,528,8,14,"yes","yes","yes",182,24
"5190",1488,33,424,4,14,"yes","no","yes",182,24
"5191",1904,66,528,8,14,"yes","no","yes",182,24
"5192",1495,50,214,4,14,"no","no","yes",182,24
"5193",1528,33,428,4,17,"no","no","yes",182,24
"5194",1545,66,528,8,14,"no","no","yes",182,24
"5195",1295,50,214,4,14,"no","no","yes",182,24
"5196",2595,33,1000,24,15,"yes","no","yes",182,24
"5197",2090,66,1000,8,14,"yes","no","yes",182,24
"5198",1590,50,214,4,15,"no","no","yes",182,24
"5199",1799,66,420,8,14,"yes","yes","yes",182,24
"5200",2539,66,1000,8,17,"no","no","yes",182,24
"5201",1329,33,212,4,15,"no","no","yes",182,24
"5202",1899,66,425,8,14,"yes","yes","yes",182,24
"5203",1940,66,528,8,15,"yes","no","yes",182,24
"5204",1538,33,424,4,14,"yes","yes","yes",182,24
"5205",2048,66,540,8,14,"yes","no","yes",182,24
"5206",2044,50,528,8,14,"yes","no","yes",182,24
"5207",2840,100,528,16,15,"yes","no","yes",182,24
"5208",1499,50,340,4,15,"yes","yes","yes",182,24
"5209",1699,33,212,8,14,"yes","yes","yes",182,24
"5210",1790,33,528,8,14,"yes","no","yes",182,24
"5211",2598,66,730,16,15,"yes","yes","yes",182,24
"5212",1898,66,420,8,14,"yes","yes","yes",182,24
"5213",2139,33,212,8,17,"yes","yes","yes",182,24
"5214",1599,66,545,4,15,"yes","no","yes",182,24
"5215",1268,33,428,4,15,"no","no","yes",182,24
"5216",2898,100,730,16,15,"yes","yes","yes",182,24
"5217",2544,50,720,16,15,"yes","no","yes",182,24
"5218",1554,50,214,4,14,"no","no","yes",182,24
"5219",2295,66,528,8,15,"yes","no","yes",182,24
"5220",2444,33,720,16,15,"yes","no","yes",182,24
"5221",2990,33,1000,24,15,"yes","yes","yes",182,24
"5222",3090,33,1000,24,15,"yes","yes","yes",182,24
"5223",1728,66,428,4,17,"no","no","yes",182,24
"5224",1695,33,528,8,14,"yes","no","yes",182,24
"5225",1998,66,730,8,15,"yes","yes","yes",182,24
"5226",1554,33,214,4,14,"no","no","yes",182,24
"5227",1940,33,1000,8,14,"yes","no","yes",182,24
"5228",1199,33,428,4,14,"no","no","yes",182,24
"5229",2198,100,420,8,14,"yes","yes","yes",182,24
"5230",2290,33,720,16,15,"yes","no","yes",158,25
"5231",1718,66,428,4,17,"no","no","yes",158,25
"5232",1568,50,428,4,15,"yes","yes","yes",158,25
"5233",2595,33,1000,24,15,"yes","no","yes",158,25
"5234",1899,66,730,8,15,"yes","no","yes",158,25
"5235",1605,66,540,8,15,"no","no","yes",158,25
"5236",2444,33,720,16,15,"yes","no","yes",158,25
"5237",1640,66,214,4,15,"no","no","yes",158,25
"5238",1490,33,214,4,15,"no","no","yes",158,25
"5239",2195,33,720,16,15,"yes","no","yes",158,25
"5240",2248,66,730,8,17,"yes","no","yes",158,25
"5241",1199,33,210,4,14,"no","no","yes",158,25
"5242",1495,33,214,4,14,"no","no","yes",158,25
"5243",1299,50,340,4,14,"yes","no","yes",158,25
"5244",1398,50,340,4,14,"yes","yes","yes",158,25
"5245",1699,33,212,8,14,"yes","yes","yes",158,25
"5246",1599,66,545,4,15,"yes","no","yes",158,25
"5247",1923,33,528,8,14,"yes","yes","yes",158,25
"5248",1680,66,540,8,15,"yes","no","yes",158,25
"5249",1848,66,545,4,17,"yes","no","yes",158,25
"5250",2345,66,720,16,15,"yes","no","yes",158,25
"5251",1295,33,214,4,14,"no","no","yes",158,25
"5252",1799,66,420,8,14,"yes","yes","yes",158,25
"5253",2423,33,720,16,15,"yes","yes","yes",158,25
"5254",3140,66,1000,24,15,"yes","yes","yes",158,25
"5255",2590,33,720,16,17,"yes","no","yes",158,25
"5256",2740,66,720,16,17,"yes","no","yes",158,25
"5257",1973,66,528,8,14,"yes","yes","yes",158,25
"5258",1940,66,528,8,14,"yes","no","yes",158,25
"5259",2295,33,720,16,15,"yes","no","yes",158,25
"5260",1999,66,730,8,15,"yes","no","yes",158,25
"5261",1989,33,212,8,15,"yes","yes","yes",158,25
"5262",1898,66,420,8,14,"yes","yes","yes",158,25
"5263",2699,100,730,16,15,"yes","no","yes",158,25
"5264",2390,33,720,16,15,"yes","no","yes",158,25
"5265",1399,33,210,4,14,"yes","yes","yes",158,25
"5266",1618,50,428,4,17,"no","no","yes",158,25
"5267",2473,66,720,16,15,"yes","yes","yes",158,25
"5268",2990,33,1000,24,15,"yes","yes","yes",158,25
"5269",1545,66,528,8,14,"no","no","yes",158,25
"5270",1644,33,214,4,14,"no","no","yes",158,25
"5271",1495,33,528,8,14,"no","no","yes",158,25
"5272",2328,100,270,4,14,"no","no","no",158,25
"5273",1885,66,730,8,15,"yes","yes","yes",158,25
"5274",1790,33,528,8,15,"yes","no","yes",158,25
"5275",1989,66,425,8,15,"yes","yes","yes",158,25
"5276",1518,33,428,4,17,"no","no","yes",158,25
"5277",1699,66,420,8,14,"yes","no","yes",158,25
"5278",2090,66,1000,8,14,"yes","no","yes",158,25
"5279",1490,33,214,4,14,"no","no","yes",158,25
"5280",1659,66,425,8,14,"yes","no","yes",158,25
"5281",1845,66,528,8,14,"yes","no","yes",158,25
"5282",1854,33,528,8,14,"yes","no","yes",158,25
"5283",2440,66,720,16,15,"yes","no","yes",158,25
"5284",1865,66,540,8,17,"no","no","yes",158,25
"5285",1295,50,214,4,14,"no","no","yes",158,25
"5286",2690,33,1000,24,15,"yes","no","yes",158,25
"5287",1590,33,214,4,14,"no","no","yes",158,25
"5288",1599,66,540,8,15,"no","no","yes",158,25
"5289",1899,66,425,8,14,"yes","yes","yes",158,25
"5290",2148,66,730,8,17,"yes","yes","yes",158,25
"5291",1590,33,214,4,15,"no","no","yes",158,25
"5292",1578,50,270,4,14,"no","no","no",158,25
"5293",1399,66,428,4,14,"no","no","yes",158,25
"5294",1828,66,270,4,14,"no","no","no",158,25
"5295",1468,66,428,4,15,"no","no","yes",158,25
"5296",2323,33,720,16,15,"yes","yes","yes",158,25
"5297",2159,66,730,8,17,"yes","no","yes",158,25
"5298",1899,33,212,8,14,"yes","yes","yes",158,25
"5299",1199,33,340,4,14,"no","no","yes",158,25
"5300",2075,66,1080,8,15,"yes","yes","yes",158,25
"5301",1795,66,540,8,15,"yes","no","yes",158,25
"5302",2254,33,720,16,15,"yes","no","yes",158,25
"5303",2944,33,1000,24,15,"yes","no","yes",158,25
"5304",1818,50,428,4,17,"yes","yes","yes",158,25
"5305",1599,66,540,8,14,"yes","no","yes",158,25
"5306",2898,100,730,16,15,"yes","yes","yes",158,25
"5307",1789,33,212,8,15,"yes","yes","yes",158,25
"5308",2598,66,730,16,15,"yes","yes","yes",158,25
"5309",2690,33,720,16,17,"yes","no","yes",158,25
"5310",1748,50,545,4,17,"yes","no","yes",158,25
"5311",2259,66,730,8,17,"yes","yes","yes",158,25
"5312",2544,33,720,16,15,"yes","no","yes",158,25
"5313",2404,66,720,16,15,"yes","no","yes",158,25
"5314",2215,66,730,16,15,"yes","yes","yes",158,25
"5315",1640,66,214,4,14,"no","no","yes",158,25
"5316",1904,66,528,8,14,"yes","no","yes",158,25
"5317",1999,100,420,8,14,"yes","no","yes",158,25
"5318",2354,33,720,16,15,"yes","no","yes",158,25
"5319",2804,66,1000,24,15,"yes","no","yes",158,25
"5320",1790,33,528,8,14,"yes","no","yes",158,25
"5321",2844,33,1000,24,15,"yes","no","yes",158,25
"5322",2099,66,425,8,17,"yes","no","yes",158,25
"5323",1698,66,540,8,14,"yes","yes","yes",158,25
"5324",1195,33,214,4,14,"no","no","yes",158,25
"5325",2529,66,364,8,14,"no","no","no",158,25
"5326",2840,66,1000,24,15,"yes","no","yes",158,25
"5327",2295,66,528,8,15,"yes","no","yes",158,25
"5328",1395,33,528,8,14,"no","no","yes",158,25
"5329",1799,66,545,8,15,"yes","no","yes",158,25
"5330",1499,50,428,4,14,"yes","yes","yes",158,25
"5331",2790,33,1000,24,15,"yes","no","yes",158,25
"5332",1899,66,730,8,15,"yes","yes","yes",158,25
"5333",2754,33,1000,24,15,"yes","no","yes",158,25
"5334",2994,66,1000,24,15,"yes","no","yes",158,25
"5335",1695,33,528,8,14,"yes","no","yes",158,25
"5336",3090,33,1000,24,15,"yes","yes","yes",158,25
"5337",2399,66,730,16,15,"yes","no","yes",158,25
"5338",2745,66,1000,24,15,"yes","no","yes",158,25
"5339",1495,50,528,8,14,"no","no","yes",158,25
"5340",2594,66,720,16,15,"yes","no","yes",158,25
"5341",2695,33,1000,24,15,"yes","no","yes",158,25
"5342",1998,66,730,8,15,"yes","yes","yes",158,25
"5343",1890,33,528,8,14,"yes","no","yes",158,25
"5344",1345,66,214,4,14,"no","no","yes",158,25
"5345",1299,50,428,4,14,"no","no","yes",158,25
"5346",3029,100,364,8,14,"no","no","no",158,25
"5347",2249,66,270,8,14,"no","no","no",158,25
"5348",1940,66,528,8,15,"yes","no","yes",158,25
"5349",1499,50,545,4,15,"yes","no","yes",158,25
"5350",2139,33,212,8,17,"yes","yes","yes",158,25
"5351",2339,66,425,8,17,"yes","yes","yes",158,25
"5352",2048,66,545,8,17,"yes","no","yes",158,25
"5353",1823,33,528,8,14,"yes","yes","yes",158,25
"5354",2019,66,1000,8,15,"yes","no","yes",158,25
"5355",1899,66,540,8,15,"yes","yes","yes",158,25
"5356",1950,66,730,8,15,"yes","yes","yes",158,25
"5357",1929,66,1000,8,14,"yes","no","yes",158,25
"5358",2198,100,420,8,14,"yes","yes","yes",158,25
"5359",1944,33,528,8,14,"yes","no","yes",158,25
"5360",1855,66,730,8,15,"yes","no","yes",158,25
"5361",1268,33,428,4,15,"no","no","yes",158,25
"5362",2094,66,528,8,14,"yes","no","yes",158,25
"5363",1754,33,528,8,14,"yes","no","yes",158,25
"5364",2654,33,1000,24,15,"yes","no","yes",158,25
"5365",1499,50,340,4,14,"yes","yes","yes",158,25
"5366",1940,33,1000,8,14,"yes","no","yes",158,25
"5367",2369,66,1000,8,17,"yes","no","yes",158,25
"5368",1890,33,528,8,15,"yes","no","yes",158,25
"5369",1794,66,214,4,14,"no","no","yes",158,25
"5370",1899,66,730,8,14,"yes","yes","yes",158,25
"5371",1545,66,214,4,14,"no","no","yes",158,25
"5372",1554,33,214,4,14,"no","no","yes",158,25
"5373",1395,33,214,4,14,"no","no","yes",158,25
"5374",2339,33,212,8,17,"yes","yes","yes",158,25
"5375",1795,33,528,8,14,"yes","no","yes",158,25
"5376",1199,33,428,4,14,"no","no","yes",158,25
"5377",1454,33,214,4,14,"no","no","yes",158,25
"5378",1545,66,540,8,15,"no","no","yes",158,25
"5379",2279,50,364,8,14,"no","no","no",158,25
"5380",2595,66,528,8,15,"yes","no","yes",158,25
"5381",1795,66,540,8,15,"yes","yes","yes",158,25
"5382",2040,33,1000,8,14,"yes","no","yes",158,25
"5383",1749,66,425,8,15,"yes","no","yes",158,25
"5384",1604,66,214,4,14,"no","no","yes",158,25
"5385",1368,50,428,4,15,"no","no","yes",158,25
"5386",2044,33,528,8,14,"yes","no","yes",158,25
"5387",1744,33,214,4,14,"no","no","yes",158,25
"5388",2238,100,545,8,17,"yes","no","yes",152,26
"5389",1698,66,540,8,14,"yes","yes","yes",152,26
"5390",1890,100,340,4,14,"no","no","yes",152,26
"5391",2495,66,364,8,14,"no","no","no",152,26
"5392",2699,100,730,16,15,"yes","no","yes",152,26
"5393",1748,50,428,4,17,"yes","yes","yes",152,26
"5394",1935,66,1000,8,14,"yes","no","yes",152,26
"5395",2304,66,720,16,15,"yes","no","yes",152,26
"5396",1454,50,340,4,14,"no","no","yes",152,26
"5397",2338,100,850,8,17,"yes","no","yes",152,26
"5398",1799,75,545,8,15,"yes","no","yes",152,26
"5399",2373,66,720,16,15,"yes","yes","yes",152,26
"5400",1929,66,1000,8,14,"yes","no","yes",152,26
"5401",2399,66,730,16,15,"yes","no","yes",152,26
"5402",1899,100,545,8,15,"yes","no","yes",152,26
"5403",2723,100,720,16,15,"yes","yes","yes",152,26
"5404",1745,66,528,8,14,"yes","no","yes",152,26
"5405",1998,66,730,8,15,"yes","yes","yes",152,26
"5406",1994,100,340,4,14,"no","no","yes",152,26
"5407",2340,66,720,16,15,"yes","no","yes",152,26
"5408",2794,100,720,16,15,"yes","no","yes",152,26
"5409",2190,100,528,8,14,"yes","no","yes",152,26
"5410",2940,50,1000,24,17,"yes","no","yes",152,26
"5411",1840,66,528,8,14,"yes","no","yes",152,26
"5412",1540,66,340,4,15,"no","no","yes",152,26
"5413",2285,100,1000,8,14,"yes","no","yes",152,26
"5414",2190,100,528,8,15,"yes","no","yes",152,26
"5415",1299,66,428,4,14,"no","no","yes",152,26
"5416",2990,66,1000,24,17,"yes","no","yes",152,26
"5417",1944,50,528,8,14,"yes","no","yes",152,26
"5418",1490,50,340,4,14,"no","no","yes",152,26
"5419",2095,100,528,8,14,"yes","no","yes",152,26
"5420",2148,100,545,8,17,"yes","no","yes",152,26
"5421",2494,66,720,16,15,"yes","no","yes",152,26
"5422",2254,50,720,16,15,"yes","no","yes",152,26
"5423",2088,66,545,8,17,"yes","no","yes",152,26
"5424",2563,100,270,4,14,"no","no","no",152,26
"5425",3123,100,1000,24,15,"yes","yes","yes",152,26
"5426",2540,50,720,16,17,"yes","no","yes",152,26
"5427",1854,100,340,4,14,"no","no","yes",152,26
"5428",2248,100,850,8,17,"yes","no","yes",152,26
"5429",1504,66,340,4,14,"no","no","yes",152,26
"5430",2364,33,212,8,17,"yes","yes","yes",152,26
"5431",1989,33,212,8,15,"yes","yes","yes",152,26
"5432",1899,66,730,8,14,"yes","yes","yes",152,26
"5433",1885,50,1000,8,14,"yes","no","yes",152,26
"5434",1823,50,528,8,14,"yes","yes","yes",152,26
"5435",2995,100,364,8,14,"no","no","no",152,26
"5436",2394,66,1000,8,17,"yes","no","yes",152,26
"5437",1789,50,212,8,15,"yes","yes","yes",152,26
"5438",1694,66,340,4,14,"no","no","yes",152,26
"5439",2338,100,545,8,17,"yes","yes","yes",152,26
"5440",2273,50,1000,24,15,"yes","yes","yes",152,26
"5441",2773,66,1000,24,15,"yes","yes","yes",152,26
"5442",1790,50,528,8,14,"yes","no","yes",152,26
"5443",2290,50,720,16,15,"yes","no","yes",152,26
"5444",2444,50,720,16,15,"yes","no","yes",152,26
"5445",1994,66,528,8,14,"yes","no","yes",152,26
"5446",1804,66,528,8,14,"yes","no","yes",152,26
"5447",2245,50,364,8,14,"no","no","no",152,26
"5448",2138,75,545,8,17,"yes","no","yes",152,26
"5449",1445,66,340,4,14,"no","no","yes",152,26
"5450",2690,100,720,16,15,"yes","no","yes",152,26
"5451",1838,50,428,4,17,"yes","yes","yes",152,26
"5452",1813,50,270,4,14,"no","no","no",152,26
"5453",2164,50,212,8,17,"yes","yes","yes",152,26
"5454",1395,50,340,4,14,"no","no","yes",152,26
"5455",1245,66,340,4,14,"no","no","yes",152,26
"5456",2364,66,425,8,17,"yes","yes","yes",152,26
"5457",1999,100,850,8,15,"yes","no","yes",152,26
"5458",2063,66,270,4,14,"no","no","no",152,26
"5459",2894,66,1000,24,15,"yes","no","yes",152,26
"5460",2019,66,1000,8,15,"yes","no","yes",152,26
"5461",1399,33,210,4,14,"yes","yes","yes",152,26
"5462",1368,66,428,4,15,"no","no","yes",152,26
"5463",2148,75,545,8,17,"yes","yes","yes",152,26
"5464",2195,66,528,8,15,"yes","no","yes",152,26
"5465",2238,75,545,8,17,"yes","yes","yes",152,26
"5466",2654,100,720,16,15,"yes","no","yes",152,26
"5467",2294,100,528,8,14,"yes","no","yes",152,26
"5468",1268,50,428,4,15,"no","no","yes",152,26
"5469",1199,33,340,4,14,"no","no","yes",152,26
"5470",2704,66,1000,24,15,"yes","no","yes",152,26
"5471",1998,66,545,8,17,"yes","no","yes",152,26
"5472",2048,75,545,8,17,"yes","no","yes",152,26
"5473",1899,75,545,8,15,"yes","yes","yes",152,26
"5474",1999,100,420,8,14,"yes","no","yes",152,26
"5475",2259,66,730,8,17,"yes","yes","yes",152,26
"5476",2195,50,720,16,15,"yes","no","yes",152,26
"5477",1499,50,545,4,15,"yes","no","yes",152,26
"5478",1199,50,428,4,14,"no","no","yes",152,26
"5479",1659,66,425,8,14,"yes","no","yes",152,26
"5480",1754,50,528,8,14,"yes","no","yes",152,26
"5481",2940,100,720,16,17,"yes","no","yes",152,26
"5482",2323,50,720,16,15,"yes","yes","yes",152,26
"5483",3054,100,1000,24,15,"yes","no","yes",152,26
"5484",2590,66,720,16,17,"yes","no","yes",152,26
"5485",3090,100,1000,24,15,"yes","no","yes",152,26
"5486",1398,50,540,8,14,"yes","yes","yes",152,26
"5487",2124,66,425,8,17,"yes","no","yes",152,26
"5488",1699,50,212,8,14,"yes","yes","yes",152,26
"5489",1748,50,545,4,17,"yes","no","yes",152,26
"5490",2598,66,730,16,15,"yes","yes","yes",152,26
"5491",1989,66,425,8,15,"yes","yes","yes",152,26
"5492",2154,100,528,8,14,"yes","no","yes",152,26
"5493",1795,100,340,4,14,"no","no","yes",152,26
"5494",2198,100,420,8,14,"yes","yes","yes",152,26
"5495",1490,50,340,4,15,"no","no","yes",152,26
"5496",1999,100,545,8,15,"yes","yes","yes",152,26
"5497",2595,100,720,16,15,"yes","no","yes",152,26
"5498",2223,100,528,8,14,"yes","yes","yes",152,26
"5499",3194,100,1000,24,15,"yes","no","yes",152,26
"5500",1890,100,340,4,15,"no","no","yes",152,26
"5501",1599,66,428,4,15,"yes","yes","yes",152,26
"5502",1899,66,540,8,15,"yes","yes","yes",152,26
"5503",2595,66,1000,24,15,"yes","no","yes",152,26
"5504",1599,66,545,4,15,"yes","no","yes",152,26
"5505",1938,66,545,4,17,"yes","no","yes",152,26
"5506",1848,66,545,4,17,"yes","no","yes",152,26
"5507",2654,50,1000,24,15,"yes","no","yes",152,26
"5508",1848,66,428,4,17,"yes","yes","yes",152,26
"5509",1599,66,540,8,15,"no","no","yes",152,26
"5510",3340,100,1000,24,17,"yes","no","yes",152,26
"5511",2740,66,1000,24,15,"yes","no","yes",152,26
"5512",2690,50,1000,24,15,"yes","no","yes",152,26
"5513",1749,66,545,8,15,"yes","no","yes",152,26
"5514",2745,100,1000,24,15,"yes","no","yes",152,26
"5515",1599,66,540,8,14,"yes","no","yes",152,26
"5516",1899,33,212,8,14,"yes","yes","yes",152,26
"5517",2898,100,730,16,15,"yes","yes","yes",152,26
"5518",2545,50,1000,24,15,"yes","no","yes",152,26
"5519",1699,66,270,4,14,"no","no","no",152,26
"5520",1199,33,210,4,14,"no","no","yes",152,26
"5521",1790,50,528,8,15,"yes","no","yes",152,26
"5522",2844,50,1000,24,15,"yes","no","yes",152,26
"5523",1499,50,428,4,15,"yes","yes","yes",152,26
"5524",1898,66,420,8,14,"yes","yes","yes",152,26
"5525",1749,66,425,8,15,"yes","no","yes",152,26
"5526",1899,66,730,8,15,"yes","no","yes",152,26
"5527",1899,66,425,8,14,"yes","yes","yes",152,26
"5528",1540,66,340,4,14,"no","no","yes",152,26
"5529",1938,66,428,4,17,"yes","yes","yes",152,26
"5530",1873,66,528,8,14,"yes","yes","yes",152,26
"5531",1699,66,420,8,14,"yes","no","yes",152,26
"5532",1799,66,420,8,14,"yes","yes","yes",152,26
"5533",1299,50,540,8,14,"yes","no","yes",152,26
"5534",2245,66,720,16,15,"yes","no","yes",152,26
"5535",1695,50,528,8,14,"yes","no","yes",152,26
"5536",1840,66,528,8,15,"yes","no","yes",152,26
"5537",2248,100,545,8,17,"yes","yes","yes",152,26
"5538",1644,50,340,4,14,"no","no","yes",152,26
"5539",1838,50,545,4,17,"yes","no","yes",152,26
"5540",2873,100,1000,24,15,"yes","yes","yes",163,27
"5541",1398,66,340,4,14,"yes","yes","yes",163,27
"5542",1899,100,540,8,15,"yes","no","yes",163,27
"5543",1845,100,528,8,14,"yes","no","yes",163,27
"5544",1754,66,528,8,14,"yes","no","yes",163,27
"5545",1799,66,420,8,14,"yes","yes","yes",163,27
"5546",1699,66,420,8,14,"yes","no","yes",163,27
"5547",2224,100,545,8,17,"yes","yes","yes",163,27
"5548",2394,50,850,16,15,"yes","no","yes",163,27
"5549",1799,66,730,8,15,"yes","no","yes",163,27
"5550",2108,75,545,8,17,"yes","no","yes",163,27
"5551",1399,33,210,4,14,"yes","yes","yes",163,27
"5552",2699,100,730,16,15,"yes","no","yes",163,27
"5553",1828,66,270,4,14,"no","no","no",163,27
"5554",1808,50,545,4,17,"yes","no","yes",163,27
"5555",1299,66,340,4,14,"yes","no","yes",163,27
"5556",2444,66,850,16,15,"yes","no","yes",163,27
"5557",1898,66,420,8,14,"yes","yes","yes",163,27
"5558",2690,66,1000,24,15,"yes","no","yes",163,27
"5559",1974,66,545,8,17,"yes","no","yes",163,27
"5560",2208,100,545,8,17,"yes","no","yes",163,27
"5561",2440,100,850,16,15,"yes","no","yes",163,27
"5562",1890,75,730,8,15,"yes","yes","yes",163,27
"5563",2673,50,1000,24,15,"yes","yes","yes",163,27
"5564",1999,100,545,8,15,"yes","yes","yes",163,27
"5565",2224,100,850,8,17,"yes","no","yes",163,27
"5566",1895,50,364,8,14,"no","no","no",163,27
"5567",1680,75,540,8,15,"yes","no","yes",163,27
"5568",1645,50,528,8,14,"yes","no","yes",163,27
"5569",1490,66,340,4,14,"no","no","yes",163,27
"5570",2095,66,528,8,15,"yes","no","yes",163,27
"5571",2473,100,850,16,15,"yes","yes","yes",163,27
"5572",1790,66,528,8,14,"yes","no","yes",163,27
"5573",1449,66,270,8,14,"no","no","yes",163,27
"5574",2024,75,545,8,17,"yes","no","yes",163,27
"5575",2290,66,850,16,15,"yes","no","yes",163,27
"5576",2890,50,1000,24,17,"yes","no","yes",163,27
"5577",2690,100,850,16,17,"yes","no","yes",163,27
"5578",2145,66,364,8,14,"no","no","no",163,27
"5579",2308,100,545,8,17,"yes","yes","yes",163,27
"5580",2094,100,528,8,14,"yes","no","yes",163,27
"5581",1644,66,340,4,14,"no","no","yes",163,27
"5582",1440,50,340,4,14,"no","no","yes",163,27
"5583",1640,100,340,4,15,"no","no","yes",163,27
"5584",1775,75,540,8,15,"yes","yes","yes",163,27
"5585",1799,100,425,8,14,"yes","no","yes",163,27
"5586",1440,50,340,4,15,"no","no","yes",163,27
"5587",1545,100,340,4,14,"no","no","yes",163,27
"5588",2994,100,1000,24,15,"yes","no","yes",163,27
"5589",1740,66,540,8,15,"yes","yes","yes",163,27
"5590",2723,66,1000,24,15,"yes","yes","yes",163,27
"5591",2840,100,1000,24,15,"yes","no","yes",163,27
"5592",2208,75,545,8,17,"yes","yes","yes",163,27
"5593",1395,66,340,4,14,"no","no","yes",163,27
"5594",2145,50,850,16,15,"yes","no","yes",163,27
"5595",2315,75,1080,16,15,"yes","yes","yes",163,27
"5596",1795,75,730,8,15,"yes","no","yes",163,27
"5597",1545,75,540,8,15,"no","no","yes",163,27
"5598",1299,66,270,4,14,"no","no","yes",163,27
"5599",1908,66,428,4,17,"yes","yes","yes",163,27
"5600",1904,100,528,8,14,"yes","no","yes",163,27
"5601",1580,66,540,8,15,"yes","no","yes",163,27
"5602",1445,66,528,8,14,"no","no","yes",163,27
"5603",1999,100,420,8,14,"yes","no","yes",163,27
"5604",2198,100,420,8,14,"yes","yes","yes",163,27
"5605",2124,100,545,8,17,"yes","no","yes",163,27
"5606",2595,66,1000,24,15,"yes","no","yes",163,27
"5607",2124,75,545,8,17,"yes","yes","yes",163,27
"5608",1459,50,425,4,14,"yes","yes","yes",163,27
"5609",1499,50,428,4,15,"yes","yes","yes",163,27
"5610",1740,50,528,8,14,"yes","no","yes",163,27
"5611",1940,100,528,8,15,"yes","no","yes",163,27
"5612",2035,100,1000,8,14,"yes","no","yes",163,27
"5613",2645,100,364,8,14,"no","no","no",163,27
"5614",1840,75,540,8,15,"yes","yes","yes",163,27
"5615",1454,66,340,4,14,"no","no","yes",163,27
"5616",1908,66,545,4,17,"yes","no","yes",163,27
"5617",1599,66,428,4,15,"yes","yes","yes",163,27
"5618",1740,50,528,8,15,"yes","no","yes",163,27
"5619",1675,66,540,8,15,"yes","yes","yes",163,27
"5620",1940,100,528,8,14,"yes","no","yes",163,27
"5621",1490,66,340,4,15,"no","no","yes",163,27
"5622",1245,66,340,4,14,"no","no","yes",163,27
"5623",2794,50,1000,24,15,"yes","no","yes",163,27
"5624",1659,66,340,4,17,"yes","no","yes",163,27
"5625",2490,50,850,16,17,"yes","no","yes",163,27
"5626",1735,50,1000,8,14,"yes","no","yes",163,27
"5627",1695,66,730,8,15,"yes","no","yes",163,27
"5628",1749,66,545,8,15,"yes","no","yes",163,27
"5629",1944,66,528,8,14,"yes","no","yes",163,27
"5630",1499,66,365,8,15,"no","no","yes",163,27
"5631",2308,100,850,8,17,"yes","no","yes",163,27
"5632",2345,100,850,16,15,"yes","no","yes",163,27
"5633",1885,66,1000,8,14,"yes","no","yes",163,27
"5634",1199,33,210,4,14,"no","no","yes",163,27
"5635",1899,75,545,8,15,"yes","yes","yes",163,27
"5636",1640,100,340,4,14,"no","no","yes",163,27
"5637",2273,50,850,16,15,"yes","yes","yes",163,27
"5638",1704,50,528,8,14,"yes","no","yes",163,27
"5639",1824,66,545,4,17,"yes","no","yes",163,27
"5640",1199,50,428,4,14,"no","no","yes",163,27
"5641",2195,66,850,16,15,"yes","no","yes",163,27
"5642",1594,50,340,4,14,"no","no","yes",163,27
"5643",2545,50,1000,24,15,"yes","no","yes",163,27
"5644",1808,50,428,4,17,"yes","yes","yes",163,27
"5645",1745,75,540,8,15,"yes","no","yes",163,27
"5646",2095,75,730,16,15,"yes","yes","yes",163,27
"5647",2640,50,1000,24,15,"yes","no","yes",163,27
"5648",1195,50,340,4,14,"no","no","yes",163,27
"5649",2058,66,545,8,17,"yes","no","yes",163,27
"5650",1395,50,528,8,14,"no","no","yes",163,27
"5651",1765,66,540,8,17,"no","no","yes",163,27
"5652",1604,100,340,4,14,"no","no","yes",163,27
"5653",1799,66,540,8,14,"yes","yes","yes",163,27
"5654",1773,50,528,8,14,"yes","yes","yes",163,27
"5655",1679,50,540,8,14,"yes","yes","yes",163,27
"5656",1578,50,270,4,14,"no","no","no",163,27
"5657",2654,66,1000,24,15,"yes","no","yes",163,27
"5658",1749,100,540,8,15,"no","no","yes",163,27
"5659",2940,66,1000,24,17,"yes","no","yes",163,27
"5660",2415,75,730,16,17,"yes","yes","yes",163,27
"5661",1794,100,340,4,14,"no","no","yes",163,27
"5662",1824,66,428,4,17,"yes","yes","yes",163,27
"5663",1799,75,545,8,15,"yes","no","yes",163,27
"5664",1949,100,730,8,14,"yes","no","yes",163,27
"5665",1999,100,850,8,15,"yes","no","yes",163,27
"5666",1865,75,540,8,17,"no","no","yes",163,27
"5667",2898,100,730,16,15,"yes","yes","yes",163,27
"5668",2328,100,270,4,14,"no","no","no",163,27
"5669",2594,100,850,16,15,"yes","no","yes",163,27
"5670",1599,66,545,4,15,"yes","no","yes",163,27
"5671",2204,50,850,16,15,"yes","no","yes",163,27
"5672",2155,75,850,16,15,"yes","yes","yes",163,27
"5673",1404,50,340,4,14,"no","no","yes",163,27
"5674",2059,66,730,8,17,"yes","no","yes",163,27
"5675",2604,50,1000,24,15,"yes","no","yes",163,27
"5676",2540,66,850,16,17,"yes","no","yes",163,27
"5677",1724,50,428,4,17,"yes","yes","yes",163,27
"5678",3090,100,1000,24,17,"yes","no","yes",163,27
"5679",2598,66,730,16,15,"yes","yes","yes",163,27
"5680",1299,66,428,4,14,"no","no","yes",163,27
"5681",1724,50,545,4,17,"yes","no","yes",163,27
"5682",1823,66,528,8,14,"yes","yes","yes",163,27
"5683",2844,66,1000,24,15,"yes","no","yes",163,27
"5684",1790,66,528,8,15,"yes","no","yes",163,27
"5685",1345,50,340,4,14,"no","no","yes",163,27
"5686",2323,66,850,16,15,"yes","yes","yes",163,27
"5687",1445,66,540,8,15,"no","no","yes",163,27
"5688",2240,50,850,16,15,"yes","no","yes",163,27
"5689",1899,100,545,8,15,"yes","no","yes",163,27
"5690",2159,66,540,8,17,"yes","yes","yes",163,27
"5691",1898,66,730,8,15,"yes","yes","yes",163,27
"5692",2254,66,850,16,15,"yes","no","yes",163,27
"5693",2745,100,1000,24,15,"yes","no","yes",163,27
"5694",1499,50,545,4,15,"yes","no","yes",163,27
"5695",1699,66,270,4,14,"no","no","no",163,27
"5696",2404,100,850,16,15,"yes","no","yes",163,27
"5697",2399,66,730,16,15,"yes","no","yes",163,27
"5698",1894,50,528,8,14,"yes","no","yes",163,27
"5699",1873,100,528,8,14,"yes","yes","yes",163,27
"5700",2804,100,1000,24,15,"yes","no","yes",163,27
"5701",1695,66,528,8,14,"yes","no","yes",163,27
"5702",1645,66,540,8,15,"yes","no","yes",163,27
"5703",1645,66,528,8,14,"yes","no","yes",132,28
"5704",1445,66,540,8,15,"no","no","yes",132,28
"5705",1799,100,420,8,14,"yes","no","yes",132,28
"5706",1823,66,528,8,14,"yes","yes","yes",132,28
"5707",1745,75,730,8,15,"yes","no","yes",132,28
"5708",1695,66,730,8,15,"yes","no","yes",132,28
"5709",1404,50,340,4,14,"no","no","yes",132,28
"5710",1715,75,540,8,15,"yes","no","yes",132,28
"5711",2540,66,850,16,17,"yes","no","yes",132,28
"5712",2940,66,1000,24,17,"yes","no","yes",132,28
"5713",2745,100,1000,24,15,"yes","no","yes",132,28
"5714",1299,66,340,4,14,"yes","no","yes",132,28
"5715",1525,75,540,8,15,"no","no","yes",132,28
"5716",2308,100,850,8,17,"yes","no","yes",132,28
"5717",2844,66,1000,24,15,"yes","no","yes",132,28
"5718",1845,100,528,8,14,"yes","no","yes",132,28
"5719",1660,75,540,8,15,"yes","no","yes",132,28
"5720",2224,100,850,8,17,"yes","no","yes",132,28
"5721",1345,50,340,4,14,"no","no","yes",132,28
"5722",1829,66,420,8,14,"no","no","no",132,28
"5723",1899,100,545,8,15,"yes","no","yes",132,28
"5724",1599,66,545,4,15,"yes","no","yes",132,28
"5725",2345,100,850,16,15,"yes","no","yes",132,28
"5726",2794,50,1000,24,15,"yes","no","yes",132,28
"5727",2208,75,545,8,17,"yes","yes","yes",132,28
"5728",1349,66,270,8,15,"no","no","yes",132,28
"5729",1454,66,340,4,14,"no","no","yes",132,28
"5730",1885,66,1000,8,14,"yes","no","yes",132,28
"5731",2994,100,1000,24,15,"yes","no","yes",132,28
"5732",1625,75,850,8,15,"no","no","yes",132,28
"5733",1773,50,528,8,14,"yes","yes","yes",132,28
"5734",1904,100,528,8,14,"yes","no","yes",132,28
"5735",2804,100,1000,24,15,"yes","no","yes",132,28
"5736",2499,100,850,16,15,"yes","no","yes",132,28
"5737",2723,66,1000,24,15,"yes","yes","yes",132,28
"5738",2404,100,850,16,15,"yes","no","yes",132,28
"5739",1735,50,1000,8,14,"yes","no","yes",132,28
"5740",1749,66,545,8,15,"yes","no","yes",132,28
"5741",1790,66,528,8,15,"yes","no","yes",132,28
"5742",1594,50,340,4,14,"no","no","yes",132,28
"5743",1695,100,540,8,15,"no","no","yes",132,28
"5744",1704,50,528,8,14,"yes","no","yes",132,28
"5745",1695,66,528,8,14,"yes","no","yes",132,28
"5746",1195,50,340,4,14,"no","no","yes",132,28
"5747",3090,100,1000,24,17,"yes","no","yes",132,28
"5748",1754,66,528,8,14,"yes","no","yes",132,28
"5749",1245,66,340,4,14,"no","no","yes",132,28
"5750",2690,100,850,16,17,"yes","no","yes",132,28
"5751",2204,50,850,16,15,"yes","no","yes",132,28
"5752",1545,100,340,4,14,"no","no","yes",132,28
"5753",1999,100,850,8,15,"yes","no","yes",132,28
"5754",1794,100,340,4,14,"no","no","yes",132,28
"5755",1799,75,545,8,15,"yes","no","yes",132,28
"5756",1899,75,545,8,15,"yes","yes","yes",132,28
"5757",1974,66,545,8,17,"yes","no","yes",132,28
"5758",1599,66,545,4,15,"yes","yes","yes",132,28
"5759",2273,50,850,16,15,"yes","yes","yes",132,28
"5760",1640,100,340,4,15,"no","no","yes",132,28
"5761",1894,50,528,8,14,"yes","no","yes",132,28
"5762",1824,66,545,4,17,"yes","no","yes",132,28
"5763",1799,66,540,8,14,"yes","yes","yes",132,28
"5764",2145,50,850,16,15,"yes","no","yes",132,28
"5765",1575,75,730,8,15,"no","no","yes",132,28
"5766",2035,100,1000,8,14,"yes","no","yes",132,28
"5767",2604,50,1000,24,15,"yes","no","yes",132,28
"5768",2654,66,1000,24,15,"yes","no","yes",132,28
"5769",1999,100,545,8,15,"yes","yes","yes",132,28
"5770",1873,100,528,8,14,"yes","yes","yes",132,28
"5771",1545,66,850,8,15,"no","no","yes",132,28
"5772",1644,66,340,4,14,"no","no","yes",132,28
"5773",1699,66,420,8,14,"yes","no","yes",132,28
"5774",2224,100,545,8,17,"yes","yes","yes",132,28
"5775",1499,66,365,8,15,"no","no","yes",132,28
"5776",2444,66,850,16,15,"yes","no","yes",132,28
"5777",1824,66,545,4,17,"yes","yes","yes",132,28
"5778",2473,100,850,16,15,"yes","yes","yes",132,28
"5779",2490,50,850,16,17,"yes","no","yes",132,28
"5780",1908,66,545,4,17,"yes","yes","yes",132,28
"5781",1635,66,540,8,15,"yes","no","yes",132,28
"5782",1940,100,528,8,15,"yes","no","yes",132,28
"5783",2399,66,850,16,15,"yes","no","yes",132,28
"5784",2595,66,1000,24,15,"yes","no","yes",132,28
"5785",2545,50,1000,24,15,"yes","no","yes",132,28
"5786",1445,66,340,8,14,"no","no","yes",132,28
"5787",1299,66,545,4,14,"no","no","yes",132,28
"5788",1495,66,730,8,15,"no","no","yes",132,28
"5789",2058,66,545,8,17,"yes","no","yes",132,28
"5790",2159,66,540,8,17,"yes","yes","yes",132,28
"5791",2673,50,1000,24,15,"yes","yes","yes",132,28
"5792",2124,100,545,8,17,"yes","no","yes",132,28
"5793",1199,50,545,4,14,"no","no","yes",132,28
"5794",1724,50,545,4,17,"yes","yes","yes",132,28
"5795",1810,75,540,8,15,"yes","yes","yes",132,28
"5796",1840,75,730,8,15,"yes","yes","yes",132,28
"5797",1398,66,340,4,14,"yes","yes","yes",132,28
"5798",1908,66,545,4,17,"yes","no","yes",132,28
"5799",2124,75,545,8,17,"yes","yes","yes",132,28
"5800",1604,100,340,4,14,"no","no","yes",132,28
"5801",1499,50,545,4,15,"yes","no","yes",132,28
"5802",2308,100,545,8,17,"yes","yes","yes",132,28
"5803",1580,66,540,8,15,"yes","no","yes",132,28
"5804",1944,66,528,8,14,"yes","no","yes",132,28
"5805",1299,66,270,4,15,"no","no","yes",132,28
"5806",2323,66,850,16,15,"yes","yes","yes",132,28
"5807",1699,66,540,8,15,"yes","yes","yes",132,28
"5808",2208,100,545,8,17,"yes","no","yes",132,28
"5809",1659,66,340,4,17,"yes","no","yes",132,28
"5810",1395,66,340,4,14,"no","no","yes",132,28
"5811",1740,50,528,8,15,"yes","no","yes",132,28
"5812",1645,50,528,8,14,"yes","no","yes",132,28
"5813",1395,50,340,8,14,"no","no","yes",132,28
"5814",1440,50,340,4,15,"no","no","yes",132,28
"5815",1755,75,540,8,15,"yes","yes","yes",132,28
"5816",1199,33,210,4,14,"no","no","yes",132,28
"5817",2254,66,850,16,15,"yes","no","yes",132,28
"5818",2394,50,850,16,15,"yes","no","yes",132,28
"5819",1808,50,545,4,17,"yes","no","yes",132,28
"5820",2594,100,850,16,15,"yes","no","yes",132,28
"5821",1808,50,545,4,17,"yes","yes","yes",132,28
"5822",1790,66,730,8,15,"yes","yes","yes",132,28
"5823",1730,66,540,8,15,"yes","yes","yes",132,28
"5824",2873,100,1000,24,15,"yes","yes","yes",132,28
"5825",1499,66,425,4,14,"yes","yes","yes",132,28
"5826",1724,50,545,4,17,"yes","no","yes",132,28
"5827",1499,50,545,4,15,"yes","yes","yes",132,28
"5828",2890,50,1000,24,17,"yes","no","yes",132,28
"5829",2108,75,545,8,17,"yes","no","yes",132,28
"5830",1675,66,540,8,15,"yes","yes","yes",132,28
"5831",2094,100,528,8,14,"yes","no","yes",132,28
"5832",1490,66,340,4,15,"no","no","yes",132,28
"5833",2024,75,545,8,17,"yes","no","yes",132,28
"5834",2195,66,850,16,15,"yes","no","yes",132,28
"5835",1666,66,270,4,14,"no","no","no",129,29
"5836",2495,50,1000,24,15,"yes","no","yes",129,29
"5837",1794,100,340,4,14,"no","no","yes",129,29
"5838",2204,100,545,8,17,"yes","yes","yes",129,29
"5839",2744,50,1000,24,15,"yes","no","yes",129,29
"5840",2182,66,364,8,14,"yes","no","no",129,29
"5841",1940,100,528,8,15,"yes","no","yes",129,29
"5842",1865,66,270,4,14,"yes","no","no",129,29
"5843",2199,66,850,16,15,"yes","yes","yes",129,29
"5844",1999,66,540,16,14,"yes","yes","yes",129,29
"5845",1574,50,270,4,14,"no","no","no",129,29
"5846",2015,100,1000,8,14,"yes","no","yes",129,29
"5847",2715,100,850,16,17,"yes","yes","yes",129,29
"5848",1773,66,528,8,14,"yes","yes","yes",129,29
"5849",2623,50,1000,24,15,"yes","yes","yes",129,29
"5850",1881,100,270,4,14,"yes","no","no",129,29
"5851",1404,66,340,4,14,"no","no","yes",129,29
"5852",1704,50,545,4,17,"yes","yes","yes",129,29
"5853",2545,66,1000,24,15,"yes","no","yes",129,29
"5854",1499,66,540,8,15,"no","no","yes",129,29
"5855",2790,50,1000,24,17,"yes","no","yes",129,29
"5856",1829,66,420,8,14,"no","no","no",129,29
"5857",1299,66,340,4,14,"yes","no","yes",129,29
"5858",2154,50,850,16,15,"yes","no","yes",129,29
"5859",1398,66,340,4,14,"yes","yes","yes",129,29
"5860",2145,66,850,16,15,"yes","no","yes",129,29
"5861",1804,66,545,4,17,"yes","yes","yes",129,29
"5862",2208,75,545,8,17,"yes","yes","yes",129,29
"5863",2640,100,850,16,17,"yes","no","yes",129,29
"5864",2004,75,545,8,17,"yes","no","yes",129,29
"5865",1604,100,340,4,14,"no","no","yes",129,29
"5866",2095,50,850,16,15,"yes","no","yes",129,29
"5867",1804,66,545,4,17,"yes","no","yes",129,29
"5868",1808,50,545,4,17,"yes","yes","yes",129,29
"5869",1999,100,545,8,15,"yes","yes","yes",129,29
"5870",2058,66,545,8,17,"yes","no","yes",129,29
"5871",1908,66,545,4,17,"yes","yes","yes",129,29
"5872",1808,50,545,4,17,"yes","no","yes",129,29
"5873",1595,50,528,8,14,"yes","no","yes",129,29
"5874",1699,66,540,8,14,"yes","yes","yes",129,29
"5875",1845,100,528,8,14,"yes","no","yes",129,29
"5876",2104,100,545,8,17,"yes","no","yes",129,29
"5877",2394,66,850,16,15,"yes","no","yes",129,29
"5878",1799,66,420,16,14,"no","no","yes",129,29
"5879",2059,66,540,8,17,"yes","yes","yes",129,29
"5880",1345,66,340,4,14,"no","no","yes",129,29
"5881",1499,50,545,4,15,"yes","yes","yes",129,29
"5882",2545,100,1260,16,15,"yes","yes","yes",129,29
"5883",1799,75,545,8,15,"yes","no","yes",129,29
"5884",1899,75,545,8,15,"yes","yes","yes",129,29
"5885",2440,66,850,16,17,"yes","no","yes",129,29
"5886",1699,66,540,8,14,"yes","yes","yes",129,29
"5887",1773,50,270,4,14,"yes","no","no",129,29
"5888",3040,100,1000,24,17,"yes","no","yes",129,29
"5889",1904,100,528,8,14,"yes","no","yes",129,29
"5890",1395,66,528,8,14,"no","no","yes",129,29
"5891",1954,66,545,8,17,"yes","no","yes",129,29
"5892",1645,66,528,8,14,"yes","no","yes",129,29
"5893",1899,100,545,8,15,"yes","no","yes",129,29
"5894",1449,66,365,8,15,"no","no","yes",129,29
"5895",1549,66,420,8,14,"no","no","yes",129,29
"5896",1640,100,340,4,15,"no","no","yes",129,29
"5897",1299,66,365,4,15,"no","no","yes",129,29
"5898",2204,100,850,8,17,"yes","no","yes",129,29
"5899",2473,100,850,16,15,"yes","yes","yes",129,29
"5900",1354,50,340,4,14,"no","no","yes",129,29
"5901",2395,100,850,16,15,"yes","yes","yes",129,29
"5902",2204,66,850,16,15,"yes","no","yes",129,29
"5903",1765,50,1000,8,14,"yes","no","yes",129,29
"5904",1749,66,540,8,14,"yes","yes","yes",129,29
"5905",1594,66,340,4,14,"no","no","yes",129,29
"5906",1908,66,545,4,17,"yes","no","yes",129,29
"5907",1654,50,528,8,14,"yes","no","yes",129,29
"5908",2208,100,545,8,17,"yes","no","yes",129,29
"5909",2344,50,850,16,15,"yes","no","yes",129,29
"5910",1723,50,528,8,14,"yes","yes","yes",129,29
"5911",2090,50,364,8,14,"yes","no","no",129,29
"5912",2554,50,1000,24,15,"yes","no","yes",129,29
"5913",2604,66,1000,24,15,"yes","no","yes",129,29
"5914",1704,66,528,8,14,"yes","no","yes",129,29
"5915",1682,100,270,4,14,"no","no","no",129,29
"5916",1545,100,340,4,14,"no","no","yes",129,29
"5917",2108,75,545,8,17,"yes","no","yes",129,29
"5918",2378,100,364,8,14,"yes","no","no",129,29
"5919",1895,100,540,8,15,"no","no","yes",129,29
"5920",2804,100,1000,24,15,"yes","no","yes",129,29
"5921",1894,66,528,8,14,"yes","no","yes",129,29
"5922",2179,100,364,8,14,"no","no","no",129,29
"5923",2594,100,850,16,15,"yes","no","yes",129,29
"5924",1499,50,545,4,15,"yes","no","yes",129,29
"5925",1390,50,340,4,15,"no","no","yes",129,29
"5926",1299,66,545,4,14,"no","no","yes",129,29
"5927",1599,66,545,4,15,"yes","no","yes",129,29
"5928",1704,50,545,4,17,"yes","no","yes",129,29
"5929",2994,100,1000,24,15,"yes","no","yes",129,29
"5930",1599,66,545,4,15,"yes","yes","yes",129,29
"5931",1873,100,528,8,14,"yes","yes","yes",129,29
"5932",1659,66,340,4,17,"yes","no","yes",129,29
"5933",1499,66,420,8,14,"no","no","yes",129,29
"5934",1983,66,364,8,14,"no","no","no",129,29
"5935",1544,50,340,4,14,"no","no","yes",129,29
"5936",1815,66,1000,8,14,"yes","no","yes",129,29
"5937",1999,100,850,8,15,"yes","no","yes",129,29
"5938",2299,100,850,16,15,"yes","yes","yes",129,29
"5939",1749,66,545,8,15,"yes","no","yes",129,29
"5940",2873,100,1000,24,15,"yes","yes","yes",129,29
"5941",2104,75,545,8,17,"yes","yes","yes",129,29
"5942",1440,66,340,4,15,"no","no","yes",129,29
"5943",2390,50,850,16,17,"yes","no","yes",129,29
"5944",1599,66,540,8,14,"yes","yes","yes",129,29
"5945",2223,50,850,16,15,"yes","yes","yes",129,29
"5946",2745,100,1000,24,15,"yes","no","yes",129,29
"5947",1690,50,528,8,15,"yes","no","yes",129,29
"5948",1740,66,528,8,15,"yes","no","yes",129,29
"5949",2094,100,528,8,14,"yes","no","yes",129,29
"5950",1699,100,540,8,14,"yes","yes","yes",129,29
"5951",1844,50,528,8,14,"yes","no","yes",129,29
"5952",2840,66,1000,24,17,"yes","no","yes",129,29
"5953",2404,100,850,16,15,"yes","no","yes",129,29
"5954",2345,100,850,16,15,"yes","no","yes",129,29
"5955",2308,100,850,8,17,"yes","no","yes",129,29
"5956",2273,66,850,16,15,"yes","yes","yes",129,29
"5957",2673,66,1000,24,15,"yes","yes","yes",129,29
"5958",1199,50,545,4,14,"no","no","yes",129,29
"5959",2794,66,1000,24,15,"yes","no","yes",129,29
"5960",2308,100,545,8,17,"yes","yes","yes",129,29
"5961",3175,100,2100,16,15,"yes","yes","yes",129,29
"5962",1295,50,340,4,14,"no","no","yes",129,29
"5963",1891,50,364,8,14,"no","no","no",129,29
"5964",1773,66,850,8,14,"yes","yes","yes",75,30
"5965",2554,50,1200,24,15,"yes","no","yes",75,30
"5966",1540,100,528,4,15,"no","no","yes",75,30
"5967",2240,66,1200,16,15,"yes","no","yes",75,30
"5968",1994,100,850,8,14,"yes","no","yes",75,30
"5969",1704,66,850,8,14,"yes","no","yes",75,30
"5970",1699,66,540,8,14,"yes","yes","yes",75,30
"5971",2744,50,1200,24,15,"yes","no","yes",75,30
"5972",1895,100,540,8,15,"no","no","yes",75,30
"5973",2304,100,1000,16,15,"yes","no","yes",75,30
"5974",2095,50,1000,16,15,"yes","no","yes",75,30
"5975",1499,66,540,8,14,"yes","no","yes",75,30
"5976",1504,100,528,4,14,"no","no","yes",75,30
"5977",2840,75,1200,24,17,"yes","no","yes",75,30
"5978",2790,50,1200,24,17,"yes","no","yes",75,30
"5979",2440,66,1000,16,17,"yes","no","yes",75,30
"5980",2154,50,1000,16,15,"yes","no","yes",75,30
"5981",2145,66,1000,16,15,"yes","no","yes",75,30
"5982",2245,100,1000,16,15,"yes","no","yes",75,30
"5983",1840,100,850,8,15,"yes","no","yes",75,30
"5984",1615,100,1000,4,14,"no","no","yes",75,30
"5985",2344,50,1000,16,15,"yes","no","yes",75,30
"5986",2495,50,1200,24,15,"yes","no","yes",75,30
"5987",1804,100,850,8,14,"yes","no","yes",75,30
"5988",1465,50,1000,4,14,"no","no","yes",75,30
"5989",1295,50,528,4,14,"no","no","yes",75,30
"5990",1745,100,850,8,14,"yes","no","yes",75,30
"5991",1723,50,850,8,14,"yes","yes","yes",75,30
"5992",1859,66,540,8,17,"yes","no","yes",75,30
"5993",2340,100,1200,16,15,"yes","no","yes",75,30
"5994",1515,66,1000,4,14,"no","no","yes",75,30
"5995",2545,66,1200,24,15,"yes","no","yes",75,30
"5996",1544,50,528,4,14,"no","no","yes",75,30
"5997",1299,66,365,8,15,"no","no","yes",75,30
"5998",1799,66,730,8,14,"yes","yes","yes",75,30
"5999",1829,66,420,8,14,"no","no","no",75,30
"6000",2645,100,1200,24,15,"yes","no","yes",75,30
"6001",2190,50,1200,16,15,"yes","no","yes",75,30
"6002",2794,75,1200,24,15,"yes","no","yes",75,30
"6003",1594,66,528,4,14,"no","no","yes",75,30
"6004",1844,50,850,8,14,"yes","no","yes",75,30
"6005",2394,66,1000,16,15,"yes","no","yes",75,30
"6006",2673,75,1200,24,15,"yes","yes","yes",75,30
"6007",2604,75,1200,24,15,"yes","no","yes",75,30
"6008",2299,100,850,16,15,"yes","yes","yes",75,30
"6009",2540,100,1000,16,17,"yes","no","yes",75,30
"6010",2223,50,1000,16,15,"yes","yes","yes",75,30
"6011",1598,66,540,8,14,"yes","yes","yes",75,30
"6012",1699,100,540,8,14,"yes","yes","yes",75,30
"6013",1345,66,528,8,14,"no","no","yes",75,30
"6014",1354,50,528,4,14,"no","no","yes",75,30
"6015",2623,50,1200,24,15,"yes","yes","yes",75,30
"6016",1699,66,545,8,14,"yes","yes","yes",75,30
"6017",1645,66,850,8,14,"yes","no","yes",75,30
"6018",1873,100,850,8,14,"yes","yes","yes",75,30
"6019",1454,66,528,4,14,"no","no","yes",75,30
"6020",1345,66,528,4,14,"no","no","yes",75,30
"6021",1599,66,540,8,14,"yes","yes","yes",75,30
"6022",1694,100,528,4,14,"no","no","yes",75,30
"6023",1894,66,850,8,14,"yes","no","yes",75,30
"6024",1595,50,850,8,14,"yes","no","yes",75,30
"6025",2373,100,1000,16,15,"yes","yes","yes",75,30
"6026",1654,50,850,8,14,"yes","no","yes",75,30
"6027",1390,50,528,4,15,"no","no","yes",75,30
"6028",1440,66,528,4,15,"no","no","yes",75,30
"6029",2059,66,540,8,17,"yes","yes","yes",75,30
"6030",1690,50,850,8,15,"yes","no","yes",75,30
"6031",2204,66,1000,16,15,"yes","no","yes",75,30
"6032",2273,66,1000,16,15,"yes","yes","yes",75,30
"6033",1499,66,540,8,14,"no","no","yes",75,30
"6034",2390,50,1000,16,17,"yes","no","yes",75,30
"6035",1740,66,850,8,15,"yes","no","yes",75,30
"6036",1445,100,528,4,14,"no","no","yes",75,30
"6037",2494,100,1000,16,15,"yes","no","yes",75,30
"6038",2199,66,850,16,15,"yes","yes","yes",75,30
"6039",2304,100,1000,16,15,"yes","no","yes",52,31
"6040",1699,66,545,8,14,"yes","yes","yes",52,31
"6041",1745,100,850,8,14,"yes","no","yes",52,31
"6042",2373,100,1000,16,15,"yes","yes","yes",52,31
"6043",1465,66,1000,4,14,"no","no","yes",52,31
"6044",1723,66,850,8,14,"yes","yes","yes",52,31
"6045",2390,66,1000,16,17,"yes","no","yes",52,31
"6046",1949,66,420,8,15,"no","no","no",52,31
"6047",2344,66,1000,16,15,"yes","no","yes",52,31
"6048",1445,100,528,4,14,"no","no","yes",52,31
"6049",1994,100,850,8,14,"yes","no","yes",52,31
"6050",1354,66,528,4,14,"no","no","yes",52,31
"6051",1540,100,528,4,15,"no","no","yes",52,31
"6052",1699,66,540,8,14,"yes","yes","yes",52,31
"6053",1544,66,528,4,14,"no","no","yes",52,31
"6054",1299,66,365,8,15,"no","no","yes",52,31
"6055",2495,66,1200,24,15,"yes","no","yes",52,31
"6056",2245,100,1000,16,15,"yes","no","yes",52,31
"6057",1295,66,528,4,14,"no","no","yes",52,31
"6058",1873,100,850,8,14,"yes","yes","yes",52,31
"6059",2223,66,1000,16,15,"yes","yes","yes",52,31
"6060",2645,100,1200,24,15,"yes","no","yes",52,31
"6061",1504,100,528,4,14,"no","no","yes",52,31
"6062",1390,66,528,4,15,"no","no","yes",52,31
"6063",2623,75,1200,24,15,"yes","yes","yes",52,31
"6064",2059,66,540,8,17,"yes","yes","yes",52,31
"6065",1699,100,540,8,14,"yes","yes","yes",52,31
"6066",1345,66,528,8,14,"no","no","yes",52,31
"6067",1809,66,540,8,17,"yes","no","yes",52,31
"6068",2790,75,1200,24,17,"yes","no","yes",52,31
"6069",2540,100,1000,16,17,"yes","no","yes",52,31
"6070",2554,75,1200,24,15,"yes","no","yes",52,31
"6071",1840,100,850,8,15,"yes","no","yes",52,31
"6072",1945,100,540,8,15,"no","no","yes",52,31
"6073",1615,100,1000,4,14,"no","no","yes",52,31
"6074",2340,100,1200,16,15,"yes","no","yes",52,31
"6075",2095,66,1000,16,15,"yes","no","yes",52,31
"6076",1804,100,850,8,14,"yes","no","yes",52,31
"6077",1844,66,850,8,14,"yes","no","yes",52,31
"6078",2494,100,1000,16,15,"yes","no","yes",52,31
"6079",1449,66,540,8,14,"yes","no","yes",52,31
"6080",2154,66,1000,16,15,"yes","no","yes",52,31
"6081",1548,66,540,8,14,"yes","yes","yes",52,31
"6082",2744,75,1200,24,15,"yes","no","yes",52,31
"6083",2190,66,1200,16,15,"yes","no","yes",52,31
"6084",2199,66,850,16,15,"yes","yes","yes",52,31
"6085",1595,66,850,8,14,"yes","no","yes",52,31
"6086",1654,66,850,8,14,"yes","no","yes",52,31
"6087",2299,100,850,16,15,"yes","yes","yes",52,31
"6088",1694,100,528,4,14,"no","no","yes",52,31
"6089",1690,66,850,8,15,"yes","no","yes",52,31
"6090",1599,66,540,8,14,"yes","yes","yes",52,31
"6091",1844,66,850,8,14,"yes","no","yes",51,32
"6092",1723,66,850,8,14,"yes","yes","yes",51,32
"6093",1699,100,540,8,14,"yes","yes","yes",51,32
"6094",2190,66,1200,16,15,"yes","no","yes",51,32
"6095",1354,66,528,4,14,"no","no","yes",51,32
"6096",1295,66,528,4,14,"no","no","yes",51,32
"6097",1649,66,540,8,14,"yes","yes","yes",51,32
"6098",2049,66,540,8,17,"yes","yes","yes",51,32
"6099",1345,66,528,8,14,"no","no","yes",51,32
"6100",1694,100,528,4,14,"no","no","yes",51,32
"6101",1949,66,420,8,15,"no","no","no",51,32
"6102",2623,75,1200,24,15,"yes","yes","yes",51,32
"6103",1690,66,850,8,15,"yes","no","yes",51,32
"6104",2223,66,1000,16,15,"yes","yes","yes",51,32
"6105",1544,66,528,4,14,"no","no","yes",51,32
"6106",1599,66,545,8,14,"yes","yes","yes",51,32
"6107",2199,66,850,16,15,"yes","yes","yes",51,32
"6108",1449,66,540,8,14,"yes","no","yes",51,32
"6109",2154,66,1000,16,15,"yes","no","yes",51,32
"6110",1504,100,528,4,14,"no","no","yes",51,32
"6111",1445,66,1000,4,14,"no","no","yes",51,32
"6112",2304,100,1000,16,15,"yes","no","yes",51,32
"6113",1994,100,850,8,14,"yes","no","yes",51,32
"6114",1548,66,540,8,14,"yes","yes","yes",51,32
"6115",2494,100,1000,16,15,"yes","no","yes",51,32
"6116",2590,75,1200,24,15,"yes","no","yes",51,32
"6117",1849,66,540,8,17,"yes","no","yes",51,32
"6118",2790,75,1200,24,17,"yes","no","yes",51,32
"6119",2299,100,850,16,15,"yes","yes","yes",51,32
"6120",2495,66,1200,24,15,"yes","no","yes",51,32
"6121",1595,66,850,8,14,"yes","no","yes",51,32
"6122",2373,100,1000,16,15,"yes","yes","yes",51,32
"6123",2645,100,1200,24,15,"yes","no","yes",51,32
"6124",2245,100,1000,16,15,"yes","no","yes",51,32
"6125",1390,66,528,4,15,"no","no","yes",51,32
"6126",1595,100,1000,4,14,"no","no","yes",51,32
"6127",2344,66,1000,16,15,"yes","no","yes",51,32
"6128",1599,66,540,8,14,"yes","yes","yes",51,32
"6129",2744,75,1200,24,15,"yes","no","yes",51,32
"6130",2540,100,1000,16,17,"yes","no","yes",51,32
"6131",1804,100,850,8,14,"yes","no","yes",51,32
"6132",1870,100,540,8,15,"no","no","yes",51,32
"6133",1840,100,850,8,15,"yes","no","yes",51,32
"6134",1745,100,850,8,14,"yes","no","yes",51,32
"6135",2340,100,1200,16,15,"yes","no","yes",51,32
"6136",1873,100,850,8,14,"yes","yes","yes",51,32
"6137",2390,66,1000,16,17,"yes","no","yes",51,32
"6138",2095,66,1000,16,15,"yes","no","yes",51,32
"6139",1540,100,528,4,15,"no","no","yes",51,32
"6140",1654,66,850,8,14,"yes","no","yes",51,32
"6141",1445,100,528,4,14,"no","no","yes",51,32
"6142",1644,100,528,4,14,"no","no","yes",40,33
"6143",2044,100,850,8,15,"yes","no","yes",40,33
"6144",1354,66,528,4,14,"no","no","yes",40,33
"6145",1345,66,528,8,14,"no","no","yes",40,33
"6146",1823,66,850,8,15,"yes","yes","yes",40,33
"6147",2790,75,1200,24,17,"yes","no","yes",40,33
"6148",2495,66,1200,24,15,"yes","no","yes",40,33
"6149",2223,75,1200,24,15,"yes","yes","yes",40,33
"6150",1454,100,528,4,14,"no","no","yes",40,33
"6151",1795,100,850,8,15,"yes","no","yes",40,33
"6152",2554,75,1200,24,15,"yes","no","yes",40,33
"6153",1695,66,850,8,15,"yes","no","yes",40,33
"6154",1923,100,850,8,15,"yes","yes","yes",40,33
"6155",2090,100,850,8,17,"yes","no","yes",40,33
"6156",1390,66,528,4,15,"no","no","yes",40,33
"6157",2095,66,1000,16,15,"yes","no","yes",40,33
"6158",1923,100,1000,16,15,"yes","yes","yes",40,33
"6159",2254,100,1000,16,15,"yes","no","yes",40,33
"6160",1990,66,850,8,17,"yes","no","yes",40,33
"6161",2154,66,1000,16,15,"yes","no","yes",40,33
"6162",2595,100,1200,24,15,"yes","no","yes",40,33
"6163",1544,66,528,4,14,"no","no","yes",40,33
"6164",1854,100,850,8,15,"yes","no","yes",40,33
"6165",2344,66,1000,16,15,"yes","no","yes",40,33
"6166",2390,66,1000,16,17,"yes","no","yes",40,33
"6167",1949,66,420,8,15,"no","no","no",40,33
"6168",1823,66,1000,16,15,"yes","yes","yes",40,33
"6169",2290,100,1200,16,15,"yes","no","yes",40,33
"6170",2195,100,1000,16,15,"yes","no","yes",40,33
"6171",1754,66,850,8,15,"yes","no","yes",40,33
"6172",1445,66,1000,4,14,"no","no","yes",40,33
"6173",1490,100,528,4,15,"no","no","yes",40,33
"6174",1944,66,850,8,15,"yes","no","yes",40,33
"6175",2490,100,1000,16,17,"yes","no","yes",40,33
"6176",1395,100,528,4,14,"no","no","yes",40,33
"6177",2444,100,1000,16,15,"yes","no","yes",40,33
"6178",1295,66,528,4,14,"no","no","yes",40,33
"6179",2744,75,1200,24,15,"yes","no","yes",40,33
"6180",1545,100,1000,4,14,"no","no","yes",40,33
"6181",2190,66,1200,16,15,"yes","no","yes",40,33
"6182",1590,66,528,8,15,"no","no","yes",39,34
"6183",2844,100,1200,24,15,"yes","no","yes",39,34
"6184",2444,100,850,16,15,"yes","no","yes",39,34
"6185",2554,66,1200,24,15,"yes","no","yes",39,34
"6186",3123,75,1600,32,15,"yes","yes","yes",39,34
"6187",1690,100,528,8,15,"no","no","yes",39,34
"6188",1844,100,528,8,14,"no","no","yes",39,34
"6189",2790,66,1200,24,17,"yes","no","yes",39,34
"6190",2390,66,850,16,17,"yes","no","yes",39,34
"6191",1699,66,630,8,15,"no","no","no",39,34
"6192",2154,66,850,16,15,"yes","no","yes",39,34
"6193",2095,66,850,16,15,"yes","no","yes",39,34
"6194",3290,75,1600,32,17,"yes","no","yes",39,34
"6195",2490,100,850,16,17,"yes","no","yes",39,34
"6196",1495,66,528,8,14,"no","no","yes",39,34
"6197",1840,100,1200,8,14,"no","no","yes",39,34
"6198",2223,66,850,16,15,"yes","yes","yes",39,34
"6199",1595,100,528,8,14,"no","no","yes",39,34
"6200",3095,100,1600,32,15,"yes","no","yes",39,34
"6201",3054,75,1600,32,15,"yes","no","yes",39,34
"6202",2885,100,1600,24,15,"yes","no","yes",39,34
"6203",2595,100,1200,24,15,"yes","no","yes",39,34
"6204",2195,100,850,16,15,"yes","no","yes",39,34
"6205",2723,100,1200,24,15,"yes","yes","yes",39,34
"6206",2995,66,1600,32,15,"yes","no","yes",39,34
"6207",2654,100,1200,24,15,"yes","no","yes",39,34
"6208",2323,100,850,16,15,"yes","yes","yes",39,34
"6209",2495,66,1200,24,15,"yes","no","yes",39,34
"6210",3244,75,1600,32,15,"yes","no","yes",39,34
"6211",2254,100,850,16,15,"yes","no","yes",39,34
"6212",2623,66,1200,24,15,"yes","yes","yes",39,34
"6213",2890,100,1200,24,17,"yes","no","yes",39,34
"6214",1740,66,1200,8,14,"no","no","yes",39,34
"6215",1554,66,528,8,14,"no","no","yes",39,34
"6216",1654,100,528,8,14,"no","no","yes",39,34
"6217",2344,66,850,16,15,"yes","no","yes",39,34
"6218",2744,66,1200,24,15,"yes","no","yes",39,34
"6219",2785,66,1600,24,15,"yes","no","yes",39,34
"6220",1744,66,528,8,14,"no","no","yes",39,34
"6221",2444,100,850,16,15,"yes","no","yes",39,35
"6222",2595,100,1200,24,15,"yes","no","yes",39,35
"6223",2844,100,1200,24,15,"yes","no","yes",39,35
"6224",1790,100,1200,8,14,"no","no","yes",39,35
"6225",1590,66,528,8,15,"no","no","yes",39,35
"6226",3244,75,1600,32,15,"yes","no","yes",39,35
"6227",3123,75,1600,32,15,"yes","yes","yes",39,35
"6228",2554,66,1200,24,15,"yes","no","yes",39,35
"6229",2323,100,850,16,15,"yes","yes","yes",39,35
"6230",2344,66,850,16,15,"yes","no","yes",39,35
"6231",2890,100,1200,24,17,"yes","no","yes",39,35
"6232",2390,66,850,16,17,"yes","no","yes",39,35
"6233",1495,66,528,8,14,"no","no","yes",39,35
"6234",1844,100,528,8,14,"no","no","yes",39,35
"6235",1699,66,630,8,15,"no","no","no",39,35
"6236",3290,75,1600,32,17,"yes","no","yes",39,35
"6237",2790,66,1200,24,17,"yes","no","yes",39,35
"6238",2845,100,1600,24,15,"yes","no","yes",39,35
"6239",1744,66,528,8,14,"no","no","yes",39,35
"6240",3095,100,1600,32,15,"yes","no","yes",39,35
"6241",1595,100,528,8,14,"no","no","yes",39,35
"6242",2254,100,850,16,15,"yes","no","yes",39,35
"6243",2723,100,1200,24,15,"yes","yes","yes",39,35
"6244",2745,66,1600,24,15,"yes","no","yes",39,35
"6245",3054,75,1600,32,15,"yes","no","yes",39,35
"6246",2744,66,1200,24,15,"yes","no","yes",39,35
"6247",1554,66,528,8,14,"no","no","yes",39,35
"6248",1654,100,528,8,14,"no","no","yes",39,35
"6249",2623,66,1200,24,15,"yes","yes","yes",39,35
"6250",2095,66,850,16,15,"yes","no","yes",39,35
"6251",2495,66,1200,24,15,"yes","no","yes",39,35
"6252",2995,66,1600,32,15,"yes","no","yes",39,35
"6253",1690,66,1200,8,14,"no","no","yes",39,35
"6254",2154,66,850,16,15,"yes","no","yes",39,35
"6255",1690,100,528,8,15,"no","no","yes",39,35
"6256",2223,66,850,16,15,"yes","yes","yes",39,35
"6257",2654,100,1200,24,15,"yes","no","yes",39,35
"6258",2195,100,850,16,15,"yes","no","yes",39,35
"6259",2490,100,850,16,17,"yes","no","yes",39,35


================================================
FILE: data/Dataset1-Media-Example-EDGES.csv
================================================
from,to,weight,type
s01,s02,10,hyperlink
s01,s02,12,hyperlink
s01,s03,22,hyperlink
s01,s04,21,hyperlink
s04,s11,22,mention
s05,s15,21,mention
s06,s17,21,mention
s08,s09,11,mention
s08,s09,12,mention
s03,s04,22,hyperlink
s04,s03,23,hyperlink
s01,s15,20,mention
s15,s01,11,hyperlink
s15,s01,11,hyperlink
s16,s17,21,mention
s16,s06,23,hyperlink
s06,s16,21,hyperlink
s09,s10,21,mention
s08,s07,21,mention
s07,s08,22,mention
s07,s10,21,hyperlink
s05,s02,21,hyperlink
s02,s03,21,hyperlink
s02,s01,23,hyperlink
s03,s01,21,hyperlink
s12,s13,22,hyperlink
s12,s14,22,mention
s14,s13,21,mention
s13,s12,21,hyperlink
s05,s09,2,hyperlink
s02,s10,5,hyperlink
s03,s12,1,hyperlink
s04,s06,1,mention
s10,s03,2,hyperlink
s03,s10,2,mention
s04,s12,3,hyperlink
s13,s17,1,mention
s06,s06,1,hyperlink
s14,s11,1,mention
s03,s11,1,hyperlink
s12,s06,2,mention
s04,s17,2,mention
s17,s04,4,hyperlink
s08,s03,2,hyperlink
s03,s08,4,hyperlink
s07,s14,4,mention
s15,s06,4,hyperlink
s15,s04,1,hyperlink
s05,s01,1,mention
s02,s09,1,hyperlink
s03,s05,1,hyperlink
s07,s03,1,mention


================================================
FILE: data/Dataset1-Media-Example-NODES.csv
================================================
id,media,media.type,type.label,audience.size
s01,NY Times,1,Newspaper,20
s02,Washington Post,1,Newspaper,25
s03,Wall Street Journal,1,Newspaper,30
s04,USA Today,1,Newspaper,32
s05,LA Times,1,Newspaper,20
s06,New York Post,1,Newspaper,50
s07,CNN,2,TV,56
s08,MSNBC,2,TV,34
s09,FOX News,2,TV,60
s10,ABC,2,TV,23
s11,BBC,2,TV,34
s12,Yahoo News,3,Online,33
s13,Google News,3,Online,23
s14,Reuters.com,3,Online,12
s15,NYTimes.com,3,Online,24
s16,WashingtonPost.com,3,Online,28
s17,AOL.com,3,Online,33


================================================
FILE: data/Dataset2-Media-User-Example-EDGES.csv
================================================
,U01,U02,U03,U04,U05,U06,U07,U08,U09,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20
s01,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
s02,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
s03,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0
s04,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0
s05,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0
s06,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0
s07,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0
s08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0
s09,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1
s10,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0


================================================
FILE: data/Dataset2-Media-User-Example-NODES.csv
================================================
id,media,media.type,media.name,audience.size
s01,NYT,1,Newspaper,20
s02,WaPo,1,Newspaper,25
s03,WSJ,1,Newspaper,30
s04,USAT,1,Newspaper,32
s05,LATimes,1,Newspaper,20
s06,CNN,2,TV,56
s07,MSNBC,2,TV,34
s08,FOX ,2,TV,60
s09,ABC,2,TV,23
s10,BBC,2,TV,34
U01,John,NA,NA,NA
U02,Mary,NA,NA,NA
U03,Paul,NA,NA,NA
U04,Ted,NA,NA,NA
U05,Tom,NA,NA,NA
U06,Kate,NA,NA,NA
U07,Ed,NA,NA,NA
U08,Anna,NA,NA,NA
U09,Dan,NA,NA,NA
U10,Nancy,NA,NA,NA
U11,Sandra,NA,NA,NA
U12,Ronda,NA,NA,NA
U13,Sheila,NA,NA,NA
U14,Jim,NA,NA,NA
U15,Jill,NA,NA,NA
U16,Jo,NA,NA,NA
U17,Brian,NA,NA,NA
U18,Jason,NA,NA,NA
U19,Lisa,NA,NA,NA
U20,Dave,NA,NA,NA


================================================
FILE: data/ItemList.csv
================================================
"","itemList"
"1","newspapers"
"2","shopping bags"
"3","chewing gum"
"4","bottled beer"
"5","sugar"
"6","root vegetables"
"7","domestic eggs"
"8","bottled beer"
"9","fruit/vegetable juice"
"10","sugar"
"11","waffles"
"12","coffee"
"13","tropical fruit,sausage,popcorn,ham"
"14","bottled water"
"15","butter milk"
"16","whole milk"
"17","curd cheese"
"18","rolls/buns"
"19","white bread"
"20","pastry"
"21","tropical fruit"
"22","pastry"
"23","rolls/buns"
"24","frankfurter"
"25","fruit/vegetable juice,citrus fruit,frankfurter,other vegetables"
"26","other vegetables,butter,domestic eggs,yogurt,canned beer,curd"
"27","dessert"
"28","other vegetables"
"29","specialty bar"
"30","ham"
"31","tropical fruit"
"32","finished products"
"33","rolls/buns"
"34","rolls/buns"
"35","beef"
"36","liquor (appetizer),frozen potato products,bathroom cleaner"
"37","detergent"
"38","rolls/buns,specialty bar,other vegetables,candy,whole milk,pork,brown bread"
"39","brown bread,other vegetables,frozen meals,oil"
"40","root vegetables"
"41","whole milk"
"42","hamburger meat"
"43","citrus fruit"
"44","sausage"
"45","frankfurter"
"46","bottled water"
"47","other vegetables"
"48","pork"
"49","soda"
"50","canned beer"
"51","beverages"
"52","whole milk,chocolate marshmallow,berries,chocolate"
"53","snack products"
"54","chocolate,frankfurter,processed cheese,tropical fruit"
"55","frankfurter,butter milk,soda"
"56","packaged fruit/vegetables"
"57","sausage"
"58","whole milk"
"59","tropical fruit"
"60","berries"
"61","frankfurter"
"62","misc. beverages"
"63","rolls/buns"
"64","dishes,tropical fruit,bottled water,bottled water"
"65","chicken"
"66","citrus fruit"
"67","red/blush wine"
"68","berries"
"69","whole milk"
"70","abrasive cleaner"
"71","sausage"
"72","onions"
"73","packaged fruit/vegetables"
"74","popcorn"
"75","bottled beer"
"76","canned beer"
"77","whole milk"
"78","root vegetables"
"79","frankfurter"
"80","specialty chocolate"
"81","sausage"
"82","sausage"
"83","curd,chocolate"
"84","chocolate"
"85","whipped/sour cream"
"86","fruit/vegetable juice"
"87","photo/film"
"88","beverages"
"89","domestic eggs"
"90","salty snack"
"91","citrus fruit"
"92","margarine"
"93","whole milk"
"94","other vegetables"
"95","pork"
"96","root vegetables"
"97","rolls/buns"
"98","fruit/vegetable juice"
"99","canned beer"
"100","chicken,newspapers"
"101","root vegetables"
"102","herbs"
"103","frankfurter"
"104","whole milk"
"105","other vegetables"
"106","grapes"
"107","rolls/buns"
"108","finished products"
"109","shopping bags"
"110","sausage"
"111","soda"
"112","whole milk"
"113","pot plants,tropical fruit,sausage,misc. beverages"
"114","frozen potato products"
"115","frankfurter"
"116","citrus fruit"
"117","other vegetables,yogurt,canned beer,ham"
"118","sausage"
"119","yogurt,flour,berries,detergent"
"120","pip fruit"
"121","packaged fruit/vegetables,rolls/buns,soda,whole milk"
"122","sausage"
"123","sausage"
"124","pastry"
"125","cereals"
"126","frankfurter"
"127","whole milk,shopping bags"
"128","chocolate"
"129","ham"
"130","whole milk"
"131","fish"
"132","hamburger meat"
"133","beef"
"134","oil"
"135","rolls/buns"
"136","pasta"
"137","oil"
"138","domestic eggs"
"139","berries"
"140","hamburger meat"
"141","root vegetables"
"142","misc. beverages"
"143","hamburger meat"
"144","root vegetables"
"145","hamburger meat"
"146","rolls/buns"
"147","rolls/buns"
"148","margarine"
"149","curd"
"150","turkey"
"151","curd"
"152","red/blush wine"
"153","pastry"
"154","frankfurter"
"155","soda"
"156","white bread"
"157","bottled water"
"158","whole milk"
"159","rolls/buns"
"160","tropical fruit"
"161","coffee"
"162","butter milk"
"163","sausage"
"164","herbs"
"165","coffee"
"166","beef,rolls/buns,other vegetables"
"167","citrus fruit"
"168","other vegetables"
"169","whole milk"
"170","grapes"
"171","tropical fruit"
"172","whole milk"
"173","other vegetables"
"174","soda"
"175","tropical fruit"
"176","flower soil/fertilizer"
"177","sparkling wine"
"178","fruit/vegetable juice"
"179","waffles,sugar,rolls/buns"
"180","whole milk"
"181","other vegetables"
"182","other vegetables,coffee"
"183","oil"
"184","sausage"
"185","citrus fruit,tropical fruit,beef,waffles"
"186","fruit/vegetable juice"
"187","pip fruit"
"188","citrus fruit"
"189","sugar"
"190","newspapers,soda,packaged fruit/vegetables,whole milk,whole milk,whole milk"
"191","bottled beer"
"192","curd cheese"
"193","canned beer"
"194","whole milk"
"195","whipped/sour cream"
"196","baking powder"
"197","whole milk"
"198","frankfurter"
"199","specialty bar"


================================================
FILE: data/MBA.csv
================================================
ID,Products
1,Product A
1,Product B
1,Product C
1,Product I
2,Product E
2,Product F
2,Product H
2,Product I
2,Product J
2,Product K
3,Product A
3,Product I
3,Product J
3,Product L
3,Product M
4,Product D
4,Product E
4,Product F
4,Product G
4,Product H
5,Product D
5,Product E
5,Product F
5,Product G
5,Product H
5,Product N
6,Product A
6,Product B
6,Product C
6,Product I
6,Product O
7,Product B
7,Product C
7,Product L
7,Product M
7,Product O
8,Product D
8,Product E
8,Product F
8,Product G
8,Product H
8,Product I
8,Product J
8,Product K
8,Product O
9,Product C
9,Product F
9,Product H
9,Product I
9,Product J
9,Product L
9,Product M
10,Product A
10,Product B
10,Product C
10,Product I
10,Product O
11,Product E
11,Product H
11,Product L
12,Product A
12,Product B
12,Product C
12,Product I
12,Product M
13,Product A
13,Product F
13,Product G
13,Product I
13,Product J
13,Product L
14,Product A
14,Product B
14,Product C
14,Product I
15,Product E
15,Product F
15,Product G
15,Product H
15,Product I
15,Product J
15,Product K


================================================
FILE: data/MBArules.csv
================================================
"rules","support","confidence","lift","count"
"1","{Product N} => {Product D}",0.0666666666666667,1,5,1
"2","{Product N} => {Product G}",0.0666666666666667,1,3,1
"3","{Product N} => {Product E}",0.0666666666666667,1,2.5,1
"4","{Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"5","{Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"6","{Product D} => {Product G}",0.2,1,3,3
"7","{Product D} => {Product E}",0.2,1,2.5,3
"8","{Product D} => {Product H}",0.2,1,2.14285714285714,3
"9","{Product D} => {Product F}",0.2,1,2.14285714285714,3
"10","{Product K} => {Product E}",0.2,1,2.5,3
"11","{Product K} => {Product J}",0.2,1,2.5,3
"12","{Product K} => {Product H}",0.2,1,2.14285714285714,3
"13","{Product K} => {Product F}",0.2,1,2.14285714285714,3
"14","{Product K} => {Product I}",0.2,1,1.36363636363636,3
"15","{Product B} => {Product A}",0.333333333333333,0.833333333333333,1.78571428571429,5
"16","{Product B} => {Product C}",0.4,1,2.14285714285714,6
"17","{Product C} => {Product B}",0.4,0.857142857142857,2.14285714285714,6
"18","{Product B} => {Product I}",0.333333333333333,0.833333333333333,1.13636363636364,5
"19","{Product G} => {Product E}",0.266666666666667,0.8,2,4
"20","{Product G} => {Product H}",0.266666666666667,0.8,1.71428571428571,4
"21","{Product G} => {Product F}",0.333333333333333,1,2.14285714285714,5
"22","{Product A} => {Product I}",0.466666666666667,1,1.36363636363636,7
"23","{Product C} => {Product I}",0.4,0.857142857142857,1.16883116883117,6
"24","{Product E} => {Product H}",0.4,1,2.14285714285714,6
"25","{Product H} => {Product E}",0.4,0.857142857142857,2.14285714285714,6
"26","{Product E} => {Product F}",0.333333333333333,0.833333333333333,1.78571428571429,5
"27","{Product J} => {Product F}",0.333333333333333,0.833333333333333,1.78571428571429,5
"28","{Product J} => {Product I}",0.4,1,1.36363636363636,6
"29","{Product H} => {Product F}",0.4,0.857142857142857,1.83673469387755,6
"30","{Product F} => {Product H}",0.4,0.857142857142857,1.83673469387755,6
"31","{Product D,Product N} => {Product G}",0.0666666666666667,1,3,1
"32","{Product G,Product N} => {Product D}",0.0666666666666667,1,5,1
"33","{Product D,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"34","{Product E,Product N} => {Product D}",0.0666666666666667,1,5,1
"35","{Product D,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"36","{Product H,Product N} => {Product D}",0.0666666666666667,1,5,1
"37","{Product D,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"38","{Product F,Product N} => {Product D}",0.0666666666666667,1,5,1
"39","{Product G,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"40","{Product E,Product N} => {Product G}",0.0666666666666667,1,3,1
"41","{Product G,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"42","{Product H,Product N} => {Product G}",0.0666666666666667,1,3,1
"43","{Product G,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"44","{Product F,Product N} => {Product G}",0.0666666666666667,1,3,1
"45","{Product E,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"46","{Product H,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"47","{Product E,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"48","{Product F,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"49","{Product H,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"50","{Product F,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"51","{Product D,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"52","{Product D,Product O} => {Product K}",0.0666666666666667,1,5,1
"53","{Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"54","{Product D,Product K} => {Product G}",0.0666666666666667,1,3,1
"55","{Product D,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"56","{Product D,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"57","{Product D,Product J} => {Product K}",0.0666666666666667,1,5,1
"58","{Product D,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"59","{Product D,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"60","{Product D,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"61","{Product D,Product I} => {Product K}",0.0666666666666667,1,5,1
"62","{Product D,Product O} => {Product G}",0.0666666666666667,1,3,1
"63","{Product G,Product O} => {Product D}",0.0666666666666667,1,5,1
"64","{Product D,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"65","{Product E,Product O} => {Product D}",0.0666666666666667,1,5,1
"66","{Product D,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"67","{Product D,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"68","{Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"69","{Product D,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"70","{Product H,Product O} => {Product D}",0.0666666666666667,1,5,1
"71","{Product D,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"72","{Product F,Product O} => {Product D}",0.0666666666666667,1,5,1
"73","{Product D,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"74","{Product D,Product I} => {Product O}",0.0666666666666667,1,3.75,1
"75","{Product D,Product G} => {Product E}",0.2,1,2.5,3
"76","{Product D,Product E} => {Product G}",0.2,1,3,3
"77","{Product D,Product J} => {Product G}",0.0666666666666667,1,3,1
"78","{Product D,Product G} => {Product H}",0.2,1,2.14285714285714,3
"79","{Product D,Product H} => {Product G}",0.2,1,3,3
"80","{Product D,Product G} => {Product F}",0.2,1,2.14285714285714,3
"81","{Product D,Product F} => {Product G}",0.2,1,3,3
"82","{Product D,Product I} => {Product G}",0.0666666666666667,1,3,1
"83","{Product D,Product J} => {Product E}",0.0666666666666667,1,2.5,1
"84","{Product D,Product E} => {Product H}",0.2,1,2.14285714285714,3
"85","{Product D,Product H} => {Product E}",0.2,1,2.5,3
"86","{Product D,Product E} => {Product F}",0.2,1,2.14285714285714,3
"87","{Product D,Product F} => {Product E}",0.2,1,2.5,3
"88","{Product D,Product I} => {Product E}",0.0666666666666667,1,2.5,1
"89","{Product D,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"90","{Product D,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"91","{Product D,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"92","{Product D,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"93","{Product D,Product H} => {Product F}",0.2,1,2.14285714285714,3
"94","{Product D,Product F} => {Product H}",0.2,1,2.14285714285714,3
"95","{Product D,Product I} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"96","{Product D,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"97","{Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"98","{Product G,Product O} => {Product K}",0.0666666666666667,1,5,1
"99","{Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"100","{Product E,Product O} => {Product K}",0.0666666666666667,1,5,1
"101","{Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"102","{Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"103","{Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"104","{Product H,Product O} => {Product K}",0.0666666666666667,1,5,1
"105","{Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"106","{Product F,Product O} => {Product K}",0.0666666666666667,1,5,1
"107","{Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"108","{Product G,Product K} => {Product E}",0.133333333333333,1,2.5,2
"109","{Product G,Product K} => {Product J}",0.133333333333333,1,2.5,2
"110","{Product G,Product K} => {Product H}",0.133333333333333,1,2.14285714285714,2
"111","{Product G,Product K} => {Product F}",0.133333333333333,1,2.14285714285714,2
"112","{Product G,Product K} => {Product I}",0.133333333333333,1,1.36363636363636,2
"113","{Product E,Product K} => {Product J}",0.2,1,2.5,3
"114","{Product J,Product K} => {Product E}",0.2,1,2.5,3
"115","{Product E,Product J} => {Product K}",0.2,1,5,3
"116","{Product E,Product K} => {Product H}",0.2,1,2.14285714285714,3
"117","{Product H,Product K} => {Product E}",0.2,1,2.5,3
"118","{Product E,Product K} => {Product F}",0.2,1,2.14285714285714,3
"119","{Product F,Product K} => {Product E}",0.2,1,2.5,3
"120","{Product E,Product K} => {Product I}",0.2,1,1.36363636363636,3
"121","{Product I,Product K} => {Product E}",0.2,1,2.5,3
"122","{Product E,Product I} => {Product K}",0.2,1,5,3
"123","{Product J,Product K} => {Product H}",0.2,1,2.14285714285714,3
"124","{Product H,Product K} => {Product J}",0.2,1,2.5,3
"125","{Product J,Product K} => {Product F}",0.2,1,2.14285714285714,3
"126","{Product F,Product K} => {Product J}",0.2,1,2.5,3
"127","{Product J,Product K} => {Product I}",0.2,1,1.36363636363636,3
"128","{Product I,Product K} => {Product J}",0.2,1,2.5,3
"129","{Product H,Product K} => {Product F}",0.2,1,2.14285714285714,3
"130","{Product F,Product K} => {Product H}",0.2,1,2.14285714285714,3
"131","{Product H,Product K} => {Product I}",0.2,1,1.36363636363636,3
"132","{Product I,Product K} => {Product H}",0.2,1,2.14285714285714,3
"133","{Product F,Product K} => {Product I}",0.2,1,1.36363636363636,3
"134","{Product I,Product K} => {Product F}",0.2,1,2.14285714285714,3
"135","{Product M,Product O} => {Product L}",0.0666666666666667,1,3,1
"136","{Product L,Product O} => {Product M}",0.0666666666666667,1,3.75,1
"137","{Product M,Product O} => {Product B}",0.0666666666666667,1,2.5,1
"138","{Product M,Product O} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"139","{Product B,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"140","{Product C,Product L} => {Product M}",0.133333333333333,1,3.75,2
"141","{Product J,Product M} => {Product L}",0.133333333333333,1,3,2
"142","{Product H,Product M} => {Product L}",0.0666666666666667,1,3,1
"143","{Product F,Product M} => {Product L}",0.0666666666666667,1,3,1
"144","{Product B,Product M} => {Product C}",0.133333333333333,1,2.14285714285714,2
"145","{Product A,Product M} => {Product I}",0.133333333333333,1,1.36363636363636,2
"146","{Product C,Product J} => {Product M}",0.0666666666666667,1,3.75,1
"147","{Product H,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"148","{Product C,Product H} => {Product M}",0.0666666666666667,1,3.75,1
"149","{Product F,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"150","{Product C,Product F} => {Product M}",0.0666666666666667,1,3.75,1
"151","{Product H,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"152","{Product F,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"153","{Product J,Product M} => {Product I}",0.133333333333333,1,1.36363636363636,2
"154","{Product H,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"155","{Product F,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"156","{Product H,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"157","{Product F,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"158","{Product L,Product O} => {Product B}",0.0666666666666667,1,2.5,1
"159","{Product B,Product L} => {Product O}",0.0666666666666667,1,3.75,1
"160","{Product L,Product O} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"161","{Product A,Product O} => {Product B}",0.133333333333333,1,2.5,2
"162","{Product B,Product O} => {Product C}",0.2,1,2.14285714285714,3
"163","{Product C,Product O} => {Product B}",0.2,1,2.5,3
"164","{Product G,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"165","{Product E,Product O} => {Product G}",0.0666666666666667,1,3,1
"166","{Product G,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"167","{Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"168","{Product G,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"169","{Product H,Product O} => {Product G}",0.0666666666666667,1,3,1
"170","{Product G,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"171","{Product F,Product O} => {Product G}",0.0666666666666667,1,3,1
"172","{Product G,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"173","{Product A,Product O} => {Product C}",0.133333333333333,1,2.14285714285714,2
"174","{Product A,Product O} => {Product I}",0.133333333333333,1,1.36363636363636,2
"175","{Product E,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"176","{Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"177","{Product E,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"178","{Product H,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"179","{Product E,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"180","{Product F,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"181","{Product E,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"182","{Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"183","{Product H,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"184","{Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"185","{Product F,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"186","{Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"187","{Product H,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"188","{Product F,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"189","{Product H,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"190","{Product F,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"191","{Product B,Product L} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"192","{Product G,Product L} => {Product A}",0.0666666666666667,1,2.14285714285714,1
"193","{Product A,Product G} => {Product L}",0.0666666666666667,1,3,1
"194","{Product G,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"195","{Product G,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"196","{Product G,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"197","{Product A,Product L} => {Product J}",0.133333333333333,1,2.5,2
"198","{Product A,Product J} => {Product L}",0.133333333333333,1,3,2
"199","{Product A,Product F} => {Product L}",0.0666666666666667,1,3,1
"200","{Product A,Product L} => {Product I}",0.133333333333333,1,1.36363636363636,2
"201","{Product C,Product J} => {Product L}",0.0666666666666667,1,3,1
"202","{Product C,Product H} => {Product L}",0.0666666666666667,1,3,1
"203","{Product C,Product F} => {Product L}",0.0666666666666667,1,3,1
"204","{Product E,Product L} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"205","{Product F,Product L} => {Product J}",0.133333333333333,1,2.5,2
"206","{Product J,Product L} => {Product I}",0.2,1,1.36363636363636,3
"207","{Product I,Product L} => {Product J}",0.2,1,2.5,3
"208","{Product F,Product L} => {Product I}",0.133333333333333,1,1.36363636363636,2
"209","{Product A,Product B} => {Product C}",0.333333333333333,1,2.14285714285714,5
"210","{Product B,Product C} => {Product A}",0.333333333333333,0.833333333333333,1.78571428571429,5
"211","{Product A,Product C} => {Product B}",0.333333333333333,1,2.5,5
"212","{Product A,Product B} => {Product I}",0.333333333333333,1,1.36363636363636,5
"213","{Product B,Product I} => {Product A}",0.333333333333333,1,2.14285714285714,5
"214","{Product B,Product C} => {Product I}",0.333333333333333,0.833333333333333,1.13636363636364,5
"215","{Product B,Product I} => {Product C}",0.333333333333333,1,2.14285714285714,5
"216","{Product C,Product I} => {Product B}",0.333333333333333,0.833333333333333,2.08333333333333,5
"217","{Product A,Product G} => {Product J}",0.0666666666666667,1,2.5,1
"218","{Product A,Product G} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"219","{Product A,Product F} => {Product G}",0.0666666666666667,1,3,1
"220","{Product A,Product G} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"221","{Product E,Product G} => {Product H}",0.266666666666667,1,2.14285714285714,4
"222","{Product G,Product H} => {Product E}",0.266666666666667,1,2.5,4
"223","{Product E,Product G} => {Product F}",0.266666666666667,1,2.14285714285714,4
"224","{Product F,Product G} => {Product E}",0.266666666666667,0.8,2,4
"225","{Product E,Product F} => {Product G}",0.266666666666667,0.8,2.4,4
"226","{Product G,Product J} => {Product F}",0.2,1,2.14285714285714,3
"227","{Product G,Product J} => {Product I}",0.2,1,1.36363636363636,3
"228","{Product G,Product I} => {Product J}",0.2,1,2.5,3
"229","{Product G,Product H} => {Product F}",0.266666666666667,1,2.14285714285714,4
"230","{Product F,Product G} => {Product H}",0.266666666666667,0.8,1.71428571428571,4
"231","{Product G,Product I} => {Product F}",0.2,1,2.14285714285714,3
"232","{Product A,Product C} => {Product I}",0.333333333333333,1,1.36363636363636,5
"233","{Product C,Product I} => {Product A}",0.333333333333333,0.833333333333333,1.78571428571429,5
"234","{Product A,Product F} => {Product J}",0.0666666666666667,1,2.5,1
"235","{Product A,Product J} => {Product I}",0.133333333333333,1,1.36363636363636,2
"236","{Product A,Product F} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"237","{Product C,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"238","{Product C,Product H} => {Product J}",0.0666666666666667,1,2.5,1
"239","{Product C,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"240","{Product C,Product F} => {Product J}",0.0666666666666667,1,2.5,1
"241","{Product C,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"242","{Product C,Product H} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"243","{Product C,Product F} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"244","{Product C,Product H} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"245","{Product C,Product F} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"246","{Product E,Product J} => {Product H}",0.2,1,2.14285714285714,3
"247","{Product E,Product J} => {Product F}",0.2,1,2.14285714285714,3
"248","{Product E,Product J} => {Product I}",0.2,1,1.36363636363636,3
"249","{Product E,Product I} => {Product J}",0.2,1,2.5,3
"250","{Product E,Product H} => {Product F}",0.333333333333333,0.833333333333333,1.78571428571429,5
"251","{Product E,Product F} => {Product H}",0.333333333333333,1,2.14285714285714,5
"252","{Product F,Product H} => {Product E}",0.333333333333333,0.833333333333333,2.08333333333333,5
"253","{Product E,Product I} => {Product H}",0.2,1,2.14285714285714,3
"254","{Product E,Product I} => {Product F}",0.2,1,2.14285714285714,3
"255","{Product H,Product J} => {Product F}",0.266666666666667,1,2.14285714285714,4
"256","{Product F,Product J} => {Product H}",0.266666666666667,0.8,1.71428571428571,4
"257","{Product H,Product J} => {Product I}",0.266666666666667,1,1.36363636363636,4
"258","{Product H,Product I} => {Product J}",0.266666666666667,1,2.5,4
"259","{Product F,Product J} => {Product I}",0.333333333333333,1,1.36363636363636,5
"260","{Product I,Product J} => {Product F}",0.333333333333333,0.833333333333333,1.78571428571429,5
"261","{Product F,Product I} => {Product J}",0.333333333333333,1,2.5,5
"262","{Product H,Product I} => {Product F}",0.266666666666667,1,2.14285714285714,4
"263","{Product F,Product I} => {Product H}",0.266666666666667,0.8,1.71428571428571,4
"264","{Product D,Product G,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"265","{Product D,Product E,Product N} => {Product G}",0.0666666666666667,1,3,1
"266","{Product E,Product G,Product N} => {Product D}",0.0666666666666667,1,5,1
"267","{Product D,Product G,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"268","{Product D,Product H,Product N} => {Product G}",0.0666666666666667,1,3,1
"269","{Product G,Product H,Product N} => {Product D}",0.0666666666666667,1,5,1
"270","{Product D,Product G,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"271","{Product D,Product F,Product N} => {Product G}",0.0666666666666667,1,3,1
"272","{Product F,Product G,Product N} => {Product D}",0.0666666666666667,1,5,1
"273","{Product D,Product E,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"274","{Product D,Product H,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"275","{Product E,Product H,Product N} => {Product D}",0.0666666666666667,1,5,1
"276","{Product D,Product E,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"277","{Product D,Product F,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"278","{Product E,Product F,Product N} => {Product D}",0.0666666666666667,1,5,1
"279","{Product D,Product H,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"280","{Product D,Product F,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"281","{Product F,Product H,Product N} => {Product D}",0.0666666666666667,1,5,1
"282","{Product E,Product G,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"283","{Product G,Product H,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"284","{Product E,Product H,Product N} => {Product G}",0.0666666666666667,1,3,1
"285","{Product E,Product G,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"286","{Product F,Product G,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"287","{Product E,Product F,Product N} => {Product G}",0.0666666666666667,1,3,1
"288","{Product G,Product H,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"289","{Product F,Product G,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"290","{Product F,Product H,Product N} => {Product G}",0.0666666666666667,1,3,1
"291","{Product E,Product H,Product N} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"292","{Product E,Product F,Product N} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"293","{Product F,Product H,Product N} => {Product E}",0.0666666666666667,1,2.5,1
"294","{Product D,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"295","{Product D,Product G,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"296","{Product D,Product G,Product O} => {Product K}",0.0666666666666667,1,5,1
"297","{Product G,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"298","{Product D,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"299","{Product D,Product E,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"300","{Product D,Product E,Product O} => {Product K}",0.0666666666666667,1,5,1
"301","{Product E,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"302","{Product D,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"303","{Product D,Product J,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"304","{Product D,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"305","{Product J,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"306","{Product D,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"307","{Product D,Product H,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"308","{Product D,Product H,Product O} => {Product K}",0.0666666666666667,1,5,1
"309","{Product H,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"310","{Product D,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"311","{Product D,Product F,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"312","{Product D,Product F,Product O} => {Product K}",0.0666666666666667,1,5,1
"313","{Product F,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"314","{Product D,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"315","{Product D,Product I,Product K} => {Product O}",0.0666666666666667,1,3.75,1
"316","{Product D,Product I,Product O} => {Product K}",0.0666666666666667,1,5,1
"317","{Product I,Product K,Product O} => {Product D}",0.0666666666666667,1,5,1
"318","{Product D,Product G,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"319","{Product D,Product E,Product K} => {Product G}",0.0666666666666667,1,3,1
"320","{Product D,Product G,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"321","{Product D,Product J,Product K} => {Product G}",0.0666666666666667,1,3,1
"322","{Product D,Product G,Product J} => {Product K}",0.0666666666666667,1,5,1
"323","{Product D,Product G,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"324","{Product D,Product H,Product K} => {Product G}",0.0666666666666667,1,3,1
"325","{Product D,Product G,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"326","{Product D,Product F,Product K} => {Product G}",0.0666666666666667,1,3,1
"327","{Product D,Product G,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"328","{Product D,Product I,Product K} => {Product G}",0.0666666666666667,1,3,1
"329","{Product D,Product G,Product I} => {Product K}",0.0666666666666667,1,5,1
"330","{Product D,Product E,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"331","{Product D,Product J,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"332","{Product D,Product E,Product J} => {Product K}",0.0666666666666667,1,5,1
"333","{Product D,Product E,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"334","{Product D,Product H,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"335","{Product D,Product E,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"336","{Product D,Product F,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"337","{Product D,Product E,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"338","{Product D,Product I,Product K} => {Product E}",0.0666666666666667,1,2.5,1
"339","{Product D,Product E,Product I} => {Product K}",0.0666666666666667,1,5,1
"340","{Product D,Product J,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"341","{Product D,Product H,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"342","{Product D,Product H,Product J} => {Product K}",0.0666666666666667,1,5,1
"343","{Product D,Product J,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"344","{Product D,Product F,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"345","{Product D,Product F,Product J} => {Product K}",0.0666666666666667,1,5,1
"346","{Product D,Product J,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"347","{Product D,Product I,Product K} => {Product J}",0.0666666666666667,1,2.5,1
"348","{Product D,Product I,Product J} => {Product K}",0.0666666666666667,1,5,1
"349","{Product D,Product H,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"350","{Product D,Product F,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"351","{Product D,Product H,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"352","{Product D,Product I,Product K} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"353","{Product D,Product H,Product I} => {Product K}",0.0666666666666667,1,5,1
"354","{Product D,Product F,Product K} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"355","{Product D,Product I,Product K} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"356","{Product D,Product F,Product I} => {Product K}",0.0666666666666667,1,5,1
"357","{Product D,Product G,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"358","{Product D,Product E,Product O} => {Product G}",0.0666666666666667,1,3,1
"359","{Product E,Product G,Product O} => {Product D}",0.0666666666666667,1,5,1
"360","{Product D,Product G,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"361","{Product D,Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"362","{Product D,Product G,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"363","{Product G,Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"364","{Product D,Product G,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"365","{Product D,Product H,Product O} => {Product G}",0.0666666666666667,1,3,1
"366","{Product G,Product H,Product O} => {Product D}",0.0666666666666667,1,5,1
"367","{Product D,Product G,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"368","{Product D,Product F,Product O} => {Product G}",0.0666666666666667,1,3,1
"369","{Product F,Product G,Product O} => {Product D}",0.0666666666666667,1,5,1
"370","{Product D,Product G,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"371","{Product D,Product I,Product O} => {Product G}",0.0666666666666667,1,3,1
"372","{Product D,Product G,Product I} => {Product O}",0.0666666666666667,1,3.75,1
"373","{Product G,Product I,Product O} => {Product D}",0.0666666666666667,1,5,1
"374","{Product D,Product E,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"375","{Product D,Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"376","{Product D,Product E,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"377","{Product E,Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"378","{Product D,Product E,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"379","{Product D,Product H,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"380","{Product E,Product H,Product O} => {Product D}",0.0666666666666667,1,5,1
"381","{Product D,Product E,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"382","{Product D,Product F,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"383","{Product E,Product F,Product O} => {Product D}",0.0666666666666667,1,5,1
"384","{Product D,Product E,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"385","{Product D,Product I,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"386","{Product D,Product E,Product I} => {Product O}",0.0666666666666667,1,3.75,1
"387","{Product E,Product I,Product O} => {Product D}",0.0666666666666667,1,5,1
"388","{Product D,Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"389","{Product D,Product H,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"390","{Product D,Product H,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"391","{Product H,Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"392","{Product D,Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"393","{Product D,Product F,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"394","{Product D,Product F,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"395","{Product F,Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"396","{Product D,Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"397","{Product D,Product I,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"398","{Product D,Product I,Product J} => {Product O}",0.0666666666666667,1,3.75,1
"399","{Product I,Product J,Product O} => {Product D}",0.0666666666666667,1,5,1
"400","{Product D,Product H,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"401","{Product D,Product F,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"402","{Product F,Product H,Product O} => {Product D}",0.0666666666666667,1,5,1
"403","{Product D,Product H,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"404","{Product D,Product I,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"405","{Product D,Product H,Product I} => {Product O}",0.0666666666666667,1,3.75,1
"406","{Product H,Product I,Product O} => {Product D}",0.0666666666666667,1,5,1
"407","{Product D,Product F,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"408","{Product D,Product I,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"409","{Product D,Product F,Product I} => {Product O}",0.0666666666666667,1,3.75,1
"410","{Product F,Product I,Product O} => {Product D}",0.0666666666666667,1,5,1
"411","{Product D,Product G,Product J} => {Product E}",0.0666666666666667,1,2.5,1
"412","{Product D,Product E,Product J} => {Product G}",0.0666666666666667,1,3,1
"413","{Product D,Product E,Product G} => {Product H}",0.2,1,2.14285714285714,3
"414","{Product D,Product G,Product H} => {Product E}",0.2,1,2.5,3
"415","{Product D,Product E,Product H} => {Product G}",0.2,1,3,3
"416","{Product D,Product E,Product G} => {Product F}",0.2,1,2.14285714285714,3
"417","{Product D,Product F,Product G} => {Product E}",0.2,1,2.5,3
"418","{Product D,Product E,Product F} => {Product G}",0.2,1,3,3
"419","{Product D,Product G,Product I} => {Product E}",0.0666666666666667,1,2.5,1
"420","{Product D,Product E,Product I} => {Product G}",0.0666666666666667,1,3,1
"421","{Product D,Product G,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"422","{Product D,Product H,Product J} => {Product G}",0.0666666666666667,1,3,1
"423","{Product D,Product G,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"424","{Product D,Product F,Product J} => {Product G}",0.0666666666666667,1,3,1
"425","{Product D,Product G,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"426","{Product D,Product G,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"427","{Product D,Product I,Product J} => {Product G}",0.0666666666666667,1,3,1
"428","{Product D,Product G,Product H} => {Product F}",0.2,1,2.14285714285714,3
"429","{Product D,Product F,Product G} => {Product H}",0.2,1,2.14285714285714,3
"430","{Product D,Product F,Product H} => {Product G}",0.2,1,3,3
"431","{Product D,Product G,Product I} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"432","{Product D,Product H,Product I} => {Product G}",0.0666666666666667,1,3,1
"433","{Product D,Product G,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"434","{Product D,Product F,Product I} => {Product G}",0.0666666666666667,1,3,1
"435","{Product D,Product E,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"436","{Product D,Product H,Product J} => {Product E}",0.0666666666666667,1,2.5,1
"437","{Product D,Product E,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"438","{Product D,Product F,Product J} => {Product E}",0.0666666666666667,1,2.5,1
"439","{Product D,Product E,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"440","{Product D,Product E,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"441","{Product D,Product I,Product J} => {Product E}",0.0666666666666667,1,2.5,1
"442","{Product D,Product E,Product H} => {Product F}",0.2,1,2.14285714285714,3
"443","{Product D,Product E,Product F} => {Product H}",0.2,1,2.14285714285714,3
"444","{Product D,Product F,Product H} => {Product E}",0.2,1,2.5,3
"445","{Product D,Product E,Product I} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"446","{Product D,Product H,Product I} => {Product E}",0.0666666666666667,1,2.5,1
"447","{Product D,Product E,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"448","{Product D,Product F,Product I} => {Product E}",0.0666666666666667,1,2.5,1
"449","{Product D,Product H,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"450","{Product D,Product F,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"451","{Product D,Product H,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"452","{Product D,Product I,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"453","{Product D,Product H,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"454","{Product D,Product F,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"455","{Product D,Product I,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"456","{Product D,Product F,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"457","{Product D,Product H,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"458","{Product D,Product F,Product I} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"459","{Product G,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"460","{Product E,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"461","{Product E,Product G,Product O} => {Product K}",0.0666666666666667,1,5,1
"462","{Product G,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"463","{Product J,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"464","{Product G,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"465","{Product G,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"466","{Product H,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"467","{Product G,Product H,Product O} => {Product K}",0.0666666666666667,1,5,1
"468","{Product G,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"469","{Product F,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"470","{Product F,Product G,Product O} => {Product K}",0.0666666666666667,1,5,1
"471","{Product G,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"472","{Product I,Product K,Product O} => {Product G}",0.0666666666666667,1,3,1
"473","{Product G,Product I,Product O} => {Product K}",0.0666666666666667,1,5,1
"474","{Product E,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"475","{Product J,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"476","{Product E,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"477","{Product E,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"478","{Product H,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"479","{Product E,Product H,Product O} => {Product K}",0.0666666666666667,1,5,1
"480","{Product E,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"481","{Product F,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"482","{Product E,Product F,Product O} => {Product K}",0.0666666666666667,1,5,1
"483","{Product E,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"484","{Product I,Product K,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"485","{Product E,Product I,Product O} => {Product K}",0.0666666666666667,1,5,1
"486","{Product J,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"487","{Product H,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"488","{Product H,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"489","{Product J,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"490","{Product F,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"491","{Product F,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"492","{Product J,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"493","{Product I,Product K,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"494","{Product I,Product J,Product O} => {Product K}",0.0666666666666667,1,5,1
"495","{Product H,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"496","{Product F,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"497","{Product F,Product H,Product O} => {Product K}",0.0666666666666667,1,5,1
"498","{Product H,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"499","{Product I,Product K,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"500","{Product H,Product I,Product O} => {Product K}",0.0666666666666667,1,5,1
"501","{Product F,Product K,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"502","{Product I,Product K,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"503","{Product F,Product I,Product O} => {Product K}",0.0666666666666667,1,5,1
"504","{Product E,Product G,Product K} => {Product J}",0.133333333333333,1,2.5,2
"505","{Product G,Product J,Product K} => {Product E}",0.133333333333333,1,2.5,2
"506","{Product E,Product G,Product J} => {Product K}",0.133333333333333,1,5,2
"507","{Product E,Product G,Product K} => {Product H}",0.133333333333333,1,2.14285714285714,2
"508","{Product G,Product H,Product K} => {Product E}",0.133333333333333,1,2.5,2
"509","{Product E,Product G,Product K} => {Product F}",0.133333333333333,1,2.14285714285714,2
"510","{Product F,Product G,Product K} => {Product E}",0.133333333333333,1,2.5,2
"511","{Product E,Product G,Product K} => {Product I}",0.133333333333333,1,1.36363636363636,2
"512","{Product G,Product I,Product K} => {Product E}",0.133333333333333,1,2.5,2
"513","{Product E,Product G,Product I} => {Product K}",0.133333333333333,1,5,2
"514","{Product G,Product J,Product K} => {Product H}",0.133333333333333,1,2.14285714285714,2
"515","{Product G,Product H,Product K} => {Product J}",0.133333333333333,1,2.5,2
"516","{Product G,Product H,Product J} => {Product K}",0.133333333333333,1,5,2
"517","{Product G,Product J,Product K} => {Product F}",0.133333333333333,1,2.14285714285714,2
"518","{Product F,Product G,Product K} => {Product J}",0.133333333333333,1,2.5,2
"519","{Product G,Product J,Product K} => {Product I}",0.133333333333333,1,1.36363636363636,2
"520","{Product G,Product I,Product K} => {Product J}",0.133333333333333,1,2.5,2
"521","{Product G,Product H,Product K} => {Product F}",0.133333333333333,1,2.14285714285714,2
"522","{Product F,Product G,Product K} => {Product H}",0.133333333333333,1,2.14285714285714,2
"523","{Product G,Product H,Product K} => {Product I}",0.133333333333333,1,1.36363636363636,2
"524","{Product G,Product I,Product K} => {Product H}",0.133333333333333,1,2.14285714285714,2
"525","{Product G,Product H,Product I} => {Product K}",0.133333333333333,1,5,2
"526","{Product F,Product G,Product K} => {Product I}",0.133333333333333,1,1.36363636363636,2
"527","{Product G,Product I,Product K} => {Product F}",0.133333333333333,1,2.14285714285714,2
"528","{Product E,Product J,Product K} => {Product H}",0.2,1,2.14285714285714,3
"529","{Product E,Product H,Product K} => {Product J}",0.2,1,2.5,3
"530","{Product H,Product J,Product K} => {Product E}",0.2,1,2.5,3
"531","{Product E,Product H,Product J} => {Product K}",0.2,1,5,3
"532","{Product E,Product J,Product K} => {Product F}",0.2,1,2.14285714285714,3
"533","{Product E,Product F,Product K} => {Product J}",0.2,1,2.5,3
"534","{Product F,Product J,Product K} => {Product E}",0.2,1,2.5,3
"535","{Product E,Product F,Product J} => {Product K}",0.2,1,5,3
"536","{Product E,Product J,Product K} => {Product I}",0.2,1,1.36363636363636,3
"537","{Product E,Product I,Product K} => {Product J}",0.2,1,2.5,3
"538","{Product I,Product J,Product K} => {Product E}",0.2,1,2.5,3
"539","{Product E,Product I,Product J} => {Product K}",0.2,1,5,3
"540","{Product E,Product H,Product K} => {Product F}",0.2,1,2.14285714285714,3
"541","{Product E,Product F,Product K} => {Product H}",0.2,1,2.14285714285714,3
"542","{Product F,Product H,Product K} => {Product E}",0.2,1,2.5,3
"543","{Product E,Product H,Product K} => {Product I}",0.2,1,1.36363636363636,3
"544","{Product E,Product I,Product K} => {Product H}",0.2,1,2.14285714285714,3
"545","{Product H,Product I,Product K} => {Product E}",0.2,1,2.5,3
"546","{Product E,Product H,Product I} => {Product K}",0.2,1,5,3
"547","{Product E,Product F,Product K} => {Product I}",0.2,1,1.36363636363636,3
"548","{Product E,Product I,Product K} => {Product F}",0.2,1,2.14285714285714,3
"549","{Product F,Product I,Product K} => {Product E}",0.2,1,2.5,3
"550","{Product E,Product F,Product I} => {Product K}",0.2,1,5,3
"551","{Product H,Product J,Product K} => {Product F}",0.2,1,2.14285714285714,3
"552","{Product F,Product J,Product K} => {Product H}",0.2,1,2.14285714285714,3
"553","{Product F,Product H,Product K} => {Product J}",0.2,1,2.5,3
"554","{Product H,Product J,Product K} => {Product I}",0.2,1,1.36363636363636,3
"555","{Product I,Product J,Product K} => {Product H}",0.2,1,2.14285714285714,3
"556","{Product H,Product I,Product K} => {Product J}",0.2,1,2.5,3
"557","{Product F,Product J,Product K} => {Product I}",0.2,1,1.36363636363636,3
"558","{Product I,Product J,Product K} => {Product F}",0.2,1,2.14285714285714,3
"559","{Product F,Product I,Product K} => {Product J}",0.2,1,2.5,3
"560","{Product F,Product H,Product K} => {Product I}",0.2,1,1.36363636363636,3
"561","{Product H,Product I,Product K} => {Product F}",0.2,1,2.14285714285714,3
"562","{Product F,Product I,Product K} => {Product H}",0.2,1,2.14285714285714,3
"563","{Product L,Product M,Product O} => {Product B}",0.0666666666666667,1,2.5,1
"564","{Product B,Product M,Product O} => {Product L}",0.0666666666666667,1,3,1
"565","{Product B,Product L,Product M} => {Product O}",0.0666666666666667,1,3.75,1
"566","{Product B,Product L,Product O} => {Product M}",0.0666666666666667,1,3.75,1
"567","{Product L,Product M,Product O} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"568","{Product C,Product M,Product O} => {Product L}",0.0666666666666667,1,3,1
"569","{Product C,Product L,Product O} => {Product M}",0.0666666666666667,1,3.75,1
"570","{Product B,Product M,Product O} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"571","{Product C,Product M,Product O} => {Product B}",0.0666666666666667,1,2.5,1
"572","{Product B,Product L,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"573","{Product B,Product C,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"574","{Product A,Product L,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"575","{Product A,Product J,Product M} => {Product L}",0.0666666666666667,1,3,1
"576","{Product A,Product L,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"577","{Product C,Product J,Product M} => {Product L}",0.0666666666666667,1,3,1
"578","{Product C,Product J,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"579","{Product H,Product L,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"580","{Product C,Product H,Product M} => {Product L}",0.0666666666666667,1,3,1
"581","{Product C,Product H,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"582","{Product F,Product L,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"583","{Product C,Product F,Product M} => {Product L}",0.0666666666666667,1,3,1
"584","{Product C,Product F,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"585","{Product C,Product I,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"586","{Product H,Product L,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"587","{Product H,Product J,Product M} => {Product L}",0.0666666666666667,1,3,1
"588","{Product H,Product J,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"589","{Product F,Product L,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"590","{Product F,Product J,Product M} => {Product L}",0.0666666666666667,1,3,1
"591","{Product J,Product L,Product M} => {Product I}",0.133333333333333,1,1.36363636363636,2
"592","{Product I,Product L,Product M} => {Product J}",0.133333333333333,1,2.5,2
"593","{Product I,Product J,Product M} => {Product L}",0.133333333333333,1,3,2
"594","{Product H,Product L,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"595","{Product F,Product L,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"596","{Product F,Product H,Product M} => {Product L}",0.0666666666666667,1,3,1
"597","{Product F,Product H,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"598","{Product H,Product L,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"599","{Product H,Product I,Product M} => {Product L}",0.0666666666666667,1,3,1
"600","{Product H,Product I,Product L} => {Product M}",0.0666666666666667,1,3.75,1
"601","{Product F,Product L,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"602","{Product F,Product I,Product M} => {Product L}",0.0666666666666667,1,3,1
"603","{Product A,Product B,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"604","{Product A,Product C,Product M} => {Product B}",0.0666666666666667,1,2.5,1
"605","{Product A,Product B,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"606","{Product B,Product I,Product M} => {Product A}",0.0666666666666667,1,2.14285714285714,1
"607","{Product B,Product I,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"608","{Product A,Product C,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"609","{Product A,Product J,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"610","{Product C,Product J,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"611","{Product C,Product H,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"612","{Product H,Product J,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"613","{Product C,Product H,Product J} => {Product M}",0.0666666666666667,1,3.75,1
"614","{Product C,Product J,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"615","{Product C,Product F,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"616","{Product F,Product J,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"617","{Product C,Product F,Product J} => {Product M}",0.0666666666666667,1,3.75,1
"618","{Product C,Product J,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"619","{Product C,Product I,Product J} => {Product M}",0.0666666666666667,1,3.75,1
"620","{Product C,Product H,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"621","{Product C,Product F,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"622","{Product F,Product H,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"623","{Product C,Product F,Product H} => {Product M}",0.0666666666666667,1,3.75,1
"624","{Product C,Product H,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"625","{Product H,Product I,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"626","{Product C,Product H,Product I} => {Product M}",0.0666666666666667,1,3.75,1
"627","{Product C,Product F,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"628","{Product F,Product I,Product M} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"629","{Product C,Product F,Product I} => {Product M}",0.0666666666666667,1,3.75,1
"630","{Product H,Product J,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"631","{Product F,Product J,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"632","{Product F,Product H,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"633","{Product H,Product J,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"634","{Product H,Product I,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"635","{Product F,Product J,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"636","{Product F,Product I,Product M} => {Product J}",0.0666666666666667,1,2.5,1
"637","{Product F,Product H,Product M} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"638","{Product H,Product I,Product M} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"639","{Product F,Product I,Product M} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"640","{Product B,Product L,Product O} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"641","{Product C,Product L,Product O} => {Product B}",0.0666666666666667,1,2.5,1
"642","{Product B,Product C,Product L} => {Product O}",0.0666666666666667,1,3.75,1
"643","{Product A,Product B,Product O} => {Product C}",0.133333333333333,1,2.14285714285714,2
"644","{Product A,Product C,Product O} => {Product B}",0.133333333333333,1,2.5,2
"645","{Product A,Product B,Product O} => {Product I}",0.133333333333333,1,1.36363636363636,2
"646","{Product B,Product I,Product O} => {Product A}",0.133333333333333,1,2.14285714285714,2
"647","{Product A,Product I,Product O} => {Product B}",0.133333333333333,1,2.5,2
"648","{Product B,Product I,Product O} => {Product C}",0.133333333333333,1,2.14285714285714,2
"649","{Product C,Product I,Product O} => {Product B}",0.133333333333333,1,2.5,2
"650","{Product E,Product G,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"651","{Product G,Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"652","{Product E,Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"653","{Product E,Product G,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"654","{Product G,Product H,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"655","{Product E,Product H,Product O} => {Product G}",0.0666666666666667,1,3,1
"656","{Product E,Product G,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"657","{Product F,Product G,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"658","{Product E,Product F,Product O} => {Product G}",0.0666666666666667,1,3,1
"659","{Product E,Product G,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"660","{Product G,Product I,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"661","{Product E,Product I,Product O} => {Product G}",0.0666666666666667,1,3,1
"662","{Product G,Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"663","{Product G,Product H,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"664","{Product H,Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"665","{Product G,Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"666","{Product F,Product G,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"667","{Product F,Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"668","{Product G,Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"669","{Product G,Product I,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"670","{Product I,Product J,Product O} => {Product G}",0.0666666666666667,1,3,1
"671","{Product G,Product H,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"672","{Product F,Product G,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"673","{Product F,Product H,Product O} => {Product G}",0.0666666666666667,1,3,1
"674","{Product G,Product H,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"675","{Product G,Product I,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"676","{Product H,Product I,Product O} => {Product G}",0.0666666666666667,1,3,1
"677","{Product F,Product G,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"678","{Product G,Product I,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"679","{Product F,Product I,Product O} => {Product G}",0.0666666666666667,1,3,1
"680","{Product A,Product C,Product O} => {Product I}",0.133333333333333,1,1.36363636363636,2
"681","{Product A,Product I,Product O} => {Product C}",0.133333333333333,1,2.14285714285714,2
"682","{Product C,Product I,Product O} => {Product A}",0.133333333333333,1,2.14285714285714,2
"683","{Product E,Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"684","{Product E,Product H,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"685","{Product H,Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"686","{Product E,Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"687","{Product E,Product F,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"688","{Product F,Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"689","{Product E,Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"690","{Product E,Product I,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"691","{Product I,Product J,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"692","{Product E,Product H,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"693","{Product E,Product F,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"694","{Product F,Product H,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"695","{Product E,Product H,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"696","{Product E,Product I,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"697","{Product H,Product I,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"698","{Product E,Product F,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"699","{Product E,Product I,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"700","{Product F,Product I,Product O} => {Product E}",0.0666666666666667,1,2.5,1
"701","{Product H,Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"702","{Product F,Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"703","{Product F,Product H,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"704","{Product H,Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"705","{Product I,Product J,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"706","{Product H,Product I,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"707","{Product F,Product J,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"708","{Product I,Product J,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"709","{Product F,Product I,Product O} => {Product J}",0.0666666666666667,1,2.5,1
"710","{Product F,Product H,Product O} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"711","{Product H,Product I,Product O} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"712","{Product F,Product I,Product O} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"713","{Product A,Product G,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"714","{Product G,Product J,Product L} => {Product A}",0.0666666666666667,1,2.14285714285714,1
"715","{Product A,Product G,Product J} => {Product L}",0.0666666666666667,1,3,1
"716","{Product A,Product G,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"717","{Product F,Product G,Product L} => {Product A}",0.0666666666666667,1,2.14285714285714,1
"718","{Product A,Product F,Product L} => {Product G}",0.0666666666666667,1,3,1
"719","{Product A,Product F,Product G} => {Product L}",0.0666666666666667,1,3,1
"720","{Product A,Product G,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"721","{Product G,Product I,Product L} => {Product A}",0.0666666666666667,1,2.14285714285714,1
"722","{Product A,Product G,Product I} => {Product L}",0.0666666666666667,1,3,1
"723","{Product G,Product J,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"724","{Product F,Product G,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"725","{Product G,Product J,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"726","{Product G,Product I,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"727","{Product F,Product G,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"728","{Product G,Product I,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"729","{Product A,Product F,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"730","{Product A,Product F,Product J} => {Product L}",0.0666666666666667,1,3,1
"731","{Product A,Product J,Product L} => {Product I}",0.133333333333333,1,1.36363636363636,2
"732","{Product A,Product I,Product L} => {Product J}",0.133333333333333,1,2.5,2
"733","{Product A,Product I,Product J} => {Product L}",0.133333333333333,1,3,2
"734","{Product A,Product F,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"735","{Product A,Product F,Product I} => {Product L}",0.0666666666666667,1,3,1
"736","{Product C,Product J,Product L} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"737","{Product C,Product H,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"738","{Product H,Product J,Product L} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"739","{Product C,Product H,Product J} => {Product L}",0.0666666666666667,1,3,1
"740","{Product C,Product J,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"741","{Product C,Product F,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"742","{Product C,Product F,Product J} => {Product L}",0.0666666666666667,1,3,1
"743","{Product C,Product J,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"744","{Product C,Product I,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"745","{Product C,Product I,Product J} => {Product L}",0.0666666666666667,1,3,1
"746","{Product C,Product H,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"747","{Product C,Product F,Product L} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"748","{Product F,Product H,Product L} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"749","{Product C,Product F,Product H} => {Product L}",0.0666666666666667,1,3,1
"750","{Product C,Product H,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"751","{Product C,Product I,Product L} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"752","{Product H,Product I,Product L} => {Product C}",0.0666666666666667,1,2.14285714285714,1
"753","{Product C,Product H,Product I} => {Product L}",0.0666666666666667,1,3,1
"754","{Product C,Product F,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"755","{Product C,Product I,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"756","{Product C,Product F,Product I} => {Product L}",0.0666666666666667,1,3,1
"757","{Product H,Product J,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"758","{Product F,Product H,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"759","{Product H,Product J,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"760","{Product H,Product I,Product L} => {Product J}",0.0666666666666667,1,2.5,1
"761","{Product F,Product J,Product L} => {Product I}",0.133333333333333,1,1.36363636363636,2
"762","{Product F,Product I,Product L} => {Product J}",0.133333333333333,1,2.5,2
"763","{Product F,Product H,Product L} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"764","{Product H,Product I,Product L} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"765","{Product A,Product B,Product C} => {Product I}",0.333333333333333,1,1.36363636363636,5
"766","{Product A,Product B,Product I} => {Product C}",0.333333333333333,1,2.14285714285714,5
"767","{Product B,Product C,Product I} => {Product A}",0.333333333333333,1,2.14285714285714,5
"768","{Product A,Product C,Product I} => {Product B}",0.333333333333333,1,2.5,5
"769","{Product A,Product G,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"770","{Product A,Product F,Product G} => {Product J}",0.0666666666666667,1,2.5,1
"771","{Product A,Product F,Product J} => {Product G}",0.0666666666666667,1,3,1
"772","{Product A,Product G,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"773","{Product A,Product G,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"774","{Product A,Product F,Product G} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"775","{Product A,Product G,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"776","{Product A,Product F,Product I} => {Product G}",0.0666666666666667,1,3,1
"777","{Product E,Product G,Product J} => {Product H}",0.133333333333333,1,2.14285714285714,2
"778","{Product G,Product H,Product J} => {Product E}",0.133333333333333,1,2.5,2
"779","{Product E,Product G,Product J} => {Product F}",0.133333333333333,1,2.14285714285714,2
"780","{Product E,Product G,Product J} => {Product I}",0.133333333333333,1,1.36363636363636,2
"781","{Product E,Product G,Product I} => {Product J}",0.133333333333333,1,2.5,2
"782","{Product E,Product G,Product H} => {Product F}",0.266666666666667,1,2.14285714285714,4
"783","{Product E,Product F,Product G} => {Product H}",0.266666666666667,1,2.14285714285714,4
"784","{Product F,Product G,Product H} => {Product E}",0.266666666666667,1,2.5,4
"785","{Product E,Product F,Product H} => {Product G}",0.266666666666667,0.8,2.4,4
"786","{Product E,Product G,Product I} => {Product H}",0.133333333333333,1,2.14285714285714,2
"787","{Product G,Product H,Product I} => {Product E}",0.133333333333333,1,2.5,2
"788","{Product E,Product G,Product I} => {Product F}",0.133333333333333,1,2.14285714285714,2
"789","{Product G,Product H,Product J} => {Product F}",0.133333333333333,1,2.14285714285714,2
"790","{Product G,Product H,Product J} => {Product I}",0.133333333333333,1,1.36363636363636,2
"791","{Product G,Product H,Product I} => {Product J}",0.133333333333333,1,2.5,2
"792","{Product F,Product G,Product J} => {Product I}",0.2,1,1.36363636363636,3
"793","{Product G,Product I,Product J} => {Product F}",0.2,1,2.14285714285714,3
"794","{Product F,Product G,Product I} => {Product J}",0.2,1,2.5,3
"795","{Product G,Product H,Product I} => {Product F}",0.133333333333333,1,2.14285714285714,2
"796","{Product A,Product F,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"797","{Product A,Product F,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"798","{Product C,Product H,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"799","{Product C,Product F,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"800","{Product C,Product F,Product H} => {Product J}",0.0666666666666667,1,2.5,1
"801","{Product C,Product H,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"802","{Product C,Product I,Product J} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"803","{Product C,Product H,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"804","{Product C,Product F,Product J} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"805","{Product C,Product I,Product J} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"806","{Product C,Product F,Product I} => {Product J}",0.0666666666666667,1,2.5,1
"807","{Product C,Product F,Product H} => {Product I}",0.0666666666666667,1,1.36363636363636,1
"808","{Product C,Product H,Product I} => {Product F}",0.0666666666666667,1,2.14285714285714,1
"809","{Product C,Product F,Product I} => {Product H}",0.0666666666666667,1,2.14285714285714,1
"810","{Product E,Product H,Product J} => {Product F}",0.2,1,2.14285714285714,3
"811","{Product E,Product F,Product J} => {Product H}",0.2,1,2.14285714285714,3
"812","{Product E,Product H,Product J} => {Product I}",0.2,1,1.36363636363636,3
"813","{Product E,Product I,Product J} => {Product H}",0.2,1,2.14285714285714,3
"814","{Product E,Product H,Product I} => {Product J}",0.2,1,2.5,3
"815","{Product E,Product F,Product J} => {Product I}",0.2,1,1.36363636363636,3
"816","{Product E,Product I,Product J} => {Product F}",0.2,1,2.14285714285714,3
"817","{Product E,Product F,Product I} => {Product J}",0.2,1,2.5,3
"818","{Product E,Product H,Product I} => {Product F}",0.2,1,2.14285714285714,3
"819","{Product E,Product F,Product I} => {Product H}",0.2,1,2.14285714285714,3
"820","{Product F,Product H,Product J} => {Product I}",0.266666666666667,1,1.36363636363636,4
"821","{Product H,Product I,Product J} => {Product F}",0.266666666666667,1,2.14285714285714,4
"822","{Product F,Product I,Product J} => {Product H}",0.266666666666667,0.8,1.71428571428571,4
"823","{Product F,Product H,Product I} => {Product J}",0.266666666666667,1,2.5,4


================================================
FILE: data/MMM_raw_data_v02.csv
================================================
Weekend,Unit_Sold,Print_Spend,Radio_Spend,TV_Spend,Promotion,Promotion_Dummy
9,2023,49099,858,51.14285714,Yes,1
227,780,48603,807,11.02857143,No,0
134,705,47249,785,11.48571429,Yes,1
60,678,46654,718,11.8,Yes,1
231,666,43688,822,10.71428571,No,0
48,715,45042,742,16.51428571,No,0
181,707,44454,737,9.31428571,Yes,1
103,710,46747,621,5.17142857,Yes,1
91,655,44564,694,5.08571429,Yes,1
44,719,47989,565,6.08571429,No,0
203,670,38676,885,5.77142857,No,0
195,622,39428,851,15.74285714,No,0
152,709,41488,767,2.71428571,No,0
79,675,46475,564,1.71428571,Yes,1
124,664,45140,591,11.48571429,Yes,1
158,680,37673,860,-0.71428571,Yes,1
21,598,48885,352,-0.14285714,No,0
8,628,39889,703,2.8,Yes,1
64,596,47563,370,5.6,No,0
224,606,39307,696,15.57142857,No,0
133,659,36706,795,2.54285714,Yes,1
54,628,40313,640,-0.51428571,No,0
105,577,35763,811,1.37142857,Yes,1
192,599,48681,272,9.22857143,Yes,1
81,569,48598,272,15.2,Yes,1
61,639,47936,295,9.14285714,No,0
109,616,48387,267,13.11428571,No,0
156,623,32886,886,-1.91428571,No,0
31,588,42767,491,2.08571429,Yes,1
198,633,40425,575,-3.94285714,No,0
173,575,42320,492,6,Yes,1
213,604,35300,774,2,Yes,1
7,605,46044,347,-3,No,0
25,599,41781,509,12.94285714,No,0
157,559,35072,777,1.94285714,No,0
162,581,38183,651,8.91428571,Yes,1
93,568,46091,330,-0.11428571,No,0
127,622,44157,391,9.8,No,0
132,636,43885,396,-0.25714286,No,0
238,581,32303,860,1.74285714,No,0
104,612,45498,325,14.71428571,No,0
219,581,44401,366,6.68571429,Yes,1
116,569,48536,203,13.68571429,No,0
138,625,43825,381,8.62857143,Yes,1
11,577,36701,648,-2.51428571,Yes,1
26,583,46352,259,4.42857143,Yes,1
6,612,41655,430,8.34285714,Yes,1
101,602,39645,507,10.31428571,Yes,1
233,538,48860,129,13.25714286,No,0
207,533,45029,261,3.11428571,Yes,1
225,540,39700,464,-2.94285714,Yes,1
140,518,33928,686,12,No,0
59,544,29155,885,9,No,0
56,541,30733,810,11.94285714,No,0
65,603,35927,598,-1.08571429,Yes,1
45,571,37643,523,6.88571429,No,0
218,517,35421,592,-5.28571429,No,0
146,568,42870,285,-3.31428571,No,0
229,542,36628,525,11.57142857,Yes,1
50,527,30595,761,13.57142857,No,0
117,587,31080,735,2.51428571,No,0
100,579,35422,548,-0.57142857,No,0
71,526,35900,522,7.37142857,No,0
24,493,43475,209,-0.71428571,Yes,1
72,564,36167,493,-0.74285714,Yes,1
14,542,44938,138,3.22857143,No,0
210,540,29065,778,-5.77142857,No,0
135,571,38215,397,12.14285714,Yes,1
206,525,29086,752,-0.94285714,No,0
220,506,26428,846,4,Yes,1
75,528,37654,394,2.97142857,No,0
52,568,34735,507,8.91428571,Yes,1
29,496,41342,233,1.85714286,No,0
99,517,29403,704,0.8,Yes,1
143,503,35773,438,6.68571429,Yes,1
150,543,31603,598,2.65714286,No,0
239,468,32552,549,-0.4,No,0
188,557,34891,437,10.45714286,Yes,1
107,470,28612,688,12.45714286,Yes,1
228,548,35204,426,-2.54285714,Yes,1
112,517,40053,226,-5.54285714,No,0
46,545,38363,290,-4.57142857,No,0
168,464,36569,354,-2.65714286,No,0
205,478,38018,291,-0.68571429,No,0
4,504,37363,316,7.31428571,Yes,1
190,477,35886,376,0.28571429,No,0
214,469,36659,323,-6.82857143,No,0
10,462,34102,420,8.14285714,No,0
141,547,30680,561,2.11428571,No,0
58,550,35321,364,-4.91428571,Yes,1
145,527,29229,598,7,Yes,1
226,516,31098,513,8.94285714,No,0
98,459,25574,720,-6.17142857,No,0
232,541,36129,295,11.8,Yes,1
5,495,36037,301,7.8,No,0
194,495,38077,208,-7.25714286,Yes,1
167,513,40035,128,-7.25714286,Yes,1
42,530,36831,253,-0.28571429,No,0
1,473,29747,535,-4.31428571,Yes,1
200,481,40057,119,10.68571429,Yes,1
94,492,40217,111,-1.34285714,No,0
125,530,39217,149,-0.37142857,No,0
97,490,22655,811,8.6,Yes,1
95,439,38326,177,1.6,Yes,1
240,515,20208,891,4.51428571,Yes,1
85,437,33618,345,2.45714286,Yes,1
113,472,23172,754,5.4,Yes,1
137,522,27701,568,-5.62857143,Yes,1
170,457,38502,139,6.37142857,No,0
180,427,27972,554,0.34285714,No,0
178,465,33244,338,8.28571429,Yes,1
171,506,36928,182,0.22857143,No,0
202,429,34629,271,-0.77142857,Yes,1
119,490,19600,874,-7.77142857,Yes,1
215,460,31167,397,2.11428571,No,0
39,505,30831,406,5.08571429,Yes,1
221,428,19763,848,0.08571429,No,0
209,418,21791,748,2.94285714,Yes,1
36,448,17983,897,-3.08571429,No,0
86,416,24036,648,2.85714286,No,0
111,452,35774,158,1.74285714,No,0
142,475,19563,802,-6.28571429,Yes,1
73,406,34579,199,-1.31428571,No,0
41,405,20212,762,-3.4,Yes,1
237,476,16963,883,6.51428571,No,0
67,471,20017,754,-3.51428571,No,0
123,471,30847,307,5.37142857,Yes,1
129,427,27574,429,3.31428571,No,0
128,440,27371,429,-6.77142857,Yes,1
38,391,17847,811,-3.77142857,Yes,1
208,477,21534,655,0.2,No,0
199,400,24161,546,9.2,Yes,1
32,437,29001,346,9.14285714,Yes,1
43,388,27420,410,5.14285714,No,0
182,400,28827,333,-2,No,0
217,430,31412,220,-1.08571429,Yes,1
155,372,19550,682,-0.17142857,Yes,1
230,399,16554,789,6.74285714,No,0
40,382,25718,413,-9.34285714,Yes,1
80,408,32630,130,4.6,Yes,1
191,394,28159,294,3.51428571,Yes,1
33,389,22739,503,4.45714286,Yes,1
16,431,23252,483,-4.54285714,Yes,1
185,403,13751,852,3.37142857,No,0
12,361,17644,694,-3.62857143,Yes,1
187,459,14664,812,6.37142857,No,0
177,406,22467,489,-7.71428571,No,0
189,434,28995,226,-6.71428571,No,0
118,416,24508,400,-0.74285714,Yes,1
62,390,30100,177,9.25714286,No,0
84,351,19123,532,-1.371428571,No,0
184,351,21729,416,-9.457142857,Yes,1
204,336,18566,535,-4.485714286,Yes,1
175,338,16173,630,1.514285714,No,0
19,392,17494,557,3.342857143,No,0
149,422,11329,801,-10.65714286,Yes,1
83,408,25527,226,-6.685714286,No,0
186,368,11355,795,-8.714285714,No,0
51,344,20593,423,-4.714285714,No,0
122,349,15159,640,-6.714285714,Yes,1
183,372,27148,153,-2.771428571,No,0
161,325,26340,190,-9.771428571,Yes,1
159,380,26834,156,-5.885714286,No,0
20,335,21378,360,-2.942857143,No,0
30,346,19448,436,3.057142857,Yes,1
179,357,9975,812,8.028571429,Yes,1
110,365,18372,479,-3.971428571,No,0
78,400,23630,248,-10.08571429,Yes,1
131,351,24608,195,5.8,Yes,1
148,304,19638,390,-7.228571429,No,0
55,384,8741,819,-2.257142857,No,0
89,313,18188,439,-11.28571429,Yes,1
37,397,26306,113,1.714285714,Yes,1
216,374,24374,186,7.685714286,Yes,1
165,354,15995,523,-5.342857143,No,0
130,352,7067,863,-5.457142857,No,0
35,306,21016,296,-11.51428571,Yes,1
68,372,6282,859,-4.714285714,No,0
174,288,19697,295,-3.885714286,Yes,1
164,286,14883,463,1.942857143,Yes,1
90,353,22662,134,-12.17142857,No,0
17,289,21040,193,-5.228571429,Yes,1
87,362,8378,696,-0.285714286,Yes,1
108,317,10784,595,-2.314285714,No,0
234,348,18322,282,-12.37142857,No,0
212,305,17999,281,-11.45714286,Yes,1
47,260,17017,313,-11.51428571,Yes,1
115,347,17485,280,-9.657142857,Yes,1
13,341,10265,564,6.342857143,Yes,1
22,275,18558,218,-2.742857143,No,0
102,260,20873,130,3.257142857,Yes,1
15,276,12626,452,-11.8,Yes,1
88,312,16413,291,-9.857142857,Yes,1
196,292,3059,821,-10.88571429,No,0
144,307,1991,854,0.057142857,Yes,1
96,269,12647,428,-7.942857143,No,0
114,254,18206,175,-13.17142857,No,0
57,303,2907,772,-4.257142857,No,0
201,302,14226,309,-1.342857143,Yes,1
82,271,9129,499,-12.45714286,No,0
53,226,11308,409,4.514285714,No,0
3,293,16124,209,-8.514285714,Yes,1
28,221,8230,502,-9.685714286,No,0
153,306,5076,623,-7.714285714,Yes,1
34,221,9146,452,-6.771428571,No,0
106,302,14965,205,-4.885714286,No,0
121,235,2776,691,1.085714286,Yes,1
2,243,16324,144,0.085714286,No,0
147,264,12106,304,-8.971428571,Yes,1
163,239,4129,621,1,No,0
27,209,14777,179,-14.11428571,No,0
236,273,12584,270,-2.114285714,Yes,1
151,229,2739,650,-1.2,No,0
18,288,9811,361,-5.257142857,Yes,1
23,274,1183,707,0.742857143,Yes,1
49,216,12910,203,-14.48571429,No,0
172,236,6727,451,-5.514285714,Yes,1
211,222,4350,543,3.485714286,Yes,1
222,269,14536,124,-7.6,Yes,1
197,214,4379,521,-8.657142857,No,0
74,240,12467,195,-13.65714286,Yes,1
77,263,1092,650,-13.65714286,No,0
66,245,11981,200,-2.771428571,No,0
69,267,2707,573,-4.771428571,No,0
160,256,2013,565,-7.028571429,No,0
235,205,12177,136,-7.171428571,No,0
70,183,4119,435,1.685714286,Yes,1
136,260,1740,534,-1.314285714,No,0
92,192,9015,220,-5.457142857,No,0
120,188,1071,480,-10.88571429,Yes,1
193,148,7384,195,-4.142857143,Yes,1
139,212,6850,181,-1.371428571,No,0
176,184,7207,165,-8.371428571,Yes,1
126,198,7333,152,-10.48571429,Yes,1
76,155,3133,261,-10.85714286,Yes,1
223,126,2367,222,-7.371428571,Yes,1
166,128,4406,129,-0.457142857,Yes,1
154,94,3566,129,-6.657142857,No,0
63,172,1923,189,-11.71428571,Yes,1
169,84,2918,120,-9.914285714,Yes,1


================================================
FILE: data/NPS Data Food Order v01.csv
================================================
Cust_Id,NPS_Score,NPS_Category,Timely_delivery,No_of_calls_in_an_Order,Loyalty_Years,C_Marital_Status,Tier_City,Mode_of_Payment,No_of_Items_in_Order,Gender,Order_Mode,Score_on_Food_Quality,Score_on_Order_Logistics,Menu_Presentation_Score,Score_on_Recommendations,Score_on_Delivery_Person_Friendliness
1,1,Detractor,N,4,5,Married,Tier1,Card,7,M,call,2,5,4,8,5
2,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,5,F,m-comm,8,10,6,4,3
3,0,Detractor,N,4,7,Married,Tier3,Cash,1,M,call,4,2,7,8,2
4,0,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,1,F,call,9,3,4,10,5
5,4,Detractor,N,4,10,Married,Tier2,Card,6,M,call,7,1,6,7,4
6,10,Promoter,N,4,7,Married,Tier1,e-Wallet,8,F,m-comm,10,6,4,2,10
7,0,Detractor,N,4,3,Married,Tier1,Cash,7,M,call,9,4,3,3,1
8,1,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,4,F,call,9,1,5,10,2
9,6,Detractor,N,4,7,Married,Tier3,Card,4,M,call,5,5,10,1,1
10,9,Promoter,N,4,5,Married,Tier3,e-Wallet,10,F,m-comm,7,10,7,7,8
11,4,Detractor,N,4,9,Married,Tier2,Cash,4,M,call,10,1,1,1,6
12,8,Passives,Y,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,6,2,1,7,3
13,2,Detractor,N,4,4,Married,Tier1,Card,3,M,call,9,3,6,6,3
14,7,Passives,N,4,9,Not Married,Tier2,e-Wallet,1,F,m-comm,9,3,9,8,7
15,6,Detractor,N,4,3,Married,Tier3,Cash,7,M,call,7,8,5,2,9
16,6,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,3,F,call,10,5,8,5,4
17,6,Detractor,N,4,3,Married,Tier2,Card,5,M,call,10,2,6,10,4
18,2,Detractor,N,4,5,Not Married,Tier3,e-Wallet,4,F,call,4,4,10,4,8
19,4,Detractor,N,4,4,Married,Tier1,Cash,4,M,call,10,2,7,6,4
20,3,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,5,F,call,10,9,8,8,3
21,8,Passives,Y,4,8,Married,Tier3,Card,4,M,call,4,7,6,6,6
22,0,Detractor,Y,4,10,Not Married,Tier1,e-Wallet,4,F,e-comm,10,9,5,1,6
23,5,Detractor,Y,4,2,Married,Tier2,Cash,1,M,e-comm,10,1,3,9,4
24,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,e-comm,10,9,5,4,8
25,0,Detractor,Y,4,1,Married,Tier1,Card,2,M,e-comm,9,10,5,3,3
26,1,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,6,F,e-comm,9,7,5,4,4
27,1,Detractor,N,4,10,Married,Tier3,Cash,4,M,call,5,6,2,1,2
28,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,10,F,m-comm,10,5,10,5,10
29,10,Promoter,Y,4,4,Married,Tier1,Card,9,M,m-comm,9,2,7,5,9
30,6,Detractor,N,4,1,Not Married,Tier3,e-Wallet,6,F,call,8,4,7,1,4
31,4,Detractor,Y,4,2,Married,Tier1,Cash,2,M,e-comm,10,8,4,2,5
32,7,Passives,N,4,6,Not Married,Tier2,Ziggy Credit,4,F,m-comm,9,7,7,8,10
33,3,Detractor,N,4,4,Married,Tier3,Card,3,M,call,7,2,4,2,4
34,7,Passives,N,4,4,Not Married,Tier1,e-Wallet,3,F,e-comm,10,6,7,10,10
35,8,Passives,Y,4,1,Married,Tier2,Cash,4,M,m-comm,10,9,9,4,2
36,4,Detractor,N,4,10,Not Married,Tier3,Ziggy Credit,4,F,call,4,6,2,3,1
37,3,Detractor,Y,4,3,Married,Tier1,Card,5,M,e-comm,10,3,8,1,3
38,6,Detractor,Y,4,9,Not Married,Tier2,e-Wallet,1,F,e-comm,9,5,3,7,4
39,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,5,9,5,7,8
40,5,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,3,F,call,10,8,10,5,4
41,7,Passives,Y,4,7,Married,Tier2,Card,4,M,m-comm,10,6,3,4,5
42,10,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,9,3,3,1,10
43,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,9,3,1,7,6
44,0,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,2,F,call,10,2,6,10,4
45,7,Passives,N,4,9,Married,Tier3,Card,4,M,call,6,9,6,3,1
46,6,Detractor,Y,4,5,Not Married,Tier1,e-Wallet,1,F,call,10,3,8,3,3
47,7,Passives,Y,4,3,Married,Tier2,Cash,7,M,m-comm,9,5,1,8,3
48,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,9,4,2,1
49,1,Detractor,N,4,8,Married,Tier1,Card,1,M,call,10,7,9,3,2
50,8,Passives,N,4,3,Not Married,Tier2,e-Wallet,4,F,m-comm,9,10,4,5,7
51,7,Passives,N,4,3,Married,Tier3,Cash,1,M,call,4,1,2,8,7
52,0,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,4,F,call,9,6,4,8,5
53,1,Detractor,N,4,5,Married,Tier2,Card,1,M,call,9,1,3,4,4
54,1,Detractor,Y,4,5,Not Married,Tier3,e-Wallet,6,F,call,6,9,1,7,9
55,6,Detractor,N,4,2,Married,Tier1,Cash,6,M,call,9,1,1,5,5
56,10,Promoter,N,4,6,Not Married,Tier1,Ziggy Credit,9,F,m-comm,10,8,2,5,9
57,1,Detractor,N,4,8,Married,Tier3,Card,4,M,call,8,7,7,6,7
58,5,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,1,F,m-comm,10,9,9,7,6
59,10,Promoter,Y,4,3,Married,Tier2,Cash,8,M,e-comm,9,4,6,1,10
60,7,Passives,Y,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,8,6,6,8,1
61,1,Detractor,Y,4,8,Married,Tier1,Card,1,M,m-comm,9,4,3,10,2
62,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,3,F,m-comm,10,2,2,4,1
63,7,Passives,N,4,2,Married,Tier3,Cash,7,M,call,5,8,2,3,2
64,5,Detractor,Y,4,2,Not Married,Tier1,Ziggy Credit,5,F,m-comm,10,4,6,3,2
65,5,Detractor,Y,4,4,Married,Tier2,Card,1,M,m-comm,9,3,1,9,5
66,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,7,7,7,2,1
67,0,Detractor,Y,4,2,Married,Tier1,Cash,1,M,m-comm,9,3,8,10,4
68,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,3,F,m-comm,9,4,6,6,1
69,10,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,10,10,3,6,10
70,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,1,5,5,7
71,9,Promoter,Y,4,7,Married,Tier1,Cash,5,M,m-comm,10,8,1,7,7
72,1,Detractor,N,4,10,Not Married,Tier3,Ziggy Credit,7,F,call,6,1,7,9,4
73,1,Detractor,Y,4,1,Married,Tier2,Card,6,M,m-comm,10,6,2,5,7
74,6,Detractor,Y,4,5,Not Married,Tier2,e-Wallet,1,F,m-comm,9,6,3,4,1
75,10,Promoter,Y,4,4,Married,Tier1,Cash,9,M,m-comm,10,6,6,2,8
76,2,Detractor,Y,4,4,Not Married,Tier1,Ziggy Credit,4,F,m-comm,9,5,1,6,5
77,0,Detractor,Y,4,9,Married,Tier2,Card,7,M,m-comm,9,6,4,7,1
78,8,Passives,Y,4,2,Not Married,Tier3,e-Wallet,1,F,call,5,6,6,5,6
79,4,Detractor,Y,4,5,Married,Tier1,Cash,2,M,m-comm,10,9,9,4,4
80,5,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,7,F,m-comm,9,2,6,3,1
81,8,Passives,Y,4,7,Married,Tier3,Card,6,M,call,4,3,9,1,8
82,3,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,7,F,m-comm,9,8,3,6,6
83,2,Detractor,Y,4,5,Married,Tier2,Cash,1,M,m-comm,10,4,5,2,3
84,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,2,F,call,5,8,5,1,1
85,2,Detractor,Y,4,9,Married,Tier1,Card,3,M,m-comm,9,4,8,2,1
86,1,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,6,F,m-comm,10,8,10,10,1
87,9,Promoter,N,4,8,Married,Tier3,Cash,2,M,call,7,7,4,10,7
88,1,Detractor,N,4,6,Married,Tier1,Ziggy Credit,10,F,e-comm,9,9,2,8,10
89,7,Passives,Y,4,6,Married,Tier2,Card,5,M,m-comm,9,2,9,3,5
90,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,3,F,call,6,5,2,4,2
91,6,Detractor,Y,4,3,Married,Tier1,Cash,3,M,m-comm,9,1,3,3,6
92,5,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,1,9,3,3
93,7,Passives,N,4,5,Married,Tier3,Card,4,M,call,4,1,4,8,1
94,3,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,1,F,m-comm,10,6,4,6,1
95,1,Detractor,Y,4,4,Married,Tier2,Cash,7,M,m-comm,9,7,6,2,6
96,7,Passives,Y,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,7,1,6,2,6
97,10,Promoter,Y,4,3,Married,Tier2,Card,10,M,m-comm,9,10,2,2,9
98,2,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,1,F,m-comm,10,8,6,10,6
99,7,Passives,N,4,5,Married,Tier3,Cash,2,M,call,4,6,4,4,1
100,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,10,4,6,10,10
101,5,Detractor,Y,4,2,Married,Tier2,Card,3,M,m-comm,10,6,3,5,5
102,1,Detractor,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,4,10,5,4,4
103,4,Detractor,Y,4,4,Married,Tier1,Cash,3,M,m-comm,10,7,6,3,1
104,1,Detractor,Y,4,7,Not Married,Tier2,Ziggy Credit,5,F,m-comm,9,9,5,8,3
105,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,8,5,3,10,3
106,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,9,1,9,5,7
107,6,Detractor,Y,4,1,Married,Tier2,Cash,1,M,m-comm,10,3,10,4,3
108,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,10,8,10,9,9
109,8,Passives,Y,4,8,Married,Tier1,Card,1,M,e-comm,10,9,4,3,3
110,6,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,7,F,m-comm,9,3,7,1,2
111,10,Promoter,Y,4,7,Married,Tier1,Cash,6,M,e-comm,10,4,5,7,9
112,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,10,4,1,10,10
113,0,Detractor,Y,4,10,Married,Tier2,Card,2,M,m-comm,9,4,10,10,6
114,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,5,8,8,1,7
115,4,Detractor,Y,4,8,Married,Tier1,Cash,4,M,m-comm,10,2,7,7,5
116,2,Detractor,Y,4,9,Not Married,Tier2,Ziggy Credit,3,F,m-comm,9,1,4,8,1
117,7,Passives,N,4,2,Married,Tier3,Card,4,M,call,7,8,3,3,10
118,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,8,8,2,9
119,9,Promoter,Y,4,5,Married,Tier1,Cash,8,M,m-comm,10,7,10,3,10
120,7,Passives,Y,4,6,Not Married,Tier3,Ziggy Credit,2,F,call,7,9,5,5,1
121,8,Passives,Y,4,7,Married,Tier1,Card,5,M,e-comm,10,8,6,5,9
122,10,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,9,9,7,7,9
123,10,Promoter,Y,4,9,Married,Tier1,Cash,6,M,m-comm,9,2,8,10,10
124,4,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,6,10,3,2
125,0,Detractor,Y,4,7,Married,Tier2,Card,6,M,m-comm,9,1,1,3,6
126,1,Detractor,N,4,1,Not Married,Tier3,e-Wallet,7,F,call,4,8,9,2,3
127,0,Detractor,Y,4,9,Married,Tier1,Cash,3,M,m-comm,10,6,6,8,5
128,9,Promoter,N,4,7,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,7,3,6,9
129,10,Promoter,Y,4,6,Married,Tier1,Card,7,M,m-comm,9,8,5,2,7
130,10,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,9,7,6,7,9
131,0,Detractor,Y,4,7,Married,Tier2,Cash,5,M,m-comm,9,2,10,7,6
132,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,4,F,call,4,8,5,1,9
133,1,Detractor,Y,4,4,Married,Tier1,Card,3,M,m-comm,9,4,1,9,4
134,5,Detractor,Y,4,10,Not Married,Tier2,e-Wallet,6,F,m-comm,10,9,3,9,2
135,8,Passives,Y,4,4,Married,Tier3,Cash,2,M,call,5,8,5,3,1
136,7,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,3,1,10,10
137,2,Detractor,Y,4,4,Married,Tier2,Card,7,M,m-comm,9,7,3,6,2
138,9,Promoter,N,4,7,Married,Tier1,e-Wallet,9,F,m-comm,10,10,8,5,10
139,9,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,10,9,9,10,10
140,6,Detractor,Y,4,1,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,3,1,8,2
141,10,Promoter,Y,4,1,Married,Tier1,Card,8,M,e-comm,10,6,5,5,7
142,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,2,F,e-comm,9,9,2,10,3
143,4,Detractor,Y,4,10,Married,Tier2,Cash,4,M,m-comm,9,3,8,5,2
144,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,7,F,call,8,9,4,1,9
145,0,Detractor,Y,4,2,Married,Tier1,Card,1,M,m-comm,9,5,9,5,5
146,3,Detractor,Y,4,4,Not Married,Tier2,e-Wallet,2,F,m-comm,9,10,4,3,6
147,10,Promoter,Y,4,7,Married,Tier1,Cash,6,M,m-comm,10,2,2,6,10
148,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,9,9,9,1,8
149,2,Detractor,Y,4,3,Married,Tier2,Card,1,M,m-comm,10,5,7,10,3
150,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,6,F,call,6,6,9,9,1
151,2,Detractor,Y,4,6,Married,Tier1,Cash,5,M,m-comm,9,4,4,2,6
152,1,Detractor,Y,4,4,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,3,10,1,4
153,10,Promoter,Y,4,8,Married,Tier1,Card,9,M,m-comm,9,6,2,5,7
154,5,Detractor,Y,4,10,Not Married,Tier1,e-Wallet,5,F,m-comm,9,7,5,10,5
155,6,Detractor,Y,4,2,Married,Tier2,Cash,5,M,m-comm,9,2,10,9,1
156,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,5,2,7,3,4
157,10,Promoter,Y,4,8,Married,Tier1,Card,5,M,e-comm,9,10,6,7,10
158,3,Detractor,Y,4,2,Not Married,Tier2,e-Wallet,4,F,m-comm,9,5,8,3,1
159,7,Passives,Y,4,10,Married,Tier3,Cash,6,M,call,4,8,2,7,5
160,2,Detractor,Y,4,8,Not Married,Tier1,Ziggy Credit,1,F,m-comm,10,2,4,9,4
161,2,Detractor,Y,4,10,Married,Tier2,Card,7,M,m-comm,10,8,7,3,4
162,7,Passives,Y,4,8,Not Married,Tier3,e-Wallet,6,F,call,5,3,8,10,2
163,1,Detractor,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,9,6,8,5
164,6,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,1,6,5,2
165,7,Passives,N,4,10,Married,Tier3,Card,4,M,call,7,5,4,6,5
166,5,Detractor,Y,4,1,Not Married,Tier1,e-Wallet,5,F,m-comm,9,9,3,3,5
167,2,Detractor,Y,4,7,Married,Tier2,Cash,7,M,m-comm,10,1,7,2,6
168,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,6,F,call,4,3,3,2,4
169,0,Detractor,Y,4,4,Married,Tier1,Card,5,M,m-comm,9,7,1,10,2
170,6,Detractor,Y,4,8,Not Married,Tier2,e-Wallet,7,F,m-comm,10,10,4,7,3
171,10,Promoter,Y,4,3,Married,Tier1,Cash,5,M,m-comm,10,5,8,7,7
172,7,Passives,N,4,2,Not Married,Tier1,Ziggy Credit,4,F,e-comm,10,2,6,7,10
173,0,Detractor,Y,4,1,Married,Tier2,Card,2,M,m-comm,10,4,1,2,4
174,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,6,6,8,9,4
175,6,Detractor,Y,4,6,Married,Tier1,Cash,1,M,m-comm,10,3,8,10,2
176,5,Detractor,Y,4,2,Not Married,Tier2,Ziggy Credit,2,F,m-comm,9,3,2,4,6
177,7,Passives,N,4,10,Married,Tier3,Card,6,M,call,6,4,3,2,8
178,1,Detractor,Y,4,4,Not Married,Tier1,e-Wallet,1,F,m-comm,10,3,1,3,4
179,9,Promoter,Y,4,2,Married,Tier1,Cash,5,M,m-comm,10,9,8,2,8
180,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,3,F,call,8,8,2,4,2
181,1,Detractor,Y,4,4,Married,Tier1,Card,3,M,m-comm,10,6,9,6,4
182,2,Detractor,Y,4,2,Not Married,Tier2,e-Wallet,1,F,m-comm,10,10,1,4,4
183,7,Passives,N,4,8,Married,Tier3,Cash,3,M,call,8,4,6,1,7
184,8,Passives,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,e-comm,10,4,9,5,2
185,7,Passives,Y,4,5,Married,Tier2,Card,5,M,m-comm,10,9,6,2,7
186,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,4,1,4,6,8
187,2,Detractor,Y,4,5,Married,Tier1,Cash,3,M,m-comm,10,2,7,8,1
188,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,9,8,9,9,9
189,7,Passives,N,4,7,Married,Tier3,Card,3,M,call,4,6,2,8,1
190,1,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,2,F,m-comm,10,8,9,3,5
191,2,Detractor,Y,4,1,Married,Tier2,Cash,5,M,m-comm,10,4,1,9,2
192,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,10,10,9,3,10
193,1,Detractor,Y,4,5,Married,Tier1,Card,7,M,m-comm,10,5,7,3,3
194,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,6,F,m-comm,10,10,9,2,3
195,9,Promoter,N,4,3,Married,Tier3,Cash,7,M,call,4,8,3,3,5
196,5,Detractor,Y,4,1,Not Married,Tier1,Ziggy Credit,4,F,m-comm,10,9,10,2,3
197,9,Promoter,Y,4,6,Married,Tier1,Card,10,M,call,10,9,5,7,9
198,8,Passives,Y,4,2,Not Married,Tier3,e-Wallet,2,F,call,7,9,6,3,9
199,4,Detractor,Y,4,8,Married,Tier1,Cash,1,M,m-comm,9,5,5,4,6
200,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,10,2,9,2,8
201,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,7,5,2,7,5
202,0,Detractor,Y,4,3,Not Married,Tier1,e-Wallet,6,F,m-comm,10,7,9,10,3
203,5,Detractor,Y,4,10,Married,Tier2,Cash,3,M,m-comm,9,9,1,7,1
204,8,Passives,Y,4,7,Not Married,Tier3,Ziggy Credit,6,F,call,8,5,7,1,8
205,5,Detractor,Y,4,1,Married,Tier1,Card,4,M,m-comm,9,5,3,1,2
206,4,Detractor,Y,4,9,Not Married,Tier2,e-Wallet,3,F,m-comm,10,1,1,7,3
207,7,Passives,N,4,6,Married,Tier3,Cash,4,M,call,4,5,8,10,6
208,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,10,2,5,4,9
209,9,Promoter,Y,4,3,Married,Tier1,Card,8,M,m-comm,10,5,5,7,7
210,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,7,8,10,3,3
211,3,Detractor,Y,4,1,Married,Tier1,Cash,1,M,m-comm,10,9,5,5,5
212,5,Detractor,Y,4,8,Not Married,Tier2,Ziggy Credit,1,F,m-comm,9,4,8,2,1
213,7,Passives,N,4,6,Married,Tier3,Card,1,M,call,5,9,4,3,7
214,9,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,9,5,10,3,9
215,3,Detractor,Y,4,3,Married,Tier2,Cash,3,M,m-comm,9,4,10,1,2
216,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,3,F,call,8,5,9,7,8
217,3,Detractor,Y,4,1,Married,Tier1,Card,6,M,m-comm,10,3,8,1,1
218,10,Promoter,Y,4,5,Married,Tier1,e-Wallet,9,F,call,9,1,7,10,7
219,8,Passives,Y,4,5,Married,Tier3,Cash,2,M,call,6,6,4,4,6
220,5,Detractor,Y,4,6,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,8,4,3,6
221,4,Detractor,Y,4,10,Married,Tier2,Card,4,M,m-comm,9,8,10,2,5
222,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,1,F,call,6,1,3,10,3
223,1,Detractor,Y,4,8,Married,Tier1,Cash,1,M,m-comm,9,7,8,5,2
224,5,Detractor,Y,4,1,Not Married,Tier2,Ziggy Credit,5,F,m-comm,10,7,5,2,5
225,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,4,6,4,2,2
226,4,Detractor,Y,4,7,Not Married,Tier1,e-Wallet,5,F,m-comm,9,2,6,4,4
227,1,Detractor,Y,4,4,Married,Tier2,Cash,1,M,m-comm,10,7,5,7,3
228,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,5,7,3,3,9
229,7,Passives,Y,4,7,Married,Tier1,Card,4,M,e-comm,9,5,4,8,10
230,5,Detractor,Y,4,3,Not Married,Tier2,e-Wallet,1,F,m-comm,10,2,4,9,4
231,7,Passives,Y,4,2,Married,Tier3,Cash,1,M,call,6,7,8,2,4
232,2,Detractor,Y,4,2,Not Married,Tier1,Ziggy Credit,2,F,m-comm,10,1,6,5,5
233,8,Passives,Y,4,9,Married,Tier2,Card,7,M,m-comm,10,2,2,5,2
234,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,9,10,5,8,7
235,10,Promoter,Y,4,4,Married,Tier3,Cash,6,M,m-comm,8,5,5,3,9
236,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,4,F,m-comm,10,7,4,7,2
237,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,5,2,2,10,2
238,1,Detractor,Y,4,1,Not Married,Tier1,e-Wallet,7,F,m-comm,9,5,9,4,5
239,9,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,9,8,1,5,8
240,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,7,F,call,4,4,2,6,9
241,10,Promoter,Y,4,9,Married,Tier1,Card,6,M,call,10,2,4,4,10
242,6,Detractor,Y,4,7,Not Married,Tier2,e-Wallet,1,F,m-comm,10,1,4,5,3
243,10,Promoter,Y,4,2,Married,Tier1,Cash,8,M,m-comm,10,3,6,2,9
244,1,Detractor,Y,4,9,Not Married,Tier1,Ziggy Credit,6,F,m-comm,10,7,10,3,5
245,7,Passives,Y,4,3,Married,Tier2,Card,1,M,m-comm,9,3,2,10,10
246,8,Passives,Y,4,8,Not Married,Tier3,e-Wallet,3,F,call,5,5,6,5,2
247,8,Passives,Y,4,2,Married,Tier1,Cash,6,M,e-comm,10,2,1,8,4
248,8,Passives,N,4,7,Not Married,Tier2,Ziggy Credit,7,F,m-comm,10,2,9,3,10
249,7,Passives,N,4,4,Married,Tier3,Card,2,M,call,5,7,5,4,6
250,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,3,F,e-comm,10,9,9,5,8
251,3,Detractor,Y,4,3,Married,Tier2,Cash,3,M,m-comm,10,2,5,5,2
252,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,2,F,call,6,9,4,6,5
253,10,Promoter,Y,4,3,Married,Tier1,Card,5,M,m-comm,9,9,2,10,8
254,4,Detractor,Y,4,10,Not Married,Tier2,e-Wallet,5,F,m-comm,9,6,2,10,4
255,9,Promoter,Y,4,9,Married,Tier1,Cash,10,M,m-comm,9,3,6,10,9
256,7,Passives,N,4,3,Not Married,Tier1,Ziggy Credit,7,F,e-comm,9,7,4,1,6
257,8,Passives,Y,4,4,Married,Tier2,Card,4,M,m-comm,9,10,1,4,6
258,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,6,F,call,7,7,1,10,3
259,3,Detractor,Y,4,3,Married,Tier1,Cash,7,M,call,10,4,8,2,3
260,4,Detractor,Y,4,7,Not Married,Tier2,Ziggy Credit,3,F,call,10,8,5,8,6
261,7,Passives,N,4,6,Married,Tier3,Card,7,M,call,8,4,6,2,7
262,6,Detractor,Y,4,9,Not Married,Tier1,e-Wallet,3,F,call,10,9,5,6,4
263,2,Detractor,Y,4,5,Married,Tier2,Cash,4,M,call,9,6,9,5,1
264,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,5,F,call,8,10,6,7,4
265,8,Passives,Y,4,7,Married,Tier1,Card,7,M,e-comm,10,4,9,1,5
266,10,Promoter,Y,4,7,Married,Tier1,e-Wallet,7,F,call,9,9,9,1,10
267,10,Promoter,Y,4,6,Married,Tier1,Cash,6,M,m-comm,10,9,3,5,9
268,7,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,e-comm,10,3,3,6,2
269,9,Promoter,Y,4,3,Married,Tier1,Card,7,M,m-comm,10,1,10,6,7
270,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,4,4,3,1,4
271,6,Detractor,Y,4,9,Married,Tier1,Cash,2,M,call,9,6,3,6,1
272,8,Passives,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,m-comm,10,7,1,1,1
273,7,Passives,N,4,3,Married,Tier3,Card,2,M,call,6,5,4,3,9
274,6,Detractor,Y,4,2,Not Married,Tier1,e-Wallet,5,F,call,9,9,8,7,5
275,8,Passives,Y,4,3,Married,Tier2,Cash,1,M,m-comm,10,3,10,2,3
276,7,Passives,Y,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,8,3,2,6,3
277,3,Detractor,N,4,7,Married,Tier1,Card,7,M,call,9,1,10,5,1
278,7,Passives,N,4,8,Not Married,Tier2,e-Wallet,4,F,m-comm,9,8,2,7,2
279,7,Passives,N,4,3,Married,Tier3,Cash,4,M,call,8,9,6,4,4
280,5,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,3,F,call,9,7,8,2,1
281,1,Detractor,N,4,8,Married,Tier2,Card,5,M,call,10,4,3,1,6
282,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,4,4,6,6,7
283,7,Passives,Y,4,2,Married,Tier1,Cash,7,M,e-comm,10,7,3,4,5
284,0,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,2,F,call,10,7,6,10,3
285,8,Passives,Y,4,2,Married,Tier3,Card,7,M,call,7,4,7,10,4
286,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,2,F,call,10,10,9,3,2
287,6,Detractor,N,4,3,Married,Tier2,Cash,1,M,call,9,4,4,8,3
288,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,2,10,6,4
289,0,Detractor,N,4,4,Married,Tier1,Card,5,M,call,9,1,7,5,4
290,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,6,F,m-comm,9,2,2,10,1
291,9,Promoter,Y,4,4,Married,Tier3,Cash,5,M,call,7,10,3,2,9
292,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,5,F,call,9,3,10,8,3
293,2,Detractor,N,4,1,Married,Tier2,Card,6,M,call,9,7,7,3,1
294,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,5,7,5,8,10
295,5,Detractor,N,4,10,Married,Tier1,Cash,4,M,call,10,10,5,7,2
296,0,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,3,F,call,9,9,2,8,4
297,7,Passives,Y,4,1,Married,Tier3,Card,2,M,call,7,1,8,3,6
298,0,Detractor,N,4,6,Not Married,Tier1,e-Wallet,1,F,call,6,5,10,2,3
299,8,Passives,Y,4,9,Married,Tier2,Cash,1,M,m-comm,9,4,9,5,2
300,8,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,1,F,call,8,8,6,4,8
301,4,Detractor,N,4,5,Married,Tier1,Card,1,M,call,10,8,9,6,6
302,1,Detractor,N,4,10,Not Married,Tier2,e-Wallet,5,F,call,7,9,4,6,5
303,7,Passives,N,4,5,Married,Tier3,Cash,1,M,call,8,1,8,8,10
304,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,3,F,call,6,6,7,10,3
305,8,Passives,Y,4,9,Married,Tier2,Card,6,M,m-comm,8,8,5,3,1
306,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,3,F,call,5,7,5,9,6
307,0,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,4,10,10,7,6
308,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,9,10,4,4,4
309,7,Passives,N,4,6,Married,Tier3,Card,6,M,call,5,3,8,9,10
310,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,8,2,3,10,3
311,0,Detractor,N,4,9,Married,Tier2,Cash,3,M,call,2,10,8,8,3
312,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,2,F,call,5,1,3,4,8
313,7,Passives,Y,4,6,Married,Tier1,Card,3,M,e-comm,5,2,10,7,3
314,1,Detractor,N,4,2,Not Married,Tier2,e-Wallet,3,F,call,9,5,6,9,3
315,7,Passives,Y,4,6,Married,Tier3,Cash,1,M,call,4,9,4,8,7
316,2,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,4,F,call,8,5,3,1,2
317,7,Passives,Y,4,3,Married,Tier2,Card,2,M,m-comm,8,4,9,7,3
318,10,Promoter,Y,4,5,Married,Tier1,e-Wallet,5,F,call,3,1,2,6,7
319,7,Passives,Y,4,9,Married,Tier1,Cash,6,M,e-comm,10,1,5,9,1
320,3,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,9,6,10,6,6
321,8,Passives,Y,4,4,Married,Tier3,Card,3,M,call,8,5,9,2,4
322,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,4,3,3,5,8
323,0,Detractor,N,4,8,Married,Tier2,Cash,7,M,call,6,5,2,7,2
324,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,1,F,call,4,3,3,8,3
325,9,Promoter,Y,4,5,Married,Tier1,Card,10,M,m-comm,2,9,7,4,10
326,10,Promoter,N,4,7,Married,Tier1,e-Wallet,9,F,m-comm,3,4,10,5,9
327,7,Passives,N,4,6,Married,Tier3,Cash,7,M,call,7,2,5,1,4
328,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,3,3,4,1,5
329,8,Passives,Y,4,2,Married,Tier2,Card,1,M,m-comm,9,5,7,6,10
330,8,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,8,7,6,5,5
331,5,Detractor,Y,4,8,Married,Tier1,Cash,2,M,call,10,2,3,4,1
332,4,Detractor,Y,4,3,Not Married,Tier2,Ziggy Credit,6,F,call,4,1,2,6,4
333,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,5,10,5,5,10
334,0,Detractor,N,4,3,Not Married,Tier1,e-Wallet,1,F,call,5,9,2,4,2
335,0,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,3,9,4,6,2
336,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,6,F,call,7,4,10,5,1
337,9,Promoter,Y,4,4,Married,Tier1,Card,5,M,m-comm,6,1,10,8,10
338,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,2,10,5,10,1
339,9,Promoter,N,4,10,Married,Tier3,Cash,7,M,call,6,10,3,9,5
340,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,7,2,5,5,6
341,6,Detractor,N,4,1,Married,Tier2,Card,5,M,call,9,7,9,10,2
342,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,6,F,call,6,4,8,5,7
343,10,Promoter,Y,4,10,Married,Tier1,Cash,8,M,m-comm,1,10,9,3,8
344,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,6,9,2,9,10
345,10,Promoter,Y,4,5,Married,Tier1,Card,9,M,m-comm,5,7,6,3,8
346,7,Passives,N,4,9,Not Married,Tier1,e-Wallet,2,F,e-comm,9,9,6,8,2
347,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,10,3,10,9,6
348,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,6,8,2,5,8
349,2,Detractor,N,4,3,Married,Tier1,Card,2,M,call,4,1,5,9,5
350,4,Detractor,N,4,8,Not Married,Tier2,e-Wallet,3,F,call,1,9,10,3,2
351,10,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,5,10,7,6,10
352,6,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,5,F,call,9,8,7,3,5
353,2,Detractor,N,4,6,Married,Tier2,Card,5,M,call,9,9,10,3,4
354,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,8,10,8,6,7
355,6,Detractor,N,4,5,Married,Tier1,Cash,2,M,call,7,5,10,5,3
356,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,10,9,3,6,7
357,7,Passives,N,4,5,Married,Tier3,Card,6,M,call,5,1,9,1,6
358,4,Detractor,N,4,6,Not Married,Tier1,e-Wallet,4,F,call,1,6,9,3,5
359,2,Detractor,N,4,9,Married,Tier2,Cash,2,M,call,4,5,7,1,3
360,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,7,1,7,5,8
361,0,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,5,5,1,4
362,6,Detractor,N,4,10,Not Married,Tier2,e-Wallet,1,F,call,9,10,5,5,4
363,9,Promoter,N,4,3,Married,Tier3,Cash,6,M,call,7,4,8,8,5
364,4,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,2,F,call,3,7,4,2,1
365,10,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,8,2,2,5,10
366,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,4,F,call,5,1,6,2,6
367,8,Passives,Y,4,3,Married,Tier1,Cash,7,M,e-comm,7,4,1,7,3
368,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,m-comm,1,9,5,7,6
369,10,Promoter,Y,4,1,Married,Tier1,Card,9,M,m-comm,2,7,8,3,7
370,2,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,2,8,4,9,3
371,3,Detractor,N,4,7,Married,Tier2,Cash,2,M,call,4,5,1,1,6
372,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,2,F,call,5,3,3,9,4
373,7,Passives,Y,4,4,Married,Tier1,Card,5,M,e-comm,3,3,2,6,1
374,4,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,8,9,4,4,5
375,9,Promoter,N,4,2,Married,Tier3,Cash,2,M,call,7,2,9,10,8
376,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,call,3,1,5,9,3
377,6,Detractor,N,4,9,Married,Tier2,Card,2,M,call,2,8,9,3,2
378,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,5,F,call,4,9,2,9,8
379,1,Detractor,N,4,7,Married,Tier1,Cash,1,M,call,4,9,10,8,6
380,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,6,F,call,3,10,8,3,2
381,9,Promoter,Y,4,3,Married,Tier1,Card,8,M,m-comm,9,9,1,7,10
382,9,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,10,1,6,6,10
383,10,Promoter,Y,4,10,Married,Tier1,Cash,8,M,m-comm,9,8,8,1,8
384,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,1,F,call,5,8,2,4,7
385,1,Detractor,N,4,8,Married,Tier1,Card,3,M,call,1,10,7,6,6
386,5,Detractor,N,4,4,Not Married,Tier2,e-Wallet,2,F,call,9,3,10,3,1
387,9,Promoter,N,4,5,Married,Tier3,Cash,1,M,call,8,9,8,1,6
388,8,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,7,F,e-comm,4,9,9,6,5
389,1,Detractor,N,4,7,Married,Tier2,Card,6,M,call,3,9,8,6,1
390,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,8,3,7,3,6
391,0,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,3,1,5,7,6
392,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,m-comm,6,7,2,3,2
393,7,Passives,N,4,10,Married,Tier3,Card,7,M,call,5,6,8,5,7
394,2,Detractor,N,4,2,Not Married,Tier1,e-Wallet,5,F,call,3,3,7,3,5
395,1,Detractor,N,4,8,Married,Tier2,Cash,4,M,call,6,1,5,8,2
396,8,Passives,Y,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,6,9,6,3,2
397,6,Detractor,N,4,7,Married,Tier1,Card,4,M,call,8,4,6,7,2
398,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,3,F,call,6,3,6,8,5
399,9,Promoter,Y,4,10,Married,Tier3,Cash,6,M,call,5,10,4,9,1
400,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,4,F,e-comm,9,8,4,10,4
401,7,Passives,Y,4,3,Married,Tier2,Card,1,M,m-comm,4,1,6,9,8
402,7,Passives,Y,4,8,Not Married,Tier3,e-Wallet,6,F,call,8,8,5,7,8
403,0,Detractor,N,4,4,Married,Tier1,Cash,1,M,call,2,1,8,8,3
404,1,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,7,F,call,6,10,3,7,4
405,9,Promoter,Y,4,8,Married,Tier1,Card,9,M,m-comm,10,7,5,7,10
406,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,7,F,call,5,4,5,5,4
407,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,6,9,4,1,6
408,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,7,10,8,9,6
409,6,Detractor,N,4,8,Married,Tier1,Card,1,M,call,6,6,3,1,4
410,4,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,8,4,2,7,3
411,9,Promoter,N,4,10,Married,Tier3,Cash,7,M,call,5,4,3,1,6
412,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,6,3,1,3,10
413,7,Passives,Y,4,9,Married,Tier2,Card,7,M,m-comm,10,5,1,9,6
414,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,7,4,7,2,7
415,6,Detractor,N,4,7,Married,Tier1,Cash,5,M,call,2,8,6,3,4
416,6,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,5,5,3,9,3
417,10,Promoter,Y,4,8,Married,Tier1,Card,8,M,m-comm,1,1,1,9,9
418,1,Detractor,N,4,4,Not Married,Tier1,e-Wallet,1,F,call,5,4,5,3,1
419,4,Detractor,N,4,1,Married,Tier2,Cash,7,M,call,10,9,4,2,4
420,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,6,F,call,5,9,2,7,4
421,9,Promoter,Y,4,2,Married,Tier1,Card,6,M,m-comm,5,4,9,8,7
422,3,Detractor,N,4,8,Not Married,Tier2,e-Wallet,6,F,call,4,7,1,3,1
423,9,Promoter,N,4,9,Married,Tier3,Cash,6,M,call,8,9,8,6,1
424,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,7,F,call,1,5,7,6,1
425,9,Promoter,Y,4,10,Married,Tier1,Card,9,M,m-comm,4,2,5,4,8
426,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,7,7,6,9,7
427,2,Detractor,N,4,3,Married,Tier1,Cash,2,M,call,3,6,9,6,4
428,1,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,7,6,2,1,5
429,9,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,5,10,6,6,7
430,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,7,F,e-comm,7,9,2,3,7
431,6,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,6,7,2,1,4
432,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,5,7,8,6,1
433,3,Detractor,N,4,6,Married,Tier1,Card,5,M,call,1,4,10,10,5
434,7,Passives,N,4,4,Not Married,Tier2,e-Wallet,5,F,m-comm,8,2,9,5,1
435,9,Promoter,N,4,4,Married,Tier3,Cash,3,M,call,5,1,9,3,5
436,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,6,5,4,6,9
437,2,Detractor,N,4,10,Married,Tier2,Card,7,M,call,2,9,4,5,4
438,8,Passives,Y,4,7,Not Married,Tier3,e-Wallet,4,F,call,6,2,9,10,9
439,3,Detractor,N,4,10,Married,Tier1,Cash,2,M,call,2,4,4,9,4
440,2,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,5,2,7,4,2
441,7,Passives,N,4,8,Married,Tier3,Card,3,M,call,7,9,3,2,10
442,7,Passives,N,4,10,Not Married,Tier1,e-Wallet,3,F,e-comm,6,3,3,4,2
443,0,Detractor,N,4,8,Married,Tier2,Cash,6,M,call,6,3,6,4,4
444,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,5,10,7,4,7
445,8,Passives,Y,4,9,Married,Tier1,Card,2,M,e-comm,10,2,6,3,3
446,6,Detractor,N,4,10,Not Married,Tier2,e-Wallet,7,F,call,3,2,1,6,6
447,9,Promoter,N,4,5,Married,Tier3,Cash,7,M,call,5,6,6,2,7
448,1,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,2,F,call,2,3,9,7,6
449,3,Detractor,N,4,3,Married,Tier2,Card,6,M,call,10,4,9,9,3
450,9,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,9,10,9,6,7
451,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,9,6,2,1,9
452,7,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,3,F,m-comm,2,2,1,9,10
453,8,Passives,Y,4,9,Married,Tier3,Card,7,M,call,4,9,3,2,2
454,0,Detractor,N,4,9,Not Married,Tier1,e-Wallet,2,F,call,5,8,4,1,6
455,9,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,9,4,3,6,7
456,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,6,F,call,7,9,8,2,1
457,4,Detractor,N,4,8,Married,Tier1,Card,3,M,call,3,9,7,10,4
458,10,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,3,9,8,9,7
459,10,Promoter,Y,4,5,Married,Tier1,Cash,5,M,m-comm,7,7,5,8,8
460,3,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,4,8,10,9,3
461,0,Detractor,N,4,2,Married,Tier2,Card,3,M,call,6,6,7,10,3
462,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,4,F,call,5,10,1,1,5
463,4,Detractor,N,4,7,Married,Tier1,Cash,4,M,call,7,5,7,3,2
464,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,3,F,call,5,4,3,10,6
465,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,7,7,10,5,6
466,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,4,F,call,1,5,4,9,6
467,4,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,7,6,7,2,4
468,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,8,6,3,10,7
469,5,Detractor,N,4,9,Married,Tier1,Card,2,M,call,7,8,9,4,2
470,9,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,1,5,5,9,8
471,9,Promoter,Y,4,2,Married,Tier3,Cash,5,M,call,4,7,5,1,6
472,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,5,9,8,7,7
473,5,Detractor,N,4,7,Married,Tier2,Card,4,M,call,2,7,4,1,6
474,8,Passives,Y,4,3,Not Married,Tier3,e-Wallet,1,F,call,4,10,6,3,7
475,5,Detractor,N,4,5,Married,Tier1,Cash,2,M,call,7,9,2,1,4
476,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,6,F,call,7,6,10,4,6
477,8,Passives,Y,4,1,Married,Tier3,Card,2,M,call,4,9,6,10,7
478,4,Detractor,N,4,2,Not Married,Tier1,e-Wallet,5,F,call,7,8,1,2,3
479,7,Passives,Y,4,2,Married,Tier2,Cash,7,M,m-comm,5,9,7,10,3
480,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,2,F,call,7,2,7,3,2
481,2,Detractor,N,4,1,Married,Tier1,Card,6,M,call,9,10,5,9,1
482,5,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,8,8,3,6,6
483,7,Passives,N,4,1,Married,Tier3,Cash,4,M,call,8,9,10,4,5
484,1,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,call,10,8,9,4,4
485,8,Passives,Y,4,6,Married,Tier2,Card,1,M,m-comm,10,3,2,1,7
486,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,1,F,call,7,3,5,8,7
487,6,Detractor,N,4,5,Married,Tier1,Cash,1,M,call,2,8,2,3,4
488,1,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,4,F,call,7,1,5,2,2
489,8,Passives,Y,4,9,Married,Tier3,Card,5,M,call,7,10,7,5,1
490,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,2,6,10,5,1
491,5,Detractor,N,4,2,Married,Tier2,Cash,2,M,call,8,9,2,5,6
492,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,1,F,call,5,5,6,3,9
493,1,Detractor,N,4,9,Married,Tier1,Card,2,M,call,2,10,10,1,4
494,4,Detractor,N,4,2,Not Married,Tier2,e-Wallet,2,F,call,4,2,10,7,1
495,7,Passives,N,4,7,Married,Tier3,Cash,5,M,call,5,9,10,6,1
496,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,9,1,7,10,8
497,2,Detractor,N,4,3,Married,Tier2,Card,3,M,call,8,2,3,8,2
498,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,7,1,9,10,3
499,4,Detractor,N,4,8,Married,Tier1,Cash,1,M,call,4,5,4,1,5
500,2,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,4,F,call,6,5,3,1,6
501,7,Passives,N,4,8,Married,Tier3,Card,2,M,call,7,6,2,2,3
502,5,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,5,1,10,10,4
503,7,Passives,Y,4,8,Married,Tier2,Cash,7,M,m-comm,8,9,7,6,2
504,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,1,F,call,6,9,4,10,9
505,2,Detractor,N,4,3,Married,Tier1,Card,2,M,call,7,5,9,1,3
506,3,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,5,1,8,8,2
507,9,Promoter,Y,4,10,Married,Tier1,Cash,6,M,m-comm,3,3,2,4,7
508,7,Passives,N,4,7,Not Married,Tier1,Ziggy Credit,3,F,e-comm,1,9,1,2,2
509,6,Detractor,N,4,10,Married,Tier2,Card,7,M,call,6,1,4,5,1
510,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,7,F,call,8,10,1,4,4
511,4,Detractor,N,4,4,Married,Tier1,Cash,3,M,call,4,1,3,7,5
512,7,Passives,N,4,6,Not Married,Tier2,Ziggy Credit,6,F,m-comm,3,7,5,3,5
513,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,5,8,5,3,1
514,2,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,5,9,9,7,3
515,7,Passives,Y,4,6,Married,Tier2,Cash,6,M,m-comm,9,8,6,3,4
516,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,8,10,8,2,6
517,2,Detractor,N,4,3,Married,Tier1,Card,1,M,call,3,9,5,6,4
518,8,Passives,N,4,7,Not Married,Tier2,e-Wallet,5,F,m-comm,10,5,5,8,8
519,7,Passives,N,4,6,Married,Tier3,Cash,3,M,call,8,1,5,6,10
520,5,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,5,F,call,9,9,5,1,5
521,2,Detractor,N,4,10,Married,Tier2,Card,2,M,call,6,5,8,1,6
522,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,4,3,6,10,4
523,8,Passives,Y,4,7,Married,Tier1,Cash,5,M,e-comm,2,4,3,10,8
524,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,call,9,4,3,4,3
525,7,Passives,N,4,4,Married,Tier3,Card,3,M,call,4,2,10,2,8
526,9,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,9,10,6,7,8
527,9,Promoter,Y,4,10,Married,Tier1,Cash,6,M,m-comm,8,9,3,4,9
528,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,7,6,3,8,10
529,4,Detractor,N,4,3,Married,Tier1,Card,7,M,call,3,6,3,7,6
530,4,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,7,8,2,9,5
531,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,4,3,4,10,7
532,7,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,7,3,8,2,6
533,4,Detractor,N,4,2,Married,Tier2,Card,2,M,call,4,2,2,1,3
534,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,5,F,call,8,7,9,3,3
535,8,Passives,Y,4,5,Married,Tier1,Cash,5,M,e-comm,4,9,1,2,2
536,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,1,F,m-comm,9,2,7,4,5
537,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,4,4,7,5,5
538,3,Detractor,N,4,9,Not Married,Tier1,e-Wallet,5,F,call,9,4,10,3,1
539,8,Passives,Y,4,10,Married,Tier2,Cash,5,M,m-comm,2,3,9,1,1
540,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,5,F,call,7,8,5,1,9
541,0,Detractor,N,4,5,Married,Tier1,Card,4,M,call,5,6,8,10,2
542,2,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,2,6,8,4,1
543,7,Passives,N,4,7,Married,Tier3,Cash,1,M,call,7,8,6,6,10
544,2,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,1,F,call,6,6,1,2,4
545,3,Detractor,N,4,5,Married,Tier2,Card,3,M,call,2,4,5,1,2
546,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,7,1,6,9,4
547,6,Detractor,Y,4,1,Married,Tier1,Cash,6,M,call,8,8,9,5,6
548,10,Promoter,Y,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,3,10,2,10,10
549,10,Promoter,N,4,8,Married,Tier1,Card,7,M,m-comm,4,10,4,5,8
550,8,Passives,N,4,4,Not Married,Tier1,e-Wallet,4,F,e-comm,7,2,8,9,6
551,7,Passives,Y,4,5,Married,Tier2,Cash,2,M,m-comm,4,8,6,9,7
552,7,Passives,Y,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,4,6,4,9,10
553,3,Detractor,N,4,7,Married,Tier1,Card,3,M,call,5,10,8,8,1
554,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,1,1,10,9,10
555,8,Passives,Y,4,2,Married,Tier3,Cash,1,M,call,4,7,7,6,5
556,6,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,4,F,call,3,8,9,10,4
557,4,Detractor,N,4,6,Married,Tier2,Card,5,M,call,3,9,7,2,3
558,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,8,10,9,8,10
559,4,Detractor,N,4,6,Married,Tier1,Cash,3,M,call,2,2,9,9,6
560,2,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,6,F,call,4,2,3,10,2
561,7,Passives,N,4,2,Married,Tier3,Card,2,M,call,6,6,3,7,4
562,3,Detractor,N,4,5,Not Married,Tier1,e-Wallet,4,F,call,7,10,2,9,5
563,0,Detractor,N,4,1,Married,Tier2,Cash,1,M,call,1,6,2,7,1
564,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,5,F,call,5,3,10,9,7
565,9,Promoter,Y,4,4,Married,Tier1,Card,6,M,m-comm,6,6,10,1,10
566,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,7,10,3,10
567,7,Passives,N,4,3,Married,Tier3,Cash,6,M,call,8,9,5,9,9
568,3,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,1,F,call,5,10,5,1,6
569,2,Detractor,N,4,5,Married,Tier2,Card,2,M,call,7,8,1,1,6
570,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,6,2,3,6,10
571,3,Detractor,N,4,1,Married,Tier1,Cash,3,M,call,10,8,1,6,1
572,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,3,9,3,6,10
573,7,Passives,Y,4,10,Married,Tier3,Card,2,M,call,4,9,7,9,5
574,3,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,2,9,3,7,4
575,9,Promoter,Y,4,7,Married,Tier1,Cash,7,M,m-comm,1,8,4,1,7
576,8,Passives,Y,4,3,Not Married,Tier3,Ziggy Credit,7,F,call,4,4,10,8,3
577,5,Detractor,N,4,4,Married,Tier1,Card,6,M,call,7,3,1,10,6
578,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,5,5,9,7,9
579,7,Passives,N,4,10,Married,Tier3,Cash,3,M,call,4,4,10,9,6
580,8,Passives,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,e-comm,4,3,4,1,3
581,0,Detractor,N,4,1,Married,Tier2,Card,2,M,call,9,4,3,2,4
582,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,9,10,2,2,10
583,1,Detractor,N,4,8,Married,Tier1,Cash,4,M,call,1,10,8,7,5
584,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,2,F,call,1,9,3,3,6
585,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,4,9,9,4,2
586,5,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,7,1,1,9,1
587,6,Detractor,N,4,2,Married,Tier2,Cash,2,M,call,10,8,4,8,3
588,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,6,3,1,9,1
589,6,Detractor,N,4,8,Married,Tier1,Card,1,M,call,8,10,3,9,1
590,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,9,5,9,3,7
591,9,Promoter,Y,4,2,Married,Tier1,Cash,10,M,m-comm,9,10,3,6,10
592,2,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,1,F,call,5,7,2,9,1
593,2,Detractor,N,4,9,Married,Tier2,Card,4,M,call,2,5,8,7,5
594,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,6,F,call,6,1,5,10,9
595,6,Detractor,N,4,5,Married,Tier1,Cash,7,M,call,8,2,6,2,1
596,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,1,F,call,8,1,9,3,2
597,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,7,9,4,9,3
598,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,7,9,6,2,4
599,6,Detractor,N,4,9,Married,Tier2,Cash,3,M,call,6,6,9,10,1
600,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,3,10,6,4,8
601,6,Detractor,N,4,5,Married,Tier1,Card,5,M,call,2,9,4,10,5
602,1,Detractor,N,4,5,Not Married,Tier2,e-Wallet,1,F,call,6,10,8,1,1
603,7,Passives,N,4,1,Married,Tier3,Cash,7,M,call,5,10,9,2,8
604,7,Passives,N,4,3,Not Married,Tier1,Ziggy Credit,2,F,e-comm,5,5,10,4,4
605,2,Detractor,N,4,3,Married,Tier2,Card,5,M,call,6,3,8,9,5
606,8,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,5,6,2,5,1
607,5,Detractor,N,4,8,Married,Tier1,Cash,4,M,call,7,1,3,6,2
608,2,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,4,F,call,3,6,1,6,1
609,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,8,7,4,9,9
610,0,Detractor,N,4,8,Not Married,Tier1,e-Wallet,2,F,call,7,1,10,4,6
611,8,Passives,Y,4,1,Married,Tier2,Cash,5,M,m-comm,5,7,6,2,2
612,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,6,7,1,9,2
613,4,Detractor,N,4,5,Married,Tier1,Card,5,M,call,10,10,6,1,3
614,8,Passives,N,4,3,Not Married,Tier2,e-Wallet,3,F,m-comm,10,4,9,6,5
615,7,Passives,N,4,6,Married,Tier3,Cash,1,M,call,4,3,1,5,4
616,6,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,6,F,call,3,4,9,3,6
617,4,Detractor,N,4,9,Married,Tier2,Card,6,M,call,10,6,3,8,6
618,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,6,F,call,7,5,1,9,6
619,2,Detractor,N,4,2,Married,Tier1,Cash,7,M,call,9,6,6,4,2
620,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,2,F,call,5,9,8,3,1
621,7,Passives,Y,4,7,Married,Tier3,Card,1,M,call,5,8,7,10,3
622,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,4,F,call,6,6,3,2,5
623,4,Detractor,N,4,3,Married,Tier2,Cash,3,M,call,10,9,9,2,2
624,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,4,F,call,7,5,4,7,1
625,10,Promoter,Y,4,3,Married,Tier1,Card,9,M,m-comm,3,9,3,1,7
626,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,6,7,9,3,9
627,7,Passives,Y,4,4,Married,Tier3,Cash,6,M,call,4,6,4,6,10
628,6,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,8,3,10,5,2
629,4,Detractor,N,4,7,Married,Tier2,Card,7,M,call,2,7,1,5,6
630,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,1,10,3,10,10
631,2,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,7,3,3,8,2
632,2,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,5,F,call,8,4,1,8,4
633,7,Passives,N,4,9,Married,Tier3,Card,3,M,call,8,2,6,8,3
634,8,Passives,N,4,5,Not Married,Tier1,e-Wallet,4,F,e-comm,6,2,4,3,6
635,3,Detractor,N,4,6,Married,Tier2,Cash,5,M,call,5,1,7,4,2
636,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,4,F,call,5,9,4,8,5
637,6,Detractor,N,4,1,Married,Tier1,Card,1,M,call,8,7,1,2,6
638,3,Detractor,N,4,4,Not Married,Tier2,e-Wallet,3,F,call,4,5,2,7,5
639,9,Promoter,Y,4,5,Married,Tier3,Cash,1,M,call,8,5,6,2,6
640,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,4,F,e-comm,10,5,9,1,10
641,3,Detractor,N,4,6,Married,Tier2,Card,6,M,call,5,4,8,6,1
642,8,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,7,10,2,4,6
643,8,Passives,Y,4,3,Married,Tier1,Cash,6,M,e-comm,10,2,7,1,3
644,4,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,call,6,10,6,4,2
645,7,Passives,N,4,8,Married,Tier3,Card,4,M,call,6,2,2,6,4
646,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,2,F,call,4,3,8,10,6
647,1,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,6,7,9,7,1
648,8,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,8,6,6,3,8
649,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,9,3,6,1,3
650,2,Detractor,N,4,4,Not Married,Tier2,e-Wallet,1,F,call,10,9,9,1,1
651,9,Promoter,N,4,5,Married,Tier3,Cash,3,M,call,6,2,3,2,8
652,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,1,2,1,7,3
653,0,Detractor,N,4,10,Married,Tier2,Card,5,M,call,10,9,8,7,6
654,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,7,9,3,8,3
655,6,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,1,2,6,10,3
656,1,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,call,8,7,10,7,6
657,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,7,3,3,6,7
658,7,Passives,N,4,9,Not Married,Tier1,e-Wallet,2,F,e-comm,9,2,5,2,2
659,10,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,5,5,9,9,7
660,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,8,1,4,7,10
661,0,Detractor,N,4,7,Married,Tier1,Card,5,M,call,8,5,1,1,5
662,4,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,2,9,3,2,4
663,9,Promoter,N,4,3,Married,Tier3,Cash,1,M,call,8,5,8,6,6
664,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,5,8,2,6,7
665,7,Passives,Y,4,5,Married,Tier2,Card,2,M,m-comm,6,3,7,3,7
666,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,6,F,call,8,4,3,1,7
667,6,Detractor,N,4,10,Married,Tier1,Cash,1,M,call,5,5,10,7,2
668,6,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,1,F,call,2,8,10,3,4
669,7,Passives,N,4,4,Married,Tier3,Card,7,M,call,7,2,1,1,10
670,6,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,8,2,3,10,2
671,5,Detractor,N,4,10,Married,Tier2,Cash,3,M,call,10,6,2,4,6
672,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,6,F,call,7,9,8,8,5
673,7,Passives,Y,4,10,Married,Tier1,Card,3,M,e-comm,7,10,5,9,6
674,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,6,F,m-comm,3,1,4,8,7
675,9,Promoter,N,4,9,Married,Tier3,Cash,4,M,call,7,1,4,8,4
676,3,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,7,3,2,7,3
677,10,Promoter,Y,4,1,Married,Tier1,Card,7,M,m-comm,1,1,5,7,8
678,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,5,6,3,9,3
679,8,Passives,Y,4,1,Married,Tier1,Cash,3,M,e-comm,3,8,2,2,2
680,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,call,7,6,1,7,6
681,7,Passives,N,4,9,Married,Tier3,Card,4,M,call,8,2,6,5,1
682,9,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,1,8,3,1,8
683,10,Promoter,Y,4,7,Married,Tier1,Cash,5,M,m-comm,9,10,1,5,10
684,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,5,7,6,2,7
685,9,Promoter,Y,4,10,Married,Tier1,Card,6,M,m-comm,4,5,6,9,10
686,10,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,2,6,6,5,9
687,9,Promoter,N,4,10,Married,Tier3,Cash,1,M,call,7,3,8,8,2
688,0,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,6,F,call,4,3,2,9,2
689,10,Promoter,Y,4,8,Married,Tier1,Card,8,M,m-comm,10,4,5,3,10
690,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,1,F,call,5,8,7,3,6
691,2,Detractor,N,4,8,Married,Tier1,Cash,7,M,call,4,6,5,7,4
692,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,m-comm,4,1,5,1,10
693,7,Passives,N,4,4,Married,Tier3,Card,7,M,call,4,10,5,3,6
694,0,Detractor,N,4,1,Not Married,Tier1,e-Wallet,7,F,call,6,7,7,10,1
695,5,Detractor,N,4,3,Married,Tier2,Cash,4,M,call,4,6,4,4,2
696,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,5,F,call,7,9,5,9,2
697,10,Promoter,Y,4,4,Married,Tier1,Card,6,M,m-comm,5,7,7,2,9
698,3,Detractor,N,4,6,Not Married,Tier2,e-Wallet,4,F,call,2,9,4,1,4
699,9,Promoter,N,4,8,Married,Tier3,Cash,7,M,call,8,9,1,7,2
700,0,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,3,F,call,5,9,4,4,6
701,4,Detractor,N,4,2,Married,Tier2,Card,3,M,call,2,8,6,5,4
702,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,5,9,2,3,1
703,5,Detractor,N,4,7,Married,Tier1,Cash,5,M,call,10,10,8,9,1
704,3,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,7,F,call,3,10,4,7,2
705,8,Passives,Y,4,6,Married,Tier3,Card,1,M,call,6,3,4,4,5
706,0,Detractor,N,4,7,Not Married,Tier1,e-Wallet,7,F,call,5,5,4,7,2
707,2,Detractor,N,4,6,Married,Tier2,Cash,7,M,call,4,10,2,1,2
708,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,1,F,call,8,5,8,6,10
709,6,Detractor,N,4,6,Married,Tier1,Card,2,M,call,5,5,6,7,6
710,3,Detractor,N,4,1,Not Married,Tier2,e-Wallet,4,F,call,9,6,5,10,6
711,9,Promoter,N,4,6,Married,Tier3,Cash,5,M,call,4,6,4,7,5
712,1,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,7,F,call,4,9,6,9,4
713,0,Detractor,N,4,8,Married,Tier2,Card,5,M,call,10,1,9,1,4
714,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,5,F,call,6,4,1,6,6
715,1,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,7,10,7,9,1
716,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,9,3,8,1,10
717,7,Passives,Y,4,7,Married,Tier3,Card,4,M,call,5,4,7,8,7
718,9,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,3,3,5,10,9
719,3,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,9,1,7,8,4
720,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,2,F,call,8,5,8,5,5
721,7,Passives,Y,4,8,Married,Tier1,Card,2,M,e-comm,7,3,2,4,7
722,10,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,6,6,3,8,10
723,9,Promoter,N,4,9,Married,Tier3,Cash,1,M,call,7,8,2,5,7
724,5,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,2,F,call,7,4,9,8,5
725,10,Promoter,Y,4,5,Married,Tier1,Card,8,M,m-comm,1,7,1,9,10
726,10,Promoter,N,4,6,Married,Tier1,e-Wallet,7,F,m-comm,3,9,9,10,9
727,4,Detractor,N,4,4,Married,Tier1,Cash,7,M,call,1,6,3,8,2
728,8,Passives,N,4,3,Not Married,Tier2,Ziggy Credit,7,F,m-comm,1,6,8,8,6
729,7,Passives,N,4,9,Married,Tier3,Card,2,M,call,8,3,2,3,2
730,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,7,F,call,8,9,2,5,2
731,3,Detractor,N,4,1,Married,Tier2,Cash,3,M,call,8,4,1,2,1
732,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,8,6,2,6,10
733,3,Detractor,N,4,2,Married,Tier1,Card,6,M,call,7,8,5,4,4
734,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,10,1,9,3,8
735,9,Promoter,N,4,7,Married,Tier3,Cash,4,M,call,5,7,8,4,8
736,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,4,9,9,9
737,8,Passives,Y,4,10,Married,Tier2,Card,4,M,m-comm,2,4,2,4,9
738,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,7,2,9,1,9
739,8,Passives,Y,4,4,Married,Tier1,Cash,1,M,e-comm,4,3,7,3,4
740,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,1,5,2,7,7
741,7,Passives,N,4,6,Married,Tier3,Card,1,M,call,8,5,6,8,2
742,9,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,4,7,5,4,9
743,0,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,2,8,4,3,6
744,8,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,4,F,call,6,6,3,2,2
745,9,Promoter,Y,4,3,Married,Tier1,Card,10,M,m-comm,8,8,1,3,8
746,1,Detractor,N,4,5,Not Married,Tier2,e-Wallet,7,F,call,9,9,2,3,3
747,9,Promoter,N,4,4,Married,Tier3,Cash,4,M,call,8,9,5,8,6
748,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,4,9,1,1,8
749,1,Detractor,N,4,8,Married,Tier2,Card,7,M,call,4,10,4,3,5
750,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,5,F,call,8,10,8,8,9
751,7,Passives,Y,4,5,Married,Tier1,Cash,3,M,e-comm,10,5,7,3,9
752,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,7,F,call,3,5,7,4,1
753,7,Passives,N,4,5,Married,Tier3,Card,6,M,call,5,7,3,6,6
754,2,Detractor,N,4,4,Not Married,Tier1,e-Wallet,7,F,call,2,9,7,2,4
755,5,Detractor,N,4,4,Married,Tier2,Cash,2,M,call,3,6,4,7,6
756,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,4,F,call,8,8,9,10,10
757,6,Detractor,N,4,9,Married,Tier1,Card,4,M,call,5,6,6,6,6
758,8,Passives,N,4,2,Not Married,Tier2,e-Wallet,6,F,m-comm,10,9,3,9,8
759,9,Promoter,Y,4,5,Married,Tier3,Cash,1,M,call,8,4,10,8,3
760,3,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,2,F,call,10,9,8,2,2
761,6,Detractor,N,4,6,Married,Tier2,Card,1,M,call,2,1,4,7,4
762,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,4,F,call,7,8,5,4,6
763,10,Promoter,Y,4,8,Married,Tier2,Cash,9,M,m-comm,5,6,10,2,7
764,2,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,5,F,call,2,4,7,3,5
765,7,Passives,Y,4,10,Married,Tier3,Card,7,M,call,7,1,2,5,5
766,4,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,3,4,7,6,3
767,0,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,7,9,10,5,4
768,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,7,4,8,7,1
769,4,Detractor,N,4,1,Married,Tier1,Card,7,M,call,8,7,10,5,1
770,5,Detractor,N,4,2,Not Married,Tier2,e-Wallet,6,F,call,9,9,4,1,6
771,9,Promoter,N,4,9,Married,Tier3,Cash,5,M,call,6,3,6,2,8
772,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,5,1,4,10,3
773,9,Promoter,Y,4,1,Married,Tier1,Card,5,M,m-comm,5,8,9,8,7
774,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,6,F,call,5,7,8,10,5
775,6,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,1,8,5,8,2
776,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,7,F,m-comm,6,3,10,4,9
777,9,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,8,5,10,10,7
778,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,2,F,call,1,3,6,10,4
779,9,Promoter,Y,4,1,Married,Tier1,Cash,9,M,m-comm,8,8,10,6,7
780,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,8,1,8,9,7
781,5,Detractor,N,4,8,Married,Tier1,Card,4,M,call,1,8,7,3,3
782,0,Detractor,N,4,1,Not Married,Tier2,e-Wallet,2,F,call,2,4,1,10,3
783,9,Promoter,N,4,5,Married,Tier3,Cash,7,M,call,4,8,10,3,9
784,2,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,1,F,call,2,1,9,8,3
785,4,Detractor,N,4,8,Married,Tier2,Card,4,M,call,7,9,3,4,3
786,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,6,F,call,6,10,1,8,3
787,3,Detractor,N,4,9,Married,Tier1,Cash,2,M,call,3,2,5,7,3
788,0,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,call,3,8,5,2,5
789,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,6,8,4,9,5
790,6,Detractor,N,4,6,Not Married,Tier1,e-Wallet,2,F,call,1,6,10,1,5
791,7,Passives,Y,4,6,Married,Tier2,Cash,5,M,m-comm,10,9,9,2,5
792,9,Promoter,N,4,6,Married,Tier2,Ziggy Credit,7,F,m-comm,2,3,7,10,9
793,8,Passives,Y,4,1,Married,Tier1,Card,1,M,e-comm,8,5,8,1,3
794,1,Detractor,N,4,6,Not Married,Tier2,e-Wallet,1,F,call,2,3,10,3,5
795,9,Promoter,Y,4,2,Married,Tier3,Cash,6,M,call,8,4,8,6,3
796,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,7,F,m-comm,3,4,8,9,9
797,10,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,10,1,3,9,7
798,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,5,4,4,2,5
799,5,Detractor,N,4,3,Married,Tier1,Cash,1,M,call,9,7,3,1,1
800,6,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,1,F,call,1,10,1,1,5
801,7,Passives,N,4,10,Married,Tier3,Card,3,M,call,7,8,8,3,7
802,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,7,10,5,8,3
803,8,Passives,Y,4,2,Married,Tier2,Cash,1,M,m-comm,7,4,8,3,8
804,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,6,F,call,4,4,9,6,6
805,10,Promoter,Y,4,7,Married,Tier1,Card,6,M,m-comm,2,10,10,3,7
806,0,Detractor,N,4,5,Not Married,Tier2,e-Wallet,7,F,call,8,9,10,9,4
807,9,Promoter,N,4,4,Married,Tier3,Cash,3,M,call,4,2,2,8,8
808,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,7,F,call,5,8,2,7,6
809,1,Detractor,N,4,2,Married,Tier2,Card,6,M,call,8,2,3,7,6
810,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,4,4,4,10,7
811,8,Passives,Y,4,10,Married,Tier1,Cash,3,M,e-comm,10,1,8,7,10
812,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,8,3,8,8,8
813,10,Promoter,Y,4,10,Married,Tier1,Card,10,M,m-comm,5,6,10,2,9
814,7,Passives,N,4,4,Not Married,Tier1,e-Wallet,6,F,e-comm,7,3,6,4,2
815,2,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,6,5,2,4,6
816,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,4,10,8,3,5
817,5,Detractor,N,4,6,Married,Tier1,Card,4,M,call,9,8,10,3,1
818,1,Detractor,N,4,9,Not Married,Tier2,e-Wallet,4,F,call,9,4,9,9,5
819,9,Promoter,N,4,6,Married,Tier3,Cash,1,M,call,5,1,4,6,6
820,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,6,2,2,10,10
821,6,Detractor,N,4,6,Married,Tier2,Card,6,M,call,5,3,6,8,6
822,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,7,F,call,4,8,8,3,5
823,5,Detractor,N,4,2,Married,Tier1,Cash,5,M,call,5,1,3,5,2
824,4,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,3,F,call,10,9,2,10,5
825,7,Passives,N,4,10,Married,Tier3,Card,5,M,call,6,1,5,5,10
826,10,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,7,4,6,2,8
827,2,Detractor,N,4,6,Married,Tier2,Cash,2,M,call,8,9,6,10,6
828,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,7,1,4,7,10
829,7,Passives,Y,4,3,Married,Tier1,Card,7,M,e-comm,1,4,2,4,8
830,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,7,6,5,3,9
831,9,Promoter,N,4,6,Married,Tier3,Cash,3,M,call,4,3,1,8,3
832,3,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,call,1,6,2,4,2
833,1,Detractor,N,4,9,Married,Tier2,Card,7,M,call,7,7,1,5,6
834,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,4,F,call,4,6,10,8,1
835,1,Detractor,N,4,4,Married,Tier1,Cash,2,M,call,2,5,5,8,3
836,0,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,4,F,call,4,4,3,4,3
837,7,Passives,N,4,8,Married,Tier3,Card,6,M,call,6,2,9,7,10
838,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,6,F,call,10,9,9,9,3
839,7,Passives,Y,4,5,Married,Tier2,Cash,1,M,m-comm,2,3,4,2,4
840,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,2,F,call,8,9,3,8,1
841,7,Passives,Y,4,4,Married,Tier1,Card,2,M,e-comm,6,1,9,2,6
842,3,Detractor,N,4,10,Not Married,Tier2,e-Wallet,1,F,call,9,5,7,10,1
843,10,Promoter,Y,4,5,Married,Tier1,Cash,9,M,m-comm,7,8,9,3,8
844,4,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,1,F,call,1,6,3,7,6
845,9,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,2,1,9,8,9
846,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,4,F,call,8,6,3,1,10
847,4,Detractor,N,4,10,Married,Tier1,Cash,5,M,call,5,6,7,9,3
848,8,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,6,F,m-comm,6,4,9,4,6
849,7,Passives,N,4,2,Married,Tier3,Card,3,M,call,5,6,10,6,1
850,2,Detractor,N,4,7,Not Married,Tier1,e-Wallet,1,F,call,8,5,7,6,6
851,5,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,10,7,8,9,2
852,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,4,10,8,5,10
853,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,5,6,2,9,4
854,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,3,F,call,6,5,8,4,5
855,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,8,3,5,7,7
856,6,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,1,F,call,4,10,7,2,2
857,3,Detractor,N,4,9,Married,Tier2,Card,5,M,call,1,4,1,3,5
858,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,6,F,call,5,9,9,2,9
859,5,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,4,10,5,1,1
860,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,6,F,m-comm,7,7,5,8,8
861,7,Passives,N,4,1,Married,Tier3,Card,2,M,call,5,6,1,1,6
862,3,Detractor,N,4,4,Not Married,Tier1,e-Wallet,3,F,call,6,4,10,3,4
863,7,Passives,Y,4,5,Married,Tier2,Cash,1,M,m-comm,2,4,7,6,3
864,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,8,5,3,2,3
865,5,Detractor,N,4,10,Married,Tier1,Card,6,M,call,5,2,2,7,3
866,0,Detractor,N,4,10,Not Married,Tier2,e-Wallet,3,F,call,7,6,7,4,5
867,9,Promoter,Y,4,10,Married,Tier3,Cash,6,M,call,5,3,9,1,9
868,3,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,4,F,call,2,5,5,10,6
869,8,Passives,Y,4,1,Married,Tier2,Card,5,M,m-comm,2,5,8,3,6
870,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,8,1,1,1,6
871,1,Detractor,N,4,3,Married,Tier1,Cash,7,M,call,5,3,2,3,6
872,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,8,3,3,9,5
873,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,5,5,9,10,6
874,7,Passives,N,4,2,Not Married,Tier1,e-Wallet,1,F,e-comm,1,10,3,9,2
875,0,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,5,5,2,1,1
876,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,10,F,m-comm,10,3,4,2,7
877,6,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,2,4,7,2
878,0,Detractor,N,4,8,Not Married,Tier2,e-Wallet,2,F,call,6,4,5,2,6
879,9,Promoter,N,4,4,Married,Tier3,Cash,1,M,call,5,9,10,5,8
880,7,Passives,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,e-comm,8,5,8,10,3
881,5,Detractor,N,4,1,Married,Tier2,Card,4,M,call,1,9,7,3,3
882,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,7,F,call,4,3,3,9,2
883,6,Detractor,N,4,7,Married,Tier1,Cash,6,M,call,4,2,4,5,3
884,7,Passives,N,4,10,Not Married,Tier2,Ziggy Credit,3,F,m-comm,2,4,1,5,10
885,7,Passives,N,4,6,Married,Tier3,Card,3,M,call,8,1,3,10,3
886,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,3,F,call,10,4,2,3,2
887,7,Passives,Y,4,2,Married,Tier2,Cash,3,M,m-comm,9,1,4,1,3
888,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,8,4,8,5,9
889,6,Detractor,N,4,6,Married,Tier1,Card,1,M,call,1,6,1,5,4
890,3,Detractor,N,4,2,Not Married,Tier2,e-Wallet,2,F,call,6,10,4,8,4
891,9,Promoter,N,4,5,Married,Tier3,Cash,1,M,call,5,8,1,4,1
892,6,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,call,6,3,9,7,4
893,3,Detractor,N,4,7,Married,Tier2,Card,1,M,call,10,8,4,5,4
894,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,2,F,call,5,3,1,2,2
895,8,Passives,Y,4,10,Married,Tier1,Cash,7,M,e-comm,8,6,10,10,9
896,1,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,4,F,call,2,10,9,1,6
897,8,Passives,Y,4,6,Married,Tier3,Card,1,M,call,8,1,8,4,1
898,8,Passives,N,4,5,Not Married,Tier1,e-Wallet,7,F,e-comm,10,4,2,3,5
899,0,Detractor,N,4,1,Married,Tier2,Cash,1,M,call,4,7,9,7,6
900,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,2,F,call,4,7,8,2,6
901,7,Passives,Y,4,6,Married,Tier1,Card,1,M,e-comm,7,9,5,4,1
902,3,Detractor,N,4,9,Not Married,Tier2,e-Wallet,2,F,call,1,4,2,1,6
903,9,Promoter,N,4,7,Married,Tier3,Cash,4,M,call,4,1,1,4,2
904,4,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,9,10,2,7,6
905,0,Detractor,N,4,3,Married,Tier2,Card,4,M,call,10,1,6,7,6
906,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,2,F,call,8,7,3,3,9
907,8,Passives,Y,4,6,Married,Tier1,Cash,4,M,e-comm,7,6,3,1,6
908,0,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,call,1,10,7,1,1
909,7,Passives,N,4,8,Married,Tier3,Card,5,M,call,8,5,2,8,3
910,7,Passives,N,4,1,Not Married,Tier1,e-Wallet,6,F,e-comm,2,10,2,5,8
911,3,Detractor,N,4,10,Married,Tier2,Cash,1,M,call,1,10,8,6,6
912,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,7,7,8,10,5
913,6,Detractor,N,4,7,Married,Tier1,Card,3,M,call,6,9,1,1,4
914,5,Detractor,N,4,10,Not Married,Tier2,e-Wallet,5,F,call,3,10,8,4,2
915,9,Promoter,Y,4,9,Married,Tier3,Cash,2,M,call,4,4,1,2,7
916,3,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,5,F,call,5,7,2,4,3
917,5,Detractor,N,4,4,Married,Tier2,Card,5,M,call,6,8,5,7,3
918,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,1,F,call,6,6,5,7,4
919,10,Promoter,Y,4,3,Married,Tier1,Cash,6,M,m-comm,9,8,1,7,9
920,7,Passives,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,m-comm,7,5,10,9,1
921,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,5,9,2,4,10
922,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,5,F,call,8,8,1,9,2
923,6,Detractor,N,4,7,Married,Tier2,Cash,4,M,call,3,7,6,4,6
924,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,7,F,call,7,3,10,1,3
925,2,Detractor,N,4,1,Married,Tier1,Card,2,M,call,9,3,3,4,2
926,1,Detractor,N,4,3,Not Married,Tier2,e-Wallet,3,F,call,3,8,1,3,1
927,9,Promoter,N,4,9,Married,Tier3,Cash,7,M,call,8,8,5,10,3
928,1,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,2,F,call,5,4,4,4,5
929,10,Promoter,Y,4,7,Married,Tier1,Card,5,M,m-comm,10,4,5,5,10
930,7,Passives,N,4,2,Not Married,Tier3,e-Wallet,7,F,call,6,7,6,5,10
931,7,Passives,Y,4,10,Married,Tier1,Cash,2,M,e-comm,10,1,6,10,7
932,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,2,5,4,8,7
933,7,Passives,N,4,7,Married,Tier3,Card,4,M,call,7,6,7,4,9
934,1,Detractor,N,4,5,Not Married,Tier1,e-Wallet,5,F,call,9,4,9,1,1
935,6,Detractor,N,4,5,Married,Tier2,Cash,4,M,call,1,2,9,7,3
936,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,4,2,6,1,7
937,7,Passives,Y,4,5,Married,Tier1,Card,4,M,e-comm,2,9,1,7,2
938,5,Detractor,N,4,9,Not Married,Tier2,e-Wallet,7,F,call,8,10,7,1,1
939,9,Promoter,N,4,9,Married,Tier3,Cash,1,M,call,6,4,5,8,8
940,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,4,4,10,8,10
941,2,Detractor,N,4,7,Married,Tier2,Card,7,M,call,9,8,10,1,5
942,9,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,10,10,5,3,9
943,5,Detractor,N,4,4,Married,Tier1,Cash,5,M,call,9,5,5,7,4
944,2,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,1,7,9,7,3
945,8,Passives,Y,4,9,Married,Tier3,Card,7,M,call,5,8,8,8,10
946,7,Passives,N,4,3,Not Married,Tier1,e-Wallet,2,F,e-comm,8,2,8,1,4
947,7,Passives,Y,4,7,Married,Tier2,Cash,5,M,m-comm,2,8,10,2,4
948,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,7,F,call,5,4,9,2,10
949,6,Detractor,N,4,8,Married,Tier1,Card,6,M,call,3,10,6,7,3
950,2,Detractor,N,4,6,Not Married,Tier2,e-Wallet,2,F,call,1,6,9,8,3
951,9,Promoter,Y,4,9,Married,Tier1,Cash,10,M,m-comm,5,9,3,8,9
952,0,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,4,F,call,2,9,8,5,6
953,4,Detractor,N,4,9,Married,Tier2,Card,2,M,call,5,10,8,2,3
954,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,4,2,8,10,8
955,9,Promoter,Y,4,1,Married,Tier1,Cash,5,M,m-comm,8,1,3,5,8
956,2,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,7,F,call,4,5,4,9,2
957,10,Promoter,Y,4,9,Married,Tier1,Card,9,M,m-comm,5,9,3,6,8
958,8,Passives,N,4,6,Not Married,Tier1,e-Wallet,5,F,e-comm,8,4,9,9,8
959,10,Promoter,Y,4,1,Married,Tier1,Cash,8,M,m-comm,7,1,7,10,8
960,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,10,4,5,3,7
961,8,Passives,Y,4,7,Married,Tier1,Card,6,M,e-comm,2,2,3,3,8
962,10,Promoter,N,4,6,Married,Tier1,e-Wallet,7,F,m-comm,3,9,5,6,8
963,9,Promoter,N,4,5,Married,Tier3,Cash,3,M,call,5,1,2,7,6
964,6,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,1,F,call,1,3,7,5,6
965,3,Detractor,N,4,3,Married,Tier2,Card,2,M,call,9,6,5,6,1
966,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,1,F,call,7,4,9,10,2
967,3,Detractor,N,4,7,Married,Tier1,Cash,1,M,call,5,8,3,6,6
968,6,Detractor,N,4,7,Not Married,Tier2,Ziggy Credit,5,F,call,4,10,10,2,4
969,7,Passives,N,4,8,Married,Tier3,Card,2,M,call,8,6,3,10,8
970,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,2,F,call,9,3,10,7,2
971,2,Detractor,N,4,7,Married,Tier2,Cash,3,M,call,7,9,1,7,6
972,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,10,6,1,7
973,9,Promoter,Y,4,1,Married,Tier1,Card,8,M,m-comm,1,2,3,6,9
974,5,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,2,9,9,8,5
975,9,Promoter,N,4,2,Married,Tier3,Cash,3,M,call,8,6,6,7,4
976,5,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,call,1,1,2,5,5
977,5,Detractor,N,4,9,Married,Tier2,Card,7,M,call,2,2,3,5,4
978,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,8,1,9,3,3
979,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,2,10,5,8,8
980,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,7,F,call,4,2,9,4,2
981,7,Passives,N,4,7,Married,Tier3,Card,7,M,call,6,6,7,1,7
982,5,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,7,9,9,9,5
983,4,Detractor,N,4,2,Married,Tier2,Cash,7,M,call,5,2,8,8,6
984,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,7,8,9,10,9
985,10,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,2,9,5,3,8
986,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,8,7,9,7,10
987,9,Promoter,N,4,10,Married,Tier3,Cash,1,M,call,8,6,1,9,1
988,7,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,e-comm,2,7,2,7,9
989,5,Detractor,N,4,2,Married,Tier2,Card,3,M,call,7,5,10,9,6
990,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,6,4,1,7,9
991,0,Detractor,N,4,6,Married,Tier1,Cash,4,M,call,2,2,8,9,1
992,8,Passives,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,m-comm,1,4,4,6,6
993,7,Passives,Y,4,1,Married,Tier3,Card,4,M,call,7,10,1,7,4
994,10,Promoter,N,4,7,Married,Tier1,e-Wallet,10,F,m-comm,7,6,1,6,8
995,2,Detractor,N,4,10,Married,Tier2,Cash,4,M,call,6,1,7,10,5
996,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,2,F,call,7,1,5,9,9
997,9,Promoter,Y,4,6,Married,Tier1,Card,6,M,m-comm,4,6,8,2,9
998,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,6,3,6,9,7
999,9,Promoter,N,4,1,Married,Tier3,Cash,7,M,call,6,8,4,10,3
1000,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,8,10,9,5,10
1001,1,Detractor,N,4,8,Married,Tier2,Card,7,M,call,3,8,3,2,6
1002,7,Passives,N,4,8,Not Married,Tier3,e-Wallet,3,F,call,7,9,1,10,4
1003,6,Detractor,N,4,8,Married,Tier1,Cash,2,M,call,8,3,1,6,4
1004,3,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,1,F,call,5,7,6,2,1
1005,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,4,1,7,8,6
1006,3,Detractor,N,4,8,Not Married,Tier1,e-Wallet,5,F,call,1,5,2,9,2
1007,4,Detractor,N,4,9,Married,Tier2,Cash,5,M,call,10,9,10,9,4
1008,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,8,1,9,10,6
1009,1,Detractor,N,4,8,Married,Tier1,Card,5,M,call,2,4,3,1,3
1010,5,Detractor,N,4,4,Not Married,Tier2,e-Wallet,6,F,call,1,9,7,7,3
1011,7,Passives,N,4,5,Married,Tier3,Cash,3,M,call,5,10,7,8,5
1012,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,2,4,10,2,8
1013,3,Detractor,N,4,6,Married,Tier2,Card,3,M,call,8,9,8,1,6
1014,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,1,2,9,6,8
1015,10,Promoter,Y,4,9,Married,Tier1,Cash,6,M,m-comm,9,9,9,8,7
1016,2,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,1,F,call,3,7,1,2,5
1017,7,Passives,N,4,9,Married,Tier3,Card,7,M,call,8,5,2,3,5
1018,7,Passives,N,4,7,Not Married,Tier1,e-Wallet,6,F,e-comm,10,2,3,2,8
1019,10,Promoter,Y,4,6,Married,Tier1,Cash,10,M,m-comm,3,5,4,10,8
1020,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,7,F,call,6,2,7,5,5
1021,2,Detractor,N,4,6,Married,Tier1,Card,3,M,call,3,10,1,1,2
1022,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,5,F,call,8,5,2,10,6
1023,7,Passives,Y,4,7,Married,Tier3,Cash,7,M,call,7,7,5,8,6
1024,2,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,6,F,call,3,6,3,1,1
1025,1,Detractor,N,4,4,Married,Tier2,Card,3,M,call,7,6,2,2,2
1026,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,4,F,call,8,1,3,8,6
1027,8,Passives,Y,4,9,Married,Tier1,Cash,5,M,e-comm,7,10,9,4,4
1028,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,3,F,call,10,2,7,10,6
1029,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,7,8,5,7,10
1030,4,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,6,3,8,8,6
1031,1,Detractor,N,4,3,Married,Tier2,Cash,5,M,call,1,4,8,3,4
1032,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,2,F,call,7,7,9,10,7
1033,5,Detractor,N,4,9,Married,Tier1,Card,1,M,call,5,2,9,10,4
1034,7,Passives,N,4,3,Not Married,Tier2,e-Wallet,6,F,m-comm,9,7,8,5,3
1035,7,Passives,Y,4,8,Married,Tier3,Cash,5,M,call,6,10,6,4,2
1036,1,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,5,9,6,10,3
1037,3,Detractor,N,4,2,Married,Tier2,Card,4,M,call,1,1,5,6,2
1038,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,7,5,5,4,10
1039,2,Detractor,N,4,8,Married,Tier1,Cash,6,M,call,8,4,2,6,1
1040,4,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,5,F,call,7,9,1,4,4
1041,8,Passives,Y,4,8,Married,Tier3,Card,3,M,call,4,8,8,4,9
1042,6,Detractor,N,4,4,Not Married,Tier1,e-Wallet,4,F,call,5,8,7,8,2
1043,0,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,6,10,10,5,2
1044,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,7,7,1,5,9
1045,8,Passives,Y,4,2,Married,Tier1,Card,3,M,e-comm,9,4,1,2,4
1046,9,Promoter,N,4,5,Married,Tier1,e-Wallet,8,F,m-comm,3,8,6,2,10
1047,7,Passives,N,4,7,Married,Tier3,Cash,6,M,call,4,2,8,4,3
1048,3,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,7,F,call,3,9,6,7,3
1049,4,Detractor,N,4,3,Married,Tier2,Card,1,M,call,4,6,7,6,3
1050,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,8,10,10,10,10
1051,4,Detractor,N,4,9,Married,Tier1,Cash,5,M,call,5,4,10,8,4
1052,1,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,3,7,1,10,6
1053,7,Passives,N,4,1,Married,Tier3,Card,6,M,call,5,5,10,2,6
1054,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,1,5,7,1,1
1055,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,5,4,4,4,1
1056,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,6,F,call,7,1,4,3,5
1057,8,Passives,Y,4,1,Married,Tier1,Card,3,M,e-comm,1,3,8,4,10
1058,9,Promoter,N,4,6,Married,Tier1,e-Wallet,5,F,m-comm,8,9,9,4,10
1059,7,Passives,N,4,9,Married,Tier3,Cash,5,M,call,7,7,3,10,7
1060,0,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,4,F,call,6,3,3,1,4
1061,6,Detractor,N,4,8,Married,Tier2,Card,2,M,call,1,10,6,8,3
1062,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,4,F,call,4,9,5,4,7
1063,8,Passives,Y,4,10,Married,Tier1,Cash,1,M,e-comm,9,10,5,7,9
1064,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,4,F,call,8,8,6,8,2
1065,9,Promoter,Y,4,10,Married,Tier1,Card,7,M,m-comm,5,5,5,1,10
1066,4,Detractor,N,4,6,Not Married,Tier1,e-Wallet,1,F,call,3,7,5,5,4
1067,6,Detractor,N,4,8,Married,Tier2,Cash,1,M,call,6,6,3,4,6
1068,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,9,5,7,4,9
1069,8,Passives,Y,4,8,Married,Tier1,Card,7,M,e-comm,2,9,7,1,10
1070,0,Detractor,N,4,10,Not Married,Tier2,e-Wallet,4,F,call,5,2,10,9,4
1071,10,Promoter,Y,4,3,Married,Tier1,Cash,7,M,m-comm,1,4,10,10,7
1072,5,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,5,F,call,9,3,3,10,5
1073,10,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,3,3,3,9,8
1074,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,5,F,call,6,5,8,4,4
1075,6,Detractor,N,4,2,Married,Tier1,Cash,6,M,call,4,2,10,10,4
1076,4,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,1,9,2,3,2
1077,7,Passives,N,4,1,Married,Tier3,Card,5,M,call,5,9,1,8,8
1078,1,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,9,8,8,10,4
1079,3,Detractor,N,4,7,Married,Tier2,Cash,2,M,call,4,1,7,7,2
1080,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,1,1,1,7,7
1081,6,Detractor,N,4,6,Married,Tier1,Card,6,M,call,6,4,5,7,6
1082,9,Promoter,N,4,5,Married,Tier1,e-Wallet,9,F,m-comm,7,10,6,5,10
1083,10,Promoter,Y,4,7,Married,Tier1,Cash,7,M,m-comm,9,7,3,9,8
1084,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,5,5,1,7,5
1085,0,Detractor,N,4,2,Married,Tier2,Card,5,M,call,7,2,1,10,6
1086,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,2,F,call,7,9,10,6,8
1087,9,Promoter,Y,4,9,Married,Tier1,Cash,9,M,m-comm,9,9,4,3,8
1088,5,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,5,F,call,2,9,1,8,4
1089,7,Passives,N,4,4,Married,Tier3,Card,3,M,call,4,6,5,6,1
1090,4,Detractor,N,4,8,Not Married,Tier1,e-Wallet,7,F,call,8,1,2,5,2
1091,9,Promoter,Y,4,9,Married,Tier1,Cash,7,M,m-comm,8,3,2,7,8
1092,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,3,F,call,5,10,6,10,8
1093,0,Detractor,N,4,3,Married,Tier1,Card,4,M,call,8,1,6,5,4
1094,3,Detractor,N,4,10,Not Married,Tier2,e-Wallet,6,F,call,1,7,5,9,1
1095,7,Passives,N,4,5,Married,Tier3,Cash,6,M,call,4,3,2,6,9
1096,10,Promoter,N,4,7,Married,Tier2,Ziggy Credit,5,F,m-comm,6,5,8,2,7
1097,4,Detractor,N,4,10,Married,Tier2,Card,7,M,call,8,2,1,6,3
1098,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,8,6,2,10,9
1099,6,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,7,2,4,4,4
1100,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,8,3,8,8,9
1101,7,Passives,N,4,1,Married,Tier3,Card,2,M,call,7,5,2,2,5
1102,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,6,F,e-comm,1,5,3,4,7
1103,8,Passives,Y,4,5,Married,Tier2,Cash,3,M,m-comm,6,8,6,6,9
1104,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,6,F,call,8,10,4,6,10
1105,1,Detractor,N,4,6,Married,Tier1,Card,4,M,call,2,2,4,1,3
1106,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,8,4,7,8,7
1107,9,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,1,10,10,5,9
1108,8,Passives,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,e-comm,1,9,1,2,9
1109,6,Detractor,N,4,6,Married,Tier2,Card,3,M,call,2,4,2,3,4
1110,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,5,F,call,7,5,8,1,8
1111,4,Detractor,N,4,10,Married,Tier1,Cash,1,M,call,10,6,7,6,2
1112,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,10,6,3,9,10
1113,7,Passives,N,4,9,Married,Tier3,Card,2,M,call,8,3,2,1,9
1114,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,5,3,10,8,9
1115,2,Detractor,N,4,8,Married,Tier2,Cash,1,M,call,2,8,5,6,6
1116,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,1,F,call,6,6,1,7,7
1117,6,Detractor,N,4,7,Married,Tier1,Card,1,M,call,8,9,6,10,5
1118,1,Detractor,N,4,4,Not Married,Tier2,e-Wallet,3,F,call,4,8,8,3,3
1119,7,Passives,N,4,6,Married,Tier3,Cash,3,M,call,4,6,2,3,1
1120,4,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,2,F,call,5,7,4,3,2
1121,0,Detractor,N,4,7,Married,Tier2,Card,2,M,call,8,1,7,6,5
1122,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,1,F,call,6,9,1,5,4
1123,8,Passives,Y,4,10,Married,Tier1,Cash,4,M,e-comm,2,5,4,5,3
1124,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,10,F,m-comm,5,8,5,5,8
1125,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,8,10,3,8,2
1126,6,Detractor,N,4,9,Not Married,Tier1,e-Wallet,6,F,call,7,1,9,9,6
1127,8,Passives,Y,4,3,Married,Tier2,Cash,4,M,m-comm,7,2,10,3,7
1128,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,4,F,call,8,7,10,7,2
1129,7,Passives,Y,4,8,Married,Tier1,Card,4,M,e-comm,8,4,6,7,8
1130,5,Detractor,N,4,8,Not Married,Tier2,e-Wallet,1,F,call,7,6,5,7,4
1131,7,Passives,N,4,8,Married,Tier3,Cash,6,M,call,4,9,7,7,3
1132,4,Detractor,N,4,8,Not Married,Tier1,Ziggy Credit,3,F,call,10,9,9,3,3
1133,1,Detractor,N,4,3,Married,Tier2,Card,3,M,call,3,9,2,10,1
1134,9,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,6,5,8,10,10
1135,3,Detractor,N,4,4,Married,Tier1,Cash,2,M,call,3,4,5,8,4
1136,5,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,7,F,call,5,6,8,8,3
1137,7,Passives,N,4,7,Married,Tier3,Card,1,M,call,8,8,7,4,4
1138,1,Detractor,N,4,9,Not Married,Tier1,e-Wallet,4,F,call,5,4,1,10,4
1139,9,Promoter,Y,4,6,Married,Tier1,Cash,9,M,m-comm,1,4,6,6,9
1140,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,5,2,2,1,7
1141,3,Detractor,N,4,3,Married,Tier1,Card,5,M,call,3,6,6,1,1
1142,0,Detractor,N,4,4,Not Married,Tier2,e-Wallet,7,F,call,3,9,5,9,1
1143,8,Passives,Y,4,8,Married,Tier3,Cash,4,M,call,4,4,10,2,8
1144,4,Detractor,N,4,3,Not Married,Tier1,Ziggy Credit,6,F,call,8,9,9,6,4
1145,5,Detractor,N,4,6,Married,Tier2,Card,1,M,call,5,3,9,7,6
1146,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,2,F,call,7,10,4,1,1
1147,1,Detractor,N,4,1,Married,Tier1,Cash,2,M,call,1,1,6,10,6
1148,5,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,10,3,8,5,4
1149,7,Passives,N,4,7,Married,Tier3,Card,3,M,call,8,2,2,1,8
1150,1,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,8,3,10,3,2
1151,5,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,2,1,2,1,2
1152,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,3,7,3,2,9
1153,10,Promoter,Y,4,9,Married,Tier1,Card,6,M,m-comm,6,8,1,5,10
1154,1,Detractor,N,4,1,Not Married,Tier2,e-Wallet,7,F,call,9,8,5,10,6
1155,7,Passives,N,4,4,Married,Tier3,Cash,6,M,call,6,1,9,5,7
1156,1,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,7,10,3,1,3
1157,4,Detractor,N,4,7,Married,Tier2,Card,5,M,call,4,5,4,4,5
1158,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,9,3,1,8,8
1159,3,Detractor,N,4,1,Married,Tier1,Cash,1,M,call,10,7,5,10,4
1160,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,5,4,10,1,4
1161,7,Passives,N,4,3,Married,Tier3,Card,3,M,call,7,8,5,6,9
1162,2,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,10,5,9,2,6
1163,6,Detractor,N,4,5,Married,Tier2,Cash,1,M,call,8,1,9,7,1
1164,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,4,F,call,7,2,5,5,2
1165,8,Passives,Y,4,8,Married,Tier1,Card,5,M,e-comm,6,1,10,9,8
1166,7,Passives,N,4,1,Not Married,Tier2,e-Wallet,4,F,m-comm,8,3,10,1,4
1167,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,7,3,10,2,4
1168,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,10,F,m-comm,7,8,5,7,9
1169,9,Promoter,Y,4,3,Married,Tier1,Card,5,M,m-comm,8,9,5,10,10
1170,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,4,3,10,2,4
1171,5,Detractor,N,4,7,Married,Tier1,Cash,2,M,call,1,7,7,6,2
1172,5,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,6,F,call,9,6,2,8,5
1173,7,Passives,N,4,2,Married,Tier3,Card,1,M,call,4,10,7,2,5
1174,3,Detractor,N,4,9,Not Married,Tier1,e-Wallet,7,F,call,8,2,10,6,6
1175,7,Passives,Y,4,7,Married,Tier2,Cash,3,M,m-comm,3,10,7,5,7
1176,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,5,F,call,7,3,3,3,10
1177,9,Promoter,Y,4,4,Married,Tier1,Card,10,M,m-comm,6,2,2,3,9
1178,4,Detractor,N,4,5,Not Married,Tier2,e-Wallet,5,F,call,9,2,1,9,2
1179,7,Passives,N,4,8,Married,Tier3,Cash,5,M,call,6,7,1,5,1
1180,0,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,9,10,8,3,2
1181,4,Detractor,N,4,1,Married,Tier2,Card,6,M,call,7,4,3,10,4
1182,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,5,F,call,7,10,6,2,3
1183,1,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,6,5,5,8,5
1184,7,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,7,F,m-comm,6,7,2,1,7
1185,9,Promoter,Y,4,1,Married,Tier1,Card,9,M,m-comm,9,7,1,3,8
1186,5,Detractor,N,4,4,Not Married,Tier1,e-Wallet,6,F,call,2,5,1,10,2
1187,2,Detractor,N,4,8,Married,Tier2,Cash,3,M,call,1,10,2,6,1
1188,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,5,9,4,6,3
1189,2,Detractor,N,4,3,Married,Tier1,Card,4,M,call,9,5,4,6,6
1190,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,7,F,m-comm,5,2,3,2,4
1191,7,Passives,N,4,3,Married,Tier3,Cash,6,M,call,8,4,2,5,6
1192,5,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,2,5,9,1,1
1193,6,Detractor,N,4,1,Married,Tier2,Card,4,M,call,3,4,9,6,6
1194,9,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,8,4,3,6,7
1195,5,Detractor,N,4,2,Married,Tier1,Cash,5,M,call,1,10,9,5,6
1196,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,3,2,3,3,7
1197,7,Passives,N,4,9,Married,Tier3,Card,5,M,call,4,5,4,1,2
1198,9,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,3,3,9,9,9
1199,6,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,8,6,10,10,2
1200,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,1,F,call,7,7,6,10,8
1201,7,Passives,Y,4,10,Married,Tier1,Card,3,M,e-comm,4,2,2,1,8
1202,7,Passives,N,4,10,Not Married,Tier2,e-Wallet,3,F,m-comm,4,2,6,7,6
1203,7,Passives,N,4,4,Married,Tier3,Cash,2,M,call,6,10,10,6,9
1204,3,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,1,10,10,10,2
1205,4,Detractor,N,4,7,Married,Tier2,Card,4,M,call,4,1,9,4,2
1206,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,2,F,call,4,1,8,4,5
1207,0,Detractor,N,4,8,Married,Tier1,Cash,5,M,call,7,6,9,4,5
1208,4,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,7,F,call,6,3,8,4,1
1209,7,Passives,N,4,10,Married,Tier3,Card,2,M,call,7,3,7,1,9
1210,10,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,2,10,6,10,10
1211,4,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,7,6,1,2,3
1212,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,5,2,8,8,6
1213,0,Detractor,N,4,5,Married,Tier1,Card,7,M,call,5,8,8,9,5
1214,2,Detractor,N,4,7,Not Married,Tier2,e-Wallet,6,F,call,2,8,4,6,2
1215,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,4,5,4,5,3
1216,1,Detractor,N,4,10,Not Married,Tier1,Ziggy Credit,3,F,call,7,1,9,9,5
1217,3,Detractor,N,4,10,Married,Tier2,Card,2,M,call,7,8,4,7,2
1218,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,3,F,call,7,4,9,2,10
1219,8,Passives,Y,4,4,Married,Tier1,Cash,4,M,e-comm,9,8,6,1,1
1220,5,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,4,F,call,3,10,7,1,2
1221,10,Promoter,Y,4,2,Married,Tier1,Card,8,M,m-comm,10,1,8,6,9
1222,8,Passives,N,4,8,Not Married,Tier1,e-Wallet,3,F,e-comm,7,1,5,4,4
1223,4,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,2,6,7,6,5
1224,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,2,F,call,4,5,2,6,5
1225,4,Detractor,N,4,10,Married,Tier1,Card,5,M,call,3,5,2,6,5
1226,0,Detractor,N,4,9,Not Married,Tier2,e-Wallet,5,F,call,10,7,1,6,5
1227,7,Passives,N,4,6,Married,Tier3,Cash,6,M,call,6,1,2,9,10
1228,6,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,6,F,call,9,1,8,1,5
1229,0,Detractor,N,4,8,Married,Tier2,Card,2,M,call,4,1,7,3,6
1230,8,Passives,N,4,8,Not Married,Tier3,e-Wallet,3,F,call,6,5,1,7,10
1231,7,Passives,Y,4,2,Married,Tier1,Cash,5,M,e-comm,10,3,5,8,6
1232,5,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,call,6,6,4,7,3
1233,8,Passives,Y,4,1,Married,Tier3,Card,3,M,call,6,7,8,3,2
1234,2,Detractor,N,4,2,Not Married,Tier1,e-Wallet,4,F,call,10,3,7,7,6
1235,2,Detractor,N,4,2,Married,Tier2,Cash,6,M,call,1,8,7,5,4
1236,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,8,2,4,4,3
1237,9,Promoter,Y,4,4,Married,Tier1,Card,10,M,m-comm,10,4,5,4,9
1238,6,Detractor,N,4,9,Not Married,Tier2,e-Wallet,1,F,call,5,7,10,9,2
1239,8,Passives,Y,4,7,Married,Tier3,Cash,1,M,call,6,2,4,6,9
1240,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,6,10,10,5,9
1241,6,Detractor,N,4,10,Married,Tier2,Card,1,M,call,10,1,5,6,1
1242,8,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,5,5,6,3,8
1243,6,Detractor,N,4,6,Married,Tier1,Cash,4,M,call,10,2,5,2,6
1244,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,2,4,9,3,8
1245,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,6,2,10,7,4
1246,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,7,8,8,7
1247,8,Passives,Y,4,9,Married,Tier2,Cash,6,M,m-comm,8,3,5,5,1
1248,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,7,2,3,8,10
1249,1,Detractor,N,4,6,Married,Tier1,Card,4,M,call,8,3,10,5,5
1250,5,Detractor,N,4,5,Not Married,Tier2,e-Wallet,4,F,call,5,3,7,5,1
1251,7,Passives,N,4,1,Married,Tier3,Cash,6,M,call,7,7,2,2,6
1252,8,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,6,F,e-comm,9,7,2,7,1
1253,0,Detractor,N,4,6,Married,Tier2,Card,1,M,call,3,9,4,6,2
1254,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,8,5,1,7,10
1255,6,Detractor,N,4,8,Married,Tier1,Cash,3,M,call,10,8,5,8,2
1256,5,Detractor,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,call,10,4,1,4,1
1257,10,Promoter,Y,4,7,Married,Tier1,Card,8,M,m-comm,10,8,8,1,8
1258,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,9,7,4,4,9
1259,6,Detractor,N,4,6,Married,Tier2,Cash,4,M,call,5,5,5,2,3
1260,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,2,F,call,8,10,2,9,2
1261,5,Detractor,N,4,5,Married,Tier1,Card,5,M,call,4,3,2,3,5
1262,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,6,F,m-comm,9,8,4,3,9
1263,7,Passives,N,4,2,Married,Tier3,Cash,6,M,call,5,9,7,2,10
1264,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,7,F,m-comm,8,1,2,4,10
1265,9,Promoter,Y,4,3,Married,Tier1,Card,9,M,m-comm,10,1,3,1,7
1266,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,7,10,7,8,2
1267,5,Detractor,N,4,9,Married,Tier1,Cash,3,M,call,9,7,2,8,4
1268,1,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,4,F,call,3,1,3,7,2
1269,7,Passives,Y,4,4,Married,Tier3,Card,5,M,call,4,1,8,8,10
1270,7,Passives,N,4,8,Not Married,Tier1,e-Wallet,2,F,e-comm,3,3,4,8,4
1271,1,Detractor,N,4,1,Married,Tier2,Cash,2,M,call,3,4,7,3,1
1272,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,7,9,2,7,6
1273,5,Detractor,N,4,4,Married,Tier1,Card,4,M,call,4,4,7,8,1
1274,6,Detractor,N,4,4,Not Married,Tier2,e-Wallet,6,F,call,8,1,3,6,1
1275,7,Passives,N,4,3,Married,Tier3,Cash,7,M,call,6,10,8,8,1
1276,1,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,3,F,call,8,1,4,2,5
1277,2,Detractor,N,4,7,Married,Tier2,Card,2,M,call,8,3,5,3,5
1278,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,7,F,call,7,6,3,5,3
1279,3,Detractor,N,4,8,Married,Tier1,Cash,2,M,call,1,4,2,1,1
1280,3,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,call,4,3,5,8,2
1281,9,Promoter,Y,4,2,Married,Tier1,Card,9,M,m-comm,10,4,7,4,8
1282,2,Detractor,N,4,1,Not Married,Tier1,e-Wallet,3,F,call,9,8,8,3,4
1283,6,Detractor,N,4,3,Married,Tier2,Cash,3,M,call,9,8,5,4,6
1284,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,4,10,2,1,8
1285,3,Detractor,N,4,10,Married,Tier1,Card,4,M,call,8,5,9,5,6
1286,9,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,1,10,3,8,9
1287,7,Passives,Y,4,5,Married,Tier3,Cash,2,M,call,5,9,2,3,5
1288,3,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,3,F,call,5,10,9,3,4
1289,7,Passives,Y,4,7,Married,Tier2,Card,3,M,m-comm,5,3,4,8,3
1290,10,Promoter,N,4,5,Married,Tier1,e-Wallet,10,F,m-comm,6,4,8,7,8
1291,7,Passives,Y,4,7,Married,Tier1,Cash,6,M,e-comm,9,1,2,5,10
1292,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,7,2,10,2,10
1293,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,4,6,2,8,4
1294,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,1,8,10,1,1
1295,4,Detractor,N,4,4,Married,Tier2,Cash,2,M,call,9,1,2,6,4
1296,8,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,5,F,call,4,2,6,7,10
1297,8,Passives,Y,4,1,Married,Tier1,Card,3,M,e-comm,7,8,3,3,9
1298,3,Detractor,N,4,9,Not Married,Tier2,e-Wallet,3,F,call,1,6,8,9,6
1299,10,Promoter,Y,4,10,Married,Tier1,Cash,7,M,m-comm,6,6,7,9,10
1300,8,Passives,N,4,5,Not Married,Tier1,Ziggy Credit,6,F,e-comm,2,9,3,9,4
1301,4,Detractor,N,4,9,Married,Tier2,Card,5,M,call,3,5,4,8,6
1302,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,4,7,2,6,8
1303,1,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,4,1,7,7,3
1304,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,5,F,m-comm,5,10,7,6,10
1305,7,Passives,N,4,10,Married,Tier3,Card,1,M,call,6,4,8,8,7
1306,6,Detractor,N,4,8,Not Married,Tier1,e-Wallet,3,F,call,2,6,8,7,2
1307,1,Detractor,N,4,5,Married,Tier2,Cash,3,M,call,5,3,6,5,5
1308,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,6,F,m-comm,7,9,10,3,10
1309,4,Detractor,N,4,3,Married,Tier1,Card,5,M,call,3,5,6,9,3
1310,4,Detractor,N,4,7,Not Married,Tier2,e-Wallet,7,F,call,5,8,9,5,1
1311,7,Passives,N,4,5,Married,Tier3,Cash,3,M,call,4,2,7,1,3
1312,8,Passives,N,4,1,Not Married,Tier1,Ziggy Credit,7,F,e-comm,8,10,4,6,4
1313,10,Promoter,Y,4,1,Married,Tier1,Card,10,M,m-comm,7,2,6,2,9
1314,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,4,F,call,4,2,10,8,5
1315,7,Passives,Y,4,1,Married,Tier1,Cash,1,M,e-comm,4,4,6,9,1
1316,0,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,7,F,call,8,10,3,1,3
1317,7,Passives,N,4,4,Married,Tier3,Card,2,M,call,8,7,6,10,5
1318,2,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,8,4,9,2,1
1319,4,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,6,4,10,10,3
1320,7,Passives,N,4,8,Not Married,Tier3,Ziggy Credit,2,F,call,5,3,5,1,8
1321,1,Detractor,N,4,1,Married,Tier1,Card,3,M,call,9,4,10,4,2
1322,7,Passives,N,4,6,Not Married,Tier2,e-Wallet,1,F,m-comm,1,10,2,8,2
1323,7,Passives,N,4,6,Married,Tier3,Cash,6,M,call,8,3,9,5,9
1324,5,Detractor,N,4,4,Not Married,Tier1,Ziggy Credit,2,F,call,10,9,1,6,3
1325,9,Promoter,Y,4,7,Married,Tier1,Card,9,M,m-comm,6,10,9,9,10
1326,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,7,1,6,7,8
1327,5,Detractor,N,4,5,Married,Tier1,Cash,6,M,call,4,6,8,8,1
1328,8,Passives,N,4,4,Not Married,Tier2,Ziggy Credit,1,F,m-comm,5,8,4,7,1
1329,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,7,2,10,8,4
1330,0,Detractor,N,4,10,Not Married,Tier1,e-Wallet,6,F,call,1,8,9,3,4
1331,9,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,6,9,6,3,9
1332,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,7,F,call,6,9,3,8,9
1333,7,Passives,Y,4,2,Married,Tier1,Card,2,M,e-comm,9,9,8,3,2
1334,6,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,7,2,1,5,4
1335,7,Passives,N,4,4,Married,Tier3,Cash,2,M,call,6,3,5,6,3
1336,2,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,1,F,call,1,8,2,8,6
1337,6,Detractor,N,4,10,Married,Tier2,Card,4,M,call,8,5,3,7,6
1338,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,3,F,call,6,6,5,2,8
1339,2,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,3,1,3,7,5
1340,5,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,6,F,call,10,1,4,7,5
1341,10,Promoter,Y,4,10,Married,Tier1,Card,9,M,m-comm,2,7,1,1,7
1342,9,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,8,9,7,6,9
1343,8,Passives,Y,4,10,Married,Tier2,Cash,6,M,m-comm,3,9,3,6,4
1344,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,7,2,7,2,9
1345,8,Passives,Y,4,6,Married,Tier1,Card,6,M,e-comm,2,1,4,7,6
1346,10,Promoter,N,4,6,Married,Tier1,e-Wallet,8,F,m-comm,10,6,5,1,10
1347,7,Passives,N,4,6,Married,Tier3,Cash,2,M,call,4,10,5,6,3
1348,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,8,5,3,6,10
1349,2,Detractor,N,4,5,Married,Tier2,Card,7,M,call,5,3,8,6,5
1350,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,6,3,7,9,1
1351,3,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,10,9,1,10,5
1352,7,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,1,F,m-comm,3,2,2,4,2
1353,10,Promoter,Y,4,10,Married,Tier1,Card,5,M,m-comm,6,2,3,6,8
1354,1,Detractor,N,4,2,Not Married,Tier1,e-Wallet,2,F,call,9,6,8,8,6
1355,9,Promoter,Y,4,7,Married,Tier1,Cash,8,M,m-comm,2,10,3,3,10
1356,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,10,F,m-comm,1,1,3,5,7
1357,4,Detractor,N,4,2,Married,Tier1,Card,7,M,call,7,5,7,6,4
1358,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,2,F,m-comm,7,2,3,8,5
1359,7,Passives,N,4,5,Married,Tier3,Cash,6,M,call,4,10,2,4,4
1360,4,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,5,F,call,2,4,1,5,5
1361,1,Detractor,N,4,9,Married,Tier2,Card,4,M,call,8,8,4,10,6
1362,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,6,5,9,10,2
1363,1,Detractor,N,4,5,Married,Tier1,Cash,6,M,call,3,1,5,3,5
1364,5,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,6,F,call,10,9,6,6,3
1365,7,Passives,Y,4,3,Married,Tier3,Card,1,M,call,5,10,6,1,10
1366,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,7,3,1,10,4
1367,10,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,1,1,9,5,7
1368,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,4,F,call,5,4,4,10,2
1369,1,Detractor,N,4,5,Married,Tier1,Card,1,M,call,1,3,6,4,6
1370,4,Detractor,N,4,9,Not Married,Tier2,e-Wallet,4,F,call,4,5,7,2,1
1371,7,Passives,N,4,4,Married,Tier3,Cash,4,M,call,4,6,1,4,10
1372,5,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,6,F,call,2,7,8,5,1
1373,6,Detractor,N,4,6,Married,Tier2,Card,7,M,call,4,1,4,1,1
1374,8,Passives,N,4,8,Not Married,Tier3,e-Wallet,2,F,call,5,3,6,5,7
1375,10,Promoter,Y,4,4,Married,Tier1,Cash,10,M,m-comm,10,8,3,1,9
1376,7,Passives,N,4,1,Not Married,Tier2,Ziggy Credit,3,F,m-comm,5,10,6,9,3
1377,7,Passives,N,4,1,Married,Tier3,Card,5,M,call,5,4,5,9,9
1378,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,10,5,3,7,10
1379,1,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,9,8,9,8,1
1380,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,1,F,call,5,8,3,6,6
1381,0,Detractor,N,4,9,Married,Tier1,Card,7,M,call,1,10,4,3,3
1382,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,5,F,m-comm,9,10,4,4,7
1383,7,Passives,N,4,2,Married,Tier3,Cash,2,M,call,8,5,1,7,9
1384,6,Detractor,N,4,5,Not Married,Tier1,Ziggy Credit,1,F,call,6,2,3,6,4
1385,0,Detractor,N,4,8,Married,Tier2,Card,7,M,call,9,7,1,9,4
1386,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,6,6,10,2,5
1387,5,Detractor,N,4,9,Married,Tier1,Cash,3,M,call,3,8,5,6,4
1388,2,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,call,8,3,7,1,6
1389,7,Passives,N,4,10,Married,Tier3,Card,2,M,call,5,1,7,5,10
1390,10,Promoter,N,4,6,Married,Tier1,e-Wallet,9,F,m-comm,7,9,10,5,9
1391,5,Detractor,N,4,6,Married,Tier2,Cash,4,M,call,3,9,2,6,1
1392,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,8,F,m-comm,1,6,3,5,7
1393,3,Detractor,N,4,4,Married,Tier1,Card,2,M,call,5,4,6,6,1
1394,1,Detractor,N,4,8,Not Married,Tier2,e-Wallet,2,F,call,1,4,6,6,5
1395,7,Passives,Y,4,4,Married,Tier3,Cash,5,M,call,5,10,4,6,5
1396,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,8,F,m-comm,8,8,6,7,8
1397,4,Detractor,N,4,9,Married,Tier2,Card,1,M,call,10,9,8,3,3
1398,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,1,F,call,4,3,3,6,2
1399,2,Detractor,N,4,3,Married,Tier1,Cash,6,M,call,5,3,6,8,2
1400,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,3,F,m-comm,8,9,10,1,10
1401,7,Passives,N,4,10,Married,Tier3,Card,4,M,call,7,8,4,3,2
1402,7,Passives,N,4,3,Not Married,Tier1,e-Wallet,6,F,e-comm,4,7,8,9,1
1403,1,Detractor,N,4,7,Married,Tier2,Cash,4,M,call,8,7,7,1,4
1404,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,2,F,call,6,1,8,4,10
1405,8,Passives,Y,4,5,Married,Tier1,Card,4,M,e-comm,4,6,5,1,9
1406,1,Detractor,N,4,4,Not Married,Tier2,e-Wallet,2,F,call,5,2,3,5,4
1407,7,Passives,N,4,8,Married,Tier3,Cash,4,M,call,6,1,5,9,8
1408,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,8,2,2,4,8
1409,9,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,3,3,5,6,8
1410,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,8,5,7,4,8
1411,8,Passives,Y,4,8,Married,Tier1,Cash,6,M,e-comm,4,1,7,2,7
1412,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,6,F,m-comm,4,4,4,5,10
1413,10,Promoter,Y,4,7,Married,Tier2,Card,8,M,m-comm,1,7,1,1,10
1414,5,Detractor,N,4,9,Not Married,Tier1,e-Wallet,1,F,call,9,9,10,10,1
1415,0,Detractor,N,4,4,Married,Tier2,Cash,3,M,call,4,2,2,4,6
1416,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,9,7,9,5,10
1417,3,Detractor,N,4,8,Married,Tier1,Card,1,M,call,4,8,6,10,4
1418,5,Detractor,N,4,8,Not Married,Tier2,e-Wallet,7,F,call,5,2,7,3,4
1419,10,Promoter,Y,4,10,Married,Tier1,Cash,10,M,m-comm,4,1,4,2,10
1420,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,9,7,2,8,8
1421,6,Detractor,N,4,9,Married,Tier2,Card,6,M,call,3,1,4,4,5
1422,7,Passives,N,4,10,Not Married,Tier3,e-Wallet,1,F,call,5,3,10,7,10
1423,7,Passives,Y,4,3,Married,Tier1,Cash,3,M,e-comm,4,4,1,5,5
1424,8,Passives,N,4,2,Not Married,Tier2,Ziggy Credit,7,F,m-comm,3,4,10,10,5
1425,7,Passives,N,4,6,Married,Tier3,Card,4,M,call,6,5,4,5,7
1426,1,Detractor,N,4,5,Not Married,Tier1,e-Wallet,7,F,call,6,1,10,7,6
1427,2,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,8,9,3,7,4
1428,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,5,F,call,8,4,6,10,10
1429,7,Passives,Y,4,8,Married,Tier1,Card,6,M,e-comm,4,6,5,9,5
1430,6,Detractor,N,4,3,Not Married,Tier2,e-Wallet,7,F,call,5,5,2,2,2
1431,7,Passives,Y,4,8,Married,Tier3,Cash,7,M,call,6,6,2,1,5
1432,6,Detractor,N,4,2,Not Married,Tier1,Ziggy Credit,1,F,call,9,10,9,3,4
1433,5,Detractor,N,4,3,Married,Tier2,Card,2,M,call,4,10,10,3,4
1434,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,5,9,3,1,9
1435,7,Passives,Y,4,10,Married,Tier1,Cash,2,M,e-comm,1,10,10,7,8
1436,1,Detractor,N,4,1,Not Married,Tier2,Ziggy Credit,2,F,call,2,9,8,4,6
1437,7,Passives,N,4,6,Married,Tier3,Card,6,M,call,4,9,4,9,4
1438,10,Promoter,N,4,5,Married,Tier2,e-Wallet,6,F,m-comm,4,6,7,1,10
1439,4,Detractor,N,4,6,Married,Tier2,Cash,5,M,call,8,5,4,3,3
1440,8,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,4,F,call,7,1,5,2,5
1441,6,Detractor,N,4,10,Married,Tier1,Card,5,M,call,8,2,4,2,6
1442,10,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,8,1,6,3,10
1443,7,Passives,Y,4,3,Married,Tier3,Cash,1,M,call,4,4,8,2,4
1444,3,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,3,F,call,8,2,9,9,1
1445,0,Detractor,N,4,6,Married,Tier2,Card,4,M,call,10,2,8,10,6
1446,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,4,1,7,9,10
1447,9,Promoter,Y,4,1,Married,Tier1,Cash,7,M,m-comm,5,2,10,8,9
1448,0,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,4,F,call,1,10,5,5,5
1449,9,Promoter,Y,4,8,Married,Tier1,Card,5,M,m-comm,8,4,5,5,7
1450,6,Detractor,N,4,1,Not Married,Tier1,e-Wallet,4,F,call,8,5,4,5,3
1451,6,Detractor,N,4,9,Married,Tier2,Cash,7,M,call,1,9,4,5,6
1452,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,8,F,m-comm,2,1,1,9,10
1453,10,Promoter,Y,4,2,Married,Tier1,Card,5,M,m-comm,6,9,4,9,9
1454,8,Passives,N,4,4,Not Married,Tier2,e-Wallet,3,F,m-comm,4,7,6,7,7
1455,7,Passives,Y,4,6,Married,Tier3,Cash,5,M,call,4,5,6,2,1
1456,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,5,6,5,9,8
1457,4,Detractor,N,4,5,Married,Tier2,Card,5,M,call,10,5,4,6,5
1458,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,5,F,call,8,6,5,5,8
1459,8,Passives,Y,4,5,Married,Tier1,Cash,6,M,e-comm,4,10,2,8,8
1460,7,Passives,N,4,5,Not Married,Tier2,Ziggy Credit,6,F,m-comm,8,6,8,4,3
1461,7,Passives,N,4,4,Married,Tier3,Card,6,M,call,6,4,2,9,4
1462,7,Passives,N,4,10,Not Married,Tier1,e-Wallet,5,F,e-comm,4,3,9,8,1
1463,7,Passives,Y,4,8,Married,Tier2,Cash,3,M,m-comm,2,10,7,9,6
1464,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,7,F,call,5,10,9,9,10
1465,9,Promoter,Y,4,3,Married,Tier1,Card,6,M,m-comm,5,7,3,9,7
1466,8,Passives,N,4,1,Not Married,Tier2,e-Wallet,4,F,m-comm,7,5,7,10,8
1467,7,Passives,N,4,7,Married,Tier3,Cash,5,M,call,7,6,7,4,1
1468,0,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,1,F,call,7,2,8,9,4
1469,9,Promoter,Y,4,4,Married,Tier1,Card,9,M,m-comm,10,2,8,1,10
1470,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,2,F,call,7,10,9,3,5
1471,9,Promoter,Y,4,9,Married,Tier1,Cash,9,M,m-comm,1,7,4,1,10
1472,6,Detractor,N,4,2,Not Married,Tier2,Ziggy Credit,4,F,call,6,6,9,5,6
1473,7,Passives,N,4,1,Married,Tier3,Card,4,M,call,5,3,5,7,2
1474,10,Promoter,N,4,6,Married,Tier1,e-Wallet,6,F,m-comm,4,9,6,2,10
1475,7,Passives,Y,4,9,Married,Tier2,Cash,4,M,m-comm,1,3,2,4,6
1476,8,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,5,F,call,6,3,3,6,7
1477,7,Passives,Y,4,2,Married,Tier1,Card,4,M,e-comm,5,1,4,2,3
1478,1,Detractor,N,4,3,Not Married,Tier2,e-Wallet,2,F,call,9,6,7,2,4
1479,8,Passives,Y,4,4,Married,Tier3,Cash,2,M,call,4,4,2,3,5
1480,3,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,8,2,8,7,3
1481,2,Detractor,N,4,2,Married,Tier2,Card,3,M,call,8,1,10,2,4
1482,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,1,10,10,5,9
1483,8,Passives,Y,4,4,Married,Tier1,Cash,7,M,e-comm,10,4,1,5,3
1484,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,6,F,m-comm,2,4,7,9,9
1485,7,Passives,N,4,1,Married,Tier3,Card,6,M,call,7,7,5,2,7
1486,4,Detractor,N,4,5,Not Married,Tier1,e-Wallet,5,F,call,6,3,4,9,6
1487,1,Detractor,N,4,1,Married,Tier2,Cash,2,M,call,7,2,10,5,4
1488,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,9,F,m-comm,10,6,2,1,8
1489,2,Detractor,N,4,2,Married,Tier1,Card,7,M,call,7,10,5,8,2
1490,6,Detractor,N,4,3,Not Married,Tier2,e-Wallet,1,F,call,6,1,6,4,4
1491,8,Passives,Y,4,7,Married,Tier3,Cash,3,M,call,8,6,5,2,9
1492,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,7,F,call,10,8,10,1,2
1493,2,Detractor,N,4,2,Married,Tier2,Card,5,M,call,4,1,2,5,5
1494,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,1,F,call,7,4,3,6,3
1495,1,Detractor,N,4,5,Married,Tier1,Cash,3,M,call,3,4,10,2,4
1496,0,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,3,7,6,8,2
1497,7,Passives,Y,4,1,Married,Tier3,Card,7,M,call,6,5,5,1,1
1498,7,Passives,N,4,5,Not Married,Tier1,e-Wallet,4,F,e-comm,1,10,6,5,2
1499,7,Passives,Y,4,10,Married,Tier2,Cash,1,M,m-comm,3,3,6,8,9
1500,7,Passives,N,4,7,Not Married,Tier3,Ziggy Credit,5,F,call,5,1,4,4,5
1501,1,Detractor,N,4,3,Married,Tier1,Card,7,M,call,10,4,4,7,6
1502,3,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,1,1,7,1,5
1503,10,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,2,9,5,7,10
1504,10,Promoter,N,4,5,Married,Tier1,Ziggy Credit,5,F,m-comm,3,3,3,8,7
1505,1,Detractor,N,4,4,Married,Tier2,Card,2,M,call,10,7,6,5,4
1506,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,3,F,call,6,9,8,4,4
1507,3,Detractor,N,4,1,Married,Tier1,Cash,6,M,call,6,5,3,1,6
1508,5,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,1,F,call,5,5,6,4,3
1509,7,Passives,N,4,7,Married,Tier3,Card,4,M,call,6,6,5,8,8
1510,5,Detractor,N,4,9,Not Married,Tier1,e-Wallet,1,F,call,1,9,9,5,3
1511,4,Detractor,N,4,7,Married,Tier2,Cash,5,M,call,9,5,10,1,3
1512,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,6,F,call,6,8,4,7,6
1513,3,Detractor,N,4,8,Married,Tier1,Card,5,M,call,3,3,10,2,1
1514,4,Detractor,N,4,4,Not Married,Tier2,e-Wallet,7,F,call,4,4,9,4,1
1515,7,Passives,N,4,1,Married,Tier3,Cash,3,M,call,4,9,3,1,4
1516,0,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,4,F,call,2,1,1,1,3
1517,3,Detractor,N,4,10,Married,Tier2,Card,6,M,call,4,10,4,1,4
1518,7,Passives,N,4,6,Not Married,Tier3,e-Wallet,7,F,call,6,6,8,8,9
1519,6,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,2,10,7,4,6
1520,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,6,F,m-comm,4,7,2,9,7
1521,7,Passives,N,4,7,Married,Tier3,Card,6,M,call,6,9,4,1,3
1522,8,Passives,N,4,3,Not Married,Tier1,e-Wallet,2,F,e-comm,8,8,4,5,3
1523,6,Detractor,N,4,2,Married,Tier2,Cash,5,M,call,7,10,5,5,5
1524,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,7,5,9,8,7
1525,5,Detractor,N,4,2,Married,Tier1,Card,3,M,call,5,3,6,8,1
1526,2,Detractor,N,4,10,Not Married,Tier2,e-Wallet,6,F,call,3,1,6,1,5
1527,7,Passives,N,4,2,Married,Tier3,Cash,7,M,call,6,1,8,6,5
1528,7,Passives,N,4,8,Not Married,Tier1,Ziggy Credit,6,F,e-comm,6,3,2,10,3
1529,5,Detractor,N,4,1,Married,Tier2,Card,3,M,call,10,7,8,1,3
1530,7,Passives,N,4,9,Not Married,Tier3,e-Wallet,6,F,call,6,10,2,9,2
1531,1,Detractor,N,4,2,Married,Tier1,Cash,2,M,call,10,9,7,8,1
1532,5,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,3,F,call,8,9,4,10,6
1533,7,Passives,N,4,5,Married,Tier3,Card,2,M,call,6,2,3,1,10
1534,10,Promoter,N,4,5,Married,Tier1,e-Wallet,6,F,m-comm,1,1,8,4,9
1535,8,Passives,Y,4,9,Married,Tier2,Cash,5,M,m-comm,7,4,10,6,10
1536,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,4,F,call,4,1,5,8,10
1537,7,Passives,Y,4,9,Married,Tier1,Card,2,M,e-comm,8,6,5,4,9
1538,7,Passives,N,4,3,Not Married,Tier2,e-Wallet,3,F,m-comm,9,10,8,10,2
1539,9,Promoter,Y,4,2,Married,Tier1,Cash,7,M,m-comm,9,1,3,6,7
1540,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,call,1,4,3,6,2
1541,1,Detractor,N,4,6,Married,Tier2,Card,7,M,call,4,2,2,1,3
1542,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,3,F,call,4,1,4,4,3
1543,10,Promoter,Y,4,2,Married,Tier1,Cash,8,M,m-comm,6,8,8,9,7
1544,1,Detractor,N,4,10,Not Married,Tier2,Ziggy Credit,2,F,call,10,9,8,1,1
1545,7,Passives,N,4,8,Married,Tier3,Card,6,M,call,6,4,3,2,3
1546,6,Detractor,N,4,7,Not Married,Tier1,e-Wallet,6,F,call,8,7,5,2,3
1547,10,Promoter,Y,4,8,Married,Tier1,Cash,8,M,m-comm,7,5,4,5,10
1548,7,Passives,N,4,5,Not Married,Tier3,Ziggy Credit,3,F,call,7,7,8,7,4
1549,2,Detractor,N,4,7,Married,Tier1,Card,4,M,call,9,1,9,5,2
1550,9,Promoter,N,4,7,Married,Tier1,e-Wallet,6,F,m-comm,1,7,3,7,9
1551,7,Passives,N,4,7,Married,Tier3,Cash,2,M,call,7,2,10,5,9
1552,1,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,7,F,call,1,4,7,5,5
1553,0,Detractor,N,4,6,Married,Tier2,Card,2,M,call,9,4,3,1,1
1554,7,Passives,N,4,3,Not Married,Tier3,e-Wallet,1,F,call,6,9,9,5,4
1555,4,Detractor,N,4,9,Married,Tier1,Cash,5,M,call,4,10,8,5,3
1556,8,Passives,N,4,8,Not Married,Tier2,Ziggy Credit,6,F,m-comm,2,5,8,7,7
1557,10,Promoter,Y,4,9,Married,Tier1,Card,7,M,m-comm,10,9,5,1,7
1558,8,Passives,N,4,7,Not Married,Tier1,e-Wallet,6,F,e-comm,3,4,5,3,5
1559,1,Detractor,N,4,4,Married,Tier2,Cash,4,M,call,9,1,1,2,2
1560,7,Passives,N,4,3,Not Married,Tier3,Ziggy Credit,4,F,call,6,6,9,6,3
1561,9,Promoter,Y,4,8,Married,Tier1,Card,10,M,m-comm,4,6,5,5,8
1562,2,Detractor,N,4,7,Not Married,Tier2,e-Wallet,2,F,call,9,9,6,9,6
1563,7,Passives,N,4,9,Married,Tier3,Cash,4,M,call,5,6,2,8,4
1564,7,Passives,N,4,4,Not Married,Tier1,Ziggy Credit,5,F,e-comm,5,3,3,4,10
1565,7,Passives,Y,4,7,Married,Tier2,Card,6,M,m-comm,9,10,6,8,2
1566,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,4,F,call,5,5,5,9,9
1567,4,Detractor,N,4,9,Married,Tier1,Cash,6,M,call,10,5,9,7,1
1568,1,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,8,9,2,5,5
1569,7,Passives,N,4,4,Married,Tier3,Card,4,M,call,7,10,7,6,4
1570,8,Passives,N,4,9,Not Married,Tier1,e-Wallet,1,F,e-comm,7,9,2,8,6
1571,8,Passives,Y,4,6,Married,Tier2,Cash,7,M,m-comm,7,8,1,8,6
1572,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,4,10,9,8,7
1573,1,Detractor,N,4,7,Married,Tier1,Card,4,M,call,2,3,3,9,2
1574,0,Detractor,N,4,8,Not Married,Tier2,e-Wallet,1,F,call,1,9,10,10,1
1575,7,Passives,Y,4,2,Married,Tier3,Cash,6,M,call,8,5,5,1,7
1576,4,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,10,1,1,2,2
1577,3,Detractor,N,4,9,Married,Tier2,Card,5,M,call,8,6,4,1,5
1578,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,3,F,call,8,5,7,2,9
1579,1,Detractor,N,4,9,Married,Tier1,Cash,7,M,call,3,4,3,2,4
1580,3,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,3,F,call,10,5,9,6,4
1581,7,Passives,N,4,1,Married,Tier3,Card,7,M,call,8,2,5,10,5
1582,10,Promoter,N,4,5,Married,Tier1,e-Wallet,7,F,m-comm,7,8,4,1,9
1583,5,Detractor,N,4,2,Married,Tier2,Cash,3,M,call,8,7,4,8,6
1584,9,Promoter,N,4,5,Married,Tier1,Ziggy Credit,7,F,m-comm,8,2,3,6,7
1585,8,Passives,Y,4,10,Married,Tier1,Card,5,M,e-comm,8,3,9,5,7
1586,7,Passives,N,4,10,Not Married,Tier2,e-Wallet,5,F,m-comm,4,5,5,4,7
1587,7,Passives,N,4,7,Married,Tier3,Cash,2,M,call,4,3,5,1,1
1588,4,Detractor,N,4,7,Not Married,Tier1,Ziggy Credit,3,F,call,10,10,2,4,2
1589,0,Detractor,N,4,10,Married,Tier2,Card,7,M,call,4,9,1,4,2
1590,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,7,F,call,5,8,2,5,2
1591,5,Detractor,N,4,7,Married,Tier1,Cash,7,M,call,3,5,10,2,4
1592,0,Detractor,N,4,5,Not Married,Tier2,Ziggy Credit,3,F,call,1,4,1,1,4
1593,9,Promoter,Y,4,5,Married,Tier1,Card,6,M,m-comm,8,6,2,1,7
1594,2,Detractor,N,4,6,Not Married,Tier1,e-Wallet,4,F,call,4,2,9,4,4
1595,4,Detractor,N,4,1,Married,Tier2,Cash,7,M,call,6,5,7,8,1
1596,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,4,F,call,7,10,9,9,5
1597,0,Detractor,N,4,8,Married,Tier1,Card,3,M,call,6,3,1,7,5
1598,4,Detractor,N,4,1,Not Married,Tier2,e-Wallet,3,F,call,6,4,6,1,6
1599,7,Passives,N,4,1,Married,Tier3,Cash,2,M,call,6,1,5,3,6
1600,10,Promoter,N,4,6,Married,Tier1,Ziggy Credit,9,F,m-comm,10,4,6,3,7
1601,0,Detractor,N,4,5,Married,Tier2,Card,5,M,call,8,1,1,5,5
1602,10,Promoter,N,4,5,Married,Tier1,e-Wallet,5,F,m-comm,7,6,9,5,8
1603,8,Passives,Y,4,9,Married,Tier1,Cash,6,M,e-comm,3,10,4,1,5
1604,5,Detractor,N,4,8,Not Married,Tier2,Ziggy Credit,5,F,call,2,2,7,10,1
1605,7,Passives,N,4,10,Married,Tier3,Card,7,M,call,5,10,4,1,3
1606,5,Detractor,N,4,8,Not Married,Tier1,e-Wallet,1,F,call,7,7,6,5,4
1607,2,Detractor,N,4,7,Married,Tier2,Cash,6,M,call,2,5,6,6,5
1608,7,Passives,N,4,1,Not Married,Tier3,Ziggy Credit,6,F,call,4,7,8,4,3
1609,5,Detractor,N,4,2,Married,Tier1,Card,5,M,call,3,2,3,2,6
1610,7,Passives,N,4,5,Not Married,Tier2,e-Wallet,3,F,m-comm,8,3,5,1,9
1611,7,Passives,Y,4,3,Married,Tier3,Cash,6,M,call,7,5,2,1,5
1612,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,9,F,m-comm,1,6,5,5,9
1613,9,Promoter,Y,4,2,Married,Tier1,Card,10,M,m-comm,7,6,2,10,10
1614,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,5,F,call,8,8,7,4,8
1615,5,Detractor,N,4,6,Married,Tier1,Cash,3,M,call,5,1,6,2,4
1616,1,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,2,F,call,8,1,6,10,1
1617,7,Passives,N,4,8,Married,Tier3,Card,1,M,call,8,8,6,10,7
1618,3,Detractor,N,4,5,Not Married,Tier1,e-Wallet,1,F,call,7,7,8,10,3
1619,3,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,4,2,5,9,1
1620,7,Passives,N,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,5,8,8,6,2
1621,2,Detractor,N,4,8,Married,Tier1,Card,4,M,call,1,6,10,4,4
1622,2,Detractor,N,4,4,Not Married,Tier2,e-Wallet,5,F,call,4,5,9,9,6
1623,7,Passives,N,4,1,Married,Tier3,Cash,5,M,call,8,7,9,9,7
1624,9,Promoter,N,4,6,Married,Tier1,Ziggy Credit,5,F,m-comm,8,8,5,8,10
1625,2,Detractor,N,4,4,Married,Tier2,Card,1,M,call,8,4,6,7,6
1626,7,Passives,N,4,1,Not Married,Tier3,e-Wallet,6,F,call,5,7,5,9,6
1627,10,Promoter,Y,4,5,Married,Tier1,Cash,9,M,m-comm,9,2,9,1,7
1628,9,Promoter,N,4,7,Not Married,Tier1,Ziggy Credit,9,F,m-comm,4,3,4,3,10
1629,7,Passives,N,4,5,Married,Tier3,Card,4,M,call,4,1,5,10,6
1630,9,Promoter,N,4,7,Married,Tier1,e-Wallet,7,F,m-comm,9,4,6,2,10
1631,7,Passives,Y,4,8,Married,Tier2,Cash,6,M,m-comm,3,2,2,1,2
1632,7,Passives,N,4,9,Not Married,Tier3,Ziggy Credit,1,F,call,7,3,5,3,8
1633,9,Promoter,Y,4,2,Married,Tier1,Card,9,M,m-comm,7,4,4,10,8
1634,6,Detractor,N,4,9,Not Married,Tier2,e-Wallet,1,F,call,3,9,8,9,6
1635,7,Passives,N,4,9,Married,Tier3,Cash,5,M,call,6,10,2,8,1
1636,7,Passives,N,4,7,Not Married,Tier1,Ziggy Credit,5,F,e-comm,8,7,1,9,7
1637,2,Detractor,N,4,2,Married,Tier2,Card,1,M,call,2,8,10,9,3
1638,7,Passives,Y,4,5,Not Married,Tier3,e-Wallet,4,F,call,5,4,10,4,10
1639,6,Detractor,N,4,6,Married,Tier1,Cash,6,M,call,6,8,3,2,5
1640,1,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,3,F,call,8,8,9,8,3
1641,7,Passives,N,4,6,Married,Tier3,Card,2,M,call,5,10,2,10,5
1642,2,Detractor,N,4,3,Not Married,Tier1,e-Wallet,4,F,call,3,7,6,3,5
1643,4,Detractor,N,4,6,Married,Tier2,Cash,6,M,call,9,9,3,2,5
1644,10,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,8,9,7,10,10
1645,0,Detractor,N,4,10,Married,Tier1,Card,2,M,call,8,8,5,6,5
1646,3,Detractor,N,4,7,Not Married,Tier2,e-Wallet,4,F,call,7,1,2,2,1
1647,8,Passives,Y,4,1,Married,Tier3,Cash,5,M,call,6,3,1,4,7
1648,4,Detractor,N,4,9,Not Married,Tier1,Ziggy Credit,1,F,call,8,9,4,3,6
1649,3,Detractor,N,4,7,Married,Tier2,Card,7,M,call,10,10,3,8,4
1650,7,Passives,N,4,4,Not Married,Tier3,e-Wallet,2,F,call,5,8,3,9,10
1651,8,Passives,Y,4,5,Married,Tier1,Cash,7,M,e-comm,9,3,9,5,3
1652,6,Detractor,N,4,9,Not Married,Tier2,Ziggy Credit,2,F,call,8,5,9,5,2
1653,7,Passives,N,4,5,Married,Tier3,Card,3,M,call,7,9,3,5,6
1654,10,Promoter,N,4,7,Married,Tier1,e-Wallet,5,F,m-comm,10,6,6,1,10
1655,4,Detractor,N,4,9,Married,Tier2,Cash,1,M,call,2,6,9,8,4
1656,10,Promoter,N,4,5,Not Married,Tier1,Ziggy Credit,5,F,m-comm,1,8,3,2,8
1657,5,Detractor,N,4,2,Married,Tier1,Card,5,M,call,6,5,8,3,6
1658,2,Detractor,N,4,2,Not Married,Tier2,e-Wallet,3,F,call,3,2,9,3,6
1659,9,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,5,8,3,8
1660,5,Detractor,N,4,1,Not Married,Tier1,Ziggy Credit,3,F,call,4,6,2,2,1
1661,10,Promoter,Y,4,7,Married,Tier1,Card,5,M,call,6,3,2,4,7
1662,7,Passives,N,4,5,Not Married,Tier3,e-Wallet,3,F,call,8,9,2,10,2
1663,7,Passives,Y,4,10,Married,Tier1,Cash,5,M,e-comm,1,1,4,1,10
1664,9,Promoter,N,4,7,Married,Tier1,Ziggy Credit,5,F,m-comm,6,5,10,7,9
1665,7,Passives,N,4,10,Married,Tier3,Card,3,M,call,5,1,8,3,9
1666,2,Detractor,N,4,4,Not Married,Tier1,e-Wallet,1,F,call,2,9,1,4,3
1667,5,Detractor,N,4,5,Married,Tier2,Cash,2,M,call,9,4,10,8,3
1668,7,Passives,N,4,2,Not Married,Tier3,Ziggy Credit,6,F,call,6,8,4,9,1
1669,9,Promoter,Y,4,6,Married,Tier1,Card,6,M,m-comm,3,3,1,4,10
1670,2,Detractor,N,4,3,Not Married,Tier2,e-Wallet,4,F,call,4,9,5,1,2
1671,7,Passives,N,4,9,Married,Tier3,Cash,4,M,call,5,9,1,9,3
1672,0,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,6,F,call,9,1,3,5,4
1673,1,Detractor,N,4,7,Married,Tier2,Card,6,M,call,8,3,10,6,5
1674,7,Passives,N,4,7,Not Married,Tier3,e-Wallet,2,F,call,7,8,10,8,2
1675,10,Promoter,Y,4,7,Married,Tier1,Cash,10,M,m-comm,10,1,8,1,10
1676,0,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,6,F,call,8,8,2,5,3
1677,7,Passives,Y,4,1,Married,Tier3,Card,2,M,call,6,9,5,2,7
1678,0,Detractor,N,4,6,Not Married,Tier1,e-Wallet,6,F,call,4,1,7,9,3
1679,7,Passives,Y,4,8,Married,Tier2,Cash,3,M,m-comm,7,1,4,10,2
1680,7,Passives,N,4,6,Not Married,Tier3,Ziggy Credit,3,F,call,5,8,7,6,2
1681,3,Detractor,N,4,10,Married,Tier1,Card,2,M,call,10,6,5,3,6
1682,5,Detractor,N,4,2,Not Married,Tier2,e-Wallet,7,F,call,5,6,10,3,2
1683,10,Promoter,Y,4,8,Married,Tier1,Cash,5,M,m-comm,10,3,10,5,7
1684,9,Promoter,Y,4,7,Married,Tier1,Ziggy Credit,8,F,call,3,7,1,1,8
1685,6,Detractor,N,4,6,Married,Tier2,Card,2,M,call,6,1,7,8,2
1686,7,Passives,Y,4,9,Not Married,Tier3,e-Wallet,7,F,call,7,8,6,8,4
1687,1,Detractor,N,4,6,Married,Tier1,Cash,5,M,call,10,6,2,10,5
1688,3,Detractor,N,4,6,Not Married,Tier2,Ziggy Credit,1,F,call,3,2,3,5,5
1689,7,Passives,N,4,2,Married,Tier3,Card,7,M,call,5,1,10,10,3
1690,10,Promoter,N,4,6,Married,Tier1,e-Wallet,10,F,m-comm,7,2,1,1,9
1691,8,Passives,Y,4,6,Married,Tier2,Cash,2,M,m-comm,5,10,9,4,2
1692,7,Passives,N,4,10,Not Married,Tier3,Ziggy Credit,3,F,call,7,10,1,4,2
1693,7,Passives,Y,4,1,Married,Tier1,Card,1,M,e-comm,2,8,7,2,2
1694,7,Passives,N,4,8,Not Married,Tier2,e-Wallet,5,F,m-comm,9,5,7,1,1
1695,7,Passives,N,4,10,Married,Tier3,Cash,1,M,call,6,1,6,9,1
1696,1,Detractor,N,4,6,Not Married,Tier1,Ziggy Credit,2,F,call,10,2,5,9,3
1697,6,Detractor,N,4,2,Married,Tier2,Card,3,M,call,9,10,6,3,2
1698,8,Passives,Y,4,5,Not Married,Tier3,e-Wallet,1,F,call,5,7,3,6,8
1699,0,Detractor,N,4,2,Married,Tier1,Cash,1,M,call,10,3,8,2,2
1700,1,Detractor,N,4,3,Not Married,Tier2,Ziggy Credit,7,F,call,2,3,10,5,5
1701,7,Passives,N,4,3,Married,Tier3,Card,2,M,call,4,2,10,8,8
1702,3,Detractor,N,4,7,Not Married,Tier1,e-Wallet,7,F,call,7,7,5,7,4
1703,2,Detractor,N,4,10,Married,Tier2,Cash,5,M,call,1,10,6,2,6
1704,7,Passives,Y,4,4,Not Married,Tier3,Ziggy Credit,7,F,call,7,10,6,10,6
1705,5,Detractor,N,4,3,Married,Tier1,Card,3,M,call,6,2,7,8,5
1706,2,Detractor,N,4,5,Not Married,Tier2,e-Wallet,6,F,call,9,10,7,6,3


================================================
FILE: data/Predict Merchant_Sales v01.csv
================================================
Merchant_Id,Annual_Sales,Merchant_Location,Zone,No_of_helpers,Daily_OPEN_hours,Years_Open,Owner's_Age,Owner's_Education,Cashier_Sales_Person_Age,Merchant_Type,Shopping_Complex_Or_Standalone
247,195,NJ City,E,1,20,18,66,High School,33,Owner,Standalone
9026,417,NJ City,S,1,10,14,43,Graduation,21,Patnership,Complex
5245,147.46,Boston,N,2,23,1,46,High School,24,Patnership,Standalone
6644,500,Boston,W,2,24,17,59,Graduation,22,Franchise,Complex
4497,111.19,NJ City,N,20,11,4,76,Graduation,22,Patnership,Standalone
2571,113,NJ City,E,1,24,14,31,High School,22,Owner,Standalone
6534,447,Boston,S,1,22,12,25,Graduation,31,Patnership,Complex
7201,393,Boston,N,1,12,16,49,Graduation,21,Patnership,Standalone
9051,434,NJ City,E,2,13,14,79,Graduation,19,Owner,Standalone
4157,183,NJ City,N,3,18,6,81,High School,32,Patnership,Standalone
3746,110,NJ City,S,2,15,21,57,High School,35,Patnership,Complex
6386,381,Boston,S,3,17,10,50,Graduation,25,Patnership,Complex
6094,283.85,Boston,S,15,8,2,80,High School,22,Patnership,Complex
4090,375.26,NJ City,S,5,13,4,60,High School,28,Patnership,Complex
9536,370,NJ City,W,3,8,18,62,Graduation,23,Franchise,Complex
7047,414,Boston,E,2,16,7,61,Graduation,27,Owner,Standalone
3199,244,NJ City,E,2,21,16,46,High School,33,Owner,Standalone
3548,122,NJ City,W,2,20,6,76,High School,31,Franchise,Complex
3694,289,NJ City,S,18,21,1,54,High School,31,Patnership,Complex
6101,467,Boston,N,2,8,23,54,Graduation,28,Patnership,Standalone
8857,69.28,NJ City,N,3,20,1,45,High School,30,Patnership,Standalone
7901,379,Boston,N,2,10,19,59,Graduation,24,Patnership,Standalone
5720,420,Boston,W,2,14,17,79,Graduation,35,Franchise,Complex
8322,487,NJ City,S,3,20,7,67,Graduation,35,Patnership,Complex
2789,193,NJ City,N,3,16,6,75,High School,25,Patnership,Standalone
7970,474,Boston,S,1,20,22,28,Graduation,21,Patnership,Complex
4478,117.18,NJ City,S,25,21,3,29,No education,19,Patnership,Complex
2109,70.5,NJ City,N,15,19,4,81,Graduation,33,Patnership,Standalone
8430,77.67,NJ City,S,17,18,2,32,Graduation,30,Patnership,Complex
6569,275.81,Boston,N,12,10,2,69,No education,22,Patnership,Standalone
4965,92.68,Boston,N,8,18,1,47,Graduation,34,Patnership,Standalone
7817,387,Boston,N,3,8,10,54,Graduation,18,Patnership,Standalone
1798,299,NJ City,S,2,20,11,56,High School,31,Patnership,Complex
3433,188,NJ City,N,3,14,24,47,High School,31,Patnership,Standalone
591,284,NJ City,E,1,18,17,39,High School,22,Owner,Standalone
1863,238,NJ City,E,2,17,16,36,High School,29,Owner,Standalone
4537,23.48,NJ City,N,9,13,4,80,High School,22,Patnership,Standalone
1544,122.44,NJ City,W,12,13,3,57,No education,28,Franchise,Complex
529,470,NJ City,N,2,20,5,51,Graduation,25,Patnership,Standalone
8065,438,NJ City,N,1,20,7,31,Graduation,35,Patnership,Standalone
3427,127.88,NJ City,E,2,24,1,74,High School,26,Owner,Standalone
4624,366,NJ City,W,1,16,5,27,Graduation,21,Franchise,Complex
1816,160,NJ City,W,2,24,7,56,High School,20,Franchise,Complex
4232,462.12,NJ City,W,1,24,14,74,No education,24,Franchise,Complex
3295,252,NJ City,E,2,20,9,77,High School,30,Owner,Standalone
6953,176.16,Boston,N,1,8,2,77,No education,29,Patnership,Standalone
9907,352,NJ City,E,1,24,18,38,Graduation,23,Owner,Standalone
7869,499,Boston,N,2,18,7,55,Graduation,20,Patnership,Standalone
3405,84.91,NJ City,N,13,10,4,50,Graduation,20,Patnership,Standalone
2119,276,NJ City,E,2,23,22,48,High School,20,Owner,Standalone
4418,220,NJ City,S,2,17,24,26,High School,27,Patnership,Complex
5843,426,Boston,E,1,19,20,84,Graduation,29,Owner,Standalone
3947,86.88,NJ City,E,24,9,3,84,No education,28,Owner,Standalone
4495,434,NJ City,E,3,14,5,39,Graduation,18,Owner,Standalone
9350,358,NJ City,S,1,21,14,47,Graduation,35,Patnership,Complex
5950,482,Boston,S,2,18,23,72,Graduation,31,Patnership,Complex
7564,490,Boston,W,2,13,15,58,Graduation,25,Franchise,Complex
3541,239,NJ City,N,1,19,9,42,High School,23,Patnership,Standalone
97,100.27,NJ City,N,18,17,4,55,High School,34,Patnership,Standalone
3745,272.24,NJ City,N,17,10,2,33,High School,28,Patnership,Standalone
1415,20.59,NJ City,N,24,19,3,63,No education,19,Owner,Standalone
2198,237,NJ City,S,2,14,11,42,High School,25,Patnership,Complex
4256,227.68,NJ City,W,8,9,3,32,No education,35,Franchise,Complex
3942,150.48,NJ City,S,4,12,1,77,Graduation,34,Patnership,Complex
7751,215.34,Boston,E,8,19,3,85,No education,31,Owner,Standalone
172,318,NJ City,W,1,17,7,41,High School,20,Franchise,Complex
370,249,NJ City,S,2,13,17,38,High School,25,Patnership,Complex
5095,489,Boston,E,3,12,10,41,Graduation,18,Owner,Standalone
7605,427,Boston,N,3,22,7,71,Graduation,20,Patnership,Standalone
2056,204.08,NJ City,W,12,22,4,44,High School,34,Franchise,Complex
3073,115,NJ City,N,1,8,17,85,High School,28,Patnership,Standalone
8017,409,Boston,N,3,8,15,66,Graduation,31,Patnership,Standalone
8240,29.53,NJ City,N,21,16,4,75,No education,22,Franchise,Complex
1080,131,NJ City,W,3,18,7,27,High School,20,Franchise,Complex
6470,77.33,Boston,S,14,10,1,33,No education,23,Patnership,Complex
6181,95.11,Boston,N,9,13,3,59,High School,31,Patnership,Standalone
2392,281,NJ City,W,1,11,7,79,High School,32,Franchise,Complex
919,162,NJ City,E,3,24,12,43,High School,33,Owner,Standalone
9770,240.75,NJ City,S,8,24,3,66,No education,27,Patnership,Complex
6106,384,Boston,S,2,12,7,63,Graduation,24,Patnership,Complex
3605,270,NJ City,N,1,10,23,64,High School,26,Patnership,Standalone
8651,125.31,NJ City,E,15,18,1,83,No education,30,Owner,Standalone
8125,234.86,NJ City,N,2,12,4,55,High School,22,Patnership,Standalone
2904,178,NJ City,W,3,24,19,74,High School,35,Franchise,Complex
9211,481.81,NJ City,E,1,21,4,74,High School,22,Owner,Standalone
4538,235.56,NJ City,S,3,24,11,35,No education,18,Patnership,Complex
2401,379,NJ City,N,3,8,5,76,Graduation,26,Patnership,Standalone
8444,474,NJ City,W,1,22,19,41,Graduation,30,Franchise,Complex
1376,487,NJ City,W,2,24,5,40,Graduation,33,Franchise,Complex
6265,385,Boston,N,2,22,8,83,Graduation,34,Patnership,Standalone
2936,119.05,NJ City,W,20,18,2,72,No education,19,Franchise,Complex
7411,44.62,Boston,N,15,23,1,51,High School,28,Owner,Standalone
9900,210.94,NJ City,W,4,19,3,70,Graduation,30,Franchise,Complex
3413,267,NJ City,N,1,8,25,40,High School,22,Patnership,Standalone
1329,406.51,NJ City,N,7,8,4,64,Graduation,19,Patnership,Standalone
9989,71.1,NJ City,N,7,16,1,37,No education,25,Patnership,Standalone
6303,350,Boston,E,1,11,25,26,Graduation,20,Owner,Standalone
3453,116,NJ City,N,2,13,18,69,High School,23,Patnership,Standalone
4374,119,NJ City,S,3,12,15,48,High School,23,Patnership,Complex
1453,247.19,NJ City,N,4,11,2,33,High School,31,Patnership,Standalone
8699,455,NJ City,E,2,9,18,46,Graduation,34,Owner,Standalone
9917,416,NJ City,N,2,22,16,67,Graduation,31,Patnership,Standalone
1247,269,NJ City,E,2,22,10,36,High School,19,Owner,Standalone
2689,174,NJ City,N,1,9,21,27,High School,35,Patnership,Standalone
1086,307,NJ City,S,3,22,21,81,High School,18,Patnership,Complex
5366,496,Boston,S,2,11,23,30,Graduation,27,Patnership,Complex
8595,426,NJ City,E,3,17,5,66,Graduation,25,Owner,Standalone
1602,50.25,NJ City,S,1,21,1,25,Graduation,19,Patnership,Complex
8115,200.25,NJ City,E,1,9,2,83,Graduation,22,Owner,Standalone
1338,64.3,NJ City,N,22,14,3,33,Graduation,33,Patnership,Complex
1242,116,NJ City,S,3,24,11,41,High School,34,Patnership,Complex
3304,243,NJ City,W,3,13,8,38,High School,28,Franchise,Complex
2948,265,NJ City,W,2,18,24,67,High School,32,Franchise,Complex
79,105,Boston,E,2,16,8,85,High School,31,Owner,Standalone
2367,174,NJ City,E,3,17,22,46,High School,24,Owner,Standalone
5530,79.05,Boston,S,20,10,1,42,High School,21,Patnership,Complex
5110,449,Boston,S,2,16,18,79,Graduation,30,Patnership,Complex
1811,170,NJ City,E,3,21,14,83,High School,28,Owner,Standalone
8650,400,NJ City,S,3,22,24,80,Graduation,32,Patnership,Complex
1466,376.99,NJ City,S,2,21,19,42,No education,28,Patnership,Complex
2157,101,NJ City,N,2,14,13,83,High School,28,Patnership,Standalone
3146,247,NJ City,S,1,18,7,59,High School,19,Patnership,Complex
6659,8.39,Boston,N,2,19,2,41,No education,25,Owner,Standalone
1967,299.59,NJ City,E,16,14,3,28,No education,30,Owner,Standalone
951,165,NJ City,E,2,18,20,63,High School,24,Owner,Standalone
7642,408,Boston,S,2,13,11,32,Graduation,21,Patnership,Complex
7013,496,Boston,N,2,10,22,41,Graduation,35,Patnership,Standalone
754,179,NJ City,S,2,22,15,53,High School,24,Patnership,Complex
8290,373,NJ City,S,2,23,24,52,Graduation,30,Patnership,Complex
4363,233,NJ City,E,3,14,11,62,High School,32,Owner,Standalone
816,154,NJ City,W,1,18,18,81,High School,30,Franchise,Complex
1278,343.28,NJ City,S,24,15,3,46,Graduation,22,Patnership,Complex
4689,27.31,NJ City,N,2,9,1,44,Graduation,26,Patnership,Standalone
2814,225,NJ City,S,1,16,23,71,High School,34,Patnership,Complex
3087,54.81,NJ City,E,2,20,4,35,Graduation,22,Owner,Standalone
5810,481,Boston,S,3,18,19,85,Graduation,31,Patnership,Complex
9164,386,NJ City,W,3,10,6,80,Graduation,23,Franchise,Complex
8245,3.34,NJ City,N,9,12,3,56,High School,23,Patnership,Standalone
5098,43.74,Boston,N,13,24,1,64,High School,35,Patnership,Complex
1494,89.26,NJ City,S,10,23,2,59,Graduation,29,Patnership,Complex
6677,363,Boston,N,2,22,20,62,Graduation,27,Patnership,Standalone
1869,211,NJ City,N,1,18,20,30,High School,21,Patnership,Standalone
513,240,NJ City,N,1,17,7,39,High School,22,Patnership,Standalone
1093,316,NJ City,N,1,18,25,36,High School,26,Patnership,Standalone
6120,479,Boston,W,3,9,7,45,Graduation,30,Franchise,Complex
1474,143,NJ City,S,1,14,24,33,High School,26,Patnership,Complex
2836,142.48,NJ City,W,21,13,1,63,High School,28,Franchise,Complex
4744,177,NJ City,W,1,13,10,67,High School,30,Franchise,Complex
7054,462,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex
6203,368,Boston,E,3,10,13,84,Graduation,29,Owner,Standalone
6977,450,Boston,N,1,19,14,84,Graduation,18,Patnership,Standalone
1423,300,NJ City,E,1,20,25,52,High School,27,Owner,Standalone
9604,351,NJ City,W,2,20,18,26,Graduation,29,Franchise,Complex
8246,11.98,NJ City,N,16,21,1,40,No education,35,Patnership,Complex
9124,160.23,NJ City,W,8,19,1,47,High School,32,Franchise,Complex
3631,127.1,NJ City,E,10,21,3,48,High School,31,Owner,Standalone
4395,117,NJ City,E,2,22,18,40,High School,25,Owner,Standalone
3839,35.99,NJ City,N,22,12,3,85,No education,24,Owner,Standalone
5016,411,Boston,W,1,22,6,70,Graduation,34,Franchise,Complex
7932,170.39,Boston,W,21,8,1,82,Graduation,31,Franchise,Complex
5386,398.73,Boston,S,3,19,1,47,High School,28,Patnership,Complex
4031,147,NJ City,E,3,23,20,67,High School,24,Owner,Standalone
34,53.18,Boston,S,17,8,4,67,High School,24,Patnership,Complex
253,203,NJ City,N,3,12,20,36,High School,19,Patnership,Standalone
3057,474,NJ City,N,1,21,5,76,Graduation,34,Patnership,Standalone
9505,480.59,NJ City,N,23,18,2,37,High School,32,Patnership,Standalone
9181,435,NJ City,N,3,22,15,81,Graduation,23,Patnership,Standalone
9126,424,NJ City,S,1,12,13,59,Graduation,19,Patnership,Complex
7610,472,Boston,S,3,19,6,42,Graduation,34,Patnership,Complex
1704,161,NJ City,W,1,16,18,68,High School,24,Franchise,Complex
5949,429,Boston,N,3,21,14,64,Graduation,35,Patnership,Standalone
8070,354,NJ City,S,3,10,12,72,Graduation,35,Patnership,Complex
8592,422,NJ City,W,3,13,20,77,Graduation,29,Franchise,Complex
3524,1.56,NJ City,N,11,16,2,66,No education,34,Franchise,Complex
6745,461,Boston,N,3,21,8,78,Graduation,31,Patnership,Standalone
3385,23.42,NJ City,N,9,22,1,41,High School,20,Patnership,Standalone
1121,124.39,NJ City,N,16,16,4,58,No education,21,Patnership,Standalone
311,288,NJ City,E,1,13,18,54,High School,21,Owner,Standalone
2005,403,NJ City,N,3,11,5,59,Graduation,30,Patnership,Standalone
1825,258,NJ City,N,2,22,10,36,High School,22,Patnership,Standalone
1322,40,NJ City,N,17,12,4,25,No education,25,Patnership,Complex
6366,465,Boston,S,1,15,24,50,Graduation,25,Patnership,Complex
6449,471,Boston,N,1,20,7,29,Graduation,25,Patnership,Standalone
4797,271,NJ City,N,1,22,16,32,High School,18,Patnership,Standalone
4289,215,NJ City,N,2,22,14,63,High School,20,Patnership,Standalone
9370,412,NJ City,S,3,19,23,29,Graduation,20,Patnership,Complex
7006,497,Boston,S,3,10,24,32,Graduation,22,Patnership,Complex
1843,106.05,NJ City,E,17,23,1,57,High School,34,Owner,Standalone
8311,21.83,NJ City,N,22,8,4,77,High School,20,Owner,Standalone
9633,418,NJ City,N,2,21,23,50,Graduation,22,Patnership,Standalone
8607,422.53,NJ City,E,13,20,3,54,Graduation,28,Owner,Standalone
7894,244.72,Boston,S,23,16,3,57,High School,20,Patnership,Complex
2355,388.74,NJ City,E,1,17,10,47,No education,24,Owner,Standalone
3378,456.79,NJ City,S,9,22,2,32,Graduation,34,Patnership,Complex
5561,354,Boston,N,3,22,23,25,Graduation,26,Patnership,Standalone
9540,472,NJ City,W,2,21,20,35,Graduation,22,Franchise,Complex
8562,383,NJ City,S,3,8,13,35,Graduation,29,Patnership,Complex
4734,278,NJ City,S,1,20,20,34,High School,23,Patnership,Complex
7182,460,Boston,S,1,12,25,37,Graduation,26,Patnership,Complex
1847,123,NJ City,E,2,16,11,57,High School,35,Owner,Standalone
2812,149,NJ City,W,1,16,13,50,High School,26,Franchise,Complex
5250,387,Boston,S,1,10,16,40,Graduation,28,Patnership,Complex
1351,303,NJ City,E,1,13,10,65,High School,18,Owner,Standalone
1206,286,NJ City,S,1,18,20,52,High School,22,Patnership,Complex
6133,485,Boston,N,1,9,18,70,Graduation,34,Patnership,Standalone
9113,498,NJ City,N,1,24,14,55,Graduation,34,Patnership,Standalone
8762,382,NJ City,S,2,11,13,58,Graduation,30,Patnership,Complex
7443,419,Boston,E,3,15,22,72,Graduation,30,Owner,Standalone
4244,121.07,NJ City,W,24,15,4,50,No education,18,Franchise,Complex
6699,358,Boston,E,2,12,25,68,Graduation,21,Owner,Standalone
3756,40.89,NJ City,N,15,22,3,60,Graduation,32,Franchise,Complex
4030,385.24,NJ City,S,3,17,3,64,High School,25,Patnership,Complex
2687,196,NJ City,E,1,16,13,74,High School,20,Owner,Standalone
7206,64.95,Boston,N,25,18,1,72,Graduation,19,Patnership,Complex
4853,126,NJ City,N,2,19,13,66,High School,18,Patnership,Standalone
6325,368,Boston,N,3,20,17,71,Graduation,22,Patnership,Standalone
4028,121,NJ City,W,3,11,21,51,High School,31,Franchise,Complex
4771,224,NJ City,E,1,8,23,45,High School,32,Owner,Standalone
8479,23.19,NJ City,N,8,16,2,53,High School,23,Owner,Standalone
2560,64.33,NJ City,N,6,19,2,40,High School,19,Franchise,Complex
1835,168,NJ City,E,2,9,25,71,High School,23,Owner,Standalone
1459,99.7,NJ City,E,19,9,2,28,High School,29,Owner,Standalone
8023,402,Boston,E,3,24,20,73,Graduation,35,Owner,Standalone
7352,416,Boston,W,3,13,24,39,Graduation,25,Franchise,Complex
5889,44.94,Boston,N,25,16,4,45,Graduation,23,Patnership,Standalone
9791,44.32,NJ City,N,5,19,3,50,No education,22,Owner,Standalone
5630,35.77,Boston,N,15,8,3,58,No education,35,Patnership,Complex
3596,19.55,NJ City,N,12,13,4,43,No education,27,Franchise,Complex
7798,467,Boston,S,2,11,6,56,Graduation,25,Patnership,Complex
3187,257.52,NJ City,E,3,21,20,59,No education,20,Owner,Standalone
1314,175,NJ City,S,2,24,18,80,High School,25,Patnership,Complex
6226,369,Boston,S,3,24,5,45,Graduation,28,Patnership,Complex
7132,92.11,Boston,W,8,24,2,73,High School,18,Franchise,Complex
9353,484,NJ City,N,3,9,14,40,Graduation,29,Patnership,Standalone
867,94.19,NJ City,E,4,20,4,30,Graduation,21,Owner,Standalone
9832,366,NJ City,W,3,22,17,29,Graduation,26,Franchise,Complex
3755,156,NJ City,E,1,19,11,30,High School,32,Owner,Standalone
8397,374.58,NJ City,N,6,15,1,44,Graduation,24,Patnership,Standalone
5183,493,Boston,E,3,16,12,78,Graduation,18,Owner,Standalone
5753,62.7,Boston,N,1,14,2,37,No education,21,Patnership,Standalone
4557,74.82,NJ City,N,23,12,1,81,Graduation,28,Patnership,Standalone
9662,462,NJ City,S,3,23,10,35,Graduation,26,Patnership,Complex
9396,426,NJ City,W,2,14,13,54,Graduation,31,Franchise,Complex
8588,448,NJ City,W,2,21,12,49,Graduation,26,Franchise,Complex
1105,6.19,NJ City,N,13,15,4,58,High School,29,Patnership,Standalone
1588,162,NJ City,W,2,10,14,48,High School,19,Franchise,Complex
2664,482.34,NJ City,W,24,17,3,53,Graduation,19,Franchise,Complex
1943,191,NJ City,E,3,11,17,78,High School,23,Owner,Standalone
661,8.61,NJ City,N,17,17,2,53,High School,29,Patnership,Standalone
4125,75.13,NJ City,N,20,12,2,48,Graduation,19,Patnership,Standalone
9718,13.05,NJ City,N,14,23,1,80,High School,34,Patnership,Complex
4885,125.37,NJ City,N,11,14,2,80,High School,22,Patnership,Standalone
7256,208,Boston,W,15,17,2,76,No education,33,Franchise,Complex
6695,453,Boston,E,2,15,5,72,Graduation,22,Owner,Standalone
5767,476,Boston,E,2,16,18,37,Graduation,28,Owner,Standalone
3791,268.34,NJ City,E,24,14,3,33,No education,30,Owner,Standalone
2027,103,NJ City,E,1,16,20,53,High School,35,Owner,Standalone
2621,155,NJ City,N,2,22,19,75,High School,29,Patnership,Standalone
8568,326.99,NJ City,W,2,23,3,33,Graduation,21,Franchise,Complex
5825,26.62,Boston,N,19,19,2,65,No education,26,Patnership,Standalone
3962,241,NJ City,S,1,9,6,82,High School,19,Patnership,Complex
2303,101,NJ City,E,2,13,22,79,High School,35,Owner,Standalone
8751,394,NJ City,E,1,10,7,38,Graduation,25,Owner,Standalone
5013,43.38,Boston,N,8,15,3,56,Graduation,19,Patnership,Standalone
3648,114.86,NJ City,W,8,23,4,74,Graduation,23,Franchise,Complex
6012,79.04,Boston,W,1,18,4,74,Graduation,35,Franchise,Complex
7319,363,Boston,E,3,12,15,30,Graduation,30,Owner,Standalone
8954,397,NJ City,S,3,8,11,57,Graduation,34,Patnership,Complex
9229,499.39,NJ City,N,23,12,2,71,High School,33,Patnership,Standalone
3314,5.56,NJ City,N,20,19,3,37,No education,31,Patnership,Complex
3576,167,NJ City,W,2,17,17,70,High School,27,Franchise,Complex
6864,472,Boston,W,1,21,13,60,Graduation,27,Franchise,Complex
6558,462,Boston,S,3,14,16,36,Graduation,33,Patnership,Complex
6996,380,Boston,W,1,16,5,49,Graduation,22,Franchise,Complex
8038,361,Boston,S,1,24,14,50,Graduation,21,Patnership,Complex
3936,101.02,NJ City,W,7,9,1,85,Graduation,23,Franchise,Complex
8960,314.37,NJ City,W,13,10,3,61,No education,23,Franchise,Complex
2777,202,NJ City,N,1,18,14,35,High School,34,Patnership,Standalone
6479,482,Boston,E,1,11,22,31,Graduation,33,Owner,Standalone
5051,4.4,Boston,N,19,8,1,40,No education,35,Owner,Standalone
4532,66.67,NJ City,N,15,16,4,30,No education,21,Franchise,Complex
8443,393,NJ City,E,1,13,23,83,Graduation,32,Owner,Standalone
9943,495,NJ City,E,3,18,24,48,Graduation,34,Owner,Standalone
6503,462,Boston,E,2,19,22,61,Graduation,31,Owner,Standalone
4732,280,NJ City,W,1,19,9,52,High School,20,Franchise,Complex
8350,446,NJ City,S,3,11,23,25,Graduation,22,Patnership,Complex
1097,4.31,NJ City,N,3,12,2,44,No education,28,Patnership,Standalone
6005,361,Boston,N,3,9,19,50,Graduation,19,Patnership,Standalone
2442,45.04,NJ City,N,8,20,2,64,Graduation,25,Patnership,Complex
4585,443.5,NJ City,N,23,11,2,44,High School,25,Patnership,Standalone
7878,398,Boston,S,3,11,8,54,Graduation,24,Patnership,Complex
5060,457,Boston,W,2,19,6,57,Graduation,33,Franchise,Complex
2195,276,NJ City,E,2,15,12,64,High School,27,Owner,Standalone
2333,160.43,NJ City,N,9,10,1,60,No education,33,Patnership,Standalone
6214,409,Boston,S,2,12,11,66,Graduation,34,Patnership,Complex
6085,480,Boston,N,3,12,20,42,Graduation,29,Patnership,Standalone
4380,111.61,NJ City,W,10,15,1,38,Graduation,30,Franchise,Complex
2201,407,NJ City,N,3,15,5,41,Graduation,28,Patnership,Standalone
4384,211,NJ City,W,3,21,6,34,High School,31,Franchise,Complex
7338,454,Boston,S,3,8,22,59,Graduation,19,Patnership,Complex
6387,453,Boston,E,3,18,15,51,Graduation,31,Owner,Standalone
5281,481,Boston,N,3,8,5,59,Graduation,26,Patnership,Standalone
8100,366,NJ City,W,3,14,6,80,Graduation,30,Franchise,Complex
6733,103.2,Boston,N,14,9,4,64,High School,26,Patnership,Standalone
2885,116.99,NJ City,N,10,24,3,26,No education,22,Patnership,Standalone
2393,189.2,NJ City,N,11,22,4,79,No education,32,Patnership,Standalone
2774,389.24,NJ City,S,18,23,2,34,No education,26,Patnership,Complex
1574,165,NJ City,S,3,11,19,73,High School,27,Patnership,Complex
74,159,Boston,S,3,16,15,71,High School,23,Patnership,Complex
2356,196,NJ City,W,3,17,11,42,High School,31,Franchise,Complex
3702,314,NJ City,S,2,19,21,63,High School,21,Patnership,Complex
7543,456.35,Boston,E,10,21,4,63,High School,30,Owner,Standalone
2441,113,NJ City,N,1,10,19,47,High School,25,Patnership,Standalone
773,270,NJ City,N,1,12,16,78,High School,22,Patnership,Standalone
3259,93.28,NJ City,E,8,9,4,56,High School,29,Owner,Standalone
8267,433,NJ City,E,2,10,10,56,Graduation,20,Owner,Standalone
6031,27.79,Boston,N,25,8,2,30,High School,35,Owner,Standalone
3981,216,NJ City,N,2,22,17,68,High School,31,Patnership,Standalone
9001,462,NJ City,N,2,8,24,49,Graduation,24,Patnership,Standalone
3697,191,NJ City,N,2,18,23,47,High School,31,Patnership,Standalone
6917,449,Boston,N,3,14,23,41,Graduation,32,Patnership,Standalone
6393,25.18,Boston,N,1,15,1,75,Graduation,19,Patnership,Standalone
3127,197,NJ City,E,3,17,16,78,High School,26,Owner,Standalone
9935,460,NJ City,E,3,18,14,65,Graduation,34,Owner,Standalone
2141,312,NJ City,N,3,21,16,60,High School,25,Patnership,Standalone
6466,443,Boston,S,2,20,10,32,Graduation,29,Patnership,Complex
372,61.02,NJ City,N,11,16,4,42,Graduation,30,Franchise,Complex
2062,350,NJ City,S,2,22,5,26,Graduation,19,Patnership,Complex
6954,492.44,Boston,S,5,8,4,59,Graduation,32,Patnership,Complex
4385,233,NJ City,N,3,14,14,44,High School,35,Patnership,Standalone
4861,306.66,NJ City,N,4,16,1,39,High School,28,Patnership,Standalone
7019,474,Boston,E,3,16,5,59,Graduation,20,Owner,Standalone
1284,26.35,NJ City,N,25,13,4,48,Graduation,28,Franchise,Complex
4814,441.56,NJ City,S,22,19,2,52,No education,27,Patnership,Complex
2944,59.01,NJ City,W,16,15,4,34,High School,27,Franchise,Complex
8449,432,NJ City,N,3,13,15,39,Graduation,21,Patnership,Standalone
4995,470.17,Boston,E,11,20,4,83,Graduation,23,Owner,Standalone
3220,14.47,NJ City,N,9,23,2,41,High School,21,Franchise,Complex
1737,106.81,NJ City,N,9,23,2,74,Graduation,32,Patnership,Standalone
4645,22.47,NJ City,N,14,16,4,26,High School,19,Patnership,Standalone
4389,351.14,NJ City,N,25,13,3,77,Graduation,22,Patnership,Standalone
645,191,NJ City,N,2,15,13,27,High School,20,Patnership,Standalone
5161,88.6,Boston,N,9,10,1,56,High School,34,Patnership,Standalone
1721,237,NJ City,N,2,21,11,58,High School,31,Patnership,Standalone
2845,303.96,NJ City,N,16,10,3,81,High School,30,Patnership,Standalone
462,113.62,NJ City,S,8,11,1,58,Graduation,27,Patnership,Complex
3482,42.57,NJ City,N,23,8,1,52,No education,24,Patnership,Complex
3829,110,NJ City,N,3,22,11,33,High School,31,Patnership,Standalone
9610,372,NJ City,S,3,13,24,67,Graduation,35,Patnership,Complex
4132,96.29,NJ City,W,3,15,4,32,High School,26,Franchise,Complex
8847,453.6,NJ City,E,5,12,2,35,Graduation,35,Owner,Standalone
6252,54.52,Boston,W,18,15,2,43,Graduation,23,Franchise,Complex
1851,94.59,NJ City,E,18,14,4,42,Graduation,33,Owner,Standalone
7625,455,Boston,N,1,21,23,59,Graduation,32,Patnership,Standalone
8901,438,NJ City,N,3,10,24,76,Graduation,29,Patnership,Standalone
5222,365,Boston,S,1,11,15,79,Graduation,19,Patnership,Complex
3847,251,NJ City,E,2,17,8,68,High School,24,Owner,Standalone
1372,43.02,NJ City,N,15,19,4,73,High School,32,Franchise,Complex
8594,442,NJ City,S,1,12,21,45,Graduation,28,Patnership,Complex
9103,500,NJ City,E,2,23,21,29,Graduation,21,Owner,Standalone
4534,131,NJ City,S,2,21,15,80,High School,34,Patnership,Complex
5321,4.57,Boston,N,6,11,3,29,No education,24,Patnership,Standalone
7946,22.61,Boston,N,8,14,1,67,No education,19,Patnership,Complex
801,74.1,NJ City,N,4,8,4,53,Graduation,33,Patnership,Standalone
3873,314,NJ City,N,1,19,10,85,High School,34,Patnership,Standalone
9563,91.21,NJ City,E,13,17,1,59,No education,24,Owner,Standalone
3632,273,NJ City,W,1,19,17,31,High School,18,Franchise,Complex
6462,82.57,Boston,S,5,17,1,47,Graduation,31,Patnership,Complex
6904,403,Boston,W,3,18,20,26,Graduation,23,Franchise,Complex
9781,489,NJ City,N,3,15,10,28,Graduation,29,Patnership,Standalone
1181,228,NJ City,N,1,15,6,25,High School,34,Patnership,Standalone
5601,166.93,Boston,N,20,8,4,64,Graduation,22,Patnership,Standalone
7164,69.08,Boston,N,17,24,4,30,Graduation,21,Franchise,Complex
6456,440.99,Boston,W,1,17,4,75,Graduation,22,Franchise,Complex
9067,475,NJ City,E,2,21,17,44,Graduation,32,Owner,Standalone
797,278,NJ City,N,2,14,22,40,High School,33,Patnership,Standalone
90,121,Boston,S,1,21,20,73,High School,35,Patnership,Complex
7532,97.32,Boston,W,14,11,3,27,No education,18,Franchise,Complex
7734,426,Boston,S,3,20,7,43,Graduation,35,Patnership,Complex
681,132,NJ City,N,2,15,25,55,High School,27,Patnership,Standalone
7599,402,Boston,E,3,10,23,73,Graduation,35,Owner,Standalone
8577,432,NJ City,N,3,22,21,58,Graduation,25,Patnership,Standalone
4621,243,NJ City,N,1,10,7,67,High School,20,Patnership,Standalone
3442,274,NJ City,S,3,13,16,67,High School,18,Patnership,Complex
1399,155,NJ City,E,1,16,12,49,High School,20,Owner,Standalone
6249,486,Boston,N,3,8,6,39,Graduation,22,Patnership,Standalone
6147,371,Boston,E,2,20,13,61,Graduation,34,Owner,Standalone
4985,35.66,Boston,N,13,19,1,72,No education,26,Patnership,Standalone
4205,179,NJ City,N,2,10,6,73,High School,22,Patnership,Standalone
6477,97.37,Boston,N,23,10,2,37,Graduation,18,Patnership,Standalone
6591,398,Boston,E,1,18,9,49,Graduation,29,Owner,Standalone
3739,270,NJ City,E,3,11,8,64,High School,24,Owner,Standalone
8108,386,NJ City,W,2,13,15,56,Graduation,30,Franchise,Complex
4055,248,NJ City,E,1,10,20,76,High School,33,Owner,Standalone
4870,179,NJ City,S,1,21,21,50,High School,35,Patnership,Complex
9495,453,NJ City,E,3,24,24,32,Graduation,31,Owner,Standalone
5385,82.59,Boston,N,10,8,1,34,Graduation,25,Patnership,Standalone
2468,107.79,NJ City,W,7,12,1,28,No education,33,Franchise,Complex
8501,376,NJ City,N,2,21,24,51,Graduation,24,Patnership,Standalone
9975,427,NJ City,E,3,17,23,38,Graduation,23,Owner,Standalone
9762,108.97,NJ City,S,4,15,2,29,Graduation,28,Patnership,Complex
5955,388,Boston,E,3,19,21,75,Graduation,27,Owner,Standalone
9543,40.93,NJ City,N,9,22,1,29,Graduation,26,Owner,Standalone
4391,255,NJ City,E,2,11,13,50,High School,28,Owner,Standalone
5773,375,Boston,N,2,23,16,60,Graduation,30,Patnership,Standalone
971,394.51,NJ City,E,20,10,4,50,No education,28,Owner,Standalone
4175,196,NJ City,E,3,22,14,31,High School,29,Owner,Standalone
7423,35.09,Boston,N,23,15,1,34,High School,29,Owner,Standalone
1485,102,NJ City,N,1,8,20,28,High School,33,Patnership,Standalone
6353,88.83,Boston,N,20,15,3,29,No education,20,Patnership,Standalone
5820,382,Boston,W,3,23,11,63,Graduation,25,Franchise,Complex
3665,96.32,NJ City,N,8,8,2,33,No education,33,Patnership,Standalone
8551,31.49,NJ City,N,15,10,2,34,High School,31,Owner,Standalone
1540,127,NJ City,W,3,11,24,77,High School,24,Franchise,Complex
5999,485,Boston,E,3,8,11,32,Graduation,19,Owner,Standalone
9487,477,NJ City,E,1,8,16,59,Graduation,23,Owner,Standalone
8011,7.77,Boston,N,13,9,2,33,High School,24,Owner,Standalone
1667,49.17,NJ City,E,12,8,1,25,No education,19,Owner,Standalone
2633,220.3,NJ City,N,9,10,1,49,No education,25,Patnership,Standalone
1636,47.13,NJ City,N,24,22,1,60,High School,26,Franchise,Complex
4168,7.2,NJ City,N,3,11,3,41,High School,24,Franchise,Complex
2130,208,NJ City,S,1,11,8,76,High School,24,Patnership,Complex
162,225,NJ City,S,2,10,24,26,High School,32,Patnership,Complex
8949,452,NJ City,N,1,11,19,43,Graduation,29,Patnership,Standalone
2318,248,NJ City,S,1,9,9,58,High School,28,Patnership,Complex
2849,76.33,NJ City,N,7,21,4,57,No education,33,Patnership,Standalone
4895,285,NJ City,E,3,22,20,67,High School,28,Owner,Standalone
845,264,NJ City,N,1,17,14,54,High School,23,Patnership,Standalone
3792,441,NJ City,W,2,11,5,40,Graduation,25,Franchise,Complex
7122,73.1,Boston,N,15,12,1,49,Graduation,22,Patnership,Complex
9692,7.69,NJ City,N,6,16,1,46,No education,19,Franchise,Complex
3634,126,NJ City,S,2,11,14,40,High School,35,Patnership,Complex
4558,66.91,NJ City,N,9,10,3,50,High School,19,Patnership,Complex
5212,482,Boston,W,3,21,11,55,Graduation,31,Franchise,Complex
7152,460,Boston,W,3,15,11,58,Graduation,27,Franchise,Complex
1518,270,NJ City,S,2,13,21,76,High School,26,Patnership,Complex
9498,411.76,NJ City,S,2,12,3,81,Graduation,19,Patnership,Complex
2160,246,NJ City,W,3,8,13,46,High School,19,Franchise,Complex
5046,431,Boston,S,2,19,11,48,Graduation,25,Patnership,Complex
3462,279,NJ City,S,1,15,14,34,High School,33,Patnership,Complex
432,66.76,NJ City,N,13,14,1,48,Graduation,34,Franchise,Complex
4077,216,NJ City,N,3,24,16,66,High School,33,Patnership,Standalone
6556,372,Boston,W,2,17,17,37,Graduation,29,Franchise,Complex
3000,312.68,NJ City,W,9,15,2,85,Graduation,21,Franchise,Complex
6342,481,Boston,S,2,8,21,32,Graduation,20,Patnership,Complex
3589,104,NJ City,N,1,23,12,67,High School,24,Patnership,Standalone
2894,27.47,NJ City,N,9,18,4,43,No education,28,Patnership,Complex
8373,440,NJ City,N,2,17,19,52,Graduation,31,Patnership,Standalone
7771,365,Boston,E,2,13,24,73,Graduation,34,Owner,Standalone
5633,437,Boston,N,2,13,14,59,Graduation,34,Patnership,Standalone
8357,420,NJ City,N,2,21,25,44,Graduation,19,Patnership,Standalone
8198,362,NJ City,S,2,21,16,61,Graduation,20,Patnership,Complex
4388,297,NJ City,W,2,19,24,30,High School,26,Franchise,Complex
2286,295,NJ City,S,2,14,6,43,High School,18,Patnership,Complex
2934,121,NJ City,S,3,22,21,82,High School,27,Patnership,Complex
7841,249.56,Boston,N,18,15,2,82,No education,34,Patnership,Standalone
1037,150,NJ City,N,2,12,6,68,High School,29,Patnership,Standalone
5966,412,Boston,S,1,13,19,82,Graduation,29,Patnership,Complex
9504,118.38,NJ City,W,15,19,4,73,Graduation,34,Franchise,Complex
8711,497,NJ City,E,2,12,19,41,Graduation,30,Owner,Standalone
1652,194.09,NJ City,W,9,19,2,66,No education,29,Franchise,Complex
9358,27.58,NJ City,N,13,18,2,47,High School,26,Patnership,Complex
6383,63.95,Boston,N,4,12,3,30,No education,23,Owner,Standalone
255,143,NJ City,E,3,21,17,45,High School,18,Owner,Standalone
2428,290,NJ City,W,3,17,10,57,High School,30,Franchise,Complex
9267,13.75,NJ City,N,11,9,4,32,Graduation,23,Owner,Standalone
2371,317.82,NJ City,E,8,18,4,82,High School,34,Owner,Standalone
6935,436,Boston,E,2,11,7,73,Graduation,19,Owner,Standalone
5514,383.16,Boston,S,17,12,2,58,Graduation,23,Patnership,Complex
1337,266,NJ City,N,3,18,21,51,High School,18,Patnership,Standalone
9163,497,NJ City,E,2,18,14,85,Graduation,21,Owner,Standalone
6500,498,Boston,W,2,17,25,40,Graduation,26,Franchise,Complex
7975,498,Boston,E,3,18,25,35,Graduation,20,Owner,Standalone
789,284,NJ City,N,3,11,16,28,High School,30,Patnership,Standalone
5234,469.41,Boston,S,21,15,3,62,No education,28,Patnership,Complex
6000,14.87,Boston,N,21,13,3,64,Graduation,20,Franchise,Complex
7037,371,Boston,N,2,17,23,33,Graduation,24,Patnership,Standalone
235,111,NJ City,E,3,14,21,82,High School,18,Owner,Standalone
1750,20.82,NJ City,N,15,20,2,67,High School,21,Patnership,Complex
2339,220,NJ City,E,3,21,17,72,High School,29,Owner,Standalone
9216,352,NJ City,W,2,13,5,70,Graduation,22,Franchise,Complex
7688,183.73,Boston,W,17,8,2,38,No education,34,Franchise,Complex
3527,115.48,NJ City,E,19,12,1,73,No education,34,Owner,Standalone
2366,82.4,NJ City,S,16,22,4,83,No education,19,Patnership,Complex
4692,186,NJ City,W,3,20,23,33,High School,19,Franchise,Complex
1461,240,NJ City,N,3,21,20,68,High School,26,Patnership,Standalone
1902,111,NJ City,S,1,13,17,40,High School,21,Patnership,Complex
6849,71.01,Boston,N,20,17,2,73,Graduation,28,Patnership,Standalone
7262,290.46,Boston,S,7,16,2,52,No education,34,Patnership,Complex
6982,493,Boston,S,2,23,12,37,Graduation,24,Patnership,Complex
6379,53.2,Boston,E,12,11,3,41,High School,34,Owner,Standalone
7700,470,Boston,W,1,13,14,27,Graduation,33,Franchise,Complex
8363,404,NJ City,E,2,16,15,82,Graduation,28,Owner,Standalone
631,305.67,NJ City,E,22,24,2,63,High School,29,Owner,Standalone
2409,134,NJ City,N,1,8,20,78,High School,25,Patnership,Standalone
7670,73.96,Boston,N,9,13,3,57,No education,25,Patnership,Complex
8008,424,Boston,W,2,11,11,39,Graduation,34,Franchise,Complex
8925,376,NJ City,N,1,13,9,59,Graduation,27,Patnership,Standalone
4390,65.09,NJ City,N,9,11,3,79,High School,21,Patnership,Complex
2891,247,NJ City,E,3,16,21,79,High School,28,Owner,Standalone
4124,209,NJ City,W,1,13,8,43,High School,23,Franchise,Complex
66,197,Boston,S,2,22,17,84,High School,23,Patnership,Complex
282,168,NJ City,S,2,23,14,63,High School,28,Patnership,Complex
6362,58.35,Boston,S,22,19,4,45,No education,35,Patnership,Complex
2033,122.31,NJ City,N,19,18,4,67,No education,21,Patnership,Standalone
65,268,Boston,N,2,12,11,50,High School,18,Patnership,Standalone
7768,469,Boston,W,1,16,10,60,Graduation,31,Franchise,Complex
3003,303,NJ City,E,3,15,10,83,High School,31,Owner,Standalone
2723,262.36,NJ City,E,11,14,4,33,No education,27,Owner,Standalone
4580,213,NJ City,W,2,9,25,85,High School,34,Franchise,Complex
8821,422.86,NJ City,N,7,9,1,68,High School,32,Patnership,Standalone
9558,451,NJ City,S,2,10,14,50,Graduation,18,Patnership,Complex
7000,388,Boston,W,3,15,17,32,Graduation,33,Franchise,Complex
7099,350,Boston,E,3,10,23,65,Graduation,33,Owner,Standalone
1176,401.7,NJ City,W,24,12,2,34,Graduation,20,Franchise,Complex
5658,460,Boston,S,2,11,19,36,Graduation,24,Patnership,Complex
980,74.93,NJ City,W,17,15,4,46,No education,33,Franchise,Complex
7602,388,Boston,S,3,17,6,44,Graduation,22,Patnership,Complex
1870,232,NJ City,S,1,10,14,72,High School,23,Patnership,Complex
9534,164.4,NJ City,S,4,24,4,42,Graduation,34,Patnership,Complex
5058,473.81,Boston,S,22,23,1,76,Graduation,29,Patnership,Complex
3471,108.01,NJ City,E,10,12,2,78,Graduation,25,Owner,Standalone
1180,108,NJ City,W,3,8,8,63,High School,30,Franchise,Complex
2486,100,NJ City,S,1,23,21,36,High School,21,Patnership,Complex
1416,71.27,NJ City,N,7,20,3,46,Graduation,29,Franchise,Complex
2927,355.38,NJ City,E,25,15,3,44,No education,28,Owner,Standalone
8630,478,NJ City,S,3,15,10,60,Graduation,30,Patnership,Complex
355,99.03,NJ City,E,1,14,4,26,High School,23,Owner,Standalone
1678,196,NJ City,S,3,8,16,43,High School,32,Patnership,Complex
173,58.86,NJ City,N,19,8,1,62,No education,33,Patnership,Standalone
2623,267,NJ City,E,3,19,23,46,High School,35,Owner,Standalone
2838,100.98,NJ City,S,22,19,3,74,Graduation,21,Patnership,Complex
8945,15.49,NJ City,N,11,11,3,37,No education,30,Patnership,Standalone
375,114,NJ City,E,2,11,7,63,High School,25,Owner,Standalone
1762,149,NJ City,S,3,14,12,49,High School,26,Patnership,Complex
5558,47.43,Boston,S,12,18,3,35,No education,25,Patnership,Complex
7306,358,Boston,S,3,19,24,31,Graduation,33,Patnership,Complex
8432,486.15,NJ City,W,25,14,4,70,No education,32,Franchise,Complex
6145,447,Boston,N,3,20,11,52,Graduation,28,Patnership,Standalone
7739,478,Boston,E,3,9,9,39,Graduation,25,Owner,Standalone
3047,150,NJ City,E,2,19,7,84,High School,22,Owner,Standalone
9973,448,NJ City,N,2,22,17,26,Graduation,26,Patnership,Standalone
6491,383,Boston,E,2,17,7,47,Graduation,32,Owner,Standalone
4802,147,NJ City,S,3,15,19,54,High School,20,Patnership,Complex
9923,364,NJ City,E,3,14,19,25,Graduation,21,Owner,Standalone
1356,460.64,NJ City,W,10,16,1,68,Graduation,30,Franchise,Complex
3196,52.45,NJ City,W,4,24,1,40,High School,34,Franchise,Complex
3135,121,NJ City,E,3,13,15,72,High School,22,Owner,Standalone
7016,363.69,Boston,W,11,12,1,52,No education,19,Franchise,Complex
3840,193,NJ City,W,1,22,25,41,High School,32,Franchise,Complex
8006,350,Boston,S,2,22,8,80,Graduation,18,Patnership,Complex
5896,415,Boston,W,1,9,14,85,Graduation,33,Franchise,Complex
3251,30.96,NJ City,N,15,24,2,40,No education,31,Owner,Standalone
9148,382,NJ City,W,1,17,5,69,Graduation,22,Franchise,Complex
9376,446,NJ City,W,1,24,21,40,Graduation,34,Franchise,Complex
6744,29.82,Boston,N,25,22,1,83,Graduation,28,Franchise,Complex
6055,114.99,Boston,E,15,23,4,78,High School,34,Owner,Standalone
8211,493,NJ City,E,1,21,5,34,Graduation,26,Owner,Standalone
7801,436,Boston,N,3,24,24,37,Graduation,25,Patnership,Standalone
4567,263,NJ City,E,1,18,18,37,High School,22,Owner,Standalone
261,211,NJ City,N,3,18,8,76,High School,30,Patnership,Standalone
441,303.7,NJ City,N,25,15,4,67,Graduation,27,Patnership,Standalone
2539,108,NJ City,E,3,14,18,84,High School,23,Owner,Standalone
3417,177,NJ City,N,1,11,20,43,High School,20,Patnership,Standalone
6735,0.88,Boston,N,12,8,1,67,Graduation,20,Owner,Standalone
7112,10.83,Boston,N,11,18,4,34,No education,22,Franchise,Complex
5502,350,Boston,S,3,19,18,28,Graduation,23,Patnership,Complex
8238,127.15,NJ City,S,9,12,2,79,Graduation,25,Patnership,Complex
7631,104.8,Boston,E,15,17,3,75,No education,34,Owner,Standalone
4515,120.95,NJ City,E,8,8,1,81,Graduation,32,Owner,Standalone
7875,82.28,Boston,E,13,18,3,55,Graduation,23,Owner,Standalone
9908,370,NJ City,W,2,10,20,43,Graduation,35,Franchise,Complex
9057,78.95,NJ City,N,18,10,3,34,Graduation,31,Patnership,Standalone
332,163,NJ City,W,1,14,21,61,High School,22,Franchise,Complex
1214,138,NJ City,S,3,18,24,84,High School,34,Patnership,Complex
4874,23.54,NJ City,N,2,21,1,41,No education,28,Patnership,Complex
8533,443,NJ City,N,3,12,16,34,Graduation,22,Patnership,Standalone
6162,372,Boston,S,3,18,21,40,Graduation,20,Patnership,Complex
2786,91.49,NJ City,S,3,8,3,69,No education,31,Patnership,Complex
1899,112,NJ City,E,1,11,13,70,High School,25,Owner,Standalone
4920,81,NJ City,W,9,16,2,38,Graduation,23,Franchise,Complex
4683,287,NJ City,E,2,20,16,35,High School,22,Owner,Standalone
6842,497,Boston,S,2,10,20,53,Graduation,24,Patnership,Complex
2738,297.1,NJ City,S,17,15,4,41,No education,20,Patnership,Complex
3198,108,NJ City,S,3,23,15,37,High School,28,Patnership,Complex
1388,398,NJ City,W,2,21,5,36,Graduation,24,Franchise,Complex
7139,483,Boston,E,1,8,20,36,Graduation,20,Owner,Standalone
1015,52.52,NJ City,E,24,13,2,68,High School,33,Owner,Standalone
5995,60,Boston,E,17,23,2,76,High School,18,Owner,Standalone
5374,453,Boston,S,2,24,13,51,Graduation,23,Patnership,Complex
5723,411,Boston,E,2,21,16,78,Graduation,19,Owner,Standalone
6765,363,Boston,N,2,14,21,60,Graduation,26,Patnership,Standalone
5442,36.1,Boston,N,8,10,2,77,Graduation,31,Patnership,Complex
4784,291,NJ City,W,1,9,15,46,High School,26,Franchise,Complex
6691,192.87,Boston,E,6,12,2,70,High School,21,Owner,Standalone
7974,405,Boston,S,2,10,6,76,Graduation,30,Patnership,Complex
9304,13.99,NJ City,N,17,8,4,81,High School,28,Franchise,Complex
7535,481,Boston,E,1,13,7,82,Graduation,27,Owner,Standalone
4319,310,NJ City,E,2,22,25,35,High School,22,Owner,Standalone
5127,43.41,Boston,N,6,17,2,78,Graduation,27,Owner,Standalone
9957,389,NJ City,N,3,21,13,57,Graduation,19,Patnership,Standalone
7226,75.22,Boston,S,19,11,4,68,No education,20,Patnership,Complex
238,250,NJ City,S,1,9,14,79,High School,35,Patnership,Complex
4043,333.93,NJ City,E,4,24,1,69,No education,27,Owner,Standalone
2477,117,NJ City,N,3,15,10,52,High School,18,Patnership,Standalone
3610,26.45,NJ City,N,10,11,4,29,High School,27,Patnership,Complex
9207,125.65,NJ City,E,3,17,4,52,Graduation,35,Owner,Standalone
3312,14.33,NJ City,N,15,18,4,78,Graduation,34,Franchise,Complex
6062,481,Boston,S,3,24,5,52,Graduation,23,Patnership,Complex
9134,427.38,NJ City,S,17,19,1,66,No education,35,Patnership,Complex
3104,148,NJ City,W,3,21,19,38,High School,26,Franchise,Complex
2418,306.33,NJ City,S,14,18,3,43,Graduation,20,Patnership,Complex
5195,499,Boston,E,2,17,13,30,Graduation,28,Owner,Standalone
1012,250,NJ City,W,2,11,15,48,High School,18,Franchise,Complex
6535,11.64,Boston,N,4,17,2,42,High School,18,Owner,Standalone
6684,320.56,Boston,W,6,11,2,57,Graduation,20,Franchise,Complex
1617,221,NJ City,N,1,10,9,66,High School,20,Patnership,Standalone
9581,388,NJ City,N,3,8,22,49,Graduation,34,Patnership,Standalone
1339,276,NJ City,E,3,14,19,71,High School,35,Owner,Standalone
1644,240,NJ City,W,2,19,18,57,High School,33,Franchise,Complex
5376,85.98,Boston,W,25,8,2,27,Graduation,31,Franchise,Complex
5076,413,Boston,W,2,23,23,31,Graduation,29,Franchise,Complex
28,171,Boston,W,1,23,14,55,High School,24,Franchise,Complex
2666,205,NJ City,S,3,16,13,63,High School,30,Patnership,Complex
1343,162,NJ City,E,3,8,13,30,High School,24,Owner,Standalone
2357,271,NJ City,N,3,22,23,44,High School,32,Patnership,Standalone
7611,131.11,Boston,E,21,16,3,57,Graduation,30,Owner,Standalone
4320,246,NJ City,W,2,17,14,26,High School,20,Franchise,Complex
1925,5.64,NJ City,N,10,20,3,31,No education,24,Patnership,Standalone
2913,258,NJ City,N,1,21,21,69,High School,34,Patnership,Standalone
7898,354,Boston,S,2,17,13,57,Graduation,25,Patnership,Complex
8490,30.16,NJ City,N,14,17,3,72,Graduation,26,Patnership,Complex
7843,431,Boston,E,3,21,25,83,Graduation,22,Owner,Standalone
8853,362,NJ City,N,1,10,23,76,Graduation,19,Patnership,Standalone
2880,319,NJ City,W,1,9,11,26,High School,23,Franchise,Complex
6653,65.96,Boston,N,22,18,2,62,No education,24,Patnership,Standalone
5684,390,Boston,W,2,10,11,43,Graduation,31,Franchise,Complex
1691,271,NJ City,E,2,14,13,28,High School,20,Owner,Standalone
3164,283,NJ City,W,2,8,20,72,High School,34,Franchise,Complex
3275,372.91,NJ City,E,24,22,2,67,No education,30,Owner,Standalone
7056,74.04,Boston,W,15,15,4,78,Graduation,29,Franchise,Complex
8064,414,NJ City,W,3,23,14,32,Graduation,26,Franchise,Complex
2489,464,NJ City,N,1,20,5,34,Graduation,30,Patnership,Standalone
4404,413,NJ City,W,3,11,5,48,Graduation,26,Franchise,Complex
3062,67.07,NJ City,N,8,12,4,58,No education,24,Patnership,Complex
4147,311,NJ City,E,1,13,12,45,High School,26,Owner,Standalone
9166,408,NJ City,S,3,24,16,51,Graduation,29,Patnership,Complex
2288,183,NJ City,W,2,23,12,68,High School,32,Franchise,Complex
1156,129,NJ City,W,3,20,22,42,High School,20,Franchise,Complex
9529,491,NJ City,N,2,15,10,74,Graduation,26,Patnership,Standalone
2084,123.74,NJ City,W,9,8,2,77,No education,29,Franchise,Complex
2321,203,NJ City,N,2,24,6,59,High School,25,Patnership,Standalone
8541,14.22,NJ City,N,14,18,3,43,Graduation,33,Patnership,Standalone
9648,428,NJ City,W,1,19,12,60,Graduation,23,Franchise,Complex
9329,361,NJ City,N,3,19,5,33,Graduation,22,Patnership,Standalone
7735,491,Boston,E,3,13,24,28,Graduation,18,Owner,Standalone
4842,73.72,NJ City,N,13,12,2,59,Graduation,30,Patnership,Complex
8537,382,NJ City,N,1,13,22,85,Graduation,26,Patnership,Standalone
3534,254,NJ City,S,2,8,13,80,High School,32,Patnership,Complex
7446,108.53,Boston,S,10,14,3,79,Graduation,18,Patnership,Complex
8570,399.46,NJ City,S,5,11,1,25,No education,28,Patnership,Complex
7177,101.23,Boston,N,23,16,2,61,High School,30,Patnership,Standalone
4984,144,Boston,W,3,10,9,75,High School,26,Franchise,Complex
8892,482,NJ City,W,2,10,20,25,Graduation,33,Franchise,Complex
1654,312,NJ City,S,1,17,16,75,High School,23,Patnership,Complex
3946,124.19,NJ City,S,3,21,3,46,High School,31,Patnership,Complex
3285,117.46,NJ City,N,13,21,4,51,Graduation,20,Patnership,Standalone
9228,445,NJ City,W,1,20,8,33,Graduation,30,Franchise,Complex
3766,13.17,NJ City,N,10,20,2,51,High School,34,Patnership,Complex
5185,273.55,Boston,N,5,19,3,77,High School,29,Patnership,Standalone
7308,11.83,Boston,N,20,24,4,40,Graduation,33,Franchise,Complex
7617,482,Boston,N,1,21,21,84,Graduation,25,Patnership,Standalone
4880,124,NJ City,W,1,24,19,54,High School,27,Franchise,Complex
9305,169.03,NJ City,N,7,16,2,81,No education,26,Patnership,Standalone
7664,417,Boston,W,3,10,18,45,Graduation,19,Franchise,Complex
5980,394.28,Boston,W,11,10,2,79,High School,20,Franchise,Complex
5524,116.09,Boston,W,2,11,1,45,High School,33,Franchise,Complex
3184,106.11,NJ City,W,8,9,2,70,High School,35,Franchise,Complex
2163,282.34,NJ City,E,16,22,3,85,Graduation,20,Owner,Standalone
1287,1.38,NJ City,N,15,24,2,56,Graduation,34,Owner,Standalone
9117,37.27,NJ City,N,14,12,3,71,Graduation,20,Patnership,Standalone
8780,213.13,NJ City,W,3,18,3,39,No education,32,Franchise,Complex
9422,441,NJ City,S,1,15,16,72,Graduation,33,Patnership,Complex
3051,191,NJ City,E,3,15,23,42,High School,18,Owner,Standalone
9035,89.12,NJ City,E,3,18,2,75,No education,30,Owner,Standalone
948,75.05,NJ City,W,8,12,2,46,Graduation,29,Franchise,Complex
7669,468,Boston,N,3,23,24,26,Graduation,30,Patnership,Standalone
3944,204,NJ City,W,1,8,12,79,High School,32,Franchise,Complex
8930,415,NJ City,S,3,18,18,36,Graduation,29,Patnership,Complex
9708,396,NJ City,W,3,9,9,55,Graduation,26,Franchise,Complex
7304,30.36,Boston,N,2,16,2,64,No education,22,Franchise,Complex
1295,173,NJ City,E,1,18,15,39,High School,34,Owner,Standalone
3784,294,NJ City,W,3,22,15,35,High School,31,Franchise,Complex
436,90.41,NJ City,W,3,12,4,59,High School,32,Franchise,Complex
1553,193,NJ City,N,3,10,16,48,High School,24,Patnership,Standalone
5697,95.3,Boston,N,19,15,1,81,Graduation,26,Patnership,Standalone
5587,403,Boston,E,3,21,9,75,Graduation,28,Owner,Standalone
2749,184,NJ City,N,1,14,9,47,High School,32,Patnership,Standalone
7281,99.19,Boston,N,14,11,3,68,Graduation,33,Patnership,Standalone
5644,89.49,Boston,W,5,8,2,26,High School,19,Franchise,Complex
2261,51.58,NJ City,N,17,24,2,54,No education,19,Patnership,Standalone
4432,127,NJ City,W,1,16,24,85,High School,21,Franchise,Complex
9286,430,NJ City,S,3,10,16,35,Graduation,34,Patnership,Complex
422,140,NJ City,S,2,10,20,32,High School,22,Patnership,Complex
4923,145,NJ City,E,2,13,14,82,High School,19,Owner,Standalone
6858,429,Boston,S,3,18,14,59,Graduation,20,Patnership,Complex
8257,460,NJ City,N,2,16,19,79,Graduation,22,Patnership,Standalone
5975,481,Boston,E,3,24,19,74,Graduation,29,Owner,Standalone
5709,490.07,Boston,N,4,17,2,29,Graduation,31,Patnership,Standalone
8514,367,NJ City,S,3,20,22,34,Graduation,35,Patnership,Complex
1738,292,NJ City,S,1,19,25,49,High School,20,Patnership,Complex
570,108,NJ City,S,3,12,10,64,High School,22,Patnership,Complex
5296,457,Boston,W,1,15,5,66,Graduation,27,Franchise,Complex
9121,387,NJ City,N,2,10,21,26,Graduation,22,Patnership,Standalone
4934,147,NJ City,S,2,18,22,84,High School,21,Patnership,Complex
7662,409,Boston,S,3,19,10,41,Graduation,22,Patnership,Complex
3014,407.91,NJ City,S,14,16,1,31,No education,27,Patnership,Complex
409,239,NJ City,N,2,18,9,39,High School,31,Patnership,Standalone
1919,198,NJ City,E,1,14,11,63,High School,20,Owner,Standalone
9072,112.83,NJ City,W,25,17,1,63,Graduation,23,Franchise,Complex
4162,104,NJ City,S,1,21,10,55,High School,25,Patnership,Complex
4414,230,NJ City,S,1,12,22,71,High School,34,Patnership,Complex
891,419,NJ City,E,2,9,5,82,Graduation,25,Owner,Standalone
2331,34.07,NJ City,N,17,16,2,58,Graduation,31,Owner,Standalone
5300,481,Boston,W,1,14,13,58,Graduation,20,Franchise,Complex
8938,500,NJ City,S,3,22,20,25,Graduation,18,Patnership,Complex
9008,428,NJ City,W,1,22,11,45,Graduation,20,Franchise,Complex
6115,453,Boston,E,1,9,23,58,Graduation,26,Owner,Standalone
1562,289,NJ City,S,2,11,24,69,High School,32,Patnership,Complex
9263,156.9,NJ City,E,23,9,4,77,No education,29,Owner,Standalone
8997,50.8,NJ City,N,9,12,3,79,Graduation,30,Patnership,Standalone
9739,452,NJ City,E,1,22,22,71,Graduation,19,Owner,Standalone
8233,432,NJ City,N,1,21,15,75,Graduation,18,Patnership,Standalone
931,195,NJ City,E,3,8,9,50,High School,24,Owner,Standalone
9335,353,NJ City,E,2,20,15,76,Graduation,25,Owner,Standalone
453,300,NJ City,N,2,14,14,53,High School,28,Patnership,Standalone
8468,27.86,NJ City,N,10,19,2,66,No education,25,Franchise,Complex
8976,486,NJ City,W,2,21,17,64,Graduation,34,Franchise,Complex
7119,286,Boston,E,1,8,2,46,Graduation,20,Owner,Standalone
5579,430.29,Boston,E,1,15,2,52,No education,21,Owner,Standalone
8845,90.15,NJ City,N,6,11,1,84,High School,34,Patnership,Standalone
7860,418,Boston,W,3,14,13,34,Graduation,24,Franchise,Complex
6681,48.42,Boston,N,10,20,2,55,Graduation,24,Patnership,Standalone
3863,230,NJ City,E,3,12,7,65,High School,29,Owner,Standalone
2382,461.99,NJ City,S,2,21,4,29,Graduation,23,Patnership,Complex
4457,255,NJ City,N,3,12,24,74,High School,26,Patnership,Standalone
8675,450,NJ City,E,2,20,8,38,Graduation,18,Owner,Standalone
1254,131,NJ City,S,3,9,13,50,High School,30,Patnership,Complex
3269,98.19,NJ City,N,17,12,4,64,No education,26,Patnership,Standalone
2150,1.27,NJ City,N,17,11,2,33,No education,22,Patnership,Complex
5348,497,Boston,W,2,22,9,44,Graduation,20,Franchise,Complex
8139,394,NJ City,E,3,17,5,73,Graduation,23,Owner,Standalone
6452,88.36,Boston,W,11,10,4,67,No education,27,Franchise,Complex
2522,287,NJ City,S,3,14,23,85,High School,25,Patnership,Complex
8331,360,NJ City,E,1,24,11,60,Graduation,26,Owner,Standalone
9859,410.17,NJ City,E,5,10,4,59,High School,35,Owner,Standalone
1868,120.44,NJ City,W,12,8,1,42,No education,26,Franchise,Complex
5692,493,Boston,W,1,21,15,52,Graduation,26,Franchise,Complex
7933,6.83,Boston,N,6,22,2,54,High School,20,Patnership,Standalone
3511,233,NJ City,E,3,18,23,82,High School,30,Owner,Standalone
6933,472,Boston,N,2,15,19,38,Graduation,31,Patnership,Standalone
2003,112.48,NJ City,E,7,21,2,63,No education,34,Owner,Standalone
3924,288,NJ City,W,3,9,20,81,High School,34,Franchise,Complex
5717,453.53,Boston,N,12,16,3,48,No education,29,Patnership,Standalone
2305,251,NJ City,N,1,12,15,27,High School,21,Patnership,Standalone
4878,296,NJ City,S,1,21,13,76,High School,21,Patnership,Complex
8841,381,NJ City,N,1,14,13,29,Graduation,32,Patnership,Standalone
4091,179,NJ City,E,1,24,7,72,High School,23,Owner,Standalone
9970,111.07,NJ City,S,15,10,4,46,High School,24,Patnership,Complex
2332,303,NJ City,W,1,20,22,81,High School,32,Franchise,Complex
5329,369,Boston,N,3,21,24,37,Graduation,18,Patnership,Standalone
4944,86.42,Boston,W,25,12,2,56,Graduation,21,Franchise,Complex
2436,101,NJ City,W,3,17,19,78,High School,18,Franchise,Complex
9754,454,NJ City,S,3,11,20,85,Graduation,19,Patnership,Complex
1572,349.01,NJ City,W,4,21,2,62,Graduation,33,Franchise,Complex
3406,234,NJ City,S,1,15,18,70,High School,34,Patnership,Complex
7043,399,Boston,E,2,23,18,35,Graduation,18,Owner,Standalone
6899,389,Boston,E,3,10,13,55,Graduation,24,Owner,Standalone
6532,427.05,Boston,W,10,16,1,29,High School,31,Franchise,Complex
9860,47.89,NJ City,W,21,13,3,59,No education,32,Franchise,Complex
2886,204,NJ City,S,2,20,17,35,High School,33,Patnership,Complex
8029,487,Boston,N,1,23,9,78,Graduation,28,Patnership,Standalone
3327,73.8,NJ City,N,1,13,3,75,Graduation,33,Owner,Standalone
6986,463,Boston,S,2,23,12,57,Graduation,19,Patnership,Complex
3991,196,NJ City,E,3,21,22,32,High School,22,Owner,Standalone
152,127.29,NJ City,W,12,15,2,32,No education,22,Franchise,Complex
9458,355,NJ City,S,1,23,24,75,Graduation,24,Patnership,Complex
103,138,NJ City,E,1,24,8,39,High School,32,Owner,Standalone
1720,129,NJ City,W,1,11,25,74,High School,32,Franchise,Complex
6720,381,Boston,W,3,17,22,73,Graduation,26,Franchise,Complex
2583,132.85,NJ City,E,17,15,1,77,Graduation,22,Owner,Standalone
955,132,NJ City,E,2,22,7,33,High School,23,Owner,Standalone
9888,16.46,NJ City,N,25,8,3,77,Graduation,24,Franchise,Complex
8280,388,NJ City,W,3,22,16,65,Graduation,35,Franchise,Complex
9690,371,NJ City,S,1,17,15,38,Graduation,26,Patnership,Complex
1522,495.07,NJ City,S,15,11,3,66,High School,27,Patnership,Complex
4111,273,NJ City,E,2,15,22,60,High School,29,Owner,Standalone
4811,207,NJ City,E,2,15,25,36,High School,32,Owner,Standalone
6257,463,Boston,N,1,15,9,69,Graduation,30,Patnership,Standalone
53,111,NY City,N,3,9,6,31,High School,23,Patnership,Standalone
2750,98.87,NJ City,S,21,24,4,26,No education,23,Patnership,Complex
8069,98.56,NJ City,N,19,11,1,79,No education,24,Patnership,Standalone
8550,398,NJ City,S,3,10,17,39,Graduation,32,Patnership,Complex
5592,406,Boston,W,2,18,19,61,Graduation,22,Franchise,Complex
6054,94.07,Boston,S,22,23,3,37,Graduation,27,Patnership,Complex
9479,14.1,NJ City,N,20,9,1,34,No education,20,Owner,Standalone
4937,279,Boston,N,1,23,10,50,High School,24,Patnership,Standalone
7927,489.69,Boston,E,13,24,1,47,High School,31,Owner,Standalone
2420,37.81,NJ City,N,16,8,4,57,No education,34,Franchise,Complex
5837,414,Boston,N,1,20,21,39,Graduation,20,Patnership,Standalone
8463,376,NJ City,E,3,10,17,66,Graduation,35,Owner,Standalone
4791,61.61,NJ City,N,11,13,1,45,Graduation,29,Owner,Standalone
9462,451,NJ City,S,2,19,21,78,Graduation,31,Patnership,Complex
4589,301,NJ City,N,2,16,21,84,High School,18,Patnership,Standalone
2472,303,NJ City,W,3,8,25,44,High School,24,Franchise,Complex
9193,33.73,NJ City,N,7,18,2,59,High School,30,Patnership,Standalone
1913,46.17,NJ City,N,25,23,2,36,No education,32,Patnership,Standalone
8153,398,NJ City,N,2,14,21,34,Graduation,32,Patnership,Standalone
7321,394,Boston,N,2,22,23,51,Graduation,21,Patnership,Standalone
7014,412,Boston,S,3,13,15,71,Graduation,32,Patnership,Complex
8058,26.52,NJ City,N,12,20,1,84,Graduation,21,Patnership,Complex
7481,396.9,Boston,N,4,11,1,48,No education,32,Patnership,Standalone
3567,166,NJ City,E,2,18,21,78,High School,22,Owner,Standalone
2911,302,NJ City,E,1,11,11,73,High School,27,Owner,Standalone
7033,439,Boston,N,3,12,24,61,Graduation,22,Patnership,Standalone
970,40.56,NJ City,N,18,13,1,36,High School,20,Patnership,Complex
9960,76.95,NJ City,W,19,12,1,31,Graduation,26,Franchise,Complex
853,177,NJ City,N,1,9,24,67,High School,34,Patnership,Standalone
8236,414,NJ City,W,2,12,12,63,Graduation,34,Franchise,Complex
4364,148,NJ City,W,3,23,9,70,High School,20,Franchise,Complex
1641,21.5,NJ City,N,14,8,3,81,Graduation,22,Patnership,Standalone
5495,112.96,Boston,E,7,15,3,54,No education,20,Owner,Standalone
8039,472,Boston,E,3,15,18,57,Graduation,33,Owner,Standalone
3856,261,NJ City,W,3,22,20,26,High School,26,Franchise,Complex
1830,264.49,NJ City,S,11,10,4,40,Graduation,24,Patnership,Complex
5740,488,Boston,W,1,8,15,59,Graduation,19,Franchise,Complex
5793,414,Boston,N,1,17,14,54,Graduation,25,Patnership,Standalone
1350,128,NJ City,S,1,9,9,38,High School,20,Patnership,Complex
4850,136,NJ City,S,1,14,20,71,High School,21,Patnership,Complex
8283,407,NJ City,E,1,22,24,35,Graduation,21,Owner,Standalone
7548,497.46,Boston,W,3,20,4,79,Graduation,21,Franchise,Complex
3600,280,NJ City,W,2,14,25,28,High School,23,Franchise,Complex
6168,338.3,Boston,W,16,10,2,71,Graduation,33,Franchise,Complex
5860,89.47,Boston,W,15,8,4,39,High School,31,Franchise,Complex
8027,200.7,Boston,E,22,15,2,74,No education,33,Owner,Standalone
3614,230,NJ City,S,2,19,15,85,High School,24,Patnership,Complex
4341,165,NJ City,N,1,17,22,36,High School,29,Patnership,Standalone
6827,34.4,Boston,N,10,12,4,49,No education,28,Owner,Standalone
4540,197,NJ City,W,3,17,24,54,High School,29,Franchise,Complex
1529,135,NJ City,N,1,9,22,57,High School,24,Patnership,Standalone
3049,158,NJ City,N,3,19,15,72,High School,34,Patnership,Standalone
8401,489,NJ City,N,2,18,6,67,Graduation,19,Patnership,Standalone
4705,213,NJ City,N,1,8,22,80,High School,19,Patnership,Standalone
7234,433,Boston,S,2,20,24,27,Graduation,18,Patnership,Complex
2232,143,NJ City,W,3,14,20,51,High School,27,Franchise,Complex
963,93.7,NJ City,E,20,13,2,69,Graduation,25,Owner,Standalone
2980,228,NJ City,W,1,19,20,35,High School,29,Franchise,Complex
9658,230.37,NJ City,S,2,22,4,44,High School,25,Patnership,Complex
3007,52.42,NJ City,E,24,19,3,53,High School,19,Owner,Standalone
7409,63.06,Boston,N,21,11,1,26,No education,21,Patnership,Standalone
2657,238,NJ City,N,2,13,16,60,High School,21,Patnership,Standalone
1771,268,NJ City,E,2,19,25,33,High School,31,Owner,Standalone
22,270,NJ City,S,3,20,8,80,High School,23,Patnership,Complex
3476,261,NJ City,W,1,24,7,67,High School,20,Franchise,Complex
5857,499,Boston,N,2,22,14,30,Graduation,19,Patnership,Standalone
9858,443,NJ City,S,2,15,8,79,Graduation,34,Patnership,Complex
2607,115,NJ City,E,3,13,16,81,High School,24,Owner,Standalone
9342,89.99,NJ City,S,22,11,4,69,Graduation,26,Patnership,Complex
2965,348.12,NJ City,N,25,8,2,46,High School,28,Patnership,Standalone
5756,275.21,Boston,W,1,8,4,25,No education,19,Franchise,Complex
3653,145,NJ City,N,1,8,20,29,High School,27,Patnership,Standalone
4358,311,NJ City,S,3,13,22,57,High School,34,Patnership,Complex
487,136.23,NJ City,E,20,18,3,55,High School,20,Owner,Standalone
5140,107.15,Boston,W,3,11,4,42,High School,29,Franchise,Complex
9120,436,NJ City,W,3,20,24,46,Graduation,22,Franchise,Complex
1505,182,NJ City,N,1,20,13,46,High School,28,Patnership,Standalone
8048,353,NJ City,W,1,12,12,42,Graduation,34,Franchise,Complex
4743,486,NJ City,E,1,12,5,70,Graduation,34,Owner,Standalone
511,49.06,NJ City,E,24,15,3,44,High School,30,Owner,Standalone
6028,228.43,Boston,W,21,20,2,80,High School,31,Franchise,Complex
1164,36.09,NJ City,N,2,8,4,31,Graduation,29,Franchise,Complex
9966,387,NJ City,S,3,15,23,54,Graduation,23,Patnership,Complex
4650,326,NJ City,S,14,17,4,74,Graduation,30,Patnership,Complex
5473,319.69,Boston,N,24,13,4,52,High School,18,Patnership,Standalone
9010,123.47,NJ City,S,1,9,1,81,High School,18,Patnership,Complex
4590,82.45,NJ City,S,25,23,2,69,Graduation,22,Patnership,Complex
6388,113.32,Boston,W,9,17,2,56,High School,35,Franchise,Complex
9674,25.33,NJ City,N,7,20,4,83,No education,29,Patnership,Complex
6987,401,Boston,E,3,17,13,41,Graduation,34,Owner,Standalone
1190,275,NJ City,S,3,8,16,70,High School,34,Patnership,Complex
8488,488,NJ City,W,1,10,22,33,Graduation,21,Franchise,Complex
3721,277,NJ City,N,3,12,9,48,High School,29,Patnership,Standalone
4271,235,NJ City,E,3,13,17,33,High School,32,Owner,Standalone
2328,6.62,NJ City,N,9,13,3,53,Graduation,20,Franchise,Complex
7213,84.56,Boston,N,18,15,3,73,High School,33,Patnership,Standalone
6658,500,Boston,S,3,22,18,82,Graduation,18,Patnership,Complex
2511,311,NJ City,E,1,15,19,25,High School,29,Owner,Standalone
1298,263,NJ City,S,1,17,11,67,High School,34,Patnership,Complex
1056,250,NJ City,W,2,16,9,56,High School,34,Franchise,Complex
189,105,NJ City,N,3,11,6,34,High School,19,Patnership,Standalone
6349,373,Boston,N,2,18,22,35,Graduation,24,Patnership,Standalone
7317,421,Boston,N,1,18,14,49,Graduation,22,Patnership,Standalone
1052,252,NJ City,W,2,16,6,32,High School,35,Franchise,Complex
623,156,NJ City,E,1,18,22,44,High School,18,Owner,Standalone
1898,132,NJ City,S,3,20,19,84,High School,31,Patnership,Complex
7501,423,Boston,N,3,10,15,75,Graduation,34,Patnership,Standalone
3085,50.4,NJ City,N,12,24,1,36,High School,30,Patnership,Standalone
8109,364,NJ City,N,2,23,25,38,Graduation,35,Patnership,Standalone
7783,81.95,Boston,E,24,21,1,85,High School,32,Owner,Standalone
5993,93.95,Boston,N,15,17,3,77,No education,35,Patnership,Standalone
233,182,NJ City,N,3,16,10,48,High School,35,Patnership,Standalone
757,389.55,NJ City,N,1,16,3,77,High School,28,Patnership,Standalone
9465,497.92,NJ City,N,5,15,1,83,Graduation,35,Patnership,Standalone
1165,303,NJ City,N,2,19,19,51,High School,25,Patnership,Standalone
2580,2.99,NJ City,N,3,10,2,69,Graduation,26,Franchise,Complex
7639,92.77,Boston,E,22,16,1,52,High School,18,Owner,Standalone
4105,139,NJ City,N,3,15,15,26,High School,31,Patnership,Standalone
8893,363,NJ City,N,2,11,15,59,Graduation,30,Patnership,Standalone
7457,435,Boston,N,2,24,6,62,Graduation,31,Patnership,Standalone
8415,497,NJ City,E,3,8,21,32,Graduation,18,Owner,Standalone
4974,142.32,Boston,S,1,13,1,53,Graduation,19,Patnership,Complex
6205,444,Boston,N,3,11,6,31,Graduation,20,Patnership,Standalone
3690,0.52,NJ City,N,14,11,4,83,Graduation,19,Patnership,Complex
2848,154,NJ City,W,1,23,19,28,High School,33,Franchise,Complex
3355,295,NJ City,E,3,16,9,79,High School,27,Owner,Standalone
7920,84.93,Boston,W,16,21,4,35,Graduation,32,Franchise,Complex
8748,383,NJ City,W,1,16,5,37,Graduation,23,Franchise,Complex
3310,316,NJ City,S,3,9,12,33,High School,35,Patnership,Complex
5257,496,Boston,N,3,15,24,43,Graduation,21,Patnership,Standalone
6800,383.18,Boston,W,13,8,3,53,No education,19,Franchise,Complex
9556,462,NJ City,W,3,22,20,57,Graduation,31,Franchise,Complex
1896,164,NJ City,W,1,17,24,43,High School,30,Franchise,Complex
7327,113.94,Boston,E,18,13,3,26,High School,18,Owner,Standalone
88,169,Boston,W,3,23,15,38,High School,33,Franchise,Complex
2637,14.07,NJ City,N,22,10,2,49,Graduation,32,Patnership,Standalone
3263,274,NJ City,E,2,8,6,30,High School,23,Owner,Standalone
4948,78.43,Boston,W,19,20,4,79,High School,28,Franchise,Complex
6928,499,Boston,W,1,8,24,75,Graduation,18,Franchise,Complex
7836,453,Boston,W,1,8,5,25,Graduation,27,Franchise,Complex
7935,429,Boston,E,3,16,20,48,Graduation,20,Owner,Standalone
2778,17.7,NJ City,N,7,9,1,66,Graduation,19,Patnership,Complex
9875,103.76,NJ City,E,9,10,1,74,No education,20,Owner,Standalone
4398,59.73,NJ City,S,9,13,3,43,Graduation,20,Patnership,Complex
6036,3.84,Boston,N,21,23,4,30,Graduation,23,Franchise,Complex
9645,11.1,NJ City,N,13,16,1,38,Graduation,31,Patnership,Standalone
7415,489,Boston,E,3,22,8,50,Graduation,21,Owner,Standalone
4832,213,NJ City,W,2,12,7,39,High School,20,Franchise,Complex
843,247,NJ City,E,2,18,13,42,High School,29,Owner,Standalone
6664,360,Boston,W,1,18,20,56,Graduation,20,Franchise,Complex
8566,377.11,NJ City,S,10,8,2,27,High School,33,Patnership,Complex
6422,370,Boston,S,3,17,24,58,Graduation,31,Patnership,Complex
86,205,Boston,S,2,10,19,79,High School,33,Patnership,Complex
6217,461,Boston,N,3,20,14,59,Graduation,28,Patnership,Standalone
7528,479,Boston,W,3,8,23,46,Graduation,33,Franchise,Complex
8719,494,NJ City,E,1,22,6,77,Graduation,33,Owner,Standalone
8469,226.39,NJ City,N,8,13,1,78,Graduation,23,Patnership,Standalone
1438,6.67,NJ City,N,2,16,3,45,High School,24,Patnership,Complex
8646,464,NJ City,S,2,16,17,44,Graduation,20,Patnership,Complex
4570,187,NJ City,S,1,17,8,40,High School,35,Patnership,Complex
2922,150,NJ City,S,2,9,12,78,High School,34,Patnership,Complex
444,262,NJ City,W,2,14,25,82,High School,24,Franchise,Complex
9015,265.59,NJ City,E,17,18,3,84,Graduation,34,Owner,Standalone
5494,476,Boston,S,2,23,17,51,Graduation,35,Patnership,Complex
2858,186,NJ City,S,1,19,10,56,High School,29,Patnership,Complex
6165,22.51,Boston,N,21,22,4,42,Graduation,27,Patnership,Standalone
5671,321.86,Boston,E,9,17,4,28,High School,33,Owner,Standalone
7976,88.06,Boston,W,19,13,1,33,No education,32,Franchise,Complex
5739,350,Boston,E,3,10,16,66,Graduation,19,Owner,Standalone
2655,292,NJ City,E,3,15,12,45,High School,25,Owner,Standalone
5307,22.88,Boston,N,25,10,3,48,Graduation,18,Owner,Standalone
214,423,NJ City,S,3,22,5,25,Graduation,31,Patnership,Complex
67,132,NJ City,E,1,20,19,63,High School,29,Owner,Standalone
8647,38.94,NJ City,N,13,18,3,67,High School,21,Owner,Standalone
8786,321.08,NJ City,S,21,8,2,81,No education,20,Patnership,Complex
8890,418,NJ City,S,3,19,16,42,Graduation,27,Patnership,Complex
7570,477,Boston,S,1,18,25,28,Graduation,32,Patnership,Complex
3038,318,NJ City,S,2,21,23,31,High School,24,Patnership,Complex
4085,112,NJ City,N,2,8,12,29,High School,26,Patnership,Standalone
5984,82.51,Boston,W,22,11,3,42,No education,24,Franchise,Complex
8624,485,NJ City,W,2,9,12,26,Graduation,32,Franchise,Complex
5092,43.21,Boston,N,22,13,3,25,High School,25,Franchise,Complex
3613,494.83,NJ City,N,4,11,2,41,High School,20,Patnership,Standalone
2970,219,NJ City,S,3,22,17,37,High School,27,Patnership,Complex
1648,7.18,NJ City,N,1,18,3,43,High School,35,Franchise,Complex
158,188,NJ City,S,3,24,17,26,High School,35,Patnership,Complex
4236,216,NJ City,W,2,21,9,74,High School,21,Franchise,Complex
2656,80.92,NJ City,W,21,12,2,59,High School,32,Franchise,Complex
8975,9.66,NJ City,N,6,19,1,62,No education,32,Owner,Standalone
8119,472,NJ City,E,1,20,5,54,Graduation,20,Owner,Standalone
457,10.78,NJ City,N,4,18,2,40,High School,26,Patnership,Standalone
1090,173,NJ City,S,3,24,6,75,High School,26,Patnership,Complex
5370,458,Boston,S,2,24,10,27,Graduation,33,Patnership,Complex
5631,476,Boston,E,3,18,23,63,Graduation,28,Owner,Standalone
4872,194,NJ City,W,2,10,22,82,High School,34,Franchise,Complex
7793,16.82,Boston,N,24,17,3,28,No education,30,Patnership,Standalone
4130,166,NJ City,S,1,9,8,73,High School,35,Patnership,Complex
3158,293,NJ City,S,1,19,23,43,High School,35,Patnership,Complex
3531,274,NJ City,E,3,15,7,81,High School,25,Owner,Standalone
3794,408.87,NJ City,S,1,10,1,66,No education,20,Patnership,Complex
8137,423,NJ City,N,1,13,19,59,Graduation,29,Patnership,Standalone
4835,338.28,NJ City,E,15,22,4,51,No education,30,Owner,Standalone
1137,103,NJ City,N,2,22,9,57,High School,23,Patnership,Standalone
85,157,Boston,N,2,18,19,56,High School,22,Patnership,Standalone
5785,23.05,Boston,N,4,18,3,29,High School,23,Patnership,Standalone
3738,403.17,NJ City,S,5,9,4,63,Graduation,27,Patnership,Complex
3129,27.59,NJ City,N,12,23,4,75,Graduation,31,Patnership,Standalone
2304,269,NJ City,W,3,21,7,55,High School,28,Franchise,Complex
7953,289.83,Boston,N,1,17,3,81,Graduation,26,Patnership,Standalone
676,151,NJ City,W,2,14,17,67,High School,22,Franchise,Complex
5426,433,Boston,S,3,23,8,39,Graduation,27,Patnership,Complex
4011,106.89,NJ City,E,17,15,1,49,Graduation,18,Owner,Standalone
8338,396,NJ City,S,2,12,7,66,Graduation,31,Patnership,Complex
5849,154.13,Boston,N,9,8,4,33,No education,35,Patnership,Standalone
3896,107,NJ City,W,3,19,17,66,High School,29,Franchise,Complex
549,7.83,NJ City,N,22,9,2,61,Graduation,26,Patnership,Standalone
3545,28.33,NJ City,N,10,19,1,34,No education,28,Patnership,Standalone
9349,40.49,NJ City,N,5,22,4,47,High School,18,Patnership,Standalone
1629,84.46,NJ City,N,8,20,3,75,Graduation,21,Patnership,Standalone
7826,365,Boston,S,3,18,12,56,Graduation,34,Patnership,Complex
3751,497,NJ City,E,3,18,5,47,Graduation,19,Owner,Standalone
3163,130.21,NJ City,E,7,19,2,55,High School,25,Owner,Standalone
4927,190,NJ City,E,3,22,12,66,High School,21,Owner,Standalone
3683,130,NJ City,E,3,11,6,25,High School,35,Owner,Standalone
1125,117.97,NJ City,N,2,17,3,28,Graduation,34,Patnership,Standalone
4587,169,NJ City,E,3,20,21,65,High School,30,Owner,Standalone
4740,281,NJ City,W,3,13,13,67,High School,23,Franchise,Complex
4518,325.85,NJ City,S,2,8,3,80,Graduation,33,Patnership,Complex
5382,374,Boston,S,3,8,14,28,Graduation,27,Patnership,Complex
2578,293,NJ City,S,3,18,17,67,High School,20,Patnership,Complex
9175,107.75,NJ City,E,17,24,3,56,High School,23,Owner,Standalone
9340,125.61,NJ City,W,19,21,3,28,High School,31,Franchise,Complex
4774,278,NJ City,S,1,9,19,77,High School,28,Patnership,Complex
9627,264.21,NJ City,E,2,12,3,42,Graduation,24,Owner,Standalone
9579,15.56,NJ City,N,16,17,1,39,Graduation,26,Owner,Standalone
5569,498.03,Boston,N,23,12,1,59,High School,25,Patnership,Standalone
6510,85.6,Boston,S,9,13,2,44,Graduation,34,Patnership,Complex
1460,16.45,NJ City,N,11,17,4,41,No education,20,Franchise,Complex
9186,62.28,NJ City,N,7,9,3,60,Graduation,22,Patnership,Complex
9291,421,NJ City,E,2,12,22,27,Graduation,28,Owner,Standalone
4044,5.62,NJ City,N,4,9,1,84,Graduation,21,Franchise,Complex
1017,137,NJ City,N,3,8,14,63,High School,23,Patnership,Standalone
285,123,NJ City,N,2,10,22,50,High School,19,Patnership,Standalone
7263,103.68,Boston,E,4,15,1,36,Graduation,24,Owner,Standalone
3165,450,NJ City,N,2,23,5,61,Graduation,20,Patnership,Standalone
2807,146,NJ City,E,1,24,21,74,High School,18,Owner,Standalone
7106,43.31,Boston,N,17,22,4,63,No education,19,Patnership,Complex
7485,81.21,Boston,N,6,12,2,44,Graduation,18,Patnership,Standalone
6670,360,Boston,S,2,23,10,68,Graduation,23,Patnership,Complex
9547,476,NJ City,E,3,11,25,63,Graduation,27,Owner,Standalone
9823,479,NJ City,E,2,12,7,40,Graduation,29,Owner,Standalone
6632,115.53,Boston,W,21,24,2,30,No education,29,Franchise,Complex
5598,384,Boston,S,1,21,9,84,Graduation,28,Patnership,Complex
6483,42.54,Boston,N,9,17,3,55,Graduation,31,Owner,Standalone
3677,233,NJ City,N,2,23,18,43,High School,19,Patnership,Standalone
2187,222,NJ City,E,3,17,19,51,High School,19,Owner,Standalone
5227,482,Boston,E,3,17,18,26,Graduation,29,Owner,Standalone
2897,1.1,NJ City,W,18,15,3,49,No education,33,Patnership,Standalone
4067,62.16,NJ City,N,21,16,3,39,No education,33,Owner,Standalone
359,85.73,NJ City,E,6,15,2,66,No education,33,Owner,Standalone
8126,415,NJ City,S,1,16,21,27,Graduation,28,Patnership,Complex
7558,476,Boston,S,3,18,13,43,Graduation,19,Patnership,Complex
6918,488,Boston,S,3,12,17,25,Graduation,32,Patnership,Complex
3257,190,NJ City,N,3,24,13,49,High School,21,Patnership,Standalone
3021,434.46,NJ City,N,16,17,4,25,Graduation,29,Patnership,Standalone
8288,377,NJ City,W,1,9,9,60,Graduation,19,Franchise,Complex
6017,357,Boston,N,2,23,6,85,Graduation,19,Patnership,Standalone
4191,395,NJ City,E,2,9,5,61,Graduation,21,Owner,Standalone
2209,244,NJ City,N,2,23,23,82,High School,28,Patnership,Standalone
7143,355.1,Boston,E,10,15,4,81,Graduation,31,Owner,Standalone
20,159.61,NY City,W,12,22,1,84,No education,34,Franchise,Complex
1779,165,NJ City,E,2,10,10,32,High School,35,Owner,Standalone
1914,114,NJ City,S,2,20,20,58,High School,27,Patnership,Complex
9309,385.03,NJ City,N,19,23,3,79,Graduation,35,Patnership,Standalone
8557,452,NJ City,N,2,14,12,34,Graduation,31,Patnership,Standalone
364,39.48,NJ City,N,5,15,2,79,High School,34,Franchise,Complex
7404,364,Boston,W,2,16,5,68,Graduation,25,Franchise,Complex
2573,78.8,NJ City,N,24,23,2,37,No education,29,Patnership,Standalone
6126,352,Boston,S,2,20,15,84,Graduation,34,Patnership,Complex
8078,262.61,NJ City,S,1,13,3,46,No education,34,Patnership,Complex
8072,429,NJ City,W,3,24,7,54,Graduation,24,Franchise,Complex
7028,472,Boston,W,3,18,8,41,Graduation,29,Franchise,Complex
9436,428.44,NJ City,W,15,12,2,44,High School,31,Franchise,Complex
3748,34.58,NJ City,N,12,19,4,68,High School,27,Franchise,Complex
9834,429,NJ City,S,2,21,25,63,Graduation,26,Patnership,Complex
3885,100,NJ City,N,3,13,14,41,High School,21,Patnership,Standalone
1978,146,NJ City,S,1,8,17,65,High School,23,Patnership,Complex
4618,53.57,NJ City,S,22,19,4,40,High School,21,Patnership,Complex
5396,383,Boston,W,3,20,17,34,Graduation,30,Franchise,Complex
7484,355,Boston,W,1,21,9,36,Graduation,20,Franchise,Complex
1768,30.57,NJ City,N,22,14,2,53,High School,26,Franchise,Complex
2406,258,NJ City,S,2,15,24,25,High School,24,Patnership,Complex
5808,399,Boston,W,2,11,21,44,Graduation,22,Franchise,Complex
7513,357,Boston,N,2,24,17,59,Graduation,28,Patnership,Standalone
4506,278.48,NJ City,S,17,18,2,58,Graduation,19,Patnership,Complex
8559,372,NJ City,E,1,24,20,80,Graduation,21,Owner,Standalone
8793,440,NJ City,N,2,12,25,77,Graduation,27,Patnership,Standalone
3194,8.55,NJ City,N,4,23,1,33,No education,24,Patnership,Complex
9170,440.21,NJ City,S,1,9,2,49,No education,28,Patnership,Complex
8187,443,NJ City,E,3,15,12,44,Graduation,19,Owner,Standalone
7960,102.2,Boston,W,6,11,3,79,High School,20,Franchise,Complex
5042,357,Boston,S,1,13,18,39,Graduation,21,Patnership,Complex
8294,458,NJ City,S,3,9,14,60,Graduation,31,Patnership,Complex
5562,351,Boston,S,2,15,23,52,Graduation,21,Patnership,Complex
1209,10.92,NJ City,N,12,20,2,78,Graduation,29,Patnership,Standalone
8750,489,NJ City,S,2,19,22,38,Graduation,28,Patnership,Complex
5650,449,Boston,S,1,21,14,31,Graduation,18,Patnership,Complex
2124,208,NJ City,W,1,24,16,71,High School,20,Franchise,Complex
8591,13.37,NJ City,N,12,20,2,37,No education,18,Owner,Standalone
6288,430,Boston,W,3,17,9,39,Graduation,27,Franchise,Complex
205,115,NJ City,N,3,20,7,40,High School,31,Patnership,Standalone
7346,368,Boston,S,2,12,6,66,Graduation,24,Patnership,Complex
5686,392,Boston,S,1,18,24,54,Graduation,21,Patnership,Complex
4198,2.71,NJ City,S,8,12,1,53,High School,27,Patnership,Complex
5331,117.9,Boston,E,11,13,3,80,Graduation,23,Owner,Standalone
1405,5.33,NJ City,N,11,20,3,25,High School,26,Patnership,Standalone
2413,143,NJ City,N,2,8,11,42,High School,25,Patnership,Standalone
8700,46.69,NJ City,N,9,19,1,26,Graduation,18,Franchise,Complex
3968,288,NJ City,W,1,22,11,30,High School,29,Franchise,Complex
976,120,NJ City,W,2,19,9,51,High School,30,Franchise,Complex
6815,87.33,Boston,E,12,15,4,85,No education,24,Owner,Standalone
6332,384,Boston,W,1,15,17,42,Graduation,19,Franchise,Complex
5634,481,Boston,S,2,24,6,53,Graduation,26,Patnership,Complex
2536,113.42,NJ City,W,1,9,2,60,High School,32,Franchise,Complex
5654,368,Boston,S,2,9,7,73,Graduation,24,Patnership,Complex
7517,436,Boston,N,1,9,22,41,Graduation,25,Patnership,Standalone
7872,490,Boston,W,1,18,14,54,Graduation,26,Franchise,Complex
2492,244,NJ City,W,3,18,15,83,High School,29,Franchise,Complex
9355,383,NJ City,E,2,9,6,48,Graduation,35,Owner,Standalone
9967,377,NJ City,E,3,9,21,75,Graduation,25,Owner,Standalone
4177,42.12,NJ City,N,25,24,4,51,High School,23,Patnership,Standalone
3580,156,NJ City,W,2,11,11,27,High School,28,Franchise,Complex
687,137,NJ City,E,1,22,14,77,High School,26,Owner,Standalone
2823,238,NJ City,E,1,21,25,40,High School,21,Owner,Standalone
358,112,NJ City,S,3,9,11,52,High School,18,Patnership,Complex
4761,21.52,NJ City,N,19,17,1,46,Graduation,25,Patnership,Standalone
3825,173,NJ City,N,2,15,24,72,High School,25,Patnership,Standalone
6737,474.49,Boston,N,16,24,1,84,No education,27,Patnership,Standalone
6417,359,Boston,N,3,18,25,29,Graduation,31,Patnership,Standalone
4483,292,NJ City,E,3,16,12,83,High School,23,Owner,Standalone
4909,158,NJ City,N,1,19,23,77,High School,26,Patnership,Standalone
128,354.2,NJ City,W,24,21,2,74,No education,20,Franchise,Complex
5334,440,Boston,S,1,19,7,51,Graduation,27,Patnership,Complex
7667,354,Boston,E,3,21,18,67,Graduation,22,Owner,Standalone
7746,406,Boston,S,2,17,13,47,Graduation,23,Patnership,Complex
7866,465,Boston,S,2,12,11,67,Graduation,22,Patnership,Complex
6542,71.68,Boston,N,19,15,3,39,No education,21,Patnership,Complex
4602,43.09,NJ City,N,13,22,1,73,Graduation,19,Patnership,Complex
2185,75.91,NJ City,N,25,11,2,51,High School,26,Patnership,Standalone
190,262,NJ City,S,1,16,13,27,High School,24,Patnership,Complex
8733,90.31,NJ City,N,2,16,4,54,Graduation,20,Patnership,Standalone
818,285.31,NJ City,S,23,15,2,58,No education,35,Patnership,Complex
1585,154,NJ City,N,3,12,7,34,High School,28,Patnership,Standalone
3288,106.63,NJ City,W,22,14,1,42,Graduation,20,Franchise,Complex
1104,107,NJ City,W,1,12,20,45,High School,18,Franchise,Complex
9666,122.48,NJ City,S,25,20,2,62,Graduation,33,Patnership,Complex
4751,267,NJ City,E,2,23,19,40,High School,18,Owner,Standalone
6463,402,Boston,E,2,19,21,44,Graduation,20,Owner,Standalone
2302,295,NJ City,S,3,15,11,59,High School,22,Patnership,Complex
5865,435,Boston,N,2,15,13,74,Graduation,34,Patnership,Standalone
8262,413,NJ City,S,3,19,16,83,Graduation,23,Patnership,Complex
1134,449.74,NJ City,S,16,15,2,47,Graduation,21,Patnership,Complex
4667,115,NJ City,E,3,18,20,25,High School,32,Owner,Standalone
384,50.19,NJ City,W,12,15,3,54,Graduation,23,Franchise,Complex
8098,437,NJ City,S,2,22,23,38,Graduation,35,Patnership,Complex
9497,57.78,NJ City,N,18,22,4,51,No education,28,Patnership,Standalone
4604,36.75,NJ City,N,6,12,1,76,No education,27,Franchise,Complex
762,295.1,NJ City,S,15,21,1,26,Graduation,30,Patnership,Complex
1893,176,NJ City,N,1,12,7,27,High School,23,Patnership,Standalone
2244,197,NJ City,W,2,22,20,59,High School,21,Franchise,Complex
3916,53.69,NJ City,W,6,18,2,65,High School,18,Franchise,Complex
9730,495.28,NJ City,S,22,23,4,35,High School,29,Patnership,Complex
3077,195,NJ City,N,1,21,12,61,High School,24,Patnership,Standalone
4159,166,NJ City,E,2,18,12,35,High School,22,Owner,Standalone
2202,258,NJ City,S,1,17,23,55,High School,34,Patnership,Complex
8504,19.8,NJ City,N,4,23,3,60,No education,19,Franchise,Complex
8349,421,NJ City,N,3,15,19,63,Graduation,22,Patnership,Standalone
3551,310,NJ City,E,1,22,13,59,High School,23,Owner,Standalone
804,199,NJ City,W,3,21,6,49,High School,21,Franchise,Complex
930,214,NJ City,S,1,8,21,45,High School,32,Patnership,Complex
5310,406,Boston,S,3,10,23,38,Graduation,19,Patnership,Complex
9078,380,NJ City,S,2,20,8,51,Graduation,28,Patnership,Complex
5270,435,Boston,S,1,21,21,41,Graduation,26,Patnership,Complex
6813,84.8,Boston,N,14,13,1,62,Graduation,31,Patnership,Standalone
4403,276,NJ City,E,3,17,20,26,High School,26,Owner,Standalone
6118,405,Boston,S,3,10,14,29,Graduation,22,Patnership,Complex
9840,463,NJ City,W,1,20,18,28,Graduation,23,Franchise,Complex
900,38.64,NJ City,N,7,24,3,26,Graduation,25,Franchise,Complex
8895,429,NJ City,E,1,22,9,60,Graduation,32,Owner,Standalone
2419,488.54,NJ City,E,21,23,4,66,High School,26,Owner,Standalone
8275,475,NJ City,E,3,18,14,35,Graduation,21,Owner,Standalone
2547,290,NJ City,E,2,22,18,49,High School,21,Owner,Standalone
274,124.82,NJ City,S,21,20,3,34,High School,20,Patnership,Complex
99,115,NJ City,E,1,8,14,28,High School,30,Owner,Standalone
3105,112,NJ City,N,2,14,15,80,High School,26,Patnership,Standalone
9857,365,NJ City,N,3,23,23,63,Graduation,32,Patnership,Standalone
1094,20.49,NJ City,N,16,17,3,36,No education,34,Patnership,Complex
7686,406,Boston,S,1,24,10,39,Graduation,34,Patnership,Complex
9693,122.08,NJ City,N,25,9,3,47,Graduation,35,Patnership,Standalone
5174,70.54,Boston,N,19,11,3,57,No education,30,Patnership,Complex
8509,473,NJ City,N,1,23,6,67,Graduation,19,Patnership,Standalone
6469,27.71,Boston,N,2,21,1,78,High School,25,Patnership,Standalone
5657,89.54,Boston,N,18,10,1,62,No education,32,Patnership,Standalone
345,280,NJ City,N,3,20,12,45,High School,31,Patnership,Standalone
4012,241,NJ City,W,3,24,20,73,High School,34,Franchise,Complex
6783,367,Boston,E,2,8,5,67,Graduation,21,Owner,Standalone
5362,471,Boston,S,2,10,15,81,Graduation,31,Patnership,Complex
336,223,NJ City,W,3,13,10,76,High School,26,Franchise,Complex
8747,496,NJ City,E,1,21,9,85,Graduation,30,Owner,Standalone
6706,434,Boston,S,2,15,24,72,Graduation,21,Patnership,Complex
7721,396,Boston,N,2,22,17,28,Graduation,19,Patnership,Standalone
2989,89.42,NJ City,N,10,11,1,52,High School,25,Patnership,Standalone
3945,304,NJ City,N,1,19,11,65,High School,23,Patnership,Standalone
7534,494,Boston,S,1,15,9,49,Graduation,19,Patnership,Complex
7612,405,Boston,W,3,13,5,56,Graduation,32,Franchise,Complex
9562,387,NJ City,S,3,16,16,67,Graduation,30,Patnership,Complex
629,64.99,NJ City,N,5,22,1,70,No education,23,Patnership,Standalone
5729,447,Boston,N,2,17,7,36,Graduation,21,Patnership,Standalone
5745,379,Boston,N,2,22,21,60,Graduation,31,Patnership,Standalone
5672,477,Boston,W,2,9,16,50,Graduation,22,Franchise,Complex
7589,369,Boston,N,2,23,11,38,Graduation,25,Patnership,Standalone
7211,492,Boston,E,2,23,14,74,Graduation,28,Owner,Standalone
7698,499,Boston,S,1,13,12,49,Graduation,32,Patnership,Complex
5237,451,Boston,N,3,16,14,78,Graduation,35,Patnership,Standalone
9449,62.58,NJ City,N,7,20,3,32,No education,22,Patnership,Standalone
9994,461,NJ City,S,2,18,25,73,Graduation,30,Patnership,Complex
9,166,Maine,N,3,21,24,73,High School,27,Patnership,Standalone
4468,90.77,NJ City,W,4,12,2,60,High School,21,Franchise,Complex
392,220,NJ City,W,1,24,22,33,High School,18,Franchise,Complex
8304,184.78,NJ City,W,6,24,4,42,Graduation,25,Franchise,Complex
8815,57.86,NJ City,E,1,19,2,48,High School,32,Owner,Standalone
7813,253.67,Boston,N,22,20,3,76,High School,32,Patnership,Standalone
9238,354,NJ City,S,3,23,23,39,Graduation,26,Patnership,Complex
7919,56.64,Boston,E,4,19,3,49,No education,33,Owner,Standalone
6423,402,Boston,E,3,24,10,27,Graduation,35,Owner,Standalone
7907,123.36,Boston,E,9,8,4,53,No education,18,Owner,Standalone
9721,354,NJ City,N,3,9,20,35,Graduation,27,Patnership,Standalone
7095,440,Boston,E,3,15,21,66,Graduation,27,Owner,Standalone
4255,0.22,NJ City,N,20,13,3,46,High School,29,Owner,Standalone
188,426,NJ City,W,1,12,5,52,Graduation,27,Franchise,Complex
1866,451.84,NJ City,S,2,8,1,25,Graduation,24,Patnership,Complex
1162,19.46,NJ City,N,14,10,2,53,High School,35,Patnership,Complex
8832,78.53,NJ City,W,20,24,2,65,Graduation,26,Franchise,Complex
4623,210,NJ City,E,3,9,21,62,High School,22,Owner,Standalone
5198,380,Boston,S,3,23,19,38,Graduation,28,Patnership,Complex
4638,47.28,NJ City,N,18,8,1,44,Graduation,28,Patnership,Complex
4591,213,NJ City,E,2,23,24,28,High School,33,Owner,Standalone
4601,289,NJ City,N,2,19,6,59,High School,18,Patnership,Standalone
3388,3.47,NJ City,N,21,13,4,36,High School,23,Franchise,Complex
9919,374,NJ City,E,3,10,6,32,Graduation,32,Owner,Standalone
704,284,NJ City,W,2,24,18,34,High School,35,Franchise,Complex
2376,145,NJ City,W,1,21,12,52,High School,26,Franchise,Complex
5271,362,Boston,E,2,14,15,44,Graduation,19,Owner,Standalone
8961,440,NJ City,N,3,9,18,81,Graduation,19,Patnership,Standalone
4129,156,NJ City,N,2,11,23,67,High School,22,Patnership,Standalone
7840,104.82,Boston,W,9,23,2,72,High School,33,Franchise,Complex
2017,260,NJ City,N,3,17,6,79,High School,26,Patnership,Standalone
3446,237,NJ City,S,2,8,7,39,High School,28,Patnership,Complex
4227,281,NJ City,E,2,11,15,72,High School,24,Owner,Standalone
9699,359,NJ City,E,3,17,11,30,Graduation,20,Owner,Standalone
9930,447,NJ City,S,1,19,20,28,Graduation,31,Patnership,Complex
2928,120,NJ City,W,3,10,19,66,High School,28,Franchise,Complex
124,45.1,NJ City,N,24,16,3,45,High School,27,Franchise,Complex
27,249,Boston,E,2,10,10,84,High School,24,Owner,Standalone
8978,361,NJ City,S,2,10,12,46,Graduation,24,Patnership,Complex
3902,283.52,NJ City,S,5,19,2,26,No education,21,Patnership,Complex
7997,395,Boston,N,2,12,13,60,Graduation,22,Patnership,Standalone
6605,428.53,Boston,N,13,16,4,34,No education,33,Patnership,Standalone
9926,432,NJ City,S,3,20,8,30,Graduation,18,Patnership,Complex
4951,13.74,Boston,N,3,16,4,66,High School,34,Owner,Standalone
8584,52.95,NJ City,W,25,8,3,83,High School,22,Franchise,Complex
6768,390,Boston,W,3,19,18,80,Graduation,30,Franchise,Complex
4214,117,NJ City,S,1,17,19,72,High School,28,Patnership,Complex
7375,426,Boston,E,1,18,7,69,Graduation,22,Owner,Standalone
8281,66.26,NJ City,N,14,9,3,70,High School,32,Patnership,Standalone
3478,271,NJ City,S,3,22,9,67,High School,35,Patnership,Complex
2728,61.25,NJ City,N,15,21,2,72,High School,24,Franchise,Complex
2854,304,NJ City,S,2,8,15,76,High School,24,Patnership,Complex
5694,365,Boston,S,3,15,6,37,Graduation,22,Patnership,Complex
563,168,NJ City,E,2,21,23,68,High School,28,Owner,Standalone
559,127,NJ City,E,1,19,21,50,High School,29,Owner,Standalone
360,44.03,NJ City,N,23,19,3,72,Graduation,29,Franchise,Complex
9501,478,NJ City,N,1,11,21,64,Graduation,22,Patnership,Standalone
3470,269,NJ City,S,3,8,25,79,High School,22,Patnership,Complex
1797,427,NJ City,N,2,18,5,53,Graduation,28,Patnership,Standalone
2180,61.3,NJ City,N,25,12,4,72,No education,35,Franchise,Complex
653,137,NJ City,N,2,9,13,48,High School,25,Patnership,Standalone
6908,440.55,Boston,W,18,22,1,65,No education,21,Franchise,Complex
1071,391.36,NJ City,E,8,14,3,37,Graduation,26,Owner,Standalone
2190,24.33,NJ City,N,6,22,4,75,Graduation,30,Patnership,Complex
6034,362,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex
6674,410,Boston,S,2,12,23,46,Graduation,31,Patnership,Complex
9022,378,NJ City,S,2,14,14,48,Graduation,22,Patnership,Complex
7386,410,Boston,S,1,23,8,84,Graduation,23,Patnership,Complex
7088,469,Boston,W,1,17,13,37,Graduation,30,Franchise,Complex
312,243,NJ City,W,2,16,13,59,High School,22,Franchise,Complex
3416,252,NJ City,W,2,8,17,43,High School,28,Franchise,Complex
3368,276,NJ City,W,1,21,8,80,High School,22,Franchise,Complex
6627,498,Boston,E,1,10,8,76,Graduation,35,Owner,Standalone
805,132,NJ City,N,1,16,23,58,High School,20,Patnership,Standalone
8356,492,NJ City,W,3,15,20,43,Graduation,21,Franchise,Complex
8694,298.9,NJ City,S,6,13,3,28,Graduation,21,Patnership,Complex
1958,68.72,NJ City,N,2,17,3,31,No education,35,Patnership,Complex
198,126,NJ City,S,1,11,25,31,High School,19,Patnership,Complex
7678,7.57,Boston,N,9,12,3,72,High School,22,Patnership,Complex
7050,371,Boston,S,1,13,13,67,Graduation,35,Patnership,Complex
3530,124.12,NJ City,S,11,24,4,69,No education,22,Patnership,Complex
2993,162,NJ City,N,3,16,11,28,High School,20,Patnership,Standalone
9689,389,NJ City,N,3,8,24,55,Graduation,30,Patnership,Standalone
6633,416,Boston,N,3,24,17,74,Graduation,30,Patnership,Standalone
831,229,NJ City,E,3,8,14,61,High School,22,Owner,Standalone
9778,493,NJ City,S,3,10,11,81,Graduation,30,Patnership,Complex
9615,367,NJ City,E,1,13,23,62,Graduation,27,Owner,Standalone
3823,356.7,NJ City,E,1,8,3,35,High School,29,Owner,Standalone
8759,118.95,NJ City,E,11,21,2,72,No education,19,Owner,Standalone
6431,463.72,Boston,E,15,8,1,37,No education,19,Owner,Standalone
7378,407,Boston,S,2,9,7,49,Graduation,27,Patnership,Complex
1942,145,NJ City,S,3,13,8,52,High School,24,Patnership,Complex
8805,395,NJ City,N,3,21,13,84,Graduation,20,Patnership,Standalone
3678,150,NJ City,S,1,17,6,46,High School,23,Patnership,Complex
195,488.82,NJ City,E,13,20,3,80,Graduation,20,Owner,Standalone
950,184,NJ City,S,1,10,14,65,High School,27,Patnership,Complex
1504,305,NJ City,W,1,13,22,37,High School,24,Franchise,Complex
7893,387,Boston,N,1,15,18,29,Graduation,27,Patnership,Standalone
7964,80.63,Boston,W,12,11,3,71,No education,33,Franchise,Complex
9099,350,NJ City,E,3,15,20,51,Graduation,33,Owner,Standalone
2991,291,NJ City,E,2,15,19,58,High School,21,Owner,Standalone
2808,134.03,NJ City,W,17,16,3,46,Graduation,25,Franchise,Complex
7067,450,Boston,E,1,13,6,72,Graduation,31,Owner,Standalone
8448,469,NJ City,W,2,8,8,77,Graduation,21,Franchise,Complex
1427,146,NJ City,E,2,10,8,29,High School,28,Owner,Standalone
272,152,NJ City,W,1,19,17,51,High School,19,Franchise,Complex
5509,356,Boston,N,1,17,8,48,Graduation,21,Patnership,Standalone
527,403,NJ City,E,2,21,5,41,Graduation,22,Owner,Standalone
4336,268.01,NJ City,W,16,15,1,63,High School,29,Franchise,Complex
911,288,NJ City,E,1,13,15,85,High School,21,Owner,Standalone
1981,222,NJ City,N,1,11,22,64,High School,18,Patnership,Standalone
2476,279,NJ City,W,2,16,13,37,High School,27,Franchise,Complex
2565,157,NJ City,N,1,11,24,82,High School,32,Patnership,Standalone
5238,366,Boston,S,1,11,14,56,Graduation,34,Patnership,Complex
2653,197,NJ City,N,2,14,20,70,High School,35,Patnership,Standalone
6841,98.8,Boston,N,13,20,1,54,High School,20,Patnership,Standalone
2844,234,NJ City,W,3,17,7,67,High School,26,Franchise,Complex
9681,376.28,NJ City,N,18,11,1,66,Graduation,25,Patnership,Standalone
1203,120,NJ City,E,3,17,17,77,High School,23,Owner,Standalone
774,24.25,NJ City,N,10,11,1,33,Graduation,25,Patnership,Complex
1523,266,NJ City,E,2,8,25,58,High School,35,Owner,Standalone
8409,455,NJ City,N,3,18,12,39,Graduation,21,Patnership,Standalone
1342,297,NJ City,S,1,17,7,62,High School,30,Patnership,Complex
1087,219.05,NJ City,E,4,24,2,84,High School,32,Owner,Standalone
8279,430,NJ City,E,3,14,16,35,Graduation,28,Owner,Standalone
8434,18.34,NJ City,N,2,18,4,58,High School,18,Patnership,Complex
181,240,NJ City,N,2,12,24,59,High School,22,Patnership,Standalone
9219,59.06,NJ City,E,15,18,4,50,Graduation,31,Owner,Standalone
5867,397,Boston,E,1,24,6,49,Graduation,23,Owner,Standalone
8718,459,NJ City,S,2,23,22,34,Graduation,29,Patnership,Complex
367,196,NJ City,E,3,20,17,77,High School,25,Owner,Standalone
3282,197,NJ City,S,1,12,9,55,High School,22,Patnership,Complex
7162,351,Boston,S,3,18,14,66,Graduation,32,Patnership,Complex
2915,105,NJ City,E,1,21,21,74,High School,23,Owner,Standalone
1259,371.92,NJ City,E,13,14,2,57,No education,23,Owner,Standalone
1029,241,NJ City,N,1,15,8,83,High School,23,Patnership,Standalone
517,22.49,NJ City,N,3,15,3,77,High School,32,Patnership,Standalone
3149,266,NJ City,N,1,22,16,29,High School,29,Patnership,Standalone
5777,453,Boston,N,2,12,18,49,Graduation,20,Patnership,Standalone
2816,411.98,NJ City,W,25,20,3,48,No education,35,Franchise,Complex
2070,218,NJ City,S,1,16,13,35,High School,22,Patnership,Complex
8439,371,NJ City,E,2,8,5,33,Graduation,27,Owner,Standalone
1323,231,NJ City,E,2,22,17,60,High School,27,Owner,Standalone
6812,375.11,Boston,W,4,10,2,47,No education,23,Franchise,Complex
6026,75.49,Boston,S,8,23,3,49,No education,26,Patnership,Complex
1803,69.48,NJ City,N,5,16,1,66,Graduation,25,Owner,Standalone
6502,18.17,Boston,N,9,19,3,48,High School,26,Patnership,Complex
8134,479,NJ City,S,1,14,13,84,Graduation,21,Patnership,Complex
1514,113.68,NJ City,S,21,16,3,26,No education,29,Patnership,Complex
4169,121,NJ City,N,1,18,25,41,High School,19,Patnership,Standalone
1637,57.38,NJ City,N,1,20,1,39,No education,20,Patnership,Standalone
2308,13.2,NJ City,N,10,16,3,30,High School,29,Franchise,Complex
8525,83.85,NJ City,N,22,24,3,68,No education,30,Patnership,Standalone
9801,404,NJ City,N,1,16,22,66,Graduation,26,Patnership,Standalone
1827,389,NJ City,E,2,19,5,58,Graduation,22,Owner,Standalone
5157,357,Boston,N,3,24,15,25,Graduation,18,Patnership,Standalone
1586,238,NJ City,S,1,22,6,67,High School,30,Patnership,Complex
6202,374,Boston,S,2,14,7,51,Graduation,30,Patnership,Complex
2649,155,NJ City,N,2,13,25,32,High School,35,Patnership,Standalone
8842,496,NJ City,S,2,15,9,56,Graduation,18,Patnership,Complex
4434,306,NJ City,S,3,24,17,50,High School,21,Patnership,Complex
7938,422,Boston,S,2,16,12,36,Graduation,35,Patnership,Complex
8324,72.96,NJ City,N,18,16,1,64,No education,22,Franchise,Complex
7573,430,Boston,N,2,15,6,60,Graduation,25,Patnership,Standalone
5704,46.23,Boston,N,2,21,2,81,High School,33,Franchise,Complex
4109,114,NJ City,N,2,16,17,72,High School,33,Patnership,Standalone
5647,113.12,Boston,E,11,10,3,42,High School,31,Owner,Standalone
437,3.16,NJ City,N,21,9,3,33,No education,22,Patnership,Standalone
7283,465,Boston,E,2,20,13,33,Graduation,24,Owner,Standalone
8450,474,NJ City,S,3,9,8,64,Graduation,31,Patnership,Complex
5,185,NY City,N,2,9,25,29,High School,32,Franchise,Complex
8498,437,NJ City,S,1,8,15,35,Graduation,24,Patnership,Complex
8882,396,NJ City,S,1,8,7,32,Graduation,29,Patnership,Complex
2519,282,NJ City,E,1,18,18,38,High School,30,Owner,Standalone
7723,465,Boston,E,1,20,18,53,Graduation,18,Owner,Standalone
1853,45.66,NJ City,N,15,24,2,49,No education,30,Patnership,Standalone
1364,113.4,NJ City,W,17,10,3,60,No education,29,Franchise,Complex
3938,82.32,NJ City,S,22,23,3,66,No education,19,Patnership,Complex
4484,113.87,NJ City,W,3,18,4,32,No education,25,Franchise,Complex
3088,291,NJ City,W,3,9,11,79,High School,34,Franchise,Complex
1619,241,NJ City,E,2,12,7,40,High School,18,Owner,Standalone
8888,370,NJ City,W,1,16,19,51,Graduation,22,Franchise,Complex
3851,93.4,NJ City,E,10,16,2,38,No education,33,Owner,Standalone
916,122.13,NJ City,W,11,11,4,55,High School,23,Franchise,Complex
9119,396.79,NJ City,E,7,18,4,47,No education,24,Owner,Standalone
5080,450,Boston,W,3,10,11,47,Graduation,18,Franchise,Complex
8913,348.33,NJ City,N,12,19,4,29,Graduation,31,Patnership,Standalone
3930,125,NJ City,S,3,19,11,34,High School,18,Patnership,Complex
1345,131,NJ City,N,3,23,20,73,High School,22,Patnership,Standalone
6529,493,Boston,N,2,17,10,64,Graduation,29,Patnership,Standalone
8517,398,NJ City,N,3,8,11,60,Graduation,33,Patnership,Standalone
2234,53.13,NJ City,S,23,15,4,82,No education,21,Patnership,Complex
9262,91.8,NJ City,S,3,8,3,69,High School,33,Patnership,Complex
2103,117.26,NJ City,E,6,10,1,35,Graduation,27,Owner,Standalone
2627,180,NJ City,E,2,21,13,66,High School,30,Owner,Standalone
8306,443,NJ City,S,1,15,24,83,Graduation,29,Patnership,Complex
690,172,NJ City,S,1,19,19,61,High School,20,Patnership,Complex
9639,405,NJ City,E,1,13,24,83,Graduation,31,Owner,Standalone
6384,473,Boston,W,2,12,6,59,Graduation,25,Franchise,Complex
7511,1.61,Boston,N,1,14,1,26,No education,23,Owner,Standalone
9941,351,NJ City,N,2,22,8,73,Graduation,22,Patnership,Standalone
8303,360,NJ City,E,3,24,18,73,Graduation,25,Owner,Standalone
1261,6.98,NJ City,N,19,14,1,52,High School,19,Patnership,Standalone
9598,362,NJ City,S,1,15,17,36,Graduation,26,Patnership,Complex
2340,47.35,NJ City,W,22,11,1,66,Graduation,25,Franchise,Complex
1188,130,NJ City,W,2,15,23,84,High School,29,Franchise,Complex
5738,476,Boston,S,3,10,9,70,Graduation,22,Patnership,Complex
7908,432,Boston,W,3,17,7,45,Graduation,22,Franchise,Complex
3435,78.43,NJ City,E,18,21,1,43,Graduation,27,Owner,Standalone
7161,457.67,Boston,N,18,19,2,62,Graduation,18,Patnership,Standalone
2259,88.45,NJ City,E,13,16,4,67,Graduation,24,Owner,Standalone
6135,5.47,Boston,N,17,11,3,35,Graduation,21,Owner,Standalone
6895,352,Boston,E,3,10,11,81,Graduation,34,Owner,Standalone
193,62.07,NJ City,N,1,24,1,60,High School,29,Patnership,Standalone
196,275,NJ City,W,1,8,21,45,High School,20,Franchise,Complex
5546,430,Boston,S,1,22,18,61,Graduation,33,Patnership,Complex
5208,432,Boston,W,3,19,6,66,Graduation,19,Franchise,Complex
6601,7.71,Boston,N,13,10,4,61,High School,23,Patnership,Standalone
3811,12.44,NJ City,N,13,23,2,41,High School,19,Owner,Standalone
719,28.8,NJ City,N,3,13,1,76,No education,19,Owner,Standalone
5952,264,Boston,W,5,20,4,40,Graduation,22,Franchise,Complex
2960,111,NJ City,W,2,8,23,67,High School,27,Franchise,Complex
4854,151.76,NJ City,S,14,18,2,69,Graduation,32,Patnership,Complex
3320,184,NJ City,W,2,8,23,36,High School,25,Franchise,Complex
5595,475,Boston,E,2,14,17,66,Graduation,24,Owner,Standalone
1685,272.67,NJ City,N,13,24,1,31,No education,18,Patnership,Standalone
3730,230.25,NJ City,S,18,15,4,80,High School,24,Patnership,Complex
5552,10.62,Boston,N,25,8,4,57,No education,27,Franchise,Complex
5253,353,Boston,N,1,15,13,73,Graduation,35,Patnership,Standalone
8686,382,NJ City,S,2,17,17,54,Graduation,35,Patnership,Complex
3252,258,NJ City,W,2,15,8,38,High School,20,Franchise,Complex
3526,112.73,NJ City,S,2,21,4,72,High School,33,Patnership,Complex
4677,109,NJ City,N,3,17,25,68,High School,35,Patnership,Standalone
686,218,NJ City,S,1,20,6,38,High School,28,Patnership,Complex
1707,29.98,NJ City,N,13,21,3,37,Graduation,31,Owner,Standalone
7572,105.9,Boston,W,12,12,1,26,Graduation,31,Franchise,Complex
1900,51.8,NJ City,W,5,8,4,81,High School,25,Franchise,Complex
7601,410,Boston,N,1,20,16,64,Graduation,21,Patnership,Standalone
1952,434.62,NJ City,W,20,17,3,79,No education,21,Franchise,Complex
2495,154,NJ City,E,3,13,24,55,High School,23,Owner,Standalone
8335,389,NJ City,E,2,13,9,55,Graduation,21,Owner,Standalone
3848,174,NJ City,W,1,21,20,34,High School,32,Franchise,Complex
5419,318.49,Boston,E,4,24,3,84,High School,31,Owner,Standalone
3140,16,NJ City,N,20,24,2,55,No education,24,Franchise,Complex
7023,132.94,Boston,E,10,22,4,83,Graduation,27,Owner,Standalone
6211,0.75,Boston,N,3,14,3,59,High School,32,Owner,Standalone
6347,408.52,Boston,E,12,18,2,75,No education,31,Owner,Standalone
2389,62.49,NJ City,N,24,14,3,82,High School,34,Patnership,Standalone
2175,16.71,NJ City,N,16,19,2,67,Graduation,24,Owner,Standalone
2324,28.17,NJ City,N,7,10,1,31,No education,21,Franchise,Complex
4641,244,NJ City,N,2,20,8,76,High School,27,Patnership,Standalone
7910,291.45,Boston,S,14,9,4,40,No education,31,Patnership,Complex
3358,150,NJ City,S,2,20,24,69,High School,18,Patnership,Complex
6894,79.93,Boston,S,7,18,4,70,Graduation,23,Patnership,Complex
5920,435,Boston,W,2,24,15,54,Graduation,28,Franchise,Complex
4310,38.86,NJ City,N,11,23,2,51,No education,31,Patnership,Complex
5197,52.48,Boston,N,12,18,3,34,High School,18,Patnership,Standalone
5945,6.69,Boston,N,19,23,1,43,No education,30,Patnership,Standalone
9230,475,NJ City,S,3,10,5,28,Graduation,33,Patnership,Complex
7049,413,Boston,N,1,18,14,41,Graduation,29,Patnership,Standalone
9845,372,NJ City,N,3,16,25,54,Graduation,24,Patnership,Standalone
8506,450,NJ City,S,1,12,20,38,Graduation,23,Patnership,Complex
8452,367,NJ City,W,1,10,25,81,Graduation,29,Franchise,Complex
40,166,NY City,W,3,24,14,27,High School,34,Franchise,Complex
6170,46.46,Boston,N,10,10,3,78,No education,34,Patnership,Complex
9883,438,NJ City,E,1,15,9,68,Graduation,18,Owner,Standalone
7879,398,Boston,E,3,18,20,57,Graduation,34,Owner,Standalone
2137,138,NJ City,N,1,14,6,40,High School,29,Patnership,Standalone
357,135.2,NJ City,N,3,14,4,40,Graduation,34,Patnership,Standalone
262,243.02,NJ City,S,2,24,4,49,High School,35,Patnership,Complex
7999,46.88,Boston,N,18,10,4,78,High School,34,Owner,Standalone
2708,272,NJ City,W,2,19,13,38,High School,34,Franchise,Complex
7081,464,Boston,N,3,24,13,66,Graduation,30,Patnership,Standalone
2236,63.52,NJ City,N,1,13,2,30,High School,35,Franchise,Complex
8184,495,NJ City,W,3,12,13,60,Graduation,18,Franchise,Complex
2346,185,NJ City,S,1,12,19,38,High School,20,Patnership,Complex
9851,390,NJ City,E,3,23,20,71,Graduation,27,Owner,Standalone
4569,104,NJ City,N,1,12,20,77,High School,20,Patnership,Standalone
6555,108.25,Boston,E,21,13,1,59,Graduation,30,Owner,Standalone
6276,466,Boston,W,1,20,25,61,Graduation,20,Franchise,Complex
8103,455,NJ City,E,2,19,23,26,Graduation,18,Owner,Standalone
5935,498,Boston,E,2,9,9,46,Graduation,26,Owner,Standalone
6742,34.75,Boston,N,12,10,3,42,High School,32,Patnership,Complex
8737,493,NJ City,N,2,14,20,35,Graduation,18,Patnership,Standalone
3857,20.14,NJ City,N,16,12,2,55,No education,20,Patnership,Standalone
3321,36.67,NJ City,N,11,17,4,55,Graduation,27,Patnership,Standalone
8271,382,NJ City,E,2,20,11,67,Graduation,20,Owner,Standalone
9464,434.77,NJ City,W,2,21,4,43,No education,32,Franchise,Complex
6374,424,Boston,S,2,17,20,61,Graduation,27,Patnership,Complex
9496,461,NJ City,W,3,14,18,77,Graduation,30,Franchise,Complex
5934,408,Boston,S,1,16,21,85,Graduation,27,Patnership,Complex
3359,100.33,NJ City,E,25,18,4,32,No education,24,Owner,Standalone
1931,298,NJ City,E,1,17,12,26,High School,33,Owner,Standalone
140,226,NJ City,W,1,20,17,43,High School,29,Franchise,Complex
5279,99.62,Boston,E,18,9,2,75,No education,31,Owner,Standalone
873,101,NJ City,N,1,22,16,51,High School,20,Patnership,Standalone
6281,469,Boston,N,1,20,15,41,Graduation,33,Patnership,Standalone
5247,419,Boston,E,3,12,12,52,Graduation,26,Owner,Standalone
8201,48.51,NJ City,N,14,18,1,30,No education,33,Patnership,Standalone
2001,227,NJ City,N,3,19,19,68,High School,20,Patnership,Standalone
6776,439,Boston,W,2,14,8,47,Graduation,30,Franchise,Complex
1915,290.21,NJ City,E,4,17,4,82,High School,23,Owner,Standalone
2981,17.39,NJ City,N,24,19,2,25,No education,21,Patnership,Standalone
6671,79.59,Boston,E,14,24,1,82,No education,19,Owner,Standalone
2635,200,NJ City,E,1,9,9,55,High School,33,Owner,Standalone
9484,346.37,NJ City,W,22,23,2,34,High School,29,Franchise,Complex
3844,280,NJ City,W,3,16,17,57,High School,32,Franchise,Complex
2240,258,NJ City,W,3,9,20,76,High School,23,Franchise,Complex
1554,10.64,NJ City,N,6,21,1,54,Graduation,22,Patnership,Complex
3975,57.81,NJ City,E,7,23,2,44,Graduation,34,Owner,Standalone
1632,22.45,NJ City,N,2,15,3,54,Graduation,34,Franchise,Complex
2528,54.07,NJ City,W,16,13,2,39,No education,18,Franchise,Complex
1686,481.11,NJ City,S,12,12,4,56,Graduation,25,Patnership,Complex
8141,0.37,NJ City,N,6,16,1,63,No education,21,Patnership,Standalone
1968,491.43,NJ City,W,16,17,1,76,Graduation,27,Franchise,Complex
5728,469,Boston,W,3,8,12,39,Graduation,32,Franchise,Complex
8739,37.19,NJ City,N,15,14,3,52,Graduation,29,Owner,Standalone
6122,100.32,Boston,S,23,13,2,40,No education,30,Patnership,Complex
2493,121.74,NJ City,N,20,12,4,52,Graduation,35,Patnership,Standalone
7444,42.32,Boston,N,16,14,3,33,High School,19,Franchise,Complex
9273,379,NJ City,N,3,20,20,69,Graduation,21,Patnership,Standalone
7508,34.78,Boston,N,15,23,1,85,No education,32,Franchise,Complex
3374,183,NJ City,S,2,17,15,41,High School,19,Patnership,Complex
6520,446,Boston,W,2,8,13,81,Graduation,35,Franchise,Complex
9728,431,NJ City,W,1,19,5,33,Graduation,35,Franchise,Complex
876,190,NJ City,W,2,13,11,76,High School,35,Franchise,Complex
5802,58.87,Boston,S,10,10,2,62,Graduation,19,Patnership,Complex
8583,100.13,NJ City,E,13,15,4,77,Graduation,32,Owner,Standalone
1526,287.5,NJ City,S,11,22,2,75,No education,26,Patnership,Complex
6656,434,Boston,W,3,22,20,56,Graduation,34,Franchise,Complex
7273,474,Boston,N,3,15,5,47,Graduation,26,Patnership,Standalone
1039,133.3,NJ City,E,22,23,3,82,High School,25,Owner,Standalone
8844,64.87,NJ City,N,5,13,4,75,Graduation,28,Franchise,Complex
9312,397,NJ City,W,1,24,14,31,Graduation,29,Franchise,Complex
1335,228,NJ City,E,1,21,24,28,High School,33,Owner,Standalone
3380,275,NJ City,W,3,10,25,79,High School,31,Franchise,Complex
7574,356,Boston,S,1,19,20,76,Graduation,27,Patnership,Complex
1770,91.46,NJ City,S,4,12,4,31,Graduation,22,Patnership,Complex
4068,223,NJ City,W,3,16,24,52,High School,21,Franchise,Complex
6622,121.96,Boston,S,4,24,3,83,High School,27,Patnership,Complex
3556,159,NJ City,W,2,11,13,85,High School,21,Franchise,Complex
8408,457,NJ City,W,1,24,25,56,Graduation,34,Franchise,Complex
6835,98.95,Boston,E,10,18,4,73,High School,26,Owner,Standalone
8007,495,Boston,E,2,17,17,69,Graduation,29,Owner,Standalone
4818,244,NJ City,S,2,11,8,52,High School,20,Patnership,Complex
7323,462,Boston,E,2,24,14,35,Graduation,24,Owner,Standalone
9122,115.65,NJ City,S,13,15,4,42,No education,35,Patnership,Complex
1533,149,NJ City,N,1,21,12,59,High School,24,Patnership,Standalone
7330,104.79,Boston,S,12,16,3,80,High School,26,Patnership,Complex
8819,386,NJ City,E,3,19,6,66,Graduation,18,Owner,Standalone
4963,227,Boston,E,3,11,7,76,High School,27,Owner,Standalone
972,177,NJ City,W,2,18,18,80,High School,18,Franchise,Complex
590,218,NJ City,S,1,12,11,53,High School,35,Patnership,Complex
2075,311,NJ City,E,3,10,21,68,High School,28,Owner,Standalone
5371,117.52,Boston,E,25,12,1,38,High School,22,Owner,Standalone
8146,437,NJ City,S,2,16,23,76,Graduation,25,Patnership,Complex
3283,177,NJ City,E,2,20,13,85,High School,23,Owner,Standalone
9401,416,NJ City,N,3,13,6,30,Graduation,31,Patnership,Standalone
2188,387,NJ City,W,3,16,5,59,Graduation,32,Franchise,Complex
1550,162,NJ City,S,3,22,14,58,High School,35,Patnership,Complex
1565,109,NJ City,N,3,17,20,58,High School,24,Patnership,Standalone
2698,261,NJ City,S,2,22,20,78,High School,26,Patnership,Complex
6238,398,Boston,S,3,23,16,53,Graduation,32,Patnership,Complex
4686,460.94,NJ City,S,21,20,3,74,Graduation,25,Patnership,Complex
8012,469,Boston,W,3,12,18,40,Graduation,27,Franchise,Complex
3177,359.02,NJ City,N,7,23,3,33,Graduation,32,Patnership,Standalone
872,265,NJ City,W,1,24,20,42,High School,26,Franchise,Complex
640,405,NJ City,W,3,22,5,62,Graduation,24,Franchise,Complex
3203,126.13,NJ City,E,6,24,2,82,No education,24,Owner,Standalone
9345,375,NJ City,N,3,17,17,67,Graduation,25,Patnership,Standalone
9231,98.4,NJ City,E,13,13,2,54,Graduation,26,Owner,Standalone
1075,151,NJ City,E,3,8,7,25,High School,24,Owner,Standalone
1435,303,NJ City,E,2,17,24,80,High School,28,Owner,Standalone
9466,460,NJ City,S,3,22,25,80,Graduation,32,Patnership,Complex
7684,412,Boston,W,3,12,12,78,Graduation,33,Franchise,Complex
2579,264,NJ City,E,1,23,14,50,High School,22,Owner,Standalone
423,49.69,NJ City,E,23,11,3,81,Graduation,32,Owner,Standalone
6901,40.2,Boston,N,1,8,4,27,High School,30,Patnership,Standalone
8435,52.7,NJ City,E,7,8,2,59,No education,27,Owner,Standalone
7524,36.25,Boston,N,1,16,1,28,Graduation,18,Franchise,Complex
2548,173.81,NJ City,W,2,8,4,36,High School,32,Franchise,Complex
7414,95.27,Boston,S,15,11,2,42,High School,25,Patnership,Complex
9242,482,NJ City,S,1,24,14,58,Graduation,23,Patnership,Complex
5236,364,Boston,W,1,18,23,63,Graduation,21,Franchise,Complex
715,224,NJ City,E,3,9,14,69,High School,32,Owner,Standalone
8336,490,NJ City,W,3,12,13,37,Graduation,29,Franchise,Complex
434,317,NJ City,S,2,15,18,81,High School,30,Patnership,Complex
9599,415.3,NJ City,E,1,15,4,65,No education,21,Owner,Standalone
4492,463.83,NJ City,W,9,10,3,58,High School,29,Franchise,Complex
5718,494,Boston,S,2,18,11,39,Graduation,21,Patnership,Complex
2452,481,NJ City,W,2,9,5,68,Graduation,22,Franchise,Complex
6857,490,Boston,N,1,19,18,75,Graduation,25,Patnership,Standalone
4069,478,NJ City,N,3,8,5,63,Graduation,33,Patnership,Standalone
1498,75.68,NJ City,S,6,10,3,33,High School,27,Patnership,Complex
9252,38.5,NJ City,N,1,15,1,46,Graduation,21,Franchise,Complex
6955,39.7,Boston,N,12,12,1,27,High School,32,Owner,Standalone
4803,83.08,NJ City,E,1,18,2,31,Graduation,22,Owner,Standalone
9139,391,NJ City,E,1,16,24,47,Graduation,25,Owner,Standalone
1420,112,NJ City,W,3,18,10,25,High School,27,Franchise,Complex
6957,436,Boston,N,2,19,19,49,Graduation,24,Patnership,Standalone
8151,422,NJ City,E,3,24,6,36,Graduation,23,Owner,Standalone
1373,197,NJ City,N,1,11,22,52,High School,18,Patnership,Standalone
5368,495,Boston,W,2,20,23,60,Graduation,27,Franchise,Complex
3659,4.45,NJ City,N,9,11,1,40,No education,18,Owner,Standalone
5152,297.03,Boston,W,20,10,4,51,High School,26,Franchise,Complex
1821,174,NJ City,N,3,14,10,36,High School,21,Patnership,Standalone
3461,93.09,NJ City,N,6,11,3,79,No education,29,Patnership,Standalone
1223,90.38,NJ City,E,25,8,4,64,No education,18,Owner,Standalone
6734,109.65,Boston,S,13,21,4,28,No education,28,Patnership,Complex
7473,424,Boston,N,3,16,12,42,Graduation,27,Patnership,Standalone
9918,495,NJ City,S,2,18,19,64,Graduation,29,Patnership,Complex
5024,367.56,Boston,W,7,22,1,31,No education,31,Franchise,Complex
7884,430,Boston,W,3,16,9,62,Graduation,30,Franchise,Complex
6646,463,Boston,S,1,12,16,32,Graduation,29,Patnership,Complex
1582,114,NJ City,S,3,16,9,74,High School,34,Patnership,Complex
7107,357,Boston,E,2,17,8,85,Graduation,22,Owner,Standalone
2569,293,NJ City,N,3,21,16,62,High School,24,Patnership,Standalone
6424,497,Boston,W,2,9,5,33,Graduation,19,Franchise,Complex
9043,93.36,NJ City,E,21,16,4,56,High School,27,Owner,Standalone
4706,153,NJ City,S,3,24,10,30,High School,25,Patnership,Complex
4691,205,NJ City,E,2,15,13,26,High School,22,Owner,Standalone
7464,105.22,Boston,W,15,15,2,81,Graduation,33,Franchise,Complex
3999,188,NJ City,E,1,19,19,59,High School,33,Owner,Standalone
5541,371,Boston,N,1,11,16,67,Graduation,34,Patnership,Standalone
7222,384,Boston,S,3,10,19,53,Graduation,20,Patnership,Complex
5500,394,Boston,W,1,23,15,71,Graduation,35,Franchise,Complex
4943,272,Boston,E,2,12,15,47,High School,18,Owner,Standalone
2465,303,NJ City,N,3,10,21,61,High School,33,Patnership,Standalone
1211,196.93,NJ City,E,17,18,3,53,No education,28,Owner,Standalone
2805,228,NJ City,N,1,20,11,54,High School,22,Patnership,Standalone
5852,390,Boston,W,2,15,23,41,Graduation,23,Franchise,Complex
8438,22.16,NJ City,N,14,13,3,74,No education,27,Patnership,Complex
6481,38.38,Boston,N,6,13,2,48,High School,23,Patnership,Standalone
7897,420,Boston,N,3,17,20,57,Graduation,23,Patnership,Standalone
4790,311,NJ City,S,1,14,15,47,High School,29,Patnership,Complex
379,201,NJ City,E,3,23,17,52,High School,21,Owner,Standalone
5850,406,Boston,S,3,19,20,33,Graduation,34,Patnership,Complex
605,312,NJ City,N,2,18,16,49,High School,35,Patnership,Standalone
7455,46.34,Boston,N,14,16,1,37,Graduation,35,Owner,Standalone
7402,476,Boston,S,2,8,21,27,Graduation,29,Patnership,Complex
5044,100.77,Boston,W,18,23,3,28,High School,20,Franchise,Complex
2327,6.22,NJ City,N,8,15,2,27,No education,22,Owner,Standalone
9750,429,NJ City,S,2,13,17,34,Graduation,21,Patnership,Complex
6182,96.03,Boston,S,18,23,4,51,No education,34,Patnership,Complex
5369,358,Boston,N,3,9,21,70,Graduation,26,Patnership,Standalone
6186,398,Boston,S,2,8,18,79,Graduation,33,Patnership,Complex
4481,222,NJ City,N,2,24,7,74,High School,20,Patnership,Standalone
905,148,NJ City,N,2,16,14,75,High School,31,Patnership,Standalone
1788,169,NJ City,W,1,21,7,62,High School,23,Franchise,Complex
6253,406,Boston,N,2,10,25,46,Graduation,27,Patnership,Standalone
3867,111.31,NJ City,E,25,9,3,26,Graduation,27,Owner,Standalone
7740,385,Boston,W,1,14,20,59,Graduation,34,Franchise,Complex
1091,235,NJ City,E,2,22,22,84,High School,34,Owner,Standalone
8812,487,NJ City,W,2,9,22,41,Graduation,34,Franchise,Complex
5170,417,Boston,S,2,18,16,67,Graduation,35,Patnership,Complex
5242,448,Boston,S,1,12,17,49,Graduation,33,Patnership,Complex
3652,319,NJ City,W,1,16,19,36,High School,34,Franchise,Complex
8629,87.03,NJ City,N,16,12,4,74,High School,23,Patnership,Standalone
1576,172,NJ City,W,1,8,6,76,High School,32,Franchise,Complex
2212,124,NJ City,W,1,8,11,46,High School,34,Franchise,Complex
3655,24.71,NJ City,N,15,17,3,37,High School,21,Owner,Standalone
4235,218,NJ City,E,3,18,6,25,High School,24,Owner,Standalone
1432,88.58,NJ City,W,17,24,4,51,High School,31,Franchise,Complex
9079,282.86,NJ City,E,10,19,2,44,High School,18,Owner,Standalone
875,74.62,NJ City,E,6,17,3,45,No education,24,Owner,Standalone
2533,158,NJ City,N,3,24,14,69,High School,27,Patnership,Standalone
7994,407,Boston,S,1,8,11,43,Graduation,19,Patnership,Complex
7797,462,Boston,N,1,12,11,52,Graduation,26,Patnership,Standalone
5318,498,Boston,S,1,12,5,74,Graduation,25,Patnership,Complex
7778,419,Boston,S,2,9,25,73,Graduation,34,Patnership,Complex
3173,256,NJ City,N,3,24,19,74,High School,19,Patnership,Standalone
9794,376,NJ City,S,2,21,17,25,Graduation,28,Patnership,Complex
6696,341.44,Boston,W,20,12,1,80,Graduation,31,Franchise,Complex
2727,100.26,NJ City,E,25,10,1,75,Graduation,34,Owner,Standalone
565,273,NJ City,N,3,8,12,62,High School,21,Patnership,Standalone
6229,91.88,Boston,N,3,18,4,52,High School,32,Patnership,Standalone
6199,379,Boston,E,1,19,25,35,Graduation,21,Owner,Standalone
8637,351,NJ City,N,1,19,9,29,Graduation,22,Patnership,Standalone
7228,367,Boston,W,2,8,25,71,Graduation,26,Franchise,Complex
6814,16.69,Boston,N,9,13,2,43,High School,18,Patnership,Complex
1642,102,NJ City,S,1,23,23,84,High School,27,Patnership,Complex
8919,448,NJ City,E,2,22,22,51,Graduation,19,Owner,Standalone
8254,24.33,NJ City,N,10,17,1,53,High School,35,Patnership,Complex
1888,100,NJ City,W,3,18,24,35,High School,31,Franchise,Complex
4971,114,Boston,E,3,11,17,34,High School,26,Owner,Standalone
1846,196,NJ City,S,3,24,9,73,High School,25,Patnership,Complex
4685,208,NJ City,N,1,12,13,67,High School,20,Patnership,Standalone
8016,403,Boston,W,2,17,17,73,Graduation,28,Franchise,Complex
8344,380,NJ City,W,3,20,14,45,Graduation,28,Franchise,Complex
8206,469,NJ City,S,1,14,10,59,Graduation,18,Patnership,Complex
4161,305,NJ City,N,1,19,14,80,High School,24,Patnership,Standalone
7507,454,Boston,E,3,11,15,59,Graduation,25,Owner,Standalone
1712,186,NJ City,W,2,15,8,29,High School,32,Franchise,Complex
7918,396,Boston,S,3,19,10,45,Graduation,33,Patnership,Complex
5422,464.11,Boston,S,17,20,1,47,High School,24,Patnership,Complex
5670,43.42,Boston,N,2,10,2,32,Graduation,27,Patnership,Complex
1793,209,NJ City,N,2,15,12,57,High School,29,Patnership,Standalone
5809,106.98,Boston,N,11,11,4,66,High School,27,Patnership,Standalone
7009,387,Boston,N,2,10,25,50,Graduation,27,Patnership,Standalone
1669,160,NJ City,N,3,18,23,85,High School,33,Patnership,Standalone
7972,392,Boston,W,1,23,15,74,Graduation,33,Franchise,Complex
8544,405,NJ City,W,3,9,14,46,Graduation,23,Franchise,Complex
1468,82.89,NJ City,W,14,12,4,70,High School,18,Franchise,Complex
5082,36.95,Boston,N,5,23,1,82,Graduation,29,Patnership,Complex
9278,108.64,NJ City,S,18,22,4,41,No education,34,Patnership,Complex
2917,207,NJ City,N,1,19,9,38,High School,31,Patnership,Standalone
3900,139,NJ City,W,1,16,11,79,High School,28,Franchise,Complex
3278,177,NJ City,S,1,14,10,52,High School,29,Patnership,Complex
9802,465,NJ City,S,1,21,15,70,Graduation,19,Patnership,Complex
7401,53.9,Boston,N,24,24,1,45,Graduation,22,Patnership,Standalone
7225,458,Boston,N,3,9,9,26,Graduation,26,Patnership,Standalone
3101,467.06,NJ City,N,2,9,3,54,No education,32,Patnership,Standalone
5642,412,Boston,S,1,16,6,82,Graduation,33,Patnership,Complex
1939,465,NJ City,E,1,9,5,73,Graduation,35,Owner,Standalone
9500,471,NJ City,W,1,11,7,68,Graduation,24,Franchise,Complex
7157,40.54,Boston,N,15,21,1,71,No education,24,Patnership,Standalone
2055,311,NJ City,E,1,14,23,56,High School,33,Owner,Standalone
2010,195,NJ City,S,2,17,20,36,High School,34,Patnership,Complex
4767,112.24,NJ City,E,5,19,3,65,Graduation,23,Owner,Standalone
6179,364,Boston,E,2,17,16,48,Graduation,25,Owner,Standalone
6563,485,Boston,E,1,19,21,77,Graduation,21,Owner,Standalone
7716,476,Boston,W,1,8,19,70,Graduation,31,Franchise,Complex
9901,102.97,NJ City,N,9,12,4,61,High School,21,Patnership,Standalone
3810,268,NJ City,S,1,16,24,26,High School,33,Patnership,Complex
5699,474.81,Boston,E,3,22,5,44,No education,27,Owner,Standalone
4490,164,NJ City,S,1,13,8,63,High School,32,Patnership,Complex
6979,75.43,Boston,E,24,11,1,85,High School,19,Owner,Standalone
6263,404,Boston,E,2,15,6,29,Graduation,23,Owner,Standalone
9594,19.32,NJ City,N,18,13,3,53,Graduation,34,Patnership,Complex
8936,384,NJ City,W,2,17,24,49,Graduation,31,Franchise,Complex
8874,119.71,NJ City,S,3,17,3,82,Graduation,20,Patnership,Complex
5925,394,Boston,N,2,24,19,53,Graduation,28,Patnership,Standalone
751,154,NJ City,E,1,20,9,37,High School,21,Owner,Standalone
7100,72.24,Boston,N,20,10,2,70,No education,23,Franchise,Complex
9006,92.9,NJ City,S,1,8,3,79,Graduation,24,Patnership,Complex
3982,149,NJ City,S,3,23,19,57,High School,26,Patnership,Complex
7066,435,Boston,S,3,10,10,58,Graduation,24,Patnership,Complex
3034,94.84,NJ City,S,20,24,4,34,High School,18,Patnership,Complex
8696,352,NJ City,W,3,16,24,51,Graduation,19,Franchise,Complex
7074,1,Boston,N,5,21,1,48,Graduation,33,Patnership,Complex
504,418.57,NJ City,W,25,19,4,68,Graduation,26,Franchise,Complex
3953,38.08,NJ City,N,20,18,4,41,No education,19,Patnership,Standalone
1375,274,NJ City,E,2,16,13,40,High School,27,Owner,Standalone
3874,116.45,NJ City,S,19,18,2,70,High School,35,Patnership,Complex
4516,57.96,NJ City,W,1,21,3,55,High School,26,Franchise,Complex
7609,435,Boston,N,1,21,24,65,Graduation,30,Patnership,Standalone
2422,76.96,NJ City,S,14,24,4,59,High School,25,Patnership,Complex
6976,325.61,Boston,W,5,8,4,44,High School,35,Franchise,Complex
2831,221,NJ City,E,1,11,19,45,High School,30,Owner,Standalone
5637,424,Boston,N,3,18,22,51,Graduation,34,Patnership,Standalone
5050,65.17,Boston,N,14,14,4,32,High School,18,Patnership,Complex
8773,377,NJ City,N,3,15,20,56,Graduation,25,Patnership,Standalone
7580,87.85,Boston,W,18,24,3,73,No education,24,Franchise,Complex
817,309,NJ City,N,3,22,25,62,High School,35,Patnership,Standalone
7895,116.92,Boston,E,7,15,2,48,No education,24,Owner,Standalone
8165,450.37,NJ City,N,4,14,3,71,No education,19,Patnership,Standalone
2364,260,NJ City,W,3,14,21,63,High School,29,Franchise,Complex
4298,5.72,NJ City,N,3,20,3,70,No education,33,Patnership,Complex
6784,320.35,Boston,W,3,17,4,80,High School,33,Franchise,Complex
7663,42.91,Boston,N,10,8,1,26,High School,23,Owner,Standalone
8922,455,NJ City,S,2,18,16,76,Graduation,27,Patnership,Complex
7138,436,Boston,S,3,23,14,48,Graduation,26,Patnership,Complex
4950,108.86,Boston,S,5,10,2,51,Graduation,31,Patnership,Complex
5743,406,Boston,E,3,21,6,31,Graduation,22,Owner,Standalone
239,363.95,NJ City,E,21,10,3,83,No education,18,Owner,Standalone
2222,176,NJ City,S,1,17,7,58,High School,22,Patnership,Complex
8663,494,NJ City,E,2,11,9,84,Graduation,19,Owner,Standalone
1292,104,NJ City,W,3,9,15,81,High School,19,Franchise,Complex
5094,379,Boston,S,1,9,17,72,Graduation,19,Patnership,Complex
3089,174,NJ City,N,1,12,8,78,High School,24,Patnership,Standalone
5880,399,Boston,W,3,22,13,78,Graduation,21,Franchise,Complex
3835,52.31,NJ City,E,4,8,3,66,High School,25,Owner,Standalone
7911,490,Boston,E,3,9,14,29,Graduation,33,Owner,Standalone
8628,412,NJ City,W,1,22,5,84,Graduation,22,Franchise,Complex
1531,376.27,NJ City,E,21,23,1,84,High School,21,Owner,Standalone
1777,162.51,NJ City,N,9,21,3,81,High School,24,Patnership,Standalone
1800,271,NJ City,W,2,21,11,72,High School,25,Franchise,Complex
9920,491.72,NJ City,W,7,23,1,30,No education,20,Franchise,Complex
2488,49.2,NJ City,W,17,12,4,44,High School,29,Franchise,Complex
3757,271,NJ City,N,1,22,8,32,High School,23,Patnership,Standalone
1073,12.17,NJ City,N,20,16,3,85,No education,30,Patnership,Standalone
4936,224,Boston,W,3,12,24,67,High School,28,Franchise,Complex
7322,353.24,Boston,S,12,24,2,56,No education,20,Patnership,Complex
7888,51.73,Boston,W,2,15,3,33,High School,26,Franchise,Complex
286,290,NJ City,S,2,8,23,36,High School,23,Patnership,Complex
7982,104.02,Boston,S,18,19,2,27,No education,18,Patnership,Complex
8094,392,NJ City,S,1,15,18,33,Graduation,19,Patnership,Complex
8617,190.61,NJ City,N,1,13,5,84,High School,22,Patnership,Standalone
6584,341.13,Boston,W,12,14,4,49,No education,32,Franchise,Complex
545,218,NJ City,N,2,18,6,40,High School,31,Patnership,Standalone
9538,434,NJ City,S,1,8,20,70,Graduation,23,Patnership,Complex
8178,57.68,NJ City,S,14,8,4,55,Graduation,18,Patnership,Complex
8917,466,NJ City,N,2,24,23,71,Graduation,31,Patnership,Standalone
1050,270,NJ City,S,2,18,13,51,High School,27,Patnership,Complex
9045,396,NJ City,N,3,22,10,43,Graduation,20,Patnership,Standalone
4681,231,NJ City,N,1,15,6,54,High School,26,Patnership,Standalone
8640,476,NJ City,W,2,22,9,27,Graduation,29,Franchise,Complex
9317,394,NJ City,N,3,19,18,84,Graduation,25,Patnership,Standalone
2963,214,NJ City,E,3,9,20,74,High School,24,Owner,Standalone
9951,414,NJ City,E,1,14,25,51,Graduation,31,Owner,Standalone
2227,234.07,NJ City,E,20,14,1,68,High School,18,Owner,Standalone
5138,475,Boston,S,3,24,11,46,Graduation,28,Patnership,Complex
5285,57.88,Boston,N,15,16,2,49,No education,33,Patnership,Standalone
4049,282,NJ City,N,1,16,10,70,High School,30,Patnership,Standalone
4290,254,NJ City,S,3,20,7,84,High School,34,Patnership,Complex
2712,118,NJ City,W,1,19,22,64,High School,25,Franchise,Complex
7800,408,Boston,W,1,12,6,63,Graduation,32,Franchise,Complex
8775,475,NJ City,E,2,12,5,47,Graduation,33,Owner,Standalone
2097,182,NJ City,N,1,22,19,43,High School,27,Patnership,Standalone
8599,48.16,NJ City,E,9,13,4,77,High School,34,Owner,Standalone
6988,93.91,Boston,W,23,13,4,65,High School,26,Franchise,Complex
8510,419,NJ City,S,1,17,14,50,Graduation,24,Patnership,Complex
4554,268,NJ City,S,1,23,18,41,High School,32,Patnership,Complex
6959,17.48,Boston,N,3,18,1,40,No education,23,Owner,Standalone
439,146,NJ City,E,3,15,9,26,High School,28,Owner,Standalone
2971,65.7,NJ City,N,20,23,1,35,High School,34,Owner,Standalone
4123,155,NJ City,E,3,21,17,30,High School,29,Owner,Standalone
4680,194,NJ City,W,2,20,15,55,High School,25,Franchise,Complex
9000,445,NJ City,W,2,12,13,39,Graduation,32,Franchise,Complex
3699,82.58,NJ City,E,9,16,3,59,Graduation,33,Owner,Standalone
968,292,NJ City,W,1,20,9,62,High School,27,Franchise,Complex
8091,438,NJ City,E,3,10,25,57,Graduation,26,Owner,Standalone
779,104.21,NJ City,E,1,16,4,76,No education,33,Owner,Standalone
6880,495,Boston,W,3,11,25,71,Graduation,32,Franchise,Complex
7459,424,Boston,E,3,20,17,29,Graduation,24,Owner,Standalone
8585,33.96,NJ City,N,17,20,1,83,No education,25,Patnership,Standalone
4501,244,NJ City,N,3,10,11,26,High School,35,Patnership,Standalone
6150,290.69,Boston,S,7,20,1,47,Graduation,33,Patnership,Complex
9058,89.22,NJ City,S,1,11,4,33,High School,21,Patnership,Complex
8502,42.57,NJ City,N,24,24,4,79,Graduation,34,Patnership,Complex
57,109,Maine,N,2,22,23,73,High School,35,Patnership,Standalone
8182,353,NJ City,S,3,13,13,82,Graduation,33,Patnership,Complex
7367,390,Boston,E,3,19,23,41,Graduation,29,Owner,Standalone
5651,455,Boston,E,1,22,9,68,Graduation,18,Owner,Standalone
7959,370,Boston,E,1,14,22,32,Graduation,23,Owner,Standalone
8542,239.72,NJ City,S,1,24,1,28,High School,29,Patnership,Complex
7545,303.55,Boston,N,6,15,2,71,Graduation,19,Patnership,Standalone
117,307,NJ City,N,3,17,23,35,High School,27,Patnership,Standalone
1959,192,NJ City,E,2,14,6,59,High School,32,Owner,Standalone
206,257.67,NJ City,S,10,20,1,43,No education,27,Patnership,Complex
6195,459,Boston,E,3,21,8,45,Graduation,20,Owner,Standalone
5560,87.27,Boston,W,3,18,1,41,High School,22,Franchise,Complex
2,11.64,NY City,N,20,22,4,55,No education,23,Franchise,Standalone
4264,292,NJ City,W,1,17,6,68,High School,28,Franchise,Complex
4577,77.41,NJ City,N,2,14,3,59,No education,32,Patnership,Standalone
9757,280.91,NJ City,N,18,17,1,47,High School,35,Patnership,Standalone
7341,431,Boston,N,3,11,12,78,Graduation,32,Patnership,Standalone
7389,268.24,Boston,N,8,24,4,78,Graduation,31,Patnership,Standalone
3786,62.25,NJ City,N,23,11,2,27,Graduation,30,Patnership,Complex
5015,377,Boston,E,2,24,20,41,Graduation,28,Owner,Standalone
5551,483,Boston,E,1,24,20,27,Graduation,23,Owner,Standalone
4925,122,NJ City,N,1,24,15,72,High School,27,Patnership,Standalone
546,266,NJ City,S,2,24,16,53,High School,19,Patnership,Complex
2721,421.57,NJ City,N,2,16,3,62,Graduation,18,Patnership,Standalone
3004,209,NJ City,W,3,17,13,38,High School,25,Franchise,Complex
1219,274,NJ City,E,2,23,14,71,High School,25,Owner,Standalone
9421,487,NJ City,N,2,18,20,56,Graduation,20,Patnership,Standalone
112,308,NJ City,W,3,24,16,68,High School,22,Franchise,Complex
9655,477,NJ City,E,3,10,13,65,Graduation,19,Owner,Standalone
6676,99.7,Boston,W,16,24,1,84,High School,33,Franchise,Complex
7288,381,Boston,W,1,15,13,58,Graduation,34,Franchise,Complex
9187,490,NJ City,E,1,15,22,26,Graduation,27,Owner,Standalone
1484,112,NJ City,W,3,15,10,30,High School,28,Franchise,Complex
6087,398,Boston,E,1,21,23,39,Graduation,20,Owner,Standalone
5244,7.42,Boston,N,20,24,3,53,Graduation,24,Franchise,Complex
781,121,NJ City,N,1,17,21,29,High School,19,Patnership,Standalone
4489,155,NJ City,N,3,23,9,26,High School,29,Patnership,Standalone
2581,299,NJ City,N,2,15,23,42,High School,34,Patnership,Standalone
378,224,NJ City,S,2,19,23,51,High School,32,Patnership,Complex
6557,468,Boston,N,2,17,9,74,Graduation,20,Patnership,Standalone
2953,262.1,NJ City,N,5,18,3,81,High School,21,Patnership,Standalone
8277,68.82,NJ City,N,3,9,4,39,Graduation,33,Patnership,Standalone
5145,390,Boston,N,3,21,7,59,Graduation,30,Patnership,Standalone
6925,0.7,Boston,N,1,19,1,47,High School,32,Patnership,Standalone
3886,310,NJ City,S,1,10,16,58,High School,20,Patnership,Complex
4464,77.47,NJ City,W,8,22,4,32,Graduation,20,Franchise,Complex
2869,155.34,NJ City,N,2,14,5,55,High School,35,Patnership,Standalone
6176,413.81,Boston,W,3,19,5,25,No education,35,Franchise,Complex
1780,159,NJ City,W,3,11,18,40,High School,24,Franchise,Complex
1347,316,NJ City,E,3,11,11,44,High School,35,Owner,Standalone
3123,308,NJ City,E,1,17,20,80,High School,19,Owner,Standalone
7398,384,Boston,S,3,14,20,36,Graduation,22,Patnership,Complex
5735,497.42,Boston,E,3,20,5,40,No education,22,Owner,Standalone
8169,459,NJ City,N,3,14,17,66,Graduation,32,Patnership,Standalone
584,242,NJ City,W,3,13,24,59,High School,35,Franchise,Complex
7955,81.16,Boston,E,20,8,4,85,No education,25,Owner,Standalone
8484,355.31,NJ City,W,24,16,3,59,Graduation,25,Franchise,Complex
9034,78.34,NJ City,S,13,23,3,57,High School,24,Patnership,Complex
494,262,NJ City,S,3,14,18,35,High School,34,Patnership,Complex
3044,216,NJ City,W,2,15,22,51,High School,28,Franchise,Complex
7488,415,Boston,W,2,20,24,44,Graduation,32,Franchise,Complex
2598,211,NJ City,S,1,16,15,84,High School,24,Patnership,Complex
5102,15.47,Boston,N,4,24,3,43,No education,18,Patnership,Complex
3716,383.32,NJ City,W,4,15,2,69,No education,19,Franchise,Complex
8622,387,NJ City,S,2,15,10,73,Graduation,21,Patnership,Complex
2470,148,NJ City,S,2,22,13,29,High School,31,Patnership,Complex
5861,468,Boston,N,3,24,20,32,Graduation,24,Patnership,Standalone
6428,498,Boston,W,3,22,15,51,Graduation,29,Franchise,Complex
8454,403,NJ City,S,2,8,23,73,Graduation,28,Patnership,Complex
6172,425.25,Boston,W,18,11,2,58,High School,22,Franchise,Complex
2278,132,NJ City,S,1,20,6,64,High School,23,Patnership,Complex
4260,313,NJ City,W,3,20,25,58,High School,29,Franchise,Complex
4356,217,NJ City,W,2,24,22,44,High School,30,Franchise,Complex
3564,115,NJ City,W,3,20,9,44,High School,30,Franchise,Complex
5913,491,Boston,N,1,14,9,77,Graduation,20,Patnership,Standalone
8174,424,NJ City,S,3,20,9,69,Graduation,23,Patnership,Complex
5071,156.45,Boston,E,21,23,2,52,High School,18,Owner,Standalone
363,184,NJ City,E,1,12,22,84,High School,27,Owner,Standalone
113,21.74,NJ City,N,6,21,2,55,No education,35,Patnership,Standalone
7129,368,Boston,N,1,18,22,30,Graduation,23,Patnership,Standalone
7977,48.86,Boston,N,24,21,4,82,Graduation,29,Patnership,Standalone
9510,496,NJ City,S,1,19,13,72,Graduation,34,Patnership,Complex
310,57.75,NJ City,S,17,22,4,37,High School,33,Patnership,Complex
9374,439,NJ City,S,3,8,22,30,Graduation,32,Patnership,Complex
713,150,NJ City,N,1,10,11,66,High School,26,Patnership,Standalone
6097,339.96,Boston,N,7,9,2,39,High School,21,Patnership,Standalone
8273,431,NJ City,N,3,14,12,60,Graduation,34,Patnership,Standalone
650,258.45,NJ City,S,9,22,2,78,No education,34,Patnership,Complex
4548,126.81,NJ City,W,22,8,4,79,Graduation,19,Franchise,Complex
5786,495,Boston,S,1,20,19,71,Graduation,19,Patnership,Complex
95,45.45,NJ City,N,12,12,1,48,No education,25,Owner,Standalone
896,97.24,NJ City,W,12,15,1,54,No education,18,Franchise,Complex
6103,32.7,Boston,N,21,18,2,50,High School,19,Owner,Standalone
889,63.97,NJ City,N,1,18,2,57,High School,20,Patnership,Standalone
2688,317,NJ City,W,1,18,18,85,High School,22,Franchise,Complex
7905,13.47,Boston,N,21,18,3,53,Graduation,23,Patnership,Standalone
7557,469,Boston,N,3,20,25,52,Graduation,27,Patnership,Standalone
414,50.18,NJ City,S,1,17,4,48,Graduation,26,Patnership,Complex
1864,228,NJ City,W,2,19,25,47,High School,23,Franchise,Complex
9320,355,NJ City,W,1,15,9,74,Graduation,33,Franchise,Complex
9654,443,NJ City,S,2,17,20,77,Graduation,25,Patnership,Complex
184,304,NJ City,W,3,13,22,53,High School,32,Franchise,Complex
8897,484,NJ City,N,2,15,23,56,Graduation,22,Patnership,Standalone
6278,500,Boston,S,2,22,15,37,Graduation,21,Patnership,Complex
4408,312,NJ City,W,1,24,19,57,High School,30,Franchise,Complex
464,55.19,NJ City,W,17,12,4,34,No education,32,Franchise,Complex
551,242,NJ City,E,2,19,6,75,High School,20,Owner,Standalone
6279,473,Boston,E,2,13,14,29,Graduation,23,Owner,Standalone
2134,245.84,NJ City,S,19,22,4,64,High School,25,Patnership,Complex
8112,473,NJ City,W,1,20,14,76,Graduation,24,Franchise,Complex
4186,240,NJ City,S,3,12,17,30,High School,22,Patnership,Complex
5202,393,Boston,S,3,19,19,54,Graduation,23,Patnership,Complex
6074,386,Boston,S,3,11,20,59,Graduation,32,Patnership,Complex
1132,197,NJ City,W,3,18,22,46,High School,34,Franchise,Complex
6878,101.66,Boston,S,17,22,2,74,No education,25,Patnership,Complex
9014,454,NJ City,S,2,16,22,82,Graduation,21,Patnership,Complex
5953,21.69,Boston,N,10,9,2,41,High School,23,Patnership,Standalone
4669,467.97,NJ City,N,11,12,3,56,High School,29,Patnership,Standalone
411,191,NJ City,E,2,22,11,32,High School,32,Owner,Standalone
6701,440,Boston,N,3,21,22,42,Graduation,32,Patnership,Standalone
1034,108.97,NJ City,S,21,23,1,59,No education,19,Patnership,Complex
3039,45.28,NJ City,N,10,15,3,55,Graduation,34,Owner,Standalone
3828,253,NJ City,W,1,17,19,75,High School,35,Franchise,Complex
8692,401,NJ City,W,2,11,6,78,Graduation,18,Franchise,Complex
6991,452,Boston,E,2,16,8,30,Graduation,21,Owner,Standalone
2506,74.43,NJ City,S,21,24,3,37,High School,34,Patnership,Complex
5590,415,Boston,S,3,21,11,47,Graduation,22,Patnership,Complex
6997,420,Boston,N,2,17,10,33,Graduation,21,Patnership,Standalone
465,123.63,NJ City,N,7,12,3,53,Graduation,34,Patnership,Standalone
683,235,NJ City,E,1,8,7,34,High School,24,Owner,Standalone
3779,52.32,NJ City,E,3,24,4,43,No education,29,Owner,Standalone
2809,96.8,NJ City,N,9,9,4,27,High School,29,Patnership,Standalone
9603,93.14,NJ City,E,20,16,4,28,Graduation,23,Owner,Standalone
473,63.76,NJ City,N,3,9,3,48,No education,33,Patnership,Standalone
5986,416,Boston,S,3,8,21,71,Graduation,30,Patnership,Complex
3418,276.79,NJ City,S,22,10,4,69,High School,30,Patnership,Complex
1511,244,NJ City,E,1,9,23,84,High School,34,Owner,Standalone
520,344.13,NJ City,W,8,14,3,80,High School,33,Franchise,Complex
6024,456,Boston,W,3,16,6,56,Graduation,22,Franchise,Complex
1573,183,NJ City,N,1,15,15,57,High School,32,Patnership,Standalone
2731,483.17,NJ City,E,15,23,4,69,High School,34,Owner,Standalone
5014,374,Boston,S,1,22,6,51,Graduation,19,Patnership,Complex
6467,392,Boston,E,2,8,18,72,Graduation,21,Owner,Standalone
9933,62.88,NJ City,N,9,22,1,56,Graduation,33,Patnership,Standalone
1724,484.46,NJ City,W,2,11,5,27,No education,30,Franchise,Complex
2799,247,NJ City,E,2,12,17,36,High School,29,Owner,Standalone
9431,372,NJ City,E,2,14,15,52,Graduation,22,Owner,Standalone
9905,116.82,NJ City,N,21,13,1,44,No education,18,Patnership,Standalone
9876,484,NJ City,W,2,23,25,78,Graduation,35,Franchise,Complex
2865,132,NJ City,N,3,15,14,30,High School,35,Patnership,Standalone
8180,378,NJ City,W,1,20,14,26,Graduation,32,Franchise,Complex
9277,414,NJ City,N,1,11,16,43,Graduation,35,Patnership,Standalone
7069,33.82,Boston,N,7,21,2,36,High School,34,Patnership,Standalone
4394,159,NJ City,S,2,22,15,37,High School,26,Patnership,Complex
8756,421,NJ City,W,3,14,25,83,Graduation,35,Franchise,Complex
6740,343.36,Boston,W,16,10,2,53,No education,18,Franchise,Complex
6799,411,Boston,E,2,22,10,83,Graduation,26,Owner,Standalone
320,193.56,NJ City,W,3,11,5,63,No education,28,Franchise,Complex
9425,380,NJ City,N,2,19,17,58,Graduation,18,Patnership,Standalone
6828,10.24,Boston,N,8,11,1,29,Graduation,32,Franchise,Complex
4756,283,NJ City,W,1,23,12,33,High School,33,Franchise,Complex
6310,181.5,Boston,S,4,23,4,78,High School,18,Patnership,Complex
3658,310,NJ City,S,1,23,17,53,High School,32,Patnership,Complex
1646,220,NJ City,S,1,20,16,67,High School,20,Patnership,Complex
2343,165,NJ City,E,1,17,21,70,High School,24,Owner,Standalone
9735,439,NJ City,E,1,16,17,43,Graduation,30,Owner,Standalone
4423,54.87,NJ City,E,25,9,4,75,High School,27,Owner,Standalone
8222,461,NJ City,S,2,19,21,82,Graduation,23,Patnership,Complex
2238,363,NJ City,S,2,23,5,57,Graduation,29,Patnership,Complex
8107,46.53,NJ City,N,5,20,1,46,High School,19,Owner,Standalone
4528,231,NJ City,W,3,17,6,58,High School,20,Franchise,Complex
7445,360,Boston,N,1,9,16,57,Graduation,26,Patnership,Standalone
3888,48.2,NJ City,W,24,21,3,76,Graduation,20,Franchise,Complex
6823,96.84,Boston,E,24,14,3,71,High School,21,Owner,Standalone
3870,136,NJ City,S,1,15,15,46,High School,26,Patnership,Complex
614,169,NJ City,S,2,18,12,66,High School,21,Patnership,Complex
9990,382,NJ City,S,3,11,18,81,Graduation,18,Patnership,Complex
3401,211,NJ City,N,2,14,16,36,High School,28,Patnership,Standalone
5314,72.18,Boston,N,6,10,4,43,High School,27,Patnership,Complex
4048,235,NJ City,W,3,14,24,59,High School,30,Franchise,Complex
836,143,NJ City,W,2,13,20,36,High School,19,Franchise,Complex
4999,404.87,Boston,E,5,20,4,52,High School,29,Owner,Standalone
8864,125,NJ City,W,24,10,4,39,No education,19,Franchise,Complex
11,32.81,NY City,N,18,22,1,38,No education,31,Owner,Standalone
8766,494,NJ City,S,14,12,18,61,Graduation,33,Patnership,Complex
4912,6.59,NJ City,N,6,23,1,80,High School,20,Franchise,Complex
4980,131.41,Boston,W,19,20,15,63,High School,30,Franchise,Complex
3603,59.41,NJ City,E,12,23,2,42,Graduation,33,Owner,Standalone
5498,470,Boston,S,13,10,17,41,Graduation,32,Patnership,Complex
8033,435,Boston,N,18,16,18,76,Graduation,27,Patnership,Standalone
6873,356.99,Boston,N,25,10,3,71,Graduation,28,Patnership,Standalone
8297,25.53,NJ City,N,3,23,3,65,No education,30,Patnership,Standalone
302,237,NJ City,S,1,15,15,51,High School,30,Patnership,Complex
7931,460,Boston,E,2,10,15,70,Graduation,29,Owner,Standalone
348,480.25,NJ City,W,19,9,7,65,High School,23,Franchise,Complex
9509,109.17,NJ City,N,13,21,1,28,No education,18,Patnership,Standalone
2199,388.95,NJ City,E,15,17,7,61,High School,25,Owner,Standalone
7031,390,Boston,E,4,19,21,33,Graduation,27,Owner,Standalone
8645,465,NJ City,N,8,18,11,79,Graduation,30,Patnership,Standalone
5505,126.75,Boston,N,8,16,1,46,Graduation,28,Patnership,Standalone
5655,480,Boston,E,15,18,17,74,Graduation,34,Owner,Standalone
2435,254.45,NJ City,E,11,23,3,78,No education,21,Owner,Standalone
3375,140,NJ City,E,2,8,14,47,High School,21,Owner,Standalone
6673,443,Boston,N,7,24,15,29,Graduation,35,Patnership,Standalone
8239,407,NJ City,E,1,14,19,50,Graduation,21,Owner,Standalone
6426,376,Boston,S,21,23,12,39,Graduation,26,Patnership,Complex
4279,215.8,NJ City,E,4,16,1,82,High School,27,Owner,Standalone
2910,341.76,NJ City,S,24,11,21,63,High School,20,Patnership,Complex
554,315.23,NJ City,S,24,11,4,34,No education,21,Patnership,Complex
4939,127.52,Boston,E,23,11,1,73,High School,25,Owner,Standalone
9337,498,NJ City,N,18,8,23,35,Graduation,23,Patnership,Standalone
572,124.05,NJ City,W,8,8,3,76,No education,34,Franchise,Complex
6751,499,Boston,E,12,23,23,26,Graduation,32,Owner,Standalone
9871,440,NJ City,E,15,14,19,36,Graduation,35,Owner,Standalone
1703,239.26,NJ City,E,19,23,13,78,High School,29,Owner,Standalone
7279,361,Boston,E,24,18,25,55,Graduation,35,Owner,Standalone
697,60.82,NJ City,N,4,19,3,47,High School,24,Patnership,Standalone
9235,112.11,NJ City,E,21,17,4,68,High School,22,Owner,Standalone
3695,169.53,NJ City,E,21,18,5,36,No education,21,Owner,Standalone
558,306.52,NJ City,S,22,15,4,69,Graduation,22,Patnership,Complex
2464,335.09,NJ City,W,17,16,2,44,High School,27,Franchise,Complex
1140,434.72,NJ City,W,9,16,8,58,High School,34,Franchise,Complex
3624,373.77,NJ City,W,19,11,18,26,High School,23,Franchise,Complex
9208,463,NJ City,W,19,24,18,77,Graduation,33,Franchise,Complex
6724,360,Boston,W,9,16,25,72,Graduation,24,Franchise,Complex
5073,441.9,Boston,N,18,23,3,55,Graduation,35,Patnership,Standalone
3985,253.01,NJ City,N,6,18,24,34,High School,33,Patnership,Standalone
9098,6.54,NJ City,N,9,16,2,66,No education,30,Patnership,Complex
1499,128.87,NJ City,E,23,23,13,31,High School,21,Owner,Standalone
1832,453.27,NJ City,W,19,11,8,43,High School,31,Franchise,Complex
4764,169.83,NJ City,W,20,18,18,70,High School,25,Franchise,Complex
9566,399,NJ City,S,9,21,20,61,Graduation,29,Patnership,Complex
8348,495,NJ City,W,5,14,23,73,Graduation,30,Franchise,Complex
5760,457,Boston,W,14,22,8,37,Graduation,24,Franchise,Complex
154,415.51,NJ City,S,10,23,8,64,High School,20,Patnership,Complex
5846,102.99,Boston,S,3,22,2,56,No education,31,Patnership,Complex
1802,295.83,NJ City,S,6,22,4,77,No education,21,Patnership,Complex
3328,295,NJ City,W,22,11,15,77,High School,21,Franchise,Complex
3238,127.41,NJ City,S,8,19,3,59,High School,31,Patnership,Complex
9435,385.01,NJ City,E,20,20,1,43,Graduation,18,Owner,Standalone
628,332.95,NJ City,W,9,19,3,59,High School,18,Franchise,Complex
2732,231,NJ City,W,1,14,11,59,High School,26,Franchise,Complex
6613,491,Boston,N,21,16,7,63,Graduation,32,Patnership,Standalone
2345,481.21,NJ City,N,15,11,21,73,High School,26,Patnership,Standalone
4101,187.24,NJ City,N,5,13,23,64,High School,33,Patnership,Standalone
1635,188,NJ City,E,2,17,17,25,High School,20,Owner,Standalone
6137,387,Boston,N,17,22,22,56,Graduation,29,Patnership,Standalone
2995,31.53,NJ City,N,22,18,1,75,High School,29,Owner,Standalone
6667,376,Boston,E,8,15,11,68,Graduation,25,Owner,Standalone
1407,10.46,NJ City,N,23,8,2,70,Graduation,32,Owner,Standalone
325,366.65,NJ City,N,4,15,20,85,High School,29,Patnership,Standalone
1216,441.75,NJ City,W,25,9,22,33,High School,21,Franchise,Complex
5822,431,Boston,S,25,21,24,76,Graduation,29,Patnership,Complex
5903,118.36,Boston,E,7,13,4,59,No education,18,Owner,Standalone
9737,296.53,NJ City,N,16,10,1,44,No education,19,Patnership,Standalone
7465,406,Boston,N,25,23,11,85,Graduation,20,Patnership,Standalone
6285,357,Boston,N,9,22,20,58,Graduation,23,Patnership,Standalone
1230,278,NJ City,S,2,15,13,67,High School,34,Patnership,Complex
92,462.54,NJ City,W,16,12,25,81,High School,26,Franchise,Complex
2572,383.74,NJ City,W,22,18,1,31,High School,18,Franchise,Complex
6587,104.1,Boston,E,7,11,4,57,No education,18,Owner,Standalone
4633,219.37,NJ City,N,14,9,16,31,High School,29,Patnership,Standalone
1352,413.07,NJ City,W,12,18,24,52,High School,35,Franchise,Complex
8384,471,NJ City,W,24,16,24,37,Graduation,22,Franchise,Complex
4606,447.53,NJ City,S,24,13,4,85,High School,35,Patnership,Complex
46,266.2,Boston,S,19,21,9,36,High School,26,Patnership,Complex
8050,374,NJ City,S,12,22,21,44,Graduation,33,Patnership,Complex
1385,58.04,NJ City,N,4,15,2,47,No education,32,Patnership,Standalone
2110,410.91,NJ City,S,15,8,8,75,High School,29,Patnership,Complex
323,204,NJ City,E,2,20,22,79,High School,27,Owner,Standalone
3361,453.7,NJ City,N,18,13,17,27,High School,26,Patnership,Standalone
6717,181.77,Boston,N,15,16,3,41,Graduation,24,Patnership,Standalone
7092,359.94,Boston,W,14,19,3,31,Graduation,35,Franchise,Complex
1949,218.78,NJ City,N,19,17,14,64,High School,32,Patnership,Standalone
6948,370,Boston,W,5,8,13,78,Graduation,30,Franchise,Complex
258,235.6,NJ City,S,18,22,14,40,High School,25,Patnership,Complex
2125,114.94,NJ City,N,21,11,2,69,High School,35,Patnership,Standalone
9102,350,NJ City,S,24,15,19,36,Graduation,26,Patnership,Complex
7867,373,Boston,E,19,10,14,71,Graduation,31,Owner,Standalone
8720,449,NJ City,W,21,8,25,56,Graduation,19,Franchise,Complex
6262,438,Boston,S,21,17,18,29,Graduation,29,Patnership,Complex
2901,408.31,NJ City,N,6,20,2,44,Graduation,32,Patnership,Standalone
6967,378,Boston,E,12,14,25,25,Graduation,21,Owner,Standalone
1736,317.93,NJ City,W,19,18,13,50,High School,31,Franchise,Complex
3086,94.06,NJ City,S,3,16,3,82,No education,34,Patnership,Complex
9841,59.6,NJ City,N,22,14,3,63,High School,20,Patnership,Standalone
1220,84.32,NJ City,W,19,13,3,85,No education,22,Franchise,Complex
4420,133.74,NJ City,W,20,17,3,46,High School,23,Franchise,Complex
8106,43.09,NJ City,N,25,11,3,64,Graduation,24,Patnership,Complex
6287,26.89,Boston,N,6,8,4,68,No education,31,Owner,Standalone
6738,278.34,Boston,S,24,13,3,75,Graduation,31,Patnership,Complex
9817,430,NJ City,N,12,14,11,30,Graduation,28,Patnership,Standalone
5037,358,Boston,N,2,17,9,33,Graduation,28,Patnership,Standalone
2410,63.54,NJ City,N,18,14,4,77,High School,19,Patnership,Complex
8351,408,NJ City,E,14,17,15,80,Graduation,32,Owner,Standalone
3366,79.68,NJ City,S,14,8,3,36,Graduation,22,Patnership,Complex
7497,496,Boston,N,12,20,22,72,Graduation,23,Patnership,Standalone
7432,67.96,Boston,N,10,18,1,61,High School,29,Franchise,Complex
24,101.09,Maine,W,2,12,1,59,Graduation,26,Franchise,Complex
8287,354,NJ City,E,17,14,20,30,Graduation,25,Owner,Standalone
5320,414.52,Boston,W,2,17,1,51,High School,24,Franchise,Complex
581,105.61,NJ City,N,7,18,2,55,No education,27,Patnership,Standalone
2138,304.3,NJ City,S,15,18,12,40,High School,27,Patnership,Complex
883,168.97,NJ City,E,10,21,24,49,High School,26,Owner,Standalone
2354,351.61,NJ City,S,13,14,25,38,High School,31,Patnership,Complex
3940,132.23,NJ City,W,16,10,18,70,High School,31,Franchise,Complex
6875,408,Boston,E,4,24,9,51,Graduation,26,Owner,Standalone
2228,130.29,NJ City,W,25,24,25,30,High School,26,Franchise,Complex
5971,75.62,Boston,E,22,11,3,45,High School,28,Owner,Standalone
8197,372.78,NJ City,N,6,8,5,61,High School,18,Patnership,Standalone
1676,329.22,NJ City,W,10,17,22,29,High School,32,Franchise,Complex
7145,490,Boston,N,10,9,10,44,Graduation,27,Patnership,Standalone
9545,471,NJ City,N,7,8,19,30,Graduation,21,Patnership,Standalone
4777,208.95,NJ City,N,18,17,16,71,High School,26,Patnership,Standalone
4158,482.05,NJ City,S,21,22,25,46,High School,35,Patnership,Complex
3439,100.34,NJ City,E,11,20,4,80,High School,22,Owner,Standalone
6560,487,Boston,W,9,16,24,40,Graduation,30,Franchise,Complex
5956,497,Boston,W,15,13,23,30,Graduation,20,Franchise,Complex
9075,458,NJ City,E,2,18,25,81,Graduation,30,Owner,Standalone
8445,357,NJ City,N,11,23,10,53,Graduation,32,Patnership,Standalone
3024,410,NJ City,W,8,18,5,33,Graduation,35,Franchise,Complex
9180,396,NJ City,W,14,17,17,49,Graduation,21,Franchise,Complex
6050,86.78,Boston,S,3,14,1,46,No education,20,Patnership,Complex
4615,148.33,NJ City,E,2,8,2,37,High School,20,Owner,Standalone
5554,94.26,Boston,S,17,13,3,31,High School,28,Patnership,Complex
6640,496,Boston,W,17,13,25,77,Graduation,29,Franchise,Complex
4267,309.49,NJ City,E,4,19,10,32,High School,19,Owner,Standalone
1218,300.03,NJ City,S,8,16,10,36,High School,32,Patnership,Complex
9318,368,NJ City,S,9,21,17,43,Graduation,34,Patnership,Complex
308,351.62,NJ City,W,5,19,22,77,High School,19,Franchise,Complex
5231,361,Boston,E,4,9,24,45,Graduation,23,Owner,Standalone
6618,479,Boston,S,21,24,15,75,Graduation,24,Patnership,Complex
3258,325.46,NJ City,S,6,9,12,67,High School,22,Patnership,Complex
5392,464,Boston,W,23,14,20,28,Graduation,31,Franchise,Complex
9874,325.77,NJ City,S,20,20,1,26,High School,34,Patnership,Complex
4074,311.56,NJ City,S,1,11,3,28,Graduation,29,Patnership,Complex
9569,495,NJ City,N,5,18,15,67,Graduation,35,Patnership,Standalone
4688,69.43,NJ City,N,25,15,1,36,No education,30,Franchise,Complex
7738,381,Boston,S,1,21,24,67,Graduation,34,Patnership,Complex
9997,379,NJ City,N,11,14,9,74,Graduation,28,Patnership,Standalone
4855,97.9,NJ City,E,25,12,3,47,High School,27,Owner,Standalone
5612,90.01,Boston,W,24,20,3,36,No education,30,Franchise,Complex
2544,27.21,NJ City,N,25,9,2,75,Graduation,29,Franchise,Complex
1501,381.03,NJ City,N,15,17,19,65,High School,33,Patnership,Standalone
8777,177.88,NJ City,N,21,8,4,37,No education,24,Patnership,Standalone
9844,119.08,NJ City,W,20,15,2,55,High School,34,Franchise,Complex
8953,494,NJ City,N,5,18,25,48,Graduation,23,Patnership,Standalone
5325,377,Boston,N,24,19,18,35,Graduation,19,Patnership,Standalone
7643,461.38,Boston,E,16,20,4,43,No education,34,Owner,Standalone
635,133.26,NJ City,E,4,17,25,38,High School,35,Owner,Standalone
288,471.69,NJ City,W,25,14,23,75,High School,31,Franchise,Complex
7345,39.02,Boston,N,19,15,1,42,High School,34,Patnership,Standalone
7450,2.39,Boston,S,12,13,3,34,High School,26,Patnership,Complex
660,85.24,NJ City,W,2,23,3,47,Graduation,23,Franchise,Complex
8249,469,NJ City,N,17,13,7,27,Graduation,24,Patnership,Standalone
6790,66.91,Boston,N,24,21,4,40,High School,23,Patnership,Complex
3303,48.83,NJ City,E,1,21,2,43,Graduation,30,Owner,Standalone
6410,490,Boston,S,19,23,21,48,Graduation,34,Patnership,Complex
2481,402.04,NJ City,N,24,11,2,81,Graduation,18,Patnership,Standalone
1848,408.87,NJ City,W,13,10,1,67,Graduation,29,Franchise,Complex
6981,380,Boston,N,22,8,12,79,Graduation,28,Patnership,Standalone
1710,432.69,NJ City,S,5,10,11,37,High School,27,Patnership,Complex
8935,358,NJ City,E,10,18,16,57,Graduation,34,Owner,Standalone
4625,435.63,NJ City,N,18,10,13,73,High School,25,Patnership,Standalone
679,350.16,NJ City,E,22,23,4,36,High School,29,Owner,Standalone
5544,376,Boston,W,13,24,5,28,Graduation,30,Franchise,Complex
5747,426,Boston,E,17,17,25,51,Graduation,29,Owner,Standalone
1778,442.65,NJ City,S,6,16,22,35,High School,22,Patnership,Complex
2462,317.54,NJ City,S,20,13,17,59,High School,23,Patnership,Complex
7732,94.49,Boston,W,4,18,2,58,High School,21,Franchise,Complex
1366,216.51,NJ City,S,12,11,24,66,High School,29,Patnership,Complex
4596,35.36,NJ City,N,18,24,3,48,Graduation,34,Franchise,Complex
7478,483,Boston,S,8,18,6,32,Graduation,33,Patnership,Complex
48,403,Boston,W,17,12,5,84,Graduation,24,Franchise,Complex
4337,100.91,NJ City,N,7,11,1,78,No education,32,Patnership,Standalone
3200,129.15,NJ City,W,23,9,19,66,High School,21,Franchise,Complex
1130,145,NJ City,S,2,15,16,68,High School,30,Patnership,Complex
7470,417,Boston,S,5,14,16,75,Graduation,21,Patnership,Complex
3644,116.59,NJ City,W,23,21,3,44,No education,24,Franchise,Complex
6893,429,Boston,N,24,19,15,59,Graduation,26,Patnership,Standalone
8110,454,NJ City,S,7,10,14,44,Graduation,20,Patnership,Complex
9094,175.93,NJ City,S,23,17,5,25,High School,25,Patnership,Complex
6763,465,Boston,E,1,22,21,85,Graduation,33,Owner,Standalone
9025,356,NJ City,N,24,9,15,64,Graduation,18,Patnership,Standalone
996,490.82,NJ City,W,4,13,22,41,High School,22,Franchise,Complex
5034,390,Boston,S,19,16,15,54,Graduation,24,Patnership,Complex
589,489.68,NJ City,N,19,20,13,31,High School,31,Patnership,Standalone
31,58.94,Boston,E,5,12,2,29,High School,18,Owner,Standalone
3492,52.43,NJ City,W,23,23,1,30,Graduation,33,Franchise,Complex
7588,430,Boston,W,11,24,18,74,Graduation,19,Franchise,Complex
6562,66.57,Boston,N,18,24,2,50,High School,25,Patnership,Complex
7369,448,Boston,N,13,23,23,37,Graduation,31,Patnership,Standalone
1671,216.4,NJ City,E,9,15,14,72,High School,35,Owner,Standalone
9112,90.6,NJ City,W,3,21,4,47,High School,27,Franchise,Complex
9736,96.43,NJ City,W,2,14,2,63,High School,25,Franchise,Complex
6427,418,Boston,E,11,17,9,45,Graduation,19,Owner,Standalone
9609,500,NJ City,N,1,11,5,75,Graduation,25,Patnership,Standalone
1202,212.07,NJ City,S,18,19,2,29,No education,27,Patnership,Complex
5856,418,Boston,W,23,10,12,38,Graduation,24,Franchise,Complex
3781,448.77,NJ City,N,19,18,15,76,High School,32,Patnership,Standalone
5565,434.29,Boston,N,19,8,4,52,Graduation,21,Patnership,Standalone
648,158.22,NJ City,W,14,21,17,68,High School,21,Franchise,Complex
5492,474,Boston,W,17,18,20,53,Graduation,26,Franchise,Complex
6213,409,Boston,N,18,9,13,78,Graduation,20,Patnership,Standalone
5306,415,Boston,S,12,18,13,84,Graduation,25,Patnership,Complex
4118,196.86,NJ City,S,20,13,7,51,High School,30,Patnership,Complex
4399,25.98,NJ City,N,13,14,3,50,High School,29,Owner,Standalone
2315,11.63,NJ City,N,16,11,1,59,No education,27,Owner,Standalone
3141,360.36,NJ City,N,16,19,19,30,High School,28,Patnership,Standalone
9341,34.57,NJ City,N,10,8,3,36,No education,18,Patnership,Standalone
8530,316.66,NJ City,S,24,19,4,75,High School,23,Patnership,Complex
7645,413,Boston,N,20,23,19,71,Graduation,29,Patnership,Standalone
7900,350.67,Boston,W,3,17,1,60,High School,22,Franchise,Complex
2375,111.79,NJ City,E,8,18,2,82,No education,19,Owner,Standalone
8573,436.01,NJ City,N,8,20,4,83,No education,31,Patnership,Standalone
5451,425,Boston,E,9,8,13,27,Graduation,33,Owner,Standalone
166,244,NJ City,S,3,18,19,79,High School,35,Patnership,Complex
1236,187.36,NJ City,W,17,13,24,56,High School,25,Franchise,Complex
7376,486,Boston,W,4,15,20,26,Graduation,35,Franchise,Complex
637,13.23,NJ City,N,16,15,2,35,High School,34,Patnership,Standalone
887,264.51,NJ City,E,12,18,15,82,High School,34,Owner,Standalone
1950,295.62,NJ City,S,5,10,14,38,High School,24,Patnership,Complex
8247,356,NJ City,E,7,24,16,57,Graduation,19,Owner,Standalone
6811,11.25,Boston,N,4,20,4,53,High School,26,Owner,Standalone
6183,63.51,Boston,N,7,21,4,31,Graduation,20,Owner,Standalone
5906,498,Boston,S,9,21,12,53,Graduation,31,Patnership,Complex
3808,102.13,NJ City,W,9,15,2,47,High School,21,Franchise,Complex
1354,321.13,NJ City,S,12,23,9,82,High School,34,Patnership,Complex
8608,359,NJ City,W,12,10,24,69,Graduation,35,Franchise,Complex
5877,25.2,Boston,N,11,18,3,75,Graduation,18,Patnership,Standalone
9410,410,NJ City,S,20,15,18,52,Graduation,35,Patnership,Complex
3969,117.52,NJ City,N,12,21,4,76,Graduation,24,Patnership,Standalone
9004,397,NJ City,W,23,19,20,50,Graduation,33,Franchise,Complex
4676,360.69,NJ City,W,20,11,16,65,High School,26,Franchise,Complex
6964,359.99,Boston,W,13,15,3,35,High School,21,Franchise,Complex
616,188.19,NJ City,W,6,13,22,42,High School,22,Franchise,Complex
1706,490.79,NJ City,S,22,22,4,31,No education,29,Patnership,Complex
5345,335.07,Boston,N,1,16,4,52,No education,19,Patnership,Standalone
5388,95.6,Boston,W,13,22,2,64,Graduation,25,Franchise,Complex
7027,41.06,Boston,N,1,19,4,41,High School,28,Owner,Standalone
5787,119.27,Boston,E,18,17,4,47,Graduation,21,Owner,Standalone
2978,137.92,NJ City,S,10,16,13,28,High School,21,Patnership,Complex
6264,16.49,Boston,N,4,8,4,43,Graduation,30,Franchise,Complex
4047,220.66,NJ City,E,15,18,14,85,High School,32,Owner,Standalone
9007,271.52,NJ City,E,21,14,1,61,High School,18,Owner,Standalone
4440,207,NJ City,W,3,20,20,70,High School,28,Franchise,Complex
4658,218.83,NJ City,S,5,8,5,70,No education,32,Patnership,Complex
2263,123.71,NJ City,E,25,20,4,74,High School,18,Owner,Standalone
5287,65.14,Boston,N,3,11,1,85,High School,33,Owner,Standalone
2912,168.73,NJ City,W,16,14,18,51,High School,18,Franchise,Complex
1118,182.35,NJ City,S,10,11,17,32,High School,20,Patnership,Complex
5608,467,Boston,W,18,21,23,74,Graduation,31,Franchise,Complex
745,307.13,NJ City,N,18,18,9,80,High School,20,Patnership,Standalone
9825,18.69,NJ City,N,7,16,1,61,Graduation,25,Patnership,Standalone
9399,499,NJ City,E,18,16,16,62,Graduation,29,Owner,Standalone
1307,139.32,NJ City,E,25,14,10,35,High School,34,Owner,Standalone
5169,49.1,Boston,N,24,21,2,55,Graduation,31,Patnership,Standalone
6190,396,Boston,S,13,11,21,29,Graduation,19,Patnership,Complex
17,168,Boston,N,2,8,19,52,High School,28,Patnership,Standalone
7253,484,Boston,N,19,23,8,67,Graduation,29,Patnership,Standalone
4284,168.14,NJ City,W,9,16,25,26,High School,31,Franchise,Complex
2864,359.71,NJ City,W,8,17,3,79,No education,23,Franchise,Complex
435,154.37,NJ City,E,13,21,20,26,High School,19,Owner,Standalone
3477,96.29,NJ City,N,25,24,4,61,Graduation,34,Patnership,Standalone
3322,230,NJ City,S,2,21,22,42,High School,35,Patnership,Complex
2861,347.57,NJ City,N,22,15,17,66,High School,29,Patnership,Standalone
8984,378,NJ City,W,13,23,9,39,Graduation,30,Franchise,Complex
3428,469.03,NJ City,W,13,20,15,61,High School,35,Franchise,Complex
2611,274.84,NJ City,E,8,24,16,56,High School,29,Owner,Standalone
5870,39.55,Boston,N,14,13,4,46,No education,35,Patnership,Complex
9751,190.39,NJ City,E,2,11,1,75,High School,31,Owner,Standalone
3594,48.88,NJ City,S,23,14,3,68,Graduation,25,Patnership,Complex
1920,250.34,NJ City,W,13,11,11,48,High School,19,Franchise,Complex
2785,280.62,NJ City,N,14,19,21,42,High School,30,Patnership,Standalone
9423,493,NJ City,E,4,21,13,62,Graduation,21,Owner,Standalone
5022,437,Boston,S,6,14,12,57,Graduation,18,Patnership,Complex
3782,368.74,NJ City,S,15,16,14,29,High School,28,Patnership,Complex
6533,350,Boston,N,4,12,11,76,Graduation,21,Patnership,Standalone
5119,422,Boston,E,10,18,11,52,Graduation,29,Owner,Standalone
1516,224.16,NJ City,W,6,22,23,56,High School,20,Franchise,Complex
3740,414.25,NJ City,W,8,8,5,38,No education,30,Franchise,Complex
476,219.27,NJ City,W,18,20,18,42,High School,32,Franchise,Complex
8687,491,NJ City,E,10,18,10,84,Graduation,18,Owner,Standalone
4257,294,NJ City,N,3,19,8,80,High School,28,Patnership,Standalone
727,110.63,NJ City,E,17,10,2,38,High School,20,Owner,Standalone
5074,477,Boston,S,11,10,25,78,Graduation,33,Patnership,Complex
8970,397,NJ City,S,17,20,24,37,Graduation,22,Patnership,Complex
7637,386,Boston,N,18,9,8,76,Graduation,28,Patnership,Standalone
1806,389.28,NJ City,S,24,19,21,81,High School,25,Patnership,Complex
2608,246.92,NJ City,W,20,12,1,35,High School,20,Franchise,Complex
5594,400,Boston,S,14,22,18,85,Graduation,35,Patnership,Complex
7958,426,Boston,S,13,24,8,31,Graduation,23,Patnership,Complex
215,273.56,NJ City,E,20,16,16,51,High School,35,Owner,Standalone
4154,318.04,NJ City,S,20,9,6,63,High School,19,Patnership,Complex
6961,378,Boston,N,14,10,20,59,Graduation,24,Patnership,Standalone
8309,363,NJ City,N,11,9,19,31,Graduation,28,Patnership,Standalone
6084,452,Boston,W,4,11,17,52,Graduation,27,Franchise,Complex
4082,93.21,NJ City,S,19,20,4,47,No education,21,Patnership,Complex
7585,461,Boston,N,4,24,14,34,Graduation,30,Patnership,Standalone
8158,455,NJ City,S,23,21,19,44,Graduation,22,Patnership,Complex
1628,457.02,NJ City,W,16,10,8,57,High School,29,Franchise,Complex
8508,95.16,NJ City,W,18,20,2,80,Graduation,31,Franchise,Complex
6581,475,Boston,N,21,23,7,67,Graduation,22,Patnership,Standalone
1035,347.53,NJ City,E,13,12,9,65,High School,22,Owner,Standalone
8656,472.01,NJ City,W,2,10,3,46,High School,24,Franchise,Complex
5462,460,Boston,S,4,8,6,42,Graduation,26,Patnership,Complex
3965,343.72,NJ City,N,22,21,9,76,High School,26,Patnership,Standalone
6816,411,Boston,W,25,14,18,58,Graduation,20,Franchise,Complex
1283,227.64,NJ City,E,24,23,20,36,High School,35,Owner,Standalone
2773,141.13,NJ City,N,4,9,4,68,High School,22,Patnership,Standalone
5387,354,Boston,E,7,18,6,45,Graduation,24,Owner,Standalone
488,377.9,NJ City,W,19,12,25,26,High School,18,Franchise,Complex
8854,266.68,NJ City,S,8,21,2,34,High School,27,Patnership,Complex
4240,113,NJ City,W,2,19,23,44,High School,28,Franchise,Complex
4992,425.23,Boston,W,8,20,7,75,High School,21,Franchise,Complex
9747,33.71,NJ City,N,20,15,1,49,Graduation,25,Owner,Standalone
2671,493.63,NJ City,E,25,11,16,63,High School,33,Owner,Standalone
9755,52.53,NJ City,E,9,9,1,56,No education,18,Owner,Standalone
8657,447,NJ City,N,18,18,18,75,Graduation,20,Patnership,Standalone
5453,61.38,Boston,N,18,11,1,27,No education,24,Patnership,Standalone
8200,413,NJ City,W,19,8,17,60,Graduation,25,Franchise,Complex
5713,392,Boston,N,12,12,6,32,Graduation,35,Patnership,Standalone
8513,448,NJ City,N,15,11,18,72,Graduation,26,Patnership,Standalone
5944,215.58,Boston,W,24,8,3,46,High School,23,Franchise,Complex
6119,372,Boston,E,17,22,14,81,Graduation,27,Owner,Standalone
5425,368,Boston,N,14,17,12,41,Graduation,25,Patnership,Standalone
5888,454,Boston,W,24,14,21,39,Graduation,33,Franchise,Complex
2181,293,NJ City,N,1,14,11,55,High School,26,Patnership,Standalone
3043,328.08,NJ City,E,23,14,2,43,High School,21,Owner,Standalone
9756,67.87,NJ City,N,20,9,2,42,Graduation,20,Franchise,Complex
6617,370,Boston,N,17,12,20,44,Graduation,26,Patnership,Standalone
145,398.42,NJ City,N,22,15,4,63,High School,22,Patnership,Standalone
556,491.49,NJ City,W,13,15,25,53,High School,35,Franchise,Complex
4171,483.38,NJ City,E,14,16,11,73,High School,33,Owner,Standalone
3029,207.18,NJ City,N,7,17,13,63,High School,22,Patnership,Standalone
6793,271.19,Boston,N,14,13,4,62,High School,24,Patnership,Standalone
9572,350,NJ City,W,5,18,20,25,Graduation,18,Franchise,Complex
9477,305.77,NJ City,N,2,20,4,62,Graduation,32,Patnership,Standalone
8172,376,NJ City,W,18,15,8,55,Graduation,30,Franchise,Complex
2460,206,NJ City,W,2,8,8,84,High School,30,Franchise,Complex
6803,283.67,Boston,E,6,23,3,46,No education,23,Owner,Standalone
1905,409.53,NJ City,N,11,23,3,83,Graduation,25,Patnership,Standalone
6041,395.46,Boston,N,4,12,4,43,No education,34,Patnership,Standalone
3591,315.97,NJ City,E,19,17,6,50,High School,34,Owner,Standalone
123,146.13,NJ City,E,23,19,6,40,High School,32,Owner,Standalone
6184,338.83,Boston,W,19,14,1,65,High School,32,Franchise,Complex
854,308.19,NJ City,S,10,11,6,63,High School,31,Patnership,Complex
8244,62.13,NJ City,N,11,23,2,57,Graduation,28,Franchise,Complex
4020,208,NJ City,W,2,13,9,73,High School,27,Franchise,Complex
4465,156.13,NJ City,N,17,11,8,25,High School,27,Patnership,Standalone
8301,406,NJ City,N,4,23,25,47,Graduation,22,Patnership,Standalone
6277,494,Boston,N,11,9,12,48,Graduation,28,Patnership,Standalone
3308,24.23,NJ City,N,13,10,2,73,No education,27,Franchise,Complex
9080,20.39,NJ City,N,23,24,4,45,No education,34,Franchise,Complex
8994,156.72,NJ City,S,20,18,2,83,Graduation,21,Patnership,Complex
3708,459.95,NJ City,W,8,21,16,63,High School,22,Franchise,Complex
4956,391.21,Boston,W,7,18,16,79,High School,23,Franchise,Complex
8036,18.03,Boston,N,18,16,2,56,No education,32,Franchise,Complex
8836,479,NJ City,W,23,24,22,29,Graduation,25,Franchise,Complex
8483,370,NJ City,E,6,13,9,36,Graduation,23,Owner,Standalone
3877,136,NJ City,N,18,18,10,52,High School,19,Patnership,Standalone
4063,108.84,NJ City,E,4,16,2,71,High School,31,Owner,Standalone
5355,417,Boston,E,14,17,23,46,Graduation,25,Owner,Standalone
7188,371,Boston,W,18,14,25,65,Graduation,28,Franchise,Complex
4006,238.4,NJ City,S,17,20,20,46,High School,33,Patnership,Complex
7266,366,Boston,S,24,12,17,65,Graduation,26,Patnership,Complex
4268,203.42,NJ City,W,5,21,7,70,High School,26,Franchise,Complex
9528,158.6,NJ City,W,18,12,1,59,Graduation,31,Franchise,Complex
5118,494,Boston,S,22,18,24,69,Graduation,22,Patnership,Complex
7328,10.41,Boston,N,8,17,4,58,No education,29,Franchise,Complex
420,321.2,NJ City,W,20,9,7,65,High School,20,Franchise,Complex
4402,51.01,NJ City,S,21,19,4,76,High School,32,Patnership,Complex
2873,432.61,NJ City,N,23,22,13,42,High School,22,Patnership,Standalone
9659,388,NJ City,E,17,20,23,57,Graduation,27,Owner,Standalone
8292,427,NJ City,W,16,24,24,53,Graduation,19,Franchise,Complex
9217,9.59,NJ City,N,14,8,1,60,High School,25,Patnership,Standalone
1702,188.98,NJ City,S,13,10,17,52,High School,30,Patnership,Complex
142,149.03,NJ City,S,7,21,10,77,High School,25,Patnership,Complex
4509,18.93,NJ City,N,3,13,2,41,Graduation,18,Patnership,Standalone
7759,460,Boston,E,25,9,7,36,Graduation,20,Owner,Standalone
8274,86.64,NJ City,S,11,23,3,80,Graduation,24,Patnership,Complex
6320,70.52,Boston,N,6,21,2,38,No education,32,Franchise,Complex
4720,174.44,NJ City,W,19,15,16,38,High School,22,Franchise,Complex
844,206.46,NJ City,W,22,20,3,85,High School,33,Franchise,Complex
512,207.26,NJ City,W,7,23,18,78,High School,33,Franchise,Complex
2029,68.29,NJ City,N,8,19,2,46,High School,33,Patnership,Standalone
5788,358,Boston,W,18,10,10,79,Graduation,32,Franchise,Complex
5430,94.78,Boston,S,22,18,1,26,Graduation,32,Patnership,Complex
8044,42.41,NJ City,N,10,20,4,42,High School,19,Franchise,Complex
4822,1.41,NJ City,N,10,10,3,31,High School,22,Patnership,Complex
2659,171,NJ City,E,2,9,13,29,High School,25,Owner,Standalone
2711,296.31,NJ City,E,8,16,8,65,High School,35,Owner,Standalone
5008,459,Boston,W,24,13,11,47,Graduation,24,Franchise,Complex
2205,176.7,NJ City,N,6,14,1,81,Graduation,19,Patnership,Standalone
1033,356.66,NJ City,N,7,23,22,31,High School,28,Patnership,Standalone
7045,474.63,Boston,N,23,21,1,78,High School,32,Patnership,Standalone
3517,488.37,NJ City,N,9,19,15,29,High School,23,Patnership,Standalone
8688,112.47,NJ City,W,16,22,1,79,Graduation,32,Franchise,Complex
186,215.17,NJ City,S,19,21,25,83,High School,20,Patnership,Complex
9602,449,NJ City,S,7,12,23,49,Graduation,30,Patnership,Complex
9531,385,NJ City,E,23,13,20,43,Graduation,26,Owner,Standalone
5593,472,Boston,N,13,11,22,39,Graduation,19,Patnership,Standalone
9088,258.55,NJ City,W,13,20,3,68,High School,21,Franchise,Complex
7383,95.75,Boston,E,13,9,1,31,Graduation,31,Owner,Standalone
5927,449,Boston,E,22,10,8,32,Graduation,24,Owner,Standalone
9457,453,NJ City,N,19,19,16,43,Graduation,32,Patnership,Standalone
5926,497,Boston,S,21,14,22,44,Graduation,27,Patnership,Complex
8369,399,NJ City,N,13,8,19,43,Graduation,23,Patnership,Standalone
39,234,NJ City,E,3,10,17,29,High School,33,Owner,Standalone
3219,231,NJ City,E,1,19,7,25,High School,23,Owner,Standalone
1729,20.55,NJ City,N,23,8,4,75,High School,29,Patnership,Standalone
3323,439.18,NJ City,E,5,10,24,80,High School,23,Owner,Standalone
9414,480.62,NJ City,S,1,23,4,79,Graduation,28,Patnership,Complex
571,141.07,NJ City,E,11,8,10,58,High School,23,Owner,Standalone
9046,106.75,NJ City,S,25,16,3,61,High School,34,Patnership,Complex
8068,36.59,NJ City,N,2,11,2,52,High School,21,Franchise,Complex
2093,383.54,NJ City,N,18,23,3,59,No education,31,Patnership,Standalone
8241,126.08,NJ City,N,6,10,4,67,Graduation,32,Patnership,Standalone
7051,124.05,Boston,E,8,14,4,50,High School,25,Owner,Standalone
7364,443,Boston,W,19,10,8,68,Graduation,21,Franchise,Complex
5589,18.39,Boston,N,14,24,4,65,Graduation,33,Patnership,Standalone
7818,420,Boston,S,4,11,22,55,Graduation,34,Patnership,Complex
2531,450.67,NJ City,E,22,10,14,63,High School,28,Owner,Standalone
3480,375.15,NJ City,W,16,18,11,26,High School,25,Franchise,Complex
1222,254,NJ City,S,2,22,7,66,High School,20,Patnership,Complex
2159,160.88,NJ City,E,16,21,14,84,High School,19,Owner,Standalone
8431,445,NJ City,E,19,16,15,83,Graduation,34,Owner,Standalone
1726,96.97,NJ City,S,20,20,1,55,High School,20,Patnership,Complex
6848,207.55,Boston,W,23,15,13,46,Graduation,27,Franchise,Complex
419,351.89,NJ City,E,15,21,18,26,High School,34,Owner,Standalone
7392,208.83,Boston,W,24,9,13,37,Graduation,27,Franchise,Complex
3761,371.15,NJ City,N,19,24,24,44,High School,21,Patnership,Standalone
7576,285.66,Boston,W,9,22,19,62,Graduation,27,Franchise,Complex
2116,98.42,NJ City,W,16,8,3,37,High School,34,Franchise,Complex
7176,71.52,Boston,N,7,12,2,60,Graduation,27,Franchise,Complex
2566,240,NJ City,S,1,16,7,77,High School,20,Patnership,Complex
8959,122.13,NJ City,E,9,18,2,73,High School,25,Owner,Standalone
1627,196,NJ City,E,1,21,22,81,High School,27,Owner,Standalone
646,464.58,NJ City,S,12,19,1,58,High School,20,Patnership,Complex
4565,406.35,NJ City,N,8,11,4,73,No education,35,Patnership,Standalone
9090,332.75,NJ City,S,25,24,17,68,Graduation,32,Patnership,Complex
6792,194.79,Boston,W,20,13,13,80,Graduation,32,Franchise,Complex
1098,495.57,NJ City,S,25,8,13,80,High School,29,Patnership,Complex
3131,392.09,NJ City,E,18,19,17,33,High School,35,Owner,Standalone
6642,406.89,Boston,S,15,24,24,79,Graduation,31,Patnership,Complex
2023,11.37,NJ City,N,10,16,4,70,High School,25,Owner,Standalone
8385,325.5,NJ City,N,10,11,21,34,Graduation,30,Patnership,Standalone
6580,333.56,Boston,W,10,11,3,38,High School,33,Franchise,Complex
9407,279.25,NJ City,E,11,16,5,72,No education,25,Owner,Standalone
5967,454.97,Boston,E,12,19,21,83,Graduation,20,Owner,Standalone
1590,25.79,NJ City,N,25,11,4,78,Graduation,33,Patnership,Complex
598,143.5,NJ City,S,20,23,12,32,High School,23,Patnership,Complex
9839,174.64,NJ City,E,15,22,8,27,Graduation,19,Owner,Standalone
3827,468.27,NJ City,E,16,12,12,26,High School,19,Owner,Standalone
8778,225.9,NJ City,S,6,18,7,85,Graduation,28,Patnership,Complex
9307,244.8,NJ City,E,7,17,13,81,Graduation,29,Owner,Standalone
1112,370.26,NJ City,W,1,11,3,42,No education,26,Franchise,Complex
2758,43.35,NJ City,N,15,20,3,79,High School,35,Patnership,Complex
9882,73.35,NJ City,N,15,24,4,58,Graduation,23,Patnership,Complex
2650,51.43,NJ City,S,23,24,3,85,High School,19,Patnership,Complex
9247,15.98,NJ City,N,10,10,3,44,High School,21,Owner,Standalone
3561,310.23,NJ City,N,13,22,8,85,High School,26,Patnership,Standalone
7114,24.42,Boston,N,25,16,3,38,High School,25,Patnership,Complex
8399,444.96,NJ City,E,19,8,13,78,Graduation,31,Owner,Standalone
7626,186.25,Boston,S,13,18,13,43,Graduation,28,Patnership,Complex
5356,396.32,Boston,W,18,20,21,72,Graduation,27,Franchise,Complex
3445,197.66,NJ City,N,24,13,22,45,High School,19,Patnership,Standalone
6528,460.2,Boston,W,8,21,16,78,Graduation,19,Franchise,Complex
4172,115.67,NJ City,W,10,17,2,62,No education,28,Franchise,Complex
9255,178.46,NJ City,E,17,16,23,36,Graduation,27,Owner,Standalone
7943,54.63,Boston,E,4,10,1,31,No education,31,Owner,Standalone
2165,40.62,NJ City,N,19,19,2,64,No education,19,Patnership,Standalone
1257,58.11,NJ City,N,23,10,1,31,Graduation,20,Patnership,Standalone
1670,70.01,NJ City,N,21,10,4,61,No education,20,Patnership,Complex
9587,449,NJ City,E,4,10,12,63,Graduation,28,Owner,Standalone
6363,14.71,Boston,N,6,18,2,31,Graduation,25,Owner,Standalone
8529,461.58,NJ City,N,9,15,21,61,Graduation,35,Patnership,Standalone
8941,75.83,NJ City,N,19,14,1,41,High School,31,Patnership,Standalone
7242,452.6,Boston,S,10,11,8,51,Graduation,34,Patnership,Complex
3,150.95,Maine,E,23,11,17,76,High School,34,Patnership,Complex
1625,416.59,NJ City,N,6,16,24,84,High School,29,Patnership,Standalone
8931,220.43,NJ City,E,16,9,17,41,Graduation,26,Owner,Standalone
1916,310.17,NJ City,W,15,17,23,41,High School,24,Franchise,Complex
9964,475.54,NJ City,W,12,22,18,25,Graduation,19,Franchise,Complex
969,249.67,NJ City,N,13,16,18,42,High School,23,Patnership,Standalone
664,171.82,NJ City,W,15,20,17,70,High School,25,Franchise,Complex
2974,138.97,NJ City,S,19,19,18,83,High School,28,Patnership,Complex
8731,303.95,NJ City,E,19,21,11,73,Graduation,25,Owner,Standalone
5463,340.64,Boston,E,22,9,25,35,Graduation,30,Owner,Standalone
1003,367.88,NJ City,E,22,21,4,43,High School,26,Owner,Standalone
9049,141.56,NJ City,N,22,19,14,81,Graduation,30,Patnership,Standalone
9352,459.89,NJ City,W,3,15,12,40,Graduation,33,Franchise,Complex
120,20.24,NJ City,N,1,19,4,55,Graduation,33,Franchise,Complex
8856,348.7,NJ City,W,6,14,2,79,Graduation,20,Franchise,Complex
8554,72.91,NJ City,N,12,16,1,67,High School,28,Patnership,Complex
9483,390.29,NJ City,E,12,15,13,81,Graduation,23,Owner,Standalone
4807,450.25,NJ City,E,19,18,16,43,High School,29,Owner,Standalone
8752,432.54,NJ City,W,25,22,23,71,Graduation,34,Franchise,Complex
2057,230,NJ City,N,3,14,15,38,High School,33,Patnership,Standalone
5121,441.33,Boston,N,1,23,4,69,Graduation,21,Patnership,Standalone
167,1.37,NJ City,N,8,15,2,85,No education,23,Owner,Standalone
6059,167.3,Boston,E,19,9,9,42,Graduation,28,Owner,Standalone
8728,492.68,NJ City,W,12,11,18,58,Graduation,31,Franchise,Complex
4783,212.61,NJ City,E,22,9,2,85,High School,24,Owner,Standalone
4738,297.67,NJ City,S,10,9,13,36,High School,27,Patnership,Complex
3525,19.95,NJ City,N,8,11,1,42,Graduation,20,Patnership,Standalone
7235,258.67,Boston,E,10,19,25,59,Graduation,33,Owner,Standalone
691,56.89,NJ City,E,12,9,4,78,High School,29,Owner,Standalone
8131,418.69,NJ City,E,18,19,10,33,Graduation,23,Owner,Standalone
2325,101.43,NJ City,N,20,16,3,51,Graduation,30,Patnership,Standalone
4133,308.69,NJ City,N,22,10,4,46,No education,35,Patnership,Standalone
3546,218.6,NJ City,S,4,15,13,76,High School,28,Patnership,Complex
5609,102.73,Boston,N,11,11,1,32,No education,30,Patnership,Standalone
2523,299.59,NJ City,E,13,21,6,29,High School,28,Owner,Standalone
6634,328.09,Boston,S,16,15,14,54,Graduation,32,Patnership,Complex
2638,477.64,NJ City,S,23,24,25,73,High School,26,Patnership,Complex
2484,429.39,NJ City,W,18,15,23,47,High School,28,Franchise,Complex
9669,353.34,NJ City,N,9,20,22,66,Graduation,34,Patnership,Standalone
5117,393.34,Boston,N,11,10,13,64,Graduation,26,Patnership,Standalone
7583,332.92,Boston,E,20,21,8,79,Graduation,18,Owner,Standalone
5508,202.62,Boston,W,23,15,25,46,Graduation,23,Franchise,Complex
8163,3,NJ City,N,4,13,1,29,Graduation,23,Owner,Standalone
4306,23.59,NJ City,N,4,18,1,31,High School,32,Patnership,Complex
8339,494.07,NJ City,E,9,12,20,32,Graduation,29,Owner,Standalone
4167,34.57,NJ City,N,15,19,1,83,Graduation,24,Owner,Standalone
9765,411.81,NJ City,N,15,24,6,26,Graduation,35,Patnership,Standalone
6271,389.37,Boston,E,25,9,13,35,Graduation,24,Owner,Standalone
4248,310.06,NJ City,W,11,23,12,51,High School,27,Franchise,Complex
5390,480.78,Boston,S,13,21,22,37,Graduation,31,Patnership,Complex
3193,466.5,NJ City,N,24,21,16,31,High School,32,Patnership,Standalone
3643,353.84,NJ City,E,24,15,11,50,High School,34,Owner,Standalone
7786,82.39,Boston,S,15,22,4,73,High School,27,Patnership,Complex
4307,202,NJ City,E,2,19,20,64,High School,23,Owner,Standalone
8148,418.62,NJ City,W,12,24,9,65,Graduation,31,Franchise,Complex
2162,311.94,NJ City,S,10,20,11,68,High School,21,Patnership,Complex
9398,140.04,NJ City,S,3,20,10,29,Graduation,28,Patnership,Complex
5900,252.59,Boston,W,5,13,20,38,Graduation,34,Franchise,Complex
1412,3.32,NJ City,N,1,19,2,84,No education,20,Franchise,Complex
4674,6.38,NJ City,N,4,12,4,79,Graduation,25,Patnership,Complex
2083,197.71,NJ City,E,16,24,21,76,High School,33,Owner,Standalone
9280,385.8,NJ City,W,17,9,18,70,Graduation,19,Franchise,Complex
8318,102.86,NJ City,S,14,8,1,75,No education,32,Patnership,Complex
9271,152.67,NJ City,E,8,18,20,49,Graduation,34,Owner,Standalone
105,172.22,NJ City,N,16,16,1,83,Graduation,20,Patnership,Standalone
2933,435.35,NJ City,N,11,14,8,63,High School,29,Patnership,Standalone
8199,154.47,NJ City,E,18,8,20,65,Graduation,21,Owner,Standalone
5981,194.93,Boston,N,25,23,13,26,Graduation,32,Patnership,Standalone
994,343.29,NJ City,S,13,22,18,30,High School,28,Patnership,Complex
7475,209.13,Boston,E,20,11,21,73,Graduation,28,Owner,Standalone
2115,53.3,NJ City,E,21,21,2,68,Graduation,24,Owner,Standalone
8995,11.91,NJ City,N,23,22,4,56,High School,18,Owner,Standalone
6870,14.64,Boston,N,7,18,4,68,Graduation,26,Patnership,Complex
271,275.4,NJ City,E,20,13,2,74,High School,26,Owner,Standalone
7981,281.48,Boston,N,5,18,18,77,Graduation,21,Patnership,Standalone
7765,153.52,Boston,N,7,21,11,47,Graduation,26,Patnership,Standalone
6501,71.44,Boston,N,18,24,1,64,Graduation,27,Patnership,Standalone
2699,60.47,NJ City,N,1,16,1,76,No education,34,Owner,Standalone
2992,238.66,NJ City,W,25,19,13,27,High School,34,Franchise,Complex
7644,171.2,Boston,W,17,23,2,62,Graduation,26,Franchise,Complex
1393,224,NJ City,N,2,18,18,49,High School,18,Patnership,Standalone
8794,367.79,NJ City,S,19,11,11,57,Graduation,19,Patnership,Complex
1823,457.16,NJ City,E,12,21,10,37,High School,21,Owner,Standalone
4027,416.06,NJ City,E,5,16,18,29,High School,29,Owner,Standalone
3434,396.56,NJ City,S,13,10,13,81,High School,34,Patnership,Complex
7988,444.47,Boston,W,14,21,11,30,Graduation,21,Franchise,Complex
5526,490.84,Boston,S,3,24,3,48,Graduation,31,Patnership,Complex
9922,164.11,NJ City,S,17,22,6,25,Graduation,22,Patnership,Complex
1095,150.43,NJ City,E,5,20,19,66,High School,26,Owner,Standalone
6822,198.98,Boston,S,9,22,17,44,Graduation,22,Patnership,Complex
5112,221.8,Boston,W,21,16,16,78,Graduation,32,Franchise,Complex
381,135.02,NJ City,N,1,21,3,39,Graduation,31,Patnership,Standalone
9617,299.26,NJ City,N,16,22,21,73,Graduation,32,Patnership,Standalone
4058,467.01,NJ City,S,24,12,22,25,High School,26,Patnership,Complex
4431,367.38,NJ City,E,23,18,17,83,High School,18,Owner,Standalone
9984,419.13,NJ City,W,18,24,19,61,Graduation,21,Franchise,Complex
3169,190,NJ City,N,1,17,18,40,High School,28,Patnership,Standalone
7909,184.38,Boston,N,7,16,11,59,Graduation,21,Patnership,Standalone
6614,141.13,Boston,S,5,8,18,62,Graduation,23,Patnership,Complex
2034,197.58,NJ City,S,9,24,20,27,High School,32,Patnership,Complex
4815,54.6,NJ City,E,15,19,3,62,Graduation,27,Owner,Standalone
696,106.9,NJ City,W,24,19,2,64,Graduation,20,Franchise,Complex
9962,267.36,NJ City,S,15,21,2,62,No education,20,Patnership,Complex
9565,414.84,NJ City,N,2,12,6,62,Graduation,32,Patnership,Standalone
6930,62.24,Boston,N,24,19,4,43,Graduation,32,Patnership,Complex
499,433.43,NJ City,E,19,13,8,77,High School,26,Owner,Standalone
2966,392.92,NJ City,S,6,16,19,46,High School,24,Patnership,Complex
3764,245.42,NJ City,W,7,16,16,77,High School,21,Franchise,Complex
9441,132.65,NJ City,N,17,24,12,43,Graduation,33,Patnership,Standalone
1542,482.68,NJ City,S,13,15,2,76,Graduation,35,Patnership,Complex
8947,478.26,NJ City,E,25,15,11,69,Graduation,22,Owner,Standalone
1320,98.61,NJ City,W,10,11,1,30,Graduation,18,Franchise,Complex
5667,429.89,Boston,E,2,14,10,39,Graduation,20,Owner,Standalone
1053,145,NJ City,N,1,18,16,63,High School,27,Patnership,Standalone
3264,304.88,NJ City,W,14,8,1,49,Graduation,18,Franchise,Complex
5791,337.42,Boston,E,5,20,1,45,High School,27,Owner,Standalone
2275,186.13,NJ City,E,23,14,21,47,High School,29,Owner,Standalone
8870,328.48,NJ City,S,19,14,12,30,Graduation,26,Patnership,Complex
6663,170.51,Boston,E,16,16,13,76,Graduation,32,Owner,Standalone
8996,478.59,NJ City,W,6,19,22,81,Graduation,22,Franchise,Complex
2326,54.52,NJ City,S,9,20,2,43,High School,22,Patnership,Complex
9157,41.57,NJ City,N,13,9,3,42,High School,20,Patnership,Standalone
6604,357.89,Boston,W,16,21,16,79,Graduation,35,Franchise,Complex
997,211.13,NJ City,N,17,20,24,57,High School,21,Patnership,Standalone
4859,280.27,NJ City,E,19,11,13,52,High School,26,Owner,Standalone
6923,141.6,Boston,E,23,16,14,31,Graduation,33,Owner,Standalone
4994,205.41,Boston,S,22,8,14,54,High School,24,Patnership,Complex
5456,92.45,Boston,W,3,20,2,33,No education,22,Franchise,Complex
9856,63.45,NJ City,N,22,17,3,69,High School,30,Franchise,Complex
8082,347.71,NJ City,S,3,10,3,74,Graduation,31,Patnership,Complex
1205,342.58,NJ City,N,23,23,6,38,High School,26,Patnership,Standalone
8173,178.85,NJ City,N,1,17,14,76,Graduation,34,Patnership,Standalone
7556,269.57,Boston,W,7,16,22,33,Graduation,35,Franchise,Complex
3871,437.86,NJ City,E,25,17,3,27,High School,22,Owner,Standalone
9224,113.08,NJ City,W,22,21,1,60,No education,28,Franchise,Complex
6189,327.25,Boston,N,1,22,25,51,Graduation,27,Patnership,Standalone
299,108.42,NJ City,E,24,14,2,37,No education,22,Owner,Standalone
6125,83.67,Boston,N,15,14,4,66,No education,31,Patnership,Standalone
933,399.27,NJ City,N,13,21,1,39,Graduation,18,Patnership,Standalone
397,222.39,NJ City,N,17,23,18,58,High School,26,Patnership,Standalone
2946,164.62,NJ City,S,16,20,4,74,Graduation,28,Patnership,Complex
4846,84.43,NJ City,S,9,11,1,71,High School,32,Patnership,Complex
3468,214.29,NJ City,W,7,19,19,48,High School,28,Franchise,Complex
7377,177.43,Boston,N,19,15,6,61,Graduation,25,Patnership,Standalone
8633,417.91,NJ City,N,24,9,23,60,Graduation,28,Patnership,Standalone
6007,156.99,Boston,E,14,12,3,79,High School,28,Owner,Standalone
3573,103,NJ City,N,1,19,24,41,High School,28,Patnership,Standalone
5626,412.55,Boston,S,8,21,22,39,Graduation,24,Patnership,Complex
9544,85.35,NJ City,W,14,9,2,63,High School,35,Franchise,Complex
799,4.24,NJ City,N,2,11,4,65,High School,35,Owner,Standalone
1146,102.03,NJ City,S,6,22,2,65,Graduation,34,Patnership,Complex
4793,221.99,NJ City,N,25,8,23,32,High School,31,Patnership,Standalone
2937,417.51,NJ City,N,6,23,6,47,High School,18,Patnership,Standalone
156,288,NJ City,W,2,8,21,38,High School,32,Franchise,Complex
7238,178.87,Boston,S,22,24,14,42,Graduation,29,Patnership,Complex
3806,46.77,NJ City,N,4,23,4,35,No education,27,Patnership,Complex
9253,176.35,NJ City,N,16,22,24,50,Graduation,27,Patnership,Standalone
5207,223.63,Boston,E,1,12,22,73,Graduation,35,Owner,Standalone
8904,387.32,NJ City,W,25,10,8,44,Graduation,29,Franchise,Complex
2846,160.57,NJ City,S,25,15,13,60,High School,21,Patnership,Complex
5855,9.23,Boston,N,2,12,2,80,No education,26,Owner,Standalone
8293,44.17,NJ City,N,9,22,1,84,High School,29,Patnership,Standalone
1948,177.94,NJ City,W,24,23,19,58,High School,19,Franchise,Complex
6200,471.73,Boston,W,8,8,6,56,Graduation,34,Franchise,Complex
7102,479.81,Boston,S,2,11,25,44,Graduation,26,Patnership,Complex
9560,413.33,NJ City,W,22,24,23,77,Graduation,33,Franchise,Complex
289,23.31,NJ City,N,22,8,2,28,High School,35,Patnership,Standalone
3340,51.34,NJ City,W,18,23,4,60,High School,22,Franchise,Complex
3696,183.34,NJ City,W,24,15,1,30,Graduation,22,Franchise,Complex
842,204.94,NJ City,S,16,21,21,54,High School,22,Patnership,Complex
543,364.26,NJ City,E,6,12,1,71,Graduation,29,Owner,Standalone
6995,6.32,Boston,N,19,23,1,74,No education,30,Owner,Standalone
1674,482.07,NJ City,S,19,13,23,27,High School,35,Patnership,Complex
8237,284.63,NJ City,N,15,19,18,49,Graduation,24,Patnership,Standalone
3016,217.14,NJ City,W,24,10,10,41,High School,29,Franchise,Complex
907,499.06,NJ City,E,25,23,11,35,High School,18,Owner,Standalone
6215,110.88,Boston,E,11,9,2,38,No education,19,Owner,Standalone
7767,454.88,Boston,E,20,24,13,27,Graduation,35,Owner,Standalone
9294,428.76,NJ City,S,20,23,5,47,Graduation,20,Patnership,Complex
9201,17.53,NJ City,N,9,12,2,29,Graduation,26,Patnership,Standalone
4294,344.87,NJ City,S,24,17,23,47,High School,26,Patnership,Complex
4170,32.08,NJ City,N,5,24,3,71,Graduation,27,Patnership,Complex
3838,369.37,NJ City,S,23,20,25,85,High School,29,Patnership,Complex
9204,476.74,NJ City,W,19,17,6,85,Graduation,18,Franchise,Complex
767,25.36,NJ City,N,6,18,4,59,No education,20,Owner,Standalone
988,348.11,NJ City,W,5,10,16,44,High School,25,Franchise,Complex
2919,445.08,NJ City,E,9,22,9,69,High School,26,Owner,Standalone
8666,10.01,NJ City,N,8,20,1,57,No education,34,Patnership,Complex
956,475.18,NJ City,W,19,8,18,33,High School,22,Franchise,Complex
4349,466.63,NJ City,N,13,8,19,34,High School,30,Patnership,Standalone
4736,216.67,NJ City,W,12,16,25,30,High School,31,Franchise,Complex
6128,414.55,Boston,W,11,17,21,52,Graduation,33,Franchise,Complex
3280,320.58,NJ City,W,16,16,13,47,High School,19,Franchise,Complex
8801,490.97,NJ City,N,20,18,20,39,Graduation,22,Patnership,Standalone
9031,468.22,NJ City,E,2,15,17,63,Graduation,27,Owner,Standalone
6754,237.13,Boston,S,1,16,10,84,Graduation,31,Patnership,Complex
906,321.16,NJ City,S,6,8,1,27,Graduation,28,Patnership,Complex
8863,49.55,NJ City,E,20,21,1,69,High School,27,Owner,Standalone
8194,73.98,NJ City,S,13,18,3,58,High School,31,Patnership,Complex
3963,283.42,NJ City,E,12,11,22,30,High School,22,Owner,Standalone
4745,134.34,NJ City,N,16,17,19,28,High School,32,Patnership,Standalone
9824,209.64,NJ City,W,7,20,7,27,Graduation,34,Franchise,Complex
5571,337.47,Boston,E,13,21,25,39,Graduation,31,Owner,Standalone
6588,201.39,Boston,W,6,23,22,39,Graduation,24,Franchise,Complex
9551,25.53,NJ City,N,13,17,1,79,No education,34,Owner,Standalone
7381,331.25,Boston,N,19,17,1,32,High School,31,Patnership,Standalone
5338,242.94,Boston,S,9,21,4,38,High School,22,Patnership,Complex
3241,211.02,NJ City,N,25,13,21,49,High School,35,Patnership,Standalone
3931,222.72,NJ City,E,19,24,8,35,High School,26,Owner,Standalone
7649,289.34,Boston,N,3,10,7,66,Graduation,28,Patnership,Standalone
3300,295.34,NJ City,W,20,13,23,60,High School,23,Franchise,Complex
4657,339.19,NJ City,N,25,16,3,44,High School,34,Patnership,Standalone
7353,31.08,Boston,N,10,22,4,79,Graduation,31,Patnership,Standalone
393,119.07,NJ City,N,15,18,3,42,Graduation,25,Patnership,Standalone
3092,331.85,NJ City,W,7,17,19,47,High School,23,Franchise,Complex
5606,223.63,Boston,S,11,19,19,70,Graduation,35,Patnership,Complex
5413,439.44,Boston,N,7,13,8,78,Graduation,23,Patnership,Standalone
8040,439.01,Boston,W,18,12,15,72,Graduation,28,Franchise,Complex
3490,169.48,NJ City,S,17,15,20,41,High School,32,Patnership,Complex
7456,239.19,Boston,W,11,15,16,49,Graduation,31,Franchise,Complex
2146,474.25,NJ City,S,22,9,17,79,High School,23,Patnership,Complex
380,66.62,NJ City,N,18,23,3,72,No education,32,Franchise,Complex
7714,391.7,Boston,S,7,22,6,83,Graduation,20,Patnership,Complex
6006,319.38,Boston,S,21,23,17,60,Graduation,31,Patnership,Complex
1894,454.29,NJ City,S,6,14,19,65,High School,24,Patnership,Complex
3059,274.33,NJ City,E,21,13,5,78,No education,20,Owner,Standalone
636,253.35,NJ City,W,14,14,15,36,High School,24,Franchise,Complex
810,17.95,NJ City,N,20,19,4,60,Graduation,28,Patnership,Complex
710,147.12,NJ City,S,24,22,14,45,High School,30,Patnership,Complex
4228,342.74,NJ City,W,10,11,6,64,High School,33,Franchise,Complex
5951,355.14,Boston,E,23,23,18,59,Graduation,31,Owner,Standalone
4527,42.02,NJ City,N,24,19,4,50,Graduation,28,Owner,Standalone
8549,103.18,NJ City,N,2,10,2,54,No education,35,Patnership,Standalone
9723,410.19,NJ City,E,12,18,7,79,Graduation,27,Owner,Standalone
7165,400.32,Boston,N,18,12,25,78,Graduation,24,Patnership,Standalone
877,304.76,NJ City,N,10,13,25,50,High School,30,Patnership,Standalone
1906,369.64,NJ City,S,12,8,21,32,High School,27,Patnership,Complex
7331,317.66,Boston,E,19,8,13,27,Graduation,26,Owner,Standalone
4801,117.09,NJ City,N,3,23,3,59,High School,30,Patnership,Standalone
3821,421.89,NJ City,N,16,10,2,66,No education,33,Patnership,Standalone
6707,321.71,Boston,E,12,15,21,81,Graduation,19,Owner,Standalone
5265,182.72,Boston,N,8,11,6,44,Graduation,21,Patnership,Standalone
9033,278.59,NJ City,N,25,14,11,25,Graduation,29,Patnership,Standalone
2210,419.17,NJ City,S,21,23,3,69,No education,20,Patnership,Complex
2782,156.08,NJ City,S,11,9,21,47,High School,19,Patnership,Complex
6547,139.93,Boston,E,16,23,2,85,High School,31,Owner,Standalone
5563,462.38,Boston,E,24,14,3,38,High School,21,Owner,Standalone
6161,151.44,Boston,N,16,22,9,52,Graduation,24,Patnership,Standalone
2685,287.67,NJ City,N,8,10,19,45,High School,31,Patnership,Standalone
9665,306.73,NJ City,N,5,13,14,84,Graduation,30,Patnership,Standalone
599,48.54,NJ City,E,6,24,1,82,No education,27,Owner,Standalone
2512,171.67,NJ City,W,20,24,13,53,High School,18,Franchise,Complex
2204,321.12,NJ City,W,13,19,24,32,High School,24,Franchise,Complex
538,488.76,NJ City,S,4,23,20,36,High School,21,Patnership,Complex
8535,15.43,NJ City,N,6,17,2,71,Graduation,29,Owner,Standalone
8395,412.16,NJ City,E,9,16,15,35,Graduation,33,Owner,Standalone
2126,81.96,NJ City,S,3,24,1,77,No education,29,Patnership,Complex
2431,341.47,NJ City,E,10,8,15,65,High School,30,Owner,Standalone
9911,314.37,NJ City,E,7,9,11,49,Graduation,22,Owner,Standalone
9237,237.67,NJ City,N,7,23,6,54,Graduation,22,Patnership,Standalone
4223,347.33,NJ City,E,9,20,3,42,No education,30,Owner,Standalone
1421,102.34,NJ City,N,2,14,4,77,No education,28,Patnership,Standalone
3347,19.41,NJ City,N,12,19,3,72,No education,35,Owner,Standalone
4838,127,NJ City,S,1,11,23,31,High School,30,Patnership,Complex
5939,447.95,Boston,E,2,13,22,55,Graduation,34,Owner,Standalone
9702,152.98,NJ City,S,17,19,7,74,Graduation,31,Patnership,Complex
6149,248.08,Boston,N,23,18,23,35,Graduation,32,Patnership,Standalone
815,460.59,NJ City,E,11,17,5,30,No education,22,Owner,Standalone
1714,71.92,NJ City,N,8,15,1,35,High School,29,Patnership,Complex
6879,419.98,Boston,E,14,8,12,53,Graduation,33,Owner,Standalone
6025,470.01,Boston,N,11,16,19,56,Graduation,25,Patnership,Standalone
6457,47.21,Boston,N,25,9,2,75,High School,30,Patnership,Standalone
4200,300.63,NJ City,W,4,12,15,61,High School,32,Franchise,Complex
5381,154.67,Boston,N,10,21,7,59,Graduation,29,Patnership,Standalone
9233,287.59,NJ City,N,6,11,21,26,Graduation,29,Patnership,Standalone
1598,10.83,NJ City,N,3,24,1,51,No education,33,Patnership,Complex
2317,220.95,NJ City,N,17,10,15,41,High School,19,Patnership,Standalone
800,226.39,NJ City,W,16,23,16,71,High School,34,Franchise,Complex
7709,291.84,Boston,N,21,20,23,47,Graduation,29,Patnership,Standalone
5898,298.66,Boston,S,15,12,15,53,Graduation,30,Patnership,Complex
5340,370.57,Boston,W,25,9,3,52,Graduation,34,Franchise,Complex
5103,413.8,Boston,E,15,18,10,42,Graduation,19,Owner,Standalone
4856,390.65,NJ City,W,7,9,24,47,High School,18,Franchise,Complex
5215,426.86,Boston,E,21,24,22,69,Graduation,35,Owner,Standalone
3520,290.15,NJ City,W,20,8,16,84,High School,24,Franchise,Complex
4184,379.2,NJ City,W,6,19,25,62,High School,26,Franchise,Complex
7441,321.45,Boston,N,23,9,24,39,Graduation,28,Patnership,Standalone
1849,417.73,NJ City,N,5,24,22,59,High School,18,Patnership,Standalone
7224,46.98,Boston,N,2,24,3,37,Graduation,25,Franchise,Complex
2914,99.84,NJ City,S,20,19,2,44,High School,18,Patnership,Complex
8785,370.61,NJ City,N,19,16,21,55,Graduation,20,Patnership,Standalone
4836,473.48,NJ City,W,24,20,14,82,High School,30,Franchise,Complex
3116,281.7,NJ City,W,8,16,23,68,High School,35,Franchise,Complex
9482,316.22,NJ City,S,7,12,2,26,No education,18,Patnership,Complex
9326,217.37,NJ City,S,11,12,7,38,Graduation,31,Patnership,Complex
9209,328.44,NJ City,N,15,23,4,61,No education,30,Patnership,Standalone
1782,359.41,NJ City,S,1,9,2,70,Graduation,26,Patnership,Complex
8226,446,NJ City,S,14,23,17,28,Graduation,20,Patnership,Complex
7094,449.8,Boston,S,3,15,3,78,No education,30,Patnership,Complex
752,183.43,NJ City,W,3,9,5,68,No education,31,Franchise,Complex
1615,266.65,NJ City,E,21,12,14,26,High School,20,Owner,Standalone
1154,265.85,NJ City,S,2,18,5,70,No education,25,Patnership,Complex
3012,396.81,NJ City,W,18,18,23,67,High School,33,Franchise,Complex
9393,298.15,NJ City,N,10,16,2,25,Graduation,29,Patnership,Standalone
2025,248.03,NJ City,N,6,23,5,31,Graduation,27,Patnership,Standalone
721,55.76,NJ City,N,18,13,4,66,High School,32,Patnership,Standalone
7472,345.3,Boston,W,6,9,11,42,Graduation,24,Franchise,Complex
9488,104.29,NJ City,W,24,21,3,79,No education,19,Franchise,Complex
1634,148.97,NJ City,S,19,8,24,58,High School,31,Patnership,Complex
3860,256.76,NJ City,W,14,15,19,55,High School,22,Franchise,Complex
3054,73.55,NJ City,N,2,23,1,54,Graduation,20,Patnership,Complex
1517,231.02,NJ City,N,2,17,2,29,No education,19,Patnership,Standalone
1450,457.02,NJ City,S,16,10,2,78,High School,31,Patnership,Complex
4608,100,NJ City,W,3,9,9,81,High School,33,Franchise,Complex
6033,368.37,Boston,N,15,18,19,67,Graduation,20,Patnership,Standalone
3536,474.7,NJ City,W,6,15,23,58,High School,18,Franchise,Complex
1239,101.68,NJ City,E,17,24,4,49,Graduation,28,Owner,Standalone
608,130.25,NJ City,W,19,8,5,45,No education,28,Franchise,Complex
6941,454.36,Boston,N,2,12,17,67,Graduation,35,Patnership,Standalone
3569,313.35,NJ City,N,8,19,1,81,No education,27,Patnership,Standalone
1756,392.65,NJ City,W,25,15,12,84,High School,26,Franchise,Complex
7733,198.78,Boston,N,24,24,18,32,Graduation,27,Patnership,Standalone
5722,306.42,Boston,S,11,12,16,38,Graduation,26,Patnership,Complex
5055,419.25,Boston,E,24,8,8,67,Graduation,25,Owner,Standalone
2606,301,NJ City,S,19,11,4,80,No education,20,Patnership,Complex
3772,343.62,NJ City,W,20,8,6,58,High School,34,Franchise,Complex
2996,188.76,NJ City,W,23,24,22,77,High School,26,Franchise,Complex
1321,263.17,NJ City,N,24,14,23,55,High School,33,Patnership,Standalone
3128,202.79,NJ City,W,23,20,24,36,High School,18,Franchise,Complex
4270,326.48,NJ City,S,10,15,21,49,High School,31,Patnership,Complex
2909,366.44,NJ City,N,21,19,5,50,No education,27,Patnership,Standalone
864,347.39,NJ City,W,13,15,21,46,High School,25,Franchise,Complex
4371,441.01,NJ City,E,6,19,7,33,High School,23,Owner,Standalone
8851,119.79,NJ City,E,24,11,1,85,High School,23,Owner,Standalone
3207,29.28,NJ City,N,22,12,2,36,Graduation,34,Owner,Standalone
6919,130.17,Boston,E,20,20,8,75,Graduation,30,Owner,Standalone
1022,97.88,NJ City,S,17,13,3,57,No education,24,Patnership,Complex
5976,176.9,Boston,W,16,17,18,60,Graduation,20,Franchise,Complex
6789,240.99,Boston,N,23,12,7,72,Graduation,35,Patnership,Standalone
5858,362.73,Boston,S,18,16,13,40,Graduation,19,Patnership,Complex
1178,308.93,NJ City,S,24,12,18,80,High School,33,Patnership,Complex
9298,28.91,NJ City,N,16,20,4,79,High School,32,Patnership,Complex
2599,218.02,NJ City,E,23,9,24,32,High School,23,Owner,Standalone
7533,183.11,Boston,N,20,21,6,50,Graduation,21,Patnership,Standalone
540,207.22,NJ City,W,19,11,15,32,High School,21,Franchise,Complex
4968,370,Boston,W,13,15,10,27,High School,19,Franchise,Complex
3686,279.07,NJ City,S,17,20,8,37,High School,23,Patnership,Complex
8701,314.85,NJ City,N,12,12,21,84,Graduation,27,Patnership,Standalone
9652,287.6,NJ City,W,20,12,4,38,High School,19,Franchise,Complex
3151,2.93,NJ City,E,15,13,1,54,High School,29,Owner,Standalone
8005,123.5,Boston,N,13,12,2,81,High School,23,Patnership,Standalone
6661,30.11,Boston,N,11,8,2,42,High School,30,Patnership,Standalone
471,409.74,NJ City,E,18,21,7,40,High School,33,Owner,Standalone
3139,82.35,NJ City,E,6,19,3,58,High School,35,Owner,Standalone
8104,308.03,NJ City,W,3,16,15,32,Graduation,25,Franchise,Complex
9475,29.8,NJ City,N,1,13,3,52,High School,27,Owner,Standalone
5391,333.15,Boston,E,3,10,8,28,Graduation,27,Owner,Standalone
7980,6.13,Boston,N,6,14,1,51,Graduation,28,Franchise,Complex
6761,119.05,Boston,N,19,19,3,84,No education,31,Patnership,Standalone
139,293.78,NJ City,E,12,18,16,45,High School,35,Owner,Standalone
9865,63.69,NJ City,N,2,22,4,80,High School,29,Patnership,Standalone
4189,47.2,NJ City,N,12,17,1,59,High School,30,Patnership,Standalone
1921,32.27,NJ City,N,7,11,1,84,High School,24,Patnership,Standalone
9667,244.41,NJ City,E,12,21,22,85,Graduation,19,Owner,Standalone
7065,68.16,Boston,N,2,21,2,70,Graduation,18,Patnership,Standalone
8526,484.02,NJ City,S,17,8,14,62,Graduation,22,Patnership,Complex
7215,459.25,Boston,E,7,12,21,40,Graduation,31,Owner,Standalone
7682,120.04,Boston,S,19,14,1,52,No education,29,Patnership,Complex
1212,305.7,NJ City,W,20,15,25,48,High School,22,Franchise,Complex
8111,220.82,NJ City,E,24,20,14,29,Graduation,32,Owner,Standalone
3156,230,NJ City,W,1,15,20,48,High School,19,Franchise,Complex
7764,495.89,Boston,W,11,20,11,42,Graduation,21,Franchise,Complex
2015,428.63,NJ City,E,8,9,11,49,High School,31,Owner,Standalone
8610,329.26,NJ City,S,16,20,14,32,Graduation,35,Patnership,Complex
1515,110.57,NJ City,E,21,23,2,77,Graduation,26,Owner,Standalone
8875,326,NJ City,E,15,23,18,41,Graduation,21,Owner,Standalone
9085,452.96,NJ City,N,9,9,4,36,High School,19,Patnership,Standalone
8062,433.12,NJ City,S,13,11,8,32,Graduation,28,Patnership,Complex
5135,407.62,Boston,E,22,18,14,76,Graduation,22,Owner,Standalone
4152,146.28,NJ City,W,15,13,25,41,High School,23,Franchise,Complex
2705,322.27,NJ City,N,20,10,16,57,High School,21,Patnership,Standalone
3316,182,NJ City,W,1,9,6,36,High School,35,Franchise,Complex
7339,380.54,Boston,E,5,10,2,76,High School,23,Owner,Standalone
813,65.69,NJ City,N,17,11,4,49,Graduation,25,Patnership,Standalone
1723,246.59,NJ City,E,10,23,15,27,High School,29,Owner,Standalone
6772,260.01,Boston,W,4,8,20,28,Graduation,18,Franchise,Complex
7789,72.31,Boston,N,11,19,1,61,High School,33,Patnership,Standalone
2988,283.49,NJ City,W,4,23,7,26,High School,20,Franchise,Complex
539,374.05,NJ City,E,6,10,11,30,High School,31,Owner,Standalone
8674,291.55,NJ City,S,24,9,16,49,Graduation,31,Patnership,Complex
1313,195.35,NJ City,N,17,10,5,71,No education,34,Patnership,Standalone
2114,39.61,NJ City,N,7,8,1,53,No education,24,Patnership,Complex
2007,4.5,NJ City,N,17,20,3,26,Graduation,31,Owner,Standalone
9454,309.72,NJ City,S,21,8,17,33,Graduation,18,Patnership,Complex
9866,26.99,NJ City,N,12,15,3,71,No education,20,Patnership,Complex
2073,446.72,NJ City,N,21,13,3,26,Graduation,24,Patnership,Standalone
7876,446.66,Boston,W,23,19,14,65,Graduation,35,Franchise,Complex
9623,334.87,NJ City,E,17,10,13,37,Graduation,23,Owner,Standalone
587,54.76,NJ City,E,16,10,3,37,No education,24,Owner,Standalone
1944,330.16,NJ City,W,11,9,15,58,High School,35,Franchise,Complex
1783,419.73,NJ City,E,11,15,2,67,High School,31,Owner,Standalone
3488,238.87,NJ City,W,18,13,6,78,High School,23,Franchise,Complex
3201,470.95,NJ City,N,4,21,10,42,High School,31,Patnership,Standalone
8738,7.19,NJ City,N,12,9,3,66,No education,26,Patnership,Complex
4303,477.58,NJ City,E,15,18,1,61,High School,19,Owner,Standalone
9456,396.39,NJ City,W,21,16,7,46,Graduation,33,Franchise,Complex
9418,463.02,NJ City,S,12,16,6,85,Graduation,22,Patnership,Complex
5213,100.03,Boston,N,22,8,2,83,No education,21,Patnership,Standalone
4215,64.89,NJ City,N,11,16,3,72,Graduation,19,Owner,Standalone
9898,131.6,NJ City,S,15,19,13,35,Graduation,31,Patnership,Complex
5283,105.16,Boston,E,13,13,1,38,Graduation,19,Owner,Standalone
1708,169,NJ City,W,1,23,18,37,High School,33,Franchise,Complex
1262,211.28,NJ City,S,9,11,8,59,High School,25,Patnership,Complex
6515,247.71,Boston,E,22,8,18,43,Graduation,26,Owner,Standalone
1363,40.79,NJ City,N,21,12,3,80,High School,18,Owner,Standalone
4155,311,NJ City,E,2,10,12,59,High School,35,Owner,Standalone
5983,178.83,Boston,E,25,9,14,51,Graduation,28,Owner,Standalone
6608,258.85,Boston,W,1,23,9,57,Graduation,32,Franchise,Complex
6247,103.42,Boston,E,23,11,4,84,High School,24,Owner,Standalone
1044,336.33,NJ City,W,12,9,23,56,High School,27,Franchise,Complex
823,3.53,NJ City,N,21,21,1,74,High School,30,Owner,Standalone
6030,73.09,Boston,N,10,22,1,51,Graduation,35,Patnership,Complex
8722,302.51,NJ City,S,7,12,8,52,Graduation,25,Patnership,Complex
8993,167,NJ City,N,14,14,3,69,No education,23,Patnership,Standalone
9174,196.15,NJ City,S,2,8,3,42,Graduation,25,Patnership,Complex
3122,497.64,NJ City,S,22,21,7,73,High School,20,Patnership,Complex
9597,337.12,NJ City,N,13,21,10,49,Graduation,28,Patnership,Standalone
1813,278,NJ City,N,2,17,20,46,High School,33,Patnership,Standalone
8202,224.29,NJ City,S,6,12,20,28,Graduation,18,Patnership,Complex
3498,167.99,NJ City,S,23,15,2,77,Graduation,24,Patnership,Complex
5864,310.7,Boston,W,12,24,16,30,Graduation,27,Franchise,Complex
5596,200.83,Boston,W,9,21,24,52,Graduation,20,Franchise,Complex
8121,101.23,NJ City,N,12,20,2,73,Graduation,29,Patnership,Standalone
2716,246.7,NJ City,W,24,19,3,83,High School,23,Franchise,Complex
8638,108.47,NJ City,S,1,15,4,84,High School,33,Patnership,Complex
5450,260.36,Boston,S,8,13,9,82,Graduation,28,Patnership,Complex
7506,152.72,Boston,S,13,10,9,70,Graduation,27,Patnership,Complex
309,283.52,NJ City,N,14,20,3,83,Graduation,32,Patnership,Standalone
4212,112.23,NJ City,W,6,16,4,83,Graduation,24,Franchise,Complex
5133,287.37,Boston,N,3,15,4,38,Graduation,20,Patnership,Standalone
8658,277.92,NJ City,S,16,8,12,28,Graduation,22,Patnership,Complex
3349,114.4,NJ City,N,18,8,3,37,High School,29,Patnership,Standalone
993,1.72,NJ City,N,9,13,4,52,Graduation,24,Patnership,Standalone
2667,250.98,NJ City,E,5,16,19,41,High School,33,Owner,Standalone
1520,364.28,NJ City,W,24,17,9,40,High School,31,Franchise,Complex
1997,55.65,NJ City,N,18,24,1,58,No education,19,Patnership,Standalone
521,127.93,NJ City,N,12,18,11,55,High School,30,Patnership,Standalone
9877,226.3,NJ City,N,17,8,10,34,Graduation,32,Patnership,Standalone
45,402.83,Boston,N,21,15,21,75,High School,30,Patnership,Standalone
8648,198.21,NJ City,W,20,24,24,77,Graduation,18,Franchise,Complex
9039,131.71,NJ City,E,7,19,18,74,Graduation,35,Owner,Standalone
3555,153.38,NJ City,E,5,12,2,48,Graduation,32,Owner,Standalone
4072,480.11,NJ City,W,12,19,9,72,High School,19,Franchise,Complex
5087,477.96,Boston,E,11,23,21,61,Graduation,31,Owner,Standalone
102,478.99,NJ City,S,7,10,24,44,High School,28,Patnership,Complex
5891,157.7,Boston,E,10,16,25,38,Graduation,34,Owner,Standalone
3423,98.7,NJ City,E,20,9,2,34,Graduation,24,Owner,Standalone
8586,306.44,NJ City,S,2,13,13,69,Graduation,34,Patnership,Complex
151,43.87,NJ City,N,23,22,3,66,High School,20,Owner,Standalone
9884,9.66,NJ City,N,1,24,4,25,No education,18,Franchise,Complex
3119,187.68,NJ City,E,23,19,15,38,High School,22,Owner,Standalone
5768,85.89,Boston,W,20,10,3,67,No education,34,Franchise,Complex
3309,74.32,NJ City,N,23,23,1,60,Graduation,35,Patnership,Standalone
5330,449.8,Boston,S,22,24,15,35,Graduation,23,Patnership,Complex
8729,490.09,NJ City,N,1,9,23,77,Graduation,29,Patnership,Standalone
5795,107.41,Boston,E,12,22,2,34,No education,25,Owner,Standalone
9250,457.86,NJ City,S,19,10,16,58,Graduation,28,Patnership,Complex
9784,174.08,NJ City,W,4,10,21,66,Graduation,33,Franchise,Complex
4610,137,NJ City,S,3,12,23,27,High School,21,Patnership,Complex
9248,318.13,NJ City,W,24,17,16,62,Graduation,19,Franchise,Complex
4000,127.02,NJ City,W,3,14,1,70,High School,21,Franchise,Complex
4014,184.54,NJ City,S,21,17,21,27,High School,28,Patnership,Complex
524,378.17,NJ City,W,13,15,24,40,High School,20,Franchise,Complex
3084,84.86,NJ City,W,3,15,1,25,Graduation,33,Franchise,Complex
7638,100,Boston,S,24,24,4,29,Graduation,29,Patnership,Complex
6107,333.22,Boston,E,14,17,18,77,Graduation,26,Owner,Standalone
4018,334.46,NJ City,S,20,21,2,71,High School,32,Patnership,Complex
9985,202.74,NJ City,N,9,9,24,72,Graduation,27,Patnership,Standalone
953,315.62,NJ City,N,21,19,16,73,High School,20,Patnership,Standalone
4451,40.55,NJ City,N,9,21,4,37,No education,28,Owner,Standalone
6389,51.26,Boston,N,11,9,1,40,No education,32,Patnership,Standalone
4940,408.06,Boston,W,4,14,9,40,High School,26,Franchise,Complex
6295,467.18,Boston,E,12,18,3,30,High School,25,Owner,Standalone
2920,414.54,NJ City,W,20,11,5,44,High School,32,Franchise,Complex
833,285.75,NJ City,N,12,23,11,40,High School,28,Patnership,Standalone
4612,118.29,NJ City,W,9,10,4,31,High School,34,Franchise,Complex
7870,430.93,Boston,S,1,10,8,49,Graduation,35,Patnership,Complex
2856,140.83,NJ City,W,20,9,12,83,High School,20,Franchise,Complex
6222,8.95,Boston,N,17,23,2,72,Graduation,29,Patnership,Complex
1577,29.01,NJ City,N,23,11,3,63,No education,28,Patnership,Standalone
9616,235.05,NJ City,W,11,24,22,51,Graduation,29,Franchise,Complex
6926,199.82,Boston,S,17,22,21,49,Graduation,32,Patnership,Complex
1715,169.94,NJ City,E,19,14,2,40,No education,32,Owner,Standalone
2260,472.92,NJ City,W,20,24,23,77,High School,24,Franchise,Complex
6989,358.53,Boston,N,12,23,7,83,Graduation,25,Patnership,Standalone
4401,100,NJ City,N,1,18,18,60,High School,31,Patnership,Standalone
470,472.36,NJ City,S,24,9,15,28,High School,18,Patnership,Complex
7197,494.02,Boston,N,20,18,2,49,Graduation,25,Patnership,Standalone
3270,165.57,NJ City,S,18,24,22,26,High School,31,Patnership,Complex
4762,375.32,NJ City,S,25,24,11,29,High School,32,Patnership,Complex
81,356.36,Boston,N,23,12,5,35,Graduation,29,Patnership,Standalone
5027,297.32,Boston,E,1,12,25,65,Graduation,31,Owner,Standalone
1934,296.35,NJ City,S,23,15,1,50,No education,22,Patnership,Complex
5624,34.39,Boston,N,20,14,2,65,No education,31,Franchise,Complex
2943,155,NJ City,E,1,21,17,69,High School,29,Owner,Standalone
3221,152.57,NJ City,N,25,18,15,35,High School,33,Patnership,Standalone
4176,121.77,NJ City,W,16,24,1,38,Graduation,32,Franchise,Complex
1567,469.5,NJ City,E,6,10,23,79,High School,32,Owner,Standalone
7776,196.94,Boston,W,21,10,3,32,Graduation,32,Franchise,Complex
8981,56.54,NJ City,N,14,24,4,57,No education,23,Patnership,Standalone
1719,111.58,NJ City,E,13,18,1,54,Graduation,23,Owner,Standalone
449,369.04,NJ City,N,17,11,18,59,High School,18,Patnership,Standalone
1659,296.01,NJ City,E,21,21,19,42,High School,27,Owner,Standalone
3284,80.36,NJ City,W,1,18,2,68,No education,23,Franchise,Complex
5332,318,Boston,W,14,23,23,82,Graduation,19,Franchise,Complex
7868,5.64,Boston,N,12,16,1,74,No education,21,Franchise,Complex
8746,450.48,NJ City,S,15,14,12,60,Graduation,20,Patnership,Complex
5830,385.57,Boston,S,24,13,22,58,Graduation,21,Patnership,Complex
523,461.93,NJ City,E,6,20,23,31,High School,18,Owner,Standalone
4387,68.47,NJ City,N,22,10,4,43,High School,19,Owner,Standalone
6001,373.78,Boston,N,21,19,7,29,Graduation,21,Patnership,Standalone
4779,379.83,NJ City,E,25,22,11,85,High School,19,Owner,Standalone
1903,56.97,NJ City,E,8,17,4,77,High School,27,Owner,Standalone
2249,81.14,NJ City,N,3,17,2,74,No education,22,Patnership,Standalone
4446,484.27,NJ City,S,15,20,20,71,High School,26,Patnership,Complex
7108,456.09,Boston,W,21,9,6,37,Graduation,22,Franchise,Complex
4491,41.45,NJ City,N,16,16,1,50,Graduation,23,Owner,Standalone
2434,132.95,NJ City,S,17,15,5,80,High School,22,Patnership,Complex
642,291,NJ City,S,2,16,20,78,High School,21,Patnership,Complex
2458,59.17,NJ City,S,19,18,4,25,High School,21,Patnership,Complex
8282,363.46,NJ City,S,14,13,12,53,Graduation,19,Patnership,Complex
5427,396.65,Boston,E,23,24,17,36,Graduation,18,Owner,Standalone
9979,324.52,NJ City,E,11,16,25,74,Graduation,29,Owner,Standalone
7737,295.78,Boston,N,16,15,23,66,Graduation,30,Patnership,Standalone
1502,451.77,NJ City,S,12,19,23,27,High School,18,Patnership,Complex
5957,262.64,Boston,N,23,14,12,44,Graduation,25,Patnership,Standalone
9630,190.46,NJ City,S,17,14,14,45,Graduation,19,Patnership,Complex
9192,457.16,NJ City,W,20,8,15,31,Graduation,19,Franchise,Complex
5083,332.55,Boston,E,2,16,7,37,Graduation,29,Owner,Standalone
6338,474.01,Boston,S,9,23,20,78,Graduation,20,Patnership,Complex
7584,57.68,Boston,W,24,19,2,82,Graduation,28,Franchise,Complex
7412,250.81,Boston,W,23,24,23,34,Graduation,33,Franchise,Complex
2740,11.06,NJ City,N,22,20,4,40,High School,18,Franchise,Complex
3578,155.1,NJ City,S,21,24,4,44,No education,28,Patnership,Complex
6951,230.24,Boston,E,16,8,5,80,Graduation,35,Owner,Standalone
6304,381.41,Boston,W,23,12,16,78,Graduation,32,Franchise,Complex
9836,327.13,NJ City,W,1,18,17,68,Graduation,20,Franchise,Complex
3976,377.08,NJ City,W,11,13,10,84,High School,35,Franchise,Complex
9789,29.82,NJ City,N,1,19,2,32,Graduation,28,Patnership,Standalone
2139,439.44,NJ City,E,13,15,15,35,High School,18,Owner,Standalone
2588,233.21,NJ City,W,17,9,16,66,High School,19,Franchise,Complex
2636,264.27,NJ City,W,11,23,23,41,High School,22,Franchise,Complex
1910,184.33,NJ City,S,11,12,11,82,High School,28,Patnership,Complex
4016,417.43,NJ City,W,21,19,19,45,High School,18,Franchise,Complex
857,226.38,NJ City,N,16,13,13,85,High School,29,Patnership,Standalone
6435,195.49,Boston,E,7,8,19,55,Graduation,30,Owner,Standalone
6647,391.35,Boston,E,20,16,7,71,Graduation,33,Owner,Standalone
9740,288.32,NJ City,W,25,15,12,65,Graduation,18,Franchise,Complex
5349,374.51,Boston,N,17,9,16,57,Graduation,31,Patnership,Standalone
9993,287.2,NJ City,N,1,8,8,25,Graduation,24,Patnership,Standalone
3121,11.59,NJ City,N,12,16,1,49,High School,21,Patnership,Standalone
613,494.51,NJ City,N,4,12,18,51,High School,24,Patnership,Standalone
600,420.46,NJ City,W,15,10,19,29,High School,34,Franchise,Complex
3192,33.68,NJ City,N,8,18,2,37,Graduation,26,Franchise,Complex
1833,445.21,NJ City,N,25,23,25,55,High School,26,Patnership,Standalone
8088,358.21,NJ City,W,7,8,7,85,Graduation,29,Franchise,Complex
1221,438.1,NJ City,N,21,20,20,41,High School,18,Patnership,Standalone
7115,415.8,Boston,E,23,23,15,73,Graduation,31,Owner,Standalone
3563,254.69,NJ City,E,10,14,24,55,High School,26,Owner,Standalone
2781,424.45,NJ City,N,4,19,16,61,High School,18,Patnership,Standalone
2000,453.15,NJ City,W,11,21,25,59,High School,34,Franchise,Complex
1316,72.31,NJ City,N,10,20,3,46,No education,18,Franchise,Complex
7086,152.76,Boston,S,8,17,13,44,Graduation,30,Patnership,Complex
8076,168.06,NJ City,W,23,19,1,52,Graduation,25,Franchise,Complex
4787,110.09,NJ City,E,3,24,2,68,No education,35,Owner,Standalone
4666,335.39,NJ City,S,2,24,4,67,High School,23,Patnership,Complex
4242,203.23,NJ City,S,14,13,15,41,High School,22,Patnership,Complex
1268,344.87,NJ City,W,13,10,1,57,No education,27,Franchise,Complex
6490,106.49,Boston,S,24,17,2,51,High School,19,Patnership,Complex
7616,265.33,Boston,W,10,17,21,83,Graduation,18,Franchise,Complex
9459,465.23,NJ City,E,19,20,12,52,Graduation,34,Owner,Standalone
2206,150.45,NJ City,S,16,22,9,31,High School,33,Patnership,Complex
3892,89.44,NJ City,W,24,19,3,58,High School,29,Franchise,Complex
3125,107,NJ City,N,2,16,19,68,High School,23,Patnership,Standalone
8555,154.75,NJ City,E,7,10,10,73,Graduation,25,Owner,Standalone
4530,456.5,NJ City,S,20,23,8,74,High School,18,Patnership,Complex
7780,404.55,Boston,W,10,8,22,60,Graduation,18,Franchise,Complex
2068,94.88,NJ City,W,21,8,1,46,High School,22,Franchise,Complex
8235,465.97,NJ City,E,4,23,23,54,Graduation,29,Owner,Standalone
2494,491.05,NJ City,S,25,13,1,49,High School,32,Patnership,Complex
3025,121.41,NJ City,N,4,9,4,42,High School,27,Patnership,Standalone
5201,321.81,Boston,N,16,20,21,54,Graduation,25,Patnership,Standalone
7565,482,Boston,N,12,20,1,61,No education,20,Patnership,Standalone
6861,180.93,Boston,N,19,18,6,59,Graduation,18,Patnership,Standalone
8942,469.33,NJ City,S,25,22,16,49,Graduation,32,Patnership,Complex
2695,355.07,NJ City,E,16,12,7,66,High School,29,Owner,Standalone
821,410.18,NJ City,N,8,15,16,62,High School,26,Patnership,Standalone
6371,203.67,Boston,E,12,16,5,43,No education,27,Owner,Standalone
5940,101.53,Boston,W,16,11,1,43,Graduation,34,Franchise,Complex
3222,81.09,NJ City,S,8,17,2,28,Graduation,34,Patnership,Complex
2014,55.51,NJ City,S,14,20,4,80,High School,30,Patnership,Complex
5586,22.26,Boston,N,18,17,4,46,Graduation,30,Patnership,Complex
9019,348.23,NJ City,E,24,10,22,67,Graduation,24,Owner,Standalone
2863,378.58,NJ City,E,22,14,20,68,High School,20,Owner,Standalone
622,98.16,NJ City,S,2,20,1,69,High School,35,Patnership,Complex
6177,114.07,Boston,N,4,12,2,66,Graduation,25,Patnership,Standalone
9076,482.17,NJ City,W,21,11,21,69,Graduation,25,Franchise,Complex
8266,305.32,NJ City,S,10,16,15,72,Graduation,20,Patnership,Complex
9064,404.8,NJ City,W,3,20,25,41,Graduation,28,Franchise,Complex
4199,75.75,NJ City,E,22,22,4,38,No education,25,Owner,Standalone
1083,154.2,NJ City,E,21,17,7,51,High School,26,Owner,Standalone
4588,352.86,NJ City,W,17,20,15,56,High School,34,Franchise,Complex
1767,391.67,NJ City,E,23,13,15,51,High School,35,Owner,Standalone
8223,261.41,NJ City,E,22,15,17,44,Graduation,21,Owner,Standalone
6044,213,Boston,W,25,18,1,50,No education,22,Franchise,Complex
8024,174.75,Boston,W,16,8,18,74,Graduation,19,Franchise,Complex
6499,335.88,Boston,E,2,9,13,25,Graduation,26,Owner,Standalone
5770,309.2,Boston,S,11,22,10,81,Graduation,25,Patnership,Complex
7087,80.46,Boston,E,21,21,1,63,High School,25,Owner,Standalone
9703,120.32,NJ City,E,3,10,2,40,High School,29,Owner,Standalone
8891,496.08,NJ City,E,16,14,15,82,Graduation,34,Owner,Standalone
1690,347.48,NJ City,S,23,14,21,59,High School,26,Patnership,Complex
7476,128.29,Boston,W,17,11,7,34,Graduation,18,Franchise,Complex
183,233.96,NJ City,E,22,18,17,39,High School,23,Owner,Standalone
106,319.15,NJ City,S,13,17,2,25,High School,35,Patnership,Complex
1308,67.73,NJ City,N,9,14,3,70,Graduation,22,Franchise,Complex
9595,8.22,NJ City,N,6,18,2,67,High School,20,Owner,Standalone
3543,114.66,NJ City,E,16,10,2,50,Graduation,23,Owner,Standalone
5210,53.05,Boston,S,13,10,1,48,No education,31,Patnership,Complex
4153,115.31,NJ City,N,9,10,3,41,High School,32,Patnership,Standalone
1228,189,NJ City,W,2,13,22,43,High School,30,Franchise,Complex
2761,490.65,NJ City,N,19,20,5,43,High School,34,Patnership,Standalone
8313,172.88,NJ City,N,22,14,1,71,Graduation,27,Patnership,Standalone
4933,181.4,NJ City,N,21,17,22,44,High School,31,Patnership,Standalone
9129,286.33,NJ City,N,23,22,20,41,Graduation,23,Patnership,Standalone
6482,20.4,Boston,N,17,20,1,46,No education,21,Patnership,Complex
5409,176.8,Boston,N,10,10,4,79,Graduation,21,Patnership,Standalone
4851,10.21,NJ City,N,19,10,4,34,Graduation,29,Owner,Standalone
110,17.25,NJ City,N,1,9,1,60,No education,26,Patnership,Complex
1804,257.93,NJ City,W,15,13,22,25,High School,23,Franchise,Complex
4350,208.97,NJ City,S,15,20,18,69,High School,22,Patnership,Complex
4541,257,NJ City,N,1,15,18,61,High School,27,Patnership,Standalone
5537,256.87,Boston,N,4,22,19,78,Graduation,28,Patnership,Standalone
880,126.86,NJ City,W,11,14,4,78,High School,23,Franchise,Complex
5377,289.83,Boston,N,18,20,24,37,Graduation,20,Patnership,Standalone
8156,167.94,NJ City,W,25,15,24,54,Graduation,20,Franchise,Complex
3208,122.89,NJ City,W,24,12,1,39,High School,28,Franchise,Complex
9588,265.66,NJ City,W,21,17,5,85,Graduation,21,Franchise,Complex
9771,271.4,NJ City,E,7,10,4,80,Graduation,18,Owner,Standalone
4224,164.39,NJ City,W,23,17,17,30,High School,28,Franchise,Complex
4250,222.35,NJ City,S,13,15,15,28,High School,24,Patnership,Complex
426,389.1,NJ City,S,10,18,16,32,High School,28,Patnership,Complex
6585,451.05,Boston,N,15,8,22,33,Graduation,21,Patnership,Standalone
7806,363.17,Boston,S,23,8,23,79,Graduation,22,Patnership,Complex
8774,169.04,NJ City,S,17,8,12,35,Graduation,20,Patnership,Complex
1640,79.06,NJ City,W,9,16,1,26,No education,28,Franchise,Complex
7772,82.86,Boston,W,5,20,2,78,No education,31,Franchise,Complex
1946,136.37,NJ City,S,6,18,2,81,No education,33,Patnership,Complex
9891,378.76,NJ City,E,8,20,2,25,Graduation,19,Owner,Standalone
9683,16.49,NJ City,N,7,22,2,83,No education,31,Owner,Standalone
3432,272.73,NJ City,W,22,17,21,38,High School,22,Franchise,Complex
5467,484.81,Boston,E,20,24,19,36,Graduation,35,Owner,Standalone
9974,120.86,NJ City,S,15,8,1,25,No education,32,Patnership,Complex
826,97.56,NJ City,S,6,17,2,54,High School,25,Patnership,Complex
33,122.01,Boston,N,3,12,4,53,Graduation,25,Patnership,Standalone
6713,445.32,Boston,N,2,15,12,82,Graduation,27,Patnership,Standalone
91,87.62,Boston,E,17,18,2,63,High School,20,Owner,Standalone
4424,295.17,NJ City,W,6,20,11,77,High School,28,Franchise,Complex
6480,191.7,Boston,W,17,10,21,28,Graduation,29,Franchise,Complex
2745,154,NJ City,N,1,20,18,79,High School,26,Patnership,Standalone
4806,117.6,NJ City,S,18,9,3,26,Graduation,28,Patnership,Complex
281,126.13,NJ City,N,3,18,4,54,No education,29,Patnership,Standalone
6380,260.23,Boston,W,2,20,15,71,Graduation,27,Franchise,Complex
4888,50.64,NJ City,W,19,19,3,35,High School,25,Franchise,Complex
3816,301.75,NJ City,W,13,15,19,38,High School,32,Franchise,Complex
8371,250.55,NJ City,E,21,16,13,64,Graduation,24,Owner,Standalone
3341,304.33,NJ City,N,11,8,9,60,High School,27,Patnership,Standalone
4986,227.78,Boston,S,20,8,12,61,High School,27,Patnership,Complex
1266,28.23,NJ City,N,1,16,3,81,Graduation,22,Patnership,Complex
8669,132.38,NJ City,N,25,11,25,52,Graduation,31,Patnership,Standalone
865,470.7,NJ City,N,2,10,4,74,High School,26,Patnership,Standalone
279,82.64,NJ City,E,13,22,1,52,Graduation,18,Owner,Standalone
2784,137.36,NJ City,W,3,12,1,27,Graduation,27,Franchise,Complex
6139,176.84,Boston,E,3,14,16,80,Graduation,31,Owner,Standalone
7536,124.95,Boston,W,6,20,2,67,Graduation,19,Franchise,Complex
3728,216,NJ City,W,3,22,8,62,High School,18,Franchise,Complex
2929,46.69,NJ City,N,9,20,3,47,High School,20,Patnership,Standalone
1319,339.57,NJ City,E,24,11,13,74,High School,30,Owner,Standalone
8899,387.9,NJ City,E,3,24,7,63,Graduation,25,Owner,Standalone
3744,367.73,NJ City,W,5,15,1,75,Graduation,23,Franchise,Complex
7940,276.85,Boston,W,16,11,9,46,Graduation,27,Franchise,Complex
1449,406.06,NJ City,N,20,23,19,51,High School,24,Patnership,Standalone
7362,77.88,Boston,S,24,9,4,48,Graduation,35,Patnership,Complex
9264,73.04,NJ City,N,4,21,2,85,Graduation,19,Franchise,Complex
6224,406.57,Boston,W,19,15,14,75,Graduation,21,Franchise,Complex
1396,60.21,NJ City,W,19,15,4,43,High School,29,Franchise,Complex
643,141.9,NJ City,E,8,14,5,51,High School,28,Owner,Standalone
1386,134.64,NJ City,S,5,14,20,67,High School,21,Patnership,Complex
5814,207.95,Boston,S,17,8,6,40,Graduation,26,Patnership,Complex
6256,87.63,Boston,W,12,18,2,47,High School,32,Franchise,Complex
1932,0.74,NJ City,N,3,18,3,38,Graduation,22,Franchise,Complex
3890,460.34,NJ City,S,13,13,10,37,High School,35,Patnership,Complex
1016,67.84,NJ City,N,13,10,4,72,No education,29,Franchise,Complex
6839,146.69,Boston,E,15,18,24,53,Graduation,19,Owner,Standalone
1985,355.85,NJ City,N,14,20,2,52,No education,28,Patnership,Standalone
8707,137.53,NJ City,E,22,15,2,42,High School,28,Owner,Standalone
6741,169.07,Boston,N,8,10,24,67,Graduation,35,Patnership,Standalone
270,305.74,NJ City,S,15,12,19,58,High School,18,Patnership,Complex
6909,424.88,Boston,N,8,21,22,73,Graduation,24,Patnership,Standalone
445,94.98,NJ City,N,10,9,2,46,High School,18,Patnership,Standalone
1000,48.86,NJ City,W,22,8,4,63,High School,32,Franchise,Complex
3134,78.67,NJ City,S,16,17,4,48,No education,18,Patnership,Complex
7418,392.43,Boston,S,25,20,20,72,Graduation,21,Patnership,Complex
9198,143.43,NJ City,S,18,10,9,31,Graduation,22,Patnership,Complex
9212,22,NJ City,N,24,21,4,54,No education,24,Franchise,Complex
2082,140.95,NJ City,S,21,12,16,31,High School,20,Patnership,Complex
5048,32.09,Boston,N,20,11,3,71,No education,21,Franchise,Complex
7311,118.45,Boston,E,3,23,2,34,Graduation,34,Owner,Standalone
4931,170.24,NJ City,E,10,20,13,53,High School,21,Owner,Standalone
5226,390.78,Boston,S,23,19,4,74,Graduation,30,Patnership,Complex
3076,105.37,NJ City,W,23,16,2,55,High School,34,Franchise,Complex
9928,225.17,NJ City,W,2,10,17,40,Graduation,28,Franchise,Complex
4866,62.56,NJ City,N,11,12,3,55,Graduation,30,Patnership,Complex
4145,179.1,NJ City,N,14,21,11,27,High School,24,Patnership,Standalone
615,176.19,NJ City,E,7,22,21,77,High School,29,Owner,Standalone
4477,494.94,NJ City,N,5,12,10,65,High School,34,Patnership,Standalone
4144,402.19,NJ City,W,5,22,23,34,High School,32,Franchise,Complex
9940,63.39,NJ City,N,2,9,2,77,High School,20,Franchise,Complex
5828,465.71,Boston,W,19,15,3,30,No education,25,Franchise,Complex
4021,140.05,NJ City,N,12,22,22,75,High School,35,Patnership,Standalone
2916,266.29,NJ City,W,20,21,6,51,High School,18,Franchise,Complex
1023,294.59,NJ City,E,19,12,11,40,High School,33,Owner,Standalone
878,320.14,NJ City,S,8,14,2,35,No education,20,Patnership,Complex
8896,78.39,NJ City,W,8,9,4,43,High School,33,Franchise,Complex
7430,96.33,Boston,S,7,20,3,83,No education,19,Patnership,Complex
4568,39.65,NJ City,N,25,17,1,36,No education,30,Franchise,Complex
4142,151,NJ City,S,2,8,20,38,High School,19,Patnership,Complex
9893,437.36,NJ City,N,7,13,3,70,No education,24,Patnership,Standalone
1886,57.94,NJ City,S,25,22,4,51,No education,22,Patnership,Complex
6154,461.29,Boston,S,4,11,1,31,High School,22,Patnership,Complex
5540,116.18,Boston,W,14,16,2,73,No education,26,Franchise,Complex
3552,83.23,NJ City,W,21,11,2,55,Graduation,27,Franchise,Complex
3022,368.32,NJ City,S,8,18,20,85,High School,28,Patnership,Complex
5375,44.04,Boston,N,11,19,3,77,No education,32,Owner,Standalone
2337,187.76,NJ City,N,15,24,8,64,High School,31,Patnership,Standalone
8407,388.98,NJ City,E,19,16,3,55,High School,23,Owner,Standalone
1658,496.6,NJ City,S,6,20,24,65,High School,35,Patnership,Complex
4597,412.77,NJ City,N,20,23,17,43,High School,21,Patnership,Standalone
7542,280.36,Boston,S,13,16,3,64,Graduation,34,Patnership,Complex
8472,404.96,NJ City,W,19,20,10,35,Graduation,21,Franchise,Complex
7057,73.53,Boston,N,17,13,2,47,High School,33,Patnership,Standalone
9297,190.12,NJ City,N,24,11,18,33,Graduation,31,Patnership,Standalone
5380,249.98,Boston,W,23,24,14,47,Graduation,32,Franchise,Complex
9600,181.2,NJ City,W,23,14,24,48,Graduation,31,Franchise,Complex
467,46.16,NJ City,N,25,21,2,38,No education,23,Owner,Standalone
506,266,NJ City,S,3,14,18,45,High School,18,Patnership,Complex
1820,113.24,NJ City,W,8,22,2,32,No education,28,Franchise,Complex
9021,404.23,NJ City,N,16,11,14,68,Graduation,26,Patnership,Standalone
3994,255.94,NJ City,S,14,16,6,85,High School,30,Patnership,Complex
1374,210.6,NJ City,S,15,24,8,45,High School,34,Patnership,Complex
7874,471.73,Boston,S,15,13,3,57,No education,18,Patnership,Complex
9110,362.15,NJ City,S,10,12,2,26,No education,18,Patnership,Complex
2316,289.59,NJ City,W,9,24,23,35,High School,34,Franchise,Complex
2710,164.66,NJ City,S,12,17,6,76,High School,18,Patnership,Complex
5260,79.62,Boston,W,19,23,1,34,High School,32,Franchise,Complex
7861,249.34,Boston,N,24,24,19,26,Graduation,26,Patnership,Standalone
8332,106.68,NJ City,W,23,19,1,79,High School,29,Franchise,Complex
2016,41.65,NJ City,N,20,18,1,64,Graduation,27,Franchise,Complex
7097,48.29,Boston,N,2,14,3,60,No education,25,Patnership,Standalone
4572,237.89,NJ City,W,2,8,4,70,Graduation,22,Franchise,Complex
3496,343.24,NJ City,W,5,24,6,33,High School,23,Franchise,Complex
4476,90.41,NJ City,W,25,12,4,33,Graduation,34,Franchise,Complex
8560,16.95,NJ City,N,11,24,2,70,High School,27,Franchise,Complex
8765,360.61,NJ City,N,2,9,12,30,Graduation,21,Patnership,Standalone
2388,471.84,NJ City,W,17,24,13,57,High School,34,Franchise,Complex
6293,198.09,Boston,N,25,12,21,74,Graduation,31,Patnership,Standalone
4034,52,NJ City,S,10,18,3,36,No education,19,Patnership,Complex
1998,71.3,NJ City,N,14,21,4,78,Graduation,33,Patnership,Complex
2241,430.29,NJ City,N,15,13,19,35,High School,21,Patnership,Standalone
1560,217.62,NJ City,W,14,18,8,36,High School,35,Franchise,Complex
2755,88.27,NJ City,E,5,23,4,57,High School,18,Owner,Standalone
5669,468.41,Boston,N,18,15,8,54,Graduation,31,Patnership,Standalone
798,47.03,NJ City,N,23,22,1,73,Graduation,27,Patnership,Complex
6544,95.72,Boston,W,25,9,2,81,High School,31,Franchise,Complex
8440,170.17,NJ City,W,23,9,1,77,High School,19,Franchise,Complex
1055,273.51,NJ City,E,5,8,1,77,No education,19,Owner,Standalone
604,20.31,NJ City,N,5,11,1,59,High School,20,Franchise,Complex
9392,489.06,NJ City,W,17,17,14,52,Graduation,32,Franchise,Complex
5428,42.13,Boston,N,3,22,1,46,High School,19,Franchise,Complex
9853,6.71,NJ City,N,19,8,2,35,High School,22,Patnership,Standalone
2752,69.08,NJ City,N,1,17,1,84,High School,28,Franchise,Complex
2860,478.6,NJ City,W,12,21,21,42,High School,28,Franchise,Complex
979,33.4,NJ City,N,8,22,4,62,High School,30,Owner,Standalone
3700,71.44,NJ City,N,4,20,2,40,High School,29,Franchise,Complex
1358,167.38,NJ City,S,6,23,3,40,No education,33,Patnership,Complex
6232,203.58,Boston,W,19,19,3,51,High School,32,Franchise,Complex
6914,107.97,Boston,S,7,21,2,74,No education,19,Patnership,Complex
2294,441.33,NJ City,S,8,16,17,64,High School,24,Patnership,Complex
4932,267.17,NJ City,W,10,8,4,81,Graduation,24,Franchise,Complex
14,180.44,Boston,S,20,17,23,50,High School,19,Patnership,Complex
3933,216,NJ City,N,2,10,17,25,High School,34,Patnership,Standalone
677,382.63,NJ City,N,25,21,1,78,No education,30,Patnership,Standalone
9978,441.83,NJ City,S,12,17,10,68,Graduation,28,Patnership,Complex
1845,354.35,NJ City,N,9,8,22,61,High School,30,Patnership,Standalone
3959,466.86,NJ City,E,12,15,14,37,High School,25,Owner,Standalone
9783,261.58,NJ City,E,24,10,11,57,Graduation,35,Owner,Standalone
9220,58.54,NJ City,W,19,12,4,69,High School,18,Franchise,Complex
4609,109.97,NJ City,N,7,16,3,58,High School,22,Patnership,Standalone
4964,99.19,Boston,W,10,24,3,40,No education,22,Franchise,Complex
3639,373.44,NJ City,E,10,9,3,52,Graduation,34,Owner,Standalone
7391,176.71,Boston,E,12,22,7,63,Graduation,23,Owner,Standalone
531,4.02,NJ City,N,4,21,2,38,Graduation,32,Owner,Standalone
2104,132.43,NJ City,W,12,12,15,80,High School,33,Franchise,Complex
2108,93.88,NJ City,W,22,11,2,73,No education,23,Franchise,Complex
8791,305.52,NJ City,E,13,23,10,84,Graduation,31,Owner,Standalone
9826,160.17,NJ City,S,4,16,10,55,Graduation,19,Patnership,Complex
8477,88.3,NJ City,N,21,23,4,46,No education,26,Patnership,Standalone
4520,346.52,NJ City,W,11,8,11,56,High School,24,Franchise,Complex
5268,475.89,Boston,W,23,14,14,71,Graduation,21,Franchise,Complex
438,189.77,NJ City,S,9,9,5,48,Graduation,29,Patnership,Complex
3769,297.31,NJ City,N,24,20,10,76,High School,19,Patnership,Standalone
9688,48.13,NJ City,W,25,15,2,71,High School,26,Franchise,Complex
8122,307.7,NJ City,S,22,23,7,39,Graduation,23,Patnership,Complex
8460,267.18,NJ City,W,14,17,5,82,Graduation,28,Franchise,Complex
2982,163.99,NJ City,S,8,20,5,40,Graduation,26,Patnership,Complex
1286,233,NJ City,S,2,17,20,33,High School,23,Patnership,Complex
6447,383.85,Boston,E,25,13,8,57,Graduation,29,Owner,Standalone
4869,270,NJ City,N,3,12,15,78,High School,27,Patnership,Standalone
2652,126.57,NJ City,W,5,10,4,66,Graduation,25,Franchise,Complex
4517,222.58,NJ City,N,18,13,12,73,High School,27,Patnership,Standalone
7555,330.66,Boston,E,19,20,10,80,Graduation,28,Owner,Standalone
8581,487.07,NJ City,N,15,18,12,75,Graduation,25,Patnership,Standalone
2233,311.02,NJ City,N,19,17,13,54,High School,29,Patnership,Standalone
3372,464.08,NJ City,W,21,15,10,70,High School,24,Franchise,Complex
9050,212.14,NJ City,S,20,14,10,26,Graduation,34,Patnership,Complex
9822,195.57,NJ City,S,14,24,22,49,Graduation,28,Patnership,Complex
6808,390.44,Boston,W,4,12,4,82,High School,35,Franchise,Complex
3002,158.02,NJ City,S,10,8,21,72,High School,29,Patnership,Complex
3237,493.77,NJ City,N,20,22,8,25,High School,19,Patnership,Standalone
8989,217.68,NJ City,N,8,24,17,44,Graduation,18,Patnership,Standalone
7489,451.75,Boston,N,9,23,14,44,Graduation,18,Patnership,Standalone
9450,405.08,NJ City,S,11,12,16,83,Graduation,24,Patnership,Complex
8955,260.58,NJ City,E,8,12,17,35,Graduation,30,Owner,Standalone
7373,17.65,Boston,N,13,9,4,67,No education,31,Patnership,Standalone
9419,242.41,NJ City,E,10,8,4,31,No education,19,Owner,Standalone
6960,90.8,Boston,W,20,8,2,77,Graduation,35,Franchise,Complex
6246,78.02,Boston,S,20,17,3,54,Graduation,30,Patnership,Complex
7454,483.77,Boston,S,14,22,12,69,Graduation,24,Patnership,Complex
1442,234.17,NJ City,S,18,19,4,41,No education,27,Patnership,Complex
2078,6.76,NJ City,N,1,24,4,36,No education,27,Patnership,Complex
6344,250.31,Boston,W,2,23,22,75,Graduation,22,Franchise,Complex
101,99.27,NJ City,N,8,10,3,61,No education,22,Patnership,Standalone
6690,253.14,Boston,S,16,15,25,32,Graduation,35,Patnership,Complex
9130,244.31,NJ City,S,4,18,2,29,High School,24,Patnership,Complex
9814,40.1,NJ City,N,2,19,3,61,High School,23,Patnership,Complex
6038,349.36,Boston,S,9,8,5,67,No education,34,Patnership,Complex
3881,314.82,NJ City,N,21,19,22,48,High School,28,Patnership,Standalone
2277,123.21,NJ City,N,13,18,1,78,Graduation,23,Patnership,Standalone
4634,198.25,NJ City,S,10,19,22,74,High School,33,Patnership,Complex
633,304.99,NJ City,N,22,12,13,82,High School,26,Patnership,Standalone
252,84.13,NJ City,W,1,16,1,53,Graduation,25,Franchise,Complex
2553,473.26,NJ City,N,14,13,7,35,High School,19,Patnership,Standalone
8680,27.23,NJ City,N,18,12,4,26,High School,35,Franchise,Complex
1677,239.95,NJ City,N,6,18,4,46,Graduation,34,Patnership,Standalone
6846,95.49,Boston,S,20,22,3,74,Graduation,18,Patnership,Complex
5144,58.27,Boston,W,14,11,3,81,No education,18,Franchise,Complex
8523,90.03,NJ City,E,18,16,4,49,Graduation,22,Owner,Standalone
7703,441.66,Boston,E,19,8,14,35,Graduation,24,Owner,Standalone
6459,180.78,Boston,E,14,14,2,38,Graduation,28,Owner,Standalone
7995,207.95,Boston,E,4,21,5,67,Graduation,34,Owner,Standalone
9969,370.38,NJ City,N,17,16,11,62,Graduation,23,Patnership,Standalone
6090,426.61,Boston,S,7,17,16,55,Graduation,28,Patnership,Complex
1999,440.87,NJ City,E,19,8,22,40,High School,26,Owner,Standalone
2129,317.46,NJ City,N,20,24,16,30,High School,18,Patnership,Standalone
3256,411.9,NJ City,W,25,10,25,59,High School,33,Franchise,Complex
7842,36.8,Boston,N,25,11,4,69,Graduation,29,Patnership,Complex
7195,220.23,Boston,E,3,8,15,57,Graduation,28,Owner,Standalone
9679,115.57,NJ City,E,15,16,3,38,High School,19,Owner,Standalone
793,48.38,NJ City,N,13,10,2,67,High School,18,Patnership,Standalone
7248,166.3,Boston,W,23,8,4,43,Graduation,25,Franchise,Complex
1810,497.39,NJ City,S,5,16,17,80,High School,19,Patnership,Complex
8166,454.56,NJ City,S,3,22,11,61,Graduation,30,Patnership,Complex
720,477.63,NJ City,W,19,16,3,81,Graduation,22,Franchise,Complex
5615,15.05,Boston,N,11,21,2,50,No education,25,Owner,Standalone
5361,421.83,Boston,N,19,10,7,47,Graduation,20,Patnership,Standalone
3616,146.81,NJ City,W,21,24,18,44,High School,25,Franchise,Complex
2117,75.44,NJ City,N,1,8,3,85,No education,24,Patnership,Standalone
8677,140.58,NJ City,N,14,13,5,61,High School,25,Patnership,Standalone
5643,204.92,Boston,E,11,22,8,40,Graduation,30,Owner,Standalone
6567,92.29,Boston,E,1,21,2,52,Graduation,28,Owner,Standalone
912,472.2,NJ City,W,11,17,23,61,High School,22,Franchise,Complex
7427,131.75,Boston,E,3,17,16,40,Graduation,30,Owner,Standalone
8798,457.12,NJ City,S,15,14,3,53,No education,33,Patnership,Complex
1293,271,NJ City,N,3,15,19,49,High School,33,Patnership,Standalone
1907,330.22,NJ City,E,6,16,24,39,High School,34,Owner,Standalone
3599,106.85,NJ City,E,5,24,3,30,No education,26,Owner,Standalone
4089,299.5,NJ City,N,8,8,25,27,High School,25,Patnership,Standalone
9038,26.03,NJ City,N,21,23,2,58,No education,23,Patnership,Complex
5528,9.44,Boston,N,18,23,1,73,No education,29,Franchise,Complex
2064,263.05,NJ City,W,15,22,17,60,High School,29,Franchise,Complex
7805,50.58,Boston,N,22,16,4,46,No education,18,Patnership,Standalone
8956,324.46,NJ City,W,11,8,16,25,Graduation,28,Franchise,Complex
5099,439.9,Boston,E,6,9,1,80,No education,27,Owner,Standalone
5142,345.36,Boston,S,25,24,17,61,Graduation,25,Patnership,Complex
7784,260.89,Boston,W,14,9,12,43,Graduation,31,Franchise,Complex
1546,20.77,NJ City,N,9,12,4,71,High School,19,Patnership,Complex
9726,294.27,NJ City,S,24,8,23,48,Graduation,23,Patnership,Complex
8128,286.97,NJ City,W,4,14,13,45,Graduation,25,Franchise,Complex
2099,489.39,NJ City,E,16,21,22,77,High School,33,Owner,Standalone
1537,391.47,NJ City,N,18,21,3,62,High School,27,Patnership,Standalone
8609,396.13,NJ City,N,5,22,14,65,Graduation,19,Patnership,Standalone
4187,144.73,NJ City,E,12,15,16,47,High School,35,Owner,Standalone
3911,221.28,NJ City,E,8,20,3,61,No education,21,Owner,Standalone
2810,213.31,NJ City,S,17,11,25,58,High School,35,Patnership,Complex
3765,9.85,NJ City,N,3,10,2,60,Graduation,25,Patnership,Standalone
1483,178.28,NJ City,E,13,11,21,48,High School,23,Owner,Standalone
1991,117,NJ City,E,3,20,19,33,High School,27,Owner,Standalone
6236,477.53,Boston,W,14,10,1,35,No education,23,Franchise,Complex
4843,172.17,NJ City,E,5,12,16,67,High School,22,Owner,Standalone
3799,157.24,NJ City,E,25,24,3,40,High School,31,Owner,Standalone
1976,105,NJ City,W,2,15,17,45,High School,32,Franchise,Complex
7782,10.1,Boston,N,8,8,3,68,Graduation,20,Patnership,Complex
5567,231.78,Boston,E,15,11,18,84,Graduation,22,Owner,Standalone
2678,173.88,NJ City,S,19,23,8,67,High School,32,Patnership,Complex
474,279.25,NJ City,S,14,10,20,36,High School,35,Patnership,Complex
3688,311.59,NJ City,W,12,21,16,76,High School,19,Franchise,Complex
4143,78.81,NJ City,E,8,16,2,75,Graduation,27,Owner,Standalone
7719,338.79,Boston,E,18,23,12,35,Graduation,25,Owner,Standalone
6645,248.02,Boston,N,19,8,2,80,Graduation,33,Patnership,Standalone
4512,283,NJ City,W,2,20,15,53,High School,33,Franchise,Complex
8020,23.67,Boston,N,15,12,4,40,High School,31,Franchise,Complex
5678,346.99,Boston,S,24,16,10,62,Graduation,25,Patnership,Complex
4369,354.7,NJ City,N,15,11,22,56,High School,27,Patnership,Standalone
7343,317.24,Boston,E,15,23,15,85,Graduation,27,Owner,Standalone
4576,478.75,NJ City,W,6,18,2,69,High School,30,Franchise,Complex
9793,111.71,NJ City,N,1,19,1,49,High School,22,Patnership,Standalone
2962,214.44,NJ City,S,9,17,4,44,High School,27,Patnership,Complex
6346,176.44,Boston,S,18,18,18,53,Graduation,25,Patnership,Complex
6506,218.67,Boston,S,13,11,11,50,Graduation,25,Patnership,Complex
1409,309.6,NJ City,N,6,20,5,56,No education,23,Patnership,Standalone
734,31.56,NJ City,N,6,13,4,45,No education,20,Patnership,Complex
84,176.3,Boston,W,23,20,3,37,Graduation,25,Franchise,Complex
7890,455.26,Boston,S,25,21,21,28,Graduation,30,Patnership,Complex
2038,481.57,NJ City,S,17,16,18,28,High School,22,Patnership,Complex
9426,446.09,NJ City,S,23,13,8,75,Graduation,34,Patnership,Complex
5018,478.2,Boston,S,20,8,13,76,Graduation,23,Patnership,Complex
2069,124.27,NJ City,N,2,19,2,74,No education,24,Patnership,Standalone
5662,383.67,Boston,S,18,19,5,73,High School,24,Patnership,Complex
5457,78.56,Boston,N,8,14,4,58,Graduation,27,Patnership,Standalone
3798,462.9,NJ City,S,17,9,21,36,High School,28,Patnership,Complex
2949,291.47,NJ City,N,14,8,24,65,High School,21,Patnership,Standalone
1072,62.33,NJ City,N,1,24,2,77,High School,26,Franchise,Complex
1123,17.79,NJ City,N,7,19,3,50,High School,24,Owner,Standalone
1656,403.97,NJ City,W,4,20,2,80,Graduation,21,Franchise,Complex
220,223.92,NJ City,W,4,14,9,47,High School,28,Franchise,Complex
3907,169.2,NJ City,E,4,17,16,41,High School,23,Owner,Standalone
5784,67.54,Boston,N,23,20,2,46,Graduation,20,Franchise,Complex
4901,442.36,NJ City,N,23,14,16,55,High School,29,Patnership,Standalone
7525,343.04,Boston,N,4,18,13,56,Graduation,27,Patnership,Standalone
2290,224.57,NJ City,S,4,8,17,44,High School,24,Patnership,Complex
2348,259,NJ City,W,1,15,23,75,High School,33,Franchise,Complex
9151,281.24,NJ City,E,13,13,9,69,Graduation,18,Owner,Standalone
3950,79.19,NJ City,S,19,13,3,37,No education,35,Patnership,Complex
9873,149.12,NJ City,N,22,10,12,83,Graduation,34,Patnership,Standalone
3144,389.9,NJ City,W,10,17,5,44,Graduation,28,Franchise,Complex
4555,292.08,NJ City,E,9,8,18,81,High School,20,Owner,Standalone
4718,453.22,NJ City,S,11,13,9,37,High School,31,Patnership,Complex
8612,188.31,NJ City,W,4,13,22,66,Graduation,27,Franchise,Complex
5171,25.28,Boston,N,14,18,3,34,No education,23,Owner,Standalone
7356,52.27,Boston,W,15,13,4,48,Graduation,29,Franchise,Complex
8494,262.32,NJ City,S,14,8,9,61,Graduation,30,Patnership,Complex
9448,416.55,NJ City,W,17,17,9,35,Graduation,22,Franchise,Complex
9769,435.06,NJ City,N,20,11,14,38,Graduation,29,Patnership,Standalone
76,71.73,Boston,N,18,18,3,53,High School,24,Franchise,Complex
30,78.5,Boston,S,15,19,3,55,Graduation,33,Patnership,Complex
8189,111.41,NJ City,N,24,14,4,77,No education,23,Patnership,Standalone
9218,489.42,NJ City,S,13,22,22,64,Graduation,33,Patnership,Complex
7496,94.06,Boston,W,20,13,1,68,No education,35,Franchise,Complex
490,26.23,NJ City,N,9,20,3,39,High School,31,Patnership,Complex
6969,319.56,Boston,N,4,15,20,48,Graduation,20,Patnership,Standalone
2787,216.56,NJ City,E,14,9,23,74,High School,30,Owner,Standalone
3707,233.89,NJ City,E,14,17,1,66,No education,21,Owner,Standalone
7292,402.75,Boston,W,13,11,6,60,Graduation,29,Franchise,Complex
2776,123.92,NJ City,W,8,10,3,72,High School,25,Franchise,Complex
4953,239.62,Boston,N,25,18,7,60,High School,24,Patnership,Standalone
2881,338.38,NJ City,N,7,9,25,64,High School,31,Patnership,Standalone
3743,275.4,NJ City,E,19,14,21,79,High School,22,Owner,Standalone
2835,109.97,NJ City,E,1,9,3,76,Graduation,30,Owner,Standalone
9672,407.59,NJ City,W,21,15,7,58,Graduation,32,Franchise,Complex
9284,357.91,NJ City,W,3,22,18,47,Graduation,22,Franchise,Complex
3319,323.55,NJ City,E,9,22,1,26,High School,27,Owner,Standalone
3880,164.12,NJ City,W,22,17,10,53,High School,22,Franchise,Complex
3497,320.94,NJ City,N,18,10,4,66,No education,30,Patnership,Standalone
7665,211.66,Boston,N,7,15,13,63,Graduation,27,Patnership,Standalone
5656,498.43,Boston,W,8,13,8,52,Graduation,30,Franchise,Complex
1074,253.17,NJ City,S,11,20,9,27,High School,21,Patnership,Complex
9503,443.62,NJ City,E,7,20,18,44,Graduation,28,Owner,Standalone
1592,488.82,NJ City,W,23,24,7,84,High School,27,Franchise,Complex
3906,337.42,NJ City,S,19,23,9,47,High School,29,Patnership,Complex
5081,470.57,Boston,N,24,14,17,53,Graduation,35,Patnership,Standalone
7422,65.74,Boston,N,13,12,3,65,Graduation,27,Patnership,Complex
9541,90.65,NJ City,N,22,21,4,35,High School,24,Patnership,Standalone
9097,424.22,NJ City,N,9,11,19,56,Graduation,29,Patnership,Standalone
3522,197.38,NJ City,S,18,14,25,54,High School,18,Patnership,Complex
1253,413.18,NJ City,N,16,14,17,66,High School,34,Patnership,Standalone
9546,275.66,NJ City,S,21,8,12,77,Graduation,22,Patnership,Complex
3460,90.37,NJ City,W,20,23,2,60,High School,29,Franchise,Complex
6992,10.85,Boston,N,24,15,1,43,No education,35,Franchise,Complex
4062,35.5,NJ City,N,10,19,4,67,Graduation,31,Patnership,Complex
6862,283.15,Boston,S,22,10,14,45,Graduation,25,Patnership,Complex
3307,329.09,NJ City,E,12,13,11,38,High School,34,Owner,Standalone
8814,30.36,NJ City,N,14,9,1,64,Graduation,24,Patnership,Complex
6377,261.24,Boston,N,21,11,14,80,Graduation,24,Patnership,Standalone
3430,181.05,NJ City,S,25,16,24,60,High School,18,Patnership,Complex
4141,268.63,NJ City,N,15,23,17,76,High School,27,Patnership,Standalone
334,43.16,NJ City,N,9,12,3,61,High School,22,Patnership,Complex
1929,34.31,NJ City,N,16,22,2,60,Graduation,24,Patnership,Standalone
8712,125.4,NJ City,W,7,14,3,51,Graduation,27,Franchise,Complex
6820,40.12,Boston,N,21,23,2,32,High School,34,Franchise,Complex
5515,287.24,Boston,E,19,14,8,53,Graduation,24,Owner,Standalone
8314,242.61,NJ City,S,15,21,18,39,Graduation,35,Patnership,Complex
2733,24.65,NJ City,N,23,15,3,65,Graduation,27,Patnership,Standalone
4088,358.62,NJ City,W,20,22,8,54,High School,24,Franchise,Complex
19,107.16,NJ City,E,14,17,4,48,High School,19,Owner,Standalone
2986,393.23,NJ City,S,8,20,18,63,High School,32,Patnership,Complex
2832,195.75,NJ City,W,1,17,4,41,Graduation,33,Franchise,Complex
3922,418.5,NJ City,S,25,21,23,52,High School,18,Patnership,Complex
4335,149,NJ City,E,3,22,12,25,High School,20,Owner,Standalone
4875,112.28,NJ City,E,13,10,3,39,Graduation,33,Owner,Standalone
9620,359.08,NJ City,W,16,19,10,26,Graduation,28,Franchise,Complex
5902,466.4,Boston,S,20,12,15,85,Graduation,25,Patnership,Complex
5812,449.86,Boston,W,17,14,20,58,Graduation,18,Franchise,Complex
4647,142.06,NJ City,E,4,19,19,66,High School,34,Owner,Standalone
5991,479.84,Boston,E,3,24,3,47,Graduation,26,Owner,Standalone
6419,431.44,Boston,E,24,8,15,54,Graduation,35,Owner,Standalone
6759,499.65,Boston,E,4,13,14,28,Graduation,34,Owner,Standalone
3160,208,NJ City,W,2,18,17,67,High School,33,Franchise,Complex
4293,135,NJ City,N,1,17,16,27,High School,26,Patnership,Standalone
6270,478.44,Boston,S,15,23,22,52,Graduation,34,Patnership,Complex
6448,377.47,Boston,W,4,10,15,85,Graduation,31,Franchise,Complex
3584,259.39,NJ City,W,11,16,2,41,No education,31,Franchise,Complex
6704,485.78,Boston,W,24,9,1,76,No education,25,Franchise,Complex
3419,324.93,NJ City,E,20,18,16,73,High School,32,Owner,Standalone
2081,316.47,NJ City,N,20,10,8,57,High School,27,Patnership,Standalone
1173,23.66,NJ City,N,21,19,2,55,Graduation,25,Patnership,Standalone
8346,359.85,NJ City,S,3,10,11,29,Graduation,22,Patnership,Complex
1066,295.2,NJ City,S,8,21,12,32,High School,19,Patnership,Complex
3977,234.6,NJ City,N,13,8,5,63,No education,23,Patnership,Standalone
8312,168.26,NJ City,W,15,14,17,58,Graduation,18,Franchise,Complex
6494,393.23,Boston,S,19,17,4,43,No education,20,Patnership,Complex
671,121.85,NJ City,E,19,18,4,30,No education,28,Owner,Standalone
3544,192.77,NJ City,W,12,23,2,38,High School,32,Franchise,Complex
2610,309.41,NJ City,S,19,22,19,31,High School,21,Patnership,Complex
9524,398.94,NJ City,W,13,8,11,76,Graduation,19,Franchise,Complex
8054,121.51,NJ City,S,9,21,1,38,No education,31,Patnership,Complex
7137,47.39,Boston,N,2,18,2,29,Graduation,22,Patnership,Standalone
3864,462.39,NJ City,W,10,9,21,82,High School,19,Franchise,Complex
6847,175.94,Boston,E,1,14,15,54,Graduation,31,Owner,Standalone
2297,298.52,NJ City,N,13,8,25,55,High School,29,Patnership,Standalone
9371,363.73,NJ City,E,17,23,18,32,Graduation,33,Owner,Standalone
508,319.9,NJ City,W,4,12,24,40,High School,32,Franchise,Complex
1871,297.59,NJ City,E,16,19,10,38,High School,26,Owner,Standalone
1157,443.85,NJ City,N,8,19,23,26,High School,23,Patnership,Standalone
9068,355.42,NJ City,W,6,21,17,60,Graduation,24,Franchise,Complex
5511,218.39,Boston,E,2,12,14,81,Graduation,27,Owner,Standalone
4556,13.47,NJ City,N,12,17,4,76,No education,28,Franchise,Complex
3607,107.59,NJ City,E,18,20,4,77,High School,24,Owner,Standalone
2647,411.9,NJ City,E,4,9,22,31,High School,35,Owner,Standalone
5116,348.58,Boston,W,9,9,25,35,Graduation,18,Franchise,Complex
8181,192.75,NJ City,N,11,18,10,30,Graduation,30,Patnership,Standalone
2938,259.64,NJ City,S,2,13,3,61,High School,30,Patnership,Complex
9017,415.52,NJ City,N,4,23,16,43,Graduation,22,Patnership,Standalone
927,470.83,NJ City,E,5,9,18,45,High School,19,Owner,Standalone
8987,168.63,NJ City,E,8,13,5,57,No education,21,Owner,Standalone
8183,276.58,NJ City,E,17,23,1,66,No education,22,Owner,Standalone
8779,276.1,NJ City,E,25,13,24,33,Graduation,18,Owner,Standalone
2306,91.54,NJ City,S,24,17,1,83,No education,28,Patnership,Complex
8518,469.62,NJ City,S,15,21,3,81,High School,28,Patnership,Complex
2798,180.18,NJ City,S,8,10,22,26,High School,27,Patnership,Complex
1457,19.87,NJ City,N,21,18,1,81,No education,19,Patnership,Standalone
4013,60.9,NJ City,N,22,23,2,41,No education,21,Patnership,Standalone
4794,148.52,NJ City,S,13,19,18,51,High School,25,Patnership,Complex
2843,484.48,NJ City,E,23,17,21,50,High School,34,Owner,Standalone
8754,272.05,NJ City,S,19,14,16,47,Graduation,20,Patnership,Complex
8252,297.61,NJ City,W,16,15,8,58,Graduation,22,Franchise,Complex
4847,89.62,NJ City,E,8,20,1,77,No education,28,Owner,Standalone
4629,33.01,NJ City,N,16,21,2,44,Graduation,34,Patnership,Standalone
9016,479.15,NJ City,W,8,8,12,85,Graduation,22,Franchise,Complex
5801,230.32,Boston,N,23,9,17,61,Graduation,35,Patnership,Standalone
8534,263.17,NJ City,S,6,23,21,52,Graduation,26,Patnership,Complex
4757,102.27,NJ City,N,11,13,3,55,No education,18,Patnership,Standalone
8671,480.25,NJ City,E,5,19,17,51,Graduation,18,Owner,Standalone
2759,285.83,NJ City,E,11,22,19,47,High School,23,Owner,Standalone
9748,393.26,NJ City,W,19,9,3,82,High School,25,Franchise,Complex
4668,180.13,NJ City,W,7,10,24,57,High School,22,Franchise,Complex
7815,156.19,Boston,E,2,17,21,47,Graduation,35,Owner,Standalone
2802,204.46,NJ City,S,16,12,25,57,High School,30,Patnership,Complex
4593,367.72,NJ City,N,13,21,20,54,High School,35,Patnership,Standalone
1742,151.5,NJ City,S,23,19,16,76,High School,31,Patnership,Complex
2035,374.41,NJ City,E,18,19,10,40,High School,22,Owner,Standalone
9104,421.72,NJ City,W,25,18,19,32,Graduation,34,Franchise,Complex
718,332.41,NJ City,S,19,12,23,51,High School,26,Patnership,Complex
8459,137.94,NJ City,E,5,20,20,35,Graduation,23,Owner,Standalone
4460,467.39,NJ City,W,4,11,15,63,High School,26,Franchise,Complex
1390,228.29,NJ City,S,12,9,13,30,High School,23,Patnership,Complex
3443,177.39,NJ City,E,13,15,14,28,High School,27,Owner,Standalone
6978,351.13,Boston,S,9,20,17,41,Graduation,32,Patnership,Complex
2797,449.24,NJ City,N,18,14,17,31,High School,27,Patnership,Standalone
5072,188.07,Boston,W,24,16,19,54,Graduation,28,Franchise,Complex
8659,349.68,NJ City,E,11,23,8,54,Graduation,29,Owner,Standalone
2404,63.7,NJ City,N,7,12,1,59,High School,28,Franchise,Complex
3205,69.39,NJ City,N,17,17,1,83,High School,29,Patnership,Standalone
2221,128.53,NJ City,N,6,16,17,76,High School,33,Patnership,Standalone
8631,393.14,NJ City,E,9,14,18,52,Graduation,21,Owner,Standalone
1264,104.89,NJ City,W,13,19,1,69,High School,27,Franchise,Complex
1096,223.37,NJ City,W,9,23,12,63,High School,28,Franchise,Complex
6078,70.55,Boston,N,7,12,2,27,Graduation,24,Patnership,Complex
9861,311.11,NJ City,N,25,15,7,36,Graduation,31,Patnership,Standalone
6056,102.16,Boston,W,18,13,4,33,No education,26,Franchise,Complex
7128,481.29,Boston,W,12,10,19,64,Graduation,27,Franchise,Complex
3997,240.18,NJ City,N,23,8,17,77,High School,35,Patnership,Standalone
7250,456.82,Boston,S,3,16,1,55,No education,30,Patnership,Complex
2338,122.7,NJ City,S,24,13,3,78,High School,26,Patnership,Complex
9988,223.33,NJ City,W,15,11,9,77,Graduation,33,Franchise,Complex
8392,0.22,NJ City,N,12,22,2,72,High School,21,Franchise,Complex
2255,418.17,NJ City,E,21,14,22,54,High School,21,Owner,Standalone
232,257.53,NJ City,W,2,22,2,27,High School,25,Franchise,Complex
5750,16.33,Boston,N,18,11,3,78,No education,26,Patnership,Complex
8916,141.7,NJ City,W,13,23,8,72,Graduation,22,Franchise,Complex
2164,172.47,NJ City,W,25,13,7,32,High School,28,Franchise,Complex
2871,88.03,NJ City,E,6,16,4,80,Graduation,32,Owner,Standalone
1539,233,NJ City,E,1,17,6,50,High School,33,Owner,Standalone
8353,332.03,NJ City,N,3,23,1,29,High School,24,Patnership,Standalone
8497,200.28,NJ City,N,1,10,20,80,Graduation,31,Patnership,Standalone
3080,19.17,NJ City,N,22,10,4,53,No education,27,Franchise,Complex
1862,137.64,NJ City,S,13,24,9,71,High School,33,Patnership,Complex
4116,96.11,NJ City,W,7,18,3,44,Graduation,19,Franchise,Complex
7117,318.42,Boston,N,7,10,15,70,Graduation,20,Patnership,Standalone
3305,108.29,NJ City,N,12,22,2,38,No education,26,Patnership,Standalone
1403,113.41,NJ City,E,21,19,4,65,No education,30,Owner,Standalone
2726,260.7,NJ City,S,9,8,11,35,High School,18,Patnership,Complex
6206,188.54,Boston,S,1,11,20,29,Graduation,27,Patnership,Complex
6194,117.66,Boston,S,17,15,4,37,No education,22,Patnership,Complex
9646,481.79,NJ City,S,1,8,9,69,Graduation,20,Patnership,Complex
5516,217.36,Boston,W,1,13,25,55,Graduation,30,Franchise,Complex
55,284.64,NJ City,E,11,22,3,60,High School,25,Owner,Standalone
1951,313,NJ City,E,13,15,16,34,High School,23,Owner,Standalone
4586,427.5,NJ City,S,18,21,25,71,High School,34,Patnership,Complex
264,23.93,NJ City,N,16,24,2,33,Graduation,31,Franchise,Complex
7802,376.63,Boston,S,4,13,12,52,Graduation,26,Patnership,Complex
4441,248.58,NJ City,N,22,23,21,64,High School,24,Patnership,Standalone
7916,466.41,Boston,W,9,23,23,63,Graduation,23,Franchise,Complex
6781,406.9,Boston,N,2,16,3,38,High School,26,Patnership,Standalone
1928,460.98,NJ City,W,10,22,18,52,High School,25,Franchise,Complex
3776,56.54,NJ City,W,4,17,3,47,No education,18,Franchise,Complex
7924,492.52,Boston,W,1,19,7,75,Graduation,34,Franchise,Complex
8427,237.4,NJ City,E,10,11,25,74,Graduation,32,Owner,Standalone
6049,99.12,Boston,N,12,13,2,27,High School,28,Patnership,Standalone
1607,488.52,NJ City,E,19,22,21,54,High School,28,Owner,Standalone
6504,90.77,Boston,W,14,24,1,44,Graduation,25,Franchise,Complex
3804,26.75,NJ City,N,7,18,2,67,Graduation,18,Franchise,Complex
5101,3.8,Boston,N,9,15,1,79,High School,32,Patnership,Standalone
8950,147.73,NJ City,S,20,19,16,73,Graduation,31,Patnership,Complex
9416,404.78,NJ City,W,21,18,23,78,Graduation,24,Franchise,Complex
3360,108.79,NJ City,W,25,8,4,26,Graduation,28,Franchise,Complex
6629,179.91,Boston,N,14,8,21,32,Graduation,33,Patnership,Standalone
3081,239.75,NJ City,N,11,12,3,62,Graduation,34,Patnership,Standalone
9626,106.83,NJ City,S,10,9,4,44,No education,20,Patnership,Complex
2008,320.6,NJ City,W,13,15,25,73,High School,27,Franchise,Complex
6331,75.68,Boston,E,17,24,3,50,High School,25,Owner,Standalone
5725,289.07,Boston,N,13,20,9,39,Graduation,21,Patnership,Standalone
3396,195.7,NJ City,W,11,23,13,39,High School,33,Franchise,Complex
1935,242.86,NJ City,E,21,8,24,54,High School,22,Owner,Standalone
77,316,Boston,N,1,8,15,83,High School,29,Patnership,Standalone
4470,39.01,NJ City,N,17,20,4,60,Graduation,33,Patnership,Complex
7121,50.47,Boston,N,2,22,3,67,No education,29,Patnership,Standalone
7856,82.52,Boston,W,1,12,4,53,No education,20,Franchise,Complex
4913,180.2,NJ City,N,8,15,2,69,No education,19,Patnership,Standalone
5566,259.77,Boston,S,10,18,15,40,Graduation,25,Patnership,Complex
3392,403.36,NJ City,W,20,19,11,61,High School,25,Franchise,Complex
58,155.3,Boston,S,12,24,18,33,High School,31,Patnership,Complex
2271,469.66,NJ City,E,18,9,10,69,High School,29,Owner,Standalone
216,305.51,NJ City,W,7,11,5,31,Graduation,28,Franchise,Complex
7581,51.18,Boston,N,25,20,3,37,Graduation,26,Patnership,Standalone
2225,436.47,NJ City,N,25,23,25,39,High School,20,Patnership,Standalone
6185,112.96,Boston,N,14,19,2,33,No education,18,Patnership,Standalone
69,400.58,Maine,N,21,8,11,66,High School,31,Patnership,Standalone
1775,53.45,NJ City,E,5,14,2,75,No education,33,Owner,Standalone
522,218,NJ City,S,2,14,25,82,High School,28,Patnership,Complex
8580,63.3,NJ City,N,22,9,1,43,Graduation,26,Franchise,Complex
922,144.62,NJ City,S,12,24,4,40,High School,21,Patnership,Complex
1030,9.78,NJ City,N,12,21,4,27,High School,18,Patnership,Complex
3069,472.87,NJ City,N,21,24,7,65,High School,32,Patnership,Standalone
3974,127.17,NJ City,S,15,10,2,31,No education,18,Patnership,Complex
8866,84.84,NJ City,S,11,15,3,75,High School,29,Patnership,Complex
1814,9.36,NJ City,N,10,24,1,44,No education,33,Patnership,Complex
706,126.46,NJ City,S,2,11,3,57,High School,24,Patnership,Complex
5466,159.47,Boston,S,24,11,9,76,Graduation,34,Patnership,Complex
4217,221.54,NJ City,N,14,15,5,67,No education,31,Patnership,Standalone
9041,179.97,NJ City,N,8,18,9,82,Graduation,31,Patnership,Standalone
119,102.64,NJ City,E,12,13,2,47,No education,34,Owner,Standalone
7827,313.82,Boston,E,19,11,8,82,Graduation,18,Owner,Standalone
4017,253.55,NJ City,N,6,15,14,35,High School,23,Patnership,Standalone
5150,461.66,Boston,S,12,12,25,38,Graduation,22,Patnership,Complex
738,77.62,NJ City,S,23,8,3,73,Graduation,19,Patnership,Complex
4603,201.22,NJ City,E,10,16,18,40,High School,25,Owner,Standalone
2503,360.74,NJ City,E,19,8,25,43,High School,35,Owner,Standalone
4494,388.76,NJ City,S,22,8,14,76,High School,21,Patnership,Complex
1443,484.02,NJ City,E,22,23,18,76,High School,28,Owner,Standalone
3513,471.63,NJ City,N,24,13,25,61,High School,21,Patnership,Standalone
8567,191.16,NJ City,E,14,11,18,74,Graduation,24,Owner,Standalone
8380,141.46,NJ City,W,15,15,17,26,Graduation,21,Franchise,Complex
9946,108.21,NJ City,S,3,20,1,44,High School,30,Patnership,Complex
223,264.33,NJ City,E,16,17,4,42,High School,26,Owner,Standalone
6367,51.88,Boston,E,1,17,3,38,High School,22,Owner,Standalone
6075,304.79,Boston,E,13,21,5,58,Graduation,23,Owner,Standalone
7329,138.27,Boston,N,2,20,5,75,Graduation,22,Patnership,Standalone
7136,338.23,Boston,W,24,18,10,55,Graduation,20,Franchise,Complex
1400,185.83,NJ City,W,16,13,13,54,High School,26,Franchise,Complex
7039,395.75,Boston,E,5,17,17,34,Graduation,22,Owner,Standalone
4346,239,NJ City,S,2,22,18,40,High School,33,Patnership,Complex
5052,320.35,Boston,W,13,17,22,51,Graduation,23,Franchise,Complex
9522,90.75,NJ City,S,22,16,3,79,Graduation,28,Patnership,Complex
4652,264,NJ City,W,1,11,18,26,High School,28,Franchise,Complex
5904,434.52,Boston,W,19,22,25,46,Graduation,33,Franchise,Complex
3352,102,NJ City,W,3,13,20,69,High School,19,Franchise,Complex
8877,146.96,NJ City,N,2,8,7,83,Graduation,29,Patnership,Standalone
8398,207.01,NJ City,S,5,11,17,25,Graduation,28,Patnership,Complex
2397,39.5,NJ City,N,19,16,4,55,Graduation,29,Patnership,Standalone
5379,417.83,Boston,E,23,24,7,77,Graduation,30,Owner,Standalone
178,381.88,NJ City,S,7,21,23,77,High School,26,Patnership,Complex
1325,412.02,NJ City,N,25,23,11,75,High School,30,Patnership,Standalone
5618,287.98,Boston,S,2,16,17,82,Graduation,29,Patnership,Complex
8698,101.08,NJ City,S,15,22,4,71,High School,25,Patnership,Complex
4792,354.85,NJ City,W,24,17,5,45,High School,32,Franchise,Complex
7990,106.47,Boston,S,13,16,2,75,High School,31,Patnership,Complex
2509,200.76,NJ City,N,13,24,18,55,High School,22,Patnership,Standalone
1189,152.84,NJ City,N,9,19,5,50,High School,27,Patnership,Standalone
848,160,NJ City,W,1,15,7,73,High School,31,Franchise,Complex
2076,350.74,NJ City,W,13,21,18,63,High School,31,Franchise,Complex
5219,432.34,Boston,E,6,15,11,52,Graduation,25,Owner,Standalone
9642,92.93,NJ City,S,19,16,4,36,Graduation,28,Patnership,Complex
5759,99.53,Boston,E,22,20,1,81,No education,30,Owner,Standalone
6721,451.02,Boston,N,15,17,20,25,Graduation,31,Patnership,Standalone
7654,416.13,Boston,S,25,12,14,69,Graduation,32,Patnership,Complex
1462,320.66,NJ City,S,7,16,25,37,High School,25,Patnership,Complex
4632,256,NJ City,W,2,15,24,82,High School,34,Franchise,Complex
1429,185.16,NJ City,N,16,14,8,29,High School,20,Patnership,Standalone
389,76.91,NJ City,N,10,12,4,69,No education,22,Patnership,Standalone
8943,486.51,NJ City,E,7,16,19,79,Graduation,30,Owner,Standalone
3197,372.62,NJ City,N,12,16,14,38,High School,29,Patnership,Standalone
8761,83.34,NJ City,N,20,21,2,36,High School,28,Patnership,Standalone
1716,149.32,NJ City,W,15,17,19,58,High School,33,Franchise,Complex
1241,126.36,NJ City,N,22,19,4,58,No education,24,Patnership,Standalone
902,337.39,NJ City,S,16,9,20,42,High School,30,Patnership,Complex
240,23.38,NJ City,N,6,19,1,48,Graduation,35,Franchise,Complex
9123,451.18,NJ City,E,10,8,20,81,Graduation,29,Owner,Standalone
698,191.15,NJ City,S,23,17,2,35,No education,26,Patnership,Complex
4413,84.59,NJ City,N,5,15,2,41,Graduation,25,Patnership,Standalone
2876,91.18,NJ City,W,4,12,1,79,No education,35,Franchise,Complex
941,164.31,NJ City,N,16,10,13,68,High School,26,Patnership,Standalone
5090,131.94,Boston,S,17,16,16,37,Graduation,28,Patnership,Complex
1114,106.53,NJ City,S,24,18,3,71,High School,28,Patnership,Complex
4071,258.54,NJ City,E,4,15,13,62,High School,32,Owner,Standalone
1882,158.2,NJ City,S,19,23,16,35,High School,20,Patnership,Complex
9346,107.77,NJ City,S,20,19,4,53,High School,18,Patnership,Complex
9065,286.38,NJ City,N,5,13,24,41,Graduation,27,Patnership,Standalone
5581,380,Boston,N,19,22,19,30,Graduation,30,Patnership,Standalone
4301,315.32,NJ City,N,4,10,13,38,High School,35,Patnership,Standalone
2921,355.12,NJ City,N,24,23,6,28,High School,34,Patnership,Standalone
5841,84.54,Boston,N,21,11,4,51,Graduation,32,Patnership,Standalone
9944,249.28,NJ City,W,12,10,24,40,Graduation,20,Franchise,Complex
2387,103,NJ City,E,1,19,8,80,High School,28,Owner,Standalone
4080,428.55,NJ City,W,25,12,14,31,High School,26,Franchise,Complex
1841,397.93,NJ City,N,22,14,14,74,High School,21,Patnership,Standalone
8473,195.3,NJ City,N,17,19,12,26,Graduation,22,Patnership,Standalone
866,389.2,NJ City,S,17,18,24,68,High School,26,Patnership,Complex
7987,299.52,Boston,E,15,13,8,47,Graduation,20,Owner,Standalone
3437,481.88,NJ City,N,10,15,25,42,High School,30,Patnership,Standalone
8907,474.77,NJ City,E,16,15,9,85,Graduation,23,Owner,Standalone
2148,289.09,NJ City,W,5,16,7,73,High School,31,Franchise,Complex
7503,238.06,Boston,E,21,11,4,29,Graduation,35,Owner,Standalone
9981,56.37,NJ City,N,10,23,1,63,Graduation,24,Patnership,Standalone
7406,79.79,Boston,S,10,14,1,82,No education,27,Patnership,Complex
8130,495.19,NJ City,S,16,19,9,57,Graduation,27,Patnership,Complex
4998,183.31,Boston,S,17,21,8,25,High School,29,Patnership,Complex
2307,72.52,NJ City,N,11,23,4,50,Graduation,25,Owner,Standalone
4979,306.17,Boston,E,22,8,23,43,High School,18,Owner,Standalone
1428,273.89,NJ City,W,5,20,14,44,High School,25,Franchise,Complex
408,274.87,NJ City,W,13,12,6,38,High School,21,Franchise,Complex
6309,249.81,Boston,N,9,13,9,45,Graduation,22,Patnership,Standalone
2524,140.63,NJ City,W,22,9,25,38,High School,22,Franchise,Complex
460,74.94,NJ City,W,19,15,4,81,High School,28,Franchise,Complex
5872,62.89,Boston,N,25,24,4,32,High School,27,Franchise,Complex
3110,493.22,NJ City,S,4,23,4,44,No education,26,Patnership,Complex
6834,499.81,Boston,S,10,13,1,32,Graduation,32,Patnership,Complex
8149,168.94,NJ City,N,14,8,22,33,Graduation,22,Patnership,Standalone
1285,216.73,NJ City,N,16,8,17,62,High School,34,Patnership,Standalone
5744,1.5,Boston,N,21,12,4,79,No education,19,Franchise,Complex
6413,175.52,Boston,N,24,21,20,71,Graduation,30,Patnership,Standalone
35,107.46,Boston,E,3,14,2,43,No education,26,Owner,Standalone
1681,395.62,NJ City,N,6,20,20,77,High School,33,Patnership,Standalone
9265,481.09,NJ City,N,25,23,24,27,Graduation,25,Patnership,Standalone
51,46.95,Boston,N,22,23,2,48,Graduation,25,Owner,Standalone
9221,290.73,NJ City,N,3,24,22,83,Graduation,30,Patnership,Standalone
9710,487.34,NJ City,S,15,11,14,56,Graduation,18,Patnership,Complex
5273,390.75,Boston,N,1,23,13,75,Graduation,31,Patnership,Standalone
9107,331.01,NJ City,E,19,9,15,57,Graduation,35,Owner,Standalone
8493,478.79,NJ City,N,20,21,6,58,Graduation,18,Patnership,Standalone
4334,278,NJ City,S,3,16,14,76,High School,18,Patnership,Complex
230,340.7,NJ City,S,16,12,5,33,No education,35,Patnership,Complex
7171,59.9,Boston,E,9,8,2,37,High School,18,Owner,Standalone
2679,433.45,NJ City,E,13,18,13,61,High School,25,Owner,Standalone
6523,156.82,Boston,E,25,13,17,74,Graduation,31,Owner,Standalone
6973,224.42,Boston,N,10,14,5,72,High School,29,Patnership,Standalone
7661,411.72,Boston,N,19,22,17,60,Graduation,28,Patnership,Standalone
9056,112.94,NJ City,W,8,16,2,84,No education,31,Franchise,Complex
6818,492.97,Boston,S,8,16,15,38,Graduation,25,Patnership,Complex
4991,410.05,Boston,E,12,23,14,79,High School,23,Owner,Standalone
6775,261.47,Boston,E,3,20,24,47,Graduation,31,Owner,Standalone
5153,76.68,Boston,N,12,20,3,50,No education,27,Patnership,Standalone
1213,152.17,NJ City,N,23,17,10,25,High School,23,Patnership,Standalone
8758,324.92,NJ City,S,16,15,1,72,High School,21,Patnership,Complex
8641,168.07,NJ City,N,4,22,9,75,Graduation,29,Patnership,Standalone
4614,87.44,NJ City,S,5,20,2,43,Graduation,29,Patnership,Complex
9906,50.24,NJ City,S,19,13,4,53,Graduation,18,Patnership,Complex
6454,112.81,Boston,S,21,20,4,81,High School,26,Patnership,Complex
6098,276.51,Boston,S,11,19,7,77,Graduation,19,Patnership,Complex
4571,106.61,NJ City,E,22,23,3,25,No education,24,Owner,Standalone
8145,48.68,NJ City,N,23,23,3,31,Graduation,32,Patnership,Standalone
2412,300.28,NJ City,W,22,21,12,73,High School,20,Franchise,Complex
2701,219.7,NJ City,N,5,10,5,51,High School,18,Patnership,Standalone
2651,173.6,NJ City,E,24,16,23,32,High School,27,Owner,Standalone
7567,393.01,Boston,E,20,13,24,68,Graduation,23,Owner,Standalone
3249,417.17,NJ City,N,17,17,6,49,High School,27,Patnership,Standalone
9359,450.94,NJ City,E,10,22,5,43,No education,20,Owner,Standalone
9695,200.4,NJ City,E,9,22,12,76,Graduation,32,Owner,Standalone
8940,16.76,NJ City,N,20,19,2,66,Graduation,21,Franchise,Complex
4229,106,NJ City,N,1,9,19,35,High School,32,Patnership,Standalone
1789,70.54,NJ City,N,9,21,1,28,High School,23,Patnership,Standalone
3459,11.71,NJ City,N,14,11,1,54,Graduation,28,Owner,Standalone
2361,148.94,NJ City,N,22,12,22,83,High School,19,Patnership,Standalone
5085,357.49,Boston,N,10,10,1,28,Graduation,35,Patnership,Standalone
3268,298.64,NJ City,W,22,19,17,42,High School,23,Franchise,Complex
3407,233.44,NJ City,E,6,24,24,38,High School,30,Owner,Standalone
9700,214.42,NJ City,W,19,10,9,55,Graduation,34,Franchise,Complex
8215,4.88,NJ City,N,2,17,4,61,High School,33,Owner,Standalone
7147,137.66,Boston,E,20,12,7,28,Graduation,34,Owner,Standalone
5575,270.4,Boston,E,10,8,6,58,Graduation,18,Owner,Standalone
8639,283.66,NJ City,E,16,21,17,28,Graduation,26,Owner,Standalone
6163,345.1,Boston,E,4,21,1,30,High School,24,Owner,Standalone
6723,104.78,Boston,E,14,13,2,64,Graduation,24,Owner,Standalone
3869,115.51,NJ City,N,24,11,3,84,No education,33,Patnership,Standalone
9800,173.39,NJ City,W,2,11,3,25,No education,35,Franchise,Complex
7657,176.81,Boston,N,19,8,20,65,Graduation,28,Patnership,Standalone
1147,360.81,NJ City,E,5,20,11,76,High School,27,Owner,Standalone
7677,392.53,Boston,N,12,24,4,25,Graduation,30,Patnership,Standalone
2039,213,NJ City,E,2,15,6,79,High School,29,Owner,Standalone
1700,25.37,NJ City,N,11,17,1,83,No education,30,Franchise,Complex
4665,370.8,NJ City,N,19,21,8,71,High School,26,Patnership,Standalone
3957,185.86,NJ City,N,19,13,22,33,High School,28,Patnership,Standalone
4699,121.08,NJ City,E,20,17,4,49,High School,32,Owner,Standalone
6376,377.43,Boston,W,23,12,18,63,Graduation,32,Franchise,Complex
9141,52.74,NJ City,N,8,20,4,66,Graduation,26,Patnership,Standalone
9096,224.74,NJ City,W,18,17,9,84,Graduation,28,Franchise,Complex
7883,115.38,Boston,E,7,16,1,59,No education,20,Owner,Standalone
1500,364.86,NJ City,W,6,23,12,26,High School,30,Franchise,Complex
9387,193.32,NJ City,E,4,9,22,56,Graduation,26,Owner,Standalone
7159,267.81,Boston,E,11,19,11,78,Graduation,26,Owner,Standalone
777,416.1,NJ City,N,16,21,17,40,High School,26,Patnership,Standalone
6209,28.67,Boston,N,10,14,2,54,No education,19,Patnership,Standalone
2646,385.21,NJ City,S,13,8,13,41,High School,27,Patnership,Complex
7571,73.31,Boston,N,1,11,3,29,No education,30,Owner,Standalone
5322,108.42,Boston,S,9,22,2,62,Graduation,32,Patnership,Complex
6250,209.16,Boston,S,1,17,9,59,Graduation,30,Patnership,Complex
4663,205,NJ City,E,1,8,6,74,High School,20,Owner,Standalone
8932,31.23,NJ City,N,15,14,4,83,High School,22,Franchise,Complex
1169,131,NJ City,N,1,15,19,39,High School,30,Patnership,Standalone
3485,430.37,NJ City,N,11,16,3,40,No education,22,Patnership,Standalone
3159,458.63,NJ City,E,6,20,19,59,High School,24,Owner,Standalone
8276,178.63,NJ City,W,1,12,1,63,No education,30,Franchise,Complex
6378,217.39,Boston,S,18,13,9,56,Graduation,28,Patnership,Complex
6966,157.81,Boston,S,8,10,3,44,Graduation,18,Patnership,Complex
1158,278.34,NJ City,S,13,9,5,52,Graduation,28,Patnership,Complex
3245,67.12,NJ City,N,11,18,4,65,No education,24,Patnership,Standalone
2092,213.43,NJ City,W,6,19,15,66,High School,18,Franchise,Complex
5333,314.27,Boston,N,22,24,15,34,Graduation,24,Patnership,Standalone
1909,41.39,NJ City,N,2,20,4,51,High School,19,Patnership,Standalone
3124,82.41,NJ City,W,21,10,1,55,High School,32,Franchise,Complex
386,241,NJ City,S,3,22,11,35,High School,26,Patnership,Complex
5931,488.81,Boston,E,25,14,7,58,Graduation,19,Owner,Standalone
1809,444.36,NJ City,N,4,10,17,39,High School,25,Patnership,Standalone
4817,77.73,NJ City,N,7,22,2,75,No education,19,Patnership,Standalone
8295,171.59,NJ City,E,22,19,5,54,Graduation,21,Owner,Standalone
23,302.16,NY City,E,3,8,1,57,No education,28,Owner,Standalone
7646,335.76,Boston,S,8,14,10,51,Graduation,23,Patnership,Complex
7477,328.14,Boston,N,11,19,14,45,Graduation,25,Patnership,Standalone
4865,396.75,NJ City,N,18,22,20,45,High School,19,Patnership,Standalone
3618,2.15,NJ City,S,4,16,3,43,Graduation,34,Patnership,Complex
1486,90.53,NJ City,S,10,24,4,73,High School,30,Patnership,Complex
6927,217.89,Boston,E,3,22,23,85,Graduation,29,Owner,Standalone
4719,53.85,NJ City,E,9,12,2,51,Graduation,35,Owner,Standalone
2900,414.38,NJ City,W,9,17,2,82,No education,27,Franchise,Complex
7325,286.85,Boston,N,9,12,17,76,Graduation,25,Patnership,Standalone
2179,125.62,NJ City,E,12,10,1,81,High School,23,Owner,Standalone
7693,322.52,Boston,N,3,17,3,29,High School,27,Patnership,Standalone
722,118.24,NJ City,S,17,24,2,74,No education,28,Patnership,Complex
4902,125,NJ City,S,3,15,13,46,High School,33,Patnership,Complex
9077,83.7,NJ City,N,8,19,3,39,No education,28,Patnership,Standalone
3474,126.48,NJ City,S,21,13,2,27,Graduation,29,Patnership,Complex
8009,482.54,Boston,N,13,23,21,32,Graduation,31,Patnership,Standalone
2216,157.55,NJ City,W,24,21,2,68,No education,19,Franchise,Complex
3118,249.13,NJ City,S,23,18,10,46,High School,28,Patnership,Complex
6404,2.93,Boston,W,1,19,3,39,No education,32,Franchise,Complex
8939,203.32,NJ City,E,9,13,21,35,Graduation,18,Owner,Standalone
1566,318,NJ City,S,1,19,12,79,High School,28,Patnership,Complex
9570,427.49,NJ City,S,10,11,14,69,Graduation,34,Patnership,Complex
6686,56.84,Boston,S,14,17,4,36,No education,22,Patnership,Complex
8887,89.91,NJ City,E,8,19,2,38,High School,34,Owner,Standalone
1569,332.99,NJ City,N,21,15,3,82,Graduation,25,Patnership,Standalone
3472,7.65,NJ City,N,24,10,1,51,High School,28,Franchise,Complex
7284,339.8,Boston,W,23,22,9,41,Graduation,20,Franchise,Complex
3535,318.34,NJ City,E,16,13,18,64,High School,25,Owner,Standalone
6753,130.89,Boston,N,6,12,16,65,Graduation,20,Patnership,Standalone
8660,450.36,NJ City,W,11,12,17,70,Graduation,33,Franchise,Complex
701,325.83,NJ City,N,22,17,19,25,High School,25,Patnership,Standalone
8830,66.54,NJ City,N,19,9,2,40,High School,30,Patnership,Complex
1982,323.27,NJ City,S,11,22,10,81,High School,19,Patnership,Complex
4945,25.16,Boston,N,13,14,2,69,High School,21,Patnership,Standalone
6067,316.67,Boston,E,24,23,7,42,Graduation,35,Owner,Standalone
4266,53.38,NJ City,S,16,17,3,55,Graduation,24,Patnership,Complex
2857,379.5,NJ City,N,8,22,10,63,High School,21,Patnership,Standalone
9571,155.62,NJ City,E,17,21,19,73,Graduation,19,Owner,Standalone
9048,164.95,NJ City,W,8,24,4,82,Graduation,18,Franchise,Complex
742,8.59,NJ City,N,11,16,1,37,High School,33,Patnership,Complex
5964,198.33,Boston,W,11,9,10,41,Graduation,21,Franchise,Complex
4828,27.64,NJ City,N,21,18,1,80,High School,28,Franchise,Complex
3858,258,NJ City,S,13,23,1,55,Graduation,32,Patnership,Complex
5228,444.26,Boston,W,22,23,11,61,Graduation,25,Franchise,Complex
918,421.33,NJ City,S,12,19,12,80,High School,25,Patnership,Complex
3130,246.01,NJ City,S,18,21,4,33,High School,26,Patnership,Complex
2178,280.92,NJ City,S,25,17,25,58,High School,26,Patnership,Complex
3608,166.15,NJ City,W,3,20,1,41,No education,23,Franchise,Complex
2018,209.28,NJ City,S,9,18,13,34,High School,31,Patnership,Complex
2408,99.59,NJ City,W,5,22,3,54,No education,35,Franchise,Complex
2086,453.56,NJ City,S,13,24,16,28,High School,31,Patnership,Complex
4046,262,NJ City,S,2,15,21,41,High School,27,Patnership,Complex
5435,107.31,Boston,E,6,16,4,48,No education,22,Owner,Standalone
8764,121.84,NJ City,W,12,15,2,71,High School,32,Franchise,Complex
7387,166.13,Boston,E,16,16,19,51,Graduation,18,Owner,Standalone
1897,80.32,NJ City,N,7,24,1,30,High School,35,Patnership,Standalone
4065,273.32,NJ City,N,4,19,19,33,High School,23,Patnership,Standalone
8285,304.41,NJ City,N,9,9,7,26,Graduation,31,Patnership,Standalone
2196,279.05,NJ City,W,10,19,3,41,Graduation,33,Franchise,Complex
3729,465.5,NJ City,N,20,19,21,54,High School,33,Patnership,Standalone
1006,430.87,NJ City,S,14,15,14,43,High School,30,Patnership,Complex
6408,367.14,Boston,W,24,20,22,53,Graduation,24,Franchise,Complex
3872,210.22,NJ City,W,24,8,4,51,No education,28,Franchise,Complex
2140,376.84,NJ City,W,20,23,15,57,High School,18,Franchise,Complex
306,143.04,NJ City,S,23,8,16,60,High School,31,Patnership,Complex
3598,239.36,NJ City,S,14,9,18,57,High School,29,Patnership,Complex
3226,233,NJ City,S,2,14,6,56,High School,20,Patnership,Complex
1019,37.27,NJ City,N,10,19,1,55,No education,32,Owner,Standalone
8138,136.99,NJ City,S,1,20,11,48,Graduation,35,Patnership,Complex
9961,3.44,NJ City,N,14,16,1,60,High School,29,Patnership,Standalone
2796,346.39,NJ City,W,13,23,17,36,High School,18,Franchise,Complex
6610,198.7,Boston,S,19,8,24,46,Graduation,30,Patnership,Complex
3216,294.87,NJ City,W,4,18,6,41,High School,19,Franchise,Complex
874,481.44,NJ City,S,7,10,7,75,High School,28,Patnership,Complex
8868,231.87,NJ City,W,10,13,24,50,Graduation,24,Franchise,Complex
7811,400.85,Boston,E,11,8,9,52,Graduation,19,Owner,Standalone
9872,469.37,NJ City,W,4,19,2,54,No education,22,Franchise,Complex
2702,90.37,NJ City,S,2,23,2,70,No education,32,Patnership,Complex
4712,348.07,NJ City,W,20,18,5,29,No education,21,Franchise,Complex
926,390.71,NJ City,S,22,18,12,37,High School,28,Patnership,Complex
7814,403.78,Boston,S,4,19,7,81,Graduation,21,Patnership,Complex
3692,225.83,NJ City,W,19,18,14,79,High School,18,Franchise,Complex
9696,157.26,NJ City,W,18,24,14,53,Graduation,33,Franchise,Complex
8831,187.27,NJ City,E,3,16,12,48,Graduation,26,Owner,Standalone
4935,110.52,NJ City,E,21,15,4,34,Graduation,21,Owner,Standalone
6974,387.08,Boston,S,22,22,2,33,No education,29,Patnership,Complex
2591,161,NJ City,E,3,18,22,44,High School,18,Owner,Standalone
840,451.2,NJ City,W,8,8,2,28,Graduation,28,Franchise,Complex
5009,451.35,Boston,N,22,19,14,82,Graduation,19,Patnership,Standalone
6819,206.62,Boston,E,5,15,1,42,Graduation,35,Owner,Standalone
9239,4.33,NJ City,N,22,18,3,42,No education,25,Owner,Standalone
5063,45.98,Boston,N,15,13,2,70,No education,30,Owner,Standalone
3785,58.1,NJ City,N,5,19,4,38,No education,31,Patnership,Standalone
9555,14.64,NJ City,N,15,18,1,84,Graduation,23,Owner,Standalone
4898,356.12,NJ City,S,20,8,15,74,High School,31,Patnership,Complex
8985,15.23,NJ City,N,7,20,1,83,Graduation,24,Patnership,Standalone
2066,16.96,NJ City,N,20,20,4,68,No education,34,Patnership,Complex
133,235,NJ City,N,1,21,25,69,High School,30,Patnership,Standalone
9818,55.75,NJ City,S,17,14,3,56,No education,23,Patnership,Complex
3491,480.29,NJ City,E,15,10,1,46,No education,23,Owner,Standalone
3154,127.9,NJ City,S,17,22,2,34,High School,21,Patnership,Complex
1299,173.68,NJ City,E,18,19,24,56,High School,18,Owner,Standalone
3620,100.38,NJ City,W,19,21,4,56,No education,27,Franchise,Complex
2194,89.67,NJ City,S,6,15,4,26,High School,22,Patnership,Complex
2604,344.83,NJ City,W,4,22,5,60,Graduation,32,Franchise,Complex
2853,131.92,NJ City,N,11,15,3,71,Graduation,34,Patnership,Standalone
6361,366.5,Boston,N,1,13,21,79,Graduation,21,Patnership,Standalone
1772,221.45,NJ City,W,13,9,23,26,High School,22,Franchise,Complex
2230,138.95,NJ City,S,1,10,3,75,High School,22,Patnership,Complex
135,103.04,NJ City,E,10,22,1,41,Graduation,33,Owner,Standalone
1860,69.5,NJ City,N,17,19,3,63,Graduation,30,Franchise,Complex
3597,228.48,NJ City,N,5,16,7,65,High School,27,Patnership,Standalone
6710,376.24,Boston,S,18,14,17,58,Graduation,27,Patnership,Complex
3628,446.69,NJ City,W,14,11,16,56,High School,35,Franchise,Complex
1092,460.38,NJ City,W,12,23,10,62,High School,23,Franchise,Complex
8781,360.66,NJ City,N,24,21,18,62,Graduation,31,Patnership,Standalone
2215,246.29,NJ City,E,11,10,13,60,High School,28,Owner,Standalone
6290,186.37,Boston,S,24,10,10,36,Graduation,19,Patnership,Complex
5061,329.57,Boston,N,23,19,9,26,Graduation,34,Patnership,Standalone
5114,302.57,Boston,S,4,13,17,80,Graduation,29,Patnership,Complex
3717,6.62,NJ City,N,2,13,3,68,Graduation,27,Patnership,Standalone
4181,286,NJ City,N,2,23,21,51,High School,23,Patnership,Standalone
932,439.06,NJ City,W,4,20,15,35,High School,32,Franchise,Complex
9724,232.01,NJ City,W,2,20,25,45,Graduation,27,Franchise,Complex
9879,175.64,NJ City,E,17,10,18,63,Graduation,20,Owner,Standalone
8606,172.32,NJ City,S,20,14,4,25,No education,32,Patnership,Complex
1237,416.73,NJ City,N,5,24,24,65,High School,28,Patnership,Standalone
9887,140.48,NJ City,E,9,16,10,43,Graduation,18,Owner,Standalone
2089,377.17,NJ City,N,24,11,20,25,High School,25,Patnership,Standalone
382,77.84,NJ City,S,13,20,3,29,High School,26,Patnership,Complex
1482,178.11,NJ City,S,20,21,6,26,High School,27,Patnership,Complex
6780,241.33,Boston,W,9,15,11,77,Graduation,33,Franchise,Complex
1818,51.95,NJ City,S,24,17,2,67,Graduation,22,Patnership,Complex
260,245.26,NJ City,W,25,19,14,75,High School,24,Franchise,Complex
8171,445.64,NJ City,E,18,20,13,37,Graduation,23,Owner,Standalone
6395,258.32,Boston,E,23,24,22,51,Graduation,30,Owner,Standalone
9092,205.51,NJ City,W,20,17,16,74,Graduation,25,Franchise,Complex
4311,442.73,NJ City,E,13,21,22,76,High School,24,Owner,Standalone
10000,159,NJ City,W,16,24,12,59,Graduation,35,Franchise,Complex
8299,330.28,NJ City,E,11,21,5,68,High School,33,Owner,Standalone
2136,307.01,NJ City,W,16,14,18,68,High School,22,Franchise,Complex
4075,315,NJ City,E,1,24,12,49,High School,33,Owner,Standalone
3846,263.78,NJ City,S,4,22,22,81,High School,32,Patnership,Complex
2446,166.09,NJ City,S,12,20,20,63,High School,29,Patnership,Complex
7243,299.7,Boston,E,6,21,15,67,Graduation,22,Owner,Standalone
7316,207.18,Boston,W,3,22,3,29,No education,18,Franchise,Complex
8615,472.84,NJ City,E,17,20,19,46,Graduation,18,Owner,Standalone
6420,83.73,Boston,W,22,11,2,53,Graduation,18,Franchise,Complex
1699,103.95,NJ City,E,6,18,2,85,High School,30,Owner,Standalone
7101,250.54,Boston,N,17,23,6,26,Graduation,34,Patnership,Standalone
9927,436.79,NJ City,E,13,15,4,27,Graduation,31,Owner,Standalone
2174,468.13,NJ City,S,11,9,10,33,High School,27,Patnership,Complex
2640,429.66,NJ City,W,13,18,12,57,High School,33,Franchise,Complex
8442,389.1,NJ City,S,12,13,17,71,Graduation,31,Patnership,Complex
9179,297.11,NJ City,E,20,24,7,36,Graduation,25,Owner,Standalone
4225,499.4,NJ City,N,14,8,24,68,High School,28,Patnership,Standalone
7091,438.74,Boston,E,14,17,17,78,Graduation,25,Owner,Standalone
293,20.49,NJ City,N,24,11,1,54,No education,34,Patnership,Standalone
5679,230.27,Boston,E,20,8,14,28,Graduation,23,Owner,Standalone
7881,187.2,Boston,N,24,11,24,80,Graduation,32,Patnership,Standalone
9983,302.9,NJ City,E,15,24,16,47,Graduation,22,Owner,Standalone
7676,317.61,Boston,W,3,15,20,48,Graduation,30,Franchise,Complex
9744,7.73,NJ City,N,7,22,2,28,Graduation,28,Franchise,Complex
4037,480.56,NJ City,N,10,17,22,54,High School,20,Patnership,Standalone
5661,356.8,Boston,N,6,19,17,65,Graduation,19,Patnership,Standalone
4958,229.5,Boston,S,17,11,15,33,High School,26,Patnership,Complex
822,322.28,NJ City,S,17,22,19,49,High School,24,Patnership,Complex
6313,226.89,Boston,N,6,17,4,77,High School,26,Patnership,Standalone
4709,151.29,NJ City,N,12,8,20,41,High School,25,Patnership,Standalone
2549,238.41,NJ City,N,21,15,4,80,No education,25,Patnership,Standalone
3464,86.31,NJ City,W,8,10,3,30,No education,22,Franchise,Complex
3020,169.5,NJ City,W,12,16,12,49,High School,28,Franchise,Complex
3010,63.17,NJ City,N,9,8,4,67,High School,22,Patnership,Complex
9532,58.82,NJ City,W,9,8,2,69,High School,25,Franchise,Complex
1758,403.92,NJ City,S,20,14,24,50,High School,22,Patnership,Complex
9924,470.37,NJ City,W,3,20,21,58,Graduation,29,Franchise,Complex
6434,319.36,Boston,S,11,9,7,28,Graduation,26,Patnership,Complex
2822,272.68,NJ City,S,20,21,4,66,No education,19,Patnership,Complex
350,232.05,NJ City,S,6,14,22,75,High School,24,Patnership,Complex
1579,240.23,NJ City,E,18,21,19,84,High School,35,Owner,Standalone
5404,363.74,Boston,W,12,22,15,54,Graduation,34,Franchise,Complex
5838,217.17,Boston,S,24,9,6,85,Graduation,28,Patnership,Complex
3061,47.13,NJ City,N,11,20,1,37,High School,21,Patnership,Standalone
4531,6.65,NJ City,N,2,9,1,25,High School,20,Owner,Standalone
4226,347.13,NJ City,S,19,12,14,78,High School,25,Patnership,Complex
54,212.17,Maine,S,4,22,22,60,High School,26,Patnership,Complex
5932,309.73,Boston,W,17,14,12,45,Graduation,25,Franchise,Complex
8402,143.25,NJ City,S,7,24,1,39,No education,19,Patnership,Complex
6460,352.3,Boston,W,9,11,24,58,Graduation,23,Franchise,Complex
2662,161.64,NJ City,S,9,22,3,27,High School,22,Patnership,Complex
9514,248.08,NJ City,S,23,16,21,69,Graduation,26,Patnership,Complex
6767,343.25,Boston,E,11,18,11,82,Graduation,33,Owner,Standalone
3447,174.36,NJ City,E,12,17,25,49,High School,21,Owner,Standalone
5840,108.91,Boston,W,6,16,1,34,No education,23,Franchise,Complex
127,274.88,NJ City,E,8,12,1,84,High School,24,Owner,Standalone
6860,345.22,Boston,W,7,16,23,54,Graduation,19,Franchise,Complex
685,455.08,NJ City,N,13,20,5,26,High School,23,Patnership,Standalone
1698,6.88,NJ City,N,5,24,3,27,Graduation,35,Patnership,Complex
7270,370.86,Boston,S,8,15,17,80,Graduation,19,Patnership,Complex
6898,234.26,Boston,S,1,20,16,62,Graduation,32,Patnership,Complex
5448,335.39,Boston,W,20,21,13,31,Graduation,32,Franchise,Complex
1171,424.19,NJ City,E,9,18,9,35,High School,29,Owner,Standalone
9270,378.94,NJ City,S,13,18,24,63,Graduation,29,Patnership,Complex
475,309.95,NJ City,E,14,22,22,53,High School,29,Owner,Standalone
3951,5.11,NJ City,N,19,16,2,32,Graduation,26,Owner,Standalone
5299,426.81,Boston,E,11,22,11,46,Graduation,22,Owner,Standalone
2840,278.56,NJ City,W,13,23,6,61,High School,32,Franchise,Complex
6939,478.98,Boston,E,12,8,5,71,Graduation,32,Owner,Standalone
8400,332.37,NJ City,W,25,14,2,28,Graduation,26,Franchise,Complex
7073,314.69,Boston,N,16,19,23,37,Graduation,28,Patnership,Standalone
126,425.63,NJ City,S,20,15,11,60,High School,20,Patnership,Complex
7044,34.32,Boston,N,16,17,2,59,Graduation,26,Franchise,Complex
1493,225.12,NJ City,N,5,24,22,76,High School,23,Patnership,Standalone
2736,442.98,NJ City,W,8,14,18,50,High School,30,Franchise,Complex
9256,257.29,NJ City,W,10,22,24,76,Graduation,25,Franchise,Complex
1175,274.33,NJ City,E,16,20,20,31,High School,33,Owner,Standalone
8802,270.1,NJ City,S,18,10,11,30,Graduation,27,Patnership,Complex
2094,303.65,NJ City,S,8,14,4,44,Graduation,22,Patnership,Complex
9439,66.98,NJ City,N,15,17,1,66,High School,32,Owner,Standalone
4447,363.01,NJ City,E,24,15,22,52,High School,30,Owner,Standalone
5475,448.69,Boston,E,14,8,19,42,Graduation,34,Owner,Standalone
2118,331.99,NJ City,S,20,24,10,26,High School,31,Patnership,Complex
4749,226.38,NJ City,N,6,12,2,28,Graduation,21,Patnership,Standalone
7914,154.57,Boston,S,16,15,13,54,Graduation,26,Patnership,Complex
5969,45.37,Boston,N,17,14,2,73,No education,27,Patnership,Standalone
791,108.19,NJ City,E,23,16,1,68,No education,35,Owner,Standalone
2247,380.66,NJ City,E,6,20,14,78,High School,34,Owner,Standalone
1179,330.88,NJ City,E,19,14,10,63,High School,25,Owner,Standalone
8084,301.79,NJ City,W,10,8,13,80,Graduation,33,Franchise,Complex
7436,432.01,Boston,W,8,22,21,37,Graduation,35,Franchise,Complex
7471,378.1,Boston,E,5,9,16,42,Graduation,26,Owner,Standalone
2956,50.55,NJ City,W,22,17,2,78,High School,25,Franchise,Complex
3397,48.14,NJ City,N,20,19,1,34,High School,26,Patnership,Standalone
3606,262.07,NJ City,S,22,10,4,27,Graduation,35,Patnership,Complex
6489,164.55,Boston,N,17,8,12,41,Graduation,35,Patnership,Standalone
8467,26.19,NJ City,N,12,14,3,40,High School,35,Owner,Standalone
2872,469.51,NJ City,W,6,17,10,84,High School,20,Franchise,Complex
7058,420.72,Boston,S,5,15,19,43,Graduation,19,Patnership,Complex
9215,192.44,NJ City,E,13,11,10,85,Graduation,25,Owner,Standalone
7144,37.45,Boston,N,6,22,2,56,High School,34,Franchise,Complex
243,52.42,NJ City,E,20,15,2,67,Graduation,19,Owner,Standalone
6316,53.08,Boston,W,20,11,4,30,High School,27,Franchise,Complex
3891,11.45,NJ City,N,20,10,4,54,Graduation,30,Owner,Standalone
6402,488.55,Boston,S,3,16,20,77,Graduation,31,Patnership,Complex
5602,352.84,Boston,S,9,14,14,25,Graduation,26,Patnership,Complex
7030,165.02,Boston,S,16,18,11,50,Graduation,26,Patnership,Complex
2074,109.8,NJ City,S,19,10,2,54,High School,26,Patnership,Complex
7083,195.18,Boston,E,14,22,6,57,Graduation,29,Owner,Standalone
1296,333.67,NJ City,W,8,23,6,84,High School,24,Franchise,Complex
9323,269.11,NJ City,E,16,23,9,53,Graduation,30,Owner,Standalone
2253,105.05,NJ City,N,2,9,1,44,Graduation,21,Patnership,Standalone
6319,258.56,Boston,E,14,14,15,70,Graduation,18,Owner,Standalone
1875,128.16,NJ City,E,22,18,9,26,High School,29,Owner,Standalone
3206,470.11,NJ City,S,11,16,1,81,No education,22,Patnership,Complex
9843,296.74,NJ City,E,10,13,3,51,Graduation,33,Owner,Standalone
2874,257.6,NJ City,S,12,23,6,26,High School,31,Patnership,Complex
6233,341.52,Boston,N,18,24,1,39,No education,21,Patnership,Standalone
4877,433.94,NJ City,N,8,22,13,49,High School,34,Patnership,Standalone
1580,83.39,NJ City,W,19,18,4,34,No education,25,Franchise,Complex
9319,245.03,NJ City,E,1,11,25,56,Graduation,22,Owner,Standalone
6392,268.94,Boston,W,14,11,10,47,Graduation,21,Franchise,Complex
1689,458.87,NJ City,N,17,15,4,25,Graduation,32,Patnership,Standalone
5424,237.07,Boston,W,11,22,24,72,Graduation,35,Franchise,Complex
1697,123.46,NJ City,N,10,10,3,36,No education,32,Patnership,Standalone
6152,127.88,Boston,W,10,13,2,37,No education,18,Franchise,Complex
7125,158.59,Boston,N,23,20,24,80,Graduation,35,Patnership,Standalone
8315,78.16,NJ City,E,24,12,4,76,No education,21,Owner,Standalone
2642,439.3,NJ City,S,22,13,4,60,No education,33,Patnership,Complex
1182,293.57,NJ City,S,10,18,7,48,High School,34,Patnership,Complex
9691,476.08,NJ City,E,11,15,10,26,Graduation,30,Owner,Standalone
574,75.3,NJ City,S,10,19,3,41,High School,18,Patnership,Complex
4286,158.12,NJ City,S,7,21,7,56,High School,33,Patnership,Complex
2801,322.86,NJ City,N,23,9,7,60,High School,34,Patnership,Standalone
1472,259.17,NJ City,W,13,19,21,38,High School,24,Franchise,Complex
5702,48.63,Boston,S,1,19,4,71,No education,35,Patnership,Complex
9111,103.4,NJ City,E,4,15,3,82,Graduation,29,Owner,Standalone
6464,403.26,Boston,W,11,9,22,52,Graduation,30,Franchise,Complex
8543,40.84,NJ City,N,15,14,2,35,No education,23,Owner,Standalone
4770,100.28,NJ City,S,21,20,3,60,Graduation,26,Patnership,Complex
6140,108.26,Boston,W,23,11,1,28,No education,26,Franchise,Complex
3913,270.08,NJ City,N,13,11,11,35,High School,33,Patnership,Standalone
632,101,NJ City,W,1,13,14,85,High School,22,Franchise,Complex
9758,451.61,NJ City,S,12,9,12,49,Graduation,28,Patnership,Complex
8456,194.22,NJ City,W,9,13,14,39,Graduation,31,Franchise,Complex
8080,300.54,NJ City,W,20,17,17,68,Graduation,30,Franchise,Complex
5481,368.46,Boston,N,3,20,21,29,Graduation,25,Patnership,Standalone
567,333.33,NJ City,E,13,22,11,39,High School,34,Owner,Standalone
1741,71.08,NJ City,N,23,22,4,55,High School,21,Patnership,Standalone
7413,494.21,Boston,N,23,19,15,81,Graduation,20,Patnership,Standalone
2246,10.63,NJ City,N,22,24,2,59,No education,26,Patnership,Complex
2142,467.12,NJ City,S,14,20,11,63,High School,26,Patnership,Complex
3836,246.95,NJ City,W,13,9,22,43,High School,30,Franchise,Complex
7290,436.51,Boston,S,21,13,18,35,Graduation,29,Patnership,Complex
4045,134.49,NJ City,N,16,23,1,55,High School,30,Patnership,Standalone
7172,16.87,Boston,N,16,19,1,38,No education,18,Franchise,Complex
3602,54.31,NJ City,S,19,22,1,29,No education,27,Patnership,Complex
4899,295,NJ City,E,2,20,7,26,High School,33,Owner,Standalone
3190,145.13,NJ City,S,20,11,14,32,High School,30,Patnership,Complex
8316,433.84,NJ City,W,18,16,18,73,Graduation,34,Franchise,Complex
8097,450.19,NJ City,N,13,18,6,71,Graduation,21,Patnership,Standalone
424,326.9,NJ City,W,20,14,16,25,High School,21,Franchise,Complex
2818,321.62,NJ City,S,11,15,25,42,High School,30,Patnership,Complex
3217,79.94,NJ City,N,11,11,4,32,High School,28,Patnership,Standalone
9516,82.24,NJ City,W,7,11,3,82,Graduation,27,Franchise,Complex
630,358.5,NJ City,S,15,18,15,82,High School,27,Patnership,Complex
3989,284.15,NJ City,N,12,8,20,73,High School,19,Patnership,Standalone
3142,388.98,NJ City,S,4,12,14,70,High School,21,Patnership,Complex
2629,465.23,NJ City,N,7,11,23,61,High School,20,Patnership,Standalone
8229,465.99,NJ City,N,14,15,3,77,Graduation,26,Patnership,Standalone
6651,145.57,Boston,E,15,11,23,70,Graduation,34,Owner,Standalone
6300,224.98,Boston,W,10,13,21,73,Graduation,34,Franchise,Complex
9146,390.98,NJ City,S,2,14,7,34,Graduation,33,Patnership,Complex
7494,222.36,Boston,S,11,9,11,77,Graduation,22,Patnership,Complex
3654,318.45,NJ City,S,21,23,2,43,Graduation,28,Patnership,Complex
8347,341.21,NJ City,E,3,15,12,60,Graduation,18,Owner,Standalone
659,293.83,NJ City,E,24,15,10,69,High School,21,Owner,Standalone
7007,364,Boston,E,16,22,5,45,No education,29,Owner,Standalone
9063,227.18,NJ City,E,11,12,3,80,Graduation,35,Owner,Standalone
6134,118.22,Boston,S,19,23,3,30,No education,26,Patnership,Complex
915,127.45,NJ City,E,20,13,1,46,Graduation,32,Owner,Standalone
2677,164.09,NJ City,N,9,16,6,50,High School,29,Patnership,Standalone
2540,362.99,NJ City,W,12,18,13,45,High School,29,Franchise,Complex
9024,291.94,NJ City,W,24,9,20,58,Graduation,25,Franchise,Complex
6980,397,Boston,W,9,18,20,76,Graduation,33,Franchise,Complex
1327,269.81,NJ City,E,7,23,22,60,High School,32,Owner,Standalone
4106,35.51,NJ City,N,11,24,3,47,No education,34,Patnership,Complex
7889,188.15,Boston,N,17,24,9,62,Graduation,22,Patnership,Standalone
7349,420.18,Boston,N,17,9,12,65,Graduation,27,Patnership,Standalone
2628,92.87,NJ City,W,14,22,2,52,Graduation,22,Franchise,Complex
1879,378.21,NJ City,E,10,22,5,58,High School,25,Owner,Standalone
4084,185.24,NJ City,W,21,20,25,51,High School,22,Franchise,Complex
9332,229.29,NJ City,W,2,17,5,29,No education,20,Franchise,Complex
1041,315.39,NJ City,N,17,8,6,81,High School,32,Patnership,Standalone
4054,307.76,NJ City,S,2,11,4,33,High School,25,Patnership,Complex
3519,61.97,NJ City,N,1,10,3,56,Graduation,34,Owner,Standalone
9720,5.83,NJ City,N,2,8,3,54,Graduation,33,Franchise,Complex
1608,223,NJ City,W,2,9,7,48,High School,27,Franchise,Complex
2497,128.81,NJ City,N,24,14,18,57,High School,22,Patnership,Standalone
8852,219.03,NJ City,W,14,14,7,50,Graduation,18,Franchise,Complex
6029,298.41,Boston,N,15,12,1,41,No education,32,Patnership,Standalone
3071,267.8,NJ City,E,15,22,22,40,High School,31,Owner,Standalone
5818,181.28,Boston,S,3,15,7,65,Graduation,33,Patnership,Complex
5988,257.65,Boston,W,1,16,8,75,Graduation,20,Franchise,Complex
402,329.46,NJ City,S,21,8,20,74,High School,20,Patnership,Complex
7089,0.26,Boston,N,7,21,4,57,Graduation,23,Patnership,Standalone
3611,478.64,NJ City,E,17,23,24,38,High School,19,Owner,Standalone
6596,171.56,Boston,W,24,17,22,59,Graduation,27,Franchise,Complex
712,349.37,NJ City,W,18,17,6,49,High School,31,Franchise,Complex
237,256.26,NJ City,N,9,18,6,51,High School,31,Patnership,Standalone
9254,495.62,NJ City,S,9,21,19,47,Graduation,25,Patnership,Complex
3875,258.13,NJ City,E,11,15,14,65,High School,27,Owner,Standalone
8218,150.05,NJ City,S,4,12,23,63,Graduation,33,Patnership,Complex
1271,232.62,NJ City,E,6,14,6,72,High School,25,Owner,Standalone
3410,22.52,NJ City,N,5,11,3,69,No education,21,Patnership,Complex
7526,102.5,Boston,S,24,24,1,32,No education,22,Patnership,Complex
9395,242.99,NJ City,E,25,18,21,41,Graduation,33,Owner,Standalone
2717,307.12,NJ City,N,21,11,19,41,High School,34,Patnership,Standalone
9471,336.95,NJ City,E,12,14,6,32,Graduation,26,Owner,Standalone
9362,210.92,NJ City,S,11,22,9,25,Graduation,31,Patnership,Complex
3583,95.82,NJ City,E,12,14,4,67,High School,24,Owner,Standalone
160,220.01,NJ City,W,6,24,10,74,High School,19,Franchise,Complex
2707,161.55,NJ City,E,12,14,18,71,High School,23,Owner,Standalone
515,420.27,NJ City,E,21,8,9,65,High School,19,Owner,Standalone
692,418.91,NJ City,W,4,24,23,80,High School,25,Franchise,Complex
4727,294.46,NJ City,E,6,24,10,45,High School,21,Owner,Standalone
9519,1.34,NJ City,N,3,16,2,38,Graduation,19,Owner,Standalone
8210,390.66,NJ City,S,17,13,10,79,Graduation,28,Patnership,Complex
4955,41.8,Boston,N,6,24,1,35,No education,23,Owner,Standalone
4066,229.84,NJ City,S,25,17,6,34,High School,21,Patnership,Complex
7608,199.47,Boston,W,12,20,24,43,Graduation,20,Franchise,Complex
9986,165.63,NJ City,S,1,24,4,38,No education,23,Patnership,Complex
6890,153.32,Boston,S,12,9,1,62,No education,27,Patnership,Complex
3068,44.03,NJ City,N,24,9,1,54,No education,28,Franchise,Complex
7835,25.53,Boston,N,5,15,2,44,No education,23,Owner,Standalone
7551,400.49,Boston,E,19,24,4,35,Graduation,23,Owner,Standalone
5125,129.95,Boston,N,19,23,1,66,High School,33,Patnership,Standalone
8196,308.66,NJ City,W,24,16,7,35,Graduation,18,Franchise,Complex
1277,162,NJ City,N,13,23,20,25,High School,22,Patnership,Standalone
7679,395.84,Boston,E,24,11,10,82,Graduation,23,Owner,Standalone
7495,51.28,Boston,E,8,10,4,27,High School,19,Owner,Standalone
3802,237,NJ City,S,2,15,19,58,High School,27,Patnership,Complex
9290,339.46,NJ City,S,21,23,3,55,No education,34,Patnership,Complex
461,60.74,NJ City,N,13,12,3,39,No education,21,Patnership,Standalone
7029,393.1,Boston,N,24,18,3,47,Graduation,29,Patnership,Standalone
116,339.76,NJ City,W,23,13,2,37,No education,24,Franchise,Complex
7839,132.09,Boston,E,19,12,20,55,Graduation,19,Owner,Standalone
4903,79.65,NJ City,E,18,15,2,41,High School,32,Owner,Standalone
3009,424.1,NJ City,N,5,22,13,61,High School,28,Patnership,Standalone
6451,242.73,Boston,E,4,13,25,33,Graduation,22,Owner,Standalone
1930,5.8,NJ City,N,8,24,2,62,High School,29,Patnership,Complex
1984,56.5,NJ City,W,18,16,1,65,High School,29,Franchise,Complex
5604,478.2,Boston,W,8,18,8,55,Graduation,25,Franchise,Complex
8965,387.49,NJ City,N,12,22,11,33,Graduation,33,Patnership,Standalone
9299,442.48,NJ City,E,8,21,17,49,Graduation,28,Owner,Standalone
2514,181.04,NJ City,S,25,24,6,76,High School,24,Patnership,Complex
8898,223.64,NJ City,S,25,19,9,75,Graduation,35,Patnership,Complex
8413,477,NJ City,N,23,8,1,74,High School,28,Patnership,Standalone
1368,35.27,NJ City,N,20,11,4,44,Graduation,27,Franchise,Complex
227,499.36,NJ City,E,19,20,12,29,High School,30,Owner,Standalone
49,9.25,Boston,N,3,9,2,33,High School,25,Patnership,Standalone
5455,387.07,Boston,E,18,8,15,57,Graduation,23,Owner,Standalone
5123,110.17,Boston,E,14,16,3,81,No education,23,Owner,Standalone
4452,25.56,NJ City,N,25,18,1,39,Graduation,31,Franchise,Complex
3345,419.07,NJ City,N,15,18,20,62,High School,32,Patnership,Standalone
6787,199.39,Boston,E,20,18,22,80,Graduation,29,Owner,Standalone
2293,134.4,NJ City,N,9,9,2,34,High School,23,Patnership,Standalone
5899,463.08,Boston,E,9,11,6,40,Graduation,31,Owner,Standalone
493,387.41,NJ City,N,6,23,22,83,High School,19,Patnership,Standalone
3899,150.65,NJ City,E,4,13,16,73,High School,32,Owner,Standalone
6711,142.66,Boston,E,21,17,23,48,Graduation,34,Owner,Standalone
8902,313.79,NJ City,S,17,23,1,45,High School,24,Patnership,Complex
8393,379.4,NJ City,N,20,11,22,84,Graduation,19,Patnership,Standalone
1290,191.04,NJ City,S,1,18,2,57,Graduation,32,Patnership,Complex
7269,410.4,Boston,N,16,8,13,67,Graduation,25,Patnership,Standalone
9032,471.14,NJ City,W,2,18,17,65,Graduation,26,Franchise,Complex
9322,382.32,NJ City,S,22,19,3,66,High School,31,Patnership,Complex
6465,56,Boston,N,13,18,3,65,Graduation,32,Patnership,Standalone
204,49.59,NJ City,W,24,8,2,29,Graduation,26,Franchise,Complex
560,14.73,NJ City,N,16,12,4,74,No education,33,Franchise,Complex
2383,343.43,NJ City,E,13,22,10,37,High School,24,Owner,Standalone
8538,36.66,NJ City,N,16,22,1,80,Graduation,34,Patnership,Complex
9992,394.74,NJ City,W,24,8,4,30,No education,33,Franchise,Complex
7690,316.51,Boston,S,15,19,4,35,High School,26,Patnership,Complex
7706,331.84,Boston,S,9,17,17,55,Graduation,21,Patnership,Complex
2186,449.37,NJ City,S,8,15,16,51,High School,30,Patnership,Complex
4798,133.81,NJ City,S,15,21,20,50,High School,21,Patnership,Complex
7773,435.02,Boston,N,11,18,2,44,Graduation,22,Patnership,Standalone
9489,459.71,NJ City,N,10,12,25,58,Graduation,31,Patnership,Standalone
5936,336.83,Boston,W,7,22,2,76,No education,22,Franchise,Complex
2584,433.78,NJ City,W,23,13,5,63,High School,32,Franchise,Complex
4315,229.4,NJ City,E,15,11,12,76,High School,21,Owner,Standalone
2009,105.26,NJ City,N,16,23,1,70,No education,32,Patnership,Standalone
1148,251.5,NJ City,W,5,16,25,48,High School,34,Franchise,Complex
8846,481.03,NJ City,S,13,15,9,41,Graduation,26,Patnership,Complex
7727,417.43,Boston,E,14,18,25,67,Graduation,32,Owner,Standalone
1955,114,NJ City,E,3,9,14,77,High School,35,Owner,Standalone
4372,34.89,NJ City,N,24,22,3,40,High School,20,Franchise,Complex
3394,338.94,NJ City,S,6,12,19,70,High School,18,Patnership,Complex
3710,300.41,NJ City,S,4,8,18,56,High School,24,Patnership,Complex
8634,16.9,NJ City,N,25,20,1,27,Graduation,27,Patnership,Complex
7655,71.95,Boston,N,2,16,1,29,No education,28,Owner,Standalone
7500,317.89,Boston,W,10,20,15,49,Graduation,21,Franchise,Complex
5477,252.24,Boston,N,16,12,19,27,Graduation,26,Patnership,Standalone
829,266.93,NJ City,N,5,18,15,40,High School,33,Patnership,Standalone
8087,2.94,NJ City,E,23,12,3,74,No education,33,Owner,Standalone
7758,370.98,Boston,S,10,23,18,70,Graduation,25,Patnership,Complex
3260,193.05,NJ City,W,13,20,13,77,High School,28,Franchise,Complex
2170,423.64,NJ City,S,17,9,1,79,High School,19,Patnership,Complex
1199,475.06,NJ City,E,8,22,25,57,High School,21,Owner,Standalone
6837,266.2,Boston,N,17,19,23,64,Graduation,27,Patnership,Standalone
1159,187.76,NJ City,E,10,24,6,36,High School,29,Owner,Standalone
1265,399.48,NJ City,N,7,24,20,54,High School,26,Patnership,Standalone
6180,150.9,Boston,W,25,24,18,74,Graduation,23,Franchise,Complex
338,5.02,NJ City,N,22,24,4,51,No education,25,Patnership,Complex
6450,7.76,Boston,N,20,8,1,31,Graduation,35,Patnership,Complex
8116,178.72,NJ City,W,4,12,4,52,High School,18,Franchise,Complex
9656,340.56,NJ City,W,15,15,19,61,Graduation,33,Franchise,Complex
8071,414.13,NJ City,E,10,11,4,32,High School,26,Owner,Standalone
5168,281.71,Boston,W,20,17,11,34,Graduation,33,Franchise,Complex
7554,28.09,Boston,N,5,9,1,74,Graduation,35,Patnership,Complex
7293,183.98,Boston,N,23,14,3,61,Graduation,20,Patnership,Standalone
256,431.55,NJ City,W,8,23,8,60,High School,20,Franchise,Complex
416,86.35,NJ City,W,19,8,1,75,No education,19,Franchise,Complex
2529,391.83,NJ City,N,13,24,24,50,High School,35,Patnership,Standalone
4302,281.38,NJ City,S,10,23,8,60,High School,24,Patnership,Complex
4957,215.15,Boston,N,11,23,8,29,High School,19,Patnership,Standalone
3095,78.17,NJ City,E,7,12,4,46,No education,25,Owner,Standalone
221,387.03,NJ City,N,21,16,1,44,No education,22,Patnership,Standalone
7653,59.89,Boston,N,15,9,2,29,Graduation,30,Patnership,Standalone
3878,14.23,NJ City,N,21,8,2,83,No education,31,Patnership,Complex
5461,229.25,Boston,N,1,18,3,68,High School,24,Patnership,Standalone
3604,69.51,NJ City,N,10,19,4,80,High School,24,Franchise,Complex
8755,180.84,NJ City,E,11,13,16,30,Graduation,26,Owner,Standalone
8161,358.08,NJ City,N,9,19,3,67,High School,33,Patnership,Standalone
2827,319.82,NJ City,E,19,16,4,76,High School,30,Owner,Standalone
3015,360.46,NJ City,E,17,20,1,33,Graduation,19,Owner,Standalone
7061,314.98,Boston,N,21,21,11,72,Graduation,24,Patnership,Standalone
674,167.04,NJ City,S,20,16,4,55,No education,20,Patnership,Complex
4730,34.19,NJ City,N,21,23,4,67,No education,23,Patnership,Complex
3630,59.83,NJ City,S,13,18,4,40,Graduation,34,Patnership,Complex
6485,297.45,Boston,N,16,10,14,45,Graduation,28,Patnership,Standalone
3100,8.79,NJ City,N,10,11,2,69,High School,23,Franchise,Complex
3451,308.52,NJ City,E,15,15,5,52,High School,21,Owner,Standalone
146,50.69,NJ City,S,18,10,4,75,No education,25,Patnership,Complex
6791,331.31,Boston,E,16,11,21,81,Graduation,19,Owner,Standalone
6142,66.34,Boston,N,25,24,1,50,High School,27,Patnership,Complex
4919,464.61,NJ City,E,25,21,21,28,High School,18,Owner,Standalone
8032,219.31,Boston,W,12,12,1,48,High School,31,Franchise,Complex
6292,497.54,Boston,W,14,11,24,30,Graduation,18,Franchise,Complex
3126,185.4,NJ City,S,13,14,9,50,High School,33,Patnership,Complex
1446,463.19,NJ City,S,20,9,20,64,High School,28,Patnership,Complex
1115,354.42,NJ City,E,5,15,25,59,High School,29,Owner,Standalone
385,418.97,NJ City,N,14,18,25,36,High School,24,Patnership,Standalone
4338,33.23,NJ City,N,18,21,4,71,Graduation,24,Patnership,Complex
9012,160.51,NJ City,W,3,18,19,78,Graduation,33,Franchise,Complex
6806,112.96,Boston,S,17,13,4,75,No education,32,Patnership,Complex
8437,86.48,NJ City,N,24,22,1,34,High School,24,Patnership,Standalone
7915,399.61,Boston,E,2,11,12,57,Graduation,31,Owner,Standalone
4086,302.97,NJ City,S,10,18,18,61,High School,22,Patnership,Complex
6294,164.52,Boston,S,24,9,10,80,Graduation,28,Patnership,Complex
36,407.36,NJ City,W,9,20,5,43,Graduation,33,Franchise,Complex
8859,193.63,NJ City,E,23,13,23,26,Graduation,26,Owner,Standalone
4496,253.14,NJ City,W,8,21,2,49,No education,33,Franchise,Complex
4628,489.4,NJ City,W,16,15,16,52,High School,21,Franchise,Complex
6370,2.99,Boston,N,24,8,4,76,High School,32,Patnership,Complex
6586,379.37,Boston,S,24,10,1,40,High School,30,Patnership,Complex
5483,399.16,Boston,E,23,19,11,39,Graduation,31,Owner,Standalone
9678,111.04,NJ City,S,2,8,4,82,Graduation,28,Patnership,Complex
7276,64.97,Boston,N,21,23,2,29,High School,22,Franchise,Complex
7227,11.27,Boston,N,13,8,1,57,Graduation,21,Owner,Standalone
7179,141.66,Boston,E,20,13,2,77,Graduation,21,Owner,Standalone
1776,162.82,NJ City,W,13,14,23,38,High School,35,Franchise,Complex
1153,493.86,NJ City,N,20,18,16,27,High School,21,Patnership,Standalone
6260,462.72,Boston,W,4,13,25,36,Graduation,20,Franchise,Complex
9232,446.88,NJ City,W,21,18,4,51,High School,25,Franchise,Complex
6068,230.24,Boston,W,23,18,9,60,Graduation,26,Franchise,Complex
5998,394.52,Boston,S,25,21,10,79,Graduation,32,Patnership,Complex
5930,24.14,Boston,N,17,21,1,73,No education,22,Patnership,Complex
7388,17.18,Boston,N,9,14,4,44,No education,34,Franchise,Complex
7371,248.91,Boston,E,10,19,5,85,Graduation,20,Owner,Standalone
3147,450.88,NJ City,E,24,15,25,40,High School,24,Owner,Standalone
3854,422.95,NJ City,S,18,12,5,34,No education,29,Patnership,Complex
9863,120.76,NJ City,E,13,13,2,27,No education,34,Owner,Standalone
6830,199.9,Boston,S,5,13,4,42,No education,29,Patnership,Complex
3932,455.38,NJ City,W,7,14,19,45,High School,19,Franchise,Complex
1336,205.97,NJ City,W,14,10,9,85,High School,30,Franchise,Complex
8326,244.62,NJ City,S,12,17,18,68,Graduation,35,Patnership,Complex
736,209.68,NJ City,W,19,18,9,41,High School,27,Franchise,Complex
2135,195.77,NJ City,E,25,12,1,60,No education,18,Owner,Standalone
13,73.48,Boston,N,25,15,2,70,High School,26,Patnership,Standalone
7170,459.81,Boston,S,2,19,18,31,Graduation,22,Patnership,Complex
2690,448.31,NJ City,S,17,10,25,76,High School,29,Patnership,Complex
3469,493.75,NJ City,N,25,14,12,67,High School,31,Patnership,Standalone
920,460.9,NJ City,W,8,11,3,32,No education,22,Franchise,Complex
9333,365.9,NJ City,N,18,14,13,29,Graduation,26,Patnership,Standalone
6715,105.75,Boston,E,4,20,1,30,High School,31,Owner,Standalone
5979,28.32,Boston,N,8,14,2,85,Graduation,20,Owner,Standalone
5039,238.38,Boston,E,12,18,25,54,Graduation,30,Owner,Standalone
4333,69.18,NJ City,N,13,11,3,58,High School,20,Patnership,Standalone
8865,299.85,NJ City,N,16,11,5,48,Graduation,20,Patnership,Standalone
7351,204.31,Boston,E,25,22,11,80,Graduation,26,Owner,Standalone
3883,33.55,NJ City,N,23,24,1,38,High School,33,Owner,Standalone
7514,403.09,Boston,S,10,8,14,36,Graduation,28,Patnership,Complex
4640,159.25,NJ City,W,16,15,23,48,High School,28,Franchise,Complex
8937,175.18,NJ City,N,5,24,22,80,Graduation,22,Patnership,Standalone
2672,362.69,NJ City,W,4,17,22,35,High School,19,Franchise,Complex
9827,473.36,NJ City,E,8,9,20,84,Graduation,34,Owner,Standalone
3030,157,NJ City,S,1,24,23,37,High School,20,Patnership,Complex
8516,77.68,NJ City,W,12,18,3,71,No education,19,Franchise,Complex
7729,92.63,Boston,N,7,16,1,79,High School,35,Patnership,Standalone
1201,293.82,NJ City,N,14,10,11,76,High School,23,Patnership,Standalone
4524,53.69,NJ City,W,2,20,1,51,Graduation,28,Franchise,Complex
8973,218.68,NJ City,N,1,22,16,35,Graduation,26,Patnership,Standalone
4339,319.45,NJ City,E,6,22,15,52,High School,18,Owner,Standalone
3529,122.61,NJ City,N,2,15,1,67,High School,30,Patnership,Standalone
9759,67.36,NJ City,N,24,24,3,40,Graduation,30,Owner,Standalone
8234,186.59,NJ City,S,24,18,4,31,No education,18,Patnership,Complex
8457,492.96,NJ City,N,2,24,19,53,Graduation,18,Patnership,Standalone
147,156.92,NJ City,E,21,14,4,65,Graduation,18,Owner,Standalone
3463,474.63,NJ City,E,9,9,2,47,High School,34,Owner,Standalone
5229,441.54,Boston,N,21,12,14,32,Graduation,29,Patnership,Standalone
5885,387.98,Boston,N,5,14,21,53,Graduation,24,Patnership,Standalone
5164,260.41,Boston,W,2,20,24,83,Graduation,31,Franchise,Complex
2368,194.69,NJ City,W,10,8,3,61,High School,31,Franchise,Complex
2779,297.08,NJ City,E,7,24,21,70,High School,26,Owner,Standalone
9149,55.78,NJ City,N,16,24,1,52,No education,34,Patnership,Standalone
6061,292.12,Boston,N,19,18,21,56,Graduation,27,Patnership,Standalone
1141,261.82,NJ City,N,16,22,9,81,High School,19,Patnership,Standalone
3376,173.76,NJ City,W,4,23,4,31,High School,24,Franchise,Complex
6648,467.49,Boston,W,3,22,15,43,Graduation,31,Franchise,Complex
917,323.66,NJ City,N,25,23,10,39,High School,21,Patnership,Standalone
2403,213,NJ City,E,1,16,18,61,High School,30,Owner,Standalone
9437,307.66,NJ City,N,2,15,21,36,Graduation,18,Patnership,Standalone
9158,69.6,NJ City,N,23,8,1,69,No education,23,Patnership,Complex
4622,96.62,NJ City,S,16,8,2,50,No education,27,Patnership,Complex
2313,497.94,NJ City,N,22,8,20,33,High School,27,Patnership,Standalone
9849,451.12,NJ City,N,9,8,3,36,Graduation,25,Patnership,Standalone
3425,163.86,NJ City,N,5,13,17,53,High School,33,Patnership,Standalone
5089,146.13,Boston,N,25,10,8,28,Graduation,20,Patnership,Standalone
7848,235.42,Boston,W,16,20,10,26,Graduation,29,Franchise,Complex
1289,306.32,NJ City,N,13,17,11,45,High School,20,Patnership,Standalone
6576,360.53,Boston,W,20,22,13,30,Graduation,23,Franchise,Complex
7816,21.74,Boston,N,1,12,3,65,High School,28,Franchise,Complex
9138,353.37,NJ City,S,3,24,23,43,Graduation,22,Patnership,Complex
5235,307.75,Boston,E,6,9,9,44,Graduation,29,Owner,Standalone
6836,444.5,Boston,W,12,11,1,45,No education,21,Franchise,Complex
4626,378.73,NJ City,S,7,15,15,71,High School,32,Patnership,Complex
3232,7.47,NJ City,N,7,23,1,41,High School,34,Franchise,Complex
2811,414.32,NJ City,E,5,21,20,26,High School,27,Owner,Standalone
4486,395.12,NJ City,S,13,11,9,66,High School,21,Patnership,Complex
7937,321.46,Boston,N,21,15,21,77,Graduation,34,Patnership,Standalone
8948,389.03,NJ City,W,16,8,15,45,Graduation,22,Franchise,Complex
2550,225.85,NJ City,S,18,11,19,40,High School,21,Patnership,Complex
5625,50.18,Boston,N,18,15,3,49,Graduation,30,Patnership,Standalone
7142,104.42,Boston,S,15,23,4,51,No education,20,Patnership,Complex
8632,285.45,NJ City,W,17,14,10,51,Graduation,19,Franchise,Complex
6341,186.69,Boston,N,23,16,22,71,Graduation,31,Patnership,Standalone
1079,343.36,NJ City,E,24,12,10,67,High School,28,Owner,Standalone
6207,30.21,Boston,N,13,8,3,26,Graduation,32,Owner,Standalone
8872,423.91,NJ City,W,3,17,11,35,Graduation,35,Franchise,Complex
7594,416.37,Boston,S,22,23,12,34,Graduation,27,Patnership,Complex
6844,176.38,Boston,W,20,17,4,36,High School,31,Franchise,Complex
295,414.48,NJ City,E,7,19,6,52,High School,21,Owner,Standalone
5358,189.26,Boston,S,20,21,21,33,Graduation,21,Patnership,Complex
9054,79.93,NJ City,S,2,15,2,65,Graduation,32,Patnership,Complex
1965,15,NJ City,N,2,18,2,69,Graduation,26,Patnership,Standalone
7512,378.18,Boston,W,17,11,14,82,Graduation,18,Franchise,Complex
7426,129.36,Boston,S,18,21,16,32,Graduation,32,Patnership,Complex
9463,212.85,NJ City,E,25,16,9,64,Graduation,29,Owner,Standalone
3262,295.21,NJ City,S,25,16,8,56,High School,35,Patnership,Complex
5771,207.07,Boston,E,15,9,12,80,Graduation,23,Owner,Standalone
8059,217.16,NJ City,E,16,20,19,28,Graduation,18,Owner,Standalone
3760,186,NJ City,W,3,19,23,82,High School,28,Franchise,Complex
8569,221.97,NJ City,N,17,18,24,48,Graduation,33,Patnership,Standalone
6769,430.3,Boston,N,18,21,4,66,High School,32,Patnership,Standalone
6509,437.98,Boston,N,23,11,24,32,Graduation,18,Patnership,Standalone
482,208.04,NJ City,S,21,19,18,68,High School,19,Patnership,Complex
3850,117.73,NJ City,S,21,12,4,72,High School,30,Patnership,Complex
2372,7.2,NJ City,N,25,16,2,31,No education,33,Franchise,Complex
8461,369.09,NJ City,N,23,8,10,67,Graduation,20,Patnership,Standalone
6123,271.3,Boston,E,15,11,9,69,Graduation,29,Owner,Standalone
1111,39.89,NJ City,N,16,14,3,65,High School,23,Owner,Standalone
8342,410.8,NJ City,S,11,11,5,76,No education,29,Patnership,Complex
9133,191.35,NJ City,N,13,22,25,47,Graduation,25,Patnership,Standalone
3574,440.57,NJ City,S,12,22,6,48,High School,23,Patnership,Complex
3365,108,NJ City,N,2,19,12,49,High School,22,Patnership,Standalone
1936,43.34,NJ City,N,6,22,3,48,High School,26,Franchise,Complex
8455,389.68,NJ City,E,11,22,18,77,Graduation,30,Owner,Standalone
882,210.5,NJ City,S,4,11,12,65,High School,31,Patnership,Complex
2467,24.55,NJ City,N,2,14,1,85,High School,31,Owner,Standalone
5272,2.2,Boston,W,11,22,2,31,High School,31,Franchise,Complex
547,333.19,NJ City,E,14,9,19,66,High School,34,Owner,Standalone
5732,114.1,Boston,W,13,19,2,75,No education,34,Franchise,Complex
4400,187.06,NJ City,W,4,19,2,52,No education,27,Franchise,Complex
2577,320.78,NJ City,N,4,17,14,84,High School,26,Patnership,Standalone
1645,8.04,NJ City,N,21,13,3,59,High School,32,Patnership,Standalone
7124,245.26,Boston,W,1,17,19,83,Graduation,29,Franchise,Complex
3379,175.67,NJ City,E,9,22,13,26,High School,35,Owner,Standalone
228,142.79,NJ City,W,17,23,7,72,High School,35,Franchise,Complex
3277,117.04,NJ City,N,20,9,2,25,High School,18,Patnership,Standalone
3635,160.91,NJ City,E,17,21,23,55,High School,34,Owner,Standalone
5258,128.81,Boston,S,22,19,9,31,Graduation,21,Patnership,Complex
7951,101.35,Boston,E,6,24,2,66,High School,26,Owner,Standalone
72,203.31,Maine,W,18,16,22,65,High School,20,Franchise,Complex
5275,93.27,Boston,E,18,21,4,42,High School,20,Owner,Standalone
2682,191.31,NJ City,S,16,17,2,69,Graduation,27,Patnership,Complex
7452,97.05,Boston,W,22,9,1,82,Graduation,21,Franchise,Complex
9772,121.16,NJ City,W,14,22,3,70,High School,35,Franchise,Complex
4083,101.84,NJ City,E,20,12,4,83,Graduation,23,Owner,Standalone
2841,257.78,NJ City,N,22,9,15,72,High School,21,Patnership,Standalone
6242,10.12,Boston,N,10,20,4,57,No education,31,Patnership,Complex
4993,146.59,Boston,N,21,16,13,75,High School,22,Patnership,Standalone
3961,465.25,NJ City,N,18,12,7,58,High School,29,Patnership,Standalone
7193,278.37,Boston,N,25,22,6,32,Graduation,27,Patnership,Standalone
8354,382.14,NJ City,S,11,14,24,68,Graduation,21,Patnership,Complex
8589,126.78,NJ City,N,25,10,3,31,Graduation,31,Patnership,Standalone
6468,77.25,Boston,W,16,19,3,56,Graduation,23,Franchise,Complex
4179,110.97,NJ City,E,23,20,3,46,Graduation,28,Owner,Standalone
155,300.76,NJ City,E,13,13,3,80,No education,18,Owner,Standalone
7361,155.59,Boston,N,23,16,19,71,Graduation,26,Patnership,Standalone
856,155.87,NJ City,W,25,24,23,62,High School,33,Franchise,Complex
9136,428.11,NJ City,W,7,12,7,52,Graduation,22,Franchise,Complex
870,100.88,NJ City,S,13,22,4,85,Graduation,19,Patnership,Complex
5523,287.76,Boston,E,22,15,14,30,Graduation,33,Owner,Standalone
9325,86.14,NJ City,N,1,12,4,64,High School,18,Patnership,Standalone
9472,471.02,NJ City,W,9,11,11,47,Graduation,35,Franchise,Complex
2737,311.59,NJ City,N,17,11,17,37,High School,29,Patnership,Standalone
6355,415.36,Boston,E,17,8,17,73,Graduation,23,Owner,Standalone
2143,91.16,NJ City,E,14,16,3,67,High School,29,Owner,Standalone
2601,326.98,NJ City,N,22,22,18,37,High School,34,Patnership,Standalone
904,8.18,NJ City,N,11,23,2,82,High School,22,Franchise,Complex
4355,483.67,NJ City,E,21,9,24,73,High School,25,Owner,Standalone
3996,379.36,NJ City,W,10,23,7,73,High School,30,Franchise,Complex
7196,179.97,Boston,W,20,18,10,50,Graduation,23,Franchise,Complex
6239,134.53,Boston,E,4,9,22,83,Graduation,31,Owner,Standalone
8265,390.05,NJ City,N,5,14,14,79,Graduation,26,Patnership,Standalone
1108,467.62,NJ City,W,4,21,14,42,High School,24,Franchise,Complex
6345,8.62,Boston,N,22,9,4,45,Graduation,20,Patnership,Standalone
5700,11.38,Boston,N,16,8,4,40,Graduation,28,Franchise,Complex
5470,363.32,Boston,S,6,24,18,58,Graduation,19,Patnership,Complex
9295,18.28,NJ City,N,25,13,3,75,High School,20,Owner,Standalone
9625,402.76,NJ City,N,18,17,15,85,Graduation,26,Patnership,Standalone
2958,405.8,NJ City,S,12,19,22,73,High School,34,Patnership,Complex
4704,369.07,NJ City,W,17,20,12,83,High School,30,Franchise,Complex
6002,268.71,Boston,S,20,8,20,45,Graduation,19,Patnership,Complex
7084,430.61,Boston,W,3,21,19,32,Graduation,31,Franchise,Complex
6929,140.54,Boston,N,5,18,6,79,Graduation,19,Patnership,Standalone
5504,315.31,Boston,W,24,24,21,65,Graduation,18,Franchise,Complex
8620,128.57,NJ City,W,10,8,11,65,Graduation,27,Franchise,Complex
9564,374.29,NJ City,W,7,15,24,50,Graduation,26,Franchise,Complex
627,298.77,NJ City,E,6,11,18,62,High School,35,Owner,Standalone
1831,469.72,NJ City,E,25,23,4,83,High School,28,Owner,Standalone
9518,181.31,NJ City,S,25,15,4,71,No education,35,Patnership,Complex
8298,410.26,NJ City,S,14,24,8,41,Graduation,35,Patnership,Complex
4900,124.1,NJ City,W,16,14,2,51,High School,34,Franchise,Complex
5407,138.23,Boston,E,18,18,24,62,Graduation,33,Owner,Standalone
3588,121.75,NJ City,W,23,9,3,79,Graduation,28,Franchise,Complex
1061,4.22,NJ City,N,14,20,4,66,No education,19,Patnership,Standalone
892,377.64,NJ City,W,23,24,1,46,High School,22,Franchise,Complex
4052,466.95,NJ City,W,7,8,20,28,High School,29,Franchise,Complex
8030,35.58,Boston,N,7,21,2,36,No education,22,Patnership,Complex
824,400.23,NJ City,W,21,24,14,51,High School,24,Franchise,Complex
6687,241.83,Boston,E,10,20,17,56,Graduation,18,Owner,Standalone
6643,390.33,Boston,E,25,22,17,71,Graduation,28,Owner,Standalone
2748,92.2,NJ City,W,1,16,4,44,Graduation,32,Franchise,Complex
1276,255.07,NJ City,W,21,20,3,29,High School,28,Franchise,Complex
7032,485.4,Boston,W,17,19,9,27,Graduation,22,Franchise,Complex
9086,375.14,NJ City,S,8,9,2,78,No education,30,Patnership,Complex
2463,133.93,NJ City,E,16,16,10,80,High School,33,Owner,Standalone
5783,205.7,Boston,E,25,14,7,26,Graduation,34,Owner,Standalone
4430,481.45,NJ City,S,24,11,17,67,High School,31,Patnership,Complex
9583,273.33,NJ City,E,12,10,3,54,High School,33,Owner,Standalone
5282,391.21,Boston,S,18,16,8,59,Graduation,26,Patnership,Complex
5487,482.74,Boston,E,9,24,8,59,Graduation,31,Owner,Standalone
4584,101.38,NJ City,W,20,17,2,53,Graduation,23,Franchise,Complex
7180,291.53,Boston,W,7,23,17,81,Graduation,30,Franchise,Complex
225,293.52,NJ City,N,25,23,7,59,High School,20,Patnership,Standalone
812,153,NJ City,W,1,16,7,26,High School,35,Franchise,Complex
885,111.82,NJ City,N,7,12,2,49,Graduation,19,Patnership,Standalone
7302,151.07,Boston,S,1,17,22,47,Graduation,23,Patnership,Complex
9909,154.66,NJ City,N,13,8,19,58,Graduation,28,Patnership,Standalone
5482,495.53,Boston,S,8,16,19,85,Graduation,32,Patnership,Complex
8101,277.25,NJ City,N,20,15,25,32,Graduation,26,Patnership,Standalone
9954,46.06,NJ City,N,15,18,3,28,Graduation,19,Patnership,Complex
5730,313.7,Boston,S,9,10,17,80,Graduation,25,Patnership,Complex
981,327.42,NJ City,N,20,23,7,50,High School,33,Patnership,Standalone
8,2.68,NY City,W,17,11,2,76,No education,31,Franchise,Complex
9842,408.85,NJ City,S,7,19,14,83,Graduation,31,Patnership,Complex
7127,164.24,Boston,E,1,8,14,36,Graduation,19,Owner,Standalone
3734,372.39,NJ City,S,22,16,3,82,No education,23,Patnership,Complex
7831,14.29,Boston,N,18,16,1,84,High School,24,Owner,Standalone
4263,17.48,NJ City,N,8,16,3,36,Graduation,33,Owner,Standalone
7287,107.51,Boston,E,8,15,4,49,Graduation,30,Owner,Standalone
2237,105.82,NJ City,N,22,8,2,45,No education,19,Patnership,Standalone
7447,219.86,Boston,E,3,24,12,65,Graduation,25,Owner,Standalone
6636,483.47,Boston,W,25,10,17,41,Graduation,23,Franchise,Complex
4760,47.86,NJ City,W,22,11,4,70,No education,33,Franchise,Complex
9921,235.93,NJ City,N,3,20,15,70,Graduation,30,Patnership,Standalone
2101,466.64,NJ City,N,24,23,21,37,High School,24,Patnership,Standalone
9446,482.6,NJ City,S,21,15,12,76,Graduation,30,Patnership,Complex
6020,242.03,Boston,W,3,17,15,58,Graduation,27,Franchise,Complex
3005,95.8,NJ City,N,12,10,1,50,No education,28,Patnership,Standalone
3409,209.45,NJ City,N,18,10,15,35,High School,26,Patnership,Standalone
2791,347.62,NJ City,E,19,20,4,25,High School,18,Owner,Standalone
7174,73.53,Boston,N,1,19,4,41,High School,27,Patnership,Complex
8308,358.17,NJ City,W,21,10,11,67,Graduation,31,Franchise,Complex
6689,395.76,Boston,N,23,10,6,37,Graduation,20,Patnership,Standalone
8952,229.12,NJ City,W,9,9,25,79,Graduation,28,Franchise,Complex
3023,70.84,NJ City,N,23,21,3,27,No education,19,Owner,Standalone
975,483.82,NJ City,E,10,10,25,64,High School,35,Owner,Standalone
361,214.32,NJ City,N,12,24,20,73,High School,22,Patnership,Standalone
1603,334.84,NJ City,E,24,10,20,52,High School,34,Owner,Standalone
5167,393.37,Boston,E,1,10,20,29,Graduation,35,Owner,Standalone
3980,336.12,NJ City,W,4,21,9,41,High School,32,Franchise,Complex
1250,315.7,NJ City,S,10,18,23,35,High School,35,Patnership,Complex
4967,356.71,Boston,E,4,14,4,75,No education,24,Owner,Standalone
5716,320.2,Boston,W,12,22,4,25,High School,25,Franchise,Complex
1317,82.75,NJ City,N,23,18,3,41,Graduation,26,Patnership,Standalone
5126,261.19,Boston,S,7,12,2,33,No education,25,Patnership,Complex
8579,346.77,NJ City,E,8,24,8,34,Graduation,27,Owner,Standalone
8251,161.1,NJ City,E,2,12,4,53,High School,34,Owner,Standalone
8053,446.98,NJ City,N,21,10,21,32,Graduation,29,Patnership,Standalone
1109,134.47,NJ City,N,20,18,19,25,High School,34,Patnership,Standalone
8367,68.9,NJ City,N,17,15,1,45,Graduation,25,Owner,Standalone
5088,334.8,Boston,W,20,22,13,45,Graduation,30,Franchise,Complex
9731,274.8,NJ City,E,15,13,2,39,No education,32,Owner,Standalone
2087,258.26,NJ City,E,9,21,14,82,High School,32,Owner,Standalone
3143,11.71,NJ City,N,20,14,3,84,No education,24,Owner,Standalone
89,139.1,Boston,N,19,16,17,74,High School,34,Patnership,Standalone
5685,307.75,Boston,N,2,24,8,59,Graduation,24,Patnership,Standalone
4772,291,NJ City,W,3,11,12,76,High School,31,Franchise,Complex
1769,372.41,NJ City,N,25,12,21,72,High School,24,Patnership,Standalone
2214,202.27,NJ City,S,17,16,8,66,High School,22,Patnership,Complex
8771,31.19,NJ City,N,1,22,2,60,No education,30,Owner,Standalone
5446,186.3,Boston,S,13,14,11,51,Graduation,24,Patnership,Complex
7846,423.45,Boston,S,11,14,9,70,Graduation,21,Patnership,Complex
9470,161.54,NJ City,S,7,10,24,30,Graduation,30,Patnership,Complex
6746,186.23,Boston,S,17,15,24,77,Graduation,20,Patnership,Complex
8203,151.92,NJ City,E,23,14,3,59,High School,35,Owner,Standalone
8837,63.44,NJ City,N,11,17,4,36,No education,24,Patnership,Standalone
400,464.37,NJ City,W,24,17,1,45,High School,25,Franchise,Complex
3837,59.17,NJ City,N,16,13,1,65,Graduation,23,Patnership,Standalone
3737,239.1,NJ City,N,8,16,14,34,High School,31,Patnership,Standalone
5506,305.14,Boston,S,23,12,3,55,High School,22,Patnership,Complex
768,163.18,NJ City,W,22,21,7,39,High School,25,Franchise,Complex
2373,471.64,NJ City,N,15,16,23,59,High School,28,Patnership,Standalone
4573,429.57,NJ City,N,6,8,15,68,High School,19,Patnership,Standalone
8602,44.92,NJ City,N,24,13,2,44,High School,18,Patnership,Complex
5230,428.95,Boston,S,18,8,12,60,Graduation,30,Patnership,Complex
3441,223.27,NJ City,N,9,13,11,25,High School,32,Patnership,Standalone
6042,160.86,Boston,S,14,15,4,38,Graduation,19,Patnership,Complex
4812,198,NJ City,W,2,11,18,32,High School,22,Franchise,Complex
8753,149.11,NJ City,N,7,18,25,28,Graduation,30,Patnership,Standalone
3841,10.52,NJ City,N,19,24,4,54,High School,30,Patnership,Standalone
7833,37.34,Boston,N,24,9,2,29,Graduation,19,Patnership,Standalone
3813,188.15,NJ City,N,23,19,6,33,High School,28,Patnership,Standalone
5354,257.37,Boston,S,3,15,8,76,Graduation,24,Patnership,Complex
7912,205.46,Boston,W,22,14,20,39,Graduation,30,Franchise,Complex
5309,82.07,Boston,N,1,11,1,74,No education,29,Patnership,Standalone
9259,199.43,NJ City,E,23,9,9,53,Graduation,21,Owner,Standalone
667,0.12,NJ City,N,22,12,1,77,High School,31,Owner,Standalone
8157,194.35,NJ City,N,25,18,10,85,Graduation,28,Patnership,Standalone
6757,488,Boston,N,11,18,6,82,Graduation,33,Patnership,Standalone
6498,309.64,Boston,S,12,11,5,29,Graduation,20,Patnership,Complex
2520,51.55,NJ City,W,6,15,2,59,Graduation,18,Franchise,Complex
8988,68.17,NJ City,N,12,11,4,76,Graduation,29,Franchise,Complex
2287,380.16,NJ City,E,5,22,23,43,High School,34,Owner,Standalone
8736,149.62,NJ City,W,17,23,3,49,Graduation,26,Franchise,Complex
6692,487.24,Boston,W,17,21,19,49,Graduation,18,Franchise,Complex
8790,296.69,NJ City,S,11,9,21,70,Graduation,28,Patnership,Complex
5859,361.87,Boston,E,24,15,18,40,Graduation,22,Owner,Standalone
8587,320.08,NJ City,E,14,13,2,83,High School,18,Owner,Standalone
1397,190,NJ City,N,1,8,17,37,High School,32,Patnership,Standalone
2959,116.12,NJ City,E,2,22,2,47,High School,32,Owner,Standalone
3558,55.88,NJ City,S,21,16,2,71,Graduation,26,Patnership,Complex
5105,266.49,Boston,N,1,12,24,57,Graduation,35,Patnership,Standalone
6824,14.98,Boston,N,11,18,4,40,No education,26,Franchise,Complex
6113,183.37,Boston,N,11,21,12,67,Graduation,25,Patnership,Standalone
1138,476.8,NJ City,S,15,10,6,65,High School,32,Patnership,Complex
578,400.4,NJ City,S,15,11,25,67,High School,25,Patnership,Complex
8333,152.2,NJ City,N,18,18,10,30,Graduation,31,Patnership,Standalone
7487,9.43,Boston,N,11,8,4,61,No education,25,Owner,Standalone
2219,126.87,NJ City,E,11,16,4,66,No education,34,Owner,Standalone
9455,346.86,NJ City,E,18,11,3,48,No education,31,Owner,Standalone
8770,369.28,NJ City,S,15,8,18,74,Graduation,18,Patnership,Complex
3685,278.5,NJ City,N,20,8,8,63,High School,28,Patnership,Standalone
7425,179.24,Boston,N,7,21,20,73,Graduation,27,Patnership,Standalone
4051,126,NJ City,E,2,11,8,34,High School,19,Owner,Standalone
7490,209.25,Boston,S,5,15,21,72,Graduation,30,Patnership,Complex
5868,418.61,Boston,W,16,19,2,48,Graduation,34,Franchise,Complex
8396,491.61,NJ City,W,2,18,10,62,Graduation,28,Franchise,Complex
6702,366.82,Boston,S,10,18,24,65,Graduation,20,Patnership,Complex
7416,165.92,Boston,W,16,14,21,30,Graduation,28,Franchise,Complex
8717,200.45,NJ City,N,25,20,11,85,Graduation,28,Patnership,Standalone
8386,163.98,NJ City,S,8,13,20,50,Graduation,33,Patnership,Complex
3132,13.72,NJ City,N,19,8,2,55,Graduation,21,Franchise,Complex
1584,341.93,NJ City,W,17,15,19,77,High School,25,Franchise,Complex
6971,436.62,Boston,E,2,24,3,39,No education,18,Owner,Standalone
9725,433.3,NJ City,N,20,16,13,27,Graduation,28,Patnership,Standalone
8886,396.52,NJ City,S,1,24,11,33,Graduation,27,Patnership,Complex
2931,464.57,NJ City,E,18,19,7,59,High School,24,Owner,Standalone
8142,192.1,NJ City,S,22,8,13,33,Graduation,19,Patnership,Complex
3289,172.01,NJ City,N,1,11,2,38,High School,24,Patnership,Standalone
7021,195.13,Boston,N,4,17,19,46,Graduation,24,Patnership,Standalone
203,487.82,NJ City,E,24,14,19,56,High School,29,Owner,Standalone
899,309.79,NJ City,E,15,17,10,50,High School,30,Owner,Standalone
5742,285.48,Boston,S,10,10,12,56,Graduation,20,Patnership,Complex
1380,128,NJ City,W,3,15,19,56,High School,23,Franchise,Complex
3956,328.87,NJ City,W,14,14,1,38,No education,24,Franchise,Complex
705,420.55,NJ City,N,23,19,1,60,Graduation,22,Patnership,Standalone
1945,434.32,NJ City,N,10,9,13,62,High School,25,Patnership,Standalone
1131,319.76,NJ City,E,25,22,12,56,High School,21,Owner,Standalone
8002,111.94,Boston,S,21,11,3,53,High School,26,Patnership,Complex
8129,114.59,NJ City,N,11,18,2,55,No education,23,Patnership,Standalone
5959,132.22,Boston,E,6,10,16,82,Graduation,29,Owner,Standalone
4386,230.56,NJ City,S,12,15,19,28,High School,26,Patnership,Complex
5824,160.19,Boston,W,6,19,1,69,High School,20,Franchise,Complex
7819,142.21,Boston,E,22,15,23,31,Graduation,33,Owner,Standalone
1731,74.68,NJ City,E,18,15,4,32,Graduation,21,Owner,Standalone
6680,496.49,Boston,W,9,19,21,37,Graduation,31,Franchise,Complex
5680,296.49,Boston,W,8,10,22,56,Graduation,23,Franchise,Complex
5572,125.39,Boston,W,2,9,3,51,High School,34,Franchise,Complex
2714,169.21,NJ City,S,21,20,5,60,No education,27,Patnership,Complex
6091,62.92,Boston,N,22,14,4,65,High School,21,Owner,Standalone
50,485.15,Boston,S,14,21,3,26,No education,30,Patnership,Complex
9892,157.26,NJ City,W,3,21,7,39,Graduation,29,Franchise,Complex
7903,139.97,Boston,E,5,17,15,32,Graduation,19,Owner,Standalone
9912,303.57,NJ City,W,19,19,11,44,Graduation,32,Franchise,Complex
9820,138.73,NJ City,W,2,23,4,76,High School,21,Franchise,Complex
7744,289.97,Boston,W,5,20,2,64,High School,26,Franchise,Complex
4475,195,NJ City,E,2,13,15,76,High School,31,Owner,Standalone
2362,205,NJ City,S,1,19,19,77,High School,32,Patnership,Complex
9339,379.16,NJ City,E,11,18,9,33,Graduation,24,Owner,Standalone
2426,177.56,NJ City,S,12,18,6,44,High School,33,Patnership,Complex
3272,462.16,NJ City,W,18,14,7,73,High School,28,Franchise,Complex
830,187.76,NJ City,S,7,18,18,64,High School,19,Patnership,Complex
4087,114.28,NJ City,E,25,11,4,85,High School,29,Owner,Standalone
6237,112.49,Boston,N,13,12,1,46,Graduation,23,Patnership,Standalone
5297,20.72,Boston,N,3,15,1,34,No education,24,Patnership,Standalone
9415,250.51,NJ City,E,10,8,19,52,Graduation,33,Owner,Standalone
9727,41.59,NJ City,N,12,24,4,62,High School,21,Owner,Standalone
869,91.64,NJ City,N,17,8,2,33,No education,30,Patnership,Standalone
4281,216.44,NJ City,N,23,23,13,77,High School,33,Patnership,Standalone
7598,475.77,Boston,S,23,19,22,68,Graduation,20,Patnership,Complex
6825,106.79,Boston,N,3,15,2,81,Graduation,24,Patnership,Standalone
3093,302.96,NJ City,N,12,8,18,44,High School,31,Patnership,Standalone
6088,177.75,Boston,W,14,9,12,63,Graduation,34,Franchise,Complex
111,137.49,NJ City,E,13,13,1,71,Graduation,32,Owner,Standalone
2438,118.44,NJ City,S,14,8,1,80,No education,26,Patnership,Complex
6043,484.66,Boston,E,25,19,4,83,High School,24,Owner,Standalone
7720,346.22,Boston,W,18,21,25,25,Graduation,18,Franchise,Complex
3680,184.23,NJ City,W,17,21,10,75,High School,34,Franchise,Complex
8152,14.63,NJ City,N,4,22,3,42,High School,32,Franchise,Complex
3503,461.85,NJ City,E,14,22,21,29,High School,26,Owner,Standalone
7344,45.1,Boston,N,17,10,4,54,Graduation,23,Franchise,Complex
4166,409.26,NJ City,S,11,14,24,39,High School,33,Patnership,Complex
8042,274.83,Boston,S,18,19,19,73,Graduation,23,Patnership,Complex
7504,148.43,Boston,W,12,22,10,76,Graduation,28,Franchise,Complex
668,306.34,NJ City,W,16,11,11,79,High School,30,Franchise,Complex
1807,430.72,NJ City,E,20,15,25,40,High School,34,Owner,Standalone
1792,47.02,NJ City,N,4,18,1,42,High School,23,Franchise,Complex
1786,314.04,NJ City,S,11,19,25,69,High School,23,Patnership,Complex
9810,258.69,NJ City,S,24,20,14,70,Graduation,34,Patnership,Complex
5878,252.44,Boston,S,10,10,21,37,Graduation,21,Patnership,Complex
8193,140.03,NJ City,N,1,13,14,46,Graduation,35,Patnership,Standalone
8908,334.13,NJ City,W,21,21,4,28,High School,18,Franchise,Complex
2893,483.73,NJ City,N,11,17,25,57,High School,19,Patnership,Standalone
4988,451.13,Boston,W,5,14,25,49,High School,28,Franchise,Complex
8855,306.54,NJ City,E,23,15,18,76,Graduation,30,Owner,Standalone
8924,260.15,NJ City,W,11,22,2,80,No education,20,Franchise,Complex
825,384.36,NJ City,N,20,15,25,30,High School,35,Patnership,Standalone
6743,174.44,Boston,E,18,16,25,44,Graduation,21,Owner,Standalone
716,213.27,NJ City,W,16,23,2,50,No education,35,Franchise,Complex
2323,316.68,NJ City,E,9,9,14,67,High School,33,Owner,Standalone
8678,264.24,NJ City,S,22,16,7,83,Graduation,30,Patnership,Complex
3861,230.68,NJ City,N,22,23,15,47,High School,28,Patnership,Standalone
3919,2.01,NJ City,E,15,23,4,43,High School,23,Owner,Standalone
1161,428.88,NJ City,N,8,19,8,74,High School,18,Patnership,Standalone
2045,115.55,NJ City,N,24,15,3,77,No education,33,Patnership,Standalone
9244,317.24,NJ City,W,2,15,21,25,Graduation,28,Franchise,Complex
8649,19.11,NJ City,N,5,13,4,34,Graduation,21,Patnership,Standalone
296,349.53,NJ City,W,7,15,5,72,No education,19,Franchise,Complex
6487,140.59,Boston,E,6,14,24,27,Graduation,20,Owner,Standalone
959,168.48,NJ City,E,15,13,22,77,High School,25,Owner,Standalone
6524,410.51,Boston,W,10,21,13,73,Graduation,27,Franchise,Complex
1808,46.03,NJ City,N,24,22,4,48,No education,33,Franchise,Complex
4507,104,NJ City,E,2,11,15,64,High School,24,Owner,Standalone
6531,281.32,Boston,E,19,9,14,54,Graduation,21,Owner,Standalone
8099,192.31,NJ City,E,22,16,11,62,Graduation,26,Owner,Standalone
7178,141.49,Boston,S,1,13,20,43,Graduation,19,Patnership,Complex
9753,442.38,NJ City,N,11,20,23,54,Graduation,33,Patnership,Standalone
2320,80.92,NJ City,W,19,17,4,81,High School,22,Franchise,Complex
2888,132,NJ City,W,3,21,10,60,High School,32,Franchise,Complex
7672,37.83,Boston,N,6,17,2,44,High School,26,Franchise,Complex
4285,472.12,NJ City,N,21,21,19,66,High School,26,Patnership,Standalone
7011,355.62,Boston,E,20,12,4,33,Graduation,35,Owner,Standalone
7961,319.14,Boston,N,4,11,19,48,Graduation,31,Patnership,Standalone
9963,190.08,NJ City,E,14,9,24,77,Graduation,19,Owner,Standalone
8944,2.72,NJ City,W,22,22,4,41,High School,19,Franchise,Complex
8858,84.32,NJ City,S,19,17,2,66,No education,34,Patnership,Complex
4466,145.6,NJ City,S,2,18,3,63,No education,31,Patnership,Complex
7527,423.89,Boston,E,24,24,19,66,Graduation,25,Owner,Standalone
7618,145.03,Boston,S,1,10,1,72,High School,19,Patnership,Complex
481,426.56,NJ City,N,14,17,18,84,High School,26,Patnership,Standalone
5075,320.89,Boston,E,16,22,19,28,Graduation,32,Owner,Standalone
1666,245.25,NJ City,S,10,18,3,61,High School,28,Patnership,Complex
7701,214.97,Boston,N,17,22,13,50,Graduation,19,Patnership,Standalone
1215,255,NJ City,E,3,19,7,28,High School,31,Owner,Standalone
1536,212.57,NJ City,W,16,24,12,45,High School,24,Franchise,Complex
6616,333.81,Boston,W,20,12,18,68,Graduation,25,Franchise,Complex
8920,139.59,NJ City,W,17,16,5,81,High School,19,Franchise,Complex
4321,196.77,NJ City,N,22,10,7,54,High School,24,Patnership,Standalone
5108,72.75,Boston,N,13,9,2,66,No education,26,Franchise,Complex
5701,318.12,Boston,N,8,24,10,68,Graduation,32,Patnership,Standalone
44,427.42,Boston,W,25,23,18,53,High School,29,Franchise,Complex
9632,11.14,NJ City,N,10,8,1,77,No education,33,Franchise,Complex
3231,443.56,NJ City,E,12,13,13,25,High School,25,Owner,Standalone
3403,183.38,NJ City,E,23,20,22,78,High School,18,Owner,Standalone
8272,232.89,NJ City,W,25,10,23,27,Graduation,32,Franchise,Complex
518,355.98,NJ City,S,22,10,22,29,High School,28,Patnership,Complex
5317,254.13,Boston,N,13,20,14,27,Graduation,35,Patnership,Standalone
8259,256.35,NJ City,E,5,11,2,48,Graduation,26,Owner,Standalone
4197,145.65,NJ City,N,4,11,10,61,High School,22,Patnership,Standalone
3117,143.46,NJ City,N,18,19,8,82,High School,22,Patnership,Standalone
4429,39.07,NJ City,N,18,9,1,80,High School,31,Patnership,Standalone
6897,374.35,Boston,N,18,15,24,41,Graduation,30,Patnership,Standalone
8248,45.84,NJ City,N,19,9,2,77,High School,18,Franchise,Complex
4600,493.01,NJ City,W,15,18,5,25,High School,18,Franchise,Complex
4262,290.93,NJ City,S,24,21,25,41,High School,24,Patnership,Complex
7929,309.99,Boston,N,12,16,14,69,Graduation,19,Patnership,Standalone
1311,93.61,NJ City,E,24,19,4,39,Graduation,24,Owner,Standalone
2022,260.41,NJ City,S,24,14,20,53,High School,31,Patnership,Complex
756,397.24,NJ City,W,5,16,17,56,High School,35,Franchise,Complex
9428,435.18,NJ City,W,24,9,2,67,No education,26,Franchise,Complex
7491,108.15,Boston,E,8,14,3,41,Graduation,25,Owner,Standalone
4327,94.86,NJ City,E,20,13,4,35,High School,34,Owner,Standalone
7085,23.05,Boston,N,22,21,2,57,No education,24,Patnership,Standalone
6010,104.46,Boston,S,14,18,3,70,High School,19,Patnership,Complex
7244,408.48,Boston,W,8,16,13,82,Graduation,23,Franchise,Complex
8769,489.76,NJ City,N,15,13,19,77,Graduation,28,Patnership,Standalone
1774,423.41,NJ City,S,5,21,15,31,High School,25,Patnership,Complex
2720,358.39,NJ City,W,12,15,18,76,High School,33,Franchise,Complex
7462,50.26,Boston,S,6,12,4,34,High School,21,Patnership,Complex
6668,248.99,Boston,W,1,8,24,71,Graduation,27,Franchise,Complex
1379,128.75,NJ City,E,22,10,10,81,High School,31,Owner,Standalone
5520,75.76,Boston,W,5,11,4,73,Graduation,27,Franchise,Complex
7372,73,Boston,N,21,21,2,74,High School,34,Franchise,Complex
3444,290,NJ City,W,3,12,13,42,High School,31,Franchise,Complex
5711,358.61,Boston,E,22,11,11,76,Graduation,24,Owner,Standalone
7236,326.08,Boston,W,22,13,9,74,Graduation,31,Franchise,Complex
56,6.86,NY City,N,3,23,3,74,No education,19,Franchise,Complex
9288,458.08,NJ City,W,16,23,24,49,Graduation,20,Franchise,Complex
5721,336.95,Boston,N,25,18,10,43,Graduation,23,Patnership,Standalone
6178,270.67,Boston,S,25,10,4,52,High School,28,Patnership,Complex
387,6.44,NJ City,N,18,12,2,74,Graduation,32,Owner,Standalone
7695,423.15,Boston,E,1,19,7,28,Graduation,27,Owner,Standalone
6624,249.44,Boston,W,9,23,12,27,Graduation,25,Franchise,Complex
3053,10.83,NJ City,N,8,22,4,85,No education,28,Patnership,Standalone
3204,105.72,NJ City,W,16,24,1,31,Graduation,18,Franchise,Complex
4188,235.48,NJ City,W,11,18,25,29,High School,20,Franchise,Complex
9682,98.56,NJ City,S,3,13,3,53,High School,20,Patnership,Complex
5973,145.85,Boston,N,21,8,18,66,Graduation,34,Patnership,Standalone
6060,455.26,Boston,W,5,12,19,81,Graduation,24,Franchise,Complex
7296,43.62,Boston,N,13,24,1,80,Graduation,32,Franchise,Complex
7079,404.85,Boston,E,14,8,2,30,No education,30,Owner,Standalone
8496,246.53,NJ City,W,14,21,19,27,Graduation,34,Franchise,Complex
6518,47.67,Boston,S,2,9,3,39,No education,35,Patnership,Complex
8154,211.08,NJ City,S,4,11,8,40,Graduation,35,Patnership,Complex
6782,195.07,Boston,S,4,16,2,51,No education,18,Patnership,Complex
9797,494.44,NJ City,N,23,22,4,48,No education,31,Patnership,Standalone
9767,190.1,NJ City,E,14,8,16,75,Graduation,22,Owner,Standalone
7838,276.29,Boston,S,6,14,19,51,Graduation,19,Patnership,Complex
6164,179.08,Boston,W,9,13,2,43,No education,20,Franchise,Complex
9161,63.12,NJ City,N,10,23,2,69,No education,33,Patnership,Standalone
9373,382.85,NJ City,N,11,16,3,41,High School,34,Patnership,Standalone
536,483.22,NJ City,W,25,23,8,37,High School,26,Franchise,Complex
7298,478.34,Boston,S,23,8,5,27,No education,25,Patnership,Complex
2482,440.74,NJ City,S,21,23,24,76,High School,30,Patnership,Complex
9949,16.72,NJ City,N,21,23,2,58,High School,31,Patnership,Standalone
4435,395.97,NJ City,E,25,15,8,79,High School,29,Owner,Standalone
9413,333.69,NJ City,N,22,14,9,78,Graduation,24,Patnership,Standalone
5614,369.89,Boston,S,11,9,6,36,Graduation,27,Patnership,Complex
3670,90.73,NJ City,S,25,12,1,43,High School,33,Patnership,Complex
301,25.01,NJ City,N,2,9,1,69,High School,19,Patnership,Standalone
5134,96.7,Boston,S,16,17,4,63,High School,29,Patnership,Complex
9093,497.19,NJ City,N,22,18,22,29,Graduation,24,Patnership,Standalone
194,87.41,NJ City,S,22,19,1,73,No education,35,Patnership,Complex
3651,163.15,NJ City,E,25,10,16,70,High School,23,Owner,Standalone
4362,462.38,NJ City,S,25,12,5,85,Graduation,22,Patnership,Complex
8132,351.73,NJ City,W,3,14,19,60,Graduation,35,Franchise,Complex
2756,121.09,NJ City,W,7,14,1,44,No education,18,Franchise,Complex
1361,192.43,NJ City,N,6,18,3,65,No education,21,Patnership,Standalone
2730,263.22,NJ City,S,7,15,16,84,High School,32,Patnership,Complex
9512,153.07,NJ City,W,21,17,13,66,Graduation,30,Franchise,Complex
8022,473.53,Boston,S,24,11,18,77,Graduation,23,Patnership,Complex
5100,231.24,Boston,W,17,12,19,49,Graduation,29,Franchise,Complex
7996,168.69,Boston,W,22,9,4,49,High School,24,Franchise,Complex
4769,119.35,NJ City,N,7,24,2,60,No education,29,Patnership,Standalone
5813,119.99,Boston,N,20,19,4,70,No education,34,Patnership,Standalone
1649,468.52,NJ City,N,18,18,17,29,High School,20,Patnership,Standalone
6942,467.93,Boston,S,4,16,11,64,Graduation,34,Patnership,Complex
305,50.11,NJ City,N,23,18,2,75,No education,34,Patnership,Standalone
6856,401.9,Boston,W,15,10,1,68,High School,35,Franchise,Complex
4754,268,NJ City,S,2,11,16,39,High School,29,Patnership,Complex
489,32.77,NJ City,N,25,14,1,50,Graduation,23,Patnership,Standalone
7906,173.99,Boston,S,11,11,10,63,Graduation,29,Patnership,Complex
6104,251.91,Boston,W,22,14,10,70,Graduation,33,Franchise,Complex
978,30.42,NJ City,N,14,9,3,47,Graduation,19,Patnership,Complex
6874,314.35,Boston,S,14,12,23,66,Graduation,26,Patnership,Complex
3302,51.28,NJ City,S,16,10,1,74,No education,27,Patnership,Complex
9996,283.37,NJ City,W,11,8,4,70,Graduation,29,Franchise,Complex
4448,261.08,NJ City,W,25,19,11,36,High School,22,Franchise,Complex
8527,361.45,NJ City,E,3,10,20,43,Graduation,19,Owner,Standalone
6998,312.85,Boston,S,18,17,8,35,Graduation,18,Patnership,Complex
861,125.8,NJ City,N,23,9,2,56,Graduation,33,Patnership,Standalone
2658,162.38,NJ City,S,25,18,17,51,High School,26,Patnership,Complex
9338,70.02,NJ City,N,18,19,1,52,No education,22,Patnership,Complex
4002,110,NJ City,S,3,22,9,61,High School,30,Patnership,Complex
6641,472.49,Boston,N,15,21,6,44,Graduation,25,Patnership,Standalone
7857,351.56,Boston,N,2,13,6,36,Graduation,18,Patnership,Standalone
3986,90,NJ City,S,18,13,1,37,No education,27,Patnership,Complex
989,280.97,NJ City,N,5,11,15,29,High School,24,Patnership,Standalone
7437,252.99,Boston,N,11,11,13,66,Graduation,32,Patnership,Standalone
4112,134.1,NJ City,W,9,11,14,26,High School,35,Franchise,Complex
552,461.11,NJ City,W,14,21,22,30,High School,31,Franchise,Complex
3663,358.85,NJ City,E,23,21,5,40,Graduation,25,Owner,Standalone
1492,306,NJ City,W,2,15,15,36,High School,20,Franchise,Complex
3458,256.03,NJ City,S,24,8,11,74,High School,27,Patnership,Complex
1859,290.81,NJ City,E,14,15,21,65,High School,21,Owner,Standalone
5132,283.83,Boston,W,11,18,25,26,Graduation,32,Franchise,Complex
6360,177.49,Boston,W,21,17,10,41,Graduation,22,Franchise,Complex
2352,453.64,NJ City,W,13,23,1,36,Graduation,23,Franchise,Complex
5617,181.62,Boston,N,5,17,11,66,Graduation,28,Patnership,Standalone
1512,157,NJ City,W,3,20,20,59,High School,30,Franchise,Complex
1424,360.73,NJ City,W,25,21,5,33,No education,34,Franchise,Complex
6559,129.15,Boston,E,12,15,18,38,Graduation,20,Owner,Standalone
641,251.45,NJ City,N,24,17,3,35,No education,23,Patnership,Standalone
618,227.62,NJ City,S,24,14,11,54,High School,19,Patnership,Complex
6486,408.39,Boston,S,6,13,11,74,Graduation,20,Patnership,Complex
5556,175.4,Boston,W,6,23,1,59,Graduation,35,Franchise,Complex
8604,22.22,NJ City,N,1,10,3,84,Graduation,18,Franchise,Complex
7692,228.02,Boston,W,6,24,4,73,Graduation,25,Franchise,Complex
1444,152.98,NJ City,W,24,8,16,60,High School,19,Franchise,Complex
6458,155.29,Boston,S,25,9,11,63,Graduation,35,Patnership,Complex
236,2.12,NJ City,W,20,14,2,59,No education,29,Franchise,Complex
7257,320.77,Boston,N,23,16,15,59,Graduation,34,Patnership,Standalone
8982,245.72,NJ City,S,8,11,19,53,Graduation,21,Patnership,Complex
7336,35.19,Boston,N,12,21,4,71,High School,35,Franchise,Complex
1746,249.13,NJ City,S,9,13,19,41,High School,28,Patnership,Complex
569,181.29,NJ City,N,12,21,13,28,High School,33,Patnership,Standalone
1594,446.11,NJ City,S,2,15,4,50,High School,30,Patnership,Complex
5454,44.44,Boston,N,14,9,1,36,Graduation,18,Patnership,Complex
8883,415.49,NJ City,E,24,23,16,76,Graduation,33,Owner,Standalone
3815,297.18,NJ City,E,9,15,22,55,High School,30,Owner,Standalone
9722,101.53,NJ City,S,12,10,1,63,No education,33,Patnership,Complex
7173,229.68,Boston,N,18,13,10,55,Graduation,28,Patnership,Standalone
25,332.8,Boston,N,18,11,18,75,High School,33,Patnership,Standalone
6505,115.36,Boston,N,1,19,3,68,High School,27,Patnership,Standalone
5636,298.8,Boston,W,5,24,6,82,Graduation,34,Franchise,Complex
3954,186.52,NJ City,S,15,11,18,39,High School,27,Patnership,Complex
4253,302,NJ City,N,3,14,13,67,High School,21,Patnership,Standalone
5800,328.63,Boston,W,12,19,7,58,Graduation,28,Franchise,Complex
6859,477.52,Boston,E,18,13,23,63,Graduation,30,Owner,Standalone
8458,171.03,NJ City,S,4,11,17,33,Graduation,28,Patnership,Complex
2558,79.93,NJ City,S,1,22,4,72,No education,27,Patnership,Complex
2686,474.3,NJ City,S,14,9,20,42,High School,26,Patnership,Complex
4213,291.18,NJ City,N,7,22,21,68,High School,30,Patnership,Standalone
6174,287.69,Boston,S,15,8,13,79,Graduation,28,Patnership,Complex
5559,256.9,Boston,E,25,19,22,55,Graduation,23,Owner,Standalone
2264,58.41,NJ City,W,8,23,3,58,No education,32,Franchise,Complex
5853,119.25,Boston,N,19,21,4,56,Graduation,32,Patnership,Standalone
7395,58.45,Boston,E,16,16,3,55,Graduation,32,Owner,Standalone
573,98.05,NJ City,N,4,10,3,47,Graduation,22,Patnership,Standalone
2751,276.58,NJ City,E,24,18,13,57,High School,28,Owner,Standalone
986,92.09,NJ City,S,1,18,1,33,No education,22,Patnership,Complex
4073,474.92,NJ City,N,15,8,13,35,High School,28,Patnership,Standalone
1062,196,NJ City,S,1,18,23,26,High School,21,Patnership,Complex
4360,272.98,NJ City,W,11,15,25,81,High School,29,Franchise,Complex
5914,19.98,Boston,N,10,14,1,30,High School,18,Patnership,Complex
526,92.82,NJ City,S,15,19,4,75,High School,29,Patnership,Complex
1812,357.48,NJ City,W,14,21,12,41,High School,26,Franchise,Complex
5137,44.49,Boston,N,23,23,2,75,High School,25,Patnership,Standalone
1198,104,NJ City,S,1,9,12,44,High School,24,Patnership,Complex
1760,302.06,NJ City,W,7,21,24,42,High School,33,Franchise,Complex
8268,341.25,NJ City,W,4,8,2,65,Graduation,19,Franchise,Complex
5122,55.81,Boston,S,3,19,1,69,High School,27,Patnership,Complex
936,2.09,NJ City,W,13,17,3,80,Graduation,29,Franchise,Complex
9059,188.26,NJ City,E,9,17,16,78,Graduation,24,Owner,Standalone
9622,118.79,NJ City,S,17,23,1,49,High School,32,Patnership,Complex
6289,446.69,Boston,N,18,13,20,36,Graduation,26,Patnership,Standalone
2925,223.29,NJ City,N,18,21,17,28,High School,35,Patnership,Standalone
5350,257.14,Boston,S,11,17,2,41,High School,30,Patnership,Complex
8135,342.63,NJ City,E,17,22,16,37,Graduation,19,Owner,Standalone
1957,442.33,NJ City,N,17,19,23,76,High School,18,Patnership,Standalone
5548,88.3,Boston,W,18,18,4,68,High School,29,Franchise,Complex
4038,379.81,NJ City,S,15,11,22,62,High School,20,Patnership,Complex
5512,170.67,Boston,W,24,23,3,85,High School,23,Franchise,Complex
7804,121.31,Boston,W,21,12,2,70,High School,30,Franchise,Complex
1246,409.05,NJ City,S,23,22,13,58,High School,34,Patnership,Complex
2824,41.56,NJ City,N,3,18,1,35,High School,25,Franchise,Complex
2310,481.8,NJ City,S,5,23,16,85,High School,20,Patnership,Complex
6058,227.66,Boston,S,12,16,2,70,High School,24,Patnership,Complex
3036,401.65,NJ City,W,15,20,25,65,High School,31,Franchise,Complex
903,134.49,NJ City,E,7,12,12,33,High School,34,Owner,Standalone
3083,50.25,NJ City,E,1,17,3,54,No education,25,Owner,Standalone
8388,7.22,NJ City,N,9,17,2,36,Graduation,25,Franchise,Complex
1791,233.44,NJ City,E,14,22,20,55,High School,31,Owner,Standalone
246,165.52,NJ City,S,25,17,18,61,High School,32,Patnership,Complex
2562,21.57,NJ City,N,24,21,3,45,Graduation,29,Patnership,Complex
3749,482.62,NJ City,N,6,14,19,85,High School,30,Patnership,Standalone
2350,142.02,NJ City,S,12,17,5,77,High School,31,Patnership,Complex
9671,409.42,NJ City,E,10,21,6,44,Graduation,22,Owner,Standalone
7705,283.28,Boston,N,20,13,10,33,Graduation,28,Patnership,Standalone
9013,43.15,NJ City,N,4,19,1,34,High School,19,Patnership,Standalone
2596,451.9,NJ City,W,25,17,18,25,High School,19,Franchise,Complex
4093,464.59,NJ City,N,18,18,6,79,High School,29,Patnership,Standalone
6130,224.98,Boston,S,23,23,21,27,Graduation,22,Patnership,Complex
5480,152.77,Boston,W,25,16,18,74,Graduation,23,Franchise,Complex
9714,303.31,NJ City,S,8,14,6,27,Graduation,33,Patnership,Complex
3210,71.39,NJ City,N,14,13,2,51,Graduation,29,Patnership,Complex
9596,386.69,NJ City,W,14,9,24,82,Graduation,24,Franchise,Complex
6947,290.9,Boston,E,9,12,2,52,No education,24,Owner,Standalone
8216,46.33,NJ City,N,24,10,2,42,No education,30,Franchise,Complex
3001,411.79,NJ City,N,9,18,6,57,High School,22,Patnership,Standalone
7828,105.77,Boston,W,11,19,3,37,High School,19,Franchise,Complex
2766,201.37,NJ City,S,16,24,12,65,High School,29,Patnership,Complex
8066,448.53,NJ City,S,7,9,9,30,Graduation,23,Patnership,Complex
7774,281.3,Boston,S,3,18,13,67,Graduation,27,Patnership,Complex
2850,242,NJ City,S,3,16,18,58,High School,30,Patnership,Complex
8026,350.37,Boston,S,2,22,1,45,High School,23,Patnership,Complex
6739,120.48,Boston,E,17,19,3,85,High School,34,Owner,Standalone
5922,176.61,Boston,S,20,24,5,69,Graduation,19,Patnership,Complex
138,227.21,NJ City,S,22,18,24,27,High School,28,Patnership,Complex
863,75.4,NJ City,E,1,15,1,61,No education,30,Owner,Standalone
7212,236.72,Boston,W,5,14,10,27,Graduation,19,Franchise,Complex
3937,75.64,NJ City,N,5,12,3,31,High School,25,Patnership,Standalone
7191,271.73,Boston,E,3,21,1,78,Graduation,24,Owner,Standalone
3521,78.57,NJ City,N,7,10,4,35,No education,18,Patnership,Standalone
1665,9.49,NJ City,N,14,11,3,84,Graduation,23,Patnership,Standalone
3801,190.16,NJ City,N,8,8,13,41,High School,33,Patnership,Standalone
9114,51.17,NJ City,S,9,17,2,35,Graduation,25,Patnership,Complex
1043,30.09,NJ City,N,12,21,2,66,No education,22,Owner,Standalone
9095,39.95,NJ City,N,4,18,3,69,No education,27,Owner,Standalone
5886,107.11,Boston,S,5,11,4,60,Graduation,34,Patnership,Complex
8910,11.73,NJ City,N,7,20,2,70,Graduation,26,Patnership,Complex
5360,341.95,Boston,W,13,14,3,70,No education,25,Franchise,Complex
4406,147.92,NJ City,S,17,8,2,59,No education,18,Patnership,Complex
2203,90.08,NJ City,E,1,13,2,43,High School,26,Owner,Standalone
6100,148.44,Boston,W,7,10,22,43,Graduation,28,Franchise,Complex
5901,269.07,Boston,N,16,11,2,35,Graduation,21,Patnership,Standalone
7385,237.61,Boston,N,23,13,24,52,Graduation,26,Patnership,Standalone
6013,13.21,Boston,N,12,18,4,62,High School,31,Patnership,Standalone
1252,338.79,NJ City,W,18,20,20,26,High School,19,Franchise,Complex
6910,236.96,Boston,S,13,11,21,50,Graduation,33,Patnership,Complex
3091,369.61,NJ City,E,24,21,16,75,High School,29,Owner,Standalone
1887,489.08,NJ City,E,9,9,9,30,High School,27,Owner,Standalone
6662,87.28,Boston,S,20,22,3,64,No education,31,Patnership,Complex
8414,85.27,NJ City,S,20,13,1,26,No education,23,Patnership,Complex
923,19.46,NJ City,N,22,22,3,30,No education,19,Owner,Standalone
9575,97.86,NJ City,E,15,22,3,73,No education,21,Owner,Standalone
5963,301.68,Boston,E,17,21,5,41,No education,24,Owner,Standalone
9037,425.28,NJ City,N,7,20,25,74,Graduation,33,Patnership,Standalone
846,303.53,NJ City,S,6,18,18,84,High School,25,Patnership,Complex
5104,112.01,Boston,W,16,22,2,82,High School,25,Franchise,Complex
6593,302.44,Boston,N,11,8,4,80,No education,29,Patnership,Standalone
7591,240.53,Boston,E,13,12,23,41,Graduation,34,Owner,Standalone
3812,182.33,NJ City,W,6,21,9,45,High School,28,Franchise,Complex
7017,68.24,Boston,N,21,24,4,80,Graduation,19,Patnership,Standalone
3917,365.72,NJ City,N,11,17,10,64,High School,26,Patnership,Standalone
5778,254.3,Boston,S,9,22,21,40,Graduation,30,Patnership,Complex
7075,232.33,Boston,E,6,9,6,54,Graduation,30,Owner,Standalone
1759,87.73,NJ City,E,6,12,3,75,High School,28,Owner,Standalone
2051,198.49,NJ City,E,15,21,23,64,High School,35,Owner,Standalone
4910,91.91,NJ City,S,8,12,1,31,No education,34,Patnership,Complex
4343,126.03,NJ City,E,3,19,4,71,No education,29,Owner,Standalone
7111,56.96,Boston,E,8,20,3,53,High School,21,Owner,Standalone
1028,258.07,NJ City,W,20,19,8,57,High School,22,Franchise,Complex
3915,357.89,NJ City,E,20,24,22,71,High School,18,Owner,Standalone
7310,163.08,Boston,S,20,12,15,55,Graduation,31,Patnership,Complex
3904,36.79,NJ City,N,15,23,4,46,High School,30,Franchise,Complex
8593,260.45,NJ City,N,10,14,3,41,High School,30,Patnership,Standalone
6798,35.78,Boston,N,9,16,1,76,Graduation,22,Patnership,Complex
8614,48,NJ City,S,25,17,3,65,High School,33,Patnership,Complex
15,52.26,Boston,E,11,10,3,83,Graduation,19,Owner,Standalone
3325,45.78,NJ City,N,8,21,1,81,High School,23,Patnership,Standalone
9913,461.86,NJ City,N,25,22,8,61,Graduation,25,Patnership,Standalone
315,230,NJ City,E,3,8,19,30,High School,30,Owner,Standalone
6160,145.8,Boston,W,23,8,10,54,Graduation,32,Franchise,Complex
354,259.66,NJ City,S,8,8,1,32,Graduation,27,Patnership,Complex
2242,198.63,NJ City,S,7,8,20,78,High School,23,Patnership,Complex
2987,387.73,NJ City,E,12,8,19,51,High School,31,Owner,Standalone
1527,437.2,NJ City,E,21,8,17,49,High School,24,Owner,Standalone
6495,233.81,Boston,E,14,17,3,60,Graduation,33,Owner,Standalone
5780,269.52,Boston,W,7,9,5,73,No education,33,Franchise,Complex
1988,442.49,NJ City,W,10,9,12,72,High School,27,Franchise,Complex
1341,149.65,NJ City,N,6,14,18,46,High School,18,Patnership,Standalone
1583,444.51,NJ City,E,14,19,20,80,High School,32,Owner,Standalone
291,314.07,NJ City,E,19,8,1,75,Graduation,31,Owner,Standalone
897,90.21,NJ City,N,16,17,1,59,Graduation,27,Patnership,Standalone
8359,38.63,NJ City,N,3,22,3,32,High School,26,Owner,Standalone
1966,134.46,NJ City,S,18,24,4,61,High School,24,Patnership,Complex
4525,53.55,NJ City,N,11,18,4,40,High School,34,Patnership,Standalone
1226,382.84,NJ City,S,17,17,2,74,No education,30,Patnership,Complex
8684,407.41,NJ City,W,16,19,19,76,Graduation,21,Franchise,Complex
7358,51.89,Boston,S,15,18,4,73,No education,22,Patnership,Complex
862,233.29,NJ City,S,21,16,3,73,High School,22,Patnership,Complex
8176,242.52,NJ City,W,14,24,25,39,Graduation,22,Franchise,Complex
5806,196.54,Boston,S,20,12,14,63,Graduation,28,Patnership,Complex
4544,23.17,NJ City,N,8,11,2,78,No education,33,Franchise,Complex
412,352.07,NJ City,W,12,14,3,38,High School,29,Franchise,Complex
6527,8.05,Boston,N,12,14,2,47,No education,30,Owner,Standalone
3384,39.89,NJ City,N,1,13,4,71,Graduation,27,Franchise,Complex
7366,155.59,Boston,S,14,18,15,27,Graduation,18,Patnership,Complex
2258,77.29,NJ City,S,13,20,4,80,No education,28,Patnership,Complex
7757,12.19,Boston,N,4,11,4,72,No education,19,Patnership,Standalone
3351,249.55,NJ City,E,14,11,8,37,High School,19,Owner,Standalone
6666,150.07,Boston,S,1,22,22,67,Graduation,25,Patnership,Complex
5364,225.31,Boston,W,14,11,11,41,Graduation,32,Franchise,Complex
2127,99.3,NJ City,E,6,21,3,78,Graduation,32,Owner,Standalone
2417,471.28,NJ City,N,8,11,6,79,High School,18,Patnership,Standalone
8090,189.99,NJ City,S,1,13,12,48,Graduation,22,Patnership,Complex
9904,449.54,NJ City,W,24,19,23,26,Graduation,19,Franchise,Complex
168,235.37,NJ City,W,9,17,23,75,High School,26,Franchise,Complex
7005,495.37,Boston,N,6,16,11,79,Graduation,32,Patnership,Standalone
298,368.61,NJ City,S,22,17,18,70,High School,26,Patnership,Complex
7509,149.87,Boston,N,5,21,21,52,Graduation,31,Patnership,Standalone
8511,253.38,NJ City,E,10,8,11,65,Graduation,25,Owner,Standalone
1911,273.17,NJ City,E,3,8,5,53,Graduation,25,Owner,Standalone
61,437.29,Boston,N,21,20,5,58,High School,34,Patnership,Standalone
6907,352.42,Boston,E,16,16,1,56,High School,19,Owner,Standalone
8880,215.39,NJ City,W,1,10,8,51,Graduation,28,Franchise,Complex
2564,497.75,NJ City,W,23,21,12,82,High School,33,Franchise,Complex
7342,18.67,Boston,N,18,12,1,56,High School,32,Patnership,Complex
4564,112.98,NJ City,W,13,11,4,32,High School,29,Franchise,Complex
1458,53.14,NJ City,S,21,22,4,62,Graduation,19,Patnership,Complex
2771,468.89,NJ City,E,14,12,21,39,High School,33,Owner,Standalone
2314,463.1,NJ City,S,17,19,5,65,High School,18,Patnership,Complex
555,197.87,NJ City,E,10,9,14,63,High School,35,Owner,Standalone
8803,401.43,NJ City,E,10,12,17,41,Graduation,21,Owner,Standalone
5176,445.21,Boston,W,18,12,6,43,Graduation,32,Franchise,Complex
3506,453.03,NJ City,S,25,19,16,65,High School,23,Patnership,Complex
9372,315.93,NJ City,W,24,23,7,27,Graduation,22,Franchise,Complex
4960,413.09,Boston,W,8,19,3,33,High School,30,Franchise,Complex
7335,281.15,Boston,E,21,20,14,64,Graduation,22,Owner,Standalone
7754,304.52,Boston,S,2,16,5,51,No education,18,Patnership,Complex
5313,202.39,Boston,N,11,21,11,75,Graduation,22,Patnership,Standalone
1855,6.4,NJ City,N,14,13,1,54,High School,24,Owner,Standalone
9734,126.83,NJ City,S,16,14,3,33,No education,18,Patnership,Complex
2243,203.57,NJ City,E,7,12,2,60,No education,18,Owner,Standalone
8878,265.99,NJ City,S,16,14,25,50,Graduation,19,Patnership,Complex
6750,386.42,Boston,S,14,20,10,72,Graduation,24,Patnership,Complex
63,168.98,Boston,E,13,24,8,78,High School,19,Owner,Standalone
726,382.83,NJ City,S,10,12,15,60,High School,18,Patnership,Complex
7796,425.31,Boston,W,6,8,7,50,Graduation,26,Franchise,Complex
1571,481.59,NJ City,E,14,9,5,29,No education,27,Owner,Standalone
8037,304.45,Boston,N,22,21,10,66,Graduation,26,Patnership,Standalone
7026,322.95,Boston,S,7,20,6,68,Graduation,35,Patnership,Complex
7630,362.1,Boston,S,24,17,9,67,Graduation,30,Patnership,Complex
2030,246.34,NJ City,S,19,20,16,29,High School,34,Patnership,Complex
788,391.94,NJ City,W,3,11,5,63,No education,26,Franchise,Complex
3763,313.03,NJ City,E,14,22,21,30,High School,27,Owner,Standalone
5146,106.29,Boston,S,10,21,4,33,High School,34,Patnership,Complex
1470,17.15,NJ City,N,4,9,2,50,Graduation,19,Patnership,Complex
4032,204,NJ City,W,3,24,6,81,High School,31,Franchise,Complex
7864,480.47,Boston,W,11,10,19,30,Graduation,18,Franchise,Complex
1722,162.59,NJ City,S,24,14,11,71,High School,21,Patnership,Complex
2554,469.11,NJ City,S,23,18,4,65,High School,20,Patnership,Complex
276,189.09,NJ City,W,3,24,4,34,Graduation,22,Franchise,Complex
2349,338.28,NJ City,N,13,16,18,83,High School,20,Patnership,Standalone
4989,211.96,Boston,N,21,11,4,81,Graduation,35,Patnership,Standalone
5965,396.53,Boston,N,1,17,10,70,Graduation,18,Patnership,Standalone
5398,56.17,Boston,S,11,14,1,38,High School,19,Patnership,Complex
5871,407.6,Boston,E,12,12,24,66,Graduation,33,Owner,Standalone
2692,167.32,NJ City,W,20,11,24,52,High School,32,Franchise,Complex
7479,345.59,Boston,E,23,24,24,32,Graduation,27,Owner,Standalone
9934,399.84,NJ City,S,24,15,2,49,High School,28,Patnership,Complex
8421,245.15,NJ City,N,7,24,6,47,Graduation,27,Patnership,Standalone
6730,389.98,Boston,S,18,15,9,84,Graduation,22,Patnership,Complex
992,354.49,NJ City,W,23,9,6,48,High School,20,Franchise,Complex
2429,215.17,NJ City,N,9,18,20,31,High School,23,Patnership,Standalone
9053,273.02,NJ City,N,18,23,20,72,Graduation,22,Patnership,Standalone
6571,278.2,Boston,E,2,23,8,57,Graduation,32,Owner,Standalone
3455,58.24,NJ City,E,2,15,4,52,No education,31,Owner,Standalone
2541,6.67,NJ City,N,9,20,3,66,Graduation,23,Patnership,Standalone
6990,80.34,Boston,S,17,13,1,28,Graduation,34,Patnership,Complex
6729,123.72,Boston,N,19,16,3,40,Graduation,22,Patnership,Standalone
5917,49.26,Boston,N,3,11,2,42,High School,30,Patnership,Standalone
9251,429.53,NJ City,E,16,12,20,44,Graduation,32,Owner,Standalone
1692,229.96,NJ City,W,7,11,8,41,High School,30,Franchise,Complex
3178,383.21,NJ City,S,17,20,1,27,High School,26,Patnership,Complex
6845,412.11,Boston,N,24,8,24,78,Graduation,28,Patnership,Standalone
6155,388.37,Boston,E,6,9,19,68,Graduation,28,Owner,Standalone
6368,133.37,Boston,W,1,19,24,25,Graduation,30,Franchise,Complex
7845,322.88,Boston,N,7,22,4,84,Graduation,19,Patnership,Standalone
8601,100.78,NJ City,N,19,14,2,26,Graduation,18,Patnership,Standalone
4273,350.77,NJ City,N,19,13,20,60,High School,33,Patnership,Standalone
7632,494.21,Boston,W,1,8,20,74,Graduation,35,Franchise,Complex
9293,296.04,NJ City,N,19,17,2,83,No education,29,Patnership,Standalone
7134,296.06,Boston,S,7,13,17,53,Graduation,29,Patnership,Complex
6655,275.09,Boston,E,1,18,20,71,Graduation,19,Owner,Standalone
4122,434.87,NJ City,S,16,8,14,74,High School,25,Patnership,Complex
3595,212.86,NJ City,E,11,21,6,51,High School,31,Owner,Standalone
3512,305.22,NJ City,W,20,21,21,47,High School,23,Franchise,Complex
1082,431.94,NJ City,S,24,15,15,43,High School,21,Patnership,Complex
5703,408.95,Boston,E,7,19,17,43,Graduation,24,Owner,Standalone
2994,458.12,NJ City,S,5,13,21,37,High School,25,Patnership,Complex
9411,16.13,NJ City,N,21,21,2,53,Graduation,33,Owner,Standalone
6944,388.78,Boston,W,18,17,14,39,Graduation,27,Franchise,Complex
1065,438.49,NJ City,N,11,22,12,64,High School,26,Patnership,Standalone
5162,364.55,Boston,S,20,18,25,33,Graduation,34,Patnership,Complex
9060,321.77,NJ City,W,2,13,9,63,Graduation,18,Franchise,Complex
2552,230.17,NJ City,W,12,13,19,79,High School,28,Franchise,Complex
1145,112.33,NJ City,N,7,12,4,78,No education,21,Patnership,Standalone
4209,250.64,NJ City,N,10,8,2,53,Graduation,21,Patnership,Standalone
349,53.01,NJ City,N,16,14,4,60,High School,35,Patnership,Standalone
4780,431.56,NJ City,W,19,8,4,47,High School,26,Franchise,Complex
3691,442.21,NJ City,E,23,16,21,33,High School,29,Owner,Standalone
2768,83.81,NJ City,W,14,17,4,26,No education,31,Franchise,Complex
6138,186.51,Boston,S,14,24,13,49,Graduation,30,Patnership,Complex
4917,211.96,NJ City,N,13,19,11,80,High School,31,Patnership,Standalone
5811,14.69,Boston,N,19,22,1,76,Graduation,24,Owner,Standalone
9314,193.69,NJ City,S,9,10,5,83,No education,31,Patnership,Complex
8520,349.85,NJ City,W,3,19,12,32,Graduation,34,Franchise,Complex
5109,263.55,Boston,N,16,16,24,35,Graduation,27,Patnership,Standalone
6261,351.67,Boston,N,10,13,2,81,Graduation,33,Patnership,Standalone
7681,49.01,Boston,N,25,14,4,27,High School,30,Patnership,Standalone
1297,481.73,NJ City,N,6,20,5,61,High School,32,Patnership,Standalone
8073,69.82,NJ City,N,2,23,3,74,Graduation,24,Patnership,Standalone
2020,227,NJ City,W,2,20,23,82,High School,28,Franchise,Complex
4239,412.1,NJ City,E,16,18,25,65,High School,35,Owner,Standalone
3337,287.84,NJ City,N,12,13,18,81,High School,34,Patnership,Standalone
7105,69.48,Boston,N,13,8,2,43,High School,35,Patnership,Standalone
6697,62.5,Boston,N,2,9,3,47,High School,31,Patnership,Standalone
9902,78.74,NJ City,S,12,17,1,34,No education,29,Patnership,Complex
3494,102.57,NJ City,S,14,11,4,57,No education,35,Patnership,Complex
6144,489.94,Boston,W,11,16,25,50,Graduation,27,Franchise,Complex
62,291.91,Boston,S,16,15,23,47,High School,29,Patnership,Complex
966,452.72,NJ City,S,5,8,2,66,Graduation,22,Patnership,Complex
8547,252.73,NJ City,E,23,11,19,42,Graduation,29,Owner,Standalone
9205,231.15,NJ City,N,4,16,15,51,Graduation,20,Patnership,Standalone
9589,227.81,NJ City,N,17,20,15,60,Graduation,25,Patnership,Standalone
8227,310.67,NJ City,E,13,13,14,51,Graduation,26,Owner,Standalone
9914,136.42,NJ City,S,21,16,25,81,Graduation,33,Patnership,Complex
6282,83.92,Boston,S,2,17,4,73,Graduation,18,Patnership,Complex
4982,257.65,Boston,S,24,16,18,57,High School,22,Patnership,Complex
2396,137.79,NJ City,W,6,19,25,57,High School,27,Franchise,Complex
4259,295.27,NJ City,E,8,21,11,50,High School,24,Owner,Standalone
2193,131.09,NJ City,N,13,21,2,38,Graduation,22,Patnership,Standalone
3987,488.54,NJ City,E,21,18,19,43,High School,34,Owner,Standalone
9408,234.65,NJ City,W,12,15,17,60,Graduation,31,Franchise,Complex
4412,385.45,NJ City,W,19,22,3,57,No education,20,Franchise,Complex
2248,22.27,NJ City,N,25,11,1,82,High School,22,Franchise,Complex
5584,310.51,Boston,W,9,16,21,28,Graduation,23,Franchise,Complex
2803,224.97,NJ City,E,17,18,25,48,High School,32,Owner,Standalone
492,257.71,NJ City,W,13,16,13,66,High School,24,Franchise,Complex
2377,449.43,NJ City,N,12,24,3,84,High School,27,Patnership,Standalone
6356,164.56,Boston,W,21,9,21,35,Graduation,30,Franchise,Complex
7063,182.52,Boston,E,22,8,23,46,Graduation,23,Owner,Standalone
1346,222,NJ City,S,2,21,16,25,High School,31,Patnership,Complex
5992,488.12,Boston,W,23,15,20,56,Graduation,28,Franchise,Complex
2713,82.28,NJ City,N,11,18,3,35,High School,19,Patnership,Standalone
4115,433.76,NJ City,E,13,9,17,62,High School,26,Owner,Standalone
949,70.92,NJ City,N,11,16,3,53,High School,23,Patnership,Standalone
4789,58.31,NJ City,N,7,10,4,52,High School,31,Patnership,Standalone
2457,132,NJ City,N,2,16,19,43,High School,25,Patnership,Standalone
8499,414.85,NJ City,E,16,23,11,53,Graduation,23,Owner,Standalone
8972,126.09,NJ City,W,22,13,2,75,No education,27,Franchise,Complex
4078,48.64,NJ City,S,7,14,4,70,High School,25,Patnership,Complex
2575,498.53,NJ City,E,18,17,15,56,High School,32,Owner,Standalone
3560,151.56,NJ City,W,13,14,20,28,High School,18,Franchise,Complex
3585,403.61,NJ City,N,21,13,6,82,High School,31,Patnership,Standalone
5269,419.69,Boston,N,5,24,18,34,Graduation,18,Patnership,Standalone
6649,413.99,Boston,N,12,17,11,37,Graduation,22,Patnership,Standalone
10,142.72,NJ City,S,24,15,25,31,High School,22,Patnership,Complex
2047,2.73,NJ City,E,9,15,1,62,High School,29,Owner,Standalone
8816,28.38,NJ City,N,1,11,4,59,No education,24,Franchise,Complex
9830,365.77,NJ City,S,13,20,17,69,Graduation,30,Patnership,Complex
7820,456.83,Boston,W,6,10,20,43,Graduation,33,Franchise,Complex
5691,499.75,Boston,E,16,20,7,39,Graduation,33,Owner,Standalone
4864,188.72,NJ City,W,7,15,7,26,High School,28,Franchise,Complex
5557,318.07,Boston,N,16,19,15,48,Graduation,28,Patnership,Standalone
9760,137.54,NJ City,W,2,17,25,63,Graduation,21,Franchise,Complex
5288,241.54,Boston,W,25,21,8,74,Graduation,29,Franchise,Complex
2668,304.28,NJ City,W,6,19,25,57,High School,29,Franchise,Complex
9154,150.57,NJ City,S,2,18,7,46,Graduation,20,Patnership,Complex
8426,73.82,NJ City,N,21,8,4,29,No education,19,Patnership,Complex
2011,239.86,NJ City,E,4,23,1,53,High School,33,Owner,Standalone
5875,372.76,Boston,E,13,12,15,71,Graduation,29,Owner,Standalone
682,299.64,NJ City,S,16,12,5,81,High School,33,Patnership,Complex
6921,168.61,Boston,N,13,9,18,36,Graduation,29,Patnership,Standalone
6492,160.06,Boston,W,24,19,19,76,Graduation,31,Franchise,Complex
4886,75.7,NJ City,S,21,9,2,69,No education,22,Patnership,Complex
9269,166.3,NJ City,N,8,13,2,79,No education,22,Patnership,Standalone
7620,286.89,Boston,W,3,11,23,30,Graduation,34,Franchise,Complex
2400,368.36,NJ City,W,16,17,1,35,Graduation,35,Franchise,Complex
9611,470.31,NJ City,E,10,10,7,73,Graduation,21,Owner,Standalone
9438,214.04,NJ City,S,22,16,20,83,Graduation,28,Patnership,Complex
8305,308.02,NJ City,N,24,16,14,72,Graduation,26,Patnership,Standalone
275,162.77,NJ City,E,5,13,25,41,High School,21,Owner,Standalone
8045,81.45,NJ City,N,9,9,3,61,No education,30,Patnership,Standalone
1624,411.69,NJ City,W,7,21,20,46,High School,23,Franchise,Complex
7619,443,Boston,E,11,20,16,66,Graduation,22,Owner,Standalone
1447,168.64,NJ City,E,25,19,1,53,High School,19,Owner,Standalone
5266,376.56,Boston,S,10,21,16,49,Graduation,21,Patnership,Complex
2111,269,NJ City,E,2,13,15,76,High School,20,Owner,Standalone
3905,189.53,NJ City,N,19,17,25,80,High School,31,Patnership,Standalone
3704,125.04,NJ City,W,2,14,1,65,No education,26,Franchise,Complex
3523,30.33,NJ City,N,23,24,3,70,High School,24,Owner,Standalone
6758,436.98,Boston,S,15,8,4,40,No education,25,Patnership,Complex
8334,463.92,NJ City,S,14,18,10,50,Graduation,20,Patnership,Complex
2516,476.91,NJ City,W,13,13,22,62,High School,19,Franchise,Complex
9653,143.45,NJ City,N,9,19,13,77,Graduation,31,Patnership,Standalone
4863,449.67,NJ City,E,8,8,10,68,High School,31,Owner,Standalone
4026,44.76,NJ City,N,17,10,2,71,Graduation,25,Patnership,Complex
3586,495.79,NJ City,S,22,14,14,49,High School,30,Patnership,Complex
2826,61.08,NJ City,N,11,8,2,44,Graduation,33,Patnership,Complex
2451,155.23,NJ City,E,10,20,21,50,High School,31,Owner,Standalone
6284,99.88,Boston,W,13,18,4,62,No education,28,Franchise,Complex
5933,70.94,Boston,N,8,20,4,33,No education,24,Patnership,Standalone
5259,310.52,Boston,E,13,23,1,53,Graduation,18,Owner,Standalone
850,73.33,NJ City,N,24,13,3,70,High School,28,Patnership,Complex
4498,305.51,NJ City,S,18,12,13,78,High School,26,Patnership,Complex
5616,281.08,Boston,W,22,17,15,53,Graduation,22,Franchise,Complex
6,418.18,Maine,S,9,16,6,49,High School,18,Patnership,Complex
6151,308.25,Boston,E,10,21,20,76,Graduation,18,Owner,Standalone
4100,368.71,NJ City,W,5,24,3,82,No education,30,Franchise,Complex
5664,54.89,Boston,W,10,14,1,56,Graduation,35,Franchise,Complex
7568,339.02,Boston,W,10,24,16,44,Graduation,24,Franchise,Complex
7460,175.84,Boston,W,3,11,25,25,Graduation,26,Franchise,Complex
7251,22.22,Boston,N,20,21,3,42,Graduation,20,Owner,Standalone
5408,488.91,Boston,W,10,19,15,49,Graduation,21,Franchise,Complex
8085,146.29,NJ City,N,23,12,23,36,Graduation,20,Patnership,Standalone
5255,169.76,Boston,E,15,15,11,32,Graduation,33,Owner,Standalone
938,154.98,NJ City,S,24,22,22,69,High School,22,Patnership,Complex
5136,10.85,Boston,N,3,9,2,68,Graduation,31,Franchise,Complex
9385,114.73,NJ City,N,11,16,3,44,High School,18,Patnership,Standalone
3797,189,NJ City,N,2,18,8,70,High School,28,Patnership,Standalone
5490,325.8,Boston,S,4,20,18,73,Graduation,18,Patnership,Complex
93,114.35,NJ City,N,22,15,3,78,Graduation,20,Patnership,Standalone
5154,264.46,Boston,S,3,18,12,29,Graduation,24,Patnership,Complex
3436,190.75,NJ City,W,4,24,11,80,High School,27,Franchise,Complex
990,182.98,NJ City,S,10,20,20,59,High School,18,Patnership,Complex
3626,212.7,NJ City,S,23,17,7,30,High School,32,Patnership,Complex
5583,215.24,Boston,E,4,20,1,53,Graduation,33,Owner,Standalone
2399,79.92,NJ City,E,8,18,2,25,No education,20,Owner,Standalone
4723,276.17,NJ City,E,18,11,18,50,High School,33,Owner,Standalone
9612,296.32,NJ City,W,7,21,13,38,Graduation,21,Franchise,Complex
4287,73.74,NJ City,N,9,20,1,39,Graduation,26,Owner,Standalone
3373,109.07,NJ City,N,4,13,3,80,High School,20,Patnership,Standalone
2739,321.02,NJ City,E,21,14,25,25,High School,25,Owner,Standalone
2498,305.01,NJ City,S,7,8,7,73,High School,23,Patnership,Complex
910,251,NJ City,S,2,11,17,83,High School,22,Patnership,Complex
5264,407.19,Boston,W,18,13,24,53,Graduation,26,Franchise,Complex
1126,305.04,NJ City,S,5,17,14,71,High School,18,Patnership,Complex
8818,120.67,NJ City,S,23,18,1,81,High School,29,Patnership,Complex
4607,364.59,NJ City,E,25,13,7,73,High School,31,Owner,Standalone
5507,476.68,Boston,E,23,8,16,69,Graduation,21,Owner,Standalone
2590,62.04,NJ City,N,22,9,4,83,High School,28,Patnership,Complex
3901,115.46,NJ City,N,22,11,3,55,High School,32,Patnership,Standalone
5443,181.1,Boston,E,12,11,14,26,Graduation,32,Owner,Standalone
4411,319.06,NJ City,E,10,21,9,76,High School,31,Owner,Standalone
8839,415.16,NJ City,E,18,21,14,51,Graduation,19,Owner,Standalone
2597,381.4,NJ City,N,6,19,25,71,High School,22,Patnership,Standalone
6111,307.18,Boston,E,9,15,9,79,Graduation,27,Owner,Standalone
7606,1.7,Boston,N,5,14,2,40,High School,33,Patnership,Complex
4354,121.98,NJ City,S,7,14,2,47,High School,31,Patnership,Complex
4976,251.73,Boston,W,13,20,7,62,High School,33,Franchise,Complex
7666,14.62,Boston,N,24,22,3,58,High School,34,Patnership,Complex
273,477.72,NJ City,N,5,14,2,52,Graduation,31,Patnership,Standalone
3973,340.69,NJ City,N,9,8,10,31,High School,32,Patnership,Standalone
534,335.13,NJ City,S,13,21,24,49,High School,34,Patnership,Complex
9867,341.86,NJ City,E,20,11,15,79,Graduation,35,Owner,Standalone
7711,8.97,Boston,N,18,13,2,74,High School,24,Owner,Standalone
2616,87.73,NJ City,W,13,13,1,27,Graduation,33,Franchise,Complex
3412,282.13,NJ City,W,14,10,19,25,High School,32,Franchise,Complex
9116,265.76,NJ City,W,11,19,10,40,Graduation,21,Franchise,Complex
1819,216.88,NJ City,E,7,16,12,52,High School,28,Owner,Standalone
9391,108.57,NJ City,E,14,19,3,58,High School,25,Owner,Standalone
8095,16.53,NJ City,N,12,22,1,69,High School,21,Owner,Standalone
1001,434.03,NJ City,N,14,19,25,81,High School,18,Patnership,Standalone
3656,240.89,NJ City,W,14,11,22,67,High School,30,Franchise,Complex
5181,210.75,Boston,N,8,18,12,36,Graduation,32,Patnership,Standalone
759,214.5,NJ City,E,19,18,17,59,High School,33,Owner,Standalone
9971,375.27,NJ City,E,19,17,10,62,Graduation,18,Owner,Standalone
132,275.59,NJ City,W,14,19,4,30,Graduation,32,Franchise,Complex
1924,8.58,NJ City,N,7,21,1,81,High School,33,Franchise,Complex
4858,330.01,NJ City,S,13,16,9,26,High School,33,Patnership,Complex
9234,395.92,NJ City,S,5,23,8,43,Graduation,27,Patnership,Complex
4883,394.98,NJ City,E,21,12,9,42,High School,35,Owner,Standalone
4292,292,NJ City,W,2,19,18,65,High School,19,Franchise,Complex
1163,286.52,NJ City,E,18,20,1,79,No education,21,Owner,Standalone
8270,368.23,NJ City,S,12,15,16,38,Graduation,21,Patnership,Complex
6299,31.52,Boston,N,1,20,4,77,No education,26,Owner,Standalone
7354,7.84,Boston,N,16,21,2,43,High School,29,Patnership,Complex
6568,122.34,Boston,W,8,23,1,65,High School,30,Franchise,Complex
5668,75.07,Boston,W,14,24,4,70,High School,26,Franchise,Complex
1876,220.37,NJ City,W,24,17,11,28,High School,26,Franchise,Complex
2725,425.76,NJ City,N,14,8,11,33,High School,35,Patnership,Standalone
2133,142.09,NJ City,N,4,11,24,70,High School,30,Patnership,Standalone
4821,344.38,NJ City,N,5,15,15,41,High School,23,Patnership,Standalone
1933,99.93,NJ City,N,5,18,1,33,High School,28,Patnership,Standalone
8783,326.35,NJ City,E,24,8,25,43,Graduation,21,Owner,Standalone
2877,444.74,NJ City,N,4,9,19,64,High School,32,Patnership,Standalone
1467,339.93,NJ City,E,21,23,20,59,High School,30,Owner,Standalone
5047,172.61,Boston,E,20,11,15,79,Graduation,20,Owner,Standalone
6298,376.35,Boston,S,10,15,11,42,Graduation,23,Patnership,Complex
8411,82.43,NJ City,E,11,12,3,45,No education,25,Owner,Standalone
8787,298.9,NJ City,E,20,17,16,45,Graduation,31,Owner,Standalone
8302,360.01,NJ City,S,5,12,25,78,Graduation,31,Patnership,Complex
9663,176.19,NJ City,E,6,9,7,58,Graduation,35,Owner,Standalone
8102,132.86,NJ City,S,6,22,22,83,Graduation,33,Patnership,Complex
9106,441.81,NJ City,S,13,13,3,38,High School,21,Patnership,Complex
7024,33.36,Boston,N,2,19,1,32,High School,29,Franchise,Complex
4163,195.09,NJ City,E,24,16,8,45,High School,34,Owner,Standalone
2440,245.42,NJ City,W,8,17,16,73,High School,23,Franchise,Complex
9315,47.56,NJ City,E,12,10,4,76,Graduation,28,Owner,Standalone
4834,258.3,NJ City,S,5,13,23,63,High School,35,Patnership,Complex
1255,466.34,NJ City,E,6,11,23,74,High School,34,Owner,Standalone
322,432.33,NJ City,S,24,11,4,53,High School,32,Patnership,Complex
717,482.63,NJ City,N,23,12,14,43,High School,28,Patnership,Standalone
8362,167.14,NJ City,S,7,18,2,30,High School,24,Patnership,Complex
3040,152.3,NJ City,W,16,15,17,28,High School,34,Franchise,Complex
639,73.63,NJ City,N,4,8,2,34,Graduation,20,Owner,Standalone
784,383.07,NJ City,W,14,14,22,57,High School,30,Franchise,Complex
1149,448.05,NJ City,N,6,15,19,28,High School,29,Patnership,Standalone
5727,340.73,Boston,E,15,11,12,84,Graduation,24,Owner,Standalone
7621,30.67,Boston,N,2,20,4,43,High School,32,Patnership,Standalone
5158,223.32,Boston,S,15,16,1,49,High School,33,Patnership,Complex
5472,430.28,Boston,W,16,22,2,71,Graduation,31,Franchise,Complex
619,240.47,NJ City,E,1,22,2,60,High School,34,Owner,Standalone
4906,299.06,NJ City,S,4,23,12,63,High School,28,Patnership,Complex
4221,140.94,NJ City,N,19,21,7,65,High School,34,Patnership,Standalone
6934,426.4,Boston,S,6,21,8,36,Graduation,24,Patnership,Complex
7403,31.92,Boston,N,8,22,3,72,No education,24,Owner,Standalone
9807,328.65,NJ City,E,7,19,1,58,Graduation,22,Owner,Standalone
4682,270.39,NJ City,S,19,23,17,69,High School,25,Patnership,Complex
9200,135.65,NJ City,W,14,19,21,37,Graduation,33,Franchise,Complex
5690,379.5,Boston,S,13,20,22,68,Graduation,30,Patnership,Complex
9197,245.79,NJ City,N,15,16,13,32,Graduation,25,Patnership,Standalone
5564,451.98,Boston,W,4,11,20,65,Graduation,19,Franchise,Complex
9716,47.32,NJ City,W,21,23,4,59,No education,21,Franchise,Complex
2765,182.66,NJ City,N,23,18,20,85,High School,35,Patnership,Standalone
2012,108.64,NJ City,W,15,15,1,46,No education,23,Franchise,Complex
2594,244.21,NJ City,S,23,13,10,26,High School,18,Patnership,Complex
3297,387.09,NJ City,N,20,19,3,66,Graduation,35,Patnership,Standalone
5232,439.48,Boston,W,18,17,21,70,Graduation,24,Franchise,Complex
4893,6.05,NJ City,N,1,18,1,30,Graduation,21,Patnership,Standalone
5389,233.03,Boston,N,4,20,17,79,Graduation,29,Patnership,Standalone
3048,341.63,NJ City,W,25,9,12,74,High School,28,Franchise,Complex
6826,25.34,Boston,N,5,14,4,81,High School,25,Patnership,Complex
2239,401.84,NJ City,E,23,16,14,67,High School,25,Owner,Standalone
5041,362.58,Boston,N,1,13,4,56,High School,26,Patnership,Standalone
6807,371.67,Boston,E,15,20,4,56,Graduation,25,Owner,Standalone
8114,82.43,NJ City,S,8,10,2,46,No education,21,Patnership,Complex
3172,432.46,NJ City,W,3,22,3,56,High School,29,Franchise,Complex
9167,454.5,NJ City,E,25,15,19,46,Graduation,27,Owner,Standalone
2715,36.01,NJ City,N,4,18,4,52,Graduation,32,Owner,Standalone
3852,333.16,NJ City,W,17,24,8,46,High School,35,Franchise,Complex
2794,320.31,NJ City,S,15,11,24,59,High School,19,Patnership,Complex
1730,192.17,NJ City,S,17,24,25,42,High School,30,Patnership,Complex
3978,94.17,NJ City,S,17,9,4,83,Graduation,29,Patnership,Complex
4514,252.59,NJ City,S,18,16,2,84,No education,24,Patnership,Complex
8188,136.77,NJ City,W,8,17,11,81,Graduation,34,Franchise,Complex
4437,5.51,NJ City,N,6,22,3,34,Graduation,32,Patnership,Standalone
3921,345.14,NJ City,N,25,24,24,78,High School,23,Patnership,Standalone
443,346.23,NJ City,E,7,16,24,27,High School,34,Owner,Standalone
3318,186.73,NJ City,S,17,19,19,32,High School,34,Patnership,Complex
1273,317.92,NJ City,N,14,14,8,30,High School,31,Patnership,Standalone
5465,17.42,Boston,N,11,13,4,40,No education,32,Patnership,Standalone
2724,206.86,NJ City,W,16,15,11,83,High School,29,Franchise,Complex
5741,422.79,Boston,N,8,19,8,48,Graduation,20,Patnership,Standalone
3261,156,NJ City,N,3,14,20,75,High School,34,Patnership,Standalone
9144,385.68,NJ City,W,4,9,10,37,Graduation,29,Franchise,Complex
130,135.99,NJ City,S,3,24,3,31,High School,28,Patnership,Complex
6938,255.47,Boston,S,16,18,23,46,Graduation,34,Patnership,Complex
4962,485.51,Boston,S,19,16,14,75,High School,24,Patnership,Complex
6348,11.42,Boston,N,1,23,3,45,Graduation,19,Franchise,Complex
6797,271.73,Boston,N,10,19,6,56,Graduation,27,Patnership,Standalone
3718,161.55,NJ City,S,15,21,2,83,High School,21,Patnership,Complex
4245,270.07,NJ City,N,22,17,14,75,High School,23,Patnership,Standalone
5706,67.11,Boston,N,1,9,4,30,Graduation,19,Patnership,Complex
1040,141.46,NJ City,W,20,12,9,48,High School,29,Franchise,Complex
4308,148.73,NJ City,W,13,8,25,80,High School,26,Franchise,Complex
7516,208.72,Boston,W,22,9,25,73,Graduation,20,Franchise,Complex
9833,489.17,NJ City,N,9,16,18,79,Graduation,32,Patnership,Standalone
1081,176.96,NJ City,N,19,21,5,35,High School,19,Patnership,Standalone
6070,155.07,Boston,S,4,20,18,25,Graduation,28,Patnership,Complex
7010,153.34,Boston,S,12,17,9,25,Graduation,29,Patnership,Complex
2734,52.83,NJ City,S,7,18,1,32,High School,25,Patnership,Complex
9982,444.61,NJ City,S,25,8,1,46,High School,32,Patnership,Complex
5028,350.42,Boston,W,1,10,9,68,Graduation,29,Franchise,Complex
3568,489.44,NJ City,W,14,24,20,29,High School,33,Franchise,Complex
4409,36.41,NJ City,N,24,19,4,70,No education,25,Patnership,Standalone
1509,304.84,NJ City,N,14,16,5,67,Graduation,23,Patnership,Standalone
655,353.57,NJ City,E,8,22,4,48,High School,18,Owner,Standalone
5120,332.52,Boston,W,23,8,17,76,Graduation,33,Franchise,Complex
1059,188.69,NJ City,E,2,9,3,50,Graduation,21,Owner,Standalone
5239,69.77,Boston,N,23,11,4,61,High School,23,Owner,Standalone
1117,57.84,NJ City,N,10,17,2,37,High School,24,Patnership,Standalone
3152,150.8,NJ City,W,24,14,23,72,High School,19,Franchise,Complex
9199,198.82,NJ City,E,9,18,14,48,Graduation,34,Owner,Standalone
2067,30.6,NJ City,N,3,18,4,79,Graduation,20,Owner,Standalone
2072,494.76,NJ City,W,8,11,8,28,High School,34,Franchise,Complex
1234,27.31,NJ City,N,3,23,2,31,High School,28,Patnership,Complex
2834,106.98,NJ City,S,13,17,3,43,No education,24,Patnership,Complex
5884,385.25,Boston,W,21,10,13,80,Graduation,24,Franchise,Complex
937,182.28,NJ City,N,20,9,2,58,High School,23,Patnership,Standalone
6327,135.15,Boston,E,3,9,8,79,Graduation,23,Owner,Standalone
3967,241.42,NJ City,E,18,19,12,37,High School,19,Owner,Standalone
5622,464.91,Boston,S,22,13,3,46,Graduation,30,Patnership,Complex
8563,90.98,NJ City,E,20,22,1,79,High School,30,Owner,Standalone
1954,155.72,NJ City,S,15,15,14,42,High School,20,Patnership,Complex
8204,222.04,NJ City,W,16,20,21,54,Graduation,25,Franchise,Complex
2167,164,NJ City,E,3,12,9,38,High School,21,Owner,Standalone
4543,84.28,NJ City,E,16,14,2,38,High School,35,Owner,Standalone
4687,406.1,NJ City,E,15,24,9,49,High School,26,Owner,Standalone
9150,194.15,NJ City,S,19,10,4,44,Graduation,20,Patnership,Complex
597,482.37,NJ City,N,25,11,8,61,High School,22,Patnership,Standalone
928,135.92,NJ City,W,16,17,2,73,High School,33,Franchise,Complex
9474,346.19,NJ City,S,6,17,15,79,Graduation,27,Patnership,Complex
8662,322.78,NJ City,S,13,15,9,38,Graduation,28,Patnership,Complex
9101,396.88,NJ City,N,15,22,8,67,Graduation,32,Patnership,Standalone
3032,113.77,NJ City,W,3,22,1,68,No education,21,Franchise,Complex
2645,288.28,NJ City,N,10,15,21,55,High School,33,Patnership,Standalone
765,346.95,NJ City,N,25,18,13,81,High School,30,Patnership,Standalone
1510,122.17,NJ City,S,7,16,3,26,High School,23,Patnership,Complex
8418,304.24,NJ City,S,22,11,21,84,Graduation,22,Patnership,Complex
3703,120.97,NJ City,E,6,13,4,26,High School,31,Owner,Standalone
37,162,NY City,N,1,22,11,27,High School,27,Patnership,Standalone
2485,422.42,NJ City,N,8,19,6,40,High School,22,Patnership,Standalone
5641,92.68,Boston,N,5,13,1,56,High School,28,Patnership,Standalone
2563,381.69,NJ City,E,21,20,11,46,High School,28,Owner,Standalone
4092,497.56,NJ City,W,18,21,13,78,High School,34,Franchise,Complex
1369,88.3,NJ City,N,21,8,4,54,High School,28,Patnership,Standalone
7110,11.07,Boston,N,15,19,4,80,Graduation,35,Patnership,Complex
8828,486.54,NJ City,W,15,24,4,76,No education,35,Franchise,Complex
9236,375.36,NJ City,W,12,12,14,81,Graduation,33,Franchise,Complex
4750,349.76,NJ City,S,18,9,25,62,High School,18,Patnership,Complex
4007,95.75,NJ City,E,18,16,3,28,No education,29,Owner,Standalone
4419,39.78,NJ City,N,9,9,2,43,Graduation,34,Owner,Standalone
2251,446.57,NJ City,E,8,21,16,65,High School,31,Owner,Standalone
1799,408.33,NJ City,E,9,18,2,54,No education,23,Owner,Standalone
4295,225.85,NJ City,E,17,21,18,64,High School,19,Owner,Standalone
5093,182.44,Boston,N,16,9,12,77,Graduation,24,Patnership,Standalone
6228,123.16,Boston,W,20,19,1,58,Graduation,29,Franchise,Complex
5513,473.96,Boston,N,8,22,3,67,No education,27,Patnership,Standalone
3910,438.19,NJ City,S,10,23,18,72,High School,22,Patnership,Complex
4907,70.04,NJ City,N,21,18,4,42,No education,30,Owner,Standalone
8809,391.28,NJ City,N,14,18,15,83,Graduation,30,Patnership,Standalone
4459,319.82,NJ City,E,18,14,3,80,High School,22,Owner,Standalone
7040,266.58,Boston,W,9,9,12,68,Graduation,19,Franchise,Complex
9804,118.96,NJ City,W,9,10,1,35,Graduation,34,Franchise,Complex
180,284.28,NJ City,W,5,16,18,34,High School,31,Franchise,Complex
9763,385.92,NJ City,E,2,21,24,62,Graduation,18,Owner,Standalone
9697,120.39,NJ City,N,12,22,1,26,High School,32,Patnership,Standalone
1451,116.47,NJ City,E,24,12,2,32,No education,29,Owner,Standalone
4472,18.22,NJ City,N,15,10,2,28,No education,20,Franchise,Complex
5043,111.31,Boston,E,3,19,4,60,Graduation,35,Owner,Standalone
5764,41.14,Boston,N,7,20,4,59,High School,32,Franchise,Complex
2224,124.24,NJ City,W,1,23,1,66,High School,24,Franchise,Complex
4342,271.71,NJ City,S,5,12,1,52,High School,35,Patnership,Complex
3060,497.51,NJ City,W,15,8,16,48,High School,32,Franchise,Complex
6444,321.62,Boston,W,11,15,9,45,Graduation,18,Franchise,Complex
8485,438.75,NJ City,N,15,10,15,57,Graduation,33,Patnership,Standalone
1160,191.08,NJ City,W,25,17,20,47,High School,24,Franchise,Complex
7468,497.84,Boston,W,11,22,23,68,Graduation,24,Franchise,Complex
3162,434.33,NJ City,S,5,17,24,32,High School,33,Patnership,Complex
8123,239.27,NJ City,E,5,17,14,29,Graduation,27,Owner,Standalone
9379,197.76,NJ City,E,22,20,14,81,Graduation,27,Owner,Standalone
2788,390.49,NJ City,W,10,23,15,84,High School,28,Franchise,Complex
2147,155,NJ City,E,3,20,12,29,High School,32,Owner,Standalone
4019,225.59,NJ City,E,20,10,3,58,No education,23,Owner,Standalone
944,472.37,NJ City,W,21,15,6,32,High School,30,Franchise,Complex
1990,477.53,NJ City,S,5,13,10,43,High School,32,Patnership,Complex
3674,138.98,NJ City,S,6,21,5,35,No education,20,Patnership,Complex
2028,128.13,NJ City,W,16,8,16,75,High School,30,Franchise,Complex
7651,464.18,Boston,E,14,12,18,60,Graduation,30,Owner,Standalone
4351,201.95,NJ City,E,21,15,1,80,High School,20,Owner,Standalone
3479,37.45,NJ City,N,21,15,3,47,No education,23,Owner,Standalone
7650,63.7,Boston,N,14,21,4,83,Graduation,25,Patnership,Complex
6727,141.32,Boston,E,10,11,17,77,Graduation,23,Owner,Standalone
3183,121.4,NJ City,E,14,19,4,46,Graduation,31,Owner,Standalone
7207,105.49,Boston,E,21,24,3,52,High School,23,Owner,Standalone
9850,371.21,NJ City,S,14,14,9,84,Graduation,34,Patnership,Complex
2665,97.22,NJ City,N,3,11,4,69,High School,35,Patnership,Standalone
4502,27.12,NJ City,N,12,17,1,48,No education,28,Patnership,Complex
468,230.17,NJ City,W,10,11,8,38,High School,23,Franchise,Complex
6931,46.25,Boston,N,22,14,3,70,High School,28,Owner,Standalone
541,448.27,NJ City,N,23,9,12,52,High School,18,Patnership,Standalone
7275,46.62,Boston,N,2,19,2,82,Graduation,27,Owner,Standalone
371,15.85,NJ City,N,2,22,1,66,No education,28,Owner,Standalone
7363,279.79,Boston,E,4,20,9,56,Graduation,30,Owner,Standalone
4679,359.99,NJ City,E,23,11,6,79,High School,33,Owner,Standalone
208,414.39,NJ City,W,13,8,15,65,High School,23,Franchise,Complex
1992,278,NJ City,W,3,8,10,56,High School,21,Franchise,Complex
2967,352.75,NJ City,E,11,23,9,42,High School,18,Owner,Standalone
9388,370.58,NJ City,W,1,22,3,33,High School,23,Franchise,Complex
3590,152.77,NJ City,S,8,18,8,52,High School,29,Patnership,Complex
1559,60.42,NJ City,N,19,23,3,50,No education,23,Owner,Standalone
319,262.09,NJ City,E,13,22,11,72,High School,25,Owner,Standalone
4450,134.34,NJ City,S,14,10,21,58,High School,32,Patnership,Complex
7018,94.48,Boston,S,14,8,1,31,High School,34,Patnership,Complex
6116,195.84,Boston,W,5,12,2,72,No education,34,Franchise,Complex
2644,400.81,NJ City,W,11,10,23,59,High School,26,Franchise,Complex
5124,474.01,Boston,W,14,23,21,52,Graduation,28,Franchise,Complex
2218,36.02,NJ City,N,8,24,3,33,High School,34,Patnership,Complex
1021,237.62,NJ City,N,8,20,3,83,High School,25,Patnership,Standalone
6838,358.22,Boston,S,6,23,19,65,Graduation,31,Patnership,Complex
6965,492.5,Boston,N,21,18,14,76,Graduation,34,Patnership,Standalone
4282,91.05,NJ City,S,6,22,3,33,High School,30,Patnership,Complex
1764,168.53,NJ City,W,23,16,17,67,High School,29,Franchise,Complex
8772,295.55,NJ City,W,1,13,7,31,Graduation,33,Franchise,Complex
3414,404.76,NJ City,S,7,17,1,35,Graduation,25,Patnership,Complex
4582,471.35,NJ City,S,12,21,20,31,High School,22,Patnership,Complex
5762,175.67,Boston,S,23,23,3,72,No education,25,Patnership,Complex
2291,324.87,NJ City,E,23,24,23,34,High School,22,Owner,Standalone
9852,274.84,NJ City,W,24,14,6,30,Graduation,20,Franchise,Complex
7318,352.93,Boston,S,16,10,6,73,Graduation,28,Patnership,Complex
4283,151.31,NJ City,E,10,22,25,39,High School,33,Owner,Standalone
3876,324.53,NJ City,W,13,17,6,38,High School,25,Franchise,Complex
4655,356.2,NJ City,E,4,19,10,36,High School,26,Owner,Standalone
5224,301.78,Boston,W,2,14,5,35,High School,28,Franchise,Complex
4535,375.21,NJ City,E,15,23,20,59,High School,26,Owner,Standalone
9972,466.19,NJ City,W,11,21,4,38,Graduation,23,Franchise,Complex
1371,241.1,NJ City,E,24,18,7,38,High School,34,Owner,Standalone
5017,435.87,Boston,N,14,17,3,60,High School,31,Patnership,Standalone
7973,283.15,Boston,N,10,16,22,63,Graduation,24,Patnership,Standalone
8446,88.21,NJ City,S,23,11,1,76,High School,33,Patnership,Complex
2173,306.99,NJ City,N,15,21,20,62,High School,24,Patnership,Standalone
335,140.19,NJ City,E,12,8,2,83,No education,20,Owner,Standalone
6166,162.83,Boston,S,4,14,11,82,Graduation,21,Patnership,Complex
2806,413,NJ City,S,16,21,19,81,High School,25,Patnership,Complex
3145,213.45,NJ City,N,24,10,6,45,High School,30,Patnership,Standalone
672,283.15,NJ City,W,17,22,9,77,High School,32,Franchise,Complex
2208,432.8,NJ City,W,20,13,24,84,High School,23,Franchise,Complex
1795,240.45,NJ City,E,24,18,17,54,High School,34,Owner,Standalone
1167,303,NJ City,E,3,14,6,26,High School,24,Owner,Standalone
3633,215.28,NJ City,N,12,24,23,25,High School,28,Patnership,Standalone
4539,241.19,NJ City,E,7,13,24,77,High School,19,Owner,Standalone
6539,133.42,Boston,E,1,17,4,41,No education,29,Owner,Standalone
2043,37.84,NJ City,N,2,9,3,38,Graduation,30,Owner,Standalone
8826,489.83,NJ City,S,19,12,21,54,Graduation,19,Patnership,Complex
6508,51.7,Boston,W,24,20,1,58,High School,21,Franchise,Complex
2507,482.68,NJ City,E,25,21,21,28,High School,35,Owner,Standalone
3052,108.92,NJ City,W,6,10,4,84,High School,25,Franchise,Complex
5436,76.06,Boston,W,23,13,3,82,Graduation,31,Franchise,Complex
894,446.73,NJ City,S,25,24,10,77,High School,21,Patnership,Complex
5652,400.73,Boston,W,19,20,22,41,Graduation,30,Franchise,Complex
3803,57.27,NJ City,E,20,11,2,69,No education,18,Owner,Standalone
6358,410.66,Boston,S,19,11,25,25,Graduation,20,Patnership,Complex
8963,10.21,NJ City,N,10,15,4,33,No education,24,Owner,Standalone
2525,172.44,NJ City,N,22,24,2,27,No education,21,Patnership,Standalone
943,54.97,NJ City,E,9,20,3,43,High School,32,Owner,Standalone
7968,88.91,Boston,W,24,23,2,80,Graduation,35,Franchise,Complex
1183,348.92,NJ City,E,25,16,11,30,High School,26,Owner,Standalone
6359,263.44,Boston,E,2,18,5,36,No education,27,Owner,Standalone
8545,447.28,NJ City,N,19,22,17,40,Graduation,28,Patnership,Standalone
52,103.36,NJ City,W,7,22,2,59,High School,23,Franchise,Complex
7822,447.18,Boston,S,24,20,7,84,Graduation,32,Patnership,Complex
8797,116.54,NJ City,N,17,10,3,29,High School,31,Patnership,Standalone
2450,339.62,NJ City,S,9,19,4,81,No education,31,Patnership,Complex
1051,312.07,NJ City,E,15,17,20,57,High School,29,Owner,Standalone
390,427.59,NJ City,S,16,18,3,63,Graduation,27,Patnership,Complex
1923,426.66,NJ City,E,23,16,4,56,Graduation,30,Owner,Standalone
7486,117.75,Boston,S,8,17,4,54,High School,25,Patnership,Complex
7561,283.61,Boston,N,17,12,7,38,Graduation,21,Patnership,Standalone
501,201.71,NJ City,N,21,23,6,27,High School,25,Patnership,Standalone
2945,391.9,NJ City,N,14,8,18,59,High School,34,Patnership,Standalone
2852,84.3,NJ City,W,14,11,1,60,No education,32,Franchise,Complex
7149,71.23,Boston,N,5,8,1,52,Graduation,29,Patnership,Standalone
129,328.94,NJ City,N,15,15,10,84,High School,29,Patnership,Standalone
4892,435.01,NJ City,W,20,13,9,82,High School,35,Franchise,Complex
9526,56.16,NJ City,S,8,22,2,34,High School,30,Patnership,Complex
4361,114.74,NJ City,N,9,21,4,68,No education,24,Patnership,Standalone
1605,11.85,NJ City,N,7,8,2,42,Graduation,30,Patnership,Standalone
8253,18.44,NJ City,N,11,23,2,44,Graduation,29,Patnership,Standalone
3215,12.98,NJ City,N,18,22,4,29,No education,30,Owner,Standalone
7433,411.39,Boston,N,11,14,17,70,Graduation,29,Patnership,Standalone
8792,353.72,NJ City,W,18,13,16,59,Graduation,29,Franchise,Complex
1334,481.22,NJ City,S,5,23,23,85,High School,24,Patnership,Complex
7658,310.09,Boston,S,9,10,14,45,Graduation,24,Patnership,Complex
9382,25.91,NJ City,N,25,18,2,38,High School,25,Patnership,Complex
3889,226.67,NJ City,N,19,21,24,34,High School,27,Patnership,Standalone
1328,106.89,NJ City,W,3,20,1,50,No education,31,Franchise,Complex
1244,473.76,NJ City,W,20,14,17,35,High School,19,Franchise,Complex
3918,215.39,NJ City,S,11,12,17,81,High School,30,Patnership,Complex
9044,468.74,NJ City,W,17,11,2,30,No education,34,Franchise,Complex
5199,422.52,Boston,E,14,18,10,25,Graduation,32,Owner,Standalone
7668,215.22,Boston,W,5,22,13,39,Graduation,34,Franchise,Complex
8725,354.12,NJ City,N,14,15,16,80,Graduation,18,Patnership,Standalone
9173,298.05,NJ City,N,7,23,5,44,No education,25,Patnership,Standalone
6046,164.56,Boston,S,1,16,1,36,High School,29,Patnership,Complex
9261,163,NJ City,N,15,17,23,28,Graduation,28,Patnership,Standalone
1815,195.21,NJ City,E,22,21,1,64,Graduation,32,Owner,Standalone
3636,412.84,NJ City,W,24,22,7,32,High School,24,Franchise,Complex
1953,324.44,NJ City,N,8,18,3,49,Graduation,25,Patnership,Standalone
755,5.82,NJ City,N,4,15,4,56,No education,31,Owner,Standalone
3726,384.97,NJ City,S,22,11,23,50,High School,20,Patnership,Complex
606,30.58,NJ City,N,2,19,4,36,Graduation,24,Patnership,Complex
6537,195.13,Boston,N,8,13,7,60,Graduation,35,Patnership,Standalone
1587,83.41,NJ City,E,10,9,2,31,Graduation,21,Owner,Standalone
7696,476.87,Boston,W,21,17,2,40,High School,35,Franchise,Complex
1850,18.78,NJ City,N,12,19,2,79,No education,28,Patnership,Complex
5674,445.96,Boston,S,2,10,11,59,Graduation,18,Patnership,Complex
1763,395.76,NJ City,E,21,24,25,51,High School,32,Owner,Standalone
8124,88.72,NJ City,W,1,23,2,66,Graduation,33,Franchise,Complex
4426,226.15,NJ City,S,17,11,8,84,High School,34,Patnership,Complex
1683,142.1,NJ City,E,23,14,7,80,High School,22,Owner,Standalone
8224,52.63,NJ City,W,15,23,2,78,High School,33,Franchise,Complex
6438,244.62,Boston,S,1,15,21,45,Graduation,26,Patnership,Complex
8423,111.09,NJ City,E,21,18,2,45,No education,18,Owner,Standalone
9143,290.45,NJ City,E,10,19,20,60,Graduation,21,Owner,Standalone
9585,482.53,NJ City,N,19,21,5,32,Graduation,29,Patnership,Standalone
7439,323.75,Boston,E,25,16,20,84,Graduation,35,Owner,Standalone
7777,314.46,Boston,N,2,15,8,52,Graduation,31,Patnership,Standalone
6553,395.2,Boston,N,9,22,9,82,Graduation,33,Patnership,Standalone
5280,75.29,Boston,W,19,23,4,71,Graduation,33,Franchise,Complex
8118,433.08,NJ City,S,15,10,14,34,Graduation,31,Patnership,Complex
8873,293.08,NJ City,N,23,15,9,30,Graduation,35,Patnership,Standalone
4839,414.94,NJ City,E,7,8,19,83,High School,32,Owner,Standalone
3031,277.91,NJ City,E,16,16,23,81,High School,27,Owner,Standalone
3111,443.88,NJ City,E,24,15,8,58,High School,32,Owner,Standalone
8300,343.76,NJ City,W,14,8,21,43,Graduation,23,Franchise,Complex
7340,275.42,Boston,W,8,20,18,72,Graduation,19,Franchise,Complex
5766,330.34,Boston,S,4,22,24,65,Graduation,34,Patnership,Complex
4330,435.19,NJ City,S,7,8,20,84,High School,22,Patnership,Complex
2250,384.3,NJ City,S,23,20,4,67,Graduation,29,Patnership,Complex
6418,448.41,Boston,S,23,8,20,57,Graduation,26,Patnership,Complex
9864,90.91,NJ City,W,1,21,4,52,Graduation,23,Franchise,Complex
377,115.04,NJ City,N,14,9,4,53,No education,24,Patnership,Standalone
4249,151.4,NJ City,N,8,16,22,38,High School,26,Patnership,Standalone
6398,29.22,Boston,N,22,24,4,75,No education,21,Patnership,Complex
6352,243.3,Boston,W,9,15,6,77,Graduation,31,Franchise,Complex
2425,108.83,NJ City,N,11,20,2,77,High School,26,Patnership,Standalone
421,216.69,NJ City,N,14,17,6,36,High School,30,Patnership,Standalone
6766,236.3,Boston,S,21,12,21,41,Graduation,20,Patnership,Complex
2955,241,NJ City,E,2,14,12,74,High School,23,Owner,Standalone
3510,206,NJ City,S,2,17,20,38,High School,29,Patnership,Complex
1168,232.12,NJ City,W,21,22,25,27,High School,33,Franchise,Complex
1144,309.86,NJ City,W,19,11,18,49,High School,23,Franchise,Complex
5790,460.98,Boston,S,14,16,16,70,Graduation,24,Patnership,Complex
4598,413.75,NJ City,S,17,10,20,56,High School,22,Patnership,Complex
3108,47.75,NJ City,W,11,13,3,58,Graduation,32,Franchise,Complex
1620,57.99,NJ City,W,14,9,3,84,Graduation,22,Franchise,Complex
9896,427.9,NJ City,W,15,12,3,42,No education,20,Franchise,Complex
4375,153.74,NJ City,E,19,12,21,68,High School,24,Owner,Standalone
1994,293,NJ City,S,2,22,20,78,High School,22,Patnership,Complex
2285,82.22,NJ City,N,4,17,3,26,No education,25,Patnership,Standalone
1107,200.2,NJ City,E,4,14,17,29,High School,20,Owner,Standalone
786,267.4,NJ City,S,6,14,10,25,High School,32,Patnership,Complex
6484,3.34,Boston,N,13,24,2,72,High School,27,Franchise,Complex
2042,139.48,NJ City,S,23,14,25,26,High School,18,Patnership,Complex
8840,452.19,NJ City,W,6,21,6,60,Graduation,26,Franchise,Complex
7299,9.69,Boston,N,15,9,4,76,Graduation,29,Owner,Standalone
5315,198.11,Boston,E,17,20,15,60,Graduation,29,Owner,Standalone
6108,301.64,Boston,W,6,12,1,28,Graduation,19,Franchise,Complex
8914,87.31,NJ City,S,10,11,2,62,High School,29,Patnership,Complex
8382,143.67,NJ City,S,14,14,5,72,Graduation,23,Patnership,Complex
744,267.66,NJ City,W,24,16,2,84,Graduation,18,Franchise,Complex
8979,137.95,NJ City,E,16,23,24,58,Graduation,21,Owner,Standalone
8964,204.81,NJ City,W,8,20,2,81,Graduation,26,Franchise,Complex
1479,271.89,NJ City,E,19,20,21,66,High School,18,Owner,Standalone
4871,138.71,NJ City,E,13,10,23,76,High School,25,Owner,Standalone
5254,360.24,Boston,S,11,19,10,79,Graduation,27,Patnership,Complex
8850,423.06,NJ City,S,16,10,6,69,Graduation,29,Patnership,Complex
6602,349.26,Boston,S,16,24,6,79,Graduation,35,Patnership,Complex
3078,256.5,NJ City,S,25,18,5,78,Graduation,30,Patnership,Complex
4254,29.91,NJ City,N,22,12,2,53,Graduation,21,Patnership,Complex
5021,36.54,Boston,N,12,23,2,69,No education,21,Patnership,Standalone
7652,1.3,Boston,N,18,23,1,62,No education,34,Franchise,Complex
2021,37.86,NJ City,N,8,13,4,65,No education,20,Patnership,Standalone
9592,341.14,NJ City,W,23,12,24,78,Graduation,21,Franchise,Complex
3235,173.55,NJ City,E,10,13,3,68,High School,31,Owner,Standalone
6956,394.09,Boston,W,16,24,1,70,No education,18,Franchise,Complex
159,122.92,NJ City,E,25,11,1,78,Graduation,31,Owner,Standalone
3018,397.59,NJ City,S,8,19,18,34,High School,28,Patnership,Complex
8749,41.2,NJ City,N,24,12,2,31,High School,23,Patnership,Standalone
9283,214.53,NJ City,E,5,9,11,65,Graduation,32,Owner,Standalone
1937,275.21,NJ City,N,14,18,19,81,High School,18,Patnership,Standalone
5536,19.81,Boston,N,4,16,1,75,High School,33,Franchise,Complex
2741,300.46,NJ City,N,13,20,16,46,High School,22,Patnership,Standalone
507,59.33,NJ City,E,6,20,4,79,Graduation,33,Owner,Standalone
1591,153.63,NJ City,E,10,11,2,45,High School,23,Owner,Standalone
1185,193.27,NJ City,N,21,14,3,33,Graduation,31,Patnership,Standalone
1784,438.7,NJ City,W,10,17,18,30,High School,35,Franchise,Complex
3336,348.05,NJ City,W,24,15,14,76,High School,32,Franchise,Complex
2866,258.12,NJ City,S,8,8,5,25,High School,34,Patnership,Complex
8881,215.35,NJ City,N,20,21,17,47,Graduation,26,Patnership,Standalone
4748,86.92,NJ City,W,9,20,3,85,No education,23,Franchise,Complex
5312,483.97,Boston,W,10,23,1,78,No education,27,Franchise,Complex
7770,390.76,Boston,S,1,14,10,50,Graduation,35,Patnership,Complex
5970,263.27,Boston,S,13,11,3,83,Graduation,18,Patnership,Complex
3949,229.41,NJ City,N,23,18,7,41,High School,26,Patnership,Standalone
2624,311.17,NJ City,W,6,16,9,26,High School,24,Franchise,Complex
5056,463.72,Boston,W,14,24,23,42,Graduation,26,Franchise,Complex
9513,446.18,NJ City,N,13,12,3,63,Graduation,30,Patnership,Standalone
6512,216.58,Boston,W,21,15,19,61,Graduation,23,Franchise,Complex
2449,394.5,NJ City,N,5,21,4,71,High School,29,Patnership,Standalone
4844,444.99,NJ City,W,10,10,5,41,No education,21,Franchise,Complex
7156,154.46,Boston,W,8,17,21,72,Graduation,34,Franchise,Complex
114,190.29,NJ City,S,25,24,4,41,Graduation,35,Patnership,Complex
2924,397.52,NJ City,W,19,11,8,70,High School,34,Franchise,Complex
2942,413.97,NJ City,S,3,16,1,80,No education,30,Patnership,Complex
5714,374.74,Boston,S,16,12,13,71,Graduation,32,Patnership,Complex
7209,204.47,Boston,N,23,13,17,48,Graduation,25,Patnership,Standalone
4561,291.73,NJ City,N,8,19,8,27,High School,21,Patnership,Standalone
6099,3.56,Boston,N,5,19,3,46,Graduation,35,Owner,Standalone
6167,355.17,Boston,E,4,8,21,64,Graduation,21,Owner,Standalone
4840,175.79,NJ City,W,13,19,22,61,High School,19,Franchise,Complex
8323,269.07,NJ City,E,4,19,22,44,Graduation,32,Owner,Standalone
472,348.36,NJ City,W,7,13,1,75,High School,27,Franchise,Complex
3064,280.27,NJ City,W,12,21,9,62,High School,24,Franchise,Complex
2556,243.61,NJ City,W,22,20,2,28,Graduation,21,Franchise,Complex
6946,358.26,Boston,S,18,9,9,38,Graduation,35,Patnership,Complex
3657,53.86,NJ City,N,14,23,3,79,Graduation,25,Patnership,Standalone
4702,465.29,NJ City,S,14,11,3,85,High School,30,Patnership,Complex
6076,294.75,Boston,W,25,10,24,78,Graduation,34,Franchise,Complex
3547,461.2,NJ City,E,16,9,16,59,High School,25,Owner,Standalone
3346,266.29,NJ City,S,10,18,4,48,High School,30,Patnership,Complex
2878,186.48,NJ City,S,13,16,3,37,High School,19,Patnership,Complex
5444,48.31,Boston,W,21,8,2,44,No education,25,Franchise,Complex
9936,455.42,NJ City,W,6,20,4,25,Graduation,23,Franchise,Complex
5030,86.48,Boston,S,12,16,4,80,No education,32,Patnership,Complex
5178,374.8,Boston,S,17,14,11,58,Graduation,35,Patnership,Complex
9738,294.02,NJ City,S,12,20,20,79,Graduation,24,Patnership,Complex
485,167.37,NJ City,N,14,20,19,27,High School,27,Patnership,Standalone
3592,185.16,NJ City,W,19,10,7,77,High School,35,Franchise,Complex
3539,153.08,NJ City,E,17,17,13,73,High School,32,Owner,Standalone
2024,407.66,NJ City,W,21,21,6,70,High School,22,Franchise,Complex
8167,217.58,NJ City,E,5,15,25,59,Graduation,19,Owner,Standalone
592,108.01,NJ City,W,19,17,4,66,High School,33,Franchise,Complex
3712,228.24,NJ City,W,11,19,1,40,High School,23,Franchise,Complex
9713,109.77,NJ City,N,6,9,1,33,No education,20,Patnership,Standalone
6755,18.82,Boston,N,8,9,3,76,No education,22,Owner,Standalone
8159,410.92,NJ City,E,7,18,2,49,No education,18,Owner,Standalone
4983,429.12,Boston,E,6,24,11,40,High School,23,Owner,Standalone
772,210.35,NJ City,W,7,17,20,72,High School,21,Franchise,Complex
2046,178,NJ City,S,3,12,12,52,High School,21,Patnership,Complex
1027,323.03,NJ City,E,7,19,10,43,High School,26,Owner,Standalone
403,474.13,NJ City,E,17,18,1,85,High School,20,Owner,Standalone
940,396.2,NJ City,W,16,16,8,64,High School,31,Franchise,Complex
6843,173,Boston,E,5,21,2,52,Graduation,21,Owner,Standalone
5294,38.59,Boston,N,10,21,3,46,No education,23,Patnership,Complex
5151,17.12,Boston,N,2,8,2,67,Graduation,25,Owner,Standalone
2171,269,NJ City,E,3,21,6,48,High School,31,Owner,Standalone
1431,228.82,NJ City,E,6,11,10,81,High School,32,Owner,Standalone
6330,80.08,Boston,S,2,15,1,26,Graduation,35,Patnership,Complex
8860,268.25,NJ City,W,14,24,1,52,High School,20,Franchise,Complex
5574,128.99,Boston,S,14,8,15,45,Graduation,26,Patnership,Complex
9732,418.38,NJ City,W,17,18,17,76,Graduation,32,Franchise,Complex
5724,239.79,Boston,W,2,14,11,75,Graduation,19,Franchise,Complex
952,250,NJ City,W,2,22,22,64,High School,24,Franchise,Complex
7231,159.69,Boston,E,5,14,22,53,Graduation,32,Owner,Standalone
480,377.89,NJ City,W,19,21,19,72,High School,23,Franchise,Complex
391,406.72,NJ City,E,8,23,16,47,High School,30,Owner,Standalone
9214,208.43,NJ City,S,16,8,18,31,Graduation,34,Patnership,Complex
1085,333.93,NJ City,N,2,11,1,28,No education,23,Patnership,Standalone
7060,200.79,Boston,W,2,21,18,78,Graduation,32,Franchise,Complex
7428,169.07,Boston,W,9,19,4,36,Graduation,32,Franchise,Complex
4546,414.03,NJ City,S,5,22,2,59,High School,22,Patnership,Complex
2890,328.79,NJ City,S,24,17,15,41,High School,27,Patnership,Complex
9677,394.58,NJ City,N,23,10,18,84,Graduation,23,Patnership,Standalone
2036,475.89,NJ City,W,22,18,21,77,High School,23,Franchise,Complex
2379,485.84,NJ City,E,18,21,6,58,High School,30,Owner,Standalone
1535,284.27,NJ City,E,11,20,19,70,High School,33,Owner,Standalone
5916,234.53,Boston,W,15,17,22,27,Graduation,19,Franchise,Complex
3673,64.77,NJ City,N,22,20,4,52,High School,21,Patnership,Standalone
5529,261.5,Boston,N,10,19,12,72,Graduation,24,Patnership,Standalone
5897,65.84,Boston,N,15,22,4,58,No education,22,Patnership,Standalone
2615,67.18,NJ City,N,9,22,1,62,No education,18,Owner,Standalone
8231,315.11,NJ City,E,20,17,25,57,Graduation,21,Owner,Standalone
4029,117.81,NJ City,N,14,12,1,71,Graduation,27,Patnership,Standalone
3398,290.27,NJ City,S,11,8,9,39,High School,20,Patnership,Complex
8556,232.28,NJ City,W,25,16,24,77,Graduation,35,Franchise,Complex
6156,142.22,Boston,W,24,9,15,46,Graduation,33,Franchise,Complex
9773,355.09,NJ City,N,22,24,11,65,Graduation,32,Patnership,Standalone
8539,151.56,NJ City,E,4,22,22,68,Graduation,28,Owner,Standalone
5393,296.12,Boston,N,14,22,1,75,No education,18,Patnership,Standalone
7852,403.41,Boston,W,21,9,1,58,High School,35,Franchise,Complex
4328,251,NJ City,W,3,15,6,49,High School,22,Franchise,Complex
8618,450.43,NJ City,S,7,24,6,82,Graduation,31,Patnership,Complex
3505,80.76,NJ City,N,10,12,4,65,High School,31,Patnership,Standalone
8849,305.15,NJ City,N,22,16,3,47,No education,22,Patnership,Standalone
8667,456.62,NJ City,E,12,18,11,33,Graduation,31,Owner,Standalone
7699,52.41,Boston,E,3,8,2,78,High School,22,Owner,Standalone
3741,52.08,NJ City,N,8,23,4,31,Graduation,25,Patnership,Standalone
1367,24.45,NJ City,N,23,13,2,39,No education,31,Owner,Standalone
5067,250.04,Boston,E,3,17,2,60,Graduation,35,Owner,Standalone
3771,28.17,NJ City,N,1,8,1,60,Graduation,26,Owner,Standalone
4265,217.24,NJ City,N,25,16,11,42,High School,24,Patnership,Standalone
8476,95.26,NJ City,W,8,21,1,43,High School,23,Franchise,Complex
2002,180.97,NJ City,S,16,24,22,31,High School,19,Patnership,Complex
8278,456.08,NJ City,S,7,24,25,74,Graduation,26,Patnership,Complex
2542,468.64,NJ City,S,25,9,1,82,High School,20,Patnership,Complex
265,273,NJ City,N,3,22,24,39,High School,20,Patnership,Standalone
8923,301.71,NJ City,E,17,16,18,75,Graduation,31,Owner,Standalone
7985,188.24,Boston,N,12,20,4,40,No education,20,Patnership,Standalone
2973,231.6,NJ City,N,16,19,8,54,High School,35,Patnership,Standalone
4876,296.47,NJ City,W,22,11,3,84,High School,33,Franchise,Complex
1507,311.62,NJ City,E,12,12,7,81,High School,30,Owner,Standalone
4322,190.51,NJ City,S,25,9,20,79,High School,18,Patnership,Complex
4788,64.68,NJ City,N,11,16,1,79,Graduation,30,Franchise,Complex
4726,121.7,NJ City,S,20,14,4,29,High School,18,Patnership,Complex
8086,259.75,NJ City,S,1,13,15,44,Graduation,22,Patnership,Complex
8697,165.4,NJ City,N,10,12,14,62,Graduation,25,Patnership,Standalone
9517,18.31,NJ City,N,10,9,1,82,High School,19,Patnership,Standalone
7858,340.59,Boston,S,14,15,11,36,Graduation,22,Patnership,Complex
2508,340.74,NJ City,W,4,22,1,33,Graduation,30,Franchise,Complex
3115,449.85,NJ City,E,7,15,20,48,High School,33,Owner,Standalone
6698,488.61,Boston,S,6,23,6,66,Graduation,23,Patnership,Complex
537,285.97,NJ City,N,14,15,4,58,Graduation,28,Patnership,Standalone
6158,262.24,Boston,S,18,15,6,74,Graduation,25,Patnership,Complex
5372,437.82,Boston,W,19,10,4,84,No education,21,Franchise,Complex
2903,414.52,NJ City,E,13,18,5,66,No education,23,Owner,Standalone
4095,10.57,NJ City,N,16,8,4,63,Graduation,25,Owner,Standalone
2052,91.25,NJ City,W,14,22,2,31,Graduation,34,Franchise,Complex
2703,230,NJ City,E,3,13,15,81,High School,32,Owner,Standalone
2780,122,NJ City,W,1,17,16,81,High School,24,Franchise,Complex
1600,282.7,NJ City,W,6,14,11,29,High School,25,Franchise,Complex
2744,317.11,NJ City,W,14,19,20,42,High School,28,Franchise,Complex
176,134.83,NJ City,W,19,19,1,78,No education,32,Franchise,Complex
8998,304.53,NJ City,S,12,19,9,79,Graduation,21,Patnership,Complex
6719,193.7,Boston,E,18,19,3,43,No education,21,Owner,Standalone
4768,197.28,NJ City,W,18,12,21,55,High School,25,Franchise,Complex
9745,150.65,NJ City,N,12,18,11,42,Graduation,20,Patnership,Standalone
2095,283.72,NJ City,E,5,23,4,84,High School,26,Owner,Standalone
1877,14.69,NJ City,N,4,24,2,65,No education,29,Patnership,Standalone
1679,303.67,NJ City,E,13,14,11,55,High School,20,Owner,Standalone
5206,443.87,Boston,S,6,13,15,38,Graduation,35,Patnership,Complex
1281,202.65,NJ City,N,6,22,19,68,High School,35,Patnership,Standalone
4891,63.46,NJ City,N,6,20,1,83,High School,26,Owner,Standalone
7278,379.02,Boston,S,18,14,21,79,Graduation,28,Patnership,Complex
9686,140.91,NJ City,S,9,9,11,44,Graduation,35,Patnership,Complex
6678,333.34,Boston,S,6,11,5,32,Graduation,22,Patnership,Complex
5545,159.23,Boston,N,3,18,13,64,Graduation,20,Patnership,Standalone
5547,448.04,Boston,E,10,18,14,32,Graduation,34,Owner,Standalone
9087,235.12,NJ City,E,21,16,20,51,Graduation,20,Owner,Standalone
5894,92.38,Boston,S,10,21,1,41,No education,30,Patnership,Complex
3313,11.48,NJ City,N,9,12,3,26,High School,23,Patnership,Standalone
2815,213,NJ City,E,1,21,13,68,High School,33,Owner,Standalone
207,105.2,NJ City,E,1,17,4,61,Graduation,29,Owner,Standalone
3714,247,NJ City,S,3,16,19,47,High School,23,Patnership,Complex
5096,256.28,Boston,W,25,18,25,58,Graduation,24,Franchise,Complex
2386,280,NJ City,S,16,11,16,48,High School,29,Patnership,Complex
1532,412.56,NJ City,W,24,12,23,47,High School,19,Franchise,Complex
700,293.84,NJ City,W,16,12,6,38,High School,32,Franchise,Complex
4102,432.68,NJ City,S,16,18,20,30,High School,23,Patnership,Complex
7563,52.65,Boston,E,14,9,2,39,Graduation,35,Owner,Standalone
7549,119.08,Boston,N,8,11,3,76,High School,26,Patnership,Standalone
4445,408,NJ City,N,22,14,13,36,High School,28,Patnership,Standalone
1622,453.45,NJ City,S,11,22,10,83,High School,18,Patnership,Complex
2475,114.74,NJ City,E,15,20,2,57,Graduation,25,Owner,Standalone
8900,416.79,NJ City,W,12,8,15,76,Graduation,32,Franchise,Complex
6442,388.39,Boston,S,4,9,10,83,Graduation,32,Patnership,Complex
5997,413.39,Boston,N,11,21,21,41,Graduation,22,Patnership,Standalone
9492,64.24,NJ City,N,22,16,3,78,Graduation,32,Franchise,Complex
9440,319.54,NJ City,W,25,16,16,27,Graduation,30,Franchise,Complex
4733,243.45,NJ City,N,21,11,23,55,High School,28,Patnership,Standalone
3928,464.88,NJ City,W,10,20,8,29,High School,34,Franchise,Complex
2019,341.69,NJ City,E,16,11,20,84,High School,31,Owner,Standalone
3783,76.28,NJ City,E,13,16,3,43,Graduation,28,Owner,Standalone
8358,20.55,NJ City,N,15,20,4,68,Graduation,24,Patnership,Complex
662,263,NJ City,S,3,12,10,37,High School,21,Patnership,Complex
8673,241.3,NJ City,N,10,24,6,56,Graduation,33,Patnership,Standalone
5510,168.31,Boston,S,11,20,8,65,Graduation,19,Patnership,Complex
3960,18.65,NJ City,N,4,9,4,60,Graduation,26,Franchise,Complex
5395,307.36,Boston,E,24,12,21,73,Graduation,20,Owner,Standalone
9525,427.86,NJ City,N,18,8,15,50,Graduation,28,Patnership,Standalone
3227,395.32,NJ City,E,16,24,17,67,High School,31,Owner,Standalone
3668,103.42,NJ City,W,20,10,1,80,No education,23,Franchise,Complex
9537,496.68,NJ City,N,16,8,2,44,Graduation,32,Patnership,Standalone
8031,473.93,Boston,E,2,24,23,83,Graduation,28,Owner,Standalone
6958,212.97,Boston,S,3,11,24,57,Graduation,19,Patnership,Complex
1631,201.15,NJ City,E,23,11,23,35,High School,23,Owner,Standalone
7312,10.68,Boston,N,12,8,3,53,High School,34,Franchise,Complex
7760,348.7,Boston,W,9,10,1,72,No education,29,Franchise,Complex
2500,184.85,NJ City,W,6,11,3,64,High School,32,Franchise,Complex
8429,324.79,NJ City,N,8,19,13,30,Graduation,34,Patnership,Standalone
3112,431.08,NJ City,W,18,13,4,52,High School,35,Franchise,Complex
4094,149.52,NJ City,S,12,12,11,63,High School,35,Patnership,Complex
3326,148.97,NJ City,S,13,15,18,38,High School,28,Patnership,Complex
5682,239.7,Boston,S,16,13,13,69,Graduation,30,Patnership,Complex
5978,288.03,Boston,S,2,19,16,48,Graduation,33,Patnership,Complex
5817,73.56,Boston,N,11,17,1,85,Graduation,20,Patnership,Standalone
5610,345.41,Boston,S,22,21,17,85,Graduation,23,Patnership,Complex
6251,145.63,Boston,E,20,18,7,85,Graduation,33,Owner,Standalone
6255,177.24,Boston,E,19,18,12,39,Graduation,30,Owner,Standalone
3671,89.76,NJ City,E,2,15,4,52,No education,19,Owner,Standalone
1530,497.63,NJ City,S,10,9,13,82,High School,20,Patnership,Complex
6051,126.87,Boston,E,10,21,2,36,Graduation,23,Owner,Standalone
2168,483.01,NJ City,W,17,23,4,41,No education,31,Franchise,Complex
1248,33.29,NJ City,N,12,10,2,29,Graduation,23,Franchise,Complex
5458,446.01,Boston,S,24,11,18,78,Graduation,29,Patnership,Complex
6326,496.44,Boston,S,5,16,8,34,Graduation,33,Patnership,Complex
4131,313.4,NJ City,E,24,15,8,29,High School,20,Owner,Standalone
6619,24.75,Boston,N,2,17,4,80,High School,34,Owner,Standalone
388,174,NJ City,W,1,15,10,33,High School,30,Franchise,Complex
5534,15.59,Boston,N,22,17,4,74,No education,21,Patnership,Complex
625,224.91,NJ City,N,15,22,16,47,High School,20,Patnership,Standalone
8092,417.14,NJ City,W,9,10,3,35,High School,24,Franchise,Complex
2586,366.17,NJ City,S,24,12,25,64,High School,21,Patnership,Complex
5035,251.61,Boston,E,21,17,8,35,Graduation,25,Owner,Standalone
9089,350.56,NJ City,N,4,17,21,39,Graduation,22,Patnership,Standalone
5491,355.02,Boston,E,15,15,22,25,Graduation,23,Owner,Standalone
8242,100.92,NJ City,S,7,22,2,85,High School,34,Patnership,Complex
2879,9.04,NJ City,N,21,10,1,66,No education,32,Owner,Standalone
6324,216.9,Boston,W,22,21,6,85,Graduation,34,Franchise,Complex
879,353.24,NJ City,E,1,23,2,45,Graduation,20,Owner,Standalone
5698,9.29,Boston,N,17,18,4,84,High School,27,Patnership,Complex
8225,22.53,NJ City,N,7,23,1,46,No education,23,Patnership,Standalone
1150,477.06,NJ City,S,14,17,5,53,High School,35,Patnership,Complex
4904,445.16,NJ City,W,10,21,16,64,High School,20,Franchise,Complex
9591,195.78,NJ City,E,16,14,14,45,Graduation,24,Owner,Standalone
3153,259.83,NJ City,N,12,11,13,67,High School,32,Patnership,Standalone
5518,399.96,Boston,S,8,18,10,54,Graduation,31,Patnership,Complex
491,84.38,NJ City,E,7,19,1,40,No education,29,Owner,Standalone
2402,55.65,NJ City,S,1,11,3,63,No education,28,Patnership,Complex
4776,263.81,NJ City,W,7,9,9,73,High School,31,Franchise,Complex
7691,344.67,Boston,E,25,15,1,62,No education,25,Owner,Standalone
6196,219.27,Boston,W,14,23,25,55,Graduation,29,Franchise,Complex
7219,233.82,Boston,E,15,21,19,71,Graduation,22,Owner,Standalone
3236,427.74,NJ City,W,18,19,10,25,High School,27,Franchise,Complex
6334,304.52,Boston,S,23,22,21,49,Graduation,28,Patnership,Complex
9792,106.6,NJ City,W,16,21,3,28,Graduation,35,Franchise,Complex
5937,40.43,Boston,N,14,9,4,59,Graduation,28,Patnership,Standalone
7133,292.18,Boston,N,15,19,5,27,No education,23,Patnership,Standalone
3046,270.69,NJ City,S,19,19,18,39,High School,27,Patnership,Complex
7189,252.48,Boston,N,5,16,14,26,Graduation,30,Patnership,Standalone
3909,320.58,NJ City,N,22,17,6,59,High School,35,Patnership,Standalone
6339,176.01,Boston,E,6,19,1,43,Graduation,34,Owner,Standalone
7451,443.72,Boston,E,19,18,3,72,No education,28,Owner,Standalone
3826,290.04,NJ City,S,15,12,13,33,High School,25,Patnership,Complex
7220,380.05,Boston,W,24,24,2,30,No education,18,Franchise,Complex
6902,34.74,Boston,N,24,19,2,62,No education,29,Patnership,Complex
1891,353.03,NJ City,E,10,18,18,68,High School,28,Owner,Standalone
8360,319.34,NJ City,W,12,15,12,85,Graduation,23,Franchise,Complex
8776,470.74,NJ City,W,7,14,4,68,High School,32,Franchise,Complex
2918,29.23,NJ City,N,2,24,1,41,No education,20,Patnership,Complex
3731,64.28,NJ City,N,25,24,3,39,No education,22,Owner,Standalone
2272,32.68,NJ City,N,20,14,1,32,High School,18,Franchise,Complex
7286,237.56,Boston,S,25,15,6,70,Graduation,34,Patnership,Complex
1796,412.02,NJ City,W,24,17,22,47,High School,21,Franchise,Complex
182,165.68,NJ City,S,18,24,3,34,No education,19,Patnership,Complex
7397,384.13,Boston,N,14,23,20,67,Graduation,35,Patnership,Standalone
442,167.38,NJ City,S,23,22,14,73,High School,25,Patnership,Complex
7795,233.45,Boston,E,24,20,13,57,Graduation,24,Owner,Standalone
5972,491.57,Boston,W,22,14,2,81,No education,33,Franchise,Complex
7722,387.69,Boston,S,11,8,10,32,Graduation,26,Patnership,Complex
1020,451.39,NJ City,W,20,24,25,74,High School,35,Franchise,Complex
2282,209,NJ City,S,1,16,7,71,High School,32,Patnership,Complex
1892,30.61,NJ City,N,9,13,2,34,No education,29,Franchise,Complex
7552,225.83,Boston,W,25,21,20,63,Graduation,19,Franchise,Complex
9210,58.17,NJ City,S,8,15,4,43,Graduation,25,Patnership,Complex
4713,415.38,NJ City,N,18,15,2,76,Graduation,35,Patnership,Standalone
9052,215.45,NJ City,W,3,10,9,63,Graduation,20,Franchise,Complex
1895,356.13,NJ City,E,25,20,14,54,High School,23,Owner,Standalone
2704,140.43,NJ City,W,11,8,1,37,High School,32,Franchise,Complex
9327,499.27,NJ City,E,17,15,14,57,Graduation,29,Owner,Standalone
1204,490.1,NJ City,W,5,19,1,75,High School,31,Franchise,Complex
7277,116.72,Boston,N,23,19,4,34,No education,25,Patnership,Standalone
3344,144.41,NJ City,W,12,13,12,39,High School,35,Franchise,Complex
8600,460.34,NJ City,W,2,22,14,37,Graduation,24,Franchise,Complex
3170,267.1,NJ City,S,4,12,14,76,High School,24,Patnership,Complex
7671,319.67,Boston,E,13,17,2,68,Graduation,21,Owner,Standalone
5646,197.39,Boston,S,23,11,13,65,Graduation,27,Patnership,Complex
8695,417.74,NJ City,E,7,13,4,51,High School,18,Owner,Standalone
4619,484.96,NJ City,E,12,24,16,77,High School,31,Owner,Standalone
8018,351.23,Boston,S,10,19,5,77,No education,26,Patnership,Complex
2619,249.18,NJ City,E,14,14,6,31,High School,35,Owner,Standalone
4471,460.16,NJ City,E,16,16,14,62,High School,33,Owner,Standalone
2898,150.34,NJ City,S,6,10,23,36,High School,26,Patnership,Complex
9848,369.46,NJ City,W,10,18,2,84,No education,19,Franchise,Complex
7104,392.6,Boston,W,20,9,7,66,Graduation,24,Franchise,Complex
4759,385.65,NJ City,E,15,22,25,51,High School,21,Owner,Standalone
7607,92.83,Boston,E,6,15,2,70,No education,21,Owner,Standalone
7569,433.72,Boston,N,5,20,21,43,Graduation,21,Patnership,Standalone
7969,150.81,Boston,N,22,22,10,79,Graduation,22,Patnership,Standalone
7123,122.02,Boston,E,15,8,2,34,High School,34,Owner,Standalone
1170,48.42,NJ City,S,20,24,4,34,Graduation,25,Patnership,Complex
2790,260.16,NJ City,S,22,23,21,34,High School,23,Patnership,Complex
1753,47.1,NJ City,N,18,20,3,48,High School,26,Patnership,Standalone
7498,264.32,Boston,S,2,18,14,64,Graduation,32,Patnership,Complex
2884,426.09,NJ City,W,23,22,19,84,High School,32,Franchise,Complex
2385,479.43,NJ City,N,7,21,6,83,High School,29,Patnership,Standalone
9894,245.46,NJ City,S,22,20,19,55,Graduation,35,Patnership,Complex
2631,480.04,NJ City,E,24,24,15,53,High School,22,Owner,Standalone
1471,419.31,NJ City,E,21,24,8,75,High School,34,Owner,Standalone
383,48.59,NJ City,E,25,14,3,74,No education,23,Owner,Standalone
6092,221.93,Boston,W,22,13,5,85,No education,19,Franchise,Complex
4064,145.16,NJ City,W,10,24,9,66,High School,23,Franchise,Complex
2617,208,NJ City,N,1,10,15,58,High School,34,Patnership,Standalone
440,157,NJ City,W,3,15,7,57,High School,35,Franchise,Complex
9694,234.63,NJ City,S,24,22,6,32,Graduation,20,Patnership,Complex
9854,205,NJ City,S,16,19,6,65,Graduation,35,Patnership,Complex
1761,51.71,NJ City,N,9,20,4,48,Graduation,30,Patnership,Standalone
5278,188.03,Boston,S,23,19,2,54,High School,25,Patnership,Complex
9451,224.51,NJ City,E,11,24,18,41,Graduation,27,Owner,Standalone
8492,296.54,NJ City,W,8,13,3,45,No education,20,Franchise,Complex
131,356.33,NJ City,E,8,21,11,56,High School,18,Owner,Standalone
1657,246.56,NJ City,N,20,22,23,38,High School,31,Patnership,Standalone
2319,196.39,NJ City,E,25,10,9,28,High School,29,Owner,Standalone
500,212.94,NJ City,W,11,8,17,68,High School,20,Franchise,Complex
7853,471.38,Boston,N,13,24,7,83,Graduation,26,Patnership,Standalone
4579,224.27,NJ City,E,24,13,1,83,High School,27,Owner,Standalone
1251,183.65,NJ City,E,24,10,11,31,High School,25,Owner,Standalone
3538,383.2,NJ City,S,24,23,12,78,High School,34,Patnership,Complex
6234,41.96,Boston,N,23,8,2,51,Graduation,25,Patnership,Complex
3465,435.71,NJ City,N,15,16,6,80,High School,32,Patnership,Standalone
9707,288.62,NJ City,E,3,22,24,77,Graduation,27,Owner,Standalone
7917,134.43,Boston,N,24,12,17,48,Graduation,20,Patnership,Standalone
1664,173.57,NJ City,W,14,8,5,43,No education,34,Franchise,Complex
7502,476.83,Boston,S,6,9,21,79,Graduation,35,Patnership,Complex
7223,256.01,Boston,E,2,22,18,58,Graduation,25,Owner,Standalone
6660,8.21,Boston,N,22,24,4,79,Graduation,34,Franchise,Complex
4799,17.88,NJ City,N,22,23,4,46,No education,28,Owner,Standalone
5412,270.99,Boston,W,15,17,12,36,Graduation,32,Franchise,Complex
174,61.53,NJ City,N,23,9,2,83,Graduation,18,Patnership,Complex
834,194,NJ City,S,2,13,17,52,High School,24,Patnership,Complex
6785,238.23,Boston,N,21,15,12,70,Graduation,18,Patnership,Standalone
3623,477.22,NJ City,E,5,9,25,36,High School,25,Owner,Standalone
6171,495.46,Boston,E,12,23,11,71,Graduation,19,Owner,Standalone
5066,141.81,Boston,S,22,19,2,76,No education,33,Patnership,Complex
2298,499.52,NJ City,S,10,15,13,80,High School,30,Patnership,Complex
3649,147.6,NJ City,N,13,22,12,78,High School,27,Patnership,Standalone
852,137,NJ City,W,3,14,6,25,High School,19,Franchise,Complex
2555,198.21,NJ City,E,24,20,4,43,No education,24,Owner,Standalone
6611,189.69,Boston,E,3,13,4,81,No education,35,Owner,Standalone
8503,175.54,NJ City,E,14,9,18,50,Graduation,19,Owner,Standalone
5873,466.94,Boston,N,6,23,17,76,Graduation,28,Patnership,Standalone
4208,134.86,NJ City,W,19,11,1,28,No education,19,Franchise,Complex
760,163.78,NJ City,W,25,10,3,63,High School,25,Franchise,Complex
1437,413.77,NJ City,N,25,11,8,36,High School,34,Patnership,Standalone
1395,330.61,NJ City,E,8,15,3,56,Graduation,29,Owner,Standalone
6760,203.91,Boston,W,17,19,11,85,Graduation,34,Franchise,Complex
8361,119.51,NJ City,N,3,17,4,76,Graduation,28,Patnership,Standalone
5960,422.8,Boston,W,12,21,24,52,Graduation,21,Franchise,Complex
2295,246,NJ City,E,1,15,23,45,High School,21,Owner,Standalone
8929,120.05,NJ City,N,4,8,4,45,High School,24,Patnership,Standalone
1013,87.17,NJ City,N,6,15,2,67,No education,24,Patnership,Standalone
9469,211.8,NJ City,N,23,12,4,73,High School,19,Patnership,Standalone
9461,305.26,NJ City,N,2,10,24,67,Graduation,25,Patnership,Standalone
1387,133.21,NJ City,E,21,23,3,36,High School,21,Owner,Standalone
9227,202.1,NJ City,E,8,8,5,31,No education,20,Owner,Standalone
6022,170.64,Boston,S,24,16,24,75,Graduation,33,Patnership,Complex
4947,467.35,Boston,E,19,15,18,25,High School,20,Owner,Standalone
2040,327.6,NJ City,W,14,11,4,81,Graduation,29,Franchise,Complex
934,292.86,NJ City,S,7,13,10,26,High School,24,Patnership,Complex
9354,135.77,NJ City,S,8,17,4,39,Graduation,30,Patnership,Complex
4848,316.02,NJ City,W,21,24,5,34,Graduation,25,Franchise,Complex
4521,415.87,NJ City,N,25,19,9,85,High School,21,Patnership,Standalone
5794,382.98,Boston,S,20,12,1,34,High School,19,Patnership,Complex
6867,455.27,Boston,E,15,17,13,56,Graduation,26,Owner,Standalone
4025,134,NJ City,N,1,21,12,30,High School,24,Patnership,Standalone
3393,475.85,NJ City,N,4,21,21,59,High School,29,Patnership,Standalone
4104,159.14,NJ City,W,25,10,14,38,High School,20,Franchise,Complex
7118,255.03,Boston,S,16,14,12,80,Graduation,32,Patnership,Complex
7109,53.26,Boston,N,17,12,2,63,No education,22,Patnership,Standalone
6385,242.53,Boston,N,10,19,24,38,Graduation,27,Patnership,Standalone
2979,87.3,NJ City,E,21,22,3,57,Graduation,33,Owner,Standalone
1693,9.07,NJ City,N,5,14,1,64,High School,33,Patnership,Standalone
1873,56.74,NJ City,N,1,10,4,33,High School,21,Patnership,Standalone
6297,460.85,Boston,N,16,12,4,66,Graduation,26,Patnership,Standalone
7991,57.71,Boston,E,6,18,1,67,No education,18,Owner,Standalone
4140,205.35,NJ City,W,19,13,7,45,High School,25,Franchise,Complex
5367,384.03,Boston,E,18,13,2,70,Graduation,34,Owner,Standalone
2997,126.26,NJ City,N,2,17,4,71,Graduation,32,Patnership,Standalone
8185,190.99,NJ City,N,24,12,20,69,Graduation,25,Patnership,Standalone
7420,204.31,Boston,W,18,21,24,46,Graduation,31,Franchise,Complex
9434,23.66,NJ City,N,25,21,3,53,No education,20,Patnership,Complex
9942,278.11,NJ City,S,13,22,5,31,Graduation,20,Patnership,Complex
9768,212.98,NJ City,W,2,10,8,35,Graduation,29,Franchise,Complex
9301,188.49,NJ City,N,22,21,9,66,Graduation,26,Patnership,Standalone
7,324.73,NJ City,E,5,21,24,44,High School,32,Owner,Standalone
562,391.57,NJ City,S,17,10,8,33,High School,20,Patnership,Complex
7382,299.85,Boston,S,9,18,16,84,Graduation,24,Patnership,Complex
2612,471.35,NJ City,W,10,10,13,54,High School,27,Franchise,Complex
2746,400.66,NJ City,S,22,12,18,26,High School,33,Patnership,Complex
8951,429.12,NJ City,E,24,15,5,79,No education,26,Owner,Standalone
248,144.01,NJ City,W,19,18,13,46,High School,22,Franchise,Complex
4646,223.04,NJ City,S,1,11,2,33,No education,18,Patnership,Complex
1106,150.47,NJ City,S,22,12,9,33,High School,19,Patnership,Complex
5292,197.62,Boston,W,24,23,14,31,Graduation,32,Franchise,Complex
2614,348.01,NJ City,S,3,22,1,64,High School,33,Patnership,Complex
8804,182.15,NJ City,W,17,9,1,38,No education,30,Franchise,Complex
2543,340.28,NJ City,E,12,22,19,64,High School,32,Owner,Standalone
1070,453.51,NJ City,S,9,12,16,27,High School,35,Patnership,Complex
7072,299.02,Boston,W,16,12,5,43,High School,32,Franchise,Complex
4673,405.79,NJ City,N,19,11,2,70,No education,29,Patnership,Standalone
6606,479.04,Boston,S,3,23,23,69,Graduation,30,Patnership,Complex
4594,310.32,NJ City,S,16,18,18,42,High School,27,Patnership,Complex
5961,297.97,Boston,N,2,10,6,73,Graduation,23,Patnership,Standalone
4613,177.46,NJ City,N,14,13,2,56,No education,28,Patnership,Standalone
9890,277.06,NJ City,S,20,12,9,76,Graduation,20,Patnership,Complex
1256,228.76,NJ City,W,7,19,25,58,High School,35,Franchise,Complex
8616,68.11,NJ City,N,20,13,2,60,Graduation,25,Franchise,Complex
2706,7.6,NJ City,N,25,15,4,38,Graduation,30,Patnership,Complex
2926,261.82,NJ City,S,9,12,6,41,High School,32,Patnership,Complex
7950,416.05,Boston,S,10,21,5,56,Graduation,34,Patnership,Complex
8760,349.14,NJ City,W,20,22,5,71,Graduation,23,Franchise,Complex
3243,492.65,NJ City,E,5,21,17,58,High School,25,Owner,Standalone
3075,439.36,NJ City,E,16,22,1,72,Graduation,27,Owner,Standalone
2335,207.52,NJ City,E,11,15,22,57,High School,19,Owner,Standalone
1838,162.15,NJ City,S,14,8,21,30,High School,25,Patnership,Complex
9405,87.26,NJ City,N,1,19,3,40,Graduation,30,Patnership,Standalone
1068,431.37,NJ City,W,13,9,11,72,High School,33,Franchise,Complex
5660,82.31,Boston,W,16,19,1,68,No education,25,Franchise,Complex
7925,116.76,Boston,N,14,20,1,80,No education,31,Patnership,Standalone
6082,152,Boston,S,18,19,24,79,Graduation,34,Patnership,Complex
9980,312.24,NJ City,W,5,16,20,34,Graduation,26,Franchise,Complex
3137,228.52,NJ City,N,23,8,25,72,High School,20,Patnership,Standalone
8980,246.25,NJ City,W,10,24,12,80,Graduation,27,Franchise,Complex
4693,311.81,NJ City,N,7,12,16,76,High School,31,Patnership,Standalone
838,246.24,NJ City,S,16,24,17,29,High School,31,Patnership,Complex
177,395.62,NJ City,N,17,9,16,63,High School,22,Patnership,Standalone
8822,377.71,NJ City,S,5,23,3,29,No education,33,Patnership,Complex
217,24.91,NJ City,N,22,24,1,54,High School,24,Patnership,Standalone
7707,235.25,Boston,E,11,18,10,52,Graduation,35,Owner,Standalone
1744,422.18,NJ City,W,21,14,13,44,High School,31,Franchise,Complex
2421,433.07,NJ City,N,14,20,11,49,High School,19,Patnership,Standalone
5765,332.31,Boston,N,11,8,9,43,Graduation,33,Patnership,Standalone
6786,470.66,Boston,S,18,9,7,79,Graduation,31,Patnership,Complex
4583,211.32,NJ City,E,24,10,1,29,No education,20,Owner,Standalone
6390,257.33,Boston,S,7,8,24,74,Graduation,25,Patnership,Complex
3429,94.48,NJ City,N,23,24,1,61,Graduation,19,Patnership,Standalone
3679,305.23,NJ City,E,14,17,20,76,High School,26,Owner,Standalone
945,8.12,NJ City,N,16,24,2,45,Graduation,33,Patnership,Standalone
7899,459.52,Boston,E,4,14,7,79,Graduation,29,Owner,Standalone
3842,473.27,NJ City,S,16,12,3,61,No education,26,Patnership,Complex
6999,219.87,Boston,E,11,20,1,27,Graduation,33,Owner,Standalone
7301,150.74,Boston,N,22,16,6,72,Graduation,31,Patnership,Standalone
7604,317.9,Boston,W,10,18,6,45,Graduation,20,Franchise,Complex
6023,327.53,Boston,E,21,14,13,48,Graduation,32,Owner,Standalone
2120,116,NJ City,W,1,19,9,63,High School,28,Franchise,Complex
7483,245.61,Boston,E,16,20,13,79,Graduation,20,Owner,Standalone
4700,26.74,NJ City,N,21,14,3,53,No education,30,Franchise,Complex
4458,315.69,NJ City,S,18,23,4,31,Graduation,18,Patnership,Complex
7660,272.81,Boston,W,19,19,25,40,Graduation,29,Franchise,Complex
5599,269.6,Boston,E,19,23,18,76,Graduation,32,Owner,Standalone
185,471.54,NJ City,N,10,15,8,39,High School,26,Patnership,Standalone
3722,378.64,NJ City,S,15,15,14,68,High School,24,Patnership,Complex
3107,384.41,NJ City,E,3,12,3,38,No education,33,Owner,Standalone
6080,321.5,Boston,W,8,15,12,78,Graduation,34,Franchise,Complex
4134,55.55,NJ City,S,14,16,1,25,Graduation,35,Patnership,Complex
1310,170.23,NJ City,S,8,8,8,70,High School,21,Patnership,Complex
2559,126,NJ City,E,1,16,18,83,High School,18,Owner,Standalone
2363,32.34,NJ City,N,18,24,3,43,No education,31,Owner,Standalone
5359,450.14,Boston,E,14,18,5,70,High School,23,Owner,Standalone
8083,171.44,NJ City,E,13,24,2,85,High School,28,Owner,Standalone
8093,38.27,NJ City,N,25,11,4,31,No education,35,Patnership,Standalone
7522,6.19,Boston,N,6,14,4,47,High School,35,Patnership,Complex
1570,335.03,NJ City,S,6,18,4,53,High School,18,Patnership,Complex
3339,121.07,NJ City,E,9,18,1,30,Graduation,25,Owner,Standalone
8745,273.29,NJ City,N,8,18,7,45,Graduation,25,Patnership,Standalone
2365,470.22,NJ City,N,14,11,23,67,High School,19,Patnership,Standalone
795,385.53,NJ City,E,25,24,8,36,High School,24,Owner,Standalone
6626,465.67,Boston,S,19,16,4,59,No education,29,Patnership,Complex
3364,120.24,NJ City,W,10,21,1,59,High School,32,Franchise,Complex
2527,192.73,NJ City,E,12,14,23,55,High School,24,Owner,Standalone
8789,124.99,NJ City,N,19,17,3,77,No education,33,Patnership,Standalone
9777,26.92,NJ City,N,11,23,4,82,Graduation,32,Patnership,Standalone
5336,82.91,Boston,W,8,20,3,47,No education,29,Franchise,Complex
7337,135.23,Boston,N,17,23,15,26,Graduation,35,Patnership,Standalone
2968,450.97,NJ City,W,19,21,14,55,High School,21,Franchise,Complex
1972,382.58,NJ City,W,4,16,22,50,High School,28,Franchise,Complex
3230,264.73,NJ City,S,14,19,13,54,High School,34,Patnership,Complex
3179,235.71,NJ City,E,15,22,13,38,High School,28,Owner,Standalone
5573,250.05,Boston,N,3,14,7,37,Graduation,19,Patnership,Standalone
5632,145.37,Boston,W,18,22,25,31,Graduation,32,Franchise,Complex
6439,39.52,Boston,N,22,13,1,28,High School,24,Owner,Standalone
7680,455.16,Boston,W,23,10,2,28,Graduation,23,Franchise,Complex
2480,196.85,NJ City,W,10,16,12,59,High School,34,Franchise,Complex
5414,332.02,Boston,S,11,20,8,30,Graduation,23,Patnership,Complex
5204,444.67,Boston,W,10,23,6,68,Graduation,35,Franchise,Complex
9715,89.38,NJ City,E,15,16,4,60,High School,22,Owner,Standalone
3106,263.67,NJ City,S,19,15,10,55,High School,34,Patnership,Complex
1036,317.26,NJ City,W,19,17,22,44,High School,32,Franchise,Complex
985,168,NJ City,N,3,15,20,25,High School,27,Patnership,Standalone
6235,295.33,Boston,E,18,13,16,72,Graduation,20,Owner,Standalone
808,394.77,NJ City,W,21,23,20,41,High School,27,Franchise,Complex
6373,288.29,Boston,N,19,23,6,48,Graduation,18,Patnership,Standalone
3698,162.16,NJ City,S,24,16,21,81,High School,22,Patnership,Complex
7155,447.02,Boston,E,21,20,20,69,Graduation,32,Owner,Standalone
4918,167.39,NJ City,S,23,21,9,52,High School,28,Patnership,Complex
7544,273.78,Boston,W,22,19,9,58,Graduation,19,Franchise,Complex
8521,336.72,NJ City,N,2,21,13,32,Graduation,27,Patnership,Standalone
5640,223.8,Boston,W,13,24,7,35,Graduation,18,Franchise,Complex
1970,295.56,NJ City,S,14,16,17,44,High School,18,Patnership,Complex
5688,172.77,Boston,W,10,22,18,26,Graduation,23,Franchise,Complex
5493,155.23,Boston,N,19,24,3,36,Graduation,20,Patnership,Standalone
675,8.61,NJ City,N,15,21,2,28,Graduation,22,Owner,Standalone
6525,205.63,Boston,N,17,23,20,39,Graduation,25,Patnership,Standalone
4393,422.42,NJ City,N,6,10,9,82,High School,18,Patnership,Standalone
3224,62.24,NJ City,N,19,9,1,34,No education,34,Franchise,Complex
3276,129,NJ City,W,3,10,12,44,High School,35,Franchise,Complex
2274,121,NJ City,S,2,19,4,74,Graduation,35,Patnership,Complex
9020,220.08,NJ City,W,23,18,3,76,No education,32,Franchise,Complex
5111,171.56,Boston,E,19,19,21,60,Graduation,26,Owner,Standalone
6621,28.98,Boston,N,7,14,2,47,Graduation,30,Patnership,Standalone
3754,318.53,NJ City,S,10,19,25,47,High School,33,Patnership,Complex
7380,298.56,Boston,W,12,12,11,50,Graduation,23,Franchise,Complex
6204,86.47,Boston,W,2,18,4,67,Graduation,19,Franchise,Complex
8428,334.49,NJ City,W,25,14,7,73,Graduation,27,Franchise,Complex
2065,82.27,NJ City,N,2,22,4,50,High School,33,Patnership,Standalone
7198,387.82,Boston,S,2,17,4,65,High School,26,Patnership,Complex
5187,260.88,Boston,E,5,10,15,27,Graduation,19,Owner,Standalone
9953,12.87,NJ City,N,24,23,1,25,No education,24,Patnership,Standalone
9109,368.94,NJ City,N,2,16,1,74,High School,20,Patnership,Standalone
2416,113.72,NJ City,W,14,17,3,84,High School,28,Franchise,Complex
458,130.83,NJ City,S,17,23,10,33,High School,32,Patnership,Complex
6267,195.85,Boston,E,1,22,23,55,Graduation,20,Owner,Standalone
5343,302.37,Boston,E,18,14,7,38,Graduation,25,Owner,Standalone
6305,470.08,Boston,N,10,24,24,71,Graduation,22,Patnership,Standalone
5429,329.02,Boston,N,3,20,16,71,Graduation,22,Patnership,Standalone
9742,290.71,NJ City,S,23,21,22,56,Graduation,24,Patnership,Complex
7264,335.41,Boston,W,9,15,17,78,Graduation,30,Franchise,Complex
8378,360.59,NJ City,S,11,19,17,53,Graduation,27,Patnership,Complex
6886,48.04,Boston,S,4,19,3,52,High School,23,Patnership,Complex
4128,268.73,NJ City,W,8,10,14,78,High School,33,Franchise,Complex
5411,481.2,Boston,E,6,15,14,66,Graduation,23,Owner,Standalone
2053,320.84,NJ City,N,24,20,1,36,High School,30,Patnership,Standalone
6962,497.81,Boston,S,24,12,15,59,Graduation,21,Patnership,Complex
1745,144.68,NJ City,N,17,20,22,63,High School,33,Patnership,Standalone
1503,287.28,NJ City,E,5,11,24,25,High School,19,Owner,Standalone
3807,66.38,NJ City,N,5,18,2,75,Graduation,18,Owner,Standalone
4510,352.07,NJ City,S,17,23,22,32,High School,23,Patnership,Complex
5943,208.4,Boston,E,13,15,1,57,Graduation,31,Owner,Standalone
9091,353.11,NJ City,E,15,15,17,71,Graduation,31,Owner,Standalone
6323,219.35,Boston,E,1,23,12,37,Graduation,27,Owner,Standalone
7863,60.36,Boston,E,10,23,2,79,Graduation,28,Owner,Standalone
5577,55.13,Boston,N,19,17,3,55,Graduation,25,Patnership,Standalone
1519,484.26,NJ City,E,21,14,12,67,High School,25,Owner,Standalone
7647,146.64,Boston,E,18,13,25,65,Graduation,20,Owner,Standalone
6169,494.76,Boston,N,11,11,19,42,Graduation,33,Patnership,Standalone
2453,311,NJ City,N,1,18,25,25,High School,24,Patnership,Standalone
5881,335.31,Boston,N,5,24,13,46,Graduation,33,Patnership,Standalone
5503,302.76,Boston,E,4,10,14,40,Graduation,21,Owner,Standalone
7956,275.1,Boston,W,2,17,10,84,Graduation,31,Franchise,Complex
7467,443.89,Boston,E,18,19,10,65,Graduation,27,Owner,Standalone
1612,193.04,NJ City,W,4,14,24,31,High School,34,Franchise,Complex
157,100,NJ City,N,3,24,19,82,High School,34,Patnership,Standalone
4868,272.26,NJ City,W,12,17,11,28,High School,25,Franchise,Complex
9938,374.4,NJ City,S,11,22,16,77,Graduation,24,Patnership,Complex
7560,95.95,Boston,W,4,11,1,42,Graduation,23,Franchise,Complex
6254,473.13,Boston,S,17,21,23,77,Graduation,34,Patnership,Complex
778,40.95,NJ City,N,5,11,4,76,High School,30,Patnership,Complex
1610,54.15,NJ City,S,16,19,1,40,No education,35,Patnership,Complex
9266,278.85,NJ City,S,17,9,5,26,No education,35,Patnership,Complex
783,14.62,NJ City,N,5,16,2,72,Graduation,22,Owner,Standalone
70,452,NJ City,S,15,12,23,54,High School,27,Patnership,Complex
4867,174.58,NJ City,E,11,19,23,26,High School,24,Owner,Standalone
5862,15.76,Boston,N,9,14,3,35,Graduation,27,Patnership,Complex
6032,202.57,Boston,W,13,15,13,34,Graduation,29,Franchise,Complex
1383,85.99,NJ City,E,1,18,1,66,Graduation,25,Owner,Standalone
8120,141.17,NJ City,W,19,15,7,38,Graduation,22,Franchise,Complex
2284,241.93,NJ City,W,17,22,1,31,High School,26,Franchise,Complex
2983,486.81,NJ City,E,5,9,5,64,High School,18,Owner,Standalone
1002,102.06,NJ City,S,9,20,2,48,Graduation,23,Patnership,Complex
1225,392.04,NJ City,N,19,10,18,83,High School,31,Patnership,Standalone
6425,255.67,Boston,N,3,11,10,61,Graduation,18,Patnership,Standalone
8727,449.93,NJ City,E,20,22,15,46,Graduation,21,Owner,Standalone
1880,486.35,NJ City,W,21,20,10,56,High School,34,Franchise,Complex
4278,147.91,NJ City,S,25,9,10,42,High School,27,Patnership,Complex
9073,14.86,NJ City,N,5,10,3,61,High School,29,Patnership,Standalone
4008,103.77,NJ City,W,22,23,4,30,Graduation,19,Franchise,Complex
187,329.82,NJ City,E,22,22,7,62,High School,35,Owner,Standalone
1638,497.45,NJ City,S,13,15,8,29,High School,34,Patnership,Complex
4211,429.32,NJ City,E,25,21,16,72,High School,32,Owner,Standalone
1382,60.88,NJ City,N,17,18,4,34,No education,24,Patnership,Complex
3011,268.27,NJ City,E,11,22,5,80,No education,20,Owner,Standalone
6538,220.01,Boston,S,18,18,1,63,High School,29,Patnership,Complex
7546,445.34,Boston,S,23,17,12,54,Graduation,23,Patnership,Complex
9226,197.42,NJ City,S,3,15,5,76,High School,30,Patnership,Complex
2570,406.68,NJ City,S,14,18,23,44,High School,18,Patnership,Complex
6638,248.04,Boston,S,12,20,8,57,Graduation,18,Patnership,Complex
5002,447.1,Boston,S,5,17,9,85,Graduation,25,Patnership,Complex
1473,424.97,NJ City,N,13,16,23,77,High School,35,Patnership,Standalone
3725,224,NJ City,N,2,11,21,58,High School,31,Patnership,Standalone
9225,161.92,NJ City,N,10,18,22,73,Graduation,20,Patnership,Standalone
2006,330.08,NJ City,S,10,15,15,74,High School,32,Patnership,Complex
3920,293.39,NJ City,W,10,23,23,66,High School,27,Franchise,Complex
7799,333.5,Boston,E,22,23,1,51,No education,35,Owner,Standalone
7103,342.62,Boston,E,9,12,23,36,Graduation,27,Owner,Standalone
3862,475.27,NJ City,S,19,24,19,29,High School,24,Patnership,Complex
6003,198.84,Boston,E,14,13,8,77,Graduation,25,Owner,Standalone
3389,271.67,NJ City,N,5,12,25,31,High School,29,Patnership,Standalone
1696,20.28,NJ City,N,9,17,4,76,High School,25,Franchise,Complex
8683,199.14,NJ City,E,15,23,9,73,Graduation,25,Owner,Standalone
4113,342.03,NJ City,N,13,24,13,81,High School,20,Patnership,Standalone
8635,275.51,NJ City,E,12,24,17,45,Graduation,32,Owner,Standalone
8000,475.13,Boston,W,1,21,17,44,Graduation,32,Franchise,Complex
2013,75.42,NJ City,N,13,24,2,26,Graduation,20,Patnership,Standalone
2279,341.53,NJ City,E,2,19,2,39,No education,33,Owner,Standalone
617,174.33,NJ City,N,22,23,21,63,High School,31,Patnership,Standalone
1639,245.62,NJ City,E,21,23,1,82,High School,18,Owner,Standalone
8179,211.11,NJ City,E,2,15,12,33,Graduation,33,Owner,Standalone
502,11.2,NJ City,N,24,18,2,77,High School,30,Patnership,Complex
6700,314.06,Boston,W,10,22,6,35,Graduation,24,Franchise,Complex
6273,210.03,Boston,N,25,10,19,53,Graduation,20,Patnership,Standalone
5400,291.64,Boston,W,1,24,6,67,Graduation,34,Franchise,Complex
6983,213.56,Boston,E,6,17,21,27,Graduation,31,Owner,Standalone
231,145.08,NJ City,E,15,14,11,35,High School,30,Owner,Standalone
2889,291.03,NJ City,N,9,21,9,40,High School,29,Patnership,Standalone
7808,478.2,Boston,W,14,8,11,63,Graduation,21,Franchise,Complex
9628,70.96,NJ City,N,3,12,1,58,High School,31,Franchise,Complex
702,66.69,NJ City,N,22,15,1,78,Graduation,30,Patnership,Complex
924,98.33,NJ City,W,24,15,2,48,Graduation,21,Franchise,Complex
4117,29.47,NJ City,N,16,15,3,67,High School,28,Patnership,Standalone
2128,366.04,NJ City,W,21,12,13,55,High School,31,Franchise,Complex
6726,327.59,Boston,S,13,15,25,83,Graduation,33,Patnership,Complex
1963,473.56,NJ City,E,7,18,20,56,High School,29,Owner,Standalone
4039,167.29,NJ City,E,4,11,17,76,High School,24,Owner,Standalone
8195,264.87,NJ City,E,10,11,20,63,Graduation,33,Owner,Standalone
2444,301.5,NJ City,W,7,24,4,35,No education,32,Franchise,Complex
6883,211.42,Boston,E,10,15,8,82,Graduation,30,Owner,Standalone
3487,272.79,NJ City,E,19,18,1,81,High School,33,Owner,Standalone
8653,7.94,NJ City,N,18,14,1,47,High School,35,Patnership,Standalone
4678,402.68,NJ City,S,3,22,5,56,High School,28,Patnership,Complex
431,412.49,NJ City,E,4,15,17,40,High School,25,Owner,Standalone
5006,44.29,Boston,N,25,16,3,83,No education,18,Patnership,Complex
4996,138.13,Boston,W,15,14,7,48,High School,32,Franchise,Complex
9785,85.45,NJ City,N,9,20,1,79,No education,21,Patnership,Standalone
3042,24.94,NJ City,N,15,13,4,80,Graduation,30,Patnership,Complex
100,282.19,NJ City,W,24,15,15,55,High School,21,Franchise,Complex
290,476.65,NJ City,S,16,17,25,48,High School,34,Patnership,Complex
4325,335.74,NJ City,N,5,19,16,71,High School,29,Patnership,Standalone
6809,147.9,Boston,N,18,13,18,81,Graduation,33,Patnership,Standalone
5580,19.25,Boston,N,8,19,2,78,Graduation,35,Franchise,Complex
5977,58.85,Boston,N,19,8,2,46,High School,25,Patnership,Standalone
8524,211.04,NJ City,W,1,15,17,71,Graduation,18,Franchise,Complex
2821,238.79,NJ City,N,14,14,15,48,High School,22,Patnership,Standalone
1136,8.24,NJ City,N,6,22,2,68,No education,34,Franchise,Complex
7204,378.53,Boston,W,17,9,14,59,Graduation,27,Franchise,Complex
4057,497.18,NJ City,N,13,17,20,38,High School,22,Patnership,Standalone
1191,299.67,NJ City,E,12,20,4,62,Graduation,27,Owner,Standalone
9272,461.96,NJ City,W,24,22,9,33,Graduation,27,Franchise,Complex
7992,38.81,Boston,N,2,10,2,77,Graduation,21,Franchise,Complex
8977,94.38,NJ City,N,1,24,3,74,High School,30,Patnership,Standalone
6018,285.86,Boston,S,17,11,9,57,Graduation,18,Patnership,Complex
6975,373.82,Boston,E,19,20,7,71,Graduation,34,Owner,Standalone
2059,262,NJ City,E,2,18,7,45,High School,21,Owner,Standalone
5915,193.32,Boston,E,23,15,13,62,Graduation,18,Owner,Standalone
7529,103,Boston,N,24,20,1,75,No education,19,Patnership,Standalone
3244,230.41,NJ City,W,16,13,14,60,High School,24,Franchise,Complex
9947,102.06,NJ City,E,12,24,3,39,No education,22,Owner,Standalone
1867,76.65,NJ City,E,3,17,3,33,High School,18,Owner,Standalone
5243,365.45,Boston,E,3,19,4,69,No education,35,Owner,Standalone
2895,365.13,NJ City,E,20,8,25,66,High School,30,Owner,Standalone
8337,355.1,NJ City,N,12,16,16,46,Graduation,30,Patnership,Standalone
6637,366.93,Boston,N,24,24,11,34,Graduation,26,Patnership,Standalone
5007,335.27,Boston,E,2,20,4,81,Graduation,23,Owner,Standalone
3817,77.66,NJ City,N,15,19,3,61,High School,26,Patnership,Standalone
3161,26.31,NJ City,N,15,24,3,34,No education,23,Patnership,Standalone
612,407.79,NJ City,W,23,11,2,59,Graduation,29,Franchise,Complex
5923,73.51,Boston,N,7,11,2,82,High School,28,Owner,Standalone
7775,410.04,Boston,E,16,20,24,73,Graduation,20,Owner,Standalone
1476,435.05,NJ City,W,19,15,1,56,Graduation,24,Franchise,Complex
5468,249.54,Boston,W,15,21,21,28,Graduation,28,Franchise,Complex
9948,98.85,NJ City,W,22,22,2,38,Graduation,25,Franchise,Complex
3211,345.05,NJ City,E,17,11,14,34,High School,29,Owner,Standalone
3995,334.06,NJ City,E,22,13,9,48,High School,30,Owner,Standalone
557,308.66,NJ City,N,8,15,18,43,High School,32,Patnership,Standalone
2183,392.52,NJ City,E,7,21,24,41,High School,29,Owner,Standalone
1304,217.92,NJ City,W,15,13,1,78,No education,30,Franchise,Complex
4382,58.98,NJ City,S,22,11,1,53,No education,29,Patnership,Complex
7303,68.54,Boston,N,5,10,1,38,High School,34,Owner,Standalone
6865,39.31,Boston,N,22,22,1,64,High School,21,Patnership,Standalone
1129,182.1,NJ City,N,8,18,2,37,High School,26,Patnership,Standalone
8710,47.73,NJ City,S,2,19,3,77,High School,18,Patnership,Complex
2177,148.94,NJ City,N,1,8,5,46,No education,26,Patnership,Standalone
9292,87.24,NJ City,W,17,13,3,46,High School,29,Franchise,Complex
9925,18.23,NJ City,N,22,14,3,72,High School,25,Patnership,Standalone
6774,335.73,Boston,S,21,19,14,30,Graduation,18,Patnership,Complex
5488,306.79,Boston,W,7,21,1,27,High School,32,Franchise,Complex
9159,412.82,NJ City,E,24,12,6,64,Graduation,23,Owner,Standalone
9631,473.88,NJ City,E,22,20,16,83,Graduation,31,Owner,Standalone
1353,249,NJ City,N,2,18,23,56,High School,24,Patnership,Standalone
9460,269.16,NJ City,W,8,9,2,67,High School,29,Franchise,Complex
3109,391.28,NJ City,N,8,22,17,75,High School,25,Patnership,Standalone
1564,116,NJ City,W,2,9,25,81,High School,34,Franchise,Complex
1973,178.97,NJ City,N,11,19,21,80,High School,22,Patnership,Standalone
5627,120.24,Boston,E,7,17,2,26,No education,25,Owner,Standalone
7148,17.37,Boston,N,14,17,2,27,No education,20,Franchise,Complex
3195,449.18,NJ City,E,15,8,22,46,High School,34,Owner,Standalone
6105,459.56,Boston,N,1,22,15,76,Graduation,27,Patnership,Standalone
1133,243.74,NJ City,N,15,22,16,73,High School,27,Patnership,Standalone
3612,60.37,NJ City,N,22,14,4,70,Graduation,35,Franchise,Complex
9752,168.58,NJ City,W,1,15,14,57,Graduation,21,Franchise,Complex
278,424.15,NJ City,S,23,24,8,83,High School,23,Patnership,Complex
5192,275.66,Boston,W,9,23,6,46,Graduation,32,Franchise,Complex
7715,52.69,Boston,E,2,23,3,45,No education,35,Owner,Standalone
82,155.86,Boston,S,16,24,20,27,High School,32,Patnership,Complex
3311,305.58,NJ City,E,14,13,20,69,High School,24,Owner,Standalone
6336,467.85,Boston,W,6,8,8,56,Graduation,28,Franchise,Complex
5059,168.71,Boston,E,7,19,8,69,Graduation,32,Owner,Standalone
6065,318.06,Boston,N,22,20,18,75,Graduation,29,Patnership,Standalone
6396,379.65,Boston,W,4,21,19,34,Graduation,21,Franchise,Complex
7160,51.91,Boston,W,13,16,1,48,No education,18,Franchise,Complex
427,295.75,NJ City,E,12,22,9,33,High School,24,Owner,Standalone
3941,421.07,NJ City,N,7,18,2,62,No education,23,Patnership,Standalone
163,449.89,NJ City,E,1,20,1,34,High School,20,Owner,Standalone
7752,375.68,Boston,W,4,16,12,28,Graduation,26,Franchise,Complex
4340,24.74,NJ City,N,18,12,3,53,No education,19,Franchise,Complex
6275,3.4,Boston,N,15,18,2,61,No education,23,Owner,Standalone
313,99.91,NJ City,N,22,22,4,48,High School,33,Patnership,Standalone
9404,297.41,NJ City,W,18,10,24,42,Graduation,35,Franchise,Complex
1049,426.83,NJ City,N,8,13,3,69,No education,21,Patnership,Standalone
1960,1.41,NJ City,W,4,19,1,71,High School,27,Franchise,Complex
3082,16.16,NJ City,N,9,22,1,77,High School,27,Patnership,Complex
7350,337.99,Boston,S,3,8,1,47,Graduation,24,Patnership,Complex
1194,234.8,NJ City,S,11,8,3,37,Graduation,23,Patnership,Complex
3681,8.99,NJ City,N,9,13,1,50,Graduation,23,Patnership,Standalone
6906,344.43,Boston,S,12,18,19,69,Graduation,32,Patnership,Complex
6952,94.37,Boston,W,7,19,2,34,High School,26,Franchise,Complex
6329,71.51,Boston,N,1,8,2,52,No education,25,Patnership,Standalone
4716,70.79,NJ City,N,8,14,1,25,Graduation,32,Franchise,Complex
8690,464.65,NJ City,S,11,8,8,30,Graduation,35,Patnership,Complex
3750,9.58,NJ City,N,9,15,4,79,Graduation,28,Patnership,Complex
3175,179.47,NJ City,E,16,12,6,34,High School,33,Owner,Standalone
267,98.98,NJ City,E,14,13,1,68,Graduation,25,Owner,Standalone
8730,327.43,NJ City,S,14,19,24,72,Graduation,31,Patnership,Complex
6493,339.73,Boston,N,11,19,22,26,Graduation,18,Patnership,Standalone
8990,307.61,NJ City,S,9,11,18,40,Graduation,26,Patnership,Complex
5708,116.85,Boston,W,20,24,2,79,No education,34,Franchise,Complex
2252,179.37,NJ City,W,6,12,15,68,High School,22,Franchise,Complex
5734,176.18,Boston,S,7,12,19,84,Graduation,32,Patnership,Complex
2334,102.23,NJ City,S,5,24,2,45,Graduation,29,Patnership,Complex
7823,439.38,Boston,E,25,14,15,65,Graduation,32,Owner,Standalone
5433,421.26,Boston,N,10,10,18,39,Graduation,31,Patnership,Standalone
9300,158.99,NJ City,W,8,10,3,26,Graduation,19,Franchise,Complex
5128,359.36,Boston,W,11,19,11,78,Graduation,24,Franchise,Complex
7745,313,Boston,N,24,8,24,36,Graduation,35,Patnership,Standalone
6889,442.12,Boston,N,3,22,9,71,Graduation,34,Patnership,Standalone
5648,41.72,Boston,N,9,24,3,39,No education,29,Franchise,Complex
7952,498.37,Boston,W,13,20,5,84,No education,19,Franchise,Complex
7025,129.46,Boston,N,16,16,17,47,Graduation,34,Patnership,Standalone
5188,456.46,Boston,W,22,20,11,64,Graduation,22,Franchise,Complex
477,116.76,NJ City,N,3,13,4,43,Graduation,31,Patnership,Standalone
8150,17.81,NJ City,N,24,9,1,56,No education,26,Patnership,Complex
2060,433.09,NJ City,W,12,15,18,46,High School,22,Franchise,Complex
8835,332.21,NJ City,E,17,22,4,54,Graduation,35,Owner,Standalone
2833,444.41,NJ City,N,23,18,17,64,High School,35,Patnership,Standalone
3509,493.89,NJ City,N,5,8,5,52,No education,25,Patnership,Standalone
4949,433.86,Boston,N,23,19,11,27,High School,35,Patnership,Standalone
8212,402.02,NJ City,W,20,10,6,44,Graduation,21,Franchise,Complex
6315,123.79,Boston,E,4,24,4,27,Graduation,28,Owner,Standalone
6157,167.49,Boston,N,20,22,8,49,Graduation,33,Patnership,Standalone
7166,302.66,Boston,S,25,13,2,51,No education,18,Patnership,Complex
6124,78.29,Boston,W,4,9,1,43,High School,22,Franchise,Complex
3335,389.75,NJ City,E,19,14,15,54,High School,35,Owner,Standalone
8034,6.24,Boston,N,4,14,2,63,Graduation,28,Patnership,Complex
1754,25.1,NJ City,N,24,23,1,54,No education,28,Patnership,Complex
5497,348.48,Boston,N,18,20,2,41,High School,22,Patnership,Standalone
3168,361.19,NJ City,W,9,11,2,41,Graduation,20,Franchise,Complex
2696,212.96,NJ City,W,22,23,25,74,High School,23,Franchise,Complex
2161,388.54,NJ City,N,21,16,19,31,High School,24,Patnership,Standalone
5553,94.95,Boston,N,8,16,4,44,Graduation,21,Patnership,Standalone
9369,218.34,NJ City,N,16,20,20,48,Graduation,19,Patnership,Standalone
656,235.52,NJ City,W,9,22,18,51,High School,25,Franchise,Complex
7466,186.79,Boston,S,10,15,12,40,Graduation,27,Patnership,Complex
7683,129.42,Boston,E,11,8,19,36,Graduation,32,Owner,Standalone
5910,478.41,Boston,S,7,22,10,42,Graduation,35,Patnership,Complex
5947,203.56,Boston,E,7,24,25,77,Graduation,29,Owner,Standalone
4938,433.51,Boston,S,16,18,17,67,High School,18,Patnership,Complex
7850,165.93,Boston,S,15,24,23,72,Graduation,19,Patnership,Complex
5346,404.46,Boston,S,12,9,13,47,Graduation,23,Patnership,Complex
4127,142.57,NJ City,E,8,21,9,82,High School,23,Owner,Standalone
7158,207.53,Boston,S,16,13,24,71,Graduation,22,Patnership,Complex
9952,475.18,NJ City,W,5,22,12,83,Graduation,19,Franchise,Complex
2473,376.01,NJ City,N,7,21,12,77,High School,28,Patnership,Standalone
9223,44.79,NJ City,N,7,21,1,83,High School,26,Owner,Standalone
5471,235.03,Boston,E,13,20,20,45,Graduation,19,Owner,Standalone
7896,260.37,Boston,W,16,15,10,52,Graduation,23,Franchise,Complex
611,327.35,NJ City,E,6,9,1,70,No education,22,Owner,Standalone
4135,278.55,NJ City,E,24,22,9,50,High School,33,Owner,Standalone
5342,338.81,Boston,S,12,9,1,36,No education,32,Patnership,Complex
3984,308.72,NJ City,W,21,22,2,76,Graduation,24,Franchise,Complex
5196,61.76,Boston,N,15,14,3,45,Graduation,21,Franchise,Complex
8969,85.95,NJ City,N,2,13,1,74,No education,21,Patnership,Standalone
259,428.94,NJ City,E,16,24,14,55,High School,25,Owner,Standalone
2545,11.36,NJ City,N,22,10,4,41,High School,22,Patnership,Standalone
6552,27.06,Boston,N,8,8,3,50,Graduation,26,Franchise,Complex
7730,218.42,Boston,S,24,23,24,85,Graduation,18,Patnership,Complex
3793,136.5,NJ City,N,24,20,20,61,High School,27,Patnership,Standalone
6136,85.23,Boston,W,11,16,3,69,High School,20,Franchise,Complex
5578,269.78,Boston,S,19,22,9,42,Graduation,32,Patnership,Complex
4070,435.84,NJ City,S,1,10,3,48,No education,26,Patnership,Complex
638,82.2,NJ City,S,15,8,1,70,No education,25,Patnership,Complex
8676,106.28,NJ City,W,17,8,3,51,Graduation,25,Franchise,Complex
9202,168.94,NJ City,S,18,16,15,78,Graduation,28,Patnership,Complex
7233,407.41,Boston,N,23,9,19,76,Graduation,18,Patnership,Standalone
3515,320.01,NJ City,E,15,8,13,47,High School,20,Owner,Standalone
2311,290.83,NJ City,E,12,23,24,29,High School,30,Owner,Standalone
6543,130.56,Boston,E,10,8,10,68,Graduation,32,Owner,Standalone
73,394.3,Boston,N,7,18,5,63,High School,31,Patnership,Standalone
5347,122.88,Boston,E,25,11,3,81,High School,26,Owner,Standalone
2634,254.03,NJ City,S,6,9,1,67,Graduation,29,Patnership,Complex
9183,85.31,NJ City,E,21,15,4,51,Graduation,35,Owner,Standalone
2423,158.77,NJ City,E,11,19,13,70,High School,25,Owner,Standalone
8049,102.16,NJ City,N,18,17,4,78,Graduation,28,Patnership,Standalone
4553,190.65,NJ City,N,10,8,15,62,High School,31,Patnership,Standalone
8561,122.48,NJ City,N,4,8,2,82,No education,26,Patnership,Standalone
9846,100.39,NJ City,S,10,9,2,35,Graduation,32,Patnership,Complex
748,62.77,NJ City,N,2,13,1,39,High School,35,Franchise,Complex
454,159.46,NJ City,S,19,16,12,50,High School,34,Patnership,Complex
8685,492.41,NJ City,N,19,13,9,70,Graduation,26,Patnership,Standalone
2969,389.41,NJ City,N,11,14,2,68,No education,19,Patnership,Standalone
4379,21.6,NJ City,N,10,12,1,43,No education,33,Owner,Standalone
5439,332.74,Boston,E,21,18,14,75,Graduation,26,Owner,Standalone
8704,319.67,NJ City,W,14,14,9,38,Graduation,30,Franchise,Complex
4160,435.81,NJ City,W,9,8,14,31,High School,35,Franchise,Complex
2648,101.95,NJ City,W,19,9,2,80,No education,32,Franchise,Complex
6937,473.66,Boston,N,21,19,5,44,High School,23,Patnership,Standalone
7326,335.83,Boston,S,3,21,18,25,Graduation,32,Patnership,Complex
3466,495.64,NJ City,S,15,8,7,51,High School,32,Patnership,Complex
2411,279.71,NJ City,E,7,13,23,74,High School,22,Owner,Standalone
5418,440.04,Boston,S,11,18,22,51,Graduation,24,Patnership,Complex
3571,265.78,NJ City,E,25,18,12,38,High School,33,Owner,Standalone
6519,99.8,Boston,E,11,17,1,80,Graduation,30,Owner,Standalone
5189,369.03,Boston,N,9,23,14,34,Graduation,33,Patnership,Standalone
9809,340.31,NJ City,N,12,23,10,40,Graduation,30,Patnership,Standalone
8703,123.95,NJ City,E,14,24,2,71,Graduation,28,Owner,Standalone
7407,137.1,Boston,E,14,9,6,71,Graduation,30,Owner,Standalone
8164,189.56,NJ City,W,10,14,17,46,Graduation,28,Franchise,Complex
7587,101.59,Boston,E,6,21,3,31,Graduation,32,Owner,Standalone
6436,390.33,Boston,W,19,19,19,30,Graduation,23,Franchise,Complex
1613,310.7,NJ City,N,7,19,4,67,No education,24,Patnership,Standalone
5305,4.6,Boston,N,16,15,3,40,High School,32,Patnership,Standalone
7463,402.64,Boston,E,9,17,9,47,Graduation,21,Owner,Standalone
1826,454.1,NJ City,S,6,24,23,69,High School,19,Patnership,Complex
7593,339.59,Boston,N,5,8,4,71,Graduation,24,Patnership,Standalone
6650,181.96,Boston,S,20,18,1,80,No education,34,Patnership,Complex
3367,249.91,NJ City,E,9,22,20,53,High School,32,Owner,Standalone
287,463.54,NJ City,E,19,17,2,83,No education,22,Owner,Standalone
1752,44.35,NJ City,N,6,16,2,53,Graduation,28,Franchise,Complex
3191,135.15,NJ City,E,8,12,15,37,High School,20,Owner,Standalone
1142,448.02,NJ City,S,23,8,5,71,No education,24,Patnership,Complex
9476,217.52,NJ City,W,20,21,22,54,Graduation,29,Franchise,Complex
6682,345.91,Boston,S,21,13,17,40,Graduation,35,Patnership,Complex
3334,484.74,NJ City,S,25,18,24,31,High School,30,Patnership,Complex
7200,457.66,Boston,W,10,24,12,79,Graduation,35,Franchise,Complex
9780,122.13,NJ City,W,10,16,2,44,Graduation,30,Franchise,Complex
1357,208.79,NJ City,N,6,23,4,64,High School,24,Patnership,Standalone
7865,302.3,Boston,N,24,17,16,51,Graduation,28,Patnership,Standalone
6675,400.47,Boston,E,11,24,21,83,Graduation,23,Owner,Standalone
1207,270.12,NJ City,E,8,23,25,63,High School,18,Owner,Standalone
118,168.85,NJ City,S,16,12,19,64,High School,34,Patnership,Complex
4694,235.2,NJ City,S,15,14,20,26,High School,24,Patnership,Complex
9303,279.93,NJ City,E,20,22,14,57,Graduation,28,Owner,Standalone
6570,342.19,Boston,S,24,12,20,49,Graduation,30,Patnership,Complex
64,455.93,Boston,W,23,9,17,33,High School,32,Franchise,Complex
1705,231.72,NJ City,N,10,16,8,54,High School,35,Patnership,Standalone
1618,468.26,NJ City,S,25,22,17,66,High School,21,Patnership,Complex
2526,259.23,NJ City,S,13,12,21,30,High School,23,Patnership,Complex
7390,371.2,Boston,S,8,19,8,66,Graduation,27,Patnership,Complex
6887,343.86,Boston,E,25,14,16,84,Graduation,26,Owner,Standalone
1128,316.82,NJ City,W,10,22,13,26,High School,24,Franchise,Complex
8966,408.6,NJ City,S,3,20,18,36,Graduation,19,Patnership,Complex
7945,392.19,Boston,N,4,21,13,54,Graduation,27,Patnership,Standalone
1217,242.23,NJ City,N,15,22,17,45,High School,29,Patnership,Standalone
5434,406.72,Boston,S,6,14,1,33,High School,25,Patnership,Complex
8269,408.34,NJ City,N,19,17,8,63,Graduation,28,Patnership,Standalone
1064,265.09,NJ City,W,4,16,13,29,High School,23,Franchise,Complex
787,418.57,NJ City,E,18,11,8,73,High School,22,Owner,Standalone
6173,424.77,Boston,N,14,24,14,59,Graduation,35,Patnership,Standalone
837,464.02,NJ City,N,11,12,17,42,High School,33,Patnership,Standalone
8258,116.31,NJ City,S,5,22,2,34,No education,23,Patnership,Complex
4299,90.51,NJ City,E,9,24,3,61,Graduation,25,Owner,Standalone
9855,251.71,NJ City,E,23,20,13,35,Graduation,20,Owner,Standalone
7873,94.99,Boston,N,13,22,1,58,High School,24,Patnership,Standalone
1489,124.34,NJ City,N,5,10,2,30,High School,25,Patnership,Standalone
368,383.01,NJ City,W,15,16,8,26,High School,32,Franchise,Complex
3008,56.69,NJ City,W,4,17,2,38,No education,27,Franchise,Complex
4845,346.18,NJ City,N,20,8,25,39,High School,24,Patnership,Standalone
3214,425.96,NJ City,S,4,23,21,64,High School,25,Patnership,Complex
3058,184,NJ City,S,1,19,9,73,High School,28,Patnership,Complex
7163,78.13,Boston,E,25,14,1,34,No education,32,Owner,Standalone
2309,85.26,NJ City,N,12,21,3,55,No education,30,Patnership,Standalone
3113,310.37,NJ City,N,24,13,25,70,High School,20,Patnership,Standalone
9363,214.8,NJ City,E,4,20,11,32,Graduation,32,Owner,Standalone
535,35.77,NJ City,N,11,16,1,25,High School,33,Owner,Standalone
6833,180.41,Boston,N,13,22,1,83,No education,25,Patnership,Standalone
6911,210.01,Boston,E,6,20,22,62,Graduation,27,Owner,Standalone
1993,116.24,NJ City,N,16,19,4,71,High School,35,Patnership,Standalone
6312,140.16,Boston,W,20,12,10,69,Graduation,27,Franchise,Complex
9240,461.56,NJ City,W,13,13,5,70,Graduation,25,Franchise,Complex
9313,32.56,NJ City,N,8,19,2,52,High School,33,Patnership,Standalone
4041,436.72,NJ City,N,10,24,18,64,High School,22,Patnership,Standalone
2301,242.25,NJ City,N,6,19,11,40,High School,31,Patnership,Standalone
331,123.5,NJ City,E,13,21,3,58,High School,20,Owner,Standalone
8507,350.48,NJ City,E,6,19,22,33,Graduation,32,Owner,Standalone
2384,192.66,NJ City,W,10,12,10,72,High School,33,Franchise,Complex
9507,144.96,NJ City,E,7,22,13,36,Graduation,26,Owner,Standalone
8486,363.08,NJ City,S,14,13,11,46,Graduation,19,Patnership,Complex
7168,238.78,Boston,W,4,23,15,70,Graduation,21,Franchise,Complex
199,326.07,NJ City,E,1,19,2,75,High School,32,Owner,Standalone
2722,488.48,NJ City,S,17,14,16,38,High School,20,Patnership,Complex
607,5.12,NJ City,N,2,24,1,53,High School,33,Owner,Standalone
9862,42.88,NJ City,N,4,12,2,50,High School,27,Patnership,Complex
1463,482.67,NJ City,E,18,12,4,80,No education,27,Owner,Standalone
38,41.18,Maine,N,2,23,2,33,No education,30,Patnership,Complex
1616,231.16,NJ City,W,14,16,10,73,High School,21,Franchise,Complex
7449,319.74,Boston,N,23,8,14,41,Graduation,25,Patnership,Standalone
26,28.93,Boston,N,16,16,3,31,No education,35,Patnership,Complex
1668,85.54,NJ City,W,14,12,1,55,Graduation,33,Franchise,Complex
484,393.99,NJ City,W,8,19,18,26,High School,27,Franchise,Complex
1672,19.75,NJ City,N,6,12,1,54,High School,28,Franchise,Complex
9249,439.34,NJ City,N,8,23,5,54,Graduation,20,Patnership,Standalone
5677,418.19,Boston,N,20,18,19,78,Graduation,18,Patnership,Standalone
3966,498.24,NJ City,S,16,11,7,80,High School,22,Patnership,Complex
3925,34.22,NJ City,N,25,17,3,30,High School,19,Patnership,Standalone
2795,15.71,NJ City,N,14,22,2,64,No education,32,Owner,Standalone
429,202.91,NJ City,N,14,8,16,48,High School,18,Patnership,Standalone
9380,393.42,NJ City,W,21,9,18,80,Graduation,28,Franchise,Complex
7575,264.98,Boston,E,6,14,9,34,Graduation,27,Owner,Standalone
6350,92.92,Boston,S,5,15,3,77,No education,26,Patnership,Complex
5779,103.49,Boston,E,5,21,3,60,High School,20,Owner,Standalone
4305,401.51,NJ City,N,13,20,10,32,High School,25,Patnership,Standalone
5831,60.66,Boston,N,7,20,1,69,No education,18,Owner,Standalone
3448,139.61,NJ City,W,4,23,19,49,High School,29,Franchise,Complex
9155,387.69,NJ City,E,24,23,7,83,Graduation,35,Owner,Standalone
3399,207.83,NJ City,E,22,9,24,51,High School,28,Owner,Standalone
7941,197.66,Boston,N,24,17,24,48,Graduation,34,Patnership,Standalone
7748,211.89,Boston,W,4,8,21,60,Graduation,20,Franchise,Complex
9675,47.93,NJ City,E,6,16,3,78,Graduation,25,Owner,Standalone
7154,41.85,Boston,N,14,17,4,28,No education,23,Patnership,Complex
9798,88.18,NJ City,S,22,20,3,56,Graduation,24,Patnership,Complex
849,289.84,NJ City,N,20,9,18,54,High School,25,Patnership,Standalone
2510,111.95,NJ City,S,23,9,4,82,No education,19,Patnership,Complex
316,398.81,NJ City,W,21,15,16,42,High School,26,Franchise,Complex
1398,332.92,NJ City,S,21,17,8,45,High School,29,Patnership,Complex
2954,327.44,NJ City,S,19,11,6,31,High School,22,Patnership,Complex
8991,489.46,NJ City,E,15,23,22,35,Graduation,35,Owner,Standalone
6590,378.61,Boston,S,18,21,22,73,Graduation,24,Patnership,Complex
300,368.91,NJ City,W,12,19,14,73,High School,20,Franchise,Complex
8228,241.98,NJ City,W,18,18,19,47,Graduation,22,Franchise,Complex
8377,268.62,NJ City,N,19,11,17,55,Graduation,35,Patnership,Standalone
7153,228.1,Boston,N,17,14,20,27,Graduation,33,Patnership,Standalone
6970,456.08,Boston,S,8,23,17,37,Graduation,21,Patnership,Complex
2091,297.95,NJ City,E,9,8,17,38,High School,33,Owner,Standalone
4461,417.56,NJ City,N,14,22,23,57,High School,35,Patnership,Standalone
5172,383.91,Boston,W,25,19,11,80,Graduation,23,Franchise,Complex
2964,221.03,NJ City,W,23,23,1,34,Graduation,25,Franchise,Complex
6146,245.91,Boston,S,25,15,5,66,No education,25,Patnership,Complex
1078,197.83,NJ City,S,25,10,22,68,High School,29,Patnership,Complex
9521,340.39,NJ City,N,20,11,14,28,Graduation,35,Patnership,Standalone
1961,100.35,NJ City,N,13,15,2,55,No education,31,Patnership,Standalone
2592,469.22,NJ City,W,15,16,2,68,Graduation,30,Franchise,Complex
4804,157.92,NJ City,W,6,9,14,31,High School,25,Franchise,Complex
3055,482.01,NJ City,E,16,22,20,83,High School,34,Owner,Standalone
4376,242.4,NJ City,W,11,20,6,83,High School,28,Franchise,Complex
763,366.57,NJ City,E,22,22,13,67,High School,33,Owner,Standalone
3660,359.77,NJ City,W,18,11,17,84,High School,22,Franchise,Complex
6497,106.48,Boston,N,14,11,2,58,No education,20,Patnership,Standalone
9082,457.31,NJ City,S,7,21,13,44,Graduation,27,Patnership,Complex
6198,468.91,Boston,S,16,18,23,29,Graduation,26,Patnership,Complex
8464,232.81,NJ City,W,21,22,5,26,High School,23,Franchise,Complex
2445,236.4,NJ City,N,5,20,14,37,High School,31,Patnership,Standalone
4529,232.06,NJ City,N,11,8,24,77,High School,32,Patnership,Standalone
6693,50.9,Boston,N,13,13,3,76,Graduation,33,Patnership,Standalone
7629,44.75,Boston,N,1,15,2,44,Graduation,32,Patnership,Standalone
4819,364.1,NJ City,E,11,19,4,31,High School,29,Owner,Standalone
3301,103.23,NJ City,N,4,13,1,72,High School,32,Patnership,Standalone
5499,113.95,Boston,E,2,11,4,39,Graduation,19,Owner,Standalone
4439,128.56,NJ City,E,16,9,3,47,No education,26,Owner,Standalone
8480,497.71,NJ City,W,13,14,18,48,Graduation,29,Franchise,Complex
5989,178.26,Boston,N,14,8,18,28,Graduation,32,Patnership,Standalone
3250,99.82,NJ City,S,4,8,1,74,High School,20,Patnership,Complex
7408,422.99,Boston,W,21,20,14,72,Graduation,25,Franchise,Complex
3701,431.85,NJ City,N,22,22,22,55,High School,31,Patnership,Standalone
5004,330.58,Boston,W,24,11,24,55,Graduation,30,Franchise,Complex
7267,222.35,Boston,E,15,24,23,80,Graduation,18,Owner,Standalone
6077,486.98,Boston,N,19,11,9,85,Graduation,26,Patnership,Standalone
9635,154.1,NJ City,E,6,10,8,73,Graduation,26,Owner,Standalone
5319,413.56,Boston,E,15,16,9,82,Graduation,30,Owner,Standalone
4915,1.05,NJ City,N,8,11,4,57,High School,31,Owner,Standalone
6892,2.88,Boston,W,24,12,3,40,High School,24,Franchise,Complex
3903,7.47,NJ City,N,14,21,1,69,Graduation,28,Owner,Standalone
9576,148.26,NJ City,W,3,13,6,45,Graduation,21,Franchise,Complex
6536,420.15,Boston,W,24,11,13,50,Graduation,34,Franchise,Complex
4473,14.36,NJ City,N,18,16,3,65,Graduation,32,Patnership,Standalone
6545,328.55,Boston,N,8,10,23,26,Graduation,33,Patnership,Standalone
7249,325.89,Boston,N,19,19,13,80,Graduation,25,Patnership,Standalone
5815,98.61,Boston,E,25,19,3,70,High School,20,Owner,Standalone
5295,59.43,Boston,E,4,21,3,62,Graduation,20,Owner,Standalone
8613,130.53,NJ City,N,24,12,12,78,Graduation,30,Patnership,Standalone
6876,233.89,Boston,W,5,23,5,38,Graduation,33,Franchise,Complex
5445,425.28,Boston,N,13,16,13,51,Graduation,20,Patnership,Standalone
6382,372.68,Boston,S,13,14,7,62,Graduation,29,Patnership,Complex
3735,424.7,NJ City,E,21,13,4,30,Graduation,30,Owner,Standalone
4009,468.64,NJ City,N,12,20,16,31,High School,25,Patnership,Standalone
5311,323.89,Boston,E,25,24,20,53,Graduation,30,Owner,Standalone
4241,161.25,NJ City,N,10,10,7,62,High School,24,Patnership,Standalone
8605,327.66,NJ City,N,10,19,25,34,Graduation,34,Patnership,Standalone
5823,488.58,Boston,E,12,8,16,71,Graduation,28,Owner,Standalone
4499,106.15,NJ City,E,14,24,1,38,No education,20,Owner,Standalone
9508,30.33,NJ City,N,10,15,2,42,High School,24,Franchise,Complex
3342,257.32,NJ City,S,21,20,19,61,High School,27,Patnership,Complex
4060,449.48,NJ City,W,16,18,5,40,High School,31,Franchise,Complex
376,88.29,NJ City,W,6,24,4,77,High School,34,Franchise,Complex
8422,186.99,NJ City,S,3,18,3,54,High School,31,Patnership,Complex
8491,461.97,NJ City,E,9,17,22,71,Graduation,20,Owner,Standalone
245,65.11,NJ City,N,22,15,1,82,No education,25,Patnership,Standalone
4500,305.4,NJ City,W,19,17,5,59,Graduation,27,Franchise,Complex
4192,408.05,NJ City,W,4,10,23,79,High School,20,Franchise,Complex
4831,154.77,NJ City,E,19,9,7,77,High School,25,Owner,Standalone
9009,197.9,NJ City,N,17,16,21,72,Graduation,27,Patnership,Standalone
7559,160.54,Boston,E,7,9,3,54,No education,34,Owner,Standalone
1294,115.72,NJ City,S,4,10,3,62,High School,28,Patnership,Complex
6795,176.96,Boston,E,16,9,7,81,Graduation,25,Owner,Standalone
4563,59.91,NJ City,E,17,15,1,53,Graduation,31,Owner,Standalone
1464,120.75,NJ City,W,12,21,2,43,Graduation,26,Franchise,Complex
197,118.58,NJ City,N,3,20,1,46,No education,35,Patnership,Standalone
8796,227.5,NJ City,W,4,10,21,40,Graduation,22,Franchise,Complex
4185,337.75,NJ City,N,16,18,19,61,High School,25,Patnership,Standalone
2567,333,NJ City,E,2,17,4,51,No education,26,Owner,Standalone
5695,34.62,Boston,N,25,15,3,75,High School,25,Owner,Standalone
9573,129.65,NJ City,N,23,16,20,68,Graduation,22,Patnership,Standalone
8160,49.31,NJ City,W,11,16,2,62,Graduation,29,Franchise,Complex
3369,192.02,NJ City,N,20,16,16,82,High School,32,Patnership,Standalone
4690,499.85,NJ City,S,1,17,5,78,High School,35,Patnership,Complex
6550,326.28,Boston,S,19,13,25,84,Graduation,32,Patnership,Complex
3475,48.38,NJ City,E,17,23,2,50,High School,21,Owner,Standalone
9108,208.22,NJ City,W,21,24,22,64,Graduation,27,Franchise,Complex
7309,408.08,Boston,N,15,15,1,68,High School,21,Patnership,Standalone
7347,209.2,Boston,E,7,9,18,80,Graduation,19,Owner,Standalone
3948,93.45,NJ City,W,23,16,1,53,Graduation,24,Franchise,Complex
9676,495.54,NJ City,W,7,18,6,58,Graduation,26,Franchise,Complex
7230,401.46,Boston,S,15,11,5,71,Graduation,27,Patnership,Complex
8319,240.39,NJ City,E,7,24,25,63,Graduation,32,Owner,Standalone
4637,477.95,NJ City,N,1,13,5,55,No education,20,Patnership,Standalone
3752,304.41,NJ City,W,19,11,18,36,High School,30,Franchise,Complex
1481,191.98,NJ City,N,21,24,12,36,High School,33,Patnership,Standalone
7566,411.02,Boston,S,4,18,22,36,Graduation,23,Patnership,Complex
5323,343.86,Boston,E,22,17,1,43,High School,28,Owner,Standalone
6394,307.96,Boston,S,16,9,10,69,Graduation,27,Patnership,Complex
2131,222.8,NJ City,E,10,17,19,41,High School,34,Owner,Standalone
1355,374.28,NJ City,E,18,12,5,57,No education,19,Owner,Standalone
9127,349.85,NJ City,E,3,23,9,42,Graduation,25,Owner,Standalone
3079,464.59,NJ City,E,9,20,2,79,High School,26,Owner,Standalone
5357,479.89,Boston,N,5,10,6,32,Graduation,35,Patnership,Standalone
5113,316.63,Boston,N,3,23,19,41,Graduation,24,Patnership,Standalone
5328,10.92,Boston,N,2,18,2,42,Graduation,26,Franchise,Complex
6446,441.55,Boston,S,15,21,1,53,No education,23,Patnership,Complex
8788,171.97,NJ City,W,22,11,12,76,Graduation,31,Franchise,Complex
6573,347.09,Boston,N,21,13,22,57,Graduation,28,Patnership,Standalone
4857,429.32,NJ City,N,7,23,3,60,Graduation,24,Patnership,Standalone
673,473.91,NJ City,N,4,10,8,67,High School,22,Patnership,Standalone
4463,402.01,NJ City,E,16,13,22,42,High School,20,Owner,Standalone
4433,292.43,NJ City,N,8,10,16,73,High School,31,Patnership,Standalone
4741,57.79,NJ City,N,24,22,2,52,High School,25,Patnership,Standalone
8603,64.15,NJ City,N,24,12,3,56,No education,31,Owner,Standalone
6478,413.52,Boston,S,13,16,3,55,High School,24,Patnership,Complex
9649,222.61,NJ City,N,16,8,7,48,Graduation,24,Patnership,Standalone
3006,118.48,NJ City,S,20,15,1,54,Graduation,35,Patnership,Complex
6071,143.24,Boston,E,14,17,25,85,Graduation,23,Owner,Standalone
3298,27.67,NJ City,N,4,21,3,77,High School,20,Patnership,Complex
5045,460.36,Boston,N,21,12,2,53,No education,23,Patnership,Standalone
8946,191.66,NJ City,S,25,19,7,47,Graduation,28,Patnership,Complex
961,56.05,NJ City,N,7,9,1,43,High School,19,Patnership,Standalone
369,218.92,NJ City,N,7,14,23,32,High School,27,Patnership,Standalone
5033,491.83,Boston,N,3,24,2,40,No education,33,Patnership,Standalone
6008,463.83,Boston,W,14,24,15,29,Graduation,33,Franchise,Complex
8296,191.87,NJ City,W,23,19,18,82,Graduation,26,Franchise,Complex
5774,407.27,Boston,S,24,8,17,35,Graduation,29,Patnership,Complex
294,283.91,NJ City,S,20,17,4,73,Graduation,18,Patnership,Complex
7794,438.54,Boston,S,19,12,3,73,Graduation,21,Patnership,Complex
4879,109.86,NJ City,E,21,20,4,77,High School,19,Owner,Standalone
7832,170.04,Boston,W,14,11,4,62,No education,26,Franchise,Complex
5423,281.73,Boston,E,12,12,8,51,Graduation,31,Owner,Standalone
8726,444.57,NJ City,S,21,18,7,61,Graduation,32,Patnership,Complex
5892,458.09,Boston,W,11,17,6,51,Graduation,35,Franchise,Complex
9176,379.03,NJ City,W,6,11,4,46,No education,24,Franchise,Complex
6924,393.71,Boston,W,8,10,21,75,Graduation,24,Franchise,Complex
6985,278.17,Boston,N,18,14,24,26,Graduation,21,Patnership,Standalone
1922,38.97,NJ City,N,21,15,3,69,No education,32,Patnership,Complex
680,433.81,NJ City,W,14,15,9,50,High School,21,Franchise,Complex
9011,96.59,NJ City,E,8,15,2,38,No education,34,Owner,Standalone
7169,36.61,Boston,N,16,15,1,45,No education,26,Patnership,Standalone
9638,314.18,NJ City,S,21,14,17,31,Graduation,20,Patnership,Complex
8824,55.54,NJ City,W,12,15,2,82,High School,29,Franchise,Complex
4190,365.26,NJ City,S,7,21,24,33,High School,32,Patnership,Complex
3353,151.81,NJ City,N,19,8,16,72,High School,26,Patnership,Standalone
5012,417.91,Boston,W,3,11,4,78,No education,31,Franchise,Complex
2281,264.21,NJ City,N,19,21,8,83,High School,22,Patnership,Standalone
5070,344.97,Boston,S,13,12,6,44,Graduation,33,Patnership,Complex
4438,223.69,NJ City,S,21,9,22,30,High School,23,Patnership,Complex
9705,79.41,NJ City,N,14,12,4,26,Graduation,32,Patnership,Standalone
6333,414.79,Boston,N,16,19,8,30,Graduation,21,Patnership,Standalone
6117,480.19,Boston,N,21,13,17,64,Graduation,34,Patnership,Standalone
9042,27.63,NJ City,N,12,20,2,68,Graduation,30,Patnership,Complex
6603,317.03,Boston,E,25,17,10,68,Graduation,18,Owner,Standalone
4454,317.64,NJ City,S,5,20,8,30,High School,29,Patnership,Complex
3537,287.57,NJ City,N,14,22,2,41,Graduation,35,Patnership,Standalone
2106,81.32,NJ City,S,12,15,1,85,Graduation,26,Patnership,Complex
3788,23.45,NJ City,N,13,9,3,77,No education,35,Franchise,Complex
3666,158.97,NJ City,S,13,13,8,63,High School,33,Patnership,Complex
2182,193.21,NJ City,S,25,9,21,63,High School,28,Patnership,Complex
7076,475.06,Boston,W,4,22,3,72,No education,32,Franchise,Complex
4233,380.44,NJ City,N,5,9,6,52,High School,31,Patnership,Standalone
7379,327.83,Boston,E,8,8,4,65,No education,28,Owner,Standalone
8768,212.83,NJ City,W,11,9,9,34,Graduation,19,Franchise,Complex
4533,168.6,NJ City,N,10,16,9,82,High School,25,Patnership,Standalone
1495,382.95,NJ City,E,8,8,17,68,High School,22,Owner,Standalone
8713,106,NJ City,N,8,8,3,71,High School,22,Patnership,Standalone
6400,44.49,Boston,N,20,23,1,55,High School,27,Franchise,Complex
3348,71.07,NJ City,N,9,9,3,54,Graduation,35,Franchise,Complex
3970,410.46,NJ City,S,13,11,14,36,High School,18,Patnership,Complex
9367,190.81,NJ City,E,11,22,7,56,Graduation,20,Owner,Standalone
7272,265.82,Boston,W,8,21,10,83,Graduation,30,Franchise,Complex
5942,340.16,Boston,S,11,22,4,50,No education,30,Patnership,Complex
5755,495.67,Boston,E,23,21,3,44,High School,34,Owner,Standalone
344,12.66,NJ City,N,10,21,4,55,No education,28,Franchise,Complex
1232,440.17,NJ City,W,19,22,11,43,High School,35,Franchise,Complex
6210,439.39,Boston,S,1,15,16,53,Graduation,18,Patnership,Complex
8665,44.88,NJ City,N,3,21,3,59,High School,34,Patnership,Standalone
1024,204.34,NJ City,W,4,12,1,65,High School,35,Franchise,Complex
4482,178.08,NJ City,S,14,13,14,74,High School,28,Patnership,Complex
9222,356.56,NJ City,S,13,8,19,76,Graduation,23,Patnership,Complex
7417,333.17,Boston,N,4,16,2,54,High School,23,Patnership,Standalone
3912,104.07,NJ City,W,5,19,2,39,Graduation,28,Franchise,Complex
107,412.4,NJ City,E,21,13,16,69,High School,23,Owner,Standalone
6230,220.53,Boston,S,14,12,2,50,No education,32,Patnership,Complex
2344,119.79,NJ City,W,1,9,2,52,High School,34,Franchise,Complex
7830,18.1,Boston,N,20,10,3,58,Graduation,34,Patnership,Complex
9828,284.45,NJ City,W,7,20,20,69,Graduation,31,Franchise,Complex
8489,347.45,NJ City,N,14,11,12,81,Graduation,23,Patnership,Standalone
4627,17.76,NJ City,N,5,13,4,27,High School,26,Owner,Standalone
3637,388.69,NJ City,N,10,12,14,73,High School,34,Patnership,Standalone
6896,8.46,Boston,N,18,23,2,55,No education,22,Franchise,Complex
4275,241.65,NJ City,E,24,13,2,53,Graduation,32,Owner,Standalone
2684,326.85,NJ City,W,18,14,10,58,High School,31,Franchise,Complex
2535,243.87,NJ City,E,8,21,12,59,High School,32,Owner,Standalone
585,391.41,NJ City,N,21,8,4,49,Graduation,33,Patnership,Standalone
731,132.94,NJ City,E,8,18,20,54,High School,32,Owner,Standalone
9361,115.47,NJ City,N,19,16,4,76,High School,18,Patnership,Standalone
4833,385.7,NJ City,N,6,18,1,84,Graduation,19,Patnership,Standalone
6197,201.31,Boston,N,18,15,5,45,No education,18,Patnership,Standalone
3723,15.59,NJ City,N,16,17,2,64,Graduation,34,Owner,Standalone
2474,295.67,NJ City,S,6,17,21,84,High School,34,Patnership,Complex
3090,24.9,NJ City,N,15,15,1,64,Graduation,35,Patnership,Complex
5715,487.35,Boston,E,8,24,4,72,Graduation,23,Owner,Standalone
5726,446.35,Boston,S,12,16,8,43,Graduation,25,Patnership,Complex
7140,196.65,Boston,W,5,22,1,29,Graduation,33,Franchise,Complex
5084,288.84,Boston,W,16,12,12,36,Graduation,18,Franchise,Complex
582,238.42,NJ City,S,23,14,13,78,High School,33,Patnership,Complex
1597,159.37,NJ City,N,13,15,15,25,High School,23,Patnership,Standalone
9813,380.51,NJ City,N,20,17,3,29,Graduation,33,Patnership,Standalone
6872,335.99,Boston,W,9,8,9,35,Graduation,26,Franchise,Complex
5924,324.02,Boston,W,5,9,12,81,Graduation,18,Franchise,Complex
5962,416.49,Boston,S,1,14,8,48,Graduation,29,Patnership,Complex
881,452.94,NJ City,N,24,11,7,65,High School,35,Patnership,Standalone
7384,230.16,Boston,W,13,9,14,59,Graduation,25,Franchise,Complex
2975,131.73,NJ City,E,13,23,8,48,High School,31,Owner,Standalone
1728,210.52,NJ City,W,15,8,23,39,High School,35,Franchise,Complex
1839,229.47,NJ City,E,23,18,24,26,High School,29,Owner,Standalone
3601,495.9,NJ City,N,10,14,17,33,High School,31,Patnership,Standalone
3645,466.46,NJ City,N,10,24,4,48,Graduation,26,Patnership,Standalone
8056,132.3,NJ City,W,22,18,15,75,Graduation,30,Franchise,Complex
4314,380.86,NJ City,S,18,16,17,52,High School,29,Patnership,Complex
1465,73.49,NJ City,N,13,18,3,43,High School,22,Patnership,Standalone
8320,271.03,NJ City,W,6,22,13,68,Graduation,22,Franchise,Complex
609,317.77,NJ City,N,24,17,1,27,Graduation,28,Patnership,Standalone
292,119.04,NJ City,W,4,20,1,40,High School,29,Franchise,Complex
9206,385.15,NJ City,S,9,12,13,34,Graduation,27,Patnership,Complex
9018,293.18,NJ City,S,23,11,17,53,Graduation,27,Patnership,Complex
5365,490.57,Boston,N,16,12,23,70,Graduation,25,Patnership,Standalone
5399,453.94,Boston,E,14,19,3,73,No education,32,Owner,Standalone
5246,43.38,Boston,N,15,14,2,38,No education,24,Patnership,Complex
303,2.32,NJ City,E,20,12,4,54,Graduation,18,Owner,Standalone
5479,309.04,Boston,E,11,19,20,50,Graduation,19,Owner,Standalone
3440,144.41,NJ City,W,14,13,2,72,No education,33,Franchise,Complex
1291,426.73,NJ City,E,15,22,23,28,High School,33,Owner,Standalone
7003,403.44,Boston,E,21,11,22,40,Graduation,24,Owner,Standalone
6565,94.51,Boston,N,15,16,3,29,High School,27,Patnership,Standalone
8470,335.84,NJ City,S,10,20,21,35,Graduation,19,Patnership,Complex
6854,409.68,Boston,S,6,23,13,73,Graduation,32,Patnership,Complex
3279,354.6,NJ City,E,23,23,7,45,High School,21,Owner,Standalone
3778,109,NJ City,S,1,20,9,63,High School,18,Patnership,Complex
7790,468.49,Boston,S,5,16,10,59,Graduation,29,Patnership,Complex
8741,472.8,NJ City,N,15,24,2,78,No education,34,Patnership,Standalone
1200,348.72,NJ City,W,17,24,16,45,High School,27,Franchise,Complex
7590,402.56,Boston,S,12,18,14,62,Graduation,30,Patnership,Complex
5905,107.34,Boston,N,18,22,2,55,High School,21,Patnership,Standalone
1983,443.45,NJ City,E,20,21,10,49,High School,25,Owner,Standalone
4654,262,NJ City,S,3,17,12,40,High School,32,Patnership,Complex
7187,109.99,Boston,E,3,13,3,65,No education,27,Owner,Standalone
5341,137.39,Boston,N,10,16,24,32,Graduation,35,Patnership,Standalone
6564,104.75,Boston,W,14,12,2,37,Graduation,35,Franchise,Complex
9433,281.48,NJ City,N,9,8,25,58,Graduation,19,Patnership,Standalone
3174,310,NJ City,S,1,16,10,80,High School,19,Patnership,Complex
5193,457.02,Boston,N,16,8,10,71,Graduation,33,Patnership,Standalone
4887,492.48,NJ City,E,9,17,16,79,High School,25,Owner,Standalone
6877,176.47,Boston,N,7,22,19,41,Graduation,30,Patnership,Standalone
3287,369.87,NJ City,E,13,14,16,30,High School,23,Owner,Standalone
6652,37.03,Boston,N,6,21,1,75,High School,18,Franchise,Complex
7396,242.59,Boston,W,4,14,7,62,Graduation,18,Franchise,Complex
2166,20.78,NJ City,N,6,21,2,34,Graduation,25,Patnership,Complex
2329,172.34,NJ City,N,7,22,13,31,High School,28,Patnership,Standalone
5821,204.98,Boston,N,24,13,4,61,High School,21,Patnership,Standalone
9835,107.08,NJ City,E,4,11,1,58,High School,28,Owner,Standalone
2370,317.21,NJ City,S,16,13,9,36,High School,30,Patnership,Complex
7001,99.21,Boston,N,17,21,3,66,No education,22,Patnership,Standalone
895,84.54,NJ City,E,15,16,3,51,High School,18,Owner,Standalone
4862,160.45,NJ City,S,13,9,11,76,High School,26,Patnership,Complex
5308,341.25,Boston,W,8,20,5,66,High School,21,Franchise,Complex
9796,14.84,NJ City,N,23,10,3,51,High School,21,Franchise,Complex
2098,485.46,NJ City,S,17,9,23,45,High School,22,Patnership,Complex
2585,14.5,NJ City,N,1,9,3,52,No education,33,Patnership,Standalone
9808,274.6,NJ City,W,8,20,10,54,Graduation,25,Franchise,Complex
2443,303,NJ City,E,1,12,6,56,High School,25,Owner,Standalone
1755,198.05,NJ City,E,10,17,8,81,High School,28,Owner,Standalone
498,134.84,NJ City,S,16,18,13,54,High School,28,Patnership,Complex
3955,230.87,NJ City,E,7,11,9,32,High School,24,Owner,Standalone
1392,296.67,NJ City,W,1,23,4,59,Graduation,35,Franchise,Complex
7623,121.32,Boston,E,7,13,4,69,Graduation,30,Owner,Standalone
5351,144.71,Boston,E,9,18,4,50,No education,24,Owner,Standalone
9790,301.05,NJ City,S,13,14,21,78,Graduation,24,Patnership,Complex
7297,389.24,Boston,N,12,15,12,36,Graduation,23,Patnership,Standalone
1872,246.83,NJ City,W,17,21,4,42,Graduation,29,Franchise,Complex
7541,285.49,Boston,N,24,24,6,27,Graduation,29,Patnership,Standalone
5611,235.02,Boston,E,3,20,20,33,Graduation,28,Owner,Standalone
8811,113.22,NJ City,E,19,14,3,60,Graduation,22,Owner,Standalone
2226,72.33,NJ City,N,9,23,4,35,Graduation,32,Patnership,Complex
5736,490.23,Boston,W,21,22,3,32,Graduation,26,Franchise,Complex
4551,372.08,NJ City,E,12,16,20,64,High School,24,Owner,Standalone
847,131.81,NJ City,E,15,21,13,59,High School,26,Owner,Standalone
6665,395.66,Boston,N,21,24,25,34,Graduation,31,Patnership,Standalone
8175,228.72,NJ City,E,6,20,12,68,Graduation,26,Owner,Standalone
2700,335.3,NJ City,W,21,15,14,43,High School,26,Franchise,Complex
201,438.49,NJ City,N,23,16,25,36,High School,29,Patnership,Standalone
5053,393.29,Boston,N,14,24,10,34,Graduation,31,Patnership,Standalone
5851,78.32,Boston,E,2,13,3,31,High School,26,Owner,Standalone
4234,201.37,NJ City,S,25,17,9,33,High School,31,Patnership,Complex
5597,477.47,Boston,N,23,20,1,27,No education,35,Patnership,Standalone
1912,59.77,NJ City,W,22,8,4,72,High School,23,Franchise,Complex
9409,99.07,NJ City,N,2,18,3,43,High School,25,Patnership,Standalone
898,436.15,NJ City,S,20,11,14,77,High School,25,Patnership,Complex
2414,417.55,NJ City,S,8,11,2,38,No education,30,Patnership,Complex
6430,413.12,Boston,S,13,22,16,79,Graduation,27,Patnership,Complex
4324,261.67,NJ City,W,8,17,16,43,High School,28,Franchise,Complex
3833,333.97,NJ City,N,7,18,4,61,No education,31,Patnership,Standalone
8885,387.92,NJ City,N,8,24,25,42,Graduation,27,Patnership,Standalone
143,225.18,NJ City,E,24,10,23,25,High School,35,Owner,Standalone
1962,103.41,NJ City,S,19,12,4,51,Graduation,29,Patnership,Complex
860,43.38,NJ City,N,21,23,2,26,No education,21,Franchise,Complex
9195,79.3,NJ City,E,8,12,1,36,Graduation,28,Owner,Standalone
5522,52.96,Boston,S,5,18,1,80,No education,28,Patnership,Complex
5223,9.96,Boston,N,22,8,2,76,Graduation,23,Owner,Standalone
8912,430.94,NJ City,W,4,15,14,46,Graduation,32,Franchise,Complex
7135,484.44,Boston,E,22,12,20,63,Graduation,32,Owner,Standalone
9582,234.79,NJ City,S,12,13,7,56,Graduation,18,Patnership,Complex
8926,438.23,NJ City,S,17,24,6,45,Graduation,25,Patnership,Complex
3370,449.01,NJ City,S,13,13,17,26,High School,23,Patnership,Complex
6779,398.6,Boston,E,20,10,4,55,No education,27,Owner,Standalone
8626,241.88,NJ City,S,20,14,17,52,Graduation,31,Patnership,Complex
7954,298.58,Boston,S,14,11,11,62,Graduation,24,Patnership,Complex
3037,154.42,NJ City,N,18,17,16,60,High School,21,Patnership,Standalone
8364,376.46,NJ City,W,9,10,13,60,Graduation,22,Franchise,Complex
9344,389.31,NJ City,W,20,8,12,38,Graduation,23,Franchise,Complex
2643,396.57,NJ City,E,5,18,5,34,Graduation,35,Owner,Standalone
2256,225.47,NJ City,W,16,8,21,79,High School,22,Franchise,Complex
6705,337.09,Boston,N,5,10,8,62,Graduation,30,Patnership,Standalone
1272,278.64,NJ City,W,11,8,22,85,High School,34,Franchise,Complex
2381,425.35,NJ City,N,25,12,3,59,No education,29,Patnership,Standalone
7442,471.97,Boston,S,5,17,16,28,Graduation,20,Patnership,Complex
7877,212,Boston,N,17,10,14,85,Graduation,32,Patnership,Standalone
8047,450.08,NJ City,E,20,20,25,45,Graduation,29,Owner,Standalone
6474,379.73,Boston,S,25,13,16,31,Graduation,27,Patnership,Complex
356,10.01,NJ City,N,11,19,4,54,No education,18,Franchise,Complex
4916,49.49,NJ City,W,5,12,2,60,No education,22,Franchise,Complex
3167,15.77,NJ City,N,6,11,2,81,No education,32,Owner,Standalone
579,292.4,NJ City,E,12,17,22,57,High School,30,Owner,Standalone
9910,482.88,NJ City,S,16,12,5,40,High School,30,Patnership,Complex
8261,117.51,NJ City,N,7,10,3,54,No education,21,Patnership,Standalone
5746,298.24,Boston,S,4,14,1,78,High School,21,Patnership,Complex
8536,66.02,NJ City,N,9,18,3,76,High School,20,Franchise,Complex
6912,22.72,Boston,N,19,12,3,26,Graduation,29,Franchise,Complex
9816,56.04,NJ City,W,10,17,4,35,Graduation,32,Franchise,Complex
5751,101.17,Boston,E,21,19,3,56,Graduation,34,Owner,Standalone
8644,160.29,NJ City,W,9,16,4,44,High School,19,Franchise,Complex
9071,117.15,NJ City,E,12,19,4,56,No education,33,Owner,Standalone
327,162.09,NJ City,E,4,12,11,69,High School,31,Owner,Standalone
7210,19.91,Boston,N,18,11,3,29,High School,32,Patnership,Complex
3242,173.66,NJ City,S,15,15,10,48,High School,33,Patnership,Complex
1333,163,NJ City,N,3,16,7,45,High School,28,Patnership,Standalone
1384,259.72,NJ City,W,9,16,5,38,High School,19,Franchise,Complex
2862,60.73,NJ City,N,10,20,4,25,Graduation,33,Patnership,Complex
3742,242.72,NJ City,S,20,13,12,81,High School,33,Patnership,Complex
3266,118.23,NJ City,S,4,8,1,25,No education,33,Patnership,Complex
9432,5.02,NJ City,N,14,17,2,74,Graduation,27,Franchise,Complex
4766,291.33,NJ City,S,13,12,10,57,High School,28,Patnership,Complex
2155,253.77,NJ City,E,10,20,6,63,High School,27,Owner,Standalone
9607,450.41,NJ City,E,6,14,1,70,High School,28,Owner,Standalone
3324,37.21,NJ City,N,21,18,3,35,Graduation,29,Franchise,Complex
7077,250.83,Boston,N,23,9,5,54,Graduation,22,Patnership,Standalone
8693,57.21,NJ City,N,14,8,4,43,No education,34,Patnership,Standalone
9493,209.46,NJ City,N,5,19,4,36,High School,19,Patnership,Standalone
3553,56.93,NJ City,N,19,8,1,83,High School,19,Patnership,Standalone
859,321.84,NJ City,E,16,9,18,54,High School,33,Owner,Standalone
6322,222.87,Boston,S,12,19,17,81,Graduation,27,Patnership,Complex
5525,282.35,Boston,N,24,10,23,57,Graduation,33,Patnership,Standalone
2189,262.7,NJ City,N,25,16,4,49,No education,29,Patnership,Standalone
8144,12.48,NJ City,N,6,16,3,37,No education,24,Franchise,Complex
3865,464.38,NJ City,N,10,23,13,55,High School,33,Patnership,Standalone
6421,445.77,Boston,N,6,12,11,26,Graduation,30,Patnership,Standalone
2145,399.78,NJ City,N,11,22,10,40,High School,29,Patnership,Standalone
7769,151.24,Boston,N,3,8,7,45,Graduation,26,Patnership,Standalone
8879,450.22,NJ City,E,8,11,17,40,Graduation,24,Owner,Standalone
4522,365.62,NJ City,S,25,15,17,56,High School,32,Patnership,Complex
6905,269.32,Boston,N,17,17,5,44,No education,32,Patnership,Standalone
6433,290.98,Boston,N,6,12,19,66,Graduation,34,Patnership,Standalone
1568,206,NJ City,W,1,16,22,75,High School,23,Franchise,Complex
9190,34.22,NJ City,N,18,20,4,52,High School,28,Patnership,Complex
9172,480.36,NJ City,W,25,13,12,75,Graduation,24,Franchise,Complex
3332,269.02,NJ City,W,8,18,15,31,High School,25,Franchise,Complex
1127,428.31,NJ City,E,24,21,13,51,High School,22,Owner,Standalone
1904,440.63,NJ City,W,23,9,20,73,High School,27,Franchise,Complex
4523,133.66,NJ City,E,25,9,19,45,High School,18,Owner,Standalone
6597,435.54,Boston,N,2,24,20,29,Graduation,19,Patnership,Standalone
5186,248.44,Boston,S,21,21,23,48,Graduation,29,Patnership,Complex
3641,458.36,NJ City,N,7,21,13,50,High School,29,Patnership,Standalone
9182,471.71,NJ City,S,4,16,6,69,Graduation,30,Patnership,Complex
6159,303.63,Boston,E,23,15,19,41,Graduation,26,Owner,Standalone
1727,92.02,NJ City,E,9,12,3,41,No education,22,Owner,Standalone
268,261.42,NJ City,W,17,11,25,33,High School,19,Franchise,Complex
2551,85.06,NJ City,E,3,15,1,56,High School,27,Owner,Standalone
7034,358.7,Boston,S,6,11,14,76,Graduation,26,Patnership,Complex
2432,349.36,NJ City,W,7,9,25,58,High School,27,Franchise,Complex
2269,145.22,NJ City,N,16,24,16,66,High School,31,Patnership,Standalone
7365,90.22,Boston,N,22,8,1,32,Graduation,26,Patnership,Standalone
5301,105.76,Boston,N,1,11,2,81,Graduation,30,Patnership,Standalone
6188,186.48,Boston,W,8,22,12,35,Graduation,19,Franchise,Complex
1302,235,NJ City,S,1,11,23,38,High School,27,Patnership,Complex
1578,402.71,NJ City,S,18,24,11,85,High School,32,Patnership,Complex
8574,337.81,NJ City,S,17,8,2,66,Graduation,34,Patnership,Complex
7936,450.57,Boston,W,21,9,4,65,High School,23,Franchise,Complex
4924,234.67,NJ City,W,7,9,11,85,High School,31,Franchise,Complex
5143,67.89,Boston,N,13,15,3,76,High School,31,Owner,Standalone
3240,173.11,NJ City,W,18,21,22,70,High School,29,Franchise,Complex
169,231.65,NJ City,N,12,10,8,51,High School,31,Patnership,Standalone
5761,427.8,Boston,N,20,8,1,51,High School,30,Patnership,Standalone
3415,399.54,NJ City,E,15,10,6,62,High School,31,Owner,Standalone
7702,47.76,Boston,S,3,16,4,31,High School,29,Patnership,Complex
6335,144.87,Boston,E,6,20,4,28,No education,21,Owner,Standalone
4365,26.95,NJ City,N,23,17,2,34,Graduation,24,Patnership,Standalone
9147,410.44,NJ City,E,11,10,1,82,Graduation,22,Owner,Standalone
6369,467.17,Boston,N,15,13,4,27,Graduation,24,Patnership,Standalone
5240,447.06,Boston,W,15,14,12,53,Graduation,27,Franchise,Complex
343,313,NJ City,E,2,18,6,41,High School,30,Owner,Standalone
7807,435.99,Boston,E,5,22,1,53,High School,23,Owner,Standalone
3554,211.92,NJ City,S,8,12,25,27,High School,30,Patnership,Complex
9987,495.22,NJ City,E,7,13,17,52,Graduation,25,Owner,Standalone
1031,131.51,NJ City,E,14,10,19,41,High School,27,Owner,Standalone
5719,212.94,Boston,E,4,19,17,31,Graduation,18,Owner,Standalone
4550,477.76,NJ City,S,4,10,13,38,High School,21,Patnership,Complex
2691,236.17,NJ City,E,25,22,6,53,High School,26,Owner,Standalone
6127,175.48,Boston,E,6,17,13,58,Graduation,29,Owner,Standalone
3830,74.28,NJ City,S,1,18,2,60,No education,33,Patnership,Complex
8170,71.05,NJ City,N,13,10,1,65,High School,35,Patnership,Complex
2096,194.97,NJ City,W,4,15,18,27,High School,21,Franchise,Complex
8578,463.72,NJ City,S,7,12,17,52,Graduation,30,Patnership,Complex
9241,51.23,NJ City,N,9,15,3,36,High School,31,Patnership,Standalone
4479,64.88,NJ City,N,21,17,1,70,Graduation,26,Owner,Standalone
5010,262.59,Boston,S,11,10,9,44,Graduation,28,Patnership,Complex
4505,241.26,NJ City,N,22,10,22,33,High School,35,Patnership,Standalone
466,378.14,NJ City,S,14,15,10,66,High School,20,Patnership,Complex
7547,18.03,Boston,N,16,22,4,67,No education,34,Owner,Standalone
1733,392.43,NJ City,N,11,16,23,65,High School,33,Patnership,Standalone
1436,198.97,NJ City,W,7,19,13,30,High School,20,Franchise,Complex
8060,173.62,NJ City,W,5,19,7,39,Graduation,18,Franchise,Complex
1661,405.77,NJ City,N,18,19,3,59,No education,34,Patnership,Standalone
7922,161.76,Boston,S,15,18,7,85,Graduation,24,Patnership,Complex
4036,463.32,NJ City,W,19,18,16,67,High School,27,Franchise,Complex
5406,73.97,Boston,S,17,11,2,37,Graduation,32,Patnership,Complex
5911,248.59,Boston,E,21,8,13,59,Graduation,31,Owner,Standalone
210,205,NJ City,S,3,10,9,82,High School,28,Patnership,Complex
6725,251.54,Boston,N,19,9,16,62,Graduation,20,Patnership,Standalone
6994,282.8,Boston,S,21,23,17,47,Graduation,20,Patnership,Complex
509,429.34,NJ City,N,13,22,6,73,High School,32,Patnership,Standalone
5476,288.56,Boston,W,15,8,20,63,Graduation,24,Franchise,Complex
330,44.9,NJ City,N,6,8,2,56,Graduation,35,Patnership,Complex
8096,444.62,NJ City,W,6,18,16,84,Graduation,25,Franchise,Complex
9929,373.2,NJ City,N,4,11,9,70,Graduation,27,Patnership,Standalone
6223,139.83,Boston,E,22,19,1,35,High School,28,Owner,Standalone
5639,468.8,Boston,E,17,10,18,66,Graduation,20,Owner,Standalone
4296,350.21,NJ City,W,13,24,8,48,High School,33,Franchise,Complex
6016,430.39,Boston,W,3,10,11,76,Graduation,22,Franchise,Complex
6021,37.56,Boston,N,4,21,4,29,Graduation,30,Patnership,Standalone
8352,384.72,NJ City,W,1,20,21,58,Graduation,30,Franchise,Complex
1865,185.72,NJ City,N,10,18,18,75,High School,22,Patnership,Standalone
3056,59.51,NJ City,W,6,16,4,40,No education,31,Franchise,Complex
3041,250.17,NJ City,N,9,17,22,53,High School,20,Patnership,Standalone
4258,429.76,NJ City,S,9,16,14,33,High School,30,Patnership,Complex
4908,53.11,NJ City,W,7,13,3,31,Graduation,34,Franchise,Complex
5410,229.54,Boston,S,10,10,13,46,Graduation,19,Patnership,Complex
9916,179.84,NJ City,W,14,17,1,26,High School,24,Franchise,Complex
3855,437.3,NJ City,E,10,8,24,73,High School,22,Owner,Standalone
5605,312.11,Boston,N,14,14,14,69,Graduation,28,Patnership,Standalone
7595,418.75,Boston,E,7,23,12,62,Graduation,28,Owner,Standalone
83,305.49,Boston,E,11,20,22,76,High School,29,Owner,Standalone
6066,211.73,Boston,S,13,21,24,47,Graduation,24,Patnership,Complex
4156,2.63,NJ City,W,21,18,4,60,High School,35,Franchise,Complex
1687,376.18,NJ City,E,2,21,1,43,High School,35,Owner,Standalone
469,237.41,NJ City,N,8,18,2,36,High School,20,Patnership,Standalone
496,5.99,NJ City,N,10,8,1,70,High School,33,Franchise,Complex
6015,232.42,Boston,E,12,9,22,61,Graduation,31,Owner,Standalone
1139,49.43,NJ City,E,21,9,4,60,No education,30,Owner,Standalone
8117,43.44,NJ City,N,7,18,4,44,No education,29,Patnership,Standalone
2061,267.47,NJ City,N,13,22,10,72,High School,18,Patnership,Standalone
2754,80.87,NJ City,S,17,9,2,55,Graduation,24,Patnership,Complex
4724,112.5,NJ City,W,2,10,2,40,No education,35,Franchise,Complex
9743,386.94,NJ City,E,2,10,17,51,Graduation,24,Owner,Standalone
7203,338.91,Boston,E,19,24,8,36,Graduation,18,Owner,Standalone
211,430.58,NJ City,E,20,8,10,43,High School,35,Owner,Standalone
7405,175.26,Boston,N,15,15,8,31,Graduation,23,Patnership,Standalone
2951,28.15,NJ City,N,13,12,2,73,No education,21,Owner,Standalone
9279,172.41,NJ City,E,23,16,8,73,Graduation,29,Owner,Standalone
9527,243.22,NJ City,E,7,15,18,45,Graduation,21,Owner,Standalone
5538,418.25,Boston,S,7,19,20,44,Graduation,26,Patnership,Complex
4575,295.28,NJ City,E,7,19,19,29,High School,35,Owner,Standalone
7126,454.26,Boston,S,11,13,24,64,Graduation,24,Patnership,Complex
8861,330.34,NJ City,N,11,13,9,26,Graduation,31,Patnership,Standalone
8974,229.07,NJ City,S,13,14,25,73,Graduation,27,Patnership,Complex
1315,375.14,NJ City,E,25,13,10,71,High School,33,Owner,Standalone
9749,244.71,NJ City,N,13,20,25,30,Graduation,29,Patnership,Standalone
1411,27.3,NJ City,N,8,10,2,73,High School,35,Owner,Standalone
6551,401.71,Boston,E,20,22,15,72,Graduation,33,Owner,Standalone
7499,218.26,Boston,E,20,22,22,37,Graduation,32,Owner,Standalone
6227,338.36,Boston,E,6,16,19,65,Graduation,20,Owner,Standalone
9027,474.61,NJ City,E,23,20,4,42,Graduation,20,Owner,Standalone
5469,134.54,Boston,N,22,22,21,71,Graduation,31,Patnership,Standalone
2461,440,NJ City,N,7,23,14,49,High School,35,Patnership,Standalone
7435,282.4,Boston,E,5,24,23,62,Graduation,19,Owner,Standalone
365,399.81,NJ City,N,15,13,9,42,High School,31,Patnership,Standalone
9805,180.81,NJ City,N,24,23,15,78,Graduation,25,Patnership,Standalone
2079,108.23,NJ City,E,8,11,3,84,Graduation,34,Owner,Standalone
8317,474.31,NJ City,N,7,10,17,37,Graduation,32,Patnership,Standalone
3381,382.33,NJ City,N,6,13,17,27,High School,28,Patnership,Standalone
753,7.52,NJ City,N,25,11,4,84,Graduation,35,Patnership,Standalone
8715,480.64,NJ City,E,6,12,7,38,Graduation,26,Owner,Standalone
9055,53.14,NJ City,E,7,24,4,82,High School,31,Owner,Standalone
6240,442.94,Boston,W,12,24,4,38,Graduation,19,Franchise,Complex
2398,141.05,NJ City,S,24,10,20,81,High School,28,Patnership,Complex
7540,87.83,Boston,W,6,19,1,63,High School,34,Franchise,Complex
6193,225.56,Boston,N,22,14,25,69,Graduation,34,Patnership,Standalone
8014,309.37,Boston,S,20,21,3,50,High School,35,Patnership,Complex
7205,130.61,Boston,N,24,24,17,75,Graduation,29,Patnership,Standalone
9897,164.87,NJ City,N,19,23,18,35,Graduation,27,Patnership,Standalone
6243,477.22,Boston,E,5,14,11,43,Graduation,35,Owner,Standalone
8825,217.82,NJ City,N,13,24,17,65,Graduation,29,Patnership,Standalone
802,449.62,NJ City,S,25,8,18,77,High School,23,Patnership,Complex
2632,213.14,NJ City,W,9,17,21,85,High School,30,Franchise,Complex
9803,214.32,NJ City,E,10,8,1,84,No education,30,Owner,Standalone
9779,164.35,NJ City,E,1,9,9,44,Graduation,28,Owner,Standalone
1227,471.59,NJ City,E,21,9,3,82,Graduation,28,Owner,Standalone
5954,299,Boston,S,5,21,18,68,Graduation,20,Patnership,Complex
59,128.43,Boston,E,25,23,21,60,High School,34,Owner,Standalone
2220,319.1,NJ City,W,1,24,5,54,Graduation,20,Franchise,Complex
2875,371.24,NJ City,E,6,22,6,55,High School,31,Owner,Standalone
5874,245.67,Boston,S,15,13,23,78,Graduation,28,Patnership,Complex
352,130.07,NJ City,W,16,19,10,67,High School,21,Franchise,Complex
9494,107.66,NJ City,S,20,17,2,78,No education,31,Patnership,Complex
7741,475.46,Boston,N,14,9,24,55,Graduation,18,Patnership,Standalone
8799,338.1,NJ City,E,24,10,16,77,Graduation,27,Owner,Standalone
347,466.67,NJ City,E,11,21,1,62,No education,32,Owner,Standalone
8668,359.4,NJ City,W,5,23,23,26,Graduation,25,Franchise,Complex
5274,205.33,Boston,S,22,13,15,52,Graduation,30,Patnership,Complex
5985,390.55,Boston,N,8,10,24,69,Graduation,25,Patnership,Standalone
5649,410.21,Boston,N,5,9,14,27,Graduation,27,Patnership,Standalone
406,281.21,NJ City,S,9,11,4,68,High School,30,Patnership,Complex
9336,492.91,NJ City,W,21,17,19,50,Graduation,23,Franchise,Complex
2829,265.91,NJ City,N,16,20,10,79,High School,30,Patnership,Standalone
946,40.01,NJ City,N,19,17,4,43,High School,28,Patnership,Complex
8576,492.11,NJ City,W,15,18,1,40,No education,24,Franchise,Complex
2694,215.76,NJ City,S,23,19,22,59,High School,33,Patnership,Complex
1402,35.74,NJ City,N,16,22,2,30,High School,24,Patnership,Complex
2360,234.84,NJ City,W,1,17,4,78,No education,28,Franchise,Complex
6728,346.21,Boston,W,3,13,17,62,Graduation,31,Franchise,Complex
2867,75.73,NJ City,E,6,8,2,68,No education,30,Owner,Standalone
6455,123.05,Boston,E,24,16,2,43,No education,29,Owner,Standalone
4392,252,NJ City,W,3,8,25,76,High School,32,Franchise,Complex
1193,302,NJ City,N,1,17,25,40,High School,29,Patnership,Standalone
3202,140.76,NJ City,S,13,19,25,49,High School,29,Patnership,Complex
7295,54.82,Boston,E,17,19,4,83,No education,24,Owner,Standalone
3853,173,NJ City,N,1,8,16,67,High School,28,Patnership,Standalone
1874,458.34,NJ City,S,11,15,18,38,High School,26,Patnership,Complex
9606,155.57,NJ City,S,2,21,25,29,Graduation,28,Patnership,Complex
42,10.15,Boston,N,6,20,1,65,Graduation,19,Patnership,Complex
5836,184.64,Boston,W,4,24,24,61,Graduation,25,Franchise,Complex
4035,460.41,NJ City,E,24,9,12,26,High School,23,Owner,Standalone
1549,211.57,NJ City,N,8,16,25,72,High School,21,Patnership,Standalone
7393,383.75,Boston,N,25,18,18,26,Graduation,24,Patnership,Standalone
1306,245.24,NJ City,S,19,22,24,75,High School,25,Patnership,Complex
9142,417.24,NJ City,S,3,11,12,37,Graduation,23,Patnership,Complex
9868,467.67,NJ City,W,13,18,6,30,Graduation,19,Franchise,Complex
561,355.27,NJ City,N,4,20,10,53,High School,22,Patnership,Standalone
4729,449.87,NJ City,N,17,13,15,39,High School,32,Patnership,Standalone
2156,120.22,NJ City,W,15,9,3,82,No education,19,Franchise,Complex
7113,490.4,Boston,N,18,19,8,72,Graduation,19,Patnership,Standalone
4467,5.12,NJ City,N,10,24,3,59,Graduation,28,Owner,Standalone
6522,212.15,Boston,S,14,15,7,41,Graduation,26,Patnership,Complex
9386,172.4,NJ City,S,16,9,12,39,Graduation,32,Patnership,Complex
6308,384.19,Boston,W,20,9,1,30,No education,24,Franchise,Complex
1980,44.17,NJ City,N,8,16,3,30,Graduation,29,Franchise,Complex
1695,100.77,NJ City,E,8,24,3,78,Graduation,26,Owner,Standalone
3382,303.5,NJ City,S,20,12,12,72,High School,25,Patnership,Complex
6657,123.22,Boston,N,6,22,2,27,Graduation,27,Patnership,Standalone
7080,220.25,Boston,W,24,17,5,62,Graduation,22,Franchise,Complex
6884,165.29,Boston,W,23,19,7,79,Graduation,23,Franchise,Complex
9258,409.38,NJ City,S,9,23,20,70,Graduation,29,Patnership,Complex
2407,90.88,NJ City,E,24,18,3,68,High School,28,Owner,Standalone
6511,453.34,Boston,E,24,9,22,78,Graduation,23,Owner,Standalone
6079,146.92,Boston,E,13,9,14,46,Graduation,18,Owner,Standalone
1528,242.05,NJ City,W,5,15,14,62,High School,24,Franchise,Complex
2763,468.1,NJ City,E,10,11,20,26,High School,30,Owner,Standalone
1680,170.03,NJ City,W,8,17,15,69,High School,32,Franchise,Complex
9774,330.63,NJ City,S,24,13,3,39,Graduation,19,Patnership,Complex
5883,238.34,Boston,E,20,19,20,56,Graduation,21,Owner,Standalone
8067,338.01,NJ City,E,12,16,16,64,Graduation,24,Owner,Standalone
8500,374.49,NJ City,W,4,8,14,73,Graduation,30,Franchise,Complex
3067,299.99,NJ City,E,23,8,23,60,High School,32,Owner,Standalone
3533,48.27,NJ City,N,20,21,3,33,No education,29,Patnership,Standalone
1524,34.7,NJ City,N,22,8,2,28,Graduation,18,Franchise,Complex
5757,288.22,Boston,N,25,11,16,63,Graduation,21,Patnership,Standalone
2347,400.3,NJ City,E,18,17,13,25,High School,30,Owner,Standalone
9955,210.8,NJ City,E,20,10,18,29,Graduation,26,Owner,Standalone
3072,208.62,NJ City,W,11,19,1,73,Graduation,27,Franchise,Complex
6286,242.9,Boston,S,9,9,21,70,Graduation,26,Patnership,Complex
9634,163.21,NJ City,S,14,23,23,85,Graduation,34,Patnership,Complex
7939,206.76,Boston,E,12,16,14,58,Graduation,28,Owner,Standalone
3402,385.11,NJ City,S,20,18,15,25,High School,18,Patnership,Complex
229,223.24,NJ City,N,11,10,8,39,High School,33,Patnership,Standalone
8813,178.43,NJ City,N,23,22,14,63,Graduation,29,Patnership,Standalone
1418,470.57,NJ City,S,17,21,18,64,High School,27,Patnership,Complex
9684,499.93,NJ City,W,11,13,24,70,Graduation,21,Franchise,Complex
41,140,Maine,N,2,22,23,44,High School,30,Patnership,Standalone
796,76.79,NJ City,W,7,20,3,34,High School,32,Franchise,Complex
9276,415.96,NJ City,W,3,20,2,43,Graduation,31,Franchise,Complex
4661,439.45,NJ City,N,7,18,6,79,High School,18,Patnership,Standalone
2292,158.1,NJ City,W,18,24,19,36,High School,21,Franchise,Complex
3831,173.63,NJ City,E,11,22,11,36,High School,22,Owner,Standalone
7469,473.74,Boston,N,24,9,17,46,Graduation,29,Patnership,Standalone
9641,193.24,NJ City,N,7,20,10,33,Graduation,21,Patnership,Standalone
5036,221.16,Boston,W,8,23,2,29,No education,32,Franchise,Complex
7062,457.84,Boston,S,19,19,17,30,Graduation,32,Patnership,Complex
8564,126.42,NJ City,W,11,18,4,78,No education,35,Franchise,Complex
5752,72.94,Boston,N,13,21,4,81,High School,26,Franchise,Complex
6453,103.84,Boston,N,24,11,2,77,Graduation,30,Patnership,Standalone
1842,454.4,NJ City,S,13,21,24,29,High School,19,Patnership,Complex
6296,368.35,Boston,W,12,10,21,59,Graduation,34,Franchise,Complex
4701,167.48,NJ City,N,24,13,1,33,Graduation,33,Patnership,Standalone
828,151.12,NJ City,W,4,15,18,28,High School,28,Franchise,Complex
771,372.88,NJ City,E,13,14,18,61,High School,27,Owner,Standalone
5091,310.97,Boston,E,12,18,4,56,Graduation,20,Owner,Standalone
5220,246.7,Boston,W,20,24,11,42,Graduation,28,Franchise,Complex
3120,499.63,NJ City,W,24,13,19,55,High School,27,Franchise,Complex
9084,243.3,NJ City,W,16,10,14,39,Graduation,25,Franchise,Complex
5990,399.44,Boston,S,23,15,25,60,Graduation,28,Patnership,Complex
4703,274.88,NJ City,E,6,20,16,28,High School,18,Owner,Standalone
4096,253.76,NJ City,W,22,21,6,51,High School,30,Franchise,Complex
5792,262.12,Boston,W,16,11,21,64,Graduation,24,Franchise,Complex
1986,141.68,NJ City,S,25,17,11,80,High School,22,Patnership,Complex
4329,421.83,NJ City,N,18,16,9,37,High School,20,Patnership,Standalone
2813,40.48,NJ City,N,9,23,3,50,No education,33,Patnership,Standalone
770,34.94,NJ City,N,25,16,2,76,No education,20,Patnership,Complex
967,210.66,NJ City,E,19,13,22,78,High School,28,Owner,Standalone
5539,98.88,Boston,E,12,16,2,62,High School,22,Owner,Standalone
9403,430.01,NJ City,E,1,13,4,84,High School,33,Owner,Standalone
9741,70.83,NJ City,N,12,10,4,35,Graduation,30,Patnership,Standalone
2192,223.89,NJ City,W,21,23,16,51,High School,33,Franchise,Complex
4107,127.28,NJ City,E,25,8,3,38,Graduation,28,Owner,Standalone
3689,372.84,NJ City,N,5,19,20,46,High School,19,Patnership,Standalone
6546,217.69,Boston,S,6,24,23,28,Graduation,22,Patnership,Complex
1975,395.72,NJ City,E,15,22,4,30,High School,29,Owner,Standalone
7717,158.59,Boston,N,7,9,23,65,Graduation,27,Patnership,Standalone
3662,259,NJ City,S,1,20,9,48,High School,21,Patnership,Complex
5683,475.44,Boston,E,3,11,16,83,Graduation,31,Owner,Standalone
2905,385.83,NJ City,N,12,20,3,80,High School,35,Patnership,Standalone
5666,316.68,Boston,S,20,17,20,41,Graduation,29,Patnership,Complex
6321,178.84,Boston,N,12,16,24,35,Graduation,32,Patnership,Standalone
7410,396.64,Boston,S,22,19,2,71,Graduation,30,Patnership,Complex
8003,48.29,Boston,E,24,14,4,84,No education,34,Owner,Standalone
9194,349.61,NJ City,S,4,19,1,85,No education,25,Patnership,Complex
7779,314.46,Boston,E,8,22,24,30,Graduation,24,Owner,Standalone
5879,446.87,Boston,E,13,20,17,62,Graduation,30,Owner,Standalone
161,333.62,NJ City,N,20,21,24,77,High School,32,Patnership,Standalone
987,37.91,NJ City,N,19,10,3,75,Graduation,22,Owner,Standalone
7962,142.88,Boston,S,21,24,9,50,Graduation,20,Patnership,Complex
1941,89.21,NJ City,N,5,21,4,35,Graduation,29,Patnership,Standalone
7431,13.78,Boston,N,10,13,1,42,Graduation,27,Owner,Standalone
2026,339.43,NJ City,S,7,8,8,70,High School,28,Patnership,Complex
6218,377.19,Boston,S,17,20,3,44,No education,25,Patnership,Complex
1785,304.64,NJ City,N,24,16,6,74,High School,25,Patnership,Standalone
2793,186.6,NJ City,N,16,9,8,53,High School,29,Patnership,Standalone
8208,39.45,NJ City,N,22,23,1,73,Graduation,30,Franchise,Complex
7245,177.05,Boston,N,21,8,16,36,Graduation,28,Patnership,Standalone
807,41.75,NJ City,N,25,9,3,65,Graduation,32,Owner,Standalone
2427,210.2,NJ City,E,7,21,6,78,High School,28,Owner,Standalone
947,190.35,NJ City,E,8,13,2,81,No education,21,Owner,Standalone
7214,409.19,Boston,S,13,23,18,64,Graduation,23,Patnership,Complex
1987,408.88,NJ City,E,3,15,4,49,High School,20,Owner,Standalone
7334,242.54,Boston,S,15,11,11,43,Graduation,26,Patnership,Complex
4644,225.52,NJ City,W,9,9,16,33,High School,22,Franchise,Complex
1956,328.39,NJ City,W,19,23,11,48,High School,23,Franchise,Complex
4001,75.37,NJ City,N,3,22,3,53,No education,26,Patnership,Standalone
1908,143.15,NJ City,W,25,11,5,71,Graduation,30,Franchise,Complex
1548,436.37,NJ City,W,12,23,8,34,High School,26,Franchise,Complex
1120,333.35,NJ City,W,23,19,15,73,High School,21,Franchise,Complex
5211,407.16,Boston,E,7,17,25,60,Graduation,19,Owner,Standalone
7579,266.25,Boston,E,8,8,19,53,Graduation,20,Owner,Standalone
983,446.46,NJ City,E,16,13,11,27,High School,25,Owner,Standalone
4456,47.54,NJ City,W,16,20,2,72,High School,25,Franchise,Complex
809,481.65,NJ City,N,4,21,21,67,High School,18,Patnership,Standalone
4201,66.63,NJ City,N,9,12,4,45,High School,22,Patnership,Standalone
5378,370.24,Boston,S,25,9,11,51,Graduation,25,Patnership,Complex
4396,12.17,NJ City,N,16,21,2,32,High School,23,Franchise,Complex
8394,298.23,NJ City,S,17,17,11,45,Graduation,24,Patnership,Complex
7070,366.82,Boston,S,8,18,13,25,Graduation,32,Patnership,Complex
4079,25.04,NJ City,N,11,13,1,84,No education,28,Owner,Standalone
4763,363.85,NJ City,E,4,23,18,28,High School,26,Owner,Standalone
1940,354.09,NJ City,W,17,24,18,60,High School,32,Franchise,Complex
115,463.9,NJ City,E,19,13,2,49,High School,34,Owner,Standalone
4436,341.6,NJ City,W,18,22,5,52,No education,34,Franchise,Complex
1861,415.31,NJ City,N,14,20,16,68,High School,21,Patnership,Standalone
8597,13.82,NJ City,N,10,19,2,42,No education,32,Patnership,Standalone
4882,242.6,NJ City,S,20,18,23,32,High School,25,Patnership,Complex
209,54.51,NJ City,N,7,15,3,55,No education,26,Patnership,Standalone
9520,476.37,NJ City,W,16,8,6,84,Graduation,32,Franchise,Complex
6993,376.34,Boston,N,6,17,5,75,Graduation,26,Patnership,Standalone
2254,440.13,NJ City,S,5,22,2,55,High School,28,Patnership,Complex
8918,105.14,NJ City,S,24,18,1,35,No education,19,Patnership,Complex
7641,102.77,Boston,N,16,11,2,84,Graduation,25,Patnership,Standalone
8636,177.16,NJ City,W,14,9,20,62,Graduation,31,Franchise,Complex
9486,207.51,NJ City,S,22,10,22,67,Graduation,21,Patnership,Complex
1662,263.6,NJ City,S,12,15,9,81,High School,26,Patnership,Complex
2172,186.27,NJ City,W,4,15,2,56,Graduation,24,Franchise,Complex
1947,276.92,NJ City,E,19,17,17,62,High School,24,Owner,Standalone
5799,103.09,Boston,E,10,16,3,59,Graduation,34,Owner,Standalone
7766,486.3,Boston,S,9,14,8,38,Graduation,27,Patnership,Complex
6340,413.5,Boston,W,9,18,1,71,High School,25,Franchise,Complex
525,167.95,NJ City,N,2,23,1,74,Graduation,20,Patnership,Standalone
9260,26.28,NJ City,N,20,15,1,64,No education,24,Franchise,Complex
9467,310.77,NJ City,E,5,18,9,47,Graduation,23,Owner,Standalone
8495,364.38,NJ City,E,5,24,14,65,Graduation,29,Owner,Standalone
8389,84.16,NJ City,N,10,12,4,80,High School,19,Patnership,Standalone
4050,150.99,NJ City,S,21,18,17,73,High School,33,Patnership,Complex
8365,29.58,NJ City,N,17,9,2,30,High School,32,Patnership,Standalone
6579,245.36,Boston,E,4,18,24,34,Graduation,26,Owner,Standalone
1837,348.1,NJ City,N,25,10,1,60,High School,19,Patnership,Standalone
6615,221.72,Boston,E,2,24,6,29,Graduation,31,Owner,Standalone
5184,204.64,Boston,W,15,8,22,72,Graduation,21,Franchise,Complex
448,400.23,NJ City,W,18,13,24,62,High School,31,Franchise,Complex
2004,209.22,NJ City,W,12,20,17,25,High School,31,Franchise,Complex
4204,269.42,NJ City,W,10,19,24,67,High School,22,Franchise,Complex
9390,455.03,NJ City,S,1,17,18,72,Graduation,25,Patnership,Complex
5832,321.89,Boston,W,13,13,10,32,Graduation,29,Franchise,Complex
7622,393.88,Boston,S,15,8,15,40,Graduation,31,Patnership,Complex
398,347.48,NJ City,S,12,18,4,28,No education,27,Patnership,Complex
9368,59.6,NJ City,W,16,11,3,74,No education,21,Franchise,Complex
8672,484.54,NJ City,W,2,13,24,77,Graduation,19,Franchise,Complex
4146,389.16,NJ City,S,11,24,12,50,High School,29,Patnership,Complex
2063,369.38,NJ City,E,4,9,2,29,No education,27,Owner,Standalone
8894,272.05,NJ City,S,23,21,12,41,Graduation,34,Patnership,Complex
5829,192.01,Boston,N,22,9,11,83,Graduation,32,Patnership,Standalone
75,382.05,Boston,E,24,16,22,45,High School,34,Owner,Standalone
9523,73.42,NJ City,N,21,9,4,60,High School,22,Owner,Standalone
1717,271.4,NJ City,N,5,18,16,85,High School,34,Patnership,Standalone
3882,75.49,NJ City,S,2,9,1,61,Graduation,21,Patnership,Complex
8462,74.75,NJ City,S,22,11,1,51,No education,26,Patnership,Complex
5946,337.15,Boston,S,13,17,15,62,Graduation,28,Patnership,Complex
4040,305,NJ City,W,3,21,18,48,High School,33,Franchise,Complex
9687,432.29,NJ City,E,10,20,7,32,Graduation,31,Owner,Standalone
8598,206.11,NJ City,S,20,12,21,46,Graduation,30,Patnership,Complex
7592,42.02,Boston,N,25,15,4,50,No education,28,Franchise,Complex
9549,150,NJ City,N,12,11,8,60,Graduation,30,Patnership,Standalone
7071,246.58,Boston,E,15,13,3,62,Graduation,18,Owner,Standalone
5441,182.08,Boston,N,21,14,25,29,Graduation,35,Patnership,Standalone
8205,101.72,NJ City,N,11,20,2,36,Graduation,26,Patnership,Standalone
6712,407.95,Boston,W,5,12,19,66,Graduation,25,Franchise,Complex
8481,203.44,NJ City,N,8,18,17,39,Graduation,30,Patnership,Standalone
5848,424.09,Boston,W,13,24,16,28,Graduation,35,Franchise,Complex
8884,152.48,NJ City,W,15,9,21,40,Graduation,24,Franchise,Complex
5996,322.92,Boston,W,14,19,24,83,Graduation,19,Franchise,Complex
5327,155.12,Boston,E,15,21,25,56,Graduation,29,Owner,Standalone
9162,214.74,NJ City,S,11,23,15,30,Graduation,22,Patnership,Complex
4739,435.31,NJ City,E,17,14,22,83,High School,25,Owner,Standalone
2447,165.59,NJ City,E,24,22,13,39,High School,24,Owner,Standalone
516,24.75,NJ City,N,18,11,1,31,Graduation,21,Franchise,Complex
5065,326.77,Boston,N,12,18,15,61,Graduation,22,Patnership,Standalone
4581,457.87,NJ City,N,6,18,22,78,High School,32,Patnership,Standalone
9289,472.36,NJ City,N,8,15,25,34,Graduation,34,Patnership,Standalone
6748,171.63,Boston,W,6,14,24,81,Graduation,35,Franchise,Complex
7659,496.21,Boston,E,8,10,2,40,Graduation,31,Owner,Standalone
5062,477.12,Boston,S,12,23,22,39,Graduation,21,Patnership,Complex
977,374.44,NJ City,N,14,14,11,27,High School,23,Patnership,Standalone
8307,297.64,NJ City,E,20,9,25,73,Graduation,34,Owner,Standalone
820,190.53,NJ City,W,20,20,23,78,High School,23,Franchise,Complex
418,357.1,NJ City,S,8,21,4,83,High School,27,Patnership,Complex
2265,61.76,NJ City,N,7,16,1,68,Graduation,18,Patnership,Standalone
9383,105.18,NJ City,E,4,16,3,82,No education,25,Owner,Standalone
6019,63.1,Boston,N,22,22,3,84,High School,19,Owner,Standalone
4183,188.3,NJ City,E,17,11,12,79,High School,28,Owner,Standalone
3033,497.3,NJ City,N,13,22,20,35,High School,26,Patnership,Standalone
9711,279.3,NJ City,E,7,19,25,30,Graduation,35,Owner,Standalone
2772,2.56,NJ City,W,14,24,1,49,Graduation,18,Franchise,Complex
3579,248.64,NJ City,E,14,18,12,54,High School,29,Owner,Standalone
8213,98.64,NJ City,N,13,10,4,25,No education,24,Patnership,Standalone
9998,163.82,NJ City,S,5,22,1,31,No education,24,Patnership,Complex
3225,123.95,NJ City,N,3,15,4,52,Graduation,23,Patnership,Standalone
4485,368.7,NJ City,N,11,19,24,38,High School,28,Patnership,Standalone
7315,304.62,Boston,E,21,23,11,31,Graduation,20,Owner,Standalone
1151,268.11,NJ City,E,7,13,25,35,High School,20,Owner,Standalone
6747,147.25,Boston,E,19,22,15,25,Graduation,32,Owner,Standalone
8013,317.35,Boston,N,9,24,25,73,Graduation,21,Patnership,Standalone
9542,140.29,NJ City,S,6,13,6,53,Graduation,26,Patnership,Complex
8260,254.77,NJ City,W,1,9,17,77,Graduation,23,Franchise,Complex
3528,280.88,NJ City,W,14,15,14,32,High School,23,Franchise,Complex
7880,396.33,Boston,W,5,10,8,61,Graduation,20,Franchise,Complex
200,251.59,NJ City,W,21,15,12,56,High School,21,Franchise,Complex
5478,100.76,Boston,S,6,23,3,61,Graduation,23,Patnership,Complex
9365,325.5,NJ City,N,23,10,6,81,Graduation,21,Patnership,Standalone
7871,126.17,Boston,E,15,18,4,34,No education,31,Owner,Standalone
9950,203.66,NJ City,S,23,18,14,58,Graduation,23,Patnership,Complex
769,326.34,NJ City,N,18,24,23,68,High School,33,Patnership,Standalone
224,102.43,NJ City,W,16,15,4,75,No education,24,Franchise,Complex
1235,116,NJ City,E,3,17,14,51,High School,28,Owner,Standalone
6578,351.54,Boston,S,17,8,25,61,Graduation,18,Patnership,Complex
5166,400.42,Boston,S,16,19,1,58,Graduation,27,Patnership,Complex
839,107.32,NJ City,E,20,20,2,36,No education,32,Owner,Standalone
6600,10.22,Boston,N,4,13,2,62,Graduation,31,Franchise,Complex
7520,415.03,Boston,W,13,21,24,85,Graduation,20,Franchise,Complex
8004,416.36,Boston,W,22,11,2,36,Graduation,30,Franchise,Complex
5252,307.69,Boston,W,14,11,23,72,Graduation,28,Franchise,Complex
1401,331.56,NJ City,N,10,15,20,67,High School,18,Patnership,Standalone
3542,277.17,NJ City,S,6,19,17,46,High School,22,Patnership,Complex
7141,492.73,Boston,N,10,16,5,69,High School,25,Patnership,Standalone
8376,351.13,NJ City,W,11,11,14,54,Graduation,23,Franchise,Complex
3228,378.86,NJ City,W,16,9,16,31,High School,31,Franchise,Complex
6461,407.93,Boston,N,16,21,1,57,No education,22,Patnership,Standalone
9580,341.32,NJ City,W,13,15,17,81,Graduation,28,Franchise,Complex
9356,167.29,NJ City,W,21,20,21,26,Graduation,30,Franchise,Complex
6900,102.51,Boston,W,2,10,1,50,Graduation,24,Franchise,Complex
3650,11.25,NJ City,N,6,11,4,55,No education,24,Patnership,Complex
1365,29.61,NJ City,N,23,23,3,41,Graduation,25,Patnership,Standalone
4114,328.21,NJ City,S,14,19,3,54,High School,23,Patnership,Complex
6069,167,Boston,N,6,10,11,41,Graduation,21,Patnership,Standalone
1378,289.26,NJ City,S,7,8,22,79,High School,29,Patnership,Complex
4194,247.02,NJ City,S,13,19,16,58,High School,29,Patnership,Complex
121,228.92,NJ City,N,24,12,5,69,High School,29,Patnership,Standalone
4443,42.3,NJ City,N,20,15,4,78,Graduation,28,Owner,Standalone
3627,19.5,NJ City,N,7,23,3,36,Graduation,23,Owner,Standalone
764,468.84,NJ City,W,14,22,23,60,High School,35,Franchise,Complex
1331,479.08,NJ City,E,4,18,23,57,High School,20,Owner,Standalone
7254,66.29,Boston,N,25,11,4,79,Graduation,26,Patnership,Complex
1732,460.93,NJ City,W,16,11,11,57,High School,30,Franchise,Complex
5613,105.16,Boston,N,18,22,2,66,Graduation,28,Patnership,Standalone
9023,236.92,NJ City,E,18,17,19,47,Graduation,19,Owner,Standalone
9881,362.43,NJ City,N,18,16,7,73,Graduation,21,Patnership,Standalone
413,49.9,NJ City,N,12,21,1,27,No education,23,Patnership,Standalone
7728,116.87,Boston,W,19,10,1,31,Graduation,28,Franchise,Complex
3371,206.05,NJ City,E,4,21,3,58,No education,29,Owner,Standalone
8286,354.82,NJ City,S,21,10,4,44,Graduation,30,Patnership,Complex
914,64.16,NJ City,N,25,20,4,75,No education,18,Patnership,Complex
6773,83.16,Boston,N,4,20,3,36,No education,27,Patnership,Standalone
6328,80.25,Boston,W,3,20,1,31,High School,18,Franchise,Complex
3572,176.77,NJ City,W,24,18,13,39,High School,23,Franchise,Complex
6089,58.2,Boston,N,22,9,2,60,No education,34,Patnership,Standalone
219,284.08,NJ City,E,10,18,18,78,High School,24,Owner,Standalone
6801,94.73,Boston,N,12,14,4,35,Graduation,25,Patnership,Standalone
9829,54.19,NJ City,N,3,16,4,32,High School,23,Patnership,Standalone
5057,222.29,Boston,N,20,9,20,68,Graduation,33,Patnership,Standalone
3884,338.86,NJ City,W,17,16,6,40,High School,22,Franchise,Complex
6437,434.98,Boston,N,13,20,3,48,No education,18,Patnership,Standalone
714,49.82,NJ City,S,4,10,4,45,Graduation,24,Patnership,Complex
7947,103.38,Boston,E,2,24,4,79,Graduation,19,Owner,Standalone
610,341.23,NJ City,S,24,20,7,58,High School,33,Patnership,Complex
4873,25.15,NJ City,N,11,22,3,46,High School,20,Patnership,Standalone
9311,419.65,NJ City,E,13,16,24,73,Graduation,26,Owner,Standalone
4508,30.12,NJ City,N,6,19,3,58,No education,31,Franchise,Complex
2289,334.82,NJ City,N,17,19,22,53,High School,25,Patnership,Standalone
192,126.66,NJ City,W,4,10,1,72,Graduation,31,Franchise,Complex
9491,499.17,NJ City,E,25,20,21,66,Graduation,25,Owner,Standalone
9105,52.96,NJ City,N,16,10,4,64,Graduation,19,Patnership,Standalone
6731,110.03,Boston,E,2,18,2,50,No education,30,Owner,Standalone
8721,374.74,NJ City,N,21,22,2,77,Graduation,31,Patnership,Standalone
8905,308.05,NJ City,N,9,9,21,51,Graduation,22,Patnership,Standalone
5582,143.96,Boston,S,14,11,21,83,Graduation,20,Patnership,Complex
7742,146.6,Boston,S,19,11,4,76,No education,18,Patnership,Complex
9429,362.7,NJ City,N,23,21,18,69,Graduation,27,Patnership,Standalone
3789,98.91,NJ City,N,20,13,2,74,Graduation,24,Patnership,Standalone
3421,428.73,NJ City,N,17,17,9,43,High School,35,Patnership,Standalone
4921,354.15,NJ City,N,5,13,1,25,High School,31,Patnership,Standalone
2031,368.24,NJ City,E,25,18,17,46,High School,20,Owner,Standalone
8230,386.39,NJ City,S,22,21,6,65,Graduation,32,Patnership,Complex
1673,340.95,NJ City,N,17,13,18,40,High School,20,Patnership,Standalone
1099,394.44,NJ City,E,21,11,20,26,High School,29,Owner,Standalone
7704,316.16,Boston,W,15,15,10,66,Graduation,21,Franchise,Complex
1042,246.73,NJ City,S,21,11,9,72,High School,31,Patnership,Complex
2515,35.35,NJ City,N,21,13,1,37,High School,30,Owner,Standalone
280,156.27,NJ City,W,22,21,10,33,High School,27,Franchise,Complex
8155,415.54,NJ City,E,1,19,7,45,Graduation,22,Owner,Standalone
1751,444.5,NJ City,E,3,10,2,64,No education,21,Owner,Standalone
5731,461.58,Boston,E,25,9,4,75,High School,21,Owner,Standalone
6283,460.9,Boston,E,8,15,16,85,Graduation,20,Owner,Standalone
1541,360.73,NJ City,N,13,19,16,62,High School,28,Patnership,Standalone
5895,403.73,Boston,E,22,24,1,82,Graduation,28,Owner,Standalone
7648,246.96,Boston,W,15,9,22,67,Graduation,29,Franchise,Complex
3780,164.32,NJ City,W,14,14,20,54,High School,32,Franchise,Complex
7247,22.02,Boston,N,3,11,4,31,No education,33,Owner,Standalone
8986,139.61,NJ City,S,11,9,7,49,Graduation,26,Patnership,Complex
8957,288.83,NJ City,N,12,9,23,37,Graduation,19,Patnership,Standalone
2792,241.1,NJ City,W,15,24,10,79,High School,21,Franchise,Complex
1604,363.21,NJ City,W,23,22,11,82,High School,20,Franchise,Complex
3290,41.68,NJ City,N,8,15,3,29,No education,26,Patnership,Complex
9152,295.92,NJ City,W,25,16,11,27,Graduation,20,Franchise,Complex
5665,469.5,Boston,N,11,21,4,29,High School,26,Patnership,Standalone
1878,118.1,NJ City,S,15,22,3,44,Graduation,28,Patnership,Complex
9384,428.01,NJ City,W,5,9,13,46,Graduation,19,Franchise,Complex
2681,370.77,NJ City,N,11,13,7,54,High School,19,Patnership,Standalone
6221,82.78,Boston,N,17,22,4,76,No education,26,Patnership,Standalone
5402,446.21,Boston,S,10,14,18,42,Graduation,29,Patnership,Complex
5437,471.8,Boston,N,16,18,14,52,Graduation,31,Patnership,Standalone
1046,212.54,NJ City,S,12,15,8,32,High School,19,Patnership,Complex
6598,368.35,Boston,S,6,12,3,79,High School,33,Patnership,Complex
4276,247.44,NJ City,W,5,15,23,37,High School,31,Franchise,Complex
4022,184.54,NJ City,S,19,11,18,58,High School,24,Patnership,Complex
6672,267.27,Boston,W,7,24,18,49,Graduation,33,Franchise,Complex
1410,296.5,NJ City,S,15,9,23,52,High School,21,Patnership,Complex
4317,131.32,NJ City,N,22,8,15,52,High School,33,Patnership,Standalone
553,426.47,NJ City,N,21,19,19,55,High School,22,Patnership,Standalone
9145,350.18,NJ City,N,9,10,16,51,Graduation,31,Patnership,Standalone
4345,150.55,NJ City,N,19,8,15,34,High School,34,Patnership,Standalone
9561,161.03,NJ City,N,10,11,24,51,Graduation,30,Patnership,Standalone
374,364.63,NJ City,S,11,18,24,31,High School,26,Patnership,Complex
8370,362.74,NJ City,S,4,14,6,35,Graduation,25,Patnership,Complex
3493,477.64,NJ City,N,18,13,19,38,High School,30,Patnership,Standalone
2054,322.32,NJ City,S,18,21,17,58,High School,33,Patnership,Complex
9420,198.7,NJ City,W,18,12,24,82,Graduation,22,Franchise,Complex
723,488.11,NJ City,E,8,21,6,29,High School,34,Owner,Standalone
8565,435.55,NJ City,N,20,19,24,29,Graduation,26,Patnership,Standalone
2530,9.12,NJ City,N,17,12,1,80,High School,23,Patnership,Complex
9443,376.47,NJ City,E,22,24,13,68,Graduation,33,Owner,Standalone
6718,444.29,Boston,S,5,8,3,38,High School,28,Patnership,Complex
3229,140.98,NJ City,N,11,22,7,80,High School,28,Patnership,Standalone
2669,98.56,NJ City,N,21,14,3,84,No education,33,Patnership,Standalone
2415,28.06,NJ City,N,15,9,3,38,Graduation,22,Owner,Standalone
9837,222.97,NJ City,N,17,19,14,34,Graduation,19,Patnership,Standalone
1224,311.49,NJ City,W,11,8,24,71,High School,24,Franchise,Complex
803,124.72,NJ City,E,21,10,4,72,No education,18,Owner,Standalone
6514,56.76,Boston,S,7,18,1,82,High School,31,Patnership,Complex
3518,174,NJ City,S,3,20,18,52,High School,25,Patnership,Complex
7048,213.17,Boston,W,15,13,17,44,Graduation,19,Franchise,Complex
2121,281.6,NJ City,N,3,11,3,47,Graduation,31,Patnership,Standalone
3625,423.75,NJ City,N,16,11,24,38,High School,19,Patnership,Standalone
4206,264.79,NJ City,S,25,12,10,74,High School,33,Patnership,Complex
7640,277.36,Boston,W,21,9,1,68,No education,25,Franchise,Complex
1054,382.97,NJ City,S,15,17,24,76,High School,21,Patnership,Complex
964,341.15,NJ City,W,14,10,20,43,High School,28,Franchise,Complex
6429,446.79,Boston,N,20,17,4,68,Graduation,34,Patnership,Standalone
3255,142,NJ City,E,1,17,14,84,High School,29,Owner,Standalone
342,247.12,NJ City,S,19,15,7,52,High School,29,Patnership,Complex
8217,448.87,NJ City,N,25,18,5,57,Graduation,25,Patnership,Standalone
4097,68.96,NJ City,N,4,18,1,53,No education,35,Patnership,Standalone
1101,37.73,NJ City,N,25,24,4,49,Graduation,29,Patnership,Standalone
741,393.11,NJ City,N,22,12,7,44,High School,21,Patnership,Standalone
684,125.36,NJ City,W,20,19,3,31,Graduation,32,Franchise,Complex
9903,121.69,NJ City,E,11,12,1,45,Graduation,26,Owner,Standalone
2353,69.92,NJ City,N,10,19,2,25,High School,20,Patnership,Standalone
5929,273.99,Boston,N,9,12,15,68,Graduation,35,Patnership,Standalone
304,387.86,NJ City,W,25,8,15,32,High School,34,Franchise,Complex
5179,13.67,Boston,N,3,10,1,75,High School,26,Owner,Standalone
5079,30.51,Boston,N,9,10,1,25,Graduation,26,Owner,Standalone
8515,200.62,NJ City,E,16,20,24,64,Graduation,22,Owner,Standalone
4415,102.38,NJ City,E,23,9,2,50,No education,23,Owner,Standalone
8343,28.75,NJ City,N,12,9,1,78,Graduation,31,Owner,Standalone
4954,37.64,Boston,N,21,22,4,77,High School,33,Patnership,Complex
5284,8.1,Boston,N,19,10,2,49,High School,24,Franchise,Complex
4422,409.16,NJ City,S,5,20,16,32,High School,35,Patnership,Complex
7993,115.81,Boston,N,14,11,4,83,High School,20,Patnership,Standalone
3473,104.1,NJ City,N,6,13,3,85,No education,34,Patnership,Standalone
3350,363.4,NJ City,S,11,9,18,71,High School,34,Patnership,Complex
2483,286.56,NJ City,E,23,21,15,32,High School,23,Owner,Standalone
6932,207.5,Boston,W,18,20,3,67,No education,30,Franchise,Complex
318,447.11,NJ City,S,9,13,16,26,High School,34,Patnership,Complex
4004,163,NJ City,W,3,13,21,83,High School,24,Franchise,Complex
7131,223.5,Boston,E,3,24,12,67,Graduation,21,Owner,Standalone
7736,487.17,Boston,W,14,11,5,61,No education,35,Franchise,Complex
9709,213.02,NJ City,N,2,22,19,58,Graduation,26,Patnership,Standalone
5543,113.97,Boston,E,18,22,4,28,No education,26,Owner,Standalone
222,306.29,NJ City,S,13,22,23,43,High School,24,Patnership,Complex
1545,433.03,NJ City,N,13,19,3,29,Graduation,26,Patnership,Standalone
2268,306.83,NJ City,W,20,17,19,42,High School,26,Franchise,Complex
6869,222.34,Boston,N,19,16,8,75,Graduation,31,Patnership,Standalone
4852,442.69,NJ City,W,13,14,10,57,High School,24,Franchise,Complex
8321,328.34,NJ City,N,22,13,24,35,Graduation,21,Patnership,Standalone
568,197.64,NJ City,W,15,12,4,44,High School,31,Franchise,Complex
6574,375.24,Boston,S,17,20,1,40,High School,27,Patnership,Complex
1348,398.02,NJ City,W,7,20,10,29,High School,20,Franchise,Complex
1926,445.4,NJ City,S,12,16,9,82,High School,28,Patnership,Complex
2391,206.65,NJ City,E,22,24,22,70,High School,27,Owner,Standalone
2471,316.88,NJ City,E,5,21,3,68,No education,20,Owner,Standalone
7851,329.06,Boston,E,4,8,19,63,Graduation,32,Owner,Standalone
6027,257.06,Boston,E,4,24,14,62,Graduation,35,Owner,Standalone
1766,118.37,NJ City,S,16,11,3,72,No education,35,Patnership,Complex
2283,341.53,NJ City,E,18,15,17,45,High School,24,Owner,Standalone
5339,307.69,Boston,E,8,21,24,63,Graduation,25,Owner,Standalone
5928,392.29,Boston,W,14,17,24,55,Graduation,30,Franchise,Complex
2296,465.32,NJ City,W,7,20,13,63,High School,28,Franchise,Complex
1979,244.12,NJ City,E,8,10,23,52,High School,31,Owner,Standalone
4304,131.89,NJ City,W,12,17,17,81,High School,26,Franchise,Complex
7192,440.36,Boston,W,17,19,9,29,Graduation,19,Franchise,Complex
3354,46.67,NJ City,N,14,14,1,58,Graduation,26,Patnership,Complex
6110,414.4,Boston,S,11,19,2,49,No education,20,Patnership,Complex
9939,322.31,NJ City,E,14,15,7,57,Graduation,26,Owner,Standalone
6045,343.69,Boston,N,24,8,20,30,Graduation,19,Patnership,Standalone
148,275.72,NJ City,W,5,12,14,61,High School,18,Franchise,Complex
9535,88.6,NJ City,E,24,17,1,32,High School,31,Owner,Standalone
583,450.83,NJ City,E,21,16,16,50,High School,30,Owner,Standalone
728,1.9,NJ City,N,25,10,4,71,No education,19,Franchise,Complex
1901,68.77,NJ City,N,24,14,4,54,No education,31,Patnership,Standalone
6810,295.86,Boston,S,10,8,7,31,Graduation,25,Patnership,Complex
5659,140.18,Boston,E,18,12,23,49,Graduation,35,Owner,Standalone
2231,468.71,NJ City,E,4,17,13,36,High School,34,Owner,Standalone
1725,144.32,NJ City,N,15,14,8,30,High School,31,Patnership,Standalone
6984,152.64,Boston,W,23,16,13,64,Graduation,18,Franchise,Complex
80,136,Boston,W,1,23,20,54,High School,29,Franchise,Complex
8810,334.82,NJ City,S,22,21,4,42,No education,35,Patnership,Complex
324,64.28,NJ City,N,4,23,2,36,Graduation,27,Franchise,Complex
4664,68.67,NJ City,N,17,20,4,39,No education,33,Franchise,Complex
8417,274.23,NJ City,N,18,9,8,65,Graduation,19,Patnership,Standalone
2837,29.68,NJ City,N,13,17,2,54,No education,27,Patnership,Standalone
9468,326.35,NJ City,W,23,21,8,59,Graduation,28,Franchise,Complex
1025,263.01,NJ City,N,20,8,6,43,High School,24,Patnership,Standalone
8621,355.91,NJ City,N,21,21,1,75,No education,27,Patnership,Standalone
4574,188,NJ City,S,3,21,11,70,High School,21,Patnership,Complex
5261,432.52,Boston,N,18,8,11,30,Graduation,18,Patnership,Standalone
4216,299.66,NJ City,W,7,19,7,25,High School,29,Franchise,Complex
8744,122.36,NJ City,W,11,14,2,62,No education,24,Franchise,Complex
3155,345.18,NJ City,E,12,8,16,28,High School,27,Owner,Standalone
3550,119.58,NJ City,S,9,13,4,70,High School,27,Patnership,Complex
12,269,Maine,W,3,23,14,42,High School,34,Franchise,Complex
9316,101.86,NJ City,W,10,9,4,27,High School,35,Franchise,Complex
1828,106.05,NJ City,W,1,14,2,26,High School,29,Franchise,Complex
4747,100.64,NJ City,E,8,20,4,67,High School,29,Owner,Standalone
5248,485.64,Boston,W,23,9,6,40,Graduation,27,Franchise,Complex
7821,4.81,Boston,N,3,21,2,64,Graduation,22,Patnership,Standalone
401,137.46,NJ City,N,13,19,5,80,No education,34,Patnership,Standalone
7834,306.82,Boston,S,18,21,11,79,Graduation,20,Patnership,Complex
4808,293.79,NJ City,W,23,21,23,69,High School,21,Franchise,Complex
4911,214.78,NJ City,E,15,19,21,48,High School,20,Owner,Standalone
5893,290.47,Boston,N,19,12,12,64,Graduation,34,Patnership,Standalone
5303,23.19,Boston,N,24,17,2,47,No education,32,Owner,Standalone
9746,133.98,NJ City,S,23,21,15,70,Graduation,21,Patnership,Complex
3501,125.16,NJ City,N,6,15,4,38,Graduation,25,Patnership,Standalone
3188,446.49,NJ City,W,1,22,4,74,No education,29,Franchise,Complex
3770,408.08,NJ City,S,6,18,6,48,High School,24,Patnership,Complex
7762,374.9,Boston,S,9,12,17,76,Graduation,30,Patnership,Complex
4366,269,NJ City,S,2,17,7,33,High School,34,Patnership,Complex
4010,195.97,NJ City,S,23,20,3,44,No education,35,Patnership,Complex
4651,270.4,NJ City,E,4,16,14,37,High School,19,Owner,Standalone
6488,218.04,Boston,W,16,19,23,52,Graduation,25,Franchise,Complex
6530,439.56,Boston,S,19,8,12,49,Graduation,29,Patnership,Complex
8558,91.14,NJ City,S,16,12,1,41,No education,35,Patnership,Complex
4715,378.07,NJ City,E,9,19,21,55,High School,25,Owner,Standalone
6607,96.86,Boston,E,1,18,3,80,High School,27,Owner,Standalone
7167,342.09,Boston,E,21,10,8,32,Graduation,23,Owner,Standalone
1829,82.39,NJ City,N,17,11,2,29,No education,25,Patnership,Standalone
6850,213.31,Boston,S,21,17,8,59,Graduation,32,Patnership,Complex
9717,118.83,NJ City,N,9,24,1,60,Graduation,25,Patnership,Standalone
9156,487.76,NJ City,W,8,13,3,64,Graduation,27,Franchise,Complex
3809,65.85,NJ City,N,19,19,1,49,No education,19,Patnership,Standalone
5440,396.43,Boston,W,12,21,22,50,Graduation,19,Franchise,Complex
1011,459.09,NJ City,E,3,19,1,56,Graduation,26,Owner,Standalone
7246,186.93,Boston,S,7,24,10,37,Graduation,33,Patnership,Complex
134,466.99,NJ City,S,24,24,1,72,No education,30,Patnership,Complex
6913,204.3,Boston,N,4,16,16,81,Graduation,33,Patnership,Standalone
2184,12.71,NJ City,N,3,10,2,38,Graduation,29,Franchise,Complex
1623,109.63,NJ City,E,20,20,2,70,Graduation,21,Owner,Standalone
353,487.12,NJ City,N,20,13,8,74,High School,24,Patnership,Standalone
8057,298.16,NJ City,N,6,16,16,27,Graduation,19,Patnership,Standalone
4513,297.22,NJ City,N,18,15,3,63,High School,26,Patnership,Standalone
326,98.41,NJ City,S,23,11,4,47,No education,26,Patnership,Complex
9028,221.6,NJ City,W,6,23,10,60,Graduation,21,Franchise,Complex
4781,204,NJ City,N,1,16,22,71,High School,25,Patnership,Standalone
5588,403.12,Boston,W,14,24,19,51,Graduation,35,Franchise,Complex
4178,241,NJ City,S,4,18,24,29,High School,26,Patnership,Complex
7291,478.77,Boston,E,3,11,15,44,Graduation,21,Owner,Standalone
9444,389.26,NJ City,W,9,22,25,73,Graduation,30,Franchise,Complex
2466,183,NJ City,S,1,10,15,40,High School,32,Patnership,Complex
5531,169.69,Boston,E,9,23,2,72,No education,20,Owner,Standalone
9347,311.11,NJ City,E,1,17,5,55,No education,30,Owner,Standalone
8571,82.47,NJ City,E,19,14,3,54,Graduation,23,Owner,Standalone
7241,422.38,Boston,N,12,8,21,67,Graduation,19,Patnership,Standalone
2223,316.31,NJ City,E,22,24,25,65,High School,18,Owner,Standalone
5190,438.69,Boston,S,11,24,4,66,Graduation,23,Patnership,Complex
1422,471.94,NJ City,S,11,8,20,63,High School,20,Patnership,Complex
9999,357.37,NJ City,E,5,21,17,41,Graduation,35,Owner,Standalone
7314,387.14,Boston,S,8,11,7,73,Graduation,33,Patnership,Complex
888,132.91,NJ City,W,23,10,14,66,High School,26,Franchise,Complex
8664,243.25,NJ City,W,19,8,17,69,Graduation,23,Franchise,Complex
973,230.45,NJ City,N,10,21,15,37,High School,30,Patnership,Standalone
4442,131.86,NJ City,S,11,17,22,75,High School,24,Patnership,Complex
3843,114.79,NJ City,E,8,11,4,55,Graduation,28,Owner,Standalone
6472,119.41,Boston,W,16,19,1,72,High School,29,Franchise,Complex
4714,220.37,NJ City,S,4,24,6,49,High School,28,Patnership,Complex
6507,274.74,Boston,E,20,8,16,70,Graduation,21,Owner,Standalone
4238,143.74,NJ City,S,6,9,19,62,High School,34,Patnership,Complex
3356,291.13,NJ City,W,13,18,1,77,No education,27,Franchise,Complex
5619,332.53,Boston,E,1,15,4,42,Graduation,34,Owner,Standalone
2245,253.41,NJ City,N,19,24,11,27,High School,22,Patnership,Standalone
2595,439.02,NJ City,E,12,22,20,56,High School,29,Owner,Standalone
1497,288,NJ City,N,3,22,17,43,High School,32,Patnership,Standalone
658,150.14,NJ City,S,17,11,11,48,High School,20,Patnership,Complex
8243,2.11,NJ City,E,10,20,2,65,No education,29,Owner,Standalone
2639,80.76,NJ City,E,5,18,4,37,No education,29,Owner,Standalone
4098,497.98,NJ City,S,20,11,17,33,High School,28,Patnership,Complex
3452,20.23,NJ City,N,1,9,4,76,No education,28,Franchise,Complex
7237,73.09,Boston,N,6,19,1,64,High School,28,Patnership,Standalone
841,59.49,NJ City,N,13,20,3,53,High School,26,Patnership,Standalone
5209,115.19,Boston,N,21,11,3,51,High School,33,Patnership,Standalone
3489,268.06,NJ City,N,20,8,11,77,High School,32,Patnership,Standalone
8962,392.63,NJ City,S,1,10,14,25,Graduation,31,Patnership,Complex
9838,439.83,NJ City,S,7,24,17,63,Graduation,24,Patnership,Complex
1155,268.85,NJ City,E,20,16,17,46,High School,24,Owner,Standalone
7186,52.2,Boston,S,12,13,3,65,High School,22,Patnership,Complex
3050,32.16,NJ City,N,13,9,1,62,No education,18,Patnership,Complex
1330,426.64,NJ City,S,22,19,16,75,High School,22,Patnership,Complex
3669,300.11,NJ City,N,23,13,1,78,Graduation,33,Patnership,Standalone
6630,1.53,Boston,N,14,21,2,60,Graduation,27,Patnership,Complex
6306,59.37,Boston,S,15,9,3,76,Graduation,35,Patnership,Complex
2899,154.06,NJ City,E,10,19,17,25,High School,32,Owner,Standalone
913,413.38,NJ City,N,8,11,13,33,High School,35,Patnership,Standalone
2908,383.94,NJ City,W,15,12,12,59,High School,29,Franchise,Complex
1089,83.95,NJ City,N,21,20,3,32,Graduation,23,Patnership,Standalone
6575,288.65,Boston,E,7,14,1,72,No education,29,Owner,Standalone
170,269.46,NJ City,S,24,14,2,79,No education,35,Patnership,Complex
2405,61.27,NJ City,N,11,13,3,45,No education,28,Patnership,Standalone
1749,62.63,NJ City,N,12,17,4,60,Graduation,28,Patnership,Standalone
5797,446,Boston,N,5,14,21,56,Graduation,27,Patnership,Standalone
7743,136.15,Boston,E,21,23,9,58,Graduation,18,Owner,Standalone
3687,420.74,NJ City,E,24,17,7,30,High School,18,Owner,Standalone
4695,198.81,NJ City,E,12,18,3,48,Graduation,20,Owner,Standalone
5555,376.19,Boston,E,3,12,1,68,No education,34,Owner,Standalone
3787,126.31,NJ City,E,12,22,4,80,High School,20,Owner,Standalone
2882,310.11,NJ City,S,20,13,15,68,High School,34,Patnership,Complex
7603,357.86,Boston,E,11,15,22,78,Graduation,21,Owner,Standalone
2454,139.64,NJ City,S,17,14,6,38,High School,22,Patnership,Complex
3483,63.56,NJ City,N,6,18,1,68,Graduation,32,Owner,Standalone
9608,321.35,NJ City,W,20,16,18,69,Graduation,22,Franchise,Complex
5432,225.08,Boston,W,23,16,24,31,Graduation,23,Franchise,Complex
7294,90.11,Boston,S,17,15,3,43,High School,30,Patnership,Complex
8724,312.33,NJ City,W,14,22,22,59,Graduation,29,Franchise,Complex
2972,431.88,NJ City,W,23,11,22,44,High School,22,Franchise,Complex
9577,276.66,NJ City,N,13,21,12,47,Graduation,26,Patnership,Standalone
6093,398.85,Boston,N,21,24,1,46,Graduation,35,Patnership,Standalone
1067,182.89,NJ City,E,12,22,14,67,High School,32,Owner,Standalone
4922,183.4,NJ City,S,12,23,9,66,High School,32,Patnership,Complex
3676,261.96,NJ City,W,4,17,23,44,High School,19,Franchise,Complex
4165,263.26,NJ City,N,20,11,24,37,High School,22,Patnership,Standalone
1538,118.48,NJ City,S,17,18,3,27,No education,32,Patnership,Complex
3819,276.83,NJ City,E,14,17,13,77,High School,23,Owner,Standalone
8512,27.21,NJ City,N,12,10,4,43,High School,32,Franchise,Complex
9128,390.02,NJ City,W,18,17,20,32,Graduation,23,Franchise,Complex
1305,35.41,NJ City,N,12,11,2,46,Graduation,33,Patnership,Standalone
3705,122.96,NJ City,N,10,16,2,44,Graduation,24,Patnership,Standalone
1119,14.96,NJ City,N,4,10,3,76,Graduation,25,Owner,Standalone
1184,41.78,NJ City,N,23,15,3,59,No education,19,Franchise,Complex
8867,103.54,NJ City,E,7,20,2,39,No education,20,Owner,Standalone
5689,421.79,Boston,N,24,17,3,47,High School,34,Patnership,Standalone
8643,59.83,NJ City,E,15,18,2,66,Graduation,25,Owner,Standalone
1647,346.1,NJ City,E,4,11,15,66,High School,28,Owner,Standalone
3988,287,NJ City,W,2,24,14,81,High School,23,Franchise,Complex
2961,299,NJ City,N,3,10,7,57,High School,24,Patnership,Standalone
6083,264.29,Boston,E,12,15,24,42,Graduation,18,Owner,Standalone
7921,439.33,Boston,N,13,15,13,61,Graduation,20,Patnership,Standalone
6441,296.97,Boston,N,1,8,11,38,Graduation,31,Patnership,Standalone
8691,271.48,NJ City,E,10,22,4,50,Graduation,24,Owner,Standalone
122,37.87,NJ City,N,3,17,1,33,No education,19,Patnership,Complex
4352,428.08,NJ City,W,20,12,1,80,No education,27,Franchise,Complex
9047,131.2,NJ City,E,5,16,7,63,Graduation,22,Owner,Standalone
4493,26.34,NJ City,N,16,9,1,54,No education,34,Patnership,Standalone
6153,249.7,Boston,N,1,13,6,54,Graduation,21,Patnership,Standalone
3774,306.64,NJ City,S,3,15,4,27,Graduation,25,Patnership,Complex
3136,286.48,NJ City,W,23,18,19,68,High School,18,Franchise,Complex
4800,59.91,NJ City,W,1,10,3,61,Graduation,35,Franchise,Complex
5416,113.23,Boston,W,19,12,3,68,High School,21,Franchise,Complex
9847,152.84,NJ City,E,9,19,12,45,Graduation,32,Owner,Standalone
4981,211.42,Boston,N,16,15,1,57,High School,24,Patnership,Standalone
3438,167.15,NJ City,S,11,24,5,37,Graduation,25,Patnership,Complex
4180,183.06,NJ City,W,22,23,5,66,High School,34,Franchise,Complex
1630,80.1,NJ City,S,22,19,3,44,High School,33,Patnership,Complex
6802,64.9,Boston,N,24,20,3,42,High School,18,Patnership,Complex
8582,177.91,NJ City,S,7,16,16,36,Graduation,30,Patnership,Complex
5994,299.96,Boston,S,8,9,21,75,Graduation,18,Patnership,Complex
4246,40.11,NJ City,N,25,21,4,37,High School,21,Patnership,Complex
5623,240.07,Boston,E,9,23,22,50,Graduation,20,Owner,Standalone
8391,170.25,NJ City,E,10,14,8,42,Graduation,30,Owner,Standalone
4367,281.53,NJ City,E,8,22,9,34,High School,18,Owner,Standalone
9657,292.5,NJ City,N,11,16,20,36,Graduation,25,Patnership,Standalone
7886,404.35,Boston,S,10,21,17,84,Graduation,34,Patnership,Complex
995,162.43,NJ City,E,9,23,1,78,No education,33,Owner,Standalone
8416,341.56,NJ City,W,21,24,12,59,Graduation,22,Franchise,Complex
670,413.36,NJ City,S,25,24,12,42,High School,27,Patnership,Complex
6554,455.89,Boston,S,2,15,17,49,Graduation,29,Patnership,Complex
3331,114.24,NJ City,E,10,21,4,55,High School,20,Owner,Standalone
5484,352.23,Boston,W,3,18,3,55,Graduation,26,Franchise,Complex
2144,204.99,NJ City,W,22,23,24,48,High School,22,Franchise,Complex
3570,479.96,NJ City,S,2,22,5,71,Graduation,27,Patnership,Complex
5696,392.75,Boston,W,10,14,8,69,Graduation,31,Franchise,Complex
1324,306.2,NJ City,W,11,24,11,52,High School,31,Franchise,Complex
6517,125.34,Boston,N,7,16,4,26,High School,35,Patnership,Standalone
575,263,NJ City,E,3,23,20,67,High School,24,Owner,Standalone
5591,321.27,Boston,E,13,20,10,41,Graduation,21,Owner,Standalone
5816,425.58,Boston,W,11,20,17,80,Graduation,34,Franchise,Complex
1790,394.4,NJ City,S,5,15,3,55,No education,33,Patnership,Complex
7749,186.2,Boston,N,12,13,7,33,Graduation,18,Patnership,Standalone
4672,133.1,NJ City,W,10,8,16,56,High School,25,Franchise,Complex
1135,473.6,NJ City,E,11,14,25,60,High School,19,Owner,Standalone
4975,127.93,Boston,E,20,15,1,26,High School,33,Owner,Standalone
7012,118.42,Boston,W,24,22,4,42,High School,30,Franchise,Complex
5781,96.81,Boston,N,10,14,3,48,Graduation,30,Patnership,Standalone
234,342.2,NJ City,S,9,21,1,27,Graduation,32,Patnership,Complex
1231,9.31,NJ City,N,20,18,4,40,High School,33,Owner,Standalone
1844,67.62,NJ City,N,7,17,4,55,No education,26,Franchise,Complex
8081,362.2,NJ City,N,15,15,22,81,Graduation,23,Patnership,Standalone
3166,165.51,NJ City,S,18,22,23,35,High School,19,Patnership,Complex
2902,411.96,NJ City,S,13,16,22,42,High School,24,Patnership,Complex
2998,84.51,NJ City,S,19,23,3,36,High School,26,Patnership,Complex
1448,435.58,NJ City,W,15,15,2,65,No education,24,Franchise,Complex
1480,178.79,NJ City,W,6,10,13,51,High School,35,Franchise,Complex
9324,388.09,NJ City,W,11,21,6,58,Graduation,22,Franchise,Complex
5077,220.71,Boston,N,9,23,10,46,Graduation,18,Patnership,Standalone
1663,46.62,NJ City,N,19,13,1,80,High School,20,Owner,Standalone
1781,453.31,NJ City,N,13,9,3,74,No education,21,Patnership,Standalone
7537,151.42,Boston,N,8,22,3,35,High School,34,Patnership,Standalone
3063,84.04,NJ City,E,5,13,1,31,Graduation,30,Owner,Standalone
8682,98.82,NJ City,S,24,20,4,64,Graduation,18,Patnership,Complex
962,43.52,NJ City,N,19,11,3,26,No education,27,Patnership,Complex
2673,340.96,NJ City,N,13,14,2,40,Graduation,27,Patnership,Standalone
8374,305.9,NJ City,S,20,19,4,72,High School,23,Patnership,Complex
4359,233.54,NJ City,E,17,13,12,43,High School,21,Owner,Standalone
8366,414.84,NJ City,S,25,8,4,37,No education,20,Patnership,Complex
9651,185.63,NJ City,E,25,18,19,33,Graduation,19,Owner,Standalone
6274,193.07,Boston,S,3,11,15,32,Graduation,25,Patnership,Complex
3795,186,NJ City,E,2,13,22,36,High School,34,Owner,Standalone
6476,409.84,Boston,W,15,11,24,62,Graduation,29,Franchise,Complex
8052,433.07,NJ City,W,15,16,14,34,Graduation,27,Franchise,Complex
8475,319.48,NJ City,E,25,12,12,33,Graduation,27,Owner,Standalone
8372,279.99,NJ City,W,9,18,6,58,Graduation,30,Franchise,Complex
1,205.93,NJ City,N,16,11,12,75,High School,34,Owner,Complex
6840,339.05,Boston,W,6,12,18,81,Graduation,32,Franchise,Complex
1406,352.87,NJ City,S,19,18,8,26,High School,35,Patnership,Complex
8708,458.34,NJ City,W,22,20,7,35,Graduation,18,Franchise,Complex
4595,30.32,NJ City,N,8,21,3,53,No education,34,Owner,Standalone
3979,123.26,NJ City,E,3,15,3,82,High School,21,Owner,Standalone
3516,436.84,NJ City,W,2,10,3,49,Graduation,26,Franchise,Complex
2448,51.41,NJ City,W,2,24,4,49,Graduation,28,Franchise,Complex
430,9.38,NJ City,N,9,10,4,81,High School,26,Patnership,Complex
6216,120.31,Boston,W,7,11,1,36,Graduation,18,Franchise,Complex
6141,434.1,Boston,N,11,23,15,37,Graduation,22,Patnership,Standalone
2977,100.32,NJ City,N,4,21,3,26,High School,19,Patnership,Standalone
1270,232,NJ City,S,2,9,15,81,High School,28,Patnership,Complex
7673,244.94,Boston,N,16,15,2,45,No education,27,Patnership,Standalone
1174,15.83,NJ City,N,20,16,3,65,High School,32,Patnership,Complex
351,18.98,NJ City,N,15,21,1,62,Graduation,29,Owner,Standalone
1414,212,NJ City,S,3,8,11,46,High School,34,Patnership,Complex
5097,95.69,Boston,N,14,17,3,71,Graduation,19,Patnership,Standalone
5276,458.2,Boston,W,8,11,8,74,Graduation,20,Franchise,Complex
4469,237.24,NJ City,N,4,10,13,49,High School,35,Patnership,Standalone
8328,197.24,NJ City,W,25,18,22,72,Graduation,21,Franchise,Complex
9650,440.28,NJ City,S,22,15,8,51,Graduation,21,Patnership,Complex
2105,385.16,NJ City,N,9,8,23,81,High School,25,Patnership,Standalone
9306,33.82,NJ City,N,3,15,4,66,Graduation,24,Patnership,Complex
1274,342.62,NJ City,S,22,18,3,82,No education,33,Patnership,Complex
6709,132.07,Boston,N,21,16,10,28,Graduation,19,Patnership,Standalone
9880,375.07,NJ City,W,14,16,3,76,High School,31,Franchise,Complex
8806,8.59,NJ City,N,9,20,3,34,High School,18,Patnership,Complex
4449,147.96,NJ City,N,18,11,13,33,High School,35,Patnership,Standalone
5827,304.62,Boston,E,7,10,25,67,Graduation,25,Owner,Standalone
78,285.14,Boston,S,7,11,14,73,High School,29,Patnership,Complex
958,200,NJ City,S,1,22,10,69,High School,26,Patnership,Complex
2775,141,NJ City,E,3,11,17,47,High School,29,Owner,Standalone
6301,240.37,Boston,N,11,16,17,82,Graduation,23,Patnership,Standalone
3185,37.32,NJ City,N,14,16,4,48,No education,32,Patnership,Standalone
7208,394.41,Boston,W,2,24,18,44,Graduation,20,Franchise,Complex
6628,19.37,Boston,N,20,21,2,76,High School,31,Franchise,Complex
8256,316.81,NJ City,W,24,13,14,58,Graduation,25,Franchise,Complex
3234,234.43,NJ City,S,4,14,14,72,High School,24,Patnership,Complex
8575,253.09,NJ City,E,7,21,19,78,Graduation,22,Owner,Standalone
9397,302.68,NJ City,N,23,10,9,70,Graduation,22,Patnership,Standalone
7755,301.67,Boston,E,6,18,5,63,Graduation,33,Owner,Standalone
2176,452.77,NJ City,W,2,24,3,34,High School,33,Franchise,Complex
1088,205.76,NJ City,W,13,19,9,40,High School,29,Franchise,Complex
9685,362.5,NJ City,N,17,18,21,29,Graduation,33,Patnership,Standalone
2088,285.88,NJ City,W,2,21,2,65,Graduation,28,Franchise,Complex
5486,423.78,Boston,S,2,18,7,28,Graduation,19,Patnership,Complex
3450,79.46,NJ City,S,18,10,1,59,Graduation,27,Patnership,Complex
3181,372.89,NJ City,N,14,20,18,47,High School,24,Patnership,Standalone
7825,462.6,Boston,N,22,18,5,33,High School,25,Patnership,Standalone
4444,62.66,NJ City,N,11,11,1,25,High School,31,Franchise,Complex
6943,281.14,Boston,E,16,14,3,29,High School,35,Owner,Standalone
1881,28.32,NJ City,N,15,14,3,38,Graduation,24,Patnership,Standalone
4928,339.83,NJ City,W,13,17,5,78,No education,19,Franchise,Complex
1743,108.94,NJ City,E,10,12,3,39,Graduation,25,Owner,Standalone
218,332.2,NJ City,S,25,22,14,35,High School,33,Patnership,Complex
8528,195.98,NJ City,W,4,12,2,53,No education,27,Franchise,Complex
7064,270.96,Boston,W,10,23,24,48,Graduation,24,Franchise,Complex
2683,348.2,NJ City,E,7,17,22,52,High School,33,Owner,Standalone
3070,418.53,NJ City,S,15,20,5,46,High School,19,Patnership,Complex
7218,259.22,Boston,S,14,22,19,43,Graduation,31,Patnership,Complex
4737,279.92,NJ City,N,7,18,15,43,High School,31,Patnership,Standalone
6635,412.53,Boston,E,1,19,25,45,Graduation,33,Owner,Standalone
8019,306.88,Boston,E,5,21,9,37,Graduation,31,Owner,Standalone
3709,480.04,NJ City,N,4,20,10,75,High School,30,Patnership,Standalone
7636,273.83,Boston,W,25,8,1,41,High School,20,Franchise,Complex
2479,423.28,NJ City,E,24,20,7,74,High School,28,Owner,Standalone
1787,471,NJ City,E,23,11,25,41,High School,29,Owner,Standalone
6208,350.9,Boston,W,11,13,17,42,Graduation,18,Franchise,Complex
3990,315.33,NJ City,S,11,22,7,75,High School,20,Patnership,Complex
1243,148.27,NJ City,E,18,9,7,64,High School,31,Owner,Standalone
456,159.75,NJ City,W,21,10,9,71,High School,24,Franchise,Complex
6950,346.9,Boston,S,16,23,18,52,Graduation,24,Patnership,Complex
6752,471.68,Boston,W,15,24,16,45,Graduation,28,Franchise,Complex
1477,43.05,NJ City,N,10,20,1,64,High School,21,Patnership,Standalone
5863,92.15,Boston,E,8,11,3,57,High School,22,Owner,Standalone
149,191.77,NJ City,N,8,17,19,62,High School,19,Patnership,Standalone
4261,26.42,NJ City,N,21,24,3,51,High School,27,Patnership,Standalone
8425,347.39,NJ City,N,6,22,8,43,Graduation,30,Patnership,Standalone
1005,292.39,NJ City,N,10,9,18,39,High School,20,Patnership,Standalone
2923,71.3,NJ City,N,6,12,2,74,High School,28,Owner,Standalone
8740,230.59,NJ City,W,2,23,5,44,High School,19,Franchise,Complex
4033,465.97,NJ City,N,7,22,7,72,High School,29,Patnership,Standalone
9870,325.42,NJ City,S,24,15,10,39,Graduation,28,Patnership,Complex
7792,135.36,Boston,W,12,11,8,49,Graduation,29,Franchise,Complex
9378,143.36,NJ City,S,18,24,22,83,Graduation,24,Patnership,Complex
8471,380.5,NJ City,E,19,18,11,57,Graduation,32,Owner,Standalone
6009,364.62,Boston,N,23,22,9,70,Graduation,21,Patnership,Standalone
740,109,NJ City,W,2,11,25,67,High School,27,Franchise,Complex
9706,57.84,NJ City,S,15,21,3,74,High School,28,Patnership,Complex
4616,118.6,NJ City,W,23,22,4,73,No education,23,Franchise,Complex
9590,419.31,NJ City,S,18,24,5,28,No education,27,Patnership,Complex
7320,41.39,Boston,N,17,9,2,72,Graduation,21,Franchise,Complex
4059,331.43,NJ City,E,17,18,14,42,High School,26,Owner,Standalone
6805,196.03,Boston,N,5,17,25,44,Graduation,35,Patnership,Standalone
7934,397.09,Boston,S,24,23,10,77,Graduation,33,Patnership,Complex
3426,346.5,NJ City,S,19,17,18,54,High School,33,Patnership,Complex
7199,393.7,Boston,E,20,18,3,42,No education,25,Owner,Standalone
9070,116.18,NJ City,S,14,10,2,63,High School,22,Patnership,Complex
3562,70.05,NJ City,N,2,21,2,67,High School,24,Patnership,Complex
212,186.64,NJ City,W,17,16,22,70,High School,20,Franchise,Complex
8623,193.87,NJ City,E,17,24,13,31,Graduation,22,Owner,Standalone
3593,128.85,NJ City,N,22,18,15,62,High School,26,Patnership,Standalone
1735,444.99,NJ City,E,12,23,8,66,High School,24,Owner,Standalone
6225,78.69,Boston,N,15,24,2,60,Graduation,18,Patnership,Standalone
8838,130.91,NJ City,S,24,13,14,50,Graduation,26,Patnership,Complex
8642,393.35,NJ City,S,4,9,24,36,Graduation,32,Patnership,Complex
4696,416.27,NJ City,W,9,20,24,25,High School,35,Franchise,Complex
4526,280,NJ City,S,2,18,25,74,High School,30,Patnership,Complex
9511,9.18,NJ City,N,12,13,4,28,High School,20,Owner,Standalone
5025,33.73,Boston,N,16,9,4,82,Graduation,19,Patnership,Standalone
6794,210.95,Boston,S,7,21,2,80,No education,18,Patnership,Complex
8833,191.11,NJ City,N,23,8,16,76,Graduation,19,Patnership,Standalone
8143,486.9,NJ City,E,19,17,23,75,Graduation,23,Owner,Standalone
399,476.07,NJ City,E,11,23,15,32,High School,35,Owner,Standalone
5854,99.58,Boston,S,12,22,3,31,High School,19,Patnership,Complex
1389,55.17,NJ City,N,22,22,3,66,Graduation,35,Patnership,Standalone
5147,195.95,Boston,E,19,24,21,76,Graduation,24,Owner,Standalone
5585,355.04,Boston,N,23,8,15,74,Graduation,28,Patnership,Standalone
7474,450.61,Boston,S,8,11,23,36,Graduation,26,Patnership,Complex
9168,48.58,NJ City,W,18,15,4,48,Graduation,32,Franchise,Complex
7282,56.24,Boston,S,5,18,1,42,High School,20,Patnership,Complex
5267,474.61,Boston,E,16,24,20,71,Graduation,25,Owner,Standalone
8889,114.32,NJ City,N,5,8,3,50,Graduation,28,Patnership,Standalone
999,443.44,NJ City,E,5,10,23,48,High School,28,Owner,Standalone
4397,364.73,NJ City,N,20,20,16,37,High School,19,Patnership,Standalone
3386,308.76,NJ City,S,8,20,5,27,No education,31,Patnership,Complex
1469,90.14,NJ City,N,21,16,1,71,No education,27,Patnership,Standalone
942,111.12,NJ City,S,4,14,4,33,Graduation,21,Patnership,Complex
8051,175.31,NJ City,E,23,17,23,60,Graduation,28,Owner,Standalone
1430,32.98,NJ City,N,19,20,1,73,No education,34,Patnership,Complex
4884,104.16,NJ City,W,5,10,4,50,Graduation,32,Franchise,Complex
5570,424.78,Boston,S,25,23,7,77,Graduation,35,Patnership,Complex
8136,463.39,NJ City,W,18,11,24,37,Graduation,19,Franchise,Complex
2770,255.39,NJ City,S,19,16,10,66,High School,30,Patnership,Complex
602,64.87,NJ City,N,20,12,1,41,No education,31,Patnership,Complex
1857,134.85,NJ City,N,25,24,23,69,High School,25,Patnership,Standalone
7052,20.71,Boston,N,5,19,3,64,No education,20,Franchise,Complex
4959,321.14,Boston,E,16,24,18,84,High School,24,Owner,Standalone
3587,126.73,NJ City,E,21,23,2,58,No education,18,Owner,Standalone
6936,196.44,Boston,W,15,21,4,67,Graduation,23,Franchise,Complex
8531,297.99,NJ City,E,1,18,25,42,Graduation,26,Owner,Standalone
1496,157.94,NJ City,W,2,19,3,35,No education,19,Franchise,Complex
7761,179.13,Boston,N,3,23,19,75,Graduation,27,Patnership,Standalone
5233,288.08,Boston,N,11,9,15,34,Graduation,28,Patnership,Standalone
165,219.52,NJ City,N,8,20,8,32,High School,25,Patnership,Standalone
855,377.84,NJ City,E,19,21,8,60,High School,26,Owner,Standalone
269,363.14,NJ City,N,20,10,24,47,High School,27,Patnership,Standalone
9899,221.1,NJ City,E,9,10,13,54,Graduation,27,Owner,Standalone
1426,77.32,NJ City,S,22,21,1,79,High School,33,Patnership,Complex
6375,421.51,Boston,E,12,15,20,36,Graduation,35,Owner,Standalone
5845,130.7,Boston,N,10,24,15,38,Graduation,21,Patnership,Standalone
3274,183,NJ City,S,2,18,7,78,High School,22,Patnership,Complex
3180,367.8,NJ City,W,10,12,12,68,High School,19,Franchise,Complex
794,368.69,NJ City,S,5,19,1,41,No education,21,Patnership,Complex
5191,333.39,Boston,E,13,8,23,65,Graduation,21,Owner,Standalone
3943,383.13,NJ City,E,19,11,20,51,High School,18,Owner,Standalone
2847,67.13,NJ City,N,13,14,4,71,Graduation,23,Owner,Standalone
2950,88.28,NJ City,S,1,23,1,42,High School,20,Patnership,Complex
4108,341.21,NJ City,W,13,12,14,70,High School,33,Franchise,Complex
4987,11.34,Boston,N,19,24,2,54,High School,34,Owner,Standalone
8140,440.81,NJ City,W,22,19,8,56,Graduation,27,Franchise,Complex
5304,442.82,Boston,W,11,19,6,50,Graduation,30,Franchise,Complex
9453,135.46,NJ City,N,8,8,15,29,Graduation,20,Patnership,Standalone
782,463.38,NJ City,S,15,22,13,64,High School,18,Patnership,Complex
9115,175.21,NJ City,E,9,17,18,62,Graduation,19,Owner,Standalone
8075,143.82,NJ City,E,5,22,2,47,No education,35,Owner,Standalone
7578,152.63,Boston,S,14,13,12,47,Graduation,33,Patnership,Complex
7518,387.85,Boston,S,6,18,16,37,Graduation,34,Patnership,Complex
4357,4.66,NJ City,N,22,15,4,81,High School,33,Patnership,Standalone
5982,431.23,Boston,S,24,17,18,64,Graduation,31,Patnership,Complex
3333,8.24,NJ City,N,22,14,4,37,Graduation,35,Patnership,Standalone
9364,354.85,NJ City,W,20,13,12,84,Graduation,21,Franchise,Complex
7202,386.98,Boston,S,4,10,10,70,Graduation,34,Patnership,Complex
4126,437.16,NJ City,S,5,15,23,74,High School,29,Patnership,Complex
8325,131.64,NJ City,N,12,8,25,38,Graduation,29,Patnership,Standalone
6272,98.16,Boston,W,11,23,4,46,No education,33,Franchise,Complex
8255,36.44,NJ City,N,4,20,3,41,No education,19,Owner,Standalone
5847,302.95,Boston,E,25,15,17,57,Graduation,21,Owner,Standalone
2906,132.79,NJ City,S,5,9,13,31,High School,32,Patnership,Complex
730,137.7,NJ City,S,14,21,2,43,High School,20,Patnership,Complex
175,303.86,NJ City,E,22,12,2,41,High School,26,Owner,Standalone
2456,238,NJ City,W,1,10,14,54,High School,34,Franchise,Complex
6716,342.37,Boston,W,21,13,5,64,No education,32,Franchise,Complex
241,109,NJ City,N,3,20,24,26,High School,30,Patnership,Standalone
3138,471.9,NJ City,S,8,20,24,70,High School,31,Patnership,Complex
2984,182.36,NJ City,W,16,14,4,74,No education,25,Franchise,Complex
5968,458.15,Boston,W,18,15,5,52,High School,18,Franchise,Complex
9782,304.63,NJ City,S,15,23,20,50,Graduation,26,Patnership,Complex
2517,302.74,NJ City,N,25,17,25,44,High School,23,Patnership,Standalone
3929,303.73,NJ City,N,13,14,7,53,High School,33,Patnership,Standalone
699,232.8,NJ City,E,22,16,14,39,High School,24,Owner,Standalone
7965,391.69,Boston,N,15,19,19,85,Graduation,24,Patnership,Standalone
5517,166.29,Boston,N,13,8,15,75,Graduation,23,Patnership,Standalone
7710,310.5,Boston,S,19,8,6,58,Graduation,27,Patnership,Complex
2546,498.11,NJ City,S,25,22,8,82,High School,32,Patnership,Complex
1077,23.24,NJ City,N,5,24,1,73,Graduation,32,Patnership,Standalone
2032,50.44,NJ City,W,12,16,1,70,High School,29,Franchise,Complex
8911,65.45,NJ City,N,8,16,4,76,High School,28,Owner,Standalone
9729,95.05,NJ City,N,10,10,4,48,Graduation,29,Patnership,Standalone
651,108,NJ City,E,2,23,22,47,High School,29,Owner,Standalone
1653,84.67,NJ City,N,1,11,3,40,Graduation,34,Patnership,Standalone
4103,133.88,NJ City,E,12,16,14,82,High School,32,Owner,Standalone
4,93.99,NJ City,W,19,9,3,42,High School,29,Owner,Standalone
2593,20.7,NJ City,N,4,15,2,26,High School,20,Patnership,Standalone
6268,176.2,Boston,W,12,8,20,40,Graduation,33,Franchise,Complex
7255,379.6,Boston,E,23,15,21,59,Graduation,21,Owner,Standalone
2887,357.86,NJ City,E,12,13,19,38,High School,23,Owner,Standalone
2532,466.53,NJ City,W,4,10,10,29,High School,19,Franchise,Complex
3706,489.87,NJ City,S,14,13,9,84,High School,30,Patnership,Complex
4889,344.2,NJ City,N,25,14,2,70,No education,26,Patnership,Standalone
4930,383.09,NJ City,S,16,24,11,30,High School,34,Patnership,Complex
3182,35.32,NJ City,N,19,12,2,31,No education,19,Patnership,Complex
2122,85.83,NJ City,S,25,19,1,27,High School,26,Patnership,Complex
5203,50.6,Boston,E,19,21,3,66,High School,33,Owner,Standalone
8168,426.91,NJ City,W,23,24,23,53,Graduation,24,Franchise,Complex
8035,481.89,Boston,E,24,23,14,82,Graduation,34,Owner,Standalone
5200,402.62,Boston,W,2,21,2,37,High School,33,Franchise,Complex
8915,454.71,NJ City,E,23,22,1,73,No education,23,Owner,Standalone
5987,133.74,Boston,E,19,17,13,64,Graduation,34,Owner,Standalone
4503,22.83,NJ City,N,2,18,2,62,Graduation,25,Owner,Standalone
2661,145,NJ City,N,1,18,12,47,High School,29,Patnership,Standalone
4519,330.93,NJ City,E,8,9,6,33,High School,23,Owner,Standalone
7862,225.62,Boston,S,25,14,9,38,Graduation,26,Patnership,Complex
2496,247.52,NJ City,W,11,11,4,82,Graduation,22,Franchise,Complex
3557,494.88,NJ City,N,12,23,6,33,High School,25,Patnership,Standalone
7785,453.09,Boston,N,8,19,13,50,Graduation,24,Patnership,Standalone
3329,166.55,NJ City,N,8,16,10,25,High School,19,Patnership,Standalone
2760,383.2,NJ City,W,19,16,19,71,High School,31,Franchise,Complex
7718,112.62,Boston,S,22,23,3,29,No education,30,Patnership,Complex
6583,356.85,Boston,E,4,18,8,80,Graduation,35,Owner,Standalone
8340,28.12,NJ City,N,1,18,4,46,Graduation,27,Franchise,Complex
693,78.38,NJ City,N,24,10,3,63,Graduation,28,Patnership,Standalone
4313,181.57,NJ City,N,7,9,2,54,No education,33,Patnership,Standalone
8800,123.32,NJ City,W,23,12,4,85,High School,28,Franchise,Complex
4373,233.03,NJ City,N,12,14,19,85,High School,26,Patnership,Standalone
2041,135.52,NJ City,N,19,12,11,67,High School,28,Patnership,Standalone
9302,295.3,NJ City,S,16,11,25,50,Graduation,23,Patnership,Complex
1260,115.5,NJ City,W,18,15,3,68,Graduation,24,Franchise,Complex
8848,10.83,NJ City,N,5,20,2,74,High School,18,Franchise,Complex
125,264,NJ City,N,2,19,15,70,High School,26,Patnership,Standalone
3897,313.68,NJ City,N,25,21,3,27,Graduation,29,Patnership,Standalone
2151,434.5,NJ City,E,20,17,12,40,High School,27,Owner,Standalone
3467,418.49,NJ City,E,17,12,7,83,High School,30,Owner,Standalone
1854,219.05,NJ City,S,12,16,1,40,Graduation,20,Patnership,Complex
9165,391.49,NJ City,N,19,18,15,48,Graduation,18,Patnership,Standalone
3998,166.16,NJ City,S,7,16,8,43,High School,25,Patnership,Complex
3248,98.78,NJ City,W,21,21,1,58,No education,22,Franchise,Complex
4309,433.41,NJ City,N,6,23,25,73,High School,29,Patnership,Standalone
5205,137.98,Boston,N,12,10,4,36,Graduation,20,Patnership,Standalone
8909,83.69,NJ City,N,9,8,2,31,No education,22,Patnership,Standalone
689,58.02,NJ City,N,18,8,4,37,No education,22,Patnership,Standalone
1614,352.6,NJ City,S,23,20,10,68,High School,27,Patnership,Complex
5175,414.88,Boston,E,25,14,23,28,Graduation,18,Owner,Standalone
8522,276.49,NJ City,S,11,14,1,73,No education,23,Patnership,Complex
9787,233.6,NJ City,E,6,16,20,61,Graduation,33,Owner,Standalone
893,408.8,NJ City,N,25,13,13,62,High School,35,Patnership,Standalone
5798,37.31,Boston,N,19,24,3,59,No education,28,Patnership,Complex
5629,270.73,Boston,N,22,13,23,39,Graduation,27,Patnership,Standalone
3834,416.12,NJ City,S,13,22,19,73,High School,24,Patnership,Complex
6881,16.84,Boston,N,20,13,1,48,No education,29,Patnership,Standalone
9915,73.75,NJ City,N,18,18,1,77,Graduation,26,Owner,Standalone
1995,162.72,NJ City,E,8,18,12,61,High School,23,Owner,Standalone
5474,66.97,Boston,N,8,23,2,69,No education,29,Patnership,Complex
8310,132.35,NJ City,S,5,24,18,64,Graduation,21,Patnership,Complex
9074,456.45,NJ City,S,17,17,16,76,Graduation,33,Patnership,Complex
5737,45.08,Boston,N,24,14,2,61,High School,32,Patnership,Standalone
9553,376.48,NJ City,N,5,13,19,28,Graduation,19,Patnership,Standalone
3621,16.33,NJ City,N,20,11,3,38,Graduation,35,Patnership,Standalone
2049,212.46,NJ City,N,16,19,3,31,Graduation,25,Patnership,Standalone
3296,48.27,NJ City,W,10,19,1,34,No education,19,Franchise,Complex
1552,365.07,NJ City,W,7,10,5,30,High School,32,Franchise,Complex
4914,144.16,NJ City,S,20,24,16,80,High School,34,Patnership,Complex
577,144.45,NJ City,N,14,18,13,75,High School,34,Patnership,Standalone
1506,381.87,NJ City,S,23,16,7,42,High School,22,Patnership,Complex
1047,245.43,NJ City,E,4,24,11,28,High School,27,Owner,Standalone
9481,120.22,NJ City,N,17,12,4,41,High School,23,Patnership,Standalone
6516,62.66,Boston,N,8,20,2,26,Graduation,24,Franchise,Complex
749,232.1,NJ City,N,17,23,3,35,No education,24,Patnership,Standalone
2680,458.7,NJ City,W,25,17,8,77,High School,20,Franchise,Complex
6863,20.47,Boston,N,7,20,4,38,No education,32,Owner,Standalone
851,480.87,NJ City,E,21,20,14,61,High School,35,Owner,Standalone
4405,169.41,NJ City,N,2,16,5,26,High School,27,Patnership,Standalone
1282,479.94,NJ City,S,15,19,1,74,High School,31,Patnership,Complex
2439,222.64,NJ City,E,6,14,13,79,High School,20,Owner,Standalone
1547,388.53,NJ City,E,5,15,17,68,High School,29,Owner,Standalone
3879,75.66,NJ City,E,22,17,4,59,Graduation,18,Owner,Standalone
410,277.68,NJ City,S,24,12,7,75,High School,28,Patnership,Complex
7989,139.91,Boston,N,3,24,9,63,Graduation,31,Patnership,Standalone
6039,113.41,Boston,E,25,14,4,57,Graduation,23,Owner,Standalone
4237,392.44,NJ City,N,4,19,11,44,High School,30,Patnership,Standalone
6473,393.1,Boston,N,25,24,8,25,Graduation,25,Patnership,Standalone
1765,190.81,NJ City,N,6,23,25,71,High School,24,Patnership,Standalone
5941,391.41,Boston,N,11,19,21,57,Graduation,24,Patnership,Standalone
9188,488.54,NJ City,W,18,18,21,26,Graduation,35,Franchise,Complex
663,438.86,NJ City,E,14,23,20,27,High School,33,Owner,Standalone
909,440.62,NJ City,N,13,22,17,31,High School,33,Patnership,Standalone
7239,235.33,Boston,E,12,8,1,67,Graduation,25,Owner,Standalone
1192,277.18,NJ City,W,11,9,4,53,High School,22,Franchise,Complex
9394,159.01,NJ City,S,8,15,18,42,Graduation,29,Patnership,Complex
2842,480.04,NJ City,S,16,13,18,33,High School,22,Patnership,Complex
479,297,NJ City,E,2,18,19,45,High School,28,Owner,Standalone
3759,433.1,NJ City,E,11,9,25,35,High School,20,Owner,Standalone
1010,394.77,NJ City,S,8,19,23,70,High School,30,Patnership,Complex
4269,219.28,NJ City,N,19,18,7,73,High School,33,Patnership,Standalone
9719,459.99,NJ City,E,8,13,13,69,Graduation,18,Owner,Standalone
9764,103.49,NJ City,W,17,19,4,35,No education,30,Franchise,Complex
2071,276.63,NJ City,E,7,11,22,70,High School,22,Owner,Standalone
9125,470.26,NJ City,N,25,22,3,54,No education,35,Patnership,Standalone
2828,170.93,NJ City,W,15,11,25,37,High School,34,Franchise,Complex
5139,237.29,Boston,E,8,11,19,85,Graduation,21,Owner,Standalone
5353,192.7,Boston,N,21,24,23,25,Graduation,28,Patnership,Standalone
8390,294.51,NJ City,S,16,21,2,77,No education,28,Patnership,Complex
6244,377.31,Boston,W,24,18,15,58,Graduation,32,Franchise,Complex
3343,476.18,NJ City,E,5,15,21,34,High School,25,Owner,Standalone
5148,385.01,Boston,W,8,17,24,30,Graduation,30,Franchise,Complex
5218,392.1,Boston,S,7,9,3,32,High School,23,Patnership,Complex
6040,381.23,Boston,W,15,18,16,25,Graduation,35,Franchise,Complex
644,311.36,NJ City,W,18,18,10,71,High School,18,Franchise,Complex
2217,19.5,NJ City,N,16,13,3,26,Graduation,29,Patnership,Standalone
483,409.13,NJ City,E,24,11,13,44,High School,34,Owner,Standalone
6762,444.49,Boston,S,9,24,7,51,Graduation,19,Patnership,Complex
5621,33.28,Boston,N,8,23,4,28,No education,31,Patnership,Standalone
4969,209.1,Boston,N,9,15,24,46,High School,21,Patnership,Standalone
6037,416.46,Boston,N,3,17,8,51,Graduation,28,Patnership,Standalone
4636,376.16,NJ City,W,12,21,11,35,High School,24,Franchise,Complex
4316,393.35,NJ City,W,4,19,2,42,No education,21,Franchise,Complex
250,439.52,NJ City,S,24,8,7,45,High School,32,Patnership,Complex
2505,83.57,NJ City,N,25,16,2,68,Graduation,27,Patnership,Standalone
2123,80.3,NJ City,E,5,8,4,45,No education,29,Owner,Standalone
9137,150.06,NJ City,N,20,14,21,78,Graduation,35,Patnership,Standalone
7597,97.53,Boston,N,7,16,2,54,High School,22,Patnership,Standalone
9424,455.23,NJ City,W,1,16,12,26,Graduation,34,Franchise,Complex
9040,340.41,NJ City,W,4,16,2,63,High School,27,Franchise,Complex
621,335.31,NJ City,N,20,24,8,51,High School,32,Patnership,Standalone
5289,164.23,Boston,N,24,13,1,65,Graduation,24,Patnership,Standalone
9036,0.94,NJ City,N,4,20,1,67,Graduation,28,Franchise,Complex
603,138.14,NJ City,E,14,19,1,27,Graduation,33,Owner,Standalone
9673,299.94,NJ City,N,10,20,20,50,Graduation,21,Patnership,Standalone
2341,101.93,NJ City,N,7,14,1,55,High School,30,Patnership,Standalone
7194,230.22,Boston,S,7,22,24,75,Graduation,35,Patnership,Complex
6771,250.75,Boston,E,3,14,3,83,Graduation,21,Owner,Standalone
3431,342.87,NJ City,E,22,20,17,84,High School,33,Owner,Standalone
3640,470.04,NJ City,W,6,12,22,59,High School,29,Franchise,Complex
1102,342.69,NJ City,S,18,19,2,43,High School,24,Patnership,Complex
96,494.33,NJ City,W,7,17,10,41,High School,27,Franchise,Complex
249,495.2,NJ City,N,8,15,1,62,Graduation,19,Patnership,Standalone
5003,400.73,Boston,E,7,14,23,58,Graduation,34,Owner,Standalone
8763,112.04,NJ City,E,3,10,1,77,Graduation,35,Owner,Standalone
5890,29.12,Boston,N,25,12,3,46,High School,29,Patnership,Complex
8041,129.77,Boston,N,17,21,23,63,Graduation,27,Patnership,Standalone
6688,499.76,Boston,W,25,20,20,30,Graduation,26,Franchise,Complex
5775,256.95,Boston,E,18,10,19,40,Graduation,34,Owner,Standalone
2213,179.11,NJ City,N,9,23,3,46,No education,33,Patnership,Standalone
6561,370.42,Boston,N,5,18,7,53,Graduation,19,Patnership,Standalone
8250,438.22,NJ City,S,12,12,25,51,Graduation,22,Patnership,Complex
929,207.38,NJ City,N,18,18,5,78,No education,31,Patnership,Standalone
649,289.02,NJ City,N,11,12,18,75,High School,22,Patnership,Standalone
6623,365.7,Boston,E,5,8,21,79,Graduation,23,Owner,Standalone
6916,384.71,Boston,W,25,22,23,25,Graduation,34,Franchise,Complex
3682,110.36,NJ City,S,1,11,3,55,High School,31,Patnership,Complex
4742,443.89,NJ City,S,7,9,2,40,No education,35,Patnership,Complex
6409,62.9,Boston,N,15,15,4,26,High School,18,Patnership,Standalone
2491,397.9,NJ City,E,4,23,1,43,High School,35,Owner,Standalone
9761,375.25,NJ City,N,24,18,21,77,Graduation,33,Patnership,Standalone
1116,381.07,NJ City,W,14,17,4,79,Graduation,20,Franchise,Complex
761,441.32,NJ City,N,21,24,15,26,High School,20,Patnership,Standalone
2985,92.83,NJ City,N,4,19,4,70,Graduation,31,Patnership,Standalone
3383,442.63,NJ City,E,25,17,21,44,High School,32,Owner,Standalone
7627,294.96,Boston,E,5,13,4,70,High School,32,Owner,Standalone
8670,408.72,NJ City,S,22,10,10,58,Graduation,32,Patnership,Complex
2270,493.73,NJ City,S,25,13,24,81,High School,26,Patnership,Complex
1280,191.44,NJ City,W,7,9,15,70,High School,18,Franchise,Complex
7844,275.54,Boston,W,8,9,11,84,Graduation,26,Franchise,Complex
7829,237.5,Boston,N,21,24,8,53,Graduation,32,Patnership,Standalone
7697,249.45,Boston,N,1,15,10,28,Graduation,24,Patnership,Standalone
3246,159.98,NJ City,S,8,19,2,41,Graduation,21,Patnership,Complex
5194,233.56,Boston,S,19,9,8,27,Graduation,32,Patnership,Complex
5501,239.22,Boston,N,3,20,21,56,Graduation,21,Patnership,Standalone
5464,198.85,Boston,W,8,20,20,60,Graduation,35,Franchise,Complex
4081,127.6,NJ City,N,10,21,1,45,High School,22,Patnership,Standalone
8074,293.58,NJ City,S,23,8,11,36,Graduation,32,Patnership,Complex
7713,285.88,Boston,N,21,21,20,25,Graduation,28,Patnership,Standalone
4332,121.69,NJ City,W,16,11,4,76,Graduation,18,Franchise,Complex
9243,53.99,NJ City,E,19,13,4,83,Graduation,19,Owner,Standalone
4810,355.17,NJ City,S,19,20,18,26,High School,19,Patnership,Complex
6853,106.59,Boston,N,7,21,4,53,High School,34,Patnership,Standalone
3390,119.39,NJ City,S,21,10,3,75,Graduation,26,Patnership,Complex
7059,100.92,Boston,E,1,21,1,46,Graduation,21,Owner,Standalone
3753,423.92,NJ City,N,22,22,14,45,High School,31,Patnership,Standalone
4966,157.92,Boston,S,11,23,23,48,High School,35,Patnership,Complex
6592,390.68,Boston,W,8,13,18,46,Graduation,33,Franchise,Complex
9530,124.58,NJ City,S,8,18,1,38,No education,29,Patnership,Complex
8433,498.36,NJ City,N,3,22,5,35,Graduation,35,Patnership,Standalone
9485,97.92,NJ City,N,16,15,1,31,No education,35,Patnership,Standalone
935,306,NJ City,E,1,23,12,62,High School,28,Owner,Standalone
7055,207.42,Boston,E,20,15,10,29,Graduation,30,Owner,Standalone
5438,437.04,Boston,S,4,13,24,82,Graduation,32,Patnership,Complex
5607,137.54,Boston,E,18,8,10,34,Graduation,24,Owner,Standalone
455,192.53,NJ City,E,14,19,15,47,High School,30,Owner,Standalone
5710,179.33,Boston,S,14,20,4,36,High School,23,Patnership,Complex
8742,457.66,NJ City,S,25,12,6,77,Graduation,30,Patnership,Complex
4617,244.2,NJ City,N,11,14,7,84,High School,22,Patnership,Standalone
6513,17.63,Boston,N,6,22,4,62,Graduation,18,Patnership,Standalone
4841,146.44,NJ City,N,18,19,16,66,High School,31,Patnership,Standalone
4219,308.11,NJ City,E,7,17,6,76,High School,25,Owner,Standalone
428,320.44,NJ City,W,5,17,6,73,High School,29,Franchise,Complex
6475,27.68,Boston,N,13,10,1,35,High School,29,Owner,Standalone
1172,174.45,NJ City,W,14,9,11,41,High School,26,Franchise,Complex
3926,389.92,NJ City,S,23,12,5,36,No education,33,Patnership,Complex
5948,234.85,Boston,W,15,8,21,74,Graduation,20,Franchise,Complex
9473,399.14,NJ City,N,3,12,10,29,Graduation,20,Patnership,Standalone
9931,382.82,NJ City,E,18,20,17,36,Graduation,26,Owner,Standalone
2630,66.92,NJ City,N,12,15,2,61,No education,31,Patnership,Complex
3028,341.58,NJ City,W,11,20,18,63,High School,32,Franchise,Complex
7539,216.71,Boston,E,8,14,5,61,Graduation,18,Owner,Standalone
5290,355.96,Boston,S,10,11,15,84,Graduation,22,Patnership,Complex
6540,184.3,Boston,W,23,20,15,73,Graduation,28,Franchise,Complex
3066,465.62,NJ City,S,19,18,9,76,High School,31,Patnership,Complex
746,450.09,NJ City,S,22,19,7,50,High School,30,Patnership,Complex
982,343.63,NJ City,S,17,14,5,34,High School,21,Patnership,Complex
4326,399.42,NJ City,S,17,9,16,60,High School,33,Patnership,Complex
3404,495.42,NJ City,W,6,22,23,44,High School,32,Franchise,Complex
8419,129.72,NJ City,E,23,8,5,39,High School,30,Owner,Standalone
7930,316.98,Boston,S,5,17,1,28,High School,20,Patnership,Complex
3758,283.3,NJ City,S,10,23,25,63,High School,33,Patnership,Complex
2342,249.86,NJ City,S,21,14,22,28,High School,23,Patnership,Complex
5384,305.17,Boston,W,2,16,14,70,Graduation,23,Franchise,Complex
1425,445.05,NJ City,N,10,17,10,42,High School,35,Patnership,Standalone
4765,227.31,NJ City,N,21,11,9,77,High School,35,Patnership,Standalone
7221,199.21,Boston,N,20,23,15,57,Graduation,20,Patnership,Standalone
7184,338.56,Boston,W,13,23,1,63,No education,30,Franchise,Complex
1822,144.87,NJ City,S,5,18,2,49,High School,28,Patnership,Complex
3958,128.65,NJ City,S,8,11,6,77,High School,35,Patnership,Complex
4731,19.55,NJ City,N,2,12,3,38,Graduation,21,Owner,Standalone
6736,181.23,Boston,W,25,11,16,31,Graduation,27,Franchise,Complex
7837,243.09,Boston,N,9,12,21,76,Graduation,20,Patnership,Standalone
8345,426.56,NJ City,N,6,11,14,37,Graduation,26,Patnership,Standalone
8113,402.37,NJ City,N,16,18,19,26,Graduation,31,Patnership,Standalone
6625,430.08,Boston,N,13,18,5,62,High School,35,Patnership,Standalone
2478,342.88,NJ City,S,21,13,17,54,High School,34,Patnership,Complex
3484,48.38,NJ City,W,23,9,3,33,High School,26,Franchise,Complex
3281,164,NJ City,N,2,18,12,50,High School,31,Patnership,Standalone
497,473.25,NJ City,N,6,21,21,41,High School,28,Patnership,Standalone
415,350.13,NJ City,E,8,19,8,72,High School,32,Owner,Standalone
5049,292.85,Boston,N,21,20,19,74,Graduation,24,Patnership,Standalone
7613,439.17,Boston,N,18,12,4,77,No education,29,Patnership,Standalone
1711,261.15,NJ City,E,17,16,9,73,High School,27,Owner,Standalone
8436,120.91,NJ City,W,15,21,1,25,Graduation,29,Franchise,Complex
9296,198.5,NJ City,W,24,12,22,59,Graduation,34,Franchise,Complex
3713,74.05,NJ City,N,17,21,2,69,No education,18,Patnership,Standalone
9445,273.38,NJ City,N,1,22,9,54,Graduation,22,Patnership,Standalone
7042,478.17,Boston,S,5,23,1,52,High School,35,Patnership,Complex
8714,114.85,NJ City,S,18,11,2,31,No education,32,Patnership,Complex
8341,0.88,NJ City,N,16,9,2,41,High School,22,Patnership,Standalone
5645,383.39,Boston,N,2,9,2,81,No education,34,Patnership,Standalone
5803,7.31,Boston,N,12,8,3,26,High School,26,Owner,Standalone
9618,162.76,NJ City,S,13,19,24,77,Graduation,25,Patnership,Complex
9083,76.93,NJ City,E,1,9,2,26,No education,35,Owner,Standalone
433,348.18,NJ City,N,21,22,13,35,High School,24,Patnership,Standalone
2990,7.19,NJ City,N,6,21,4,26,No education,22,Patnership,Complex
1852,29.44,NJ City,N,7,19,4,51,High School,34,Franchise,Complex
4722,10.03,NJ City,N,13,15,1,58,Graduation,24,Patnership,Complex
7983,303.35,Boston,E,13,21,18,51,Graduation,33,Owner,Standalone
9660,85.4,NJ City,W,14,9,4,64,Graduation,29,Franchise,Complex
9321,253.93,NJ City,N,7,18,20,47,Graduation,34,Patnership,Standalone
2336,185.15,NJ City,W,6,18,14,38,High School,23,Franchise,Complex
1288,261,NJ City,W,2,22,21,34,High School,19,Franchise,Complex
5909,149.63,Boston,N,3,8,22,38,Graduation,30,Patnership,Standalone
3102,256.55,NJ City,S,12,11,8,44,High School,34,Patnership,Complex
3330,431.3,NJ City,S,12,11,24,82,High School,30,Patnership,Complex
5420,193.01,Boston,W,15,24,5,65,No education,22,Franchise,Complex
8186,488.45,NJ City,S,14,22,22,32,Graduation,25,Patnership,Complex
2868,435.94,NJ City,W,9,17,19,79,High School,33,Franchise,Complex
8441,338.49,NJ City,N,21,15,11,76,Graduation,32,Patnership,Standalone
7515,4.41,Boston,N,14,19,1,59,Graduation,31,Owner,Standalone
8383,306.68,NJ City,E,9,20,7,59,Graduation,25,Owner,Standalone
3114,297.11,NJ City,S,4,22,5,30,Graduation,24,Patnership,Complex
9593,141.12,NJ City,N,9,19,3,64,No education,30,Patnership,Standalone
307,425.5,NJ City,E,13,24,21,68,High School,35,Owner,Standalone
7803,33.5,Boston,N,8,10,2,83,Graduation,34,Owner,Standalone
1152,346.29,NJ City,W,23,13,6,52,High School,34,Franchise,Complex
1312,207.5,NJ City,W,5,12,23,46,High School,25,Franchise,Complex
9287,213.14,NJ City,E,9,15,24,64,Graduation,20,Owner,Standalone
337,388.85,NJ City,N,6,20,20,71,High School,33,Patnership,Standalone
6722,123.5,Boston,S,6,24,2,37,No education,30,Patnership,Complex
9554,216.86,NJ City,S,12,17,11,39,Graduation,33,Patnership,Complex
3887,497.87,NJ City,E,17,21,15,67,High School,20,Owner,Standalone
2534,206.72,NJ City,S,17,14,23,27,High School,22,Patnership,Complex
7913,382.19,Boston,N,19,10,18,66,Graduation,31,Patnership,Standalone
505,19.86,NJ City,N,18,18,2,72,High School,21,Patnership,Standalone
4099,405.38,NJ City,E,23,16,7,64,High School,22,Owner,Standalone
9191,200.68,NJ City,E,9,20,20,85,Graduation,34,Owner,Standalone
4370,454.96,NJ City,S,11,8,11,43,High School,27,Patnership,Complex
1561,165.57,NJ City,N,7,13,19,50,High School,33,Patnership,Standalone
2058,15.98,NJ City,N,8,14,1,35,Graduation,26,Patnership,Complex
766,330.19,NJ City,S,19,24,15,45,High School,22,Patnership,Complex
1269,407.02,NJ City,N,7,10,8,82,High School,25,Patnership,Standalone
8732,163.5,NJ City,W,17,24,12,52,Graduation,28,Franchise,Complex
8654,234.57,NJ City,S,20,24,24,41,Graduation,29,Patnership,Complex
6219,27.97,Boston,N,23,16,2,62,Graduation,22,Owner,Standalone
4231,169.88,NJ City,E,4,13,22,27,High School,26,Owner,Standalone
8162,365.1,NJ City,S,4,14,12,48,Graduation,21,Patnership,Complex
6851,436.81,Boston,E,10,8,2,77,No education,25,Owner,Standalone
1300,169.96,NJ City,W,6,9,16,77,High School,29,Franchise,Complex
7849,336.06,Boston,N,5,14,24,66,Graduation,18,Patnership,Standalone
8105,27.64,NJ City,N,1,10,1,35,No education,28,Patnership,Standalone
9348,319.69,NJ City,W,10,11,19,53,Graduation,18,Franchise,Complex
5705,12.78,Boston,N,7,13,3,25,No education,28,Patnership,Standalone
7300,214.25,Boston,W,18,8,9,63,Graduation,22,Franchise,Complex
5958,412.59,Boston,S,12,22,14,48,Graduation,20,Patnership,Complex
153,364.51,NJ City,N,17,17,25,84,High School,34,Patnership,Standalone
3672,210.47,NJ City,W,19,10,18,54,High School,30,Franchise,Complex
3213,349.9,NJ City,N,10,22,19,83,High School,33,Patnership,Standalone
4427,228.57,NJ City,E,13,10,24,53,High School,33,Owner,Standalone
3582,137,NJ City,S,3,10,14,48,High School,22,Patnership,Complex
5748,94.87,Boston,W,3,15,1,38,Graduation,26,Franchise,Complex
68,259.54,NY City,W,16,22,20,31,High School,18,Franchise,Complex
4136,119,NJ City,W,3,8,18,29,High School,24,Franchise,Complex
1249,300.64,NJ City,N,18,14,2,58,High School,20,Patnership,Standalone
4182,173.87,NJ City,S,25,18,14,41,High School,25,Patnership,Complex
3820,443.92,NJ City,W,17,17,8,60,High School,26,Franchise,Complex
4753,493.49,NJ City,N,7,15,10,65,High School,25,Patnership,Standalone
2154,395.62,NJ City,S,15,23,9,37,High School,35,Patnership,Complex
6191,239.83,Boston,E,7,17,12,50,Graduation,22,Owner,Standalone
7550,473.93,Boston,S,16,22,17,71,Graduation,35,Patnership,Complex
8191,44.3,NJ City,N,15,9,1,64,High School,24,Owner,Standalone
3581,26.8,NJ City,N,10,15,2,39,No education,28,Patnership,Standalone
6831,302.68,Boston,E,16,8,21,26,Graduation,23,Owner,Standalone
6443,342.31,Boston,E,17,21,14,29,Graduation,21,Owner,Standalone
5373,32.27,Boston,N,12,18,2,79,Graduation,35,Patnership,Standalone
8025,4.48,Boston,N,11,12,3,60,Graduation,34,Patnership,Standalone
2568,365.46,NJ City,W,25,12,11,82,High School,21,Franchise,Complex
2718,439.72,NJ City,S,4,18,22,81,High School,19,Patnership,Complex
4207,205,NJ City,E,2,13,14,55,High School,25,Owner,Standalone
1757,442.62,NJ City,N,15,13,21,85,High School,22,Patnership,Standalone
5086,374.04,Boston,S,14,18,5,80,High School,31,Patnership,Complex
4042,165.55,NJ City,S,24,23,25,74,High School,32,Patnership,Complex
4827,215,NJ City,E,3,23,8,39,High School,22,Owner,Standalone
340,356.78,NJ City,W,12,24,4,83,High School,22,Franchise,Complex
1556,136.97,NJ City,W,15,13,6,55,High School,22,Franchise,Complex
6548,11.05,Boston,N,15,13,2,45,No education,33,Franchise,Complex
6248,133.33,Boston,W,7,15,6,38,Graduation,32,Franchise,Complex
2941,256.48,NJ City,N,24,16,23,68,High School,23,Patnership,Standalone
9375,301.74,NJ City,E,1,23,17,69,Graduation,25,Owner,Standalone
2048,284.43,NJ City,W,1,18,1,44,No education,26,Franchise,Complex
2100,25.76,NJ City,N,1,15,1,34,Graduation,26,Franchise,Complex
2753,207.69,NJ City,N,19,18,24,62,High School,33,Patnership,Standalone
3189,199,NJ City,N,2,9,23,62,High School,21,Patnership,Standalone
1377,26.86,NJ City,N,10,17,3,68,Graduation,32,Patnership,Standalone
486,273,NJ City,S,1,10,11,56,High School,20,Patnership,Complex
5324,426.9,Boston,W,8,13,7,82,Graduation,32,Franchise,Complex
5165,492.48,Boston,N,17,19,24,34,Graduation,29,Patnership,Standalone
9257,403.91,NJ City,N,14,21,17,49,Graduation,18,Patnership,Standalone
3457,227.15,NJ City,N,7,11,17,55,High School,27,Patnership,Standalone
164,470.5,NJ City,W,7,10,19,84,High School,28,Franchise,Complex
6415,311.92,Boston,E,13,15,14,42,Graduation,35,Owner,Standalone
6405,286.25,Boston,N,22,20,7,42,Graduation,25,Patnership,Standalone
7429,101.8,Boston,N,11,12,3,52,High School,31,Patnership,Standalone
1824,52.65,NJ City,W,9,20,2,85,Graduation,27,Franchise,Complex
9131,303.79,NJ City,E,22,20,20,41,Graduation,28,Owner,Standalone
3894,125.95,NJ City,S,10,13,1,33,Graduation,32,Patnership,Complex
4381,365.35,NJ City,N,21,24,2,31,High School,19,Patnership,Standalone
5397,306.05,Boston,N,24,12,18,82,Graduation,21,Patnership,Standalone
3822,399.7,NJ City,S,23,24,4,37,Graduation,27,Patnership,Complex
7419,441.57,Boston,E,15,21,5,34,Graduation,25,Owner,Standalone
4711,174.86,NJ City,E,6,17,2,52,High School,19,Owner,Standalone
694,205,NJ City,S,3,16,20,27,High School,28,Patnership,Complex
2262,415.62,NJ City,S,13,20,25,27,High School,29,Patnership,Complex
7324,470.66,Boston,W,18,10,20,79,Graduation,19,Franchise,Complex
3094,318.92,NJ City,S,23,19,25,79,High School,27,Patnership,Complex
9644,314.66,NJ City,W,23,23,17,25,Graduation,28,Franchise,Complex
4708,445.62,NJ City,W,17,13,21,33,High School,23,Franchise,Complex
5882,38.95,Boston,N,4,9,2,56,No education,27,Patnership,Complex
8679,357.9,NJ City,E,1,24,9,67,Graduation,29,Owner,Standalone
1748,360.21,NJ City,W,20,21,1,61,No education,33,Franchise,Complex
4825,494.81,NJ City,N,7,12,1,72,High School,20,Patnership,Standalone
1817,324.79,NJ City,N,25,13,10,39,High School,21,Patnership,Standalone
6679,426.94,Boston,E,17,19,18,55,Graduation,22,Owner,Standalone
4488,282.62,NJ City,W,23,14,6,25,High School,32,Franchise,Complex
6354,255.91,Boston,S,7,20,11,27,Graduation,23,Patnership,Complex
2158,50.03,NJ City,S,6,22,3,31,High School,33,Patnership,Complex
6414,391.82,Boston,S,15,15,16,46,Graduation,18,Patnership,Complex
586,328.22,NJ City,S,11,21,12,71,High School,31,Patnership,Complex
729,187.96,NJ City,N,16,20,10,57,High School,30,Patnership,Standalone
9733,283.66,NJ City,N,9,18,9,82,Graduation,18,Patnership,Standalone
9586,173.83,NJ City,S,20,14,16,32,Graduation,24,Patnership,Complex
8999,241.42,NJ City,E,10,9,10,75,Graduation,25,Owner,Standalone
4243,460.97,NJ City,E,6,17,24,57,High School,31,Owner,Standalone
5805,159.35,Boston,N,8,16,22,59,Graduation,25,Patnership,Standalone
954,115.64,NJ City,S,18,13,3,55,Graduation,30,Patnership,Complex
6714,193.48,Boston,S,23,16,15,26,Graduation,31,Patnership,Complex
242,475.22,NJ City,S,8,10,13,74,High School,30,Patnership,Complex
8921,370.12,NJ City,N,19,19,8,70,Graduation,26,Patnership,Standalone
4824,96.43,NJ City,W,22,16,3,67,Graduation,25,Franchise,Complex
9636,332.37,NJ City,W,21,13,8,76,Graduation,24,Franchise,Complex
1557,452.53,NJ City,N,3,16,2,72,Graduation,22,Patnership,Standalone
3299,278,NJ City,E,2,9,10,81,High School,33,Owner,Standalone
9308,11.86,NJ City,N,19,14,1,40,No education,22,Franchise,Complex
5177,329.41,Boston,N,22,11,10,71,Graduation,35,Patnership,Standalone
7694,170.75,Boston,S,16,16,19,63,Graduation,33,Patnership,Complex
6888,192.15,Boston,W,22,23,5,60,Graduation,29,Franchise,Complex
5326,293.9,Boston,S,14,13,12,35,Graduation,27,Patnership,Complex
7967,313.95,Boston,E,24,16,18,37,Graduation,19,Owner,Standalone
6852,219.15,Boston,W,15,15,4,50,Graduation,33,Franchise,Complex
87,485.04,Boston,E,9,23,9,49,High School,26,Owner,Standalone
871,376.68,NJ City,E,7,14,16,76,High School,23,Owner,Standalone
9177,60.3,NJ City,N,1,14,3,77,Graduation,20,Patnership,Standalone
5844,310.65,Boston,W,5,12,8,28,Graduation,34,Franchise,Complex
2330,458.43,NJ City,S,23,11,1,67,No education,29,Patnership,Complex
550,145.05,NJ City,S,23,10,20,35,High School,33,Patnership,Complex
9442,264.02,NJ City,S,9,10,20,41,Graduation,28,Patnership,Complex
1701,106.89,NJ City,N,22,10,4,57,Graduation,32,Patnership,Standalone
4547,166.98,NJ City,E,21,22,10,49,High School,27,Owner,Standalone
8043,299.64,Boston,E,3,20,15,34,Graduation,34,Owner,Standalone
2469,164.81,NJ City,N,8,19,15,47,High School,32,Patnership,Standalone
1834,369.91,NJ City,S,20,16,23,36,High School,21,Patnership,Complex
3622,189.66,NJ City,S,20,12,8,71,High School,18,Patnership,Complex
144,37.4,NJ City,N,20,9,1,53,Graduation,22,Franchise,Complex
5344,391.07,Boston,W,21,13,14,27,Graduation,34,Franchise,Complex
1434,243.44,NJ City,S,25,23,19,49,High School,31,Patnership,Complex
9381,487.77,NJ City,N,16,9,8,57,Graduation,30,Patnership,Standalone
9704,443.11,NJ City,W,12,22,15,82,Graduation,30,Franchise,Complex
5807,220.96,Boston,E,9,12,11,45,Graduation,30,Owner,Standalone
8133,238.33,NJ City,N,8,12,13,42,Graduation,31,Patnership,Standalone
4752,82.48,NJ City,W,20,23,3,75,Graduation,32,Franchise,Complex
3927,366.1,NJ City,E,10,18,19,64,High School,23,Owner,Standalone
1440,159.35,NJ City,W,13,23,19,46,High School,31,Franchise,Complex
4659,312.24,NJ City,E,21,17,16,37,High School,30,Owner,Standalone
7305,134.08,Boston,N,3,13,21,67,Graduation,27,Patnership,Standalone
6708,377.39,Boston,W,23,8,8,82,Graduation,26,Franchise,Complex
747,335.12,NJ City,E,11,16,20,78,High School,35,Owner,Standalone
9100,162.33,NJ City,W,1,17,3,27,High School,23,Franchise,Complex
3773,89.7,NJ City,N,19,13,1,83,No education,23,Patnership,Standalone
8933,159.26,NJ City,N,9,22,2,34,No education,29,Patnership,Standalone
5417,92.08,Boston,N,25,24,2,36,No education,23,Patnership,Standalone
9574,5.52,NJ City,N,3,23,3,82,High School,19,Patnership,Complex
6014,106.25,Boston,S,13,14,4,76,No education,34,Patnership,Complex
4148,365.13,NJ City,W,12,21,9,66,High School,32,Franchise,Complex
5040,120.71,Boston,W,8,21,2,62,Graduation,30,Franchise,Complex
4174,121.14,NJ City,S,17,17,3,72,High School,31,Patnership,Complex
2359,60.14,NJ City,E,17,9,2,31,High School,27,Owner,Standalone
7004,201.6,Boston,W,10,9,17,30,Graduation,24,Franchise,Complex
1048,376.36,NJ City,W,20,21,8,85,High School,33,Franchise,Complex
7979,449.93,Boston,E,8,22,9,66,Graduation,28,Owner,Standalone
921,424.46,NJ City,N,13,21,8,64,High School,27,Patnership,Standalone
2112,331.2,NJ City,W,14,18,1,48,Graduation,29,Franchise,Complex
5782,69.07,Boston,N,16,18,3,69,High School,28,Patnership,Complex
1360,173.44,NJ City,W,15,14,21,66,High School,20,Franchise,Complex
9680,116.44,NJ City,W,1,22,1,26,No education,33,Franchise,Complex
2152,357.94,NJ City,W,19,20,16,66,High School,31,Franchise,Complex
1989,337.41,NJ City,N,20,19,2,49,Graduation,32,Patnership,Standalone
1258,474.52,NJ City,S,7,18,18,77,High School,32,Patnership,Complex
1709,317,NJ City,N,1,22,13,84,High School,19,Patnership,Standalone
3824,154.63,NJ City,W,7,12,18,79,High School,28,Franchise,Complex
7519,312.11,Boston,E,17,8,9,25,Graduation,21,Owner,Standalone
2378,484.4,NJ City,S,25,21,11,51,High School,26,Patnership,Complex
542,175.87,NJ City,S,16,11,15,69,High School,19,Patnership,Complex
2930,293.38,NJ City,S,9,8,4,81,No education,30,Patnership,Complex
1195,16.86,NJ City,N,14,14,4,67,High School,28,Owner,Standalone
8655,371.8,NJ City,E,19,19,13,74,Graduation,28,Owner,Standalone
8055,193.38,NJ City,E,9,15,12,68,Graduation,24,Owner,Standalone
4670,300,NJ City,S,1,22,6,61,High School,26,Patnership,Complex
1009,170,NJ City,N,3,17,19,78,High School,32,Patnership,Standalone
708,15.69,NJ City,N,22,13,3,26,Graduation,23,Franchise,Complex
7333,237.38,Boston,N,3,16,12,32,Graduation,20,Patnership,Standalone
2589,190.36,NJ City,N,6,24,13,47,High School,20,Patnership,Standalone
6129,445.9,Boston,N,24,9,12,84,Graduation,34,Patnership,Standalone
6922,326.16,Boston,S,25,15,18,39,Graduation,23,Patnership,Complex
1267,142.67,NJ City,E,6,16,23,63,High School,25,Owner,Standalone
9360,126.97,NJ City,W,13,22,1,43,Graduation,29,Franchise,Complex
4786,90.34,NJ City,S,23,22,4,35,High School,19,Patnership,Complex
9559,67.88,NJ City,N,12,13,1,27,High School,22,Owner,Standalone
9584,225.14,NJ City,W,3,18,21,42,Graduation,23,Franchise,Complex
2102,376.92,NJ City,S,15,16,10,30,High School,19,Patnership,Complex
8661,104.17,NJ City,N,19,10,3,35,Graduation,33,Patnership,Standalone
451,265.54,NJ City,E,25,12,22,81,High School,29,Owner,Standalone
7634,425.55,Boston,S,11,16,14,67,Graduation,34,Patnership,Complex
1938,300,NJ City,S,3,21,22,36,High School,34,Patnership,Complex
1433,210.82,NJ City,N,4,12,20,39,High School,19,Patnership,Standalone
4562,68.37,NJ City,N,6,18,3,57,No education,18,Patnership,Complex
7260,153.73,Boston,W,9,8,23,66,Graduation,19,Franchise,Complex
3507,252.55,NJ City,E,21,20,13,38,High School,30,Owner,Standalone
1740,305.63,NJ City,W,3,10,4,53,Graduation,19,Franchise,Complex
7078,151.91,Boston,S,12,17,2,47,High School,34,Patnership,Complex
1032,389.99,NJ City,W,17,10,15,33,High School,31,Franchise,Complex
1650,197.11,NJ City,S,12,9,18,28,High School,32,Patnership,Complex
8406,219.5,NJ City,S,6,17,8,72,Graduation,24,Patnership,Complex
9427,79.63,NJ City,E,8,8,1,47,High School,23,Owner,Standalone
7348,11.59,Boston,N,5,17,1,39,High School,24,Franchise,Complex
6968,17.71,Boston,N,18,21,2,25,No education,34,Franchise,Complex
8820,354.28,NJ City,W,6,12,14,32,Graduation,33,Franchise,Complex
519,206.82,NJ City,E,11,9,12,26,High School,19,Owner,Standalone
2587,495.04,NJ City,E,8,24,3,54,High School,35,Owner,Standalone
1060,114.95,NJ City,W,11,19,2,43,High School,22,Franchise,Complex
5869,242.89,Boston,N,11,14,1,55,High School,20,Patnership,Standalone
7628,387.17,Boston,W,5,24,16,74,Graduation,20,Franchise,Complex
7624,379.57,Boston,W,14,10,24,80,Graduation,23,Franchise,Complex
3186,269.93,NJ City,S,5,21,14,42,High School,31,Patnership,Complex
3411,492.01,NJ City,E,9,9,20,84,High School,19,Owner,Standalone
3720,435.96,NJ City,W,14,22,3,47,Graduation,24,Franchise,Complex
3362,177.12,NJ City,S,6,23,21,64,High School,34,Patnership,Complex
8147,256.96,NJ City,E,15,22,13,60,Graduation,24,Owner,Standalone
8876,89.66,NJ City,W,24,20,3,29,No education,31,Franchise,Complex
9330,385.98,NJ City,S,21,14,19,85,Graduation,27,Patnership,Complex
2675,477.55,NJ City,E,5,10,10,48,High School,23,Owner,Standalone
4758,329.01,NJ City,S,11,23,13,53,High School,27,Patnership,Complex
1694,274,NJ City,S,3,19,18,75,High School,21,Patnership,Complex
5918,254.88,Boston,S,19,9,23,76,Graduation,34,Patnership,Complex
1575,28.05,NJ City,N,14,10,1,31,Graduation,30,Owner,Standalone
6915,128.7,Boston,E,19,14,9,43,Graduation,19,Owner,Standalone
4611,320.46,NJ City,E,10,19,23,38,High School,27,Owner,Standalone
4348,5.4,NJ City,N,22,10,3,42,High School,19,Franchise,Complex
8387,180.24,NJ City,E,7,8,1,83,No education,20,Owner,Standalone
5019,115.89,Boston,E,1,11,2,63,Graduation,33,Owner,Standalone
6175,376.26,Boston,E,20,13,4,49,High School,32,Owner,Standalone
7265,497.06,Boston,N,4,9,19,44,Graduation,34,Patnership,Standalone
3800,219.6,NJ City,W,14,11,19,29,High School,19,Franchise,Complex
8540,472.7,NJ City,W,10,24,2,76,No education,33,Franchise,Complex
5758,194.88,Boston,S,1,13,4,47,High School,29,Patnership,Complex
6011,27.29,Boston,N,12,22,1,70,No education,35,Owner,Standalone
3715,250,NJ City,E,6,21,8,60,High School,23,Owner,Standalone
2767,432.96,NJ City,E,5,23,15,79,High School,26,Owner,Standalone
2800,306.37,NJ City,W,9,21,14,69,High School,30,Franchise,Complex
6595,90.73,Boston,E,8,22,4,33,High School,33,Owner,Standalone
6749,319.21,Boston,N,22,12,10,36,Graduation,24,Patnership,Standalone
7957,110.27,Boston,N,14,23,3,52,High School,27,Patnership,Standalone
7229,107.08,Boston,N,10,8,3,51,No education,28,Patnership,Standalone
6764,30.93,Boston,N,21,13,4,33,No education,35,Franchise,Complex
5023,152.91,Boston,E,20,18,8,62,Graduation,29,Owner,Standalone
3098,434.32,NJ City,S,4,10,4,62,No education,24,Patnership,Complex
9030,313.89,NJ City,S,11,19,4,36,Graduation,29,Patnership,Complex
7090,16.68,Boston,N,21,11,3,40,High School,32,Patnership,Complex
1856,311.95,NJ City,W,21,9,10,69,High School,23,Franchise,Complex
6307,446.85,Boston,E,15,22,20,30,Graduation,21,Owner,Standalone
3790,130.83,NJ City,S,18,8,7,76,High School,26,Patnership,Complex
1113,83.27,NJ City,N,10,15,3,49,Graduation,18,Patnership,Standalone
4247,480.28,NJ City,E,8,24,19,63,High School,22,Owner,Standalone
4785,443.21,NJ City,N,6,13,16,75,High School,26,Patnership,Standalone
8405,162.35,NJ City,N,16,16,11,60,Graduation,25,Patnership,Standalone
3223,388.49,NJ City,E,11,11,4,53,High School,18,Owner,Standalone
1057,256.42,NJ City,N,22,14,5,61,High School,32,Patnership,Standalone
2602,264.27,NJ City,S,19,16,3,65,High School,21,Patnership,Complex
9328,56.05,NJ City,W,18,10,4,57,High School,23,Franchise,Complex
6245,44.53,Boston,N,19,14,4,40,No education,28,Patnership,Standalone
7510,146.46,Boston,S,19,19,6,71,Graduation,20,Patnership,Complex
7577,372.6,Boston,N,19,11,17,59,Graduation,29,Patnership,Standalone
4347,231.53,NJ City,E,11,15,18,78,High School,23,Owner,Standalone
329,248,NJ City,N,3,12,25,40,High School,28,Patnership,Standalone
5663,1.4,Boston,N,5,24,3,58,No education,27,Owner,Standalone
5262,372.33,Boston,S,19,8,1,41,Graduation,19,Patnership,Complex
2050,7.82,NJ City,N,25,9,1,49,High School,18,Patnership,Complex
750,299.57,NJ City,S,6,14,10,40,High School,30,Patnership,Complex
4417,344.78,NJ City,N,2,8,2,34,High School,25,Patnership,Standalone
5804,290.92,Boston,W,6,16,12,32,Graduation,26,Franchise,Complex
7041,98.07,Boston,N,8,22,2,51,Graduation,33,Patnership,Standalone
6566,317.86,Boston,S,3,23,18,29,Graduation,30,Patnership,Complex
886,141.69,NJ City,S,15,24,23,78,High School,30,Patnership,Complex
3265,235,NJ City,N,2,21,6,63,High School,27,Patnership,Standalone
5675,149.73,Boston,E,5,15,6,38,Graduation,20,Owner,Standalone
141,287,NJ City,N,3,9,13,76,High School,28,Patnership,Standalone
2504,8.41,NJ City,N,7,17,2,83,No education,27,Franchise,Complex
5401,300.5,Boston,N,1,15,22,49,Graduation,26,Patnership,Standalone
2883,239.37,NJ City,E,24,13,24,31,High School,24,Owner,Standalone
109,157.06,NJ City,N,22,22,18,57,High School,30,Patnership,Standalone
532,462.48,NJ City,W,4,15,22,79,High School,29,Franchise,Complex
5001,91.78,Boston,N,16,16,4,66,Graduation,27,Patnership,Standalone
1589,349.38,NJ City,N,19,10,12,49,High School,29,Patnership,Standalone
2374,15.15,NJ City,N,16,14,4,78,High School,25,Patnership,Complex
446,183.76,NJ City,S,16,13,5,54,No education,33,Patnership,Complex
3292,308.17,NJ City,W,10,19,2,38,High School,26,Franchise,Complex
593,488.2,NJ City,N,13,21,14,33,High School,18,Patnership,Standalone
3218,44.8,NJ City,N,14,12,4,67,No education,21,Patnership,Complex
4277,360.19,NJ City,N,23,12,13,66,High School,28,Patnership,Standalone
594,76.95,NJ City,S,25,21,2,70,Graduation,28,Patnership,Complex
1456,390.19,NJ City,W,15,9,22,46,High School,25,Franchise,Complex
2424,101.13,NJ City,W,1,16,3,65,Graduation,35,Franchise,Complex
4648,89.14,NJ City,W,2,23,4,64,High School,26,Franchise,Complex
3495,115.93,NJ City,E,13,16,4,61,Graduation,19,Owner,Standalone
4416,65.1,NJ City,N,12,12,2,78,Graduation,20,Franchise,Complex
7258,436.68,Boston,S,25,13,24,73,Graduation,32,Patnership,Complex
5834,307.71,Boston,S,8,10,10,66,Graduation,28,Patnership,Complex
136,234.76,NJ City,W,16,23,10,52,High School,19,Franchise,Complex
4121,196.17,NJ City,N,7,20,10,38,High School,25,Patnership,Standalone
4511,459.12,NJ City,E,12,11,22,80,High School,33,Owner,Standalone
8412,53.02,NJ City,W,6,24,3,34,Graduation,23,Franchise,Complex
5754,286.99,Boston,S,7,14,10,60,Graduation,25,Patnership,Complex
1885,430.96,NJ City,N,7,9,23,65,High School,32,Patnership,Standalone
9775,495.41,NJ City,E,6,17,25,43,Graduation,34,Owner,Standalone
5403,403.14,Boston,E,5,15,18,55,Graduation,30,Owner,Standalone
3895,285.32,NJ City,E,5,14,6,58,High School,18,Owner,Standalone
1883,135.02,NJ City,E,12,17,19,69,High School,34,Owner,Standalone
5638,354.78,Boston,S,23,23,9,51,Graduation,35,Patnership,Complex
9795,79.33,NJ City,E,23,24,3,38,Graduation,22,Owner,Standalone
5106,42.96,Boston,N,5,21,4,80,Graduation,30,Patnership,Complex
9878,196.36,NJ City,S,5,17,19,69,Graduation,23,Patnership,Complex
4331,194.55,NJ City,E,5,19,8,83,High School,27,Owner,Standalone
6096,164.66,Boston,W,2,18,17,47,Graduation,25,Franchise,Complex
5833,420.39,Boston,N,2,11,7,83,Graduation,24,Patnership,Standalone
4202,225.26,NJ City,S,14,9,9,38,High School,19,Patnership,Complex
8061,386.36,NJ City,N,20,9,21,77,Graduation,24,Patnership,Standalone
2613,150,NJ City,N,3,24,21,65,High School,22,Patnership,Standalone
2499,10.75,NJ City,N,17,8,2,74,Graduation,29,Owner,Standalone
6403,412.73,Boston,E,18,13,14,30,Graduation,35,Owner,Standalone
7750,117.94,Boston,S,24,14,4,35,High School,27,Patnership,Complex
8823,237.37,NJ City,E,23,14,24,69,Graduation,25,Owner,Standalone
8028,252.99,Boston,W,5,13,13,36,Graduation,19,Franchise,Complex
3027,375.16,NJ City,E,23,9,16,43,High School,31,Owner,Standalone
3391,111.05,NJ City,E,5,9,4,58,High School,18,Owner,Standalone
7038,267.11,Boston,S,4,18,6,43,Graduation,31,Patnership,Complex
9956,253.13,NJ City,W,8,21,19,85,Graduation,21,Franchise,Complex
1508,203.33,NJ City,W,22,17,14,66,High School,20,Franchise,Complex
4721,119,NJ City,N,2,13,7,35,High School,21,Patnership,Standalone
9539,353.93,NJ City,E,16,15,5,43,No education,35,Owner,Standalone
1445,168,NJ City,N,1,21,10,57,High School,33,Patnership,Standalone
8505,112.58,NJ City,N,21,23,3,40,Graduation,25,Patnership,Standalone
3500,402.13,NJ City,W,18,21,15,49,High School,22,Franchise,Complex
4973,244.02,Boston,N,23,16,14,75,High School,20,Patnership,Standalone
8590,487.01,NJ City,S,9,11,11,44,Graduation,32,Patnership,Complex
733,450.79,NJ City,N,5,20,20,41,High School,28,Patnership,Standalone
150,268.21,NJ City,S,18,11,17,43,High School,19,Patnership,Complex
2609,172.98,NJ City,N,8,8,20,28,High School,18,Patnership,Standalone
1747,294.54,NJ City,E,4,16,16,80,High School,33,Owner,Standalone
1884,150.3,NJ City,W,23,15,11,72,High School,33,Franchise,Complex
9499,447.41,NJ City,E,9,20,3,26,High School,25,Owner,Standalone
4300,323.02,NJ City,W,5,10,24,46,High School,21,Franchise,Complex
8782,448.51,NJ City,S,23,13,11,74,Graduation,34,Patnership,Complex
4318,10.02,NJ City,N,9,21,4,81,High School,24,Patnership,Complex
6612,36.76,Boston,N,10,16,2,29,Graduation,19,Franchise,Complex
4735,362.87,NJ City,E,15,14,1,54,High School,29,Owner,Standalone
7421,119.12,Boston,N,25,22,2,58,No education,28,Patnership,Standalone
3577,27.01,NJ City,N,12,10,1,85,High School,19,Patnership,Standalone
9196,427.81,NJ City,W,13,16,12,72,Graduation,23,Franchise,Complex
6121,78.01,Boston,N,21,20,4,80,High School,25,Patnership,Standalone
6399,178.98,Boston,E,16,10,4,50,Graduation,31,Owner,Standalone
7505,365.35,Boston,N,24,16,25,68,Graduation,35,Patnership,Standalone
5291,428.93,Boston,E,2,18,18,84,Graduation,20,Owner,Standalone
9799,276.96,NJ City,E,14,16,1,57,High School,18,Owner,Standalone
775,63.72,NJ City,N,21,8,3,25,High School,34,Owner,Standalone
6756,314.98,Boston,W,1,10,18,38,Graduation,20,Franchise,Complex
8474,40.05,NJ City,N,22,13,3,39,No education,33,Patnership,Complex
9932,260.53,NJ City,W,16,12,25,37,Graduation,22,Franchise,Complex
6871,414.39,Boston,E,8,24,8,85,Graduation,25,Owner,Standalone
7374,53.24,Boston,S,13,9,2,64,Graduation,22,Patnership,Complex
2719,316.02,NJ City,E,1,8,2,55,High School,26,Owner,Standalone
8403,224.16,NJ City,E,23,18,22,35,Graduation,35,Owner,Standalone
9640,102.07,NJ City,W,23,14,4,58,High School,23,Franchise,Complex
7357,347.9,Boston,N,1,20,9,65,Graduation,32,Patnership,Standalone
8329,80.09,NJ City,N,10,8,2,78,High School,22,Patnership,Standalone
9629,494.44,NJ City,N,25,23,5,80,No education,31,Patnership,Standalone
5452,353.79,Boston,W,5,11,5,45,High School,31,Franchise,Complex
2312,85.02,NJ City,W,1,16,3,55,No education,24,Franchise,Complex
1688,1.36,NJ City,N,20,15,3,45,No education,25,Franchise,Complex
5600,225.58,Boston,W,1,11,11,78,Graduation,27,Franchise,Complex
3638,186,NJ City,S,2,13,19,52,High School,35,Patnership,Complex
8519,66.42,NJ City,N,9,20,2,26,No education,20,Owner,Standalone
8807,108.01,NJ City,E,7,12,1,39,No education,26,Owner,Standalone
8869,292.7,NJ City,N,13,12,7,68,Graduation,19,Patnership,Standalone
3306,262.93,NJ City,S,25,22,22,35,High School,32,Patnership,Complex
564,44.83,NJ City,N,8,22,1,57,Graduation,29,Franchise,Complex
2907,264.2,NJ City,E,12,12,18,52,High School,34,Owner,Standalone
9621,341.24,NJ City,N,5,16,16,29,Graduation,20,Patnership,Standalone
7261,399.33,Boston,N,11,14,2,60,High School,25,Patnership,Standalone
957,354.03,NJ City,N,23,8,21,61,High School,31,Patnership,Standalone
1840,182.6,NJ City,W,8,13,22,31,High School,35,Franchise,Complex
7892,197.82,Boston,W,6,13,11,42,Graduation,34,Franchise,Complex
4639,460.22,NJ City,E,10,14,16,72,High School,25,Owner,Standalone
1836,116.36,NJ City,W,10,17,3,62,Graduation,32,Franchise,Complex
137,17.78,NJ City,N,17,19,3,63,No education,28,Patnership,Standalone
3642,18.03,NJ City,N,8,12,4,35,Graduation,28,Patnership,Complex
8834,159.76,NJ City,S,6,8,25,54,Graduation,30,Patnership,Complex
9003,391.9,NJ City,E,7,10,2,53,Graduation,30,Owner,Standalone
1858,236.4,NJ City,S,13,14,20,39,High School,23,Patnership,Complex
5449,285.42,Boston,N,16,21,8,85,Graduation,32,Patnership,Standalone
9886,66.54,NJ City,N,13,15,2,67,High School,31,Patnership,Complex
2169,104.06,NJ City,N,3,18,2,79,Graduation,25,Patnership,Standalone
1581,424.09,NJ City,N,10,11,1,85,Graduation,29,Patnership,Standalone
1534,98.88,NJ City,S,18,20,3,31,High School,30,Patnership,Complex
4881,337.02,NJ City,N,9,22,15,53,High School,27,Patnership,Standalone
739,233.51,NJ City,E,12,21,21,78,High School,27,Owner,Standalone
396,317.65,NJ City,W,15,18,9,67,High School,32,Franchise,Complex
6778,221.31,Boston,S,15,10,23,78,Graduation,19,Patnership,Complex
8089,379.75,NJ City,N,21,9,11,62,Graduation,34,Patnership,Standalone
3026,221.86,NJ City,S,1,9,1,48,No education,33,Patnership,Complex
6343,252.79,Boston,E,10,13,24,27,Graduation,22,Owner,Standalone
8221,362.7,NJ City,N,9,16,13,33,Graduation,32,Patnership,Standalone
2149,366.27,NJ City,N,21,9,24,82,High School,33,Patnership,Standalone
4487,208.2,NJ City,E,5,16,10,27,High School,35,Owner,Standalone
1332,4.87,NJ City,N,5,22,2,47,Graduation,18,Franchise,Complex
4193,196.93,NJ City,N,20,21,9,71,High School,27,Patnership,Standalone
620,91.49,NJ City,W,7,22,4,54,No education,19,Franchise,Complex
18,425.42,Boston,S,24,20,25,64,High School,27,Patnership,Complex
4970,50.83,Boston,S,3,17,4,32,No education,30,Patnership,Complex
1245,138.13,NJ City,N,5,18,22,37,High School,30,Patnership,Standalone
9895,2.12,NJ City,E,4,23,3,41,High School,35,Owner,Standalone
3805,260.27,NJ City,N,14,22,6,80,High School,20,Patnership,Standalone
6407,134.51,Boston,E,14,13,9,69,Graduation,26,Owner,Standalone
1413,47.27,NJ City,N,13,18,3,60,Graduation,32,Patnership,Standalone
1563,324.2,NJ City,E,13,14,1,65,Graduation,32,Owner,Standalone
4941,291.44,Boston,N,18,19,25,41,High School,33,Patnership,Standalone
4643,344.5,NJ City,E,25,15,19,47,High School,20,Owner,Standalone
1076,394.87,NJ City,W,19,10,17,33,High School,24,Franchise,Complex
2743,380.95,NJ City,E,14,9,9,39,High School,19,Owner,Standalone
2820,299.21,NJ City,W,12,13,8,41,High School,32,Franchise,Complex
758,455.76,NJ City,S,4,13,1,31,No education,28,Patnership,Complex
6521,352.98,Boston,N,11,21,19,65,Graduation,22,Patnership,Standalone
2839,59.39,NJ City,E,14,17,4,41,High School,24,Owner,Standalone
580,498.23,NJ City,W,20,22,15,40,High School,18,Franchise,Complex
339,156.54,NJ City,E,18,9,20,56,High School,34,Owner,Standalone
6057,85.83,Boston,N,25,11,2,47,Graduation,20,Patnership,Standalone
9081,259.7,NJ City,N,22,24,24,45,Graduation,29,Patnership,Standalone
3395,42.21,NJ City,N,10,18,3,72,No education,30,Owner,Standalone
4110,351.59,NJ City,S,10,13,23,85,High School,28,Patnership,Complex
3747,161.25,NJ City,E,14,19,10,71,High School,19,Owner,Standalone
9140,494.16,NJ City,W,15,15,24,81,Graduation,33,Franchise,Complex
5653,203.12,Boston,N,16,11,2,46,High School,23,Patnership,Standalone
7944,316.9,Boston,W,8,22,20,62,Graduation,18,Franchise,Complex
3422,23.62,NJ City,N,21,18,1,83,No education,35,Patnership,Complex
9366,157.35,NJ City,S,8,11,20,25,Graduation,32,Patnership,Complex
3099,17.02,NJ City,N,4,18,3,70,Graduation,20,Owner,Standalone
3338,327.45,NJ City,S,14,15,25,29,High School,31,Patnership,Complex
4474,273.05,NJ City,S,21,14,20,49,High School,22,Patnership,Complex
7904,310.24,Boston,W,13,14,20,34,Graduation,28,Franchise,Complex
9977,302.97,NJ City,N,21,23,4,85,No education,33,Patnership,Standalone
1240,39.11,NJ City,N,14,11,3,58,High School,24,Franchise,Complex
1303,173,NJ City,E,3,16,10,70,High School,19,Owner,Standalone
3619,73.37,NJ City,N,15,9,3,47,High School,24,Owner,Standalone
7687,50.19,Boston,E,14,22,2,52,High School,34,Owner,Standalone
4890,30.03,NJ City,N,20,21,1,78,Graduation,19,Patnership,Complex
7232,21.54,Boston,N,19,10,4,57,No education,27,Franchise,Complex
8451,390.09,NJ City,E,24,22,3,43,Graduation,29,Owner,Standalone
9275,48.62,NJ City,E,19,22,3,38,No education,29,Owner,Standalone
6095,235.33,Boston,E,23,13,21,71,Graduation,25,Owner,Standalone
8817,149.92,NJ City,N,20,13,22,55,Graduation,18,Patnership,Standalone
4542,59.22,NJ City,S,25,14,3,72,Graduation,29,Patnership,Complex
362,392.8,NJ City,S,5,15,16,75,High School,32,Patnership,Complex
9400,269.04,NJ City,W,15,19,5,65,High School,27,Franchise,Complex
1490,44.72,NJ City,N,3,22,4,67,No education,34,Patnership,Complex
2892,279.83,NJ City,W,12,22,24,43,High School,35,Franchise,Complex
4164,186.67,NJ City,W,15,24,16,71,High School,27,Franchise,Complex
7438,393.92,Boston,S,24,17,21,51,Graduation,33,Patnership,Complex
7948,101.25,Boston,W,9,10,1,82,High School,24,Franchise,Complex
3133,195.92,NJ City,N,6,14,25,52,High School,28,Patnership,Standalone
1439,209.14,NJ City,E,25,12,16,56,High School,35,Owner,Standalone
3357,274.66,NJ City,N,11,13,10,49,High School,33,Patnership,Standalone
6048,4.35,Boston,N,14,16,1,39,Graduation,31,Franchise,Complex
4053,496.53,NJ City,N,18,16,16,84,High School,19,Patnership,Standalone
9062,261.69,NJ City,S,20,23,11,63,Graduation,35,Patnership,Complex
4024,469.5,NJ City,W,24,17,1,38,High School,33,Franchise,Complex
7810,192.66,Boston,S,17,19,15,49,Graduation,24,Patnership,Complex
4592,335.85,NJ City,W,10,14,10,65,High School,27,Franchise,Complex
9184,370.79,NJ City,W,20,15,10,51,Graduation,29,Franchise,Complex
7098,122.3,Boston,S,17,18,3,44,Graduation,21,Patnership,Complex
8063,228.8,NJ City,E,6,15,17,44,Graduation,30,Owner,Standalone
8291,439.23,NJ City,E,25,24,3,68,No education,32,Owner,Standalone
1555,470.9,NJ City,E,14,16,25,67,High School,25,Owner,Standalone
3609,273.32,NJ City,N,14,15,9,75,High School,27,Patnership,Standalone
4150,28.57,NJ City,N,19,16,3,27,High School,35,Patnership,Complex
2735,111.14,NJ City,E,6,11,1,37,No education,33,Owner,Standalone
3486,269.55,NJ City,S,18,24,9,43,High School,29,Patnership,Complex
8743,352.98,NJ City,E,9,17,22,26,Graduation,21,Owner,Standalone
3377,452.97,NJ City,N,16,19,19,68,High School,28,Patnership,Standalone
5733,151.34,Boston,N,24,11,10,45,Graduation,18,Patnership,Standalone
8706,137.25,NJ City,S,10,19,3,33,Graduation,35,Patnership,Complex
3646,0.48,NJ City,N,1,12,4,31,High School,18,Patnership,Complex
2618,369.27,NJ City,S,18,9,21,77,High School,32,Patnership,Complex
6318,199.12,Boston,S,13,11,25,69,Graduation,33,Patnership,Complex
417,199.89,NJ City,N,19,19,12,42,High School,30,Patnership,Standalone
544,50.52,NJ City,W,13,12,2,80,High School,34,Franchise,Complex
5842,479.73,Boston,S,13,20,13,68,Graduation,35,Patnership,Complex
9959,1.18,NJ City,N,14,13,2,58,No education,23,Owner,Standalone
709,230.03,NJ City,N,25,22,13,85,High School,20,Patnership,Standalone
6241,33.19,Boston,N,9,24,2,34,High School,31,Patnership,Standalone
8077,182.66,NJ City,N,23,18,6,77,Graduation,23,Patnership,Standalone
3983,93.63,NJ City,E,17,19,2,47,No education,23,Owner,Standalone
7855,45.39,Boston,N,10,19,3,31,High School,24,Owner,Standalone
7633,459.38,Boston,N,3,15,4,31,High School,20,Patnership,Standalone
8482,72.98,NJ City,N,2,17,3,82,High School,32,Patnership,Complex
7963,455.62,Boston,E,22,15,3,63,High School,27,Owner,Standalone
7538,383.51,Boston,S,24,22,6,49,Graduation,23,Patnership,Complex
9619,301.39,NJ City,E,17,20,9,84,Graduation,23,Owner,Standalone
8420,487.84,NJ City,W,25,9,25,43,Graduation,30,Franchise,Complex
1684,470.95,NJ City,W,24,15,20,26,High School,35,Franchise,Complex
1488,292.57,NJ City,W,15,17,21,36,High School,35,Franchise,Complex
3868,83.84,NJ City,W,16,24,1,41,High School,25,Franchise,Complex
6365,245.55,Boston,N,3,17,7,84,Graduation,34,Patnership,Standalone
2947,262.81,NJ City,E,6,16,23,84,High School,30,Owner,Standalone
5383,262.09,Boston,E,11,20,4,36,High School,30,Owner,Standalone
790,148,NJ City,S,2,20,20,25,High School,31,Patnership,Complex
254,0.67,NJ City,N,11,17,1,32,No education,33,Patnership,Complex
4698,167.63,NJ City,S,5,22,15,84,High School,20,Patnership,Complex
4222,101.47,NJ City,S,22,14,1,29,High School,23,Patnership,Complex
666,243,NJ City,S,1,9,10,33,High School,22,Patnership,Complex
2044,307,NJ City,W,3,8,18,80,High School,28,Franchise,Complex
2080,7.8,NJ City,N,8,23,1,61,High School,31,Franchise,Complex
3273,356.62,NJ City,N,6,15,24,44,High School,25,Patnership,Standalone
8424,267.68,NJ City,W,2,13,2,47,Graduation,24,Franchise,Complex
9452,338.72,NJ City,W,7,14,24,30,Graduation,26,Franchise,Complex
5459,453.82,Boston,E,7,14,18,63,Graduation,27,Owner,Standalone
2455,222.85,NJ City,E,25,12,14,57,High School,27,Owner,Standalone
2433,292.41,NJ City,N,6,22,13,63,High School,22,Patnership,Standalone
7185,355.27,Boston,N,14,18,7,57,Graduation,32,Patnership,Standalone
2859,26.68,NJ City,N,5,21,4,72,Graduation,24,Owner,Standalone
2459,102.98,NJ City,E,25,21,4,27,No education,28,Owner,Standalone
7440,338.88,Boston,W,3,8,9,65,Graduation,25,Franchise,Complex
7190,154.56,Boston,S,1,24,21,66,Graduation,28,Patnership,Complex
8379,321.32,NJ City,E,10,16,13,33,Graduation,19,Owner,Standalone
3777,244,NJ City,N,3,11,11,40,High School,30,Patnership,Standalone
7332,6.7,Boston,N,5,14,2,46,Graduation,31,Franchise,Complex
3684,437.2,NJ City,W,8,11,2,78,Graduation,32,Franchise,Complex
5293,470.02,Boston,N,3,10,4,82,High School,30,Patnership,Standalone
7175,375.74,Boston,E,21,23,8,33,Graduation,25,Owner,Standalone
3972,405.72,NJ City,W,17,10,1,42,Graduation,25,Franchise,Complex
3424,293,NJ City,W,3,19,8,69,High School,27,Franchise,Complex
7480,275.15,Boston,W,19,16,5,78,High School,33,Franchise,Complex
8709,379.18,NJ City,N,11,20,14,56,Graduation,31,Patnership,Standalone
1004,262.09,NJ City,W,23,20,17,82,High School,21,Franchise,Complex
5054,45.8,Boston,N,4,8,2,31,No education,33,Patnership,Complex
7685,469.12,Boston,N,5,8,3,70,No education,27,Patnership,Standalone
6266,421.81,Boston,S,1,13,4,34,No education,28,Patnership,Complex
6788,72.47,Boston,N,21,20,4,65,No education,29,Franchise,Complex
1964,280.24,NJ City,W,25,10,19,71,High School,20,Franchise,Complex
3239,452.65,NJ City,E,9,16,23,26,High School,33,Owner,Standalone
6109,415.25,Boston,N,22,21,4,51,High School,28,Patnership,Standalone
108,237.69,NJ City,W,18,20,17,80,High School,32,Franchise,Complex
9945,165,NJ City,N,13,22,14,51,Graduation,26,Patnership,Standalone
3045,465.03,NJ City,N,13,20,22,56,High School,24,Patnership,Standalone
9701,208.05,NJ City,N,7,23,9,81,Graduation,20,Patnership,Standalone
6364,95.93,Boston,W,19,8,4,84,High School,20,Franchise,Complex
6683,494.72,Boston,E,24,14,9,79,Graduation,30,Owner,Standalone
6201,123.97,Boston,N,9,18,2,48,Graduation,35,Patnership,Standalone
2620,385.01,NJ City,W,10,17,23,58,High School,19,Franchise,Complex
4297,96.67,NJ City,N,23,22,1,80,High School,18,Patnership,Standalone
4642,340.09,NJ City,S,6,12,6,43,High School,31,Patnership,Complex
3566,436.53,NJ City,S,21,22,22,63,High School,30,Patnership,Complex
8934,1.66,NJ City,N,13,19,1,50,Graduation,21,Patnership,Complex
5693,179.12,Boston,N,8,14,21,28,Graduation,19,Patnership,Standalone
1521,167.6,NJ City,N,10,14,6,26,High School,20,Patnership,Standalone
7971,343.3,Boston,E,3,17,18,45,Graduation,29,Owner,Standalone
4599,200.92,NJ City,E,9,21,15,34,High School,23,Owner,Standalone
2390,128.91,NJ City,S,4,19,1,59,No education,26,Patnership,Complex
8627,269.03,NJ City,E,17,9,24,44,Graduation,24,Owner,Standalone
7582,326.31,Boston,S,24,8,15,41,Graduation,29,Patnership,Complex
2622,47.92,NJ City,S,14,10,3,84,Graduation,31,Patnership,Complex
5866,436.57,Boston,S,22,12,23,37,Graduation,20,Patnership,Complex
9069,397.03,NJ City,N,17,23,4,64,Graduation,21,Patnership,Standalone
5673,363.76,Boston,N,16,24,16,38,Graduation,21,Patnership,Standalone
7763,42.06,Boston,N,16,18,3,78,No education,32,Owner,Standalone
984,433.51,NJ City,W,22,10,6,29,High School,26,Franchise,Complex
263,308.71,NJ City,E,3,23,2,69,No education,19,Owner,Standalone
4138,29.71,NJ City,N,21,21,4,56,High School,35,Patnership,Complex
5576,54.49,Boston,W,1,23,2,61,No education,19,Franchise,Complex
4775,476.12,NJ City,E,3,13,5,79,No education,25,Owner,Standalone
9786,416.32,NJ City,S,2,10,9,84,Graduation,29,Patnership,Complex
4552,127.22,NJ City,W,20,18,1,68,High School,32,Franchise,Complex
6891,431.19,Boston,E,25,11,24,83,Graduation,34,Owner,Standalone
1143,219.11,NJ City,E,18,10,5,60,Graduation,20,Owner,Standalone
4725,274,NJ City,N,1,10,18,36,High School,35,Patnership,Standalone
4137,57.72,NJ City,N,8,13,3,57,Graduation,18,Patnership,Standalone
4455,462.22,NJ City,E,11,10,1,40,Graduation,35,Owner,Standalone
3736,49.74,NJ City,W,25,8,2,58,High School,18,Franchise,Complex
743,490.1,NJ City,E,5,14,24,56,High School,32,Owner,Standalone
8552,131.56,NJ City,W,7,10,21,49,Graduation,20,Franchise,Complex
3565,154.26,NJ City,N,20,16,16,66,High School,25,Patnership,Standalone
7008,284.01,Boston,W,10,15,17,26,Graduation,31,Franchise,Complex
5681,7.45,Boston,N,10,9,4,79,No education,21,Patnership,Standalone
1279,339.53,NJ City,E,11,21,17,35,High School,31,Owner,Standalone
4977,221.04,Boston,N,21,14,13,56,High School,21,Patnership,Standalone
2235,17.12,NJ City,N,4,10,3,61,Graduation,18,Owner,Standalone
9213,207.61,NJ City,N,22,14,23,53,Graduation,18,Patnership,Standalone
6397,190.95,Boston,N,21,8,4,38,High School,20,Patnership,Standalone
647,27.23,NJ City,N,8,17,4,62,No education,26,Owner,Standalone
9132,55.66,NJ City,W,25,12,1,61,Graduation,24,Franchise,Complex
5796,123.94,Boston,W,16,16,2,31,Graduation,24,Franchise,Complex
8355,376.71,NJ City,E,2,17,7,50,Graduation,35,Owner,Standalone
7394,6.21,Boston,N,20,14,4,35,No education,35,Patnership,Complex
4620,219.06,NJ City,W,10,21,8,84,High School,31,Franchise,Complex
3661,145.25,NJ City,N,18,21,5,39,High School,32,Patnership,Standalone
4280,52.22,NJ City,W,11,15,4,31,No education,31,Franchise,Complex
2207,385.5,NJ City,E,20,18,9,77,High School,19,Owner,Standalone
3233,202.06,NJ City,N,11,15,6,50,High School,28,Patnership,Standalone
321,66.77,NJ City,N,10,13,3,46,Graduation,18,Patnership,Standalone
2077,398.03,NJ City,N,9,12,11,46,High School,31,Patnership,Standalone
4816,29.11,NJ City,N,9,22,4,55,High School,26,Franchise,Complex
735,448.04,NJ City,E,19,21,23,69,High School,24,Owner,Standalone
1601,449.13,NJ City,N,10,18,21,70,High School,28,Patnership,Standalone
9811,186.85,NJ City,E,21,21,13,27,Graduation,29,Owner,Standalone
7725,266.51,Boston,N,9,24,20,56,Graduation,22,Patnership,Standalone
1069,112.7,NJ City,N,23,20,1,52,High School,27,Patnership,Standalone
6053,126.71,Boston,N,12,22,2,73,No education,19,Patnership,Standalone
5628,76.05,Boston,W,13,13,4,82,Graduation,22,Franchise,Complex
7978,97.78,Boston,S,18,13,4,85,High School,18,Patnership,Complex
8375,0.93,NJ City,N,25,11,2,62,No education,32,Owner,Standalone
7986,345.33,Boston,S,7,12,14,58,Graduation,21,Patnership,Complex
695,181.23,NJ City,E,17,18,10,44,High School,24,Owner,Standalone
4990,322.77,Boston,S,6,15,4,71,High School,20,Patnership,Complex
4425,307,NJ City,N,2,17,22,42,High School,35,Patnership,Standalone
5130,58.05,Boston,S,18,20,1,74,Graduation,24,Patnership,Complex
366,434.25,NJ City,S,10,23,20,75,High School,20,Patnership,Complex
478,290.06,NJ City,S,20,15,7,51,High School,34,Patnership,Complex
9502,236.16,NJ City,S,11,13,5,26,High School,25,Patnership,Complex
7787,390.22,Boston,E,4,12,24,45,Graduation,32,Owner,Standalone
6654,187.45,Boston,S,5,19,3,35,Graduation,24,Patnership,Complex
171,370.97,NJ City,E,25,10,9,49,High School,35,Owner,Standalone
2395,253.76,NJ City,E,18,13,4,85,High School,20,Owner,Standalone
1394,35.59,NJ City,N,2,19,3,54,No education,20,Patnership,Complex
5519,136.16,Boston,E,8,23,11,36,Graduation,31,Owner,Standalone
8548,400.93,NJ City,W,3,13,4,52,High School,31,Franchise,Complex
7712,242.2,Boston,W,22,16,16,62,Graduation,29,Franchise,Complex
9118,195.84,NJ City,S,6,11,22,43,Graduation,22,Patnership,Complex
7902,93.01,Boston,S,19,24,4,81,Graduation,31,Patnership,Complex
5789,14.55,Boston,N,16,8,1,40,No education,24,Patnership,Standalone
7530,196.82,Boston,S,17,24,13,83,Graduation,26,Patnership,Complex
5912,240.14,Boston,W,17,20,2,30,No education,19,Franchise,Complex
191,459.08,NJ City,E,6,9,10,84,High School,32,Owner,Standalone
5149,371.75,Boston,N,3,10,24,69,Graduation,31,Patnership,Standalone
5159,209.01,Boston,E,16,24,11,53,Graduation,19,Owner,Standalone
6620,49.34,Boston,W,7,21,4,71,No education,27,Franchise,Complex
9869,380.5,NJ City,N,21,17,19,34,Graduation,24,Patnership,Standalone
2191,47.9,NJ City,E,9,18,1,57,High School,34,Owner,Standalone
9171,208.18,NJ City,E,25,22,1,81,Graduation,29,Owner,Standalone
1609,44.63,NJ City,N,20,15,1,32,High School,28,Patnership,Standalone
1525,7.11,NJ City,N,15,19,4,68,High School,29,Patnership,Standalone
1596,283.6,NJ City,W,17,10,13,57,High School,30,Franchise,Complex
7966,246.22,Boston,S,3,17,13,55,Graduation,19,Patnership,Complex
1340,338.15,NJ City,W,12,23,14,77,High School,33,Franchise,Complex
7885,180.54,Boston,N,6,13,11,31,Graduation,33,Patnership,Standalone
3664,498.49,NJ City,W,19,23,15,54,High School,20,Franchise,Complex
1441,235.41,NJ City,N,14,21,6,57,High School,19,Patnership,Standalone
9968,147.07,NJ City,W,11,13,22,35,Graduation,20,Franchise,Complex
9506,117.15,NJ City,S,10,19,4,34,No education,20,Patnership,Complex
4377,169.73,NJ City,N,20,12,14,27,High School,26,Patnership,Standalone
6882,5.54,Boston,N,9,14,1,74,Graduation,24,Patnership,Complex
8553,150.3,NJ City,N,25,22,21,82,Graduation,20,Patnership,Standalone
1058,96.01,NJ City,S,8,14,3,49,No education,24,Patnership,Complex
2107,322.55,NJ City,E,25,17,8,47,High School,25,Owner,Standalone
4778,218,NJ City,S,3,17,23,72,High School,32,Patnership,Complex
8209,388.3,NJ City,N,21,10,15,67,Graduation,21,Patnership,Standalone
7216,205.42,Boston,W,6,8,8,30,Graduation,18,Franchise,Complex
7448,466.56,Boston,W,21,21,14,42,Graduation,29,Franchise,Complex
4120,196,NJ City,W,18,20,13,64,High School,21,Franchise,Complex
4630,254.82,NJ City,S,25,14,3,28,High School,21,Patnership,Complex
2952,11.31,NJ City,N,18,17,4,74,Graduation,28,Franchise,Complex
737,203,NJ City,N,2,21,6,64,High School,22,Patnership,Standalone
2276,469.93,NJ City,W,13,17,6,73,High School,26,Franchise,Complex
3103,234,NJ City,E,2,10,19,74,High School,34,Owner,Standalone
7847,52.1,Boston,E,4,24,4,44,No education,18,Owner,Standalone
3952,161.07,NJ City,W,23,10,5,66,High School,20,Franchise,Complex
226,21.35,NJ City,N,14,24,4,26,High School,35,Patnership,Complex
9430,138.19,NJ City,S,13,11,7,63,Graduation,29,Patnership,Complex
665,112.47,NJ City,N,5,24,3,85,No education,35,Patnership,Standalone
3291,84.28,NJ City,E,5,18,2,57,Graduation,23,Owner,Standalone
8487,354.85,NJ City,E,25,15,16,34,Graduation,34,Owner,Standalone
9668,295.56,NJ City,W,25,8,19,61,Graduation,32,Franchise,Complex
43,394.95,Boston,E,12,13,25,81,High School,25,Owner,Standalone
1309,249.31,NJ City,N,8,11,2,66,High School,23,Patnership,Standalone
5763,260.5,Boston,E,7,17,5,29,Graduation,33,Owner,Standalone
8220,401.66,NJ City,W,21,15,16,81,Graduation,23,Franchise,Complex
9377,495.24,NJ City,N,12,9,18,57,Graduation,29,Patnership,Standalone
5249,373.95,Boston,N,17,19,20,60,Graduation,24,Patnership,Standalone
3617,427.12,NJ City,N,19,20,15,45,High School,27,Patnership,Standalone
6639,347.11,Boston,E,18,23,21,53,Graduation,34,Owner,Standalone
7523,336.93,Boston,E,7,22,2,59,No education,32,Owner,Standalone
2037,262,NJ City,N,3,17,7,55,High School,19,Patnership,Standalone
9647,428.61,NJ City,E,7,19,23,42,Graduation,34,Owner,Standalone
4671,495.35,NJ City,E,25,16,19,76,High School,27,Owner,Standalone
827,290.21,NJ City,E,4,22,22,58,High School,20,Owner,Standalone
6432,199.16,Boston,W,6,8,17,69,Graduation,30,Franchise,Complex
5405,56.95,Boston,N,18,22,2,83,No education,22,Patnership,Standalone
7259,248.44,Boston,E,8,22,22,49,Graduation,28,Owner,Standalone
5277,125.3,Boston,N,9,9,4,26,Graduation,19,Patnership,Standalone
404,101.62,NJ City,W,20,12,2,80,No education,25,Franchise,Complex
7562,488.01,Boston,S,5,16,22,35,Graduation,20,Patnership,Complex
2090,311,NJ City,S,2,18,14,39,High School,18,Patnership,Complex
8829,236.03,NJ City,N,21,8,17,37,Graduation,22,Patnership,Standalone
8001,88.33,Boston,N,7,19,2,34,Graduation,35,Patnership,Standalone
1166,346.97,NJ City,S,16,9,7,43,High School,35,Patnership,Complex
6572,338.79,Boston,W,22,14,24,60,Graduation,19,Franchise,Complex
1660,319.67,NJ City,W,12,14,21,38,High School,18,Franchise,Complex
6081,263.68,Boston,N,25,11,21,80,Graduation,25,Patnership,Standalone
2626,47.63,NJ City,S,10,24,2,85,High School,23,Patnership,Complex
7854,228.51,Boston,S,5,16,21,62,Graduation,33,Patnership,Complex
1008,5.16,NJ City,N,19,15,4,49,Graduation,22,Franchise,Complex
4462,70.14,NJ City,N,18,22,3,55,High School,31,Patnership,Complex
5921,342.83,Boston,N,9,8,13,56,Graduation,19,Patnership,Standalone
60,187.42,Boston,W,8,19,16,53,High School,32,Franchise,Complex
5026,203.07,Boston,S,25,17,8,49,Graduation,30,Patnership,Complex
8289,108.47,NJ City,N,8,8,3,53,Graduation,25,Patnership,Standalone
213,9.15,NJ City,N,9,18,3,75,Graduation,20,Patnership,Standalone
6541,126.38,Boston,N,20,23,1,43,High School,35,Patnership,Standalone
1177,142,NJ City,N,3,24,12,50,High School,19,Patnership,Standalone
9490,147.78,NJ City,S,23,9,16,56,Graduation,28,Patnership,Complex
5068,354.1,Boston,W,10,10,15,66,Graduation,21,Franchise,Complex
3675,215.74,NJ City,E,22,9,2,68,Graduation,27,Owner,Standalone
3647,374.4,NJ City,E,8,19,25,36,High School,32,Owner,Standalone
9637,206.44,NJ City,N,2,24,18,54,Graduation,33,Patnership,Standalone
283,241,NJ City,E,1,10,16,27,High School,21,Owner,Standalone
5676,100.47,Boston,W,9,9,3,83,Graduation,31,Franchise,Complex
5363,216.11,Boston,E,11,20,16,48,Graduation,21,Owner,Standalone
7035,225.27,Boston,E,7,22,7,72,Graduation,31,Owner,Standalone
4559,487.7,NJ City,E,14,19,10,61,High School,33,Owner,Standalone
4015,70.09,NJ City,N,18,22,3,61,High School,31,Owner,Standalone
7285,148.53,Boston,N,5,14,21,36,Graduation,22,Patnership,Standalone
5521,340.24,Boston,N,25,16,18,52,Graduation,27,Patnership,Standalone
9806,404.17,NJ City,S,9,14,24,44,Graduation,31,Patnership,Complex
2830,342.93,NJ City,S,16,21,9,55,High School,27,Patnership,Complex
8808,409.54,NJ City,W,9,21,3,52,Graduation,26,Franchise,Complex
7307,375.79,Boston,E,11,11,1,49,No education,35,Owner,Standalone
2582,391.96,NJ City,S,12,22,20,66,High School,26,Patnership,Complex
4707,179.52,NJ City,E,10,15,25,74,High School,28,Owner,Standalone
9812,218.84,NJ City,W,10,17,19,46,Graduation,28,Franchise,Complex
3796,52.58,NJ City,W,14,14,4,31,High School,30,Franchise,Complex
7689,73.78,Boston,N,3,24,1,77,Graduation,23,Patnership,Standalone
2280,176.67,NJ City,W,9,9,18,73,High School,18,Franchise,Complex
2819,132.38,NJ City,E,23,14,23,42,High School,25,Owner,Standalone
5776,298.21,Boston,W,25,8,15,31,Graduation,28,Franchise,Complex
5919,101.65,Boston,E,12,21,2,43,Graduation,22,Owner,Standalone
7355,488.66,Boston,E,8,17,5,31,No education,32,Owner,Standalone
6577,396.22,Boston,N,8,14,5,83,High School,33,Patnership,Standalone
776,71.57,NJ City,N,9,12,1,80,No education,31,Franchise,Complex
1063,265.06,NJ City,E,16,21,11,63,High School,31,Owner,Standalone
7002,324.85,Boston,S,25,24,15,62,Graduation,23,Patnership,Complex
884,284.8,NJ City,W,25,9,1,57,No education,32,Franchise,Complex
1007,367.99,NJ City,E,20,24,1,55,No education,23,Owner,Standalone
1675,250.84,NJ City,E,5,19,17,53,High School,33,Owner,Standalone
5155,287.76,Boston,E,5,16,10,61,Graduation,32,Owner,Standalone
9815,392.18,NJ City,E,20,20,2,85,No education,29,Owner,Standalone
297,103.27,NJ City,N,18,18,3,83,Graduation,31,Patnership,Standalone
3508,61.38,NJ City,N,1,24,4,66,High School,24,Franchise,Complex
1611,105,NJ City,E,3,8,3,72,Graduation,27,Owner,Standalone
2437,8.34,NJ City,N,16,24,4,65,High School,20,Patnership,Standalone
5217,154.41,Boston,N,16,19,6,63,Graduation,21,Patnership,Standalone
8681,401.5,NJ City,N,9,12,1,48,No education,30,Patnership,Standalone
6694,163.77,Boston,S,10,22,18,58,Graduation,28,Patnership,Complex
9351,374.26,NJ City,E,13,19,10,50,Graduation,25,Owner,Standalone
678,167.65,NJ City,S,15,18,21,76,High School,31,Patnership,Complex
8079,334.87,NJ City,E,1,17,18,62,Graduation,30,Owner,Standalone
9135,186.65,NJ City,E,17,22,1,40,Graduation,24,Owner,Standalone
2257,436.17,NJ City,N,7,16,9,77,High School,29,Patnership,Standalone
510,294.96,NJ City,S,3,23,2,66,Graduation,28,Patnership,Complex
7068,391.27,Boston,W,2,24,17,62,Graduation,22,Franchise,Complex
328,167.52,NJ City,W,15,10,19,53,High School,22,Franchise,Complex
9643,480.76,NJ City,E,8,18,20,84,Graduation,18,Owner,Standalone
9402,91.23,NJ City,S,5,23,3,85,Graduation,21,Patnership,Complex
6280,177.23,Boston,W,5,23,19,52,Graduation,33,Franchise,Complex
1404,355.16,NJ City,W,23,13,10,62,High School,33,Franchise,Complex
3814,23.49,NJ City,N,2,12,2,85,High School,34,Patnership,Complex
5550,153.14,Boston,S,13,18,5,54,Graduation,34,Patnership,Complex
6372,360.38,Boston,W,11,13,19,52,Graduation,34,Franchise,Complex
528,306.76,NJ City,W,25,19,16,27,High School,28,Franchise,Complex
4353,335.84,NJ City,N,7,14,4,34,Graduation,23,Patnership,Standalone
2999,316.53,NJ City,E,15,9,20,30,High School,32,Owner,Standalone
2132,53.36,NJ City,W,23,14,2,34,No education,33,Franchise,Complex
9712,295.63,NJ City,W,7,14,12,67,Graduation,28,Franchise,Complex
7313,446.82,Boston,N,3,12,10,70,Graduation,35,Patnership,Standalone
2430,333.05,NJ City,S,14,16,11,29,High School,30,Patnership,Complex
8906,392.2,NJ City,S,22,19,6,41,Graduation,27,Patnership,Complex
4717,134,NJ City,N,1,21,15,57,High School,26,Patnership,Standalone
4813,272.23,NJ City,N,8,16,10,83,High School,29,Patnership,Standalone
6381,193.17,Boston,N,5,18,12,48,Graduation,28,Patnership,Standalone
2513,81.54,NJ City,N,12,16,4,61,No education,35,Patnership,Standalone
2574,76.77,NJ City,S,14,19,4,25,Graduation,29,Patnership,Complex
9568,237.79,NJ City,W,9,23,4,38,High School,31,Franchise,Complex
1026,462.12,NJ City,S,14,10,13,85,High School,22,Patnership,Complex
2518,212.94,NJ City,S,18,16,12,33,High School,27,Patnership,Complex
3732,410.05,NJ City,W,11,23,9,74,High School,28,Franchise,Complex
7280,387.24,Boston,W,13,19,20,62,Graduation,31,Franchise,Complex
3939,297.41,NJ City,E,5,10,24,68,High School,21,Owner,Standalone
2660,105.15,NJ City,W,23,19,2,39,No education,20,Franchise,Complex
2764,82.04,NJ City,W,24,18,2,42,High School,18,Franchise,Complex
8466,61.45,NJ City,N,18,22,4,33,Graduation,32,Patnership,Complex
4076,250.93,NJ City,W,20,11,11,70,High School,25,Franchise,Complex
1208,448.26,NJ City,W,13,18,15,30,High School,24,Franchise,Complex
1918,269.7,NJ City,S,17,14,17,26,High School,21,Patnership,Complex
6496,81.75,Boston,W,12,20,1,36,High School,18,Franchise,Complex
2729,450.52,NJ City,N,6,10,4,31,No education,28,Patnership,Standalone
6148,333.75,Boston,W,21,23,25,43,Graduation,20,Franchise,Complex
9937,33.04,NJ City,N,22,8,1,68,High School,20,Patnership,Standalone
7923,28.76,Boston,N,3,12,2,79,Graduation,33,Owner,Standalone
1718,232.64,NJ City,S,20,24,14,44,High School,28,Patnership,Complex
9245,287.98,NJ City,N,14,16,22,70,Graduation,18,Patnership,Standalone
3859,142,NJ City,E,3,22,16,54,High School,21,Owner,Standalone
1773,468.39,NJ City,N,24,20,3,35,Graduation,26,Patnership,Standalone
8652,322.5,NJ City,W,13,18,22,67,Graduation,34,Franchise,Complex
9550,246.6,NJ City,S,16,22,5,73,High School,29,Patnership,Complex
425,200,NJ City,N,1,17,22,53,High School,34,Patnership,Standalone
8177,315.62,NJ City,N,5,13,2,47,No education,20,Patnership,Standalone
8784,495.03,NJ City,W,17,18,23,34,Graduation,26,Franchise,Complex
3096,124.32,NJ City,W,16,15,3,61,Graduation,20,Franchise,Complex
576,243.98,NJ City,W,25,23,1,84,Graduation,31,Franchise,Complex
707,106.43,NJ City,E,24,14,3,62,No education,23,Owner,Standalone
9160,120.09,NJ City,W,23,24,2,51,High School,35,Franchise,Complex
8827,63.22,NJ City,N,9,21,4,36,High School,24,Owner,Standalone
2783,65.91,NJ City,N,22,24,3,47,No education,28,Owner,Standalone
1621,435.72,NJ City,N,19,11,12,79,High School,21,Patnership,Standalone
5489,74.27,Boston,N,22,23,3,70,No education,29,Patnership,Standalone
939,240.85,NJ City,E,10,18,7,67,High School,31,Owner,Standalone
1233,1.78,NJ City,N,6,13,4,43,Graduation,26,Patnership,Standalone
4578,54.1,NJ City,S,24,22,3,53,Graduation,18,Patnership,Complex
1513,155.3,NJ City,N,10,16,3,41,High School,20,Patnership,Standalone
8992,152.82,NJ City,W,14,13,24,55,Graduation,33,Franchise,Complex
6291,424.23,Boston,E,14,19,8,44,Graduation,25,Owner,Standalone
5535,436.92,Boston,E,5,11,22,36,Graduation,19,Owner,Standalone
7926,231,Boston,S,12,10,16,50,Graduation,24,Patnership,Complex
7151,434.91,Boston,E,22,14,18,55,Graduation,21,Owner,Standalone
2674,130.94,NJ City,S,10,18,21,54,High School,31,Patnership,Complex
3148,450.9,NJ City,W,22,23,17,61,High School,33,Franchise,Complex
1977,257.49,NJ City,N,13,9,13,31,High School,20,Patnership,Standalone
5078,314.89,Boston,S,12,12,15,33,Graduation,24,Patnership,Complex
991,336.02,NJ City,E,15,22,5,76,High School,30,Owner,Standalone
9885,161.06,NJ City,N,10,20,16,64,Graduation,34,Patnership,Standalone
9965,61.19,NJ City,N,25,11,1,33,No education,28,Patnership,Standalone
7615,309.51,Boston,E,3,14,15,38,Graduation,23,Owner,Standalone
5707,448.41,Boston,E,3,10,6,26,Graduation,21,Owner,Standalone
6311,161.91,Boston,E,25,11,10,78,Graduation,30,Owner,Standalone
1593,227.26,NJ City,N,13,8,20,66,High School,32,Patnership,Standalone
1452,161.66,NJ City,W,6,22,9,35,High School,24,Franchise,Complex
3540,393.68,NJ City,W,12,20,8,56,High School,26,Franchise,Complex
7859,274.71,Boston,E,3,11,9,39,Graduation,25,Owner,Standalone
5533,195.44,Boston,N,7,19,15,39,Graduation,26,Patnership,Standalone
9613,37.68,NJ City,N,12,21,2,57,High School,26,Patnership,Standalone
4897,184.22,NJ City,N,15,21,6,71,High School,35,Patnership,Standalone
5163,205.43,Boston,E,9,14,22,82,Graduation,34,Owner,Standalone
9389,203.45,NJ City,N,12,9,13,63,Graduation,21,Patnership,Standalone
2825,327.04,NJ City,N,14,8,12,58,High School,27,Patnership,Standalone
5064,446.45,Boston,W,22,11,6,34,Graduation,34,Franchise,Complex
6114,160.35,Boston,S,23,17,13,60,Graduation,19,Patnership,Complex
6220,402.38,Boston,W,2,13,3,48,High School,24,Franchise,Complex
7596,275.57,Boston,W,25,14,23,81,Graduation,20,Franchise,Complex
8968,280.82,NJ City,W,7,8,18,33,Graduation,35,Franchise,Complex
8021,143.51,Boston,N,15,11,5,58,No education,19,Patnership,Standalone
6631,346.9,Boston,E,3,10,25,68,Graduation,30,Owner,Standalone
6192,436.19,Boston,W,8,12,18,49,Graduation,33,Franchise,Complex
6594,161.33,Boston,S,7,20,15,66,Graduation,28,Patnership,Complex
595,394.15,NJ City,E,21,18,9,71,High School,35,Owner,Standalone
395,224.57,NJ City,E,25,19,5,38,No education,25,Owner,Standalone
7274,128.8,Boston,S,17,21,16,40,Graduation,22,Patnership,Complex
6732,55.33,Boston,W,23,16,2,39,Graduation,24,Franchise,Complex
9478,324.69,NJ City,S,3,15,17,70,Graduation,28,Patnership,Complex
6868,125.5,Boston,W,10,17,1,66,High School,35,Franchise,Complex
4796,18.25,NJ City,N,1,8,1,72,No education,18,Franchise,Complex
9991,311.91,NJ City,E,10,14,1,44,High School,26,Owner,Standalone
1643,164.68,NJ City,E,22,13,25,51,High School,30,Owner,Standalone
4312,110.83,NJ City,W,24,12,2,46,High School,31,Franchise,Complex
1362,355.18,NJ City,S,16,18,6,53,High School,24,Patnership,Complex
98,428.65,NJ City,S,24,15,19,35,High School,22,Patnership,Complex
3504,106.03,NJ City,W,5,23,4,78,Graduation,22,Franchise,Complex
588,33.88,NJ City,N,2,12,2,27,Graduation,23,Franchise,Complex
9976,7.06,NJ City,N,22,23,2,51,High School,30,Franchise,Complex
7022,131.93,Boston,S,7,9,2,63,No education,33,Patnership,Complex
6102,118.2,Boston,S,18,19,1,39,Graduation,27,Patnership,Complex
4605,314.34,NJ City,N,8,12,2,70,Graduation,19,Patnership,Standalone
3971,198.81,NJ City,E,13,10,4,46,No education,19,Owner,Standalone
8702,241.26,NJ City,S,4,10,1,47,No education,21,Patnership,Complex
3733,233.88,NJ City,N,12,13,25,75,High School,26,Patnership,Standalone
4545,264.76,NJ City,N,24,8,25,46,High School,32,Patnership,Standalone
2487,478.33,NJ City,E,18,16,24,71,High School,31,Owner,Standalone
394,21.31,NJ City,N,1,11,1,25,High School,35,Patnership,Complex
6855,5.22,Boston,N,24,16,1,81,Graduation,27,Owner,Standalone
6052,356.44,Boston,W,16,11,10,61,Graduation,22,Franchise,Complex
6406,34.7,Boston,N,25,15,2,39,High School,18,Patnership,Complex
624,373.09,NJ City,W,17,8,22,73,High School,30,Franchise,Complex
4151,448.33,NJ City,E,19,14,12,70,High School,21,Owner,Standalone
32,281.83,Boston,W,21,12,23,66,High School,30,Franchise,Complex
6401,96.07,Boston,N,23,11,3,80,No education,18,Patnership,Standalone
2939,261.35,NJ City,E,9,10,15,81,High School,31,Owner,Standalone
9153,420.41,NJ City,N,7,18,19,64,Graduation,26,Patnership,Standalone
7756,65.02,Boston,N,25,14,3,33,High School,34,Franchise,Complex
3667,184.73,NJ City,E,10,12,17,52,High School,32,Owner,Standalone
6777,42.68,Boston,N,11,15,2,50,Graduation,29,Patnership,Standalone
8232,376.42,NJ City,W,23,20,16,54,Graduation,21,Franchise,Complex
2817,286.17,NJ City,N,9,15,3,54,Graduation,20,Patnership,Standalone
9447,46.75,NJ City,N,17,13,3,51,Graduation,25,Owner,Standalone
3176,499.81,NJ City,W,14,14,22,72,High School,24,Franchise,Complex
6445,193,Boston,N,22,8,18,33,Graduation,23,Patnership,Standalone
8795,290.54,NJ City,E,14,21,11,31,Graduation,32,Owner,Standalone
8381,209.32,NJ City,N,16,12,24,71,Graduation,28,Patnership,Standalone
832,172.02,NJ City,W,17,12,24,42,High School,23,Franchise,Complex
8734,122.52,NJ City,S,6,12,4,51,High School,32,Patnership,Complex
251,416.86,NJ City,E,10,13,20,81,High School,34,Owner,Standalone
5485,32,Boston,N,20,9,3,40,High School,24,Patnership,Standalone
9268,105.84,NJ City,W,3,20,2,62,High School,21,Franchise,Complex
3934,229.28,NJ City,S,13,13,17,64,High School,35,Patnership,Complex
2576,405.61,NJ City,W,3,12,5,47,No education,35,Franchise,Complex
21,181.33,Maine,N,9,15,2,76,Graduation,18,Patnership,Standalone
3294,496.65,NJ City,S,17,24,20,39,High School,32,Patnership,Complex
2870,339.21,NJ City,S,8,23,9,75,High School,34,Patnership,Complex
4635,302.72,NJ City,E,17,13,24,85,High School,18,Owner,Standalone
8843,74.77,NJ City,E,15,9,3,30,No education,24,Owner,Standalone
7809,492.58,Boston,N,10,10,24,85,Graduation,28,Patnership,Standalone
9357,26.91,NJ City,N,2,11,2,73,Graduation,21,Patnership,Standalone
6903,54.62,Boston,E,6,21,1,43,Graduation,34,Owner,Standalone
8716,121.86,NJ City,W,6,11,1,63,High School,22,Franchise,Complex
9578,404.81,NJ City,S,8,24,2,82,No education,26,Patnership,Complex
407,271,NJ City,E,2,15,7,28,High School,31,Owner,Standalone
7183,13.26,Boston,N,13,9,2,49,High School,24,Owner,Standalone
5839,175.38,Boston,E,9,24,4,26,High School,35,Owner,Standalone
9661,378.03,NJ City,N,10,9,18,35,Graduation,30,Patnership,Standalone
8757,313.08,NJ City,N,10,13,17,29,Graduation,34,Patnership,Standalone
4061,293.77,NJ City,N,12,16,2,48,No education,24,Patnership,Standalone
1370,347.05,NJ City,S,4,10,1,54,No education,22,Patnership,Complex
8010,451.79,Boston,S,24,18,11,73,Graduation,22,Patnership,Complex
7724,46.34,Boston,N,1,19,1,61,No education,20,Franchise,Complex
8015,258.87,Boston,E,20,11,18,56,Graduation,24,Owner,Standalone
4196,85.61,NJ City,W,19,13,3,68,No education,33,Franchise,Complex
1417,312.76,NJ City,N,7,19,2,73,High School,18,Patnership,Standalone
5298,257.46,Boston,S,11,16,23,76,Graduation,34,Patnership,Complex
4023,324.72,NJ City,E,15,18,18,67,High School,33,Owner,Standalone
8219,465.62,NJ City,E,16,15,7,50,Graduation,27,Owner,Standalone
7268,57.47,Boston,W,23,21,2,84,No education,31,Franchise,Complex
5532,116.75,Boston,W,7,13,4,79,Graduation,23,Franchise,Complex
2358,8.28,NJ City,N,9,8,2,71,Graduation,21,Patnership,Complex
8264,389.83,NJ City,W,10,23,22,50,Graduation,26,Franchise,Complex
7791,497.98,Boston,E,16,13,23,47,Graduation,31,Owner,Standalone
5214,490.41,Boston,S,6,8,24,65,Graduation,24,Patnership,Complex
652,158.51,NJ City,W,7,10,11,80,High School,23,Franchise,Complex
5549,40.33,Boston,N,21,11,1,48,No education,31,Patnership,Standalone
7531,276.64,Boston,E,3,19,23,31,Graduation,35,Owner,Standalone
6259,106.87,Boston,E,19,23,4,52,High School,33,Owner,Standalone
3893,82.51,NJ City,N,18,23,4,73,No education,22,Patnership,Standalone
2940,284.07,NJ City,W,10,23,8,40,High School,19,Franchise,Complex
6258,222.42,Boston,S,19,23,24,48,Graduation,32,Patnership,Complex
5069,358.88,Boston,N,5,11,12,35,Graduation,28,Patnership,Standalone
965,145.23,NJ City,N,9,15,4,28,No education,27,Patnership,Standalone
2153,389.81,NJ City,N,10,15,13,67,High School,27,Patnership,Standalone
732,60.48,NJ City,N,17,8,4,55,Graduation,31,Franchise,Complex
4428,277,NJ City,W,2,12,9,76,High School,20,Franchise,Complex
6945,473.74,Boston,N,19,19,8,48,Graduation,31,Patnership,Standalone
7887,117.11,Boston,E,18,13,2,49,Graduation,18,Owner,Standalone
4005,183.47,NJ City,N,8,20,16,34,High School,25,Patnership,Standalone
3866,460.27,NJ City,S,21,12,7,51,High School,23,Patnership,Complex
8767,327.85,NJ City,E,1,23,12,82,Graduation,23,Owner,Standalone
6963,180.03,Boston,E,8,23,12,65,Graduation,33,Owner,Standalone
4946,459.13,Boston,S,20,19,14,51,High School,22,Patnership,Complex
463,286.14,NJ City,E,8,10,14,34,High School,25,Owner,Standalone
5974,6.81,Boston,N,5,12,4,60,High School,29,Patnership,Complex
7600,437.13,Boston,W,24,12,16,77,Graduation,19,Franchise,Complex
7788,199.53,Boston,W,7,11,13,79,Graduation,32,Franchise,Complex
6589,104.93,Boston,N,5,10,3,35,High School,18,Patnership,Standalone
1186,348.67,NJ City,S,17,16,5,40,High School,29,Patnership,Complex
6112,78.97,Boston,W,21,24,3,76,High School,34,Franchise,Complex
16,240.1,Boston,W,11,24,22,65,High School,31,Franchise,Complex
8478,62.51,NJ City,N,16,15,4,75,Graduation,31,Patnership,Complex
341,255.07,NJ City,N,13,17,18,39,High School,31,Patnership,Standalone
4896,67.66,NJ City,N,15,23,2,36,Graduation,28,Franchise,Complex
2394,422.26,NJ City,S,10,17,19,38,High School,25,Patnership,Complex
8410,453.47,NJ City,S,3,13,19,28,Graduation,23,Patnership,Complex
8572,205.67,NJ City,W,20,20,24,31,Graduation,31,Franchise,Complex
6866,138.22,Boston,S,25,21,5,35,No education,22,Patnership,Complex
6269,378.29,Boston,N,25,24,19,39,Graduation,24,Patnership,Standalone
5687,474.51,Boston,E,21,19,25,65,Graduation,34,Owner,Standalone
9958,253.33,NJ City,S,20,8,10,41,Graduation,28,Patnership,Complex
6609,334.21,Boston,N,23,11,17,30,Graduation,22,Patnership,Standalone
5107,379.66,Boston,E,21,11,5,70,High School,31,Owner,Standalone
3845,351.87,NJ City,N,15,12,17,35,High School,31,Patnership,Standalone
8971,273.42,NJ City,E,19,21,7,36,Graduation,30,Owner,Standalone
3035,394.56,NJ City,E,18,16,1,60,No education,29,Owner,Standalone
7949,149.86,Boston,N,10,18,9,30,Graduation,28,Patnership,Standalone
8368,243.35,NJ City,W,13,18,24,76,Graduation,32,Franchise,Complex
5251,303.34,Boston,E,25,11,6,28,Graduation,29,Owner,Standalone
4755,346.05,NJ City,E,24,14,4,56,Graduation,23,Owner,Standalone
7096,459.49,Boston,W,1,14,19,46,Graduation,18,Franchise,Complex
4675,71.68,NJ City,N,6,18,1,51,High School,25,Owner,Standalone
3549,175.39,NJ City,N,7,24,19,47,High School,19,Patnership,Standalone
6004,309.2,Boston,W,14,17,14,84,Graduation,33,Franchise,Complex
5182,241.53,Boston,S,17,10,9,52,Graduation,19,Patnership,Complex
2557,114,NJ City,N,2,15,8,75,High School,34,Patnership,Standalone
9624,220.47,NJ City,W,11,11,13,31,Graduation,20,Franchise,Complex
5568,226.59,Boston,W,15,19,16,56,Graduation,31,Franchise,Complex
5032,348.8,Boston,W,19,19,25,64,Graduation,21,Franchise,Complex
1045,110.61,NJ City,N,13,12,1,26,High School,34,Patnership,Standalone
3150,251.81,NJ City,S,23,20,7,63,High School,26,Patnership,Complex
5011,189.16,Boston,E,10,9,1,70,High School,27,Owner,Standalone
7998,182.41,Boston,S,1,11,5,50,Graduation,24,Patnership,Complex
2300,304.62,NJ City,W,22,10,20,31,High School,21,Franchise,Complex
8927,47.28,NJ City,N,4,9,1,69,No education,24,Owner,Standalone
6526,387.11,Boston,S,9,10,10,32,Graduation,21,Patnership,Complex
257,439.39,NJ City,N,14,9,6,76,High School,34,Patnership,Standalone
1974,79.16,NJ City,S,25,21,2,40,Graduation,19,Patnership,Complex
1805,124,NJ City,N,3,11,10,29,High School,30,Patnership,Standalone
71,436.56,NY City,E,24,8,11,41,High School,23,Owner,Standalone
960,153.85,NJ City,W,6,11,17,82,High School,25,Franchise,Complex
908,315.67,NJ City,W,15,22,8,41,High School,34,Franchise,Complex
3013,337.2,NJ City,N,11,22,8,35,High School,24,Patnership,Standalone
3499,420.47,NJ City,E,6,24,11,60,High School,21,Owner,Standalone
7731,220.06,Boston,E,7,23,16,82,Graduation,25,Owner,Standalone
8862,170.91,NJ City,S,6,10,22,62,Graduation,33,Patnership,Complex
4421,258.36,NJ City,N,21,16,21,60,High School,28,Patnership,Standalone
2851,456.39,NJ City,E,18,16,15,59,High School,20,Owner,Standalone
3719,166.67,NJ City,E,6,22,11,75,High School,32,Owner,Standalone
5542,204.5,Boston,S,13,11,16,30,Graduation,30,Patnership,Complex
868,147,NJ City,W,3,8,6,74,High School,34,Franchise,Complex
5907,499.17,Boston,E,6,9,15,30,Graduation,33,Owner,Standalone
1014,152.89,NJ City,S,12,16,2,30,Graduation,27,Patnership,Complex
6821,164.7,Boston,N,3,21,18,46,Graduation,18,Patnership,Standalone
1454,183.27,NJ City,S,7,15,25,47,High School,33,Patnership,Complex
4139,109.24,NJ City,E,12,12,2,71,No education,23,Owner,Standalone
4368,433.7,NJ City,W,11,13,6,38,High School,28,Franchise,Complex
835,479.58,NJ City,E,8,8,4,74,High School,26,Owner,Standalone
5286,335.28,Boston,S,14,24,12,44,Graduation,33,Patnership,Complex
9515,217.39,NJ City,E,1,22,6,79,Graduation,32,Owner,Standalone
725,430,NJ City,N,10,16,6,69,High School,31,Patnership,Standalone
8625,463.43,NJ City,N,15,21,18,68,Graduation,31,Patnership,Standalone
3775,7.02,NJ City,N,7,20,4,64,High School,24,Owner,Standalone
9331,163.97,NJ City,E,2,13,5,27,High School,28,Owner,Standalone
4929,93.78,NJ City,N,14,9,3,27,Graduation,23,Patnership,Standalone
9819,495.08,NJ City,E,24,17,19,80,Graduation,31,Owner,Standalone
9548,381.73,NJ City,W,3,8,9,31,Graduation,35,Franchise,Complex
4849,126.6,NJ City,N,6,22,2,39,High School,32,Patnership,Standalone
4119,41.52,NJ City,N,11,12,4,83,Graduation,33,Owner,Standalone
4378,481.39,NJ City,S,4,8,10,40,High School,21,Patnership,Complex
792,378.48,NJ City,W,21,20,12,82,High School,29,Franchise,Complex
7781,151.94,Boston,N,2,22,24,84,Graduation,29,Patnership,Standalone
9601,52.71,NJ City,N,16,12,3,34,High School,19,Patnership,Standalone
7020,24.42,Boston,N,1,14,4,68,Graduation,25,Franchise,Complex
3575,33.92,NJ City,N,20,13,4,67,No education,33,Owner,Standalone
5938,405.96,Boston,S,25,24,6,52,Graduation,20,Patnership,Complex
4978,235,Boston,S,3,13,16,38,High School,27,Patnership,Complex
4805,322.09,NJ City,N,15,10,14,60,High School,25,Patnership,Standalone
4220,122.78,NJ City,W,8,18,1,75,No education,29,Franchise,Complex
1419,76.86,NJ City,E,9,12,4,45,Graduation,29,Owner,Standalone
890,102.51,NJ City,S,17,17,2,41,No education,27,Patnership,Complex
3456,417.1,NJ City,W,20,19,6,62,High School,27,Franchise,Complex
9189,122.19,NJ City,N,2,12,3,81,Graduation,18,Patnership,Standalone
4773,321.97,NJ City,N,23,22,5,41,Graduation,30,Patnership,Standalone
3818,1.69,NJ City,N,6,21,3,40,No education,22,Patnership,Complex
7146,411.24,Boston,S,11,20,13,85,Graduation,29,Patnership,Complex
4323,120.2,NJ City,E,9,16,3,50,Graduation,20,Owner,Standalone
4631,432.7,NJ City,E,16,13,18,49,High School,19,Owner,Standalone
4823,473.48,NJ City,E,23,20,1,85,No education,21,Owner,Standalone
314,87.05,NJ City,S,11,22,2,72,No education,33,Patnership,Complex
8546,365.2,NJ City,S,24,18,4,36,No education,27,Patnership,Complex
3908,211.56,NJ City,W,5,15,12,81,High School,30,Franchise,Complex
4251,415.29,NJ City,E,21,11,15,53,High School,23,Owner,Standalone
9552,149.96,NJ City,W,7,23,15,79,Graduation,30,Franchise,Complex
7675,127.82,Boston,E,17,22,2,82,High School,19,Owner,Standalone
1599,318.53,NJ City,E,12,8,10,51,High School,23,Owner,Standalone
2351,312,NJ City,E,2,22,21,78,High School,27,Owner,Standalone
4809,290.74,NJ City,N,14,17,6,40,High School,18,Patnership,Standalone
450,397.16,NJ City,S,15,11,25,64,High School,18,Patnership,Complex
4274,491.52,NJ City,S,19,12,9,36,High School,21,Patnership,Complex
3559,395.75,NJ City,E,4,16,3,80,High School,27,Owner,Standalone
5256,244.04,Boston,W,17,11,12,33,Graduation,27,Franchise,Complex
9605,61.88,NJ City,N,1,12,3,54,No education,22,Patnership,Standalone
2299,136,NJ City,E,3,8,24,73,High School,18,Owner,Standalone
3271,206.63,NJ City,E,21,18,20,72,High School,18,Owner,Standalone
2976,496.86,NJ City,W,15,22,12,51,High School,33,Franchise,Complex
4383,178.44,NJ City,E,14,8,9,29,High School,34,Owner,Standalone
3762,269,NJ City,S,2,23,19,67,High School,23,Patnership,Complex
1318,11.63,NJ City,N,11,17,2,47,High School,32,Patnership,Complex
6063,432.39,Boston,E,23,18,7,41,Graduation,34,Owner,Standalone
4272,403.27,NJ City,W,15,20,16,62,High School,21,Franchise,Complex
9061,123.21,NJ City,N,3,16,2,36,High School,35,Patnership,Standalone
9005,299.61,NJ City,N,2,23,22,47,Graduation,25,Patnership,Standalone
1543,12.15,NJ City,N,9,9,2,77,High School,26,Owner,Standalone
7130,477.24,Boston,S,13,17,16,28,Graduation,27,Patnership,Complex
6047,24.52,Boston,N,15,17,4,63,No education,22,Owner,Standalone
6416,187.33,Boston,W,12,8,19,73,Graduation,31,Franchise,Complex
819,199.56,NJ City,E,10,24,2,31,Graduation,30,Owner,Standalone
4997,111,Boston,N,1,17,23,52,High School,34,Patnership,Standalone
266,347.18,NJ City,S,14,17,3,43,No education,21,Patnership,Complex
1633,11.78,NJ City,N,13,16,1,66,High School,32,Patnership,Standalone
3724,202.53,NJ City,W,20,15,1,31,High School,33,Franchise,Complex
724,201.65,NJ City,W,25,21,14,44,High School,25,Franchise,Complex
5887,260.81,Boston,E,24,9,13,65,Graduation,34,Owner,Standalone
9776,426.17,NJ City,W,3,9,21,30,Graduation,33,Franchise,Complex
5431,469.7,Boston,E,7,21,18,51,Graduation,27,Owner,Standalone
3615,61.88,NJ City,N,12,17,4,76,Graduation,29,Owner,Standalone
6086,327.55,Boston,S,13,12,11,52,Graduation,22,Patnership,Complex
9995,468.77,NJ City,E,1,14,22,82,Graduation,26,Owner,Standalone
3247,64.71,NJ City,N,21,24,3,81,High School,34,Owner,Standalone
1349,31.99,NJ City,N,15,8,1,29,No education,30,Patnership,Standalone
7036,188.41,Boston,W,8,19,7,50,Graduation,21,Franchise,Complex
7252,151.39,Boston,W,14,15,15,69,Graduation,20,Franchise,Complex
5769,429.6,Boston,N,15,20,5,83,Graduation,24,Patnership,Standalone
7614,52.68,Boston,S,14,18,2,33,Graduation,33,Patnership,Complex
8596,192.54,NJ City,W,3,22,13,57,Graduation,30,Franchise,Complex
925,237.82,NJ City,N,23,13,10,40,High School,25,Patnership,Standalone
8327,92.55,NJ City,E,10,23,2,47,No education,34,Owner,Standalone
5876,19.74,Boston,N,23,19,2,79,No education,25,Franchise,Complex
3914,499.6,NJ City,S,14,18,8,85,High School,33,Patnership,Complex
9417,430.68,NJ City,N,24,17,7,25,Graduation,22,Patnership,Standalone
2229,160.86,NJ City,N,9,17,21,84,High School,21,Patnership,Standalone
4653,424.5,NJ City,N,25,8,9,85,High School,19,Patnership,Standalone
346,331.36,NJ City,S,12,14,10,72,High School,34,Patnership,Complex
3514,298.42,NJ City,S,8,20,16,81,High School,30,Patnership,Complex
9406,369.02,NJ City,S,5,11,4,51,High School,18,Patnership,Complex
2762,48.91,NJ City,S,22,10,1,67,No education,31,Patnership,Complex
2670,275.7,NJ City,S,8,24,4,48,Graduation,32,Patnership,Complex
4003,176,NJ City,E,3,20,21,66,High School,34,Owner,Standalone
4656,169.85,NJ City,W,12,14,2,27,Graduation,25,Franchise,Complex
1739,40.06,NJ City,N,20,19,3,55,No education,26,Owner,Standalone
1100,343.79,NJ City,W,15,10,7,36,High School,24,Franchise,Complex
6391,451.38,Boston,E,13,13,25,66,Graduation,26,Owner,Standalone
5835,287.87,Boston,E,18,15,24,57,Graduation,28,Owner,Standalone
277,120.5,NJ City,N,10,16,3,27,High School,21,Patnership,Standalone
4961,485.2,Boston,N,19,16,24,56,High School,19,Patnership,Standalone
9246,168.01,NJ City,S,7,16,24,45,Graduation,34,Patnership,Complex
3502,191.74,NJ City,S,15,10,7,44,High School,26,Patnership,Complex
5216,243.73,Boston,W,25,11,10,64,Graduation,22,Franchise,Complex
6073,292.77,Boston,N,22,11,5,60,High School,28,Patnership,Standalone
5415,214.14,Boston,E,5,22,10,58,Graduation,35,Owner,Standalone
2502,62.66,NJ City,N,6,9,3,51,Graduation,24,Patnership,Complex
8465,499.86,NJ City,N,19,9,24,64,Graduation,23,Patnership,Standalone
7928,212.55,Boston,W,1,14,16,68,Graduation,25,Franchise,Complex
4549,348.94,NJ City,N,18,17,23,80,High School,28,Patnership,Standalone
7674,80.2,Boston,S,18,24,1,56,Graduation,20,Patnership,Complex
5241,167.76,Boston,N,7,14,11,40,Graduation,35,Patnership,Standalone
3363,299.35,NJ City,E,4,17,3,30,Graduation,29,Owner,Standalone
6972,107.46,Boston,W,8,16,2,46,Graduation,19,Franchise,Complex
7181,239.96,Boston,N,19,12,18,80,Graduation,19,Patnership,Standalone
5180,499.33,Boston,W,19,13,25,72,Graduation,32,Franchise,Complex
669,60.06,NJ City,N,15,19,3,32,Graduation,21,Patnership,Standalone
5749,42.63,Boston,N,13,21,2,56,High School,35,Patnership,Standalone
3727,365.06,NJ City,E,7,24,4,50,High School,22,Owner,Standalone
4697,159.24,NJ City,N,11,21,12,29,High School,25,Patnership,Standalone
1263,359.67,NJ City,E,18,23,24,59,High School,20,Owner,Standalone
3254,245.67,NJ City,S,12,18,8,50,High School,26,Patnership,Complex
9282,330.86,NJ City,S,15,20,3,73,Graduation,31,Patnership,Complex
1927,418.02,NJ City,E,21,15,17,55,High School,30,Owner,Standalone
1103,247.67,NJ City,E,20,17,24,58,High School,29,Owner,Standalone
5031,321.81,Boston,E,15,19,15,62,Graduation,19,Owner,Standalone
495,218,NJ City,E,3,19,7,40,High School,26,Owner,Standalone
1196,113.01,NJ City,W,14,19,4,39,No education,32,Franchise,Complex
3935,498.08,NJ City,E,10,22,16,38,High School,20,Owner,Standalone
5337,47.47,Boston,N,12,22,1,60,Graduation,31,Patnership,Standalone
1455,140,NJ City,E,9,23,13,62,High School,35,Owner,Standalone
7120,162.68,Boston,W,7,14,16,27,Graduation,32,Franchise,Complex
3267,398.92,NJ City,E,12,14,14,64,High School,35,Owner,Standalone
7217,295.94,Boston,N,20,17,18,63,Graduation,29,Patnership,Standalone
6317,304.77,Boston,N,3,20,6,63,Graduation,21,Patnership,Standalone
5335,415.83,Boston,E,18,18,9,56,Graduation,33,Owner,Standalone
459,77.82,NJ City,E,22,24,4,34,Graduation,34,Owner,Standalone
6337,185.03,Boston,N,10,8,23,66,Graduation,26,Patnership,Standalone
2267,471.18,NJ City,E,19,24,18,73,High School,26,Owner,Standalone
4894,302.66,NJ City,S,3,11,5,44,High School,24,Patnership,Complex
3408,332.39,NJ City,W,15,19,8,46,High School,29,Franchise,Complex
596,38.3,NJ City,N,13,11,2,41,No education,25,Franchise,Complex
7984,170.92,Boston,W,18,22,19,61,Graduation,27,Franchise,Complex
1551,233.81,NJ City,E,24,13,14,28,High School,31,Owner,Standalone
4407,148.72,NJ City,E,16,18,11,69,High School,32,Owner,Standalone
1238,342.46,NJ City,S,21,22,11,34,High School,32,Patnership,Complex
5000,338.2,Boston,W,22,18,9,70,Graduation,33,Franchise,Complex
6796,370.11,Boston,W,9,21,9,35,Graduation,21,Franchise,Complex
4566,483.5,NJ City,S,12,21,18,41,High School,28,Patnership,Complex
9281,201.03,NJ City,N,22,14,3,44,No education,18,Patnership,Standalone
7882,343.77,Boston,S,6,12,5,73,High School,35,Patnership,Complex
5527,421.19,Boston,E,8,23,23,79,Graduation,19,Owner,Standalone
2521,404.18,NJ City,N,5,8,24,60,High School,31,Patnership,Standalone
8903,134.49,NJ City,E,19,9,8,43,Graduation,28,Owner,Standalone
373,188,NJ City,N,21,16,7,67,High School,33,Patnership,Standalone
3532,247,NJ City,W,1,23,22,28,High School,27,Franchise,Complex
7492,124.3,Boston,W,20,12,3,49,High School,34,Franchise,Complex
5447,173.95,Boston,E,3,19,24,77,Graduation,25,Owner,Standalone
2697,67.65,NJ City,N,2,18,4,53,Graduation,33,Patnership,Standalone
2769,197.13,NJ City,N,11,22,13,55,High School,33,Patnership,Standalone
5421,414.46,Boston,N,17,8,5,31,Graduation,29,Patnership,Standalone
9533,447.44,NJ City,N,25,10,23,79,Graduation,24,Patnership,Standalone
6949,199.91,Boston,N,20,15,20,25,Graduation,24,Patnership,Standalone
7116,298.41,Boston,W,15,16,19,37,Graduation,25,Franchise,Complex
9567,264.32,NJ City,E,18,24,16,25,Graduation,24,Owner,Standalone
7461,153.21,Boston,N,23,16,2,56,Graduation,24,Patnership,Standalone
1475,416.02,NJ City,E,13,23,12,56,High School,23,Owner,Standalone
3449,185.05,NJ City,N,6,9,24,37,High School,29,Patnership,Standalone
5141,274.9,Boston,N,20,10,6,61,Graduation,19,Patnership,Standalone
9412,116.08,NJ City,W,18,23,1,76,High School,18,Franchise,Complex
2538,94.17,NJ City,S,1,14,2,46,Graduation,22,Patnership,Complex
3317,271.74,NJ City,N,6,11,8,52,High School,25,Patnership,Standalone
6231,358.5,Boston,E,11,21,23,51,Graduation,21,Owner,Standalone
5603,385.18,Boston,E,11,13,24,37,Graduation,29,Owner,Standalone
6817,92.95,Boston,N,19,21,2,66,High School,22,Patnership,Standalone
2380,140.11,NJ City,W,14,11,18,74,High School,34,Franchise,Complex
8611,444.22,NJ City,E,2,13,13,38,Graduation,35,Owner,Standalone
6703,198.27,Boston,E,12,11,23,68,Graduation,23,Owner,Standalone
3065,443,NJ City,N,19,22,2,55,No education,27,Patnership,Standalone
1391,196,NJ City,E,3,23,25,83,High School,21,Owner,Standalone
3420,248.71,NJ City,W,12,12,9,25,High School,30,Franchise,Complex
4173,299.33,NJ City,N,15,24,17,65,High School,29,Patnership,Standalone
1595,91.5,NJ City,E,13,16,1,30,No education,30,Owner,Standalone
9831,264.67,NJ City,E,7,14,4,79,Graduation,34,Owner,Standalone
1381,117.74,NJ City,N,15,14,4,67,High School,35,Patnership,Standalone
806,175.12,NJ City,S,5,14,1,79,No education,18,Patnership,Complex
7082,307.44,Boston,S,19,17,1,59,No education,28,Patnership,Complex
2322,96.25,NJ City,S,17,10,1,28,Graduation,28,Patnership,Complex
333,195.19,NJ City,N,11,20,4,83,Graduation,34,Patnership,Standalone
9670,230.48,NJ City,S,23,22,15,53,Graduation,28,Patnership,Complex
2501,278.94,NJ City,N,22,17,17,43,High School,18,Patnership,Standalone
8735,84.06,NJ City,E,12,11,1,31,No education,29,Owner,Standalone
2855,450.96,NJ City,E,13,9,12,34,High School,33,Owner,Standalone
2709,168.77,NJ City,N,14,16,8,81,High School,20,Patnership,Standalone
8404,186.88,NJ City,W,10,14,15,60,Graduation,22,Franchise,Complex
2600,434.99,NJ City,W,14,22,5,43,No education,18,Franchise,Complex
4056,206.03,NJ City,W,7,11,13,59,High School,31,Franchise,Complex
9178,406.25,NJ City,S,9,8,4,59,High School,32,Patnership,Complex
514,79.92,NJ City,S,11,23,1,43,High School,35,Patnership,Complex
4252,397.85,NJ City,W,14,11,15,85,High School,32,Franchise,Complex
3157,117.53,NJ City,N,16,18,2,79,High School,22,Patnership,Standalone
1038,255.38,NJ City,S,18,11,2,53,Graduation,19,Patnership,Complex
2663,314.67,NJ City,E,16,9,15,28,High School,31,Owner,Standalone
3992,93.3,NJ City,W,25,15,2,55,No education,24,Franchise,Complex
548,383.75,NJ City,W,6,12,18,36,High School,18,Franchise,Complex
5225,180.29,Boston,N,18,9,19,84,Graduation,31,Patnership,Standalone
6314,43.28,Boston,N,1,20,2,61,No education,29,Patnership,Complex
6549,213.25,Boston,N,4,17,18,84,Graduation,24,Patnership,Standalone
2757,88.97,NJ City,N,22,22,1,81,Graduation,28,Patnership,Standalone
2603,320.99,NJ City,E,25,9,20,27,High School,29,Owner,Standalone
8723,405.84,NJ City,E,21,22,6,53,Graduation,28,Owner,Standalone
711,87.82,NJ City,E,23,20,4,65,Graduation,29,Owner,Standalone
5115,441.74,Boston,E,24,14,19,34,Graduation,19,Owner,Standalone
6804,430.6,Boston,W,16,21,2,29,Graduation,30,Franchise,Complex
703,194.03,NJ City,E,13,17,12,63,High School,24,Owner,Standalone
8619,458.58,NJ City,E,12,10,21,84,Graduation,33,Owner,Standalone
1651,83.3,NJ City,E,22,16,3,80,High School,23,Owner,Standalone
9889,490.33,NJ City,N,18,22,8,55,Graduation,20,Patnership,Standalone
9002,192.08,NJ City,S,5,20,15,43,Graduation,31,Patnership,Complex
2896,345.65,NJ City,W,11,23,7,74,High School,34,Franchise,Complex
4820,103.65,NJ City,W,24,16,3,71,No education,25,Franchise,Complex
4860,22.92,NJ City,N,13,19,2,28,Graduation,18,Franchise,Complex
4972,54.36,Boston,W,14,14,1,29,High School,30,Franchise,Complex
7635,334.06,Boston,E,8,15,17,61,Graduation,31,Owner,Standalone
9310,339.53,NJ City,S,9,15,21,36,Graduation,33,Patnership,Complex
6412,238.57,Boston,W,17,21,17,64,Graduation,26,Franchise,Complex
6920,491.6,Boston,W,4,23,5,62,No education,26,Franchise,Complex
4480,266.09,NJ City,W,15,18,18,47,High School,35,Franchise,Complex
3454,307.48,NJ City,S,19,13,7,52,High School,19,Patnership,Complex
1487,235,NJ City,E,3,10,21,68,High School,19,Owner,Standalone
5173,225.59,Boston,N,5,15,3,69,High School,24,Patnership,Standalone
3017,412.77,NJ City,N,23,19,13,74,High School,23,Patnership,Standalone
6471,362.9,Boston,E,22,23,1,52,Graduation,20,Owner,Standalone
1084,22.59,NJ City,N,20,23,1,30,High School,33,Franchise,Complex
8284,332.07,NJ City,W,3,11,23,77,Graduation,27,Franchise,Complex
6072,57.9,Boston,W,9,11,4,39,Graduation,31,Franchise,Complex
6131,416.4,Boston,E,25,12,15,57,Graduation,32,Owner,Standalone
6357,52.34,Boston,N,23,23,1,31,Graduation,22,Patnership,Standalone
8532,86.89,NJ City,W,13,14,1,80,Graduation,24,Franchise,Complex
5038,476.17,Boston,S,22,13,13,69,Graduation,21,Patnership,Complex
1210,252.06,NJ City,S,6,13,24,62,High School,35,Patnership,Complex
4662,191.95,NJ City,S,10,15,23,27,High School,26,Patnership,Complex
566,106.4,NJ City,S,7,13,2,39,No education,21,Patnership,Complex
9185,248.11,NJ City,N,21,21,5,36,No education,29,Patnership,Standalone
5029,226.12,Boston,N,4,12,11,75,Graduation,34,Patnership,Standalone
7289,80.79,Boston,N,16,10,2,55,No education,31,Patnership,Standalone
6940,119.84,Boston,W,3,8,3,29,High School,20,Franchise,Complex
4504,177.24,NJ City,W,23,18,12,63,High School,33,Franchise,Complex
5460,167.43,Boston,W,24,10,24,81,Graduation,20,Franchise,Complex
447,235.92,NJ City,E,16,17,1,33,Graduation,31,Owner,Standalone
4195,206,NJ City,E,3,8,24,84,High School,33,Owner,Standalone
7240,64.89,Boston,N,8,18,4,68,High School,23,Franchise,Complex
8871,114.09,NJ City,E,23,20,3,85,Graduation,30,Owner,Standalone
6351,79.36,Boston,E,20,20,3,64,Graduation,23,Owner,Standalone
3711,349.2,NJ City,E,12,9,23,69,High School,19,Owner,Standalone
2747,292.92,NJ City,E,13,12,24,26,High School,23,Owner,Standalone
2957,293.48,NJ City,N,25,17,15,72,High School,27,Patnership,Standalone
858,229.04,NJ City,S,16,18,10,37,High School,35,Patnership,Complex
657,71.11,NJ City,N,21,17,4,63,Graduation,22,Patnership,Standalone
3832,12.93,NJ City,N,1,15,1,71,High School,20,Franchise,Complex
1917,27.76,NJ City,N,15,12,2,85,Graduation,21,Patnership,Standalone
6582,46.95,Boston,N,4,23,2,57,Graduation,30,Patnership,Complex
5316,412.13,Boston,W,8,22,13,34,Graduation,34,Franchise,Complex
179,90.72,NJ City,E,15,8,3,39,No education,19,Owner,Standalone
6885,167.22,Boston,N,5,22,12,40,Graduation,23,Patnership,Standalone
2676,243.35,NJ City,W,16,20,15,38,High School,22,Franchise,Complex
2935,169.46,NJ City,E,19,8,8,80,High School,32,Owner,Standalone
7434,82.81,Boston,S,3,23,2,51,Graduation,28,Patnership,Complex
4410,12.4,NJ City,N,19,9,3,77,Graduation,32,Patnership,Complex
7046,251.47,Boston,S,16,9,9,51,Graduation,35,Patnership,Complex
7424,447.19,Boston,W,4,24,19,31,Graduation,28,Franchise,Complex
4453,379.93,NJ City,N,12,9,2,60,High School,23,Patnership,Standalone
4218,232.61,NJ City,S,16,14,1,50,Graduation,34,Patnership,Complex
7726,393.15,Boston,S,9,11,22,58,Graduation,26,Patnership,Complex
8967,116.28,NJ City,E,9,12,4,36,Graduation,28,Owner,Standalone
1491,131.14,NJ City,E,5,19,18,59,High School,33,Owner,Standalone
1969,169.49,NJ City,N,14,10,11,65,High School,35,Patnership,Standalone
3964,170.23,NJ City,W,17,21,3,82,High School,20,Franchise,Complex
7586,43.9,Boston,N,12,11,1,80,No education,35,Patnership,Complex
2561,408.66,NJ City,N,15,22,21,70,High School,19,Patnership,Standalone
5908,258.37,Boston,W,24,12,15,66,Graduation,28,Franchise,Complex
3993,266.4,NJ City,N,8,20,2,41,Graduation,20,Patnership,Standalone
3293,304.58,NJ City,N,16,8,10,75,High School,27,Patnership,Standalone
4829,185.06,NJ City,N,22,16,21,75,High School,31,Patnership,Standalone
6669,121.66,Boston,N,18,19,4,83,Graduation,35,Patnership,Standalone
5160,347.67,Boston,W,1,10,11,29,Graduation,18,Franchise,Complex
1110,479.73,NJ City,S,17,18,12,28,High School,35,Patnership,Complex
7656,79.86,Boston,W,4,15,2,81,Graduation,24,Franchise,Complex
5221,61.35,Boston,N,14,10,1,64,High School,22,Patnership,Standalone
780,108.7,NJ City,W,18,11,1,48,Graduation,27,Franchise,Complex
9788,128.06,NJ City,W,24,24,1,79,No education,18,Franchise,Complex
4837,264.46,NJ City,N,24,22,15,61,High School,29,Patnership,Standalone
1801,466.04,NJ City,N,16,23,18,45,High School,30,Patnership,Standalone
7812,26.36,Boston,N,9,18,4,84,Graduation,18,Franchise,Complex
7015,258.79,Boston,E,22,22,8,57,Graduation,23,Owner,Standalone
3286,201.03,NJ City,S,3,23,3,54,High School,27,Patnership,Complex
1408,69.53,NJ City,N,17,14,1,31,High School,29,Franchise,Complex
7521,147.1,Boston,N,21,23,3,69,Graduation,23,Patnership,Standalone
654,73.7,NJ City,N,9,17,1,57,Graduation,33,Patnership,Complex
1229,351.47,NJ City,N,16,8,17,39,High School,18,Patnership,Standalone
3767,452.07,NJ City,E,13,22,17,62,High School,25,Owner,Standalone
4795,418.83,NJ City,E,16,18,18,77,High School,30,Owner,Standalone
3019,88.86,NJ City,E,15,15,3,61,High School,24,Owner,Standalone
5352,391.36,Boston,W,8,12,15,40,Graduation,25,Franchise,Complex
1558,417.63,NJ City,S,12,16,9,74,High School,32,Patnership,Complex
1124,390.37,NJ City,W,21,18,18,76,High School,26,Franchise,Complex
530,424.11,NJ City,S,24,16,21,37,High School,34,Patnership,Complex
8127,252.31,NJ City,E,23,14,24,37,Graduation,30,Owner,Standalone
7053,71.36,Boston,N,9,20,2,65,Graduation,24,Patnership,Standalone
2804,470.58,NJ City,W,7,20,25,72,High School,30,Franchise,Complex
94,273.94,NJ City,S,15,20,3,37,High School,31,Patnership,Complex
5772,96.14,Boston,W,20,14,1,39,Graduation,25,Franchise,Complex
2932,495.22,NJ City,W,25,15,13,62,High School,30,Franchise,Complex
1713,451.7,NJ City,N,4,17,22,25,High School,32,Patnership,Standalone
9029,329.68,NJ City,N,6,21,4,57,No education,29,Patnership,Standalone
2537,48.61,NJ City,N,2,9,1,46,No education,35,Patnership,Standalone
8453,20.85,NJ City,N,9,18,2,30,No education,30,Patnership,Standalone
1326,30.36,NJ City,N,12,18,2,26,Graduation,31,Patnership,Complex
7891,138.46,Boston,E,19,19,20,53,Graduation,27,Owner,Standalone
9334,109.28,NJ City,S,24,15,4,60,High School,20,Patnership,Complex
1996,347.02,NJ City,W,16,9,13,38,High School,31,Franchise,Complex
3253,110.84,NJ City,N,25,9,4,35,High School,30,Patnership,Standalone
4203,14.24,NJ City,N,7,20,1,48,Graduation,27,Owner,Standalone
2641,303.7,NJ City,N,13,16,10,31,High School,27,Patnership,Standalone
634,275.65,NJ City,S,9,9,15,60,High School,20,Patnership,Complex
2200,391.11,NJ City,W,8,11,22,59,High School,25,Franchise,Complex
8705,36.96,NJ City,N,16,9,1,56,No education,29,Patnership,Standalone
5819,328.44,Boston,E,12,16,3,53,No education,22,Owner,Standalone
6064,95.96,Boston,W,22,14,4,71,High School,19,Franchise,Complex
1734,155.46,NJ City,S,25,22,4,39,Graduation,26,Patnership,Complex
6599,492.82,Boston,E,3,11,11,50,Graduation,30,Owner,Standalone
1301,417.53,NJ City,N,18,13,23,33,High School,31,Patnership,Standalone
5496,327.01,Boston,W,6,13,18,52,Graduation,24,Franchise,Complex
2266,482.22,NJ City,S,19,22,9,46,High School,21,Patnership,Complex
4830,490.19,NJ City,S,17,13,25,27,High School,33,Patnership,Complex
7708,197.03,Boston,W,12,9,21,58,Graduation,24,Franchise,Complex
4560,45.69,NJ City,N,4,16,4,76,Graduation,35,Franchise,Complex
4782,422.82,NJ City,S,23,14,7,80,High School,20,Patnership,Complex
8958,295.01,NJ City,S,18,21,4,61,Graduation,21,Patnership,Complex
3315,185.28,NJ City,E,4,10,22,71,High School,18,Owner,Standalone
4660,122.11,NJ City,W,7,22,3,68,High School,20,Franchise,Complex
1889,39.84,NJ City,N,10,12,2,38,No education,26,Patnership,Standalone
974,311.05,NJ City,S,19,10,16,31,High School,19,Patnership,Complex
7824,334.66,Boston,W,22,16,20,40,Graduation,33,Franchise,Complex
4926,219,NJ City,S,3,10,13,53,High School,30,Patnership,Complex
6685,53.91,Boston,N,13,16,3,75,High School,26,Patnership,Standalone
4710,292.99,NJ City,S,9,18,3,39,Graduation,27,Patnership,Complex
9203,447.17,NJ City,E,18,18,4,81,No education,25,Owner,Standalone
8046,366.52,NJ City,S,6,13,1,46,Graduation,27,Patnership,Complex
6440,287.86,Boston,W,5,14,23,40,Graduation,27,Franchise,Complex
244,151.04,NJ City,W,24,22,18,81,High School,26,Franchise,Complex
317,312.21,NJ City,N,15,17,1,38,No education,31,Patnership,Standalone
4344,407.67,NJ City,W,10,17,13,59,High School,32,Franchise,Complex
533,390.97,NJ City,N,10,18,14,34,High School,18,Patnership,Standalone
3212,336.11,NJ City,W,17,12,20,59,High School,23,Franchise,Complex
2211,337.32,NJ City,E,1,15,3,42,Graduation,20,Owner,Standalone
4649,302.92,NJ City,N,13,12,16,63,High School,29,Patnership,Standalone
9698,15.98,NJ City,N,14,16,4,84,No education,21,Patnership,Complex
1655,480.3,NJ City,E,18,8,20,49,High School,22,Owner,Standalone
5394,26.45,Boston,N,20,18,4,52,Graduation,35,Patnership,Complex
1890,243.07,NJ City,S,21,8,23,71,High School,19,Patnership,Complex
785,424.61,NJ City,N,10,10,1,62,No education,28,Patnership,Standalone
452,298.39,NJ City,W,9,13,16,82,High School,18,Franchise,Complex
9557,311.15,NJ City,N,24,11,25,80,Graduation,34,Patnership,Standalone
5131,343.3,Boston,E,10,14,24,49,Graduation,21,Owner,Standalone
6035,499.88,Boston,E,22,11,2,74,No education,27,Owner,Standalone
4149,43.8,NJ City,N,15,18,1,61,Graduation,30,Patnership,Standalone
7271,327.63,Boston,E,25,13,21,40,Graduation,22,Owner,Standalone
5263,102.89,Boston,E,6,16,4,62,High School,35,Owner,Standalone
4288,201.89,NJ City,W,4,23,21,85,High School,35,Franchise,Complex
1682,333.76,NJ City,S,23,23,3,30,No education,24,Patnership,Complex
8928,253.05,NJ City,W,17,8,10,49,Graduation,32,Franchise,Complex
814,478.98,NJ City,S,13,22,25,46,High School,25,Patnership,Complex
7360,39.21,Boston,N,15,18,2,28,High School,30,Franchise,Complex
29,349.03,Boston,N,4,17,20,32,High School,23,Patnership,Standalone
5712,361.21,Boston,W,17,8,21,42,Graduation,22,Franchise,Complex
6411,405.54,Boston,E,1,20,19,25,Graduation,28,Owner,Standalone
3923,190.08,NJ City,E,12,21,21,67,High School,20,Owner,Standalone
4746,227.79,NJ City,S,8,10,21,78,High School,22,Patnership,Complex
5156,26.63,Boston,N,7,12,1,41,No education,19,Franchise,Complex
3481,292.16,NJ City,N,13,21,11,77,High School,21,Patnership,Standalone
7150,305.97,Boston,S,15,17,22,53,Graduation,26,Patnership,Complex
601,161.32,NJ City,N,24,10,2,82,High School,19,Patnership,Standalone
9614,263.38,NJ City,S,6,18,18,38,Graduation,29,Patnership,Complex
1359,0.3,NJ City,N,16,19,4,73,Graduation,33,Owner,Standalone
2490,259,NJ City,S,3,22,6,46,High School,18,Patnership,Complex
8330,48.85,NJ City,S,13,18,1,37,No education,27,Patnership,Complex
2197,124.59,NJ City,N,22,17,3,77,High School,31,Patnership,Standalone
104,196.29,NJ City,W,4,24,25,67,High School,21,Franchise,Complex
7370,383.6,Boston,S,3,12,16,55,Graduation,28,Patnership,Complex
8447,42.57,NJ City,N,23,15,1,38,No education,34,Owner,Standalone
1344,495.74,NJ City,W,6,23,19,49,High School,34,Franchise,Complex
3693,62.24,NJ City,N,8,19,2,81,Graduation,25,Patnership,Standalone
1018,71.84,NJ City,N,11,16,2,54,High School,33,Patnership,Complex
2605,13.65,NJ City,N,6,9,4,40,High School,20,Patnership,Standalone
3849,297.52,NJ City,N,10,17,17,77,High School,28,Patnership,Standalone
405,467.63,NJ City,N,18,16,15,28,High School,24,Patnership,Standalone
7553,8.06,Boston,N,12,21,2,81,No education,19,Patnership,Standalone
7482,13.72,Boston,N,11,21,3,49,Graduation,27,Patnership,Complex
8207,284.41,NJ City,E,13,15,5,34,No education,27,Owner,Standalone
3171,351.54,NJ City,E,19,20,21,72,High School,31,Owner,Standalone
688,402.29,NJ City,W,25,14,8,27,High School,24,Franchise,Complex
5020,139.89,Boston,W,25,11,9,79,Graduation,32,Franchise,Complex
4942,289.12,Boston,S,11,11,17,47,High School,18,Patnership,Complex
2113,463.62,NJ City,N,17,13,13,29,High School,21,Patnership,Standalone
9169,107.16,NJ City,N,9,15,4,48,High School,23,Patnership,Standalone
6212,118.9,Boston,W,5,24,1,61,No education,27,Franchise,Complex
3629,12.77,NJ City,N,20,13,4,31,No education,19,Patnership,Standalone
4826,358.19,NJ City,S,10,19,15,68,High School,28,Patnership,Complex
8689,435.57,NJ City,N,24,9,22,80,Graduation,18,Patnership,Standalone
7493,305.23,Boston,N,14,22,18,67,Graduation,30,Patnership,Standalone
1971,118,NJ City,E,1,12,17,68,High School,28,Owner,Standalone
5826,396.97,Boston,S,18,12,14,54,Graduation,25,Patnership,Complex
9480,414.59,NJ City,W,2,16,7,49,Graduation,20,Franchise,Complex
5005,204.97,Boston,N,19,8,3,83,High School,32,Patnership,Standalone
9285,0.86,NJ City,N,14,22,3,30,Graduation,29,Patnership,Standalone
7747,419.17,Boston,E,10,20,21,54,Graduation,19,Owner,Standalone
3400,225.99,NJ City,W,21,18,8,28,High School,30,Franchise,Complex
5129,380.47,Boston,N,14,8,25,42,Graduation,25,Patnership,Standalone
3387,367.35,NJ City,E,4,21,11,81,High School,24,Owner,Standalone
998,70.75,NJ City,N,11,9,2,82,No education,32,Patnership,Complex
7093,194.19,Boston,N,13,14,9,84,Graduation,32,Patnership,Standalone
7368,136.21,Boston,W,1,16,19,77,Graduation,31,Franchise,Complex
4230,37.89,NJ City,N,3,8,4,66,Graduation,30,Patnership,Complex
1626,163.63,NJ City,S,18,22,25,59,High School,33,Patnership,Complex
1122,314.96,NJ City,S,5,19,14,85,High School,22,Patnership,Complex
2742,81.05,NJ City,S,8,11,1,61,Graduation,29,Patnership,Complex
3209,302.48,NJ City,N,23,9,4,66,No education,26,Patnership,Standalone
1275,298.19,NJ City,E,10,12,6,36,High School,28,Owner,Standalone
9664,435.61,NJ City,W,2,11,9,63,Graduation,24,Franchise,Complex
47,94.1,Boston,E,6,16,2,43,No education,31,Owner,Standalone
202,50.09,NJ City,S,20,24,1,74,High School,32,Patnership,Complex
9066,143.72,NJ City,S,3,12,25,25,Graduation,31,Patnership,Complex
6187,263.65,Boston,E,8,21,4,33,High School,33,Owner,Standalone
4905,325,NJ City,N,21,12,6,49,High School,23,Patnership,Standalone
4210,316.46,NJ City,S,15,9,13,59,High School,25,Patnership,Complex
7359,95.93,Boston,E,16,11,3,84,Graduation,26,Owner,Standalone
8983,402.87,NJ City,E,25,18,11,78,Graduation,18,Owner,Standalone
7399,257.17,Boston,E,7,20,25,69,Graduation,20,Owner,Standalone
9821,64.34,NJ City,N,10,18,3,55,No education,35,Patnership,Standalone
7453,162.93,Boston,N,8,8,7,62,Graduation,26,Patnership,Standalone
4728,263.83,NJ City,W,13,16,11,42,High School,30,Franchise,Complex
9274,414.83,NJ City,S,23,14,15,66,Graduation,35,Patnership,Complex
2369,4.68,NJ City,N,14,22,3,51,No education,18,Patnership,Standalone
8192,126.48,NJ City,W,16,16,3,36,No education,32,Franchise,Complex
2625,40.33,NJ City,N,2,18,2,42,Graduation,27,Patnership,Standalone
6829,477.77,Boston,N,14,22,9,39,Graduation,33,Patnership,Standalone
8190,199.26,NJ City,S,6,10,13,57,Graduation,33,Patnership,Complex
7458,410.57,Boston,S,15,11,11,74,Graduation,21,Patnership,Complex
8263,484.48,NJ City,E,6,16,2,47,High School,19,Owner,Standalone
6132,276.31,Boston,W,17,24,10,59,Graduation,27,Franchise,Complex
8214,277.73,NJ City,S,4,11,15,35,Graduation,18,Patnership,Complex
626,123,NJ City,S,1,23,15,45,High School,27,Patnership,Complex
2654,472.16,NJ City,S,12,17,16,42,High School,35,Patnership,Complex
6832,486.14,Boston,W,8,9,10,67,Graduation,33,Franchise,Complex
1606,388.74,NJ City,S,9,12,13,46,High School,23,Patnership,Complex
1794,163.27,NJ City,S,13,19,25,25,High School,29,Patnership,Complex
3097,183.45,NJ City,N,18,23,5,32,High School,35,Patnership,Standalone
7753,36.91,Boston,N,16,18,2,42,High School,24,Patnership,Standalone
1197,314.67,NJ City,N,15,14,13,84,High School,23,Patnership,Standalone
9766,416,NJ City,S,9,23,22,36,Graduation,19,Patnership,Complex
3898,183.83,NJ City,S,13,22,15,30,High School,22,Patnership,Complex
4291,23.78,NJ City,N,21,20,2,53,High School,21,Owner,Standalone
6770,29.81,Boston,N,7,21,4,77,No education,22,Patnership,Complex
6302,4.99,Boston,N,18,11,1,80,No education,27,Patnership,Complex
5635,8.26,Boston,N,18,21,2,68,High School,31,Owner,Standalone
3074,473.93,NJ City,S,11,15,16,57,High School,19,Patnership,Complex
7400,256.82,Boston,W,2,20,9,62,Graduation,27,Franchise,Complex
503,31.38,NJ City,N,25,8,4,79,No education,32,Owner,Standalone
2693,179,NJ City,N,1,13,22,76,High School,20,Patnership,Standalone
9343,373.47,NJ City,E,2,22,15,58,Graduation,28,Owner,Standalone
5302,62.78,Boston,N,3,21,2,71,High School,23,Patnership,Complex
3768,425.43,NJ City,W,23,16,13,31,High School,24,Franchise,Complex
7942,161.77,Boston,S,13,13,1,56,High School,23,Patnership,Complex
811,453.14,NJ City,E,23,8,7,38,High School,23,Owner,Standalone
1187,196.21,NJ City,E,5,22,3,70,No education,22,Owner,Standalone
901,81.19,NJ City,N,24,17,3,60,High School,27,Patnership,Standalone
4684,11.69,NJ City,N,1,21,4,78,High School,35,Franchise,Complex
284,19.65,NJ City,N,9,11,2,68,No education,33,Franchise,Complex
4536,278,NJ City,W,1,14,20,31,High School,18,Franchise,Complex
5620,442.51,Boston,W,6,19,5,51,High School,18,Franchise,Complex
6143,219.34,Boston,E,23,23,2,74,No education,21,Owner,Standalone
2085,308.46,NJ City,N,25,8,6,81,High School,22,Patnership,Standalone
1478,125,NJ City,S,2,11,14,26,High School,31,Patnership,Complex
2273,91.63,NJ City,N,21,23,3,33,No education,18,Patnership,Standalone
4952,91.91,Boston,W,14,11,2,28,No education,25,Franchise,Complex


================================================
FILE: data/Prostate_Cancer.csv
================================================
id,diagnosis_result,radius,texture,perimeter,area,smoothness,compactness,symmetry,fractal_dimension
1,M,23,12,151,954,0.143,0.278,0.242,0.079
2,B,9,13,133,1326,0.143,0.079,0.181,0.057
3,M,21,27,130,1203,0.125,0.16,0.207,0.06
4,M,14,16,78,386,0.07,0.284,0.26,0.097
5,M,9,19,135,1297,0.141,0.133,0.181,0.059
6,B,25,25,83,477,0.128,0.17,0.209,0.076
7,M,16,26,120,1040,0.095,0.109,0.179,0.057
8,M,15,18,90,578,0.119,0.165,0.22,0.075
9,M,19,24,88,520,0.127,0.193,0.235,0.074
10,M,25,11,84,476,0.119,0.24,0.203,0.082
11,M,24,21,103,798,0.082,0.067,0.153,0.057
12,M,17,15,104,781,0.097,0.129,0.184,0.061
13,B,14,15,132,1123,0.097,0.246,0.24,0.078
14,M,12,22,104,783,0.084,0.1,0.185,0.053
15,M,12,13,94,578,0.113,0.229,0.207,0.077
16,M,22,19,97,659,0.114,0.16,0.23,0.071
17,M,10,16,95,685,0.099,0.072,0.159,0.059
18,M,15,14,108,799,0.117,0.202,0.216,0.074
19,M,20,14,130,1260,0.098,0.103,0.158,0.054
20,B,17,11,87,566,0.098,0.081,0.189,0.058
21,B,16,14,86,520,0.108,0.127,0.197,0.068
22,B,17,24,60,274,0.102,0.065,0.182,0.069
23,M,20,27,103,704,0.107,0.214,0.252,0.07
24,M,19,12,137,1404,0.094,0.102,0.177,0.053
25,M,9,13,110,905,0.112,0.146,0.2,0.063
26,M,19,27,116,913,0.119,0.228,0.304,0.074
27,M,10,24,97,645,0.105,0.187,0.225,0.069
28,M,16,24,122,1094,0.094,0.107,0.17,0.057
29,M,15,15,102,732,0.108,0.17,0.193,0.065
30,M,11,16,115,955,0.098,0.116,0.174,0.061
31,M,11,22,125,1088,0.106,0.189,0.218,0.062
32,M,23,26,78,441,0.111,0.152,0.23,0.078
33,M,20,18,113,899,0.12,0.15,0.225,0.064
34,M,11,21,128,1162,0.094,0.172,0.185,0.063
35,M,16,23,107,807,0.104,0.156,0.2,0.065
36,M,10,13,110,870,0.096,0.134,0.19,0.057
37,M,18,12,94,633,0.098,0.11,0.189,0.061
38,B,21,11,83,524,0.09,0.038,0.147,0.059
39,M,11,15,96,699,0.094,0.051,0.157,0.055
40,M,10,14,88,559,0.102,0.126,0.172,0.064
41,M,24,16,86,563,0.082,0.06,0.178,0.056
42,M,19,27,72,371,0.123,0.122,0.19,0.069
43,M,11,11,128,1104,0.091,0.219,0.231,0.063
44,M,15,21,87,545,0.104,0.144,0.197,0.068
45,M,10,15,85,532,0.097,0.105,0.175,0.062
46,M,18,11,124,1076,0.11,0.169,0.191,0.06
47,B,22,12,52,202,0.086,0.059,0.177,0.065
48,M,20,14,86,535,0.116,0.123,0.213,0.068
49,B,20,21,78,449,0.103,0.091,0.168,0.06
50,B,25,11,87,561,0.088,0.077,0.181,0.057
51,B,19,25,75,428,0.086,0.05,0.15,0.059
52,B,19,22,87,572,0.077,0.061,0.135,0.06
53,B,25,15,76,438,0.083,0.048,0.187,0.061
54,M,14,26,120,1033,0.115,0.149,0.209,0.063
55,M,18,25,97,713,0.091,0.071,0.162,0.057
56,B,18,13,73,409,0.095,0.055,0.192,0.059
57,M,10,19,126,1152,0.105,0.127,0.192,0.06
58,M,17,20,96,657,0.114,0.137,0.203,0.068
59,B,22,15,83,527,0.081,0.038,0.182,0.055
60,B,23,26,54,225,0.098,0.053,0.168,0.072
61,B,15,18,65,312,0.113,0.081,0.274,0.07
62,B,25,15,55,222,0.124,0.09,0.183,0.068
63,M,12,22,96,646,0.105,0.201,0.195,0.073
64,B,24,17,59,261,0.077,0.088,0.234,0.07
65,M,16,19,83,499,0.112,0.126,0.191,0.066
66,M,11,21,97,668,0.117,0.148,0.195,0.067
67,B,12,13,60,269,0.104,0.078,0.172,0.069
68,B,18,12,72,394,0.081,0.047,0.152,0.057
69,B,16,17,59,251,0.107,0.141,0.211,0.08
70,B,17,21,81,503,0.098,0.052,0.159,0.057
71,M,21,18,124,1130,0.09,0.103,0.158,0.055
72,B,9,26,59,244,0.098,0.153,0.19,0.09
73,M,21,12,114,929,0.107,0.183,0.193,0.065
74,M,22,25,90,584,0.101,0.128,0.166,0.066
75,B,18,13,79,471,0.092,0.068,0.172,0.059
76,M,21,18,104,818,0.092,0.084,0.18,0.054
77,B,10,17,88,559,0.129,0.105,0.24,0.066
78,M,11,21,120,1006,0.107,0.215,0.215,0.067
79,M,16,18,144,1245,0.129,0.345,0.291,0.081
80,B,22,16,83,506,0.099,0.095,0.172,0.06
81,B,10,18,74,402,0.11,0.094,0.184,0.07
82,B,17,21,86,520,0.108,0.154,0.194,0.069
83,M,10,15,172,1878,0.106,0.267,0.183,0.068
84,M,20,14,129,1132,0.122,0.179,0.163,0.072
85,B,25,21,77,443,0.097,0.072,0.208,0.06
86,M,14,13,121,1075,0.099,0.105,0.213,0.06
87,M,19,26,94,648,0.094,0.099,0.208,0.056
88,M,19,11,122,1076,0.09,0.121,0.195,0.056
89,B,11,11,80,466,0.088,0.094,0.193,0.064
90,B,12,23,96,652,0.113,0.134,0.212,0.063
91,B,23,27,95,663,0.09,0.086,0.169,0.059
92,M,10,12,100,728,0.092,0.104,0.172,0.061
93,B,14,14,85,552,0.074,0.051,0.139,0.053
94,B,10,17,87,555,0.102,0.082,0.164,0.057
95,M,22,26,100,706,0.104,0.155,0.186,0.063
96,M,23,16,132,1264,0.091,0.131,0.21,0.056
97,B,22,14,78,451,0.105,0.071,0.19,0.066
98,B,19,27,62,295,0.102,0.053,0.135,0.069
99,B,21,24,74,413,0.09,0.075,0.162,0.066
100,M,16,27,94,643,0.098,0.114,0.188,0.064


================================================
FILE: data/Rules_20.csv
================================================
"confidence","support"


================================================
FILE: data/Sales.csv
================================================
Month-Year,Sales
1/1/2003,141
2/1/2003,157
3/1/2003,185
4/1/2003,199
5/1/2003,203
6/1/2003,189
7/1/2003,207
8/1/2003,207
9/1/2003,171
10/1/2003,150
11/1/2003,138
12/1/2003,165
1/1/2004,145
2/1/2004,168
3/1/2004,197
4/1/2004,208
5/1/2004,210
6/1/2004,209
7/1/2004,238
8/1/2004,238
9/1/2004,199
10/1/2004,168
11/1/2004,152
12/1/2004,196
1/1/2005,183
2/1/2005,200
3/1/2005,249
4/1/2005,251
5/1/2005,289
6/1/2005,249
7/1/2005,279
8/1/2005,279
9/1/2005,232
10/1/2005,204
11/1/2005,194
12/1/2005,232
1/1/2006,215
2/1/2006,239
3/1/2006,270
4/1/2006,279
5/1/2006,307
6/1/2006,305
7/1/2006,322
8/1/2006,339
9/1/2006,263
10/1/2006,241
11/1/2006,229
12/1/2006,272
1/1/2007,247
2/1/2007,261
3/1/2007,330
4/1/2007,362
5/1/2007,385
6/1/2007,340
7/1/2007,370
8/1/2007,381
9/1/2007,299
10/1/2007,266
11/1/2007,239
12/1/2007,281
1/1/2008,257
2/1/2008,250
3/1/2008,329
4/1/2008,350
5/1/2008,393
6/1/2008,370
7/1/2008,423
8/1/2008,410
9/1/2008,326
10/1/2008,289
11/1/2008,270
12/1/2008,321
1/1/2009,305
2/1/2009,310
3/1/2009,374
4/1/2009,414
5/1/2009,454
6/1/2009,441
7/1/2009,510
8/1/2009,486
9/1/2009,393
10/1/2009,345
11/1/2009,315
12/1/2009,389
1/1/2010,358
2/1/2010,368
3/1/2010,444
4/1/2010,482
5/1/2010,534
6/1/2010,524
7/1/2010,578
8/1/2010,567
9/1/2010,447
10/1/2010,386
11/1/2010,360
12/1/2010,428
1/1/2011,397
2/1/2011,400
3/1/2011,498
4/1/2011,536
5/1/2011,596
6/1/2011,591
7/1/2011,651
8/1/2011,654
9/1/2011,509
10/1/2011,437
11/1/2011,406
12/1/2011,470
1/1/2012,428
2/1/2012,423
3/1/2012,507
4/1/2012,536
5/1/2012,610
6/1/2012,609
7/1/2012,687
8/1/2012,707
9/1/2012,509
10/1/2012,452
11/1/2012,412
12/1/2012,472
1/1/2013,454
2/1/2013,455
3/1/2013,568
4/1/2013,610
5/1/2013,706
6/1/2013,661
7/1/2013,767
8/1/2013,783
9/1/2013,583
10/1/2013,513
11/1/2013,481
12/1/2013,567
1/1/2014,525
2/1/2014,520
3/1/2014,587
4/1/2014,710
5/1/2014,793
6/1/2014,749
7/1/2014,871
8/1/2014,848
9/1/2014,640
10/1/2014,581
11/1/2014,519
12/1/2014,605


================================================
FILE: data/Sales_files/frameworks-95aff0b550d3fe338b645a4deebdcb1b.css
================================================
/*!
 * GitHub Light v0.4.1
 * Copyright (c) 2012 - 2017 GitHub, Inc.
 * Licensed under MIT (https://github.com/primer/github-syntax-theme-generator/blob/master/LICENSE)
 */.pl-c{color:#6a737d}.pl-c1,.pl-s .pl-v{color:#005cc5}.pl-e,.pl-en{color:#6f42c1}.pl-smi,.pl-s .pl-s1{color:#24292e}.pl-ent{color:#22863a}.pl-k{color:#d73a49}.pl-s,.pl-pds,.pl-s .pl-pse .pl-s1,.pl-sr,.pl-sr .pl-cce,.pl-sr .pl-sre,.pl-sr .pl-sra{color:#032f62}.pl-v,.pl-smw{color:#e36209}.pl-bu{color:#b31d28}.pl-ii{color:#fafbfc;background-color:#b31d28}.pl-c2{color:#fafbfc;background-color:#d73a49}.pl-c2::before{content:"^M"}.pl-sr .pl-cce{font-weight:bold;color:#22863a}.pl-ml{color:#735c0f}.pl-mh,.pl-mh .pl-en,.pl-ms{font-weight:bold;color:#005cc5}.pl-mi{font-style:italic;color:#24292e}.pl-mb{font-weight:bold;color:#24292e}.pl-md{color:#b31d28;background-color:#ffeef0}.pl-mi1{color:#22863a;background-color:#f0fff4}.pl-mc{color:#e36209;background-color:#ffebda}.pl-mi2{color:#f6f8fa;background-color:#005cc5}.pl-mdr{font-weight:bold;color:#6f42c1}.pl-ba{color:#586069}.pl-sg{color:#959da5}.pl-corl{text-decoration:underline;color:#032f62}.CodeMirror{font-family:monospace;height:300px;color:black;direction:ltr}.CodeMirror-lines{padding:4px 0}.CodeMirror pre{padding:0 4px}.CodeMirror-scrollbar-filler,.CodeMirror-gutter-filler{background-color:white}.CodeMirror-gutters{border-right:1px solid #ddd;background-color:#f7f7f7;white-space:nowrap}.CodeMirror-linenumber{padding:0 3px 0 5px;min-width:20px;text-align:right;color:#999;white-space:nowrap}.CodeMirror-guttermarker{color:black}.CodeMirror-guttermarker-subtle{color:#999}.CodeMirror-cursor{border-left:1px solid black;border-right:none;width:0}.CodeMirror div.CodeMirror-secondarycursor{border-left:1px solid silver}.cm-fat-cursor .CodeMirror-cursor{width:auto;border:0 !important;background:#7e7}.cm-fat-cursor div.CodeMirror-cursors{z-index:1}.cm-fat-cursor-mark{background-color:rgba(20,255,20,0.5);animation:blink 1.06s steps(1) infinite}.cm-animate-fat-cursor{width:auto;border:0;animation:blink 1.06s steps(1) infinite;background-color:#7e7}@keyframes blink{0%{}50%{background-color:transparent}100%{}}.cm-tab{display:inline-block;text-decoration:inherit}.CodeMirror-rulers{position:absolute;left:0;right:0;top:-50px;bottom:-20px;overflow:hidden}.CodeMirror-ruler{border-left:1px solid #ccc;top:0;bottom:0;position:absolute}.cm-s-default .cm-header{color:blue}.cm-s-default .cm-quote{color:#090}.cm-negative{color:#d44}.cm-positive{color:#292}.cm-header,.cm-strong{font-weight:bold}.cm-em{font-style:italic}.cm-link{text-decoration:underline}.cm-strikethrough{text-decoration:line-through}.cm-s-default .cm-keyword{color:#708}.cm-s-default .cm-atom{color:#219}.cm-s-default .cm-number{color:#164}.cm-s-default .cm-def{color:#00f}.cm-s-default .cm-variable-2{color:#05a}.cm-s-default .cm-variable-3,.cm-s-default .cm-type{color:#085}.cm-s-default .cm-comment{color:#a50}.cm-s-default .cm-string{color:#a11}.cm-s-default .cm-string-2{color:#f50}.cm-s-default .cm-meta{color:#555}.cm-s-default .cm-qualifier{color:#555}.cm-s-default .cm-builtin{color:#30a}.cm-s-default .cm-bracket{color:#997}.cm-s-default .cm-tag{color:#170}.cm-s-default .cm-attribute{color:#00c}.cm-s-default .cm-hr{color:#999}.cm-s-default .cm-link{color:#00c}.cm-s-default .cm-error{color:#f00}.cm-invalidchar{color:#f00}.CodeMirror-composing{border-bottom:2px solid}div.CodeMirror span.CodeMirror-matchingbracket{color:#0b0}div.CodeMirror span.CodeMirror-nonmatchingbracket{color:#a22}.CodeMirror-matchingtag{background:rgba(255,150,0,0.3)}.CodeMirror-activeline-background{background:#e8f2ff}.CodeMirror{position:relative;overflow:hidden;background:white}.CodeMirror-scroll{overflow:scroll !important;margin-bottom:-30px;margin-right:-30px;padding-bottom:30px;height:100%;outline:none;position:relative}.CodeMirror-sizer{position:relative;border-right:30px solid transparent}.CodeMirror-vscrollbar,.CodeMirror-hscrollbar,.CodeMirror-scrollbar-filler,.CodeMirror-gutter-filler{position:absolute;z-index:6;display:none}.CodeMirror-vscrollbar{right:0;top:0;overflow-x:hidden;overflow-y:scroll}.CodeMirror-hscrollbar{bottom:0;left:0;overflow-y:hidden;overflow-x:scroll}.CodeMirror-scrollbar-filler{right:0;bottom:0}.CodeMirror-gutter-filler{left:0;bottom:0}.CodeMirror-gutters{position:absolute;left:0;top:0;min-height:100%;z-index:3}.CodeMirror-gutter{white-space:normal;height:100%;display:inline-block;vertical-align:top;margin-bottom:-30px}.CodeMirror-gutter-wrapper{position:absolute;z-index:4;background:none !important;border:none !important}.CodeMirror-gutter-background{position:absolute;top:0;bottom:0;z-index:4}.CodeMirror-gutter-elt{position:absolute;cursor:default;z-index:4}.CodeMirror-gutter-wrapper ::-moz-selection{background-color:transparent}.CodeMirror-gutter-wrapper ::selection{background-color:transparent}.CodeMirror-gutter-wrapper ::-moz-selection{background-color:transparent}.CodeMirror-lines{cursor:text;min-height:1px}.CodeMirror pre{border-radius:0;border-width:0;background:transparent;font-family:inherit;font-size:inherit;margin:0;white-space:pre;word-wrap:normal;line-height:inherit;color:inherit;z-index:2;position:relative;overflow:visible;-webkit-tap-highlight-color:transparent;-webkit-font-variant-ligatures:contextual;font-variant-ligatures:contextual}.CodeMirror-wrap pre{word-wrap:break-word;white-space:pre-wrap;word-break:normal}.CodeMirror-linebackground{position:absolute;left:0;right:0;top:0;bottom:0;z-index:0}.CodeMirror-linewidget{position:relative;z-index:2;padding:0.1px}.CodeMirror-rtl pre{direction:rtl}.CodeMirror-code{outline:none}.CodeMirror-scroll,.CodeMirror-sizer,.CodeMirror-gutter,.CodeMirror-gutters,.CodeMirror-linenumber{box-sizing:content-box}.CodeMirror-measure{position:absolute;width:100%;height:0;overflow:hidden;visibility:hidden}.CodeMirror-cursor{position:absolute;pointer-events:none}.CodeMirror-measure pre{position:static}div.CodeMirror-cursors{visibility:hidden;position:relative;z-index:3}div.CodeMirror-dragcursors{visibility:visible}.CodeMirror-focused div.CodeMirror-cursors{visibility:visible}.CodeMirror-selected{background:#d9d9d9}.CodeMirror-focused .CodeMirror-selected{background:#d7d4f0}.CodeMirror-crosshair{cursor:crosshair}.CodeMirror-line::-moz-selection,.CodeMirror-line>span::-moz-selection,.CodeMirror-line>span>span::-moz-selection{background:#d7d4f0}.CodeMirror-line::selection,.CodeMirror-line>span::selection,.CodeMirror-line>span>span::selection{background:#d7d4f0}.CodeMirror-line::-moz-selection,.CodeMirror-line>span::-moz-selection,.CodeMirror-line>span>span::-moz-selection{background:#d7d4f0}.cm-searching{background-color:#ffa;background-color:rgba(255,255,0,0.4)}.cm-force-border{padding-right:.1px}@media print{.CodeMirror div.CodeMirror-cursors{visibility:hidden}}.cm-tab-wrap-hack:after{content:''}span.CodeMirror-selectedtext{background:none}.CodeMirror-dialog{position:absolute;left:0;right:0;background:inherit;z-index:15;padding:.1em .8em;overflow:hidden;color:inherit}.CodeMirror-dialog-top{border-bottom:1px solid #eee;top:0}.CodeMirror-dialog-bottom{border-top:1px solid #eee;bottom:0}.CodeMirror-dialog input{border:none;outline:none;background:transparent;width:20em;color:inherit;font-family:monospace}.CodeMirror-dialog button{font-size:70%}.CodeMirror-merge{position:relative;border:1px solid #ddd;white-space:pre}.CodeMirror-merge,.CodeMirror-merge .CodeMirror{height:350px}.CodeMirror-merge-2pane .CodeMirror-merge-pane{width:47%}.CodeMirror-merge-2pane .CodeMirror-merge-gap{width:6%}.CodeMirror-merge-3pane .CodeMirror-merge-pane{width:31%}.CodeMirror-merge-3pane .CodeMirror-merge-gap{width:3.5%}.CodeMirror-merge-pane{display:inline-block;white-space:normal;vertical-align:top}.CodeMirror-merge-pane-rightmost{position:absolute;right:0px;z-index:1}.CodeMirror-merge-gap{z-index:2;display:inline-block;height:100%;box-sizing:border-box;overflow:hidden;border-left:1px solid #ddd;border-right:1px solid #ddd;position:relative;background:#f8f8f8}.CodeMirror-merge-scrolllock-wrap{position:absolute;bottom:0;left:50%}.CodeMirror-merge-scrolllock{position:relative;left:-50%;cursor:pointer;color:#555;line-height:1}.CodeMirror-merge-scrolllock:after{content:"\21db\00a0\00a0\21da"}.CodeMirror-merge-scrolllock.CodeMirror-merge-scrolllock-enabled:after{content:"\21db\21da"}.CodeMirror-merge-copybuttons-left,.CodeMirror-merge-copybuttons-right{position:absolute;left:0;top:0;right:0;bottom:0;line-height:1}.CodeMirror-merge-copy{position:absolute;cursor:pointer;color:#44c;z-index:3}.CodeMirror-merge-copy-reverse{position:absolute;cursor:pointer;color:#44c}.CodeMirror-merge-copybuttons-left .CodeMirror-merge-copy{left:2px}.CodeMirror-merge-copybuttons-right .CodeMirror-merge-copy{right:2px}.CodeMirror-merge-r-inserted,.CodeMirror-merge-l-inserted{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAACCAYAAACddGYaAAAAGUlEQVQI12MwuCXy3+CWyH8GBgYGJgYkAABZbAQ9ELXurwAAAABJRU5ErkJggg==);background-position:bottom left;background-repeat:repeat-x}.CodeMirror-merge-r-deleted,.CodeMirror-merge-l-deleted{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAACCAYAAACddGYaAAAAGUlEQVQI12M4Kyb2/6yY2H8GBgYGJgYkAABURgPz6Ks7wQAAAABJRU5ErkJggg==);background-position:bottom left;background-repeat:repeat-x}.CodeMirror-merge-r-chunk{background:#ffffe0}.CodeMirror-merge-r-chunk-start{border-top:1px solid #ee8}.CodeMirror-merge-r-chunk-end{border-bottom:1px solid #ee8}.CodeMirror-merge-r-connect{fill:#ffffe0;stroke:#ee8;stroke-width:1px}.CodeMirror-merge-l-chunk{background:#eef}.CodeMirror-merge-l-chunk-start{border-top:1px solid #88e}.CodeMirror-merge-l-chunk-end{border-bottom:1px solid #88e}.CodeMirror-merge-l-connect{fill:#eef;stroke:#88e;stroke-width:1px}.CodeMirror-merge-l-chunk.CodeMirror-merge-r-chunk{background:#dfd}.CodeMirror-merge-l-chunk-start.CodeMirror-merge-r-chunk-start{border-top:1px solid #4e4}.CodeMirror-merge-l-chunk-end.CodeMirror-merge-r-chunk-end{border-bottom:1px solid #4e4}.CodeMirror-merge-collapsed-widget:before{content:"(...)"}.CodeMirror-merge-collapsed-widget{cursor:pointer;color:#88b;background:#eef;border:1px solid #ddf;font-size:90%;padding:0 3px;border-radius:4px}.CodeMirror-merge-collapsed-line .CodeMirror-gutter-elt{display:none}/*!
 * GitHub Light v0.4.2
 * Copyright (c) 2012 - 2017 GitHub, Inc.
 * Licensed under MIT (https://github.com/primer/github-syntax-theme-generator/blob/master/LICENSE)
 */.cm-s-github-light.CodeMirror{background:#fff;color:#24292e}.cm-s-github-light .CodeMirror-gutters{background:#fff;border-right-width:0}.cm-s-github-light .CodeMirror-guttermarker{color:white}.cm-s-github-light .CodeMirror-guttermarker-subtle{color:#d0d0d0}.cm-s-github-light .CodeMirror-linenumber{color:#959da5;padding:0 16px 0 16px}.cm-s-github-light .CodeMirror-cursor{border-left:1px solid #24292e}.cm-s-github-light div.CodeMirror-selected,.cm-s-github-light .CodeMirror-line::-moz-selection,.cm-s-github-light .CodeMirror-line>span::-moz-selection,.cm-s-github-light .CodeMirror-line>span>span::-moz-selection,.cm-s-github-light .CodeMirror-line::-moz-selection,.cm-s-github-light .CodeMirror-line>span::-moz-selection,.cm-s-github-light .CodeMirror-line>span>span::-moz-selection{background:#c8c8fa}.cm-s-github-light div.CodeMirror-selected,.cm-s-github-light .CodeMirror-line::selection,.cm-s-github-light .CodeMirror-line>span::selection,.cm-s-github-light .CodeMirror-line>span>span::selection,.cm-s-github-light .CodeMirror-line::-moz-selection,.cm-s-github-light .CodeMirror-line>span::-moz-selection,.cm-s-github-light .CodeMirror-line>span>span::-moz-selection{background:#c8c8fa}.cm-s-github-light .CodeMirror-activeline-background{background:#fafbfc}.cm-s-github-light .CodeMirror-matchingbracket{text-decoration:underline;color:#24292e !important}.cm-s-github-light .CodeMirror-lines{font-family:"SFMono-Regular", Consolas, "Liberation Mono", Menlo, Courier, monospace;font-size:12px;background:#fff;line-height:1.5}.cm-s-github-light .cm-comment{color:#6a737d}.cm-s-github-light .cm-constant{color:#005cc5}.cm-s-github-light .cm-entity{font-weight:normal;font-style:normal;text-decoration:none;color:#6f42c1}.cm-s-github-light .cm-keyword{font-weight:normal;font-style:normal;text-decoration:none;color:#d73a49}.cm-s-github-light .cm-storage{color:#d73a49}.cm-s-github-light .cm-string{font-weight:normal;font-style:normal;text-decoration:none;color:#032f62}.cm-s-github-light .cm-support{font-weight:normal;font-style:normal;text-decoration:none;color:#005cc5}.cm-s-github-light .cm-variable{font-weight:normal;font-style:normal;text-decoration:none;color:#e36209}details-dialog{position:fixed;margin:10vh auto;top:0;left:50%;transform:translateX(-50%);z-index:999;max-height:80vh;max-width:90vw;width:448px}.octicon{display:inline-block;vertical-align:text-top;fill:currentColor}body.intent-mouse [role="button"]:focus,body.intent-mouse button:focus,body.intent-mouse summary:focus{outline:none}.CodeMirror{height:calc(100vh - 1px)}.file-editor-textarea{width:100%;padding:5px 4px;font:12px "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;resize:vertical;border:0;border-radius:0;outline:none}.container-preview .tabnav-tabs{margin:-6px 0 -6px -11px}.container-preview .tabnav-tabs .tabnav-tab{padding:12px 15px;border-radius:0}.container-preview .tabnav-tabs>.selected:first-child{border-top-left-radius:3px}.container-preview .tabnav-tabs .selected{font-weight:600}.container-preview.template-editor .commit-create,.container-preview.template-editor .file-actions{display:block}.container-preview.template-editor .show-code,.container-preview.template-editor .commit-preview,.container-preview.template-editor .loading-preview-msg,.container-preview.template-editor .no-changes-preview-msg,.container-preview.template-editor .error-preview-msg{display:none}.container-preview.render-editor .commit-create,.container-preview.render-editor .file-actions{display:block}.container-preview.render-editor .template-editor,.container-preview.render-editor .show-code,.container-preview.render-editor .commit-preview,.container-preview.render-editor .loading-preview-msg,.container-preview.render-editor .no-changes-preview-msg,.container-preview.render-editor .error-preview-msg{display:none}.container-preview.show-code .commit-create,.container-preview.show-code .file-actions{display:block}.container-preview.show-code .template-editor,.container-preview.show-code .render-editor,.container-preview.show-code .commit-preview,.container-preview.show-code .loading-preview-msg,.container-preview.show-code .no-changes-preview-msg,.container-preview.show-code .error-preview-msg{display:none}.container-preview:not(.show-code) .commit-create,.container-preview:not(.show-code) .file-actions{display:none}.container-preview.loading-preview .loading-preview-msg{display:block}.container-preview.loading-preview .template-editor,.container-preview.loading-preview .render-editor,.container-preview.loading-preview .no-changes-preview-msg,.container-preview.loading-preview .error-preview-msg,.container-preview.loading-preview .commit-preview{display:none}.container-preview.show-preview .commit-preview{display:block}.container-preview.show-preview .template-editor,.container-preview.show-preview .render-editor,.container-preview.show-preview .loading-preview-msg,.container-preview.show-preview .no-changes-preview-msg,.container-preview.show-preview .error-preview-msg{display:none}.container-preview.no-changes-preview .no-changes-preview-msg{display:block}.container-preview.no-changes-preview .template-editor,.container-preview.no-changes-preview .render-editor,.container-preview.no-changes-preview .loading-preview-msg,.container-preview.no-changes-preview .error-preview-msg,.container-preview.no-changes-preview .commit-preview{display:none}.container-preview.error-preview .error-preview-msg{display:block}.container-preview.error-preview .template-editor,.container-preview.error-preview .render-editor,.container-preview.error-preview .loading-preview-msg,.container-preview.error-preview .no-changes-preview-msg,.container-preview.error-preview .commit-preview{display:none}.container-preview p.preview-msg{padding:30px;font-size:16px}.CodeMirror-merge-header{height:30px}.CodeMirror-merge-header .CodeMirror-merge-pane{height:30px;line-height:30px}.cm-s-github-light .merge-gutter{width:14px}.conflict-background+.CodeMirror-gutter-wrapper .CodeMirror-linenumber{background-color:#fffbdd}.conflict-gutter-marker{background-color:#fffbdd}.conflict-gutter-marker::after,.conflict-gutter-marker::before{position:absolute;left:-1px;content:"";background-color:#d73a49}.conflict-gutter-marker-start::after,.conflict-gutter-marker-end::after{width:1px;height:10px}.conflict-gutter-marker-start::before,.conflict-gutter-marker-middle::before,.conflict-gutter-marker-end::before{width:10px;height:1px}.conflict-gutter-marker-start::after{bottom:0}.conflict-gutter-marker-end::after{top:0}.conflict-gutter-marker-start::before{top:7px}.conflict-gutter-marker-end::before{bottom:7px}.conflict-gutter-marker-line::after,.conflict-gutter-marker-middle::after{width:1px;height:18px}.conflict-gutter-marker-middle::before{top:9px}.form-group .draggable-box{height:20px;opacity:0}.form-group .draggable-box:hover{cursor:move}.form-group .edit-action{opacity:0.6}.form-group .draggable-box-icon{margin-left:-4px}.form-group .form-field-hover{background-color:none;border:1px solid rgba(149,157,165,0)}.form-group:hover{cursor:pointer}.form-group:hover .draggable-box{opacity:0.2}.form-group:hover .edit-action{opacity:0.7}.form-group:hover .form-field-hover{border:1px solid #959da5}.placeholder-box{border:1px solid #959da5}.template-previews{max-width:768px}.template-previews .Box .expand-group{display:none;height:0}.template-previews .Box .dismiss-preview-button{display:none}.template-previews .Box.expand-preview .expand-group{display:block;height:100%;transition:height 3s}.template-previews .Box.expand-preview .preview-button{display:none}.template-previews .Box.expand-preview .dismiss-preview-button{display:inline}.additional-options{display:none}.edit-labels{display:none}.preview-section{display:block}.edit-section{display:none}.Box .section-focus .preview-section{display:none}.Box .section-focus .edit-section{display:block}.user-select-contain{-ms-user-select:element;-webkit-user-select:contain;-moz-user-select:contain;user-select:contain}.zeroclipboard-link{padding:0;margin:0;color:#0366d6;cursor:pointer;background:none;border:0}.zeroclipboard-link .octicon{display:block}image-crop{display:block}image-crop.nesw{cursor:nesw-resize}image-crop.nwse{cursor:nwse-resize}image-crop.nesw .crop-box,image-crop.nwse .crop-box{cursor:inherit}image-crop[loaded] .crop-image{display:block}image-crop[loaded] [data-loading-slot],image-crop .crop-image{display:none}image-crop .crop-wrapper{position:relative;font-size:0}image-crop .crop-container{user-select:none;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;position:absolute;overflow:hidden;z-index:1;top:0;width:100%;height:100%}image-crop .crop-box{position:absolute;border:1px dashed #fff;box-sizing:border-box;cursor:move}image-crop .crop-outline{position:absolute;top:0;bottom:0;left:0;right:0;outline:600px solid rgba(0,0,0,0.3)}image-crop .handle{position:absolute}image-crop .handle:before{position:absolute;display:block;padding:4px;transform:translate(-50%, -50%);content:' ';background:#fff;border:1px solid #767676}image-crop .ne{top:0;right:0;cursor:nesw-resize}image-crop .nw{top:0;left:0;cursor:nwse-resize}image-crop .se{bottom:0;right:0;cursor:nwse-resize}image-crop .sw{bottom:0;left:0;cursor:nesw-resize}/*!
 * Primer-core
 * http://primer.github.io
 *
 * Released under MIT license. Copyright (c) 2018 GitHub Inc.
 *//*! normalize.css v4.1.1 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,main,menu,nav,section{display:block}summary{display:list-item}audio,canvas,progress,video{display:inline-block}audio:not([controls]){display:none;height:0}progress{vertical-align:baseline}template,[hidden]{display:none}a{background-color:transparent}a:active,a:hover{outline-width:0}abbr[title]{border-bottom:none;text-decoration:underline;-webkit-text-decoration:underline dotted;text-decoration:underline dotted}b,strong{font-weight:inherit}b,strong{font-weight:bolder}dfn{font-style:italic}h1{font-size:2em;margin:0.67em 0}mark{background-color:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-0.25em}sup{top:-0.5em}img{border-style:none}svg:not(:root){overflow:hidden}code,kbd,pre,samp{font-family:monospace, monospace;font-size:1em}figure{margin:1em 40px}hr{box-sizing:content-box;height:0;overflow:visible}button,input,select,textarea{font:inherit;margin:0}optgroup{font-weight:bold}button,input{overflow:visible}button,select{text-transform:none}button,html [type="button"],[type="reset"],[type="submit"]{-webkit-appearance:button}button::-moz-focus-inner,[type="button"]::-moz-focus-inner,[type="reset"]::-moz-focus-inner,[type="submit"]::-moz-focus-inner{border-style:none;padding:0}button:-moz-focusring,[type="button"]:-moz-focusring,[type="reset"]:-moz-focusring,[type="submit"]:-moz-focusring{outline:1px dotted ButtonText}fieldset{border:1px solid #c0c0c0;margin:0 2px;padding:0.35em 0.625em 0.75em}legend{box-sizing:border-box;color:inherit;display:table;max-width:100%;padding:0;white-space:normal}textarea{overflow:auto}[type="checkbox"],[type="radio"]{box-sizing:border-box;padding:0}[type="number"]::-webkit-inner-spin-button,[type="number"]::-webkit-outer-spin-button{height:auto}[type="search"]{-webkit-appearance:textfield;outline-offset:-2px}[type="search"]::-webkit-search-cancel-button,[type="search"]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-input-placeholder{color:inherit;opacity:0.54}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}*{box-sizing:border-box}input,select,textarea,button{font-family:inherit;font-size:inherit;line-height:inherit}body{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-size:14px;line-height:1.5;color:#24292e;background-color:#fff}a{color:#0366d6;text-decoration:none}a:hover{text-decoration:underline}b,strong{font-weight:600}hr,.rule{height:0;margin:15px 0;overflow:hidden;background:transparent;border:0;border-bottom:1px solid #dfe2e5}hr::before,.rule::before{display:table;content:""}hr::after,.rule::after{display:table;clear:both;content:""}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}button{cursor:pointer;border-radius:0}details summary{cursor:pointer}details:not([open])>*:not(summary){display:none !important}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:0}h1{font-size:32px;font-weight:600}h2{font-size:24px;font-weight:600}h3{font-size:20px;font-weight:600}h4{font-size:16px;font-weight:600}h5{font-size:14px;font-weight:600}h6{font-size:12px;font-weight:600}p{margin-top:0;margin-bottom:10px}small{font-size:90%}blockquote{margin:0}ul,ol{padding-left:0;margin-top:0;margin-bottom:0}ol ol,ul ol{list-style-type:lower-roman}ul ul ol,ul ol ol,ol ul ol,ol ol ol{list-style-type:lower-alpha}dd{margin-left:0}tt,code{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px}pre{margin-top:0;margin-bottom:0;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px}.octicon{vertical-align:text-bottom}.Box{background-color:#fff;border:1px solid #d1d5da;border-radius:3px}.Box--condensed{line-height:1.25}.Box--condensed .Box-header{padding:8px 16px}.Box--condensed .Box-body{padding:8px 16px}.Box--condensed .Box-footer{padding:8px 16px}.Box--condensed .Box-btn-octicon.btn-octicon{padding:8px 16px;margin:-8px -16px;line-height:1.25}.Box--condensed .Box-row{padding:8px 16px}.Box--spacious .Box-header{padding:24px;line-height:1.25}.Box--spacious .Box-title{font-size:20px}.Box--spacious .Box-body{padding:24px}.Box--spacious .Box-footer{padding:24px}.Box--spacious .Box-btn-octicon.btn-octicon{padding:24px;margin:-24px -24px}.Box--spacious .Box-row{padding:24px}.Box-header{padding:16px;margin:-1px -1px 0 -1px;background-color:#f6f8fa;border-color:#d1d5da;border-style:solid;border-width:1px;border-top-left-radius:3px;border-top-right-radius:3px}.Box-title{font-size:14px;font-weight:600}.Box-body{padding:16px;border-bottom:1px solid #e1e4e8}.Box-body:last-of-type{margin-bottom:-1px;border-bottom-right-radius:2px;border-bottom-left-radius:2px}.Box-row{padding:16px;margin-top:-1px;list-style-type:none;border-top:1px solid #e1e4e8}.Box-row:first-of-type{border-top-color:transparent;border-top-left-radius:2px;border-top-right-radius:2px}.Box-row:last-of-type{border-bottom-right-radius:2px;border-bottom-left-radius:2px}.Box-row.Box-row--unread,.Box-row.unread{box-shadow:2px 0 0 #0366d6 inset}.Box-row.navigation-focus .Box-row--drag-button{color:#0366d6;cursor:-webkit-grab;cursor:grab;opacity:100}.Box-row.navigation-focus.is-dragging .Box-row--drag-button{cursor:-webkit-grabbing;cursor:grabbing}.Box-row.navigation-focus.sortable-chosen{background-color:#fafbfc}.Box-row.navigation-focus.sortable-ghost{background-color:#f6f8fa}.Box-row.navigation-focus.sortable-ghost .Box-row--drag-hide{opacity:0}.Box-row--focus-gray.navigation-focus{background-color:#f6f8fa}.Box-row--focus-blue.navigation-focus{background-color:#f1f8ff}.Box-row--hover-gray:hover{background-color:#f6f8fa}.Box-row--hover-blue:hover{background-color:#f1f8ff}@media (min-width: 768px){.Box-row-link{color:#24292e;text-decoration:none}.Box-row-link:hover{color:#0366d6;text-decoration:none}}.Box-row--drag-button{opacity:0}.Box-footer{padding:16px;margin-top:-1px;border-top:1px solid #e1e4e8}.Box--scrollable{max-height:324px;overflow:scroll}.Box--blue{border-color:#c8e1ff}.Box--blue .Box-header{background-color:#f1f8ff;border-color:#c8e1ff}.Box--blue .Box-body{border-color:#c8e1ff}.Box--blue .Box-row{border-color:#c8e1ff}.Box--blue .Box-footer{border-color:#c8e1ff}.Box--danger{border-color:#d73a49}.Box--danger .Box-row:first-of-type{border-color:#d73a49}.Box--danger .Box-body:last-of-type{border-color:#d73a49}.Box-header--blue{background-color:#f1f8ff;border-color:#c8e1ff}.Box-row--yellow{background-color:#fffbdd}.Box-row--blue{background-color:#f1f8ff}.Box-row--gray{background-color:#f6f8fa}.Box-btn-octicon.btn-octicon{padding:16px 16px;margin:-16px -16px;line-height:1.5}.breadcrumb-item{display:inline-block;margin-left:-0.35em;white-space:nowrap;list-style:none}.breadcrumb-item::after{padding-right:.5em;padding-left:.5em;color:#e1e4e8;content:"/"}.breadcrumb-item:first-child{margin-left:0}.breadcrumb-item-selected::after{content:none}.btn{position:relative;display:inline-block;padding:6px 12px;font-size:14px;font-weight:600;line-height:20px;white-space:nowrap;vertical-align:middle;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-repeat:repeat-x;background-position:-1px -1px;background-size:110% 110%;border:1px solid rgba(27,31,35,0.2);border-radius:0.25em;-webkit-appearance:none;-moz-appearance:none;appearance:none}.btn i{font-style:normal;font-weight:500;opacity:0.75}.btn .octicon{vertical-align:text-top}.btn .Counter{color:#586069;text-shadow:none;background-color:rgba(27,31,35,0.1)}.btn:hover{text-decoration:none;background-repeat:repeat-x}.btn:focus{outline:0}.btn:disabled,.btn.disabled{cursor:default;background-position:0 0}.btn:active,.btn.selected{background-image:none}.btn{color:#24292e;background-color:#eff3f6;background-image:linear-gradient(-180deg, #fafbfc 0%, #eff3f6 90%)}.btn:focus,.btn.focus{box-shadow:0 0 0 0.2em rgba(3,102,214,0.3)}.btn:hover,.btn.hover{background-color:#e6ebf1;background-image:linear-gradient(-180deg, #f0f3f6 0%, #e6ebf1 90%);background-position:-.5em;border-color:rgba(27,31,35,0.35)}.btn:active,.btn.selected,[open]>.btn{background-color:#e9ecef;background-image:none;border-color:rgba(27,31,35,0.35);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn:disabled,.btn.disabled{color:rgba(36,41,46,0.4);background-color:#eff3f6;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-primary{color:#fff;background-color:#28a745;background-image:linear-gradient(-180deg, #34d058 0%, #28a745 90%)}.btn-primary:focus,.btn-primary.focus{box-shadow:0 0 0 0.2em rgba(52,208,88,0.4)}.btn-primary:hover,.btn-primary.hover{background-color:#269f42;background-image:linear-gradient(-180deg, #2fcb53 0%, #269f42 90%);background-position:-.5em;border-color:rgba(27,31,35,0.5)}.btn-primary:active,.btn-primary.selected,[open]>.btn-primary{background-color:#279f43;background-image:none;border-color:rgba(27,31,35,0.5);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn-primary:disabled,.btn-primary.disabled{color:rgba(255,255,255,0.75);background-color:#94d3a2;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-primary .Counter{color:#29b249;background-color:#fff}.btn-purple{color:#fff;background-color:#643ab0;background-image:linear-gradient(-180deg, #7e55c7 0%, #643ab0 90%)}.btn-purple:focus,.btn-purple.focus{box-shadow:0 0 0 0.2em rgba(126,85,199,0.4)}.btn-purple:hover,.btn-purple.hover{background-color:#5f37a8;background-image:linear-gradient(-180deg, #784ec5 0%, #5f37a8 90%);background-position:-.5em;border-color:rgba(27,31,35,0.5)}.btn-purple:active,.btn-purple.selected,[open]>.btn-purple{background-color:#613ca4;background-image:none;border-color:rgba(27,31,35,0.5);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn-purple:disabled,.btn-purple.disabled{color:rgba(255,255,255,0.75);background-color:#b19cd7;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-purple .Counter{color:#683cb8;background-color:#fff}.btn-blue{color:#fff;background-color:#0361cc;background-image:linear-gradient(-180deg, #0679fc 0%, #0361cc 90%)}.btn-blue:focus,.btn-blue.focus{box-shadow:0 0 0 0.2em rgba(6,121,252,0.4)}.btn-blue:hover,.btn-blue.hover{background-color:#035cc2;background-image:linear-gradient(-180deg, #0374f4 0%, #035cc2 90%);background-position:-.5em;border-color:rgba(27,31,35,0.5)}.btn-blue:active,.btn-blue.selected,[open]>.btn-blue{background-color:#045cc1;background-image:none;border-color:rgba(27,31,35,0.5);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn-blue:disabled,.btn-blue.disabled{color:rgba(255,255,255,0.75);background-color:#81b0e5;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-blue .Counter{color:#0366d6;background-color:#fff}.btn-danger{color:#cb2431;background-color:#fafbfc;background-image:linear-gradient(-180deg, #fafbfc 0%, #eff3f6 90%)}.btn-danger:focus{box-shadow:0 0 0 0.2em rgba(203,36,49,0.4)}.btn-danger:hover{color:#fff;background-color:#cb2431;background-image:linear-gradient(-180deg, #de4450 0%, #cb2431 90%);border-color:rgba(27,31,35,0.5)}.btn-danger:hover .Counter{color:#fff}.btn-danger:active,.btn-danger.selected,[open]>.btn-danger{color:#fff;background-color:#b5202c;background-image:none;border-color:rgba(27,31,35,0.5);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn-danger:disabled,.btn-danger.disabled{color:rgba(203,36,49,0.4);background-color:#eff3f6;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-outline{color:#0366d6;background-color:#fff;background-image:none}.btn-outline .Counter{background-color:rgba(27,31,35,0.07)}.btn-outline:hover,.btn-outline:active,.btn-outline.selected,[open]>.btn-outline{color:#fff;background-color:#0366d6;background-image:none;border-color:#0366d6}.btn-outline:hover .Counter,.btn-outline:active .Counter,.btn-outline.selected .Counter,[open]>.btn-outline .Counter{color:#0366d6;background-color:#fff}.btn-outline:focus{border-color:#0366d6;box-shadow:0 0 0 0.2em rgba(3,102,214,0.4)}.btn-outline:disabled,.btn-outline.disabled{color:rgba(27,31,35,0.3);background-color:#fff;border-color:rgba(27,31,35,0.15);box-shadow:none}.btn-with-count{float:left;border-top-right-radius:0;border-bottom-right-radius:0}.btn-sm{padding:3px 10px;font-size:12px;line-height:20px}.btn-large{padding:.75em 1.25em;font-size:inherit;border-radius:6px}.hidden-text-expander{display:block}.hidden-text-expander.inline{position:relative;top:-1px;display:inline-block;margin-left:5px;line-height:0}.hidden-text-expander a,.ellipsis-expander{display:inline-block;height:12px;padding:0 5px 5px;font-size:12px;font-weight:600;line-height:6px;color:#444d56;text-decoration:none;vertical-align:middle;background:#dfe2e5;border:0;border-radius:1px}.hidden-text-expander a:hover,.ellipsis-expander:hover{text-decoration:none;background-color:#c6cbd1}.hidden-text-expander a:active,.ellipsis-expander:active{color:#fff;background-color:#2188ff}.social-count{float:left;padding:3px 10px;font-size:12px;font-weight:600;line-height:20px;color:#24292e;vertical-align:middle;background-color:#fff;border:1px solid rgba(27,31,35,0.2);border-left:0;border-top-right-radius:3px;border-bottom-right-radius:3px}.social-count:hover,.social-count:active{text-decoration:none}.social-count:hover{color:#0366d6;cursor:pointer}.btn-block{display:block;width:100%;text-align:center}.btn-link{display:inline-block;padding:0;font-size:inherit;color:#0366d6;text-decoration:none;white-space:nowrap;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-color:transparent;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none}.btn-link:hover{text-decoration:underline}.btn-link:disabled,.btn-link:disabled:hover{color:rgba(88,96,105,0.5);cursor:default}.details-reset>summary{list-style:none}.details-reset>summary::before{display:none}.details-reset>summary::-webkit-details-marker{display:none}.BtnGroup{display:inline-block;vertical-align:middle}.BtnGroup::before{display:table;content:""}.BtnGroup::after{display:table;clear:both;content:""}.BtnGroup+.BtnGroup,.BtnGroup+.btn{margin-left:5px}.BtnGroup-item{position:relative;float:left;border-right-width:0;border-radius:0}.BtnGroup-item:first-child{border-top-left-radius:3px;border-bottom-left-radius:3px}.BtnGroup-item:last-child{border-right-width:1px;border-top-right-radius:3px;border-bottom-right-radius:3px}.BtnGroup-item.selected,.BtnGroup-item:focus,.BtnGroup-item:active,.BtnGroup-item:hover{border-right-width:1px}.BtnGroup-item.selected+.BtnGroup-item,.BtnGroup-item.selected+.BtnGroup-parent .BtnGroup-item,.BtnGroup-item.selected+.BtnGroup-form .BtnGroup-item,.BtnGroup-item:focus+.BtnGroup-item,.BtnGroup-item:focus+.BtnGroup-parent .BtnGroup-item,.BtnGroup-item:focus+.BtnGroup-form .BtnGroup-item,.BtnGroup-item:active+.BtnGroup-item,.BtnGroup-item:active+.BtnGroup-parent .BtnGroup-item,.BtnGroup-item:active+.BtnGroup-form .BtnGroup-item,.BtnGroup-item:hover+.BtnGroup-item,.BtnGroup-item:hover+.BtnGroup-parent .BtnGroup-item,.BtnGroup-item:hover+.BtnGroup-form .BtnGroup-item{border-left-width:0}.BtnGroup-parent,.BtnGroup-form{float:left}.BtnGroup-parent:first-child .BtnGroup-item,.BtnGroup-form:first-child .BtnGroup-item{border-top-left-radius:3px;border-bottom-left-radius:3px}.BtnGroup-parent:last-child .BtnGroup-item,.BtnGroup-form:last-child .BtnGroup-item{border-right-width:1px;border-top-right-radius:3px;border-bottom-right-radius:3px}.BtnGroup-parent .BtnGroup-item,.BtnGroup-form .BtnGroup-item{border-right-width:0;border-radius:0}.BtnGroup-parent.selected .BtnGroup-item,.BtnGroup-parent:focus .BtnGroup-item,.BtnGroup-parent:active .BtnGroup-item,.BtnGroup-parent:hover .BtnGroup-item,.BtnGroup-form.selected .BtnGroup-item,.BtnGroup-form:focus .BtnGroup-item,.BtnGroup-form:active .BtnGroup-item,.BtnGroup-form:hover .BtnGroup-item{border-right-width:1px}.BtnGroup-parent.selected+.BtnGroup-item,.BtnGroup-parent.selected+.BtnGroup-parent .BtnGroup-item,.BtnGroup-parent.selected+.BtnGroup-form .BtnGroup-item,.BtnGroup-parent:focus+.BtnGroup-item,.BtnGroup-parent:focus+.BtnGroup-parent .BtnGroup-item,.BtnGroup-parent:focus+.BtnGroup-form .BtnGroup-item,.BtnGroup-parent:active+.BtnGroup-item,.BtnGroup-parent:active+.BtnGroup-parent .BtnGroup-item,.BtnGroup-parent:active+.BtnGroup-form .BtnGroup-item,.BtnGroup-parent:hover+.BtnGroup-item,.BtnGroup-parent:hover+.BtnGroup-parent .BtnGroup-item,.BtnGroup-parent:hover+.BtnGroup-form .BtnGroup-item,.BtnGroup-form.selected+.BtnGroup-item,.BtnGroup-form.selected+.BtnGroup-parent .BtnGroup-item,.BtnGroup-form.selected+.BtnGroup-form .BtnGroup-item,.BtnGroup-form:focus+.BtnGroup-item,.BtnGroup-form:focus+.BtnGroup-parent .BtnGroup-item,.BtnGroup-form:focus+.BtnGroup-form .BtnGroup-item,.BtnGroup-form:active+.BtnGroup-item,.BtnGroup-form:active+.BtnGroup-parent .BtnGroup-item,.BtnGroup-form:active+.BtnGroup-form .BtnGroup-item,.BtnGroup-form:hover+.BtnGroup-item,.BtnGroup-form:hover+.BtnGroup-parent .BtnGroup-item,.BtnGroup-form:hover+.BtnGroup-form .BtnGroup-item{border-left-width:0}.TableObject{display:table}.TableObject-item{display:table-cell;width:1%;white-space:nowrap;vertical-align:middle}.TableObject-item--primary{width:99%}fieldset{padding:0;margin:0;border:0}label{font-weight:600}.form-control,.form-select{min-height:34px;padding:6px 8px;font-size:16px;line-height:20px;color:#24292e;vertical-align:middle;background-color:#fff;background-repeat:no-repeat;background-position:right 8px center;border:1px solid #d1d5da;border-radius:3px;outline:none;box-shadow:inset 0 1px 2px rgba(27,31,35,0.075)}.form-control.focus,.form-control:focus,.form-select.focus,.form-select:focus{border-color:#2188ff;outline:none;box-shadow:inset 0 1px 2px rgba(27,31,35,0.075),0 0 0 0.2em rgba(3,102,214,0.3)}@media (min-width: 768px){.form-control,.form-select{font-size:14px}}.input-contrast{background-color:#fafbfc}.input-contrast:focus{background-color:#fff}:-ms-input-placeholder{color:#6a737d}::-ms-input-placeholder{color:#6a737d}::placeholder{color:#6a737d}.input-sm{min-height:28px;padding-top:3px;padding-bottom:3px;font-size:12px;line-height:20px}.input-lg{padding:4px 10px;font-size:16px}.input-block{display:block;width:100%}.input-monospace{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace}.input-hide-webkit-autofill::-webkit-contacts-auto-fill-button{position:absolute;right:0;display:none !important;pointer-events:none;visibility:hidden}.form-checkbox{padding-left:20px;margin:15px 0;vertical-align:middle}.form-checkbox label em.highlight{position:relative;left:-4px;padding:2px 4px;font-style:normal;background:#fffbdd;border-radius:3px}.form-checkbox input[type=checkbox],.form-checkbox input[type=radio]{float:left;margin:5px 0 0 -20px;vertical-align:middle}.form-checkbox .note{display:block;margin:0;font-size:12px;font-weight:400;color:#586069}.form-checkbox-details{display:none}.form-checkbox-details-trigger:checked ~ * .form-checkbox-details,.form-checkbox-details-trigger:checked ~ .form-checkbox-details{display:block}.hfields{margin:15px 0}.hfields::before{display:table;content:""}.hfields::after{display:table;clear:both;content:""}.hfields .form-group{float:left;margin:0 30px 0 0}.hfields .form-group dt label{display:inline-block;margin:5px 0 0;color:#586069}.hfields .form-group dt img{position:relative;top:-2px}.hfields .btn{float:left;margin:28px 25px 0 -20px}.hfields .form-select{margin-top:5px}input::-webkit-outer-spin-button,input::-webkit-inner-spin-button{margin:0;-webkit-appearance:none;appearance:none}.form-actions::before{display:table;content:""}.form-actions::after{display:table;clear:both;content:""}.form-actions .btn{float:right}.form-actions .btn+.btn{margin-right:5px}.form-warning{padding:8px 10px;margin:10px 0;font-size:14px;color:#735c0f;background:#fffbdd;border:1px solid #d9d0a5;border-radius:3px}.form-warning p{margin:0;line-height:1.5}.form-warning a{font-weight:600}.form-select{display:inline-block;max-width:100%;height:34px;padding-right:24px;padding-right:8px \9;background:#fff url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAUCAMAAACzvE1FAAAADFBMVEUzMzMzMzMzMzMzMzMKAG/3AAAAA3RSTlMAf4C/aSLHAAAAPElEQVR42q3NMQ4AIAgEQTn//2cLdRKppSGzBYwzVXvznNWs8C58CiussPJj8h6NwgorrKRdTvuV9v16Afn0AYFOB7aYAAAAAElFTkSuQmCC") no-repeat right 8px center;background-image:none \9;background-size:8px 10px;-webkit-appearance:none;-moz-appearance:none;appearance:none}.form-select::-ms-expand{opacity:0}.form-select[multiple]{height:auto}.select-sm{height:28px;min-height:28px;padding-top:3px;padding-bottom:3px;font-size:12px}.select-sm[multiple]{height:auto;min-height:0}.form-group{margin:15px 0}.form-group .form-control{width:440px;max-width:100%;margin-right:5px;background-color:#fafbfc}.form-group .form-control:focus{background-color:#fff}.form-group .form-control.shorter{width:130px}.form-group .form-control.short{width:250px}.form-group .form-control.long{width:100%}.form-group textarea.form-control{width:100%;height:200px;min-height:200px}.form-group textarea.form-control.short{height:50px;min-height:50px}.form-group dt{margin:0 0 6px}.form-group label{position:relative}.form-group.flattened dt{float:left;margin:0;line-height:32px}.form-group.flattened dd{line-height:32px}.form-group dd h4{margin:4px 0 0}.form-group dd h4.is-error{color:#cb2431}.form-group dd h4.is-success{color:#28a745}.form-group dd h4+.note{margin-top:0}.form-group.required dt label::after{padding-left:5px;color:#cb2431;content:"*"}.form-group .success,.form-group .error,.form-group .indicator{display:none;font-size:12px;font-weight:600}.form-group.loading{opacity:0.5}.form-group.loading .indicator{display:inline}.form-group.loading .spinner{display:inline-block;vertical-align:middle}.form-group.successful .success{display:inline;color:#28a745}.form-group.warn .warning,.form-group.warn .error,.form-group.errored .warning,.form-group.errored .error{position:absolute;z-index:10;display:block;max-width:450px;padding:5px 8px;margin:4px 0 0;font-size:13px;font-weight:400;border-style:solid;border-width:1px;border-radius:3px}.form-group.warn .warning::after,.form-group.warn .warning::before,.form-group.warn .error::after,.form-group.warn .error::before,.form-group.errored .warning::after,.form-group.errored .warning::before,.form-group.errored .error::after,.form-group.errored .error::before{position:absolute;bottom:100%;left:10px;z-index:15;width:0;height:0;pointer-events:none;content:" ";border:solid transparent}.form-group.warn .warning::after,.form-group.warn .error::after,.form-group.errored .warning::after,.form-group.errored .error::after{border-width:5px}.form-group.warn .warning::before,.form-group.warn .error::before,.form-group.errored .warning::before,.form-group.errored .error::before{margin-left:-1px;border-width:6px}.form-group.warn .warning{color:#735c0f;background-color:#fffbdd;border-color:#d9d0a5}.form-group.warn .warning::after{border-bottom-color:#fffbdd}.form-group.warn .warning::before{border-bottom-color:#d9d0a5}.form-group.errored label{color:#cb2431}.form-group.errored .error{color:#86181d;background-color:#ffdce0;border-color:#cea0a5}.form-group.errored .error::after{border-bottom-color:#ffdce0}.form-group.errored .error::before{border-bottom-color:#cea0a5}.note{min-height:17px;margin:4px 0 2px;font-size:12px;color:#586069}.note .spinner{margin-right:3px;vertical-align:middle}dl.form-group>dd .form-control.is-autocheck-loading,dl.form-group>dd .form-control.is-autocheck-successful,dl.form-group>dd .form-control.is-autocheck-errored{padding-right:30px}dl.form-group>dd .form-control.is-autocheck-loading{background-image:url("/images/spinners/octocat-spinner-16px.gif")}dl.form-group>dd .form-control.is-autocheck-successful{background-image:url("/images/modules/ajax/success.png")}dl.form-group>dd .form-control.is-autocheck-errored{background-image:url("/images/modules/ajax/error.png")}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){dl.form-group>dd .form-control.is-autocheck-loading,dl.form-group>dd .form-control.is-autocheck-successful,dl.form-group>dd .form-control.is-autocheck-errored{background-size:16px 16px}dl.form-group>dd .form-control.is-autocheck-loading{background-image:url("/images/spinners/octocat-spinner-32.gif")}dl.form-group>dd .form-control.is-autocheck-successful{background-image:url("/images/modules/ajax/success@2x.png")}dl.form-group>dd .form-control.is-autocheck-errored{background-image:url("/images/modules/ajax/error@2x.png")}}.status-indicator{display:inline-block;width:16px;height:16px;margin-left:5px}.status-indicator .octicon{display:none}.status-indicator-success::before{content:""}.status-indicator-success .octicon-check{display:inline-block;color:#28a745;fill:#28a745}.status-indicator-success .octicon-x{display:none}.status-indicator-failed::before{content:""}.status-indicator-failed .octicon-check{display:none}.status-indicator-failed .octicon-x{display:inline-block;color:#cb2431;fill:#d73a49}.status-indicator-loading{width:16px;background:url("/images/spinners/octocat-spinner-32-EAF2F5.gif") 0 0 no-repeat;background-size:16px}.inline-form{display:inline-block}.inline-form .btn-plain{background-color:transparent;border:0}.drag-and-drop{padding:7px 10px;margin:0;font-size:13px;line-height:16px;color:#586069;background-color:#fafbfc;border:1px solid #c3c8cf;border-top:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.drag-and-drop .default,.drag-and-drop .loading,.drag-and-drop .error{display:none}.drag-and-drop .error{color:#cb2431}.drag-and-drop img{vertical-align:top}.is-default .drag-and-drop .default{display:inline-block}.is-uploading .drag-and-drop .loading{display:inline-block}.is-bad-file .drag-and-drop .bad-file{display:inline-block}.is-duplicate-filename .drag-and-drop .duplicate-filename{display:inline-block}.is-too-big .drag-and-drop .too-big{display:inline-block}.is-hidden-file .drag-and-drop .hidden-file{display:inline-block}.is-empty .drag-and-drop .empty{display:inline-block}.is-bad-permissions .drag-and-drop .bad-permissions{display:inline-block}.is-repository-required .drag-and-drop .repository-required{display:inline-block}.drag-and-drop-error-info{font-weight:400;color:#586069}.drag-and-drop-error-info a{color:#0366d6}.is-failed .drag-and-drop .failed-request{display:inline-block}.manual-file-chooser{position:absolute;width:240px;padding:5px;margin-left:-80px;cursor:pointer;opacity:0.0001}.manual-file-chooser:hover+.manual-file-chooser-text{text-decoration:underline}.btn .manual-file-chooser{top:0;padding:0;line-height:34px}.upload-enabled textarea{display:block;border-bottom:1px dashed #dfe2e5;border-bottom-right-radius:0;border-bottom-left-radius:0}.upload-enabled.focused{border-radius:3px;box-shadow:inset 0 1px 2px rgba(27,31,35,0.075),0 0 0 0.2em rgba(3,102,214,0.3)}.upload-enabled.focused .form-control{box-shadow:none}.upload-enabled.focused .drag-and-drop{border-color:#4a9eff}.dragover textarea,.dragover .drag-and-drop{box-shadow:#c9ff00 0 0 3px}.write-content{position:relative}.previewable-comment-form{position:relative}.previewable-comment-form .tabnav{position:relative;padding:8px 8px 0}.previewable-comment-form .comment{border:1px solid #c3c8cf}.previewable-comment-form .comment-form-error{margin-bottom:8px}.previewable-comment-form .write-content,.previewable-comment-form .preview-content{display:none;margin:0 8px 8px}.previewable-comment-form.write-selected .write-content,.previewable-comment-form.preview-selected .preview-content{display:block}.previewable-comment-form textarea{display:block;width:100%;min-height:100px;max-height:500px;padding:8px;resize:vertical}.form-action-spacious{margin-top:10px}div.composer{margin-top:0;border:0}.composer .comment-form-textarea{height:200px;min-height:200px}.composer .tabnav{margin:0 0 10px}h2.account{margin:15px 0 0;font-size:18px;font-weight:400;color:#586069}p.explain{position:relative;font-size:12px;color:#586069}p.explain strong{color:#24292e}p.explain .octicon{margin-right:5px;color:#959da5}p.explain .minibutton{top:-4px;float:right}.form-group label{position:static}.input-group{display:table}.input-group .form-control{position:relative;width:100%}.input-group .form-control:focus{z-index:2}.input-group .form-control+.btn{margin-left:0}.input-group.inline{display:inline-table}.input-group .form-control,.input-group-button{display:table-cell}.input-group-button{width:1%;vertical-align:middle}.input-group .form-control:first-child,.input-group-button:first-child .btn{border-top-right-radius:0;border-bottom-right-radius:0}.input-group-button:first-child .btn{margin-right:-1px}.input-group .form-control:last-child,.input-group-button:last-child .btn{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-button:last-child .btn{margin-left:-1px}.container{width:980px;margin-right:auto;margin-left:auto}.container::before{display:table;content:""}.container::after{display:table;clear:both;content:""}.container-md{max-width:768px;margin-right:auto;margin-left:auto}.container-lg{max-width:1012px;margin-right:auto;margin-left:auto}.container-xl{max-width:1280px;margin-right:auto;margin-left:auto}.columns{margin-right:-10px;margin-left:-10px}.columns::before{display:table;content:""}.columns::after{display:table;clear:both;content:""}.column{float:left;padding-right:10px;padding-left:10px}.one-third{width:33.333333%}.two-thirds{width:66.666667%}.one-fourth{width:25%}.one-half{width:50%}.three-fourths{width:75%}.one-fifth{width:20%}.four-fifths{width:80%}.centered{display:block;float:none;margin-right:auto;margin-left:auto}.col-1{width:8.33333%}.col-2{width:16.66667%}.col-3{width:25%}.col-4{width:33.33333%}.col-5{width:41.66667%}.col-6{width:50%}.col-7{width:58.33333%}.col-8{width:66.66667%}.col-9{width:75%}.col-10{width:83.33333%}.col-11{width:91.66667%}.col-12{width:100%}@media (min-width: 544px){.col-sm-1{width:8.33333%}.col-sm-2{width:16.66667%}.col-sm-3{width:25%}.col-sm-4{width:33.33333%}.col-sm-5{width:41.66667%}.col-sm-6{width:50%}.col-sm-7{width:58.33333%}.col-sm-8{width:66.66667%}.col-sm-9{width:75%}.col-sm-10{width:83.33333%}.col-sm-11{width:91.66667%}.col-sm-12{width:100%}}@media (min-width: 768px){.col-md-1{width:8.33333%}.col-md-2{width:16.66667%}.col-md-3{width:25%}.col-md-4{width:33.33333%}.col-md-5{width:41.66667%}.col-md-6{width:50%}.col-md-7{width:58.33333%}.col-md-8{width:66.66667%}.col-md-9{width:75%}.col-md-10{width:83.33333%}.col-md-11{width:91.66667%}.col-md-12{width:100%}}@media (min-width: 1012px){.col-lg-1{width:8.33333%}.col-lg-2{width:16.66667%}.col-lg-3{width:25%}.col-lg-4{width:33.33333%}.col-lg-5{width:41.66667%}.col-lg-6{width:50%}.col-lg-7{width:58.33333%}.col-lg-8{width:66.66667%}.col-lg-9{width:75%}.col-lg-10{width:83.33333%}.col-lg-11{width:91.66667%}.col-lg-12{width:100%}}@media (min-width: 1280px){.col-xl-1{width:8.33333%}.col-xl-2{width:16.66667%}.col-xl-3{width:25%}.col-xl-4{width:33.33333%}.col-xl-5{width:41.66667%}.col-xl-6{width:50%}.col-xl-7{width:58.33333%}.col-xl-8{width:66.66667%}.col-xl-9{width:75%}.col-xl-10{width:83.33333%}.col-xl-11{width:91.66667%}.col-xl-12{width:100%}}.gutter{margin-right:-16px;margin-left:-16px}.gutter>[class*="col-"]{padding-right:16px !important;padding-left:16px !important}.gutter-condensed{margin-right:-8px;margin-left:-8px}.gutter-condensed>[class*="col-"]{padding-right:8px !important;padding-left:8px !important}.gutter-spacious{margin-right:-24px;margin-left:-24px}.gutter-spacious>[class*="col-"]{padding-right:24px !important;padding-left:24px !important}@media (min-width: 544px){.gutter-sm{margin-right:-16px;margin-left:-16px}.gutter-sm>[class*="col-"]{padding-right:16px !important;padding-left:16px !important}.gutter-sm-condensed{margin-right:-8px;margin-left:-8px}.gutter-sm-condensed>[class*="col-"]{padding-right:8px !important;padding-left:8px !important}.gutter-sm-spacious{margin-right:-24px;margin-left:-24px}.gutter-sm-spacious>[class*="col-"]{padding-right:24px !important;padding-left:24px !important}}@media (min-width: 768px){.gutter-md{margin-right:-16px;margin-left:-16px}.gutter-md>[class*="col-"]{padding-right:16px !important;padding-left:16px !important}.gutter-md-condensed{margin-right:-8px;margin-left:-8px}.gutter-md-condensed>[class*="col-"]{padding-right:8px !important;padding-left:8px !important}.gutter-md-spacious{margin-right:-24px;margin-left:-24px}.gutter-md-spacious>[class*="col-"]{padding-right:24px !important;padding-left:24px !important}}@media (min-width: 1012px){.gutter-lg{margin-right:-16px;margin-left:-16px}.gutter-lg>[class*="col-"]{padding-right:16px !important;padding-left:16px !important}.gutter-lg-condensed{margin-right:-8px;margin-left:-8px}.gutter-lg-condensed>[class*="col-"]{padding-right:8px !important;padding-left:8px !important}.gutter-lg-spacious{margin-right:-24px;margin-left:-24px}.gutter-lg-spacious>[class*="col-"]{padding-right:24px !important;padding-left:24px !important}}@media (min-width: 1280px){.gutter-xl{margin-right:-16px;margin-left:-16px}.gutter-xl>[class*="col-"]{padding-right:16px !important;padding-left:16px !important}.gutter-xl-condensed{margin-right:-8px;margin-left:-8px}.gutter-xl-condensed>[class*="col-"]{padding-right:8px !important;padding-left:8px !important}.gutter-xl-spacious{margin-right:-24px;margin-left:-24px}.gutter-xl-spacious>[class*="col-"]{padding-right:24px !important;padding-left:24px !important}}.offset-1{margin-left:8.33333%}.offset-2{margin-left:16.66667%}.offset-3{margin-left:25%}.offset-4{margin-left:33.33333%}.offset-5{margin-left:41.66667%}.offset-6{margin-left:50%}.offset-7{margin-left:58.33333%}.offset-8{margin-left:66.66667%}.offset-9{margin-left:75%}.offset-10{margin-left:83.33333%}.offset-11{margin-left:91.66667%}@media (min-width: 544px){.offset-sm-1{margin-left:8.33333%}.offset-sm-2{margin-left:16.66667%}.offset-sm-3{margin-left:25%}.offset-sm-4{margin-left:33.33333%}.offset-sm-5{margin-left:41.66667%}.offset-sm-6{margin-left:50%}.offset-sm-7{margin-left:58.33333%}.offset-sm-8{margin-left:66.66667%}.offset-sm-9{margin-left:75%}.offset-sm-10{margin-left:83.33333%}.offset-sm-11{margin-left:91.66667%}}@media (min-width: 768px){.offset-md-1{margin-left:8.33333%}.offset-md-2{margin-left:16.66667%}.offset-md-3{margin-left:25%}.offset-md-4{margin-left:33.33333%}.offset-md-5{margin-left:41.66667%}.offset-md-6{margin-left:50%}.offset-md-7{margin-left:58.33333%}.offset-md-8{margin-left:66.66667%}.offset-md-9{margin-left:75%}.offset-md-10{margin-left:83.33333%}.offset-md-11{margin-left:91.66667%}}@media (min-width: 1012px){.offset-lg-1{margin-left:8.33333%}.offset-lg-2{margin-left:16.66667%}.offset-lg-3{margin-left:25%}.offset-lg-4{margin-left:33.33333%}.offset-lg-5{margin-left:41.66667%}.offset-lg-6{margin-left:50%}.offset-lg-7{margin-left:58.33333%}.offset-lg-8{margin-left:66.66667%}.offset-lg-9{margin-left:75%}.offset-lg-10{margin-left:83.33333%}.offset-lg-11{margin-left:91.66667%}}@media (min-width: 1280px){.offset-xl-1{margin-left:8.33333%}.offset-xl-2{margin-left:16.66667%}.offset-xl-3{margin-left:25%}.offset-xl-4{margin-left:33.33333%}.offset-xl-5{margin-left:41.66667%}.offset-xl-6{margin-left:50%}.offset-xl-7{margin-left:58.33333%}.offset-xl-8{margin-left:66.66667%}.offset-xl-9{margin-left:75%}.offset-xl-10{margin-left:83.33333%}.offset-xl-11{margin-left:91.66667%}}.menu{margin-bottom:15px;list-style:none;background-color:#fff;border:1px solid #d1d5da;border-radius:3px}.menu-item{position:relative;display:block;padding:8px 10px;border-bottom:1px solid #e1e4e8}.menu-item:first-child{border-top:0;border-top-left-radius:2px;border-top-right-radius:2px}.menu-item:first-child::before{border-top-left-radius:2px}.menu-item:last-child{border-bottom:0;border-bottom-right-radius:2px;border-bottom-left-radius:2px}.menu-item:last-child::before{border-bottom-left-radius:2px}.menu-item:hover{text-decoration:none;background-color:#f6f8fa}.menu-item.selected{font-weight:600;color:#24292e;cursor:default;background-color:#fff}.menu-item.selected::before{position:absolute;top:0;bottom:0;left:0;width:2px;content:"";background-color:#e36209}.menu-item .octicon{width:16px;margin-right:5px;color:#24292e;text-align:center}.menu-item .Counter{float:right;margin-left:5px}.menu-item .menu-warning{float:right;color:#86181d}.menu-item .avatar{float:left;margin-right:5px}.menu-item.alert .Counter{color:#cb2431}.menu-heading{display:block;padding:8px 10px;margin-top:0;margin-bottom:0;font-size:13px;font-weight:600;line-height:20px;color:#586069;background-color:#f3f5f8;border-bottom:1px solid #e1e4e8}.menu-heading:hover{text-decoration:none}.menu-heading:first-child{border-top-left-radius:2px;border-top-right-radius:2px}.menu-heading:last-child{border-bottom:0;border-bottom-right-radius:2px;border-bottom-left-radius:2px}.tabnav{margin-top:0;margin-bottom:15px;border-bottom:1px solid #d1d5da}.tabnav .Counter{margin-left:5px}.tabnav-tabs{margin-bottom:-1px}.tabnav-tab{display:inline-block;padding:8px 12px;font-size:14px;line-height:20px;color:#586069;text-decoration:none;background-color:transparent;border:1px solid transparent;border-bottom:0}.tabnav-tab.selected{color:#24292e;background-color:#fff;border-color:#d1d5da;border-radius:3px 3px 0 0}.tabnav-tab:hover,.tabnav-tab:focus{color:#24292e;text-decoration:none}.tabnav-extra{display:inline-block;padding-top:10px;margin-left:10px;font-size:12px;color:#586069}.tabnav-extra>.octicon{margin-right:2px}a.tabnav-extra:hover{color:#0366d6;text-decoration:none}.tabnav-btn{margin-left:10px}.filter-list{list-style-type:none}.filter-list.small .filter-item{padding:4px 10px;margin:0 0 2px;font-size:12px}.filter-list.pjax-active .filter-item{color:#586069;background-color:transparent}.filter-list.pjax-active .filter-item.pjax-active{color:#fff;background-color:#0366d6}.filter-item{position:relative;display:block;padding:8px 10px;margin-bottom:5px;overflow:hidden;font-size:14px;color:#586069;text-decoration:none;text-overflow:ellipsis;white-space:nowrap;cursor:pointer;border-radius:3px}.filter-item:hover{text-decoration:none;background-color:#eaecef}.filter-item.selected{color:#fff;background-color:#0366d6}.filter-item .count{float:right;font-weight:600}.filter-item .bar{position:absolute;top:2px;right:0;bottom:2px;z-index:-1;display:inline-block;background-color:#eff3f6}.subnav{margin-bottom:20px}.subnav::before{display:table;content:""}.subnav::after{display:table;clear:both;content:""}.subnav-bordered{padding-bottom:20px;border-bottom:1px solid #eaecef}.subnav-flush{margin-bottom:0}.subnav-item{position:relative;float:left;padding:6px 14px;font-weight:600;line-height:20px;color:#586069;border:1px solid #e1e4e8}.subnav-item+.subnav-item{margin-left:-1px}.subnav-item:hover,.subnav-item:focus{text-decoration:none;background-color:#f6f8fa}.subnav-item.selected,.subnav-item.selected:hover,.subnav-item.selected:focus{z-index:2;color:#fff;background-color:#0366d6;border-color:#0366d6}.subnav-item:first-child{border-top-left-radius:3px;border-bottom-left-radius:3px}.subnav-item:last-child{border-top-right-radius:3px;border-bottom-right-radius:3px}.subnav-search{position:relative;margin-left:10px}.subnav-search-input{width:320px;padding-left:30px;color:#586069}.subnav-search-input-wide{width:500px}.subnav-search-icon{position:absolute;top:9px;left:8px;display:block;color:#c6cbd1;text-align:center;pointer-events:none}.subnav-search-context .btn{color:#444d56;border-top-right-radius:0;border-bottom-right-radius:0}.subnav-search-context .btn:hover,.subnav-search-context .btn:focus,.subnav-search-context .btn:active,.subnav-search-context .btn.selected{z-index:2}.subnav-search-context+.subnav-search{margin-left:-1px}.subnav-search-context+.subnav-search .subnav-search-input{border-top-left-radius:0;border-bottom-left-radius:0}.subnav-search-context .select-menu-modal-holder{z-index:30}.subnav-search-context .select-menu-modal{width:220px}.subnav-search-context .select-menu-item-icon{color:inherit}.subnav-spacer-right{padding-right:10px}.UnderlineNav{display:flex;justify-content:space-between;border-bottom:1px solid #e1e4e8}.UnderlineNav-body{display:flex;margin-bottom:-1px}.UnderlineNav-item{padding:16px 8px;margin-right:16px;font-size:14px;line-height:1.5;color:#586069;text-align:center;border-bottom:2px solid transparent}.UnderlineNav-item:hover,.UnderlineNav-item:focus{color:#24292e;text-decoration:none;border-bottom-color:#d1d5da;transition:0.2s ease}.UnderlineNav-item:hover .UnderlineNav-octicon,.UnderlineNav-item:focus .UnderlineNav-octicon{color:#6a737d}.UnderlineNav-item.selected{font-weight:600;color:#24292e;border-bottom-color:#e36209}.UnderlineNav-item.selected .UnderlineNav-octicon{color:#6a737d}.UnderlineNav--right{justify-content:flex-end}.UnderlineNav--right .UnderlineNav-item{margin-right:0;margin-left:16px}.UnderlineNav--right .UnderlineNav-actions{flex:1 1 auto}.UnderlineNav-actions{align-self:center}.UnderlineNav--full{display:block}.UnderlineNav-octicon{color:#959da5}.UnderlineNav-container{display:flex;justify-content:space-between}.pagination::before{display:table;content:""}.pagination::after{display:table;clear:both;content:""}.pagination a,.pagination span,.pagination em{position:relative;float:left;padding:7px 12px;margin-left:-1px;font-size:13px;font-style:normal;font-weight:600;color:#0366d6;white-space:nowrap;vertical-align:middle;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background:#fff;border:1px solid #e1e4e8}.pagination a:first-child,.pagination span:first-child,.pagination em:first-child{margin-left:0;border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination a:last-child,.pagination span:last-child,.pagination em:last-child{border-top-right-radius:3px;border-bottom-right-radius:3px}.pagination a:hover,.pagination a:focus,.pagination span:hover,.pagination span:focus,.pagination em:hover,.pagination em:focus{z-index:2;text-decoration:none;background-color:#eff3f6;border-color:#e1e4e8}.pagination .selected{z-index:3}.pagination .current,.pagination .current:hover{z-index:3;color:#fff;background-color:#0366d6;border-color:#0366d6}.pagination .gap,.pagination .disabled,.pagination .gap:hover,.pagination .disabled:hover{color:#d1d5da;cursor:default;background-color:#fafbfc}.paginate-container{margin-top:20px;margin-bottom:15px;text-align:center}.paginate-container .pagination{display:inline-block}.tooltipped{position:relative}.tooltipped::after{position:absolute;z-index:1000000;display:none;padding:.5em .75em;font:normal normal 11px/1.5 -apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";-webkit-font-smoothing:subpixel-antialiased;color:#fff;text-align:center;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-wrap:break-word;white-space:pre;pointer-events:none;content:attr(aria-label);background:#1b1f23;border-radius:3px;opacity:0}.tooltipped::before{position:absolute;z-index:1000001;display:none;width:0;height:0;color:#1b1f23;pointer-events:none;content:"";border:6px solid transparent;opacity:0}@keyframes tooltip-appear{from{opacity:0}to{opacity:1}}.tooltipped:hover::before,.tooltipped:hover::after,.tooltipped:active::before,.tooltipped:active::after,.tooltipped:focus::before,.tooltipped:focus::after{display:inline-block;text-decoration:none;animation-name:tooltip-appear;animation-duration:.1s;animation-fill-mode:forwards;animation-timing-function:ease-in;animation-delay:.4s}.tooltipped-no-delay:hover::before,.tooltipped-no-delay:hover::after,.tooltipped-no-delay:active::before,.tooltipped-no-delay:active::after,.tooltipped-no-delay:focus::before,.tooltipped-no-delay:focus::after{animation-delay:0s}.tooltipped-multiline:hover::after,.tooltipped-multiline:active::after,.tooltipped-multiline:focus::after{display:table-cell}.tooltipped-s::after,.tooltipped-se::after,.tooltipped-sw::after{top:100%;right:50%;margin-top:6px}.tooltipped-s::before,.tooltipped-se::before,.tooltipped-sw::before{top:auto;right:50%;bottom:-7px;margin-right:-6px;border-bottom-color:#1b1f23}.tooltipped-se::after{right:auto;left:50%;margin-left:-16px}.tooltipped-sw::after{margin-right:-16px}.tooltipped-n::after,.tooltipped-ne::after,.tooltipped-nw::after{right:50%;bottom:100%;margin-bottom:6px}.tooltipped-n::before,.tooltipped-ne::before,.tooltipped-nw::before{top:-7px;right:50%;bottom:auto;margin-right:-6px;border-top-color:#1b1f23}.tooltipped-ne::after{right:auto;left:50%;margin-left:-16px}.tooltipped-nw::after{margin-right:-16px}.tooltipped-s::after,.tooltipped-n::after{transform:translateX(50%)}.tooltipped-w::after{right:100%;bottom:50%;margin-right:6px;transform:translateY(50%)}.tooltipped-w::before{top:50%;bottom:50%;left:-7px;margin-top:-6px;border-left-color:#1b1f23}.tooltipped-e::after{bottom:50%;left:100%;margin-left:6px;transform:translateY(50%)}.tooltipped-e::before{top:50%;right:-7px;bottom:50%;margin-top:-6px;border-right-color:#1b1f23}.tooltipped-align-right-1::after,.tooltipped-align-right-2::after{right:0;margin-right:0}.tooltipped-align-right-1::before{right:10px}.tooltipped-align-right-2::before{right:15px}.tooltipped-align-left-1::after,.tooltipped-align-left-2::after{left:0;margin-left:0}.tooltipped-align-left-1::before{left:5px}.tooltipped-align-left-2::before{left:10px}.tooltipped-multiline::after{width:-webkit-max-content;width:-moz-max-content;width:max-content;max-width:250px;word-wrap:break-word;white-space:pre-line;border-collapse:separate}.tooltipped-multiline.tooltipped-s::after,.tooltipped-multiline.tooltipped-n::after{right:auto;left:50%;transform:translateX(-50%)}.tooltipped-multiline.tooltipped-w::after,.tooltipped-multiline.tooltipped-e::after{right:100%}@media screen and (min-width: 0\0){.tooltipped-multiline::after{width:250px}}.tooltipped-sticky::before,.tooltipped-sticky::after{display:inline-block}.tooltipped-sticky.tooltipped-multiline::after{display:table-cell}.css-truncate.css-truncate-target,.css-truncate .css-truncate-target{display:inline-block;max-width:125px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;vertical-align:top}.css-truncate.expandable.zeroclipboard-is-hover .css-truncate-target,.css-truncate.expandable.zeroclipboard-is-hover.css-truncate-target,.css-truncate.expandable:hover .css-truncate-target,.css-truncate.expandable:hover.css-truncate-target{max-width:10000px !important}.anim-fade-in{animation-name:fade-in;animation-duration:1s;animation-timing-function:ease-in-out}.anim-fade-in.fast{animation-duration:300ms}@keyframes fade-in{0%{opacity:0}100%{opacity:1}}.anim-fade-out{animation-name:fade-out;animation-duration:1s;animation-timing-function:ease-out}.anim-fade-out.fast{animation-duration:0.3s}@keyframes fade-out{0%{opacity:1}100%{opacity:0}}.anim-fade-up{opacity:0;animation-name:fade-up;animation-duration:0.3s;animation-fill-mode:forwards;animation-timing-function:ease-out;animation-delay:1s}@keyframes fade-up{0%{opacity:0.8;transform:translateY(100%)}100%{opacity:1;transform:translateY(0)}}.anim-fade-down{animation-name:fade-down;animation-duration:0.3s;animation-fill-mode:forwards;animation-timing-function:ease-in}@keyframes fade-down{0%{opacity:1;transform:translateY(0)}100%{opacity:0.5;transform:translateY(100%)}}.anim-grow-x{width:0%;animation-name:grow-x;animation-duration:0.3s;animation-fill-mode:forwards;animation-timing-function:ease;animation-delay:0.5s}@keyframes grow-x{to{width:100%}}.anim-shrink-x{animation-name:shrink-x;animation-duration:0.3s;animation-fill-mode:forwards;animation-timing-function:ease-in-out;animation-delay:0.5s}@keyframes shrink-x{to{width:0%}}.anim-scale-in{animation-name:scale-in;animation-duration:0.15s;animation-timing-function:cubic-bezier(0.2, 0, 0.13, 1.5)}@keyframes scale-in{0%{opacity:0;transform:scale(0.5)}100%{opacity:1;transform:scale(1)}}.anim-pulse{animation-name:pulse;animation-duration:2s;animation-timing-function:linear;animation-iteration-count:infinite}@keyframes pulse{0%{opacity:0.3}10%{opacity:1}100%{opacity:0.3}}.anim-pulse-in{animation-name:pulse-in;animation-duration:0.5s}@keyframes pulse-in{0%{transform:scale3d(1, 1, 1)}50%{transform:scale3d(1.1, 1.1, 1.1)}100%{transform:scale3d(1, 1, 1)}}.hover-grow{transition:transform 0.3s;-webkit-backface-visibility:hidden;backface-visibility:hidden}.hover-grow:hover{transform:scale(1.025)}.border{border:1px #e1e4e8 solid !important}.border-top{border-top:1px #e1e4e8 solid !important}.border-right{border-right:1px #e1e4e8 solid !important}.border-bottom{border-bottom:1px #e1e4e8 solid !important}.border-left{border-left:1px #e1e4e8 solid !important}.border-y{border-top:1px #e1e4e8 solid !important;border-bottom:1px #e1e4e8 solid !important}.border-dashed{border-style:dashed !important}.border-blue{border-color:#0366d6 !important}.border-blue-light{border-color:#c8e1ff !important}.border-green{border-color:#34d058 !important}.border-green-light{border-color:#a2cbac !important}.border-red{border-color:#d73a49 !important}.border-red-light{border-color:#cea0a5 !important}.border-purple{border-color:#6f42c1 !important}.border-yellow{border-color:#d9d0a5 !important}.border-gray-light{border-color:#eaecef !important}.border-gray-dark{border-color:#d1d5da !important}.border-black-fade{border-color:rgba(27,31,35,0.15) !important}.border-0{border:0 !important}.border-top-0{border-top:0 !important}.border-right-0{border-right:0 !important}.border-bottom-0{border-bottom:0 !important}.border-left-0{border-left:0 !important}.rounded-0{border-radius:0 !important}.rounded-1{border-radius:3px !important}.rounded-2{border-radius:6px !important}.circle{border-radius:50% !important}.box-shadow{box-shadow:0 1px 1px rgba(27,31,35,0.1) !important}.box-shadow-medium{box-shadow:0 1px 5px rgba(27,31,35,0.15) !important}.box-shadow-large{box-shadow:0 1px 15px rgba(27,31,35,0.15) !important}.box-shadow-extra-large{box-shadow:0 10px 50px rgba(27,31,35,0.07) !important}.box-shadow-none{box-shadow:none !important}.bg-white{background-color:#fff !important}.bg-blue{background-color:#0366d6 !important}.bg-blue-light{background-color:#f1f8ff !important}.bg-gray-dark{background-color:#24292e !important}.bg-gray{background-color:#f6f8fa !important}.bg-gray-light{background-color:#fafbfc !important}.bg-green{background-color:#28a745 !important}.bg-green-light{background-color:#dcffe4 !important}.bg-red{background-color:#d73a49 !important}.bg-red-light{background-color:#ffdce0 !important}.bg-yellow{background-color:#ffd33d !important}.bg-yellow-light{background-color:#fff5b1 !important}.bg-purple{background-color:#6f42c1 !important}.bg-purple-light{background-color:#f5f0ff !important}.bg-shade-gradient{background-image:linear-gradient(180deg, rgba(27,31,35,0.065), rgba(27,31,35,0)) !important;background-repeat:no-repeat !important;background-size:100% 200px !important}.text-blue{color:#0366d6 !important}.text-red{color:#cb2431 !important}.text-gray-light{color:#6a737d !important}.text-gray{color:#586069 !important}.text-gray-dark{color:#24292e !important}.text-green{color:#28a745 !important}.text-orange{color:#a04100 !important}.text-orange-light{color:#e36209 !important}.text-purple{color:#6f42c1 !important}.text-white{color:#fff !important}.text-inherit{color:inherit !important}.text-pending{color:#b08800 !important}.bg-pending{color:#dbab09 !important}.link-gray{color:#586069 !important}.link-gray:hover{color:#0366d6 !important}.link-gray-dark{color:#24292e !important}.link-gray-dark:hover{color:#0366d6 !important}.link-hover-blue:hover{color:#0366d6 !important}.muted-link{color:#586069 !important}.muted-link:hover{color:#0366d6 !important;text-decoration:none}.details-overlay[open]>summary::before{position:fixed;top:0;right:0;bottom:0;left:0;z-index:80;display:block;cursor:default;content:" ";background:transparent}.details-overlay-dark[open]>summary::before{z-index:99;background:rgba(27,31,35,0.5)}.flex-row{flex-direction:row !important}.flex-row-reverse{flex-direction:row-reverse !important}.flex-column{flex-direction:column !important}.flex-wrap{flex-wrap:wrap !important}.flex-nowrap{flex-wrap:nowrap !important}.flex-justify-start{justify-content:flex-start !important}.flex-justify-end{justify-content:flex-end !important}.flex-justify-center{justify-content:center !important}.flex-justify-between{justify-content:space-between !important}.flex-justify-around{justify-content:space-around !important}.flex-items-start{align-items:flex-start !important}.flex-items-end{align-items:flex-end !important}.flex-items-center{align-items:center !important}.flex-items-baseline{align-items:baseline !important}.flex-items-stretch{align-items:stretch !important}.flex-content-start{align-content:flex-start !important}.flex-content-end{align-content:flex-end !important}.flex-content-center{align-content:center !important}.flex-content-between{align-content:space-between !important}.flex-content-around{align-content:space-around !important}.flex-content-stretch{align-content:stretch !important}.flex-auto{flex:1 1 auto !important}.flex-shrink-0{flex-shrink:0 !important}.flex-self-auto{align-self:auto !important}.flex-self-start{align-self:flex-start !important}.flex-self-end{align-self:flex-end !important}.flex-self-center{align-self:center !important}.flex-self-baseline{align-self:baseline !important}.flex-self-stretch{align-self:stretch !important}.flex-item-equal{flex-grow:1;flex-basis:0}@media (min-width: 544px){.flex-sm-row{flex-direction:row !important}.flex-sm-row-reverse{flex-direction:row-reverse !important}.flex-sm-column{flex-direction:column !important}.flex-sm-wrap{flex-wrap:wrap !important}.flex-sm-nowrap{flex-wrap:nowrap !important}.flex-sm-justify-start{justify-content:flex-start !important}.flex-sm-justify-end{justify-content:flex-end !important}.flex-sm-justify-center{justify-content:center !important}.flex-sm-justify-between{justify-content:space-between !important}.flex-sm-justify-around{justify-content:space-around !important}.flex-sm-items-start{align-items:flex-start !important}.flex-sm-items-end{align-items:flex-end !important}.flex-sm-items-center{align-items:center !important}.flex-sm-items-baseline{align-items:baseline !important}.flex-sm-items-stretch{align-items:stretch !important}.flex-sm-content-start{align-content:flex-start !important}.flex-sm-content-end{align-content:flex-end !important}.flex-sm-content-center{align-content:center !important}.flex-sm-content-between{align-content:space-between !important}.flex-sm-content-around{align-content:space-around !important}.flex-sm-content-stretch{align-content:stretch !important}.flex-sm-auto{flex:1 1 auto !important}.flex-sm-shrink-0{flex-shrink:0 !important}.flex-sm-self-auto{align-self:auto !important}.flex-sm-self-start{align-self:flex-start !important}.flex-sm-self-end{align-self:flex-end !important}.flex-sm-self-center{align-self:center !important}.flex-sm-self-baseline{align-self:baseline !important}.flex-sm-self-stretch{align-self:stretch !important}.flex-sm-item-equal{flex-grow:1;flex-basis:0}}@media (min-width: 768px){.flex-md-row{flex-direction:row !important}.flex-md-row-reverse{flex-direction:row-reverse !important}.flex-md-column{flex-direction:column !important}.flex-md-wrap{flex-wrap:wrap !important}.flex-md-nowrap{flex-wrap:nowrap !important}.flex-md-justify-start{justify-content:flex-start !important}.flex-md-justify-end{justify-content:flex-end !important}.flex-md-justify-center{justify-content:center !important}.flex-md-justify-between{justify-content:space-between !important}.flex-md-justify-around{justify-content:space-around !important}.flex-md-items-start{align-items:flex-start !important}.flex-md-items-end{align-items:flex-end !important}.flex-md-items-center{align-items:center !important}.flex-md-items-baseline{align-items:baseline !important}.flex-md-items-stretch{align-items:stretch !important}.flex-md-content-start{align-content:flex-start !important}.flex-md-content-end{align-content:flex-end !important}.flex-md-content-center{align-content:center !important}.flex-md-content-between{align-content:space-between !important}.flex-md-content-around{align-content:space-around !important}.flex-md-content-stretch{align-content:stretch !important}.flex-md-auto{flex:1 1 auto !important}.flex-md-shrink-0{flex-shrink:0 !important}.flex-md-self-auto{align-self:auto !important}.flex-md-self-start{align-self:flex-start !important}.flex-md-self-end{align-self:flex-end !important}.flex-md-self-center{align-self:center !important}.flex-md-self-baseline{align-self:baseline !important}.flex-md-self-stretch{align-self:stretch !important}.flex-md-item-equal{flex-grow:1;flex-basis:0}}@media (min-width: 1012px){.flex-lg-row{flex-direction:row !important}.flex-lg-row-reverse{flex-direction:row-reverse !important}.flex-lg-column{flex-direction:column !important}.flex-lg-wrap{flex-wrap:wrap !important}.flex-lg-nowrap{flex-wrap:nowrap !important}.flex-lg-justify-start{justify-content:flex-start !important}.flex-lg-justify-end{justify-content:flex-end !important}.flex-lg-justify-center{justify-content:center !important}.flex-lg-justify-between{justify-content:space-between !important}.flex-lg-justify-around{justify-content:space-around !important}.flex-lg-items-start{align-items:flex-start !important}.flex-lg-items-end{align-items:flex-end !important}.flex-lg-items-center{align-items:center !important}.flex-lg-items-baseline{align-items:baseline !important}.flex-lg-items-stretch{align-items:stretch !important}.flex-lg-content-start{align-content:flex-start !important}.flex-lg-content-end{align-content:flex-end !important}.flex-lg-content-center{align-content:center !important}.flex-lg-content-between{align-content:space-between !important}.flex-lg-content-around{align-content:space-around !important}.flex-lg-content-stretch{align-content:stretch !important}.flex-lg-auto{flex:1 1 auto !important}.flex-lg-shrink-0{flex-shrink:0 !important}.flex-lg-self-auto{align-self:auto !important}.flex-lg-self-start{align-self:flex-start !important}.flex-lg-self-end{align-self:flex-end !important}.flex-lg-self-center{align-self:center !important}.flex-lg-self-baseline{align-self:baseline !important}.flex-lg-self-stretch{align-self:stretch !important}.flex-lg-item-equal{flex-grow:1;flex-basis:0}}@media (min-width: 1280px){.flex-xl-row{flex-direction:row !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column{flex-direction:column !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-justify-start{justify-content:flex-start !important}.flex-xl-justify-end{justify-content:flex-end !important}.flex-xl-justify-center{justify-content:center !important}.flex-xl-justify-between{justify-content:space-between !important}.flex-xl-justify-around{justify-content:space-around !important}.flex-xl-items-start{align-items:flex-start !important}.flex-xl-items-end{align-items:flex-end !important}.flex-xl-items-center{align-items:center !important}.flex-xl-items-baseline{align-items:baseline !important}.flex-xl-items-stretch{align-items:stretch !important}.flex-xl-content-start{align-content:flex-start !important}.flex-xl-content-end{align-content:flex-end !important}.flex-xl-content-center{align-content:center !important}.flex-xl-content-between{align-content:space-between !important}.flex-xl-content-around{align-content:space-around !important}.flex-xl-content-stretch{align-content:stretch !important}.flex-xl-auto{flex:1 1 auto !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-self-auto{align-self:auto !important}.flex-xl-self-start{align-self:flex-start !important}.flex-xl-self-end{align-self:flex-end !important}.flex-xl-self-center{align-self:center !important}.flex-xl-self-baseline{align-self:baseline !important}.flex-xl-self-stretch{align-self:stretch !important}.flex-xl-item-equal{flex-grow:1;flex-basis:0}}.position-static{position:static !important}.position-relative{position:relative !important}.position-absolute{position:absolute !important}.position-fixed{position:fixed !important}.top-0{top:0 !important}.right-0{right:0 !important}.bottom-0{bottom:0 !important}.left-0{left:0 !important}.v-align-middle{vertical-align:middle !important}.v-align-top{vertical-align:top !important}.v-align-bottom{vertical-align:bottom !important}.v-align-text-top{vertical-align:text-top !important}.v-align-text-bottom{vertical-align:text-bottom !important}.v-align-baseline{vertical-align:baseline !important}.overflow-hidden{overflow:hidden !important}.overflow-scroll{overflow:scroll !important}.overflow-auto{overflow:auto !important}.clearfix::before{display:table;content:""}.clearfix::after{display:table;clear:both;content:""}.float-right{float:right !important}.float-left{float:left !important}.float-none{float:none !important}@media (min-width: 544px){.float-sm-left{float:left !important}.float-sm-right{float:right !important}.float-sm-none{float:none !important}}@media (min-width: 768px){.float-md-left{float:left !important}.float-md-right{float:right !important}.float-md-none{float:none !important}}@media (min-width: 1012px){.float-lg-left{float:left !important}.float-lg-right{float:right !important}.float-lg-none{float:none !important}}@media (min-width: 1280px){.float-xl-left{float:left !important}.float-xl-right{float:right !important}.float-xl-none{float:none !important}}.width-fit{max-width:100% !important}.width-full{width:100% !important}.height-fit{max-height:100% !important}.height-full{height:100% !important}.min-width-0{min-width:0 !important}.direction-rtl{direction:rtl !important}.direction-ltr{direction:ltr !important}@media (min-width: 544px){.direction-sm-rtl{direction:rtl !important}.direction-sm-ltr{direction:ltr !important}}@media (min-width: 768px){.direction-md-rtl{direction:rtl !important}.direction-md-ltr{direction:ltr !important}}@media (min-width: 1012px){.direction-lg-rtl{direction:rtl !important}.direction-lg-ltr{direction:ltr !important}}@media (min-width: 1280px){.direction-xl-rtl{direction:rtl !important}.direction-xl-ltr{direction:ltr !important}}.m-0{margin:0 !important}.mt-0{margin-top:0 !important}.mr-0{margin-right:0 !important}.mb-0{margin-bottom:0 !important}.ml-0{margin-left:0 !important}.mt-n0{margin-top:-0 !important}.mr-n0{margin-right:-0 !important}.mb-n0{margin-bottom:-0 !important}.ml-n0{margin-left:-0 !important}.mx-0{margin-right:0 !important;margin-left:0 !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.m-1{margin:4px !important}.mt-1{margin-top:4px !important}.mr-1{margin-right:4px !important}.mb-1{margin-bottom:4px !important}.ml-1{margin-left:4px !important}.mt-n1{margin-top:-4px !important}.mr-n1{margin-right:-4px !important}.mb-n1{margin-bottom:-4px !important}.ml-n1{margin-left:-4px !important}.mx-1{margin-right:4px !important;margin-left:4px !important}.my-1{margin-top:4px !important;margin-bottom:4px !important}.m-2{margin:8px !important}.mt-2{margin-top:8px !important}.mr-2{margin-right:8px !important}.mb-2{margin-bottom:8px !important}.ml-2{margin-left:8px !important}.mt-n2{margin-top:-8px !important}.mr-n2{margin-right:-8px !important}.mb-n2{margin-bottom:-8px !important}.ml-n2{margin-left:-8px !important}.mx-2{margin-right:8px !important;margin-left:8px !important}.my-2{margin-top:8px !important;margin-bottom:8px !important}.m-3{margin:16px !important}.mt-3{margin-top:16px !important}.mr-3{margin-right:16px !important}.mb-3{margin-bottom:16px !important}.ml-3{margin-left:16px !important}.mt-n3{margin-top:-16px !important}.mr-n3{margin-right:-16px !important}.mb-n3{margin-bottom:-16px !important}.ml-n3{margin-left:-16px !important}.mx-3{margin-right:16px !important;margin-left:16px !important}.my-3{margin-top:16px !important;margin-bottom:16px !important}.m-4{margin:24px !important}.mt-4{margin-top:24px !important}.mr-4{margin-right:24px !important}.mb-4{margin-bottom:24px !important}.ml-4{margin-left:24px !important}.mt-n4{margin-top:-24px !important}.mr-n4{margin-right:-24px !important}.mb-n4{margin-bottom:-24px !important}.ml-n4{margin-left:-24px !important}.mx-4{margin-right:24px !important;margin-left:24px !important}.my-4{margin-top:24px !important;margin-bottom:24px !important}.m-5{margin:32px !important}.mt-5{margin-top:32px !important}.mr-5{margin-right:32px !important}.mb-5{margin-bottom:32px !important}.ml-5{margin-left:32px !important}.mt-n5{margin-top:-32px !important}.mr-n5{margin-right:-32px !important}.mb-n5{margin-bottom:-32px !important}.ml-n5{margin-left:-32px !important}.mx-5{margin-right:32px !important;margin-left:32px !important}.my-5{margin-top:32px !important;margin-bottom:32px !important}.m-6{margin:40px !important}.mt-6{margin-top:40px !important}.mr-6{margin-right:40px !important}.mb-6{margin-bottom:40px !important}.ml-6{margin-left:40px !important}.mt-n6{margin-top:-40px !important}.mr-n6{margin-right:-40px !important}.mb-n6{margin-bottom:-40px !important}.ml-n6{margin-left:-40px !important}.mx-6{margin-right:40px !important;margin-left:40px !important}.my-6{margin-top:40px !important;margin-bottom:40px !important}.mx-auto{margin-right:auto !important;margin-left:auto !important}@media (min-width: 544px){.m-sm-0{margin:0 !important}.mt-sm-0{margin-top:0 !important}.mr-sm-0{margin-right:0 !important}.mb-sm-0{margin-bottom:0 !important}.ml-sm-0{margin-left:0 !important}.mt-sm-n0{margin-top:-0 !important}.mr-sm-n0{margin-right:-0 !important}.mb-sm-n0{margin-bottom:-0 !important}.ml-sm-n0{margin-left:-0 !important}.mx-sm-0{margin-right:0 !important;margin-left:0 !important}.my-sm-0{margin-top:0 !important;margin-bottom:0 !important}}@media (min-width: 544px){.m-sm-1{margin:4px !important}.mt-sm-1{margin-top:4px !important}.mr-sm-1{margin-right:4px !important}.mb-sm-1{margin-bottom:4px !important}.ml-sm-1{margin-left:4px !important}.mt-sm-n1{margin-top:-4px !important}.mr-sm-n1{margin-right:-4px !important}.mb-sm-n1{margin-bottom:-4px !important}.ml-sm-n1{margin-left:-4px !important}.mx-sm-1{margin-right:4px !important;margin-left:4px !important}.my-sm-1{margin-top:4px !important;margin-bottom:4px !important}}@media (min-width: 544px){.m-sm-2{margin:8px !important}.mt-sm-2{margin-top:8px !important}.mr-sm-2{margin-right:8px !important}.mb-sm-2{margin-bottom:8px !important}.ml-sm-2{margin-left:8px !important}.mt-sm-n2{margin-top:-8px !important}.mr-sm-n2{margin-right:-8px !important}.mb-sm-n2{margin-bottom:-8px !important}.ml-sm-n2{margin-left:-8px !important}.mx-sm-2{margin-right:8px !important;margin-left:8px !important}.my-sm-2{margin-top:8px !important;margin-bottom:8px !important}}@media (min-width: 544px){.m-sm-3{margin:16px !important}.mt-sm-3{margin-top:16px !important}.mr-sm-3{margin-right:16px !important}.mb-sm-3{margin-bottom:16px !important}.ml-sm-3{margin-left:16px !important}.mt-sm-n3{margin-top:-16px !important}.mr-sm-n3{margin-right:-16px !important}.mb-sm-n3{margin-bottom:-16px !important}.ml-sm-n3{margin-left:-16px !important}.mx-sm-3{margin-right:16px !important;margin-left:16px !important}.my-sm-3{margin-top:16px !important;margin-bottom:16px !important}}@media (min-width: 544px){.m-sm-4{margin:24px !important}.mt-sm-4{margin-top:24px !important}.mr-sm-4{margin-right:24px !important}.mb-sm-4{margin-bottom:24px !important}.ml-sm-4{margin-left:24px !important}.mt-sm-n4{margin-top:-24px !important}.mr-sm-n4{margin-right:-24px !important}.mb-sm-n4{margin-bottom:-24px !important}.ml-sm-n4{margin-left:-24px !important}.mx-sm-4{margin-right:24px !important;margin-left:24px !important}.my-sm-4{margin-top:24px !important;margin-bottom:24px !important}}@media (min-width: 544px){.m-sm-5{margin:32px !important}.mt-sm-5{margin-top:32px !important}.mr-sm-5{margin-right:32px !important}.mb-sm-5{margin-bottom:32px !important}.ml-sm-5{margin-left:32px !important}.mt-sm-n5{margin-top:-32px !important}.mr-sm-n5{margin-right:-32px !important}.mb-sm-n5{margin-bottom:-32px !important}.ml-sm-n5{margin-left:-32px !important}.mx-sm-5{margin-right:32px !important;margin-left:32px !important}.my-sm-5{margin-top:32px !important;margin-bottom:32px !important}}@media (min-width: 544px){.m-sm-6{margin:40px !important}.mt-sm-6{margin-top:40px !important}.mr-sm-6{margin-right:40px !important}.mb-sm-6{margin-bottom:40px !important}.ml-sm-6{margin-left:40px !important}.mt-sm-n6{margin-top:-40px !important}.mr-sm-n6{margin-right:-40px !important}.mb-sm-n6{margin-bottom:-40px !important}.ml-sm-n6{margin-left:-40px !important}.mx-sm-6{margin-right:40px !important;margin-left:40px !important}.my-sm-6{margin-top:40px !important;margin-bottom:40px !important}}@media (min-width: 768px){.m-md-0{margin:0 !important}.mt-md-0{margin-top:0 !important}.mr-md-0{margin-right:0 !important}.mb-md-0{margin-bottom:0 !important}.ml-md-0{margin-left:0 !important}.mt-md-n0{margin-top:-0 !important}.mr-md-n0{margin-right:-0 !important}.mb-md-n0{margin-bottom:-0 !important}.ml-md-n0{margin-left:-0 !important}.mx-md-0{margin-right:0 !important;margin-left:0 !important}.my-md-0{margin-top:0 !important;margin-bottom:0 !important}}@media (min-width: 768px){.m-md-1{margin:4px !important}.mt-md-1{margin-top:4px !important}.mr-md-1{margin-right:4px !important}.mb-md-1{margin-bottom:4px !important}.ml-md-1{margin-left:4px !important}.mt-md-n1{margin-top:-4px !important}.mr-md-n1{margin-right:-4px !important}.mb-md-n1{margin-bottom:-4px !important}.ml-md-n1{margin-left:-4px !important}.mx-md-1{margin-right:4px !important;margin-left:4px !important}.my-md-1{margin-top:4px !important;margin-bottom:4px !important}}@media (min-width: 768px){.m-md-2{margin:8px !important}.mt-md-2{margin-top:8px !important}.mr-md-2{margin-right:8px !important}.mb-md-2{margin-bottom:8px !important}.ml-md-2{margin-left:8px !important}.mt-md-n2{margin-top:-8px !important}.mr-md-n2{margin-right:-8px !important}.mb-md-n2{margin-bottom:-8px !important}.ml-md-n2{margin-left:-8px !important}.mx-md-2{margin-right:8px !important;margin-left:8px !important}.my-md-2{margin-top:8px !important;margin-bottom:8px !important}}@media (min-width: 768px){.m-md-3{margin:16px !important}.mt-md-3{margin-top:16px !important}.mr-md-3{margin-right:16px !important}.mb-md-3{margin-bottom:16px !important}.ml-md-3{margin-left:16px !important}.mt-md-n3{margin-top:-16px !important}.mr-md-n3{margin-right:-16px !important}.mb-md-n3{margin-bottom:-16px !important}.ml-md-n3{margin-left:-16px !important}.mx-md-3{margin-right:16px !important;margin-left:16px !important}.my-md-3{margin-top:16px !important;margin-bottom:16px !important}}@media (min-width: 768px){.m-md-4{margin:24px !important}.mt-md-4{margin-top:24px !important}.mr-md-4{margin-right:24px !important}.mb-md-4{margin-bottom:24px !important}.ml-md-4{margin-left:24px !important}.mt-md-n4{margin-top:-24px !important}.mr-md-n4{margin-right:-24px !important}.mb-md-n4{margin-bottom:-24px !important}.ml-md-n4{margin-left:-24px !important}.mx-md-4{margin-right:24px !important;margin-left:24px !important}.my-md-4{margin-top:24px !important;margin-bottom:24px !important}}@media (min-width: 768px){.m-md-5{margin:32px !important}.mt-md-5{margin-top:32px !important}.mr-md-5{margin-right:32px !important}.mb-md-5{margin-bottom:32px !important}.ml-md-5{margin-left:32px !important}.mt-md-n5{margin-top:-32px !important}.mr-md-n5{margin-right:-32px !important}.mb-md-n5{margin-bottom:-32px !important}.ml-md-n5{margin-left:-32px !important}.mx-md-5{margin-right:32px !important;margin-left:32px !important}.my-md-5{margin-top:32px !important;margin-bottom:32px !important}}@media (min-width: 768px){.m-md-6{margin:40px !important}.mt-md-6{margin-top:40px !important}.mr-md-6{margin-right:40px !important}.mb-md-6{margin-bottom:40px !important}.ml-md-6{margin-left:40px !important}.mt-md-n6{margin-top:-40px !important}.mr-md-n6{margin-right:-40px !important}.mb-md-n6{margin-bottom:-40px !important}.ml-md-n6{margin-left:-40px !important}.mx-md-6{margin-right:40px !important;margin-left:40px !important}.my-md-6{margin-top:40px !important;margin-bottom:40px !important}}@media (min-width: 1012px){.m-lg-0{margin:0 !important}.mt-lg-0{margin-top:0 !important}.mr-lg-0{margin-right:0 !important}.mb-lg-0{margin-bottom:0 !important}.ml-lg-0{margin-left:0 !important}.mt-lg-n0{margin-top:-0 !important}.mr-lg-n0{margin-right:-0 !important}.mb-lg-n0{margin-bottom:-0 !important}.ml-lg-n0{margin-left:-0 !important}.mx-lg-0{margin-right:0 !important;margin-left:0 !important}.my-lg-0{margin-top:0 !important;margin-bottom:0 !important}}@media (min-width: 1012px){.m-lg-1{margin:4px !important}.mt-lg-1{margin-top:4px !important}.mr-lg-1{margin-right:4px !important}.mb-lg-1{margin-bottom:4px !important}.ml-lg-1{margin-left:4px !important}.mt-lg-n1{margin-top:-4px !important}.mr-lg-n1{margin-right:-4px !important}.mb-lg-n1{margin-bottom:-4px !important}.ml-lg-n1{margin-left:-4px !important}.mx-lg-1{margin-right:4px !important;margin-left:4px !important}.my-lg-1{margin-top:4px !important;margin-bottom:4px !important}}@media (min-width: 1012px){.m-lg-2{margin:8px !important}.mt-lg-2{margin-top:8px !important}.mr-lg-2{margin-right:8px !important}.mb-lg-2{margin-bottom:8px !important}.ml-lg-2{margin-left:8px !important}.mt-lg-n2{margin-top:-8px !important}.mr-lg-n2{margin-right:-8px !important}.mb-lg-n2{margin-bottom:-8px !important}.ml-lg-n2{margin-left:-8px !important}.mx-lg-2{margin-right:8px !important;margin-left:8px !important}.my-lg-2{margin-top:8px !important;margin-bottom:8px !important}}@media (min-width: 1012px){.m-lg-3{margin:16px !important}.mt-lg-3{margin-top:16px !important}.mr-lg-3{margin-right:16px !important}.mb-lg-3{margin-bottom:16px !important}.ml-lg-3{margin-left:16px !important}.mt-lg-n3{margin-top:-16px !important}.mr-lg-n3{margin-right:-16px !important}.mb-lg-n3{margin-bottom:-16px !important}.ml-lg-n3{margin-left:-16px !important}.mx-lg-3{margin-right:16px !important;margin-left:16px !important}.my-lg-3{margin-top:16px !important;margin-bottom:16px !important}}@media (min-width: 1012px){.m-lg-4{margin:24px !important}.mt-lg-4{margin-top:24px !important}.mr-lg-4{margin-right:24px !important}.mb-lg-4{margin-bottom:24px !important}.ml-lg-4{margin-left:24px !important}.mt-lg-n4{margin-top:-24px !important}.mr-lg-n4{margin-right:-24px !important}.mb-lg-n4{margin-bottom:-24px !important}.ml-lg-n4{margin-left:-24px !important}.mx-lg-4{margin-right:24px !important;margin-left:24px !important}.my-lg-4{margin-top:24px !important;margin-bottom:24px !important}}@media (min-width: 1012px){.m-lg-5{margin:32px !important}.mt-lg-5{margin-top:32px !important}.mr-lg-5{margin-right:32px !important}.mb-lg-5{margin-bottom:32px !important}.ml-lg-5{margin-left:32px !important}.mt-lg-n5{margin-top:-32px !important}.mr-lg-n5{margin-right:-32px !important}.mb-lg-n5{margin-bottom:-32px !important}.ml-lg-n5{margin-left:-32px !important}.mx-lg-5{margin-right:32px !important;margin-left:32px !important}.my-lg-5{margin-top:32px !important;margin-bottom:32px !important}}@media (min-width: 1012px){.m-lg-6{margin:40px !important}.mt-lg-6{margin-top:40px !important}.mr-lg-6{margin-right:40px !important}.mb-lg-6{margin-bottom:40px !important}.ml-lg-6{margin-left:40px !important}.mt-lg-n6{margin-top:-40px !important}.mr-lg-n6{margin-right:-40px !important}.mb-lg-n6{margin-bottom:-40px !important}.ml-lg-n6{margin-left:-40px !important}.mx-lg-6{margin-right:40px !important;margin-left:40px !important}.my-lg-6{margin-top:40px !important;margin-bottom:40px !important}}@media (min-width: 1280px){.m-xl-0{margin:0 !important}.mt-xl-0{margin-top:0 !important}.mr-xl-0{margin-right:0 !important}.mb-xl-0{margin-bottom:0 !important}.ml-xl-0{margin-left:0 !important}.mt-xl-n0{margin-top:-0 !important}.mr-xl-n0{margin-right:-0 !important}.mb-xl-n0{margin-bottom:-0 !important}.ml-xl-n0{margin-left:-0 !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}}@media (min-width: 1280px){.m-xl-1{margin:4px !important}.mt-xl-1{margin-top:4px !important}.mr-xl-1{margin-right:4px !important}.mb-xl-1{margin-bottom:4px !important}.ml-xl-1{margin-left:4px !important}.mt-xl-n1{margin-top:-4px !important}.mr-xl-n1{margin-right:-4px !important}.mb-xl-n1{margin-bottom:-4px !important}.ml-xl-n1{margin-left:-4px !important}.mx-xl-1{margin-right:4px !important;margin-left:4px !important}.my-xl-1{margin-top:4px !important;margin-bottom:4px !important}}@media (min-width: 1280px){.m-xl-2{margin:8px !important}.mt-xl-2{margin-top:8px !important}.mr-xl-2{margin-right:8px !important}.mb-xl-2{margin-bottom:8px !important}.ml-xl-2{margin-left:8px !important}.mt-xl-n2{margin-top:-8px !important}.mr-xl-n2{margin-right:-8px !important}.mb-xl-n2{margin-bottom:-8px !important}.ml-xl-n2{margin-left:-8px !important}.mx-xl-2{margin-right:8px !important;margin-left:8px !important}.my-xl-2{margin-top:8px !important;margin-bottom:8px !important}}@media (min-width: 1280px){.m-xl-3{margin:16px !important}.mt-xl-3{margin-top:16px !important}.mr-xl-3{margin-right:16px !important}.mb-xl-3{margin-bottom:16px !important}.ml-xl-3{margin-left:16px !important}.mt-xl-n3{margin-top:-16px !important}.mr-xl-n3{margin-right:-16px !important}.mb-xl-n3{margin-bottom:-16px !important}.ml-xl-n3{margin-left:-16px !important}.mx-xl-3{margin-right:16px !important;margin-left:16px !important}.my-xl-3{margin-top:16px !important;margin-bottom:16px !important}}@media (min-width: 1280px){.m-xl-4{margin:24px !important}.mt-xl-4{margin-top:24px !important}.mr-xl-4{margin-right:24px !important}.mb-xl-4{margin-bottom:24px !important}.ml-xl-4{margin-left:24px !important}.mt-xl-n4{margin-top:-24px !important}.mr-xl-n4{margin-right:-24px !important}.mb-xl-n4{margin-bottom:-24px !important}.ml-xl-n4{margin-left:-24px !important}.mx-xl-4{margin-right:24px !important;margin-left:24px !important}.my-xl-4{margin-top:24px !important;margin-bottom:24px !important}}@media (min-width: 1280px){.m-xl-5{margin:32px !important}.mt-xl-5{margin-top:32px !important}.mr-xl-5{margin-right:32px !important}.mb-xl-5{margin-bottom:32px !important}.ml-xl-5{margin-left:32px !important}.mt-xl-n5{margin-top:-32px !important}.mr-xl-n5{margin-right:-32px !important}.mb-xl-n5{margin-bottom:-32px !important}.ml-xl-n5{margin-left:-32px !important}.mx-xl-5{margin-right:32px !important;margin-left:32px !important}.my-xl-5{margin-top:32px !important;margin-bottom:32px !important}}@media (min-width: 1280px){.m-xl-6{margin:40px !important}.mt-xl-6{margin-top:40px !important}.mr-xl-6{margin-right:40px !important}.mb-xl-6{margin-bottom:40px !important}.ml-xl-6{margin-left:40px !important}.mt-xl-n6{margin-top:-40px !important}.mr-xl-n6{margin-right:-40px !important}.mb-xl-n6{margin-bottom:-40px !important}.ml-xl-n6{margin-left:-40px !important}.mx-xl-6{margin-right:40px !important;margin-left:40px !important}.my-xl-6{margin-top:40px !important;margin-bottom:40px !important}}.p-0{padding:0 !important}.pt-0{padding-top:0 !important}.pr-0{padding-right:0 !important}.pb-0{padding-bottom:0 !important}.pl-0{padding-left:0 !important}.px-0{padding-right:0 !important;padding-left:0 !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.p-1{padding:4px !important}.pt-1{padding-top:4px !important}.pr-1{padding-right:4px !important}.pb-1{padding-bottom:4px !important}.pl-1{padding-left:4px !important}.px-1{padding-right:4px !important;padding-left:4px !important}.py-1{padding-top:4px !important;padding-bottom:4px !important}.p-2{padding:8px !important}.pt-2{padding-top:8px !important}.pr-2{padding-right:8px !important}.pb-2{padding-bottom:8px !important}.pl-2{padding-left:8px !important}.px-2{padding-right:8px !important;padding-left:8px !important}.py-2{padding-top:8px !important;padding-bottom:8px !important}.p-3{padding:16px !important}.pt-3{padding-top:16px !important}.pr-3{padding-right:16px !important}.pb-3{padding-bottom:16px !important}.pl-3{padding-left:16px !important}.px-3{padding-right:16px !important;padding-left:16px !important}.py-3{padding-top:16px !important;padding-bottom:16px !important}.p-4{padding:24px !important}.pt-4{padding-top:24px !important}.pr-4{padding-right:24px !important}.pb-4{padding-bottom:24px !important}.pl-4{padding-left:24px !important}.px-4{padding-right:24px !important;padding-left:24px !important}.py-4{padding-top:24px !important;padding-bottom:24px !important}.p-5{padding:32px !important}.pt-5{padding-top:32px !important}.pr-5{padding-right:32px !important}.pb-5{padding-bottom:32px !important}.pl-5{padding-left:32px !important}.px-5{padding-right:32px !important;padding-left:32px !important}.py-5{padding-top:32px !important;padding-bottom:32px !important}.p-6{padding:40px !important}.pt-6{padding-top:40px !important}.pr-6{padding-right:40px !important}.pb-6{padding-bottom:40px !important}.pl-6{padding-left:40px !important}.px-6{padding-right:40px !important;padding-left:40px !important}.py-6{padding-top:40px !important;padding-bottom:40px !important}@media (min-width: 544px){.p-sm-0{padding:0 !important}.pt-sm-0{padding-top:0 !important}.pr-sm-0{padding-right:0 !important}.pb-sm-0{padding-bottom:0 !important}.pl-sm-0{padding-left:0 !important}.px-sm-0{padding-right:0 !important;padding-left:0 !important}.py-sm-0{padding-top:0 !important;padding-bottom:0 !important}}@media (min-width: 544px){.p-sm-1{padding:4px !important}.pt-sm-1{padding-top:4px !important}.pr-sm-1{padding-right:4px !important}.pb-sm-1{padding-bottom:4px !important}.pl-sm-1{padding-left:4px !important}.px-sm-1{padding-right:4px !important;padding-left:4px !important}.py-sm-1{padding-top:4px !important;padding-bottom:4px !important}}@media (min-width: 544px){.p-sm-2{padding:8px !important}.pt-sm-2{padding-top:8px !important}.pr-sm-2{padding-right:8px !important}.pb-sm-2{padding-bottom:8px !important}.pl-sm-2{padding-left:8px !important}.px-sm-2{padding-right:8px !important;padding-left:8px !important}.py-sm-2{padding-top:8px !important;padding-bottom:8px !important}}@media (min-width: 544px){.p-sm-3{padding:16px !important}.pt-sm-3{padding-top:16px !important}.pr-sm-3{padding-right:16px !important}.pb-sm-3{padding-bottom:16px !important}.pl-sm-3{padding-left:16px !important}.px-sm-3{padding-right:16px !important;padding-left:16px !important}.py-sm-3{padding-top:16px !important;padding-bottom:16px !important}}@media (min-width: 544px){.p-sm-4{padding:24px !important}.pt-sm-4{padding-top:24px !important}.pr-sm-4{padding-right:24px !important}.pb-sm-4{padding-bottom:24px !important}.pl-sm-4{padding-left:24px !important}.px-sm-4{padding-right:24px !important;padding-left:24px !important}.py-sm-4{padding-top:24px !important;padding-bottom:24px !important}}@media (min-width: 544px){.p-sm-5{padding:32px !important}.pt-sm-5{padding-top:32px !important}.pr-sm-5{padding-right:32px !important}.pb-sm-5{padding-bottom:32px !important}.pl-sm-5{padding-left:32px !important}.px-sm-5{padding-right:32px !important;padding-left:32px !important}.py-sm-5{padding-top:32px !important;padding-bottom:32px !important}}@media (min-width: 544px){.p-sm-6{padding:40px !important}.pt-sm-6{padding-top:40px !important}.pr-sm-6{padding-right:40px !important}.pb-sm-6{padding-bottom:40px !important}.pl-sm-6{padding-left:40px !important}.px-sm-6{padding-right:40px !important;padding-left:40px !important}.py-sm-6{padding-top:40px !important;padding-bottom:40px !important}}@media (min-width: 768px){.p-md-0{padding:0 !important}.pt-md-0{padding-top:0 !important}.pr-md-0{padding-right:0 !important}.pb-md-0{padding-bottom:0 !important}.pl-md-0{padding-left:0 !important}.px-md-0{padding-right:0 !important;padding-left:0 !important}.py-md-0{padding-top:0 !important;padding-bottom:0 !important}}@media (min-width: 768px){.p-md-1{padding:4px !important}.pt-md-1{padding-top:4px !important}.pr-md-1{padding-right:4px !important}.pb-md-1{padding-bottom:4px !important}.pl-md-1{padding-left:4px !important}.px-md-1{padding-right:4px !important;padding-left:4px !important}.py-md-1{padding-top:4px !important;padding-bottom:4px !important}}@media (min-width: 768px){.p-md-2{padding:8px !important}.pt-md-2{padding-top:8px !important}.pr-md-2{padding-right:8px !important}.pb-md-2{padding-bottom:8px !important}.pl-md-2{padding-left:8px !important}.px-md-2{padding-right:8px !important;padding-left:8px !important}.py-md-2{padding-top:8px !important;padding-bottom:8px !important}}@media (min-width: 768px){.p-md-3{padding:16px !important}.pt-md-3{padding-top:16px !important}.pr-md-3{padding-right:16px !important}.pb-md-3{padding-bottom:16px !important}.pl-md-3{padding-left:16px !important}.px-md-3{padding-right:16px !important;padding-left:16px !important}.py-md-3{padding-top:16px !important;padding-bottom:16px !important}}@media (min-width: 768px){.p-md-4{padding:24px !important}.pt-md-4{padding-top:24px !important}.pr-md-4{padding-right:24px !important}.pb-md-4{padding-bottom:24px !important}.pl-md-4{padding-left:24px !important}.px-md-4{padding-right:24px !important;padding-left:24px !important}.py-md-4{padding-top:24px !important;padding-bottom:24px !important}}@media (min-width: 768px){.p-md-5{padding:32px !important}.pt-md-5{padding-top:32px !important}.pr-md-5{padding-right:32px !important}.pb-md-5{padding-bottom:32px !important}.pl-md-5{padding-left:32px !important}.px-md-5{padding-right:32px !important;padding-left:32px !important}.py-md-5{padding-top:32px !important;padding-bottom:32px !important}}@media (min-width: 768px){.p-md-6{padding:40px !important}.pt-md-6{padding-top:40px !important}.pr-md-6{padding-right:40px !important}.pb-md-6{padding-bottom:40px !important}.pl-md-6{padding-left:40px !important}.px-md-6{padding-right:40px !important;padding-left:40px !important}.py-md-6{padding-top:40px !important;padding-bottom:40px !important}}@media (min-width: 1012px){.p-lg-0{padding:0 !important}.pt-lg-0{padding-top:0 !important}.pr-lg-0{padding-right:0 !important}.pb-lg-0{padding-bottom:0 !important}.pl-lg-0{padding-left:0 !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}}@media (min-width: 1012px){.p-lg-1{padding:4px !important}.pt-lg-1{padding-top:4px !important}.pr-lg-1{padding-right:4px !important}.pb-lg-1{padding-bottom:4px !important}.pl-lg-1{padding-left:4px !important}.px-lg-1{padding-right:4px !important;padding-left:4px !important}.py-lg-1{padding-top:4px !important;padding-bottom:4px !important}}@media (min-width: 1012px){.p-lg-2{padding:8px !important}.pt-lg-2{padding-top:8px !important}.pr-lg-2{padding-right:8px !important}.pb-lg-2{padding-bottom:8px !important}.pl-lg-2{padding-left:8px !important}.px-lg-2{padding-right:8px !important;padding-left:8px !important}.py-lg-2{padding-top:8px !important;padding-bottom:8px !important}}@media (min-width: 1012px){.p-lg-3{padding:16px !important}.pt-lg-3{padding-top:16px !important}.pr-lg-3{padding-right:16px !important}.pb-lg-3{padding-bottom:16px !important}.pl-lg-3{padding-left:16px !important}.px-lg-3{padding-right:16px !important;padding-left:16px !important}.py-lg-3{padding-top:16px !important;padding-bottom:16px !important}}@media (min-width: 1012px){.p-lg-4{padding:24px !important}.pt-lg-4{padding-top:24px !important}.pr-lg-4{padding-right:24px !important}.pb-lg-4{padding-bottom:24px !important}.pl-lg-4{padding-left:24px !important}.px-lg-4{padding-right:24px !important;padding-left:24px !important}.py-lg-4{padding-top:24px !important;padding-bottom:24px !important}}@media (min-width: 1012px){.p-lg-5{padding:32px !important}.pt-lg-5{padding-top:32px !important}.pr-lg-5{padding-right:32px !important}.pb-lg-5{padding-bottom:32px !important}.pl-lg-5{padding-left:32px !important}.px-lg-5{padding-right:32px !important;padding-left:32px !important}.py-lg-5{padding-top:32px !important;padding-bottom:32px !important}}@media (min-width: 1012px){.p-lg-6{padding:40px !important}.pt-lg-6{padding-top:40px !important}.pr-lg-6{padding-right:40px !important}.pb-lg-6{padding-bottom:40px !important}.pl-lg-6{padding-left:40px !important}.px-lg-6{padding-right:40px !important;padding-left:40px !important}.py-lg-6{padding-top:40px !important;padding-bottom:40px !important}}@media (min-width: 1280px){.p-xl-0{padding:0 !important}.pt-xl-0{padding-top:0 !important}.pr-xl-0{padding-right:0 !important}.pb-xl-0{padding-bottom:0 !important}.pl-xl-0{padding-left:0 !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}}@media (min-width: 1280px){.p-xl-1{padding:4px !important}.pt-xl-1{padding-top:4px !important}.pr-xl-1{padding-right:4px !important}.pb-xl-1{padding-bottom:4px !important}.pl-xl-1{padding-left:4px !important}.px-xl-1{padding-right:4px !important;padding-left:4px !important}.py-xl-1{padding-top:4px !important;padding-bottom:4px !important}}@media (min-width: 1280px){.p-xl-2{padding:8px !important}.pt-xl-2{padding-top:8px !important}.pr-xl-2{padding-right:8px !important}.pb-xl-2{padding-bottom:8px !important}.pl-xl-2{padding-left:8px !important}.px-xl-2{padding-right:8px !important;padding-left:8px !important}.py-xl-2{padding-top:8px !important;padding-bottom:8px !important}}@media (min-width: 1280px){.p-xl-3{padding:16px !important}.pt-xl-3{padding-top:16px !important}.pr-xl-3{padding-right:16px !important}.pb-xl-3{padding-bottom:16px !important}.pl-xl-3{padding-left:16px !important}.px-xl-3{padding-right:16px !important;padding-left:16px !important}.py-xl-3{padding-top:16px !important;padding-bottom:16px !important}}@media (min-width: 1280px){.p-xl-4{padding:24px !important}.pt-xl-4{padding-top:24px !important}.pr-xl-4{padding-right:24px !important}.pb-xl-4{padding-bottom:24px !important}.pl-xl-4{padding-left:24px !important}.px-xl-4{padding-right:24px !important;padding-left:24px !important}.py-xl-4{padding-top:24px !important;padding-bottom:24px !important}}@media (min-width: 1280px){.p-xl-5{padding:32px !important}.pt-xl-5{padding-top:32px !important}.pr-xl-5{padding-right:32px !important}.pb-xl-5{padding-bottom:32px !important}.pl-xl-5{padding-left:32px !important}.px-xl-5{padding-right:32px !important;padding-left:32px !important}.py-xl-5{padding-top:32px !important;padding-bottom:32px !important}}@media (min-width: 1280px){.p-xl-6{padding:40px !important}.pt-xl-6{padding-top:40px !important}.pr-xl-6{padding-right:40px !important}.pb-xl-6{padding-bottom:40px !important}.pl-xl-6{padding-left:40px !important}.px-xl-6{padding-right:40px !important;padding-left:40px !important}.py-xl-6{padding-top:40px !important;padding-bottom:40px !important}}.p-responsive{padding-right:16px !important;padding-left:16px !important}@media (min-width: 544px){.p-responsive{padding-right:40px !important;padding-left:40px !important}}@media (min-width: 1012px){.p-responsive{padding-right:16px !important;padding-left:16px !important}}.h1{font-size:26px !important}@media (min-width: 768px){.h1{font-size:32px !important}}.h2{font-size:22px !important}@media (min-width: 768px){.h2{font-size:24px !important}}.h3{font-size:18px !important}@media (min-width: 768px){.h3{font-size:20px !important}}.h4{font-size:16px !important}.h5{font-size:14px !important}.h6{font-size:12px !important}.h1,.h2,.h3,.h4,.h5,.h6{font-weight:600 !important}.f1{font-size:26px !important}@media (min-width: 768px){.f1{font-size:32px !important}}.f2{font-size:22px !important}@media (min-width: 768px){.f2{font-size:24px !important}}.f3{font-size:18px !important}@media (min-width: 768px){.f3{font-size:20px !important}}.f4{font-size:16px !important}@media (min-width: 768px){.f4{font-size:16px !important}}.f5{font-size:14px !important}.f6{font-size:12px !important}.f00-light{font-size:40px !important;font-weight:300 !important}@media (min-width: 768px){.f00-light{font-size:48px !important}}.f0-light{font-size:32px !important;font-weight:300 !important}@media (min-width: 768px){.f0-light{font-size:40px !important}}.f1-light{font-size:26px !important;font-weight:300 !important}@media (min-width: 768px){.f1-light{font-size:32px !important}}.f2-light{font-size:22px !important;font-weight:300 !important}@media (min-width: 768px){.f2-light{font-size:24px !important}}.f3-light{font-size:18px !important;font-weight:300 !important}@media (min-width: 768px){.f3-light{font-size:20px !important}}.text-small{font-size:12px !important}.lead{margin-bottom:30px;font-size:20px;font-weight:300;color:#586069}.lh-condensed-ultra{line-height:1 !important}.lh-condensed{line-height:1.25 !important}.lh-default{line-height:1.5 !important}.lh-0{line-height:0 !important}.text-right{text-align:right !important}.text-left{text-align:left !important}.text-center{text-align:center !important}@media (min-width: 544px){.text-sm-right{text-align:right !important}.text-sm-left{text-align:left !important}.text-sm-center{text-align:center !important}}@media (min-width: 768px){.text-md-right{text-align:right !important}.text-md-left{text-align:left !important}.text-md-center{text-align:center !important}}@media (min-width: 1012px){.text-lg-right{text-align:right !important}.text-lg-left{text-align:left !important}.text-lg-center{text-align:center !important}}@media (min-width: 1280px){.text-xl-right{text-align:right !important}.text-xl-left{text-align:left !important}.text-xl-center{text-align:center !important}}.text-normal{font-weight:400 !important}.text-bold{font-weight:600 !important}.text-italic{font-style:italic !important}.text-uppercase{text-transform:uppercase !important}.text-underline{text-decoration:underline !important}.no-underline{text-decoration:none !important}.no-wrap{white-space:nowrap !important}.ws-normal{white-space:normal !important}.wb-break-all{word-break:break-all !important}.text-emphasized{font-weight:600;color:#24292e}.list-style-none{list-style:none !important}.text-shadow-dark{text-shadow:0 1px 1px rgba(27,31,35,0.25),0 1px 25px rgba(27,31,35,0.75)}.text-shadow-light{text-shadow:0 1px 0 rgba(255,255,255,0.5)}.text-mono{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace}.user-select-none{-webkit-user-select:none !important;-moz-user-select:none !important;-ms-user-select:none !important;user-select:none !important}.v-hidden{visibility:hidden !important}.v-visible{visibility:visible !important}.d-table{display:table !important}.d-table-cell{display:table-cell !important}.table-fixed{table-layout:fixed !important}.d-block{display:block !important}.d-inline{display:inline !important}.d-inline-block{display:inline-block !important}.d-flex{display:flex !important}.d-inline-flex{display:inline-flex !important}.d-none{display:none !important}@media (min-width: 544px){.d-sm-table{display:table !important}.d-sm-table-cell{display:table-cell !important}.d-sm-block{display:block !important}.d-sm-inline{display:inline !important}.d-sm-inline-block{display:inline-block !important}.d-sm-flex{display:flex !important}.d-sm-inline-flex{display:inline-flex !important}.d-sm-none{display:none !important}}@media (min-width: 768px){.d-md-table{display:table !important}.d-md-table-cell{display:table-cell !important}.d-md-block{display:block !important}.d-md-inline{display:inline !important}.d-md-inline-block{display:inline-block !important}.d-md-flex{display:flex !important}.d-md-inline-flex{display:inline-flex !important}.d-md-none{display:none !important}}@media (min-width: 1012px){.d-lg-table{display:table !important}.d-lg-table-cell{display:table-cell !important}.d-lg-block{display:block !important}.d-lg-inline{display:inline !important}.d-lg-inline-block{display:inline-block !important}.d-lg-flex{display:flex !important}.d-lg-inline-flex{display:inline-flex !important}.d-lg-none{display:none !important}}@media (min-width: 1280px){.d-xl-table{display:table !important}.d-xl-table-cell{display:table-cell !important}.d-xl-block{display:block !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}}@media (max-width: 544px){.hide-sm{display:none !important}}@media (min-width: 544px) and (max-width: 768px){.hide-md{display:none !important}}@media (min-width: 768px) and (max-width: 1012px){.hide-lg{display:none !important}}@media (min-width: 1012px){.hide-xl{display:none !important}}.sr-only{position:absolute;width:1px;height:1px;padding:0;overflow:hidden;clip:rect(0, 0, 0, 0);word-wrap:normal;border:0}.show-on-focus{position:absolute;width:1px;height:1px;margin:0;overflow:hidden;clip:rect(1px, 1px, 1px, 1px)}.show-on-focus:focus{z-index:20;width:auto;height:auto;clip:auto}
/*# sourceMappingURL=frameworks-afd119c67567f2c750f08c93d4211333.css.map */


================================================
FILE: data/Sales_files/frameworks-b3cd8fa1481bc34c4b18cf307ca75438.js.download
================================================
!function(e){var t={};function n(e){if(e in t)return t[e];throw new Error("dependency not found: "+e)}e.define=function(r,o,i){if(r in t)console.error("module redefined: "+r);else{var a;arguments.length<3&&(i=o,o=[]);var s=[];for(a=0;a<o.length;a++)s.push(n(o[a]));var u="github-rollup-frameworks-bootstrap"===r;try{if(t[r]=i.apply(this,s),u){var c,l=t[r];for(c in l){var f=c.replace(/___/g,"@").replace(/__/g,"/").replace(/_/g,"-");t[f]=l[c]}}}catch(t){throw t.message="evaluating module "+r+": "+t.message,u&&(e.define=function(){}),t}}}}(self),define("github-rollup-frameworks-bootstrap",function(){"use strict";function e(){var e=this,t=0;return{next:function(){return{done:t===e.length,value:e[t++]}}}}Symbol.iterator||(Symbol.iterator=Symbol()),Array.prototype[Symbol.iterator]||(Array.prototype[Symbol.iterator]=e),NodeList.prototype[Symbol.iterator]||Object.defineProperty(NodeList.prototype,Symbol.iterator,{enumerable:!1,configurable:!0,get:function(){return e}}),HTMLCollection.prototype[Symbol.iterator]||Object.defineProperty(HTMLCollection.prototype,Symbol.iterator,{enumerable:!1,configurable:!0,get:function(){return e}}),window.Headers&&!Headers.prototype[Symbol.iterator]&&(Headers.prototype[Symbol.iterator]=function(){var e=[];return this.forEach(function(t,n){e.push([n,t])}),e[Symbol.iterator]()});var t="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function n(){throw new Error("Dynamic requires are not currently supported by rollup-plugin-commonjs")}function r(e,t){return e(t={exports:{}},t.exports),t.exports}!function e(t,r,o){function i(s,u){if(!r[s]){if(!t[s]){if(!u&&n)return n(s,!0);if(a)return a(s,!0);var c=new Error("Cannot find module '"+s+"'");throw c.code="MODULE_NOT_FOUND",c}var l=r[s]={exports:{}};t[s][0].call(l.exports,function(e){var n=t[s][1][e];return i(n||e)},l,l.exports,e,t,r,o)}return r[s].exports}for(var a=n,s=0;s<o.length;s++)i(o[s]);return i}({1:[function(e,t,n){e("./index").polyfill()},{"./index":2}],2:[function(e,t,n){function r(e,t){if(void 0===e||null===e)throw new TypeError("Cannot convert first argument to object");for(var n=Object(e),r=1;r<arguments.length;r++){var o=arguments[r];if(void 0!==o&&null!==o)for(var i=Object.keys(Object(o)),a=0,s=i.length;a<s;a++){var u=i[a],c=Object.getOwnPropertyDescriptor(o,u);void 0!==c&&c.enumerable&&(n[u]=o[u])}}return n}t.exports={assign:r,polyfill:function(){Object.assign||Object.defineProperty(Object,"assign",{enumerable:!1,configurable:!0,writable:!0,value:r})}}},{}]},{},[1]);r(function(e){!function(t){var n,r=Object.prototype.hasOwnProperty,o="function"==typeof Symbol?Symbol:{},i=o.iterator||"@@iterator",a=o.toStringTag||"@@toStringTag",s=t.regeneratorRuntime;if(s)e.exports=s;else{(s=t.regeneratorRuntime=e.exports).wrap=p;var u="suspendedStart",c="suspendedYield",l="executing",f="completed",d={},h=b.prototype=m.prototype;g.prototype=h.constructor=b,b.constructor=g,b[a]=g.displayName="GeneratorFunction",s.isGeneratorFunction=function(e){var t="function"==typeof e&&e.constructor;return!!t&&(t===g||"GeneratorFunction"===(t.displayName||t.name))},s.mark=function(e){return Object.setPrototypeOf?Object.setPrototypeOf(e,b):(e.__proto__=b,a in e||(e[a]="GeneratorFunction")),e.prototype=Object.create(h),e},s.awrap=function(e){return new w(e)},y(E.prototype),s.async=function(e,t,n,r){var o=new E(p(e,t,n,r));return s.isGeneratorFunction(t)?o:o.next().then(function(e){return e.done?e.value:o.next()})},y(h),h[i]=function(){return this},h[a]="Generator",h.toString=function(){return"[object Generator]"},s.keys=function(e){var t=[];for(var n in e)t.push(n);return t.reverse(),function n(){for(;t.length;){var r=t.pop();if(r in e)return n.value=r,n.done=!1,n}return n.done=!0,n}},s.values=T,x.prototype={constructor:x,reset:function(e){if(this.prev=0,this.next=0,this.sent=n,this.done=!1,this.delegate=null,this.tryEntries.forEach(k),!e)for(var t in this)"t"===t.charAt(0)&&r.call(this,t)&&!isNaN(+t.slice(1))&&(this[t]=n)},stop:function(){this.done=!0;var e=this.tryEntries[0].completion;if("throw"===e.type)throw e.arg;return this.rval},dispatchException:function(e){if(this.done)throw e;var t=this;function n(n,r){return a.type="throw",a.arg=e,t.next=n,!!r}for(var o=this.tryEntries.length-1;o>=0;--o){var i=this.tryEntries[o],a=i.completion;if("root"===i.tryLoc)return n("end");if(i.tryLoc<=this.prev){var s=r.call(i,"catchLoc"),u=r.call(i,"finallyLoc");if(s&&u){if(this.prev<i.catchLoc)return n(i.catchLoc,!0);if(this.prev<i.finallyLoc)return n(i.finallyLoc)}else if(s){if(this.prev<i.catchLoc)return n(i.catchLoc,!0)}else{if(!u)throw new Error("try statement without catch or finally");if(this.prev<i.finallyLoc)return n(i.finallyLoc)}}}},abrupt:function(e,t){for(var n=this.tryEntries.length-1;n>=0;--n){var o=this.tryEntries[n];if(o.tryLoc<=this.prev&&r.call(o,"finallyLoc")&&this.prev<o.finallyLoc){var i=o;break}}i&&("break"===e||"continue"===e)&&i.tryLoc<=t&&t<=i.finallyLoc&&(i=null);var a=i?i.completion:{};return a.type=e,a.arg=t,i?this.next=i.finallyLoc:this.complete(a),d},complete:function(e,t){if("throw"===e.type)throw e.arg;"break"===e.type||"continue"===e.type?this.next=e.arg:"return"===e.type?(this.rval=e.arg,this.next="end"):"normal"===e.type&&t&&(this.next=t)},finish:function(e){for(var t=this.tryEntries.length-1;t>=0;--t){var n=this.tryEntries[t];if(n.finallyLoc===e)return this.complete(n.completion,n.afterLoc),k(n),d}},catch:function(e){for(var t=this.tryEntries.length-1;t>=0;--t){var n=this.tryEntries[t];if(n.tryLoc===e){var r=n.completion;if("throw"===r.type){var o=r.arg;k(n)}return o}}throw new Error("illegal catch attempt")},delegateYield:function(e,t,n){return this.delegate={iterator:T(e),resultName:t,nextLoc:n},d}}}function p(e,t,r,o){var i=Object.create((t||m).prototype),a=new x(o||[]);return i._invoke=function(e,t,r){var o=u;return function(i,a){if(o===l)throw new Error("Generator is already running");if(o===f){if("throw"===i)throw a;return j()}for(;;){var s=r.delegate;if(s){if("return"===i||"throw"===i&&s.iterator[i]===n){r.delegate=null;var h=s.iterator.return;if(h){var p=v(h,s.iterator,a);if("throw"===p.type){i="throw",a=p.arg;continue}}if("return"===i)continue}var p=v(s.iterator[i],s.iterator,a);if("throw"===p.type){r.delegate=null,i="throw",a=p.arg;continue}i="next",a=n;var m=p.arg;if(!m.done)return o=c,m;r[s.resultName]=m.value,r.next=s.nextLoc,r.delegate=null}if("next"===i)r.sent=o===c?a:n;else if("throw"===i){if(o===u)throw o=f,a;r.dispatchException(a)&&(i="next",a=n)}else"return"===i&&r.abrupt("return",a);o=l;var p=v(e,t,r);if("normal"===p.type){o=r.done?f:c;var m={value:p.arg,done:r.done};if(p.arg!==d)return m;r.delegate&&"next"===i&&(a=n)}else"throw"===p.type&&(o=f,i="throw",a=p.arg)}}}(e,r,a),i}function v(e,t,n){try{return{type:"normal",arg:e.call(t,n)}}catch(e){return{type:"throw",arg:e}}}function m(){}function g(){}function b(){}function y(e){["next","throw","return"].forEach(function(t){e[t]=function(e){return this._invoke(t,e)}})}function w(e){this.arg=e}function E(e){function t(n,r,o,i){var a=v(e[n],e,r);if("throw"!==a.type){var s=a.arg,u=s.value;return u instanceof w?Promise.resolve(u.arg).then(function(e){t("next",e,o,i)},function(e){t("throw",e,o,i)}):Promise.resolve(u).then(function(e){s.value=e,o(s)},i)}i(a.arg)}var n;"object"==typeof process&&process.domain&&(t=process.domain.bind(t)),this._invoke=function(e,r){function o(){return new Promise(function(n,o){t(e,r,n,o)})}return n=n?n.then(o,o):o()}}function _(e){var t={tryLoc:e[0]};1 in e&&(t.catchLoc=e[1]),2 in e&&(t.finallyLoc=e[2],t.afterLoc=e[3]),this.tryEntries.push(t)}function k(e){var t=e.completion||{};t.type="normal",delete t.arg,e.completion=t}function x(e){this.tryEntries=[{tryLoc:"root"}],e.forEach(_,this),this.reset(!0)}function T(e){if(e){var t=e[i];if(t)return t.call(e);if("function"==typeof e.next)return e;if(!isNaN(e.length)){var o=-1,a=function t(){for(;++o<e.length;)if(r.call(e,o))return t.value=e[o],t.done=!1,t;return t.value=n,t.done=!0,t};return a.next=a}}return{next:j}}function j(){return{value:n,done:!0}}}("object"==typeof t?t:"object"==typeof window?window:"object"==typeof self?self:t)});window.requestIdleCallback=window.requestIdleCallback||function(e){var t=Date.now();return setTimeout(function(){e({didTimeout:!1,timeRemaining:function(){return Math.max(0,50-(Date.now()-t))}})},1)},window.cancelIdleCallback=window.cancelIdleCallback||function(e){clearTimeout(e)};var o=function(e,t,n,r,o,i,a,s){if(!e){var u;if(void 0===t)u=new Error("Minified exception occurred; use the non-minified dev environment for the full error message and additional helpful warnings.");else{var c=[n,r,o,i,a,s],l=0;(u=new Error(t.replace(/%s/g,function(){return c[l++]}))).name="Invariant Violation"}throw u.framesToPop=1,u}},i="interactive"===document.readyState||"complete"===document.readyState?Promise.resolve():new Promise(function(e){document.addEventListener("DOMContentLoaded",function(){e()})}),a="complete"===document.readyState?Promise.resolve():new Promise(function(e){window.addEventListener("load",e)}),s=Object.freeze({ready:i,loaded:a}),u="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},c=function(e){return function(){var t=e.apply(this,arguments);return new Promise(function(e,n){return function r(o,i){try{var a=t[o](i),s=a.value}catch(e){return void n(e)}if(!a.done)return Promise.resolve(s).then(function(e){r("next",e)},function(e){r("throw",e)});e(s)}("next")})}},l=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},f=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),d=function e(t,n,r){null===t&&(t=Function.prototype);var o=Object.getOwnPropertyDescriptor(t,n);if(void 0===o){var i=Object.getPrototypeOf(t);return null===i?void 0:e(i,n,r)}if("value"in o)return o.value;var a=o.get;return void 0!==a?a.call(r):void 0},h=function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)},p=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t},v=function(){return function(e,t){if(Array.isArray(e))return e;if(Symbol.iterator in Object(e))return function(e,t){var n=[],r=!0,o=!1,i=void 0;try{for(var a,s=e[Symbol.iterator]();!(r=(a=s.next()).done)&&(n.push(a.value),!t||n.length!==t);r=!0);}catch(e){o=!0,i=e}finally{try{!r&&s.return&&s.return()}finally{if(o)throw i}}return n}(e,t);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),m=function(e){if(Array.isArray(e)){for(var t=0,n=Array(e.length);t<e.length;t++)n[t]=e[t];return n}return Array.from(e)},g=[],b="text/plain",y=["application/x-www-form-urlencoded","multipart/form-data",b];var w=!1;"sendBeacon"in window.navigator?w=!0:navigator.sendBeacon=function(e,t){return t instanceof Blob?((n=t,r=new FileReader,i=new Promise(function(e,t){r.onload=function(){o("string"==typeof r.result,"app/assets/modules/github/send-beacon.js:125"),e(r.result)},r.onerror=function(){t(r.error)}}),r.readAsText(n),i).then(function(n){var r="string"==typeof t.type&&""!==t.type?t.type:b;k(e,n,r)}),!0):(k(e,t,b),!0);var n,r,i};var E=void 0,_=!1;function k(e,t,n){var r={url:String(e),data:t,type:n};if(_){var o=j()||[];o.push(r),O(o)}else g.push(r),E&&clearTimeout(E),E=setTimeout(function(){x(g),g.length=0},20);return!0}function x(e){var t=!0,n=!1,r=void 0;try{for(var o,i=e[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){var a=o.value,s=new XMLHttpRequest;try{s.open("POST",a.url,!0),s.setRequestHeader("Content-Type",a.type),s.send(a.data)}catch(e){}}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}}window.addEventListener("beforeunload",function(e){setTimeout(function(){e.defaultPrevented||(_=!0,E&&clearTimeout(E),g.length>0&&O(g))})}),c(regeneratorRuntime.mark(function e(){var t;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,i;case 2:(t=j())&&(x(t),L());case 4:case"end":return e.stop()}},e,this)}))();var T="send-beacon-queue";function j(){var e=void 0;try{e=sessionStorage.getItem(T)}catch(e){}if(e)return JSON.parse(e)}function O(e){var t=JSON.stringify(e);try{sessionStorage.setItem(T,t)}catch(e){}}function L(){try{sessionStorage.removeItem(T)}catch(e){}}var S=Object.freeze({guaranteedPost:function(e,t,n){w&&navigator.sendBeacon&&function(e){return e=e.split(";")[0],y.indexOf(e)>-1}(n)?navigator.sendBeacon(e,new Blob([t],{type:n})):k(e,t,n)}});r(function(e){var t=!1,n=!1;function r(){}try{var o=Object.create({},{passive:{get:function(){t=!0}},once:{get:function(){n=!0}}});window.addEventListener("test",r,o),window.removeEventListener("test",r,o)}catch(e){}var i=e.exports=function(e){var o=e.addEventListener,i=e.removeEventListener,a=new WeakMap;e.addEventListener=function(e,i,s){if(void 0===s||!0===s||!1===s)return o.call(this,e,i,s);var u=i,c="boolean"==typeof s?{capture:s}:s||{},l=Boolean(c.passive),f=Boolean(c.once),d=Boolean(c.capture),h=u;!n&&f&&(u=function(t){this.removeEventListener(e,i,c),h.call(this,t)}),!t&&l&&(u=function(e){e.preventDefault=r,h.call(this,e)}),a.has(this)||a.set(this,new WeakMap);var p=a.get(this);p.has(i)||p.set(i,[]);var v=1*l+2*f+4*d;p.get(i)[v]=u,o.call(this,e,u,d)},e.removeEventListener=function(e,t,n){var r=Boolean("object"==typeof n?n.capture:n),o=a.get(this);if(!o)return i.call(this,e,t,n);var s=o.get(t);if(!s)return i.call(this,e,t,n);for(var u in s){var c=Boolean(4&u);c===r&&i.call(this,e,s[u],c)}}};t&&n||("undefined"!=typeof EventTarget?i(EventTarget.prototype):(i(Text.prototype),i(HTMLElement.prototype),i(HTMLDocument.prototype),i(Window.prototype),i(XMLHttpRequest.prototype)))}),r(function(e,t){!function(){var t=window,n=document;e.exports={polyfill:function(){if(!("scrollBehavior"in n.documentElement.style&&!0!==t.__forceSmoothScrollPolyfill__)){var e,r=t.HTMLElement||t.Element,o=468,i=(e=t.navigator.userAgent,new RegExp(["MSIE ","Trident/","Edge/"].join("|")).test(e)?1:0),a={scroll:t.scroll||t.scrollTo,scrollBy:t.scrollBy,elementScroll:r.prototype.scroll||u,scrollIntoView:r.prototype.scrollIntoView},s=t.performance&&t.performance.now?t.performance.now.bind(t.performance):Date.now;t.scroll=t.scrollTo=function(){void 0!==arguments[0]&&(!0!==c(arguments[0])?p.call(t,n.body,void 0!==arguments[0].left?~~arguments[0].left:t.scrollX||t.pageXOffset,void 0!==arguments[0].top?~~arguments[0].top:t.scrollY||t.pageYOffset):a.scroll.call(t,void 0!==arguments[0].left?arguments[0].left:"object"!=typeof arguments[0]?arguments[0]:t.scrollX||t.pageXOffset,void 0!==arguments[0].top?arguments[0].top:void 0!==arguments[1]?arguments[1]:t.scrollY||t.pageYOffset))},t.scrollBy=function(){void 0!==arguments[0]&&(c(arguments[0])?a.scrollBy.call(t,void 0!==arguments[0].left?arguments[0].left:"object"!=typeof arguments[0]?arguments[0]:0,void 0!==arguments[0].top?arguments[0].top:void 0!==arguments[1]?arguments[1]:0):p.call(t,n.body,~~arguments[0].left+(t.scrollX||t.pageXOffset),~~arguments[0].top+(t.scrollY||t.pageYOffset)))},r.prototype.scroll=r.prototype.scrollTo=function(){if(void 0!==arguments[0])if(!0!==c(arguments[0])){var e=arguments[0].left,t=arguments[0].top;p.call(this,this,void 0===e?this.scrollLeft:~~e,void 0===t?this.scrollTop:~~t)}else{if("number"==typeof arguments[0]&&void 0===arguments[1])throw new SyntaxError("Value couldn't be converted");a.elementScroll.call(this,void 0!==arguments[0].left?~~arguments[0].left:"object"!=typeof arguments[0]?~~arguments[0]:this.scrollLeft,void 0!==arguments[0].top?~~arguments[0].top:void 0!==arguments[1]?~~arguments[1]:this.scrollTop)}},r.prototype.scrollBy=function(){void 0!==arguments[0]&&(!0!==c(arguments[0])?this.scroll({left:~~arguments[0].left+this.scrollLeft,top:~~arguments[0].top+this.scrollTop,behavior:arguments[0].behavior}):a.elementScroll.call(this,void 0!==arguments[0].left?~~arguments[0].left+this.scrollLeft:~~arguments[0]+this.scrollLeft,void 0!==arguments[0].top?~~arguments[0].top+this.scrollTop:~~arguments[1]+this.scrollTop))},r.prototype.scrollIntoView=function(){if(!0!==c(arguments[0])){var e=function(e){var t;do{t=(e=e.parentNode)===n.body}while(!1===t&&!1===d(e));return t=null,e}(this),r=e.getBoundingClientRect(),o=this.getBoundingClientRect();e!==n.body?(p.call(this,e,e.scrollLeft+o.left-r.left,e.scrollTop+o.top-r.top),"fixed"!==t.getComputedStyle(e).position&&t.scrollBy({left:r.left,top:r.top,behavior:"smooth"})):t.scrollBy({left:o.left,top:o.top,behavior:"smooth"})}else a.scrollIntoView.call(this,void 0===arguments[0]||arguments[0])}}function u(e,t){this.scrollLeft=e,this.scrollTop=t}function c(e){if(null===e||"object"!=typeof e||void 0===e.behavior||"auto"===e.behavior||"instant"===e.behavior)return!0;if("object"==typeof e&&"smooth"===e.behavior)return!1;throw new TypeError("behavior member of ScrollOptions "+e.behavior+" is not a valid value for enumeration ScrollBehavior.")}function l(e,t){return"Y"===t?e.clientHeight+i<e.scrollHeight:"X"===t?e.clientWidth+i<e.scrollWidth:void 0}function f(e,n){var r=t.getComputedStyle(e,null)["overflow"+n];return"auto"===r||"scroll"===r}function d(e){var t=l(e,"Y")&&f(e,"Y"),n=l(e,"X")&&f(e,"X");return t||n}function h(e){var n,r,i,a,u=(s()-e.startTime)/o;a=u=u>1?1:u,n=.5*(1-Math.cos(Math.PI*a)),r=e.startX+(e.x-e.startX)*n,i=e.startY+(e.y-e.startY)*n,e.method.call(e.scrollable,r,i),r===e.x&&i===e.y||t.requestAnimationFrame(h.bind(t,e))}function p(e,r,o){var i,c,l,f,d=s();e===n.body?(i=t,c=t.scrollX||t.pageXOffset,l=t.scrollY||t.pageYOffset,f=a.scroll):(i=e,c=e.scrollLeft,l=e.scrollTop,f=u),h({scrollable:i,method:f,startTime:d,startX:c,startY:l,x:r,y:o})}}}}()}).polyfill;function A(e,t){var n=e.head;o(n,"document.head was not initialized -- app/assets/modules/github/metadata.js:13");var r=!0,i=!1,a=void 0;try{for(var s,u=n.getElementsByTagName("meta")[Symbol.iterator]();!(r=(s=u.next()).done);r=!0){var c=s.value;if(o(c instanceof HTMLMetaElement,"getElementsByTagName('meta') returned non-meta element -- app/assets/modules/github/metadata.js:18"),c.name===t)return c.content||""}}catch(e){i=!0,a=e}finally{try{!r&&u.return&&u.return()}finally{if(i)throw a}}return""}var M=Object.freeze({getMetadataByName:A});function C(e){var t=A(e,"expected-hostname");return!!t&&t.replace(/\.$/,"").split(".").slice(-2).join(".")!==e.location.hostname.replace(/\.$/,"").split(".").slice(-2).join(".")}var D=Object.freeze({default:C}),P=0;function H(e){V()&&I(function(e){var t=e.message,n=e.filename,r=e.lineno,o=e.colno;return B(e.error,{message:t,filename:n,lineno:r,colno:o})}(e))}function R(e){V()&&e.promise&&e.promise.catch(function(e){var t={};e&&e.response instanceof Response&&(t.dataRemoteUrl=e.response.url),q(e,t)})}function N(e){var t=e.body||{},n=new Error("ReportingObserverError");I({bucket:"github-js-reports",error:{columnNumber:t.columnNumber,fileName:""===t.sourceFile?n.fileName:t.sourceFile,lineNumber:t.lineNumber,message:t.message||n.message,name:t.id,stack:n.stack},url:e.url})}function q(e){I(B(e,arguments.length>1&&void 0!==arguments[1]?arguments[1]:{}))}function I(e){var t=A(document,"browser-errors-url");t&&(P++,window.fetch(t,{method:"post",body:JSON.stringify(e)}).catch(function(){}))}var F=(new Date).getTime();function B(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=void 0;return e&&(n={columnNumber:e.columnNumber,fileName:e.fileName,lineNumber:e.lineNumber,message:e.message,name:e.name,stack:e.stack,toString:e.toString()}),Object.assign({},{error:n,url:window.location.href,readyState:document.readyState,referrer:document.referrer,timeSinceLoad:Math.round((new Date).getTime()-F),extensionScripts:JSON.stringify(function(){for(var e=document.getElementsByTagName("script"),t=[],n=0,r=e.length;n<r;n++){var o=e[n];/^(?:chrome-extension|file):/.test(o.src)&&t.push(o.src)}return t}().sort()),user:function(){var e=document.querySelector("meta[name=user-login]");if(e instanceof HTMLMetaElement)return e.content}()},t)}var z=!1;function V(){return!z&&P<10&&"undefined"!=typeof customElements&&"undefined"!=typeof Intl&&"undefined"!=typeof MutationObserver&&!C(document)}window.addEventListener("pageshow",function(){z=!1}),window.addEventListener("pagehide",function(){z=!0});var X,W=Object.freeze({reportEvent:H,reportPromiseRejectionEvent:R,reportIntervention:N,reportError:q});function U(){if(!(this instanceof U))return new U;this.size=0,this.uid=0,this.selectors=[],this.indexes=Object.create(this.indexes),this.activeIndexes=[]}window.addEventListener("error",H),window.addEventListener("unhandledrejection",R),"#b00m"===window.location.hash&&setTimeout(function(){throw new Error("b00m")}),(X=window.ReportingObserver)&&new X(function(e){var t=!0,n=!1,r=void 0;try{for(var o,i=e[Symbol.iterator]();!(t=(o=i.next()).done);t=!0)N(o.value)}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}},{buffered:!0}).observe(),"undefined"==typeof Reflect&&(window.Reflect={construct:function(e,t,n){void 0===n&&(n=e);var r=n.prototype||Object.prototype,o=Object.create(r);return Function.prototype.apply.call(e,o,t)||o}});var Y=window.document.documentElement,G=Y.matches||Y.webkitMatchesSelector||Y.mozMatchesSelector||Y.oMatchesSelector||Y.msMatchesSelector;U.prototype.matchesSelector=function(e,t){return G.call(e,t)},U.prototype.querySelectorAll=function(e,t){return t.querySelectorAll(e)},U.prototype.indexes=[];var K=/^#((?:[\w\u00c0-\uFFFF\-]|\\.)+)/g;U.prototype.indexes.push({name:"ID",selector:function(e){var t;if(t=e.match(K))return t[0].slice(1)},element:function(e){if(e.id)return[e.id]}});var $=/^\.((?:[\w\u00c0-\uFFFF\-]|\\.)+)/g;U.prototype.indexes.push({name:"CLASS",selector:function(e){var t;if(t=e.match($))return t[0].slice(1)},element:function(e){var t=e.className;if(t){if("string"==typeof t)return t.split(/\s/);if("object"==typeof t&&"baseVal"in t)return t.baseVal.split(/\s/)}}});var J,Z=/^((?:[\w\u00c0-\uFFFF\-]|\\.)+)/g;U.prototype.indexes.push({name:"TAG",selector:function(e){var t;if(t=e.match(Z))return t[0].toUpperCase()},element:function(e){return[e.nodeName.toUpperCase()]}}),U.prototype.indexes.default={name:"UNIVERSAL",selector:function(){return!0},element:function(){return[!0]}},J="function"==typeof window.Map?window.Map:function(){function e(){this.map={}}return e.prototype.get=function(e){return this.map[e+" "]},e.prototype.set=function(e,t){this.map[e+" "]=t},e}();var Q=/((?:\((?:\([^()]+\)|[^()]+)+\)|\[(?:\[[^\[\]]*\]|['"][^'"]*['"]|[^\[\]'"]+)+\]|\\.|[^ >+~,(\[\\]+)+|[>+~])(\s*,\s*)?((?:.|\r|\n)*)/g;function ee(e,t){var n,r,o,i,a,s,u=(e=e.slice(0).concat(e.default)).length,c=t,l=[];do{if(Q.exec(""),(o=Q.exec(c))&&(c=o[3],o[2]||!c))for(n=0;n<u;n++)if(a=(s=e[n]).selector(o[1])){for(r=l.length,i=!1;r--;)if(l[r].index===s&&l[r].key===a){i=!0;break}i||l.push({index:s,key:a});break}}while(o);return l}function te(e,t){var n,r,o;for(n=0,r=e.length;n<r;n++)if(o=e[n],t.isPrototypeOf(o))return o}function ne(e,t){return e.id-t.id}U.prototype.logDefaultIndexUsed=function(){},U.prototype.add=function(e,t){var n,r,o,i,a,s,u,c,l=this.activeIndexes,f=this.selectors;if("string"==typeof e){for(n={id:this.uid++,selector:e,data:t},u=ee(this.indexes,e),r=0;r<u.length;r++)i=(c=u[r]).key,(a=te(l,o=c.index))||((a=Object.create(o)).map=new J,l.push(a)),o===this.indexes.default&&this.logDefaultIndexUsed(n),(s=a.map.get(i))||(s=[],a.map.set(i,s)),s.push(n);this.size++,f.push(e)}},U.prototype.remove=function(e,t){if("string"==typeof e){var n,r,o,i,a,s,u,c,l=this.activeIndexes,f={},d=1===arguments.length;for(n=ee(this.indexes,e),o=0;o<n.length;o++)for(r=n[o],i=l.length;i--;)if(s=l[i],r.index.isPrototypeOf(s)){if(u=s.map.get(r.key))for(a=u.length;a--;)(c=u[a]).selector!==e||!d&&c.data!==t||(u.splice(a,1),f[c.id]=!0);break}this.size-=Object.keys(f).length}},U.prototype.queryAll=function(e){if(!this.selectors.length)return[];var t,n,r,o,i,a,s,u,c={},l=[],f=this.querySelectorAll(this.selectors.join(", "),e);for(t=0,r=f.length;t<r;t++)for(i=f[t],n=0,o=(a=this.matches(i)).length;n<o;n++)c[(u=a[n]).id]?s=c[u.id]:(s={id:u.id,selector:u.selector,data:u.data,elements:[]},c[u.id]=s,l.push(s)),s.elements.push(i);return l.sort(ne)},U.prototype.matches=function(e){if(!e)return[];var t,n,r,o,i,a,s,u,c,l,f,d=this.activeIndexes,h={},p=[];for(t=0,o=d.length;t<o;t++)if(u=(s=d[t]).element(e))for(n=0,i=u.length;n<i;n++)if(c=s.map.get(u[n]))for(r=0,a=c.length;r<a;r++)!h[f=(l=c[r]).id]&&this.matchesSelector(e,l.selector)&&(h[f]=!0,p.push(l));return p.sort(ne)};var re={},oe={},ie=new WeakMap,ae=new WeakMap,se=new WeakMap,ue=Object.getOwnPropertyDescriptor(Event.prototype,"currentTarget");function ce(e,t,n){var r=e[t];return e[t]=function(){return n.apply(e,arguments),r.apply(e,arguments)},e}function le(){ie.set(this,!0)}function fe(){ie.set(this,!0),ae.set(this,!0)}function de(){return se.get(this)||null}function he(e,t){ue&&Object.defineProperty(e,"currentTarget",{configurable:!0,enumerable:!0,get:t||ue.get})}function pe(e){var t=(1===e.eventPhase?oe:re)[e.type];if(t){var n=function(e,t,n){var r=[],o=t;do{if(1!==o.nodeType)break;var i=e.matches(o);if(i.length){var a={node:o,observers:i};n?r.unshift(a):r.push(a)}}while(o=o.parentElement);return r}(t,e.target,1===e.eventPhase);if(n.length){ce(e,"stopPropagation",le),ce(e,"stopImmediatePropagation",fe),he(e,de);for(var r=0,o=n.length;r<o&&!ie.get(e);r++){var i=n[r];se.set(e,i.node);for(var a=0,s=i.observers.length;a<s&&!ae.get(e);a++)i.observers[a].data.call(i.node,e)}se.delete(e),he(e)}}}function ve(e,t,n){var r=!!(arguments.length>3&&void 0!==arguments[3]?arguments[3]:{}).capture,o=r?oe:re,i=o[e];i||(i=new U,o[e]=i,document.addEventListener(e,pe,r)),i.add(t,n)}function me(e,t,n){return e.dispatchEvent(new CustomEvent(t,{bubbles:!0,cancelable:!0,detail:n}))}var ge=Object.freeze({on:ve,off:function(e,t,n){var r=!!(arguments.length>3&&void 0!==arguments[3]?arguments[3]:{}).capture,o=r?oe:re,i=o[e];i&&(i.remove(t,n),i.size||(delete o[e],document.removeEventListener(e,pe,r)))},fire:me}),be=new WeakMap;var ye,we=(ye=c(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(e.t0=be.get(t),e.t0){e.next=7;break}return e.t1=Ee,e.next=5,_e(t,"codeEditor:ready");case 5:e.t2=e.sent,e.t0=(0,e.t1)(e.t2);case 7:return e.abrupt("return",e.t0);case 8:case"end":return e.stop()}},e,this)})),function(e){return ye.apply(this,arguments)});function Ee(e){o(e instanceof CustomEvent,"app/assets/modules/github/code-editor.js:18");var t=e.detail.editor;return be.set(e.target,t),t}function _e(e,t){return new Promise(function(n){e.addEventListener(t,n,{once:!0})})}ve("codeEditor:ready",".js-code-editor",Ee);var ke=Object.freeze({getCodeEditor:function(e){return be.get(e)},getAsyncCodeEditor:we});function xe(e,t){var n=void 0;return function(){var r=this,o=arguments;clearTimeout(n),n=setTimeout(function(){clearTimeout(n),e.apply(r,o)},t)}}document.addEventListener("click",function(e){if(e.target instanceof Element){var t=e.target.closest("a[data-confirm], input[type=submit][data-confirm], input[type=checkbox][data-confirm], button[data-confirm]");if(t){var n=t.getAttribute("data-confirm");n&&(t instanceof HTMLInputElement&&t.hasAttribute("data-confirm-checked")&&!t.checked||confirm(n)||(e.stopImmediatePropagation(),e.preventDefault()))}}},!0);var Te=Object.freeze({default:xe});var je=Object.freeze({default:function(e,t){var n=0,r=void 0;return function(){for(var o=this,i=arguments.length,a=Array(i),s=0;s<i;s++)a[s]=arguments[s];var u=Date.now()-n;u<t?(clearTimeout(r),r=setTimeout(function(){n=Date.now(),e.apply(o,a)},t-u)):(n=Date.now(),e.apply(this,a))}}});function Oe(e,t,n){var r=n||HTMLElement,o=e.closest(t);if(o instanceof r)return o;throw new Error("Element not found: <"+r.name+"> "+t)}function Le(e,t,n){var r=n||HTMLElement,o=e.querySelector(t);if(o instanceof r)return o;throw new Error("Element not found: <"+r.name+"> "+t)}function Se(e,t,n){var r=n||HTMLElement,o=[],i=!0,a=!1,s=void 0;try{for(var u,c=e.querySelectorAll(t)[Symbol.iterator]();!(i=(u=c.next()).done);i=!0){var l=u.value;l instanceof r&&o.push(l)}}catch(e){a=!0,s=e}finally{try{!i&&c.return&&c.return()}finally{if(a)throw s}}return o}var Ae=Object.freeze({closest:Oe,query:Le,querySelectorAll:Se,namedItem:function(e,t,n){var r=n||HTMLInputElement,o=e.elements.namedItem(t);if(o instanceof r)return o;throw new Error("Element not found by name: <"+r.name+"> "+t)}}),Me=[],Ce=0;function De(e){c(regeneratorRuntime.mark(function t(){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return Me.push(e),t.next=3,i;case 3:Pe();case 4:case"end":return t.stop()}},t,this)}))()}function Pe(){var e=Ce;Ce=Me.length,He(Me.slice(e),null,window.location.href)}function He(e,t,n){var r=window.location.hash.slice(1),o={oldURL:t,newURL:n,target:r&&document.getElementById(r)},i=!0,a=!1,s=void 0;try{for(var u,c=e[Symbol.iterator]();!(i=(u=c.next()).done);i=!0){u.value.call(null,o)}}catch(e){a=!0,s=e}finally{try{!i&&c.return&&c.return()}finally{if(a)throw s}}}De.clear=function(){Me.length=Ce=0};var Re=window.location.href;window.addEventListener("popstate",function(){Re=window.location.href}),window.addEventListener("hashchange",function(e){var t=window.location.href;try{He(Me,e.oldURL||Re,t)}finally{Re=t}});var Ne=null;document.addEventListener("pjax:start",function(){Ne=window.location.href}),document.addEventListener("pjax:end",function(){He(Me,Ne,window.location.href)});var qe=Object.freeze({default:De}),Ie="ontransitionend"in window;function Fe(e,t){if(Ie){var n=Array.from(e.querySelectorAll(".js-transitionable"));e.classList.add("js-transitionable")&&n.push(e);var r=function(e){var t=Be(e);e.addEventListener("transitionend",function(){e.style.display="",e.style.visibility="",t&&ze(e,function(){e.style.height=""})},{once:!0}),e.style.boxSizing="content-box",e.style.display="block",e.style.visibility="visible",t&&ze(e,function(){e.style.height=getComputedStyle(e).height}),e.offsetHeight},o=!0,i=!1,a=void 0;try{for(var s,u=n[Symbol.iterator]();!(o=(s=u.next()).done);o=!0){r(p=s.value)}}catch(e){i=!0,a=e}finally{try{!o&&u.return&&u.return()}finally{if(i)throw a}}t();var c=!0,l=!1,f=void 0;try{for(var d,h=n[Symbol.iterator]();!(c=(d=h.next()).done);c=!0){var p;if(Be(p=d.value)){var v=getComputedStyle(p).height;p.style.boxSizing="",p.style.height="0px"===v?p.scrollHeight+"px":"0px"}}}catch(e){l=!0,f=e}finally{try{!c&&h.return&&h.return()}finally{if(l)throw f}}}else t()}function Be(e){return"height"===getComputedStyle(e).transitionProperty}function ze(e,t){e.style.transition="none",t(),e.offsetHeight,e.style.transition=""}function Ve(e){var t=e.getAttribute("data-details-container")||".js-details-container",n=Oe(e,t);e.getAttribute("data-initial-state")||e.setAttribute("data-initial-state",e.getAttribute("aria-expanded")||"false"),Fe(n,function(){n.classList.toggle("open"),n.classList.toggle("Details--on"),e.setAttribute("aria-expanded",(e.getAttribute("data-initial-state")!==n.classList.contains("Details--on").toString()).toString()),Promise.resolve().then(function(){!function(e){var t=e.querySelectorAll("input[autofocus], textarea[autofocus]"),n=t[t.length-1];n&&document.activeElement!==n&&n.focus()}(n),function(e){e.classList.contains("tooltipped")&&(e.classList.remove("tooltipped"),e.addEventListener("mouseleave",function(){return e.classList.add("tooltipped")},{once:!0}))}(e),function(e){var t=e.closest(".js-edit-repository-meta");t instanceof HTMLFormElement&&t.reset()}(e);var t=new CustomEvent("details:toggled",{bubbles:!0,cancelable:!1,detail:{open:n.classList.contains("Details--on")}});n.dispatchEvent(t)})})}function Xe(e){for(var t=!1,n=e.parentElement;n;)n.classList.contains("Details-content--shown")&&(t=!0),n.classList.contains("js-details-container")&&(n.classList.toggle("open",!t),n.classList.toggle("Details--on",!t),t=!1),n=n.parentElement}ve("click",".js-details-target",function(e){var t=e.currentTarget;o(t instanceof HTMLElement,"app/assets/modules/github/details.js:128"),Ve(t),e.preventDefault()}),De(function(e){var t=e.target;t&&Xe(t)});var We=Object.freeze({toggleDetailsTarget:Ve,isDetailsTargetExpanded:function(e){var t=Oe(e,e.getAttribute("data-details-container")||".js-details-container").classList;return t.contains("Details--on")||t.contains("open")},ensureExpanded:Xe}),Ue=function(){var e=c(regeneratorRuntime.mark(function e(t){var n,r,i,a,s,u,c;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=Le(document,"#site-details-dialog",HTMLTemplateElement),i=r.content.cloneNode(!0),a=Le(i,"details"),s=Le(a,"details-dialog"),u=Le(a,".js-details-dialog-spinner"),t.dialogClass&&(n=s.classList).add.apply(n,m(t.dialogClass.split(" "))),o(document.body,"app/assets/modules/github/details-dialog.js:20"),document.body.append(i),e.next=10,t.content;case 10:return c=e.sent,u.remove(),s.prepend(c),setTimeout(function(){a.addEventListener("toggle",function(){me(s,"dialog:remove"),a.remove()},{once:!0})}),e.abrupt("return",s);case 15:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),Ye=Object.freeze({dialog:Ue});function Ge(e){var t=e,n=t.ownerDocument;if(n&&t.offsetParent){var r=n.defaultView.HTMLElement;if(t!==n.body){for(;t!==n.body;){if(!(t.parentElement instanceof r))return;t=t.parentElement;var o=getComputedStyle(t),i=o.position,a=o.overflowY,s=o.overflowX;if("fixed"===i||"auto"===a||"auto"===s||"scroll"===a||"scroll"===s)break}return t instanceof Document?null:t}}}function Ke(e,t){var n=t,r=e.ownerDocument;if(r&&r.body){var o=r.documentElement;if(o&&e!==o){var i=$e(e,n);if(i){var a=(n=i._container)===r.documentElement?{top:r.defaultView.pageYOffset,left:r.defaultView.pageXOffset}:{top:n.scrollTop,left:n.scrollLeft},s=i.top-a.top,u=i.left-a.left,c=n.clientHeight,l=n.clientWidth;return{top:s,left:u,bottom:c-(s+e.offsetHeight),right:l-(u+e.offsetWidth),height:c,width:l}}}}}function $e(e,t){var n=e,r=n.ownerDocument;if(r){var o=r.documentElement;if(o){var i=r.body;if(i){for(var a=r.defaultView.HTMLElement,s=0,u=0,c=n.offsetHeight,l=n.offsetWidth;n!==r.body&&n!==t;){if(s+=n.offsetTop||0,u+=n.offsetLeft||0,!(n.offsetParent instanceof a))return;n=n.offsetParent}var f=void 0,d=void 0,h=void 0;if(t&&t!==r&&t!==r.defaultView&&t!==r.documentElement&&t!==r.body){if(!(t instanceof a))return;h=t,f=t.scrollHeight,d=t.scrollWidth}else h=o,f=function(e,t){return Math.max(e.scrollHeight,t.scrollHeight,e.offsetHeight,t.offsetHeight,t.clientHeight)}(i,o),d=function(e,t){return Math.max(e.scrollWidth,t.scrollWidth,e.offsetWidth,t.offsetWidth,t.clientWidth)}(i,o);return{top:s,left:u,bottom:f-(s+c),right:d-(u+l),_container:h}}}}}var Je=Object.freeze({offset:function(e){var t=e.getBoundingClientRect();return{top:t.top+window.pageYOffset,left:t.left+window.pageXOffset}},overflowParent:Ge,overflowOffset:Ke,positionedOffset:$e});var Ze=Object.freeze({microtask:function(){return Promise.resolve()},animationFrame:function(){return new Promise(window.requestAnimationFrame)}});function Qe(){if("Intl"in window)try{return(new window.Intl.DateTimeFormat).resolvedOptions().timeZone}catch(e){if(!(e instanceof RangeError))throw e}}var et=Object.freeze({default:Qe});function tt(e,t){return!!(e&&t in e&&(n=e[t],"function"==typeof n&&n.toString().match(/native code/)));var n}var nt={beacon:tt(window.navigator,"sendBeacon"),customElements:tt(window.customElements,"define"),highResolutionTime:tt(window.performance,"now"),stringEndsWith:tt(String.prototype,"endsWith"),stringStartsWith:tt(String.prototype,"startsWith"),timezone:!!Qe(),userTimingEntries:tt(window.performance,"getEntries"),userTimingMark:tt(window.performance,"mark")},rt=Object.freeze({default:nt});function ot(e){var t=e.querySelector("meta[name=html-safe-nonce]");if(null==t||!(t instanceof HTMLMetaElement))throw new Error("could not find html-safe-nonce on document");var n=t.content;if(n)return n;throw new Error("could not find html-safe-nonce on document")}var it=function(e){function t(e,n){l(this,t);var r=p(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e+" for HTTP "+n.status));return r.response=n,r}return h(t,e),t}(Error);function at(e,t){var n=t.headers.get("content-type")||"";if(!n.startsWith("text/html"))throw new it("expected response with text/html, but was "+n,t);var r=t.headers.get("x-html-safe");if(!r)throw new it("missing X-HTML-Safe nonce",t);if(r!==e)throw new it("response X-HTML-Safe nonce did not match",t)}var st=Object.freeze({getDocumentHtmlSafeNonce:ot,verifyResponseHtmlSafeNonce:at});function ut(e,t){if("function"!=typeof HTMLTemplateElement.bootstrap){var n=e.createElement("template");return n.innerHTML=t,e.importNode(n.content,!0)}var r=e.createDocumentFragment(),i=e.implementation.createHTMLDocument(void 0);o(i.body,"app/assets/modules/github/parse-html.js:27"),i.body.innerHTML=t,o(i.body.childNodes,"app/assets/modules/github/parse-html.js:30");var a=Array.from(i.body.childNodes),s=!0,u=!1,c=void 0;try{for(var l,f=a[Symbol.iterator]();!(s=(l=f.next()).done);s=!0){var d=l.value;r.appendChild(d)}}catch(e){u=!0,c=e}finally{try{!s&&f.return&&f.return()}finally{if(u)throw c}}return"function"==typeof HTMLTemplateElement.bootstrap&&HTMLTemplateElement.bootstrap(r),r}var ct=Object.freeze({parseHTML:ut}),lt=function(e){function t(e){l(this,t);var n=e.statusText?" "+e.statusText:"",r=p(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,"HTTP "+e.status+n));return r.response=e,r}return h(t,e),t}(Error);function ft(e){if(e.status>=200&&e.status<300)return e;throw new lt(e)}function dt(e){return e.json()}function ht(e){return e.text()}function pt(e,t){var n=t?Object.assign({},t):{};n.credentials||(n.credentials="same-origin");var r=new Request(e,n);if(r.headers.append("X-Requested-With","XMLHttpRequest"),/#csrf-token=/.test(r.url))throw new TypeError("URL with encoded CSRF token was passed to fetch() without using the csrfRequest(url) helper");return r}function vt(e,t){var n=pt(e,t);return self.fetch(n).then(ft)}function mt(e,t){var n=pt(e,t);return self.fetch(n).then(ft).then(ht)}function gt(e,t){var n=pt(e,t);return n.headers.set("Accept","application/json"),self.fetch(n).then(ft).then(dt)}var bt=function(){var e=c(regeneratorRuntime.mark(function e(t,n,r){var o,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return o=pt(n,r),e.next=3,self.fetch(o);case 3:return ft(i=e.sent),at(ot(t),i),e.t0=ut,e.t1=t,e.next=10,i.text();case 10:return e.t2=e.sent,e.abrupt("return",(0,e.t0)(e.t1,e.t2));case 12:case"end":return e.stop()}},e,this)}));return function(t,n,r){return e.apply(this,arguments)}}();var yt=Object.freeze({csrfRequest:function(e,t){var n=new URL(e,window.location.origin),r=t?Object.assign({},t):{},o=n.hash.match(/^#csrf-token=([A-Za-z0-9+/=]+)$/);if(!o)throw new TypeError("Expected csrfRequest(url) to have an associated #csrf-token");n.hash="",e=n.toString(),r.mode="same-origin";var i=new Request(e,r);return i.headers.append("Scoped-CSRF-Token",o[1]),i},fetch:vt,fetchText:mt,fetchJSON:gt,fetchForm:function(e){return vt(e.action,{method:e.method,body:new FormData(e)})},fetchSafeDocumentFragment:bt,fetchPoll:function(e,t){return new Promise(function(n,r){!function o(i){vt(e,t).then(function(e){switch(e.status){case 200:n(e);break;case 202:setTimeout(function(){return o(1.5*i)},i);break;default:r(new lt(e))}},r)}(1e3)})}}),wt="function"==typeof FormData&&"entries"in FormData.prototype?function(e){return Array.from(new FormData(e).entries())}:function(e){for(var t=[],n=e.elements,r=0;r<n.length;r++){var o=n[r],i=o.tagName.toUpperCase();if("SELECT"===i||"TEXTAREA"===i||"INPUT"===i){var a=o.type,s=o.name;if(s&&!o.disabled&&"submit"!==a&&"reset"!==a&&"button"!==a&&("radio"!==a&&"checkbox"!==a||o.checked))if("SELECT"===i)for(var u=o.getElementsByTagName("option"),c=0;c<u.length;c++){var l=u[c];l.selected&&t.push([s,l.value])}else"file"===a?(console.warn("form-data-entries could not serialize <input type=file>",o),t.push([s,""])):t.push([s,o.value])}}return t};function Et(e){var t=e.querySelector("input.is-submit-button-value");return t instanceof HTMLInputElement?t:null}function _t(e){var t=e.closest("form");if(t instanceof HTMLFormElement){var n=Et(t);if(e.name){var r=e.matches("input[type=submit]")?"Submit":"",o=e.value||r;n||((n=document.createElement("input")).type="hidden",n.classList.add("is-submit-button-value"),t.prepend(n)),n.name=e.name,n.value=o}else n&&n.remove()}}var kt=Object.freeze({findPersistedSubmitButtonValue:Et,persistSubmitButtonValue:_t});function xt(e,t,n){return e.dispatchEvent(new CustomEvent(t,{bubbles:!0,cancelable:n}))}function Tt(e,t){t&&_t(t),xt(e,"submit",!0)&&e.submit()}function jt(e){if(!(e instanceof HTMLElement))return!1;var t=e.nodeName.toLowerCase(),n=(e.getAttribute("type")||"").toLowerCase();return"select"===t||"textarea"===t||"input"===t&&"submit"!==n&&"reset"!==n||e.isContentEditable}function Ot(e){var t=new URLSearchParams,n=!0,r=!1,o=void 0;try{for(var i,a=wt(e)[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value,u=v(s,2),c=u[0],l=u[1];t.append(c,l)}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}return t.toString()}var Lt=Object.freeze({submit:Tt,changeValue:function(e,t){if("boolean"==typeof t){if(!(e instanceof HTMLInputElement))throw new TypeError("only checkboxes can be set to boolean value");e.checked=t}else{if("checkbox"===e.type)throw new TypeError("checkbox can't be set to string value");e.value=t}xt(e,"change",!1)},fillFormValues:function(e,t){for(var n in t){var r=t[n],o=e.elements.namedItem(n);o instanceof HTMLInputElement?o.value=r:o instanceof HTMLTextAreaElement&&(o.value=r)}},isFormField:jt,serialize:Ot});function St(e){return At(e,Mt(arguments.length>1&&void 0!==arguments[1]?arguments[1]:location.hash))}function At(e,t){if(""!==t)return e.getElementById(t)||e.getElementsByName(t)[0]}function Mt(e){try{return decodeURIComponent(e.slice(1))}catch(e){return""}}var Ct=Object.freeze({findFragmentTarget:St,findElementByFragmentName:At,decodeFragmentValue:Mt});!function(){var e=function(e){this.w=e||[]};e.prototype.set=function(e){this.w[e]=!0},e.prototype.encode=function(){for(var e=[],t=0;t<this.w.length;t++)this.w[t]&&(e[Math.floor(t/6)]^=1<<t%6);for(t=0;t<e.length;t++)e[t]="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_".charAt(e[t]||0);return e.join("")+"~"};var t=new e;function n(e){t.set(e)}var r=function(t,n){var r=new e(i(t));r.set(n),t.set(it,r.w)},o=function(n){n=i(n),n=new e(n);for(var r=t.w.slice(),o=0;o<n.w.length;o++)r[o]=r[o]||n.w[o];return new e(r).encode()},i=function(e){return e=e.get(it),s(e)||(e=[]),e},a=function(e){return"function"==typeof e},s=function(e){return"[object Array]"==Object.prototype.toString.call(Object(e))},u=function(e){return void 0!=e&&-1<(e.constructor+"").indexOf("String")},c=function(e,t){return 0==e.indexOf(t)},l=function(e){return e?e.replace(/^[\s\xa0]+|[\s\xa0]+$/g,""):""},f=function(e){var t=k.createElement("img");return t.width=1,t.height=1,t.src=e,t},d=function(){},h=function(e){return encodeURIComponent instanceof Function?encodeURIComponent(e):(n(28),e)},p=function(e,t,r,o){try{e.addEventListener?e.addEventListener(t,r,!!o):e.attachEvent&&e.attachEvent("on"+t,r)}catch(e){n(27)}},v=/^[\w\-:/.?=&%!]+$/,m=function(e,t,n,r){e&&(n?(r="",t&&v.test(t)&&(r=' id="'+t+'"'),v.test(e)&&k.write("<script"+r+' src="'+e+'"><\/script>')):((n=k.createElement("script")).type="text/javascript",n.async=!0,n.src=e,r&&(n.onload=r),t&&(n.id=t),(e=k.getElementsByTagName("script")[0]).parentNode.insertBefore(n,e)))},g=function(){return"https:"==k.location.protocol},b=function(e,t){return(e=e.match("(?:&|#|\\?)"+h(t).replace(/([.*+?^=!:${}()|\[\]\/\\])/g,"\\$1")+"=([^&#]*)"))&&2==e.length?e[1]:""},y=function(){var e=""+k.location.hostname;return 0==e.indexOf("www.")?e.substring(4):e},w=function(e,t){if(1==t.length&&null!=t[0]&&"object"==typeof t[0])return t[0];for(var n={},r=Math.min(e.length+1,t.length),o=0;o<r;o++){if("object"==typeof t[o]){for(var i in t[o])t[o].hasOwnProperty(i)&&(n[i]=t[o][i]);break}o<e.length&&(n[e[o]]=t[o])}return n},E=function(){this.keys=[],this.values={},this.m={}};E.prototype.set=function(e,t,n){this.keys.push(e),n?this.m[":"+e]=t:this.values[":"+e]=t},E.prototype.get=function(e){return this.m.hasOwnProperty(":"+e)?this.m[":"+e]:this.values[":"+e]},E.prototype.map=function(e){for(var t=0;t<this.keys.length;t++){var n=this.keys[t],r=this.get(n);r&&e(n,r)}};var _=window,k=document,x=window,T=function(e){var t=x._gaUserPrefs;if(t&&t.ioo&&t.ioo()||e&&!0===x["ga-disable-"+e])return!0;try{var n=x.external;if(n&&n._gaUserPrefs&&"oo"==n._gaUserPrefs)return!0}catch(e){}return!1},j=function(e){var t=[],n=k.cookie.split(";");e=new RegExp("^\\s*"+e+"=\\s*(.*?)\\s*$");for(var r=0;r<n.length;r++){var o=n[r].match(e);o&&t.push(o[1])}return t},O=function(e,t,r,o,i,a){if(!(i=!T(i)&&!(A.test(k.location.hostname)||"/"==r&&S.test(o))))return!1;if(t&&1200<t.length&&(t=t.substring(0,1200),n(24)),r=e+"="+t+"; path="+r+"; ",a&&(r+="expires="+new Date((new Date).getTime()+a).toGMTString()+"; "),o&&"none"!=o&&(r+="domain="+o+";"),o=k.cookie,k.cookie=r,!(o=o!=k.cookie))e:{for(e=j(e),o=0;o<e.length;o++)if(t==e[o]){o=!0;break e}o=!1}return o},L=function(e){return h(e).replace(/\(/g,"%28").replace(/\)/g,"%29")},S=/^(www\.)?google(\.com?)?(\.[a-z]{2})?$/,A=/(^|\.)doubleclick\.net$/i,M=function(){return(le||g()?"https:":"http:")+"//www.google-analytics.com"},C=function(e,t,n){if(n=n||d,2036>=t.length)D(e,t,n);else{if(!(8192>=t.length))throw R("len",t.length),new function(e){this.name="len",this.message=e+"-8192"}(t.length);H(e,t,n)||P(e,t,n)||D(e,t,n)}},D=function(e,t,n){var r=f(e+"?"+t);r.onload=r.onerror=function(){r.onload=null,r.onerror=null,n()}},P=function(e,t,n){var r=_.XMLHttpRequest;if(!r)return!1;var o=new r;return"withCredentials"in o&&(o.open("POST",e,!0),o.withCredentials=!0,o.setRequestHeader("Content-Type","text/plain"),o.onreadystatechange=function(){4==o.readyState&&(n(),o=null)},o.send(t),!0)},H=function(e,t,n){return!!_.navigator.sendBeacon&&(!!_.navigator.sendBeacon(e,t)&&(n(),!0))},R=function(e,t,n){1<=100*Math.random()||T("?")||(e=["t=error","_e="+e,"_v=j48","sr=1"],t&&e.push("_f="+t),n&&e.push("_m="+h(n.substring(0,100))),e.push("aip=1"),e.push("z="+G()),D(M()+"/collect",e.join("&"),d))},N=function(e){var t=_.gaData=_.gaData||{};return t[e]=t[e]||{}},q=function(){this.M=[]};function I(e){if(100!=e.get(Ht)&&$n(ee(e,_t))%1e4>=100*te(e,Ht))throw"abort"}function F(e){if(T(ee(e,Tt)))throw"abort"}function B(){var e=k.location.protocol;if("http:"!=e&&"https:"!=e)throw"abort"}function z(e){try{_.navigator.sendBeacon?n(42):_.XMLHttpRequest&&"withCredentials"in new _.XMLHttpRequest&&n(40)}catch(e){}e.set(ot,o(e),!0),e.set(ye,te(e,ye)+1);var t=[];Z.map(function(n,r){r.F&&(void 0!=(n=e.get(n))&&n!=r.defaultValue&&("boolean"==typeof n&&(n*=1),t.push(r.F+"="+h(""+n))))}),t.push("z="+K()),e.set(me,t.join("&"),!0)}function V(e){var t=ee(e,qt)||M()+"/collect",n=ee(e,be);if(!n&&e.get(ge)&&(n="beacon"),n){var r=ee(e,me),o=(o=e.get(ve))||d;"image"==n?D(t,r,o):"xhr"==n&&P(t,r,o)||"beacon"==n&&H(t,r,o)||C(t,r,o)}else C(t,ee(e,me),e.get(ve));t=e.get(Tt),n=(t=N(t)).hitcount,t.hitcount=n?n+1:1,t=e.get(Tt),delete N(t).pending_experiments,e.set(ve,d,!0)}function X(e){var t;(_.gaData=_.gaData||{}).expId&&e.set(Ke,(_.gaData=_.gaData||{}).expId),(_.gaData=_.gaData||{}).expVar&&e.set($e,(_.gaData=_.gaData||{}).expVar);var n=e.get(Tt);if(n=N(n).pending_experiments){var r=[];for(t in n)n.hasOwnProperty(t)&&n[t]&&r.push(encodeURIComponent(t)+"."+encodeURIComponent(n[t]));t=r.join("!")}else t=void 0;t&&e.set(Je,t,!0)}function W(){if(_.navigator&&"preview"==_.navigator.loadPurpose)throw"abort"}function U(e){var t=_.gaDevIds;s(t)&&0!=t.length&&e.set("&did",t.join(","),!0)}function Y(e){if(!e.get(Tt))throw"abort"}q.prototype.add=function(e){this.M.push(e)},q.prototype.D=function(e){try{for(var t=0;t<this.M.length;t++){var n=e.get(this.M[t]);n&&a(n)&&n.call(_,e)}}catch(e){}(t=e.get(ve))!=d&&a(t)&&(e.set(ve,d,!0),setTimeout(t,10))};var G=function(){return Math.round(2147483647*Math.random())},K=function(){try{var e=new Uint32Array(1);return _.crypto.getRandomValues(e),2147483647&e[0]}catch(e){return G()}};function $(e){var t=te(e,tt);if(500<=t&&n(15),"transaction"!=(r=ee(e,pe))&&"item"!=r){var r=te(e,rt),o=(new Date).getTime(),i=te(e,nt);if(0==i&&e.set(nt,o),0<(i=Math.round(2*(o-i)/1e3))&&(r=Math.min(r+i,20),e.set(nt,o)),0>=r)throw"abort";e.set(rt,--r)}e.set(tt,++t)}var J=function(){this.data=new E},Z=new E,Q=[];J.prototype.get=function(e){var t=oe(e),n=this.data.get(e);return t&&void 0==n&&(n=a(t.defaultValue)?t.defaultValue():t.defaultValue),t&&t.Z?t.Z(this,e,n):n};var ee=function(e,t){return void 0==(e=e.get(t))?"":""+e},te=function(e,t){return void 0==(e=e.get(t))||""===e?0:1*e};J.prototype.set=function(e,t,n){if(e)if("object"==typeof e)for(var r in e)e.hasOwnProperty(r)&&ne(this,r,e[r],n);else ne(this,e,t,n)};var ne=function(e,t,n,r){if(void 0!=n)switch(t){case Tt:Tn.test(n)}var o=oe(t);o&&o.o?o.o(e,t,n,r):e.data.set(t,n,r)},re=function(e,t,n,r,o){this.name=e,this.F=t,this.Z=r,this.o=o,this.defaultValue=n},oe=function(e){var t=Z.get(e);if(!t)for(var n=0;n<Q.length;n++){var r=Q[n],o=r[0].exec(e);if(o){t=r[1](o),Z.set(t.name,t);break}}return t},ie=function(e,t,n,r,o){return e=new re(e,t,n,r,o),Z.set(e.name,e),e.name},ae=function(e,t){Q.push([new RegExp("^"+e+"$"),t])},se=function(e,t,n){return ie(e,t,n,void 0,ue)},ue=function(){},ce=u(window.GoogleAnalyticsObject)&&l(window.GoogleAnalyticsObject)||"ga",le=!1,fe=se("apiVersion","v"),de=se("clientVersion","_v");ie("anonymizeIp","aip");var he=ie("adSenseId","a"),pe=ie("hitType","t"),ve=ie("hitCallback"),me=ie("hitPayload");ie("nonInteraction","ni"),ie("currencyCode","cu"),ie("dataSource","ds");var ge=ie("useBeacon",void 0,!1),be=ie("transport");ie("sessionControl","sc",""),ie("sessionGroup","sg"),ie("queueTime","qt");var ye=ie("_s","_s");ie("screenName","cd");var we=ie("location","dl",""),Ee=ie("referrer","dr"),_e=ie("page","dp","");ie("hostname","dh");var ke=ie("language","ul"),xe=ie("encoding","de");ie("title","dt",function(){return k.title||void 0}),ae("contentGroup([0-9]+)",function(e){return new re(e[0],"cg"+e[1])});var Te=ie("screenColors","sd"),je=ie("screenResolution","sr"),Oe=ie("viewportSize","vp"),Le=ie("javaEnabled","je"),Se=ie("flashVersion","fl");ie("campaignId","ci"),ie("campaignName","cn"),ie("campaignSource","cs"),ie("campaignMedium","cm"),ie("campaignKeyword","ck"),ie("campaignContent","cc");var Ae=ie("eventCategory","ec"),Me=ie("eventAction","ea"),Ce=ie("eventLabel","el"),De=ie("eventValue","ev"),Pe=ie("socialNetwork","sn"),He=ie("socialAction","sa"),Re=ie("socialTarget","st"),Ne=ie("l1","plt"),qe=ie("l2","pdt"),Ie=ie("l3","dns"),Fe=ie("l4","rrt"),Be=ie("l5","srt"),ze=ie("l6","tcp"),Ve=ie("l7","dit"),Xe=ie("l8","clt"),We=ie("timingCategory","utc"),Ue=ie("timingVar","utv"),Ye=ie("timingLabel","utl"),Ge=ie("timingValue","utt");ie("appName","an"),ie("appVersion","av",""),ie("appId","aid",""),ie("appInstallerId","aiid",""),ie("exDescription","exd"),ie("exFatal","exf");var Ke=ie("expId","xid"),$e=ie("expVar","xvar"),Je=ie("exp","exp"),Ze=ie("_utma","_utma"),Qe=ie("_utmz","_utmz"),et=ie("_utmht","_utmht"),tt=ie("_hc",void 0,0),nt=ie("_ti",void 0,0),rt=ie("_to",void 0,20);ae("dimension([0-9]+)",function(e){return new re(e[0],"cd"+e[1])}),ae("metric([0-9]+)",function(e){return new re(e[0],"cm"+e[1])}),ie("linkerParam",void 0,void 0,function(e){var t=un(e=e.get(_t),0);return"_ga=1."+h(t+"."+e)},ue);var ot=ie("usage","_u"),it=ie("_um");ie("forceSSL",void 0,void 0,function(){return le},function(e,t,r){n(34),le=!!r});var at=ie("_j1","jid");ae("\\&(.*)",function(e){var t=new re(e[0],e[1]),n=function(e){var t;return Z.map(function(n,r){r.F==e&&(t=r)}),t&&t.name}(e[0].substring(1));return n&&(t.Z=function(e){return e.get(n)},t.o=function(e,t,r,o){e.set(n,r,o)},t.F=void 0),t});var st=se("_oot"),ut=ie("previewTask"),ct=ie("checkProtocolTask"),lt=ie("validationTask"),ft=ie("checkStorageTask"),dt=ie("historyImportTask"),ht=ie("samplerTask"),pt=ie("_rlt"),vt=ie("buildHitTask"),mt=ie("sendHitTask"),gt=ie("ceTask"),bt=ie("devIdTask"),yt=ie("timingTask"),wt=ie("displayFeaturesTask"),Et=se("name"),_t=se("clientId","cid"),kt=se("clientIdTime"),xt=ie("userId","uid"),Tt=se("trackingId","tid"),jt=se("cookieName",void 0,"_ga"),Ot=se("cookieDomain"),Lt=se("cookiePath",void 0,"/"),St=se("cookieExpires",void 0,63072e3),At=se("legacyCookieDomain"),Mt=se("legacyHistoryImport",void 0,!0),Ct=se("storage",void 0,"cookie"),Dt=se("allowLinker",void 0,!1),Pt=se("allowAnchor",void 0,!0),Ht=se("sampleRate","sf",100),Rt=se("siteSpeedSampleRate",void 0,1),Nt=se("alwaysSendReferrer",void 0,!1),qt=ie("transportUrl"),It=ie("_r","_r");function Ft(e,t,r,o){t[e]=function(){try{return o&&n(o),r.apply(this,arguments)}catch(t){throw R("exc",e,t&&t.name),t}}}var Bt=function(e,t,n){this.V=e,this.fa=t,this.$=!1,this.oa=n,this.ea=1},zt=function(e,t){var n;if(e.fa&&e.$)return 0;if(e.$=!0,t){if(e.oa&&te(t,e.oa))return te(t,e.oa);if(0==t.get(Rt))return 0}return 0==e.V?0:(void 0===n&&(n=K()),0==n%e.V?Math.floor(n/e.V)%e.ea+1:0)};var Vt=function(e){var t={};if(Xt(t)||Wt(t)){var n=t[Ne];void 0==n||1/0==n||isNaN(n)||(0<n?(Ut(t,Ie),Ut(t,ze),Ut(t,Be),Ut(t,qe),Ut(t,Fe),Ut(t,Ve),Ut(t,Xe),e(t)):p(_,"load",function(){Vt(e)},!1))}},Xt=function(e){var t;if(!(t=(t=_.performance||_.webkitPerformance)&&t.timing))return!1;var n=t.navigationStart;return 0!=n&&(e[Ne]=t.loadEventStart-n,e[Ie]=t.domainLookupEnd-t.domainLookupStart,e[ze]=t.connectEnd-t.connectStart,e[Be]=t.responseStart-t.requestStart,e[qe]=t.responseEnd-t.responseStart,e[Fe]=t.fetchStart-n,e[Ve]=t.domInteractive-n,e[Xe]=t.domContentLoadedEventStart-n,!0)},Wt=function(e){if(_.top!=_)return!1;var t=_.external,n=t&&t.onloadT;return t&&!t.isValidLoadTime&&(n=void 0),2147483648<n&&(n=void 0),0<n&&t.setPageReadyTime(),void 0!=n&&(e[Ne]=n,!0)},Ut=function(e,t){var n=e[t];(isNaN(n)||1/0==n||0>n)&&(e[t]=void 0)},Yt=function(e){return function(t){if("pageview"==t.get(pe)&&!e.I){e.I=!0;var n=function(e){var t=Math.min(te(e,Rt),100);return!($n(ee(e,_t))%100>=t)}(t);t=0<b(t.get(we),"gclid").length,(n||t)&&Vt(function(t){e.send(n?"timing":"adtiming",t)})}}},Gt=!1,Kt=function(e){if("cookie"==ee(e,Ct)){var t=ee(e,jt),r=Zt(e),o=tn(ee(e,Lt)),i=en(ee(e,Ot)),a=1e3*te(e,St),s=ee(e,Tt);if("auto"!=i)O(t,r,o,i,s,a)&&(Gt=!0);else{var u;if(n(32),r=[],4!=(i=y().split(".")).length||(u=i[i.length-1],parseInt(u,10)!=u)){for(u=i.length-2;0<=u;u--)r.push(i.slice(u).join("."));r.push("none"),u=r}else u=["none"];for(var c=0;c<u.length;c++)if(i=u[c],e.data.set(Ot,i),r=Zt(e),O(t,r,o,i,s,a))return void(Gt=!0);e.data.set(Ot,"auto")}}},$t=function(e){if("cookie"==ee(e,Ct)&&!Gt&&(Kt(e),!Gt))throw"abort"},Jt=function(e){if(e.get(Mt)){var t=ee(e,Ot),r=ee(e,At)||y(),o=rn("__utma",r,t);o&&(n(19),e.set(et,(new Date).getTime(),!0),e.set(Ze,o.R),(t=rn("__utmz",r,t))&&o.hash==t.hash&&e.set(Qe,t.R))}},Zt=function(e){var t=L(ee(e,_t)),n=en(ee(e,Ot)).split(".").length;return 1<(e=nn(ee(e,Lt)))&&(n+="-"+e),["GA1",n,t].join(".")},Qt=function(e,t,n){for(var r,o=[],i=[],a=0;a<e.length;a++){var s=e[a];s.H[n]==t?o.push(s):void 0==r||s.H[n]<r?(i=[s],r=s.H[n]):s.H[n]==r&&i.push(s)}return 0<o.length?o:i},en=function(e){return 0==e.indexOf(".")?e.substr(1):e},tn=function(e){return e?(1<e.length&&e.lastIndexOf("/")==e.length-1&&(e=e.substr(0,e.length-1)),0!=e.indexOf("/")&&(e="/"+e),e):"/"},nn=function(e){return"/"==(e=tn(e))?1:e.split("/").length};function rn(e,t,n){"none"==t&&(t="");var r=[],o=j(e);e="__utma"==e?6:2;for(var i=0;i<o.length;i++){var a=(""+o[i]).split(".");a.length>=e&&r.push({hash:a[0],R:o[i],O:a})}if(0!=r.length)return 1==r.length?r[0]:on(t,r)||on(n,r)||on(null,r)||r[0]}function on(e,t){var n;null==e?n=e=1:(n=$n(e),e=$n(c(e,".")?e.substring(1):"."+e));for(var r=0;r<t.length;r++)if(t[r].hash==n||t[r].hash==e)return t[r]}var an=new RegExp(/^https?:\/\/([^\/:]+)/),sn=/(.*)([?&#])(?:_ga=[^&#]*)(?:&?)(.*)/;function un(e,t){var n=new Date,r=_.navigator,o=r.plugins||[];for(e=[e,r.userAgent,n.getTimezoneOffset(),n.getYear(),n.getDate(),n.getHours(),n.getMinutes()+t],t=0;t<o.length;++t)e.push(o[t].description);return $n(e.join("."))}var cn=function(e){n(48),this.target=e,this.T=!1};cn.prototype.ca=function(e,t){if(e.tagName){if("a"==e.tagName.toLowerCase())return void(e.href&&(e.href=ln(this,e.href,t)));if("form"==e.tagName.toLowerCase())return fn(this,e)}if("string"==typeof e)return ln(this,e,t)};var ln=function(e,t,n){(o=sn.exec(t))&&3<=o.length&&(t=o[1]+(o[3]?o[2]+o[3]:"")),e=e.target.get("linkerParam");var r=t.indexOf("?"),o=t.indexOf("#");return n?t+=(-1==o?"#":"&")+e:(n=-1==r?"?":"&",t=-1==o?t+(n+e):t.substring(0,o)+n+e+t.substring(o)),t.replace(/&+_ga=/,"&_ga=")},fn=function(e,t){if(t&&t.action){var n=e.target.get("linkerParam").split("=")[1];if("get"==t.method.toLowerCase()){e=t.childNodes||[];for(var r=0;r<e.length;r++)if("_ga"==e[r].name)return void e[r].setAttribute("value",n);(e=k.createElement("input")).setAttribute("type","hidden"),e.setAttribute("name","_ga"),e.setAttribute("value",n),t.appendChild(e)}else"post"==t.method.toLowerCase()&&(t.action=ln(e,t.action))}};function dn(e,t){if(t==k.location.hostname)return!1;for(var n=0;n<e.length;n++)if(e[n]instanceof RegExp){if(e[n].test(t))return!0}else if(0<=t.indexOf(e[n]))return!0;return!1}cn.prototype.S=function(e,t,r){function o(r){try{var o;r=r||_.event;e:{var a=r.target||r.srcElement;for(r=100;a&&0<r;){if(a.href&&a.nodeName.match(/^a(?:rea)?$/i)){o=a;break e}a=a.parentNode,r--}o={}}("http:"==o.protocol||"https:"==o.protocol)&&dn(e,o.hostname||"")&&o.href&&(o.href=ln(i,o.href,t))}catch(e){n(26)}}var i=this;this.T||(this.T=!0,p(k,"mousedown",o,!1),p(k,"keyup",o,!1)),r&&p(k,"submit",function(t){if((t=(t=t||_.event).target||t.srcElement)&&t.action){var n=t.action.match(an);n&&dn(e,n[1])&&fn(i,t)}})};var hn,pn=/^(GTM|OPT)-[A-Z0-9]+$/,vn=/;_gaexp=[^;]*/g,mn=/;((__utma=)|([^;=]+=GAX?\d+\.))[^;]*/g,gn=function(e,t,r){this.U=at,this.aa=t,(t=r)||(t=(t=ee(e,Et))&&"t0"!=t?En.test(t)?"_gat_"+L(ee(e,Tt)):"_gat_"+L(t):"_gat"),this.Y=t,zt(new Bt(10),e)&&(n(30),this.pa=!0)},bn=function(e,t){t.get(e.U)||("1"==j(e.Y)[0]?t.set(e.U,"",!0):t.set(e.U,""+G(),!0))},yn=function(e,t){if(t.get(e.U)){var n=6e5;e.pa&&(n/=10),O(e.Y,"1",t.get(Lt),t.get(Ot),t.get(Tt),n)}},wn=function(e,t){if(t.get(e.U)){var n=new E,r=function(e){oe(e).F&&n.set(oe(e).F,t.get(e))};r(fe),r(de),r(Tt),r(_t),r(xt),r(e.U),n.set(oe(ot).F,o(t));var i=e.aa;n.map(function(e,t){i+=h(e)+"=",i+=h(""+t)+"&"}),i+="z="+G(),f(i),t.set(e.U,"",!0)}},En=/^gtm\d+$/,_n=function(e,t){var n;(e=e.b).get("dcLoaded")||(r(e,29),(t=t||{})[jt]&&(n=L(t[jt])),function(e,t){var n=t.get(vt);t.set(vt,function(t){bn(e,t);var r=n(t);return yn(e,t),r});var r=t.get(mt);t.set(mt,function(t){var n=r(t);return wn(e,t),n})}(t=new gn(e,"https://stats.g.doubleclick.net/r/collect?t=dc&aip=1&_r=3&",n),e),e.set("dcLoaded",!0))},kn=function(e){if(!e.get("dcLoaded")&&"cookie"==e.get(Ct)){r(e,51);var t=new gn(e);bn(t,e),yn(t,e),e.get(t.U)&&(e.set(It,1,!0),e.set(qt,M()+"/r/collect",!0))}},xn=function(e){return e?(1*e).toFixed(3):"0"},Tn=/^(UA|YT|MO|GP)-(\d+)-(\d+)$/,jn=function(e){function t(e,t){i.b.data.set(e,t)}function r(e,n){t(e,n),i.filters.add(e)}function o(e,t,r){zt(new Bt(1e4,!0,t),i.b)&&(e=j(e))&&0<e.length&&n(r)}var i=this;this.b=new J,this.filters=new q,t(Et,e[Et]),t(Tt,l(e[Tt])),t(jt,e[jt]),t(Ot,e[Ot]||y()),t(Lt,e[Lt]),t(St,e[St]),t(At,e[At]),t(Mt,e[Mt]),t(Dt,e[Dt]),t(Pt,e[Pt]),t(Ht,e[Ht]),t(Rt,e[Rt]),t(Nt,e[Nt]),t(Ct,e[Ct]),t(xt,e[xt]),t(kt,e[kt]),t(fe,1),t(de,"j48"),r(st,F),r(ut,W),r(ct,B),r(lt,Y),r(ft,$t),r(dt,Jt),r(ht,I),r(pt,$),r(gt,X),r(bt,U),r(wt,kn),r(vt,z),r(mt,V),r(yt,Yt(this)),On(this.b,e[_t]),Ln(this.b),this.b.set(he,function(){var e=_.gaGlobal=_.gaGlobal||{};return e.hid=e.hid||G()}()),function(e,t,n){if(!hn){var r;r=k.location.hash;var o=_.name,i=/^#?gaso=([^&]*)/;(o=(r=(r=r&&r.match(i)||o&&o.match(i))?r[1]:j("GASO")[0]||"")&&r.match(/^(?:!([-0-9a-z.]{1,40})!)?([-.\w]{10,1200})$/i))&&(O("GASO",""+r,n,t,e,0),window._udo||(window._udo=t),window._utcp||(window._utcp=n),e=o[1],m("https://www.google.com/analytics/web/inpage/pub/inpage.js?"+(e?"prefix="+e+"&":"")+G(),"_gasojs")),hn=!0}}(this.b.get(Tt),this.b.get(Ot),this.b.get(Lt)),this.ra=new Bt(1e4,!0,"gaexp10"),o("_gid","gacookie11",41),o("_gaid","gacookie12",44)},On=function(e,t){if("cookie"==ee(e,Ct)){var r;Gt=!1;e:{var o=j(ee(e,jt));if(o&&!(1>o.length)){r=[];for(var i=0;i<o.length;i++){var a,s=(a=o[i].split(".")).shift();("GA1"==s||"1"==s)&&1<a.length?(1==(s=a.shift().split("-")).length&&(s[1]="1"),s[0]*=1,s[1]*=1,a={H:s,s:a.join(".")}):a=void 0,a&&r.push(a)}if(1==r.length){n(13),r=r[0].s;break e}if(0!=r.length){if(n(14),o=en(ee(e,Ot)).split(".").length,1==(r=Qt(r,o,0)).length){r=r[0].s;break e}o=nn(ee(e,Lt)),r=(r=Qt(r,o,1))[0]&&r[0].s;break e}n(12)}r=void 0}r||(r=ee(e,Ot),void 0!=(r=rn("__utma",o=ee(e,At)||y(),r))?(n(10),r=r.O[1]+"."+r.O[2]):r=void 0),r&&(e.data.set(_t,r),Gt=!0)}if(r=e.get(Pt),(i=b(k.location[r?"href":"search"],"_ga"))&&(e.get(Dt)?-1==(r=i.indexOf("."))?n(22):(o=i.substring(r+1),"1"!=i.substring(0,r)?n(22):-1==(r=o.indexOf("."))?n(22):(i=o.substring(0,r))!=un(r=o.substring(r+1),0)&&i!=un(r,-1)&&i!=un(r,-2)?n(23):(n(11),e.data.set(_t,r))):n(21)),t&&(n(9),e.data.set(_t,h(t))),!e.get(_t))if(t=(t=_.gaGlobal&&_.gaGlobal.vid)&&-1!=t.search(/^(?:utma\.)?\d+\.\d+$/)?t:void 0)n(17),e.data.set(_t,t);else{for(n(8),r=(t=_.navigator.userAgent+(k.cookie?k.cookie:"")+(k.referrer?k.referrer:"")).length,o=_.history.length;0<o;)t+=o--^r++;e.data.set(_t,[G()^2147483647&$n(t),Math.round((new Date).getTime()/1e3)].join("."))}Kt(e)},Ln=function(e){var t=_.navigator,r=_.screen,o=k.location;if(e.set(Ee,function(e){var t=k.referrer;if(/^https?:\/\//i.test(t)){if(e)return t;e="//"+k.location.hostname;var n=t.indexOf(e);if(!(5!=n&&6!=n||"/"!=(e=t.charAt(n+e.length))&&"?"!=e&&""!=e&&":"!=e))return;return t}}(e.get(Nt))),o){var i=o.pathname||"";"/"!=i.charAt(0)&&(n(31),i="/"+i),e.set(we,o.protocol+"//"+o.hostname+i+o.search)}r&&e.set(je,r.width+"x"+r.height),r&&e.set(Te,r.colorDepth+"-bit");r=k.documentElement;var a=(i=k.body)&&i.clientWidth&&i.clientHeight,s=[];if(r&&r.clientWidth&&r.clientHeight&&("CSS1Compat"===k.compatMode||!a)?s=[r.clientWidth,r.clientHeight]:a&&(s=[i.clientWidth,i.clientHeight]),r=0>=s[0]||0>=s[1]?"":s.join("x"),e.set(Oe,r),e.set(Se,function(){var e,t,n;if((n=(n=_.navigator)?n.plugins:null)&&n.length)for(var r=0;r<n.length&&!t;r++){var o=n[r];-1<o.name.indexOf("Shockwave Flash")&&(t=o.description)}if(!t)try{t=(e=new ActiveXObject("ShockwaveFlash.ShockwaveFlash.7")).GetVariable("$version")}catch(e){}if(!t)try{e=new ActiveXObject("ShockwaveFlash.ShockwaveFlash.6"),t="WIN 6,0,21,0",e.AllowScriptAccess="always",t=e.GetVariable("$version")}catch(e){}if(!t)try{t=(e=new ActiveXObject("ShockwaveFlash.ShockwaveFlash")).GetVariable("$version")}catch(e){}return t&&(e=t.match(/[\d]+/g))&&3<=e.length&&(t=e[0]+"."+e[1]+" r"+e[2]),t||void 0}()),e.set(xe,k.characterSet||k.charset),e.set(Le,t&&"function"==typeof t.javaEnabled&&t.javaEnabled()||!1),e.set(ke,(t&&(t.language||t.browserLanguage)||"").toLowerCase()),o&&e.get(Pt)&&(t=k.location.hash)){for(t=t.split(/[?&#]+/),o=[],r=0;r<t.length;++r)(c(t[r],"utm_id")||c(t[r],"utm_campaign")||c(t[r],"utm_source")||c(t[r],"utm_medium")||c(t[r],"utm_term")||c(t[r],"utm_content")||c(t[r],"gclid")||c(t[r],"dclid")||c(t[r],"gclsrc"))&&o.push(t[r]);0<o.length&&(t="#"+o.join("&"),e.set(we,e.get(we)+t))}};jn.prototype.get=function(e){return this.b.get(e)},jn.prototype.set=function(e,t){this.b.set(e,t)};var Sn={pageview:[_e],event:[Ae,Me,Ce,De],social:[Pe,He,Re],timing:[We,Ue,Ge,Ye]};jn.prototype.send=function(e){var t,r;1>arguments.length||("string"==typeof arguments[0]?(t=arguments[0],r=[].slice.call(arguments,1)):(t=arguments[0]&&arguments[0][pe],r=arguments),t&&((r=w(Sn[t]||[],r))[pe]=t,this.b.set(r,void 0,!0),this.filters.D(this.b),this.b.data.m={},zt(this.ra,this.b)&&function(e){var t=_.performance;if(t&&t.getEntriesByName){n(35);var r="https://www.google-analytics.com/analytics.js?wpid="+e;m(r,void 0,void 0,function(){try{var o=1,i=t.getEntriesByName("https://www.google-analytics.com/analytics.js");i&&0!=i.length||(i=t.getEntriesByName("http://www.google-analytics.com/analytics.js"),o=0);var a=t.getEntriesByName(r);if(i&&1==i.length&&a&&1==a.length){n(37);var s=i[0],u=a[0],c={tid:e,ad:xn(s.duration),bd:xn(u.duration),ar:xn(s.responseEnd-s.requestStart),br:xn(u.responseEnd-u.requestStart),an:xn(s.domainLookupEnd-s.domainLookupStart),bn:xn(u.domainLookupEnd-u.domainLookupStart),ac:xn(s.connectEnd-s.connectStart),bc:xn(u.connectEnd-u.connectStart),as:o};for(var l in(o=[]).push("_v=j48"),o.push("id=10"),c)c.hasOwnProperty(l)&&o.push(l+"="+h(c[l]));o.push("z="+G()),D("https://www.google-analytics.com/u/d",o.join("&"),d)}}catch(e){}})}}(this.b.get(Tt))))},jn.prototype.ma=function(e,t){var n=this;In(e,n,t)||(Bn(e,function(){In(e,n,t)}),Fn(String(n.get(Et)),e,void 0,t,!0))};var An,Mn,Cn,Dn,Pn=function(e){return"prerender"!=k.visibilityState&&(e(),!0)},Hn=function(e){if(!Pn(e)){n(16);var t=!1,r=function(){if(!t&&Pn(e)){t=!0;var n=r,o=k;o.removeEventListener?o.removeEventListener("visibilitychange",n,!1):o.detachEvent&&o.detachEvent("onvisibilitychange",n)}};p(k,"visibilitychange",r)}},Rn=/^(?:(\w+)\.)?(?:(\w+):)?(\w+)$/,Nn=function(e){if(a(e[0]))this.u=e[0];else{var t=Rn.exec(e[0]);if(null!=t&&4==t.length&&(this.c=t[1]||"t0",this.K=t[2]||"",this.C=t[3],this.a=[].slice.call(e,1),this.K||(this.A="create"==this.C,this.i="require"==this.C,this.g="provide"==this.C,this.ba="remove"==this.C),this.i&&(3<=this.a.length?(this.X=this.a[1],this.W=this.a[2]):this.a[1]&&(u(this.a[1])?this.X=this.a[1]:this.W=this.a[1]))),t=e[1],e=e[2],!this.C)throw"abort";if(this.i&&(!u(t)||""==t))throw"abort";if(this.g&&(!u(t)||""==t||!a(e)))throw"abort";if(qn(this.c)||qn(this.K))throw"abort";if(this.g&&"t0"!=this.c)throw"abort"}};function qn(e){return 0<=e.indexOf(".")||0<=e.indexOf(":")}An=new E,Cn=new E,Dn=new E,Mn={ec:45,ecommerce:46,linkid:47};var In=function(e,t,n){t==Un||t.get(Et);var r=An.get(e);return!!a(r)&&(t.plugins_=t.plugins_||new E,!!t.plugins_.get(e)||(t.plugins_.set(e,new r(t,n||{})),!0))},Fn=function(e,t,r,o,i){if(!a(An.get(t))&&!Cn.get(t)){if(Mn.hasOwnProperty(t)&&n(Mn[t]),pn.test(t)){if(n(52),!(e=Un.j(e)))return!0;o={id:t,B:(r=o||{}).dataLayer||"dataLayer",ia:!!e.get("anonymizeIp"),na:i,G:!1},e.get("&gtm")==t&&(o.G=!0);var s=String(e.get("name"));"t0"!=s&&(o.target=s),T(String(e.get("trackingId")))||(o.ja=String(e.get(_t)),o.ka=Number(e.get(kt)),r=r.palindrome?mn:vn,r=(r=k.cookie.replace(/^|(; +)/g,";").match(r))?r.sort().join("").substring(1):void 0,o.la=r,o.qa=b(e.b.get(we)||"","gclid")),e=o.B,r=(new Date).getTime(),_[e]=_[e]||[],r={"gtm.start":r},i||(r.event="gtm.js"),_[e].push(r),r=function(e){function t(e,t){t&&(n+="&"+e+"="+h(t))}var n="https://www.google-analytics.com/gtm/js?id="+h(e.id);return"dataLayer"!=e.B&&t("l",e.B),t("t",e.target),t("cid",e.ja),t("cidt",e.ka),t("gac",e.la),t("aip",e.ia),e.na&&t("m","sync"),t("cycle",e.G),e.qa&&t("gclid",e.qa),n}(o)}!r&&Mn.hasOwnProperty(t)?(n(39),r=t+".js"):n(43),r&&(r&&0<=r.indexOf("/")||(r=(le||g()?"https:":"http:")+"//www.google-analytics.com/plugins/ua/"+r),e=(o=Xn(r)).protocol,r=k.location.protocol,("https:"==e||e==r||"http:"==e&&"http:"==r)&&Vn(o)&&(m(o.url,void 0,i),Cn.set(t,!0)))}},Bn=function(e,t){var n=Dn.get(e)||[];n.push(t),Dn.set(e,n)},zn=function(e,t){An.set(e,t),t=Dn.get(e)||[];for(var n=0;n<t.length;n++)t[n]();Dn.set(e,[])},Vn=function(e){var t=Xn(k.location.href);return!!c(e.url,"https://www.google-analytics.com/gtm/js?id=")||!(e.query||0<=e.url.indexOf("?")||0<=e.path.indexOf("://"))&&(e.host==t.host&&e.port==t.port||(t="http:"==e.protocol?80:443,!("www.google-analytics.com"!=e.host||(e.port||t)!=t||!c(e.path,"/plugins/"))))},Xn=function(e){function t(e){var t=(e.hostname||"").split(":")[0].toLowerCase(),n=(e.protocol||"").toLowerCase();n=1*e.port||("http:"==n?80:"https:"==n?443:"");return e=e.pathname||"",c(e,"/")||(e="/"+e),[t,""+n,e]}var n=k.createElement("a");n.href=k.location.href;var r=(n.protocol||"").toLowerCase(),o=t(n),i=n.search||"",a=r+"//"+o[0]+(o[1]?":"+o[1]:"");return c(e,"//")?e=r+e:c(e,"/")?e=a+e:!e||c(e,"?")?e=a+o[2]+(e||i):0>e.split("/")[0].indexOf(":")&&(e=a+o[2].substring(0,o[2].lastIndexOf("/"))+"/"+e),n.href=e,r=t(n),{protocol:(n.protocol||"").toLowerCase(),host:r[0],port:r[1],path:r[2],query:n.search||"",url:e||""}},Wn={ga:function(){Wn.f=[]}};Wn.ga(),Wn.D=function(e){var t=Wn.J.apply(Wn,arguments);t=Wn.f.concat(t);for(Wn.f=[];0<t.length&&!Wn.v(t[0])&&(t.shift(),!(0<Wn.f.length)););Wn.f=Wn.f.concat(t)},Wn.J=function(e){for(var t=[],n=0;n<arguments.length;n++)try{var r=new Nn(arguments[n]);r.g?zn(r.a[0],r.a[1]):(r.i&&(r.ha=Fn(r.c,r.a[0],r.X,r.W)),t.push(r))}catch(e){}return t},Wn.v=function(e){try{if(e.u)e.u.call(_,Un.j("t0"));else{var t=e.c==ce?Un:Un.j(e.c);if(e.A)"t0"!=e.c||Un.create.apply(Un,e.a);else if(e.ba)Un.remove(e.c);else if(t)if(e.i){if(e.ha&&(e.ha=Fn(e.c,e.a[0],e.X,e.W)),!In(e.a[0],t,e.W))return!0}else if(e.K){var n=e.C,r=e.a,o=t.plugins_.get(e.K);o[n].apply(o,r)}else t[e.C].apply(t,e.a)}}catch(e){}};var Un=function(e){n(1),Wn.D.apply(Wn,[arguments])};Un.h={},Un.P=[],Un.L=0,Un.answer=42;var Yn=[Tt,Ot,Et];Un.create=function(e){var t=w(Yn,[].slice.call(arguments));t[Et]||(t[Et]="t0");var n=""+t[Et];return Un.h[n]?Un.h[n]:(t=new jn(t),Un.h[n]=t,Un.P.push(t),t)},Un.remove=function(e){for(var t=0;t<Un.P.length;t++)if(Un.P[t].get(Et)==e){Un.P.splice(t,1),Un.h[e]=null;break}},Un.j=function(e){return Un.h[e]},Un.getAll=function(){return Un.P.slice(0)},Un.N=function(){"ga"!=ce&&n(49);var e=_[ce];if(!e||42!=e.answer){if(Un.L=e&&e.l,Un.loaded=!0,Ft("create",t=_[ce]=Un,t.create),Ft("remove",t,t.remove),Ft("getByName",t,t.j,5),Ft("getAll",t,t.getAll,6),Ft("get",t=jn.prototype,t.get,7),Ft("set",t,t.set,4),Ft("send",t,t.send),Ft("requireSync",t,t.ma),Ft("get",t=J.prototype,t.get),Ft("set",t,t.set),!g()&&!le){e:{for(var t=k.getElementsByTagName("script"),r=0;r<t.length&&100>r;r++){var o=t[r].src;if(o&&0==o.indexOf("https://www.google-analytics.com/analytics")){n(33),t=!0;break e}}t=!1}t&&(le=!0)}g()||le||!zt(new Bt(1e4))||(n(36),le=!0),(_.gaplugins=_.gaplugins||{}).Linker=cn,t=cn.prototype,zn("linker",cn),Ft("decorate",t,t.ca,20),Ft("autoLink",t,t.S,25),zn("displayfeatures",_n),zn("adfeatures",_n),e=e&&e.q,s(e)?Wn.D.apply(Un,e):n(50)}},Un.da=function(){for(var e=Un.getAll(),t=0;t<e.length;t++)e[t].get(Et)};var Gn=Un.N,Kn=_[ce];function $n(e){var t,n,r=1;if(e)for(r=0,n=e.length-1;0<=n;n--)r=0!=(t=266338304&(r=(r<<6&268435455)+(t=e.charCodeAt(n))+(t<<14)))?r^t>>21:r;return r}Kn&&Kn.r?Gn():Hn(Gn),Hn(function(){Wn.D(["provide","render",d])})}(window),function(){var e=window,t="push",n="length",r="prototype",o=function(e){if(e.get&&e.set){this.clear();var t=e.get("buildHitTask");e.set("buildHitTask",l(this,t)),e.set("_rlt",f(this,e.get("_rlt")))}},i={action:"pa",promoAction:"promoa",id:"ti",affiliation:"ta",revenue:"tr",tax:"tt",shipping:"ts",coupon:"tcc",step:"cos",label:"col",option:"col",options:"col",list:"pal",listSource:"pls"},a={id:"id",name:"nm",brand:"br",category:"ca",variant:"va",position:"ps",price:"pr",quantity:"qt",coupon:"cc","dimension(\\d+)":"cd","metric(\\d+)":"cm"},s={id:"id",name:"nm",creative:"cr",position:"ps"},u="detail checkout checkout_option click add remove purchase refund".split(" ");o[r].clear=function(){this.b=void 0,this.f=[],this.a=[],this.g=[],this.d=void 0},o[r].h=function(e,t){var n=t||{};"promo_click"==e?n.promoAction="click":n.action=e,this.b=d(n)},o[r].j=function(e){(e=d(e))&&this.f[t](e)},o[r].i=function(e){var r=d(e);if(r){var o,i=e.list||"";e=e.listSource||"";for(var a=0;a<this.a[n];a++)if(this.a[a].name==i){o=this.a[a];break}o||(o=new function(e,t){this.name=e,this.source=t,this.e=[]}(i,e),this.a[t](o)),o.e[t](r)}},o[r].c=function(e){(e=d(e))&&this.g[t](e)};var c=function(e,t,o){if("[object Array]"==Object[r].toString.call(Object(e)))for(var i=0;i<e[n];i++)t.call(o,e[i])};o[r].data=function(e){if(e&&e.ecommerce){(e=e.ecommerce).promoView&&c(e.promoView.promotions,this.c,this),e.promoClick&&(this.h("promo_click",e.promoClick.actionField),c(e.promoClick.promotions,this.c,this));for(var t=0;t<u[n];t++){var r=e[u[t]];if(r){this.h(u[t],r.actionField),c(r.products,this.j,this);break}}c(e.impressions,this.i,this),e.currencyCode&&(this.d=e.currencyCode)}};var l=function(e,t){return function(r){var o,u,c;for(e.b&&h(i,e.b,r,"&"),o=0;o<e.f[n];o++)u="&pr"+(o+1),h(a,e.f[o],r,u);for(o=0;o<e.a[n];o++){u="&il"+(o+1),(c=e.a[o]).name&&r.set(u+"nm",c.name,!0),c.source&&r.set(u+"ls",c.source,!0);for(var l=0;l<c.e[n];l++)h(a,c.e[l],r,u+"pi"+(l+1))}for(o=0;o<e.g[n];o++)u="&promo"+(o+1),h(s,e.g[o],r,u);return e.d&&r.set("&cu",e.d,!0),e.clear(),t(r)}},f=function(e,t){return function(n){var r=e.b&&e.b.action;if("purchase"!=r&&"refund"!=r)return t(n)}},d=function(e){var t=0,n={};if(e&&"object"==typeof e)for(var r in e)e.hasOwnProperty(r)&&(n[r]=e[r],t++);return t?n:void 0},h=function(e,t,n,r){for(var o in t)if(t.hasOwnProperty(o))for(var i in e)if(e.hasOwnProperty(i)){var a=o.match("^"+i+"$");a&&n.set(r+e[i]+a.slice(1).join(""),t[o],!0)}};!function(){e.gaplugins=e.gaplugins||{},e.gaplugins.EC=o,o[r].setAction=o[r].h,o[r].addProduct=o[r].j,o[r].addImpression=o[r].i,o[r].addPromo=o[r].c,o[r].clear=o[r].clear,o[r].data=o[r].data;var n=e.GoogleAnalyticsObject||"ga";e[n]=e[n]||function(){(e[n].q=e[n].q||[])[t](arguments)},e[n]("provide","ec",o)}()}(),window.ga||(window.ga=function(){window.ga.q.push(arguments)},window.ga.q=[]);var Dt=Object.freeze({trackPageview:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.page=e,window.ga("send","pageview",t)},setDimension:function(e,t){window.ga("set",e,t)},setGlobalLocation:function(e){window.ga("set",{location:e})},setGlobalTitle:function(e){window.ga("set",{title:e})},setGlobalAccount:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};window.ga("create",e,t,n),window.ga("set","transport","sendBeacon"in window.navigator?"beacon":"xhr")},providePlugin:function(e,t){var n=window[window.GoogleAnalyticsObject||"ga"];"function"==typeof n&&n("provide",e,t)},requirePlugin:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};window.ga(function(){window.ga("require",e,t)})},trackEvent:function(e){void 0===e.interactive&&(e.interactive=!0),window.ga("send","event",e.category,e.action,e.label,e.value,{nonInteraction:!e.interactive})}});function Pt(e){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return Ht(e)||function(e,t){var n=e.ownerDocument.activeElement;if(!n)return!1;if(t&&n===e)return!1;return jt(n)&&e===n||e.contains(n)}(e,t)||function(e){return e.matches(":active")}(e)||function(e){return!(!e.closest(".is-dirty")&&!e.querySelector(".is-dirty"))}(e)}function Ht(e){var t=!0,n=!1,r=void 0;try{for(var o,i=e.querySelectorAll("input, textarea")[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){var a=o.value;if((a instanceof HTMLInputElement||a instanceof HTMLTextAreaElement)&&Rt(a))return!0}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}return!1}function Rt(e){if(e instanceof HTMLInputElement&&("checkbox"===e.type||"radio"===e.type)){if(e.checked!==e.defaultChecked)return!0}else if(e.value!==e.defaultValue)return!0;return!1}var Nt=Object.freeze({hasInteractions:Pt,hasDirtyFields:Ht}),qt=function(){function e(t){l(this,e),this.children=[],this.parent=t}return f(e,[{key:"delete",value:function(e){var t=this.children.indexOf(e);return-1!==t&&(this.children=this.children.slice(0,t).concat(this.children.slice(t+1)),0===this.children.length&&this.parent.delete(this),!0)}},{key:"add",value:function(e){return this.children.push(e),this}}]),e}(),It=function(){function e(t){l(this,e),this.parent=null,this.children={},this.parent=t||null}return f(e,[{key:"get",value:function(e){return this.children[e]}},{key:"insert",value:function(t){for(var n=this,r=0;r<t.length;r+=1){var o=t[r],i=n.get(o);if(r===t.length-1)return i instanceof e&&(n.delete(i),i=null),i||(i=new qt(n),n.children[o]=i),i;i instanceof qt&&(i=null),i||(i=new e(n),n.children[o]=i),n=i}return n}},{key:"delete",value:function(e){for(var t in this.children){if(this.children[t]===e){var n=delete this.children[t];return 0===Object.keys(this.children).length&&this.parent&&this.parent.delete(this),n}}return!1}}]),e}(),Ft=Object.freeze({Leaf:qt,RadixTrie:It});function Bt(e){return(e.ctrlKey?"Control+":"")+(e.altKey?"Alt+":"")+(e.metaKey?"Meta+":"")+e.key}var zt=Object.freeze({default:Bt}),Vt=null,Xt=null,Wt=[];function Ut(e,t){var n=[];function r(){var e=n;n=[],t(e)}return function(){for(var t=arguments.length,o=Array(t),i=0;i<t;i++)o[i]=arguments[i];n.push(o),1===n.length&&Yt(e,r)}}function Yt(e,t){Xt||(Xt=new MutationObserver(Gt)),Vt||(Vt=e.createElement("div"),Xt.observe(Vt,{attributes:!0})),Wt.push(t),Vt.setAttribute("data-twiddle",""+Date.now())}function Gt(){var e=Wt;Wt=[];for(var t=0;t<e.length;t++)try{e[t]()}catch(e){setTimeout(function(){throw e},0)}}var Kt=new WeakMap,$t=new WeakMap,Jt=new WeakMap,Zt=new WeakMap;function Qt(e,t){for(var n=0;n<t.length;n++){var r=t[n],o=r[0],i=r[1],a=r[2];o===an?(en(a,i),tn(a,i)):o===sn?nn(a,i):o===un&&rn(e.observers,i)}}function en(e,t){if(t instanceof e.elementConstructor){var n=Kt.get(t);if(n||(n=[],Kt.set(t,n)),-1===n.indexOf(e.id)){var r=void 0;if(e.initialize&&(r=e.initialize.call(void 0,t)),r){var o=$t.get(t);o||(o={},$t.set(t,o)),o[""+e.id]=r}n.push(e.id)}}}function tn(e,t){if(t instanceof e.elementConstructor){var n=Zt.get(t);if(n||(n=[],Zt.set(t,n)),-1===n.indexOf(e.id)){e.elements.push(t);var r=$t.get(t),o=r?r[""+e.id]:null;if(o&&o.add&&o.add.call(void 0,t),e.subscribe){var i=e.subscribe.call(void 0,t);if(i){var a=Jt.get(t);a||(a={},Jt.set(t,a)),a[""+e.id]=i}}e.add&&e.add.call(void 0,t),n.push(e.id)}}}function nn(e,t){if(t instanceof e.elementConstructor){var n=Zt.get(t);if(n){var r=e.elements.indexOf(t);if(-1!==r&&e.elements.splice(r,1),-1!==(r=n.indexOf(e.id))){var o=$t.get(t),i=o?o[""+e.id]:null;if(i&&i.remove&&i.remove.call(void 0,t),e.subscribe){var a=Jt.get(t),s=a?a[""+e.id]:null;s&&s.unsubscribe&&s.unsubscribe()}e.remove&&e.remove.call(void 0,t),n.splice(r,1)}0===n.length&&Zt.delete(t)}}}function rn(e,t){var n=Zt.get(t);if(n){for(var r=n.slice(0),o=0;o<r.length;o++){var i=e[r[o]];if(i){var a=i.elements.indexOf(t);-1!==a&&i.elements.splice(a,1);var s=$t.get(t),u=s?s[""+i.id]:null;u&&u.remove&&u.remove.call(void 0,t);var c=Jt.get(t),l=c?c[""+i.id]:null;l&&l.unsubscribe&&l.unsubscribe(),i.remove&&i.remove.call(void 0,t)}}Zt.delete(t)}}var on=null;var an=1,sn=2,un=3;function cn(e,t,n){for(var r=0;r<n.length;r++){var o=n[r];"childList"===o.type?(ln(e,t,o.addedNodes),fn(e,t,o.removedNodes)):"attributes"===o.type&&dn(e,t,o.target)}(function(e){if(null===on){var t=e.createElement("div"),n=e.createElement("div"),r=e.createElement("div");t.appendChild(n),n.appendChild(r),t.innerHTML="",on=r.parentNode!==n}return on})(e.ownerDocument)&&function(e,t){for(var n=0;n<e.observers.length;n++){var r=e.observers[n];if(r)for(var o=r.elements,i=0;i<o.length;i++){var a=o[i];a.parentNode||t.push([un,a])}}}(e,t)}function ln(e,t,n){for(var r=0;r<n.length;r++){var o=n[r];if("matches"in o)for(var i=e.selectorSet.matches(o),a=0;a<i.length;a++){var s=i[a].data;t.push([an,o,s])}if("querySelectorAll"in o)for(var u=e.selectorSet.queryAll(o),c=0;c<u.length;c++)for(var l=u[c],f=l.data,d=l.elements,h=0;h<d.length;h++)t.push([an,d[h],f])}}function fn(e,t,n){for(var r=0;r<n.length;r++){var o=n[r];if("querySelectorAll"in o){t.push([un,o]);for(var i=o.querySelectorAll("*"),a=0;a<i.length;a++)t.push([un,i[a]])}}}function dn(e,t,n){if("matches"in n)for(var r=e.selectorSet.matches(n),o=0;o<r.length;o++){var i=r[o].data;t.push([an,n,i])}if("querySelectorAll"in n){var a=Zt.get(n);if(a)for(var s=0;s<a.length;s++){var u=e.observers[a[s]];u&&(e.selectorSet.matchesSelector(n,u.selector)||t.push([sn,n,u]))}}}var hn="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},pn=0;function vn(e){var t,n,r;this.rootNode=9===e.nodeType?e.documentElement:e,this.ownerDocument=9===e.nodeType?e:e.ownerDocument,this.observers=[],this.selectorSet=new U,this.mutationObserver=new MutationObserver(function(e,t){var n=[];cn(e,n,t),Qt(e,n)}.bind(this,this)),this._scheduleAddRootNodes=Ut(this.ownerDocument,function(e){var t=[];ln(e,t,[e.rootNode]),Qt(e,t)}.bind(this,this)),this._handleThrottledChangedTargets=Ut(this.ownerDocument,function(e,t){var n=[];(function(e,t,n){for(var r=0;r<n.length;r++)for(var o=n[r],i=o.form?o.form.elements:e.rootNode.querySelectorAll("input"),a=0;a<i.length;a++)dn(e,t,i[a])})(e,n,t),Qt(e,n)}.bind(this,this)),this.rootNode.addEventListener("change",function(e,t){e._handleThrottledChangedTargets(t.target)}.bind(this,this),!1),t=this.ownerDocument,n=function(e){e.mutationObserver.observe(e.rootNode,{childList:!0,attributes:!0,subtree:!0}),e._scheduleAddRootNodes()}.bind(this,this),"interactive"===(r=t.readyState)||"complete"===r?Yt(t,n):t.addEventListener("DOMContentLoaded",Yt(t,n))}vn.prototype.disconnect=function(){this.mutationObserver.disconnect()},vn.prototype.observe=function(e,t){var n=void 0;"function"==typeof t?n={selector:e,initialize:t}:"object"===(void 0===t?"undefined":hn(t))?(n=t).selector=e:n=e;var r=this,o={id:pn++,selector:n.selector,initialize:n.initialize,add:n.add,remove:n.remove,subscribe:n.subscribe,elements:[],elementConstructor:n.hasOwnProperty("constructor")?n.constructor:Element,abort:function(){r._abortObserving(o)}};return this.selectorSet.add(o.selector,o),this.observers[o.id]=o,this._scheduleAddRootNodes(),o},vn.prototype._abortObserving=function(e){for(var t=e.elements,n=0;n<t.length;n++)nn(e,t[n]);this.selectorSet.remove(e.selector,e),delete this.observers[e.id]},vn.prototype.triggerObservers=function(e){var t=[];!function(e,t,n){if("querySelectorAll"in n){dn(e,t,n);for(var r=n.querySelectorAll("*"),o=0;o<r.length;o++)dn(e,t,r[o])}}(this,t,e),Qt(this,t)};var mn=void 0;function gn(){return mn||(mn=new vn(window.document)),mn}function bn(){var e;return(e=gn()).observe.apply(e,arguments)}var yn=Object.freeze({default:vn,getDocumentObserver:gn,observe:bn,triggerObservers:function(){var e;return(e=gn()).triggerObservers.apply(e,arguments)}}),wn=new It;function En(e){return e.split(",").map(function(e){return e.split(" ")})}var _n=wn,kn=null;function xn(){kn=null,_n=wn}document.addEventListener("keydown",function(e){if(!(e.target instanceof Node&&jt(e.target))){null!=kn&&clearTimeout(kn),kn=setTimeout(xn,1500);var t,n=_n.get(Bt(e));if(n)return _n=n,n instanceof qt?(me(e.target,"hotkey:activate",{originalEvent:e}),jt(t=n.children[n.children.length-1])?t.focus():(t instanceof HTMLAnchorElement&&t.href||"BUTTON"===t.tagName||"SUMMARY"===t.tagName)&&t.click(),e.preventDefault(),void xn()):void 0;else xn()}});var Tn=new WeakMap;bn("[data-hotkey]",{add:function(e){var t=En(e.getAttribute("data-hotkey")||"").map(function(t){return wn.insert(t).add(e)});Tn.set(e,t)},remove:function(e){var t=Tn.get(e);if(t&&t.length){var n=!0,r=!1,o=void 0;try{for(var i,a=t[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;s&&s.delete(e)}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}}});var jn=Object.freeze({expandHotkeyToEdges:En}),On=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}();function Ln(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(Ln.prototype,HTMLElement.prototype),Object.setPrototypeOf(Ln,HTMLElement);var Sn=new WeakMap;function An(e,t){setTimeout(function(){var n=t.ownerDocument.createEvent("Event");n.initEvent(e,!1,!1),t.dispatchEvent(n)},0)}function Mn(e){return Cn(e).then(function(t){var n=e.parentNode;n&&(e.insertAdjacentHTML("afterend",t),n.removeChild(e))},function(){e.classList.add("is-error")})}function Cn(e){var t=e.src,n=Sn.get(e);return n&&n.src===t?n.data:(n=t?e.load():Promise.reject(new Error("missing src")),Sn.set(e,{src:t,data:n}),n)}var Dn=function(e){function t(){return function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}(this,(t.__proto__||Object.getPrototypeOf(t)).call(this))}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,Ln),On(t,[{key:"attributeChangedCallback",value:function(e){"src"===e&&this._attached&&Mn(this)}},{key:"connectedCallback",value:function(){this._attached=!0,this.src&&Mn(this)}},{key:"disconnectedCallback",value:function(){this._attached=!1}},{key:"request",value:function(){var e=this.src;if(!e)throw new Error("missing src");return new Request(e,{method:"GET",credentials:"same-origin",headers:{Accept:"text/html"}})}},{key:"load",value:function(){var e=this;return Promise.resolve().then(function(){return An("loadstart",e),e.fetch(e.request())}).then(function(e){if(200!==e.status)throw new Error("Failed to load resource: the server responded with a status of "+e.status);var t=e.headers.get("Content-Type");if(!t||!t.match(/^text\/html/))throw new Error("Failed to load resource: expected text/html but was "+t);return e}).then(function(e){return e.text()}).then(function(t){return An("load",e),An("loadend",e),t},function(t){throw An("error",e),An("loadend",e),t})}},{key:"fetch",value:function(e){function t(t){return e.apply(this,arguments)}return t.toString=function(){return e.toString()},t}(function(e){return fetch(e)})},{key:"src",get:function(){var e=this.getAttribute("src");if(e){var t=this.ownerDocument.createElement("a");return t.href=e,t.href}return""},set:function(e){e?this.setAttribute("src",e):this.removeAttribute("src")}},{key:"data",get:function(){return Cn(this)}}],[{key:"observedAttributes",get:function(){return["src"]}}]),t}();window.customElements.get("include-fragment")||(window.IncludeFragmentElement=Dn,window.customElements.define("include-fragment",Dn)),IncludeFragmentElement.prototype.fetch=vt;var Pn=Object.freeze({pluralize:function(e,t){return t+(e>1||0===e?"s":"")},pluralizeNode:function(e,t){var n=1===e?"data-singular-string":"data-plural-string",r=t.getAttribute(n);null!=r&&(t.textContent=r)}}),Hn=Object.freeze({weekdays:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],months:["January","February","March","April","May","June","July","August","September","October","November","December"]}),Rn=null;function Nn(e){Rn&&qn(Rn),me(e,"menu:activate")&&(document.addEventListener("keydown",Fn),document.addEventListener("click",In),Rn=e,Fe(e,function(){e.classList.add("active");var t=e.querySelector(".js-menu-content [tabindex]");t&&t.focus();var n=e.querySelector(".js-menu-target");n&&(n.setAttribute("aria-expanded","true"),n.hasAttribute("data-no-toggle")||n.classList.add("selected"))}),me(e,"menu:activated"))}function qn(e){me(e,"menu:deactivate")&&(document.removeEventListener("keydown",Fn),document.removeEventListener("click",In),Rn=null,Fe(e,function(){e.classList.remove("active");var t=e.querySelector(".js-menu-content");t&&t.setAttribute("aria-expanded","false");var n=e.querySelector(".js-menu-target");n&&(n.setAttribute("aria-expanded","false"),n.hasAttribute("data-no-toggle")||n.classList.remove("selected"))}),me(e,"menu:deactivated"))}function In(e){if(Rn){var t=e.target;o(t instanceof Element,"app/assets/modules/github/menu.js:111");var n=t.closest("#facebox, .facebox-overlay, details[open], details-dialog"),r=!!n&&!n.contains(Rn);Rn.contains(t)||r||(e.preventDefault(),qn(Rn))}}function Fn(e){if(Rn){var t=document.activeElement;t&&"Escape"===e.key&&(Rn.contains(t)&&t.blur(),e.preventDefault(),qn(Rn))}}ve("click",".js-menu-container",function(e){var t=e.currentTarget;o(t instanceof HTMLElement,"app/assets/modules/github/menu.js:156"),e.target.closest(".js-menu-target")instanceof HTMLElement?(e.preventDefault(),t===Rn?qn(t):Nn(t)):e.target.closest(".js-menu-content")||t===Rn&&(e.preventDefault(),qn(t))}),ve("click",".js-menu-container .js-menu-close",function(e){var t=e.currentTarget.closest(".js-menu-container");o(t instanceof HTMLElement,"expected container to be .js-menu-container -- app/assets/modules/github/menu.js:183"),qn(t),e.preventDefault()}),bn(".js-menu-container.active",{add:function(){var e=document.body;o(e,"app/assets/modules/github/menu.js:192"),e.classList.add("menu-active")},remove:function(){var e=document.body;o(e,"app/assets/modules/github/menu.js:197"),e.classList.remove("menu-active")}});var Bn=Object.freeze({activate:Nn,deactivate:qn}),zn=function e(t){var n=this;l(this,e),this.closed=!1,this.unsubscribe=function(){t(),n.closed=!0}};function Vn(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]&&arguments[3];return e.addEventListener(t,n,r),new zn(function(){e.removeEventListener(t,n,r)})}var Xn=Object.freeze({fromEvent:Vn});function Wn(e){var t=Un(e);if(t)return t.defaultView}function Un(e){return e?9===e.nodeType?e:e.ownerDocument?e.ownerDocument:e.document?e.document:null:null}var Yn=Object.freeze({getWindow:Wn,getDocument:Un});function Gn(e,t){var n=Un(e);if(n){e!==n&&e!==n.defaultView&&e!==n.documentElement&&e!==n.body||(e=n);var r=n.defaultView.Document,i=n.defaultView.HTMLElement;if(null==t&&(t={}),null==t.top&&null==t.left)if(t.target){var a=$e(t.target,e);null!=a&&(t.top=a.top,t.left=a.left)}else if(e instanceof i){var s=$e(e,e.offsetParent);e=e.offsetParent,null!=s&&(t.top=s.top,t.left=s.left)}if(e instanceof r||e===n.body){var u=null!=t.top?t.top:n.defaultView.pageYOffset,c=null!=t.left?t.left:n.defaultView.pageXOffset;n.defaultView.scrollTo(c,u)}else o(e instanceof i,"container is not HTMLElement -- app/assets/modules/github/scrollto.js:62"),null!=t.top&&(e.scrollTop=t.top),null!=t.left&&(e.scrollLeft=t.left)}}var Kn=Object.freeze({default:Gn});function $n(e){return!function(e){return e.offsetWidth<=0&&e.offsetHeight<=0}(e)}var Jn=Object.freeze({default:$n}),Zn=navigator.userAgent.match(/Macintosh/),Qn=Zn?"metaKey":"ctrlKey",er=Zn?"Meta":"Control",tr=!1,nr={x:0,y:0};function rr(e){o(e instanceof MouseEvent,"app/assets/modules/github/navigation.js:142"),nr.x===e.clientX&&nr.y===e.clientY||(tr=!1),nr={x:e.clientX,y:e.clientY}}function or(e){if(!tr){var t=e.currentTarget;if(o(t instanceof HTMLElement,"app/assets/modules/github/navigation.js:157"),t.closest(".js-active-navigation-container")){o(e.target instanceof Element,"app/assets/modules/github/navigation.js:161");var n=e.target.closest(".js-navigation-item");n&&mr(n,t)}}}bn(".js-navigation-container",{subscribe:function(e){return Vn(e,"mousemove",rr)}}),bn(".js-navigation-container",{subscribe:function(e){return Vn(e,"mouseover",or)}});var ir=0;function ar(e){if(!(e.target!==document.body&&e.target instanceof HTMLElement)||e.target.classList.contains("js-navigation-enable")){tr=!0;var t=gr(),n=void 0;if(t)n=me(t.querySelector('.js-navigation-item[aria-selected="true"]')||t,"navigation:keydown",{hotkey:Bt(e),originalEvent:e,originalTarget:e.target});n||e.preventDefault()}}function sr(e){me(e.currentTarget,"navigation:open",{modifierKey:e.modifierKey||e.altKey||e.ctrlKey||e.metaKey,shiftKey:e.shiftKey})||e.preventDefault()}function ur(e){var t=gr();e!==t&&(t&&cr(t),e.classList.add("js-active-navigation-container"))}function cr(e){e.classList.remove("js-active-navigation-container")}bn(".js-active-navigation-container",{add:function(){1===++ir&&document.addEventListener("keydown",ar)},remove:function(){0===--ir&&document.removeEventListener("keydown",ar)}}),ve("navigation:keydown",".js-active-navigation-container",function(e){o(e instanceof CustomEvent,"app/assets/modules/github/navigation.js:229");var t=e.currentTarget,n=e.detail.originalTarget.matches("input, textarea"),r=e.target;if(r.classList.contains("js-navigation-item"))if(n){if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":pr(r,t);break;case"Control+p":hr(r,t)}switch(Bt(e.detail.originalEvent)){case"ArrowUp":hr(r,t);break;case"ArrowDown":pr(r,t);break;case"Enter":case er+"+Enter":vr(r,e.detail.originalEvent[Qn])}}else{if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":pr(r,t);break;case"Control+p":hr(r,t);break;case"Alt+v":!function(e,t){var n=br(t),r=n.indexOf(e),o=Ge(e);if(null==o)return;var i=void 0,a=void 0;for(;(i=n[r-1])&&(a=Ke(i,o))&&a.top>=0;)r--;if(i){var s=mr(i,t);if(s)return;wr(o,i)}}(r,t);break;case"Control+v":!function(e,t){var n=br(t),r=n.indexOf(e),o=Ge(e);if(null==o)return;var i=void 0,a=void 0;for(;(i=n[r+1])&&(a=Ke(i,o))&&a.bottom>=0;)r++;if(i){var s=mr(i,t);if(s)return;wr(o,i)}}(r,t)}switch(Bt(e.detail.originalEvent)){case"j":case"J":pr(r,t);break;case"k":case"K":hr(r,t);break;case"o":case"Enter":case er+"+Enter":vr(r,e.detail[Qn])}}else{var i=br(t)[0];if(i)if(n){if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":mr(i,t)}switch(Bt(e.detail.originalEvent)){case"ArrowDown":mr(i,t)}}else{if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":case"Control+v":mr(i,t)}switch(Bt(e.detail.originalEvent)){case"j":mr(i,t)}}}if(n){if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":case"Control+p":e.preventDefault()}switch(Bt(e.detail.originalEvent)){case"ArrowUp":case"ArrowDown":e.preventDefault();break;case"Enter":e.preventDefault()}}else{if(Zn)switch(Bt(e.detail.originalEvent)){case"Control+n":case"Control+p":case"Control+v":case"Alt+v":e.preventDefault()}switch(Bt(e.detail.originalEvent)){case"j":case"k":e.preventDefault();break;case"o":case"Enter":case Qn+"+Enter":e.preventDefault()}}}),ve("click",".js-active-navigation-container .js-navigation-item",function(e){o(e instanceof MouseEvent,"app/assets/modules/github/navigation.js:385"),sr(e)}),ve("navigation:keyopen",".js-active-navigation-container .js-navigation-item",function(e){o(e instanceof CustomEvent,"app/assets/modules/github/navigation.js:391");var t=e.currentTarget.classList.contains("js-navigation-open")?e.currentTarget:e.currentTarget.querySelector(".js-navigation-open");if(t){if(e.detail.modifierKey)window.open(t.href,"_blank"),window.focus();else{var n=void 0;try{n=new MouseEvent("click",{bubbles:!0,cancelable:!0})}catch(e){(n=document.createEvent("MouseEvent")).initEvent("click",!0,!0)}t.dispatchEvent(n)&&t.click()}e.preventDefault()}else sr(e)});var lr=[];function fr(e,t){t||(t=e);var n=br(e)[0],r=t.closest(".js-navigation-item")||n;if(ur(e),r instanceof HTMLElement){if(mr(r,e))return;var i=Ge(r);o(i,"app/assets/modules/github/navigation.js:498"),Er(i,r)}}function dr(e){var t=e.querySelectorAll(".js-navigation-item[aria-selected]"),n=!0,r=!1,o=void 0;try{for(var i,a=t[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;s.classList.remove("navigation-focus"),s.setAttribute("aria-selected","false")}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}function hr(e,t){var n=br(t),r=n[n.indexOf(e)-1];if(r){if(mr(r,t))return;var i=Ge(r);o(i,"app/assets/modules/github/navigation.js:546"),"page"===yr(t)?wr(i,r):Er(i,r)}}function pr(e,t){var n=br(t),r=n[n.indexOf(e)+1];if(r){if(mr(r,t))return;var i=Ge(r);o(i,"app/assets/modules/github/navigation.js:573"),"page"===yr(t)?wr(i,r):Er(i,r)}}function vr(e,t){null==t&&(t=!1),me(e,"navigation:keyopen",{modifierKey:t})}function mr(e,t){return!me(e,"navigation:focus")||(dr(t),e.classList.add("navigation-focus"),e.setAttribute("aria-selected","true"),!1)}function gr(){return document.querySelector(".js-active-navigation-container")}function br(e){return Array.from(e.querySelectorAll(".js-navigation-item")).filter($n)}function yr(e){return e.getAttribute("data-navigation-scroll")||"item"}function wr(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:"smooth",r=Ke(t,e);r&&(r.bottom<=0?t.scrollIntoView({behavior:n,block:"start"}):r.top<=0&&t.scrollIntoView({behavior:n,block:"end"}))}function Er(e,t){var n=$e(t,e),r=Ke(t,e);null!=n&&null!=r&&(r.bottom<=0&&document.body?Gn(e,{top:(null!=e.offsetParent?e.scrollHeight:document.body.scrollHeight)-(n.bottom+r.height)}):r.top<=0&&Gn(e,{top:n.top}))}var _r=Object.freeze({activate:ur,push:function(e){var t=gr();t&&lr.push(t),ur(e)},pop:function(e){cr(e),dr(e);var t=lr.pop();t&&ur(t)},focus:fr,clear:dr,refocus:function(e,t){dr(e),fr(e,t)}}),kr=14594616e5;var xr=Object.freeze({formatNumber:function(e){return(""+e).replace(/(^|[^\w.])(\d{4,})/g,function(e,t,n){return t+n.replace(/\d(?=(?:\d\d\d)+(?!\d))/g,"$&,")})},parseFormattedNumber:function(e){return"string"==typeof e&&(e=e.replace(/,/g,"")),parseFloat(e)}}),Tr=!1,jr=new U;function Or(e){var t=e.target;if(t instanceof HTMLElement&&t.nodeType!==Node.DOCUMENT_NODE){var n=!0,r=!1,o=void 0;try{for(var i,a=jr.matches(t)[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){i.value.data.call(null,t)}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}}function Lr(e,t){Tr||(Tr=!0,document.addEventListener("focus",Or,!0)),jr.add(e,t),document.activeElement&&document.activeElement.matches(e)&&t(document.activeElement)}var Sr=Object.freeze({onFocus:Lr,onKey:function(e,t,n){function r(t){t.currentTarget.removeEventListener(e,n),t.currentTarget.removeEventListener("blur",r)}Lr(t,function(t){t.addEventListener(e,n),t.addEventListener("blur",r)})},onInput:function(e,t){function n(e){e.currentTarget.removeEventListener("input",t),e.currentTarget.removeEventListener("blur",n)}Lr(e,function(e){e.addEventListener("input",t),e.addEventListener("blur",n)})}}),Ar=[],Mr=0,Cr=function(){var e={_id:(new Date).getTime()};return Hr(e),e}();function Dr(){return Cr}function Pr(){return history.length-1+Mr}function Hr(e){Cr=e;var t=location.href;Ar[Pr()]={url:t,state:Cr},Ar.length=history.length;var n=new CustomEvent("statechange",{bubbles:!1,cancelable:!1});n.state=Cr,window.dispatchEvent(n)}function Rr(e,t){var n={_id:t};if(e)for(var r in e)n[r]=e[r];return n}function Nr(){return(new Date).getTime()}function qr(e,t,n){Mr=0,e=Rr(e,Nr()),history.pushState(e,t,n),Hr(e)}function Ir(e,t,n){e=Rr(e,Dr()._id),history.replaceState(e,t,n),Hr(e)}window.addEventListener("popstate",function(e){e.state&&e.state._id&&(e.state._id<Dr()._id?Mr--:Mr++,Hr(e.state))},!0),window.addEventListener("hashchange",function(){if(history.length>Ar.length){var e=Rr({},Nr());history.replaceState(e,"",location.href),Hr(e)}},!0);var Fr=Object.freeze({getState:Dr,pushState:qr,replaceState:Ir,getBackURL:function(){var e=Ar[Pr()-1];if(e)return e.url},getForwardURL:function(){var e=Ar[Pr()+1];if(e)return e.url}});function Br(){}var zr=function(){function e(){l(this,e),this.previousReceiver={resolve:Br,reject:Br}}return f(e,[{key:"push",value:function(e){var t=this;return this.previousReceiver.resolve=this.previousReceiver.reject=Br,new Promise(function(n,r){var o={resolve:n,reject:r};t.previousReceiver=o,e.then(function(){o.resolve.apply(this,arguments)},function(){o.reject.apply(this,arguments)})})}}]),e}(),Vr=Object.freeze({default:zr});function Xr(e,t,n){return o(e instanceof t,"undefined -- app/assets/modules/github/typecast.js:17"),e}var Wr=Object.freeze({default:Xr});function Ur(e){for(var t=[];e&&(t.push(Yr(e)),e!==Un(e)&&!e.id);)e=e.parentNode;return t.reverse().join(" > ")}function Yr(e){if(e===window)return"window";var t=[e.nodeName.toLowerCase()],n=e.id;if(n&&t.push("#"+n),"function"==typeof e.getAttribute&&e.getAttribute("class")){var r=(e.getAttribute("class")||"").trim().split(/\s+/).join(".");r&&t.push("."+r)}return t.join("")}var Gr=Object.freeze({default:Ur}),Kr=new WeakMap;function $r(e,t){var n=function(e,t){var n=Se(e,"link[rel=pjax-prefetch]",HTMLLinkElement),r=!0,o=!1,i=void 0;try{for(var a,s=n[Symbol.iterator]();!(r=(a=s.next()).done);r=!0){var u=a.value;if(u.href===t)return u}}catch(e){o=!0,i=e}finally{try{!r&&s.return&&s.return()}finally{if(o)throw i}}}(e,t);if(n){var r=Kr.get(n);return n.remove(),Kr.delete(n),r}}var Jr=Object.freeze({popPrefetchResponseForRequest:$r,setPrefetchResponse:function(e,t){Kr.set(e,t)}}),Zr={container:null,timeout:650,push:!0,replace:!1,type:"GET",dataType:"html",scrollTo:0},Qr=20,eo=void 0,to=new zr;function no(e,t,n){return e.dispatchEvent(new CustomEvent(t,{bubbles:!0,cancelable:!0,detail:n}))}function ro(e){oo({url:e.url,container:e.container})}function oo(e){var t,n=(t=c(regeneratorRuntime.mark(function e(t){var n,o,i,c,l,f,d,h,p,v,m,g,b;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return n=eo,o=xo(),i=t.headers.get("X-PJAX-Version"),e.next=5,t.text();case 5:if(c=e.sent,l=bo(c,t,r),f=l.contents,d=fo(l.url),a&&(d.hash=a,l.url=d.href),!o||!i||o===i){e.next=13;break}return io(l.url),e.abrupt("return");case 13:if(f){e.next=16;break}return io(l.url),e.abrupt("return");case 16:if(eo={id:null!=r.id?r.id:co(),url:l.url,title:l.title,container:u,fragment:r.fragment,timeout:r.timeout},!0!==r.push&&!0!==r.replace||Ir(eo,l.title,l.url),h=document.activeElement,p=null!=r.container&&r.container.contains(h),h&&p)try{h.blur()}catch(e){}l.title&&(document.title=l.title),no(s,"pjax:beforeReplace",{contents:f,state:eo,previousState:n}),mo(s,f),(v=Se(s,"input[autofocus], textarea[autofocus]").pop())&&document.activeElement!==v&&v.focus(),yo(l.scripts),m=r.scrollTo,a&&(g=St(document,a))&&(b=g.getBoundingClientRect(),m=b.top+window.pageYOffset),"number"==typeof m&&window.scrollTo(window.pageXOffset,m),no(s,"pjax:success"),no(s,"pjax:complete"),no(s,"pjax:end");case 33:case"end":return e.stop()}},e,this)})),function(e){return t.apply(this,arguments)}),r={url:"",container:null};Object.assign(r,Zr,e),o("string"==typeof r.url,"app/assets/modules/github/pjax.js:177"),r.requestUrl=r.url;var i=fo(r.url),a=i.hash,s=r.container;o(s,"app/assets/modules/github/pjax.js:183");var u=po(s);"GET"===r.type&&(i.search+=(i.search?"&":"")+"_pjax="+encodeURIComponent(u),r.url=i.toString()),eo||Ir(eo={id:co(),url:window.location.href,title:document.title,container:u,fragment:r.fragment,timeout:r.timeout},eo.title,eo.url),ro.options=r,o("string"==typeof r.requestUrl,"app/assets/modules/github/pjax.js:324");var l,f,d=$r(s,r.requestUrl);d||(o(r.url,"app/assets/modules/github/pjax.js:327"),d=vt(r.url,{method:r.type,body:r.data,headers:{Accept:"text/html","X-PJAX":"true","X-PJAX-Container":u}}),"GET"===r.type&&"number"==typeof r.timeout&&r.timeout>0&&(d=Promise.race([d,new Promise(function(e,t){setTimeout(function(){no(s,"pjax:timeout")&&t(new Error("timeout"))},r.timeout)})]))),!0===r.push&&!0!==r.replace&&(l=eo.id,f=lo(s),wo[l]=f,_o.push(l),ko(Eo,0),ko(_o,Qr),o("string"==typeof r.requestUrl,"app/assets/modules/github/pjax.js:355"),qr(null,"",r.requestUrl)),no(s,"pjax:start",{url:r.url}),no(s,"pjax:send"),to.push(d).then(n,function(e){var t=r.requestUrl;o("string"==typeof t,"app/assets/modules/github/pjax.js:197"),e.response&&(t=go(e.response,t));var n=no(s,"pjax:error");"GET"===r.type&&n&&io(t),no(s,"pjax:complete"),no(s,"pjax:end")})}function io(e){o(eo,"app/assets/modules/github/pjax.js:406"),Ir(null,"",eo.url),window.location.replace(e)}var ao=!0,so=window.location.href,uo=window.history.state;function co(){return(new Date).getTime()}function lo(e){var t=e.cloneNode(!0);return[po(e),Array.from(t.childNodes)]}function fo(e){var t=document.createElement("a");return t.href=e,t}function ho(e){return e.href.replace(/#.*/,"")}function po(e){if(e.id)return"#"+e.id;throw new Error("pjax container has no id")}function vo(e,t,n){var r=[],o=!0,i=!1,a=void 0;try{for(var s,u=e[Symbol.iterator]();!(o=(s=u.next()).done);o=!0){var c=s.value;c instanceof Element&&(c instanceof n&&c.matches(t)&&r.push(c),r=r.concat(Se(c,t,n)))}}catch(e){i=!0,a=e}finally{try{!o&&u.return&&u.return()}finally{if(i)throw a}}return r}function mo(e,t){e.innerHTML="";var n=!0,r=!1,o=void 0;try{for(var i,a=t[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;null!=s&&e.appendChild(s)}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}function go(e,t){var n,r=e.headers.get("X-PJAX-URL");return r?((n=fo(r)).search=n.search.replace(/([?&])(_pjax|_)=[^&]*/g,""),n.href.replace(/\?($|#)/,"$1")):t}function bo(e,t,n){o("string"==typeof n.requestUrl,"app/assets/modules/github/pjax.js:612");var r={url:go(t,n.requestUrl),title:""},i=/<html/i.test(e);if("text/html"!==(t.headers.get("Content-Type")||"").split(";",1)[0].trim())return r;var a=void 0,s=void 0;if(i){var u=e.match(/<head[^>]*>([\s\S.]*)<\/head>/i),c=e.match(/<body[^>]*>([\s\S.]*)<\/body>/i);a=u?Array.from(ut(document,u[0]).childNodes):[],s=c?Array.from(ut(document,c[0]).childNodes):[]}else a=s=Array.from(ut(document,e).childNodes);if(0===s.length)return r;var l=vo(a,"title",HTMLTitleElement);r.title=l.length>0?l[l.length-1].textContent:"";var f=void 0;if(n.fragment){if("body"===n.fragment)f=s;else{var d=vo(s,n.fragment,Element);f=d.length>0?[d[0]]:[]}if(f.length&&("body"===n.fragment?r.contents=f:r.contents=f.reduce(function(e,t){return e.concat(Array.from(t.childNodes))},[]),!r.title)){var h=f[0];h instanceof Element&&(r.title=h.getAttribute("title")||h.getAttribute("data-title")||"")}}else i||(r.contents=s);if(r.contents){r.contents=r.contents.filter(function(e){return!(e instanceof Element)||!e.matches("title")});var p=!0,v=!1,m=void 0;try{for(var g,b=r.contents[Symbol.iterator]();!(p=(g=b.next()).done);p=!0){var y=g.value;if(y instanceof Element){var w=!0,E=!1,_=void 0;try{for(var k,x=y.querySelectorAll("title")[Symbol.iterator]();!(w=(k=x.next()).done);w=!0){k.value.remove()}}catch(e){E=!0,_=e}finally{try{!w&&x.return&&x.return()}finally{if(E)throw _}}}}}catch(e){v=!0,m=e}finally{try{!p&&b.return&&b.return()}finally{if(v)throw m}}var T=vo(r.contents,"script[src]",HTMLScriptElement),j=!0,O=!1,L=void 0;try{for(var S,A=T[Symbol.iterator]();!(j=(S=A.next()).done);j=!0){S.value.remove()}}catch(e){O=!0,L=e}finally{try{!j&&A.return&&A.return()}finally{if(O)throw L}}r.scripts=T,o(r.contents,"app/assets/modules/github/pjax.js:704"),r.contents=r.contents.filter(function(e){return-1===T.indexOf(e)})}return r.title&&(r.title=r.title.trim()),r}function yo(e){if(e){var t=Se(document,"script[src]",HTMLScriptElement),n=function(e){var n=e.src;if(t.some(function(e){return e.src===n}))return{v:void 0};var r=document.createElement("script"),o=e.getAttribute("type");o&&(r.type=o),r.src=n,document.head&&document.head.appendChild(r)},r=!0,o=!1,i=void 0;try{for(var a,s=e[Symbol.iterator]();!(r=(a=s.next()).done);r=!0){var c=n(a.value);if("object"===(void 0===c?"undefined":u(c)))return c.v}}catch(e){o=!0,i=e}finally{try{!r&&s.return&&s.return()}finally{if(o)throw i}}}}uo&&uo.container&&(eo=uo),"state"in window.history&&(ao=!1);var wo={},Eo=[],_o=[];function ko(e,t){for(;e.length>t;)delete wo[e.shift()]}function xo(){var e=!0,t=!1,n=void 0;try{for(var r,o=document.getElementsByTagName("meta")[Symbol.iterator]();!(e=(r=o.next()).done);e=!0){var i=r.value,a=i.getAttribute("http-equiv");if(a&&"X-PJAX-VERSION"===a.toUpperCase())return i.content}}catch(e){t=!0,n=e}finally{try{!e&&o.return&&o.return()}finally{if(t)throw n}}}window.addEventListener("popstate",function(e){ao||to.push(Promise.resolve(new Response));var t=eo,n=e.state,r=void 0;if(n&&n.container){if(ao&&so===n.url)return;if(t){if(t.id===n.id)return;r=t.id<n.id?"forward":"back"}var o=wo[n.id]||[],i=document.querySelector(o[0]||n.container),a=o[1];if(i){t&&function(e,t,n){var r=void 0,o=void 0;wo[t]=n,"forward"===e?(r=_o,o=Eo):(r=Eo,o=_o),r.push(t),(t=o.pop())&&delete wo[t],ko(r,Qr)}(r,t.id,lo(i)),no(i,"pjax:popstate",{state:n,direction:r});var s={id:n.id,url:n.url,container:i,push:!1,fragment:n.fragment,timeout:n.timeout,scrollTo:!1};a?(no(i,"pjax:start"),eo=n,n.title&&(document.title=n.title),no(i,"pjax:beforeReplace",{contents:a,state:n,previousState:t}),mo(i,a),no(i,"pjax:end")):oo(s),i.offsetHeight}else io(location.href)}ao=!1});var To=Object.freeze({click:function(e,t){var n=Xr(e.currentTarget,HTMLAnchorElement);if(!(0!==e.button||e.metaKey||e.ctrlKey||e.shiftKey||e.altKey||location.protocol!==n.protocol||location.hostname!==n.hostname||n.href.indexOf("#")>-1&&ho(n)===ho(location)||e.defaultPrevented)){var r={url:n.href,container:null,target:n};Object.assign(r,t),no(n,"pjax:click",{options:r,relatedEvent:e})&&(oo(r),e.preventDefault(),no(n,"pjax:clicked",{options:r}))}},submit:function(e,t){var n=Xr(e.currentTarget,HTMLFormElement),r={type:(n.method||"GET").toUpperCase(),url:n.action,container:null,target:n};if(Object.assign(r,t),"GET"===r.type){if(n.querySelector("input[type=file]"))return;o("string"==typeof r.url,"app/assets/modules/github/pjax.js:142");var i=fo(r.url);i.search+=(i.search?"&":"")+Ot(n),r.url=i.toString()}else r.data=new FormData(n);oo(r),e.preventDefault()},default:ro,fetch:function(e,t){var n=e.closest("[data-pjax-container]");if(!n)throw new Error("no pjax container for "+Ur(e));var r=po(n),o=fo(e.href);return o.search+=(o.search?"&":"")+"_pjax="+encodeURIComponent(r),vt(o.href,{headers:Object.assign({Accept:"text/html","X-PJAX":"true","X-PJAX-Container":r},t&&t.headers)})},getState:function(){return eo}});function jo(e){return new Promise(function(t){setTimeout(function(){t()},e)})}var Oo=Object.freeze({delay:jo}),Lo=function(e){function t(){return l(this,t),p(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return h(t,e),f(t,[{key:"fetch",value:function(){var e=c(regeneratorRuntime.mark(function e(n){var r,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,d(t.prototype.__proto__||Object.getPrototypeOf(t.prototype),"fetch",this).call(this,n);case 2:if(202!==(r=e.sent).status){e.next=9;break}return e.next=6,jo(o);case 6:return e.abrupt("return",this.fetch(n,1.5*o));case 9:return e.abrupt("return",r);case 10:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}()}]),t}(IncludeFragmentElement);window.customElements.get("poll-include-fragment")||(window.PollIncludeFragmentElement=Lo,window.customElements.define("poll-include-fragment",Lo));var So,Ao=function(){var e=c(regeneratorRuntime.mark(function e(){var t,n,r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return t=Le(document,"link[rel=sso-modal]",HTMLLinkElement),e.next=3,Ue({content:bt(document,t.href),dialogClass:"sso-modal"});case 3:return n=e.sent,r=null,window.external.ssoComplete=function(e){e.error?Do(r=!1):(Do(r=!0),Po(e.expiresAround),window.focus()),window.external.ssoComplete=null},e.next=8,Co(n);case 8:if(r){e.next=10;break}throw new Error("sso prompt canceled");case 10:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),Mo=(So=c(regeneratorRuntime.mark(function e(){var t,n,r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(t=document.querySelector("link[rel=sso-session]"),n=document.querySelector("meta[name=sso-expires-around]"),t){e.next=4;break}return e.abrupt("return",!0);case 4:if(Ho(n)){e.next=6;break}return e.abrupt("return",!0);case 6:return r=Xr(t,HTMLLinkElement).href,e.abrupt("return",gt(r));case 8:case"end":return e.stop()}},e,this)})),function(){return So.apply(this,arguments)});function Co(e){return new Promise(function(t){e.addEventListener("dialog:remove",t,{once:!0})})}function Do(e){var t=document.querySelector(".sso-modal");t&&(t.classList.remove("success","error"),e?t.classList.add("success"):t.classList.add("error"))}function Po(e){var t=document.querySelector("meta[name=sso-expires-around]");t&&t.setAttribute("content",e)}function Ho(e){if(!e)return!0;var t=parseInt(Xr(e,HTMLMetaElement).content);return(new Date).getTime()/1e3>t}bn(".js-sso-modal-complete",function(e){if(window.opener&&window.opener.external.ssoComplete){var t=e.getAttribute("data-error"),n=e.getAttribute("data-expires-around");window.opener.external.ssoComplete({error:t,expiresAround:n}),window.close()}else{var r=e.getAttribute("data-fallback-url");window.location=r}});var Ro=null;function No(){Ro=null}var qo=c(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,Mo();case 2:if(e.sent){e.next=7;break}return Ro||(Ro=Ao().then(No).catch(No)),e.next=7,Ro;case 7:case"end":return e.stop()}},e,this)})),Io=Object.freeze({default:qo}),Fo=function(){var e=c(regeneratorRuntime.mark(function e(t,n,r,o){var i,a,s,u,c,l,f,d,h=this;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:i=!1,a=regeneratorRuntime.mark(function e(t){var a,s,u,c,l,f;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return a=Vo(),s=v(a,2),u=s[0],c=s[1],f={text:l=function(){return i=!0,c(),o},json:function(){return r.headers.set("Accept","application/json"),l()},html:function(){return r.headers.set("Accept","text/html"),l()}},e.next=5,Promise.race([u,t.data.call(null,n,f,r)]);case 5:case"end":return e.stop()}},e,h)}),s=!0,u=!1,c=void 0,e.prev=5,l=t[Symbol.iterator]();case 7:if(s=(f=l.next()).done){e.next=13;break}return d=f.value,e.delegateYield(a(d),"t0",10);case 10:s=!0,e.next=7;break;case 13:e.next=19;break;case 15:e.prev=15,e.t1=e.catch(5),u=!0,c=e.t1;case 19:e.prev=19,e.prev=20,!s&&l.return&&l.return();case 22:if(e.prev=22,!u){e.next=25;break}throw c;case 25:return e.finish(22);case 26:return e.finish(19);case 27:return e.abrupt("return",i);case 28:case"end":return e.stop()}},e,this,[[5,15,19,27],[20,,22,26]])}));return function(t,n,r,o){return e.apply(this,arguments)}}(),Bo=function(){var e=c(regeneratorRuntime.mark(function e(t){var n,r,o,i,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,qo();case 2:return e.next=4,window.fetch(t.url,{method:t.method,body:null!==t.body?t.body:void 0,headers:t.headers,credentials:"same-origin"});case 4:return n=e.sent,e.t0=n.url,e.t1=n.status,e.t2=n.statusText,e.t3=n.headers,i={url:e.t0,status:e.t1,statusText:e.t2,headers:e.t3,text:"",get json(){return n=this,r=JSON.parse(n.text),delete n.json,n.json=r,n.json},get html(){return n=this,at(ot(document),n),o=ut(document,n.text),delete n.html,n.html=o,n.html}},e.next=12,n.text();case 12:if(a=e.sent,i.text=a,!(i.status<300)){e.next=18;break}return e.abrupt("return",i);case 18:throw new zo("request failed",i);case 19:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),zo=function(e){function t(e,n){l(this,t);var r=p(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e));return r.response=n,r}return h(t,e),t}(Error);function Vo(){var e=void 0,t=void 0,n=new Promise(function(n,r){e=n,t=r});return o("function"==typeof e,"app/assets/modules/github/remote-form.js:28"),o("function"==typeof t,"app/assets/modules/github/remote-form.js:29"),[n,e,t]}var Xo=void 0,Wo=[];function Uo(e,t){Xo||(Xo=new U,document.addEventListener("submit",Yo)),Xo.add(e,t)}function Yo(e){var t=Xr(e.target,HTMLFormElement),n=Xo&&Xo.matches(t);if(n&&0!==n.length){var r=function(e){var t={method:e.method||"GET",url:e.action,headers:new Headers({"X-Requested-With":"XMLHttpRequest"}),body:null};if("GET"===t.method.toUpperCase()){var n=Ot(e);n&&(t.url+=(~t.url.indexOf("?")?"&":"?")+n)}else t.body=new FormData(function(e){if(Go){for(var t=void 0,n=e.elements.length-1;n>=0&&((t=e.elements[n])instanceof HTMLButtonElement||t instanceof HTMLInputElement&&("submit"===t.type||"reset"===t.type||"text"===t.type&&!t.name));)n--;if(t instanceof HTMLInputElement&&(("checkbox"===t.type||"radio"===t.type)&&!t.checked||"file"===t.type)){var r=document.createElement("input");r.type="hidden",r.name="_ie11fix",e.append(r)}}return e}(e));return t}(t),o=Vo(),i=v(o,3),a=i[0],s=i[1],u=i[2];e.preventDefault(),Fo(n,t,r,a).then(function(e){e?Bo(r).then(s,u).catch(function(){}).then(function(){var e=!0,n=!1,r=void 0;try{for(var o,i=Wo[Symbol.iterator]();!(e=(o=i.next()).done);e=!0){(0,o.value)(t)}}catch(e){n=!0,r=e}finally{try{!e&&i.return&&i.return()}finally{if(n)throw r}}}):t.submit()},function(e){t.submit(),setTimeout(function(){throw e})})}}var Go=/\bTrident\/.+\brv:11\./.test(navigator.userAgent);var Ko=Object.freeze({afterRemote:function(e){Wo.push(e)},remoteForm:Uo,remoteUninstall:function(e,t){Xo&&Xo.remove(e,t)}}),$o=function(){var e=c(regeneratorRuntime.mark(function e(t){var n,r,i,a,s,u,c,l,f,d,h,p,v;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=t.currentTarget,o(n instanceof Element,"app/assets/modules/github/select-menu/loading.js:38"),n.classList.remove("js-load-contents"),n.classList.add("is-loading"),n.classList.remove("has-error"),r=n.getAttribute("data-contents-url"),o(r,"app/assets/modules/github/select-menu/loading.js:45"),i=new URL(r,window.location.origin),!(a=Jo.get(n))){e.next=31;break}for(s=new URLSearchParams(i.search.slice(1)),u=!0,c=!1,l=void 0,e.prev=14,f=a[Symbol.iterator]();!(u=(d=f.next()).done);u=!0)h=d.value,s.append(h[0],h[1]);e.next=22;break;case 18:e.prev=18,e.t0=e.catch(14),c=!0,l=e.t0;case 22:e.prev=22,e.prev=23,!u&&f.return&&f.return();case 25:if(e.prev=25,!c){e.next=28;break}throw l;case 28:return e.finish(25);case 29:return e.finish(22);case 30:i.search=s.toString();case 31:return p=n.querySelector(".js-select-menu-deferred-content"),v=void 0,e.prev=33,e.next=36,qo();case 36:if(!p){e.next=42;break}return e.next=39,mt(i);case 39:v=e.sent,e.next=45;break;case 42:return e.next=44,gt(i);case 44:v=e.sent;case 45:e.next=51;break;case 47:return e.prev=47,e.t1=e.catch(33),n.classList.add("has-error"),e.abrupt("return");case 51:return e.prev=51,n.classList.remove("is-loading"),e.finish(51);case 54:p?p.innerHTML=v:me(n,"selectmenu:data",{data:v}),(n.classList.contains("active")||n.hasAttribute("open"))&&me(n,"selectmenu:load");case 56:case"end":return e.stop()}},e,this,[[14,18,22,30],[23,,25,29],[33,47,51,54]])}));return function(t){return e.apply(this,arguments)}}(),Jo=new WeakMap;bn(".js-select-menu.js-load-contents",{add:function(e){e.addEventListener("mouseenter",$o),e.addEventListener("menu:activate",$o)},remove:function(e){e.removeEventListener("mouseenter",$o),e.removeEventListener("menu:activate",$o)}});var Zo=Object.freeze({setLoadingData:function(e,t){Jo.set(e,t)}});var Qo,ei=Object.freeze({getItem:function(e){try{return sessionStorage.getItem(e)}catch(e){return}},setItem:function(e,t){try{sessionStorage.setItem(e,t)}catch(e){}},removeItem:function(e){try{sessionStorage.removeItem(e)}catch(e){}}}),ti=function(){var e=c(regeneratorRuntime.mark(function e(){var t,n,r,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(t=document.body,o(t,"app/assets/modules/github/sudo.js:15"),n=Le(document,"link[rel=sudo-modal]",HTMLLinkElement),!((r=document.querySelector(".js-sudo-prompt"))instanceof HTMLTemplateElement)){e.next=8;break}return e.abrupt("return",r);case 8:if(!n){e.next=16;break}return e.next=11,bt(document,n.href);case 11:return i=e.sent,t.appendChild(i),e.abrupt("return",Le(document,".js-sudo-prompt",HTMLTemplateElement));case 16:throw new Error("couldn't load sudo prompt");case 17:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),ni=function(){var e=c(regeneratorRuntime.mark(function e(){var t,n,r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!ri){e.next=2;break}return e.abrupt("return",!1);case 2:return ri=!0,t=!1,e.next=6,ti();case 6:return n=e.sent,e.next=9,Ue({content:n.content.cloneNode(!0)});case 9:return r=e.sent,Uo(".js-sudo-form",function(){var e=c(regeneratorRuntime.mark(function e(n,r){var o;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.prev=0,e.next=3,r.text();case 3:e.next=22;break;case 5:if(e.prev=5,e.t0=e.catch(0),e.t0.response){e.next=9;break}throw e.t0;case 9:o=void 0,e.t1=e.t0.response.status,e.next=401===e.t1?13:429===e.t1?15:17;break;case 13:return o="Incorrect password.",e.abrupt("break",18);case 15:return o="Too many password attempts. Please wait and try again later.",e.abrupt("break",18);case 17:o="Failed to receive a response. Please try again later.";case 18:return Le(n,".js-sudo-error").textContent=o,Le(n,".js-sudo-error").hidden=!1,Le(n,".js-sudo-password",HTMLInputElement).value="",e.abrupt("return");case 22:t=!0,Oe(n,"details").removeAttribute("open");case 24:case"end":return e.stop()}},e,this,[[0,5]])}));return function(t,n){return e.apply(this,arguments)}}()),e.next=13,new Promise(function(e){r.addEventListener("dialog:remove",function(){ri=!1,e()},{once:!0})});case 13:return e.abrupt("return",t);case 14:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),ri=!1,oi=function(){var e=c(regeneratorRuntime.mark(function e(t){var n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,gt("/sessions/in_sudo.json?requested_access_level="+t);case 2:if(n=e.sent,e.t0=n,e.t0){e.next=8;break}return e.next=7,ni();case 7:e.t0=e.sent;case 8:return e.abrupt("return",e.t0);case 9:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();ve("click","[data-sudo-required]",(Qo=c(regeneratorRuntime.mark(function e(t){var n,r,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return t.preventDefault(),n=t.currentTarget,o(n instanceof HTMLElement,"app/assets/modules/github/sudo.js:91"),a=n.getAttribute("data-sudo-required"),o(r="low"===a||"medium"===a||"high"===a||"two_factor"===a?a:null,"app/assets/modules/github/sudo.js:93"),e.next=7,gt("/sessions/in_sudo.json?requested_access_level="+r);case 7:if(i=e.sent,e.t0=i,e.t0){e.next=13;break}return e.next=12,ni();case 12:e.t0=e.sent;case 13:if(!e.t0){e.next=16;break}n.removeAttribute("data-sudo-required"),n.click();case 16:case"end":return e.stop()}var a},e,this)})),function(e){return Qo.apply(this,arguments)}));var ii=Object.freeze({default:oi}),ai=new WeakMap,si=null;function ui(e,t){return e.closest("task-lists")===t.closest("task-lists")}function ci(e){if(e.currentTarget===e.target){var t=e.currentTarget;if(t instanceof Element){var n=t.closest(".contains-task-list");if(n&&(t.classList.add("is-ghost"),e.dataTransfer&&e.dataTransfer.setData("text/plain",t.textContent.trim()),t.parentElement)){var r=Array.from(t.parentElement.children),o=r.indexOf(t);si={didDrop:!1,dragging:t,dropzone:t,sourceList:n,sourceSibling:r[o+1]||null,sourceIndex:o}}}}}function li(e){if(si){var t=e.currentTarget;t instanceof Element&&(ui(si.dragging,t)?(e.preventDefault(),e.dataTransfer&&(e.dataTransfer.dropEffect="move"),si.dropzone!==t&&(si.dragging.classList.add("is-dragging"),si.dropzone=t,!function(e,t){if(e.parentNode===t.parentNode)for(var n=e;n;){if(n===t)return!0;n=n.previousElementSibling}return!1}(si.dragging,t)?t.after(si.dragging):t.before(si.dragging))):e.stopPropagation())}}function fi(e){if(si){var t=e.currentTarget;if(t instanceof Element&&(si.didDrop=!0,si.dragging.parentElement)){var n=Array.from(si.dragging.parentElement.children).indexOf(si.dragging),r=t.closest(".contains-task-list");if(r&&(si.sourceIndex!==n||si.sourceList!==r)){si.sourceList===r&&si.sourceIndex<n&&n++;var o={list:si.sourceList,index:si.sourceIndex},i={list:r,index:n},a=ai.get(si.dragging);a&&a({src:o,dst:i})}}}}function di(){si&&(si.dragging.classList.remove("is-dragging"),si.dragging.classList.remove("is-ghost"),si.didDrop||si.sourceList.insertBefore(si.dragging,si.sourceSibling),si=null)}function hi(e){if(si){var t=e.currentTarget;t instanceof Element&&(ui(si.dragging,t)?(e.preventDefault(),e.dataTransfer&&(e.dataTransfer.dropEffect="move")):e.stopPropagation())}}var pi=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},vi=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),mi=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t};function gi(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(gi.prototype,HTMLElement.prototype),Object.setPrototypeOf(gi,HTMLElement);var bi=new WeakMap,yi=function(e){function t(){pi(this,t);var e=mi(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.addEventListener("change",function(t){var n=t.target;n instanceof HTMLInputElement&&n.classList.contains("task-list-item-checkbox")&&e.dispatchEvent(new CustomEvent("task-lists:check",{bubbles:!0,detail:{position:function(e){var t=Oi(e);if(!t)throw new Error(".contains-task-list not found");var n=Array.from(t.querySelectorAll("li")).indexOf(e.closest(".task-list-item"));return[Ai(t),n]}(n),checked:n.checked}}))}),bi.set(e,new MutationObserver(Li.bind(null,e))),e}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,gi),vi(t,[{key:"connectedCallback",value:function(){var e=bi.get(this);e&&e.observe(this,{childList:!0,subtree:!0}),Li(this)}},{key:"disconnectedCallback",value:function(){var e=bi.get(this);e&&e.disconnect()}},{key:"attributeChangedCallback",value:function(e,t,n){if(t!==n)switch(e){case"disabled":Si(this)}}},{key:"disabled",get:function(){return this.hasAttribute("disabled")},set:function(e){e?this.setAttribute("disabled",""):this.removeAttribute("disabled")}},{key:"sortable",get:function(){return this.hasAttribute("sortable")},set:function(e){e?this.setAttribute("sortable",""):this.removeAttribute("sortable")}}],[{key:"observedAttributes",get:function(){return["disabled"]}}]),t}(),wi=document.createElement("template");wi.innerHTML='\n  <span class="handle">\n    <svg class="drag-handle" aria-hidden="true" width="16" height="15" version="1.1" viewBox="0 0 16 15">\n      <path d="M12,4V5H4V4h8ZM4,8h8V7H4V8Zm0,3h8V10H4v1Z"></path>\n    </svg>\n  </span>';var Ei=new WeakMap;function _i(e){if(!Ei.get(e)){Ei.set(e,!0);var t=e.closest("task-lists");if(t instanceof yi&&!(t.querySelectorAll(".task-list-item").length<=1)){var n=wi.content.cloneNode(!0),r=n.querySelector(".handle");if(e.prepend(n),!r)throw new Error("handle not found");r.addEventListener("mouseenter",Ci),r.addEventListener("mouseleave",Di),function(e,t){ai.set(e,t),e.addEventListener("dragstart",ci),e.addEventListener("dragenter",li),e.addEventListener("dragend",di),e.addEventListener("drop",fi),e.addEventListener("dragover",hi)}(e,Mi),e.addEventListener("mouseenter",ki),e.addEventListener("mouseleave",xi)}}}function ki(e){var t=e.currentTarget;if(t instanceof Element){var n=t.closest("task-lists");n instanceof yi&&n.sortable&&!n.disabled&&t.classList.add("hovered")}}function xi(e){var t=e.currentTarget;t instanceof Element&&t.classList.remove("hovered")}function Ti(e){var t=e.parentElement;return t?t.closest(".contains-task-list"):null}function ji(e){return Ti(e)===Oi(e)}function Oi(e){var t=Ti(e);return t?Oi(t)||t:null}function Li(e){var t=e.querySelectorAll(".contains-task-list > .task-list-item"),n=!0,r=!1,o=void 0;try{for(var i,a=t[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;ji(s)&&_i(s)}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}Si(e)}function Si(e){var t=!0,n=!1,r=void 0;try{for(var o,i=e.querySelectorAll(".task-list-item")[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){o.value.classList.toggle("enabled",!e.disabled)}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}var a=!0,s=!1,u=void 0;try{for(var c,l=e.querySelectorAll(".task-list-item-checkbox")[Symbol.iterator]();!(a=(c=l.next()).done);a=!0){var f=c.value;f instanceof HTMLInputElement&&(f.disabled=e.disabled)}}catch(e){s=!0,u=e}finally{try{!a&&l.return&&l.return()}finally{if(s)throw u}}}function Ai(e){var t=e.parentElement;if(!t)throw new Error("parent not found");return Array.from(t.children).filter(function(e){return"OL"===e.nodeName||"UL"===e.nodeName}).indexOf(e)}function Mi(e){var t=e.src,n=e.dst,r=t.list.closest("task-lists");r&&r.dispatchEvent(new CustomEvent("task-lists:move",{bubbles:!0,detail:{src:[Ai(t.list),t.index],dst:[Ai(n.list),n.index]}}))}function Ci(e){var t=e.currentTarget;if(t instanceof Element){var n=t.closest(".task-list-item");if(n){var r=n.closest("task-lists");r instanceof yi&&r.sortable&&!r.disabled&&n.setAttribute("draggable","true")}}}function Di(e){if(!si){var t=e.currentTarget;if(t instanceof Element){var n=t.closest(".task-list-item");n&&n.setAttribute("draggable","false")}}}function Pi(e){if(e.querySelector(".js-task-list-field")){var t=!0,n=!1,r=void 0;try{for(var o,i=Se(e,"task-lists",yi)[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){o.value.disabled=!1}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}}}function Hi(e,t,n){var r=Le(e,".js-comment-update",HTMLFormElement);!function(e){var t=!0,n=!1,r=void 0;try{for(var o,i=Se(e,"task-lists",yi)[Symbol.iterator]();!(t=(o=i.next()).done);t=!0)o.value.disabled=!0}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}}(e);var i=r.elements.namedItem("task_list_track");i&&i.remove();var a=r.elements.namedItem("task_list_operation");a&&a.remove();var s=document.createElement("input");s.setAttribute("type","hidden"),s.setAttribute("name","task_list_track"),s.setAttribute("value",t),r.appendChild(s);var u=document.createElement("input");if(u.setAttribute("type","hidden"),u.setAttribute("name","task_list_operation"),u.setAttribute("value",JSON.stringify(n)),r.appendChild(u),!r.elements.namedItem("task_list_key")){var c=Le(r,".js-task-list-field").getAttribute("name");o(c,"app/assets/modules/github/task-list.js:127");var l=c.split("[")[0],f=document.createElement("input");f.setAttribute("type","hidden"),f.setAttribute("name","task_list_key"),f.setAttribute("value",l),r.appendChild(f)}e.classList.remove("is-comment-stale"),Tt(r)}window.customElements.get("task-lists")||(window.TaskListsElement=yi,window.customElements.define("task-lists",yi)),bn(".js-task-list-container .js-task-list-field",function(e){Pi(Oe(e,".js-task-list-container"))}),ve("task-lists:move","task-lists",function(e){o(e instanceof CustomEvent,"app/assets/modules/github/task-list.js:68");var t=e.detail,n=t.src,r=t.dst;Hi(Oe(e.currentTarget,".js-task-list-container"),"reordered",{operation:"move",src:n,dst:r})}),ve("task-lists:check","task-lists",function(e){o(e instanceof CustomEvent,"app/assets/modules/github/task-list.js:75");var t=e.detail,n=t.position,r=t.checked;Hi(Oe(e.currentTarget,".js-task-list-container"),"checked:"+(r?1:0),{operation:"check",position:n,checked:r})}),Uo(".js-task-list-container .js-comment-update",function(){var e=c(regeneratorRuntime.mark(function e(t,n){var r,o,i,a,s,u,c,l,f,d,h;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=Oe(t,".js-task-list-container"),(o=t.elements.namedItem("task_list_track"))&&o.remove(),(i=t.elements.namedItem("task_list_operation"))&&i.remove(),a=void 0,e.prev=6,e.next=9,n.json();case 9:a=e.sent,e.next=17;break;case 12:e.prev=12,e.t0=e.catch(6),s=void 0;try{s=JSON.parse(e.t0.response.text)}catch(e){}s&&s.stale?(u=s.updated_markdown,c=s.updated_html,l=s.version,u&&c&&l&&(f=Le(r,".js-comment-body"),d=Le(r,".js-body-version"),h=Le(r,".js-task-list-field",HTMLTextAreaElement),f.innerHTML=c,h.value=u,r.setAttribute("data-body-version",l),d instanceof HTMLInputElement&&(d.value=l))):window.location.reload();case 17:a&&(i&&a.json.source&&(Le(r,".js-task-list-field",HTMLTextAreaElement).value=a.json.source),Pi(r));case 18:case"end":return e.stop()}},e,this,[[6,12]])}));return function(t,n){return e.apply(this,arguments)}}());var Ri=Object.freeze({enableTaskList:Pi});function Ni(e){return e.dispatchEvent(new CustomEvent("change",{bubbles:!0,cancelable:!1}))}var qi=Object.freeze({getUtf8StringLength:function(e){var t=e.split("‍"),n=0,r=!0,o=!1,i=void 0;try{for(var a,s=t[Symbol.iterator]();!(r=(a=s.next()).done);r=!0){var u=a.value;n+=Array.from(u.split(/[\ufe00-\ufe0f]/).join("")).length}}catch(e){o=!0,i=e}finally{try{!r&&s.return&&s.return()}finally{if(o)throw i}}return n/t.length},replaceText:function(e,t,n){var r=e.value.substring(0,e.selectionEnd),o=e.value.substring(e.selectionEnd);r=r.replace(t,n),o=o.replace(t,n),e.value=r+o,e.selectionStart=r.length,e.selectionEnd=r.length,Ni(e)},insertText:function(e,t){var n=e.selectionEnd,r=e.value.substring(0,n),o=e.value.substring(n),i=""===e.value||r.match(/\n$/)?"":"\n";e.value=r+i+t+o,e.selectionStart=n+t.length,e.selectionEnd=n+t.length,Ni(e),e.focus()}}),Ii=new WeakMap;function Fi(e){var t=Ii.get(e);o(t,"app/assets/modules/github/throttled-input.js:24"),null!=t.timer&&clearTimeout(t.timer),t.timer=setTimeout(function(){null!=t.timer&&(t.timer=null),t.inputed=!1,t.listener.call(null,e)},t.wait)}function Bi(e){var t=Ii.get(e.currentTarget);o(t,"app/assets/modules/github/throttled-input.js:36"),t.keypressed=!0,null!=t.timer&&clearTimeout(t.timer)}function zi(e){var t=Ii.get(e.currentTarget);o(t,"app/assets/modules/github/throttled-input.js:45"),o(e.currentTarget instanceof HTMLInputElement||e.currentTarget instanceof HTMLTextAreaElement,"app/assets/modules/github/throttled-input.js:46"),t.keypressed=!1,t.inputed&&Fi(e.currentTarget)}function Vi(e){var t=Ii.get(e.currentTarget);o(t,"app/assets/modules/github/throttled-input.js:55"),o(e.currentTarget instanceof HTMLInputElement||e.currentTarget instanceof HTMLTextAreaElement,"app/assets/modules/github/throttled-input.js:56"),t.inputed=!0,t.keypressed||Fi(e.currentTarget)}var Xi=Object.freeze({addThrottledInputEventListener:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Ii.set(e,{keypressed:!1,inputed:!1,timer:void 0,listener:t,wait:null!=n.wait?n.wait:100}),e.addEventListener("keydown",Bi),e.addEventListener("keyup",zi),e.addEventListener("input",Vi)},removeThrottledInputEventListener:function(e,t){e.removeEventListener("keydown",Bi),e.removeEventListener("keyup",zi),e.removeEventListener("input",Vi);var n=Ii.get(e);n&&(null!=n.timer&&n.listener===t&&clearTimeout(n.timer),Ii.delete(e))},dispatchThrottledInputEvent:function(e){var t=Ii.get(e);t&&t.listener.call(null,e)}});function Wi(e,t,n){if(0===t&&0===n)return[0,0];var r=Ui(e);Gn(e,{top:r.top+n,left:r.left+t});var o=Ui(e);return[o.left-r.left,o.top-r.top]}function Ui(e){if(e.offsetParent&&e instanceof HTMLElement)return{top:e.scrollTop,left:e.scrollLeft};var t=Wn(e);return{top:null!=t?t.pageYOffset:0,left:null!=t?t.pageXOffset:0}}var Yi=Object.freeze({default:Wi});function Gi(e){return new Promise(function(t){if(e===window&&((n=Ki)<kr?self.performance.now()-n:Date.now()-n)>500)requestAnimationFrame(function(){return t()});else{var n,r=xe(function(){e.removeEventListener("scroll",r,{capture:!0,passive:!0}),t()},500);e.addEventListener("scroll",r,{capture:!0,passive:!0}),r()}})}var Ki=0;window.addEventListener("scroll",function(e){var t;t=e.timeStamp,Ki=!t||t<kr&&!(t%1)?Date.now():t},{capture:!0,passive:!0});var $i=function(){var e=c(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,Gi(window);case 2:return e.abrupt("return",Zi(Ji(),t));case 3:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();function Ji(){if(document.activeElement!==document.body)return document.activeElement;var e=document.querySelectorAll(":hover"),t=e.length;return t?e[t-1]:void 0}function Zi(e,t){if(!e)return t();var n=function(e){var t=[];for(;e instanceof HTMLElement;){var n=e.getBoundingClientRect(),r=n.top,o=n.left;t.push({element:e,top:r,left:o}),e=e.parentElement}return t}(e),r=t.call(e),o=function(e){var t=document.documentElement;if(!t)return;var n=!0,r=!1,o=void 0;try{for(var i,a=e[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;if(t.contains(s.element))return s}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}(n);if(o){e=o.element;var i=o.top,a=o.left,s=e.getBoundingClientRect(),u=s.top;return function(e,t,n){for(var r=Ge(e),o=0,i=0;r;){var a=Wi(r,t-o,n-i),s=v(a,2),u=s[0];if(i+=s[1],(o+=u)===t&&i===n)break;r=Ge(r)}}(e,s.left-a,u-i),r}}var Qi=Object.freeze({preserveInteractivePosition:$i,preservingScrollPosition:Zi});function ea(e,t){return new Promise(function(n,r){e.onload=function(){200===e.status?n(e.responseText):r(new Error("XMLHttpRequest "+e.statusText))},e.onerror=r,e.send(t||null)})}var ta=new WeakMap,na=function(){var e=c(regeneratorRuntime.mark(function e(t){var n,r,o,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!ta.get(t)){e.next=2;break}return e.abrupt("return");case 2:if(n=new XMLHttpRequest,r=t.getAttribute("data-url"),o=t.hasAttribute("data-retain-focus"),null!=r){e.next=7;break}throw new Error("Element must have `data-url` attribute");case 7:return n.open("GET",r),n.setRequestHeader("Accept","text/html"),n.setRequestHeader("X-Requested-With","XMLHttpRequest"),ta.set(t,n),e.prev=11,e.next=14,ea(n);case 14:if(i=e.sent,!Pt(t,o)){e.next=17;break}throw new Error("element had interactions");case 17:return e.abrupt("return",oa(t,i,o));case 20:e.prev=20,e.t0=e.catch(11),"XMLHttpRequest abort"!==e.t0.message&&console.warn("Failed to update content",t,e.t0);case 23:return e.prev=23,ta.delete(t),e.finish(23);case 26:case"end":return e.stop()}},e,this,[[11,20,23,26]])}));return function(t){return e.apply(this,arguments)}}(),ra=function(){var e=c(regeneratorRuntime.mark(function e(t,n){var r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return(r=ta.get(t))&&r.abort(),e.abrupt("return",oa(t,n));case 3:case"end":return e.stop()}},e,this)}));return function(t,n){return e.apply(this,arguments)}}();function oa(e,t){var n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return $i(function(){var r=ut(document,t.trim()),o=n&&e===e.ownerDocument.activeElement?r.querySelector("*"):null;e.replaceWith(r),o&&o.focus()})}var ia=Object.freeze({updateContent:na,replaceContent:ra}),aa=r(function(e){var t;t=function(){if("undefined"==typeof window||!window.document)return function(){throw new Error("Sortable.js requires a window with a document")};var e,t,n,r,o,i,a,s,u,c,l,f,d,h,p,v,m,g,b,y,w,E,_={},k=/\s+/g,x=/left|right|inline/,T="Sortable"+(new Date).getTime(),j=window,O=j.document,L=j.parseInt,S=j.setTimeout,A=j.jQuery||j.Zepto,M=j.Polymer,C=!1,D="draggable"in O.createElement("div"),P=!navigator.userAgent.match(/(?:Trident.*rv[ :]?11\.|msie)/i)&&((E=O.createElement("x")).style.cssText="pointer-events:auto","auto"===E.style.pointerEvents),H=!1,R=Math.abs,N=Math.min,q=[],I=[],F=function(){return!1},B=ie(function(e,t,n){if(n&&t.scroll){var r,o,i,a,s,f,d=n[T],h=t.scrollSensitivity,p=t.scrollSpeed,v=e.clientX,m=e.clientY,g=window.innerWidth,b=window.innerHeight;if(c!==n&&(u=t.scroll,c=n,l=t.scrollFn,!0===u)){u=n;do{if(u.offsetWidth<u.scrollWidth||u.offsetHeight<u.scrollHeight)break}while(u=u.parentNode)}u&&(r=u,o=u.getBoundingClientRect(),i=(R(o.right-v)<=h)-(R(o.left-v)<=h),a=(R(o.bottom-m)<=h)-(R(o.top-m)<=h)),i||a||(a=(b-m<=h)-(m<=h),((i=(g-v<=h)-(v<=h))||a)&&(r=j)),_.vx===i&&_.vy===a&&_.el===r||(_.el=r,_.vx=i,_.vy=a,clearInterval(_.pid),r&&(_.pid=setInterval(function(){if(f=a?a*p:0,s=i?i*p:0,"function"==typeof l)return l.call(d,s,f,e);r===j?j.scrollTo(j.pageXOffset+s,j.pageYOffset+f):(r.scrollTop+=f,r.scrollLeft+=s)},24)))}},30),z=function(e){function t(e,t){return null!=e&&!0!==e||null!=(e=n.name)?"function"==typeof e?e:function(n,r){var o=r.options.group.name;return t?e:e&&(e.join?e.indexOf(o)>-1:o==e)}:F}var n={},r=e.group;r&&"object"==typeof r||(r={name:r}),n.name=r.name,n.checkPull=t(r.pull,!0),n.checkPut=t(r.put),n.revertClone=r.revertClone,e.group=n};try{window.addEventListener("test",null,Object.defineProperty({},"passive",{get:function(){C={capture:!1,passive:!1}}}))}catch(e){}function V(e,t){if(!e||!e.nodeType||1!==e.nodeType)throw"Sortable: `el` must be HTMLElement, and not "+{}.toString.call(e);this.el=e,this.options=t=ae({},t),e[T]=this;var n={group:null,sort:!0,disabled:!1,store:null,handle:null,scroll:!0,scrollSensitivity:30,scrollSpeed:10,draggable:/[uo]l/i.test(e.nodeName)?"li":">*",ghostClass:"sortable-ghost",chosenClass:"sortable-chosen",dragClass:"sortable-drag",ignore:"a, img",filter:null,preventOnFilter:!0,animation:0,setData:function(e,t){e.setData("Text",t.textContent)},dropBubble:!1,dragoverBubble:!1,dataIdAttr:"data-id",delay:0,forceFallback:!1,fallbackClass:"sortable-fallback",fallbackOnBody:!1,fallbackTolerance:0,fallbackOffset:{x:0,y:0},supportPointer:!1!==V.supportPointer};for(var r in n)!(r in t)&&(t[r]=n[r]);for(var o in z(t),this)"_"===o.charAt(0)&&"function"==typeof this[o]&&(this[o]=this[o].bind(this));this.nativeDraggable=!t.forceFallback&&D,Y(e,"mousedown",this._onTapStart),Y(e,"touchstart",this._onTapStart),t.supportPointer&&Y(e,"pointerdown",this._onTapStart),this.nativeDraggable&&(Y(e,"dragover",this),Y(e,"dragenter",this)),I.push(this._onDragOver),t.store&&this.sort(t.store.get(this))}function X(t,n){"clone"!==t.lastPullMode&&(n=!0),o&&o.state!==n&&($(o,"display",n?"none":""),n||o.state&&(t.options.group.revertClone?(i.insertBefore(o,a),t._animate(e,o)):i.insertBefore(o,e)),o.state=n)}function W(e,t,n){if(e){n=n||O;do{if(">*"===t&&e.parentNode===n||oe(e,t))return e}while(e=U(e))}return null}function U(e){var t=e.host;return t&&t.nodeType?t:e.parentNode}function Y(e,t,n){e.addEventListener(t,n,C)}function G(e,t,n){e.removeEventListener(t,n,C)}function K(e,t,n){if(e)if(e.classList)e.classList[n?"add":"remove"](t);else{var r=(" "+e.className+" ").replace(k," ").replace(" "+t+" "," ");e.className=(r+(n?" "+t:"")).replace(k," ")}}function $(e,t,n){var r=e&&e.style;if(r){if(void 0===n)return O.defaultView&&O.defaultView.getComputedStyle?n=O.defaultView.getComputedStyle(e,""):e.currentStyle&&(n=e.currentStyle),void 0===t?n:n[t];t in r||(t="-webkit-"+t),r[t]=n+("string"==typeof n?"":"px")}}function J(e,t,n){if(e){var r=e.getElementsByTagName(t),o=0,i=r.length;if(n)for(;o<i;o++)n(r[o],o);return r}return[]}function Z(e,t,n,r,i,a,s,u){e=e||t[T];var c=O.createEvent("Event"),l=e.options,f="on"+n.charAt(0).toUpperCase()+n.substr(1);c.initEvent(n,!0,!0),c.to=i||t,c.from=a||t,c.item=r||t,c.clone=o,c.oldIndex=s,c.newIndex=u,t.dispatchEvent(c),l[f]&&l[f].call(e,c)}function Q(e,t,n,r,o,i,a,s){var u,c,l=e[T],f=l.options.onMove;return(u=O.createEvent("Event")).initEvent("move",!0,!0),u.to=t,u.from=e,u.dragged=n,u.draggedRect=r,u.related=o||t,u.relatedRect=i||t.getBoundingClientRect(),u.willInsertAfter=s,e.dispatchEvent(u),f&&(c=f.call(l,u,a)),c}function ee(e){e.draggable=!1}function te(){H=!1}function ne(e){for(var t=e.tagName+e.className+e.src+e.href+e.textContent,n=t.length,r=0;n--;)r+=t.charCodeAt(n);return r.toString(36)}function re(e,t){var n=0;if(!e||!e.parentNode)return-1;for(;e&&(e=e.previousElementSibling);)"TEMPLATE"===e.nodeName.toUpperCase()||">*"!==t&&!oe(e,t)||n++;return n}function oe(e,t){if(e){var n=(t=t.split(".")).shift().toUpperCase(),r=new RegExp("\\s("+t.join("|")+")(?=\\s)","g");return!(""!==n&&e.nodeName.toUpperCase()!=n||t.length&&((" "+e.className+" ").match(r)||[]).length!=t.length)}return!1}function ie(e,t){var n,r;return function(){void 0===n&&(n=arguments,r=this,S(function(){1===n.length?e.call(r,n[0]):e.apply(r,n),n=void 0},t))}}function ae(e,t){if(e&&t)for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);return e}function se(e){return S(e,0)}function ue(e){return clearTimeout(e)}return V.prototype={constructor:V,_onTapStart:function(t){var n,r=this,o=this.el,i=this.options,a=i.preventOnFilter,u=t.type,c=t.touches&&t.touches[0],l=(c||t).target,f=t.target.shadowRoot&&t.path&&t.path[0]||l,d=i.filter;if(function(e){q.length=0;var t=e.getElementsByTagName("input"),n=t.length;for(;n--;){var r=t[n];r.checked&&q.push(r)}}(o),!e&&!(/mousedown|pointerdown/.test(u)&&0!==t.button||i.disabled)&&!f.isContentEditable&&(l=W(l,i.draggable,o))&&s!==l){if(n=re(l,i.draggable),"function"==typeof d){if(d.call(this,t,l,this))return Z(r,f,"filter",l,o,o,n),void(a&&t.preventDefault())}else if(d&&(d=d.split(",").some(function(e){if(e=W(f,e.trim(),o))return Z(r,e,"filter",l,o,o,n),!0})))return void(a&&t.preventDefault());i.handle&&!W(f,i.handle,o)||this._prepareDragStart(t,c,l,n)}},_prepareDragStart:function(r,o,u,c){var l,f=this,d=f.el,h=f.options,v=d.ownerDocument;u&&!e&&u.parentNode===d&&(b=r,i=d,e=u,h.handleReplacedDragElement&&(t=u.getAttribute("id")),n=e.parentNode,a=e.nextSibling,s=u,m=h.group,p=c,this._lastX=(o||r).clientX,this._lastY=(o||r).clientY,e.style["will-change"]="all",l=function(){f._disableDelayedDrag(),e.draggable=f.nativeDraggable,K(e,h.chosenClass,!0),f._triggerDragStart(r,o),Z(f,i,"choose",e,i,i,p)},h.ignore.split(",").forEach(function(t){J(e,t.trim(),ee)}),Y(v,"mouseup",f._onDrop),Y(v,"touchend",f._onDrop),Y(v,"touchcancel",f._onDrop),Y(v,"selectstart",f),h.supportPointer&&Y(v,"pointercancel",f._onDrop),h.delay?(Y(v,"mouseup",f._disableDelayedDrag),Y(v,"touchend",f._disableDelayedDrag),Y(v,"touchcancel",f._disableDelayedDrag),Y(v,"mousemove",f._disableDelayedDrag),Y(v,"touchmove",f._disableDelayedDrag),h.supportPointer&&Y(v,"pointermove",f._disableDelayedDrag),f._dragStartTimer=S(l,h.delay)):l())},_disableDelayedDrag:function(){var e=this.el.ownerDocument;clearTimeout(this._dragStartTimer),G(e,"mouseup",this._disableDelayedDrag),G(e,"touchend",this._disableDelayedDrag),G(e,"touchcancel",this._disableDelayedDrag),G(e,"mousemove",this._disableDelayedDrag),G(e,"touchmove",this._disableDelayedDrag),G(e,"pointermove",this._disableDelayedDrag)},_triggerDragStart:function(t,n){(n=n||("touch"==t.pointerType?t:null))?(b={target:e,clientX:n.clientX,clientY:n.clientY},this._onDragStart(b,"touch")):this.nativeDraggable?(Y(e,"dragend",this),Y(i,"dragstart",this._onDragStart)):this._onDragStart(b,!0);try{O.selection?se(function(){O.selection.empty()}):window.getSelection().removeAllRanges()}catch(e){}},_dragStarted:function(){if(i&&e){var t=this.options;K(e,t.ghostClass,!0),K(e,t.dragClass,!1),V.active=this,Z(this,i,"start",e,i,i,p)}else this._nulling()},_emulateDragOver:function(){if(y){if(this._lastX===y.clientX&&this._lastY===y.clientY)return;this._lastX=y.clientX,this._lastY=y.clientY,P||$(r,"display","none");var e=O.elementFromPoint(y.clientX,y.clientY),t=e,n=I.length;if(e&&e.shadowRoot&&(t=e=e.shadowRoot.elementFromPoint(y.clientX,y.clientY)),t)do{if(t[T]){for(;n--;)I[n]({clientX:y.clientX,clientY:y.clientY,target:e,rootEl:t});break}e=t}while(t=t.parentNode);P||$(r,"display","")}},_onTouchMove:function(e){if(b){var t=this.options,n=t.fallbackTolerance,o=t.fallbackOffset,i=e.touches?e.touches[0]:e,a=i.clientX-b.clientX+o.x,s=i.clientY-b.clientY+o.y,u=e.touches?"translate3d("+a+"px,"+s+"px,0)":"translate("+a+"px,"+s+"px)";if(!V.active){if(n&&N(R(i.clientX-this._lastX),R(i.clientY-this._lastY))<n)return;this._dragStarted()}this._appendGhost(),w=!0,y=i,$(r,"webkitTransform",u),$(r,"mozTransform",u),$(r,"msTransform",u),$(r,"transform",u),e.preventDefault()}},_appendGhost:function(){if(!r){var t,n=e.getBoundingClientRect(),o=$(e),a=this.options;K(r=e.cloneNode(!0),a.ghostClass,!1),K(r,a.fallbackClass,!0),K(r,a.dragClass,!0),$(r,"top",n.top-L(o.marginTop,10)),$(r,"left",n.left-L(o.marginLeft,10)),$(r,"width",n.width),$(r,"height",n.height),$(r,"opacity","0.8"),$(r,"position","fixed"),$(r,"zIndex","100000"),$(r,"pointerEvents","none"),a.fallbackOnBody&&O.body.appendChild(r)||i.appendChild(r),t=r.getBoundingClientRect(),$(r,"width",2*n.width-t.width),$(r,"height",2*n.height-t.height)}},_onDragStart:function(t,n){var r=t.dataTransfer,o=this.options;this._offUpEvents(),m.checkPull(this,this,e,t),K(e,o.dragClass,!0),n?("touch"===n?(Y(O,"touchmove",this._onTouchMove),Y(O,"touchend",this._onDrop),Y(O,"touchcancel",this._onDrop),o.supportPointer&&(Y(O,"pointermove",this._onTouchMove),Y(O,"pointerup",this._onDrop))):(Y(O,"mousemove",this._onTouchMove),Y(O,"mouseup",this._onDrop)),this._loopId=setInterval(this._emulateDragOver,50)):(r&&(r.effectAllowed="move",o.setData&&o.setData.call(this,r,e)),Y(O,"drop",this),this._dragStartId=se(this._dragStarted))},_onDragOver:function(s){var u,c,l,p,v=this.el,b=this.options,y=b.group,E=V.active,_=m===y,k=!1,j=b.sort;if(void 0!==s.preventDefault&&(s.preventDefault(),!b.dragoverBubble&&s.stopPropagation()),!e.animated&&(w=!0,b.handleReplacedDragElement&&!e.parentNode&&t&&K(e=O.getElementById(t)||e,this.options.ghostClass,!0),E&&!b.disabled&&(_?j||(p=!i.contains(e)):g===this||(E.lastPullMode=m.checkPull(this,E,e,s))&&y.checkPut(this,E,e,s))&&(void 0===s.rootEl||s.rootEl===this.el))){if(B(s,b,this.el),H)return;if(u=W(s.target,b.draggable,v),c=e.getBoundingClientRect(),g!==this&&(g=this,k=!0),p)return X(E,!0),n=i,void(o||a?i.insertBefore(e,o||a):j||i.appendChild(e));if(0===v.children.length||v.children[0]===r||v===s.target&&function(e,t){var n=e.lastElementChild.getBoundingClientRect();return t.clientY-(n.top+n.height)>5||t.clientX-(n.left+n.width)>5}(v,s)){if(0!==v.children.length&&v.children[0]!==r&&v===s.target&&(u=v.lastElementChild),u){if(u.animated)return;l=u.getBoundingClientRect()}X(E,_),!1!==Q(i,v,e,c,u,l,s)&&(e.contains(v)||(v.appendChild(e),n=v),this._animate(c,e),u&&this._animate(l,u))}else if(u&&!u.animated&&u!==e&&void 0!==u.parentNode[T]){f!==u&&(f=u,d=$(u),h=$(u.parentNode));var L=(l=u.getBoundingClientRect()).right-l.left,A=l.bottom-l.top,M=x.test(d.cssFloat+d.display)||"flex"==h.display&&0===h["flex-direction"].indexOf("row"),C=u.offsetWidth>e.offsetWidth,D=u.offsetHeight>e.offsetHeight,P=(M?(s.clientX-l.left)/L:(s.clientY-l.top)/A)>.5,R=u.nextElementSibling,N=!1;if(M){var q=e.offsetTop,I=u.offsetTop;N=q===I?u.previousElementSibling===e&&!C||P&&C:u.previousElementSibling===e||e.previousElementSibling===u?(s.clientY-l.top)/A>.5:I>q}else k||(N=R!==e&&!D||P&&D);var F=Q(i,v,e,c,u,l,s,N);!1!==F&&(1!==F&&-1!==F||(N=1===F),H=!0,S(te,30),X(E,_),e.contains(v)||(N&&!R?v.appendChild(e):u.parentNode.insertBefore(e,N?R:u)),n=e.parentNode,this._animate(c,e),this._animate(l,u))}}},_animate:function(e,t){var n=this.options.animation;if(n){var r=t.getBoundingClientRect();1===e.nodeType&&(e=e.getBoundingClientRect()),$(t,"transition","none"),$(t,"transform","translate3d("+(e.left-r.left)+"px,"+(e.top-r.top)+"px,0)"),t.offsetWidth,$(t,"transition","all "+n+"ms"),$(t,"transform","translate3d(0,0,0)"),clearTimeout(t.animated),t.animated=S(function(){$(t,"transition",""),$(t,"transform",""),t.animated=!1},n)}},_offUpEvents:function(){var e=this.el.ownerDocument;G(O,"touchmove",this._onTouchMove),G(O,"pointermove",this._onTouchMove),G(e,"mouseup",this._onDrop),G(e,"touchend",this._onDrop),G(e,"pointerup",this._onDrop),G(e,"touchcancel",this._onDrop),G(e,"pointercancel",this._onDrop),G(e,"selectstart",this)},_onDrop:function(t){var s=this.el,u=this.options;clearInterval(this._loopId),clearInterval(_.pid),clearTimeout(this._dragStartTimer),ue(this._cloneId),ue(this._dragStartId),G(O,"mouseover",this),G(O,"mousemove",this._onTouchMove),this.nativeDraggable&&(G(O,"drop",this),G(s,"dragstart",this._onDragStart)),this._offUpEvents(),t&&(w&&(t.preventDefault(),!u.dropBubble&&t.stopPropagation()),r&&r.parentNode&&r.parentNode.removeChild(r),i!==n&&"clone"===V.active.lastPullMode||o&&o.parentNode&&o.parentNode.removeChild(o),e&&(this.nativeDraggable&&G(e,"dragend",this),ee(e),e.style["will-change"]="",K(e,this.options.ghostClass,!1),K(e,this.options.chosenClass,!1),Z(this,i,"unchoose",e,n,i,p),i!==n?(v=re(e,u.draggable))>=0&&(Z(null,n,"add",e,n,i,p,v),Z(this,i,"remove",e,n,i,p,v),Z(null,n,"sort",e,n,i,p,v),Z(this,i,"sort",e,n,i,p,v)):e.nextSibling!==a&&(v=re(e,u.draggable))>=0&&(Z(this,i,"update",e,n,i,p,v),Z(this,i,"sort",e,n,i,p,v)),V.active&&(null!=v&&-1!==v||(v=p),Z(this,i,"end",e,n,i,p,v),this.save()))),this._nulling()},_nulling:function(){i=e=n=r=a=o=s=u=c=b=y=w=v=f=d=g=m=V.active=null,q.forEach(function(e){e.checked=!0}),q.length=0},handleEvent:function(t){switch(t.type){case"drop":case"dragend":this._onDrop(t);break;case"dragover":case"dragenter":e&&(this._onDragOver(t),function(e){e.dataTransfer&&(e.dataTransfer.dropEffect="move");e.preventDefault()}(t));break;case"mouseover":this._onDrop(t);break;case"selectstart":t.preventDefault()}},toArray:function(){for(var e,t=[],n=this.el.children,r=0,o=n.length,i=this.options;r<o;r++)W(e=n[r],i.draggable,this.el)&&t.push(e.getAttribute(i.dataIdAttr)||ne(e));return t},sort:function(e){var t={},n=this.el;this.toArray().forEach(function(e,r){var o=n.children[r];W(o,this.options.draggable,n)&&(t[e]=o)},this),e.forEach(function(e){t[e]&&(n.removeChild(t[e]),n.appendChild(t[e]))})},save:function(){var e=this.options.store;e&&e.set(this)},closest:function(e,t){return W(e,t||this.options.draggable,this.el)},option:function(e,t){var n=this.options;if(void 0===t)return n[e];n[e]=t,"group"===e&&z(n)},destroy:function(){var e=this.el;e[T]=null,G(e,"mousedown",this._onTapStart),G(e,"touchstart",this._onTapStart),G(e,"pointerdown",this._onTapStart),this.nativeDraggable&&(G(e,"dragover",this),G(e,"dragenter",this)),Array.prototype.forEach.call(e.querySelectorAll("[draggable]"),function(e){e.removeAttribute("draggable")}),I.splice(I.indexOf(this._onDragOver),1),this._onDrop(),this.el=e=null}},Y(O,"touchmove",function(e){V.active&&e.preventDefault()}),V.utils={on:Y,off:G,css:$,find:J,is:function(e,t){return!!W(e,t,e)},extend:ae,throttle:ie,closest:W,toggleClass:K,clone:function(e){return M&&M.dom?M.dom(e).cloneNode(!0):A?A(e).clone(!0)[0]:e.cloneNode(!0)},index:re,nextTick:se,cancelNextTick:ue},V.create=function(e,t){return new V(e,t)},V.version="1.7.0",V},e.exports=t()}),sa=Object.freeze({default:aa,__moduleExports:aa});var ua=new WeakMap;function ca(e,t){var n=new XMLHttpRequest;return n.open("GET",t,!0),n.setRequestHeader("Accept","text/html; fragment"),function(e,t){var n=ua.get(e);n&&n.abort();ua.set(e,t);var r=function(){return ua.delete(e)},o=function(e){return new Promise(function(t,n){e.onload=function(){e.status>=200&&e.status<300?t(e.responseText):n(new Error(e.responseText))},e.onerror=n,e.send()})}(t);return o.then(r,r),o}(e,n)}var la=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},fa=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),da=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t},ha=navigator.userAgent.match(/Macintosh/),pa=function(){function e(t,n,r){var o,i,a;la(this,e),this.container=t,this.input=n,this.results=r,this.results.hidden=!0,this.input.setAttribute("autocomplete","off"),this.input.setAttribute("spellcheck","false"),this.mouseDown=!1,this.onInputChange=(o=this.onInputChange.bind(this),i=300,a=void 0,function(){for(var e=arguments.length,t=Array(e),n=0;n<e;n++)t[n]=arguments[n];var r=this;clearTimeout(a),a=setTimeout(function(){clearTimeout(a),o.apply(r,t)},i)}),this.onResultsClick=this.onResultsClick.bind(this),this.onResultsMouseDown=this.onResultsMouseDown.bind(this),this.onInputBlur=this.onInputBlur.bind(this),this.onInputFocus=this.onInputFocus.bind(this),this.onKeydown=this.onKeydown.bind(this),this.input.addEventListener("keydown",this.onKeydown),this.input.addEventListener("focus",this.onInputFocus),this.input.addEventListener("blur",this.onInputBlur),this.input.addEventListener("input",this.onInputChange),this.results.addEventListener("mousedown",this.onResultsMouseDown),this.results.addEventListener("click",this.onResultsClick)}return fa(e,[{key:"destroy",value:function(){this.input.removeEventListener("keydown",this.onKeydown),this.input.removeEventListener("focus",this.onInputFocus),this.input.removeEventListener("blur",this.onInputBlur),this.input.removeEventListener("input",this.onInputChange),this.results.removeEventListener("mousedown",this.onResultsMouseDown),this.results.removeEventListener("click",this.onResultsClick)}},{key:"sibling",value:function(e){var t=Array.from(this.results.querySelectorAll('[role="option"]')),n=this.results.querySelector('[aria-selected="true"]'),r=t.indexOf(n),o=e?t[r+1]:t[r-1],i=e?t[0]:t[t.length-1];return o||i}},{key:"select",value:function(e){var t=!0,n=!1,r=void 0;try{for(var o,i=this.results.querySelectorAll('[aria-selected="true"]')[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){o.value.removeAttribute("aria-selected")}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}e.setAttribute("aria-selected","true"),this.input.setAttribute("aria-activedescendant",e.id),function(e,t){(function(e,t){var n=e.scrollTop,r=n+e.clientHeight,o=t.offsetTop,i=o+t.clientHeight;return o>=n&&i<=r})(e,t)||(e.scrollTop=t.offsetTop)}(this.results,e)}},{key:"onKeydown",value:function(e){switch(e.key){case"Escape":this.container.open&&(this.container.open=!1,e.stopPropagation(),e.preventDefault());break;case"ArrowDown":var t=this.sibling(!0);t&&this.select(t),e.preventDefault();break;case"ArrowUp":var n=this.sibling(!1);n&&this.select(n),e.preventDefault();break;case"n":if(ha&&e.ctrlKey){var r=this.sibling(!0);r&&this.select(r),e.preventDefault()}break;case"p":if(ha&&e.ctrlKey){var o=this.sibling(!1);o&&this.select(o),e.preventDefault()}break;case"Tab":var i=this.results.querySelector('[aria-selected="true"]');i&&this.commit(i);break;case"Enter":var a=this.results.querySelector('[aria-selected="true"]');a&&this.container.open&&(this.commit(a),e.preventDefault())}}},{key:"onInputFocus",value:function(){this.fetchResults()}},{key:"onInputBlur",value:function(){this.mouseDown||(this.container.open=!1)}},{key:"commit",value:function(e){if("true"!==e.getAttribute("aria-disabled")){if(e instanceof HTMLAnchorElement)return e.click(),void(this.container.open=!1);var t=e.getAttribute("data-autocomplete-value")||e.textContent;this.container.value=t,this.container.open=!1}}},{key:"onResultsClick",value:function(e){if(e.target instanceof Element){var t=e.target.closest('[role="option"]');t&&this.commit(t)}}},{key:"onResultsMouseDown",value:function(){var e=this;this.mouseDown=!0,this.results.addEventListener("mouseup",function(){return e.mouseDown=!1},{once:!0})}},{key:"onInputChange",value:function(){this.container.removeAttribute("value"),this.fetchResults()}},{key:"identifyOptions",value:function(){var e=0,t=!0,n=!1,r=void 0;try{for(var o,i=this.results.querySelectorAll('[role="option"]:not([id])')[Symbol.iterator]();!(t=(o=i.next()).done);t=!0){o.value.id=this.results.id+"-option-"+e++}}catch(e){n=!0,r=e}finally{try{!t&&i.return&&i.return()}finally{if(n)throw r}}}},{key:"fetchResults",value:function(){var e=this,t=this.input.value.trim();if(t){var n=this.container.src;if(n){var r=new URL(n,window.location.href),o=new URLSearchParams(r.search.slice(1));o.append("q",t),r.search=o.toString(),this.container.dispatchEvent(new CustomEvent("loadstart")),ca(this.input,r.toString()).then(function(t){e.results.innerHTML=t,e.identifyOptions();var n=!!e.results.querySelector('[role="option"]');e.container.open=n,e.container.dispatchEvent(new CustomEvent("load")),e.container.dispatchEvent(new CustomEvent("loadend"))}).catch(function(){e.container.dispatchEvent(new CustomEvent("error")),e.container.dispatchEvent(new CustomEvent("loadend"))})}}else this.container.open=!1}},{key:"open",value:function(){this.results.hidden&&(this.results.hidden=!1,this.container.setAttribute("aria-expanded","true"),this.container.dispatchEvent(new CustomEvent("toggle",{detail:{input:this.input,results:this.results}})))}},{key:"close",value:function(){this.results.hidden||(this.results.hidden=!0,this.input.removeAttribute("aria-activedescendant"),this.container.setAttribute("aria-expanded","false"),this.container.dispatchEvent(new CustomEvent("toggle",{detail:{input:this.input,results:this.results}})))}}]),e}();function va(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(va.prototype,HTMLElement.prototype),Object.setPrototypeOf(va,HTMLElement);var ma=new WeakMap,ga=function(e){function t(){return la(this,t),da(this,(t.__proto__||Object.getPrototypeOf(t)).call(this))}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,va),fa(t,[{key:"connectedCallback",value:function(){var e=this.getAttribute("aria-owns");if(e){var t=this.querySelector("input"),n=document.getElementById(e);t instanceof HTMLInputElement&&n&&(ma.set(this,new pa(this,t,n)),this.setAttribute("role","combobox"),this.setAttribute("aria-haspopup","listbox"),this.setAttribute("aria-expanded","false"),t.setAttribute("aria-autocomplete","list"),t.setAttribute("aria-controls",e),n.setAttribute("role","listbox"))}}},{key:"disconnectedCallback",value:function(){var e=ma.get(this);e&&(e.destroy(),ma.delete(this))}},{key:"attributeChangedCallback",value:function(e,t,n){if(t!==n){var r=ma.get(this);if(r)switch(e){case"open":null===n?r.close():r.open();break;case"value":null!==n&&(r.input.value=n),this.dispatchEvent(new CustomEvent("change",{bubbles:!0}))}}}},{key:"src",get:function(){return this.getAttribute("src")||""},set:function(e){this.setAttribute("src",e)}},{key:"value",get:function(){return this.getAttribute("value")||""},set:function(e){this.setAttribute("value",e)}},{key:"open",get:function(){return this.hasAttribute("open")},set:function(e){e?this.setAttribute("open",""):this.removeAttribute("open")}}],[{key:"observedAttributes",get:function(){return["open","value"]}}]),t}();window.customElements.get("auto-complete")||(window.AutocompleteElement=ga,window.customElements.define("auto-complete",ga));var ba=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}();function ya(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(ya.prototype,HTMLElement.prototype),Object.setPrototypeOf(ya,HTMLElement);var wa=function(e){function t(){return function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}(this,(t.__proto__||Object.getPrototypeOf(t)).call(this))}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,ya),ba(t,[{key:"connectedCallback",value:function(){var e=this;this.setAttribute("role","menu");var t=this.parentElement;if(t){var n=t.querySelector("summary");n&&n.setAttribute("aria-haspopup","menu"),t.addEventListener("click",Ta),t.addEventListener("keydown",ja),t.addEventListener("toggle",function(){if(e.src){var n=e.querySelector("include-fragment");n&&(n.addEventListener("loadend",_a.bind(null,t)),n.src=e.src)}},{once:!0}),t.addEventListener("toggle",Ea),t.addEventListener("toggle",_a.bind(null,t))}}},{key:"src",get:function(){return this.getAttribute("src")||""},set:function(e){this.setAttribute("src",e)}}]),t}();function Ea(e){var t=e.currentTarget;if(t instanceof Element&&t.hasAttribute("open")){var n=!0,r=!1,o=void 0;try{for(var i,a=document.querySelectorAll("details[open] > details-menu")[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value.closest("details");s&&s!==t&&!s.contains(t)&&s.removeAttribute("open")}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}}function _a(e){if(e.open){var t=e.querySelector("[autofocus]");t&&t.focus()}}function ka(e,t){var n=Array.from(e.querySelectorAll('[role^="menuitem"]:not([hidden]):not([disabled]):not([aria-disabled="true"])')),r=document.activeElement,o=n.indexOf(r),i=t?n[o+1]:n[o-1],a=t?n[0]:n[n.length-1];return i||a}var xa=navigator.userAgent.match(/Macintosh/);function Ta(e){var t=e.target;if(t instanceof Element){var n=e.currentTarget;if(n instanceof Element&&t.closest("details")===n){var r=t.closest('[role^="menuitem"]');r&&function(e,t){if(e.hasAttribute("disabled")||"true"===e.getAttribute("aria-disabled"))return;if(!e.dispatchEvent(new CustomEvent("details-menu-select",{bubbles:!0,cancelable:!0})))return;(function(e,t){var n=t.querySelector("[data-menu-button]");if(n){var r=function(e){if(!e)return null;var t=e.hasAttribute("data-menu-button-text")?e:e.querySelector("[data-menu-button-text]");return t?t.getAttribute("data-menu-button-text")||t.textContent:null}(e);if(r)n.textContent=r;else{var o=function(e){if(!e)return null;var t=e.hasAttribute("data-menu-button-contents")?e:e.querySelector("[data-menu-button-contents]");return t?t.innerHTML:null}(e);o&&(n.innerHTML=o)}}})(e,t),function(e,t){if(function(e){var t=e.getAttribute("role");return"menuitemradio"===t||"menuitemcheckbox"===t}(e)){if("menuitemradio"===e.getAttribute("role")){var n=!0,r=!1,o=void 0;try{for(var i,a=t.querySelectorAll('[role="menuitemradio"]')[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;s.setAttribute("aria-checked","false")}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}e.setAttribute("aria-checked","true")}}(e,t),"menuitemcheckbox"!==e.getAttribute("role")&&Oa(t);e.dispatchEvent(new CustomEvent("details-menu-selected",{bubbles:!0}))}(r,n)}}}function ja(e){var t,n=e.currentTarget;if(!n.querySelector("details[open]"))switch(e.key){case"Escape":Oa(n),e.preventDefault();break;case"ArrowDown":var r=ka(n,!0);r&&r.focus(),e.preventDefault();break;case"ArrowUp":var o=ka(n,!1);o&&o.focus(),e.preventDefault();break;case"n":if(xa&&e.ctrlKey){var i=ka(n,!0);i&&i.focus(),e.preventDefault()}break;case"p":if(xa&&e.ctrlKey){var a=ka(n,!1);a&&a.focus(),e.preventDefault()}break;case" ":case"Enter":var s=document.activeElement;s&&("menuitem"===(t=s.getAttribute("role"))||"menuitemcheckbox"===t||"menuitemradio"===t)&&s.closest("details")===n&&(e.preventDefault(),e.stopPropagation(),s.click())}}function Oa(e){e.open=!1;var t=e.querySelector("summary");t&&t.focus()}window.customElements.get("details-menu")||(window.DetailsMenuElement=wa,window.customElements.define("details-menu",wa));var La=/\bWindows NT 6.1\b/.test(navigator.userAgent),Sa=/\bWindows NT 6.2\b/.test(navigator.userAgent),Aa=/\bWindows NT 6.3\b/.test(navigator.userAgent),Ma=/\bLinux\b/.test(navigator.userAgent),Ca=/\bFreeBSD\b/.test(navigator.userAgent);var Da=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},Pa=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),Ha=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t};function Ra(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(Ra.prototype,HTMLElement.prototype),Object.setPrototypeOf(Ra,HTMLElement);var Na=function(e){function t(){return Da(this,t),Ha(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,Ra),Pa(t,[{key:"connectedCallback",value:function(){if(null===this.image&&(La||Sa||Aa||Ma||Ca)){this.textContent="";var e=function(e){var t=document.createElement("img");return t.className="emoji",t.alt=e.getAttribute("alias")||"",t.height=20,t.width=20,t}(this);e.src=this.getAttribute("fallback-src")||"",this.appendChild(e)}}},{key:"image",get:function(){return this.firstElementChild instanceof HTMLImageElement?this.firstElementChild:null}}]),t}();window.customElements.get("g-emoji")||(window.GEmojiElement=Na,window.customElements.define("g-emoji",Na));var qa=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}();function Ia(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(Ia.prototype,HTMLElement.prototype),Object.setPrototypeOf(Ia,HTMLElement);var Fa=document.createElement("template");function Ba(e){var t=e.currentTarget;if(t.dragStartX&&t.dragStartY){var n=Math.min(Math.max(0,t.box.offsetLeft+e.pageX-t.dragStartX),t.image.width-t.box.offsetWidth),r=Math.min(Math.max(0,t.box.offsetTop+e.pageY-t.dragStartY),t.image.height-t.box.offsetHeight);t.box.style.left=n+"px",t.box.style.top=r+"px",Ya(t,{x:n,y:r,width:t.box.offsetWidth,height:t.box.offsetHeight})}t.dragStartX=e.pageX,t.dragStartY=e.pageY}function za(e){var t=e.target.closest("image-crop"),n=t.getBoundingClientRect();Xa(t,e.pageX-t.startX-n.left-window.pageXOffset,e.pageY-t.startY-n.top-window.pageYOffset)}function Va(e){var t=e.currentTarget.closest("image-crop");if(e.target.hasAttribute("data-direction")){var n=e.target.getAttribute("data-direction");t.addEventListener("mousemove",za),["nw","se"].indexOf(n)>=0&&t.classList.add("nwse"),["ne","sw"].indexOf(n)>=0&&t.classList.add("nesw"),t.startX=t.box.offsetLeft+(["se","ne"].indexOf(n)>=0?0:t.box.offsetWidth),t.startY=t.box.offsetTop+(["se","sw"].indexOf(n)>=0?0:t.box.offsetHeight),za(e)}else t.addEventListener("mousemove",Ba)}function Xa(e,t,n){var r=Math.max(Math.abs(t),Math.abs(n),e.minWidth);r=Math.min(r,n>0?e.image.height-e.startY:e.startY,t>0?e.image.width-e.startX:e.startX);var o=Math.round(Math.max(0,t>0?e.startX:e.startX-r)),i=Math.round(Math.max(0,n>0?e.startY:e.startY-r));e.box.style.left=o+"px",e.box.style.top=i+"px",e.box.style.width=r+"px",e.box.style.height=r+"px",Ya(e,{x:o,y:i,width:r,height:r})}function Wa(e){var t=e.currentTarget.closest("image-crop");t.loaded=!0;var n=e.target,r=Math.round(n.clientWidth>n.clientHeight?n.clientHeight:n.clientWidth);t.startX=(n.clientWidth-r)/2,t.startY=(n.clientHeight-r)/2,Xa(t,r,r)}function Ua(e){var t=e.currentTarget;t.dragStartX=t.dragStartY=null,t.classList.remove("nwse","nesw"),t.removeEventListener("mousemove",za),t.removeEventListener("mousemove",Ba)}function Ya(e,t){var n=e.image.naturalWidth/e.image.width;for(var r in t){var o=Math.round(t[r]*n);t[r]=o;var i=e.querySelector("[data-image-crop-input='"+r+"']");i&&(i.value=o)}e.dispatchEvent(new CustomEvent("image-crop-change",{bubbles:!0,detail:t}))}Fa.innerHTML='\n  <div class="crop-wrapper">\n    <img width="100%" class="crop-image" alt="">\n    <div class="crop-container">\n      <div data-crop-box class="crop-box">\n        <div class="crop-outline"></div>\n        <div data-direction="nw" class="handle nw"></div>\n        <div data-direction="ne" class="handle ne"></div>\n        <div data-direction="sw" class="handle sw"></div>\n        <div data-direction="se" class="handle se"></div>\n      </div>\n    </div>\n  </div>\n';var Ga=function(e){function t(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t);var e=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.startX=null,e.startY=null,e.minWidth=10,e}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,Ia),qa(t,[{key:"connectedCallback",value:function(){this.constructed||(this.constructed=!0,this.appendChild(document.importNode(Fa.content,!0)),this.image=this.querySelector("img"),this.box=this.querySelector("[data-crop-box]"),this.image.addEventListener("load",Wa),this.addEventListener("mouseleave",Ua),this.addEventListener("mouseup",Ua),this.box.addEventListener("mousedown",Va),this.src&&(this.image.src=this.src))}},{key:"attributeChangedCallback",value:function(e,t,n){"src"===e&&(this.loaded=!1,this.image&&(this.image.src=n))}},{key:"src",get:function(){return this.getAttribute("src")},set:function(e){e?this.setAttribute("src",e):this.removeAttribute("src")}},{key:"loaded",get:function(){return this.hasAttribute("loaded")},set:function(e){e?this.setAttribute("loaded",""):this.removeAttribute("loaded")}}],[{key:"observedAttributes",get:function(){return["src"]}}]),t}();window.customElements.get("image-crop")||window.customElements.define("image-crop",Ga);var Ka=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},$a=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}();function Ja(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function Za(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}function Qa(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}function es(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(es.prototype,HTMLElement.prototype),Object.setPrototypeOf(es,HTMLElement);var ts=new WeakMap,ns=function(e){function t(){Ja(this,t);var e,n=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this)),r=function(){var e=ts.get(n);e&&_s(n,e)};return n.addEventListener("keydown",(e=r,function(t){" "!==t.key&&"Enter"!==t.key||(t.preventDefault(),e(t))})),n.addEventListener("click",r),n}return Qa(t,es),$a(t,[{key:"connectedCallback",value:function(){this.hasAttribute("tabindex")||this.setAttribute("tabindex","0"),this.hasAttribute("role")||this.setAttribute("role","button")}},{key:"click",value:function(){var e=ts.get(this);e&&_s(this,e)}}]),t}(),rs=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"### "}),e}return Qa(t,ns),t}();window.customElements.get("md-header")||(window.MarkdownHeaderButtonElement=rs,window.customElements.define("md-header",rs));var os=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.setAttribute("hotkey","b"),ts.set(e,{prefix:"**",suffix:"**",trimFirst:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-bold")||(window.MarkdownBoldButtonElement=os,window.customElements.define("md-bold",os));var is=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.setAttribute("hotkey","i"),ts.set(e,{prefix:"_",suffix:"_",trimFirst:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-italic")||(window.MarkdownItalicButtonElement=is,window.customElements.define("md-italic",is));var as=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"> ",multiline:!0,surroundWithNewlines:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-quote")||(window.MarkdownQuoteButtonElement=as,window.customElements.define("md-quote",as));var ss=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"`",suffix:"`",blockPrefix:"```",blockSuffix:"```"}),e}return Qa(t,ns),t}();window.customElements.get("md-code")||(window.MarkdownCodeButtonElement=ss,window.customElements.define("md-code",ss));var us=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.setAttribute("hotkey","k"),ts.set(e,{prefix:"[",suffix:"](url)",replaceNext:"url",scanFor:"https?://"}),e}return Qa(t,ns),t}();window.customElements.get("md-link")||(window.MarkdownLinkButtonElement=us,window.customElements.define("md-link",us));var cs=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"- ",multiline:!0,surroundWithNewlines:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-unordered-list")||(window.MarkdownUnorderedListButtonElement=cs,window.customElements.define("md-unordered-list",cs));var ls=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"1. ",multiline:!0,orderedList:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-ordered-list")||(window.MarkdownOrderedListButtonElement=ls,window.customElements.define("md-ordered-list",ls));var fs=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.setAttribute("hotkey","L"),ts.set(e,{prefix:"- [ ] ",multiline:!0,surroundWithNewlines:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-task-list")||(window.MarkdownTaskListButtonElement=fs,window.customElements.define("md-task-list",fs));var ds=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"@",prefixSpace:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-mention")||(window.MarkdownMentionButtonElement=ds,window.customElements.define("md-mention",ds));var hs=function(e){function t(){Ja(this,t);var e=Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return ts.set(e,{prefix:"#",prefixSpace:!0}),e}return Qa(t,ns),t}();window.customElements.get("md-ref")||(window.MarkdownRefButtonElement=hs,window.customElements.define("md-ref",hs));var ps=navigator.userAgent.match(/Macintosh/)?"Meta":"Control",vs=function(e){function t(){return Ja(this,t),Za(this,(t.__proto__||Object.getPrototypeOf(t)).call(this))}return Qa(t,es),$a(t,[{key:"connectedCallback",value:function(){var e=function(e,t){if(t.metaKey&&"Meta"===ps||t.ctrlKey&&"Control"===ps){var n=e.querySelector('[hotkey="'+t.key+'"]');n&&(n.click(),t.preventDefault())}}.bind(null,this);this.field&&(this.field.addEventListener("keydown",e),ms.set(this,e))}},{key:"disconnectedCallback",value:function(){var e=ms.get(this);e&&this.field&&(this.field.removeEventListener("keydown",e),ms.delete(this))}},{key:"field",get:function(){var e=this.getAttribute("for");if(e){var t=document.getElementById(e);return t instanceof HTMLTextAreaElement?t:null}}}]),t}(),ms=new WeakMap;function gs(e){return e.trim().split("\n").length>1}function bs(e,t){return Array(t+1).join(e)}window.customElements.get("markdown-toolbar")||(window.MarkdownToolbarElement=vs,window.customElements.define("markdown-toolbar",vs));var ys=null;function ws(e,t){var n=e.value.slice(e.selectionStart,e.selectionEnd),r=void 0;r=t.orderedList?function(e){var t=/^\d+\.\s+/,n=void 0,r=void 0,o=e.value.slice(e.selectionStart,e.selectionEnd),i=o.split("\n");if(i.every(function(e){return t.test(e)}))i=i.map(function(e){return e.replace(t,"")}),o=i.join("\n");else{i=function(){var e=void 0,t=void 0,n=void 0,r=[];for(n=e=0,t=i.length;e<t;n=++e){var o=i[n];r.push(n+1+". "+o)}return r}(),o=i.join("\n");var a=Es(e),s=a.newlinesToAppend,u=a.newlinesToPrepend;r=e.selectionStart+s.length,n=r+o.length,o=s+o+u}return{text:o,selectionStart:r,selectionEnd:n}}(e):t.multiline&&gs(n)?function(e,t){var n=t.prefix,r=t.suffix,o=t.surroundWithNewlines,i=e.value.slice(e.selectionStart,e.selectionEnd),a=e.selectionStart,s=e.selectionEnd,u=i.split("\n");if(u.every(function(e){return e.startsWith(n)&&e.endsWith(r)}))i=u.map(function(e){return e.slice(n.length,e.length-r.length)}).join("\n"),s=a+i.length;else if(i=u.map(function(e){return n+e+r}).join("\n"),o){var c=Es(e),l=c.newlinesToAppend,f=c.newlinesToPrepend;a+=l.length,s=a+i.length,i=l+i+f}return{text:i,selectionStart:a,selectionEnd:s}}(e,t):function(e,t){var n=void 0,r=void 0,o=t.prefix,i=t.suffix,a=t.blockPrefix,s=t.blockSuffix,u=t.replaceNext,c=t.prefixSpace,l=t.scanFor,f=t.surroundWithNewlines,d=e.selectionStart,h=e.selectionEnd,p=e.value.slice(e.selectionStart,e.selectionEnd),v=gs(p)&&a.length>0?a+"\n":o,m=gs(p)&&s.length>0?"\n"+s:i;if(c){var g=e.value[e.selectionStart-1];0===e.selectionStart||null==g||g.match(/\s/)||(v=" "+v)}p=function(e,t,n){if(e.selectionStart===e.selectionEnd)e.selectionStart=function(e,t){for(;e[t]&&null!=e[t-1]&&!e[t-1].match(/\s/);)t--;return t}(e.value,e.selectionStart),e.selectionEnd=function(e,t){for(;e[t]&&!e[t].match(/\s/);)t++;return t}(e.value,e.selectionEnd);else{var r=e.selectionStart-t.length,o=e.selectionEnd+n.length,i=e.value.slice(r,e.selectionStart)===t,a=e.value.slice(e.selectionEnd,o)===n;i&&a&&(e.selectionStart=r,e.selectionEnd=o)}return e.value.slice(e.selectionStart,e.selectionEnd)}(e,v,m);var b=e.selectionStart,y=e.selectionEnd,w=u.length>0&&m.indexOf(u)>-1&&p.length>0;if(f){var E=Es(e);n=E.newlinesToAppend,r=E.newlinesToPrepend,v=n+o,m+=r}if(p.startsWith(v)&&p.endsWith(m)){var _=p.slice(v.length,p.length-m.length);if(d===h){var k=d-v.length;k=Math.max(k,b),k=Math.min(k,b+_.length),b=y=k}else y=b+_.length;return{text:_,selectionStart:b,selectionEnd:y}}if(w){if(l.length>0&&p.match(l)){m=m.replace(u,p);var x=v+m;return b=y=b+v.length,{text:x,selectionStart:b,selectionEnd:y}}var T=v+p+m;return b=b+v.length+p.length+m.indexOf(u),y=b+u.length,{text:T,selectionStart:b,selectionEnd:y}}var j=v+p+m;b=d+v.length,y=h+v.length;var O=p.match(/^\s*|\s*$/g);if(t.trimFirst&&O){var L=O[0]||"",S=O[1]||"";j=L+v+p.trim()+m+S,b+=L.length,y-=S.length}return{text:j,selectionStart:b,selectionEnd:y}}(e,t),function(e,t){var n=t.text,r=t.selectionStart,o=t.selectionEnd,i=e.selectionStart,a=e.value.slice(0,i),s=e.value.slice(e.selectionEnd);if(null===ys||!0===ys){e.contentEditable="true";try{ys=document.execCommand("insertText",!1,n)}catch(e){ys=!1}e.contentEditable="false"}if(ys&&!e.value.slice(0,e.selectionStart).endsWith(n)&&(ys=!1),!ys){try{document.execCommand("ms-beginUndoUnit")}catch(e){}e.value=a+n+s;try{document.execCommand("ms-endUndoUnit")}catch(e){}e.dispatchEvent(new CustomEvent("input",{bubbles:!0,cancelable:!0}))}null!=r&&null!=o?e.setSelectionRange(r,o):e.setSelectionRange(i,e.selectionEnd)}(e,r)}function Es(e){var t=e.value.slice(0,e.selectionStart),n=e.value.slice(e.selectionEnd),r=t.match(/\n*$/),o=n.match(/^\n*/),i=r?r[0].length:0,a=o?o[0].length:0,s=void 0,u=void 0;return t.match(/\S/)&&i<2&&(s=bs("\n",2-i)),n.match(/\S/)&&a<2&&(u=bs("\n",2-a)),null==s&&(s=""),null==u&&(u=""),{newlinesToAppend:s,newlinesToPrepend:u}}function _s(e,t){var n=e.closest("markdown-toolbar");if(n instanceof vs){var r=Ka({},{prefix:"",suffix:"",blockPrefix:"",blockSuffix:"",multiline:!1,replaceNext:"",prefixSpace:!1,scanFor:"",surroundWithNewlines:!1,orderedList:!1,trimFirst:!1},t),o=n.field;o&&(o.focus(),ws(o,r))}}var ks=["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],xs=["January","February","March","April","May","June","July","August","September","October","November","December"];function Ts(e){return("0"+e).slice(-2)}function js(e,t){var n=e.getDay(),r=e.getDate(),o=e.getMonth(),i=e.getFullYear(),a=e.getHours(),s=e.getMinutes(),u=e.getSeconds();return t.replace(/%([%aAbBcdeHIlmMpPSwyYZz])/g,function(t){var c=void 0;switch(t[1]){case"%":return"%";case"a":return ks[n].slice(0,3);case"A":return ks[n];case"b":return xs[o].slice(0,3);case"B":return xs[o];case"c":return e.toString();case"d":return Ts(r);case"e":return r;case"H":return Ts(a);case"I":return Ts(js(e,"%l"));case"l":return 0===a||12===a?12:(a+12)%12;case"m":return Ts(o+1);case"M":return Ts(s);case"p":return a>11?"PM":"AM";case"P":return a>11?"pm":"am";case"S":return Ts(u);case"w":return n;case"y":return Ts(i%100);case"Y":return i;case"Z":return(c=e.toString().match(/\((\w+)\)$/))?c[1]:"";case"z":return(c=e.toString().match(/\w([+-]\d\d\d\d) /))?c[1]:""}})}function Os(e){var t=void 0;return function(){if(t)return t;if("Intl"in window)try{return t=new Intl.DateTimeFormat(void 0,e)}catch(e){if(!(e instanceof RangeError))throw e}}}var Ls=null,Ss=Os({day:"numeric",month:"short"});function As(){if(null!==Ls)return Ls;var e=Ss();if(e){var t=e.format(new Date(0));return Ls=!!t.match(/^\d/)}return!1}var Ms=null,Cs=Os({day:"numeric",month:"short",year:"numeric"});var Ds=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},Ps=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),Hs=function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)},Rs=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t};function Ns(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(Ns.prototype,HTMLElement.prototype),Object.setPrototypeOf(Ns,HTMLElement);var qs=function(e){function t(){return Ds(this,t),Rs(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return Hs(t,Ns),Ps(t,[{key:"attributeChangedCallback",value:function(e,t,n){if("datetime"===e){var r=Date.parse(n);this._date=isNaN(r)?null:new Date(r)}var o=this.getFormattedTitle();o&&!this.hasAttribute("title")&&this.setAttribute("title",o);var i=this.getFormattedDate();i&&(this.textContent=i)}},{key:"getFormattedTitle",value:function(){if(this._date){var e=Is();if(e)return e.format(this._date);try{return this._date.toLocaleString()}catch(e){if(e instanceof RangeError)return this._date.toString();throw e}}}}],[{key:"observedAttributes",get:function(){return["datetime","day","format","hour","minute","month","second","title","weekday","year"]}}]),t}(),Is=Os({day:"numeric",month:"short",year:"numeric",hour:"numeric",minute:"2-digit",timeZoneName:"short"}),Fs=new WeakMap,Bs=function(e){function t(){return Ds(this,t),Rs(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return Hs(t,qs),Ps(t,[{key:"attributeChangedCallback",value:function(e,n,r){"hour"!==e&&"minute"!==e&&"second"!==e&&"time-zone-name"!==e||Fs.delete(this),function e(t,n,r){null===t&&(t=Function.prototype);var o=Object.getOwnPropertyDescriptor(t,n);if(void 0===o){var i=Object.getPrototypeOf(t);return null===i?void 0:e(i,n,r)}if("value"in o)return o.value;var a=o.get;return void 0!==a?a.call(r):void 0}(t.prototype.__proto__||Object.getPrototypeOf(t.prototype),"attributeChangedCallback",this).call(this,e,n,r)}},{key:"getFormattedDate",value:function(){if(this._date)return((function(e){var t={weekday:{short:"%a",long:"%A"},day:{numeric:"%e","2-digit":"%d"},month:{short:"%b",long:"%B"},year:{numeric:"%Y","2-digit":"%y"}},n=As()?"weekday day month year":"weekday month day, year";for(var r in t){var o=t[r][e.getAttribute(r)];n=n.replace(r,o||"")}return n=n.replace(/(\s,)|(,\s$)/,""),js(e._date,n).replace(/\s+/," ").trim()}(this)||"")+" "+(function(e){var t={hour:e.getAttribute("hour"),minute:e.getAttribute("minute"),second:e.getAttribute("second"),timeZoneName:e.getAttribute("time-zone-name")};for(var n in t)t[n]||delete t[n];if(0===Object.keys(t).length)return;var r=Fs.get(e);r||(r=Os(t),Fs.set(e,r));var o=r();if(o)return o.format(e._date);var i=t.second?"%H:%M:%S":"%H:%M";return js(e._date,i)}(this)||"")).trim()}}]),t}();window.customElements.get("local-time")||(window.LocalTimeElement=Bs,window.customElements.define("local-time",Bs));var zs=function(){function e(t){Ds(this,e),this.date=t}return Ps(e,[{key:"toString",value:function(){var e=this.timeElapsed();if(e)return e;var t=this.timeAhead();return t||"on "+this.formatDate()}},{key:"timeElapsed",value:function(){var e=(new Date).getTime()-this.date.getTime(),t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24);return e>=0&&o<30?this.timeAgoFromMs(e):null}},{key:"timeAhead",value:function(){var e=this.date.getTime()-(new Date).getTime(),t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24);return e>=0&&o<30?this.timeUntil():null}},{key:"timeAgo",value:function(){var e=(new Date).getTime()-this.date.getTime();return this.timeAgoFromMs(e)}},{key:"timeAgoFromMs",value:function(e){var t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24),i=Math.round(o/30),a=Math.round(i/12);return e<0?"just now":t<10?"just now":t<45?t+" seconds ago":t<90?"a minute ago":n<45?n+" minutes ago":n<90?"an hour ago":r<24?r+" hours ago":r<36?"a day ago":o<30?o+" days ago":o<45?"a month ago":i<12?i+" months ago":i<18?"a year ago":a+" years ago"}},{key:"microTimeAgo",value:function(){var e=(new Date).getTime()-this.date.getTime(),t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24),i=Math.round(o/30),a=Math.round(i/12);return n<1?"1m":n<60?n+"m":r<24?r+"h":o<365?o+"d":a+"y"}},{key:"timeUntil",value:function(){var e=this.date.getTime()-(new Date).getTime();return this.timeUntilFromMs(e)}},{key:"timeUntilFromMs",value:function(e){var t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24),i=Math.round(o/30),a=Math.round(i/12);return i>=18?a+" years from now":i>=12?"a year from now":o>=45?i+" months from now":o>=30?"a month from now":r>=36?o+" days from now":r>=24?"a day from now":n>=90?r+" hours from now":n>=45?"an hour from now":t>=90?n+" minutes from now":t>=45?"a minute from now":t>=10?t+" seconds from now":"just now"}},{key:"microTimeUntil",value:function(){var e=this.date.getTime()-(new Date).getTime(),t=Math.round(e/1e3),n=Math.round(t/60),r=Math.round(n/60),o=Math.round(r/24),i=Math.round(o/30),a=Math.round(i/12);return o>=365?a+"y":r>=24?o+"d":n>=60?r+"h":n>1?n+"m":"1m"}},{key:"formatDate",value:function(){var e,t=As()?"%e %b":"%b %e";return e=this.date,(new Date).getUTCFullYear()!==e.getUTCFullYear()&&(t+=function(){if(null!==Ms)return Ms;var e=Cs();if(e){var t=e.format(new Date(0));return Ms=!!t.match(/\d,/)}return!0}()?", %Y":" %Y"),js(this.date,t)}},{key:"formatTime",value:function(){var e=Vs();return e?e.format(this.date):js(this.date,"%l:%M%P")}}]),e}(),Vs=Os({hour:"numeric",minute:"2-digit"}),Xs=function(e){function t(){return Ds(this,t),Rs(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return Hs(t,qs),Ps(t,[{key:"getFormattedDate",value:function(){if(this._date)return new zs(this._date).toString()}},{key:"connectedCallback",value:function(){Ws.push(this),Us||(Ys(),Us=setInterval(Ys,6e4))}},{key:"disconnectedCallback",value:function(){var e=Ws.indexOf(this);-1!==e&&Ws.splice(e,1),Ws.length||Us&&(clearInterval(Us),Us=null)}}]),t}(),Ws=[],Us=void 0;function Ys(){var e,t=void 0,n=void 0;for(n=0,e=Ws.length;n<e;n++)(t=Ws[n]).textContent=t.getFormattedDate()}window.customElements.get("relative-time")||(window.RelativeTimeElement=Xs,window.customElements.define("relative-time",Xs));var Gs=function(e){function t(){return Ds(this,t),Rs(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return Hs(t,Xs),Ps(t,[{key:"getFormattedDate",value:function(){if(this._date)return"micro"===this.getAttribute("format")?new zs(this._date).microTimeAgo():new zs(this._date).timeAgo()}}]),t}();window.customElements.get("time-ago")||(window.TimeAgoElement=Gs,window.customElements.define("time-ago",Gs));var Ks=function(e){function t(){return Ds(this,t),Rs(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return Hs(t,Xs),Ps(t,[{key:"getFormattedDate",value:function(){if(this._date)return"micro"===this.getAttribute("format")?new zs(this._date).microTimeUntil():new zs(this._date).timeUntil()}}]),t}();function $s(e,t){if(!Zs(e,t.textContent)){var n=getSelection();if(null!=n){n.removeAllRanges();var r=document.createRange();r.selectNodeContents(t),n.addRange(r),document.execCommand("copy"),n.removeAllRanges()}}}function Js(e,t){if(!Zs(e,t)){var n=document.body;if(n){var r=function(e){var t=document.createElement("pre");return t.style.width="1px",t.style.height="1px",t.style.position="fixed",t.style.top="5px",t.textContent=e,t}(t);n.appendChild(r),$s(e,r),n.removeChild(r)}}}function Zs(e,t){var n=navigator.clipboard;return!!n&&(n.writeText(t).then(function(){e.dispatchEvent(new CustomEvent("copy",{bubbles:!0}))}),!0)}window.customElements.get("time-until")||(window.TimeUntilElement=Ks,window.customElements.define("time-until",Ks));var Qs=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},eu=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),tu=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t};function nu(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}function ru(e){var t=e.getAttribute("for"),n=e.getAttribute("value");n?Js(e,n):t&&function(e,t){var n=e.ownerDocument.getElementById(t);if(!n)return;n instanceof HTMLInputElement||n instanceof HTMLTextAreaElement?"hidden"===n.type?Js(e,n.value):function(e,t){if(!Zs(e,t.value)){t.select(),document.execCommand("copy");var n=getSelection();null!=n&&n.removeAllRanges()}}(e,n):n instanceof HTMLAnchorElement&&n.hasAttribute("href")?Js(e,n.href):$s(e,n)}(e,t)}function ou(e){var t=e.currentTarget;t instanceof HTMLElement&&ru(t)}function iu(e){if(" "===e.key||"Enter"===e.key){var t=e.currentTarget;t instanceof HTMLElement&&(e.preventDefault(),ru(t))}}function au(e){e.currentTarget.addEventListener("keydown",iu)}function su(e){e.currentTarget.removeEventListener("keydown",iu)}Object.setPrototypeOf(nu.prototype,HTMLElement.prototype),Object.setPrototypeOf(nu,HTMLElement);var uu=function(e){function t(){Qs(this,t);var e=tu(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return e.addEventListener("click",ou),e.addEventListener("focus",au),e.addEventListener("blur",su),e}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,nu),eu(t,[{key:"connectedCallback",value:function(){this.hasAttribute("tabindex")||this.setAttribute("tabindex","0"),this.hasAttribute("role")||this.setAttribute("role","button")}},{key:"value",get:function(){return this.getAttribute("value")||""},set:function(e){this.setAttribute("value",e)}}]),t}();window.customElements.get("clipboard-copy")||(window.ClipboardCopyElement=uu,window.customElements.define("clipboard-copy",uu));var cu=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}();function lu(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(lu.prototype,HTMLElement.prototype),Object.setPrototypeOf(lu,HTMLElement);var fu="[data-close-dialog]",du="a, input, button, textarea, select, summary";function hu(e){var t=e.currentTarget;t instanceof Element&&("Escape"===e.key?(t.removeAttribute("open"),e.stopPropagation()):"Tab"===e.key&&function(e){if(!(e.currentTarget instanceof Element))return;var t=e.currentTarget.querySelector("details-dialog");if(!t)return;e.preventDefault();var n=Array.from(t.querySelectorAll(du)).filter(pu),r=e.shiftKey?-1:1,o=n.filter(function(e){return e.matches(":focus")})[0],i=n.length-1;if(o){var a=n.indexOf(o);if(-1!==a){var s=a+r;s>=0&&(i=s%n.length)}}n[i].focus()}(e))}function pu(e){return!(e.disabled||e.hidden||e.type&&"hidden"===e.type)}function vu(e){var t=e.currentTarget;if(t instanceof Element){var n=t.querySelector("details-dialog");if(n instanceof gu)if(t.hasAttribute("open"))document.activeElement&&mu.set(n,{details:t,activeElement:document.activeElement}),function(e){var t=e.querySelector("[autofocus]");t||(t=e,e.setAttribute("tabindex","-1")),t.focus()}(n),t.addEventListener("keydown",hu);else{var r=!0,o=!1,i=void 0;try{for(var a,s=n.querySelectorAll("form")[Symbol.iterator]();!(r=(a=s.next()).done);r=!0){var u=a.value;u instanceof HTMLFormElement&&u.reset()}}catch(e){o=!0,i=e}finally{try{!r&&s.return&&s.return()}finally{if(o)throw i}}var c=function(e,t){var n=mu.get(t);return n&&n.activeElement instanceof HTMLElement?n.activeElement:e.querySelector("summary")}(t,n);c&&c.focus(),t.removeEventListener("keydown",hu)}}}var mu=new WeakMap,gu=function(e){function t(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t);var e=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t}(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return mu.set(e,{details:null,activeElement:null}),e.addEventListener("click",function(e){var t=e.target;if(t instanceof Element){var n=t.closest("details");n&&t.closest(fu)&&n.removeAttribute("open")}}),e}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)}(t,lu),cu(t,null,[{key:"CLOSE_ATTR",get:function(){return"data-close-dialog"}},{key:"CLOSE_SELECTOR",get:function(){return fu}},{key:"INPUT_SELECTOR",get:function(){return du}}]),cu(t,[{key:"connectedCallback",value:function(){this.setAttribute("role","dialog");var e=mu.get(this);if(e){var t=this.parentElement;if(t){var n=t.querySelector("summary");n&&n.setAttribute("aria-haspopup","dialog"),t.addEventListener("toggle",vu),e.details=t}}}},{key:"disconnectedCallback",value:function(){var e=mu.get(this);e&&e.details&&(e.details.removeEventListener("toggle",vu),e.details=null)}},{key:"toggle",value:function(e){var t=mu.get(this);if(t){var n=t.details;n&&(e?n.setAttribute("open","open"):n.removeAttribute("open"))}}}]),t}();window.customElements.get("details-dialog")||(window.DetailsDialogElement=gu,window.customElements.define("details-dialog",gu));var bu=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},yu=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),wu=function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)},Eu=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t},_u=function(e){function t(e,n,r){bu(this,t);var o=Eu(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return o.statusCode=e,o.responseText=n,o.contentType=r,o}return wu(t,e),t}(Error);function ku(){return Reflect.construct(HTMLElement,[],this.__proto__.constructor)}Object.setPrototypeOf(ku.prototype,HTMLElement.prototype),Object.setPrototypeOf(ku,HTMLElement);var xu=new WeakMap,Tu=new WeakMap,ju=function(e){function t(){bu(this,t);var e,n,r,o=Eu(this,(t.__proto__||Object.getPrototypeOf(t)).call(this));return o.boundCheck=(e=o.check.bind(o),n=300,r=void 0,function(){for(var t=arguments.length,o=Array(t),i=0;i<t;i++)o[i]=arguments[i];var a=this;clearTimeout(r),r=setTimeout(function(){clearTimeout(r),e.apply(a,o)},n)}),o}return wu(t,ku),yu(t,[{key:"connectedCallback",value:function(){var e=this.querySelector("input");e instanceof HTMLInputElement&&(this.input=e,this.input.addEventListener("change",this.boundCheck),this.input.addEventListener("input",this.boundCheck),this.input.autocomplete="off",this.input.spellcheck=!1)}},{key:"disconnectedCallback",value:function(){this.input&&(this.input.removeEventListener("change",this.boundCheck),this.input.removeEventListener("input",this.boundCheck),this.input.setCustomValidity(""))}},{key:"check",value:function(){var e=this;if(!this.src)throw new Error("missing src");if(!this.csrf)throw new Error("missing csrf");var t=new FormData;t.append("authenticity_token",this.csrf),t.append("value",this.input.value);var n=t.entries?[].concat(function(e){if(Array.isArray(e)){for(var t=0,n=Array(e.length);t<e.length;t++)n[t]=e[t];return n}return Array.from(e)}(t.entries())).sort().toString():null;if(!n||n!==Tu.get(this.input))if(Tu.set(this.input,n),this.input.dispatchEvent(new CustomEvent("autocheck:send",{detail:{body:t},bubbles:!0})),this.input.value.trim()){var r=function(){e.dispatchEvent(new CustomEvent("loadend")),e.input.dispatchEvent(new CustomEvent("autocheck:complete",{bubbles:!0}))};this.required&&this.input.setCustomValidity("Verifying…"),this.dispatchEvent(new CustomEvent("loadstart")),function(e,t,n){var r=xu.get(e);r&&r.abort();var o=function(){return xu.delete(e)},i=new XMLHttpRequest;xu.set(e,i),i.open("POST",n,!0),i.setRequestHeader("Accept","text/html; fragment");var a=function(e,t){return new Promise(function(n,r){e.onload=function(){e.status>=200&&e.status<300?n(e.responseText):r(new _u(e.status,e.responseText,e.getResponseHeader("Content-Type")))},e.onerror=function(){r(new _u(e.status,e.responseText,e.getResponseHeader("Content-Type")))},e.send(t)})}(i,t);return a.then(o,o),a}(this.input,t,this.src).then(function(t){e.dispatchEvent(new CustomEvent("load"));var n=t?t.trim():null;e.required&&e.input.setCustomValidity(""),e.input.dispatchEvent(new CustomEvent("autocheck:success",{detail:{message:n},bubbles:!0}))}).catch(function(t){e.required&&e.input.setCustomValidity(Ou(t)||"Something went wrong"),e.dispatchEvent(new CustomEvent("error")),e.input.dispatchEvent(new CustomEvent("autocheck:error",{detail:{message:Ou(t)},bubbles:!0}))}).then(r,r)}else this.input.dispatchEvent(new CustomEvent("autocheck:complete",{bubbles:!0}))}},{key:"src",get:function(){var e=this.getAttribute("src");if(!e)return"";var t=this.ownerDocument.createElement("a");return t.href=e,t.href},set:function(e){this.setAttribute("src",e)}},{key:"csrf",get:function(){return this.getAttribute("csrf")||""},set:function(e){this.setAttribute("csrf",e)}},{key:"required",get:function(){return this.hasAttribute("required")},set:function(e){this.input.required=e,e?this.setAttribute("required",""):this.removeAttribute("required")}}]),t}();function Ou(e){if(422===e.statusCode&&e.responseText&&e.contentType.includes("text/html; fragment"))return e.responseText}window.customElements.get("auto-check")||(window.AutoCheckElement=ju,window.customElements.define("auto-check",ju));var Lu=new Map,Su=function(){function e(t,n,r){var o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Wu;l(this,e),this.strings=t,this.values=n,this.type=r,this.partCallback=o}return f(e,[{key:"getHTML",value:function(){for(var e,t,n=this.strings.length-1,r="",o=!0,i=0;i<n;i++){var a=this.strings[i];r+=a;var s=(void 0,void 0,t=(e=a).lastIndexOf(">"),e.indexOf("<",t+1)>-1?e.length:t);r+=(o=s>-1?s<a.length:o)?Du:Cu}return r+=this.strings[n]}},{key:"getTemplateElement",value:function(){var e=document.createElement("template");return e.innerHTML=this.getHTML(),e}}]),e}(),Au=function(e){function t(){return l(this,t),p(this,(t.__proto__||Object.getPrototypeOf(t)).apply(this,arguments))}return h(t,Su),f(t,[{key:"getHTML",value:function(){return"<svg>"+d(t.prototype.__proto__||Object.getPrototypeOf(t.prototype),"getHTML",this).call(this)+"</svg>"}},{key:"getTemplateElement",value:function(){var e=d(t.prototype.__proto__||Object.getPrototypeOf(t.prototype),"getTemplateElement",this).call(this),n=e.content,r=n.firstChild;return n.removeChild(r),Yu(n,r.firstChild),e}}]),t}();function Mu(e){var t=Lu.get(e.type);void 0===t&&(t=new Map,Lu.set(e.type,t));var n=t.get(e.strings);return void 0===n&&(n=new qu(e,e.getTemplateElement()),t.set(e.strings,n)),n}var Cu="{{lit-"+String(Math.random()).slice(2)+"}}",Du="\x3c!--"+Cu+"--\x3e",Pu=new RegExp(Cu+"|"+Du),Hu=/[ \x09\x0a\x0c\x0d]([^\0-\x1F\x7F-\x9F \x09\x0a\x0c\x0d"'>=/]+)[ \x09\x0a\x0c\x0d]*=[ \x09\x0a\x0c\x0d]*(?:[^ \x09\x0a\x0c\x0d"'`<>=]*|"[^"]*|'[^']*)$/;var Ru=function e(t,n,r,o,i){l(this,e),this.type=t,this.index=n,this.name=r,this.rawName=o,this.strings=i},Nu=function(e){return-1!==e.index},qu=function e(t,n){l(this,e),this.parts=[],this.element=n;for(var r=this.element.content,o=document.createTreeWalker(r,133,null,!1),i=-1,a=0,s=[],u=void 0,c=void 0;o.nextNode();){i++,u=c;var f=c=o.currentNode;if(1===f.nodeType){if(!f.hasAttributes())continue;for(var d=f.attributes,h=0,p=0;p<d.length;p++)d[p].value.indexOf(Cu)>=0&&h++;for(;h-- >0;){var v=t.strings[a],m=Hu.exec(v)[1],g=d.getNamedItem(m),b=g.value.split(Pu);this.parts.push(new Ru("attribute",i,g.name,m,b)),f.removeAttribute(g.name),a+=b.length-1}}else if(3===f.nodeType){var y=f.nodeValue;if(y.indexOf(Cu)<0)continue;var w=f.parentNode,E=y.split(Pu),_=E.length-1;a+=_;for(var k=0;k<_;k++)w.insertBefore(""===E[k]?document.createComment(""):document.createTextNode(E[k]),f),this.parts.push(new Ru("node",i++));w.insertBefore(""===E[_]?document.createComment(""):document.createTextNode(E[_]),f),s.push(f)}else if(8===f.nodeType&&f.nodeValue===Cu){var x=f.parentNode,T=f.previousSibling;null===T||T!==u||T.nodeType!==Node.TEXT_NODE?x.insertBefore(document.createComment(""),f):i--,this.parts.push(new Ru("node",i++)),s.push(f),null===f.nextSibling?x.insertBefore(document.createComment(""),f):i--,c=u,a++}}var j=!0,O=!1,L=void 0;try{for(var S,A=s[Symbol.iterator]();!(j=(S=A.next()).done);j=!0){var M=S.value;M.parentNode.removeChild(M)}}catch(e){O=!0,L=e}finally{try{!j&&A.return&&A.return()}finally{if(O)throw L}}},Iu=function(e,t){return Fu(t)?(t=t(e),Bu):null===t?void 0:t},Fu=function(e){return"function"==typeof e&&!0===e.__litDirective},Bu={},zu=function(e){return null===e||!("object"===(void 0===e?"undefined":u(e))||"function"==typeof e)},Vu=function(){function e(t,n,r,o){l(this,e),this.instance=t,this.element=n,this.name=r,this.strings=o,this.size=o.length-1,this._previousValues=[]}return f(e,[{key:"_interpolate",value:function(e,t){for(var n=this.strings,r=n.length-1,o="",i=0;i<r;i++){o+=n[i];var a=Iu(this,e[t+i]);if(a&&a!==Bu&&(Array.isArray(a)||"string"!=typeof a&&a[Symbol.iterator])){var s=!0,u=!1,c=void 0;try{for(var l,f=a[Symbol.iterator]();!(s=(l=f.next()).done);s=!0){o+=l.value}}catch(e){u=!0,c=e}finally{try{!s&&f.return&&f.return()}finally{if(u)throw c}}}else o+=a}return o+n[r]}},{key:"_equalToPreviousValues",value:function(e,t){for(var n=t;n<t+this.size;n++)if(this._previousValues[n]!==e[n]||!zu(e[n]))return!1;return!0}},{key:"setValue",value:function(e,t){if(!this._equalToPreviousValues(e,t)){var n=this.strings,r=void 0;2===n.length&&""===n[0]&&""===n[1]?(r=Iu(this,e[t]),Array.isArray(r)&&(r=r.join(""))):r=this._interpolate(e,t),r!==Bu&&this.element.setAttribute(this.name,r),this._previousValues=e}}}]),e}(),Xu=function(){function e(t,n,r){l(this,e),this.instance=t,this.startNode=n,this.endNode=r,this._previousValue=void 0}return f(e,[{key:"setValue",value:function(e){if((e=Iu(this,e))!==Bu)if(zu(e)){if(e===this._previousValue)return;this._setText(e)}else e instanceof Su?this._setTemplateResult(e):Array.isArray(e)||e[Symbol.iterator]?this._setIterable(e):e instanceof Node?this._setNode(e):void 0!==e.then?this._setPromise(e):this._setText(e)}},{key:"_insert",value:function(e){this.endNode.parentNode.insertBefore(e,this.endNode)}},{key:"_setNode",value:function(e){this._previousValue!==e&&(this.clear(),this._insert(e),this._previousValue=e)}},{key:"_setText",value:function(e){var t=this.startNode.nextSibling;e=void 0===e?"":e,t===this.endNode.previousSibling&&t.nodeType===Node.TEXT_NODE?t.textContent=e:this._setNode(document.createTextNode(e)),this._previousValue=e}},{key:"_setTemplateResult",value:function(e){var t=this.instance._getTemplate(e),n=void 0;this._previousValue&&this._previousValue.template===t?n=this._previousValue:(n=new Uu(t,this.instance._partCallback,this.instance._getTemplate),this._setNode(n._clone()),this._previousValue=n),n.update(e.values)}},{key:"_setIterable",value:function(t){Array.isArray(this._previousValue)||(this.clear(),this._previousValue=[]);var n=this._previousValue,r=0,o=!0,i=!1,a=void 0;try{for(var s,u=t[Symbol.iterator]();!(o=(s=u.next()).done);o=!0){var c=s.value,l=n[r];if(void 0===l){var f=this.startNode;if(r>0)f=n[r-1].endNode=document.createTextNode(""),this._insert(f);l=new e(this.instance,f,this.endNode),n.push(l)}l.setValue(c),r++}}catch(e){i=!0,a=e}finally{try{!o&&u.return&&u.return()}finally{if(i)throw a}}if(0===r)this.clear(),this._previousValue=void 0;else if(r<n.length){var d=n[r-1];n.length=r,this.clear(d.endNode.previousSibling),d.endNode=this.endNode}}},{key:"_setPromise",value:function(e){var t=this;this._previousValue=e,e.then(function(n){t._previousValue===e&&t.setValue(n)})}},{key:"clear",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:this.startNode;Gu(this.startNode.parentNode,e.nextSibling,this.endNode)}}]),e}(),Wu=function(e,t,n){if("attribute"===t.type)return new Vu(e,n,t.name,t.strings);if("node"===t.type)return new Xu(e,n,n.nextSibling);throw new Error("Unknown part type "+t.type)},Uu=function(){function e(t,n,r){l(this,e),this._parts=[],this.template=t,this._partCallback=n,this._getTemplate=r}return f(e,[{key:"update",value:function(e){var t=0,n=!0,r=!1,o=void 0;try{for(var i,a=this._parts[Symbol.iterator]();!(n=(i=a.next()).done);n=!0){var s=i.value;s?void 0===s.size?(s.setValue(e[t]),t++):(s.setValue(e,t),t+=s.size):t++}}catch(e){r=!0,o=e}finally{try{!n&&a.return&&a.return()}finally{if(r)throw o}}}},{key:"_clone",value:function(){var e=this.template.element.content.cloneNode(!0),t=this.template.parts;if(t.length>0)for(var n=document.createTreeWalker(e,133,null,!1),r=-1,o=0;o<t.length;o++){var i=t[o],a=Nu(i);if(a)for(;r<i.index;)r++,n.nextNode();this._parts.push(a?this._partCallback(this,i,n.currentNode):void 0)}return e}}]),e}(),Yu=function(e,t){for(var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null,o=t;o!==n;){var i=o.nextSibling;e.insertBefore(o,r),o=i}},Gu=function(e,t){for(var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,r=t;r!==n;){var o=r.nextSibling;e.removeChild(r),r=o}};return{delegated_events:ge,form_data_entries:wt,lit_html:Object.freeze({templateCaches:Lu,html:function(e){for(var t=arguments.length,n=Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];return new Su(e,n,"html")},svg:function(e){for(var t=arguments.length,n=Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];return new Au(e,n,"svg")},TemplateResult:Su,SVGTemplateResult:Au,defaultTemplateFactory:Mu,render:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:Mu,r=n(e),o=t.__templateInstance;if(void 0===o||o.template!==r||o._partCallback!==e.partCallback){o=new Uu(r,e.partCallback,n),t.__templateInstance=o;var i=o._clone();o.update(e.values),Gu(t,t.firstChild),t.appendChild(i)}else o.update(e.values)},TemplatePart:Ru,isTemplatePartActive:Nu,Template:qu,getValue:Iu,directive:function(e){return e.__litDirective=!0,e},noChange:Bu,directiveValue:Bu,AttributePart:Vu,NodePart:Xu,defaultPartCallback:Wu,TemplateInstance:Uu,reparentNodes:Yu,removeNodes:Gu}),github__code_editor:ke,github__debounce:Te,github__throttle:je,github__details:We,github__details_dialog:Ye,github__dimensions:Je,github__document_ready:s,github__eventloop_tasks:Ze,github__failbot:W,github__feature_detection:rt,github__fetch:yt,github__form:Lt,github__fragment_target:Ct,github__google_analytics:Dt,github__has_interactions:Nt,github__hash_change:qe,github__history:Fr,github__hotkey:zt,github__hotkey_map:jn,github__html_safe:st,github__inflector:Pn,github__inspect:Gr,github__locale:Hn,github__menu:Bn,github__metadata:M,github__navigation:_r,github__node:Yn,github__number_helpers:xr,github__onfocus:Sr,github__parse_html:ct,github__pjax:To,github__pjax__prefetch:Jr,github__preserve_position:Qi,github__proxy_site_detection:D,github__query_selector:Ae,github__radix_trie:Ft,github__remote_form:Ko,github__remote_submit:kt,github__restrict_tab_behavior:Object.freeze({default:function(e){var t=e.currentTarget;if(o(t instanceof HTMLElement,"app/assets/modules/github/restrict-tab-behavior.js:9"),"Tab"===Bt(e)){e.preventDefault();var n=Array.from(t.querySelectorAll("input, button, textarea")).filter(function(e){if(e instanceof HTMLInputElement||e instanceof HTMLTextAreaElement||e instanceof HTMLButtonElement)return!e.disabled&&$n(e)}),r=e.shiftKey?-1:1,i=n.length-1;if(document.activeElement){var a=n.indexOf(document.activeElement);if(-1!==a){var s=a+r;s>=0&&(i=s%n.length)}}n[i].focus()}}}),github__scrollby:Yi,github__scrollto:Kn,github__select_menu__loading:Zo,github__send_beacon:S,github__session_storage:ei,github__sliding_promise_queue:Vr,github__sso:Io,github__subscription:Xn,github__sudo:ii,github__task_list:Ri,github__text:qi,github__throttled_input:Xi,github__timers:Oo,github__timezone:et,github__typecast:Wr,github__updatable_content:ia,github__visible:Jn,invariant:o,selector_observer:yn,___github__sortablejs:sa}});

//# sourceMappingURL=frameworks-12be30dcbe69e704d110d6bebe91912c.js.map


================================================
FILE: data/Sales_files/github-542f291c828bb453339765ba3a54c144.js.download
================================================
define("github-rollup-github-bootstrap",["github/query-selector","selector-observer","delegated-events","github/onfocus","invariant","github/sliding-promise-queue","github/fetch","github/parse-html","github/debounce","github/remote-form","github/form","github/inspect","github/failbot","github/throttled-input","github/history","github/eventloop-tasks","github/proxy-site-detection","github/metadata","github/send-beacon","github/document-ready","github/feature-detection","github/subscription","github/updatable-content","github/text","github/has-interactions","github/pjax","github/session-storage","lit-html","github/navigation","github/typecast","github/hotkey","github/fragment-target","github/visible","github/preserve-position","github/sudo","github/hash-change","github/google-analytics","github/number-helpers","github/inflector","github/details","github/menu","form-data-entries","github/select-menu/loading","@github/sortablejs","github/sso","github/details-dialog","github/remote-submit","github/pjax/prefetch","github/code-editor","github/html-safe","github/timers","github/timezone","github/task-list"],function(e,t,r,n,a,s,o,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k,E,T,q,S,A,M,H,R,I,C,_,F,P,N,O,D,B,U,z,W,V,$,J,K,G,X,Q,Y){"use strict";a=a&&a.hasOwnProperty("default")?a.default:a,s=s&&s.hasOwnProperty("default")?s.default:s,u=u&&u.hasOwnProperty("default")?u.default:u,d=d&&d.hasOwnProperty("default")?d.default:d,g=g&&g.hasOwnProperty("default")?g.default:g,j=j&&j.hasOwnProperty("default")?j.default:j;var Z="default"in E?E.default:E;function ee(t){var r=e.query(t,'input[type="radio"]',HTMLInputElement),n=r.hasAttribute("data-already-member"),a=e.closest(t,"form");if(a.classList.toggle("is-member",n),a.classList.toggle("is-not-member",!n),r.hasAttribute("data-member-type")){var s=r.getAttribute("data-member-type");if(s){var o=e.query(a,'input[type="hidden"][name="member_type"]',HTMLInputElement);o&&(o.value=s)}}}A=A&&A.hasOwnProperty("default")?A.default:A,M=M&&M.hasOwnProperty("default")?M.default:M,R=R&&R.hasOwnProperty("default")?R.default:R,C=C&&C.hasOwnProperty("default")?C.default:C,_=_&&_.hasOwnProperty("default")?_.default:_,B=B&&B.hasOwnProperty("default")?B.default:B,z=z&&z.hasOwnProperty("default")?z.default:z,W=W&&W.hasOwnProperty("default")?W.default:W,Q=Q&&Q.hasOwnProperty("default")?Q.default:Q,t.observe(".js-account-membership",function(t){var r=e.query(t,".select-menu-item.selected",HTMLElement);r&&ee(r)}),r.on("selectmenu:selected",".js-account-membership .select-menu-item",function(e){ee(e.currentTarget)});var te=function(e){return function(){var t=e.apply(this,arguments);return new Promise(function(e,r){return function n(a,s){try{var o=t[a](s),i=o.value}catch(e){return void r(e)}if(!o.done)return Promise.resolve(i).then(function(e){n("next",e)},function(e){n("throw",e)});e(i)}("next")})}},re=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},ne=function(){function e(e,t){for(var r=0;r<t.length;r++){var n=t[r];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(e,n.key,n)}}return function(t,r,n){return r&&e(t.prototype,r),n&&e(t,n),t}}(),ae=function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function, not "+typeof t);e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,enumerable:!1,writable:!0,configurable:!0}}),t&&(Object.setPrototypeOf?Object.setPrototypeOf(e,t):e.__proto__=t)},se=function(e,t){if(!e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!t||"object"!=typeof t&&"function"!=typeof t?e:t},oe=function(){return function(e,t){if(Array.isArray(e))return e;if(Symbol.iterator in Object(e))return function(e,t){var r=[],n=!0,a=!1,s=void 0;try{for(var o,i=e[Symbol.iterator]();!(n=(o=i.next()).done)&&(r.push(o.value),!t||r.length!==t);n=!0);}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return r}(e,t);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),ie=function(e,t){return Object.freeze(Object.defineProperties(e,{raw:{value:Object.freeze(t)}}))},ue=function(e){if(Array.isArray(e)){for(var t=0,r=Array(e.length);t<e.length;t++)r[t]=e[t];return r}return Array.from(e)};function ce(){var t=e.querySelectorAll(document,"input[type=text].js-advanced-search-prefix",HTMLInputElement),r=e.querySelectorAll(document,"select.js-advanced-search-prefix",HTMLSelectElement),n=e.querySelectorAll(document,".js-advanced-search-prefix:checked",HTMLInputElement),a=[].concat(ue(de(t)),ue(de(r)),ue(de(n))),s=a.reduce(function(e,t){return t.value&&t.type&&e[t.type]++,e},{Repositories:0,Users:0,Code:0,Issues:0}),o=a.reduce(function(e,t){return(e+" "+(r=t,n=r.prefix,a=r.value,""===n?"":a?""+n+a:"")).trim();var r,n,a},""),i=e.query(document,".js-advanced-search-input",HTMLInputElement).value;e.query(document,".js-type-value",HTMLInputElement).value=function(e){var t=0,r="Repositories";for(var n in e)e[n]>t&&(t=e[n],r=n);return r}(s),e.query(document,".js-search-query",HTMLInputElement).value=(i+" "+o).trim();var u=e.query(document,".js-advanced-query");u.innerHTML="",u.textContent=o;var c=document.createElement("span");c.textContent=i.trim(),u.prepend(c," ")}function le(e){return-1!==e.search(/\s/g)?'"'+e+'"':e}function de(e){return e.map(function(e){var t=e.value.trim(),r=e.getAttribute("data-search-prefix");a(null!=r,"app/assets/modules/github/advanced-search.js:82");var n=e.getAttribute("data-search-type");return""===r?{prefix:r,value:t,type:n}:-1!==t.search(/,/g)&&"location"!==r?t.split(/,/).map(function(e){return{prefix:r,value:le(e.trim()),type:n}}):{prefix:r,value:le(t),type:n}}).reduce(function(e,t){return e.concat(t)},[])}n.onInput(".js-advanced-search-prefix",function(){ce()}),r.on("change",".js-advanced-search-prefix",ce),n.onFocus(".js-advanced-search-input",function(t){var r=e.closest(t,".js-advanced-search-label");r.classList.add("focus"),t.addEventListener("blur",function(){return r.classList.remove("focus")},{once:!0})}),t.observe(".js-advanced-search-input",function(){ce()});var me,pe=new s;function fe(e){return a(e instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:16"),e.querySelectorAll(".js-integrations-install-repo-picked .js-repository-picker-result").length}function ve(e){a(e instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:32");var t=e.querySelector("input.js-no-repositories-radio"),r=e.querySelector("input.js-all-repositories-radio");return a(r instanceof HTMLInputElement,"app/assets/modules/github/apps/installation-repo-selection.js:35"),t instanceof HTMLInputElement&&t.checked||r.checked||function(e){return fe(e)>0}(e)}function ge(){return document.querySelector(".js-apps-install-select-repositories-container")}function he(){var e=ge();return e&&e.querySelector(".js-filterable-field")}function ye(e){return function(){var t=e.querySelector("button.js-integrations-install-form-submit");a(t instanceof HTMLButtonElement,"app/assets/modules/github/apps/installation-repo-selection.js:53"),t.disabled=!ve(e);var r=e.querySelector(".flash");r&&r.classList.toggle("d-none",!function(e){var t=+e.getAttribute("data-max-repos");if(t)return fe(e)>=t}(e))}}function be(){var e=0;0!==document.querySelectorAll(".js-integrations-install-repo-picked:not(.d-none)").length&&(e=document.querySelectorAll(".js-repository-picker-result:not(.d-none)").length);var t="";e>0&&(t="Selected "+e+" "+(e>1?"repositories":"repository"));var r=document.querySelector(".js-integration-total-repos");a(r instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:78"),r.textContent=t}function je(e,t){var r=e.querySelector(".js-app-logo-with-bgcolor");r&&(r.style.backgroundColor="#"+t)}function Le(e,t){var r=e.nextElementSibling;if(r)return a(r instanceof HTMLElement,"app/assets/modules/github/find-next-element-sibling.js:8"),r.classList.contains(t)?r:Le(r,t)}t.observe(".js-integrations-install-form",function(t){var n,s=(n=te(regeneratorRuntime.mark(function e(t){var r,n,s,u,d,m,p,f,v,g,h,y,b;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.prev=0,r=he(),n=r&&r.getAttribute("data-alternate-search-url"),a(n,"app/assets/modules/github/apps/installation-repo-selection.js:121"),s=Array.prototype.slice.call(document.querySelectorAll(".js-integrations-install-repo-picked .js-selected-repository-field")).map(function(e){return a(e instanceof HTMLInputElement,"app/assets/modules/github/apps/installation-repo-selection.js:126"),e.value}),n+=~n.indexOf("?")?"&":"?",n+="q="+encodeURIComponent(t),e.next=9,pe.push(o.fetchText(n));case 9:for(u=e.sent,d=i.parseHTML(document,u.trim()),m=!0,p=!1,f=void 0,e.prev=14,v=d.querySelectorAll(".js-navigation-item")[Symbol.iterator]();!(m=(g=v.next()).done);m=!0)h=g.value,y=h.getAttribute("data-autocomplete-value"),s.includes(y)&&h.remove();e.next=22;break;case 18:e.prev=18,e.t0=e.catch(14),p=!0,f=e.t0;case 22:e.prev=22,e.prev=23,!m&&v.return&&v.return();case 25:if(e.prev=25,!p){e.next=28;break}throw f;case 28:return e.finish(25);case 29:return e.finish(22);case 30:for(;c.firstChild;)c.removeChild(c.firstChild);c.append(d);case 32:return e.prev=32,(b=ge())&&b.classList.remove("is-loading"),l=null,e.finish(32);case 37:case"end":return e.stop()}},e,this,[[0,,32,37],[14,18,22,30],[23,,25,29]])})),function(e){return n.apply(this,arguments)}),u=ge();if(u){var c=u.querySelector(".js-navigation-container");a(c,"app/assets/modules/github/apps/installation-repo-selection.js:86"),t.addEventListener("change",ye(t)),r.on("selectmenu:selected",".js-apps-install-select-repositories-container",function(r){r.target.classList.add("d-none");var n=t.querySelector(".js-integrations-install-repo-picked"),o=r.target.querySelector(".js-repository-picker-result");a(o instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:97");var u=i.parseHTML(document,o.outerHTML.trim()).querySelector(".js-repository-picker-result");a(u instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:101"),u.classList.remove("d-none"),a(n,"app/assets/modules/github/apps/installation-repo-selection.js:103"),n.insertBefore(u,n.firstChild),e.query(document,".js-min-repository-error").classList.add("d-none"),be(),ye(t).call(),s("")});var l=null;r.on("filterable:change",".js-apps-install-select-repositories-container",function(e){if(!l){l=e;var t=ge();t&&t.classList.add("is-loading"),c.classList.contains("filterable-active")?setTimeout(function(){var e=he();if(e instanceof HTMLInputElement){var t=e.value.trim();s(t)}},1e3):(t&&t.classList.remove("is-loading"),l=null)}}),r.on("click",".js-repository-picker-remove",function(r){var n=r.target.closest(".js-repository-picker-result");a(n instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:192");var o=r.target.closest("svg.js-repository-picker-remove");if(o){var i=o.getAttribute("data-repo-id");if(i){var u=ge(),c=u&&u.querySelector('.js-navigation-item[data-repo-id="'+i+'"]');c&&(c.classList.remove("selected"),c.classList.remove("d-none"))}}if(n.remove(),0===e.query(document,".js-integrations-install-repo-picked").children.length){var l=Array.from(document.querySelectorAll(".js-min-repository-error")),d=!0,m=!1,p=void 0;try{for(var f,v=l[Symbol.iterator]();!(d=(f=v.next()).done);d=!0){f.value.classList.remove("d-none")}}catch(e){m=!0,p=e}finally{try{!d&&v.return&&v.return()}finally{if(m)throw p}}}return be(),s(""),ye(t).call()}),r.on("click",".js-apps-install-select-repositories-container .js-menu-target",function(){return e.query(document,".js-select-repositories-radio",HTMLInputElement).checked=!0,ye(t).call()}),r.on("click",".js-no-repositories-radio",function(){e.query(document,".js-integrations-install-repo-picked, .js-min-repository-error").classList.add("d-none"),be()}),r.on("click",".js-all-repositories-radio",function(){e.query(document,".js-integrations-install-repo-picked, .js-min-repository-error").classList.add("d-none"),be()}),r.on("click",".js-select-repositories-radio",function(){e.query(document,".js-integrations-install-repo-picked").classList.remove("d-none"),be()})}}),r.on("submit",".js-integrations-install-form",function(e){var t=e.target.querySelector(".js-all-repositories-radio");if(t instanceof HTMLInputElement)if(t.checked){var r=Array.from(e.target.querySelectorAll('input[name="repository_ids[]"]')),n=!0,s=!1,o=void 0;try{for(var i,u=r[Symbol.iterator]();!(n=(i=u.next()).done);n=!0){i.value.remove()}}catch(e){s=!0,o=e}finally{try{!n&&u.return&&u.return()}finally{if(s)throw o}}}else{var c=ge();if(!c)return;var l=c.querySelector(".js-navigation-container");for(a(l instanceof HTMLElement,"app/assets/modules/github/apps/installation-repo-selection.js:260");l.firstChild;)l.removeChild(l.firstChild)}}),c.remoteForm(".js-app-bgcolor-form",(me=te(regeneratorRuntime.mark(function e(t,r){var n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=t.querySelector(".js-app-bgcolor-save-notice")){e.next=3;break}return e.abrupt("return");case 3:return a=void 0,e.prev=4,e.next=7,r.html();case 7:a=e.sent,e.next=13;break;case 10:e.prev=10,e.t0=e.catch(4),n.classList.remove("visible");case 13:a&&(n.classList.add("visible"),setTimeout(function(){return n.classList.remove("visible")},1500));case 14:case"end":return e.stop()}},e,this,[[4,10]])})),function(e,t){return me.apply(this,arguments)})),n.onInput(".js-app-bgcolor-input",function(t){var r=t.target;a(r instanceof HTMLInputElement,"app/assets/modules/github/apps.js:46");var n=e.closest(r,"form",HTMLFormElement),s=r.value.replace(/^#/,"");if(s.length<1)return r.classList.remove("text-red"),void je(n,r.defaultValue);r.checkValidity()?(r.classList.remove("text-red"),je(n,s),n.classList.contains("js-app-bgcolor-form")&&u(function(){return function(e,t){t.checkValidity()&&l.submit(e)}(n,r)},400)()):(r.classList.add("text-red"),je(n,r.defaultValue))}),r.on("click",".js-banner .js-next",function(t){var r=e.closest(t.currentTarget,".js-banner"),n=e.query(r,".js-dismiss",HTMLButtonElement),s=e.query(r,".js-next",HTMLButtonElement),o=e.query(r,".js-page.d-block"),i=Le(o,"js-page");a(i,"app/assets/modules/github/banner.js:43"),o.classList.remove("d-block"),o.classList.add("d-none"),i.classList.remove("d-none"),i.classList.add("d-block"),Le(i,"js-page")||(s.classList.add("d-none"),n.classList.remove("d-none"))}),t.observe(".js-banner",function(t){var r=e.query(t,".js-dismiss",HTMLButtonElement);if(t.querySelectorAll(".js-page").length>0){var n=e.query(t,".js-next",HTMLButtonElement),a=e.query(t,".js-page.d-block",HTMLDivElement);a&&!Le(a,"js-page")?r.classList.remove("d-none"):n.classList.remove("d-none")}else r.classList.remove("d-none")});var we=function(e){function t(e){re(this,t);var r=se(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e));return r.name="DataRemoteError",r.message=e,r}return ae(t,e),t}(Error);function xe(){var e=document.getElementById("ajax-error-message");e&&e.classList.add("visible")}function ke(){var e=document.getElementById("ajax-error-message");e&&e.classList.remove("visible")}r.on("deprecatedAjaxError","[data-remote]",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/behaviors/ajax-error.js:35");var t=e.detail,r=t.error,n=t.status,s=t.text;if(e.currentTarget===e.target&&"abort"!==r&&"canceled"!==r){var o="."+e.currentTarget.className.split(" ").sort().join("."),i=new we(r+" ("+n+") from "+o);if(m.reportError(i,{dataRemoteTarget:d(e.currentTarget),dataRemoteMethod:e.currentTarget.getAttribute("method")||"GET",dataRemoteUrl:e.currentTarget.href||e.currentTarget.action||window.location.href,dataRemoteType:e.currentTarget.getAttribute("data-type")||"intelligent guess"}),/<html/.test(s))throw xe(),e.stopImmediatePropagation(),i;setTimeout(function(){if(!e.defaultPrevented)throw xe(),i},0)}}),r.on("deprecatedAjaxSend","[data-remote]",function(){ke()}),r.on("click",".js-ajax-error-dismiss",function(){ke()}),r.on("deprecatedAjaxSend","[data-remote]",function(e){e.currentTarget===e.target&&(e.defaultPrevented||e.currentTarget.classList.add("loading"))}),r.on("deprecatedAjaxComplete","[data-remote]",function(e){e.currentTarget===e.target&&e.currentTarget.classList.remove("loading")}),c.remoteForm("form.js-ajax-pagination, .js-ajax-pagination form",function(){var t=te(regeneratorRuntime.mark(function t(n,a){var s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=e.closest(n,".js-ajax-pagination"),o=void 0,t.prev=2,t.next=5,a.html();case 5:o=t.sent,t.next=16;break;case 8:if(t.prev=8,t.t0=t.catch(2),!t.t0.response||404!==t.t0.response.status){t.next=15;break}return s.remove(),t.abrupt("return");case 15:throw t.t0;case 16:s.replaceWith(o.html),r.fire(n,"page:loaded");case 18:case"end":return t.stop()}},t,this,[[2,8]])}));return function(e,r){return t.apply(this,arguments)}}()),function(){var e=document.createElement("input");if(!("checkValidity"in e&&"setCustomValidity"in e&&(e.required=!0,e.value="hi",e.cloneNode().checkValidity()))){var t=new WeakMap,r=!0,n=!1,a=void 0;try{for(var s,o=["HTMLFormElement","HTMLInputElement","HTMLTextAreaElement","HTMLSelectElement","HTMLButtonElement","HTMLFieldSetElement","HTMLOutputElement"][Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;window[i]&&(window[i].prototype.checkValidity=u,"HTMLFormElement"!==i&&(window[i].prototype.setCustomValidity=c))}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}function u(){var e=function(){if(this instanceof HTMLFormElement||this instanceof HTMLFieldSetElement){var e=!0,r=!1,n=void 0;try{for(var a,s=this.elements[Symbol.iterator]();!(e=(a=s.next()).done);e=!0)if(!a.value.checkValidity())return!1}catch(e){r=!0,n=e}finally{try{!e&&s.return&&s.return()}finally{if(r)throw n}}return!0}if(this instanceof HTMLInputElement&&"hidden"===this.type)return!0;if(t.get(this))return!1;if(this.hasAttribute("required")&&!this.value)return!1;var o=this.getAttribute("pattern");if(null!=o&&this.value){var i=new RegExp("^(?:"+o+")$");if(0!==this.value.search(i))return!1}return!0}.call(this);if(!e){var r=new CustomEvent("invalid",{bubbles:!1,cancelable:!0});this.dispatchEvent(r)}return e}function c(e){t.set(this,e)}}();var Ee=["input[pattern]","input[required]","textarea[required]","input[data-required-change]","textarea[data-required-change]"].join(",");n.onFocus(Ee,function(e){a(e instanceof HTMLInputElement||e instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/html-validation.js:21");var t=e.checkValidity();function r(){var r=e.checkValidity();r!==t&&e.form&&qe(e.form),t=r}e.addEventListener("input",r),e.addEventListener("blur",function t(){e.removeEventListener("input",r),e.removeEventListener("blur",t)})});var Te=new WeakMap;function qe(t){var r=t.checkValidity(),n=!0,a=!1,s=void 0;try{for(var o,i=e.querySelectorAll(t,"button[data-disable-invalid]",HTMLButtonElement)[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.disabled=!r}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}t.observe("button[data-disable-invalid]",{constructor:HTMLButtonElement,initialize:function(e){var t=e.form;t&&(!function(e){Te.get(e)||(e.addEventListener("change",function(){return qe(e)}),Te.set(e,!0))}(t),e.disabled=!t.checkValidity())}}),t.observe("input[data-required-change], textarea[data-required-change]",function(e){function t(){e.setCustomValidity(e.value===e.defaultValue?"unchanged":"")}a(e instanceof HTMLInputElement||e instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/html-validation.js:68"),e.addEventListener("input",t),e.addEventListener("change",t),t(),e.form&&qe(e.form)}),document.addEventListener("reset",function(e){if(e.target instanceof HTMLFormElement){var t=e.target;setTimeout(function(){return qe(t)})}}),r.on("submit","form.js-normalize-submit",function(e){var t=e.currentTarget;a(t instanceof HTMLFormElement,"app/assets/modules/github/behaviors/html-validation.js:97"),t.checkValidity()||e.preventDefault()});var Se=new WeakMap;function Ae(e,t){t.classList.remove("is-loading","successed","errored","warn"),e.classList.remove("is-autocheck-loading","is-autocheck-successful","is-autocheck-errored");var r=t.querySelector("p.note");if(r){var n=Se.get(r);n&&(r.innerHTML=n)}var a=t.querySelector("dd.error");a&&a.remove();var s=t.querySelector("dd.warning");s&&s.remove()}function Me(e){var t=e.closest("form");if(t){var r=t.querySelector(".js-auto-complete-button");r instanceof HTMLButtonElement&&(r.disabled=!e.value)}}t.observe("auto-check",function(t){var r=e.query(t,"input",HTMLInputElement),n=e.closest(r,"dl.form-group"),s=r.form;a(s,"app/assets/modules/github/behaviors/autocheck.js:16");var o=n.querySelector("p.note");o&&Se.set(o,o.innerHTML),t.addEventListener("loadstart",function(){Ae(r,n),n.classList.add("is-loading"),r.classList.add("is-autocheck-loading"),qe(s)}),t.addEventListener("loadend",function(){n.classList.remove("is-loading"),r.classList.remove("is-autocheck-loading")}),r.addEventListener("autocheck:success",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/behaviors/autocheck.js:35"),r.classList.add("is-autocheck-successful"),n.classList.add("successed"),qe(s);var t=e.detail.message;if(t)if(o)o.innerHTML=t;else{var i=document.createElement("dd");i.classList.add("warning"),i.innerHTML=t,n.append(i),n.classList.add("warn")}}),r.addEventListener("autocheck:error",function(e){if(a(e instanceof CustomEvent,"app/assets/modules/github/behaviors/autocheck.js:56"),r.classList.add("is-autocheck-errored"),n.classList.add("errored"),qe(s),o)o.innerHTML=e.detail.message||"Something went wrong";else{var t=e.detail.message||"Something went wrong",i=document.createElement("dd");i.classList.add("error"),i.innerHTML=t,n.append(i)}}),r.addEventListener("input",function(){r.value||Ae(r,n)})}),t.observe("auto-complete",function(e){e.addEventListener("loadstart",function(){return e.classList.add("is-auto-complete-loading")}),e.addEventListener("loadend",function(){return e.classList.remove("is-auto-complete-loading")})}),t.observe("auto-complete",{constructor:AutocompleteElement,initialize:Me}),r.on("change","auto-complete",function(e){a(e.currentTarget instanceof AutocompleteElement,"app/assets/modules/github/behaviors/autocomplete.js:18"),Me(e.currentTarget)});var He=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,i,u,c;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=t.form,a(r,"app/assets/modules/github/behaviors/autosearch-form.js:34"),r.classList.add("is-sending"),e.prev=3,n=l.serialize(r),s=(r.action+"&"+n).replace(/[?&]/,"?"),e.next=8,Re.push(o.fetchText(s));case 8:i=e.sent,u=r.getAttribute("data-results-container"),(c=u?document.getElementById(u):null)&&(c.innerHTML=i),f.replaceState(null,"","?"+n);case 13:return e.prev=13,r.classList.remove("is-sending"),e.finish(13);case 16:case"end":return e.stop()}},e,this,[[3,,13,16]])}));return function(t){return e.apply(this,arguments)}}(),Re=new s;n.onFocus(".js-autosearch-field",function(e){var t=e;a(t instanceof HTMLInputElement,"app/assets/modules/github/behaviors/autosearch-form.js:54"),p.addThrottledInputEventListener(t,He),t.addEventListener("blur",function(){return p.removeThrottledInputEventListener(t,He)},{once:!0})}),n.onFocus("input[data-autoselect]",function(){var e=te(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return a(t instanceof HTMLInputElement,"app/assets/modules/github/behaviors/autoselect.js:10"),e.next=3,v.microtask();case 3:t.select();case 4:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}()),r.on("change","form[data-autosubmit]",function(e){var t=e.currentTarget;a(t instanceof HTMLFormElement,"app/assets/modules/github/behaviors/autosubmit.js:20"),l.submit(t)});var Ie=function(){var e=te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.loaded;case 2:null==Fe&&(Fe=requestIdleCallback(Pe));case 3:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),Ce=[];function _e(e){void 0===e.timestamp&&(e.timestamp=(new Date).getTime()),Ce.push(e),Ie()}var Fe=null;function Pe(){if(Fe=null,!g(document)){var e=h.getMetadataByName(document,"browser-stats-url");e&&(y.guaranteedPost(e,JSON.stringify({stats:Ce}),"application/json"),Ce=[])}}function Ne(e){e.preventDefault(),e.stopPropagation()}_e({features:j}),t.observe("a.btn.disabled",{subscribe:function(e){return L.fromEvent(e,"click",Ne)}});function Oe(e){try{return JSON.parse(window.localStorage.getItem(e))}catch(e){return{}}}function De(e,t){try{window.localStorage.setItem(e,JSON.stringify(t))}catch(e){}}function Be(){var e={},t=!0,r=!1,n=void 0;try{for(var a,s=document.getElementsByTagName("script")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;(m=o.src.match(/\/([\w-]+)-[0-9a-f]{32,}\.js$/))&&(e[m[1]+".js"]=o.src)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}var i=!0,u=!1,c=void 0;try{for(var l,d=document.getElementsByTagName("link")[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m,p=l.value;(m=p.href.match(/\/([\w-]+)-[0-9a-f]{32,}\.css$/))&&(e[m[1]+".css"]=p.href)}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}return e}te(regeneratorRuntime.mark(function e(){var t,r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.loaded;case 2:t=Oe("bundle-urls")||{},De("bundle-urls",r=Be()),(n=Object.keys(r).filter(function(e){var n=r[e];return t[e]!==n})).length&&_e({downloadedBundles:n});case 7:case"end":return e.stop()}},e,void 0)}))();var Ue,ze,We,Ve,$e,Je=function(){return function(e,t){if(Array.isArray(e))return e;if(Symbol.iterator in Object(e))return function(e,t){var r=[],n=!0,a=!1,s=void 0;try{for(var o,i=e[Symbol.iterator]();!(n=(o=i.next()).done)&&(r.push(o.value),!t||r.length!==t);n=!0);}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return r}(e,t);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}();function Ke(e){var t=e.querySelector(".js-comment-form-error");t&&(t.style.display="none")}function Ge(e,t){var r="You can't comment at this time";if(t.response&&422===t.response.status){var n=t.response.json;n.errors&&(r+=" — your comment "+n.errors.join(", "))}r+=". ";var a=e.querySelector(".js-comment-form-error");a&&(a.style.display="block",a.classList.remove("d-none"),a.textContent=r)}function Xe(t){var r=t.currentTarget;a(r instanceof HTMLIFrameElement,"app/assets/modules/github/behaviors/commenting/app-toolbar.js:61");var n=e.closest(r,".js-write-bucket"),s=document.querySelector(".js-write-bucket.comment-app-focused");if(null!==s){if(s.isSameNode(n))return;s.classList.toggle("comment-app-focused"),n.classList.toggle("comment-app-focused")}else n.classList.contains("comment-app-focused")||n.classList.add("comment-app-focused")}function Qe(e,t){var r=e.closest(".js-write-bucket");r&&r.classList.toggle("focused",t)}function Ye(e){var t=e.currentTarget;t instanceof Element&&Qe(t,!1)}t.observe(".js-check-all-container",{subscribe:function(e){var t=!1,r=null;function n(e,t,r,n){t instanceof HTMLInputElement&&(t.indeterminate=n,t.checked!==r&&(t.checked=r,setTimeout(function(){var r=new CustomEvent("change",{bubbles:!0,cancelable:!1,detail:{relatedTarget:e}});t.dispatchEvent(r)})))}function a(a){var s=a.target;s instanceof Element&&(s.hasAttribute("data-check-all")?function(t){if(t instanceof CustomEvent){var a=t.detail.relatedTarget;if(a&&a.hasAttribute("data-check-all-item"))return}var s=t.target;if(s instanceof HTMLInputElement){r=null;var i=!0,u=!1,c=void 0;try{for(var l,d=e.querySelectorAll("[data-check-all-item]")[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;n(s,m,s.checked,!1)}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}o()}}(a):s.hasAttribute("data-check-all-item")&&function(a){if(a instanceof CustomEvent){var s=a.detail.relatedTarget;if(s.hasAttribute("data-check-all")||s.hasAttribute("data-check-all-item"))return}var i=a.target;if(i instanceof HTMLInputElement){var u=e.querySelector("[data-check-all]");if(u){var c=Array.from(e.querySelectorAll("[data-check-all-item]"));if(t&&r){var l=[c.indexOf(r),c.indexOf(i)].sort(),d=Je(l,2),m=d[0],p=d[1],f=!0,v=!1,g=void 0;try{for(var h,y=c.slice(m,+p+1||9e9)[Symbol.iterator]();!(f=(h=y.next()).done);f=!0){var b=h.value;n(i,b,i.checked,!1)}}catch(e){v=!0,g=e}finally{try{!f&&y.return&&y.return()}finally{if(v)throw g}}}t=!1,r=i;var j=c.length,L=c.filter(function(e){return e instanceof HTMLInputElement&&e.checked}).length;n(i,u,L===j,j>L&&L>0),o()}}}(a))}function s(e){e.target instanceof Element&&e.target.hasAttribute("data-check-all-item")&&(t=e.shiftKey)}function o(){var t=e.querySelector("[data-check-all-count]");if(t){var r=e.querySelectorAll("[data-check-all-item]:checked").length;t.textContent=r.toString()}}return e.addEventListener("mousedown",s),e.addEventListener("change",a),{unsubscribe:function(){e.removeEventListener("mousedown",s),e.removeEventListener("change",a)}}}}),c.remoteForm(".js-new-comment-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,d,m,p,f,v;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=void 0,Ke(r),t.prev=2,t.next=5,n.json();case 5:a=t.sent,t.next=11;break;case 8:t.prev=8,t.t0=t.catch(2),Ge(r,t.t0);case 11:if(a){t.next=13;break}return t.abrupt("return");case 13:for(r.reset(),s=!0,o=!1,i=void 0,t.prev=17,u=e.querySelectorAll(r,".js-resettable-field",HTMLInputElement)[Symbol.iterator]();!(s=(c=u.next()).done);s=!0)d=c.value,l.changeValue(d,d.getAttribute("data-reset-value")||"");t.next=25;break;case 21:t.prev=21,t.t1=t.catch(17),o=!0,i=t.t1;case 25:t.prev=25,t.prev=26,!s&&u.return&&u.return();case 28:if(t.prev=28,!o){t.next=31;break}throw i;case 31:return t.finish(28);case 32:return t.finish(25);case 33:for(p in e.query(r,".js-write-tab").click(),m=a.json.updateContent)f=m[p],(v=document.querySelector(p))?w.replaceContent(v,f):console.warn("couldn't find "+p+" for immediate update");case 37:case"end":return t.stop()}},t,this,[[2,8],[17,21,25,33],[26,,28,32]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("comment-app:render",".js-write-bucket",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/behaviors/commenting/app-toolbar.js:41"),a(t.target instanceof Element,"app/assets/modules/github/behaviors/commenting/app-toolbar.js:42");var r=e.query(t.target,"textarea",HTMLTextAreaElement);if(null!=t.detail.markdown){var n=t.detail.markdown;x.insertText(r,n)}else t.preventDefault(),t.stopImmediatePropagation()}),t.observe(".js-comment-app-iframe",{subscribe:function(e){return L.fromEvent(e,"mouseover",Xe)}}),t.observe(".js-comment-app-iframe",{subscribe:function(t){return L.fromEvent(t,"load",function(t){var r=t.currentTarget;a(r instanceof HTMLIFrameElement,"app/assets/modules/github/behaviors/commenting/app-toolbar.js:80");var n,s,o,i,u=e.closest(r,"details");n=u,s=!0,o=!1,i=e.query(n,".js-comment-app-iframe"),e.query(n,".js-comment-app-spinner").hidden=!o,i.hidden=!s})}}),t.observe(".js-comment-app-iframe",{constructor:HTMLIFrameElement,subscribe:function(t){var r=e.closest(t,"details");return L.fromEvent(r,"toggle",function(){var e=t.getAttribute("data-src");r.hasAttribute("open")&&e&&t.src!==e&&(t.src=e)})}}),window.addEventListener("message",function(e){var t=document.querySelector(".js-comment-app-details[open]");if(t){var n=t.querySelector(".js-comment-app"),a=n&&n.getAttribute("data-host");if(a&&e.origin===new URL(a).origin){var s=t.closest(".js-write-bucket");if(s){var o=s.closest("form");if(!(e.data instanceof Object))return;o instanceof HTMLFormElement&&function(e,t){if(t.markdown){var n=e.closest(".js-write-bucket");n&&r.fire(n,"comment-app:render",{markdown:t.markdown})}!0===t.minimize&&e.removeAttribute("open")}(t,e.data)}}}}),t.observe(".js-comment-and-button",{constructor:HTMLButtonElement,initialize:function(t){a(t.form,"app/assets/modules/github/behaviors/commenting/close.js:29");var r=e.query(t.form,".js-comment-field"),n=t.textContent,s=!1;function o(e){var r=e.currentTarget;a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/commenting/close.js:36");var o=r.value.trim();o!==s&&(s=o,t.textContent=o?t.getAttribute("data-comment-text")||"":n)}return{add:function(){r.addEventListener("input",o),r.addEventListener("change",o)},remove:function(){r.removeEventListener("input",o),r.removeEventListener("change",o)}}}}),r.on("click",".js-comment-edit-button",function(t){var n=e.closest(t.currentTarget,".js-comment");n.classList.add("is-comment-editing"),e.query(n,".js-write-tab").click();var s=e.query(n,".js-comment-field");a(s instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/commenting/edit.js:18"),s.focus(),r.fire(s,"change");var o=t.currentTarget.closest(".js-dropdown-details");o&&o.removeAttribute("open")}),r.on("click",".js-comment-hide-button",function(t){var r=e.closest(t.currentTarget,".js-comment").querySelector(".js-minimize-comment");r&&r.classList.remove("d-none");var n=t.currentTarget.closest(".js-dropdown-details");n&&n.removeAttribute("open")}),r.on("click",".js-comment-hide-minimize-form",function(t){e.closest(t.currentTarget,".js-minimize-comment").classList.add("d-none")}),r.on("click",".js-comment-cancel-button",function(t){var r=e.closest(t.currentTarget,"form",HTMLFormElement),n=t.currentTarget.getAttribute("data-confirm-text")||"";if(k.hasDirtyFields(r)&&!confirm(n))return!1;var s=!0,o=!1,i=void 0;try{for(var u,c=r.querySelectorAll("input, textarea")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;a(l instanceof HTMLInputElement||l instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/commenting/edit.js:55"),l.value=l.defaultValue}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}var d=t.currentTarget.closest(".js-comment");d&&d.classList.remove("is-comment-editing")}),c.remoteForm(".js-comment-delete, .js-comment .js-comment-update, .js-issue-update, .js-comment-minimize, .js-comment-unminimize",function(t,r,n){var a=e.closest(t,".js-comment");a.classList.add("is-comment-loading");var s=a.getAttribute("data-body-version");s&&n.headers.set("X-Body-Version",s)}),c.remoteForm(".js-comment .js-comment-update",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=void 0,i=e.closest(r,".js-comment"),t.prev=2,t.next=5,n.json();case 5:s=t.sent,t.next=19;break;case 8:if(t.prev=8,t.t0=t.catch(2),422!==t.t0.response.status){t.next=18;break}if(!(u=JSON.parse(t.t0.response.text)).errors){t.next=16;break}return(c=i.querySelector(".js-comment-update-error"))&&(c.textContent="There was an error posting your comment: "+u.errors.join(", "),c.style.display="block"),t.abrupt("return");case 16:t.next=19;break;case 18:throw t.t0;case 19:if(s){t.next=21;break}return t.abrupt("return");case 21:for(l=s.json,(d=i.querySelector(".js-comment-body"))&&(d.innerHTML=l.body),(m=i.querySelector(".js-comment-update-error"))&&(m.style.display="none"),i.setAttribute("data-body-version",l.newBodyVersion),(p=i.querySelector(".js-body-version"))instanceof HTMLInputElement&&(p.value=l.newBodyVersion),f=!0,v=!1,g=void 0,t.prev=32,h=i.querySelectorAll("input, textarea")[Symbol.iterator]();!(f=(y=h.next()).done);f=!0)b=y.value,a(b instanceof HTMLInputElement||b instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/commenting/edit.js:119"),b.defaultValue=b.value;t.next=40;break;case 36:t.prev=36,t.t1=t.catch(32),v=!0,g=t.t1;case 40:t.prev=40,t.prev=41,!f&&h.return&&h.return();case 43:if(t.prev=43,!v){t.next=46;break}throw g;case 46:return t.finish(43);case 47:return t.finish(40);case 48:if(i.classList.remove("is-comment-stale","is-comment-editing"),!((j=i.querySelector(".js-comment-edit-history"))instanceof IncludeFragmentElement)){t.next=54;break}j.setAttribute("src",l.editUrl),t.next=61;break;case 54:if(!(L=i.querySelector(".js-comment-fragment"))){t.next=61;break}return t.next=58,o.fetchSafeDocumentFragment(document,l.editUrl);case 58:w=t.sent,L.innerHTML="",L.append(w);case 61:case"end":return t.stop()}},t,this,[[2,8],[32,36,40,48],[41,,43,47]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-comment .js-comment-delete, .js-comment .js-comment-update, .js-comment-minimize, .js-comment-unminimize",(Ue=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.closest(r,".js-comment"),t.prev=1,t.next=4,n.text();case 4:t.next=15;break;case 6:if(t.prev=6,t.t0=t.catch(1),422!==t.t0.response.status){t.next=14;break}s=void 0;try{s=JSON.parse(t.t0.response.text)}catch(e){}s&&s.stale&&a.classList.add("is-comment-stale"),t.next=15;break;case 14:throw t.t0;case 15:a.classList.remove("is-comment-loading");case 16:case"end":return t.stop()}},t,this,[[1,6]])})),function(e,t){return Ue.apply(this,arguments)})),c.remoteForm(".js-comment-delete",(ze=te(regeneratorRuntime.mark(function t(r,n){var s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:(s=r.closest(".js-comment-delete-container"))||(s=r.closest(".js-comment-container")||r.closest(".js-line-comments"))&&1!==s.querySelectorAll(".js-comment").length&&(s=e.closest(r,".js-comment")),a(s,"app/assets/modules/github/behaviors/commenting/edit.js:178"),s.remove();case 6:case"end":return t.stop()}},t,this)})),function(e,t){return ze.apply(this,arguments)})),c.remoteForm(".js-issue-update",(We=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.closest(r,".js-details-container")).classList.remove("open"),t.next=4,n.json();case 4:for(s=t.sent,null!=(o=s.json).issue_title&&(e.query(a,".js-issue-title").textContent=o.issue_title,(i=a.closest(".js-issues-results"))&&(u=i.querySelector(".js-merge-pull-request textarea"))instanceof HTMLTextAreaElement&&u.value===u.defaultValue&&(u.value=u.defaultValue=o.issue_title)),document.title=o.page_title,c=!0,l=!1,d=void 0,t.prev=11,m=r.elements[Symbol.iterator]();!(c=(p=m.next()).done);c=!0)((f=p.value)instanceof HTMLInputElement||f instanceof HTMLTextAreaElement)&&(f.defaultValue=f.value);t.next=19;break;case 15:t.prev=15,t.t0=t.catch(11),l=!0,d=t.t0;case 19:t.prev=19,t.prev=20,!c&&m.return&&m.return();case 22:if(t.prev=22,!l){t.next=25;break}throw d;case 25:return t.finish(22);case 26:return t.finish(19);case 27:case"end":return t.stop()}},t,this,[[11,15,19,27],[20,,22,26]])})),function(e,t){return We.apply(this,arguments)})),c.remoteForm(".js-comment-minimize",(Ve=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:a=e.closest(r,".js-comment"),(s=a.querySelector(".js-minimize-comment"))&&s.classList.add("d-none"),(o=e.closest(r,".unminimized-comment")).classList.add("d-none"),o.classList.remove("js-comment"),i=e.closest(r,".js-minimizable-comment-group"),(u=i.querySelector(".minimized-comment"))&&u.classList.remove("d-none"),u&&u.classList.add("js-comment");case 12:case"end":return t.stop()}},t,this)})),function(e,t){return Ve.apply(this,arguments)})),c.remoteForm(".js-comment-unminimize",($e=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:a=e.closest(r,".js-minimizable-comment-group"),(s=a.querySelector(".unminimized-comment"))&&s.classList.remove("d-none"),s&&s.classList.add("js-comment"),(o=a.querySelector(".minimized-comment"))&&o.classList.add("d-none"),o&&o.classList.remove("js-comment");case 9:case"end":return t.stop()}},t,this)})),function(e,t){return $e.apply(this,arguments)})),n.onFocus(".js-comment-field",function(e){Qe(e,!0),e.addEventListener("blur",Ye,{once:!0})});var Ze=function(){var e=te(regeneratorRuntime.mark(function e(t,n,a,s){var o,i,u,c,l,d,m,p;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(o={text:n,original_line:null,path:null,authenticity_token:et(t)},a&&(o.original_line=a),s&&(o.path=s),i={url:t.getAttribute("data-preview-url")||"",data:o},r.fire(t,"preview:setup",i),u=JSON.stringify(i),c=nt.get(t)||[],l=oe(c,2),d=l[0],m=l[1],d!==u){e.next=9;break}return e.abrupt("return",m);case 9:return at=!1,(p=rt.push(st(i))).then(function(){at=!0}),nt.set(t,[u,p]),e.abrupt("return",p);case 14:case"end":return e.stop()}},e,this)}));return function(t,r,n,a){return e.apply(this,arguments)}}();function et(t){var r=t.getAttribute("data-preview-authenticity-token"),n=e.closest(t,"form",HTMLFormElement).elements.namedItem("authenticity_token");return null!=r?r:n instanceof HTMLInputElement?n.value:void 0}function tt(t){var r=e.closest(t,".js-previewable-comment-form"),n=t.classList.contains("js-preview-tab");if(n){var a=e.query(r,".js-write-bucket");e.query(r,".js-preview-body").style.minHeight=a.clientHeight+"px"}r.classList.toggle("preview-selected",n),r.classList.toggle("write-selected",!n);var s=e.query(r,".tabnav-tab.selected");s.setAttribute("aria-selected","false"),s.classList.remove("selected"),t.classList.add("selected"),t.setAttribute("aria-selected","true");var o=e.query(r,".js-write-tab");return n?o.setAttribute("data-hotkey","Control+P,Meta+P"):o.removeAttribute("data-hotkey"),r}r.on("click",".js-write-tab",function(t){var r=t.currentTarget,n=tt(r);setTimeout(function(){e.query(n,".js-comment-field").focus()});var a=e.closest(r,".js-previewable-comment-form").querySelector("markdown-toolbar");null!=a&&a.classList.remove("d-none")}),r.on("click",".js-preview-tab",function(t){var r=t.currentTarget,n=tt(r);setTimeout(function(){ot(n)});var a=e.closest(r,".js-previewable-comment-form").querySelector("markdown-toolbar");null!=a&&a.classList.add("d-none"),t.stopPropagation(),t.preventDefault()}),r.on("preview:render",".js-previewable-comment-form",function(t){var r=tt(e.query(t.target,".js-preview-tab"));setTimeout(function(){ot(r)})});var rt=new s,nt=new WeakMap,at=!1;function st(e){var t=new FormData;for(var r in e.data)null!==e.data[r]&&t.append(r,e.data[r]);return o.fetchText(e.url,{method:"post",body:t})}function ot(t){var r=e.query(t,".js-comment-field",HTMLTextAreaElement),n=e.query(t,".comment-body"),a=t.querySelector(".js-original-line"),s=t.querySelector(".js-path"),o=a instanceof HTMLInputElement?a.value:null,i=s instanceof HTMLInputElement?s.value:null;Ze(t,r.value,o,i).then(function(e){n.innerHTML=e||"<p>Nothing to preview</p>"}),at||(n.innerHTML="<p>Loading preview&hellip;</p>")}t.observe(".js-preview-tab",function(t){var r=void 0,n=void 0,a=void 0,s=void 0;t.addEventListener("mouseenter",function(){if(!r){r=e.closest(t,".js-previewable-comment-form"),n=e.query(r,".js-comment-field",HTMLTextAreaElement);var o=r.querySelector(".js-original-line"),i=r.querySelector(".js-path");a=o instanceof HTMLInputElement?o.value:null,s=i instanceof HTMLInputElement?i.value:null}Ze(r,n.value,a,s)})}),n.onKey("keydown",".js-comment-field",function(t){var r=t.target;if(a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/commenting/preview.js:220"),(t.ctrlKey||t.metaKey)&&"P"===t.key){var n=e.closest(r,".js-previewable-comment-form");n.classList.contains("write-selected")&&(r.blur(),n.dispatchEvent(new CustomEvent("preview:render",{bubbles:!0,cancelable:!1})),t.preventDefault(),t.stopImmediatePropagation())}});var it=new WeakMap,ut=["input[type=submit][data-disable-with]","button[data-disable-with]"].join(", ");function ct(e,t){e instanceof HTMLInputElement?e.value=t:e.innerHTML=t}function lt(e){var t=!0,r=!1,n=void 0;try{for(var s,o=e.querySelectorAll(ut)[Symbol.iterator]();!(t=(s=o.next()).done);t=!0){var i=s.value,u=it.get(i);null!=u&&(a(i instanceof HTMLInputElement||i instanceof HTMLButtonElement,"app/assets/modules/github/behaviors/disable-with.js:56"),ct(i,u),i.hasAttribute("data-disable-invalid")&&!e.checkValidity()||(i.disabled=!1),it.delete(i))}}catch(e){r=!0,n=e}finally{try{!t&&o.return&&o.return()}finally{if(r)throw n}}}r.on("submit","form",function(e){var t,r=!0,n=!1,s=void 0;try{for(var o,i=e.currentTarget.querySelectorAll(ut)[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){var u=o.value;a(u instanceof HTMLInputElement||u instanceof HTMLButtonElement,"app/assets/modules/github/behaviors/disable-with.js:40"),it.set(u,(t=u)instanceof HTMLInputElement?t.value||"Submit":t.innerHTML||"");var c=u.getAttribute("data-disable-with");c&&ct(u,c),u.disabled=!0}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}},{capture:!0}),r.on("deprecatedAjaxComplete","form",function(e){var t=e.currentTarget,r=e.target;a(t instanceof HTMLFormElement,"app/assets/modules/github/behaviors/disable-with.js:70"),t===r&&lt(t)}),c.afterRemote(lt),r.on("menu:activate",".js-select-menu",function(e){e.currentTarget.classList.add("is-dirty")}),r.on("menu:deactivate",".js-select-menu",function(e){e.currentTarget.classList.remove("is-dirty")});var dt={OS:"Meta",Win:"Meta",Windows:"Meta",Scroll:"ScrollLock",SpaceBar:" ",Left:"ArrowLeft",Right:"ArrowRight",Down:"ArrowDown",Up:"ArrowUp",Del:"Delete",Esc:"Escape"};var mt=Object.getOwnPropertyDescriptor(KeyboardEvent.prototype,"key");if(mt){var pt=function(e){return mt.get.apply(e)};/Macintosh.*Safari/.test(navigator.userAgent)&&(pt=function(e){return function(e,t){return t&&/^[a-z]$/.test(e)?e.toUpperCase():e}(mt.get.apply(e),e.shiftKey)}),Object.defineProperty(KeyboardEvent.prototype,"key",{enumerable:!0,configurable:!0,get:function(){return e=pt(this),dt[e]||e;var e}})}var ft=new WeakMap;function vt(e){return ft.has(e)}function gt(t,r,n){var s=n.limit,o=e.query(t,"template",HTMLTemplateElement),i={},u=!0,c=!1,l=void 0;try{for(var d,m=e.querySelectorAll(t,"input[type=hidden]",HTMLInputElement)[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){var p=d.value;i[""+p.name+p.value]=p}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}for(var f=o.nextElementSibling;f;){var v=f;f=v.nextElementSibling,v.classList.contains("selected")||v.classList.contains("select-menu-divider")?v.classList.add("d-none"):v.remove()}var g=ft.get(t);a(null!=g,"app/assets/modules/github/substring-memory-filter-list.js:52");var h=0,y=document.createDocumentFragment(),b=t.querySelector(".js-divider-suggestions"),j=t.querySelector(".js-divider-rest");function L(t){var n=!(null!=s&&h>=s)&&function(e){return(e.login+" "+e.name).toLowerCase().trim()}(t).indexOf(r)>=0;if(n||t.selected){var a=function(t,r,n){if(null!=t.element)return t.element;var a=r.content.cloneNode(!0),s=e.query(a,"input[type=checkbox]",HTMLInputElement);t.type&&(s.name="reviewer_"+t.type+"_ids[]");s.value=t.id;var o=""+s.name+t.id,i=t.selected;n[o]&&(i=!0,n[o].remove(),delete n[o]);var u=e.query(a,".js-navigation-item");i&&(u.classList.add("selected"),s.checked=!0);t.disabled&&u.classList.add("disabled");var c=a.querySelector(".js-username");c&&(c.textContent=t.login);var l=a.querySelector(".js-description");l&&(l.textContent=t.name);var d=a.querySelector(".js-extended-description");d&&(t.description?d.textContent=t.description:d.remove());return e.query(a,".js-avatar",HTMLImageElement).src=t.avatar,t.element=u,t.element}(t,o,i);a.classList.toggle("d-none",!n),n&&h++,y.appendChild(a)}}if(b&&g.suggestions){var w=!0,x=!1,k=void 0;try{for(var E,T=g.suggestions[Symbol.iterator]();!(w=(E=T.next()).done);w=!0){L(E.value)}}catch(e){x=!0,k=e}finally{try{!w&&T.return&&T.return()}finally{if(x)throw k}}y.childNodes.length&&(b.after(y),b.classList.toggle("d-none",0===h),y=document.createDocumentFragment())}var q=h,S=!0,A=!1,M=void 0;try{for(var H,R=g.users[Symbol.iterator]();!(S=(H=R.next()).done);S=!0){L(H.value)}}catch(e){A=!0,M=e}finally{try{!S&&R.return&&R.return()}finally{if(A)throw M}}return t.append(y),j&&j.classList.toggle("d-none",h===q||0===q),h}function ht(e,t,r){var n=wt(e,t);n&&-1===t.indexOf("/")&&(n+=wt(e.substring(e.lastIndexOf("/")+1),t));return n}function yt(e,t){var r=function(){for(var r=[],n=0,a=e.length;n<a;n++){var s=e[n],o=ht(s,t);o&&r.push([s,o])}return r}();r.sort(bt);for(var n=[],a=0,s=r.length;a<s;a++){var o=r[a];n.push(o[0])}return n}function bt(e,t){var r=e[0],n=t[0],a=e[1],s=t[1];return a>s?-1:a<s?1:r<n?-1:r>n?1:0}function jt(e){for(var t=e.toLowerCase().split(""),r="",n=0;n<t.length;n++){var a=t[n].replace(/[\\^$*+?.()|[\]{}]/g,"\\$&");r+=0===n?"(.*)("+a+")":"([^"+a+"]*?)("+a+")"}return new RegExp(r+"(.*?)$","i")}function Lt(e,t,r){var n=e.innerHTML.trim();if(t){null==r&&(r=jt(t));var a=n.match(r);if(!a)return;var s=!1;n=[];var o,i=void 0,u=void 0;for(i=u=1,o=a.length;1<=o?u<o:u>o;i=1<=o?++u:--u){var c=a[i];c&&(i%2==0?s||(n.push("<mark>"),s=!0):s&&(n.push("</mark>"),s=!1),n.push(c))}e.innerHTML=n.join("")}else{var l=n.replace(/<\/?mark>/g,"");n!==l&&(e.innerHTML=l)}}function wt(e,t){if(e===t)return 1;var r,n=e.length,a=0,s=0,o=void 0,i=void 0;for(o=i=0,r=t.length;i<r;o=++i){var u=t[o],c=e.indexOf(u.toLowerCase()),l=e.indexOf(u.toUpperCase()),d=Math.min(c,l),m=d>-1?d:Math.max(c,l);if(-1===m)return 0;a+=.1,e[m]===u&&(a+=.1),0===m&&(a+=.8,0===o&&(s=1))," "===e.charAt(m-1)&&(a+=.8),e=e.substring(m+1,n)}var p=t.length,f=a/p,v=(f*(p/n)+f)/2;return s&&v+.1<1&&(v+=.1),v}var xt=new WeakMap,kt=new WeakMap,Et=new WeakMap;function Tt(e,t,r){var n=void 0;if(null==r&&(r={}),!e)return 0;t=t.toLowerCase();var a=null!=r.content?r.content:St,s=null!=r.text?r.text:At,o=null!=r.score?r.score:ht,i=r.limit;!0===r.mark?n=Mt:null!=r.mark&&null!=r.mark.call&&(n=r.mark);var u=xt.get(e),c=Array.from(e.children);u||(u=Array.from(e.children),xt.set(e,u));var l=!0,d=!1,m=void 0;try{for(var p,f=c[Symbol.iterator]();!(l=(p=f.next()).done);l=!0){var v=p.value;e.removeChild(v),v.style.display=""}}catch(e){d=!0,m=e}finally{try{!l&&f.return&&f.return()}finally{if(d)throw m}}var g=document.createDocumentFragment(),h=0,y=0;if(t){var b=Array.from(u),j=!0,L=!1,w=void 0;try{for(var x,k=b[Symbol.iterator]();!(j=(x=k.next()).done);j=!0){var E=x.value;null==kt.get(E)&&kt.set(E,s(a(E))),Et.set(E,String(o(kt.get(E)||"",t,E)))}}catch(e){L=!0,w=e}finally{try{!j&&k.return&&k.return()}finally{if(L)throw w}}b.sort(qt);var T=jt(t),q=!0,S=!1,A=void 0;try{for(var M,H=b[Symbol.iterator]();!(q=(M=H.next()).done);q=!0){var R=M.value;if((null==i||h<i)&&parseFloat(Et.get(R))>0){if(y++,n instanceof Function){var I=a(R);n(I),n(I,t,T)}g.appendChild(R)}h++}}catch(e){S=!0,A=e}finally{try{!q&&H.return&&H.return()}finally{if(S)throw A}}}else{var C=!0,_=!1,F=void 0;try{for(var P,N=u[Symbol.iterator]();!(C=(P=N.next()).done);C=!0){var O=P.value;(null==i||h<i)&&(y++,n instanceof Function&&n(a(O)),g.appendChild(O)),h++}}catch(e){_=!0,F=e}finally{try{!C&&N.return&&N.return()}finally{if(_)throw F}}}e.appendChild(g);var D=e.querySelectorAll(".js-divider"),B=!0,U=!1,z=void 0;try{for(var W,V=D[Symbol.iterator]();!(B=(W=V.next()).done);B=!0){W.value.classList.toggle("d-none",Boolean(t&&t.trim().length>0))}}catch(e){U=!0,z=e}finally{try{!B&&V.return&&V.return()}finally{if(U)throw z}}return y}function qt(e,t){var r=parseFloat(Et.get(e)),n=parseFloat(Et.get(t)),a=kt.get(e)||"",s=kt.get(t)||"";return r>n?-1:r<n?1:a<s?-1:a>s?1:0}function St(e){return e}function At(e){return e.hasAttribute("data-filter-value")?(e.getAttribute("data-filter-value")||"").toLowerCase().trim():e.textContent.toLowerCase().trim()}var Mt=Lt;function Ht(e){return e.textContent.toLowerCase().trim()}function Rt(e,t){var r=e.innerHTML;if(t){var n=new RegExp(t,"i");e.innerHTML=r.replace(n,"<mark>$&</mark>")}else{var a=r.replace(/<\/?mark>/g,"");r!==a&&(e.innerHTML=a)}}function It(e){return e.textContent.toLowerCase().trim()}function Ct(e,t){var r=e.innerHTML;if(t){var n=new RegExp(t,"i");e.innerHTML=r.replace(n,"<mark>$&</mark>")}else{var a=r.replace(/<\/?mark>/g,"");r!==a&&(e.innerHTML=a)}}function _t(e,t){var r=e.hasAttribute("data-filterable-highlight"),n=parseInt(e.getAttribute("data-filterable-limit"),10)||null,a=0;switch(e.getAttribute("data-filterable-type")){case"fuzzy":a=Tt(e,t,{mark:r,limit:n});break;case"substring":a=function(e,t,r){var n=void 0;if(null==r&&(r={}),e){t=t.toLowerCase();var a=null!=r.text?r.text:It,s=r.limit,o=Array.from(e.children).filter(function(e){return!e.classList.contains("select-menu-no-results")});!0===r.mark?n=Ct:"function"==typeof r.mark&&(n=r.mark);var i=0,u=!0,c=!1,l=void 0;try{for(var d,m=o[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){var p=d.value;-1!==a(p).indexOf(t)?null!=s&&i>=s?(p.style.display="none",p.hidden=!0):(i++,p.style.display="",p.hidden=!1,n&&(n(p),n(p,t))):(p.style.display="none",p.hidden=!0)}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}return i}}(e,t,{mark:r,limit:n});break;case"substring-memory":a=gt(e,t,{limit:n});break;default:a=function(e,t,r){var n=void 0;if(null==r&&(r={}),e){t=t.toLowerCase();var a=null!=r.text?r.text:Ht,s=r.limit;!0===r.mark?n=Rt:"function"==typeof r.mark&&(n=r.mark);var o=0,i=!0,u=!1,c=void 0;try{for(var l,d=e.children[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;0===a(m).indexOf(t)?null!=s&&o>=s?(m.style.display="none",m.hidden=!0):(o++,m.style.display="",m.hidden=!1,n&&(n(m),n(m,t))):(m.style.display="none",m.hidden=!0)}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}return o}}(e,t,{mark:r,limit:n})}e.classList.toggle("filterable-active",t.length>0),e.classList.toggle("filterable-empty",0===a)}t.observe(".js-filterable-field",{constructor:HTMLInputElement,initialize:function(e){var t,n,a=(t=te(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(o!==t.value){e.next=2;break}return e.abrupt("return");case 2:return o=t.value,e.next=5,v.microtask();case 5:r.fire(t,"filterable:change");case 6:case"end":return e.stop()}},e,this)})),function(e){return t.apply(this,arguments)}),s=(n=te(regeneratorRuntime.mark(function t(){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return o=e.value,t.next=3,v.microtask();case 3:r.fire(e,"filterable:change");case 4:case"end":return t.stop()}},t,this)})),function(){return n.apply(this,arguments)}),o=e.value;return{add:function(e){e.addEventListener("focus",s),p.addThrottledInputEventListener(e,a),document.activeElement===e&&s()},remove:function(e){e.removeEventListener("focus",s),p.removeThrottledInputEventListener(e,a)}}}}),r.on("filterable:change",".js-filterable-field",function(e){var t=e.currentTarget;a(t instanceof HTMLInputElement,"app/assets/modules/github/behaviors/filterable.js:84");var r=t.value.trim().toLowerCase(),n=document.querySelectorAll("[data-filterable-for="+t.id+"]"),s=!0,o=!1,i=void 0;try{for(var u,c=n[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;if("substring-memory"!==l.getAttribute("data-filterable-type")||vt(l)){_t(l,r);var d=new CustomEvent("filterable:change",{bubbles:!0,cancelable:!1,detail:{inputField:t}});l.dispatchEvent(d)}}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}),document.addEventListener("selectmenu:data",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/behaviors/filterable.js:138");var t=e.target,n=e.detail;a(t instanceof HTMLElement,"app/assets/modules/github/behaviors/filterable.js:140");var s=t.querySelector('[data-filterable-type="substring-memory"]');if(s){!function(e,t){ft.set(e,t)}(s,n.data);var o=t.querySelector(".js-filterable-field");o&&o===document.activeElement&&r.fire(o,"filterable:change")}}),r.on("click",".js-flash-close",function(e){var t=e.currentTarget.closest(".flash-messages"),r=e.currentTarget.closest(".flash");a(r,"app/assets/modules/github/behaviors/flash.js:24"),r.remove(),t&&!t.querySelector(".flash")&&t.remove()});var Ft=new WeakMap;function Pt(e){return null==e.getAttribute("data-pjax-preserve-scroll")&&0}function Nt(e){for(var t=e;t;){var r=t.getAttribute("data-pjax");if(r&&"true"!==r)return document.querySelector(r);t=t.parentElement&&t.parentElement.closest("[data-pjax]")}return e.closest("[data-pjax-container]")}document.addEventListener("focus",function(e){var t=e.target;Ft.get(t)||(r.fire(t,"focusin:delay"),Ft.set(t,!0))},{capture:!0}),document.addEventListener("blur",function(e){setTimeout(function(){var t=e.target;t!==document.activeElement&&(r.fire(t,"focusout:delay"),Ft.delete(e.target))},200)},{capture:!0}),c.remoteForm(".js-immediate-updates",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s,o,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.json();case 2:if(n=e.sent,a=n.json.updateContent)for(s in a)o=a[s],(i=document.querySelector(s))&&w.replaceContent(i,o);case 5:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),n.onKey("keydown","div.btn-sm, span.btn-sm",function(e){a(e.target instanceof HTMLElement,"app/assets/modules/github/behaviors/minibutton-accessibility.js:7"),"Enter"===e.key&&(e.target.click(),e.preventDefault())}),c.remoteForm(".js-notice-dismiss",function(){var t=te(regeneratorRuntime.mark(function t(r,n){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.text();case 2:e.closest(r,".js-notice").remove();case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-permalink-shortcut",function(e){var t=e.currentTarget;a(t instanceof HTMLAnchorElement,"app/assets/modules/github/behaviors/permalink.js:16");try{f.replaceState(null,"",t.href+window.location.hash)}catch(e){window.location=t.href+window.location.hash}e.preventDefault()}),r.on("click","[data-pjax] a, a[data-pjax]",function(e){a(e instanceof MouseEvent,"app/assets/modules/github/behaviors/pjax.js:54");var t=e.currentTarget;if(t instanceof HTMLAnchorElement){if(null!=t.getAttribute("data-skip-pjax"))return;if(null!=t.getAttribute("data-remote"))return;var r=Nt(t);r&&E.click(e,{container:r,scrollTo:Pt(t)})}}),r.on("submit","form[data-pjax]",function(e){a(e instanceof Event,"app/assets/modules/github/behaviors/pjax.js:75");var t=e.target,r=Nt(t);r&&E.submit(e,{container:r,scrollTo:Pt(t)})}),r.on("change","select[data-pjax]",function(e){var t=e.currentTarget;a(t instanceof HTMLSelectElement,"app/assets/modules/github/behaviors/pjax.js:88");var r=Nt(t);r&&Z({url:t.value,container:r})}),te(regeneratorRuntime.mark(function e(){var t,r,n,a,s,o,i,u,c;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return c=function(){a&&clearTimeout(a),i(100),t.classList.remove("is-loading")},u=function e(){0===n&&(n=12),i(Math.min(n+3,95)),a=setTimeout(e,500)},i=function(e){0===e&&(null==s&&(s=getComputedStyle(r).transition),r.style.transition="none"),n=e,r.style.width=n+"%",0===e&&(r.clientWidth,r.style.transition=s||"")},o=function(){i(0),t.classList.add("is-loading"),a=setTimeout(u,0)},e.next=6,b.ready;case 6:if(t=document.getElementById("js-pjax-loader-bar")){e.next=9;break}return e.abrupt("return");case 9:if((r=t.firstElementChild)instanceof HTMLElement){e.next=12;break}return e.abrupt("return");case 12:n=0,a=null,s=null,document.addEventListener("pjax:start",o),document.addEventListener("pjax:end",c),document.addEventListener("pjax:timeout",function(e){e.preventDefault()});case 18:case"end":return e.stop()}},e,this)}))();var Ot=function(){var e=te(regeneratorRuntime.mark(function e(){var t,r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,v.microtask();case 2:if(window.performance.getEntriesByName(Ut).length){e.next=4;break}return e.abrupt("return");case 4:if(window.performance.mark(zt),window.performance.measure(Bt,Ut,zt),t=window.performance.getEntriesByName(Bt),r=t.pop(),n=r?r.duration:null){e.next=11;break}return e.abrupt("return");case 11:Dt&&_e({requestUrl:Dt,pjaxDuration:Math.round(n)}),Wt();case 13:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),Dt=null,Bt="last_pjax_request",Ut="pjax_start",zt="pjax_end";function Wt(){window.performance.clearMarks(Ut),window.performance.clearMarks(zt),window.performance.clearMeasures(Bt)}function Vt(e,t){return e.split("/",3).join("/")===t.split("/",3).join("/")}window.performance.getEntriesByName&&(document.addEventListener("pjax:start",function(e){e instanceof CustomEvent&&e.detail&&e.detail.url&&(window.performance.mark(Ut),Dt=e.detail.url)}),document.addEventListener("pjax:end",Ot)),document.addEventListener("pjax:click",function(e){if(window.onbeforeunload)return e.preventDefault()}),r.on("pjax:click","#js-repo-pjax-container a[href]",function(e){a(e.currentTarget instanceof HTMLAnchorElement,"app/assets/modules/github/behaviors/pjax/exceptions.js:25");var t=e.currentTarget.pathname;Vt(t,location.pathname)?function(e){var t=e.split("/")[3];return-1!==["projects","releases","tags","wiki"].indexOf(t)&&Vt(e,location.pathname)}(t)&&e.preventDefault():e.preventDefault()}),r.on("pjax:click",".js-comment-body",function(e){var t=e.target;t instanceof HTMLAnchorElement&&"files"===t.pathname.split("/")[3]&&e.preventDefault()});var $t={};function Jt(e){for(var t=arguments.length,r=Array(t>1?t-1:0),n=1;n<t;n++)r[n-1]=arguments[n];return r.some(function(t){return e.classList.contains(t)})}function Kt(e){if(null===e.parentNode||!(e.parentNode instanceof HTMLElement))throw new Error;for(var t=e.parentNode.children,r=0;r<t.length;++r)if(t[r]===e)return r;return 0}te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.ready;case 2:$t[document.location.pathname]=Array.from(document.querySelectorAll("head [data-pjax-transient]"));case 3:case"end":return e.stop()}},e,void 0)}))(),document.addEventListener("pjax:beforeReplace",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/behaviors/pjax/head.js:14");for(var t=e.detail.contents,r=0;r<t.length;r++){var n=t[r];if(n)if("pjax-head"===n.id)$t[document.location.pathname]=Array.from(n.children),t[r]=null;else if("js-flash-container"===n.id){var s=document.querySelector("#js-flash-container");s&&s.replaceWith(n),t[r]=null}}}),document.addEventListener("pjax:end",function(){var e=$t[document.location.pathname];if(e){var t=document.head;a(t,"app/assets/modules/github/behaviors/pjax/head.js:35");var r=!0,n=!1,s=void 0;try{for(var o,i=document.querySelectorAll("head [data-pjax-transient]")[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){o.value.remove()}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}var u=!0,c=!1,l=void 0;try{for(var d,m=e[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){var p=d.value;p.matches("title, script, link[rel=stylesheet]")?p.matches("link[rel=stylesheet]")&&t.append(p):(p.setAttribute("data-pjax-transient",""),t.append(p))}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}}}),n.onKey("keydown",".js-quick-submit",function(e){var t=e.target;if(a(t instanceof HTMLInputElement||t instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/quick-submit.js:14"),(e.ctrlKey||e.metaKey)&&"Enter"===e.key){var r=t.form;a(r instanceof HTMLFormElement,"app/assets/modules/github/behaviors/quick-submit.js:18");var n=r.querySelector("input[type=submit], button[type=submit]");if(e.shiftKey){var s=r.querySelector(".js-quick-submit-alternative");(s instanceof HTMLInputElement||s instanceof HTMLButtonElement)&&!s.disabled&&l.submit(r,s)}else(n instanceof HTMLInputElement||n instanceof HTMLButtonElement)&&n.disabled||l.submit(r);e.preventDefault()}});var Gt=0;var Xt={INPUT:function(e){return e instanceof HTMLInputElement&&e.checked?"[x] ":"[ ] "},CODE:function(e){var t=e.textContent;if(!e.parentNode)throw new Error;return"PRE"===e.parentNode.nodeName?(e.textContent=t.replace(/^/gm,"    "),e.textContent):t.indexOf("`")>=0?"`` "+t+" ``":"`"+t+"`"},PRE:function(e){if(!(e.parentNode&&e.parentNode instanceof HTMLElement))throw new Error;var t=e.parentNode;return"DIV"===t.nodeName&&t.classList.contains("highlight")&&(e.textContent=e.textContent.replace(/^/gm,"    "),e.append("\n\n")),e},STRONG:function(e){return"**"+e.textContent+"**"},EM:function(e){return"_"+e.textContent+"_"},BLOCKQUOTE:function(e){var t=e.textContent.trim().replace(/^/gm,"> "),r=document.createElement("pre");return r.textContent=t+"\n\n",r},A:function(e){var t=e.textContent;if(Jt(e,"issue-link","user-mention","team-mention"))return t;if(/^https?:/.test(t)&&t===e.getAttribute("href"))return t;var r=e.getAttribute("href");if(!r)throw new Error;return"["+t+"]("+r+")"},IMG:function(e){var t=e.getAttribute("alt");if(!t)throw new Error;if(Jt(e,"emoji"))return t;var r=e.getAttribute("src");if(!r)throw new Error;return"!["+t+"]("+r+")"},LI:function(e){var t,r,n,a=e.parentNode;if(!a)throw new Error;if(r=(t=e).childNodes[0],n=t.childNodes[1],!(r&&t.childNodes.length<3)||("OL"!==r.nodeName&&"UL"!==r.nodeName||n&&(n.nodeType!==Node.TEXT_NODE||n.textContent.trim())))switch(a.nodeName){case"UL":e.prepend("* ");break;case"OL":if(Gt>0&&!a.previousSibling){var s=Kt(e)+Gt+1;e.prepend(s+"\\. ")}else e.prepend(Kt(e)+1+". ")}return e},OL:function(e){var t=document.createElement("li");return t.appendChild(document.createElement("br")),e.append(t),e},H1:function(e){var t=parseInt(e.nodeName.slice(1));return e.prepend(Array(t+1).join("#")+" "),e},UL:function(e){return e}};Xt.UL=Xt.OL;for(var Qt=2;Qt<=6;++Qt)Xt["H"+Qt]=Xt.H1;function Yt(e,t){for(var r=document.createNodeIterator(e,NodeFilter.SHOW_ELEMENT,function(e){return e.nodeName in Xt&&(function(e){return"IMG"===e.nodeName||null!=e.firstChild}(e)||function(e){return"INPUT"===e.nodeName&&e instanceof HTMLInputElement&&"checkbox"===e.type}(e))?NodeFilter.FILTER_ACCEPT:NodeFilter.FILTER_SKIP}),n=[],a=r.nextNode();a;)a instanceof HTMLElement&&n.push(a),a=r.nextNode();n.reverse();var s=!0,o=!1,i=void 0;try{for(var u,c=n[Symbol.iterator]();!(s=(u=c.next()).done);s=!0)t(a=u.value,Xt[a.nodeName](a))}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}var Zt=new WeakMap,er=0;function tr(e){if(!function(e){return e.defaultPrevented||"r"!==e.key||e.metaKey||e.altKey||e.shiftKey||e.ctrlKey||e.target instanceof HTMLElement&&(t=e.target,r=t.nodeName.toLowerCase(),n=(t.getAttribute("type")||"").toLowerCase(),"select"===r||"textarea"===r||"input"===r&&"submit"!==n&&"reset"!==n||t.isContentEditable);var t,r,n}(e)){var t=window.getSelection(),r=t.toString().trim();if(r){var n=t.focusNode;if(n&&(n.nodeType!==Node.ELEMENT_NODE&&(n=n.parentNode),n instanceof Element)){var a=function(e){for(var t=e;t=t.parentElement;)if(Zt.has(t))return t}(n);if(a){if(a.hasAttribute("data-quote-markdown"))try{r=function(e,t){var r=document.body;if(!r)return"";var n=document.createElement("div");n.appendChild(t),n.style.cssText="position:absolute;left:-9999px;",r.appendChild(n);var a=void 0;try{var s=document.createRange();s.selectNodeContents(n),e.removeAllRanges(),e.addRange(s),a=e.toString(),e.removeAllRanges()}finally{r.removeChild(n)}return a||""}(t,function(e){var t=e.getRangeAt(0).cloneContents();if(Gt=0,!(e.anchorNode&&e.anchorNode.parentNode&&e.anchorNode.parentNode instanceof HTMLElement))throw new Error("selection's anchorNode and parentNode must not be null");var r=e.anchorNode.parentNode.closest("li");if(r){if(!r.parentNode)throw new Error;if("OL"===r.parentNode.nodeName&&(Gt=Kt(r)),!t.querySelector("li")){var n=document.createElement("li");if(!r.parentNode)throw new Error;var a=document.createElement(r.parentNode.nodeName);n.appendChild(t),a.appendChild(n),(t=document.createDocumentFragment()).appendChild(a)}}return Yt(t,function(e,t){return e.replaceWith(t)}),t}(t)).replace(/^\n+/,"").replace(/\s+$/,"")}catch(e){setTimeout(function(){throw e})}if(a.dispatchEvent(new CustomEvent("quote-selection",{bubbles:!0,cancelable:!0,detail:{selection:t,selectionText:r}}))){var s=Array.from(a.querySelectorAll("textarea")).filter(rr)[0];if(s instanceof HTMLTextAreaElement){var o="> "+r.replace(/\n/g,"\n> ")+"\n\n";s.value&&(o=s.value+"\n\n"+o),s.value=o,s.focus(),s.selectionStart=s.value.length,s.scrollTop=s.scrollHeight,e.preventDefault()}}else e.preventDefault()}}}}}function rr(e){return!(e.offsetWidth<=0&&e.offsetHeight<=0)}t.observe(".js-quote-selection-container",{subscribe:function(e){var t;return t=e,er+=Zt.has(t)?0:1,Zt.set(t,1),document.addEventListener("keydown",tr),{unsubscribe:function(){var t;t=e,er-=Zt.has(t)?1:0,Zt.delete(t),er||document.removeEventListener("keydown",tr)}}}}),t.observe(".has-removed-contents",function(){var e=void 0;return{add:function(t){e=Array.from(t.childNodes);var n=!0,a=!1,s=void 0;try{for(var o,i=e[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;t.removeChild(u)}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}var c=t.closest("form");c&&r.fire(c,"change")},remove:function(t){var n=!0,a=!1,s=void 0;try{for(var o,i=e[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;t.appendChild(u)}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}var c=t.closest("form");c&&r.fire(c,"change")}}});var nr=function(){var e=te(regeneratorRuntime.mark(function e(t){var n,a,s,o,i,u,c,l,d,m,p,f,g,h,y,b,j,L,w;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n="session-resume:"+t,a=T.getItem(n)){e.next=4;break}return e.abrupt("return");case 4:for(T.removeItem(n),s=[],o=!0,i=!1,u=void 0,e.prev=9,c=JSON.parse(a)[Symbol.iterator]();!(o=(l=c.next()).done);o=!0)d=l.value,m=oe(d,2),p=m[0],f=m[1],r.fire(document,"session:resume",{targetId:p,targetValue:f})&&(g=document.getElementById(p))&&(g instanceof HTMLInputElement||g instanceof HTMLTextAreaElement)&&g.value===g.defaultValue&&(g.value=f,s.push(g));e.next=17;break;case 13:e.prev=13,e.t0=e.catch(9),i=!0,u=e.t0;case 17:e.prev=17,e.prev=18,!o&&c.return&&c.return();case 20:if(e.prev=20,!i){e.next=23;break}throw u;case 23:return e.finish(20);case 24:return e.finish(17);case 25:return e.next=27,v.microtask();case 27:for(h=!0,y=!1,b=void 0,e.prev=30,j=s[Symbol.iterator]();!(h=(L=j.next()).done);h=!0)w=L.value,r.fire(w,"change");e.next=38;break;case 34:e.prev=34,e.t1=e.catch(30),y=!0,b=e.t1;case 38:e.prev=38,e.prev=39,!h&&j.return&&j.return();case 41:if(e.prev=41,!y){e.next=44;break}throw b;case 44:return e.finish(41);case 45:return e.finish(38);case 46:case"end":return e.stop()}},e,this,[[9,13,17,25],[18,,20,24],[30,34,38,46],[39,,41,45]])}));return function(t){return e.apply(this,arguments)}}();function ar(e){return e=e||window.location,h.getMetadataByName(document,"session-resume-id")||e.pathname}var sr=null;function or(e){var t="session-resume:"+e,r=[],n=!0,a=!1,s=void 0;try{for(var o,i=document.querySelectorAll(".js-session-resumable")[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;(u instanceof HTMLInputElement||u instanceof HTMLTextAreaElement)&&r.push(u)}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}var c=r.filter(function(e){return function(e){return e.id&&e.value!==e.defaultValue&&e.form!==sr}(e)}).map(function(e){return[e.id,e.value]});c.length&&T.setItem(t,JSON.stringify(c))}window.addEventListener("submit",function(){var e=te(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return sr=t.target,e.next=3,v.microtask();case 3:t.defaultPrevented&&(sr=null);case 4:case"end":return e.stop()}},e,void 0)}));return function(t){return e.apply(this,arguments)}}(),{capture:!0}),window.addEventListener("pageshow",function(){nr(ar())}),window.addEventListener("pjax:end",function(){nr(ar())}),window.addEventListener("pagehide",function(){or(ar())}),window.addEventListener("pjax:beforeReplace",function(e){var t=e.detail.previousState,r=t?t.url:null;if(r)or(ar(new URL(r)));else{var n=new Error("pjax:beforeReplace event.detail.previousState.url is undefined");setTimeout(function(){throw n})}}),t.observe("textarea.js-size-to-fit",{constructor:HTMLTextAreaElement,subscribe:function(e){var t=null,r=!1,n=void 0,a=void 0,s=void 0;function o(t){if(n!==t.clientX||a!==t.clientY){var i=e.style.height;s&&s!==i&&(r=!0,e.style.maxHeight="",e.removeEventListener("mousemove",o)),s=i}n=t.clientX,a=t.clientY}var i=e.ownerDocument,u=i.documentElement;function c(){if(!(r||e.value===t||e.offsetWidth<=0&&e.offsetHeight<=0)){var n=function(){for(var t=0,r=e;r!==i.body&&null!==r;)t+=r.offsetTop||0,r=r.offsetParent;var n=t-i.defaultView.pageYOffset;return{top:n,bottom:u.clientHeight-(n+e.offsetHeight)}}(),a=n.top,o=n.bottom;if(!(a<0||o<0)){var c=Number(getComputedStyle(e).height.replace(/px/,""))+o;e.style.maxHeight=c-100+"px";var l=e.parentElement;if(l instanceof HTMLElement){var d=l.style.height;l.style.height=getComputedStyle(l).height,e.style.height="auto",e.style.height=e.scrollHeight+"px",l.style.height=d,s=e.style.height}t=e.value}}}function l(){r=!1,e.style.height="",e.style.maxHeight=""}e.addEventListener("mousemove",o),e.addEventListener("input",c),e.addEventListener("change",c);var d=e.form;return d&&d.addEventListener("reset",l),e.value&&c(),{unsubscribe:function(){e.removeEventListener("mousemove",o),e.removeEventListener("input",c),e.removeEventListener("change",c),d&&d.removeEventListener("reset",l)}}}}),c.remoteForm(".js-social-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:for(a=t.sent,s=e.closest(r,".js-social-container"),o=!0,i=!1,u=void 0,t.prev=7,c=s.querySelectorAll(".js-social-count")[Symbol.iterator]();!(o=(l=c.next()).done);o=!0)l.value.textContent=a.json.count;t.next=15;break;case 11:t.prev=11,t.t0=t.catch(7),i=!0,u=t.t0;case 15:t.prev=15,t.prev=16,!o&&c.return&&c.return();case 18:if(t.prev=18,!i){t.next=21;break}throw u;case 21:return t.finish(18);case 22:return t.finish(15);case 23:case"end":return t.stop()}},t,this,[[7,11,15,23],[16,,18,22]])}));return function(e,r){return t.apply(this,arguments)}}());var ir=["position:absolute;","overflow:auto;","word-wrap:break-word;","top:0px;","left:-9999px;"],ur=["box-sizing","font-family","font-size","font-style","font-variant","font-weight","height","letter-spacing","line-height","max-height","min-height","padding-bottom","padding-left","padding-right","padding-top","border-bottom","border-left","border-right","border-top","text-decoration","text-indent","text-transform","width","word-spacing"],cr=new WeakMap;function lr(t){var r=function(e,t){var r=e.nodeName.toLowerCase();if("textarea"!==r&&"input"!==r)throw new Error("expected textField to a textarea or input");var n=cr.get(e);if(n&&n.parentElement===e.parentElement)n.innerHTML="";else{n=document.createElement("div"),cr.set(e,n);var s=window.getComputedStyle(e),o=ir.slice(0);"textarea"===r?o.push("white-space:pre-wrap;"):o.push("white-space:nowrap;");for(var i=0,u=ur.length;i<u;i++){var c=ur[i];o.push(c+":"+s.getPropertyValue(c)+";")}n.style.cssText=o.join(" ")}var l=void 0;!1!==t&&((l=document.createElement("span")).style.cssText="position: absolute;",l.className="text-field-mirror-marker",l.innerHTML="&nbsp;");var d=void 0,m=void 0;if("number"==typeof t){var p=e.value.substring(0,t);p&&(d=document.createTextNode(p)),(p=e.value.substring(t))&&(m=document.createTextNode(p))}else{var f=e.value;f&&(d=document.createTextNode(f))}return d&&n.appendChild(d),l&&n.appendChild(l),m&&n.appendChild(m),n.parentElement||(a(e.parentElement,"textField must have a parentElement to mirror -- app/assets/modules/github/text-field-mirror.js:111"),e.parentElement.insertBefore(n,e)),n.scrollTop=e.scrollTop,n.scrollLeft=e.scrollLeft,n}(t,arguments.length>1&&void 0!==arguments[1]?arguments[1]:t.selectionEnd),n=e.query(r,".text-field-mirror-marker"),s=r.getBoundingClientRect(),o=n.getBoundingClientRect();return setTimeout(function(){r.remove()},5e3),{top:o.top-s.top,left:o.left-s.left}}var dr=ie(["",""],["",""]),mr=ie(['\n        <li class="js-navigation-item"\n          data-value="','"\n          data-text="','"\n          data-mentionable-type="','"\n          data-mentionable-id="','">\n          <span>',"</span>\n          <small>","</small>\n        </li>"],['\n        <li class="js-navigation-item"\n          data-value="','"\n          data-text="','"\n          data-mentionable-type="','"\n          data-mentionable-id="','">\n          <span>',"</span>\n          <small>","</small>\n        </li>"]),pr=new WeakMap,fr={},vr=function(){function e(t){re(this,e),this.setup=this.setup.bind(this),this.teardown=this.teardown.bind(this),this.textarea=t.input,this.types=t.types,this.suggester=t.suggester,this.repositionManually=t.repositionManually,this.teardownManually=t.teardownManually,this.onActivate=t.onActivate,this.suggestions=t.suggestions||document.createElement("div"),this.disable=t.disable,this.deactivateHandler=this.deactivate.bind(this),this.pasteHandler=this.onPaste.bind(this),this.inputHandler=this.onInput.bind(this),this.keyDownHandler=this.onNavigationKeyDown.bind(this),this.navigationOpenHandler=this.onNavigationOpen.bind(this)}return ne(e,[{key:"setup",value:function(){var e=this.textarea.form;a(e,"app/assets/modules/github/suggester.js:131"),e.addEventListener("reset",this.deactivateHandler),this.textarea.addEventListener("paste",this.pasteHandler),this.textarea.addEventListener("input",this.inputHandler),this.suggester.addEventListener("navigation:keydown",this.keyDownHandler),this.suggester.addEventListener("navigation:open",this.navigationOpenHandler),null==this.teardownManually&&this.textarea.addEventListener("focusout:delay",this.teardown),this.loadSuggestions()}},{key:"teardown",value:function(){this.deactivate();var e=this.textarea.form;a(e,"app/assets/modules/github/suggester.js:150"),e.removeEventListener("reset",this.deactivateHandler),this.textarea.removeEventListener("paste",this.pasteHandler),this.textarea.removeEventListener("input",this.inputHandler),this.suggester.removeEventListener("navigation:keydown",this.keyDownHandler),this.suggester.removeEventListener("navigation:open",this.navigationOpenHandler),this.textarea.removeEventListener("focusout:delay",this.teardown),this.onSuggestionsLoaded=function(){return null}}},{key:"onPaste",value:function(){this.deactivate(),this.justPasted=!0}},{key:"onInput",value:function(){if(!this.justPasted)return!this.checkQuery()&&void 0;this.justPasted=!1}},{key:"onNavigationKeyDown",value:function(e){var t=e.detail;if(e.target instanceof HTMLElement&&e.target.hasAttribute("data-value"))switch(t.hotkey){case"Tab":this.onNavigationOpen(e),e.preventDefault();break;case"Escape":this.deactivate(),e.stopImmediatePropagation(),e.preventDefault()}}},{key:"_getDataValue",value:function(e){return this.currentSearch.type.getValue?this.currentSearch.type.getValue(e):e.getAttribute("data-value")}},{key:"_findIndexOfPick",value:function(e,t){var r=1,n=!0,a=!1,s=void 0;try{for(var o,i=e[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;if(this._getDataValue(u)===t)return r;r++}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return-1}},{key:"logMention",value:function(e,t,r,n){var a=r.getAttribute("data-mentionable-type");if(a){var s=r.getAttribute("data-mentionable-id");if(s){var i=new FormData;return i.append("authenticity_token",t),i.append("mentionable_type",a),i.append("mentionable_id",s),i.append("query_string",n),o.fetch(e,{method:"POST",body:i})}}}},{key:"onNavigationOpen",value:function(e){var t=this,r=A(e.target,HTMLElement);if(r.hasAttribute("data-value")){var n=this._getDataValue(r),a=this.textarea.value.substring(0,this.currentSearch.endIndex),s=this.textarea.value.substring(this.currentSearch.endIndex);this.currentSearch.type.onSelection?this.currentSearch.type.onSelection(n):(a=a.replace(this.currentSearch.type.match,this.currentSearch.type.replace.replace("$value",n)),this.textarea.value=a+s),this.deactivate(),this.textarea.focus(),this.textarea.selectionStart=a.length,this.textarea.selectionEnd=a.length;var o=this.suggester.getAttribute("data-log-mention-url");if(o){var i=this.suggester.getAttribute("data-log-mention-authenticity-token");i&&requestIdleCallback(function(){te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.prev=0,e.next=3,t.logMention(o,i,r,t.currentSearch.query);case 3:e.next=7;break;case 5:e.prev=5,e.t0=e.catch(0);case 7:case"end":return e.stop()}},e,t,[[0,5]])}))()})}}}},{key:"mentionData",value:function(){var e=this.suggestions.querySelector("[data-mentions-json]");if(e){var t=pr.get(e);if(!t){var r=e.getAttribute("data-mentions-json");a(r,"app/assets/modules/github/suggester.js:292"),t=JSON.parse(r),pr.set(e,t)}return t}}},{key:"checkQuery",value:function(){var e=this,t=this.searchQuery();if(t){var r="mention"===t.type.typeid?this.mentionData():null;if(this.currentSearch&&this.currentSearch===t.query)return;return this.currentSearch=t,this.search(t.type,t.query,r).then(function(r){r?e.activate(t.startIndex):e.deactivate()}),this.currentSearch.query}this.currentSearch=null,this.deactivate()}},{key:"renderResults",value:function(e,t){var r,n=function(e){var t="user"===e.type?e.login:e.name,r="user"===e.type?e.name:e.description,n=t+" "+r;return q.html(mr,t,n,e.type,e.id,t,r)};q.render((r=t,q.html(dr,r.map(n))),e)}},{key:"activate",value:function(e){if(this.textarea===document.activeElement){if(this.onActivate&&this.onActivate(this.suggester),null==this.repositionManually){var t=lr(this.textarea,e+1),r=t.top,n=t.left;this.suggester.style.top=r+"px",this.suggester.style.left=n+"px"}this.suggester.hidden&&(this.suggester.hidden=!1,this.textarea.classList.add("js-navigation-enable"),S.push(this.suggester),S.focus(this.suggester))}}},{key:"deactivate",value:function(){if(!this.suggester.hidden){this.suggester.hidden=!0;var e=this.suggester.querySelector(".suggestions");e&&(e.style.display="none"),this.textarea.classList.remove("js-navigation-enable"),S.pop(this.suggester)}}},{key:"search",value:function(){var e=te(regeneratorRuntime.mark(function e(t,r,n){var a,s,o,i;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,t.search(this.suggestions,r,n);case 2:if(a=e.sent,s=a.element,o=a.results,!((o&&Array.isArray(o)?o.length:o)>0&&s)){e.next=16;break}return i=s.cloneNode(!0),n&&Array.isArray(o)&&this.renderResults(i,o),this.suggester.innerHTML="",this.suggester.appendChild(i),i.style.display="block",S.focus(this.suggester),e.abrupt("return",!0);case 16:return e.abrupt("return",!1);case 17:case"end":return e.stop()}},e,this)}));return function(t,r,n){return e.apply(this,arguments)}}()},{key:"searchQuery",value:function(){var e=this.textarea.selectionEnd,t=this.textarea.value.substring(0,e);if(!this.disable||!this.disable(t))for(var r in this.types){var n=this.types[r],a=t.match(n.match);if(a)return n.normalizeMatch?n.normalizeMatch(n,e,a):this.normalizeMatch(n,e,a)}}},{key:"normalizeMatch",value:function(e,t,r){var n=r[2];return{type:e,text:n,query:r[3],startIndex:t-n.length,endIndex:t}}},{key:"loadSuggestions",value:function(){var e=te(regeneratorRuntime.mark(function e(){var t,r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!this.suggestions.hasChildNodes()){e.next=2;break}return e.abrupt("return");case 2:if(t=this.suggester.getAttribute("data-url")){e.next=5;break}return e.abrupt("return");case 5:return r=fr[t]||(fr[t]=o.fetchText(t)),e.next=8,r;case 8:return n=e.sent,e.abrupt("return",this.onSuggestionsLoaded(n));case 10:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}()},{key:"onSuggestionsLoaded",value:function(e){var t=i.parseHTML(document,e);if(this.suggestions.appendChild(t),document.activeElement===this.textarea)return this.currentSearch=null,this.checkQuery()}}]),e}(),gr=new WeakMap,hr=new WeakMap;function yr(e,t,r){t=t.toLowerCase();var n=r.text,a=null!=r.score?r.score:ht,s=e;if(t){s=[];var o=!0,i=!1,u=void 0;try{for(var c,l=e[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){var d=c.value;null==gr.get(d)&&gr.set(d,n(d));var m=a(gr.get(d)||"",t,d);hr.set(d,m),m>0&&s.push(d)}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}s.sort(br)}return null!=r.limit?s.slice(0,r.limit):s}function br(e,t){var r=parseFloat(hr.get(e)),n=parseFloat(hr.get(t)),a=gr.get(e)||"",s=gr.get(t)||"";return r>n?-1:r<n?1:a<s?-1:a>s?1:0}function jr(e,t,r){var n=r[3],a=r[4];return{type:e,text:n,query:a,startIndex:t-a.length,endIndex:t}}var Lr={mention:{typeid:"mention",match:/(^|\s|\()(@([a-z0-9\-_/]*))$/i,replace:"$1@$value ",search:function(e,t,r){var n=Ar(t),s=e.querySelector("ul.mention-suggestions"),o=void 0;return o=r?yr(r,t,{limit:5,score:function(e,t,r){var a=n.score(e),s=r.score;return"number"==typeof s?a*s:a},text:function(e){return e.login?(e.login+" "+e.name).trim().toLowerCase():(e.name+" "+e.description).trim().toLowerCase()}}):Tt(s,t,{limit:5,text:kr,score:function(e,t,r){var s=n.score(e);a(r,"app/assets/modules/github/behaviors/suggester.js:95");var o=r.getAttribute("data-mentionable-score");return null!==o?s*parseFloat(o):s}}),Promise.resolve({element:s,results:o})}},auditLogUser:{typeid:"auditLogUser",match:/(^|\s)((-?actor:|-?user:)([a-z0-9\-+_]*))$/i,replace:"$1$3$value ",search:function(e,t){var r=e.querySelector("ul.user-suggestions"),n=Tt(r,t,{limit:5});return Promise.resolve({element:r,results:n})},normalizeMatch:jr},auditLogOrg:{typeid:"auditLogOrg",match:/(^|\s)((-?org:)([a-z0-9\-+_]*))$/i,replace:"$1$3$value ",search:function(e,t){var r=e.querySelector("ul.org-suggestions"),n=Tt(r,t,{limit:5});return Promise.resolve({element:r,results:n})},normalizeMatch:jr},auditLogAction:{typeid:"auditLogAction",match:/(^|\s)((-?action:)([a-z0-9.\-+_]*))$/i,replace:"$1$3$value ",search:function(e,t){var r=e.querySelector("ul.action-suggestions"),n=Tt(r,t,{limit:5});return Promise.resolve({element:r,results:n})},normalizeMatch:jr},auditLogRepo:{typeid:"auditLogRepo",match:/(^|\s)((-?repo:)([a-z0-9/\-+_]*))$/i,replace:"$1$3$value ",search:function(e,t){var r=e.querySelector("ul.repo-suggestions"),n=Tt(r,t,{limit:5});return Promise.resolve({element:r,results:n})},normalizeMatch:jr},auditLogCountry:{typeid:"auditLogCountry",match:/(^|\s)((-?country:)([a-z0-9\-+_]*))$/i,replace:"$1$3$value ",search:function(e,t){var r=e.querySelector("ul.country-suggestions"),n=Tt(r,t,{limit:5});return Promise.resolve({element:r,results:n})},normalizeMatch:jr},emoji:{typeid:"emoji",match:/(^|\s|\()(:([a-z0-9\-+_]*))$/i,replace:"$1$value ",getValue:function(e){if(e.hasAttribute("data-use-colon-emoji"))return e.getAttribute("data-value");var t=e.firstElementChild;return t&&"G-EMOJI"===t.tagName&&!t.firstElementChild?t.textContent:e.getAttribute("data-value")},search:function(e,t){var r=e.querySelector("ul.emoji-suggestions"),n=Tt(r,t=" "+t.toLowerCase().replace(/_/g," "),{limit:5,text:xr,score:Tr});return Promise.resolve({element:r,results:n})}},search:{typeid:"search",match:/(^|\s)([a-z-]*)$/i,replace:"$1$value",getValue:function(e){return e.getAttribute("data-value")},search:function(e,t){var r=e.querySelector("ul.search-suggestions"),n=Tt(r,t,{limit:10,text:function(e){return e.getAttribute("data-text")||""},score:Er});return Promise.resolve({element:r,results:n})},normalizeMatch:function(e,t,r){var n=r[2];return{type:e,text:n,query:n,startIndex:t-n.length,endIndex:t}}},hashed:{typeid:"issue",match:/(^|\s|\()(#([a-z0-9\-_/]*))$/i,replace:"$1#$value ",search:function(e,t){var r=void 0,n=e.querySelector("ul.hashed-suggestions"),a=Tt(n,t,{limit:5,text:kr,score:/^\d+$/.test(t)?(r=new RegExp("\\b"+t),function(e){return function(e,t){var r=e.search(t);return r>-1?1e3-r:0}(e,r)}):Ar(t).score});return Promise.resolve({element:n,results:a})}}},wr={};function xr(e){var t=e.getAttribute("data-emoji-name")||"";return wr[t]=" "+kr(e).replace(/_/g," "),t}function kr(e){return(e.getAttribute("data-text")||"").trim().toLowerCase()}function Er(e,t){var r=e.indexOf(t);return-1===r?0:1/(r+1)}function Tr(e,t){var r=wr[e].indexOf(t);return r>-1?1e3-r:0}function qr(e,t,r){var n,a=void 0,s=void 0,o=[];for(a=s=1,n=t.length;1<=n?s<n:s>n;a=1<=n?++s:--s){if(-1===(r=e.indexOf(t[a],r)))return;o.push(r++)}return o}function Sr(){return 2}function Ar(e){var t=void 0;if(e){var r=e.toLowerCase().split("");t=function(t){if(!t)return 0;var n=function(e,t){var r,n=void 0,a=void 0,s=void 0,o=function(e,t){for(var r=0,n=[];(r=e.indexOf(t,r))>-1;)n.push(r++);return n}(e,t[0]);if(0!==o.length){if(1===t.length)return[o[0],1,[]];for(s=null,a=0,r=o.length;a<r;a++){var i=o[a];if(n=qr(e,t,i+1)){var u=n[n.length-1]-i;(!s||u<s[1])&&(s=[i,u,n])}}return s}}(t,r);if(!n)return 0;var a=e.length/n[1];return a/=n[0]/2+1}}else t=Sr;return{score:t}}function Mr(e){var t=e.match(/`{3,}/g);return t||(t=function(e){return e.replace(/`{3,}[^`]*\n(.+)?\n`{3,}/g,"")}(e).match(/`/g)),null!=t&&t.length%2}r.on("focusin:delay",".js-suggester-field",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement||r instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/suggester.js:422");var n=e.closest(r,".js-suggester-container"),s=e.query(n,".js-suggester");new vr({input:r,suggester:s,types:Lr,disable:Mr}).setup()});var Hr=function(){function t(e){re(this,t),this.container=e.container,this.selections=e.selections,this.inputWrap=e.inputWrap,this.input=e.input,this.tagTemplate=e.tagTemplate,a(this.input.form,"app/assets/modules/github/behaviors/tag-input.js:62"),this.form=this.input.form,this.autoComplete=e.autoComplete}return ne(t,[{key:"setup",value:function(){var e=this;this.container.addEventListener("click",function(t){var r=t.target;a(r instanceof HTMLElement,"app/assets/modules/github/behaviors/tag-input.js:71"),r.matches(".js-remove")?e.removeTag(t):e.onFocus()}),this.input.addEventListener("focus",this.onFocus.bind(this)),this.input.addEventListener("blur",this.onBlur.bind(this)),this.input.addEventListener("keydown",this.onKeyDown.bind(this)),this.form.addEventListener("submit",this.onSubmit.bind(this)),this.autoComplete.addEventListener("change",function(){e.selectTag(e.autoComplete.value)})}},{key:"onFocus",value:function(){this.inputWrap.classList.add("focus"),this.input!==document.activeElement&&this.input.focus()}},{key:"onBlur",value:function(){this.inputWrap.classList.remove("focus"),this.autoComplete.open||this.onSubmit()}},{key:"onSubmit",value:function(){this.input.value&&(this.selectTag(this.input.value),this.autoComplete.open=!1)}},{key:"onKeyDown",value:function(e){switch(M(e)){case"Backspace":this.onBackspace();break;case"Enter":case"Tab":this.taggifyValueWhenSuggesterHidden(e);break;case",":case" ":this.taggifyValue(e)}}},{key:"taggifyValueWhenSuggesterHidden",value:function(e){!this.autoComplete.open&&this.input.value&&(e.preventDefault(),this.selectTag(this.input.value))}},{key:"taggifyValue",value:function(e){this.input.value&&(e.preventDefault(),this.selectTag(this.input.value),this.autoComplete.open=!1)}},{key:"selectTag",value:function(e){var t=this.normalizeTag(e),n=this.selectedTags();t&&n.indexOf(t)<0&&(this.selections.appendChild(this.templateTag(t)),this.input.value="",r.fire(this.form,"tags:changed"))}},{key:"removeTag",value:function(t){var n=t.target;a(n instanceof HTMLElement,"app/assets/modules/github/behaviors/tag-input.js:161"),t.preventDefault(),e.closest(n,".js-tag-input-tag").remove(),r.fire(this.form,"tags:changed")}},{key:"templateTag",value:function(t){var r=this.tagTemplate.cloneNode(!0);return e.query(r,"input",HTMLInputElement).value=t,e.query(r,".js-placeholder-tag-name").replaceWith(t),r.classList.remove("d-none","js-template"),r}},{key:"normalizeTag",value:function(e){return e.toLowerCase().trim().replace(/[\s,']+/g,"-")}},{key:"onBackspace",value:function(){if(!this.input.value){var e=this.selections.querySelector("li:last-child .js-remove");e&&e.click()}}},{key:"selectedTags",value:function(){var t=e.querySelectorAll(this.selections,"input",HTMLInputElement);return Array.from(t).map(function(e){return e.value}).filter(function(e){return e.length>0})}}]),t}();t.observe(".js-tag-input-container",{constructor:HTMLElement,initialize:function(t){new Hr({container:t,inputWrap:e.query(t,".js-tag-input-wrapper"),input:e.query(t,'input[type="text"], input:not([type])',HTMLInputElement),selections:e.query(t,".js-tag-input-selected-tags"),tagTemplate:e.query(t,".js-template"),autoComplete:e.query(t,"auto-complete",AutocompleteElement)}).setup()}});var Rr=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,i,u,c,l,d,m,p,f,v,g;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(r=t.currentTarget,a(r instanceof HTMLElement,"app/assets/modules/github/behaviors/team-members.js:10"),n=r.getAttribute("data-url")){e.next=5;break}return e.abrupt("return");case 5:for(s=o.fetchJSON(n),i=r.getAttribute("data-id"),a(i,"app/assets/modules/github/behaviors/team-members.js:17"),u=document.querySelectorAll(".js-team-mention[data-id='"+i+"']"),c=!0,l=!1,d=void 0,e.prev=12,m=u[Symbol.iterator]();!(c=(p=m.next()).done);c=!0)p.value.removeAttribute("data-url");e.next=20;break;case 16:e.prev=16,e.t0=e.catch(12),l=!0,d=e.t0;case 20:e.prev=20,e.prev=21,!c&&m.return&&m.return();case 23:if(e.prev=23,!l){e.next=26;break}throw d;case 26:return e.finish(23);case 27:return e.finish(20);case 28:return e.prev=28,e.next=31,s;case 31:0===(f=e.sent).total?f.members.push("This team has no members"):f.total>f.members.length&&f.members.push(f.total-f.members.length+" more"),Ir(u,Cr(f.members)),e.next=42;break;case 36:e.prev=36,e.t1=e.catch(28),v=e.t1.response?e.t1.response.status:500,g=r.getAttribute(404===v?"data-permission-text":"data-error-text"),a(g,"app/assets/modules/github/behaviors/team-members.js:35"),Ir(u,g);case 42:case"end":return e.stop()}},e,this,[[12,16,20,28],[21,,23,27],[28,36]])}));return function(t){return e.apply(this,arguments)}}();function Ir(e,t){var r=!0,n=!1,a=void 0;try{for(var s,o=e[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;i.setAttribute("aria-label",t),i.classList.add("tooltipped","tooltipped-s","tooltipped-multiline")}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}function Cr(e){if(0===e.length)return"";if(1===e.length)return e[0];if(2===e.length)return e.join(" and ");var t=e[e.length-1];return e.slice(0,-1).concat("and "+t).join(", ")}function _r(e){return new Promise(function(t){function r(){e.hasFocus()&&(t(),e.removeEventListener("visibilitychange",r),window.removeEventListener("focus",r),window.removeEventListener("blur",r))}e.addEventListener("visibilitychange",r),window.addEventListener("focus",r),window.addEventListener("blur",r),r()})}t.observe(".js-team-mention",function(e){e.addEventListener("mouseenter",Rr)});var Fr=function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(r.elements.push(t),1!==r.elements.length){e.next=6;break}return window.addEventListener("scroll",r.scrollHandler,{capture:!0,passive:!0}),e.next=5,_r(document);case 5:Dr(r);case 6:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}(),Pr=0,Nr=-1;function Or(e){var t=e.getBoundingClientRect(),r=window.innerHeight,n=window.innerWidth;if(0===t.height)return!1;if(t.height<r)return t.top>=0&&t.left>=0&&t.bottom<=r&&t.right<=n;var a=Math.ceil(r/2);return t.top>=0&&t.top+a<r}function Dr(e){var t=!0,r=!1,n=void 0;try{for(var a,s=e.elements[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;Or(o)?e.in.call(o,o,e):e.out&&e.out.call(o,o,e)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}function Br(e,r){var n={id:Pr++,selector:e,in:r,out:null,elements:[],checkPending:!1,scrollHandler:function(){!function(e){document.hasFocus()&&window.scrollY!==Nr&&(Nr=window.scrollY,e.checkPending||(e.checkPending=!0,window.requestAnimationFrame(function(){e.checkPending=!1,Dr(e)})))}(n)}};t.observe(e,{add:function(e){Fr(e,n)},remove:function(e){!function(e,t){var r=t.elements.indexOf(e);-1!==r&&t.elements.splice(r,1),0===t.elements.length&&window.removeEventListener("scroll",t.scrollHandler,{capture:!0,passive:!0})}(e,n)}})}var Ur=0;function zr(e){return e.classList.remove("js-unread-item","unread-item")}Br(".js-unread-item",function(e){zr(e)}),t.observe(".js-unread-item",{add:function(){Ur++},remove:function(){0===--Ur&&function(){if(document.hasFocus()){var e=document.querySelector(".js-timeline-marker-form");e&&e instanceof HTMLFormElement&&l.submit(e)}}()}}),r.on("socket:message",".js-discussion",function(e){if(e.currentTarget===e.target){var t=!0,r=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-unread-item")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){zr(a.value)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}});var Wr=0,Vr=/^\(\d+\)\s+/;function $r(){var e=Wr?"("+Wr+") ":"";document.title.match(Vr)?document.title=document.title.replace(Vr,e):document.title=""+e+document.title}function Jr(){if(!document.querySelector(":target")){var e=H.decodeFragmentValue(location.hash),t=H.findElementByFragmentName(document,"user-content-"+e);t&&t.scrollIntoView()}}function Kr(e){a(e.target instanceof Element,"app/assets/modules/github/behaviors/will-transition-once.js:26"),e.target.classList.remove("will-transition-once")}t.observe(".js-unread-item",{add:function(){Wr++,$r()},remove:function(){Wr--,$r()}}),window.addEventListener("hashchange",Jr),document.addEventListener("pjax:success",Jr),te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.ready;case 2:Jr();case 3:case"end":return e.stop()}},e,this)}))(),r.on("click","a[href]",function(e){var t=e.currentTarget;a(t instanceof HTMLAnchorElement,"app/assets/modules/github/behaviors/user-content.js:45"),t.href===location.href&&location.hash.length>1&&setTimeout(function(){e.defaultPrevented||Jr()})}),t.observe(".will-transition-once",{constructor:HTMLElement,subscribe:function(e){return L.fromEvent(e,"transitionend",Kr)}});var Gr=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,a,s,i,u,c,l,d,m,p,v,g,h,y,b,j,L,w,x,k,T,q;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=t.getAttribute("data-item-name")||"items",n=parseInt(t.getAttribute("data-item-minimum"))||0,a=parseInt(t.getAttribute("data-item-count"))||0,s=Math.max(n,parseInt(t.value)||0),i=s>300,(u=document.querySelector(".js-purchase-button"))instanceof HTMLButtonElement&&(u.disabled=0===s||i),(c=document.querySelector(".js-downgrade-button"))instanceof HTMLButtonElement&&(c.disabled=s===a),l=new URL(t.getAttribute("data-url")||"",window.location.origin),(d=new URLSearchParams(l.search.slice(1))).append(r,s.toString()),document.querySelector(".js-transform-user")&&d.append("transform_user","1"),l.search=d.toString(),e.next=17,Xr.push(o.fetchJSON(l));case 17:m=e.sent,(p=document.querySelector(".js-contact-us"))&&p.classList.toggle("d-none",!i),(v=document.querySelector(".js-payment-summary"))&&v.classList.toggle("d-none",i),(g=document.querySelector(".js-billing-section"))&&g.classList.toggle("has-removed-contents",m.free),(h=document.querySelector(".js-upgrade-info"))&&h.classList.toggle("d-none",s<=0),(y=document.querySelector(".js-downgrade-info"))&&y.classList.toggle("d-none",s>=0),(b=document.querySelector(".js-extra-seats-line-item"))&&b.classList.toggle("d-none",m.no_additional_seats),j=m.selectors,e.t0=regeneratorRuntime.keys(j);case 32:if((e.t1=e.t0()).done){e.next=55;break}for(L=e.t1.value,w=!0,x=!1,k=void 0,e.prev=37,T=document.querySelectorAll(L)[Symbol.iterator]();!(w=(q=T.next()).done);w=!0)q.value.textContent=j[L];e.next=45;break;case 41:e.prev=41,e.t2=e.catch(37),x=!0,k=e.t2;case 45:e.prev=45,e.prev=46,!w&&T.return&&T.return();case 48:if(e.prev=48,!x){e.next=51;break}throw k;case 51:return e.finish(48);case 52:return e.finish(45);case 53:e.next=32;break;case 55:f.replaceState(E.getState(),"",m.url);case 56:case"end":return e.stop()}},e,this,[[37,41,45,53],[46,,48,52]])}));return function(t){return e.apply(this,arguments)}}(),Xr=new s;t.observe(".js-addon-purchase-field",{constructor:HTMLInputElement,add:function(e){R(e)&&Gr(e),p.addThrottledInputEventListener(e,function(){Gr(e)})}}),t.observe(".js-addon-downgrade-field",{constructor:HTMLInputElement,add:function(e){R(e)&&Gr(e),e.addEventListener("change",function(){Gr(e)})}});var Qr=function(){var t=te(regeneratorRuntime.mark(function t(n,a,s){var i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return i=e.query(document,".js-repo-health-check",HTMLFormElement),e.query(i,".js-repo-health-name",HTMLInputElement).value=s,n.classList.remove("d-none"),n.classList.add("is-loading"),a.setCustomValidity("checking"),r.fire(a,"change"),t.next=9,o.fetchSafeDocumentFragment(document,i.action,{method:"POST",body:new FormData(i)});case 9:u=t.sent,(c=e.query(n,".js-repo-health-results")).innerHTML="",c.appendChild(u),n.classList.remove("is-loading"),a.setCustomValidity(""),r.fire(a,"change");case 16:case"end":return t.stop()}},t,this)}));return function(e,r,n){return t.apply(this,arguments)}}();t.observe(".js-repo-health",function(t){var r=e.closest(t,"form",HTMLFormElement),n=e.query(r,".js-comment-field",HTMLTextAreaElement),a=e.query(r,".js-repo-name",HTMLInputElement);function s(){Qr(t,n,a.value)}"hidden"===a.type?n.addEventListener("focus",s):a.addEventListener("change",s)});var Yr=null,Zr=300,en=[".",".","."],tn=0,rn=new WeakMap;function nn(){var t=e.query(document,".js-audit-log-export-status");return rn.set(t,t.textContent),Yr=setInterval(function(){var e=en.slice(0,tn).join("");t.textContent="Exporting"+e,tn>=3?tn=0:tn+=1},Zr),e.query(document,".js-audit-log-export-button").classList.add("disabled")}function an(){e.query(document,".js-audit-log-export-button").classList.remove("disabled");var t=e.query(document,".js-audit-log-export-status");t.textContent=rn.get(t)||"",null!==Yr&&clearInterval(Yr),tn=0}function sn(){an();var e=document.getElementById("ajax-error-message");e&&e.classList.add("visible")}c.remoteForm(".js-audit-log-export-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return nn(),n=void 0,e.prev=2,e.next=5,r.json();case 5:n=e.sent,e.next=12;break;case 8:return e.prev=8,e.t0=e.catch(2),sn(),e.abrupt("return");case 12:return a=n.json,e.prev=13,e.next=16,o.fetchPoll(a.job_url);case 16:e.next=22;break;case 18:return e.prev=18,e.t1=e.catch(13),sn(),e.abrupt("return");case 22:t=a.export_url,an(),window.location=t;case 23:case"end":return e.stop()}var t},e,this,[[2,8],[13,18]])}));return function(t,r){return e.apply(this,arguments)}}()),r.on("navigation:open",".audit-search-form .js-suggester",function(t){var r=e.closest(t.currentTarget,"form",HTMLFormElement);l.submit(r)});var on=null,un=new s;function cn(t){var r=t.form;a(r,"app/assets/modules/github/branches.js:44");var n=e.closest(r,".js-branches"),s=n.querySelectorAll(".js-branches-subnav .js-subnav-item"),i=n.querySelector(".js-branches-subnav .js-subnav-item.selected"),u=e.query(n,".js-branches-subnav .js-branches-all"),c=r.getAttribute("data-results-container");on||(on=i);var l=t.value.trim().length>0,d=function(e){var t=e.form;if(a(t,"app/assets/modules/github/branches.js:23"),e.value.trim()){var r=new URL(t.action,window.location.origin),n=new URLSearchParams(r.search.slice(1)),s=t.elements.namedItem("utf8");return s instanceof HTMLInputElement&&n.append("utf8",s.value),n.append("query",e.value),r.search=n.toString(),r.toString()}var o=t.getAttribute("data-reset-url");return a(o,"app/assets/modules/github/branches.js:38"),o}(t);function m(){n.classList.remove("is-loading")}un.push(o.fetchSafeDocumentFragment(document,d)).then(function(e){f.replaceState(null,"",d);var t=c?document.getElementById(c):null;t&&(t.innerHTML="",t.appendChild(e))}).then(m,m),n.classList.toggle("is-search-mode",l),n.classList.add("is-loading");var p=!0,v=!1,g=void 0;try{for(var h,y=s[Symbol.iterator]();!(p=(h=y.next()).done);p=!0){h.value.classList.remove("selected")}}catch(e){v=!0,g=e}finally{try{!p&&y.return&&y.return()}finally{if(v)throw g}}l?u.classList.add("selected"):on&&(on.classList.add("selected"),on=null)}function ln(t){var r=e.closest(t,".js-branch-row").getAttribute("data-branch-name"),n=e.closest(t,".js-branches").querySelectorAll(".js-branch-row");return Array.from(n).filter(function(e){return e.getAttribute("data-branch-name")===r})}t.observe(".js-branch-search-field",{constructor:HTMLInputElement,initialize:function(e){p.addThrottledInputEventListener(e,cn)},subscribe:function(e){return L.fromEvent(e,"keyup",function(t){var r,n;a(t instanceof KeyboardEvent,"app/assets/modules/github/branches.js:105"),"Escape"===M(t)&&(n=(r=e).value.trim(),r.value="",n&&cn(r),e.blur())})}}),r.on("submit",".js-branch-search",function(e){return e.preventDefault()}),r.on("click",".js-clear-branch-search",function(t){var n=t.currentTarget;a(n instanceof HTMLButtonElement,"app/assets/modules/github/branches.js:119"),a(n.form,"app/assets/modules/github/branches.js:120");var s=e.query(n.form,".js-branch-search-field",HTMLInputElement);s.focus(),s.value="",r.fire(s,"input")}),c.remoteForm(".js-branch-destroy, .js-branch-restore",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:for((a=e.query(r,"button[type=submit]")).blur(),a.classList.remove("tooltipped"),s=ln(r),o=!0,i=!1,u=void 0,t.prev=7,c=s[Symbol.iterator]();!(o=(l=c.next()).done);o=!0)l.value.classList.add("loading");t.next=15;break;case 11:t.prev=11,t.t0=t.catch(7),i=!0,u=t.t0;case 15:t.prev=15,t.prev=16,!o&&c.return&&c.return();case 18:if(t.prev=18,!i){t.next=21;break}throw u;case 21:return t.finish(18);case 22:return t.finish(15);case 23:return d=!1,t.prev=24,t.next=27,n.text();case 27:t.next=33;break;case 29:t.prev=29,t.t1=t.catch(24),d=!0,location.reload();case 33:for(t.prev=33,a.classList.add("tooltipped"),m=!0,p=!1,f=void 0,t.prev=38,v=s[Symbol.iterator]();!(m=(g=v.next()).done);m=!0)g.value.classList.remove("loading");t.next=46;break;case 42:t.prev=42,t.t2=t.catch(38),p=!0,f=t.t2;case 46:t.prev=46,t.prev=47,!m&&v.return&&v.return();case 49:if(t.prev=49,!p){t.next=52;break}throw f;case 52:return t.finish(49);case 53:return t.finish(46);case 54:return t.finish(33);case 55:if(d){t.next=76;break}for(h=r.classList.contains("js-branch-destroy"),y=!0,b=!1,j=void 0,t.prev=60,L=s[Symbol.iterator]();!(y=(w=L.next()).done);y=!0)w.value.classList.toggle("is-deleted",h);t.next=68;break;case 64:t.prev=64,t.t3=t.catch(60),b=!0,j=t.t3;case 68:t.prev=68,t.prev=69,!y&&L.return&&L.return();case 71:if(t.prev=71,!b){t.next=74;break}throw j;case 74:return t.finish(71);case 75:return t.finish(68);case 76:case"end":return t.stop()}},t,this,[[7,11,15,23],[16,,18,22],[24,29,33,55],[38,42,46,54],[47,,49,53],[60,64,68,76],[69,,71,75]])}));return function(e,r){return t.apply(this,arguments)}}());var dn=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,a,s,i,u,c,l,d;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=t.target,n=r.querySelector(".js-bulk-actions"),a=Array.from(r.querySelectorAll(".js-bulk-actions-toggle:checked")),s=a.map(function(e){return e.closest(".js-bulk-actions-item").getAttribute("data-bulk-actions-id")}).sort(),i=r.getAttribute("data-bulk-actions-url"),u=r.getAttribute("data-bulk-actions-parameter"),c=s.map(function(e){return u+"[]="+e}).join("&"),l=i+"?"+c,e.next=10,mn.push(o.fetchText(l));case 10:d=e.sent,0===a.length?(n.innerHTML=d,pn(r)):(pn(r),n.innerHTML=d);case 12:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),mn=new s;function pn(e){var t=document.querySelector(".js-membership-tabs");if(t){var r=e.querySelectorAll(".js-bulk-actions-toggle:checked");t.classList.toggle("d-none",r.length>0)}}r.on("change",".js-bulk-actions-toggle",function(t){var n=t.currentTarget,a=e.closest(n,".js-bulk-actions-container");r.fire(a,"bulk-actions:update")}),r.on("bulk-actions:update",".js-bulk-actions-container",u(dn,100)),r.on("change",".js-select-initial-admins",function(t){if(t.target===t.currentTarget){var r=t.target;a(r instanceof AutocompleteElement,"app/assets/modules/github/businesses.js:12");var n=e.closest(r,"form");r.value&&(!function(t,r){var n=e.query(t,".js-business-admin-login-list",HTMLInputElement);n.value=n.value+" "+r;var a=e.query(t,".js-initial-admins",HTMLTableElement),s=e.query(t,".js-admin-template-row",HTMLTableRowElement).cloneNode(!0);s.setAttribute("data-value",r),e.query(s,".js-admin-login",HTMLElement).textContent=r,a.appendChild(s),s.classList.remove("d-none"),a.classList.remove("d-none")}(n,r.value),r.value="")}}),r.on("click",".js-deselect-admin",function(t){var r=A(t.target,HTMLButtonElement);!function(t,r){var n=e.closest(r,"tr"),a=n.getAttribute("data-value"),s=e.query(t,".js-business-admin-login-list",HTMLInputElement);null!=a&&(s.value=s.value.replace(a,""));n.remove()}(e.closest(r,"form"),r)}),r.on("click",".js-preview-sign-in-message",function(t){var r=e.query(document,"#custom_messages_sign_in_message",HTMLTextAreaElement).value;e.query(document,"#sign_in_message",HTMLInputElement).value=r;var n=A(t.currentTarget,HTMLButtonElement);a(n.form,"app/assets/modules/github/businesses.js:66"),n.form.submit()}),r.on("click",".js-preview-auth-provider-name",function(t){var r=e.query(document,"#custom_messages_auth_provider_name",HTMLInputElement).value;e.query(document,"#auth_provider_name",HTMLInputElement).value=r;var n=A(t.currentTarget,HTMLButtonElement);a(n.form,"app/assets/modules/github/businesses.js:75"),l.submit(n.form)}),r.on("click",".js-preview-suspended-message",function(t){var r=e.query(document,"#custom_messages_suspended_message",HTMLTextAreaElement).value;e.query(document,"#suspended_message",HTMLInputElement).value=r;var n=A(t.currentTarget,HTMLButtonElement);a(n.form,"app/assets/modules/github/businesses.js:84"),n.form.submit()}),top!==window&&(alert("For security reasons, framing is not allowed."),top.location.replace(document.location));var fn=/\bChrome\//.test(navigator.userAgent)&&!/\bEdge\//.test(navigator.userAgent),vn=/Macintosh.*Safari/.test(navigator.userAgent),gn=void 0,hn=!1;function yn(){gn=document.activeElement,document.body&&(fn||vn)&&document.body.classList.toggle("intent-mouse",hn)}document.addEventListener("mousedown",function(){hn=!0,gn===document.activeElement&&yn()},{capture:!0}),document.addEventListener("keydown",function(){hn=!1},{capture:!0}),document.addEventListener("focusin",yn,{capture:!0}),r.on("click",".js-new-user-contrib-example",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(!(n=e.query(document,".js-calendar-graph")).classList.contains("sample-graph")){t.next=3;break}return t.abrupt("return");case 3:return n.classList.add("sample-graph"),s=r.currentTarget.getAttribute("data-url"),a(s,"app/assets/modules/github/calendar-sample.js:16"),i=void 0,t.prev=7,t.next=10,o.fetchText(s);case 10:i=t.sent,t.next=17;break;case 13:return t.prev=13,t.t0=t.catch(7),n.classList.remove("sample-graph"),t.abrupt("return");case 17:(u=document.createElement("div")).innerHTML=i,c=n.querySelector(".js-calendar-graph-svg"),a(c,"app/assets/modules/github/calendar-sample.js:33"),c.replaceWith(u.children[0]);case 22:case"end":return t.stop()}},t,this,[[7,13]])}));return function(e){return t.apply(this,arguments)}}());var bn={}.hasOwnProperty,jn=[].slice;function Ln(e){if(console&&console.warn)return console.warn(e)}var wn={host:"collector.githubapp.com",type:"page_view",dimensions:{},measures:{},context:{},actor:{},image:new Image,performance:{},expectedPerformanceTimingKeys:["connectEnd","connectStart","domComplete","domContentLoadedEventEnd","domContentLoadedEventStart","domInteractive","domLoading","domainLookupEnd","domainLookupStart","fetchStart","loadEventEnd","loadEventStart","navigationStart","redirectEnd","redirectStart","requestStart","responseEnd","responseStart","secureConnectionStart","unloadEventEnd","unloadEventStart"],recordPageView:function(){return this.applyMetaTags(),null!=this.app&&(null==this.host?(Ln("Host not set, you are doing something wrong"),!1):(this.image.src=this._src(),this._clearPerformance(),!0))},setHost:function(e){this.host=e},setApp:function(e){this.app=e},setDimensions:function(e){this.dimensions=e},addDimensions:function(e){var t=void 0;null==this.dimensions&&(this.dimensions={});var r=[];for(t in e)if(bn.call(e,t)){var n=e[t];r.push(this.dimensions[t]=n)}return r},setMeasures:function(e){this.measures=e},addMeasures:function(e){var t=void 0;null==this.measures&&(this.measures={});var r=[];for(t in e)if(bn.call(e,t)){var n=e[t];r.push(this.measures[t]=n)}return r},setContext:function(e){this.context=e},addContext:function(e){var t=void 0;null==this.context&&(this.context={});var r=[];for(t in e)if(bn.call(e,t)){var n=e[t];r.push(this.context[t]=n)}return r},setActor:function(e){this.actor=e},push:function(e){return this.applyCall(e)},enablePerformance:function(){this.performance=this._performanceTiming()},_recordSrc:function(e,t,r,n){return"//"+this.host+"/"+this.app+"/"+e+"?"+this._queryString(t,r,n)},_src:function(){return"//"+this.host+"/"+this.app+"/"+this.type+"?"+this._queryString()},_queryString:function(e,t,r){var n=void 0,a=void 0,s=function(){var e=this._params(),t=[];for(n in e)a=e[n],t.push("dimensions["+n+"]="+a);return t}.call(this);return s.push(this._encodeObject("dimensions",this._merge(this.dimensions,e))),s.push(this._encodeObject("measures",this._merge(this.measures,t))),null!=this.performance&&s.push(this._encodeObject("measures",{performance_timing:this.performance})),s.push(this._encodeObject("context",this._merge(this.context,r))),s.push(this._actor()),s.push(this._encodeObject("dimensions",{cid:this._clientId()})),s.join("&")},_clearPerformance:function(){this.performance=null},_performanceTiming:function(){var e,t=void 0,r=void 0;if(null==window.performance||null==window.performance.timing||null==window.performance.timing.navigationStart)return null;var n={},a=this.expectedPerformanceTimingKeys;for(t=0,e=a.length;t<e;t++){var s=a[t];n[s]=null!=(r=window.performance.timing[s])?r:0}var o=[],i=n.navigationStart;for(var u in n){var c=n[u],l=0===c?null:c-i;o.push(l)}return"1-"+o.join("-")},_params:function(){return{page:this._encode(this._page()),title:this._encode(this._title()),referrer:this._encode(this._referrer()),user_agent:this._encode(this._agent()),screen_resolution:this._encode(this._screenResolution()),pixel_ratio:this._encode(this._pixelRatio()),browser_resolution:this._encode(this._browserResolution()),tz_seconds:this._encode(this._tzSeconds()),timestamp:(new Date).getTime()}},_page:function(){try{var e=document.querySelector("meta[name=octolytics-location]");return e instanceof HTMLMetaElement?document.location.origin+e.content:document.location.href}catch(e){}},_title:function(){try{return document.title}catch(e){}},_referrer:function(){var e=void 0;e="";try{e=window.top.document.referrer}catch(t){if(window.parent)try{e=window.parent.document.referrer}catch(e){}}return""===e&&(e=document.referrer),e},_agent:function(){try{return navigator.userAgent}catch(e){}},_screenResolution:function(){try{return screen.width+"x"+screen.height}catch(e){return"unknown"}},_pixelRatio:function(){return window.devicePixelRatio},_browserResolution:function(){var e=void 0,t=void 0;try{return t=0,e=0,"number"==typeof window.innerWidth?(t=window.innerWidth,e=window.innerHeight):null!=document.documentElement&&null!=document.documentElement.clientWidth?(t=document.documentElement.clientWidth,e=document.documentElement.clientHeight):null!=document.body&&null!=document.body.clientWidth&&(t=document.body.clientWidth,e=document.body.clientHeight),t+"x"+e}catch(e){return"unknown"}},_tzSeconds:function(){try{return-60*(new Date).getTimezoneOffset()}catch(e){return""}},_merge:function(){var e,t=void 0,r=void 0,n=1<=arguments.length?jn.call(arguments,0):[],a={};for(t=0,e=n.length;t<e;t++){var s=n[t];for(r in s){var o=s[r];a[r]=o}}return a},_encodeObject:function(e,t){var r=void 0,n=void 0,a=void 0,s=[];if(null!=Array.isArray&&Array.isArray(t)||"[object Array]"===Object.prototype.toString.call(t))for(r=0,n=t.length;r<n;r++){var o=t[r];s.push(this._encodeObject(e+"[]",o))}else if(t===Object(t))for(a in t)s.push(this._encodeObject(e+"["+a+"]",t[a]));else s.push(e+"="+this._encode(t));return s.join("&")},_actor:function(){var e=void 0,t=void 0,r=void 0,n=[],a=this.actor;for(t in a){var s=a[t],o="dimensions[actor_"+t+"]";if(s.join)for(e=0,r=s.length;e<r;e++){var i=s[e];n.push(o+"[]="+this._encode(i))}else n.push(o+"="+this._encode(s))}return n.join("&")},_getCookie:function(e){var t,r=void 0,n=[],a=document.cookie.split(";");for(r=0,t=a.length;r<t;r++){var s=a[r].trim().split("=");if(!(s.length<2)){var o=s[0],i=s[1];o===e&&n.push({key:o,value:i})}}return n},_clientId:function(){var e=void 0;return""===(e=this._getClientId())&&(e=this._setClientId()),e},_getClientId:function(){var e,t=void 0,r=void 0,n=this._getCookie("_octo"),a=[];for(r=0,e=n.length;r<e;r++){var s=n[r].value.split(".");if("GH1"===s.shift()&&s.length>1){var o=s.shift().split("-");1===o.length&&(o[1]="1"),o[0]*=1,o[1]*=1,t=s.join("."),a.push([o,t])}}return t="",a.length>0&&(t=a.sort().reverse()[0][1]),t},_setClientId:function(){var e=(new Date).getTime(),t=Math.round(Math.random()*(Math.pow(2,31)-1))+"."+Math.round(e/1e3),r="GH1.1."+t,n=new Date(e+63072e6).toUTCString(),a=document.domain;if(null==a)throw new Error("Unable to get document domain");var s="."+a.split(".").reverse().slice(0,2).reverse().join(".");return document.cookie="_octo="+r+"; expires="+n+"; path=/; domain="+s,t},_encode:function(e){return null!=e?window.encodeURIComponent(e):""},applyQueuedCalls:function(e){var t,r=void 0,n=[];for(r=0,t=e.length;r<t;r++){var a=e[r];n.push(this.applyCall(a))}return n},applyCall:function(e){var t=e[0],r=e.slice(1);return this[t]?this[t].apply(this,r):Ln(t+" is not a valid method")},applyMetaTags:function(){var e=this.loadMetaTags();return e.host&&this.setHost(e.host),e.app&&this.setApp(e.app),this._objectIsEmpty(e.actor)||this.setActor(e.actor),this.addDimensions(e.dimensions),this.addMeasures(e.measures),this.addContext(e.context)},loadMetaTags:function(){var e,t=void 0,r={dimensions:{},measures:{},context:{},actor:{}},n=document.getElementsByTagName("meta");for(t=0,e=n.length;t<e;t++){var a=n[t];if(a.name&&a.content){var s=a.name.match(this.octolyticsMetaTagName);if(s)switch(s[1]){case"host":r.host=a.content;break;case"app-id":case"app":r.app=a.content;break;case"dimension":this._addField(r.dimensions,s[2],a);break;case"measure":this._addField(r.measures,s[2],a);break;case"context":this._addField(r.context,s[2],a);break;case"actor":this._addField(r.actor,s[2],a)}}}return r},_addField:function(e,t,r){r.attributes["data-array"]?(null==e[t]&&(e[t]=[]),e[t].push(r.content)):e[t]=r.content},_objectIsEmpty:function(e){var t=void 0;for(t in e)if(bn.call(e,t))return!1;return!0},octolyticsMetaTagName:/^octolytics-(host|app-id|app|dimension|measure|context|actor)-?(.*)/};if(window._octo){if(window._octo.slice){var xn=window._octo.slice(0);window._octo=wn,window._octo.applyQueuedCalls(xn)}}else window._octo=wn;function kn(e){var t=e.getAttribute("data-fieldname");return a("string"==typeof t,"app/assets/modules/github/community.js:11"),document.querySelectorAll('span[data-fieldname="'+t+'"]')}function En(e,t){if(e.value){var r=!0,n=!1,a=void 0;try{for(var s,o=t[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.textContent=e.value}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}}document.addEventListener("keydown",function(e){"Escape"===e.key&&(e.target===document.body&&(window.location.hash.startsWith("#issue")||window.location.hash.startsWith("#pullrequestreview")||window.location.hash.startsWith("#discussion"))&&I.preserveInteractivePosition(function(){window.location.hash=""}))}),n.onFocus(".js-template-form-input",function(e){a(e instanceof HTMLInputElement,"app/assets/modules/github/community.js:30");var t=kn(e);function r(){En(e,t)}function n(e){var r=!0,n=!1,a=void 0;try{for(var s,o=t[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.classList.toggle("CommunityTemplate-highlight--focus",e)}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}n(!0),e.addEventListener("input",r),e.addEventListener("blur",function t(){n(!1),e.removeEventListener("input",r),e.removeEventListener("blur",t)})}),r.on("click",".js-template-highlight",function(t){var r,n;(r=t.currentTarget,n=r.getAttribute("data-fieldname"),a("string"==typeof n,"app/assets/modules/github/community.js:17"),e.query(document,'input[data-fieldname="'+n+'"]',HTMLInputElement)).focus()}),t.observe(".js-templates",function(){var t=!0,r=!1,n=void 0;try{for(var a,s=e.querySelectorAll(document,".js-template-form-input",HTMLInputElement)[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;En(o,kn(o))}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}});var Tn=["Code review","Issues","Pull requests","Commits"];function qn(e,t){var r=Tn.indexOf(e),n=Tn.indexOf(t);return r>-1&&n>-1?r<n?-1:r>n?1:0:e.localeCompare(t)}function Sn(e,t){var r=e.getBoundingClientRect();return(t.getBoundingClientRect().width-r.width)/2}function An(e,t){var r=e.querySelector("svg");if(r){var n=r.querySelector(".js-highlight-blob"),s=Math.floor(e.clientWidth),o=Math.max(s,250),i=o,u=r.querySelector(".js-highlight-label-top"),c=r.querySelector(".js-highlight-label-bottom"),l=r.querySelector(".js-highlight-label-right"),d=r.querySelector(".js-highlight-label-left"),m=r.querySelector(".js-highlight-percent-top"),p=r.querySelector(".js-highlight-percent-bottom"),f=r.querySelector(".js-highlight-percent-right"),v=r.querySelector(".js-highlight-percent-left"),g=Object.keys(t.counts).slice(0,4).sort(qn),h=document.createElement("title"),y=document.createElement("title"),b=document.createElement("title"),j=document.createElement("title"),L=r.querySelector(".js-highlight-x-axis"),w=r.querySelector(".js-highlight-y-axis"),x=r.querySelector(".js-highlight-top-ellipse"),k=r.querySelector(".js-highlight-right-ellipse"),E=r.querySelector(".js-highlight-bottom-ellipse"),T=r.querySelector(".js-highlight-left-ellipse"),q=e.querySelector(".js-activity-overview-graph-spinner"),S=r.querySelector("g");a(S,"app/assets/modules/github/contributions-spider-graph.js:82"),a(n,"app/assets/modules/github/contributions-spider-graph.js:83"),a(u,"app/assets/modules/github/contributions-spider-graph.js:84"),a(c,"app/assets/modules/github/contributions-spider-graph.js:85"),a(l,"app/assets/modules/github/contributions-spider-graph.js:86"),a(d,"app/assets/modules/github/contributions-spider-graph.js:87"),a(m,"app/assets/modules/github/contributions-spider-graph.js:88"),a(p,"app/assets/modules/github/contributions-spider-graph.js:89"),a(f,"app/assets/modules/github/contributions-spider-graph.js:90"),a(v,"app/assets/modules/github/contributions-spider-graph.js:91"),a(L,"app/assets/modules/github/contributions-spider-graph.js:92"),a(w,"app/assets/modules/github/contributions-spider-graph.js:93"),a(x,"app/assets/modules/github/contributions-spider-graph.js:94"),a(k,"app/assets/modules/github/contributions-spider-graph.js:95"),a(E,"app/assets/modules/github/contributions-spider-graph.js:96"),a(T,"app/assets/modules/github/contributions-spider-graph.js:97"),a(q,"app/assets/modules/github/contributions-spider-graph.js:98"),S.removeAttribute("transform"),r.classList.remove("d-none"),r.classList.add("d-block"),q.classList.remove("d-block"),q.classList.add("d-none"),u.innerHTML="",l.innerHTML="",c.innerHTML="",d.innerHTML="",m.innerHTML="&nbsp;",f.innerHTML="&nbsp;",p.innerHTML="&nbsp;",v.innerHTML="&nbsp;";var A=g[0],M=g[1],H=g[2],R=g[3],I=t.counts[A],C=t.counts[M],_=t.counts[H],F=t.counts[R];h.textContent=I.toString(),u.appendChild(h),y.textContent=_.toString(),c.appendChild(y);var P=null,N=function(e,r){var n=t.percentages[e];if(n>0)return P=r,n+"%"},O=N(A,m);O&&(m.textContent=O);var D=N(M,f);D&&(f.textContent=D);var B=N(H,p);B&&(p.textContent=B);var U=N(R,v);U&&(v.textContent=U);var z=o/2,W=o/2;u.appendChild(document.createTextNode(A)),u.setAttribute("dx",z.toString()),c.appendChild(document.createTextNode(H)),c.setAttribute("dx",W.toString()),m.setAttribute("dx",z.toString()),p.setAttribute("dx",W.toString());var V=u.getBoundingClientRect().height,$=0;P&&($=P.getBoundingClientRect().height),j.textContent=F.toString(),d.appendChild(j),b.textContent=C.toString(),l.appendChild(b);var J=i/2+V/4,K=i/2+V/4;t.percentages[R]>0&&(J=J+$/2+2.5),t.percentages[M]>0&&(K=K+$/2+2.5),d.appendChild(document.createTextNode(R)),d.setAttribute("dy",J.toString()),l.appendChild(document.createTextNode(M)),l.setAttribute("dy",K.toString());var G=J-V,X=K-V;v.setAttribute("dy",G.toString()),f.setAttribute("dy",X.toString());var Q=t.percentages[A]/100,Y=t.percentages[H]/100,Z=t.percentages[R]/100,ee=t.percentages[M]/100,te=Math.max(Q,Y,Z,ee),re=function(e){return 1*e/te+0};Q=re(Q),Y=re(Y),Z=re(Z),ee=re(ee);var ne=function(e){var t=0,r=!0,n=!1,a=void 0;try{for(var s,o=e[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value.getBoundingClientRect();i.width>t&&(t=i.width)}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}return t}([d,l,u,c])+10,ae=Math.max(ne,V),se=o/2,oe=i/2,ie=se,ue=ae,ce=se,le=i-ae,de=ae,me=oe,pe=o-ae,fe=oe,ve=se-de,ge=oe-ue,he=ue+(1-Q)*ge,ye=Math.max(le-(1-Y)*ge,oe),be=de+(1-Z)*ve,je=pe-(1-ee)*ve;L.setAttribute("x1",de.toString()),L.setAttribute("y1",oe.toString()),L.setAttribute("x2",pe.toString()),L.setAttribute("y2",oe.toString()),w.setAttribute("x1",se.toString()),w.setAttribute("y1",ue.toString()),w.setAttribute("x2",se.toString()),w.setAttribute("y2",le.toString()),he+4<oe&&(he+=4),ye-4>oe&&(ye-=4),be+4<se&&(be+=4),je-4>se&&(je-=4);var Le=ue-10,we=pe+10,xe=le+10+8;t.percentages[H]>0&&(xe+=$);var ke=de-12;u.setAttribute("dy",Le.toString()),l.setAttribute("dx",we.toString()),c.setAttribute("dy",xe.toString()),d.setAttribute("dx",ke.toString());var Ee=ke-Sn(v,d),Te=we+Sn(f,l);v.setAttribute("dx",Ee.toString()),f.setAttribute("dx",Te.toString());var qe=Le-V,Se=xe-V;m.setAttribute("dy",qe.toString()),p.setAttribute("dy",Se.toString());x.setAttribute("cx",ie.toString()),x.setAttribute("cy",(he-2).toString()),k.setAttribute("cx",(je+2).toString()),k.setAttribute("cy",fe.toString()),E.setAttribute("cx",ce.toString()),E.setAttribute("cy",(ye+2).toString()),T.setAttribute("cx",(be-2).toString()),T.setAttribute("cy",me.toString()),0===I&&x.classList.add("d-none"),0===C&&k.classList.add("d-none"),0===_&&E.classList.add("d-none"),0===F&&T.classList.add("d-none");var Ae=S.getBoundingClientRect(),Me=r.getBoundingClientRect(),He=0-(Ae.top-Me.top),Re=0-(Ae.left-Me.left);S.setAttribute("transform","translate("+Re+", "+He+")");var Ie=Math.ceil(Ae.width),Ce=Math.ceil(Ae.height);r.setAttribute("width",Ie.toString()),r.setAttribute("height",Ce.toString());var _e="M"+ie+","+he+" "+[[je,fe],[ce,ye],[be,me]].map(function(e){return"L"+e[0]+","+e[1]}).join(" ")+" z";n.setAttribute("d",_e)}}function Mn(e){var t=e.getAttribute("data-hydro-view"),r=e.getAttribute("data-hydro-view-hmac");a(t,"app/assets/modules/github/hydro-tracking.js:22"),a(r,"app/assets/modules/github/hydro-tracking.js:23"),_e({hydroEventPayload:t,hydroEventHmac:r})}function Hn(t){var r=t.map(function(e){return e.priority}),n=Math.min.apply(Math,ue(r)),a=t.find(function(e){return e.priority===n});a&&e.query(a.el,".js-loader").classList.remove("d-none")}t.observe(".js-activity-overview-graph-container",function(e){var t=e.getAttribute("data-counts"),r=e.getAttribute("data-percentages");a(t,"app/assets/modules/github/contributions-spider-graph.js:347"),a(r,"app/assets/modules/github/contributions-spider-graph.js:348"),An(e,{counts:JSON.parse(t),percentages:JSON.parse(r)})}),r.on("click","[data-hydro-click]",function(e){var t=e.currentTarget,r=t.getAttribute("data-hydro-click"),n=t.getAttribute("data-hydro-click-hmac");a(r,"app/assets/modules/github/hydro-tracking.js:12"),a(n,"app/assets/modules/github/hydro-tracking.js:13"),_e({hydroEventPayload:r,hydroEventHmac:n})}),t.observe(".dashboard-sidebar.js-sticky",{constructor:HTMLElement,initialize:function(e){if(!e.classList.contains("is-placeholder")){var t=!0,r=function(){var r=e.getBoundingClientRect(),n=window.innerHeight,a=0===r.top;a?a&&t&&(t=!1,e.style.height=n+"px",e.style.width="auto"):(t=!0,e.style.width="auto",e.style.height=n-r.top+"px")};document.addEventListener("scroll",r,{passive:!0}),window.addEventListener("resize",function(){t=!0,r()},{passive:!0}),r()}}}),r.on("details:toggled",".js-news-feed-event-group",function(e){if(a(e instanceof CustomEvent,"app/assets/modules/github/dashboard.js:49"),e.detail.open){var t=e.currentTarget,r=!0,n=!1,s=void 0;try{for(var o,i=t.querySelectorAll("[data-hydro-view]")[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){Mn(o.value)}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}}});var Rn=document.querySelectorAll(".js-dashboard-deferred");if(Rn.length)try{!function(e){var t=Array.from(e).map(function(e){var t=e.getAttribute("data-src"),r=e.getAttribute("data-priority");return a(t,"missing required data-src -- app/assets/modules/github/dashboard.js:72"),a(r,"missing required data-priority -- app/assets/modules/github/dashboard.js:73"),{url:t,priority:r=parseInt(r,10),el:e,loaded:!1}}),r=function(e){var r=e.url,n=e.el;o.fetchSafeDocumentFragment(document,r).then(function(r){n.replaceWith(r),e.loaded=!0,Hn(t.filter(function(e){return!e.loaded}))}).catch(function(){n.classList.add("is-error")})},n=!0,s=!1,i=void 0;try{for(var u,c=t[Symbol.iterator]();!(n=(u=c.next()).done);n=!0)r(u.value)}catch(e){s=!0,i=e}finally{try{!n&&c.return&&c.return()}finally{if(s)throw i}}Hn(t)}(Rn)}catch(e){}function In(t){var r=t.currentTarget;a(r instanceof HTMLElement,"app/assets/modules/github/dashboard/discover-repositories.js:20"),e.query(r,".js-dismiss-repository-recommendation").classList.remove("d-none")}function Cn(t){var r=t.currentTarget;a(r instanceof HTMLElement,"app/assets/modules/github/dashboard/discover-repositories.js:26"),e.query(r,".js-dismiss-repository-recommendation").classList.add("d-none")}Br(".js-discover-repositories .js-ajax-pagination",function(e){a(e instanceof HTMLFormElement,"app/assets/modules/github/dashboard/discover-repositories.js:11"),e.classList.contains("is-loading")||(e.classList.add("is-loading"),l.submit(e))}),t.observe(".js-repository-recommendation",{add:function(e){e.addEventListener("mouseover",In),e.addEventListener("mouseout",Cn)},remove:function(e){e.removeEventListener("mouseover",In),e.removeEventListener("mouseout",Cn)}}),c.remoteForm(".js-dismiss-repository-recommendation",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.closest(r,".js-repository-recommendation-and-restore"),s=e.query(a,".js-restore-repo-rec-container"),o=e.query(a,".js-repository-recommendation"),t.next=5,n.html();case 5:t.sent&&(o.classList.add("d-none"),s.classList.remove("d-none"),s.style.display="block");case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-restore-repo-rec-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.closest(r,".js-repository-recommendation-and-restore"),s=e.query(a,".js-restore-repo-rec-container"),o=e.query(a,".js-repository-recommendation"),t.next=5,n.html();case 5:t.sent&&(s.classList.add("d-none"),o.classList.remove("d-none"),o.style.display="block");case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-show-more-recent-items",function(e){A(e.currentTarget,HTMLButtonElement).classList.add("d-none")}),t.observe(".js-recent-activity-container",function(){var e=document.querySelector(".js-all-activity-header");e&&e.classList.remove("d-none")});var _n=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,C("high");case 2:return n=e.query(document,".js-delegated-account-recovery-submit",HTMLButtonElement),a=e.query(document,".js-create-recovery-token-form",HTMLFormElement),r.classList.remove("failed"),r.classList.add("loading"),n.disabled=!0,t.prev=7,t.next=10,o.fetchForm(a);case 10:return s=t.sent,t.next=13,s.json();case 13:i=t.sent,l.fillFormValues(r,{token:i.token,state:i.state_url}),r.submit(),t.next=23;break;case 18:t.prev=18,t.t0=t.catch(7),r.classList.remove("loading"),r.classList.add("failed"),n.disabled=!1;case 23:case"end":return t.stop()}},t,this,[[7,18]])}));return function(e){return t.apply(this,arguments)}}();r.on("submit","form.js-post-recovery-token",function(e){var t=e.currentTarget;a(t instanceof HTMLFormElement,"app/assets/modules/github/delegated-account-recovery.js:44"),e.preventDefault(),_n(t)}),t.observe("form.js-recovery-provider-auto-redirect",{constructor:HTMLFormElement,initialize:function(e){e.submit()}}),r.on("click",".js-sub-dependencies",function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,i,u,c,l,d,m=t.currentTarget;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(a(m instanceof HTMLElement,"app/assets/modules/github/dependencies.js:8"),n=(r=m).getAttribute("data-loaded"),s=r.querySelector(".js-expanded"),i=r.querySelector(".js-collapsed"),u=r.closest(".js-details-container"),s&&s.classList.toggle("d-none"),i&&i.classList.toggle("d-none"),u&&u.classList.toggle("bg-gray-light"),n){e.next=18;break}return r.setAttribute("data-loaded","true"),c=r.getAttribute("data-sub-dependency-url"),l=r.closest(".js-dependency"),a(c,"app/assets/modules/github/dependencies.js:24"),e.next=16,o.fetchSafeDocumentFragment(document,c);case 16:d=e.sent,l&&l.after(d);case 18:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}());var Fn=function(){var e=te(regeneratorRuntime.mark(function e(t){var n,s,o=t.currentTarget;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return a(o instanceof HTMLElement,"app/assets/modules/github/details-element.js:45"),n=o,s=o.hasAttribute("open"),r.fire(n,s?"menu:activate":"menu:deactivate"),e.next=6,v.microtask();case 6:r.fire(n,s?"menu:activated":"menu:deactivated");case 7:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),Pn=null;function Nn(e){var t=e.currentTarget;a(t instanceof HTMLElement,"app/assets/modules/github/details-element.js:27"),t.hasAttribute("open")?(Pn&&Pn!==t&&Pn.removeAttribute("open"),Pn=t):t===Pn&&(Pn=null)}document.addEventListener("keydown",function(e){"Escape"===e.key&&Pn&&Pn.removeAttribute("open")}),t.observe(".js-dropdown-details",{subscribe:function(e){return L.fromEvent(e,"toggle",Nn)}}),t.observe("details.js-select-menu",{subscribe:function(e){return L.fromEvent(e,"toggle",Fn)}}),r.on("menu:deactivate","details.js-select-menu",function(e){e.currentTarget.hasAttribute("open")&&e.currentTarget.removeAttribute("open")}),t.observe("[data-deferred-details-content-url]",function(t){t.addEventListener("toggle",function(){var r=t.getAttribute("data-deferred-details-content-url")||"";e.query(t,"include-fragment",IncludeFragmentElement).src=r},{once:!0})}),r.on("click","[data-toggle-for]",function(e){var t=e.currentTarget.getAttribute("data-toggle-for")||"",r=document.getElementById(t);r&&(r.hasAttribute("open")?r.removeAttribute("open"):r.setAttribute("open","open"))}),t.observe("details.select-menu details-menu include-fragment",function(e){var t=e.closest("details");t&&(e.addEventListener("loadstart",function(){t.classList.add("is-loading")}),e.addEventListener("loadend",function(){t.classList.remove("is-loading")}))}),t.observe("details details-menu .js-filterable-field",{constructor:HTMLInputElement,add:function(e){var t=e.closest("details");t&&t.addEventListener("toggle",function(){t.hasAttribute("open")||(e.value="",r.fire(e,"filterable:change"))})}}),_(function(e){var t=e.target;if(t&&!t.closest("summary"))for(var r=t.parentElement;r;)(r=r.closest("details"))&&(r.hasAttribute("open")||r.setAttribute("open",""),r=r.parentElement)});var On=!1,Dn=[];function Bn(){Dn.length?On||(window.addEventListener("resize",Un,{passive:!0}),document.addEventListener("scroll",Un,{passive:!0}),On=!0):(window.removeEventListener("resize",Un,{passive:!0}),document.removeEventListener("scroll",Un,{passive:!0}),On=!1)}function Un(){var e=!0,t=!1,r=void 0;try{for(var n,a=Dn[Symbol.iterator]();!(e=(n=a.next()).done);e=!0){var s=n.value;if(s.element.offsetHeight>0){var o=s.element,i=s.placeholder,u=s.top,c=o.getBoundingClientRect();if(i){var l=i.getBoundingClientRect();o.classList.contains("is-stuck")?l.top>parseInt(u)?Wn(s):Vn(s):c.top<=parseInt(u)&&zn(s)}else c.top<=parseInt(u)?zn(s):Wn(s)}}}catch(e){t=!0,r=e}finally{try{!e&&a.return&&a.return()}finally{if(t)throw r}}}function zn(e){var t=e.element,r=e.placeholder,n=e.top;if(r){var a=t.getBoundingClientRect();t.style.top=n.toString(),t.style.left=a.left+"px",t.style.width=a.width+"px",t.style.marginTop="0",t.style.position="fixed",r.style.display="block"}t.classList.add("is-stuck")}function Wn(e){var t=e.element,r=e.placeholder;r&&(t.style.position="static",t.style.marginTop=r.style.marginTop,r.style.display="none"),t.classList.remove("is-stuck")}function Vn(e){var t=e.element,r=e.placeholder,n=e.offsetParent,a=e.top;if(r){var s=t.getBoundingClientRect(),o=r.getBoundingClientRect();if(t.style.left=o.left+"px",t.style.width=o.width+"px",n){var i=n.getBoundingClientRect();i.bottom<s.height+parseInt(a)&&(t.style.top=i.bottom-s.height+"px")}}}function $n(e){if(function(e){var t=window.getComputedStyle(e).position;return/sticky/.test(t)}(e))return null;var t=e.previousElementSibling;if(t&&t.classList.contains("is-placeholder"))return a(t instanceof HTMLElement,"previousElement must be an HTMLElement -- app/assets/modules/github/sticky.js:127"),t;var r=document.createElement("div");return r.style.visibility="hidden",r.style.display="none",r.style.height=window.getComputedStyle(e).height,r.className=e.className,r.classList.remove("js-sticky"),r.classList.add("is-placeholder"),a(e.parentNode,"Element must be inserted into the dom -- app/assets/modules/github/sticky.js:140"),e.parentNode.insertBefore(r,e)}function Jn(e){var t=e.ownerDocument;e.scrollIntoView(),t.defaultView.scrollBy(0,-Kn(t))}function Kn(e){Un();var t=e.querySelectorAll(".js-sticky-offset-scroll");return Math.max.apply(Math,ue(Array.from(t).map(function(e){var t=e.getBoundingClientRect(),r=t.top,n=t.height;return 0===r?n:0})))}function Gn(e){var t=Xn();if(t){Qn(e,t);var r=function(e,t){var r=function(e,t){var r=/^(diff-[0-9a-f]{32})(?:[L|R]\d+)?$/.exec(t);if(!r)return;var n=r[1],a=e.querySelector("a[name='"+n+"']");if(!a)return;var s=a.nextElementSibling;if(s&&!s.querySelector(".js-diff-load-container"))return;return s}(e,t);if(r)return r;return function(e,t){var r=/^(?:r|commitcomment-)(\d+)$/.exec(t);if(!r)return;var n=r[1],a=e.querySelector("#diff-with-comment-"+n);if(!a)return;return a.closest(".js-file")}(e,t)}(e,t);r&&(Jn(r),Zn(r))}}function Xn(){return window.location.hash.slice(1)}function Qn(e,t){var r=H.findElementByFragmentName(e.ownerDocument,t);r&&e.contains(r)&&Jn(r)}function Yn(t){var r=t.currentTarget;a(r instanceof Element,"app/assets/modules/github/diffs/progressive.js:146"),e.query(r,".js-diff-progressive-spinner").classList.add("d-none"),e.query(r,".js-diff-progressive-retry").classList.remove("d-none")}function Zn(t){var r=e.query(t,".js-diff-entry-loader",IncludeFragmentElement),n=e.query(t,".js-diff-placeholder",Element),a=e.query(t,"button.js-diff-load",HTMLButtonElement),s=e.query(t,".js-button-text");n.setAttribute("fill","url('#animated-diff-gradient')"),s.textContent=a.getAttribute("data-disable-with")||"",a.disabled=!0;var o=new URL(r.getAttribute("data-fragment-url")||"",window.location.origin);return r.src=o.toString(),r.data}function ea(t){var r=t.currentTarget;a(r instanceof Element,"app/assets/modules/github/diffs/progressive.js:175"),e.query(r,".js-diff-load-button-container").classList.add("d-none"),e.query(r,".js-diff-load-retry").classList.remove("d-none")}function ta(e){var t=e.currentTarget;a(t instanceof IncludeFragmentElement,"app/assets/modules/github/diffs/progressive.js:188"),t.setAttribute("data-url",t.src),t.removeAttribute("src")}function ra(e){e.src=e.getAttribute("data-url")||"",e.removeAttribute("data-url")}function na(){var t=!0,r=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-newsletter-frequency-choice.selected")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){a.value.classList.remove("selected")}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}var o=e.query(document,".js-newsletter-frequency-radio:enabled:checked");e.closest(o,".js-newsletter-frequency-choice").classList.add("selected")}t.observe(".js-sticky",{constructor:HTMLElement,add:function(e){!function(e){var t=$n(e),r=window.getComputedStyle(e).position;e.style.position="static";var n=e.offsetParent;e.style.position="fixed";var a=window.getComputedStyle(e).top,s={element:e,placeholder:t,offsetParent:n,top:"auto"===a?0:a};e.style.position=r,Dn.push(s)}(e),Un(),Bn()},remove:function(e){!function(e){var t=Dn.map(function(e){return e.element}).indexOf(e);Dn.splice(t,1)}(e),Bn()}}),t.observe(".js-diff-progressive-container",function(e){Gn(e);var t=e.querySelector(".js-diff-progressive-loader");t&&(t.addEventListener("load",function(){Gn(e)}),t.addEventListener("error",ta),t.addEventListener("error",Yn))}),r.on("click",".js-diff-progressive-retry .js-retry-button",function(t){var r=e.closest(t.currentTarget,".js-diff-progressive-loader",IncludeFragmentElement);!function(t){e.query(t,".js-diff-progressive-spinner").classList.remove("d-none"),e.query(t,".js-diff-progressive-retry").classList.add("d-none")}(r),ra(r)}),t.observe(".js-diff-load-container",function(t){var r=t.querySelector(".js-diff-entry-loader");r&&(r.addEventListener("load",function(){e.closest(t,".js-file").classList.remove("hide-file-notes-toggle");var r=Xn();r&&Qn(t,r)}),r.addEventListener("error",ta),r.addEventListener("error",ea))}),r.on("click",".js-diff-load",function(t){t.target.classList.contains("js-ignore-this")||Zn(e.closest(t.currentTarget,".js-diff-load-container"))}),r.on("click",".js-diff-load-retry .js-retry-button",function(t){var r=e.closest(t.currentTarget,".js-diff-entry-loader",IncludeFragmentElement);!function(t){e.query(t,".js-diff-load-button-container").classList.remove("d-none"),e.query(t,".js-diff-load-retry").classList.add("d-none")}(r),ra(r)}),r.on("click",".js-rendered, .js-source",function(t){var r=t.currentTarget;r.classList.contains("selected")?t.preventDefault():(function(t){var r=e.closest(t,".js-file-header"),n=t.classList.contains("js-rendered"),s=t.classList.contains("js-source"),o=r.getAttribute("data-short-path");a(o,"app/assets/modules/github/diffs/prose.js:39");var i=r.getAttribute("data-anchor");a(i,"app/assets/modules/github/diffs/prose.js:41");var u=new URL(window.location.href,window.location.origin),c=new URLSearchParams(u.search.slice(1));u.hash=i,n?c.set("short_path",o):s&&c.delete("short_path");u.search=c.toString(),f.replaceState(null,"",u.toString())}(r),function(e){var t=!0,r=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-rendered, .js-source")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;o.classList.remove("selected")}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}e.classList.add("selected")}(r))}),c.remoteForm(".js-prose-diff-toggle-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.closest(r,".js-details-container"),s=e.query(a,".js-file-content"),t.next=4,n.html();case 4:for(o=t.sent;s.lastChild;)s.removeChild(s.lastChild);s.append(o.html),a.classList.toggle("display-rich-diff"),a.classList.toggle("show-inline-notes");case 9:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-diff-style-toggle input",function(t){var r=e.closest(t.target,".js-diff-settings"),n=!0,a=!1,s=void 0;try{for(var o,i=e.querySelectorAll(r,".js-diff-style-toggle label")[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.classList.toggle("selected")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}),r.on("click",".js-toc-retry",function(e){var t=e.target.closest(".js-select-menu");null!=t&&(t.classList.add("js-load-contents","is-loading"),t.classList.remove("has-error"))}),t.observe(".js-subscription-toggle",function(){na()}),r.on("change",".js-newsletter-frequency-radio",function(){na()}),c.remoteForm(".js-subscription-toggle",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.text();case 2:(a=e.query(r,".selected .notice")).classList.add("visible"),setTimeout(function(){a.classList.remove("visible")},2e3);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-explore-newsletter-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=t.sent,e.closest(r,".js-explore-newsletter-container").replaceWith(a.html);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),_(function(){Kn(document)&&function(e){var t=H.findFragmentTarget(e);t&&Jn(t)}(document)});var aa=function e(t){re(this,e),this.name=t.getAttribute("data-theme-name")||"",this.slug=t.getAttribute("data-theme-slug")||"",this.gem=t.getAttribute("data-theme-gem")||"",this.selected=t.classList.contains("selected"),this.baseHref=t.getAttribute("href")||""},sa=function(){function t(){var r=this;re(this,t),this.pagePreview=e.query(document,"#page-preview"),this.contextLoader=e.query(document,".theme-picker-spinner"),this.fullPicker=e.query(document,".theme-picker-thumbs"),this.miniPicker=e.query(document,".theme-picker-controls"),this.scrollBackwardsLink=e.query(document,".theme-toggle-full-left",HTMLButtonElement),this.scrollForwardsLink=e.query(document,".theme-toggle-full-right",HTMLButtonElement),this.themeLinksContainer=e.query(this.fullPicker,".js-theme-selector"),this.themeLinks=e.querySelectorAll(this.themeLinksContainer,".theme-selector-thumbnail"),this.themes=this.themeLinks.map(function(e){var t=new aa(e);return t.selected&&(r.selectedTheme=t),t}),this.scrolledPage=0,this.selectedTheme=this.selectedTheme||this.themes[0],this.pagePreview.addEventListener("load",function(){return r.onPagePreviewLoad()}),this.scrollBackwardsLink.addEventListener("click",function(){return r.scrollThemeLinksContainer(-1)}),this.scrollForwardsLink.addEventListener("click",function(){return r.scrollThemeLinksContainer(1)}),e.query(document,".theme-picker-prev").addEventListener("click",function(e){return r.onThemeNavPrevClick(e)}),e.query(document,".theme-picker-next").addEventListener("click",function(e){return r.onThemeNavNextClick(e)});var n=!0,a=!1,s=void 0;try{for(var o,i=this.themeLinks[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.addEventListener("click",function(e){return r.onThemeLinkClick(e)})}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}e.query(document,".theme-picker-view-toggle").addEventListener("click",function(e){return r.onHideClick(e)});var u=document.querySelector("#page-edit");u&&u.addEventListener("click",function(e){return r.onEditClick(e)}),e.query(document,"#page-publish").addEventListener("click",function(e){return r.onPublishClick(e)}),this.theme(this.selectedTheme),this.updateScrollLinks()}return ne(t,[{key:"onPagePreviewLoad",value:function(){this.contextLoader.classList.remove("visible")}},{key:"onThemeNavPrevClick",value:function(e){this.theme(this.prevTheme()),e.preventDefault()}},{key:"onThemeNavNextClick",value:function(e){this.theme(this.nextTheme()),e.preventDefault()}},{key:"onThemeLinkClick",value:function(e){this.theme(this.themeForLink(e.currentTarget)),e.preventDefault()}},{key:"onHideClick",value:function(e){var t=e.currentTarget;a(t instanceof HTMLElement,"app/assets/modules/github/generated-pages-theme-picker.js:98"),this.fullPicker.classList.toggle("d-none"),this.miniPicker.classList.toggle("d-none"),this.scrollToTheme(this.theme(),!1),t.classList.toggle("open")}},{key:"onEditClick",value:function(t){l.submit(e.query(document,"#page-edit-form",HTMLFormElement)),t.preventDefault()}},{key:"onPublishClick",value:function(t){var r=e.query(document,"#page-publish-form",HTMLFormElement),n=this.theme();n&&(e.query(document,'input[name="page[theme_slug]"]',HTMLInputElement).value=n.slug),l.submit(r),t.preventDefault()}},{key:"scrollThemeLinksContainer",value:function(e){this.scrollToTheme(this.themes[e<0?0:6]),this.updateScrollLinks()}},{key:"updateScrollLinks",value:function(){var e=0===this.scrolledPage;this.scrollBackwardsLink.disabled=e,this.scrollBackwardsLink.classList.toggle("disabled",e),this.scrollForwardsLink.disabled=!e,this.scrollForwardsLink.classList.toggle("disabled",!e)}},{key:"selectedThemeIndex",value:function(){return this.themes.indexOf(this.selectedTheme)}},{key:"prevTheme",value:function(){var e=(this.selectedThemeIndex()-1)%this.themes.length;return e<0&&(e+=this.themes.length),this.themes[e]}},{key:"nextTheme",value:function(){return this.themes[(this.selectedThemeIndex()+1)%this.themes.length]}},{key:"themeForLink",value:function(e){return this.themes[this.themeLinks.indexOf(e)]}},{key:"linkForTheme",value:function(e){return this.themeLinks[this.themes.indexOf(e)]}},{key:"scrollToTheme",value:function(e){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];this.scrolledPage=Math.floor(this.themes.indexOf(e)/6);var r=0;if(this.scrolledPage>0){var n=this.linkForTheme(this.themes[6*this.scrolledPage]);r=n.offsetLeft}var a,s=this.themeLinksContainer,o=r-s.scrollLeft;if(t&&0!==o){a=function(e){if(!(e<0)){var t=s.scrollLeft;return s.scrollLeft=Math[r-t>0?"min":"max"](t+o*(e/150),r),t!==s.scrollLeft&&void 0}},new Promise(function(e){var t=performance.now();!function r(){window.requestAnimationFrame(function(n){!1!==a(n-t)?(t=n,r()):e()})}()})}else s.scrollLeft=r}},{key:"theme",value:function(t){if(!t)return this.selectedTheme;this.selectedTheme=t,this.showPreviewFor(t);var r=!0,n=!1,a=void 0;try{for(var s,o=this.themeLinks[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.classList.remove("selected")}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}this.linkForTheme(t).classList.add("selected"),this.scrollToTheme(t),e.query(this.miniPicker,".js-theme-name").textContent=t.name}},{key:"showPreviewFor",value:function(t){if(this.contextLoader.classList.add("visible"),t.gem)this.pagePreview.setAttribute("src",t.baseHref);else{var r=e.query(this.fullPicker,"form",HTMLFormElement);e.query(document,'input[name="theme_slug"]',HTMLInputElement).value=t.slug,l.submit(r)}}}]),t}();function oa(){var e=document.querySelector(".js-get-repo-modal");if(e){e.classList.remove("is-downloading");var t=!0,r=!1,n=void 0;try{for(var a,s=e.querySelectorAll(".js-modal-download-mac, .js-modal-download-windows, .js-modal-download-xcode, .js-modal-download-visual-studio")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;o.classList.remove("d-block"),o.classList.add("d-none")}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}}function ia(e){var t=document.querySelectorAll(e);if(t.length>0)return t[t.length-1]}function ua(){return((t=ia("meta[name=analytics-location]"))instanceof HTMLMetaElement?t.content:window.location.pathname)+function(){var t="";ia("meta[name=analytics-location-query-strip]")||(t=window.location.search);var r=ia("meta[name=analytics-location-params]");r instanceof HTMLMetaElement&&(t+=(t?"&":"?")+r.content);var n=!0,a=!1,s=void 0;try{for(var o,i=e.querySelectorAll(document,"meta[name=analytics-param-rename]",HTMLMetaElement)[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value.content.split(":",2);t=t.replace(new RegExp("(^|[?&])"+u[0]+"($|=)","g"),"$1"+u[1]+"$2")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return t}();var t}function ca(){var t=window.location.protocol+"//"+window.location.host+ua();F.setGlobalLocation(t),F.setGlobalTitle(ia("meta[name=analytics-location]")?"(masked)":document.title);var r=ia("meta[name=analytics-ec-payload]");r instanceof HTMLMetaElement&&la(r.content);var n=T.getItem("ga-deferred");n&&(la(n),T.removeItem("ga-deferred"));var a=!0,s=!1,o=void 0;try{for(var i,u=e.querySelectorAll(document,"meta.js-ga-set",HTMLMetaElement)[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;F.setDimension(c.name,c.content)}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}}function la(e){if(e){var t=!0,r=!1,n=void 0;try{for(var a,s=JSON.parse(e)[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;window.ga.apply(null,o)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}}function da(){var e=!0,t=!1,r=void 0;try{for(var n,a=document.querySelectorAll("meta[name=analytics-virtual-pageview]")[Symbol.iterator]();!(e=(n=a.next()).done);e=!0){var s=n.value;s instanceof HTMLMetaElement&&F.trackPageview(s.content,{title:""})}}catch(e){t=!0,r=e}finally{try{!e&&a.return&&a.return()}finally{if(t)throw r}}F.trackPageview()}function ma(e){var t=e.trim().split(/\s*,\s*/),r=oe(t,4),n=r[0],a=r[1],s=r[2],o=r[3],i={category:n,action:a,label:s};return o&&(i.value=Number(o)),i}t.observe("#theme-picker-wrap",function(){new sa}),r.on("click",".email-hidden-toggle",function(e){var t=e.currentTarget.nextElementSibling;a(t instanceof HTMLElement,"app/assets/modules/github/gfm.js:11"),t.style.display="",t.classList.toggle("expanded"),e.preventDefault()}),r.on("click",".js-git-clone-help-container .js-git-clone-help-switcher",function(t){var r=t.currentTarget,n=e.closest(r,".js-git-clone-help-container"),a=r.getAttribute("data-url")||"";if(e.query(n,".js-git-clone-help-field",HTMLInputElement).value=a,r.matches(".js-git-protocol-clone-url")){var s=!0,o=!1,i=void 0;try{for(var u,c=n.querySelectorAll(".js-git-clone-help-text")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.textContent=a}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}var l=n.querySelector(".js-clone-url-button.selected");l&&l.classList.remove("selected"),e.closest(r,".js-clone-url-button").classList.add("selected")}),r.on("click",".js-get-repo",function(t){var r=t.currentTarget.getAttribute("data-open-app");a(r,"app/assets/modules/github/git-clone-help.js:31"),function(e,t){e.classList.add("is-downloading");var r=void 0;switch(t){case"mac":r=e.querySelector(".js-modal-download-mac");break;case"windows":r=e.querySelector(".js-modal-download-windows");break;case"xcode":r=e.querySelector(".js-modal-download-xcode");break;case"visual-studio":r=e.querySelector(".js-modal-download-visual-studio")}r&&(r.classList.remove("d-none"),r.classList.add("d-block"))}(e.closest(t.currentTarget,".js-get-repo-modal"),r)}),r.on("toggle",".js-get-repo-select-menu",oa,{capture:!0}),r.on("click",".js-get-repo-modal-download-back",oa),F.providePlugin("octolyticsPlugin",function(e){var t=e.get("sendHitTask");e.set("sendHitTask",function(e){t(e),"event"===e.get("hitType")&&null!=window._octo&&y.guaranteedPost("//"+window._octo.host+"/collect",e.get("hitPayload"),"application/x-www-form-urlencoded")})});var pa=document.querySelector("meta[name=google-analytics]");function fa(t){if(window._octo){var r=Math.floor((new Date).getTime()/1e3);t.timestamp=r;if(document.head&&document.head.querySelector('meta[name="octolytics-event-url"]')){var n=e.query(document.head,'meta[name="octolytics-event-url"]',HTMLMetaElement).content;y.guaranteedPost(n,JSON.stringify(t),"text/plain")}}}pa&&(a(pa instanceof HTMLMetaElement,"app/assets/modules/github/google-analytics-tracking.js:20"),F.setGlobalAccount(pa.content,"auto"),F.requirePlugin("octolyticsPlugin"),F.requirePlugin("ec"),ca()),te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.ready;case 2:da();case 3:case"end":return e.stop()}},e,this)}))(),document.addEventListener("pjax:complete",function(){setTimeout(function(){ca(),da()},20)},!1),t.observe("[data-ga-load]",function(e){var t=ma(e.getAttribute("data-ga-load")||"");t.interactive=!1,F.trackEvent(t)}),t.observe("meta[name=analytics-event]",{constructor:HTMLMetaElement,initialize:function(e){var t=ma(e.content);t.interactive=!1,F.trackEvent(t)}}),r.on("click","[data-ga-click]",function(e){la(e.currentTarget.getAttribute("data-ga-ec"));var t=ma(e.currentTarget.getAttribute("data-ga-click")||"");F.trackEvent(t)},{capture:!0}),r.on("click","[data-ga-deferred]",function(e){T.setItem("ga-deferred",e.currentTarget.getAttribute("data-ga-deferred")||"")},{capture:!0}),r.on("change","[data-ga-change]",function(e){la(e.currentTarget.getAttribute("data-ga-ec"));var t=ma(e.currentTarget.getAttribute("data-ga-change")||"");F.trackEvent(t)},{capture:!0}),r.on("click",".js-skip-to-content",function(e){var t=document.getElementById("start-of-content");if(t){var r=t.nextElementSibling;r instanceof HTMLElement&&(r.setAttribute("tabindex","-1"),r.focus())}e.preventDefault()}),r.on("click",".js-video-play, .js-video-close",function(t){t.preventDefault();var r=t.currentTarget,n=r.classList.contains("js-video-play"),a=e.closest(r,".js-video-container"),s=e.query(a,".js-video-iframe",HTMLIFrameElement),o=document.querySelector(".js-video-bg");n?s.src=s.getAttribute("data-src")||"":s.removeAttribute("src"),a.classList.toggle("is-expanded",n),null!=o&&o.classList.toggle("is-expanded",n),function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=e.getBoundingClientRect(),n=r.top-t,a=r.bottom-window.innerHeight+t;n<0?window.scrollBy(0,n):a>0&&window.scrollBy(0,a)}(s,20)}),r.on("click","[data-octo-click]",function(e){if(window._octo){var t=e.currentTarget,r=t.getAttribute("data-octo-click")||"",n={};n.event_type=r;var a={},s={},o={},i=[];t.hasAttribute("data-octo-dimensions")&&(i=(t.getAttribute("data-octo-dimensions")||"").split(","));var u=document.head?document.head.querySelectorAll('meta[name^="octolytics-"]'):[],c=!0,l=!1,d=void 0;try{for(var m,p=u[Symbol.iterator]();!(c=(m=p.next()).done);c=!0){var f=m.value,v=A(f,HTMLMetaElement);if(v.name.startsWith("octolytics-dimension-"))a[v.name.replace(/^octolytics-dimension-/,"")]=v.content;else if(v.name.startsWith("octolytics-measure-")){s[v.name.replace(/^octolytics-measure-/,"")]=v.content}else if(v.name.startsWith("octolytics-context-")){o[v.name.replace(/^octolytics-context-/,"")]=v.content}else if(v.name.startsWith("octolytics-actor-")){a[v.name.replace(/^octolytics-/,"").replace(/-/g,"_")]=v.content}else if(v.name.startsWith("octolytics-")){n[v.name.replace(/^octolytics-/,"").replace(/-/g,"_")]=v.content}}}catch(e){l=!0,d=e}finally{try{!c&&p.return&&p.return()}finally{if(l)throw d}}if(t.hasAttribute("data-ga-click")){var g=(t.getAttribute("data-ga-click")||"").split(",").map(function(e){return e.trim()});a.category=g[0],a.action=g[1]}var h=!0,y=!1,b=void 0;try{for(var j,L=i[Symbol.iterator]();!(h=(j=L.next()).done);h=!0){var w=j.value.split(":");a[w.shift()]=w.join(":")}}catch(e){y=!0,b=e}finally{try{!h&&L.return&&L.return()}finally{if(y)throw b}}n.dimensions=a,n.measures=s,n.context=o,fa(n)}});var va=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return ya[n="/hovercards?user_id="+t+"&subject="+r]||(ya[n]=o.fetchSafeDocumentFragment(document,n)),e.abrupt("return",ya[n]);case 3:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}(),ga=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,s,o,i,u;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=t.currentTarget,a(n instanceof HTMLElement,"app/assets/modules/github/hovercards.js:206"),ba!==n){e.next=4;break}return e.abrupt("return");case 4:if(!n.closest(".js-hovercard-content")){e.next=6;break}return e.abrupt("return");case 6:return Aa(),ba=n,ja=document.activeElement,s=Ra(n)||"",o=n.getAttribute("data-hovercard-user-id")||"",i=void 0,e.prev=12,u=new Promise(function(e){return setTimeout(e,r,0)}),e.next=16,va(o,s);case 16:return i=e.sent,e.next=19,u;case 19:e.next=28;break;case 21:if(e.prev=21,e.t0=e.catch(12),!(e.t0.response&&404===e.t0.response.status||/X-HTML-Safe/.test(e.t0.message))){e.next=27;break}return e.abrupt("return");case 27:throw e.t0;case 28:n===ba&&(Ha(i,n),t instanceof KeyboardEvent&&ha&&ha.focus());case 29:case"end":return e.stop()}},e,this,[[12,21]])}));return function(t,r){return e.apply(this,arguments)}}(),ha=document.querySelector(".js-hovercard-content"),ya={},ba=void 0,ja=void 0,La=void 0,wa=12,xa=24,ka=xa-7,Ea=16,Ta=100,qa=250;function Sa(e){return"Popover-message--"+e}function Aa(){ha&&(ha.style.display="none",ha.children[0].innerHTML="",ja=null,ba=null)}function Ma(e,t){if(ha){ha.style.visibility="hidden",ha.style.display="block",t.classList.remove(Sa("bottom-left"),Sa("bottom-right"),Sa("right-top"),Sa("right-bottom"),Sa("top-left"),Sa("top-right"));var r=function(e){a(ha,"app/assets/modules/github/hovercards.js:88");var t=ha.getBoundingClientRect(),r=t.width,n=t.height,s=e.getBoundingClientRect(),o=s.left,i=s.top,u=s.height,c=s.width,l=i>n;if(e.classList.contains("js-hovercard-left")){var d=i+u/2;return{containerTop:l?d-n+ka+Ea/2:d-ka-Ea/2,containerLeft:o-r-wa,contentClassSuffix:l?"right-bottom":"right-top"}}var m=window.innerWidth-o>r,p=o+c/2;return{containerTop:l?i-n-wa:i+u+wa,containerLeft:m?p-xa:p-r+xa,contentClassSuffix:l?m?"bottom-left":"bottom-right":m?"top-left":"top-right"}}(e),n=r.containerTop,s=r.containerLeft,o=r.contentClassSuffix;t.classList.add(Sa(o)),ha.style.top=n+window.pageYOffset+"px",ha.style.left=s+window.pageXOffset+"px",ha.style.visibility=""}}function Ha(t,r){if(ha){var n=ha.children[0];n.innerHTML="";var a=document.createElement("div"),s=!0,o=!1,i=void 0;try{for(var u,c=t.children[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;a.appendChild(l.cloneNode(!0))}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}n.appendChild(a),Ma(r,n),function(t){setTimeout(function(){if(document.body&&document.body.contains(t)){var r=e.query(t,"[data-hovercard-tracking]").getAttribute("data-hovercard-tracking");if(r){var n={event_type:"user-hovercard-load"};n.dimensions=JSON.parse(r),fa(n)}Mn(e.query(t,"[data-hydro-view]"))}},500)}(a),ha.style.display="block"}}function Ra(e){var t=e.closest("[data-hovercard-subject-tag]");if(t)return t.getAttribute("data-hovercard-subject-tag");var r=document.head&&document.head.querySelector('meta[name="hovercard-subject-tag"]');return r?r.getAttribute("content"):void 0}function Ia(e){ga(e,qa)}function Ca(e){if(ba){if(e instanceof MouseEvent&&e.relatedTarget instanceof HTMLElement){var t=e.relatedTarget;if(t.closest(".js-hovercard-content")||t.closest("[data-hovercard-user-id]"))return}else e instanceof KeyboardEvent&&ja&&ja.focus();Aa()}}function _a(e){var t=ba;La=setTimeout(function(){ba===t&&Ca(e)},Ta)}function Fa(e){switch(a(e instanceof KeyboardEvent,"app/assets/modules/github/hovercards.js:282"),e.key){case"h":case"H":ga(e);break;case"Escape":Ca(e)}}function Pa(){La&&clearTimeout(La)}function Na(){var e=document.getElementById("js-update-integration-permissions");e&&e.removeAttribute("disabled")}ha&&(t.observe("[data-hovercard-user-id]",{subscribe:function(e){return L.fromEvent(e,"mouseover",Ia)}}),t.observe("[data-hovercard-user-id]",{subscribe:function(e){return L.fromEvent(e,"mouseout",_a)}}),t.observe("[data-hovercard-user-id]",{subscribe:function(e){return L.fromEvent(e,"keyup",Fa)}}),t.observe("[data-hovercard-user-id]",{add:function(e){var t=[].concat(ue(e.querySelectorAll("a")));"A"===e.tagName&&t.push(e);var r=!0,n=!1,a=void 0;try{for(var s,o=t[Symbol.iterator]();!(r=(s=o.next()).done);r=!0)s.value.setAttribute("aria-describedby","hovercard-aria-description")}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}},remove:function(e){ba===e&&Aa()}}),t.observe(".js-hovercard-content",{subscribe:function(e){return L.fromEvent(e,"mouseover",Pa)}}),t.observe(".js-hovercard-content",{subscribe:function(e){return L.fromEvent(e,"mouseout",Ca)}}),t.observe(".js-hovercard-content",{subscribe:function(e){return L.fromEvent(e,"keyup",Fa)}}),r.on("menu:activated",".js-select-menu",Aa),window.addEventListener("statechange",Aa)),t.observe(".js-scrollnav-listener",function(t){window.addEventListener("scroll",function(){!function(t){var r=e.query(t,".js-scrollnav").getBoundingClientRect().height,n=!0,a=!1,s=void 0;try{for(var o,i=t.querySelectorAll(".js-section")[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value,c=u.getBoundingClientRect(),l=r>=c.top,d=r<=c.top+c.height;if(l&&d){var m=t.querySelector('.js-scrollnav-item[href="#'+u.id+'"]');if(m&&!m.classList.contains("selected")){var p=!0,f=!1,v=void 0;try{for(var g,h=t.querySelectorAll(".js-scrollnav-item")[Symbol.iterator]();!(p=(g=h.next()).done);p=!0)g.value.classList.remove("selected")}catch(e){f=!0,v=e}finally{try{!p&&h.return&&h.return()}finally{if(f)throw v}}m.classList.add("selected")}}}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}(t)},{passive:!0})}),r.on("change",".js-integration-permissions-selector [id^=integration_permission_]",function(t){var r=t.currentTarget,n=r.getAttribute("data-permission"),a=r.getAttribute("data-resource")||"",s=e.querySelectorAll(document,'.js-integration-hook-event[data-resource="'+a+'"]',HTMLInputElement),o=e.querySelectorAll(document,".js-integration-single-file-resource",HTMLInputElement),i=e.querySelectorAll(document,'.js-dropdown-container[data-resource="'+a+'"]');if(Na(),"none"!==n){var u=e.querySelectorAll(document,".js-integration-hook-event-permission-error[data-resource='"+a+"']"),c=!0,l=!1,d=void 0;try{for(var m,p=u[Symbol.iterator]();!(c=(m=p.next()).done);c=!0){m.value.classList.add("d-none")}}catch(e){l=!0,d=e}finally{try{!c&&p.return&&p.return()}finally{if(l)throw d}}var f=e.querySelectorAll(document,".js-integration-single-file-permission-error"),v=!0,g=!1,h=void 0;try{for(var y,b=f[Symbol.iterator]();!(v=(y=b.next()).done);v=!0){y.value.classList.add("d-none")}}catch(e){g=!0,h=e}finally{try{!v&&b.return&&b.return()}finally{if(g)throw h}}var j=!0,L=!1,w=void 0;try{for(var x,k=s[Symbol.iterator]();!(j=(x=k.next()).done);j=!0){x.value.readOnly=!1}}catch(e){L=!0,w=e}finally{try{!j&&k.return&&k.return()}finally{if(L)throw w}}var E=!0,T=!1,q=void 0;try{for(var S,A=o[Symbol.iterator]();!(E=(S=A.next()).done);E=!0){S.value.readOnly=!1}}catch(e){T=!0,q=e}finally{try{!E&&A.return&&A.return()}finally{if(T)throw q}}e.closest(r,".js-list-group-item").classList.remove("disabled");var M=!0,H=!1,R=void 0;try{for(var I,C=i[Symbol.iterator]();!(M=(I=C.next()).done);M=!0){I.value.classList.remove("d-none")}}catch(e){H=!0,R=e}finally{try{!M&&C.return&&C.return()}finally{if(H)throw R}}}else{var _=!0,F=!1,P=void 0;try{for(var N,O=i[Symbol.iterator]();!(_=(N=O.next()).done);_=!0){N.value.classList.add("anim-fade-in","d-none")}}catch(e){F=!0,P=e}finally{try{!_&&O.return&&O.return()}finally{if(F)throw P}}e.closest(r,".js-list-group-item").classList.add("disabled");var D=!0,B=!1,U=void 0;try{for(var z,W=s[Symbol.iterator]();!(D=(z=W.next()).done);D=!0){var V=z.value;V.readOnly=!0,V.checked=!1}}catch(e){B=!0,U=e}finally{try{!D&&W.return&&W.return()}finally{if(B)throw U}}if("single_file"===a){var $=!0,J=!1,K=void 0;try{for(var G,X=o[Symbol.iterator]();!($=(G=X.next()).done);$=!0){var Q=G.value;Q.readOnly=!0,Q.value=""}}catch(e){J=!0,K=e}finally{try{!$&&X.return&&X.return()}finally{if(J)throw K}}}}}),r.on("change",".js-integration-permissions-selector [name^=integration]",Na),r.on("click",".js-integration-permissions-selector .js-integration-hook-event",function(t){var r=t.currentTarget;if(a(r instanceof HTMLInputElement,"app/assets/modules/github/integrations.js:79"),r.readOnly){var n=e.closest(r,".js-send-events");e.query(n,".js-integration-hook-event-permission-error").classList.remove("d-none"),t.preventDefault()}}),r.on("click",".js-integration-permissions-selector .js-integration-single-file-resource",function(t){var r=t.currentTarget;if(a(r instanceof HTMLInputElement,"app/assets/modules/github/integrations.js:91"),r.readOnly){var n=e.closest(r,".js-single-file");e.query(n,".js-integration-single-file-permission-error").classList.remove("d-none"),t.preventDefault()}});var Oa=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,i,u,c,l,d,m,p,f,v,g=t.currentTarget;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(a(g instanceof HTMLElement,"app/assets/modules/github/issues/issue-link.js:12"),r=g.getAttribute("data-url")){e.next=4;break}return e.abrupt("return");case 4:for(n=g.getAttribute("data-id")||"",s=g.textContent,i=document.querySelectorAll(".js-issue-link[data-id='"+n+"']"),u=!0,c=!1,l=void 0,e.prev=10,d=i[Symbol.iterator]();!(u=(m=d.next()).done);u=!0)m.value.removeAttribute("data-url");e.next=18;break;case 14:e.prev=14,e.t0=e.catch(10),c=!0,l=e.t0;case 18:e.prev=18,e.prev=19,!u&&d.return&&d.return();case 21:if(e.prev=21,!c){e.next=24;break}throw l;case 24:return e.finish(21);case 25:return e.finish(18);case 26:return e.prev=26,e.next=29,o.fetchJSON(r);case 29:p=e.sent,Da(i,s+", "+p.title),e.next=38;break;case 33:e.prev=33,e.t1=e.catch(26),f=(null!=e.t1.response?e.t1.response.status:void 0)||500,v=function(){switch(f){case 404:return g.getAttribute("data-permission-text");default:return g.getAttribute("data-error-text")}}(),Da(i,v||"");case 38:case"end":return e.stop()}},e,this,[[10,14,18,26],[19,,21,25],[26,33]])}));return function(t){return e.apply(this,arguments)}}();function Da(e,t){var r=!0,n=!1,a=void 0;try{for(var s,o=e[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;i.classList.add("tooltipped","tooltipped-ne"),i.setAttribute("aria-label",t)}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}function Ba(e){var t=/^([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(function(e){return String(e).toLowerCase().replace(/[^0-9a-f]/g,"")}(e));if(t)return[parseInt(t[1],16),parseInt(t[2],16),parseInt(t[3],16)]}t.observe(".js-issue-link",{subscribe:function(e){return L.fromEvent(e,"mouseenter",Oa)}});var Ua="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function za(e,t){return e(t={exports:{}},t.exports),t.exports}var Wa=za(function(e,t){var r;r=function(){var e=null,t={};s("monochrome",null,[[0,0],[100,0]]),s("red",[-26,18],[[20,100],[30,92],[40,89],[50,85],[60,78],[70,70],[80,60],[90,55],[100,50]]),s("orange",[19,46],[[20,100],[30,93],[40,88],[50,86],[60,85],[70,70],[100,70]]),s("yellow",[47,62],[[25,100],[40,94],[50,89],[60,86],[70,84],[80,82],[90,80],[100,75]]),s("green",[63,178],[[30,100],[40,90],[50,85],[60,81],[70,74],[80,64],[90,50],[100,40]]),s("blue",[179,257],[[20,100],[30,86],[40,80],[50,74],[60,60],[70,52],[80,44],[90,39],[100,35]]),s("purple",[258,282],[[20,100],[30,87],[40,79],[50,70],[60,65],[70,59],[80,52],[90,45],[100,42]]),s("pink",[283,334],[[20,100],[30,90],[40,86],[60,84],[80,80],[90,75],[100,73]]);var r=function(s){if(void 0!==(s=s||{}).seed&&null!==s.seed&&s.seed===parseInt(s.seed,10))e=s.seed;else if("string"==typeof s.seed)e=function(e){for(var t=0,r=0;r!==e.length&&!(t>=Number.MAX_SAFE_INTEGER);r++)t+=e.charCodeAt(r);return t}(s.seed);else{if(void 0!==s.seed&&null!==s.seed)throw new TypeError("The seed value must be an integer or string");e=null}var u,c,l;if(null!==s.count&&void 0!==s.count){var d=s.count,m=[];for(s.count=null;d>m.length;)e&&s.seed&&(s.seed+=1),m.push(r(s));return s.count=d,m}return u=function(e){var r=a(function(e){if("number"==typeof parseInt(e)){var r=parseInt(e);if(r<360&&r>0)return[r,r]}if("string"==typeof e)if(t[e]){var n=t[e];if(n.hueRange)return n.hueRange}else if(e.match(/^#?([0-9A-F]{3}|[0-9A-F]{6})$/i)){var a=function(e){e=3===(e=e.replace(/^#/,"")).length?e.replace(/(.)/g,"$1$1"):e;var t=parseInt(e.substr(0,2),16)/255,r=parseInt(e.substr(2,2),16)/255,n=parseInt(e.substr(4,2),16)/255,a=Math.max(t,r,n),s=a-Math.min(t,r,n),o=a?s/a:0;switch(a){case t:return[(r-n)/s%6*60||0,o,a];case r:return[60*((n-t)/s+2)||0,o,a];case n:return[60*((t-r)/s+4)||0,o,a]}}(e)[0];return[a,a]}return[0,360]}(e.hue));r<0&&(r=360+r);return r}(s),c=function(e,t){if("monochrome"===t.hue)return 0;if("random"===t.luminosity)return a([0,100]);var r=function(e){return n(e).saturationRange}(e),s=r[0],o=r[1];switch(t.luminosity){case"bright":s=55;break;case"dark":s=o-10;break;case"light":o=55}return a([s,o])}(u,s),l=function(e,t,r){var s=function(e,t){for(var r=n(e).lowerBounds,a=0;a<r.length-1;a++){var s=r[a][0],o=r[a][1],i=r[a+1][0],u=r[a+1][1];if(t>=s&&t<=i){var c=(u-o)/(i-s),l=o-c*s;return c*t+l}}return 0}(e,t),o=100;switch(r.luminosity){case"dark":o=s+20;break;case"light":s=(o+s)/2;break;case"random":s=0,o=100}return a([s,o])}(u,c,s),function(e,t){switch(t.format){case"hsvArray":return e;case"hslArray":return i(e);case"hsl":var r=i(e);return"hsl("+r[0]+", "+r[1]+"%, "+r[2]+"%)";case"hsla":var n=i(e),a=t.alpha||Math.random();return"hsla("+n[0]+", "+n[1]+"%, "+n[2]+"%, "+a+")";case"rgbArray":return o(e);case"rgb":var s=o(e);return"rgb("+s.join(", ")+")";case"rgba":var u=o(e),a=t.alpha||Math.random();return"rgba("+u.join(", ")+", "+a+")";default:return function(e){var t=o(e);function r(e){var t=e.toString(16);return 1==t.length?"0"+t:t}return"#"+r(t[0])+r(t[1])+r(t[2])}(e)}}([u,c,l],s)};function n(e){for(var r in e>=334&&e<=360&&(e-=360),t){var n=t[r];if(n.hueRange&&e>=n.hueRange[0]&&e<=n.hueRange[1])return t[r]}return"Color not found"}function a(t){if(null===e)return Math.floor(t[0]+Math.random()*(t[1]+1-t[0]));var r=t[1]||1,n=t[0]||0,a=(e=(9301*e+49297)%233280)/233280;return Math.floor(n+a*(r-n))}function s(e,r,n){var a=n[0][0],s=n[n.length-1][0],o=n[n.length-1][1],i=n[0][1];t[e]={hueRange:r,lowerBounds:n,saturationRange:[a,s],brightnessRange:[o,i]}}function o(e){var t=e[0];0===t&&(t=1),360===t&&(t=359),t/=360;var r=e[1]/100,n=e[2]/100,a=Math.floor(6*t),s=6*t-a,o=n*(1-r),i=n*(1-s*r),u=n*(1-(1-s)*r),c=256,l=256,d=256;switch(a){case 0:c=n,l=u,d=o;break;case 1:c=i,l=n,d=o;break;case 2:c=o,l=n,d=u;break;case 3:c=o,l=i,d=n;break;case 4:c=u,l=o,d=n;break;case 5:c=n,l=o,d=i}var m=[Math.floor(255*c),Math.floor(255*l),Math.floor(255*d)];return m}function i(e){var t=e[0],r=e[1]/100,n=e[2]/100,a=(2-r)*n;return[t,Math.round(r*n/(a<1?a:2-a)*1e4)/100,a/2*100]}return r}(),e&&e.exports&&(t=e.exports=r),t.randomColor=r}),Va=(Wa.randomColor,function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f,v,g,h;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.closest(".js-label-preview-container")){t.next=3;break}return t.abrupt("return");case 3:if(s=e.closest(r,".js-label-form",HTMLFormElement),i=s.querySelector(".js-new-label-error"),u=s.getAttribute("data-label-id"),c=e.query(n,".js-label-preview",HTMLElement),l=as(s,c),d=ns(s),m=rs(s),p=c.getAttribute("data-url-template"),a("string"==typeof p,"app/assets/modules/github/issues/labels.js:231"),f=ts(p,l,d,m,u),!n.hasAttribute("data-last-preview-url")){t.next=18;break}if(v=n.getAttribute("data-last-preview-url"),a("string"==typeof v,"app/assets/modules/github/issues/labels.js:236"),f!==v){t.next=18;break}return t.abrupt("return");case 18:return g=void 0,t.prev=19,t.next=22,o.fetchSafeDocumentFragment(document,f);case 22:g=t.sent,t.next=33;break;case 25:return t.prev=25,t.t0=t.catch(19),t.next=29,t.t0.response.json();case 29:return h=t.sent,Ya(s,h),i&&(i.textContent=h.message,i.hidden=!1),t.abrupt("return");case 33:i&&(i.textContent="",i.hidden=!0),Za(s),c.innerHTML="",c.appendChild(g),n.setAttribute("data-last-preview-url",f);case 38:case"end":return t.stop()}},t,this,[[19,25]])}));return function(e){return t.apply(this,arguments)}}());function $a(e,t){var r=function(e){var t=Ba(e);if(t)return(299*t[0]+587*t[1]+114*t[2])/1e3}(t);null!=r&&(r<150?(e.classList.remove("text-gray-dark"),e.classList.add("text-white")):(e.classList.remove("text-white"),e.classList.add("text-gray-dark")))}function Ja(e,t){var r=t;"#"!==r.charAt(0)&&(r="#"+r),e.style.backgroundColor=r;var n=e.querySelector(".js-new-label-color-icon");n&&$a(n,t)}function Ka(t,r){t.blur();var n=e.closest(t,"form");e.query(n,".js-new-label-color-input",HTMLInputElement).value=r,Ja(e.query(n,".js-new-label-color",HTMLButtonElement),r)}function Ga(e){var t=Array.from(e.querySelectorAll(".js-navigation-item")).filter(function(e){return""===e.style.display});if(!(t.length>2)&&t.every(function(e){return e.classList.contains("js-label-options")})){var r=t.filter(function(e){return e.classList.contains("js-add-label-button")})[0];if(r){var n=!0,a=!1,s=void 0;try{for(var o,i=e.querySelectorAll('[aria-selected="true"]')[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.setAttribute("aria-selected","false")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}r.setAttribute("aria-selected","true")}}}function Xa(t){var r=function(e){return e.hasAttribute("data-maxlength")?parseInt(e.getAttribute("data-maxlength")):e.maxLength}(t);!function(t,r,n){var a=n.closest(".js-label-characters-remaining-container");if(a){var s=e.query(a,".js-label-characters-remaining"),o=String(s.getAttribute("data-suffix")),i=r-x.getUtf8StringLength(t);i<=20?(s.textContent=i+" "+o,s.classList.toggle("text-red",i<=5),s.hidden=!1):s.hidden=!0}}(t.value,r,t)}function Qa(t,r,n){var a=r.querySelector(t);a&&(n?function(t,r){e.closest(t,".js-label-error-container").classList.add("errored"),t.textContent=r,t.classList.remove("d-none")}(a,n[0]):function(t){e.closest(t,".js-label-error-container").classList.remove("errored"),t.classList.add("d-none")}(a))}function Ya(e,t){Qa(".js-label-name-error",e,t.name),Qa(".js-label-description-error",e,t.description),Qa(".js-label-color-error",e,t.color)}function Za(e){Qa(".js-label-name-error",e,null),Qa(".js-label-description-error",e,null),Qa(".js-label-color-error",e,null)}function es(t){var r=t.querySelectorAll(".js-label-characters-remaining-container"),n=!0,a=!1,s=void 0;try{for(var o,i=r[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;Xa(e.query(u,".js-label-characters-remaining-field",HTMLInputElement))}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}function ts(e,t,r,n,a){var s=new URL(""+e+encodeURIComponent(t),window.location.origin),o=new URLSearchParams(s.search.slice(1));return o.append("color",r),n&&o.append("description",n),a&&o.append("id",a),s.search=o.toString(),s.toString()}function rs(e){var t=null,r=e.querySelector(".js-new-label-description-input");return r instanceof HTMLInputElement&&r.value.trim().length>0&&(t=r.value.trim()),t}function ns(t){var r=e.query(t,".js-new-label-color-input",HTMLInputElement);return r.checkValidity()?r.value.trim().replace(/^#/,""):"ededed"}function as(t,r){var n=e.query(t,".js-new-label-name-input",HTMLInputElement).value.trim();return n.length<1&&(n=r.getAttribute("data-default-name"),a("string"==typeof n,"app/assets/modules/github/issues/labels.js:211")),n}function ss(t,r){e.closest(t,".js-details-container").classList.toggle("is-empty",r)}function os(t){var r=e.query(document,".js-labels-count"),n=P.parseFormattedNumber(r.textContent)+t;r.textContent=P.formatNumber(n);var a=e.query(document,".js-labels-label");return N.pluralizeNode(n,a),n}n.onInput(".js-label-filter-field",function(t){var r=t.target;a(r instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:295");var n=e.closest(r,".js-select-menu").querySelector(".js-new-label-name");if(n){var s=r.value.trim();n.textContent=s}}),t.observe(".js-label-characters-remaining-field",{constructor:HTMLInputElement,initialize:Xa}),r.on("filterable:change",".js-filterable-issue-labels",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/issues/labels.js:313");var r=e.closest(t.target,".js-issue-labels-menu-content"),n=r.querySelector(".js-new-label-modal, .js-add-label-button");if(n){var s=t.detail.inputField.value.trim().toLowerCase(),o=Array.from(r.querySelectorAll(".js-navigation-item")).filter(function(e){return""===e.style.display}),i=s.length>0;if(i){var u=!0,c=!1,l=void 0;try{for(var d,m=o[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){var p=d.value.querySelector('input[type="checkbox"][name="issue[labels][]"]');if(p)if(p.getAttribute("data-label-name").toLowerCase()===s){i=!1;break}}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}}n.classList.toggle("d-none",!i),Ga(r)}}),r.on("navigation:focus",".js-label-options",function(e){var t=e.target.closest(".js-filterable-issue-labels");t instanceof HTMLElement&&Ga(t)}),n.onFocus(".js-new-label-color-input",function(t){var r=e.closest(t,"form"),n=e.query(r,".js-new-label-swatches");n.hidden=!1,t.addEventListener("blur",function(){n.hidden=!0},{once:!0})}),n.onInput(".js-new-label-color-input",function(t){var r=t.target;a(r instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:372");var n=r.value.trim();if(!(n.length<1))if(0!==n.indexOf("#")&&(n="#"+n,r.value=n),r.checkValidity()){r.classList.remove("text-red");var s=e.closest(r,"form");Ja(e.query(s,".js-new-label-color",HTMLButtonElement),n)}else r.classList.add("text-red")}),n.onKey("keyup",".js-new-label-color-input",function(t){var n=t.target;a(n instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:396");var s=n.value.trim();if(0!==s.indexOf("#")&&(s="#"+s,n.value=s),n.checkValidity()){var o=e.closest(n,"form");Ja(e.query(o,".js-new-label-color",HTMLButtonElement),s)}r.fire(n,"change",!1),Za(e.closest(n,"form",HTMLFormElement))}),n.onKey("keyup",".js-new-label-description-input",function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:417");var r=t.form;a(r,"app/assets/modules/github/issues/labels.js:419"),Za(r)}),n.onKey("keyup",".js-new-label-color-input",function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:425");var r=t.form;a(r,"app/assets/modules/github/issues/labels.js:427"),Za(r)}),n.onKey("keydown",".js-label-filter-field",function(t){if("Enter"===t.key){var r=e.query(document,".js-issue-labels-menu-content").querySelector('.js-label-options[aria-selected="true"]');r&&r.click()}}),r.on("click",".js-new-label-color",function(e){var t=e.currentTarget;a(t instanceof HTMLButtonElement,"app/assets/modules/github/issues/labels.js:448"),Ka(t,Wa()),Va(t)}),r.on("mousedown",".js-new-label-color-swatch",function(t){var r=t.currentTarget;a(r instanceof HTMLButtonElement,"app/assets/modules/github/issues/labels.js:456");var n=r.getAttribute("data-color");a(n,"must have data-color attribute -- app/assets/modules/github/issues/labels.js:458"),Ka(r,n),Va(r),e.closest(r,".js-new-label-swatches").hidden=!0}),r.on("toggle",".js-new-label-modal",function(t){t.target.hasAttribute("open")&&function(t){var r=t.querySelector(".js-new-label-name-input");if(!r)return;a(r instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:483");var n=e.query(t,".js-new-label-color-input",HTMLInputElement),s=Wa();if(n.value.length>0)for(;n.value===s;)s=Wa();n.value=s;var o=e.query(t,".js-new-label-color",HTMLButtonElement);Ja(o,s);var i=e.query(document,".js-new-label-name").textContent;l.changeValue(r,i),Xa(r),Va(o)}(t.target)},{capture:!0}),c.remoteForm(".js-new-label-modal-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.query(r,".js-new-label-error"),s=void 0,t.prev=2,t.next=5,n.html();case 5:s=t.sent,t.next=13;break;case 8:t.prev=8,t.t0=t.catch(2),o=t.t0.response.json,a.textContent=o.message,a.hidden=!1;case 13:if(s){t.next=15;break}return t.abrupt("return");case 15:a.hidden=!0,S.clear(e.query(document,".js-issue-labels-menu-content")),e.query(document,".js-new-label-modal").removeAttribute("open"),e.query(document,".js-filterable-issue-labels").prepend(s.html),(i=e.query(document,".js-label-filter-field",HTMLInputElement)).value=i.defaultValue,i.focus();case 23:case"end":return t.stop()}},t,this,[[2,8]])}));return function(e,r){return t.apply(this,arguments)}}()),n.onInput(".js-label-characters-remaining-field",function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:540"),Xa(t)}),r.on("navigation:keydown",".js-label-emoji-suggester",function(t){var r=e.closest(t.target,".js-suggester-container",HTMLElement);Xa(e.query(r,".js-suggester-field",HTMLInputElement))}),r.on("click",".js-edit-label-cancel",function(t){var r=e.closest(t.target,"form",HTMLFormElement);Za(r),r.reset();var n=e.query(r,".js-new-label-color-input",HTMLInputElement),a=n.value;e.query(r,".js-new-label-color",HTMLButtonElement).style.backgroundColor=a,es(r),Va(n);var s=t.currentTarget.closest(".labels-list-item");if(s){e.query(s,".js-update-label",HTMLElement).classList.add("d-none");var o=s.querySelector(".js-label-preview");if(o)o.classList.add("d-none"),e.query(s,".js-label-link",HTMLElement).classList.remove("d-none");var i=s.querySelectorAll(".js-hide-on-label-edit.d-none"),u=!0,c=!1,l=void 0;try{for(var d,m=i[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){d.value.classList.remove("d-none")}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}}}),c.remoteForm(".js-update-label",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=void 0,t.prev=1,t.next=4,n.html();case 4:a=t.sent,t.next=12;break;case 7:return t.prev=7,t.t0=t.catch(1),s=t.t0.response.json,Ya(r,s),t.abrupt("return");case 12:Za(r),e.closest(r,".labels-list-item").replaceWith(a.html);case 15:case"end":return t.stop()}},t,this,[[1,7]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-create-label",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=void 0,t.prev=1,t.next=4,n.html();case 4:a=t.sent,t.next=12;break;case 7:return t.prev=7,t.t0=t.catch(1),s=t.t0.response.json,Ya(r,s),t.abrupt("return");case 12:r.reset(),Za(r),e.query(document,".js-label-list").prepend(a.html),os(1),ss(r,!1),Ka(e.query(r,".js-new-label-color",HTMLButtonElement),Wa()),Va(e.query(r,".js-new-label-name-input",HTMLInputElement)),es(r),(o=r.closest(".js-details-container"))instanceof HTMLElement&&O.toggleDetailsTarget(o);case 23:case"end":return t.stop()}},t,this,[[1,7]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-toggle-label-delete",function(t){var r=e.closest(t.currentTarget,".labels-list-item"),n=e.query(r,".js-label-delete-container",HTMLElement),a=e.querySelectorAll(r,".js-hide-on-label-delete",HTMLElement),s=n.classList.contains("d-none");n.classList.toggle("d-none");var o=!0,i=!1,u=void 0;try{for(var c,l=a[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){c.value.classList.toggle("d-none",s)}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}}),r.on("click",".js-details-target-new-label",function(){var t=e.query(document,".js-create-label");e.query(t,".js-new-label-name-input",HTMLInputElement).focus()}),r.on("click",".js-edit-label",function(t){var r=e.closest(t.currentTarget,".labels-list-item"),n=e.query(r,".js-update-label",HTMLElement);n.classList.remove("d-none"),e.query(n,".js-new-label-name-input",HTMLInputElement).focus();var a=r.querySelector(".js-label-preview");a&&(a.classList.remove("d-none"),e.query(r,".js-label-link",HTMLElement).classList.add("d-none"));var s=e.querySelectorAll(r,".js-hide-on-label-edit",HTMLElement),o=!0,i=!1,u=void 0;try{for(var c,l=s[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){c.value.classList.add("d-none")}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}}),c.remoteForm(".js-delete-label",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return r.classList.add("loading"),t.next=3,n.text();case 3:r.classList.remove("loading"),a=os(-1),ss(r,0===a),e.closest(r,".labels-list-item").remove();case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}());var is=u(function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:271"),Va(t)},500);n.onInput(".js-new-label-name-input",is),n.onInput(".js-new-label-description-input",is),n.onInput(".js-new-label-color-input",is),r.on("navigation:keydown",".js-label-emoji-suggester",function(t){var r=e.closest(t.target,".js-suggester-container",HTMLElement),n=e.query(r,".js-suggester-field",HTMLInputElement);Va(n)}),r.on("click",".js-label-emoji-suggester .js-navigation-item",function(t){var r=e.closest(t.target,".js-suggester-container",HTMLElement),n=e.query(r,".js-suggester-field",HTMLInputElement);Va(n)}),n.onKey("keypress",".js-new-label-name-input",function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/issues/labels.js:715");var r=parseInt(t.getAttribute("data-maxlength"));x.getUtf8StringLength(t.value)>=r&&e.preventDefault()}),_(function(e){var t=e.newURL,r=t.match(/\/issues#issue\/(\d+)$/);if(r){var n=r[1];window.location=t.replace(/\/?#issue\/.+/,"/"+n)}}),_(function(e){var t=e.newURL,r=t.match(/\/issues#issue\/(\d+)\/comment\/(\d+)$/);if(r){var n=r[1],a=r[2];window.location=t.replace(/\/?#issue\/.+/,"/"+n+"#issuecomment-"+a)}}),t.observe(".js-issue-row .js-issues-list-check:checked",{add:function(t){e.closest(t,".js-issue-row").classList.add("selected")},remove:function(t){e.closest(t,".js-issue-row").classList.remove("selected")}}),r.on("navigation:keydown",".js-issue-row",function(e){var t,r;a(e instanceof CustomEvent,"app/assets/modules/github/issues/list.js:28"),"x"===e.detail.hotkey&&(t=e.currentTarget,(r=t.querySelector(".js-issues-list-check"))instanceof HTMLInputElement&&l.changeValue(r,!r.checked),e.preventDefault(),e.stopPropagation())}),n.onFocus("#js-issues-search",function(e){a(e instanceof HTMLInputElement,"app/assets/modules/github/issues/list.js:40"),e.value=e.value}),r.on("selectmenu:selected",".js-saved-reply-container",function(t){var r=e.query(t.target,".js-saved-reply-body"),n=r.textContent.trim(),a=e.closest(t.currentTarget,".js-previewable-comment-form"),s=e.query(a,"textarea.js-comment-field",HTMLTextAreaElement);x.insertText(s,n),e.query(a,"input.js-saved-reply-id",HTMLInputElement).value=r.getAttribute("data-saved-reply-id")||""}),n.onKey("keydown",".js-saved-reply-shortcut-comment-field",function(t){if("Control+."===M(t)){a(t.target instanceof HTMLElement,"app/assets/modules/github/issues/replies.js:26");var r=e.closest(t.target,".js-previewable-comment-form"),n=e.query(r,".js-saved-reply-container");D.activate(n),t.preventDefault(),F.trackEvent({category:"Markdown Toolbar",action:"shortcut",label:"saved reply"})}}),n.onKey("keydown",".js-saved-reply-filter-input",function(t){if(/^Control\+[1-9]$/.test(M(t))){a(t.target instanceof HTMLElement,"app/assets/modules/github/issues/replies.js:41");var r=e.closest(t.target,".js-previewable-comment-form").querySelectorAll(".js-navigation-item"),n=Number(t.key),s=r[n-1];s&&(s.click(),t.preventDefault(),F.trackEvent({category:"Saved Replies",action:"shortcut",label:"saved reply number "+n}))}});var us=function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,i,u,c,l;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=ms(r),n&&s.append(n.name,n.value),null==(i=r.getAttribute("data-authenticity-token"))&&(u=e.closest(r,"form",HTMLFormElement).elements.namedItem("authenticity_token"))instanceof HTMLInputElement&&(i=u.value),a(i,"app/assets/modules/github/issues/sidebar.js:107"),s.append("authenticity_token",i),c=r.getAttribute("data-url"),a(c,"app/assets/modules/github/issues/sidebar.js:113"),t.next=10,o.fetchText(c,{method:"post",body:s});case 10:l=t.sent,ds(e.closest(r,".js-discussion-sidebar-item"),l);case 12:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}();function cs(e){e instanceof HTMLFormElement?l.submit(e):us(e)}function ls(e){var t=e.currentTarget;a(t instanceof Element,"app/assets/modules/github/issues/sidebar.js:21");var r=t.closest(".js-issue-sidebar-form")||t.querySelector(".js-issue-sidebar-form");a(r,"app/assets/modules/github/issues/sidebar.js:23"),cs(r)}function ds(e,t){e.replaceWith(i.parseHTML(document,t))}function ms(t){var r=e.closest(t,"form",HTMLFormElement),n=B(r),a=new FormData,s=!0,o=!1,i=void 0;try{for(var u,c=n[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value,d=oe(l,2),m=d[0],p=d[1];t.contains(ps(r,m,p))&&a.append(m,p)}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}return a}function ps(e,t,r){var n=!0,a=!1,s=void 0;try{for(var o,i=e.elements[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;if((u instanceof HTMLInputElement||u instanceof HTMLTextAreaElement||u instanceof HTMLButtonElement)&&u.name===t&&u.value===r)return u}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}r.on("selectmenu:selected",".js-discussion-sidebar-item",function(t){var r=e.query(t.currentTarget,".js-issue-sidebar-form"),n=t.target,a=e.closest(n,".js-select-menu"),s=a.hasAttribute("data-multiple");if(n.hasAttribute("data-clear-assignees")){var o=e.querySelectorAll(a,'input[name="issue[user_assignee_ids][]"]:checked',HTMLInputElement),i=!0,u=!1,c=void 0;try{for(var l,d=o[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;m.disabled=!1,m.checked=!1}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}cs(r)}else if(s){var p=Number(a.getAttribute("data-max-options"));if(p){var f=a.querySelectorAll('input[type="checkbox"]:checked').length;e.query(a,".js-max-warning").classList.toggle("d-none",f<=p)}a.addEventListener("menu:deactivate",ls,{once:!0})}else cs(r)}),c.remoteForm(".js-issue-sidebar-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:s=t.sent,a(r.parentNode instanceof HTMLElement,"app/assets/modules/github/issues/sidebar.js:60"),e.closest(r,".js-discussion-sidebar-item").replaceWith(s.html);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click","div.js-issue-sidebar-form .js-suggested-reviewer",function(t){var r=t.currentTarget;a(r instanceof HTMLButtonElement,"app/assets/modules/github/issues/sidebar.js:67");var n=e.closest(r,".js-issue-sidebar-form");us(n,{name:r.name,value:r.value}),t.preventDefault()}),r.on("click","div.js-issue-sidebar-form .js-issue-assign-self",function(t){var r=t.currentTarget;a(r instanceof HTMLButtonElement,"app/assets/modules/github/issues/sidebar.js:77");var n=e.closest(r,".js-issue-sidebar-form");us(n,{name:r.name,value:r.value}),t.preventDefault()}),c.remoteForm(".js-pages-preview-toggle-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:a=t.sent,e.query(r,"button.btn").textContent=a.json.new_button_value;case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-project-menu-checkbox",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/issues/sidebar.js:123");var n=e.closest(r,".js-project-menu-container"),s=!0,o=!1,i=void 0;try{for(var u,c=e.querySelectorAll(n,".js-project-menu-checkbox",HTMLInputElement)[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;l!==r&&(l.name===r.name&&(l.checked=r.checked,e.closest(l,".select-menu-item").classList.toggle("selected",l.checked)))}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}),r.on("click",".js-prompt-dismiss",function(t){e.closest(t.currentTarget,".js-prompt").remove()}),r.on("change",".js-issues-list-check",function(){var t=!!document.querySelector(".js-issues-list-check:checked");e.query(document,"#js-issues-toolbar").classList.toggle("triage-mode",t)}),r.on("change",".js-issues-list-check",function(){var t=e.querySelectorAll(document,".js-issues-list-check:checked",HTMLInputElement).map(function(e){return[e.name,e.value]}),r=document.querySelectorAll("#js-issues-toolbar .js-issues-toolbar-triage .js-select-menu"),n=!0,a=!1,s=void 0;try{for(var o,i=r[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;U.setLoadingData(u,t),u.classList.add("js-load-contents")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}),r.on("selectmenu:selected",".js-issues-toolbar-triage .js-navigation-item",function(t){var r=t.currentTarget,n=e.closest(r,".js-menu-container").hasAttribute("data-submits-hash"),s=e.closest(r,"form"),o=r.classList.contains("selected"),i=r.getAttribute("data-name");a(i,"app/assets/modules/github/issues/triage.js:44");var u=r.getAttribute("data-value");a(null!=u,"app/assets/modules/github/issues/triage.js:46");var c=document.createElement("input");c.setAttribute("type","hidden"),n?(c.setAttribute("name",i+"["+u+"]"),c.setAttribute("value",o?"1":"0")):(c.setAttribute("name",i),c.setAttribute("value",o?u:"")),Promise.resolve().then(function(){var e=r.closest(".js-menu-container");e instanceof HTMLElement&&D.deactivate(e)}),e.query(s,".js-issues-triage-fields").appendChild(c),s.classList.add("will-submit")}),r.on("menu:deactivate",".js-issues-toolbar-triage .js-menu-container",function(e){var t=e.currentTarget,r=t.querySelector("form.will-submit");if(r instanceof HTMLFormElement){t.classList.add("is-loading");var n=r.action,s=r.method;o.fetchJSON(n,{method:s,body:new FormData(r)}).then(function(e){return o.fetchPoll(e.job.url,{headers:{accept:"application/json"}})}).then(function(){a(t instanceof HTMLElement,"app/assets/modules/github/issues/triage.js:87"),D.deactivate(t),location.reload()}).catch(function(){t.classList.add("has-error")}),r.classList.remove("will-submit"),e.preventDefault()}});c.remoteForm(".js-undo-issue-event-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,s,o;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:for(n=t.getAttribute("action"),a("string"==typeof n,"app/assets/modules/github/issues/undo-timeline-event.js:8"),t.remove(),s=document.querySelectorAll('.js-undo-issue-event-form[action="'+n+'"]'),o=0;o<s.length;o++)s[o].remove();case 7:case"end":return e.stop()}},e,void 0)}));return function(t,r){return e.apply(this,arguments)}}());var fs=function(e){function t(e){re(this,t);var r=se(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e));return r.name="JumpToParseError",r}return ae(t,e),t}(Error),vs=function(e){function t(e){re(this,t);var r=se(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e.message));return r.name="JumpToPageViewError",r.stack=e.stack,r.message,r}return ae(t,e),t}(Error);function gs(e){var t=void 0;switch(e.type){case"Project":a(e.owner,"app/assets/modules/github/jump-to/page-key-helpers.js:9"),a(null!==e.number&&void 0!==e.number,"app/assets/modules/github/jump-to/page-key-helpers.js:10"),t=bs(e.owner.name,""+e.number);break;case"Repository":t=ys.apply(void 0,ue(e.name.split("/")));break;case"Team":t=hs.apply(void 0,ue(e.name.split("/")));break;default:throw new Error("Invalid Suggestion type: "+e.type)}return t}function hs(e,t){return"team:"+e+"/"+t}function ys(e,t){return"repository:"+e+"/"+t}function bs(e,t){return"project:"+e+"/"+t}var js={frequency:.6,recency:.4};function Ls(e,t,r){var n=t.get(e)||0,a=r.get(e)||0;return n*js.frequency+a*js.recency}function ws(e){var t=[].concat(ue(Object.keys(e))).reduce(function(t,r){return t+e[r].visitCount},0);return new Map(Object.keys(e).map(function(r){return[r,e[r].visitCount/t]}))}function xs(e){var t=[].concat(ue(Object.keys(e))).sort(function(t,r){return e[t].lastVisitedAt-e[r].lastVisitedAt}),r=t.length;return new Map(t.map(function(e,t){return[e,(t+1)/r]}))}var ks="jump_to:page_views",Es=/^\/orgs\/([a-z0-9-]+)\/teams\/([\w-]+)/,Ts=[/^\/([^/]+)\/([^/]+)\/?$/,/^\/([^/]+)\/([^/]+)\/blob/,/^\/([^/]+)\/([^/]+)\/tree/,/^\/([^/]+)\/([^/]+)\/issues/,/^\/([^/]+)\/([^/]+)\/pulls?/,/^\/([^/]+)\/([^/]+)\/pulse/],qs=[["organization",/^\/orgs\/([a-z0-9-]+)\/projects\/([0-9-]+)/],["repository",/^\/([^/]+)\/([^/]+)\/projects\/([0-9-]+)/]],Ss=/^(team|repository|project):[^/]+\/[^/]+(\/([^/]+))?$/,As=100;function Ms(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Cs();if(!e)return{};var t=e.getItem(ks);if(!t)return{};var r=void 0;try{r=JSON.parse(t)}catch(t){var n=new fs("Failed to parse jump-to localStorage contents");return m.reportError(n),Is({},e),{}}var a={};for(var s in r)if(s.match(Ss))a[s]=r[s];else{var o=new fs("Invalid jump-to pageview map key from localStorage");m.reportError(o)}return a}function Hs(e){return Object.keys(e).length<=As?e:function(e){var t=ws(e),r=xs(e);return Object.keys(e).map(function(e){return{pageKey:e,score:Ls(e,t,r)}}).sort(function(e,t){return t.score-e.score}).map(function(e){return e.pageKey})}(e).slice(0,As/2).reduce(function(t,r){return t[r]=e[r],t},{})}function Rs(e,t){var r=Ms(t),n=Math.floor(Date.now()/1e3),a=r[e]||{lastVisitedAt:n,visitCount:0};a.visitCount+=1,a.lastVisitedAt=n,r[e]=a,Is(Hs(r),t)}function Is(e,t){t.setItem(ks,JSON.stringify(e))}var Cs=function(){var e="test-storage-availability";try{return window.localStorage.setItem(e,e),window.localStorage.removeItem(e),window.localStorage}catch(e){return null}};function _s(){var e=new FormData,t=!0,r=!1,n=void 0;try{for(var a,s=Object.keys(Ms())[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;e.append("variables[pageViews][]",o)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}return e}function Fs(e){if(e.data.errors)return[];var t=e.data.suggestions.nodes,r=1,n=!0,a=!1,s=void 0;try{for(var o,i=t[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;u.rank=r++,u.pageKey=gs(u),"Team"===u.type&&(u.name="@"+u.name)}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return t}function Ps(e,t,r){var n=new URL(e,window.location.origin),a=new URLSearchParams(n.search.slice(1));a.set("q",t),r&&a.set("unscoped_q",t);var s=new URLSearchParams(window.location.search).get("type");return s&&a.set("type",s),n.search=a.toString(),n.toString()}var Ns=void 0;function Os(e){return Ns||(Ns=h.getMetadataByName(document,"enabled-features").split(",")),-1!==Ns.indexOf(e)}var Ds={};function Bs(t){var r=t.getAttribute("data-target-type");if(a(r,"app/assets/modules/github/jump-to/tracking.js:19"),"Search"===r){var n=e.query(document,".js-site-search-form",HTMLFormElement),s=n.getAttribute("data-scope-type");s&&zs({dimensions:{scope_id:parseInt(n.getAttribute("data-scope-id")),scope_type:s}}),Us("search")}else zs({dimensions:{target_id:parseInt(t.getAttribute("data-target-id")),target_type:r},measures:{client_rank:parseInt(t.getAttribute("data-client-rank")),server_rank:parseInt(t.getAttribute("data-server-rank"))}}),Us("click")}function Us(e){var t=parseInt(h.getMetadataByName(document,"octolytics-actor-id"));if(!t)return!1;Ds.dimensions=Ds.dimensions||{},Ds.dimensions.actor_id=t;var r=Ds.dimensions&&Ds.dimensions.session_id;return("menu-activation"!==e||!r)&&(!("menu-activation"!==e&&!r)&&("menu-activation"===e&&zs({dimensions:{session_id:r="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx".replace(/[xy]/g,function(e){var t=16*Math.random()|0,r="x"===e?t:3&t|8;return r.toString(16)})}}),Ds.event_type="jump-to-"+e,a(r,"app/assets/modules/github/jump-to/tracking.js:77"),function(e){Os("JUMP_TO_LOGGING")&&console.log(JSON.stringify(e));fa(e)}(Ds),"menu-deactivation"!==e&&"click"!==e&&"search"!==e||(Ds={}),!0))}function zs(e){e.context&&(Ds.context=Object.assign(Ds.context||{},e.context),Ds.dimensions=Object.assign(Ds.dimensions||{},e.context)),e.dimensions&&(Ds.dimensions=Object.assign(Ds.dimensions||{},e.dimensions)),e.measures&&(Ds.measures=Object.assign(Ds.measures||{},e.measures))}function Ws(e){return!!(e?e.closest(".js-jump-to-field"):document.querySelector(".js-jump-to-field"))}function Vs(){return!!h.getMetadataByName(document,"user-login")}function $s(e){var t=e.getAttribute("data-jump-to-suggestions-path");return a(t,"app/assets/modules/github/jump-to/controller.js:35"),window.location.origin+t}function Js(t,r){var n=t.value.trim(),s=function(e){var t=(arguments.length>1&&void 0!==arguments[1]?arguments[1]:null)||Ms(),r=ws(t),n=xs(t);return e.map(function(e){return{suggestion:e,score:Ls(e.pageKey,r,n)}}).sort(function(e,t){return t.score-e.score}).map(function(e){return e.suggestion})}(function(e,t,r){var n=e.replace(/\s/g,"");return t=t.filter(function(e){return e.path!==r}),n?yr(t,n,{text:function(e){return e.name}}):t}(n,r,window.location.pathname)),o=s.slice(0,7);zs({measures:{result_count:s.length,display_count:o.length,filter_count:r.length-s.length},context:{query:n,display_set:o.map(function(e){return[e.type,e.databaseId]})}}),function(t,r,n){t.classList.add("js-navigation-enable");var s=Zs(".js-jump-to-suggestions-template-container"),o=document.createDocumentFragment(),i=!(!t.form||!t.form.getAttribute("data-scope-type"));if(r){var u=t.form&&t.form.getAttribute("action");a(u,"app/assets/modules/github/jump-to/render.js:44");var c=Ys(s,{type:"Search",href:Ps(u,r,i),text:r,queryText:r,isScoped:i});o.appendChild(c)}if(n.length<1&&!r)!function(){if(!Vs())return;var t=Zs(".js-jump-to-no-results-template-container").cloneNode(!0);t.classList.remove("d-none");var r=e.query(document,".js-jump-to-suggestions-results-container");r.innerHTML="",r.appendChild(t)}();else{if(r&&i){var l=t.form&&t.form.getAttribute("data-unscoped-search-url");a(l,"app/assets/modules/github/jump-to/render.js:61");var d=Ys(s,{type:"Search",href:Ps(l,r,!1),text:r,queryText:r,isScoped:!1});o.appendChild(d)}n.forEach(function(e,t){o.appendChild(Ys(s,{type:e.type,href:e.path,text:e.name,queryText:r,avatarUrl:e.avatarUrl,targetId:e.databaseId,clientRank:t,serverRank:e.rank}))});var m=e.query(document,".js-jump-to-suggestions-results-container");m.innerHTML="",m.appendChild(o)}}(t,n,o),Us("menu-activation")||Us("query")}function Ks(e){e.classList.add("js-navigation-enable"),e.classList.add("jump-to-field-active")}function Gs(t){e.query(document,".js-jump-to-suggestions-container").classList.remove("d-none"),t.classList.add("jump-to-dropdown-visible")}function Xs(){e.query(document,".js-jump-to-suggestions-container").classList.add("d-none"),e.query(document,".js-jump-to-field").classList.remove("jump-to-dropdown-visible"),Us("menu-deactivation")}function Qs(t,r){var n=e.query(t,".js-jump-to-octicon"),a=e.query(n,r,SVGElement);n.classList.remove("d-none"),a.classList.remove("d-none")}function Ys(t,r){var n=t.cloneNode(!0),a=e.query(n,".js-jump-to-suggestion-path",HTMLAnchorElement);a.href=r.href,a.setAttribute("data-target-type",r.type),"Search"!==r.type&&(a.setAttribute("data-target-id",""+r.targetId),a.setAttribute("data-client-rank",""+r.clientRank),a.setAttribute("data-server-rank",""+r.serverRank));var s=e.query(n,".js-jump-to-suggestion-name");switch(s.textContent=r.text,s.setAttribute("aria-label",r.text),"Search"!==r.type&&Lt(s,r.queryText.replace(/\s/g,"")),r.type){case"Team":var o=e.query(n,".js-jump-to-suggestion-avatar",HTMLImageElement);o.alt=r.text,o.src=r.avatarUrl?r.avatarUrl:"",o.classList.remove("d-none");break;case"Project":Qs(n,".js-jump-to-octicon-project");break;case"Repository":Qs(n,".js-jump-to-octicon-repo");break;case"Search":Qs(n,".js-jump-to-octicon-search")}if("Search"===r.type){var i=e.query(n,".js-jump-to-badge-search");i.classList.remove("d-none"),r.isScoped?e.query(i,".js-jump-to-badge-search-text-default").classList.remove("d-none"):e.query(i,".js-jump-to-badge-search-text-global").classList.remove("d-none")}else{e.query(n,".js-jump-to-badge-jump").classList.remove("d-none")}return n}function Zs(t){var r=e.query(document,".js-jump-to-suggestions-container");return A(e.query(r,t).firstElementChild,HTMLElement)}var eo=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=$s(t),n=_s(),e.prev=2,e.next=5,o.fetchJSON(o.csrfRequest(r,{method:"POST",body:n}));case 5:return a=e.sent,e.abrupt("return",Fs(a));case 9:if(e.prev=9,e.t0=e.catch(2),!e.t0.response||422!==e.t0.response.status){e.next=15;break}return e.abrupt("return",[]);case 15:throw e.t0;case 16:case"end":return e.stop()}},e,this,[[2,9]])}));return function(t){return e.apply(this,arguments)}}(),to=function(){var e=te(regeneratorRuntime.mark(function e(t){var r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return Ks(r=A(t,HTMLInputElement,".js-jump-to-field should always be an input")),Vs()&&Gs(r),ro||(ro=eo(r)),e.t0=ao,e.t1=r,e.next=8,ro;case 8:e.t2=e.sent,(0,e.t0)(e.t1,e.t2);case 10:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),ro=void 0;t.observe(".js-jump-to-field",{constructor:HTMLInputElement,add:function(e){try{!function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Cs();if(t){var r=e.match(Es);if(r)Rs(hs(r[1],r[2]),t);else{for(var n=void 0,s=0,o=qs.length;s<o;s++){var i=oe(qs[s],2),u=i[0],c=i[1];if(n=e.match(c)){var l=void 0,d=void 0;switch(u){case"organization":l=n[1],d=n[2];break;case"repository":l=n[1]+"/"+n[2],d=n[3]}return a(l,"app/assets/modules/github/jump-to/page-views.js:104"),a(d,"app/assets/modules/github/jump-to/page-views.js:105"),void Rs(bs(l,d),t)}}for(var m=void 0,p=0,f=Ts.length;p<f;p++)if(m=e.match(Ts[p]))return void Rs(ys(m[1],m[2]),t)}}}(window.location.pathname)}catch(e){m.reportError(new vs(e))}p.addThrottledInputEventListener(e,te(regeneratorRuntime.mark(function t(){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.t0=ao,t.t1=e,t.next=4,ro;case 4:t.t2=t.sent,(0,t.t0)(t.t1,t.t2);case 6:case"end":return t.stop()}},t,this)}))),document.activeElement&&document.activeElement===e&&to(e)}}),n.onFocus(".js-jump-to-field",to),r.on("navigation:keydown",".js-site-search-form",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/jump-to.js:87");var r=t.currentTarget.querySelector('.js-navigation-item[aria-selected="true"]');switch(t.detail.hotkey){case"Enter":r?Bs(e.query(r,".js-jump-to-suggestion-path",HTMLElement)):l.submit(A(t.currentTarget,HTMLFormElement));break;case"Escape":e.query(t.currentTarget,".js-jump-to-field",HTMLInputElement).blur(),Xs()}});var no=void 0;function ao(t,r){Js(t,r);var n=t.value.trim(),a=e.query(document,".js-jump-to-suggestions-results-container"),s=!Vs();n&&s?Gs(t):s&&Xs(),n?S.focus(a):S.activate(a)}function so(e){var t=e.currentTarget;a(t instanceof HTMLInputElement||t instanceof HTMLTextAreaElement,"app/assets/modules/github/length-limited-input-with-warning.js:35");var r=parseInt(t.getAttribute("data-input-max-length"),10),n=parseInt(t.getAttribute("data-warning-length"),10)||5,s=t.value.replace(/(\r\n|\n|\r)/g,"\r\n"),o=r-s.length;if(o<=0){var i=s.substr(0,r);i.endsWith("\r")?(i=i.substr(0,r-1),o=1):o=0,t.value=i}var u=t.getAttribute("data-warning-text");a(u,"app/assets/modules/github/length-limited-input-with-warning.js:70");var c=t.closest(".js-length-limited-input-container");a(c,"app/assets/modules/github/length-limited-input-with-warning.js:74");var l=c.querySelector(".js-length-limited-input-warning");a(l,"app/assets/modules/github/length-limited-input-with-warning.js:78"),o<=n?(l.textContent=u.replace(new RegExp("{{remaining}}","g"),""+o),l.classList.remove("d-none")):(l.textContent="",l.classList.add("d-none"))}function oo(t){var r=t.currentTarget,n=e.query(r,".js-milestone-edit-cancel"),a=n.getAttribute("data-confirm-changes");a&&(k.hasDirtyFields(r)?n.setAttribute("data-confirm",a):n.removeAttribute("data-confirm"))}function io(e,t){r.on("click",e+" .js-sortable-button",function(){var r=this.closest(e),n=this.getAttribute("data-direction"),a=Array.from(r.parentElement.children).indexOf(r);"up"===n?r.previousElementSibling.insertAdjacentElement("beforebegin",r):"down"===n&&r.nextElementSibling&&r.nextElementSibling.insertAdjacentElement("afterend",r);var s=Array.from(r.parentElement.children).indexOf(r);this.focus(),t({oldIndex:a,newIndex:s,item:r})})}r.on("focusout",".js-jump-to",function(){var t=e.query(document,".js-jump-to-field",HTMLInputElement);no=setTimeout(function(){!function(e){e.classList.remove("js-navigation-enable"),e.classList.remove("jump-to-field-active")}(t),Xs()},200)}),r.on("focusin",".js-jump-to",function(){clearTimeout(no),Gs(e.query(document,".js-jump-to-field",HTMLInputElement))}),r.on("click",".js-jump-to-suggestion-path",function(t){var r,n,a,s,o=A(t.currentTarget,HTMLAnchorElement);if("Search"===o.getAttribute("data-target-type")){var i=e.query(document,".js-jump-to-field",HTMLInputElement);o.href=(r=i.value.trim(),n=o.href,a=new URL(n),(s=new URLSearchParams(a.search.slice(1))).get("q")&&s.set("q",r),s.get("unscoped_q")&&s.set("unscoped_q",r),a.search=s.toString(),a.toString())}Bs(o)}),r.on("submit",".js-site-search-form",function(e){if(Ws()){var t=e.target;t.getAttribute("data-scoped-search-url")&&zs({scope_type:t.getAttribute("data-scope-type"),scope_id:t.getAttribute("data-scope-id")}),Us("search")}}),r.on("click",".js-details-target-new-label",function(t){var r=Le(A(t.target,HTMLElement),"Popover");if(r){var n=e.query(r,"form",HTMLFormElement);l.submit(n)}}),r.on("click",".js-segmented-nav-button",function(t){t.preventDefault();var r=t.currentTarget,n=r.getAttribute("data-selected-tab");a(n,"app/assets/modules/github/legacy/pages/site/features.js:12");var s=e.closest(r,".js-segmented-nav"),o=s.parentElement;a(o,"app/assets/modules/github/legacy/pages/site/features.js:16");var i=!0,u=!1,c=void 0;try{for(var l,d=e.querySelectorAll(s,".js-segmented-nav-button")[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){l.value.classList.remove("selected")}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}r.classList.add("selected");var m=!0,p=!1,f=void 0;try{for(var v,g=e.querySelectorAll(o,".js-selected-nav-tab")[Symbol.iterator]();!(m=(v=g.next()).done);m=!0){v.value.classList.remove("active")}}catch(e){p=!0,f=e}finally{try{!m&&g.return&&g.return()}finally{if(p)throw f}}e.query(document,"."+n).classList.add("active")}),t.observe(".js-length-limited-input",{add:function(e){e.addEventListener("input",so),e.addEventListener("change",so)},remove:function(e){e.removeEventListener("input",so),e.removeEventListener("change",so)}}),t.observe("link[rel=prefetch-viewed]",{initialize:function(){requestIdleCallback(function(){fetch(location.href,{method:"HEAD",credentials:"same-origin",headers:{Purpose:"prefetch-viewed"}})})}}),r.on("click",".js-manage-requests-tabs-item",function(t){var r=t.currentTarget,n=e.closest(r,".js-manage-memberships-container");e.query(n,".js-manage-invitations-tabs-item").classList.remove("selected"),r.classList.add("selected");var a=e.query(n,".js-manage-invitations-list"),s=e.query(n,".js-manage-requests-list");a.classList.add("d-none"),s.classList.remove("d-none")}),r.on("click",".js-manage-invitations-tabs-item",function(t){var r=t.currentTarget,n=e.closest(r,".js-manage-memberships-container");e.query(n,".js-manage-requests-tabs-item").classList.remove("selected"),r.classList.add("selected");var a=e.query(n,".js-manage-requests-list"),s=e.query(n,".js-manage-invitations-list");a.classList.add("d-none"),s.classList.remove("d-none")}),r.on("change",".js-milestone-edit-form",oo),r.on("click",".js-milestone-edit-form",oo);var uo=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!k.hasInteractions(t)){e.next=2;break}return e.abrupt("return");case 2:return r=t.getAttribute("data-url"),a(r,"app/assets/modules/github/milestone-dragging.js:28"),e.next=6,o.fetchSafeDocumentFragment(document,r);case 6:n=e.sent,I.preserveInteractivePosition(function(){t.replaceWith(n)});case 8:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),co=new WeakMap;r.on("socket:message",".js-milestone-issues",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(r=A(r,CustomEvent),n=A(r.currentTarget,Element),a=r.detail.data,"1"!==(s=e.query(n,".js-milestone-issues-container")).getAttribute("data-is-sorting")){t.next=7;break}return s.removeAttribute("data-is-sorting"),t.abrupt("return");case 7:return t.next=9,W();case 9:(o=document.querySelector(".js-client-uid"))instanceof HTMLInputElement&&o.value===a.client_uid||uo(n);case 11:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),c.remoteForm(".js-milestone-sort-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:a=t.sent,s=a.json,(o=e.query(r,".js-milestone-reorder-feedback")).textContent="",s.error?e.query(r,".js-milestone-changed").classList.remove("d-none"):(e.query(r,".js-timestamp",HTMLInputElement).value=s.updated_at,o.textContent=o.getAttribute("data-success-text")||"");case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}());var lo=u(function(t){var r=t.newIndex,n=t.item,a=e.closest(n,".js-milestone-issues-container"),s=n.getAttribute("data-id"),o=function(e,t){return e.querySelectorAll(".js-draggable-issue")[t]}(a,r-1),i=o&&o.getAttribute("data-id"),u=e.closest(a,".js-milestone-sort-form");e.query(u,".js-item-id",HTMLInputElement).value=s,e.query(u,".js-prev-id",HTMLInputElement).value=i||"",F.trackEvent({category:"Milestone",action:"reorder",label:t.trackingLabel||"drag-and-drop"}),a.setAttribute("data-is-sorting","1"),l.submit(u)},200);function mo(t,r){lo({item:r,newIndex:Array.from(t.querySelectorAll(".js-draggable-issue")).indexOf(r),trackingLabel:"keyboard-shortcut"}),S.refocus(e.closest(r,".js-navigation-container"),r)}function po(e,t){var r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-setting-toggle .js-status-indicator")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.classList.remove("status-indicator-success","status-indicator-loading","status-indicator-failed")}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}e.classList.add(t)}function fo(){var e=document.querySelector(".js-org-reinstate-forms"),t=document.querySelectorAll(".js-org-reinstate-option:checked");if(e&&1===t.length){var r=t[0].getAttribute("data-form");a(r,"app/assets/modules/github/orgs/invitations.js:37");var n=e.getElementsByClassName("js-togglable-form"),s=!0,o=!1,i=void 0;try{for(var u,c=n[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.classList.add("d-none")}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}var l=document.getElementById(r);a(l,"app/assets/modules/github/orgs/invitations.js:45"),l.classList.remove("d-none")}}io(".js-draggable-issue",lo),r.on("navigation:keydown",".js-draggable-issues-container .js-draggable-issue",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/milestone-dragging.js:113");var r=t.currentTarget,n=e.closest(r,".js-draggable-issues-container");if("J"===t.detail.hotkey){var s=r.nextElementSibling;s&&(s.after(r),mo(n,r),t.preventDefault(),t.stopPropagation())}else if("K"===t.detail.hotkey){var o=r.previousElementSibling;o&&(o.before(r),mo(n,r),t.preventDefault(),t.stopPropagation())}}),t.observe(".js-draggable-issues-container",{add:function(e){if(!co.has(e)){var t=z.create(e,{animation:150,item:".js-draggable-issue",handle:".js-drag-handle",onUpdate:lo,chosenClass:"is-dragging"});co.set(e,t)}},remove:function(e){var t=co.get(e);t&&t.destroy()}}),r.on("submit",".js-mobile-preference-form",function(t){e.query(t.currentTarget,".js-mobile-preference-anchor-field",HTMLInputElement).value=window.location.hash.substr(1)}),c.remoteForm(".js-setting-toggle",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return po(a=e.query(r,".js-status-indicator"),"status-indicator-loading"),t.prev=2,t.next=5,n.text();case 5:t.next=10;break;case 7:t.prev=7,t.t0=t.catch(2),po(a,"status-indicator-failed");case 10:po(a,"status-indicator-success");case 11:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-toggle-email-settings input",function(){var t=e.query(document,".js-notification-emails"),r=document.querySelector(".js-toggle-email-settings input:checked");t.classList.toggle("d-none",!r)}),c.remoteForm(".js-unignore-form, .js-ignore-form, .js-unsubscribe-form, .js-subscribe-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.closest(r,".js-subscription-row")).classList.add("loading"),t.prev=2,t.next=5,n.text();case 5:t.next=13;break;case 7:t.prev=7,t.t0=t.catch(2),a.classList.remove("loading"),(s=e.query(r,".btn-sm")).classList.add("btn-danger"),s.setAttribute("title",a.getAttribute("data-error-message")||"");case 13:a.classList.remove("loading"),r.classList.contains("js-unignore-form")||r.classList.contains("js-unsubscribe-form")?a.classList.add("unsubscribed"):(r.classList.contains("js-ignore-form")||r.classList.contains("js-subscribe-form"))&&a.classList.remove("unsubscribed");case 15:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-thread-subscribe-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.text();case 2:a=t.sent,s=e.closest(r,".js-thread-subscription-status"),a&&w.replaceContent(s,a.text);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),t.observe("#js-oauth-authorize-btn",function(e){_r(document).then(function(){setTimeout(function(){a(e instanceof HTMLButtonElement,"app/assets/modules/github/oauth.js:10"),e.disabled=!1},1e3)})}),te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.ready;case 2:window._octo.push(["enablePerformance"]),window._octo.push(["recordPageView"]);case 4:case"end":return e.stop()}},e,this)}))(),document.addEventListener("pjax:complete",function(){window._octo.push(["recordPageView"])}),r.on("change",".js-two-factor-needs-enforced",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/orgs/2fa.js:36"),e.query(document,".js-confirm-2fa-modal").classList.toggle("d-none",!r.checked),e.query(document,".js-2fa-save-button").classList.toggle("d-none",r.checked)}),t.observe(".js-two-factor-enforcement-poller",function(e){e.addEventListener("load",function(){window.location.reload()})}),r.on("click",".js-invitations-team-suggestions-view-all",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f,v;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=r.currentTarget,s=n.getAttribute("data-url"),a(s,"app/assets/modules/github/orgs/invitations.js:12"),i=e.querySelectorAll(document,".js-invitation-toggle-team:checked",HTMLInputElement).map(function(e){return e.value}),t.next=6,o.fetchSafeDocumentFragment(document,s);case 6:for(u=t.sent,(c=e.closest(n,"ul")).innerHTML="",c.appendChild(u),l=!0,d=!1,m=void 0,t.prev=13,p=i[Symbol.iterator]();!(l=(f=p.next()).done);l=!0)v=f.value,e.query(c,'.js-invitation-toggle-team[value="'+v+'"]',HTMLInputElement).checked=!0;t.next=21;break;case 17:t.prev=17,t.t0=t.catch(13),d=!0,m=t.t0;case 21:t.prev=21,t.prev=22,!l&&p.return&&p.return();case 24:if(t.prev=24,!d){t.next=27;break}throw m;case 27:return t.finish(24);case 28:return t.finish(21);case 29:case"end":return t.stop()}},t,this,[[13,17,21,29],[22,,24,28]])}));return function(e){return t.apply(this,arguments)}}()),r.on("change",".js-org-reinstate-option",fo),t.observe(".js-org-reinstate-forms",fo);var vo=function(){var t=te(regeneratorRuntime.mark(function t(r,n,s,i){var u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return u=r.getAttribute("data-url"),a(u,"app/assets/modules/github/orgs/members.js:90"),c=new URL(u,window.location.origin),(l=new URLSearchParams(c.search.slice(1))).append("member",n),l.append("action_type",s),i&&l.append("return_to",i),c.search=l.toString(),go(!0),e.query(document,".js-add-team-member-form").hidden=!0,t.next=12,o.fetchSafeDocumentFragment(document,c);case 12:d=t.sent,go(!1),r.innerHTML="",r.appendChild(d);case 16:case"end":return t.stop()}},t,this)}));return function(e,r,n,a){return t.apply(this,arguments)}}();function go(t){e.query(document,".js-add-members-loading-state").hidden=!t,e.query(document,".js-add-members-modal-content").hidden=t}function ho(){e.query(document,".js-save-member-privileges-button-container").classList.toggle("member-privilege-radios-preserved",yo()&&bo()&&jo())}function yo(){return""===e.query(document,".js-customize-member-privileges-default-repository-permission-radio:checked",HTMLInputElement).value}function bo(){return"0"===e.query(document,".js-customize-member-privileges-repository-creation-radio:checked",HTMLInputElement).value}function jo(){return"secret"===e.query(document,".js-customize-member-privileges-team-privacy-radio:checked",HTMLInputElement).value}function Lo(t){for(var r in t.selectors){var n=t.selectors[r],a=document.querySelector(r);n&&a&&(a.textContent=n)}var s=100===t.filled_seats_percent;e.query(document,".js-live-update-seats-percent").style.width=t.filled_seats_percent+"%",e.query(document,".js-need-more-seats").classList.toggle("d-none",!s),e.query(document,".js-add-org-member-form").classList.toggle("d-none",s)}r.on("click",".js-membership-tab",function(t){var r=t.currentTarget.getAttribute("data-membership")||"",n=e.query(document,".js-member-filter-field",HTMLInputElement),a=n.value,s=new RegExp("membership:[a-z-]+"),o=a.toString().trim().replace(s,"");n.value=(o+" "+r).replace(/\s\s/," "),n.focus(),p.dispatchThrottledInputEvent(n),e.query(document,".js-membership-tabs").classList.remove("selected"),t.currentTarget.classList.add("selected")}),r.on("click",".js-member-search-filter",function(t){t.preventDefault();var r=t.currentTarget.getAttribute("data-filter")||"",n=e.closest(t.currentTarget,".js-select-menu").getAttribute("data-filter-on")||"",a=e.query(document,".js-member-filter-field",HTMLInputElement),s=a.value,o=new RegExp(n+":[a-z]+"),i=s.toString().trim().replace(o,"");a.value=(i+" "+r).replace(/\s\s/," "),a.focus(),p.dispatchThrottledInputEvent(a)}),r.on("submit",".js-needs-interstitial .js-approve-membership-request",function(t){var r=t.currentTarget;a(r instanceof HTMLFormElement,"app/assets/modules/github/orgs/members.js:57");var n=e.query(r,"[data-member-name]").getAttribute("data-member-name");a(n,"app/assets/modules/github/orgs/members.js:60");var s=r.getAttribute("data-action-type");a(s,"app/assets/modules/github/orgs/members.js:63");var o=e.closest(r,".js-add-members-container");t.preventDefault(),vo(o,n,s,null)}),r.on("submit",".js-needs-interstitial.js-add-team-member-form",function(t){var r=t.currentTarget;a(r instanceof HTMLFormElement,"app/assets/modules/github/orgs/members.js:72");var n=e.namedItem(r,"member");if(n.value){var s=r.getAttribute("data-action-type");a(s,"app/assets/modules/github/orgs/members.js:78");var o=e.query(document,".js-add-members-container");t.preventDefault();var i=r.getAttribute("data-return-to");vo(o,n.value,s,i)}}),t.observe("#add-team-member",function(t){var r=e.query(t,"details-dialog"),n=r.innerHTML;t.addEventListener("toggle",function(e){var t=e.currentTarget;a(t instanceof HTMLElement,"app/assets/modules/github/orgs/members.js:118"),t.hasAttribute("open")||(r.innerHTML=n)})}),c.remoteForm(".js-add-org-member-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,u,c,l,d,m,p,f,v;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.query(document,".js-member-list"),r.classList.add("is-sending"),(s=document.querySelector(".flash-messages"))&&s.remove(),o=void 0,t.prev=5,t.next=8,n.json();case 8:o=t.sent,t.next=17;break;case 11:if(t.prev=11,t.t0=t.catch(5),t.t0.response||t.t0.response.json){t.next=15;break}return t.abrupt("return");case 15:return a.insertAdjacentHTML("beforebegin",t.t0.response.json.message_html),t.abrupt("return");case 17:if(r.classList.remove("is-sending"),e.query(r,"auto-complete",AutocompleteElement).value="",u=e.query(i.parseHTML(document,o.json.list_item_html),"*"),!(c=u.getAttribute("data-login"))){t.next=49;break}l=!0,d=!1,m=void 0,t.prev=25,p=a.children[Symbol.iterator]();case 27:if(l=(f=p.next()).done){t.next=35;break}if((v=f.value).getAttribute("data-login")!==c){t.next=32;break}return v.remove(),t.abrupt("break",35);case 32:l=!0,t.next=27;break;case 35:t.next=41;break;case 37:t.prev=37,t.t1=t.catch(25),d=!0,m=t.t1;case 41:t.prev=41,t.prev=42,!l&&p.return&&p.return();case 44:if(t.prev=44,!d){t.next=47;break}throw m;case 47:return t.finish(44);case 48:return t.finish(41);case 49:a.prepend(u);case 50:case"end":return t.stop()}},t,this,[[5,11],[25,37,41,49],[42,,44,48]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-customize-member-privileges-default-repository-permission-radio",function(){e.query(document,".js-migrate-ability-list-item-default-repository-permission").classList.toggle("migrate-ability-not-possible",yo()),ho()}),r.on("change",".js-customize-member-privileges-repository-creation-radio",function(){e.query(document,".js-migrate-ability-list-item-members-can-create-repositories").classList.toggle("migrate-ability-not-possible",bo()),ho()}),r.on("change",".js-customize-member-privileges-team-privacy-radio",function(){e.query(document,".js-migrate-ability-list-item-team-privacy").classList.toggle("migrate-ability-not-possible",jo()),ho()}),t.observe(".js-save-member-privileges-button-container",ho),r.on("click",".js-org-signup-duration-change",function(t){a(t instanceof MouseEvent,"app/assets/modules/github/orgs/new.js:13");var r=t.currentTarget,n=e.query(document,".js-plan-choice:checked",HTMLInputElement),s=e.query(document,"#js-pjax-container"),o=new URLSearchParams;o.append("plan_duration",r.getAttribute("data-plan-duration")||""),o.append("plan",n.value);var i=e.query(document,".js-new-organization-name",HTMLInputElement);i.value&&o.append("login",i.value);var u=e.query(document,".js-new-organization-billing-email",HTMLInputElement);u.value&&o.append("billing_email",u.value),document.querySelector(".js-transform-user")&&o.append("transform_user","1");var c=t.currentTarget;a(c instanceof HTMLAnchorElement,"app/assets/modules/github/orgs/new.js:29"),c.search="?"+o.toString(),E.click(t,{scrollTo:!1,container:s})}),n.onInput(".js-new-organization-name",function(t){var r=t.target;a(r instanceof HTMLInputElement,"app/assets/modules/github/orgs/new.js:40");var n=e.closest(r,"dd").querySelector(".js-field-hint-name");n&&("innerText"in n?n.innerText=r.value:n.textContent=r.value)}),n.onInput(".js-company-name-input",function(t){var r=t.target;a(r instanceof HTMLInputElement,"app/assets/modules/github/orgs/new.js:63");var n=r.form;a(n,"app/assets/modules/github/orgs/new.js:66");var s=n.querySelector(".js-company-name-text");if(s){var o=e.query(n,".js-corp-tos-link"),i=n.querySelector(".js-tos-link");i&&(i.classList.add("d-none"),i.setAttribute("aria-hidden","true")),o.classList.remove("d-none"),o.setAttribute("aria-hidden","false");var u=" on behalf of your company, "+r.value;"innerText"in s?s.innerText=u:s.textContent=u}}),t.observe(".js-company-owned:not(:checked)",{constructor:HTMLInputElement,add:function(t){var r=t.form;a(r,"app/assets/modules/github/orgs/new.js:105");var n=e.query(r,".js-company-name-input",HTMLInputElement),s=e.query(document,".js-company-name-text"),o=e.query(document,".js-corp-tos-link"),i=e.query(document,".js-tos-link");t.getAttribute("data-optional")&&n.removeAttribute("required"),l.changeValue(n,""),i.classList.remove("d-none"),i.setAttribute("aria-hidden","false"),o.classList.add("d-none"),o.setAttribute("aria-hidden","true"),"innerText"in s?s.innerText="":s.textContent=""}}),t.observe(".js-company-owned:checked",{constructor:HTMLInputElement,add:function(t){var n=t.form;a(n,"app/assets/modules/github/orgs/new.js:140");var s=e.query(n,".js-company-name-input");s.setAttribute("required","required"),r.fire(s,"change")}}),t.observe(".js-plan-choice",{constructor:HTMLInputElement,add:function(t){var r=t;r.addEventListener("change",function(){var t=document.querySelector(".js-new-business-section");t&&r.checked&&(t.classList.add("has-removed-contents"),e.query(document,".js-company-owned",HTMLInputElement).disabled=!1)})}}),t.observe(".js-show-new-business-fields",{constructor:HTMLInputElement,add:function(e){var t=e;t.addEventListener("change",function(){var e=document.querySelector(".js-new-business-section");e&&t.checked&&e.classList.remove("has-removed-contents")})}}),t.observe(".js-company-owned-autoselect",{constructor:HTMLInputElement,add:function(t){var r=t;function n(){if(r.checked&&r.form){var t=e.query(r.form,".js-company-owned",HTMLInputElement);l.changeValue(t,!0),document.querySelectorAll(".js-new-business-section").length>0&&(t.disabled=!0)}}r.addEventListener("change",n),n()}}),c.remoteForm(".js-org-list-item .js-org-remove-item",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return e.closest(r,".js-org-list-item").classList.add("d-none"),t.prev=1,t.next=4,n.text();case 4:t.next=13;break;case 6:if(t.prev=6,t.t0=t.catch(1),e.closest(r,".js-org-list-item").classList.remove("d-none"),!(a=r.getAttribute("data-error-message"))){t.next=13;break}return alert(a),t.abrupt("return");case 13:e.closest(r,".js-org-list-item").remove();case 14:case"end":return t.stop()}},t,this,[[1,6]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-select-repo-permission",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return t.classList.remove("was-successful"),t.classList.add("is-loading"),e.next=4,r.json();case 4:n=e.sent,t.classList.remove("is-loading"),t.classList.add("was-successful"),(a=t.closest(".js-org-repo"))&&(s=n.json,a.classList.toggle("with-higher-access",s.members_with_higher_access));case 9:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-per-seat-invite-field, .js-per-seat-invite .js-org-remove-item",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.json();case 2:Lo(e.sent.json);case 4:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}());var wo=function(){var t=te(regeneratorRuntime.mark(function t(){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,V.dialog({content:e.query(document,"#disable-saml-confirmation",HTMLTemplateElement).content.cloneNode(!0)});case 2:t.sent.addEventListener("dialog:remove",Ro);case 4:case"end":return t.stop()}},t,this)}));return function(){return t.apply(this,arguments)}}();function xo(){return e.query(document,".js-saml-provider-settings-form",HTMLFormElement)}function ko(){return xo().querySelector(".js-saml-form-inputs")}function Eo(){return e.query(document,".js-org-saml-confirm-enforcement-hidden",HTMLInputElement)}function To(){return"1"===e.query(document,".js-org-saml-previously-enforced",HTMLInputElement).value}function qo(e){e&&e.classList.remove("d-none")}function So(){return To()&&!e.query(document,".js-org-enable-saml",HTMLInputElement).checked}function Ao(){return e.query(document,".is-submit-button-value",HTMLInputElement)}function Mo(){return e.query(document,".js-org-saml-enforce",HTMLInputElement).checked&&"0"===Eo().value&&!To()&&"1"===e.query(document,".js-org-has-unlinked-saml-members",HTMLInputElement).value}function Ho(){xo().submit()}function Ro(){var e=document.querySelector(".js-org-enable-saml");e&&e instanceof HTMLInputElement&&(e.checked=!0,qo(ko()))}r.on("click",".js-org-enable-saml",function(e){var t;a(e.currentTarget instanceof HTMLInputElement,"app/assets/modules/github/orgs/security.js:107"),e.currentTarget.checked?qo(ko()):(t=ko())&&t.classList.add("d-none")}),r.on("click",".js-saml-submit",function(e){e.preventDefault(),a(e.currentTarget instanceof HTMLButtonElement,"app/assets/modules/github/orgs/security.js:124"),$.persistSubmitButtonValue(e.currentTarget),l.submit(xo())}),r.on("click",".js-org-saml-confirm-enforce-button",function(){Eo().value="true",l.submit(xo())}),r.on("submit",".js-saml-provider-settings-form",function(t){t.preventDefault(),"test_settings"===Ao().name?Ho():"save_settings"===Ao().name&&(So()?wo():Mo()?V.dialog({content:e.query(document,"#enforce-saml-confirmation",HTMLTemplateElement).content.cloneNode(!0)}):Ho())});var Io=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,i,u,c,l,d,m,p,f,v,g,h,y,b,j;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:n=e.closest(r,".js-team-add-user-form"),a=e.query(n,".js-team-add-user-name",HTMLInputElement),s=a.value,a.value="",i=e.query(document,".js-team-user-logins"),u=!0,c=!1,l=void 0,t.prev=8,d=i.querySelectorAll("li")[Symbol.iterator]();case 10:if(u=(m=d.next()).done){t.next=17;break}if(m.value.getAttribute("data-login")!==s){t.next=14;break}return t.abrupt("return");case 14:u=!0,t.next=10;break;case 17:t.next=23;break;case 19:t.prev=19,t.t0=t.catch(8),c=!0,l=t.t0;case 23:t.prev=23,t.prev=24,!u&&d.return&&d.return();case 26:if(t.prev=26,!c){t.next=29;break}throw l;case 29:return t.finish(26);case 30:return t.finish(23);case 31:return t.next=33,C("low");case 33:return p=new URL(n.getAttribute("data-template-url")||"",window.location.origin),(f=new URLSearchParams(p.search.slice(1))).append("member",s),p.search=f.toString(),a.focus(),t.next=40,o.fetchSafeDocumentFragment(document,p);case 40:for(v=t.sent,i.appendChild(v),g=!0,h=!1,y=void 0,t.prev=45,b=e.querySelectorAll(document,".js-login-field",HTMLInputElement)[Symbol.iterator]();!(g=(j=b.next()).done);g=!0)j.value.disabled=!1;t.next=53;break;case 49:t.prev=49,t.t1=t.catch(45),h=!0,y=t.t1;case 53:t.prev=53,t.prev=54,!g&&b.return&&b.return();case 56:if(t.prev=56,!h){t.next=59;break}throw y;case 59:return t.finish(56);case 60:return t.finish(53);case 61:n.classList.add("d-none");case 62:case"end":return t.stop()}},t,this,[[8,19,23,31],[24,,26,30],[45,49,53,61],[54,,56,60]])}));return function(e){return t.apply(this,arguments)}}();function Co(t,r){e.query(t,".js-add-child-team-loading-state").hidden=!r,e.query(t,".js-add-child-team-modal-content").hidden=r}function _o(t){var r=e.query(t,".js-inline-comment-form-container");r.classList.add("open"),e.query(r,".js-write-tab").click(),e.query(r,".js-comment-field").focus()}function Fo(t){t.reset();var n=e.closest(t,".js-inline-comment-form-container");n.classList.remove("open"),r.fire(n,"inlinecomment:collapse")}r.on("click",".js-team-remove-user",function(t){t.preventDefault();var r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-team-add-user-form")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.classList.remove("d-none")}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}e.query(document,".js-team-add-user-name").focus(),e.closest(t.currentTarget,"li").remove()}),r.on("click",".js-team-add-user-button",function(e){e.preventDefault(),Io(e.currentTarget)}),r.on("click",".js-rename-owners-team-next-btn",function(){var t=e.query(document,".js-rename-owners-team-about-content");t.hidden=!t.hidden;var r=e.query(document,".js-rename-owners-team-rename-form");r.hidden=!r.hidden}),r.on("change",".js-add-child-team-completer",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(r.target===r.currentTarget){t.next=2;break}return t.abrupt("return");case 2:if(n=r.currentTarget,a(n instanceof AutocompleteElement,"app/assets/modules/github/orgs/team-add-child.js:17"),n.value){t.next=6;break}return t.abrupt("return");case 6:return Co(s=e.closest(n,".js-add-team-container"),!0),i=e.closest(n,".js-add-child-team-form"),u=i.getAttribute("data-change-parent-summary-path"),a(u,"app/assets/modules/github/orgs/team-add-child.js:25"),t.next=13,o.fetchSafeDocumentFragment(document,u);case 13:c=t.sent,e.query(i,".js-add-team-search").hidden=!0,e.query(i,".js-add-team-warning").append(c),Co(s,!1);case 17:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),r.on("click",".js-expandable-team-breadcrumbs .js-team-breadcrumb-trigger",function(){var t=te(regeneratorRuntime.mark(function t(r){var n;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(n=e.closest(r.target,".js-expandable-team-breadcrumbs")).classList.add("is-loading"),t.prev=2,t.next=5,w.updateContent(n);case 5:t.next=9;break;case 7:t.prev=7,t.t0=t.catch(2);case 9:n.classList.remove("is-loading");case 10:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e){return t.apply(this,arguments)}}()),r.on("click",".js-toggle-inline-comment-form",function(t){_o(e.closest(t.currentTarget,".js-line-comments"))}),r.on("quote-selection",".js-line-comments",function(e){_o(e.currentTarget)}),n.onKey("keydown",".js-inline-comment-form-container form .js-comment-field",function(t){var r=t.target;(a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/behaviors/inline-comment.js:37"),r.classList.contains("js-navigation-enable"))||"Escape"===t.key&&0===r.value.length&&(Fo(e.closest(r,"form",HTMLFormElement)),t.preventDefault())}),r.on("click",".js-hide-inline-comment-form",function(t){var r=e.closest(t.currentTarget,"form",HTMLFormElement);!k.hasDirtyFields(r)||confirm(t.target.getAttribute("data-confirm-cancel-text")||"")?Fo(r):t.preventDefault()});var Po,No,Oo=(Po=te(regeneratorRuntime.mark(function e(){var t;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.loaded;case 2:(t=document.querySelector(".js-show-discussion .js-mark-notification-form"))instanceof HTMLFormElement&&l.submit(t);case 4:case"end":return e.stop()}},e,this)})),function(){return Po.apply(this,arguments)});function Do(t){var r=t.closest(".js-teams-write-a-post");return r?function(t){var r=e.query(t,".js-team-discussions-post-compose");r.classList.contains("d-none")||(r.classList.add("d-none"),e.query(t,".js-post-placeholder").classList.remove("d-none"),t.classList.remove("active")),e.query(t,".js-comment-form-error").classList.add("d-none")}(r):t.closest(".js-teams-reply-to-post")?function(e){a(e instanceof HTMLFormElement,"app/assets/modules/github/orgs/team-discussions.js:31"),Fo(e)}(t):void 0}function Bo(e){var t=Jo(e);if(t){a(/\[private\]/.test(t.name),"app/assets/modules/github/orgs/team-discussions.js:67");var n=t.getAttribute("data-original-value"),s=e.querySelector("1"===n?".js-discussion-post-select-private":".js-discussion-post-select-public");s&&r.fire(s,"navigation:open")}}function Uo(e){var t=e.closest(".js-teams-reply-to-post"),r=t&&t.querySelector(".js-comment-form-error");r&&r.classList.add("d-none")}function zo(e,t){e.textContent=(parseInt(e.textContent)+t).toString()}function Wo(e){e.addEventListener("transitionend",function(t){t instanceof TransitionEvent&&"opacity"===t.propertyName&&e.remove()},{once:!0}),e.classList.add("fade-out")}function Vo(t,r){t.classList.add("d-none"),a(t.parentElement,"app/assets/modules/github/orgs/team-discussions.js:224"),e.query(t.parentElement,r).classList.remove("d-none")}function $o(t,r){var n=Jo(A(t.target.closest(".js-comment-update")||t.target.closest(".js-new-comment-form"),HTMLFormElement));a(n,"app/assets/modules/github/orgs/team-discussions.js:258"),a(/\[private\]/.test(n.name),"app/assets/modules/github/orgs/team-discussions.js:259"),n.value=r;var s=t.target.closest(".js-discussion-post");if(s){var o=e.query(s,".js-discussion-post-privacy-icon");"1"===r?(s.classList.add("discussion-post-private"),o.classList.remove("d-none")):(s.classList.remove("discussion-post-private"),o.classList.add("d-none"))}}function Jo(e){var t=e.querySelector(".js-discussion-post-privacy-select");if(t){var r=t.querySelector("input");if(r)return a(r instanceof HTMLInputElement,"app/assets/modules/github/orgs/team-discussions.js:282"),r}}r.on("click",".js-team-discussions-team-description-toggle",function(){e.query(document,".js-team-discussions-team-description").classList.toggle("d-none"),e.query(document,".js-team-discussions-team-description-form").classList.toggle("d-none")}),r.on("click",".js-team-discussions-post-toggle",function(t){var r=e.closest(t.target,".js-teams-write-a-post");r.classList.contains("active")||(r.classList.add("active"),e.query(r,".js-team-discussions-post-compose").classList.remove("d-none"),e.query(r,".js-post-placeholder").classList.add("d-none"),e.query(r,".js-title-field").focus())}),r.on("click",".js-hide-post-form",function(t){var r=e.closest(t.target,".js-new-comment-form",HTMLFormElement);!k.hasDirtyFields(r)||confirm(t.target.getAttribute("data-confirm-cancel-text")||"")?(Do(t.target),Bo(r)):t.preventDefault()}),r.on("click",".js-hide-inline-comment-form",function(e){Uo(e.target)}),c.remoteForm(".js-new-comment-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:Do(t),Bo(t),Uo(t);case 5:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-team-discussions-team-description-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=7;break;case 5:t.prev=5,t.t0=t.catch(0);case 7:a=e.query(document,".js-team-discussions-team-description"),s=e.query(a,".description"),o=e.query(document,".js-team-discussions-team-description-field",HTMLTextAreaElement),a.classList.toggle("d-none"),r.classList.toggle("d-none"),o.value.trim()?(s.textContent=o.value,o.defaultValue=o.value):(s.textContent="This team has no description",o.defaultValue="");case 13:case"end":return t.stop()}},t,this,[[0,5]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-comment-pin",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.query(r,"button[type=submit]")).blur(),t.next=4,n.text();case 4:s=e.query(r,'input[name="team_discussion[pinned]"]',HTMLInputElement),o=document.querySelector("#pinned_posts_counter"),i=a.getAttribute("aria-label")||"",u=a.getAttribute("data-alternate-aria-label")||"",a.setAttribute("data-alternate-aria-label",i),a.setAttribute("aria-label",u),a.classList.toggle("pinned"),c=a.classList.contains("pinned"),o&&(zo(o,c?1:-1),l=e.closest(o,".js-pinned-post-tab"),!c&&l.classList.contains("selected")&&Wo(e.closest(r,".js-comment-delete-container"))),s.value=c?"0":"1";case 14:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-comment-delete",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:n=t.closest(".discussion-post"),(a=n&&n.querySelector(".js-comment-pin button[type=submit]"))&&a.classList.contains("pinned")&&(s=document.querySelector("#pinned_posts_counter"))&&zo(s,-1);case 5:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),r.on("quote-selection",".js-discussion-post",function(t){var r=e.query(t.target,".js-inline-comment-form-container");r.classList.add("open"),e.query(r,".js-write-tab").click(),e.query(r,".js-comment-field").focus()}),c.remoteForm(".js-comment-subscribe",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:Vo(t,".js-comment-unsubscribe");case 3:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-comment-unsubscribe",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:Vo(t,".js-comment-subscribe");case 3:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-discussion-post-update",(No=te(regeneratorRuntime.mark(function e(t,r){var n,a,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=t.closest(".js-comment")){e.next=3;break}return e.abrupt("return");case 3:if(a=n.querySelector(".js-comment-title")){e.next=6;break}return e.abrupt("return");case 6:return e.next=8,r.json();case 8:s=e.sent,a.textContent=s.json.title;case 10:case"end":return e.stop()}},e,this)})),function(e,t){return No.apply(this,arguments)})),r.on("selectmenu:selected",".js-discussion-post-select-private",function(e){$o(e,"1")}),r.on("selectmenu:selected",".js-discussion-post-select-public",function(e){$o(e,"0")}),Oo();var Ko=function(){var e=te(regeneratorRuntime.mark(function e(t,r,n){var a,s,i,u,c,l;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!r.length){e.next=24;break}for(a=!0,s=!1,i=void 0,e.prev=4,u=r[Symbol.iterator]();!(a=(c=u.next()).done);a=!0)c.value.classList.remove("d-none");e.next=12;break;case 8:e.prev=8,e.t0=e.catch(4),s=!0,i=e.t0;case 12:e.prev=12,e.prev=13,!a&&u.return&&u.return();case 15:if(e.prev=15,!s){e.next=18;break}throw i;case 18:return e.finish(15);case 19:return e.finish(12);case 20:t.classList.add("is-open"),Xo=!1,e.next=30;break;case 24:return e.next=26,o.fetchSafeDocumentFragment(document,n);case 26:l=e.sent,t.after(l),t.classList.add("is-open"),Xo=!1;case 30:case"end":return e.stop()}},e,this,[[4,8,12,20],[13,,15,19]])}));return function(t,r,n){return e.apply(this,arguments)}}(),Go=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=r.getAttribute("data-contents-url"),a(n,"app/assets/modules/github/orgs/team-hierarchy.js:87"),s=new URL(n,window.location.origin),(i=new URLSearchParams(s.search.slice(1))).append("query",r.value.trim()),s.search=i.toString(),u=e.query(document,".js-select-team-results"),t.next=9,o.fetchSafeDocumentFragment(document,s);case 9:c=t.sent,u.innerHTML="",u.append(c);case 12:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}(),Xo=!1;function Qo(t){var r=e.closest(t,"form",HTMLFormElement),n=r.querySelector(".js-selected-team-id"),a=n instanceof HTMLInputElement&&n.value,s=e.query(r,".js-team-privacy-closed"),o=e.query(r,".js-team-privacy-secret"),i=e.query(s,"input",HTMLInputElement),u=e.query(o,"input",HTMLInputElement);o.classList.toggle("tooltipped",!!a),o.classList.toggle("text-gray",!!a),u.disabled=!!a,a&&(i.checked=!0)}function Yo(e,t){var r=e.value;e.value=t.textContent,t.textContent=r}function Zo(e){var t=e.match(/#?(?:L)(\d+)/g);return t?t.map(function(e){return parseInt(e.replace(/\D/g,""))}):[]}function ei(e){return{lineRange:Zo(e),anchorPrefix:function(e){var t=e.match(/(file-.+?-)L\d+?/i);return t?t[1]:""}(e)}}function ti(e,t){return e-t}r.on("click",".js-open-child-team",function(t){if(!Xo){Xo=!0;var r=t.currentTarget.getAttribute("data-parent-team-slug");if(r){var n=document.querySelectorAll('.js-child-team[data-parent-team-slug="'+r+'"]'),s=e.closest(t.currentTarget,".js-team-row");if(s.classList.contains("is-open"))t.currentTarget.setAttribute("aria-expanded","false"),function(e,t){(function e(t){var r=!0;var n=!1;var a=void 0;try{for(var s,o=t[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value,u=String(i.getAttribute("data-team-slug")),c=document.querySelectorAll('.js-child-team[data-parent-team-slug="'+u+'"]');e(c),i.classList.remove("is-open"),i.classList.add("d-none")}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}})(t),e.classList.remove("is-open"),Xo=!1}(s,n);else{t.currentTarget.setAttribute("aria-expanded","true");var o=t.currentTarget.getAttribute("data-child-team-url");a(o,"app/assets/modules/github/orgs/team-hierarchy.js:29"),Ko(s,n,o)}}}}),r.on("click",".js-show-more-child-teams",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(!Xo){t.next=2;break}return t.abrupt("return");case 2:return Xo=!0,n=r.currentTarget,s=e.closest(n,".js-team-row"),i=n.getAttribute("data-child-team-url"),a(i,"app/assets/modules/github/orgs/team-hierarchy.js:74"),u=s.parentNode,t.next=10,o.fetchSafeDocumentFragment(document,i);case 10:c=t.sent,s.before(c),u&&u.removeChild(s),Xo=!1;case 14:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),t.observe(".js-selected-team-field",{constructor:HTMLInputElement,initialize:function(e){p.addThrottledInputEventListener(e,Go)}}),r.on("menu:activated",".js-select-team-menu",function(e){var t=e.target.querySelector(".js-selected-team-field");t&&t.focus()}),r.on("selectmenu:selected",".js-select-team",function(t){var r=t.currentTarget,n=r.getAttribute("data-selected-team-id")||"",a=e.query(document,"#selected-team-id",HTMLInputElement),s=e.query(r,".js-selected-team-name").innerHTML,o=e.query(document,"#selected-team-name",HTMLInputElement);n!==a.value&&(a.value=n,o.value=s)}),r.on("selectmenu:selected",".js-clear-team-selection",function(){var t=e.query(document,".js-select-button");t.textContent=t.getAttribute("data-placeholder")||"Select team"}),n.onInput(".js-team-ldap-group-field",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/orgs/team-import.js:11"),e.closest(r,".form-group").classList.remove("errored")}),r.on("change",".js-import-ldap-group-completer",function(t){if(t.target===t.currentTarget){var r=t.target;a(r instanceof AutocompleteElement,"app/assets/modules/github/orgs/team-import.js:19");var n=e.closest(r,".js-ldap-group-adder"),s=e.query(r,".js-team-ldap-group-field",HTMLInputElement),o=e.query(n,".js-team-ldap-dn-field",HTMLInputElement);if(o.value="",r.value){var i=JSON.parse(r.value),u=i.dn,c=i.cn;s.value=c,o.value=u}}}),c.remoteForm(".js-ldap-group-adder",function(){var t=te(regeneratorRuntime.mark(function t(r,n,a){var s,o,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a.headers.set("X-Context","import"),s=e.closest(r,".js-ldap-import-groups-container"),o=void 0,t.prev=3,t.next=6,n.html();case 6:o=t.sent,t.next=13;break;case 9:return t.prev=9,t.t0=t.catch(3),t.t0.response&&e.query(r,".form-group").classList.add("errored"),t.abrupt("return");case 13:i=document.querySelector(".js-import-form-actions"),s.classList.remove("is-empty"),e.query(s,".js-ldap-imported-groups").prepend(o.html),r.reset(),e.query(r,".js-team-ldap-group-field").focus(),i&&i.classList.remove("d-none");case 19:case"end":return t.stop()}},t,this,[[3,9]])}));return function(e,r,n){return t.apply(this,arguments)}}()),r.on("submit",".js-team-remove-group",function(t){e.closest(t.currentTarget,".js-team").classList.add("is-removing"),e.query(document,".js-team-ldap-group-field").focus()}),c.remoteForm(".js-team-remove-group",function(){var t=te(regeneratorRuntime.mark(function t(r,n){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=9;break;case 5:return t.prev=5,t.t0=t.catch(0),e.closest(r,".js-team").classList.remove("is-removing"),t.abrupt("return");case 9:e.closest(r,".js-team").remove(),document.querySelector(".js-team:not(.is-removing)")||(e.query(document,".js-ldap-import-groups-container").classList.add("is-empty"),e.query(document,".js-import-form-actions").classList.add("d-none"));case 11:case"end":return t.stop()}},t,this,[[0,5]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-edit-team",function(t){var r=e.closest(t.currentTarget,".js-team");r.classList.contains("is-removing")||(r.classList.add("is-editing"),e.query(r,".js-team-name-field").focus())}),r.on("click",".js-cancel-team-edit",function(t){var r=e.closest(t.currentTarget,".js-team"),n=e.query(r,".js-team-form",HTMLFormElement);r.classList.remove("is-editing"),n.reset()}),c.remoteForm(".js-team-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n,a){var s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a.headers.set("X-Context","import"),t.next=3,n.html();case 3:s=t.sent,e.closest(r,".js-team").replaceWith(s.html);case 6:case"end":return t.stop()}},t,this)}));return function(e,r,n){return t.apply(this,arguments)}}()),r.on("click",".js-team-search-filter",function(t){t.preventDefault();var r=t.currentTarget.getAttribute("data-filter")||"",n=e.closest(t.currentTarget,".js-select-menu").getAttribute("data-filter-on")||"",a=e.query(document,".js-team-search-field",HTMLInputElement),s=new RegExp(n+":[a-z]+"),o=a.value.trim().replace(s,"");a.value=(o+" "+r).replace(/\s\s/," "),a.focus(),p.dispatchThrottledInputEvent(a)}),t.observe(".js-select-team-menu",function(e){Qo(e)}),r.on("selectmenu:selected",".js-select-team-menu",function(e){Qo(e.currentTarget)}),r.on("click",".js-create-team-button",function(t){var r=e.closest(t.currentTarget,"form"),n=r.querySelector(".js-already-selected-team-name");if(n instanceof HTMLInputElement){var s=r.querySelector(".js-original-team-visibility"),i=r.querySelector(".js-team-privacy-secret");if(i){var u=e.query(i,"input",HTMLInputElement),c=function(e){return e.value!==e.getAttribute("data-original-team-name")}(n),l=function(e,t){return null!=e&&t.checked&&!["","SECRET"].includes(e.getAttribute("data-original-team-visibility"))}(s,u);if(c||l){t.preventDefault();var d=r.getAttribute("data-important-changes-summary-url");a(d,"app/assets/modules/github/orgs/team-new.js:66");var m=new URL(d,window.location.origin),p=new URLSearchParams(m.search.slice(1));p.append("parent_team",n.value),l&&p.append("visibility_changed",l.toString()),c&&p.append("parent_changed",c.toString()),m.search=p.toString(),V.dialog({content:o.fetchSafeDocumentFragment(document,m),dialogClass:"p-3"})}}}}),r.on("click",".js-confirm-edit-team",function(){e.query(document,".js-org-team-form",HTMLFormElement).submit()}),r.on("change",".js-team-project-completer",function(t){if(t.target===t.currentTarget){var r=t.currentTarget;a(r instanceof AutocompleteElement,"app/assets/modules/github/orgs/team-projects.js:11");var n=e.closest(t.currentTarget,"form",HTMLFormElement),s=e.namedItem(n,"project_id"),o=e.query(n,".js-team-project-complete-field",HTMLInputElement);if(r.value){var i=JSON.parse(r.value),u=i.name,c=i.id;s.value=c,o.value=u}else s.value=""}}),t.observe(".js-org-transform-poller",function(e){var t=e.getAttribute("data-redirect-url");e.addEventListener("load",function(){window.location.href=t})}),r.on("click","#load-readme",function(e){var t=document.getElementById("gollum-editor-body");a(t instanceof HTMLTextAreaElement,"app/assets/modules/github/pages-composer.js:8");var r=document.getElementById("editor-body-buffer"),n=document.getElementById("undo-load-readme");a(r&&n,"app/assets/modules/github/pages-composer.js:12");var s=r.textContent,o=e.currentTarget.getAttribute("data-readme-name");a(o,"app/assets/modules/github/pages-composer.js:16"),Yo(t,r),a(e.currentTarget instanceof HTMLButtonElement,"app/assets/modules/github/pages-composer.js:19"),e.currentTarget.disabled=!0,e.currentTarget.textContent=o+" loaded",n.classList.remove("d-none"),t.addEventListener("input",function e(){t.value!==s&&(n.classList.add("d-none"),t.removeEventListener("input",e))})}),r.on("click","#undo-load-readme",function(e){var t=document.getElementById("gollum-editor-body");a(t instanceof HTMLTextAreaElement,"app/assets/modules/github/pages-composer.js:36");var r=document.getElementById("editor-body-buffer");a(r,"app/assets/modules/github/pages-composer.js:39"),Yo(t,r);var n=document.getElementById("load-readme");a(n instanceof HTMLButtonElement,"app/assets/modules/github/pages-composer.js:43");var s=n.getAttribute("data-readme-name");a(s,"app/assets/modules/github/pages-composer.js:46"),n.disabled=!1,n.textContent="Load "+s,e.currentTarget.classList.add("d-none")});var ri=!1;function ni(){var t=ei(window.location.hash);!function(e){var t=e.lineRange,r=e.anchorPrefix,n=document.querySelectorAll(".js-file-line");if(0!==n.length){var a=!0,s=!1,o=void 0;try{for(var i,u=n[Symbol.iterator]();!(a=(i=u.next()).done);a=!0)i.value.classList.remove("highlighted")}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}for(var c=t[0];c<=t[t.length-1];c++){var l=document.querySelector("#"+r+"LC"+c);l&&l.classList.add("highlighted")}}}(t),function(){var e=document.querySelector(".js-file-line-actions");if(!e)return;var t=document.querySelectorAll(".js-file-line.highlighted"),r=t[0];if(r){!function(e){var t=[],r=!0,n=!1,a=void 0;try{for(var s,o=e[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;t.push(i.textContent)}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}var u=document.getElementById("js-copy-lines");if(u instanceof ClipboardCopyElement){u.textContent="Copy "+N.pluralize(e.length,"line"),u.value=t.join("\n");var c="Blob, copyLines, numLines:"+e.length.toString();u.setAttribute("data-ga-click",c)}}(t),function(e){var t=document.getElementById("js-view-git-blame");if(!t)return;t.setAttribute("data-ga-click","Blob, viewGitBlame, numLines:"+e.toString())}(t.length);var n=function(e){var t=document.querySelector(".js-permalink-shortcut");if(t instanceof HTMLAnchorElement){var r=""+t.href+window.location.hash,n=document.getElementById("js-copy-permalink");if(n instanceof ClipboardCopyElement){n.value=r;var a="Blob, copyPermalink, numLines:"+e.toString();n.setAttribute("data-ga-click",a)}return r}}(t.length);n&&function(e,t){var r=document.getElementById("js-new-issue");if(r instanceof HTMLAnchorElement){if(!r.href)return;var n=new URL(r.href,window.location.origin),a=new URLSearchParams(n.search.slice(1));a.set("permalink",e),n.search=a.toString(),r.href=n.toString(),r.setAttribute("data-ga-click","Blob, newIssue, numLines:"+t.toString())}}(n,t.length),e.style.top=r.offsetTop-2+"px",e.classList.remove("d-none")}else e.classList.add("d-none")}();var r=t.lineRange,n=t.anchorPrefix,a=document.querySelector("#"+n+"LC"+r[0]);!ri&&a&&(a.scrollIntoView(),e.closest(a,".blob-wrapper, .js-blob-wrapper").scrollLeft=0);ri=!1}function ai(e){var t=e.currentTarget,r=t.getAttribute("data-original-text");r&&(t.textContent="Copied!",setTimeout(function(){t.textContent=r},2e3))}function si(e){var t=e.match(/#(diff-[a-f0-9]+)([L|R])(\d+)-?([L|R])?(\d+)?$/i);if(null!=t&&6===t.length)return t;var r=e.match(/#(discussion-diff-[0-9]+)([L|R])(\d+)-?([L|R])?(\d+)$/i);return null!=r&&6===r.length?r:null}_(function(){if(document.querySelector(".js-file-line-container")){setTimeout(ni,0);var e=window.location.hash,t=!0,r=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-update-url-with-hash")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;if(o instanceof HTMLAnchorElement)o.hash=e;else if(o instanceof HTMLFormElement){var i=new URL(o.action);i.hash=e,o.action=i.toString()}}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}}),r.on("click","#js-copy-permalink",ai),r.on("click","#js-copy-lines",ai),r.on("click",".js-line-number",function(e){a(e.currentTarget instanceof Element,"app/assets/modules/github/pages/blob/lines.js:160");var t,r,n=ei(e.currentTarget.id);if(e instanceof MouseEvent&&e.shiftKey){var s=Zo(window.location.hash);s.length>0&&n.lineRange.unshift(s[0])}t=function(){window.location.hash=function(e){var t=e.lineRange,r=e.anchorPrefix;switch(t.sort(ti),t.length){case 1:return"#"+r+"L"+t[0];case 2:return"#"+r+"L"+t[0]+"-L"+t[1];default:return"#"}}(n)},r=window.scrollY,ri=!0,t(),window.scrollTo(0,r)}),r.on("submit",".js-jump-to-line-form",function(t){var r=e.query(t.currentTarget,".js-jump-to-line-field",HTMLInputElement).value.replace(/[^\d-]/g,"").split("-").map(function(e){return parseInt(e,10)}).filter(function(e){return e>0}).sort(function(e,t){return e-t});r.length&&(window.location.hash="L"+r.join("-L")),t.preventDefault()}),n.onInput(".js-csv-filter-field",function(e){a(e.target instanceof HTMLInputElement,"app/assets/modules/github/pages/blob/csv.js:7");var t=e.target.value.toLowerCase(),r=!0,n=!1,s=void 0;try{for(var o,i=document.querySelectorAll(".js-csv-data tbody tr")[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){var u=o.value;u.hidden=!!t&&!u.textContent.toLowerCase().includes(t)}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}}),r.on("navigation:keydown",".js-commits-list-item",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/pages/commits.js:9"),"c"===t.detail.hotkey&&e.query(t.target,".js-navigation-open").click()}),r.on("menu:activated",".js-diffbar-commits-menu",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pages/commits.js:16");var t=e.currentTarget.querySelector(".in-range");t&&S.focus(e.currentTarget,t)});var oi=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s,o,i,u,c,l,d,m,p,f,v,g;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=t.anchor,a=t.side,s=t.line,o=t.lastLine,i=t.hashFragment,u=t.partialHashFragment,c=H.findElementByFragmentName(document,n)){e.next=4;break}return e.abrupt("return");case 4:if(l=c.nextElementSibling){e.next=7;break}return e.abrupt("return");case 7:if((d=li(l,a,s,o)).length){e.next=27;break}if(!(m=H.findElementByFragmentName(document,i))){e.next=13;break}return ui(m),e.abrupt("return");case 13:if(p=l.querySelector(".js-diff-load-container")){e.next=16;break}return e.abrupt("return");case 16:return e.prev=16,e.next=19,Zn(p);case 19:(f=H.findElementByFragmentName(document,i))instanceof HTMLElement&&ui(f),e.next=26;break;case 23:e.prev=23,e.t0=e.catch(16),ui(l);case 26:return e.abrupt("return");case 27:return e.next=29,ci(d,u);case 29:v=1,g=H.findElementByFragmentName(document,i),li(l,a,s,o).length?oi(t,r):g?ui(g):r<v&&oi(t,r+1);case 33:case"end":return e.stop()}},e,this,[[16,23]])}));return function(t,r){return e.apply(this,arguments)}}(),ii=function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,i,u,c,l,d,m,p,f,v;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=void 0,n?(i="R"===n.slice(-1)?"data-right-range":"data-left-range",u=r.getAttribute(i)||"",c=parseInt(u.split("-")[0],10),s=n+c):s=r.hash.slice(1),l=r.getAttribute("data-url"),a(l,"app/assets/modules/github/pages/diffs/expander.js:142"),d=new URL(l,window.location.origin),(m=new URLSearchParams(d.search.slice(1))).append("anchor",s),d.search=m.toString(),t.next=10,o.fetchSafeDocumentFragment(document,d);case 10:p=t.sent,f=e.closest(r,".js-expandable-line"),g=".file-diff-line",h=void 0,h=f.nextElementSibling,v=h instanceof HTMLElement&&h.matches(g)?h:null,I.preservingScrollPosition(v,function(){f.replaceWith(p)});case 14:case"end":return t.stop()}var g,h},t,this)}));return function(e,r){return t.apply(this,arguments)}}();function ui(e){O.ensureExpanded(e),Jn(e)}function ci(e,t){return Promise.all(e.map(function(e){return ii(e,t)}))}function li(t,r,n,a){var s=parseInt(n,10),o=parseInt(a,10);return e.querySelectorAll(t,".js-expand",HTMLAnchorElement).filter(function(e){var t="R"===r?"data-right-range":"data-left-range",n=(e.getAttribute(t)||"").split("-"),a=parseInt(n[0],10),i=parseInt(n[1],10);return a<=s&&s<=i||(s<=a&&i<=o||a<=o&&o<=i)})}function di(e,t){var r=e.nextElementSibling;return r&&r.matches(t)?r:null}function mi(t,r){var n=e.query(t,".js-comment-form-error"),a=void 0;a=r.errors?Array.isArray(r.errors)?r.errors.join(", "):r.errors:"There was an error posting your comment.",n.textContent=a,n.style.display="block",n.classList.remove("d-none")}function pi(e){var t=e.querySelector(".js-toggle-file-notes");t instanceof HTMLInputElement&&l.changeValue(t,!0)}function fi(){var e=!0,t=!1,r=void 0;try{for(var n,a=document.querySelectorAll(".file .js-inline-comments-container")[Symbol.iterator]();!(e=(n=a.next()).done);e=!0){var s=n.value,o=s.querySelectorAll(".js-comments-holder > *").length>0,i=s.querySelector(".js-inline-comment-form-container"),u=!!i&&i.classList.contains("open");o||u||s.remove()}}catch(e){t=!0,r=e}finally{try{!e&&a.return&&a.return()}finally{if(t)throw r}}}function vi(t){var r=e.query(document,t).firstElementChild;a(r,"app/assets/modules/github/pages/diffs/line-comments.js:215");var n=r.cloneNode(!0),s=n.querySelector("textarea");return s instanceof HTMLTextAreaElement&&(s.value=""),n}function gi(t,r){var n=!0,a=!1,s=void 0;try{for(var o,i=["type","path","position","line","side","original-line"][Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value,c=t.elements.namedItem(u);if(c instanceof HTMLInputElement){var l=r.getAttribute("data-"+u)||"";c.value=l}}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}var d=r.getAttribute("data-position")||"",m=r.getAttribute("data-anchor")||"",p=e.query(t,".js-comment-field"),f=p.id.replace(/^r\d+ /,"").replace("${anchor}",m).replace("${position}",d),v=!0,g=!1,h=void 0;try{for(var y,b=t.querySelectorAll('[for="'+p.id+'"]')[Symbol.iterator]();!(v=(y=b.next()).done);v=!0){y.value.setAttribute("for",f)}}catch(e){g=!0,h=e}finally{try{!v&&b.return&&b.return()}finally{if(g)throw h}}p.id=f}function hi(e){return Math.floor(e/2)}function yi(e,t){var r=e.parentElement;if(r){var n=r.children,a=void 0;if(4===n.length)for(var s=0,o=n.length;s<o;s++){n[s]===e&&(a=hi(s))}for(var i=0,u=n.length;i<u;i++){var c=n[i];null!=a&&hi(i)!==a||c.classList.toggle("is-hovered",t)}}}_(function(){var e=function(e){if(!e)return;var t=si(e);if(!t)return;var r=t[1],n=t[2],a=t[3],s=t[5];return{anchor:r,side:n,line:a,lastLine:s,hashFragment:r+n+a,partialHashFragment:r+n}}(window.location.hash);e&&(H.findElementByFragmentName(document,e.hashFragment)&&!e.lastLine||oi(e,0))}),r.on("click",".js-expand",function(e){e.preventDefault(),a(e.currentTarget instanceof HTMLAnchorElement,"app/assets/modules/github/pages/diffs/expander.js:122"),ii(e.currentTarget,null)}),r.on("click",".js-add-single-line-comment",function(t){var r=t.currentTarget;a(r instanceof Element,"app/assets/modules/github/pages/diffs/line-comments.js:17"),pi(e.closest(r,".file"));var n=function(t){var r=e.closest(t,"tr"),n=di(r,".js-inline-comments-container");if(n instanceof Element){var s=e.query(r,"button.js-add-line-comment"),o=Array.from(n.querySelectorAll(".js-inline-comment-form")).pop();return a(o instanceof HTMLFormElement,"app/assets/modules/github/pages/diffs/line-comments.js:52"),gi(o,s),n}}(r)||function(t){var r=e.closest(t,"tr"),n=vi("#js-inline-comments-single-container-template"),a=n.querySelector(".js-inline-comment-form");a instanceof HTMLFormElement&&gi(a,t);return r.after(n),n}(r);_o(Array.from(n.querySelectorAll(".js-line-comments")).pop())}),r.on("click",".js-toggle-new-thread-form",function(t){var r=e.closest(t.currentTarget,"tr"),n=Array.from(r.querySelectorAll(".js-line-comments")).pop(),s=e.query(n,".js-inline-comment-form",HTMLFormElement),o=r.previousElementSibling,i=t.currentTarget.getAttribute("data-side");a(o,"app/assets/modules/github/pages/diffs/line-comments.js:35");var u="right"===i?".blob-code-addition":".blob-code-deletion";gi(s,e.query(o,u+" button.js-add-line-comment")),_o(n)}),r.on("click",".js-add-split-line-comment",function(t){var r=t.currentTarget;pi(e.closest(r,".file"));var n,s,o=e.closest(r,"tr"),i="addition"===r.getAttribute("data-type")?"js-addition":"js-deletion",u=function(e,t,r){var n=e.querySelector(".js-line-comments."+t);if(n){var s=Array.from(n.querySelectorAll(".js-inline-comment-form")).pop();return a(s instanceof HTMLFormElement,"app/assets/modules/github/pages/diffs/line-comments.js:247"),gi(s,r),n}var o=vi("#js-inline-comments-split-form-container-template");o.classList.add(t);var i=o.querySelector(".js-inline-comment-form");i instanceof HTMLFormElement&&gi(i,r);var u=e.querySelectorAll("."+t);u[u.length-1].after(o);var c=!0,l=!1,d=void 0;try{for(var m,p=u[Symbol.iterator]();!(c=(m=p.next()).done);c=!0){var f=m.value;f.remove()}}catch(e){l=!0,d=e}finally{try{!c&&p.return&&p.return()}finally{if(l)throw d}}return o}((s=di(n=o,".js-inline-comments-container"))||(s=vi("#js-inline-comments-split-container-template"),n.after(s),s),i,r);_o(Array.from(u.querySelectorAll(".js-line-comments")).pop())}),c.remoteForm(".js-inline-comment-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,o,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=void 0,t.prev=1,t.next=4,n.json();case 4:s=t.sent,t.next=16;break;case 7:if(t.prev=7,t.t0=t.catch(1),!t.t0.response){t.next=15;break}o=void 0;try{o=t.t0.response.json}catch(e){}if(!o){t.next=15;break}return mi(r,o),t.abrupt("return");case 15:throw t.t0;case 16:u=s.json,c=u.inline_comment,l=r.closest(".js-line-comments"),c&&(a(l,"app/assets/modules/github/pages/diffs/line-comments.js:119"),e.query(l,".js-comments-holder").append(i.parseHTML(document,c))),(d=u.inline_comment_thread)&&(a(l,"app/assets/modules/github/pages/diffs/line-comments.js:126"),l.replaceWith(i.parseHTML(document,d))),Fo(r);case 23:case"end":return t.stop()}},t,this,[[1,7]])}));return function(e,r){return t.apply(this,arguments)}}()),document.addEventListener("session:resume",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pages/diffs/line-comments.js:183"),function(e){var t=e.match(/^new_inline_comment_(?:discussion|diff)_(?:[\w-]+)_(\d+)_(\d+)$/)||[],r=oe(t,2),n=r[0],a=r[1];if(n){var s=document.querySelector(".js-inline-comment-form input[name='in_reply_to'][value='"+a+"']");if(s){var o=s.closest(".js-line-comments");o&&_o(o)}}}(e.detail.targetId),function(e){var t=e.match(/^new_inline_comment_diff_(?:[\w-]+)_(\d+)$/)||[],r=oe(t,2),n=r[0],a=r[1];if(n){var s=document.querySelector(".js-add-line-comment[data-anchor='"+n+"'][data-position='"+a+"']");s&&s.click()}}(e.detail.targetId)}),t.observe(".js-comment",{remove:fi}),document.addEventListener("inlinecomment:collapse",function(){fi()}),t.observe(".diff-table",function(e){var t=null;function r(e){a(e instanceof MouseEvent,"app/assets/modules/github/pages/diffs/line-highlight.js:43"),t&&yi(t,!1),t=null}function n(e){a(e instanceof MouseEvent,"app/assets/modules/github/pages/diffs/line-highlight.js:51"),t&&yi(t,!1),e.target instanceof HTMLElement&&(t=e.target.closest("td.blob-code"))&&yi(t,!0)}return{add:function(){e.addEventListener("mouseenter",r),e.addEventListener("mouseleave",r),e.addEventListener("mouseover",n)},remove:function(){e.removeEventListener("mouseenter",r),e.removeEventListener("mouseleave",r),e.removeEventListener("mouseover",n)}}}),r.on("click",".js-linkable-line-number",function(e){window.location.hash=e.currentTarget.id,e.preventDefault()});var bi=null;function ji(e){return Math.floor(e/2)}function Li(){var e=void 0,t=void 0,r=void 0,n=void 0,s=void 0,o=void 0,i=void 0,u=void 0;if(bi){for(r=0,s=bi.length;r<s;r++)(e=bi[r]).classList.remove("selected-line");bi=null}var c=si(window.location.hash);if(c){var l=c[1]+c[2]+c[3];if(l&&(i=H.findElementByFragmentName(document,l)),i&&i.classList.contains("js-linkable-line-number")){var d=i.parentNode;a(d instanceof HTMLElement,"app/assets/modules/github/pages/diffs/linkable-line-number.js:64");var m=d.children;if(4===m.length)for(t=n=0,o=m.length;n<o;t=++n)(e=m[t])===i&&(u=ji(t));bi=function(){var r,n=void 0,a=[];for(t=n=0,r=m.length;n<r;t=++n)e=m[t],null!=u&&ji(t)!==u||(e.classList.toggle("selected-line"),a.push(e));return a}()}}}function wi(){var e=document.body;a(e,"app/assets/modules/github/pages/diffs/split.js:9");var t=document.querySelector("meta[name=diff-view]"),r=t&&t instanceof HTMLMetaElement?t.content:"",n=document.querySelector(".file-diff-split"),s=document.querySelector(".CommunityTemplate-header"),o=!!("split"===r&&n||document.querySelector(".wants-full-width-container"));e.classList.toggle("full-width",o);var i=!!s;e.classList.toggle("full-width-p0",i)}function xi(e){var t=e.parentElement;a(t,"app/assets/modules/github/pages/diffs/tr-collapsing.js:19");var r=t.querySelectorAll("td.js-line-comments").length,n=t.querySelectorAll("td.js-line-comments.is-collapsed").length;t.classList.toggle("is-collapsed",n>0&&r===n)}function ki(t){var r=e.querySelectorAll(document,".js-hook-event-checkbox",HTMLInputElement),n=!0,a=!1,s=void 0;try{for(var o,i=r[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;u.checked=u.matches(t)}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}}function Ei(e){e.classList.contains("read")||(e.classList.toggle("unread"),e.classList.toggle("read"))}_(Li),t.observe(".blob-expanded",Li),t.observe(".js-diff-progressive-loader",function(e){e.addEventListener("load",Li)}),t.observe(".js-diff-entry-loader",function(e){e.addEventListener("load",Li)}),r.on("click",".js-rich-diff.collapsed .js-expandable",function(e){e.preventDefault();var t=e.target.closest(".js-rich-diff");a(t,"app/assets/modules/github/pages/diffs/prose-diff.js:9"),t.classList.remove("collapsed")}),r.on("click",".js-show-rich-diff",function(e){var t=e.currentTarget.closest(".js-warn-no-visible-changes");if(t){t.classList.add("d-none");var r=t.parentElement;a(r,"app/assets/modules/github/pages/diffs/prose-diff.js:20");var n=r.querySelector(".js-no-rich-changes");n&&n.classList.remove("d-none")}}),t.observe("meta[name=diff-view]",{add:wi,remove:wi}),t.observe(".file-diff-split",{add:wi,remove:wi}),t.observe(".js-compare-tab.selected",{add:wi,remove:wi}),t.observe(".wants-full-width-container",{add:wi,remove:wi}),t.observe(".CommunityTemplate-header",{add:wi,remove:wi}),r.on("change",".js-toggle-file-notes",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/pages/diffs/toggle-file-notes.js:15"),e.closest(r,".file").classList.toggle("show-inline-notes",r.checked)}),r.on("click",".js-toggle-all-file-notes",function(t){var r=e.querySelectorAll(document,".js-toggle-file-notes",HTMLInputElement),n=r.some(function(e){return e.checked}),a=!0,s=!1,o=void 0;try{for(var i,u=r[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;l.changeValue(c,!n)}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}t.preventDefault()}),t.observe(".js-inline-comments-container",function(e){var t=void 0,r=e.closest(".file");if(r)return{add:t=function(){var e=null!=r.querySelector(".js-inline-comments-container");r.classList.toggle("has-inline-notes",e)},remove:t}}),t.observe("td.js-line-comments.is-collapsed",{add:xi,remove:xi}),t.observe(".js-hook-url-field",{constructor:HTMLInputElement,add:function(t){function r(){var r=t.form;a(r,"app/assets/modules/github/pages/hooks.js:19");var n=void 0;try{n=new URL(t.value)}catch(e){}var s=r.querySelector(".js-invalid-url-notice");s&&(s.hidden=!!(""===t.value||n&&/^https?:/.test(n.protocol))),e.query(r,".js-ssl-hook-fields").hidden=!(n&&"https:"===n.protocol)}p.addThrottledInputEventListener(t,r),r()}}),r.on("change",".js-hook-event-choice",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/pages/hooks.js:49");var n=r.checked&&"custom"===r.value,s=r.closest(".js-hook-events-field");(s&&s.classList.toggle("is-custom",n),r.checked)&&(n?e.query(document,".js-hook-wildcard-event",HTMLInputElement).checked=!1:"push"===r.value?ki('[value="push"]'):"all"===r.value&&ki(".js-hook-wildcard-event"))}),r.on("details:toggled",".js-hook-secret",function(t){var r=t.currentTarget,n=e.query(r,"input[type=password]",HTMLInputElement);r.classList.contains("open")&&n.focus()}),r.on("click",".js-hook-delivery-container .js-tabnav-tab",function(t){var r=t.currentTarget,n=e.closest(r,".js-hook-delivery-container"),s=!0,o=!1,i=void 0;try{for(var u,c=n.querySelectorAll(".js-tabnav-tab, .js-tabnav-tabcontent")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.classList.remove("selected")}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}r.classList.add("selected");var l=r.getAttribute("data-tab-target");a(l,"app/assets/modules/github/pages/hooks.js:88");var d=n.querySelector('.js-tabnav-tabcontent[data-tab-name="'+l+'"]');d&&d.classList.add("selected")}),r.on("click",".js-hook-deliveries-pagination-button",function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=t.currentTarget,a(r instanceof HTMLButtonElement,"app/assets/modules/github/pages/hooks.js:97"),r.disabled=!0,n=r.parentElement,a(n,"app/assets/modules/github/pages/hooks.js:100"),s=r.getAttribute("data-url"),a(s,"app/assets/modules/github/pages/hooks.js:103"),e.t0=n,e.next=10,o.fetchSafeDocumentFragment(document,s);case 10:e.t1=e.sent,e.t0.before.call(e.t0,e.t1),n.remove();case 13:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}()),c.remoteForm(".js-redeliver-hook-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return s=void 0,t.prev=1,t.next=4,n.html();case 4:s=t.sent,t.next=12;break;case 7:return t.prev=7,t.t0=t.catch(1),a(r.parentElement,"app/assets/modules/github/pages/hooks.js:115"),r.classList.add("failed"),t.abrupt("return");case 12:o=r.getAttribute("data-delivery-guid")||"",i=e.query(document,'.js-hook-delivery-container[data-delivery-guid="'+o+'"]'),u=i.parentElement,a(u,"app/assets/modules/github/pages/hooks.js:123"),i.replaceWith(s.html),e.query(u,"poll-include-fragment").addEventListener("load",function(){var t=e.query(u,".js-hook-delivery-container"),r=e.query(u,".js-item-status"),n=t.getAttribute("data-status-class");r.classList.remove("success","pending","failure"),n&&r.classList.add(n);var a=u.querySelector(".js-item-status-tooltip"),s=t.getAttribute("data-status-message");a&&s&&a.setAttribute("aria-label",s)});case 19:case"end":return t.stop()}},t,this,[[1,7]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-test-hook-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.query(document,".js-test-hook-message")).classList.remove("error","success"),t.prev=2,t.next=5,n.text();case 5:t.next=20;break;case 7:if(t.prev=7,t.t0=t.catch(2),a.classList.add("error"),s=e.query(a,".js-test-hook-message-errors"),null==t.t0.response){t.next=18;break}return t.next=14,t.t0.response.json();case 14:o=t.sent,s.textContent=o.errors,t.next=19;break;case 18:s.textContent=s.getAttribute("data-network-error-message")||"";case 19:return t.abrupt("return");case 20:a.classList.add("success");case 21:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-hook-enforcement-select .js-navigation-item",function(t){var r=t.currentTarget,n=e.query(t.currentTarget,".js-value"),a=e.closest(r,"form",HTMLFormElement),s=e.query(a,".js-enforcement-value",HTMLInputElement),o=n.textContent.split("_"),i=oe(o,2),u=i[0],c=i[1];s.value=u;var d=a.querySelector(".js-final-value");d instanceof HTMLInputElement&&(d.value=c?"false":"true"),l.submit(a)}),r.on("click",".js-notification-target",function(t){(a(t instanceof MouseEvent,"app/assets/modules/github/pages/notifications.js:19"),0===t.button)&&Ei(e.closest(t.currentTarget,".js-notification"))}),c.remoteForm(".js-delete-notification",function(){var t=te(regeneratorRuntime.mark(function t(r,n){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:Ei(e.closest(r,".js-notification"));case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-mute-notification",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:Ei(a=e.closest(r,".js-notification")),a.classList.toggle("muted");case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-unmute-notification",function(){var t=te(regeneratorRuntime.mark(function t(r,n){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:e.closest(r,".js-notification").classList.toggle("muted");case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-mark-visible-as-read",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:for(a=e.closest(r,".js-notifications-browser"),s=a.querySelectorAll(".unread"),o=!0,i=!1,u=void 0,t.prev=7,c=s[Symbol.iterator]();!(o=(l=c.next()).done);o=!0)(d=l.value).classList.remove("unread"),d.classList.add("read");t.next=15;break;case 11:t.prev=11,t.t0=t.catch(7),i=!0,u=t.t0;case 15:t.prev=15,t.prev=16,!o&&c.return&&c.return();case 18:if(t.prev=18,!i){t.next=21;break}throw u;case 21:return t.finish(18);case 22:return t.finish(15);case 23:(m=a.querySelector(".js-mark-visible-as-read"))&&m.classList.add("mark-all-as-read-confirmed"),(p=a.querySelector(".js-mark-as-read-confirmation"))&&p.classList.add("mark-all-as-read-confirmed");case 27:case"end":return t.stop()}},t,this,[[7,11,15,23],[16,,18,22]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-mark-remaining-as-read",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=e.closest(r,".js-notifications-browser"),(s=a.querySelector(".js-mark-remaining-as-read"))&&s.classList.add("d-none"),(o=a.querySelector(".js-mark-remaining-as-read-confirmation"))&&o.classList.remove("d-none");case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-delete-saved-notification-thread",function(){var t=te(regeneratorRuntime.mark(function t(r,n){return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:e.closest(r,".js-notification").classList.toggle("read");case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("navigation:keydown",".js-notification",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pages/notifications.js:92");var t=e.currentTarget;switch(e.detail.hotkey){case"I":case"e":case"y":l.submit(t.querySelector(".js-delete-notification")),e.preventDefault(),e.stopPropagation();break;case"M":case"m":l.submit(t.querySelector(".js-mute-notification")),e.preventDefault(),e.stopPropagation()}}),r.on("navigation:keyopen",".js-notification",function(e){Ei(e.currentTarget)}),c.remoteForm(".js-notifications-subscription",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.query(r,".js-spinner")).classList.remove("d-none"),t.prev=2,t.next=5,n.html();case 5:t.next=9;break;case 7:t.prev=7,t.t0=t.catch(2);case 9:a.classList.add("d-none");case 10:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-toggler-container .js-set-approval-state",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.closest(r,".js-toggler-container")).classList.add("loading"),t.next=4,n.json();case 4:1===(s=t.sent).json.approval_state?a.classList.add("on"):2===s.json.approval_state&&(a.classList.add("revoked"),a.classList.remove("on")),a.classList.remove("loading");case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-request-access-approval-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:n=t.getAttribute("data-container-id"),a(n,"app/assets/modules/github/pages/oauth.js:24"),s=document.getElementById(n),a(s,"app/assets/modules/github/pages/oauth.js:26"),s.classList.add("on");case 7:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}());var Ti=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.getAttribute("data-results")){t.next=3;break}return t.abrupt("return");case 3:if(s=document.getElementById(n)){t.next=6;break}return t.abrupt("return");case 6:if(i=qi.get(s)){t.next=28;break}if(!Si){t.next=10;break}return t.abrupt("return");case 10:return Si=!0,u=s.getAttribute("data-url"),a(u,"app/assets/modules/github/pages/repositories/tree-finder.js:33"),c=void 0,t.prev=14,t.next=17,o.fetchJSON(u);case 17:c=t.sent,t.next=24;break;case 20:return t.prev=20,t.t0=t.catch(14),Si=!1,t.abrupt("return");case 24:return Si=!1,qi.set(s,c.paths),Ti(r),t.abrupt("return");case 28:for(l=e.query(s,".js-tree-browser-result-template").firstElementChild,a(l,"app/assets/modules/github/pages/repositories/tree-finder.js:48"),d=e.query(s,".js-tree-finder-results"),m=r.value,p=void 0,f=void 0,m?(p=jt(m),f=yt(i,m)):f=i,s.classList.toggle("filterable-empty",!f.length),v=document.createDocumentFragment(),g=!0,h=!1,y=void 0,t.prev=40,b=f.slice(0,50)[Symbol.iterator]();!(g=(j=b.next()).done);g=!0)L=j.value,w=l.cloneNode(!0),x=e.query(w,".js-tree-finder-path",HTMLAnchorElement),(k=new URL(x.href)).pathname=k.pathname+"/"+encodeURI(L),x.href=k.href,x.textContent=L,Lt(x,m,p),v.append(w);t.next=48;break;case 44:t.prev=44,t.t1=t.catch(40),h=!0,y=t.t1;case 48:t.prev=48,t.prev=49,!g&&b.return&&b.return();case 51:if(t.prev=51,!h){t.next=54;break}throw y;case 54:return t.finish(51);case 55:return t.finish(48);case 56:d.innerHTML="",d.append(v),S.focus(d);case 59:case"end":return t.stop()}},t,this,[[14,20],[40,44,48,56],[49,,51,55]])}));return function(e){return t.apply(this,arguments)}}(),qi=new WeakMap,Si=!1;n.onKey("keydown",".js-tree-finder-field",function(e){"Escape"===e.key&&(e.preventDefault(),history.back())}),t.observe(".js-tree-finder-field",{constructor:HTMLInputElement,initialize:function(e){function t(){Ti(e)}return t(),{add:function(e){p.addThrottledInputEventListener(e,t),e.focus()},remove:function(e){p.removeThrottledInputEventListener(e,t)}}}}),document.addEventListener("pjax:end",function(){var e=document.querySelector('meta[name="selected-link"]'),t=e&&e.getAttribute("value");if(t){var r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-sidenav-container-pjax .js-selected-navigation-item")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value,u=(i.getAttribute("data-selected-links")||"").split(" ").indexOf(t)>=0;i.classList.toggle("selected",u)}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}}),n.onFocus(".js-email-notice-trigger",function(t){var r=e.querySelectorAll(document,".js-email-notice"),n=!0,a=!1,s=void 0;try{for(var o,i=r[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.classList.add("notice-highlight")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}t.addEventListener("blur",function(){var e=!0,t=!1,n=void 0;try{for(var a,s=r[Symbol.iterator]();!(e=(a=s.next()).done);e=!0){a.value.classList.remove("notice-highlight")}}catch(e){t=!0,n=e}finally{try{!e&&s.return&&s.return()}finally{if(t)throw n}}})}),t.observe(".js-plan-choice:checked",{add:function(e){var t=e.closest(".plan-row");t&&t.classList.add("selected")},remove:function(e){var t=e.closest(".plan-row");t&&t.classList.remove("selected")}}),t.observe(".js-setup-organization:checked",{add:function(){var t=e.query(document,".js-choose-plan-submit");t.getAttribute("data-default-text")||t.setAttribute("data-default-text",t.textContent),t.textContent=t.getAttribute("data-org-text")||""},remove:function(){var t=e.query(document,".js-choose-plan-submit");t.textContent=t.getAttribute("data-default-text")||""}});var Ai=new WeakMap;function Mi(t){var r=t.target;a(r instanceof HTMLElement,"app/assets/modules/github/pages/signup.js:65");var n=e.closest(r,"form");if(r.closest("input[type=text]")&&!Ai.get(n)){var s=e.query(n,".js-signup-source",HTMLInputElement);F.trackEvent({category:"Signup",action:"Attempt",label:s.value}),Ai.set(n,!0)}}t.observe(".js-signup-form",{subscribe:function(e){return L.fromEvent(e,"input",Mi)}}),t.observe(".js-octocaptcha-parent",function(t){var r=e.query(t,".js-octocaptcha-spinner"),n=e.query(t,".js-octocaptcha-success"),a=e.query(t,".js-octocaptcha-token",HTMLInputElement),s=e.query(t,"#signup_button",HTMLButtonElement),o=e.query(t,".js-octocaptcha-frame-container"),i=e.query(t,".js-octocaptcha-frame"),u=a.getAttribute("data-octocaptcha-url"),c=!1,l=function(){c||(c=!0,r.classList.add("d-none"),n.classList.remove("d-none"),s.disabled=!1)};setTimeout(l,2e4),i.addEventListener("error",l),window.addEventListener("message",function(e){if(e.origin===u){var t=e.data&&e.data.event;"captcha-loaded"===t?c||(c=!0,r.classList.add("d-none"),o.classList.remove("v-hidden","position-absolute")):"captcha-complete"===t?(a.value=e.data.sessionToken,s.disabled=!1):"captcha-suppressed"===t&&l()}})});var Hi=null;r.on("pjax:click",".js-pjax-capture-input",function(){Hi=function(e){var t=e.createElement("textarea");return t.style.position="fixed",t.style.top="0",t.style.left="0",t.style.opacity="0",a(e.body,"app/assets/modules/github/capture-keypresses.js:18"),e.body.appendChild(t),t.focus(),function(){return t.blur(),t.remove(),t.value}}(document)}),r.on("pjax:end","#js-repo-pjax-container",function(){if(Hi){var e=Hi(),t=document.querySelector(".js-pjax-restore-captured-input");t instanceof HTMLInputElement&&e&&l.changeValue(t,e),Hi=null}}),r.on("pjax:click",".js-pjax-history-navigate",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pjax/history-navigate.js:14"),e.currentTarget.href===f.getBackURL()?(history.back(),e.detail.relatedEvent.preventDefault(),e.preventDefault()):e.currentTarget.href===f.getForwardURL()&&(history.forward(),e.detail.relatedEvent.preventDefault(),e.preventDefault())}),t.observe("link[rel=pjax-prefetch]",{constructor:HTMLLinkElement,initialize:function(e){var t=E.fetch(e,{headers:{Purpose:"prefetch"}});J.setPrefetchResponse(e,t)}});var Ri=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(r=document.getElementById("js-contribution-activity")){e.next=3;break}return e.abrupt("return");case 3:return r.classList.add("loading"),e.next=6,o.fetchSafeDocumentFragment(document,t);case 6:n=e.sent,r.classList.remove("loading"),r.innerHTML="",r.append(n);case 10:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),Ii=function(){var t=te(regeneratorRuntime.mark(function t(r,n,s){var i,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return i=e.query(document,".js-calendar-graph"),u=i.getAttribute("data-graph-url"),a(u,"app/assets/modules/github/profile/contributions.js:162"),c=new URL(u,window.location.origin),l=Bi(c.search.slice(1),{from:r,to:n,org:s}),c.search=l.toString(),t.next=8,o.fetchSafeDocumentFragment(document,c.toString());case 8:d=t.sent,e.query(document,".js-yearly-contributions").replaceWith(d);case 10:case"end":return t.stop()}},t,this)}));return function(e,r,n){return t.apply(this,arguments)}}(),Ci=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=document.getElementById("year-list-container")){e.next=3;break}return e.abrupt("return");case 3:return r.append("year_list","1"),t.search=r.toString(),e.next=7,o.fetchSafeDocumentFragment(document,t.toString());case 7:a=e.sent,n.innerHTML="",n.append(a);case 10:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}(),_i=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,o,i,u;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=$i(),n=Ki(),a(n.first,"app/assets/modules/github/profile/contributions.js:365"),a(n.last,"app/assets/modules/github/profile/contributions.js:366"),s=new Date(n.first),o=new Date(n.last),e.next=8,Ii(s,o,t);case 8:r.first&&r.last&&(i=new Date(r.first),u=new Date(r.last),Wi(i,u));case 9:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),Fi=null;function Pi(){var t=e.query(document,".js-calendar-graph").getAttribute("data-url");return a(t,"app/assets/modules/github/profile/contributions.js:19"),new URL(t,window.location.origin)}function Ni(e){var t=e.target;a(t instanceof Element,"app/assets/modules/github/profile/contributions.js:58"),t.matches("rect.day")&&(Oi(),function(e){var t=document.body;a(t,"app/assets/modules/github/profile/contributions.js:91");var r=e.getAttribute("data-date");a(r,"app/assets/modules/github/profile/contributions.js:94");var n=function(e,t){var r=Di[t.getUTCMonth()].slice(0,3)+" "+t.getUTCDate()+", "+t.getUTCFullYear(),n=0===e?"No":P.formatNumber(e),a=document.createElement("div");a.classList.add("svg-tip","svg-tip-one-line"),a.style.pointerEvents="none";var s=document.createElement("strong");return s.textContent=n+" "+N.pluralize(e,"contribution"),a.append(s," on "+r),a}(parseInt(e.getAttribute("data-count")),Qi(r));t.appendChild(n);var s=e.getBoundingClientRect(),o=s.left+window.pageXOffset-n.offsetWidth/2+s.width/2,i=s.bottom+window.pageYOffset-n.offsetHeight-2*s.height;n.style.top=i+"px",n.style.left=o+"px"}(t))}function Oi(){var e=document.querySelector(".svg-tip");e&&e.remove()}t.observe(".js-calendar-graph-svg",function(t){var r=e.closest(t,".js-calendar-graph");r.addEventListener("mouseover",Ni),r.addEventListener("mouseout",Oi);var n=r.getAttribute("data-from");n&&(n=Fi=Qi(n));var a=r.getAttribute("data-to");a&&(a=Qi(a))}),r.on("click",".js-calendar-graph rect.day",function(e){a(e instanceof MouseEvent,"app/assets/modules/github/profile/contributions.js:40");var t=e.currentTarget,r=t.closest(".js-calendar-graph").getAttribute("data-org")||"",n=t.getAttribute("data-date"),s=t.classList.contains("active");(a(n,"app/assets/modules/github/profile/contributions.js:46"),s)?Zi(Yi()):function(e,t,r){var n=void 0,a=void 0;if(Fi&&t){var s=Fi.getTime(),o=s-26784e5,i=s+26784e5,u=e>Fi?[Fi,e]:[e,Fi],c=oe(u,2);n=c[0],a=c[1],n=new Date(Math.max(n,o)),a=new Date(Math.min(a,i)),Fi=null}else Fi=a=n=e;Wi(n,a);var l=Pi(),d=Bi(l.search.slice(1),{from:n,to:a,org:r});d.append("tab","overview"),l.search=d.toString(),Ri(l.toString())}(Qi(n),e.shiftKey,r)});var Di=["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"];function Bi(e,t){var r=new URLSearchParams(e);r.delete("from"),r.delete("to"),r.delete("org");var n=t.fromStr;t.from&&(n=Xi(t.from)),n&&r.append("from",n);var a=t.toStr;t.to&&(a=Xi(t.to)),a&&r.append("to",a);var s=t.org;return s&&r.append("org",s),r}function Ui(e,t){var r=(e.getAttribute("class")||"").trim().split(" ").filter(function(e){return e!==t});e.setAttribute("class",r.join(" "))}function zi(e,t){var r=(e.getAttribute("class")||"")+" "+t;e.setAttribute("class",r.trim())}function Wi(t,r){var n=e.query(document,".js-calendar-graph"),a=n.querySelectorAll("rect.day"),s=!0,o=!1,i=void 0;try{for(var u,c=a[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){Ui(u.value,"active")}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}if(n.classList.remove("days-selected"),t||r){n.classList.add("days-selected");var l=!0,d=!1,m=void 0;try{for(var p,f=a[Symbol.iterator]();!(l=(p=f.next()).done);l=!0){var v=p.value;g(v)&&zi(v,"active")}}catch(e){d=!0,m=e}finally{try{!l&&f.return&&f.return()}finally{if(d)throw m}}}function g(e){var n=Qi(e.getAttribute("data-date")||"").getTime();return t&&r?t.getTime()<=n&&n<=r.getTime():t?n===t.getTime():void 0}}function Vi(){var e=new URLSearchParams(window.location.search.slice(1));return{first:e.get("from"),last:e.get("to")}}function $i(){var t=e.query(document,".js-calendar-graph").querySelectorAll("rect.active"),r=t[0],n=t[t.length-1];return{first:r=r&&r.getAttribute("data-date"),last:n=n&&n.getAttribute("data-date")}}function Ji(){var e=new URL(Yi()),t=new URLSearchParams(e.search.slice(1));return{first:t.get("from"),last:t.get("to")}}function Ki(){var t=e.query(document,".js-calendar-graph");return{first:t.getAttribute("data-from"),last:t.getAttribute("data-to")}}function Gi(e){return("0"+e).slice(-2)}function Xi(e){return e.getUTCFullYear()+"-"+Gi(e.getUTCMonth()+1)+"-"+Gi(e.getUTCDate())}function Qi(e){var t=e.split("-").map(function(e){return parseInt(e,10)}),r=oe(t,3),n=r[0],a=r[1],s=r[2];return new Date(Date.UTC(n,a-1,s))}function Yi(){var t=e.query(document,".js-profile-timeline-year-list .js-year-link.selected");return a(t instanceof HTMLAnchorElement,"app/assets/modules/github/profile/contributions.js:448"),t.href||""}function Zi(e){var t=new URL(e).search,r=new URLSearchParams(t.slice(1)),n=r.get("org"),a=new Date(r.get("from")),s=new Date(r.get("to"));Ii(a,s,n);var o=new Pi,i=Bi(o.search.slice(1),{from:a,to:s,org:n});i.append("tab","overview"),o.search=i.toString(),Ri(o.toString())}function eu(e){var t=e.closest(".js-details-container");t&&t.classList.add("open");var r=e.getBoundingClientRect(),n=window.scrollY+r.top-62-10;window.scrollTo(0,n)}r.on("click",".js-org-filter-link",function(e){e.stopPropagation(),e.preventDefault();var t=e.currentTarget;a(t instanceof HTMLAnchorElement,"app/assets/modules/github/profile/contributions.js:385");var r=t.closest(".js-org-filter-links-container");a(r,"app/assets/modules/github/profile/contributions.js:387");var n=r.querySelector(".js-org-filter-link.selected"),s=new URL(t.href,window.location.origin),o=new URLSearchParams(s.search.slice(1)).get("org"),i=function(){var e=[$i,Vi,Ji],t=void 0,r=void 0,n=void 0,s=!0,o=!1,i=void 0;try{for(var u,c=e[Symbol.iterator]();!(s=(u=c.next()).done);s=!0)if((n=(0,u.value)()).last){t=n.first,r=n.last;break}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}return a(t,"app/assets/modules/github/profile/contributions.js:296"),a(r,"app/assets/modules/github/profile/contributions.js:297"),{first:t,last:r}}();a(i.first,"app/assets/modules/github/profile/contributions.js:394"),a(i.last,"app/assets/modules/github/profile/contributions.js:395");var u=new Date(i.first),c=new Date(i.last);n&&n.classList.remove("selected"),t!==n&&t.classList.add("selected"),_i(o);var l=Pi(),d=Bi(l.search.slice(1),{org:o,from:u,to:c});l.search=d.toString(),Ri(l.toString()),Ci(l,d),f.pushState(null,"",l.toString())}),r.on("click",".js-year-link",function(t){t.stopPropagation(),t.preventDefault();var r=t.target;a(r instanceof HTMLAnchorElement,"app/assets/modules/github/profile/contributions.js:431");var n=r.closest("ul");a(n instanceof HTMLElement,"app/assets/modules/github/profile/contributions.js:434"),e.query(n,".js-year-link.selected").classList.remove("selected"),r.classList.add("selected"),Zi(r.href),f.pushState(null,"",r.href)}),function(){var e=window.location.hash;if(e&&!(e.indexOf("#event-")<0)){var t=e.slice(1,e.length),r=document.getElementById(t);r&&eu(r)}}(),window.addEventListener("hashchange",function(e){var t=e.newURL||window.location.href,r=t.slice(t.indexOf("#")+1,t.length),n=document.getElementById(r);n&&(e.stopPropagation(),eu(n))}),c.remoteForm(".js-show-more-timeline-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,o,i,u,c,l,d,m,p,v;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.text();case 2:(s=document.querySelector(".js-show-more-timeline-form"))&&(o=s.getAttribute("data-year"),a(null!=o,"Missing attribute `data-year` -- app/assets/modules/github/profile/contributions.js:511"),i=e.query(document,".js-year-link.selected"),u=A(document.getElementById("year-link-"+o),HTMLElement),i.classList.remove("selected"),u.classList.add("selected"),c=r.getAttribute("data-year"),o!==c&&(l=s.getAttribute("data-from"),a(l,"app/assets/modules/github/profile/contributions.js:523"),d=new Date(l),m=s.getAttribute("data-to"),a(m,"app/assets/modules/github/profile/contributions.js:526"),p=new Date(m),v=s.getAttribute("data-org"),Ii(d,p,v))),document.title=r.getAttribute("data-title")||"",f.pushState(null,"",r.getAttribute("data-url")||"");case 6:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}());var tu=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,o,i,u,c,l,d,m,p=r.currentTarget;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=e.query(p,".js-pinned-repos-selection-list"),t.next=3,v.microtask();case 3:for(a=e.querySelectorAll(n,"input[type=checkbox]",HTMLInputElement),s=a.filter(function(e){return e.checked}).length,o=parseInt(n.getAttribute("data-max-repo-count"),10),i=!0,u=!1,c=void 0,t.prev=9,l=a[Symbol.iterator]();!(i=(d=l.next()).done);i=!0)(m=d.value).disabled=s===o&&!m.checked;t.next=17;break;case 13:t.prev=13,t.t0=t.catch(9),u=!0,c=t.t0;case 17:t.prev=17,t.prev=18,!i&&l.return&&l.return();case 20:if(t.prev=20,!u){t.next=23;break}throw c;case 23:return t.finish(20);case 24:return t.finish(17);case 25:ru(e.closest(n,".js-pinned-repos-selection-form",HTMLFormElement),s,o);case 27:case"end":return t.stop()}},t,this,[[9,13,17,25],[18,,20,24]])}));return function(e){return t.apply(this,arguments)}}();function ru(t,r,n){var s=e.query(t,".js-remaining-pinned-repos-count"),o=s.getAttribute("data-remaining-label");a(o,"app/assets/modules/github/profile/pinned-repositories.js:29");var i=n-r;s.textContent=i+" "+o,s.classList.toggle("text-red",i<1)}function nu(t){return{name:e.query(t,".js-repo").textContent.toLowerCase().trim(),pinned:e.query(t,'input[type="checkbox"]',HTMLInputElement).checked,owner:t.classList.contains("js-owned-repo"),contributor:t.classList.contains("js-contributed-repo"),element:t}}function au(t){var r=A(t.target,Element),n=e.closest(r,".js-pinned-repos-selection-form"),a=e.query(n,".js-pinned-repos-filter",HTMLInputElement).value.toLowerCase().trim(),s=function(t){if(function(e){return!e.querySelector(".js-pinned-repo-source")}(t))return{owners:!0,contributors:!0};var r=e.querySelectorAll(t,".js-pinned-repo-source:checked",HTMLInputElement),n=r.map(function(e){return e.value}),a=n.indexOf("owned")>-1,s=n.indexOf("contributed")>-1,o=!0,i=!1,u=void 0;try{for(var c,l=r[Symbol.iterator]();!(o=(c=l.next()).done);o=!0)c.value.disabled=a!==s}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}return{owners:a,contributors:s}}(n),o=Array.from(n.querySelectorAll(".js-pinned-repos-selection")).map(nu).map(function(e){return{matched:function(e,t,r){if(e.pinned)return!0;var n=!t||e.name.indexOf(t)>-1,a=e.owner&&r.owners,s=e.contributor&&r.contributors;return n&&(a||s)}(e,a,s),candidate:e}}),i=!0,u=!1,c=void 0;try{for(var l,d=o[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value,p=m.matched;m.candidate.element.classList.toggle("d-none",!p)}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}var f=o.some(function(e){return e.matched});e.query(n,".js-no-repos-message").classList.toggle("d-none",f)}n.onKey("keydown",".js-pinned-repos-filter",function(e){a(e instanceof KeyboardEvent,"app/assets/modules/github/profile/pinned-repositories.js:128"),"Enter"===M(e)&&e.preventDefault()}),n.onInput(".js-pinned-repos-filter",au),r.on("change",".js-pinned-repos-filter",au),r.on("search",".js-pinned-repos-filter",au),r.on("change",".js-pinned-repo-source",au),r.on("change",".js-pinned-repos-selection-form",tu),r.on("reset",".js-pinned-repos-selection-form",tu);var su=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.oldIndex,s=r.newIndex,i=r.item,n!==s){t.next=3;break}return t.abrupt("return");case 3:return u=e.closest(i,".js-pinned-repos-reorder-form",HTMLFormElement),c=e.closest(u,".js-pinned-repos-reorder-container"),l=e.query(c,".js-pinned-repos-spinner"),(d=e.query(c,".js-pinned-repos-reorder-message")).textContent="",l.style.display="inline-block",a(ou,"app/assets/modules/github/profile/pinned-repository-reordering.js:37"),ou.option("disabled",!0),t.prev=11,t.next=14,o.fetchText(u.action,{method:u.method,body:new FormData(u)});case 14:d.textContent=d.getAttribute("data-success-text")||"",l.style.display="none",ou.option("disabled",!1),t.next=25;break;case 19:t.prev=19,t.t0=t.catch(11),d.textContent=d.getAttribute("data-error-text")||"",m=i.parentNode,a(m,"app/assets/modules/github/profile/pinned-repository-reordering.js:52"),iu?m.insertBefore(i,iu):m.appendChild(i);case 25:case"end":return t.stop()}},t,this,[[11,19]])}));return function(e){return t.apply(this,arguments)}}(),ou=null,iu=null;function uu(e){var t=e.item,r=e.oldIndex;iu=t.parentNode.children[r+1]}t.observe(".js-pinned-repos-reorder-list",function(e){ou=z.create(e,{animation:150,item:".js-pinned-repo-list-item",handle:".js-pinned-repository-reorder",onUpdate:su,onStart:uu,chosenClass:"is-dragging"})}),r.on("submit",".js-pinned-repos-reorder-form",function(e){e.preventDefault()}),io(".js-pinned-repo-list-item",su),r.on("click",".js-user-profile-bio-toggle",function(){e.query(document,".js-user-profile-bio").classList.toggle("d-none"),e.query(document,".js-user-profile-bio-form").classList.toggle("d-none"),e.query(document,".js-update-bio-error").classList.add("d-none")}),c.remoteForm(".js-user-profile-bio-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=e.query(r,".js-update-bio-error"),s=void 0,t.prev=2,t.next=5,n.json();case 5:s=t.sent,t.next=14;break;case 8:return t.prev=8,t.t0=t.catch(2),o=t.t0.response.json,a.textContent=o.message,a.classList.remove("d-none"),t.abrupt("return");case 14:if(s){t.next=16;break}return t.abrupt("return");case 16:i=s.json.bioHtml,u=e.query(document,".js-user-profile-bio"),c=e.query(u,".js-user-profile-bio-contents"),l=e.query(u,".js-user-profile-bio-toggle"),c.innerHTML=i,l.textContent=i?"Edit bio":"Add a bio",c.classList.toggle("d-none",!i),(d=e.query(document,".js-user-profile-bio-field",HTMLTextAreaElement)).defaultValue=d.value,u.classList.toggle("d-none"),r.classList.toggle("d-none");case 27:case"end":return t.stop()}},t,this,[[2,8]])}));return function(e,r){return t.apply(this,arguments)}}()),t.observe(".js-user-profile-sticky-fields.is-stuck",function(){var t=e.query(document,".js-user-profile-sticky-bar");return{add:function(){t.classList.add("is-stuck")},remove:function(){t.classList.remove("is-stuck")}}}),t.observe(".js-user-profile-follow-button.is-stuck",function(){var t=e.query(document,".js-user-profile-sticky-bar");return{add:function(){t.classList.add("is-follow-stuck")},remove:function(){t.classList.remove("is-follow-stuck")}}}),t.observe(".js-user-profile-following-toggle .js-toggler-container.on",function(){return{add:function(){var e=document.querySelector(".js-user-profile-following-mini-toggle .js-toggler-container");e&&e.classList.add("on")},remove:function(){var e=document.querySelector(".js-user-profile-following-mini-toggle .js-toggler-container");e&&e.classList.remove("on")}}}),t.observe(".js-user-profile-following-mini-toggle .js-toggler-container.on",function(){var t=e.query(document,".js-user-profile-following-toggle .js-toggler-container");return{add:function(){t.classList.add("on")},remove:function(){t.classList.remove("on")}}});var cu=document.querySelector("meta[name=js-proxy-site-detection-payload]"),lu=document.querySelector("meta[name=expected-hostname]");if(cu instanceof HTMLMetaElement&&lu instanceof HTMLMetaElement&&g(document)){var du={url:window.location.href,expectedHostname:lu.content,documentHostname:document.location.hostname,proxyPayload:cu.content},mu=new Error,pu={};pu.$__=btoa(JSON.stringify(du)),m.reportError(mu,pu)}r.on("details-menu-select",".js-pull-base-branch-item",function(t){t.preventDefault();var r=e.query(document,".js-change-base-template",HTMLTemplateElement).content.cloneNode(!0);e.query(r,".js-pull-change-base-branch-field",HTMLInputElement).value=t.currentTarget.getAttribute("data-branch")||"",V.dialog({content:r})});var fu=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(n=function(e){a(e.target instanceof HTMLElement,"app/assets/modules/github/pulls/commits-range-selection.js:61"),vu(t,r,e.target)},r=t.querySelector('.js-navigation-item[aria-selected="true"]')){e.next=4;break}return e.abrupt("return");case 4:return vu(t,r,r),t.addEventListener("navigation:focus",n),e.next=8,new Promise(function(e){return window.addEventListener("keyup",e,{once:!0})});case 8:t.removeEventListener("navigation:focus",n),gu(t);case 10:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();function vu(e,t,r){var n=Array.from(e.querySelectorAll(".js-navigation-item")),a=n.indexOf(t),s=n.indexOf(r);if(-1===a)throw new Error("Couldn't find startIndex in container");if(-1===s)throw new Error("Couldn't find endItem in container");if(gu(e),n[s].classList.add("is-last-in-range"),a>s){var o=[s,a];a=o[0],s=o[1]}var i=!0,u=!1,c=void 0;try{for(var l,d=n[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){l.value.classList.add("js-navigation-open")}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}var m=!0,p=!1,f=void 0;try{for(var v,g=n.slice(a,s+1)[Symbol.iterator]();!(m=(v=g.next()).done);m=!0){var h=v.value;h.classList.add("is-range-selected"),h.classList.remove("js-navigation-open")}}catch(e){p=!0,f=e}finally{try{!m&&g.return&&g.return()}finally{if(p)throw f}}}function gu(e){var t=!0,r=!1,n=void 0;try{for(var a,s=e.querySelectorAll(".js-navigation-item")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){a.value.classList.remove("is-range-selected","is-last-in-range")}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}r.on("navigation:open",".js-diffbar-commits-list .js-navigation-item",function(e){if(a(e instanceof CustomEvent,"app/assets/modules/github/pulls/commits-range-selection.js:83"),e.detail.shiftKey){e.preventDefault();var t=e.currentTarget.closest(".js-diffbar-commits-menu");if(e.currentTarget.classList.contains("is-range-selected")){e.stopPropagation();var r=t.querySelectorAll(".js-navigation-item.is-range-selected"),n=r[0],s=r[r.length-1],o=t.getAttribute("data-range-url"),i=n.getAttribute("data-parent-commit"),u=s.getAttribute("data-commit"),c=i&&u?i+".."+u:u,l=o.replace("$range",c),d=document.getElementById("js-repo-pjax-container");a(d,"app/assets/modules/github/pulls/commits-range-selection.js:105"),Z({url:l,container:d})}else e.stopImmediatePropagation(),fu(t)}}),r.on("click",".js-compare-tab",function(t){var r=!0,n=!1,s=void 0;try{for(var o,i=document.querySelectorAll(".js-compare-tab.selected")[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){o.value.classList.remove("selected")}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}t.currentTarget.classList.add("selected");var u=!0,c=!1,l=void 0;try{for(var d,m=document.querySelectorAll("#commits_bucket, #files_bucket, #commit_comments_bucket")[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){d.value.classList.add("d-none")}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}a(t.currentTarget instanceof HTMLAnchorElement,"app/assets/modules/github/pulls/compare.js:21");var p=t.currentTarget.hash;e.query(document,p).classList.remove("d-none"),t.preventDefault()}),_(function(t){var r=t.target;if(r instanceof HTMLElement){var n=r.closest("#commits_bucket, #files_bucket, #commit_comments_bucket");n&&n instanceof HTMLElement&&!R(n)&&e.query(document,'.js-compare-tab[href="#'+n.id+'"]').click()}}),r.on("click",".js-toggle-range-editor-cross-repo",function(){e.query(document,".js-range-editor").classList.toggle("is-cross-repo")}),r.on("pjax:click",".js-range-editor",function(e){var t=document.querySelector(".js-compare-pr");if(t&&t.classList.contains("open")){a(e instanceof CustomEvent,"app/assets/modules/github/pulls/compare.js:52");var r=e.detail.options,n=new URL(r.url);n.search.match(/expand=1/)||(n.search+=(n.search?"&":"")+"expand=1",r.url=n.toString())}}),r.on("navigation:open",".js-commitish-form",function(e){var t=e.currentTarget;a(t instanceof HTMLFormElement,"app/assets/modules/github/pulls/compare.js:64");var r=document.createElement("input");r.type="hidden",r.name="new_compare_ref";var n=t.querySelector(".js-new-item-name");a(n,"app/assets/modules/github/pulls/compare.js:69"),r.value=n.textContent,t.appendChild(r),l.submit(t)}),t.observe(".js-compare-pr.open",{add:function(){var e=document.body;a(e,"app/assets/modules/github/pulls/compare.js:79"),e.classList.add("is-pr-composer-expanded")},remove:function(){var e=document.body;a(e,"app/assets/modules/github/pulls/compare.js:84"),e.classList.remove("is-pr-composer-expanded")}}),r.on("change",".js-collab-checkbox",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/pulls/composer.js:9");var n=r.form;a(n,"app/assets/modules/github/pulls/composer.js:11");var s=!0,o=!1,i=void 0;try{for(var u,c=n.querySelectorAll(".errored")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.classList.remove("errored")}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}var l=e.closest(r,".js-collab-option"),d=e.query(l,".js-status-indicator");d.classList.remove("status-indicator-success","status-indicator-failed"),d.classList.add("status-indicator-loading")}),c.remoteForm(".js-collab-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k,E,T,q;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=46;break;case 5:for(t.prev=5,t.t0=t.catch(0),a=!0,s=!1,o=void 0,t.prev=10,i=r.querySelectorAll(".status-indicator-loading")[Symbol.iterator]();!(a=(u=i.next()).done);a=!0)(c=u.value).classList.remove("status-indicator-loading"),c.classList.add("status-indicator-failed"),(l=e.closest(c,".js-collab-option")).classList.add("errored"),(d=e.query(l,".js-collab-checkbox",HTMLInputElement)).checked=!d.checked;t.next=18;break;case 14:t.prev=14,t.t1=t.catch(10),s=!0,o=t.t1;case 18:t.prev=18,t.prev=19,!a&&i.return&&i.return();case 21:if(t.prev=21,!s){t.next=24;break}throw o;case 24:return t.finish(21);case 25:return t.finish(18);case 26:for(m=!0,p=!1,f=void 0,t.prev=29,v=r.querySelectorAll(".status-indicator-success")[Symbol.iterator]();!(m=(g=v.next()).done);m=!0)g.value.classList.remove("status-indicator-success");t.next=37;break;case 33:t.prev=33,t.t2=t.catch(29),p=!0,f=t.t2;case 37:t.prev=37,t.prev=38,!m&&v.return&&v.return();case 40:if(t.prev=40,!p){t.next=43;break}throw f;case 43:return t.finish(40);case 44:return t.finish(37);case 45:return t.abrupt("return");case 46:for(h=!0,y=!1,b=void 0,t.prev=49,j=r.querySelectorAll(".errored")[Symbol.iterator]();!(h=(L=j.next()).done);h=!0)L.value.classList.remove("errored");t.next=57;break;case 53:t.prev=53,t.t3=t.catch(49),y=!0,b=t.t3;case 57:t.prev=57,t.prev=58,!h&&j.return&&j.return();case 60:if(t.prev=60,!y){t.next=63;break}throw b;case 63:return t.finish(60);case 64:return t.finish(57);case 65:for(w=!0,x=!1,k=void 0,t.prev=68,E=r.querySelectorAll(".status-indicator-loading")[Symbol.iterator]();!(w=(T=E.next()).done);w=!0)(q=T.value).classList.remove("status-indicator-loading"),q.classList.add("status-indicator-success");t.next=76;break;case 72:t.prev=72,t.t4=t.catch(68),x=!0,k=t.t4;case 76:t.prev=76,t.prev=77,!w&&E.return&&E.return();case 79:if(t.prev=79,!x){t.next=82;break}throw k;case 82:return t.finish(79);case 83:return t.finish(76);case 84:case"end":return t.stop()}},t,this,[[0,5],[10,14,18,26],[19,,21,25],[29,33,37,45],[38,,40,44],[49,53,57,65],[58,,60,64],[68,72,76,84],[77,,79,83]])}));return function(e,r){return t.apply(this,arguments)}}()),t.observe(".js-timeline-item > .discussion-item.discussion-commits",{constructor:HTMLElement,add:function(t){if(!t.querySelector(".discussion-item-header")){var r=t.closest(".js-timeline-item");if(r instanceof HTMLElement){var n=r.previousElementSibling;n instanceof HTMLElement&&n.querySelector(".discussion-item.discussion-commits")&&function(t,r){var n=e.query(t,".timeline-commits"),a=r.querySelectorAll(".timeline-commits > .commit"),s=!0,o=!1,i=void 0;try{for(var u,c=a[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;n.appendChild(l)}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}r.remove()}(n,r)}}}});var hu=void 0;function yu(e){var t=document.querySelector("head .js-site-favicon");t instanceof HTMLLinkElement&&(null==hu&&(hu=t.href),t.href=e)}t.observe("[data-favicon-override]",{add:function(e){var t=e.getAttribute("data-favicon-override");a("string"==typeof t,"app/assets/modules/github/pulls/favicon-status.js:27"),setTimeout(function(){return yu(t)})},remove:function(){null!=hu&&yu(hu)}}),r.on("click",".js-file-header .js-details-target",function(t){if(t instanceof MouseEvent&&t.altKey&&t.currentTarget instanceof HTMLElement){var r=t.currentTarget,n=e.closest(r,".js-details-container");n.addEventListener("details:toggled",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pulls/file-collapsing.js:18");var t=e.detail.open;I.preservingScrollPosition(n,function(){var e=!0,r=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-file.js-details-container")[Symbol.iterator]();!(e=(s=o.next()).done);e=!0){var i=s.value;i!==n&&(i.classList.toggle("open",t),i.classList.toggle("Details--on",t))}}catch(e){r=!0,a=e}finally{try{!e&&o.return&&o.return()}finally{if(r)throw a}}})},{once:!0})}}),_(function(){var e=window.location.hash.slice(1);if(e){var t=document.getElementsByName(e)[0];if(t){var r=t.nextElementSibling;r&&r.matches(".js-file.js-details-container")&&(r.classList.add("open"),r.classList.add("Details--on"))}}}),r.on("details:toggled",".js-pull-merging",function(t){var r=t.currentTarget,n=e.querySelectorAll(r,".js-merge-pull-request"),a=n.some(R),s=!0,o=!1,i=void 0;try{for(var u,c=n[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.classList.toggle("is-dirty",a)}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}),c.remoteForm(".js-merge-pull-request",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:t.reset(),t.classList.remove("is-dirty");case 4:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),document.addEventListener("session:resume",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/pulls/merge.js:35");var t=document.getElementById(e.detail.targetId);if(t){var r=t.closest(".js-merge-pull-request");if(r){var n=r.closest(".js-details-container");n&&n.classList.add("open")}}}),r.on("change",".js-merge-method",function(t){var r=t.currentTarget,n=e.closest(r,".js-merge-pr");a(r instanceof HTMLInputElement,"app/assets/modules/github/pulls/merge.js:48"),n.classList.toggle("is-merging","merge"===r.value),n.classList.toggle("is-squashing","squash"===r.value),n.classList.toggle("is-rebasing","rebase"===r.value);var s=e.querySelectorAll(n,".js-merge-pull-request .js-merge-commit-button",HTMLButtonElement),o=!0,i=!1,u=void 0;try{for(var c,l=s[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){var d=c.value;d.type=r.value===d.value?"submit":"button"}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}var m=e.closest(n,".js-pull-merging"),p=m.getAttribute("data-url")||"";p=p.replace(/merge_type=(\w+)/,"merge_type="+r.value),m.setAttribute("data-url",p)}),r.on("change",".js-merge-button-toggle",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/pulls/merge.js:65");var n=e.closest(r,".js-merge-pr"),s=!r.checked,o=!0,i=!1,u=void 0;try{for(var c,l=e.querySelectorAll(n,".js-merge-commit-button",HTMLButtonElement)[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){c.value.disabled=s}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}}),r.on("navigation:open",".js-merge-method-menu .js-navigation-item",function(t){var r=t.currentTarget,n=e.closest(r,".js-merge-pr"),a=e.query(n,".js-merge-title",HTMLInputElement),s=e.query(n,".js-merge-message",HTMLTextAreaElement);a.defaultValue===a.value&&(a.defaultValue=r.getAttribute("data-input-title-value")||""),s.defaultValue===s.value&&(s.defaultValue=r.getAttribute("data-input-message-value")||"")}),r.on("details:toggled",".js-merge-pr",function(e){var t=e.currentTarget.querySelector(".js-merge-message");t&&r.fire(t,"change")}),r.on("click",".js-pull-merging a[href]",function(e){var t=e.currentTarget;a(t instanceof HTMLElement,"app/assets/modules/github/pulls/merge.js:97"),hn&&t.blur()}),c.remoteForm(".js-handle-pull-merging-errors",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=10;break;case 5:t.prev=5,t.t0=t.catch(0),(a=e.closest(r,".js-pull-merging")).classList.add("is-error"),422===t.t0.response.status&&(s=a.querySelector(".js-pull-merging-error"))&&t.t0.response.html&&s.replaceWith(t.t0.response.html);case 10:case"end":return t.stop()}},t,this,[[0,5]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-pull-merging-refresh",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,i,u,c,l,d,m=r.currentTarget;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=e.closest(m,".js-pull-merging"),a=new URL(n.getAttribute("data-url")||"",window.location.origin),s=new URLSearchParams(a.search.slice(1)),(i=document.getElementById("merge_title_field"))instanceof HTMLInputElement&&(u=i.value,s.set("commit_title",u)),(c=document.getElementById("merge_message_field"))instanceof HTMLTextAreaElement&&(l=c.value,s.set("commit_message",l)),a.search=s.toString(),t.next=10,o.fetchText(a.toString());case 10:d=t.sent,n.outerHTML=d;case 12:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}());var bu=!1;function ju(){for(var e=document.querySelectorAll(".pull-request-ref-restore"),t=0;t<e.length;t++)e[t].classList.toggle("last",t===e.length-1)}function Lu(){var e=null!=document.querySelector("#js-pull-restorable"),t=document.querySelector(".js-pull-discussion-timeline");t&&t.classList.toggle("is-pull-restorable",e)}function wu(){var t=document.querySelector(".js-reviews-container");t&&setTimeout(function(){return function(t){var r=e.closest(t,".js-review-state-classes"),n=r.querySelectorAll(".js-pending-review-comment").length;r.classList.toggle("is-review-pending",n>0);var a=!0,s=!1,o=void 0;try{for(var i,u=document.querySelectorAll(".js-pending-review-comment-count")[Symbol.iterator]();!(a=(i=u.next()).done);a=!0)i.value.textContent=String(n)}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}var c=!0,l=!1,d=void 0;try{for(var m,p=document.querySelectorAll(".js-pending-comment-count-type")[Symbol.iterator]();!(c=(m=p.next()).done);c=!0){var f=m.value;N.pluralizeNode(n,f)}}catch(e){l=!0,d=e}finally{try{!c&&p.return&&p.return()}finally{if(l)throw d}}if(n>0){var v=e.query(t,".js-reviews-toggle");v.classList.add("anim-pulse-in"),v.addEventListener("animationend",function(){return v.classList.remove("anim-pulse-in")},{once:!0})}}(t)})}document.addEventListener("keydown",function(e){"Alt"===e.key&&(bu=!0)}),document.addEventListener("keyup",function(e){"Alt"===e.key&&(bu=!1)}),r.on("click",".js-toggle-outdated-comments",function(t){if((t instanceof MouseEvent&&t.altKey||bu)&&t.currentTarget instanceof HTMLElement){var r=t.currentTarget,n=e.closest(r,"details");setTimeout(function(){I.preservingScrollPosition(r,a)})}function a(){var t=n.hasAttribute("open"),a=!0,s=!1,o=void 0;try{for(var i,u=document.querySelectorAll(".js-toggle-outdated-comments")[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;if(c!==r){var l=e.closest(c,"details");t?l.setAttribute("open",""):l.removeAttribute("open")}}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}}}),t.observe(".pull-request-ref-restore",{add:ju,remove:ju}),t.observe("#js-pull-restorable",{add:Lu,remove:Lu}),c.remoteForm(".js-inline-comment-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:wu();case 3:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-pending-review-comment .js-comment-delete",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:wu();case 3:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-resolvable-timeline-thread-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=t.sent,e.closest(r,".js-resolvable-timeline-thread-container").replaceWith(a.html);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-resolvable-thread-toggler",function(t){var r=e.closest(t.target,".js-resolvable-timeline-thread-container");e.closest(t.target,".js-resolvable-thread-toggler-container").classList.toggle("border-bottom"),e.query(r,".js-resolvable-thread-contents").classList.toggle("d-none")}),document.addEventListener("pjax:end",function(){var e=!0,t=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-pull-refresh-on-pjax")[Symbol.iterator]();!(e=(a=s.next()).done);e=!0){var o=a.value;r.fire(o,"socket:message",{})}}catch(e){t=!0,n=e}finally{try{!e&&s.return&&s.return()}finally{if(t)throw n}}});var xu="```suggestion",ku="```",Eu=/^[+-]/;function Tu(t){var r,n,s,o,i=function(t){var r=t.closest(".js-inline-comments-container");if(r){var n=t.closest(".js-line-comments.line-comments"),s=null;n&&(s=n.classList.contains("js-addition")?"right":"left");var o=r.previousElementSibling;a(o,"app/assets/modules/github/pulls/suggested-changes.js:30");var i=[];if("TR"===o.tagName)i=Array.from(o.children);else{var u=o.querySelectorAll("tr"),c=Array.from(u).pop();i=Array.from(c.children)}var l=i.filter(function(e){var t=e.classList;return t.contains("code-review")||t.contains("blob-code")}),d="right"===s?l.pop():l.shift();if(d)return e.query(d,".blob-code-inner").textContent.replace(Eu,"")}}(t);if(null!=i){var u=e.closest(t,".js-suggester-container"),c=e.query(u,".js-comment-field",HTMLTextAreaElement),d=(r=i,s="\n"===(n=c).value.split("").pop(),o=""===n.value.trim()||s?"":"\n",o+=xu+"\n",o+=r+"\n",o+=ku),m=c.value.length+d.length-("\n"+ku).length;l.changeValue(c,c.value+d),c.focus(),c.setSelectionRange(m,m)}}function qu(t){var r=e.closest(t,".js-inline-comments-container"),n=r.previousElementSibling;a(n,"app/assets/modules/github/pulls/suggested-changes.js:185");var s=n.querySelectorAll(".js-linkable-line-number");if(s){var o=s[s.length-1].getAttribute("data-line-number");a(o,"app/assets/modules/github/pulls/suggested-changes.js:189");var i=r.querySelectorAll(".js-suggested-changes-container .js-suggested-changes-num"),u=i[0],c=Array.from(i).slice(1,i.length);if(u&&u.setAttribute("data-line-number",o),c.length){var l=0,d=!0,m=!1,p=void 0;try{for(var f,v=c[Symbol.iterator]();!(d=(f=v.next()).done);d=!0){var g=f.value,h=parseInt(o,10)+l;g.setAttribute("data-line-number",""+h),l++}}catch(e){m=!0,p=e}finally{try{!d&&v.return&&v.return()}finally{if(m)throw p}}}}}function Su(t){var r=t.target;a(r instanceof HTMLElement,"app/assets/modules/github/reactions.js:32");var n=r.getAttribute("data-reaction-label");a(n,"app/assets/modules/github/reactions.js:35");var s=e.closest(r,".js-add-reaction-popover");e.query(s,".js-reaction-description").textContent=n}function Au(t){a(t.target instanceof HTMLElement,"app/assets/modules/github/reactions.js:43");var r=e.closest(t.target,".js-add-reaction-popover");e.query(r,".js-reaction-description").textContent="Pick your reaction"}r.on("click",".js-suggested-change-toolbar-item",function(e){Tu(e.target)}),r.on("click",".js-refresh-after-suggestion",function(){window.location.reload()}),t.observe(".js-preview-body .js-accept-changes",{add:function(t){var r=t.closest(".js-suggested-changes-container");if(!r)return t.remove();var n=r.querySelector(".js-suggest-changes-validation"),s=e.closest(t,".js-suggested-changes-blob");if(!n)return t.remove();qu(t);var o=t.previousElementSibling;a(o,"app/assets/modules/github/pulls/suggested-changes.js:105");var i=o.querySelector(".js-blob-code-deletion"),u=o.querySelector(".js-blob-code-addition");if(i&&u){var c=i.textContent;if(u.textContent===c){var l=n.cloneNode(!0);l.classList.add("d-flex");var d=s.parentNode;a(d,"app/assets/modules/github/pulls/suggested-changes.js:120"),d.insertBefore(l,s.nextSibling)}t.remove()}}}),t.observe(".js-comment-body .js-accept-changes",{add:function(t){var r=t.closest(".js-suggested-changes-container");if(!r)return t.remove();var n=r.querySelector(".js-suggested-changes-template");if(!(n instanceof HTMLTemplateElement))return t.remove();var a=n.content.cloneNode(!0),s=e.query(a,".js-suggested-change-form"),o=t.closest(".js-suggested-changes-blob"),i=e.query(a,".js-disabled-apply-suggestion-button");if(qu(t),o){var u=o.querySelector(".js-blob-code-deletion"),c=o.querySelectorAll(".js-blob-code-addition");if(!u||0===c.length)return;var l=u.textContent,d=Array.from(c).map(function(e){return e.textContent});if(l===d)e.query(a,"details").remove(),i.classList.remove("d-none");else if(!(1===d.length&&""===d[0].trim())){var m=!0,p=!1,f=void 0;try{for(var v,g=d[Symbol.iterator]();!(m=(v=g.next()).done);m=!0){var h=v.value,y=document.createElement("input");y.setAttribute("type","hidden"),y.setAttribute("name","value[]"),y.value=h,s.appendChild(y)}}catch(e){p=!0,f=e}finally{try{!m&&g.return&&g.return()}finally{if(p)throw f}}}}t.replaceWith(a)}}),c.remoteForm(".js-suggested-change-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:200===t.sent.status&&(a=e.closest(r,".js-suggested-change-form-container"),s=e.closest(r,"details"),o=e.query(a,".js-suggestion-accepted"),(i=e.closest(r,".js-suggested-changes-contents"))&&((u=e.query(i,".js-refresh")).hidden=!1,a.prepend(u),o.classList.remove("d-none"),s.remove()));case 4:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-apply-suggestion-button",function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:(r=t.currentTarget.parentElement)&&(n=r.querySelector(".js-suggestion-commit-message"))&&setTimeout(function(){return n.focus()},1);case 2:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}()),r.on("click",".js-dismiss-suggested-change-onboarding-notice",function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s,i,u,c,l,d;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=t.currentTarget.getAttribute("data-url"),a(r,"app/assets/modules/github/pulls/suggested-changes.js:241"),(n=new FormData).append("notice","suggested_changes_onboarding_prompt"),e.next=6,o.fetchText(o.csrfRequest(r,{method:"post",body:n}));case 6:for(s=document.querySelectorAll(".js-suggested-change-onboarding-notice"),i=!0,u=!1,c=void 0,e.prev=10,l=s[Symbol.iterator]();!(i=(d=l.next()).done);i=!0)d.value.remove();e.next=18;break;case 14:e.prev=14,e.t0=e.catch(10),u=!0,c=e.t0;case 18:e.prev=18,e.prev=19,!i&&l.return&&l.return();case 21:if(e.prev=21,!u){e.next=24;break}throw c;case 24:return e.finish(21);case 25:return e.finish(18);case 26:case"end":return e.stop()}},e,this,[[10,14,18,26],[19,,21,25]])}));return function(t){return e.apply(this,arguments)}}()),c.remoteForm(".js-pick-reaction",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.json();case 2:a=t.sent,(s=r.closest(".js-menu-container"))instanceof HTMLElement&&D.deactivate(s),o=e.closest(r,".js-comment"),u=e.query(o,".js-reactions-container"),c=e.query(o,".js-comment-header-reaction-button"),l=i.parseHTML(document,a.json.reactions_container.trim()),d=i.parseHTML(document,a.json.comment_header_reaction_button.trim()),u.replaceWith(l),c.replaceWith(d),o.classList.remove("is-reacting");case 13:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("toggle",".js-reaction-popover-container",function(t){var r=t.currentTarget.hasAttribute("open"),n=!0,a=!1,s=void 0;try{for(var o,i=t.target.querySelectorAll(".js-reaction-option-item")[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;r?(u.addEventListener("mouseenter",Su),u.addEventListener("mouseleave",Au)):(u.removeEventListener("mouseenter",Su),u.removeEventListener("mouseleave",Au))}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}e.closest(t.target,".js-comment").classList.toggle("is-reacting",r)},{capture:!0});var Mu=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(r.value){t.next=2;break}return t.abrupt("return");case 2:if(r.value!==Cu.get(r)){t.next=4;break}return t.abrupt("return");case 4:return Iu("loading"),Cu.set(r,r.value),n=r.getAttribute("data-url"),a(n,"app/assets/modules/github/releases.js:142"),s=new URL(n,window.location.origin),(i=new URLSearchParams(s.search.slice(1))).append("tag_name",r.value),s.search=i.toString(),t.prev=12,t.next=15,o.fetchJSON(s);case 15:"duplicate"===(u=t.sent).status&&parseInt(r.getAttribute("data-existing-id"))===parseInt(u.release_id)?Iu("valid"):(e.query(document,".js-release-tag .js-edit-release-link").setAttribute("href",u.url),Iu(u.status)),t.next=22;break;case 19:t.prev=19,t.t0=t.catch(12),Iu("invalid");case 22:case"end":return t.stop()}},t,this,[[12,19]])}));return function(e){return t.apply(this,arguments)}}(),Hu=function(){var t=te(regeneratorRuntime.mark(function t(n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=A(n.form,HTMLFormElement),e.query(a,"#release_draft",HTMLInputElement).value="1",Ru(n,"saving"),t.prev=4,t.next=7,o.fetchJSON(a.action,{method:a.method,body:new FormData(a)});case 7:return s=t.sent,Ru(n,"saved"),setTimeout(Ru,5e3,n,"default"),r.fire(a,"release:saved",{release:s}),t.abrupt("return",s);case 14:throw t.prev=14,t.t0=t.catch(4),Ru(n,"failed"),t.t0;case 18:case"end":return t.stop()}},t,this,[[4,14]])}));return function(e){return t.apply(this,arguments)}}();function Ru(e,t){var r=!0,n=!1,a=void 0;try{for(var s,o=e.querySelectorAll(".js-save-draft-button-state")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;i.hidden=i.getAttribute("data-state")!==t}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}e.disabled="saving"===t}function Iu(e){var t=document.querySelector(".js-release-target-wrapper");if(null!=t){switch(e){case"valid":t.classList.add("d-none");break;case"loading":break;default:t.classList.remove("d-none")}var r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-tag-status-message")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;i.hidden=i.getAttribute("data-state")!==e}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}}r.on("click",".js-save-draft",function(e){var t=A(e.currentTarget,HTMLButtonElement);Hu(t),e.preventDefault()}),r.on("click",".js-timeline-tags-expander",function(t){var r=A(t.currentTarget,HTMLElement);e.closest(r,".js-timeline-tags").classList.remove("is-collapsed")}),r.on("release:saved",".js-release-form",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/releases.js:58");var r=t.detail.release,n=t.currentTarget,s=n.getAttribute("data-repo-url"),o=r.update_url||_u("tag",s,r.tag_name);(n.setAttribute("action",o),r.update_authenticity_token)&&(n.querySelector("input[name=authenticity_token]").value=r.update_authenticity_token);var i=r.edit_url||_u("edit",s,r.tag_name);f.replaceState(E.getState(),document.title,i);var u=document.querySelector("#delete_release_confirm form");if(u){var c=r.delete_url||_u("tag",s,r.tag_name);if(u.setAttribute("action",c),r.delete_authenticity_token)e.query(u,"input[name=authenticity_token]",HTMLInputElement).value=r.delete_authenticity_token}var l=n.querySelector("#release_id");if(!l.value){l.value=r.id;var d=document.createElement("input");d.type="hidden",d.name="_method",d.value="put",n.appendChild(d)}}),r.on("click",".js-publish-release",function(){e.query(document,"#release_draft",HTMLInputElement).value="0"});var Cu=new WeakMap;function _u(e,t,r){return t+"/releases/"+e+"/"+r}function Fu(t){var r=e.closest(t,"form",HTMLFormElement).querySelector(".js-previewable-comment-form");if(r){var n=r.getAttribute("data-base-preview-url");n||(n=String(r.getAttribute("data-preview-url")),r.setAttribute("data-base-preview-url",n));var a=e.querySelectorAll(t,'input[name="release[tag_name]"], input[name="release[target_commitish]"]:checked',HTMLInputElement),s=new URL(n,window.location.origin),o=new URLSearchParams(s.search.slice(1)),i=!0,u=!1,c=void 0;try{for(var l,d=a[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;m.value&&o.append(m.name,m.value)}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}s.search=o.toString(),r.setAttribute("data-preview-url",s.toString())}}t.observe("input.js-release-tag-field",{constructor:HTMLInputElement,initialize:function(e){Mu(e),e.addEventListener("blur",function(){Mu(e)})}}),r.on("change",".js-release-tag",function(e){Fu(e.currentTarget)}),t.observe(".js-release-form .js-previewable-comment-form",function(t){Fu(e.query(e.closest(t,"form"),".js-release-tag"))});var Pu=["is-render-pending","is-render-ready","is-render-loading","is-render-loaded"],Nu=new WeakMap;function Ou(e){if(null!=e){var t=Nu.get(e);null!=t&&(t.load=t.hello=null,t.helloTimer&&(clearTimeout(t.helloTimer),t.helloTimer=null),t.loadTimer&&(clearTimeout(t.loadTimer),t.loadTimer=null))}}function Du(e){var t;if(null!=e)return(t=e.classList).remove.apply(t,Pu),e.classList.add("is-render-failed"),Ou(e)}function Bu(){return!0}function Uu(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Bu;return function(){if(R(e)&&!e.classList.contains("is-render-ready")&&!e.classList.contains("is-render-failed")&&!e.classList.contains("is-render-failed-fatally")&&(!t||t()))return Du(e)}}t.observe(".js-render-target",{constructor:HTMLElement,initialize:function(e){var t=Nu.get(e);null!=t&&t.load||(Ou(e),function(e){if(!Nu.get(e)){var t={load:null,hello:null,helloTimer:null,loadTimer:null};t.load=Date.now(),t.helloTimer=setTimeout(Uu(e,function(){return!t.hello}),1e4),t.loadTimer=setTimeout(Uu(e),45e3),Nu.set(e,t)}}(e),e.classList.add("is-render-automatic"),e.classList.add("is-render-requested"))}}),window.addEventListener("message",function(e){var t=e.data;if(t){var n=function(e){if("string"==typeof e)try{return JSON.parse(e)}catch(e){return}}(t)||t;if("string"==typeof n.type||"render"===n.type){n.type;if("string"==typeof n.identity){var a=n.identity;if("string"==typeof n.body){var s=n.body,o=function(e){return Array.from(document.querySelectorAll(".js-render-target")).filter(function(t){return!e||t.getAttribute("data-identity")===e})[0]}(a);if(o)if(e.origin===o.getAttribute("data-host"))!function(e,t,n,a,s){var o,i,u;switch(a){case"hello":var c=Nu.get(e)||{untimed:!0};c.hello=Date.now();var l=function(e){var t=e.querySelector("iframe");return t instanceof HTMLIFrameElement?t.contentWindow:null}(e);if(l&&"function"==typeof l.postMessage&&l.postMessage(JSON.stringify({type:"render:cmd",body:{cmd:"ack",ack:!0}}),"*"),l&&"function"==typeof l.postMessage&&l.postMessage(JSON.stringify({type:"render:cmd",body:{cmd:"branding",branding:!1}}),"*"),e.classList.contains("is-local")&&l&&"function"==typeof l.postMessage){var d=l,m=e.closest(".js-code-editor"),p=m instanceof HTMLElement?K.getCodeEditor(m):null,f=e.getAttribute("data-data");if(p){var v=null,g=function(){var e=p.code();if(e!==v){v=e;var t=JSON.stringify({type:"render:data",body:e});d.postMessage(t,"*")}};p.textarea.addEventListener("change",g),g()}else if(f){var h=JSON.stringify({type:"render:data",body:f});d.postMessage(h,"*")}}break;case"error":return Du(e);case"error:fatal":return Du(e),e.classList.add("is-render-failed-fatal");case"error:invalid":return Du(e),e.classList.add("is-render-failed-invalid");case"loading":return(o=e.classList).remove.apply(o,Pu),e.classList.add("is-render-loading");case"loaded":return(i=e.classList).remove.apply(i,Pu),e.classList.add("is-render-loaded");case"ready":(u=e.classList).remove.apply(u,Pu),e.classList.add("is-render-ready"),s&&"number"==typeof s.height&&(e.style.height=s.height+"px");break;case"resize":s&&"number"==typeof s.height&&(e.style.height=s.height+"px");break;case"data":r.fire(e,"render:data",s);break;default:;}}(o,0,0,s,null!=n.payload?n.payload:void 0)}}}}}),r.on("change",".js-repository-import-owner-container input",function(t){var r=t.currentTarget,n=r.getAttribute("data-upsell")||"",a=r.getAttribute("data-billing-url")||"";e.query(document,".js-repository-import-billing-url",HTMLAnchorElement).href=a,e.query(document,".js-repository-import-upsell").classList.toggle("d-none","false"===n),e.query(document,".js-repository-import-no-upsell").classList.toggle("d-none","true"===n)}),r.on("socket:message",".repository-import",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/repo-import.js:19");var t=e.detail.data;t.redirect_to&&(document.location.href=t.redirect_to,e.stopImmediatePropagation())}),r.on("change","input.js-repository-import-lfs-opt",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/repo-import.js:28");var n=parseInt(r.getAttribute("data-percent-used")),s=e.closest(r,".js-repository-import-lfs-container"),o=r.getAttribute("data-used")||"";e.query(s,".js-repository-import-lfs-warn").classList.toggle("d-none",!(n>100)),e.query(s,".js-usage-bar").classList.toggle("exceeded",n>=100),e.query(s,".js-usage-bar").setAttribute("aria-label",n+"%"),e.query(s,".js-repository-import-lfs-progress").style.width=n+"%",e.query(s,"span.js-usage-text").textContent=o}),c.remoteForm(".js-repository-import-author-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=t.sent,e.closest(r,".js-repository-import-author").replaceWith(a.html);case 5:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-repository-import-projects-cancel-button",function(){var t=e.query(document,".js-repository-import-projects-cancel-form",HTMLFormElement);l.submit(t)});var zu=!1;function Wu(){return"false"===e.query(document,".js-privacy-toggle:checked",HTMLInputElement).value}function Vu(){var t=e.query(document,".js-repo-name");r.fire(t,"change");var n=e.query(document,".js-owner-container"),a=e.query(n,'[aria-checked="true"]');if(!zu){var s=e.query(document,".js-privacy-toggle[value=false]",HTMLInputElement),o=e.query(document,".js-privacy-toggle[value=true]",HTMLInputElement);"private"===a.getAttribute("data-default")?(s.checked=!0,r.fire(s,"change")):(o.checked=!0,r.fire(o,"change"))}!function(e){var t=!0,r=!1,n=void 0;try{for(var a,s=document.querySelectorAll(".js-with-permission-fields")[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;o.hidden=!e}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}var i=!0,u=!1,c=void 0;try{for(var l,d=document.querySelectorAll(".js-without-permission-fields")[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;m.hidden=e}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}var p=document.querySelector(".errored"),f=document.querySelector("dl.warn");p&&(p.hidden=!e);f&&(f.hidden=!e)}("yes"===a.getAttribute("data-permission")),function(){var t=document.querySelector("#js-upgrade-container");if(!t)return;var r=e.query(document,"#js-payment-methods-form");t.firstElementChild&&r.appendChild(t.firstElementChild);var n=e.query(document,"input[name=owner]:checked",HTMLInputElement).value,a=r.querySelector('.js-upgrade[data-login="'+n+'"]');a&&t.appendChild(a)}(),$u();var i=document.querySelector(".js-quick-install-container");if(i){var u=e.query(i,".js-quick-install-divider");u.hidden=!0;var c=e.query(document,"input[name=owner]:checked",HTMLInputElement).parentElement;if(c){var l=c.querySelector(".js-quick-install-list-template");if(l instanceof HTMLTemplateElement){var d=e.query(i,".js-account-apps");d.innerHTML="",d.append(l.content.cloneNode(!0)),l.children.length>0&&(u.hidden=!1)}}}}function $u(e){var t=document.querySelector("#js-upgrade-container");if(t){var r=t.querySelector(".js-billing-section"),n=t.querySelector(".js-confirm-upgrade-checkbox"),s=e?e.target:null;s||(s=document.querySelector(".js-privacy-toggle:checked")),a(s instanceof HTMLInputElement,"app/assets/modules/github/repo-new.js:86"),"false"===s.value?(t.hidden=!1,r&&r.classList.remove("has-removed-contents"),n&&(A(n,HTMLInputElement).checked=!0)):(t.hidden=!0,r&&r.classList.add("has-removed-contents"),n&&(A(n,HTMLInputElement).checked=!1)),Ju()}}function Ju(){var t=e.query(document,"#new_repository"),r=e.query(t,".js-repo-name").classList.contains("is-autocheck-successful");Wu()&&(r=r&&function(){var e=document.querySelector("#js-upgrade-container");if(!e)return!0;var t=e.querySelector(".js-confirm-upgrade-checkbox");if(t instanceof HTMLInputElement&&!t.checked)return!1;var r=e.querySelector(".js-zuora-billing-info");if(r&&r.classList.contains("d-none"))return!1;return!0}()),e.query(t,"button[type=submit]",HTMLButtonElement).disabled=!r}t.observe("#js-upgrade-container .js-zuora-billing-info:not(.d-none)",Ju),t.observe(".page-new-repo",function(){zu=Wu();var e=document.querySelector("#js-upgrade-container");e&&(e.hidden=!0),Vu()}),r.on("click",".js-reponame-suggestion",function(t){var n=e.query(document,".js-repo-name",HTMLInputElement);n.value=t.currentTarget.textContent,r.fire(n,"input",!1)}),r.on("click",".js-privacy-toggle",function(){zu=!0}),r.on("change",".js-privacy-toggle",$u),r.on("change",".js-owner-radio-button",Vu),r.on("change","#js-upgrade-container input",Ju),n.onInput("#js-upgrade-container input",Ju),r.on("autocheck:send","#repository_name",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/repo-new.js:178");var r=A(t.currentTarget,HTMLInputElement).form;a(r,"app/assets/modules/github/repo-new.js:181");var n=e.query(r,"input[name=owner]:checked",HTMLInputElement).value;t.detail.body.append("owner",n)}),r.on("autocheck:complete","#repository_name",function(){Ju()}),r.on("click",".js-toggle-lang-stats",function(){var t=e.query(document,".js-stats-switcher-viewport"),r=0!==t.scrollTop?"is-revealing-overview":"is-revealing-lang-stats";t.classList.toggle(r)});var Ku=new WeakMap,Gu=100;function Xu(e){var t=Ku.get(e)||0;if(!(t>Gu)){var r=e.querySelector(".js-more-repos-form");r&&(Ku.set(e,t+1),l.submit(A(r,HTMLFormElement)))}}function Qu(e){var t=e.querySelector(".js-your-repositories-search");t&&r.fire(t,"filterable:change")}function Yu(e){var t=e.getAttribute("data-url"),r=e.getAttribute("data-query-name");a("string"==typeof t&&"string"==typeof r,"app/assets/modules/github/repo-list.js:76"),f.replaceState(null,"",function(e,t,r){var n=new URL(e,window.location.origin),a=new URLSearchParams(n.search.slice(1));return t.length<1?a.delete(r):a.set(r,t),n.search=a.toString(),n.toString()}(t,e.value.trim(),r))}function Zu(t,r){if(!Ws(t)){var n=e.query(document,".js-site-search-form",HTMLFormElement);e.query(document,".js-site-search").classList.toggle("scoped-search",r);var s=void 0,o=void 0;r?(s=n.getAttribute("data-scoped-search-url"),o=t.getAttribute("data-scoped-placeholder")):(s=n.getAttribute("data-unscoped-search-url"),o=t.getAttribute("data-unscoped-placeholder")),a("string"==typeof s,"app/assets/modules/github/search/site.js:28"),a("string"==typeof o,"app/assets/modules/github/search/site.js:29"),n.setAttribute("action",s),t.setAttribute("placeholder",o)}}n.onFocus(".js-your-repositories-search",function(t){Xu(e.closest(t,".js-repos-container"))}),t.observe(".js-your-repositories-search",{constructor:HTMLInputElement,initialize:function(t){t.defaultValue.trim().length>0&&Qu(e.closest(t,".js-repos-container"))}}),n.onInput(".js-your-repositories-search",function(e){Yu(A(e.target,HTMLInputElement))}),t.observe(".js-more-repos-form",function(t){var r=e.closest(t,".js-repos-container");(function(e){var t=e.querySelector(".js-your-repositories-search");return!!t&&(document.activeElement===t||A(t,HTMLInputElement).defaultValue.trim().length>0)})(r)&&Xu(r),t.addEventListener("page:loaded",function(){Qu(r)})}),r.on("pjax:end",".js-repos-container",function(e){var t=A(e.target,HTMLElement);!function(e){Ku.set(e,0)}(t),Qu(t)}),t.observe(".js-codesearch-nav",function(e){var t=e.querySelector(".selected");if(t){var r=t.offsetLeft,n=t.offsetWidth,a=r+n,s=window.innerWidth;(a-e.scrollLeft>s||a<e.scrollLeft)&&(e.scrollLeft=r-s/2+n/2)}}),t.observe(".js-codesearch-count",function(e){var t=e.getAttribute("data-search-type"),r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-codesearch-deferred-count")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value;if(t&&i.getAttribute("data-search-type")===t){var u=e.cloneNode(!0);u.classList.remove("js-codesearch-count"),i.replaceWith(u)}}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}),n.onKey("keyup",".js-site-search-field",function(e){var t=e.target;a(t instanceof HTMLInputElement,"app/assets/modules/github/search/site.js:36");var r=0===t.value.length;r&&"Backspace"===e.key&&t.classList.contains("is-clearable")&&Zu(t,!1),r&&"Escape"===e.key&&Zu(t,!0),t.classList.toggle("is-clearable",r)}),n.onFocus(".js-site-search-focus",function(t){var r=e.closest(t,".js-chromeless-input-container");r.classList.add("focus"),t.addEventListener("blur",function e(){r.classList.remove("focus"),a(t instanceof HTMLInputElement,"app/assets/modules/github/search/site.js:55"),0===t.value.length&&t.classList.contains("js-site-search-field")&&Zu(t,!0),t.removeEventListener("blur",e)})}),r.on("submit",".js-site-search-form",function(t){e.query(t.target,".js-site-search-type-field",HTMLInputElement).value=new URLSearchParams(window.location.search).get("type")});var ec,tc=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,i,u,c,l,d,m,p,f;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.getAttribute("data-contents-url"),a=Os("SEARCH_SUGGESTIONS"),n){t.next=4;break}return t.abrupt("return");case 4:return s=e.query(document,".js-search-suggester"),i=r.value.slice(0,r.selectionEnd).match(/\S*$/),u=i?i[0]:"",c=new URL(n,window.location.origin),(l=new URLSearchParams).append("query",u),c.search=l.toString(),t.next=13,o.fetchText(c.toString());case 13:if(""!==(d=t.sent).trim()){t.next=17;break}return nc(),t.abrupt("return");case 17:s.innerHTML=d,a&&s.classList.remove("d-none"),S.activate(e.query(s,".js-navigation-container")),m=e.query(s,".js-search-suggester-helper"),p=r.value.match(/(^|\s)[^\s:]+$/),f=A(m.parentElement,HTMLElement),p&&(m.textContent=r.value),f.classList.toggle("d-none",a&&!p);case 25:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}();function rc(e){var t=e.value.slice(0,e.selectionEnd),r=e.value.slice(e.selectionEnd),n=0===e.value.trim().length,a=t.match(/(^|\s+)[^\s:]+$/)&&r.match(/^(\s|$)/);return n||a}function nc(){e.query(document,".js-search-suggester").classList.add("d-none")}function ac(t){var r=t.target,n=e.closest(r,".js-navigation-item"),a=e.query(document,".js-search-suggester-field",HTMLInputElement),s=e.query(document,".js-search-suggester"),o=n.getAttribute("data-value")||"",i=a.value.slice(0,a.selectionEnd).replace(/\S+$/,""),u=a.value.slice(a.selectionEnd);s.classList.contains("d-none")?l.submit(e.query(document,"#search_form",HTMLFormElement)):(t.preventDefault(),a.value=i+o+u,rc(a)?tc(a):nc())}function sc(t,r){var n=e.query(r,".js-repo-access-error");n.textContent=t,n.classList.remove("d-none")}function oc(){var e=!0,t=!1,r=void 0;try{for(var n,a=document.querySelectorAll(".js-repo-access-error")[Symbol.iterator]();!(e=(n=a.next()).done);e=!0){var s=n.value;s.textContent="",s.classList.add("d-none")}}catch(e){t=!0,r=e}finally{try{!e&&a.return&&a.return()}finally{if(t)throw r}}}function ic(e){return e.classList.toggle("is-empty",!e.querySelector(".js-repo-access-entry"))}function uc(){var t=document.getElementById("collaborators");t&&(e.query(t,".js-add-new-collab",HTMLButtonElement).disabled=!0)}function cc(t){var r=document.querySelector(".js-repo-access-team-select");if(r){var n=0,a=!0,s=!1,o=void 0;try{for(var i,u=r.querySelectorAll(".js-repo-access-team-select-option")[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;t===c.getAttribute("data-team-id")&&(c.classList.add("has-access"),c.classList.remove("selected")),c.classList.contains("has-access")||n++}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}0===n&&e.closest(r,".js-repo-access-group").classList.add("no-form")}}function lc(t){var r=document.querySelector(".js-repo-access-team-select");if(r){var n=r.querySelector("[data-team-id='"+t+"']");n&&n.classList.remove("has-access"),e.closest(r,".js-repo-access-group").classList.remove("no-form")}}function dc(t){var r=e.query(t,".js-authorized-pushers");if(r.getAttribute("data-limit")){var n=parseInt(r.getAttribute("data-limit")),a=r.querySelectorAll(".js-authorized-user-or-team").length;r.classList.toggle("at-limit",a>=n)}}t.observe(".js-search-suggester-field",{constructor:HTMLInputElement,initialize:function(e){tc(e),p.addThrottledInputEventListener(e,function(){rc(e)?tc(e):nc()})}}),r.on("focusin",".js-search-suggester-field",function(e){var t=A(e.currentTarget,HTMLInputElement);rc(t)?tc(t):nc()}),r.on("focusout",".js-search-suggester-field",function(){nc()}),r.on("mousedown",".js-search-suggester",ac),r.on("navigation:keydown",".js-search-suggester",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/search-suggester.js:104");var r=t.currentTarget.querySelector('.js-search-suggester .js-navigation-item[aria-selected="true"]');switch(t.detail.hotkey){case"Enter":r?ac(t):l.submit(e.query(document,"#search_form",HTMLFormElement));break;case"Tab":r&&ac(t);break;case"ArrowLeft":case"ArrowRight":case"Escape":nc()}}),r.on("selectmenu:selected",".js-repo-access-team-select",function(t){var r=t.currentTarget,n=e.query(r,".js-repo-access-team-select-option.selected").getAttribute("data-team-id")||"",s=e.query(e.closest(r,".js-repo-access-group"),".js-add-repo-access-field",HTMLInputElement);s.value=n,a(s.form,"app/assets/modules/github/settings/repository-collabs.js:86"),l.submit(s.form)}),c.remoteForm(".js-add-repo-access-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return oc(),t.next=3,n.json();case 3:if(a=t.sent,s=a.json,o=void 0,i=e.closest(r,".js-repo-access-group"),u=e.query(r,".js-add-repo-access-field",HTMLInputElement),o="collaborators"===i.id?i.querySelector(".js-repo-access-list-invites"):i.querySelector(".js-repo-access-list"),c=u.value,u.value="",!s.error){t.next=14;break}return sc(s.error,i),t.abrupt("return");case 14:uc(),m=void 0,(m=document.querySelector("#teams .js-add-new-collab"))instanceof HTMLButtonElement&&(m.disabled=!0),o&&o.insertAdjacentHTML("beforeend",s.html),ic(i),(s.seats||0===s.seats)&&(l=1===s.seats?"seat":"seats",(d=document.getElementById("available-seats"))&&(d.innerHTML=s.seats+" "+l)),"teams"===i.id&&cc(c);case 20:case"end":return t.stop()}var m},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-remove-repo-access-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(!r.hasAttribute("data-skip-remote")){t.next=2;break}return t.abrupt("return");case 2:return t.prev=2,t.next=5,n.text();case 5:t.next=11;break;case 7:return t.prev=7,t.t0=t.catch(2),sc(r.getAttribute("data-error-message")||"",e.closest(r,".js-repo-access-group")),t.abrupt("return");case 11:oc(),a=e.closest(r,".js-repo-access-entry"),"teams"===(s=e.closest(r,".js-repo-access-group")).id&&lc(a.getAttribute("data-team-id")||""),a.remove(),ic(s);case 17:case"end":return t.stop()}},t,this,[[2,7]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change","input.js-repo-data-opt-in",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:16");var n=e.closest(r,".js-repo-data-services"),s=r.checked,o=!0,i=!1,u=void 0;try{for(var c,l=n.querySelectorAll(".js-repo-data-options")[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){c.value.classList.toggle("active",s)}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}var d=!0,m=!1,p=void 0;try{for(var f,v=n.querySelectorAll(".js-repo-data-option")[Symbol.iterator]();!(d=(f=v.next()).done);d=!0){var g=f.value;s?g.removeAttribute("disabled"):(g.setAttribute("disabled","disabled"),g.removeAttribute("checked"))}}catch(e){m=!0,p=e}finally{try{!d&&v.return&&v.return()}finally{if(m)throw p}}}),r.on("change","input.js-default-branch",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:40");var n=e.query(document,".js-default-branch-confirmation",HTMLInputElement);e.query(document,".js-update-branch-dialog").hidden=r.value===n.getAttribute("data-original-value"),n.value=r.value}),r.on("change",".js-repo-features-form input[type=checkbox]",function(t){var r=t.currentTarget,n=e.closest(r,".js-repo-option"),a=e.query(n,".js-status-indicator");a.classList.remove("status-indicator-success","status-indicator-failed"),a.classList.add("status-indicator-loading")}),c.remoteForm(".js-repo-features-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f,v,g,h,y;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=void 0,t.prev=1,t.next=4,n.html();case 4:a=t.sent,t.next=29;break;case 7:for(t.prev=7,t.t0=t.catch(1),s=!0,o=!1,i=void 0,t.prev=12,u=e.querySelectorAll(r,".status-indicator-loading")[Symbol.iterator]();!(s=(c=u.next()).done);s=!0)(l=c.value).classList.remove("status-indicator-loading"),l.classList.add("status-indicator-failed"),d=e.closest(l,".js-repo-option"),(m=e.query(d,"input[type=checkbox]",HTMLInputElement)).checked=!m.checked;t.next=20;break;case 16:t.prev=16,t.t1=t.catch(12),o=!0,i=t.t1;case 20:t.prev=20,t.prev=21,!s&&u.return&&u.return();case 23:if(t.prev=23,!o){t.next=26;break}throw i;case 26:return t.finish(23);case 27:return t.finish(20);case 28:return t.abrupt("return");case 29:for(p=!0,f=!1,v=void 0,t.prev=32,g=e.querySelectorAll(r,".status-indicator-loading")[Symbol.iterator]();!(p=(h=g.next()).done);p=!0)(y=h.value).classList.remove("status-indicator-loading"),y.classList.add("status-indicator-success");t.next=40;break;case 36:t.prev=36,t.t2=t.catch(32),f=!0,v=t.t2;case 40:t.prev=40,t.prev=41,!p&&g.return&&g.return();case 43:if(t.prev=43,!f){t.next=46;break}throw v;case 46:return t.finish(43);case 47:return t.finish(40);case 48:e.query(document,".js-repo-nav").replaceWith(a.html);case 49:case"end":return t.stop()}},t,this,[[1,7],[12,16,20,28],[21,,23,27],[32,36,40,48],[41,,43,47]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-merge-features-form input[type=checkbox]",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:77");var n=r.form;a(n,"app/assets/modules/github/settings/repository-options.js:80");var s=!0,o=!1,i=void 0;try{for(var u,c=n.querySelectorAll(".errored")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){u.value.classList.remove("errored")}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}var l=e.closest(r,".js-repo-option"),d=e.query(l,".js-status-indicator");d.classList.remove("status-indicator-success","status-indicator-failed"),d.classList.add("status-indicator-loading")}),c.remoteForm(".js-merge-features-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k,E,T,q,S,A,M,H,R,I,C,_,F,P;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=65;break;case 5:for(t.prev=5,t.t0=t.catch(0),a=!0,s=!1,o=void 0,t.prev=10,i=r.querySelectorAll(".js-select-one-warning")[Symbol.iterator]();!(a=(u=i.next()).done);a=!0)u.value.classList.remove("d-none");t.next=18;break;case 14:t.prev=14,t.t1=t.catch(10),s=!0,o=t.t1;case 18:t.prev=18,t.prev=19,!a&&i.return&&i.return();case 21:if(t.prev=21,!s){t.next=24;break}throw o;case 24:return t.finish(21);case 25:return t.finish(18);case 26:for(c=!0,l=!1,d=void 0,t.prev=29,m=r.querySelectorAll(".status-indicator-loading")[Symbol.iterator]();!(c=(p=m.next()).done);c=!0)(f=p.value).classList.remove("status-indicator-loading"),f.classList.add("status-indicator-failed"),(v=e.closest(f,".js-repo-option")).classList.add("errored"),(g=e.query(v,"input[type=checkbox]",HTMLInputElement)).checked=!g.checked;t.next=37;break;case 33:t.prev=33,t.t2=t.catch(29),l=!0,d=t.t2;case 37:t.prev=37,t.prev=38,!c&&m.return&&m.return();case 40:if(t.prev=40,!l){t.next=43;break}throw d;case 43:return t.finish(40);case 44:return t.finish(37);case 45:for(h=!0,y=!1,b=void 0,t.prev=48,j=r.querySelectorAll(".status-indicator-success")[Symbol.iterator]();!(h=(L=j.next()).done);h=!0)L.value.classList.remove("status-indicator-success");t.next=56;break;case 52:t.prev=52,t.t3=t.catch(48),y=!0,b=t.t3;case 56:t.prev=56,t.prev=57,!h&&j.return&&j.return();case 59:if(t.prev=59,!y){t.next=62;break}throw b;case 62:return t.finish(59);case 63:return t.finish(56);case 64:return t.abrupt("return");case 65:for(w=!0,x=!1,k=void 0,t.prev=68,E=r.querySelectorAll(".errored")[Symbol.iterator]();!(w=(T=E.next()).done);w=!0)T.value.classList.remove("errored");t.next=76;break;case 72:t.prev=72,t.t4=t.catch(68),x=!0,k=t.t4;case 76:t.prev=76,t.prev=77,!w&&E.return&&E.return();case 79:if(t.prev=79,!x){t.next=82;break}throw k;case 82:return t.finish(79);case 83:return t.finish(76);case 84:for(q=!0,S=!1,A=void 0,t.prev=87,M=r.querySelectorAll(".js-select-one-warning")[Symbol.iterator]();!(q=(H=M.next()).done);q=!0)H.value.classList.add("d-none");t.next=95;break;case 91:t.prev=91,t.t5=t.catch(87),S=!0,A=t.t5;case 95:t.prev=95,t.prev=96,!q&&M.return&&M.return();case 98:if(t.prev=98,!S){t.next=101;break}throw A;case 101:return t.finish(98);case 102:return t.finish(95);case 103:for(R=!0,I=!1,C=void 0,t.prev=106,_=r.querySelectorAll(".status-indicator-loading")[Symbol.iterator]();!(R=(F=_.next()).done);R=!0)(P=F.value).classList.remove("status-indicator-loading"),P.classList.add("status-indicator-success");t.next=114;break;case 110:t.prev=110,t.t6=t.catch(106),I=!0,C=t.t6;case 114:t.prev=114,t.prev=115,!R&&_.return&&_.return();case 117:if(t.prev=117,!I){t.next=120;break}throw C;case 120:return t.finish(117);case 121:return t.finish(114);case 122:case"end":return t.stop()}},t,this,[[0,5],[10,14,18,26],[19,,21,25],[29,33,37,45],[38,,40,44],[48,52,56,64],[57,,59,63],[68,72,76,84],[77,,79,83],[87,91,95,103],[96,,98,102],[106,110,114,122],[115,,117,121]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change","input.js-required-status-toggle",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:139");var n=e.closest(r,".js-protected-branch-settings");e.query(n,".js-required-statuses").classList.toggle("d-none",!r.checked)}),r.on("change","input.js-required-status-checkbox",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:146");var n=e.closest(r,".js-protected-branches-item");e.query(n,".js-required-status-badge").classList.toggle("d-none",!r.checked)}),r.on("change",".js-authorized-branch-pushers-toggle, input.js-authorized-review-dismisser-toggle",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/repository-options.js:156");var n=e.closest(r,".js-protected-branch-options"),s=e.query(n,".js-authorized-pushers");s.classList.toggle("d-none",!r.checked),e.query(s,".js-add-protected-branch-user-or-team input",HTMLInputElement).focus()}),r.on("change",".js-protected-branch-include-admin-toggle",function(t){var r=t.currentTarget,n=e.closest(r,".js-protected-branch-settings").querySelectorAll(".js-protected-branch-admin-permission"),a=!0,s=!1,o=void 0;try{for(var i,u=n[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;c.classList.toggle("d-none"),c.classList.toggle("active",!c.classList.contains("d-none"))}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}}),r.on("change",".js-add-protected-branch-user-or-team",(ec=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(r.target===r.currentTarget){t.next=2;break}return t.abrupt("return");case 2:if(n=r.currentTarget,a(n instanceof AutocompleteElement,"app/assets/modules/github/settings/repository-options.js:188"),s=n.value){t.next=7;break}return t.abrupt("return");case 7:if(n.value="",i=e.closest(n,".js-protected-branch-options"),u=e.query(i,".js-authorized-users-and-teams"),!(c=u.querySelector("div[data-user-or-team-name='"+s+"']"))){t.next=14;break}return e.query(c,".js-protected-branch-pusher").classList.add("user-already-added"),t.abrupt("return");case 14:return l=n.getAttribute("data-add-url"),a(l,"app/assets/modules/github/settings/repository-options.js:203"),d=new URL(l,window.location.origin),(m=new URLSearchParams(d.search.slice(1))).append("item",s),d.search=m.toString(),t.t0=u,t.next=23,o.fetchSafeDocumentFragment(document,d);case 23:t.t1=t.sent,t.t0.append.call(t.t0,t.t1),dc(i);case 26:case"end":return t.stop()}},t,this)})),function(e){return ec.apply(this,arguments)})),r.on("click",".js-remove-authorized-user-or-team",function(t){var r=t.currentTarget,n=e.closest(r,".js-protected-branch-options");e.closest(r,".js-authorized-user-or-team").remove(),dc(n)}),t.observe("#pages-cname-field",{constructor:HTMLInputElement,add:function(t){p.addThrottledInputEventListener(t,function(){e.query(document,".js-pages-cname-save-btn",HTMLButtonElement).disabled=t.value===t.defaultValue})}}),r.on("selectmenu:selected",".js-pages-source",function(t){var r=t.currentTarget,n=e.query(document,".js-pages-source-btn-text"),s=n.getAttribute("data-original-text")===n.textContent;e.query(document,".js-pages-source-save-btn",HTMLButtonElement).disabled=s;var o=document.querySelector(".js-pages-theme-source-value");if(o&&o instanceof HTMLInputElement){var i=e.query(r,".selected input",HTMLInputElement).value,u=e.query(document,".js-pages-theme-source-note"),c=e.query(document,".js-pages-theme-source-note-value");if("none"===i){var l=o.getAttribute("data-original-value"),d=c.getAttribute("data-original-text");a(null!=l&&null!=d,"Missing attributes [`data-original-value`, `data-original-text`] -- app/assets/modules/github/settings/repository-options.js:252"),o.value=l,c.textContent=d,u.classList.remove("hide-note")}else o.value=i,c.textContent=n.textContent,s?u.classList.add("hide-note"):u.classList.remove("hide-note")}}),t.observe(".js-pages-https-status-form",{constructor:HTMLFormElement,add:function(e){var t=this,r=new Request(e.action,{method:e.method,body:new FormData(e)});te(regeneratorRuntime.mark(function n(){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,o.fetchSafeDocumentFragment(document,r);case 3:a=t.sent,e.replaceWith(a),t.next=13;break;case 7:if(t.prev=7,t.t0=t.catch(0),!t.t0.response||404!==t.t0.response.status){t.next=12;break}t.next=13;break;case 12:throw t.t0;case 13:case"end":return t.stop()}},n,t,[[0,7]])}))()}}),t.observe(".js-enable-btn",{constructor:HTMLButtonElement,add:function(e){e.disabled=!1,e.classList.remove("tooltipped"),e.removeAttribute("aria-label")}}),r.on("click",".js-repo-team-suggestions-view-all",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=e.closest(r.currentTarget,"ul"),s=r.currentTarget.getAttribute("data-url"),a(s,"app/assets/modules/github/settings/repository-settings.js:11"),i=e.querySelectorAll(document,".js-repo-toggle-team:checked",HTMLInputElement).map(function(e){return e.value}),t.next=6,o.fetchSafeDocumentFragment(document,s);case 6:for(u=t.sent,n.innerHTML="",n.append(u),c=!0,l=!1,d=void 0,t.prev=12,m=i[Symbol.iterator]();!(c=(p=m.next()).done);c=!0)f=p.value,e.query(n,'.js-repo-toggle-team[value="'+f+'"]',HTMLInputElement).checked=!0;t.next=20;break;case 16:t.prev=16,t.t0=t.catch(12),l=!0,d=t.t0;case 20:t.prev=20,t.prev=21,!c&&m.return&&m.return();case 23:if(t.prev=23,!l){t.next=26;break}throw d;case 26:return t.finish(23);case 27:return t.finish(20);case 28:case"end":return t.stop()}},t,this,[[12,16,20,28],[21,,23,27]])}));return function(e){return t.apply(this,arguments)}}()),c.remoteForm(".js-saved-reply-delete",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,s,o;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:n=t.closest(".js-saved-reply-container"),a(n,"app/assets/modules/github/settings/saved-replies.js:10"),s=n.querySelectorAll(".js-saved-reply-list-item").length,n.classList.toggle("has-replies",s>1),o=t.closest(".js-saved-reply-list-item"),a(o,"app/assets/modules/github/settings/saved-replies.js:16"),o.remove();case 9:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-user-sessions-revoke",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,s,o;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:n=t.closest(".js-user-sessions-container"),s=t.closest(".js-user-session"),a(s,"app/assets/modules/github/settings/user-sessions.js:12"),s.remove(),n&&(o=n.querySelectorAll(".js-user-session").length,n.classList.toggle("has-active-sessions",o>0));case 7:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}());var mc=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(t){e.next=2;break}return e.abrupt("return");case 2:if(r=t.getAttribute("data-url")){e.next=5;break}return e.abrupt("return");case 5:return n=void 0,e.prev=6,e.next=9,o.fetchSafeDocumentFragment(document,r);case 9:n=e.sent,t.innerHTML="",t.append(n),e.next=17;break;case 14:e.prev=14,e.t0=e.catch(6),window.location.reload();case 17:case"end":return e.stop()}},e,this,[[6,14]])}));return function(t){return e.apply(this,arguments)}}();function pc(t,r){e.query(t,".js-add-new-blocked-user",HTMLButtonElement).disabled=!r}function fc(e){var t=e.currentTarget.querySelector("input.js-filterable-field");t&&t.focus()}function vc(t){var r=e.closest(t.currentTarget,".js-select-menu").querySelector(".js-navigation-container"),n=t.currentTarget;r&&n instanceof HTMLElement&&S.refocus(r,n)}function gc(e){var t=e.currentTarget.querySelectorAll(".js-select-menu-tab"),r=t[0],n=!0,a=!1,s=void 0;try{for(var o,i=t[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;if(u.classList.contains("selected")){r=u;break}}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}r&&hc(r)}function hc(e){var t=e.closest(".js-select-menu");a(t instanceof HTMLElement,"app/assets/modules/github/select-menu/tabs.js:87");var r=!0,n=!1,s=void 0;try{for(var o,i=t.querySelectorAll(".js-select-menu-tab")[Symbol.iterator]();!(r=(o=i.next()).done);r=!0){var u=o.value;u!==e&&(u.classList.remove("selected"),u.setAttribute("aria-current","false"))}}catch(e){n=!0,s=e}finally{try{!r&&i.return&&i.return()}finally{if(n)throw s}}e.classList.add("selected"),e.setAttribute("aria-current","true")}function yc(e,t){var n=e.getAttribute("data-tab-filter"),s=e.closest(".js-select-menu");a(s,"app/assets/modules/github/select-menu/tabs.js:109");var o=s.querySelectorAll(".js-select-menu-tab-bucket"),i=!0,u=!1,c=void 0;try{for(var l,d=o[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){var m=l.value;m.getAttribute("data-tab-filter")===n&&(m.classList.toggle("selected",t),t&&r.fire(m,"selectmenu:tabchange"))}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}}t.observe(".js-email-global-unsubscribe-form",function(t){e.query(t,".js-email-global-unsubscribe-submit",HTMLInputElement).disabled=!0}),r.on("change",".js-email-global-unsubscribe-form",function(t){var r=t.currentTarget,n=e.query(r,".js-email-global-unsubscribe-submit",HTMLInputElement),a=e.querySelectorAll(r,".js-email-global-unsubscribe",HTMLInputElement),s=Array.from(a).some(function(e){return e.checked!==e.defaultChecked});n.disabled=!s}),c.remoteForm(".js-verify-ssh-key",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=e.closest(r,"li"),e.query(a,".js-unverified-user-key-notice").remove(),e.query(a,".js-user-key-icon").classList.remove("unverified-user-key"),r.remove();case 6:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-leave-collaborated-repo",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:s=r.getAttribute("data-repo-id"),a(s,"app/assets/modules/github/settings/user-settings.js:43"),e.query(document,".js-collab-repo[data-repo-id='"+s+"']").remove(),document.querySelector(".js-collab-repo")||(e.query(document,".js-collaborated-repos-empty").hidden=!1,e.query(document,".js-collaborated-repos").hidden=!0);case 6:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-newsletter-unsubscribe-form",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s,o,i,u;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,r.text();case 2:for(n=!0,a=!1,s=void 0,e.prev=5,o=document.querySelectorAll(".js-newsletter-unsubscribe-message")[Symbol.iterator]();!(n=(i=o.next()).done);n=!0)(u=i.value).hidden=!u.hidden;e.next=13;break;case 9:e.prev=9,e.t0=e.catch(5),a=!0,s=e.t0;case 13:e.prev=13,e.prev=14,!n&&o.return&&o.return();case 16:if(e.prev=16,!a){e.next=19;break}throw s;case 19:return e.finish(16);case 20:return e.finish(13);case 21:case"end":return e.stop()}},e,this,[[5,9,13,21],[14,,16,20]])}));return function(t,r){return e.apply(this,arguments)}}()),c.remoteForm(".js-revoke-access-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,o,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:s=r.getAttribute("data-id"),o=r.getAttribute("data-type-name"),a(s,"app/assets/modules/github/settings/user-settings.js:63"),a(o,"app/assets/modules/github/settings/user-settings.js:64"),(i=e.query(document,".js-revoke-item[data-type='"+o+"'][data-id='"+s+"']")).remove(),i.classList.contains("new-token")&&e.query(document,".js-flash-new-token").remove();case 9:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-delete-oauth-application-image",function(){var t=e.query(document,".js-delete-oauth-application-image-form",HTMLFormElement);l.submit(t)}),r.on("click",".js-new-callback",function(t){t.preventDefault();var r=t.currentTarget,n=e.closest(r,".js-callback-urls"),s=e.query(n,".js-callback-url").cloneNode(!0);s.classList.remove("is-default-callback"),e.query(s,"input",HTMLInputElement).value="",n.classList.add("has-many");var o=r.parentNode;a(o instanceof HTMLElement,"app/assets/modules/github/settings/user-settings.js:93"),o.insertBefore(s,r)}),r.on("click",".js-delete-callback",function(t){t.preventDefault();var r=t.currentTarget;e.closest(r,".js-callback-url").remove();var n=e.closest(r,".js-callback-urls");e.querySelectorAll(n,".js-callback-url",HTMLElement).length<=1&&n.classList.remove("has-many")}),r.on("click",".js-oauth-application-whitelist .js-deny-this-request",function(t){var r=t.currentTarget,n=r.nextElementSibling;a(n instanceof HTMLInputElement,"app/assets/modules/github/settings/user-settings.js:115"),n.value="denied",l.submit(e.closest(r,".js-org-application-access-form",HTMLFormElement))}),r.on("change",".js-checkbox-scope",function(t){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/settings/user-settings.js:124");var n=e.closest(r,".js-check-scope-container"),s=e.querySelectorAll(n,".js-checkbox-scope",HTMLInputElement),o=!0,i=!1,u=void 0;try{for(var c,l=s[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){var d=c.value;d!==r&&(d.checked=r.checked,d.disabled=r.checked)}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}}),r.on("click",".js-generate-integration-key",function(){var t=e.query(document,".js-integration-key-management-wrapper");t.classList.add("downloading"),t.classList.contains("js-integration-key-multi")&&(t.classList.add("has-keys"),setTimeout(function(){var t=e.query(document,".js-integration-keys-container",HTMLElement);t&&mc(t)},1e3))}),c.remoteForm(".js-remove-integration-key",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var s,o,i,u,c,l;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.prev=0,t.next=3,n.text();case 3:t.next=23;break;case 5:if(t.prev=5,t.t0=t.catch(0),t.t0.response){t.next=9;break}throw t.t0;case 9:e.closest(r,"details").removeAttribute("open"),s=e.query(document,".js-integration-key-management-wrapper"),o=e.query(s,".js-error-container"),i=e.query(o,".js-error-message"),t.t1=t.t0.response.status,t.next=500===t.t1?16:404===t.t1?18:20;break;case 16:return i.textContent="Oops, something went wrong.",t.abrupt("break",21);case 18:return i.textContent="Oops, that key could not be found.",t.abrupt("break",21);case 20:i.textContent=t.t0.response.json.message;case 21:return s.classList.add("error"),t.abrupt("return");case 23:u=r.getAttribute("data-key-id"),a(u,"app/assets/modules/github/settings/user-settings.js:199"),(c=document.getElementById(u))&&(l=e.closest(c,".js-integration-keys-container",HTMLElement))&&mc(l);case 27:case"end":return t.stop()}},t,this,[[0,5]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-error-dismiss",function(){var t=e.query(document,".js-integration-keys-container",HTMLElement);t&&mc(t)}),t.observe(".js-block-users-form",function(e){pc(e,!1)}),c.remoteForm(".js-block-users-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=t.sent,s=e.query(document,".js-user-block-settings-list"),o=e.query(s,".js-blocked-list"),pc(r,!1),i=e.query(r,".js-add-blocked-user-field",HTMLInputElement),l.changeValue(i,""),u=s,document.querySelector(".js-blocked-user-list")||e.query(u,".blankslate").classList.add("d-none"),o.prepend(a.html);case 10:case"end":return t.stop()}var u},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("change",".js-add-blocked-user-completer",function(t){if(t.target===t.currentTarget){var r=t.target;a(r instanceof AutocompleteElement,"app/assets/modules/github/settings/user-settings.js:250"),pc(e.closest(r,".js-block-users-form"),!!r.value)}}),t.observe("body.js-print-popup",function(){window.print(),setTimeout(window.close,1e3)}),r.on("deprecatedAjaxSuccess",".js-select-menu:not([data-multiple])",function(e){a(e.currentTarget instanceof HTMLElement,"app/assets/modules/github/select-menu/ajax.js:20"),D.deactivate(e.currentTarget)}),r.on("deprecatedAjaxSend",".js-select-menu:not([data-multiple])",function(e){e.currentTarget.classList.add("is-loading")}),r.on("deprecatedAjaxComplete",".js-select-menu",function(e){e.currentTarget.classList.remove("is-loading")}),r.on("deprecatedAjaxError",".js-select-menu",function(e){e.currentTarget.classList.add("has-error")}),r.on("menu:deactivate",".js-select-menu",function(e){e.currentTarget.classList.remove("is-loading","has-error")}),r.on("click",".js-auto-submit-select-menu",function(t){var r=function(t){var r=e.query(t,".js-value"),n=e.closest(t,"form",HTMLFormElement);return e.query(n,".js-item-value",HTMLInputElement).value=r.textContent,n}(t.currentTarget);l.submit(r)}),r.on("navigation:open",".js-select-menu:not([data-multiple]) .js-navigation-item",function(t){var n=t.currentTarget;if(r.fire(n,"selectmenu:select")){var a=e.closest(n,".js-select-menu"),s=a.querySelector(".js-navigation-item.selected");s&&s.classList.remove("selected"),n.classList.add("selected"),n.classList.remove("indeterminate");var o=!0,i=!1,u=void 0;try{for(var c,d=e.querySelectorAll(n,"input[type=radio], input[type=checkbox]",HTMLInputElement)[Symbol.iterator]();!(o=(c=d.next()).done);o=!0){var m=c.value;l.changeValue(m,!0)}}catch(e){i=!0,u=e}finally{try{!o&&d.return&&d.return()}finally{if(i)throw u}}r.fire(n,"selectmenu:selected"),a.classList.contains("is-loading")||D.deactivate(a)}}),r.on("navigation:open",".js-select-menu[data-multiple] .js-navigation-item",function(t){var n=t.currentTarget;if(r.fire(n,"selectmenu:select")){var a=n.classList.contains("selected");n.classList.toggle("selected"),n.classList.remove("indeterminate");var s=!0,o=!1,i=void 0;try{for(var u,c=e.querySelectorAll(n,"input[type=radio], input[type=checkbox]",HTMLInputElement)[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var d=u.value;l.changeValue(d,!a)}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}r.fire(n,"selectmenu:selected")}}),r.on("selectmenu:select",".js-select-menu .js-navigation-item.disabled",function(e){e.preventDefault()}),r.on("selectmenu:selected",".js-select-menu .js-navigation-item",function(t){var r=t.currentTarget,n=e.closest(r,".js-select-menu"),a=r.querySelector(".js-select-button-text");if(a){var s=n.querySelector(".js-select-button");s&&(s.innerHTML=a.innerHTML)}var o=r.querySelector(".js-select-menu-item-gravatar");if(o){var i=n.querySelector(".js-select-button-gravatar");i&&(i.innerHTML=o.innerHTML)}}),r.on("selectmenu:change",".js-select-menu .select-menu-list",function(e){var t=e.currentTarget,r=Array.from(t.querySelectorAll(".js-navigation-item")),n=!0,a=!1,s=void 0;try{for(var o,i=r[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){o.value.classList.remove("last-visible")}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}var u=r.filter(R).pop();if(u&&u.classList.add("last-visible"),!t.hasAttribute("data-filterable-for")){var c=e.target.classList.contains("filterable-empty");t.classList.toggle("filterable-empty",c)}}),r.on("menu:activated",".js-select-menu",fc),r.on("selectmenu:load",".js-select-menu",fc),r.on("menu:deactivate",".js-select-menu",function(e){var t=e.currentTarget,n=t.querySelector("input.js-filterable-field");n instanceof HTMLInputElement&&(n.value="",r.fire(n,"filterable:change"));var s=!0,o=!1,i=void 0;try{for(var u,c=t.querySelectorAll(".js-navigation-item.selected")[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value,d=l.querySelector("input[type=radio], input[type=checkbox]");d&&(a(d instanceof HTMLInputElement,"app/assets/modules/github/select-menu/filterable.js:34"),l.classList.toggle("selected",d.checked))}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}var m=document.activeElement;if(m&&t.contains(m))try{m.blur()}catch(e){}}),r.on("menu:activate",".js-select-menu",function(e){var t=e.currentTarget.querySelector(":focus");t&&t.blur();var r=e.currentTarget.querySelector(".js-menu-target");r&&r.classList.add("selected");var n=e.currentTarget.querySelector(".js-navigation-container");n&&S.push(n)}),r.on("menu:deactivate",".js-select-menu",function(e){var t=e.currentTarget.querySelector(".js-menu-target");t&&t.classList.remove("selected");var r=e.currentTarget.querySelector(".js-navigation-container");r&&S.pop(r)}),r.on("filterable:change",".js-select-menu .select-menu-list",vc),r.on("selectmenu:tabchange",".js-select-menu .select-menu-list",vc),r.on("filterable:change",".js-select-menu .select-menu-list, details-menu .select-menu-list",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/select-menu/new.js:22");var n=t.currentTarget,s=n.querySelector(".js-new-item-form");s&&function(t,r,n){var a=n.length>0&&!function(e,t){var r=!0,n=!1,a=void 0;try{for(var s,o=e.querySelectorAll(".js-select-button-text, .js-select-menu-filter-text")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value,u=i.textContent.toLowerCase().trim();if(u===t.toLowerCase())return!0}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}return!1}(t,n);if(t.classList.toggle("is-showing-new-item-form",a),!a)return;var s=e.query(r,".js-new-item-name");"innerText"in s?s.innerText=n:s.textContent=n;var o=r.querySelector(".js-new-item-value");(o instanceof HTMLInputElement||o instanceof HTMLButtonElement)&&(o.value=n)}(n,s,t.detail.inputField.value),r.fire(t.target,"selectmenu:change")}),r.on("menu:activate",".js-select-menu",gc),r.on("selectmenu:load",".js-select-menu",gc),r.on("click",".js-select-menu .js-select-menu-tab",function(e){var t=e.currentTarget,r=t.closest(".js-select-menu");a(r instanceof HTMLElement,"app/assets/modules/github/select-menu/tabs.js:63"),hc(t);var n=r.querySelector(".js-filterable-field");if(n){var s=t.getAttribute("data-filter-placeholder");s&&n.setAttribute("placeholder",s),n.focus()}e.stopPropagation(),e.preventDefault()}),t.observe(".js-select-menu .js-select-menu-tab.selected",{add:function(e){yc(e,!0)},remove:function(e){yc(e,!1)}});function bc(){var e=document.body;a(e,"app/assets/modules/github/sessions/two-factor.js:13"),e.classList.add("is-sending"),e.classList.remove("is-sent","is-not-sent")}function jc(){var e=document.body;a(e,"app/assets/modules/github/sessions/two-factor.js:21"),e.classList.add("is-sent"),e.classList.remove("is-sending")}function Lc(t){var r=document.body;a(r,"app/assets/modules/github/sessions/two-factor.js:29"),t&&(e.query(document,".js-sms-error").textContent=t),r.classList.add("is-not-sent"),r.classList.remove("is-sending")}c.remoteForm(".js-send-auth-code",function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return bc(),n=void 0,e.prev=2,e.next=5,r.text();case 5:n=e.sent,e.next=11;break;case 8:e.prev=8,e.t0=e.catch(2),Lc(e.t0.response.text);case 11:n&&jc();case 12:case"end":return e.stop()}},e,void 0,[[2,8]])}));return function(t,r){return e.apply(this,arguments)}}()),r.on("click",".js-send-two-factor-code",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k,E;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=A(r.currentTarget,HTMLButtonElement),s=n.form,a(s,"app/assets/modules/github/sessions/two-factor.js:57"),i=e.query(s,".js-country-code-select",HTMLSelectElement).value,u=e.query(s,".js-sms-number",HTMLInputElement).value,c=i+" "+u,bc(),null==(l=n.getAttribute("data-authenticity-token"))&&(d=A(s.elements.namedItem("authenticity_token"),HTMLInputElement),l=d.value),(m=new FormData).append("number",c),m.append("authenticity_token",l),t.prev=12,p=n.getAttribute("data-url"),a(p,"app/assets/modules/github/sessions/two-factor.js:78"),t.next=17,o.fetch(p,{method:"post",body:m});case 17:for(jc(),f=!0,v=!1,g=void 0,t.prev=21,h=s.querySelectorAll(".js-2fa-enable")[Symbol.iterator]();!(f=(y=h.next()).done);f=!0)((b=y.value)instanceof HTMLInputElement||b instanceof HTMLButtonElement)&&(b.disabled=!1);t.next=29;break;case 25:t.prev=25,t.t0=t.catch(21),v=!0,g=t.t0;case 29:t.prev=29,t.prev=30,!f&&h.return&&h.return();case 32:if(t.prev=32,!v){t.next=35;break}throw g;case 35:return t.finish(32);case 36:return t.finish(29);case 37:e.query(s,".js-2fa-otp").focus(),t.next=66;break;case 40:if(t.prev=40,t.t1=t.catch(12),!t.t1.response){t.next=47;break}return t.next=45,t.t1.response.text();case 45:Lc(t.sent);case 47:for(j=!0,L=!1,w=void 0,t.prev=50,x=s.querySelectorAll(".js-2fa-enable")[Symbol.iterator]();!(j=(k=x.next()).done);j=!0)((E=k.value)instanceof HTMLInputElement||E instanceof HTMLButtonElement)&&(E.disabled=!0);t.next=58;break;case 54:t.prev=54,t.t2=t.catch(50),L=!0,w=t.t2;case 58:t.prev=58,t.prev=59,!j&&x.return&&x.return();case 61:if(t.prev=61,!L){t.next=64;break}throw w;case 64:return t.finish(61);case 65:return t.finish(58);case 66:case"end":return t.stop()}},t,this,[[12,40],[21,25,29,37],[30,,32,36],[50,54,58,66],[59,,61,65]])}));return function(e){return t.apply(this,arguments)}}()),r.on("click",".js-enable-enable-two-factor-auth-button",function(){var t=e.query(document,".js-enable-two-factor-auth-button",HTMLButtonElement);t.disabled=!1,t.removeAttribute("aria-label"),t.classList.remove("tooltipped")}),t.observe(".js-two-factor-sms-fallback-button",function(t){t.addEventListener("toggle",function(t){var r=t.currentTarget;a(r instanceof Element,"app/assets/modules/github/sessions/two-factor.js:114");var n=!0,s=!1,o=void 0;try{for(var i,u=r.querySelectorAll(".flash")[Symbol.iterator]();!(n=(i=u.next()).done);n=!0){i.value.hidden=!0}}catch(e){s=!0,o=e}finally{try{!n&&u.return&&u.return()}finally{if(s)throw o}}e.query(r,".js-configure-sms-fallback").hidden=!1,e.query(r,".js-verify-sms-fallback").hidden=!0})}),c.remoteForm(".js-two-factor-set-sms-fallback",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return a=void 0,t.prev=1,t.next=4,n.text();case 4:a=t.sent,t.next=14;break;case 7:switch(t.prev=7,t.t0=t.catch(1),s=e.query(r,".js-configure-sms-fallback"),o=e.query(r,".js-verify-sms-fallback"),i=s.hidden?o:s,u=e.query(i,".flash"),t.t0.response.status){case 422:case 429:u.textContent=t.t0.response.text,u.hidden=!1}case 14:if(!a){t.next=24;break}t.t1=a.status,t.next=200===t.t1?18:201===t.t1?18:202===t.t1?20:24;break;case 18:return window.location.reload(),t.abrupt("break",24);case 20:return e.query(r,".js-configure-sms-fallback").hidden=!0,e.query(r,".js-verify-sms-fallback").hidden=!1,e.query(r,".js-fallback-otp").focus(),t.abrupt("break",24);case 24:case"end":return t.stop()}},t,void 0,[[1,7]])}));return function(e,r){return t.apply(this,arguments)}}()),t.observe("meta[name=user-login]",{constructor:HTMLMetaElement,initialize:function(t){var n=t.content,a=String(!!n.length);try{window.localStorage.setItem("logged-in",a)}catch(e){return}window.addEventListener("storage",function(t){if(t.storageArea===window.localStorage&&"logged-in"===t.key&&("true"===t.newValue||"false"===t.newValue)&&t.newValue!==a){a=t.newValue;var n=e.query(document,".js-stale-session-flash");n.classList.toggle("is-signed-in","true"===a),n.classList.toggle("is-signed-out","false"===a),n.classList.remove("d-none"),window.addEventListener("popstate",function(e){null!=e.state.container&&location.reload()}),r.on("submit","form",function(e){e.preventDefault()})}})}}),t.observe(".js-contact-javascript-flag",{constructor:HTMLInputElement,add:function(e){e.value="true"}}),r.on("socket:message",".js-notification-indicator",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/site/header-notifications.js:10");var t=e.currentTarget,r=e.detail.data;t.setAttribute("aria-label",r.aria_label),t.setAttribute("data-ga-click",r.ga_click),t.querySelector("span").setAttribute("class",r.span_class)});var wc=function(){var e=te(regeneratorRuntime.mark(function e(){var t;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!xc){e.next=2;break}return e.abrupt("return");case 2:return xc=!0,t="/site/keyboard_shortcuts?url="+window.location.pathname,e.next=6,V.dialog({content:o.fetchSafeDocumentFragment(document,t),dialogClass:"width-full container-xl"});case 6:e.sent.addEventListener("dialog:remove",function(){xc=!1},{once:!0});case 8:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}(),xc=!1;r.on("click",".js-keyboard-shortcuts",wc),document.addEventListener("keydown",function(e){e.target instanceof Node&&l.isFormField(e.target)||"?"===M(e)&&wc()}),r.on("click",".js-see-all-keyboard-shortcuts",function(e){var t=e.currentTarget;t instanceof HTMLButtonElement&&(t.disabled=!0);var r=!0,n=!1,a=void 0;try{for(var s,o=document.querySelectorAll(".js-hidden-shortcut-pane")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){s.value.style.display="table-row-group"}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}});var kc=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,o,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=h.getMetadataByName(document,"site-status-api-url"),t.next=3,window.fetch(n);case 3:return a=t.sent,t.next=6,a.json();case 6:(s=t.sent).status&&"good"!==s.status&&(e.query(r,".js-site-status-message").textContent=s.body,e.query(r,".js-site-status-time").setAttribute("datetime",s.created_on),o=e.query(r,".flash"),i="major"===s.status?"flash-error":"flash-warn",o.classList.add(i),r.classList.remove("d-none"));case 8:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}();t.observe(".js-site-status-container",{initialize:function(e){kc(e)}}),n.onFocus(".js-skip-password-autofill",function(e){a(e instanceof HTMLInputElement,"app/assets/modules/github/skip-autofill.js:9"),e.type="password"}),r.on("click",".js-smoothscroll-anchor",function(e){var t=e.currentTarget;if(t instanceof HTMLAnchorElement){var r=H.findFragmentTarget(document,t.hash);r&&(r.scrollIntoView({behavior:"smooth"}),e.preventDefault())}});var Ec=void 0;te(regeneratorRuntime.mark(function e(){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!Os("SERVICE_WORKER")||!navigator.serviceWorker){e.next=11;break}return e.prev=1,e.next=4,navigator.serviceWorker.register("/service-worker.js");case 4:Ec=e.sent,e.next=10;break;case 7:e.prev=7,e.t0=e.catch(1),console.error(e.t0);case 10:Ec&&console.log("Service worker registration successful: ",Ec);case 11:case"end":return e.stop()}},e,this,[[1,7]])}))(),r.on("click",".js-subscribe-to-web-push",function(t){if(window.PushManager){t.preventDefault();var r=A(t.currentTarget,HTMLButtonElement);a(r.form,"app/assets/modules/github/service-worker-registration.js:37");var n=e.query(r.form,"[name=authenticity_token]",HTMLInputElement).value;fetch("/web-push/public-key",{credentials:"same-origin"}).then(function(e){return e.json()}).then(function(e){return e.vapid_public_key}).then(function(e){var t=new Uint8Array(e);return Ec.pushManager.subscribe({userVisibleOnly:!0,applicationServerKey:t})}).then(function(e){e=e.toJSON();var t=new FormData;return t.append("authenticity_token",n),t.append("endpoint",e.endpoint),t.append("p256dh",e.keys.p256dh),t.append("auth",e.keys.auth),fetch("/web-push-subscription",{method:"POST",body:t,credentials:"same-origin"})}).then(function(e){return console.log(e)}).catch(function(e){console.error(e)})}});var Tc=za(function(e,t){var r,n;r=function(e,t){var r=function(n,a,s){var o,i,u={close:function(){}},c=this,l=0,d=-1,m=!1,p=!1,f=Object.assign({},r.defaultOptions,"function"==typeof s?{shouldReconnect:s}:s);if("number"!=typeof f.timeout)throw new Error("timeout must be the number of milliseconds to timeout a connection attempt");if("function"!=typeof f.shouldReconnect)throw new Error("shouldReconnect must be a function that returns the number of milliseconds to wait for a reconnect attempt, or null or undefined to not reconnect.");function v(){i&&(clearTimeout(i),i=null)}["bufferedAmount","url","readyState","protocol","extensions"].forEach(function(e){Object.defineProperty(c,e,{get:function(){return u[e]}})});var g=function(e){m&&(v(),j(e))},h=function(){m=!0,u.close(1e3)},y=!1;function b(){y&&(e.removeEventListener("online",g),e.removeEventListener("offline",h),y=!1)}function j(e){if(!f.shouldReconnect.handle1000&&1e3===e.code||p)l=0;else if(!1!==t.onLine){var r=f.shouldReconnect(e,c);"number"==typeof r&&(i=setTimeout(L,r))}else m=!0}function L(){i=null,(u=new WebSocket(n,a||void 0)).binaryType=c.binaryType,l++,c.dispatchEvent(Object.assign(new CustomEvent("connecting"),{attempts:l,reconnects:d})),o=setTimeout(function(){o=null,b(),c.dispatchEvent(Object.assign(new CustomEvent("timeout"),{attempts:l,reconnects:d}))},f.timeout),["open","close","message","error"].forEach(function(e){u.addEventListener(e,function(t){c.dispatchEvent(t);var r=c["on"+e];if("function"==typeof r)return r.apply(c,arguments)})}),f.ignoreConnectivityEvents||y||(e.addEventListener("online",g),e.addEventListener("offline",h),y=!0)}c.send=function(){return u.send.apply(u,arguments)},c.close=function(e,t){return"number"!=typeof e&&(t=e,e=1e3),v(),m=!1,p=!0,b(),u.close(e,t)},c.open=function(){u.readyState!==WebSocket.OPEN&&u.readyState!==WebSocket.CONNECTING&&(v(),m=!1,p=!1,L())},Object.defineProperty(c,"listeners",{value:{open:[function(e){o&&(clearTimeout(o),o=null),e.reconnects=++d,e.attempts=l,l=0,m=!1}],close:[j]}}),Object.defineProperty(c,"attempts",{get:function(){return l},enumerable:!0}),Object.defineProperty(c,"reconnects",{get:function(){return d},enumerable:!0}),f.automaticOpen&&L()};return r.defaultOptions={timeout:4e3,shouldReconnect:function(e,t){if(1008!==e.code&&1011!==e.code)return[0,3e3,1e4][t.attempts]},ignoreConnectivityEvents:!1,automaticOpen:!0},r.prototype.binaryType="blob",r.prototype.addEventListener=function(e,t){e in this.listeners||(this.listeners[e]=[]),this.listeners[e].push(t)},r.prototype.removeEventListener=function(e,t){if(e in this.listeners)for(var r=this.listeners[e],n=0,a=r.length;n<a;n++)if(r[n]===t)return void r.splice(n,1)},r.prototype.dispatchEvent=function(e){if(e.type in this.listeners)for(var t=this.listeners[e.type],r=0,n=t.length;r<n;r++)t[r].call(this,e)},r},n="undefined"!=typeof window?window:Ua,e.exports=r(n,navigator)}),qc={},Sc={},Ac=null;function Mc(e){var t=e.getAttribute("data-channel");return t?t.split(/\s+/):[]}window.WebSocket&&t.observe(".js-socket-channel[data-channel]",{add:function(e){Ac||(Ac=function(){var e=document.head&&document.head.querySelector("link[rel=web-socket]");if(e){a(e instanceof HTMLLinkElement,"Link must be of type HTMLLinkElement -- app/assets/modules/github/socket-channel.js:36");var t=new Tc(e.href,void 0,{automaticOpen:!1,shouldReconnect:function(e,t){if(!(t.reconnects<=5)&&1008!==e.code&&1011!==e.code){if(1013===e.code)return 6e3;if(1012===e.code)return 5e3;var r=5*Math.random();return 1e3*Math.pow(2,t.attempts)*r}}});t.addEventListener("open",function(){for(var e in qc)t.send("subscribe:"+e)}),t.addEventListener("message",function(e){var t=JSON.parse(e.data),n=t[0],a=t[1];if(n&&a){var s=Sc[n]||[],o=!0,i=!1,u=void 0;try{for(var c,l=s[Symbol.iterator]();!(o=(c=l.next()).done);o=!0){var d=c.value;r.fire(d,"socket:message",{data:a,name:n})}}catch(e){i=!0,u=e}finally{try{!o&&l.return&&l.return()}finally{if(i)throw u}}}});try{t.open()}catch(e){if(e.name&&"SecurityError"!==e.name)throw new Error("error opening WebSocket: "+e.message+" ("+e.name+")");return null}return t}}());var t=Ac;if(t){var n=!0,s=!1,o=void 0;try{for(var i,u=Mc(e)[Symbol.iterator]();!(n=(i=u.next()).done);n=!0){var c=i.value;t.readyState!==WebSocket.OPEN||qc[c]||t.send("subscribe:"+c),qc[c]=!0,null==Sc[c]&&(Sc[c]=[]),Sc[c].push(e)}}catch(e){s=!0,o=e}finally{try{!n&&u.return&&u.return()}finally{if(s)throw o}}}},remove:function(e){var t=!0,r=!1,n=void 0;try{for(var a,s=Mc(e)[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value,i=Sc[o];if(i){var u=i.indexOf(e);-1!==u&&i.splice(u,1)}}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}}),t.observe("form.js-auto-replay-enforced-sso-request",{constructor:HTMLFormElement,initialize:function(e){l.submit(e)}});var Hc=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.querySelector(".js-dropdown-details"),(s=r.querySelector(".js-status-dropdown-menu")||r.closest(".js-status-dropdown-menu"))instanceof HTMLElement){t.next=4;break}return t.abrupt("return");case 4:return i=e.query(s,".js-status-loader"),u=e.query(s,".js-status-loading"),c=e.query(s,".js-status-error"),l=i.getAttribute("data-contents-url"),a(l,"app/assets/modules/github/statuses.js:86"),u.classList.remove("d-none"),c.classList.add("d-none"),d=void 0,t.prev=12,t.next=15,W();case 15:return t.next=17,o.fetchSafeDocumentFragment(document,l);case 17:d=t.sent,t.next=24;break;case 20:t.prev=20,t.t0=t.catch(12),u.classList.add("d-none"),c.classList.remove("d-none");case 24:d&&(i.replaceWith(d),e.query(s,".js-details-container").classList.add("open"),n&&s.classList.contains("js-append-menu-to-body")&&Rc(A(document.body,HTMLElement),s,n));case 25:case"end":return t.stop()}},t,this,[[12,20]])}));return function(e){return t.apply(this,arguments)}}();function Rc(e,t,r){var n=e.getBoundingClientRect().height,a=t.getBoundingClientRect(),s=r.getBoundingClientRect(),o=s.top;o+a.height+10>=n&&(o=Math.max(n-a.height-10,0));var i=s.right;null!=r.closest(".js-build-status-to-the-left")&&(i=Math.max(s.left-a.width-10,0)),t.style.top=o+"px",t.style.left=i+"px",t.style.right="auto"}function Ic(e){var t=e.currentTarget;a(t instanceof Element,"app/assets/modules/github/statuses.js:114"),Hc(t)}r.on("toggle",".js-build-status .js-dropdown-details",function(t){var r=t.currentTarget,n=r.querySelector(".js-status-dropdown-menu");if(n&&(r.addEventListener("toggle",s),n.classList.contains("js-close-menu-on-scroll")&&window.addEventListener("scroll",o,{capture:!0,passive:!0}),n.classList.remove("d-none"),e.query(n,".js-details-container").classList.add("open"),n.classList.contains("js-append-menu-to-body"))){var a=A(document.body,HTMLElement);a.appendChild(n),Rc(a,n,r)}function s(){r.hasAttribute("open")||i()}function o(e){n.contains(e.target)||i()}function i(){r.removeAttribute("open"),n.classList.add("d-none"),r.appendChild(n),r.removeEventListener("toggle",s),window.removeEventListener("scroll",o)}},{capture:!0}),r.on("click",".js-status-retry",function(e){var t=e.currentTarget;Hc(t)}),t.observe(".js-build-status",{add:function(e){e.addEventListener("mouseenter",Ic,{once:!0})},remove:function(e){e.removeEventListener("mouseenter",Ic)}});var Cc=!1;function _c(e,n){var s,o=(s=te(regeneratorRuntime.mark(function e(t){var s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(Cc){e.next=6;break}return t.preventDefault(),t.stopImmediatePropagation(),e.next=5,C(n);case 5:try{s=t.target,a(s instanceof HTMLFormElement,"app/assets/modules/github/sudo-required.js:22"),Cc=!0,l.submit(s),Cc=!1}catch(e){r.fire(t.target,"sudo:failed",{error:e})}case 6:case"end":return e.stop()}},e,this)})),function(e){return s.apply(this,arguments)});t.observe(e,{add:function(e){e.addEventListener("submit",o)},remove:function(e){e.removeEventListener("submit",o)}})}function Fc(t,n){var a=e.closest(t,".js-survey-question-form"),s=e.query(a,"input.js-survey-other-text",HTMLInputElement);a.classList.toggle("is-other-selected",n),s.hidden=!n,n?(s.required=!0,s.focus()):s.required=!1,r.fire(s,"change")}_c("form.js-sudo-required","low"),_c("form.js-high-risk-sudo-required","high"),r.on("change","select.js-survey-select",function(e){var t=e.currentTarget;a(t instanceof HTMLSelectElement,"app/assets/modules/github/survey.js:24"),Fc(t,t.options[t.selectedIndex].classList.contains("js-survey-option-other"))}),r.on("change","input.js-survey-radio",function(e){var t=e.currentTarget;a(t instanceof HTMLInputElement,"app/assets/modules/github/survey.js:30"),Fc(t,t.classList.contains("js-survey-radio-other"))});var Pc=function(){var t=te(regeneratorRuntime.mark(function t(){var r,n,s,u,c,l,d,m,p,f,v,g,h,y;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(r=e.query(document,".js-tagsearch-popover"),n=e.query(r,".js-tagsearch-popover-content"),s=e.query(r,".js-tagsearch-popover-loader"),u=document.getSelection(),a(null!=u,"Selection must not be null -- app/assets/modules/github/tagsearch.js:17"),!u.isCollapsed){t.next=9;break}return r.classList.add("d-none"),D.deactivate(r),t.abrupt("return");case 9:if((c=u.toString().trim())&&!c.match(/\n|\s|[();&.=",]/)){t.next=12;break}return t.abrupt("return");case 12:if(null!=u.anchorNode&&u.anchorNode.parentNode instanceof HTMLElement){t.next=14;break}return t.abrupt("return");case 14:if(u.anchorNode.parentNode.closest(".js-file-line-container")){t.next=17;break}return t.abrupt("return");case 17:if(l=u.getRangeAt(0).getClientRects()[0],r.style.position="absolute",r.style.top=window.scrollY+l.bottom+6+"px",r.style.left=window.scrollX+l.left+l.width/2+"px",n.innerHTML="",s.classList.remove("d-none"),r.classList.remove("d-none"),D.activate(r),d=r.getAttribute("data-tagsearch-url"),m=r.getAttribute("data-tagsearch-path"),p=r.getAttribute("data-tagsearch-ref"),null!=d&&null!=m&&null!=p){t.next=30;break}throw new Error("Missing attribute `data-tagsearch-url");case 30:return f=new URL(d,window.location.origin),(v=new URLSearchParams).set("q",c),v.set("context",m),v.set("ref",p),f.search=v.toString(),t.prev=36,t.next=39,o.fetchPoll(f);case 39:return g=t.sent,G.verifyResponseHtmlSafeNonce(G.getDocumentHtmlSafeNonce(document),g),t.t0=i.parseHTML,t.t1=document,t.next=45,g.text();case 45:t.t2=t.sent,h=(0,t.t0)(t.t1,t.t2),s.classList.add("d-none"),n.innerHTML="",n.append(h),t.next=57;break;case 52:t.prev=52,t.t3=t.catch(36),y=e.query(r,".js-tagsearch-popover-error"),s.classList.add("d-none"),y.classList.remove("d-none");case 57:case"end":return t.stop()}},t,this,[[36,52]])}));return function(){return t.apply(this,arguments)}}(),Nc=u(Pc,200);t.observe(".js-tagsearch-popover",{add:function(){document.addEventListener("selectionchange",Nc)},remove:function(){document.removeEventListener("selectionchange",Nc)}});var Oc=function(){var t=te(regeneratorRuntime.mark(function t(){var r,n,s,i,u,c,l,d,m,p,f;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(c=function(){n===r.value&&Uc&&(Uc.value=n)},u=function(t){if(n===r.value){var a=t.normalized_ref;if(i.innerHTML=null==t.message_html?"":t.message_html,!a)e.query(i,"code").textContent=s;Uc&&(Uc.value=a)}},r=e.query(document,".js-quick-pull-new-branch-name",HTMLInputElement),n=r.value,null!=(s=r.getAttribute("data-generated-branch"))){t.next=7;break}throw new Error("Missing attribute `data-generated-branch`");case 7:return i=e.query(document,".js-quick-pull-normalization-info"),l=r.getAttribute("data-check-authenticity-token")||"",(d=new FormData).append("ref",n),d.append("authenticity_token",l),m=r.getAttribute("data-check-url"),a(m,"app/assets/modules/github/template-editor.js:57"),p=o.fetchJSON(m,{method:"post",body:d}),t.next=17,Bc.push(p);case 17:f=t.sent;try{u(f)}catch(e){c()}case 19:case"end":return t.stop()}},t,this)}));return function(){return t.apply(this,arguments)}}(),Dc=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x,k,E,T;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:r.classList.add("is-loading"),Jc(),n=e.query(r,'[name="filename"]',HTMLInputElement).value,a={},s=!0,u=!1,c=void 0,t.prev=7,l=e.querySelectorAll(document,".js-template-form",HTMLFormElement)[Symbol.iterator]();case 9:if(s=(d=l.next()).done){t.next=35;break}for(m=d.value,p=e.query(m,'[name="filename"]',HTMLInputElement).value,a[p]={},f=!0,v=!1,g=void 0,t.prev=16,h=B(m)[Symbol.iterator]();!(f=(y=h.next()).done);f=!0)b=y.value,j=oe(b,2),L=j[0],w=j[1],a[p][L]=w;t.next=24;break;case 20:t.prev=20,t.t0=t.catch(16),v=!0,g=t.t0;case 24:t.prev=24,t.prev=25,!f&&h.return&&h.return();case 27:if(t.prev=27,!v){t.next=30;break}throw g;case 30:return t.finish(27);case 31:return t.finish(24);case 32:s=!0,t.next=9;break;case 35:t.next=41;break;case 37:t.prev=37,t.t1=t.catch(7),u=!0,c=t.t1;case 41:t.prev=41,t.prev=42,!s&&l.return&&l.return();case 44:if(t.prev=44,!u){t.next=47;break}throw c;case 47:return t.finish(44);case 48:return t.finish(41);case 49:return t.next=51,o.fetchJSON(r.action,{method:"POST",body:JSON.stringify({current:n,templates:a})});case 51:x=t.sent,k=i.parseHTML(document,x.html),e.closest(r,".js-template-preview").replaceWith(k),E=e.query(document,".js-hidden-template-fields"),(T=E.querySelector('[data-filename="'+x.filename+'"]'))instanceof HTMLInputElement?T.value=x.markdown:((T=document.createElement("input")).type="hidden",T.name="templates[]["+x.filename+"]",T.setAttribute("data-filename",x.filename),T.value=x.markdown,E.append(T)),r.classList.remove("is-loading"),Jc();case 59:case"end":return t.stop()}},t,this,[[7,37,41,49],[16,20,24,32],[25,,27,31],[42,,44,48]])}));return function(e){return t.apply(this,arguments)}}(),Bc=new s,Uc=null,zc=u(Oc,200);function Wc(){return document.querySelectorAll(".js-template-editor").length>0}function Vc(e){e.classList.toggle("section-focus")}function $c(){return document.querySelectorAll(".js-template-form.is-loading").length>0?"loading":document.querySelectorAll(".js-template-form.is-errored").length>0?"error":"ok"}function Jc(){var t=e.query(document,".js-commit-templates-form",HTMLFormElement),r=e.query(t,".js-blob-submit",HTMLButtonElement),n=e.query(document,".js-template-commit-form-error-message"),a=e.query(document,".js-template-commit-form-loading-message");switch($c()){case"loading":n.classList.add("d-none"),a.classList.remove("d-none"),t.setAttribute("disabled","disabled"),r.setAttribute("disabled","disabled");break;case"error":n.classList.remove("d-none"),a.classList.add("d-none"),t.setAttribute("disabled","disabled"),r.setAttribute("disabled","disabled");break;default:n.classList.add("d-none"),a.classList.add("d-none"),t.removeAttribute("disabled"),r.removeAttribute("disabled")}}function Kc(){var t=!0,r=!1,n=void 0;try{for(var a,s=e.querySelectorAll(document,".js-template-form",HTMLFormElement)[Symbol.iterator]();!(t=(a=s.next()).done);t=!0){var o=a.value;Dc(o)}}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}function Gc(e){if(!function(e){var t=void 0;try{t=new URL(e.url)}catch(e){return!0}return t.host!==window.location.host}(e)&&!function(e){return/[?&]_pjax=/.test(e.url)}(e)){var t,r=null!=(t=document.querySelector(".js-timeline-marker"))?t.getAttribute("data-last-modified"):null;r&&e.headers.set("X-Timeline-Last-Modified",r)}}t.observe(".js-custom-fields-drag-container",function(e){z.create(e,{draggable:".js-custom-field-draggable",handle:".js-draggable-box-icon"})}),r.on("change",".js-quick-pull-choice-option",function(t){if(Wc()){var r=t.currentTarget;a(r instanceof HTMLInputElement,"app/assets/modules/github/template-editor.js:86"),e.query(document,".js-commit-branch-name").classList.toggle("d-none","quick-pull"!==r.value);var n=e.query(document,".js-quick-pull-new-branch-name");"quick-pull"===r.value?n.setAttribute("required","required"):n.removeAttribute("required")}}),n.onInput(".js-quick-pull-new-branch-name",function(t){if(Wc()){var r=A(t.target,HTMLInputElement).value;e.query(document,".js-quick-pull-target-branch",HTMLInputElement).value=r,r.length&&zc()}}),n.onInput(".js-synced-template-input",function(t){var r=A(t.target,HTMLInputElement),n=r.getAttribute("data-sync");if(n){var a=e.closest(r,".js-sync-container"),s=e.querySelectorAll(a,'[data-sync-with="'+n+'"]'),o=r.value;if(""!==o.trim()){var i=!0,u=!1,c=void 0;try{for(var l,d=s[Symbol.iterator]();!(i=(l=d.next()).done);i=!0){l.value.textContent=r.value}}catch(e){u=!0,c=e}finally{try{!i&&d.return&&d.return()}finally{if(u)throw c}}if("name"===n)e.query(a,".js-sync-filename",HTMLInputElement).value=(o.replace(/[^\w]/g,"-")+".md").toLowerCase()}else{var m=!0,p=!1,f=void 0;try{for(var v,g=s[Symbol.iterator]();!(m=(v=g.next()).done);m=!0){var h=v.value,y=h.getAttribute("data-sync-blank");y&&(h.innerHTML='<span class="text-gray">'+y+"</span>")}}catch(e){p=!0,f=e}finally{try{!m&&g.return&&g.return()}finally{if(p)throw f}}}}}),r.on("submit",".js-template-form",function(e){e.preventDefault();var t=A(e.currentTarget,HTMLFormElement);Dc(t)}),r.on("click",".js-toggle-template-commit",function(){var t=e.query(document,".js-template-commit-pane");t.classList.toggle("d-none"),t.classList.contains("d-none")||Kc()}),r.on("submit",".js-commit-templates-form",function(e){"ok"!==$c()&&e.preventDefault(),Jc()}),r.on("click",".js-refresh-template-content",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,a,s,i,u,c,l;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(n=r.currentTarget,a=e.closest(n,".js-template-form"),(s=e.query(a,".js-template-content-preview")).innerHTML='<span class="text-gray">Loading preview...</span>',i=e.query(a,".js-template-content-textarea",HTMLTextAreaElement).value,u=n.getAttribute("data-markdown-preview-url")){t.next=8;break}return t.abrupt("return");case 8:return(c=new FormData).append("markdown",i),t.next=12,o.fetchText(u,{method:"POST",body:c});case 12:l=t.sent,s.innerHTML=l;case 14:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),r.on("click",".js-custom-template-toggle",function(t){var r=t.target,n=e.closest(r,".js-template-preview"),a=!n.classList.contains("expand-preview");if(function(){var e=document.querySelectorAll(".js-template-preview"),t=!0,r=!1,n=void 0;try{for(var a,s=e[Symbol.iterator]();!(t=(a=s.next()).done);t=!0)a.value.classList.remove("expand-preview")}catch(e){r=!0,n=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw n}}}(),a)n.classList.add("expand-preview"),n.scrollIntoView({behavior:"smooth",block:"start"});else{var s=e.query(n,".js-template-form",HTMLFormElement);Dc(s)}}),r.on("click",".js-remove-template-button",function(t){var r=e.closest(t.target,".js-template-preview"),n=r.getAttribute("data-filename");if(!n)throw new Error("No template filename");var a=e.query(document,".js-hidden-template-fields").querySelector('[data-filename="'+n+'"]');a&&a.remove(),r.remove(),Kc()}),r.on("click",".js-append-custom-field-option",function(t){var r=t.target,n=r.getAttribute("data-source");if(n){var a=e.query(document,"#"+n);r.insertAdjacentHTML("beforebegin",a.innerHTML)}}),r.on("click",".js-remove-custom-field-option",function(t){e.closest(t.target,".js-custom-field-option").remove()}),r.on("click",".js-edit-custom-field",function(t){Vc(e.closest(t.target,".js-custom-field-editor"))}),r.on("click",".js-edit-custom-field-header",function(t){e.closest(t.target,".js-custom-field-header").classList.toggle("section-focus")}),r.on("click",".js-remove-custom-field",function(t){e.closest(t.target,".js-custom-field-editor").remove()}),r.on("click",".js-add-template-button",function(t){var r=t.currentTarget,n=e.query(document,".js-templates-container"),a=r.getAttribute("data-source");if(a){var s=e.query(document,"#"+a);n.insertAdjacentHTML("beforeend",s.innerHTML)}}),r.on("click",".js-add-custom-field-button",function(t){var r=t.currentTarget,n=e.closest(r,".js-template-preview"),a=r.getAttribute("data-source");if(a){var s=e.query(document,"#"+a),o=i.parseHTML(document,s.innerHTML).firstElementChild;if(o instanceof HTMLElement)Vc(o),e.query(n,".js-custom-fields-container").insertAdjacentElement("beforeend",o)}}),c.remoteForm(".js-needs-timeline-marker-header",function(e,t,r){Gc(r)}),r.on("deprecatedAjaxSend","[data-remote]",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/timeline/marker.js:50"),Gc(e.detail.request)});var Xc=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,a,s,i,u,c,l,d,m,p;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(r=document.getElementById("js-timeline-progressive-loader")){e.next=3;break}return e.abrupt("return");case 3:if(n=r.getAttribute("data-timeline-item-src")){e.next=6;break}return e.abrupt("return");case 6:return a=new URL(n,window.location.origin),(s=new URLSearchParams(a.search.slice(1))).append("anchor",t),a.search=s.toString(),i=void 0,e.prev=11,e.next=14,o.fetchSafeDocumentFragment(document,a);case 14:i=e.sent,e.next=20;break;case 17:return e.prev=17,e.t0=e.catch(11),e.abrupt("return");case 20:if(u=i.querySelector(".js-timeline-item")){e.next=23;break}return e.abrupt("return");case 23:if(c=u.getAttribute("data-gid")){e.next=26;break}return e.abrupt("return");case 26:(l=document.querySelector(".js-timeline-item[data-gid='"+c+"']"))?(l.replaceWith(u),(d=document.getElementById(t))&&Qc(d)):((m=document.getElementById("progressive-timeline-item-container"))&&m.replaceWith(i),(p=document.getElementById(t))&&Qc(p));case 28:case"end":return e.stop()}},e,this,[[11,17]])}));return function(t){return e.apply(this,arguments)}}();function Qc(e){!function(e){var t=e.closest("details, .js-details-container");if(!t)return;a(t instanceof HTMLElement,"app/assets/modules/github/timeline/progressive.js:86"),"DETAILS"===t.nodeName?t.setAttribute("open","open"):O.isDetailsTargetExpanded(t)||O.toggleDetailsTarget(t)}(e);var t=e.querySelector("[href='#"+e.id+"']");t&&t.click()}function Yc(){return window.location.hash.slice(1)}te(regeneratorRuntime.mark(function e(){var t;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.ready;case 2:if(t=Yc()){e.next=5;break}return e.abrupt("return");case 5:document.getElementById(t)||Xc(t);case 7:case"end":return e.stop()}},e,void 0)}))(),t.observe(".js-timeline-progressive-focus-container",function(e){var t=Yc();if(t){var r=document.getElementById(t);r&&e.contains(r)&&Qc(r)}});var Zc="navigationStart";function el(){var e={};e.crossBrowserLoadEvent=Date.now();var t=window.performance&&window.performance.timing;if(t)for(var r in t){var n=t[r];"number"==typeof n&&(e[r]=n)}else{var a=function(){var e=T.getItem(Zc);if(e)return parseInt(e,10)}();null!=a&&(e.simulatedNavigationStart=a)}var s=function(){if(window.performance&&"timeOrigin"in window.performance&&window.performance.getEntriesByType){var e=window.performance.getEntriesByType("paint");if(e.length){var t=e.find(function(e){return"first-paint"===e.name});if(t)return Math.round((performance.timeOrigin+t.startTime)/1e6)}}}();return null!=s&&(e.chromeFirstPaintTime=s),e}function tl(){var e=[],t=window.performance;if(t&&t.getEntriesByType){var r=!0,n=!1,a=void 0;try{for(var s,o=t.getEntriesByType("resource")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value,u={};for(var c in e.push(u),i){var l=i[c];"number"!=typeof l&&"string"!=typeof l||(u[c]=l)}}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}return e}function rl(){var e=[],t=window.performance;if(t&&t.getEntriesByType){var r=!0,n=!1,a=void 0;try{for(var s,o=t.getEntriesByType("navigation")[Symbol.iterator]();!(r=(s=o.next()).done);r=!0){var i=s.value,u={};for(var c in e.push(u),i){var l=i[c];"number"!=typeof l&&"string"!=typeof l||(u[c]=l)}}}catch(e){n=!0,a=e}finally{try{!r&&o.return&&o.return()}finally{if(n)throw a}}}var d=el();return d&&0===e.length&&e.push({name:location.href,entryType:"navigation",startTime:0,duration:d.loadEventEnd-d.navigationStart,initiatorType:"navigation",nextHopProtocol:"http/1.1",type:"navigate",redirectStart:d.redirectStart,redirectEnd:d.redirectEnd,fetchStart:d.fetchStart,domainLookupStart:d.domainLookupStart,domainLookupEnd:d.domainLookupEnd,connectStart:d.connectStart,connectEnd:d.connectEnd,secureConnectionStart:d.secureConnectionStart,requestStart:d.requestStart,responseStart:d.responseStart,responseEnd:d.responseEnd,unloadEventStart:d.unloadEventStart,unloadEventEnd:d.unloadEventEnd,domInteractive:d.domInteractive,domContentLoadedEventStart:d.domContentLoadedEventStart,domContentLoadedEventEnd:d.domContentLoadedEventEnd,domComplete:d.domComplete,loadEventStart:d.loadEventStart,loadEventEnd:d.loadEventEnd}),e}window.performance||window.performance.timing||window.addEventListener("pagehide",function(){T.setItem(Zc,Date.now().toString())}),function(){var e=te(regeneratorRuntime.mark(function e(){var t,r;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,b.loaded;case 2:return e.next=4,X.delay(0);case 4:(t=tl()).length&&_e({resourceTimings:t}),(r=rl()).length&&_e({navigationTimings:r});case 8:case"end":return e.stop()}},e,this)}));return function(){return e.apply(this,arguments)}}()();r.on("click",".js-toggler-container .js-toggler-target",function(e){(a(e instanceof MouseEvent,"app/assets/modules/github/toggler.js:41"),a(e.currentTarget,"app/assets/modules/github/toggler.js:42"),0===e.button)&&e.currentTarget.closest(".js-toggler-container").classList.toggle("on")}),c.remoteForm(".js-toggler-container",function(){var e=te(regeneratorRuntime.mark(function e(t,r){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return t.classList.remove("success","error"),t.classList.add("loading"),e.prev=2,e.next=5,r.text();case 5:t.classList.add("success"),e.next=11;break;case 8:e.prev=8,e.t0=e.catch(2),t.classList.add("error");case 11:return e.prev=11,t.classList.remove("loading"),e.finish(11);case 14:case"end":return e.stop()}},e,void 0,[[2,8,11,14]])}));return function(t,r){return e.apply(this,arguments)}}());var nl=function(){var t=te(regeneratorRuntime.mark(function t(){var r,n,s;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return il(!1),r=e.query(document,"#topics-list-container"),n=r.getAttribute("data-url"),a(n,"`data-url` must exist -- app/assets/modules/github/topics.js:55"),t.next=6,o.fetchSafeDocumentFragment(document,n);case 6:s=t.sent,r.innerHTML="",r.appendChild(s);case 9:case"end":return t.stop()}},t,this)}));return function(){return t.apply(this,arguments)}}(),al=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(r=t.querySelector(".js-topic-suggestions-container")){e.next=3;break}return e.abrupt("return");case 3:return n=r.getAttribute("data-url"),a(n,"`data-url` must exist -- app/assets/modules/github/topics.js:69"),e.next=7,o.fetchSafeDocumentFragment(document,n);case 7:s=e.sent,r.innerHTML="",r.appendChild(s);case 10:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();function sl(t){var r=e.closest(t,".js-topic-save-notice-container"),n=e.query(r,".js-repo-topics-save-notice");n.classList.remove("d-none"),n.classList.add("d-inline-block","anim-fade-in"),setTimeout(function(){n.classList.remove("d-inline-block"),n.classList.add("d-none")},1900)}function ol(t){var r=e.query(t,".js-topic-suggestions-box");r.querySelector(".js-topic-suggestion")||r.remove()}function il(t){var r=e.query(document,".js-repo-meta-container"),n=e.query(document,"#topics-list-container"),a=e.query(r,".js-repo-meta-edit");e.query(r,".js-edit-repo-meta-button").classList.toggle("d-none",t),n.classList.toggle("d-none",t),a.classList.toggle("d-none",t)}function ul(){}c.remoteForm(".js-accept-topic-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=e.closest(r,".js-topic-form-area"),s=e.closest(r,".js-topic-suggestion"),o=e.query(a,".js-template"),i=e.query(a,".js-tag-input-selected-tags"),u=o.cloneNode(!0),c=e.query(s,'input[name="input[name]"]',HTMLInputElement).value,e.query(u,"input",HTMLInputElement).value=c,e.query(u,".js-placeholder-tag-name").replaceWith(c),u.classList.remove("d-none","js-template"),i.appendChild(u),s.remove(),al(a),ol(a),sl(r);case 16:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-repo-topics-form-done",function(e){a(e.target instanceof HTMLElement,"app/assets/modules/github/topics.js:103"),O.toggleDetailsTarget(e.target),nl()}),c.remoteForm(".js-decline-topic-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:sl(r),a=e.closest(r,".js-topic-form-area"),e.closest(r,".js-topic-suggestion").remove(),al(a),ol(a);case 8:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),c.remoteForm(".js-repo-topics-edit-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a,s,o,i,u,c,l,d,m,p,f,v,g,h,y,b,j,L,w,x;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:for((a=e.closest(r,".js-topic-form-area")).classList.remove("errored"),e.query(a,".js-topic-error").textContent="",s=!0,o=!1,i=void 0,t.prev=6,u=a.querySelectorAll(".js-tag-input-tag.invalid-topic")[Symbol.iterator]();!(s=(c=u.next()).done);s=!0)c.value.classList.remove("invalid-topic");t.next=14;break;case 10:t.prev=10,t.t0=t.catch(6),o=!0,i=t.t0;case 14:t.prev=14,t.prev=15,!s&&u.return&&u.return();case 17:if(t.prev=17,!o){t.next=20;break}throw i;case 20:return t.finish(17);case 21:return t.finish(14);case 22:return t.prev=22,t.next=25,n.json();case 25:sl(r),al(a),t.next=78;break;case 29:if(t.prev=29,t.t1=t.catch(22),(l=t.t1.response.json).message&&(a.classList.add("errored"),e.query(a,".js-topic-error").textContent=l.message),!l.invalidTopics){t.next=78;break}d=e.querySelectorAll(r,".js-topic-input",HTMLInputElement),m=!0,p=!1,f=void 0,t.prev=38,v=l.invalidTopics[Symbol.iterator]();case 40:if(m=(g=v.next()).done){t.next=64;break}for(h=g.value,y=!0,b=!1,j=void 0,t.prev=45,L=d[Symbol.iterator]();!(y=(w=L.next()).done);y=!0)(x=w.value).value===h&&e.closest(x,".js-tag-input-tag").classList.add("invalid-topic");t.next=53;break;case 49:t.prev=49,t.t2=t.catch(45),b=!0,j=t.t2;case 53:t.prev=53,t.prev=54,!y&&L.return&&L.return();case 56:if(t.prev=56,!b){t.next=59;break}throw j;case 59:return t.finish(56);case 60:return t.finish(53);case 61:m=!0,t.next=40;break;case 64:t.next=70;break;case 66:t.prev=66,t.t3=t.catch(38),p=!0,f=t.t3;case 70:t.prev=70,t.prev=71,!m&&v.return&&v.return();case 73:if(t.prev=73,!p){t.next=76;break}throw f;case 76:return t.finish(73);case 77:return t.finish(70);case 78:case"end":return t.stop()}},t,this,[[6,10,14,22],[15,,17,21],[22,29],[38,66,70,78],[45,49,53,61],[54,,56,60],[71,,73,77]])}));return function(e,r){return t.apply(this,arguments)}}()),r.on("tags:changed",".js-repo-topics-edit-form",function(e){l.submit(A(e.target,HTMLFormElement))}),r.on("click",".js-repo-topics-form-toggle",function(t){var r=e.closest(t.target,".js-repo-meta-container"),n=r.querySelector(".js-repo-topics-form-fragment");if(n){n.classList.remove("d-none");var s=n.getAttribute("data-url");a(s,"`data-url` must exist -- app/assets/modules/github/topics.js:173"),n.setAttribute("src",s)}e.query(r,".js-repository-topics-container").classList.contains("open")?il(!0):nl()}),r.on("click",".js-edit-repo-meta-toggle",function(t){var r=e.closest(t.target,".js-repo-meta-container"),n=e.query(r,".js-repo-meta-edit").classList.contains("open"),a=r.querySelector(".js-repository-topics-container");a&&a.classList.toggle("d-none",n)}),r.on("click",".js-reveal-hidden-topics",function(e){var t=e.target,r=A(t.closest(".js-hidden-topics-container"),HTMLElement).querySelector(".js-hidden-topics");r&&(r.classList.remove("d-none"),t.remove())}),t.observe(".js-touch-events",{add:function(e){e.addEventListener("click",ul)},remove:function(e){e.removeEventListener("click",ul)}});var cl=function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:n=e.closest(r,".unfurl"),s=e.closest(r,"[data-unfurl-authenticity-token]").getAttribute("data-unfurl-authenticity-token"),a(s,"app/assets/modules/github/unfurls.js:17"),o=r.getAttribute("data-id"),a(o,"app/assets/modules/github/unfurls.js:20"),(i=new XMLHttpRequest).onload=function(){i.status>=200&&i.status<300&&n.remove()},i.open("POST","/content_reference_attachments/hide"),(u=new FormData).append("id",o),u.append("authenticity_token",s),i.send(u);case 12:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}();function ll(){return!!window.u2f||"true"===h.getMetadataByName(document,"u2f-support")}r.on("click",".js-hide-unfurl",function(e){e.preventDefault(),cl(e.currentTarget)}),requestIdleCallback(function(){var e=Q();if(e){var t="https:"===location.protocol?"secure":"";document.cookie="tz="+encodeURIComponent(e)+"; path=/; "+t}}),function(){var e="chrome"in window&&window.navigator.userAgent.indexOf("Edge")<0;if(!("u2f"in window)&&e){var t,r=window.u2f={};r.EXTENSION_ID="kmendfapggjehodndflmmgagdbamhnfd",r.MessageTypes={U2F_REGISTER_REQUEST:"u2f_register_request",U2F_REGISTER_RESPONSE:"u2f_register_response",U2F_SIGN_REQUEST:"u2f_sign_request",U2F_SIGN_RESPONSE:"u2f_sign_response",U2F_GET_API_VERSION_REQUEST:"u2f_get_api_version_request",U2F_GET_API_VERSION_RESPONSE:"u2f_get_api_version_response"},r.ErrorCodes={OK:0,OTHER_ERROR:1,BAD_REQUEST:2,CONFIGURATION_UNSUPPORTED:3,DEVICE_INELIGIBLE:4,TIMEOUT:5},r.getMessagePort=function(e){if("undefined"!=typeof chrome&&chrome.runtime){var t={type:r.MessageTypes.U2F_SIGN_REQUEST,signRequests:[]};chrome.runtime.sendMessage(r.EXTENSION_ID,t,function(){chrome.runtime.lastError?r.getIframePort_(e):r.getChromeRuntimePort_(e)})}else r.isAndroidChrome_()?r.getAuthenticatorPort_(e):r.isIosChrome_()?r.getIosPort_(e):r.getIframePort_(e)},r.isAndroidChrome_=function(){var e=navigator.userAgent;return-1!=e.indexOf("Chrome")&&-1!=e.indexOf("Android")},r.isIosChrome_=function(){return["iPhone","iPad","iPod"].indexOf(navigator.platform)>-1},r.getChromeRuntimePort_=function(e){var t=chrome.runtime.connect(r.EXTENSION_ID,{includeTlsChannelId:!0});setTimeout(function(){e(new r.WrappedChromeRuntimePort_(t))},0)},r.getAuthenticatorPort_=function(e){setTimeout(function(){e(new r.WrappedAuthenticatorPort_)},0)},r.getIosPort_=function(e){setTimeout(function(){e(new r.WrappedIosPort_)},0)},r.WrappedChromeRuntimePort_=function(e){this.port_=e},r.formatSignRequest_=function(e,n,a,s,o){if(void 0===t||t<1.1){for(var i=[],u=0;u<a.length;u++)i[u]={version:a[u].version,challenge:n,keyHandle:a[u].keyHandle,appId:e};return{type:r.MessageTypes.U2F_SIGN_REQUEST,signRequests:i,timeoutSeconds:s,requestId:o}}return{type:r.MessageTypes.U2F_SIGN_REQUEST,appId:e,challenge:n,registeredKeys:a,timeoutSeconds:s,requestId:o}},r.formatRegisterRequest_=function(e,n,a,s,o){if(void 0===t||t<1.1){for(var i=0;i<a.length;i++)a[i].appId=e;var u=[];for(i=0;i<n.length;i++)u[i]={version:n[i].version,challenge:a[0],keyHandle:n[i].keyHandle,appId:e};return{type:r.MessageTypes.U2F_REGISTER_REQUEST,signRequests:u,registerRequests:a,timeoutSeconds:s,requestId:o}}return{type:r.MessageTypes.U2F_REGISTER_REQUEST,appId:e,registerRequests:a,registeredKeys:n,timeoutSeconds:s,requestId:o}},r.WrappedChromeRuntimePort_.prototype.postMessage=function(e){this.port_.postMessage(e)},r.WrappedChromeRuntimePort_.prototype.addEventListener=function(e,t){var r=e.toLowerCase();"message"==r||"onmessage"==r?this.port_.onMessage.addListener(function(e){t({data:e})}):console.error("WrappedChromeRuntimePort only supports onMessage")},r.WrappedAuthenticatorPort_=function(){this.requestId_=-1,this.requestObject_=null},r.WrappedAuthenticatorPort_.prototype.postMessage=function(e){var t=r.WrappedAuthenticatorPort_.INTENT_URL_BASE_+";S.request="+encodeURIComponent(JSON.stringify(e))+";end";document.location=t},r.WrappedAuthenticatorPort_.prototype.getPortType=function(){return"WrappedAuthenticatorPort_"},r.WrappedAuthenticatorPort_.prototype.addEventListener=function(e,t){if("message"==e.toLowerCase()){window.addEventListener("message",this.onRequestUpdate_.bind(this,t),!1)}else console.error("WrappedAuthenticatorPort only supports message")},r.WrappedAuthenticatorPort_.prototype.onRequestUpdate_=function(e,t){var r=JSON.parse(t.data),n=(r.intentURL,r.errorCode,null);r.hasOwnProperty("data")&&(n=JSON.parse(r.data)),e({data:n})},r.WrappedAuthenticatorPort_.INTENT_URL_BASE_="intent:#Intent;action=com.google.android.apps.authenticator.AUTHENTICATE",r.WrappedIosPort_=function(){},r.WrappedIosPort_.prototype.postMessage=function(e){var t=JSON.stringify(e),r="u2f://auth?"+encodeURI(t);location.replace(r)},r.WrappedIosPort_.prototype.getPortType=function(){return"WrappedIosPort_"},r.WrappedIosPort_.prototype.addEventListener=function(e,t){"message"!==e.toLowerCase()&&console.error("WrappedIosPort only supports message")},r.getIframePort_=function(e){var t="chrome-extension://"+r.EXTENSION_ID,n=document.createElement("iframe");n.src=t+"/u2f-comms.html",n.setAttribute("style","display:none"),document.body.appendChild(n);var a=new MessageChannel,s=function(t){"ready"==t.data?(a.port1.removeEventListener("message",s),e(a.port1)):console.error('First event on iframe port was not "ready"')};a.port1.addEventListener("message",s),a.port1.start(),n.addEventListener("load",function(){n.contentWindow.postMessage("init",t,[a.port2])})},r.EXTENSION_TIMEOUT_SEC=30,r.port_=null,r.waitingForPort_=[],r.reqCounter_=0,r.callbackMap_={},r.getPortSingleton_=function(e){r.port_?e(r.port_):(0==r.waitingForPort_.length&&r.getMessagePort(function(e){for(r.port_=e,r.port_.addEventListener("message",r.responseHandler_);r.waitingForPort_.length;)r.waitingForPort_.shift()(r.port_)}),r.waitingForPort_.push(e))},r.responseHandler_=function(e){var t=e.data,n=t.requestId;if(n&&r.callbackMap_[n]){var a=r.callbackMap_[n];delete r.callbackMap_[n],a(t.responseData)}else console.error("Unknown or missing requestId in response.")},r.sign=function(e,n,a,s,o){void 0===t?r.getApiVersion(function(i){t=void 0===i.js_api_version?0:i.js_api_version,console.log("Extension JS API Version: ",t),r.sendSignRequest(e,n,a,s,o)}):r.sendSignRequest(e,n,a,s,o)},r.sendSignRequest=function(e,t,n,a,s){r.getPortSingleton_(function(o){var i=++r.reqCounter_;r.callbackMap_[i]=a;var u=void 0!==s?s:r.EXTENSION_TIMEOUT_SEC,c=r.formatSignRequest_(e,t,n,u,i);o.postMessage(c)})},r.register=function(e,n,a,s,o){void 0===t?r.getApiVersion(function(i){t=void 0===i.js_api_version?0:i.js_api_version,console.log("Extension JS API Version: ",t),r.sendRegisterRequest(e,n,a,s,o)}):r.sendRegisterRequest(e,n,a,s,o)},r.sendRegisterRequest=function(e,t,n,a,s){r.getPortSingleton_(function(o){var i=++r.reqCounter_;r.callbackMap_[i]=a;var u=void 0!==s?s:r.EXTENSION_TIMEOUT_SEC,c=r.formatRegisterRequest_(e,n,t,u,i);o.postMessage(c)})},r.getApiVersion=function(e,t){r.getPortSingleton_(function(n){if(n.getPortType){var a;switch(n.getPortType()){case"WrappedIosPort_":case"WrappedAuthenticatorPort_":a=1.1;break;default:a=0}e({js_api_version:a})}else{var s=++r.reqCounter_;r.callbackMap_[s]=e;var o={type:r.MessageTypes.U2F_GET_API_VERSION_REQUEST,timeoutSeconds:void 0!==t?t:r.EXTENSION_TIMEOUT_SEC,requestId:s};n.postMessage(o)}})}}}();var dl=function(e){function t(e,r){re(this,t);var n=se(this,(t.__proto__||Object.getPrototypeOf(t)).call(this,e));return n.code=r,n}return ae(t,e),t}(Error);function ml(){var t=e.query(document,".js-u2f-auth-form",HTMLFormElement),n=e.query(t,".js-u2f-auth-response",HTMLInputElement),a=t.getAttribute("data-app-id"),s=t.getAttribute("data-challenge"),o=t.getAttribute("data-sign-requests");if(null!=o){var i=JSON.parse(o),u=!0,c=!1,l=void 0;try{for(var d,m=document.querySelectorAll(".js-u2f-error")[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){d.value.classList.add("d-none")}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}var p=document.querySelector(".js-u2f-login-waiting");null!=p&&p.classList.remove("d-none"),function(){for(var e=arguments.length,t=Array(e),r=0;r<e;r++)t[r]=arguments[r];return new Promise(function(e,r){var n;(n=window.u2f).sign.apply(n,t.concat([function(t){null!=t.errorCode&&0!==t.errorCode?r(new dl("Signing request failed",t.errorCode)):e(t)}]))})}(a,s,i).then(function(e){n.value=JSON.stringify(e),r.fire(t,"submit")&&t.submit()},function(e){var t=".js-u2f-auth-error";switch(e.code){case 4:t=".js-u2f-auth-not-registered-error";break;case 5:t=".js-u2f-auth-timeout"}var r=document.querySelector(t);null!=r&&r.classList.remove("d-none"),null!=p&&p.classList.add("d-none")})}}function pl(e,t,r){if(null==r){var n=e.getAttribute(t);return null==n?null:JSON.parse(n)}e.setAttribute(t,JSON.stringify(r))}function fl(t){return pl(e.query(document,".js-add-u2f-registration-form",HTMLFormElement),"data-sign-requests",t)}function vl(t){return pl(e.query(document,".js-add-u2f-registration-form",HTMLFormElement),"data-register-requests",t)}function gl(e){e.register_requests&&vl(e.register_requests),e.sign_requests&&fl(e.sign_requests)}function hl(t,r){var n=e.query(document,".js-new-u2f-registration");n.classList.add("is-showing-error"),n.classList.remove("is-sending");var a=!0,s=!1,o=void 0;try{for(var i,u=n.querySelectorAll(".js-u2f-error")[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){i.value.classList.add("d-none")}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}var c=e.query(n,t);null!=r&&(c.textContent=r),c.classList.remove("d-none")}function yl(t){C("high").then(function(){var r=e.query(document,".js-add-u2f-registration-form",HTMLFormElement);A(r.elements.namedItem("response"),HTMLInputElement).value=JSON.stringify(t),o.fetchJSON(r.action,{method:r.method,body:new FormData(r)}).then(function(t){gl(t),e.query(document,".js-new-u2f-registration").classList.remove("is-sending","is-active"),e.query(document,".js-u2f-registration-nickname-field",HTMLInputElement).value="",function(t){var r=document.createElement("div");r.innerHTML=t;var n=r.firstChild;null!=n&&e.query(document,".js-u2f-registrations").appendChild(n)}(t.registration)}).catch(function(e){e.response?e.response.json().then(function(e){gl(e),hl(".js-u2f-server-error",e.error)}):hl(".js-u2f-network-error")})})}function bl(){var t=e.query(document,".js-new-u2f-registration");t.classList.add("is-sending"),t.classList.remove("is-showing-error");var r=e.query(document,".js-add-u2f-registration-form").getAttribute("data-app-id");if(null==r)throw new Error("invalid appId");(function(){for(var e=arguments.length,t=Array(e),r=0;r<e;r++)t[r]=arguments[r];return new Promise(function(e,r){var n;(n=window.u2f).register.apply(n,t.concat([function(t){null!=t.errorCode&&0!==t.errorCode?r(new dl("Device registration failed",t.errorCode)):e(t)}]))})})(r,vl(),fl()).then(yl).catch(function(e){var t=".js-u2f-other-error";switch(e.code){case 4:t=".js-u2f-registered-error";break;case 5:t=".js-u2f-timeout-error"}hl(t)})}function jl(e){var t=e.dataTransfer;if(t&&!function(e){return Array.from(e.types).indexOf("Files")>=0}(t)&&kl(t)){e.stopPropagation(),e.preventDefault();var r=e.currentTarget;a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/paste-markdown-image-link.js:29");var n=!0,s=!1,o=void 0;try{for(var i,u=El(t).map(xl)[Symbol.iterator]();!(n=(i=u.next()).done);n=!0){var c=i.value;x.insertText(r,c)}}catch(e){s=!0,o=e}finally{try{!n&&u.return&&u.return()}finally{if(s)throw o}}}}function Ll(e){var t=e.dataTransfer;t&&(t.dropEffect="link")}function wl(e){var t=e.clipboardData;if(t&&kl(t)){e.stopPropagation(),e.preventDefault();var r=e.currentTarget;a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/paste-markdown-image-link.js:49");var n=!0,s=!1,o=void 0;try{for(var i,u=El(t).map(xl)[Symbol.iterator]();!(n=(i=u.next()).done);n=!0){var c=i.value;x.insertText(r,c)}}catch(e){s=!0,o=e}finally{try{!n&&u.return&&u.return()}finally{if(s)throw o}}}}function xl(e){return t=e.split(".").pop().toLowerCase(),["gif","png","jpg","jpeg"].indexOf(t)>-1?"\n![]("+e+")\n":e;var t}function kl(e){return Array.from(e.types).indexOf("text/uri-list")>=0}function El(e){return(e.getData("text/uri-list")||"").split("\r\n")}function Tl(e){var t=e.dataTransfer;if(t&&!function(e){return Array.from(e.types).indexOf("Files")>=0}(t)){var r=Hl(t);if(r){e.stopPropagation(),e.preventDefault();var n=e.currentTarget;a(n instanceof HTMLTextAreaElement,"app/assets/modules/github/paste-markdown-table.js:32"),x.insertText(n,Ml(r))}}}function ql(e){var t=e.dataTransfer;t&&(t.dropEffect="copy")}function Sl(e){if(e.clipboardData){var t=Hl(e.clipboardData);if(t){e.stopPropagation(),e.preventDefault();var r=e.currentTarget;a(r instanceof HTMLTextAreaElement,"app/assets/modules/github/paste-markdown-table.js:51"),x.insertText(r,Ml(t))}}}function Al(e){return e.textContent.trim().replace(/\|/g,"\\|").replace(/\n/g," ")||" "}function Ml(e){var t,r=Array.from(e.querySelectorAll("tr")),n=(t=r.shift(),Array.from(t.querySelectorAll("td, th")).map(Al)),a=n.map(function(){return"--"});return"\n"+(n.join(" | ")+"\n"+a.join(" | ")+"\n")+r.map(function(e){return Array.from(e.querySelectorAll("td")).map(Al).join(" | ")}).join("\n")+"\n\n"}function Hl(e){if(-1!==Array.from(e.types).indexOf("text/html")){var t=e.getData("text/html");if(/<table/i.test(t)){var r=i.parseHTML(document,t).querySelector("table");if(r&&!r.closest(".js-comment"))return/\b(js|blob|diff)-./.test(r.className)?null:r}}}r.on("click",".js-u2f-auth-retry",function(){ml()}),t.observe(".js-u2f-auth-form-body",function(e){e.classList.toggle("unavailable",!ll()),ll()&&ml()}),c.remoteForm(".js-u2f-registration-delete",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return(a=e.closest(r,".js-u2f-registration")).classList.add("is-sending"),t.next=4,n.json();case 4:gl(t.sent.json),a.remove();case 7:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),r.on("click",".js-add-u2f-registration-link",function(){var t=e.query(document,".js-new-u2f-registration");t.classList.add("is-active"),t.classList.remove("is-showing-error"),e.query(document,".js-u2f-registration-nickname-field",HTMLInputElement).focus()}),r.on("click",".js-u2f-register-retry",function(){bl()}),r.on("submit",".js-add-u2f-registration-form",function(e){e.preventDefault(),bl()}),t.observe(".js-u2f-box",function(e){e.classList.toggle("available",ll())}),r.on("socket:message",".js-updatable-content",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/updatable-content-observer.js:17");var t=e.detail.data||{},r=t.gid,n=t.wait,s=void 0;if(r&&e.target.getAttribute("data-gid")===r?s=e.target:r?s=e.target.querySelector('[data-url][data-gid="'+r+'"]'):e.currentTarget===e.target&&(s=e.target),s instanceof HTMLElement){var o=w.updateContent.bind(null,s);null==n?o():setTimeout(o,n)}}),r.on("upload:setup",".js-upload-avatar-image",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/upload/avatar.js:9");var t=e.detail.policyRequest,r=e.currentTarget.getAttribute("data-alambic-organization"),n=e.currentTarget.getAttribute("data-alambic-owner-type"),s=e.currentTarget.getAttribute("data-alambic-owner-id");r&&t.body.append("organization_id",r),n&&t.body.append("owner_type",n),s&&t.body.append("owner_id",s)}),r.on("upload:complete",".js-upload-avatar-image",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/upload/avatar.js:26");var t="/settings/avatars/"+e.detail.result.id;V.dialog({content:o.fetchSafeDocumentFragment(document,t)})});var Rl=function(){function e(t){re(this,e),this.dataview=new DataView(t),this.pos=0}return ne(e,null,[{key:"fromFile",value:function(t){return new Promise(function(r,n){var s=new FileReader;s.onload=function(){a(s.result instanceof ArrayBuffer,"app/assets/modules/github/png-scanner.js:38"),r(new e(s.result))},s.onerror=function(){n(s.error)},s.readAsArrayBuffer(t)})}}]),ne(e,[{key:"advance",value:function(e){this.pos+=e}},{key:"readInt",value:function(e){var t=this,r=function(){switch(e){case 1:return t.dataview.getUint8(t.pos);case 2:return t.dataview.getUint16(t.pos);case 4:return t.dataview.getUint32(t.pos);default:throw new Error("bytes parameter must be 1, 2 or 4")}}();return this.advance(e),r}},{key:"readChar",value:function(){return this.readInt(1)}},{key:"readShort",value:function(){return this.readInt(2)}},{key:"readLong",value:function(){return this.readInt(4)}},{key:"readString",value:function(e){for(var t=[],r=0;r<e;r++)t.push(String.fromCharCode(this.readChar()));return t.join("")}},{key:"scan",value:function(e){if(2303741511!==this.readLong())throw new Error("invalid PNG");for(this.advance(4);;){var t=this.readLong(),r=this.readString(4),n=this.pos+t+4;if(!1===e.call(this,r,t)||"IEND"===r)break;this.pos=n}}}]),e}(),Il=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if("image/png"===t.type){e.next=2;break}return e.abrupt("return",null);case 2:return r=t.slice(0,10240,t.type),e.next=5,Rl.fromFile(r);case 5:return n=e.sent,a={width:0,height:0,ppi:1},n.scan(function(e){switch(e){case"IHDR":return a.width=this.readLong(),a.height=this.readLong(),!0;case"pHYs":var t=this.readLong(),r=this.readLong(),n=void 0;return 1===this.readChar()&&(n=.0254),n&&(a.ppi=Math.round((t+r)/2*n)),!1;case"IDAT":return!1}return!0}),e.abrupt("return",a);case 9:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();function Cl(e){return(_l(e)?"!":"")+"[Uploading "+e.name+"…]()"}function _l(e){return["image/gif","image/png","image/jpg","image/jpeg"].indexOf(e.type)>-1}t.observe(".js-paste-markdown",{add:function(e){e.addEventListener("dragover",ql),e.addEventListener("drop",Tl),e.addEventListener("paste",Sl)},remove:function(e){e.removeEventListener("dragover",ql),e.removeEventListener("drop",Tl),e.removeEventListener("paste",Sl)}}),t.observe(".js-paste-markdown",{add:function(e){e.addEventListener("dragover",Ll),e.addEventListener("drop",jl),e.addEventListener("paste",wl)},remove:function(e){e.removeEventListener("dragover",Ll),e.removeEventListener("drop",jl),e.removeEventListener("paste",wl)}});function Fl(t){var r=e.closest(t,"form",HTMLFormElement);return e.query(r,"#release_id",HTMLInputElement).value}r.on("upload:setup",".js-upload-markdown-image",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/markdown.js:55");var r=e.query(t.currentTarget,".js-comment-field",HTMLTextAreaElement);r.setCustomValidity("uploading"),x.insertText(r,Cl(t.detail.file)+"\n")}),r.on("upload:complete",".js-upload-markdown-image",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,o,i,u;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return u=function(e){var t="["+n.file.name+"]("+n.policy.asset.href+")";if(_l(n.file)){var r=function(e){return e.toLowerCase().replace(/[^a-z0-9\-_]+/gi,".").replace(/\.{2,}/g,".").replace(/^\.|\.$/gi,"")}(n.policy.asset.name).replace(/\.[^.]+$/,"").replace(/\./g," "),a=n.policy.asset.href;if(e&&144===e.ppi)t='<img width="'+Math.round(e.width/2)+'" alt="'+r+'" src="'+a+'">';else t="!["+r+"]("+a+")"}o.setCustomValidity(""),x.replaceText(o,i,t)},a(r instanceof CustomEvent,"app/assets/modules/github/upload/markdown.js:62"),n=r.detail,s=r.currentTarget,o=e.query(s,".js-comment-field",HTMLTextAreaElement),i=Cl(n.file),t.prev=6,t.t0=u,t.next=10,Il(n.file);case 10:t.t1=t.sent,(0,t.t0)(t.t1),t.next=18;break;case 14:t.prev=14,t.t2=t.catch(6),u(),setTimeout(function(){throw t.t2});case 18:case"end":return t.stop()}},t,this,[[6,14]])}));return function(e){return t.apply(this,arguments)}}()),r.on("upload:error",".js-upload-markdown-image",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/markdown.js:97");var r=e.query(t.currentTarget,".js-comment-field",HTMLTextAreaElement),n=Cl(t.detail.file);r.setCustomValidity(""),x.replaceText(r,n,"")}),r.on("upload:invalid",".js-upload-markdown-image",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/markdown.js:105");var r=e.query(t.currentTarget,".js-comment-field",HTMLTextAreaElement),n=Cl(t.detail.file);r.setCustomValidity(""),x.replaceText(r,n,"")}),r.on("upload:setup",".js-upload-marketplace-listing-screenshot",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/upload/marketplace-listing-screenshot.js:9");var t=e.detail.policyRequest,r=e.currentTarget.getAttribute("data-marketplace-listing-id");r&&t.body.append("marketplace_listing_id",r)}),r.on("upload:complete",".js-upload-marketplace-listing-screenshot",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=r.currentTarget.getAttribute("data-screenshots-url"),a(n,"`data-screenshots-url` must exist -- app/assets/modules/github/upload/marketplace-listing-screenshot.js:21"),s=e.query(document,".js-marketplace-listing-screenshots-container"),t.next=5,o.fetchSafeDocumentFragment(document,n);case 5:i=t.sent,s.innerHTML="",s.appendChild(i);case 8:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),r.on("upload:setup",".js-upload-marketplace-listing-hero-card-background",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/upload/marketplace-listing-hero-card-background.js:8");var t=e.detail.policyRequest,r=e.currentTarget.getAttribute("data-marketplace-listing-id");r&&t.body.append("marketplace_listing_id",r)}),r.on("upload:complete",".js-upload-marketplace-listing-hero-card-background",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/marketplace-listing-hero-card-background.js:19");var r=t.detail,n=r.policy;e.query(document,".js-marketplace-listing-hero-card-background-id",HTMLInputElement).value=n.asset.id||r.result.id;var s=e.query(document,".js-hero-listing-container"),o=n.asset.href||r.result.href;s.style.backgroundImage="url("+o+")"}),r.on("click",".js-release-remove-file",function(t){var r=e.closest(t.currentTarget,".js-release-file");r.classList.add("delete"),e.query(r,"input.destroy",HTMLInputElement).value="true"}),r.on("click",".js-release-undo-remove-file",function(t){var r=e.closest(t.currentTarget,".js-release-file");r.classList.remove("delete"),e.query(r,"input.destroy",HTMLInputElement).value=""});var Pl=null;function Nl(t,r){var n=A(r.body,FormData);n.append("release_id",Fl(t));var a=e.querySelectorAll(document,".js-releases-field .js-release-file.delete .id",HTMLInputElement);if(a.length){var s=a.map(function(e){return e.value});n.append("deletion_candidates",s.join(","))}}r.on("release:saved",".js-release-form",function(t){var r=t.currentTarget;Pl=null;var n=!1,a=!0,s=!1,o=void 0;try{for(var i,u=r.querySelectorAll(".js-releases-field .js-release-file")[Symbol.iterator]();!(a=(i=u.next()).done);a=!0){var c=i.value;c.classList.contains("delete")?c.remove():c.classList.contains("js-template")||(n=!0)}}catch(e){s=!0,o=e}finally{try{!a&&u.return&&u.return()}finally{if(s)throw o}}var l=e.query(r,".js-releases-field");l.classList.toggle("not-populated",!n),l.classList.toggle("is-populated",n)}),r.on("upload:setup",".js-upload-release-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/release-file.js:60");var r=t.detail,n=r.policyRequest,s=r.preprocess,o=t.currentTarget;if(Fl(o))Nl(o,n);else{if(!Pl){var i=e.query(document,".js-save-draft",HTMLButtonElement);Pl=Hu(i)}var u=Nl.bind(null,o,n);s.push(Pl.then(u))}}),r.on("upload:start",".js-upload-release-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/release-file.js:83");var r=t.detail.policy;e.query(t.currentTarget,".js-upload-meter").classList.remove("d-none");var n=r.asset.replaced_asset;if(n){var s=!0,o=!1,i=void 0;try{for(var u,c=e.querySelectorAll(document,".js-releases-field .js-release-file .id",HTMLInputElement)[Symbol.iterator]();!(s=(u=c.next()).done);s=!0){var l=u.value;Number(l.value)===n&&e.closest(l,".js-release-file").remove()}}catch(e){o=!0,i=e}finally{try{!s&&c.return&&c.return()}finally{if(o)throw i}}}}),r.on("upload:complete",".js-upload-release-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/release-file.js:100");var r=t.detail,n=r.policy,s=e.query(document,".js-releases-field"),o=e.query(s,".js-template").cloneNode(!0);o.classList.remove("d-none","js-template"),e.query(o,"input.id",HTMLInputElement).value=n.asset.id||r.result.id;var i=n.asset.name||n.asset.href.split("/").pop(),u=!0,c=!1,l=void 0;try{for(var d,m=o.querySelectorAll(".js-release-asset-filename")[Symbol.iterator]();!(u=(d=m.next()).done);u=!0){var p=d.value;p instanceof HTMLInputElement?p.value=i:p.textContent=i}}catch(e){c=!0,l=e}finally{try{!u&&m.return&&m.return()}finally{if(c)throw l}}var f=n.asset.size?"("+(n.asset.size/1048576).toFixed(2)+" MB)":"";e.query(o,".js-release-asset-filesize").textContent=f,s.appendChild(o),s.classList.remove("not-populated"),s.classList.add("is-populated"),e.query(t.currentTarget,".js-upload-meter").classList.add("d-none")}),r.on("upload:progress",".js-upload-release-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/release-file.js:133"),e.query(t.currentTarget,".js-upload-meter").style.width=t.detail.percent+"%"});var Ol=function(){var t=te(regeneratorRuntime.mark(function t(r){var n;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return n=r.getAttribute("data-redirect-url"),t.prev=1,t.next=4,o.fetchPoll(r.getAttribute("data-poll-url")||"");case 4:window.location=n,t.next=11;break;case 7:t.prev=7,t.t0=t.catch(1),e.query(document,".js-manifest-ready-check").classList.add("d-none"),e.query(document,".js-manifest-ready-check-failed").classList.remove("d-none");case 11:case"end":return t.stop()}},t,this,[[1,7]])}));return function(e){return t.apply(this,arguments)}}(),Dl=null,Bl=new WeakMap;function Ul(t,r){var n=e.closest(t,".js-upload-manifest-file-container"),a=e.query(n,".js-upload-progress");a.classList.add("active"),t.classList.add("is-progress-bar");var s=e.query(a,".js-upload-meter-text");e.query(s,".js-upload-meter-range-start").textContent=String(r.batch.uploaded+1),e.query(s,".js-upload-meter-range-end").textContent=String(r.batch.size)}function zl(t){t.classList.remove("is-progress-bar");var r=e.closest(t,".js-upload-manifest-file-container");e.query(r,".js-upload-progress").classList.remove("active"),e.query(r,".js-upload-meter-text .js-upload-meter-filename").textContent=""}function Wl(){var e=void 0,t=void 0,r=new Promise(function(r,n){e=r,t=n});return a(e,"app/assets/modules/github/upload/upload-manifest-file.js:140"),a(t,"app/assets/modules/github/upload/upload-manifest-file.js:141"),[r,e,t]}function Vl(e){return e._path?e._path+"/"+e.name:e.name}function $l(e){zl(e.currentTarget)}r.on("upload:drop:setup",".js-upload-manifest-file",function(e){a(e instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:53");var t=e.detail.files,r=parseInt(e.currentTarget.getAttribute("data-directory-upload-max-files"),10);t.length>r&&(e.preventDefault(),e.currentTarget.classList.add("is-too-many"))}),r.on("upload:drop:setup",".js-upload-manifest-tree-view",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:63"),t.preventDefault();var r=t.detail.upload,n=e.query(document,"#js-repo-pjax-container");n.addEventListener("pjax:success",function(){r(n.querySelector(".js-uploadable-container"))},{once:!0});var s=t.currentTarget.getAttribute("data-drop-url");a(s,"app/assets/modules/github/upload/upload-manifest-file.js:79"),Z({url:s,container:n})}),r.on("upload:setup",".js-upload-manifest-file",function(){var t=te(regeneratorRuntime.mark(function t(r){var n,s,i,u,c,l,d,m,p,f,v,g,h,y;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:if(c=function(){s.body.append("upload_manifest_id",Bl.get(u))},a(r instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:84"),n=r.detail,s=n.policyRequest,i=n.preprocess,Ul(u=r.currentTarget,r.detail),!Bl.get(u)){t.next=8;break}return c(),t.abrupt("return");case 8:if(!Dl){t.next=11;break}return i.push(Dl.then(c)),t.abrupt("return");case 11:return l=e.closest(u,".js-upload-manifest-file-container"),d=l.querySelector(".js-upload-manifest-form"),Dl=o.fetchJSON(d.action,{method:d.method,body:new FormData(d)}),m=Wl(),p=oe(m,2),f=p[0],v=p[1],i.push(f.then(c)),t.next=18,Dl;case 18:g=t.sent,h=e.query(document,".js-manifest-commit-form",HTMLFormElement),y=h.elements.namedItem("manifest_id"),a(y instanceof HTMLInputElement,"app/assets/modules/github/upload/upload-manifest-file.js:123"),y.value=g.upload_manifest.id,Bl.set(u,g.upload_manifest.id),Dl=null,v();case 26:case"end":return t.stop()}},t,this)}));return function(e){return t.apply(this,arguments)}}()),r.on("upload:start",".js-upload-manifest-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:154");var r=t.detail,n=e.closest(t.currentTarget,".js-upload-manifest-file-container"),s=e.query(n,".js-upload-progress"),o=e.query(s,".js-upload-meter-text");e.query(o,".js-upload-meter-range-start").textContent=r.batch.uploaded+1,e.query(o,".js-upload-meter-filename").textContent=Vl(r.file)}),r.on("upload:complete",".js-upload-manifest-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:169");var r=t.detail,n=e.query(document,".js-manifest-commit-file-template",HTMLTableElement).rows[0].cloneNode(!0);e.query(n,".name").textContent=Vl(r.file);var s=r.policy.asset.id||r.result.id,o=e.query(n,".js-remove-manifest-file-form",HTMLFormElement).elements.namedItem("file_id");a(o instanceof HTMLInputElement,"app/assets/modules/github/upload/upload-manifest-file.js:185"),o.value=s;var i=e.query(document,".js-manifest-file-list");i.classList.remove("d-none"),t.currentTarget.classList.add("is-file-list"),e.query(document,".js-upload-progress").classList.add("is-file-list"),e.query(i,".js-manifest-file-list-root").appendChild(n),r.batch.isFinished()&&zl(t.currentTarget)}),r.on("upload:progress",".js-upload-manifest-file",function(t){a(t instanceof CustomEvent,"app/assets/modules/github/upload/upload-manifest-file.js:203");var r=t.detail,n=e.closest(t.currentTarget,".js-upload-manifest-file-container");e.query(n,".js-upload-meter").style.width=r.batch.percent()+"%"}),r.on("upload:error",".js-upload-manifest-file",$l),r.on("upload:invalid",".js-upload-manifest-file",$l),c.remoteForm(".js-remove-manifest-file-form",function(){var t=te(regeneratorRuntime.mark(function t(r,n){var a;return regeneratorRuntime.wrap(function(t){for(;;)switch(t.prev=t.next){case 0:return t.next=2,n.html();case 2:a=e.closest(r,".js-manifest-file-list-root"),e.closest(r,".js-manifest-file-entry").remove(),a.hasChildNodes()||(e.closest(a,".js-manifest-file-list").classList.add("d-none"),e.query(document,".js-upload-manifest-file").classList.remove("is-file-list"),e.query(document,".js-upload-progress").classList.remove("is-file-list"));case 6:case"end":return t.stop()}},t,this)}));return function(e,r){return t.apply(this,arguments)}}()),t.observe(".js-manifest-ready-check",{initialize:function(e){Ol(e)}});var Jl=function(){function e(t){re(this,e),this.files=Array.from(t),this.percentages=this.files.map(function(){return 0}),this.size=this.files.length,this.total=this.files.reduce(function(e,t){return e+t.size},0),this.uploaded=0}return ne(e,[{key:"percent",value:function(){var e=this,t=this.files.map(function(t,r){return t.size*e.percentages[r]/100}).reduce(function(e,t){return e+t});return Math.round(t/this.total*100)}},{key:"progress",value:function(e,t){var r=this.files.indexOf(e);this.percentages[r]=t}},{key:"completed",value:function(){this.uploaded+=1}},{key:"isFinished",value:function(){return this.uploaded===this.files.length}}]),e}(),Kl=["is-default","is-uploading","is-bad-file","is-duplicate-filename","is-too-big","is-too-many","is-hidden-file","is-failed","is-bad-dimensions","is-empty","is-bad-permissions","is-repository-required","is-bad-format"];function Gl(e,t){var r;(r=e.classList).remove.apply(r,Kl),e.classList.add(t)}var Xl=new(function(){function e(){re(this,e),this.uploads=[],this.busy=!1}return ne(e,[{key:"upload",value:function(e){this.uploads.push(e),this.process()}},{key:"process",value:function(){var e=this;if(!this.busy&&0!==this.uploads.length){var t=this.uploads.shift();this.busy=!0;var r=new XMLHttpRequest;for(var n in r.open("POST",t.to,!0),t.header)r.setRequestHeader(n,t.header[n]);r.onloadstart=function(){t.start()},r.onload=function(){204===r.status?t.complete({}):201===r.status?t.complete(JSON.parse(r.responseText)):t.error({status:r.status,body:r.responseText}),e.busy=!1,e.process()},r.onerror=function(){t.error({status:0,body:""}),e.busy=!1,e.uploads=[]},r.upload.onprogress=function(e){if(e.lengthComputable){var r=Math.round(e.loaded/e.total*100);t.progress(r)}};var a=new FormData;for(var s in t.sameOrigin&&a.append("authenticity_token",t.csrf),t.form)a.append(s,t.form[s]);a.append("file",t.file),r.send(a)}}}]),e}()),Ql=function(){var e=te(regeneratorRuntime.mark(function e(t,n){var a,s,i,u,c,l,d,m,p,f,v,g,h;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:a=!0,s=!1,i=void 0,e.prev=3,u=t.files[Symbol.iterator]();case 5:if(a=(c=u.next()).done){e.next=37;break}if(l=c.value,d=Yl(l,n),m=[],r.fire(n,"upload:setup",{batch:t,file:l,policyRequest:d.options,preprocess:m})){e.next=11;break}return e.abrupt("return");case 11:return e.prev=11,e.next=14,Promise.all(m);case 14:return e.next=16,o.fetchJSON(d.url,d.options);case 16:p=e.sent,Xl.upload(Zl(t,l,p,n)),e.next=34;break;case 20:if(e.prev=20,e.t0=e.catch(11),r.fire(n,"upload:invalid",{batch:t,file:l,error:e.t0}),!e.t0.response){e.next=32;break}return e.next=26,e.t0.response.text();case 26:f=e.sent,v=e.t0.response.status,g=td({status:v,body:f},l),Gl(n,g),e.next=34;break;case 32:h=td({status:0,body:""}),Gl(n,h);case 34:a=!0,e.next=5;break;case 37:e.next=43;break;case 39:e.prev=39,e.t1=e.catch(3),s=!0,i=e.t1;case 43:e.prev=43,e.prev=44,!a&&u.return&&u.return();case 46:if(e.prev=46,!s){e.next=49;break}throw i;case 49:return e.finish(46);case 50:return e.finish(43);case 51:case"end":return e.stop()}},e,this,[[3,39,43,51],[11,20],[44,,46,50]])}));return function(t,r){return e.apply(this,arguments)}}();function Yl(e,t){var r=t.getAttribute("data-upload-policy-url");a(r,"app/assets/modules/github/upload/queue.js:75");var n=t.getAttribute("data-upload-repository-id"),s=t.getAttribute("data-upload-policy-authenticity-token");null==s&&(s=ed(t));var o=new FormData;return o.append("name",e.name),o.append("size",String(e.size)),o.append("content_type",e.type),o.append("authenticity_token",s),n&&o.append("repository_id",n),e._path&&o.append("directory",String(e._path)),{url:r,options:{method:"post",body:o,headers:{}}}}function Zl(e,t,n,a){var s=n.upload_authenticity_token;null==s&&(s=ed(a));var i=n.asset_upload_authenticity_token;return null==i&&(i=ed(a)),{file:t,to:n.upload_url,form:n.form,header:n.header,sameOrigin:n.same_origin,csrf:s,start:function(){Gl(a,"is-uploading"),r.fire(a,"upload:start",{batch:e,file:t,policy:n})},progress:function(n){e.progress(t,n),r.fire(a,"upload:progress",{batch:e,file:t,percent:n})},complete:function(s){if(e.completed(),s&&s.href&&(n.asset||(n.asset={}),n.asset.href=s.href),n.asset_upload_url&&n.asset_upload_url.length>0){var u=new FormData;u.append("authenticity_token",i),o.fetchJSON(n.asset_upload_url,{method:"put",body:u})}r.fire(a,"upload:complete",{batch:e,file:t,policy:n,result:s}),Gl(a,"is-default")},error:function(s){r.fire(a,"upload:error",{batch:e,file:t,policy:n});var o=td(s);Gl(a,o)}}}function ed(t){var r=e.closest(t,"form",HTMLFormElement);return A(r.elements.namedItem("authenticity_token"),HTMLInputElement).value}function td(e,t){if(400===e.status)return"is-bad-file";if(422!==e.status)return"is-failed";var r=JSON.parse(e.body);if(!r||!r.errors)return"is-failed";var n=!0,a=!1,s=void 0;try{for(var o,i=r.errors[Symbol.iterator]();!(n=(o=i.next()).done);n=!0){var u=o.value;switch(u.field){case"size":var c=t?t.size:null;return null!=c&&0===parseInt(c)?"is-empty":"is-too-big";case"file_count":return"is-too-many";case"width":case"height":return"is-bad-dimensions";case"name":return"already_exists"===u.code?"is-duplicate-filename":"is-bad-file";case"content_type":return"is-bad-file";case"uploader_id":return"is-bad-permissions";case"repository_id":return"is-repository-required";case"format":return"is-bad-format"}}}catch(e){a=!0,s=e}finally{try{!n&&i.return&&i.return()}finally{if(a)throw s}}return"is-failed"}var rd=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!r.getFilesAndDirectories){e.next=8;break}return e.next=3,r.getFilesAndDirectories();case 3:return n=e.sent,a=ud(n).map(function(e){return rd(r.path,e)}),e.abrupt("return",Promise.all(a));case 8:return r._path=t,e.abrupt("return",Promise.resolve(r));case 10:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}(),nd=function(){var e=te(regeneratorRuntime.mark(function e(t){return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.t0=id,e.next=3,rd("",t);case 3:return e.t1=e.sent,e.abrupt("return",(0,e.t0)(e.t1));case 5:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}(),ad=function(){var e=te(regeneratorRuntime.mark(function e(t,r){var n,a,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:if(!r.isDirectory){e.next=8;break}return e.next=3,ld(r);case 3:return n=e.sent,a=ud(n).map(function(e){return ad(r.fullPath,e)}),e.abrupt("return",Promise.all(a));case 8:return e.next=10,cd(r);case 10:return(s=e.sent)._path=t,e.abrupt("return",s);case 13:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}(),sd=function(){var e=te(regeneratorRuntime.mark(function e(t){var r,n;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return r=Array.from(t.items).map(function(e){return e.webkitGetAsEntry()}),n=ud(r).map(function(e){return ad("",e)}),e.t0=id,e.next=5,Promise.all(n);case 5:return e.t1=e.sent,e.abrupt("return",(0,e.t0)(e.t1));case 7:case"end":return e.stop()}},e,this)}));return function(t){return e.apply(this,arguments)}}();function od(e,t){return t&&e.getFilesAndDirectories?nd(e):t&&function(e){return e.items&&Array.from(e.items).some(function(e){return e.webkitGetAsEntry&&e.webkitGetAsEntry().isDirectory})}(e)?sd(e):Promise.resolve(ud(Array.from(e.files)))}function id(e){return e.reduce(function(e,t){return e.concat(Array.isArray(t)?id(t):t)},[])}function ud(e){return Array.from(e).filter(function(e){return!function(e){return e.name.startsWith(".")}(e)})}function cd(e){return new Promise(function(t,r){e.file(t,r)})}function ld(e){return new Promise(function(t,r){e.createReader().readEntries(t,r)})}var dd=function(){var e=te(regeneratorRuntime.mark(function e(t,n){var a,s;return regeneratorRuntime.wrap(function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,od(n,t.hasAttribute("data-directory-upload"));case 2:if((a=e.sent).length){e.next=6;break}return Gl(t,"is-hidden-file"),e.abrupt("return");case 6:s=pd.bind(null,a),!r.fire(t,"upload:drop:setup",{upload:s,files:a})||pd(a,t);case 9:case"end":return e.stop()}},e,this)}));return function(t,r){return e.apply(this,arguments)}}();function md(e){return Array.from(e.types).indexOf("Files")>=0}function pd(e,t){var r=new Jl(e);Ql(r,t)}function fd(e){var t=e.dataTransfer;t&&md(t)&&e.preventDefault()}function vd(e){var t=e.dataTransfer;t&&md(t)&&e.preventDefault()}var gd=null;function hd(e){if(!Ed){var t=e.currentTarget;a(t instanceof Element,"app/assets/modules/github/uploads.js:68"),gd&&clearTimeout(gd),gd=setTimeout(function(){return t.classList.remove("dragover")},200);var r=e.dataTransfer;r&&md(r)&&(r.dropEffect="copy",t.classList.add("dragover"),e.stopPropagation(),e.preventDefault())}}function yd(e){e.dataTransfer&&(e.dataTransfer.dropEffect="none"),a(e.currentTarget instanceof Element,"app/assets/modules/github/uploads.js:91"),e.currentTarget.classList.remove("dragover"),e.stopPropagation(),e.preventDefault()}function bd(e){e.target instanceof Element&&e.target.classList.contains("js-document-dropzone")&&(a(e.currentTarget instanceof Element,"app/assets/modules/github/uploads.js:102"),e.currentTarget.classList.remove("dragover"))}function jd(e){var t=document.body;a(t,"app/assets/modules/github/uploads.js:127");var r=e.currentTarget;a(r instanceof Element,"app/assets/modules/github/uploads.js:130"),r.classList.remove("dragover"),t.classList.remove("dragover");var n=e.dataTransfer;n&&md(n)&&(dd(r,n),e.stopPropagation(),e.preventDefault())}function Ld(e){if(e.clipboardData&&e.clipboardData.items){var t=Array.from(e.clipboardData.items).map(function(e){return[e,function(e){switch(e){case"image/gif":return"image.gif";case"image/png":return"image.png";case"image/jpeg":return"image.jpg"}}(e.type)]}).filter(function(e){return e[1]}).shift();if(t){var r=oe(t,1)[0].getAsFile();a(e.currentTarget instanceof HTMLElement,"app/assets/modules/github/uploads.js:170"),a(r,"app/assets/modules/github/uploads.js:171"),pd([r],e.currentTarget),e.preventDefault()}}}function wd(e){var t=e.target;t instanceof HTMLInputElement&&t.classList.contains("js-manual-file-chooser")&&(a(e.currentTarget instanceof Element,"app/assets/modules/github/uploads.js:187"),pd(t.files,e.currentTarget),t.value="")}function xd(t){var r=t.currentTarget;a(r instanceof HTMLElement,"app/assets/modules/github/uploads.js:199"),Gl(r.classList.contains("js-uploadable-container")?r:e.query(r,".js-uploadable-container"),"is-default")}var kd=0,Ed=!1;function Td(){Ed=!0}function qd(){Ed=!1}function Sd(e){var t,r=e.currentTarget;a(r instanceof Element,"app/assets/modules/github/warn-unsaved-changes.js:25"),k.hasDirtyFields(r)?(t=r.getAttribute("data-warn-unsaved-changes")||"Changes you made may not be saved.",window.onbeforeunload=function(e){return e.returnValue=t,t}):Ad()}function Ad(){window.onbeforeunload=null}t.observe(".js-document-dropzone",{add:function(e){var t=document.body;a(t,"app/assets/modules/github/uploads.js:226"),t.addEventListener("dragstart",Td),t.addEventListener("dragend",qd),t.addEventListener("dragenter",hd),t.addEventListener("dragover",hd),t.addEventListener("dragleave",bd),e.addEventListener("drop",jd)},remove:function(e){var t=document.body;a(t,"app/assets/modules/github/uploads.js:237"),t.removeEventListener("dragstart",Td),t.removeEventListener("dragend",qd),t.removeEventListener("dragenter",hd),t.removeEventListener("dragover",hd),t.removeEventListener("dragleave",bd),e.removeEventListener("drop",jd)}}),t.observe(".js-uploadable-container",{add:function(e){0==kd++&&(document.addEventListener("drop",fd),document.addEventListener("dragover",vd)),e.addEventListener("dragenter",hd),e.addEventListener("dragover",hd),e.addEventListener("dragleave",yd),e.addEventListener("drop",jd),e.addEventListener("paste",Ld),e.addEventListener("change",wd);var t=e.closest("form");null!=t&&t.addEventListener("reset",xd)},remove:function(e){0==--kd&&(document.removeEventListener("drop",fd),document.removeEventListener("dragover",vd)),e.removeEventListener("dragenter",hd),e.removeEventListener("dragover",hd),e.removeEventListener("dragleave",yd),e.removeEventListener("drop",jd),e.removeEventListener("paste",Ld),e.removeEventListener("change",wd);var t=e.closest("form");null!=t&&t.removeEventListener("reset",xd)}}),!function(){var e=document.createElement("div");e.style.cssText="-ms-user-select: element; user-select: contain;";var t=e;return"element"===t.msUserSelect||"contain"===t.userSelect}()&&window.getSelection&&r.on("click",".user-select-contain",function(e){var t=e.currentTarget,r=window.getSelection();if(r.rangeCount){var n=r.getRangeAt(0).commonAncestorContainer;t.contains(n)||r.selectAllChildren(t)}}),t.observe("[data-warn-unsaved-changes]",{add:function(e){e.addEventListener("input",Sd),e.addEventListener("change",Sd),e.addEventListener("submit",Ad)},remove:function(e){e.removeEventListener("input",Sd),e.removeEventListener("change",Sd),e.removeEventListener("submit",Ad)}}),r.on("click",["form button:not([type])","form button[type=submit]","form input[type=submit]"].join(", "),function(e){var t=e.currentTarget;a(t instanceof HTMLButtonElement||t instanceof HTMLInputElement,"app/assets/modules/github/remote.js:157"),t.form&&!e.defaultPrevented&&$.persistSubmitButtonValue(t)}),c.remoteForm("form[data-remote]",function(e,t,n){"json"===e.getAttribute("data-type")&&n.headers.set("Accept","application/json"),r.fire(e,"deprecatedAjaxSend",{request:n}),t.text().catch(function(e){if(e.response)return e.response;throw e}).then(function(t){t.status<300?r.fire(e,"deprecatedAjaxSuccess"):r.fire(e,"deprecatedAjaxError",{error:t.statusText,status:t.status,text:t.text})},function(t){r.fire(e,"deprecatedAjaxError",{error:t.message,status:0,text:null})}).then(function(){r.fire(e,"deprecatedAjaxComplete")})}),r.on("deprecatedAjaxComplete","form",function(e){var t=e.currentTarget;a(t instanceof HTMLFormElement,"app/assets/modules/github/remote.js:168");var r=$.findPersistedSubmitButtonValue(t);r&&r.remove()}),c.afterRemote(function(e){var t=$.findPersistedSubmitButtonValue(e);t&&t.remove()})});

//# sourceMappingURL=github-e6e60edf10ff602befa0bcc137129d93.js.map


================================================
FILE: data/Sales_files/github-cdaf214b636e7d0581fce94eda9de4bd.css
================================================
/*!
 * Primer-product
 * http://primer.github.io
 *
 * Released under MIT license. Copyright (c) 2018 GitHub Inc.
 */.flash{position:relative;padding:16px;color:#032f62;background-color:#dbedff;border:1px solid rgba(27,31,35,0.15);border-radius:3px}.flash p:last-child{margin-bottom:0}.flash-messages{margin-bottom:24px}.flash-close{float:right;padding:16px;margin:-16px;color:inherit;text-align:center;cursor:pointer;background:none;border:0;-webkit-appearance:none;-moz-appearance:none;appearance:none;opacity:0.6}.flash-close:hover{opacity:1}.flash-action{float:right;margin-top:-3px;margin-left:24px}.flash-warn{color:#735c0f;background-color:#fffbdd;border-color:rgba(27,31,35,0.15)}.flash-error{color:#86181d;background-color:#ffdce0;border-color:rgba(27,31,35,0.15)}.flash-success{color:#165c26;background-color:#dcffe4;border-color:rgba(27,31,35,0.15)}.flash-full{margin-top:-1px;border-width:1px 0;border-radius:0}.warning{padding:.5em;margin-bottom:0.8em;font-weight:600;background-color:#fffbdd}.avatar{display:inline-block;overflow:hidden;line-height:1;vertical-align:middle;border-radius:3px}.avatar-small{border-radius:2px}.avatar-link{float:left;line-height:1}.avatar-group-item{display:inline-block;margin-bottom:3px}.avatar-parent-child{position:relative}.avatar-child{position:absolute;right:-15%;bottom:-9%;background-color:#fff;border-radius:2px;box-shadow:-2px -2px 0 rgba(255,255,255,0.8)}.avatar-stack{display:inline-block;white-space:nowrap}.avatar-stack .avatar{position:relative;z-index:2;display:inline-block;width:20px;height:20px;box-sizing:content-box;margin-right:-15px;background-color:#fff;border-right:1px solid #fff;border-radius:2px;transition:margin 0.1s ease-in-out}.avatar-stack .avatar:only-child{background-color:transparent}.avatar-stack .avatar:first-child{z-index:3}.avatar-stack .avatar:last-child{z-index:1;margin-right:0;border-right:0}.avatar-stack:hover .avatar{margin-right:3px}.avatar-stack:hover .avatar:last-child{margin-right:0}.AvatarStack{position:relative;min-width:26px;height:20px}.AvatarStack .AvatarStack-body{position:absolute}.AvatarStack.AvatarStack--two{min-width:36px}.AvatarStack.AvatarStack--three-plus{min-width:46px}.AvatarStack-body{display:flex;background:#fff}.AvatarStack-body .avatar{position:relative;z-index:2;display:flex;width:20px;height:20px;box-sizing:content-box;margin-right:-11px;background-color:#fff;border-right:1px solid #fff;border-radius:2px;transition:margin 0.1s ease-in-out}.AvatarStack-body .avatar:first-child{z-index:3}.AvatarStack-body .avatar:last-child{z-index:1;border-right:0}.AvatarStack-body .avatar img{border-radius:2px}.AvatarStack-body .avatar:nth-child(n+4){display:none;opacity:0}.AvatarStack-body:hover .avatar{margin-right:3px}.AvatarStack-body:hover .avatar:nth-child(n+4){display:flex;opacity:1}.AvatarStack-body:hover .avatar-more{display:none !important}.avatar.avatar-more{z-index:1;margin-right:0;background:#f6f8fa}.avatar.avatar-more::before,.avatar.avatar-more::after{position:absolute;display:block;height:20px;content:"";border-radius:2px;outline:1px solid #fff}.avatar.avatar-more::before{width:17px;background:#e1e4e8}.avatar.avatar-more::after{width:14px;background:#d1d5da}.AvatarStack--right .AvatarStack-body{right:0;flex-direction:row-reverse}.AvatarStack--right .AvatarStack-body:hover .avatar{margin-right:0;margin-left:3px}.AvatarStack--right .avatar.avatar-more{background:#d1d5da}.AvatarStack--right .avatar.avatar-more::before{width:5px}.AvatarStack--right .avatar.avatar-more::after{width:2px;background:#f6f8fa}.AvatarStack--right .avatar{margin-right:0;margin-left:-11px;border-right:0;border-left:1px solid #fff}.CircleBadge{display:flex;align-items:center;justify-content:center;background-color:#fff;border-radius:50%;box-shadow:0 1px 5px rgba(27,31,35,0.15)}.CircleBadge-icon{max-width:60% !important;height:auto !important;max-height:55% !important}.CircleBadge--small{width:56px;height:56px}.CircleBadge--medium{width:96px;height:96px}.CircleBadge--large{width:128px;height:128px}.DashedConnection{position:relative}.DashedConnection::before{position:absolute;top:50%;left:0;width:100%;content:"";border-bottom:2px dashed #e1e4e8}.DashedConnection .CircleBadge{position:relative}.blankslate{position:relative;padding:32px;text-align:center;background-color:#fafbfc;border:1px solid #e1e4e8;border-radius:3px;box-shadow:inset 0 0 10px rgba(27,31,35,0.05)}.blankslate code{padding:2px 5px 3px;font-size:14px;background:#fff;border:1px solid #eaecef;border-radius:3px}.blankslate-icon{margin-right:4px;margin-bottom:8px;margin-left:4px;color:#a3aab1}.blankslate-capped{border-radius:0 0 3px 3px}.blankslate-spacious{padding:80px 40px}.blankslate-narrow{width:485px;margin:0 auto}.blankslate-large h3{margin:16px 0;font-size:20px}.blankslate-large p{font-size:16px}.blankslate-clean-background{background:none;border:0;box-shadow:none}.branch-name{display:inline-block;padding:2px 6px;font:12px "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;color:rgba(27,31,35,0.6);background-color:#eaf5ff;border-radius:3px}.branch-name .octicon{margin:1px -2px 0 0;color:#a8bbd0}a.branch-name{color:#0366d6}.labels{position:relative}.label,.Label{display:inline-block;padding:3px 4px;font-size:12px;font-weight:600;line-height:1;color:#fff;border-radius:2px;box-shadow:inset 0 -1px 0 rgba(27,31,35,0.12)}.label:hover,.Label:hover{text-decoration:none}.Label--gray{color:#586069;background-color:#eaecef}.Label--outline{margin-top:-1px;margin-bottom:-1px;font-weight:400;color:#586069;background-color:transparent;border:1px solid rgba(27,31,35,0.15);box-shadow:none}.Label--outline-green{color:#28a745;border:1px solid #34d058}.Label--gray-darker{background-color:#6a737d}.Label--orange{background-color:#d15704}.state,.State{display:inline-block;padding:4px 8px;font-weight:600;line-height:20px;color:#fff;text-align:center;background-color:#6a737d;border-radius:3px}.State--green{background-color:#2cbe4e}.State--purple{background-color:#6f42c1}.State--red{background-color:#cb2431}.State--small{padding:.125em 4px;font-size:12px}.State--small .octicon{width:1em}.Counter{display:inline-block;padding:2px 5px;font-size:12px;font-weight:600;line-height:1;color:#586069;background-color:rgba(27,31,35,0.08);border-radius:20px}.Counter--gray-light{color:#24292e;background-color:rgba(27,31,35,0.15)}.Counter--gray{color:#fff;background-color:#6a737d}.markdown-body{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-size:16px;line-height:1.5;word-wrap:break-word}.markdown-body::before{display:table;content:""}.markdown-body::after{display:table;clear:both;content:""}.markdown-body>*:first-child{margin-top:0 !important}.markdown-body>*:last-child{margin-bottom:0 !important}.markdown-body a:not([href]){color:inherit;text-decoration:none}.markdown-body .absent{color:#cb2431}.markdown-body .anchor{float:left;padding-right:4px;margin-left:-20px;line-height:1}.markdown-body .anchor:focus{outline:none}.markdown-body p,.markdown-body blockquote,.markdown-body ul,.markdown-body ol,.markdown-body dl,.markdown-body table,.markdown-body pre{margin-top:0;margin-bottom:16px}.markdown-body hr{height:.25em;padding:0;margin:24px 0;background-color:#e1e4e8;border:0}.markdown-body blockquote{padding:0 1em;color:#6a737d;border-left:0.25em solid #dfe2e5}.markdown-body blockquote>:first-child{margin-top:0}.markdown-body blockquote>:last-child{margin-bottom:0}.markdown-body kbd{display:inline-block;padding:3px 5px;font-size:11px;line-height:10px;color:#444d56;vertical-align:middle;background-color:#fafbfc;border:solid 1px #c6cbd1;border-bottom-color:#959da5;border-radius:3px;box-shadow:inset 0 -1px 0 #959da5}.markdown-body h1,.markdown-body h2,.markdown-body h3,.markdown-body h4,.markdown-body h5,.markdown-body h6{margin-top:24px;margin-bottom:16px;font-weight:600;line-height:1.25}.markdown-body h1 .octicon-link,.markdown-body h2 .octicon-link,.markdown-body h3 .octicon-link,.markdown-body h4 .octicon-link,.markdown-body h5 .octicon-link,.markdown-body h6 .octicon-link{color:#1b1f23;vertical-align:middle;visibility:hidden}.markdown-body h1:hover .anchor,.markdown-body h2:hover .anchor,.markdown-body h3:hover .anchor,.markdown-body h4:hover .anchor,.markdown-body h5:hover .anchor,.markdown-body h6:hover .anchor{text-decoration:none}.markdown-body h1:hover .anchor .octicon-link,.markdown-body h2:hover .anchor .octicon-link,.markdown-body h3:hover .anchor .octicon-link,.markdown-body h4:hover .anchor .octicon-link,.markdown-body h5:hover .anchor .octicon-link,.markdown-body h6:hover .anchor .octicon-link{visibility:visible}.markdown-body h1 tt,.markdown-body h1 code,.markdown-body h2 tt,.markdown-body h2 code,.markdown-body h3 tt,.markdown-body h3 code,.markdown-body h4 tt,.markdown-body h4 code,.markdown-body h5 tt,.markdown-body h5 code,.markdown-body h6 tt,.markdown-body h6 code{font-size:inherit}.markdown-body h1{padding-bottom:0.3em;font-size:2em;border-bottom:1px solid #eaecef}.markdown-body h2{padding-bottom:0.3em;font-size:1.5em;border-bottom:1px solid #eaecef}.markdown-body h3{font-size:1.25em}.markdown-body h4{font-size:1em}.markdown-body h5{font-size:0.875em}.markdown-body h6{font-size:0.85em;color:#6a737d}.markdown-body ul,.markdown-body ol{padding-left:2em}.markdown-body ul.no-list,.markdown-body ol.no-list{padding:0;list-style-type:none}.markdown-body ul ul,.markdown-body ul ol,.markdown-body ol ol,.markdown-body ol ul{margin-top:0;margin-bottom:0}.markdown-body li{word-wrap:break-all}.markdown-body li>p{margin-top:16px}.markdown-body li+li{margin-top:.25em}.markdown-body dl{padding:0}.markdown-body dl dt{padding:0;margin-top:16px;font-size:1em;font-style:italic;font-weight:600}.markdown-body dl dd{padding:0 16px;margin-bottom:16px}.markdown-body table{display:block;width:100%;overflow:auto}.markdown-body table th{font-weight:600}.markdown-body table th,.markdown-body table td{padding:6px 13px;border:1px solid #dfe2e5}.markdown-body table tr{background-color:#fff;border-top:1px solid #c6cbd1}.markdown-body table tr:nth-child(2n){background-color:#f6f8fa}.markdown-body table img{background-color:transparent}.markdown-body img{max-width:100%;box-sizing:content-box;background-color:#fff}.markdown-body img[align=right]{padding-left:20px}.markdown-body img[align=left]{padding-right:20px}.markdown-body .emoji{max-width:none;vertical-align:text-top;background-color:transparent}.markdown-body span.frame{display:block;overflow:hidden}.markdown-body span.frame>span{display:block;float:left;width:auto;padding:7px;margin:13px 0 0;overflow:hidden;border:1px solid #dfe2e5}.markdown-body span.frame span img{display:block;float:left}.markdown-body span.frame span span{display:block;padding:5px 0 0;clear:both;color:#24292e}.markdown-body span.align-center{display:block;overflow:hidden;clear:both}.markdown-body span.align-center>span{display:block;margin:13px auto 0;overflow:hidden;text-align:center}.markdown-body span.align-center span img{margin:0 auto;text-align:center}.markdown-body span.align-right{display:block;overflow:hidden;clear:both}.markdown-body span.align-right>span{display:block;margin:13px 0 0;overflow:hidden;text-align:right}.markdown-body span.align-right span img{margin:0;text-align:right}.markdown-body span.float-left{display:block;float:left;margin-right:13px;overflow:hidden}.markdown-body span.float-left span{margin:13px 0 0}.markdown-body span.float-right{display:block;float:right;margin-left:13px;overflow:hidden}.markdown-body span.float-right>span{display:block;margin:13px auto 0;overflow:hidden;text-align:right}.markdown-body code,.markdown-body tt{padding:0.2em 0.4em;margin:0;font-size:85%;background-color:rgba(27,31,35,0.05);border-radius:3px}.markdown-body code br,.markdown-body tt br{display:none}.markdown-body del code{text-decoration:inherit}.markdown-body pre{word-wrap:normal}.markdown-body pre>code{padding:0;margin:0;font-size:100%;word-break:normal;white-space:pre;background:transparent;border:0}.markdown-body .highlight{margin-bottom:16px}.markdown-body .highlight pre{margin-bottom:0;word-break:normal}.markdown-body .highlight pre,.markdown-body pre{padding:16px;overflow:auto;font-size:85%;line-height:1.45;background-color:#f6f8fa;border-radius:3px}.markdown-body pre code,.markdown-body pre tt{display:inline;max-width:auto;padding:0;margin:0;overflow:visible;line-height:inherit;word-wrap:normal;background-color:transparent;border:0}.markdown-body .csv-data td,.markdown-body .csv-data th{padding:5px;overflow:hidden;font-size:12px;line-height:1;text-align:left;white-space:nowrap}.markdown-body .csv-data .blob-num{padding:10px 8px 9px;text-align:right;background:#fff;border:0}.markdown-body .csv-data tr{border-top:0}.markdown-body .csv-data th{font-weight:600;background:#f6f8fa;border-top:0}.Popover{position:absolute;z-index:100}.Popover-message{position:relative;width:232px;margin-right:auto;margin-left:auto}.Popover-message::before,.Popover-message::after{position:absolute;left:50%;display:inline-block;content:""}.Popover-message::before{top:-16px;margin-left:-9px;border:8px solid transparent;border-bottom-color:rgba(27,31,35,0.15)}.Popover-message::after{top:-14px;margin-left:-8px;border:7px solid transparent;border-bottom-color:#fff}.Popover-message--bottom::before,.Popover-message--bottom::after,.Popover-message--bottom-right::before,.Popover-message--bottom-right::after,.Popover-message--bottom-left::before,.Popover-message--bottom-left::after{top:auto;border-bottom-color:transparent}.Popover-message--bottom::before,.Popover-message--bottom-right::before,.Popover-message--bottom-left::before{bottom:-16px;border-top-color:rgba(27,31,35,0.15)}.Popover-message--bottom::after,.Popover-message--bottom-right::after,.Popover-message--bottom-left::after{bottom:-14px;border-top-color:#fff}.Popover-message--top-right,.Popover-message--bottom-right{right:-9px;margin-right:0}.Popover-message--top-right::before,.Popover-message--top-right::after,.Popover-message--bottom-right::before,.Popover-message--bottom-right::after{left:auto;margin-left:0}.Popover-message--top-right::before,.Popover-message--bottom-right::before{right:20px}.Popover-message--top-right::after,.Popover-message--bottom-right::after{right:21px}.Popover-message--top-left,.Popover-message--bottom-left{left:-9px;margin-left:0}.Popover-message--top-left::before,.Popover-message--top-left::after,.Popover-message--bottom-left::before,.Popover-message--bottom-left::after{left:24px;margin-left:0}.Popover-message--top-left::after,.Popover-message--bottom-left::after{left:25px}.Popover-message--right::before,.Popover-message--right::after,.Popover-message--right-top::before,.Popover-message--right-top::after,.Popover-message--right-bottom::before,.Popover-message--right-bottom::after,.Popover-message--left::before,.Popover-message--left::after,.Popover-message--left-top::before,.Popover-message--left-top::after,.Popover-message--left-bottom::before,.Popover-message--left-bottom::after{top:50%;left:auto;margin-left:0;border-bottom-color:transparent}.Popover-message--right::before,.Popover-message--right-top::before,.Popover-message--right-bottom::before,.Popover-message--left::before,.Popover-message--left-top::before,.Popover-message--left-bottom::before{margin-top:-9px}.Popover-message--right::after,.Popover-message--right-top::after,.Popover-message--right-bottom::after,.Popover-message--left::after,.Popover-message--left-top::after,.Popover-message--left-bottom::after{margin-top:-8px}.Popover-message--right::before,.Popover-message--right-top::before,.Popover-message--right-bottom::before{right:-16px;border-left-color:rgba(27,31,35,0.15)}.Popover-message--right::after,.Popover-message--right-top::after,.Popover-message--right-bottom::after{right:-14px;border-left-color:#fff}.Popover-message--left::before,.Popover-message--left-top::before,.Popover-message--left-bottom::before{left:-16px;border-right-color:rgba(27,31,35,0.15)}.Popover-message--left::after,.Popover-message--left-top::after,.Popover-message--left-bottom::after{left:-14px;border-right-color:#fff}.Popover-message--right-top::before,.Popover-message--right-top::after,.Popover-message--left-top::before,.Popover-message--left-top::after{top:24px}.Popover-message--right-bottom::before,.Popover-message--right-bottom::after,.Popover-message--left-bottom::before,.Popover-message--left-bottom::after{top:auto}.Popover-message--right-bottom::before,.Popover-message--left-bottom::before{bottom:16px}.Popover-message--right-bottom::after,.Popover-message--left-bottom::after{bottom:17px}@media (min-width: 544px){.Popover-message--large{min-width:320px}}.Subhead{display:flex;padding-bottom:8px;margin-bottom:16px;border-bottom:1px #e1e4e8 solid;flex-flow:row wrap}.Subhead--spacious{margin-top:40px}.Subhead-heading{font-size:24px;font-weight:normal;flex:1 1 auto}.Subhead-heading--danger{font-weight:600;color:#cb2431}.Subhead-description{font-size:14px;color:#586069;flex:1 100%}.Subhead-actions{align-self:center;justify-content:flex-end}.table-list{display:table;width:100%;color:#6a737d;table-layout:fixed;border-bottom:1px solid #e1e4e8}.table-list ol{list-style-type:decimal}.table-list-bordered{border-bottom-color:#eaecef}.table-list-bordered .table-list-cell:first-child{border-left:1px solid #eaecef}.table-list-bordered .table-list-cell:last-child{border-right:1px solid #eaecef}.table-list-item{position:relative;display:table-row;list-style:none}.table-list-item.unread .table-list-cell:first-child{box-shadow:2px 0 0 #0366d6 inset}.table-list-cell{position:relative;display:table-cell;padding:8px 10px;font-size:12px;vertical-align:top;border-top:1px solid #eaecef}.table-list-cell.flush-left{padding-left:0}.table-list-cell.flush-right{padding-right:0}.table-list-cell-checkbox{width:30px;padding-right:0;padding-left:0;text-align:center}.table-list-header{position:relative;margin-top:20px;background-color:#f6f8fa;border:1px solid #e1e4e8;border-radius:3px 3px 0 0}.table-list-header::before{display:table;content:""}.table-list-header::after{display:table;clear:both;content:""}.table-list-header .btn-link{position:relative;display:inline-block;padding-top:13px;padding-bottom:13px;font-weight:400}.table-list-heading{margin-left:10px}.table-list-header-select-all{float:left;width:30px;padding:12px 10px;margin-right:5px;margin-left:-1px;text-align:center}.table-list-header-meta{display:inline-block;padding-top:13px;padding-bottom:13px;color:#586069}.table-list-header-toggle h4{padding:12px 0}.table-list-filters:first-child .table-list-header-toggle:first-child{padding-left:16px}.table-list-header-toggle.states .selected{font-weight:600}.table-list-header-toggle .btn-link{color:#586069}.table-list-header-toggle .btn-link .octicon{margin-right:2px}.table-list-header-toggle .btn-link:hover{color:#24292e;text-decoration:none}.table-list-header-toggle .btn-link.selected,.table-list-header-toggle .btn-link.selected:hover{color:#24292e}.table-list-header-toggle .btn-link+.btn-link{margin-left:10px}.table-list-header-toggle .btn-link:disabled,.table-list-header-toggle .btn-link.disabled{pointer-events:none;opacity:0.5}.table-list-header-toggle .select-menu{position:relative}.table-list-header-toggle .select-menu-item.selected{font-weight:600}.table-list-header-toggle .select-menu-button{padding-right:15px;padding-left:15px}.table-list-header-toggle .select-menu-button:hover,.table-list-header-toggle .select-menu-button.selected,.table-list-header-toggle .select-menu-button.selected:hover{color:#24292e}.table-list-header-toggle .select-menu-modal-holder{right:10px}.table-list-header-toggle .select-menu-modal-holder .select-menu-modal{margin-top:-1px}.table-list-header-next{margin-top:20px;margin-bottom:-1px}.table-list-header-next .table-list-header-select-all{padding-left:14px}.table-list-header-next .select-all-dropdown{padding-top:10px;padding-bottom:10px}.bulk-actions-header{position:-webkit-sticky;position:sticky;top:0;z-index:32;height:50px}.table-list-triage{display:none}.triage-mode .table-list-filters{display:none}.triage-mode .table-list-triage{display:block}.ajax-pagination-form .ajax-pagination-btn{width:100%;padding:6px;margin-top:20px;font-weight:600;color:#0366d6;background:#fff;border:1px solid #e1e4e8;border-radius:3px}.ajax-pagination-form .ajax-pagination-btn:hover,.ajax-pagination-form .ajax-pagination-btn:focus{background-color:#f6f8fa}.ajax-pagination-form.loading .ajax-pagination-btn{text-indent:-3000px;background-color:#f6f8fa;background-image:url("/images/spinners/octocat-spinner-16px-EAF2F5.gif");background-repeat:no-repeat;background-position:center center;border-color:#d1d5da}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){.ajax-pagination-form.loading .ajax-pagination-btn{background-image:url("/images/spinners/octocat-spinner-32-EAF2F5.gif");background-size:16px auto}}.autocomplete-results{position:absolute;z-index:99;width:100%;max-height:20em;overflow-y:auto;font-size:13px;list-style:none;background:#fff;border-radius:3px;box-shadow:0 0 5px rgba(27,31,35,0.3)}.autocomplete-item{display:block;padding:5px;overflow:hidden;font-weight:600;text-decoration:none;text-overflow:ellipsis;white-space:nowrap;cursor:pointer}.autocomplete-item:hover,.autocomplete-item[aria-selected="true"],.autocomplete-item.selected,.autocomplete-item.navigation-focus{color:#fff;text-decoration:none;background-color:#0366d6}.autocomplete-item:hover .organization-member,.autocomplete-item:hover .ldap-group-dn,.autocomplete-item[aria-selected="true"] .organization-member,.autocomplete-item[aria-selected="true"] .ldap-group-dn,.autocomplete-item.selected .organization-member,.autocomplete-item.selected .ldap-group-dn,.autocomplete-item.navigation-focus .organization-member,.autocomplete-item.navigation-focus .ldap-group-dn{color:#f6f8fa}.autocomplete-item .secondary-label{font-weight:400}.autocomplete-item .organization-member{float:right;padding-top:1px;color:#6a737d}.is-auto-complete-loading .form-control{padding-right:30px;background-image:url("/images/spinners/octocat-spinner-32.gif");background-size:16px}.suggester-container{position:absolute;top:0;left:0;z-index:30;transform:translateZ(0)}.suggester{position:relative;top:0;left:0;min-width:180px;margin-top:20px;cursor:pointer;background:#fff;border:1px solid #dfe2e5;border-radius:3px;box-shadow:0 0 5px rgba(27,31,35,0.1)}.suggester ul{padding:0;margin:0;list-style:none}.suggester li{display:block;padding:5px 10px;font-weight:600;border-bottom:1px solid #dfe2e5}.suggester li small{font-weight:400;color:#586069}.suggester li:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.suggester li:first-child a{border-top-left-radius:3px;border-top-right-radius:3px}.suggester li[aria-selected="true"],.suggester li.navigation-focus{color:#fff;text-decoration:none;background:#0366d6}.suggester li[aria-selected="true"] small,.suggester li.navigation-focus small{color:#fff}.Box--overlay{width:448px;margin-right:auto;margin-left:auto;background-color:#fff;background-clip:padding-box;border-color:#444d56;box-shadow:0 0 18px rgba(0,0,0,0.4)}.Box--overlay .Box-header{margin:0;border-width:0;border-bottom-width:1px;border-top-left-radius:2px;border-top-right-radius:2px}.Box-overlay--narrow{width:320px}.Box-overlay--wide{width:640px}.Overlay{position:fixed;top:0;right:0;bottom:0;left:0;z-index:99;overflow-y:auto}.Overlay-table-full{display:table;width:100%;height:100%;table-layout:fixed}.Overlay-cell-middle{display:table-cell;width:100%;height:100%;padding:32px;vertical-align:middle}.bg-transparent-dark{background-color:rgba(0,0,0,0.5)}.transition-in-out{transition:opacity 0.2s ease-in-out}.transition-in-out[hidden]{opacity:0}.Box-body.scrollable-overlay{max-height:400px;overflow-y:scroll}.Box-body .help{padding-top:8px;margin:0;color:#586069;text-align:center}.boxed-group{position:relative;margin-bottom:30px;border-radius:3px}.boxed-group .Counter{color:#fff;background-color:rgba(47,54,61,0.5)}.boxed-group.flush .boxed-group-inner{padding:0}.boxed-group.condensed .boxed-group-inner{padding:0;font-size:12px}.boxed-group>h3,.boxed-group .heading{display:block;padding:9px 10px 10px;margin:0;font-size:14px;line-height:17px;background-color:#f6f8fa;border:1px solid rgba(27,31,35,0.15);border-bottom:0;border-radius:3px 3px 0 0}.boxed-group>h3 a,.boxed-group .heading a{color:inherit}.boxed-group>h3 a.boxed-group-breadcrumb,.boxed-group .heading a.boxed-group-breadcrumb{font-weight:400;color:#586069;text-decoration:none}.boxed-group>h3 .avatar,.boxed-group .heading .avatar{margin-top:-4px}.boxed-group .tabnav.heading{padding:0}.boxed-group .tabnav.heading .tabnav-tab.selected{border-top:0}.boxed-group .tabnav.heading li:first-child .selected{border-left-color:#fff;border-top-left-radius:3px}.boxed-group .tabnav-tab{border-top:0;border-radius:0}.boxed-group code.heading{font-size:12px}.boxed-group.dangerzone>h3{color:#fff;background-color:#d73a49;border:1px solid #9e1c23}.boxed-group.dangerzone .boxed-group-inner{border-top:0}.boxed-group.condensed>h3{padding:6px 6px 7px;font-size:12px}.boxed-group.condensed>h3 .octicon{padding:0 6px 0 2px}.one-half .boxed-group,.dashboard-sidebar .boxed-group{margin-bottom:20px}.boxed-group .bleed-flush{width:100%;padding:0 10px;margin-left:-10px}.boxed-group .compact{margin-top:10px;margin-bottom:10px}.boxed-group-inner{padding:10px;color:#586069;background:#fff;border:1px solid #d1d5da;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.boxed-group-inner .markdown-body{padding:20px 10px 10px;font-size:13px}.boxed-group-inner.markdown-body{padding-top:10px;padding-bottom:10px}.boxed-group-inner.seamless{padding:0}.boxed-group-inner .tabnav{padding-right:10px;padding-left:10px;margin-right:-10px;margin-left:-10px}.boxed-group-inner .tabnav-tab.selected{border-top:1px solid #dfe2e5}.boxed-group-inner .help{padding:1em 10px 1em 35px;margin:1em -10px -10px;clear:both;color:#586069;border-top:1px solid #dfe2e5}.boxed-group-inner .help .octicon{margin-right:5px;margin-left:-25px}.boxed-group-inner .boxed-group-list+.help{margin-top:0}.boxed-action{float:right;margin-left:10px}.boxed-group-action{position:relative;z-index:2;float:right;margin:5px 10px 0 0}.boxed-group-action.flush{margin-top:0;margin-right:0}.field-with-errors{display:inline}.compact-options{margin:-6px 0 13px}.compact-options>li{display:inline-block;margin:0 12px 0 0;font-weight:600;list-style-type:none}.compact-options>li label{float:left}.compact-options>li .spinner{display:block;float:left;width:16px;height:16px;margin-left:5px}.boxed-group-list{margin:0;list-style:none}.boxed-group-list:first-child>li:first-child{border-top:0}.boxed-group-list>li{display:block;padding:5px 10px;margin-right:-10px;margin-left:-10px;line-height:23px;border-bottom:1px solid #e1e4e8}.boxed-group-list>li:first-child{border-top:1px solid #dfe2e5}.boxed-group-list>li:last-of-type{border-bottom:0}.boxed-group-list>li.selected{background:#dcffe4}.boxed-group-list>li.approved .btn-sm,.boxed-group-list>li.rejected .btn-sm{display:none}.boxed-group-list>li.rejected a{text-decoration:line-through}.boxed-group-list>li .avatar{margin-top:-2px;margin-right:4px}.boxed-group-list>li .octicon{width:24px;margin-right:4px}.boxed-group-list>li .btn-sm{float:right;margin:-1px 0 0 10px}.boxed-group-list>li .BtnGroup{float:right}.boxed-group-list>li .BtnGroup .btn-sm{float:left}.boxed-group.flush .boxed-group-list li{width:auto;padding-right:0;padding-left:0;margin-left:0}.boxed-group-list.standalone{margin-top:-1px}.boxed-group-list.standalone>li:first-child{border-top:0}.boxed-group-standalone{margin-top:-10px;margin-bottom:-10px}.boxed-group-standalone>li:last-child{border-radius:0 0 2px 2px}.boxed-group-table{width:100%;text-align:left}.boxed-group-table tr:last-child td{border-bottom:0}.boxed-group-table th{padding:9px;background-color:#fafbfc;border-bottom:1px solid #eaecef}.boxed-group-table td{padding:9px;vertical-align:top;border-bottom:1px solid #eaecef}.ajax-error-message{position:fixed;top:-200px;left:50%;z-index:9999;display:none;width:974px;margin:0 3px;margin-left:-487px;transition:top 0.5s ease-in-out}.ajax-error-message.visible{top:0;display:block}.ajax-error-message>.octicon-alert{vertical-align:text-top}.boxed-group-warning{padding:10px 15px;margin:-10px -10px 10px;color:rgba(27,31,35,0.85);background-color:#fffbdd;border-color:rgba(27,31,35,0.15);border-style:solid;border-width:1px 0}.boxed-group-warning .btn-sm{margin:-5px 0}.boxed-group-warning:first-child{border-top:0}.breadcrumb{margin-bottom:10px;font-size:18px;color:#586069}.breadcrumb .separator::before,.breadcrumb .separator::after{content:" "}.breadcrumb strong.final-path{color:#24292e}.breadcrumb .zeroclipboard-button{display:inline-block;margin-left:5px}.breadcrumb .repo-root{font-weight:600}.breadcrumb .octicon{vertical-align:-2px}.editor-license-template,.editor-code-of-conduct-template,.editor-flow-template,.editor-gitignore-template{position:relative;top:3px;display:block;float:right;font-size:14px}.editor-license-template .select-menu-git-ignore,.editor-code-of-conduct-template .select-menu-git-ignore,.editor-flow-template .select-menu-git-ignore,.editor-gitignore-template .select-menu-git-ignore{right:0}.starring-container .unstarred,.starring-container.on .starred{display:block}.starring-container.on .unstarred,.starring-container .starred{display:none}.starring-container.loading{opacity:0.5}.user-following-container .follow,.user-following-container.on .unfollow{display:inline-block}.user-following-container.on .follow,.user-following-container .unfollow{display:none}.user-following-container.loading{opacity:0.5}.members .user-following-container{float:right}.close-button{padding:0;background:transparent;border:0;outline:none}.btn-invisible{color:#0366d6;background-color:#fff;background-image:none;border:0}.btn-invisible:hover,.btn-invisible:active,.btn-invisible:focus,.btn-invisible.selected,.btn-invisible.zeroclipboard-is-hover,.btn-invisible.zeroclipboard-is-active{color:#0366d6;background:none;outline:none;box-shadow:none}.btn-octicon{display:inline-block;padding:5px;margin-left:5px;line-height:1;color:#586069;vertical-align:middle;background:transparent;border:0}.btn-octicon:hover{color:#0366d6}.btn-octicon.disabled{color:#959da5;cursor:default}.btn-octicon.disabled:hover{color:#959da5}.btn-octicon-danger:hover{color:#cb2431}.btn-states .btn-state-alternate{display:none}.btn-states:hover .btn-state-default{display:none}.btn-states:hover .btn-state-alternate{display:inline-block}.commit-ref{position:relative;display:inline-block;padding:0 5px;font:0.75em/2 "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;color:#274466;white-space:nowrap;background-color:#eff7ff;border-radius:3px}.commit-ref .user{color:#044289}a.commit-ref:hover{text-decoration:none;background-color:#dbedff}.capped-cards{list-style:none}.capped-cards .capped-card{float:left;width:450px}.capped-card{margin:10px;list-style:none;border:1px solid #dfe2e5;border-radius:2px}.capped-card::before{display:table;content:""}.capped-card::after{display:table;clear:both;content:""}.capped-card:nth-child(odd){margin-left:0}.capped-card:nth-child(even){margin-right:0}.capped-card h3{padding:10px;margin:0;line-height:100%;border-bottom:1px solid #eaecef}.capped-card>p{display:block;padding:0 10px 10px;margin:0;font-size:15px;line-height:100%;color:#586069;border-bottom:1px solid #eaecef}.capped-card-content{display:block;background:#f6f8fa}.capped-card-content::before{display:table;content:""}.capped-card-content::after{display:table;clear:both;content:""}.details-collapse .collapse{position:relative;display:none;height:0;overflow:hidden;transition:height 0.35s ease-in-out}.details-collapse.open .collapse{display:block;height:auto;overflow:visible}.comment .email-format{line-height:1.5}.previewable-edit .previewable-comment-form{display:none}.previewable-edit .previewable-comment-form::before{display:table;content:""}.previewable-edit .previewable-comment-form::after{display:table;clear:both;content:""}.previewable-edit .previewable-comment-form .tabnav-tabs{display:inline-block}.previewable-edit .previewable-comment-form .form-actions{float:right;margin-right:10px;margin-bottom:10px}.previewable-edit.is-comment-editing .timeline-comment-header{display:none}.is-comment-editing .previewable-comment-form{display:block}.is-comment-editing .timeline-comment-actions,.is-comment-editing .edit-comment-hide{display:none}.is-comment-loading .previewable-comment-form{opacity:0.5}.is-comment-stale .comment-form-stale{display:block}.comment-body{width:100%;padding:15px;overflow:visible;font-size:14px}.comment-body .highlight{overflow:visible !important;background-color:transparent}.comment-form-textarea{width:100%;max-width:100%;height:100px;min-height:100px;margin:0;line-height:1.6}.comment-form-textarea.dragover{border:solid 1px #0366d6}.discussion-topic-header{position:relative;padding:10px;word-wrap:break-word}.comment-form-error,.comment-form-stale{display:none;padding:15px 10px;margin:10px;color:#86181d;background-color:#ffdce0;border:1px solid rgba(27,31,35,0.15);border-radius:3px}.comment-form-error.comment-form-bottom,.comment-form-stale.comment-form-bottom{margin-bottom:10px}.email-format{line-height:1.5em !important}.email-format div{white-space:pre-wrap}.email-format .email-hidden-reply{display:none;white-space:pre-wrap}.email-format .email-hidden-reply.expanded{display:block}.email-format .email-quoted-reply,.email-format .email-signature-reply{padding:0 15px;margin:15px 0;color:#586069;border-left:4px solid #dfe2e5}.email-format .email-hidden-toggle a{display:inline-block;height:12px;padding:0 9px;font-size:12px;font-weight:600;line-height:6px;color:#444d56;text-decoration:none;vertical-align:middle;background:#dfe2e5;border-radius:1px}.email-format .email-hidden-toggle a:hover{background-color:#c6cbd1}.email-format .email-hidden-toggle a:active{color:#fff;background-color:#0366d6}.comment-email-format div{white-space:normal}.comment-email-format .email-hidden-reply{display:none;white-space:normal}.comment-email-format .email-hidden-reply.expanded{display:block}.comment-email-format blockquote,.comment-email-format p{margin:0}.blankslate.conversation-limited{padding:20px 0 10px;margin:15px}.locked-conversation .write-tab,.locked-conversation .preview-tab{color:#c6cbd1}.commit-form{position:relative;padding:15px;border:1px solid #dfe2e5;border-radius:3px}.commit-form::after,.commit-form::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.commit-form::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#fff}.commit-form::before{border-width:8px;border-right-color:#dfe2e5}.commit-form .input-block{margin-top:10px;margin-bottom:10px}.commit-form-avatar{float:left;margin-left:-64px;border-radius:3px}.commit-form-actions::before{display:table;content:""}.commit-form-actions::after{display:table;clear:both;content:""}.commit-form-actions .BtnGroup{margin-right:5px}.commit-form-actions .check-for-fork{line-height:34px}.merge-commit-message{resize:vertical}.commit-sha{padding:0.2em 0.4em;font-size:90%;font-weight:400;background-color:#f6f8fa;border:1px solid #eaecef;border-radius:0.2em}.commit-partial-notice{margin-top:20px;margin-bottom:20px}.commit-paginate-container{float:right;margin:-5px 0 0;text-align:inherit}.commit .commit-title,.commit .commit-title a{color:#444d56}.commit .commit-title.blank,.commit .commit-title.blank a{color:rgba(27,31,35,0.3)}.commit .commit-title .issue-link{font-weight:600;color:#0366d6}.commit .sha-block,.commit .sha{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px}.commit.open .commit-desc{display:block}.commit-link{font-weight:400;color:#0366d6}.commit-email-flash{display:inline}.commit-desc{display:none}.commit-desc pre{max-width:700px;margin-top:10px;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:11px;line-height:1.45;color:#444d56;white-space:pre-wrap}.commit-desc+.commit-branches{padding-top:8px;margin-top:2px;border-top:solid 1px rgba(27,31,35,0.15)}.commit-author-section{color:#24292e}.commit-author-section span.user-mention{font-weight:400}.commit-tease{position:relative;padding:10px;margin-bottom:-1px;font-size:13px;line-height:20px;color:#586069;background-color:#f1f8ff;border:1px solid #c8e1ff;border-radius:3px;border-bottom-right-radius:0;border-bottom-left-radius:0}.commit-tease .muted-link{color:inherit}.commit-tease .loader{float:left;margin:2px 5px 0 2px}.commit-tease .message{color:inherit}.commit-tease .avatar{margin-top:-1px}.commit-tease.open .commit-desc{display:block}.branch-infobar+.commit-tease{border-top-left-radius:0;border-top-right-radius:0}.commit-tease-comments{margin-right:15px}.commit-tease-sha{display:inline-block;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:90%;color:#444d56}.commit-tease-contributors{padding:5px 10px;margin:10px -10px -10px;background-color:#fff;border-top:1px solid #bedfff;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.commit-tease-contributors::before{display:table;content:""}.commit-tease-contributors::after{display:table;clear:both;content:""}.commit-tease-contributors .avatar-link{float:left;margin-right:3px}.commit-tease-contributors .loader-loading{margin:2px 5px 2px 0}.commit-tease-contributors.error .loader-loading{display:none}.commit-tease-contributors.error .loader-error{display:block}.commits-listing{position:relative;padding-bottom:20px;margin-bottom:15px}.commits-listing::before{position:absolute;top:0;bottom:0;left:14px;z-index:-1;display:block;width:2px;content:"";background-color:#eff1f3}.commits-listing .discussion-item-icon{margin-right:5px;margin-left:-1px}.commits-listing .timeline-commits{padding-left:8px;margin-bottom:20px}.commits-listing .timeline-commits:last-child{margin-bottom:0}.commits-listing-padded{padding-left:39px}.commit-group{margin-top:10px;list-style-type:none}.commit-group-title{margin-top:15px;margin-left:-31px;color:#586069}.commit-group-title .octicon-git-commit{margin-right:17px;color:#c6cbd1;background:#fff}.commits-list-item[aria-selected="true"],.commits-list-item.navigation-focus{background:#f6fbff}.commits-list-item .commit-title{margin:0;font-size:15px;font-weight:600;color:#24292e}.commits-list-item .commit-meta{margin-top:1px;font-weight:400;color:#586069}.commits-list-item .status .octicon{height:14px;line-height:14px}.commits-list-item .commit-author{color:#586069}.commits-list-item .octicon-arrow-right{margin:0 3px}.commits-list-item .btn-outline{margin-top:2px}.commits-list-item .commit-desc pre{margin-top:5px;margin-bottom:10px;color:#586069}.commits-list-item .commit-desc pre a{word-break:break-word}.commits-list-item em{padding:3px;font-style:normal;font-weight:600;background-color:rgba(255,247,140,0.5);border-radius:3px}.commit-indicator{margin-left:4px}.commit-links-cell{width:335px;text-align:right}.commit-links-group{margin-right:5px}.commits-list-item+.commits-list-item{border-top:1px solid #e1e4e8}.timeline-commits{width:100%;margin-top:5px;border-collapse:separate}.timeline-commits+.timeline-commits{margin-top:15px}.timeline-commits td{padding-top:4px;padding-right:8px;padding-bottom:4px;font-size:12px;line-height:16px;vertical-align:top;background-color:transparent}.discussion-item .timeline-commits .commit-author{display:none}.timeline-commits .author{font-weight:600;color:#444d56}.timeline-commits .commit-message{max-width:550px;min-height:0;flex-grow:2;white-space:normal}.timeline-commits .commit-message>code a{color:#444d56}.timeline-commits .commit-message>code a:hover{color:#0366d6}.timeline-commits .commit-desc pre{overflow:visible;color:#586069}.timeline-commits .hidden-text-expander .ellipsis-expander{height:13px;background-color:#eaecef}.timeline-commits .hidden-text-expander .ellipsis-expander:hover{color:#fff;background-color:#0366d6}.timeline-commits .commit-sig-status{width:60px;margin-left:auto}.timeline-commits .commit-ci-status{width:16px}.timeline-commits .commit-ci-status .octicon-primitive-dot{width:9px;margin-right:3px;margin-left:2px}.timeline-commits .commit-meta{width:50px}.commit-icon{color:#d1d5da}.commit-icon .octicon{background-color:#fff}.commit-id{color:#959da5}.commit-id:hover{color:#0366d6}.full-commit{padding:8px 8px 0;margin:10px 0;font-size:14px;background:#eaf5ff;border:1px solid rgba(27,31,35,0.15);border-radius:3px}.full-commit:first-child{margin-top:0}.full-commit .btn-outline,.full-commit .btn-outline:disabled{background-color:transparent;border-color:rgba(27,31,35,0.15)}.full-commit .btn-outline:not(:disabled):hover{color:#005cc5;border-color:#005cc5}.full-commit p.commit-title{margin:0 0 8px;font-size:18px;font-weight:600;color:#05264c}.full-commit .branches-list{display:inline;margin-right:10px;margin-left:2px;vertical-align:middle;list-style:none}.full-commit .branches-list li{display:inline-block;padding-left:3px;font-weight:600;color:#444d56}.full-commit .branches-list li::before{padding-right:6px;font-weight:400;content:"+"}.full-commit .branches-list li:first-child{padding-left:0}.full-commit .branches-list li:first-child::before{padding-right:0;content:""}.full-commit .branches-list li.loading{font-weight:400;color:#6a737d}.full-commit .branches-list li.pull-request{font-weight:400;color:#6a737d}.full-commit .branches-list li.pull-request::before{margin-left:-8px;content:""}.full-commit .branches-list li.pull-request-error{margin-bottom:-1px}.full-commit .branches-list li a{color:inherit}.full-commit .commit-meta{padding:8px;margin-right:-8px;margin-left:-8px;background:#fff;border-top:1px solid rgba(27,31,35,0.15);border-bottom-right-radius:3px;border-bottom-left-radius:3px}.full-commit .sha-block{margin-left:16px;font-size:12px;line-height:24px;color:#586069}.full-commit .sha-block>.sha{color:#444d56}.full-commit .sha-block>a{color:#444d56;text-decoration:none;border-bottom:1px dotted #c6cbd1}.full-commit .sha-block>a:hover{border-bottom:1px solid #444d56}.full-commit .commit-desc{display:block;margin:-5px 0 10px}.full-commit .commit-desc pre{max-width:100%;overflow:visible;font-size:13px;word-wrap:break-word}.branches-tag-list{display:inline;margin-right:10px;margin-left:2px;vertical-align:middle;list-style:none}.branches-tag-list .more-commit-details,.branches-tag-list.open .hidden-text-expander{display:none}.branches-tag-list.open .more-commit-details{display:inline-block}.branches-tag-list li{display:inline-block;padding-left:3px}.branches-tag-list li:first-child{padding-left:0;font-weight:600;color:#444d56}.branches-tag-list li.loading{font-weight:400;color:#6a737d}.branches-tag-list li.abbrev-tags{cursor:pointer}.branches-tag-list li a,.branches-tag-list li .ellipsis-expander{color:inherit}.branches-tag-list li .ellipsis-expander{background-color:#d3e2f4}.branches-tag-list li .ellipsis-expander:hover{background-color:#d6e2f1}.commit-branches{min-height:18px;margin-top:-6px;margin-bottom:8px;font-size:12px;color:#6a737d;vertical-align:middle}.commit-branches .octicon{vertical-align:middle}.commit-loader .loader-error{display:none;margin:0;font-size:12px;font-weight:600;color:#cb2431}.commit-loader.is-error .loader-loading{display:none}.commit-loader.is-error .loader-error{display:block}.commit-comments-heading{max-width:780px;margin-bottom:15px}.commit-comment-count{display:inline-block;margin-right:15px;margin-bottom:0}.commit-build-statuses{position:relative;display:inline-block;text-align:left}.commit-build-statuses .octicon-primitive-dot{width:10px}.commit-build-statuses.active .dropdown-menu-content{display:block}.commit-build-statuses.active .tooltipped::before,.commit-build-statuses.active .tooltipped::after{display:none}.commit-build-statuses .dropdown-menu{min-width:400px;max-width:500px;padding-top:0;padding-bottom:0}.commit-build-statuses .dropdown-menu .merge-status-list{max-height:170px;border-bottom:0}.commit-build-statuses .dropdown-menu-w,.commit-build-statuses .dropdown-menu-e{top:-11px}.commit-build-statuses .merge-status-item:last-child{border-radius:0 0 2px 2px}.dropdown-signed-commit .dropdown-menu{width:260px;margin-top:8px;font-size:13px;line-height:1.4}.dropdown-signed-commit .dropdown-menu::after{border-bottom-color:#f6f8fa}.dropdown-signed-commit .dropdown-menu-w{top:-28px;margin-top:0}.dropdown-signed-commit .dropdown-menu-w::after{border-bottom-color:transparent;border-left-color:#f6f8fa}.signed-commit-header{padding:12px;line-height:1.3;white-space:normal;border-collapse:separate;background-color:#f6f8fa;border-bottom:solid 1px #e1e4e8;border-top-left-radius:3px;border-top-right-radius:3px}.signed-commit-header .octicon-verified{color:#28a745}.signed-commit-header .octicon-unverified{color:#959da5}.signed-commit-signer{padding-right:12px;padding-left:12px;margin-top:12px;border-collapse:separate}.signed-commit-footer{padding:12px;font-size:12px;line-height:1.5}.signed-commit-cert-info{margin-bottom:6px}.signed-commit-cert-info td{vertical-align:top}.signed-commit-cert-info td:first-child{width:44px;padding-right:12px}.signed-commit-badge{display:inline-block;padding:1px 4px;font-size:10px;color:#586069;vertical-align:middle;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background:none;border:solid 1px #e1e4e8;border-radius:2px}.signed-commit-badge:hover{text-decoration:none;border-color:#c6cbd1}.signed-commit-badge.verified{color:#22863a}.signed-commit-badge.verified:hover{border-color:#28a745}.signed-commit-badge-small{margin-top:-2px;margin-right:3px}.signed-commit-badge-medium{padding:3px 8px;font-size:12px;border-radius:3px}.signed-commit-badge-large{padding:6px 12px;margin-top:2px;margin-right:9px;font-size:13px;line-height:20px;border-radius:3px}.signed-commit-verified-label{color:#1e7e34;white-space:nowrap}.signed-commit-signer-name{font-size:14px;text-align:left}.signed-commit-signer-name .signer{display:block;font-weight:600;color:#24292e}.table-of-contents{margin:15px 0}.table-of-contents li{padding:7px 0;list-style-type:none}.table-of-contents li+li{border-top:1px solid #eaecef}.table-of-contents li>.octicon{margin-right:3px}.table-of-contents .toc-diff-stats{padding-left:20px;line-height:26px}.table-of-contents .toc-diff-stats .octicon{float:left;margin-top:3px;margin-left:-20px;color:#c6cbd1}.table-of-contents .toc-diff-stats .btn-link{font-weight:600}.table-of-contents .toc-diff-stats+.content{padding-top:5px}.table-of-contents .octicon-diff-removed{color:#cb2431}.table-of-contents .octicon-diff-renamed{color:#6a737d}.table-of-contents .octicon-diff-modified{color:#dbab09}.table-of-contents .octicon-diff-added{color:#28a745}.toc-select .select-menu-modal{width:420px}.toc-select .select-menu-item .css-truncate{max-width:290px}.toc-select .select-menu-item-heading,.toc-select .select-menu-item-text{color:#24292e}.toc-select .select-menu-item-icon.octicon-diff-removed{color:#cb2431}.toc-select .select-menu-item-icon.octicon-diff-renamed{color:#6a737d}.toc-select .select-menu-item-icon.octicon-diff-modified{color:#dbab09}.toc-select .select-menu-item-icon.octicon-diff-added{color:#28a745}.toc-select[aria-selected="true"] .select-menu-item-heading,.toc-select[aria-selected="true"] .select-menu-item-text,.toc-select[aria-selected="true"] .text-green,.toc-select[aria-selected="true"] .text-red,.toc-select[aria-selected="true"] .text-gray-light,.toc-select[aria-selected="true"] .octicon-diff-removed,.toc-select[aria-selected="true"] .octicon-diff-renamed,.toc-select[aria-selected="true"] .octicon-diff-modified,.toc-select[aria-selected="true"] .octicon-diff-added,.toc-select[aria-selected="true"] .diffstat,.toc-select .navigation-focus .select-menu-item-heading,.toc-select .navigation-focus .select-menu-item-text,.toc-select .navigation-focus .text-green,.toc-select .navigation-focus .text-red,.toc-select .navigation-focus .text-gray-light,.toc-select .navigation-focus .octicon-diff-removed,.toc-select .navigation-focus .octicon-diff-renamed,.toc-select .navigation-focus .octicon-diff-modified,.toc-select .navigation-focus .octicon-diff-added,.toc-select .navigation-focus .diffstat{color:#fff !important}.select-menu-item+.semantic-toc-file,.semantic-toc-symbol:not(.last-visible){overflow:visible !important}.semantic-toc-symbol:not(.last-visible)::after,.select-menu-item+.semantic-toc-file::after{position:absolute;left:0;width:100%;height:16px;pointer-events:none;content:" ";background-color:#f6f8fa;border-color:#e1e4e8;border-style:solid;border-width:1px 0}.semantic-toc-symbol:not(.last-visible){margin-bottom:16px}.semantic-toc-symbol:not(.last-visible)::after{bottom:-18px}.select-menu-item+.semantic-toc-file{margin-top:16px}.select-menu-item+.semantic-toc-file::after{top:-18px}.filterable-active .select-menu-item-nested{padding-left:16px !important;margin-left:0}.filterable-active .semantic-toc-symbol{margin-bottom:0}.filterable-active .semantic-toc-file{margin-top:0}.filterable-active .semantic-toc-symbol::after,.filterable-active .semantic-toc-file::after{display:none}.conversation-list-heading{height:0;margin:35px 0 10px;font-size:16px;font-weight:400;color:#6a737d;text-align:center;border-bottom:1px solid #dfe2e5}.conversation-list-heading .inner{position:relative;top:-10px;display:inline-block;padding:0 5px;background:#fff}.simple-conversation-list{margin:15px 0;font-size:13px;color:#6a737d}.simple-conversation-list>li{padding:11px 0 8px;margin:0;list-style-type:none;border-top:1px solid #f6f8fa}.simple-conversation-list>li:first-child{border-top:0}.simple-conversation-list>li .title{font-weight:600}.simple-conversation-list>li .num{color:#6a737d}.simple-conversation-list>li .state{padding-top:2px;padding-bottom:2px;margin-top:-3px;margin-right:3px}.simple-conversation-list>li .meta{float:right;margin-left:10px}.simple-conversation-list.varied-states>li .state{float:left;width:80px;margin-left:-90px}.copyable-terminal{position:relative;padding:10px 55px 10px 10px;background-color:#f6f8fa;border-radius:3px}.copyable-terminal-content{overflow:auto}.copyable-terminal-button{position:absolute;top:5px;right:5px}.copyable-terminal-button .zeroclipboard-button{float:right}.copyable-terminal-button .zeroclipboard-button .octicon{padding-left:1px;margin:0 auto}.Details--on .Details-content--shown{display:none !important}.Details:not(.Details--on) .Details-content--hidden{display:none !important}.Details-element[open]>summary .Details-content--closed{display:none !important}.Details-element:not([open])>summary .Details-content--open{display:none !important}.details-overlay[open]>.dropdown-item:hover{color:inherit;background:#fff}details-menu{display:block}.blob-wrapper{overflow-x:auto;overflow-y:hidden;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.blob-wrapper-embedded{max-height:240px;overflow-y:auto}.diff-table{width:100%;border-collapse:separate}.diff-table .line-comments{padding:10px;vertical-align:top;border-top:1px solid #e1e4e8}.diff-table .line-comments:first-child+.empty-cell{border-left-width:1px}.diff-table tr:not(:last-child) .line-comments{border-top:1px solid #e1e4e8;border-bottom:1px solid #e1e4e8}.blob-num{width:1%;min-width:50px;padding-right:10px;padding-left:10px;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px;line-height:20px;color:rgba(27,31,35,0.3);text-align:right;white-space:nowrap;vertical-align:top;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.blob-num:hover{color:rgba(27,31,35,0.6)}.blob-num::before{content:attr(data-line-number)}.blob-num.non-expandable{cursor:default}.blob-num.non-expandable:hover{color:rgba(27,31,35,0.3)}.blob-code{position:relative;padding-right:10px;padding-left:10px;line-height:20px;vertical-align:top}.blob-code-inner{overflow:visible;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px;color:#24292e;word-wrap:normal;white-space:pre}.blob-code-inner .x-first{border-top-left-radius:0.2em;border-bottom-left-radius:0.2em}.blob-code-inner .x-last{border-top-right-radius:0.2em;border-bottom-right-radius:0.2em}.blob-code-inner::before{content:" "}.blob-code-inner.highlighted{background-color:#fffbdd}.blob-code-marker-addition::before{content:"+ "}.blob-code-marker-deletion::before{content:"- "}.blob-code-marker-context::before{content:"  "}.soft-wrap .diff-table{table-layout:fixed}.soft-wrap .blob-code{padding-left:18px;text-indent:-7px}.soft-wrap .blob-code-inner{word-wrap:break-word;white-space:pre-wrap}.soft-wrap .no-nl-marker{display:none}.soft-wrap .add-line-comment{margin-left:-28px}.blob-num-hunk,.blob-code-hunk,.blob-num-expandable,.blob-code-expandable{color:rgba(27,31,35,0.5);vertical-align:middle}.blob-num-hunk,.blob-num-expandable{background-color:#dbedff}.blob-code-hunk,.blob-code-expandable{padding-top:4px;padding-bottom:4px;background-color:#f1f8ff;border-width:1px 0}.blob-expanded .blob-num,.blob-expanded .blob-code{background-color:#fafbfc}.blob-expanded+tr:not(.blob-expanded) .blob-num,.blob-expanded+tr:not(.blob-expanded) .blob-code{border-top:1px solid #eaecef}.blob-expanded .blob-num-hunk{border-top:1px solid #eaecef}tr:not(.blob-expanded)+.blob-expanded .blob-num,tr:not(.blob-expanded)+.blob-expanded .blob-code{border-top:1px solid #eaecef}.blob-num-expandable{padding:0;font-size:12px;text-align:center}.blob-num-expandable .octicon{vertical-align:top}.blob-num-expandable .diff-expander{display:block;width:auto;height:auto;padding:4px 11px 4px 10px;margin-right:-1px;color:#586069;cursor:pointer}.blob-num-expandable .diff-expander:hover{color:#fff;text-shadow:none;background-color:#0366d6;border-color:#0366d6}.blob-code-addition{background-color:#e6ffed}.blob-code-addition .x{color:#24292e;background-color:#acf2bd}.blob-num-addition{background-color:#cdffd8;border-color:#bef5cb}.blob-code-deletion{background-color:#ffeef0}.blob-code-deletion .x{color:#24292e;background-color:#fdb8c0}.blob-num-deletion{background-color:#ffdce0;border-color:#fdaeb7}.selected-line.blob-code{background-color:#fffbdd}.selected-line.blob-code .x{background-color:transparent}.selected-line.blob-num{background-color:#fff5b1;border-color:#ffea7f}.add-line-comment{position:relative;z-index:5;float:left;width:22px;height:22px;margin:-2px -10px -2px -20px;line-height:21px;color:#fff;text-align:center;text-indent:0;cursor:pointer;background-color:#0366d6;background-image:linear-gradient(#0372ef, #0366d6);border-radius:3px;box-shadow:0 1px 4px rgba(27,31,35,0.15);opacity:0;transition:transform 0.1s ease-in-out;transform:scale(0.8, 0.8)}.add-line-comment:hover{transform:scale(1, 1)}.is-hovered .add-line-comment,.add-line-comment:focus{opacity:1}.add-line-comment .octicon{vertical-align:text-top;pointer-events:none}.add-line-comment.octicon-check{background:#333;opacity:1}.inline-comment-form{border:1px solid #dfe2e5;border-radius:3px}.inline-review-comment{margin-top:0 !important;margin-bottom:10px !important}.inline-review-comment .gc:first-child+tr .blob-num,.inline-review-comment .gc:first-child+tr .blob-code{padding-top:5px}.inline-review-comment tr:last-child{border-bottom-right-radius:2px;border-bottom-left-radius:2px}.inline-review-comment tr:last-child .blob-num,.inline-review-comment tr:last-child .blob-code{padding-bottom:8px}.inline-review-comment tr:last-child .blob-num:first-child,.inline-review-comment tr:last-child .blob-code:first-child{border-bottom-left-radius:2px}.inline-review-comment tr:last-child .blob-num:last-child,.inline-review-comment tr:last-child .blob-code:last-child{border-bottom-right-radius:2px}.timeline-inline-comments{width:100%;table-layout:fixed}.timeline-inline-comments .inline-comments,.show-inline-notes .inline-comments{display:table-row}.inline-comments{display:none}.inline-comments.is-collapsed{display:none}.inline-comments .line-comments.is-collapsed{visibility:hidden}.inline-comments .line-comments+.blob-num{border-left-width:1px}.inline-comments .timeline-comment{margin-bottom:10px}.inline-comments .inline-comment-form,.inline-comments .inline-comment-form-container{max-width:780px}.comment-holder{max-width:780px}.line-comments+.line-comments,.empty-cell+.line-comments{border-left:1px solid #eaecef}.inline-comment-form-container .inline-comment-form,.inline-comment-form-container.open .inline-comment-form-actions{display:none}.inline-comment-form-container .inline-comment-form-actions,.inline-comment-form-container.open .inline-comment-form{display:block}body.split-diff .container,body.split-diff .container-lg,body.full-width .container,body.full-width .container-lg{width:100%;max-width:none;padding-right:20px;padding-left:20px}body.split-diff .repository-content,body.full-width .repository-content{width:100%}body.split-diff .new-pr-form,body.full-width .new-pr-form{max-width:980px}body.split-diff .new-pr-form .discussion-sidebar,body.full-width .new-pr-form .discussion-sidebar{width:200px}.file-diff-split{table-layout:fixed}.file-diff-split .blob-code+.blob-num{border-left:1px solid #f6f8fa}.file-diff-split .blob-code-inner{word-wrap:break-word;white-space:pre-wrap}.file-diff-split .empty-cell{cursor:default;background-color:#fafbfc;border-right-color:#eaecef}.submodule-diff-stats .octicon-diff-removed{color:#cb2431}.submodule-diff-stats .octicon-diff-renamed{color:#677a85}.submodule-diff-stats .octicon-diff-modified{color:#d0b44c}.submodule-diff-stats .octicon-diff-added{color:#28a745}.BlobToolbar{left:-17px}.BlobToolbar-dropdown{margin-left:-2px}.diffstat{font-size:12px;font-weight:600;color:#586069;white-space:nowrap;cursor:default}.block-diff-deleted,.block-diff-added,.block-diff-neutral{display:inline-block;width:8px;height:8px;margin-left:1px}.block-diff-deleted,.text-red .block-diff-neutral{background-color:#cb2431}.block-diff-added,.block-diff-neutral{background-color:#2cbe4e}.block-diff-neutral{background-color:#d1d5da}.discussion-timeline{position:relative;float:left;width:760px}.discussion-timeline::before{position:absolute;top:0;bottom:0;left:79px;z-index:-1;display:block;width:2px;content:"";background-color:#e6ebf1}.discussion-timeline.team-discussion-timeline::before{bottom:24px;left:20px;z-index:auto;background-color:#e1e4e8}.discussion-timeline.team-discussion-timeline .blankslate{background:#fff}.discussion-sidebar{top:0;z-index:21;float:right;width:200px}.discussion-sidebar-item{padding-top:15px;font-size:12px;color:#586069}.discussion-sidebar-item .btn .octicon{margin-right:0}.discussion-sidebar-item+.discussion-sidebar-item{margin-top:15px;border-top:1px solid #e6ebf1}.discussion-sidebar-item .select-menu{position:relative}.discussion-sidebar-item .select-menu-modal-holder{top:25px;right:-1px;left:auto}.discussion-sidebar-heading{margin-bottom:10px;font-size:12px;line-height:16px;color:#586069}.discussion-sidebar-toggle{padding:5px 0;margin:-5px 0 5px}.discussion-sidebar-toggle .octicon{float:right;color:#c3c8cf}.discussion-sidebar-toggle:hover{color:#0366d6;text-decoration:none;cursor:pointer}.discussion-sidebar-toggle:hover .octicon{color:inherit}button.discussion-sidebar-toggle{display:block;width:100%;font-weight:600;text-align:left;background:none;border:0}.sidebar-milestone .progress-bar,.sidebar-projects .progress-bar{height:8px;margin-bottom:2px;border-radius:2px}.milestone-name{display:block;margin-top:5px;font-weight:600;color:#586069}.milestone-name .css-truncate-target{max-width:100%}.milestone-name:hover{color:#0366d6;text-decoration:none}.sidebar-assignee .css-truncate-target{max-width:110px}.sidebar-assignee .assignee{font-weight:600;color:#586069;vertical-align:middle}.sidebar-assignee .assignee:hover{color:#0366d6;text-decoration:none}.sidebar-assignee .reviewers-status-icon{width:14px}.sidebar-assignee .octicon{margin-top:2px}.sidebar-notifications{position:relative}.sidebar-notifications .thread-subscription-status{padding:0;margin:0;border:0}.sidebar-notifications .thread-subscription-status .thread-subscribe-form{display:block}.sidebar-notifications .thread-subscription-status .octicon-radio-tower{display:none}.sidebar-notifications .thread-subscription-status .reason{padding:0;margin:5px 0 0}.sidebar-notifications .thread-subscription-status .btn-sm{display:block;width:100%}.participation .participant-avatar{float:left;margin:3px 0 0 3px}.participation a{color:#586069}.participation a:hover{color:#0366d6;text-decoration:none}.participation-avatars{margin-left:-3px}.participation-avatars::before{display:table;content:""}.participation-avatars::after{display:table;clear:both;content:""}.participation-more{float:left;margin:6px 0 0;line-height:14px}.inline-comment-form .form-actions,.timeline-new-comment .form-actions{padding:0 10px 10px}.inline-comment-form::before{display:table;content:""}.inline-comment-form::after{display:table;clear:both;content:""}.inline-comment-form .tabnav-tabs{display:inline-block}.inline-comment-form .form-actions{float:right}.gh-header-actions{float:right;margin-top:3px}.gh-header-actions .btn-sm{float:left;margin-left:5px}.gh-header-actions .btn-sm .octicon{margin-right:0}.gh-header .gh-header-edit{display:none}.gh-header .gh-header-meta .base-ref{display:inline-block}.gh-header .gh-header-meta .commit-ref-dropdown{display:none}.gh-header.open .gh-header-show{display:none}.gh-header.open .gh-header-edit{display:block}.gh-header.open .gh-header-meta .base-ref{display:none}.gh-header.open .gh-header-meta .commit-ref-dropdown{display:inline-block;margin-top:-3px;margin-bottom:-1px;vertical-align:top}.gh-header-title{margin-right:150px;margin-bottom:0;font-weight:400;line-height:1.125;word-wrap:break-word}.gh-header-no-access .gh-header-title{margin-right:0}.gh-header-number{font-weight:300;color:#a3aab1;letter-spacing:-1px}.gh-header-edit::before{display:table;content:""}.gh-header-edit::after{display:table;clear:both;content:""}.gh-header-edit .edit-issue-title{float:left;width:760px;padding:7px 10px;margin-right:10px;font-size:16px;background-color:#fafbfc}.gh-header-edit .edit-issue-title:focus{background-color:#fff}.gh-header-edit .btn{float:left;padding:7px 15px}.gh-header-edit .btn-link{float:left;margin:7px 10px}.gh-header-meta{padding-bottom:20px;margin-top:8px;font-size:14px;line-height:20px;color:#586069;border-bottom:1px solid #e6ebf1}.gh-header.issue .gh-header-meta{margin-bottom:15px}.gh-header.pull .gh-header-meta{padding-bottom:0;border-bottom:0}.gh-header-meta .TableObject-item--primary{word-wrap:break-word;white-space:normal}.gh-header-meta .TableObject-item--primary .commit-ref .css-truncate-target,.gh-header-meta .TableObject-item--primary .commit-ref:hover .css-truncate-target{max-width:780px !important}.gh-header-meta .State{margin-right:8px}.gh-header-meta .avatar{float:left;margin-top:-3px;margin-right:5px}.gh-header-meta .author{font-weight:600;color:#586069}.gh-header-meta .noun{text-transform:lowercase}.tabnav-pr{margin:15px 0 20px;border-color:#e1e4e8}.tabnav-pr .tabnav-tab{position:relative;padding:9px 14px;font-size:13px;color:#586069}.tabnav-pr .tabnav-tab.selected{color:#24292e;border-color:#e1e4e8}.timeline-comment-wrapper>.timeline-comment::after,.timeline-comment-wrapper>.timeline-comment::before,.timeline-new-comment .timeline-comment::after,.timeline-new-comment .timeline-comment::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.timeline-comment-wrapper>.timeline-comment::after,.timeline-new-comment .timeline-comment::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#f6f8fa}.timeline-comment-wrapper>.timeline-comment::before,.timeline-new-comment .timeline-comment::before{border-width:8px;border-right-color:#d1d5da}.timeline-comment-wrapper{position:relative;padding-left:60px;margin-top:15px;margin-bottom:15px;border-top:2px solid #fff;border-bottom:2px solid #fff}.timeline-comment-wrapper .timeline-comment.current-user::after,.timeline-comment-wrapper .timeline-comment.current-user::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.timeline-comment-wrapper .timeline-comment.current-user::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#f1f8ff}.timeline-comment-wrapper .timeline-comment.current-user::before{border-width:8px;border-right-color:#c0d3eb}.timeline-comment-wrapper .timeline-comment.unread-item::after,.timeline-comment-wrapper .timeline-comment.unread-item::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.timeline-comment-wrapper .timeline-comment.unread-item::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#fffdef}.timeline-comment-wrapper .timeline-comment.unread-item::before{border-width:8px;border-right-color:#e5d999}.timeline-comment-avatar{float:left;margin-left:-60px;border-radius:3px}.timeline-comment{position:relative;background-color:#fff;border:1px solid #d1d5da;border-radius:3px}.timeline-comment.will-transition-once{transition:border-color .65s ease-in-out}.timeline-comment.will-transition-once .timeline-comment-header{transition:background-color .65s ease,border-bottom-color .65s ease-in-out}.timeline-comment.will-transition-once .timeline-comment-label{transition:border-color .65s ease-in-out}.timeline-comment.will-transition-once::before,.timeline-comment.will-transition-once::after{transition:border-right-color .65s ease-in-out}.timeline-comment.current-user{border-color:#c0d3eb}.timeline-comment.current-user .timeline-comment-header{background-color:#f1f8ff;border-bottom-color:#c0d3eb}.timeline-comment.current-user .timeline-comment-label{border-color:#c0d3eb}.timeline-comment.current-user .previewable-comment-form .comment-form-head.tabnav{color:#c0d3eb;background-color:#f1f8ff;border-bottom-color:#c0d3eb}.timeline-comment.unread-item{border-color:#e5d999}.timeline-comment.unread-item .timeline-comment-header{background-color:#fffdef;border-bottom-color:#e5d999}.timeline-comment.unread-item .timeline-comment-label{border-color:#e5d999}.timeline-comment.unread-item .previewable-comment-form .comment-form-head.tabnav{color:#e5d999;background-color:#fffdef;border-bottom-color:#e5d999}.timeline-comment:empty{display:none}.timeline-comment .comment+.comment{border-top:1px solid #e1e4e8}.timeline-comment .comment+.comment::before,.timeline-comment .comment+.comment::after{display:none}.timeline-comment .comment+.comment .timeline-comment-header{border-top-left-radius:0;border-top-right-radius:0}.timeline-comment-group:target .timeline-comment{border-color:#2188ff;box-shadow:0 0 0 0.2em #c8e1ff}.timeline-comment-group:target .timeline-comment::before{filter:drop-shadow(-0.25em 0 0 #c8e1ff);border-right-color:#2188ff}.review-comment:target{border:1px solid #2188ff;border-radius:3px;box-shadow:0 0 0 0.2em #c8e1ff}.timeline-comment:target .review-summary{box-shadow:0 0 0 0.2em #c8e1ff}.timeline-comment:target .review-summary::before{filter:drop-shadow(0 -0.28em 0 #c8e1ff);border-bottom-color:#2188ff}.timeline-comment:target .review-summary .comment-body{border:1px solid #2188ff;border-radius:3px}.timeline-comment-header{padding-right:15px;padding-left:15px;color:#586069;background-color:#f6f8fa;border-bottom:1px solid #d1d5da;border-top-left-radius:3px;border-top-right-radius:3px}.timeline-comment-header:only-child{border-bottom:0;border-radius:3px}.timeline-comment-header .author{color:#586069}.timeline-comment-header .timestamp{color:inherit;white-space:nowrap}.timeline-comment-header .timestamp.timestamp-edited{cursor:default}.timeline-comment-header code{word-break:break-all}.comment-type-icon{color:inherit}.timeline-comment-label{float:right;padding:2px 5px;margin:8px 0 0 10px;font-size:12px;cursor:default;border:1px solid rgba(27,31,35,0.1);border-radius:3px}.timeline-comment-label-spammy{color:#cb2431;border-color:#d73a49}.timeline-comment-header-text{max-width:78%;padding-top:10px;padding-bottom:10px}.timeline-comment-header-text code a{color:#586069}.timeline-comment-actions{float:right;margin-right:-5px;margin-left:10px}.timeline-comment-actions .show-more-popover.dropdown-menu-sw{right:-6px;margin-top:-5px}.timeline-comment-action{display:inline-block;padding:10px 5px;color:inherit;opacity:0.5}.timeline-comment-action:hover,.timeline-comment-action:focus{color:#0366d6;text-decoration:none;opacity:1}.timeline-comment-action .octicon-check{height:16px;font-size:18px}.timeline-comment-action.disabled{color:#b1b7bc;cursor:default}.timeline-comment-action.disabled:hover{color:#b1b7bc}.compare-tab-comments .timeline-comment-actions{display:none}.timeline-new-comment{max-width:780px;margin-bottom:0}.timeline-new-comment .comment-form-head{margin-bottom:10px}.timeline-new-comment .previewable-comment-form .comment-body{padding:5px 5px 15px;border-bottom:1px solid #e1e4e8}.discussion-item+.discussion-item,.discussion-item-review+.discussion-item{padding-top:15px;border-top:1px solid #e6ebf1}.discussion-item+.discussion-item-review{padding-top:8px;border-top:1px solid #e6ebf1}.discussion-item{position:relative;padding-left:25px;margin:15px 0 15px 79px}.discussion-item .author{font-weight:600;color:#586069}.discussion-item .timestamp{color:inherit;white-space:nowrap}.discussion-item.open .discussion-item-details{display:block}.discussion-item.open .discussion-item-toggler-opened{display:inline}.discussion-item.open .discussion-item-toggler-closed{display:none}.discussion-item .code-owner{color:inherit}.discussion-item-details{display:none}.discussion-item-deployed{padding-top:15px;border-top:1px solid #e6ebf1}.discussion-item-toggler-opened{display:none}.discussion-item-icon{float:left;width:32px;height:32px;margin-top:-7px;margin-left:-40px;line-height:28px;color:#586069;text-align:center;background-color:#e6ebf1;border:2px solid #fff;border-radius:50%}.discussion-item-icon .octicon-pencil{font-size:14px}.discussion-item-header{min-height:30px;padding-top:5px;padding-bottom:5px;line-height:22px;color:#586069;word-wrap:break-word}.discussion-item-header .avatar{width:16px;height:16px}.discussion-item-header .discussion-item-private{vertical-align:-1px}.discussion-item-header:last-child{padding-bottom:0}.discussion-item-header .commit-ref{font-size:85%;vertical-align:baseline}.discussion-item-header .btn-outline{float:right;padding:4px 8px;margin-top:-5px;margin-left:10px}.discussion-item-body{margin-top:5px}.discussion-item-footer{font-size:12px}.discussion-item-link{color:#586069}.discussion-item-link:hover{color:#0366d6}.discussion-item-entity{font-weight:600;color:#24292e}.discussion-item-entity:hover{color:#0366d6;text-decoration:none}.discussion-item-ref-title .issue-num{font-weight:400;color:#586069}.discussion-item-ref-title .title-link{color:#24292e}.discussion-item-ref-title .title-link:hover{color:#0366d6;text-decoration:none}.discussion-item-ref-title .title-link:hover .issue-num{color:inherit}.discussion-item-context-icon{display:inline-block;margin-left:10px;line-height:22px}.discussion-item-help{color:#586069}.discussion-item-help:hover{color:#0366d6}.discussion-item-private{color:rgba(27,31,35,0.7)}.discussion-item-rollup-ref .State{margin-top:2px}.discussion-item-rollup-ref .discussion-item-context-icon{margin-top:2px}.discussion-item-reopened .discussion-item-icon,.discussion-item-review.is-approved.is-writer .discussion-item-icon{color:#fff;background-color:#2cbe4e}.discussion-item-closed .discussion-item-icon,.discussion-item-review.is-rejected.is-writer .discussion-item-icon{color:#fff;background-color:#cb2431}.discussion-item-head_ref_deleted .discussion-item-icon{padding-left:1px;color:#fff;background-color:#6a737d}.discussion-item-locked .discussion-item-icon,.discussion-item-unlocked .discussion-item-icon{color:#fff;background-color:#24292e}.discussion-item-integrations-callout .discussion-item-icon{color:#fff;background-color:#428ade}.discussion-item-integrations-callout .pull-request-integrations-dismiss{padding:4px 8px;margin:-3px 0 0 10px;color:#586069}.pull-request-integrations-title{margin:0;font-size:15px;color:#24292e}.pull-request-integrations-body{margin-top:5px;color:#586069}.discussion-item .renamed-was,.discussion-item .renamed-is{font-weight:600;color:#24292e}.discussion-commits .discussion-item-icon{padding-top:1px}.discussion-commits .discussion-item-body{margin-top:0;margin-left:-31px}.discussion-item-changes-marker{margin-bottom:0}.discussion-item-changes-marker .discussion-item-icon{color:#fff;background-color:#0366d6}.discussion-item-changes-marker+.discussion-commits{padding-top:0;margin-top:0;border-top:0}.discussion-item-changes-marker+.discussion-commits .discussion-item-icon{display:none}.new-discussion-timeline .previewable-comment-form .comment-form-head.tabnav{padding:6px 10px 0;background:#f6f8fa;border-radius:3px 3px 0 0}.new-discussion-timeline .previewable-comment-form .comment{border:0}.new-discussion-timeline .previewable-comment-form .comment-body{padding:5px 5px 15px;background-color:transparent;border-bottom:1px solid #e1e4e8}.new-discussion-timeline .previewable-comment-form .timeline-comment .timeline-comment-actions{display:none}.new-discussion-timeline .closed-banner{position:relative;height:19px;margin:15px 0 -15px;overflow:visible;background:#e6ebf1;border-bottom:15px solid #fff;border-radius:0}.new-discussion-timeline .composer .timeline-comment{margin-bottom:10px}.new-discussion-timeline .composer .timeline-comment::after{border-right-color:#fff}.new-discussion-timeline .composer .comment-form-head.tabnav{padding-top:0;background-color:#fff}.discussion-timeline-actions{background-color:#fff;border-top:2px solid #e6ebf1}.discussion-timeline-actions .merge-pr{padding-top:0;border-top:0}.discussion-timeline-actions .thread-subscription-status{margin-top:20px}.discussion-timeline-actions .thread-subscription-status .octicon-radio-tower{display:none}.discussion-item-merged .discussion-item-icon{padding-left:1px;color:#fff;background-color:#6f42c1}.discussion-item-merged.open .discussion-item-footer{display:none}.discussion-item-merged.open .discussion-item-details{margin-top:5px;margin-bottom:10px;border:1px solid #d1d5da;border-radius:3px}.discussion-item-merged.open .discussion-item-details-header{padding:12px 15px;margin-top:0;margin-bottom:0;font-size:inherit;border-top:1px solid #d1d5da}.discussion-item-merged.open .discussion-item-details-header:first-child{border-top:0}.discussion-item-merged.open .merge-status-list{max-height:370px;margin:0;border-top-color:#d1d5da}.discussion-item-merged.open .merge-status-item{padding-left:15px}.pagination-loader-container{background-color:#fff;background-image:url("/images/modules/pulls/progressive-disclosure-line@2x.png");background-repeat:repeat-x;background-position:left center;background-size:32px}.progressive-timeline-loader-container{padding-left:64px}.deployment-icon .octicon-rocket{position:relative;bottom:-1px;left:-1px}.environment-name{color:#586069}.deployment-meta{font-size:12px;color:#586069}.deployment-meta .octicon{color:#c3c8cf}.deployment-status-label{display:inline-block;padding:1px 4px;margin-top:-2px;margin-right:3px;font-size:10px;line-height:16px;color:#6a737d;vertical-align:middle;background:none;border:solid 1px #e1e4e8;border-radius:2px}.deployment-status-label.is-error,.deployment-status-label.is-failure{color:#cb2431;text-align:center}.deployment-status-label.is-active{color:#28a745}.deployment-status-label.is-pending,.deployment-status-label.is-queued,.deployment-status-label.is-in_progress{color:#b08800}.discussion-item-review{padding-left:0;margin:20px 0 20px 80px;border:0}.discussion-item-review .discussion-item{margin-left:-30px}.discussion-item-review .discussion-item-copy{max-width:460px}.discussion-item-review .discussion-item-body{margin-left:25px}.discussion-item-review .file-header{padding:2px 12px;background-color:#fafbfc;border-bottom:1px solid #d1d5da;border-top-left-radius:3px;border-top-right-radius:3px}.discussion-item-review .blob-wrapper{border-bottom-right-radius:0;border-bottom-left-radius:0}.discussion-item-review .timeline-comment{background-color:transparent;border:0}.discussion-item-review .timeline-comment::before,.discussion-item-review .timeline-comment::after{display:none}.discussion-item-review .timeline-comment .timeline-comment-header{padding-right:5px;background-color:inherit;border:0}.discussion-item-review .timeline-comment .discussion-item-icon{margin-right:8px;margin-left:-11px}.discussion-item-review .timeline-comment-avatar{margin-top:-7px;margin-left:-80px}.discussion-item-review.is-pending .form-actions{margin-right:10px;margin-bottom:5px}.discussion-item-review.is-pending .file,.discussion-item-review.is-pending .file-header,.discussion-item-review.is-pending .tabnav-tab.selected,.discussion-item-review.is-pending .comment-form-head.tabnav{border-color:#d9d0a5}.discussion-item-review.is-pending .file-header,.discussion-item-review.is-pending .comment-form-head.tabnav{background-color:#fffdef}.discussion-item-review-comment{border-bottom:1px solid #e1e4e8}.discussion-item-review-comment:last-child{border-bottom:0}.review-summary{position:relative;margin-left:-20px}.review-summary::after,.review-summary::before{position:absolute;left:11px;content:" ";border-color:transparent transparent #fff;border-style:solid;border-width:8px}.review-summary::after{top:-15px;border-bottom-color:#fff}.review-summary::before{top:-16px;border-bottom-color:#d1d5da}.discussion-item-icon-gray{background-color:#e6ebf1 !important}.dropdown{position:relative}.dropdown.active .dropdown-menu-content{display:block;pointer-events:all}.dropdown-caret{display:inline-block;width:0;height:0;vertical-align:middle;content:"";border:4px solid;border-right-color:transparent;border-bottom-color:transparent;border-left-color:transparent}.dropdown-menu{position:absolute;top:100%;left:0;z-index:100;width:160px;padding-top:5px;padding-bottom:5px;margin-top:2px;list-style:none;background-color:#fff;background-clip:padding-box;border:1px solid rgba(27,31,35,0.15);border-radius:4px;box-shadow:0 3px 12px rgba(27,31,35,0.15)}.dropdown-menu::before,.dropdown-menu::after{position:absolute;display:inline-block;content:""}.dropdown-menu::before{border:8px solid transparent;border-bottom-color:rgba(27,31,35,0.15)}.dropdown-menu::after{border:7px solid transparent;border-bottom-color:#fff}.dropdown-menu>ul{list-style:none}.dropdown-menu-no-overflow{width:auto}.dropdown-menu-no-overflow .dropdown-item{padding:4px 15px;overflow:visible;text-overflow:inherit}.dropdown-item{display:block;padding:4px 10px 4px 15px;overflow:hidden;color:#24292e;text-overflow:ellipsis;white-space:nowrap}.dropdown-item:focus,.dropdown-item:hover,.dropdown-item.zeroclipboard-is-hover{color:#fff;text-decoration:none;background-color:#0366d6;outline:none}.dropdown-item:focus>.octicon,.dropdown-item:hover>.octicon,.dropdown-item.zeroclipboard-is-hover>.octicon{color:inherit;opacity:1}.dropdown-item.btn-link{width:100%;text-align:left}.dropdown-signout{width:100%;text-align:left;background:none;border:0}.dropdown-divider{height:1px;margin:8px 1px;background-color:#e1e4e8}.dropdown-header{padding:4px 15px;font-size:12px;color:#586069}.dropdown-menu-content{display:none}.dropdown-menu-content.anim-scale-in{position:relative;z-index:100;pointer-events:none}.dropdown-menu-w{top:0;right:100%;left:auto;width:auto;margin-top:0;margin-right:10px}.dropdown-menu-w::before{top:10px;right:-16px;left:auto;border-color:transparent;border-left-color:rgba(27,31,35,0.15)}.dropdown-menu-w::after{top:11px;right:-14px;left:auto;border-color:transparent;border-left-color:#fff}.dropdown-menu-e{top:0;left:100%;width:auto;margin-top:0;margin-left:10px}.dropdown-menu-e::before{top:10px;left:-16px;border-color:transparent;border-right-color:rgba(27,31,35,0.15)}.dropdown-menu-e::after{top:11px;left:-14px;border-color:transparent;border-right-color:#fff}.dropdown-menu-ne{top:auto;bottom:100%;left:0;margin-bottom:3px}.dropdown-menu-ne::before,.dropdown-menu-ne::after{top:auto;right:auto}.dropdown-menu-ne::before{bottom:-8px;left:9px;border-top:8px solid rgba(27,31,35,0.15);border-right:8px solid transparent;border-bottom:0;border-left:8px solid transparent}.dropdown-menu-ne::after{bottom:-7px;left:10px;border-top:7px solid #fff;border-right:7px solid transparent;border-bottom:0;border-left:7px solid transparent}.dropdown-menu-s{right:50%;left:auto;transform:translateX(50%)}.dropdown-menu-s::before{top:-16px;right:50%;transform:translateX(50%)}.dropdown-menu-s::after{top:-14px;right:50%;transform:translateX(50%)}.dropdown-menu-sw{right:0;left:auto}.dropdown-menu-sw::before{top:-16px;right:9px;left:auto}.dropdown-menu-sw::after{top:-14px;right:10px;left:auto}.dropdown-menu-se::before{top:-16px;left:9px}.dropdown-menu-se::after{top:-14px;left:10px}g-emoji{font-family:"Apple Color Emoji", "Segoe UI", "Segoe UI Emoji", "Segoe UI Symbol";font-size:1.2em;font-weight:400;line-height:20px;vertical-align:middle}g-emoji img{width:1em;height:1em}body.emoji-size-boost g-emoji{margin-right:3px}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){body.emoji-size-boost g-emoji{margin-right:0;font-size:1.4em}}.emoji-icon{display:inline-block;width:20px;height:20px;vertical-align:middle;background-repeat:no-repeat;background-size:20px 20px}.emoji-result{display:inline-block;height:20px;font-size:18px;font-weight:400;vertical-align:middle}.facebox{position:absolute;top:0;left:0;z-index:100;padding-bottom:40px}.facebox ul{margin-bottom:15px;margin-left:25px}.facebox .suggester ul{margin:0}.facebox-popup{position:relative;background-color:#fff;background-clip:padding-box;border:1px solid rgba(27,31,35,0.25);border-radius:5px;box-shadow:0 0 18px rgba(27,31,35,0.4)}.facebox-content{width:455px;padding:15px}.facebox-content::before{display:table;content:""}.facebox-content::after{display:table;clear:both;content:""}.facebox-close{position:absolute;top:8px;right:5px;padding:10px;cursor:pointer;background-color:transparent;border:0;opacity:0.25;-webkit-appearance:none;-moz-appearance:none;appearance:none}.facebox-close:hover{opacity:1}.facebox-overlay{position:fixed;top:0;left:0;width:100%;height:100%}.facebox-overlay-hide{z-index:-100}.facebox-overlay-active{z-index:99}.facebox-header{padding:15px;margin:-15px -15px 15px;font-size:18px;font-weight:400;border-bottom:1px solid #e1e4e8}.facebox-header:focus{outline:none}.facebox-footer{padding:10px 15px;margin:0 -15px -15px;text-align:right;background:#fafbfc;border-top:1px solid #e1e4e8;border-bottom-right-radius:5px;border-bottom-left-radius:5px}.facebox-footer .help{margin:0;color:#586069;text-align:center}.facebox-alert{padding:10px 15px;margin:-16px -15px 15px;color:#735c0f;background-color:#ffea7f;border-color:rgba(176,136,0,0.3);border-style:solid;border-width:1px 0}.keyboard-shortcuts{float:right}.keyboard-mappings{font-size:12px;color:#444d56}.keyboard-mappings th{padding-top:25px;font-size:14px;line-height:1.5;color:#24292e;text-align:left}.keyboard-mappings tbody:first-child tr:first-child th{padding-top:0}.keyboard-mappings td{padding-top:3px;padding-bottom:3px;line-height:20px;vertical-align:top}.keyboard-mappings .keys{padding-right:10px;color:#586069;text-align:right;white-space:nowrap}.linejump .linejump-input{width:340px;background-color:#fafbfc}.linejump .linejump-input,.linejump .btn{padding:10px 15px;font-size:16px}.linejump+.facebox-close{top:18px}.repo-transfer-tip{margin-bottom:0}.flash-banner{position:fixed;top:0;z-index:90;width:100%;border-top:0;border-right:0;border-left:0;border-radius:0}.signed-in-tab-flash,.signed-out-tab-flash{display:none}.stale-session-flash.is-signed-in .signed-in-tab-flash{display:inline}.stale-session-flash.is-signed-out .signed-out-tab-flash{display:inline}.org-privileges-tour-dismiss{padding:4px 8px;margin:-3px 0 0 10px;color:#586069}.footer-octicon{color:#c6cbd1}.footer-octicon:hover{color:#959da5}.page-responsive .footer .footer-octicon{display:none}@media (min-width: 1012px){.page-responsive .footer .footer-octicon{display:block}}.gollum-editor .comment-form-head.tabnav{border:1px solid #dfe2e5}.gollum-editor .gollum-editor-body{height:390px;resize:vertical}.active .gollum-editor-function-buttons{display:block !important}include-fragment,poll-include-fragment{display:block}kbd{display:inline-block;padding:3px 5px;font:11px "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;line-height:10px;color:#444d56;vertical-align:middle;background-color:#fafbfc;border:solid 1px #d1d5da;border-bottom-color:#c6cbd1;border-radius:3px;box-shadow:inset 0 -1px 0 #c6cbd1}.badmono{font-family:sans-serif;font-weight:600}.IssueLabel{height:20px;padding:0.15em 4px;font-size:12px;font-weight:600;line-height:15px;border-radius:2px;box-shadow:inset 0 -1px 0 rgba(27,31,35,0.12)}.IssueLabel .g-emoji{position:relative;top:-0.05em;display:inline-block;font-size:1em;line-height:1}.IssueLabel:hover{text-decoration:none}.IssueLabel--big{padding:0 8px;font-size:16px;font-weight:600;line-height:2;border-radius:3px;transition:opacity 0.2s linear}.IssueLabel--big .g-emoji{display:inline-block;margin-top:-1px}.IssueLabel--big:hover{opacity:0.85}.list-group-item{position:relative;display:block;padding:8px 10px 10px 40px;margin-bottom:-1px;border:1px solid #e1e4e8}.list-group-item::before{display:table;content:""}.list-group-item::after{display:table;clear:both;content:""}.list-group-item a:hover{text-decoration:none}.list-group-item:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.list-group-item.closed{background-color:#fafbfc}.list-group-item.selectable{padding-left:60px}.list-group-item.selected{background-color:#fffdef}.list-group-item[aria-selected="true"],.list-group-item.navigation-focus{background-color:#f1f8ff}.list-group-item .list-group-item-summary a{color:#586069}.list-group-item .list-group-item-summary a.quiet{color:#6a737d}.list-group-item .status{position:relative;top:2px;float:right;margin-right:-9px}.list-group-item .type-icon{position:relative;top:1px;width:16px;text-align:center;vertical-align:middle}.list-group-item .assignee{float:right}.list-group-item .assignee img{display:block;border-radius:2px}.list-group-item .labels{top:-2px;display:inline-block;margin-bottom:-2px;margin-left:4px}.list-group-item-name{margin:0 60px 2px 0;font-size:15px;line-height:1.3;word-wrap:break-word}.list-group-item-name .type-icon{float:left;margin-top:1px;margin-left:-24px}.list-group-item-link{color:#24292e}.closed.octicon,.reverted.octicon{color:#cb2431}.open.octicon{color:#28a745}.merged.octicon{color:#6f42c1}.list-group-item-summary{margin-top:2px}.list-group-item-summary p{margin:0 0 5px}.standalone .list-group-item-summary p{margin-bottom:0}.animated-ellipsis-container{display:inline-block;width:12px;height:12px;overflow:hidden;transform:translateZ(0)}.animated-ellipsis-container>.animated-ellipsis{display:inline-block;overflow:hidden;vertical-align:bottom;animation:ellipsis 1s infinite}@keyframes ellipsis{from{width:2px}to{width:12px}}.large-loading-area{padding:100px 0;text-align:center}.user-mention,.team-mention{font-weight:600;color:#24292e;white-space:nowrap}.pagehead{position:relative;padding-top:20px;padding-bottom:20px;margin-bottom:20px}@media (min-width: 1012px){.pagehead{border-bottom:1px solid #e1e4e8}}.pagehead.admin{background:url("/images/modules/pagehead/background-yellowhatch-v3.png") 0 0 repeat-x}.pagehead ul.pagehead-actions{position:relative;z-index:31;float:right;margin:0}.pagehead .path-divider{margin:0 0.25em}.pagehead h1{margin-top:0;margin-bottom:0;font-size:20px;font-weight:400;line-height:28px}.pagehead h1 .avatar{margin-top:-2px;margin-right:9px;margin-bottom:-2px}.pagehead .account-switcher{margin-top:-3px;margin-bottom:-3px}.pagehead .underline-nav{height:69px;margin-top:-20px;margin-bottom:-20px}.pagehead-heading{color:inherit}.pagehead-actions>li{float:left;margin:0 10px 0 0;font-size:11px;color:#24292e;list-style-type:none}.pagehead-actions>li:last-child{margin-right:0}.pagehead-actions .octicon-mute{color:#cb2431}.pagehead-actions .select-menu{position:relative}.pagehead-actions .select-menu::before{display:table;content:""}.pagehead-actions .select-menu::after{display:table;clear:both;content:""}.pagehead-actions .select-menu-modal-holder{top:100%}.pagehead-tabs-item{float:left;padding:8px 15px 11px;color:#586069;white-space:nowrap;border:solid transparent;border-width:3px 1px 1px;border-radius:3px 3px 0 0}.pagehead-tabs-item .octicon{color:rgba(27,31,35,0.3)}.pagehead-tabs-item .Counter{color:#444d56}.pagehead-tabs-item:hover{color:#24292e;text-decoration:none}.pagehead-tabs-item.selected{font-weight:600;color:#24292e;background-color:#fff;border-color:#e36209 #e1e4e8 transparent}.pagehead-tabs-item.selected>.octicon{color:inherit}.repohead.experiment-repo-nav{padding-bottom:0;background-color:#fafbfc}.repohead .repohead-details-container{margin-bottom:20px}.repohead.mirror h1,.repohead.fork h1{height:auto;margin-top:-5px;margin-bottom:15px}.repohead h1{position:relative;float:left;max-width:635px;padding-left:18px;font-size:18px;line-height:26px;color:#586069}.repohead h1.private .octicon{color:rgba(27,31,35,0.5)}.repohead h1 .octicon{position:absolute;top:0;left:0;margin-top:5px;color:#959da5}.repohead .octicon-mirror{left:-3px}.repohead .octicon-lock{top:10px}.repohead .fork-flag,.repohead .mirror-flag{display:block;font-size:11px;line-height:10px;white-space:nowrap}.reponav{position:relative;top:1px;margin-top:-5px}.reponav::before{display:table;content:""}.reponav::after{display:table;clear:both;content:""}.reponav-dropdown{position:relative;float:left}.reponav-dropdown.active .dropdown-menu-content{display:block}.reponav-item{float:left;padding:7px 15px 8px;color:#586069;white-space:nowrap;border:solid transparent;border-width:3px 1px 1px;border-radius:3px 3px 0 0}.reponav-item .octicon{color:rgba(27,31,35,0.3)}.reponav-item .Counter{color:#444d56}.reponav-item:hover,.reponav-item:focus{color:#24292e;text-decoration:none}.reponav-item.selected{color:#24292e;background-color:#fff;border-color:#e36209 #e1e4e8 transparent}.reponav-item.selected>.octicon{color:inherit}.reponav-wrapper{position:relative;z-index:2;overflow-y:hidden;background-color:#24292e}.reponav-wrapper .reponav{top:0;padding-right:8px;padding-left:8px;margin-top:0;-webkit-overflow-scrolling:touch;overflow-x:auto;color:rgba(255,255,255,0.75)}.reponav-wrapper .reponav-item{display:inline-block;float:none;padding:4px 8px 16px;color:rgba(255,255,255,0.75);border:0}.reponav-wrapper .reponav-item .Counter{color:inherit;background-color:rgba(255,255,255,0.15)}.reponav-wrapper .reponav-item.selected{font-weight:600;color:#fff;background-color:transparent;border:0}.progress-bar{display:block;height:15px;overflow:hidden;background-color:#eaecef;border-radius:3px}.progress-bar .progress{display:block;height:100%;background-color:#2cbe4e}.reverse-progress-container{position:relative;height:3px;background-color:#e1e4e8;background-image:linear-gradient(to right, #28a745, #005cc5, #3a1d6e, #cb2431, #f66a0a);background-size:100% 3px}.reverse-progress-bar{position:absolute;right:0;height:100%;background-color:#e1e4e8}.progress-bar-small{height:10px}.progress-bar-inline .progress-bar{width:100%;border-color:#e1e4e8;border-style:solid;border-width:1px;border-top:0;border-radius:0}.steps{display:table;width:100%;padding:0;margin:30px auto 0;overflow:hidden;list-style:none;border:1px solid #dfe2e5;border-radius:3px;box-shadow:0 1px 3px rgba(27,31,35,0.05)}.steps li{display:table-cell;width:33.3%;padding:10px 15px;color:#c6cbd1;cursor:default;background-color:#fafbfc;border-left:1px solid #dfe2e5}.steps li.current{color:#24292e;background-color:#fff}.steps li.current .octicon{color:#0366d6}.steps li .octicon{float:left;margin-right:15px;margin-bottom:5px}.steps li .step{display:block}.steps li:first-child{border-left:0}.steps .complete{color:#586069}.steps .complete .octicon{color:#28a745}.prose-diff .anchor{display:none}.prose-diff .show-rich-diff{color:#0366d6;text-decoration:none;cursor:pointer}.prose-diff .show-rich-diff:hover{text-decoration:underline}.prose-diff.collapsed .rich-diff-level-zero.expandable{cursor:pointer}.prose-diff.collapsed .rich-diff-level-zero.expandable .vicinity{display:block}.prose-diff.collapsed .rich-diff-level-zero.expandable .unchanged:not(.vicinity){display:none}.prose-diff.collapsed .rich-diff-level-zero.expandable .octicon{display:block;margin:20px auto;color:#d1d5da}.prose-diff.collapsed .rich-diff-level-zero.expandable:hover .octicon{color:#24292e}.prose-diff.collapsed .rich-diff-level-zero.expandable:only-child::before{font-size:18px;color:#d1d5da;content:"Sorry, no visible changes to display."}.prose-diff.collapsed .rich-diff-level-zero.expandable:only-child:hover::before{color:#24292e}.prose-diff.collapsed .rich-diff-level-zero.expandable>.removed,.prose-diff.collapsed .rich-diff-level-zero.expandable>del{display:none;text-decoration:none}.prose-diff .markdown-body{padding:30px;padding-left:15px}.prose-diff .markdown-body>ins{box-shadow:inset 4px 0 0 #2cbe4e}.prose-diff .markdown-body>del{text-decoration:none;box-shadow:inset 4px 0 0 #cb2431}.prose-diff .markdown-body>ins,.prose-diff .markdown-body>del{display:block;border-radius:0}.prose-diff .markdown-body>ins>.rich-diff-level-zero,.prose-diff .markdown-body>ins>.rich-diff-level-one,.prose-diff .markdown-body>del>.rich-diff-level-zero,.prose-diff .markdown-body>del>.rich-diff-level-one{margin-left:15px}.prose-diff .markdown-body>ins:first-child *,.prose-diff .markdown-body>del:first-child *{margin-top:0}.prose-diff .rich-diff-level-zero.added{box-shadow:inset 4px 0 0 #2cbe4e}.prose-diff .rich-diff-level-zero.removed{box-shadow:inset 4px 0 0 #cb2431}.prose-diff .rich-diff-level-zero.changed{box-shadow:inset 4px 0 0 #f9c513}.prose-diff .rich-diff-level-zero.unchanged,.prose-diff .rich-diff-level-zero.vicinity{margin-left:15px}.prose-diff .rich-diff-level-zero.added,.prose-diff .rich-diff-level-zero.removed,.prose-diff .rich-diff-level-zero.changed{display:block;border-radius:0}.prose-diff .rich-diff-level-zero.added>.rich-diff-level-one,.prose-diff .rich-diff-level-zero.removed>.rich-diff-level-one,.prose-diff .rich-diff-level-zero.changed>.rich-diff-level-one{margin-left:15px}.prose-diff .rich-diff-level-zero.added:first-child *,.prose-diff .rich-diff-level-zero.removed:first-child *,.prose-diff .rich-diff-level-zero.changed:first-child *{margin-top:0}.prose-diff :not(.changed)>:not(.github-user-ins):not(.github-user-del)>.removed,.prose-diff :not(.changed)>:not(.github-user-ins):not(.github-user-del)>del{text-decoration:none}.prose-diff .changed del,.prose-diff .changed del pre,.prose-diff .changed del code,.prose-diff .changed del>div,.prose-diff .changed .removed,.prose-diff .changed .removed pre,.prose-diff .changed .removed code,.prose-diff .changed .removed>div{color:#cb2431;text-decoration:line-through;background:#ffdce0}.prose-diff .changed ins,.prose-diff .changed ins code,.prose-diff .changed ins pre,.prose-diff .changed .added{background:#e6ffed;border-bottom:1px solid #2cbe4e}.prose-diff>.markdown-body .github-user-ins{text-decoration:underline}.prose-diff>.markdown-body .github-user-del{text-decoration:line-through}.prose-diff>.markdown-body li ul.added{background:#e6ffed}.prose-diff>.markdown-body li ul.removed{color:#cb2431;background:#ffeef0}.prose-diff>.markdown-body li ul.removed:not(.github-user-ins){text-decoration:line-through}.prose-diff>.markdown-body li.added.moved-up .octicon,.prose-diff>.markdown-body li.added.moved-down .octicon{margin-right:5px;margin-left:5px;color:#d1d5da}.prose-diff>.markdown-body li.added.moved{background:#fffdef}.prose-diff>.markdown-body li.removed.moved{display:none}.prose-diff>.markdown-body pre{padding:10px 20px}.prose-diff>.markdown-body th.changed,.prose-diff>.markdown-body td.changed{background:#fffdef;border-left-color:#dfe2e5}.prose-diff>.markdown-body :not(li.moved).removed{color:#cb2431;text-decoration:line-through;background:#ffeef0}.prose-diff>.markdown-body :not(.github-user-ins):not(li.moved).removed{text-decoration:line-through}.prose-diff>.markdown-body :not(li.moved).added,.prose-diff>.markdown-body li:not(.moved).added{background:#e6ffed}.prose-diff>.markdown-body :not(.github-user-del):not(li.moved).added li:not(.moved):not(.github-user-del).added{text-decoration:none}.prose-diff>.markdown-body li:not(.moved).removed{color:#cb2431;background:#ffeef0}.prose-diff>.markdown-body li:not(.moved):not(.github-user-ins).removed{text-decoration:line-through}.prose-diff>.markdown-body .added,.prose-diff>.markdown-body ins+.added,.prose-diff>.markdown-body ins{border-top:0;border-bottom:0}.prose-diff>.markdown-body .added:not(.github-user-del):not(.github-user-ins),.prose-diff>.markdown-body ins+.added:not(.github-user-del):not(.github-user-ins),.prose-diff>.markdown-body ins:not(.github-user-del):not(.github-user-ins){text-decoration:none}.prose-diff>.markdown-body img.added,.prose-diff>.markdown-body img.removed{border-style:solid;border-width:1px}.prose-diff>.markdown-body ins pre:not(.github-user-del):not(.github-user-ins),.prose-diff>.markdown-body ins code:not(.github-user-del):not(.github-user-ins),.prose-diff>.markdown-body ins>div:not(.github-user-del):not(.github-user-ins){text-decoration:none}.prose-diff>.markdown-body ul>ins,.prose-diff>.markdown-body ul>del{display:block;padding:0}.prose-diff>.markdown-body .added>li,.prose-diff>.markdown-body .removed>li{margin-top:0;margin-bottom:0}span.changed_tag,em.changed_tag,strong.changed_tag,b.changed_tag,i.changed_tag,code.changed_tag{border-bottom:1px dotted #6a737d;border-radius:0}a.added_href,a.changed_href,span.removed_href{border-bottom:1px dotted #6a737d;border-radius:0}.diff-view .file-type-prose .rich-diff{display:none}.diff-view .display-rich-diff .rich-diff{display:block}.diff-view .display-rich-diff .file-diff{display:none}.diff-view .display-rich-diff.has-inline-notes .show-file-notes{display:none}.protip{margin-top:20px;text-align:center}.protip code{padding:2px;background-color:#f6f8fa;border-radius:3px}.protip-callout{padding:8px 10px;margin-bottom:20px;color:#444d56;text-align:left;border:solid 1px #eaecef;border-radius:3px}.radio-group::before{display:table;content:""}.radio-group::after{display:table;clear:both;content:""}.radio-label{float:left;padding:8px 10px 8px 35px;margin-left:-1px;color:#24292e;cursor:pointer;border:1px solid #d1d5da}:checked+.radio-label{position:relative;z-index:1;border-color:#0366d6}.radio-label .octicon{padding-right:5px}.radio-label:first-of-type{margin-left:0;border-top-left-radius:3px;border-bottom-left-radius:3px}.radio-label:last-of-type{padding-right:16px;border-top-right-radius:3px;border-bottom-right-radius:3px}.radio-input{z-index:3;float:left;margin:14px -35px 0 14px}.add-reaction-btn{opacity:0;transition:opacity 0.1s ease-in-out}.add-reaction-plus-icon{margin-right:-1px}.reaction-popover-container{display:inline-block}.reaction-popover-container[open] .add-reaction-btn{opacity:1}.add-reaction-popover.dropdown-menu{width:220px}.add-reaction-popover.dropdown-menu-ne{bottom:100%;left:6px;margin-bottom:3px}.reaction-popover-form .loading-spinner{display:none;float:right}.reaction-popover-form.loading .loading-spinner{display:inline}.add-reactions-options::before{display:table;content:""}.add-reactions-options::after{display:table;clear:both;content:""}.add-reactions-options-item{float:left;width:34px;line-height:29px;transition:transform 0.15s cubic-bezier(0.2, 0, 0.13, 2);transform:scale(1)}.add-reactions-options-item:hover,.add-reactions-options-item:focus{text-decoration:none;transform:scale(1.2)}.add-reactions-options-item:active{background-color:#dbedff}.comment-reactions::before{display:table;content:""}.comment-reactions::after{display:table;clear:both;content:""}.comment-reactions .reaction-popover-container{z-index:100}.comment-reactions.has-reactions{border-top:1px solid #e1e4e8}.comment-reactions.has-reactions:hover .add-reaction-btn{opacity:1}.comment-reactions .user-has-reacted{background-color:#f1f8ff}.comment-reactions .add-reaction-btn{border-right:0}.reaction-summary-item{float:left;padding:9px 15px 7px;line-height:18px;border-right:1px solid #e1e4e8}.reaction-summary-item:hover,.reaction-summary-item:focus{text-decoration:none}.comment-reactions-options .reaction-summary-item:first-child{border-bottom-left-radius:2px}.render-container{padding:30px;line-height:0;text-align:center;background:#dfe2e5}.render-container .render-viewer{display:none;width:100%;height:100%;border:0}.render-container .octospinner{display:none}.render-container .render-viewer-error,.render-container .render-viewer-fatal,.render-container .render-viewer-invalid{display:none}.render-container.is-render-automatic .octospinner{display:inline-block}.render-container.is-render-requested .octospinner{display:inline-block}.render-container.is-render-requested.is-render-failed .render-viewer-error{display:inline-block}.render-container.is-render-requested.is-render-failed .render-viewer,.render-container.is-render-requested.is-render-failed .render-viewer-fatal,.render-container.is-render-requested.is-render-failed .render-viewer-invalid,.render-container.is-render-requested.is-render-failed .octospinner{display:none}.render-container.is-render-requested.is-render-failed-fatal .render-viewer-fatal{display:inline-block}.render-container.is-render-requested.is-render-failed-fatal .render-viewer,.render-container.is-render-requested.is-render-failed-fatal .render-viewer-error,.render-container.is-render-requested.is-render-failed-fatal .render-viewer-invalid,.render-container.is-render-requested.is-render-failed-fatal .octospinner{display:none}.render-container.is-render-requested.is-render-failed-invalid .render-viewer-invalid{display:inline-block}.render-container.is-render-requested.is-render-failed-invalid .render-viewer,.render-container.is-render-requested.is-render-failed-invalid .render-viewer-error,.render-container.is-render-requested.is-render-failed-invalid .render-viewer-fatal,.render-container.is-render-requested.is-render-failed-invalid .octospinner{display:none}.render-container.is-render-ready.is-render-requested:not(.is-render-failed){height:500px;padding:0;background:none}.render-container.is-render-ready.is-render-requested:not(.is-render-failed) .render-viewer{display:block}.render-container.is-render-ready.is-render-requested:not(.is-render-failed) .render-viewer-error,.render-container.is-render-ready.is-render-requested:not(.is-render-failed) .render-viewer-fatal,.render-container.is-render-ready.is-render-requested:not(.is-render-failed) .octospinner{display:none}.render-notice{padding:20px 15px;font-size:14px;color:#444d56;background-color:#f6f8fa;border-color:rgba(27,31,35,0.15)}.select-menu-button::after{display:inline-block;width:0;height:0;vertical-align:-2px;content:"";border:4px solid;border-right-color:transparent;border-bottom-color:transparent;border-left-color:transparent}.select-menu-button.icon-only{padding-left:7px}.select-menu-button.primary::after{border-top-color:#fff}.select-menu-button.primary::after:active{background-color:#28a745}.select-menu-button-large::after{margin-left:0.25em;border-width:0.33em}.select-menu .spinner{float:left;margin:4px 0 0 -24px}.select-menu.active .select-menu-modal-holder{display:block}.select-menu.select-menu-modal-right{position:relative}.select-menu.select-menu-modal-right .select-menu-modal-holder{right:0}.select-menu .select-menu-clear-item{display:block}.select-menu .select-menu-clear-item .octicon{color:inherit}.select-menu .select-menu-clear-item+.select-menu-no-results{display:none !important}.select-menu.is-loading .select-menu-loading-overlay{display:block}.select-menu.is-loading .select-menu-modal{min-height:200px}.select-menu.has-error .select-menu-error{display:block}.select-menu-error{display:none}.select-menu-loading-overlay{position:absolute;top:0;z-index:5;display:none;width:100%;height:100%;background-color:rgba(255,255,255,0.8);border:1px solid transparent;border-radius:5px}.select-menu-loading-overlay .octicon-octoface{position:absolute;top:50%;left:50%;margin:-16px 0 0 -16px}.select-menu-modal-holder{position:absolute;z-index:30;display:none}.select-menu-modal{position:relative;width:300px;margin-top:4px;margin-bottom:20px;overflow:hidden;font-size:12px;color:#586069;background-color:#fff;background-clip:padding-box;border:1px solid rgba(27,31,35,0.15);border-radius:3px;box-shadow:0 3px 12px rgba(27,31,35,0.15)}.select-menu-modal-narrow{width:200px}.select-menu-header,.select-menu-divider{padding:8px 10px;line-height:16px;background:#f6f8fa;border-bottom:1px solid #e1e4e8}.select-menu-header .select-menu-title,.select-menu-divider{font-weight:600;color:#24292e}.select-menu-divider{margin-top:-1px;border-top:1px solid #e1e4e8}.select-menu-header .close-button,.select-menu-header .octicon{display:block;float:right;color:#c6cbd1;cursor:pointer}.select-menu-header .close-button:hover,.select-menu-header .octicon:hover{color:#444d56}.select-menu-header:focus{outline:none}.select-menu-filters{background-color:#f6f8fa}.select-menu-text-filter{padding:10px 10px 0}.select-menu-text-filter:first-child:last-child{padding-bottom:10px;border-bottom:1px solid #dfe2e5}.select-menu-text-filter input{display:block;width:100%;max-width:100%;padding:5px;border:1px solid #dfe2e5;border-radius:3px}.select-menu-text-filter input:-ms-input-placeholder{color:#a3aab1}.select-menu-text-filter input::-ms-input-placeholder{color:#a3aab1}.select-menu-text-filter input::placeholder{color:#a3aab1}.select-menu-tabs{padding:10px 10px 0;border-bottom:1px solid #dfe2e5}.select-menu-tabs ul{position:relative;bottom:-1px}.select-menu-tabs .select-menu-tab{display:inline-block}.select-menu-tabs a,.select-menu-tabs .select-menu-tab-nav{display:inline-block;padding:4px 8px 2px;font-size:11px;font-weight:600;color:#6a737d;text-decoration:none;cursor:pointer;background:transparent;border:1px solid transparent;border-radius:3px 3px 0 0}.select-menu-tabs a:hover,.select-menu-tabs .select-menu-tab-nav:hover{color:#24292e}.select-menu-tabs a.selected,.select-menu-tabs .select-menu-tab-nav.selected{color:#24292e;background-color:#fff;border-color:#dfe2e5;border-bottom-color:#fff}.select-menu-list{position:relative;max-height:400px;overflow:auto}.select-menu-list.select-menu-tab-bucket{display:none}.select-menu-list.select-menu-tab-bucket.selected{display:block}.select-menu-list.is-showing-new-item-form .select-menu-new-item-form{display:block}.select-menu-list.is-showing-new-item-form .select-menu-no-results,.select-menu-list.is-showing-new-item-form .select-menu-clear-item{display:none}.select-menu-blankslate{padding:16px;text-align:center}.select-menu-blankslate svg{display:block;margin-right:auto;margin-bottom:9px;margin-left:auto;fill:#879099}.select-menu-blankslate h3{font-size:14px;color:#24292e}.select-menu-blankslate p{width:195px;margin-right:auto;margin-bottom:0;margin-left:auto}.select-menu-item{display:block;padding:8px 8px 8px 30px;overflow:hidden;color:inherit;cursor:pointer;border-bottom:1px solid #eaecef}.select-menu-item .select-menu-item-text .octicon-x{display:none;float:right;margin:1px 10px 0 0;opacity:0.6}.select-menu-item:hover{text-decoration:none}.select-menu-item.select-menu-item-template{display:none}.select-menu-item.disabled,.select-menu-item.disabled.selected{color:#6a737d;cursor:default}.select-menu-item.disabled .description,.select-menu-item.disabled.selected .description{color:#6a737d}.select-menu-item.disabled.opaque,.select-menu-item.disabled.selected.opaque{opacity:0.7}.select-menu-item.disabled .select-menu-item-gravatar,.select-menu-item.disabled.selected .select-menu-item-gravatar{opacity:0.5}.select-menu-item .octicon{vertical-align:middle}.select-menu-item .octicon-check,.select-menu-item input[type="radio"]:not(:checked)+.octicon-check{visibility:hidden}.select-menu-item input[type="radio"]{display:none}.select-menu-item:focus{outline:none}.select-menu-item:hover,.select-menu-item:hover.selected,.select-menu-item:hover.select-menu-action,.select-menu-item:hover .description-inline,.select-menu-item:focus,.select-menu-item:focus.selected,.select-menu-item:focus.select-menu-action,.select-menu-item:focus .description-inline,.select-menu-item.navigation-focus,.select-menu-item.navigation-focus.selected,.select-menu-item.navigation-focus.select-menu-action,.select-menu-item.navigation-focus .description-inline,.select-menu-item[aria-selected="true"],.select-menu-item[aria-selected="true"].selected,.select-menu-item[aria-selected="true"].select-menu-action,.select-menu-item[aria-selected="true"] .description-inline{color:#fff;background-color:#0366d6}.select-menu-item:hover>.octicon,.select-menu-item:hover.selected>.octicon,.select-menu-item:hover.select-menu-action>.octicon,.select-menu-item:hover .description-inline>.octicon,.select-menu-item:focus>.octicon,.select-menu-item:focus.selected>.octicon,.select-menu-item:focus.select-menu-action>.octicon,.select-menu-item:focus .description-inline>.octicon,.select-menu-item.navigation-focus>.octicon,.select-menu-item.navigation-focus.selected>.octicon,.select-menu-item.navigation-focus.select-menu-action>.octicon,.select-menu-item.navigation-focus .description-inline>.octicon,.select-menu-item[aria-selected="true"]>.octicon,.select-menu-item[aria-selected="true"].selected>.octicon,.select-menu-item[aria-selected="true"].select-menu-action>.octicon,.select-menu-item[aria-selected="true"] .description-inline>.octicon{color:#fff}.select-menu-item:hover .description,.select-menu-item:hover .description-warning,.select-menu-item:hover.selected .description,.select-menu-item:hover.selected .description-warning,.select-menu-item:hover.select-menu-action .description,.select-menu-item:hover.select-menu-action .description-warning,.select-menu-item:hover .description-inline .description,.select-menu-item:hover .description-inline .description-warning,.select-menu-item:focus .description,.select-menu-item:focus .description-warning,.select-menu-item:focus.selected .description,.select-menu-item:focus.selected .description-warning,.select-menu-item:focus.select-menu-action .description,.select-menu-item:focus.select-menu-action .description-warning,.select-menu-item:focus .description-inline .description,.select-menu-item:focus .description-inline .description-warning,.select-menu-item.navigation-focus .description,.select-menu-item.navigation-focus .description-warning,.select-menu-item.navigation-focus.selected .description,.select-menu-item.navigation-focus.selected .description-warning,.select-menu-item.navigation-focus.select-menu-action .description,.select-menu-item.navigation-focus.select-menu-action .description-warning,.select-menu-item.navigation-focus .description-inline .description,.select-menu-item.navigation-focus .description-inline .description-warning,.select-menu-item[aria-selected="true"] .description,.select-menu-item[aria-selected="true"] .description-warning,.select-menu-item[aria-selected="true"].selected .description,.select-menu-item[aria-selected="true"].selected .description-warning,.select-menu-item[aria-selected="true"].select-menu-action .description,.select-menu-item[aria-selected="true"].select-menu-action .description-warning,.select-menu-item[aria-selected="true"] .description-inline .description,.select-menu-item[aria-selected="true"] .description-inline .description-warning{color:#fff}.select-menu-item[aria-selected="true"].disabled,.select-menu-item.navigation-focus.disabled{color:rgba(255,255,255,0.5)}.select-menu-item[aria-selected="true"].disabled .description,.select-menu-item.navigation-focus.disabled .description{color:rgba(255,255,255,0.6)}.select-menu-item>.octicon-dash{display:none}.select-menu-item.indeterminate>.octicon-check{display:none}.select-menu-item.indeterminate>.octicon-dash{display:block}.select-menu-item[aria-checked="true"] .octicon-check,.select-menu-item input:checked+.octicon-check{color:inherit;visibility:visible}.select-menu-item.selected{color:#24292e}.select-menu-item.selected .description{color:#586069}.select-menu-item.selected>.octicon{color:#24292e}.select-menu-item.selected .octicon-check{color:inherit;visibility:visible}.select-menu-item.selected .select-menu-item-text .octicon-x{display:block;color:inherit}.select-menu.label-select-menu .select-menu-item:active{background-color:transparent !important}.select-menu-item a{color:inherit;text-decoration:none}.select-menu-item .hidden-select-button-text{display:none}.select-menu-item .css-truncate-target{max-width:100%}.select-menu-item-parent{pointer-events:none}.select-menu-item-parent:hover{cursor:default}.select-menu-item-parent.navigation-focus,.select-menu-item-parent.navigation-focus.selected,.select-menu-item-parent[aria-selected="true"],.select-menu-item-parent[aria-selected="true"].selected{color:#24292e;background-color:#f1f8ff}.select-menu-item-parent.navigation-focus .octicon-check,.select-menu-item-parent.navigation-focus.selected .octicon-check,.select-menu-item-parent[aria-selected="true"] .octicon-check,.select-menu-item-parent[aria-selected="true"].selected .octicon-check{color:#24292e}.select-menu-item-icon{float:left;margin-left:-20px}form.select-menu-item>div:first-child{display:none !important}.select-menu-list:last-child .select-menu-item:last-child,.select-menu-item.last-visible{border-bottom:0;border-radius:0 0 3px 3px}.select-menu-action{font-weight:400;color:#444d56}.select-menu-action>.octicon{color:inherit}.select-menu-action:hover{color:#0366d6}.select-menu-no-results{display:none;padding:9px;color:#586069;cursor:auto}.select-menu-list.filterable-empty .select-menu-no-results,.select-menu-no-results:only-child{display:block}.select-menu-button-gravatar,.select-menu-item-gravatar{width:20px;overflow:hidden;line-height:0}.select-menu-button-gravatar img,.select-menu-item-gravatar img{display:inline-block;width:20px;height:20px;border-radius:3px}.select-menu-item-gravatar{float:left;width:20px;height:20px;margin-right:8px;border-radius:2px}.select-menu-button-gravatar{float:left;margin-right:5px}.select-menu-item-text{display:block;text-align:left}.select-menu-item-text .description{display:block;max-width:265px;font-size:12px;color:#586069}.select-menu-item-text .description-inline{font-size:10px;color:#586069}.select-menu-item-text .description-warning{color:#cb2431}.select-menu-item-heading{display:block;margin-top:0;margin-bottom:0;font-size:14px;font-weight:600}.select-menu-item-heading .description{display:inline;font-weight:400}.select-menu-new-item-form{display:none}.select-menu-new-item-form .octicon{color:#0366d6}.modal-backdrop{display:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}body.menu-active .modal-backdrop{position:fixed;top:0;left:0;z-index:20;display:block;width:100vw;height:100vh}.authors-2 .AvatarStack{min-width:36px !important}.authors-3 .AvatarStack{min-width:46px !important}[aria-selected="true"] .AvatarStack-body,.navigation-focus .AvatarStack-body{background:#f6fbff}.commit-tease .AvatarStack-body{background:#f1f8ff}.blame-commit .AvatarStack{margin-top:3px}.unfurl{padding:0 1em;border-left:.25em solid #d1d5da}.unfurl-icon{width:15px;height:15px}.hide-unfurl{display:none}.editable-comment .hide-unfurl{display:block}body{min-width:1012px;word-wrap:break-word}.page-content{padding-top:20px}.container-sm{max-width:544px}.marketing .pagehead h1{font-size:30px}.marketing .pagehead p{margin-top:4px;margin-bottom:0;font-size:14px;color:#586069}.marketing .pagehead ul.actions{margin-top:10px}.marketing h2 .secure{float:right;padding:1px 0;font-size:11px;font-weight:600;color:#28a745;text-transform:uppercase}.marketing .questions p{font-size:14px}.marketing-section{position:relative;padding-top:80px;padding-bottom:80px;font-size:16px;line-height:1.5;text-align:center;border-bottom:1px solid #e5e5e5}.marketing-section::before{display:table;content:""}.marketing-section::after{display:table;clear:both;content:""}.marketing-section h3{font-size:21px;font-weight:400}.marketing-hero-octicon{position:relative;width:100px;height:100px;margin:0 auto 15px;text-align:center;border:solid 1px #e5e5e5;border-radius:50px}.marketing-hero-octicon .octicon{margin-top:22px;color:#0366d6}.marketing-hero-octicon .octicon-checklist{position:relative;right:-3px}.hanging-icon-list li{padding-left:25px;margin:10px 0;font-size:14px;list-style-type:none}.hanging-icon-list .octicon{float:left;margin-top:3px;margin-left:-25px;color:#586069}.hanging-icon-list .octicon-check{color:#28a745}.hanging-icon-list .octicon-x{color:#cb2431}.nonprofit-head{padding:100px 0 120px}.heart{position:absolute;top:40%;left:50%;width:12px;height:12px;margin-left:400px;background:#83d6c0;box-shadow:140px 30px 0 #efa, 120px -120px 0 #aded84, 220px -60px 0 #ded, 30px 240px 0 #ada, 60px -60px 0 #d76666, 60px -30px 0 #ff846f, 60px 0 0 #f9a7a7, 60px 30px 0 #ffc8c8, 60px 60px 0 #ffd8d8, 30px 60px 0 #baf2ca, 30px 30px 0 #98eaac, 30px 0 0 #80d896, 30px -30px 0 #6dd085, 30px -60px 0 #55be6f, 0 -60px 0 #4cc2a7, 0 -30px 0 #73d3b9, 0 30px 0 #93e3cd, 0 60px 0 #adf9e4, -30px 60px 0 #ffe1b9, -30px 30px 0 #ffd194, -30px 0 0 #ffc86f, -60px 0 0 #fd9ff0, -60px 30px 0 #ffbaf7, -60px 60px 0 #fccdf7, -180px 60px 0 #9df;transform:rotate(45deg)}.heart:first-child{margin-left:-400px;transform:rotate(-45deg),scaleX(-1)}.octo-earth{position:absolute;bottom:-150px;left:50%;margin-left:-120px;animation:rotate 20s infinite linear}@keyframes rotate{0%{transform:rotate(0)}100%{transform:rotate(-360deg)}}.btn-blurple{color:#fff;background-color:#3b41af;background-image:linear-gradient(-180deg, #6570e2 0%, #3b41af 90%)}.btn-blurple:focus,.btn-blurple.focus{box-shadow:0 0 0 0.2em rgba(101,112,226,0.4)}.btn-blurple:hover,.btn-blurple.hover{background-color:#383ea7;background-image:linear-gradient(-180deg, #5c68e0 0%, #383ea7 90%);background-position:-.5em;border-color:rgba(27,31,35,0.5)}.btn-blurple:active,.btn-blurple.selected,[open]>.btn-blurple{background-color:#3a43bb;background-image:none;border-color:rgba(27,31,35,0.5);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn-blurple:disabled,.btn-blurple.disabled{color:rgba(255,255,255,0.75);background-color:#9da0d7;background-image:none;border-color:rgba(27,31,35,0.2);box-shadow:none}.btn-blurple .Counter{color:#4350dc;background-color:#fff}.integrations-hero-octicon.marketing-hero-octicon{width:75px;height:75px;border-width:5px}.integrations-hero-octicon.marketing-hero-octicon .octicon{margin-top:15px}.marketing-blue-octicon{color:#34acbf;border-color:#34acbf}.marketing-blue-octicon .octicon{color:#34acbf}.marketing-turquoise-octicon{color:#75bbb6;border-color:#75bbb6}.marketing-turquoise-octicon .octicon{color:#75bbb6}.marketing-purple-octicon{color:#b086b7;border-color:#b086b7}.marketing-purple-octicon .octicon{color:#b086b7}.marketing-graphic{position:relative}.intgrs-dir .marketing-graphic{padding-right:0;margin:0}.intgrs-dir .footer{margin-top:40px}.intgrs-dir-section h2{margin-top:0;margin-bottom:20px;font-size:26px;font-weight:300}.intgrs-dir-intro{padding:40px 0;margin:0;text-align:left;background-image:linear-gradient(-110deg, #48227d 0%, #2f569c 100%);border-bottom:0}.pagehead+.intgrs-dir-intro{margin-top:-20px}.intgrs-dir-intro .directory-header-back{margin-top:10px;font-size:18px;color:#fff}.intgrs-dir-intro .directory-header-back:hover{color:#d7def1;text-decoration:none}.intgrs-dir-intro .directory-header-back .octicon{vertical-align:middle}.intgrs-dir-intro .directory-header-back .header-link{color:#96dad9}.intgrs-dir-intro .column{padding-right:40px;padding-left:0}.intgrs-dir-intro .directory-tag-line{margin-bottom:0;font-size:28px;font-weight:400;color:#fff}.intgrs-dir-intro .lead{margin-top:10px;margin-bottom:6px;font-size:18px;font-weight:400;color:#d7def1}.intgrs-lstng-search{display:inline-block;width:33%;margin-left:20px}.intgrs-lstng-search .subnav-search-input{width:100%}.intgrs-lstng-categories-container{display:inline-block;float:left;width:20%}.intgrs-lstng-categories-container .intgrs-lstng-categories{top:0}.intgrs-lstng-categories-container .filter-item{padding:6px 10px;margin-right:-10px;margin-left:-10px}.intgrs-lstng-container{display:inline-block;width:80%;text-align:left}.intgrs-lstng-item{position:relative;display:inline-flex;width:30.8%;font-size:14px;border:1px solid #ededed;border-radius:4px;transition:border-color 0.15s ease 0s, transform 0.15s ease 0s, box-shadow 0.15s ease 0s, color 0.15s ease 0s}.intgrs-lstng-item:hover{border-color:#51a7e8;box-shadow:0 0 5px rgba(81,167,232,0.5);transform:scale(1.05)}.intgrs-lstng-item .intgrs-lstng-logo{display:block;margin:0 auto 10px}.intgrs-lstng-item .draft-tag{position:absolute;top:-1px;left:10px}.intgrs-lstng-item-link{display:block;width:100%;height:181px;padding-top:20px}.intgrs-lstng-item-link:hover{text-decoration:none}.intgrs-lstng-item-link:hover .intgrs-lstng-item-header{color:#0366d6}.intgrs-lstng-item-header{margin:15px 10px 0;font-size:14px;font-weight:600;color:#333}.intgrs-lstng-item-description{position:relative;height:2.8em;padding:0 10px;margin-top:5px;overflow:hidden;font-size:13px;color:#586069}.intgrs-lstng-item-description::after{position:absolute;right:0;bottom:0;padding:0 15px 0 20px;color:transparent;content:"\00a0";background-image:linear-gradient(to right, rgba(255,255,255,0), #fff 80%)}.intgr-admin-link{position:relative;display:inline-block;height:25px;padding-left:23px;font-size:13px;vertical-align:middle;border:1px solid #ededed;border-radius:3px}.intgr-admin-link.draft-tag{padding-left:25px;border:0}.intgr-admin-link.draft-tag .octicon,.intgr-admin-link.draft-tag:hover .octicon{color:#fff}.intgr-admin-link.draft-tag:hover{text-decoration:none;background-color:#000}.intgr-admin-link:hover .octicon{color:#0366d6}.intgr-admin-link .octicon{position:absolute;top:3px;left:5px;color:#586069}.intgr-feat-header{position:relative;width:85%;padding:0 65px 10px;color:#d7def1}.intgr-feat-header .intgr-admin-link{border-color:rgba(215,222,241,0.6)}.intgr-feat-header .intgr-admin-link .octicon{color:#d7def1}.intgr-feat-header .intgr-admin-link:hover .octicon{color:#fff}.intgr-feat-header .marketing-hero-octicon{position:absolute;top:0;left:5px;width:50px;height:50px;border-width:3px}.intgr-feat-header .marketing-hero-octicon .octicon{margin-top:11px}.intgr-feat-header h2{margin:0;font-size:25px;line-height:50px;color:#fff}.intgr-feat-header p{max-width:580px;margin:0;font-size:18px}.integrations-breadcrumb{display:inline-block;font-weight:400;color:#8296cc}.integrations-breadcrumb-link{line-height:0;color:#d7def1}.integrations-breadcrumb-link:hover{color:#fff;text-decoration:none}.integrations-auth-wrapper{width:511px;margin:60px auto}.integrations-auth-header{font-size:20px;text-align:center}.integrations-permissions-group dt{font-size:18px;font-weight:400}.integrations-permissions-group .integrations-permission{position:relative;padding-left:22px;margin-bottom:10px;list-style-type:none}.integrations-permissions-group .integrations-permission .octicon{position:absolute;top:1px;left:0;margin-right:10px}.integrations-install-target .select-menu{vertical-align:middle}.integrations-install-target input[type=radio]{margin-right:10px}.integrations-install-target .flash{background-color:transparent}.integrations-install-target .flash-error{background-color:transparent;border:0}.integrations-install-target .octicon-lock,.integrations-install-target .octicon-repo{margin-right:3px}.integrations-install-target .octicon-lock{color:rgba(27,31,35,0.7)}.integrations-install-target .private{background-color:#fff9ea}.integrations-install-target [aria-selected="true"].private,.integrations-install-target .navigation-focus.private{background-color:#4078c0}.integrations-install-target [aria-selected="true"].octicon-lock,.integrations-install-target .navigation-focus .octicon-lock{color:inherit}.integrations-setup-note{margin:10px 0}.listgroup-item{line-height:inherit}.listgroup-item.disabled{background-color:#fafbfc}.listgroup-item.disabled .listgroup-item-title{color:#333}.integration-key-management-wrapper .integration-key-downloading{display:none}.integration-key-management-wrapper .integration-key-list{display:none}.integration-key-management-wrapper .flash-error{display:none}.integration-key-management-wrapper .blankslate{margin-bottom:30px}.integration-key-management-wrapper .action .deletable{display:none}.integration-key-management-wrapper .action .undeletable{display:block}.integration-key-management-wrapper.multi-keys .action .deletable{display:block}.integration-key-management-wrapper.multi-keys .action .undeletable{display:none}.integration-key-management-wrapper.error .flash-error{display:block}.integration-key-management-wrapper.error .integration-key{opacity:0.5}.integration-key-management-wrapper.error .action .deletable{display:none}.integration-key-management-wrapper.error .action .undeletable{display:block}.integration-key-management-wrapper.downloading .blankslate{display:none}.integration-key-management-wrapper.downloading .integration-key-downloading{display:block}.integration-key-management-wrapper.downloading .integration-key-list{display:block}.integration-key-management-wrapper.downloading .integration-key-management{display:none}.integration-key-management-wrapper.has-keys .blankslate{display:none}.integration-key-management-wrapper.has-keys .integration-key-list{display:block}.link-small{color:#767676;transition:color 500ms ease}.listgroup-item:hover .link-small{color:#0366d6}.social-callout-twitter:hover .social-callout-twitter-logo{background-image:url("/images/icons/twitter-white.png")}.social-callout-twitter-logo{display:inline-block;width:32px;height:32px;vertical-align:middle;background:url("/images/icons/twitter.png") 0 0 no-repeat;background-size:32px auto}.accessibility-error{outline:10px solid #d73a49 !important}.account-membership-form .become-a-member,.account-membership-form .already-a-member{display:none}.account-membership-form.is-member .already-a-member{display:block}.account-membership-form.is-not-member .become-a-member{display:block}.billing-plans tbody td{width:25%;vertical-align:middle}.billing-plans .current{background-color:#f2ffed}.billing-plans .name{font-size:14px;font-weight:600;color:#333}.billing-plans .coupon{font-size:12px}.billing-plans .coupon td{color:#fff;background-color:#28a745}.billing-plans .coupon .text-right{white-space:nowrap}.billing-plans .coupon.expiring td{background-color:#df6e00}.billing-plans .coupon.expiring .coupon-label::after{border-bottom-color:#df6e00}.billing-plans tbody>.selected{background-color:#fdffce}.coupon-label{position:relative;padding:9px;margin:-9px}.coupon-label::after{position:absolute;bottom:100%;left:15px;width:0;height:0;pointer-events:none;content:" ";border:solid transparent;border-width:5px;border-bottom-color:#28a745}.boxed-group-table .toggle-currency{font-size:11px;font-weight:400}.has-removed-contents{display:none}.currency-notice{margin-bottom:10px}.org-login{margin-top:-30px;margin-bottom:30px}.org-login img{width:450px;padding:1px;margin:10px -25px;border:1px solid #ccc}.plan-notice{padding:10px;margin-bottom:0;border-top:1px solid #eee}.member-list-item .member-username{display:inline}.member-list-item .member-link{display:inline}.actor-and-action{font-weight:600}.vertical-separator{margin-right:8px;margin-left:5px;border-left:1px solid gray}.audit-log-search .audit-search-form{margin-bottom:10px}.audit-log-search .audit-results-actions{margin:15px 0}.audit-log-search .audit-search-clear{margin-bottom:0}.auth-form{width:340px;margin:0 auto}.auth-form .form-group.warn .warning,.auth-form .form-group.warn .error,.auth-form .form-group.errored .warning,.auth-form .form-group.errored .error{max-width:274px}.auth-form-header{padding:10px 20px;margin:0;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.3);background-color:#829aa8;border:1px solid #768995;border-radius:3px 3px 0 0}.auth-form-header h1{font-size:16px}.auth-form-header h1 a{color:#fff}.auth-form-header .octicon{position:absolute;top:10px;right:20px;color:rgba(0,0,0,0.4);text-shadow:0 1px 0 rgba(255,255,255,0.1)}.auth-form-message{max-height:140px;padding:20px 20px 10px;overflow-y:scroll;border:1px solid #d8dee2;border-radius:3px}.auth-form-message ol,.auth-form-message ul{padding-left:inherit;margin-bottom:inherit}.auth-form-body{padding:20px;font-size:14px;background-color:#fff;border:1px solid #d8dee2;border-top:0;border-radius:0 0 3px 3px}.auth-form-body .input-block{margin-top:5px;margin-bottom:15px}.auth-form-body p{margin:10px 0}.auth-form-body ol,.auth-form-body ul{padding-left:inherit;margin-bottom:inherit}.two-factor-help{position:relative;padding:10px 10px 10px 36px;margin:60px 0 auto auto;border:1px solid #eaeaea;border-radius:3px}.two-factor-help h4{margin-top:0;margin-bottom:5px}.two-factor-help .octicon-device-mobile{position:absolute;top:10px;left:10px}.two-factor-help .octicon-key{position:absolute;left:10px}.two-factor-help ul{list-style-type:none}.u2f-send-code-spinner{position:relative;bottom:2px;display:none;vertical-align:bottom}.loading .u2f-send-code-spinner{display:inline}.u2f-login-spinner{top:2px}.u2f-auth-header{padding-bottom:10px;margin-bottom:20px;border-bottom:1px solid #eaeaea}.auth-form-body .u2f-auth-form-body{padding:0}.u2f-auth-form-body{padding:30px 30px 20px;text-align:center}.u2f-auth-form-body button{margin-top:20px}.u2f-auth-form-body .u2f-enabled{display:block}.u2f-auth-form-body .u2f-disabled{display:none}.u2f-auth-form-body.unavailable .u2f-enabled{display:none}.u2f-auth-form-body.unavailable .u2f-disabled{display:block}.u2f-auth-icon{color:#aaa}.flash.sms-error,.flash.sms-success{display:none;margin:0 0 10px}.is-sent .sms-success{display:block}.is-sent .sms-error{display:none}.is-not-sent .sms-success{display:none}.is-not-sent .sms-error{display:block}.session-authentication{background-color:#f9f9f9}.session-authentication .header-logged-out{background-color:transparent;border-bottom:0}.session-authentication .header-logo{color:#333}.session-authentication .flash{padding:15px 20px;margin:0 auto;margin-bottom:10px;font-size:13px;border-style:solid;border-width:1px;border-radius:5px}.session-authentication .flash .container{width:auto}.session-authentication .flash .flash-close{height:40px}.session-authentication .flash.is-signed-in,.session-authentication .flash.is-signed-out{width:100%;border-top:0;border-right:0;border-left:0;border-radius:0}.session-authentication .auth-form label{display:block;margin-bottom:7px}.session-authentication .auth-form .btn{margin-top:20px}.session-authentication .auth-form .u2f-error{margin-bottom:0}.session-authentication .label-link{float:right;font-size:12px}.session-authentication .auth-form-header{margin-bottom:15px;color:#333;text-align:center;text-shadow:none;background-color:transparent;border:0}.session-authentication .auth-form-header h1{font-size:24px;font-weight:300;letter-spacing:-0.5px}.session-authentication .auth-form-body{border-top:1px solid #d8dee2;border-radius:5px}.session-authentication .auth-form-body.u2f-auth-form-body{padding:20px}.session-authentication .create-account-callout{padding:15px 20px;text-align:center;border:1px solid #d8dee2;border-radius:5px}.session-authentication .two-factor-help{padding:0 0 0 20px;margin-top:20px;border:0}.session-authentication .two-factor-help .octicon-device-mobile{top:3px;left:0}.session-authentication .two-factor-help .octicon-key{top:0;left:0}.session-authentication.enterprise .header-logged-out{padding:48px 0 28px;background-color:transparent}.session-authentication.hosted .header-logged-out{padding:40px 0 20px;background-color:transparent}.cvv-hint{position:relative}.cvv-hint:hover .cvv-hint-tooltip{display:block}.cvv-hint-tooltip{position:absolute;left:-115px;z-index:1000;display:none;padding:15px;background-color:#fff;border:1px solid #d0d0d0;border-radius:3px;transform:translateX(-100%) translateY(-50%)}.credit-card{position:relative;width:250px;height:150px;padding:20px;margin-top:5px;background-color:#f5f5f5;border-radius:10px}.credit-card.amex{margin-top:15px}.credit-card.amex .title{position:relative;top:-5px;z-index:1;font-family:"Arial Black", "Arial Bold", Gadget, sans-serif;color:#6a737d;text-align:center;letter-spacing:1px;transform:scale(1.3, 1)}.credit-card.amex .card-number{position:relative;display:inline-block;margin-top:40px;font-size:15px;white-space:nowrap}.credit-card.amex .gladiator{position:absolute;top:50px;left:50%;width:70px;height:80px;margin-left:-35px;background-color:#f5f5f5;border:3px solid #fff;border-radius:50%;box-shadow:0 0 1px #aaa}.credit-card.normal .strap{height:20px;margin:-5px -20px 15px;background-color:#555}.credit-card.normal .signature{display:inline-block;width:150px;height:30px;font-family:"Brush Script MT", cursive;font-size:17px;line-height:33px;color:#767676;text-indent:10px;letter-spacing:-1px;white-space:nowrap;background-color:#fff}.credit-card .cvv{position:relative;top:-10px;left:-7px;display:inline-block;padding:2px 5px;font-family:monospace;font-size:10px;line-height:1;text-align:center;border:2px solid #f00;border-top-left-radius:20px 10px;border-top-right-radius:20px 10px;border-bottom-right-radius:20px 10px;border-bottom-left-radius:20px 10px}.credit-card .cvv span{position:absolute;right:100%;margin-right:5px;color:#586069}.credit-card .text{display:block;font-family:monospace;font-size:7px;font-weight:600;line-height:1.1;text-transform:uppercase}.billing-addon-items table input{width:5em}.billing-addon-items td{vertical-align:middle;border-bottom:0}.billing-addon-items td.fixed{width:150px}.billing-addon-items td.black{color:#000}.billing-addon-items tr{border-bottom:1px solid #eee}.billing-addon-items tr:last-child{border-bottom-width:0}.billing-addon-items tr:nth-child(even){background-color:#fafbfc}.billing-addon-items tr.total-row{color:#cb2431;background-color:#fff}.billing-addon-items .new-addon-items{margin-left:5px}.billing-addon-items .addon-cost{color:#6a737d}.billing-addon-items .discounted-original-price{color:#666}.billing-addon-items .form-submit,.billing-addon-items .payment-method{margin-left:8px}.billing-addon-items .payment-summary{margin-right:8px;margin-left:8px}.billing-credit-card .javascript-disabled-overlay{position:absolute;top:0;left:0;z-index:1;display:none;width:100%;height:100%;background-color:#fff;opacity:0.5}.billing-credit-card.disabled .javascript-disabled-overlay,.billing-credit-card.unsupported .javascript-disabled-overlay{display:block}.billing-actions{padding-bottom:10px}.billing-heres-what-appears{margin:0 0 10px}.billing-extra-box{padding-left:10px;margin:10px 0;border-left:3px solid #eee}.billing-vat-box{padding-left:10px;margin:10px 0;border-left:3px solid #eee}.billing-section .action-button{float:right;margin-bottom:5px;margin-left:10px}.billing-section .section-label{position:absolute;width:85px;font-weight:400;color:#586069;text-align:right}.billing-section .section-title{width:100px}.billing-section .section-content{margin-left:100px;color:#333}.billing-section:last-child{border-bottom:0}.billing-section .usage-bar{max-width:304px}.usage-bar{width:100%;margin:5px 0 0;background:#eee;border-radius:20px}.usage-bar.exceeded .progress{background:#d73a49}.usage-bar .progress{position:relative;max-width:100%;height:5px;background:#67d07c;border-radius:20px;transition:width 0.3s}.usage-bar .progress.no-highlight{background:#959da5}.live-update-seats-usage{margin:10px 0 5px}.billing-per-seat-callout{position:relative}.lfs-data-pack-field{margin:-6px 0}.packs-table .desc{width:1%;white-space:nowrap}.lfs-data-icon{color:#586069;text-align:center}.lfs-data-icon.dark{color:#333}.lfs-data-icon.octicon-database{margin-right:3px;margin-left:2px}.setup-wrapper .paypal-container{margin-bottom:30px}.setup-wrapper .paypal-logged-in .paypal-container{margin-bottom:10px}.payment-methods{position:relative}.payment-methods .selected-payment-method{display:none}.payment-methods .selected-payment-method::before{display:table;content:""}.payment-methods .selected-payment-method::after{display:table;clear:both;content:""}.payment-methods .selected-payment-method.active{display:block}.payment-methods .form-group dd .card-select-number{width:300px}.payment-methods .form-group dd .form-control.short.input-vat{width:300px}.payment-methods .pay-with-header{margin:5px 0}.payment-methods .pay-with-paypal .setup-creditcard-form,.payment-methods .pay-with-paypal .paypal-form-actions,.payment-methods .pay-with-paypal .terms,.payment-methods .pay-with-paypal .paypal-signed-in,.payment-methods .pay-with-paypal .paypal-down-flash,.payment-methods .pay-with-paypal .loading-paypal-spinner{display:none}.payment-methods.paypal-loading .loading-paypal-spinner{display:block}.payment-methods.paypal-down .paypal-down-flash{display:block}.payment-methods.paypal-logged-in .paypal-sign-in{display:none}.payment-methods.paypal-logged-in .setup-creditcard-form,.payment-methods.paypal-logged-in .paypal-form-actions,.payment-methods.paypal-logged-in .terms,.payment-methods.paypal-logged-in .paypal-signed-in{display:block}.payment-methods.has-paypal-account .paypal-sign-in{display:none}.payment-methods.has-paypal-account .paypal-signed-in{display:block}.paypal-label{margin:15px 0 10px;font-weight:600}.paypal-container{display:inline-block;margin-bottom:15px;vertical-align:top;background-color:#f9f9f9;border-radius:4px}.braintree-paypal-loggedin{padding:11px 16px !important;background-position:12px 50% !important;border:1px solid #ddd !important;border-radius:4px}.bt-pp-name{margin-left:20px !important}.bt-pp-email{margin-left:15px !important}.bt-pp-cancel{font-size:0 !important;line-height:1 !important;color:#a00 !important;text-decoration:none !important}.payment-history .id,.payment-history .date,.payment-history .receipt,.payment-history .status,.payment-history .amount{white-space:nowrap}.payment-history .break-all{word-break:break-all}.payment-history .receipt{text-align:center}.payment-history .currency,.payment-history .status{color:#586069}.payment-history .status-icon{width:14px;text-align:center}.payment-history .succeeded .status{color:#28a745}.payment-history .refunded,.payment-history .failed{background:#f6f8fa}.payment-history .refunded td,.payment-history .failed td{opacity:0.5}.payment-history .refunded .receipt,.payment-history .refunded .status,.payment-history .failed .receipt,.payment-history .failed .status{opacity:1}.payment-history .refunded .status{color:#6a737d}.payment-history .failed .status{color:#cb2431}.paypal-icon{margin:0 2px 0 1px;vertical-align:middle}.inline-form-action{display:inline}.boxed-group .boxed-group-content{margin:10px}.currency-container .local-currency,.currency-container .local-currency-block{display:none}.currency-container.open .local-currency{display:inline}.currency-container.open .local-currency-block{display:block}.currency-container.open .default-currency{display:none}.strong-label{display:inline-block;margin-bottom:5px;font-weight:600}.discounted-original-price{font-weight:400;color:#586069;text-decoration:line-through}.billing-manager-input{width:500px}.billing-manager-banner{padding:30px 20px;margin-bottom:30px;overflow:hidden;background:#f9f9f9;border-bottom:1px solid #eee}.billing-manager-banner .container{position:relative}.billing-manager-banner-text{margin-left:210px;font-size:14px;color:#555}.billing-manager-banner-text .btn{margin-top:8px;margin-right:8px}.billing-manager-banner-title{font-size:12px;font-weight:600;color:#586069}.billing-manager-icon{position:absolute;top:-35px;left:0;width:180px;height:180px;font-size:180px;color:#e0e0e0}.golden-ticket-banner{margin-top:30px;margin-bottom:15px;text-align:center;border-top:1px solid #e6d445}.golden-ticket{height:60px;margin-top:-30px}.golden-ticket-button{float:left;width:50%;padding:30px 20px;font-size:18px;font-weight:400}.golden-ticket-button .octicon{margin-right:10px;vertical-align:middle}.golden-ticket-button:first-child{border-radius:3px 0 0 3px}.golden-ticket-button:last-child{border-left:0;border-radius:0 3px 3px 0}.golden-ticket-confirm .setup-header{text-align:center;border:0}.seats-change-arrow{margin:0 10px}.billing-note-block{margin:15px 0}.plan-choice{position:relative;display:block;padding:15px;padding-left:40px;font-weight:400;background-color:#fafbfc;border:1px solid #e0e0e0}.plan-choice.open,.plan-choice.selected{background-color:#fff}.plan-choice--experiment{cursor:pointer;transition:transform 0.3s, box-shadow 0.3s, border-color 0.3s}.plan-choice--experiment.open,.plan-choice--experiment.selected{border-color:#d1d5da;box-shadow:0 1px 15px rgba(27,31,35,0.15);transform:scale(1.025)}.plan-choice--experiment.open .plan-choice-icon,.plan-choice--experiment.selected .plan-choice-icon{background-color:#28a745;box-shadow:0 1px 1px rgba(27,31,35,0.1)}.plan-choice--experiment.open .plan-choice-icon .octicon,.plan-choice--experiment.selected .plan-choice-icon .octicon{transform:scale(1)}.plan-choice--experiment.plan-choice--blue.open,.plan-choice--experiment.plan-choice--blue.selected{border-color:#2188ff}.plan-choice--experiment.plan-choice--blue.open .plan-choice-icon,.plan-choice--experiment.plan-choice--blue.selected .plan-choice-icon{background-color:#0366d6}.plan-choice--experiment.plan-choice--green.open,.plan-choice--experiment.plan-choice--green.selected{border-color:#34d058}.plan-choice--experiment.plan-choice--green.open .plan-choice-icon,.plan-choice--experiment.plan-choice--green.selected .plan-choice-icon{background-color:#28a745}.plan-choice--experiment.plan-choice--purple.open,.plan-choice--experiment.plan-choice--purple.selected{border-color:#8a63d2}.plan-choice--experiment.plan-choice--purple.open .plan-choice-icon,.plan-choice--experiment.plan-choice--purple.selected .plan-choice-icon{background-color:#6f42c1}.plan-choice-icon{transition:box-shadow 0.3s}.plan-choice-icon .octicon{transition:transform 0.2s;transform:scale(0.5)}.plan-choice-top{border-radius:3px 3px 0 0}.plan-choice-bottom{margin-bottom:20px;border-top:0;border-radius:0 0 3px 3px}.plan-choice-radio{position:absolute;top:18px;left:15px}.plan-choice-exp{margin-top:5px;font-size:12px;color:#6a737d}.seat-field{width:50px;margin-right:5px}.billing-form-title{font-size:16px}.billing-line-items{margin-top:10px}.billing-line-item{padding:10px 0;font-size:12px;list-style:none;border-top:1px solid #e0e0e0}.billing-line-item::before{display:table;content:""}.billing-line-item::after{display:table;clear:both;content:""}.billing-line-item-last{font-weight:600;border-top-width:3px}.line-item-value{float:right}.condensed-payment-methods .vat-field,.condensed-payment-methods .cc-field,.condensed-payment-methods .expiration-field,.condensed-payment-methods .country-field,.condensed-payment-methods .vat-field{margin-top:15px}.condensed-payment-methods .vat-field{width:100%;margin-bottom:0}.condensed-payment-methods .cc-field{width:42%}.condensed-payment-methods .expiration-field,.condensed-payment-methods .state-field{width:30%}.condensed-payment-methods .expiration-field{white-space:nowrap}.condensed-payment-methods .expiration-field .select{width:40%}.condensed-payment-methods .expiration-divider{width:20%;line-height:30px;text-align:center}.condensed-payment-methods .cvv-field,.condensed-payment-methods .postcode-field{width:28%;margin-top:15px;margin-bottom:0}.condensed-payment-methods .state-field.column,.condensed-payment-methods .postcode-field.column{margin-top:15px;margin-bottom:0}.condensed-payment-methods .country-field{width:42%}.condensed-payment-methods .state-field{width:30%}.condensed-payment-methods .is-international .country-field{width:72%}.condensed-payment-methods .is-international.no-postcodes .country-field{width:100%}.zuora-billing-section.PaymentMethod--creditcard:not(.has-removed-contents) ~ .SignUpContinueActions{display:none}.zuora-billing-section.PaymentMethod--creditcard-added ~ .SignUpContinueActions{display:block}.zuora-billing-section.PaymentMethod--paypal ~ .SignUpContinueActions{display:block}.blame-commit{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.blame-commit[data-heat="1"]{border-right:2px solid #f66a0a}.blame-commit[data-heat="2"]{border-right:2px solid rgba(246,106,10,0.9)}.blame-commit[data-heat="3"]{border-right:2px solid rgba(246,106,10,0.8)}.blame-commit[data-heat="4"]{border-right:2px solid rgba(246,106,10,0.7)}.blame-commit[data-heat="5"]{border-right:2px solid rgba(246,106,10,0.6)}.blame-commit[data-heat="6"]{border-right:2px solid rgba(246,106,10,0.5)}.blame-commit[data-heat="7"]{border-right:2px solid rgba(246,106,10,0.4)}.blame-commit[data-heat="8"]{border-right:2px solid rgba(246,106,10,0.3)}.blame-commit[data-heat="9"]{border-right:2px solid rgba(246,106,10,0.2)}.blame-commit[data-heat="10"]{border-right:2px solid rgba(246,106,10,0.1)}.heat[data-heat="1"]{background:#f66a0a}.heat[data-heat="2"]{background:rgba(246,106,10,0.9)}.heat[data-heat="3"]{background:rgba(246,106,10,0.8)}.heat[data-heat="4"]{background:rgba(246,106,10,0.7)}.heat[data-heat="5"]{background:rgba(246,106,10,0.6)}.heat[data-heat="6"]{background:rgba(246,106,10,0.5)}.heat[data-heat="7"]{background:rgba(246,106,10,0.4)}.heat[data-heat="8"]{background:rgba(246,106,10,0.3)}.heat[data-heat="9"]{background:rgba(246,106,10,0.2)}.heat[data-heat="10"]{background:rgba(246,106,10,0.1)}.blame-commit-date{font-size:11px;line-height:25px;flex-shrink:0}.blame-commit-date[data-heat="1"]{color:#c24e00}.blame-commit-date[data-heat="2"]{color:#ac571f}.blame-commit-date[data-heat="3"]{color:#a35b2c}.blame-commit-date[data-heat="4"]{color:#9a5f38}.blame-commit-date[data-heat="5"]{color:#926245}.blame-commit-date[data-heat="6"]{color:#896651}.blame-commit-date[data-heat="7"]{color:#806a5e}.blame-commit-date[data-heat="8"]{color:#776d6a}.blame-commit-date[data-heat="9"]{color:#6e7177}.blame-commit-date[data-heat="10"]{color:#6a737d}.line-age-legend .heat{width:2px;height:10px;margin:2px 1px 0}.blame-breadcrumb .css-truncate-target{max-width:680px}.blame-commit-info{width:450px;height:26px}.blame-commit-content{flex-grow:2;overflow:hidden}.blame-commit-message{text-overflow:ellipsis}.blame-commit-message .message.blank{color:rgba(27,31,35,0.3)}.blob-reblame{min-width:24px;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.reblame-link{padding-top:2px;color:#d1d5da}.blame-hunk g-emoji{font-size:14px !important}.blame-hunk:hover .reblame-link{color:#6a737d}.blame-container .blame-blob-num,.blame-container .blob-code-inner{padding-top:3px;padding-bottom:3px}.blame-container .blob-code-inner{flex-grow:1}.editor-abort{display:inline;font-size:14px}.blob-interaction-bar{position:relative;background-color:#f2f2f2;border-bottom:1px solid #e5e5e5}.blob-interaction-bar::before{display:table;content:""}.blob-interaction-bar::after{display:table;clear:both;content:""}.blob-interaction-bar .octicon-search{position:absolute;top:10px;left:10px;font-size:12px;color:#586069}.blob-filter{width:100%;padding:4px 20px 5px 30px;font-size:12px;border:0;border-radius:0;outline:none}.blob-filter:focus{outline:none}.html-blob{margin-bottom:15px}.TagsearchPopover-content{width:inherit;max-width:600px}.license-summary-octicon{color:#959da5}.rule-type-permissions{color:#28a745}.rule-type-conditions{color:#0366d6}.rule-type-limitations{color:#d73a49}.check-for-fork.is-error .check-for-fork-loading{display:none}.check-for-fork.is-error .check-for-fork-error{display:inline-block}.check-for-fork-error{display:none}.commit-form-avatar{margin-left:-64px}.file-commit-form{padding-left:64px}.commit-form::after,.commit-form::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.commit-form::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#fff}.commit-form::before{border-width:8px;border-right-color:#ddd}.quick-pull-new-branch-icon{top:9px;left:10px}.code-formatting-menu{width:260px}.merge-pr{padding-top:10px;margin:20px 0 0;border-top:1px solid #d1d5da}.merge-pr.open .merge-branch-form{display:block}.merge-pr.open .branch-action{display:none}.status-heading{margin-bottom:1px}.merge-status-list{max-height:0;padding:0;margin:15px -15px -16px -55px;overflow-y:auto;border:solid #e1e4e8;border-width:1px 0 0;transition:max-height 0.25s ease-in-out}.statuses-toggle-opened{display:none}.merge-status-item{position:relative;padding:10px 15px;background-color:#fafbfc;border-bottom:1px solid #e1e4e8}.merge-status-item:last-child{border-bottom:0}.merge-status-item .css-truncate-target{max-width:100%}.merge-status-item .dismiss-review-form{display:none}.merge-status-item.open .review-status-item{display:none !important}.merge-status-item.open .dismiss-review-form{display:block}.status-meta{color:#586069}.status-meta-file-name{padding:0.2em 0.4em;margin:0;font-size:85%;background-color:rgba(27,31,35,0.05);border-radius:3px}.status-actions{margin-left:auto}.branch-action-item-icon{float:left;margin-left:-40px}.merge-status-icon{width:30px;margin-right:12px}.merge-pr-more-commits{margin-top:10px;margin-bottom:10px;margin-left:60px;font-size:12px;color:#586069}.branch-action{padding-left:60px;margin-top:15px;margin-bottom:15px}.branch-action .merge-branch-heading{margin-bottom:4px}.branch-action-icon{float:left;width:44px;height:44px;padding:6px;margin-left:-60px;color:#fff;text-align:center;border-radius:3px}.branch-action-body{position:relative;background-color:#fff;border:1px solid #d1d5da;border-radius:3px}.branch-action-body::after,.branch-action-body::before{position:absolute;top:11px;right:100%;left:-16px;display:block;width:0;height:0;pointer-events:none;content:" ";border-color:transparent;border-style:solid solid outset}.branch-action-body::after{margin-top:1px;margin-left:2px;border-width:7px;border-right-color:#fff}.branch-action-body::before{border-width:8px;border-right-color:#d1d5da}.branch-action-body .spinner{display:block;float:left;width:32px;height:32px;margin-right:15px;background:url("/images/spinners/octocat-spinner-32.gif") no-repeat}.branch-action-body .merge-message,.branch-action-body .merge-branch-form{padding:15px;background-color:#fafbfc;border-top:1px solid #e1e4e8;border-bottom-right-radius:2px;border-bottom-left-radius:2px}.post-merge-message{padding:15px}.branch-action-item{padding:15px 15px 15px 55px;font-size:13px;line-height:1.4}.branch-action-item+.branch-action-item,.branch-action-item+.mergeability-details{border-top:1px solid #e1e4e8}.branch-action-item.open>.merge-status-list{max-height:215px;margin-bottom:-15px}.branch-action-item.open .statuses-toggle-opened{display:inline}.branch-action-item.open .statuses-toggle-closed{display:none}.branch-action-btn{margin-left:15px}.branch-action-item-simple{padding-left:15px}.branch-action-item-simple .merge-status-list{margin-left:-15px}.branch-action-item-simple .merge-status-item{padding-left:12px}.branch-action-state-clean .branch-action-icon{background-color:#2cbe4e}.branch-action-state-clean .branch-action-body{border-color:#34d058}.branch-action-state-clean .branch-action-body::before{border-right-color:#34d058}.branch-action-state-unknown .branch-action-icon,.branch-action-state-unstable .branch-action-icon{background-color:#dbab09}.branch-action-state-unknown .branch-action-body,.branch-action-state-unstable .branch-action-body{border-color:#e2cc7a}.branch-action-state-unknown .branch-action-body::before,.branch-action-state-unstable .branch-action-body::before{border-right-color:#e2cc7a}.branch-action-state-merged .branch-action-icon{background-color:#6f42c1}.branch-action-state-merged .branch-action-body{border-color:#d5c8ed}.branch-action-state-merged .branch-action-body::before{border-right-color:#d5c8ed}.branch-action-state-dirty .branch-action-icon,.branch-action-state-closed-dirty .branch-action-icon,.is-rebasing .branch-action-state-dirty-if-rebasing .branch-action-icon{background-color:#6a737d}.branch-action-state-dirty .branch-action-body,.branch-action-state-closed-dirty .branch-action-body,.is-rebasing .branch-action-state-dirty-if-rebasing .branch-action-body{border-color:#d1d5da}.branch-action-state-dirty .branch-action-body::before,.branch-action-state-closed-dirty .branch-action-body::before,.is-rebasing .branch-action-state-dirty-if-rebasing .branch-action-body::before{border-right-color:#d1d5da}.branch-action-state-error .branch-action-icon{background-color:#d73a49}.branch-action-state-error .branch-action-body{border-color:#f97583}.branch-action-state-error .branch-action-body::before{border-right-color:#f97583}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){.branch-action-body .spinner{background-image:url("/images/spinners/octocat-spinner-64.gif");background-size:32px 32px}}.merge-branch-form{display:none;margin:15px 0}.merge-branch-form .commit-form{border-color:#85e89d}.merge-branch-form .commit-form::before{border-right-color:#e1e4e8}.merge-branch-form.error .commit-form,.merge-branch-form.danger .commit-form{border-color:#f97583}.merge-branch-form.error .commit-form::before,.merge-branch-form.danger .commit-form::before{border-right-color:#f97583}.merge-button-matrix-merge-form .merge-branch-form{display:block}.completeness-indicator{width:30px;height:30px;text-align:center}.completeness-indicator .octicon{display:block;margin-top:7px;margin-right:auto;margin-left:auto}.completeness-indicator .octicon-alert{margin-top:6px}.completeness-indicator-success{color:#fff;background-color:#2cbe4e;border-radius:50%}.completeness-indicator-error{color:#fff;background-color:#cb2431;border-radius:50%}.completeness-indicator-problem{color:#fff;background-color:#6a737d;border-radius:50%}.pull-merging .pull-merging-error{display:none}.pull-merging.is-error .pull-merging-error{display:block}.pull-merging.is-error .merge-pr{display:none}.RecentBranches{background-color:#fffbdd;border:1px solid rgba(27,31,35,0.15);border-radius:3px}.RecentBranches-item{line-height:28px;color:#735c0f}.RecentBranches-item+.RecentBranches-item{border-top:1px solid rgba(27,31,35,0.05)}.RecentBranches-item-link{color:#735c0f}.RecentBranches-item-link.css-truncate-target{max-width:400px}.range-editor{position:relative;padding:5px 15px 5px 40px;margin-top:15px;margin-bottom:15px;background-color:#fafbfc;border:1px solid #e1e4e8;border-radius:3px}.range-editor .dots{font-size:16px}.range-editor .select-menu{position:relative;display:inline-block}.range-editor .select-menu.fork-suggester{display:none}.range-editor .branch-name{line-height:22px}.range-editor .branch .css-truncate-target,.range-editor .fork-suggester .css-truncate-target{max-width:180px}.range-editor .pre-mergability{display:inline-block;padding:5px;line-height:26px;vertical-align:middle}.range-editor .pre-mergability .octicon{vertical-align:text-bottom}.range-editor.is-cross-repo .select-menu.fork-suggester{display:inline-block}.range-editor-icon{float:left;margin-top:10px;margin-left:-25px;color:#586069}.gh-header-new-pr{margin-bottom:15px}.gh-header-new-pr .gh-header-meta{padding-bottom:0;margin-top:5px;border-bottom:0}.gh-header-new-pr .branch-name{display:inline}.compare-pr-header{display:none}.is-pr-composer-expanded .compare-show-header{display:none}.is-pr-composer-expanded .compare-pr-header{display:block}.range-cross-repo-pair{display:inline-block;padding:5px;white-space:nowrap}ul.comparison-list{width:350px;margin:25px auto 15px;font-size:14px;text-align:left;background:#fff;border:1px solid #dfe2e5;border-radius:3px}ul.comparison-list>li{padding:7px 10px;list-style-type:none;border-top:1px solid #eaecef}ul.comparison-list>li a{font-weight:600}ul.comparison-list>li em{float:right;font-style:normal;color:#586069}ul.comparison-list>li .octicon{position:relative;top:1px;color:#a3aab1}ul.comparison-list>li .css-truncate-target{max-width:200px}ul.comparison-list>li.title{font-size:12px;font-weight:600;color:#6a737d;text-transform:uppercase;background:#fafbfc;border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.branches .page-header{margin-bottom:20px}.branches .clear-search{display:none}.branches .loading-overlay{position:absolute;top:0;z-index:20;display:none;width:100%;height:100%;padding-top:50px;text-align:center;background-color:rgba(255,255,255,0.7)}.branches .loading-overlay .spinner{display:inline-block}.branches.is-search-mode .clear-search{display:inline-block}.branches.is-loading .loading-overlay{display:block}.branches .status{display:inline-block;width:16px;text-align:center}.branches .status .octicon{position:relative}.branches .status .octicon-primitive-dot{width:10px}.branches .branch-actions{position:relative;top:-4px;right:-4px;float:right}.branches .branch-actions form{display:inline}.branches .branch-actions .octicon{width:16px;text-align:center}.branch-groups{position:relative}.branch-group{width:100%;margin-bottom:20px;border-radius:3px}.branch-group::before{display:table;content:""}.branch-group::after{display:table;clear:both;content:""}.branch-group-heading{padding:6px 12px;background:#f6f8fa;border:1px solid #dfe2e5;border-bottom:0}.branch-group-heading+.branch-summary{border-top:1px solid #dfe2e5}.branch-group-heading .branch-name{color:#fff;background:#6a737d}.branch-group-name{font-weight:600;color:#586069}.branch-group-heading:first-child,.branch-summary:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.branch-group-heading:last-child,.branch-summary:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.branches-view-switcher{display:inline-block;vertical-align:middle}.branch-search{position:relative;float:right;vertical-align:middle}.branch-search .clear-search{position:absolute;top:9px;right:12px;color:#6a737d}.branch-search-field{width:250px;padding-right:25px}.no-results-message{padding:12px;color:rgba(27,31,35,0.5);text-align:center;border:1px solid #dfe2e5;border-radius:0 0 3px 3px}.branch-summary{padding:12px;color:rgba(27,31,35,0.5);border:1px solid #dfe2e5;border-bottom:0}.branch-summary:last-child{border-bottom:1px solid #dfe2e5}.branch-summary .branch-spinner{display:none;vertical-align:text-bottom}.branch-summary.loading .branch-delete-icon{display:none}.branch-summary.loading .branch-spinner{display:inline-block}.branch-summary.is-deleted .existing-branch-summary{display:none}.branch-summary.is-deleted .deleted-branch-summary{display:block}.deleted-branch-summary{display:none}.deleted-branch-summary .css-truncate-target{max-width:500px}.deleted-branch-summary .branch-name{text-decoration:line-through;opacity:0.5}.deleted-branch-summary .branch-spinner{position:relative;top:4px;right:5px;float:right}.branch-delete{display:inline-block;margin:4px 2px 0 8px;color:#cb2431}.branch-delete.disabled{color:#dfe2e5}.more-branches{display:block;width:100%;padding:6px;color:#0366d6;text-align:center;text-decoration:none;background:#eaf5ff;border:1px solid rgba(27,31,35,0.05);border-radius:0 0 3px 3px}.more-branches:hover{background:#dbedff}.more-branches .octicon{position:relative;top:1px;margin-left:5px}.branch-details{display:inline-block;width:490px;margin-right:10px}.branch-details .css-truncate-target{max-width:240px}.branch-details .octicon-shield{margin-right:2px}.branch-meta{font-size:12px;line-height:20px;color:#6a737d}.default-label{display:inline-block;width:150px;text-align:center;vertical-align:top}.default-label .sha{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace}.default-label .sha .ellipses{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";color:inherit}.default-label .sha .octicon{padding-right:4px}.branch-a-b-count{display:inline-block;vertical-align:middle}.branch-a-b-count .count-half{position:relative;float:left;width:90px;padding-bottom:6px;text-align:right}.branch-a-b-count .count-half:last-child{text-align:left;border-left:1px solid #959da5}.branch-a-b-count .count-value{position:relative;top:-1px;display:block;padding:0 3px;font-size:10px}.branch-a-b-count .bar{position:absolute;min-width:3px;height:4px}.branch-a-b-count .meter{position:absolute;height:4px;background-color:#c6cbd1}.branch-a-b-count .meter.zero{background-color:transparent}.branch-a-b-count .bar-behind{right:0;border-radius:3px 0 0 3px}.branch-a-b-count .bar-behind .meter{right:0;border-radius:3px 0 0 3px}.branch-a-b-count .bar-ahead{left:0;border-radius:0 3px 3px 0}.branch-a-b-count .bar-ahead .meter{border-radius:0 3px 3px 0}.branch-a-b-count .bar-ahead.even,.branch-a-b-count .bar-behind.even{min-width:2px;background:#eaecef}.businesses-toolbar .subnav-search{width:240px;margin-right:20px}.custom-messages .add-message{flex:1}.custom-messages .add-message .Subhead-heading{flex:0 0 auto}.custom-messages .add-message p{flex:1;line-height:21px}.custom-messages .tabnav{height:23px;padding:0;border:0}.custom-messages .write-content{margin-right:0;margin-left:0}.custom-messages .timeline-comment{margin-top:-25px;background-color:inherit;border:0}.custom-messages .comment-form-textarea{min-height:200px}.custom-messages .flash{margin-bottom:10px;overflow-y:hidden}body.full-width-p-0 .new-discussion-timeline{padding:0 !important;border-bottom:1px #e1e4e8 solid}body.full-width-p-0 .footer .mt-6{margin-top:0 !important;border-top:0 !important}body.full-width-p-0 .tabnav-pr .tabnav-extra{margin-right:24px}body.full-width-p-0 .tabnav-pr .tabnav-tabs{margin-left:16px}.checks-toolbar-commit-message.css-truncate{max-width:300px}.checks-index-group .octicon{vertical-align:text-top}.checks-index-item:first-child::before{position:absolute;top:0;left:0;z-index:1;width:100%;height:5px;content:" ";background-image:linear-gradient(to bottom, rgba(47,54,61,0.1) 0%, rgba(255,255,255,0) 100%)}.checks-index-item:hover{background-color:#fff !important}.checks-index-item.selected{background-color:#fff !important}.checks-index-item.selected::after{position:absolute;top:0;bottom:0;left:0;display:block;width:3px;content:" ";background-color:#e36209}.checks-index-item .css-truncate-target{max-width:220px}.checks-index-item-icon{width:16px}.selected .checks-index-label{font-weight:600;color:#24292e}.checks-summary-meta .octicon{width:16px}.checks-results-items .octicon-fold{display:none}.checks-results-items .Details--on .octicon-fold{display:inline-block}.checks-results-items .Details--on .octicon-unfold{display:none}.check-annotation{border-left:0;border-top-left-radius:0;border-bottom-left-radius:0}.check-annotation::after{position:absolute;top:-1px;bottom:-1px;left:0;display:block;width:4px;content:" "}.check-annotation .annotation-actions{top:4px;right:8px}.check-annotation .annotation-octicon{width:16px}.check-annotation.Details--on .Details-content--hidden{display:block !important}.diff-table .check-annotation{max-width:962px}.annotation-title{word-break:break-all}.check-annotation-failure::after{background-color:#d73a49}.check-annotation-failure .annotation-title{color:#d73a49}.check-annotation-warning::after{background-color:#ffd33d}.check-annotation-warning .octicon-alert{color:#dbab09}.check-annotation-warning .annotation-title{color:#b08800}.check-annotation-notice::after{background-color:#6a737d}.check-annotation-notice .annotation-title{color:#6a737d}.neutral-check{color:#959da5}.code-frequency .addition{fill:#2cbe4e;fill-opacity:1}.code-frequency .deletion{fill:#cb2431;fill-opacity:1}.cadd{font-weight:600;color:#28a745}.cdel{font-weight:600;color:#cb2431}.code-list .file-box{border:1px #e1e4e8 solid;border-radius:3px}.code-list em{padding:2px;margin:0 -2px;font-style:normal;font-weight:600;color:#24292e;background-color:rgba(255,255,140,0.5);border-radius:3px}.code-list .title{min-height:24px;margin:-3px 0 10px 38px;font-weight:600;line-height:1.2}.code-list .repo-specific .title,.code-list .repo-specific .full-path{margin-left:0}.code-list .match-count,.code-list .updated-at{margin:0;font-weight:400}.code-list .language{float:right;margin-left:10px;font-size:12px;color:rgba(51,51,51,0.75)}.code-list .avatar{float:left}.code-list .code-list-item+.code-list-item{border-top:1px solid #eee}.code-list .blob-num{padding:0}.code-list .blob-num::before{content:normal}.code-list .blob-num a{padding:0 10px;color:inherit}.code-list .blob-num a:hover{color:#0366d6}.code-list .blob-code{white-space:pre-wrap}.code-list .divider .blob-num,.code-list .divider .blob-code{padding-top:0;padding-bottom:0;cursor:default;background-color:#f8fafd}.code-list .divider .blob-num{height:18px;padding:0 10px;line-height:15px;background-color:#f0f5fa}.code-list .full-path{margin:0 0 0 40px}.code-list .full-path .octicon-repo{color:#586069}.code-list .full-path .octicon-lock{color:#e9dba4}.code-list .full-path a{color:#6a737d}.code-list-item-private .file-box{border:1px solid #fadda5}.code-list-item-private .blob-num{background-color:#fffdef;border-right:1px solid #fadda5}.code-list-item-private .blob-num a{color:rgba(27,31,35,0.7)}.code-list-item-private .divider .blob-num,.code-list-item-private .divider .blob-code{color:rgba(27,31,35,0.7);background-color:#fffdef}.codesearch-head.pagehead h1{width:250px;line-height:33px}.search-autocomplete-dropdown{min-width:240px}.search-autocomplete-dropdown .autocomplete-category-heading::after{position:absolute;width:100%;height:1px;margin-top:8px;content:"";background-color:#e1e4e8}.search-autocomplete-dropdown .autocomplete-keyword-only{background:#f6f8fa}.search-autocomplete-dropdown .autocomplete-text{color:#24292e}.search-autocomplete-dropdown .navigation-item{cursor:pointer}.search-autocomplete-dropdown .navigation-item[aria-selected="true"],.search-autocomplete-dropdown .navigation-item.navigation-focus{color:#fff;background-color:#0366d6;border-radius:3px}.search-autocomplete-dropdown .navigation-item[aria-selected="true"] .autocomplete-text,.search-autocomplete-dropdown .navigation-item.navigation-focus .autocomplete-text{color:#fff}@media (min-width: 768px){.advanced-search-form .flattened dt{width:230px}.advanced-search-form .flattened dt label{font-weight:400}.advanced-search-form .flattened dd{margin-left:250px}.advanced-search-form .form-checkbox{margin-left:250px}}.codesearch-results .code-list .title a{word-wrap:break-word}.codesearch-results .repo-list-item{border-bottom:0}.codesearch-results .repo-list-item+.repo-list-item{border-top:1px #e1e4e8 solid}.search-form-fluid .TableObject-item--primary{position:relative;padding-right:8px}.search-form-fluid .completed-query{position:absolute;top:7px;right:8px;left:8px;z-index:1;margin:0;overflow:hidden;white-space:nowrap}.search-form-fluid .completed-query span{opacity:0}.search-form-fluid .search-page-label{position:relative;display:block;font-weight:400;cursor:text}.search-form-fluid .search-page-label.focus .completed-query{opacity:0.6}.search-form-fluid .search-page-input{position:relative;z-index:2;min-height:0;padding:0;margin:0;background:none;border:0;box-shadow:none}.search-form-fluid .search-page-input:focus{box-shadow:none}.sort-bar{padding-bottom:20px;margin-bottom:20px;border-bottom:1px solid #f1f1f1}.sort-bar .sort-label{padding-right:5px;font-size:13px;font-weight:200;color:#666}.sort-bar .select-menu{float:right}.topics-row-container{height:30px;overflow:hidden}@media (max-width: 544px){.codesearch-pagination-container a:not(.next_page):not(.previous_page),.codesearch-pagination-container .gap{display:none}.codesearch-pagination-container .previous_page,.codesearch-pagination-container .next_page{width:100%}.codesearch-pagination-container .current{color:#586069;background:#fff;border-color:#e1e4e8}.codesearch-pagination-container .current::after{content:" of " attr(data-total-pages)}}.comment-apps-container{border-top-width:0}.comment-apps-container .comment-app-details[open]>.comment-app{color:#fff;background-color:#0366d6}.comment-apps .select-menu{bottom:36px}.comment-apps .select-menu .select-menu-button{background-color:#e1e4e8;box-shadow:0 0 0 4px #fafbfc}.comment-apps .select-menu .select-menu-button.selected{color:#fff;background-color:#0366d6}.comment-apps .select-menu .select-menu-button::after{display:none}.comment-apps .select-menu-modal{z-index:90}.write-content.focused .select-menu-button{box-shadow:0 0 0 4px #fff}.commit-activity-graphs .dots{display:none}.commit-activity-master{margin-top:20px}.is-graph-loading .commit-activity-master{display:none}rect{shape-rendering:crispedges}rect.max{fill:#f9c513}g.bar{fill:#28a745}g.mini{fill:#fb8532}g.active rect{fill:#b31d28}circle.focus{fill:#444d56}.dot text{fill:#444d56;stroke:none}.CommunityTemplate-header{height:40px}.CommunityTemplate-markdown{height:800px;overflow-y:scroll;font-size:14px}.CommunityTemplate-highlight{padding:2px 4px;margin:0;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px;font-style:normal;font-weight:600;color:#24292e;cursor:pointer;background-color:#ffd33d;border-radius:3px}.CommunityTemplate-highlight--focus{color:#fff;background-color:#0366d6}.community-checklist .progress-bar{background:linear-gradient(to right, #ffd33d, #c5e300, #28a745);background-color:transparent}.community-checklist .progress{float:right;background-color:#f6f8fa}.community-checklist .checklist-dot{color:#dbab09}body.full-width-p0 .container{width:100%;padding-right:20px;padding-left:20px}body.full-width-p0 .container.new-discussion-timeline{padding-right:0;padding-left:0}.compare-cutoff,.diff-cutoff{padding:8px 0;margin:5px 0;font-weight:600;color:#4c4a42;text-align:center;background-color:#fff9ea;border:solid 1px #dfd8c2;border-radius:3px}span.no-nl-marker{position:relative;color:#cb2431;vertical-align:middle}.symlink .no-nl-marker{display:none}.existing-pull{margin:10px 0}.existing-pull .list-group-item::before{display:table;content:""}.existing-pull .list-group-item::after{display:table;clear:both;content:""}.existing-pull .existing-pull-contents{float:left;width:680px}.existing-pull .existing-pull-button{float:right;margin-top:3px}.existing-pull .existing-pull-number{font-weight:400;color:#aaa}.existing-pull .css-truncate{max-width:700px}.existing-pull .css-truncate p{display:inline}.compare-pr-placeholder{padding:15px;margin:15px 0;font-size:14px;color:#4c4a42;background-color:#fff9ea;border:solid 1px #dfd8c2;border-radius:3px}.compare-pr-placeholder p{margin:7px 0;color:#6d6c60}.compare-pr-placeholder .btn{margin-right:10px;margin-bottom:-2px}.compare-pr-placeholder .help-link{padding:3px;margin-top:5px;margin-right:-3px;color:#9c997d;text-decoration:none}.compare-pr .new-pr-form{display:none}.compare-pr .contributing{display:none}.compare-pr.open .compare-pr-placeholder{display:none}.compare-pr.open .new-pr-form{display:block}.compare-pr.open .contributing{display:block}.contact-github textarea{height:100px;resize:vertical}.contact-github .contact-checklist>li{margin:5px 0 5px 18px;list-style-position:outside}.documentation-results-wrapper{position:relative;top:-19px}.documentation-results{position:absolute;top:0;z-index:2;width:400px;margin-top:5px;box-shadow:0 0 5px rgba(0,0,0,0.2)}.documentation-results ul{width:100%}.documentation-results ul li:first-child a{border-top-left-radius:3px;border-top-right-radius:3px}.documentation-results .documentation-results-footer a{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.documentation-results a{display:block;padding:5px 10px;font-weight:600;color:#333;text-decoration:none;cursor:pointer;background-color:#fff;border:solid #ddd;border-width:0 1px 1px;outline:none}.documentation-results a:hover{color:#fff;background-color:#3586c3}.documentation-results a.selected{color:#fff;background-color:#3586c3}ul.documentation-results-group{list-style-type:none}.contact-form-extras{display:none}.select-menu-item{text-align:left;background-color:#fff;border-top:0;border-right:0;border-left:0}.timeline-jump-to-menu{left:-85px;z-index:80}.contributions-setting-menu{z-index:80;width:330px}.calendar-graph{height:126px;padding:5px 0 0;text-align:center}.calendar-graph.days-selected rect.day{opacity:0.5}.calendar-graph.days-selected rect.day.active{opacity:1}.calendar-graph .dots{width:64px;height:64px;margin:20px auto 0}.calendar-graph text.month{font-size:10px;fill:#767676}.calendar-graph text.wday{font-size:9px;fill:#767676}.graph-before-activity-overview{border-top-left-radius:3px;border-top-right-radius:3px}.activity-overview-box{border-top-left-radius:0;border-top-right-radius:0}.contrib-footer{padding:0 10px 12px;font-size:11px}.contrib-legend{float:right}.contrib-legend .legend{position:relative;bottom:-1px;display:inline-block;margin:0 5px;list-style:none}.contrib-legend .legend li{display:inline-block;width:10px;height:10px}.contrib-square{font-size:22px;line-height:0.9;color:#cce888}.contribution-activity h2{margin:30px 0 15px;font-size:18px;font-weight:400}.contribution-activity .select-menu-button{position:relative;top:-4px}.contribution-activity.loading .contribution-activity-listing{display:none}.contribution-activity.loading .contribution-activity-show-more{display:none}.contribution-activity.loading .contribution-activity-spinner{display:block}.contribution-activity-spinner{display:none;width:64px;height:64px;margin:20px auto 0}.contribution-activity-spinner.next{margin:0 auto}ul.simple-conversation-list a.meta{color:#586069}li.contribution{padding:10px 0;list-style:none}li.contribution h3{display:inline-block;margin:0;font-size:14px}li.contribution .cmeta{display:block;font-size:12px}li.contribution .d{color:#c00}li.contribution .a{color:#8cac29}li.contribution .num{color:#586069}.activity-overview-axis,.activity-overview-point{stroke:#6cb267}.halloween-activity-overview .activity-overview-axis,.halloween-activity-overview .activity-overview-point{stroke:#fe9600}.activity-overview-label{fill:#586069}.activity-overview-percentage{font-size:10px;fill:#959da5}.tint-box{position:relative;margin-bottom:10px;background:#f6f8fa;border-radius:6px}.tint-box.transparent{background:#fff}.tint-box .activity{padding-top:100px;margin-top:0}.contrib-person path{fill:#fb8532}.contrib-person .midlabel{fill:#959da5}.logged-out.enter-coupon{background-color:#f9f9f9}.logged-out.enter-coupon .coupon-form-body{margin-bottom:-20px;background-image:none;box-shadow:0 1px 3px rgba(0,0,0,0.075),inset 1px 0 #fff,0 0 200px #fff}.logged-out.enter-coupon .header-logged-out{background-color:#fff}.coupons .setup-plans td img{margin-top:-2px;vertical-align:middle}.coupons .coupon-form-body{width:270px;padding:20px;margin:100px auto 60px;font-size:14px;text-align:center;background-color:#fff;background-image:linear-gradient(#fefefe, #fafafa);border:1px solid #ccc;border-radius:3px;box-shadow:0 1px 3px rgba(0,0,0,0.075),inset 1px 0 #fff}.coupons .coupon-form-body .input-block{margin-bottom:15px}.coupons .coupon-form-body .btn{display:block;width:100%}.coupon-icon{width:80px;height:80px;margin:0 auto 15px;color:#0366d6;border:1px solid #dedede;border-radius:40px}.coupon-icon .octicon{margin-top:15px;margin-right:2px}.coupons-list-options{margin-bottom:15px}.coupons-list-options .select-menu,.coupons-list-options .BtnGroup{display:inline-block;margin-right:10px}.coupons-list-options .pagination{float:right;margin:0}.user-repos .mini-repo-list-item{padding-right:6px}.user-repos .mini-repo-list-item .repo-and-owner{max-width:100%}.user-repos .mini-repo-list-item .owner{max-width:145px}.repo-private-icon{fill:#dbab09}.dashboard-rollup-items>.dashboard-rollup-item{border-top:1px solid #e1e4e8}@keyframes broadCastMaskFade{0%{opacity:0}30%{opacity:1}70%{opacity:1}100%{opacity:0}}.github-jobs-logo:hover{text-decoration:none}.github-jobs-logo strong{display:inline-block;width:62px;height:12px;text-indent:-9999px;vertical-align:middle;background:url("/images/modules/jobs/logo.png") 0 0 no-repeat;background-size:62px auto}.dashboard h1{margin-bottom:0.5em;font-size:160%}.dashboard h1 a{font-size:70%;font-weight:400}.dashboard .notice{padding:15px;margin-top:0;margin-bottom:0;text-align:center}.news-full,.page-profile .news{float:none;width:auto}.dashboard-break-word{-webkit-hyphens:auto;-ms-hyphens:auto;hyphens:auto;word-break:break-word}.news .error-message{display:none}.news .is-error .loading-message{display:none}.news .is-error .error-message{display:block}.news .bio g-emoji,.news .repo-description g-emoji{display:inline-block}.account-switcher-truncate-override{width:calc(100% - 36px);max-width:100% !important}.dashboard-notice{position:relative;padding:15px 15px 15px 55px;margin-bottom:20px;font-size:14px;background-color:#fafbfc;border:solid 1px #d1d5da;border-radius:3px}.dashboard-notice .dismiss{position:absolute;top:10px;right:10px;width:16px;height:16px;color:#959da5;cursor:pointer}.dashboard-notice .dismiss:hover{color:#586069}.dashboard-notice .notice-icon{position:absolute;top:15px;left:15px}.dashboard-notice .octicon-organization{color:#0366d6}.dashboard-notice h2{margin-top:9px;margin-bottom:16px;font-size:18px;font-weight:400;color:#24292e}.dashboard-notice p.no-title{padding-right:5px}.dashboard-notice .inset-figure{float:right;margin-bottom:15px;margin-left:20px}.dashboard-notice ul{margin-left:18px}.dashboard-notice li{padding-bottom:15px}.dashboard-notice .coupon{padding:10px;margin:15px 0;font-size:20px;font-weight:600;text-align:center;background:#fff;border:1px dashed #dbedff}.dashboards-overview-lead{width:700px}.dashboards-overview-cards .boxed-group{width:100%;margin:10px 0}.dashboards-overview-cards .boxed-group .graph-canvas path{stroke-opacity:0.5}.dashboards-overview-cards .is-no-activity .blankslate{display:block}.dashboards-overview-cards .is-no-activity .dashboards-overview-graph{display:none}.dashboards-overview-cards .blankslate{display:none;padding-top:47px;background-color:#fff;border:0;box-shadow:none}.dashboards-overview-cards .octicon-arrow-down,.dashboards-overview-cards .octicon-arrow-up{display:none}.dashboards-overview-cards .is-increase .octicon-arrow-up{display:inline-block}.dashboards-overview-cards .is-decrease .octicon-arrow-down{display:inline-block}.dashboards-overview-cards .octicon-arrow-down{color:#cb2431}.dashboards-overview-cards .octicon-arrow-up{color:#1db34f}.dashboards-overview-cards .graph-canvas .dots{padding:43px 0}.dashboards-overview-cards .summary-stats{height:78px}.dashboards-overview-cards .summary-stats .metric-1{color:#1db34f}.dashboards-overview-cards .summary-stats .metric-2{color:#0366d6}.dashboards-overview-cards .summary-stats .totals-num{margin:0 7px}.dashboards-overview-cards .summary-stats .single{width:100%}.dashboards-overview-graph{height:160px}.dashboards-overview-graph path{fill:none;stroke-width:2}.dashboards-overview-graph path.metric-1{stroke:#1db34f}.dashboards-overview-graph path.metric-2{stroke:#1d7fb3}.dashboards-overview-graph .y line{stroke:#1db34f}.dashboards-overview-graph .y.unique line{stroke:#1d7fb3}.dashboards-overview-graph .overlay{fill-opacity:0}.metric-1 circle{fill:#1db34f;stroke:#fff;stroke-width:2}.dots.metric-2 circle{fill:#1d7fb3;stroke:#fff;stroke-width:2}dl.form.developer-select-account{margin-top:0}.developer-wrapper .setup-info-module .features-list{margin-left:16px}.developer-wrapper .setup-info-module .features-list .octicon{margin-left:-17px}.developer-thanks h2{font-size:38px;font-weight:400}.developer-thanks .hook{margin-top:2px;margin-bottom:30px;font-size:18px;font-weight:300;color:#666}.developer-thanks-image{position:relative;bottom:-45px;float:left;width:400px}.developer-thanks-section{margin:130px 0 0 470px}.developer-next-steps{font-size:18px;font-weight:300;list-style:none}.developer-next-steps li{margin-top:10px}.developer-next-steps li:first-child{margin-top:0}.developer-next-steps .octicon{margin-right:10px;color:#28a745;vertical-align:middle}.donut-chart>.error,.donut-chart>.cancelled,.donut-chart>.action_required,.donut-chart>.timed_out,.donut-chart>.failure{fill:#cb2431}.donut-chart>.expected,.donut-chart>.queued,.donut-chart>.in_progress,.donut-chart>.pending{fill:#dbab09}.donut-chart>.success{fill:#2cbe4e}.donut-chart>.neutral{fill:#959da5}.survey-question-form .other-text-form,.survey-question-form .other-text-form-block{display:none;margin-top:0}.survey-question-form.is-other-selected .other-text-form{display:inline-block}.survey-question-form.is-other-selected .other-text-form-block{display:block}.setup-header .large-file-storage-header{font-size:44px}.early-acccess-setup-form .form-group{margin-top:0;margin-bottom:30px}.early-acccess-setup-form select{display:block;width:200px}.early-access-setup-list{padding:0 15px 15px;margin:0;font-size:14px}.early-access-setup-list .early-access-setup-list-item{margin-top:10px;margin-left:20px}.early-access-setup-list .early-access-setup-list-item:first-child{margin-top:0}.early-access-thanks-wrapper{position:relative;z-index:1;height:80vh;margin-bottom:-41px;background-color:#fcfcfc;border-bottom:1px solid #ddd}.early-access-thanks-content{position:relative;top:50%;width:500px;margin:0 auto;transform:translateY(-50%)}.early-access-thanks-title{margin-top:0;font-weight:400}.early-access-thanks-lead{margin-top:0;margin-bottom:0}.eap-error-state-title{margin-top:0}.ghe-license-status{padding:40px 0;font-size:16px;text-align:center}.ghe-license-status .octocat{width:225px;margin-bottom:20px}.ghe-license-status h1{margin-bottom:10px}.ghe-license-status p{margin-bottom:5px;color:#586069}.ghe-license-expiry-icon{margin:5px 10px 0 0;color:#ddb38a}.explore-content{margin-top:-15px}.explore-content .blankslate{margin-top:15px}.explore-pjax-container{position:relative}.repo-snipit{display:inline-block;margin-top:7px}.repo-snipit:hover{text-decoration:none}.repo-snipit .octicon{font-size:14px;color:#586069}.repo-snipit-name{max-width:200px;color:#666}.repo-snipit-description{max-width:300px;color:#586069}.repo-snipit:hover .repo-snipit-name,.repo-snipit:hover .repo-snipit-description{color:#0366d6}.newsletter-frequency-choice{font-weight:400;cursor:pointer;border:1px solid #eee;border-radius:4px}.newsletter-frequency-choice .notice{position:absolute;right:0;bottom:1em;left:0;z-index:-1;font-weight:600;color:#28a745;text-align:center;opacity:0}.newsletter-frequency-choice .notice.visible{bottom:-2em;opacity:1;transition:opacity 0.15s ease-in-out}.newsletter-frequency-choice h3{padding:10px;margin:0;font-weight:400;text-align:center;background-color:#fafbfc;border-bottom:1px solid #eee}.newsletter-frequency-choice h3 input{position:relative;top:-2px;margin:0 3px 0 -19px}.newsletter-frequency-choice p{margin:15px}.newsletter-frequency-choice:hover{border-color:#0366d6}.newsletter-frequency-choice:hover h3{color:#fff;background-color:#0366d6;border-color:#0366d6}.newsletter-frequency-choice.selected{border-color:#28a745;box-shadow:0 0 5px rgba(0,0,0,0.2)}.newsletter-frequency-choice.selected h3{color:#fff;background-color:#28a745;border-color:#28a745}@media (min-width: 1012px){.col-lg-9 .Collection{max-width:744px}}@media (min-width: 1012px){.Spotlight{margin-top:100px}}.Spotlight-text{z-index:1}.Spotlight-blur{z-index:0;opacity:0.9}@media (min-width: 1012px){.Spotlight-blur{opacity:1;-webkit-mask-image:linear-gradient(0deg, #000, #000 75%, transparent);mask-image:linear-gradient(0deg, #000, #000 75%, transparent)}}.exploregrid{display:flex;flex-wrap:wrap;justify-content:flex-start;align-items:stretch;list-style:none}.exploregrid-item-wrap{display:flex;flex-direction:column}.exploregrid-item{flex:1;position:relative;display:block;height:100%;padding-top:24px;padding-bottom:64px;font-size:14px;color:#555;border:1px solid rgba(0,0,0,0.075);border-bottom-color:rgba(0,0,0,0.125);border-radius:4px;box-shadow:0 1px 2px rgba(0,0,0,0.05),0 5px 10px rgba(0,0,0,0.05);transition:border-color 0.1s ease-in-out, box-shadow 0.1s ease-in-out}.exploregrid-item:hover{text-decoration:none;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.15);box-shadow:0 1px 3px rgba(0,0,0,0.05),0 8px 15px rgba(0,0,0,0.1)}.exploregrid-item:hover .exploregrid-item-title{color:#0366d6}.exploregrid-item:hover .exploregrid-item-header{opacity:1}.exploregrid-item-mini{float:left;width:100%;height:auto;margin:0 0 15px}.exploregrid-item-mini .exploregrid-item-header{height:10px}.exploregrid-item-mini .exploregrid-item-title{font-size:16px}.exploregrid-item-header{position:absolute;top:0;left:0;width:100%;height:18px;background-color:#f5f5f5;border:1px solid rgba(0,0,0,0.1);border-top-left-radius:4px;border-top-right-radius:4px;opacity:0.8;transition:opacity 0.1s ease-in-out}.exploregrid-item-title{margin-top:0;margin-bottom:5px;font-size:20px;font-weight:400;line-height:1.2;color:#24292e}.fakelogin{position:fixed;top:0;z-index:1000;width:100%;font-size:14px;line-height:34px;color:#fff;text-align:center;text-shadow:0 -1px 0 rgba(153,0,0,0.25);background-image:linear-gradient(#dc5f59, #b33630);border-bottom:1px solid #900}.fakelogin+.Header{margin-top:35px}.fakelogin+.server-stats{margin-top:35px}.fakelogin .cancel-impersonation{color:#fff;text-decoration:underline}.file{position:relative;margin-top:16px;margin-bottom:16px;border:1px solid #ddd;border-radius:3px}.file .data.empty{padding:5px 10px;color:#586069}.file:not(.open) .file-header.file-header--expandable{border-bottom:0}.file .data.suppressed,.file.open .image{display:none}.file.open .data.suppressed{display:block}.file .image{position:relative;padding:30px;text-align:center;background-color:#ddd}.file .image table{margin:0 auto}.file .image td{padding:0 5px;color:#888;text-align:center;vertical-align:top}.file .image td img{max-width:100%}.file .image .border-wrap{position:relative;display:inline-block;line-height:0;background-color:#fff;border:1px solid #6a737d}.file .image a{display:inline-block;line-height:0}.file .image img,.file .image canvas{max-width:600px;background:url("/images/modules/commit/trans_bg.gif") right bottom #eee;border:1px solid #fff}.file .image .view img,.file .image .view canvas{position:relative;top:0;right:0;max-width:inherit;background:url("/images/modules/commit/trans_bg.gif") right bottom #eee}.file .image .view>span{vertical-align:middle}.file .empty{background:none}.file-header{padding:5px 10px;background-color:#fafbfc;border-bottom:1px solid #e1e4e8;border-top-left-radius:2px;border-top-right-radius:2px}.file-header::before{display:table;content:""}.file-header::after{display:table;clear:both;content:""}.file-actions{float:right;padding-top:2px;font-size:13px}.file-actions select{margin-left:5px}.file-info{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px;line-height:32px}.file-info .octicon{vertical-align:text-bottom}.file-info-divider{display:inline-block;width:1px;height:18px;margin-right:3px;margin-left:3px;vertical-align:middle;background-color:#ddd}.file-mode{text-transform:capitalize}.show-file-notes{display:none}.comments-indicator{display:none}.has-inline-notes .show-file-notes{display:inline-block;margin-right:10px;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.has-inline-notes.hide-file-notes-toggle .show-file-notes{display:none !important}.has-inline-notes.hide-file-notes-toggle .comments-indicator{display:inline-block}.file-blankslate{border:0;border-radius:0 0 2px 2px}.diff-progressive-loader{min-height:150px}.load-diff-button,.load-diff-retry{z-index:1;min-height:32px}.diff-placeholder-svg{clip:rect(1px, 1px, 1px, 1px);-webkit-clip-path:inset(50%);clip-path:inset(50%)}.hidden-diff-reason{z-index:2}.axis{font-size:10px}.axis line{stroke:rgba(27,31,35,0.1);shape-rendering:crispedges}.axis path{display:none}.axis .zero line{stroke:#0366d6;stroke-dasharray:3 3;stroke-width:1.5}.graphs .is-graph-loading{min-height:500px}.graphs.wheader h2{padding:1px}.graphs .area{fill:#28a745;fill-opacity:0.5}.graphs .path{fill:none;stroke:#28a745;stroke-opacity:1;stroke-width:2px}.graphs .dot{fill:#28a745;stroke:#1e7e34;stroke-width:2px}.graphs .dot.padded{stroke:#fff;stroke-width:1px}.graphs .dot.padded circle:hover{fill:#0366d6}.graphs .d3-tip{fill:#24292e}.graphs .d3-tip text{font-size:11px;fill:#fff}.graphs .dir{float:right;padding-top:5px;font-size:12px;font-weight:400;line-height:100%;color:#6a737d}.graphs .selection .overlay{visibility:none}.graphs .selection .selection{fill:#24292e;fill-opacity:0.1;stroke:#24292e;stroke-dasharray:3 3;stroke-opacity:0.4;stroke-width:1px;shape-rendering:crispedges}.graph-filter h3{display:inline-block;font-size:24px;font-weight:300}.graph-filter .info{margin-bottom:20px;color:#586069}h2.ghead::after{display:block;height:0;clear:both;visibility:hidden;content:"."}.graph-canvas .activity{width:400px;padding:10px;margin:100px auto 0;color:#2f363d;text-align:center;border-radius:3px}.graph-canvas .dots{margin:0 auto}.graph-canvas>.activity{display:none}.graph-loading,.graph-error,.graph-no-usable-data,.graph-empty{display:none}.graph-canvas.is-graph-loading>.activity,.graph-canvas.is-graph-without-usable-data>.activity,.graph-canvas.is-graph-empty>.activity{display:block}.is-graph-loading .graph-loading,.is-graph-empty .graph-empty,.is-graph-without-usable-data .graph-no-usable-data,.is-graph-load-error .graph-error{display:block}.svg-tip{position:absolute;z-index:99999;padding:10px;font-size:12px;color:#959da5;text-align:center;background:rgba(0,0,0,0.8);border-radius:3px}.svg-tip strong{color:#dfe2e5}.svg-tip.is-visible{display:block}.svg-tip::after{position:absolute;bottom:-10px;left:50%;width:5px;height:5px;box-sizing:border-box;margin:0 0 0 -5px;content:" ";border:5px solid transparent;border-top-color:rgba(0,0,0,0.8)}.svg-tip.comparison{padding:0;text-align:left;pointer-events:none}.svg-tip.comparison .title{display:block;padding:10px;margin:0;font-weight:600;line-height:1;pointer-events:none}.svg-tip.comparison ul{margin:0;white-space:nowrap;list-style:none}.svg-tip.comparison li{display:inline-block;padding:10px}.svg-tip.comparison .metric-1{border-top:3px solid #28a745;border-right:1px solid #24292e}.svg-tip.comparison .metric-2{border-top:3px solid #2188ff}.svg-tip-one-line{white-space:nowrap}.LoadingDependencies{position:absolute;left:0;width:100%;animation:fadeOut;animation-duration:0.6s;animation-fill-mode:forwards;animation-timing-function:ease-in}.LoadingDependencies--loading{position:relative}.LoadingDependencies--loading .octicon{opacity:0;animation:dropBox;animation-duration:1.25s;animation-fill-mode:forwards;animation-timing-function:linear;animation-delay:1s;animation-iteration-count:infinite}.LoadingDependencies--loading .octicon:nth-child(2){position:absolute;left:calc(50% - 27px);animation-delay:1.61s}@keyframes fadeOut{0%{opacity:1}100%{opacity:0}}@keyframes dropBox{0%{opacity:1;transform:translateY(-110%)}7%{opacity:1;transform:translateY(20%);transform:translateX(0)}80%{opacity:1}100%{opacity:0;transform:translateX(-250%)}}.getting-started .main-content .container{width:420px}.getting-started .list-group-item{background:#fff}.getting-started .list-group-item .octicon{color:#bbb}.getting-started .list-group-item .octicon-lead{width:30px}.getting-started .list-group-item:hover,.getting-started .list-group-item:focus{background:#fafbfc}.getting-started .link-text{color:#24292e}.getting-started .link-text strong{color:#0366d6}.topic-tag{display:inline-block;padding:0.3em 0.9em;margin:0 0.5em 0.5em 0;white-space:nowrap;background-color:#f1f8ff;border-radius:3px}.topic-tag-link:hover{text-decoration:none;background-color:#def}.delete-topic-button{display:inline-block;width:26px;color:#6a737d;background-color:#f1f8ff;border-top:0;border-right:0;border-bottom:0;border-left:1px solid #b4d9ff;border-top-right-radius:3px;border-bottom-right-radius:3px}.delete-topic-button:hover{background-color:#def}.invalid-topic .delete-topic-button{color:#86181d;background-color:#ffdce0;border-left-color:#cea0a5}.invalid-topic .delete-topic-button:hover{background-color:#ffc8ce}.topic-tag-action{display:inline-flex;align-items:center;padding-left:0.8em;margin:0.4em 0.4em 0 0;background-color:#f1f8ff;border-radius:3px}.topic-tag-action.invalid-topic{color:#86181d;background-color:#ffdce0;border-color:#cea0a5}.topic-tag-action .add-topic-button,.topic-tag-action .remove-topic-button{display:inline-block;width:26px;font-size:15px;color:#6a737d;background-color:#f1f8ff;border-top:0;border-right:0;border-bottom:0;border-left:1px solid #b4d9ff}.topic-tag-action .add-topic-button:hover,.topic-tag-action .remove-topic-button:hover{color:#fff}.topic-tag-action .add-topic-button:hover{background-color:#28a745}.topic-tag-action .remove-topic-button{border-right:0;border-top-right-radius:3px;border-bottom-right-radius:3px}.topic-tag-action .remove-topic-button:hover{background-color:#d73a49}.topic-input-container .tag-input{width:908px;cursor:text}.topic-input-container .tag-input.org-repo-tag-input{width:100%}.topic-input-container .tag-input .tag-input-inner{min-height:26px;background-image:none}.topic-input-container .topic-tag{margin-top:2px}.repository-topics-container .list-topics-container{display:inline-flex;flex-wrap:wrap;align-items:baseline}.repository-topics-container .manage-topics-container{display:none}.repository-topics-container.open .list-topics-container{display:none}.repository-topics-container.open .manage-topics-container{display:block}.topic .css-truncate-target{max-width:75%}.topic-list .topic-list-item+.topic-list-item{border-top:1px #e1e4e8 solid}.team-breadcrumb .team-breadcrumb-item{display:inline-block}.team-breadcrumb .team-breadcrumb-item::after{padding-right:0.5em;padding-left:0.5em;color:#e1e4e8;content:"/"}.team-breadcrumb .team-breadcrumb-item-selected::after{content:none}.team-discussions-container{min-height:100vh}.team-left-column{max-width:100%}@media (min-width: 768px){.team-left-column{max-width:350px}}.team-left-column .team-avatar{width:80px;height:80px}@media (min-width: 768px){.team-left-column .team-avatar{width:140px;height:140px}}.team-discussions{max-width:768px}.team-discussions .previewable-comment-form .comment{border:0}.discussion-post{opacity:1;transition:opacity 400ms}.discussion-post .timeline-comment::after,.discussion-post .timeline-comment::before{display:none}.discussion-post .post-author{margin-top:-6px}.discussion-post .post-author-timestamp{margin-top:-3px}.discussion-post.fade-out{opacity:0}.discussion-post .timeline-inline-comments{background:#fafbfc}.discussion-post .team-discussion-timeline::before{bottom:20px}.discussion-post .reply-comment:first-child{border-top:1px solid #e1e4e8}.discussion-post .reply-comment:first-child .review-comment{padding-top:16px}.discussion-post .reply-comment .review-comment{padding:8px 16px}.discussion-post .reply-comment .review-comment.is-comment-editing{padding:0;background:#fafbfc}.discussion-post .comment .comment-reactions{margin-left:16px;border-top:0}.discussion-post .comment .reaction-summary-item{margin-bottom:16px}.discussion-post .reaction-summary-item:not(.add-reaction-btn){padding:0 8px;font-size:12px;line-height:26px;border:1px solid #d2dff0;border-radius:3px}.discussion-post .reaction-summary-item:not(.add-reaction-btn) .emoji{font-size:16px;vertical-align:sub}.discussion-post .reaction-summary-item:not(.add-reaction-btn)+.reaction-summary-item{margin-left:8px}.discussion-post .reply-comments-holder{position:relative}.discussion-post .reply-comments-holder::before{position:absolute;top:51px;bottom:0;left:29px;width:2px;content:"";background-color:#e1e4e8}.discussion-post .add-reaction-btn{padding:4px 10px}.discussion-post .pin-btn:disabled{pointer-events:none}.discussion-post .pinned{color:#f66a0a;opacity:1}.discussion-post .loading-spinner{display:none;float:left;margin-top:12px}.discussion-post .loading .loading-spinner{display:block}.discussion-post ~ .blankslate{display:none}.team-discussion-new-post .review-thread-reply-button:disabled{cursor:inherit;background-color:#f6f8fa;border:0;box-shadow:none}.team-project-suggestion-number{font-weight:300;color:#a3aab1}.team-discussion-nav-disabled{pointer-events:none}.header-search-wrapper{display:table;width:100%;max-width:100%;min-height:0;padding:0;font-weight:400;vertical-align:middle}.header-search-wrapper.header-search-wrapper-jump-to .header-search-scope{width:-webkit-fit-content;width:-moz-fit-content;width:fit-content}.header-search-input{display:table-cell;width:100%;min-height:26px;padding-top:0;padding-bottom:0;background:none;border:0;box-shadow:none}.header-search-input:focus{border:0;box-shadow:none}.header-search-input:focus ~ .header-search-key-slash{display:none !important}.header-search-input::-ms-clear{display:none}.header-search-scope{display:none;width:1%;padding-right:8px;padding-left:8px;font-size:12px;color:#586069;white-space:nowrap;vertical-align:middle;border-right:1px solid #eaecef;border-top-left-radius:3px;border-bottom-left-radius:3px}.header-search-scope:empty+.header-search-input{width:100%}.header-search-scope:hover{color:#fff;background-color:rgba(255,255,255,0.12)}.scoped-search .header-search-scope{display:table-cell}.scoped-search .form-control.focus .header-search-scope{color:#0366d6;background-color:#c0ddfe;border-color:#8ec2fd}.jump-to-field-active{color:#24292e !important;background-color:#fafbfc}.jump-to-field-active:-ms-input-placeholder{color:#586069 !important}.jump-to-field-active::-ms-input-placeholder{color:#586069 !important}.jump-to-field-active::placeholder{color:#586069 !important}.jump-to-field-active ~ .header-search-key-slash{display:none}.jump-to-field-active.jump-to-dropdown-visible{border-bottom-right-radius:0;border-bottom-left-radius:0}.jump-to-suggestions{top:30px;left:0;z-index:35;width:100%;border-top-left-radius:0;border-top-right-radius:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px;box-shadow:0 4px 10px rgba(0,0,0,0.1)}.jump-to-suggestions-path{min-width:0;min-height:44px;color:#1b1f23}.jump-to-suggestions-path .jump-to-octicon{width:28px;color:#6a737d}.jump-to-suggestions-path .jump-to-suggestion-name{max-width:none}.jump-to-suggestions-path mark{font-weight:600;background-color:transparent}.jump-to-suggestions-results-container .navigation-item{border-bottom:1px solid #e1e4e8}.jump-to-suggestions-results-container .navigation-item:last-child{border-bottom:0}.jump-to-suggestions-results-container .d-on-nav-focus{display:none}.jump-to-suggestions-results-container [aria-selected="true"] .jump-to-octicon,.jump-to-suggestions-results-container .navigation-focus .jump-to-octicon{color:#fff}.jump-to-suggestions-results-container [aria-selected="true"] .jump-to-suggestions-path,.jump-to-suggestions-results-container .navigation-focus .jump-to-suggestions-path{color:#fff;background:#0366d6}.jump-to-suggestions-results-container [aria-selected="true"] mark,.jump-to-suggestions-results-container .navigation-focus mark{color:#fff}.jump-to-suggestions-results-container [aria-selected="true"] .d-on-nav-focus,.jump-to-suggestions-results-container .navigation-focus .d-on-nav-focus{display:block}.Header{z-index:32;padding-top:12px;padding-bottom:12px;color:rgba(255,255,255,0.75);background-color:#24292e}.server-stats+.Header{box-shadow:inset 0 1px 0 rgba(255,255,255,0.075)}.Header .header-search-scope{font-size:inherit;line-height:28px;color:rgba(255,255,255,0.75);border-right-color:#282e34}.Header .header-search-wrapper{min-height:30px;font-size:inherit;color:#fff;background-color:rgba(255,255,255,0.125);border:0;box-shadow:none}.Header .header-search-wrapper .truncate-repo-scope{max-width:110px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.Header .header-search-wrapper.focus{background-color:rgba(255,255,255,0.175);box-shadow:none}.Header .header-search-wrapper.focus .header-search-scope{color:#fff;background-color:rgba(255,255,255,0.075);border-right-color:#282e34}.Header .header-search-input{min-height:30px;font-size:16px;color:inherit}@media (min-width: 1012px){.Header .header-search-input{font-size:inherit}}.Header .header-search-input:-ms-input-placeholder{color:rgba(255,255,255,0.75)}.Header .header-search-input::-ms-input-placeholder{color:rgba(255,255,255,0.75)}.Header .header-search-input::placeholder{color:rgba(255,255,255,0.75)}.Header .dropdown-menu{width:300px}.Header .platform-nav .dropdown-menu{width:500px}.page-responsive .Header .platform-nav .dropdown-menu{width:auto}@media (min-width: 1012px){.page-responsive .Header .platform-nav .dropdown-menu{width:500px}}.Header .notification-indicator:hover::after{content:none}@media (min-width: 1012px){.Header .notification-indicator:hover::after{content:attr(aria-label)}}.page-responsive .HeaderMenu{display:none;clear:both}@media (min-width: 1012px){.page-responsive .HeaderMenu{display:block;clear:none}}.page-responsive .HeaderMenu .header-search{float:none;width:auto;max-width:none;padding-top:16px;padding-bottom:16px;margin-right:0;border-top:1px solid #444d56}@media (min-width: 1012px){.page-responsive .HeaderMenu .header-search{float:left;width:300px;padding-top:4px;padding-bottom:4px;border-top:0}}.open .HeaderMenu{display:block}.HeaderMenu .header-search{width:300px;padding-top:3px;padding-bottom:3px;font-size:13px}.page-responsive .HeaderMenu--experiment{z-index:100;width:300px;overflow:auto;background-color:#fff;box-shadow:0 10px 50px rgba(27,31,35,0.15)}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment{width:auto;overflow:visible;background-color:transparent;box-shadow:none}}.page-responsive .HeaderMenu--experiment .HeaderMenu-item{border-bottom:1px solid #eaecef}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .HeaderMenu-item{border-bottom:0 solid transparent}}.page-responsive .HeaderMenu--experiment .HeaderMenu-target{color:#586069 !important}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .HeaderMenu-target{color:#fff !important}}.page-responsive .HeaderMenu--experiment .dropdown-menu{position:static;width:auto;border:0 solid transparent;box-shadow:none}.page-responsive .HeaderMenu--experiment .dropdown-menu::before,.page-responsive .HeaderMenu--experiment .dropdown-menu::after{content:none}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .dropdown-menu{position:absolute;width:300px;border:1px solid rgba(27,31,35,0.15);box-shadow:0 3px 12px rgba(27,31,35,0.15)}.page-responsive .HeaderMenu--experiment .dropdown-menu::before,.page-responsive .HeaderMenu--experiment .dropdown-menu::after{content:""}}.page-responsive .HeaderMenu--experiment .dropdown-menu-s{transform:none}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .dropdown-menu-s{transform:translateX(50%)}}.page-responsive .HeaderMenu--experiment .header-search{width:auto;padding-top:0;padding-bottom:0;border-top:0}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .header-search{width:240px}}.page-responsive .HeaderMenu--experiment .header-search-key-slash{opacity:0}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .header-search-key-slash{opacity:1}}.page-responsive .HeaderMenu--experiment .header-search-input{border:1px solid #eaecef}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .header-search-input{border:0}}.page-responsive .HeaderMenu--experiment .header-search-input:-ms-input-placeholder{color:#586069 !important}.page-responsive .HeaderMenu--experiment .header-search-input::-ms-input-placeholder{color:#586069 !important}.page-responsive .HeaderMenu--experiment .header-search-input::placeholder{color:#586069 !important}@media (min-width: 1012px){.page-responsive .HeaderMenu--experiment .header-search-input:-ms-input-placeholder{color:rgba(255,255,255,0.75) !important}.page-responsive .HeaderMenu--experiment .header-search-input::-ms-input-placeholder{color:rgba(255,255,255,0.75) !important}.page-responsive .HeaderMenu--experiment .header-search-input::placeholder{color:rgba(255,255,255,0.75) !important}}.HeaderMenu--experiment .header-search{width:240px}.read-only-mode-banner{text-align:center;background-color:#f8e45f;border-bottom-color:#f6dc2e}.header-logo-invertocat{margin:-1px 15px -1px -2px;color:#fff;white-space:nowrap}.header-logo-invertocat .octicon-mark-github{float:left}.header-logo-invertocat:hover{color:#fff;text-decoration:none}.header-logo-subbrand{float:left;margin-left:6px;font-size:16px;font-weight:600;line-height:30px}.header-logo-wordmark{position:relative;float:left;height:26px;margin-right:15px;color:#fff}.header-logo-wordmark .octicon{float:left}.header-logo-wordmark:hover{color:#fff}.header-actions{float:right;margin-top:-3px;margin-bottom:-3px}.header-actions .btn{margin-left:5px}.notification-indicator{color:rgba(255,255,255,0.75)}.notification-indicator .mail-status{position:absolute;top:-6px;left:6px;z-index:2;display:none;width:14px;height:14px;color:#fff;background-image:linear-gradient(#54a3ff, #006eed);background-clip:padding-box;border:2px solid #24292e;border-radius:50%}.notification-indicator .mail-status.unread{display:inline-block}.notification-indicator:hover .mail-status{text-decoration:none;background-color:#0366d6}.HeaderNavlink{display:block;color:#fff}.page-responsive .HeaderNavlink{border-top:1px solid #444d56}@media (min-width: 1012px){.page-responsive .HeaderNavlink{border-top:0}}.HeaderNavlink:hover,.HeaderNavlink:focus{color:rgba(255,255,255,0.75);text-decoration:none}.HeaderNavlink:hover .dropdown-caret,.HeaderNavlink:focus .dropdown-caret{border-top-color:rgba(255,255,255,0.75)}.HeaderNavlink.selected{color:#fff}.HeaderNavlink.selected.js-menu-target{border-bottom-color:transparent}.HeaderNavlink.selected.tooltipped::before,.HeaderNavlink.selected.tooltipped::after{display:none}.user-nav .octicon{width:16px}.user-nav .octicon-plus{margin-right:1px}.user-nav .dropdown-menu{width:180px;margin-top:8px}.header-nav-current-user{padding-bottom:0;font-size:inherit}.header-nav-current-user .css-truncate-target{max-width:100%}.header-nav-current-user .user-profile-link{color:#24292e}.unsupported-browser{color:#211e14;background-image:linear-gradient(#feefae, #fae692);border-bottom:1px solid #b3a569}.mobile-banner button.switch-to-mobile{display:block;width:100%;padding:30px 0 45px;font-size:60px;font-weight:600;color:#eaeaea;text-align:center;background-color:#444;border:0}.hooks-listing .boxed-group-action.select-menu{z-index:auto}.hooks-listing .boxed-group-inner{padding:0 10px;margin-bottom:10px}.hook-item a:hover{text-decoration:none}.hook-item .item-status{float:left;width:16px;margin-right:8px;text-align:center}.hook-item .description{color:#6a737d}.hook-item .description .css-truncate-target{max-width:160px}.hook-item .icon-for-success,.hook-item .icon-for-failure,.hook-item .icon-for-pending,.hook-item .icon-for-mute,.hook-item .icon-for-inactive{display:none}.hook-item.success .icon-for-success{display:inline-block;color:#28a745}.hook-item.failure .icon-for-failure{display:inline-block;color:#cb2431}.hook-item.pending .icon-for-pending{display:inline-block;color:#6a737d}.hook-item.inactive .icon-for-inactive{display:inline-block;color:#6a737d}.hook-item.mute .icon-for-mute{display:inline-block;color:#6a737d}.hook-item .icon-for-enabled,.hook-item .icon-for-disabled{display:none}.hook-item.enabled .icon-for-enabled{display:inline-block;color:#28a745}.hook-item.disabled .icon-for-disabled{display:inline-block;color:#ccc}.hook-item .hook-error-message{margin-left:24px;color:#cb2431}.hook-url.css-truncate-target{max-width:360px}.hook-events-field .hook-event-selector{display:none}.hook-events-field.is-custom .hook-event-selector{display:block}.hook-event-selector{margin-left:10px}.hook-event{display:inline-block;width:310px;padding:5px 0 5px 30px;margin:0}.hook-event p{font-weight:400}.hook-event-choice{font-weight:400}.hooks-oap-warning{margin-top:0}.hooks-oap-warning ul{margin:10px 0}.hooks-oap-warning ul li{margin-left:16px}.hook-secret .hook-secret-standin{display:block}.hook-secret .hook-secret-field{display:none}.hook-secret.open .hook-secret-standin{display:none}.hook-secret.open .hook-secret-field{display:block}.hook-deliveries-list .error-message{padding:7px 4px;margin:10px 0}.hook-deliveries-list .error-message .octicon{position:relative;top:1px}.hook-deliveries-list .spinner{display:inline-block;margin:0;vertical-align:top}.hook-deliveries-list .hook-delivery-item:hover{background-color:transparent}.hook-deliveries-list .item-status{display:inline-block;width:16px;margin-right:5px;text-align:center}.hook-deliveries-list .item-status .icon-for-success,.hook-deliveries-list .item-status .icon-for-failure,.hook-deliveries-list .item-status .icon-for-pending{display:none}.hook-deliveries-list .item-status.success{color:#28a745;visibility:visible}.hook-deliveries-list .item-status.success .icon-for-success{display:inline-block}.hook-deliveries-list .item-status.failure{color:#cb2431}.hook-deliveries-list .item-status.failure .icon-for-failure{display:inline-block}.hook-deliveries-list .item-status.pending{color:#6a737d}.hook-deliveries-list .item-status.pending .icon-for-pending{display:inline-block}.boxed-group-list li.hook-delivery-item{padding:10px}.hook-deliveries-list .error-message,.hook-delivery-loader .error-message{display:none}.hook-deliveries-list.is-error .loading-message,.hook-delivery-loader.is-error .loading-message{display:none}.hook-deliveries-list.is-error .error-message,.hook-delivery-loader.is-error .error-message{display:block}.hook-delivery-time{float:right;margin-right:10px;font-size:10px;color:#6a737d}.hook-delivery-guid{display:inline-block;padding:2px 6px;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:12px;color:rgba(0,0,0,0.5);background-color:rgba(209,227,237,0.5);border-radius:3px}.hook-delivery-guid .octicon{margin:1px -2px 0 0;color:#b0c4ce}.hook-delivery-actions{padding-top:1px}.boxed-group-list>li.hook-delivery-item .btn-sm{margin:0}.boxed-group span.animated-ellipsis-container,.boxed-group span.animated-ellipsis{padding:0}.boxed-group .animated-ellipsis-container{line-height:1.3}.hook-delivery-container .tabnav-tabcontent{display:none}.hook-delivery-container .tabnav-tabcontent.selected{display:block}.hook-delivery-container hr{margin:10px 0}.hook-delivery-container pre{padding:7px 12px;margin:10px 0;overflow:auto;font-size:13px;line-height:1.5;background-color:#f8f8f8;border:1px solid #ddd;border-radius:3px}.hook-delivery-container .tabnav{margin:10px 0}.hook-delivery-container h4.remote-call-header{margin:20px 0 10px;border-bottom:1px solid #959da5}.hook-delivery-response-status{display:inline-block;padding:4px 6px 3px;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:10px;line-height:1.1;color:#fff;background-color:#d73a49;border-radius:3px}.hook-delivery-response-status[data-response-status^="2"]{background-color:#28a745}.redelivery-dialog .pending-message{display:block}.redelivery-dialog .failure-message{display:none}.redelivery-dialog.failed{color:#9c2400;background-image:linear-gradient(#f8d8d8, #efd0d0);border-color:#da9797}.redelivery-dialog.failed .pending-message{display:none}.redelivery-dialog.failed .failure-message{display:block}.test-hook-message .success-message,.test-hook-message .error-message{display:none;margin-top:10px}.test-hook-message.success .success-message{display:block}.test-hook-message.error .error-message{display:block}.item-name{float:left;font-weight:600}.integration-meta-head{font-size:16px;color:#586069}.integrations-select-repos{max-height:138px;overflow-y:scroll;border-radius:3px}.integrations-select-repos .mini-repo-list-item{padding:8px 64px 8px 30px}.integrations-select-repos .mini-repo-list-item:hover .repo,.integrations-select-repos .mini-repo-list-item:hover .owner{text-decoration:none}.integrations-select-repos .mini-repo-list-item .css-truncate-target{max-width:345px}.integrations-select-repos::-webkit-scrollbar{width:10px}.integrations-select-repos::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,0.5);border:solid #fff 2px;border-radius:6px;box-shadow:0 0 1px rgba(255,255,255,0.5)}.integrations-select-repos::-webkit-scrollbar-track-piece{background:transparent}.integrations-repository-picker{position:relative;top:-5px;width:440px}.integrations-repository-picker .select-menu-modal{width:440px}.integrations-repository-picker .autocomplete-results{border:0}.integrations-repository-picker .select-menu .search-status{display:none}.integrations-repository-picker .select-menu.is-loading .search-status{display:block}.integrations-repository-picker .subnav-search{margin-bottom:5px;margin-left:0}.integrations-repository-picker .flash{padding:10px;margin-bottom:10px}dl.form-group>dd .integrations-repository-picker .form-control{margin-right:0}.integrations-repository-picker .octicon-x{position:absolute;top:8px;right:10px;cursor:pointer}.target-avatar{position:relative;top:-2px}.issue-list em{padding:3px;font-style:normal;font-weight:600;background-color:rgba(255,255,140,0.5);border-radius:3px}.issue-list .title{min-height:24px;padding:0;margin:0 80px 10px 0;font-size:18px;font-weight:400;line-height:24px;word-wrap:break-word}.issue-list .title .octicon{position:absolute;top:-4px;left:0;color:#888}.issue-list .title .closed.octicon{color:#cb2431}.issue-list .title .open.octicon{color:#28a745}.issue-list .title .merged.octicon{color:#6f42c1}.issue-list .description{margin:0 0 10px;overflow:hidden;line-height:20px}.issue-list-item+.issue-list-item{border-top:1px #e1e4e8 solid}.issues-reset-query-wrapper{margin-bottom:20px}.label-link:hover{text-decoration:none}.issues-reset-query{font-weight:600;color:#586069}.issues-reset-query:hover{color:#0366d6;text-decoration:none}.issues-reset-query:hover .issues-reset-query-icon{background-color:#0366d6}.issues-reset-query-icon{width:18px;height:18px;padding:1px;margin-right:3px;color:#fff;text-align:center;background-color:#6a737d;border-radius:3px}.table-list-milestones .table-list-cell{padding:15px 20px}.table-list-milestones .stat{display:inline-block;font-size:14px;font-weight:600;line-height:1.2;color:#555;white-space:nowrap}.table-list-milestones .stat a{color:inherit}.table-list-milestones .stat+.stat{margin-left:15px}.table-list-milestones .stat-label{font-weight:400;color:#586069}.milestone-title{width:500px}.milestone-title-link{margin-top:0;margin-bottom:5px;font-size:24px;font-weight:400;line-height:1.2}.milestone-title-link a{color:#333}.milestone-title-link a:hover{color:#0366d6}.milestone-progress{width:420px}.milestone-progress .progress-bar{margin-top:7px;margin-bottom:12px}.milestone-meta{font-size:14px}.milestone-meta-item{display:inline-block;margin-right:10px}.milestone-meta-item .octicon{width:16px;text-align:center}.milestone-description-html{display:none}.milestone-description{margin-top:5px}.milestone-description .expand-more{color:#0366d6;cursor:pointer}.milestone-description .expand-more:hover{text-decoration:underline}.milestone-description.open .milestone-description-plaintext{display:none}.milestone-description.open .milestone-description-html{display:block}.issue-reorder-warning{z-index:110}.task-progress{color:#586069;text-decoration:none;vertical-align:top}.task-progress .octicon{margin-right:5px;color:#999;vertical-align:bottom}.task-progress .progress-bar{display:inline-block;width:80px;height:5px;vertical-align:2px;background-color:#eee}.task-progress .progress-bar .progress{background-color:#ccc}.task-progress-counts{display:inline-block;margin-right:6px;margin-left:-2px;font-size:12px}a.task-progress:hover{color:#0366d6}a.task-progress:hover .octicon{color:inherit}a.task-progress:hover .progress-bar .progress{background-color:#0366d6}.issues-listing{position:relative}.issues-listing .octocat-search{position:absolute;right:0;height:250px;margin:-132px -4px;transform:scaleX(-1)}.issue-meta-section .octicon{color:#ccc;vertical-align:bottom}.issue-milestone{max-width:240px}.issue-milestone .css-truncate-target{max-width:100px}.milestone-link .octicon{font-size:14px}.milestone-link:hover .octicon{color:inherit}.new-issue-form{padding-top:20px;margin-top:15px;border-top:1px solid #ddd}.new-issue-form::before{display:table;content:""}.new-issue-form::after{display:table;clear:both;content:""}.new-issue-form .discussion-timeline::before{display:none}.new-pr-form{margin-top:15px;margin-bottom:20px}.new-pr-form::before{display:table;content:""}.new-pr-form::after{display:table;clear:both;content:""}.new-pr-form .discussion-timeline::before{display:none}.new-pr-form .discussion-sidebar{position:relative}.label-select-menu .description{margin-left:19px}.label-select-menu .color{display:inline-block;width:14px;height:14px;margin-top:-1px;margin-right:2px;vertical-align:middle;border-radius:3px}.label-select-menu .selected .select-menu-item-icon{color:inherit !important}.label-select-menu .selected:active{background-color:transparent !important}.label-select-menu .select-menu-item{position:relative}.label-select-menu .select-menu-item[aria-selected="true"],.label-select-menu .select-menu-item.navigation-focus{color:inherit;background-color:#f4f4f4}.label-select-menu .select-menu-item[aria-selected="true"] .select-menu-item-icon,.label-select-menu .select-menu-item.navigation-focus .select-menu-item-icon{color:transparent}.label-select-menu>form{position:relative}.closed-banner{height:7px;margin:15px 0 15px 60px;overflow:hidden;background:url("/images/modules/comments/closed_pattern.gif");border-radius:3px}.subnav .btn+.issues-search{padding-right:10px;border-right:1px solid #eee}.reaction-sort-item{float:left;width:39px;padding:5px;margin-top:5px;text-align:center;pointer-events:all;border:solid 1px transparent;border-radius:3px;opacity:0.7}.reaction-sort-item:hover,.reaction-sort-item.selected:hover,.reaction-sort-item[aria-selected="true"],.reaction-sort-item.navigation-focus{text-decoration:none;background-color:#0366d6;opacity:1}.reaction-sort-item.selected{background-color:#f1f8ff;border-color:#0366d6;opacity:1}body.emoji-size-boost .reaction-sort-item g-emoji{margin-left:-3px}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){body.emoji-size-boost .reaction-sort-item g-emoji{margin-left:0}}.issue-keyword{border-bottom:1px dotted #959da5}.issue-keyword:hover{border-bottom:0}.new-label-color-dimensions{width:24px;height:24px}.select-menu-item[aria-selected="true"]>.octicon.label-options-icon,.select-menu-item.navigation-focus>.octicon.label-options-icon{color:#24292e}.new-label-color-input:invalid{color:#cb2431}.repository-lang-stats{position:relative}.repository-lang-stats ol.repository-lang-stats-numbers li{display:table-cell;width:1%;padding:10px 5px;text-align:center;white-space:nowrap;border-bottom:0}.repository-lang-stats ol.repository-lang-stats-numbers li span.percent{float:none}.repository-lang-stats ol.repository-lang-stats-numbers li>a,.repository-lang-stats ol.repository-lang-stats-numbers li>span{font-weight:600;color:#6a737d;text-decoration:none}.repository-lang-stats ol.repository-lang-stats-numbers li .lang{color:#24292e}.repository-lang-stats ol.repository-lang-stats-numbers li .language-color{display:inline-block;width:10px;height:10px;border-radius:50%}.repository-lang-stats ol.repository-lang-stats-numbers li a:hover{background:transparent}.stats-switcher-viewport{height:41px;overflow:hidden}.stats-switcher-viewport .stats-switcher-wrapper{position:relative;top:0;transition:top 0.25s ease-in-out}.stats-switcher-viewport.is-revealing-lang-stats .stats-switcher-wrapper{top:-41px}.stats-switcher-viewport.is-revealing-overview .stats-switcher-wrapper{top:41px}.repository-lang-stats-graph{width:100%;overflow:hidden;white-space:nowrap;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;border:1px solid #ddd;border-top:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.repository-lang-stats-graph .language-color{line-height:8px;text-indent:-9999px}.repository-lang-stats-graph .language-color:first-child{border-bottom-left-radius:2px}.repository-lang-stats-graph .language-color:last-child{border-bottom-right-radius:2px}.facebox-loading,.octocat-spinner{min-height:64px;background-image:url("/images/spinners/octocat-spinner-64.gif");background-repeat:no-repeat;background-position:center center}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){.facebox-loading,.octocat-spinner{background-image:url("/images/spinners/octocat-spinner-128.gif");background-size:64px 64px}}.MarketplaceJumbotron{background-color:#2f363d;background-image:url("/images/modules/marketplace/bg-hero.svg");background-repeat:repeat-y;background-position:center top;background-size:150% auto}@media (min-width: 768px){.MarketplaceJumbotron{background-repeat:no-repeat;background-size:cover}}.CircleBadge--feature{position:relative;top:0;transition:top 0.15s ease-in, box-shadow 0.12s ease-in}.MarketplaceFeature-wrap{-webkit-overflow-scrolling:touch;overflow-x:auto}.MarketplaceFeature{min-width:250px}.MarketplaceFeature-text{opacity:0.7;transition:opacity 0.12s ease-in}.MarketplaceFeature-link:hover .CircleBadge--feature{top:-3px;box-shadow:0 3px 8px 0 rgba(0,0,0,0.2)}.MarketplaceFeature-link:hover .MarketplaceFeature-text{opacity:1}.MarketplaceFeature-link:active .CircleBadge--feature{top:0;box-shadow:0}.MarketplaceSideNav{-webkit-overflow-scrolling:touch;background-color:#f6f8fa}@media (min-width: 768px){.MarketplaceSideNav{background-color:#fff;border-right:1px #e1e4e8 solid}}.ScreenshotCarousel{border:1px #e1e4e8 solid;border-radius:3px}.ScreenshotCarousel-screenshot{display:none;padding:16px}.ScreenshotCarousel-screenshot.active{display:block}.ScreenshotCarousel-nav{display:flex;overflow-x:auto;align-items:top;box-shadow:inset 0 1px 0 #e1e4e8}.ScreenshotCarousel-navitem{width:20%;min-width:120px;padding:16px;cursor:pointer;border-right:1px #e1e4e8 solid}.ScreenshotCarousel-navitem:last-child{border-right:0}.ScreenshotCarousel-navitem.selected{background-color:#f6f8fa;box-shadow:inset 0 0 4px rgba(36,41,46,0.15)}.marketplace-plan-edit-unit,.marketplace-plan-edit-unit-label,.marketplace-plan-edit-price{display:none}.marketplace-plan-enable-free-trial{display:none}.marketplace-plan-editor[data-model="FLAT_RATE"] .marketplace-plan-edit-price{display:block}.marketplace-plan-editor[data-model="FLAT_RATE"] .marketplace-plan-enable-free-trial{display:block}.marketplace-plan-editor[data-model="PER_UNIT"] .marketplace-plan-edit-price,.marketplace-plan-editor[data-model="PER_UNIT"] .marketplace-plan-edit-unit{display:block}.marketplace-plan-editor[data-model="PER_UNIT"] .marketplace-plan-edit-unit-label{display:inline}.marketplace-plan-editor[data-model="PER_UNIT"] .marketplace-plan-enable-free-trial{display:block}.marketplace-listing-screenshot-container{width:175px;min-height:175px;background-repeat:no-repeat;background-position:center center;background-size:cover}.marketplace-listing-screenshot-container .marketplace-listing-screenshot{min-width:100%;min-height:100%;opacity:0}.marketplace-listing-screenshot-zoom{display:none;cursor:move}.marketplace-listing-details-sidebar{order:2}@media (min-width: 768px){.marketplace-listing-details-sidebar{order:1}}.marketplace-listing-details-description{order:1}@media (min-width: 768px){.marketplace-listing-details-description{order:2}}.marketplace-listing-screenshot-link{height:100px;cursor:move}.marketplace-listing-screenshot-link:hover .marketplace-listing-screenshot-zoom,.marketplace-listing-screenshot-link:focus .marketplace-listing-screenshot-zoom{top:0;left:0;display:block;width:100%;height:100%;padding-top:28px;background-color:rgba(255,255,255,0.75)}.marketplace-integratable-logo{width:40px;height:40px}.marketplace-listing-save-notice,.marketplace-listing-save-error{display:none;opacity:0;transition:opacity 0.15s linear}.marketplace-listing-save-notice.visible,.marketplace-listing-save-error.visible{display:inline-block;opacity:1}.marketplace-listing-screenshot-delete-form{position:absolute;bottom:-24px;width:100%;text-align:center}.marketplace-plan-dollar-field-container .price-note{display:none}.marketplace-plan-dollar-field-container.is-errored .price-note{display:block}.marketplace-plan-dollar-field-container.is-errored .form-control{border-color:#d73a49}.marketplace-plan-emphasis{color:#24292e}.selected .marketplace-plan-emphasis{color:#fff}.marketplace-plan-unit-name-preview::before{content:"per "}.marketplace-plan-per-time{clear:right}.marketplace-billing-modal{width:540px;max-height:90vh}.marketplace-listing-markdown,.marketplace-url-link{word-wrap:break-word;white-space:pre-wrap}.marketplace-listing-markdown{line-height:1.4}.integrations-promo-bg{background-image:url("/images/modules/works-with/build-your-own.svg");background-repeat:no-repeat;background-size:0 0}@media (min-width: 544px){.integrations-promo-bg{background-position:top right;background-size:auto 100%}}.marketplace-product-callout{border-color:#d1d5da !important}.marketplace-product-callout::before,.marketplace-product-callout::after{display:none}.marketplace-product-callout .branch-action-item-icon{color:#586069;background-color:#e1e4e8}.filter-item.selected .Label--outline-white{color:#fff;border-color:#fff}.MarketplaceEdit-body{min-height:570px}.MarketplaceEdit-body .pricing-model-selector{width:calc(100% - 12px);max-width:100% !important}.MarketplaceEdit-body .Box-row--pending{box-shadow:2px 0 0 #f9c513 inset}.MarketplaceEdit-body .menu{border-right:0;border-left:0;border-radius:0}.MarketplaceEdit-body .menu-item{padding:12px 16px;background:#fafbfc}.MarketplaceEdit-body .menu-item.selected{background:#fff}.MarketplaceEdit-body .menu-item:hover{background:#f6f8fa}.MarketplaceEdit-body .menu-item.selected::before{position:absolute;top:0;bottom:0;left:0;width:3px;content:"";background-color:#f66a0a}.MarketplaceEdit-body .menu-item:first-child::before{border-top-left-radius:0}.MarketplaceEdit-body .CircleIcon{display:inline-block;width:32px;height:32px;font-weight:bold;line-height:32px;color:#586069;text-align:center;background:#e6ebf1;border-radius:100%}.MarketplaceEdit-body .CircleIcon .octicon{display:inline-block}.MarketplaceEdit-body .CircleIcon--success{color:#fff;background:#28a745}.MarketplaceInsights-graph .insights-month .tick:nth-child(2n){visibility:hidden}.CircleBadge--tiny{width:32px;height:32px}.CircleBadge--github{position:relative}.CircleBadge--github.CircleBadge--large::after{right:5px;bottom:5px}.CircleBadge--github.CircleBadge--small::after{right:-5px;bottom:-5px}.CircleBadge--github::after{position:absolute;right:0;bottom:0;display:block;width:22px;height:22px;padding:3px;line-height:0;content:"";background:#fff url("data:image/svg+xml;base64,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") center no-repeat;border-radius:100px}.select-search-icon{position:absolute;top:19px;left:18px}body.min-width-0.page-responsive .flash-full .container{width:100%;max-width:980px}.member-list-item .table-list-cell-checkbox{width:30px}.member-list-item.adminable .member-info{padding-left:5px}.member-list-item .member-avatar-cell{width:58px}.member-list-item .dropdown-menu-content{text-align:left}.member-info{width:450px}.member-info .css-truncate-target{max-width:430px}.member-meta .select-menu-modal{width:310px}.member-meta .select-menu-modal-holder{right:0;text-align:left}.triage-mode .none-selected{display:none}.member-role-menu .select-menu-item-text{padding-right:8px}.merge-branch-heading{margin:0;line-height:1;color:#24292e}.merge-branch-description{margin-right:160px;margin-bottom:-5px;line-height:1.6em;color:#586069}.merge-branch-description .zeroclipboard-link .octicon{top:2px}.alt-merge-options{display:inline-block;margin-bottom:0;margin-left:4px;vertical-align:middle}.merged .merge-branch-description .commit-ref .css-truncate-target{max-width:180px}.merge-branch-prh-output{margin-top:10px}.merge-branch-form{display:none;padding-left:64px}.merge-branch.open .merge-branch-form{display:block}.merge-branch.open .merge-message{display:none}.merge-branch-manually{display:none;padding-top:15px;margin-top:14px;background-color:transparent;border-top:1px solid #ddd}.merge-branch-manually p{margin-bottom:0}.merge-branch-manually h3{margin-bottom:10px}.merge-branch-manually .intro{padding-bottom:10px;margin-top:0}.merge-branch-manually .step{margin:15px 0 5px}.merge-branch-manually .url-box{padding:0;margin-left:0;border:0}.merge-branch-manually .copyable-terminal{background-color:#f2f2f2}.open .merge-branch-manually{display:block}.select-menu-merge-method{width:310px}.merge-pr.is-rebasing .commit-form-fields{margin-top:-110px;opacity:0;transition:opacity 0.15s linear 0s, margin-top 0.25s ease 0.1s}.merge-pr .btn-group-merge,.merge-pr.is-squashing .btn-group-squash,.merge-pr.is-rebasing .btn-group-rebase{display:inline-block}.merge-pr .merging-body,.merge-pr .rebasing-body,.merge-pr .squashing-body{display:none}.merge-pr.is-merging .merging-body,.merge-pr.is-rebasing .rebasing-body,.merge-pr.is-squashing .squashing-body{display:block}.merge-pr .btn-group-squash,.merge-pr .btn-group-rebase,.merge-pr.is-squashing .btn-group-merge,.merge-pr.is-rebasing .btn-group-merge{display:none;margin-left:0}.commit-form-fields{transition:opacity 0.15s linear 0.1s, margin-top 0.25s ease 0s}.unavailable-merge-method{display:block;margin-top:6px;color:#a04100}[aria-selected="true"].disabled .unavailable-merge-method,.navigation-focus.disabled .unavailable-merge-method{color:#fff}.network .network-tree{vertical-align:middle}.network .gravatar{margin-right:4px;vertical-align:middle;border-radius:3px}.network .octicon{display:inline-block;width:16px;margin-left:2px;text-align:center;vertical-align:middle}.network .current-repository{background-color:#fff5b1}.page-new-repo .octicon-repo{color:#959da5}.page-new-repo .octicon-lock{color:rgba(115,92,15,0.5)}.page-new-repo ul.repo-templates{margin:10px 0}.page-new-repo ul.repo-templates>li{display:inline-block;margin:0 10px 0 0;list-style-type:none}.page-new-repo ul.repo-templates .select-menu{float:left}.page-new-repo .form-checkbox .octicon{float:left;margin-right:5px}.page-new-repo .form-checkbox.disabled{color:#6a737d}.page-new-repo .license-info{float:left;margin-top:5px;margin-left:10px;color:#0366d6}.new-repo-container{width:700px;margin:40px auto 0}.new-repo-container .Subhead{margin-bottom:30px}.owner-reponame dl.form-group{margin-top:5px;margin-bottom:0}.owner-reponame .slash{float:left;padding-top:32px;margin:0 8px;font-size:21px;color:#586069}.reponame-suggestion{color:#34631a;cursor:pointer}.upgrade-upsell{padding-left:33px}.cc-upgrade{padding-left:20px}.featured-license{font-weight:600}.license-container{padding-left:15px;border-left:1px solid #ccc}.news .account-switcher{margin-bottom:20px}.news .release{margin-top:0;margin-bottom:0}.news blockquote{color:#586069}.news h1{margin-bottom:0}.news .alert{position:relative;padding:0 0 1em 45px;overflow:hidden;border-top:1px solid #eff3f6}.news .alert .commits{padding-left:40px}.news .alert .css-truncate.css-truncate-target,.news .alert .css-truncate .css-truncate-target{max-width:180px}.news .alert p{margin:0}.news .alert .markdown-body blockquote{padding:0 0 0 40px;border:0 none}.news .alert .octicon{color:#959da5}.news .alert .dashboard-event-icon{position:absolute;top:18px;left:22px;transform:translateX(-50%)}.news .alert .body{padding:1em 0 0;overflow:hidden;font-size:14px;border-bottom:0}.news .alert .time{font-size:12px;color:#959da5}.news .alert .title{padding:0;font-weight:600}.news .alert .title .subtle{color:#959da5}.news .alert .gravatar{float:left;margin-right:0.6em;line-height:0;background-color:#fff;border-radius:3px}.news .alert .simple .title{display:inline-block;font-size:13px;font-weight:400;color:#586069}.news .alert .simple .time{display:inline-block}.news .alert .branch-link{position:relative;top:-2px;margin:0;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;color:#4183c4}.news .alert .branch-link .octicon{display:none}.news .alert:first-child{border-top:0}.news .alert:first-child .body{padding-top:0}.news .alert:first-child .dashboard-event-icon{top:0}.news .github-welcome .done{color:#586069;text-decoration:line-through}.news .commits li{margin-top:0.15em;list-style-type:none}.news .commits li.more{padding-top:2px;font-size:11px}.news .commits li .committer{display:none;padding-left:0.5em}.news .commits li img{margin:0 1px 0 0;vertical-align:middle;background-color:#fff;border-radius:2px}.news .commits li img.emoji{padding:0;margin:0;border:0}.news .commits li .message{display:inline-block;max-width:390px;margin-top:2px;overflow:hidden;font-size:13px;line-height:1.3;text-overflow:ellipsis;white-space:nowrap;vertical-align:top}.news div.message,.news li blockquote{display:inline;font-size:13px;color:#586069}.notification-routing .notification-email .edit-link{margin-right:10px;font-weight:600}.notification-routing .notification-email .btn-sm{float:none;margin:-2px 0 0}.notification-routing .notification-email .edit-form{display:none}.notification-routing .notification-email.open .edit-form{display:block}.notification-routing .notification-email.open .email-display{display:none}.notifications .list-group-item{padding-top:8px;padding-bottom:8px;padding-left:35px;border-width:1px 0 0}.notifications .list-group-item:first-child{border:0}.notifications .list-group-item-name{display:block;max-width:400px;font-size:14px;line-height:1.5em}.notifications .list-group-item-name a{display:block;max-width:460px}.notifications .notifications-more{padding:0}.notifications .notifications-more>a{display:block;padding:10px 15px;font-weight:600;color:#0366d6;text-align:center}.notifications .notifications-more>a:hover{text-decoration:underline}.notifications .read .type-icon{color:#586069}.notifications .read .list-group-item-name>a{color:#586069}.notifications .read .notification-actions{color:#586069}.notifications .read .avatar-stack{opacity:0.5}.notifications .read .undo{display:block}.notifications .read .delete{visibility:hidden}.notifications .read[aria-selected="true"],.notifications .read.navigation-focus{background-color:#f5f9fc}.notifications .muted .unmute{display:block}.notifications .muted .mute{display:none}.notifications .unmute{display:none}.notifications-sso-prompt{padding:0}.notifications-sso-prompt[aria-selected="true"],.notifications-sso-prompt.navigation-focus{background-color:#f5f9fc}.notifications-sso-prompt a{display:block;padding:10px 15px;color:#586069;text-align:center;background:none;border:0;outline:none}.notifications-sso-prompt a:hover{text-decoration:none}.notifications-sso-prompt em{font-style:normal;color:#0366d6}.notifications-sso-prompt a:hover em{text-decoration:underline}.notifications-sso-prompt strong{font-weight:bold}.type-icon-state-none{color:#586069}.type-icon-state-open{color:#28a745}.type-icon-state-closed{color:#cb2431}.type-icon-state-merged{color:#6f42c1}.notifications-list{float:left;width:100%}.notifications-list .notifications-repo-link{max-width:500px}.notifications-list .boxed-group .notification-success{position:absolute;right:3px;width:220px;margin-top:4px;font-size:13px;color:#28a745;text-align:right;visibility:hidden;opacity:0;transition:opacity 0.35s ease-in-out, transform 0.35s ease-in-out;transform:translateX(10px)}.notifications-list .mark-all-as-read{padding:2px 6px 5px 10px;margin-top:0;margin-right:0;line-height:20px;color:#586069;background-color:transparent;border:0}.notifications-list .mark-all-as-read-confirmed .notification-success{visibility:visible;opacity:1;transform:translateX(0)}.notifications-list .mark-all-as-read-confirmed .mark-all-as-read{visibility:hidden}.notifications-list .confirmation{max-height:0;padding:0;overflow:hidden;color:#666;text-align:center;opacity:0;transition:opacity 0.4s ease-in-out, max-height 0.4s ease-in-out, padding 0.4s ease-in-out}.notifications-list .confirmation+.list-group-item{margin-top:-1px;border-top-color:#d5d5d5}.notifications-list .confirmation.mark-all-as-read-confirmed{max-height:300px;padding:10px 0;opacity:1}.notification-actions{position:absolute;top:8px;right:10px;list-style:none}.notification-actions li{float:right;margin-left:10px;font-size:16px;line-height:20px}.notification-actions .age{width:120px;font-size:12px;color:#586069}.notification-actions .undo{position:absolute;top:0;display:none}.notification-actions .btn-link{padding-right:5px;padding-left:5px;line-height:inherit;color:#586069}.notification-actions .btn-link:hover{color:#0366d6;text-decoration:none}.repo-subscription-container{width:600px;margin-right:auto;margin-left:auto}.repo-subscription-container .spinner{float:right}.repo-subscription-container h2{margin-top:24px;margin-bottom:16px;font-size:22px;font-weight:400}.repo-subscription-container .intro{font-size:14px;color:#666}.repo-subscription-label{display:inline-block}.subscriptions-content .repo-icon{margin-right:5px;color:#666;vertical-align:middle}.subscriptions-content .repo-list form{display:inline}.subscriptions-content .repo-list .only-loading{display:none}.subscriptions-content .repo-list .loading .only-loading{display:inline-block}.subscriptions-content .repo-list .only-unsubed{display:none}.subscriptions-content .repo-list .unsubscribed .only-unsubed{display:inline}.subscriptions-content .repo-list .unsubscribed .only-subed{display:none}.subscriptions-content .repo-list .only-unignored{display:none}.subscriptions-content .repo-list .unsubscribed .only-unignored{display:inline}.subscriptions-content .repo-list .unsubscribed .only-ignored{display:none}.thread-subscription-status{padding:10px;margin:40px 0 20px;color:#586069;background-color:#fff;border:1px solid #eee;border-radius:3px}.thread-subscription-status .octicon-radio-tower{margin-right:10px;margin-left:4px;color:#ccc;vertical-align:middle}.thread-subscription-status .btn-sm>.octicon{margin-right:1px}.thread-subscription-status .reason{display:inline-block;margin:0 10px;vertical-align:middle}.thread-subscription-status .thread-subscribe-form{display:inline-block;vertical-align:middle}.subscription .loading{opacity:0.5}.oauth-permissions-details{position:relative;padding:15px;margin:0;list-style:none;border-bottom:1px solid #f2f2f2}.oauth-permissions-details:first-child{border-radius:3px 3px 0 0}.oauth-permissions-details:last-child{border:0;border-radius:0 0 3px 3px}.oauth-permissions-details.oauth-public-data-only{border-radius:3px}.oauth-permissions-details .markdown-body{font-size:13px}.oauth-permissions-details .content{display:none;margin-left:45px}.oauth-permissions-details .content .form-checkbox{margin-left:0}.oauth-permissions-details .content .form-checkbox:last-child{margin-bottom:0}.oauth-permissions-details .octicon{float:left;color:#586069;text-align:center}.oauth-permissions-details .permission-help{font-size:13px}.oauth-permissions-details .permission-help ul{padding-left:20px;margin:1em 0}.oauth-permissions-details .permission-summary{margin-left:45px}.oauth-permissions-details .permission-summary .access-details{position:relative;color:#586069}.oauth-permissions-details .permission-summary em.highlight{position:relative;padding:2px 3px;margin-right:-2px;margin-left:-3px;font-style:normal;color:#4c4a42;background:#fff9ea;border-radius:3px}.oauth-permissions-details .permission-title{display:block;color:#000}.oauth-permissions-details a.btn-sm{float:right;margin-top:4px}.oauth-permissions-details.open a.btn-sm{background-color:#dcdcdc;background-image:none;border-color:#b5b5b5;box-shadow:inset 0 2px 4px rgba(0,0,0,0.15)}.oauth-permissions-details.open .content{display:block}.oauth-permissions-details.default:not(.delete) .no-access,.oauth-permissions-details.default:not(.delete) .default-access,.oauth-permissions-details.none .no-access,.oauth-permissions-details.none .default-access{display:inline}.oauth-permissions-details.default:not(.delete) .access-details,.oauth-permissions-details.default:not(.delete) .permission-title,.oauth-permissions-details.none .access-details,.oauth-permissions-details.none .permission-title{color:#6a737d}.oauth-permissions-details.default:not(.delete) .octicon,.oauth-permissions-details.none .octicon{color:#ccc}.oauth-permissions-details.default .default-access{display:inline}.oauth-permissions-details.full .full-access{display:inline}.oauth-details-toggle{position:absolute;top:0;right:0;padding:20px 15px}.oauth-details-toggle .octicon-chevron-up{display:none}.open .oauth-details-toggle .octicon-chevron-down{display:none}.open .oauth-details-toggle .octicon-chevron-up{display:block}.oauth-user-permissions .full-access,.oauth-user-permissions .limited-access,.oauth-user-permissions .limited-access-emails-followers,.oauth-user-permissions .limited-access-emails-profile,.oauth-user-permissions .limited-access-followers-profile,.oauth-user-permissions .limited-access-profile,.oauth-user-permissions .limited-access-followers,.oauth-user-permissions .limited-access-emails,.oauth-user-permissions .no-access{display:none}.oauth-user-permissions.limited.limited-email .limited-access-emails{display:inline}.oauth-user-permissions.limited.limited-email.limited-profile .limited-access-emails,.oauth-user-permissions.limited.limited-email.limited-profile .limited-access-profile{display:none}.oauth-user-permissions.limited.limited-email.limited-profile .limited-access-emails-profile{display:inline}.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-emails,.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-profile,.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-followers,.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-emails-profile,.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-emails-followers,.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access-followers-profile{display:none}.oauth-user-permissions.limited.limited-email.limited-profile.limited-follow .limited-access{display:inline}.oauth-user-permissions.limited.limited-email.limited-follow .limited-access-emails,.oauth-user-permissions.limited.limited-email.limited-follow .limited-access-followers{display:none}.oauth-user-permissions.limited.limited-email.limited-follow .limited-access-emails-followers{display:inline}.oauth-user-permissions.limited.limited-follow .limited-access-followers{display:inline}.oauth-user-permissions.limited.limited-follow.limited-profile .limited-access-followers,.oauth-user-permissions.limited.limited-follow.limited-profile .limited-access-profile{display:none}.oauth-user-permissions.limited.limited-follow.limited-profile .limited-access-followers-profile{display:inline}.oauth-user-permissions.limited.limited-profile .limited-access-profile{display:inline}.oauth-repo-permissions .default-access,.oauth-repo-permissions .public-access,.oauth-repo-permissions .limited-repo-invite-access,.oauth-repo-permissions .full-access{display:none}.oauth-repo-permissions.public .public-access{display:inline}.oauth-delete-repo-permissions .octicon-alert{color:#cb2431}.oauth-repo-status-permissions .no-access,.oauth-repo-status-permissions .full-access,.oauth-repo-deployment-permissions .no-access,.oauth-repo-deployment-permissions .full-access{display:none}.oauth-notifications-permissions .no-access,.oauth-notifications-permissions .read-access,.oauth-notifications-permissions .via-public-access,.oauth-notifications-permissions .via-full-access{display:none}.oauth-notifications-permissions.read .read-access{display:inline}.oauth-notifications-permissions.via-public .via-public-access{display:inline}.oauth-notifications-permissions.via-public .octicon{display:none}.oauth-notifications-permissions.via-full .via-full-access{display:inline}.oauth-gist-permissions .no-access,.oauth-gist-permissions .full-access{display:none}.oauth-granular-permissions .no-access,.oauth-granular-permissions .read-access,.oauth-granular-permissions .write-access,.oauth-granular-permissions .full-access{display:none}.oauth-granular-permissions.none .no-access{display:inline}.oauth-granular-permissions.read .read-access{display:inline}.oauth-granular-permissions.write .write-access{display:inline}.oauth-granular-permissions.full .full-access{display:inline}.oauth-no-description{color:#586069}.oauth-org-access-details a:hover{text-decoration:none}.oauth-org-access-details .boxed-group-inner{border:0;border-radius:3px}.oauth-org-access-details .oauth-org-item{line-height:24px}.oauth-org-access-details .oauth-org-item:first-child{border-radius:3px 3px 0 0}.oauth-org-access-details .oauth-org-item .loading-indicator{display:none;margin:4px}.oauth-org-access-details .oauth-org-item.on{background:#fff}.oauth-org-access-details .oauth-org-item.on:hover{background:#ffe}.oauth-org-access-details .oauth-org-item.on .authorized-tools{display:block}.oauth-org-access-details .oauth-org-item.on .unauthorized-tools{display:none}.oauth-org-access-details .oauth-org-item.on strong{color:#24292e}.oauth-org-access-details .oauth-org-item.on .octicon-check{display:inline}.oauth-org-access-details .oauth-org-item.on .octicon-x{display:none}.oauth-org-access-details .oauth-org-item.revoked{background:#fff}.oauth-org-access-details .oauth-org-item.revoked .unauthorized-tools,.oauth-org-access-details .oauth-org-item.revoked .authorized-tools{display:none}.oauth-org-access-details .oauth-org-item.revoked .octicon-x{color:#cb2431}.oauth-org-access-details .oauth-org-item.loading .unauthorized-tools,.oauth-org-access-details .oauth-org-item.loading .authorized-tools{display:none}.oauth-org-access-details .oauth-org-item.loading .loading-indicator{display:block}.oauth-org-access-details .oauth-org-item .authorized-tools{display:none}.oauth-org-access-details .oauth-org-item .unauthorized-tools{display:block}.oauth-org-access-details .btn{line-height:1.5em}.oauth-org-access-details .octicon{color:#979797}.oauth-org-access-details .octicon-check{display:none;color:#28a745}.oauth-org-access-details .octicon-x{display:inline}.oauth-org-access-details .octicon-x.org-access-denied{color:#cb2431}.permission-title{margin-top:0}.oauth-application-whitelist h2{display:inline-block}.oauth-application-whitelist .request-info{display:block}.oauth-application-whitelist .request-info strong{display:inline-block;color:#24292e}.oauth-application-whitelist .request-info .application-description{display:none}.oauth-application-whitelist .request-info.open .application-description{display:block}.oauth-application-whitelist .avatar{margin-top:0}.oauth-application-whitelist .requestor{font-weight:600}.oauth-application-whitelist .octicon-alert{color:#a04100}.oauth-application-whitelist .octicon-check,.oauth-application-whitelist .approved-request{color:#28a745}.oauth-application-whitelist .denied-request{color:#cb2431}.oauth-application-whitelist .request-indicator{margin-left:10px}.oauth-application-whitelist .edit-link{color:#6a737d}.oauth-application-whitelist .edit-link:hover{color:#0366d6}.oauth-application-whitelist .boxed-group-list{margin-top:1em}.oauth-application-whitelist .boxed-group-list li{padding:10px}.boxed-group-inner .oauth-application-info{margin-bottom:10px}.oauth-application-info .application-title{font-size:30px;color:#24292e}.oauth-application-info .application-description{margin-top:3px;margin-bottom:0}.oauth-application-info .app-info{display:inline-block;margin-right:10px;color:#6a737d}.oauth-application-info .app-info .octicon{margin-right:5px}.oauth-application-info .listgroup-item{line-height:inherit}.oauth-application-info .app-denied,.oauth-application-info .app-approved{margin-left:10px;font-size:13px;font-weight:400;white-space:nowrap}.oauth-application-info .app-approved,.oauth-application-info .octicon-check{color:#28a745}.oauth-application-info .app-denied,.oauth-application-info .octicon-x{color:#a04100}.restrict-oauth-access-button{margin-right:20px}.restrict-oauth-access-info{margin-bottom:40px;font-size:15px}.restrict-oauth-access-list{padding-left:25px}.restrict-oauth-access-list li{margin-bottom:10px}.restrict-oauth-access-list li:last-child{margin-bottom:0}.app-transfer-actions form{display:inline}.oauth-border{border-bottom:1px solid #e5e5e5}.oauth-border:last-child{border:0}.developer-app-item .developer-app-avatar-cell{width:60px}.developer-app-item .developer-app-name{font-size:15px;font-weight:600;line-height:1.2;color:#24292e}.developer-app-item .developer-app-name:hover{color:#0366d6;text-decoration:none}.developer-app-item .developer-app-info-cell{padding-left:0}.developer-app-item .developer-app-list-meta{margin-top:3px;margin-bottom:2px;font-weight:400;color:#6a737d}.org-transfer-requests{margin:10px 0 20px}.octocaptcha-element{width:450px;height:310px}.octocaptcha-frame{width:450px;height:340px}.invitation-2fa-banner{margin-right:-24px;margin-left:-24px}.sign-up-via-invitation .bleed-flush{width:100%;padding:0 20px;margin-left:-20px;border-color:#d8dee2}.sign-up-via-invitation label{font-size:13px}.orghead{padding-top:20px;padding-bottom:0;margin-bottom:20px;color:#666;background-color:#fafbfc;border-bottom:1px solid #eee}.orghead .edit-org{position:relative;top:-6px;display:inline-block;padding:3px 5px;font-size:14px;color:#aaa;border:1px solid #e5e5e5;border-radius:3px}.orghead .edit-org:hover{color:#0366d6;background-color:#fff}.orghead .edit-org .octicon{font-size:14px}.orghead .orgnav{position:relative;top:1px;margin-top:10px}.org-header-wrapper .TableObject-item--primary,.org-repos .TableObject-item--primary{white-space:normal}.org-header-wrapper .avatar{display:block;width:100px}.org-header-wrapper .TableObject-item--primary{padding-left:20px}.org-name{font-weight:400;color:#333}.org-description{margin-top:0;margin-bottom:8px;font-size:16px;line-height:1.25}.org-header-meta{font-size:12px;line-height:1.5;list-style:none}.org-header-meta .meta-item{display:inline-block;max-width:100%;padding-right:18px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.org-header-meta .octicon{position:relative;top:1px;margin-right:2px;color:#ccc;vertical-align:text-bottom}.org-header-meta.has-email.has-blog .meta-item,.org-header-meta.has-email.has-location .meta-item,.org-header-meta.has-blog.has-location .meta-item{max-width:278px}.org-header-meta.has-email.has-blog.has-location .meta-item{max-width:186px}.org-profile .member-badge{display:block;padding-top:0;padding-bottom:0;border-top:0}.audit-log-map-container{position:relative;margin-bottom:20px}.audit-log-map-container .activity{position:absolute;top:120px;left:340px;z-index:99999;display:none;text-align:center}.audit-log-map-container .is-graph-loading .activity{display:block}.audit-search-form{position:relative;margin-bottom:20px}.audit-search-form::before{display:table;content:""}.audit-search-form::after{display:table;clear:both;content:""}.audit-search-form .subnav-search-input{width:250px}.audit-log-map{height:277px;overflow:hidden;background-color:#0366d6;border-radius:3px;box-shadow:inset 1px 1px 0 rgba(0,0,0,0.2)}.map-background{pointer-events:all;fill:#0366d6;cursor:-webkit-grab;cursor:grab}.land{fill:none;stroke:#256aae;stroke-width:2;shape-rendering:crispedges}.country{fill:#d7c7ad;shape-rendering:crispedges;cursor:pointer}.country.hk{stroke:#a5967e}.country:hover{fill:#c8b28e}.country.active{fill:#f6e5ca}.borders{fill:none;stroke:#a5967e;shape-rendering:crispedges}.graticule{pointer-events:none;fill:none;stroke:#fff;stroke-opacity:0.2;shape-rendering:crispedges}.graticule :nth-child(2n){stroke-dasharray:2, 2}.security-map-legend circle{fill-opacity:0;stroke:#fff;stroke-width:1.5}.security-map-legend text{font-size:10px;fill:#fff;text-anchor:end}.security-map-legend .link{stroke:#fff;stroke-width:1.5}.audit-point{pointer-events:none;fill:#d73a49;fill-opacity:0.8;stroke:#d73a49}.country-info{position:absolute;top:10px;right:10px;padding:10px;pointer-events:none;background:rgba(255,255,255,0.9);border-radius:2px;opacity:0}.audit-log-search .member-info{width:300px}.audit-log-search .member-info .member-avatar{float:left;margin-right:15px}.audit-log-search .member-info .member-link{display:block}.audit-log-search .member-info .member-list-avatar{margin-right:0}.audit-log-search .member-info .ghost{display:inline-block;color:#586069}.audit-log-search .blankslate{border-top-left-radius:0;border-top-right-radius:0}.audit-log-search .export-phrase{margin:5px 0}.audit-results-actions{overflow:auto}.audit-search-clear{float:left;margin-bottom:20px;border:0}.audit-search-clear .issues-reset-query{margin-bottom:0}.audit-type{width:200px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.audit-type .octicon{margin-right:3px;font-weight:400;vertical-align:bottom}.audit-type .repo{color:#a04100}.audit-type .team{color:#28a745}.audit-type .user{color:#6f42c1}.audit-type .oauth_access{color:#cb2431}.audit-type .hook{color:#e1bf4e}.export-actions{display:inline-block;margin-left:10px}.export-actions a{margin-top:-3px;color:#6a737d}.export-actions a:hover{color:#0366d6;text-decoration:none}.export-actions .select-menu-button::after{position:absolute;top:50%;right:15px;margin-top:-2px}.export-actions .select-menu-modal{width:111px}.export-actions .select-menu-item-text{text-align:center}.export-phrase{margin-top:5px}.export-phrase pre{padding-left:10px;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;white-space:pre-wrap;border-left:1px solid #eee}.audit-log-export-button{width:110px;height:30px;transition:0.25s width ease-in-out}.audit-log-export-button .loader{position:absolute;top:50%;left:11px;display:none;margin-top:-9px}.audit-log-export-button .octicon{position:absolute;top:50%;left:11px;margin-top:-9px}.audit-log-export-button .audit-log-export-status{position:absolute;top:4px;left:35px}.audit-log-export-button.disabled{width:125px}.audit-log-export-button.disabled::after{display:none}.audit-log-export-button.disabled .octicon{display:none}.audit-log-export-button.disabled .loader{display:block}.two-factor-enforcement-form .loading-spinner{float:left;margin:0 0 0 -20px;vertical-align:middle}.saml-enabled-banner-container{background-image:linear-gradient(-1deg, #fff 0%, #f5f5f5 100%)}.saml-settings-form .test-status-indicator{width:30px;height:30px;margin-top:-4px;border-radius:50%}.saml-settings-form .test-status-indicator .octicon{display:block;margin-top:7px;margin-right:auto;margin-left:auto}.saml-settings-form .form-group.errored{margin-bottom:40px}.saml-settings-form .test-status-indicator-error{color:#fff;background-color:#cb2431}.saml-settings-form .test-status-indicator-success{color:#fff;background-color:#28a745}.saml-settings-form .details-container .method-field{display:none}.saml-settings-form .details-container .method-label{font-weight:400}.saml-settings-form .details-container .details-target{cursor:pointer}.saml-settings-form .details-container.open .method-value,.saml-settings-form .details-container.open .details-target{display:none}.saml-settings-form .details-container.open .method-field{display:inline-block}.saml-settings-form .saml-enforcement-disabled{opacity:0.5}.form-group .form-control.saml-certificate-field{width:440px;height:150px;min-height:0}.member-avatar-group{margin:-1px}.member-avatar-group::before{display:table;content:""}.member-avatar-group::after{display:table;clear:both;content:""}.member-avatar{float:left;margin:1px}.member-row{width:100%;min-height:40px;padding-bottom:15px;margin-top:15px;font-size:14px;color:#333;border-bottom:1px solid #eee}.member-row::before{display:table;content:""}.member-row::after{display:table;clear:both;content:""}.member-row a:hover{color:#0366d6;text-decoration:none}.member-row:last-child{padding-bottom:0;border-bottom:0}.member-row .member-name{display:block;padding-left:50px}.member-row .member-fullname{display:block;padding-left:50px;word-wrap:break-word}.member-fullname{color:#586069}.org-toolbar.disabled{pointer-events:none}.org-toolbar .subnav-search{width:320px;margin-right:20px;margin-left:0}.org-toolbar .subnav-search-context+.subnav-search{margin-left:-1px}.org-toolbar .subnav-search-input{width:100%}.org-toolbar-next{margin-bottom:24px}.org-toolbar-next .subnav-search{width:240px}.auto-search-group{position:relative}.auto-search-group .auto-search-input{padding-left:30px}.auto-search-group .spinner,.auto-search-group>.octicon{position:absolute;left:10px;z-index:5;width:16px;height:16px}.auto-search-group .spinner{top:9px;background-color:#fff}.auto-search-group>.octicon{top:10px;font-size:14px;color:#bbb;text-align:center}.org-list .list-item{position:relative;padding-top:15px;padding-bottom:15px;border-bottom:1px solid #eee}.org-list .list-item::before{display:table;content:""}.org-list .list-item::after{display:table;clear:both;content:""}.org-repos .blankslate{margin-top:15px}.org-repos-mini{padding:0;margin:0}.org-repos-mini .org-repo-mini-item:first-child .org-repo-mini-cell{border-top:0}.org-repos-mini .org-repo-name{margin-top:0;margin-bottom:0;font-size:14px;word-wrap:break-word}.org-repos-mini .org-repo-name .octicon-repo{color:#586069}.org-repos-mini .org-repo-name .octicon-lock{color:rgba(115,92,15,0.5)}.org-repos-mini .org-repo-name .repo-prefix{font-weight:400}.org-repos-mini .org-repo-name .repo-slash{display:inline-block;margin-right:-4px;margin-left:-4px}.org-repo-mini-cell{padding-top:15px;padding-bottom:15px;vertical-align:middle}.org-repo-meta{width:165px}.org-repo-meta .access-level{cursor:default}.org-repo-access-level{text-align:center}.org-repo-manage{width:270px}.with-higher-access .table-list-cell-checkbox{vertical-align:top}.permission-level-cell .select-menu-button{width:78px;text-align:left}.permission-level-cell .select-menu-button::after{position:absolute;top:10px;right:10px}.permission-level-cell .spinner,.permission-level-cell .permission-success-icon{float:none;opacity:0;transition:opacity 0.2s ease-in-out}.permission-level-cell .permission-success-icon{color:#28a745}.permission-level-cell .is-loading .spinner,.permission-level-cell .was-successful .permission-success-icon{opacity:1}.select-menu-option-title{margin-top:0;margin-bottom:0}.reinstate-org-member{position:relative;width:500px;margin:40px auto}.reinstate-org-member .reinstate-lead{margin-bottom:30px;font-size:16px}.reinstate-org-member label{cursor:pointer}.reinstate-org-member .reinstate-detail-container{margin:15px 0}.reinstate-org-member .reinstate-title{color:#333}.reinstate-org-member .reinstate-title .octicon{width:16px;margin-right:10px;color:#999}.add-member-wrapper{position:relative;width:500px;margin:40px auto}.add-member-wrapper .available-seats{color:#586069}.add-member-wrapper .buy-more-link{margin-right:5px}.cancel-invitation-form{margin-top:-34px}.add-member-lead{font-size:16px}.add-member-roles{margin:30px 0}.add-member-roles .role-make-member{display:block;margin-bottom:10px}.add-member-team-list{display:block;margin-bottom:30px;list-style:none}.add-member-team-list .team-row-header{padding:10px 20px 10px 0;font-size:12px;text-align:right;background:#fafafa;border-top-left-radius:3px;border-top-right-radius:3px}.add-member-team-list .team-row-header .team-row-teams{margin-right:245px}.add-member-team-list .team-row-header .team-row-members{margin-right:35px}.add-member-team-list .team{display:block;font-weight:400;cursor:pointer}.add-member-team-list .team:first-child{border-top:1px solid #f2f2f2}.add-member-team-list .team .btn-sm{float:right}.add-member-team-list .team-info{max-width:80%;color:#000;text-decoration:none}.add-member-team-list .team-meta{margin-top:2px;margin-bottom:2px;color:#586069}.add-member-team-list .team-description{width:260px;margin-top:2px;margin-bottom:2px}.add-member-team-list .team-toggler .turn-on{display:inline-block}.add-member-team-list .team-toggler.on .turn-off{display:inline-block}.add-member-team-list .team-toggler .turn-off{display:none}.add-member-team-list .team-toggler.on .turn-on{display:none}.team-list-footer{padding:10px 0;text-align:center;border-top:1px solid #e5e5e5}.team-list-footer .show-all-link .octicon{margin-left:5px;color:#586069}.invite-team-member-list .team{cursor:default}.invite-team-member-list .team:first-child .table-list-cell{border-top:0}.invite-team-member-list .table-list-cell-checkbox{width:42px}.invite-team-member-list .team-toggler{padding-top:12px;padding-right:3px}.invite-team-member-list .team-info{width:260px;padding:10px 15px 10px 0}.invite-team-member-list .team-description{display:block;padding-top:0;padding-bottom:0;font-weight:400}.invite-team-member-list .team-meta{width:100px;text-align:left;vertical-align:middle}.invite-team-member-list .team-link{color:#0366d6;text-align:right}.migration-jumbotron{height:70vh;min-height:450px;max-height:650px}.migration-jumbotron,.migration-sub-header{position:relative;margin-top:-1px;background-color:#3f4851;background-image:linear-gradient(#3f4851 0, #282d33 100%)}.migration-jumbotron::after,.migration-sub-header::after{position:absolute;top:0;right:0;bottom:0;left:0;z-index:1;content:" ";background-image:url("/images/modules/orgs/dots-bg.png");background-repeat:repeat;background-size:80%;opacity:0.75}.migration-jumbotron-content{position:relative;top:50%;z-index:2;width:980px;padding:100px 60px;margin:0 auto;text-align:center;transform:translateY(-50%)}.migration-jumbotron-octicons{height:60px;margin-bottom:20px;text-align:center}.migration-jumbotron-octicon-item{position:relative;display:inline-block;width:60px;height:60px;margin-right:10px;margin-left:10px;background-image:linear-gradient(-45deg, #6f42c1 30%, #f66a0a 100%);border-radius:50px}.migration-jumbotron-octicon-item::after{position:absolute;top:1px;right:1px;bottom:1px;left:1px;content:"";background-color:#383f47;border-radius:50px}.migration-jumbotron-octicon-item .octicon{position:relative;z-index:2;margin-top:16px;color:rgba(255,255,255,0.9)}.migration-jumbotron-title,.migration-jumbotron-lead,.migration-sub-title,.migration-sub-lead{color:#fff;text-shadow:0 1px 1px rgba(0,0,0,0.05)}.migration-jumbotron-title,.migration-section-title{margin-bottom:10px;font-weight:300}.migration-jumbotron-title{font-size:40px}.migration-section-title{margin-top:0;font-size:30px}.migration-jumbotron-lead{margin-top:0;font-size:24px;opacity:0.85}.migration-section-lead{margin-top:20px;margin-bottom:20px}.migration-jumbotron-btn{padding:12px 18px;font-size:16px;color:#6f42c1;background-color:#fff;border-width:0;box-shadow:0 3px 3px rgba(0,0,0,0.05)}.migration-jumbotron-btn:hover{color:#6f42c1;background-color:#eee}.migration-section{padding-top:100px;padding-bottom:100px;overflow:hidden;border-bottom:1px solid #ddd}.migration-feature-list{margin-top:30px;margin-bottom:20px;overflow:hidden;font-size:14px;color:#586069;list-style:none}.migration-feature-list::before{display:block;width:100px;margin-bottom:30px;content:"";border-top:1px solid #ddd}.migration-feature-list .octicon{width:22px;margin-left:-3px;color:#586069;text-align:center}.migration-feature-list-item{float:left;width:50%;margin-bottom:15px}.migration-section-grey{background-color:#fcfcfc}.migration-illustration-wrapper::before{display:table;content:""}.migration-illustration-wrapper::after{display:table;clear:both;content:""}.migration-illustration{width:700px;margin-top:-30px;margin-bottom:-50px;border:1px solid #ddd;border-radius:3px;box-shadow:0 0 15px rgba(0,0,0,0.05)}.migration-illustration-left{float:right;margin-right:50px}.migration-illustration-right{float:left;margin-left:50px}.migration-section-privileges{padding-top:80px;padding-bottom:80px}.migration-footer{position:relative;z-index:1;padding-top:60px;padding-bottom:60px;margin-bottom:-41px;border-bottom:1px solid #ddd}.migration-footer-content{width:800px;margin:0 auto;text-align:center}.migration-footer-title,.migration-footer-lead{margin-bottom:0}.migration-footer-lead{margin-top:10px}.migration-footer-btn{margin-top:20px}.migration-sub-header{padding-top:40px;padding-bottom:40px;margin-bottom:40px}.org-settings-updating{padding:15px;margin-top:0;margin-bottom:30px;background-color:#fff;border:1px solid #ddd;border-radius:3px}.org-settings-updating .spinner{display:inline-block;margin-top:-2px;vertical-align:middle}.org-disabled-settings{pointer-events:none;opacity:0.5}.migration-sub-header-content{width:68%}.migration-sub-title{margin-bottom:0}.migration-sub-lead{margin-top:10px;margin-bottom:0}.migration-org-avatar{margin-top:6px;margin-right:72px;border:3px solid #fff;border-radius:3px}.org-migration-settings-sidebar{position:-webkit-sticky;position:sticky;top:20px}.org-migration-settings-sidebar .migrate-org-roles{margin-top:0;margin-bottom:10px}.org-migration-settings-sidebar .preserve-member-privileges-btn{display:none}.org-migration-settings-sidebar .member-privilege-radios-preserved .preserve-member-privileges-btn{display:block}.org-migration-settings-sidebar .member-privilege-radios-preserved .save-member-privileges-btn{display:none}.org-migration-settings-section{position:relative;padding-bottom:50px;margin-right:60px;margin-bottom:50px;border-bottom:1px solid #ddd}.org-migration-settings-section:last-child{padding-bottom:0;margin-bottom:0;border-bottom:0}.org-migration-settings-section .disabled{pointer-events:none;opacity:0.5}.org-migration-settings-section .spinner{display:inline-block;margin-bottom:-3px}.org-migration-settings-icon{position:absolute;left:-45px;color:#ccc}.org-migration-settings-title{margin-bottom:0;font-size:22px;font-weight:400}.org-migration-settings-info{margin-top:5px;margin-bottom:30px;font-size:16px;color:#586069}.migrate-owners-wrapper{position:relative;min-height:550px}.migrate-owners-content-about{position:absolute;top:0;left:50%;width:700px;margin:30px auto 0;text-align:center;transition:opacity 0.2s ease-in-out, transform 0.3s ease-in-out;transform:translate(-50%, 0)}.migrate-owners-title{font-size:35px;font-weight:400}.migrate-owners-lead{margin-top:0;margin-bottom:20px}.migrate-owners-content-rename{width:520px;margin:auto}.delete-owners-button{color:#586069}.delete-owners-button:hover{color:#cb2431}.migrate-org-roles{display:table;width:100%;margin-top:-20px;border:1px solid #ddd;border-radius:3px}.migrate-org-roles .tooltipped::after{width:150px;white-space:normal}.no-avatars-roles-matrix .migrate-org-roles{margin-top:5px}.migrate-org-roles-item{display:table-cell;width:33.33%;border-right:1px solid #ddd}.migrate-org-roles-item:last-child{border-right:0}.migrate-ability-list{margin:15px 0;list-style:none}.migrate-ability-list-item{padding-top:5px;padding-bottom:5px;margin:0 20px;font-size:14px}.migrate-ability-list-item:first-child{border-top:0}.migrate-ability-list-item .octicon-check,.migrate-ability-list-item .octicon-x{width:15px}.migrate-ability-list-item .octicon-check{color:#28a745}.migrate-ability-list-item .octicon-x{color:#aaa}.migrate-ability-list-item .octicon-question{font-size:12px;color:#555}.migrate-ability-not-possible{color:#586069}.default-repository-permission .octicon-x,.members-can-create-repositories .octicon-x,.team-privacy .octicon-x{display:none}.default-repository-permission.migrate-ability-not-possible .octicon-x,.members-can-create-repositories.migrate-ability-not-possible .octicon-x,.team-privacy.migrate-ability-not-possible .octicon-x{display:inline-block}.default-repository-permission.migrate-ability-not-possible .octicon-check,.members-can-create-repositories.migrate-ability-not-possible .octicon-check,.team-privacy.migrate-ability-not-possible .octicon-check{display:none}.migrate-org-roles-header{padding:15px 20px;border-bottom:1px solid #ddd}.migrate-org-roles-title{margin-top:0;margin-bottom:0;font-size:18px;font-weight:400}.migrate-org-roles-lead{margin-top:4px;margin-bottom:0;font-size:14px;color:#586069}.migrate-org-roles-count{padding:10px 20px;color:#586069;border-top:1px solid #ddd}.migrate-org-avatar-list{margin-top:5px;margin-bottom:10px}.migrate-org-avatar-list::before{display:table;content:""}.migrate-org-avatar-list::after{display:table;clear:both;content:""}.migrate-org-avatar-list .migrate-org-avatar,.migrate-org-avatar-list .migrate-org-avatar-empty{float:left;margin-left:2px}.migrate-org-avatar-list .migrate-org-avatar:first-child,.migrate-org-avatar-list .migrate-org-avatar-empty:first-child{margin-left:0}.migrate-org-avatar-list .migrate-org-avatar-empty{width:30px;height:30px;border-radius:3px}.migrate-org-avatar-list .migrate-org-more-ellipsis,.migrate-org-avatar-list .migrate-org-zero{font-size:18px;line-height:30px;color:#586069;text-align:center}.migrate-org-avatar-list .migrate-org-more-ellipsis{font-weight:600;line-height:20px;background-color:#f5f5f5}.migrate-org-avatar-list .migrate-org-zero{color:#586069;border:1px dashed #ddd}.migrate-org-avatar-list .migrate-org-more-ellipsis:hover{text-decoration:none}.migrate-org-avatar-list .tooltipped::after{width:auto;white-space:nowrap}.menu-item .org-avatar,.menu-item .org-octicon-credit-card{position:absolute}.menu-item .org-octicon-credit-card{right:0}.org-settings-link{display:block;padding:0 30px;word-wrap:break-word}.team-stats{padding-right:15px;padding-left:15px;margin-right:-15px;margin-bottom:-15px;margin-left:-15px;border-top:1px solid #eee}.stats-group{display:table;width:100%;table-layout:fixed}.stats-group-stat{display:table-cell;padding-top:10px;padding-bottom:10px;padding-left:15px;font-size:12px;color:#586069;text-transform:uppercase}.stats-group-stat:first-child{padding-left:0;border-right:1px solid #eee}.stats-group-stat:hover,.stats-group-stat:hover .stat-number{color:#0366d6;text-decoration:none}.stats-group-stat.no-link:hover{color:#586069;text-decoration:none}.stats-group-stat.no-link:hover .stat-number{color:#333}.stat-number{display:block;font-size:16px;color:#333}.permission-title{margin-top:0}.add-to-org-wrapper{width:500px}.invite-member-results ul{margin:0}.invite-member-header{text-align:center}.add-to-org-title{font-size:21px;font-weight:400;line-height:normal}.team-member-list{list-style:none}.team-member-list .table-list-cell{padding-top:15px;padding-bottom:15px}.team-member-list .team-member-content{margin-left:50px}.team-member-list .team-member-username{margin:0;font-size:14px;font-weight:600;line-height:20px}.team-member-list .Label--gray,.team-member-list .Label--outline{cursor:default}.team-member-list .invite-icon{width:28px;color:#959da5}.menu-item-danger,.menu-item-danger.selected{color:#cb2431}.menu-item-danger:hover,.menu-item-danger[aria-selected="true"],.menu-item-danger.navigation-focus,.menu-item-danger.selected:hover,.menu-item-danger.selected[aria-selected="true"],.menu-item-danger.selected.navigation-focus{color:#fff;background:#d73a49}.team-member-list-avatar{float:left;margin-right:10px}.team-member-list-avatar .octicon{width:40px;color:#959da5}.org-team-form .disabled{opacity:0.5}.org-team-form .css-truncate-target{max-width:250px}.confirm-removal-container .private-fork-count{margin-top:0;font-size:12px;font-weight:400;color:#586069}.confirm-removal-container .deleting-private-forks-warning{position:relative;padding-left:26px}.confirm-removal-container .deleting-private-forks-warning .octicon{position:absolute;top:2px;left:0;color:#cb2431}.confirm-removal-list-container{margin-bottom:15px;border:1px solid #eaeaea;border-radius:3px}.confirm-removal-list-item{padding:10px;margin:0;font-size:14px;font-weight:600;border-top:1px solid #eaeaea}.confirm-removal-list-item:first-child{border-top:0}.confirm-removal-team .octicon,.confirm-removal-repo .octicon{margin-right:3px;color:#586069}.team-repo-access-list{max-height:245px}.manage-member-meta{list-style:none}.manage-member-meta-item{margin-top:12px;color:#586069}.manage-member-meta-item:first-child{margin-top:0}.manage-member-meta-item .btn-link{color:#586069}.manage-member-meta-item>.octicon{width:14px;margin-right:5px;color:#586069;text-align:center}.manage-member-meta-item>.octicon-alert{color:#a04100}.member-two-factor-disabled{color:#cb2431}.manage-member-button{margin-bottom:10px}.org-person-repo-header{margin-top:0}.org-person-repo-search{margin-top:5px;margin-right:5px}.org-user-notice-title{margin-top:0;margin-bottom:0}.org-user-notice-content{margin-top:10px;margin-bottom:10px;font-size:14px}.org-user-notice-content strong{color:#333}.org-user-notice-content:last-child{margin-bottom:0}.org-user-notice-content .octicon{color:#586069}.org-user-notice-icon{float:right;margin:10px 10px 20px;font-size:45px;color:#ccc}.manage-repo-access-header{margin-top:30px;margin-bottom:30px}.manage-repo-access-header::before{display:table;content:""}.manage-repo-access-header::after{display:table;clear:both;content:""}.manage-repo-access-header .btn{margin-top:8px}.manage-repo-access-header .tooltipped::after{width:250px;white-space:normal}.manage-repo-access-heading{margin-top:-2px;margin-bottom:0;font-size:24px;font-weight:400}.manage-repo-access-lead{margin-top:3px;margin-bottom:0;font-size:16px;color:#586069}.manage-repo-access-group{background-color:#fff;border:1px solid #ddd;border-radius:3px}.manage-repo-access-title{padding:12px 15px;margin-top:0;margin-bottom:0;font-size:14px;background-color:#f8f8f8;border-bottom:1px solid #ddd;border-radius:3px 3px 0 0}.manage-repo-access-wrapper{position:relative;padding-left:25px}.manage-repo-access-wrapper::before{position:absolute;top:15px;bottom:15px;left:20px;z-index:1;display:block;width:2px;content:"";background-color:#eee}.manage-repo-access-icon{position:relative;z-index:2;float:left;padding-top:2px;padding-bottom:2px;margin-top:-3px;margin-left:-25px;background:#fff}.manage-repo-access-icon .octicon{font-size:14px;color:#ccc}.manage-repo-access-list{list-style:none}.manage-repo-access-list-item{padding:15px}.manage-repo-access-list-item:last-child{border-bottom:0;border-radius:0 0 3px 3px}.manage-repo-access-teams-group{margin-top:-20px;list-style:none;border:1px solid #ddd;border-radius:3px}.manage-repo-access-team-item{border-top:1px solid #eee}.manage-repo-access-team-item:first-child{border-top:0}.manage-repo-access-description{margin-top:3px;margin-bottom:0;overflow:hidden;text-overflow:ellipsis;word-wrap:break-word;white-space:nowrap}.manage-repo-access-not-active{color:#24292e;background-color:#fafbfc}.manage-repo-access-not-active .manage-repo-access-icon{background:#f9f9f9}.manage-access-remove-footer{padding:15px;border-top:1px solid #ddd}.manage-access-remove-footer .tooltipped::after{width:250px;white-space:normal}.manage-access-none{margin:20px 50px;text-align:center}.ldap-group-dn{display:block;font-weight:400;color:#aaa}.ldap-import-groups-container .blankslate{display:none}.ldap-import-groups-container.is-empty .blankslate{display:block}.ldap-import-groups-container.is-empty .ldap-memberships-list{display:none}.ldap-memberships-list{margin-bottom:30px}.ldap-memberships-list .table-list-cell{padding-top:10px;padding-bottom:10px;font-size:13px;vertical-align:middle}.ldap-memberships-list .table-list-cell:last-child{width:92px}.ldap-memberships-list .ldap-list-team-name{width:380px}.ldap-memberships-list .ldap-group-dn{font-size:11px}.ldap-memberships-list .ldap-mention-as{width:260px}.ldap-memberships-list .edit{position:absolute;padding:10px;margin-left:-33px;color:#0366d6;cursor:pointer}.ldap-memberships-list .edit-fields{display:none}.ldap-memberships-list .is-editing .edit-hide{display:none}.ldap-memberships-list .is-editing .edit-fields{display:block}.ldap-memberships-list .is-editing .spinner{margin-left:15px;vertical-align:middle}.ldap-memberships-list .is-removing{opacity:0.25}.ldap-memberships-list .is-removing .edit{opacity:0.5}.team-name-field{height:33px}.ldap-import-form-actions{margin-top:30px}.invited .team-member-list{margin:-20px 0}.invited .team-member-list .list-item{padding:10px 0;border-bottom:1px solid #eee}.invited .team-member-list .list-item::before{display:table;content:""}.invited .team-member-list .list-item::after{display:table;clear:both;content:""}.invited .team-member-list .list-item:last-of-type{border:0}.invited .team-member-list .list-item .edit-invitation{float:right;margin-top:6px}.invited-banner{margin-bottom:20px}.invited-banner::before{display:table;content:""}.invited-banner::after{display:table;clear:both;content:""}.invited-banner .btn-sm{float:right;margin-left:5px}.invited-banner p{font-size:15px;line-height:1.6}.invited-banner .inviter-link{font-weight:600}.manage-member-sso-sessions.has-active-sessions .blankslate{display:none}.manage-memberships-nav{position:relative;top:1px;margin-top:10px}.manage-memberships-tabs-item{color:#586069;white-space:nowrap;cursor:pointer;border:solid transparent;border-width:3px 1px 1px;border-radius:3px 3px 0 0}.manage-memberships-tabs-item:hover{color:#333}.manage-memberships-tabs-item.selected{font-weight:600;color:#24292e;background-color:#fff;border-bottom:2px solid #d26911}.owner-select-grid{margin-left:-8px}.owner-select-grid::before{display:table;content:""}.owner-select-grid::after{display:table;clear:both;content:""}.owner-select-target{float:left;padding:10px;margin:0 10px 20px;font-weight:600;text-align:center;cursor:pointer;background-color:#f2f2f2;border:0;border-radius:3px}.owner-select-target:hover,.owner-select-target:focus{color:#fff;background-color:#0366d6}.owner-select-target:active{color:#fff;background-color:#024ea4}.owner-select-target .css-truncate-target{max-width:90px}.owner-select-target.disabled{color:#6a737d;cursor:not-allowed}.owner-select-target.disabled .user-mention{color:#6a737d}.owner-select-target.disabled .owner-select-avatar{opacity:0.3}.owner-select-avatar{display:block;margin-bottom:9px}.theme-picker{margin-bottom:-1px;background-color:#fff;background-clip:padding-box;border-bottom:1px solid rgba(0,0,0,0.1);box-shadow:0 5px 10px rgba(0,0,0,0.1)}.theme-picker>.container{position:relative;overflow:hidden;text-align:center}.theme-picker-thumbs{border-bottom:1px solid #eee}.theme-toggle{width:32px;height:32px;padding:0;color:#ccc;background:none;border:0}.theme-toggle:hover{color:#0084c8;text-decoration:none}.theme-toggle.disabled,.theme-toggle.disabled:hover{color:#ccc;cursor:not-allowed;opacity:0.3}.theme-toggle-full-left,.theme-toggle-full-right{position:absolute;top:50px;overflow:hidden}.theme-toggle-full-left{left:4px}.theme-toggle-full-right{right:4px}.theme-selector{height:102px;margin:15px 46px}.theme-selector-thumbnail{padding:2px;border:1px solid #ddd}.theme-selector-thumbnail:hover{text-decoration:none;background-color:#f5f5f5}.theme-selector-thumbnail.selected{padding:3px;background-color:#0366d6;border:0}.theme-selector-thumbnail.selected .theme-selector-img{border:1px solid #fff}.theme-selector-img{width:126px;height:96px;border-radius:1px}.theme-picker-spinner{position:absolute;top:16px;left:50%;margin-left:-16px;background-color:#fff;opacity:0;transition:0.2s, opacity ease-in-out}.theme-picker-spinner.visible{opacity:1}.theme-picker-spinner.visible ~ .theme-picker-controls .theme-name{opacity:0}.theme-picker-view-toggle{float:left}.theme-picker-view-toggle .for-hiding{display:none}.theme-picker-view-toggle.open .for-hiding{display:inline}.theme-picker-view-toggle.open .for-showing{display:none}.theme-picker-controls{position:absolute;top:15px;left:50%;width:220px;margin-left:-110px;line-height:34px;text-align:center}.theme-picker-controls .theme-toggle{vertical-align:middle}.theme-name{display:inline-block;margin-right:10px;margin-left:10px;font-size:20px;line-height:1;vertical-align:middle}.page-preview{z-index:-100;display:block;width:100%;height:6000px;padding:0;background-color:#fff;border:0}.pinned-repos-spinner{position:relative;top:2px;left:6px}.pinned-repos-reorder-error{padding-left:6px;font-size:12px;font-weight:400;color:#86181d}.pinned-repos-setting-link{font-size:13px;font-weight:400}.pinned-repo-name{color:#6a737d}.pinned-repo-checkbox:checked+.pinned-repo-name{color:#24292e;background-color:#f1f8ff}.org-pinned-repos-list.pinned-repos-list{justify-content:flex-start}.org-pinned-repo-item.pinned-repo-item{width:319px;margin-right:11px}.org-pinned-repo-item.pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item{margin-right:11px}.org-pinned-repo-item.pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item{margin-right:0}.org-pinned-repo-item.pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item+.org-pinned-repo-item{margin-right:0}@media print{#serverstats,.Header,.header-search,.site-search,.reponav,.comment::before,.comment::after,.footer,.pagehead-actions,.discussion-timeline-actions,.timeline-comment-actions,.timeline-new-comment,.thread-subscription-status,.lock-toggle-link,.header,.pr-review-tools,.file-actions,.js-expandable-line,.pr-toolbar-shadow,.pr-toolbar.is-placeholder,.language-color{display:none !important}.repository-lang-stats-graph{height:0}.btn:not(.btn-outline){color:#24292e !important;background:none}p,.comment h2{page-break-inside:avoid}.markdown-body h2{page-break-after:avoid}.topic-tag{padding:0}.topic-tag::before{content:"#";margin-right:-2px}.blob-num{border-right:2px solid #e1e4e8}.blob-num-deletion{border-right-color:#d73a49}.blob-num-addition{border-right-color:#28a745}.blob-code-addition .x{border-bottom:2px solid #28a745;border-radius:0px}.blob-code-deletion .x{border-bottom:2px solid #d73a49;border-radius:0px}.pr-toolbar.is-stuck{position:static !important;width:100% !important}.block-diff-neutral{border:4px solid #d1d5da}.block-diff-deleted{border:4px solid #d73a49}.block-diff-added{border:4px solid #28a745}.State{border:1px #e1e4e8 solid;color:#1b1f23;border-color:none;background:none}.State--green{border:1px solid #2cbe4e;color:#28a745}.State--purple{border:1px solid #6f42c1;color:#6f42c1}.State--red{border:1px solid #cb2431;color:#cb2431}.markdown-body pre>code{white-space:pre-wrap}}.pjax-loader-bar{position:fixed;top:0;left:0;z-index:40;opacity:0;transition:opacity 0.4s linear 0.4s}.pjax-loader-bar .progress{position:fixed;top:0;left:0;height:2px;background:#77b6ff;box-shadow:0 0 10px rgba(119,182,255,0.7);transition:width 0.4s ease}.pjax-loader-bar.is-loading{opacity:1;transition:none}.project-full-screen .pagehead,.project-full-screen .Header{display:block}@media (min-width: 544px){.project-full-screen .pagehead,.project-full-screen .Header{display:none}}.project-full-screen .project-header{padding-top:10px;padding-bottom:10px;color:rgba(255,255,255,0.75)}@media (min-width: 544px){.project-full-screen .project-header{background-color:#24292e}}.project-full-screen .project-header .project-header-link{color:rgba(255,255,255,0.75) !important}.project-full-screen .project-header .project-header-link:hover{color:#fff !important}.project-full-screen .project-header .pending-cards-status{border-color:#24292e}@media (min-width: 544px){.project-full-screen .card-filter-input{color:#fff;background-color:rgba(255,255,255,0.125);border:0;outline:none;box-shadow:none}.project-full-screen .card-filter-input:-ms-input-placeholder{color:rgba(255,255,255,0.7)}.project-full-screen .card-filter-input::-ms-input-placeholder{color:rgba(255,255,255,0.7)}.project-full-screen .card-filter-input::placeholder{color:rgba(255,255,255,0.7)}.project-full-screen .card-filter-input:focus{background-color:rgba(255,255,255,0.175)}}.project-header{background-color:#eff1f3}@media (min-width: 544px){.project-header{background-color:#fff}}.project-header .select-menu-modal-holder{z-index:500}.project-updated-message{top:6px;left:50%;z-index:50;transform:translate(-50%, 0)}.pending-cards-status{top:-2px;right:-9px;z-index:2;width:14px;height:14px;background-image:linear-gradient(#54a3ff, #006eed);background-clip:padding-box;border:2px solid #fff}.project-columns{overflow-x:auto}@media (min-width: 544px){.project-columns-container{height:0;overflow-x:visible !important}}.project-column{min-width:100%;max-width:100%;background-color:#eff1f3;border-width:0 !important;border-radius:0 !important}.project-column:focus{outline:none}@media (min-width: 544px){.project-column{min-width:355px;max-width:355px;border-width:1px !important;border-radius:6px !important}.project-column:focus{border-color:#2188ff !important;box-shadow:0 0 0 0.2em rgba(3,102,214,0.3)}}.project-column.moving{background-color:#f1f8ff !important;box-shadow:0 0 0 0.2em rgba(3,102,214,0.3);transform:translateX(4px) translateY(-4px)}.project-column .automation-summary-tooltip::after{max-width:190px !important}.new-project-column{width:315px;border-color:#959da5 !important}.project-search-form .loading-indicator{top:21px;right:21px;display:none}.project-search-form.loading .loading-indicator{display:inline-block}.sortable-ghost{background-color:#f6f8fa;opacity:0.5}.project-card .project-reference-markdown>p,.project-card:last-child{margin-bottom:0 !important}.project-card:first-child{margin-top:8px !important}@media (min-width: 544px){.project-card:first-child{margin-top:3px !important}}.project-card ul,.project-card ol{margin-bottom:8px;margin-left:16px}.project-card .contains-task-list{margin-left:24px}.project-card:hover{border-color:#d1d5da !important;box-shadow:0 1px 3px rgba(106,115,125,0.3) !important}.project-card:focus{outline:none}@media (min-width: 544px){.project-card:focus{border-color:#2188ff !important;box-shadow:0 0 0 0.2em rgba(3,102,214,0.3) !important}}.project-card.moving{background-color:#f1f8ff !important;box-shadow:0 0 0 0.2em rgba(3,102,214,0.3) !important;transform:translateX(4px) translateY(0)}.archived-project-cards-pane .project-card .archived-header{display:flex !important;color:#735c0f}.archived-project-cards-pane .project-card .archive-dropdown-item{display:none}.issue-card.draggable{cursor:move}.issue-card .issue-card-label{max-width:50%}.issue-card .issue-card-label::after{max-width:150px}.issue-card .avatar-stack:hover .from-avatar{margin-right:-4px}.issue-card pre{word-wrap:break-word;white-space:pre-wrap}@keyframes show-pane{0%{transform:translateX(390px)}100%{transform:translateX(0)}}.project-pane{z-index:30;background-clip:padding-box;box-shadow:-3px 0 5px rgba(36,41,46,0.05)}@media (min-width: 544px){.project-pane{position:absolute !important;width:360px !important;height:auto !important;animation:show-pane 0.2s cubic-bezier(0, 0, 0, 1)}}.project-pane .redacted-activity{cursor:help;border-bottom:1px dotted #959da5}.project-pane .project-body-markdown p:last-child,.project-pane .project-body-markdown ul:last-child,.project-pane .project-body-markdown ol:last-child{margin-bottom:0}.project-pane-close{color:rgba(27,31,35,0.5)}.project-pane-close:hover{color:rgba(27,31,35,0.7)}.project-note-form textarea{resize:vertical}.card-menu-container .dropdown-menu,.column-menu-container .dropdown-menu{min-width:180px}.card-octicon{top:6px;left:10px}.card-note-octicon{top:8px}.is-sending .auto-search-group .chooser-spinner{top:15px;right:21px;left:auto}.card-filter-input{width:0}@media (min-width: 544px){.card-filter-input{width:290px}}.card-filter-autocomplete-dropdown{z-index:500;float:none;min-width:240px;max-height:240px;cursor:pointer}.card-filter-autocomplete-dropdown [aria-selected="true"],.card-filter-autocomplete-dropdown .navigation-focus{color:#fff !important;background-color:#0366d6;border-radius:3px}.card-filter-autocomplete-dropdown [aria-selected="true"] .autocomplete-text-qualifier,.card-filter-autocomplete-dropdown .navigation-focus .autocomplete-text-qualifier{color:#fff !important}.projects-reset-query:hover .projects-reset-query-icon{background-color:#0366d6}.projects-reset-query-icon{width:18px;height:18px;padding:1px;background-color:#6a737d}.project-small-menu-dropdown::before,.project-small-menu-dropdown::after{display:none}.project-header-controls,.project-header-search{flex-grow:1}@media (min-width: 1012px){.project-header-controls,.project-header-search{flex-grow:0}}.project-header-subnav-search{flex-grow:1}@media (min-width: 544px){.project-header-subnav-search{flex-grow:0}}.project-modal{max-width:90vw}.project-page .application-main{flex-shrink:0 !important}@media (min-width: 544px){.project-page .application-main{flex-shrink:1 !important}}.project-page .reponav-small{display:none !important}@media (min-width: 544px){.project-page .reponav-small{display:block !important}}@media (min-width: 1012px){.project-page .reponav-small{display:none !important}}.project-edit-mode .column-menu-container,.project-edit-mode .column-menu-item{display:none !important}.project-edit-mode .project-move-actions{display:flex !important}.push-board-over{padding-right:0 !important;transition:all 0.2s ease}@media (min-width: 544px){.push-board-over{padding-right:360px !important}}.project-touch-scrolling{-webkit-overflow-scrolling:touch}.project-page.menu-active .project-touch-scrolling{-webkit-overflow-scrolling:auto}.projects-comment-form .comment-md-support-link{float:none;width:100%;text-align:center}.projects-comment-form .comment-form-actions{width:100%;padding:8px 16px;margin:4px 0 !important}.projects-comment-form .comment-form-actions button{width:100%;margin:4px 0 !important}.projects-comment-form .comment-form-head{padding:0 !important;margin:0 !important;border-bottom:0}.projects-comment-form .comment-form-head .tabnav-tabs{padding:8px 8px 0}.projects-comment-form .comment-form-head .toolbar-commenting{width:100%;background-color:#fff;border-top:1px solid #d1d5da}.projects-comment-form .comment-form-head .toolbar-commenting .toolbar-group{margin:10px 7px 5px !important}.projects-comment-form .comment-form-head::after{display:block;clear:both;content:" "}.projects-comment-form .comment-form-textarea{height:250px !important}.projects-comment-form .preview-content{margin:0;border-top:1px solid #d1d5da}.projects-comment-form .preview-content .comment-body{padding:16px}.project-issue-body-wrapper{max-height:200px;overflow:hidden}.Details--on .project-issue-body-wrapper{max-height:none;overflow:visible}.project-issue-body-blur{height:32px;background:linear-gradient(to top, #fff, rgba(255,255,255,0))}.Details--on .project-issue-body-blur{height:0}.project-comment-title-hover .comment-action,.project-comment-body-hover .comment-action{opacity:0}.project-comment-title-hover:hover .comment-action,.project-comment-body-hover:hover .comment-action{opacity:1}.project-comment-title-hover .octicon-pencil,.project-comment-body-hover .octicon-pencil{color:rgba(27,31,35,0.5)}.project-comment-title-hover .octicon-pencil:hover,.project-comment-body-hover .octicon-pencil:hover{color:#0366d6}.project-comment-body-reaction .timeline-comment-action{padding:4px 8px}.project-comment-reactions .reaction-summary-item{padding:8px}.project-comment-reactions .reaction-summary-item g-emoji{margin:0 !important}.vcard-names{line-height:1}.vcard-fullname{font-size:26px;line-height:30px}.vcard-username{font-size:20px;font-style:normal;font-weight:300;line-height:24px;color:#666}.vcard-details{list-style:none}.vcard-details .css-truncate.css-truncate-target{width:100%;max-width:100%}.vcard-details .css-truncate.css-truncate-target div{overflow:hidden;text-overflow:ellipsis}.vcard-detail{padding-left:24px;font-size:14px}.vcard-detail .octicon{float:left;width:16px;margin-top:3px;margin-left:-24px;color:#6a737d;text-align:center}.member-badge{padding-left:24px;font-size:14px;color:#0366d6}.member-badge .octicon{float:left;width:16px;margin-left:-24px;color:#6a737d;text-align:center}.member-badge+.member-badge{padding-top:0;margin-top:-3px;border-top:0}.new-user-avatar-cta{font-size:14px;color:#244f79;background-color:#f1f6fb;border:solid 1px #d0e5f8;border-radius:3px}.user-profile-bio{margin-bottom:12px;overflow:hidden;font-size:14px;color:#6a737d}.form-group .form-control.user-profile-bio-field{width:440px;height:5.35em;min-height:0}.user-profile-bio-field-container,.user-profile-company-field-container{position:relative}.user-profile-bio-message{margin:5px 0 0;font-size:12px;color:#86181d}.vcard-detail{padding-left:22px}.vcard-detail .octicon{margin-left:-22px}.member-badge{font-size:13px}.user-profile-sticky-bar{position:fixed;top:0;z-index:90;width:233px;word-break:break-all;pointer-events:none;opacity:0;transition:0.2s}.user-profile-sticky-bar::after{position:fixed;top:0;right:0;left:0;z-index:100;display:none;height:54px;content:"";background-color:#fff;border-bottom:solid 1px #d1d5da;box-shadow:0 1px 2px rgba(0,0,0,0.075);opacity:0;transition:0.2s}.user-profile-sticky-bar.is-stuck{pointer-events:auto;opacity:1}.user-profile-sticky-bar.is-stuck::after{display:block;opacity:1}.user-profile-mini-vcard{position:relative;top:1px;z-index:110;height:54px}.user-profile-mini-avatar{width:32px}.mini-follow-button{padding:0 8px;line-height:1.5;opacity:0;transition:0.2s}.is-follow-stuck .mini-follow-button{opacity:1}.user-profile-following-container .user-following-container.on .follow,.user-profile-following-container .user-following-container .unfollow{display:none}.user-profile-following-container .user-following-container .follow,.user-profile-following-container .user-following-container.on .unfollow{display:block}.vcard-names-container{top:0}.vcard-names-container.is-stuck{pointer-events:none}.vcard-names-container.is-stuck .vcard-names{opacity:0}.vcard-names-container.is-stuck::after{opacity:1}.blocked-user-notice{width:100%;padding:8px 16px;color:#cb2431;text-align:center;border:solid 1px #d73a49;border-radius:3px}.user-profile-nav{background-color:#fff;border-bottom:solid 1px #d1d5da}.user-profile-nav.is-stuck{z-index:90}.pinned-repos-list{display:flex;list-style:none;flex-wrap:wrap;justify-content:space-between}.pinned-repo-item{display:flex;width:359px}.pinned-repo-item .pinned-repository-handle{padding-top:1px;color:#586069}.pinned-repo-item .pinned-repository-handle:hover{cursor:-webkit-grab;cursor:grab}.pinned-repo-item.is-dragging,.pinned-repo-item.is-dragging .pinned-repository-handle{cursor:-webkit-grabbing;cursor:grabbing}.pinned-repo-item.is-dragging{background-color:#f1f8ff}.pinned-repo-item.sortable-ghost{background-color:#f1f8ff;opacity:0}.pinned-repo-item.empty{border-style:dashed;border-width:1px;align-items:center;justify-content:center}.pinned-repo-item-content{display:flex;width:100%;flex-direction:column}.pinned-repo-desc{flex:1 0 auto}.pinned-repo-meta{display:inline-block}.pinned-repo-meta+.pinned-repo-meta{margin-left:16px}.user-repo-search-results-summary{white-space:normal}.profile-timeline.discussion-timeline{float:none}.profile-timeline.discussion-timeline .profile-timeline-month-heading::after{position:absolute;top:12px;z-index:-1;width:100%;height:1px;content:"";background-color:#eaecef}.profile-timeline.discussion-timeline::before{left:15px;background-color:#eaecef}.profile-timeline.discussion-timeline .contribution-joined-github,.profile-timeline.discussion-timeline .contribution-first-pull-request,.profile-timeline.discussion-timeline .contribution-first-repository,.profile-timeline.discussion-timeline .contribution-first-issue{margin-top:-16px}.profile-break-word{-webkit-hyphens:auto;-ms-hyphens:auto;hyphens:auto;word-break:break-word}.profile-timeline-year-list{top:74px}.profile-timeline-card-wrapper{border-top:solid 2px #fff;border-bottom:solid 2px #fff}.profile-timeline-card .issue-meta-section .progress-bar{width:40px}.profile-timeline-card .issue-meta-section .octicon{color:#6a737d;vertical-align:middle}.profile-rollup-wrapper .css-truncate-target{max-width:85%}.profile-rollup-wrapper .progress-bar{height:8px;border-radius:2px}.profile-rollup-wrapper+.profile-rollup-wrapper{border-top:1px solid #eaecef}.profile-timeline-card-wrapper+.profile-timeline-card-wrapper{margin-top:24px}.profile-rollup-wrapper+.profile-timeline-card-wrapper{margin-top:0}.profile-timeline-month-heading+.profile-timeline-card-wrapper{margin-top:24px}.profile-rollup-summarized+.profile-rollup-summarized{border-top:1px solid #eaecef}.profile-rollup-icon{display:inline-block;width:16px;text-align:center}.profile-rollup-content{display:none}.profile-rollup-content .content-title:hover .link-gray-dark-underline{text-decoration:underline}.vertical-text-bottom{vertical-align:text-bottom}.profile-rollup-summarized.open>.profile-rollup-content,.profile-rollup-summarized.open .profile-rollup-toggle-closed,.profile-rollup-wrapper.open>.profile-rollup-content,.profile-rollup-wrapper.open .profile-rollup-toggle-closed{display:block}.profile-rollup-summarized.open .profile-rollup-toggle-open,.profile-rollup-wrapper.open .profile-rollup-toggle-open{display:none}.profile-rollup-toggle-closed{display:none}.profile-rollup-toggle{color:#6a737d;cursor:pointer}.profile-rollup-toggle:hover{color:#0366d6}.pull-request-tab-content{display:none}.pull-request-tab-content.is-visible{display:block}.discussion-timeline p.explain{margin:0;font-size:12px}.pull-request-ref-restore{display:none}.pull-request-ref-restore .animated-ellipsis-container{line-height:16px}.pull-request-ref-restore-text{display:block}.pull-discussion-timeline.is-pull-restorable .pull-request-ref-restore.last{display:block}.stale-files-tab{margin-bottom:10px}.files-bucket{margin-bottom:15px}.full-width .diffbar .container,.split-diff .diffbar .container{padding-right:0;padding-left:0}.pr-toolbar{position:-webkit-sticky;position:sticky;top:0;z-index:29;height:60px;padding:0 20px;margin:-20px -20px 0;background-color:#fff}.pr-toolbar .stale-files-tab{float:left;padding:5px 10px;margin-top:-5px;margin-bottom:-5px;color:#a04100;background-color:#fff8f2;border-radius:3px}.pr-toolbar .subset-files-tab{float:left;padding:5px 10px;margin-top:-5px;margin-bottom:-5px;font-size:13px;color:#0366d6;background-color:#e6f1f6;border-radius:3px}.pr-toolbar .stale-files-tab-link{font-weight:600;color:inherit}.pr-toolbar.is-stuck+.pr-toolbar-shadow{position:fixed;top:60px;right:0;left:0;z-index:28;display:block;height:5px;content:"";background:linear-gradient(rgba(0,0,0,0.075), rgba(0,0,0,0.001)) repeat-x 0 0;border-top:1px solid rgba(0,0,0,0.15)}.pr-toolbar .float-right .diffbar-item{margin-right:0}.pr-toolbar .float-right .diffbar-item+.diffbar-item{margin-left:20px}.files-next-bucket .file,.files-next-bucket .full-commit{margin-top:0;margin-bottom:20px}.diffbar{height:20px;padding-top:20px;padding-bottom:20px;background-color:#fff}.diffbar::before{display:table;content:""}.diffbar::after{display:table;clear:both;content:""}.diffbar .container{width:auto}.diffbar .table-of-contents{margin-bottom:0}.diffbar .table-of-contents ol{margin-bottom:-15px}.diffbar .table-of-contents li{border-top:1px solid #eee}.diffbar .table-of-contents li:first-child{border-top:0}.diffbar [aria-selected="true"] .text-emphasized,.diffbar .navigation-focus .text-emphasized{color:#fff}.diffbar-range-menu .select-menu-modal{width:380px}.diffbar-range-menu .css-truncate-target{max-width:280px}.diffbar-range-menu .select-menu-item:not(.select-menu-action){padding:8px 10px}.diffbar-range-menu .emoji{vertical-align:bottom}.diffbar-range-menu .in-range:not([aria-selected="true"]):not(.is-range-selected),.diffbar-range-menu .in-range:not(.navigation-focus):not(.is-range-selected){background-color:#f1f8ff;border-bottom-color:rgba(27,31,35,0.15)}.diffbar-range-menu .is-range-selected{color:#735c0f;cursor:default;background-color:#fffbdd;border-bottom-color:rgba(38,44,49,0.15);outline:none}.diffbar-range-menu .is-range-selected .text-emphasized{color:#776521}.diffbar-range-menu .is-range-selected .description{color:inherit}.diffbar-range-menu .is-last-in-range{cursor:pointer;background-color:#fff5b1}.diffbar-item{float:left;margin-right:20px;font-size:13px;line-height:20px;vertical-align:middle}.fork-collab-tip{width:270px;margin-top:-30px}.fork-collab-tip.dropdown-menu-s{margin-top:10px}.fork-collab-link{margin-left:15px}.conflict-resolver .conflict-loader,.conflict-resolver.loading .resolve-file-form{display:none}.conflict-resolver .resolve-file-form,.conflict-resolver.loading .conflict-loader{display:block}.conflict-resolver.loading{position:relative;height:calc(100vh + 51px);padding-top:50px;border:1px #e1e4e8 solid}.conflict-resolver .file-header{padding:9px 10px}.conflicts-nav{height:100vh;-ms-overflow-style:-ms-autohiding-scrollbar;border-width:0 0 1px}.conflict-nav-item .discussion-item-icon{display:none}.conflict-nav-item.resolved .discussion-item-icon{display:block;margin-left:-5px}.conflict-nav-item.resolved .octicon-file-code{display:none}.conflict-nav-item.selected::before{border-radius:0}.conflict-nav-item .octicon{width:22px}.conflict-nav-item .css-truncate-target{max-width:100%}.is-resolved .file-actions{display:none}.is-resolved .resolved-notice{display:block}.resolved-notice{display:none}.finish-review-label,.add-comment-label,.review-cancel-button,.is-review-pending .start-review-label{display:none}.start-review-label,.is-review-pending .finish-review-label,.is-review-pending .add-comment-label{display:inline-block}.is-review-pending .review-simple-reply-button{display:none}.is-review-pending .review-cancel-button{display:block}.is-review-pending .review-title-default{display:none}.is-review-pending .review-title-with-count{display:block}.review-title-default{display:block}.review-title-with-count{display:none}.pr-review-tools{margin-top:-4px}.pr-review-tools .Counter{display:none}.is-review-pending .pr-review-tools .Counter{display:inline-block}.pull-request-review-menu{top:30px;width:400px}.pull-request-review-menu::after,.pull-request-review-menu::before{display:none}.pull-request-review-menu .select-menu-header{border-radius:3px 3px 0 0}.pull-request-review-menu .form-actions{border-radius:0 0 3px 3px}.pull-request-review-menu .comment-form-head{display:none}.review-thread{border:1px #e1e4e8 solid;border-radius:3px}.review-thread:not(:last-child){margin-bottom:16px}.last-review-thread{padding:10px 10px 0;margin:10px -10px 0;border-top:1px solid #e1e4e8}.review-comment-contents{margin-left:44px}.review-comment-contents .timestamp{color:inherit;white-space:nowrap}.review-comment::after,.review-comment-loader::after,.review-comment.is-comment-editing::after{position:absolute;top:31px;left:29px;z-index:-1;width:3px;height:100%;content:"";background-color:#f6f8fa}.review-comment{position:relative;padding:8px 16px}.review-comment:first-child{padding-top:16px}.review-comment:last-child{padding-bottom:16px}.review-comment .comment-body,.review-comment .comment-reactions{padding:0}.review-comment .comment-body{padding-top:4px}.review-comment .comment-body .suggested-change-form-container:nth-last-of-type(2){margin-bottom:0 !important}.review-comment .comment-reactions{margin-top:5px;border-top:0}.review-comment .comment-reactions .add-reaction-btn{padding:4px 10px}.review-comment .comment-reactions.has-reactions{margin-top:12px}.review-comment .add-reaction-popover.dropdown-menu-sw,.review-comment .show-more-popover.dropdown-menu-sw{right:-5px;margin-top:5px}.review-comment .add-reaction-popover.dropdown-menu-ne{left:9px}.review-comment .reaction-summary-item:not(.add-reaction-btn){padding:0 8px;font-size:12px;line-height:26px;border:1px solid #d2dff0;border-radius:3px}.review-comment .reaction-summary-item:not(.add-reaction-btn) .emoji{font-size:16px;vertical-align:sub}.review-comment .reaction-summary-item:not(.add-reaction-btn)+.reaction-summary-item{margin-left:8px}.review-comment:last-child::after,.review-comment:last-child .review-comment-contents::after{display:none}.review-comment .timeline-comment-actions{opacity:0}.review-comment:hover .timeline-comment-actions,.review-comment.is-reacting .timeline-comment-actions{opacity:1}.review-comment .timeline-comment-action{padding:0 5px}.review-comment .timeline-comment-label{float:none;margin-top:0}.review-comment .is-comment-editing{position:relative;background-color:#fff;border:1px #e1e4e8 solid;border-radius:3px}.review-comment .is-comment-editing::after{top:100%;bottom:0;left:19px;height:20px}.review-comment .is-comment-editing .timeline-comment-actions,.review-comment .is-comment-editing .edit-comment-hide{display:none}.review-comment .is-comment-editing .previewable-comment-form{display:block}.review-comment.is-comment-loading .previewable-comment-form{opacity:0.5}.review-comment.is-comment-stale .comment-form-stale{display:block}.timeline-comment.is-comment-editing .discussion-item-header{display:none}.discussion-item-body .outdated-comment .hide-outdated-button{display:none}.discussion-item-body .outdated-comment.file{margin:4px 0}.discussion-item-body .outdated-comment .file-header{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.discussion-item-body .outdated-comment[open] .hide-outdated-button{display:block}.discussion-item-body .outdated-comment[open] .show-outdated-button{display:none}.discussion-item-body .outdated-comment[open] .file-header{border-bottom:1px #e1e4e8 solid;border-bottom-right-radius:0;border-bottom-left-radius:0}.discussion-item-body .outdated-comment .blob-code{position:static}.discussion-item-body .outdated-comment-label{height:26px;margin-top:3px;line-height:25px}.discussion-item-body .outdated-comment-label .octicon{top:1px}.review-thread-reply{padding:8px 16px;background-color:#f6f8fa;border-top:1px #e1e4e8 solid;border-radius:0 0 3px 3px}.discussion-item-review .review-thread-reply{margin-bottom:0;border-width:1px 0 0}.review-thread-reply .inline-comment-form{margin:-8px -16px;background-color:#fff;border:0}.review-thread-reply-button{display:inline-block;min-height:28px;padding:3px 8px;margin-left:8px;cursor:text}.review-summary-form-wrapper{display:none;margin-bottom:24px;margin-left:-20px;background-color:#fff;border:1px #e1e4e8 solid;border-radius:3px}.is-pending .review-summary-form-wrapper,.is-comment-editing .review-summary-form-wrapper{display:block}.is-pending .review-summary-form-wrapper{border-color:#d9d0a5}.tooltipped-left::after{right:auto;left:0}.tooltipped-left::before{right:auto;left:0}.discussion-item-review .edit-review{opacity:0;transition:opacity 0.15s ease 0s}.discussion-item-review:hover .edit-review{opacity:1}.diffstat-summary{padding:0 20px 0 0;font-size:16px;line-height:1.8;color:#586069;text-align:left;vertical-align:middle;border-radius:3px}.diffstat-summary a{color:#555}.diffstat-summary strong{color:#333}.authors-and-code .insertions{color:#28a745}.authors-and-code .deletions{color:#cb2431}.authors-and-code .section{display:table-cell;width:459px;height:150px}.pulse-authors-graph{position:relative;height:150px}.pulse-authors-graph>svg{width:100%}.pulse-authors-graph .dots{position:absolute;top:40px;right:0;left:0;width:64px;height:64px;margin:0 auto}.pulse-authors-graph .bar rect{fill:#f66a0a;fill-opacity:0.7}.pulse-authors-graph .bar rect:hover{fill-opacity:1}.summary-stats{display:table;width:100%;table-layout:fixed}.summary-stats li{display:table-cell;color:#586069;text-align:center;border-left:1px solid #e1e4e8}.summary-stats li a{display:block;padding-bottom:10px;color:#586069;text-decoration:none}.summary-stats li a:hover{background:#fafafa}.summary-stats li .octicon-git-pull-request{color:#6f42c1}.summary-stats li .octicon-git-branch{color:#28a745}.summary-stats li .octicon-issue-closed{color:#cb2431}.summary-stats li .octicon-issue-opened{color:#28a745}.summary-stats li:first-child{border-left:0;border-bottom-left-radius:3px}.summary-stats li .num{display:block;padding-top:10px;font-size:16px;font-weight:600;color:#000}.pulse-sections{margin-top:20px;clear:both}.pulse-section{padding:0;clear:both;font-size:14px;color:#666}.pulse-section p{margin-top:20px}.readme.contributing>div{max-height:250px;overflow:auto}.readme .markdown-body,.readme .plain{padding:45px;word-wrap:break-word;background-color:#fff;border:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.readme .plain pre{font-size:15px;white-space:pre-wrap}.file .readme .markdown-body{padding:45px;border:0;border-radius:0}.file .readme table[data-table-type="yaml-metadata"]{font-size:12px;line-height:1}.file .readme table[data-table-type="yaml-metadata"] table{margin:0}.release-timeline-tags .tag-timeline-date::after{position:absolute;top:50%;right:-7px;z-index:10;display:block;width:12px;height:12px;box-sizing:border-box;margin-top:-6px;content:" ";background-color:#e1e4e8;border:2px solid #fff;border-radius:6px}.release-timeline-tags.is-collapsed .expander{display:block !important}.release-timeline-tags.is-collapsed>.collapsable{display:none}.Label--draft{color:#cb2431;border-color:#d73a49}.Label--prerelease{color:#f66a0a;border-color:#f66a0a}.uploaded-files{border-top-left-radius:3px;border-top-right-radius:3px}.uploaded-files.not-populated+.drop-target p{border-top:1px #c3c8cf;border-top-left-radius:3px;border-top-right-radius:3px}.uploaded-files.is-populated{border:1px solid #ddd;border-bottom-color:#e5e5e5}.uploaded-files.is-populated+.drop-target p{border-top:0;border-top-left-radius:0;border-top-right-radius:0}.uploaded-files>li.delete{background:#f9f9f9}.uploaded-files>li.delete:nth-child(2){border-top-left-radius:3px;border-top-right-radius:3px}.uploaded-files>li.delete .delete-pending{display:block !important}.uploaded-files>li.delete .live{display:none !important}.uploaded-files>li:nth-child(2){border-top:0 !important}.uploaded-files .remove:hover{color:#cb2431 !important}.upload-progress{height:3px;margin-top:3px;border-radius:30px;box-shadow:0 1px 1px #fff,inset 0 1px 1px rgba(255,255,255,0.5)}.upload-progress .upload-meter{background-image:linear-gradient(#8dd2f7, #58b8f4);border-radius:30px}.release-entry{border-bottom:1px #e1e4e8 solid}.release-entry:last-child{border-bottom:0}@media (min-width: 768px){.release-tag-field{width:130px !important}.release-main-section{border-left:2px solid #e1e4e8}.release-entry{border-bottom:0}}.repo-file-upload-progress{position:relative;height:0;overflow:hidden;color:#246;background:#f7fbfe;border-bottom-right-radius:3px;border-bottom-left-radius:3px;box-shadow:0 0 0 1px #d0dbe7 inset;transition:height 0.2s ease-out}.repo-file-upload-progress.active{height:50px}.repo-file-upload-progress.is-file-list{border-bottom-right-radius:0;border-bottom-left-radius:0}.repo-file-upload-progress .repo-file-upload-meter{position:absolute;top:1px;left:1px;width:0;height:48px;background:#d8e8f7}.repo-file-upload-progress .repo-file-upload-meter-text{position:absolute;top:7px;left:10px}.repo-file-upload-progress .repo-file-upload-meter-text .repo-file-upload-info{display:block;font-weight:600}.repo-file-upload-progress .repo-file-upload-meter-text .repo-file-upload-meter-filename{display:block;margin:0}.manifest-commit-form{margin-top:20px}.repo-file-upload-outline{width:100%;height:100%}.repo-file-upload-target{position:relative;padding-top:100px;padding-bottom:80px;color:#666}.repo-file-upload-target.is-progress-bar,.repo-file-upload-target.is-uploading{border-bottom:0;border-bottom-right-radius:0;border-bottom-left-radius:0}.repo-file-upload-target.is-file-list{border-bottom-right-radius:0;border-bottom-left-radius:0}.repo-file-upload-target.is-uploading .repo-file-upload-text.initial-text,.repo-file-upload-target.is-failed .repo-file-upload-text.initial-text,.repo-file-upload-target.is-default .repo-file-upload-text.initial-text{display:none}.repo-file-upload-target.is-uploading .repo-file-upload-text.alternate-text,.repo-file-upload-target.is-failed .repo-file-upload-text.alternate-text,.repo-file-upload-target.is-default .repo-file-upload-text.alternate-text{display:block}.repo-file-upload-target.is-uploading.dragover .repo-file-upload-text,.repo-file-upload-target.is-failed.dragover .repo-file-upload-text,.repo-file-upload-target.is-default.dragover .repo-file-upload-text{display:none}.repo-file-upload-target .repo-file-upload-text.initial-text{display:block}.repo-file-upload-target .repo-file-upload-text.alternate-text{display:none}.repo-file-upload-target .repo-file-upload-text,.repo-file-upload-target .repo-file-upload-drop-text{margin-bottom:5px}.repo-file-upload-target .repo-file-upload-choose{display:inline-block;margin-top:0;font-size:18px}.repo-file-upload-target .manual-file-chooser{margin-left:0}.repo-file-upload-target .repo-file-upload-outline{position:absolute;top:3%;left:1%;width:98%;height:94%}.repo-file-upload-target.is-failed .repo-file-upload-outline,.repo-file-upload-target.is-bad-file .repo-file-upload-outline,.repo-file-upload-target.is-too-big .repo-file-upload-outline,.repo-file-upload-target.is-too-many .repo-file-upload-outline,.repo-file-upload-target.is-empty .repo-file-upload-outline{height:85%}.repo-file-upload-target.dragover .repo-file-upload-text{display:none}.repo-file-upload-target.dragover .repo-file-upload-choose{visibility:hidden}.repo-file-upload-target.dragover .repo-file-upload-drop-text{display:block}.repo-file-upload-target.dragover .repo-file-upload-outline{border:6px #ddd dashed;border-radius:5px}.repo-file-upload-target .repo-file-upload-drop-text{display:none}.repo-file-upload-errors{display:none}.repo-file-upload-errors .error{display:none}.is-failed .repo-file-upload-errors,.is-bad-file .repo-file-upload-errors,.is-too-big .repo-file-upload-errors,.is-too-many .repo-file-upload-errors,.is-hidden-file .repo-file-upload-errors,.is-empty .repo-file-upload-errors{position:absolute;right:0;bottom:0;left:0;display:block;padding:5px 8px;line-height:1.5;text-align:left;background-color:#fff;border-top:1px solid #e5e5e5;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.is-file-list .repo-file-upload-errors{border-bottom-right-radius:0;border-bottom-left-radius:0}.is-failed .repo-file-upload-errors .failed-request,.is-bad-file .repo-file-upload-errors .failed-request{display:inline-block}.is-too-big .repo-file-upload-errors .too-big{display:inline-block}.is-hidden-file .repo-file-upload-errors .hidden-file{display:inline-block}.is-too-many .repo-file-upload-errors .too-many{display:inline-block}.is-empty .repo-file-upload-errors .empty{display:inline-block}.repo-file-upload-tree-target{position:fixed;top:0;left:0;z-index:1000;width:100%;height:100%;padding:12px;color:#666;visibility:hidden;background:radial-gradient(ellipse at center, #fff 0%, rgba(255,255,255,0.85) 65%, rgba(255,255,255,0.85) 100%);opacity:0;transition:visibility 0.2s, opacity 0.2s}.repo-file-upload-tree-target .repo-file-upload-outline{border:6px #ddd dashed;border-radius:5px}.dragover .repo-file-upload-tree-target{visibility:visible;opacity:1}.dragover .repo-file-upload-tree-target .repo-file-upload-slate{top:50%;opacity:1}.dragover .repo-file-upload-tree-target .files-lg>.file-graph{opacity:1;transform:translateX(4px)}.dragover .repo-file-upload-tree-target .files-lg>.file-zip{opacity:1;transform:translateX(9px)}.dragover .repo-file-upload-tree-target .files-lg>.file-generic{opacity:1;transform:translateX(-4px)}.dragover .repo-file-upload-tree-target .files-lg>.file-acrobat{opacity:1;transform:translateX(-9px)}.repo-file-upload-slate{position:absolute;top:50%;width:100%;text-align:center;transform:translateY(-50%)}.repo-file-upload-slate h2{margin-top:5px}.repo-file-upload-slate .file-graph{opacity:0;transition:opacity 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275),transform 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275);transform:translateX(50px)}.repo-file-upload-slate .file-zip{opacity:0;transition:opacity 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275),transform 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275);transform:translateX(90px)}.repo-file-upload-slate .file-generic{opacity:0;transition:opacity 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275),transform 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275);transform:translateX(-50px)}.repo-file-upload-slate .file-acrobat{opacity:0;transition:opacity 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275),transform 0.2s 0.12s cubic-bezier(0.175, 0.885, 0.32, 1.275);transform:translateX(-90px)}.repo-file-upload-file-wrap .name{color:#888}.repo-file-upload-file-wrap .actions{width:50px;padding-right:10px;text-align:right}.repo-file-upload-file-wrap .remove-file{color:#888}.repo-file-upload-file-wrap .remove-file:hover{color:#666}.repo-upload-breadcrumb{margin-bottom:18px}.labels-list .blankslate{display:none}.labels-list .table-list-header{display:block}.labels-list.is-empty .blankslate{display:block}.labels-list.is-empty .table-list-header{display:none}.labels-list-item .table-list-cell{width:100%}.label-description{padding:8px 16px}.label-delete-confirmation{line-height:34px}.labels-list-action{display:block;float:left;padding:8px 10px;color:#586069}.labels-list-action .octicon{margin-right:2px}.labels-list-action .octicon-pencil{font-size:14px}.sidebar-labels .sidebar-labels-style{margin-top:3px}.label-select-menu-item .g-emoji{font-size:12px;line-height:1;vertical-align:baseline}.new-label{padding:10px;margin-bottom:15px;background-color:#fafbfc;border:1px solid #e5e5e5;border-radius:3px}.new-label .label-edit::before{display:table;content:""}.new-label .label-edit::after{display:table;clear:both;content:""}.label-edit::before{display:table;content:""}.label-edit::after{display:table;clear:both;content:""}.label-edit label{display:block;margin-bottom:8px}.label-edit .error{float:left;margin-top:8px;margin-left:10px;color:#f00}.label-edit.is-valid .octicon-check{display:block}.label-edit.loading{display:block}.label-delete-form{display:inline}.label-delete-form.loading .label-delete-spinner{display:block}.label-delete-spinner{display:none;float:left;margin-top:10px;margin-right:10px}.label-characters-remaining{color:#586069}.emoji-size-boost .IssueLabel .g-emoji{top:0;font-size:1.15em;line-height:1.1}.repo-list{position:relative}.repo-list-item{position:relative;padding-top:30px;padding-bottom:30px;list-style:none;border-bottom:1px solid #eee}.repo-list-item-with-avatar{padding-left:42px}.repo-list-item-hanging-avatar{float:left;margin-left:-42px}.mini-repo-list-item{position:relative;display:block;padding:6px 64px 6px 30px;font-size:14px;border-top:1px solid #d1d5da}.mini-repo-list-item:hover{text-decoration:none}.mini-repo-list-item:hover .repo,.mini-repo-list-item:hover .owner{text-decoration:underline}.mini-repo-list-item .repo-icon{float:left;margin-top:2px;margin-left:-20px;color:#666}.mini-repo-list-item .repo-and-owner{max-width:220px}.mini-repo-list-item .owner{max-width:110px}.mini-repo-list-item .repo{font-weight:600}.mini-repo-list-item .stars{position:absolute;top:0;right:10px;margin-top:6px;font-size:12px;color:#6a737d}.mini-repo-list-item .repo-description{display:block;max-width:100%;font-size:12px;line-height:21px;color:#586069}.private .mini-repo-list-item{background-color:#fffdef}.private .mini-repo-list-item .repo-icon{color:rgba(27,31,35,0.7)}.filter-bar{padding:10px;background-color:#fafbfc;border-bottom:1px solid #e5e5e5}.filter-bar::before{display:table;content:""}.filter-bar::after{display:table;clear:both;content:""}.user-repos .filter-bar{text-align:center}.url-box{width:100%;height:26px;padding:10px 10px 0;margin-top:10px;margin-left:-10px;border-top:1px solid #ddd}.url-box p{float:left;height:26px;margin:0 0 0 5px;line-height:26px}.url-box p strong{color:#000}.clone-url-button{display:table-cell;width:1%;vertical-align:top}.clone-url-button:first-child .clone-url-link{border-top-left-radius:3px;border-bottom-left-radius:3px}.clone-url-button>.clone-url-link{position:relative;display:block;padding:0 9px;margin-right:-1px;font-size:11px;font-weight:600;line-height:26px;color:#333;text-decoration:none;white-space:nowrap;cursor:pointer;background-image:linear-gradient(#fafafa, #eaeaea);border:1px solid #ccc}.clone-url-button>.clone-url-link:hover,.clone-url-button>.clone-url-link:active{z-index:3;color:#fff;text-decoration:none;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-image:linear-gradient(#599bcd, #3072b3);border-color:#2a65a0}.clone-url-button>.clone-url-link:active{background-color:#3072b3;background-image:none;border-color:#25588c;box-shadow:inset 0 3px 5px rgba(0,0,0,0.15)}.clone-url-button>.clone-url-link:focus{outline:0}.clone-url-button+.clone-url-button>.clone-url-link{box-shadow:inset 1px 0 0 #fff}.clone-url-button+.clone-url-button>.clone-url-link:hover{box-shadow:none}.clone-url-button+.clone-url-button>.clone-url-link:active{box-shadow:inset 0 3px 5px rgba(0,0,0,0.15)}.clone-url-button.selected>.clone-url-link,.clone-url-button.selected>.clone-url-link:hover{z-index:2;color:#333;text-shadow:0 1px 0 rgba(255,255,255,0.6);background-color:#ccc;background-image:linear-gradient(#ccc, #d5d5d5);border-color:#bbb;box-shadow:inset 0 2px 3px rgba(0,0,0,0.075)}.url-box-clippy .zeroclipboard-button{margin-left:0 !important;border-left:0;border-radius:0 3px 3px 0}.repository-import .repository-import-label{display:block;margin-bottom:8px;font-size:18px;font-weight:400}.repository-import .state-header{font-size:16px}.text-yellow{color:rgba(115,92,15,0.5)}.timeout{width:auto;height:300px;padding:0;margin:20px 0;background-color:transparent;border:0}.timeout h3{padding-top:100px;color:#586069}.overall-summary{position:relative;margin-bottom:10px;border:1px solid #dfe2e5;border-radius:3px}.overall-summary-bottomless{margin-bottom:0;border-bottom:0;border-radius:3px 3px 0 0}.numbers-summary{display:table;width:100%;table-layout:fixed}.numbers-summary li{display:table-cell;padding:0;text-align:center;white-space:nowrap}.numbers-summary a,.numbers-summary .nolink{display:block;padding:10px 0;color:#586069;text-decoration:none}.numbers-summary .octicon{color:#6a737d}.numbers-summary a:hover{color:#0366d6}.numbers-summary a:hover .num{color:inherit}.repo-language-color{position:relative;top:1px;display:inline-block;width:12px;height:12px;border-radius:50%}.repository-meta{margin:0 0 20px;clear:both;font-size:16px;color:#586069}.repository-meta::before{display:table;content:""}.repository-meta::after{display:table;clear:both;content:""}.repository-meta .repo-description-field{width:540px}.repository-meta .repo-website-field{width:300px}.repository-meta .repository-meta-content{display:inline-block}.repository-meta .edit-repository-meta{display:none;margin-bottom:5px;font-size:13px}.repository-meta .edit-repository-meta .field{display:inline-block;margin-right:5px}.repository-meta .edit-repository-meta label{display:block;margin-bottom:6px;font-weight:600;color:#24292e}.repository-meta.open .repository-meta-content,.repository-meta.open .edit-link{display:none}.repository-meta.open .edit-repository-meta{display:block;margin-bottom:20px}.experiment-repo-nav .capped-cards .capped-card{width:480px}.experiment-repo-nav .new-issue-form{padding-top:0;border-top:0}.experiment-repo-nav.repohead .repo-mirror{padding-left:20px}.iconbutton .octicon{margin-right:0}.file-navigation::before{display:table;content:""}.file-navigation::after{display:table;clear:both;content:""}.file-navigation.in-mid-page{margin-top:15px}.file-navigation .select-menu-button .css-truncate-target{max-width:200px}.file-navigation .branch-select-menu{margin-right:6px;margin-bottom:10px}.file-navigation .new-pull-request-btn{float:left}.file-navigation .breadcrumb{float:left;margin-top:0;margin-left:5px;font-size:16px;line-height:26px}.file-navigation+.breadcrumb{margin-bottom:10px}.file-navigation .BtnGroup{margin-bottom:10px;margin-left:10px}.file-navigation .get-repo-select-menu{margin-left:6px}.file-navigation .get-repo-modal{top:6px;width:352px}.file-navigation .get-repo-modal .https-clone-options{display:block}.file-navigation .get-repo-modal .ssh-clone-options{display:none}.file-navigation .get-repo-modal.on .https-clone-options{display:none}.file-navigation .get-repo-modal.on .ssh-clone-options{display:block}.file-navigation .get-repo-modal .get-repo-modal-options{display:block}.file-navigation .get-repo-modal.is-downloading .get-repo-modal-options{display:none}.file-navigation .get-repo-modal .animated-ellipsis-container{width:16px;height:16px}.file-navigation .get-repo-modal .animated-ellipsis-container>.animated-ellipsis{animation:ellipsis 1s infinite}@keyframes ellipsis{from{width:2px}to{width:16px}}.file-navigation .btn-change-protocol{font-size:12px}.file-navigation .get-repo-decription-text{font-size:13px}.file-navigation .get-repo-btn{float:left;width:50%;padding:10px 0;text-align:center;border:0;border-top:1px solid #e1e4e8;border-radius:0}.file-navigation .get-repo-btn:first-child{border-right:1px solid #e1e4e8;border-bottom-left-radius:3px}.file-navigation .get-repo-btn:nth-child(1):nth-last-child(3){border-radius:0}.file-navigation .get-repo-btn:last-child{border-bottom-right-radius:3px}.file-navigation .get-repo-btn.btn-block,.file-navigation .get-repo-btn:only-child{width:100%;border-right:0;border-radius:0 0 3px 3px}.file-navigation .clone-options{padding:8px 16px 16px}.file-wrap,.details-truncate-file-list.Details--on .file-wrap{max-height:none;margin-bottom:10px;overflow-y:hidden;border:1px solid #dfe2e5;border-top:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.file-wrap .include-fragment-error,.details-truncate-file-list.Details--on .file-wrap .include-fragment-error{display:none}.file-wrap.is-error .include-fragment-error,.details-truncate-file-list.Details--on .file-wrap.is-error .include-fragment-error{display:table-row}.details-truncate-file-list .file-wrap{max-height:185px;margin-bottom:0;overflow-y:scroll;border-bottom-right-radius:0;border-bottom-left-radius:0}.details-truncate-file-list .file-wrap .files{border-bottom-right-radius:0;border-bottom-left-radius:0}.details-truncate-file-list .btn-truncate-file-list{background:#fff;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.details-truncate-file-list .btn-truncate-file-list:hover{background:#f6f8fa}table.files{width:100%;background:#fff;border-radius:2px}table.files tr[aria-selected="true"] td,table.files tr.navigation-focus td{background:#f6f8fa}table.files td{padding:6px 3px;line-height:20px;border-top:1px solid #eaecef}table.files td.icon{width:17px;padding-right:2px;padding-left:10px;color:rgba(3,47,98,0.55)}table.files td.icon .octicon-file-directory{color:rgba(3,47,98,0.5)}table.files td.icon .spinner{position:relative;top:3px;display:none;margin-top:-3px;margin-left:-2px}table.files td .simplified-path{color:#6a737d}table.files td .css-truncate{max-width:100%}table.files td.content{max-width:180px}table.files td.message{max-width:442px;padding-left:10px;overflow:hidden;color:#6a737d}table.files td.message .emoji{vertical-align:top}table.files td.message a{color:#6a737d}table.files td.message a:hover{color:#0366d6}table.files td.age{width:125px;padding-right:10px;color:#6a737d;text-align:right;white-space:nowrap}table.files .message .tooltipped::before,table.files .message .tooltipped::after{display:none}table.files tr.is-loading td.icon .octicon{display:none}table.files tr.is-loading td.icon .spinner{display:inline-block}table.files tr.up-tree{border-top:1px solid #eaecef;border-bottom:1px solid #eaecef}table.files tr.up-tree a{padding:3px 6px;margin-left:-3px;font-weight:600;border-radius:2px}table.files tr.up-tree a:hover{background-color:#dfe2e5}table.files tbody tr:first-child td{border-top:0}.branch-infobar{padding:8px;color:#586069;background-color:#fafbfc;border:solid #dfe2e5;border-width:1px 1px 0;border-top-left-radius:3px;border-top-right-radius:3px}.branch-infobar::before{display:table;content:""}.branch-infobar::after{display:table;clear:both;content:""}.branch-infobar .muted-link{display:inline-block;margin-left:10px}.branch-infobar .muted-link .octicon{color:#959da5}.branch-infobar .muted-link:hover .octicon{color:inherit}.spinner-forking{display:block;margin:20px auto 40px}.prereceive-feedback{padding:15px;margin-bottom:15px;border:1px solid #dfe2e5;border-left:3px solid #caa21a;border-radius:3px}.prereceive-feedback-heading{margin-top:0;margin-bottom:10px;color:#b08800}.file-navigation-options{float:right;margin-left:3px}.file-navigation-options.active .dropdown-menu-content{display:block}.file-navigation-options .dropdown-menu{width:360px;padding:15px}.file-navigation-options .dropdown-divider{margin:15px -15px}.file-navigation-option{position:relative;display:inline-block;margin-left:3px}.file-navigation-option .select-menu{display:inline-block;margin-right:0;margin-bottom:0;vertical-align:middle}.file-navigation-option .select-menu-button .octicon:only-child{margin-left:2px}.file-navigation-option .zeroclipboard-button{padding-right:8px}.file-navigation-option .input-group{width:290px}.file-navigation-option .input-group .form-control{width:calc(100% + 2px);height:28px;min-height:0;margin-right:-1px;margin-left:-1px;border-radius:0}.file-navigation-option .input-group .select-menu-button{position:relative;z-index:2}.profile-picture{margin:10px 0 0}.profile-picture>p{float:left;margin:0;line-height:30px}.profile-picture>img{float:left;margin:0 10px 0 0;border-radius:3px}.app-owner{margin:15px 0 0}.edit-profile-avatar{width:200px}.edit-profile-avatar .drag-and-drop{padding:0;color:#666;border-width:0}.edit-profile-avatar input{cursor:pointer}.edit-profile-avatar.is-bad-file{border:0}.edit-profile-avatar .manual-file-chooser{position:absolute;top:0;left:0;height:34px;padding:0;cursor:pointer}.button-change-avatar{overflow:hidden}.profile-picture-spinner{background-image:url("/images/spinners/octocat-spinner-128.gif");background-repeat:no-repeat;background-position:center;background-size:64px 64px}.avatar-upload .flash{width:100%;padding:30px 15px;border:dashed 1px #d73a49;box-shadow:none}.avatar-upload .upload-state{display:none;padding:10px 0}.avatar-upload .upload-state p{margin:0;font-size:12px;color:#586069}.avatar-upload .avatar-upload .octicon{display:inline-block}.is-uploading .avatar-upload .loading{display:block;padding:0}.is-uploading .avatar-upload .loading img{vertical-align:top}.is-uploading .avatar-upload .button-change-avatar{display:none}.is-bad-file .avatar-upload .bad-file{display:block;margin:0}.is-too-big .avatar-upload .too-big{display:block;margin:0}.is-bad-dimensions .avatar-upload .bad-dimensions{display:block;margin:0}.is-bad-format .avatar-upload .bad-format{display:block;margin:0}.is-failed .avatar-upload .failed-request{display:block;margin:0}.is-empty .avatar-upload .file-empty{display:block;margin:0}dl.new-email-form{padding:10px 10px 0;margin:0 -10px 10px;border-top:1px solid #e5e5e5}.email-actions{float:right}.email-actions>span{float:left}.email-actions form{display:inline}.email-actions span.label{padding:0 10px;font-size:13px;color:#586069}.email-actions .octicon-alert{color:#ca5633}.boxed-group .fork-flag{margin-left:16px;font-size:12px;color:#586069}.selected-user-key{background-color:#fffdef}.user-key-type{padding-right:20px;padding-left:10px;text-align:center}.user-key-badge{display:block;padding-right:5px;padding-left:5px;margin-top:3px;font-size:12px;line-height:1.4;border:solid 1px #ddd;border-radius:3px}.user-key-email-badge{display:inline-table;margin-right:4px}.user-key-email{display:table-cell;padding:1px 5px;font-size:12px;line-height:1.4;border:solid 1px #ddd;border-radius:3px}.user-key-email.unverified{border-top-right-radius:0;border-bottom-right-radius:0}.user-key-email-unverified{display:table-cell;padding-right:5px;padding-left:5px;font-size:11px;color:#666;background-color:#ecebec;border:solid 1px #ddd;border-left:0;border-top-right-radius:3px;border-bottom-right-radius:3px}.user-key-details{width:400px;line-height:1.6;white-space:normal}.user-key-details code{font-size:13px}.recent-user-key{color:#28a745}.recent-user-key-access{color:#1e7e34}.unverified-user-key,.unverified-user-key-notice{color:#cb2431}.notification-center .overview{padding:0 10px 10px;border-bottom:1px solid #ddd}.oauth-app-info-container .float-left-container{float:left;text-align:left}.oauth-app-info-container .float-right-container{float:right;text-align:right}.oauth-app-info-container dl.keys{margin:10px 0}.oauth-app-info-container dl.keys dt{margin-top:10px;font-weight:600;color:#586069}.oauth-app-info-container dl.keys dd{font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;color:#333}.oauth-app-info-container .user-count{font-size:30px;font-weight:300;color:#586069}.logo-uploader-container{display:block}.logo-upload{position:relative;display:inline-block}.logo-upload a.delete,.logo-upload span.delete{position:absolute;left:88px;display:none;padding:8px 10px}.logo-upload a.delete:hover,.logo-upload span.delete:hover{color:#cb2431}.logo-upload-container{display:inline-block}.logo-upload-container .logo-upload-label .manual-file-chooser{top:0;left:0;width:130px;height:34px;padding:0;margin-left:0;cursor:pointer}.logo-upload-container .upload-state{padding:10px 0}.logo-upload-container .upload-state p{margin:0;font-size:12px;color:#586069}.logo-box{display:inline-block;float:left;width:120px;height:120px;background-color:#eee;border:1px solid #ccc;border-radius:3px}.logo-box img{display:none;width:118px;height:118px;border-radius:3px}.logo-placeholder{color:#586069;text-align:center;text-shadow:0 1px 0 #fff}.logo-placeholder p{margin:0;font-size:14px}.has-uploaded-logo .logo-placeholder,.has-uploaded-logo .or{display:none}.has-uploaded-logo:hover a.delete,.has-uploaded-logo:hover span.delete{display:block}.has-uploaded-logo .logo-box img{display:block}.saved-reply-form{border:1px solid #ddd;border-radius:3px}.saved-reply-form .tabnav-tab.selected{border-radius:3px 3px 0 0}.saved-reply-form .form-actions{margin-right:10px;margin-bottom:8px}.saved-reply-form .comment{border:0}.saved-reply-form .comment-body{padding:5px 5px 15px;background-color:transparent;border-bottom:1px solid #eee}.saved-reply-settings-container.has-replies .listgroup{display:block}.saved-reply-settings-container.has-replies .blankslate{display:none}.saved-reply-settings-container .listgroup{display:none}.access-token{border-bottom:1px solid #e5e5e5}.access-token:last-child{border:0}.access-token .last-used{margin-right:10px}.access-token .zeroclipboard-link{display:inline-block;vertical-align:text-bottom}.access-token.new-token{background-color:rgba(108,198,68,0.1)}.access-token.new-token .octicon-check{color:#28a745}.access-token .token-description{max-width:450px;color:#24292e}.access-token .token{font-size:14px}.access-token .sso-ready-badge{display:inline-block;padding:1px 4px;font-size:10px;line-height:16px;color:#767676;vertical-align:middle;background:none;border:1px solid #e5e5e5;border-radius:2px}.access-token .sso-ready-badge:focus{outline:none}.access-token .sso-ready-badge:hover{border-color:#ccc}.access-token .sso-ready-badge.ready{color:#55a532}.access-token .sso-ready-badge.ready:hover{border-color:#55a532}.access-token .credential-authorization-statuses{position:relative;display:inline-block}.access-token .credential-authorization-statuses.active .dropdown-menu-content{display:block}.access-token .credential-authorization-statuses .dropdown-menu{top:-4px;min-width:400px;max-width:500px;padding-top:0;padding-bottom:0}.access-token .credential-authorization-menu{padding:15px;font-size:13px;line-height:1.4}.access-token .credential-authorization-menu .status-heading{color:#24292e}.access-token .credential-authorization-status-list{max-height:235px;padding:0;margin:15px -15px -15px;overflow:scroll;border-top:1px solid #eee;transition:max-height 0.25s ease-in-out}.access-token .credential-authorization-status-item{position:relative;padding:10px 15px 10px 12px;background-color:#fafbfc;border-bottom:1px solid #eee}.access-token .credential-authorization-status-item:last-child{border-bottom:0;border-radius:0 0 2px 2px}.access-token .credential-authorization-status-icon{width:30px;margin-right:10px}.access-token .credential-authorization-status-avatar{position:relative;top:5px}.regenerate-token-cta .btn-danger{margin-left:30px}.personal-access-tokens-group{padding:0 10px}.personal-access-tokens label{display:inline}.personal-access-tokens label p{display:inline-block;margin:0;font-size:13px;font-weight:400}.personal-access-tokens .child-scopes{margin-left:20px;list-style:none}.personal-access-tokens .child-scopes .token-scope{width:180px;font-weight:400}.personal-access-tokens .child-scopes .child-scopes{margin-left:0}.token-scope{display:inline-block;width:200px;padding:2px 0;margin:0;font-size:13px;color:#24292e}.token-scope input{margin-right:5px}.callback-urls dl dd .form-control{width:100%}.callback-urls.has-many .callback-url-action-cell{display:table-cell}.callback-description{margin-top:20px}.callback-description .octicon{padding-left:0}.callback-url .label{display:none;width:64px;text-align:center}.callback-url.is-default-callback .label{display:inline-block}.callback-url.is-default-callback .btn{display:none}.callback-url-wrap{display:table;width:100%}.callback-url-field-cell{display:table-cell}.callback-url-action-cell{display:none;width:70px;text-align:right}.boxed-group.application-show-group dl.form-group>dd .form-control.wide{width:460px}.boxed-group.application-show-group dl.form-group>dd .form-control.short{height:50px;min-height:50px}.application-show-group .errored .note{display:none}.application-show-group .drag-and-drop{padding:0;text-align:left;background-color:transparent;border:0}.application-show-group .drag-and-drop img{margin-bottom:1px;vertical-align:bottom}.application-show-group .drag-and-drop span{padding:0}.application-show-group .dragover .logo-box{box-shadow:#c9ff00 0 0 3px}.application-show-group .is-uploading .loading{display:inline-block}.application-show-group .is-uploading .default{display:none}.application-show-group .is-failed .failed-request{display:inline-block}.application-show-group .is-failed .default{display:none}.application-show-group .is-bad-file .bad-file{display:inline-block}.application-show-group .is-bad-file .default{display:none}.application-show-group .is-too-big .file-too-big{display:inline-block}.application-show-group .is-too-big .default{display:none}.application-show-group .is-bad-format .bad-format{display:inline-block}.application-show-group .is-bad-format .default{display:none}.application-show-group .is-default .default{display:block}.post-recovery-token .octospinner,.post-recovery-token .create-recovery-token-error{display:none}.post-recovery-token.loading .octospinner{display:block}.post-recovery-token.failed .create-recovery-token-error{display:block}.security-history .security-history-timestamp{float:right;color:#586069}table.security-history-detail{width:100%;font-size:12px}table.security-history-detail td{max-width:200px;word-wrap:break-word}.settings-email .email-actions .settings-remove-email{float:right;padding-right:7px;padding-left:7px;margin-left:5px;line-height:24px;color:#cb2431;cursor:pointer}.settings-email .email-actions .settings-remove-email.settings-disabled-remove-email{display:block;color:#6a737d}.settings-email .octicon-info{margin-left:5px}.settings-email .css-truncate-target{max-width:300px}.email-preference-exceptions{font-size:12px}.email-preference-exceptions h5{margin:15px 0 5px;color:#666}.email-preference-exceptions .exception-list{list-style:none}.email-preference-exceptions .exception{max-width:400px;padding:5px;padding-left:0;border-top:1px solid #eee}.email-preference-exceptions .exception:last-child{border-bottom:1px solid #eee}.email-preference-exceptions.opt-in-list{display:none}.transactional-only .email-preference-exceptions.opt-in-list{display:block}.transactional-only .email-preference-exceptions.opt-out-list{display:none}.two-factor-intro{width:675px;margin:40px auto 0}.two-factor-intro .two-factor-graphic{margin:20px 0}.two-factor-intro .two-factor-explain{padding:0;margin:0 0 40px;font-size:13px;list-style:none}.two-factor-intro .two-factor-explain li{float:left;padding:0;margin:0}.two-factor-intro .two-factor-explain .step-one{width:185px;margin-right:36px}.two-factor-intro .two-factor-explain .step-two{width:230px;margin-right:42px}.two-factor-intro .two-factor-explain .step-three{width:180px}.two-factor-graphic{width:675px;height:135px;background-image:url("/images/modules/settings/2fa_guide.png");background-repeat:no-repeat}.two-factor-recovery-codes{margin:30px 0;font-family:"SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;font-size:21px}.two-factor-recovery-code-mark{width:24px;height:24px;font-size:24px;line-height:16px;color:#bbb}.two-factor-recovery-code{display:inline-block;width:48%;line-height:1.6;text-align:center}.two-factor-recovery-code::before{position:relative;top:-3px;margin-right:10px;font-size:10px;color:#d8d8d8;content:"\25CF"}.recovery-codes-saving-options{margin-left:35px}.recovery-codes-saving-options .recovery-code-save-button{width:115px;margin-right:15px;text-align:center}.recovery-codes-warning{margin:0 -15px}.recovery-codes-warning .recovery-codes-warning-octicon{height:40px;margin-right:15px}@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and (min--moz-device-pixel-ratio: 2), only screen and (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio: 2), only screen and (min-resolution: 192dpi), only screen and (min-resolution: 2dppx){.two-factor-graphic{background-image:url("/images/modules/settings/2fa_guide@2x.png");background-size:675px 135px}}.btn-two-factor-state{min-width:70px}.sms-or-app{width:100%;padding:40px 0 0;margin:0;border-top:1px solid #ddd}.sms-or-app::before{display:table;content:""}.sms-or-app::after{display:table;clear:both;content:""}.sms-or-app li{float:left;width:325px;padding:0;list-style:none}.sms-or-app li:first-child{margin-right:25px}.sms-or-app li .btn{display:block;width:100%;height:100%;padding-top:12px;padding-bottom:12px;margin:10px 0;font-size:15px;text-align:center}.sms-or-app small{font-size:80%}.app-only{padding:20px 0 0}.app-only li{float:none;width:auto}.app-only li .btn{display:inline-block;width:auto;padding-right:20px;padding-left:20px}.two-factor-wrapper{width:500px;margin:40px auto}.two-factor-lead{font-size:16px}.two-factor-qr-code{text-align:center}.two-factor-steps{padding:15px 15px 0;margin:30px 0;border:1px solid #eee;border-radius:3px}.two-factor-steps .error-icon{position:relative;top:2px;left:5px;color:#cb2431}.two-factor-steps .sent-message{position:relative;top:2px;left:5px;color:#28a745}.container.two-factor-toggle{width:700px}.sms-country-code-field{padding-right:10px}.two-factor-toggle{margin-top:40px}.two-factor-toggle .two-factor-status{padding:20px 0;margin:0 0 20px;color:#586069;border-bottom:1px solid #eaeaea}.two-factor-toggle .two-factor-status p{margin:0}.two-factor-toggle .two-factor-status .btn{position:relative;top:-3px;float:right}.two-factor-toggle .two-factor-on{padding:3px 5px;margin-right:5px;color:#fff;text-shadow:0 1px 1px rgba(0,0,0,0.1);background-color:#28a745;border-radius:2px}.two-factor-settings-group{position:relative;padding:0 0 20px 220px;margin:0 0 20px;border-bottom:1px solid #ddd}.two-factor-settings-group>h3{position:absolute;left:0;width:200px;font-size:14px}.two-factor-settings-group>h3 .octicon{position:absolute;left:-24px;color:#cb2431}.two-factor-settings-group li{line-height:1.5;list-style:none}.u2f-registrations{padding-left:24px}.u2f-registration{position:relative;padding-bottom:8px;margin-bottom:8px;border-bottom:1px solid #f8f8f8}.u2f-registration.is-sending .u2f-registration-delete{display:none}.u2f-registration.is-sending .spinner{position:relative;top:3px}.u2f-registration-nickname{font-weight:600}.u2f-registration-icon{position:absolute;left:-24px;color:rgba(47,38,6,0.5)}.new-u2f-registration{position:relative}.new-u2f-registration .add-u2f-registration-form{display:none;margin-bottom:10px}.new-u2f-registration.is-active .add-u2f-registration-link{display:none}.new-u2f-registration.is-active .add-u2f-registration-form{display:block}.new-u2f-registration .u2f-request-interaction,.new-u2f-registration .u2f-request-error{display:none}.new-u2f-registration.is-sending .u2f-request-interaction{display:block}.new-u2f-registration.is-showing-error .u2f-request-error{display:block}.new-u2f-registration .u2f-error-icon{font-size:64px}.u2f-box .u2f-sorry{display:block}.u2f-box .new-u2f-registration{display:none}.u2f-box.available .u2f-sorry{display:none}.u2f-box.available .new-u2f-registration{display:block}.github-access-banner{position:relative;padding:10px 20px 10px 70px;margin:0 0 20px;font-size:14px;border:1px solid #ddd;border-radius:3px}.github-access-banner .octicon{position:absolute;top:20px;left:20px;color:#cb2431}.error-icon,.spinner,.sent-message,.sms-error-message{display:none}.sms-error-message{margin-top:15px}.is-sending .spinner{display:inline-block}.is-sent .sent-message{display:inline-block}.is-not-sent .sms-error-message{display:block}.is-not-sent .error-icon{display:inline-block}.qr-code-table{display:inline-block;padding:20px;margin:30px auto;border:1px solid #eee;border-radius:3px;box-shadow:0 2px 2px 0 rgba(0,0,0,0.04)}.qr-code-table tr{background:transparent;border:0}.qr-code-table th,.qr-code-table td{padding:0;border:0}.qr-code-table td{width:3px;height:3px}.qr-code-table .black{background:#000}.qr-code-table .white{background:#fff}.confirmation-phrase{font-style:italic;font-weight:400}.do-not-copy-me{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.session-device{position:relative;padding:15px;line-height:18px;color:#586069}.session-device .btn{float:right;margin-top:4px}.session-device .session-state-indicator{float:left;width:8px;height:8px;margin-top:10px;border-radius:5px}.session-device .session-state-indicator.recent{background-color:#28a745;box-shadow:0 0 10px rgba(108,198,68,0.5)}.session-device .session-state-indicator.not-recent{background-image:linear-gradient(#aaa, #ccc);box-shadow:0 1px 0 #fff}.session-device .session-icon{float:left;width:32px;margin-top:1px;margin-left:15px;color:#bbb;text-align:center}.session-device .sessions-more-info{margin-top:10px}.session-device.session-current{margin-top:0;background-color:#fff}.session-device.session-current .sessions-more-info{color:#586069}.session-device.session-current .sessions-more-info::after{border-top-color:#fff}.session-device.session-current .octicon{color:#586069}.session-details{margin-left:70px}.session-details:hover .octicon{color:#0366d6;cursor:pointer}.session-details.open .sessions-more-info{display:block}.collaborators .collab-list{border-bottom-width:0}.collaborators .collab-list-item:first-child .collab-list-cell{border-top-width:0}.collaborators .collab-list-cell{padding-top:15px;padding-bottom:15px;vertical-align:middle}.collaborators .collab-meta{width:140px}.collaborators .collab-remove{padding-right:20px;text-align:right}.collaborators .collab-remove .remove-link{color:#586069}.collaborators .collab-remove .remove-link:hover{color:#cb2431}.collaborators .collab-team-link{width:300px}.collaborators .collab-team-link:hover{text-decoration:none}.collaborators .collab-team-link .avatar{float:left;margin-top:1px;margin-right:10px}.collaborators .collab-team-link.disabled{pointer-events:none}.collaborators .collab-info{height:100%;color:#666}.collaborators .collab-info .description{padding-right:50px;margin-top:3px;margin-bottom:3px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.collaborators .collab-info .collab-name{display:block;font-size:14px}.collaborators .collab-info .collab-message{position:relative;top:25%;display:block}.collaborators .copy-invite-modal{width:352px}.collaborators .cancel-invite-cell{width:113px;padding-left:0}.access-sub-heading{float:right;font-weight:400;line-height:1.4;color:#586069}.access-form-wrapper{padding:10px;background-color:#fcfcfc;border-top:1px solid #ddd;border-radius:0 0 3px 3px}.access-flash{padding:8px;margin-right:10px;margin-bottom:10px;margin-left:10px}.repo-access-group .blankslate{display:none}.repo-access-group.is-empty .blankslate{display:block}.repo-access-group.no-form .add-team-form{display:none}.repo-access-group .select-menu-item.has-access{display:none}.oauth-pending-deletion-list-item{background-color:#fafbfc;box-shadow:inset 0 0 8px #eee}.oauth-pending-deletion-list-item:hover{background-color:#fafbfc}.oauth-pending-deletion-list-item .oauth-pending-deletion{display:inline-block;width:19%;line-height:30px}.oauth-pending-deletion-list-item .active{display:none}.oauth-pending-deletion{display:none;width:100%}.boxed-group-list .access-level{color:#586069}.boxed-group-list .access-level.css-truncate-target{max-width:500px}.settings-next{font-size:14px;line-height:1.5}.settings-next label{font-size:14px}.settings-next .note{font-size:13px}.settings-next .form-checkbox input[type="radio"],.settings-next .form-checkbox input[type="checkbox"]{margin-top:4px}dl.form-group>dd textarea.compact{height:100px;min-height:0}.form-hr{margin-top:15px;margin-bottom:15px;border-bottom-color:#e5e5e5}.listgroup{list-style:none;border:1px solid #e5e5e5;border-radius:3px}.listgroup-item{min-height:inherit;padding:10px;font-size:13px;line-height:26px;color:#586069}.listgroup-item::before{display:table;content:""}.listgroup-item::after{display:table;clear:both;content:""}.listgroup-item+.listgroup-item{border-top:1px solid #e5e5e5}.listgroup-item.listgroup-item-preview{line-height:inherit}.listgroup-item.listgroup-item-preview .BtnGroup{margin-top:5px}.listgroup-item .css-truncate-target{max-width:615px}.listgroup-item-title{display:block;font-weight:600}.listgroup-item-body{display:block}.listgroup-header{border-top:0;border-bottom:1px solid #e5e5e5}.listgroup-overflow{max-height:240px;overflow-y:auto;background-color:#f5f5f5}.listgroup-sm .listgroup-item{padding-top:5px;padding-bottom:5px}.protected-branches{margin-top:15px;margin-bottom:15px}.protected-branch-options,.repo-data-options{margin-left:20px;opacity:0.5}.protected-branch-options.active,.repo-data-options.active{opacity:1}.protected-branch-reviews.on .require-code-owner-review,.protected-branch-reviews.on .reviews-dismiss-on-push,.protected-branch-reviews.on .reviews-include-dismiss,.protected-branch-reviews.on .require-approving-reviews{display:block}.protected-branch-reviews .require-code-owner-review,.protected-branch-reviews .reviews-dismiss-on-push,.protected-branch-reviews .reviews-include-dismiss,.protected-branch-reviews .require-approving-reviews{display:none}.protected-branch-orgs-and-repo-admins{padding:10px;background-color:#fafbfc}.authorized-pushers{width:440px}.authorized-pushers .add-protected-branch-user-or-team{display:block}.authorized-pushers .user-or-team-limit-reached{display:none;padding:10px;font-size:13px}.authorized-pushers.at-limit .add-protected-branch-user-or-team{display:none}.authorized-pushers.at-limit .user-or-team-limit-reached{display:block;width:440px}.protected-branch-authorized-pushers-table{margin-top:10px}.protected-branch-authorized-pushers-table .boxed-group-inner{max-height:350px;overflow-y:auto}.protected-branch-authorized-pushers-table .table-list{border-bottom:0}.protected-branch-authorized-pushers-table .table-list-cell{vertical-align:middle}.protected-branch-authorized-pushers-table .table-list-cell:first-child{width:100%}.protected-branch-authorized-pushers-table .avatar,.protected-branch-authorized-pushers-table .octicon-jersey,.protected-branch-authorized-pushers-table .octicon-organization{width:36px;margin-right:10px;text-align:center}.protected-branch-pusher{color:#333}.user-already-added::after{display:inline-block;padding:1px 5px;margin-left:6px;font-size:11px;line-height:1.4;color:#fff;content:"Already added";background-color:#d15704;border-radius:3px}.protected-branch-admin-permission{float:left;padding:3px;margin:-2px 0 -2px -4px;line-height:normal;border:1px solid transparent;border-radius:3px}.protected-branch-admin-permission.active{animation:toggle-color 1s ease-in-out 0s}@keyframes toggle-color{0%{background-color:transparent}50%{color:#4c4a42;background-color:#fff9ea;border-color:#dfd8c2}100%{background-color:transparent}}.automated-check-options{margin-top:10px}.automated-check-options .listgroup-item label{font-size:inherit}.automated-check-options .listgroup-item input[type="checkbox"]{float:none;margin-top:-2px;margin-right:5px;margin-left:0}.automated-check-options .label{margin-top:4px}.repository-merge-features .form-group.errored label{color:inherit}.repository-merge-features .form-group.errored .error{position:inherit;padding:0;margin-top:0;margin-left:6px;font-size:11px;color:#cb2431;background:transparent;border:0}.repository-merge-features .form-group.errored .error::before,.repository-merge-features .form-group.errored .error::after{display:none}.logged-out.signup .header-actions .primary,.logged-out.signup .footer,.logged-out.signup .header-search{display:none}.setup-wrapper{width:750px;padding-top:30px;margin:0 auto}.setup-wrapper::before{display:table;content:""}.setup-wrapper::after{display:table;clear:both;content:""}.setup-header{padding-bottom:20px;margin:0 auto 30px;overflow:hidden;text-align:left;border-bottom:1px solid #ddd}.setup-header h1{margin-top:0;margin-bottom:0;font-size:45px;font-weight:400;line-height:1.1;letter-spacing:-1px}.setup-header h1 .octicon{color:#bbb}.setup-header .lead{margin-top:2px;margin-bottom:0;font-size:21px}.setup-header .lead a{color:#586069}.setup-header .lead a:hover{color:#0366d6;text-decoration:none}.setup-org{padding-bottom:0;border-bottom:0}.setup-main{float:left;width:450px}.setup-main.without-secondary{margin-left:150px}.setup-secondary{float:right;width:250px}.setup-secondary .info{padding-top:0;padding-bottom:0;margin-top:-10px;font-size:12px;line-height:18px;color:#586069;text-align:center}.setup-info-module{margin-bottom:30px;background-color:#fff;border:1px solid #ccc;border-radius:3px;box-shadow:0 1px 3px rgba(0,0,0,0.075)}.setup-info-module h2{padding:15px;margin-bottom:15px;overflow:hidden;font-size:16px;border-bottom:1px solid #ddd}.setup-info-module h2 .price{float:right;font-weight:600;color:#586069}.setup-info-module h3{padding:0 15px;margin:0 0 -7px;font-size:14px}.setup-info-module p{padding:0 15px;margin:15px 0}.setup-info-module .setup-info-note{padding:1px 0;margin:0;background:#f9f9f9;border-top:1px solid #e0e0e0}.features-list{padding:0 15px 15px;margin:0;font-size:14px;list-style:none}.features-list li{margin-top:10px}.features-list li:first-child{margin-top:0}.features-list .list-divider{margin:15px -15px;border-top:1px solid #eee}.features-list .octicon-check{margin-right:5px;color:#60b044}.features-list .octicon-question{font-size:12px;color:#555}.features-list .tooltipped::after{width:250px;white-space:normal}.features-list.features-list-org{padding-bottom:0}.setup-form-container .setup-form-title{font-size:16px}.setup-form-container .secure{float:right;margin-top:2px;font-size:11px;color:#60b044;text-transform:uppercase}.setup-form-container hr{margin-top:25px;margin-bottom:25px}.setup-form-container .form-actions{padding-top:0;padding-bottom:0;text-align:left}.team-member-container{margin-bottom:20px}.team-member-container .team-member-username{line-height:1.2}.setup-form{padding-bottom:15px}.setup-form .form-group dd .form-control{width:100%}.setup-form .form-group dd .form-control.short{width:250px}.setup-form dd{position:relative}.setup-form dd .octicon{position:absolute;top:8px;right:25px}.setup-form .octicon-alert{color:#cb2431}.setup-form .octicon-check{color:#28a745}.setup-form .tos-info,.setup-form .setup-organization-next{margin:15px 0;border-top:1px solid #eee;border-bottom:1px solid #eee}.setup-form .tos-info{padding:15px 0}.setup-form .setup-organization-next{padding-top:15px;padding-bottom:15px}.setup-form .setup-plans{border-collapse:separate;border:1px solid #d1d5da}.setup-form .setup-plans tr.selected{background-color:#f1f8ff}.setup-form .setup-plans .name{font-weight:600}.setup-form .setup-plans .choose-plan input[type="radio"]{display:none}.setup-form .new-business-initial-admins td{padding:5px 10px;border:1px solid #f5f5f5}.setup-creditcard-form .cc-extras{margin-bottom:15px}.setup-creditcard-form .expiration-form select{width:80px}.setup-creditcard-form .expiration-form,.setup-creditcard-form .cvv-form,.setup-creditcard-form .country-form,.setup-creditcard-form .state-form{float:left;margin:0;word-wrap:normal}.setup-creditcard-form .country-form,.setup-creditcard-form .postal-code-form{margin-top:0;margin-bottom:15px}.setup-creditcard-form .form-group dd .input-cvv{width:80px}.setup-creditcard-form .form-group select.select-country{width:182px;margin-right:5px}.setup-creditcard-form .form-group select:invalid{color:#6a737d}.setup-creditcard-form .form-group select.select-state{width:113px}.setup-creditcard-form .form-group .card-select-number-field{width:100%}.setup-creditcard-form .form-group .input-vat{width:288px}.setup-creditcard-form .form-group .card-select-number-field-icons{top:1px;right:2px}.setup-creditcard-form .form-group .card-select-number-field-icons>.placeholder{top:8px;right:10px}.setup-creditcard-form .form-group .card-select-number-field-icons>.card{height:20px;margin-top:6px;margin-right:5px;margin-bottom:6px}.setup-creditcard-form .form-group .card-select-number-field-icons>.card.enabled{display:block}.setup-creditcard-form .form-group input.input-postal-code{width:180px}.setup-creditcard-form.is-vat-country .vat-field{display:block}.setup-creditcard-form.is-international .form-group select.select-country{width:300px}.setup-creditcard-form.is-international .state-form{display:none}.setup-creditcard-form.no-postcodes .postal-code-form{display:none}.setup-creditcard-form dd .octicon-credit-card{position:inherit}.setup-creditcard-form .enter-new-card{display:none}.setup-creditcard-form.has-credit-card .enter-new-card{display:inline-block}.setup-creditcard-form.has-credit-card .card-select-number-field-icons,.setup-creditcard-form.has-credit-card .card-select-number-field,.setup-creditcard-form.has-credit-card .cancel-enter-new-card{display:none}.setup-creditcard-form .vat-field{display:none}.setup-creditcard-form .vat-field.prefilled{display:block}.setup-creditcard-form .help-text{font-size:80%;font-weight:400;color:#586069}.org-creation-questions,.user-identification-questions{float:none;width:auto}.org-creation-questions .question,.user-identification-questions .question{margin-bottom:30px}.org-creation-questions .question-title,.user-identification-questions .question-title{padding-right:40px;margin-bottom:10px;font-size:14px;line-height:1.5}.org-creation-questions .question-title i,.user-identification-questions .question-title i{font-size:13px;font-style:normal;font-weight:400;color:#767676}.org-creation-questions .response-group label,.user-identification-questions .response-group label{font-weight:400}.org-creation-questions .form-checkbox,.user-identification-questions .form-checkbox{margin:8px 0}.org-creation-questions .other-field .form-control,.user-identification-questions .other-field .form-control{margin-top:10px}.org-creation-questions .other-field input+.form-control,.user-identification-questions .other-field input+.form-control{display:none}.org-creation-questions .other-field input:checked+.form-control,.user-identification-questions .other-field input:checked+.form-control{display:block}.org-creation-questions .alternate-action,.user-identification-questions .alternate-action{margin-left:10px;line-height:34px}.org-creation-questions .disclaimer,.user-identification-questions .disclaimer{margin:40px 0 0;text-align:center}.user-identification-questions{margin-top:40px}.user-identification-questions .form-checkbox{float:left;width:250px}.shelf{padding-top:20px;margin-bottom:20px;background-color:#fefefe;border-bottom:1px solid #eff1f3}.shelf .container{position:relative}.shelf-title{margin:0;font-size:30px;font-weight:400}.shelf-content{width:800px;margin:50px auto;text-align:center}.shelf-lead{margin-top:10px;margin-bottom:30px;font-size:18px;color:#586069}.shelf-dismiss{position:absolute;top:0;right:20px;font-size:12px;color:#959da5}.shelf-dismiss:hover{color:#0366d6;text-decoration:none}.shelf-dismiss .close-button{width:28px;height:28px;padding:3px 5px;color:rgba(27,31,35,0.7);border:1px solid rgba(27,31,35,0.15);border-radius:28px}.shelf-dismiss .close-button:hover{color:#24292e;border-color:rgba(27,31,35,0.3)}.shelf-cta{padding:10px 50px;font-size:16px}.intro-shelf{margin-top:0;color:rgba(27,31,35,0.85);background-image:linear-gradient(180deg, rgba(255,255,255,0) 60%, #fff),linear-gradient(70deg, #dbedff 32%, #ebfff0);border-bottom:#fff}.intro-shelf .shelf-lead{color:rgba(27,31,35,0.7)}.orgs-help-shelf{padding-top:20px;padding-bottom:20px;margin-top:-20px;margin-bottom:20px;border-bottom:1px solid #eff1f3}.orgs-help-shelf .orgs-help-title{font-size:30px;font-weight:400}.orgs-help-shelf-content{width:800px;margin:50px auto;text-align:center}.orgs-help-shelf-content .orgs-help-lead{padding-right:45px;padding-left:45px;font-size:18px}.orgs-help-shelf-content .orgs-help-divider{display:block;width:150px;margin:40px auto;content:"";border-top:1px solid #d1d5da}.orgs-help-lead{margin-top:10px;margin-bottom:30px;color:#586069}.orgs-help-items{margin-bottom:40px}.orgs-help-item-octicon{width:70px;height:70px;margin:0 auto 15px;text-align:center;background-color:#fff;border:solid 1px #e1e4e8;border-radius:50px}.orgs-help-item-octicon .octicon{margin-top:20px;color:#0366d6}.orgs-help-item-title{margin-bottom:10px;font-weight:400}.orgs-help-item-content{margin-top:0;font-size:14px;color:#586069}.orgs-help-dismiss{float:right;margin-top:5px;margin-right:10px;font-size:12px;color:#586069}.orgs-help-dismiss:hover{color:#0366d6;text-decoration:none}.orgs-help-dismiss .octicon{position:relative;top:1px}.orgs-help-title{margin-top:0;margin-bottom:0}.collection-page .column.main{margin-right:260px !important}.collection-page .column.sidebar{width:240px}.collection-search-results em{padding:0.1em;background-color:#faffa6}.draft-tag{padding:5px 10px;font-weight:600;color:#eee;background-color:#404040}.showcase-page-pattern{position:relative;z-index:-1;height:100px;margin-top:-21px;margin-bottom:-70px}.showcase-page-pattern::after{position:absolute;top:0;right:0;bottom:0;left:0;display:block;content:"";background-image:linear-gradient(180deg, rgba(255,255,255,0.85), #fff)}.showcase-page-repo-list{border-top:1px solid #eee}.repo-health .repo-health-loading{display:none;font-style:italic}.repo-health.is-loading .repo-health-loading{display:block}.repo-health.is-loading .repo-health-results{display:none}.sortable-button-item:first-of-type .sortable-button[data-direction="up"],.sortable-button-item:last-of-type .sortable-button[data-direction="down"]{display:none}.org-sso{width:340px;margin:0 auto}.org-sso .sso-title{font-size:24px;font-weight:300;letter-spacing:-0.5px}.org-sso .org-sso-panel{padding:20px;background-color:#fff;border:1px #e1e4e8 solid;border-radius:5px}.org-sso .sso-recovery-callout{padding:15px 10px;text-align:center;border:1px solid #d1d5da;border-radius:5px}.sso-modal{padding:16px}.sso-modal .org-sso{width:auto}.sso-modal .org-sso .org-sso-panel{border:0}.sso-modal .sso-prompt-success,.sso-modal .sso-prompt-error{display:none}.sso-modal.success .sso-prompt-default{display:none}.sso-modal.success .sso-prompt-success{display:block}.sso-modal.error .sso-prompt-default{display:none}.sso-modal.error .sso-prompt-error{display:block}.sso-modal.error .flash-error{margin-right:-35px;margin-left:-35px;border-right:0;border-left:0;border-radius:0}.tab-size[data-tab-size="1"]{-moz-tab-size:1;tab-size:1}.tab-size[data-tab-size="2"]{-moz-tab-size:2;tab-size:2}.tab-size[data-tab-size="3"]{-moz-tab-size:3;tab-size:3}.tab-size[data-tab-size="4"]{-moz-tab-size:4;tab-size:4}.tab-size[data-tab-size="5"]{-moz-tab-size:5;tab-size:5}.tab-size[data-tab-size="6"]{-moz-tab-size:6;tab-size:6}.tab-size[data-tab-size="7"]{-moz-tab-size:7;tab-size:7}.tab-size[data-tab-size="8"]{-moz-tab-size:8;tab-size:8}.tab-size[data-tab-size="9"]{-moz-tab-size:9;tab-size:9}.tab-size[data-tab-size="10"]{-moz-tab-size:10;tab-size:10}.tab-size[data-tab-size="11"]{-moz-tab-size:11;tab-size:11}.tab-size[data-tab-size="12"]{-moz-tab-size:12;tab-size:12}.tag-input-container{position:relative}.tag-input-container .suggester{position:absolute;z-index:100;width:100%;margin-top:-1px}.tag-input-container ul{list-style:none}.tag-input input{float:left;padding-left:2px;margin:0;background:none;border:0;box-shadow:none}.tag-input input:focus{box-shadow:none}.tag-input .tag-input-tag{float:left;margin-right:5px}.tag-input .tag-input-tag:hover{background-color:#eee}.tag-input-tag{position:relative;padding:5px 30px 5px 10px;background:#eee;border-radius:3px}.tag-input-tag:hover{background-color:#ddd}.tag-input-tag .remove{position:absolute;top:6px;right:6px;display:block;width:18px;height:18px;font-size:15px;line-height:16px;color:#fff;text-align:center;text-decoration:none;cursor:pointer;background:#c8c8c8;border-radius:18px}.tag-input-tag .remove:hover{background:#bd2c00}.task-list-item{list-style-type:none}.task-list-item label{font-weight:400}.task-list-item.enabled label{cursor:pointer}.task-list-item+.task-list-item{margin-top:3px}.task-list-item .handle{display:none}.task-list-item-checkbox{margin:0 0.2em 0.25em -1.6em;vertical-align:middle}.reorderable-task-lists .markdown-body .contains-task-list{padding:0}.reorderable-task-lists .markdown-body li:not(.task-list-item){margin-left:26px}.reorderable-task-lists .markdown-body ol:not(.contains-task-list) li,.reorderable-task-lists .markdown-body ul:not(.contains-task-list) li{margin-left:0}.reorderable-task-lists .markdown-body li p{margin-top:0}.reorderable-task-lists .markdown-body .task-list-item{padding-right:15px;padding-left:42px;margin-right:-15px;margin-left:-15px;border:1px solid transparent}.reorderable-task-lists .markdown-body .task-list-item+.task-list-item{margin-top:0}.reorderable-task-lists .markdown-body .task-list-item .contains-task-list{padding-top:4px}.reorderable-task-lists .markdown-body .task-list-item .handle{display:block;float:left;width:20px;padding:2px 0 0 2px;margin-left:-43px;opacity:0}.reorderable-task-lists .markdown-body .task-list-item .drag-handle{fill:#333}.reorderable-task-lists .markdown-body .task-list-item.hovered{background:#fafafa;border-top-color:#ededed;border-bottom-color:#ededed}.reorderable-task-lists .markdown-body .task-list-item.hovered>.handle{opacity:1}.reorderable-task-lists .markdown-body .task-list-item.is-dragging{opacity:0}.reorderable-task-lists .markdown-body .task-list-item.is-ghost{border-right-color:#ededed;border-left-color:#ededed}.review-comment-contents .markdown-body .task-list-item{padding-left:42px;margin-right:-12px;margin-left:-12px;border-top-left-radius:3px;border-bottom-left-radius:3px}.review-comment-contents .markdown-body .task-list-item.hovered{border-left-color:#ededed}.task-run .icon-for-success,.task-run .icon-for-failure,.task-run .icon-for-pending,.task-run .icon-for-inactive{display:none}.task-run.success .icon-for-success{display:inline-block;color:#28a745}.task-run.failure .icon-for-failure{display:inline-block;color:#cb2431}.task-run.pending .icon-for-pending{display:inline-block;color:#6a737d}.task-run.inactive .icon-for-inactive{display:inline-block;color:#6a737d}.task-run .task-run-name{display:inline-block;max-width:40%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;vertical-align:bottom}.team-label-ldap{display:inline-block;padding:0 9px;line-height:25px;color:#586069;text-transform:uppercase;cursor:default;border:1px solid #eaeaea;border-radius:3px;box-shadow:none}.team-label-ldap.header-label-ldap{padding:3px 5px}.team-member-ellipsis{width:25px;height:25px;line-height:24px;color:#586069}.team-member-ellipsis:hover{color:#0366d6;background:#f6f8fa}.team-listing .table-list-cell{padding-top:10px;padding-bottom:10px;vertical-align:middle}.team-listing .table-list-cell-checkbox{width:30px;padding-right:0;padding-left:10px}.team-listing .nested-teams-checkbox{padding-left:3px}.team-listing .nested-teams-checkbox.show{padding-right:11px}.team-listing .nested-teams-checkbox.indent-1{padding-left:30px}.team-listing .nested-teams-checkbox.indent-2{padding-left:54px}.team-listing .nested-teams-checkbox.indent-3{padding-left:78px}.team-listing .nested-teams-checkbox.indent-4{padding-left:102px}.team-listing .nested-teams-checkbox.indent-5{padding-left:126px}.team-listing .nested-teams-checkbox.indent-6{padding-left:150px}.team-listing .nested-teams-checkbox.indent-7{padding-left:174px}.team-listing .nested-teams-checkbox.indent-8{padding-left:198px}.team-listing .nested-teams-checkbox.indent-9{padding-left:222px}.team-listing .nested-teams-checkbox.indent-10{padding-left:246px}.team-listing .nested-teams-checkbox.indent-11{padding-left:270px}.team-listing .nested-teams-checkbox.indent-12{padding-left:294px}.team-listing .nested-teams-checkbox.indent-13{padding-left:318px}.team-listing .nested-teams-checkbox.indent-14{padding-left:342px}.team-listing .nested-teams-checkbox.indent-15{padding-left:366px}.team-listing .team-info{width:280px}.team-listing .nested-team-info{width:650px}.team-listing .nested-team-name{max-width:268px}.team-listing .team-name{line-height:1.3;color:#0366d6}.team-listing .description{display:block}.team-listing .shortened-teams-avatars{margin-left:auto}.team-listing .shortened-teams-avatars.width-0{width:300px}.team-listing .shortened-teams-avatars.width-1{width:233px}.team-listing .shortened-teams-avatars.width-2{width:167px}.team-listing .shortened-teams-avatars.width-3{width:99px}.team-listing .team-meta{font-size:14px}.team-listing .team-members-count{width:124px;text-align:right}.team-listing .team-show-more-cell{display:block;width:980px}.team-listing .team-show-more{display:block;width:110px;height:30px;padding-top:6px;margin:auto;color:#0366d6;text-align:center;text-decoration:none;cursor:pointer}.team-listing .team-buttons{width:130px;text-align:right}.team-listing .octicon-chevron-up{display:none}.team-listing .octicon-wrapper{display:inline-block;width:10px}.team-listing .is-open.root-team{background-color:#fafbfc}.team-listing .is-open .expand-nested-team{font-weight:bold;color:#24292e}.team-listing .is-open .octicon-chevron-down{transform:rotate(180deg)}.team-listing .expand-nested-team{color:#6a737d;cursor:pointer}.team-listing .expand-nested-team:hover{color:#0366d6}.team-listing .team-menu{padding-right:15px}.traffic-graph{min-height:150px}.traffic-graph .activity{margin-top:0}.traffic-graph .activity .dots{margin-top:40px}.traffic-graph .path{fill:none;stroke-width:2}.traffic-graph path.total{stroke:#28a745}.traffic-graph path.unique{stroke:#005cc5}.traffic-graph .axis .tick:first-of-type line{stroke:#28a745;stroke-width:2px}.traffic-graph .y line{stroke:#28a745}.traffic-graph .y.unique line{stroke:#005cc5}.traffic-graph .overlay{fill-opacity:0}.uniques-graph .axis .tick:last-child line{stroke:#005cc5;stroke-width:2px}.svg-tip .date{color:#fff}.top-domains .dots{display:block;margin:167px auto 0}.top-domains-icon{display:inline-block;margin-right:5px;vertical-align:middle}table.capped-list{width:100%;line-height:100%}table.capped-list th{padding:8px;text-align:left;background:#f6f8fa;border-bottom:1px solid #dfe2e5}table.capped-list td{padding:8px;font-size:12px;border-bottom:1px solid #eaecef}table.capped-list th.middle,table.capped-list td.middle{text-align:center}table.capped-list .favicon{width:16px;height:16px;margin:0 5px;vertical-align:middle}table.capped-list .octicon{margin-right:10px;color:#444d56;vertical-align:-1px}table.capped-list tr:nth-child(even){background-color:#fafbfc}.capped-list-label{max-width:200px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.traffic-graph-stats{border-top:1px solid #dfe2e5}.traffic-graph-stats .summary-stats{width:100%}.traffic-graph-stats .summary-stats::before{display:table;content:""}.traffic-graph-stats .summary-stats::after{display:table;clear:both;content:""}.traffic-graph-stats .summary-stats li{display:block;float:left;width:50%;padding-bottom:10px}.totals circle{fill:#28a745;stroke:#fff;stroke-width:2}.uniques circle{fill:#005cc5;stroke:#fff;stroke-width:2}.top-lists .is-loading{margin:40px;text-align:center}ul.web-views li{width:140px}ul.clones li{width:170px}.tree-browser{width:100%;margin:0;border-right:0;border-bottom:1px solid #cacaca;border-left:0}.tree-browser td{padding:7px 3px;color:#484848;white-space:nowrap;vertical-align:middle;background:#f8f8f8;border-bottom:1px solid #eee}.tree-browser td.icon{width:17px;padding-right:2px;padding-left:10px}.tree-browser td:first-child{border-left:1px solid #cacaca}.tree-browser td:last-child{border-right:1px solid #cacaca}.tree-browser td a.message{color:#484848}.tree-browser td span.ref{color:#aaa}.tree-browser img{vertical-align:text-bottom}.tree-browser tbody tr:last-child td{border-bottom:0}.tree-browser .history{float:right;padding-right:5px}.tree-browser .octicon-chevron-right{color:transparent}.tree-browser tr[aria-selected="true"] td,.tree-browser tr.navigation-focus td{background-color:#fff}.tree-browser tr[aria-selected="true"] td .octicon-chevron-right,.tree-browser tr.navigation-focus td .octicon-chevron-right{color:#0366d6}.tree-browser .octicon-file-directory{color:rgba(3,102,214,0.75)}.tree-browser .octicon-file-submodule{color:#3cbf5e}.tree-browser .octicon-file{color:#586069}.tree-browser .content{max-width:220px}.tree-browser .message{max-width:420px}.tree-browser .css-truncate-target{max-width:100%}.tree-browser-result .css-truncate-target{max-width:870px}.tree-browser-result mark{font-weight:600;color:#0366d6;background-color:transparent}.tree-finder-input,.tree-finder-input:focus{padding-left:0;margin-left:5px;font-size:inherit;line-height:inherit;border:0;outline:none;box-shadow:none;-webkit-appearance:none;-moz-appearance:none;appearance:none}.tree-finder .no-results{display:none}.tree-finder .no-results th{text-align:center}.tree-finder tr td.icon{cursor:pointer}.tree-finder .tree-browser{border-top:1px solid #cacaca}.filterable-empty+.no-results{display:block}.toolbar-commenting{float:right}.toolbar-commenting .dropdown.active .dropdown-menu-content{display:block}.toolbar-commenting .dropdown-menu-s{width:100px}.toolbar-commenting .dropdown-item{font-weight:600;line-height:1em;background:none;border:0}.toolbar-commenting .dropdown-item:hover{color:#0366d6}.toolbar-commenting .dropdown-item:focus{color:#0366d6;outline:none}.toolbar-item{display:block;float:left;padding:4px 5px;color:#586069;background:none;border:0}.toolbar-item.dropdown,.toolbar-item.select-menu{padding:0}.toolbar-item .select-menu-modal{margin-top:2px}.toolbar-item .select-menu-item{padding-left:8px}.toolbar-item .menu-target{display:block;padding:4px 5px;color:#586069;background:none;border:0}.toolbar-item .menu-target:hover,.toolbar-item:hover{color:#0366d6}.toolbar-item .menu-target:focus,.toolbar-item:focus{color:#0366d6;outline:none}.toolbar-item .dropdown-caret{margin-left:-3px}.toolbar-item:disabled{color:#ddd}.toolbar-item .octicon-link,.toolbar-item .octicon-tasklist{margin-left:-3px}.toolbar-item .octicon-mention{margin-left:-4px}.toolbar-item .octicon-bold{margin-left:-2px}.toolbar-group{display:inline-block;margin-left:20px}.toolbar-group:first-child{margin-left:0}.typeahead-result{position:relative;display:block;min-width:100%;padding:10px;margin-top:0;color:#333;cursor:pointer}.typeahead-result::before{display:table;content:""}.typeahead-result::after{display:table;clear:both;content:""}.typeahead-result:first-child{border-top:0}.typeahead-result:focus,.typeahead-result:hover,.typeahead-result[aria-selected="true"],.typeahead-result.navigation-focus{text-decoration:none}.typeahead-result[aria-selected="true"],.typeahead-result:hover,.typeahead-result.navigation-focus{color:#fff;background-color:#0366d6}.typeahead-result[aria-selected="true"] .octicon-plus,.typeahead-result:hover .octicon-plus,.typeahead-result.navigation-focus .octicon-plus{color:#fff}.typeahead-result.disabled{pointer-events:none;opacity:0.5}.member-suggestion{padding-left:44px}.member-suggestion .avatar{float:left;margin-right:10px;margin-left:-34px}.member-suggestion .member-suggestion-info{width:90%;margin-top:2px;margin-bottom:0;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.member-suggestion .member-name{font-size:12px;color:#586069}.member-suggestion .member-email{margin-top:0;margin-bottom:0}.member-suggestion .octicon-plus,.member-suggestion .octicon-check{position:absolute;top:50%;right:15px;margin-top:-8px;color:#ddd}.member-suggestion .already-member-note,.member-suggestion .non-member-note,.member-suggestion .non-member-action{margin-top:0;margin-bottom:0;color:#586069}.member-suggestion .non-member-action{display:none}.member-suggestion[aria-selected="true"] .member-name,.member-suggestion[aria-selected="true"] .non-member-note,.member-suggestion[aria-selected="true"] .already-member-note,.member-suggestion[aria-selected="true"] .non-member-action,.member-suggestion[aria-selected="true"] .member-email,.member-suggestion:hover .member-name,.member-suggestion:hover .non-member-note,.member-suggestion:hover .already-member-note,.member-suggestion:hover .non-member-action,.member-suggestion:hover .member-email,.member-suggestion.navigation-focus .member-name,.member-suggestion.navigation-focus .non-member-note,.member-suggestion.navigation-focus .already-member-note,.member-suggestion.navigation-focus .non-member-action,.member-suggestion.navigation-focus .member-email{color:#fff}.member-suggestion[aria-selected="true"] .non-member-note,.member-suggestion:hover .non-member-note,.member-suggestion.navigation-focus .non-member-note{display:none}.member-suggestion[aria-selected="true"] .non-member-action,.member-suggestion:hover .non-member-action,.member-suggestion.navigation-focus .non-member-action{display:block}.member-suggestion[aria-selected="true"] .octicon,.member-suggestion:hover .octicon,.member-suggestion.navigation-focus .octicon{color:#fff}.member-suggestion.not-a-member .member-info,.member-suggestion.disabled .member-info{margin-top:-2px}.non-member-result{padding-left:31px}.team-suggestion{padding-left:32px}.team-suggestion .octicon{float:left;margin-top:2px;margin-left:-22px}.team-suggestion .team-suggestion-info{margin:2px 0 0}.team-suggestion .team-suggestion-info .css-truncate-target{max-width:none}.team-suggestion .team-size,.team-suggestion .team-description{font-size:12px;color:#586069}.team-suggestion[aria-selected="true"] .team-size,.team-suggestion[aria-selected="true"] .team-description,.team-suggestion.navigation-focus .team-size,.team-suggestion.navigation-focus .team-description{color:#fff}.email-suggestion{padding-left:32px}.email-suggestion .octicon-mail{margin-left:-20px;color:#959da5}.email-suggestion .member-suggestion-info{margin-top:1px}.repo-access-add-team .team-name{font-size:13px}.repo-access-add-team .team-description{display:block}.repo-access-add-team .team-size,.repo-access-add-team .team-description{font-size:12px;color:#586069}.repo-access-add-team[aria-selected="true"] .team-size,.repo-access-add-team[aria-selected="true"] .team-description,.repo-access-add-team.navigation-focus .team-size,.repo-access-add-team.navigation-focus .team-description{color:#fff}#user-content-toc{overflow:visible}#user-content-toc tr{border-top:0}#user-content-toc td{padding:0 20px;background-color:#f7f7f7;border:0;border-radius:3px}#user-content-toc ul{padding-left:0;font-weight:600;list-style:none}#user-content-toc ul li{padding-left:0.2em}#user-content-toc ul ul{font-weight:400}#user-content-toc ul ul li::before{float:left;margin-top:-0.2em;margin-right:0.2em;font-size:1.2em;line-height:1;color:#aaa;content:"\231e"}#user-content-toc ul ul ul{padding-left:0.9em}#user-content-toctitle h2{margin-top:1em;margin-bottom:0.5em;font-size:1.25em;border-bottom:0}.user-list em{padding:3px;font-style:normal;font-weight:600;background-color:rgba(255,255,140,0.5);border-radius:3px}.user-list .avatar{position:absolute;top:0;left:0}.user-list-info{min-height:48px;padding:0;font-size:18px;font-weight:400;line-height:20px}.user-list-meta{overflow:hidden}.user-list-item+.user-list-item{border-top:1px solid #e1e4e8}.follow-list{list-style-type:none}.follow-list .follow-list-item{width:305px;height:100px;padding-bottom:20px;margin-right:20px;margin-bottom:20px}.follow-list .follower-list-align-top{vertical-align:top}.follow-list .flagged-banner{width:75px;padding:3px 0;font-size:10px;font-weight:600;color:#fff;text-transform:uppercase;background-color:#cb2431;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.follow-list .follow-list-name{margin-bottom:1px;font-weight:400}.follow-list .follow-list-name a{color:inherit}.follow-list .follow-list-info{margin-bottom:0.6em;font-size:12px;color:#586069}.follow-list .css-truncate.css-truncate-target{max-width:190px}.wiki-list em{padding:3px;font-style:normal;font-weight:600;background-color:rgba(255,255,140,0.5);border-radius:3px}.wiki-list .avatar{float:left}.wiki-list .title{min-height:24px;margin:-3px 0 10px 38px;font-weight:600;line-height:1.2}.wiki-list .title .updated-at{font-weight:400}.wiki-list .repo-specific .title,.wiki-list .repo-specific .full-path{margin-left:0}.wiki-list .description{margin:0 0 10px;overflow:hidden;line-height:20px}.wiki-list .wiki-list-item+.wiki-list-item{border-top:1px #e1e4e8 solid}@media (min-width: 768px){.wiki-rightbar{width:230px !important}}.wiki-rightbar .markdown-body .anchor{display:none}.wiki-rightbar .markdown-body h1{font-size:1.6em}.wiki-rightbar .markdown-body h2{font-size:1.4em}.wiki-rightbar p:last-child,.wiki-rightbar ul:last-child,.wiki-rightbar ol:last-child{margin-bottom:0}.wiki-footer .markdown-body,.wiki-rightbar .markdown-body{font-size:13px}.wiki-footer .markdown-body.wiki-writable>:nth-child(2),.wiki-rightbar .markdown-body.wiki-writable>:nth-child(2){margin-top:0 !important}.wiki-footer .markdown-body img{background:none}.wiki-pages-box .wiki-more-pages{display:none}.wiki-pages-box.wiki-show-more .wiki-more-pages,.wiki-pages-box .filterable-active .wiki-more-pages{display:block}.wiki-pages-box.wiki-show-more .wiki-more-pages-link,.wiki-pages-box .filterable-active .wiki-more-pages-link{display:none}
/*# sourceMappingURL=github-874c3f3bbed02d4c8eacb8d8b673ada4.css.map */


================================================
FILE: data/Segmentation_Data v01.csv
================================================
Cust_id,Age,Annual_Income_Lacs,Twitter User,Number of Vehicles,Gender,Married,Daily hours of work,Affluent_Lifestyle,No of Dependents,Urban_residence,Social,Outstation_Travel_last_year
9400,15,1,1,4,0,1,12,1,6,1,1,1
8418,15,1.7,1,3,0,1,12,1,4,1,1,4
10559,15,1.8,1,2,0,1,12,1,3,1,1,4
9924,15,1.8,1,2,0,1,15,1,6,1,1,6
9823,15,2.1,1,5,0,1,13,1,3,1,1,1
8995,15,2.1,1,1,0,1,12,1,5,1,1,3
10472,15,2.3,1,4,0,1,9,1,7,1,1,7
2480,15,2.7,1,4,0,1,12,0,5,1,1,2
6744,15,2.8,1,1,0,1,9,1,1,1,1,3
9571,15,3.8,1,2,0,1,10,1,2,1,1,4
1422,15,3.8,1,3,0,1,14,0,8,1,1,0
7839,15,4,1,4,0,1,9,1,6,1,1,1
9375,15,4.9,1,2,0,1,14,1,4,1,1,1
1457,15,4.9,1,2,0,1,12,0,6,1,1,2
1351,15,5,1,1,0,1,10,0,3,1,1,4
1454,15,5.1,1,2,0,1,12,0,3,1,1,0
9624,15,5.5,1,3,0,1,12,1,3,1,1,2
8314,15,5.6,1,4,0,1,12,1,5,1,1,0
6074,15,5.9,1,3,0,1,12,1,8,1,1,5
6750,15,5.9,1,5,0,1,15,1,1,1,1,5
10808,15,6.2,1,4,0,1,14,1,3,1,1,3
4600,15,6.3,1,1,0,1,15,0,8,1,1,3
4599,15,6.4,1,5,0,1,10,0,7,1,1,1
4252,15,6.5,1,3,0,1,10,0,2,1,1,6
8096,15,6.5,1,4,0,1,11,1,5,1,1,6
1834,15,6.6,1,4,0,1,10,0,5,1,1,0
9393,15,6.8,1,5,0,1,14,1,5,1,1,1
8120,15,8.2,1,3,0,1,11,1,7,1,1,0
7414,15,8.7,1,3,0,1,12,1,7,1,1,7
8894,15,8.7,1,3,0,1,11,1,7,1,1,6
64,15,8.9,1,2,0,1,15,0,1,1,1,5
1981,15,9.1,1,2,0,1,11,0,2,1,1,1
1892,15,9.3,1,2,0,1,12,0,8,1,1,5
295,15,9.4,1,4,0,1,11,0,4,1,1,1
1402,15,9.8,1,4,0,1,12,0,3,1,1,1
490,15,10.1,1,4,0,1,12,0,7,1,1,10
4071,15,10.3,1,5,0,1,14,0,6,1,1,1
9132,15,10.7,1,2,0,1,13,1,6,1,1,7
8642,15,11.5,1,4,0,1,15,1,4,1,1,7
2113,15,11.6,1,2,0,1,11,0,8,1,1,1
10273,15,11.9,1,3,0,1,12,1,3,1,1,1
3134,15,11.9,1,5,0,1,12,0,2,1,1,3
5000,15,12.2,1,4,0,1,11,0,3,1,1,3
3961,15,12.2,1,2,0,1,10,0,6,1,1,3
3621,15,12.6,1,3,0,1,14,0,6,1,1,0
6450,15,12.7,1,1,0,1,12,1,2,1,1,8
691,15,12.9,1,4,0,1,11,0,5,1,1,3
4838,15,12.9,1,5,0,1,12,0,3,1,1,4
3850,15,13.4,1,3,0,1,14,0,2,1,1,1
4818,15,14,1,4,0,1,14,0,6,1,1,9
1272,15,14.5,1,4,0,1,12,0,3,1,1,2
6530,15,14.6,1,3,0,1,9,1,6,1,1,10
4512,15,15.1,1,3,0,1,13,0,6,1,1,4
3595,15,15.1,1,2,0,1,11,0,1,1,1,0
5026,15,15.9,1,1,0,1,10,0,3,1,1,7
4539,15,16.1,1,1,0,1,13,0,5,1,1,3
7422,15,16.2,1,4,0,1,10,1,8,1,1,10
6960,15,16.4,1,1,0,1,9,1,1,1,1,9
1677,15,16.7,1,1,0,1,13,0,2,1,1,0
1411,15,16.8,1,1,0,1,12,0,1,1,1,4
10816,15,16.8,1,3,0,1,14,1,6,1,1,8
3090,15,16.9,1,5,0,1,10,0,8,1,1,6
642,15,17.5,1,3,0,1,10,0,2,1,1,10
8174,15,17.7,1,5,0,1,9,1,2,1,1,0
9297,15,18,1,2,0,1,12,1,7,1,1,1
3548,15,18.3,1,5,0,1,15,0,1,1,1,3
5848,15,19,1,3,0,1,13,1,4,1,1,1
3370,15,19.1,1,2,0,1,12,0,1,1,1,7
7932,15,19.4,1,1,0,1,11,1,3,1,1,8
9477,15,19.6,1,5,0,1,10,1,2,1,1,0
2332,15,20,1,5,0,1,13,0,7,1,1,2
10404,15,21.1,1,3,0,1,13,1,1,1,1,7
7979,15,21.3,1,5,0,1,9,1,2,1,1,3
10624,15,21.5,1,4,0,1,9,1,7,1,1,7
3254,15,22,1,3,0,1,14,0,6,1,1,1
4632,15,22.1,1,5,0,1,12,0,2,1,1,1
8384,15,22.5,1,3,0,1,12,1,4,1,1,9
1340,15,22.9,1,3,0,1,13,0,7,1,1,3
2684,15,23.4,1,1,0,1,12,0,1,1,1,0
3111,15,23.6,1,3,0,1,15,0,3,1,1,0
4425,15,23.8,1,2,0,1,14,0,6,1,1,2
3044,15,24.1,1,3,0,1,10,0,5,1,1,2
634,15,24.1,1,4,0,1,9,0,6,1,1,2
10275,15,24.2,1,4,0,1,13,1,3,1,1,2
1067,15,24.4,1,5,0,1,15,0,7,1,1,1
7983,15,24.4,1,3,0,1,10,1,1,1,1,4
4337,15,24.6,1,1,0,1,12,0,4,1,1,1
3977,15,25.3,1,1,0,1,12,0,8,1,1,4
10846,15,26.4,1,4,0,1,13,1,1,1,1,8
4335,15,26.5,1,2,0,1,10,0,8,1,1,3
3832,15,26.7,1,3,0,1,13,0,3,1,1,8
10798,15,26.9,1,5,0,1,13,1,5,1,1,9
3741,15,27.1,1,3,0,1,12,0,1,1,1,2
7886,15,27.1,1,5,0,1,9,1,2,1,1,8
10857,15,27.6,1,2,0,1,12,1,4,1,1,1
7995,15,27.6,1,1,0,1,12,1,1,1,1,3
1936,15,28.8,1,2,0,1,9,0,4,1,1,7
2786,15,28.8,1,5,0,1,9,0,8,1,1,0
8650,15,29.1,1,1,0,1,10,1,2,1,1,5
10263,15,29.2,1,3,0,1,12,1,5,1,1,4
2813,15,29.2,1,5,0,1,12,0,4,1,1,4
7380,15,29.4,1,5,0,1,12,1,6,1,1,6
1202,15,29.5,1,4,0,1,15,0,1,1,1,2
10544,15,29.6,1,5,0,1,11,1,3,1,1,5
3778,15,29.7,1,5,0,1,12,0,3,1,1,3
9618,15,29.8,1,3,0,1,14,1,4,1,1,9
1828,15,29.9,1,2,0,1,9,0,1,1,1,5
8906,15,29.9,1,5,0,1,13,1,5,1,1,7
3779,16,0.2,1,1,0,1,12,0,3,1,1,0
278,16,0.6,1,1,0,1,13,0,3,1,1,4
3959,16,0.8,1,1,0,1,12,0,8,1,1,3
2930,16,0.8,1,5,0,1,12,0,5,1,1,1
7715,16,0.8,1,5,0,1,14,1,3,1,1,0
3252,16,1,1,4,0,1,15,0,4,1,1,3
5869,16,1.2,1,2,0,1,9,1,4,1,1,1
4666,16,1.6,1,3,0,1,12,0,4,1,1,3
7625,16,1.7,1,3,0,1,15,1,3,1,1,4
1094,16,1.8,1,5,0,1,15,0,6,1,1,1
1502,16,1.9,1,4,0,1,10,0,1,1,1,6
2398,16,1.9,1,5,0,1,10,0,1,1,1,4
8129,16,2.1,1,2,0,1,9,1,4,1,1,3
8972,16,2.2,1,2,0,1,15,1,2,1,1,9
4776,16,2.3,1,5,0,1,11,0,3,1,1,0
6652,16,2.3,1,4,0,1,13,1,8,1,1,2
10744,16,2.3,1,3,0,1,12,1,7,1,1,8
7234,16,2.4,1,4,0,1,12,1,8,1,1,5
9596,16,2.7,1,1,0,1,14,1,2,1,1,6
1756,16,2.8,1,5,0,1,12,0,8,1,1,1
9960,16,2.8,1,4,0,1,12,1,8,1,1,1
2310,16,3.1,1,3,0,1,9,0,7,1,1,2
8782,16,3.3,1,4,0,1,12,1,6,1,1,7
10022,16,3.8,1,1,0,1,15,1,1,1,1,6
2564,16,4.2,1,3,0,1,9,0,5,1,1,6
5834,16,4.3,1,5,0,1,10,1,8,1,1,2
8488,16,4.7,1,2,0,1,10,1,5,1,1,10
8813,16,5.4,1,1,0,1,9,1,4,1,1,3
4394,16,6,1,2,0,1,13,0,5,1,1,5
1966,16,6.3,1,5,0,1,15,0,2,1,1,0
7026,16,6.6,1,3,0,1,10,1,4,1,1,10
4362,16,6.7,1,1,0,1,12,0,5,1,1,4
8297,16,6.9,1,4,0,1,14,1,2,1,1,0
2521,16,7.1,1,2,0,1,10,0,5,1,1,3
9002,16,7.3,1,1,0,1,10,1,4,1,1,9
10485,16,7.4,1,1,0,1,15,1,3,1,1,1
9948,16,7.4,1,1,0,1,12,1,3,1,1,2
4992,16,7.5,1,5,0,1,12,0,6,1,1,1
6664,16,7.8,1,3,0,1,15,1,2,1,1,10
392,16,7.9,1,4,0,1,12,0,5,1,1,7
5920,16,8.1,1,5,0,1,12,1,1,1,1,10
10442,16,8.3,1,2,0,1,15,1,3,1,1,2
8246,16,8.4,1,3,0,1,12,1,3,1,1,0
7037,16,8.8,1,5,0,1,9,1,8,1,1,2
838,16,9.3,1,4,0,1,13,0,3,1,1,4
8803,16,9.8,1,5,0,1,15,1,8,1,1,4
4654,16,10,1,3,0,1,13,0,7,1,1,4
5123,16,10.3,1,1,0,1,12,0,1,1,1,3
4885,16,10.4,1,1,0,1,11,0,7,1,1,1
3736,16,10.7,1,1,0,1,14,0,2,1,1,1
10118,16,11.4,1,3,0,1,13,1,7,1,1,1
8254,16,11.8,1,4,0,1,12,1,2,1,1,3
5790,16,11.9,1,5,0,1,11,1,8,1,1,8
2356,16,12.1,1,5,0,1,12,0,5,1,1,5
1450,16,12.2,1,2,0,1,14,0,7,1,1,8
8216,16,12.2,1,5,0,1,10,1,5,1,1,5
1241,16,12.7,1,4,0,1,11,0,6,1,1,2
6774,16,13.2,1,2,0,1,13,1,4,1,1,9
5964,16,13.6,1,5,0,1,12,1,8,1,1,2
8068,16,13.6,1,2,0,1,15,1,4,1,1,5
3360,16,14,1,2,0,1,10,0,1,1,1,5
4379,16,14,1,3,0,1,12,0,5,1,1,1
6964,16,15.2,1,2,0,1,10,1,6,1,1,0
8048,16,15.3,1,5,0,1,13,1,5,1,1,6
10326,16,15.7,1,2,0,1,12,1,6,1,1,1
3846,16,16,1,3,0,1,15,0,2,1,1,2
8808,16,16.3,1,5,0,1,12,1,4,1,1,5
8102,16,16.5,1,3,0,1,14,1,6,1,1,8
2134,16,17,1,1,0,1,13,0,7,1,1,3
4963,16,17.2,1,2,0,1,12,0,2,1,1,0
2196,16,18.2,1,5,0,1,12,0,4,1,1,7
9381,16,18.2,1,4,0,1,10,1,3,1,1,0
6577,16,18.9,1,1,0,1,14,1,5,1,1,4
4916,16,19,1,3,0,1,12,0,6,1,1,8
9092,16,19.2,1,3,0,1,15,1,8,1,1,9
5078,16,19.4,1,2,0,1,14,0,3,1,1,1
8829,16,19.5,1,3,0,1,14,1,6,1,1,3
3812,16,19.7,1,5,0,1,12,0,1,1,1,0
2756,16,19.8,1,4,0,1,10,0,1,1,1,2
5068,16,19.8,1,5,0,1,13,0,1,1,1,1
6483,16,20.1,1,5,0,1,15,1,4,1,1,4
2576,16,20.3,1,4,0,1,11,0,2,1,1,2
1925,16,20.5,1,5,0,1,12,0,3,1,1,2
1020,16,20.7,1,5,0,1,11,0,8,1,1,3
7535,16,21.1,1,1,0,1,10,1,7,1,1,2
2699,16,21.6,1,3,0,1,12,0,3,1,1,1
8428,16,21.6,1,3,0,1,11,1,1,1,1,9
10730,16,21.7,1,5,0,1,15,1,8,1,1,2
9076,16,22.3,1,1,0,1,11,1,6,1,1,7
6331,16,22.5,1,5,0,1,11,1,8,1,1,2
5161,16,22.9,1,3,0,1,14,0,4,1,1,4
7758,16,23.1,1,3,0,1,9,1,8,1,1,9
1657,16,23.3,1,4,0,1,14,0,6,1,1,1
8713,16,24.1,1,5,0,1,15,1,4,1,1,4
2728,16,24.1,1,2,0,1,12,0,4,1,1,7
3877,16,24.5,1,3,0,1,9,0,4,1,1,3
394,16,24.5,1,3,0,1,9,0,2,1,1,0
1484,16,24.7,1,2,0,1,14,0,7,1,1,3
5917,16,24.9,1,1,0,1,10,1,8,1,1,2
993,16,25.2,1,2,0,1,12,0,7,1,1,2
7174,16,25.2,1,3,0,1,9,1,1,1,1,0
3057,16,25.2,1,2,0,1,9,0,2,1,1,2
2696,16,25.2,1,3,0,1,12,0,7,1,1,2
7188,16,25.4,1,5,0,1,15,1,3,1,1,4
3923,16,25.5,1,4,0,1,14,0,7,1,1,2
452,16,25.6,1,4,0,1,14,0,7,1,1,9
6394,16,26,1,2,0,1,13,1,5,1,1,7
8049,16,26.1,1,1,0,1,13,1,2,1,1,4
3428,16,26.2,1,1,0,1,12,0,7,1,1,10
828,16,26.2,1,3,0,1,13,0,7,1,1,1
4353,16,26.3,1,1,0,1,10,0,2,1,1,4
5168,16,26.7,1,3,0,1,10,0,4,1,1,1
972,16,26.9,1,2,0,1,15,0,8,1,1,8
3120,16,27.4,1,4,0,1,9,0,8,1,1,4
8470,16,27.4,1,1,0,1,13,1,6,1,1,4
3522,16,27.5,1,3,0,1,12,0,1,1,1,8
7789,16,27.7,1,2,0,1,15,1,5,1,1,4
3584,16,27.8,1,3,0,1,9,0,6,1,1,9
2000,16,27.9,1,1,0,1,13,0,3,1,1,1
3065,16,28.1,1,5,0,1,12,0,2,1,1,4
2898,16,28.5,1,4,0,1,15,0,7,1,1,3
3602,16,28.6,1,3,0,1,12,0,5,1,1,8
3996,16,29,1,1,0,1,13,0,6,1,1,8
7572,16,29.3,1,5,0,1,12,1,1,1,1,6
3413,17,0.3,1,2,0,1,10,0,2,1,1,2
6359,17,0.4,1,2,0,1,11,1,1,1,1,0
7160,17,0.8,1,3,0,1,12,1,1,1,1,8
7388,17,1.3,1,5,0,1,12,1,2,1,1,9
4810,17,1.5,1,2,0,1,9,0,5,1,1,8
7619,17,2.9,1,1,0,1,9,1,2,1,1,4
8550,17,3.1,1,3,0,1,9,1,6,1,1,10
9848,17,3.1,1,1,0,1,13,1,2,1,1,3
10558,17,3.4,1,2,0,1,13,1,2,1,1,4
3461,17,3.9,1,2,0,1,11,0,5,1,1,0
7309,17,4,1,4,0,1,9,1,4,1,1,3
10836,17,4.1,1,4,0,1,12,1,3,1,1,1
4938,17,4.3,1,2,0,1,10,0,5,1,1,5
4791,17,5.1,1,2,0,1,11,0,4,1,1,2
4442,17,5.3,1,5,0,1,12,0,7,1,1,8
737,17,5.3,1,1,0,1,15,0,6,1,1,1
9900,17,6,1,3,0,1,11,1,7,1,1,3
4168,17,6.3,1,2,0,1,12,0,4,1,1,1
5908,17,7.1,1,3,0,1,12,1,1,1,1,1
9862,17,7.2,1,2,0,1,10,1,7,1,1,4
3394,17,7.9,1,2,0,1,11,0,5,1,1,4
2581,17,8,1,5,0,1,13,0,5,1,1,3
1220,17,8,1,2,0,1,11,0,6,1,1,2
4464,17,8,1,4,0,1,15,0,4,1,1,8
1022,17,8.8,1,2,0,1,13,0,6,1,1,4
4166,17,9.4,1,2,0,1,10,0,7,1,1,2
8564,17,9.4,1,1,0,1,12,1,5,1,1,7
3754,17,9.7,1,1,0,1,14,0,2,1,1,5
9295,17,9.8,1,2,0,1,12,1,3,1,1,2
2673,17,10.1,1,1,0,1,12,0,7,1,1,3
3814,17,10.2,1,1,0,1,13,0,2,1,1,6
9190,17,10.2,1,1,0,1,12,1,7,1,1,9
4952,17,10.4,1,2,0,1,10,0,7,1,1,5
6669,17,10.6,1,5,0,1,12,1,7,1,1,1
3300,17,10.6,1,1,0,1,10,0,1,1,1,7
8518,17,10.9,1,4,0,1,12,1,8,1,1,1
5878,17,10.9,1,3,0,1,11,1,6,1,1,7
5006,17,11.5,1,5,0,1,14,0,8,1,1,1
3082,17,11.8,1,3,0,1,12,0,8,1,1,0
4188,17,12.3,1,1,0,1,12,0,8,1,1,9
1324,17,12.5,1,1,0,1,12,0,1,1,1,7
3242,17,12.6,1,5,0,1,14,0,3,1,1,9
5736,17,12.8,1,4,0,1,14,1,4,1,1,9
2905,17,12.8,1,2,0,1,14,0,5,1,1,3
103,17,12.9,1,1,0,1,11,0,2,1,1,0
2296,17,14.2,1,1,0,1,13,0,2,1,1,1
4470,17,15.3,1,5,0,1,10,0,6,1,1,2
536,17,15.9,1,3,0,1,9,0,7,1,1,9
8790,17,16.1,1,5,0,1,9,1,2,1,1,5
10637,17,16.1,1,5,0,1,10,1,3,1,1,4
10055,17,16.2,1,2,0,1,9,1,1,1,1,1
2698,17,17,1,2,0,1,14,0,7,1,1,3
1119,17,17.3,1,2,0,1,14,0,7,1,1,2
4752,17,17.4,1,5,0,1,14,0,4,1,1,6
9018,17,17.5,1,3,0,1,12,1,2,1,1,8
10178,17,18.1,1,4,0,1,11,1,5,1,1,5
128,17,18.4,1,1,0,1,11,0,7,1,1,10
4767,17,19,1,5,0,1,11,0,6,1,1,4
8036,17,19.4,1,1,0,1,15,1,1,1,1,0
10338,17,19.4,1,4,0,1,12,1,4,1,1,8
10074,17,19.6,1,5,0,1,10,1,5,1,1,2
6738,17,19.8,1,5,0,1,9,1,1,1,1,10
4367,17,20.5,1,3,0,1,13,0,2,1,1,4
6160,17,20.5,1,3,0,1,13,1,7,1,1,7
6258,17,21.8,1,4,0,1,12,1,2,1,1,3
5130,17,22.1,1,5,0,1,9,0,7,1,1,0
2420,17,22.5,1,1,0,1,9,0,7,1,1,5
8358,17,22.7,1,3,0,1,11,1,1,1,1,1
5108,17,23,1,5,0,1,12,0,2,1,1,10
108,17,23.1,1,2,0,1,11,0,1,1,1,1
2231,17,23.1,1,1,0,1,11,0,3,1,1,2
6491,17,23.3,1,2,0,1,14,1,6,1,1,0
3219,17,24.4,1,5,0,1,12,0,2,1,1,0
650,17,24.5,1,4,0,1,12,0,1,1,1,6
9237,17,24.9,1,5,0,1,14,1,3,1,1,4
9612,17,25.1,1,2,0,1,11,1,7,1,1,7
4295,17,25.4,1,1,0,1,14,0,3,1,1,4
5231,17,25.5,1,3,0,1,9,0,6,1,1,3
2111,17,26.2,1,4,0,1,9,0,7,1,1,4
5571,17,26.5,1,5,0,1,9,1,6,1,1,4
10443,17,27.3,1,1,0,1,10,1,5,1,1,0
4035,17,27.4,1,1,0,1,13,0,5,1,1,3
900,17,27.6,1,2,0,1,14,0,2,1,1,0
1368,17,27.7,1,5,0,1,9,0,4,1,1,8
3104,17,27.7,1,4,0,1,9,0,4,1,1,4
5538,17,27.8,1,3,0,1,11,1,3,1,1,10
6043,17,28.3,1,5,0,1,9,1,8,1,1,1
2523,17,28.3,1,4,0,1,13,0,7,1,1,4
3358,17,28.4,1,4,0,1,9,0,1,1,1,7
8574,17,28.4,1,5,0,1,9,1,2,1,1,4
6634,17,28.7,1,2,0,1,9,1,4,1,1,0
2580,17,29.1,1,1,0,1,10,0,7,1,1,0
1399,17,29.2,1,2,0,1,9,0,5,1,1,1
7344,17,29.4,1,5,0,1,12,1,5,1,1,3
7468,17,29.7,1,4,0,1,11,1,8,1,1,1
2634,18,0.2,1,1,0,1,12,0,7,1,1,6
3549,18,1.1,1,2,0,1,15,0,2,1,1,0
124,18,1.2,1,5,0,1,15,0,6,1,1,8
5866,18,1.3,1,3,0,1,14,1,7,1,1,8
8708,18,1.4,1,3,0,1,11,1,3,1,1,8
9247,18,1.7,1,3,0,1,13,1,7,1,1,4
7741,18,1.9,1,2,0,1,15,1,2,1,1,0
9207,18,2.5,1,1,0,1,12,1,2,1,1,1
6977,18,2.6,1,4,0,1,15,1,8,1,1,1
9084,18,3.2,1,3,0,1,12,1,5,1,1,0
10513,18,3.4,1,2,0,1,14,1,8,1,1,2
7452,18,3.5,1,4,0,1,10,1,7,1,1,4
8037,18,3.9,1,2,0,1,9,1,3,1,1,3
3894,18,3.9,1,1,0,1,13,0,3,1,1,0
1976,18,4,1,4,0,1,12,0,6,1,1,4
3144,18,4.3,1,1,0,1,14,0,6,1,1,6
9887,18,4.6,1,4,0,1,9,1,4,1,1,4
1335,18,4.7,1,4,0,1,13,0,7,1,1,1
10494,18,4.8,1,3,0,1,11,1,5,1,1,0
8099,18,4.9,1,1,0,1,10,1,3,1,1,1
6171,18,5,1,5,0,1,13,1,8,1,1,2
2808,18,5,1,5,0,1,12,0,6,1,1,2
3604,18,5.1,1,4,0,1,9,0,8,1,1,2
5671,18,5.2,1,5,0,1,12,1,5,1,1,0
8346,18,5.7,1,2,0,1,10,1,6,1,1,5
6913,18,6.2,1,3,0,1,12,1,1,1,1,2
8085,18,6.2,1,1,0,1,9,1,4,1,1,0
1236,18,6.3,1,1,0,1,9,0,7,1,1,0
4730,18,6.4,1,3,0,1,14,0,6,1,1,2
8964,18,6.6,1,4,0,1,10,1,7,1,1,9
1919,18,6.9,1,3,0,1,13,0,4,1,1,4
7838,18,7.3,1,2,0,1,12,1,6,1,1,1
8878,18,8,1,4,0,1,9,1,7,1,1,8
1590,18,8.1,1,4,0,1,12,0,8,1,1,10
5348,18,8.4,1,3,0,1,14,0,6,1,1,2
9233,18,8.8,1,3,0,1,12,1,3,1,1,2
4490,18,9.7,1,2,0,1,10,0,2,1,1,10
7454,18,10,1,1,0,1,10,1,1,1,1,3
7763,18,10.2,1,1,0,1,12,1,1,1,1,2
5140,18,10.3,1,4,0,1,15,0,6,1,1,0
10477,18,10.5,1,3,0,1,14,1,1,1,1,1
5686,18,11.2,1,4,0,1,12,1,6,1,1,8
7946,18,11.2,1,2,0,1,13,1,5,1,1,3
486,18,11.6,1,5,0,1,12,0,5,1,1,1
7639,18,11.8,1,3,0,1,11,1,5,1,1,1
2461,18,12,1,2,0,1,12,0,4,1,1,3
646,18,12,1,5,0,1,15,0,1,1,1,2
5216,18,12.2,1,4,0,1,14,0,7,1,1,8
5874,18,12.4,1,1,0,1,11,1,8,1,1,0
976,18,12.5,1,3,0,1,10,0,6,1,1,2
8705,18,12.7,1,2,0,1,11,1,7,1,1,3
6787,18,12.8,1,1,0,1,9,1,7,1,1,0
10662,18,12.9,1,3,0,1,11,1,7,1,1,9
9726,18,13.2,1,5,0,1,15,1,7,1,1,0
527,18,13.3,1,5,0,1,10,0,4,1,1,1
8976,18,13.6,1,5,0,1,11,1,3,1,1,1
872,18,14,1,5,0,1,15,0,8,1,1,2
6970,18,14.1,1,2,0,1,14,1,6,1,1,2
2214,18,14.3,1,3,0,1,11,0,8,1,1,6
2248,18,14.6,1,2,0,1,9,0,6,1,1,2
8637,18,14.7,1,5,0,1,9,1,4,1,1,3
4034,18,15.1,1,1,0,1,15,0,6,1,1,0
6765,18,15.5,1,5,0,1,12,1,6,1,1,4
864,18,16,1,5,0,1,12,0,3,1,1,8
2110,18,16.4,1,3,0,1,13,0,3,1,1,1
9073,18,16.5,1,3,0,1,15,1,3,1,1,2
8656,18,16.6,1,3,0,1,15,1,2,1,1,8
8486,18,17,1,2,0,1,15,1,4,1,1,8
10859,18,17.4,1,5,0,1,11,1,1,1,1,2
2304,18,17.7,1,4,0,1,10,0,7,1,1,8
3490,18,17.9,1,1,0,1,14,0,5,1,1,4
10703,18,18.1,1,5,0,1,10,1,5,1,1,2
10452,18,18.2,1,3,0,1,12,1,7,1,1,4
4494,18,18.3,1,3,0,1,9,0,2,1,1,6
118,18,18.7,1,4,0,1,9,0,8,1,1,6
977,18,19.5,1,2,0,1,11,0,2,1,1,1
8882,18,19.5,1,4,0,1,9,1,5,1,1,7
2940,18,19.6,1,2,0,1,9,0,6,1,1,0
5624,18,19.6,1,4,0,1,13,1,7,1,1,3
3492,18,19.7,1,1,0,1,15,0,1,1,1,6
8280,18,19.7,1,2,0,1,13,1,3,1,1,9
10194,18,20.3,1,5,0,1,12,1,8,1,1,7
3938,18,20.7,1,2,0,1,12,0,8,1,1,7
4332,18,21,1,4,0,1,12,0,1,1,1,6
8580,18,21.1,1,4,0,1,15,1,2,1,1,2
5673,18,21.4,1,5,0,1,14,1,5,1,1,1
9797,18,22.1,1,1,0,1,15,1,2,1,1,3
9331,18,22.3,1,4,0,1,15,1,6,1,1,0
5574,18,22.9,1,4,0,1,13,1,5,1,1,5
2084,18,23.1,1,5,0,1,11,0,1,1,1,10
436,18,23.4,1,2,0,1,12,0,2,1,1,8
1133,18,23.6,1,2,0,1,14,0,4,1,1,3
1371,18,23.6,1,2,0,1,12,0,4,1,1,0
2732,18,23.9,1,3,0,1,12,0,3,1,1,8
6434,18,23.9,1,2,0,1,15,1,6,1,1,8
6528,18,23.9,1,5,0,1,14,1,2,1,1,8
844,18,24,1,1,0,1,12,0,8,1,1,10
4032,18,24.2,1,3,0,1,14,0,3,1,1,6
1712,18,24.2,1,4,0,1,11,0,3,1,1,1
4899,18,24.8,1,3,0,1,14,0,7,1,1,2
7456,18,24.9,1,4,0,1,9,1,4,1,1,1
9060,18,25.2,1,5,0,1,12,1,3,1,1,4
628,18,25.4,1,2,0,1,13,0,3,1,1,0
1458,18,25.4,1,1,0,1,13,0,6,1,1,3
10779,18,25.7,1,5,0,1,14,1,7,1,1,1
8874,18,25.8,1,2,0,1,13,1,2,1,1,0
1126,18,26,1,5,0,1,10,0,6,1,1,1
1134,18,26.1,1,5,0,1,14,0,8,1,1,1
4737,18,26.1,1,5,0,1,12,0,8,1,1,4
1380,18,26.7,1,5,0,1,10,0,8,1,1,7
8930,18,27.3,1,4,0,1,9,1,4,1,1,2
7927,18,27.3,1,2,0,1,15,1,1,1,1,3
7080,18,27.7,1,2,0,1,11,1,5,1,1,7
5665,18,28,1,3,0,1,15,1,8,1,1,2
7972,18,28.2,1,3,0,1,14,1,7,1,1,5
9007,18,28.3,1,2,0,1,14,1,4,1,1,1
1346,18,28.6,1,5,0,1,12,0,7,1,1,0
8695,18,29.7,1,4,0,1,14,1,6,1,1,2
9536,18,29.9,1,1,0,1,9,1,7,1,1,1
3866,19,0.2,1,3,0,1,12,0,1,1,1,0
9480,19,0.3,1,3,0,1,9,1,5,1,1,7
4482,19,0.7,1,3,0,1,14,0,7,1,1,4
2002,19,0.7,1,2,0,1,9,0,8,1,1,8
1824,19,0.9,1,4,0,1,15,0,5,1,1,7
878,19,1.1,1,1,0,1,9,0,8,1,1,5
5258,19,1.5,1,3,0,1,12,0,5,1,1,0
10689,19,1.6,1,1,0,1,9,1,6,1,1,4
4718,19,1.9,1,4,0,1,15,0,4,1,1,1
10623,19,1.9,1,4,0,1,12,1,7,1,1,1
3498,19,1.9,1,3,0,1,12,0,6,1,1,4
8609,19,2,1,1,0,1,14,1,7,1,1,0
7486,19,2.6,1,1,0,1,9,1,3,1,1,4
2518,19,2.7,1,2,0,1,14,0,4,1,1,10
438,19,3.4,1,5,0,1,15,0,2,1,1,3
7351,19,3.7,1,1,0,1,12,1,3,1,1,3
4658,19,3.7,1,5,0,1,11,0,7,1,1,9
338,19,3.9,1,2,0,1,11,0,2,1,1,6
10421,19,4.4,1,5,0,1,12,1,2,1,1,2
7192,19,5.1,1,5,0,1,10,1,3,1,1,7
7480,19,5.8,1,4,0,1,13,1,7,1,1,9
928,19,5.9,1,3,0,1,13,0,1,1,1,7
7958,19,5.9,1,5,0,1,15,1,5,1,1,0
9817,19,5.9,1,5,0,1,12,1,1,1,1,4
5553,19,6.1,1,4,0,1,9,1,4,1,1,0
5558,19,6.2,1,5,0,1,11,1,1,1,1,9
7218,19,6.9,1,4,0,1,13,1,4,1,1,10
9826,19,7.6,1,1,0,1,9,1,2,1,1,2
3907,19,7.6,1,4,0,1,11,0,3,1,1,2
4159,19,7.6,1,5,0,1,15,0,4,1,1,2
3690,19,8,1,1,0,1,9,0,1,1,1,2
5486,19,8.7,1,5,0,1,13,1,1,1,1,7
1390,19,8.7,1,5,0,1,13,0,4,1,1,2
7987,19,8.9,1,4,0,1,15,1,3,1,1,0
6811,19,8.9,1,5,0,1,9,1,7,1,1,3
2068,19,9.2,1,4,0,1,12,0,2,1,1,10
2760,19,9.2,1,1,0,1,13,0,2,1,1,6
1870,19,9.7,1,2,0,1,10,0,3,1,1,7
8114,19,9.8,1,3,0,1,14,1,6,1,1,1
4475,19,10.1,1,5,0,1,12,0,1,1,1,3
6618,19,10.6,1,4,0,1,10,1,7,1,1,4
2275,19,10.8,1,2,0,1,10,0,4,1,1,0
4706,19,11.3,1,5,0,1,12,0,5,1,1,5
10427,19,11.4,1,4,0,1,12,1,7,1,1,0
9644,19,11.4,1,3,0,1,15,1,3,1,1,2
4039,19,11.5,1,4,0,1,11,0,7,1,1,2
2344,19,11.9,1,3,0,1,11,0,6,1,1,10
1928,19,12.1,1,3,0,1,10,0,5,1,1,3
6466,19,12.1,1,5,0,1,12,1,5,1,1,7
8547,19,12.1,1,1,0,1,10,1,6,1,1,2
9356,19,12.5,1,3,0,1,9,1,2,1,1,2
5870,19,12.8,1,2,0,1,15,1,4,1,1,10
5264,19,12.8,1,5,0,1,11,0,2,1,1,8
10378,19,12.9,1,3,0,1,11,1,4,1,1,10
8020,19,13,1,3,0,1,14,1,3,1,1,7
6876,19,13.2,1,5,0,1,10,1,7,1,1,10
2776,19,13.6,1,1,0,1,15,0,1,1,1,1
2583,19,14,1,4,0,1,9,0,7,1,1,0
6714,19,15.1,1,3,0,1,10,1,7,1,1,3
7941,19,15.2,1,3,0,1,10,1,5,1,1,3
8514,19,15.4,1,2,0,1,12,1,1,1,1,10
9198,19,15.5,1,3,0,1,11,1,1,1,1,2
4219,19,16.1,1,3,0,1,13,0,4,1,1,3
1666,19,16.3,1,4,0,1,14,0,1,1,1,4
4110,19,16.3,1,4,0,1,12,0,7,1,1,2
1816,19,16.8,1,1,0,1,11,0,6,1,1,7
9072,19,16.9,1,1,0,1,10,1,1,1,1,7
9043,19,17,1,4,0,1,13,1,4,1,1,4
3268,19,17.5,1,4,0,1,14,0,1,1,1,10
9763,19,17.6,1,4,0,1,12,1,3,1,1,3
556,19,17.6,1,3,0,1,14,0,4,1,1,4
9812,19,17.6,1,5,0,1,12,1,3,1,1,6
6543,19,18.2,1,4,0,1,12,1,8,1,1,1
4057,19,18.6,1,5,0,1,12,0,8,1,1,3
8577,19,18.7,1,4,0,1,15,1,2,1,1,1
575,19,18.7,1,5,0,1,11,0,7,1,1,3
9066,19,19.1,1,5,0,1,11,1,2,1,1,9
635,19,19.4,1,1,0,1,12,0,3,1,1,4
6681,19,19.4,1,3,0,1,12,1,6,1,1,2
636,19,19.6,1,4,0,1,15,0,5,1,1,0
3018,19,19.8,1,1,0,1,11,0,7,1,1,8
2818,19,19.9,1,3,0,1,14,0,4,1,1,7
6343,19,20,1,4,0,1,11,1,5,1,1,3
9042,19,20.1,1,4,0,1,10,1,4,1,1,0
5300,19,20.1,1,1,0,1,12,0,3,1,1,3
2140,19,20.8,1,4,0,1,14,0,5,1,1,7
3620,19,20.9,1,2,0,1,15,0,2,1,1,2
2545,19,20.9,1,1,0,1,13,0,4,1,1,2
2862,19,21.1,1,5,0,1,13,0,6,1,1,9
6832,19,21.7,1,4,0,1,12,1,3,1,1,0
1836,19,22.3,1,3,0,1,12,0,8,1,1,10
2434,19,22.5,1,1,0,1,13,0,2,1,1,7
6237,19,22.6,1,4,0,1,10,1,3,1,1,3
2096,19,22.7,1,4,0,1,10,0,7,1,1,8
9397,19,23,1,2,0,1,12,1,5,1,1,0
3478,19,23.2,1,1,0,1,11,0,6,1,1,2
8160,19,23.2,1,5,0,1,12,1,5,1,1,8
3407,19,24.2,1,3,0,1,15,0,2,1,1,1
10231,19,24.2,1,1,0,1,14,1,8,1,1,0
10286,19,24.2,1,3,0,1,10,1,6,1,1,6
8732,19,24.7,1,2,0,1,11,1,1,1,1,5
8673,19,24.8,1,4,0,1,12,1,5,1,1,3
6976,19,25,1,3,0,1,12,1,8,1,1,10
6860,19,25.2,1,2,0,1,14,1,8,1,1,5
7584,19,25.7,1,3,0,1,12,1,2,1,1,1
412,19,25.7,1,4,0,1,12,0,7,1,1,2
3529,19,25.9,1,1,0,1,11,0,4,1,1,0
3889,19,25.9,1,3,0,1,9,0,4,1,1,4
1291,19,26.2,1,2,0,1,11,0,1,1,1,3
6812,19,26.3,1,2,0,1,15,1,3,1,1,1
5388,19,26.5,1,5,0,1,15,0,7,1,1,1
3611,19,26.7,1,5,0,1,13,0,6,1,1,2
6802,19,27.3,1,3,0,1,12,1,6,1,1,10
6170,19,27.3,1,4,0,1,10,1,8,1,1,9
7328,19,27.5,1,2,0,1,12,1,2,1,1,3
9436,19,27.6,1,4,0,1,10,1,5,1,1,3
1964,19,27.9,1,5,0,1,10,0,6,1,1,2
9370,19,28.2,1,1,0,1,13,1,1,1,1,0
6795,19,28.9,1,1,0,1,12,1,2,1,1,4
6826,19,29.2,1,2,0,1,9,1,8,1,1,6
4568,19,29.5,1,5,0,1,11,0,4,1,1,9
7412,20,0.1,1,2,0,1,12,1,6,1,1,4
6886,20,0.4,1,4,0,1,11,1,4,1,1,1
5838,20,0.4,1,3,0,1,15,1,1,1,1,5
5191,20,0.4,1,1,0,1,12,0,1,1,1,0
1746,20,0.4,1,1,0,1,14,0,2,1,1,6
462,20,1.3,1,2,0,1,9,0,8,1,1,5
4114,20,1.4,1,4,0,1,14,0,1,1,1,6
7236,20,1.5,1,1,0,1,11,1,2,1,1,2
6214,20,2,1,2,0,1,9,1,7,1,1,9
4686,20,2.4,1,2,0,1,9,0,7,1,1,1
5007,20,2.6,1,5,0,1,12,0,6,1,1,0
6220,20,2.7,1,4,0,1,10,1,2,1,1,0
5580,20,2.8,1,4,0,1,13,1,2,1,1,7
1580,20,3.5,1,3,0,1,11,0,7,1,1,8
3644,20,4.2,1,5,0,1,13,0,1,1,1,7
8965,20,4.2,1,2,0,1,12,1,6,1,1,1
1406,20,4.5,1,2,0,1,15,0,1,1,1,4
9254,20,4.6,1,4,0,1,14,1,2,1,1,0
2046,20,5.1,1,5,0,1,14,0,6,1,1,3
8192,20,5.4,1,3,0,1,13,1,4,1,1,6
8142,20,5.6,1,4,0,1,11,1,4,1,1,4
2069,20,5.7,1,2,0,1,13,0,4,1,1,1
9156,20,6,1,3,0,1,12,1,5,1,1,0
494,20,6,1,1,0,1,11,0,8,1,1,3
10189,20,6,1,3,0,1,11,1,4,1,1,1
5901,20,6.2,1,4,0,1,14,1,4,1,1,2
3950,20,6.3,1,5,0,1,10,0,8,1,1,4
8794,20,6.3,1,1,0,1,10,1,1,1,1,7
2795,20,6.7,1,3,0,1,12,0,2,1,1,3
4028,20,6.9,1,4,0,1,11,0,5,1,1,1
6048,20,7.1,1,4,0,1,14,1,6,1,1,3
3140,20,7.1,1,3,0,1,11,0,6,1,1,0
2603,20,7.1,1,5,0,1,11,0,7,1,1,2
3656,20,7.2,1,3,0,1,11,0,5,1,1,3
2744,20,7.2,1,2,0,1,15,0,1,1,1,0
780,20,7.4,1,1,0,1,9,0,4,1,1,0
10635,20,7.7,1,3,0,1,15,1,8,1,1,2
6056,20,7.9,1,2,0,1,9,1,6,1,1,4
9544,20,8,1,5,0,1,14,1,4,1,1,8
46,20,8,1,4,0,1,11,0,4,1,1,1
1751,20,8,1,4,0,1,15,0,5,1,1,0
5456,20,8.4,1,3,0,1,9,1,3,1,1,3
860,20,8.7,1,3,0,1,11,0,3,1,1,10
417,20,9,1,4,0,1,10,0,7,1,1,1
9856,20,9.3,1,4,0,1,14,1,5,1,1,4
7536,20,9.7,1,4,0,1,15,1,8,1,1,8
5351,20,10,1,2,0,1,12,0,3,1,1,1
10698,20,10.2,1,3,0,1,13,1,5,1,1,10
5692,20,10.2,1,3,0,1,11,1,3,1,1,2
5112,20,10.9,1,4,0,1,10,0,6,1,1,3
1108,20,10.9,1,4,0,1,14,0,2,1,1,0
1461,20,11,1,3,0,1,15,0,5,1,1,2
2120,20,11.1,1,3,0,1,15,0,4,1,1,1
7386,20,11.5,1,3,0,1,12,1,7,1,1,6
2906,20,11.8,1,2,0,1,12,0,2,1,1,9
6312,20,11.9,1,1,0,1,12,1,6,1,1,5
920,20,12.1,1,2,0,1,12,0,3,1,1,6
468,20,12.7,1,2,0,1,11,0,6,1,1,1
7124,20,12.9,1,1,0,1,12,1,1,1,1,0
9771,20,13.1,1,5,0,1,9,1,4,1,1,1
8405,20,13.4,1,5,0,1,10,1,4,1,1,2
1975,20,13.4,1,4,0,1,13,0,7,1,1,3
416,20,14,1,2,0,1,15,0,4,1,1,4
7764,20,14.5,1,5,0,1,14,1,7,1,1,3
1161,20,14.6,1,2,0,1,15,0,3,1,1,4
3998,20,14.9,1,4,0,1,9,0,5,1,1,4
4393,20,15,1,3,0,1,10,0,4,1,1,3
10153,20,15.2,1,1,0,1,10,1,2,1,1,0
10370,20,15.3,1,4,0,1,11,1,6,1,1,0
4065,20,15.4,1,5,0,1,15,0,2,1,1,3
9105,20,15.6,1,4,0,1,12,1,3,1,1,1
6349,20,15.8,1,4,0,1,14,1,6,1,1,4
4136,20,15.8,1,1,0,1,15,0,1,1,1,8
4826,20,15.9,1,1,0,1,10,0,2,1,1,2
6907,20,16,1,1,0,1,14,1,8,1,1,2
8988,20,16.3,1,2,0,1,13,1,2,1,1,9
5278,20,16.4,1,5,0,1,15,0,1,1,1,3
8365,20,16.4,1,5,0,1,10,1,1,1,1,2
7791,20,17,1,2,0,1,14,1,8,1,1,1
5954,20,17,1,4,0,1,9,1,5,1,1,9
4298,20,17.5,1,3,0,1,10,0,2,1,1,3
9894,20,17.6,1,4,0,1,14,1,1,1,1,6
49,20,18,1,2,0,1,10,0,3,1,1,3
7006,20,18.1,1,1,0,1,12,1,8,1,1,5
3308,20,18.7,1,5,0,1,10,0,7,1,1,10
9606,20,18.8,1,5,0,1,12,1,6,1,1,3
3166,20,18.9,1,3,0,1,9,0,1,1,1,3
5209,20,19.2,1,3,0,1,11,0,1,1,1,4
6636,20,19.2,1,4,0,1,9,1,7,1,1,3
7620,20,19.3,1,3,0,1,13,1,1,1,1,9
7183,20,19.6,1,1,0,1,12,1,5,1,1,1
6965,20,19.6,1,1,0,1,14,1,7,1,1,4
1917,20,19.8,1,1,0,1,15,0,5,1,1,2
5226,20,20.1,1,1,0,1,11,0,5,1,1,4
3784,20,20.2,1,4,0,1,10,0,4,1,1,5
7420,20,20.6,1,5,0,1,15,1,8,1,1,3
5428,20,20.6,1,2,0,1,13,1,7,1,1,2
6404,20,21.3,1,2,0,1,14,1,2,1,1,8
5262,20,21.5,1,4,0,1,9,0,6,1,1,0
2051,20,21.8,1,1,0,1,9,0,8,1,1,3
5041,20,22,1,1,0,1,15,0,8,1,1,4
7172,20,22.8,1,1,0,1,12,1,4,1,1,10
3486,20,23.7,1,4,0,1,12,0,5,1,1,2
6499,20,23.7,1,1,0,1,9,1,7,1,1,3
2415,20,24.1,1,5,0,1,10,0,1,1,1,0
10298,20,24.2,1,5,0,1,11,1,1,1,1,10
8079,20,24.4,1,2,0,1,12,1,4,1,1,4
7126,20,24.6,1,4,0,1,12,1,5,1,1,9
601,20,24.7,1,2,0,1,12,0,1,1,1,4
6628,20,24.8,1,4,0,1,9,1,7,1,1,3
1101,20,25.1,1,4,0,1,15,0,7,1,1,1
3367,20,25.7,1,1,0,1,12,0,1,1,1,0
7474,20,25.7,1,5,0,1,14,1,5,1,1,1
5426,20,25.8,1,3,0,1,13,1,2,1,1,2
3576,20,26.2,1,4,0,1,15,0,4,1,1,9
10414,20,26.3,1,5,0,1,12,1,1,1,1,0
7649,20,26.5,1,5,0,1,11,1,2,1,1,3
9374,20,26.7,1,2,0,1,13,1,8,1,1,10
6544,20,27.2,1,2,0,1,15,1,5,1,1,7
6656,20,27.5,1,3,0,1,12,1,1,1,1,0
5516,20,27.5,1,3,0,1,15,1,3,1,1,4
5554,20,27.6,1,5,0,1,9,1,4,1,1,7
6658,20,27.6,1,2,0,1,10,1,3,1,1,6
1578,20,27.8,1,3,0,1,10,0,3,1,1,4
6956,20,27.9,1,5,0,1,12,1,2,1,1,8
8668,20,28.6,1,4,0,1,11,1,4,1,1,7
3724,20,28.7,1,1,0,1,12,0,4,1,1,4
6057,20,28.7,1,5,0,1,15,1,4,1,1,0
9011,20,28.8,1,4,0,1,9,1,2,1,1,4
351,20,28.9,1,1,0,1,11,0,7,1,1,3
10436,20,29,1,2,0,1,12,1,3,1,1,8
2094,20,29,1,2,0,1,12,0,8,1,1,7
9358,20,29.4,1,3,0,1,12,1,5,1,1,1
6144,20,29.6,1,3,0,1,11,1,3,1,1,8
5856,21,0.3,1,1,0,1,13,1,1,1,1,5
8044,21,1.5,1,4,0,1,11,1,8,1,1,0
716,21,1.5,1,2,0,1,11,0,4,1,1,9
1378,21,1.7,1,2,0,1,12,0,6,1,1,4
6234,21,2.7,1,5,0,1,10,1,3,1,1,7
9758,21,2.8,1,3,0,1,12,1,5,1,1,5
524,21,2.9,1,3,0,1,15,0,3,1,1,8
7733,21,3,1,3,0,1,15,1,4,1,1,2
1010,21,3.8,1,1,0,1,10,0,7,1,1,4
10820,21,4,1,3,0,1,15,1,1,1,1,8
3336,21,4.2,1,5,0,1,9,0,4,1,1,2
3294,21,4.7,1,2,0,1,15,0,6,1,1,3
4451,21,5.8,1,1,0,1,11,0,6,1,1,2
8370,21,6,1,5,0,1,15,1,5,1,1,3
8467,21,6.1,1,5,0,1,10,1,4,1,1,3
8553,21,6.5,1,4,0,1,10,1,5,1,1,1
3642,21,6.8,1,1,0,1,9,0,3,1,1,3
1604,21,6.9,1,5,0,1,15,0,4,1,1,3
7549,21,7.1,1,4,0,1,11,1,3,1,1,1
2312,21,7.7,1,1,0,1,10,0,2,1,1,6
1418,21,8.1,1,5,0,1,9,0,6,1,1,1
3354,21,8.4,1,1,0,1,14,0,7,1,1,10
3862,21,8.4,1,5,0,1,9,0,4,1,1,5
4989,21,8.9,1,1,0,1,10,0,7,1,1,1
2426,21,8.9,1,5,0,1,13,0,8,1,1,9
10198,21,9,1,3,0,1,13,1,5,1,1,4
4581,21,9.5,1,1,0,1,15,0,1,1,1,2
1877,21,9.7,1,4,0,1,12,0,4,1,1,4
221,21,9.7,1,5,0,1,11,0,6,1,1,4
582,21,9.9,1,3,0,1,15,0,7,1,1,5
10040,21,10,1,4,0,1,13,1,3,1,1,4
764,21,10.3,1,4,0,1,15,0,8,1,1,5
3546,21,10.4,1,2,0,1,15,0,5,1,1,7
4402,21,11.1,1,2,0,1,14,0,1,1,1,7
2916,21,11.5,1,3,0,1,11,0,8,1,1,8
63,21,11.7,1,1,0,1,14,0,7,1,1,1
5636,21,11.8,1,5,0,1,13,1,2,1,1,6
6453,21,12,1,4,0,1,13,1,4,1,1,3
1058,21,12,1,5,0,1,12,0,1,1,1,10
630,21,12.4,1,2,0,1,10,0,8,1,1,5
3852,21,12.8,1,2,0,1,11,0,8,1,1,7
10480,21,13.1,1,4,0,1,14,1,5,1,1,0
6261,21,13.2,1,1,0,1,12,1,6,1,1,1
3315,21,13.2,1,4,0,1,11,0,4,1,1,3
903,21,13.2,1,5,0,1,14,0,5,1,1,0
10848,21,13.2,1,5,0,1,13,1,8,1,1,1
3234,21,13.2,1,3,0,1,11,0,5,1,1,7
6019,21,13.4,1,2,0,1,12,1,3,1,1,4
9852,21,14.1,1,2,0,1,10,1,1,1,1,1
3859,21,14.1,1,5,0,1,15,0,7,1,1,2
10529,21,14.2,1,2,0,1,12,1,2,1,1,0
6244,21,14.3,1,5,0,1,9,1,7,1,1,8
10464,21,14.6,1,2,0,1,9,1,8,1,1,0
5025,21,14.9,1,5,0,1,9,0,2,1,1,1
3960,21,15,1,5,0,1,10,0,8,1,1,1
5948,21,15,1,1,0,1,13,1,3,1,1,5
1938,21,15.4,1,4,0,1,12,0,3,1,1,0
6897,21,16.1,1,5,0,1,9,1,5,1,1,1
3266,21,16.2,1,5,0,1,13,0,3,1,1,3
881,21,16.2,1,1,0,1,11,0,4,1,1,3
3982,21,16.4,1,3,0,1,14,0,5,1,1,3
9888,21,16.9,1,3,0,1,13,1,1,1,1,4
9984,21,18,1,3,0,1,14,1,3,1,1,3
1278,21,18,1,2,0,1,10,0,1,1,1,9
7493,21,18.1,1,5,0,1,10,1,7,1,1,4
4721,21,18.6,1,5,0,1,12,0,4,1,1,1
10428,21,18.8,1,2,0,1,10,1,7,1,1,6
1556,21,19.8,1,2,0,1,14,0,8,1,1,3
948,21,20.5,1,2,0,1,12,0,8,1,1,10
8406,21,20.7,1,2,0,1,12,1,6,1,1,10
3715,21,20.8,1,1,0,1,14,0,3,1,1,3
5478,21,21.1,1,5,0,1,12,1,8,1,1,1
3100,21,21.2,1,1,0,1,12,0,3,1,1,6
2032,21,21.7,1,2,0,1,15,0,5,1,1,1
10358,21,22,1,3,0,1,13,1,8,1,1,0
414,21,22.2,1,5,0,1,12,0,2,1,1,6
7125,21,22.2,1,5,0,1,12,1,6,1,1,0
7319,21,22.3,1,3,0,1,10,1,6,1,1,0
4009,21,22.7,1,4,0,1,9,0,3,1,1,1
9678,21,22.8,1,5,0,1,15,1,4,1,1,9
10743,21,23.4,1,3,0,1,14,1,1,1,1,4
9886,21,23.4,1,2,0,1,14,1,3,1,1,10
6566,21,23.6,1,5,0,1,9,1,4,1,1,7
2658,21,23.7,1,3,0,1,13,0,2,1,1,5
10420,21,24,1,3,0,1,11,1,1,1,1,2
1546,21,24.1,1,5,0,1,10,0,4,1,1,5
1525,21,24.2,1,1,0,1,12,0,4,1,1,3
3264,21,24.5,1,1,0,1,15,0,1,1,1,10
3845,21,24.8,1,5,0,1,9,0,1,1,1,2
4462,21,24.9,1,4,0,1,14,0,6,1,1,2
10226,21,24.9,1,4,0,1,10,1,4,1,1,8
6760,21,25.7,1,3,0,1,9,1,7,1,1,9
8400,21,25.8,1,3,0,1,12,1,2,1,1,4
4164,21,26,1,1,0,1,9,0,1,1,1,6
10641,21,26.2,1,2,0,1,14,1,6,1,1,1
8733,21,26.6,1,4,0,1,11,1,7,1,1,3
6274,21,26.7,1,2,0,1,11,1,8,1,1,2
6556,21,26.8,1,4,0,1,12,1,6,1,1,2
8776,21,27,1,1,0,1,14,1,3,1,1,2
1639,21,27.2,1,2,0,1,14,0,3,1,1,1
8088,21,27.3,1,2,0,1,12,1,2,1,1,6
968,21,27.5,1,1,0,1,13,0,7,1,1,9
3365,21,27.7,1,3,0,1,14,0,5,1,1,4
9562,21,28.7,1,2,0,1,12,1,5,1,1,4
7228,21,28.8,1,5,0,1,14,1,1,1,1,3
2396,21,29.2,1,2,0,1,12,0,1,1,1,4
9988,21,29.2,1,1,0,1,12,1,6,1,1,5
4852,21,29.8,1,1,0,1,14,0,5,1,1,9
1949,21,29.8,1,5,0,1,13,0,3,1,1,2
2330,21,30,1,3,0,1,9,0,3,1,1,6
9857,22,0.2,1,5,0,1,15,1,8,1,1,1
6708,22,0.9,1,5,0,1,10,1,2,1,1,5
2778,22,1,1,4,0,1,15,0,5,1,1,7
35,22,1.3,1,1,0,1,12,0,8,1,1,0
2811,22,1.5,1,2,0,1,12,0,5,1,1,2
10258,22,1.9,1,2,0,1,14,1,8,1,1,4
10061,22,2.8,1,3,0,1,12,1,8,1,1,1
8361,22,3.3,1,4,0,1,14,1,2,1,1,4
1649,22,3.7,1,5,0,1,12,0,3,1,1,0
1972,22,3.7,1,2,0,1,12,0,7,1,1,4
9884,22,3.9,1,5,0,1,11,1,6,1,1,1
2715,22,4,1,2,0,1,12,0,7,1,1,1
7312,22,4.4,1,4,0,1,12,1,6,1,1,10
9635,22,4.7,1,4,0,1,12,1,4,1,1,1
7634,22,4.7,1,2,0,1,15,1,6,1,1,2
6722,22,4.7,1,4,0,1,12,1,5,1,1,7
2021,22,4.9,1,3,0,1,14,0,2,1,1,3
9031,22,4.9,1,3,0,1,10,1,4,1,1,0
8235,22,5.1,1,3,0,1,13,1,2,1,1,2
8340,22,5.4,1,5,0,1,11,1,6,1,1,2
4649,22,5.4,1,1,0,1,12,0,8,1,1,3
752,22,5.4,1,2,0,1,15,0,7,1,1,2
8666,22,5.4,1,5,0,1,14,1,7,1,1,6
3089,22,6.4,1,5,0,1,13,0,3,1,1,1
1153,22,7.2,1,4,0,1,12,0,3,1,1,0
6408,22,7.6,1,3,0,1,9,1,8,1,1,8
4342,22,7.7,1,2,0,1,12,0,5,1,1,3
6006,22,8.6,1,1,0,1,15,1,5,1,1,6
10408,22,8.7,1,2,0,1,11,1,5,1,1,9
6818,22,8.7,1,5,0,1,9,1,5,1,1,2
6366,22,8.8,1,2,0,1,12,1,2,1,1,8
5439,22,8.8,1,1,0,1,10,1,3,1,1,1
6323,22,9.7,1,3,0,1,12,1,1,1,1,0
3372,22,10.1,1,5,0,1,12,0,5,1,1,0
5620,22,10.5,1,5,0,1,12,1,4,1,1,3
4029,22,10.6,1,5,0,1,14,0,4,1,1,1
8716,22,10.9,1,4,0,1,12,1,3,1,1,2
1315,22,10.9,1,2,0,1,14,0,2,1,1,0
6428,22,10.9,1,1,0,1,12,1,2,1,1,7
1392,22,11.1,1,2,0,1,15,0,1,1,1,5
66,22,11.6,1,5,0,1,15,0,6,1,1,9
2104,22,11.7,1,3,0,1,14,0,4,1,1,1
3430,22,12,1,4,0,1,12,0,8,1,1,7
8039,22,12.3,1,5,0,1,12,1,1,1,1,1
10481,22,12.7,1,5,0,1,10,1,5,1,1,1
8670,22,13,1,2,0,1,14,1,5,1,1,1
4312,22,13.7,1,2,0,1,11,0,2,1,1,6
680,22,13.9,1,3,0,1,11,0,4,1,1,6
294,22,14.1,1,3,0,1,9,0,2,1,1,0
4303,22,14.4,1,1,0,1,11,0,4,1,1,4
6979,22,14.5,1,1,0,1,10,1,3,1,1,0
2472,22,15.3,1,2,0,1,13,0,4,1,1,5
1009,22,15.4,1,5,0,1,12,0,5,1,1,0
9616,22,15.5,1,3,0,1,11,1,8,1,1,7
7194,22,15.9,1,4,0,1,15,1,3,1,1,6
5364,22,15.9,1,5,0,1,12,0,7,1,1,2
7890,22,16.6,1,5,0,1,11,1,6,1,1,10
8720,22,16.8,1,5,0,1,15,1,1,1,1,1
9188,22,16.8,1,5,0,1,12,1,2,1,1,9
4060,22,17.2,1,1,0,1,9,0,3,1,1,4
3709,22,17.3,1,2,0,1,10,0,6,1,1,0
6840,22,18,1,5,0,1,12,1,1,1,1,0
5732,22,18.3,1,3,0,1,10,1,3,1,1,1
375,22,18.5,1,2,0,1,15,0,1,1,1,2
6384,22,18.7,1,2,0,1,14,1,6,1,1,9
4685,22,18.7,1,5,0,1,14,0,3,1,1,4
7692,22,18.9,1,5,0,1,14,1,1,1,1,6
5524,22,19.9,1,5,0,1,13,1,5,1,1,3
8680,22,20.1,1,4,0,1,14,1,3,1,1,10
5709,22,20.7,1,3,0,1,12,1,4,1,1,1
5462,22,21.2,1,5,0,1,11,1,4,1,1,1
5824,22,21.6,1,2,0,1,14,1,8,1,1,7
6344,22,21.6,1,2,0,1,14,1,2,1,1,5
2997,22,22.1,1,2,0,1,13,0,7,1,1,0
2455,22,22.3,1,5,0,1,14,0,7,1,1,0
4378,22,22.3,1,5,0,1,10,0,5,1,1,10
2616,22,22.4,1,2,0,1,15,0,3,1,1,4
5064,22,23.5,1,3,0,1,15,0,4,1,1,5
8443,22,23.5,1,3,0,1,14,1,5,1,1,0
4120,22,23.6,1,5,0,1,9,0,3,1,1,10
1948,22,23.6,1,3,0,1,15,0,1,1,1,7
5138,22,24,1,4,0,1,15,0,2,1,1,2
504,22,24.7,1,1,0,1,12,0,6,1,1,5
9611,22,24.9,1,4,0,1,11,1,1,1,1,1
7750,22,25,1,1,0,1,10,1,2,1,1,5
9008,22,25.5,1,5,0,1,9,1,8,1,1,6
3145,22,25.6,1,5,0,1,12,0,4,1,1,0
6548,22,26.2,1,3,0,1,15,1,6,1,1,6
10045,22,26.7,1,4,0,1,10,1,6,1,1,4
2270,22,27.1,1,1,0,1,11,0,8,1,1,7
3587,22,27.6,1,5,0,1,15,0,8,1,1,0
8084,22,27.6,1,4,0,1,12,1,1,1,1,8
5135,22,28.2,1,5,0,1,11,0,2,1,1,4
1308,22,28.8,1,4,0,1,10,0,5,1,1,10
3174,22,29,1,3,0,1,12,0,3,1,1,8
10815,22,29.5,1,4,0,1,12,1,7,1,1,0
8402,23,0.3,1,1,0,1,13,1,5,1,1,8
6239,23,0.5,1,4,0,1,13,1,1,1,1,1
4947,23,0.8,1,1,0,1,9,0,6,1,1,4
110,23,1.2,1,1,0,1,10,0,3,1,1,0
6586,23,1.3,1,5,0,1,14,1,7,1,1,0
1514,23,2.1,1,4,0,1,13,0,1,1,1,3
5469,23,2.5,1,5,0,1,15,1,5,1,1,3
5711,23,2.8,1,2,0,1,15,1,1,1,1,2
1684,23,3.1,1,1,0,1,9,0,7,1,1,3
6254,23,3.4,1,1,0,1,14,1,2,1,1,0
8110,23,3.5,1,2,0,1,12,1,6,1,1,10
517,23,3.6,1,4,0,1,12,0,7,1,1,2
1182,23,3.8,1,1,0,1,11,0,2,1,1,2
10554,23,4,1,5,0,1,15,1,5,1,1,7
5953,23,4.5,1,2,0,1,12,1,2,1,1,0
2913,23,4.7,1,5,0,1,13,0,8,1,1,3
9633,23,5,1,3,0,1,12,1,4,1,1,0
883,23,5.1,1,4,0,1,15,0,7,1,1,1
10292,23,5.2,1,5,0,1,15,1,1,1,1,8
4976,23,5.5,1,2,0,1,12,0,8,1,1,0
8132,23,5.8,1,5,0,1,12,1,4,1,1,6
939,23,6,1,1,0,1,13,0,5,1,1,3
2948,23,6.3,1,1,0,1,14,0,5,1,1,0
6742,23,6.4,1,3,0,1,14,1,4,1,1,0
9283,23,6.5,1,3,0,1,10,1,5,1,1,2
2822,23,6.5,1,3,0,1,10,0,4,1,1,9
9012,23,6.8,1,2,0,1,12,1,3,1,1,5
2633,23,7,1,2,0,1,14,0,8,1,1,0
6351,23,7,1,1,0,1,10,1,2,1,1,2
3042,23,7.3,1,4,0,1,10,0,6,1,1,7
5432,23,7.5,1,4,0,1,13,1,6,1,1,4
2334,23,8.4,1,2,0,1,13,0,4,1,1,8
1784,23,8.5,1,2,0,1,15,0,3,1,1,2
380,23,9,1,1,0,1,12,0,4,1,1,6
3831,23,9.1,1,5,0,1,11,0,3,1,1,3
2655,23,9.1,1,4,0,1,10,0,2,1,1,4
8204,23,9.3,1,5,0,1,11,1,2,1,1,7
1200,23,9.7,1,4,0,1,9,0,6,1,1,8
3114,23,9.8,1,1,0,1,9,0,7,1,1,4
3720,23,9.8,1,3,0,1,15,0,7,1,1,6
5228,23,10,1,3,0,1,13,0,4,1,1,1
4894,23,10.5,1,4,0,1,10,0,7,1,1,10
1488,23,10.7,1,4,0,1,9,0,1,1,1,9
9126,23,11.4,1,1,0,1,14,1,8,1,1,2
8188,23,11.7,1,2,0,1,12,1,2,1,1,9
2589,23,11.8,1,2,0,1,14,0,7,1,1,4
9366,23,11.8,1,2,0,1,12,1,8,1,1,2
4646,23,11.9,1,5,0,1,11,0,1,1,1,2
4116,23,12,1,3,0,1,12,0,7,1,1,0
2984,23,12.5,1,5,0,1,9,0,7,1,1,7
6054,23,13.2,1,4,0,1,15,1,3,1,1,5
1967,23,13.3,1,5,0,1,9,0,7,1,1,4
1791,23,13.6,1,1,0,1,15,0,1,1,1,4
800,23,13.7,1,1,0,1,15,0,2,1,1,8
946,23,13.8,1,2,0,1,11,0,6,1,1,4
8244,23,13.9,1,4,0,1,14,1,7,1,1,6
5904,23,13.9,1,5,0,1,14,1,5,1,1,3
2618,23,14.2,1,3,0,1,15,0,4,1,1,9
612,23,14.3,1,5,0,1,15,0,2,1,1,10
4106,23,14.4,1,5,0,1,15,0,6,1,1,5
2719,23,14.6,1,1,0,1,12,0,1,1,1,2
5600,23,14.8,1,2,0,1,15,1,8,1,1,6
8797,23,15.1,1,3,0,1,12,1,6,1,1,4
9832,23,15.1,1,3,0,1,10,1,4,1,1,9
9062,23,15.9,1,3,0,1,12,1,6,1,1,1
884,23,15.9,1,4,0,1,11,0,7,1,1,6
9218,23,16.2,1,3,0,1,12,1,7,1,1,10
7782,23,17,1,1,0,1,14,1,2,1,1,2
792,23,17.1,1,4,0,1,14,0,5,1,1,5
5994,23,17.2,1,2,0,1,10,1,8,1,1,2
1019,23,17.6,1,1,0,1,10,0,7,1,1,2
7083,23,17.8,1,5,0,1,14,1,3,1,1,1
9225,23,17.9,1,4,0,1,9,1,6,1,1,2
7277,23,18.1,1,2,0,1,11,1,7,1,1,4
2490,23,18.3,1,4,0,1,15,0,6,1,1,5
7254,23,18.7,1,3,0,1,14,1,3,1,1,10
1096,23,18.8,1,5,0,1,13,0,8,1,1,2
4444,23,19,1,3,0,1,12,0,3,1,1,7
5446,23,19.1,1,5,0,1,10,1,1,1,1,2
9291,23,19.1,1,2,0,1,12,1,7,1,1,1
8456,23,19.2,1,2,0,1,11,1,8,1,1,7
8097,23,19.3,1,2,0,1,14,1,3,1,1,3
2646,23,19.4,1,4,0,1,9,0,7,1,1,3
4970,23,19.6,1,4,0,1,14,0,2,1,1,1
7988,23,20,1,5,0,1,11,1,3,1,1,10
579,23,20.5,1,5,0,1,14,0,1,1,1,1
3350,23,20.6,1,2,0,1,13,0,8,1,1,7
1252,23,20.7,1,4,0,1,12,0,4,1,1,10
9284,23,20.7,1,2,0,1,11,1,6,1,1,8
5380,23,21.5,1,4,0,1,14,0,1,1,1,9
5652,23,21.6,1,3,0,1,12,1,8,1,1,6
1296,23,21.8,1,2,0,1,12,0,7,1,1,5
2348,23,22.5,1,3,0,1,11,0,3,1,1,10
4582,23,22.8,1,2,0,1,10,0,5,1,1,4
234,23,22.8,1,3,0,1,15,0,1,1,1,4
7831,23,24.3,1,2,0,1,10,1,8,1,1,1
3148,23,24.6,1,1,0,1,12,0,2,1,1,0
5939,23,24.7,1,5,0,1,15,1,2,1,1,3
9330,23,24.9,1,3,0,1,12,1,2,1,1,8
6158,23,25.3,1,1,0,1,14,1,8,1,1,9
6034,23,25.7,1,5,0,1,13,1,6,1,1,1
4414,23,26,1,3,0,1,11,0,2,1,1,2
4671,23,26.1,1,3,0,1,12,0,8,1,1,2
6108,23,26.2,1,5,0,1,13,1,4,1,1,9
10196,23,26.4,1,2,0,1,12,1,5,1,1,0
8892,23,27.1,1,1,0,1,13,1,3,1,1,6
4524,23,27.2,1,4,0,1,15,0,5,1,1,7
268,23,27.3,1,4,0,1,15,0,7,1,1,7
10218,23,27.5,1,2,0,1,12,1,4,1,1,9
10264,23,27.7,1,2,0,1,10,1,7,1,1,9
3666,23,27.8,1,4,0,1,9,0,5,1,1,6
154,23,28.2,1,2,0,1,12,0,4,1,1,8
9700,23,28.8,1,5,0,1,11,1,5,1,1,8
4200,23,28.8,1,1,0,1,12,0,2,1,1,6
8522,23,29.3,1,3,0,1,15,1,8,1,1,0
7537,23,29.4,1,2,0,1,15,1,5,1,1,2
2607,23,29.5,1,4,0,1,12,0,2,1,1,1
7719,23,29.6,1,4,0,1,9,1,7,1,1,1
1031,23,29.7,1,4,0,1,9,0,5,1,1,4
4656,24,0.2,1,5,0,1,15,0,1,1,1,9
531,24,0.4,1,1,0,1,12,0,8,1,1,3
4189,24,1.3,1,3,0,1,12,0,8,1,1,3
5466,24,2.2,1,5,0,1,12,1,1,1,1,3
1699,24,2.8,1,3,0,1,9,0,4,1,1,2
1199,24,3,1,3,0,1,15,0,8,1,1,3
4688,24,3.1,1,4,0,1,9,0,5,1,1,8
9324,24,3.2,1,4,0,1,10,1,6,1,1,1
6322,24,4,1,4,0,1,14,1,8,1,1,8
7310,24,4.9,1,1,0,1,14,1,4,1,1,7
5105,24,5.2,1,4,0,1,11,0,4,1,1,0
1046,24,5.4,1,3,0,1,11,0,7,1,1,0
8008,24,5.8,1,1,0,1,15,1,1,1,1,2
7278,24,6.4,1,2,0,1,11,1,5,1,1,6
9488,24,7,1,1,0,1,15,1,3,1,1,2
5766,24,7.5,1,2,0,1,12,1,5,1,1,3
7826,24,7.9,1,2,0,1,12,1,7,1,1,1
6106,24,8,1,2,0,1,12,1,1,1,1,1
7506,24,9.1,1,4,0,1,9,1,3,1,1,5
1510,24,9.6,1,4,0,1,12,0,6,1,1,2
6858,24,9.6,1,1,0,1,9,1,1,1,1,1
5517,24,10,1,3,0,1,11,1,4,1,1,4
4559,24,10.1,1,2,0,1,12,0,8,1,1,4
5722,24,10.5,1,5,0,1,12,1,6,1,1,3
2876,24,10.5,1,5,0,1,12,0,6,1,1,9
1270,24,10.5,1,4,0,1,12,0,5,1,1,0
9773,24,10.6,1,2,0,1,9,1,8,1,1,3
8681,24,11.1,1,4,0,1,13,1,4,1,1,4
9696,24,11.3,1,1,0,1,11,1,6,1,1,1
5200,24,12.4,1,4,0,1,12,0,7,1,1,2
2710,24,12.8,1,3,0,1,12,0,4,1,1,2
8938,24,12.9,1,4,0,1,13,1,2,1,1,2
7909,24,13.3,1,2,0,1,11,1,1,1,1,1
1362,24,13.4,1,4,0,1,15,0,7,1,1,4
7306,24,13.6,1,1,0,1,10,1,4,1,1,4
7518,24,14,1,5,0,1,12,1,6,1,1,7
4888,24,14.3,1,2,0,1,15,0,4,1,1,8
8040,24,14.9,1,1,0,1,13,1,2,1,1,10
8311,24,15,1,1,0,0,15,1,1,1,1,5
8219,24,15,1,2,0,0,15,1,2,1,1,7
247,24,15.1,1,5,0,0,10,0,1,1,1,5
8299,24,15.1,1,3,0,0,13,1,1,1,1,6
919,24,15.1,1,1,0,0,13,0,2,1,1,8
2185,24,15.1,1,5,0,0,9,0,2,1,1,7
905,24,15.2,1,4,0,0,11,0,0,1,1,9
3381,24,15.2,1,2,0,0,14,0,2,1,1,7
4291,24,15.2,1,5,0,0,15,0,2,1,1,5
8363,24,15.3,1,4,0,1,13,1,5,1,1,0
5799,24,15.3,1,4,0,0,11,1,0,1,1,10
8665,24,15.3,1,2,0,0,9,1,0,1,1,5
2691,24,15.3,1,4,0,0,9,0,2,1,1,8
10371,24,15.3,1,3,0,0,13,1,1,1,1,6
69,24,15.3,1,2,0,0,15,0,2,1,1,9
2197,24,15.3,1,2,0,0,15,0,0,1,1,5
1641,24,15.3,1,1,0,0,12,0,0,1,1,6
989,24,15.3,1,2,0,0,14,0,0,1,1,6
6581,24,15.3,1,2,0,0,13,1,1,1,1,7
8436,24,15.4,1,3,0,1,10,1,4,1,1,6
2251,24,15.4,1,3,0,0,15,0,0,1,1,7
10677,24,15.4,1,1,0,0,9,1,1,1,1,7
5997,24,15.5,1,4,0,0,12,1,2,1,1,7
7007,24,15.5,1,2,0,0,15,1,1,1,1,8
10215,24,15.6,1,1,0,0,15,1,2,1,1,8
571,24,15.6,1,5,0,0,13,0,1,1,1,7
10563,24,15.6,1,1,0,0,13,1,0,1,1,6
7947,24,15.7,1,4,0,0,14,1,2,1,1,6
3235,24,15.7,1,5,0,0,14,0,1,1,1,5
9519,24,15.7,1,2,0,0,9,1,2,1,1,10
8411,24,15.8,1,5,0,0,9,1,1,1,1,7
2163,24,15.8,1,4,0,0,14,0,1,1,1,5
10339,24,15.8,1,1,0,0,15,1,2,1,1,6
2247,24,15.8,1,2,0,0,9,0,0,1,1,7
7695,24,15.8,1,5,0,0,9,1,0,1,1,10
5225,24,15.9,1,5,0,0,12,0,1,1,1,5
9561,24,15.9,1,1,0,0,14,1,2,1,1,8
1627,24,15.9,1,5,0,0,10,0,0,1,1,5
8975,24,15.9,1,2,0,0,9,1,2,1,1,8
10817,24,15.9,1,4,0,0,9,1,2,1,1,5
4905,24,16,1,2,0,0,14,0,2,1,1,7
7133,24,16,1,2,0,0,14,1,2,1,1,7
8757,24,16,1,2,0,0,15,1,2,1,1,9
6875,24,16,1,2,0,0,9,1,0,1,1,6
3969,24,16,1,1,0,0,14,0,1,1,1,5
2955,24,16.1,1,1,0,0,9,0,0,1,1,6
157,24,16.1,1,1,0,0,12,0,1,1,1,6
7345,24,16.1,1,5,0,0,10,1,1,1,1,5
6097,24,16.1,1,1,0,0,13,1,1,1,1,7
7783,24,16.1,1,5,0,0,9,1,1,1,1,9
7333,24,16.1,1,5,0,0,11,1,0,1,1,6
2423,24,16.2,1,1,0,0,12,0,1,1,1,5
795,24,16.2,1,4,0,0,11,0,1,1,1,5
1177,24,16.3,1,2,0,1,14,0,1,1,1,2
4261,24,16.3,1,3,0,0,13,0,1,1,1,5
6287,24,16.3,1,1,0,0,10,1,1,1,1,10
4103,24,16.4,1,5,0,0,12,0,2,1,1,7
6519,24,16.4,1,2,0,0,10,1,1,1,1,6
9935,24,16.4,1,2,0,0,15,1,0,1,1,5
10699,24,16.4,1,1,0,0,9,1,0,1,1,8
1607,24,16.5,1,2,0,0,9,0,1,1,1,8
7473,24,16.5,1,5,0,0,10,1,0,1,1,9
3713,24,16.6,1,3,0,0,12,0,0,1,1,6
10573,24,16.6,1,1,0,0,9,1,1,1,1,5
1929,24,16.6,1,4,0,0,12,0,0,1,1,5
1933,24,16.6,1,5,0,0,14,0,1,1,1,6
8625,24,16.6,1,1,0,0,14,1,0,1,1,9
431,24,16.6,1,2,0,0,12,0,1,1,1,9
8141,24,16.6,1,4,0,0,14,1,1,1,1,9
9205,24,16.6,1,2,0,0,9,1,0,1,1,6
5355,24,16.6,1,3,0,0,10,0,0,1,1,8
3355,24,16.6,1,1,0,0,15,0,0,1,1,6
1678,24,16.7,1,1,0,1,12,0,3,1,1,10
2617,24,16.7,1,3,0,0,15,0,1,1,1,9
9061,24,16.7,1,4,0,0,11,1,1,1,1,10
1879,24,16.7,1,2,0,0,13,0,2,1,1,6
6175,24,16.8,1,2,0,0,13,1,2,1,1,5
8885,24,16.8,1,1,0,0,10,1,1,1,1,9
1564,24,16.8,1,5,0,1,14,0,4,1,1,0
5637,24,16.8,1,5,0,0,9,1,1,1,1,9
9759,24,16.9,1,1,0,0,15,1,2,1,1,8
8799,24,16.9,1,4,0,1,12,1,2,1,1,3
8769,24,16.9,1,1,0,0,14,1,2,1,1,6
1862,24,16.9,1,1,0,1,14,0,1,1,1,7
3761,24,16.9,1,5,0,0,12,0,0,1,1,8
10683,24,17,1,3,0,0,14,1,0,1,1,5
4007,24,17,1,4,0,0,12,0,2,1,1,9
8171,24,17,1,1,0,1,12,1,7,1,1,1
6857,24,17,1,2,0,0,11,1,0,1,1,10
2081,24,17,1,4,0,0,12,0,2,1,1,8
203,24,17.1,1,1,0,0,10,0,1,1,1,9
6937,24,17.1,1,1,0,0,10,1,1,1,1,5
7058,24,17.1,1,5,0,1,12,1,5,1,1,1
4675,24,17.2,1,4,0,0,12,0,1,1,1,5
5787,24,17.2,1,4,0,0,13,1,1,1,1,8
5321,24,17.2,1,2,0,0,12,0,2,1,1,6
4569,24,17.3,1,5,0,0,12,0,0,1,1,10
4069,24,17.3,1,3,0,0,13,0,0,1,1,6
8273,24,17.3,1,3,0,0,10,1,0,1,1,5
6737,24,17.4,1,3,0,0,15,1,1,1,1,8
7559,24,17.4,1,5,0,0,14,1,0,1,1,5
1011,24,17.4,1,4,0,0,12,0,0,1,1,7
1121,24,17.4,1,1,0,0,10,0,1,1,1,5
9917,24,17.4,1,1,0,0,11,1,1,1,1,6
10467,24,17.5,1,3,0,0,14,1,1,1,1,8
9733,24,17.5,1,4,0,0,12,1,2,1,1,9
1427,24,17.5,1,2,0,0,12,0,1,1,1,9
8557,24,17.6,1,5,0,0,10,1,1,1,1,10
10567,24,17.6,1,4,0,0,13,1,1,1,1,6
5401,24,17.6,1,4,0,0,12,0,2,1,1,8
4239,24,17.6,1,2,0,0,10,0,0,1,1,7
1021,24,17.6,1,1,0,0,14,0,2,1,1,8
10687,24,17.6,1,3,0,0,11,1,0,1,1,5
10173,24,17.7,1,3,0,0,13,1,1,1,1,5
917,24,17.8,1,2,0,0,14,0,2,1,1,8
7235,24,17.8,1,4,0,0,12,1,0,1,1,10
10063,24,17.8,1,5,0,0,11,1,1,1,1,8
6631,24,17.9,1,3,0,0,13,1,1,1,1,5
8253,24,17.9,1,5,0,0,11,1,1,1,1,10
999,24,17.9,1,5,0,0,10,0,2,1,1,5
1935,24,17.9,1,2,0,0,11,0,2,1,1,10
7759,24,17.9,1,1,0,0,12,1,1,1,1,10
2917,24,17.9,1,1,0,0,14,0,2,1,1,7
9145,24,17.9,1,1,0,0,11,1,2,1,1,7
4595,24,17.9,1,3,0,0,13,0,1,1,1,6
583,24,17.9,1,2,0,0,12,0,0,1,1,8
10255,24,17.9,1,5,0,0,12,1,1,1,1,6
5111,24,17.9,1,3,0,0,11,0,2,1,1,6
4679,24,18,1,5,0,0,11,0,0,1,1,8
2857,24,18,1,5,0,0,9,0,2,1,1,7
10713,24,18,1,5,0,0,11,1,0,1,1,5
7211,24,18,1,3,0,0,12,1,0,1,1,7
4479,24,18.1,1,1,0,0,10,0,0,1,1,8
229,24,18.1,1,3,0,0,15,0,0,1,1,7
3851,24,18.1,1,2,0,0,12,0,1,1,1,8
6093,24,18.1,1,3,0,0,12,1,2,1,1,9
2761,24,18.1,1,5,0,0,15,0,0,1,1,8
1349,24,18.1,1,1,0,0,9,0,2,1,1,8
7677,24,18.1,1,2,0,0,9,1,0,1,1,7
3605,24,18.2,1,2,0,0,10,0,1,1,1,9
6063,24,18.2,1,2,0,0,15,1,2,1,1,9
9881,24,18.2,1,3,0,0,11,1,2,1,1,9
2772,24,18.2,1,4,0,1,10,0,3,1,1,4
3085,24,18.2,1,3,0,0,13,0,2,1,1,7
5763,24,18.2,1,3,0,0,14,1,1,1,1,6
2128,24,18.3,1,5,0,1,10,0,1,1,1,8
2615,24,18.3,1,5,0,0,10,0,0,1,1,7
2283,24,18.3,1,4,0,0,11,0,0,1,1,7
6127,24,18.3,1,4,0,0,12,1,2,1,1,7
1405,24,18.3,1,2,0,0,12,0,2,1,1,10
4719,24,18.3,1,1,0,0,9,0,0,1,1,5
7786,24,18.3,1,3,0,1,11,1,4,1,1,9
6341,24,18.3,1,1,0,0,12,1,0,1,1,6
2514,24,18.4,1,4,0,1,13,0,3,1,1,1
6251,24,18.4,1,2,0,0,14,1,2,1,1,5
3107,24,18.4,1,4,0,0,13,0,0,1,1,10
1621,24,18.5,1,1,0,0,9,0,2,1,1,5
1745,24,18.5,1,1,0,0,12,0,2,1,1,7
6357,24,18.5,1,2,0,0,14,1,2,1,1,9
6881,24,18.5,1,1,0,0,11,1,2,1,1,8
2747,24,18.5,1,2,0,0,12,0,0,1,1,10
7407,24,18.6,1,3,0,0,12,1,1,1,1,7
4933,24,18.6,1,3,0,0,12,0,0,1,1,10
1221,24,18.6,1,3,0,0,12,0,0,1,1,5
6645,24,18.6,1,2,0,0,11,1,2,1,1,7
7427,24,18.7,1,3,0,0,9,1,0,1,1,10
2191,24,18.7,1,4,0,0,13,0,2,1,1,5
6073,24,18.7,1,4,0,0,12,1,0,1,1,7
637,24,18.8,1,1,0,0,10,0,1,1,1,9
7159,24,18.8,1,3,0,0,12,1,2,1,1,9
1681,24,18.8,1,4,0,0,10,0,0,1,1,9
3083,24,18.8,1,5,0,0,14,0,0,1,1,7
5261,24,18.8,1,3,0,0,13,0,2,1,1,9
9385,24,18.8,1,3,0,0,12,1,2,1,1,6
1739,24,18.8,1,3,0,0,15,0,1,1,1,8
8959,24,18.8,1,2,0,0,13,1,2,1,1,10
4971,24,18.8,1,2,0,0,15,0,2,1,1,5
10737,24,18.8,1,3,0,0,11,1,2,1,1,5
10813,24,18.9,1,3,0,0,12,1,1,1,1,6
1027,24,18.9,1,3,0,0,9,0,1,1,1,9
3071,24,18.9,1,2,0,0,15,0,2,1,1,6
10778,24,18.9,1,3,0,1,11,1,3,1,1,1
2481,24,19,1,2,0,0,14,0,1,1,1,8
6541,24,19,1,3,0,0,14,1,0,1,1,8
783,24,19,1,2,0,0,10,0,1,1,1,9
5633,24,19,1,1,0,0,12,1,1,1,1,9
10679,24,19.1,1,4,0,0,12,1,2,1,1,5
4973,24,19.1,1,2,0,0,12,0,1,1,1,8
1343,24,19.1,1,5,0,0,11,0,1,1,1,7
1137,24,19.2,1,1,0,0,9,0,0,1,1,5
10389,24,19.2,1,1,0,0,14,1,2,1,1,7
5483,24,19.2,1,1,0,0,11,1,0,1,1,7
1375,24,19.2,1,3,0,0,11,0,1,1,1,10
2121,24,19.2,1,2,0,0,13,0,2,1,1,5
7491,24,19.2,1,2,0,0,12,1,1,1,1,10
1219,24,19.2,1,4,0,0,9,0,0,1,1,9
6383,24,19.3,1,1,0,0,9,1,0,1,1,9
205,24,19.3,1,3,0,0,11,0,2,1,1,6
3550,24,19.3,1,5,0,1,9,0,3,1,1,1
6887,24,19.3,1,1,0,0,12,1,2,1,1,8
9989,24,19.3,1,1,0,0,12,1,2,1,1,10
523,24,19.3,1,5,0,1,14,0,6,1,1,2
1183,24,19.4,1,1,0,0,15,0,1,1,1,7
2263,24,19.4,1,2,0,0,12,0,1,1,1,10
10745,24,19.4,1,5,0,0,12,1,0,1,1,10
2327,24,19.5,1,1,0,0,14,0,1,1,1,9
10095,24,19.5,1,1,0,0,9,1,0,1,1,7
5837,24,19.6,1,1,0,0,12,1,0,1,1,10
10043,24,19.6,1,3,0,0,15,1,2,1,1,8
10611,24,19.6,1,2,0,0,13,1,2,1,1,8
7571,24,19.7,1,2,0,0,14,1,0,1,1,9
10241,24,19.7,1,4,0,0,9,1,2,1,1,10
2085,24,19.7,1,4,0,0,9,0,0,1,1,9
4031,24,19.7,1,4,0,0,12,0,0,1,1,6
2447,24,19.8,1,5,0,0,13,0,0,1,1,6
8289,24,19.8,1,2,0,0,11,1,2,1,1,7
10553,24,19.8,1,3,0,0,12,1,1,1,1,6
1029,24,19.8,1,1,0,0,15,0,0,1,1,8
5909,24,19.9,1,1,0,0,10,1,1,1,1,10
9549,24,19.9,1,1,0,0,15,1,1,1,1,8
7221,24,19.9,1,5,0,0,13,1,0,1,1,9
10607,24,20,1,5,0,0,12,1,2,1,1,5
1173,24,20,1,1,0,0,14,0,1,1,1,8
7269,24,20,1,2,0,0,11,1,0,1,1,9
6741,24,20,1,5,0,1,12,1,3,1,1,4
6585,24,20,1,2,0,0,10,1,2,1,1,10
243,24,20.1,1,1,0,0,11,0,2,1,1,10
8067,24,20.1,1,5,0,0,12,1,0,1,1,7
1617,24,20.2,1,5,0,0,10,0,1,1,1,9
6273,24,20.2,1,4,0,0,9,1,2,1,1,7
1593,24,20.2,1,1,0,0,12,0,1,1,1,9
7215,24,20.2,1,1,0,0,12,1,1,1,1,10
8531,24,20.2,1,2,0,0,12,1,1,1,1,5
10765,24,20.3,1,1,0,0,12,1,2,1,1,10
911,24,20.3,1,1,0,0,15,0,2,1,1,6
1409,24,20.3,1,2,0,0,14,0,2,1,1,9
9827,24,20.4,1,4,0,0,10,1,2,1,1,6
8387,24,20.4,1,1,0,0,14,1,1,1,1,9
9365,24,20.4,1,5,0,0,15,1,1,1,1,7
6935,24,20.5,1,3,0,0,15,1,0,1,1,5
3095,24,20.5,1,1,0,0,10,0,0,1,1,8
2625,24,20.5,1,3,0,0,9,0,1,1,1,5
9545,24,20.5,1,1,0,0,10,1,1,1,1,5
4525,24,20.5,1,1,0,0,10,0,1,1,1,8
4847,24,20.5,1,2,0,0,12,0,2,1,1,7
6807,24,20.5,1,2,0,0,13,1,2,1,1,6
7603,24,20.6,1,4,0,0,9,1,1,1,1,10
1169,24,20.6,1,4,0,0,13,0,0,1,1,8
10233,24,20.6,1,1,0,0,12,1,2,1,1,6
9113,24,20.6,1,1,0,0,13,1,1,1,1,7
7661,24,20.7,1,5,0,0,14,1,0,1,1,5
3455,24,20.7,1,2,0,0,10,0,1,1,1,8
8879,24,20.7,1,5,0,0,13,1,2,1,1,8
9599,24,20.8,1,5,0,0,12,1,2,1,1,10
1851,24,20.8,1,5,0,0,11,0,0,1,1,8
1671,24,20.9,1,5,0,0,10,0,0,1,1,7
1053,24,20.9,1,3,0,0,13,0,0,1,1,5
4771,24,20.9,1,4,0,0,9,0,1,1,1,6
10811,24,21,1,5,0,0,14,1,2,1,1,5
6143,24,21,1,4,0,0,14,1,1,1,1,7
8517,24,21,1,3,0,0,9,1,1,1,1,6
10818,24,21,1,3,0,1,14,1,7,1,1,2
2311,24,21,1,3,0,0,11,0,1,1,1,5
5727,24,21.1,1,1,0,0,9,1,0,1,1,6
9021,24,21.1,1,5,0,0,9,1,0,1,1,7
3356,24,21.2,1,2,0,1,11,0,5,1,1,3
935,24,21.2,1,5,0,0,12,0,0,1,1,8
9507,24,21.2,1,3,0,0,14,1,1,1,1,7
5277,24,21.2,1,3,0,0,11,0,1,1,1,8
5681,24,21.2,1,5,0,0,9,1,1,1,1,6
6233,24,21.2,1,5,0,0,14,1,1,1,1,6
4123,24,21.2,1,2,0,0,11,0,0,1,1,9
389,24,21.2,1,5,0,0,14,0,2,1,1,10
6574,24,21.3,1,1,0,1,12,1,4,1,1,0
8131,24,21.3,1,2,0,0,13,1,0,1,1,5
5503,24,21.3,1,1,0,0,15,1,1,1,1,5
8491,24,21.3,1,1,0,0,12,1,1,1,1,10
4879,24,21.4,1,3,0,0,12,0,1,1,1,7
7609,24,21.4,1,1,0,0,12,1,1,1,1,7
10782,24,21.5,1,3,0,1,14,1,7,1,1,8
907,24,21.5,1,1,0,0,11,0,0,1,1,8
695,24,21.5,1,2,0,0,13,0,0,1,1,7
9035,24,21.6,1,4,0,0,10,1,1,1,1,6
5807,24,21.6,1,4,0,0,9,1,0,1,1,10
125,24,21.6,1,4,0,0,11,0,2,1,1,7
4563,24,21.7,1,2,0,0,14,0,1,1,1,10
8935,24,21.7,1,3,0,0,12,1,2,1,1,5
10175,24,21.7,1,4,0,0,12,1,1,1,1,10
8307,24,21.7,1,3,0,0,12,1,1,1,1,8
655,24,21.7,1,4,0,0,15,0,2,1,1,8
4555,24,21.7,1,1,0,0,14,0,2,1,1,7
9261,24,21.7,1,1,0,0,14,1,1,1,1,8
2435,24,21.7,1,2,0,0,12,0,2,1,1,8
7547,24,21.7,1,4,0,0,13,1,1,1,1,5
3093,24,21.8,1,5,0,0,11,0,2,1,1,9
9037,24,21.8,1,1,0,0,15,1,1,1,1,10
2169,24,21.8,1,3,0,0,9,0,2,1,1,9
735,24,21.9,1,2,0,0,15,0,1,1,1,8
7021,24,21.9,1,4,0,0,12,1,2,1,1,6
3873,24,21.9,1,4,0,1,13,0,4,1,1,2
171,24,21.9,1,4,0,0,15,0,0,1,1,5
5393,24,21.9,1,3,0,0,12,0,2,1,1,8
10139,24,22,1,2,0,0,10,1,2,1,1,9
7343,24,22,1,3,0,0,12,1,1,1,1,10
3781,24,22,1,2,0,0,12,0,1,1,1,6
1227,24,22,1,4,0,0,15,0,2,1,1,9
10065,24,22,1,1,0,0,13,1,1,1,1,6
10223,24,22,1,5,0,0,13,1,1,1,1,7
4583,24,22,1,2,0,0,10,0,0,1,1,10
8761,24,22,1,4,0,0,9,1,2,1,1,8
5743,24,22,1,3,0,0,13,1,1,1,1,9
47,24,22.1,1,1,0,0,14,0,0,1,1,8
3035,24,22.1,1,2,0,0,12,0,1,1,1,5
3171,24,22.1,1,3,0,0,15,0,2,1,1,6
3759,24,22.2,1,1,0,0,15,0,2,1,1,9
5341,24,22.2,1,2,0,0,10,0,2,1,1,9
1545,24,22.2,1,3,0,0,15,0,2,1,1,7
4149,24,22.2,1,2,0,0,15,0,2,1,1,10
5197,24,22.2,1,4,0,1,10,0,8,1,1,1
8285,24,22.4,1,4,0,0,13,1,1,1,1,7
5694,24,22.4,1,5,0,1,9,1,7,1,1,7
5151,24,22.4,1,1,0,0,12,0,2,1,1,7
4061,24,22.4,1,5,0,0,14,0,0,1,1,6
1939,24,22.4,1,3,0,0,15,0,2,1,1,8
1585,24,22.5,1,4,0,0,11,0,1,1,1,9
3575,24,22.5,1,4,0,0,12,0,1,1,1,5
8551,24,22.5,1,3,0,0,12,1,1,1,1,10
4689,24,22.5,1,2,0,1,10,0,6,1,1,4
4109,24,22.6,1,2,0,0,9,0,1,1,1,7
3038,24,22.6,1,2,0,1,11,0,7,1,1,0
2653,24,22.6,1,2,0,0,11,0,0,1,1,5
8181,24,22.6,1,4,0,0,12,1,1,1,1,7
4183,24,22.7,1,2,0,0,14,0,2,1,1,6
5623,24,22.7,1,1,0,0,14,1,2,1,1,8
67,24,22.8,1,1,0,0,13,0,2,1,1,6
3309,24,22.8,1,1,0,0,12,0,2,1,1,5
8969,24,22.8,1,4,0,0,9,1,1,1,1,10
5033,24,22.8,1,1,0,0,12,0,0,1,1,6
7727,24,22.8,1,4,0,0,12,1,2,1,1,7
777,24,22.9,1,3,0,0,9,0,1,1,1,10
1570,24,22.9,1,2,0,1,9,0,3,1,1,4
8563,24,22.9,1,5,0,0,11,1,2,1,1,5
5509,24,22.9,1,2,0,0,10,1,0,1,1,8
2909,24,23,1,1,0,0,11,0,0,1,1,7
2249,24,23,1,1,0,0,9,0,1,1,1,6
4297,24,23.1,1,3,0,0,14,0,0,1,1,9
10741,24,23.1,1,4,0,0,14,1,1,1,1,5
4345,24,23.1,1,3,0,0,10,0,2,1,1,10
9661,24,23.1,1,5,0,0,13,1,1,1,1,7
1691,24,23.2,1,1,0,0,12,0,0,1,1,7
7951,24,23.2,1,1,0,0,9,1,1,1,1,10
8851,24,23.3,1,1,0,0,15,1,1,1,1,8
8997,24,23.3,1,3,0,0,12,1,0,1,1,8
4256,24,23.3,1,5,0,1,12,0,8,1,1,9
9321,24,23.3,1,1,0,0,12,1,0,1,1,8
9893,24,23.3,1,4,0,0,11,1,1,1,1,9
4041,24,23.3,1,4,0,0,9,0,0,1,1,10
6117,24,23.3,1,4,0,0,10,1,1,1,1,9
5323,24,23.3,1,1,0,0,15,0,0,1,1,5
7683,24,23.3,1,2,0,0,12,1,2,1,1,10
6291,24,23.3,1,1,0,0,12,1,2,1,1,8
1049,24,23.4,1,2,0,0,13,0,2,1,1,10
3241,24,23.4,1,1,0,0,11,0,1,1,1,9
5903,24,23.4,1,4,0,0,13,1,2,1,1,9
1687,24,23.4,1,5,0,0,9,0,1,1,1,10
3819,24,23.4,1,2,0,0,10,0,1,1,1,10
7841,24,23.5,1,3,0,0,10,1,1,1,1,10
887,24,23.5,1,1,0,0,11,0,2,1,1,5
10855,24,23.5,1,1,0,0,9,1,2,1,1,10
659,24,23.5,1,3,0,0,13,0,1,1,1,10
10027,24,23.5,1,2,0,0,14,1,2,1,1,6
7223,24,23.5,1,2,0,0,15,1,0,1,1,10
10127,24,23.6,1,4,0,0,9,1,1,1,1,10
3989,24,23.6,1,1,0,0,15,0,2,1,1,6
4125,24,23.6,1,1,0,0,10,0,2,1,1,5
4575,24,23.6,1,3,0,0,13,0,0,1,1,10
10015,24,23.7,1,2,0,0,15,1,0,1,1,10
5113,24,23.7,1,2,0,0,14,0,0,1,1,7
5911,24,23.7,1,5,0,1,15,1,7,1,1,3
4627,24,23.7,1,1,0,0,11,0,0,1,1,8
10117,24,23.7,1,2,0,0,12,1,0,1,1,7
647,24,23.7,1,4,0,0,14,0,1,1,1,6
2915,24,23.7,1,1,0,0,15,0,2,1,1,10
4603,24,23.7,1,1,0,0,12,0,2,1,1,8
2609,24,23.7,1,1,0,0,15,0,2,1,1,6
7573,24,23.8,1,3,0,0,15,1,0,1,1,7
7113,24,23.8,1,4,0,0,10,1,2,1,1,8
10773,24,23.8,1,3,0,1,12,1,6,1,1,1
4637,24,23.8,1,5,0,0,12,0,0,1,1,6
6565,24,23.9,1,3,0,0,12,1,1,1,1,5
10235,24,23.9,1,1,0,0,12,1,2,1,1,6
623,24,23.9,1,5,0,0,12,0,1,1,1,9
3369,24,23.9,1,5,0,0,12,0,2,1,1,5
3479,24,24,1,2,0,0,9,0,2,1,1,10
7255,24,24,1,1,0,0,12,1,1,1,1,6
424,24,24,1,1,0,1,9,0,4,1,1,3
9591,24,24,1,1,0,0,10,1,2,1,1,6
5211,24,24,1,1,0,0,10,0,2,1,1,6
473,24,24.1,1,3,0,0,12,0,0,1,1,8
6257,24,24.1,1,3,0,0,15,1,0,1,1,10
10537,24,24.1,1,2,0,0,9,1,2,1,1,7
1961,24,24.1,1,4,0,0,15,0,0,1,1,8
4249,24,24.2,1,1,0,0,9,0,2,1,1,8
9597,24,24.2,1,5,0,0,12,1,0,1,1,9
1901,24,24.2,1,2,0,0,13,0,2,1,1,7
6721,24,24.2,1,4,0,0,11,1,0,1,1,6
8301,24,24.2,1,2,0,0,9,1,2,1,1,10
5363,24,24.3,1,3,0,0,9,0,0,1,1,6
6709,24,24.3,1,1,0,0,11,1,0,1,1,10
10561,24,24.3,1,5,0,0,11,1,2,1,1,10
1047,24,24.3,1,3,0,0,13,0,2,1,1,9
1793,24,24.3,1,1,0,0,10,0,1,1,1,9
5251,24,24.3,1,2,0,0,9,0,2,1,1,9
333,24,24.3,1,4,0,0,13,0,2,1,1,6
6623,24,24.3,1,3,0,0,13,1,2,1,1,5
2005,24,24.4,1,3,0,0,12,0,2,1,1,5
729,24,24.4,1,5,0,0,9,0,2,1,1,9
2213,24,24.4,1,4,0,0,12,0,1,1,1,8
10329,24,24.5,1,2,0,0,13,1,0,1,1,5
5049,24,24.5,1,3,0,0,9,0,1,1,1,7
425,24,24.5,1,5,0,0,13,0,1,1,1,7
2861,24,24.6,1,1,0,0,12,0,2,1,1,6
9559,24,24.6,1,3,0,0,13,1,1,1,1,8
5983,24,24.6,1,3,0,0,10,1,0,1,1,10
10769,24,24.6,1,4,0,0,12,1,1,1,1,10
1309,24,24.6,1,4,0,0,9,0,2,1,1,8
1087,24,24.6,1,5,0,0,10,0,2,1,1,7
3151,24,24.7,1,2,0,0,14,0,0,1,1,5
5309,24,24.7,1,4,0,0,9,0,2,1,1,5
10644,24,24.7,1,3,0,1,13,1,2,1,1,4
9627,24,24.7,1,2,0,0,10,1,2,1,1,7
3747,24,24.7,1,2,0,0,15,0,1,1,1,6
8323,24,24.8,1,4,0,0,11,1,2,1,1,7
5067,24,24.8,1,1,0,0,15,0,2,1,1,9
9315,24,24.9,1,1,0,0,9,1,2,1,1,10
7489,24,24.9,1,1,0,0,9,1,2,1,1,6
8333,24,25,1,1,0,0,13,1,1,1,1,8
193,24,25,1,3,0,0,9,0,2,1,1,8
2750,24,25,1,4,0,1,10,0,7,1,1,4
327,24,25.1,1,2,0,0,12,0,2,1,1,7
1495,24,25.1,1,5,0,0,12,0,1,1,1,7
4395,24,25.1,1,3,0,1,15,0,3,1,1,0
7629,24,25.1,1,3,0,0,15,1,2,1,1,6
8005,24,25.1,1,1,0,0,9,1,1,1,1,8
6345,24,25.2,1,3,0,0,12,1,1,1,1,10
3973,24,25.2,1,3,0,0,10,0,1,1,1,5
6621,24,25.2,1,3,0,0,12,1,2,1,1,8
269,24,25.3,1,3,0,0,15,0,0,1,1,8
6219,24,25.3,1,3,0,0,14,1,1,1,1,5
9017,24,25.3,1,3,0,0,14,1,1,1,1,7
10405,24,25.3,1,4,0,0,12,1,2,1,1,9
9573,24,25.3,1,1,0,0,9,1,1,1,1,6
387,24,25.3,1,5,0,1,12,0,4,1,1,2
8445,24,25.4,1,5,0,0,13,1,0,1,1,8
489,24,25.4,1,3,0,0,13,0,0,1,1,10
4517,24,25.5,1,5,0,0,15,0,1,1,1,9
8485,24,25.5,1,4,0,0,9,1,1,1,1,9
5581,24,25.5,1,5,0,0,12,1,2,1,1,10
8359,24,25.5,1,1,0,0,15,1,2,1,1,6
9029,24,25.5,1,3,0,0,14,1,2,1,1,8
10087,24,25.6,1,2,0,0,10,1,0,1,1,7
6461,24,25.6,1,3,0,0,12,1,0,1,1,6
1,24,25.6,1,3,0,0,14,0,0,1,1,7
2805,24,25.6,1,1,0,0,11,0,2,1,1,7
9271,24,25.7,1,2,0,0,15,1,0,1,1,6
10229,24,25.7,1,5,0,0,13,1,1,1,1,7
3403,24,25.7,1,3,0,0,11,0,0,1,1,10
8723,24,25.7,1,2,0,0,10,1,1,1,1,10
7997,24,25.7,1,1,0,0,13,1,2,1,1,6
1873,24,25.7,1,5,0,0,9,0,0,1,1,10
6069,24,25.7,1,5,0,0,9,1,1,1,1,9
1987,24,25.8,1,2,0,0,14,0,2,1,1,6
1043,24,25.8,1,3,0,0,15,0,0,1,1,10
9949,24,25.8,1,1,0,0,10,1,2,1,1,6
8946,24,25.8,1,1,0,1,14,1,8,1,1,6
2963,24,25.9,1,2,0,0,15,0,0,1,1,9
10785,24,25.9,1,5,0,0,15,1,1,1,1,9
7419,24,25.9,1,3,0,0,12,1,1,1,1,5
599,24,25.9,1,2,0,0,13,0,0,1,1,5
2591,24,25.9,1,1,0,0,11,0,1,1,1,8
9435,24,26,1,5,0,0,10,1,1,1,1,5
10323,24,26,1,3,0,0,9,1,2,1,1,7
10169,24,26,1,1,0,0,11,1,0,1,1,8
7873,24,26,1,3,0,0,15,1,0,1,1,6
3993,24,26,1,2,0,0,9,0,1,1,1,9
7013,24,26,1,4,0,0,9,1,0,1,1,5
2357,24,26,1,4,0,0,11,0,1,1,1,7
2661,24,26,1,2,0,0,11,0,1,1,1,9
839,24,26,1,5,0,0,12,0,0,1,1,6
4911,24,26,1,4,0,0,14,0,2,1,1,10
3027,24,26.1,1,5,0,0,14,0,1,1,1,8
9240,24,26.1,1,2,0,1,12,1,7,1,1,5
4201,24,26.1,1,1,0,0,11,0,2,1,1,7
6691,24,26.1,1,3,0,0,14,1,1,1,1,6
10111,24,26.1,1,3,0,0,11,1,0,1,1,10
6545,24,26.2,1,3,0,0,14,1,0,1,1,6
383,24,26.2,1,3,0,0,9,0,0,1,1,7
7581,24,26.3,1,5,0,0,12,1,0,1,1,10
2369,24,26.3,1,5,0,0,12,0,0,1,1,6
8447,24,26.3,1,2,0,0,15,1,1,1,1,7
2029,24,26.3,1,4,0,0,12,0,0,1,1,7
9445,24,26.3,1,5,0,0,12,1,2,1,1,8
2293,24,26.3,1,1,0,0,14,0,1,1,1,8
8091,24,26.3,1,1,0,0,14,1,0,1,1,7
660,24,26.3,1,4,0,1,12,0,3,1,1,8
9489,24,26.3,1,1,0,0,12,1,2,1,1,6
7233,24,26.4,1,5,0,0,11,1,0,1,1,7
10245,24,26.4,1,2,0,0,12,1,0,1,1,7
3127,24,26.5,1,1,0,0,13,0,2,1,1,8
705,24,26.5,1,2,0,0,14,0,2,1,1,5
7009,24,26.5,1,5,0,0,11,1,0,1,1,6
4713,24,26.5,1,5,0,0,12,0,0,1,1,8
9329,24,26.6,1,2,0,0,12,1,1,1,1,5
6571,24,26.6,1,5,0,0,10,1,1,1,1,8
7717,24,26.6,1,2,0,0,14,1,2,1,1,10
5679,24,26.6,1,3,0,0,9,1,2,1,1,9
8783,24,26.6,1,1,0,0,13,1,1,1,1,8
6205,24,26.6,1,2,0,0,10,1,2,1,1,10
4547,24,26.7,1,3,0,0,11,0,1,1,1,9
6133,24,26.7,1,5,0,0,12,1,2,1,1,8
2161,24,26.7,1,1,0,0,12,0,1,1,1,10
5851,24,26.8,1,5,0,0,10,1,2,1,1,9
10425,24,26.8,1,1,0,0,13,1,1,1,1,8
9803,24,26.8,1,4,0,0,10,1,1,1,1,8
1299,24,26.8,1,5,0,0,9,0,1,1,1,6
10281,24,26.8,1,1,0,0,12,1,1,1,1,8
1125,24,26.9,1,4,0,0,13,0,2,1,1,6
7299,24,26.9,1,5,0,0,12,1,0,1,1,7
4127,24,26.9,1,2,0,0,15,0,2,1,1,8
3267,24,26.9,1,2,0,0,11,0,1,1,1,10
2925,24,27,1,5,0,0,14,0,0,1,1,6
7617,24,27,1,3,0,0,12,1,1,1,1,5
4317,24,27,1,3,0,0,12,0,2,1,1,6
4907,24,27.1,1,2,0,0,9,0,0,1,1,5
8073,24,27.1,1,2,0,0,11,1,2,1,1,8
5923,24,27.2,1,3,0,0,11,1,2,1,1,9
5601,24,27.2,1,5,0,0,10,1,0,1,1,7
1437,24,27.3,1,2,0,1,14,0,8,1,1,4
10300,24,27.4,1,5,0,1,11,1,4,1,1,8
10181,24,27.4,1,5,0,0,12,1,2,1,1,6
7887,24,27.5,1,1,0,0,9,1,2,1,1,5
1971,24,27.5,1,2,0,0,11,0,1,1,1,5
7029,24,27.5,1,4,0,0,9,1,0,1,1,7
10455,24,27.5,1,4,0,0,12,1,0,1,1,5
6471,24,27.6,1,5,0,0,9,1,1,1,1,10
7281,24,27.6,1,4,0,0,9,1,1,1,1,9
4413,24,27.6,1,2,0,0,13,0,1,1,1,5
7443,24,27.6,1,5,0,0,15,1,2,1,1,5
4895,24,27.6,1,2,0,0,10,0,0,1,1,10
6485,24,27.7,1,1,0,0,12,1,1,1,1,6
3443,24,27.7,1,1,0,0,12,0,0,1,1,6
1697,24,27.7,1,5,0,0,12,0,1,1,1,10
2443,24,27.8,1,5,0,0,9,0,1,1,1,9
9149,24,27.8,1,1,0,0,10,1,0,1,1,10
3415,24,27.8,1,4,0,0,13,0,0,1,1,9
1486,24,27.8,1,3,0,1,12,0,2,1,1,1
10759,24,27.8,1,2,0,0,15,1,1,1,1,7
1819,24,27.8,1,4,0,0,11,0,1,1,1,5
6281,24,27.9,1,1,0,0,13,1,2,1,1,8
3997,24,27.9,1,1,0,0,15,0,0,1,1,8
6353,24,27.9,1,4,0,0,11,1,1,1,1,9
7151,24,27.9,1,1,0,0,12,1,1,1,1,8
4185,24,27.9,1,2,0,0,15,0,0,1,1,6
71,24,28,1,2,0,0,12,0,0,1,1,10
3211,24,28,1,2,0,0,15,0,2,1,1,10
9807,24,28,1,1,0,0,12,1,0,1,1,9
4619,24,28.1,1,2,0,0,11,0,2,1,1,6
3402,24,28.1,1,3,0,1,9,0,4,1,1,2
10353,24,28.2,1,5,0,0,10,1,0,1,1,6
5533,24,28.3,1,1,0,0,13,1,2,1,1,6
8410,24,28.3,1,3,0,1,13,1,3,1,1,5
2215,24,28.3,1,5,0,0,12,0,1,1,1,10
4043,24,28.3,1,5,0,0,9,0,0,1,1,10
6037,24,28.3,1,2,0,0,15,1,2,1,1,7
9055,24,28.4,1,5,0,0,10,1,1,1,1,9
4277,24,28.4,1,5,0,0,12,0,0,1,1,10
8905,24,28.4,1,5,0,0,10,1,2,1,1,5
9013,24,28.5,1,2,0,0,12,1,0,1,1,6
475,24,28.5,1,3,0,0,15,0,0,1,1,9
5001,24,28.5,1,5,0,1,13,0,8,1,1,0
9587,24,28.6,1,5,0,0,12,1,0,1,1,9
9791,24,28.6,1,1,0,0,10,1,0,1,1,9
7056,24,28.6,1,2,0,1,10,1,5,1,1,7
5303,24,28.6,1,4,0,0,12,0,2,1,1,7
2711,24,28.6,1,1,0,0,12,0,0,1,1,7
587,24,28.6,1,1,0,0,13,0,2,1,1,10
287,24,28.6,1,2,0,0,10,0,2,1,1,5
5631,24,28.7,1,2,0,0,9,1,0,1,1,5
3839,24,28.7,1,3,0,0,14,0,0,1,1,5
8315,24,28.7,1,1,0,0,9,1,1,1,1,7
8525,24,28.8,1,4,0,0,10,1,2,1,1,9
711,24,28.8,1,5,0,0,15,0,1,1,1,10
1831,24,28.8,1,5,0,0,13,0,0,1,1,10
8111,24,28.8,1,2,0,0,9,1,2,1,1,6
10617,24,28.8,1,5,0,0,10,1,1,1,1,6
4523,24,28.8,1,3,0,1,11,0,5,1,1,0
6425,24,28.8,1,5,0,0,12,1,1,1,1,9
7967,24,28.9,1,3,0,0,9,1,0,1,1,6
9249,24,28.9,1,2,0,0,13,1,0,1,1,5
1181,24,28.9,1,2,0,0,10,0,0,1,1,7
2701,24,28.9,1,2,0,0,11,0,0,1,1,9
5173,24,28.9,1,5,0,0,12,0,1,1,1,5
7483,24,28.9,1,1,0,0,9,1,1,1,1,7
5825,24,28.9,1,2,0,0,9,1,0,1,1,10
455,24,29,1,2,0,0,9,0,2,1,1,8
1569,24,29,1,2,0,0,13,0,0,1,1,9
5259,24,29,1,3,0,0,10,0,1,1,1,6
5253,24,29,1,1,0,0,9,0,1,1,1,8
3737,24,29.1,1,1,0,0,13,0,1,1,1,6
603,24,29.1,1,4,0,0,15,0,2,1,1,5
4869,24,29.2,1,2,0,0,11,0,2,1,1,6
951,24,29.2,1,4,0,0,14,0,0,1,1,9
1179,24,29.3,1,2,0,0,13,0,2,1,1,9
5399,24,29.3,1,5,0,0,13,0,1,1,1,8
1823,24,29.3,1,4,0,0,10,0,0,1,1,8
81,24,29.3,1,2,0,0,12,0,1,1,1,10
8899,24,29.3,1,2,0,0,12,1,1,1,1,7
2529,24,29.4,1,5,0,0,10,0,1,1,1,5
4129,24,29.5,1,4,0,0,11,0,2,1,1,7
3225,24,29.5,1,2,0,0,10,0,1,1,1,6
4795,24,29.5,1,2,0,0,11,0,1,1,1,10
6761,24,29.5,1,2,0,0,14,1,2,1,1,10
8917,24,29.6,1,4,0,0,12,1,2,1,1,9
195,24,29.6,1,3,0,0,9,0,1,1,1,9
8151,24,29.7,1,4,0,0,9,1,1,1,1,7
7917,24,29.7,1,2,0,0,13,1,2,1,1,7
10489,24,29.7,1,2,0,0,14,1,1,1,1,6
9279,24,29.7,1,1,0,0,12,1,2,1,1,7
6985,24,29.7,1,5,0,0,15,1,1,1,1,7
2067,24,29.7,1,1,0,0,10,0,0,1,1,9
8847,24,29.7,1,1,0,0,10,1,2,1,1,8
8727,24,29.8,1,2,0,0,14,1,1,1,1,6
355,24,29.8,1,4,0,0,10,0,1,1,1,8
1821,24,29.8,1,3,0,0,12,0,0,1,1,7
1365,24,29.8,1,5,0,0,15,0,1,1,1,10
2973,24,29.8,1,2,0,0,12,0,0,1,1,9
423,24,29.8,1,5,0,0,13,0,1,1,1,6
6901,24,29.8,1,3,0,0,13,1,2,1,1,7
6767,24,29.8,1,1,0,0,14,1,0,1,1,5
8077,24,29.9,1,2,0,0,11,1,1,1,1,8
3019,24,29.9,1,1,0,1,15,0,2,1,1,0
2217,24,30,1,4,0,0,15,0,0,1,1,7
3283,25,0.1,1,3,0,1,9,0,3,1,1,1
7138,25,0.5,1,5,0,1,12,1,7,1,1,4
9158,25,0.7,1,1,0,1,10,1,4,1,1,7
1940,25,0.8,1,5,0,1,15,0,1,1,1,7
7296,25,1,1,1,0,1,12,1,7,1,1,3
5206,25,1.3,1,1,0,1,15,0,5,1,1,5
6082,25,2.1,1,4,0,1,12,1,1,1,1,0
7892,25,2.3,1,2,0,1,10,1,7,1,1,4
2800,25,2.5,1,1,0,1,12,0,6,1,1,3
3742,25,2.7,1,4,0,1,10,0,6,1,1,10
446,25,2.8,1,1,0,1,9,0,5,1,1,8
4270,25,3.9,1,5,0,1,13,0,4,1,1,1
8932,25,4.4,1,3,0,1,12,1,6,1,1,7
574,25,4.5,1,4,0,1,15,0,7,1,1,1
6324,25,4.7,1,5,0,1,14,1,4,1,1,5
8607,25,4.8,1,4,0,1,14,1,3,1,1,2
10282,25,5.3,1,5,0,1,9,1,8,1,1,10
8217,25,5.5,1,3,0,1,10,1,6,1,1,2
9910,25,5.8,1,2,0,1,15,1,1,1,1,8
7548,25,6.2,1,5,0,1,9,1,6,1,1,10
2188,25,6.5,1,2,0,1,10,0,4,1,1,2
3440,25,6.5,1,1,0,1,12,0,3,1,1,6
6114,25,6.6,1,5,0,1,10,1,3,1,1,5
5338,25,6.7,1,2,0,1,12,0,5,1,1,1
5828,25,7,1,4,0,1,12,1,7,1,1,8
5528,25,7.4,1,4,0,1,11,1,2,1,1,2
4786,25,7.5,1,3,0,1,15,0,6,1,1,7
3192,25,8.1,1,1,0,1,14,0,2,1,1,2
5090,25,8.6,1,3,0,1,9,0,3,1,1,1
7734,25,8.7,1,3,0,1,9,1,6,1,1,7
7674,25,9.2,1,4,0,1,13,1,2,1,1,0
722,25,9.2,1,2,0,1,11,0,2,1,1,8
7934,25,9.4,1,4,0,1,11,1,3,1,1,9
8742,25,10,1,2,0,1,13,1,4,1,1,3
10790,25,10.1,1,1,0,1,12,1,5,1,1,2
1500,25,10.4,1,1,0,1,15,0,1,1,1,2
9034,25,10.8,1,1,0,1,9,1,4,1,1,6
8771,25,10.9,1,4,0,1,15,1,4,1,1,0
7991,25,11.1,1,5,0,1,12,1,6,1,1,3
9275,25,11.1,1,5,0,1,13,1,4,1,1,2
967,25,11.2,1,5,0,1,12,0,7,1,1,4
9005,25,11.2,1,5,0,1,12,1,6,1,1,1
8740,25,11.6,1,1,0,1,14,1,2,1,1,3
2355,25,12.9,1,2,0,1,14,0,4,1,1,3
4913,25,13.1,1,1,0,1,14,0,7,1,1,1
4392,25,13.2,1,5,0,1,14,0,6,1,1,1
2726,25,13.4,1,5,0,1,15,0,4,1,1,2
1006,25,13.5,1,5,0,1,14,0,6,1,1,4
1223,25,13.6,1,1,0,1,11,0,4,1,1,2
6161,25,13.7,1,4,0,1,13,1,7,1,1,2
7688,25,14,1,4,0,1,13,1,1,1,1,6
8144,25,14.3,1,2,0,1,12,1,5,1,1,7
10294,25,14.4,1,4,0,1,10,1,2,1,1,6
4002,25,15,1,5,0,1,12,0,6,1,1,3
9401,25,15,1,2,0,0,13,1,1,1,1,10
9123,25,15,1,3,0,0,13,1,2,1,1,5
4051,25,15,1,1,0,0,12,0,2,1,1,6
707,25,15,1,3,0,0,10,0,0,1,1,10
7499,25,15,1,4,0,0,15,1,2,1,1,8
5269,25,15.1,1,4,0,0,14,0,1,1,1,9
5925,25,15.1,1,5,0,0,10,1,0,1,1,5
2833,25,15.1,1,3,0,0,10,0,0,1,1,8
113,25,15.1,1,3,0,0,15,0,1,1,1,6
9255,25,15.2,1,1,0,0,10,1,2,1,1,7
7173,25,15.2,1,2,0,0,10,1,1,1,1,9
10265,25,15.2,1,4,0,0,14,1,0,1,1,10
8999,25,15.2,1,3,0,0,15,1,1,1,1,7
6387,25,15.2,1,5,0,0,13,1,2,1,1,6
5588,25,15.2,1,5,0,1,12,1,5,1,1,8
9001,25,15.3,1,1,0,0,15,1,0,1,1,5
7469,25,15.3,1,5,0,0,12,1,2,1,1,6
10377,25,15.3,1,1,0,0,15,1,1,1,1,7
2329,25,15.3,1,4,0,0,9,0,1,1,1,6
7833,25,15.3,1,3,0,0,12,1,1,1,1,9
10667,25,15.3,1,2,0,1,11,1,7,1,1,1
2167,25,15.3,1,2,0,0,15,0,1,1,1,6
10441,25,15.3,1,2,0,0,15,1,1,1,1,10
4247,25,15.3,1,3,0,0,9,0,1,1,1,6
7593,25,15.4,1,1,0,0,10,1,2,1,1,7
2035,25,15.4,1,1,0,0,9,0,0,1,1,8
3807,25,15.5,1,4,0,0,14,0,2,1,1,6
7705,25,15.5,1,1,0,0,12,1,2,1,1,7
5699,25,15.5,1,5,0,0,14,1,0,1,1,8
8348,25,15.5,1,4,0,1,15,1,6,1,1,8
6087,25,15.5,1,4,0,0,12,1,2,1,1,6
19,25,15.5,1,1,0,0,12,0,1,1,1,9
2353,25,15.6,1,2,0,0,12,0,2,1,1,8
7977,25,15.6,1,2,0,0,12,1,0,1,1,9
6536,25,15.7,1,2,0,1,13,1,8,1,1,3
8197,25,15.7,1,5,0,0,12,1,1,1,1,8
2391,25,15.7,1,1,0,0,15,0,2,1,1,6
4267,25,15.7,1,3,0,0,14,0,0,1,1,8
10819,25,15.8,1,4,0,0,12,1,0,1,1,9
5659,25,15.8,1,5,0,0,13,1,2,1,1,7
10333,25,15.8,1,1,0,0,12,1,2,1,1,10
5339,25,15.9,1,3,0,1,13,0,6,1,1,2
3833,25,15.9,1,1,0,0,12,0,0,1,1,6
2407,25,15.9,1,1,0,0,10,0,2,1,1,5
8541,25,15.9,1,4,0,0,12,1,1,1,1,10
10538,25,15.9,1,5,0,1,15,1,5,1,1,2
5157,25,15.9,1,2,0,1,10,0,8,1,1,0
101,25,16,1,4,0,0,13,0,2,1,1,6
1615,25,16.1,1,5,0,0,12,0,2,1,1,6
10729,25,16.1,1,1,0,0,12,1,0,1,1,6
9441,25,16.1,1,2,0,0,9,1,2,1,1,9
9253,25,16.2,1,5,0,0,13,1,1,1,1,8
9667,25,16.2,1,5,0,0,10,1,1,1,1,6
10033,25,16.2,1,4,0,0,14,1,2,1,1,7
1431,25,16.2,1,5,0,0,13,0,2,1,1,6
3311,25,16.2,1,5,0,0,11,0,2,1,1,10
4543,25,16.2,1,5,0,0,9,0,1,1,1,8
1729,25,16.2,1,1,0,0,15,0,2,1,1,10
5459,25,16.2,1,1,0,0,12,1,0,1,1,9
8497,25,16.3,1,1,0,0,14,1,1,1,1,5
2473,25,16.3,1,5,0,0,14,0,1,1,1,10
3942,25,16.3,1,5,0,1,13,0,4,1,1,1
2503,25,16.4,1,3,0,0,12,0,2,1,1,8
2741,25,16.4,1,1,0,0,11,0,2,1,1,8
4797,25,16.4,1,2,0,0,12,0,0,1,1,7
1911,25,16.4,1,1,0,0,12,0,1,1,1,9
3327,25,16.4,1,5,0,0,13,0,0,1,1,8
4111,25,16.4,1,4,0,0,14,0,1,1,1,9
3499,25,16.5,1,5,0,0,9,0,0,1,1,9
5663,25,16.5,1,2,0,0,11,1,0,1,1,8
10757,25,16.5,1,2,0,0,15,1,0,1,1,9
10461,25,16.5,1,3,0,0,9,1,1,1,1,7
1329,25,16.5,1,5,0,0,15,0,1,1,1,8
3943,25,16.6,1,4,0,0,11,0,1,1,1,10
10491,25,16.6,1,4,0,0,15,1,1,1,1,5
6045,25,16.6,1,3,0,0,15,1,1,1,1,6
4167,25,16.7,1,4,0,0,12,0,0,1,1,10
8135,25,16.7,1,5,0,0,9,1,0,1,1,10
7587,25,16.7,1,4,0,0,10,1,2,1,1,7
8653,25,16.7,1,4,0,0,13,1,0,1,1,10
4841,25,16.8,1,4,0,0,13,0,0,1,1,10
9975,25,16.8,1,1,0,0,14,1,2,1,1,7
4819,25,16.8,1,5,0,0,10,0,0,1,1,10
8179,25,16.8,1,2,0,0,10,1,2,1,1,7
7453,25,16.9,1,5,0,0,14,1,1,1,1,8
3963,25,17,1,1,0,0,14,0,1,1,1,10
3545,25,17,1,4,0,0,11,0,2,1,1,9
909,25,17,1,2,0,0,14,0,1,1,1,5
6591,25,17,1,1,0,0,11,1,0,1,1,9
9150,25,17,1,5,0,1,13,1,4,1,1,7
3445,25,17.1,1,5,0,0,13,0,1,1,1,9
1419,25,17.1,1,4,0,0,15,0,1,1,1,10
3585,25,17.1,1,4,0,0,11,0,2,1,1,7
5819,25,17.1,1,5,0,0,12,1,2,1,1,5
133,25,17.1,1,1,0,0,11,0,2,1,1,9
8529,25,17.2,1,1,0,0,14,1,0,1,1,5
2853,25,17.2,1,3,0,0,12,0,0,1,1,6
10509,25,17.2,1,2,0,0,10,1,1,1,1,9
1115,25,17.3,1,3,0,0,13,0,1,1,1,9
1447,25,17.3,1,2,0,0,9,0,0,1,1,8
3184,25,17.3,1,4,0,1,12,0,4,1,1,1
10835,25,17.3,1,3,0,0,13,1,1,1,1,6
4119,25,17.3,1,3,0,0,13,0,0,1,1,10
7653,25,17.4,1,5,0,0,9,1,2,1,1,7
5,25,17.4,1,2,0,0,13,0,1,1,1,6
1807,25,17.4,1,1,0,0,13,0,2,1,1,7
403,25,17.4,1,2,0,0,9,0,1,1,1,5
5057,25,17.5,1,3,0,0,15,0,0,1,1,5
10823,25,17.5,1,5,0,0,12,1,0,1,1,8
10069,25,17.5,1,1,0,0,9,1,1,1,1,9
1907,25,17.5,1,1,0,0,12,0,0,1,1,8
6415,25,17.5,1,5,0,0,12,1,1,1,1,10
9647,25,17.6,1,2,0,0,12,1,2,1,1,7
8419,25,17.6,1,5,0,0,15,1,0,1,1,9
1703,25,17.6,1,3,0,0,12,0,1,1,1,7
8639,25,17.6,1,3,0,0,10,1,2,1,1,9
9323,25,17.6,1,2,0,0,13,1,0,1,1,7
7329,25,17.7,1,4,0,0,11,1,1,1,1,5
461,25,17.7,1,5,0,0,14,0,0,1,1,10
6215,25,17.7,1,2,0,0,9,1,1,1,1,6
959,25,17.7,1,3,0,0,13,0,2,1,1,9
4763,25,17.7,1,3,0,0,12,0,0,1,1,7
6713,25,17.8,1,3,0,0,9,1,0,1,1,6
7323,25,17.8,1,2,0,0,14,1,1,1,1,9
1189,25,17.8,1,3,0,0,11,0,2,1,1,8
3687,25,17.8,1,4,0,0,14,0,1,1,1,9
573,25,17.9,1,1,0,0,12,0,1,1,1,5
5405,25,17.9,1,4,0,0,10,0,0,1,1,9
8459,25,17.9,1,4,0,0,12,1,0,1,1,9
5687,25,17.9,1,3,0,0,13,1,0,1,1,8
10515,25,17.9,1,4,0,0,14,1,2,1,1,10
1711,25,17.9,1,5,0,0,9,0,1,1,1,6
7835,25,17.9,1,5,0,0,12,1,0,1,1,8
9891,25,18,1,5,0,0,11,1,0,1,1,7
5417,25,18,1,5,0,0,12,0,1,1,1,7
6949,25,18,1,4,0,0,9,1,0,1,1,9
3879,25,18.1,1,4,0,0,15,0,2,1,1,5
33,25,18.1,1,2,0,0,11,0,0,1,1,7
3013,25,18.1,1,4,0,0,9,0,1,1,1,8
10001,25,18.1,1,5,0,0,9,1,2,1,1,8
10581,25,18.1,1,4,0,0,10,1,2,1,1,5
7627,25,18.1,1,3,0,0,14,1,0,1,1,5
5531,25,18.2,1,1,0,0,9,1,0,1,1,5
5937,25,18.2,1,3,0,0,14,1,0,1,1,5
4707,25,18.2,1,2,0,0,14,0,1,1,1,9
5005,25,18.3,1,1,0,0,15,0,0,1,1,6
379,25,18.3,1,2,0,0,13,0,0,1,1,8
1055,25,18.3,1,4,0,0,9,0,0,1,1,6
8183,25,18.3,1,3,0,0,13,1,1,1,1,9
7303,25,18.3,1,1,0,0,11,1,1,1,1,7
2676,25,18.3,1,5,0,1,15,0,5,1,1,9
2769,25,18.3,1,4,0,0,10,0,1,1,1,6
1033,25,18.4,1,2,0,0,15,0,1,1,1,9
9889,25,18.4,1,1,0,0,12,1,0,1,1,10
1305,25,18.4,1,5,0,0,14,0,2,1,1,7
9045,25,18.4,1,3,0,1,12,1,6,1,1,1
8633,25,18.4,1,4,0,0,12,1,1,1,1,8
9024,25,18.4,1,1,0,1,12,1,1,1,1,1
2276,25,18.5,1,4,0,1,15,0,6,1,1,10
6131,25,18.6,1,2,0,0,15,1,1,1,1,5
6971,25,18.6,1,2,0,0,10,1,1,1,1,6
7139,25,18.6,1,5,0,0,15,1,0,1,1,9
123,25,18.6,1,2,0,0,12,0,1,1,1,10
10555,25,18.6,1,2,0,0,15,1,1,1,1,7
7373,25,18.7,1,3,0,0,14,1,2,1,1,8
5145,25,18.7,1,1,0,0,12,0,1,1,1,6
4241,25,18.8,1,1,0,0,12,0,0,1,1,8
9231,25,18.8,1,2,0,0,13,1,2,1,1,6
6607,25,18.8,1,3,0,0,13,1,0,1,1,7
1757,25,18.8,1,4,0,0,13,0,0,1,1,7
3263,25,18.8,1,4,0,0,13,0,1,1,1,5
1063,25,18.9,1,5,0,0,11,0,2,1,1,10
9171,25,18.9,1,5,0,0,9,1,2,1,1,10
5013,25,18.9,1,2,0,0,9,0,0,1,1,5
3379,25,18.9,1,5,0,0,13,0,1,1,1,10
4897,25,18.9,1,4,0,0,13,0,1,1,1,6
8943,25,19,1,5,0,0,15,1,0,1,1,7
5263,25,19,1,4,0,0,14,0,2,1,1,9
2495,25,19,1,1,0,0,10,0,2,1,1,6
10865,25,19.1,1,1,0,0,13,1,2,1,1,10
4668,25,19.1,1,1,0,1,9,0,8,1,1,5
1655,25,19.1,1,4,0,0,15,0,1,1,1,10
2297,25,19.2,1,3,0,0,10,0,2,1,1,6
3401,25,19.2,1,2,0,0,15,0,1,1,1,5
6423,25,19.2,1,4,0,0,14,1,0,1,1,6
8657,25,19.2,1,5,0,0,15,1,2,1,1,5
1369,25,19.2,1,5,0,0,11,0,2,1,1,10
8267,25,19.3,1,2,0,0,13,1,0,1,1,5
9552,25,19.3,1,1,0,1,14,1,6,1,1,5
4750,25,19.3,1,2,0,1,10,0,8,1,1,1
1601,25,19.3,1,2,0,0,10,0,2,1,1,5
5297,25,19.3,1,5,0,0,15,0,0,1,1,5
2129,25,19.3,1,5,0,0,10,0,2,1,1,9
6501,25,19.4,1,4,0,0,15,1,0,1,1,10
4915,25,19.4,1,2,0,0,15,0,1,1,1,9
6967,25,19.4,1,3,0,0,15,1,1,1,1,10
4507,25,19.4,1,2,0,0,12,0,1,1,1,6
8113,25,19.4,1,5,0,0,14,1,0,1,1,8
3273,25,19.4,1,1,0,0,14,0,0,1,1,5
3723,25,19.5,1,2,0,0,10,0,0,1,1,8
241,25,19.5,1,5,0,0,13,0,2,1,1,9
9945,25,19.5,1,5,0,0,15,1,1,1,1,6
6525,25,19.5,1,1,0,0,14,1,2,1,1,9
4709,25,19.5,1,3,0,0,9,0,1,1,1,10
2941,25,19.5,1,4,0,0,12,0,0,1,1,9
31,25,19.6,1,5,0,1,14,0,1,1,1,1
2405,25,19.6,1,1,0,0,10,0,0,1,1,6
7821,25,19.6,1,3,0,0,13,1,0,1,1,8
3495,25,19.6,1,5,0,0,10,0,0,1,1,6
8929,25,19.7,1,4,0,0,10,1,1,1,1,8
10735,25,19.7,1,5,0,0,15,1,1,1,1,5
6289,25,19.7,1,2,0,0,12,1,1,1,1,7
9903,25,19.7,1,4,0,0,11,1,0,1,1,7
7691,25,19.7,1,4,0,0,11,1,1,1,1,9
7101,25,19.8,1,4,0,0,9,1,2,1,1,9
1327,25,19.8,1,5,0,0,15,0,1,1,1,10
373,25,19.8,1,4,0,0,14,0,2,1,1,8
3527,25,19.9,1,4,0,0,12,0,2,1,1,5
9905,25,19.9,1,2,0,0,9,1,2,1,1,8
6797,25,19.9,1,4,0,0,12,1,2,1,1,6
7011,25,19.9,1,3,0,0,15,1,1,1,1,8
127,25,19.9,1,2,0,0,12,0,1,1,1,8
185,25,20,1,2,0,0,15,0,0,1,1,6
3635,25,20,1,2,0,0,12,0,1,1,1,6
4965,25,20,1,4,0,0,11,0,0,1,1,5
1106,25,20,1,1,0,1,14,0,3,1,1,10
9389,25,20,1,2,0,0,14,1,2,1,1,5
9741,25,20.1,1,3,0,0,11,1,2,1,1,9
8825,25,20.1,1,1,0,0,12,1,2,1,1,7
7405,25,20.1,1,2,0,0,11,1,1,1,1,9
181,25,20.2,1,5,0,0,12,0,1,1,1,8
10141,25,20.2,1,2,0,0,10,1,1,1,1,8
8417,25,20.2,1,5,0,0,9,1,2,1,1,9
6065,25,20.2,1,2,0,0,12,1,0,1,1,7
2449,25,20.2,1,2,0,1,12,0,4,1,1,2
3179,25,20.3,1,3,0,0,10,0,0,1,1,6
4834,25,20.3,1,3,0,1,11,0,2,1,1,9
1663,25,20.3,1,1,0,0,15,0,2,1,1,10
9691,25,20.3,1,5,0,0,13,1,2,1,1,9
10271,25,20.4,1,2,0,0,10,1,2,1,1,8
3105,25,20.5,1,2,0,1,13,0,8,1,1,0
6189,25,20.5,1,3,0,0,13,1,0,1,1,5
10289,25,20.5,1,3,0,0,10,1,1,1,1,8
5745,25,20.5,1,2,0,0,15,1,2,1,1,9
855,25,20.6,1,4,0,0,11,0,2,1,1,10
2089,25,20.6,1,2,0,0,11,0,2,1,1,10
6339,25,20.6,1,1,0,0,12,1,1,1,1,8
4273,25,20.6,1,5,0,0,13,0,1,1,1,6
5777,25,20.6,1,4,0,0,12,1,1,1,1,6
10579,25,20.6,1,4,0,0,12,1,1,1,1,6
7713,25,20.6,1,1,0,0,12,1,1,1,1,5
3955,25,20.7,1,5,0,0,13,0,2,1,1,7
7065,25,20.7,1,5,0,0,9,1,2,1,1,5
2959,25,20.7,1,1,0,0,9,0,1,1,1,9
2953,25,20.7,1,3,0,0,9,0,0,1,1,7
53,25,20.7,1,5,0,0,13,0,1,1,1,9
8859,25,20.7,1,2,0,0,13,1,0,1,1,9
237,25,20.7,1,2,0,0,12,0,1,1,1,10
2335,25,20.8,1,1,0,0,9,0,2,1,1,8
10165,25,20.8,1,3,0,0,10,1,2,1,1,7
4663,25,20.8,1,2,0,1,12,0,8,1,1,4
1539,25,20.8,1,5,0,0,14,0,2,1,1,10
8453,25,20.8,1,4,0,0,11,1,2,1,1,9
2533,25,20.8,1,3,0,0,15,0,2,1,1,9
9151,25,20.9,1,3,0,1,14,1,7,1,1,3
3801,25,21,1,2,0,0,15,0,1,1,1,10
4377,25,21,1,2,0,0,13,0,0,1,1,8
1993,25,21,1,1,0,0,14,0,1,1,1,7
1251,25,21,1,5,0,0,12,0,1,1,1,10
1625,25,21,1,4,0,0,11,0,2,1,1,5
273,25,21,1,3,0,0,12,0,2,1,1,10
4601,25,21.1,1,2,0,0,14,0,0,1,1,5
6675,25,21.1,1,5,0,0,9,1,2,1,1,5
2613,25,21.1,1,2,0,0,11,0,0,1,1,7
239,25,21.1,1,5,0,0,10,0,2,1,1,10
5407,25,21.2,1,3,0,0,12,0,0,1,1,8
4177,25,21.2,1,1,0,0,11,0,0,1,1,5
7311,25,21.2,1,3,0,0,9,1,2,1,1,5
521,25,21.2,1,5,0,0,15,0,2,1,1,8
3345,25,21.2,1,4,0,0,9,0,1,1,1,6
9475,25,21.3,1,5,0,0,11,1,0,1,1,10
4845,25,21.3,1,3,0,0,15,0,0,1,1,5
2895,25,21.3,1,1,0,0,12,0,2,1,1,5
2269,25,21.3,1,5,0,0,9,0,1,1,1,7
4505,25,21.3,1,3,0,0,13,0,0,1,1,5
5993,25,21.4,1,5,0,0,12,1,2,1,1,10
7031,25,21.4,1,2,0,0,11,1,0,1,1,8
9299,25,21.4,1,4,0,0,12,1,1,1,1,7
105,25,21.4,1,1,0,0,11,0,1,1,1,6
161,25,21.4,1,4,0,0,11,0,0,1,1,10
3561,25,21.4,1,4,0,0,13,0,0,1,1,9
1988,25,21.4,1,1,0,1,11,0,6,1,1,6
7205,25,21.4,1,2,0,0,14,1,1,1,1,9
8543,25,21.4,1,4,0,0,11,1,1,1,1,6
8271,25,21.5,1,5,0,0,12,1,0,1,1,9
8051,25,21.5,1,4,0,0,10,1,1,1,1,10
1647,25,21.6,1,5,0,0,11,0,1,1,1,10
8056,25,21.7,1,1,0,1,15,1,7,1,1,3
9137,25,21.7,1,2,0,0,14,1,1,1,1,9
7385,25,21.7,1,5,0,0,13,1,0,1,1,8
2469,25,21.7,1,3,0,0,14,0,2,1,1,9
2749,25,21.7,1,1,0,0,12,0,0,1,1,8
8741,25,21.8,1,3,0,0,14,1,2,1,1,9
2291,25,21.8,1,5,0,0,10,0,1,1,1,9
8207,25,21.8,1,5,0,0,10,1,2,1,1,5
1035,25,21.8,1,4,0,0,9,0,1,1,1,9
8559,25,21.8,1,5,0,0,13,1,1,1,1,7
5449,25,21.8,1,3,0,0,12,1,2,1,1,7
8991,25,21.9,1,4,0,0,13,1,1,1,1,8
9639,25,22,1,3,0,0,12,1,2,1,1,9
6301,25,22,1,4,0,0,9,1,1,1,1,7
7861,25,22,1,4,0,0,12,1,0,1,1,8
10619,25,22,1,5,0,0,14,1,0,1,1,5
4871,25,22,1,3,0,0,11,0,0,1,1,6
7219,25,22,1,3,0,0,12,1,0,1,1,6
91,25,22,1,5,0,0,12,0,2,1,1,8
2687,25,22,1,5,0,0,10,0,2,1,1,6
2785,25,22,1,3,0,0,10,0,1,1,1,6
3185,25,22.1,1,3,0,0,12,0,1,1,1,7
1985,25,22.1,1,3,0,0,12,0,1,1,1,7
1883,25,22.1,1,4,0,0,12,0,1,1,1,5
10657,25,22.1,1,3,0,0,13,1,0,1,1,5
833,25,22.1,1,1,0,0,13,0,1,1,1,8
9615,25,22.1,1,2,0,0,14,1,0,1,1,8
539,25,22.1,1,5,0,0,13,0,1,1,1,5
4139,25,22.2,1,4,0,0,12,0,1,1,1,6
2511,25,22.2,1,5,0,0,11,0,0,1,1,8
9305,25,22.2,1,1,0,0,12,1,0,1,1,9
3783,25,22.3,1,5,0,0,15,0,2,1,1,7
3735,25,22.3,1,2,0,0,12,0,0,1,1,8
341,25,22.3,1,5,0,0,11,0,0,1,1,9
3991,25,22.4,1,1,0,0,12,0,2,1,1,10
899,25,22.4,1,3,0,0,9,0,2,1,1,9
10397,25,22.4,1,4,0,0,10,1,2,1,1,7
6687,25,22.5,1,4,0,0,14,1,2,1,1,7
10147,25,22.5,1,3,0,0,15,1,0,1,1,7
10409,25,22.5,1,3,0,0,12,1,0,1,1,10
9211,25,22.5,1,2,0,0,12,1,0,1,1,8
7891,25,22.5,1,1,0,0,13,1,0,1,1,5
2961,25,22.5,1,5,0,0,12,0,2,1,1,7
6883,25,22.6,1,1,0,0,15,1,2,1,1,10
6330,25,22.6,1,5,0,1,9,1,6,1,1,7
3063,25,22.6,1,4,0,0,13,0,0,1,1,6
4161,25,22.6,1,2,0,0,13,0,2,1,1,8
1204,25,22.7,1,1,0,1,12,0,1,1,1,3
6841,25,22.7,1,3,0,0,14,1,2,1,1,5
6225,25,22.7,1,5,0,0,15,1,2,1,1,5
4587,25,22.7,1,3,0,0,14,0,0,1,1,6
1787,25,22.7,1,2,0,0,12,0,1,1,1,8
3869,25,22.7,1,1,0,0,12,0,1,1,1,9
5987,25,22.7,1,3,0,0,15,1,1,1,1,9
7577,25,22.7,1,2,0,0,11,1,0,1,1,5
5155,25,22.8,1,1,0,0,13,0,2,1,1,5
6689,25,22.8,1,3,0,0,12,1,2,1,1,7
559,25,22.8,1,5,0,0,11,0,0,1,1,9
10705,25,22.9,1,1,0,0,12,1,1,1,1,8
1603,25,22.9,1,4,0,0,14,0,0,1,1,5
631,25,22.9,1,1,0,0,11,0,2,1,1,10
36,25,22.9,1,2,0,1,14,0,5,1,1,1
2775,25,22.9,1,2,0,0,14,0,0,1,1,8
5955,25,22.9,1,3,0,0,10,1,1,1,1,8
5284,25,23,1,5,0,1,10,0,7,1,1,10
3856,25,23,1,1,0,1,10,0,2,1,1,7
8462,25,23,1,5,0,1,10,1,6,1,1,1
6719,25,23,1,1,0,0,10,1,1,1,1,5
8241,25,23.1,1,2,0,0,11,1,1,1,1,7
2848,25,23.1,1,2,0,1,9,0,2,1,1,2
3073,25,23.1,1,4,0,0,12,0,1,1,1,9
1533,25,23.2,1,5,0,0,13,0,2,1,1,9
5829,25,23.2,1,3,0,0,13,1,0,1,1,7
2929,25,23.2,1,5,0,0,14,0,2,1,1,9
7467,25,23.2,1,4,0,0,14,1,1,1,1,6
8399,25,23.2,1,4,0,0,12,1,0,1,1,10
5383,25,23.2,1,3,0,0,10,0,0,1,1,7
3097,25,23.3,1,5,0,0,12,0,0,1,1,9
5097,25,23.3,1,5,0,0,10,0,2,1,1,7
7171,25,23.3,1,5,0,0,15,1,1,1,1,6
4195,25,23.4,1,3,0,0,11,0,1,1,1,6
207,25,23.4,1,4,0,0,15,0,2,1,1,5
5789,25,23.4,1,4,0,0,11,1,1,1,1,9
6321,25,23.4,1,1,0,0,12,1,2,1,1,7
1855,25,23.4,1,1,0,0,10,0,1,1,1,6
7711,25,23.4,1,2,0,0,12,1,2,1,1,9
931,25,23.4,1,1,0,0,15,0,2,1,1,8
2849,25,23.4,1,2,0,0,12,0,2,1,1,9
4290,25,23.5,1,3,0,1,15,0,3,1,1,0
827,25,23.5,1,3,0,0,14,0,1,1,1,9
10711,25,23.5,1,5,0,0,14,1,1,1,1,9
3537,25,23.5,1,2,0,0,15,0,1,1,1,9
9417,25,23.5,1,5,0,0,14,1,2,1,1,9
6017,25,23.5,1,5,0,0,9,1,2,1,1,8
2445,25,23.5,1,5,0,0,11,0,1,1,1,5
5926,25,23.5,1,2,0,1,11,1,8,1,1,3
2513,25,23.5,1,1,0,0,12,0,2,1,1,8
5377,25,23.6,1,1,0,0,9,0,2,1,1,5
957,25,23.6,1,2,0,0,12,0,1,1,1,9
6991,25,23.6,1,5,0,1,12,1,6,1,1,2
7647,25,23.6,1,3,0,0,13,1,0,1,1,10
1385,25,23.6,1,1,0,0,15,0,2,1,1,5
3295,25,23.6,1,1,0,0,10,0,2,1,1,7
8185,25,23.6,1,3,0,0,15,1,1,1,1,7
9455,25,23.7,1,3,0,0,12,1,2,1,1,10
259,25,23.7,1,4,0,0,10,0,1,1,1,10
2974,25,23.7,1,5,0,1,15,0,4,1,1,5
9747,25,23.8,1,3,0,0,10,1,0,1,1,9
10781,25,23.8,1,3,0,0,15,1,0,1,1,9
9333,25,23.8,1,1,0,0,11,1,1,1,1,5
2759,25,23.8,1,2,0,1,12,0,6,1,1,4
9629,25,23.9,1,2,0,0,10,1,0,1,1,7
1953,25,23.9,1,1,0,0,15,0,1,1,1,6
4875,25,24,1,3,0,0,9,0,1,1,1,6
4339,25,24,1,1,0,0,10,0,2,1,1,10
5017,25,24,1,3,0,1,9,0,8,1,1,2
9809,25,24,1,4,0,0,11,1,1,1,1,5
7121,25,24,1,1,0,0,9,1,2,1,1,7
3953,25,24.1,1,3,0,0,15,0,1,1,1,8
10519,25,24.1,1,5,0,0,13,1,0,1,1,7
661,25,24.2,1,5,0,0,15,0,1,1,1,8
5077,25,24.2,1,3,0,0,12,0,1,1,1,7
1777,25,24.2,1,2,0,0,13,0,0,1,1,7
279,25,24.2,1,5,0,0,11,0,2,1,1,9
5217,25,24.2,1,5,0,0,15,0,0,1,1,8
8737,25,24.2,1,3,0,0,12,1,1,1,1,9
4329,25,24.2,1,2,0,0,14,0,0,1,1,6
10217,25,24.3,1,2,0,0,14,1,0,1,1,5
785,25,24.3,1,5,0,0,12,0,1,1,1,9
3338,25,24.3,1,5,0,1,12,0,3,1,1,3
6495,25,24.3,1,4,0,0,9,1,0,1,1,5
5741,25,24.3,1,3,0,0,9,1,0,1,1,7
3039,25,24.3,1,2,0,0,12,0,2,1,1,5
3800,25,24.4,1,4,0,1,12,0,4,1,1,8
9921,25,24.4,1,5,0,0,12,1,0,1,1,9
4598,25,24.4,1,5,0,1,10,0,1,1,1,1
10599,25,24.5,1,1,0,0,12,1,2,1,1,7
8927,25,24.5,1,2,0,0,10,1,1,1,1,5
7487,25,24.5,1,1,0,0,9,1,0,1,1,10
7061,25,24.5,1,2,0,0,12,1,0,1,1,7
751,25,24.5,1,1,0,0,13,0,2,1,1,5
7274,25,24.6,1,2,0,1,11,1,6,1,1,5
5495,25,24.6,1,5,0,0,10,1,1,1,1,5
3599,25,24.7,1,3,0,0,10,0,2,1,1,6
2579,25,24.7,1,1,0,0,11,0,2,1,1,9
4307,25,24.7,1,3,0,0,12,0,2,1,1,10
5129,25,24.8,1,3,0,0,13,0,0,1,1,6
5815,25,24.8,1,3,0,0,14,1,0,1,1,5
7059,25,24.8,1,4,0,0,9,1,1,1,1,5
6299,25,24.9,1,1,0,0,14,1,1,1,1,9
3025,25,24.9,1,3,0,0,12,0,2,1,1,6
3861,25,24.9,1,4,0,0,13,0,1,1,1,8
3473,25,24.9,1,3,0,0,9,0,0,1,1,6
3535,25,25,1,2,0,0,15,0,2,1,1,10
799,25,25,1,2,0,0,12,0,1,1,1,10
9335,25,25,1,5,0,0,15,1,2,1,1,5
8265,25,25,1,1,0,0,14,1,0,1,1,8
5018,25,25,1,4,0,1,11,0,1,1,1,5
4975,25,25,1,2,0,0,11,0,2,1,1,9
8391,25,25,1,3,0,0,14,1,0,1,1,9
3477,25,25,1,5,0,0,12,0,1,1,1,6
10403,25,25.1,1,2,0,0,13,1,0,1,1,7
1383,25,25.1,1,1,0,0,9,0,1,1,1,7
9025,25,25.2,1,1,0,0,12,1,1,1,1,9
7869,25,25.2,1,4,0,0,10,1,2,1,1,7
1455,25,25.2,1,4,0,0,12,0,2,1,1,10
2055,25,25.2,1,3,0,0,14,0,1,1,1,8
1086,25,25.2,1,4,0,1,14,0,2,1,1,5
3653,25,25.3,1,2,0,0,10,0,1,1,1,10
2465,25,25.3,1,1,0,0,15,0,2,1,1,8
3341,25,25.3,1,1,0,0,13,0,2,1,1,9
8223,25,25.3,1,3,0,0,14,1,0,1,1,9
985,25,25.3,1,5,0,0,14,0,1,1,1,7
8395,25,25.4,1,3,0,0,10,1,2,1,1,7
5109,25,25.4,1,4,0,0,13,0,1,1,1,6
5595,25,25.4,1,1,0,0,13,1,2,1,1,10
6049,25,25.4,1,2,0,0,9,1,0,1,1,8
4749,25,25.4,1,1,0,0,12,0,0,1,1,9
5875,25,25.4,1,3,0,0,11,1,0,1,1,9
2143,25,25.4,1,5,0,0,11,0,1,1,1,7
4631,25,25.5,1,1,0,0,14,0,1,1,1,10
1467,25,25.5,1,3,0,0,10,0,1,1,1,7
2998,25,25.5,1,2,0,1,14,0,8,1,1,8
7961,25,25.5,1,5,0,0,9,1,2,1,1,5
1071,25,25.5,1,5,0,0,9,0,2,1,1,7
8826,25,25.5,1,2,0,1,15,1,2,1,1,7
991,25,25.5,1,2,0,0,12,0,1,1,1,6
3485,25,25.5,1,4,0,0,12,0,1,1,1,7
2007,25,25.5,1,3,0,0,14,0,0,1,1,6
9961,25,25.6,1,4,0,0,9,1,1,1,1,10
10143,25,25.6,1,5,0,0,15,1,2,1,1,9
1105,25,25.7,1,3,0,0,11,0,0,1,1,8
8855,25,25.7,1,5,0,0,14,1,0,1,1,9
719,25,25.7,1,4,0,0,12,0,2,1,1,9
4263,25,25.8,1,4,0,0,13,0,1,1,1,7
9353,25,25.8,1,1,0,0,15,1,1,1,1,7
4981,25,25.8,1,5,0,0,12,0,1,1,1,10
9459,25,25.9,1,2,0,0,14,1,1,1,1,7
6861,25,25.9,1,1,0,0,9,1,2,1,1,8
8117,25,25.9,1,5,0,0,15,1,0,1,1,7
2755,25,25.9,1,3,0,0,11,0,1,1,1,10
8953,25,25.9,1,2,0,0,14,1,2,1,1,10
1387,25,25.9,1,3,0,0,10,0,2,1,1,7
7321,25,25.9,1,1,0,0,14,1,2,1,1,7
147,25,26.1,1,2,0,0,12,0,1,1,1,8
6629,25,26.1,1,5,0,0,11,1,1,1,1,6
1307,25,26.2,1,1,0,0,11,0,0,1,1,5
10319,25,26.2,1,2,0,0,15,1,0,1,1,10
4787,25,26.2,1,3,0,0,12,0,0,1,1,5
6075,25,26.2,1,1,0,0,11,1,0,1,1,6
2893,25,26.3,1,4,0,0,10,0,2,1,1,5
6221,25,26.3,1,1,0,0,12,1,1,1,1,9
4485,25,26.3,1,2,0,0,10,0,2,1,1,6
1073,25,26.4,1,5,0,0,12,0,1,1,1,5
9069,25,26.4,1,5,0,0,12,1,0,1,1,10
9177,25,26.4,1,1,0,0,15,1,1,1,1,9
1507,25,26.4,1,5,0,0,12,0,1,1,1,6
1339,25,26.4,1,5,0,0,13,0,0,1,1,8
2569,25,26.4,1,3,0,0,10,0,0,1,1,10
9551,25,26.4,1,4,0,0,13,1,0,1,1,10
405,25,26.4,1,5,0,0,11,0,2,1,1,6
5728,25,26.4,1,1,0,1,9,1,8,1,1,8
7774,25,26.5,1,5,0,1,11,1,5,1,1,10
10695,25,26.5,1,5,0,0,14,1,0,1,1,8
4759,25,26.6,1,3,0,0,14,0,2,1,1,10
7747,25,26.6,1,4,0,0,9,1,0,1,1,10
1723,25,26.6,1,5,0,0,9,0,2,1,1,6
6557,25,26.6,1,5,0,0,9,1,1,1,1,8
6651,25,26.7,1,4,0,0,11,1,0,1,1,6
4805,25,26.7,1,1,0,0,12,0,1,1,1,10
7701,25,26.7,1,5,0,0,12,1,2,1,1,7
3657,25,26.7,1,5,0,0,14,0,1,1,1,5
8313,25,26.7,1,1,0,0,12,1,2,1,1,9
7097,25,26.8,1,2,0,0,14,1,0,1,1,5
6023,25,26.8,1,3,0,0,11,1,2,1,1,7
4433,25,26.8,1,4,0,0,9,0,0,1,1,7
9995,25,26.9,1,1,0,0,13,1,0,1,1,9
2821,25,26.9,1,2,0,0,15,0,1,1,1,9
7481,25,26.9,1,3,0,0,15,1,0,1,1,8
7569,25,26.9,1,1,0,0,13,1,0,1,1,10
2907,25,26.9,1,4,0,0,11,0,1,1,1,7
1487,25,27,1,3,0,0,12,0,2,1,1,10
2839,25,27,1,5,0,0,14,0,0,1,1,6
7655,25,27,1,4,0,0,13,1,1,1,1,7
2773,25,27,1,3,0,0,10,0,2,1,1,7
9738,25,27,1,2,0,1,15,1,6,1,1,0
2777,25,27,1,2,0,0,15,0,1,1,1,6
10525,25,27.1,1,2,0,0,13,1,1,1,1,5
308,25,27.1,1,2,0,1,13,0,4,1,1,10
7785,25,27.1,1,3,0,0,14,1,2,1,1,6
3137,25,27.1,1,4,0,0,10,0,1,1,1,5
13,25,27.1,1,1,0,0,12,0,2,1,1,9
7243,25,27.1,1,3,0,0,14,1,0,1,1,9
6191,25,27.2,1,1,0,0,10,1,0,1,1,10
9537,25,27.2,1,1,0,0,14,1,1,1,1,5
4623,25,27.2,1,2,0,0,10,0,0,1,1,10
45,25,27.2,1,5,0,0,14,0,1,1,1,10
1023,25,27.2,1,1,0,0,15,0,1,1,1,6
9243,25,27.2,1,5,0,1,13,1,5,1,1,1
1899,25,27.3,1,5,0,0,12,0,1,1,1,6
1459,25,27.3,1,3,0,0,15,0,0,1,1,5
4245,25,27.3,1,4,0,0,10,0,0,1,1,9
1721,25,27.3,1,5,0,0,12,0,2,1,1,8
337,25,27.3,1,3,0,0,9,0,1,1,1,9
7919,25,27.3,1,1,0,0,14,1,2,1,1,5
6911,25,27.3,1,5,0,0,13,1,2,1,1,8
6601,25,27.4,1,3,0,0,10,1,0,1,1,9
2277,25,27.4,1,1,0,0,9,0,2,1,1,6
1235,25,27.5,1,4,0,0,11,0,1,1,1,5
2605,25,27.5,1,1,0,0,10,0,0,1,1,5
5641,25,27.5,1,1,0,0,9,1,0,1,1,10
3571,25,27.6,1,3,0,0,13,0,0,1,1,8
2025,25,27.6,1,2,0,0,9,0,0,1,1,7
1093,25,27.6,1,1,0,0,10,0,1,1,1,9
3881,25,27.7,1,1,0,0,14,0,1,1,1,10
4181,25,27.7,1,4,0,0,11,0,0,1,1,10
4717,25,27.7,1,3,0,0,11,0,1,1,1,7
10867,25,27.7,1,4,0,0,10,1,2,1,1,8
3799,25,27.7,1,1,0,0,9,0,0,1,1,7
4743,25,27.8,1,2,0,0,10,0,1,1,1,6
305,25,27.8,1,5,0,0,14,0,0,1,1,7
8773,25,27.8,1,1,0,0,13,1,1,1,1,10
8687,25,27.9,1,4,0,0,10,1,2,1,1,7
3480,25,27.9,1,4,0,1,12,0,2,1,1,1
2133,25,27.9,1,4,0,0,9,0,2,1,1,9
437,25,27.9,1,2,0,0,14,0,1,1,1,5
4647,25,27.9,1,4,0,0,15,0,1,1,1,5
10655,25,27.9,1,4,0,0,10,1,1,1,1,8
3173,25,28,1,4,0,0,14,0,1,1,1,8
3289,25,28,1,4,0,0,15,0,0,1,1,10
7485,25,28,1,3,0,0,12,1,2,1,1,8
429,25,28,1,2,0,0,9,0,0,1,1,10
7429,25,28,1,5,0,0,14,1,1,1,1,5
3931,25,28.1,1,5,0,0,9,0,0,1,1,9
4589,25,28.1,1,3,0,0,13,0,2,1,1,8
8937,25,28.1,1,3,0,0,12,1,0,1,1,5
4861,25,28.1,1,2,0,0,12,0,1,1,1,9
10861,25,28.1,1,2,0,0,12,1,1,1,1,7
4453,25,28.1,1,5,0,1,14,0,1,1,1,2
6051,25,28.2,1,5,0,0,15,1,2,1,1,5
169,25,28.2,1,2,0,0,13,0,1,1,1,5
9553,25,28.2,1,4,0,0,12,1,1,1,1,10
2333,25,28.2,1,3,0,0,13,0,0,1,1,10
6559,25,28.2,1,4,0,0,15,1,2,1,1,7
9085,25,28.2,1,3,0,0,13,1,2,1,1,9
6033,25,28.2,1,5,0,0,14,1,0,1,1,9
303,25,28.3,1,4,0,0,11,0,0,1,1,6
10134,25,28.3,1,4,0,1,9,1,4,1,1,5
2573,25,28.3,1,2,0,0,12,0,2,1,1,6
9987,25,28.3,1,4,0,0,11,1,2,1,1,5
7447,25,28.3,1,2,0,0,12,1,0,1,1,8
3175,25,28.3,1,2,0,0,14,0,2,1,1,6
2677,25,28.4,1,4,0,0,9,0,0,1,1,6
8523,25,28.4,1,1,0,0,12,1,2,1,1,5
10237,25,28.5,1,3,0,0,14,1,1,1,1,8
1871,25,28.5,1,3,0,0,12,0,1,1,1,8
10792,25,28.6,1,5,0,1,14,1,8,1,1,6
3017,25,28.6,1,1,0,0,11,0,1,1,1,9
3515,25,28.6,1,2,0,0,15,0,2,1,1,9
9819,25,28.6,1,1,0,0,15,1,1,1,1,10
861,25,28.6,1,4,0,0,10,0,0,1,1,9
8993,25,28.7,1,5,0,0,15,1,0,1,1,7
4811,25,28.7,1,2,0,0,13,0,0,1,1,5
5575,25,28.7,1,3,0,0,13,1,2,1,1,6
3419,25,28.7,1,3,0,0,12,0,1,1,1,9
2253,25,28.7,1,1,0,0,15,0,0,1,1,6
1983,25,28.8,1,2,0,0,10,0,1,1,1,8
5667,25,28.8,1,1,0,0,15,1,2,1,1,9
3453,25,28.8,1,5,0,0,12,0,0,1,1,9
10102,25,28.8,1,3,0,1,12,1,7,1,1,1
2205,25,28.8,1,1,0,0,9,0,2,1,1,6
7933,25,28.8,1,1,0,0,10,1,2,1,1,5
4667,25,28.8,1,5,0,0,13,0,0,1,1,5
10221,25,28.8,1,3,0,0,9,1,1,1,1,7
5035,25,28.8,1,2,0,0,15,0,1,1,1,6
725,25,28.9,1,2,0,0,13,0,0,1,1,8
1377,25,28.9,1,3,0,0,10,0,0,1,1,6
6701,25,28.9,1,4,0,0,13,1,0,1,1,5
2563,25,29,1,4,0,0,11,0,0,1,1,8
5281,25,29,1,3,0,0,9,0,0,1,1,9
6725,25,29,1,2,0,0,9,1,0,1,1,5
7389,25,29,1,1,0,0,12,1,1,1,1,7
5975,25,29,1,3,0,0,12,1,2,1,1,8
10527,25,29,1,2,0,0,13,1,2,1,1,7
2411,25,29,1,4,0,0,12,0,0,1,1,9
10827,25,29.1,1,1,0,0,11,1,0,1,1,5
4980,25,29.1,1,2,0,1,13,0,6,1,1,0
6025,25,29.1,1,3,0,0,14,1,2,1,1,6
9203,25,29.1,1,3,0,0,15,1,1,1,1,5
4063,25,29.1,1,4,0,0,12,0,0,1,1,7
5723,25,29.2,1,1,0,0,15,1,2,1,1,8
10493,25,29.2,1,3,0,0,12,1,2,1,1,7
10789,25,29.2,1,4,0,0,12,1,0,1,1,6
1417,25,29.2,1,5,0,0,11,0,0,1,1,6
873,25,29.2,1,2,0,0,13,0,1,1,1,5
3269,25,29.2,1,1,0,0,12,0,2,1,1,5
9023,25,29.2,1,3,0,0,12,1,2,1,1,8
9175,25,29.3,1,4,0,0,10,1,0,1,1,7
4309,25,29.3,1,1,0,0,12,0,2,1,1,8
5611,25,29.3,1,1,0,0,15,1,0,1,1,10
4625,25,29.3,1,5,0,0,13,0,0,1,1,8
8831,25,29.4,1,4,0,0,14,1,1,1,1,9
449,25,29.4,1,5,0,0,9,0,2,1,1,5
3170,25,29.4,1,5,0,1,9,0,4,1,1,1
5897,25,29.4,1,2,0,0,13,1,1,1,1,6
8867,25,29.5,1,4,0,1,15,1,5,1,1,2
7801,25,29.5,1,1,0,0,14,1,2,1,1,7
6365,25,29.5,1,3,0,0,9,1,0,1,1,10
9319,25,29.5,1,5,0,0,15,1,1,1,1,7
7779,25,29.6,1,4,0,0,12,1,1,1,1,6
2863,25,29.6,1,4,0,0,12,0,1,1,1,10
5079,25,29.6,1,2,0,0,10,0,2,1,1,8
7703,25,29.6,1,3,0,0,12,1,2,1,1,6
1045,25,29.7,1,1,0,0,14,0,1,1,1,5
7971,25,29.7,1,2,0,0,14,1,1,1,1,8
5295,25,29.7,1,3,0,0,14,0,2,1,1,6
5307,25,29.8,1,1,0,0,10,0,0,1,1,8
2991,25,29.9,1,1,0,0,9,0,2,1,1,9
4537,25,29.9,1,2,0,0,10,0,1,1,1,8
8071,25,29.9,1,5,0,0,9,1,0,1,1,5
8105,25,29.9,1,2,0,0,12,1,2,1,1,7
9239,25,29.9,1,2,0,0,12,1,2,1,1,10
4415,25,29.9,1,5,0,0,9,0,2,1,1,5
8735,25,30,1,1,0,0,12,1,2,1,1,10
529,25,30,1,2,0,0,9,0,0,1,1,9
1163,26,0.6,1,1,0,1,9,0,7,1,1,3
7665,26,0.6,1,1,0,1,11,1,4,1,1,0
615,26,0.8,1,3,0,1,15,0,4,1,1,2
2380,26,1.1,1,2,0,1,10,0,5,1,1,6
2102,26,1.2,1,3,0,1,15,0,6,1,1,5
2919,26,1.5,1,4,0,1,9,0,1,1,1,1
570,26,2,1,3,0,1,10,0,2,1,1,9
8578,26,2,1,2,0,1,12,1,1,1,1,10
2260,26,2.3,1,2,0,1,13,0,6,1,1,6
102,26,2.4,1,4,0,1,13,0,1,1,1,2
3462,26,3.1,1,3,0,1,12,0,5,1,1,4
9338,26,3.4,1,2,0,1,13,1,1,1,1,9
7369,26,3.7,1,2,0,1,10,1,1,1,1,1
6116,26,4.1,1,5,0,1,15,1,6,1,1,1
8382,26,4.6,1,1,0,1,12,1,4,1,1,9
533,26,5.2,1,3,0,1,11,0,4,1,1,4
4331,26,5.2,1,3,0,1,9,0,1,1,1,1
4473,26,5.7,1,1,0,1,12,0,2,1,1,4
4070,26,6.1,1,2,0,1,10,0,4,1,1,8
8176,26,6.1,1,3,0,1,13,1,3,1,1,5
5756,26,7.4,1,3,0,1,9,1,2,1,1,10
1192,26,7.5,1,2,0,1,12,0,5,1,1,2
5412,26,7.9,1,3,0,1,12,0,2,1,1,3
5036,26,7.9,1,5,0,1,14,0,5,1,1,7
826,26,8,1,2,0,1,9,0,3,1,1,6
3422,26,8.2,1,4,0,1,15,0,5,1,1,1
2371,26,8.3,1,1,0,1,13,0,6,1,1,3
9946,26,8.4,1,3,0,1,14,1,6,1,1,6
10804,26,8.5,1,1,0,1,12,1,6,1,1,2
2265,26,8.9,1,2,0,1,12,0,4,1,1,3
6164,26,8.9,1,2,0,1,9,1,1,1,1,2
8157,26,8.9,1,1,0,1,10,1,6,1,1,3
596,26,10.1,1,5,0,1,12,0,5,1,1,0
10824,26,10.4,1,3,0,1,11,1,5,1,1,10
10070,26,10.6,1,1,0,1,12,1,7,1,1,10
6611,26,10.6,1,1,0,1,15,1,7,1,1,1
2476,26,11.3,1,5,0,1,13,0,2,1,1,9
7638,26,12.4,1,4,0,1,12,1,7,1,1,0
2560,26,12.8,1,4,0,1,14,0,2,1,1,1
5583,26,13.4,1,3,0,1,12,1,2,1,1,4
7195,26,14.1,1,2,0,1,12,1,5,1,1,0
1090,26,14.2,1,3,0,1,10,0,5,1,1,9
4066,26,14.7,1,4,0,1,13,0,5,1,1,7
7199,26,14.8,1,3,0,1,12,1,2,1,1,1
727,26,15,1,3,0,0,12,0,1,1,1,7
1577,26,15,1,3,0,0,14,0,1,1,1,5
1595,26,15,1,4,0,0,10,0,1,1,1,10
6853,26,15,1,3,0,0,14,1,1,1,1,9
9423,26,15.2,1,1,0,0,15,1,1,1,1,10
1159,26,15.2,1,4,0,0,9,0,1,1,1,8
3139,26,15.2,1,4,0,0,10,0,0,1,1,5
7019,26,15.2,1,5,0,0,11,1,1,1,1,8
8567,26,15.2,1,4,0,0,15,1,2,1,1,6
2489,26,15.2,1,2,0,0,12,0,0,1,1,7
8509,26,15.3,1,1,0,0,12,1,1,1,1,9
5491,26,15.3,1,1,0,0,10,1,1,1,1,9
3123,26,15.3,1,3,0,0,9,0,1,1,1,7
6183,26,15.3,1,5,0,0,13,1,0,1,1,9
9101,26,15.3,1,1,0,0,14,1,1,1,1,10
4101,26,15.3,1,2,0,0,11,0,1,1,1,5
6773,26,15.4,1,4,0,0,14,1,1,1,1,10
803,26,15.4,1,1,0,0,14,0,1,1,1,5
1157,26,15.4,1,5,0,0,12,0,2,1,1,8
5535,26,15.4,1,5,0,0,14,1,2,1,1,6
6135,26,15.5,1,5,0,0,11,1,2,1,1,5
9697,26,15.5,1,3,0,0,11,1,1,1,1,6
5015,26,15.5,1,4,0,0,10,0,0,1,1,6
8963,26,15.5,1,1,0,0,12,1,0,1,1,8
2245,26,15.5,1,3,0,0,11,0,0,1,1,5
10549,26,15.5,1,2,0,0,14,1,0,1,1,8
9655,26,15.5,1,3,0,0,13,1,2,1,1,8
1080,26,15.6,1,1,0,1,12,0,1,1,1,0
4107,26,15.6,1,1,0,0,12,0,1,1,1,8
3793,26,15.6,1,4,0,0,10,0,2,1,1,7
4815,26,15.7,1,5,0,0,12,0,2,1,1,5
5881,26,15.7,1,3,0,0,12,1,1,1,1,7
4163,26,15.7,1,5,0,0,10,0,0,1,1,9
6891,26,15.7,1,3,0,0,13,1,1,1,1,9
5677,26,15.7,1,1,0,0,15,1,1,1,1,8
4657,26,15.7,1,3,0,1,15,0,1,1,1,3
1091,26,15.8,1,2,0,0,13,0,1,1,1,9
4883,26,15.8,1,5,0,0,14,0,1,1,1,7
5731,26,15.8,1,2,0,0,14,1,0,1,1,10
8347,26,15.9,1,2,0,0,15,1,2,1,1,10
5054,26,15.9,1,4,0,1,13,0,5,1,1,5
7657,26,15.9,1,2,0,0,12,1,2,1,1,5
3425,26,15.9,1,1,0,0,13,0,1,1,1,9
9693,26,15.9,1,1,0,0,11,1,2,1,1,9
2939,26,16,1,2,0,0,13,0,2,1,1,9
4215,26,16,1,5,0,0,9,0,0,1,1,6
7849,26,16,1,1,0,0,13,1,1,1,1,8
7239,26,16,1,3,0,0,13,1,2,1,1,7
5985,26,16,1,3,0,0,12,1,0,1,1,5
9563,26,16,1,4,0,0,12,1,0,1,1,8
6715,26,16,1,2,0,1,13,1,5,1,1,3
8651,26,16.1,1,4,0,0,12,1,0,1,1,7
6253,26,16.1,1,1,0,0,10,1,2,1,1,9
4862,26,16.1,1,3,0,1,12,0,5,1,1,9
2933,26,16.1,1,2,0,0,13,0,2,1,1,5
3613,26,16.1,1,4,0,0,14,0,1,1,1,9
4500,26,16.2,1,5,0,1,11,0,3,1,1,4
5557,26,16.2,1,2,0,0,9,1,0,1,1,8
9141,26,16.2,1,2,0,0,12,1,1,1,1,6
1535,26,16.3,1,1,0,0,11,0,0,1,1,5
3947,26,16.3,1,4,0,0,9,0,2,1,1,8
4695,26,16.3,1,1,0,0,12,0,1,1,1,5
4557,26,16.3,1,1,0,0,11,0,2,1,1,9
8537,26,16.3,1,4,0,0,13,1,2,1,1,9
5989,26,16.3,1,5,0,0,11,1,0,1,1,10
6933,26,16.4,1,2,0,0,13,1,1,1,1,9
2831,26,16.4,1,2,0,0,12,0,0,1,1,7
10187,26,16.4,1,3,0,0,14,1,0,1,1,5
10083,26,16.4,1,1,0,0,9,1,1,1,1,6
8909,26,16.4,1,1,0,0,12,1,1,1,1,10
2139,26,16.5,1,1,0,0,14,0,1,1,1,10
2593,26,16.5,1,4,0,0,12,0,2,1,1,9
8499,26,16.5,1,2,0,0,14,1,1,1,1,10
7737,26,16.5,1,3,0,1,13,1,2,1,1,2
7819,26,16.5,1,5,0,0,10,1,0,1,1,9
10359,26,16.5,1,4,0,0,11,1,1,1,1,7
1659,26,16.6,1,1,0,0,10,0,1,1,1,10
5593,26,16.6,1,4,0,0,11,1,2,1,1,9
8979,26,16.7,1,1,0,0,9,1,2,1,1,9
9811,26,16.7,1,3,0,0,11,1,0,1,1,5
10501,26,16.8,1,4,0,0,13,1,2,1,1,7
8549,26,16.8,1,3,0,0,10,1,2,1,1,7
5915,26,16.9,1,3,0,0,13,1,0,1,1,8
6535,26,16.9,1,3,0,0,12,1,2,1,1,9
8392,26,16.9,1,4,0,1,9,1,6,1,1,3
3,26,17,1,5,0,0,15,0,1,1,1,9
6095,26,17,1,5,0,0,12,1,0,1,1,7
7045,26,17.1,1,5,0,0,13,1,1,1,1,9
85,26,17.1,1,5,0,0,13,0,1,1,1,7
4049,26,17.1,1,5,0,0,15,0,0,1,1,7
4001,26,17.1,1,3,0,0,12,0,0,1,1,5
597,26,17.1,1,5,0,0,12,0,0,1,1,5
5381,26,17.1,1,3,0,0,15,0,1,1,1,10
6241,26,17.2,1,1,0,0,15,1,1,1,1,10
9447,26,17.2,1,4,0,0,12,1,1,1,1,9
9929,26,17.2,1,1,0,0,15,1,2,1,1,5
5055,26,17.2,1,2,0,1,13,0,4,1,1,4
3669,26,17.2,1,4,0,0,11,0,2,1,1,6
4327,26,17.2,1,3,0,0,9,0,1,1,1,9
9675,26,17.2,1,3,0,0,13,1,1,1,1,6
1243,26,17.2,1,2,0,0,12,0,1,1,1,6
1799,26,17.3,1,1,0,0,14,0,2,1,1,10
8699,26,17.3,1,3,0,0,10,1,2,1,1,7
4693,26,17.3,1,4,0,0,13,0,2,1,1,9
4439,26,17.4,1,3,0,0,13,0,1,1,1,8
10777,26,17.4,1,3,0,0,13,1,0,1,1,8
10775,26,17.4,1,2,0,0,11,1,2,1,1,8
6871,26,17.4,1,1,0,0,9,1,0,1,1,10
10471,26,17.4,1,1,0,0,14,1,1,1,1,6
4887,26,17.4,1,5,0,0,9,0,0,1,1,5
2439,26,17.4,1,1,0,0,15,0,2,1,1,6
9805,26,17.5,1,3,0,0,14,1,0,1,1,6
1353,26,17.5,1,4,0,0,12,0,2,1,1,7
421,26,17.5,1,1,0,0,12,0,0,1,1,8
7117,26,17.5,1,5,0,0,11,1,2,1,1,5
131,26,17.6,1,2,0,0,15,0,0,1,1,8
4019,26,17.6,1,4,0,0,15,0,0,1,1,7
5089,26,17.6,1,4,0,0,9,0,0,1,1,6
10451,26,17.6,1,3,0,0,15,1,2,1,1,6
3685,26,17.7,1,2,0,0,10,0,1,1,1,7
1313,26,17.7,1,5,0,0,15,0,1,1,1,6
8707,26,17.7,1,4,0,0,12,1,0,1,1,10
10129,26,17.7,1,3,0,0,12,1,1,1,1,6
10505,26,17.7,1,5,0,0,10,1,1,1,1,5
7889,26,17.7,1,5,0,0,15,1,0,1,1,7
1517,26,17.8,1,1,0,0,12,0,2,1,1,9
5243,26,17.9,1,4,0,0,11,0,0,1,1,10
5357,26,17.9,1,2,0,0,9,0,0,1,1,9
9963,26,17.9,1,4,0,0,10,1,0,1,1,5
3643,26,17.9,1,2,0,0,12,0,0,1,1,7
481,26,17.9,1,2,0,0,15,0,0,1,1,5
2153,26,17.9,1,2,0,0,15,0,1,1,1,5
7471,26,17.9,1,1,0,0,11,1,0,1,1,7
9041,26,17.9,1,2,0,0,10,1,0,1,1,8
6696,26,17.9,1,2,0,1,11,1,8,1,1,9
8809,26,17.9,1,1,0,0,9,1,1,1,1,5
3885,26,18,1,3,0,0,12,0,2,1,1,5
8787,26,18,1,2,0,1,12,1,1,1,1,2
6393,26,18,1,1,0,0,13,1,1,1,1,6
2985,26,18,1,4,0,0,12,0,1,1,1,9
4363,26,18,1,2,0,0,10,0,2,1,1,8
7161,26,18,1,3,0,0,9,1,0,1,1,7
4571,26,18,1,5,0,0,10,0,2,1,1,7
4929,26,18.1,1,4,0,0,11,0,1,1,1,8
3871,26,18.1,1,5,0,0,12,0,1,1,1,5
853,26,18.1,1,1,0,0,12,0,0,1,1,10
3337,26,18.1,1,3,0,0,10,0,2,1,1,6
5999,26,18.2,1,2,0,0,12,1,2,1,1,10
8023,26,18.3,1,4,0,0,14,1,2,1,1,5
1889,26,18.3,1,4,0,0,11,0,2,1,1,6
3281,26,18.3,1,5,0,0,13,0,2,1,1,7
553,26,18.4,1,3,0,0,9,0,2,1,1,8
4282,26,18.4,1,3,0,1,14,0,4,1,1,5
2019,26,18.4,1,3,0,0,12,0,2,1,1,10
8441,26,18.4,1,2,0,0,12,1,0,1,1,6
4265,26,18.4,1,4,0,0,9,0,2,1,1,7
3197,26,18.5,1,1,0,0,12,0,0,1,1,10
10739,26,18.5,1,4,0,0,10,1,0,1,1,6
8083,26,18.5,1,5,0,1,14,1,1,1,1,2
1113,26,18.5,1,5,0,0,9,0,2,1,1,5
6615,26,18.5,1,5,0,0,10,1,2,1,1,7
1833,26,18.5,1,3,0,0,15,0,0,1,1,10
7341,26,18.6,1,5,0,0,15,1,1,1,1,9
4617,26,18.6,1,2,0,0,12,0,2,1,1,8
9139,26,18.6,1,2,0,0,11,1,1,1,1,8
3009,26,18.6,1,1,0,0,10,0,0,1,1,5
2313,26,18.6,1,2,0,0,14,0,1,1,1,8
7935,26,18.7,1,3,0,0,10,1,2,1,1,5
6204,26,18.7,1,2,0,1,9,1,6,1,1,8
8193,26,18.7,1,1,0,1,14,1,2,1,1,1
10473,26,18.7,1,5,0,0,13,1,1,1,1,6
3717,26,18.7,1,1,0,0,13,0,1,1,1,10
5879,26,18.7,1,3,0,0,12,1,2,1,1,6
1391,26,18.8,1,1,0,0,12,0,2,1,1,9
9687,26,18.8,1,4,0,0,10,1,1,1,1,7
8810,26,18.8,1,4,0,1,12,1,8,1,1,2
4521,26,18.9,1,1,0,0,12,0,2,1,1,7
9979,26,18.9,1,5,0,0,12,1,1,1,1,5
7745,26,18.9,1,4,0,0,12,1,2,1,1,6
217,26,18.9,1,3,0,0,13,0,1,1,1,10
10037,26,18.9,1,5,0,0,12,1,2,1,1,10
4863,26,18.9,1,2,0,0,15,0,2,1,1,5
9882,26,19,1,2,0,1,10,1,3,1,1,3
5697,26,19,1,2,0,0,14,1,2,1,1,7
9531,26,19,1,1,0,0,14,1,2,1,1,5
9785,26,19,1,3,0,0,12,1,1,1,1,5
1667,26,19,1,3,0,0,12,0,0,1,1,5
1867,26,19,1,3,0,0,13,0,1,1,1,8
9165,26,19,1,5,0,0,9,1,1,1,1,9
201,26,19,1,2,0,0,10,0,0,1,1,6
4807,26,19,1,5,0,0,14,0,2,1,1,10
2693,26,19.1,1,4,0,0,12,0,0,1,1,9
7201,26,19.1,1,3,0,0,12,1,2,1,1,9
4333,26,19.1,1,2,0,0,14,0,0,1,1,9
5493,26,19.1,1,2,0,0,9,1,0,1,1,9
4677,26,19.1,1,3,0,0,9,0,2,1,1,10
8124,26,19.1,1,2,0,1,11,1,1,1,1,2
9799,26,19.1,1,2,0,0,15,1,2,1,1,7
5627,26,19.1,1,4,0,0,10,1,2,1,1,9
4025,26,19.2,1,3,0,0,12,0,2,1,1,8
6959,26,19.3,1,1,0,0,10,1,1,1,1,9
7953,26,19.3,1,3,0,0,9,1,1,1,1,6
4509,26,19.3,1,3,0,0,14,0,0,1,1,8
1553,26,19.3,1,3,0,0,10,0,0,1,1,5
7185,26,19.5,1,4,0,0,12,1,0,1,1,5
1959,26,19.5,1,2,0,0,12,0,0,1,1,10
4209,26,19.5,1,3,0,0,12,0,0,1,1,9
10019,26,19.6,1,4,0,0,14,1,1,1,1,8
1977,26,19.6,1,3,0,0,13,0,1,1,1,7
4417,26,19.6,1,2,0,0,15,0,0,1,1,6
7475,26,19.6,1,3,0,0,15,1,2,1,1,10
7517,26,19.6,1,1,0,0,11,1,2,1,1,6
9923,26,19.7,1,5,0,0,15,1,1,1,1,9
2559,26,19.7,1,1,0,0,15,0,2,1,1,6
7847,26,19.7,1,3,0,0,13,1,1,1,1,7
8029,26,19.7,1,5,0,0,10,1,2,1,1,6
5785,26,19.7,1,5,0,0,14,1,0,1,1,9
2363,26,19.8,1,2,0,0,11,0,2,1,1,5
3427,26,19.8,1,4,0,0,15,0,1,1,1,6
1769,26,19.8,1,4,0,0,15,0,2,1,1,5
5705,26,19.8,1,1,0,0,12,1,2,1,1,5
6335,26,19.8,1,1,0,0,12,1,2,1,1,9
3679,26,19.8,1,5,0,0,12,0,0,1,1,9
5369,26,19.9,1,4,0,0,10,0,0,1,1,8
7557,26,19.9,1,3,0,0,12,1,0,1,1,5
5941,26,20,1,4,0,0,11,1,0,1,1,7
8971,26,20,1,1,0,0,12,1,1,1,1,5
8621,26,20,1,4,0,0,12,1,2,1,1,6
2801,26,20,1,4,0,0,14,0,0,1,1,5
9387,26,20,1,4,0,0,10,1,0,1,1,5
9957,26,20,1,3,0,0,11,1,1,1,1,7
8911,26,20,1,3,0,0,12,1,2,1,1,6
4778,26,20.1,1,3,0,1,14,0,5,1,1,1
3823,26,20.1,1,2,0,0,11,0,0,1,1,7
5783,26,20.1,1,5,0,0,15,1,0,1,1,8
863,26,20.1,1,1,0,0,11,0,1,1,1,9
1863,26,20.2,1,3,0,0,10,0,2,1,1,5
10577,26,20.2,1,3,0,0,14,1,0,1,1,9
10580,26,20.2,1,4,0,1,12,1,7,1,1,9
3055,26,20.2,1,2,0,0,13,0,1,1,1,9
7047,26,20.3,1,5,0,0,11,1,1,1,1,6
4113,26,20.3,1,3,0,0,14,0,1,1,1,7
10761,26,20.3,1,3,0,0,14,1,2,1,1,5
8987,26,20.4,1,5,0,0,14,1,0,1,1,8
2441,26,20.4,1,3,0,0,13,0,1,1,1,9
10049,26,20.4,1,5,0,0,9,1,1,1,1,5
4645,26,20.4,1,3,0,0,15,0,0,1,1,9
2817,26,20.4,1,4,0,0,12,0,0,1,1,5
43,26,20.4,1,2,0,0,10,0,1,1,1,7
7041,26,20.5,1,2,0,0,14,1,2,1,1,9
6685,26,20.5,1,4,0,0,10,1,1,1,1,5
5515,26,20.5,1,5,0,0,13,1,0,1,1,9
8483,26,20.6,1,1,0,0,11,1,2,1,1,9
1237,26,20.6,1,5,0,0,13,0,1,1,1,7
8921,26,20.7,1,3,0,0,12,1,0,1,1,8
8337,26,20.7,1,5,0,0,14,1,2,1,1,10
5187,26,20.7,1,3,0,0,12,0,2,1,1,10
6107,26,20.7,1,1,0,0,11,1,0,1,1,7
4437,26,20.9,1,3,0,0,13,0,0,1,1,10
7245,26,20.9,1,5,0,0,9,1,2,1,1,10
7374,26,20.9,1,2,0,1,9,1,6,1,1,5
3649,26,21,1,4,0,0,15,0,0,1,1,9
1441,26,21,1,4,0,0,13,0,2,1,1,9
3207,26,21,1,4,0,0,13,0,2,1,1,8
10059,26,21,1,1,0,0,12,1,1,1,1,5
87,26,21.1,1,2,0,0,11,0,1,1,1,7
11,26,21.1,1,3,0,0,14,0,1,1,1,10
7371,26,21.1,1,2,0,0,9,1,2,1,1,7
5747,26,21.1,1,3,0,0,10,1,0,1,1,9
8805,26,21.1,1,3,0,0,13,1,2,1,1,7
6163,26,21.2,1,4,0,0,13,1,0,1,1,9
747,26,21.2,1,1,0,0,9,0,0,1,1,6
6885,26,21.2,1,5,0,0,11,1,1,1,1,8
1117,26,21.3,1,2,0,0,10,0,0,1,1,9
7421,26,21.3,1,1,0,0,14,1,2,1,1,7
365,26,21.3,1,1,0,0,15,0,2,1,1,8
1007,26,21.4,1,4,0,0,12,0,2,1,1,7
3373,26,21.4,1,5,0,0,15,0,2,1,1,8
4697,26,21.4,1,3,0,0,14,0,1,1,1,6
1957,26,21.4,1,2,0,0,15,0,2,1,1,10
6925,26,21.4,1,2,0,0,10,1,1,1,1,7
3357,26,21.5,1,2,0,0,13,0,2,1,1,6
10137,26,21.5,1,2,0,0,11,1,0,1,1,7
6031,26,21.5,1,4,0,0,13,1,2,1,1,7
5385,26,21.5,1,2,0,0,11,0,2,1,1,6
10023,26,21.6,1,5,0,0,12,1,2,1,1,7
10510,26,21.6,1,5,0,1,12,1,7,1,1,9
5229,26,21.7,1,5,0,0,15,0,1,1,1,5
5927,26,21.7,1,2,0,0,12,1,0,1,1,10
9049,26,21.7,1,1,0,0,9,1,0,1,1,7
8755,26,21.7,1,3,0,0,13,1,1,1,1,5
841,26,21.7,1,3,0,0,13,0,2,1,1,10
3927,26,21.7,1,3,0,0,14,0,0,1,1,7
7709,26,21.7,1,3,0,0,9,1,1,1,1,10
4455,26,21.8,1,5,0,0,12,0,1,1,1,6
8199,26,21.8,1,1,0,0,14,1,0,1,1,7
10343,26,21.9,1,4,0,0,12,1,2,1,1,8
2457,26,21.9,1,4,0,0,12,0,1,1,1,8
1205,26,21.9,1,1,0,0,9,0,1,1,1,5
4783,26,21.9,1,3,0,0,13,0,0,1,1,8
9801,26,22,1,5,0,0,12,1,0,1,1,7
1693,26,22,1,3,0,0,10,0,0,1,1,8
8177,26,22,1,5,0,0,12,1,2,1,1,8
7308,26,22,1,3,0,1,10,1,1,1,1,1
9439,26,22,1,2,0,0,9,1,2,1,1,7
6573,26,22.1,1,1,0,0,14,1,1,1,1,6
3119,26,22.1,1,2,0,0,14,0,1,1,1,6
1069,26,22.1,1,1,0,0,15,0,0,1,1,7
3053,26,22.2,1,5,0,0,11,0,2,1,1,8
3169,26,22.2,1,5,0,0,12,0,0,1,1,7
4609,26,22.2,1,5,0,0,14,0,0,1,1,8
925,26,22.3,1,5,0,0,15,0,1,1,1,9
8145,26,22.3,1,1,0,0,14,1,0,1,1,5
6465,26,22.3,1,4,0,0,10,1,0,1,1,9
8989,26,22.3,1,3,0,0,12,1,2,1,1,8
10439,26,22.3,1,4,0,0,9,1,0,1,1,8
5255,26,22.3,1,1,0,0,15,0,0,1,1,5
6449,26,22.4,1,4,0,0,15,1,0,1,1,5
5835,26,22.5,1,3,0,0,10,1,0,1,1,10
3387,26,22.5,1,3,0,0,12,0,2,1,1,6
5635,26,22.5,1,3,0,0,10,1,2,1,1,8
3507,26,22.5,1,1,0,0,13,0,0,1,1,6
6961,26,22.5,1,3,0,0,15,1,0,1,1,8
8293,26,22.5,1,1,0,0,13,1,0,1,1,7
923,26,22.5,1,3,0,0,14,0,1,1,1,7
7403,26,22.6,1,1,0,0,12,1,1,1,1,9
9943,26,22.6,1,1,0,0,9,1,2,1,1,7
567,26,22.7,1,2,0,0,12,0,0,1,1,9
8753,26,22.7,1,3,0,0,11,1,2,1,1,9
4541,26,22.7,1,1,0,0,12,0,0,1,1,8
1995,26,22.7,1,3,0,0,12,0,0,1,1,9
3983,26,22.8,1,2,0,0,9,0,0,1,1,8
7057,26,22.8,1,2,0,0,9,1,0,1,1,8
3577,26,22.8,1,2,0,0,13,0,1,1,1,6
2554,26,22.8,1,3,0,1,15,0,3,1,1,2
8259,26,22.8,1,2,0,0,12,1,0,1,1,10
8247,26,22.8,1,5,0,0,15,1,1,1,1,10
3645,26,22.8,1,3,0,0,11,0,2,1,1,7
7207,26,22.9,1,4,0,0,13,1,0,1,1,9
8239,26,22.9,1,3,0,0,10,1,1,1,1,8
8437,26,22.9,1,1,0,0,14,1,2,1,1,10
6777,26,22.9,1,1,0,0,14,1,2,1,1,10
1171,26,22.9,1,1,0,0,12,0,1,1,1,10
9899,26,23,1,2,0,0,14,1,2,1,1,9
7431,26,23,1,2,0,0,13,1,2,1,1,5
4653,26,23,1,3,0,0,11,0,1,1,1,9
8663,26,23,1,5,0,0,13,1,0,1,1,8
10135,26,23.1,1,4,0,0,12,1,0,1,1,6
7,26,23.1,1,3,0,0,15,0,1,1,1,10
7739,26,23.1,1,4,0,0,11,1,2,1,1,6
2037,26,23.1,1,3,0,0,13,0,1,1,1,10
9908,26,23.1,1,3,0,1,9,1,5,1,1,8
805,26,23.2,1,5,0,0,11,0,0,1,1,6
8215,26,23.2,1,5,0,0,12,1,2,1,1,10
8597,26,23.3,1,4,0,0,12,1,0,1,1,7
6835,26,23.3,1,2,0,0,12,1,1,1,1,7
7275,26,23.3,1,5,0,0,12,1,1,1,1,5
1275,26,23.4,1,3,0,0,10,0,1,1,1,6
4925,26,23.4,1,3,0,0,15,0,2,1,1,5
3489,26,23.4,1,3,0,0,12,0,0,1,1,5
2301,26,23.5,1,4,0,0,14,0,0,1,1,10
10251,26,23.5,1,5,0,0,12,1,2,1,1,10
5437,26,23.5,1,2,0,0,12,1,0,1,1,7
6079,26,23.5,1,3,0,0,13,1,1,1,1,7
10171,26,23.6,1,1,0,0,9,1,1,1,1,10
7875,26,23.6,1,5,0,0,12,1,0,1,1,7
5857,26,23.6,1,3,0,0,12,1,2,1,1,6
2207,26,23.6,1,1,0,0,15,0,0,1,1,9
1808,26,23.7,1,1,0,1,10,0,4,1,1,0
10291,26,23.7,1,2,0,0,9,1,1,1,1,7
3051,26,23.7,1,3,0,0,10,0,2,1,1,10
2273,26,23.8,1,3,0,0,15,0,1,1,1,6
983,26,23.8,1,3,0,0,10,0,0,1,1,6
4237,26,23.8,1,3,0,0,13,0,1,1,1,6
7565,26,23.8,1,4,0,0,13,1,2,1,1,9
5125,26,23.8,1,4,0,0,15,0,0,1,1,5
3688,26,23.8,1,1,0,1,10,0,1,1,1,5
369,26,23.8,1,3,0,0,13,0,2,1,1,8
2227,26,23.9,1,4,0,0,9,0,0,1,1,8
1165,26,24,1,5,0,0,13,0,2,1,1,9
3941,26,24,1,3,0,1,13,0,6,1,1,1
106,26,24,1,3,0,1,13,0,1,1,1,8
859,26,24.1,1,3,0,0,14,0,1,1,1,10
9161,26,24.1,1,4,0,0,11,1,1,1,1,5
6751,26,24.1,1,4,0,0,14,1,0,1,1,6
6381,26,24.1,1,4,0,0,11,1,0,1,1,10
1147,26,24.2,1,3,0,0,15,0,0,1,1,9
10303,26,24.2,1,2,0,0,13,1,2,1,1,10
8385,26,24.2,1,4,0,0,13,1,0,1,1,7
9915,26,24.3,1,2,0,0,12,1,1,1,1,10
9147,26,24.3,1,3,0,0,13,1,2,1,1,9
2393,26,24.3,1,1,0,0,12,0,1,1,1,5
6373,26,24.3,1,2,0,0,10,1,2,1,1,8
2647,26,24.3,1,1,0,0,12,0,0,1,1,5
10831,26,24.4,1,1,0,0,12,1,1,1,1,5
1193,26,24.4,1,2,0,0,9,0,0,1,1,6
5175,26,24.4,1,3,0,0,13,0,1,1,1,5
7073,26,24.4,1,2,0,0,15,1,2,1,1,7
2705,26,24.5,1,2,0,0,12,0,1,1,1,10
3471,26,24.6,1,4,0,0,10,0,0,1,1,8
8209,26,24.6,1,5,0,0,10,1,2,1,1,10
7035,26,24.6,1,4,0,0,11,1,2,1,1,7
4849,26,24.6,1,2,0,0,9,0,1,1,1,7
1445,26,24.6,1,1,0,0,15,0,0,1,1,9
8231,26,24.7,1,3,0,0,14,1,1,1,1,6
9449,26,24.7,1,4,0,0,15,1,2,1,1,8
7645,26,24.7,1,5,0,0,9,1,1,1,1,5
2243,26,24.8,1,4,0,0,14,0,1,1,1,8
5448,26,24.8,1,1,0,1,12,1,6,1,1,8
145,26,24.8,1,5,0,1,12,0,3,1,1,4
6953,26,24.8,1,4,0,0,10,1,1,1,1,6
1611,26,24.9,1,4,0,0,10,0,1,1,1,8
6845,26,24.9,1,5,0,0,9,1,0,1,1,8
4469,26,24.9,1,4,0,0,10,0,2,1,1,7
6055,26,24.9,1,3,0,0,12,1,1,1,1,10
2173,26,24.9,1,5,0,0,15,0,0,1,1,9
8981,26,24.9,1,3,0,0,11,1,2,1,1,9
6927,26,24.9,1,2,0,1,10,1,7,1,1,3
10293,26,25,1,2,0,0,13,1,2,1,1,8
2768,26,25,1,4,0,1,13,0,4,1,1,2
7943,26,25,1,2,0,0,12,1,2,1,1,7
2871,26,25,1,1,0,0,15,0,0,1,1,6
5721,26,25,1,1,0,0,13,1,1,1,1,7
8203,26,25,1,5,0,0,11,1,0,1,1,5
165,26,25.1,1,3,0,0,13,0,2,1,1,6
5949,26,25.1,1,4,0,0,14,1,1,1,1,10
7863,26,25.1,1,2,0,0,14,1,2,1,1,6
7157,26,25.1,1,3,0,0,13,1,2,1,1,6
6360,26,25.1,1,2,0,1,12,1,2,1,1,9
4075,26,25.2,1,2,0,0,14,0,1,1,1,6
6825,26,25.2,1,2,0,0,9,1,0,1,1,10
3421,26,25.2,1,3,0,0,12,0,2,1,1,5
10003,26,25.2,1,3,0,0,10,1,1,1,1,10
4954,26,25.2,1,4,0,1,15,0,8,1,1,4
5299,26,25.3,1,1,0,0,14,0,2,1,1,10
5227,26,25.3,1,3,0,0,10,0,1,1,1,9
2659,26,25.3,1,4,0,0,14,0,0,1,1,8
10047,26,25.3,1,1,0,0,15,1,2,1,1,5
7925,26,25.3,1,4,0,0,12,1,2,1,1,7
9499,26,25.4,1,3,0,0,11,1,1,1,1,10
55,26,25.4,1,4,0,0,13,0,1,1,1,10
1747,26,25.4,1,5,0,0,9,0,1,1,1,8
299,26,25.5,1,3,0,0,10,0,0,1,1,8
6633,26,25.5,1,1,0,0,11,1,1,1,1,6
95,26,25.6,1,3,0,1,12,0,2,1,1,0
151,26,25.6,1,5,0,0,10,0,2,1,1,9
4549,26,25.6,1,4,0,0,10,0,1,1,1,6
2707,26,25.6,1,5,0,0,13,0,2,1,1,10
4463,26,25.7,1,1,0,0,11,0,1,1,1,7
6759,26,25.7,1,4,0,0,10,1,2,1,1,10
4983,26,25.7,1,1,0,0,14,0,2,1,1,9
8261,26,25.8,1,4,0,0,10,1,2,1,1,6
411,26,25.8,1,3,0,0,9,0,2,1,1,6
8375,26,25.8,1,5,0,0,15,1,1,1,1,7
7752,26,25.8,1,1,0,1,9,1,1,1,1,4
1261,26,25.8,1,3,0,0,12,0,1,1,1,5
4121,26,25.8,1,4,0,0,14,0,1,1,1,5
3691,26,25.8,1,3,0,0,12,0,2,1,1,9
3913,26,25.8,1,3,0,0,12,0,2,1,1,5
6320,26,25.9,1,5,0,1,12,1,4,1,1,6
9465,26,25.9,1,5,0,1,15,1,3,1,1,1
6139,26,25.9,1,5,0,0,12,1,0,1,1,6
2851,26,25.9,1,4,0,0,9,0,2,1,1,6
6783,26,25.9,1,5,0,0,13,1,0,1,1,10
2119,26,26,1,4,0,0,15,0,0,1,1,8
2323,26,26,1,2,0,0,9,0,2,1,1,9
4955,26,26,1,3,0,0,13,0,2,1,1,8
5285,26,26,1,5,0,0,10,0,1,1,1,5
6679,26,26.1,1,1,0,0,14,1,1,1,1,7
5481,26,26.1,1,4,0,1,13,1,3,1,1,3
9453,26,26.1,1,2,0,0,12,1,2,1,1,7
10151,26,26.2,1,2,0,0,10,1,1,1,1,10
1099,26,26.2,1,3,0,0,12,0,1,1,1,5
3811,26,26.2,1,4,0,0,12,0,0,1,1,6
6869,26,26.2,1,4,0,0,12,1,0,1,1,9
3975,26,26.2,1,3,0,0,9,0,2,1,1,9
671,26,26.3,1,1,0,0,15,0,0,1,1,7
3389,26,26.3,1,3,0,0,11,0,1,1,1,8
3195,26,26.3,1,1,0,0,15,0,1,1,1,9
10201,26,26.3,1,5,0,0,10,1,0,1,1,5
8751,26,26.4,1,2,0,0,9,1,0,1,1,8
9116,26,26.4,1,5,0,1,12,1,2,1,1,0
9199,26,26.4,1,3,0,0,14,1,2,1,1,8
10299,26,26.4,1,4,0,0,12,1,0,1,1,7
6081,26,26.4,1,3,0,0,10,1,2,1,1,6
9357,26,26.5,1,1,0,0,9,1,2,1,1,6
10317,26,26.5,1,4,0,0,11,1,2,1,1,10
3722,26,26.5,1,2,0,1,13,0,5,1,1,1
1249,26,26.5,1,2,0,0,11,0,1,1,1,6
8821,26,26.5,1,2,0,0,11,1,2,1,1,7
7225,26,26.5,1,1,0,0,9,1,1,1,1,9
8504,26,26.5,1,3,0,1,13,1,3,1,1,8
6229,26,26.6,1,3,0,0,10,1,0,1,1,9
2419,26,26.6,1,4,0,0,10,0,2,1,1,7
1731,26,26.6,1,3,0,0,10,0,0,1,1,7
6963,26,26.6,1,4,0,0,12,1,0,1,1,7
7633,26,26.6,1,1,0,0,13,1,1,1,1,6
1095,26,26.6,1,1,0,0,11,0,0,1,1,9
5739,26,26.7,1,4,0,0,9,1,2,1,1,6
10787,26,26.7,1,2,0,0,14,1,2,1,1,7
4687,26,26.7,1,3,0,0,13,0,1,1,1,7
7699,26,26.7,1,1,0,0,13,1,0,1,1,6
3633,26,26.7,1,1,0,0,12,0,0,1,1,8
5455,26,26.7,1,3,0,0,11,1,2,1,1,9
5761,26,26.7,1,3,0,0,10,1,0,1,1,5
4877,26,26.8,1,5,0,0,15,0,2,1,1,8
1619,26,26.8,1,4,0,0,12,0,2,1,1,5
10197,26,26.9,1,1,0,0,11,1,1,1,1,8
10109,26,26.9,1,4,0,0,14,1,2,1,1,5
4853,26,26.9,1,5,0,0,12,0,2,1,1,7
2187,26,26.9,1,4,0,0,12,0,0,1,1,7
7077,26,26.9,1,2,0,0,12,1,1,1,1,10
8415,26,27,1,2,0,0,10,1,1,1,1,7
8335,26,27,1,4,0,0,10,1,0,1,1,8
1817,26,27,1,4,0,0,12,0,1,1,1,8
1097,26,27.1,1,3,0,0,13,0,1,1,1,5
5391,26,27.1,1,4,0,0,10,0,1,1,1,8
9121,26,27.1,1,2,0,0,11,1,2,1,1,7
4077,26,27.1,1,1,0,0,9,0,0,1,1,7
6337,26,27.1,1,2,0,0,13,1,2,1,1,6
4519,26,27.1,1,4,0,0,15,0,2,1,1,6
3015,26,27.1,1,2,0,0,12,0,0,1,1,5
7167,26,27.1,1,5,0,0,10,1,0,1,1,6
477,26,27.1,1,3,0,0,14,0,1,1,1,5
3497,26,27.2,1,1,0,0,13,0,2,1,1,7
10755,26,27.3,1,4,0,0,11,1,2,1,1,6
4155,26,27.3,1,4,0,0,13,0,1,1,1,5
8777,26,27.3,1,2,0,0,13,1,2,1,1,6
251,26,27.4,1,4,0,0,10,0,1,1,1,10
10327,26,27.4,1,5,0,0,12,1,0,1,1,5
3363,26,27.4,1,4,0,0,12,0,0,1,1,9
6635,26,27.4,1,1,0,0,10,1,2,1,1,8
2267,26,27.4,1,2,0,0,10,0,1,1,1,10
892,26,27.4,1,2,0,1,12,0,6,1,1,7
693,26,27.4,1,2,0,0,15,0,1,1,1,8
649,26,27.4,1,5,0,0,11,0,0,1,1,6
8689,26,27.4,1,1,0,0,10,1,0,1,1,10
10675,26,27.4,1,3,0,0,12,1,0,1,1,9
5649,26,27.4,1,5,0,0,14,1,0,1,1,9
2491,26,27.5,1,2,0,0,14,0,0,1,1,8
1429,26,27.5,1,3,0,0,13,0,2,1,1,7
10717,26,27.5,1,2,0,0,10,1,0,1,1,6
8585,26,27.5,1,5,0,0,11,1,1,1,1,9
4605,26,27.5,1,3,0,0,9,0,0,1,1,8
252,26,27.5,1,2,0,1,11,0,8,1,1,1
8469,26,27.5,1,5,0,0,12,1,2,1,1,7
7749,26,27.5,1,1,0,0,9,1,0,1,1,6
4611,26,27.5,1,4,0,0,12,0,2,1,1,10
3559,26,27.6,1,4,0,0,14,0,1,1,1,9
2987,26,27.6,1,5,0,0,12,0,1,1,1,9
1895,26,27.6,1,5,0,0,12,0,0,1,1,10
5207,26,27.6,1,1,0,0,14,0,1,1,1,8
7913,26,27.6,1,2,0,0,15,1,0,1,1,5
2459,26,27.7,1,3,0,0,11,0,1,1,1,7
6867,26,27.8,1,1,0,0,11,1,1,1,1,10
3847,26,27.8,1,5,0,0,10,0,2,1,1,6
3729,26,27.8,1,5,0,0,12,0,0,1,1,6
6397,26,27.8,1,1,0,0,11,1,0,1,1,6
6511,26,27.9,1,3,0,0,14,1,0,1,1,9
7147,26,27.9,1,2,0,0,15,1,0,1,1,6
4987,26,27.9,1,2,0,0,12,0,0,1,1,5
9815,26,27.9,1,2,0,0,13,1,2,1,1,9
9991,26,28,1,1,0,0,15,1,2,1,1,10
3377,26,28,1,3,0,0,10,0,1,1,1,5
6319,26,28,1,2,0,0,14,1,2,1,1,7
3777,26,28,1,4,0,0,14,0,1,1,1,9
6421,26,28.1,1,2,0,0,14,1,1,1,1,10
5325,26,28.1,1,1,0,0,10,0,2,1,1,5
3121,26,28.1,1,5,0,0,12,0,2,1,1,5
6047,26,28.1,1,3,0,0,9,1,0,1,1,5
8731,26,28.1,1,2,0,0,10,1,0,1,1,10
2229,26,28.1,1,5,0,0,12,0,1,1,1,5
4387,26,28.1,1,5,0,0,12,0,2,1,1,5
7175,26,28.1,1,1,0,0,12,1,2,1,1,9
1407,26,28.2,1,3,0,0,10,0,0,1,1,10
5795,26,28.2,1,3,0,0,11,1,1,1,1,8
2903,26,28.2,1,5,0,0,15,0,1,1,1,7
2179,26,28.2,1,2,0,0,14,0,0,1,1,6
7401,26,28.2,1,5,0,0,9,1,2,1,1,7
10663,26,28.2,1,2,0,0,12,1,0,1,1,7
1503,26,28.2,1,5,0,0,15,0,0,1,1,6
7721,26,28.2,1,4,0,0,14,1,0,1,1,5
687,26,28.2,1,4,0,0,12,0,0,1,1,9
5435,26,28.3,1,4,0,0,13,1,1,1,1,9
1013,26,28.3,1,2,0,0,13,0,2,1,1,6
254,26,28.3,1,2,0,1,14,0,3,1,1,5
10523,26,28.3,1,4,0,0,10,1,2,1,1,8
7267,26,28.4,1,1,0,0,12,1,0,1,1,6
6137,26,28.4,1,5,0,0,11,1,0,1,1,7
5729,26,28.4,1,2,0,0,9,1,1,1,1,9
9107,26,28.4,1,4,0,0,10,1,2,1,1,7
10067,26,28.4,1,4,0,0,12,1,2,1,1,6
6643,26,28.4,1,3,0,0,15,1,2,1,1,9
4775,26,28.4,1,2,0,0,14,0,1,1,1,8
2177,26,28.5,1,1,0,0,15,0,2,1,1,9
7843,26,28.5,1,1,0,0,13,1,1,1,1,6
4515,26,28.5,1,1,0,0,9,0,2,1,1,10
10569,26,28.5,1,2,0,0,12,1,2,1,1,9
457,26,28.5,1,2,0,0,12,0,1,1,1,9
6167,26,28.6,1,1,0,0,10,1,1,1,1,9
7687,26,28.6,1,4,0,0,9,1,2,1,1,7
9831,26,28.6,1,2,0,0,11,1,2,1,1,10
6377,26,28.6,1,5,0,1,12,1,3,1,1,4
9263,26,28.6,1,4,0,0,10,1,2,1,1,5
821,26,28.6,1,4,0,0,12,0,1,1,1,10
7191,26,28.6,1,5,0,0,14,1,2,1,1,5
3229,26,28.6,1,5,0,0,10,0,0,1,1,7
6315,26,28.7,1,1,0,0,12,1,1,1,1,8
10801,26,28.7,1,3,0,0,14,1,1,1,1,7
10547,26,28.8,1,4,0,0,15,1,2,1,1,9
3699,26,28.8,1,3,0,0,10,0,0,1,1,8
5717,26,28.8,1,5,0,1,14,1,1,1,1,4
9115,26,28.8,1,2,0,0,13,1,2,1,1,9
4221,26,28.8,1,5,0,0,12,0,0,1,1,5
4321,26,28.9,1,3,0,0,12,0,0,1,1,5
6593,26,28.9,1,5,0,0,13,1,0,1,1,5
10760,26,28.9,1,1,0,1,15,1,6,1,1,2
5669,26,28.9,1,2,0,0,12,1,2,1,1,10
3033,26,28.9,1,5,0,0,12,0,0,1,1,6
6435,26,28.9,1,1,0,0,10,1,1,1,1,9
5603,26,29,1,2,0,0,14,1,2,1,1,9
7417,26,29,1,4,0,0,15,1,1,1,1,9
6252,26,29,1,3,0,1,12,1,3,1,1,7
3631,26,29,1,4,0,0,14,0,1,1,1,7
2373,26,29,1,2,0,0,15,0,2,1,1,6
4313,26,29.1,1,2,0,0,12,0,0,1,1,10
485,26,29.1,1,5,0,0,15,0,2,1,1,9
8619,26,29.1,1,3,0,0,14,1,0,1,1,5
2031,26,29.2,1,1,0,0,9,0,2,1,1,8
9543,26,29.2,1,1,0,0,13,1,2,1,1,6
3129,26,29.2,1,5,0,0,12,0,1,1,1,5
8785,26,29.3,1,4,0,0,9,1,0,1,1,10
1719,26,29.3,1,5,0,1,14,0,5,1,1,3
8817,26,29.3,1,3,0,0,9,1,0,1,1,5
10648,26,29.3,1,5,0,1,14,1,1,1,1,6
8863,26,29.3,1,1,0,0,11,1,0,1,1,10
7315,26,29.3,1,5,0,0,14,1,2,1,1,9
9643,26,29.4,1,1,0,0,12,1,2,1,1,9
625,26,29.5,1,1,0,0,12,0,2,1,1,6
3411,26,29.5,1,3,0,0,14,0,0,1,1,7
3359,26,29.5,1,2,0,0,13,0,1,1,1,7
4829,26,29.5,1,3,0,0,13,0,1,1,1,7
4231,26,29.6,1,3,0,0,9,0,0,1,1,10
5222,26,29.6,1,1,0,1,10,0,7,1,1,9
8945,26,29.6,1,1,0,0,14,1,0,1,1,8
3331,26,29.6,1,1,0,0,12,0,0,1,1,7
10565,26,29.6,1,1,0,0,15,1,1,1,1,9
5579,26,29.7,1,5,0,0,13,1,0,1,1,6
9583,26,29.7,1,4,0,1,10,1,2,1,1,0
3163,26,29.7,1,5,0,0,12,0,2,1,1,10
745,26,29.7,1,1,0,0,9,0,0,1,1,7
7291,26,29.8,1,1,0,0,15,1,2,1,1,8
1631,26,29.8,1,3,0,0,15,0,1,1,1,6
1665,26,29.8,1,2,0,0,12,0,1,1,1,6
3227,26,29.8,1,3,0,0,12,0,1,1,1,8
10693,26,29.9,1,4,0,0,12,1,2,1,1,6
6791,26,29.9,1,1,0,0,13,1,0,1,1,5
5318,26,29.9,1,3,0,1,14,0,4,1,1,2
4091,26,29.9,1,1,0,0,14,0,1,1,1,7
3757,26,29.9,1,4,0,0,13,0,1,1,1,7
3435,26,29.9,1,3,0,0,9,0,1,1,1,8
8994,26,30,1,2,0,1,12,1,5,1,1,3
9227,26,30,1,3,0,0,12,1,2,1,1,10
10367,26,30,1,4,0,0,9,1,2,1,1,6
5808,27,0.2,1,3,0,1,15,1,3,1,1,10
9626,27,1.1,1,2,0,1,9,1,8,1,1,1
2856,27,1.5,1,5,0,1,9,0,2,1,1,6
319,27,1.5,1,2,0,1,13,0,6,1,1,0
9010,27,2.1,1,3,0,1,12,1,8,1,1,10
7040,27,2.7,1,4,0,1,15,1,4,1,1,3
9938,27,2.9,1,3,0,1,12,1,2,1,1,7
6042,27,2.9,1,2,0,1,12,1,2,1,1,10
9214,27,3.9,1,3,0,1,9,1,1,1,1,6
10598,27,4.1,1,1,0,1,13,1,7,1,1,3
9302,27,4.5,1,3,0,1,10,1,6,1,1,9
10722,27,4.7,1,1,0,1,14,1,4,1,1,3
9440,27,5.5,1,4,0,1,15,1,4,1,1,4
4884,27,6.3,1,5,0,1,12,0,2,1,1,0
6122,27,6.4,1,2,0,1,11,1,5,1,1,5
4698,27,7,1,5,0,1,13,0,3,1,1,10
4173,27,7,1,4,0,1,10,0,6,1,1,4
8320,27,7.2,1,1,0,1,11,1,8,1,1,7
4536,27,7.4,1,1,0,1,14,0,2,1,1,7
4682,27,7.4,1,2,0,1,12,0,4,1,1,4
7836,27,7.5,1,1,0,1,15,1,3,1,1,10
1648,27,8.4,1,2,0,1,11,0,8,1,1,5
3230,27,9,1,3,0,1,10,0,5,1,1,6
804,27,9.6,1,3,0,1,14,0,7,1,1,4
10214,27,9.8,1,5,0,1,10,1,7,1,1,8
10304,27,10.6,1,4,0,1,13,1,3,1,1,6
4386,27,10.7,1,4,0,1,14,0,8,1,1,0
6292,27,11,1,1,0,1,11,1,7,1,1,4
10642,27,11.8,1,2,0,1,9,1,2,1,1,2
8094,27,11.8,1,4,0,1,10,1,5,1,1,4
1557,27,11.9,1,4,0,1,10,0,3,1,1,3
10809,27,12.1,1,3,0,1,10,1,6,1,1,2
9184,27,12.2,1,2,0,1,10,1,4,1,1,8
7497,27,12.6,1,4,0,1,12,1,1,1,1,1
718,27,12.7,1,4,0,1,13,0,8,1,1,8
9442,27,13.1,1,5,0,1,9,1,6,1,1,4
5791,27,13.3,1,3,0,1,12,1,8,1,1,3
3366,27,13.4,1,1,0,1,14,0,1,1,1,7
10594,27,13.7,1,4,0,1,13,1,8,1,1,2
5952,27,14.1,1,2,0,1,15,1,8,1,1,9
1761,27,14.3,1,5,0,1,12,0,8,1,1,2
9458,27,14.3,1,4,0,1,11,1,4,1,1,9
564,27,14.6,1,3,0,1,12,0,3,1,1,8
1168,27,15,1,2,0,1,9,0,3,1,1,2
8147,27,15,1,4,0,0,12,1,2,1,1,5
9565,27,15,1,4,0,0,11,1,2,1,1,10
9245,27,15,1,4,0,0,14,1,2,1,1,7
8569,27,15,1,1,0,0,13,1,1,1,1,6
2103,27,15.1,1,3,0,0,15,0,2,1,1,6
7809,27,15.1,1,1,0,0,10,1,2,1,1,6
1297,27,15.1,1,3,0,0,11,0,2,1,1,6
5805,27,15.1,1,2,0,0,10,1,2,1,1,7
7479,27,15.1,1,2,0,0,12,1,2,1,1,6
9181,27,15.2,1,4,0,0,9,1,0,1,1,8
3135,27,15.2,1,5,0,0,13,0,1,1,1,9
10321,27,15.2,1,1,0,0,13,1,0,1,1,5
3313,27,15.3,1,2,0,0,12,0,0,1,1,6
7989,27,15.4,1,3,0,0,12,1,2,1,1,10
8473,27,15.5,1,3,0,0,12,1,0,1,1,10
6799,27,15.5,1,1,0,0,13,1,0,1,1,6
6197,27,15.5,1,4,0,1,15,1,3,1,1,4
6987,27,15.6,1,1,0,0,14,1,1,1,1,8
10257,27,15.6,1,2,0,0,9,1,0,1,1,10
5029,27,15.7,1,1,0,0,15,0,0,1,1,7
5038,27,15.7,1,4,0,1,9,0,2,1,1,9
10479,27,15.7,1,4,0,0,11,1,2,1,1,9
3049,27,15.7,1,4,0,0,11,0,0,1,1,7
5086,27,15.8,1,1,0,1,14,0,3,1,1,6
9649,27,15.8,1,2,0,0,11,1,2,1,1,7
4478,27,15.9,1,2,0,1,12,0,8,1,1,9
2791,27,15.9,1,1,0,0,14,0,2,1,1,9
9063,27,15.9,1,1,0,0,14,1,1,1,1,9
479,27,15.9,1,2,0,0,13,0,1,1,1,9
2097,27,15.9,1,3,0,0,14,0,1,1,1,8
8501,27,16,1,1,0,0,12,1,1,1,1,9
4389,27,16.1,1,5,0,0,15,0,1,1,1,6
2181,27,16.1,1,3,0,0,12,0,0,1,1,9
8317,27,16.1,1,1,0,0,13,1,2,1,1,8
2463,27,16.1,1,5,0,0,14,0,0,1,1,6
5753,27,16.1,1,4,0,0,12,1,0,1,1,6
9301,27,16.2,1,4,0,0,10,1,1,1,1,9
297,27,16.2,1,5,0,0,15,0,1,1,1,9
2966,27,16.2,1,2,0,1,11,0,5,1,1,0
669,27,16.3,1,3,0,0,12,0,1,1,1,8
9292,27,16.3,1,4,0,1,10,1,3,1,1,2
8691,27,16.3,1,2,0,0,9,1,1,1,1,6
9849,27,16.3,1,3,0,0,12,1,1,1,1,5
8765,27,16.4,1,1,0,0,11,1,1,1,1,7
581,27,16.4,1,4,0,0,12,0,2,1,1,10
813,27,16.4,1,5,0,0,12,0,2,1,1,7
4615,27,16.4,1,4,0,0,9,0,1,1,1,6
3301,27,16.4,1,5,0,0,11,0,0,1,1,6
10631,27,16.5,1,4,0,0,13,1,1,1,1,10
2679,27,16.5,1,2,0,0,15,0,2,1,1,5
5147,27,16.5,1,3,0,0,14,0,1,1,1,9
5957,27,16.5,1,2,0,1,11,1,5,1,1,3
1453,27,16.5,1,2,0,0,15,0,2,1,1,9
1317,27,16.6,1,4,0,0,10,0,1,1,1,9
4467,27,16.6,1,4,0,0,9,0,2,1,1,5
9179,27,16.6,1,2,0,0,9,1,2,1,1,10
7063,27,16.6,1,5,0,0,12,1,0,1,1,7
3979,27,16.6,1,5,0,0,9,0,0,1,1,7
2368,27,16.6,1,2,0,1,14,0,4,1,1,10
5527,27,16.7,1,5,0,0,12,1,0,1,1,10
4385,27,16.7,1,4,0,0,11,0,0,1,1,6
249,27,16.7,1,2,0,0,10,0,1,1,1,10
3883,27,16.7,1,5,0,1,12,0,6,1,1,4
2383,27,16.7,1,4,0,0,9,0,1,1,1,8
3434,27,16.7,1,5,0,1,14,0,5,1,1,9
189,27,16.7,1,4,0,0,12,0,0,1,1,6
4097,27,16.9,1,4,0,0,10,0,1,1,1,10
10103,27,16.9,1,3,0,0,12,1,1,1,1,5
8243,27,16.9,1,4,0,0,11,1,1,1,1,7
3333,27,16.9,1,2,0,0,12,0,0,1,1,7
6249,27,16.9,1,3,0,0,9,1,1,1,1,5
7105,27,16.9,1,2,0,0,13,1,0,1,1,7
9103,27,16.9,1,4,0,0,10,1,1,1,1,5
10075,27,16.9,1,5,0,0,11,1,1,1,1,6
7153,27,16.9,1,1,0,0,15,1,0,1,1,8
5137,27,17,1,2,0,0,13,0,0,1,1,9
945,27,17,1,1,0,0,12,0,2,1,1,9
6705,27,17,1,4,0,0,10,1,0,1,1,8
1289,27,17.1,1,5,0,0,11,0,1,1,1,10
10429,27,17.1,1,3,0,0,15,1,2,1,1,9
2649,27,17.1,1,3,0,0,14,0,2,1,1,5
9717,27,17.1,1,4,0,0,15,1,1,1,1,7
2725,27,17.1,1,5,0,0,15,0,2,1,1,6
3133,27,17.1,1,3,0,0,12,0,1,1,1,8
3821,27,17.1,1,4,0,0,13,0,2,1,1,6
3837,27,17.1,1,3,0,0,11,0,1,1,1,5
600,27,17.2,1,3,0,1,9,0,5,1,1,5
5591,27,17.2,1,3,0,0,10,1,1,1,1,5
6505,27,17.2,1,2,0,0,11,1,2,1,1,6
4401,27,17.2,1,4,0,0,12,0,0,1,1,8
5395,27,17.2,1,1,0,0,12,0,1,1,1,5
1709,27,17.2,1,5,0,0,9,0,0,1,1,9
6893,27,17.3,1,2,0,0,12,1,1,1,1,9
1231,27,17.3,1,3,0,0,14,0,2,1,1,6
4227,27,17.3,1,2,0,0,13,0,0,1,1,7
2255,27,17.3,1,4,0,0,15,0,0,1,1,10
5485,27,17.5,1,4,0,0,15,1,0,1,1,5
9276,27,17.5,1,2,0,1,11,1,2,1,1,6
1129,27,17.5,1,2,0,0,9,0,1,1,1,6
9529,27,17.5,1,3,0,0,13,1,2,1,1,6
755,27,17.5,1,2,0,0,12,0,1,1,1,8
7413,27,17.6,1,2,0,0,12,1,1,1,1,10
8849,27,17.6,1,3,0,0,13,1,0,1,1,8
5185,27,17.6,1,2,0,0,12,0,1,1,1,5
2977,27,17.6,1,4,0,0,12,0,2,1,1,5
5513,27,17.6,1,1,0,0,13,1,0,1,1,7
2557,27,17.7,1,2,0,0,9,0,2,1,1,5
1135,27,17.7,1,4,0,0,10,0,1,1,1,10
345,27,17.7,1,5,0,0,11,0,0,1,1,9
7751,27,17.8,1,1,0,1,15,1,3,1,1,0
3003,27,17.8,1,2,0,0,10,0,2,1,1,6
7197,27,17.8,1,1,0,0,9,1,0,1,1,9
3213,27,17.8,1,4,0,0,15,0,2,1,1,7
3555,27,17.8,1,4,0,0,10,0,2,1,1,6
9613,27,17.8,1,4,0,0,11,1,2,1,1,8
9407,27,17.9,1,5,0,0,9,1,0,1,1,7
7111,27,17.9,1,5,0,0,10,1,1,1,1,5
8845,27,17.9,1,2,0,0,15,1,1,1,1,10
6692,27,17.9,1,3,0,1,14,1,8,1,1,8
10557,27,17.9,1,2,0,0,14,1,1,1,1,8
10305,27,17.9,1,3,0,0,13,1,1,1,1,5
6245,27,17.9,1,5,0,0,15,1,2,1,1,5
6071,27,18,1,5,0,0,11,1,0,1,1,5
5331,27,18.1,1,1,0,0,14,0,2,1,1,10
4435,27,18.1,1,4,0,0,11,0,1,1,1,8
3023,27,18.2,1,4,0,0,12,0,1,1,1,7
2387,27,18.2,1,3,0,0,12,0,0,1,1,7
1947,27,18.2,1,3,0,0,9,0,1,1,1,9
1909,27,18.2,1,2,0,0,9,0,1,1,1,9
9609,27,18.2,1,5,0,0,10,1,1,1,1,10
9533,27,18.3,1,1,0,0,12,1,0,1,1,8
4135,27,18.3,1,2,0,0,12,0,1,1,1,8
5769,27,18.3,1,5,0,0,11,1,0,1,1,5
7615,27,18.3,1,1,0,0,10,1,2,1,1,7
3167,27,18.3,1,4,0,0,12,0,2,1,1,5
5619,27,18.4,1,3,0,0,10,1,0,1,1,9
8719,27,18.4,1,5,0,0,10,1,0,1,1,8
7342,27,18.4,1,4,0,1,15,1,3,1,1,2
2947,27,18.4,1,1,0,0,10,0,2,1,1,6
10383,27,18.4,1,3,0,0,12,1,0,1,1,5
4779,27,18.4,1,1,0,0,10,0,0,1,1,7
843,27,18.4,1,3,0,1,12,0,6,1,1,4
7879,27,18.4,1,4,0,0,9,1,2,1,1,6
4641,27,18.4,1,5,0,0,11,0,2,1,1,6
3659,27,18.5,1,3,0,0,10,0,0,1,1,8
1565,27,18.5,1,4,0,0,15,0,1,1,1,9
1141,27,18.5,1,5,0,0,15,0,1,1,1,5
4085,27,18.5,1,3,0,0,10,0,1,1,1,5
8377,27,18.5,1,3,0,0,12,1,0,1,1,6
2599,27,18.5,1,1,0,0,12,0,2,1,1,10
7859,27,18.5,1,5,0,0,9,1,1,1,1,6
2307,27,18.6,1,2,0,0,15,0,2,1,1,8
8636,27,18.6,1,1,0,1,12,1,2,1,1,10
6177,27,18.6,1,4,0,0,12,1,1,1,1,7
5257,27,18.6,1,2,0,0,9,0,2,1,1,5
7529,27,18.6,1,3,0,0,11,1,1,1,1,6
3965,27,18.7,1,5,0,0,12,0,0,1,1,10
4207,27,18.7,1,1,0,1,13,0,3,1,1,4
8449,27,18.7,1,3,0,0,15,1,0,1,1,7
1941,27,18.7,1,2,0,1,14,0,6,1,1,1
4357,27,18.7,1,5,0,0,12,0,2,1,1,10
419,27,18.7,1,1,0,0,9,0,1,1,1,7
1547,27,18.7,1,2,0,0,14,0,2,1,1,6
4639,27,18.7,1,1,0,0,11,0,1,1,1,10
947,27,18.8,1,3,0,0,12,0,0,1,1,6
5505,27,18.8,1,5,0,0,14,1,2,1,1,6
9451,27,18.8,1,4,0,0,14,1,2,1,1,8
2721,27,18.8,1,2,0,0,11,0,2,1,1,5
7689,27,18.8,1,2,0,0,12,1,1,1,1,9
381,27,18.8,1,1,0,0,12,0,1,1,1,6
6531,27,18.9,1,5,0,0,13,1,2,1,1,10
3303,27,18.9,1,3,0,0,14,0,0,1,1,5
831,27,18.9,1,4,0,0,11,0,1,1,1,8
4003,27,18.9,1,1,0,0,11,0,2,1,1,8
10793,27,18.9,1,1,0,0,11,1,2,1,1,7
4308,27,18.9,1,4,0,1,13,0,5,1,1,0
2105,27,18.9,1,1,0,0,12,0,0,1,1,6
871,27,18.9,1,1,0,0,12,0,2,1,1,9
2515,27,19,1,5,0,0,9,0,2,1,1,10
8119,27,19,1,1,0,0,11,1,1,1,1,5
6863,27,19,1,5,0,0,15,1,0,1,1,5
1683,27,19,1,4,0,0,12,0,1,1,1,9
10391,27,19,1,1,0,0,11,1,2,1,1,7
1913,27,19,1,3,0,0,11,0,1,1,1,10
9983,27,19,1,2,0,0,12,1,0,1,1,5
6639,27,19,1,4,0,0,13,1,1,1,1,5
8869,27,19.1,1,3,0,0,14,1,0,1,1,6
7771,27,19.1,1,1,0,0,15,1,1,1,1,6
2401,27,19.1,1,5,0,0,9,0,1,1,1,7
3647,27,19.1,1,1,0,0,14,0,2,1,1,5
9925,27,19.1,1,4,0,0,12,1,1,1,1,7
9419,27,19.2,1,4,0,0,13,1,2,1,1,8
6259,27,19.2,1,3,0,0,13,1,2,1,1,10
2261,27,19.2,1,3,0,0,15,0,1,1,1,9
8425,27,19.2,1,1,0,0,12,1,2,1,1,5
7127,27,19.2,1,3,0,0,15,1,1,1,1,6
6575,27,19.2,1,1,0,0,13,1,1,1,1,5
10767,27,19.2,1,5,0,0,13,1,0,1,1,5
3125,27,19.3,1,1,0,0,9,0,2,1,1,8
6747,27,19.3,1,2,0,0,11,1,1,1,1,8
8629,27,19.3,1,4,0,0,14,1,1,1,1,10
4355,27,19.3,1,1,0,0,13,0,2,1,1,8
1143,27,19.4,1,5,0,0,14,0,2,1,1,6
8331,27,19.4,1,2,0,0,12,1,2,1,1,6
9641,27,19.4,1,2,0,0,12,1,0,1,1,10
6041,27,19.5,1,4,0,0,9,1,1,1,1,8
7137,27,19.6,1,1,0,0,13,1,0,1,1,7
5027,27,19.6,1,5,0,0,15,0,0,1,1,7
2127,27,19.6,1,4,0,0,10,0,0,1,1,8
4083,27,19.6,1,4,0,0,13,0,0,1,1,8
1381,27,19.7,1,4,0,0,12,0,1,1,1,7
8662,27,19.8,1,4,0,1,12,1,5,1,1,7
5149,27,19.8,1,3,0,0,11,0,0,1,1,8
7599,27,19.9,1,3,0,0,9,1,1,1,1,9
7757,27,19.9,1,5,0,0,11,1,1,1,1,8
10453,27,19.9,1,5,0,0,14,1,1,1,1,5
3221,27,19.9,1,5,0,0,9,0,1,1,1,7
1509,27,19.9,1,2,0,0,11,0,1,1,1,9
5543,27,19.9,1,1,0,0,12,1,1,1,1,6
4217,27,19.9,1,5,0,0,10,0,2,1,1,8
5389,27,20,1,5,0,0,9,0,2,1,1,10
5119,27,20,1,2,0,0,12,0,2,1,1,5
7601,27,20,1,5,0,0,13,1,1,1,1,9
9567,27,20,1,2,0,0,12,1,1,1,1,7
427,27,20,1,4,0,0,11,0,1,1,1,6
107,27,20,1,5,0,0,15,0,0,1,1,9
8351,27,20,1,5,0,0,15,1,2,1,1,9
10093,27,20,1,3,0,0,13,1,0,1,1,10
8907,27,20.1,1,1,0,0,9,1,1,1,1,8
7575,27,20.1,1,3,0,0,12,1,0,1,1,6
10593,27,20.1,1,4,0,1,14,1,6,1,1,4
2183,27,20.1,1,4,0,0,10,0,0,1,1,7
4715,27,20.1,1,1,0,0,10,0,1,1,1,8
2949,27,20.2,1,4,0,1,9,0,1,1,1,0
353,27,20.2,1,1,0,0,10,0,1,1,1,8
253,27,20.2,1,3,0,0,12,0,1,1,1,7
3155,27,20.2,1,4,0,0,11,0,1,1,1,7
6763,27,20.2,1,4,0,0,12,1,1,1,1,10
3257,27,20.2,1,5,0,0,12,0,0,1,1,9
2209,27,20.2,1,5,0,0,13,0,0,1,1,8
10774,27,20.3,1,2,0,1,15,1,1,1,1,10
5081,27,20.3,1,3,0,0,13,0,0,1,1,7
4293,27,20.3,1,3,0,0,9,0,0,1,1,10
8661,27,20.3,1,3,0,0,15,1,0,1,1,6
495,27,20.4,1,3,0,0,9,0,1,1,1,10
4527,27,20.4,1,1,0,0,14,0,0,1,1,7
8397,27,20.4,1,3,0,0,14,1,2,1,1,7
5559,27,20.4,1,3,0,0,12,1,0,1,1,8
9969,27,20.4,1,5,0,0,15,1,0,1,1,8
9787,27,20.4,1,3,0,0,11,1,1,1,1,8
6375,27,20.5,1,3,0,0,12,1,2,1,1,9
7366,27,20.5,1,3,0,1,12,1,5,1,1,6
593,27,20.5,1,4,0,0,12,0,0,1,1,6
1001,27,20.5,1,4,0,0,12,0,0,1,1,7
5367,27,20.5,1,2,0,0,12,0,0,1,1,8
8973,27,20.5,1,5,0,0,12,1,2,1,1,9
7213,27,20.6,1,4,0,0,11,1,1,1,1,5
6729,27,20.6,1,1,0,0,12,1,0,1,1,8
3579,27,20.6,1,5,0,0,15,0,2,1,1,6
8035,27,20.6,1,3,0,0,9,1,2,1,1,5
7509,27,20.7,1,2,0,0,9,1,2,1,1,10
3617,27,20.7,1,3,0,0,14,0,1,1,1,10
3079,27,20.7,1,5,0,0,12,0,2,1,1,5
3047,27,20.7,1,1,0,0,13,0,0,1,1,9
9469,27,20.8,1,2,0,0,14,1,0,1,1,9
7055,27,20.8,1,2,0,0,13,1,1,1,1,9
1491,27,20.8,1,4,0,0,9,0,1,1,1,7
8811,27,20.8,1,3,0,0,12,1,0,1,1,6
1695,27,20.9,1,1,0,0,14,0,1,1,1,5
4269,27,20.9,1,5,0,0,13,0,2,1,1,9
2165,27,21,1,4,0,0,12,0,0,1,1,9
9781,27,21,1,4,0,0,12,1,2,1,1,7
4931,27,21.1,1,1,0,0,10,0,0,1,1,8
7495,27,21.1,1,1,0,0,15,1,2,1,1,7
6419,27,21.1,1,3,0,0,10,1,2,1,1,5
4365,27,21.2,1,2,0,0,13,0,2,1,1,5
9433,27,21.2,1,2,0,0,12,1,2,1,1,8
6745,27,21.2,1,2,0,0,13,1,0,1,1,9
4271,27,21.2,1,4,0,0,9,0,0,1,1,7
6555,27,21.2,1,2,0,0,12,1,1,1,1,8
9065,27,21.2,1,4,0,0,15,1,1,1,1,6
9911,27,21.2,1,3,0,0,9,1,2,1,1,8
2063,27,21.3,1,3,0,0,15,0,0,1,1,8
5444,27,21.3,1,4,0,1,10,1,5,1,1,4
2351,27,21.3,1,1,0,0,13,0,1,1,1,6
2059,27,21.3,1,2,0,0,9,0,0,1,1,8
5737,27,21.3,1,4,0,0,9,1,1,1,1,5
8729,27,21.3,1,4,0,0,10,1,2,1,1,10
8477,27,21.4,1,2,0,0,10,1,2,1,1,10
9077,27,21.4,1,3,0,0,12,1,2,1,1,10
4591,27,21.4,1,5,0,0,12,0,2,1,1,6
3835,27,21.4,1,4,0,0,11,0,1,1,1,7
6039,27,21.5,1,3,0,0,15,1,0,1,1,8
10597,27,21.5,1,4,0,0,11,1,2,1,1,10
8763,27,21.5,1,3,0,0,14,1,1,1,1,10
10548,27,21.5,1,1,0,1,9,1,1,1,1,9
9411,27,21.5,1,3,0,0,12,1,0,1,1,7
613,27,21.6,1,2,0,0,13,0,0,1,1,6
3957,27,21.6,1,4,0,0,12,0,1,1,1,8
673,27,21.6,1,4,0,0,9,0,1,1,1,10
4213,27,21.7,1,5,0,0,10,0,1,1,1,5
4495,27,21.7,1,5,0,0,9,0,0,1,1,6
3553,27,21.7,1,3,0,0,12,0,0,1,1,7
4937,27,21.7,1,4,0,0,14,0,0,1,1,10
8325,27,21.8,1,1,0,0,9,1,0,1,1,5
9761,27,21.8,1,1,0,0,10,1,2,1,1,10
7907,27,21.8,1,2,0,0,15,1,0,1,1,9
3245,27,21.8,1,4,0,0,12,0,1,1,1,8
6207,27,21.9,1,1,0,0,14,1,1,1,1,10
6467,27,21.9,1,4,0,0,11,1,0,1,1,8
10279,27,21.9,1,1,0,0,12,1,2,1,1,9
5781,27,21.9,1,4,0,0,11,1,1,1,1,9
5467,27,21.9,1,2,0,0,12,1,1,1,1,7
1575,27,21.9,1,3,0,0,13,0,1,1,1,9
4253,27,21.9,1,1,0,0,13,0,0,1,1,5
10723,27,21.9,1,2,0,0,12,1,1,1,1,9
3503,27,21.9,1,3,0,0,12,0,2,1,1,5
1499,27,21.9,1,2,0,1,12,0,4,1,1,0
7623,27,21.9,1,1,0,0,9,1,2,1,1,6
1041,27,22,1,2,0,0,11,0,0,1,1,7
10709,27,22,1,3,0,0,12,1,2,1,1,8
5291,27,22,1,1,0,0,12,0,2,1,1,9
5487,27,22.1,1,4,0,0,15,1,1,1,1,5
8679,27,22.1,1,4,0,0,9,1,0,1,1,7
10295,27,22.1,1,1,0,0,14,1,0,1,1,5
1813,27,22.1,1,3,0,0,9,0,2,1,1,8
7827,27,22.1,1,2,0,0,12,1,2,1,1,10
2837,27,22.1,1,4,0,0,15,0,2,1,1,8
709,27,22.2,1,3,0,0,12,0,1,1,1,5
7905,27,22.2,1,4,0,0,11,1,1,1,1,10
6697,27,22.2,1,2,0,0,12,1,1,1,1,5
10325,27,22.3,1,2,0,0,13,1,2,1,1,5
9663,27,22.4,1,5,0,0,12,1,0,1,1,6
10144,27,22.4,1,4,0,1,12,1,1,1,1,9
9026,27,22.4,1,4,0,1,14,1,5,1,1,1
10575,27,22.4,1,5,0,0,9,1,2,1,1,9
167,27,22.5,1,4,0,0,12,0,1,1,1,7
3888,27,22.5,1,5,0,1,12,0,5,1,1,0
6385,27,22.5,1,4,0,0,13,1,0,1,1,8
7131,27,22.5,1,4,0,0,12,1,1,1,1,9
2783,27,22.5,1,2,0,0,10,0,1,1,1,10
1767,27,22.6,1,1,0,0,9,0,2,1,1,8
9191,27,22.6,1,2,0,0,15,1,0,1,1,5
2347,27,22.7,1,5,0,0,14,0,1,1,1,8
6699,27,22.7,1,5,0,0,15,1,0,1,1,5
4957,27,22.7,1,2,0,0,12,0,1,1,1,10
4738,27,22.7,1,2,0,1,15,0,3,1,1,10
6667,27,22.7,1,2,0,0,9,1,1,1,1,5
1395,27,22.7,1,5,0,0,15,0,0,1,1,10
5715,27,22.7,1,2,0,0,11,1,1,1,1,9
8451,27,22.8,1,2,0,0,14,1,2,1,1,8
5519,27,22.8,1,4,0,0,14,1,2,1,1,10
8510,27,22.8,1,3,0,1,10,1,8,1,1,4
1123,27,22.8,1,2,0,0,13,0,1,1,1,9
4789,27,22.8,1,1,0,0,15,0,0,1,1,10
8889,27,22.9,1,2,0,0,15,1,0,1,1,7
741,27,22.9,1,4,0,0,10,0,0,1,1,10
7993,27,22.9,1,1,0,0,14,1,2,1,1,5
9665,27,22.9,1,2,0,0,13,1,2,1,1,10
9585,27,22.9,1,2,0,0,12,1,2,1,1,10
7339,27,22.9,1,2,0,0,9,1,1,1,1,9
2381,27,23,1,4,0,0,12,0,1,1,1,9
4843,27,23,1,3,0,0,13,0,0,1,1,10
8480,27,23.1,1,4,0,1,9,1,4,1,1,0
10681,27,23.1,1,4,0,0,14,1,1,1,1,7
901,27,23.1,1,2,0,0,12,0,1,1,1,6
10199,27,23.1,1,2,0,0,12,1,2,1,1,8
3899,27,23.1,1,1,0,0,11,0,2,1,1,7
5425,27,23.1,1,5,0,0,11,1,1,1,1,8
7399,27,23.2,1,2,0,0,15,1,2,1,1,7
1155,27,23.2,1,1,0,0,11,0,0,1,1,5
5865,27,23.2,1,5,0,0,15,1,2,1,1,6
6671,27,23.2,1,2,0,0,11,1,1,1,1,8
2601,27,23.2,1,3,0,0,12,0,1,1,1,6
2289,27,23.2,1,5,0,0,12,0,1,1,1,5
1197,27,23.3,1,5,0,0,12,0,2,1,1,9
10247,27,23.3,1,3,0,0,15,1,0,1,1,10
9931,27,23.4,1,2,0,0,11,1,2,1,1,10
5043,27,23.4,1,3,0,0,9,0,1,1,1,5
501,27,23.4,1,4,0,0,12,0,0,1,1,8
7899,27,23.4,1,2,0,0,11,1,1,1,1,6
9971,27,23.5,1,2,0,0,13,1,1,1,1,10
3615,27,23.5,1,4,0,0,12,0,2,1,1,10
3695,27,23.5,1,5,0,0,12,0,1,1,1,8
3364,27,23.6,1,2,0,1,12,0,4,1,1,0
6141,27,23.6,1,3,0,0,15,1,2,1,1,8
4053,27,23.6,1,5,0,0,9,0,2,1,1,8
7313,27,23.6,1,2,0,0,10,1,1,1,1,9
1869,27,23.6,1,4,0,0,10,0,2,1,1,7
6311,27,23.6,1,1,0,0,13,1,1,1,1,9
3865,27,23.6,1,5,0,0,12,0,0,1,1,10
3165,27,23.6,1,4,0,0,9,0,1,1,1,8
8017,27,23.7,1,2,0,0,12,1,2,1,1,7
8749,27,23.7,1,2,0,0,12,1,2,1,1,10
8167,27,23.7,1,5,0,0,13,1,1,1,1,8
6507,27,23.7,1,5,0,0,14,1,1,1,1,9
2341,27,23.7,1,4,0,0,14,0,2,1,1,8
3061,27,23.8,1,4,0,0,12,0,0,1,1,9
2829,27,23.8,1,3,0,0,12,0,2,1,1,10
4323,27,23.8,1,5,0,0,10,0,0,1,1,10
1225,27,23.9,1,2,0,0,14,0,1,1,1,8
2855,27,23.9,1,3,0,0,12,0,0,1,1,10
10639,27,24,1,5,0,0,11,1,2,1,1,7
10025,27,24,1,2,0,0,12,1,0,1,1,6
10853,27,24,1,2,0,0,11,1,1,1,1,5
6865,27,24,1,5,0,0,9,1,1,1,1,9
1501,27,24.1,1,3,0,0,12,0,2,1,1,10
2487,27,24.1,1,5,0,0,12,0,1,1,1,9
3449,27,24.1,1,2,0,0,13,0,0,1,1,9
4449,27,24.2,1,1,0,0,12,0,1,1,1,5
10077,27,24.3,1,1,0,0,10,1,0,1,1,10
2471,27,24.3,1,3,0,0,14,0,2,1,1,8
8711,27,24.3,1,1,0,0,11,1,2,1,1,7
6743,27,24.3,1,1,0,0,13,1,0,1,1,9
9777,27,24.3,1,5,0,0,10,1,2,1,1,6
4369,27,24.4,1,2,0,0,13,0,0,1,1,10
1567,27,24.4,1,5,0,0,12,0,2,1,1,9
471,27,24.4,1,3,0,0,14,0,2,1,1,7
2547,27,24.4,1,2,0,0,15,0,0,1,1,6
9821,27,24.4,1,2,0,0,15,1,2,1,1,6
5803,27,24.6,1,1,0,0,11,1,0,1,1,9
7143,27,24.6,1,4,0,0,12,1,2,1,1,8
10183,27,24.6,1,5,0,0,14,1,0,1,1,6
1591,27,24.6,1,3,0,0,12,0,0,1,1,6
1815,27,24.6,1,1,0,0,13,0,1,1,1,8
3921,27,24.7,1,4,0,0,10,0,2,1,1,10
6839,27,24.7,1,1,0,0,13,1,0,1,1,10
4341,27,24.7,1,2,0,0,15,0,2,1,1,10
3581,27,24.7,1,1,0,0,12,0,0,1,1,8
10603,27,24.7,1,2,0,0,9,1,1,1,1,7
4093,27,24.8,1,5,0,0,12,0,1,1,1,6
4381,27,24.8,1,1,0,0,11,0,0,1,1,8
6152,27,24.8,1,1,0,1,15,1,4,1,1,2
9965,27,24.8,1,1,0,0,13,1,0,1,1,9
323,27,24.9,1,3,0,0,13,0,1,1,1,7
8647,27,24.9,1,2,0,0,15,1,2,1,1,10
9138,27,24.9,1,5,0,1,9,1,7,1,1,7
6223,27,24.9,1,4,0,0,11,1,1,1,1,5
7263,27,24.9,1,1,0,0,11,1,2,1,1,7
1955,27,24.9,1,3,0,0,14,0,1,1,1,7
7679,27,25,1,5,0,0,12,1,0,1,1,8
10659,27,25,1,3,0,0,12,1,1,1,1,10
4179,27,25.1,1,2,0,0,13,0,0,1,1,6
9695,27,25.1,1,2,0,0,11,1,0,1,1,5
5811,27,25.1,1,3,0,0,12,1,0,1,1,10
6603,27,25.2,1,3,0,0,13,1,1,1,1,8
2331,27,25.2,1,4,0,0,14,0,0,1,1,10
1443,27,25.3,1,1,0,0,9,0,2,1,1,9
6549,27,25.3,1,5,0,0,13,1,0,1,1,5
6333,27,25.3,1,5,0,0,10,1,1,1,1,7
491,27,25.3,1,3,0,0,14,0,2,1,1,9
4489,27,25.3,1,5,0,0,13,0,2,1,1,5
9287,27,25.3,1,4,0,0,9,1,0,1,1,10
7521,27,25.4,1,1,0,0,12,1,2,1,1,7
4279,27,25.4,1,2,0,0,14,0,0,1,1,10
8667,27,25.4,1,4,0,0,9,1,1,1,1,10
10081,27,25.4,1,2,0,0,11,1,2,1,1,5
9645,27,25.4,1,4,0,0,12,1,2,1,1,7
2935,27,25.5,1,3,0,0,12,0,2,1,1,8
4299,27,25.5,1,4,0,0,9,0,0,1,1,10
4889,27,25.5,1,2,0,0,11,0,2,1,1,6
9257,27,25.5,1,2,0,0,14,1,2,1,1,8
759,27,25.5,1,2,0,0,11,0,2,1,1,7
399,27,25.6,1,2,0,0,13,0,0,1,1,10
5176,27,25.6,1,4,0,1,12,0,4,1,1,4
5091,27,25.6,1,2,0,0,10,0,1,1,1,6
10449,27,25.7,1,3,0,0,13,1,1,1,1,9
1923,27,25.7,1,2,0,0,10,0,1,1,1,6
4773,27,25.8,1,5,0,0,10,0,1,1,1,8
2535,27,25.8,1,2,0,0,14,0,0,1,1,8
617,27,25.8,1,1,0,0,10,0,2,1,1,10
3655,27,25.8,1,1,0,0,13,0,1,1,1,10
5965,27,25.8,1,1,0,0,14,1,0,1,1,7
8767,27,25.9,1,1,0,0,9,1,0,1,1,7
6313,27,25.9,1,5,0,0,15,1,2,1,1,5
1811,27,25.9,1,5,0,1,11,0,7,1,1,3
3705,27,25.9,1,2,0,0,14,0,2,1,1,5
3277,27,26,1,1,0,0,12,0,2,1,1,9
8429,27,26,1,4,0,0,12,1,0,1,1,8
3293,27,26,1,1,0,1,15,0,7,1,1,4
5031,27,26,1,5,0,0,11,0,1,1,1,8
5242,27,26,1,1,0,1,14,0,1,1,1,10
2875,27,26,1,2,0,0,10,0,2,1,1,10
10459,27,26.1,1,2,0,0,15,1,2,1,1,9
8781,27,26.1,1,1,0,0,14,1,1,1,1,5
7589,27,26.1,1,3,0,0,15,1,2,1,1,5
9595,27,26.1,1,1,0,0,12,1,1,1,1,5
8123,27,26.1,1,2,0,0,12,1,2,1,1,10
2145,27,26.1,1,5,0,0,15,0,1,1,1,6
393,27,26.1,1,5,0,0,14,0,2,1,1,7
8103,27,26.1,1,1,0,0,12,1,1,1,1,5
7729,27,26.1,1,4,0,0,11,1,0,1,1,6
7579,27,26.2,1,1,0,0,13,1,2,1,1,6
5075,27,26.3,1,1,0,0,13,0,1,1,1,5
9095,27,26.3,1,5,0,0,15,1,1,1,1,7
4565,27,26.3,1,3,0,0,12,0,0,1,1,5
6433,27,26.3,1,3,0,0,9,1,1,1,1,8
2993,27,26.3,1,1,0,0,12,0,2,1,1,5
2723,27,26.3,1,4,0,0,11,0,0,1,1,7
10097,27,26.3,1,4,0,0,11,1,2,1,1,7
5945,27,26.4,1,5,0,0,10,1,0,1,1,9
8139,27,26.4,1,4,0,0,12,1,0,1,1,10
1425,27,26.4,1,2,0,0,14,0,0,1,1,10
5039,27,26.4,1,3,0,0,13,0,1,1,1,5
2845,27,26.5,1,1,0,0,15,0,2,1,1,5
9737,27,26.5,1,2,0,0,12,1,2,1,1,8
8109,27,26.5,1,4,0,0,9,1,1,1,1,6
7375,27,26.5,1,5,0,0,10,1,0,1,1,9
1551,27,26.5,1,2,0,0,14,0,1,1,1,6
9099,27,26.5,1,5,0,0,12,1,2,1,1,10
2493,27,26.5,1,4,0,0,14,0,1,1,1,9
2083,27,26.5,1,5,0,0,12,0,2,1,1,8
743,27,26.5,1,1,0,0,15,0,1,1,1,7
9795,27,26.5,1,4,0,0,10,1,1,1,1,7
187,27,26.6,1,2,0,0,9,0,0,1,1,10
609,27,26.7,1,1,0,0,12,0,0,1,1,7
3259,27,26.7,1,5,0,0,13,0,2,1,1,6
2927,27,26.7,1,3,0,0,10,0,0,1,1,9
3523,27,26.7,1,5,0,0,11,0,0,1,1,10
3875,27,26.7,1,2,0,0,13,0,2,1,1,7
6781,27,26.8,1,4,0,0,10,1,2,1,1,5
5311,27,26.8,1,1,0,0,12,0,0,1,1,9
6703,27,26.8,1,3,0,0,12,1,2,1,1,10
545,27,26.9,1,3,0,0,13,0,2,1,1,9
5943,27,26.9,1,3,0,0,13,1,2,1,1,6
2815,27,27,1,5,0,0,9,0,1,1,1,6
2611,27,27,1,1,0,0,12,0,1,1,1,7
2159,27,27,1,4,0,0,9,0,2,1,1,9
4545,27,27,1,5,0,0,11,0,1,1,1,6
8163,27,27.1,1,5,0,0,9,1,2,1,1,6
3113,27,27.1,1,5,0,0,15,0,0,1,1,10
8055,27,27.1,1,4,0,0,12,1,0,1,1,5
9109,27,27.2,1,5,0,0,11,1,0,1,1,6
6905,27,27.2,1,2,0,0,12,1,0,1,1,9
7583,27,27.2,1,3,0,0,10,1,1,1,1,10
605,27,27.2,1,5,0,0,14,0,1,1,1,6
3395,27,27.2,1,4,0,0,13,0,1,1,1,10
6325,27,27.3,1,2,0,0,9,1,0,1,1,8
5776,27,27.3,1,1,0,1,14,1,3,1,1,1
6379,27,27.3,1,4,0,0,12,1,0,1,1,8
2713,27,27.3,1,4,0,0,12,0,1,1,1,9
4701,27,27.3,1,5,0,0,12,0,2,1,1,9
2575,27,27.3,1,1,0,0,15,0,2,1,1,10
7959,27,27.4,1,4,0,0,11,1,2,1,1,9
9901,27,27.4,1,4,0,0,9,1,1,1,1,8
4409,27,27.4,1,1,0,0,15,0,0,1,1,6
4259,27,27.4,1,1,0,0,10,0,2,1,1,7
3177,27,27.4,1,3,0,0,11,0,2,1,1,8
9087,27,27.4,1,3,0,0,12,1,2,1,1,5
9217,27,27.5,1,1,0,0,10,1,2,1,1,10
8081,27,27.5,1,1,0,0,13,1,2,1,1,10
5373,27,27.5,1,3,0,0,12,0,2,1,1,7
6957,27,27.5,1,3,0,0,12,1,0,1,1,6
507,27,27.5,1,1,0,0,12,0,0,1,1,5
4251,27,27.6,1,1,0,0,11,0,2,1,1,9
1965,27,27.7,1,5,0,0,13,0,2,1,1,9
9947,27,27.7,1,3,0,0,10,1,0,1,1,6
4441,27,27.7,1,4,0,0,13,0,0,1,1,6
51,27,27.8,1,2,0,0,11,0,0,1,1,9
487,27,27.8,1,2,0,0,13,0,1,1,1,8
2531,27,27.8,1,5,0,0,11,0,0,1,1,10
6986,27,27.8,1,1,0,1,12,1,4,1,1,9
5703,27,27.8,1,1,0,0,9,1,0,1,1,10
773,27,27.9,1,3,0,0,12,0,2,1,1,6
9702,27,27.9,1,1,0,1,11,1,5,1,1,9
1881,27,28,1,3,0,0,11,0,2,1,1,5
3525,27,28,1,5,0,0,13,0,1,1,1,6
6487,27,28,1,3,0,0,14,1,0,1,1,8
1463,27,28,1,3,0,0,12,0,0,1,1,5
3339,27,28,1,2,0,0,10,0,1,1,1,6
7357,27,28,1,4,0,1,10,1,1,1,1,0
7868,27,28.1,1,2,0,1,11,1,2,1,1,1
6361,27,28.1,1,3,0,0,13,1,1,1,1,6
4809,27,28.1,1,1,0,0,12,0,2,1,1,7
5647,27,28.1,1,3,0,0,14,1,0,1,1,7
9977,27,28.1,1,1,0,0,14,1,1,1,1,5
9167,27,28.1,1,3,0,0,12,1,1,1,1,10
10125,27,28.2,1,3,0,0,12,1,1,1,1,6
8587,27,28.2,1,4,0,0,12,1,0,1,1,5
7331,27,28.2,1,4,0,0,9,1,1,1,1,5
5265,27,28.2,1,4,0,0,9,0,2,1,1,9
513,27,28.2,1,3,0,0,12,0,1,1,1,8
1550,27,28.2,1,2,0,1,12,0,1,1,1,1
5319,27,28.2,1,5,0,0,12,0,1,1,1,10
511,27,28.2,1,2,0,1,12,0,2,1,1,3
3601,27,28.3,1,4,0,0,15,0,0,1,1,10
4873,27,28.3,1,5,0,0,11,0,2,1,1,6
1079,27,28.3,1,3,0,0,9,0,2,1,1,9
3945,27,28.3,1,5,0,0,14,0,1,1,1,9
779,27,28.3,1,4,0,0,14,0,1,1,1,5
7823,27,28.3,1,3,0,0,9,1,0,1,1,5
2945,27,28.4,1,3,0,0,13,0,2,1,1,7
2117,27,28.4,1,3,0,0,14,0,2,1,1,7
8275,27,28.4,1,3,0,0,12,1,1,1,1,7
9885,27,28.4,1,4,0,0,9,1,1,1,1,9
8015,27,28.5,1,5,0,0,12,1,1,1,1,5
5645,27,28.5,1,4,0,0,11,1,0,1,1,6
5219,27,28.5,1,2,0,0,13,0,2,1,1,9
4165,27,28.5,1,3,0,0,12,0,1,1,1,6
2049,27,28.5,1,2,0,0,13,0,2,1,1,8
1829,27,28.5,1,3,0,0,14,0,0,1,1,6
6429,27,28.5,1,3,0,0,10,1,1,1,1,5
4881,27,28.6,1,5,0,0,13,0,2,1,1,8
5607,27,28.6,1,1,0,0,12,1,2,1,1,10
3949,27,28.6,1,2,0,0,15,0,1,1,1,7
5653,27,28.6,1,5,0,0,11,1,0,1,1,10
10379,27,28.6,1,4,0,0,10,1,0,1,1,8
5287,27,28.6,1,4,0,0,9,0,2,1,1,6
2389,27,28.7,1,4,0,0,13,0,1,1,1,6
3693,27,28.7,1,4,0,0,12,0,1,1,1,8
4533,27,28.7,1,3,0,0,15,0,1,1,1,9
10771,27,28.7,1,4,0,0,11,1,2,1,1,7
8779,27,28.7,1,2,0,0,12,1,2,1,1,8
6129,27,28.7,1,2,0,0,11,1,2,1,1,5
10643,27,28.7,1,2,0,0,14,1,0,1,1,5
7335,27,28.7,1,1,0,1,11,1,7,1,1,1
3855,27,28.8,1,3,0,0,10,0,1,1,1,8
4391,27,28.8,1,2,0,0,10,0,0,1,1,5
2843,27,28.8,1,3,0,0,15,0,1,1,1,7
6809,27,28.8,1,2,0,0,9,1,2,1,1,5
8011,27,28.8,1,5,0,0,12,1,2,1,1,8
4229,27,28.8,1,3,0,0,9,0,0,1,1,7
4977,27,28.8,1,2,0,0,12,0,2,1,1,6
1839,27,28.9,1,3,0,0,13,0,1,1,1,6
3291,27,28.9,1,2,0,0,12,0,0,1,1,7
1505,27,28.9,1,5,0,0,11,0,1,1,1,8
9871,27,28.9,1,2,0,0,15,1,2,1,1,9
10701,27,28.9,1,3,0,0,12,1,0,1,1,7
9579,27,29,1,3,0,0,12,1,1,1,1,10
41,27,29,1,5,0,0,12,0,2,1,1,6
1435,27,29.1,1,2,0,0,12,0,1,1,1,10
2645,27,29.1,1,4,0,0,12,0,0,1,1,6
9421,27,29.2,1,4,0,0,9,1,2,1,1,7
10435,27,29.2,1,3,0,0,11,1,0,1,1,8
2565,27,29.2,1,2,0,0,15,0,0,1,1,7
3863,27,29.2,1,2,0,0,14,0,0,1,1,10
4597,27,29.2,1,2,0,0,13,0,2,1,1,9
1701,27,29.2,1,2,0,0,11,0,1,1,1,8
3059,27,29.2,1,3,0,0,12,0,1,1,1,9
9824,27,29.2,1,5,0,1,13,1,7,1,1,6
4153,27,29.3,1,2,0,0,12,0,1,1,1,7
2075,27,29.3,1,2,0,0,13,0,1,1,1,10
1555,27,29.3,1,2,0,0,12,0,1,1,1,7
5885,27,29.4,1,3,0,0,12,1,2,1,1,8
8355,27,29.4,1,1,0,0,11,1,0,1,1,10
4921,27,29.4,1,3,0,0,10,0,0,1,1,6
850,27,29.4,1,5,0,1,13,0,1,1,1,10
3958,27,29.5,1,2,0,1,9,0,3,1,1,5
4211,27,29.5,1,1,0,0,9,0,1,1,1,8
7273,27,29.5,1,4,0,0,12,1,0,1,1,6
3817,27,29.5,1,2,0,0,11,0,0,1,1,9
7707,27,29.6,1,5,0,0,10,1,1,1,1,8
9467,27,29.6,1,2,0,0,14,1,1,1,1,9
1085,27,29.6,1,3,0,0,9,0,0,1,1,10
6617,27,29.6,1,1,0,0,9,1,2,1,1,6
4691,27,29.6,1,2,0,0,15,0,2,1,1,6
4997,27,29.7,1,5,0,0,13,0,0,1,1,8
9311,27,29.7,1,3,0,0,12,1,0,1,1,5
5969,27,29.7,1,1,0,0,14,1,2,1,1,9
5707,27,29.7,1,2,0,0,12,1,2,1,1,5
5971,27,29.7,1,2,0,0,9,1,2,1,1,9
309,27,29.7,1,4,0,0,12,0,2,1,1,10
5143,27,29.7,1,1,0,0,15,0,1,1,1,5
5045,27,29.7,1,1,0,0,12,0,1,1,1,9
1139,27,29.7,1,3,0,0,14,0,2,1,1,7
3375,27,29.8,1,5,0,0,14,0,0,1,1,9
2709,27,29.8,1,3,0,0,15,0,0,1,1,8
5329,27,29.8,1,1,0,0,13,0,0,1,1,10
9209,27,29.8,1,2,0,0,12,1,1,1,1,5
9215,27,29.9,1,4,0,0,15,1,0,1,1,8
5817,27,29.9,1,1,0,0,12,1,2,1,1,9
915,27,29.9,1,2,0,0,15,0,0,1,1,8
2151,27,29.9,1,1,0,0,12,0,2,1,1,7
10465,27,30,1,5,0,0,12,1,1,1,1,6
3001,27,30,1,1,0,0,14,0,1,1,1,8
2643,27,30,1,4,0,0,9,0,2,1,1,5
5132,28,0.6,1,3,0,1,15,0,5,1,1,0
10205,28,1.1,1,2,0,1,14,1,6,1,1,3
7962,28,1.1,1,2,0,1,14,1,6,1,1,4
1492,28,1.4,1,2,0,1,10,0,7,1,1,7
2510,28,1.5,1,1,0,1,14,0,7,1,1,4
3853,28,2,1,1,0,1,12,0,3,1,1,2
6077,28,2.2,1,1,0,1,15,1,2,1,1,4
4214,28,3.2,1,2,0,1,12,0,8,1,1,7
5414,28,3.3,1,4,0,1,12,0,5,1,1,4
357,28,3.5,1,2,0,1,12,0,4,1,1,3
1832,28,3.5,1,3,0,1,9,0,4,1,1,1
699,28,5.1,1,4,0,1,11,0,1,1,1,2
8221,28,5.3,1,4,0,1,9,1,6,1,1,2
6723,28,5.4,1,5,0,1,14,1,7,1,1,3
1464,28,5.4,1,1,0,1,10,0,2,1,1,9
4910,28,5.5,1,1,0,1,11,0,8,1,1,2
9456,28,5.9,1,5,0,1,12,1,3,1,1,0
1005,28,6,1,1,0,1,14,0,4,1,1,0
8678,28,6,1,3,0,1,15,1,1,1,1,7
3603,28,6.3,1,2,0,1,13,0,6,1,1,4
3328,28,6.8,1,2,0,1,10,0,7,1,1,5
9080,28,7.2,1,2,0,1,12,1,1,1,1,7
331,28,7.2,1,2,0,1,12,0,5,1,1,2
6015,28,7.8,1,5,0,1,10,1,7,1,1,2
6354,28,8.1,1,1,0,1,11,1,2,1,1,2
420,28,8.4,1,4,0,1,12,0,2,1,1,8
7773,28,8.8,1,4,0,1,15,1,5,1,1,1
10056,28,9.2,1,3,0,1,11,1,6,1,1,5
9972,28,9.5,1,3,0,1,9,1,8,1,1,0
6616,28,9.9,1,4,0,1,15,1,7,1,1,9
10797,28,10.7,1,3,0,1,13,1,1,1,1,3
1541,28,11,1,4,0,1,11,0,3,1,1,2
6372,28,11.4,1,2,0,1,10,1,4,1,1,6
6910,28,11.9,1,1,0,1,12,1,4,1,1,10
1180,28,13.5,1,4,0,1,12,0,6,1,1,2
644,28,13.8,1,1,0,1,11,0,4,1,1,4
2071,28,13.9,1,1,0,1,9,0,2,1,1,2
9376,28,14.2,1,3,0,1,15,1,8,1,1,8
4092,28,14.6,1,2,0,1,12,0,5,1,1,9
9210,28,14.7,1,4,0,1,10,1,4,1,1,5
6260,28,14.7,1,4,0,1,14,1,4,1,1,4
9866,28,14.8,1,5,0,1,12,1,1,1,1,9
1367,28,15,1,3,0,0,14,0,2,1,1,6
1653,28,15,1,3,0,0,12,0,2,1,1,9
199,28,15,1,1,0,0,9,0,1,1,1,6
3045,28,15.1,1,3,0,0,11,0,0,1,1,8
4375,28,15.1,1,4,0,0,12,0,1,1,1,6
1167,28,15.1,1,5,0,0,13,0,0,1,1,9
5877,28,15.1,1,3,0,0,10,1,1,1,1,7
10211,28,15.2,1,5,0,1,15,1,5,1,1,4
2641,28,15.2,1,1,0,0,9,0,1,1,1,5
5839,28,15.2,1,4,0,0,11,1,0,1,1,10
7355,28,15.2,1,1,0,0,12,1,1,1,1,10
2597,28,15.2,1,1,0,0,12,0,0,1,1,8
10309,28,15.2,1,2,0,0,13,1,2,1,1,8
2479,28,15.2,1,4,0,0,11,0,0,1,1,7
1242,28,15.2,1,4,0,1,13,0,5,1,1,2
6497,28,15.3,1,1,0,0,12,1,0,1,1,7
1003,28,15.3,1,2,0,0,12,0,2,1,1,9
3459,28,15.3,1,4,0,0,12,0,0,1,1,6
4017,28,15.3,1,3,0,0,9,0,1,1,1,5
807,28,15.3,1,2,0,0,12,0,2,1,1,8
1613,28,15.3,1,4,0,0,12,0,1,1,1,9
5127,28,15.3,1,1,0,0,13,0,2,1,1,9
9511,28,15.4,1,2,0,0,11,1,0,1,1,8
7765,28,15.5,1,1,0,0,10,1,0,1,1,10
4699,28,15.5,1,4,0,0,13,0,1,1,1,5
3189,28,15.5,1,2,0,0,14,0,2,1,1,10
9775,28,15.5,1,5,0,0,14,1,1,1,1,10
1018,28,15.5,1,1,0,1,14,0,8,1,1,10
3321,28,15.5,1,4,0,0,15,0,2,1,1,5
10665,28,15.6,1,5,0,0,12,1,0,1,1,8
9913,28,15.6,1,4,0,0,9,1,0,1,1,5
3147,28,15.6,1,5,0,0,11,0,0,1,1,8
7563,28,15.7,1,5,0,0,12,1,2,1,1,7
5463,28,15.7,1,1,0,0,12,1,1,1,1,5
8793,28,15.7,1,1,0,0,12,1,0,1,1,9
4827,28,15.7,1,3,0,0,12,0,0,1,1,7
9703,28,15.7,1,3,0,0,12,1,1,1,1,6
2598,28,15.7,1,1,0,1,10,0,4,1,1,0
5656,28,15.7,1,2,0,1,14,1,3,1,1,8
1257,28,15.7,1,1,0,0,10,0,1,1,1,10
6105,28,15.7,1,5,0,0,12,1,1,1,1,8
6109,28,15.8,1,5,0,0,14,1,2,1,1,6
7305,28,15.8,1,3,0,0,10,1,2,1,1,6
7459,28,15.8,1,4,0,0,14,1,1,1,1,9
2061,28,15.8,1,2,0,0,10,0,0,1,1,6
9483,28,15.9,1,2,0,0,15,1,1,1,1,8
6457,28,15.9,1,3,0,0,13,1,0,1,1,9
3043,28,15.9,1,4,0,1,9,0,3,1,1,3
8675,28,15.9,1,2,0,0,10,1,1,1,1,7
2989,28,16,1,2,0,0,9,0,0,1,1,10
3353,28,16,1,2,0,1,10,0,2,1,1,3
5421,28,16,1,1,0,0,14,1,2,1,1,7
10195,28,16,1,2,0,0,13,1,2,1,1,8
1679,28,16,1,1,0,0,12,0,1,1,1,7
10051,28,16,1,1,0,0,15,1,2,1,1,7
7123,28,16,1,4,0,0,11,1,2,1,1,8
8887,28,16.1,1,4,0,0,12,1,1,1,1,6
8739,28,16.1,1,5,0,1,11,1,6,1,1,1
1605,28,16.1,1,5,0,0,12,0,0,1,1,8
10423,28,16.2,1,5,0,1,12,1,8,1,1,0
6833,28,16.2,1,3,0,0,9,1,1,1,1,10
4079,28,16.2,1,5,0,0,9,0,0,1,1,7
10697,28,16.3,1,1,0,0,12,1,2,1,1,10
8033,28,16.3,1,1,0,0,12,1,1,1,1,8
10085,28,16.3,1,2,0,0,12,1,0,1,1,7
7377,28,16.3,1,2,0,0,12,1,0,1,1,6
451,28,16.3,1,1,0,0,13,0,1,1,1,5
969,28,16.4,1,1,0,0,11,0,2,1,1,9
9111,28,16.4,1,1,0,0,15,1,2,1,1,6
8524,28,16.5,1,1,0,1,15,1,4,1,1,4
1713,28,16.5,1,1,0,0,9,0,0,1,1,6
8961,28,16.5,1,3,0,0,9,1,1,1,1,9
9855,28,16.5,1,2,0,0,10,1,0,1,1,9
1727,28,16.5,1,2,0,0,12,0,1,1,1,10
10845,28,16.5,1,4,0,0,12,1,0,1,1,9
2879,28,16.5,1,5,0,0,12,0,1,1,1,7
10499,28,16.5,1,4,0,0,11,1,2,1,1,9
5489,28,16.6,1,4,0,0,14,1,2,1,1,6
10849,28,16.6,1,4,0,0,12,1,1,1,1,9
4289,28,16.6,1,4,0,0,12,0,0,1,1,10
5693,28,16.6,1,3,0,0,13,1,1,1,1,9
3209,28,16.6,1,2,0,0,15,0,0,1,1,7
913,28,16.7,1,1,0,0,13,0,0,1,1,7
8201,28,16.7,1,5,0,0,13,1,0,1,1,7
2733,28,16.7,1,5,0,0,14,0,2,1,1,6
1563,28,16.7,1,1,0,0,10,0,1,1,1,5
9431,28,16.8,1,1,0,0,15,1,0,1,1,8
179,28,16.8,1,2,0,0,11,0,0,1,1,7
9195,28,16.8,1,1,0,0,12,1,1,1,1,7
9277,28,16.8,1,5,0,0,12,1,1,1,1,5
611,28,16.8,1,2,0,0,13,0,0,1,1,6
3307,28,16.9,1,5,0,0,11,0,2,1,1,5
1581,28,16.9,1,3,0,0,10,0,2,1,1,7
3841,28,16.9,1,5,0,0,10,0,0,1,1,8
6855,28,17,1,2,0,0,14,1,2,1,1,7
8655,28,17,1,2,0,0,14,1,1,1,1,10
771,28,17,1,3,0,0,12,0,0,1,1,6
10651,28,17,1,2,0,0,12,1,0,1,1,6
5883,28,17,1,4,0,0,10,1,2,1,1,9
9731,28,17,1,5,0,0,12,1,1,1,1,7
8827,28,17,1,4,0,0,14,1,0,1,1,5
7490,28,17,1,2,0,1,12,1,3,1,1,9
2799,28,17.1,1,1,0,0,12,0,1,1,1,5
5651,28,17.1,1,2,0,0,10,1,0,1,1,8
8413,28,17.1,1,5,0,0,15,1,1,1,1,8
1490,28,17.1,1,2,0,1,12,0,8,1,1,7
4727,28,17.1,1,5,0,0,12,0,1,1,1,5
4857,28,17.1,1,1,0,0,10,0,1,1,1,10
3447,28,17.2,1,3,0,0,13,0,2,1,1,6
8435,28,17.3,1,2,0,0,15,1,0,1,1,8
3815,28,17.3,1,4,0,0,14,0,2,1,1,8
2827,28,17.3,1,4,0,0,12,0,1,1,1,5
3501,28,17.3,1,2,0,0,9,0,2,1,1,9
2781,28,17.3,1,1,0,0,12,0,1,1,1,9
8213,28,17.3,1,5,0,0,14,1,2,1,1,10
765,28,17.4,1,3,0,0,14,0,0,1,1,5
325,28,17.4,1,1,0,0,10,0,2,1,1,6
963,28,17.4,1,4,0,0,12,0,2,1,1,10
10605,28,17.5,1,5,0,0,14,1,2,1,1,7
1765,28,17.5,1,5,0,0,12,0,2,1,1,8
10799,28,17.5,1,5,0,0,12,1,0,1,1,9
1279,28,17.6,1,1,0,0,13,0,2,1,1,5
7203,28,17.6,1,5,0,0,11,1,2,1,1,5
9027,28,17.6,1,1,0,0,11,1,0,1,1,9
2869,28,17.6,1,1,0,0,11,0,2,1,1,5
2972,28,17.6,1,3,0,1,12,0,3,1,1,1
1775,28,17.6,1,4,0,0,10,0,2,1,1,9
6222,28,17.7,1,1,0,1,9,1,3,1,1,7
2199,28,17.7,1,2,0,0,13,0,2,1,1,10
1612,28,17.8,1,2,0,1,12,0,5,1,1,6
1061,28,17.8,1,5,0,0,10,0,0,1,1,6
6919,28,17.8,1,4,0,0,12,1,1,1,1,6
5689,28,17.8,1,4,0,0,12,1,0,1,1,10
3827,28,17.8,1,3,0,0,12,0,1,1,1,8
4945,28,17.9,1,3,0,0,14,0,0,1,1,5
7813,28,17.9,1,2,0,0,10,1,1,1,1,7
6247,28,17.9,1,1,0,0,15,1,1,1,1,10
10177,28,17.9,1,2,0,0,15,1,2,1,1,6
5809,28,17.9,1,3,0,0,15,1,0,1,1,5
1229,28,17.9,1,2,0,0,9,0,2,1,1,8
7135,28,18,1,2,0,0,9,1,2,1,1,6
8575,28,18,1,2,0,0,11,1,2,1,1,9
1991,28,18,1,4,0,0,13,0,0,1,1,5
6829,28,18,1,3,0,0,12,1,2,1,1,5
6169,28,18,1,5,0,0,12,1,0,1,1,7
6515,28,18.1,1,5,0,0,9,1,2,1,1,10
9897,28,18.1,1,4,0,0,13,1,1,1,1,7
8234,28,18.1,1,5,0,1,13,1,4,1,1,4
7673,28,18.1,1,4,0,0,11,1,1,1,1,6
4927,28,18.1,1,3,0,0,14,0,0,1,1,9
302,28,18.1,1,1,0,1,13,0,5,1,1,8
10031,28,18.1,1,1,0,0,13,1,2,1,1,10
3825,28,18.1,1,3,0,0,14,0,1,1,1,5
2669,28,18.2,1,3,0,0,13,0,2,1,1,5
7769,28,18.2,1,3,0,0,11,1,1,1,1,5
9837,28,18.3,1,4,0,0,13,1,2,1,1,9
385,28,18.3,1,2,0,0,11,0,0,1,1,10
9281,28,18.3,1,1,0,0,10,1,2,1,1,10
1725,28,18.4,1,1,0,0,10,0,1,1,1,7
9782,28,18.4,1,1,0,1,14,1,3,1,1,1
1209,28,18.4,1,1,0,0,10,0,2,1,1,6
4825,28,18.4,1,2,0,0,14,0,0,1,1,8
1388,28,18.5,1,3,0,1,11,0,5,1,1,4
2497,28,18.5,1,4,0,0,15,0,0,1,1,10
5847,28,18.5,1,5,0,0,11,1,2,1,1,10
8865,28,18.5,1,3,0,0,11,1,1,1,1,7
10457,28,18.6,1,2,0,0,13,1,2,1,1,9
2077,28,18.6,1,4,0,0,10,0,2,1,1,6
409,28,18.6,1,1,0,0,13,0,1,1,1,6
767,28,18.6,1,4,0,0,15,0,1,1,1,10
1397,28,18.6,1,1,0,0,10,0,2,1,1,6
10419,28,18.6,1,4,0,0,10,1,0,1,1,10
9941,28,18.7,1,1,0,0,9,1,1,1,1,8
4501,28,18.7,1,2,0,0,10,0,1,1,1,5
2377,28,18.7,1,2,0,0,12,0,1,1,1,10
2675,28,18.7,1,2,0,0,10,0,0,1,1,5
3739,28,18.8,1,1,0,0,14,0,0,1,1,10
3505,28,18.9,1,4,0,0,11,0,1,1,1,10
8367,28,18.9,1,5,0,0,12,1,1,1,1,10
1643,28,19,1,5,0,0,12,0,1,1,1,10
6951,28,19,1,3,0,0,10,1,0,1,1,6
5457,28,19.1,1,3,0,0,9,1,2,1,1,7
10365,28,19.1,1,5,0,0,11,1,0,1,1,7
7795,28,19.2,1,2,0,0,12,1,2,1,1,7
3703,28,19.3,1,4,0,0,11,0,2,1,1,6
9569,28,19.3,1,5,0,0,13,1,2,1,1,6
2309,28,19.3,1,3,0,0,10,0,0,1,1,10
8195,28,19.3,1,5,0,0,11,1,0,1,1,8
10185,28,19.4,1,2,0,0,12,1,0,1,1,9
6665,28,19.4,1,4,0,0,11,1,2,1,1,7
2171,28,19.4,1,3,0,0,11,0,1,1,1,10
377,28,19.4,1,1,0,0,11,0,2,1,1,7
57,28,19.4,1,3,0,0,9,0,2,1,1,10
5661,28,19.4,1,2,0,0,14,1,1,1,1,5
10707,28,19.5,1,4,0,0,10,1,1,1,1,6
1285,28,19.5,1,2,0,0,14,0,2,1,1,8
6403,28,19.5,1,2,0,0,9,1,1,1,1,9
4949,28,19.5,1,1,0,0,11,0,0,1,1,6
5445,28,19.6,1,5,0,0,15,1,2,1,1,9
9539,28,19.6,1,1,0,0,14,1,1,1,1,8
1217,28,19.6,1,2,0,0,14,0,2,1,1,10
8697,28,19.6,1,1,0,0,10,1,0,1,1,7
5599,28,19.6,1,2,0,0,10,1,2,1,1,5
2195,28,19.7,1,1,0,0,15,0,1,1,1,8
10805,28,19.7,1,3,0,0,12,1,2,1,1,5
8925,28,19.7,1,2,0,0,13,1,1,1,1,6
8533,28,19.7,1,5,0,0,10,1,2,1,1,7
3143,28,19.8,1,4,0,0,11,0,1,1,1,6
2011,28,19.8,1,1,0,0,11,0,0,1,1,5
10363,28,19.8,1,5,0,0,13,1,0,1,1,6
4923,28,19.9,1,5,0,0,14,0,2,1,1,5
1213,28,19.9,1,2,0,0,11,0,0,1,1,6
2883,28,19.9,1,5,0,0,15,0,1,1,1,8
8881,28,20,1,1,0,0,11,1,1,1,1,7
1323,28,20,1,2,0,0,15,0,1,1,1,5
6733,28,20,1,2,0,0,12,1,2,1,1,6
10601,28,20.1,1,5,0,0,11,1,0,1,1,5
1393,28,20.1,1,2,0,0,9,0,2,1,1,6
8571,28,20.1,1,4,0,0,12,1,1,1,1,8
5021,28,20.1,1,2,0,0,10,0,0,1,1,5
3181,28,20.1,1,1,0,0,12,0,1,1,1,7
9997,28,20.2,1,5,0,0,9,1,1,1,1,9
2417,28,20.3,1,1,0,0,10,0,1,1,1,9
9657,28,20.3,1,2,0,0,15,1,0,1,1,7
3937,28,20.3,1,1,0,0,11,0,1,1,1,7
7337,28,20.4,1,4,0,0,10,1,0,1,1,5
9221,28,20.4,1,4,0,0,15,1,2,1,1,5
7533,28,20.4,1,3,0,0,14,1,1,1,1,5
9727,28,20.4,1,3,0,0,10,1,2,1,1,5
4935,28,20.5,1,3,0,0,12,0,2,1,1,9
1103,28,20.5,1,2,0,0,11,0,2,1,1,6
6053,28,20.5,1,4,0,0,12,1,2,1,1,5
2397,28,20.6,1,5,0,0,9,0,0,1,1,10
2271,28,20.6,1,5,0,0,15,0,1,1,1,5
9173,28,20.7,1,4,0,0,13,1,0,1,1,7
2027,28,20.7,1,4,0,0,15,0,1,1,1,10
6989,28,20.7,1,2,0,0,10,1,1,1,1,10
7807,28,20.8,1,4,0,0,12,1,1,1,1,10
9223,28,20.8,1,3,0,0,14,1,2,1,1,5
5639,28,20.8,1,4,0,0,11,1,1,1,1,9
6329,28,20.8,1,4,0,0,10,1,2,1,1,5
8747,28,20.8,1,3,0,0,14,1,2,1,1,7
893,28,20.8,1,4,0,0,10,0,0,1,1,6
5713,28,20.8,1,1,0,0,12,1,0,1,1,5
1931,28,20.8,1,3,0,0,14,0,0,1,1,8
5427,28,20.8,1,1,0,0,14,1,2,1,1,9
10647,28,20.8,1,2,0,0,9,1,1,1,1,10
1597,28,21,1,1,0,0,10,0,0,1,1,10
2835,28,21,1,2,0,0,15,0,2,1,1,9
1479,28,21,1,1,0,0,12,0,2,1,1,7
5177,28,21,1,5,0,0,9,0,2,1,1,8
5011,28,21,1,4,0,0,11,0,0,1,1,8
4233,28,21.1,1,5,0,0,11,0,1,1,1,5
8503,28,21.1,1,4,0,0,11,1,0,1,1,5
6851,28,21.1,1,3,0,0,15,1,2,1,1,5
3323,28,21.1,1,3,0,0,9,0,1,1,1,5
5617,28,21.1,1,3,0,0,14,1,2,1,1,5
8535,28,21.2,1,3,0,0,12,1,0,1,1,7
9776,28,21.2,1,4,0,1,10,1,3,1,1,7
6059,28,21.2,1,3,0,0,15,1,2,1,1,9
5735,28,21.2,1,5,0,0,15,1,1,1,1,7
7039,28,21.2,1,3,0,0,14,1,2,1,1,7
1207,28,21.2,1,1,0,0,15,0,2,1,1,10
5165,28,21.2,1,3,0,0,11,0,0,1,1,5
723,28,21.2,1,1,0,0,12,0,2,1,1,5
4799,28,21.3,1,1,0,0,15,0,1,1,1,5
3765,28,21.3,1,3,0,0,14,0,1,1,1,8
5587,28,21.3,1,1,0,0,12,1,2,1,1,7
5871,28,21.4,1,2,0,0,9,1,2,1,1,7
7743,28,21.4,1,2,0,0,9,1,1,1,1,5
2587,28,21.4,1,4,0,0,14,0,2,1,1,9
543,28,21.4,1,2,0,0,13,0,2,1,1,6
5477,28,21.5,1,1,0,0,10,1,2,1,1,9
7130,28,21.5,1,1,0,1,15,1,7,1,1,5
1303,28,21.5,1,1,0,0,15,0,2,1,1,6
3795,28,21.5,1,5,0,0,11,0,2,1,1,5
8009,28,21.5,1,3,0,0,11,1,1,1,1,10
7085,28,21.5,1,1,0,0,12,1,1,1,1,10
8383,28,21.5,1,1,0,0,9,1,1,1,1,8
4419,28,21.7,1,1,0,0,12,0,1,1,1,6
9877,28,21.7,1,4,0,0,15,1,2,1,1,7
7361,28,21.8,1,1,0,0,12,1,2,1,1,10
4147,28,21.8,1,3,0,0,15,0,0,1,1,5
343,28,21.8,1,2,0,0,12,0,1,1,1,5
8581,28,21.8,1,2,0,0,10,1,1,1,1,6
6217,28,21.8,1,4,0,0,15,1,1,1,1,7
230,28,21.8,1,4,0,1,9,0,1,1,1,1
321,28,21.8,1,3,0,0,15,0,2,1,1,8
6527,28,21.9,1,5,0,0,15,1,2,1,1,8
3417,28,22,1,4,0,0,10,0,2,1,1,7
6647,28,22,1,1,0,0,9,1,2,1,1,7
6145,28,22.1,1,5,0,0,13,1,0,1,1,6
6061,28,22.1,1,3,0,0,14,1,2,1,1,5
10399,28,22.1,1,2,0,0,12,1,0,1,1,8
5475,28,22.2,1,1,0,0,14,1,2,1,1,9
10053,28,22.2,1,4,0,0,10,1,1,1,1,8
5289,28,22.2,1,3,0,0,11,0,0,1,1,8
5003,28,22.2,1,3,0,0,9,0,1,1,1,10
3247,28,22.2,1,4,0,0,15,0,2,1,1,8
37,28,22.3,1,1,0,0,9,0,0,1,1,7
10585,28,22.3,1,3,0,0,14,1,0,1,1,7
9959,28,22.4,1,1,0,0,10,1,0,1,1,8
8875,28,22.4,1,4,0,0,15,1,2,1,1,5
5551,28,22.4,1,4,0,0,9,1,0,1,1,7
3981,28,22.4,1,2,0,0,12,0,2,1,1,5
8211,28,22.5,1,2,0,0,13,1,0,1,1,8
8161,28,22.5,1,4,0,0,12,1,2,1,1,9
5919,28,22.5,1,4,0,0,11,1,2,1,1,6
5333,28,22.5,1,3,0,0,12,0,1,1,1,7
10417,28,22.5,1,2,0,0,11,1,2,1,1,5
5059,28,22.6,1,2,0,0,9,0,2,1,1,8
7445,28,22.6,1,4,0,0,9,1,2,1,1,8
3915,28,22.7,1,2,0,0,13,0,0,1,1,10
4373,28,22.7,1,4,0,0,12,0,1,1,1,9
6213,28,22.8,1,4,0,0,12,1,1,1,1,5
2303,28,22.8,1,3,0,0,13,0,2,1,1,7
697,28,22.9,1,1,0,0,12,0,1,1,1,7
9495,28,22.9,1,5,0,0,12,1,0,1,1,6
10011,28,22.9,1,4,0,0,9,1,2,1,1,8
9183,28,22.9,1,1,0,0,12,1,0,1,1,5
5887,28,22.9,1,2,0,0,9,1,2,1,1,9
549,28,22.9,1,4,0,0,12,0,2,1,1,6
8631,28,22.9,1,3,0,0,11,1,0,1,1,9
10851,28,23,1,3,0,0,12,1,1,1,1,7
5695,28,23,1,4,0,0,10,1,0,1,1,9
9325,28,23,1,4,0,0,10,1,1,1,1,10
9523,28,23,1,5,0,0,12,1,0,1,1,9
8643,28,23,1,3,0,0,11,1,2,1,1,6
1471,28,23,1,4,0,0,10,0,1,1,1,10
7888,28,23,1,5,0,1,12,1,6,1,1,4
3951,28,23.1,1,5,0,0,15,0,2,1,1,6
641,28,23.1,1,2,0,0,12,0,2,1,1,8
1325,28,23.1,1,5,0,1,15,0,3,1,1,4
10099,28,23.1,1,4,0,0,9,1,0,1,1,6
7251,28,23.2,1,2,0,1,15,1,1,1,1,1
10469,28,23.2,1,5,0,0,15,1,1,1,1,10
8115,28,23.2,1,3,0,0,13,1,0,1,1,8
5757,28,23.2,1,1,0,0,13,1,0,1,1,7
8305,28,23.2,1,4,0,0,13,1,0,1,1,10
829,28,23.3,1,4,0,0,15,0,2,1,1,5
6035,28,23.3,1,3,0,0,12,1,0,1,1,6
2013,28,23.3,1,3,0,0,11,0,1,1,1,10
6551,28,23.3,1,2,0,0,10,1,2,1,1,10
4953,28,23.3,1,2,0,0,10,0,1,1,1,5
3891,28,23.4,1,1,0,0,12,0,1,1,1,9
1025,28,23.5,1,2,0,0,10,0,0,1,1,5
7893,28,23.5,1,1,0,0,12,1,1,1,1,7
8759,28,23.5,1,5,0,0,10,1,1,1,1,9
5063,28,23.6,1,5,0,0,14,0,2,1,1,10
6455,28,23.6,1,3,0,0,13,1,0,1,1,8
9343,28,23.6,1,1,0,0,10,1,0,1,1,6
6269,28,23.6,1,4,0,0,12,1,1,1,1,6
9185,28,23.7,1,5,0,0,12,1,2,1,1,6
6941,28,23.7,1,1,0,0,14,1,2,1,1,10
3429,28,23.7,1,3,0,0,11,0,2,1,1,5
3091,28,23.7,1,1,0,0,14,0,0,1,1,9
5101,28,23.7,1,2,0,0,12,0,1,1,1,6
835,28,23.8,1,2,0,0,12,0,1,1,1,7
10483,28,23.8,1,3,0,0,11,1,0,1,1,8
5507,28,23.8,1,5,0,0,9,1,1,1,1,8
1661,28,23.8,1,3,0,0,15,0,0,1,1,6
8795,28,23.8,1,2,0,0,12,1,1,1,1,10
9581,28,23.9,1,2,0,0,12,1,2,1,1,7
8614,28,23.9,1,2,0,1,10,1,7,1,1,4
10385,28,23.9,1,5,0,0,12,1,0,1,1,8
5335,28,24,1,5,0,0,12,0,2,1,1,6
4757,28,24,1,3,0,0,15,0,1,1,1,7
7075,28,24,1,1,0,0,14,1,2,1,1,6
3279,28,24,1,5,0,0,10,0,0,1,1,10
7081,28,24,1,2,0,0,12,1,1,1,1,6
4661,28,24,1,4,0,0,12,0,0,1,1,7
8915,28,24.1,1,5,0,0,14,1,0,1,1,6
4941,28,24.1,1,3,0,0,14,0,2,1,1,10
3573,28,24.1,1,3,0,0,15,0,0,1,1,9
7543,28,24.1,1,2,0,0,11,1,2,1,1,8
8873,28,24.1,1,5,0,0,15,1,2,1,1,6
9373,28,24.1,1,2,0,0,12,1,0,1,1,9
8189,28,24.2,1,1,0,0,11,1,1,1,1,8
3697,28,24.2,1,5,0,0,9,0,2,1,1,5
6599,28,24.2,1,4,0,0,11,1,1,1,1,9
9721,28,24.2,1,1,0,0,9,1,2,1,1,5
6843,28,24.3,1,4,0,0,12,1,0,1,1,8
9683,28,24.3,1,2,0,0,10,1,1,1,1,7
7817,28,24.3,1,2,0,0,10,1,2,1,1,6
5497,28,24.3,1,2,0,0,11,1,0,1,1,8
233,28,24.3,1,5,0,0,15,0,1,1,1,10
9337,28,24.3,1,5,0,0,13,1,0,1,1,5
809,28,24.4,1,2,0,0,13,0,0,1,1,8
6085,28,24.4,1,4,0,0,15,1,2,1,1,9
6441,28,24.5,1,1,0,0,12,1,2,1,1,7
1783,28,24.5,1,3,0,0,10,0,2,1,1,5
1345,28,24.5,1,2,0,1,12,0,8,1,1,1
5139,28,24.5,1,2,0,0,10,0,2,1,1,7
8768,28,24.5,1,1,0,1,9,1,3,1,1,7
6185,28,24.5,1,3,0,0,9,1,2,1,1,9
1515,28,24.6,1,3,0,0,9,0,2,1,1,6
6805,28,24.6,1,3,0,0,11,1,1,1,1,9
877,28,24.6,1,2,0,0,12,0,2,1,1,10
753,28,24.7,1,2,0,0,12,0,0,1,1,6
7903,28,24.7,1,5,0,0,14,1,1,1,1,9
6003,28,24.8,1,3,0,0,13,1,2,1,1,7
5657,28,24.8,1,4,0,0,11,1,2,1,1,10
8947,28,24.8,1,1,0,0,11,1,1,1,1,5
9607,28,24.8,1,2,0,0,11,1,0,1,1,7
4477,28,24.9,1,1,0,0,12,0,0,1,1,8
9471,28,25,1,1,0,0,10,1,1,1,1,5
8227,28,25,1,2,0,0,12,1,2,1,1,10
865,28,25,1,5,0,0,9,0,2,1,1,9
7761,28,25,1,5,0,0,15,1,1,1,1,9
2235,28,25.1,1,5,0,0,10,0,0,1,1,5
9303,28,25.1,1,4,0,0,15,1,0,1,1,5
1389,28,25.1,1,5,0,0,10,0,0,1,1,8
5071,28,25.1,1,3,0,0,14,0,2,1,1,9
8919,28,25.1,1,3,0,0,10,1,2,1,1,6
4483,28,25.1,1,1,0,0,10,0,2,1,1,7
997,28,25.2,1,3,0,0,10,0,1,1,1,9
7805,28,25.2,1,2,0,0,13,1,1,1,1,10
8519,28,25.2,1,3,0,0,14,1,1,1,1,6
5891,28,25.2,1,2,0,0,12,1,0,1,1,8
2885,28,25.2,1,5,0,0,14,0,1,1,1,8
7181,28,25.3,1,1,0,0,12,1,2,1,1,5
7637,28,25.3,1,5,0,0,12,1,0,1,1,5
3521,28,25.4,1,4,0,0,11,0,2,1,1,9
7921,28,25.4,1,1,0,0,10,1,0,1,1,10
8957,28,25.5,1,1,0,0,13,1,1,1,1,7
9241,28,25.5,1,1,0,0,15,1,0,1,1,10
9783,28,25.5,1,3,0,0,10,1,0,1,1,9
627,28,25.6,1,3,0,0,10,0,1,1,1,10
5931,28,25.6,1,5,0,0,12,1,2,1,1,7
7643,28,25.6,1,2,0,0,11,1,0,1,1,8
8373,28,25.6,1,5,0,0,10,1,2,1,1,8
3661,28,25.7,1,3,0,0,12,0,0,1,1,6
6764,28,25.7,1,3,0,1,14,1,4,1,1,5
5115,28,25.7,1,4,0,0,10,0,0,1,1,8
1283,28,25.7,1,2,0,0,14,0,0,1,1,9
5061,28,25.7,1,5,0,0,11,0,0,1,1,8
5913,28,25.7,1,5,0,0,12,1,2,1,1,6
6753,28,25.7,1,3,0,0,10,1,1,1,1,6
129,28,25.8,1,3,0,0,13,0,1,1,1,9
245,28,25.8,1,1,0,0,14,0,2,1,1,7
1963,28,25.8,1,1,0,0,14,0,0,1,1,8
93,28,25.9,1,3,0,0,14,0,1,1,1,6
6915,28,25.9,1,2,0,0,15,1,2,1,1,8
9347,28,25.9,1,2,0,0,10,1,1,1,1,10
1481,28,25.9,1,1,0,0,12,0,0,1,1,9
5189,28,25.9,1,5,0,0,10,0,0,1,1,7
7528,28,25.9,1,5,0,1,11,1,4,1,1,1
990,28,26,1,3,0,1,11,0,8,1,1,6
2477,28,26,1,4,0,0,13,0,1,1,1,10
9067,28,26,1,1,0,0,9,1,0,1,1,8
7129,28,26.1,1,5,0,0,12,1,0,1,1,6
7053,28,26.1,1,1,0,0,13,1,2,1,1,9
6479,28,26.1,1,5,0,0,15,1,1,1,1,7
6823,28,26.1,1,4,0,0,15,1,2,1,1,8
6877,28,26.2,1,3,0,0,13,1,0,1,1,7
2995,28,26.2,1,2,0,0,10,0,2,1,1,10
2739,28,26.2,1,3,0,0,13,0,2,1,1,10
8379,28,26.2,1,5,0,0,9,1,2,1,1,9
6309,28,26.2,1,1,0,0,12,1,0,1,1,8
3641,28,26.3,1,5,0,0,10,0,1,1,1,8
2771,28,26.4,1,5,0,0,15,0,0,1,1,8
2577,28,26.4,1,5,0,0,9,0,1,1,1,5
2881,28,26.4,1,3,0,0,13,0,0,1,1,7
9608,28,26.4,1,2,0,1,14,1,3,1,1,1
3707,28,26.4,1,2,0,0,12,0,0,1,1,10
2379,28,26.4,1,1,0,0,14,0,0,1,1,8
5823,28,26.4,1,1,0,1,13,1,7,1,1,4
9405,28,26.5,1,5,0,0,12,1,2,1,1,8
7393,28,26.5,1,4,0,0,9,1,1,1,1,8
8977,28,26.6,1,1,0,0,12,1,2,1,1,8
5313,28,26.6,1,4,0,0,13,0,2,1,1,9
2475,28,26.6,1,4,0,0,12,0,1,1,1,10
8693,28,26.6,1,5,0,0,14,1,0,1,1,6
5479,28,26.6,1,5,0,0,12,1,2,1,1,10
4655,28,26.7,1,5,0,0,12,0,0,1,1,10
6785,28,26.7,1,4,0,0,12,1,2,1,1,10
10381,28,26.7,1,4,0,0,12,1,2,1,1,7
2483,28,26.7,1,4,0,0,15,0,0,1,1,6
9779,28,26.7,1,3,0,0,12,1,1,1,1,10
1519,28,26.7,1,2,0,0,12,0,2,1,1,8
2241,28,26.8,1,2,0,0,9,0,2,1,1,10
10017,28,26.8,1,4,0,0,14,1,1,1,1,9
1321,28,26.8,1,3,0,0,9,0,2,1,1,10
293,28,26.8,1,1,0,0,14,0,0,1,1,5
255,28,26.8,1,5,0,0,14,0,2,1,1,7
8801,28,26.9,1,3,0,0,11,1,0,1,1,7
9485,28,26.9,1,3,0,0,13,1,1,1,1,5
7911,28,27,1,3,0,0,14,1,1,1,1,8
8463,28,27,1,1,0,0,12,1,2,1,1,5
6255,28,27,1,3,0,1,12,1,2,1,1,0
6283,28,27.1,1,2,0,0,14,1,2,1,1,9
5095,28,27.1,1,1,0,0,12,0,1,1,1,8
1361,28,27.1,1,5,0,0,12,0,2,1,1,10
6993,28,27.1,1,3,0,0,11,1,0,1,1,9
4131,28,27.1,1,3,0,0,15,0,2,1,1,5
4197,28,27.1,1,5,0,0,15,0,2,1,1,8
9765,28,27.2,1,2,0,0,13,1,1,1,1,7
6101,28,27.2,1,3,0,0,12,1,2,1,1,8
6409,28,27.3,1,2,0,0,11,1,1,1,1,6
3725,28,27.3,1,2,0,0,12,0,0,1,1,9
9479,28,27.3,1,5,0,0,12,1,2,1,1,9
4347,28,27.4,1,1,0,0,11,0,0,1,1,7
3967,28,27.4,1,1,0,0,11,0,2,1,1,6
4047,28,27.4,1,1,0,0,10,0,2,1,1,9
5273,28,27.4,1,3,0,0,12,0,2,1,1,5
9699,28,27.4,1,3,0,1,10,1,5,1,1,0
6142,28,27.4,1,1,0,1,9,1,4,1,1,10
6975,28,27.5,1,2,0,1,15,1,4,1,1,4
5961,28,27.5,1,5,0,0,12,1,0,1,1,5
5963,28,27.5,1,5,0,0,12,1,0,1,1,9
7855,28,27.5,1,5,0,0,12,1,0,1,1,9
8465,28,27.6,1,4,0,0,9,1,1,1,1,9
378,28,27.6,1,2,0,1,9,0,3,1,1,7
3319,28,27.6,1,4,0,0,14,0,0,1,1,10
2343,28,27.6,1,4,0,1,14,0,4,1,1,4
2825,28,27.6,1,4,0,0,13,0,2,1,1,9
5288,28,27.7,1,4,0,1,15,0,8,1,1,7
721,28,27.7,1,2,0,0,13,0,1,1,1,7
6837,28,27.7,1,3,0,0,10,1,2,1,1,6
10105,28,27.8,1,2,0,0,12,1,0,1,1,7
5361,28,27.8,1,2,0,0,9,0,0,1,1,8
2901,28,27.8,1,5,0,0,14,0,2,1,1,5
9503,28,27.8,1,4,0,0,12,1,2,1,1,6
1589,28,27.8,1,2,0,0,13,0,2,1,1,7
7461,28,27.8,1,3,0,0,15,1,2,1,1,5
50,28,27.8,1,1,0,1,10,0,5,1,1,8
3785,28,27.9,1,4,0,0,10,0,1,1,1,6
10615,28,27.9,1,2,0,0,9,1,2,1,1,10
3343,28,27.9,1,5,0,0,9,0,1,1,1,6
5415,28,27.9,1,3,0,0,12,0,1,1,1,9
9251,28,27.9,1,4,0,0,12,1,1,1,1,9
5371,28,27.9,1,2,0,0,12,0,1,1,1,6
6305,28,28,1,4,0,0,10,1,1,1,1,8
1175,28,28,1,2,0,0,12,0,0,1,1,7
2299,28,28,1,1,0,0,15,0,2,1,1,7
6523,28,28,1,1,0,0,12,1,0,1,1,6
10640,28,28,1,4,0,1,10,1,6,1,1,6
633,28,28.1,1,5,0,0,12,0,2,1,1,10
3433,28,28.1,1,1,0,0,14,0,0,1,1,5
5237,28,28.1,1,2,0,0,9,0,2,1,1,5
2053,28,28.2,1,4,0,0,11,0,0,1,1,9
3191,28,28.2,1,3,0,0,15,0,0,1,1,8
1837,28,28.2,1,1,0,0,12,0,0,1,1,5
315,28,28.2,1,2,0,0,12,0,2,1,1,8
6181,28,28.3,1,2,0,0,13,1,1,1,1,8
10863,28,28.3,1,5,0,0,13,1,2,1,1,6
8279,28,28.3,1,4,0,0,13,1,0,1,1,5
2943,28,28.4,1,2,0,0,11,0,2,1,1,7
703,28,28.4,1,4,0,0,12,0,1,1,1,10
9371,28,28.4,1,3,0,0,15,1,2,1,1,6
2337,28,28.4,1,2,0,0,13,0,2,1,1,10
10571,28,28.4,1,2,0,0,10,1,2,1,1,10
3813,28,28.5,1,3,0,0,12,0,0,1,1,8
4281,28,28.5,1,1,0,0,10,0,1,1,1,6
8245,28,28.5,1,5,0,0,10,1,2,1,1,7
4579,28,28.5,1,2,0,0,11,0,2,1,1,5
7209,28,28.6,1,1,0,0,12,1,0,1,1,10
7249,28,28.6,1,4,0,0,12,1,0,1,1,5
9951,28,28.6,1,1,0,0,13,1,0,1,1,5
7957,28,28.6,1,5,0,0,12,1,1,1,1,5
987,28,28.7,1,5,0,0,10,0,1,1,1,7
5053,28,28.7,1,5,0,0,12,0,2,1,1,7
6157,28,28.8,1,5,0,0,12,1,1,1,1,5
585,28,28.8,1,3,0,0,12,0,2,1,1,6
2753,28,28.8,1,5,0,0,12,0,2,1,1,6
9033,28,28.9,1,3,0,0,14,1,2,1,1,7
8291,28,28.9,1,5,0,0,12,1,1,1,1,6
3629,28,28.9,1,3,0,0,12,0,1,1,1,7
8047,28,28.9,1,3,0,0,12,1,0,1,1,8
7439,28,28.9,1,2,0,0,9,1,0,1,1,7
8303,28,28.9,1,4,0,0,11,1,2,1,1,6
2246,28,29,1,4,0,1,12,0,7,1,1,10
7325,28,29,1,5,0,0,15,1,2,1,1,8
1281,28,29,1,1,0,0,14,0,0,1,1,7
10829,28,29.1,1,4,0,0,12,1,2,1,1,8
965,28,29.1,1,2,0,0,12,0,1,1,1,7
6439,28,29.1,1,2,0,0,12,1,0,1,1,8
7247,28,29.1,1,5,0,0,12,1,1,1,1,7
354,28,29.2,1,5,0,1,12,0,1,1,1,3
10119,28,29.2,1,2,0,0,13,1,2,1,1,8
10341,28,29.2,1,4,0,0,10,1,2,1,1,5
7937,28,29.2,1,5,0,0,15,1,2,1,1,10
7567,28,29.3,1,1,0,0,11,1,1,1,1,5
10447,28,29.3,1,4,0,0,9,1,1,1,1,8
2971,28,29.4,1,3,0,0,13,0,1,1,1,9
3607,28,29.4,1,1,0,0,14,0,1,1,1,5
851,28,29.4,1,5,0,0,12,0,1,1,1,6
1743,28,29.4,1,2,0,0,13,0,2,1,1,7
4027,28,29.5,1,2,0,0,12,0,1,1,1,10
4733,28,29.5,1,2,0,0,11,0,2,1,1,7
10632,28,29.6,1,3,0,1,11,1,3,1,1,6
5933,28,29.6,1,3,0,1,14,1,6,1,1,1
9535,28,29.6,1,3,0,0,11,1,0,1,1,6
1927,28,29.7,1,1,0,0,11,0,2,1,1,7
2221,28,29.7,1,1,0,0,9,0,2,1,1,5
4199,28,29.7,1,1,0,0,14,0,0,1,1,7
3619,28,29.7,1,5,0,0,15,0,1,1,1,5
10747,28,29.7,1,3,0,1,15,1,4,1,1,4
5471,28,29.7,1,3,0,0,12,1,2,1,1,7
4967,28,29.8,1,3,0,0,14,0,2,1,1,6
9361,28,29.8,1,2,0,0,10,1,0,1,1,6
9541,28,29.8,1,4,0,0,14,1,2,1,1,8
25,28,29.8,1,5,0,0,11,0,2,1,1,8
5701,28,29.8,1,1,0,0,13,1,2,1,1,6
7908,28,29.9,1,2,0,1,13,1,1,1,1,0
4493,28,29.9,1,5,0,0,11,0,1,1,1,5
17,28,29.9,1,2,0,0,12,0,2,1,1,6
10783,28,29.9,1,1,0,0,11,1,0,1,1,6
4015,28,29.9,1,1,0,0,9,0,2,1,1,10
265,28,29.9,1,5,0,0,12,0,1,1,1,10
5337,28,29.9,1,2,0,0,13,0,0,1,1,10
1915,28,30,1,3,0,0,13,0,1,1,1,10
139,29,0.1,1,5,0,1,12,0,2,1,1,0
2681,29,0.8,1,4,0,1,12,0,6,1,1,0
1523,29,0.9,1,2,0,1,11,0,7,1,1,3
10306,29,1.1,1,4,0,1,12,1,4,1,1,1
6900,29,1.3,1,5,0,1,12,1,6,1,1,1
5163,29,2,1,2,0,1,13,0,3,1,1,0
5556,29,2.3,1,5,0,1,14,1,6,1,1,8
5326,29,2.3,1,5,0,1,12,0,5,1,1,7
10148,29,2.9,1,5,0,1,14,1,5,1,1,8
4254,29,3.2,1,3,0,1,15,0,1,1,1,7
8942,29,3.2,1,4,0,1,13,1,3,1,1,7
9829,29,3.3,1,1,0,1,9,1,5,1,1,3
5476,29,3.7,1,1,0,1,10,1,7,1,1,9
6084,29,4.2,1,1,0,1,14,1,8,1,1,7
6216,29,4.4,1,3,0,1,10,1,6,1,1,8
8626,29,4.5,1,2,0,1,12,1,3,1,1,0
7049,29,4.6,1,4,0,1,10,1,2,1,1,1
4078,29,4.6,1,5,0,1,9,0,7,1,1,7
220,29,5,1,5,0,1,9,0,2,1,1,2
8593,29,5.4,1,5,0,1,11,1,1,1,1,1
9057,29,5.5,1,2,0,1,9,1,6,1,1,0
9166,29,5.6,1,4,0,1,9,1,8,1,1,5
2359,29,6.4,1,2,0,1,9,0,7,1,1,3
9071,29,6.5,1,4,0,1,15,1,4,1,1,4
1970,29,6.6,1,5,0,1,13,0,3,1,1,4
4361,29,6.7,1,1,0,1,15,0,8,1,1,2
5710,29,6.8,1,3,0,1,11,1,2,1,1,5
9296,29,7.6,1,1,0,1,11,1,6,1,1,2
1462,29,7.9,1,1,0,1,15,0,2,1,1,1
4676,29,8.3,1,2,0,1,13,0,3,1,1,3
2727,29,8.3,1,5,0,1,13,0,3,1,1,0
2340,29,8.8,1,4,0,1,9,0,1,1,1,4
6187,29,9,1,5,0,1,15,1,4,1,1,0
5793,29,9.2,1,4,0,1,11,1,3,1,1,0
9064,29,9.3,1,1,0,1,15,1,5,1,1,7
3193,29,10,1,1,0,1,13,0,7,1,1,3
6268,29,10.2,1,5,0,1,9,1,8,1,1,6
4613,29,10.4,1,5,0,1,12,0,6,1,1,2
1860,29,10.6,1,5,0,1,13,0,5,1,1,6
5436,29,10.9,1,3,0,1,10,1,4,1,1,10
956,29,11,1,5,0,1,12,0,2,1,1,1
8526,29,11.1,1,4,0,1,9,1,3,1,1,4
2508,29,11.2,1,2,0,1,13,0,3,1,1,1
1145,29,11.5,1,5,0,1,11,0,1,1,1,2
2636,29,12.1,1,3,0,1,11,0,1,1,1,4
7107,29,12.1,1,1,0,1,11,1,1,1,1,2
9637,29,12.3,1,5,0,1,10,1,1,1,1,2
4280,29,12.3,1,4,0,1,10,0,7,1,1,7
10684,29,12.6,1,2,0,1,14,1,1,1,1,0
10738,29,12.9,1,4,0,1,10,1,8,1,1,0
6562,29,13.1,1,5,0,1,13,1,3,1,1,10
4503,29,13.2,1,2,0,1,10,0,2,1,1,1
7008,29,13.5,1,3,0,1,10,1,5,1,1,0
5906,29,13.5,1,3,0,1,10,1,2,1,1,6
5806,29,13.7,1,2,0,1,9,1,6,1,1,1
5461,29,13.7,1,4,0,1,14,1,5,1,1,1
5861,29,13.9,1,3,0,1,13,1,5,1,1,3
10259,29,13.9,1,5,0,1,12,1,6,1,1,3
407,29,14.3,1,1,0,1,12,0,5,1,1,1
10753,29,15.2,1,3,0,1,13,1,6,1,1,1
6688,29,15.4,1,1,0,1,13,1,5,1,1,1
674,29,15.4,1,2,0,1,12,0,5,1,1,4
6695,29,15.6,1,2,0,1,9,1,1,1,1,3
2090,29,15.6,1,5,0,1,11,0,8,1,1,3
74,29,16.2,1,1,0,1,13,0,5,1,1,9
5205,29,16.2,1,3,0,1,9,0,4,1,1,3
7071,29,16.2,1,3,0,1,11,1,7,1,1,3
10012,29,16.6,1,3,0,1,11,1,4,1,1,3
7428,29,16.9,1,5,0,1,9,1,2,1,1,3
8404,29,17,1,4,0,1,10,1,3,1,1,7
10219,29,17.3,1,4,0,1,12,1,1,1,1,1
1798,29,17.4,1,2,0,1,15,0,4,1,1,6
159,29,17.5,1,1,0,1,9,0,4,1,1,3
1366,29,17.7,1,4,0,1,10,0,5,1,1,6
3206,29,17.9,1,4,0,1,12,0,4,1,1,0
3194,29,17.9,1,2,0,1,10,0,6,1,1,3
3320,29,18.1,1,4,0,1,11,0,4,1,1,2
2132,29,18.5,1,4,0,1,10,0,4,1,1,7
6736,29,18.6,1,5,0,1,12,1,5,1,1,4
3954,29,18.7,1,5,0,1,11,0,7,1,1,2
5349,29,18.9,1,2,0,1,15,0,4,1,1,1
2118,29,18.9,1,1,0,1,10,0,7,1,1,5
2180,29,19.1,1,4,0,1,15,0,8,1,1,9
6209,29,19.2,1,3,0,1,13,1,3,1,1,3
9360,29,20.2,1,3,0,1,15,1,2,1,1,1
9082,29,20.3,1,1,0,1,12,1,2,1,1,6
1707,29,21.6,1,1,0,1,9,0,4,1,1,1
7654,29,21.8,1,4,0,1,11,1,3,1,1,3
4735,29,21.9,1,3,0,1,14,0,3,1,1,1
7610,29,22,1,1,0,1,12,1,5,1,1,2
9768,29,22.4,1,1,0,1,10,1,2,1,1,10
2233,29,22.9,1,1,0,1,14,0,7,1,1,3
6460,29,23.3,1,3,0,1,12,1,2,1,1,10
8354,29,23.6,1,4,0,1,9,1,6,1,1,2
8472,29,23.7,1,3,0,1,11,1,5,1,1,6
9834,29,24,1,4,0,1,12,1,2,1,1,7
10284,29,24,1,3,0,1,12,1,6,1,1,8
6276,29,24,1,2,0,1,13,1,7,1,1,8
9383,29,24.1,1,5,0,1,15,1,7,1,1,3
1396,29,24.5,1,3,0,1,10,0,6,1,1,10
6793,29,24.7,1,1,0,1,15,1,8,1,1,3
3909,29,25,1,2,0,1,15,0,4,1,1,3
6314,29,25.1,1,5,0,1,15,1,1,1,1,7
1960,29,25.4,1,4,0,1,13,0,3,1,1,1
9926,29,25.7,1,1,0,1,10,1,3,1,1,3
9679,29,25.9,1,5,0,1,9,1,8,1,1,3
8042,29,25.9,1,5,0,1,12,1,5,1,1,8
9308,29,26.7,1,3,0,1,13,1,4,1,1,3
10542,29,27,1,2,0,1,12,1,5,1,1,10
10296,29,27.4,1,3,0,1,11,1,1,1,1,0
5235,29,27.4,1,3,0,1,11,0,3,1,1,3
2865,29,27.6,1,2,0,1,12,0,2,1,1,2
7616,29,28,1,3,0,1,10,1,8,1,1,6
7078,29,28.7,1,5,0,1,11,1,2,1,1,7
9532,29,28.8,1,3,0,1,11,1,8,1,1,9
7574,29,28.9,1,1,0,1,12,1,8,1,1,7
8750,29,29,1,2,0,1,11,1,4,1,1,3
7494,29,29,1,2,0,1,10,1,8,1,1,4
3238,29,29.5,1,5,0,1,13,0,2,1,1,2
1690,29,29.9,1,2,0,1,11,0,6,1,1,0
7942,30,0.2,1,4,0,1,8,1,8,1,1,10
7340,30,0.4,1,2,0,1,6,1,5,1,1,3
2430,30,0.6,1,1,0,1,5,0,3,1,1,9
6642,30,0.7,1,1,0,1,7,1,1,1,1,10
6626,30,1,1,3,0,1,6,1,5,1,1,7
8164,30,1.1,1,1,0,1,6,1,8,1,1,4
8064,30,1.6,1,1,0,1,6,1,3,1,1,9
9430,30,1.8,1,1,0,1,7,1,8,1,1,4
4182,30,2.4,1,4,0,1,7,0,6,1,1,0
7870,30,2.4,1,3,0,1,8,1,3,1,1,0
2988,30,2.4,1,3,0,1,6,0,6,1,1,1
5698,30,3.4,1,3,0,1,7,1,2,1,1,6
3412,30,3.5,1,1,0,1,8,0,7,1,1,1
61,30,3.7,1,2,0,1,7,0,5,1,1,3
4544,30,3.8,1,4,0,1,8,0,8,1,1,1
7177,30,3.9,1,5,0,1,8,1,2,1,1,3
2965,30,4.2,1,3,0,1,5,0,5,1,1,0
190,30,4.3,1,1,0,1,6,0,4,1,1,2
3258,30,4.5,1,5,0,1,7,0,6,1,1,6
5024,30,4.6,1,1,0,1,5,0,3,1,1,1
9846,30,4.8,1,2,0,1,8,1,2,1,1,7
542,30,5,1,5,0,1,8,0,4,1,1,2
3636,30,5.3,1,4,0,1,7,0,7,1,1,6
2384,30,5.4,1,4,0,1,6,0,1,1,1,9
1946,30,5.7,1,5,0,1,8,0,1,1,1,7
9380,30,5.9,1,5,0,1,8,1,1,1,1,2
10532,30,6,1,4,0,1,7,1,6,1,1,5
8778,30,6.3,1,2,0,1,5,1,4,1,1,3
2586,30,6.4,1,5,0,1,7,0,1,1,1,3
1640,30,6.6,1,2,0,1,6,0,7,1,1,8
3010,30,6.7,1,2,0,1,5,0,1,1,1,9
6346,30,7.1,1,2,0,1,5,1,1,1,1,4
9980,30,7.2,1,2,0,1,7,1,7,1,1,5
6027,30,7.2,1,1,0,1,7,1,7,1,1,4
6740,30,7.8,1,3,0,1,6,1,6,1,1,5
6296,30,7.9,1,5,0,1,7,1,5,1,1,10
6872,30,8,1,1,0,1,8,1,8,1,1,1
4440,30,8.3,1,5,0,1,7,0,8,1,1,0
5674,30,8.9,1,1,0,1,7,1,1,1,1,2
5402,30,9.1,1,5,0,1,7,0,8,1,1,8
140,30,9.2,1,5,0,1,7,0,6,1,1,7
3627,30,9.2,1,4,0,1,5,0,4,1,1,2
1016,30,9.3,1,5,0,1,7,0,6,1,1,5
1789,30,9.4,1,1,0,1,5,0,3,1,1,1
8922,30,10.1,1,2,0,1,5,1,4,1,1,8
7912,30,10.7,1,5,0,1,6,1,4,1,1,6
6606,30,10.8,1,3,0,1,7,1,3,1,1,2
5708,30,11.5,1,3,0,1,5,1,4,1,1,5
4246,30,11.6,1,5,0,1,7,0,8,1,1,1
5960,30,12.1,1,2,0,1,6,1,4,1,1,8
5991,30,12.2,1,5,0,1,6,1,4,1,1,2
10166,30,12.8,1,3,0,1,8,1,6,1,1,0
1233,30,12.8,1,4,0,1,6,0,2,1,1,0
3678,30,13.2,1,3,0,1,7,0,7,1,1,4
10692,30,13.6,1,2,0,1,8,1,6,1,1,5
8122,30,13.9,1,4,0,1,8,1,2,1,1,10
10130,30,14.2,1,3,0,1,5,1,8,1,1,2
7796,30,14.3,1,4,0,1,8,1,5,1,1,0
9497,30,14.6,1,1,0,1,7,1,4,1,1,0
329,30,14.8,1,2,0,1,7,0,6,1,1,2
3749,30,15.7,1,3,0,1,7,0,4,1,1,3
10026,30,15.7,1,5,0,1,6,1,5,1,1,1
1446,30,15.7,1,5,0,1,6,0,2,1,1,10
698,30,15.8,1,4,0,1,5,0,6,1,1,2
9746,30,17.1,1,2,0,1,6,1,7,1,1,8
3928,30,17.3,1,3,0,1,5,0,2,1,1,6
6396,30,18,1,3,0,1,5,1,4,1,1,8
2522,30,18,1,3,0,1,7,0,2,1,1,5
9367,30,18,1,3,0,1,8,1,4,1,1,3
5199,30,18.2,1,3,0,1,7,0,1,1,1,3
2718,30,18.2,1,2,0,1,8,0,3,1,1,4
10248,30,18.4,1,1,0,1,6,1,3,1,1,6
248,30,18.4,1,4,0,1,5,0,4,1,1,7
4260,30,18.5,1,1,0,1,5,0,1,1,1,5
9019,30,18.6,1,1,0,1,5,1,5,1,1,3
2774,30,18.8,1,4,0,1,7,0,8,1,1,3
7182,30,18.9,1,3,0,1,8,1,8,1,1,3
3744,30,19,1,1,0,1,8,0,2,1,1,4
9512,30,19,1,1,0,1,7,1,2,1,1,3
9446,30,19.3,1,1,0,1,7,1,5,1,1,4
9698,30,19.4,1,5,0,1,5,1,2,1,1,5
2302,30,19.6,1,1,0,1,8,0,8,1,1,6
6800,30,19.6,1,2,0,1,5,1,1,1,1,3
8792,30,19.9,1,4,0,1,7,1,7,1,1,10
7914,30,20.1,1,3,0,1,7,1,2,1,1,5
8128,30,20.6,1,4,0,1,5,1,2,1,1,10
9517,30,20.9,1,2,0,1,7,1,2,1,1,3
4354,30,21,1,3,0,1,7,0,4,1,1,4
3099,30,21.1,1,5,0,1,6,0,2,1,1,2
3393,30,21.3,1,5,0,1,7,0,4,1,1,3
5562,30,21.4,1,1,0,1,8,1,5,1,1,3
8701,30,21.4,1,1,0,1,5,1,6,1,1,4
8725,30,22.2,1,4,0,1,5,1,3,1,1,0
5452,30,22.2,1,5,0,1,8,1,1,1,1,4
7002,30,23.9,1,4,0,1,8,1,4,1,1,6
848,30,24,1,5,0,1,8,0,4,1,1,9
9620,30,24.2,1,4,0,1,8,1,8,1,1,4
9222,30,24.6,1,3,0,1,8,1,6,1,1,1
10386,30,24.9,1,2,0,1,7,1,7,1,1,5
7700,30,25.6,1,3,0,1,5,1,2,1,1,7
1774,30,25.6,1,3,0,1,7,0,3,1,1,7
5283,30,25.7,1,1,0,1,5,0,1,1,1,2
1945,30,26.4,1,5,0,1,5,0,5,1,1,4
262,30,27.2,1,1,0,1,5,0,3,1,1,3
4286,30,27.3,1,3,0,1,8,0,2,1,1,9
9953,30,28.2,1,4,0,1,5,1,6,1,1,4
8572,30,28.3,1,3,0,1,8,1,2,1,1,9
1866,30,28.3,1,5,0,1,8,0,5,1,1,7
3305,30,28.6,1,3,0,1,6,0,2,1,1,1
9078,30,28.7,1,4,0,1,8,1,1,1,1,7
6717,30,28.9,1,5,0,1,7,1,3,1,1,1
1318,30,28.9,1,4,0,1,5,0,7,1,1,10
782,30,29.2,1,1,0,1,8,0,3,1,1,6
2484,30,29.2,1,1,0,1,8,0,4,1,1,6
4382,30,29.2,1,4,0,1,5,0,3,1,1,4
7288,30,29.6,1,2,0,1,8,1,3,1,1,4
8837,30,29.7,1,1,0,1,8,1,7,1,1,3
5876,31,0.6,1,4,0,1,6,1,1,1,1,6
8065,31,0.7,1,3,0,1,7,1,8,1,1,2
4562,31,1,1,4,0,1,8,0,5,1,1,6
9836,31,1.1,1,1,0,1,8,1,1,1,1,10
9526,31,1.7,1,4,0,1,7,1,2,1,1,0
5596,31,2.7,1,4,0,1,5,1,8,1,1,7
3572,31,3.2,1,2,0,1,5,0,2,1,1,5
5532,31,3.9,1,3,0,1,6,1,1,1,1,7
209,31,4.4,1,5,0,1,5,0,1,1,1,0
9490,31,4.7,1,1,0,1,5,1,5,1,1,7
3976,31,5.1,1,3,0,1,5,0,4,1,1,0
10278,31,5.2,1,2,0,1,8,1,7,1,1,7
6710,31,5.3,1,2,0,1,7,1,8,1,1,5
6140,31,5.3,1,4,0,1,6,1,1,1,1,5
2743,31,5.4,1,2,0,1,7,0,3,1,1,4
3730,31,5.5,1,4,0,1,8,0,6,1,1,6
5166,31,6.2,1,3,0,1,6,0,5,1,1,8
6336,31,7.4,1,5,0,1,5,1,3,1,1,5
2770,31,7.4,1,4,0,1,5,0,4,1,1,0
6594,31,7.6,1,2,0,1,6,1,2,1,1,2
3240,31,7.9,1,2,0,1,7,0,5,1,1,5
3886,31,8.1,1,4,0,1,8,0,3,1,1,9
6610,31,8.9,1,4,0,1,6,1,3,1,1,1
3244,31,8.9,1,5,0,1,6,0,1,1,1,7
313,31,9.1,1,3,0,1,7,0,1,1,1,3
5804,31,9.2,1,4,0,1,8,1,1,1,1,1
6044,31,9.8,1,1,0,1,8,1,4,1,1,0
5552,31,9.9,1,2,0,1,5,1,6,1,1,0
4577,31,9.9,1,4,0,1,8,0,3,1,1,2
8601,31,10,1,5,0,1,6,1,1,1,1,1
5234,31,10.8,1,1,0,1,8,0,1,1,1,7
8568,31,10.9,1,1,0,1,5,1,3,1,1,0
10373,31,11.4,1,5,0,1,7,1,6,1,1,0
2868,31,11.5,1,5,0,1,7,0,5,1,1,9
10658,31,11.5,1,4,0,1,5,1,1,1,1,1
5646,31,11.9,1,3,0,1,6,1,2,1,1,3
3829,31,12,1,1,0,1,6,0,6,1,1,4
1065,31,13,1,5,0,1,6,0,1,1,1,2
88,31,13.6,1,5,0,1,8,0,7,1,1,0
1778,31,13.8,1,2,0,1,6,0,8,1,1,0
1256,31,14,1,4,0,1,6,0,3,1,1,1
3838,31,14.5,1,1,0,1,5,0,4,1,1,4
7015,31,14.6,1,5,0,1,6,1,1,1,1,3
10086,31,14.9,1,3,0,1,6,1,7,1,1,7
2803,31,15.2,1,4,0,1,5,0,6,1,1,0
6022,31,15.3,1,3,0,1,6,1,2,1,1,10
484,31,15.3,1,4,0,1,6,0,5,1,1,3
10649,31,16.1,1,1,0,1,8,1,6,1,1,0
4016,31,16.7,1,1,0,1,6,0,1,1,1,5
2986,31,16.8,1,1,0,1,5,0,6,1,1,7
9394,31,17.9,1,3,0,1,8,1,3,1,1,9
8556,31,18.3,1,2,0,1,6,1,1,1,1,7
7214,31,18.5,1,4,0,1,7,1,5,1,1,0
2686,31,19.4,1,5,0,1,7,0,2,1,1,5
5563,31,19.4,1,1,0,1,7,1,6,1,1,4
1482,31,19.5,1,5,0,1,7,0,1,1,1,0
3878,31,19.5,1,3,0,1,7,0,5,1,1,3
7944,31,19.5,1,3,0,1,5,1,8,1,1,3
7648,31,20.1,1,5,0,1,7,1,2,1,1,3
4160,31,20.8,1,5,0,1,5,0,1,1,1,3
1558,31,20.9,1,2,0,1,8,0,3,1,1,1
6852,31,21.1,1,3,0,1,7,1,3,1,1,6
8455,31,21.3,1,3,0,1,5,1,6,1,1,2
3464,31,22.4,1,1,0,1,6,0,4,1,1,9
10191,31,22.8,1,3,0,1,6,1,3,1,1,3
3926,31,22.9,1,5,0,1,7,0,3,1,1,6
7799,31,23,1,5,0,1,7,1,1,1,1,3
384,31,23.7,1,5,0,1,8,0,7,1,1,4
6083,31,24.1,1,1,0,1,8,1,7,1,1,2
2890,31,24.2,1,2,0,1,7,0,8,1,1,5
8956,31,24.3,1,1,0,1,8,1,1,1,1,8
7824,31,24.5,1,3,0,1,6,1,3,1,1,4
10500,31,24.5,1,1,0,1,8,1,3,1,1,7
10521,31,25.2,1,2,0,1,7,1,6,1,1,3
9238,31,25.8,1,3,0,1,7,1,1,1,1,3
5518,31,25.9,1,4,0,1,6,1,8,1,1,4
8901,31,25.9,1,2,0,1,8,1,2,1,1,2
4683,31,26,1,2,0,1,6,0,6,1,1,4
8149,31,26.1,1,1,0,1,7,1,5,1,1,2
598,31,26.4,1,3,0,1,6,0,1,1,1,5
5198,31,26.4,1,2,0,1,6,0,6,1,1,4
3068,31,26.4,1,2,0,1,8,0,3,1,1,5
2614,31,26.5,1,1,0,1,6,0,4,1,1,8
6831,31,26.8,1,1,0,1,5,1,8,1,1,3
9399,31,27.1,1,4,0,1,7,1,1,1,1,0
442,31,27.9,1,5,0,1,8,0,6,1,1,6
7262,31,28.3,1,2,0,1,7,1,4,1,1,2
1493,31,28.8,1,4,0,1,5,0,2,1,1,0
2942,31,28.8,1,4,0,1,8,0,8,1,1,8
9650,31,29.2,1,1,0,1,8,1,7,1,1,5
1822,31,29.3,1,5,0,1,5,0,4,1,1,10
4296,31,29.3,1,1,0,1,5,0,4,1,1,1
9163,31,29.6,1,2,0,1,8,1,6,1,1,3
4660,31,29.7,1,3,0,1,8,0,1,1,1,5
5872,32,0.6,1,1,0,1,7,1,1,1,1,7
10133,32,0.9,1,2,0,1,6,1,1,1,1,4
1838,32,0.9,1,1,0,1,8,0,3,1,1,0
112,32,1.9,1,5,0,1,5,0,2,1,1,8
7330,32,2,1,4,0,1,6,1,6,1,1,2
9228,32,2.6,1,3,0,1,7,1,5,1,1,2
1456,32,2.8,1,5,0,1,6,0,8,1,1,10
1668,32,2.8,1,5,0,1,5,0,2,1,1,9
4208,32,3,1,2,0,1,7,0,3,1,1,1
9226,32,3,1,3,0,1,7,1,1,1,1,8
8840,32,3.1,1,2,0,1,8,1,3,1,1,1
8296,32,3.6,1,3,0,1,7,1,7,1,1,4
7198,32,3.8,1,4,0,1,5,1,8,1,1,10
3956,32,3.8,1,2,0,1,6,0,4,1,1,1
10182,32,4.3,1,2,0,1,6,1,2,1,1,0
3481,32,4.7,1,4,0,1,6,0,2,1,1,2
8371,32,5.1,1,4,0,1,7,1,7,1,1,4
1680,32,5.4,1,4,0,1,7,0,2,1,1,7
4162,32,5.5,1,3,0,1,7,0,3,1,1,2
3239,32,5.8,1,1,0,1,6,0,6,1,1,4
1944,32,5.8,1,1,0,1,5,0,6,1,1,7
4994,32,6.2,1,3,0,1,6,0,6,1,1,8
4054,32,6.2,1,3,0,1,6,0,3,1,1,5
2896,32,6.8,1,4,0,1,5,0,3,1,1,9
2685,32,7.2,1,5,0,1,6,0,2,1,1,1
7284,32,7.6,1,3,0,1,8,1,5,1,1,1
10048,32,7.8,1,5,0,1,5,1,5,1,1,6
7410,32,8.2,1,2,0,1,5,1,3,1,1,3
10204,32,8.9,1,1,0,1,7,1,6,1,1,10
1630,32,9.4,1,4,0,1,6,0,4,1,1,2
3196,32,10.7,1,2,0,1,5,0,4,1,1,6
3980,32,11.2,1,5,0,1,6,0,1,1,1,7
5640,32,11.3,1,1,0,1,7,1,1,1,1,5
4248,32,11.5,1,5,0,1,8,0,6,1,1,10
374,32,11.6,1,4,0,1,7,0,1,1,1,8
6884,32,11.7,1,3,0,1,7,1,6,1,1,4
8928,32,11.7,1,1,0,1,5,1,3,1,1,0
5914,32,12.7,1,1,0,1,7,1,1,1,1,1
3316,32,13.1,1,4,0,1,7,0,6,1,1,6
3454,32,13.4,1,3,0,1,7,0,8,1,1,9
9282,32,13.5,1,5,0,1,5,1,5,1,1,10
3466,32,13.7,1,5,0,1,5,0,6,1,1,7
9725,32,13.9,1,4,0,1,8,1,4,1,1,3
8820,32,14,1,2,0,1,7,1,1,1,1,10
2762,32,14,1,1,0,1,5,0,3,1,1,9
9874,32,14.5,1,2,0,1,5,1,6,1,1,4
8648,32,15.1,1,1,0,1,6,1,7,1,1,3
8321,32,16.2,1,3,0,1,7,1,4,1,1,0
2714,32,16.2,1,4,0,1,6,0,8,1,1,5
4628,32,16.8,1,2,0,1,8,0,8,1,1,8
1795,32,17.1,1,4,0,1,8,0,4,1,1,2
4760,32,17.1,1,5,0,1,8,0,1,1,1,4
6969,32,17.2,1,5,0,1,8,1,7,1,1,1
10724,32,17.3,1,4,0,1,5,1,5,1,1,6
6173,32,17.5,1,2,0,1,8,1,6,1,1,1
7558,32,17.6,1,4,0,1,6,1,2,1,1,10
4112,32,17.8,1,4,0,1,5,0,2,1,1,9
1734,32,18.1,1,3,0,1,8,0,2,1,1,4
4305,32,18.1,1,5,0,1,7,0,8,1,1,1
1635,32,18.2,1,3,0,1,6,0,5,1,1,0
3096,32,18.4,1,1,0,1,5,0,3,1,1,6
4301,32,18.4,1,4,0,1,7,0,8,1,1,3
10140,32,18.4,1,2,0,1,6,1,8,1,1,7
7406,32,18.4,1,4,0,1,7,1,4,1,1,7
4837,32,18.7,1,1,0,1,8,0,5,1,1,0
6200,32,18.8,1,4,0,1,6,1,7,1,1,5
2281,32,18.9,1,2,0,1,5,0,6,1,1,0
285,32,19.1,1,1,0,1,7,0,1,1,1,1
5981,32,19.3,1,1,0,1,6,1,6,1,1,0
361,32,19.5,1,3,0,1,6,0,5,1,1,2
6029,32,19.7,1,4,0,1,7,1,5,1,1,2
563,32,20.2,1,5,0,1,7,0,4,1,1,2
6486,32,20.8,1,1,0,1,8,1,6,1,1,0
6620,32,21.1,1,4,0,1,8,1,5,1,1,2
1720,32,21.5,1,5,0,1,5,0,7,1,1,5
5221,32,21.9,1,4,0,1,5,0,4,1,1,0
3205,32,21.9,1,1,0,1,6,0,4,1,1,2
8494,32,22.1,1,3,0,1,7,1,8,1,1,5
5275,32,22.8,1,4,0,1,5,0,7,1,1,2
6355,32,22.8,1,2,0,1,5,1,5,1,1,2
5482,32,23,1,3,0,1,8,1,6,1,1,0
6766,32,23.3,1,3,0,1,6,1,5,1,1,1
619,32,23.4,1,2,0,1,5,0,6,1,1,3
2370,32,23.4,1,2,0,1,5,0,4,1,1,4
2632,32,23.5,1,3,0,1,8,0,8,1,1,0
9028,32,23.6,1,5,0,1,7,1,8,1,1,6
10614,32,24,1,1,0,1,7,1,2,1,1,10
9135,32,24.9,1,3,0,1,5,1,3,1,1,4
2352,32,25,1,5,0,1,8,0,3,1,1,2
10082,32,25.2,1,1,0,1,6,1,5,1,1,10
4400,32,25.2,1,5,0,1,6,0,3,1,1,2
6195,32,25.8,1,3,0,1,6,1,2,1,1,1
10207,32,25.8,1,3,0,1,6,1,2,1,1,3
5978,32,26,1,5,0,1,5,1,2,1,1,6
9934,32,26.2,1,4,0,1,8,1,6,1,1,9
2922,32,26.4,1,5,0,1,8,0,3,1,1,2
10700,32,26.4,1,3,0,1,7,1,7,1,1,1
7754,32,26.5,1,3,0,1,8,1,6,1,1,8
2101,32,26.6,1,1,0,1,6,0,3,1,1,2
1372,32,27.3,1,4,0,1,7,0,4,1,1,6
10669,32,27.3,1,3,0,1,7,1,5,1,1,2
7426,32,27.5,1,3,0,1,5,1,1,1,1,10
8026,32,27.5,1,2,0,1,7,1,1,1,1,0
10388,32,28,1,4,0,1,8,1,8,1,1,2
7631,32,28.3,1,4,0,1,8,1,4,1,1,2
5332,32,28.8,1,1,0,1,7,0,3,1,1,2
9878,32,28.9,1,4,0,1,7,1,1,1,1,8
7289,32,29,1,2,0,1,8,1,6,1,1,3
8431,32,29.1,1,3,0,1,5,1,5,1,1,4
3654,32,29.2,1,2,0,1,8,0,7,1,1,5
4812,32,29.5,1,1,0,1,6,0,7,1,1,3
9406,32,29.6,1,1,0,1,6,1,7,1,1,2
3458,32,29.9,1,5,0,1,7,0,3,1,1,1
5676,33,0.3,1,1,0,1,5,1,8,1,1,7
8343,33,0.9,1,5,0,1,6,1,7,1,1,0
5597,33,1,1,1,0,1,8,1,5,1,1,4
10432,33,1.1,1,2,0,1,5,1,2,1,1,10
2724,33,2,1,5,0,1,7,0,1,1,1,0
10322,33,2.2,1,4,0,1,7,1,5,1,1,4
6836,33,2.3,1,5,0,1,6,1,1,1,1,5
7229,33,3,1,5,0,1,5,1,4,1,1,2
8166,33,3.4,1,2,0,1,7,1,1,1,1,5
4399,33,3.8,1,2,0,1,6,0,2,1,1,0
3614,33,4.8,1,4,0,1,5,0,5,1,1,7
1930,33,5,1,2,0,1,7,0,6,1,1,3
98,33,5.3,1,1,0,1,6,0,4,1,1,2
7675,33,5.5,1,5,0,1,8,1,8,1,1,3
3208,33,5.6,1,4,0,1,7,0,3,1,1,4
7542,33,5.9,1,2,0,1,6,1,1,1,1,6
7362,33,6,1,3,0,1,8,1,3,1,1,9
2388,33,6.1,1,4,0,1,6,0,8,1,1,1
1932,33,6.6,1,2,0,1,5,0,3,1,1,10
7984,33,6.6,1,1,0,1,6,1,1,1,1,9
4918,33,7.3,1,5,0,1,5,0,8,1,1,1
4262,33,7.5,1,2,0,1,8,0,1,1,1,8
3077,33,8,1,3,0,1,7,0,7,1,1,1
4033,33,8.3,1,4,0,1,5,0,8,1,1,4
3220,33,8.8,1,1,0,1,6,0,1,1,1,4
1516,33,8.9,1,2,0,1,5,0,6,1,1,2
9602,33,9.3,1,4,0,1,6,1,6,1,1,5
1151,33,9.8,1,4,0,1,8,0,1,1,1,3
499,33,10.3,1,3,0,1,7,0,3,1,1,3
4670,33,10.4,1,1,0,1,7,0,6,1,1,4
3574,33,10.6,1,4,0,1,8,0,8,1,1,2
1758,33,11.5,1,3,0,1,5,0,5,1,1,8
6115,33,11.5,1,5,0,1,5,1,1,1,1,1
7408,33,11.5,1,4,0,1,7,1,6,1,1,1
9506,33,12,1,3,0,1,7,1,4,1,1,3
6430,33,12,1,5,0,1,5,1,2,1,1,10
386,33,12,1,3,0,1,8,0,3,1,1,8
941,33,12.6,1,4,0,1,8,0,6,1,1,0
4447,33,12.6,1,1,0,1,5,0,7,1,1,3
9229,33,12.9,1,4,0,1,8,1,3,1,1,1
3849,33,13,1,5,0,1,5,0,3,1,1,4
10578,33,13.2,1,4,0,1,7,1,4,1,1,5
1670,33,14,1,1,0,1,6,0,2,1,1,10
482,33,14.1,1,3,0,1,8,0,7,1,1,9
1710,33,14.5,1,1,0,1,8,0,3,1,1,5
8414,33,14.6,1,5,0,1,7,1,8,1,1,4
281,33,14.8,1,4,0,1,7,0,8,1,1,1
8258,33,15,1,5,0,1,7,1,8,1,1,10
1864,33,15.1,1,3,0,1,6,0,6,1,1,2
10476,33,15.7,1,4,0,1,7,1,3,1,1,1
1468,33,15.7,1,1,0,1,8,0,4,1,1,4
7918,33,16.6,1,3,0,1,8,1,4,1,1,1
10826,33,16.8,1,1,0,1,5,1,3,1,1,6
7560,33,17.1,1,2,0,1,8,1,8,1,1,6
1109,33,17.3,1,4,0,1,8,0,3,1,1,1
100,33,18.1,1,3,0,1,7,0,6,1,1,1
2432,33,18.1,1,4,0,1,7,0,5,1,1,2
6502,33,18.2,1,4,0,1,5,1,3,1,1,4
4310,33,18.2,1,1,0,1,8,0,5,1,1,0
10595,33,18.2,1,4,0,1,5,1,4,1,1,0
4844,33,18.3,1,4,0,1,5,0,6,1,1,5
9589,33,18.5,1,5,0,1,6,1,4,1,1,3
2635,33,18.9,1,4,0,1,5,0,4,1,1,4
2540,33,19.5,1,5,0,1,8,0,8,1,1,2
347,33,19.5,1,3,0,1,5,0,7,1,1,3
6080,33,19.7,1,3,0,1,6,1,6,1,1,7
8137,33,20.3,1,4,0,1,8,1,2,1,1,3
5100,33,20.7,1,1,0,1,7,0,7,1,1,3
995,33,20.8,1,4,0,1,6,0,6,1,1,4
8850,33,20.9,1,2,0,1,8,1,4,1,1,7
152,33,21,1,5,0,1,8,0,3,1,1,5
204,33,21.2,1,1,0,1,7,0,6,1,1,10
10552,33,21.2,1,5,0,1,5,1,7,1,1,8
82,33,21.3,1,4,0,1,5,0,8,1,1,6
8823,33,21.6,1,2,0,1,5,1,3,1,1,2
2631,33,22.3,1,2,0,1,5,0,3,1,1,3
7756,33,22.5,1,2,0,1,7,1,3,1,1,7
8858,33,23.7,1,2,0,1,7,1,3,1,1,2
8448,33,23.8,1,2,0,1,6,1,3,1,1,7
10036,33,23.8,1,5,0,1,8,1,5,1,1,1
5762,33,23.8,1,3,0,1,8,1,1,1,1,4
2814,33,24.3,1,2,0,1,8,0,7,1,1,8
1337,33,24.4,1,1,0,1,5,0,8,1,1,4
400,33,24.4,1,2,0,1,6,0,2,1,1,10
18,33,25,1,4,0,1,5,0,4,1,1,2
10780,33,25.1,1,2,0,1,7,1,7,1,1,5
6693,33,25.3,1,1,0,1,5,1,5,1,1,0
8726,33,26.3,1,2,0,1,5,1,6,1,1,6
5956,33,26.5,1,4,0,1,5,1,6,1,1,8
3451,33,26.7,1,1,0,1,5,0,7,1,1,4
10168,33,27.2,1,2,0,1,8,1,3,1,1,7
6125,33,27.4,1,2,0,1,7,1,5,1,1,1
4084,33,27.6,1,1,0,1,5,0,3,1,1,3
4158,33,27.8,1,3,0,1,8,0,3,1,1,7
8075,33,28.5,1,4,0,1,6,1,1,1,1,3
9981,33,28.8,1,2,0,1,5,1,4,1,1,1
2622,33,29.2,1,4,0,1,8,0,5,1,1,7
4180,33,29.3,1,1,0,1,6,0,3,1,1,0
8516,33,29.4,1,3,0,1,6,1,6,1,1,1
856,33,29.9,1,2,0,1,8,0,2,1,1,6
9148,34,0.9,1,2,0,1,7,1,1,1,1,3
7864,34,1,1,4,0,1,5,1,2,1,1,0
1608,34,1,1,1,0,1,8,0,8,1,1,9
8134,34,1.4,1,2,0,1,5,1,1,1,1,6
9212,34,1.4,1,2,0,1,5,1,3,1,1,9
90,34,1.5,1,2,0,1,5,0,8,1,1,7
1132,34,2.3,1,2,0,1,7,0,8,1,1,10
3906,34,2.5,1,1,0,1,5,0,7,1,1,5
10192,34,2.6,1,3,0,1,5,1,6,1,1,4
7798,34,2.7,1,1,0,1,7,1,5,1,1,8
4285,34,2.8,1,4,0,1,8,0,4,1,1,2
2014,34,3,1,4,0,1,8,0,5,1,1,8
5098,34,3.7,1,1,0,1,5,0,1,1,1,5
2639,34,4.3,1,2,0,1,6,0,2,1,1,2
6822,34,4.4,1,3,0,1,8,1,4,1,1,9
9460,34,4.5,1,4,0,1,5,1,5,1,1,6
2436,34,4.7,1,5,0,1,5,0,3,1,1,10
541,34,4.8,1,3,0,1,5,0,2,1,1,3
6868,34,5.2,1,3,0,1,5,1,1,1,1,8
80,34,5.3,1,3,0,1,6,0,3,1,1,10
2294,34,5.9,1,2,0,1,8,0,1,1,1,8
4936,34,6.1,1,3,0,1,7,0,7,1,1,2
1120,34,6.2,1,1,0,1,7,0,6,1,1,1
7722,34,6.6,1,3,0,1,8,1,6,1,1,3
7767,34,6.8,1,2,0,1,7,1,1,1,1,0
3243,34,7.1,1,2,0,1,5,0,7,1,1,4
5423,34,7.2,1,1,0,1,6,1,3,1,1,3
2904,34,7.2,1,3,0,1,5,0,4,1,1,8
9079,34,7.3,1,4,0,1,7,1,7,1,1,2
7253,34,7.4,1,4,0,1,5,1,8,1,1,0
1234,34,7.6,1,2,0,1,6,0,1,1,1,7
10645,34,7.7,1,2,0,1,6,1,7,1,1,2
10568,34,8.1,1,1,0,1,6,1,3,1,1,10
1843,34,8.6,1,3,0,1,5,0,8,1,1,0
9248,34,9,1,5,0,1,8,1,4,1,1,6
1904,34,9,1,4,0,1,6,0,6,1,1,0
1146,34,9.1,1,3,0,1,8,0,4,1,1,10
6398,34,9.2,1,3,0,1,5,1,1,1,1,7
10862,34,9.2,1,3,0,1,7,1,1,1,1,10
4550,34,9.3,1,4,0,1,8,0,5,1,1,8
10315,34,9.6,1,2,0,1,6,1,1,1,1,4
1886,34,9.9,1,1,0,1,8,0,4,1,1,3
4851,34,10.1,1,4,0,1,7,0,4,1,1,0
1301,34,10.9,1,4,0,1,5,0,3,1,1,4
4423,34,11.3,1,4,0,1,6,0,5,1,1,1
1277,34,11.4,1,4,0,1,6,0,4,1,1,0
3008,34,11.5,1,4,0,1,8,0,6,1,1,3
3974,34,11.9,1,4,0,1,7,0,7,1,1,8
7082,34,12.2,1,5,0,1,6,1,1,1,1,0
10446,34,12.2,1,2,0,1,6,1,6,1,1,6
8021,34,12.3,1,4,0,1,7,1,4,1,1,3
8728,34,12.3,1,3,0,1,5,1,8,1,1,0
5685,34,12.5,1,3,0,1,8,1,1,1,1,3
9443,34,12.8,1,4,0,1,5,1,1,1,1,1
2464,34,13.1,1,3,0,1,7,0,5,1,1,5
1194,34,14,1,1,0,1,8,0,1,1,1,4
10458,34,14.2,1,4,0,1,8,1,1,1,1,0
10560,34,14.4,1,4,0,1,6,1,2,1,1,4
7852,34,14.4,1,3,0,1,5,1,2,1,1,3
6978,34,14.5,1,3,0,1,7,1,5,1,1,5
7915,34,15.1,1,1,0,1,7,1,6,1,1,0
215,34,15.6,1,5,0,1,5,0,3,1,1,1
6474,34,15.7,1,3,0,1,6,1,6,1,1,2
6104,34,15.8,1,4,0,1,7,1,5,1,1,7
2086,34,16.4,1,4,0,1,6,0,8,1,1,6
1744,34,16.5,1,1,0,1,8,0,5,1,1,2
8583,34,16.8,1,1,0,1,6,1,5,1,1,4
2062,34,16.9,1,3,0,1,8,0,8,1,1,1
6298,34,17.1,1,2,0,1,8,1,8,1,1,10
5434,34,17.4,1,3,0,1,5,1,6,1,1,5
208,34,17.5,1,2,0,1,8,0,4,1,1,2
9168,34,17.6,1,4,0,1,5,1,7,1,1,7
4222,34,17.9,1,2,0,1,6,0,3,1,1,3
4939,34,18.3,1,3,0,1,6,0,5,1,1,3
5062,34,18.4,1,2,0,1,6,0,3,1,1,2
10184,34,18.7,1,3,0,1,5,1,3,1,1,6
2665,34,18.9,1,4,0,1,6,0,5,1,1,1
3745,34,19.1,1,3,0,1,8,0,5,1,1,3
8461,34,19.3,1,3,0,1,5,1,1,1,1,4
10007,34,19.4,1,2,0,1,7,1,4,1,1,3
802,34,19.6,1,3,0,1,5,0,5,1,1,2
10146,34,19.7,1,4,0,1,6,1,8,1,1,4
9651,34,19.9,1,3,0,1,5,1,5,1,1,3
10332,34,19.9,1,3,0,1,6,1,7,1,1,5
5774,34,20.5,1,2,0,1,5,1,6,1,1,4
1403,34,20.5,1,2,0,1,6,0,5,1,1,1
7186,34,20.8,1,5,0,1,5,1,6,1,1,1
9860,34,21.4,1,4,0,1,8,1,4,1,1,1
5511,34,21.4,1,3,0,1,7,1,7,1,1,0
1528,34,21.4,1,5,0,1,7,0,6,1,1,3
10106,34,21.7,1,4,0,1,6,1,6,1,1,5
2017,34,22,1,1,0,1,5,0,2,1,1,0
4226,34,22,1,3,0,1,8,0,3,1,1,0
1332,34,22.1,1,3,0,1,5,0,4,1,1,9
5246,34,22.3,1,2,0,1,7,0,4,1,1,4
2794,34,22.4,1,3,0,1,5,0,6,1,1,9
3037,34,22.4,1,3,0,1,7,0,2,1,1,1
8492,34,23.3,1,1,0,1,7,1,6,1,1,0
6227,34,23.5,1,3,0,1,8,1,2,1,1,1
8877,34,23.5,1,3,0,1,5,1,8,1,1,1
6350,34,23.8,1,2,0,1,8,1,7,1,1,4
9260,34,24.1,1,5,0,1,8,1,2,1,1,6
1336,34,24.9,1,1,0,1,8,0,7,1,1,4
6147,34,25,1,2,0,1,5,1,7,1,1,4
7217,34,25,1,5,0,1,8,1,6,1,1,3
8345,34,25.3,1,2,0,1,6,1,8,1,1,1
7356,34,25.8,1,2,0,1,5,1,4,1,1,2
8016,34,26.2,1,5,0,1,5,1,1,1,1,10
7822,34,26.6,1,2,0,1,5,1,8,1,1,9
1912,34,27.2,1,5,0,1,8,0,1,1,1,4
2018,34,27.3,1,3,0,1,8,0,4,1,1,3
4712,34,27.4,1,3,0,1,8,0,7,1,1,3
9290,34,27.5,1,1,0,1,6,1,8,1,1,1
4946,34,29,1,2,0,1,7,0,7,1,1,6
1357,34,29.2,1,4,0,1,5,0,7,1,1,2
6683,34,29.5,1,4,0,1,5,1,8,1,1,2
7524,34,29.6,1,2,0,1,8,1,3,1,1,5
9348,34,29.7,1,3,0,1,6,1,2,1,1,0
5512,35,0.4,1,2,0,1,5,1,5,1,1,7
10005,35,1,1,4,0,1,6,1,6,1,1,0
7992,35,1.5,1,1,0,1,7,1,2,1,1,6
5208,35,1.5,1,2,0,1,8,0,5,1,1,3
8807,35,2.2,1,5,0,1,8,1,6,1,1,4
8754,35,2.2,1,5,0,1,6,1,2,1,1,8
7017,35,2.5,1,2,0,1,8,1,2,1,1,0
364,35,2.6,1,4,0,1,5,0,3,1,1,10
9928,35,2.7,1,5,0,1,8,1,8,1,1,7
144,35,2.8,1,2,0,1,5,0,7,1,1,0
5696,35,2.9,1,5,0,1,7,1,1,1,1,6
2282,35,2.9,1,5,0,1,5,0,3,1,1,6
9872,35,3.1,1,1,0,1,7,1,4,1,1,5
7438,35,3.4,1,2,0,1,8,1,2,1,1,10
4714,35,3.4,1,2,0,1,7,0,6,1,1,9
9730,35,3.5,1,4,0,1,5,1,7,1,1,0
5488,35,3.5,1,2,0,1,8,1,3,1,1,10
8832,35,3.6,1,3,0,1,6,1,4,1,1,2
6067,35,3.6,1,5,0,1,6,1,5,1,1,0
1622,35,3.7,1,1,0,1,5,0,8,1,1,1
5868,35,3.7,1,2,0,1,7,1,4,1,1,7
1586,35,3.7,1,3,0,1,8,0,8,1,1,3
7550,35,3.8,1,5,0,1,6,1,4,1,1,5
6100,35,4.2,1,2,0,1,5,1,6,1,1,6
6242,35,4.3,1,2,0,1,6,1,6,1,1,8
10163,35,4.4,1,5,0,1,5,1,6,1,1,2
6410,35,4.6,1,4,0,1,5,1,3,1,1,4
5193,35,4.8,1,3,0,1,5,0,5,1,1,4
4108,35,4.8,1,1,0,1,8,0,1,1,1,6
276,35,5.1,1,2,0,1,7,0,8,1,1,0
5474,35,5.4,1,2,0,1,8,1,6,1,1,6
8458,35,5.4,1,5,0,1,5,1,3,1,1,3
2050,35,5.5,1,3,0,1,7,0,2,1,1,8
7088,35,6.5,1,3,0,1,5,1,6,1,1,8
6801,35,7.1,1,5,0,1,8,1,1,1,1,3
6469,35,7.1,1,3,0,1,8,1,6,1,1,1
223,35,7.1,1,5,0,1,7,0,7,1,1,2
1645,35,7.8,1,1,0,1,8,0,7,1,1,0
1768,35,7.9,1,2,0,1,7,0,4,1,1,0
4430,35,8.6,1,2,0,1,7,0,8,1,1,3
6153,35,8.8,1,1,0,1,7,1,7,1,1,1
7118,35,9.4,1,5,0,1,7,1,7,1,1,8
1044,35,10.2,1,1,0,1,5,0,8,1,1,2
9564,35,10.3,1,2,0,1,7,1,4,1,1,8
6590,35,10.5,1,1,0,1,6,1,7,1,1,6
3867,35,10.5,1,4,0,1,7,0,5,1,1,4
731,35,10.6,1,1,0,1,5,0,4,1,1,1
1384,35,10.7,1,3,0,1,6,0,4,1,1,10
9270,35,10.8,1,5,0,1,7,1,5,1,1,1
2012,35,10.8,1,1,0,1,8,0,7,1,1,5
3510,35,11.1,1,2,0,1,5,0,6,1,1,7
6290,35,11.5,1,2,0,1,6,1,6,1,1,9
3536,35,11.6,1,3,0,1,5,0,8,1,1,3
2638,35,11.7,1,4,0,1,7,0,5,1,1,5
5144,35,4,1,1,0,1,5,0,6,1,1,3
2444,35,4,1,5,0,1,6,0,3,1,1,6
4836,35,6,1,5,0,1,8,0,7,1,1,2
1186,35,2,1,5,0,1,5,0,8,1,1,2
402,35,5,1,3,0,1,8,0,7,1,1,7
4010,35,6,1,3,0,1,8,0,6,1,1,2
4745,35,1,1,2,0,1,6,0,3,1,1,3
3344,35,6,1,1,0,1,7,0,2,1,1,10
7227,35,4,1,4,0,1,5,1,3,1,1,1
1136,35,4,1,2,0,1,5,0,2,1,1,2
4284,35,6,1,1,0,1,8,0,6,1,1,1
3992,35,4,1,4,0,1,5,0,1,1,1,7
3146,35,7,1,5,0,1,8,0,3,1,1,10
6230,35,2,1,5,0,1,5,1,5,1,1,2
5849,35,7,1,2,0,1,6,1,8,1,1,1
5051,35,6,1,2,0,1,6,0,6,1,1,3
3696,35,3,1,4,0,1,7,0,4,1,1,4
6193,35,1,1,5,0,1,6,1,6,1,1,1
3232,35,7,1,3,0,1,7,0,1,1,1,1
8876,35,6,1,1,0,1,6,1,1,1,1,4
6690,35,3,1,1,0,1,8,1,5,1,1,9
10621,35,1,1,2,0,1,6,1,8,1,1,4
4621,35,3,1,1,0,1,8,0,7,1,1,0
2236,35,2,1,5,0,1,6,0,3,1,1,5
459,35,7,1,2,0,1,5,0,8,1,1,4
3376,35,4,1,3,0,1,8,0,6,1,1,2
6438,35,2,1,3,0,1,8,1,1,1,1,5
6192,35,2,1,4,0,1,6,1,5,1,1,6
3217,35,1,1,3,0,1,7,0,7,1,1,2
3418,35,7,1,1,0,1,5,0,1,1,1,6
8508,35,7,1,1,0,1,6,1,4,1,1,3
3719,35,7,1,4,0,1,6,0,3,1,1,0
3136,35,2,1,2,0,1,8,0,8,1,1,9
7882,35,5,1,5,0,1,7,1,7,1,1,7
3712,35,2,1,1,0,1,7,0,5,1,1,8
2226,35,2,1,3,0,1,5,0,3,1,1,8
8092,35,2,1,2,0,1,8,1,8,1,1,3
7298,35,3,1,5,0,1,6,1,2,1,1,4
3868,35,6,1,1,0,1,5,0,5,1,1,5
4756,35,6,1,4,0,1,7,0,2,1,1,3
10497,35,5,1,3,0,1,5,1,1,1,1,2
4272,35,5,1,3,0,1,6,0,8,1,1,6
8599,35,7,1,1,0,1,7,1,6,1,1,2
7090,35,2,1,3,0,1,6,1,5,1,1,5
9920,35,1,1,1,0,1,8,1,2,1,1,1
4203,35,2,1,2,0,1,6,0,8,1,1,2
5396,35,6,1,3,0,1,5,0,7,1,1,8
5840,35,6,1,2,0,1,8,1,6,1,1,2
5286,35,1,1,4,0,1,8,0,5,1,1,1
7526,35,2,1,3,0,1,5,1,7,1,1,4
10592,35,7,1,2,0,1,7,1,5,1,1,2
4986,35,6,1,5,0,1,6,0,4,1,1,3
8210,35,4,1,3,0,1,7,1,4,1,1,8
2164,35,2,1,5,0,1,5,0,5,1,1,10
4094,35,3,1,2,0,1,7,0,3,1,1,0
918,35,6,1,3,0,1,7,0,5,1,1,9
8276,35,7,1,4,0,1,7,1,8,1,1,7
9525,35,7,1,3,0,1,5,1,8,1,1,4
7842,35,3,1,2,0,1,7,1,5,1,1,4
8263,35,6,1,2,0,1,6,1,8,1,1,1
5376,35,4,1,1,0,1,7,0,2,1,1,7
4266,35,3,1,3,0,1,5,0,5,1,1,8
1038,35,7,1,4,0,1,7,0,6,1,1,4
2395,35,5,1,2,0,1,6,0,1,1,1,0
7931,35,6,1,2,0,1,7,1,3,1,1,0
1990,35,7,1,5,0,1,8,0,5,1,1,6
10498,35,1,1,3,0,1,5,1,2,1,1,10
10157,35,5,1,3,0,1,7,1,7,1,1,0
2918,35,3,1,3,0,1,5,0,8,1,1,1
5037,35,2,1,4,0,1,5,0,1,1,1,2
2125,35,2,1,5,0,1,6,0,8,1,1,2
7851,35,4,1,5,0,1,5,1,6,1,1,3
8819,35,2,1,1,0,1,8,1,3,1,1,4
5040,35,3,1,3,0,1,7,0,7,1,1,2
788,35,5,1,1,0,1,5,0,6,1,1,2
8101,35,2,1,2,0,1,8,1,1,1,1,0
10850,35,3,1,1,0,1,8,1,3,1,1,6
10239,35,5,1,5,0,1,5,1,3,1,1,1
4040,36,0.2,1,3,0,1,7,0,2,1,1,0
1624,36,0.2,1,2,0,1,7,0,5,1,1,9
7640,36,0.2,1,1,0,1,5,1,8,1,1,3
4497,36,0.2,1,4,0,1,7,0,7,1,1,0
8939,36,1.4,1,2,0,1,7,1,6,1,1,3
7982,36,1.7,1,4,0,1,8,1,5,1,1,5
8910,36,2,1,3,0,1,8,1,5,1,1,10
2325,36,2.4,1,5,0,1,7,0,4,1,1,4
4528,36,3.4,1,5,0,1,8,0,4,1,1,8
9508,36,3.5,1,1,0,1,6,1,6,1,1,6
7910,36,3.9,1,5,0,1,7,1,2,1,1,1
3201,36,4.5,1,5,0,1,5,0,5,1,1,3
1172,36,4.5,1,4,0,1,7,0,8,1,1,10
10150,36,4.8,1,2,0,1,8,1,1,1,1,9
4964,36,5.1,1,1,0,1,7,0,1,1,1,3
6316,36,6,1,5,0,1,7,1,1,1,1,10
5542,36,6.5,1,3,0,1,8,1,8,1,1,7
4710,36,6.9,1,1,0,1,6,0,6,1,1,8
5719,36,7.1,1,1,0,1,6,1,7,1,1,0
6111,36,7.1,1,1,0,1,6,1,6,1,1,3
5306,36,7.3,1,5,0,1,6,0,2,1,1,7
2324,36,7.3,1,1,0,1,8,0,8,1,1,8
1530,36,7.4,1,1,0,1,7,0,6,1,1,8
8606,36,7.4,1,1,0,1,7,1,4,1,1,10
5900,36,7.5,1,5,0,1,6,1,6,1,1,0
1521,36,8,1,3,0,1,5,0,2,1,1,4
7411,36,8.2,1,3,0,1,6,1,3,1,1,1
3016,36,8.3,1,4,0,1,7,0,1,1,1,2
9472,36,8.3,1,3,0,1,7,1,1,1,1,5
2558,36,8.4,1,1,0,1,7,0,4,1,1,9
10502,36,8.5,1,1,0,1,5,1,7,1,1,0
8700,36,8.6,1,2,0,1,7,1,2,1,1,4
7714,36,8.7,1,2,0,1,5,1,4,1,1,3
7510,36,8.8,1,5,0,1,6,1,2,1,1,2
8495,36,9.4,1,2,0,1,7,1,6,1,1,1
5907,36,9.9,1,5,0,1,7,1,4,1,1,3
5202,36,10.1,1,4,0,1,5,0,4,1,1,2
10475,36,10.9,1,3,0,1,8,1,3,1,1,4
4908,36,11,1,5,0,1,6,0,3,1,1,10
3689,36,11.9,1,5,0,1,5,0,4,1,1,3
1750,36,7,1,4,0,1,6,0,8,1,1,7
1689,36,1,1,4,0,1,8,0,3,1,1,3
8034,36,3,1,4,0,1,7,1,1,1,1,5
2606,36,5,1,1,0,1,5,0,5,1,1,7
4872,36,6,1,5,0,1,5,0,7,1,1,10
2168,36,2,1,4,0,1,6,0,7,1,1,6
1752,36,1,1,5,0,1,5,0,5,1,1,4
9102,36,6,1,3,0,1,8,1,3,1,1,0
441,36,4,1,5,0,1,8,0,1,1,1,1
4302,36,5,1,1,0,1,6,0,1,1,1,2
29,36,6,1,4,0,1,6,0,4,1,1,2
4130,36,3,1,1,0,1,6,0,5,1,1,4
4429,36,3,1,2,0,1,6,0,7,1,1,1
5882,36,3,1,1,0,1,8,1,8,1,1,7
8310,36,1,1,3,0,1,6,1,4,1,1,4
6533,36,3,1,1,0,1,5,1,2,1,1,0
9089,36,5,1,5,0,1,7,1,7,1,1,2
3755,36,1,1,5,0,1,7,0,6,1,1,2
7671,36,7,1,5,0,1,6,1,1,1,1,2
5614,36,2,1,3,0,1,8,1,2,1,1,9
7505,36,5,1,3,0,1,8,1,8,1,1,3
1531,36,2,1,3,0,1,5,0,3,1,1,1
3329,36,7,1,2,0,1,8,0,6,1,1,2
520,36,7,1,4,0,1,8,0,7,1,1,0
2406,36,2,1,1,0,1,5,0,8,1,1,9
1274,36,5,1,4,0,1,6,0,1,1,1,9
8565,36,7,1,2,0,1,7,1,6,1,1,3
6279,36,6,1,3,0,1,5,1,4,1,1,2
8835,36,4,1,5,0,1,5,1,1,1,1,1
6711,36,6,1,2,0,1,7,1,2,1,1,0
4132,36,5,1,3,0,1,8,0,6,1,1,8
4755,36,2,1,4,0,1,5,0,4,1,1,1
3630,36,4,1,5,0,1,6,0,6,1,1,9
3826,36,5,1,2,0,1,5,0,4,1,1,4
342,36,3,1,2,0,1,8,0,5,1,1,9
6563,36,4,1,1,0,1,7,1,4,1,1,3
1352,36,3,1,2,0,1,7,0,1,1,1,1
5142,36,7,1,1,0,1,7,0,7,1,1,0
2872,36,4,1,3,0,1,6,0,2,1,1,7
9939,36,7,1,3,0,1,6,1,3,1,1,0
1875,36,2,1,4,0,1,8,0,7,1,1,4
4968,36,7,1,4,0,1,8,0,1,1,1,0
4492,36,1,1,4,0,1,8,0,6,1,1,5
8334,36,3,1,1,0,1,8,1,1,1,1,8
1623,36,1,1,3,0,1,5,0,4,1,1,2
9557,36,4,1,2,0,1,6,1,8,1,1,3
5938,36,6,1,4,0,1,8,1,7,1,1,7
4762,36,5,1,4,0,1,6,0,1,1,1,7
5312,36,7,1,2,0,1,5,0,5,1,1,0
781,36,4,1,1,0,1,8,0,7,1,1,1
2220,36,6,1,1,0,1,5,0,5,1,1,10
8352,36,7,1,4,0,1,5,1,2,1,1,4
3716,36,3,1,1,0,1,8,0,5,1,1,1
3066,36,2,1,3,0,1,5,0,7,1,1,0
2305,36,4,1,3,0,1,8,0,1,1,1,3
7514,36,4,1,1,0,1,7,1,4,1,1,0
4192,36,3,1,1,0,1,7,0,3,1,1,6
2367,36,2,1,5,0,1,8,0,7,1,1,2
578,36,6,1,2,0,1,7,0,8,1,1,8
817,36,7,1,4,0,1,5,0,6,1,1,1
7923,36,6,1,1,0,1,7,1,1,1,1,3
4602,36,3,1,1,0,1,8,0,5,1,1,4
7628,36,6,1,5,0,1,7,1,3,1,1,10
10488,36,3,1,5,0,1,6,1,6,1,1,8
9970,36,3,1,1,0,1,8,1,8,1,1,10
5802,36,6,1,2,0,1,6,1,2,1,1,8
10206,36,4,1,4,0,1,7,1,3,1,1,8
10167,36,5,1,4,0,1,8,1,5,1,1,0
8786,36,6,1,5,0,1,8,1,2,1,1,1
3738,36,5,1,3,0,1,7,0,3,1,1,0
10346,36,7,1,4,0,1,6,1,2,1,1,9
5023,36,2,1,3,0,1,8,0,2,1,1,3
10668,36,3,1,5,0,1,6,1,6,1,1,10
5195,36,3,1,1,0,1,8,0,2,1,1,4
4046,36,6,1,3,0,1,8,0,6,1,1,6
9674,36,4,1,3,0,1,8,1,2,1,1,0
10625,36,6,1,4,0,1,5,1,4,1,1,2
10123,36,4,1,5,0,1,5,1,3,1,1,2
5368,36,6,1,4,0,1,6,0,1,1,1,4
10071,37,0.6,1,1,0,1,8,1,8,1,1,1
1334,37,0.7,1,3,0,1,5,0,2,1,1,8
10576,37,1,1,5,0,1,5,1,1,1,1,0
4037,37,1.3,1,4,0,1,5,0,4,1,1,3
1513,37,1.4,1,2,0,1,6,0,2,1,1,0
2830,37,1.9,1,3,0,1,7,0,6,1,1,8
8061,37,2.4,1,3,0,1,6,1,3,1,1,3
6716,37,2.9,1,4,0,1,7,1,4,1,1,3
5748,37,3.3,1,3,0,1,7,1,7,1,1,1
7442,37,3.5,1,2,0,1,6,1,7,1,1,6
7590,37,4.1,1,3,0,1,6,1,4,1,1,1
8862,37,4.2,1,4,0,1,5,1,1,1,1,8
6032,37,4.8,1,1,0,1,5,1,8,1,1,10
8214,37,5,1,1,0,1,7,1,8,1,1,2
7906,37,5.6,1,4,0,1,7,1,2,1,1,8
9252,37,5.6,1,5,0,1,5,1,1,1,1,8
8374,37,5.7,1,3,0,1,8,1,6,1,1,10
4096,37,5.8,1,1,0,1,7,0,2,1,1,5
2734,37,6.4,1,3,0,1,7,0,8,1,1,10
9030,37,6.8,1,4,0,1,5,1,7,1,1,8
10328,37,6.9,1,3,0,1,5,1,5,1,1,7
8582,37,7,1,1,0,1,5,1,7,1,1,1
8649,37,7.5,1,5,0,1,5,1,5,1,1,3
8853,37,7.6,1,1,0,1,8,1,3,1,1,0
4922,37,8.1,1,5,0,1,7,0,5,1,1,2
7024,37,8.3,1,1,0,1,7,1,2,1,1,1
4985,37,9,1,5,0,1,5,0,4,1,1,0
3298,37,9.1,1,1,0,1,8,0,1,1,1,6
2588,37,9.2,1,2,0,1,8,0,6,1,1,4
170,37,9.3,1,2,0,1,8,0,7,1,1,2
155,37,9.9,1,5,0,1,8,0,8,1,1,0
7397,37,9.9,1,2,0,1,8,1,1,1,1,2
7395,37,10,1,5,0,1,6,1,7,1,1,3
2731,37,10.1,1,1,0,1,6,0,8,1,1,4
5099,37,10.2,1,1,0,1,8,0,3,1,1,4
8118,37,10.5,1,3,0,1,5,1,3,1,1,3
9749,37,10.9,1,5,0,1,6,1,7,1,1,2
8306,37,11.6,1,4,0,1,8,1,4,1,1,9
3798,37,11.9,1,2,0,1,8,0,8,1,1,9
774,37,12,1,3,0,1,5,0,5,1,1,1
8269,37,3,1,1,0,1,5,1,4,1,1,4
7955,37,2,1,1,0,1,6,1,5,1,1,2
232,37,3,1,5,0,1,6,0,2,1,1,7
2842,37,2,1,4,0,1,6,0,7,1,1,9
390,37,2,1,2,0,1,6,0,1,1,1,8
9124,37,3,1,3,0,1,7,1,2,1,1,2
8598,37,2,1,2,0,1,8,1,6,1,1,8
10646,37,3,1,1,0,1,6,1,6,1,1,9
2860,37,7,1,1,0,1,8,0,3,1,1,8
4909,37,7,1,5,0,1,8,0,3,1,1,1
9524,37,7,1,1,0,1,7,1,4,1,1,8
1850,37,2,1,5,0,1,7,0,6,1,1,0
6102,37,3,1,1,0,1,7,1,2,1,1,8
8257,37,3,1,2,0,1,8,1,5,1,1,0
3324,37,5,1,1,0,1,7,0,1,1,1,6
9090,37,2,1,4,0,1,6,1,7,1,1,8
8338,37,2,1,5,0,1,8,1,2,1,1,4
9723,37,4,1,4,0,1,8,1,1,1,1,2
1451,37,3,1,1,0,1,8,0,6,1,1,4
9816,37,2,1,1,0,1,8,1,3,1,1,10
10533,37,1,1,5,0,1,5,1,5,1,1,1
6506,37,7,1,2,0,1,7,1,5,1,1,0
717,37,3,1,1,0,1,6,0,2,1,1,2
464,37,2,1,4,0,1,6,0,4,1,1,9
8344,37,3,1,3,0,1,5,1,2,1,1,7
10746,37,1,1,2,0,1,7,1,4,1,1,8
4154,37,7,1,2,0,1,7,0,5,1,1,3
622,37,4,1,1,0,1,6,0,8,1,1,1
2358,37,1,1,4,0,1,7,0,1,1,1,3
1602,37,5,1,2,0,1,5,0,8,1,1,10
3547,37,1,1,1,0,1,6,0,8,1,1,1
3544,37,5,1,4,0,1,6,0,7,1,1,9
576,37,3,1,5,0,1,5,0,3,1,1,7
10671,37,6,1,4,0,1,8,1,8,1,1,2
10313,37,4,1,5,0,1,6,1,2,1,1,4
8312,37,1,1,3,0,1,8,1,3,1,1,6
8326,37,5,1,5,0,1,7,1,3,1,1,3
2527,37,7,1,1,0,1,5,0,2,1,1,4
1572,37,4,1,2,0,1,6,0,5,1,1,6
2767,37,1,1,5,0,1,6,0,4,1,1,4
3609,37,1,1,3,0,1,8,0,4,1,1,4
3318,37,2,1,3,0,1,5,0,8,1,1,0
7720,37,6,1,5,0,1,5,1,6,1,1,8
1077,37,7,1,4,0,1,8,0,6,1,1,3
10337,37,3,1,3,0,1,5,1,8,1,1,1
7162,37,7,1,4,0,1,5,1,3,1,1,7
8270,37,6,1,1,0,1,8,1,8,1,1,1
6068,37,4,1,5,0,1,6,1,2,1,1,10
2600,37,7,1,5,0,1,8,0,7,1,1,9
3925,37,4,1,3,0,1,6,0,1,1,1,0
7766,37,4,1,5,0,1,6,1,1,1,1,1
970,37,4,1,1,0,1,6,0,7,1,1,3
5996,38,0.2,1,1,0,1,8,1,1,1,1,2
1618,38,0.3,1,5,0,1,6,0,2,1,1,10
6156,38,0.3,1,2,0,1,6,1,1,1,1,5
10318,38,0.6,1,1,0,1,7,1,6,1,1,6
7232,38,1.3,1,5,0,1,5,1,2,1,1,3
5065,38,1.4,1,4,0,1,5,0,3,1,1,4
10311,38,1.6,1,2,0,1,5,1,2,1,1,3
5271,38,1.7,1,4,0,1,6,0,7,1,1,2
1394,38,2.1,1,1,0,1,8,0,4,1,1,8
5148,38,2.5,1,3,0,1,8,0,4,1,1,3
10361,38,2.6,1,1,0,1,5,1,2,1,1,4
3348,38,2.6,1,1,0,1,5,0,1,1,1,6
8548,38,2.7,1,1,0,1,7,1,4,1,1,9
10608,38,2.9,1,2,0,1,7,1,8,1,1,7
1688,38,3.2,1,3,0,1,5,0,7,1,1,10
7790,38,3.4,1,5,0,1,6,1,8,1,1,4
7150,38,3.5,1,2,0,1,6,1,3,1,1,3
3500,38,3.8,1,5,0,1,6,0,6,1,1,7
2887,38,3.9,1,5,0,1,6,0,2,1,1,3
8706,38,4,1,3,0,1,6,1,7,1,1,5
10039,38,4.1,1,2,0,1,7,1,7,1,1,2
1465,38,4.1,1,5,0,1,6,0,4,1,1,1
5655,38,4.1,1,2,0,1,8,1,1,1,1,3
9213,38,5.3,1,4,0,1,6,1,7,1,1,4
1986,38,5.5,1,3,0,1,6,0,2,1,1,4
8446,38,5.6,1,4,0,1,8,1,2,1,1,3
6918,38,6.1,1,4,0,1,5,1,1,1,1,9
8692,38,6.2,1,1,0,1,5,1,2,1,1,10
5935,38,6.8,1,1,0,1,5,1,7,1,1,0
9839,38,6.9,1,2,0,1,6,1,5,1,1,2
3593,38,7.3,1,3,0,1,6,0,2,1,1,3
7023,38,7.4,1,2,0,1,8,1,1,1,1,2
9598,38,7.4,1,1,0,1,5,1,6,1,1,9
4117,38,7.4,1,4,0,1,5,0,5,1,1,3
787,38,7.5,1,2,0,1,6,0,3,1,1,4
8658,38,7.7,1,1,0,1,8,1,4,1,1,2
7434,38,7.9,1,2,0,1,8,1,3,1,1,0
3351,38,8.4,1,4,0,1,7,0,5,1,1,1
8936,38,9,1,2,0,1,8,1,4,1,1,9
8490,38,9.3,1,2,0,1,8,1,5,1,1,3
4893,38,10,1,2,0,1,5,0,5,1,1,2
4865,38,10.3,1,2,0,1,6,0,4,1,1,3
10392,38,10.6,1,5,0,1,8,1,7,1,1,6
10696,38,10.6,1,1,0,1,8,1,4,1,1,6
8891,38,10.8,1,4,0,1,8,1,8,1,1,3
7770,38,11,1,1,0,1,6,1,6,1,1,2
2604,38,11,1,3,0,1,7,0,8,1,1,4
3248,38,11.4,1,4,0,1,5,0,7,1,1,9
2894,38,1,1,2,0,1,5,0,6,1,1,8
10250,38,5,1,4,0,1,7,1,7,1,1,7
9457,38,3,1,5,0,1,7,1,4,1,1,1
349,38,6,1,1,0,1,5,0,4,1,1,2
1878,38,6,1,1,0,1,6,0,6,1,1,9
2954,38,2,1,2,0,1,5,0,8,1,1,7
7538,38,5,1,3,0,1,6,1,4,1,1,9
8542,38,2,1,4,0,1,7,1,6,1,1,2
56,38,1,1,3,0,1,6,0,2,1,1,7
1420,38,1,1,5,0,1,5,0,2,1,1,3
10504,38,5,1,2,0,1,5,1,3,1,1,3
9119,38,2,1,5,0,1,8,1,4,1,1,0
560,38,2,1,4,0,1,8,0,4,1,1,2
2470,38,7,1,1,0,1,8,0,7,1,1,0
9341,38,7,1,4,0,1,5,1,2,1,1,4
1906,38,2,1,4,0,1,7,0,8,1,1,3
5016,38,6,1,1,0,1,5,0,7,1,1,2
6597,38,1,1,3,0,1,7,1,4,1,1,2
8369,38,2,1,4,0,1,7,1,5,1,1,3
8552,38,4,1,2,0,1,6,1,7,1,1,3
3511,38,1,1,4,0,1,7,0,5,1,1,0
3362,38,4,1,1,0,1,5,0,1,1,1,6
4498,38,1,1,1,0,1,5,0,3,1,1,0
6771,38,2,1,5,0,1,8,1,8,1,1,4
9919,38,7,1,4,0,1,8,1,1,1,1,4
3944,38,5,1,5,0,1,5,0,4,1,1,2
1098,38,1,1,5,0,1,7,0,2,1,1,1
10260,38,7,1,1,0,1,8,1,7,1,1,5
5022,38,5,1,4,0,1,5,0,5,1,1,0
10758,38,3,1,3,0,1,6,1,4,1,1,0
7929,38,7,1,1,0,1,6,1,5,1,1,3
3110,38,5,1,1,0,1,7,0,1,1,1,5
3534,38,3,1,2,0,1,6,0,8,1,1,9
9504,38,1,1,2,0,1,8,1,3,1,1,7
9354,38,5,1,3,0,1,7,1,8,1,1,2
86,38,4,1,5,0,1,7,0,6,1,1,9
5431,38,3,1,5,0,1,6,1,8,1,1,0
2424,38,7,1,3,0,1,8,0,4,1,1,6
4531,38,5,1,4,0,1,5,0,1,1,1,3
5429,38,2,1,1,0,1,5,1,2,1,1,3
5102,38,2,1,4,0,1,5,0,3,1,1,6
4481,38,2,1,1,0,1,6,0,3,1,1,3
6151,38,3,1,2,0,1,6,1,3,1,1,1
7261,38,1,1,2,0,1,7,1,7,1,1,3
283,38,5,1,4,0,1,6,0,6,1,1,0
3132,38,4,1,3,0,1,5,0,3,1,1,0
5582,38,3,1,5,0,1,5,1,6,1,1,7
222,38,5,1,3,0,1,8,0,6,1,1,9
9386,38,6,1,3,0,1,7,1,4,1,1,6
8423,38,5,1,1,0,1,7,1,8,1,1,4
7738,38,4,1,1,0,1,8,1,7,1,1,5
10246,38,1,1,4,0,1,8,1,6,1,1,7
5496,38,4,1,5,0,1,6,1,4,1,1,7
2826,38,2,1,1,0,1,5,0,4,1,1,0
6443,38,2,1,4,0,1,6,1,5,1,1,4
1415,38,3,1,2,0,1,7,0,8,1,1,0
3382,38,2,1,5,0,1,8,0,7,1,1,1
134,38,1,1,3,0,1,8,0,5,1,1,4
2394,38,3,1,5,0,1,7,0,5,1,1,0
7317,38,1,1,3,0,1,7,1,8,1,1,4
9259,38,6,1,1,0,1,7,1,6,1,1,4
796,39,0.2,1,3,0,1,7,0,5,1,1,0
770,39,0.4,1,3,0,1,8,0,6,1,1,8
4764,39,0.9,1,4,0,1,8,0,8,1,1,9
3060,39,1.3,1,4,0,1,7,0,3,1,1,0
4726,39,1.5,1,4,0,1,6,0,5,1,1,0
433,39,2.1,1,2,0,1,8,0,7,1,1,3
6553,39,2.4,1,4,0,1,6,1,4,1,1,1
2158,39,2.5,1,5,0,1,8,0,8,1,1,0
6447,39,2.5,1,5,0,1,8,1,4,1,1,4
2700,39,3.2,1,2,0,1,7,0,2,1,1,0
8240,39,3.5,1,5,0,1,5,1,8,1,1,10
6030,39,3.7,1,4,0,1,6,1,4,1,1,7
9418,39,4.1,1,5,0,1,5,1,5,1,1,4
3036,39,4.7,1,4,0,1,7,0,1,1,1,8
4513,39,4.7,1,1,0,1,6,0,1,1,1,1
4978,39,5.3,1,4,0,1,7,0,5,1,1,6
6588,39,5.5,1,4,0,1,8,1,3,1,1,3
2376,39,5.6,1,5,0,1,5,0,6,1,1,7
8187,39,6.1,1,2,0,1,8,1,8,1,1,4
4408,39,6.5,1,3,0,1,8,0,1,1,1,4
6134,39,6.6,1,1,0,1,8,1,2,1,1,7
10208,39,6.7,1,4,0,1,6,1,2,1,1,3
5276,39,6.9,1,2,0,1,8,0,3,1,1,0
4465,39,7.2,1,3,0,1,7,0,5,1,1,0
626,39,7.4,1,1,0,1,6,0,1,1,1,1
4099,39,7.5,1,4,0,1,8,0,1,1,1,2
9890,39,7.8,1,3,0,1,5,1,2,1,1,1
10725,39,8.3,1,5,0,1,6,1,5,1,1,0
2820,39,8.4,1,2,0,1,6,0,8,1,1,5
4876,39,8.7,1,1,0,1,8,0,4,1,1,4
2285,39,9.2,1,1,0,1,6,0,5,1,1,2
225,39,9.4,1,4,0,1,8,0,4,1,1,0
1920,39,9.7,1,5,0,1,6,0,5,1,1,2
10174,39,9.8,1,4,0,1,8,1,8,1,1,5
3046,39,10,1,4,0,1,6,0,2,1,1,3
665,39,10.1,1,1,0,1,8,0,4,1,1,4
6724,39,11.4,1,5,0,1,8,1,8,1,1,0
9767,39,11.4,1,3,0,1,8,1,5,1,1,4
8362,39,11.7,1,1,0,1,6,1,2,1,1,3
8760,39,11.7,1,2,0,1,8,1,2,1,1,8
2278,39,12,1,4,0,1,6,0,7,1,1,8
1386,39,12,1,1,0,1,5,0,5,1,1,0
9216,39,12,1,4,0,1,6,1,6,1,1,3
6086,39,12,1,3,0,1,7,1,8,1,1,8
6052,39,6,1,4,0,1,5,1,5,1,1,7
786,39,2,1,3,0,1,7,0,6,1,1,8
6938,39,1,1,5,0,1,6,1,5,1,1,4
4648,39,2,1,1,0,1,5,0,6,1,1,8
6990,39,2,1,4,0,1,7,1,8,1,1,0
10158,39,1,1,2,0,1,6,1,3,1,1,3
7268,39,7,1,4,0,1,8,1,8,1,1,5
258,39,7,1,3,0,1,6,0,7,1,1,6
9528,39,6,1,5,0,1,6,1,7,1,1,7
3041,39,1,1,2,0,1,8,0,6,1,1,4
7981,39,1,1,1,0,1,6,1,3,1,1,4
9268,39,5,1,4,0,1,8,1,5,1,1,7
9558,39,3,1,3,0,1,7,1,3,1,1,1
10128,39,6,1,2,0,1,8,1,6,1,1,4
2428,39,7,1,1,0,1,6,0,7,1,1,4
1287,39,5,1,5,0,1,7,0,6,1,1,2
7580,39,7,1,1,0,1,5,1,4,1,1,3
6815,39,7,1,2,0,1,8,1,8,1,1,1
2100,39,2,1,1,0,1,6,0,4,1,1,3
5122,39,4,1,2,0,1,7,0,2,1,1,5
8082,39,3,1,3,0,1,7,1,2,1,1,8
4901,39,1,1,2,0,1,6,0,1,1,1,3
10382,39,2,1,5,0,1,8,1,8,1,1,7
8532,39,7,1,3,0,1,8,1,4,1,1,0
1265,39,6,1,2,0,1,7,0,6,1,1,2
5390,39,3,1,1,0,1,7,0,5,1,1,10
5342,39,7,1,5,0,1,7,0,5,1,1,6
4388,39,2,1,3,0,1,6,0,1,1,1,9
138,39,7,1,2,0,1,7,0,5,1,1,6
8238,39,7,1,5,0,1,8,1,3,1,1,5
10664,39,2,1,5,0,1,6,1,6,1,1,10
3330,39,2,1,5,0,1,6,0,4,1,1,4
3138,39,6,1,4,0,1,6,0,6,1,1,7
3004,39,4,1,2,0,1,6,0,8,1,1,3
7845,39,2,1,4,0,1,6,1,1,1,1,4
9666,39,4,1,3,0,1,7,1,2,1,1,10
2150,39,6,1,5,0,1,6,0,4,1,1,10
5850,39,6,1,1,0,1,5,1,1,1,1,9
6931,39,6,1,2,0,1,7,1,5,1,1,4
358,39,5,1,4,0,1,7,0,4,1,1,4
7180,39,6,1,5,0,1,8,1,2,1,1,2
2286,39,3,1,4,0,1,6,0,3,1,1,4
6481,39,4,1,4,0,1,8,1,8,1,1,4
16,39,3,1,2,0,1,5,0,5,1,1,10
4048,39,5,1,2,0,1,7,0,6,1,1,5
2144,39,4,1,5,0,1,5,0,7,1,1,9
6596,39,1,1,5,0,1,7,1,7,1,1,5
1844,39,5,1,2,0,1,7,0,3,1,1,7
9104,39,7,1,1,0,1,5,1,1,1,1,6
8242,39,6,1,2,0,1,8,1,4,1,1,8
6659,39,6,1,4,0,1,8,1,3,1,1,0
7477,39,5,1,2,0,1,8,1,1,1,1,1
5220,39,6,1,4,0,1,8,0,5,1,1,8
6920,39,7,1,4,0,1,5,1,1,1,1,10
7646,39,2,1,1,0,1,5,1,1,1,1,0
2065,39,5,1,4,0,1,7,0,4,1,1,4
8844,39,1,1,1,0,1,8,1,4,1,1,0
3994,39,2,1,3,0,1,7,0,8,1,1,10
7437,39,3,1,4,0,1,5,1,3,1,1,4
3183,39,3,1,3,0,1,8,0,5,1,1,0
174,39,2,1,1,0,1,5,0,5,1,1,0
246,39,6,1,3,0,1,5,0,8,1,1,8
710,39,1,1,1,0,1,7,0,2,1,1,0
3124,39,5,1,4,0,1,5,0,8,1,1,9
8814,39,4,1,4,0,1,6,1,8,1,1,7
8528,40,0.5,1,5,0,1,5,1,8,1,1,8
8256,40,0.8,1,4,0,1,8,1,1,1,1,0
9927,40,1.6,1,3,0,1,6,1,4,1,1,4
1686,40,1.7,1,5,0,1,5,0,7,1,1,9
6646,40,1.7,1,2,0,1,6,1,7,1,1,7
4634,40,1.8,1,4,0,1,7,0,4,1,1,8
8586,40,2.6,1,4,0,1,5,1,1,1,1,8
2595,40,2.7,1,2,0,1,7,0,6,1,1,3
3508,40,4.2,1,1,0,1,6,0,3,1,1,4
7336,40,4.3,1,4,0,1,5,1,2,1,1,5
2375,40,4.6,1,5,0,1,6,0,3,1,1,4
5921,40,5.1,1,2,0,1,5,1,1,1,1,3
3346,40,5.2,1,2,0,1,6,0,6,1,1,4
6917,40,5.3,1,5,0,1,8,1,5,1,1,2
1885,40,5.7,1,5,0,1,6,0,5,1,1,0
8041,40,5.8,1,1,0,1,5,1,2,1,1,0
9180,40,6.5,1,1,0,1,6,1,4,1,1,1
6136,40,6.6,1,5,0,1,7,1,2,1,1,4
7433,40,6.7,1,5,0,1,7,1,3,1,1,3
6755,40,6.9,1,2,0,1,5,1,2,1,1,2
6399,40,7.4,1,2,0,1,7,1,4,1,1,1
6600,40,7.5,1,1,0,1,5,1,2,1,1,7
8364,40,7.7,1,3,0,1,8,1,5,1,1,9
7605,40,8,1,2,0,1,7,1,2,1,1,1
8045,40,8.1,1,1,0,1,6,1,1,1,1,3
4578,40,8.2,1,3,0,1,6,0,2,1,1,10
10355,40,8.6,1,4,0,1,6,1,2,1,1,4
6522,40,8.9,1,3,0,1,8,1,1,1,1,5
4240,40,9,1,4,0,1,7,0,7,1,1,2
2135,40,9.2,1,3,0,1,7,0,8,1,1,2
5110,40,9.3,1,1,0,1,5,0,3,1,1,2
715,40,10,1,2,0,1,5,0,8,1,1,1
2683,40,10.3,1,5,0,1,5,0,4,1,1,4
7142,40,10.5,1,4,0,1,8,1,4,1,1,10
1996,40,10.5,1,4,0,1,8,0,1,1,1,6
5862,40,10.8,1,1,0,1,6,1,4,1,1,2
7348,40,10.9,1,1,0,1,8,1,5,1,1,10
7010,40,11.5,1,2,0,1,6,1,3,1,1,4
4708,40,12,1,1,0,1,6,0,2,1,1,6
10352,40,7,1,4,0,1,7,1,5,1,1,4
7465,40,4,1,5,0,1,8,1,7,1,1,3
6468,40,5,1,3,0,1,6,1,4,1,1,1
5976,40,4,1,2,0,1,7,1,8,1,1,10
592,40,1,1,4,0,1,8,0,6,1,1,3
675,40,5,1,3,0,1,6,0,5,1,1,1
1037,40,3,1,3,0,1,7,0,2,1,1,3
1868,40,7,1,2,0,1,6,0,5,1,1,10
6119,40,3,1,4,0,1,5,1,1,1,1,3
346,40,3,1,1,0,1,5,0,7,1,1,0
7676,40,7,1,1,0,1,8,1,6,1,1,0
4892,40,4,1,1,0,1,6,0,8,1,1,10
6746,40,3,1,5,0,1,5,1,8,1,1,10
9789,40,3,1,2,0,1,8,1,3,1,1,4
5800,40,5,1,1,0,1,6,1,2,1,1,9
2539,40,5,1,2,0,1,6,0,5,1,1,2
7202,40,1,1,5,0,1,7,1,6,1,1,5
930,40,5,1,5,0,1,6,0,1,1,1,2
9843,40,1,1,2,0,1,6,1,4,1,1,2
8950,40,6,1,2,0,1,7,1,8,1,1,10
8555,40,5,1,1,0,1,5,1,8,1,1,4
7498,40,3,1,4,0,1,6,1,8,1,1,3
5502,40,5,1,4,0,1,5,1,1,1,1,4
9719,40,1,1,3,0,1,6,1,8,1,1,4
760,40,2,1,2,0,1,6,0,6,1,1,9
638,40,6,1,1,0,1,7,0,4,1,1,1
1827,40,1,1,5,0,1,7,0,8,1,1,1
768,40,1,1,3,0,1,5,0,2,1,1,0
7594,40,7,1,3,0,1,8,1,6,1,1,0
7016,40,5,1,3,0,1,7,1,8,1,1,7
7102,40,3,1,2,0,1,6,1,3,1,1,5
2099,40,1,1,3,0,1,6,0,4,1,1,3
5814,40,3,1,2,0,1,5,1,6,1,1,6
9289,40,5,1,1,0,1,7,1,7,1,1,0
2170,40,2,1,4,0,1,7,0,1,1,1,6
121,40,4,1,4,0,1,6,0,2,1,1,4
10422,40,7,1,2,0,1,5,1,4,1,1,7
5972,40,2,1,5,0,1,8,1,6,1,1,9
10203,40,5,1,1,0,1,5,1,8,1,1,1
966,40,5,1,1,0,1,7,0,5,1,1,2
3158,40,5,1,1,0,1,5,0,4,1,1,1
10369,40,4,1,1,0,1,7,1,3,1,1,1
10406,40,6,1,2,0,1,5,1,8,1,1,9
422,40,3,1,3,0,1,8,0,8,1,1,10
4874,40,6,1,1,0,1,7,0,1,1,1,9
2571,40,1,1,5,0,1,7,0,7,1,1,2
10267,40,4,1,2,0,1,8,1,2,1,1,3
3054,40,1,1,3,0,1,5,0,1,1,1,2
1762,40,2,1,5,0,1,7,0,1,1,1,8
4798,40,5,1,4,0,1,6,0,5,1,1,1
5973,40,3,1,4,0,1,7,1,6,1,1,4
720,40,2,1,5,0,1,8,0,8,1,1,0
9670,40,2,1,2,0,1,7,1,5,1,1,5
8013,40,5,1,2,0,1,8,1,4,1,1,0
8914,40,4,1,4,0,1,8,1,3,1,1,2
1433,40,5,1,1,0,1,5,0,4,1,1,3
2566,40,6,1,5,0,1,5,0,6,1,1,5
6778,40,1,1,2,0,1,6,1,1,1,1,9
7746,40,7,1,1,0,1,7,1,8,1,1,1
8001,41,0.4,1,4,0,1,7,1,4,1,1,0
2779,41,1.2,1,2,0,1,6,0,7,1,1,4
6236,41,1.4,1,2,0,1,8,1,1,1,1,9
9936,41,1.8,1,2,0,1,7,1,7,1,1,1
9326,41,2.1,1,2,0,1,8,1,8,1,1,7
1295,41,2.1,1,1,0,1,5,0,7,1,1,2
6864,41,2.3,1,2,0,1,8,1,1,1,1,9
10630,41,2.3,1,3,0,1,6,1,4,1,1,8
6748,41,2.4,1,2,0,1,7,1,3,1,1,1
1154,41,2.5,1,5,0,1,8,0,7,1,1,1
2202,41,2.5,1,4,0,1,6,0,3,1,1,9
9128,41,2.9,1,2,0,1,7,1,1,1,1,5
6630,41,3.2,1,3,0,1,8,1,5,1,1,3
1698,41,3.3,1,5,0,1,6,0,1,1,1,3
9378,41,3.5,1,3,0,1,7,1,4,1,1,6
1028,41,3.8,1,2,0,1,5,0,8,1,1,1
7525,41,3.8,1,3,0,1,8,1,7,1,1,2
7896,41,4.2,1,1,0,1,5,1,8,1,1,5
7430,41,4.3,1,5,0,1,8,1,2,1,1,2
3854,41,4.6,1,5,0,1,7,0,6,1,1,4
10763,41,5,1,4,0,1,7,1,6,1,1,0
2650,41,5.2,1,5,0,1,6,0,8,1,1,10
1413,41,5.5,1,5,0,1,8,0,8,1,1,4
1076,41,5.6,1,1,0,1,6,0,7,1,1,5
7300,41,5.7,1,4,0,1,6,1,7,1,1,9
558,41,5.7,1,1,0,1,5,0,2,1,1,8
8498,41,6.4,1,2,0,1,7,1,6,1,1,6
8401,41,6.7,1,1,0,1,7,1,4,1,1,2
2551,41,6.8,1,5,0,1,8,0,8,1,1,3
9070,41,6.8,1,5,0,1,8,1,1,1,1,10
9098,41,7.2,1,1,0,1,6,1,7,1,1,2
6368,41,7.3,1,4,0,1,6,1,3,1,1,9
2234,41,7.5,1,4,0,1,5,0,7,1,1,4
5643,41,7.5,1,3,0,1,5,1,2,1,1,1
8058,41,7.9,1,3,0,1,8,1,5,1,1,9
1198,41,7.9,1,2,0,1,8,0,2,1,1,6
256,41,8.2,1,2,0,1,8,0,1,1,1,8
5152,41,8.3,1,2,0,1,8,0,5,1,1,0
7735,41,8.5,1,5,0,1,6,1,4,1,1,0
10016,41,9.2,1,5,0,1,6,1,2,1,1,1
10673,41,9.5,1,4,0,1,6,1,3,1,1,0
6916,41,9.8,1,3,0,1,5,1,6,1,1,10
6704,41,10,1,3,0,1,6,1,7,1,1,1
9309,41,10.5,1,5,0,1,6,1,1,1,1,1
6882,41,10.8,1,5,0,1,5,1,5,1,1,8
1376,41,10.8,1,4,0,1,8,0,1,1,1,1
4212,41,11.1,1,4,0,1,5,0,2,1,1,7
2258,41,11.2,1,3,0,1,6,0,4,1,1,5
9918,41,11.5,1,5,0,1,7,1,7,1,1,9
2952,41,11.5,1,2,0,1,6,0,8,1,1,6
4725,41,11.6,1,5,0,1,5,0,7,1,1,4
7731,41,11.7,1,2,0,1,8,1,8,1,1,3
2956,41,11.7,1,3,0,1,6,0,6,1,1,6
10712,41,11.8,1,2,0,1,5,1,2,1,1,8
1074,41,12,1,3,0,1,8,0,4,1,1,6
1271,41,2,1,5,0,1,6,0,5,1,1,2
8618,41,1,1,4,0,1,6,1,8,1,1,8
10714,41,7,1,5,0,1,5,1,8,1,1,0
9621,41,4,1,4,0,1,8,1,7,1,1,4
7466,41,5,1,3,0,1,5,1,6,1,1,4
3917,41,6,1,1,0,1,7,0,6,1,1,4
7072,41,1,1,2,0,1,8,1,3,1,1,8
8815,41,5,1,4,0,1,6,1,4,1,1,0
9052,41,4,1,3,0,1,5,1,8,1,1,5
7803,41,2,1,3,0,1,6,1,8,1,1,3
6202,41,4,1,3,0,1,7,1,4,1,1,2
7724,41,2,1,2,0,1,6,1,7,1,1,5
10110,41,4,1,2,0,1,7,1,1,1,1,4
3182,41,6,1,1,0,1,7,0,4,1,1,8
9745,41,3,1,5,0,1,8,1,1,1,1,4
3911,41,6,1,4,0,1,6,0,5,1,1,3
7458,41,6,1,4,0,1,8,1,1,1,1,1
3005,41,1,1,3,0,1,8,0,3,1,1,1
8688,41,7,1,1,0,1,6,1,5,1,1,6
8394,41,2,1,4,0,1,7,1,6,1,1,2
4460,41,4,1,5,0,1,8,0,5,1,1,10
572,41,1,1,2,0,1,5,0,3,1,1,4
2678,41,4,1,1,0,1,8,0,4,1,1,3
2088,41,7,1,5,0,1,8,0,6,1,1,8
9349,41,4,1,4,0,1,5,1,1,1,1,0
6091,41,6,1,3,0,1,6,1,1,1,1,4
7470,41,7,1,2,0,1,7,1,2,1,1,4
6118,41,7,1,4,0,1,8,1,7,1,1,1
4340,41,5,1,2,0,1,8,0,7,1,1,10
2672,41,4,1,3,0,1,6,0,5,1,1,4
8224,41,6,1,1,0,1,8,1,6,1,1,10
6529,41,1,1,5,0,1,7,1,4,1,1,1
10014,41,7,1,1,0,1,8,1,4,1,1,6
7379,41,3,1,5,0,1,8,1,1,1,1,4
1350,41,7,1,1,0,1,5,0,6,1,1,3
9170,41,4,1,5,0,1,5,1,3,1,1,7
6470,41,2,1,4,0,1,7,1,6,1,1,0
5610,41,5,1,2,0,1,5,1,7,1,1,2
7484,41,6,1,2,0,1,6,1,1,1,1,8
4842,41,2,1,2,0,1,7,0,5,1,1,0
3456,41,6,1,2,0,1,8,0,4,1,1,10
6395,41,7,1,1,0,1,6,1,5,1,1,1
5666,41,4,1,2,0,1,5,1,8,1,1,8
2338,41,5,1,5,0,1,7,0,2,1,1,10
3122,41,4,1,2,0,1,5,0,8,1,1,6
7666,41,1,1,1,0,1,5,1,3,1,1,1
5141,41,2,1,3,0,1,6,0,2,1,1,4
1474,41,5,1,2,0,1,5,0,8,1,1,1
1538,41,4,1,3,0,1,7,0,8,1,1,4
4050,41,6,1,5,0,1,5,0,2,1,1,0
8686,41,4,1,5,0,1,8,1,2,1,1,9
736,41,7,1,5,0,1,8,0,1,1,1,3
3542,41,6,1,4,0,1,8,0,7,1,1,0
734,41,5,1,5,0,1,8,0,7,1,1,3
5360,41,7,1,5,0,1,7,0,7,1,1,3
6040,41,6,1,3,0,1,6,1,5,1,1,3
2804,41,4,1,5,0,1,6,0,6,1,1,8
2951,41,5,1,1,0,1,7,0,5,1,1,3
7611,41,2,1,1,0,1,7,1,5,1,1,2
4330,41,6,1,5,0,1,7,0,4,1,1,7
8366,41,1,1,1,0,1,5,1,5,1,1,8
1201,41,7,1,4,0,1,5,0,1,1,1,2
4487,41,6,1,4,0,1,5,0,1,1,1,0
10517,41,1,1,4,0,1,5,1,6,1,1,0
6572,41,4,1,5,0,1,6,1,8,1,1,2
4118,41,3,1,4,0,1,7,0,3,1,1,4
8454,41,4,1,2,0,1,8,1,1,1,1,1
5247,41,5,1,4,0,1,5,0,1,1,1,2
2452,42,0.4,1,2,0,1,8,0,6,1,1,6
4418,42,0.8,1,5,0,1,8,0,3,1,1,6
639,42,1,1,4,0,1,6,0,1,1,1,2
8125,42,1.3,1,1,0,1,5,1,3,1,1,0
5296,42,1.4,1,3,0,1,5,0,7,1,1,4
119,42,1.6,1,2,0,1,6,0,1,1,1,0
3312,42,1.9,1,2,0,1,7,0,3,1,1,7
9648,42,2,1,3,0,1,6,1,2,1,1,3
9986,42,2.1,1,1,0,1,5,1,3,1,1,0
8104,42,2.2,1,5,0,1,6,1,6,1,1,8
5836,42,2.2,1,2,0,1,8,1,3,1,1,10
6698,42,2.8,1,3,0,1,7,1,3,1,1,8
2702,42,2.9,1,2,0,1,8,0,4,1,1,0
10213,42,2.9,1,4,0,1,7,1,3,1,1,4
8502,42,3.3,1,1,0,1,6,1,3,1,1,5
7103,42,3.7,1,2,0,1,7,1,1,1,1,3
6096,42,3.8,1,4,0,1,5,1,5,1,1,10
10486,42,4.6,1,4,0,1,7,1,2,1,1,1
5683,42,4.7,1,2,0,1,8,1,8,1,1,2
7287,42,4.8,1,2,0,1,7,1,3,1,1,0
10054,42,5.2,1,2,0,1,5,1,6,1,1,6
7854,42,5.2,1,3,0,1,5,1,8,1,1,1
9892,42,5.3,1,4,0,1,6,1,8,1,1,6
8173,42,5.3,1,5,0,1,5,1,6,1,1,2
2980,42,5.5,1,5,0,1,8,0,3,1,1,6
10821,42,5.5,1,1,0,1,6,1,8,1,1,3
1156,42,5.8,1,2,0,1,5,0,3,1,1,9
176,42,6.1,1,5,0,1,5,0,2,1,1,2
5214,42,6.3,1,4,0,1,8,0,4,1,1,8
2006,42,6.4,1,1,0,1,6,0,3,1,1,7
685,42,6.6,1,3,0,1,6,0,5,1,1,1
4062,42,6.9,1,4,0,1,7,0,1,1,1,9
8444,42,7.5,1,5,0,1,5,1,6,1,1,0
9415,42,7.9,1,3,0,1,8,1,3,1,1,1
4722,42,8,1,5,0,1,6,0,6,1,1,8
594,42,8,1,3,0,1,6,0,1,1,1,4
1884,42,8.2,1,3,0,1,8,0,7,1,1,2
3117,42,8.4,1,1,0,1,7,0,2,1,1,0
1410,42,8.5,1,4,0,1,7,0,4,1,1,4
2314,42,9.1,1,2,0,1,6,0,6,1,1,1
6286,42,9.2,1,5,0,1,6,1,1,1,1,0
5858,42,9.9,1,3,0,1,8,1,8,1,1,5
9462,42,10.9,1,4,0,1,6,1,8,1,1,7
6050,42,11,1,1,0,1,6,1,5,1,1,7
8791,42,11.1,1,2,0,1,8,1,7,1,1,0
9658,42,11.7,1,4,0,1,8,1,4,1,1,10
1952,42,11.8,1,2,0,1,6,0,5,1,1,0
4268,42,3,1,4,0,1,5,0,5,1,1,1
9638,42,6,1,4,0,1,7,1,3,1,1,0
7858,42,5,1,2,0,1,5,1,2,1,1,4
9582,42,1,1,4,0,1,7,1,6,1,1,8
9642,42,7,1,5,0,1,8,1,7,1,1,1
3985,42,6,1,1,0,1,7,0,6,1,1,4
9155,42,1,1,5,0,1,5,1,7,1,1,0
6772,42,3,1,1,0,1,8,1,6,1,1,2
4662,42,6,1,1,0,1,7,0,2,1,1,2
1782,42,6,1,1,0,1,5,0,1,1,1,9
6784,42,4,1,5,0,1,6,1,3,1,1,2
7302,42,5,1,3,0,1,8,1,3,1,1,3
8000,42,7,1,4,0,1,8,1,8,1,1,2
2298,42,2,1,5,0,1,6,0,6,1,1,6
9463,42,3,1,4,0,1,5,1,2,1,1,0
1900,42,2,1,5,0,1,6,0,2,1,1,3
7435,42,7,1,1,0,1,5,1,8,1,1,3
6819,42,6,1,5,0,1,8,1,2,1,1,1
1726,42,3,1,2,0,1,5,0,4,1,1,10
2460,42,3,1,2,0,1,5,0,7,1,1,5
4926,42,7,1,1,0,1,7,0,1,1,1,5
114,42,6,1,1,0,1,6,0,2,1,1,5
7165,42,7,1,4,0,1,6,1,7,1,1,4
6676,42,6,1,3,0,1,7,1,4,1,1,5
7092,42,7,1,2,0,1,8,1,1,1,1,8
3022,42,2,1,5,0,1,8,0,6,1,1,4
757,42,1,1,3,0,1,7,0,8,1,1,1
3704,42,2,1,5,0,1,5,0,5,1,1,9
6190,42,6,1,3,0,1,5,1,3,1,1,0
4823,42,5,1,4,0,1,7,0,3,1,1,1
2570,42,1,1,3,0,1,8,0,4,1,1,0
9143,42,6,1,4,0,1,5,1,3,1,1,4
3711,42,1,1,2,0,1,5,0,7,1,1,2
9122,42,2,1,2,0,1,8,1,3,1,1,8
9160,42,6,1,2,0,1,7,1,5,1,1,9
2553,42,1,1,1,0,1,8,0,1,1,1,4
2060,42,3,1,4,0,1,7,0,5,1,1,7
7811,42,7,1,1,0,1,7,1,5,1,1,3
2544,42,6,1,5,0,1,5,0,2,1,1,3
9998,42,7,1,2,0,1,8,1,1,1,1,7
3118,42,1,1,2,0,1,7,0,3,1,1,10
3887,42,5,1,1,0,1,6,0,2,1,1,2
8507,42,2,1,1,0,1,8,1,2,1,1,3
3282,42,1,1,1,0,1,7,0,3,1,1,8
10091,42,5,1,3,0,1,7,1,2,1,1,2
2914,42,1,1,1,0,1,7,0,8,1,1,6
9841,42,1,1,2,0,1,8,1,2,1,1,1
5936,42,4,1,3,0,1,6,1,1,1,1,7
8360,42,1,1,3,0,1,8,1,5,1,1,2
5629,42,7,1,5,0,1,5,1,6,1,1,1
6168,42,2,1,3,0,1,7,1,1,1,1,9
9593,42,3,1,1,0,1,5,1,3,1,1,0
5233,42,2,1,2,0,1,8,0,2,1,1,1
2306,42,3,1,4,0,1,5,0,7,1,1,3
1072,42,1,1,3,0,1,5,0,8,1,1,8
1845,42,1,1,4,0,1,6,0,1,1,1,1
5514,42,2,1,3,0,1,8,1,4,1,1,2
1741,42,6,1,2,0,1,7,0,8,1,1,2
6768,42,7,1,3,0,1,5,1,5,1,1,4
9753,42,3,1,4,0,1,7,1,3,1,1,0
9372,42,6,1,2,0,1,5,1,4,1,1,4
1448,42,5,1,4,0,1,7,0,3,1,1,7
3663,43,0.6,1,2,0,1,7,0,5,1,1,1
5365,43,0.7,1,5,0,1,8,0,8,1,1,1
6789,43,1.7,1,2,0,1,5,1,1,1,1,4
6758,43,1.7,1,2,0,1,8,1,5,1,1,2
2967,43,1.8,1,3,0,1,7,0,8,1,1,4
9412,43,2.1,1,5,0,1,6,1,6,1,1,7
9402,43,2.2,1,5,0,1,7,1,4,1,1,3
8025,43,3,1,3,0,1,5,1,5,1,1,4
7158,43,3.1,1,4,0,1,5,1,8,1,1,9
4854,43,3.5,1,4,0,1,5,0,8,1,1,3
3764,43,4.1,1,1,0,1,7,0,5,1,1,2
9954,43,4.7,1,4,0,1,7,1,7,1,1,0
4205,43,5.2,1,1,0,1,8,0,6,1,1,1
9515,43,5.4,1,2,0,1,8,1,6,1,1,0
798,43,6,1,4,0,1,5,0,6,1,1,0
10057,43,6.1,1,1,0,1,5,1,1,1,1,3
7898,43,6.1,1,1,0,1,6,1,1,1,1,3
8060,43,6.3,1,2,0,1,6,1,4,1,1,1
8931,43,6.4,1,3,0,1,5,1,8,1,1,2
5196,43,6.6,1,2,0,1,5,0,2,1,1,4
6948,43,7,1,4,0,1,6,1,8,1,1,7
7145,43,7.1,1,3,0,1,8,1,5,1,1,2
750,43,7.3,1,5,0,1,8,0,5,1,1,7
9359,43,7.3,1,4,0,1,5,1,3,1,1,4
8904,43,7.4,1,5,0,1,8,1,8,1,1,10
7206,43,7.4,1,4,0,1,6,1,1,1,1,5
10629,43,7.4,1,4,0,1,6,1,6,1,1,2
2738,43,7.7,1,1,0,1,8,0,6,1,1,1
5327,43,7.9,1,1,0,1,7,0,2,1,1,4
9774,43,8,1,2,0,1,7,1,3,1,1,2
6278,43,8,1,2,0,1,6,1,7,1,1,10
4398,43,8,1,3,0,1,6,0,8,1,1,7
10837,43,8.1,1,5,0,1,7,1,5,1,1,4
2844,43,8.1,1,3,0,1,7,0,8,1,1,1
1520,43,9.2,1,2,0,1,6,0,3,1,1,5
10814,43,9.3,1,1,0,1,6,1,1,1,1,1
7285,43,9.9,1,1,0,1,8,1,6,1,1,2
96,43,10,1,5,0,1,5,0,5,1,1,10
4243,43,10.4,1,1,0,1,8,0,1,1,1,3
6810,43,10.4,1,5,0,1,5,1,8,1,1,10
4145,43,10.5,1,2,0,1,8,0,5,1,1,2
8140,43,10.7,1,5,0,1,5,1,1,1,1,7
6297,43,11.6,1,5,0,1,7,1,7,1,1,4
1218,43,11.6,1,3,0,1,5,0,1,1,1,4
10530,43,3,1,5,0,1,6,1,7,1,1,4
2543,43,3,1,2,0,1,5,0,2,1,1,4
2374,43,2,1,1,0,1,6,0,7,1,1,0
8200,43,1,1,2,0,1,8,1,5,1,1,1
210,43,2,1,3,0,1,7,0,1,1,1,2
4036,43,3,1,1,0,1,7,0,3,1,1,8
9202,43,4,1,4,0,1,5,1,5,1,1,8
3287,43,3,1,1,0,1,5,0,6,1,1,3
10600,43,1,1,4,0,1,5,1,8,1,1,6
7280,43,6,1,2,0,1,8,1,6,1,1,5
591,43,1,1,2,0,1,8,0,4,1,1,4
7472,43,5,1,1,0,1,5,1,6,1,1,6
1543,43,6,1,1,0,1,6,0,3,1,1,3
819,43,2,1,3,0,1,5,0,2,1,1,4
7684,43,5,1,4,0,1,7,1,3,1,1,6
236,43,1,1,3,0,1,7,0,7,1,1,4
3626,43,2,1,1,0,1,7,0,2,1,1,10
7820,43,3,1,5,0,1,8,1,2,1,1,4
2740,43,3,1,3,0,1,7,0,7,1,1,5
7259,43,4,1,2,0,1,8,1,6,1,1,3
6739,43,7,1,3,0,1,5,1,8,1,1,0
363,43,2,1,1,0,1,5,0,2,1,1,0
6550,43,3,1,2,0,1,8,1,8,1,1,9
9766,43,7,1,1,0,1,6,1,2,1,1,7
7814,43,1,1,3,0,1,6,1,5,1,1,5
9364,43,1,1,2,0,1,7,1,3,1,1,0
5419,43,6,1,3,0,1,8,1,1,1,1,0
1518,43,2,1,4,0,1,7,0,4,1,1,2
3999,43,6,1,3,0,1,5,0,7,1,1,3
8002,43,6,1,4,0,1,7,1,3,1,1,10
7503,43,3,1,3,0,1,5,1,4,1,1,3
1554,43,6,1,4,0,1,6,0,3,1,1,8
5841,43,7,1,4,0,1,8,1,5,1,1,2
6128,43,1,1,2,0,1,7,1,4,1,1,8
9513,43,6,1,5,0,1,8,1,5,1,1,4
2026,43,5,1,1,0,1,6,0,7,1,1,7
7360,43,3,1,2,0,1,6,1,3,1,1,3
3400,43,2,1,4,0,1,8,0,1,1,1,4
9473,43,4,1,2,0,1,7,1,4,1,1,2
4567,43,7,1,4,0,1,8,0,2,1,1,2
3670,43,7,1,4,0,1,6,0,3,1,1,3
9566,43,6,1,5,0,1,6,1,4,1,1,7
1632,43,2,1,3,0,1,7,0,1,1,1,10
3901,43,3,1,4,0,1,8,0,5,1,1,4
1738,43,5,1,4,0,1,8,0,5,1,1,8
4635,43,2,1,4,0,1,6,0,7,1,1,1
7276,43,2,1,4,0,1,5,1,8,1,1,9
3006,43,7,1,4,0,1,5,0,6,1,1,7
10066,43,3,1,5,0,1,7,1,3,1,1,7
2498,43,4,1,3,0,1,5,0,6,1,1,7
6401,43,1,1,4,0,1,5,1,7,1,1,3
9873,43,6,1,3,0,1,8,1,7,1,1,4
10240,43,5,1,4,0,1,8,1,6,1,1,1
6364,43,7,1,5,0,1,5,1,5,1,1,6
7152,43,7,1,4,0,1,7,1,7,1,1,1
6780,43,7,1,2,0,1,5,1,4,1,1,9
2509,43,3,1,2,0,1,6,0,2,1,1,2
7880,43,5,1,3,0,1,6,1,3,1,1,5
8043,43,2,1,1,0,1,8,1,8,1,1,1
9933,43,7,1,4,0,1,8,1,1,1,1,1
1548,43,4,1,3,0,1,8,0,1,1,1,9
7363,43,7,1,5,0,1,6,1,8,1,1,2
2152,43,7,1,3,0,1,7,0,8,1,1,2
3591,44,0.1,1,3,0,1,6,0,8,1,1,0
1847,44,0.2,1,1,0,1,6,0,2,1,1,4
1770,44,0.2,1,3,0,1,5,0,3,1,1,7
6094,44,0.7,1,3,0,1,8,1,7,1,1,2
1728,44,2,1,4,0,1,8,0,1,1,1,4
2076,44,2.2,1,3,0,1,7,0,2,1,1,9
1052,44,2.3,1,4,0,1,7,0,5,1,1,10
10528,44,2.4,1,5,0,1,5,1,2,1,1,0
2345,44,2.5,1,2,0,1,8,0,3,1,1,0
1048,44,2.6,1,5,0,1,5,0,6,1,1,3
3431,44,2.6,1,3,0,1,7,0,5,1,1,4
1609,44,2.8,1,4,0,1,6,0,5,1,1,0
10546,44,3,1,3,0,1,5,1,7,1,1,9
7973,44,3.1,1,5,0,1,5,1,3,1,1,0
7060,44,3.3,1,3,0,1,8,1,2,1,1,7
1306,44,3.6,1,2,0,1,5,0,6,1,1,0
7952,44,3.9,1,2,0,1,5,1,7,1,1,1
8671,44,4.3,1,2,0,1,8,1,4,1,1,0
2934,44,4.6,1,5,0,1,6,0,3,1,1,8
1651,44,4.9,1,3,0,1,5,0,6,1,1,3
10002,44,4.9,1,3,0,1,6,1,8,1,1,2
6524,44,5,1,4,0,1,7,1,1,1,1,10
197,44,5.1,1,1,0,1,5,0,3,1,1,0
4988,44,5.2,1,4,0,1,7,0,2,1,1,5
7922,44,5.3,1,2,0,1,5,1,6,1,1,7
10833,44,5.5,1,2,0,1,8,1,1,1,1,3
2526,44,5.7,1,2,0,1,8,0,5,1,1,7
5843,44,6.1,1,4,0,1,8,1,8,1,1,3
8294,44,6.2,1,3,0,1,5,1,7,1,1,2
950,44,6.2,1,4,0,1,5,0,2,1,1,1
7134,44,6.8,1,2,0,1,6,1,2,1,1,6
8220,44,6.9,1,3,0,1,5,1,8,1,1,6
7282,44,6.9,1,5,0,1,7,1,3,1,1,3
1856,44,7.4,1,2,0,1,7,0,7,1,1,0
1280,44,7.4,1,1,0,1,8,0,1,1,1,2
5890,44,8.1,1,3,0,1,8,1,1,1,1,4
10101,44,8.3,1,5,0,1,7,1,1,1,1,2
7871,44,8.8,1,2,0,1,7,1,8,1,1,3
6480,44,9,1,5,0,1,7,1,7,1,1,10
8715,44,9.6,1,4,0,1,8,1,2,1,1,1
9437,44,9.8,1,3,0,1,7,1,5,1,1,3
4669,44,10.6,1,4,0,1,5,0,8,1,1,3
10686,44,11.4,1,5,0,1,7,1,5,1,1,6
10200,44,11.8,1,3,0,1,8,1,2,1,1,1
2886,44,3,1,1,0,1,8,0,6,1,1,10
9056,44,5,1,4,0,1,5,1,5,1,1,2
9660,44,6,1,4,0,1,8,1,6,1,1,0
3352,44,4,1,1,0,1,7,0,2,1,1,10
8784,44,2,1,1,0,1,6,1,2,1,1,4
1740,44,3,1,5,0,1,6,0,2,1,1,6
182,44,2,1,2,0,1,7,0,7,1,1,4
9320,44,1,1,5,0,1,6,1,2,1,1,7
8028,44,3,1,4,0,1,6,1,6,1,1,1
3506,44,4,1,5,0,1,5,0,5,1,1,3
9398,44,2,1,1,0,1,5,1,3,1,1,7
4468,44,6,1,4,0,1,7,0,2,1,1,8
961,44,6,1,1,0,1,7,0,8,1,1,1
5308,44,6,1,1,0,1,5,0,6,1,1,4
1893,44,3,1,2,0,1,8,0,5,1,1,4
8895,44,5,1,3,0,1,8,1,5,1,1,3
3818,44,2,1,1,0,1,8,0,8,1,1,3
4769,44,7,1,1,0,1,8,0,2,1,1,1
7354,44,7,1,1,0,1,5,1,3,1,1,9
5270,44,7,1,1,0,1,8,0,8,1,1,0
1485,44,2,1,1,0,1,8,0,8,1,1,1
8019,44,4,1,2,0,1,7,1,5,1,1,2
9280,44,3,1,2,0,1,7,1,6,1,1,6
3335,44,2,1,1,0,1,8,0,6,1,1,3
8493,44,5,1,5,0,1,7,1,6,1,1,4
4370,44,5,1,3,0,1,6,0,4,1,1,0
6870,44,4,1,5,0,1,8,1,6,1,1,10
1000,44,5,1,1,0,1,5,0,7,1,1,7
5104,44,1,1,2,0,1,6,0,1,1,1,6
6902,44,6,1,5,0,1,6,1,4,1,1,7
953,44,6,1,1,0,1,6,0,4,1,1,3
10444,44,6,1,1,0,1,5,1,2,1,1,6
10261,44,1,1,1,0,1,5,1,6,1,1,1
5397,44,4,1,5,0,1,6,0,7,1,1,2
498,44,1,1,2,0,1,8,0,2,1,1,5
4606,44,6,1,1,0,1,8,0,6,1,1,6
1804,44,5,1,5,0,1,7,0,4,1,1,0
10768,44,7,1,5,0,1,7,1,4,1,1,10
2938,44,5,1,5,0,1,7,0,5,1,1,3
7070,44,7,1,4,0,1,8,1,6,1,1,6
7364,44,4,1,1,0,1,6,1,3,1,1,0
10520,44,5,1,2,0,1,7,1,5,1,1,3
8212,44,7,1,5,0,1,5,1,4,1,1,3
7668,44,3,1,4,0,1,5,1,2,1,1,0
4572,44,2,1,5,0,1,6,0,2,1,1,1
10092,44,7,1,5,0,1,6,1,6,1,1,8
9438,44,4,1,5,0,1,8,1,3,1,1,6
9844,44,4,1,2,0,1,7,1,7,1,1,6
936,44,1,1,5,0,1,8,0,1,1,1,3
2538,44,7,1,5,0,1,5,0,6,1,1,5
8098,44,2,1,5,0,1,7,1,3,1,1,4
1412,44,4,1,1,0,1,6,0,8,1,1,3
8108,44,6,1,4,0,1,5,1,8,1,1,6
955,44,2,1,5,0,1,6,0,6,1,1,2
10354,44,6,1,4,0,1,5,1,5,1,1,9
3698,44,1,1,1,0,1,7,0,8,1,1,10
2079,44,6,1,3,0,1,6,0,6,1,1,3
9081,44,5,1,4,0,1,5,1,7,1,1,4
3590,44,7,1,1,0,1,7,0,4,1,1,2
4098,45,0.5,1,3,0,1,8,0,6,1,1,5
2016,45,0.6,1,3,0,1,7,0,3,1,1,6
9486,45,0.8,1,4,0,1,6,1,4,1,1,6
9038,45,0.9,1,1,0,1,8,1,4,1,1,2
5172,45,0.9,1,2,0,1,8,0,8,1,1,6
9450,45,1.1,1,5,0,1,8,1,4,1,1,5
6914,45,1.2,1,3,0,1,7,1,1,1,1,4
4928,45,1.8,1,5,0,1,8,0,3,1,1,7
9300,45,1.8,1,1,0,1,5,1,1,1,1,9
200,45,2.2,1,2,0,1,5,0,6,1,1,10
1891,45,2.2,1,5,0,1,8,0,6,1,1,1
3618,45,2.2,1,5,0,1,6,0,2,1,1,5
9728,45,2.3,1,2,0,1,8,1,5,1,1,7
5615,45,3.2,1,5,0,1,5,1,8,1,1,0
5508,45,3.3,1,5,0,1,7,1,3,1,1,0
5605,45,3.4,1,1,0,1,6,1,5,1,1,4
8588,45,3.8,1,3,0,1,7,1,4,1,1,6
9194,45,3.8,1,1,0,1,5,1,8,1,1,9
7068,45,4.1,1,4,0,1,5,1,7,1,1,5
3930,45,4.1,1,4,0,1,5,0,1,1,1,0
4650,45,4.3,1,2,0,1,7,0,2,1,1,3
4416,45,4.5,1,3,0,1,8,0,2,1,1,5
8180,45,4.6,1,5,0,1,6,1,8,1,1,1
4690,45,4.6,1,1,0,1,5,0,4,1,1,1
9994,45,5.4,1,2,0,1,6,1,7,1,1,0
6318,45,5.8,1,1,0,1,7,1,3,1,1,2
10348,45,5.8,1,1,0,1,5,1,3,1,1,2
7794,45,5.9,1,2,0,1,6,1,6,1,1,8
2657,45,5.9,1,2,0,1,7,0,5,1,1,0
9550,45,6.2,1,1,0,1,6,1,8,1,1,2
3250,45,7.1,1,1,0,1,5,0,6,1,1,2
9604,45,7.4,1,1,0,1,6,1,7,1,1,6
552,45,7.9,1,5,0,1,5,0,2,1,1,4
2602,45,8.1,1,2,0,1,7,0,7,1,1,0
7098,45,8.1,1,2,0,1,6,1,3,1,1,2
4729,45,8.4,1,5,0,1,7,0,3,1,1,2
876,45,8.5,1,5,0,1,7,0,6,1,1,8
3624,45,8.7,1,5,0,1,8,0,2,1,1,2
6718,45,8.8,1,4,0,1,8,1,1,1,1,4
10653,45,8.9,1,2,0,1,6,1,7,1,1,0
10398,45,9.1,1,3,0,1,7,1,4,1,1,0
6498,45,9.4,1,2,0,1,5,1,1,1,1,0
2095,45,9.5,1,4,0,1,7,0,4,1,1,2
8595,45,9.6,1,2,0,1,8,1,1,1,1,4
9492,45,9.7,1,4,0,1,6,1,4,1,1,10
3752,45,9.9,1,2,0,1,7,0,3,1,1,2
3439,45,10.7,1,5,0,1,5,0,2,1,1,0
2433,45,10.8,1,2,0,1,8,0,5,1,1,0
301,45,11.3,1,3,0,1,8,0,3,1,1,3
4674,45,11.6,1,1,0,1,8,0,5,1,1,1
5356,45,11.8,1,3,0,1,8,0,5,1,1,1
10682,45,7,1,4,0,1,5,1,5,1,1,2
6058,45,4,1,1,0,1,5,1,6,1,1,8
5069,45,2,1,4,0,1,6,0,7,1,1,1
2530,45,7,1,3,0,1,5,0,5,1,1,9
6638,45,4,1,5,0,1,6,1,8,1,1,8
7554,45,1,1,2,0,1,5,1,8,1,1,2
8505,45,3,1,1,0,1,5,1,1,1,1,2
2737,45,3,1,3,0,1,8,0,1,1,1,1
6267,45,3,1,5,0,1,8,1,1,1,1,0
992,45,5,1,2,0,1,8,0,7,1,1,0
832,45,2,1,2,0,1,8,0,5,1,1,1
3952,45,3,1,2,0,1,5,0,1,1,1,3
6472,45,6,1,5,0,1,8,1,2,1,1,4
4958,45,4,1,5,0,1,7,0,8,1,1,0
2819,45,5,1,3,0,1,8,0,6,1,1,1
6496,45,6,1,4,0,1,8,1,4,1,1,2
3556,45,7,1,3,0,1,8,0,2,1,1,8
532,45,6,1,3,0,1,7,0,6,1,1,9
6720,45,2,1,4,0,1,5,1,2,1,1,5
1344,45,6,1,4,0,1,7,0,7,1,1,3
4900,45,5,1,1,0,1,6,0,4,1,1,6
7730,45,2,1,2,0,1,6,1,1,1,1,3
4530,45,7,1,5,0,1,5,0,1,1,1,2
9350,45,1,1,4,0,1,6,1,1,1,1,1
1475,45,6,1,2,0,1,5,0,3,1,1,3
5353,45,6,1,4,0,1,7,0,1,1,1,0
4610,45,5,1,3,0,1,6,0,8,1,1,10
3916,45,2,1,3,0,1,7,0,3,1,1,10
10445,45,4,1,5,0,1,5,1,7,1,1,0
6644,45,7,1,1,0,1,6,1,2,1,1,10
6731,45,5,1,2,0,1,6,1,5,1,1,0
9792,45,2,1,5,0,1,5,1,5,1,1,9
6332,45,1,1,3,0,1,8,1,3,1,1,5
5644,45,7,1,1,0,1,5,1,7,1,1,7
4020,45,7,1,3,0,1,7,0,5,1,1,5
10749,45,6,1,2,0,1,6,1,2,1,1,4
7237,45,5,1,2,0,1,8,1,3,1,1,0
5750,45,5,1,1,0,1,5,1,1,1,1,0
6660,45,6,1,2,0,1,7,1,1,1,1,5
5398,45,6,1,5,0,1,7,0,3,1,1,2
9520,45,7,1,4,0,1,8,1,7,1,1,5
3314,45,3,1,3,0,1,6,0,2,1,1,0
5362,45,5,1,5,0,1,7,0,1,1,1,2
10100,45,3,1,5,0,1,6,1,8,1,1,6
6270,45,6,1,2,0,1,7,1,3,1,1,7
3882,45,3,1,2,0,1,5,0,3,1,1,3
10224,45,6,1,3,0,1,8,1,1,1,1,0
6684,45,6,1,4,0,1,6,1,8,1,1,7
1540,45,7,1,3,0,1,7,0,1,1,1,3
408,45,3,1,3,0,1,7,0,4,1,1,8
9096,45,2,1,4,0,1,8,1,3,1,1,10
9094,45,3,1,3,0,1,8,1,7,1,1,5
9909,45,6,1,1,0,1,8,1,7,1,1,2
2066,45,6,1,4,0,1,5,0,7,1,1,2
1042,45,1,1,3,0,1,7,0,6,1,1,10
9482,45,4,1,5,0,1,5,1,5,1,1,7
5867,45,4,1,5,0,1,6,1,4,1,1,3
3432,45,5,1,3,0,1,6,0,4,1,1,7
5930,45,3,1,2,0,1,7,1,3,1,1,5
311,45,4,1,1,0,1,7,0,8,1,1,3
3463,45,6,1,1,0,1,6,0,4,1,1,3
2413,45,3,1,2,0,1,6,0,4,1,1,3
5668,45,6,1,2,0,1,5,1,5,1,1,9
1685,45,7,1,4,0,1,5,0,2,1,1,1
1059,45,3,1,1,0,1,6,0,7,1,1,4
3332,45,6,1,5,0,1,8,0,8,1,1,7
4052,45,6,1,4,0,1,5,0,1,1,1,6
1212,45,3,1,3,0,1,7,0,2,1,1,1
2422,45,2,1,5,0,1,6,0,4,1,1,6
3442,46,0.2,1,4,0,1,8,0,1,1,1,8
658,46,0.3,1,5,0,1,5,0,6,1,1,6
5895,46,0.6,1,5,0,1,5,1,3,1,1,3
4128,46,0.7,1,5,0,1,5,0,7,1,1,3
5499,46,0.7,1,3,0,1,5,1,1,1,1,4
8466,46,0.8,1,5,0,1,5,1,8,1,1,0
5764,46,1.1,1,1,0,1,5,1,3,1,1,1
10236,46,1.2,1,3,0,1,6,1,1,1,1,8
4012,46,1.5,1,3,0,1,8,0,6,1,1,8
8860,46,1.5,1,2,0,1,7,1,4,1,1,5
339,46,1.8,1,4,0,1,6,0,4,1,1,2
2717,46,2.1,1,3,0,1,8,0,4,1,1,0
6464,46,2.2,1,2,0,1,6,1,8,1,1,9
10531,46,2.5,1,4,0,1,5,1,1,1,1,0
3763,46,3.1,1,1,0,1,7,0,4,1,1,4
8984,46,3.8,1,1,0,1,5,1,3,1,1,5
10732,46,3.9,1,3,0,1,7,1,8,1,1,5
9625,46,4.9,1,4,0,1,8,1,8,1,1,0
5056,46,5.4,1,2,0,1,5,0,2,1,1,7
10360,46,5.9,1,2,0,1,5,1,1,1,1,6
1573,46,5.9,1,3,0,1,6,0,7,1,1,3
10574,46,6,1,1,0,1,8,1,3,1,1,3
6413,46,6.3,1,3,0,1,8,1,5,1,1,3
78,46,6.6,1,1,0,1,5,0,6,1,1,3
2528,46,7.6,1,3,0,1,8,0,2,1,1,5
10402,46,7.9,1,4,0,1,8,1,1,1,1,10
4801,46,7.9,1,3,0,1,8,0,2,1,1,1
1979,46,7.9,1,5,0,1,5,0,5,1,1,0
6892,46,8.2,1,3,0,1,8,1,8,1,1,0
5822,46,8.3,1,1,0,1,8,1,4,1,1,1
1511,46,8.6,1,3,0,1,5,0,8,1,1,4
2788,46,8.7,1,2,0,1,7,0,5,1,1,1
10490,46,9.1,1,4,0,1,7,1,5,1,1,10
1559,46,9.1,1,1,0,1,8,0,4,1,1,2
2923,46,9.2,1,5,0,1,6,0,8,1,1,3
9898,46,9.8,1,2,0,1,7,1,2,1,1,7
3493,46,10,1,5,0,1,5,0,4,1,1,3
5980,46,10.2,1,1,0,1,8,1,6,1,1,5
6752,46,10.2,1,2,0,1,7,1,3,1,1,3
59,46,10.6,1,2,0,1,5,0,3,1,1,4
6613,46,10.7,1,2,0,1,6,1,5,1,1,3
1742,46,11.2,1,1,0,1,5,0,7,1,1,10
5181,46,11.2,1,2,0,1,7,0,7,1,1,0
2975,46,11.9,1,4,0,1,6,0,3,1,1,3
4073,46,12,1,4,0,1,6,0,7,1,1,3
5203,46,3,1,5,0,1,5,0,3,1,1,0
6769,46,7,1,1,0,1,7,1,2,1,1,2
2592,46,1,1,1,0,1,6,0,8,1,1,4
6026,46,5,1,4,0,1,7,1,2,1,1,1
8573,46,3,1,4,0,1,6,1,1,1,1,4
7940,46,2,1,4,0,1,8,1,2,1,1,2
624,46,7,1,5,0,1,6,0,8,1,1,8
6782,46,1,1,4,0,1,6,1,4,1,1,1
231,46,7,1,1,0,1,5,0,4,1,1,3
9594,46,4,1,5,0,1,5,1,7,1,1,10
3278,46,7,1,3,0,1,7,0,7,1,1,3
4450,46,2,1,5,0,1,5,0,6,1,1,10
10044,46,2,1,4,0,1,5,1,1,1,1,10
654,46,5,1,5,0,1,7,0,7,1,1,7
4100,46,4,1,1,0,1,6,0,3,1,1,0
1842,46,2,1,1,0,1,7,0,1,1,1,0
10394,46,2,1,5,0,1,5,1,7,1,1,9
4424,46,3,1,4,0,1,5,0,8,1,1,9
6682,46,2,1,3,0,1,5,1,5,1,1,5
6148,46,5,1,2,0,1,8,1,5,1,1,6
5520,46,5,1,3,0,1,6,1,5,1,1,6
4932,46,6,1,4,0,1,5,0,1,1,1,6
10496,46,2,1,4,0,1,7,1,2,1,1,8
316,46,4,1,5,0,1,5,0,1,1,1,5
3904,46,7,1,3,0,1,6,0,8,1,1,3
6277,46,3,1,2,0,1,6,1,5,1,1,2
8496,46,7,1,5,0,1,7,1,4,1,1,7
9542,46,4,1,3,0,1,7,1,6,1,1,2
8834,46,3,1,1,0,1,7,1,4,1,1,4
10018,46,2,1,5,0,1,6,1,8,1,1,8
6280,46,5,1,1,0,1,7,1,1,1,1,8
10277,46,5,1,4,0,1,7,1,2,1,1,0
7949,46,2,1,2,0,1,5,1,6,1,1,0
8866,46,3,1,2,0,1,6,1,4,1,1,6
3519,46,4,1,4,0,1,5,0,6,1,1,0
7862,46,3,1,1,0,1,8,1,1,1,1,10
7170,46,6,1,3,0,1,6,1,7,1,1,10
2184,46,2,1,1,0,1,7,0,6,1,1,6
10364,46,2,1,1,0,1,8,1,2,1,1,0
8390,46,6,1,4,0,1,5,1,5,1,1,6
3589,46,2,1,3,0,1,6,0,3,1,1,2
7682,46,6,1,4,0,1,7,1,3,1,1,1
3161,46,5,1,5,0,1,7,0,5,1,1,4
5131,46,6,1,2,0,1,5,0,8,1,1,1
9232,46,2,1,5,0,1,6,1,8,1,1,4
3265,46,1,1,5,0,1,6,0,6,1,1,0
3514,46,3,1,2,0,1,5,0,2,1,1,6
10564,46,6,1,1,0,1,6,1,5,1,1,9
7564,46,7,1,1,0,1,5,1,6,1,1,5
2147,46,7,1,4,0,1,7,0,5,1,1,0
6542,46,5,1,4,0,1,7,1,5,1,1,8
7732,46,3,1,2,0,1,8,1,3,1,1,2
4824,46,5,1,1,0,1,7,0,1,1,1,2
6661,46,2,1,2,0,1,5,1,3,1,1,1
7621,46,5,1,1,0,1,5,1,6,1,1,1
10107,46,1,1,5,0,1,5,1,8,1,1,1
5788,46,7,1,5,0,1,6,1,6,1,1,4
5779,46,2,1,2,0,1,6,1,6,1,1,1
2765,46,5,1,2,0,1,6,0,3,1,1,2
4427,46,2,1,2,0,1,8,0,1,1,1,1
6803,46,1,1,3,0,1,5,1,8,1,1,1
6947,46,3,1,2,0,1,6,1,7,1,1,0
2561,46,2,1,4,0,1,6,0,8,1,1,4
9706,46,5,1,4,0,1,6,1,7,1,1,10
4045,46,7,1,1,0,1,7,0,6,1,1,3
2697,46,6,1,2,0,1,6,0,7,1,1,0
7238,46,5,1,4,0,1,5,1,7,1,1,6
4095,46,1,1,5,0,1,5,0,2,1,1,0
6943,46,6,1,1,0,1,8,1,8,1,1,4
5812,46,7,1,4,0,1,6,1,3,1,1,5
1050,46,3,1,3,0,1,6,0,6,1,1,10
474,46,5,1,1,0,1,5,0,5,1,1,5
10013,46,3,1,3,0,1,7,1,7,1,1,2
8856,46,1,1,2,0,1,5,1,5,1,1,2
1184,47,0.2,1,4,0,1,7,0,7,1,1,2
2982,47,0.4,1,1,0,1,8,0,2,1,1,10
2745,47,0.6,1,4,0,1,8,0,7,1,1,1
5622,47,1.2,1,2,0,1,5,1,1,1,1,6
2836,47,1.5,1,1,0,1,5,0,6,1,1,6
7902,47,1.8,1,2,0,1,8,1,1,1,1,3
4817,47,2.2,1,3,0,1,7,0,4,1,1,0
2080,47,2.5,1,3,0,1,7,0,6,1,1,8
3168,47,2.6,1,4,0,1,6,0,8,1,1,6
10487,47,3.3,1,4,0,1,8,1,7,1,1,1
4156,47,3.4,1,1,0,1,8,0,7,1,1,0
10742,47,4.1,1,3,0,1,6,1,2,1,1,9
4074,47,4.3,1,3,0,1,5,0,5,1,1,8
1310,47,4.6,1,2,0,1,6,0,3,1,1,9
910,47,4.8,1,3,0,1,8,0,3,1,1,6
5621,47,5,1,3,0,1,7,1,7,1,1,2
4765,47,5.4,1,5,0,1,7,0,4,1,1,4
9368,47,5.7,1,2,0,1,7,1,3,1,1,4
1054,47,7.3,1,1,0,1,5,0,2,1,1,7
8330,47,7.3,1,3,0,1,6,1,8,1,1,8
7829,47,7.5,1,1,0,1,6,1,5,1,1,4
8933,47,7.9,1,3,0,1,5,1,4,1,1,3
395,47,8.6,1,2,0,1,6,0,7,1,1,0
9327,47,8.6,1,5,0,1,5,1,1,1,1,1
2562,47,8.9,1,3,0,1,5,0,8,1,1,10
2009,47,9,1,3,0,1,7,0,8,1,1,3
4244,47,9.2,1,3,0,1,5,0,6,1,1,5
8770,47,9.4,1,4,0,1,7,1,1,1,1,8
1759,47,9.6,1,3,0,1,6,0,1,1,1,3
5700,47,10,1,4,0,1,8,1,1,1,1,0
8830,47,10.4,1,4,0,1,6,1,7,1,1,4
9332,47,10.5,1,5,0,1,8,1,6,1,1,2
6827,47,10.7,1,5,0,1,8,1,8,1,1,3
3726,47,11.4,1,4,0,1,7,0,3,1,1,1
704,47,11.4,1,2,0,1,8,0,4,1,1,8
2878,47,11.5,1,3,0,1,5,0,8,1,1,9
2020,47,11.6,1,3,0,1,7,0,7,1,1,8
3622,47,11.7,1,5,0,1,8,0,4,1,1,4
9505,47,11.8,1,4,0,1,8,1,6,1,1,3
9235,47,11.9,1,3,0,1,8,1,2,1,1,0
5827,47,12,1,2,0,1,6,1,7,1,1,0
3809,47,1,1,1,0,1,8,0,5,1,1,0
5458,47,6,1,1,0,1,8,1,7,1,1,1
2149,47,4,1,3,0,1,6,0,5,1,1,2
6484,47,7,1,1,0,1,5,1,3,1,1,9
4454,47,2,1,5,0,1,6,0,5,1,1,6
3648,47,4,1,4,0,1,7,0,4,1,1,8
3098,47,7,1,1,0,1,5,0,2,1,1,0
3903,47,6,1,4,0,1,8,0,6,1,1,0
4316,47,3,1,3,0,1,8,0,1,1,1,10
6327,47,7,1,2,0,1,6,1,2,1,1,2
3011,47,4,1,2,0,1,5,0,1,1,1,1
6678,47,1,1,2,0,1,8,1,2,1,1,3
7226,47,5,1,1,0,1,8,1,3,1,1,2
8644,47,5,1,1,0,1,6,1,8,1,1,5
1250,47,7,1,4,0,1,6,0,1,1,1,2
5529,47,6,1,2,0,1,6,1,7,1,1,3
9744,47,3,1,4,0,1,7,1,8,1,1,1
2921,47,6,1,2,0,1,8,0,6,1,1,0
5382,47,2,1,5,0,1,8,0,4,1,1,6
10426,47,4,1,5,0,1,6,1,8,1,1,7
7986,47,1,1,4,0,1,5,1,4,1,1,8
784,47,5,1,2,0,1,8,0,4,1,1,4
9414,47,6,1,1,0,1,6,1,8,1,1,6
6307,47,2,1,2,0,1,5,1,7,1,1,3
4520,47,3,1,3,0,1,7,0,2,1,1,7
890,47,4,1,2,0,1,6,0,2,1,1,8
8272,47,3,1,5,0,1,5,1,8,1,1,0
1532,47,7,1,5,0,1,5,0,3,1,1,2
10733,47,2,1,1,0,1,5,1,2,1,1,1
10678,47,7,1,3,0,1,8,1,2,1,1,8
2568,47,3,1,5,0,1,6,0,2,1,1,1
10347,47,1,1,3,0,1,6,1,5,1,1,2
4768,47,2,1,5,0,1,5,0,4,1,1,5
40,47,1,1,4,0,1,6,0,8,1,1,7
10030,47,1,1,1,0,1,7,1,5,1,1,1
1512,47,6,1,5,0,1,7,0,2,1,1,9
9316,47,6,1,3,0,1,5,1,7,1,1,3
7076,47,7,1,3,0,1,7,1,2,1,1,9
2023,47,1,1,5,0,1,7,0,4,1,1,2
10830,47,3,1,5,0,1,8,1,7,1,1,6
8594,47,4,1,4,0,1,6,1,2,1,1,3
7994,47,7,1,4,0,1,8,1,8,1,1,0
620,47,7,1,4,0,1,5,0,7,1,1,8
874,47,7,1,2,0,1,6,0,3,1,1,7
10572,47,6,1,1,0,1,6,1,4,1,1,6
10582,47,3,1,1,0,1,8,1,1,1,1,5
9351,47,1,1,5,0,1,6,1,3,1,1,3
5859,47,4,1,3,0,1,6,1,2,1,1,4
5967,47,2,1,3,0,1,5,1,2,1,1,0
2274,47,7,1,3,0,1,5,0,6,1,1,10
7457,47,4,1,5,0,1,7,1,1,1,1,4
9294,47,6,1,4,0,1,8,1,1,1,1,8
8319,47,6,1,5,0,1,5,1,6,1,1,0
9014,47,3,1,2,0,1,7,1,5,1,1,1
8764,47,4,1,4,0,1,5,1,7,1,1,2
6226,47,6,1,5,0,1,6,1,2,1,1,6
10350,47,1,1,1,0,1,5,1,5,1,1,9
7440,47,3,1,3,0,1,6,1,6,1,1,6
8262,47,2,1,2,0,1,8,1,7,1,1,0
4300,47,4,1,5,0,1,7,0,6,1,1,3
8968,47,7,1,3,0,1,5,1,2,1,1,3
5928,47,5,1,2,0,1,8,1,2,1,1,4
7630,47,2,1,2,0,1,5,1,2,1,1,10
450,47,4,1,1,0,1,6,0,3,1,1,4
404,47,3,1,5,0,1,5,0,1,1,1,5
3876,47,5,1,4,0,1,7,0,7,1,1,1
150,47,3,1,5,0,1,7,0,1,1,1,4
1333,47,7,1,3,0,1,6,0,2,1,1,3
460,47,4,1,2,0,1,6,0,8,1,1,9
10058,47,5,1,3,0,1,5,1,6,1,1,5
2666,47,6,1,2,0,1,5,0,3,1,1,0
1594,47,2,1,3,0,1,6,0,6,1,1,6
9293,47,2,1,5,0,1,5,1,3,1,1,4
1262,47,1,1,1,0,1,6,0,4,1,1,2
1442,47,6,1,1,0,1,5,0,5,1,1,8
270,48,0.2,1,3,0,1,7,0,8,1,1,9
5951,48,0.3,1,1,0,1,8,1,3,1,1,0
4196,48,0.5,1,3,0,1,8,0,4,1,1,5
9046,48,0.6,1,1,0,1,7,1,8,1,1,9
6888,48,1,1,4,0,1,7,1,8,1,1,4
4624,48,1.3,1,5,0,1,5,0,6,1,1,10
9851,48,1.7,1,4,0,1,5,1,4,1,1,1
5392,48,1.8,1,5,0,1,6,0,5,1,1,8
10543,48,1.9,1,5,0,1,5,1,5,1,1,1
7231,48,2,1,5,0,1,6,1,7,1,1,0
3483,48,2.1,1,3,0,1,7,0,4,1,1,4
7265,48,2.2,1,5,0,1,5,1,3,1,1,4
148,48,2.2,1,1,0,1,6,0,4,1,1,1
9172,48,2.6,1,5,0,1,5,1,5,1,1,4
2453,48,2.8,1,5,0,1,5,0,8,1,1,3
1282,48,3.6,1,2,0,1,5,0,4,1,1,8
1676,48,4,1,5,0,1,8,0,6,1,1,3
7448,48,4.4,1,5,0,1,8,1,8,1,1,2
1926,48,6,1,1,0,1,5,0,5,1,1,6
4990,48,6,1,4,0,1,6,0,2,1,1,0
6196,48,6.6,1,5,0,1,5,1,5,1,1,10
10202,48,7.1,1,1,0,1,7,1,8,1,1,6
1036,48,7.6,1,1,0,1,5,0,5,1,1,8
9688,48,7.7,1,5,0,1,6,1,5,1,1,4
6754,48,7.8,1,5,0,1,5,1,1,1,1,9
274,48,7.8,1,5,0,1,5,0,6,1,1,7
8641,48,7.9,1,3,0,1,6,1,3,1,1,0
8368,48,8.4,1,3,0,1,5,1,4,1,1,1
228,48,8.5,1,1,0,1,8,0,8,0,1,2
1797,48,8.8,1,3,0,1,5,0,3,0,1,3
10230,48,8.9,1,2,0,1,6,1,5,0,1,2
8190,48,9.9,1,1,0,1,6,1,3,0,1,3
9444,48,10,1,2,0,1,6,1,1,0,1,0
9708,48,10.2,1,3,0,1,8,1,7,0,1,9
7320,48,10.4,1,1,0,1,8,1,3,0,1,0
1452,48,10.4,1,5,0,1,8,0,4,0,1,6
9074,48,10.8,1,3,0,1,6,1,6,0,1,2
7184,48,11.2,1,4,0,1,7,1,1,0,1,10
9623,48,11.6,1,1,0,1,6,1,2,0,1,2
9201,48,3,1,1,0,1,8,1,1,0,1,4
672,48,2,1,2,0,1,6,0,2,0,1,6
8949,48,4,1,3,0,1,8,1,4,0,1,1
4584,48,3,1,1,0,1,8,0,5,0,1,1
9540,48,4,1,2,0,1,5,1,4,0,1,4
8903,48,5,1,4,0,1,8,1,8,0,1,4
2852,48,5,1,3,0,1,7,0,7,0,1,2
9556,48,7,1,1,0,1,5,1,8,0,1,3
104,48,6,1,5,0,1,8,0,5,0,1,1
2957,48,1,1,2,0,1,6,0,2,0,1,3
9867,48,7,1,3,0,1,6,1,7,0,1,0
9619,48,6,1,4,0,1,5,1,6,0,1,3
5494,48,2,1,1,0,1,6,1,4,0,1,10
9813,48,2,1,2,0,1,6,1,1,0,1,4
8589,48,1,1,5,0,1,8,1,5,0,1,3
9974,48,4,1,2,0,1,6,1,6,0,1,7
3533,48,6,1,1,0,1,7,0,6,0,1,1
8440,48,4,1,1,0,1,5,1,5,0,1,8
2902,48,5,1,4,0,1,6,0,5,0,1,2
9790,48,7,1,3,0,1,5,1,2,0,1,4
1999,48,7,1,2,0,1,7,0,4,0,1,1
5826,48,4,1,5,0,1,7,1,4,0,1,10
1014,48,7,1,5,0,1,5,0,3,0,1,10
1552,48,5,1,5,0,1,7,0,2,0,1,10
8471,48,7,1,3,0,1,7,1,7,0,1,4
2802,48,7,1,1,0,1,6,0,5,0,1,4
240,48,7,1,4,0,1,6,0,8,0,1,1
5613,48,3,1,1,0,1,6,1,1,0,1,4
24,48,7,1,1,0,1,5,0,7,0,1,2
6376,48,3,1,1,0,1,5,1,3,0,1,7
5982,48,5,1,2,0,1,7,1,2,0,1,7
10212,48,3,1,5,0,1,5,1,6,0,1,6
5182,48,7,1,2,0,1,7,0,1,0,1,3
1472,48,2,1,2,0,1,7,0,3,0,1,10
4553,48,3,1,3,0,1,7,0,6,0,1,4
10262,48,4,1,4,0,1,7,1,4,0,1,5
10145,48,3,1,5,0,1,5,1,7,0,1,0
4780,48,2,1,3,0,1,5,0,7,0,1,0
7884,48,3,1,5,0,1,5,1,4,0,1,7
6235,48,4,1,2,0,1,7,1,2,0,1,4
5786,48,5,1,2,0,1,5,1,1,0,1,7
6580,48,6,1,2,0,1,5,1,5,0,1,4
4374,48,2,1,1,0,1,6,0,5,0,1,4
3610,48,3,1,3,0,1,5,0,7,0,1,4
7444,48,2,1,2,0,1,8,1,6,0,1,7
9937,48,4,1,3,0,1,6,1,8,0,1,2
10285,48,6,1,3,0,1,5,1,4,0,1,2
8158,48,3,1,1,0,1,6,1,1,0,1,3
5386,48,5,1,4,0,1,8,0,7,0,1,0
4404,48,4,1,4,0,1,7,0,6,0,1,2
3802,48,5,1,4,0,1,8,0,1,0,1,2
6418,48,2,1,3,0,1,7,1,3,0,1,0
6727,48,4,1,2,0,1,6,1,1,0,1,1
4784,48,7,1,1,0,1,6,0,6,0,1,8
7681,48,4,1,4,0,1,8,1,2,0,1,0
9672,48,2,1,5,0,1,5,1,3,0,1,10
6552,48,5,1,2,0,1,6,1,7,0,1,0
6521,48,5,1,4,0,1,7,1,2,0,1,1
8640,48,5,1,5,0,1,7,1,7,0,1,5
3396,48,7,1,1,0,1,6,0,6,0,1,8
6224,48,1,1,4,0,1,6,1,7,0,1,7
3675,48,5,1,5,0,1,7,0,5,0,1,0
4644,48,6,1,5,0,1,8,0,3,0,1,5
9547,48,1,1,4,0,1,7,1,7,0,1,0
9760,48,6,1,5,0,1,5,1,3,0,1,1
663,48,2,1,4,0,1,7,0,1,0,1,1
9186,48,5,1,5,0,1,7,1,3,0,1,5
9662,48,4,1,2,0,1,6,1,2,0,1,8
758,48,6,1,2,0,1,5,0,4,0,1,2
3383,49,0.4,1,5,0,1,8,0,7,0,1,0
9187,49,0.5,1,2,0,1,8,1,6,0,1,0
7441,49,0.6,1,4,0,1,6,1,4,0,1,3
9404,49,0.6,1,2,0,1,6,1,6,0,1,2
1849,49,0.6,1,3,0,1,8,0,2,0,1,3
5404,49,1,1,2,0,1,6,0,1,0,1,10
7030,49,1,1,3,0,1,7,1,3,0,1,7
3786,49,1.2,1,5,0,1,8,0,6,0,1,10
7596,49,1.7,1,3,0,1,7,1,6,0,1,5
9006,49,1.8,1,1,0,1,5,1,6,0,1,4
9715,49,1.9,1,4,0,1,8,1,3,0,1,1
7087,49,2.6,1,3,0,1,8,1,8,0,1,3
84,49,2.6,1,2,0,1,6,0,8,0,1,6
4359,49,2.9,1,5,0,1,6,0,1,0,1,4
2519,49,3.2,1,4,0,1,7,0,4,0,1,0
7978,49,3.3,1,2,0,1,5,1,8,0,1,3
3684,49,3.6,1,3,0,1,8,0,8,0,1,3
5256,49,3.6,1,3,0,1,5,0,3,0,1,8
3202,49,3.8,1,5,0,1,8,0,6,0,1,0
1111,49,4,1,1,0,1,8,0,6,0,1,3
6284,49,4.3,1,5,0,1,5,1,8,0,1,6
537,49,4.3,1,2,0,1,8,0,5,0,1,2
6442,49,4.9,1,2,0,1,8,1,1,0,1,1
5418,49,5,1,1,0,1,8,0,5,0,1,9
8069,49,5.6,1,2,0,1,7,1,8,0,1,0
465,49,5.6,1,4,0,1,6,0,8,0,1,2
10335,49,5.6,1,4,0,1,5,1,6,0,1,1
226,49,5.8,1,1,0,1,5,0,1,0,1,4
6098,49,6,1,3,0,1,6,1,8,0,1,6
5742,49,6,1,1,0,1,8,1,8,0,1,3
1476,49,6,1,5,0,1,5,0,2,0,1,1
9461,49,6.6,1,1,0,1,7,1,5,0,1,1
8202,49,6.7,1,3,0,1,8,1,8,0,1,9
5752,49,6.7,1,2,0,1,7,1,6,0,1,0
5506,49,7.4,1,2,0,1,5,1,2,0,1,7
8464,49,7.5,1,2,0,1,7,1,8,0,1,10
10541,49,7.9,1,5,0,1,5,1,5,0,1,2
5050,49,8.8,1,2,0,1,8,0,4,0,1,8
3210,49,9,1,4,0,1,8,0,5,0,1,6
10424,49,9.4,1,5,0,1,6,1,8,0,1,6
10802,49,10.4,1,4,0,1,5,1,5,0,1,5
2478,49,10.5,1,1,0,1,6,0,7,0,1,10
2620,49,10.8,1,3,0,1,5,0,4,0,1,9
2574,49,10.9,1,5,0,1,5,0,2,0,1,10
858,49,11.1,1,5,0,1,5,0,6,0,1,3
2108,49,11.2,1,5,0,1,5,0,3,0,1,6
9575,49,11.2,1,3,0,1,8,1,5,0,1,0
2516,49,11.4,1,3,0,1,7,0,1,0,1,2
3797,49,6,1,2,0,1,7,0,5,0,1,4
2798,49,2,1,3,0,1,8,0,3,0,1,2
2619,49,1,1,1,0,1,7,0,3,0,1,2
3616,49,2,1,1,0,1,8,0,2,0,1,7
7552,49,7,1,3,0,1,8,1,5,0,1,8
8076,49,6,1,3,0,1,8,1,2,0,1,0
5820,49,2,1,3,0,1,6,1,3,0,1,4
3187,49,6,1,5,0,1,5,0,3,0,1,2
4566,49,2,1,4,0,1,6,0,2,0,1,6
2542,49,6,1,4,0,1,5,0,2,0,1,3
7295,49,2,1,2,0,1,5,1,5,0,1,1
10786,49,1,1,1,0,1,8,1,3,0,1,8
4412,49,5,1,4,0,1,8,0,2,0,1,0
3271,49,1,1,3,0,1,8,0,8,0,1,4
10368,49,2,1,4,0,1,5,1,4,0,1,0
7812,49,2,1,4,0,1,8,1,6,0,1,1
4325,49,4,1,4,0,1,7,0,8,0,1,0
8282,49,6,1,1,0,1,7,1,5,0,1,1
5350,49,1,1,3,0,1,7,0,7,0,1,3
3731,49,4,1,2,0,1,5,0,5,0,1,2
9735,49,3,1,2,0,1,6,1,7,0,1,2
6004,49,2,1,4,0,1,7,1,8,0,1,7
6090,49,4,1,4,0,1,5,1,3,0,1,6
9748,49,4,1,2,0,1,7,1,6,0,1,8
6569,49,5,1,1,0,1,5,1,3,0,1,1
3092,49,4,1,5,0,1,7,0,2,0,1,4
3896,49,6,1,2,0,1,6,0,5,0,1,5
3984,49,4,1,4,0,1,8,0,5,0,1,6
4898,49,6,1,3,0,1,5,0,5,0,1,5
5977,49,3,1,4,0,1,8,1,7,0,1,1
2882,49,5,1,2,0,1,7,0,6,0,1,0
9193,49,3,1,2,0,1,5,1,4,0,1,0
8479,49,6,1,4,0,1,5,1,5,0,1,3
2524,49,3,1,5,0,1,8,0,7,0,1,9
2189,49,6,1,4,0,1,7,0,3,0,1,4
8205,49,4,1,2,0,1,5,1,4,0,1,1
10492,49,6,1,2,0,1,5,1,2,0,1,5
8324,49,4,1,2,0,1,8,1,5,0,1,6
5416,49,7,1,1,0,1,8,0,5,0,1,6
816,49,2,1,5,0,1,6,0,8,0,1,10
5490,49,4,1,4,0,1,8,1,6,0,1,9
9369,49,1,1,1,0,1,6,1,2,0,1,0
2608,49,5,1,3,0,1,7,0,5,0,1,10
4943,49,3,1,2,0,1,7,0,4,0,1,1
4169,49,4,1,1,0,1,5,0,1,0,1,1
6362,49,3,1,2,0,1,8,1,3,0,1,8
183,49,1,1,1,0,1,6,0,7,0,1,3
296,49,6,1,4,0,1,6,0,7,0,1,8
1576,49,5,1,4,0,1,8,0,8,0,1,9
5046,49,2,1,4,0,1,5,0,1,0,1,8
4090,49,5,1,5,0,1,7,0,3,0,1,7
2390,49,1,1,5,0,1,5,0,6,0,1,2
7936,49,4,1,4,0,1,7,1,6,0,1,2
6476,49,2,1,4,0,1,8,1,1,0,1,3
6509,49,3,1,3,0,1,5,1,4,0,1,4
6821,49,2,1,4,0,1,6,1,1,0,1,4
5889,49,5,1,4,0,1,7,1,1,0,1,1
6149,49,5,1,2,0,1,7,1,7,0,1,2
544,49,3,1,1,0,1,6,0,7,0,1,0
5855,49,3,1,2,0,1,7,1,6,0,1,0
9864,49,1,1,4,0,1,8,1,3,0,1,9
4174,49,4,1,2,0,1,5,0,4,0,1,8
1214,49,6,1,1,0,1,5,0,3,0,1,6
8484,49,7,1,1,0,1,7,1,2,0,1,8
8857,49,3,1,5,0,1,8,1,7,0,1,2
5958,49,3,1,5,0,1,7,1,4,0,1,6
4411,49,4,1,3,0,1,8,0,6,0,1,1
4421,50,0.3,1,1,0,1,7,0,8,0,1,2
3502,50,0.5,1,5,0,1,7,0,8,0,1,2
2758,50,0.7,1,5,0,1,6,0,7,0,1,2
6001,50,0.7,1,2,0,1,6,1,7,0,1,2
8659,50,1.2,1,5,0,1,5,1,5,0,1,4
469,50,1.5,1,3,0,1,5,0,2,0,1,3
4304,50,1.6,1,5,0,1,5,0,1,0,1,10
9136,50,1.9,1,1,0,1,8,1,5,0,1,3
12,50,2.1,1,5,0,1,5,0,8,0,1,10
8198,50,2.3,1,3,0,1,7,1,3,0,1,1
6456,50,2.4,1,2,0,1,6,1,4,0,1,5
4534,50,2.5,1,4,0,1,6,0,6,0,1,0
6303,50,2.6,1,1,0,1,5,1,2,0,1,3
9127,50,2.8,1,4,0,1,5,1,4,0,1,2
4187,50,2.9,1,5,0,1,8,0,6,0,1,0
2780,50,2.9,1,3,0,1,7,0,6,0,1,5
6706,50,2.9,1,1,0,1,6,1,3,0,1,2
10810,50,2.9,1,5,0,1,8,1,3,0,1,3
6388,50,3.1,1,4,0,1,7,1,6,0,1,5
7686,50,3.3,1,5,0,1,8,1,8,0,1,8
8086,50,3.9,1,3,0,1,8,1,7,0,1,9
8772,50,4.1,1,2,0,1,7,1,5,0,1,9
8302,50,4.3,1,4,0,1,8,1,7,0,1,10
146,50,4.5,1,4,0,1,8,0,5,0,1,1
2838,50,5.1,1,3,0,1,6,0,3,0,1,1
2695,50,5.2,1,5,0,1,8,0,1,0,1,2
5072,50,5.9,1,1,0,1,6,0,6,0,1,6
1921,50,6.2,1,2,0,1,8,0,6,0,1,3
5372,50,6.3,1,4,0,1,6,0,1,0,1,7
5924,50,6.3,1,2,0,1,7,1,4,0,1,4
3597,50,7,1,4,0,1,6,0,5,0,1,3
8248,50,7,1,1,0,1,7,1,8,0,1,7
4055,50,7.2,1,3,0,1,8,0,8,0,1,2
3964,50,7.2,1,5,0,1,8,0,8,0,1,6
8515,50,7.3,1,4,0,1,7,1,5,0,1,1
4458,50,7.8,1,5,0,1,7,0,2,0,1,4
7613,50,7.9,1,1,0,1,8,1,1,0,1,2
8255,50,7.9,1,5,0,1,6,1,4,0,1,4
5549,50,8.1,1,3,0,1,8,1,4,0,1,4
562,50,8.2,1,2,0,1,6,0,5,0,1,10
3012,50,8.2,1,4,0,1,8,0,6,0,1,10
4580,50,8.5,1,2,0,1,5,0,1,0,1,4
5320,50,8.7,1,4,0,1,6,0,3,0,1,3
1114,50,8.7,1,1,0,1,6,0,5,0,1,5
7432,50,8.7,1,5,0,1,7,1,7,0,1,1
5184,50,9,1,4,0,1,5,0,2,0,1,8
3399,50,9.9,1,4,0,1,5,0,7,0,1,4
77,50,10.4,1,4,0,1,6,0,5,0,1,3
3255,50,10.6,1,1,0,1,7,0,5,0,1,3
9673,50,10.8,1,1,0,1,7,1,1,0,1,3
2729,50,10.8,1,3,0,1,7,0,1,0,1,2
4044,50,11.3,1,5,0,1,8,0,5,0,1,8
3246,50,11.3,1,4,0,1,6,0,5,0,1,5
3787,50,12,1,4,0,1,5,0,1,0,1,2
5230,50,4,1,3,0,1,6,0,7,0,1,6
2204,50,5,1,5,0,1,7,0,4,0,1,9
566,50,7,1,2,0,1,5,0,1,0,1,4
5995,50,6,1,2,0,1,8,1,5,0,1,4
2626,50,5,1,3,0,1,5,0,2,0,1,8
6854,50,3,1,4,0,1,5,1,6,0,1,5
5541,50,5,1,5,0,1,7,1,8,0,1,4
4026,50,1,1,1,0,1,7,0,1,0,1,10
5880,50,3,1,1,0,1,8,1,2,0,1,7
8789,50,5,1,3,0,1,6,1,3,0,1,1
7762,50,5,1,1,0,1,5,1,6,0,1,2
1614,50,1,1,2,0,1,5,0,2,0,1,7
9262,50,3,1,3,0,1,7,1,8,0,1,2
3285,50,2,1,4,0,1,7,0,8,0,1,4
10029,50,6,1,3,0,1,7,1,6,0,1,1
5526,50,2,1,1,0,1,7,1,3,0,1,3
6374,50,5,1,4,0,1,7,1,7,0,1,10
7523,50,7,1,5,0,1,8,1,7,0,1,4
7660,50,6,1,2,0,1,8,1,1,0,1,4
8422,50,7,1,2,0,1,7,1,5,0,1,7
10301,50,1,1,4,0,1,5,1,5,0,1,3
10791,50,6,1,2,0,1,5,1,5,0,1,1
10634,50,7,1,5,0,1,7,1,7,0,1,1
406,50,2,1,1,0,1,8,0,8,0,1,5
690,50,5,1,4,0,1,8,0,6,0,1,0
2208,50,2,1,3,0,1,6,0,1,0,1,10
6582,50,5,1,5,0,1,7,1,3,0,1,3
6794,50,4,1,1,0,1,8,1,6,0,1,10
5706,50,2,1,2,0,1,6,1,3,0,1,8
3325,50,3,1,2,0,1,5,0,6,0,1,1
6653,50,5,1,2,0,1,8,1,1,0,1,0
3796,50,4,1,1,0,1,8,0,4,0,1,8
6880,50,2,1,3,0,1,5,1,8,0,1,3
4461,50,7,1,2,0,1,5,0,5,0,1,2
10193,50,4,1,5,0,1,8,1,7,0,1,0
6560,50,3,1,3,0,1,5,1,3,0,1,8
8432,50,1,1,4,0,1,7,1,6,0,1,9
9940,50,5,1,3,0,1,5,1,3,0,1,7
1247,50,6,1,4,0,1,5,0,3,0,1,1
6272,50,7,1,3,0,1,5,1,6,0,1,3
6587,50,7,1,3,0,1,7,1,8,0,1,4
4364,50,2,1,1,0,1,8,0,8,0,1,10
5801,50,5,1,1,0,1,7,1,7,0,1,3
2706,50,3,1,5,0,1,6,0,2,0,1,7
7164,50,4,1,5,0,1,6,1,5,0,1,4
2897,50,7,1,2,0,1,7,0,3,0,1,0
6673,50,6,1,5,0,1,8,1,7,0,1,1
5126,50,4,1,3,0,1,5,0,3,0,1,10
2138,50,1,1,1,0,1,7,0,3,0,1,6
8616,50,6,1,5,0,1,6,1,4,0,1,5
9500,50,1,1,5,0,1,8,1,2,0,1,1
94,50,2,1,2,0,1,8,0,4,0,1,1
3914,50,3,1,2,0,1,8,0,8,0,1,2
5720,50,1,1,5,0,1,8,1,6,0,1,3
7693,50,5,1,2,0,1,7,1,1,0,1,2
9068,50,3,1,3,0,1,7,1,5,0,1,7
6792,50,2,1,5,0,1,8,1,3,0,1,6
5845,50,2,1,1,0,1,7,1,8,0,1,4
8327,50,4,1,2,0,1,7,1,1,0,1,4
6184,50,2,1,4,0,1,5,1,8,0,1,4
2421,50,2,1,4,0,1,6,0,6,0,1,3
1166,50,6,1,5,0,1,7,0,4,0,1,7
8250,50,2,1,1,0,1,7,1,1,0,1,2
1956,50,2,1,4,0,1,7,0,8,0,1,1
6668,50,7,1,4,0,1,5,1,5,0,1,2
5992,50,1,1,4,0,1,8,1,1,0,1,3
4622,50,2,1,4,0,1,5,0,3,0,1,3
766,51,0.4,0,3,0,1,6,0,8,0,1,1
7376,51,1.2,0,2,0,1,8,1,1,0,1,5
5618,51,1.3,0,4,0,1,5,1,2,0,1,2
8538,51,1.3,0,5,0,1,5,1,6,0,1,9
2958,51,1.8,0,2,0,1,8,0,6,0,1,10
7694,51,2.1,0,3,0,1,5,1,3,0,1,7
5282,51,2.6,0,2,0,1,8,0,4,0,1,6
3692,51,3.3,0,2,0,1,5,0,7,0,1,3
396,51,3.3,0,2,0,1,8,0,6,0,1,0
10280,51,3.7,0,1,0,1,7,1,4,0,1,4
5749,51,3.9,0,5,0,1,8,1,5,0,1,1
3224,51,4.1,0,1,0,1,7,0,8,0,1,10
4314,51,4.3,0,4,0,1,7,0,2,0,1,10
7592,51,4.4,0,3,0,1,5,1,7,0,1,0
8318,51,4.7,0,3,0,1,5,1,5,0,1,8
9990,51,4.7,0,1,0,1,7,1,1,0,1,5
2130,51,4.8,0,2,0,1,5,0,7,0,1,6
22,51,4.9,0,1,0,1,6,0,4,0,1,6
440,51,5,0,3,0,1,6,0,7,0,1,9
2284,51,5.1,0,2,0,1,5,0,8,0,1,2
4703,51,5.2,0,2,0,1,5,0,3,0,1,4
4920,51,5.2,0,4,0,1,6,0,6,0,1,10
3261,51,5.3,0,5,0,1,7,0,6,0,1,2
10073,51,5.7,0,1,0,1,5,1,4,0,1,3
8228,51,5.7,0,4,0,1,5,1,6,0,1,5
10731,51,5.9,0,2,0,1,8,1,6,0,1,1
326,51,6.4,0,1,0,1,8,0,3,0,1,6
8951,51,6.5,0,3,0,1,5,1,2,0,1,2
7618,51,7.3,0,3,0,1,7,1,5,0,1,10
1246,51,7.3,0,4,0,1,7,0,2,0,1,10
8027,51,7.4,0,3,0,1,6,1,5,0,1,1
7025,51,7.7,0,2,0,1,7,1,5,0,1,3
5572,51,8,0,4,0,1,5,1,8,0,1,2
6013,51,8,0,1,0,1,8,1,3,0,1,0
3153,51,8.1,0,1,0,1,8,0,7,0,1,4
2142,51,8.5,0,2,0,1,8,0,3,0,1,7
9709,51,8.6,0,1,0,1,5,1,6,0,1,0
2454,51,8.6,0,4,0,1,8,0,7,0,1,3
10484,51,9,0,3,0,1,5,1,2,0,1,6
9875,51,9.5,0,3,0,1,6,1,7,0,1,2
2525,51,9.6,0,4,0,1,7,0,6,0,1,4
9464,51,9.8,0,4,0,1,6,1,6,0,1,1
8268,51,10,0,3,0,1,7,1,2,0,1,10
2807,51,10.1,0,3,0,1,7,0,8,0,1,3
5984,51,10.6,0,5,0,1,8,1,4,0,1,7
7787,51,10.9,0,1,0,1,5,1,5,0,1,1
8852,51,11,0,5,0,1,7,1,1,0,1,7
5578,51,11.1,0,2,0,1,6,1,2,0,1,3
4822,51,11.1,0,2,0,1,6,0,3,0,1,10
10556,51,11.2,0,2,0,1,6,1,6,0,1,8
4912,51,11.4,0,3,0,1,5,0,3,0,1,4
10706,51,11.5,0,2,0,1,8,1,7,0,1,2
8804,51,11.5,0,4,0,1,8,1,4,0,1,4
10545,51,11.7,0,1,0,1,8,1,5,0,1,3
3024,51,7,0,1,0,1,5,0,5,0,1,2
1773,51,1,0,1,0,1,6,0,3,0,1,4
6348,51,3,0,4,0,1,7,1,2,0,1,10
6612,51,4,0,2,0,1,6,1,8,0,1,3
10478,51,7,0,5,0,1,5,1,6,0,1,3
1664,51,2,0,1,0,1,6,0,7,0,1,8
5642,51,1,0,5,0,1,5,1,3,0,1,9
3770,51,5,0,1,0,1,8,0,2,0,1,10
318,51,1,0,3,0,1,8,0,7,0,1,7
7612,51,4,0,2,0,1,7,1,8,0,1,7
7860,51,3,0,1,0,1,7,1,2,0,1,6
3753,51,3,0,3,0,1,8,0,2,0,1,4
6275,51,6,0,5,0,1,8,1,4,0,1,4
9996,51,4,0,3,0,1,5,1,6,0,1,10
7149,51,3,0,3,0,1,5,1,3,0,1,0
6847,51,1,0,1,0,1,7,1,8,0,1,3
8590,51,6,0,4,0,1,6,1,3,0,1,6
397,51,5,0,3,0,1,8,0,5,0,1,1
2532,51,6,0,3,0,1,5,0,2,0,1,10
6828,51,3,0,3,0,1,5,1,7,0,1,7
1992,51,3,0,5,0,1,5,0,1,0,1,6
7834,51,7,0,2,0,1,6,1,6,0,1,4
4474,51,6,0,4,0,1,6,0,3,0,1,0
370,51,4,0,5,0,1,6,0,2,0,1,4
1526,51,4,0,1,0,1,5,0,7,0,1,1
7003,51,1,0,1,0,1,6,1,7,0,1,4
4206,51,2,0,1,0,1,7,0,6,0,1,7
348,51,6,0,5,0,1,7,0,6,0,1,3
10076,51,4,0,3,0,1,6,1,7,0,1,7
6627,51,2,0,4,0,1,8,1,8,0,1,3
7867,51,7,0,1,0,1,8,1,2,0,1,0
978,51,3,0,3,0,1,5,0,8,0,1,0
6968,51,5,0,1,0,1,8,1,1,0,1,5
4814,51,2,0,2,0,1,5,0,3,0,1,7
10357,51,2,0,4,0,1,5,1,8,0,1,0
9724,51,2,0,2,0,1,8,1,5,0,1,10
998,51,6,0,4,0,1,5,0,1,0,1,6
8900,51,5,0,3,0,1,8,1,5,0,1,8
5844,51,4,0,3,0,1,6,1,3,0,1,9
10864,51,3,0,1,0,1,7,1,3,0,1,7
9930,51,2,0,1,0,1,7,1,3,0,1,0
5569,51,4,0,4,0,1,8,1,4,0,1,2
5451,51,1,0,3,0,1,5,1,5,0,1,2
8527,51,1,0,1,0,1,7,1,8,0,1,3
9197,51,1,0,4,0,1,6,1,5,0,1,4
4956,51,6,0,3,0,1,5,0,1,0,1,8
1582,51,3,0,3,0,1,8,0,4,0,1,7
9973,51,4,0,4,0,1,8,1,3,0,1,4
9796,51,7,0,4,0,1,6,1,4,0,1,10
214,51,3,0,5,0,1,5,0,7,0,1,4
2336,51,3,0,5,0,1,8,0,8,0,1,3
9985,51,1,0,4,0,1,8,1,7,0,1,0
7970,51,7,0,5,0,1,7,1,3,0,1,1
4276,51,3,0,2,0,1,7,0,7,0,1,10
2792,51,5,0,5,0,1,8,0,7,0,1,3
10550,51,2,0,3,0,1,5,1,6,0,1,8
7014,51,6,0,3,0,1,7,1,7,0,1,2
7818,51,2,0,5,0,1,6,1,3,0,1,0
8420,51,3,0,5,0,1,8,1,7,0,1,2
1974,51,6,0,2,0,1,5,0,3,0,1,4
6475,51,4,0,4,0,1,8,1,4,0,1,0
6211,51,5,0,3,0,1,8,1,6,0,1,4
2218,51,5,0,2,0,1,5,0,1,0,1,3
8983,51,5,0,5,0,1,8,1,5,0,1,4
8329,51,2,0,2,0,1,6,1,3,0,1,0
132,51,1,0,5,0,1,7,0,4,0,1,4
2126,51,6,0,1,0,1,8,0,1,0,1,6
5232,51,7,0,1,0,1,7,0,3,0,1,1
9118,51,5,0,1,0,1,7,1,3,0,1,7
3768,51,2,0,5,0,1,5,0,5,0,1,9
5076,51,4,0,4,0,1,5,0,7,0,1,5
5718,51,3,0,2,0,1,7,1,8,0,1,9
726,52,1,0,1,0,1,6,0,1,0,1,1
5292,52,1.1,0,3,0,1,7,0,8,0,1,5
2682,52,1.3,0,5,0,1,7,0,4,0,1,5
6954,52,1.3,0,3,0,1,6,1,4,0,1,8
9091,52,1.5,0,2,0,1,8,1,4,0,1,0
5573,52,1.5,0,5,0,1,5,1,2,0,1,4
8433,52,1.7,0,4,0,1,8,1,6,0,1,4
7667,52,1.8,0,2,0,1,5,1,1,0,1,0
7775,52,2,0,1,0,1,8,1,3,0,1,1
7034,52,2.3,0,1,0,1,6,1,2,0,1,3
3326,52,2.5,0,5,0,1,5,0,6,0,1,1
7856,52,2.7,0,2,0,1,7,1,5,0,1,3
4023,52,2.7,0,2,0,1,6,0,1,0,1,0
7663,52,2.8,0,2,0,1,7,1,7,0,1,1
9653,52,3.3,0,3,0,1,6,1,4,0,1,3
1943,52,3.7,0,3,0,1,8,0,2,0,1,0
9521,52,4.1,0,3,0,1,6,1,6,0,1,3
388,52,4.2,0,2,0,1,6,0,7,0,1,10
1264,52,4.5,0,2,0,1,6,0,5,0,1,2
1364,52,4.6,0,4,0,1,7,0,4,0,1,5
5833,52,4.7,0,3,0,1,7,1,5,0,1,4
3126,52,5.1,0,1,0,1,5,0,3,0,1,5
5632,52,6.2,0,1,0,1,8,1,4,0,1,3
2690,52,7,0,5,0,1,8,0,7,0,1,8
7704,52,7,0,1,0,1,7,1,7,0,1,3
7865,52,7.6,0,4,0,1,8,1,3,0,1,2
367,52,8.2,0,5,0,1,8,0,6,0,1,1
5301,52,8.2,0,4,0,1,8,0,8,0,1,4
5893,52,8.5,0,3,0,1,8,1,2,0,1,3
8592,52,8.6,0,4,0,1,8,1,3,0,1,6
2137,52,8.7,0,1,0,1,6,0,2,0,1,4
2022,52,8.7,0,1,0,1,7,0,6,0,1,2
3606,52,9.2,0,3,0,1,6,0,2,0,1,0
3934,52,9.3,0,3,0,1,7,0,2,0,1,1
5608,52,9.8,0,2,0,1,7,1,3,0,1,4
5884,52,10.5,0,4,0,1,5,1,5,0,1,0
4319,52,10.7,0,1,0,1,5,0,4,0,1,4
5047,52,11.4,0,5,0,1,8,0,6,0,1,3
1338,52,11.7,0,2,0,1,5,0,3,0,1,8
2629,52,2,0,1,0,1,8,0,2,0,1,2
8442,52,4,0,1,0,1,8,1,8,0,1,5
3156,52,4,0,3,0,1,6,0,3,0,1,5
8150,52,6,0,4,0,1,8,1,6,0,1,0
6293,52,3,0,1,0,1,7,1,2,0,1,3
6622,52,1,0,4,0,1,8,1,8,0,1,5
8357,52,1,0,4,0,1,6,1,2,0,1,2
2680,52,5,0,4,0,1,5,0,3,0,1,7
2623,52,2,0,5,0,1,8,0,1,0,1,0
2378,52,7,0,2,0,1,8,0,1,0,1,6
7240,52,6,0,3,0,1,5,1,5,0,1,5
7266,52,1,0,4,0,1,5,1,1,0,1,2
1439,52,4,0,5,0,1,8,0,1,0,1,2
9036,52,4,0,5,0,1,8,1,6,0,1,1
445,52,3,0,4,0,1,6,0,5,0,1,3
3848,52,1,0,4,0,1,7,0,8,0,1,8
516,52,1,0,4,0,1,7,0,6,0,1,4
9786,52,5,0,1,0,1,6,1,4,0,1,1
1527,52,2,0,4,0,1,8,0,4,0,1,2
3109,52,1,0,4,0,1,5,0,7,0,1,1
6672,52,7,0,1,0,1,7,1,4,0,1,1
4056,52,7,0,5,0,1,5,0,7,0,1,9
9793,52,6,0,1,0,1,5,1,7,0,1,2
7797,52,2,0,5,0,1,8,1,1,0,1,1
6124,52,5,0,3,0,1,7,1,8,0,1,5
3728,52,2,0,3,0,1,6,0,2,0,1,5
7507,52,7,0,5,0,1,6,1,1,0,1,0
3222,52,2,0,5,0,1,7,0,5,0,1,6
7307,52,7,0,2,0,1,5,1,7,0,1,0
2494,52,6,0,1,0,1,7,0,1,0,1,3
10312,52,1,0,3,0,1,5,1,3,0,1,10
3565,52,7,0,4,0,1,7,0,3,0,1,4
954,52,6,0,1,0,1,6,0,1,0,1,7
7848,52,2,0,4,0,1,5,1,3,0,1,10
3924,52,2,0,2,0,1,7,0,1,0,1,8
898,52,5,0,1,0,1,7,0,8,0,1,8
8264,52,2,0,4,0,1,8,1,6,0,1,6
1628,52,5,0,1,0,1,7,0,3,0,1,7
10456,52,5,0,2,0,1,8,1,1,0,1,0
4846,52,2,0,2,0,1,6,0,3,0,1,0
8457,52,2,0,3,0,1,6,1,5,0,1,2
7032,52,6,0,5,0,1,5,1,6,0,1,6
6922,52,6,0,5,0,1,8,1,1,0,1,7
2789,52,1,0,4,0,1,8,0,6,0,1,4
9420,52,4,0,3,0,1,6,1,3,0,1,5
9522,52,3,0,2,0,1,7,1,8,0,1,2
4287,52,3,0,5,0,1,6,0,7,0,1,0
10372,52,4,0,2,0,1,5,1,5,0,1,1
6256,52,1,0,5,0,1,5,1,7,0,1,1
5344,52,4,0,5,0,1,6,0,1,0,1,3
6444,52,6,0,3,0,1,7,1,2,0,1,4
10418,53,0.4,0,1,0,1,5,1,8,0,1,1
2790,53,0.6,0,2,0,1,8,0,7,0,1,2
6,53,1.9,0,3,0,1,5,0,2,0,1,1
8295,53,1.9,0,2,0,1,6,1,2,0,1,4
5585,53,2.1,0,2,0,1,5,1,3,0,1,1
6420,53,2.5,0,2,0,1,6,1,8,0,1,8
4636,53,2.6,0,2,0,1,5,0,2,0,1,2
3076,53,2.7,0,4,0,1,8,0,4,0,1,9
7650,53,3.2,0,2,0,1,8,1,2,0,1,3
1196,53,3.6,0,3,0,1,8,0,8,0,1,2
7297,53,3.7,0,5,0,1,8,1,3,0,1,4
8818,53,3.9,0,1,0,1,6,1,2,0,1,3
9278,53,4.1,0,2,0,1,7,1,5,0,1,10
4758,53,5.1,0,4,0,1,8,0,7,0,1,7
7928,53,5.1,0,3,0,1,5,1,2,0,1,1
4390,53,5.6,0,1,0,1,7,0,6,0,1,9
10602,53,5.9,0,4,0,1,7,1,3,0,1,8
3212,53,5.9,0,2,0,1,7,0,8,0,1,0
8758,53,6.5,0,3,0,1,5,1,4,0,1,9
3215,53,6.9,0,3,0,1,6,0,2,0,1,4
7659,53,7.3,0,4,0,1,6,1,8,0,1,4
3874,53,8.2,0,2,0,1,8,0,5,0,1,2
2264,53,8.3,0,2,0,1,7,0,2,0,1,10
9304,53,9,0,1,0,1,6,1,6,0,1,4
6788,53,9.4,0,5,0,1,5,1,7,0,1,0
4630,53,10.2,0,1,0,1,7,0,8,0,1,1
5347,53,10.5,0,3,0,1,6,0,6,0,1,0
4142,53,10.6,0,2,0,1,5,0,3,0,1,3
3509,53,12,0,5,0,1,7,0,7,0,1,2
6889,53,4,0,4,0,1,8,1,5,0,1,4
8960,53,4,0,2,0,1,5,1,6,0,1,4
3204,53,1,0,1,0,1,8,0,4,0,1,2
4146,53,6,0,3,0,1,8,0,5,0,1,2
4383,53,2,0,3,0,1,8,0,6,0,1,0
9780,53,3,0,4,0,1,7,1,1,0,1,7
9509,53,1,0,2,0,1,8,1,8,0,1,4
984,53,1,0,2,0,1,7,0,6,0,1,6
3672,53,2,0,3,0,1,7,0,2,0,1,5
1537,53,1,0,5,0,1,8,0,4,0,1,2
4835,53,4,0,1,0,1,8,0,6,0,1,4
153,53,3,0,5,0,1,6,0,5,0,1,1
8007,53,7,0,4,0,1,6,1,5,0,1,3
4396,53,3,0,5,0,1,5,0,6,0,1,10
2911,53,5,0,5,0,1,5,0,2,0,1,3
3080,53,6,0,3,0,1,6,0,1,0,1,7
9051,53,1,0,3,0,1,7,1,1,0,1,0
1104,53,6,0,3,0,1,6,0,8,0,1,2
7706,53,2,0,5,0,1,7,1,1,0,1,10
4499,53,1,0,4,0,1,7,0,4,0,1,4
10719,53,5,0,2,0,1,5,1,3,0,1,3
3392,53,7,0,1,0,1,7,0,5,0,1,3
525,53,2,0,3,0,1,8,0,2,0,1,3
5818,53,4,0,4,0,1,6,1,3,0,1,3
4068,53,4,0,2,0,1,8,0,2,0,1,7
7544,53,2,0,4,0,1,6,1,6,0,1,8
9840,53,1,0,5,0,1,5,1,1,0,1,9
973,53,3,0,5,0,1,8,0,5,0,1,1
643,53,2,0,3,0,1,6,0,6,0,1,1
3280,53,2,0,4,0,1,5,0,7,0,1,10
8736,53,2,0,5,0,1,8,1,4,0,1,9
5525,53,3,0,3,0,1,7,1,6,0,1,2
38,53,1,0,5,0,1,5,0,1,0,1,8
9020,53,4,0,4,0,1,5,1,4,0,1,7
1568,53,1,0,4,0,1,7,0,4,0,1,2
4573,53,3,0,5,0,1,8,0,3,0,1,0
929,53,1,0,5,0,1,7,0,8,0,1,2
4802,53,7,0,1,0,1,8,0,8,0,1,2
886,53,5,0,4,0,1,5,0,6,0,1,3
8281,53,3,0,2,0,1,8,1,6,0,1,3
652,53,7,0,4,0,1,5,0,8,0,1,1
548,53,1,0,1,0,1,8,0,1,0,1,0
756,53,7,0,2,0,1,8,0,4,0,1,0
797,53,1,0,2,0,1,5,0,8,0,1,4
746,53,4,0,1,0,1,8,0,2,0,1,6
6619,53,3,0,5,0,1,7,1,1,0,1,2
10847,53,4,0,2,0,1,6,1,3,0,1,1
3160,53,6,0,3,0,1,7,0,5,0,1,7
7939,53,6,0,3,0,1,8,1,2,0,1,1
10468,53,1,0,3,0,1,7,1,5,0,1,7
5201,53,6,0,3,0,1,5,0,2,0,1,4
443,53,7,0,4,0,1,8,0,6,0,1,0
3087,53,2,0,2,0,1,6,0,7,0,1,3
9574,53,4,0,3,0,1,7,1,3,0,1,1
2567,53,7,0,3,0,1,6,0,4,0,1,0
4919,53,4,0,1,0,1,8,0,8,0,1,2
7314,53,4,0,3,0,1,7,1,1,0,1,4
2810,53,5,0,5,0,1,5,0,2,0,1,0
6912,53,5,0,1,0,1,6,1,5,0,1,9
4535,53,4,0,2,0,1,8,0,8,0,1,0
8153,53,5,0,2,0,1,8,1,3,0,1,3
8955,53,5,0,5,0,1,8,1,2,0,1,3
849,53,3,0,3,0,1,6,0,4,0,1,4
2999,53,3,0,5,0,1,8,0,3,0,1,4
4542,53,3,0,5,0,1,6,0,7,0,1,2
5616,53,4,0,3,0,1,8,1,6,0,1,0
5932,53,1,0,3,0,1,5,1,4,0,1,0
10345,53,5,0,4,0,1,6,1,4,0,1,2
5902,53,5,0,4,0,1,6,1,1,0,1,8
1978,53,7,0,3,0,1,8,0,2,0,1,10
10041,54,0.5,0,1,0,1,8,1,7,0,1,2
1358,54,0.7,0,4,0,1,6,0,5,0,1,3
6866,54,0.9,0,2,0,1,5,1,8,0,1,4
7051,54,1.2,0,4,0,1,6,1,2,0,1,3
2920,54,1.2,0,2,0,1,8,0,3,0,1,0
3706,54,1.3,0,5,0,1,6,0,4,0,1,7
9842,54,1.4,0,4,0,1,5,1,6,0,1,6
2402,54,2,0,3,0,1,7,0,2,0,1,0
6934,54,2.1,0,2,0,1,6,1,2,0,1,10
5990,54,2.3,0,3,0,1,8,1,8,0,1,1
7606,54,2.3,0,1,0,1,8,1,3,0,1,5
10098,54,2.4,0,1,0,1,7,1,4,0,1,1
7449,54,2.7,0,3,0,1,7,1,4,0,1,3
6561,54,3.1,0,1,0,1,6,1,1,0,1,2
4816,54,3.6,0,4,0,1,7,0,5,0,1,6
8168,54,4.2,0,5,0,1,7,1,2,0,1,0
2644,54,4.5,0,2,0,1,8,0,7,0,1,1
3002,54,5,0,5,0,1,6,0,1,0,1,4
4702,54,5.2,0,3,0,1,6,0,5,0,1,0
8880,54,5.4,0,4,0,1,7,1,5,0,1,1
9865,54,6.3,0,5,0,1,5,1,6,0,1,4
1600,54,6.3,0,3,0,1,8,0,3,0,1,2
7293,54,6.7,0,2,0,1,5,1,7,0,1,0
7872,54,6.8,0,2,0,1,5,1,5,0,1,7
10511,54,6.8,0,4,0,1,7,1,4,0,1,2
8603,54,7,0,5,0,1,5,1,4,0,1,4
10156,54,7.1,0,2,0,1,6,1,1,0,1,1
6178,54,7.3,0,1,0,1,5,1,3,0,1,3
7945,54,7.3,0,4,0,1,7,1,6,0,1,0
616,54,7.3,0,2,0,1,6,0,6,0,1,1
2098,54,7.4,0,4,0,1,8,0,6,0,1,9
7112,54,7.7,0,5,0,1,5,1,3,0,1,9
7930,54,7.8,0,2,0,1,5,1,3,0,1,10
7409,54,7.9,0,3,0,1,6,1,6,0,1,0
62,54,7.9,0,4,0,1,7,0,2,0,1,7
9117,54,8,0,1,0,1,8,1,1,0,1,1
8222,54,8,0,2,0,1,5,1,8,0,1,0
10508,54,8.1,0,5,0,1,5,1,4,0,1,7
1812,54,8.8,0,5,0,1,6,0,3,0,1,0
2924,54,9.3,0,2,0,1,7,0,8,0,1,4
5316,54,9.4,0,1,0,1,5,0,6,0,1,7
9530,54,9.5,0,2,0,1,8,1,5,0,1,4
1012,54,10.1,0,5,0,1,8,0,2,0,1,0
791,54,10.2,0,5,0,1,8,0,3,0,1,2
6930,54,10.4,0,5,0,1,6,1,6,0,1,3
2300,54,11.1,0,2,0,1,6,0,6,0,1,3
4746,54,11.2,0,1,0,1,6,0,7,0,1,10
10121,54,11.5,0,1,0,1,5,1,8,0,1,3
8172,54,11.9,0,2,0,1,6,1,3,0,1,4
2671,54,1,0,2,0,1,5,0,7,0,1,1
3101,54,6,0,4,0,1,8,0,2,0,1,0
5594,54,2,0,4,0,1,7,1,6,0,1,1
8316,54,4,0,5,0,1,5,1,4,0,1,8
5156,54,2,0,3,0,1,6,0,7,0,1,4
10516,54,7,0,2,0,1,6,1,3,0,1,1
5134,54,6,0,2,0,1,7,0,5,0,1,8
10694,54,6,0,3,0,1,5,1,1,0,1,7
7832,54,6,0,3,0,1,8,1,5,0,1,6
4860,54,3,0,2,0,1,5,0,1,0,1,3
1424,54,7,0,3,0,1,7,0,6,0,1,4
7938,54,1,0,2,0,1,5,1,2,0,1,0
916,54,7,0,5,0,1,5,0,4,0,1,10
7800,54,7,0,3,0,1,8,1,7,0,1,0
4680,54,4,0,1,0,1,8,0,5,0,1,6
9586,54,6,0,1,0,1,7,1,8,0,1,7
9250,54,5,0,1,0,1,5,1,6,0,1,1
6062,54,7,0,2,0,1,6,1,5,0,1,8
5590,54,2,0,3,0,1,8,1,6,0,1,4
7324,54,2,0,2,0,1,6,1,1,0,1,5
8738,54,3,0,1,0,1,8,1,3,0,1,10
1341,54,3,0,5,0,1,6,0,7,0,1,3
10589,54,7,0,4,0,1,7,1,1,0,1,4
9502,54,3,0,4,0,1,7,1,8,0,1,2
2222,54,4,0,3,0,1,7,0,3,0,1,2
5816,54,1,0,1,0,1,6,1,6,0,1,10
5832,54,2,0,5,0,1,6,1,6,0,1,2
1347,54,1,0,3,0,1,6,0,3,0,1,4
3638,54,6,0,5,0,1,7,0,7,0,1,0
3154,54,7,0,5,0,1,5,0,7,0,1,2
5294,54,2,0,3,0,1,7,0,6,0,1,3
4438,54,6,0,5,0,1,8,0,5,0,1,5
4705,54,6,0,2,0,1,7,0,6,0,1,4
7960,54,3,0,3,0,1,5,1,2,0,1,7
1872,54,1,0,3,0,1,8,0,8,0,1,8
8696,54,2,0,5,0,1,6,1,6,0,1,1
584,54,3,0,2,0,1,7,0,2,0,1,8
6285,54,1,0,5,0,1,6,1,5,0,1,3
694,54,1,0,5,0,1,6,0,7,0,1,5
4225,54,2,0,1,0,1,7,0,6,0,1,1
4306,54,4,0,1,0,1,8,0,1,0,1,3
569,54,3,0,5,0,1,5,0,8,0,1,4
7326,54,5,0,3,0,1,6,1,3,0,1,10
10308,54,3,0,5,0,1,8,1,2,0,1,9
5223,54,6,0,1,0,1,5,0,3,0,1,0
4144,54,7,0,5,0,1,8,0,7,0,1,8
4235,54,7,0,4,0,1,5,0,4,0,1,4
7806,54,4,0,1,0,1,8,1,7,0,1,2
2692,54,3,0,3,0,1,7,0,3,0,1,7
8089,54,5,0,4,0,1,6,1,5,0,1,2
10415,54,3,0,1,0,1,6,1,1,0,1,2
568,54,2,0,3,0,1,5,0,2,0,1,10
9822,54,6,0,5,0,1,6,1,8,0,1,8
3131,54,3,0,4,0,1,6,0,5,0,1,0
10807,54,1,0,1,0,1,7,1,7,0,1,3
7708,54,2,0,3,0,1,7,1,5,0,1,7
6174,54,5,0,4,0,1,7,1,8,0,1,1
4753,54,7,0,2,0,1,5,0,2,0,1,3
2840,54,2,0,5,0,1,6,0,5,0,1,3
6894,54,7,0,3,0,1,5,1,8,0,1,3
9640,54,1,0,4,0,1,7,1,8,0,1,4
2322,55,0.4,0,4,0,1,5,0,6,0,1,5
10080,55,0.6,0,1,0,1,7,1,3,0,1,1
10702,55,0.8,0,1,0,1,6,1,8,0,1,1
10828,55,1,0,3,0,1,6,1,7,0,1,4
7042,55,1.2,0,1,0,1,6,1,7,0,1,3
1494,55,1.3,0,2,0,1,5,0,4,0,1,5
7874,55,1.3,0,2,0,1,7,1,6,0,1,9
6526,55,2.1,0,3,0,1,7,1,6,0,1,9
4820,55,2.2,0,5,0,1,8,0,2,0,1,8
879,55,3.1,0,1,0,1,6,0,2,0,1,2
614,55,3.3,0,5,0,1,6,0,6,0,1,3
4150,55,3.5,0,1,0,1,5,0,8,0,1,3
3760,55,3.6,0,1,0,1,7,0,3,0,1,6
932,55,3.7,0,5,0,1,7,0,8,0,1,8
5484,55,3.9,0,4,0,1,8,1,7,0,1,4
519,55,3.9,0,4,0,1,7,0,2,0,1,4
432,55,4,0,5,0,1,5,0,6,0,1,1
897,55,4.6,0,4,0,1,6,0,7,0,1,1
4472,55,4.7,0,1,0,1,7,0,2,0,1,7
5950,55,5.1,0,4,0,1,8,1,7,0,1,9
8196,55,5.1,0,2,0,1,5,1,4,0,1,10
10718,55,5.3,0,2,0,1,5,1,8,0,1,0
10586,55,5.3,0,1,0,1,7,1,6,0,1,1
3700,55,5.4,0,3,0,1,7,0,3,0,1,1
2154,55,5.5,0,4,0,1,7,0,3,0,1,3
8610,55,5.8,0,1,0,1,6,1,4,0,1,6
9039,55,5.8,0,2,0,1,6,1,5,0,1,4
3820,55,5.9,0,5,0,1,7,0,3,0,1,9
2627,55,5.9,0,4,0,1,8,0,6,0,1,3
9345,55,6.1,0,3,0,1,6,1,5,0,1,1
1566,55,6.7,0,4,0,1,7,0,6,0,1,9
7614,55,7.2,0,3,0,1,8,1,5,0,1,8
7257,55,8.8,0,4,0,1,5,1,8,0,1,0
8992,55,9.1,0,2,0,1,5,1,7,0,1,5
4850,55,9.1,0,3,0,1,5,0,2,0,1,4
5498,55,9.2,0,2,0,1,6,1,4,0,1,6
3990,55,9.3,0,4,0,1,7,0,5,0,1,3
4777,55,9.8,0,5,0,1,5,0,6,0,1,2
9680,55,9.9,0,5,0,1,6,1,4,0,1,0
1188,55,10,0,2,0,1,8,0,3,0,1,5
8178,55,10,0,5,0,1,7,1,2,0,1,3
8617,55,10.1,0,5,0,1,6,1,8,0,1,4
10708,55,10.7,0,5,0,1,6,1,4,0,1,2
2123,55,5,0,3,0,1,8,0,6,0,1,3
9162,55,6,0,3,0,1,7,1,7,0,1,3
3070,55,7,0,2,0,1,5,0,8,0,1,1
4088,55,5,0,2,0,1,8,0,4,0,1,4
8570,55,4,0,5,0,1,6,1,2,0,1,10
7626,55,4,0,5,0,1,5,1,3,0,1,0
5314,55,5,0,1,0,1,6,0,1,0,1,0
3637,55,6,0,4,0,1,7,0,1,0,1,1
9200,55,7,0,5,0,1,8,1,2,0,1,7
6700,55,4,0,1,0,1,8,1,3,0,1,5
1414,55,2,0,2,0,1,8,0,2,0,1,8
6099,55,3,0,5,0,1,6,1,4,0,1,2
1064,55,7,0,2,0,1,6,0,3,0,1,4
1656,55,6,0,3,0,1,8,0,3,0,1,9
1230,55,6,0,3,0,1,6,0,7,0,1,10
1504,55,7,0,3,0,1,5,0,5,0,1,8
1790,55,1,0,2,0,1,5,0,4,0,1,5
8948,55,4,0,2,0,1,5,1,5,0,1,7
8146,55,1,0,4,0,1,7,1,8,0,1,6
9632,55,4,0,4,0,1,7,1,3,0,1,3
2148,55,5,0,2,0,1,6,0,3,0,1,9
2425,55,2,0,4,0,1,8,0,3,0,1,3
5020,55,7,0,4,0,1,5,0,6,0,1,2
5179,55,5,0,3,0,1,5,0,5,0,1,1
3426,55,1,0,1,0,1,8,0,6,0,1,3
10754,55,7,0,2,0,1,8,1,1,0,1,9
1810,55,1,0,3,0,1,5,0,2,0,1,6
9684,55,3,0,1,0,1,6,1,8,0,1,2
6648,55,5,0,1,0,1,7,1,1,0,1,2
9835,55,6,0,1,0,1,6,1,5,0,1,0
4514,55,4,0,1,0,1,8,0,2,0,1,2
702,55,2,0,4,0,1,8,0,7,0,1,2
5093,55,7,0,2,0,1,7,0,1,0,1,1
5087,55,3,0,2,0,1,5,0,4,0,1,3
1360,55,3,0,1,0,1,7,0,4,0,1,8
8278,55,5,0,5,0,1,6,1,2,0,1,4
6604,55,4,0,1,0,1,5,1,2,0,1,4
2172,55,3,0,2,0,1,7,0,3,0,1,5
1319,55,2,0,3,0,1,8,0,4,0,1,0
742,55,5,0,5,0,1,8,0,6,0,1,2
4678,55,4,0,3,0,1,7,0,5,0,1,10
1669,55,4,0,3,0,1,5,0,3,0,1,4
1714,55,4,0,1,0,1,8,0,8,0,1,4
5442,55,3,0,2,0,1,7,1,2,0,1,3
2652,55,2,0,3,0,1,5,0,6,0,1,6
927,55,7,0,5,0,1,5,0,1,0,1,3
3274,55,3,0,3,0,1,7,0,3,0,1,8
6378,55,3,0,5,0,1,5,1,4,0,1,0
4456,55,4,0,3,0,1,7,0,4,0,1,2
126,55,1,0,5,0,1,6,0,7,0,1,1
2864,55,4,0,1,0,1,5,0,1,0,1,2
4223,55,3,0,5,0,1,6,0,2,0,1,2
8024,55,7,0,2,0,1,5,1,8,0,1,8
7777,55,4,0,5,0,1,5,1,4,0,1,1
335,55,3,0,5,0,1,7,0,2,0,1,3
1672,55,2,0,4,0,1,6,0,8,0,1,7
4633,55,4,0,3,0,1,8,0,8,0,1,0
10838,56,0.4,0,4,0,1,8,1,2,0,1,6
5370,56,0.6,0,1,0,1,8,0,5,0,1,1
10302,56,0.6,0,5,0,1,5,1,2,0,1,4
6632,56,1.3,0,1,0,1,6,1,8,0,1,7
5180,56,1.4,0,4,0,1,7,0,4,0,1,2
4133,56,2.2,0,3,0,1,8,0,5,0,1,4
6121,56,2.8,0,1,0,1,5,1,5,0,1,2
2847,56,3.6,0,3,0,1,5,0,6,0,1,1
7710,56,3.8,0,1,0,1,7,1,2,0,1,4
6609,56,3.9,0,4,0,1,8,1,4,0,1,1
7810,56,4,0,3,0,1,7,1,6,0,1,3
9501,56,4.1,0,2,0,1,6,1,7,0,1,4
8416,56,4.6,0,2,0,1,6,1,7,0,1,1
3340,56,4.7,0,1,0,1,6,0,1,0,1,2
4833,56,4.9,0,2,0,1,7,0,6,0,1,3
3237,56,5,0,1,0,1,8,0,8,0,1,2
2136,56,5.9,0,4,0,1,7,0,6,0,1,4
8426,56,6,0,5,0,1,7,1,8,0,1,8
2448,56,6.2,0,2,0,1,8,0,8,0,1,4
5947,56,6.4,0,4,0,1,7,1,8,0,1,0
6890,56,6.7,0,3,0,1,5,1,2,0,1,4
8694,56,7.1,0,2,0,1,7,1,5,0,1,9
2534,56,7.3,0,1,0,1,8,0,4,0,1,4
1148,56,7.4,0,2,0,1,5,0,3,0,1,5
4961,56,7.7,0,4,0,1,5,0,6,0,1,0
7885,56,7.8,0,4,0,1,8,1,1,0,1,1
4744,56,7.9,0,3,0,1,6,0,8,0,1,0
2072,56,8,0,4,0,1,5,0,7,0,1,5
1138,56,8.2,0,4,0,1,7,0,8,0,1,7
4720,56,8.3,0,3,0,1,5,0,1,0,1,8
6666,56,8.6,0,4,0,1,6,1,1,0,1,0
1524,56,8.8,0,2,0,1,7,0,8,0,1,6
5384,56,8.9,0,4,0,1,6,0,4,0,1,5
3334,56,9.3,0,4,0,1,6,0,5,0,1,2
629,56,9.7,0,4,0,1,7,0,2,0,1,3
188,56,9.8,0,4,0,1,7,0,7,0,1,0
2288,56,9.9,0,1,0,1,5,0,6,0,1,5
9772,56,10,0,1,0,1,7,1,7,0,1,3
10858,56,10.7,0,3,0,1,8,1,7,0,1,6
116,56,11,0,5,0,1,7,0,3,0,1,2
2354,56,11,0,4,0,1,7,0,4,0,1,0
3302,56,11.1,0,5,0,1,6,0,3,0,1,3
3968,56,11.2,0,3,0,1,5,0,4,0,1,6
10120,56,11.4,0,5,0,1,8,1,6,0,1,7
391,56,11.6,0,1,0,1,5,0,6,0,1,4
1088,56,11.8,0,5,0,1,7,0,8,0,1,10
10626,56,12,0,3,0,1,5,1,5,0,1,10
10721,56,12.8,0,5,0,1,5,1,1,0,1,0
607,56,13,0,5,0,1,6,0,7,0,1,0
3828,56,13.1,0,5,0,1,5,0,4,0,1,9
921,56,14,0,5,0,1,7,0,3,0,1,4
4193,56,14.1,0,3,0,1,6,0,2,0,1,1
3586,56,14.2,0,1,0,1,5,0,4,0,1,4
122,56,14.2,0,5,0,1,8,0,2,0,1,3
4867,56,14.2,0,3,0,1,6,0,5,0,1,0
4511,56,14.5,0,5,0,1,7,0,5,0,1,4
10656,56,14.5,0,3,0,1,5,1,5,0,1,10
8424,56,14.6,0,2,0,1,6,1,3,0,1,4
7350,56,14.9,0,2,0,1,8,1,6,0,1,10
2846,56,15.4,0,1,0,1,5,0,1,0,1,8
1216,56,15.5,0,2,0,1,7,0,3,0,1,6
6844,56,15.9,0,5,0,1,6,1,4,0,1,3
1081,56,16,0,5,0,1,5,0,8,0,1,4
2156,56,16,0,5,0,1,5,0,1,0,1,7
4405,56,16,0,2,0,1,5,0,6,0,1,2
4006,56,16.1,0,3,0,1,8,0,1,0,1,4
8476,56,16.5,0,1,0,1,5,1,7,0,1,1
5730,56,16.5,0,5,0,1,7,1,3,0,1,6
2115,56,16.9,0,5,0,1,5,0,2,0,1,0
9182,56,17.6,0,5,0,1,6,1,1,0,1,9
9870,56,17.9,0,2,0,1,6,1,5,0,1,6
9701,56,17.9,0,4,0,1,5,1,5,0,1,4
4694,56,18,0,3,0,1,8,0,4,0,1,8
3322,56,18,0,2,0,1,5,0,4,0,1,8
2888,56,18.1,0,3,0,1,8,0,6,0,1,5
6702,56,18.2,0,2,0,1,8,1,2,0,1,4
812,56,18.6,0,3,0,1,7,0,3,0,1,7
4979,56,18.9,0,2,0,1,5,0,5,0,1,2
3141,56,19,0,5,0,1,7,0,4,0,1,0
3457,56,19.3,0,3,0,1,7,0,1,0,1,4
4134,56,19.3,0,1,0,1,6,0,8,0,1,10
8376,56,19.4,0,4,0,1,5,1,7,0,1,5
6482,56,19.5,0,5,0,1,7,1,8,0,1,4
651,56,19.6,0,1,0,1,7,0,3,0,1,0
9322,56,19.7,0,2,0,1,8,1,6,0,1,10
2073,56,19.9,0,5,0,1,7,0,4,0,1,1
10234,56,20.4,0,1,0,1,5,1,3,0,1,3
9634,56,20.4,0,4,0,1,7,1,1,0,1,2
5892,56,20.9,0,1,0,1,5,1,3,0,1,6
6896,56,20.9,0,4,0,1,5,1,5,0,1,6
6570,56,21.5,0,3,0,1,6,1,4,0,1,10
10825,56,22,0,3,0,1,5,1,4,0,1,4
8284,56,22.3,0,3,0,1,6,1,3,0,1,7
1140,56,22.4,0,3,0,1,6,0,3,0,1,6
8283,56,22.9,0,2,0,1,5,1,7,0,1,3
2964,56,23.4,0,5,0,1,6,0,7,0,1,3
8380,56,23.7,0,5,0,1,5,1,1,0,1,10
8403,56,23.9,0,4,0,1,8,1,3,0,1,1
3494,56,24.1,0,5,0,1,7,0,4,0,1,2
7283,56,24.3,0,2,0,1,7,1,3,0,1,1
5759,56,24.6,0,2,0,1,8,1,2,0,1,2
1342,56,24.6,0,2,0,1,8,0,1,0,1,1
8579,56,24.9,0,2,0,1,5,1,7,0,1,4
280,56,24.9,0,5,0,1,7,0,4,0,1,8
5044,56,25.3,0,5,0,1,7,0,2,0,1,0
5510,56,25.8,0,5,0,1,8,1,5,0,1,2
1032,56,26.5,0,4,0,1,5,0,8,0,1,5
117,56,26.8,0,5,0,1,5,0,1,0,1,2
9676,56,27.2,0,2,0,1,7,1,5,0,1,9
9286,56,27.3,0,1,0,1,7,1,3,0,1,6
8159,56,27.4,0,2,0,1,5,1,3,0,1,3
7004,56,27.5,0,3,0,1,5,1,6,0,1,6
5628,56,27.6,0,1,0,1,6,1,4,0,1,5
3384,56,27.8,0,3,0,1,7,0,8,0,1,1
2431,56,27.9,0,3,0,1,8,0,7,0,1,4
1958,56,28.2,0,3,0,1,5,0,3,0,1,0
7545,56,28.2,0,2,0,1,8,1,2,0,1,2
5293,56,28.3,0,1,0,1,6,0,1,0,1,2
1534,56,28.3,0,3,0,1,7,0,1,0,1,5
8175,56,28.7,0,1,0,1,6,1,5,0,1,1
1057,56,29,0,1,0,1,7,0,2,0,1,3
8752,57,0.2,0,4,0,1,8,1,3,0,1,5
2131,57,0.2,0,2,0,1,5,0,3,0,1,2
1636,57,0.3,0,4,0,1,6,0,1,0,1,5
6808,57,0.6,0,1,0,1,6,1,1,0,1,0
1483,57,0.8,0,4,0,1,5,0,8,0,1,0
4283,57,1.8,0,3,0,1,7,0,4,0,1,4
4593,57,1.9,0,5,0,1,8,0,5,0,1,1
5792,57,2,0,2,0,1,5,1,2,0,1,6
1444,57,2.7,0,2,0,1,7,0,7,0,1,4
6641,57,3,0,4,0,1,5,1,4,0,1,4
286,57,3.5,0,1,0,1,7,0,7,0,1,8
9267,57,3.7,0,2,0,1,5,1,6,0,1,4
8328,57,4.1,0,5,0,1,6,1,2,0,1,4
5584,57,4.4,0,1,0,1,7,1,5,0,1,2
10784,57,5.1,0,5,0,1,6,1,4,0,1,7
538,57,5.1,0,5,0,1,8,0,1,0,1,8
9711,57,5.2,0,3,0,1,7,1,1,0,1,2
2520,57,5.9,0,5,0,1,5,0,8,0,1,1
1880,57,6,0,4,0,1,7,0,4,0,1,10
300,57,6.8,0,4,0,1,8,0,2,0,1,8
4346,57,6.9,0,5,0,1,6,0,5,0,1,0
6998,57,7.7,0,3,0,1,6,1,2,0,1,8
2008,57,7.9,0,5,0,1,6,0,4,0,1,8
6712,57,8.7,0,2,0,1,7,1,8,0,1,2
3803,57,8.7,0,3,0,1,5,0,4,0,1,1
3594,57,9,0,5,0,1,6,0,5,0,1,8
2546,57,9.1,0,3,0,1,7,0,7,0,1,1
9858,57,10.7,0,2,0,1,6,1,7,0,1,8
6380,57,10.9,0,1,0,1,8,1,1,0,1,2
7383,57,10.9,0,2,0,1,5,1,4,0,1,4
4998,57,11.1,0,3,0,1,8,0,8,0,1,8
7670,57,11.1,0,5,0,1,8,1,4,0,1,0
6478,57,11.3,0,4,0,1,6,1,5,0,1,5
2252,57,11.3,0,4,0,1,8,0,1,0,1,7
6369,57,11.5,0,2,0,1,6,1,6,0,1,3
3662,57,11.5,0,4,0,1,6,0,1,0,1,5
426,57,11.6,0,5,0,1,7,0,3,0,1,3
7069,57,12.1,0,4,0,1,7,1,2,0,1,3
732,57,12.1,0,2,0,1,6,0,6,0,1,0
6407,57,12.3,0,2,0,1,8,1,1,0,1,2
5738,57,12.6,0,3,0,1,6,1,7,0,1,9
3538,57,12.7,0,3,0,1,8,0,4,0,1,1
4751,57,12.8,0,5,0,1,7,0,1,0,1,1
8169,57,12.9,0,2,0,1,5,1,8,0,1,0
604,57,12.9,0,5,0,1,8,0,7,0,1,7
2950,57,13.5,0,2,0,1,7,0,4,0,1,8
6076,57,13.8,0,4,0,1,5,1,8,0,1,6
1260,57,13.9,0,3,0,1,8,0,6,0,1,1
5238,57,13.9,0,2,0,1,8,0,4,0,1,2
2892,57,14.2,0,2,0,1,6,0,7,0,1,10
1806,57,14.5,0,4,0,1,6,0,5,0,1,0
3256,57,14.5,0,5,0,1,7,0,4,0,1,1
10727,57,14.6,0,1,0,1,6,1,5,0,1,3
9654,57,14.6,0,2,0,1,6,1,4,0,1,10
10320,57,14.9,0,2,0,1,7,1,6,0,1,4
2106,57,15,0,4,0,1,5,0,2,0,1,10
10269,57,15.1,0,3,0,1,6,1,3,0,1,2
10852,57,15.2,0,1,0,1,8,1,2,0,1,0
4781,57,15.4,0,3,0,1,7,0,7,0,1,3
1382,57,15.4,0,3,0,1,6,0,3,0,1,9
4087,57,15.7,0,5,0,1,6,0,8,0,1,1
824,57,15.7,0,5,0,1,7,0,2,0,1,8
5268,57,15.8,0,2,0,1,5,0,2,0,1,4
3112,57,15.9,0,4,0,1,7,0,2,0,1,3
5010,57,16.1,0,5,0,1,5,0,6,0,1,10
4859,57,16.2,0,4,0,1,7,0,3,0,1,4
3088,57,16.9,0,4,0,1,5,0,8,0,1,7
975,57,16.9,0,1,0,1,6,0,1,0,1,4
496,57,17,0,4,0,1,8,0,4,0,1,8
3686,57,17,0,1,0,1,7,0,3,0,1,1
3050,57,17.1,0,3,0,1,6,0,5,0,1,0
8054,57,17.1,0,2,0,1,6,1,5,0,1,1
7516,57,17.4,0,5,0,1,5,1,7,0,1,9
5726,57,17.9,0,4,0,1,6,1,4,0,1,8
5159,57,18.2,0,3,0,1,8,0,1,0,1,1
10155,57,18.4,0,3,0,1,6,1,8,0,1,4
10507,57,18.6,0,5,0,1,5,1,6,0,1,2
8170,57,18.7,0,4,0,1,5,1,8,0,1,2
160,57,19.2,0,2,0,1,7,0,1,0,1,1
1320,57,19.3,0,3,0,1,5,0,6,0,1,7
9664,57,19.3,0,5,0,1,6,1,1,0,1,1
6503,57,19.4,0,2,0,1,8,1,3,0,1,2
4948,57,19.5,0,1,0,1,7,0,3,0,1,9
7716,57,20.1,0,3,0,1,5,1,8,0,1,8
3816,57,20.4,0,5,0,1,7,0,3,0,1,6
10390,57,20.7,0,2,0,1,8,1,5,0,1,1
10252,57,21.8,0,3,0,1,7,1,5,0,1,6
3890,57,21.9,0,4,0,1,7,0,3,0,1,7
6879,57,22.1,0,4,0,1,7,1,5,0,1,1
5218,57,22.3,0,4,0,1,5,0,7,0,1,6
1989,57,22.5,0,4,0,1,8,0,1,0,1,3
10400,57,23,0,4,0,1,5,1,4,0,1,6
2823,57,23.3,0,2,0,1,5,0,1,0,1,2
7332,57,23.9,0,5,0,1,5,1,7,0,1,1
1092,57,23.9,0,3,0,1,7,0,8,0,1,5
7455,57,24,0,2,0,1,5,1,3,0,1,0
9818,57,24.2,0,2,0,1,8,1,7,0,1,6
6657,57,24.2,0,1,0,1,7,1,6,0,1,0
986,57,24.3,0,2,0,1,5,0,4,0,1,6
5194,57,24.4,0,1,0,1,5,0,6,0,1,5
6830,57,24.5,0,1,0,1,8,1,4,0,1,0
6848,57,24.5,0,5,0,1,8,1,8,0,1,5
6431,57,24.5,0,4,0,1,8,1,8,0,1,2
1263,57,24.9,0,4,0,1,7,0,8,0,1,0
10225,57,26.1,0,2,0,1,6,1,6,0,1,0
5440,57,27.5,0,4,0,1,8,1,4,0,1,2
5842,57,27.6,0,3,0,1,8,1,4,0,1,7
845,57,27.7,0,2,0,1,8,0,3,0,1,2
4072,57,27.7,0,1,0,1,5,0,8,0,1,4
8500,57,27.8,0,4,0,1,8,1,3,0,1,10
1776,57,27.8,0,3,0,1,8,0,2,0,1,6
5094,57,27.9,0,2,0,1,6,0,2,0,1,0
9850,57,27.9,0,4,0,1,8,1,2,0,1,4
430,57,28,0,1,0,1,5,0,7,0,1,5
2688,57,28.1,0,4,0,1,6,0,3,0,1,4
4607,57,28.4,0,3,0,1,7,0,8,0,1,2
5169,57,28.5,0,3,0,1,7,0,5,0,1,0
1705,57,28.8,0,4,0,1,8,0,5,0,1,0
7241,57,28.9,0,4,0,1,7,1,2,0,1,1
683,57,28.9,0,2,0,1,7,0,6,0,1,0
3632,57,29.1,0,2,0,1,8,0,7,0,1,9
2668,57,29.1,0,2,0,1,8,0,5,0,1,1
4152,57,29.3,0,1,0,1,6,0,6,0,1,8
7338,57,29.4,0,2,0,1,8,1,6,0,1,0
1068,57,29.5,0,3,0,1,8,0,6,0,1,5
4218,58,0.7,0,3,0,1,6,0,4,0,1,9
5566,58,1.2,0,3,0,1,5,1,7,0,1,7
7271,58,1.4,0,5,0,1,7,1,2,0,1,4
6490,58,1.5,0,1,0,1,8,1,3,0,1,9
8888,58,2.3,0,4,0,1,7,1,7,0,1,5
2256,58,2.4,0,1,0,1,5,0,6,0,1,10
5555,58,2.5,0,1,0,1,5,1,2,0,1,0
9408,58,3.6,0,3,0,1,7,1,2,0,1,2
681,58,3.9,0,3,0,1,7,0,7,0,1,0
8861,58,3.9,0,3,0,1,7,1,7,0,1,4
580,58,4.3,0,5,0,1,6,0,6,0,1,6
822,58,5.4,0,2,0,1,8,0,3,0,1,5
2410,58,5.7,0,1,0,1,6,0,2,0,1,2
7451,58,6.7,0,5,0,1,8,1,3,0,1,0
1962,58,7,0,1,0,1,7,0,7,0,1,0
4315,58,7.1,0,1,0,1,5,0,1,0,1,1
2039,58,7.4,0,3,0,1,5,0,4,0,1,3
6138,58,7.5,0,4,0,1,5,1,6,0,1,4
7212,58,8.3,0,3,0,1,6,1,1,0,1,5
5114,58,8.6,0,4,0,1,6,0,7,0,1,8
9093,58,8.8,0,5,0,1,7,1,2,0,1,4
6520,58,9.6,0,1,0,1,6,1,4,0,1,8
3072,58,9.8,0,3,0,1,6,0,6,0,1,6
712,58,10.1,0,1,0,1,6,0,1,0,1,7
1592,58,11,0,5,0,1,5,0,4,0,1,0
7301,58,11.2,0,3,0,1,7,1,5,0,1,4
5279,58,11.5,0,4,0,1,7,0,4,0,1,3
3830,58,11.8,0,5,0,1,8,0,8,0,1,5
6066,58,12,0,1,0,1,7,1,1,0,1,1
5896,58,12,0,1,0,1,5,1,7,0,1,6
8584,58,12.1,0,1,0,1,8,1,8,0,1,8
8842,58,12.2,0,1,0,1,6,1,1,0,1,9
2467,58,12.2,0,2,0,1,6,0,7,0,1,0
434,58,12.5,0,5,0,1,6,0,3,0,1,2
6194,58,12.9,0,1,0,1,5,1,5,0,1,4
1562,58,12.9,0,2,0,1,5,0,4,0,1,8
2689,58,13.3,0,4,0,1,6,0,6,0,1,2
5378,58,13.5,0,3,0,1,6,0,5,0,1,7
10430,58,13.7,0,2,0,1,8,1,6,0,1,4
4457,58,14,0,5,0,1,5,0,3,0,1,2
2660,58,14.1,0,3,0,1,8,0,1,0,1,2
554,58,14.3,0,1,0,1,8,0,6,0,1,6
7425,58,15.1,0,1,0,1,7,1,1,0,1,1
8452,58,15.2,0,4,0,1,6,1,1,0,1,4
3592,58,15.2,0,5,0,1,6,0,2,0,1,5
4556,58,15.3,0,5,0,1,5,0,8,0,1,3
10190,58,15.7,0,2,0,1,5,1,3,0,1,9
7005,58,15.8,0,3,0,1,8,1,3,0,1,3
8746,58,16,0,2,0,1,8,1,8,0,1,1
5212,58,16.2,0,3,0,1,5,0,2,0,1,8
6046,58,16.3,0,3,0,1,6,1,3,0,1,10
3272,58,17.1,0,3,0,1,6,0,2,0,1,3
6554,58,17.1,0,2,0,1,5,1,5,0,1,8
2045,58,17.7,0,1,0,1,6,0,8,0,1,1
3834,58,18.2,0,3,0,1,6,0,4,0,1,8
6446,58,18.5,0,3,0,1,5,1,4,0,1,0
2427,58,19.2,0,1,0,1,7,0,2,0,1,0
3750,58,19.3,0,2,0,1,8,0,6,0,1,9
6494,58,20.1,0,4,0,1,6,1,7,0,1,7
4351,58,20.2,0,2,0,1,5,0,6,0,1,1
5846,58,20.2,0,1,0,1,6,1,2,0,1,6
9592,58,21,0,2,0,1,6,1,4,0,1,3
8136,58,21.3,0,3,0,1,6,1,1,0,1,4
5470,58,21.4,0,3,0,1,7,1,5,0,1,4
4640,58,22,0,5,0,1,5,0,6,0,1,1
5411,58,22.2,0,5,0,1,8,0,7,0,1,3
6159,58,22.5,0,1,0,1,8,1,8,0,1,1
526,58,22.8,0,3,0,1,5,0,6,0,1,0
1716,58,23,0,1,0,1,6,0,7,0,1,2
2155,58,23.2,0,3,0,1,5,0,7,0,1,0
3972,58,23.3,0,1,0,1,7,0,4,0,1,4
7033,58,23.4,0,4,0,1,7,1,5,0,1,0
10249,58,23.5,0,1,0,1,8,1,2,0,1,2
1374,58,23.8,0,5,0,1,6,0,1,0,1,8
3773,58,24.1,0,4,0,1,6,0,8,0,1,2
4586,58,24.3,0,5,0,1,8,0,7,0,1,5
242,58,24.5,0,1,0,1,5,0,5,0,1,2
8018,58,24.6,0,2,0,1,7,1,7,0,1,6
9800,58,24.9,0,4,0,1,5,1,4,0,1,2
4338,58,25.2,0,3,0,1,5,0,4,0,1,7
5164,58,25.3,0,5,0,1,5,0,3,0,1,2
7576,58,25.3,0,2,0,1,6,1,8,0,1,8
4972,58,25.3,0,4,0,1,8,0,2,0,1,6
1118,58,25.4,0,3,0,1,5,0,4,0,1,4
10609,58,26.9,0,4,0,1,6,1,3,0,1,2
9478,58,26.9,0,2,0,1,8,1,8,0,1,7
9334,58,27.4,0,4,0,1,6,1,6,0,1,1
3416,58,27.6,0,2,0,1,7,0,6,0,1,2
250,58,27.7,0,2,0,1,5,0,7,0,1,5
1755,58,27.9,0,3,0,1,5,0,2,0,1,0
815,58,28.1,0,1,0,1,5,0,5,0,1,0
2036,58,28.4,0,4,0,1,5,0,5,0,1,3
8985,58,29,0,5,0,1,7,1,5,0,1,0
1330,58,29.1,0,4,0,1,8,0,1,0,1,5
3391,58,29.4,0,5,0,1,7,0,5,0,1,2
1626,58,29.5,0,4,0,1,5,0,1,0,1,5
5171,58,29.6,0,5,0,1,8,0,6,0,1,2
8780,58,29.7,0,4,0,1,7,1,7,0,1,5
3290,58,29.8,0,1,0,1,7,0,2,0,1,3
2748,59,0.2,0,4,0,1,6,0,1,0,1,6
4137,59,0.4,0,3,0,1,5,0,1,0,1,3
7079,59,0.7,0,2,0,1,5,1,5,0,1,0
6208,59,0.9,0,5,0,1,5,1,4,0,1,10
3567,59,1.1,0,4,0,1,7,0,2,0,1,0
9129,59,1.3,0,2,0,1,6,1,3,0,1,4
5501,59,1.4,0,3,0,1,7,1,1,0,1,3
2416,59,1.4,0,5,0,1,8,0,7,0,1,6
9204,59,1.9,0,4,0,1,8,1,4,0,1,2
2091,59,2,0,2,0,1,8,0,8,0,1,1
4643,59,2.4,0,4,0,1,6,0,3,0,1,0
5648,59,2.7,0,3,0,1,5,1,7,0,1,0
4089,59,2.7,0,4,0,1,6,0,4,0,1,2
7546,59,4.4,0,1,0,1,8,1,8,0,1,4
7216,59,4.9,0,1,0,1,8,1,4,0,1,6
10551,59,5.2,0,2,0,1,8,1,2,0,1,0
914,59,5.5,0,3,0,1,6,0,2,0,1,4
6282,59,5.6,0,4,0,1,7,1,5,0,1,5
8615,59,5.7,0,2,0,1,7,1,7,0,1,2
8560,59,6.2,0,5,0,1,5,1,8,0,1,0
5085,59,6.7,0,1,0,1,6,0,7,0,1,0
1290,59,6.9,0,4,0,1,8,0,4,0,1,2
1650,59,7.2,0,4,0,1,5,0,7,0,1,5
10242,59,8.4,0,1,0,1,8,1,1,0,1,9
1561,59,8.4,0,5,0,1,8,0,6,0,1,2
7830,59,8.8,0,3,0,1,7,1,6,0,1,7
10006,59,9,0,1,0,1,6,1,2,0,1,6
1062,59,9,0,1,0,1,7,0,2,0,1,10
505,59,9.1,0,4,0,1,6,0,7,0,1,2
10186,59,9.2,0,2,0,1,6,1,7,0,1,3
6008,59,9.3,0,5,0,1,6,1,6,0,1,7
10164,59,9.4,0,4,0,1,5,1,8,0,1,4
5684,59,9.4,0,1,0,1,8,1,5,0,1,1
4318,59,9.5,0,4,0,1,8,0,1,0,1,0
5522,59,9.6,0,1,0,1,5,1,2,0,1,1
3056,59,9.8,0,3,0,1,5,0,2,0,1,7
10788,59,9.8,0,2,0,1,6,1,1,0,1,4
6477,59,10,0,4,0,1,7,1,6,0,1,1
2670,59,10.3,0,3,0,1,7,0,1,0,1,6
9178,59,10.4,0,3,0,1,8,1,3,0,1,8
645,59,10.7,0,1,0,1,7,0,3,0,1,0
410,59,11.5,0,1,0,1,5,0,6,0,1,3
8714,59,11.5,0,4,0,1,7,1,8,0,1,3
3733,59,11.8,0,3,0,1,8,0,8,0,1,2
6538,59,11.9,0,5,0,1,5,1,6,0,1,4
7760,59,12,0,5,0,1,7,1,8,0,1,1
10000,59,12,0,3,0,1,6,1,7,0,1,3
9932,59,12.1,0,1,0,1,5,1,7,0,1,2
2889,59,12.5,0,4,0,1,5,0,1,0,1,0
621,59,12.6,0,3,0,1,5,0,7,0,1,2
97,59,12.8,0,5,0,1,5,0,6,0,1,1
8148,59,13,0,4,0,1,5,1,4,0,1,10
7781,59,13.8,0,3,0,1,5,1,6,0,1,1
6982,59,14.3,0,4,0,1,6,1,2,0,1,1
7804,59,14.7,0,4,0,1,8,1,7,0,1,4
9306,59,14.8,0,2,0,1,7,1,4,0,1,3
9555,59,15,0,5,0,1,8,1,3,0,1,3
2867,59,15.3,0,1,0,1,5,0,7,0,1,2
6370,59,15.5,0,4,0,1,6,1,4,0,1,0
5778,59,15.6,0,3,0,1,6,1,1,0,1,7
9982,59,16.2,0,3,0,1,7,1,2,0,1,1
8996,59,16.7,0,4,0,1,6,1,1,0,1,7
8545,59,17.3,0,5,0,1,7,1,7,0,1,2
7392,59,17.7,0,5,0,1,8,1,7,0,1,4
4000,59,17.7,0,2,0,1,5,0,8,0,1,5
3680,59,18.3,0,4,0,1,7,0,4,0,1,9
7110,59,18.5,0,2,0,1,8,1,7,0,1,7
5734,59,18.7,0,5,0,1,8,1,1,0,1,10
4448,59,19.1,0,4,0,1,5,0,5,0,1,1
8230,59,20,0,3,0,1,8,1,7,0,1,7
5266,59,20.3,0,1,0,1,6,0,2,0,1,5
1779,59,20.4,0,5,0,1,8,0,6,0,1,3
8978,59,20.7,0,5,0,1,5,1,6,0,1,7
1185,59,21.1,0,4,0,1,6,0,8,0,1,3
949,59,21.1,0,2,0,1,8,0,3,0,1,3
7980,59,21.3,0,4,0,1,8,1,5,0,1,0
3748,59,21.3,0,5,0,1,8,0,8,0,1,9
2500,59,21.4,0,2,0,1,6,0,4,0,1,0
257,59,21.4,0,3,0,1,6,0,4,0,1,4
3864,59,21.4,0,1,0,1,5,0,2,0,1,8
4238,59,21.5,0,5,0,1,6,0,5,0,1,0
8812,59,21.9,0,2,0,1,8,1,8,0,1,2
10770,59,22.3,0,1,0,1,5,1,6,0,1,6
4502,59,23.8,0,4,0,1,6,0,4,0,1,3
714,59,24.1,0,1,0,1,5,0,3,0,1,7
4711,59,24.8,0,1,0,1,6,0,5,0,1,4
1015,59,25.2,0,1,0,1,5,0,5,0,1,2
5174,59,25.6,0,4,0,1,5,0,8,0,1,8
1544,59,25.6,0,2,0,1,8,0,5,0,1,9
54,59,26,0,5,0,1,7,0,8,0,1,7
6206,59,26,0,2,0,1,6,1,3,0,1,10
1772,59,26.1,0,3,0,1,8,0,8,0,1,2
5680,59,26.4,0,3,0,1,7,1,7,0,1,5
2507,59,26.4,0,2,0,1,7,0,1,0,1,3
8967,59,26.6,0,3,0,1,5,1,6,0,1,1
810,59,26.9,0,1,0,1,5,0,1,0,1,10
9154,59,26.9,0,3,0,1,7,1,8,0,1,7
10740,59,27.1,0,4,0,1,8,1,3,0,1,7
10362,59,28.3,0,1,0,1,6,1,7,0,1,2
2960,59,28.8,0,2,0,1,7,0,1,0,1,4
8450,59,29.1,0,3,0,1,5,1,1,0,1,8
8654,59,29.8,0,1,0,1,5,1,6,0,1,6
7476,60,0.2,0,4,0,1,5,1,5,0,1,6
1644,60,0.3,0,5,0,1,5,0,4,0,1,6
1788,60,0.6,0,2,0,1,5,0,2,0,1,0
5962,60,0.8,0,1,0,1,7,1,7,0,1,4
9883,60,0.9,0,4,0,1,6,1,8,0,1,4
10160,60,1.5,0,3,0,1,6,1,5,0,1,1
586,60,1.5,0,1,0,1,5,0,4,0,1,3
3650,60,1.6,0,2,0,1,7,0,8,0,1,5
2996,60,1.8,0,3,0,1,8,0,7,0,1,8
6756,60,2.3,0,5,0,1,5,1,5,0,1,10
667,60,2.6,0,4,0,1,5,0,3,0,1,1
8236,60,2.7,0,5,0,1,7,1,3,0,1,9
6493,60,2.7,0,3,0,1,6,1,6,0,1,3
3562,60,2.8,0,3,0,1,7,0,1,0,1,0
2841,60,3,0,1,0,1,8,0,7,0,1,0
1968,60,3.2,0,5,0,1,7,0,8,0,1,0
6265,60,3.2,0,2,0,1,8,1,6,0,1,1
2290,60,3.3,0,2,0,1,8,0,3,0,1,9
4436,60,3.4,0,5,0,1,6,0,5,0,1,7
10149,60,3.5,0,4,0,1,5,1,5,0,1,3
8266,60,3.8,0,5,0,1,7,1,3,0,1,10
4692,60,3.8,0,1,0,1,8,0,2,0,1,6
3517,60,4,0,2,0,1,8,0,8,0,1,1
9976,60,4.2,0,2,0,1,5,1,8,0,1,9
8600,60,4.9,0,3,0,1,6,1,1,0,1,9
413,60,5.3,0,4,0,1,8,0,5,0,1,0
10448,60,5.4,0,5,0,1,6,1,7,0,1,8
8191,60,5.9,0,1,0,1,6,1,6,0,1,1
1675,60,6.5,0,3,0,1,8,0,5,0,1,4
5245,60,6.8,0,4,0,1,6,0,4,0,1,2
9256,60,6.8,0,4,0,1,7,1,8,0,1,7
4969,60,6.8,0,1,0,1,5,0,8,0,1,3
4522,60,6.9,0,4,0,1,8,0,8,0,1,2
6489,60,7.3,0,1,0,1,6,1,4,0,1,4
10832,60,7.3,0,4,0,1,5,1,3,0,1,10
3767,60,8.3,0,2,0,1,6,0,6,0,1,1
7304,60,8.4,0,3,0,1,8,1,6,0,1,3
10856,60,8.6,0,4,0,1,7,1,1,0,1,5
8774,60,8.6,0,3,0,1,6,1,2,0,1,4
8389,60,8.8,0,4,0,1,5,1,4,0,1,1
9656,60,9.7,0,2,0,1,7,1,7,0,1,3
6598,60,9.9,0,4,0,1,8,1,2,0,1,3
1195,60,10.8,0,3,0,1,5,0,5,0,1,3
3218,60,11,0,2,0,1,8,0,3,0,1,4
6999,60,11.3,0,4,0,1,6,1,4,0,1,4
1432,60,11.3,0,5,0,1,8,0,5,0,1,4
7635,60,11.5,0,5,0,1,8,1,6,0,1,1
4959,60,11.8,0,2,0,1,6,0,2,0,1,4
4326,60,12.1,0,4,0,1,6,0,6,0,1,9
5260,60,12.3,0,1,0,1,6,0,6,0,1,4
7270,60,12.4,0,4,0,1,5,1,3,0,1,7
415,60,12.6,0,2,0,1,8,0,6,0,1,2
6946,60,13,0,3,0,1,6,1,3,0,1,2
2450,60,13.1,0,3,0,1,5,0,2,0,1,2
6981,60,13.2,0,2,0,1,8,1,7,0,1,2
10463,60,14.7,0,5,0,1,7,1,6,0,1,0
7200,60,14.8,0,1,0,1,5,1,6,0,1,5
5740,60,14.9,0,3,0,1,7,1,4,0,1,5
3513,60,15.3,0,2,0,1,8,0,5,0,1,3
5544,60,15.3,0,2,0,1,8,1,8,0,1,8
9751,60,16.2,0,4,0,1,7,1,3,0,1,0
5116,60,16.2,0,2,0,1,7,0,8,0,1,10
6352,60,16.8,0,2,0,1,6,1,7,0,1,6
2850,60,17.3,0,2,0,1,6,0,3,0,1,0
444,60,17.4,0,2,0,1,7,0,5,0,1,10
1754,60,17.7,0,1,0,1,7,0,1,0,1,4
1908,60,17.8,0,4,0,1,5,0,7,0,1,9
996,60,18.3,0,5,0,1,8,0,6,0,1,0
2708,60,18.3,0,4,0,1,5,0,7,0,1,9
8916,60,18.3,0,2,0,1,5,1,8,0,1,6
1284,60,18.9,0,4,0,1,5,0,7,0,1,9
8100,60,19.1,0,2,0,1,7,1,8,0,1,5
1294,60,19.6,0,3,0,1,7,0,5,0,1,8
10094,60,19.8,0,3,0,1,6,1,7,0,1,6
9734,60,20.7,0,3,0,1,8,1,1,0,1,3
10654,60,20.7,0,1,0,1,8,1,4,0,1,3
7256,60,20.8,0,1,0,1,7,1,6,0,1,4
3467,60,21,0,5,0,1,6,0,7,0,1,3
6250,60,21.3,0,2,0,1,7,1,2,0,1,0
10290,60,21.4,0,5,0,1,6,1,1,0,1,9
1421,60,21.6,0,2,0,1,6,0,6,0,1,3
5979,60,21.7,0,4,0,1,7,1,3,0,1,4
3317,60,21.9,0,4,0,1,5,0,5,0,1,3
9518,60,22,0,2,0,1,5,1,4,0,1,5
964,60,22.6,0,4,0,1,6,0,4,0,1,8
3804,60,22.7,0,1,0,1,8,0,8,0,1,1
9340,60,22.8,0,3,0,1,6,1,5,0,1,8
6779,60,23.3,0,5,0,1,8,1,8,0,1,3
6547,60,23.7,0,4,0,1,8,1,8,0,1,4
5771,60,25,0,2,0,1,5,1,6,0,1,3
4104,60,25.3,0,2,0,1,6,0,5,0,1,4
8252,60,25.5,0,2,0,1,8,1,8,0,1,3
10633,60,25.6,0,3,0,1,5,1,4,0,1,1
6862,60,26.3,0,1,0,1,7,1,8,0,1,2
9192,60,26.5,0,1,0,1,8,1,3,0,1,0
8434,60,26.5,0,1,0,1,7,1,8,0,1,4
814,60,26.6,0,4,0,1,5,0,1,0,1,10
2880,60,26.6,0,3,0,1,5,0,2,0,1,5
1100,60,26.7,0,4,0,1,5,0,6,0,1,10
10268,60,26.8,0,5,0,1,7,1,2,0,1,1
4030,60,27.4,0,1,0,1,8,0,8,0,1,4
744,60,27.5,0,3,0,1,5,0,7,0,1,8
2328,60,28,0,5,0,1,5,0,1,0,1,9
7816,60,28,0,3,0,1,7,1,5,0,1,6
1780,60,28,0,4,0,1,7,0,2,0,1,5
7780,60,28.5,0,5,0,1,8,1,4,0,1,10
3920,60,28.6,0,1,0,1,5,0,5,0,1,10
2266,60,28.6,0,3,0,1,7,0,7,0,1,3
3226,60,28.7,0,3,0,1,6,0,7,0,1,1
6973,60,29.1,0,5,0,1,7,1,1,0,1,3
3152,60,29.2,0,5,0,1,8,0,4,0,1,10
6899,61,0.5,0,1,0,1,5,1,2,0,1,3
5310,61,0.8,0,2,0,1,6,0,3,0,1,2
8544,61,0.8,0,4,0,1,8,1,5,0,1,3
3918,61,1.4,0,5,0,1,5,0,2,0,1,7
8386,61,2,0,5,0,1,7,1,5,0,1,0
4882,61,2.2,0,1,0,1,5,0,3,0,1,6
5073,61,2.6,0,5,0,1,5,0,4,0,1,2
1954,61,2.6,0,3,0,1,7,0,1,0,1,1
9762,61,2.7,0,4,0,1,7,1,6,0,1,0
5447,61,2.8,0,2,0,1,6,1,6,0,1,1
9798,61,3,0,4,0,1,5,1,5,0,1,8
8155,61,3.2,0,2,0,1,6,1,4,0,1,0
6092,61,3.2,0,4,0,1,5,1,2,0,1,9
3409,61,3.5,0,5,0,1,8,0,6,0,1,4
1176,61,3.5,0,2,0,1,8,0,4,0,1,6
7482,61,4,0,5,0,1,5,1,7,0,1,6
9130,61,4.3,0,4,0,1,7,1,1,0,1,7
7698,61,4.7,0,1,0,1,6,1,5,0,1,10
9770,61,4.8,0,4,0,1,7,1,5,0,1,10
2339,61,4.8,0,3,0,1,8,0,4,0,1,0
1924,61,4.8,0,4,0,1,7,0,3,0,1,2
8249,61,4.9,0,1,0,1,8,1,2,0,1,1
2932,61,5.6,0,1,0,1,5,0,7,0,1,1
10068,61,5.9,0,1,0,1,8,1,5,0,1,6
7566,61,5.9,0,5,0,1,7,1,4,0,1,5
4250,61,6.1,0,2,0,1,8,0,1,0,1,8
7954,61,6.2,0,1,0,1,5,1,7,0,1,8
5154,61,6.7,0,1,0,1,7,0,6,0,1,1
789,61,7,0,1,0,1,7,0,7,0,1,0
2412,61,7.7,0,5,0,1,8,0,2,0,1,8
14,61,7.7,0,3,0,1,8,0,2,0,1,1
1771,61,7.8,0,5,0,1,6,0,5,0,1,3
1162,61,7.8,0,5,0,1,7,0,3,0,1,8
4510,61,8.1,0,5,0,1,5,0,3,0,1,2
6391,61,8.1,0,1,0,1,6,1,8,0,1,2
763,61,8.5,0,2,0,1,5,0,4,0,1,1
7501,61,8.6,0,1,0,1,7,1,2,0,1,0
4124,61,8.6,0,4,0,1,8,0,3,0,1,0
2203,61,8.6,0,3,0,1,7,0,2,0,1,1
224,61,8.7,0,4,0,1,7,0,3,0,1,4
1449,61,9.5,0,4,0,1,6,0,6,0,1,1
7396,61,9.6,0,5,0,1,8,1,3,0,1,0
8646,61,9.8,0,3,0,1,7,1,2,0,1,8
3406,61,10,0,5,0,1,8,0,6,0,1,2
4148,61,10.5,0,2,0,1,8,0,5,0,1,4
9434,61,10.6,0,2,0,1,8,1,3,0,1,5
2720,61,11.5,0,4,0,1,7,0,7,0,1,1
2784,61,11.5,0,3,0,1,5,0,6,0,1,1
7588,61,11.6,0,1,0,1,6,1,2,0,1,9
7084,61,11.7,0,3,0,1,6,1,8,0,1,2
5002,61,12.3,0,1,0,1,5,0,1,0,1,2
515,61,12.4,0,2,0,1,6,0,5,0,1,0
3895,61,12.6,0,1,0,1,5,0,6,0,1,4
10672,61,12.7,0,4,0,1,5,1,3,0,1,4
10806,61,12.8,0,5,0,1,6,1,3,0,1,10
8766,61,12.8,0,5,0,1,5,1,8,0,1,7
9516,61,13.1,0,1,0,1,7,1,2,0,1,2
4172,61,13.5,0,2,0,1,5,0,2,0,1,1
3450,61,13.6,0,3,0,1,6,0,1,0,1,4
9470,61,13.7,0,4,0,1,5,1,8,0,1,8
4138,61,14.8,0,2,0,1,7,0,7,0,1,9
8460,61,15.1,0,1,0,1,5,1,3,0,1,8
8561,61,15.3,0,3,0,1,7,1,8,0,1,3
7279,61,15.6,0,4,0,1,6,1,5,0,1,1
7624,61,15.8,0,1,0,1,6,1,3,0,1,1
3262,61,16.4,0,5,0,1,8,0,8,0,1,10
708,61,16.5,0,2,0,1,5,0,2,0,1,4
3021,61,16.6,0,5,0,1,6,0,1,0,1,0
3746,61,17,0,5,0,1,7,0,6,0,1,10
483,61,17,0,1,0,1,8,0,6,0,1,3
3651,61,17.1,0,5,0,1,5,0,5,0,1,2
6103,61,17.2,0,3,0,1,6,1,6,0,1,0
1825,61,17.3,0,5,0,1,6,0,7,0,1,1
7220,61,17.4,0,2,0,1,7,1,3,0,1,6
5324,61,17.6,0,5,0,1,5,0,6,0,1,10
692,61,17.7,0,2,0,1,5,0,2,0,1,5
10650,61,18.4,0,3,0,1,8,1,1,0,1,1
3371,61,19.1,0,3,0,1,6,0,4,0,1,4
4982,61,19.6,0,1,0,1,8,0,4,0,1,0
958,61,19.7,0,1,0,1,6,0,3,0,1,5
6514,61,20.1,0,1,0,1,6,1,6,0,1,1
4406,61,20.1,0,5,0,1,7,0,5,0,1,6
149,61,20.1,0,4,0,1,7,0,8,0,1,2
10839,61,20.2,0,4,0,1,7,1,4,0,1,2
6814,61,20.9,0,2,0,1,5,1,4,0,1,9
689,61,21.2,0,1,0,1,8,0,1,0,1,2
8536,61,21.4,0,1,0,1,8,1,7,0,1,10
9896,61,21.7,0,3,0,1,6,1,2,0,1,10
1937,61,22.1,0,1,0,1,7,0,4,0,1,4
8162,61,22.3,0,5,0,1,6,1,3,0,1,4
9100,61,22.4,0,3,0,1,8,1,1,0,1,1
1522,61,22.6,0,4,0,1,6,0,4,0,1,7
9236,61,23.1,0,2,0,1,8,1,1,0,1,8
895,61,23.3,0,4,0,1,5,0,4,0,1,0
3014,61,23.4,0,2,0,1,8,0,7,0,1,4
267,61,23.6,0,2,0,1,7,0,8,0,1,3
4808,61,23.7,0,2,0,1,5,0,6,0,1,8
4864,61,23.8,0,4,0,1,5,0,2,0,1,3
8870,61,23.9,0,3,0,1,8,1,1,0,1,3
359,61,24.1,0,3,0,1,8,0,6,0,1,3
5066,61,24.2,0,3,0,1,5,0,3,0,1,1
5167,61,24.8,0,1,0,1,8,0,2,0,1,0
5162,61,24.9,0,3,0,1,5,0,4,0,1,7
5464,61,25.2,0,3,0,1,6,1,5,0,1,7
3361,61,26.1,0,5,0,1,8,0,7,0,1,4
3108,61,27.1,0,3,0,1,6,0,6,0,1,0
6306,61,27.2,0,2,0,1,8,1,5,0,1,6
8032,61,27.3,0,5,0,1,5,1,8,0,1,4
7120,61,27.4,0,3,0,1,6,1,2,0,1,0
10316,61,27.8,0,4,0,1,5,1,3,0,1,9
9743,61,28.1,0,4,0,1,8,1,4,0,1,3
4360,61,28.1,0,5,0,1,6,0,5,0,1,4
2287,61,28.1,0,5,0,1,7,0,7,0,1,2
6589,61,28.4,0,5,0,1,8,1,7,0,1,1
6984,61,28.9,0,3,0,1,6,1,7,0,1,10
10618,61,28.9,0,3,0,1,5,1,7,0,1,2
10396,61,29,0,3,0,1,8,1,3,0,1,8
5577,62,0.7,0,1,0,1,7,1,3,0,1,0
7562,62,0.8,0,4,0,1,5,1,5,0,1,10
4866,62,0.8,0,3,0,1,7,0,7,0,1,1
6624,62,0.9,0,4,0,1,7,1,8,0,1,4
5547,62,1.1,0,3,0,1,7,1,4,0,1,4
1916,62,1.4,0,1,0,1,5,0,2,0,1,10
2978,62,1.5,0,4,0,1,8,0,2,0,1,3
8974,62,2.3,0,2,0,1,8,1,8,0,1,4
3810,62,2.7,0,5,0,1,7,0,1,0,1,5
6452,62,2.7,0,5,0,1,5,1,2,0,1,6
5267,62,2.8,0,3,0,1,7,0,1,0,1,4
6846,62,2.9,0,2,0,1,5,1,5,0,1,0
9742,62,3.1,0,4,0,1,7,1,3,0,1,3
4594,62,5,0,5,0,1,7,0,7,0,1,0
1800,62,5.1,0,5,0,1,7,0,8,0,1,4
8520,62,5.3,0,1,0,1,6,1,3,0,1,2
3552,62,5.3,0,3,0,1,7,0,2,0,1,3
4684,62,5.5,0,3,0,1,6,0,8,0,1,0
5328,62,5.5,0,1,0,1,5,0,1,0,1,9
9015,62,5.7,0,2,0,1,5,1,5,0,1,3
4934,62,5.8,0,3,0,1,6,0,8,0,1,5
10431,62,6,0,4,0,1,5,1,3,0,1,2
4608,62,6.2,0,4,0,1,7,0,6,0,1,9
6358,62,6.4,0,2,0,1,8,1,1,0,1,6
2232,62,6.4,0,2,0,1,6,0,6,0,1,0
3438,62,7.2,0,2,0,1,5,0,6,0,1,10
5492,62,7.3,0,4,0,1,5,1,3,0,1,5
10159,62,7.6,0,5,0,1,8,1,8,0,1,1
4491,62,7.6,0,5,0,1,8,0,5,0,1,1
4080,62,8.2,0,4,0,1,8,0,5,0,1,8
2116,62,8.3,0,3,0,1,5,0,3,0,1,5
3766,62,8.4,0,4,0,1,6,0,3,0,1,4
5714,62,8.4,0,2,0,1,8,1,1,0,1,5
6537,62,8.5,0,4,0,1,6,1,4,0,1,0
9810,62,8.6,0,1,0,1,7,1,8,0,1,0
1599,62,8.6,0,5,0,1,8,0,6,0,1,1
6950,62,8.8,0,2,0,1,7,1,5,0,1,5
10244,62,8.9,0,4,0,1,5,1,1,0,1,1
3233,62,9.3,0,1,0,1,8,0,7,0,1,2
8634,62,9.4,0,4,0,1,8,1,8,0,1,10
10331,62,9.6,0,3,0,1,8,1,8,0,1,2
9942,62,10.3,0,2,0,1,5,1,1,0,1,4
1416,62,11,0,2,0,1,7,0,5,0,1,4
9993,62,11.1,0,5,0,1,5,1,5,0,1,0
4940,62,11.9,0,4,0,1,5,0,7,0,1,5
10344,62,12.4,0,3,0,1,5,1,2,0,1,3
1130,62,12.6,0,4,0,1,5,0,4,0,1,6
7436,62,13.2,0,2,0,1,6,1,1,0,1,9
6728,62,13.4,0,2,0,1,7,1,6,0,1,7
8393,62,13.6,0,2,0,1,8,1,1,0,1,3
7378,62,13.9,0,3,0,1,7,1,6,0,1,10
7632,62,14.2,0,4,0,1,8,1,8,0,1,8
1178,62,14.9,0,4,0,1,7,0,5,0,1,10
6909,62,15.4,0,3,0,1,6,1,7,0,1,1
2466,62,15.8,0,4,0,1,6,0,3,0,1,5
934,62,15.9,0,5,0,1,6,0,8,0,1,2
3186,62,16.5,0,1,0,1,6,0,5,0,1,6
5775,62,16.7,0,5,0,1,5,1,2,0,1,3
10032,62,16.8,0,1,0,1,6,1,7,0,1,3
4748,62,17.1,0,4,0,1,7,0,7,0,1,2
8530,62,17.1,0,2,0,1,6,1,5,0,1,4
1857,62,17.5,0,5,0,1,7,0,6,0,1,4
6707,62,17.8,0,4,0,1,5,1,5,0,1,2
2040,62,17.9,0,5,0,1,5,0,4,0,1,9
2316,62,17.9,0,5,0,1,5,0,5,0,1,1
2228,62,18.8,0,4,0,1,8,0,1,0,1,3
1102,62,18.9,0,4,0,1,6,0,3,0,1,5
922,62,19.5,0,5,0,1,7,0,2,0,1,5
5773,62,19.6,0,3,0,1,8,1,2,0,1,0
5798,62,20,0,5,0,1,5,1,4,0,1,1
653,62,20.7,0,2,0,1,7,0,7,0,1,4
7246,62,20.8,0,5,0,1,6,1,2,0,1,4
32,62,20.9,0,5,0,1,5,0,6,0,1,0
3474,62,21.1,0,3,0,1,6,0,8,0,1,1
7950,62,21.8,0,3,0,1,6,1,1,0,1,5
10228,62,23,0,1,0,1,6,1,5,0,1,7
940,62,23.4,0,4,0,1,7,0,4,0,1,9
9590,62,23.5,0,2,0,1,6,1,2,0,1,3
3299,62,24.7,0,3,0,1,6,0,1,0,1,0
10715,62,25.2,0,5,0,1,7,1,2,0,1,0
8934,62,25.5,0,2,0,1,8,1,1,0,1,5
3966,62,25.7,0,5,0,1,6,0,3,0,1,10
5886,62,26,0,3,0,1,8,1,7,0,1,3
9712,62,26.3,0,4,0,1,5,1,4,0,1,1
7446,62,26.5,0,2,0,1,7,1,2,0,1,5
3558,62,26.6,0,5,0,1,8,0,2,0,1,9
6463,62,27,0,2,0,1,5,1,3,0,1,1
1253,62,27.1,0,2,0,1,6,0,1,0,1,2
4724,62,27.2,0,3,0,1,7,0,1,0,1,9
8717,62,27.2,0,4,0,1,7,1,7,0,1,0
5873,62,27.7,0,2,0,1,7,1,1,0,1,3
2240,62,28.1,0,2,0,1,5,0,4,0,1,3
3115,62,28.4,0,4,0,1,5,0,5,0,1,1
962,63,0.7,0,4,0,1,6,0,8,0,1,2
10008,63,1.1,0,3,0,1,6,1,8,0,1,2
2736,63,1.2,0,3,0,1,8,0,3,0,1,2
463,63,1.2,0,3,0,1,7,0,2,0,1,2
6504,63,1.5,0,4,0,1,8,1,3,0,1,2
820,63,2.3,0,4,0,1,6,0,8,0,1,2
2556,63,2.6,0,2,0,1,8,0,1,0,1,7
3052,63,2.9,0,5,0,1,8,0,5,0,1,10
9058,63,3.2,0,1,0,1,8,1,1,0,1,3
8152,63,3.4,0,5,0,1,7,1,5,0,1,5
9426,63,3.7,0,3,0,1,8,1,4,0,1,3
2198,63,3.8,0,2,0,1,6,0,3,0,1,3
1854,63,4.2,0,3,0,1,7,0,2,0,1,10
2042,63,4.4,0,5,0,1,6,0,4,0,1,10
2308,63,4.4,0,4,0,1,8,0,6,0,1,7
4508,63,4.7,0,2,0,1,6,0,8,0,1,0
6212,63,5,0,5,0,1,8,1,2,0,1,10
7001,63,5,0,1,0,1,7,1,2,0,1,4
9752,63,5,0,3,0,1,7,1,5,0,1,3
5274,63,5.7,0,2,0,1,6,0,6,0,1,1
4184,63,5.8,0,5,0,1,5,0,3,0,1,0
632,63,5.9,0,3,0,1,6,0,8,0,1,8
7193,63,6,0,1,0,1,8,1,8,0,1,2
3297,63,6.6,0,2,0,1,8,0,5,0,1,1
2976,63,6.9,0,2,0,1,5,0,5,0,1,0
6113,63,7.2,0,2,0,1,8,1,1,0,1,1
8112,63,7.5,0,4,0,1,5,1,4,0,1,3
5780,63,8,0,5,0,1,8,1,5,0,1,8
700,63,8.7,0,4,0,1,5,0,1,0,1,10
1574,63,8.9,0,4,0,1,8,0,2,0,1,6
428,63,9.3,0,2,0,1,8,0,1,0,1,10
4564,63,9.6,0,1,0,1,6,0,3,0,1,9
2716,63,9.6,0,3,0,1,8,0,1,0,1,5
10324,63,9.7,0,1,0,1,7,1,7,0,1,9
5400,63,9.9,0,4,0,1,5,0,5,0,1,10
7658,63,11,0,1,0,1,8,1,2,0,1,7
8439,63,11,0,3,0,1,8,1,5,0,1,2
1401,63,11.2,0,5,0,1,8,0,3,0,1,3
3216,63,11.3,0,5,0,1,8,0,5,0,1,10
275,63,11.5,0,4,0,1,6,0,2,0,1,1
606,63,12.5,0,4,0,1,6,0,7,0,1,2
2418,63,12.7,0,4,0,1,7,0,8,0,1,4
8050,63,13.5,0,1,0,1,7,1,3,0,1,9
7222,63,14.6,0,1,0,1,7,1,7,0,1,7
3995,63,14.6,0,3,0,1,5,0,7,0,1,3
4115,63,14.7,0,5,0,1,5,0,6,0,1,4
1239,63,14.7,0,2,0,1,8,0,5,0,1,2
733,63,14.8,0,5,0,1,8,0,5,0,1,3
503,63,14.8,0,4,0,1,8,0,1,0,1,2
6942,63,14.9,0,5,0,1,6,1,6,0,1,4
3870,63,15,0,2,0,1,8,0,2,0,1,10
4178,63,15.2,0,2,0,1,6,0,1,0,1,4
1722,63,15.7,0,4,0,1,7,0,6,0,1,3
3986,63,15.9,0,3,0,1,8,0,5,0,1,2
1269,63,16,0,5,0,1,6,0,1,0,1,2
4848,63,16.2,0,4,0,1,5,0,1,0,1,3
7148,63,16.2,0,3,0,1,5,1,5,0,1,2
175,63,16.4,0,2,0,1,6,0,5,0,1,1
4761,63,16.6,0,5,0,1,8,0,3,0,1,4
4642,63,16.7,0,2,0,1,8,0,7,0,1,3
5034,63,16.7,0,3,0,1,8,0,2,0,1,5
10253,63,16.9,0,2,0,1,6,1,7,0,1,2
9716,63,17.2,0,2,0,1,7,1,4,0,1,3
6392,63,17.6,0,2,0,1,6,1,4,0,1,8
7372,63,17.7,0,1,0,1,6,1,4,0,1,8
4157,63,18,0,5,0,1,6,0,8,0,1,2
7089,63,18.3,0,3,0,1,5,1,1,0,1,2
1017,63,18.3,0,2,0,1,5,0,4,0,1,2
3674,63,18.6,0,3,0,1,7,0,2,0,1,9
8274,63,18.7,0,3,0,1,5,1,8,0,1,2
1753,63,19.3,0,2,0,1,6,0,6,0,1,0
2910,63,19.4,0,4,0,1,7,0,6,0,1,1
5570,63,19.5,0,1,0,1,5,1,7,0,1,0
1918,63,20.3,0,1,0,1,6,0,8,0,1,3
889,63,20.8,0,1,0,1,7,0,1,0,1,2
10090,63,20.8,0,1,0,1,5,1,2,0,1,6
453,63,20.9,0,1,0,1,6,0,1,0,1,1
7086,63,21,0,3,0,1,6,1,4,0,1,4
2038,63,21,0,4,0,1,8,0,6,0,1,3
2834,63,21.2,0,1,0,1,5,0,4,0,1,9
7541,63,21.7,0,3,0,1,7,1,2,0,1,3
2082,63,21.8,0,1,0,1,6,0,8,0,1,6
8745,63,22.2,0,3,0,1,5,1,3,0,1,3
2364,63,22.7,0,3,0,1,5,0,7,0,1,10
2438,63,22.7,0,5,0,1,5,0,4,0,1,10
8756,63,22.9,0,2,0,1,7,1,1,0,1,2
1818,63,23.5,0,2,0,1,8,0,8,0,1,0
943,63,23.6,0,3,0,1,6,0,2,0,1,3
4856,63,23.7,0,4,0,1,5,0,7,0,1,9
3150,63,23.7,0,3,0,1,8,0,3,0,1,4
9474,63,23.9,0,1,0,1,7,1,3,0,1,6
4198,63,24,0,4,0,1,6,0,6,0,1,0
1708,63,24.1,0,4,0,1,8,0,1,0,1,0
8012,63,24.4,0,3,0,1,8,1,2,0,1,0
1876,63,24.4,0,3,0,1,5,0,4,0,1,6
9847,63,24.6,0,4,0,1,5,1,7,0,1,1
9912,63,24.7,0,5,0,1,7,1,2,0,1,5
3970,63,25,0,3,0,1,8,0,5,0,1,2
3789,63,25.1,0,4,0,1,8,0,2,0,1,1
2174,63,25.3,0,2,0,1,7,0,8,0,1,5
3159,63,25.3,0,3,0,1,8,0,8,0,1,3
10416,63,25.4,0,4,0,1,7,1,6,0,1,3
4352,63,25.9,0,3,0,1,8,0,8,0,1,8
6317,63,26.4,0,5,0,1,6,1,8,0,1,3
8341,63,26.6,0,2,0,1,5,1,4,0,1,1
4422,63,27,0,3,0,1,6,0,7,0,1,0
1903,63,27.1,0,5,0,1,5,0,4,0,1,2
7553,63,27.3,0,5,0,1,8,1,1,0,1,1
9605,63,27.4,0,5,0,1,8,1,5,0,1,0
3180,63,27.5,0,5,0,1,7,0,1,0,1,5
4604,63,27.7,0,2,0,1,7,0,1,0,1,10
8612,63,28,0,4,0,1,7,1,7,0,1,3
5586,63,28,0,1,0,1,5,1,6,0,1,9
10138,63,28.1,0,1,0,1,7,1,3,0,1,10
6302,63,28.2,0,1,0,1,6,1,5,0,1,8
10142,63,28.3,0,2,0,1,5,1,5,0,1,8
5170,63,29,0,5,0,1,8,0,3,0,1,7
8010,63,29.3,0,1,0,1,8,1,1,0,1,5
3102,63,29.8,0,1,0,1,8,0,4,0,1,8
9554,64,0.3,0,2,0,1,8,1,8,0,1,2
10342,64,0.7,0,2,0,1,5,1,6,0,1,0
7963,64,0.9,0,3,0,1,8,1,3,0,1,1
1994,64,0.9,0,5,0,1,7,0,7,0,1,5
3824,64,1.8,0,5,0,1,7,0,1,0,1,5
4024,64,2.1,0,3,0,1,7,0,4,0,1,1
291,64,2.5,0,1,0,1,8,0,3,0,1,3
6445,64,2.5,0,2,0,1,8,1,2,0,1,4
3708,64,2.7,0,1,0,1,5,0,6,0,1,4
2900,64,2.8,0,2,0,1,5,0,2,0,1,5
10591,64,2.8,0,3,0,1,7,1,2,0,1,4
730,64,3,0,3,0,1,6,0,3,0,1,1
9820,64,4,0,1,0,1,8,1,6,0,1,8
2074,64,5,0,2,0,1,7,0,3,0,1,6
7866,64,5.1,0,3,0,1,7,1,3,0,1,4
5012,64,5.1,0,1,0,1,8,0,2,0,1,2
3448,64,5.2,0,2,0,1,5,0,5,0,1,10
7586,64,5.5,0,2,0,1,7,1,5,0,1,3
7504,64,5.7,0,5,0,1,6,1,2,0,1,3
8078,64,6,0,5,0,1,8,1,2,0,1,8
5254,64,6.2,0,2,0,1,7,0,3,0,1,0
4410,64,6.3,0,1,0,1,6,0,7,0,1,5
7260,64,6.7,0,3,0,1,5,1,6,0,1,8
10114,64,6.7,0,1,0,1,6,1,3,0,1,8
5354,64,6.8,0,4,0,1,6,0,8,0,1,3
10834,64,6.8,0,4,0,1,8,1,3,0,1,1
4540,64,6.9,0,3,0,1,5,0,2,0,1,5
2109,64,7.3,0,1,0,1,8,0,7,0,1,3
158,64,7.4,0,2,0,1,8,0,7,0,1,1
608,64,7.7,0,1,0,1,6,0,1,0,1,8
10462,64,8.5,0,1,0,1,5,1,7,0,1,8
2216,64,8.5,0,3,0,1,8,0,3,0,1,8
9131,64,8.6,0,1,0,1,5,1,4,0,1,0
9802,64,8.8,0,3,0,1,8,1,6,0,1,4
3296,64,8.9,0,2,0,1,7,0,4,0,1,7
5998,64,9.2,0,2,0,1,6,1,7,0,1,8
8690,64,9.2,0,3,0,1,7,1,3,0,1,10
5733,64,9.4,0,2,0,1,7,1,5,0,1,1
9794,64,9.5,0,2,0,1,6,1,8,0,1,7
109,64,9.5,0,5,0,1,8,0,3,0,1,2
2640,64,9.6,0,5,0,1,8,0,3,0,1,4
4821,64,9.8,0,5,0,1,8,0,4,0,1,1
1549,64,10.3,0,2,0,1,6,0,6,0,1,3
6790,64,10.5,0,4,0,1,7,1,5,0,1,8
2092,64,10.9,0,3,0,1,7,0,4,0,1,0
4278,64,11.1,0,4,0,1,5,0,3,0,1,1
10116,64,11.2,0,4,0,1,5,1,2,0,1,0
2182,64,11.6,0,5,0,1,8,0,3,0,1,9
3884,64,11.8,0,4,0,1,7,0,8,0,1,3
1497,64,12.2,0,4,0,1,5,0,2,0,1,1
8824,64,12.3,0,2,0,1,7,1,3,0,1,4
7678,64,12.4,0,5,0,1,7,1,4,0,1,2
7530,64,12.6,0,4,0,1,7,1,7,0,1,5
3423,64,12.8,0,1,0,1,6,0,1,0,1,2
5797,64,12.9,0,1,0,1,6,1,2,0,1,2
4999,64,13.4,0,1,0,1,6,0,8,0,1,0
2624,64,13.6,0,4,0,1,6,0,3,0,1,9
7064,64,14.8,0,3,0,1,5,1,2,0,1,0
196,64,14.9,0,2,0,1,5,0,8,0,1,7
5550,64,15.1,0,1,0,1,8,1,6,0,1,8
5083,64,15.3,0,1,0,1,8,0,3,0,1,1
2178,64,15.5,0,4,0,1,7,0,4,0,1,5
5682,64,15.6,0,5,0,1,7,1,3,0,1,3
5918,64,16.3,0,4,0,1,7,1,5,0,1,5
8576,64,16.4,0,2,0,1,8,1,4,0,1,9
5658,64,17.4,0,2,0,1,6,1,1,0,1,8
4716,64,17.5,0,5,0,1,5,0,1,0,1,2
9967,64,18.2,0,3,0,1,7,1,3,0,1,2
6694,64,18.6,0,3,0,1,5,1,3,0,1,7
9681,64,18.7,0,1,0,1,7,1,6,0,1,4
6000,64,18.7,0,5,0,1,6,1,3,0,1,10
3203,64,18.8,0,3,0,1,6,0,1,0,1,4
4830,64,18.9,0,5,0,1,6,0,5,0,1,5
3064,64,19,0,1,0,1,8,0,1,0,1,6
418,64,19,0,5,0,1,5,0,2,0,1,0
7897,64,19.3,0,3,0,1,7,1,7,0,1,2
8980,64,19.5,0,1,0,1,5,1,7,0,1,6
6686,64,20,0,4,0,1,5,1,6,0,1,5
9740,64,20.3,0,3,0,1,8,1,1,0,1,10
9264,64,20.4,0,5,0,1,7,1,1,0,1,2
9230,64,20.8,0,4,0,1,8,1,4,0,1,10
6972,64,20.9,0,4,0,1,6,1,4,0,1,8
6962,64,21.1,0,3,0,1,8,1,6,0,1,8
6120,64,21.5,0,4,0,1,5,1,2,0,1,4
3424,64,21.5,0,5,0,1,7,0,7,0,1,2
1288,64,22.3,0,5,0,1,6,0,2,0,1,7
266,64,22.4,0,2,0,1,5,0,4,0,1,0
4585,64,23,0,1,0,1,8,0,3,0,1,0
7702,64,23.1,0,1,0,1,6,1,6,0,1,3
4828,64,23.1,0,2,0,1,8,0,8,0,1,4
9879,64,23.8,0,1,0,1,8,1,6,0,1,2
9258,64,23.9,0,2,0,1,7,1,3,0,1,1
3676,64,24.6,0,4,0,1,5,0,4,0,1,1
9580,64,24.8,0,2,0,1,7,1,1,0,1,8
2763,64,25.1,0,3,0,1,6,0,5,0,1,2
2010,64,25.3,0,4,0,1,7,0,7,0,1,1
3582,64,25.6,0,3,0,1,5,0,7,0,1,2
1478,64,26.1,0,3,0,1,8,0,1,0,1,4
2782,64,26.1,0,1,0,1,7,0,5,0,1,10
4102,64,26.4,0,5,0,1,7,0,5,0,1,6
2621,64,26.4,0,3,0,1,6,0,1,0,1,2
4576,64,26.8,0,2,0,1,7,0,1,0,1,6
2437,64,26.8,0,1,0,1,8,0,6,0,1,3
8430,64,26.9,0,3,0,1,6,1,2,0,1,5
7463,64,27.2,0,5,0,1,8,1,8,0,1,3
5821,64,27.3,0,4,0,1,6,1,6,0,1,4
772,64,27.4,0,5,0,1,8,0,7,0,1,8
3162,64,27.7,0,3,0,1,7,0,5,0,1,6
3682,64,28.1,0,3,0,1,6,0,8,0,1,8
2468,64,28.1,0,1,0,1,6,0,3,0,1,9
6440,64,28.4,0,5,0,1,7,1,7,0,1,3
6650,64,28.4,0,4,0,1,6,1,1,0,1,3
6179,64,28.5,0,2,0,1,5,1,6,0,1,0
4878,64,28.6,0,4,0,1,8,0,2,0,1,7
9713,64,28.7,0,3,0,1,8,1,3,0,1,0
1764,64,29,0,4,0,1,5,0,3,0,1,8
9481,64,29.1,0,1,0,1,7,1,1,0,1,4
4659,64,29.3,0,3,0,1,7,0,5,0,1,2
1208,64,29.9,0,4,0,1,7,0,2,0,1,5
9003,65,0.4,0,3,0,1,6,1,4,0,1,4
10210,65,0.9,0,2,0,1,6,1,1,0,1,1
10566,65,0.9,0,5,0,1,7,1,2,0,1,7
83,65,1.3,0,3,0,1,7,0,3,0,1,1
2047,65,1.3,0,1,0,1,5,0,5,0,1,4
3062,65,1.4,0,2,0,1,6,0,8,0,1,7
448,65,1.5,0,3,0,1,8,0,4,0,1,1
1874,65,2.1,0,3,0,1,8,0,5,0,1,3
1652,65,2.7,0,2,0,1,6,0,5,0,1,8
330,65,3.1,0,1,0,1,8,0,7,0,1,4
6906,65,3.5,0,1,0,1,5,1,3,0,1,6
7022,65,4.6,0,1,0,1,6,1,7,0,1,8
836,65,4.7,0,1,0,1,7,0,7,0,1,9
9808,65,4.9,0,1,0,1,6,1,2,0,1,0
1717,65,4.9,0,5,0,1,8,0,1,0,1,3
7597,65,5.2,0,3,0,1,8,1,7,0,1,1
4126,65,5.5,0,5,0,1,6,0,8,0,1,6
3664,65,5.7,0,4,0,1,5,0,3,0,1,5
8487,65,7.3,0,4,0,1,5,1,3,0,1,4
7208,65,7.7,0,1,0,1,6,1,5,0,1,1
2043,65,8,0,1,0,1,7,0,7,0,1,4
6576,65,8.6,0,4,0,1,6,1,4,0,1,5
1466,65,8.6,0,5,0,1,7,0,1,0,1,1
7964,65,9.3,0,1,0,1,7,1,4,0,1,9
550,65,9.3,0,2,0,1,5,0,6,0,1,5
3623,65,9.5,0,4,0,1,8,0,6,0,1,0
2239,65,9.6,0,4,0,1,6,0,8,0,1,1
5986,65,9.6,0,5,0,1,8,1,1,0,1,4
9234,65,10,0,5,0,1,5,1,7,0,1,10
7141,65,10.7,0,3,0,1,8,1,2,0,1,2
8003,65,11.1,0,2,0,1,7,1,1,0,1,4
10072,65,11.2,0,1,0,1,8,1,4,0,1,9
1293,65,11.4,0,2,0,1,6,0,2,0,1,2
9714,65,12,0,3,0,1,8,1,4,0,1,10
3069,65,12.7,0,4,0,1,7,0,8,0,1,0
2315,65,13.2,0,4,0,1,8,0,6,0,1,4
6154,65,13.4,0,1,0,1,8,1,1,0,1,0
1796,65,13.5,0,5,0,1,5,0,8,0,1,8
9682,65,14.1,0,1,0,1,7,1,5,0,1,5
1460,65,15,0,4,0,1,5,0,7,0,1,5
9388,65,15.2,0,3,0,1,8,1,6,0,1,3
6078,65,16,0,3,0,1,5,1,1,0,1,5
8396,65,16.1,0,1,0,1,7,1,4,0,1,2
5121,65,16.4,0,4,0,1,5,0,8,0,1,2
10356,65,16.5,0,1,0,1,8,1,4,0,1,8
9224,65,16.8,0,2,0,1,5,1,1,0,1,6
10524,65,17.5,0,3,0,1,5,1,5,0,1,7
8062,65,17.8,0,2,0,1,6,1,3,0,1,5
7190,65,17.9,0,4,0,1,6,1,8,0,1,10
2703,65,18.1,0,1,0,1,8,0,7,0,1,3
6516,65,18.3,0,1,0,1,8,1,1,0,1,9
2176,65,18.6,0,1,0,1,6,0,6,0,1,8
8954,65,18.6,0,2,0,1,5,1,6,0,1,10
4210,65,18.8,0,5,0,1,6,0,5,0,1,9
8288,65,19.2,0,3,0,1,6,1,3,0,1,10
9729,65,19.4,0,1,0,1,7,1,2,0,1,1
6417,65,20.2,0,1,0,1,7,1,4,0,1,0
4886,65,20.2,0,3,0,1,6,0,1,0,1,2
5966,65,20.3,0,1,0,1,6,1,3,0,1,6
7723,65,20.9,0,4,0,1,7,1,2,0,1,0
1254,65,20.9,0,5,0,1,8,0,2,0,1,1
9757,65,21.1,0,1,0,1,7,1,2,0,1,4
4868,65,21.5,0,1,0,1,5,0,5,0,1,9
8378,65,21.7,0,4,0,1,6,1,5,0,1,1
9600,65,22.4,0,5,0,1,6,1,8,0,1,7
806,65,22.5,0,1,0,1,5,0,1,0,1,5
4785,65,22.8,0,2,0,1,8,0,2,0,1,3
5272,65,22.9,0,5,0,1,8,0,1,0,1,7
8277,65,23,0,3,0,1,7,1,2,0,1,3
7966,65,23.2,0,3,0,1,5,1,7,0,1,0
1735,65,24,0,2,0,1,8,0,5,0,1,1
3908,65,24.7,0,2,0,1,7,0,8,0,1,6
2015,65,25.7,0,1,0,1,5,0,4,0,1,0
6014,65,25.8,0,5,0,1,5,1,3,0,1,10
7570,65,26.1,0,3,0,1,5,1,1,0,1,9
1206,65,26.3,0,1,0,1,7,0,6,0,1,4
3190,65,26.7,0,1,0,1,7,0,6,0,1,10
10534,65,26.8,0,5,0,1,7,1,4,0,1,10
9487,65,26.8,0,3,0,1,5,1,6,0,1,4
6994,65,26.9,0,3,0,1,8,1,5,0,1,0
10078,65,27.4,0,2,0,1,8,1,3,0,1,9
470,65,27.9,0,5,0,1,5,0,4,0,1,1
818,65,28,0,4,0,1,7,0,7,0,1,5
3157,65,28.1,0,3,0,1,5,0,3,0,1,1
9395,65,28.2,0,4,0,1,6,1,7,0,1,4
9838,65,28.7,0,5,0,1,5,1,6,0,1,7
2664,65,28.8,0,3,0,1,6,0,4,0,1,9
480,65,29.1,0,4,0,1,6,0,3,0,1,9
3368,65,29.5,0,3,0,1,5,0,2,0,1,4
10610,65,29.7,0,3,0,1,6,1,6,0,1,9
4242,66,0.4,0,1,0,1,6,0,5,0,1,5
8828,66,0.6,0,4,0,1,6,1,6,0,1,8
8645,66,1.3,0,5,0,1,8,1,1,0,1,3
510,66,1.9,0,1,0,1,6,0,2,0,1,1
6903,66,2.5,0,5,0,1,5,1,7,0,1,3
666,66,2.8,0,5,0,1,8,0,2,0,1,7
7196,66,3.5,0,5,0,1,5,1,2,0,1,1
2157,66,3.6,0,3,0,1,7,0,5,0,1,0
9377,66,3.6,0,5,0,1,7,1,4,0,1,4
166,66,3.7,0,5,0,1,6,0,7,0,1,3
6850,66,3.9,0,4,0,1,6,1,1,0,1,7
8800,66,4.3,0,1,0,1,8,1,5,0,1,3
8304,66,4.3,0,3,0,1,6,1,4,0,1,2
842,66,4.4,0,1,0,1,8,0,7,0,1,7
4548,66,4.8,0,5,0,1,6,0,5,0,1,6
3756,66,5,0,3,0,1,7,0,4,0,1,1
10470,66,5.5,0,5,0,1,5,1,4,0,1,4
6540,66,6.1,0,5,0,1,5,1,8,0,1,9
2350,66,6.6,0,5,0,1,6,0,3,0,1,2
10351,66,6.8,0,3,0,1,7,1,1,0,1,1
4236,66,7.4,0,1,0,1,6,0,8,0,1,0
1638,66,8.2,0,3,0,1,7,0,4,0,1,2
6662,66,8.3,0,1,0,1,7,1,4,0,1,8
6637,66,8.6,0,4,0,1,8,1,6,0,1,3
10131,66,8.7,0,4,0,1,7,1,2,0,1,2
9588,66,8.7,0,4,0,1,5,1,2,0,1,1
3714,66,8.8,0,2,0,1,8,0,2,0,1,8
9484,66,8.8,0,3,0,1,8,1,6,0,1,2
7904,66,8.9,0,5,0,1,5,1,5,0,1,9
3149,66,9.3,0,2,0,1,5,0,3,0,1,2
870,66,9.4,0,1,0,1,8,0,2,0,1,7
7641,66,9.6,0,3,0,1,7,1,7,0,1,3
8912,66,9.8,0,1,0,1,7,1,4,0,1,5
2112,66,9.9,0,3,0,1,8,0,4,0,1,2
9174,66,10.1,0,1,0,1,5,1,4,0,1,2
2442,66,10.3,0,5,0,1,5,0,7,0,1,8
9344,66,10.3,0,1,0,1,5,1,5,0,1,2
9603,66,11.1,0,2,0,1,5,1,1,0,1,0
9133,66,11.2,0,2,0,1,6,1,1,0,1,2
3524,66,11.3,0,4,0,1,6,0,5,0,1,1
7900,66,11.8,0,3,0,1,5,1,7,0,1,4
3658,66,11.9,0,2,0,1,8,0,6,0,1,7
808,66,13.8,0,3,0,1,5,0,4,0,1,10
1232,66,14.3,0,3,0,1,6,0,5,0,1,4
8116,66,14.5,0,1,0,1,5,1,8,0,1,10
9958,66,15.4,0,1,0,1,8,1,2,0,1,1
3794,66,15.5,0,5,0,1,6,0,4,0,1,4
5473,66,16.1,0,5,0,1,5,1,6,0,1,3
2244,66,16.8,0,4,0,1,5,0,4,0,1,3
7513,66,17.2,0,2,0,1,5,1,6,0,1,4
2990,66,17.8,0,3,0,1,6,0,6,0,1,1
1112,66,18,0,4,0,1,5,0,7,0,1,1
5252,66,18,0,4,0,1,5,0,2,0,1,8
4664,66,18.1,0,4,0,1,5,0,5,0,1,7
7492,66,18.3,0,1,0,1,7,1,3,0,1,10
10795,66,18.6,0,5,0,1,5,1,7,0,1,1
6932,66,18.7,0,3,0,1,6,1,7,0,1,7
1890,66,18.9,0,1,0,1,6,0,8,0,1,0
314,66,19.2,0,1,0,1,8,0,4,0,1,5
7359,66,19.3,0,4,0,1,8,1,3,0,1,3
10866,66,19.5,0,3,0,1,6,1,1,0,1,10
8087,66,19.5,0,4,0,1,5,1,4,0,1,3
9016,66,20,0,4,0,1,7,1,3,0,1,0
6539,66,20.7,0,3,0,1,7,1,2,0,1,4
7318,66,20.8,0,2,0,1,6,1,2,0,1,10
866,66,21.1,0,4,0,1,8,0,6,0,1,2
6334,66,21.3,0,4,0,1,6,1,7,0,1,9
10512,66,21.6,0,5,0,1,7,1,5,0,1,6
4496,66,22.1,0,5,0,1,8,0,3,0,1,3
8833,66,22.3,0,3,0,1,5,1,8,0,1,0
5534,66,23,0,3,0,1,5,1,4,0,1,10
9494,66,23.2,0,5,0,1,7,1,2,0,1,9
3491,66,23.2,0,1,0,1,5,0,8,0,1,2
9491,66,24.9,0,3,0,1,7,1,1,0,1,0
5784,66,25,0,3,0,1,7,1,3,0,1,7
3718,66,25.8,0,4,0,1,7,0,3,0,1,1
8409,66,25.8,0,3,0,1,6,1,7,0,1,0
340,66,26.1,0,5,0,1,8,0,7,0,1,6
4322,66,26.4,0,3,0,1,7,0,5,0,1,1
2259,66,26.7,0,5,0,1,7,0,4,0,1,1
1634,66,27.3,0,3,0,1,8,0,7,0,1,8
2317,66,27.3,0,1,0,1,5,0,7,0,1,2
2578,66,27.5,0,1,0,1,8,0,6,0,1,7
5712,66,27.5,0,3,0,1,7,1,3,0,1,8
10764,66,27.7,0,5,0,1,6,1,2,0,1,3
4538,66,27.9,0,5,0,1,5,0,8,0,1,9
2326,66,28.5,0,2,0,1,8,0,5,0,1,7
1682,66,28.8,0,2,0,1,7,0,1,0,1,2
1286,66,29.3,0,4,0,1,8,0,4,0,1,8
9176,66,29.4,0,5,0,1,6,1,7,0,1,4
8843,66,29.8,0,2,0,1,8,1,4,0,1,3
7672,67,0.8,0,2,0,1,7,1,3,0,1,8
1529,67,0.9,0,3,0,1,5,0,7,0,1,0
7520,67,1.2,0,5,0,1,6,1,8,0,1,5
701,67,2,0,1,0,1,6,0,5,0,1,4
8512,67,2.2,0,5,0,1,6,1,7,0,1,6
143,67,2.2,0,2,0,1,8,0,7,0,1,3
5598,67,3,0,4,0,1,5,1,1,0,1,6
5215,67,3.7,0,2,0,1,7,0,4,0,1,1
9266,67,3.7,0,4,0,1,6,1,8,0,1,10
4788,67,4,0,5,0,1,5,0,4,0,1,8
8194,67,4.1,0,3,0,1,5,1,6,0,1,3
9339,67,4.4,0,1,0,1,6,1,8,0,1,4
6924,67,4.6,0,2,0,1,5,1,2,0,1,1
4431,67,4.6,0,1,0,1,5,0,1,0,1,2
988,67,5,0,1,0,1,6,0,1,0,1,1
3677,67,5.1,0,1,0,1,6,0,1,0,1,3
7802,67,5.2,0,5,0,1,7,1,6,0,1,10
7224,67,5.3,0,3,0,1,7,1,7,0,1,5
10330,67,5.3,0,3,0,1,6,1,7,0,1,9
6849,67,6.6,0,2,0,1,6,1,2,0,1,4
9628,67,6.8,0,4,0,1,6,1,6,0,1,9
6817,67,6.8,0,3,0,1,5,1,4,0,1,4
9390,67,7,0,3,0,1,6,1,2,0,1,5
713,67,7,0,5,0,1,7,0,6,0,1,2
7600,67,7.1,0,2,0,1,7,1,7,0,1,4
3740,67,7.1,0,5,0,1,5,0,3,0,1,6
3386,67,7.5,0,4,0,1,7,0,6,0,1,1
2190,67,7.5,0,4,0,1,5,0,7,0,1,7
213,67,8.2,0,5,0,1,5,0,4,0,1,0
6813,67,9.2,0,1,0,1,8,1,7,0,1,0
6199,67,9.3,0,1,0,1,8,1,4,0,1,1
6011,67,9.4,0,2,0,1,6,1,3,0,1,1
4984,67,9.5,0,5,0,1,5,0,8,0,1,1
173,67,9.6,0,4,0,1,5,0,7,0,1,3
5190,67,10,0,3,0,1,7,0,6,0,1,0
2870,67,10.2,0,5,0,1,7,0,4,0,1,5
2797,67,10.6,0,2,0,1,5,0,1,0,1,0
6584,67,10.8,0,2,0,1,7,1,1,0,1,1
2787,67,10.9,0,1,0,1,5,0,4,0,1,0
3788,67,11.1,0,2,0,1,6,0,1,0,1,2
4800,67,11.2,0,5,0,1,6,0,1,0,1,6
1606,67,11.6,0,5,0,1,5,0,2,0,1,2
10020,67,11.7,0,2,0,1,6,1,5,0,1,1
5150,67,11.8,0,2,0,1,5,0,8,0,1,8
3857,67,11.8,0,5,0,1,5,0,3,0,1,1
5460,67,11.8,0,4,0,1,6,1,3,0,1,9
6605,67,12.4,0,1,0,1,7,1,4,0,1,3
7551,67,12.7,0,5,0,1,7,1,3,0,1,4
1584,67,12.9,0,3,0,1,6,0,1,0,1,1
9999,67,13.3,0,1,0,1,5,1,6,0,1,4
1302,67,13.4,0,1,0,1,8,0,8,0,1,4
6462,67,13.8,0,3,0,1,5,1,1,0,1,5
6995,67,13.9,0,4,0,1,7,1,8,0,1,2
5662,67,14.6,0,1,0,1,5,1,1,0,1,0
7519,67,15.6,0,5,0,1,8,1,8,0,1,3
3228,67,15.9,0,4,0,1,7,0,2,0,1,7
5922,67,16.2,0,3,0,1,8,1,8,0,1,1
10840,67,16.6,0,5,0,1,7,1,2,0,1,3
4951,67,16.6,0,3,0,1,8,0,4,0,1,3
5678,67,16.6,0,5,0,1,7,1,1,0,1,7
9206,67,16.9,0,3,0,1,8,1,1,0,1,6
6367,67,17.1,0,5,0,1,5,1,2,0,1,2
5796,67,17.6,0,5,0,1,8,1,6,0,1,3
3176,67,17.7,0,3,0,1,7,0,5,0,1,7
1210,67,18.2,0,3,0,1,6,0,5,0,1,10
39,67,18.7,0,4,0,1,6,0,8,0,1,4
7508,67,19,0,1,0,1,8,1,8,0,1,5
4488,67,19.3,0,3,0,1,6,0,5,0,1,1
6762,67,19.7,0,3,0,1,5,1,1,0,1,8
4420,67,19.9,0,2,0,1,6,0,3,0,1,4
6295,67,20,0,1,0,1,7,1,5,0,1,3
1861,67,20.1,0,1,0,1,6,0,6,0,1,2
1897,67,20.1,0,5,0,1,8,0,5,0,1,1
4105,67,20.1,0,1,0,1,6,0,1,0,1,1
5609,67,20.3,0,4,0,1,6,1,4,0,1,4
8868,67,21.5,0,4,0,1,6,1,7,0,1,3
6304,67,21.7,0,2,0,1,8,1,1,0,1,3
2757,67,22.1,0,1,0,1,6,0,6,0,1,1
2225,67,22.9,0,1,0,1,5,0,5,0,1,2
10009,67,23.1,0,1,0,1,6,1,5,0,1,0
2409,67,23.5,0,1,0,1,8,0,4,0,1,3
7094,67,24.6,0,2,0,1,6,1,2,0,1,10
679,67,24.8,0,5,0,1,5,0,2,0,1,4
2537,67,24.8,0,1,0,1,6,0,4,0,1,2
6532,67,25,0,2,0,1,7,1,5,0,1,5
7828,67,25.3,0,3,0,1,6,1,2,0,1,6
8482,67,25.7,0,1,0,1,7,1,8,0,1,9
8066,67,25.7,0,1,0,1,6,1,8,0,1,1
4747,67,26.2,0,3,0,1,7,0,6,0,1,2
4058,67,26.5,0,3,0,1,5,0,3,0,1,2
3172,67,26.6,0,3,0,1,5,0,6,0,1,9
5088,67,26.7,0,1,0,1,5,0,3,0,1,4
5352,67,27,0,4,0,1,7,0,3,0,1,5
1244,67,27.8,0,2,0,1,7,0,5,0,1,5
7622,67,28.1,0,2,0,1,5,1,6,0,1,3
10661,67,28.2,0,4,0,1,7,1,5,0,1,3
2146,67,28.4,0,1,0,1,6,0,2,0,1,9
4258,67,28.9,0,5,0,1,7,0,4,0,1,0
8251,67,29.5,0,3,0,1,8,1,7,0,1,4
5241,67,29.5,0,2,0,1,7,0,1,0,1,3
670,67,29.8,0,5,0,1,5,0,1,0,1,2
4766,68,0.2,0,4,0,1,5,0,8,0,1,4
2752,68,0.6,0,5,0,1,5,0,5,0,1,10
184,68,0.7,0,5,0,1,5,0,3,0,1,0
6928,68,0.9,0,5,0,1,7,1,4,0,1,10
2812,68,1,0,3,0,1,7,0,3,0,1,0
2212,68,1,0,2,0,1,8,0,2,0,1,3
3504,68,1.1,0,1,0,1,8,0,4,0,1,1
6798,68,1.2,0,5,0,1,7,1,6,0,1,0
4950,68,1.2,0,2,0,1,5,0,7,0,1,0
8154,68,1.8,0,4,0,1,8,1,2,0,1,4
502,68,1.9,0,2,0,1,5,0,1,0,1,2
4067,68,2.3,0,4,0,1,5,0,6,0,1,4
6246,68,2.6,0,3,0,1,8,1,7,0,1,8
9114,68,2.8,0,3,0,1,6,1,6,0,1,6
1258,68,3,0,2,0,1,6,0,6,0,1,7
3634,68,3.2,0,1,0,1,7,0,4,0,1,1
10254,68,3.7,0,1,0,1,8,1,8,0,1,9
9950,68,3.9,0,4,0,1,5,1,8,0,1,6
21,68,3.9,0,3,0,1,6,0,2,0,1,3
8308,68,4.3,0,1,0,1,6,1,3,0,1,7
191,68,4.4,0,4,0,1,6,0,6,0,1,0
4143,68,4.4,0,3,0,1,5,0,1,0,1,4
320,68,4.6,0,2,0,1,7,0,2,0,1,1
4176,68,4.8,0,4,0,1,6,0,5,0,1,9
8506,68,4.8,0,2,0,1,6,1,6,0,1,9
4186,68,5.1,0,1,0,1,5,0,2,0,1,0
10616,68,5.3,0,4,0,1,6,1,2,0,1,7
1359,68,5.4,0,2,0,1,8,0,6,0,1,2
5754,68,5.5,0,2,0,1,7,1,1,0,1,8
6512,68,5.6,0,3,0,1,6,1,3,0,1,9
5634,68,5.7,0,2,0,1,8,1,4,0,1,1
8632,68,6,0,4,0,1,5,1,1,0,1,2
2093,68,6.8,0,1,0,1,5,0,7,0,1,1
5240,68,6.8,0,3,0,1,7,0,3,0,1,0
728,68,7.3,0,2,0,1,6,0,1,0,1,6
10750,68,7.9,0,4,0,1,5,1,8,0,1,0
3476,68,8.2,0,3,0,1,7,0,5,0,1,2
6110,68,8.4,0,5,0,1,5,1,5,0,1,9
30,68,9.2,0,1,0,1,7,0,6,0,1,7
1853,68,9.5,0,3,0,1,5,0,1,0,1,0
5521,68,9.5,0,3,0,1,5,1,8,0,1,3
5794,68,9.7,0,1,0,1,7,1,5,0,1,10
44,68,10,0,5,0,1,6,0,6,0,1,3
9718,68,10.5,0,1,0,1,5,1,8,0,1,7
3762,68,11,0,5,0,1,5,0,5,0,1,1
7808,68,11.2,0,1,0,1,5,1,7,0,1,9
10172,68,11.4,0,4,0,1,5,1,7,0,1,4
4344,68,11.4,0,3,0,1,5,0,8,0,1,2
2648,68,12.1,0,2,0,1,7,0,5,0,1,8
5004,68,12.5,0,1,0,1,5,0,1,0,1,8
5946,68,12.5,0,4,0,1,5,1,7,0,1,5
3487,68,12.6,0,2,0,1,7,0,1,0,1,1
9720,68,12.9,0,2,0,1,5,1,8,0,1,7
2194,68,13,0,4,0,1,5,0,3,0,1,5
5178,68,13.2,0,3,0,1,7,0,5,0,1,1
10216,68,13.4,0,5,0,1,6,1,3,0,1,4
1998,68,13.8,0,4,0,1,7,0,2,0,1,1
5894,68,13.9,0,5,0,1,5,1,7,0,1,5
3484,68,14.1,0,1,0,1,7,0,2,0,1,0
7119,68,14.3,0,3,0,1,8,1,4,0,1,0
8156,68,14.8,0,2,0,1,6,1,3,0,1,2
8682,68,14.9,0,1,0,1,8,1,4,0,1,5
9144,68,15.4,0,5,0,1,7,1,2,0,1,2
6966,68,15.5,0,3,0,1,7,1,3,0,1,7
7264,68,15.9,0,1,0,1,7,1,4,0,1,5
8982,68,15.9,0,1,0,1,6,1,4,0,1,0
4570,68,16,0,4,0,1,8,0,8,0,1,6
272,68,16.2,0,1,0,1,7,0,7,0,1,2
5454,68,16.3,0,1,0,1,6,1,2,0,1,4
4616,68,16.7,0,2,0,1,8,0,4,0,1,9
2238,68,16.9,0,2,0,1,6,0,1,0,1,8
2186,68,17,0,1,0,1,8,0,2,0,1,1
6411,68,17.3,0,3,0,1,7,1,2,0,1,2
9083,68,18.1,0,4,0,1,6,1,2,0,1,0
811,68,18.2,0,4,0,1,8,0,2,0,1,1
3405,68,18.5,0,5,0,1,5,0,5,0,1,4
6018,68,18.7,0,3,0,1,6,1,2,0,1,3
5183,68,19.2,0,5,0,1,6,0,3,0,1,2
10454,68,19.8,0,1,0,1,7,1,5,0,1,4
9153,68,20,0,3,0,1,8,1,6,0,1,4
3946,68,20.6,0,3,0,1,5,0,7,0,1,8
2766,68,21,0,2,0,1,7,0,5,0,1,7
8709,68,21.1,0,5,0,1,5,1,6,0,1,0
1814,68,21.2,0,1,0,1,5,0,8,0,1,4
2462,68,21.9,0,3,0,1,6,0,5,0,1,10
8539,68,22.1,0,3,0,1,7,1,8,0,1,4
4793,68,22.4,0,4,0,1,6,0,7,0,1,0
10766,68,22.8,0,3,0,1,8,1,4,0,1,3
10401,68,22.8,0,2,0,1,6,1,8,0,1,4
10514,68,22.9,0,4,0,1,5,1,1,0,1,7
9169,68,22.9,0,4,0,1,7,1,6,0,1,2
1083,68,23.1,0,2,0,1,8,0,6,0,1,4
6338,68,23.3,0,4,0,1,8,1,6,0,1,3
4082,68,23.9,0,5,0,1,7,0,8,0,1,3
847,68,24.2,0,5,0,1,8,0,2,0,1,2
3094,68,24.9,0,4,0,1,8,0,2,0,1,5
6923,68,24.9,0,4,0,1,5,1,6,0,1,0
6021,68,24.9,0,1,0,1,7,1,8,0,1,3
7725,68,25.5,0,3,0,1,7,1,8,0,1,1
6996,68,25.6,0,3,0,1,5,1,4,0,1,3
6089,68,26,0,2,0,1,6,1,6,0,1,3
9097,68,26.3,0,4,0,1,6,1,4,0,1,1
3673,68,26.6,0,2,0,1,7,0,3,0,1,3
7598,68,27.2,0,3,0,1,5,1,5,0,1,5
8350,68,27.3,0,3,0,1,7,1,7,0,1,2
23,68,27.6,0,2,0,1,5,0,4,0,1,4
1268,68,27.7,0,1,0,1,7,0,8,0,1,8
5530,68,27.9,0,1,0,1,7,1,4,0,1,8
7591,68,28,0,5,0,1,7,1,6,0,1,4
4191,68,28.4,0,5,0,1,7,0,1,0,1,0
9572,68,28.5,0,2,0,1,5,1,1,0,1,6
8702,68,28.8,0,2,0,1,8,1,4,0,1,7
3084,68,29,0,4,0,1,5,0,7,0,1,5
2541,68,29.1,0,1,0,1,6,0,5,0,1,2
7104,68,29.1,0,1,0,1,6,1,6,0,1,5
1131,68,29.2,0,4,0,1,8,0,2,0,1,3
4612,68,29.3,0,2,0,1,6,0,7,0,1,0
7837,68,29.4,0,2,0,1,6,1,4,0,1,4
5568,68,29.6,0,4,0,1,6,1,6,0,1,3
5830,68,29.7,0,4,0,1,8,1,4,0,1,6
3034,69,0.1,0,2,0,1,7,0,5,0,1,5
3488,69,0.2,0,1,0,1,5,0,1,0,1,3
7424,69,0.5,0,1,0,1,7,1,4,0,1,7
7697,69,0.6,0,3,0,1,5,1,6,0,1,0
9952,69,0.6,0,4,0,1,5,1,6,0,1,10
4232,69,1.4,0,4,0,1,8,0,8,0,1,8
7416,69,1.7,0,3,0,1,8,1,6,0,1,2
10518,69,1.7,0,2,0,1,6,1,5,0,1,9
522,69,2,0,2,0,1,6,0,8,0,1,9
8604,69,2.5,0,1,0,1,6,1,1,0,1,9
1238,69,2.6,0,2,0,1,5,0,1,0,1,6
8059,69,2.7,0,4,0,1,8,1,5,0,1,0
2365,69,3.1,0,3,0,1,6,0,8,0,1,1
4343,69,3.3,0,5,0,1,7,0,2,0,1,1
9863,69,3.5,0,5,0,1,7,1,6,0,1,2
1039,69,4.5,0,3,0,1,8,0,4,0,1,0
10266,69,5.5,0,2,0,1,5,1,2,0,1,7
9601,69,5.5,0,3,0,1,8,1,2,0,1,4
7726,69,6.1,0,3,0,1,7,1,3,0,1,6
72,69,6.4,0,5,0,1,6,0,3,0,1,2
3106,69,6.9,0,1,0,1,7,0,6,0,1,10
10860,69,6.9,0,5,0,1,5,1,1,0,1,10
2828,69,7.1,0,5,0,1,8,0,8,0,1,0
290,69,7.5,0,2,0,1,8,0,8,0,1,7
5340,69,7.8,0,2,0,1,7,0,3,0,1,9
7512,69,8,0,5,0,1,7,1,6,0,1,7
456,69,8.5,0,4,0,1,6,0,1,0,1,2
4704,69,8.8,0,1,0,1,8,0,3,0,1,7
9962,69,9.1,0,4,0,1,7,1,2,0,1,9
2806,69,9.1,0,1,0,1,5,0,6,0,1,5
1973,69,9.2,0,3,0,1,7,0,5,0,1,3
8566,69,9.4,0,3,0,1,8,1,2,0,1,4
5910,69,9.6,0,1,0,1,6,1,2,0,1,6
9448,69,10.2,0,4,0,1,5,1,4,0,1,7
5408,69,10.2,0,1,0,1,5,0,3,0,1,1
8838,69,10.2,0,1,0,1,5,1,8,0,1,5
2730,69,10.8,0,4,0,1,6,0,5,0,1,5
2512,69,11.4,0,4,0,1,6,0,2,0,1,2
10562,69,11.9,0,4,0,1,8,1,6,0,1,0
3880,69,12.4,0,1,0,1,8,0,3,0,1,1
10272,69,13.1,0,2,0,1,8,1,5,0,1,1
10843,69,13.2,0,2,0,1,5,1,7,0,1,3
4311,69,13.2,0,4,0,1,8,0,8,0,1,1
10060,69,13.8,0,2,0,1,8,1,2,0,1,8
10752,69,13.9,0,1,0,1,5,1,8,0,1,9
3551,69,14.5,0,4,0,1,8,0,8,0,1,2
10604,69,15.1,0,1,0,1,8,1,8,0,1,8
7038,69,15.1,0,4,0,1,7,1,5,0,1,6
2735,69,15.4,0,1,0,1,7,0,1,0,1,2
6009,69,15.7,0,3,0,1,8,1,7,0,1,3
8721,69,15.9,0,2,0,1,6,1,3,0,1,1
5523,69,15.9,0,2,0,1,5,1,3,0,1,0
676,69,16.2,0,3,0,1,8,0,7,0,1,8
3436,69,17.2,0,5,0,1,8,0,6,0,1,0
1228,69,17.5,0,4,0,1,7,0,5,0,1,5
684,69,17.7,0,4,0,1,6,0,7,0,1,5
3472,69,18.1,0,3,0,1,5,0,7,0,1,3
555,69,18.2,0,4,0,1,7,0,5,0,1,3
9498,69,19,0,5,0,1,5,1,4,0,1,6
3665,69,19.3,0,4,0,1,6,0,5,0,1,0
7272,69,19.4,0,1,0,1,5,1,3,0,1,1
2474,69,19.5,0,5,0,1,6,0,4,0,1,2
3030,69,19.6,0,2,0,1,7,0,1,0,1,0
4255,69,20.1,0,2,0,1,6,0,4,0,1,0
2192,69,20.2,0,5,0,1,8,0,4,0,1,6
9576,69,20.2,0,5,0,1,7,1,6,0,1,6
6162,69,20.6,0,5,0,1,6,1,4,0,1,9
577,69,20.6,0,5,0,1,8,0,7,0,1,4
10612,69,20.8,0,4,0,1,6,1,5,0,1,9
10366,69,21.1,0,4,0,1,7,1,5,0,1,4
3292,69,21.1,0,2,0,1,8,0,5,0,1,2
1763,69,21.3,0,2,0,1,8,0,1,0,1,3
823,69,21.6,0,4,0,1,6,0,6,0,1,1
2392,69,21.9,0,1,0,1,7,0,8,0,1,4
4839,69,22.3,0,2,0,1,6,0,3,0,1,4
2983,69,22.9,0,1,0,1,5,0,4,0,1,1
1314,69,23,0,5,0,1,5,0,8,0,1,9
7895,69,23.6,0,4,0,1,5,1,1,0,1,2
4723,69,23.6,0,3,0,1,6,0,8,0,1,3
547,69,23.7,0,2,0,1,8,0,7,0,1,3
6294,69,23.7,0,1,0,1,7,1,6,0,1,7
1662,69,23.9,0,1,0,1,8,0,7,0,1,5
2446,69,24.1,0,3,0,1,6,0,3,0,1,8
9968,69,24.1,0,5,0,1,7,1,7,0,1,10
4794,69,24.3,0,4,0,1,7,0,5,0,1,6
6389,69,24.8,0,4,0,1,5,1,2,0,1,2
8130,69,25,0,5,0,1,8,1,7,0,1,4
2548,69,25,0,1,0,1,8,0,2,0,1,3
7531,69,25.3,0,4,0,1,8,1,1,0,1,3
10122,69,25.4,0,4,0,1,5,1,6,0,1,2
8475,69,25.6,0,1,0,1,8,1,7,0,1,0
9108,69,25.7,0,2,0,1,8,1,2,0,1,0
7685,69,25.8,0,3,0,1,7,1,3,0,1,0
2674,69,26.2,0,5,0,1,8,0,6,0,1,6
7956,69,26.7,0,3,0,1,7,1,4,0,1,5
6955,69,26.8,0,4,0,1,6,1,8,0,1,1
2056,69,26.8,0,2,0,1,7,0,3,0,1,0
9242,69,27.5,0,3,0,1,7,1,1,0,1,0
6726,69,27.5,0,5,0,1,5,1,3,0,1,4
981,69,27.9,0,4,0,1,7,0,8,0,1,3
9317,69,28.1,0,5,0,1,5,1,2,0,1,0
10115,69,28.2,0,2,0,1,5,1,4,0,1,2
7999,69,28.5,0,3,0,1,8,1,4,0,1,0
5942,69,28.9,0,1,0,1,7,1,2,0,1,3
1733,69,28.9,0,4,0,1,6,0,3,0,1,3
10274,69,29,0,2,0,1,7,1,4,0,1,2
271,69,29.7,0,1,0,1,8,0,8,0,1,2
4376,70,0.3,0,2,0,1,5,0,2,0,1,4
20,70,0.5,0,3,0,1,8,0,5,0,1,8
65,70,0.5,0,2,0,1,6,0,6,0,1,0
6231,70,0.7,0,3,0,1,7,1,8,0,1,4
7556,70,0.9,0,3,0,1,7,1,2,0,1,0
1902,70,1.3,0,2,0,1,6,0,7,0,1,9
168,70,1.4,0,2,0,1,7,0,8,0,1,2
3342,70,1.7,0,3,0,1,5,0,6,0,1,9
2044,70,2.5,0,3,0,1,6,0,6,0,1,2
10222,70,2.5,0,1,0,1,8,1,7,0,1,10
2994,70,2.9,0,3,0,1,7,0,2,0,1,5
7095,70,3.3,0,4,0,1,6,1,3,0,1,4
4428,70,3.5,0,5,0,1,8,0,2,0,1,4
5638,70,3.8,0,5,0,1,8,1,2,0,1,2
7876,70,4.2,0,1,0,1,7,1,2,0,1,0
4832,70,4.2,0,4,0,1,5,0,1,0,1,0
7252,70,4.2,0,5,0,1,5,1,5,0,1,4
10349,70,4.5,0,5,0,1,7,1,5,0,1,0
10084,70,4.7,0,5,0,1,6,1,3,0,1,3
9468,70,5.1,0,4,0,1,6,1,5,0,1,0
9778,70,5.5,0,5,0,1,7,1,7,0,1,0
1785,70,6.2,0,3,0,1,5,0,1,0,1,2
8006,70,6.3,0,1,0,1,6,1,3,0,1,6
27,70,6.5,0,1,0,1,5,0,6,0,1,4
6416,70,6.5,0,2,0,1,7,1,3,0,1,2
4558,70,6.7,0,1,0,1,8,0,7,0,1,10
4596,70,6.7,0,2,0,1,8,0,5,0,1,5
1732,70,7.1,0,5,0,1,8,0,8,0,1,6
10162,70,7.2,0,3,0,1,8,1,3,0,1,9
8534,70,7.6,0,5,0,1,6,1,8,0,1,7
9876,70,7.9,0,1,0,1,7,1,5,0,1,9
7924,70,7.9,0,1,0,1,8,1,8,0,1,5
264,70,8,0,1,0,1,5,0,4,0,1,4
6940,70,8.2,0,3,0,1,6,1,1,0,1,9
8846,70,8.4,0,2,0,1,6,1,3,1,1,2
7062,70,8.4,0,3,0,1,6,1,7,1,1,9
749,70,8.9,0,4,0,1,7,0,4,1,1,4
682,70,8.9,0,5,0,1,5,0,2,1,1,0
7920,70,9,0,4,0,1,5,1,1,1,1,3
2319,70,9.8,0,5,0,1,5,0,4,1,1,4
7290,70,10.1,0,4,0,1,5,1,3,1,1,3
2926,70,10.1,0,1,0,1,6,0,2,1,1,4
7502,70,10.4,0,5,0,1,7,1,4,1,1,6
8611,70,10.7,0,2,0,1,8,1,5,1,1,0
3860,70,10.8,0,3,0,1,8,0,8,1,1,6
7753,70,11,0,4,0,1,5,1,2,1,1,4
8540,70,11.1,0,4,0,1,7,1,3,1,1,5
9922,70,11.4,0,1,0,1,6,1,8,1,1,0
5107,70,11.5,0,1,0,1,5,0,7,1,1,2
6732,70,11.6,0,1,0,1,7,1,8,1,1,2
1826,70,12.1,0,4,0,1,6,0,2,1,1,4
618,70,12.4,0,4,0,1,8,0,3,1,1,3
1477,70,12.7,0,1,0,1,8,0,2,1,1,1
4445,70,13.1,0,4,0,1,7,0,1,1,1,3
9784,70,13.2,0,3,0,1,8,1,4,1,1,0
3465,70,13.8,0,5,0,1,5,0,4,1,1,0
7091,70,13.9,0,2,0,1,5,1,8,1,1,1
1696,70,14.3,0,1,0,1,8,0,7,1,1,7
3598,70,14.4,0,2,0,1,8,0,4,1,1,2
1982,70,15,0,1,0,1,5,0,4,1,1,4
9956,70,15.6,0,4,0,1,5,1,3,1,1,4
7358,70,15.7,0,3,0,1,7,1,3,1,1,2
1950,70,15.8,0,2,0,1,7,0,1,1,1,4
10674,70,16,0,5,0,1,6,1,5,1,1,0
7488,70,16.5,0,4,0,1,6,1,6,1,1,8
10,70,16.6,0,5,0,1,7,0,8,1,1,6
3898,70,16.8,0,5,0,1,5,0,8,1,1,1
7036,70,16.8,0,2,0,1,6,1,4,1,1,0
2360,70,17.1,0,1,0,1,7,0,5,1,1,8
6146,70,17.6,0,1,0,1,8,1,3,1,1,3
194,70,17.8,0,3,0,1,7,0,1,1,1,3
1629,70,18,0,4,0,1,8,0,2,1,1,4
7998,70,18.1,0,2,0,1,8,1,7,1,1,6
1127,70,18.3,0,3,0,1,8,0,5,1,1,0
2408,70,18.9,0,3,0,1,6,0,4,1,1,6
2656,70,19.3,0,2,0,1,5,0,7,1,1,7
3578,70,19.7,0,4,0,1,8,0,5,1,1,0
1026,70,19.9,0,3,0,1,8,0,5,1,1,6
7696,70,20.2,0,5,0,1,7,1,1,1,1,0
7755,70,20.4,0,1,0,1,5,1,2,1,1,0
1060,70,20.8,0,1,0,1,5,0,1,1,1,9
3782,70,20.8,0,1,0,1,6,0,4,1,1,6
1300,70,20.9,0,1,0,1,7,0,5,1,1,9
2346,70,21.1,0,4,0,1,6,0,8,1,1,7
8990,70,21.3,0,1,0,1,8,1,7,1,1,4
2087,70,21.5,0,2,0,1,5,0,2,1,1,0
4320,70,21.6,0,1,0,1,6,0,5,1,1,4
7144,70,21.7,0,1,0,1,8,1,6,1,1,9
3130,70,21.8,0,4,0,1,8,0,6,1,1,3
4334,70,21.9,0,3,0,1,7,0,2,1,1,2
869,70,22.1,0,3,0,1,8,0,5,1,1,3
8478,70,22.6,0,4,0,1,7,1,7,1,1,8
178,70,22.7,0,3,0,1,7,0,6,1,1,10
7334,70,23.1,0,3,0,1,8,1,6,1,1,5
2585,70,23.2,0,1,0,1,6,0,8,1,1,4
1426,70,23.3,0,1,0,1,8,0,8,1,1,9
7176,70,23.8,0,4,0,1,6,1,6,1,1,6
1255,70,23.9,0,3,0,1,5,0,1,1,1,3
9861,70,24.2,0,3,0,1,7,1,5,1,1,3
2250,70,24.4,0,3,0,1,5,0,5,1,1,8
10756,70,24.9,0,4,0,1,7,1,3,1,1,10
2028,70,25.2,0,3,0,1,7,0,5,1,1,4
5724,70,25.7,0,4,0,1,7,1,6,1,1,2
565,70,26.5,0,4,0,1,8,0,6,1,1,4
1715,70,26.7,0,2,0,1,5,0,4,1,1,2
8685,70,26.9,0,4,0,1,7,1,7,1,1,4
9269,70,27.3,0,5,0,1,8,1,5,1,1,1
2517,70,27.4,0,1,0,1,6,0,2,1,1,4
472,70,27.4,0,1,0,1,5,0,5,1,1,3
8698,70,27.8,0,1,0,1,8,1,3,1,1,1
9425,70,28,0,3,0,1,7,1,6,1,1,4
10310,70,28.2,0,3,0,1,8,1,6,1,1,0
1579,70,28.3,0,2,0,1,6,0,4,1,1,2
6228,70,29,0,3,0,1,7,1,7,1,1,2
2651,70,29.1,0,3,0,1,8,0,2,1,1,0
6820,70,29.4,0,5,0,1,5,1,2,1,1,7
7368,70,29.8,0,2,0,1,5,1,5,1,1,5
8660,70,29.9,0,1,0,1,8,1,4,1,1,5
362,70,29.9,0,3,0,1,6,0,5,1,1,5
1702,71,0.2,0,3,0,1,7,0,3,1,1,6
7109,71,1,0,4,0,1,8,1,3,1,1,4
7740,71,1,0,4,0,1,8,1,1,1,1,7
10288,71,1.2,0,1,0,1,8,1,7,1,1,4
2596,71,1.3,0,1,0,1,8,0,5,1,1,8
6070,71,1.7,0,1,0,1,8,1,2,1,1,6
2824,71,1.9,0,5,0,1,8,0,8,1,1,1
5334,71,2,0,4,0,1,6,0,2,1,1,1
10680,71,2.1,0,5,0,1,5,1,5,1,1,8
1066,71,2.3,0,1,0,1,6,0,8,1,1,7
10628,71,2.5,0,4,0,1,7,1,1,1,1,5
2403,71,2.6,0,2,0,1,7,0,5,1,1,1
10822,71,2.9,0,3,0,1,7,1,6,1,1,0
10113,71,3.1,0,5,0,1,6,1,2,1,1,0
6974,71,3.2,0,4,0,1,6,1,1,1,1,2
906,71,3.3,0,5,0,1,5,0,4,1,1,10
10736,71,3.3,0,4,0,1,6,1,7,1,1,1
9722,71,3.8,0,5,0,1,8,1,7,1,1,3
5888,71,3.9,0,5,0,1,6,1,7,1,1,1
9379,71,4.2,0,5,0,1,5,1,4,1,1,0
5988,71,4.4,0,4,0,1,8,1,3,1,1,8
10590,71,4.6,0,2,0,1,8,1,8,1,1,2
9868,71,4.7,0,5,0,1,7,1,6,1,1,7
8839,71,5,0,5,0,1,7,1,4,1,1,1
9694,71,5,0,1,0,1,6,1,5,1,1,6
500,71,5.7,0,4,0,1,5,0,7,1,1,9
4858,71,5.7,0,3,0,1,8,0,7,1,1,9
3772,71,6.7,0,1,0,1,7,0,5,1,1,2
6734,71,6.8,0,5,0,1,8,1,6,1,1,9
8511,71,7.1,0,3,0,1,7,1,2,1,1,0
5604,71,7.3,0,3,0,1,6,1,6,1,1,9
2764,71,7.6,0,4,0,1,7,0,3,1,1,3
3518,71,7.7,0,3,0,1,7,0,5,1,1,6
5672,71,7.7,0,4,0,1,8,1,5,1,1,2
8481,71,8.3,0,1,0,1,6,1,6,1,1,0
5929,71,8.4,0,3,0,1,7,1,6,1,1,3
7244,71,8.6,0,5,0,1,8,1,2,1,1,2
7515,71,8.9,0,2,0,1,5,1,6,1,1,1
8664,71,9.2,0,5,0,1,6,1,2,1,1,5
4638,71,9.4,0,2,0,1,5,0,8,1,1,10
862,71,10.3,0,4,0,1,7,0,5,1,1,4
136,71,10.4,0,5,0,1,5,0,7,1,1,0
4995,71,10.4,0,1,0,1,8,0,5,1,1,4
4516,71,10.7,0,3,0,1,8,0,7,1,1,10
1107,71,10.8,0,1,0,1,6,0,5,1,1,4
5813,71,11.5,0,2,0,1,8,1,3,1,1,0
3702,71,11.7,0,1,0,1,7,0,8,1,1,5
5546,71,11.8,0,4,0,1,5,1,1,1,1,4
9510,71,12.3,0,3,0,1,7,1,3,1,1,5
8421,71,12.5,0,3,0,1,6,1,1,1,1,4
7916,71,12.6,0,3,0,1,7,1,7,1,1,8
4831,71,12.6,0,1,0,1,8,0,7,1,1,2
8998,71,12.6,0,3,0,1,5,1,7,1,1,5
9009,71,12.6,0,4,0,1,7,1,2,1,1,0
7347,71,12.6,0,1,0,1,5,1,3,1,1,1
7728,71,12.9,0,5,0,1,5,1,7,1,1,6
846,71,13.1,0,5,0,1,6,0,1,1,1,7
9140,71,13.5,0,3,0,1,6,1,3,1,1,9
1024,71,13.6,0,5,0,1,5,0,6,1,1,4
9692,71,13.6,0,1,0,1,5,1,1,1,1,2
1692,71,13.6,0,2,0,1,5,0,5,1,1,2
9125,71,14,0,2,0,1,8,1,6,1,1,1
6010,71,14.3,0,2,0,1,5,1,4,1,1,7
1830,71,14.3,0,5,0,1,7,0,7,1,1,5
9496,71,14.4,0,5,0,1,7,1,4,1,1,2
3000,71,14.4,0,5,0,1,5,0,8,1,1,2
960,71,14.5,0,5,0,1,6,0,8,1,1,8
3640,71,14.6,0,3,0,1,5,0,8,1,1,9
5567,71,15.6,0,3,0,1,7,1,6,1,1,1
1245,71,15.7,0,4,0,1,8,0,7,1,1,2
2751,71,15.7,0,2,0,1,6,0,4,1,1,4
2912,71,16,0,2,0,1,8,0,3,1,1,9
3468,71,16.2,0,2,0,1,5,0,5,1,1,1
830,71,16.3,0,3,0,1,6,0,1,1,1,3
7500,71,16.4,0,5,0,1,6,1,5,1,1,1
4021,71,17,0,4,0,1,7,0,6,1,1,1
9754,71,17.2,0,3,0,1,6,1,2,1,1,2
5248,71,17.3,0,2,0,1,5,0,3,1,1,5
7840,71,17.4,0,4,0,1,5,1,1,1,1,4
10256,71,17.6,0,1,0,1,7,1,8,1,1,5
6926,71,17.8,0,4,0,1,6,1,5,1,1,7
9196,71,17.9,0,5,0,1,6,1,7,1,1,6
4966,71,18,0,4,0,1,6,0,2,1,1,3
4903,71,18.4,0,2,0,1,7,0,6,1,1,1
9880,71,18.6,0,3,0,1,8,1,4,1,1,0
5690,71,18.7,0,4,0,1,6,1,6,1,1,6
6908,71,19,0,5,0,1,7,1,6,1,1,7
9313,71,20.3,0,2,0,1,8,1,2,1,1,2
8521,71,20.4,0,5,0,1,6,1,2,1,1,1
3539,71,21,0,2,0,1,5,0,4,1,1,1
9631,71,21.6,0,2,0,1,6,1,3,1,1,1
4384,71,21.7,0,2,0,1,6,0,2,1,1,10
7793,71,21.7,0,1,0,1,6,1,6,1,1,0
7561,71,21.8,0,4,0,1,5,1,6,1,1,2
4772,71,21.8,0,2,0,1,8,0,5,1,1,0
4204,71,22.3,0,1,0,1,5,0,5,1,1,5
7965,71,22.5,0,1,0,1,8,1,1,1,1,1
10243,71,23.1,0,2,0,1,6,1,7,1,1,1
5564,71,23.4,0,5,0,1,6,1,8,1,1,10
5664,71,23.6,0,5,0,1,7,1,7,1,1,5
8287,71,24.1,0,2,0,1,5,1,7,1,1,2
6422,71,24.2,0,1,0,1,6,1,8,1,1,5
5537,71,24.8,0,5,0,1,6,1,3,1,1,4
8070,71,24.8,0,3,0,1,8,1,4,1,1,10
9546,71,24.9,0,3,0,1,6,1,3,1,1,7
5302,71,25,0,3,0,1,5,0,2,1,1,10
6126,71,25.2,0,2,0,1,8,1,1,1,1,5
8596,71,25.2,0,2,0,1,5,1,8,1,1,10
9630,71,25.3,0,3,0,1,6,1,7,1,1,0
9424,71,25.6,0,5,0,1,8,1,7,1,1,4
5290,71,25.6,0,1,0,1,6,0,7,1,1,1
8730,71,25.6,0,1,0,1,6,1,6,1,1,6
60,71,25.9,0,1,0,1,7,0,3,1,1,3
2057,71,26,0,2,0,1,5,0,3,1,1,3
7116,71,26.1,0,3,0,1,7,1,5,1,1,5
6414,71,26.6,0,5,0,1,8,1,4,1,1,10
9342,71,26.7,0,2,0,1,7,1,1,1,1,8
3897,71,26.7,0,5,0,1,5,0,6,1,1,0
6546,71,27.8,0,1,0,1,6,1,1,1,1,10
2382,71,28.5,0,5,0,1,5,0,7,1,1,10
350,71,28.5,0,5,0,1,5,0,8,1,1,4
9750,71,28.6,0,5,0,1,7,1,1,1,1,2
551,71,29,0,1,0,1,8,0,6,1,1,0
52,71,29.1,0,5,0,1,7,0,5,1,1,1
2114,72,0.5,0,1,0,1,8,0,3,1,1,7
5974,72,0.6,0,4,0,1,8,1,4,1,1,7
2712,72,0.6,0,5,0,1,8,0,2,1,1,8
8883,72,0.8,0,2,0,1,8,1,1,1,1,4
3164,72,1,0,4,0,1,6,0,2,1,1,2
6473,72,1.2,0,5,0,1,6,1,1,1,1,3
9493,72,1.4,0,4,0,1,7,1,1,1,1,2
5358,72,1.5,0,1,0,1,7,0,7,1,1,9
5465,72,1.5,0,1,0,1,8,1,3,1,1,4
2662,72,1.6,0,3,0,1,6,0,2,1,1,1
6263,72,1.7,0,1,0,1,6,1,8,1,1,2
1560,72,2.2,0,2,0,1,7,0,2,1,1,1
186,72,2.8,0,5,0,1,7,0,7,1,1,4
75,72,3.1,0,2,0,1,8,0,5,1,1,4
8053,72,3.7,0,1,0,1,7,1,2,1,1,2
439,72,3.7,0,3,0,1,7,0,3,1,1,0
1865,72,4.4,0,2,0,1,7,0,4,1,1,2
4018,72,5.3,0,2,0,1,5,0,1,1,1,9
1004,72,6.2,0,4,0,1,8,0,8,1,1,9
9054,72,7,0,3,0,1,5,1,5,1,1,1
6060,72,7.7,0,3,0,1,6,1,2,1,1,6
3721,72,8.3,0,5,0,1,7,0,7,1,1,1
211,72,8.4,0,4,0,1,5,0,8,1,1,1
9814,72,8.4,0,4,0,1,5,1,5,1,1,3
8093,72,8.5,0,5,0,1,6,1,1,1,1,2
5755,72,8.6,0,3,0,1,5,1,7,1,1,0
7402,72,9.2,0,1,0,1,8,1,1,1,1,2
1694,72,9.4,0,1,0,1,7,0,2,1,1,10
8412,72,10,0,5,0,1,5,1,2,1,1,6
7853,72,10.1,0,2,0,1,6,1,6,1,1,2
9668,72,10.4,0,5,0,1,5,1,7,1,1,5
3668,72,10.9,0,5,0,1,8,0,3,1,1,8
244,72,11.2,0,4,0,1,8,0,4,1,1,7
9409,72,11.7,0,1,0,1,7,1,8,1,1,2
3771,72,12,0,2,0,1,8,0,4,1,1,0
10690,72,12.3,0,3,0,1,8,1,3,1,1,10
9355,72,12.3,0,1,0,1,7,1,3,1,1,0
9106,72,12.7,0,1,0,1,6,1,1,1,1,4
8836,72,13,0,2,0,1,8,1,6,1,1,8
6248,72,13.1,0,4,0,1,7,1,8,1,1,5
4739,72,13.2,0,1,0,1,5,0,7,1,1,3
3758,72,13.3,0,5,0,1,5,0,6,1,1,1
10079,72,13.7,0,1,0,1,6,1,6,1,1,4
164,72,14,0,5,0,1,6,0,4,1,1,2
6264,72,14,0,5,0,1,7,1,8,1,1,3
9382,72,14.4,0,3,0,1,6,1,8,1,1,1
8734,72,14.6,0,1,0,1,7,1,4,1,1,2
8908,72,14.6,0,4,0,1,6,1,8,1,1,5
7093,72,15.1,0,3,0,1,8,1,3,1,1,2
9059,72,15.2,0,1,0,1,6,1,6,1,1,0
4022,72,15.2,0,5,0,1,8,0,2,1,1,9
7353,72,15.4,0,4,0,1,7,1,5,1,1,2
688,72,15.4,0,2,0,1,7,0,4,1,1,1
6513,72,15.4,0,4,0,1,6,1,3,1,1,0
2,72,15.7,0,2,0,1,5,0,3,1,1,5
506,72,15.9,0,5,0,1,8,0,7,1,1,10
8143,72,16.6,0,1,0,1,8,1,6,1,1,0
7349,72,16.6,0,2,0,1,7,1,2,1,1,4
8260,72,16.6,0,3,0,1,8,1,2,1,1,3
1075,72,16.6,0,3,0,1,8,0,3,1,1,4
904,72,16.6,0,1,0,1,8,0,5,1,1,6
3806,72,16.9,0,3,0,1,5,0,7,1,1,0
5280,72,17.4,0,1,0,1,7,0,8,1,1,9
4216,72,17.9,0,5,0,1,7,0,6,1,1,6
4620,72,18.3,0,1,0,1,6,0,2,1,1,4
1158,72,18.4,0,1,0,1,7,0,3,1,1,3
1587,72,19.3,0,5,0,1,6,0,5,1,1,2
5343,72,19.4,0,1,0,1,5,0,4,1,1,1
8106,72,19.6,0,4,0,1,5,1,1,1,1,8
4552,72,19.6,0,2,0,1,5,0,1,1,1,3
512,72,19.7,0,4,0,1,6,0,2,1,1,5
7532,72,19.9,0,3,0,1,6,1,6,1,1,6
7990,72,20.4,0,3,0,1,6,1,3,1,1,7
4774,72,20.7,0,1,0,1,6,0,8,1,1,9
10587,72,20.7,0,4,0,1,5,1,7,1,1,2
352,72,20.9,0,1,0,1,6,0,2,1,1,0
73,72,21.5,0,4,0,1,8,0,8,1,1,4
2162,72,21.5,0,1,0,1,7,0,6,1,1,4
6024,72,21.8,0,5,0,1,5,1,8,1,1,5
8672,72,21.9,0,2,0,1,7,1,3,1,1,5
219,72,21.9,0,5,0,1,8,0,3,1,1,2
4896,72,22,0,3,0,1,7,0,2,1,1,0
3058,72,22.2,0,3,0,1,6,0,8,1,1,7
1266,72,22.3,0,4,0,1,7,0,1,1,1,10
6232,72,22.5,0,3,0,1,8,1,6,1,1,0
6786,72,22.8,0,3,0,1,5,1,7,1,1,2
68,72,22.9,0,4,0,1,5,0,6,1,1,6
3199,72,23,0,4,0,1,5,0,2,1,1,2
8848,72,23.2,0,1,0,1,7,1,6,1,1,8
7462,72,23.4,0,3,0,1,6,1,3,1,1,2
6958,72,23.7,0,1,0,1,7,1,6,1,1,4
9346,72,23.9,0,1,0,1,8,1,4,1,1,5
7642,72,24.2,0,1,0,1,6,1,3,1,1,0
4013,72,24.7,0,3,0,1,7,0,6,1,1,3
6567,72,25.8,0,1,0,1,7,1,2,1,1,3
6980,72,26,0,3,0,1,6,1,3,1,1,0
7166,72,26.8,0,2,0,1,6,1,4,1,1,3
3734,72,26.9,0,3,0,1,7,0,3,1,1,5
1298,72,26.9,0,4,0,1,6,0,5,1,1,3
6458,72,27.1,0,3,0,1,8,1,6,1,1,0
3251,72,27.3,0,1,0,1,6,0,6,1,1,2
9219,72,27.4,0,2,0,1,5,1,8,1,1,0
7136,72,27.9,0,2,0,1,8,1,6,1,1,10
162,72,27.9,0,5,0,1,8,0,6,1,1,8
2224,72,28.2,0,1,0,1,5,0,8,1,1,6
7108,72,28.6,0,5,0,1,5,1,5,1,1,10
212,72,29.1,0,5,0,1,8,0,5,1,1,7
6002,72,29.9,0,1,0,1,5,1,4,1,1,2
6363,73,0.1,0,4,0,1,8,1,3,1,1,1
3646,73,0.2,0,5,0,1,8,0,3,1,1,0
1781,73,0.7,0,1,0,1,8,0,8,1,1,0
1718,73,0.9,0,3,0,1,7,0,5,1,1,7
982,73,1,0,2,0,1,8,0,5,1,1,6
10238,73,1.3,0,5,0,1,5,1,4,1,1,3
2502,73,1.3,0,3,0,1,8,0,8,1,1,0
324,73,1.7,0,5,0,1,6,0,8,1,1,10
7985,73,1.8,0,2,0,1,6,1,6,1,1,0
6997,73,2.1,0,1,0,1,7,1,6,1,1,1
5239,73,2.3,0,2,0,1,8,0,8,1,1,0
289,73,2.7,0,5,0,1,7,0,7,1,1,4
1914,73,3.5,0,1,0,1,8,0,1,1,1,10
7122,73,4.1,0,2,0,1,7,1,1,1,1,3
738,73,4.1,0,4,0,1,6,0,7,1,1,2
5443,73,4.6,0,4,0,1,5,1,7,1,1,4
4696,73,4.6,0,3,0,1,6,0,4,1,1,2
1160,73,4.6,0,4,0,1,5,0,5,1,1,3
10726,73,5,0,5,0,1,6,1,1,1,1,8
3026,73,5.3,0,4,0,1,6,0,3,1,1,10
3971,73,5.8,0,1,0,1,8,0,3,1,1,3
8924,73,5.9,0,3,0,1,6,1,4,1,1,6
9514,73,5.9,0,2,0,1,7,1,1,1,1,4
762,73,6,0,3,0,1,5,0,6,1,1,7
7163,73,6.1,0,3,0,1,6,1,8,1,1,2
540,73,6.1,0,3,0,1,8,0,1,1,1,7
1934,73,6.5,0,5,0,1,8,0,3,1,1,1
10720,73,6.7,0,3,0,1,8,1,3,1,1,5
372,73,7,0,5,0,1,7,0,3,1,1,1
640,73,7,0,3,0,1,8,0,4,1,1,7
5250,73,7.2,0,4,0,1,6,0,4,1,1,4
9454,73,7.4,0,4,0,1,7,1,2,1,1,2
7788,73,8,0,1,0,1,6,1,2,1,1,0
282,73,8.6,0,4,0,1,5,0,6,1,1,2
5422,73,9.2,0,1,0,1,8,1,8,1,1,10
5746,73,9.2,0,2,0,1,5,1,3,1,1,0
3596,73,9.7,0,1,0,1,7,0,8,1,1,2
7367,73,10.5,0,3,0,1,5,1,3,1,1,0
9208,73,10.7,0,1,0,1,8,1,3,1,1,5
6558,73,11.1,0,3,0,1,5,1,7,1,1,4
3398,73,11.7,0,1,0,1,7,0,8,1,1,7
4224,73,11.8,0,5,0,1,7,0,2,1,1,8
5560,73,12,0,2,0,1,7,1,4,1,1,1
304,73,12.3,0,1,0,1,6,0,1,1,1,9
5480,73,12.3,0,5,0,1,5,1,7,1,1,10
4813,73,12.4,0,4,0,1,6,0,7,1,1,4
9690,73,12.4,0,3,0,1,5,1,2,1,1,2
1610,73,12.5,0,4,0,1,7,0,2,1,1,6
3905,73,12.7,0,4,0,1,5,0,3,1,1,4
6432,73,13.5,0,3,0,1,6,1,2,1,1,9
944,73,13.6,0,2,0,1,7,0,6,1,1,0
2366,73,14.2,0,2,0,1,8,0,8,1,1,4
1174,73,14.6,0,4,0,1,6,0,4,1,1,7
6873,73,14.7,0,1,0,1,5,1,3,1,1,4
10297,73,14.9,0,5,0,1,7,1,2,1,1,4
5691,73,15,0,1,0,1,6,1,2,1,1,3
6730,73,15,0,3,0,1,8,1,4,1,1,3
3441,73,15.2,0,4,0,1,8,0,4,1,1,3
8513,73,15.4,0,2,0,1,5,1,6,1,1,2
8074,73,15.7,0,1,0,1,7,1,3,1,1,7
5153,73,15.8,0,2,0,1,8,0,4,1,1,1
2458,73,16.4,0,4,0,1,7,0,7,1,1,4
5576,73,17.5,0,5,0,1,7,1,6,1,1,1
1404,73,17.7,0,3,0,1,5,0,7,1,1,9
92,73,17.8,0,3,0,1,7,0,4,1,1,0
5768,73,17.9,0,4,0,1,7,1,2,1,1,7
8627,73,18.5,0,4,0,1,7,1,7,1,1,4
9044,73,18.8,0,5,0,1,5,1,3,1,1,6
971,73,19.1,0,3,0,1,7,0,8,1,1,1
3922,73,19.4,0,5,0,1,6,0,1,1,1,5
2663,73,19.4,0,3,0,1,8,0,5,1,1,1
6856,73,20.1,0,3,0,1,7,1,7,1,1,2
4574,73,20.2,0,3,0,1,8,0,3,1,1,0
3842,73,20.4,0,3,0,1,6,0,5,1,1,9
2931,73,20.4,0,5,0,1,7,0,3,1,1,2
216,73,20.4,0,2,0,1,7,0,5,1,1,10
8872,73,20.6,0,3,0,1,7,1,4,1,1,1
2141,73,20.7,0,2,0,1,5,0,6,1,1,0
9955,73,21.4,0,1,0,1,5,1,1,1,1,1
6405,73,21.5,0,1,0,1,7,1,6,1,1,1
9314,73,22,0,2,0,1,5,1,8,1,1,1
610,73,22.2,0,3,0,1,7,0,1,1,1,1
7294,73,22.2,0,2,0,1,7,1,7,1,1,7
10064,73,22.2,0,2,0,1,6,1,5,1,1,7
202,73,22.3,0,3,0,1,8,0,1,1,1,6
6132,73,22.5,0,3,0,1,8,1,2,1,1,0
5374,73,22.5,0,5,0,1,7,0,6,1,1,8
8226,73,22.6,0,3,0,1,7,1,3,1,1,3
6130,73,22.9,0,4,0,1,7,1,4,1,1,8
7020,73,22.9,0,3,0,1,6,1,7,1,1,10
366,73,24.1,0,4,0,1,6,0,8,1,1,6
26,73,24.3,0,1,0,1,6,0,1,1,1,3
7511,73,24.3,0,5,0,1,8,1,4,1,1,4
8635,73,24.4,0,5,0,1,7,1,7,1,1,2
5592,73,25.2,0,3,0,1,8,1,4,1,1,10
9391,73,25.2,0,1,0,1,7,1,5,1,1,2
4403,73,25.2,0,1,0,1,6,0,1,1,1,3
6952,73,25.7,0,2,0,1,8,1,2,1,1,10
1588,73,25.8,0,2,0,1,7,0,1,1,1,6
3560,73,25.9,0,4,0,1,6,0,8,1,1,3
4740,73,26.1,0,4,0,1,7,0,2,1,1,0
6936,73,26.1,0,2,0,1,5,1,6,1,1,10
6150,73,26.7,0,3,0,1,6,1,7,1,1,10
1142,73,26.8,0,5,0,1,8,0,5,1,1,7
801,73,27,0,4,0,1,6,0,7,1,1,3
4471,73,27.1,0,4,0,1,7,0,7,1,1,3
590,73,27.7,0,2,0,1,7,0,8,1,1,4
4904,73,27.9,0,5,0,1,6,0,7,1,1,4
775,73,28,0,1,0,1,6,0,6,1,1,3
5317,73,28,0,1,0,1,5,0,4,1,1,2
686,73,28.1,0,4,0,1,8,0,2,1,1,10
5305,73,28.5,0,4,0,1,6,0,5,1,1,4
156,73,28.6,0,5,0,1,8,0,7,1,1,4
10376,73,28.8,0,2,0,1,5,1,1,1,1,8
8388,73,28.9,0,3,0,1,5,1,5,1,1,5
6518,73,29,0,3,0,1,7,1,5,1,1,2
5536,73,29,0,2,0,1,5,1,2,1,1,7
938,73,29,0,2,0,1,8,0,4,1,1,9
4962,73,29.2,0,4,0,1,5,0,5,1,1,5
5944,73,29.4,0,2,0,1,6,1,5,1,1,7
5120,73,29.5,0,2,0,1,8,0,4,1,1,6
10506,73,29.6,0,1,0,1,8,1,4,1,1,3
10460,73,29.8,0,2,0,1,5,1,8,1,1,7
4504,74,0.2,0,1,0,1,7,0,5,1,1,0
5304,74,0.2,0,2,0,1,6,0,4,1,1,3
4234,74,0.4,0,3,0,1,7,0,7,1,1,0
7322,74,0.9,0,4,0,1,7,1,3,1,1,10
9452,74,0.9,0,3,0,1,7,1,8,1,1,4
5060,74,1.2,0,4,0,1,6,0,1,1,1,5
4452,74,1.3,0,2,0,1,5,0,1,1,1,1
9410,74,1.8,0,1,0,1,8,1,3,1,1,2
6427,74,2.4,0,4,0,1,5,1,1,1,1,2
5080,74,2.4,0,2,0,1,7,0,6,1,1,2
880,74,2.6,0,2,0,1,8,0,8,1,1,3
3040,74,2.6,0,2,0,1,7,0,1,1,1,2
2858,74,2.9,0,4,0,1,5,0,1,1,1,2
8558,74,3.2,0,2,0,1,8,1,6,1,1,0
4122,74,3.3,0,1,0,1,6,0,6,1,1,1
288,74,3.4,0,2,0,1,6,0,5,1,1,6
9307,74,3.4,0,2,0,1,6,1,4,1,1,1
4480,74,3.5,0,4,0,1,7,0,3,1,1,5
10540,74,3.7,0,3,0,1,6,1,4,1,1,7
1273,74,3.7,0,3,0,1,8,0,5,1,1,4
5438,74,4,0,2,0,1,8,1,7,1,1,3
8126,74,4,0,3,0,1,7,1,8,1,1,6
1322,74,4.2,0,1,0,1,7,0,4,1,1,0
6804,74,4.2,0,5,0,1,8,1,6,1,1,7
7154,74,4.7,0,4,0,1,5,1,3,1,1,9
5744,74,4.9,0,5,0,1,5,1,4,1,1,4
10180,74,5.2,0,2,0,1,7,1,8,1,1,7
4651,74,5.4,0,3,0,1,5,0,8,1,1,1
4782,74,5.5,0,1,0,1,7,0,5,1,1,8
10307,74,5.9,0,2,0,1,5,1,3,1,1,3
994,74,5.9,0,4,0,1,7,0,6,1,1,9
668,74,6.5,0,2,0,1,8,0,4,1,1,7
2536,74,7,0,2,0,1,7,0,4,1,1,2
5670,74,7,0,3,0,1,8,1,8,1,1,8
7846,74,7.2,0,4,0,1,8,1,8,1,1,10
7534,74,7.2,0,4,0,1,5,1,7,1,1,1
3919,74,7.5,0,1,0,1,6,0,8,1,1,0
8913,74,7.8,0,2,0,1,5,1,4,1,1,4
2582,74,7.8,0,5,0,1,5,0,2,1,1,2
6842,74,9.1,0,2,0,1,8,1,5,1,1,3
9964,74,9.2,0,1,0,1,6,1,3,1,1,4
9318,74,9.4,0,5,0,1,5,1,8,1,1,4
493,74,9.7,0,1,0,1,6,0,1,1,1,0
10393,74,9.8,0,1,0,1,8,1,1,1,1,2
7248,74,9.9,0,4,0,1,8,1,7,1,1,6
8884,74,10.2,0,3,0,1,7,1,7,1,1,2
10751,74,10.6,0,1,0,1,5,1,8,1,1,3
6595,74,10.8,0,4,0,1,8,1,3,1,1,2
5366,74,11.2,0,2,0,1,6,0,4,1,1,7
902,74,11.3,0,2,0,1,6,0,7,1,1,2
2041,74,11.5,0,3,0,1,5,0,7,1,1,3
1473,74,12.3,0,4,0,1,8,0,7,1,1,0
1980,74,12.5,0,1,0,1,8,0,3,1,1,5
9622,74,12.7,0,4,0,1,6,1,3,1,1,1
5136,74,13.1,0,2,0,1,6,0,6,1,1,0
9992,74,13.2,0,4,0,1,5,1,5,1,1,8
4349,74,13.3,0,2,0,1,7,0,8,1,1,1
2891,74,13.5,0,2,0,1,8,0,8,1,1,0
3032,74,13.6,0,3,0,1,6,0,4,1,1,10
4336,74,13.7,0,5,0,1,6,0,3,1,1,6
2572,74,13.8,0,1,0,1,5,0,5,1,1,9
206,74,13.9,0,5,0,1,6,0,4,1,1,2
7585,74,13.9,0,4,0,1,6,1,2,1,1,4
312,74,14.2,0,5,0,1,5,0,6,1,1,5
2877,74,14.3,0,2,0,1,5,0,8,1,1,0
5970,74,14.9,0,2,0,1,7,1,7,1,1,3
5561,74,15.1,0,1,0,1,5,1,1,1,1,0
5106,74,15.2,0,4,0,1,5,0,4,1,1,8
9527,74,15.3,0,1,0,1,8,1,6,1,1,0
9578,74,15.3,0,5,0,1,5,1,1,1,1,6
10411,74,15.5,0,5,0,1,8,1,7,1,1,2
4059,74,15.9,0,1,0,1,7,0,5,1,1,4
7568,74,16,0,3,0,1,7,1,7,1,1,10
1400,74,16.4,0,2,0,1,8,0,4,1,1,7
3639,74,17,0,1,0,1,5,0,7,1,1,1
7881,74,17,0,3,0,1,5,1,8,1,1,4
1267,74,17.2,0,2,0,1,5,0,8,1,1,0
3936,74,17.3,0,4,0,1,5,0,7,1,1,5
1841,74,17.5,0,3,0,1,6,0,4,1,1,0
3048,74,17.5,0,2,0,1,6,0,3,1,1,1
7404,74,17.8,0,1,0,1,6,1,4,1,1,6
7784,74,18.4,0,5,0,1,5,1,7,1,1,4
2630,74,18.5,0,2,0,1,7,0,1,1,1,1
9710,74,18.6,0,3,0,1,6,1,7,1,1,10
1894,74,18.6,0,3,0,1,7,0,7,1,1,5
3939,74,18.7,0,1,0,1,7,0,7,1,1,2
371,74,19.7,0,3,0,1,7,0,4,1,1,1
5765,74,20.1,0,5,0,1,5,1,7,1,1,4
1766,74,20.4,0,4,0,1,7,0,7,1,1,1
8080,74,20.5,0,4,0,1,8,1,7,1,1,1
2590,74,21.3,0,4,0,1,8,0,1,1,1,2
3900,74,21.4,0,4,0,1,8,0,1,1,1,6
6328,74,21.6,0,5,0,1,8,1,2,1,1,7
4356,74,22.3,0,1,0,1,6,0,1,1,1,6
8022,74,22.4,0,3,0,1,7,1,2,1,1,8
10495,74,22.7,0,4,0,1,7,1,8,1,1,4
5472,74,22.9,0,3,0,1,8,1,6,1,1,9
3470,74,23,0,2,0,1,8,0,7,1,1,5
979,74,23.2,0,1,0,1,6,0,2,1,1,1
2962,74,23.3,0,3,0,1,5,0,8,1,1,8
8630,74,23.3,0,5,0,1,8,1,3,1,1,6
7464,74,23.5,0,1,0,1,6,1,6,1,1,1
4652,74,24.3,0,5,0,1,7,0,3,1,1,8
1008,74,24.4,0,4,0,1,8,0,7,1,1,4
2070,74,24.5,0,4,0,1,7,0,6,1,1,10
6677,74,24.7,0,5,0,1,8,1,4,1,1,2
9671,74,24.9,0,2,0,1,6,1,4,1,1,1
528,74,25.1,0,5,0,1,5,0,6,1,1,6
5387,74,25.3,0,4,0,1,8,0,4,1,1,3
3380,74,25.4,0,4,0,1,8,0,4,1,1,2
6749,74,25.7,0,4,0,1,6,1,8,1,1,2
4996,74,26,0,5,0,1,5,0,1,1,1,10
3516,74,26.2,0,4,0,1,5,0,7,1,1,2
8,74,26.4,0,4,0,1,6,0,2,1,1,0
10038,74,26.5,0,1,0,1,5,1,6,1,1,10
3840,74,26.7,0,2,0,1,8,0,5,1,1,3
1596,74,26.8,0,4,0,1,8,0,5,1,1,5
10283,74,27,0,5,0,1,6,1,8,1,1,3
561,74,27.6,0,5,0,1,5,0,7,1,1,4
8886,74,28.1,0,4,0,1,7,1,8,1,1,4
3200,74,28.2,0,1,0,1,6,0,2,1,1,3
8902,74,28.3,0,3,0,1,6,1,5,1,1,10
5716,74,28.4,0,3,0,1,8,1,2,1,1,2
5028,74,29.3,0,5,0,1,6,0,5,1,1,0
6517,74,30,0,5,0,1,8,1,7,1,1,4
1423,75,0.5,0,5,0,1,6,0,5,1,1,4
10776,75,1,0,4,0,1,7,1,7,1,1,5
7286,75,1.1,0,3,0,1,8,1,4,1,1,10
2937,75,2.6,0,3,0,1,5,0,4,1,1,4
3067,75,2.8,0,2,0,1,7,0,8,1,1,0
1276,75,3.1,0,4,0,1,6,0,4,1,1,2
656,75,3.3,0,5,0,1,8,0,2,1,1,8
3568,75,3.7,0,5,0,1,5,0,2,1,1,1
10539,75,3.8,0,4,0,1,7,1,3,1,1,1
3540,75,4.9,0,5,0,1,7,0,6,1,1,0
7096,75,5.1,0,5,0,1,5,1,5,1,1,1
740,75,5.2,0,3,0,1,7,0,7,1,1,2
8628,75,5.4,0,1,0,1,6,1,5,1,1,6
9152,75,5.6,0,1,0,1,5,1,5,1,1,7
6155,75,5.8,0,1,0,1,8,1,7,1,1,1
6824,75,5.8,0,4,0,1,7,1,6,1,1,1
6210,75,6.1,0,4,0,1,5,1,7,1,1,8
2361,75,6.2,0,2,0,1,8,0,6,1,1,2
7652,75,7.1,0,4,0,1,5,1,8,1,1,3
8182,75,7.1,0,1,0,1,5,1,5,1,1,2
2262,75,7.4,0,3,0,1,5,0,8,1,1,8
2078,75,7.5,0,1,0,1,5,0,4,1,1,2
7690,75,7.5,0,5,0,1,7,1,3,1,1,7
7044,75,7.5,0,4,0,1,5,1,6,1,1,1
10588,75,8.3,0,2,0,1,8,1,1,1,1,7
1480,75,8.3,0,2,0,1,6,0,7,1,1,8
2342,75,8.3,0,4,0,1,6,0,4,1,1,5
4484,75,8.4,0,2,0,1,8,0,5,1,1,6
8841,75,8.5,0,3,0,1,7,1,7,1,1,2
5298,75,8.6,0,3,0,1,6,0,6,1,1,7
4560,75,8.7,0,4,0,1,5,0,2,1,1,10
8349,75,9.6,0,4,0,1,5,1,8,1,1,2
7210,75,10.7,0,2,0,1,8,1,4,1,1,9
9825,75,10.9,0,5,0,1,8,1,6,1,1,1
5934,75,11.9,0,1,0,1,6,1,3,1,1,7
7128,75,12,0,2,0,1,7,1,4,1,1,1
6859,75,12.2,0,5,0,1,8,1,8,1,1,0
8684,75,12.2,0,4,0,1,5,1,2,1,1,7
6757,75,12.8,0,5,0,1,6,1,4,1,1,3
8788,75,12.9,0,2,0,1,8,1,2,1,1,6
163,75,13,0,3,0,1,5,0,1,1,1,0
5725,75,13.2,0,5,0,1,7,1,8,1,1,3
2242,75,13.9,0,4,0,1,5,0,3,1,1,1
8133,75,14,0,1,0,1,5,1,8,1,1,4
336,75,14.2,0,2,0,1,6,0,7,1,1,10
6180,75,14.3,0,1,0,1,5,1,1,1,1,1
5612,75,14.5,0,1,0,1,8,1,4,1,1,6
9413,75,15.4,0,4,0,1,7,1,7,1,1,4
8722,75,15.5,0,4,0,1,8,1,3,1,1,0
4590,75,15.6,0,2,0,1,8,0,6,1,1,8
7187,75,15.7,0,5,0,1,6,1,5,1,1,3
678,75,16.1,0,1,0,1,5,0,5,1,1,7
3526,75,16.7,0,3,0,1,8,0,8,1,1,2
2866,75,17,0,2,0,1,6,0,3,1,1,7
4944,75,17.7,0,1,0,1,7,0,3,1,1,4
1292,75,17.8,0,4,0,1,7,0,6,1,1,10
2122,75,18.2,0,5,0,1,6,0,6,1,1,4
3652,75,18.3,0,5,0,1,6,0,1,1,1,5
1536,75,18.3,0,5,0,1,8,0,5,1,1,2
6988,75,18.6,0,3,0,1,5,1,5,1,1,2
4171,75,18.8,0,4,0,1,5,0,8,1,1,4
3774,75,18.8,0,1,0,1,8,0,5,1,1,10
180,75,18.9,0,5,0,1,7,0,8,1,1,9
6654,75,19,0,3,0,1,8,1,1,1,1,4
5545,75,19.7,0,3,0,1,8,1,4,1,1,3
48,75,20.4,0,3,0,1,5,0,8,1,1,4
6680,75,20.6,0,3,0,1,7,1,4,1,1,6
9707,75,21.1,0,3,0,1,8,1,3,1,1,2
5770,75,21.8,0,2,0,1,7,1,3,1,1,4
6016,75,22.6,0,4,0,1,5,1,2,1,1,1
4588,75,23.1,0,2,0,1,5,0,2,1,1,7
8898,75,23.9,0,1,0,1,7,1,5,1,1,8
6451,75,24.3,0,5,0,1,8,1,1,1,1,2
3078,75,24.6,0,2,0,1,6,0,4,1,1,9
7582,75,24.7,0,3,0,1,5,1,8,1,1,0
778,75,25,0,4,0,1,8,0,3,1,1,6
557,75,25.2,0,5,0,1,5,0,7,1,1,2
4190,75,25.4,0,2,0,1,7,0,8,1,1,2
7043,75,25.4,0,2,0,1,8,1,3,1,1,1
9362,75,26,0,3,0,1,7,1,6,1,1,7
9040,75,26,0,2,0,1,5,1,2,1,1,9
8926,75,26.1,0,1,0,1,5,1,8,1,1,6
2052,75,26.2,0,2,0,1,8,0,3,1,1,10
1637,75,26.4,0,1,0,1,8,0,1,1,1,2
3236,75,26.4,0,5,0,1,6,0,6,1,1,2
9859,75,26.5,0,2,0,1,6,1,7,1,1,4
2058,75,26.6,0,1,0,1,7,0,8,1,1,7
2386,75,26.9,0,1,0,1,8,0,8,1,1,10
9669,75,27,0,2,0,1,7,1,1,1,1,3
3580,75,27.1,0,3,0,1,6,0,4,1,1,4
4592,75,27.2,0,4,0,1,5,0,3,1,1,10
4257,75,27.3,0,3,0,1,8,0,7,1,1,0
9869,75,27.7,0,1,0,1,5,1,7,1,1,4
7540,75,28.1,0,2,0,1,6,1,5,1,1,3
10176,75,28.4,0,3,0,1,8,1,2,1,1,8
10772,75,29,0,1,0,1,6,1,7,1,1,2
2936,75,29,0,1,0,1,6,0,8,1,1,4
2160,75,29.3,0,3,0,1,7,0,8,1,1,6
534,75,29.4,0,1,0,1,6,0,4,1,1,10
1730,76,0.4,0,5,0,1,6,0,3,1,1,8
4064,76,0.4,0,3,0,1,8,0,6,1,1,6
1128,76,0.6,0,1,0,1,7,0,8,1,1,0
2372,76,1.1,0,1,0,1,5,0,1,1,1,6
5548,76,1.3,0,2,0,1,7,1,5,1,1,7
3780,76,1.9,0,5,0,1,8,0,5,1,1,7
9298,76,2.6,0,3,0,1,6,1,3,1,1,3
3532,76,3.1,0,5,0,1,8,0,3,1,1,4
9244,76,3.3,0,3,0,1,8,1,5,1,1,7
7815,76,4.2,0,3,0,1,8,1,2,1,1,0
1354,76,4.2,0,5,0,1,7,0,5,1,1,0
6300,76,4.7,0,5,0,1,7,1,6,1,1,5
677,76,5.1,0,3,0,1,7,0,7,1,1,2
7346,76,5.1,0,2,0,1,7,1,5,1,1,1
172,76,5.2,0,2,0,1,5,0,3,1,1,7
7418,76,5.4,0,1,0,1,6,1,8,1,1,5
8669,76,5.6,0,2,0,1,5,1,8,1,1,3
5810,76,6.5,0,4,0,1,5,1,7,1,1,5
2210,76,6.9,0,4,0,1,7,0,5,1,1,7
5625,76,7,0,5,0,1,8,1,1,1,1,1
1328,76,7.1,0,4,0,1,7,0,6,1,1,8
1348,76,7.1,0,2,0,1,5,0,3,1,1,0
2295,76,7.5,0,2,0,1,6,0,2,1,1,2
4806,76,7.7,0,2,0,1,5,0,6,1,1,0
7415,76,7.7,0,2,0,1,7,1,4,1,1,1
10466,76,7.8,0,4,0,1,7,1,4,1,1,4
8724,76,7.9,0,4,0,1,8,1,4,1,1,0
2486,76,8.2,0,1,0,1,5,0,2,1,1,8
10124,76,8.4,0,1,0,1,7,1,4,1,1,3
8796,76,8.6,0,4,0,1,8,1,1,1,1,3
8893,76,8.7,0,2,0,1,8,1,5,1,1,1
1760,76,8.7,0,2,0,1,5,0,2,1,1,3
9689,76,8.8,0,4,0,1,5,1,4,1,1,0
6663,76,10.2,0,3,0,1,5,1,6,1,1,0
7602,76,10.2,0,4,0,1,7,1,5,1,1,3
4014,76,10.5,0,1,0,1,5,0,6,1,1,8
706,76,10.7,0,1,0,1,8,0,6,1,1,2
7365,76,11,0,3,0,1,7,1,6,1,1,2
10503,76,11.5,0,2,0,1,5,1,3,1,1,4
7106,76,11.6,0,5,0,1,5,1,3,1,1,8
5782,76,12,0,4,0,1,8,1,3,1,1,9
10803,76,12.5,0,1,0,1,7,1,8,1,1,2
5751,76,12.8,0,3,0,1,7,1,2,1,1,2
9476,76,12.9,0,5,0,1,7,1,3,1,1,4
6775,76,13,0,5,0,1,5,1,3,1,1,0
3530,76,13.6,0,2,0,1,6,0,3,1,1,4
4732,76,13.8,0,5,0,1,6,0,4,1,1,0
9577,76,14.4,0,3,0,1,7,1,7,1,1,4
3374,76,14.8,0,1,0,1,7,0,6,1,1,6
933,76,14.8,0,2,0,1,7,0,8,1,1,2
9396,76,15.6,0,2,0,1,8,1,8,1,1,6
8676,76,15.6,0,4,0,1,7,1,5,1,1,0
1122,76,16,0,1,0,1,7,0,3,1,1,10
6945,76,16.1,0,1,0,1,8,1,8,1,1,3
1852,76,16.6,0,5,0,1,6,0,2,1,1,2
9944,76,16.7,0,1,0,1,5,1,4,1,1,6
9053,76,16.8,0,5,0,1,8,1,4,1,1,2
1508,76,16.9,0,1,0,1,5,0,7,1,1,8
4614,76,16.9,0,1,0,1,7,0,5,1,1,5
6340,76,17.1,0,2,0,1,6,1,4,1,1,10
2992,76,17.6,0,3,0,1,8,0,2,1,1,0
7258,76,17.8,0,4,0,1,8,1,3,1,1,4
7901,76,18.2,0,2,0,1,7,1,3,1,1,1
8121,76,19,0,2,0,1,7,1,3,1,1,2
5379,76,19.2,0,3,0,1,7,0,3,1,1,2
7651,76,19.3,0,3,0,1,5,1,5,1,1,0
2223,76,19.4,0,1,0,1,8,0,8,1,1,3
6583,76,20,0,1,0,1,7,1,5,1,1,3
9004,76,20.2,0,5,0,1,6,1,5,1,1,6
696,76,20.5,0,1,0,1,5,0,2,1,1,4
6436,76,20.6,0,1,0,1,6,1,2,1,1,2
2048,76,20.6,0,2,0,1,5,0,8,1,1,8
317,76,20.8,0,1,0,1,7,0,3,1,1,0
5626,76,21.6,0,5,0,1,5,1,7,1,1,8
8286,76,21.7,0,2,0,1,8,1,4,1,1,4
1082,76,21.9,0,1,0,1,8,0,3,1,1,0
10440,76,22,0,3,0,1,8,1,6,1,1,9
8290,76,22.3,0,1,0,1,6,1,4,1,1,7
1436,76,23.6,0,5,0,1,5,0,4,1,1,2
2555,76,23.8,0,1,0,1,5,0,5,1,1,0
10482,76,24.1,0,5,0,1,7,1,2,1,1,5
7744,76,24.2,0,3,0,1,7,1,1,1,1,1
10004,76,24.2,0,1,0,1,6,1,2,1,1,2
7382,76,24.5,0,1,0,1,7,1,2,1,1,9
9134,76,24.7,0,1,0,1,8,1,8,1,1,10
3437,76,24.8,0,1,0,1,6,0,2,1,1,3
7054,76,25,0,1,0,1,6,1,5,1,1,0
10112,76,25.3,0,5,0,1,5,1,6,1,1,4
1203,76,25.4,0,5,0,1,5,0,8,1,1,3
896,76,25.5,0,1,0,1,8,0,4,1,1,4
2501,76,25.7,0,4,0,1,5,0,5,1,1,0
4741,76,26.1,0,2,0,1,7,0,3,1,1,1
4906,76,26.1,0,1,0,1,7,0,8,1,1,3
5968,76,27,0,1,0,1,7,1,7,1,1,1
9804,76,27.4,0,5,0,1,6,1,7,1,1,0
4561,76,27.8,0,1,0,1,7,0,8,1,1,1
3420,76,28,0,4,0,1,8,0,1,1,1,1
588,76,28.2,0,2,0,1,8,0,5,1,1,10
8918,76,29,0,1,0,1,7,1,4,1,1,8
794,76,29,0,4,0,1,8,0,6,1,1,8
10688,76,29.1,0,5,0,1,8,1,3,1,1,7
4840,76,29.1,0,5,0,1,8,0,1,1,1,9
10336,76,29.2,0,3,0,1,5,1,4,1,1,7
5192,76,29.8,0,3,0,1,6,0,4,1,1,9
4202,77,0.2,0,1,0,1,7,0,3,1,1,9
2054,77,0.3,0,4,0,1,5,0,7,1,1,2
3414,77,0.6,0,4,0,1,7,0,3,1,1,8
1489,77,0.7,0,5,0,1,8,0,6,1,1,0
3727,77,0.7,0,3,0,1,5,0,4,1,1,0
8624,77,2,0,2,0,1,6,1,4,1,1,9
2612,77,2.3,0,2,0,1,8,0,1,1,1,0
885,77,2.3,0,2,0,1,7,0,8,1,1,1
9328,77,2.3,0,1,0,1,8,1,7,1,1,5
10748,77,2.6,0,4,0,1,8,1,5,1,1,5
6201,77,3,0,2,0,1,6,1,3,1,1,1
2268,77,3.3,0,2,0,1,6,0,6,1,1,5
2899,77,3.7,0,2,0,1,6,0,7,1,1,3
3701,77,3.8,0,5,0,1,8,0,1,1,1,0
6437,77,3.8,0,2,0,1,6,1,2,1,1,1
3843,77,4.2,0,4,0,1,5,0,8,1,1,1
10021,77,4.3,0,2,0,1,8,1,6,1,1,2
6112,77,4.5,0,5,0,1,8,1,4,1,1,7
3288,77,4.8,0,5,0,1,7,0,7,1,1,6
3872,77,4.9,0,2,0,1,8,0,2,1,1,4
5394,77,5,0,1,0,1,7,0,1,1,1,5
7115,77,5.1,0,4,0,1,5,1,3,1,1,0
5346,77,5.1,0,4,0,1,8,0,2,1,1,10
1616,77,5.1,0,3,0,1,6,0,7,1,1,3
662,77,5.4,0,3,0,1,5,0,7,1,1,8
6674,77,5.7,0,3,0,1,7,1,1,1,1,6
1110,77,5.8,0,2,0,1,6,0,5,1,1,5
8683,77,5.8,0,1,0,1,5,1,6,1,1,3
3893,77,6,0,5,0,1,8,0,8,1,1,4
7844,77,6,0,3,0,1,6,1,1,1,1,1
3249,77,6.3,0,4,0,1,7,0,7,1,1,1
8030,77,6.8,0,5,0,1,7,1,8,1,1,8
888,77,7,0,1,0,1,6,0,3,1,1,9
7748,77,7.1,0,4,0,1,8,1,3,1,1,3
2946,77,7.1,0,2,0,1,8,0,5,1,1,6
3270,77,7.8,0,1,0,1,7,0,8,1,1,6
793,77,7.9,0,4,0,1,6,0,2,1,1,1
10622,77,7.9,0,2,0,1,5,1,3,1,1,0
10704,77,7.9,0,5,0,1,6,1,1,1,1,1
10104,77,8.4,0,4,0,1,5,1,8,1,1,4
3284,77,8.9,0,3,0,1,5,0,5,1,1,3
6500,77,9.1,0,2,0,1,6,1,2,1,1,0
5322,77,9.2,0,1,0,1,7,0,5,1,1,9
8798,77,9.5,0,1,0,1,6,1,5,1,1,1
10096,77,9.6,0,3,0,1,5,1,6,1,1,6
8292,77,9.9,0,1,0,1,7,1,2,1,1,10
6165,77,10,0,4,0,1,8,1,1,1,1,2
5330,77,10.2,0,5,0,1,8,0,6,1,1,7
466,77,10.6,0,2,0,1,6,0,1,1,1,1
3231,77,10.6,0,2,0,1,7,0,6,1,1,0
34,77,10.9,0,5,0,1,8,0,1,1,1,1
6203,77,11,0,1,0,1,8,1,4,1,1,0
1620,77,11.4,0,4,0,1,8,0,2,1,1,9
5854,77,12,0,3,0,1,7,1,6,1,1,1
3408,77,12.6,0,4,0,1,8,0,5,1,1,4
2400,77,12.6,0,2,0,1,7,0,1,1,1,9
2219,77,13.2,0,1,0,1,6,0,6,1,1,4
4081,77,13.3,0,2,0,1,7,0,4,1,1,0
1859,77,13.4,0,3,0,1,6,0,2,1,1,0
9189,77,13.6,0,5,0,1,8,1,1,1,1,1
9610,77,13.6,0,5,0,1,7,1,6,1,1,6
10395,77,14.2,0,5,0,1,8,1,6,1,1,0
28,77,14.2,0,4,0,1,6,0,4,1,1,8
2385,77,14.5,0,5,0,1,8,0,2,1,1,4
4993,77,14.6,0,3,0,1,7,0,7,1,1,4
7100,77,15.2,0,3,0,1,6,1,3,1,1,3
2550,77,16.2,0,4,0,1,5,0,3,1,1,7
6400,77,16.4,0,4,0,1,6,1,7,1,1,2
3683,77,16.5,0,1,0,1,8,0,1,1,1,1
2816,77,16.6,0,3,0,1,7,0,1,1,1,7
595,77,17.3,0,4,0,1,6,0,1,1,1,2
9560,77,17.5,0,1,0,1,8,1,1,1,1,9
3260,77,17.6,0,3,0,1,8,0,6,1,1,8
3475,77,17.8,0,4,0,1,7,0,8,1,1,1
5070,77,17.9,0,3,0,1,6,0,7,1,1,8
5688,77,18.1,0,3,0,1,7,1,5,1,1,8
10179,77,18.7,0,1,0,1,8,1,5,1,1,3
10010,77,18.9,0,5,0,1,7,1,3,1,1,10
2030,77,19.5,0,5,0,1,6,0,8,1,1,8
1802,77,19.7,0,2,0,1,8,0,1,1,1,2
852,77,19.7,0,4,0,1,8,0,5,1,1,10
142,77,20,0,3,0,1,8,0,3,1,1,6
356,77,20,0,1,0,1,7,0,3,1,1,7
1724,77,20.1,0,5,0,1,8,0,3,1,1,2
1801,77,20.2,0,4,0,1,8,0,4,1,1,2
9032,77,20.3,0,1,0,1,6,1,6,1,1,9
4380,77,20.7,0,3,0,1,6,0,5,1,1,0
3452,77,21.2,0,1,0,1,8,0,7,1,1,9
3566,77,21.7,0,5,0,1,6,0,8,1,1,2
7527,77,21.8,0,5,0,1,5,1,3,1,1,3
3775,77,22.6,0,4,0,1,6,0,2,1,1,0
5453,77,22.7,0,2,0,1,5,1,8,1,1,1
3103,77,22.8,0,1,0,1,7,0,7,1,1,2
1326,77,23.1,0,4,0,1,7,0,2,1,1,2
1654,77,23.4,0,1,0,1,7,0,4,1,1,10
7370,77,23.6,0,4,0,1,6,1,2,1,1,6
8620,77,23.6,0,1,0,1,8,1,5,1,1,1
9384,77,23.9,0,4,0,1,5,1,6,1,1,1
894,77,24.2,0,3,0,1,8,0,3,1,1,6
235,77,24.4,0,1,0,1,7,0,6,1,1,2
3444,77,24.7,0,2,0,1,5,0,1,1,1,3
6816,77,24.9,0,3,0,1,7,1,6,1,1,10
9806,77,25.2,0,5,0,1,7,1,8,1,1,9
3892,77,25.5,0,3,0,1,8,0,6,1,1,10
4924,77,25.5,0,4,0,1,5,0,7,1,1,4
2908,77,25.7,0,1,0,1,8,0,4,1,1,6
8923,77,26.3,0,3,0,1,5,1,6,1,1,3
6895,77,26.7,0,4,0,1,7,1,2,1,1,3
5188,77,26.7,0,5,0,1,5,0,8,1,1,9
3496,77,26.9,0,4,0,1,6,0,6,1,1,6
8605,77,27,0,3,0,1,5,1,5,1,1,4
4672,77,27.7,0,1,0,1,8,0,2,1,1,6
6806,77,28.3,0,5,0,1,6,1,4,1,1,2
2200,77,28.4,0,5,0,1,7,0,5,1,1,6
5606,77,28.5,0,1,0,1,6,1,7,1,1,7
2969,77,29.2,0,4,0,1,6,0,3,1,1,1
6488,77,29.5,0,5,0,1,6,1,2,1,1,3
10670,77,29.8,0,1,0,1,8,1,1,1,1,8
3385,77,29.8,0,5,0,1,7,0,4,1,1,1
1898,77,29.9,0,3,0,1,6,0,6,1,1,10
5403,78,0.1,0,4,0,1,8,0,6,1,1,3
9978,78,0.2,0,2,0,1,8,1,3,1,1,9
9534,78,0.2,0,5,0,1,6,1,1,1,1,10
7048,78,0.4,0,4,0,1,7,1,6,1,1,8
3482,78,0.5,0,5,0,1,8,0,5,1,1,2
2237,78,0.9,0,3,0,1,6,0,7,1,1,2
6412,78,1,0,3,0,1,5,1,7,1,1,1
7028,78,1.4,0,2,0,1,5,1,7,1,1,5
6649,78,1.5,0,2,0,1,5,1,3,1,1,0
8057,78,1.6,0,5,0,1,7,1,3,1,1,0
10161,78,1.7,0,5,0,1,8,1,5,1,1,1
4446,78,1.7,0,3,0,1,5,0,1,1,1,3
10035,78,2.1,0,3,0,1,8,1,8,1,1,4
10132,78,2.5,0,3,0,1,8,1,4,1,1,6
4770,78,2.5,0,4,0,1,7,0,5,1,1,9
6005,78,4.1,0,2,0,1,5,1,6,1,1,0
9427,78,4.7,0,1,0,1,7,1,1,1,1,2
9086,78,4.9,0,2,0,1,6,1,1,1,1,4
8356,78,5,0,1,0,1,6,1,8,1,1,7
7140,78,5.3,0,1,0,1,8,1,7,1,1,6
8372,78,5.4,0,4,0,1,5,1,6,1,1,3
514,78,5.5,0,4,0,1,6,0,2,1,1,10
2970,78,5.9,0,4,0,1,8,0,6,1,1,3
7391,78,6.4,0,5,0,1,6,1,1,1,1,2
3671,78,6.7,0,4,0,1,5,0,7,1,1,4
4175,78,6.8,0,4,0,1,8,0,7,1,1,1
5602,78,7.1,0,2,0,1,5,1,8,1,1,10
6240,78,7.2,0,3,0,1,5,1,2,1,1,9
9652,78,7.3,0,2,0,1,8,1,7,1,1,6
9614,78,7.4,0,1,0,1,7,1,7,1,1,10
10794,78,7.7,0,2,0,1,5,1,1,1,1,8
9677,78,7.8,0,3,0,1,8,1,5,1,1,0
5210,78,7.9,0,4,0,1,8,0,5,1,1,7
1794,78,7.9,0,3,0,1,7,0,2,1,1,0
79,78,8.6,0,5,0,1,8,0,1,1,1,0
4274,78,9.3,0,2,0,1,6,0,2,1,1,9
5118,78,9.3,0,1,0,1,5,0,4,1,1,6
2832,78,9.5,0,1,0,1,6,0,8,1,1,2
8854,78,9.6,0,2,0,1,8,1,1,1,1,2
10716,78,9.7,0,4,0,1,8,1,3,1,1,10
6939,78,10.1,0,2,0,1,6,1,1,1,1,0
2594,78,10.1,0,4,0,1,8,0,3,1,1,9
8703,78,10.8,0,5,0,1,7,1,4,1,1,0
3978,78,11.1,0,2,0,1,5,0,2,1,1,10
664,78,11.1,0,4,0,1,6,0,5,1,1,2
2552,78,11.6,0,5,0,1,7,0,8,1,1,5
1951,78,12.4,0,1,0,1,7,0,5,1,1,4
5504,78,12.6,0,2,0,1,6,1,6,1,1,4
9432,78,12.7,0,2,0,1,7,1,2,1,1,1
7607,78,12.9,0,4,0,1,7,1,7,1,1,1
5424,78,12.9,0,1,0,1,7,1,7,1,1,8
4004,78,13,0,2,0,1,6,0,3,1,1,3
5539,78,13.2,0,4,0,1,7,1,8,1,1,4
6921,78,13.2,0,2,0,1,5,1,1,1,1,1
4891,78,13.5,0,4,0,1,8,0,3,1,1,4
5864,78,13.5,0,5,0,1,7,1,8,1,1,7
8095,78,14.4,0,3,0,1,5,1,7,1,1,3
1997,78,14.5,0,2,0,1,8,0,5,1,1,2
6608,78,14.7,0,2,0,1,6,1,7,1,1,1
4275,78,15,0,1,0,1,5,0,8,1,1,0
137,78,15.2,0,5,0,1,7,0,4,1,1,4
10450,78,15.2,0,5,0,1,8,1,3,1,1,1
9110,78,15.3,0,4,0,1,6,1,8,1,1,6
7776,78,15.4,0,5,0,1,5,1,3,1,1,1
5589,78,15.5,0,4,0,1,7,1,7,1,1,1
9907,78,16.2,0,3,0,1,7,1,6,1,1,2
5146,78,16.4,0,5,0,1,5,0,7,1,1,1
3306,78,16.6,0,1,0,1,8,0,8,1,1,6
3007,78,16.8,0,1,0,1,6,0,8,1,1,3
1896,78,16.8,0,3,0,1,6,0,6,1,1,4
3858,78,17,0,2,0,1,6,0,8,1,1,5
3612,78,17,0,1,0,1,8,0,1,1,1,3
130,78,17,0,2,0,1,8,0,4,1,1,0
8890,78,17,0,5,0,1,7,1,6,1,1,5
4870,78,17.6,0,3,0,1,5,0,6,1,1,4
10276,78,17.9,0,4,0,1,8,1,4,1,1,10
7046,78,18.4,0,5,0,1,8,1,4,1,1,2
368,78,18.6,0,4,0,1,5,0,8,1,1,10
5052,78,19,0,4,0,1,5,0,8,1,1,6
382,78,19.1,0,3,0,1,6,0,3,1,1,4
3198,78,19.4,0,1,0,1,5,0,5,1,1,10
535,78,19.7,0,4,0,1,5,0,1,1,1,3
10691,78,19.8,0,3,0,1,7,1,6,1,1,0
6878,78,20.8,0,3,0,1,8,1,6,1,1,10
9220,78,20.8,0,4,0,1,7,1,8,1,1,1
6904,78,20.9,0,5,0,1,8,1,5,1,1,1
10407,78,20.9,0,5,0,1,8,1,2,1,1,0
8206,78,21.2,0,3,0,1,7,1,7,1,1,10
227,78,21.5,0,1,0,1,6,0,2,1,1,2
478,78,21.7,0,3,0,1,7,0,3,1,1,6
2504,78,21.9,0,3,0,1,8,0,5,1,1,10
7974,78,22.1,0,1,0,1,5,1,2,1,1,10
4434,78,22.2,0,3,0,1,5,0,3,1,1,7
8229,78,22.4,0,2,0,1,8,1,6,1,1,0
238,78,22.4,0,4,0,1,6,0,2,1,1,4
6386,78,22.5,0,1,0,1,6,1,7,1,1,5
4292,78,22.8,0,5,0,1,5,0,8,1,1,6
332,78,23,0,5,0,1,6,0,3,1,1,10
3569,78,23.4,0,3,0,1,7,0,6,1,1,1
8966,78,23.4,0,4,0,1,6,1,4,1,1,9
3543,78,23.5,0,4,0,1,5,0,7,1,1,1
8554,78,23.7,0,2,0,1,6,1,4,1,1,7
5375,78,24.3,0,2,0,1,6,0,4,1,1,4
5074,78,24.8,0,4,0,1,6,0,3,1,1,7
7894,78,25,0,4,0,1,7,1,8,1,1,8
8309,78,25.1,0,3,0,1,6,1,5,1,1,2
4372,78,25.5,0,5,0,1,8,0,3,1,1,6
7680,78,25.5,0,1,0,1,5,1,6,1,1,4
6508,78,25.8,0,5,0,1,8,1,2,1,1,1
10314,78,26,0,1,0,1,6,1,2,1,1,0
10089,78,26.6,0,5,0,1,8,1,3,1,1,3
8748,78,26.8,0,3,0,1,6,1,1,1,1,3
7168,78,27,0,1,0,1,5,1,7,1,1,1
3378,78,27.1,0,3,0,1,5,0,2,1,1,9
4991,78,27.5,0,4,0,1,7,0,3,1,1,0
2506,78,27.6,0,1,0,1,7,0,2,1,1,9
3020,78,27.6,0,5,0,1,5,0,8,1,1,1
4673,78,28,0,3,0,1,5,0,1,1,1,3
2349,78,28.3,0,3,0,1,8,0,6,1,1,1
6288,78,28.6,0,2,0,1,6,1,5,1,1,10
1498,78,29.1,0,3,0,1,6,0,1,1,1,1
2754,78,29.3,0,4,0,1,8,0,2,1,1,9
9336,78,29.4,0,5,0,1,5,1,6,1,1,1
4358,78,29.7,0,3,0,1,5,0,7,1,1,7
9904,78,29.8,0,2,0,1,6,1,8,1,1,7
882,79,0.4,0,2,0,1,6,0,7,1,1,1
4518,79,0.8,0,4,0,1,5,0,2,1,1,2
4526,79,0.9,0,2,0,1,5,0,8,1,1,6
1905,79,1.4,0,3,0,1,6,0,4,1,1,2
980,79,2.1,0,2,0,1,7,0,2,1,1,5
9685,79,2.3,0,3,0,1,6,1,7,1,1,4
2642,79,2.3,0,3,0,1,6,0,7,1,1,2
5406,79,2.4,0,4,0,1,6,0,5,1,1,1
5084,79,2.7,0,3,0,1,7,0,8,1,1,5
6578,79,2.7,0,1,0,1,6,1,2,1,1,2
6944,79,3,0,1,0,1,8,1,4,1,1,2
4880,79,3,0,3,0,1,6,0,1,1,1,0
3902,79,3.2,0,2,0,1,5,0,5,1,1,0
10666,79,3.3,0,2,0,1,5,1,1,1,1,5
1030,79,3.5,0,5,0,1,6,0,7,1,1,7
8489,79,4.7,0,4,0,1,5,1,3,1,1,4
6382,79,5.3,0,3,0,1,8,1,3,1,1,6
10841,79,5.9,0,2,0,1,7,1,8,1,1,1
8562,79,5.9,0,5,0,1,7,1,8,1,1,5
5160,79,6,0,5,0,1,6,0,1,1,1,1
748,79,6.3,0,1,0,1,5,0,6,1,1,6
10287,79,6.5,0,1,0,1,8,1,5,1,1,3
7390,79,6.6,0,1,0,1,5,1,5,1,1,9
5863,79,7.1,0,5,0,1,7,1,6,1,1,2
8468,79,7.2,0,2,0,1,8,1,3,1,1,2
5019,79,7.3,0,1,0,1,7,0,7,1,1,3
5940,79,7.4,0,2,0,1,7,1,6,1,1,7
6983,79,7.6,0,2,0,1,6,1,6,1,1,4
111,79,7.7,0,5,0,1,7,0,8,1,1,1
3667,79,7.8,0,3,0,1,5,0,1,1,1,2
1496,79,8.3,0,3,0,1,7,0,1,1,1,5
3128,79,8.6,0,3,0,1,5,0,4,1,1,0
9428,79,8.6,0,4,0,1,7,1,2,1,1,0
9914,79,8.7,0,4,0,1,5,1,1,1,1,3
6898,79,9.2,0,5,0,1,5,1,4,1,1,1
4974,79,9.4,0,5,0,1,5,0,2,1,1,1
6088,79,9.4,0,4,0,1,5,1,6,1,1,9
7926,79,9.5,0,2,0,1,6,1,1,1,1,8
7398,79,9.5,0,1,0,1,6,1,2,1,1,3
9285,79,9.9,0,1,0,1,5,1,3,1,1,1
7496,79,10.3,0,4,0,1,7,1,6,1,1,5
5042,79,10.6,0,4,0,1,5,0,5,1,1,4
8958,79,10.9,0,3,0,1,5,1,6,1,1,3
454,79,11.3,0,1,0,1,6,0,1,1,1,3
2362,79,11.8,0,5,0,1,8,0,2,1,1,1
7948,79,11.9,0,5,0,1,8,1,4,1,1,10
5413,79,12,0,2,0,1,6,0,1,1,1,0
7968,79,12.1,0,4,0,1,6,1,1,1,1,5
9048,79,12.3,0,4,0,1,7,1,5,1,1,6
6602,79,12.4,0,2,0,1,5,1,2,1,1,7
6072,79,12.5,0,1,0,1,6,1,6,1,1,3
8052,79,12.6,0,1,0,1,6,1,1,1,1,8
15,79,12.7,0,4,0,1,8,0,2,1,1,2
5898,79,12.9,0,2,0,1,7,1,6,1,1,2
1820,79,13.1,0,5,0,1,5,0,5,1,1,3
3912,79,14,0,4,0,1,7,0,2,1,1,0
7976,79,14,0,5,0,1,5,1,4,1,1,1
3388,79,14.7,0,1,0,1,5,0,7,1,1,10
9845,79,14.7,0,2,0,1,6,1,7,1,1,4
2742,79,14.8,0,5,0,1,7,0,7,1,1,8
1598,79,15.5,0,4,0,1,8,0,5,1,1,5
2667,79,15.7,0,4,0,1,5,0,8,1,1,0
10052,79,16.1,0,1,0,1,5,1,3,1,1,0
1470,79,16.4,0,4,0,1,6,0,6,1,1,2
4140,79,16.5,0,3,0,1,7,0,4,1,1,1
4476,79,16.6,0,3,0,1,6,0,8,1,1,8
6188,79,17.2,0,3,0,1,6,1,3,1,1,8
6342,79,17.5,0,4,0,1,6,1,4,1,1,7
2549,79,17.5,0,1,0,1,8,0,1,1,1,1
1846,79,17.5,0,3,0,1,5,0,1,1,1,6
2166,79,17.7,0,3,0,1,6,0,2,1,1,9
4629,79,18.2,0,2,0,1,8,0,4,1,1,1
3792,79,18.4,0,2,0,1,5,0,7,1,1,4
4902,79,18.6,0,5,0,1,6,0,2,1,1,5
9830,79,18.9,0,5,0,1,5,1,1,1,1,0
7644,79,18.9,0,2,0,1,8,1,2,1,1,2
9392,79,19.3,0,4,0,1,8,1,2,1,1,8
10126,79,19.3,0,5,0,1,5,1,8,1,1,9
10413,79,19.7,0,4,0,1,5,1,4,1,1,4
6218,79,19.7,0,2,0,1,6,1,7,1,1,4
7850,79,19.8,0,3,0,1,7,1,1,1,1,2
5704,79,19.9,0,2,0,1,5,1,6,1,1,3
3031,79,20.1,0,3,0,1,5,0,5,1,1,2
1910,79,20.2,0,4,0,1,8,0,2,1,1,6
1440,79,20.3,0,1,0,1,5,0,3,1,1,5
2292,79,20.3,0,2,0,1,7,0,5,1,1,7
4407,79,20.6,0,5,0,1,6,0,7,1,1,2
5124,79,20.7,0,4,0,1,8,0,2,1,1,0
1792,79,20.8,0,4,0,1,8,0,6,1,1,2
4551,79,21,0,2,0,1,5,0,1,1,1,0
4546,79,21,0,2,0,1,8,0,1,1,1,8
7778,79,21.1,0,1,0,1,6,1,6,1,1,8
6568,79,21.2,0,1,0,1,7,1,8,1,1,8
2809,79,21.6,0,1,0,1,6,0,3,1,1,0
9112,79,22,0,1,0,1,8,1,4,1,1,9
5409,79,22.5,0,2,0,1,6,0,5,1,1,0
1051,79,23.5,0,2,0,1,5,0,6,1,1,0
2064,79,23.5,0,4,0,1,6,0,7,1,1,5
10227,79,23.8,0,5,0,1,7,1,6,1,1,1
1316,79,24.4,0,5,0,1,5,0,3,1,1,8
2272,79,24.9,0,2,0,1,8,0,8,1,1,7
4532,79,24.9,0,3,0,1,5,0,1,1,1,8
2318,79,25.2,0,1,0,1,6,0,2,1,1,4
1259,79,25.4,0,5,0,1,7,0,7,1,1,1
4731,79,25.5,0,3,0,1,6,0,6,1,1,2
2279,79,25.7,0,1,0,1,7,0,4,1,1,4
8718,79,25.9,0,4,0,1,6,1,4,1,1,6
2859,79,26.3,0,1,0,1,5,0,7,1,1,2
3541,79,26.5,0,1,0,1,5,0,3,1,1,2
3390,79,26.7,0,1,0,1,8,0,5,1,1,5
76,79,26.9,0,1,0,1,8,0,6,1,1,1
7718,79,27,0,5,0,1,5,1,4,1,1,10
9466,79,27.7,0,3,0,1,8,1,2,1,1,9
6670,79,27.8,0,1,0,1,7,1,7,1,1,1
9902,79,27.9,0,1,0,1,7,1,4,1,1,0
9756,79,27.9,0,1,0,1,6,1,7,1,1,4
9274,79,27.9,0,5,0,1,5,1,8,1,1,10
1070,79,28,0,2,0,1,6,0,2,1,1,10
5767,79,28.1,0,1,0,1,7,1,6,1,1,3
8332,79,28.6,0,2,0,1,5,1,7,1,1,3
2637,79,28.8,0,4,0,1,7,0,5,1,1,2
2694,79,29.2,0,4,0,1,7,0,7,1,1,4
7230,79,29.2,0,2,0,1,7,1,4,1,1,8
10583,79,29.6,0,3,0,1,7,1,4,1,1,2
10522,80,0.6,0,4,0,1,6,1,8,1,1,6
6038,80,1.2,0,4,0,1,6,1,8,1,1,5
9646,80,1.4,0,5,0,1,7,1,1,1,1,0
1887,80,1.5,0,5,0,1,7,0,5,1,1,2
4529,80,1.7,0,3,0,1,8,0,2,1,1,4
3805,80,1.7,0,2,0,1,7,0,4,1,1,3
4220,80,1.8,0,2,0,1,6,0,2,1,1,3
8743,80,2.4,0,4,0,1,5,1,6,1,1,4
5249,80,2.6,0,3,0,1,8,0,1,1,1,0
3116,80,2.8,0,1,0,1,8,0,1,1,1,7
8710,80,3.1,0,1,0,1,6,1,5,1,1,9
9312,80,3.3,0,5,0,1,7,1,3,1,1,0
5916,80,4,0,1,0,1,7,1,5,1,1,6
3528,80,4.1,0,1,0,1,5,0,5,1,1,5
3404,80,4.2,0,5,0,1,7,0,6,1,1,1
509,80,4.4,0,5,0,1,7,0,1,1,1,3
9429,80,4.6,0,5,0,1,7,1,6,1,1,2
7450,80,4.9,0,1,0,1,6,1,1,1,1,4
8677,80,4.9,0,5,0,1,8,1,2,1,1,0
10270,80,5.2,0,1,0,1,8,1,1,1,1,1
1187,80,5.4,0,1,0,1,7,0,6,1,1,2
3769,80,5.4,0,2,0,1,5,0,4,1,1,2
2193,80,5.6,0,2,0,1,7,0,6,1,1,4
7792,80,5.8,0,1,0,1,7,1,1,1,1,0
8427,80,6.3,0,2,0,1,8,1,1,1,1,3
648,80,6.9,0,3,0,1,5,0,4,1,1,6
1969,80,6.9,0,1,0,1,7,0,6,1,1,4
3608,80,7.1,0,1,0,1,5,0,3,1,1,1
9142,80,7.4,0,4,0,1,7,1,7,1,1,1
10676,80,7.4,0,1,0,1,6,1,6,1,1,10
1642,80,7.6,0,2,0,1,7,0,6,1,1,7
5758,80,7.6,0,5,0,1,5,1,8,1,1,8
2254,80,7.8,0,3,0,1,5,0,1,1,1,4
6238,80,8,0,1,0,1,8,1,4,1,1,10
2429,80,8,0,5,0,1,8,0,7,1,1,2
3929,80,8.3,0,4,0,1,6,0,8,1,1,1
6390,80,8.8,0,4,0,1,6,1,3,1,1,8
9022,80,9.1,0,3,0,1,7,1,7,1,1,2
7656,80,9.1,0,5,0,1,7,1,7,1,1,5
2584,80,9.5,0,2,0,1,7,0,5,1,1,9
9403,80,9.6,0,2,0,1,6,1,7,1,1,2
9769,80,9.8,0,5,0,1,7,1,5,1,1,4
9422,80,10.1,0,1,0,1,5,1,2,1,1,7
7204,80,10.1,0,1,0,1,5,1,5,1,1,6
1144,80,10.2,0,3,0,1,7,0,8,1,1,8
1542,80,10.5,0,2,0,1,7,0,4,1,1,10
10410,80,10.5,0,1,0,1,6,1,4,1,1,6
2873,80,10.5,0,1,0,1,5,0,3,1,1,1
6614,80,10.9,0,3,0,1,6,1,6,1,1,3
2944,80,11,0,2,0,1,8,0,6,1,1,10
6166,80,11.2,0,5,0,1,8,1,1,1,1,10
10384,80,11.4,0,5,0,1,5,1,5,1,1,3
6655,80,11.7,0,1,0,1,6,1,5,1,1,1
8218,80,11.7,0,4,0,1,6,1,4,1,1,10
7522,80,11.9,0,2,0,1,6,1,3,1,1,9
2496,80,12.1,0,5,0,1,5,0,5,1,1,9
7178,80,12.5,0,4,0,1,5,1,3,1,1,2
9659,80,12.7,0,1,0,1,5,1,8,1,1,4
2003,80,13.5,0,1,0,1,8,0,4,1,1,1
8712,80,13.5,0,2,0,1,6,1,6,1,1,7
4736,80,13.5,0,3,0,1,5,0,4,1,1,9
9272,80,13.6,0,1,0,1,7,1,1,1,1,6
1571,80,13.7,0,4,0,1,6,0,5,1,1,3
4742,80,13.8,0,5,0,1,7,0,4,1,1,4
5032,80,13.9,0,2,0,1,7,0,4,1,1,7
602,80,14,0,5,0,1,8,0,5,1,1,7
8652,80,14.2,0,2,0,1,7,1,7,1,1,10
6454,80,14.3,0,4,0,1,8,1,5,1,1,7
6735,80,14.3,0,4,0,1,6,1,4,1,1,4
1583,80,14.9,0,5,0,1,7,0,6,1,1,4
5213,80,15.1,0,1,0,1,6,0,8,1,1,3
1215,80,15.3,0,5,0,1,6,0,6,1,1,1
8802,80,15.5,0,4,0,1,6,1,2,1,1,10
10380,80,15.5,0,4,0,1,7,1,5,1,1,2
7169,80,16.1,0,3,0,1,7,1,3,1,1,0
10536,80,16.1,0,4,0,1,6,1,8,1,1,2
5500,80,16.2,0,3,0,1,6,1,3,1,1,5
2034,80,16.6,0,1,0,1,7,0,4,1,1,7
1224,80,16.7,0,4,0,1,5,0,5,1,1,7
5082,80,16.9,0,2,0,1,6,0,3,1,1,0
9705,80,16.9,0,1,0,1,7,1,1,1,1,1
3791,80,17.2,0,3,0,1,5,0,4,1,1,1
761,80,17.4,0,4,0,1,8,0,6,1,1,0
9764,80,18.2,0,4,0,1,7,1,4,1,1,8
8353,80,18.4,0,4,0,1,7,1,3,1,1,4
9853,80,18.6,0,3,0,1,8,1,5,1,1,1
3304,80,19.4,0,4,0,1,5,0,2,1,1,5
2211,80,19.5,0,1,0,1,8,0,5,1,1,2
10844,80,19.6,0,2,0,1,6,1,2,1,1,8
1438,80,19.7,0,3,0,1,8,0,4,1,1,2
9050,80,20.3,0,1,0,1,5,1,3,1,1,1
6310,80,20.6,0,4,0,1,7,1,2,1,1,8
8090,80,20.6,0,2,0,1,7,1,1,1,1,9
5430,80,20.9,0,2,0,1,5,1,2,1,1,7
7179,80,21.1,0,5,0,1,6,1,3,1,1,0
10606,80,21.2,0,3,0,1,6,1,3,1,1,2
739,80,22.6,0,5,0,1,5,0,4,1,1,1
10596,80,22.7,0,1,0,1,6,1,8,1,1,5
1646,80,23,0,2,0,1,8,0,4,1,1,7
8962,80,23.2,0,4,0,1,6,1,1,1,1,8
10374,80,23.4,0,5,0,1,5,1,7,1,1,10
8602,80,24.1,0,2,0,1,7,1,5,1,1,10
7742,80,24.3,0,5,0,1,7,1,1,1,1,2
4792,80,25,0,3,0,1,6,0,7,1,1,7
3932,80,25.8,0,3,0,1,6,0,5,1,1,10
8896,80,25.8,0,4,0,1,8,1,2,1,1,1
3563,80,26.4,0,3,0,1,5,0,8,1,1,4
497,80,26.4,0,3,0,1,7,0,3,1,1,2
924,80,26.4,0,4,0,1,7,0,1,1,1,8
344,80,26.6,0,5,0,1,5,0,6,1,1,3
10046,80,27.2,0,1,0,1,5,1,8,1,1,3
4790,80,27.3,0,4,0,1,8,0,7,1,1,0
4194,80,27.3,0,4,0,1,7,0,6,1,1,9
10535,80,27.3,0,4,0,1,6,1,6,1,1,3
1149,80,27.9,0,3,0,1,5,0,8,1,1,0
5244,80,28.3,0,1,0,1,6,0,1,1,1,8
8944,80,28.7,0,2,0,1,7,1,4,1,1,6
3349,80,28.9,0,5,0,1,6,0,3,1,1,0
488,80,29.1,0,3,0,1,5,0,7,1,1,8
6028,80,29.1,0,5,0,1,8,1,4,1,1,7
7877,80,29.3,0,1,0,1,5,1,2,1,1,2
6262,81,0.3,0,5,0,1,8,1,7,1,1,4
8232,81,0.6,0,4,0,1,8,1,5,1,1,5
4141,81,1,0,5,0,1,8,0,6,1,1,2
9966,81,1.1,0,5,0,1,8,1,7,1,1,2
1373,81,1.2,0,4,0,1,7,0,2,1,1,4
10050,81,1.3,0,2,0,1,5,1,8,1,1,7
6326,81,1.5,0,2,0,1,8,1,4,1,1,3
3935,81,2.3,0,5,0,1,6,0,2,1,1,0
10188,81,2.4,0,1,0,1,7,1,8,1,1,6
9288,81,2.6,0,2,0,1,7,1,8,1,1,2
3253,81,3.1,0,5,0,1,8,0,5,1,1,1
6172,81,3.2,0,2,0,1,5,1,3,1,1,9
7018,81,3.7,0,3,0,1,5,1,1,1,1,2
776,81,3.9,0,5,0,1,6,0,8,1,1,4
10434,81,3.9,0,5,0,1,8,1,2,1,1,1
7292,81,4.2,0,3,0,1,7,1,8,1,1,3
10812,81,4.3,0,4,0,1,8,1,8,1,1,0
4011,81,4.5,0,5,0,1,5,0,8,1,1,1
3625,81,5,0,2,0,1,8,0,1,1,1,1
790,81,5.3,0,4,0,1,5,0,5,1,1,7
322,81,5.3,0,5,0,1,5,0,5,1,1,8
8208,81,5.5,0,1,0,1,6,1,4,1,1,4
3681,81,6.4,0,1,0,1,7,0,1,1,1,1
8342,81,6.5,0,2,0,1,6,1,1,1,1,4
4803,81,6.6,0,2,0,1,6,0,1,1,1,4
8298,81,6.6,0,2,0,1,8,1,8,1,1,10
926,81,6.7,0,1,0,1,8,0,5,1,1,8
6424,81,7,0,3,0,1,7,1,1,1,1,9
1674,81,7.1,0,1,0,1,6,0,3,1,1,5
376,81,7.3,0,4,0,1,5,0,7,1,1,2
198,81,8.4,0,5,0,1,6,0,1,1,1,1
518,81,8.7,0,4,0,1,7,0,5,1,1,9
4917,81,8.9,0,3,0,1,5,0,8,1,1,4
4681,81,9.1,0,5,0,1,6,0,6,1,1,0
467,81,9.2,0,2,0,1,8,0,8,1,1,0
2230,81,9.2,0,3,0,1,5,0,6,1,1,6
3751,81,9.5,0,3,0,1,7,0,6,1,1,1
974,81,9.9,0,2,0,1,8,0,5,1,1,10
7539,81,10.1,0,5,0,1,6,1,7,1,1,3
4076,81,10.7,0,4,0,1,5,0,8,1,1,10
7000,81,10.8,0,1,0,1,5,1,7,1,1,0
4170,81,11,0,3,0,1,7,0,2,1,1,3
5336,81,11.1,0,5,0,1,5,0,4,1,1,6
2793,81,11.4,0,1,0,1,6,0,3,1,1,1
328,81,11.7,0,5,0,1,8,0,7,1,1,3
4432,81,11.9,0,1,0,1,7,0,4,1,1,10
6007,81,12.1,0,3,0,1,7,1,5,1,1,2
5048,81,12.2,0,3,0,1,7,0,1,1,1,2
9570,81,12.4,0,1,0,1,7,1,3,1,1,3
8336,81,12.5,0,5,0,1,7,1,5,1,1,7
6271,81,13.3,0,4,0,1,7,1,8,1,1,4
4042,81,13.4,0,1,0,1,5,0,6,1,1,0
7825,81,13.5,0,4,0,1,8,1,6,1,1,1
2320,81,13.6,0,5,0,1,8,0,6,1,1,8
306,81,13.6,0,3,0,1,8,0,5,1,1,5
2884,81,13.7,0,4,0,1,5,0,3,1,1,10
7969,81,13.7,0,1,0,1,7,1,4,1,1,1
1164,81,14.3,0,4,0,1,5,0,3,1,1,5
7423,81,14.3,0,5,0,1,8,1,1,1,1,1
2981,81,14.4,0,2,0,1,8,0,6,1,1,2
5096,81,14.7,0,4,0,1,5,0,1,1,1,9
5204,81,14.8,0,1,0,1,5,0,4,1,1,1
7027,81,15,0,2,0,1,8,1,2,1,1,0
10387,81,15.4,0,2,0,1,8,1,2,1,1,0
2488,81,15.7,0,3,0,1,7,0,3,1,1,0
8941,81,15.8,0,5,0,1,5,1,2,1,1,1
3142,81,15.9,0,1,0,1,6,0,7,1,1,7
401,81,16.7,0,3,0,1,5,0,7,1,1,1
2979,81,17.3,0,4,0,1,8,0,4,1,1,3
1398,81,17.6,0,3,0,1,5,0,1,1,1,3
5565,81,18.4,0,1,0,1,6,1,6,1,1,1
6510,81,18.8,0,5,0,1,8,1,2,1,1,9
9,81,19.1,0,5,0,1,5,0,5,1,1,1
8546,81,19.5,0,4,0,1,8,1,4,1,1,8
1658,81,19.6,0,1,0,1,6,0,6,1,1,7
6459,81,19.7,0,5,0,1,5,1,4,1,1,1
9854,81,19.8,0,3,0,1,7,1,1,1,1,4
2485,81,20.1,0,1,0,1,7,0,6,1,1,4
1056,81,20.3,0,5,0,1,8,0,2,1,1,10
9833,81,20.3,0,4,0,1,7,1,7,1,1,0
4459,81,20.8,0,5,0,1,5,0,2,1,1,3
141,81,21.1,0,3,0,1,8,0,4,1,1,4
10136,81,21.1,0,2,0,1,8,1,8,1,1,7
5315,81,21.1,0,5,0,1,7,0,4,1,1,4
5117,81,21.2,0,2,0,1,5,0,8,1,1,2
10152,81,21.2,0,2,0,1,5,1,1,1,1,10
3694,81,21.4,0,1,0,1,6,0,2,1,1,6
6838,81,21.8,0,2,0,1,5,1,6,1,1,8
1379,81,22,0,4,0,1,8,0,4,1,1,2
4914,81,22,0,2,0,1,6,0,4,1,1,2
310,81,22.1,0,5,0,1,6,0,4,1,1,4
908,81,22.3,0,1,0,1,6,0,7,1,1,8
912,81,22.5,0,1,0,1,5,0,4,1,1,4
447,81,22.8,0,5,0,1,6,0,6,1,1,0
2628,81,23.3,0,1,0,1,7,0,8,1,1,4
8438,81,24.1,0,3,0,1,6,1,7,1,1,9
8184,81,24.2,0,1,0,1,6,1,5,1,1,5
868,81,24.2,0,3,0,1,7,0,2,1,1,8
8138,81,24.4,0,5,0,1,6,1,7,1,1,3
9265,81,24.5,0,1,0,1,7,1,6,1,1,2
1040,81,24.9,0,5,0,1,6,0,6,1,1,8
1469,81,25,0,4,0,1,5,0,7,1,1,3
3028,81,25.1,0,1,0,1,7,0,3,1,1,9
5654,81,25.2,0,3,0,1,8,1,3,1,1,6
6929,81,25.9,0,5,0,1,5,1,3,1,1,3
4368,81,26,0,1,0,1,6,0,1,1,1,6
3910,81,26.2,0,2,0,1,8,0,1,1,1,5
1700,81,26.3,0,2,0,1,6,0,4,1,1,4
1942,81,26.6,0,4,0,1,8,0,4,1,1,7
5014,81,26.7,0,4,0,1,6,0,1,1,1,8
7146,81,26.9,0,3,0,1,6,1,6,1,1,2
135,81,27.1,0,4,0,1,7,0,1,1,1,4
4008,81,27.5,0,1,0,1,8,0,3,1,1,8
840,81,27.7,0,3,0,1,5,0,7,1,1,2
3808,81,27.7,0,4,0,1,5,0,2,1,1,5
3074,81,27.9,0,3,0,1,6,0,6,1,1,5
769,81,28.3,0,3,0,1,6,0,5,1,1,2
3512,81,29.1,0,2,0,1,7,0,4,1,1,3
3214,81,29.2,0,5,0,1,5,0,7,1,1,5
8986,81,29.3,0,4,0,1,8,1,5,1,1,10
8970,81,29.6,0,4,0,1,7,1,8,1,1,0
1311,81,29.7,0,1,0,1,8,0,1,1,1,1
7189,82,0.5,0,4,0,1,8,1,8,1,1,0
4890,82,0.6,0,2,0,1,5,0,1,1,1,2
10685,82,1.7,0,1,0,1,5,1,5,1,1,3
9047,82,1.8,0,5,0,1,8,1,1,1,1,1
9906,82,1.9,0,5,0,1,8,1,4,1,1,2
458,82,2.1,0,2,0,1,5,0,5,1,1,3
7772,82,2.2,0,2,0,1,8,1,2,1,1,4
6308,82,2.2,0,2,0,1,6,1,2,1,1,8
3940,82,2.7,0,2,0,1,5,0,3,1,1,5
4348,82,2.8,0,1,0,1,6,0,5,1,1,3
9157,82,3.2,0,3,0,1,8,1,5,1,1,2
8623,82,3.4,0,1,0,1,5,1,4,1,1,2
4038,82,3.6,0,5,0,1,8,0,7,1,1,9
2722,82,3.7,0,2,0,1,8,0,4,1,1,7
2492,82,3.7,0,3,0,1,7,0,4,1,1,2
5912,82,3.7,0,5,0,1,5,1,5,1,1,7
5702,82,4,0,2,0,1,7,1,8,1,1,4
3446,82,4,0,2,0,1,5,0,3,1,1,3
3844,82,4.8,0,3,0,1,6,0,1,1,1,1
8474,82,5.2,0,5,0,1,8,1,5,1,1,6
1888,82,5.3,0,3,0,1,5,0,7,1,1,2
1150,82,5.5,0,2,0,1,5,0,6,1,1,8
292,82,5.7,0,4,0,1,6,0,5,1,1,6
2001,82,5.9,0,2,0,1,7,0,5,1,1,1
10584,82,6.2,0,4,0,1,8,1,4,1,1,0
3178,82,6.8,0,1,0,1,8,0,5,1,1,5
9739,82,7.3,0,5,0,1,8,1,6,1,1,1
7595,82,7.3,0,2,0,1,7,1,8,1,1,4
4397,82,7.4,0,5,0,1,7,0,1,1,1,3
8940,82,7.4,0,4,0,1,7,1,7,1,1,8
9755,82,7.5,0,4,0,1,8,1,4,1,1,0
10220,82,7.5,0,1,0,1,7,1,1,1,1,3
3347,82,7.6,0,4,0,1,8,0,4,1,1,4
7050,82,7.8,0,5,0,1,6,1,6,1,1,0
4665,82,8.2,0,5,0,1,6,0,6,1,1,4
99,82,8.5,0,2,0,1,6,0,7,1,1,4
10636,82,8.5,0,5,0,1,7,1,8,1,1,5
5852,82,8.6,0,1,0,1,6,1,8,1,1,3
875,82,8.7,0,2,0,1,8,0,8,1,1,0
8127,82,8.8,0,5,0,1,6,1,7,1,1,0
5675,82,8.8,0,1,0,1,5,1,3,1,1,2
3836,82,9.1,0,4,0,1,8,0,7,1,1,4
3276,82,9.4,0,2,0,1,5,0,4,1,1,0
3776,82,9.8,0,1,0,1,7,0,2,1,1,7
4466,82,9.9,0,1,0,1,8,0,2,1,1,0
5450,82,10.3,0,2,0,1,7,1,8,1,1,2
10474,82,11.1,0,5,0,1,7,1,7,1,1,7
8165,82,11.2,0,2,0,1,6,1,1,1,1,0
5359,82,11.2,0,5,0,1,5,0,8,1,1,1
8704,82,11.4,0,5,0,1,7,1,2,1,1,10
1840,82,11.6,0,1,0,1,5,0,1,1,1,4
4486,82,11.7,0,4,0,1,5,0,6,1,1,5
8952,82,12.4,0,1,0,1,6,1,8,1,1,3
398,82,12.7,0,3,0,1,7,0,7,1,1,0
3933,82,13.4,0,3,0,1,6,0,5,1,1,2
1240,82,13.4,0,5,0,1,5,0,7,1,1,6
10042,82,14,0,3,0,1,6,1,1,1,1,4
8638,82,14.1,0,5,0,1,5,1,8,1,1,3
42,82,15.9,0,1,0,1,6,0,5,1,1,4
6770,82,16,0,4,0,1,7,1,5,1,1,7
1370,82,16,0,5,0,1,7,0,5,1,1,3
1706,82,16.3,0,2,0,1,5,0,3,1,1,6
10340,82,17.1,0,4,0,1,6,1,2,1,1,10
2654,82,17.2,0,2,0,1,8,0,8,1,1,5
10652,82,17.5,0,2,0,1,7,1,8,1,1,9
1124,82,17.6,0,5,0,1,5,0,2,1,1,9
10108,82,17.7,0,3,0,1,6,1,7,1,1,5
6534,82,17.8,0,2,0,1,8,1,1,1,1,10
4855,82,18.5,0,4,0,1,8,0,6,1,1,2
4443,82,18.9,0,1,0,1,6,0,5,1,1,1
10728,82,19.1,0,1,0,1,8,1,6,1,1,5
546,82,19.3,0,5,0,1,6,0,4,1,1,4
5236,82,19.8,0,2,0,1,8,0,3,1,1,8
6625,82,19.8,0,4,0,1,5,1,1,1,1,1
3790,82,20.3,0,1,0,1,6,0,4,1,1,0
1835,82,20.6,0,4,0,1,5,0,5,1,1,3
6123,82,20.7,0,3,0,1,7,1,2,1,1,3
2404,82,21.2,0,1,0,1,6,0,7,1,1,0
6796,82,21.6,0,3,0,1,6,1,3,1,1,10
298,82,21.8,0,4,0,1,6,0,8,1,1,2
5860,82,22.5,0,2,0,1,8,1,5,1,1,3
3275,82,22.5,0,2,0,1,5,0,4,1,1,0
7878,82,23.1,0,5,0,1,6,1,7,1,1,7
58,82,24.4,0,1,0,1,5,0,5,1,1,9
1356,82,24.4,0,2,0,1,6,0,6,1,1,0
2854,82,24.4,0,4,0,1,8,0,8,1,1,7
6266,82,24.7,0,3,0,1,8,1,5,1,1,0
3460,82,24.9,0,4,0,1,6,0,8,1,1,4
1078,82,25.7,0,5,0,1,8,0,7,1,1,9
8237,82,25.8,0,1,0,1,7,1,2,1,1,4
8674,82,26.3,0,4,0,1,5,1,8,1,1,9
9686,82,26.9,0,5,0,1,5,1,2,1,1,3
7074,82,27,0,4,0,1,7,1,6,1,1,6
4426,82,27,0,5,0,1,8,0,7,1,1,10
10854,82,27.3,0,2,0,1,6,1,1,1,1,8
837,82,27.7,0,2,0,1,7,0,2,1,1,1
1408,82,27.7,0,2,0,1,5,0,4,1,1,7
3410,82,28.2,0,4,0,1,5,0,4,1,1,7
8031,82,28.3,0,4,0,1,7,1,8,1,1,3
5772,82,28.7,0,1,0,1,6,1,8,1,1,0
10024,82,29.4,0,2,0,1,8,1,8,1,1,1
2746,82,29.8,0,2,0,1,7,0,3,1,1,8
4328,83,0.7,0,3,0,1,6,0,6,1,1,3
7066,83,1.3,0,1,0,1,7,1,7,1,1,6
6243,83,1.4,0,3,0,1,7,1,4,1,1,1
9636,83,2,0,4,0,1,8,1,1,1,1,6
4700,83,2.2,0,2,0,1,6,0,6,1,1,0
492,83,2.8,0,5,0,1,7,0,2,1,1,0
1430,83,3,0,2,0,1,7,0,1,1,1,9
8339,83,3.2,0,1,0,1,8,1,3,1,1,2
4796,83,3.2,0,4,0,1,7,0,6,1,1,4
2280,83,3.5,0,2,0,1,5,0,5,1,1,2
6406,83,3.6,0,1,0,1,7,1,2,1,1,1
7132,83,3.6,0,2,0,1,6,1,6,1,1,3
10062,83,4,0,2,0,1,6,1,3,1,1,5
1922,83,4.1,0,3,0,1,6,0,7,1,1,10
3743,83,4.4,0,2,0,1,8,0,6,1,1,4
8608,83,4.6,0,5,0,1,8,1,4,1,1,6
10796,83,4.7,0,2,0,1,5,1,1,1,1,4
5345,83,5,0,1,0,1,8,0,5,1,1,1
5008,83,5.4,0,4,0,1,6,0,2,1,1,0
854,83,5.4,0,1,0,1,5,0,1,1,1,5
5433,83,5.4,0,3,0,1,6,1,7,1,1,0
9164,83,5.6,0,4,0,1,5,1,7,1,1,4
6036,83,5.6,0,3,0,1,8,1,8,1,1,5
10627,83,5.9,0,2,0,1,6,1,3,1,1,4
2004,83,6.1,0,2,0,1,5,0,6,1,1,2
4960,83,6.1,0,1,0,1,7,0,6,1,1,8
3188,83,6.2,0,5,0,1,5,0,2,1,1,7
6182,83,6.3,0,5,0,1,6,1,6,1,1,9
2505,83,6.5,0,1,0,1,5,0,7,1,1,3
10438,83,6.7,0,1,0,1,7,1,4,1,1,9
5959,83,7.1,0,5,0,1,8,1,2,1,1,3
5468,83,7.8,0,3,0,1,7,1,3,1,1,8
7975,83,7.8,0,3,0,1,7,1,5,1,1,0
263,83,8.3,0,3,0,1,5,0,2,1,1,1
5058,83,8.6,0,5,0,1,6,0,2,1,1,2
6448,83,9.1,0,4,0,1,7,1,8,1,1,6
5441,83,9.4,0,4,0,1,6,1,4,1,1,0
5030,83,9.4,0,5,0,1,7,0,2,1,1,5
3988,83,9.4,0,4,0,1,7,0,8,1,1,3
4366,83,10.4,0,1,0,1,6,0,3,1,1,0
8806,83,11.1,0,2,0,1,5,1,6,1,1,3
6012,83,11.3,0,4,0,1,7,1,2,1,1,6
6776,83,12,0,3,0,1,7,1,8,1,1,7
3029,83,12,0,3,0,1,6,0,6,1,1,2
6874,83,12.4,0,4,0,1,5,1,5,1,1,2
360,83,13,0,5,0,1,5,0,6,1,1,2
7768,83,13.5,0,5,0,1,7,1,1,1,1,10
1984,83,13.7,0,5,0,1,5,0,1,1,1,1
6402,83,13.9,0,3,0,1,7,1,5,1,1,4
7400,83,14,0,1,0,1,8,1,7,1,1,1
2968,83,14.3,0,2,0,1,7,0,2,1,1,1
4942,83,14.6,0,4,0,1,6,0,2,1,1,0
9916,83,14.7,0,2,0,1,5,1,1,1,1,7
1749,83,15,0,5,0,1,6,0,2,1,1,4
9584,83,15.2,0,1,0,1,7,1,2,1,1,10
2610,83,15.6,0,5,0,1,5,0,1,1,1,1
7712,83,15.9,0,4,0,1,7,1,2,1,1,5
7156,83,16.1,0,4,0,1,5,1,4,1,1,1
8038,83,16.4,0,5,0,1,6,1,1,1,1,1
9310,83,16.6,0,5,0,1,5,1,2,1,1,3
261,83,16.6,0,1,0,1,7,0,3,1,1,4
8046,83,16.7,0,3,0,1,7,1,1,1,1,7
5899,83,16.7,0,5,0,1,8,1,3,1,1,0
4350,83,16.8,0,2,0,1,7,0,5,1,1,10
8622,83,17,0,1,0,1,6,1,4,1,1,3
6356,83,17.4,0,4,0,1,6,1,3,1,1,1
7012,83,17.7,0,3,0,1,8,1,2,1,1,6
942,83,17.8,0,4,0,1,6,0,1,1,1,3
834,83,18.1,0,4,0,1,7,0,5,1,1,9
10734,83,18.1,0,2,0,1,7,1,2,1,1,1
6198,83,19,0,3,0,1,6,1,1,1,1,9
2451,83,19.1,0,5,0,1,5,0,4,1,1,1
1312,83,19.2,0,5,0,1,8,0,5,1,1,2
9704,83,19.3,0,1,0,1,6,1,3,1,1,4
2321,83,19.3,0,3,0,1,8,0,8,1,1,0
8014,83,19.6,0,4,0,1,8,1,8,1,1,7
260,83,19.7,0,2,0,1,7,0,4,1,1,8
10437,83,19.7,0,3,0,1,7,1,4,1,1,4
4264,83,21,0,2,0,1,6,0,6,1,1,10
1355,83,21.1,0,2,0,1,7,0,4,1,1,4
115,83,21.4,0,4,0,1,7,0,6,1,1,3
7555,83,21.5,0,2,0,1,6,1,6,1,1,0
5410,83,21.7,0,2,0,1,6,0,2,1,1,1
4804,83,21.9,0,3,0,1,6,0,7,1,1,2
5420,83,22.6,0,2,0,1,5,1,8,1,1,0
1152,83,23.3,0,5,0,1,6,0,2,1,1,6
4554,83,23.6,0,4,0,1,7,0,8,1,1,1
6064,83,23.9,0,1,0,1,5,1,4,1,1,9
4294,83,24,0,1,0,1,8,0,2,1,1,6
3081,83,24.3,0,2,0,1,5,0,6,1,1,0
284,83,24.9,0,3,0,1,6,0,6,1,1,0
1002,83,25,0,5,0,1,8,0,1,1,1,3
2414,83,25.7,0,2,0,1,8,0,5,1,1,8
2399,83,25.8,0,2,0,1,7,0,6,1,1,4
1786,83,26,0,5,0,1,7,0,5,1,1,7
3987,83,26.2,0,1,0,1,7,0,2,1,1,1
4626,83,26.4,0,5,0,1,7,0,5,1,1,7
7857,83,26.4,0,3,0,1,5,1,7,1,1,4
754,83,26.4,0,1,0,1,6,0,2,1,1,9
6640,83,26.8,0,1,0,1,5,1,3,1,1,5
6020,83,26.9,0,1,0,1,6,1,4,1,1,3
5630,83,27.1,0,3,0,1,7,1,4,1,1,2
177,83,27.2,0,1,0,1,7,0,6,1,1,2
857,83,27.2,0,3,0,1,5,0,4,1,1,0
1633,83,28.1,0,2,0,1,6,0,4,1,1,2
7067,83,28.4,0,2,0,1,8,1,2,1,1,2
952,83,28.8,0,3,0,1,8,0,4,1,1,9
7250,83,29,0,4,0,1,7,1,8,1,1,4
277,83,29.4,0,2,0,1,8,0,7,1,1,1
9416,83,29.4,0,2,0,1,6,1,8,1,1,9
1737,83,29.5,0,5,0,1,7,0,6,1,1,3
891,84,0.2,0,1,0,1,5,0,3,1,1,4
6992,84,0.5,0,5,0,1,8,1,3,1,1,7
9352,84,1.4,0,1,0,1,5,1,3,1,1,6
825,84,1.8,0,3,0,1,6,0,8,1,1,4
476,84,1.9,0,1,0,1,7,0,6,1,1,9
8186,84,2,0,3,0,1,8,1,5,1,1,5
1084,84,2.1,0,1,0,1,7,0,3,1,1,9
120,84,2.2,0,3,0,1,6,0,3,1,1,4
1211,84,2.3,0,4,0,1,6,0,7,1,1,4
7478,84,2.4,0,2,0,1,5,1,7,1,1,7
1704,84,2.5,0,3,0,1,7,0,3,1,1,1
9828,84,2.7,0,2,0,1,5,1,2,1,1,0
4230,84,3.2,0,3,0,1,7,0,3,1,1,7
2874,84,3.3,0,4,0,1,8,0,7,1,1,1
3948,84,3.4,0,4,0,1,7,0,6,1,1,5
8225,84,3.7,0,2,0,1,5,1,6,1,1,3
10232,84,3.9,0,5,0,1,8,1,6,1,1,9
5224,84,4.2,0,5,0,1,5,0,8,1,1,9
1034,84,4.5,0,4,0,1,6,0,1,1,1,6
7242,84,4.9,0,5,0,1,8,1,5,1,1,9
8408,84,5.2,0,5,0,1,6,1,1,1,1,6
1673,84,5.6,0,3,0,1,7,0,2,1,1,0
5133,84,5.7,0,3,0,1,6,0,8,1,1,1
1660,84,5.8,0,3,0,1,6,0,6,1,1,10
867,84,5.9,0,2,0,1,7,0,1,1,1,3
6371,84,6.1,0,3,0,1,8,1,8,1,1,0
6579,84,6.3,0,1,0,1,7,1,6,1,1,0
435,84,6.4,0,4,0,1,7,0,3,1,1,2
4506,84,6.7,0,5,0,1,8,0,8,1,1,2
1226,84,7.1,0,1,0,1,8,0,8,1,1,10
7387,84,7.2,0,1,0,1,6,1,4,1,1,2
4086,84,8.2,0,2,0,1,6,0,1,1,1,9
5540,84,8.3,0,1,0,1,8,1,8,1,1,8
8322,84,8.4,0,3,0,1,5,1,6,1,1,2
7114,84,9,0,4,0,1,8,1,1,1,1,5
4,84,9.7,0,3,0,1,7,0,1,1,1,4
3710,84,9.7,0,2,0,1,8,0,8,1,1,9
3732,84,10,0,1,0,1,8,0,3,1,1,9
7316,84,10.3,0,1,0,1,5,1,3,1,1,6
2482,84,10.5,0,4,0,1,5,0,3,1,1,10
2024,84,10.6,0,3,0,1,5,0,2,1,1,9
8864,84,11.2,0,3,0,1,5,1,4,1,1,5
9273,84,11.2,0,2,0,1,6,1,1,1,1,4
10375,84,11.4,0,4,0,1,7,1,8,1,1,2
10028,84,11.5,0,2,0,1,8,1,5,1,1,6
7381,84,12,0,1,0,1,7,1,2,1,1,4
8381,84,12.2,0,5,0,1,5,1,3,1,1,3
1748,84,12.4,0,3,0,1,7,0,7,1,1,10
9088,84,12.4,0,1,0,1,5,1,4,1,1,9
1858,84,12.6,0,3,0,1,7,0,4,1,1,9
8004,84,12.6,0,3,0,1,6,1,4,1,1,2
3962,84,12.8,0,3,0,1,6,0,3,1,1,4
7155,84,13,0,3,0,1,7,1,1,1,1,2
4288,84,13.1,0,4,0,1,8,0,5,1,1,5
8871,84,13.3,0,2,0,1,5,1,6,1,1,1
10433,84,13.4,0,1,0,1,6,1,6,1,1,1
8816,84,13.5,0,5,0,1,5,1,5,1,1,5
2499,84,13.7,0,1,0,1,7,0,6,1,1,0
7636,84,13.7,0,4,0,1,8,1,3,1,1,9
8822,84,14.1,0,2,0,1,6,1,8,1,1,2
9568,84,14.3,0,2,0,1,7,1,1,1,1,3
2928,84,14.9,0,1,0,1,7,0,6,1,1,9
8775,84,15,0,3,0,1,7,1,4,1,1,2
3822,84,15.4,0,4,0,1,6,0,8,1,1,0
10088,84,15.5,0,3,0,1,7,1,7,1,1,6
10412,84,16,0,5,0,1,7,1,7,1,1,5
9732,84,16.3,0,4,0,1,6,1,1,1,1,7
6564,84,16.3,0,3,0,1,6,1,7,1,1,6
9000,84,16.9,0,1,0,1,7,1,3,1,1,4
7099,84,17,0,2,0,1,7,1,5,1,1,1
5186,84,17.4,0,3,0,1,5,0,2,1,1,3
4618,84,17.9,0,2,0,1,6,0,2,1,1,0
1248,84,18,0,5,0,1,6,0,2,1,1,7
1304,84,18.2,0,2,0,1,8,0,1,1,1,6
3600,84,18.3,0,5,0,1,6,0,7,1,1,3
7052,84,18.7,0,3,0,1,5,1,8,1,1,0
2704,84,18.8,0,2,0,1,5,0,3,1,1,7
7996,84,19,0,4,0,1,6,1,4,1,1,2
8072,84,19.1,0,3,0,1,5,1,4,1,1,6
3286,84,19.4,0,1,0,1,5,0,4,1,1,9
10638,84,19.5,0,5,0,1,6,1,6,1,1,6
1089,84,19.7,0,4,0,1,8,0,5,1,1,3
1882,84,19.8,0,5,0,1,7,0,8,1,1,1
3588,84,19.8,0,3,0,1,5,0,4,1,1,0
8107,84,20.4,0,2,0,1,7,1,7,1,1,4
218,84,20.4,0,3,0,1,7,0,6,1,1,4
5009,84,20.4,0,4,0,1,8,0,8,1,1,4
5158,84,20.6,0,3,0,1,8,0,6,1,1,0
1428,84,20.6,0,2,0,1,6,0,8,1,1,6
6176,84,20.7,0,4,0,1,5,1,7,1,1,0
2440,84,21.1,0,1,0,1,5,0,2,1,1,8
2175,84,21.4,0,4,0,1,7,0,1,1,1,2
3086,84,21.5,0,5,0,1,7,0,4,1,1,7
724,84,21.6,0,1,0,1,6,0,1,1,1,9
9120,84,21.6,0,5,0,1,8,1,5,1,1,2
5650,84,21.7,0,4,0,1,6,1,6,1,1,2
10762,84,21.7,0,4,0,1,6,1,7,1,1,10
3520,84,22.6,0,4,0,1,7,0,7,1,1,4
4151,84,22.6,0,1,0,1,6,0,7,1,1,1
70,84,23,0,1,0,1,7,0,8,1,1,4
1170,84,23.3,0,5,0,1,5,0,6,1,1,7
10620,84,23.5,0,2,0,1,8,1,4,1,1,7
7883,84,23.7,0,3,0,1,7,1,3,1,1,2
8591,84,23.8,0,5,0,1,6,1,8,1,1,3
307,84,23.9,0,3,0,1,6,0,8,1,1,3
1803,84,24.9,0,3,0,1,7,0,4,1,1,4
9617,84,25.4,0,2,0,1,8,1,4,1,1,3
10170,84,25.6,0,4,0,1,7,1,4,1,1,1
4005,84,25.9,0,3,0,1,6,0,4,1,1,2
6347,84,26.2,0,4,0,1,8,1,4,1,1,1
3075,84,26.7,0,4,0,1,8,0,5,1,1,1
9895,84,26.8,0,3,0,1,5,1,2,1,1,3
8920,84,27.1,0,4,0,1,7,1,5,1,1,9
3583,84,27.1,0,4,0,1,8,0,7,1,1,4
5660,84,27.2,0,2,0,1,7,1,6,1,1,6
3570,84,27.6,0,3,0,1,7,0,5,1,1,0
3557,84,28.4,0,1,0,1,5,0,6,1,1,4
4371,84,28.7,0,3,0,1,5,0,3,1,1,2
1805,84,29,0,1,0,1,7,0,5,1,1,4
5853,84,29.4,0,4,0,1,7,1,2,1,1,3
4228,85,0.6,0,5,0,1,7,0,1,1,1,0
7352,85,0.6,0,1,0,1,8,1,6,1,1,3
9538,85,1.1,0,5,0,1,5,1,3,1,1,8
5103,85,1.5,0,4,0,1,8,0,4,1,1,0
2201,85,1.8,0,2,0,1,5,0,8,1,1,4
1331,85,2.8,0,4,0,1,6,0,1,1,1,4
334,85,3.3,0,3,0,1,6,0,8,1,1,7
1190,85,3.4,0,5,0,1,7,0,4,1,1,10
10209,85,3.9,0,4,0,1,6,1,8,1,1,3
7604,85,4.1,0,4,0,1,6,1,7,1,1,0
2107,85,4.2,0,4,0,1,6,0,7,1,1,4
3310,85,4.6,0,4,0,1,8,0,2,1,1,9
3554,85,4.7,0,2,0,1,5,0,5,1,1,9
5905,85,5.1,0,3,0,1,5,1,4,1,1,4
9159,85,6.1,0,4,0,1,6,1,5,1,1,4
6186,85,6.3,0,5,0,1,8,1,4,1,1,8
7578,85,6.8,0,4,0,1,6,1,1,1,1,7
9363,85,7.4,0,2,0,1,5,1,5,1,1,2
2456,85,8.2,0,5,0,1,8,0,1,1,1,5
508,85,8.3,0,4,0,1,8,0,5,1,1,3
8063,85,8.6,0,2,0,1,6,1,8,1,1,3
10034,85,10,0,2,0,1,8,1,8,1,1,7
8300,85,10.2,0,1,0,1,7,1,8,1,1,1
3628,85,10.4,0,3,0,1,8,0,2,1,1,8
8762,85,10.7,0,3,0,1,5,1,3,1,1,2
9736,85,10.7,0,5,0,1,6,1,8,1,1,8
10842,85,10.7,0,1,0,1,8,1,6,1,1,4
7384,85,10.8,0,2,0,1,6,1,7,1,1,5
8233,85,10.8,0,3,0,1,7,1,6,1,1,2
89,85,11,0,4,0,1,6,0,7,1,1,1
1848,85,11.2,0,3,0,1,6,0,2,1,1,6
3397,85,11.2,0,3,0,1,7,0,5,1,1,0
7664,85,11.4,0,5,0,1,7,1,7,1,1,0
7394,85,11.5,0,4,0,1,7,1,5,1,1,3
8613,85,12.2,0,3,0,1,8,1,3,1,1,4
6426,85,12.2,0,2,0,1,7,1,7,1,1,3
10570,85,12.2,0,4,0,1,7,1,1,1,1,4
3469,85,12.6,0,5,0,1,5,0,7,1,1,0
3531,85,12.7,0,4,0,1,6,0,5,1,1,2
4728,85,12.7,0,2,0,1,5,0,1,1,1,3
10334,85,12.8,0,4,0,1,7,1,8,1,1,10
1191,85,13,0,1,0,1,7,0,8,1,1,0
7736,85,13.3,0,4,0,1,8,1,6,1,1,0
4754,85,13.8,0,3,0,1,5,0,8,1,1,10
9246,85,13.8,0,2,0,1,6,1,4,1,1,9
589,85,14,0,1,0,1,6,0,8,1,1,1
10660,85,14.5,0,3,0,1,5,1,7,1,1,7
7608,85,14.8,0,5,0,1,6,1,1,1,1,9
8744,85,15,0,1,0,1,8,1,8,1,1,5
9788,85,15,0,5,0,1,6,1,2,1,1,3
2796,85,15.5,0,4,0,1,5,0,4,1,1,9
5128,85,15.6,0,5,0,1,7,0,8,1,1,10
6492,85,16,0,5,0,1,6,1,8,1,1,4
9146,85,16.4,0,5,0,1,6,1,6,1,1,7
1434,85,17.2,0,5,0,1,5,0,5,1,1,4
6834,85,17.7,0,3,0,1,8,1,3,1,1,7
4734,85,17.9,0,2,0,1,5,0,3,1,1,9
3564,85,18.5,0,2,0,1,5,0,2,1,1,1
4930,85,18.6,0,3,0,1,6,0,3,1,1,9
1116,85,19,0,3,0,1,7,0,4,1,1,2
1363,85,19.2,0,3,0,1,8,0,4,1,1,3
7662,85,19.2,0,5,0,1,5,1,8,1,1,4
8407,85,19.3,0,1,0,1,6,1,1,1,1,4
2033,85,19.5,0,5,0,1,5,0,7,1,1,1
2206,85,19.7,0,2,0,1,5,0,1,1,1,9
4324,85,20.1,0,2,0,1,7,0,5,1,1,9
10800,85,20.2,0,3,0,1,8,1,7,1,1,4
9548,85,20.6,0,4,0,1,8,1,1,1,1,8
657,85,20.6,0,5,0,1,7,0,7,1,1,3
10526,85,20.8,0,2,0,1,5,1,4,1,1,4
7460,85,21.1,0,3,0,1,5,1,5,1,1,0
8897,85,21.4,0,4,0,1,6,1,4,1,1,3
7327,85,21.7,0,5,0,1,5,1,4,1,1,3
7669,85,21.7,0,4,0,1,7,1,6,1,1,2
1809,85,21.8,0,2,0,1,7,0,4,1,1,3
5092,85,21.9,0,5,0,1,8,0,2,1,1,2
2257,85,22.8,0,2,0,1,8,0,2,1,1,1
3660,85,23.1,0,3,0,1,6,0,1,1,1,9
10613,85,23.4,0,1,0,1,5,1,6,1,1,4
5831,85,23.6,0,4,0,1,5,1,7,1,1,1
530,85,24.2,0,2,0,1,6,0,7,1,1,0
1222,85,24.5,0,5,0,1,6,0,1,1,1,10
192,85,24.7,0,2,0,1,5,0,3,1,1,6
937,85,25.1,0,1,0,1,7,0,3,1,1,1
10710,85,25.6,0,4,0,1,8,1,7,1,1,3
5760,85,25.6,0,5,0,1,8,1,2,1,1,6
9075,85,25.9,0,5,0,1,5,1,4,1,1,3
8398,85,26.2,0,3,0,1,6,1,5,1,1,5
1506,85,26.3,0,3,0,1,7,0,5,1,1,10
3223,85,26.5,0,2,0,1,6,0,6,1,1,1
6592,85,28.3,0,5,0,1,8,1,8,1,1,9
1736,85,29.4,0,3,0,1,6,0,4,1,1,0
2124,85,29.7,0,5,0,1,8,0,6,1,1,3
10154,85,29.8,0,1,0,1,6,1,7,1,1,5


================================================
FILE: data/Social_Network_Ads.csv
================================================
User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
15570769,Female,26,80000,0
15606274,Female,26,52000,0
15746139,Male,20,86000,0
15704987,Male,32,18000,0
15628972,Male,18,82000,0
15697686,Male,29,80000,0
15733883,Male,47,25000,1
15617482,Male,45,26000,1
15704583,Male,46,28000,1
15621083,Female,48,29000,1
15649487,Male,45,22000,1
15736760,Female,47,49000,1
15714658,Male,48,41000,1
15599081,Female,45,22000,1
15705113,Male,46,23000,1
15631159,Male,47,20000,1
15792818,Male,49,28000,1
15633531,Female,47,30000,1
15744529,Male,29,43000,0
15669656,Male,31,18000,0
15581198,Male,31,74000,0
15729054,Female,27,137000,1
15573452,Female,21,16000,0
15776733,Female,28,44000,0
15724858,Male,27,90000,0
15713144,Male,35,27000,0
15690188,Female,33,28000,0
15689425,Male,30,49000,0
15671766,Female,26,72000,0
15782806,Female,27,31000,0
15764419,Female,27,17000,0
15591915,Female,33,51000,0
15772798,Male,35,108000,0
15792008,Male,30,15000,0
15715541,Female,28,84000,0
15639277,Male,23,20000,0
15798850,Male,25,79000,0
15776348,Female,27,54000,0
15727696,Male,30,135000,1
15793813,Female,31,89000,0
15694395,Female,24,32000,0
15764195,Female,18,44000,0
15744919,Female,29,83000,0
15671655,Female,35,23000,0
15654901,Female,27,58000,0
15649136,Female,24,55000,0
15775562,Female,23,48000,0
15807481,Male,28,79000,0
15642885,Male,22,18000,0
15789109,Female,32,117000,0
15814004,Male,27,20000,0
15673619,Male,25,87000,0
15595135,Female,23,66000,0
15583681,Male,32,120000,1
15605000,Female,59,83000,0
15718071,Male,24,58000,0
15679760,Male,24,19000,0
15654574,Female,23,82000,0
15577178,Female,22,63000,0
15595324,Female,31,68000,0
15756932,Male,25,80000,0
15726358,Female,24,27000,0
15595228,Female,20,23000,0
15782530,Female,33,113000,0
15592877,Male,32,18000,0
15651983,Male,34,112000,1
15746737,Male,18,52000,0
15774179,Female,22,27000,0
15667265,Female,28,87000,0
15655123,Female,26,17000,0
15595917,Male,30,80000,0
15668385,Male,39,42000,0
15709476,Male,20,49000,0
15711218,Male,35,88000,0
15798659,Female,30,62000,0
15663939,Female,31,118000,1
15694946,Male,24,55000,0
15631912,Female,28,85000,0
15768816,Male,26,81000,0
15682268,Male,35,50000,0
15684801,Male,22,81000,0
15636428,Female,30,116000,0
15809823,Male,26,15000,0
15699284,Female,29,28000,0
15786993,Female,29,83000,0
15709441,Female,35,44000,0
15710257,Female,35,25000,0
15582492,Male,28,123000,1
15575694,Male,35,73000,0
15756820,Female,28,37000,0
15766289,Male,27,88000,0
15593014,Male,28,59000,0
15584545,Female,32,86000,0
15675949,Female,33,149000,1
15672091,Female,19,21000,0
15801658,Male,21,72000,0
15706185,Female,26,35000,0
15789863,Male,27,89000,0
15720943,Male,26,86000,0
15697997,Female,38,80000,0
15665416,Female,39,71000,0
15660200,Female,37,71000,0
15619653,Male,38,61000,0
15773447,Male,37,55000,0
15739160,Male,42,80000,0
15689237,Male,40,57000,0
15679297,Male,35,75000,0
15591433,Male,36,52000,0
15642725,Male,40,59000,0
15701962,Male,41,59000,0
15811613,Female,36,75000,0
15741049,Male,37,72000,0
15724423,Female,40,75000,0
15574305,Male,35,53000,0
15678168,Female,41,51000,0
15697020,Female,39,61000,0
15610801,Male,42,65000,0
15745232,Male,26,32000,0
15722758,Male,30,17000,0
15792102,Female,26,84000,0
15675185,Male,31,58000,0
15801247,Male,33,31000,0
15725660,Male,30,87000,0
15638963,Female,21,68000,0
15800061,Female,28,55000,0
15578006,Male,23,63000,0
15668504,Female,20,82000,0
15687491,Male,30,107000,1
15610403,Female,28,59000,0
15741094,Male,19,25000,0
15807909,Male,19,85000,0
15666141,Female,18,68000,0
15617134,Male,35,59000,0
15783029,Male,30,89000,0
15622833,Female,34,25000,0
15746422,Female,24,89000,0
15750839,Female,27,96000,1
15749130,Female,41,30000,0
15779862,Male,29,61000,0
15767871,Male,20,74000,0
15679651,Female,26,15000,0
15576219,Male,41,45000,0
15699247,Male,31,76000,0
15619087,Female,36,50000,0
15605327,Male,40,47000,0
15610140,Female,31,15000,0
15791174,Male,46,59000,0
15602373,Male,29,75000,0
15762605,Male,26,30000,0
15598840,Female,32,135000,1
15744279,Male,32,100000,1
15670619,Male,25,90000,0
15599533,Female,37,33000,0
15757837,Male,35,38000,0
15697574,Female,33,69000,0
15578738,Female,18,86000,0
15762228,Female,22,55000,0
15614827,Female,35,71000,0
15789815,Male,29,148000,1
15579781,Female,29,47000,0
15587013,Male,21,88000,0
15570932,Male,34,115000,0
15794661,Female,26,118000,0
15581654,Female,34,43000,0
15644296,Female,34,72000,0
15614420,Female,23,28000,0
15609653,Female,35,47000,0
15594577,Male,25,22000,0
15584114,Male,24,23000,0
15673367,Female,31,34000,0
15685576,Male,26,16000,0
15774727,Female,31,71000,0
15694288,Female,32,117000,1
15603319,Male,33,43000,0
15759066,Female,33,60000,0
15814816,Male,31,66000,0
15724402,Female,20,82000,0
15571059,Female,33,41000,0
15674206,Male,35,72000,0
15715160,Male,28,32000,0
15730448,Male,24,84000,0
15662067,Female,19,26000,0
15779581,Male,29,43000,0
15662901,Male,19,70000,0
15689751,Male,28,89000,0
15667742,Male,34,43000,0
15738448,Female,30,79000,0
15680243,Female,20,36000,0
15745083,Male,26,80000,0
15708228,Male,35,22000,0
15628523,Male,35,39000,0
15708196,Male,49,74000,0
15735549,Female,39,134000,1
15809347,Female,41,71000,0
15660866,Female,58,101000,1
15766609,Female,47,47000,0
15654230,Female,55,130000,1
15794566,Female,52,114000,0
15800890,Female,40,142000,1
15697424,Female,46,22000,0
15724536,Female,48,96000,1
15735878,Male,52,150000,1
15707596,Female,59,42000,0
15657163,Male,35,58000.0,0
15622478,Male,47.0,43000,0
15779529,Female,60,108000,1
15636023,Male,49,65000,0
15582066,Male,40,78000,0
15666675,Female,46,96000,0
15732987,Male,59,143000,1
15789432,Female,41,80000,0
15663161,Male,35,91000,1
15694879,Male,37,144000,1
15593715,Male,60,102000,1
15575002,Female,35,60000,0
15622171,Male,37,53000,0
15795224,Female,36,126000,1
15685346,Male,56,133000,1
15691808,Female,40,72000,0
15721007,Female,42,80000,1
15794253,Female,35,147000,1
15694453,Male,39,42000,0
15813113,Male,40,107000,1
15614187,Male,49,86000,1
15619407,Female,38,112000,0
15646227,Male,46,79000,1
15660541,Male,40,57000,0
15753874,Female,37,80000,0
15617877,Female,46,82000,0
15772073,Female,53,143000,1
15701537,Male,42,149000,1
15736228,Male,38,59000,0
15780572,Female,50,88000,1
15769596,Female,56,104000,1
15586996,Female,41,72000,0
15722061,Female,51,146000,1
15638003,Female,35,50000,0
15775590,Female,57,122000,1
15730688,Male,41,52000,0
15753102,Female,35,97000,1
15810075,Female,44,39000,0
15723373,Male,37,52000,0
15795298,Female,48,134000,1
15584320,Female,37,146000,1
15724161,Female,50,44000,0
15750056,Female,52,90000,1
15609637,Female,41,72000,0
15794493,Male,40,57000,0
15569641,Female,58,95000,1
15815236,Female,45,131000,1
15811177,Female,35,77000,0
15680587,Male,36,144000,1
15672821,Female,55,125000,1
15767681,Female,35,72000,0
15600379,Male,48,90000,1
15801336,Female,42,108000,1
15721592,Male,40,75000,0
15581282,Male,37,74000,0
15746203,Female,47,144000,1
15583137,Male,40,61000,0
15680752,Female,43,133000,0
15688172,Female,59,76000,1
15791373,Male,60,42000,1
15589449,Male,39,106000,1
15692819,Female,57,26000,1
15727467,Male,57,74000,1
15734312,Male,38,71000,0
15764604,Male,49,88000,1
15613014,Female,52,38000,1
15759684,Female,50,36000,1
15609669,Female,59,88000,1
15685536,Male,35,61000,0
15750447,Male,37,70000,1
15663249,Female,52,21000,1
15638646,Male,48,141000,0
15734161,Female,37,93000,1
15631070,Female,37,62000,0
15761950,Female,48,138000,1
15649668,Male,41,79000,0
15713912,Female,37,78000,1
15586757,Male,39,134000,1
15596522,Male,49,89000,1
15625395,Male,55,39000,1
15760570,Male,37,77000,0
15566689,Female,35,57000,0
15725794,Female,36,63000,0
15673539,Male,42,73000,1
15705298,Female,43,112000,1
15675791,Male,45,79000,0
15747043,Male,46,117000,1
15736397,Female,58,38000,1
15678201,Male,48,74000,1
15720745,Female,37,137000,1
15637593,Male,37,79000,1
15598070,Female,40,60000,0
15787550,Male,42,54000,0
15603942,Female,51,134000,0
15733973,Female,47,113000,1
15596761,Male,36,125000,1
15652400,Female,38,50000,0
15717893,Female,42,70000,0
15622585,Male,39,96000,1
15733964,Female,38,50000,0
15753861,Female,49,141000,1
15747097,Female,39,79000,0
15594762,Female,39,75000,1
15667417,Female,54,104000,1
15684861,Male,35,55000,0
15742204,Male,45,32000,1
15623502,Male,36,60000,0
15774872,Female,52,138000,1
15611191,Female,53,82000,1
15674331,Male,41,52000,0
15619465,Female,48,30000,1
15575247,Female,48,131000,1
15695679,Female,41,60000,0
15713463,Male,41,72000,0
15785170,Female,42,75000,0
15796351,Male,36,118000,1
15639576,Female,47,107000,1
15693264,Male,38,51000,0
15589715,Female,48,119000,1
15769902,Male,42,65000,0
15587177,Male,40,65000,0
15814553,Male,57,60000,1
15601550,Female,36,54000,0
15664907,Male,58,144000,1
15612465,Male,35,79000,0
15810800,Female,38,55000,0
15665760,Male,39,122000,1
15588080,Female,53,104000,1
15776844,Male,35,75000,0
15717560,Female,38,65000,0
15629739,Female,47,51000,1
15729908,Male,47,105000,1
15716781,Female,41,63000,0
15646936,Male,53,72000,1
15768151,Female,54,108000,1
15579212,Male,39,77000,0
15721835,Male,38,61000,0
15800515,Female,38,113000,1
15591279,Male,37,75000,0
15587419,Female,42,90000,1
15750335,Female,37,57000,0
15699619,Male,36,99000,1
15606472,Male,60,34000,1
15778368,Male,54,70000,1
15671387,Female,41,72000,0
15573926,Male,40,71000,1
15709183,Male,42,54000,0
15577514,Male,43,129000,1
15778830,Female,53,34000,1
15768072,Female,47,50000,1
15768293,Female,42,79000,0
15654456,Male,42,104000,1
15807525,Female,59,29000,1
15574372,Female,58,47000,1
15671249,Male,46,88000,1
15779744,Male,38,71000,0
15624755,Female,54,26000,1
15611430,Female,60,46000,1
15774744,Male,60,83000,1
15629885,Female,39,73000,0
15708791,Male,59,130000,1
15793890,Female,37,80000,0
15646091,Female,46,32000,1
15596984,Female,46,74000,0
15800215,Female,42,53000,0
15577806,Male,41,87000,1
15749381,Female,58,23000,1
15683758,Male,42,64000,0
15670615,Male,48,33000,1
15715622,Female,44,139000,1
15707634,Male,49,28000,1
15806901,Female,57,33000,1
15775335,Male,56,60000,1
15724150,Female,49,39000,1
15627220,Male,39,71000,0
15672330,Male,47,34000,1
15668521,Female,48,35000,1
15807837,Male,48,33000,1
15592570,Male,47,23000,1
15748589,Female,45,45000,1
15635893,Male,60,42000,1
15757632,Female,39,59000,0
15691863,Female,46,41000,1
15706071,Male,51,23000,1
15654296,Female,50,20000,1
15755018,Male,36,33000,0
15594041,Female,49,36000,1

================================================
FILE: data/StudentPassFail.csv
================================================
studentid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender,select
s01,1,1,1,1,1,1,1,1
s02,1,1,1,1,1,1,1,1
s03,0,0,0,0,0,0,0,0
s04,0,1,0,0,0,0,0,0
s05,1,1,1,0,1,1,1,1
s06,1,1,1,1,0,0,0,0
s07,0,0,0,0,0,0,0,0
s08,1,1,1,1,0,0,0,1
s09,1,1,1,1,1,1,1,1
s10,0,1,0,0,0,0,0,0
s11,0,1,0,1,0,0,0,0
s12,0,0,0,0,0,0,0,0
s13,1,1,1,1,1,1,1,1
s14,1,1,0,1,1,0,0,0
s15,1,1,1,0,1,0,0,1
s16,1,1,0,0,1,1,1,1
s17,1,0,0,1,1,1,1,0
s18,0,0,0,0,1,1,0,0
s19,0,0,0,0,0,0,1,0
s20,1,0,1,1,1,1,1,1
s21,1,1,0,1,1,1,1,0
s22,1,1,1,0,1,1,1,1
s23,1,1,1,1,0,0,1,1
s24,1,1,1,1,1,1,0,0
s25,0,0,0,1,0,0,0,0
s26,0,1,1,1,0,0,0,0
s27,0,0,1,0,0,0,0,0
s28,1,1,1,1,0,1,1,1
s29,1,1,1,1,1,0,1,1
s30,1,1,1,1,0,1,1,1
s31,1,1,1,0,1,1,1,1
s32,1,0,0,1,1,1,1,0
s33,0,0,0,0,0,0,1,0
s34,1,1,1,1,1,1,1,1
s35,1,1,1,1,1,1,1,1
s36,0,1,0,0,1,1,1,0
s37,0,0,0,0,0,0,0,0
s38,0,0,0,0,0,0,0,0
s39,1,1,0,1,1,1,1,1
s40,0,0,0,0,0,0,0,0
s41,1,0,1,1,1,1,1,1
s42,1,0,0,0,0,0,0,0
s43,0,0,0,0,1,0,0,0
s44,0,1,0,0,0,0,1,0
s45,1,1,0,1,1,1,1,1
s46,1,1,1,0,1,0,1,0
s47,0,0,0,0,0,0,0,0
s48,0,0,1,0,0,1,0,0
s49,0,1,1,0,1,1,1,0
s50,0,0,0,0,0,1,0,0

================================================
FILE: data/StudentTid1.csv
================================================
"tid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender"
"s1,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s2,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s3,No,No,No,No,No,No,No,No"
"s4,No,Yes,No,No,No,No,No,No"
"s5,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes"
"s6,Yes,Yes,Yes,Yes,No,No,No,No"
"s7,No,No,No,No,No,No,No,No"
"s8,Yes,Yes,Yes,Yes,No,No,No,Yes"
"s9,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s10,No,Yes,No,No,No,No,No,No"
"s11,No,Yes,No,Yes,No,No,No,No"
"s12,No,No,No,No,No,No,No,No"
"s13,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s14,Yes,Yes,No,Yes,Yes,No,No,No"
"s15,Yes,Yes,Yes,No,Yes,No,No,Yes"
"s16,Yes,Yes,No,No,Yes,Yes,Yes,Yes"
"s17,Yes,No,No,Yes,Yes,Yes,Yes,No"
"s18,No,No,No,No,Yes,Yes,No,No"
"s19,No,No,No,No,No,No,Yes,No"
"s20,Yes,No,Yes,Yes,Yes,Yes,Yes,Yes"
"s21,Yes,Yes,No,Yes,Yes,Yes,Yes,No"
"s22,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes"
"s23,Yes,Yes,Yes,Yes,No,No,Yes,Yes"
"s24,Yes,Yes,Yes,Yes,Yes,Yes,No,No"
"s25,No,No,No,Yes,No,No,No,No"
"s26,No,Yes,Yes,Yes,No,No,No,No"
"s27,No,No,Yes,No,No,No,No,No"
"s28,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes"
"s29,Yes,Yes,Yes,Yes,Yes,No,Yes,Yes"
"s30,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes"
"s31,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes"
"s32,Yes,No,No,Yes,Yes,Yes,Yes,No"
"s33,No,No,No,No,No,No,Yes,No"
"s34,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s35,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes"
"s36,No,Yes,No,No,Yes,Yes,Yes,No"
"s37,No,No,No,No,No,No,No,No"
"s38,No,No,No,No,No,No,No,No"
"s39,Yes,Yes,No,Yes,Yes,Yes,Yes,Yes"
"s40,No,No,No,No,No,No,No,No"
"s41,Yes,No,Yes,Yes,Yes,Yes,Yes,Yes"
"s42,Yes,No,No,No,No,No,No,No"
"s43,No,No,No,No,Yes,No,No,No"
"s44,No,Yes,No,No,No,No,Yes,No"
"s45,Yes,Yes,No,Yes,Yes,Yes,Yes,Yes"
"s46,Yes,Yes,Yes,No,Yes,No,Yes,No"
"s47,No,No,No,No,No,No,No,No"
"s48,No,No,Yes,No,No,Yes,No,No"
"s49,No,Yes,Yes,No,Yes,Yes,Yes,No"
"s50,No,No,No,No,No,Yes,No,No"

================================================
FILE: data/StudentTid2.csv
================================================
"studentch,bigdata,wmc,cc,pr,adbms,crypto"
"c1,Yes,Yes,Yes,Yes,Yes,Yes"
"c2,Yes,Yes,Yes,Yes,Yes,Yes"
"c3,No,No,No,No,No,No"
"c4,No,Yes,No,No,No,No"
"c5,Yes,Yes,Yes,No,Yes,Yes"
"c6,Yes,Yes,Yes,Yes,No,No"
"c7,No,No,No,No,No,No"
"c8,Yes,Yes,Yes,Yes,No,No"
"c9,Yes,Yes,Yes,Yes,Yes,Yes"
"c10,No,Yes,No,No,No,No"
"c11,No,Yes,No,Yes,No,No"
"c12,No,No,No,No,No,No"
"c13,Yes,Yes,Yes,Yes,Yes,Yes"
"c14,Yes,Yes,No,Yes,Yes,No"
"c15,Yes,Yes,Yes,Yes,Yes,Yes"
"c16,Yes,Yes,Yes,Yes,Yes,Yes"
"c17,Yes,Yes,Yes,Yes,Yes,Yes"
"c18,No,No,No,No,No,No"
"c19,No,No,No,No,No,No"
"c20,Yes,Yes,Yes,Yes,Yes,Yes"
"c21,Yes,Yes,Yes,Yes,Yes,Yes"
"c22,Yes,Yes,Yes,Yes,Yes,Yes"
"c23,Yes,Yes,Yes,Yes,Yes,Yes"
"c24,Yes,Yes,Yes,Yes,Yes,Yes"
"c25,No,No,No,No,No,No"
"c26,No,Yes,Yes,Yes,No,No"
"c27,No,No,Yes,No,No,No"
"c28,Yes,Yes,Yes,Yes,Yes,Yes"
"c29,Yes,Yes,Yes,Yes,Yes,Yes"
"c30,Yes,Yes,Yes,Yes,Yes,Yes"
"c31,Yes,Yes,Yes,Yes,Yes,Yes"
"c32,Yes,Yes,Yes,Yes,Yes,Yes"
"c33,No,No,No,No,No,No"
"c34,Yes,Yes,Yes,Yes,Yes,Yes"
"c35,Yes,Yes,Yes,Yes,Yes,Yes"
"c36,No,Yes,No,No,Yes,Yes"
"c37,No,No,No,No,No,No"
"c38,No,No,No,No,No,No"
"c39,Yes,Yes,Yes,Yes,Yes,Yes"
"c40,No,No,No,No,No,No"
"c41,Yes,Yes,Yes,Yes,Yes,Yes"
"c42,Yes,No,No,No,No,No"
"c43,No,No,No,No,No,No"
"c44,No,No,No,No,No,No"
"c45,Yes,Yes,Yes,Yes,Yes,Yes"
"c46,Yes,Yes,Yes,Yes,Yes,Yes"
"c47,No,No,No,No,No,No"
"c48,No,No,No,No,No,No"
"c49,Yes,Yes,Yes,Yes,Yes,Yes"
"c50,No,No,No,No,No,No"

================================================
FILE: data/airpsng.csv
================================================
"","seasonal","trend","remainder"
"1",-25.4977183397736,127.187348437257,10.3103699025161
"2",-35.2209348015702,126.649531986286,26.5714028152841
"3",-3.02747801177543,126.111715535315,8.91576247646078
"4",-8.29905418525078,126.198931785072,11.1001224001792
"5",-5.73728881319017,126.286148034828,0.451140778361719
"6",32.3366341296942,126.733023088781,-24.0696572184753
"7",70.243881537013,127.179898142734,-49.4237796797467
"8",68.0494327275685,127.416216448501,-47.4656491760699
"9",17.4383260373817,127.652534754269,-9.09086079165087
"10",-21.0634315333585,129.018589081415,11.0448424519439
"11",-57.4818505136532,130.38464340856,31.0972071050931
"12",-31.7405155016501,132.361835620351,17.3786798812987
"13",-25.4977183397736,134.339027832143,6.15869050763092
"14",-35.2209348015702,135.108357729297,26.1125770722728
"15",-3.02747801177543,135.877687626452,8.14979038532329
"16",-8.29905418525078,135.945405997676,7.35364818757489
"17",-5.73728881319017,136.0131243689,-5.27583555570945
"18",32.3366341296942,137.209320297481,-20.545954427175
"19",70.243881537013,138.405516226062,-38.6493977630751
"20",68.0494327275685,141.311406001181,-39.3608387287493
"21",17.4383260373817,144.217295776299,-3.65562181368119
"22",-21.0634315333585,148.337064919027,5.7263666143316
"23",-57.4818505136532,152.456834061754,19.0250164518989
"24",-31.7405155016501,156.12565625991,15.6148592417402
"25",-25.4977183397736,159.794478458065,10.7032398817081
"26",-35.2209348015702,161.691434451555,23.5295003500156
"27",-3.02747801177543,163.588390445044,17.4390875667319
"28",-8.29905418525078,164.601673192288,6.69738099296282
"29",-5.73728881319017,165.614955939532,12.1223328736578
"30",32.3366341296942,167.228457233176,-21.56509136287
"31",70.243881537013,168.841958526819,-40.0858400638321
"32",68.0494327275685,171.207023262775,-40.2564559903438
"33",17.4383260373817,173.572087998732,-7.01041403611322
"34",-21.0634315333585,176.552637711269,6.51079382208968
"35",-57.4818505136532,179.533187423806,23.9486630898471
"36",-31.7405155016501,182.627766111254,15.1127493903959
"37",-25.4977183397736,185.722344798702,10.7753735410714
"38",-35.2209348015702,187.816317517393,27.4046172841769
"39",-3.02747801177543,189.910290236084,6.11718777569118
"40",-8.29905418525078,191.131167423307,-1.83211323805639
"41",-5.73728881319017,192.35204461053,-3.61475579733994
"42",32.3366341296942,194.065816612842,-8.40245074253599
"43",70.243881537013,195.779588615153,-36.0234701521664
"44",68.0494327275685,198.781387188149,-24.8308199157176
"45",17.4383260373817,201.783185761145,-10.2215117985265
"46",-21.0634315333585,206.068688304712,5.99474322864663
"47",-57.4818505136532,210.354190848279,19.1276596653743
"48",-31.7405155016501,214.184877683673,11.5556378179768
"49",-25.4977183397736,218.015564519068,3.482153820706
"50",-35.2209348015702,220.126339421712,11.0945953798577
"51",-3.02747801177543,222.237114324357,16.7903636874181
"52",-8.29905418525078,222.937566087235,20.361488098016
"53",-5.73728881319017,223.638017850112,11.099270963078
"54",32.3366341296942,223.792819906663,-13.1294540363569
"55",70.243881537013,223.947621963213,-30.1915035002262
"56",68.0494327275685,223.944274995632,-19.9937077232004
"57",17.4383260373817,223.940928028051,-4.37925406543229
"58",-21.0634315333585,224.594749908819,7.46868162453964
"59",-57.4818505136532,225.248571789587,12.2332787240661
"60",-31.7405155016501,227.021501512322,5.71901398932818
"61",-25.4977183397736,228.794431235057,0.703287104716935
"62",-35.2209348015702,230.553505904556,-7.33257110298547
"63",-3.02747801177543,232.312580574055,5.71489743772082
"64",-8.29905418525078,233.832832018355,1.46622216689607
"65",-5.73728881319017,235.353083462655,4.38420535053535
"66",32.3366341296942,237.704099926798,-6.04073405649189
"67",70.243881537013,240.05511639094,-8.29899792795351
"68",68.0494327275685,243.246548965287,-18.2959816928552
"69",17.4383260373817,246.437981539633,-4.87630757701459
"70",-21.0634315333585,250.103656704049,-0.0402251706909169
"71",-57.4818505136532,253.769331868466,6.71251864518734
"72",-31.7405155016501,258.083606891498,2.65690861015162
"73",-25.4977183397736,262.397881914531,5.09983642524259
"74",-35.2209348015702,266.754513995415,1.46642080615493
"75",-3.02747801177543,271.111146076299,-1.08366806452392
"76",-8.29905418525078,274.838409823356,2.46064436189482
"77",-5.73728881319017,278.565673570413,-2.82838475722247
"78",32.3366341296942,282.086164202862,0.577201667444172
"79",70.243881537013,285.606654835311,8.14946362767643
"80",68.0494327275685,289.351499353568,-10.400932081136
"81",17.4383260373817,293.096343871825,1.46533009079377
"82",-21.0634315333585,297.076039019802,-2.01260748644347
"83",-57.4818505136532,301.055734167779,-6.57388365412612
"84",-31.7405155016501,305.406362440275,4.33415306137465
"85",-25.4977183397736,309.756990712771,-0.259272372997827
"86",-35.2209348015702,314.029480875506,-1.80854607393582
"87",-3.02747801177543,318.30197103824,1.72550697353495
"88",-8.29905418525078,321.707069380599,-0.408015195348185
"89",-5.73728881319017,325.112167722957,-1.37487890976729
"90",32.3366341296942,327.651388720406,14.0119771498995
"91",70.243881537013,330.190609717855,12.5655087451319
"92",68.0494327275685,332.483104445992,4.46746282643971
"93",17.4383260373817,334.775599174129,2.78607478848983
"94",-21.0634315333585,337.349295153346,-10.2858636199874
"95",-57.4818505136532,339.922991132563,-11.4411406189101
"96",-31.7405155016501,343.561769587394,-5.82125408574382
"97",-25.4977183397736,347.200548042224,-6.7028297024508
"98",-35.2209348015702,351.911285203933,-15.6903504023633
"99",-3.02747801177543,356.622022365642,2.40545564613296
"100",-8.29905418525078,360.990024627648,-4.69097044239697
"101",-5.73728881319017,365.358026889653,-4.62073807646289
"102",32.3366341296942,368.22531885991,21.4380470103961
"103",70.243881537013,371.092610830166,23.6635076328207
"104",68.0494327275685,372.201712805953,26.7488544664784
"105",17.4383260373817,373.31081478174,13.2508591808784
"106",-21.0634315333585,373.068928913618,-5.00549738025944
"107",-57.4818505136532,372.827043045496,-10.3451925318427
"108",-31.7405155016501,373.166347081905,-5.42583158025531
"109",-25.4977183397736,373.505651118315,-8.00793277854126
"110",-35.2209348015702,375.303700005273,-22.0827652037024
"111",-3.02747801177543,377.10174889223,-12.0742708804547
"112",-8.29905418525078,379.176588330239,-22.8775341449881
"113",-5.73728881319017,381.251427768248,-12.5141389550575
"114",32.3366341296942,382.873413959425,19.7899519108811
"115",70.243881537013,384.495400150601,36.2607183123855
"116",68.0494327275685,386.41290168182,50.5376655906113
"117",17.4383260373817,388.330403213039,-1.76872925042073
"118",-21.0634315333585,390.674565056982,-10.6111335236239
"119",-57.4818505136532,393.018726900926,-25.5368763872724
"120",-31.7405155016501,396.341601828568,-27.6010863269179
"121",-25.4977183397736,399.66447675621,-14.1667584164366
"122",-35.2209348015702,404.903486498249,-27.6825516966784
"123",-3.02747801177543,410.142496240287,-1.11501822851147
"124",-8.29905418525078,416.344962920592,-12.0459087353409
"125",-5.73728881319017,422.547429600896,3.1898592122937
"126",32.3366341296942,427.78965355225,11.8737123180561
"127",70.243881537013,433.031877503603,44.7242409593841
"128",68.0494327275685,436.436237135998,54.5143301364332
"129",17.4383260373817,439.840596768394,5.72107719422462
"130",-21.0634315333585,442.375500213607,-14.3120686802483
"131",-57.4818505136532,444.91040365882,-25.4285531451666
"132",-31.7405155016501,448.640181617913,-11.8996661162626
"133",-25.4977183397736,452.369959577005,-9.87224123723178
"134",-35.2209348015702,457.812129900788,-31.5911950992179
"135",-3.02747801177543,463.254300224571,-41.2268222127952
"136",-8.29905418525078,467.610423079392,1.68863110585846
"137",-5.73728881319017,471.966545934214,5.77074287897614
"138",32.3366341296942,475.608298961987,27.0550669083188
"139",70.243881537013,479.25005198976,72.5060664732271
"140",68.0494327275685,483.189402069829,54.7611652026028
"141",17.4383260373817,487.128752149897,3.43292181272085
"142",-21.0634315333585,490.75301975182,-8.6895882184611
"143",-57.4818505136532,494.377287353742,-46.8954368400885
"144",-31.7405155016501,497.429905051779,-33.6893895501286


================================================
FILE: data/ar14.csv
================================================
graduation,attendance,assignment,unittest,result
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor
Good,Good,Good,Good,Good
Good,Good,Good,Good,Good
Avg,Avg,Avg,Avg,Avg
Poor,Poor,Poor,Poor,Poor

================================================
FILE: data/ar14b.csv
================================================
graduation,attendance,assignment,unittest,result
87,57,68,65,73
66,87,86,64,81
51,82,62,95,55
85,94,81,64,54
84,84,63,79,76
66,95,51,79,85
55,95,71,60,79
65,65,78,82,61
84,67,53,94,65
71,87,87,81,63
50,81,65,67,63
92,85,69,80,76
58,53,93,91,94
71,83,73,87,51
74,70,82,64,89
56,93,65,82,70
92,90,91,58,56
72,50,81,79,80
76,64,73,54,92
57,93,95,72,73
63,62,60,91,64
78,70,61,66,72
64,77,87,88,70
92,87,83,67,72
65,54,76,55,53
89,51,76,55,56
69,84,74,84,62
88,70,64,68,91
54,94,95,72,77

================================================
FILE: data/arulesfin.csv
================================================
"","lhs","rhs","support","confidence","lift","count"
"1","{Bonds,FD,NRI Banking}","{Credit Card}",0.0066815144766147,1,6.8030303030303,3
"2","{Car Insurance,Debit Card,Mobile Banking}","{Savings Account}",0.0066815144766147,1,6.23611111111111,3
"3","{Credit Card,FD,NRI Banking}","{Bonds}",0.0066815144766147,1,6.23611111111111,3
"4","{Bonds,Debit Card,General Insurance}","{FD}",0.0066815144766147,1,6.06756756756757,3
"5","{Bonds,Credit Card,NRI Banking}","{FD}",0.0066815144766147,1,6.06756756756757,3
"6","{Car Insurance,Debit Card,Savings Account}","{Mobile Banking}",0.0066815144766147,1,5.47560975609756,3
"7","{Bonds,FD,General Insurance}","{Debit Card}",0.0066815144766147,0.75,4.95220588235294,3
"8","{Debit Card,FD,General Insurance}","{Bonds}",0.0066815144766147,0.75,4.67708333333333,3
"9","{Bonds,Credit Card,FD}","{NRI Banking}",0.0066815144766147,0.75,4.15740740740741,3
"10","{Bonds,Debit Card,FD}","{General Insurance}",0.0066815144766147,0.6,3.592,3
"11","{NPS,Savings Account}","{Stocks}",0.0066815144766147,0.5,3.45384615384615,3
"12","{Debit Card,NRI Banking}","{General Insurance}",0.0089086859688196,0.571428571428571,3.42095238095238,4
"13","{NPS,PPF}","{Credit Card}",0.0089086859688196,0.5,3.40151515151515,4
"14","{Debit Card,Mobile Banking,Savings Account}","{Car Insurance}",0.0066815144766147,0.6,3.32592592592593,3
"15","{Car Insurance,Credit Card}","{NRI Banking}",0.0089086859688196,0.571428571428571,3.16754850088183,4
"16","{Credit Card,FD}","{Bonds}",0.0089086859688196,0.5,3.11805555555556,4
"17","{Credit Card,Mobile Banking}","{Savings Account}",0.0066815144766147,0.5,3.11805555555556,3
"18","{Car Insurance,Mobile Banking}","{Savings Account}",0.0155902004454343,0.5,3.11805555555556,7
"19","{Debit Card,Savings Account}","{Mobile Banking}",0.0111358574610245,0.555555555555556,3.0420054200542,5
"20","{General Insurance,PPF}","{FD}",0.0089086859688196,0.5,3.03378378378378,4
"21","{Credit Card,Savings Account}","{NRI Banking}",0.0066815144766147,0.5,2.7716049382716,3
"22","{Credit Card,Mobile Banking}","{NRI Banking}",0.0066815144766147,0.5,2.7716049382716,3
"23","{Credit Card,Savings Account}","{Mobile Banking}",0.0066815144766147,0.5,2.73780487804878,3
"24","{Car Insurance,Savings Account}","{Mobile Banking}",0.0155902004454343,0.5,2.73780487804878,7


================================================
FILE: data/attendance1.csv
================================================
RollNo,Student,4-Aug-17,5-Aug-17,6-Aug-17,8-Aug-17,8-Aug-17,12-Aug-17,19-Aug-17,20-Aug-17,26-Aug-17,27-Aug-17,30-Aug-17,3-Sep-17,5-Sep-17,6-Sep-17,8-Sep-17,9-Sep-17,10-Sep-17,12-Sep-17,16-Sep-17,17-Sep-17,20-Sep-17,22-Sep-17,8-Oct-17,9-Oct-17,11-Oct-17,14-Oct-17,15-Oct-17,16-Oct-17,27-Oct-17,3-Nov-17,5-Nov-17,7-Nov-17,8-Nov-17,10-Nov-17,11-Nov-17,13-Nov-17,15-Nov-17,17-Nov-17,19-Nov-17,24-Nov-17
120030102001,S001,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,P,P,P,P,A,A,P,P,P,P,P,P,A
120030102002,S002,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,A,P,P,A,A,P,A,P,P,A,P,P,P,P,A,A,A,P,A,A,P,P,P,A
120030102003,S003,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P
120030102004,S004,P,A,P,P,P,P,P,A,A,A,A,P,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102007,S005,A,A,A,A,A,A,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,A,A,A,P,P,P,P,P,A,P,P,P
120030102008,S006,A,A,P,P,P,P,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,P,P,P,A,A,A,A,A,A,P,P,P,A,A,A,A,A
120030102009,S007,P,A,P,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102011,S008,A,p,P,A,A,P,P,P,P,P,p,P,A,A,P,P,P,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,P
120030102013,S009,A,A,P,A,A,P,A,P,A,P,A,P,P,A,P,A,A,P,P,P,A,P,P,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,P,P
120030102014,S010,A,A,P,P,P,A,P,A,A,P,A,P,A,A,P,A,P,A,A,A,A,P,A,P,P,A,A,A,A,A,P,A,A,P,P,A,A,P,P,A
120030102015,S011,A,P,A,P,P,P,A,P,P,P,A,P,P,A,A,A,P,P,P,A,A,P,A,P,A,P,A,P,A,P,P,A,A,P,P,A,P,P,A,A
120030102016,S012,A,P,A,P,P,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,A
120030102017,S013,A,A,A,A,A,A,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,A,A,P,P,P,P,P,P,P,P,P,P,P
120030102018,S014,A,A,A,P,P,P,A,P,P,P,p,P,A,A,P,P,P,A,P,A,A,P,A,A,P,A,P,A,A,A,P,A,A,P,A,A,P,P,P,P
120030102019,S015,A,P,P,P,P,P,P,A,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,P,A,P,P,P,P,P,P,P
120030102020,S016,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,A,A,P,P,P,A,P,P,P,P,P,P,A
120030102021,S017,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,A,A,A,P,P,A,P,P,P,P
120030102022,S018,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,P,A,A,P,A,A,A,A,A,A
120030102023,S019,A,A,P,A,A,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,P,A,A,A,P,A,A,P,A,A,A,P,A,P
120030102024,S020,P,P,P,P,P,P,P,P,P,P,A,A,A,A,A,A,P,P,P,P,A,P,A,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102025,S021,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102026,S022,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102027,S023,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,A,P,P,A
120030102028,S024,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102029,S025,A,A,A,A,A,P,P,P,A,P,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,A,A,A,A
120030102030,S026,A,P,A,A,A,P,P,P,P,P,A,A,A,A,P,P,P,P,P,A,A,P,A,P,A,A,P,A,P,A,P,A,A,P,P,A,A,P,A,P
120030102031,S027,A,A,P,A,A,P,P,A,P,A,A,A,A,A,A,A,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,A,P,P,A,A,P,P,P
120030102032,S028,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102033,S029,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102034,S030,P,P,P,P,P,P,P,P,P,P,p,P,A,A,P,P,P,P,P,P,A,A,A,P,P,P,A,P,P,P,A,A,A,P,P,P,P,P,A,P
120030102035,S031,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,A,A,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P
120030102036,S032,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A
120030102037,S033,A,A,A,A,A,P,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,P,A,A,A,A,A,A,A,P,A,A
120030102038,S034,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102039,S035,A,P,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A
120030102040,S036,A,A,A,A,A,A,A,A,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P
120030102041,S037,A,A,A,A,A,A,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,A,A,P,A,A,A,P,A,P,A,A,P,A,A,P,A,P
120030102042,S038,P,A,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102043,S039,P,P,P,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,A,A,A,P,A,P,P,P,P,P,A,P,P,A,A,P,P,A,A,P,P,A
120030102044,S040,A,A,A,A,A,P,P,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,P,P,P,A,P,A,P,A,A,P,P,A,A,A,P,A,A
120030102045,S041,P,P,A,A,A,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102047,S042,A,A,A,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,P,A,A,A,A,A,A,A,P,A,A,A,A,A,A,P,P,A,A,P,A,A
120030102048,S043,A,A,A,A,A,P,A,A,P,P,A,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102049,S044,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102050,S045,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,A,A,P,P,P,P,P,P,P
120030103023,S046,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,P
620030102001,S047,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
110030102040,S048,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
10030102040,S049,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
110030102033,S050,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,P,P,A,A,A,A,P,A,A,A,A

================================================
FILE: data/attendance2.csv
================================================
RollNo,Student,D1,D2,D3,D4,D5,D6,D7,D8,D9,D10,D11,D12,D13,D14,D15,D16,D17,D18,D19,D20,D21,D22,D23,D24,D25,D26,D27,D28,D29,D30,D31,D32,D33,D34,D35,D36,D37,D38,D39,D40
120030102001,S001,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,P,P,P,P,A,A,P,P,P,P,P,P,A
120030102002,S002,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,A,A,P,P,A,A,P,A,P,P,A,P,P,P,P,A,A,A,P,A,A,P,P,P,A
120030102003,S003,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P
120030102004,S004,P,A,P,P,P,P,P,A,A,A,A,P,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102007,S005,A,A,A,A,A,A,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,A,A,A,P,P,P,P,P,A,P,P,P
120030102008,S006,A,A,P,P,P,P,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,P,P,P,A,A,A,A,A,A,P,P,P,A,A,A,A,A
120030102009,S007,P,A,P,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102011,S008,A,p,P,A,A,P,P,P,P,P,p,P,A,A,P,P,P,P,P,A,A,P,A,P,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,P
120030102013,S009,A,A,P,A,A,P,A,P,A,P,A,P,P,A,P,A,A,P,P,P,A,P,P,P,P,P,P,P,P,P,P,P,A,P,P,P,A,P,P,P
120030102014,S010,A,A,P,P,P,A,P,A,A,P,A,P,A,A,P,A,P,A,A,A,A,P,A,P,P,A,A,A,A,A,P,A,A,P,P,A,A,P,P,A
120030102015,S011,A,P,A,P,P,P,A,P,P,P,A,P,P,A,A,A,P,P,P,A,A,P,A,P,A,P,A,P,A,P,P,A,A,P,P,A,P,P,A,A
120030102016,S012,A,P,A,P,P,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,P,A,A
120030102017,S013,A,A,A,A,A,A,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,A,A,P,P,P,P,P,P,P,P,P,P,P
120030102018,S014,A,A,A,P,P,P,A,P,P,P,p,P,A,A,P,P,P,A,P,A,A,P,A,A,P,A,P,A,A,A,P,A,A,P,A,A,P,P,P,P
120030102019,S015,A,P,P,P,P,P,P,A,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,P,A,P,P,P,P,P,P,P
120030102020,S016,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,A,A,P,P,P,A,P,P,P,P,P,P,A
120030102021,S017,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,A,A,A,P,P,A,P,P,P,P
120030102022,S018,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,P,P,A,A,P,A,P,P,A,P,A,A,A,P,A,A,P,A,A,A,A,A,A
120030102023,S019,A,A,P,A,A,P,A,A,A,A,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,P,A,A,A,P,A,A,P,A,A,A,P,A,P
120030102024,S020,P,P,P,P,P,P,P,P,P,P,A,A,A,A,A,A,P,P,P,P,A,P,A,P,P,A,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102025,S021,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102026,S022,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102027,S023,A,A,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,A,P,P,A
120030102028,S024,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102029,S025,A,A,A,A,A,P,P,P,A,P,A,P,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,A,A,A,A
120030102030,S026,A,P,A,A,A,P,P,P,P,P,A,A,A,A,P,P,P,P,P,A,A,P,A,P,A,A,P,A,P,A,P,A,A,P,P,A,A,P,A,P
120030102031,S027,A,A,P,A,A,P,P,A,P,A,A,A,A,A,A,A,A,P,P,P,A,P,A,P,A,P,P,A,P,A,P,P,A,P,P,A,A,P,P,P
120030102032,S028,A,P,P,P,P,P,P,P,P,P,A,P,P,A,P,A,P,A,P,P,A,P,A,P,P,P,P,A,A,P,A,P,A,P,P,P,P,P,P,A
120030102033,S029,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102034,S030,P,P,P,P,P,P,P,P,P,P,p,P,A,A,P,P,P,P,P,P,A,A,A,P,P,P,A,P,P,P,A,A,A,P,P,P,P,P,A,P
120030102035,S031,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,A,A,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P
120030102036,S032,A,A,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A
120030102037,S033,A,A,A,A,A,P,P,A,P,A,A,A,A,A,A,A,A,A,P,A,A,P,A,P,A,A,A,A,A,P,A,A,A,A,A,A,A,P,A,A
120030102038,S034,P,P,P,P,P,P,P,P,P,P,p,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102039,S035,A,P,P,P,P,P,P,P,A,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,A,P,A,A,A,P,P,P,P,P,P,A
120030102040,S036,A,A,A,A,A,A,A,A,P,P,p,P,P,A,P,P,P,P,P,P,A,P,P,P,A,A,P,P,A,P,P,P,P,P,P,P,A,P,P,P
120030102041,S037,A,A,A,A,A,A,P,P,P,P,A,P,A,A,A,A,A,A,P,P,A,P,A,A,A,P,A,A,A,P,A,P,A,A,P,A,A,P,A,P
120030102042,S038,P,A,P,P,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102043,S039,P,P,P,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,A,A,A,P,A,P,P,P,P,P,A,P,P,A,A,P,P,A,A,P,P,A
120030102044,S040,A,A,A,A,A,P,P,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,P,P,P,A,P,A,P,A,A,P,P,A,A,A,P,A,A
120030102045,S041,P,P,A,A,A,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
120030102047,S042,A,A,A,A,A,P,P,P,P,P,A,P,A,A,P,P,P,A,P,A,A,A,A,A,A,A,P,A,A,A,A,A,A,P,P,A,A,P,A,A
120030102048,S043,A,A,A,A,A,P,A,A,P,P,A,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102049,S044,A,A,A,A,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
120030102050,S045,P,P,P,P,P,P,P,P,P,P,A,P,A,A,P,A,P,P,P,P,A,P,A,P,P,P,P,A,P,P,P,A,A,P,P,P,P,P,P,P
120030103023,S046,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,P
620030102001,S047,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
110030102040,S048,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A
10030102040,S049,P,P,P,P,P,P,P,P,P,P,A,P,P,A,P,P,P,P,P,P,A,P,A,P,P,P,P,P,P,P,P,P,A,P,P,P,P,P,P,P
110030102033,S050,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,A,P,A,P,A,A,A,A,A,A,A,P,P,A,A,A,A,P,A,A,A,A

================================================
FILE: data/attrition.csv
================================================
"","Emp ID","satisfaction_level","last_evaluation","number_project","average_montly_hours","time_spend_company","Work_accident","promotion_last_5years","dept","salary","status"
"1",2001,0.58,0.74,4,215,3,0,0,"sales","low","exist"
"2",2002,0.82,0.67,2,202,3,0,0,"sales","low","exist"
"3",2003,0.45,0.69,5,193,3,0,0,"sales","low","exist"
"4",2004,0.78,0.82,5,247,3,0,0,"sales","low","exist"
"5",2005,0.49,0.6,3,214,2,0,0,"sales","low","exist"
"6",2006,0.36,0.95,3,206,4,0,0,"sales","low","exist"
"7",2007,0.54,0.37,2,176,2,0,0,"sales","low","exist"
"8",2008,0.99,0.91,5,136,4,0,0,"sales","low","exist"
"9",2009,0.5,0.75,6,127,3,0,0,"sales","low","exist"
"10",2010,0.74,0.64,4,268,3,0,0,"sales","low","exist"
"11",2011,0.56,0.58,4,258,3,0,0,"sales","medium","exist"
"12",2012,0.34,0.39,2,136,3,0,0,"sales","medium","exist"
"13",2013,0.48,0.94,5,255,6,0,0,"accounting","medium","exist"
"14",2014,0.73,0.62,3,218,3,0,0,"accounting","medium","exist"
"15",2015,0.59,0.87,3,268,4,0,0,"accounting","medium","exist"
"16",2016,0.81,0.57,3,224,2,0,0,"hr","medium","exist"
"17",2017,0.9,0.66,3,231,3,0,0,"hr","medium","exist"
"18",2018,0.41,0.84,6,191,6,0,0,"hr","medium","exist"
"19",2019,0.89,0.92,4,165,5,0,0,"hr","medium","exist"
"20",2020,0.48,0.84,4,252,3,0,0,"technical","medium","exist"
"21",2021,0.79,0.97,5,266,2,0,0,"technical","medium","exist"
"22",2022,0.98,0.66,5,248,3,0,0,"technical","medium","exist"
"23",2023,0.75,0.7,4,144,4,0,0,"technical","high","exist"
"24",2024,1,0.41,4,174,3,0,0,"technical","low","exist"
"25",2025,0.24,0.82,5,179,6,0,0,"technical","medium","exist"
"26",2026,0.84,0.43,6,246,4,0,0,"technical","medium","exist"
"27",2027,0.56,0.86,4,201,3,1,0,"technical","medium","exist"
"28",2028,0.92,0.93,4,208,3,0,0,"technical","medium","exist"
"29",2029,0.61,0.98,3,267,3,0,0,"technical","low","exist"
"30",2030,0.84,0.77,4,262,4,0,0,"technical","low","exist"
"31",2031,0.85,0.59,3,235,3,0,0,"support","low","exist"
"32",2032,0.67,0.57,2,160,4,0,0,"support","low","exist"
"33",2033,0.54,0.94,4,267,4,0,0,"support","low","exist"
"34",2034,0.75,0.56,5,175,4,0,0,"support","low","exist"
"35",2035,0.82,0.79,4,224,2,0,0,"support","low","exist"
"36",2036,0.76,0.6,4,177,2,0,0,"support","low","exist"
"37",2037,0.19,0.53,6,191,4,0,0,"support","low","exist"
"38",2038,0.61,0.41,3,138,3,0,0,"support","low","exist"
"39",2039,0.51,0.8,3,218,2,1,0,"support","low","exist"
"40",2040,0.52,0.88,3,179,2,1,0,"support","low","exist"
"41",2041,0.74,0.58,3,241,3,0,0,"support","low","exist"
"42",2042,0.98,0.91,4,240,3,0,0,"technical","low","exist"
"43",2043,0.71,0.92,3,202,4,0,0,"technical","low","exist"
"44",2044,0.33,0.88,6,260,3,0,0,"technical","low","exist"
"45",2045,0.98,0.97,3,196,3,0,0,"management","low","exist"
"46",2046,0.52,0.59,2,176,3,1,0,"IT","low","exist"
"47",2047,0.84,0.65,2,140,3,0,0,"IT","low","exist"
"48",2048,0.87,0.5,3,242,2,0,0,"IT","low","exist"
"49",2049,0.48,0.85,3,279,4,0,0,"IT","low","exist"
"50",2050,0.58,0.55,4,202,3,0,0,"IT","medium","exist"
"51",2051,0.58,0.84,5,228,3,0,0,"product_mng","medium","exist"
"52",2052,0.73,0.69,4,171,3,0,0,"product_mng","medium","exist"
"53",2053,0.68,0.54,4,153,3,0,0,"product_mng","medium","exist"
"54",2054,0.41,0.68,3,165,3,1,0,"product_mng","medium","exist"
"55",2055,0.85,0.6,3,182,3,0,0,"IT","medium","exist"
"56",2056,0.54,0.7,5,239,5,0,0,"RandD","medium","exist"
"57",2057,0.81,0.61,5,231,2,0,0,"RandD","medium","exist"
"58",2058,0.7,0.52,4,255,3,0,0,"RandD","medium","exist"
"59",2059,0.63,0.66,4,237,2,1,0,"RandD","medium","exist"
"60",2060,0.68,0.54,3,251,2,0,0,"RandD","medium","exist"
"61",2061,0.7,0.53,4,178,2,0,0,"marketing","medium","exist"
"62",2062,0.82,0.65,4,148,3,0,0,"sales","high","exist"
"63",2063,0.72,0.94,4,240,4,0,0,"accounting","low","exist"
"64",2064,0.77,0.78,3,269,3,0,0,"support","medium","exist"
"65",2065,0.86,0.91,4,147,3,0,0,"technical","medium","exist"
"66",2066,0.15,0.97,3,198,5,0,0,"management","medium","exist"
"67",2067,0.81,0.99,5,143,3,0,0,"marketing","medium","exist"
"68",2068,0.93,0.98,3,238,2,0,0,"marketing","low","exist"
"69",2069,0.62,0.74,4,213,4,0,0,"marketing","low","exist"
"70",2070,0.53,0.81,3,226,3,1,0,"sales","low","exist"
"71",2071,0.86,0.99,3,169,2,1,0,"sales","low","exist"
"72",2072,0.92,0.65,4,238,2,0,0,"sales","low","exist"
"73",2073,0.97,0.83,4,202,3,0,0,"sales","low","exist"
"74",2074,0.39,0.78,2,205,6,1,0,"sales","low","exist"
"75",2075,0.45,0.66,3,111,4,0,0,"sales","low","exist"
"76",2076,0.41,0.47,4,104,3,0,0,"sales","low","exist"
"77",2077,0.51,0.69,3,212,3,0,0,"sales","low","exist"
"78",2078,0.74,0.62,4,236,4,0,0,"sales","low","exist"
"79",2079,0.69,0.57,5,245,2,1,0,"sales","low","exist"
"80",2080,0.84,0.64,4,267,4,0,0,"sales","low","exist"
"81",2081,0.69,0.66,5,106,5,0,0,"sales","low","exist"
"82",2082,0.93,0.53,5,198,3,0,0,"sales","low","exist"
"83",2083,0.33,0.45,6,239,3,0,0,"sales","low","exist"
"84",2084,0.25,0.65,5,220,3,0,0,"sales","low","exist"
"85",2085,0.63,0.59,5,224,3,0,0,"sales","low","exist"
"86",2086,0.81,0.62,3,100,3,0,0,"sales","low","exist"
"87",2087,0.12,0.87,4,244,5,0,0,"sales","low","exist"
"88",2088,0.52,0.66,4,139,3,0,0,"sales","low","exist"
"89",2089,0.57,0.51,2,152,2,0,0,"accounting","medium","exist"
"90",2090,0.84,0.58,4,208,3,0,0,"accounting","medium","exist"
"91",2091,0.6,0.95,5,205,3,0,0,"accounting","medium","exist"
"92",2092,0.73,0.44,2,194,6,0,0,"hr","medium","exist"
"93",2093,0.2,0.58,3,209,5,0,0,"hr","medium","exist"
"94",2094,0.58,0.9,3,212,3,0,0,"hr","medium","exist"
"95",2095,0.48,0.56,2,151,3,0,0,"hr","medium","exist"
"96",2096,0.54,0.67,4,282,6,0,0,"technical","medium","exist"
"97",2097,0.86,1,4,256,3,0,0,"technical","medium","exist"
"98",2098,0.94,0.83,2,185,3,1,0,"technical","medium","exist"
"99",2099,0.76,0.74,5,132,3,0,0,"technical","medium","exist"
"100",2100,0.61,0.95,5,233,3,0,0,"technical","medium","exist"
"101",2101,0.56,0.94,4,215,2,0,0,"technical","high","exist"
"102",2102,1,0.74,3,220,4,0,0,"technical","low","exist"
"103",2103,0.15,0.53,6,222,3,0,0,"technical","medium","exist"
"104",2104,0.19,0.58,5,182,2,0,0,"technical","medium","exist"
"105",2105,0.17,0.73,5,258,4,0,0,"technical","medium","exist"
"106",2106,0.71,0.57,3,209,2,0,0,"technical","medium","exist"
"107",2107,0.86,0.79,3,242,2,0,0,"support","low","exist"
"108",2108,0.59,0.88,4,155,3,1,0,"support","low","exist"
"109",2109,0.74,0.76,5,104,4,0,0,"support","low","exist"
"110",2110,0.98,0.92,4,201,3,1,0,"support","low","exist"
"111",2111,0.93,0.75,5,143,3,0,0,"support","low","exist"
"112",2112,1,0.92,5,161,3,1,0,"support","low","exist"
"113",2113,0.59,0.81,4,200,2,0,0,"support","low","exist"
"114",2114,0.98,0.55,4,255,2,0,0,"support","low","exist"
"115",2115,0.35,0.5,5,227,2,0,0,"support","low","exist"
"116",2116,0.42,0.96,3,270,6,0,0,"support","low","exist"
"117",2117,0.61,0.85,5,230,3,0,0,"support","low","exist"
"118",2118,0.78,0.72,5,270,3,1,0,"technical","low","exist"
"119",2119,0.93,0.52,4,200,3,0,0,"technical","low","exist"
"120",2120,0.5,0.95,5,207,3,0,0,"technical","low","exist"
"121",2121,0.67,0.51,5,182,3,0,0,"management","low","exist"
"122",2122,0.75,0.85,4,234,3,0,0,"IT","low","exist"
"123",2123,0.79,0.51,4,237,2,0,0,"IT","low","exist"
"124",2124,0.84,0.89,4,187,2,1,0,"IT","low","exist"
"125",2125,0.72,0.5,3,257,6,0,0,"IT","low","exist"
"126",2126,0.57,0.48,2,194,2,0,0,"IT","low","exist"
"127",2127,0.73,0.52,4,162,3,0,0,"product_mng","low","exist"
"128",2128,0.74,0.58,4,148,2,0,0,"product_mng","medium","exist"
"129",2129,0.52,0.83,4,210,2,0,0,"product_mng","medium","exist"
"130",2130,0.56,0.76,3,213,2,1,0,"product_mng","medium","exist"
"131",2131,0.76,0.68,4,189,2,1,0,"IT","medium","exist"
"132",2132,0.82,0.93,4,185,2,0,0,"RandD","medium","exist"
"133",2133,0.76,0.83,3,186,2,1,0,"RandD","medium","exist"
"134",2134,0.62,0.59,3,128,3,0,0,"RandD","medium","exist"
"135",2135,0.48,0.8,4,268,3,0,0,"RandD","medium","exist"
"136",2136,0.64,0.77,3,213,3,1,0,"RandD","medium","exist"
"137",2137,0.74,0.82,4,142,2,0,0,"marketing","medium","exist"
"138",2138,0.52,0.43,2,199,2,0,0,"sales","medium","exist"
"139",2139,0.67,0.5,4,157,2,0,0,"accounting","medium","exist"
"140",2140,0.71,0.76,5,172,2,1,0,"support","high","exist"
"141",2141,0.72,0.63,3,176,3,1,0,"technical","low","exist"
"142",2142,0.33,0.58,2,183,2,0,0,"management","medium","exist"
"143",2143,0.91,0.56,4,270,2,0,0,"marketing","medium","exist"
"144",2144,0.88,0.68,5,157,4,1,0,"marketing","medium","exist"
"145",2145,0.96,0.6,4,185,3,0,0,"marketing","medium","exist"
"146",2146,0.97,0.68,3,167,3,0,0,"sales","low","exist"
"147",2147,0.27,0.59,5,226,5,0,0,"sales","low","exist"
"148",2148,0.65,0.64,3,223,4,0,0,"sales","low","exist"
"149",2149,0.68,0.73,3,257,3,0,0,"sales","low","exist"
"150",2150,0.68,0.46,4,143,3,0,0,"sales","low","exist"
"151",2151,0.69,0.74,3,215,2,0,0,"sales","low","exist"
"152",2152,0.79,0.99,3,194,4,0,0,"sales","low","exist"
"153",2153,0.74,0.92,5,193,3,0,0,"sales","low","exist"
"154",2154,0.8,0.83,3,163,3,0,0,"sales","low","exist"
"155",2155,0.38,0.94,5,252,5,0,0,"sales","low","exist"
"156",2156,0.26,0.83,3,168,3,0,0,"sales","low","exist"
"157",2157,0.81,0.86,3,231,3,0,0,"sales","low","exist"
"158",2158,0.67,0.54,2,141,2,0,0,"sales","low","exist"
"159",2159,0.55,0.81,4,260,2,0,0,"sales","low","exist"
"160",2160,0.87,0.71,3,132,2,0,0,"sales","low","exist"
"161",2161,0.46,0.69,2,159,2,0,0,"sales","low","exist"
"162",2162,0.63,0.57,4,177,3,1,0,"sales","low","exist"
"163",2163,0.54,0.96,4,248,3,0,0,"sales","low","exist"
"164",2164,1,0.49,3,185,2,0,0,"sales","low","exist"
"165",2165,0.97,0.66,4,149,2,0,0,"accounting","low","exist"
"166",2166,0.9,0.92,3,152,3,0,0,"accounting","low","exist"
"167",2167,0.75,0.7,3,129,3,0,0,"accounting","medium","exist"
"168",2168,0.92,0.84,4,208,2,0,0,"hr","medium","exist"
"169",2169,0.8,0.94,4,136,2,0,0,"hr","medium","exist"
"170",2170,0.57,0.81,3,142,2,0,0,"hr","medium","exist"
"171",2171,0.81,0.94,3,225,4,0,0,"hr","medium","exist"
"172",2172,0.64,0.6,3,143,3,0,0,"technical","medium","exist"
"173",2173,0.71,0.54,4,215,3,0,0,"technical","medium","exist"
"174",2174,0.35,0.58,3,229,6,1,0,"technical","medium","exist"
"175",2175,0.88,0.81,5,193,5,0,0,"technical","medium","exist"
"176",2176,0.13,0.59,5,160,5,0,0,"technical","medium","exist"
"177",2177,0.82,0.73,4,195,5,1,0,"technical","medium","exist"
"178",2178,0.17,0.92,4,189,2,0,0,"technical","medium","exist"
"179",2179,0.21,0.82,4,207,5,0,0,"technical","high","exist"
"180",2180,0.89,0.47,4,108,3,0,0,"technical","low","exist"
"181",2181,0.2,0.72,6,224,4,0,0,"technical","medium","exist"
"182",2182,0.99,0.81,5,180,3,1,0,"technical","medium","exist"
"183",2183,0.26,0.85,6,152,4,0,0,"support","medium","exist"
"184",2184,0.22,0.53,4,244,2,0,0,"support","medium","exist"
"185",2185,0.79,0.84,3,176,3,0,0,"support","low","exist"
"186",2186,0.73,0.79,4,145,2,1,0,"support","low","exist"
"187",2187,0.83,0.54,3,149,3,0,0,"support","low","exist"
"188",2188,0.42,0.54,3,122,4,0,0,"support","low","exist"
"189",2189,0.18,0.8,2,110,5,0,0,"support","low","exist"
"190",2190,0.92,0.91,4,222,2,0,0,"support","low","exist"
"191",2191,0.87,0.52,3,237,3,0,0,"support","low","exist"
"192",2192,0.72,0.65,4,224,3,0,0,"support","low","exist"
"193",2193,0.64,0.58,5,115,5,0,0,"support","low","exist"
"194",2194,1,0.66,4,180,3,0,0,"technical","low","exist"
"195",2195,0.83,0.65,4,162,3,0,0,"technical","low","exist"
"196",2196,0.98,0.58,4,136,3,0,0,"technical","low","exist"
"197",2197,0.7,0.87,3,260,2,0,0,"management","low","exist"
"198",2198,0.9,0.79,4,150,2,0,0,"IT","low","exist"
"199",2199,0.55,0.99,4,248,3,0,0,"IT","low","exist"
"200",2200,0.78,0.84,3,233,3,1,0,"IT","low","exist"
"201",2201,0.89,0.53,5,272,3,0,0,"IT","low","exist"
"202",2202,0.17,0.59,3,197,5,0,0,"IT","low","exist"
"203",2203,0.14,0.64,5,164,5,0,0,"product_mng","low","exist"
"204",2204,0.85,0.57,4,216,2,0,0,"product_mng","low","exist"
"205",2205,0.84,0.79,4,266,3,1,0,"product_mng","low","exist"
"206",2206,0.7,0.69,3,102,4,1,0,"product_mng","medium","exist"
"207",2207,0.16,0.98,5,284,5,0,0,"IT","medium","exist"
"208",2208,0.51,0.69,3,145,2,1,0,"RandD","medium","exist"
"209",2209,0.6,0.89,3,167,4,0,0,"RandD","medium","exist"
"210",2210,0.5,0.63,3,172,2,0,0,"RandD","medium","exist"
"211",2211,0.43,0.39,5,198,5,0,0,"RandD","medium","exist"
"212",2212,0.5,0.7,4,201,4,0,0,"RandD","medium","exist"
"213",2213,0.91,0.89,4,197,4,0,0,"marketing","medium","exist"
"214",2214,0.65,0.93,4,270,2,0,0,"sales","medium","exist"
"215",2215,0.59,0.52,2,149,3,0,0,"accounting","medium","exist"
"216",2216,0.89,0.56,3,256,3,0,0,"support","medium","exist"
"217",2217,0.97,0.6,3,162,3,0,0,"technical","medium","exist"
"218",2218,0.56,0.97,5,163,2,0,0,"management","high","exist"
"219",2219,0.76,0.93,3,266,3,1,0,"marketing","low","exist"
"220",2220,0.28,0.55,4,208,4,0,0,"marketing","medium","exist"
"221",2221,0.75,0.51,4,138,4,0,0,"marketing","medium","exist"
"222",2222,0.78,0.81,4,232,3,0,0,"sales","medium","exist"
"223",2223,0.26,0.63,6,100,4,0,0,"sales","medium","exist"
"224",2224,0.53,0.72,2,172,5,0,0,"sales","low","exist"
"225",2225,0.25,0.41,3,133,6,1,0,"sales","low","exist"
"226",2226,0.82,0.51,3,234,3,0,0,"sales","low","exist"
"227",2227,0.71,0.57,2,183,4,0,0,"sales","low","exist"
"228",2228,0.61,0.95,4,174,4,0,0,"sales","low","exist"
"229",2229,0.89,0.68,3,175,2,0,0,"sales","low","exist"
"230",2230,0.57,0.78,3,109,3,1,0,"sales","low","exist"
"231",2231,0.93,0.8,4,248,3,0,0,"sales","low","exist"
"232",2232,0.61,0.84,5,104,4,0,0,"sales","low","exist"
"233",2233,0.56,0.62,3,154,2,0,0,"sales","low","exist"
"234",2234,0.7,0.89,6,214,2,0,0,"sales","low","exist"
"235",2235,0.9,0.64,4,209,4,0,0,"sales","low","exist"
"236",2236,0.15,0.74,6,212,2,0,0,"sales","low","exist"
"237",2237,0.39,0.36,3,168,3,1,0,"sales","low","exist"
"238",2238,0.74,0.72,4,176,3,0,0,"sales","low","exist"
"239",2239,0.7,0.61,4,163,4,1,0,"sales","low","exist"
"240",2240,0.72,0.93,4,148,2,0,0,"sales","low","exist"
"241",2241,0.61,0.97,3,137,3,0,0,"accounting","low","exist"
"242",2242,0.96,1,5,162,3,0,0,"accounting","low","exist"
"243",2243,0.7,0.59,4,216,3,0,0,"accounting","low","exist"
"244",2244,0.92,0.49,3,240,2,0,0,"hr","low","exist"
"245",2245,0.72,0.56,4,176,2,0,0,"hr","medium","exist"
"246",2246,0.53,0.75,6,192,6,0,0,"hr","medium","exist"
"247",2247,0.67,0.85,3,160,4,0,0,"hr","medium","exist"
"248",2248,0.78,0.8,4,194,2,1,0,"technical","medium","exist"
"249",2249,0.53,0.75,4,239,2,1,0,"technical","medium","exist"
"250",2250,0.9,0.48,4,204,3,0,0,"technical","medium","exist"
"251",2251,0.16,0.9,5,258,3,0,0,"technical","medium","exist"
"252",2252,0.62,0.38,3,257,3,0,0,"technical","medium","exist"
"253",2253,0.62,0.98,4,137,3,0,0,"technical","medium","exist"
"254",2254,0.22,0.52,6,175,4,0,0,"technical","medium","exist"
"255",2255,0.91,0.82,3,183,3,0,0,"technical","medium","exist"
"256",2256,0.87,0.74,4,190,4,0,0,"technical","medium","exist"
"257",2257,0.95,0.69,3,225,2,0,0,"technical","high","exist"
"258",2258,0.99,0.75,3,215,3,0,0,"technical","low","exist"
"259",2259,0.99,0.57,3,176,4,1,0,"support","medium","exist"
"260",2260,0.77,0.99,4,153,3,1,0,"support","medium","exist"
"261",2261,0.75,0.68,3,150,2,1,0,"support","medium","exist"
"262",2262,0.83,0.54,4,259,5,0,0,"support","medium","exist"
"263",2263,0.61,0.39,3,99,2,0,0,"support","low","exist"
"264",2264,0.91,0.97,3,167,2,0,0,"support","low","exist"
"265",2265,0.47,0.64,3,192,3,0,0,"support","low","exist"
"266",2266,0.77,0.61,5,146,3,0,0,"support","low","exist"
"267",2267,0.55,0.51,3,190,3,0,0,"support","low","exist"
"268",2268,0.32,0.48,5,246,3,0,0,"support","low","exist"
"269",2269,0.96,0.67,6,190,3,0,0,"support","low","exist"
"270",2270,0.72,0.79,5,260,2,0,0,"technical","low","exist"
"271",2271,0.8,0.9,4,136,3,0,0,"technical","low","exist"
"272",2272,0.61,0.55,4,231,3,0,0,"technical","low","exist"
"273",2273,0.97,0.88,3,204,2,0,0,"management","low","exist"
"274",2274,0.63,0.93,4,201,3,0,0,"IT","low","exist"
"275",2275,0.92,0.92,3,159,3,0,0,"IT","low","exist"
"276",2276,0.94,0.74,5,171,3,0,0,"IT","low","exist"
"277",2277,0.79,0.72,6,240,4,0,0,"IT","low","exist"
"278",2278,0.75,0.73,2,152,4,0,0,"IT","low","exist"
"279",2279,0.78,0.99,3,151,3,1,0,"product_mng","low","exist"
"280",2280,0.96,0.45,6,232,2,1,0,"product_mng","low","exist"
"281",2281,0.65,0.68,4,128,5,0,0,"product_mng","low","exist"
"282",2282,0.18,0.94,3,187,6,1,0,"product_mng","low","exist"
"283",2283,0.94,0.51,3,160,2,0,0,"IT","low","exist"
"284",2284,0.84,0.79,4,259,3,0,0,"RandD","medium","exist"
"285",2285,0.67,0.54,2,136,2,0,0,"RandD","medium","exist"
"286",2286,0.71,0.5,4,253,3,0,0,"RandD","medium","exist"
"287",2287,0.56,0.64,3,260,3,0,0,"RandD","medium","exist"
"288",2288,0.29,0.56,5,231,6,0,0,"RandD","medium","exist"
"289",2289,0.47,0.9,3,101,2,1,0,"marketing","medium","exist"
"290",2290,0.4,0.69,2,174,3,0,0,"sales","medium","exist"
"291",2291,0.81,0.82,4,167,2,0,0,"accounting","medium","exist"
"292",2292,0.96,0.99,3,148,3,0,0,"support","medium","exist"
"293",2293,0.99,0.75,6,139,5,1,0,"technical","medium","exist"
"294",2294,0.75,0.77,4,136,3,0,0,"management","medium","exist"
"295",2295,0.75,0.74,4,153,2,0,0,"marketing","medium","exist"
"296",2296,1,0.86,4,161,2,0,0,"marketing","high","exist"
"297",2297,0.52,0.53,2,163,2,0,0,"marketing","low","exist"
"298",2298,0.98,0.74,3,164,3,0,0,"sales","medium","exist"
"299",2299,0.6,0.64,2,137,5,0,0,"sales","medium","exist"
"300",2300,0.38,0.44,3,137,3,0,0,"sales","medium","exist"
"301",2301,0.51,0.41,6,106,5,0,0,"sales","medium","exist"
"302",2302,0.91,0.61,2,272,2,0,0,"sales","low","exist"
"303",2303,0.56,0.62,5,238,3,0,0,"sales","low","exist"
"304",2304,0.58,0.69,4,223,4,0,0,"sales","low","exist"
"305",2305,0.51,0.53,3,201,2,0,0,"sales","low","exist"
"306",2306,0.91,0.55,6,97,4,0,0,"sales","low","exist"
"307",2307,0.8,0.98,2,232,6,1,0,"sales","low","exist"
"308",2308,0.55,0.83,4,199,3,0,0,"sales","low","exist"
"309",2309,0.62,0.53,3,141,3,0,0,"sales","low","exist"
"310",2310,0.62,0.6,3,171,2,0,0,"sales","low","exist"
"311",2311,0.87,0.58,4,212,3,0,0,"sales","low","exist"
"312",2312,0.65,0.5,5,270,2,0,0,"sales","low","exist"
"313",2313,0.51,0.64,3,267,2,0,0,"sales","low","exist"
"314",2314,0.98,0.77,3,134,2,1,0,"sales","low","exist"
"315",2315,0.13,0.43,4,165,5,0,0,"sales","low","exist"
"316",2316,0.78,0.76,5,168,4,1,0,"sales","low","exist"
"317",2317,0.6,0.98,3,262,2,0,0,"accounting","low","exist"
"318",2318,0.68,0.69,3,185,2,0,0,"accounting","low","exist"
"319",2319,0.55,0.84,3,237,3,0,0,"accounting","low","exist"
"320",2320,0.99,0.79,4,192,3,0,0,"hr","low","exist"
"321",2321,0.92,0.68,5,236,2,0,0,"hr","low","exist"
"322",2322,1,0.65,4,202,4,1,0,"hr","low","exist"
"323",2323,0.77,0.93,4,171,2,0,0,"hr","medium","exist"
"324",2324,0.86,0.7,5,160,3,0,0,"technical","medium","exist"
"325",2325,0.89,0.84,2,252,3,0,0,"technical","medium","exist"
"326",2326,0.58,0.55,5,206,3,0,0,"technical","medium","exist"
"327",2327,0.56,0.66,3,212,2,0,0,"technical","medium","exist"
"328",2328,0.38,0.64,3,111,3,0,0,"technical","medium","exist"
"329",2329,0.62,0.64,3,240,2,0,0,"technical","medium","exist"
"330",2330,0.66,0.77,2,171,2,0,0,"technical","medium","exist"
"331",2331,0.3,0.44,3,129,2,0,0,"technical","medium","exist"
"332",2332,0.82,0.83,3,271,2,0,0,"technical","medium","exist"
"333",2333,0.96,0.68,4,162,2,0,0,"technical","medium","exist"
"334",2334,0.66,0.95,3,191,3,0,0,"technical","medium","exist"
"335",2335,0.79,0.5,5,176,3,0,0,"support","high","exist"
"336",2336,0.97,0.77,3,182,2,1,0,"support","low","exist"
"337",2337,0.59,0.65,3,226,3,0,0,"support","medium","exist"
"338",2338,0.57,0.48,4,161,3,0,0,"support","medium","exist"
"339",2339,0.64,0.53,4,163,3,0,0,"support","medium","exist"
"340",2340,0.14,0.51,5,173,4,0,0,"support","medium","exist"
"341",2341,0.48,0.55,3,228,2,0,0,"support","low","exist"
"342",2342,0.78,1,3,139,3,0,0,"support","low","exist"
"343",2343,0.96,0.62,5,128,5,0,0,"support","low","exist"
"344",2344,0.82,0.97,3,115,2,1,0,"support","low","exist"
"345",2345,0.94,0.9,5,191,4,0,0,"support","low","exist"
"346",2346,0.95,0.66,4,183,3,0,0,"technical","low","exist"
"347",2347,0.59,0.43,3,173,3,0,0,"technical","low","exist"
"348",2348,0.69,0.89,4,174,2,0,0,"technical","low","exist"
"349",2349,0.74,0.72,3,213,3,0,0,"management","low","exist"
"350",2350,0.67,0.67,4,192,4,0,0,"IT","low","exist"
"351",2351,0.83,0.52,3,167,2,1,0,"IT","low","exist"
"352",2352,0.81,0.85,3,263,3,1,0,"IT","low","exist"
"353",2353,0.54,0.73,2,100,3,0,0,"IT","low","exist"
"354",2354,0.89,0.83,3,164,3,0,0,"IT","low","exist"
"355",2355,0.79,0.74,5,172,2,0,0,"product_mng","low","exist"
"356",2356,0.46,0.58,4,171,3,0,0,"product_mng","low","exist"
"357",2357,0.99,0.93,4,236,3,0,0,"product_mng","low","exist"
"358",2358,0.75,0.9,5,186,4,0,0,"product_mng","low","exist"
"359",2359,0.93,0.82,4,175,3,0,0,"IT","low","exist"
"360",2360,0.65,0.6,5,227,3,0,0,"RandD","low","exist"
"361",2361,0.19,0.63,4,142,6,0,0,"RandD","low","exist"
"362",2362,0.48,0.61,2,121,2,0,0,"RandD","medium","exist"
"363",2363,0.95,0.64,5,234,3,0,0,"RandD","medium","exist"
"364",2364,0.92,0.77,4,185,3,0,0,"RandD","medium","exist"
"365",2365,0.84,0.54,4,160,3,0,0,"marketing","medium","exist"
"366",2366,0.37,0.63,4,153,3,1,0,"sales","medium","exist"
"367",2367,0.22,0.74,3,199,6,0,0,"accounting","medium","exist"
"368",2368,0.64,0.54,3,166,2,0,0,"support","medium","exist"
"369",2369,0.72,0.88,2,247,3,0,0,"technical","medium","exist"
"370",2370,0.48,0.69,4,245,3,0,0,"management","medium","exist"
"371",2371,0.12,0.55,5,242,4,0,0,"marketing","medium","exist"
"372",2372,0.78,0.98,5,158,2,0,0,"marketing","medium","exist"
"373",2373,0.71,0.74,3,163,3,1,0,"marketing","medium","exist"
"374",2374,0.38,0.69,3,99,3,1,0,"sales","high","exist"
"375",2375,0.57,0.85,4,164,3,0,0,"sales","low","exist"
"376",2376,0.72,0.51,3,160,3,0,0,"sales","medium","exist"
"377",2377,0.6,0.57,2,184,3,0,0,"sales","medium","exist"
"378",2378,0.61,0.55,5,266,2,0,0,"sales","medium","exist"
"379",2379,0.67,0.64,4,190,2,0,0,"sales","medium","exist"
"380",2380,0.97,0.97,5,192,2,0,0,"sales","low","exist"
"381",2381,0.22,0.6,3,205,6,1,0,"sales","low","exist"
"382",2382,0.15,0.53,4,205,5,1,0,"sales","low","exist"
"383",2383,0.6,0.6,3,258,3,0,0,"sales","low","exist"
"384",2384,0.15,0.8,5,151,2,1,0,"sales","low","exist"
"385",2385,0.5,0.81,3,148,2,0,0,"sales","low","exist"
"386",2386,0.9,0.67,3,179,2,0,0,"sales","low","exist"
"387",2387,0.84,0.51,6,141,2,1,0,"sales","low","exist"
"388",2388,0.74,0.78,5,216,2,0,0,"sales","low","exist"
"389",2389,0.72,0.51,3,235,2,0,0,"sales","low","exist"
"390",2390,0.93,0.63,3,160,4,1,0,"sales","low","exist"
"391",2391,0.54,0.69,3,141,4,0,0,"sales","low","exist"
"392",2392,0.87,0.65,4,246,2,1,0,"sales","low","exist"
"393",2393,0.19,0.98,5,226,4,1,0,"accounting","low","exist"
"394",2394,0.33,0.4,4,212,2,1,0,"accounting","low","exist"
"395",2395,0.94,0.93,4,220,3,0,0,"accounting","low","exist"
"396",2396,0.77,0.49,4,266,2,0,0,"hr","low","exist"
"397",2397,0.48,0.82,3,183,2,0,0,"hr","low","exist"
"398",2398,0.7,0.74,5,263,3,1,0,"hr","low","exist"
"399",2399,0.54,0.93,4,161,4,1,0,"hr","low","exist"
"400",2400,0.61,0.98,4,199,2,0,0,"technical","low","exist"
"401",2401,0.97,0.4,4,258,4,1,0,"technical","medium","exist"
"402",2402,0.6,0.85,3,209,2,1,0,"technical","medium","exist"
"403",2403,0.93,0.84,5,135,3,0,0,"technical","medium","exist"
"404",2404,0.48,0.69,4,222,2,0,0,"technical","medium","exist"
"405",2405,0.16,0.76,5,192,3,0,0,"technical","medium","exist"
"406",2406,0.18,0.75,3,250,3,0,0,"technical","medium","exist"
"407",2407,0.84,0.75,3,187,3,1,0,"technical","medium","exist"
"408",2408,0.69,0.63,4,217,3,0,0,"technical","medium","exist"
"409",2409,0.22,0.88,4,213,3,1,0,"technical","medium","exist"
"410",2410,0.83,0.52,4,273,3,0,0,"technical","medium","exist"
"411",2411,0.58,0.5,2,132,3,0,0,"support","medium","exist"
"412",2412,0.61,0.62,4,140,3,1,0,"support","medium","exist"
"413",2413,0.67,0.5,4,173,2,1,0,"support","high","exist"
"414",2414,0.56,0.76,4,189,2,0,0,"support","low","exist"
"415",2415,0.74,0.74,3,156,3,0,0,"support","medium","exist"
"416",2416,0.92,0.97,4,238,5,1,0,"support","medium","exist"
"417",2417,0.81,0.68,5,230,2,0,0,"support","medium","exist"
"418",2418,0.48,0.49,4,242,2,1,0,"support","medium","exist"
"419",2419,0.73,0.72,4,197,3,0,0,"support","low","exist"
"420",2420,0.97,0.66,6,164,5,0,0,"support","low","exist"
"421",2421,0.15,0.51,6,248,5,0,0,"support","low","exist"
"422",2422,0.69,0.76,5,255,6,0,0,"technical","low","exist"
"423",2423,0.61,0.68,5,225,4,0,0,"technical","low","exist"
"424",2424,0.86,0.58,3,151,2,0,0,"technical","low","exist"
"425",2425,0.55,0.88,4,252,3,0,0,"management","low","exist"
"426",2426,0.9,0.74,4,206,4,1,0,"IT","low","exist"
"427",2427,0.65,0.4,2,141,2,0,0,"IT","low","exist"
"428",2428,0.81,0.92,5,259,3,0,0,"IT","low","exist"
"429",2429,0.65,0.86,5,250,3,0,0,"IT","low","exist"
"430",2430,0.47,0.86,4,169,6,0,0,"IT","low","exist"
"431",2431,0.93,0.53,3,200,3,1,0,"product_mng","low","exist"
"432",2432,0.77,0.9,4,104,5,0,0,"product_mng","low","exist"
"433",2433,0.87,0.82,6,176,3,0,0,"product_mng","low","exist"
"434",2434,0.87,0.84,5,137,2,0,0,"product_mng","low","exist"
"435",2435,0.65,0.75,2,151,3,0,0,"IT","low","exist"
"436",2436,0.21,0.7,6,130,6,1,0,"RandD","low","exist"
"437",2437,0.75,0.59,4,199,2,0,0,"RandD","low","exist"
"438",2438,0.72,0.86,4,191,2,0,0,"RandD","low","exist"
"439",2439,0.88,0.63,3,273,3,1,0,"RandD","low","exist"
"440",2440,0.66,0.58,3,205,3,0,0,"RandD","medium","exist"
"441",2441,0.8,0.75,3,181,3,0,0,"marketing","medium","exist"
"442",2442,0.22,0.55,4,261,3,1,0,"sales","medium","exist"
"443",2443,0.92,0.69,3,192,3,0,0,"accounting","medium","exist"
"444",2444,0.54,0.77,4,271,3,0,0,"support","medium","exist"
"445",2445,0.91,0.56,4,158,3,0,0,"technical","medium","exist"
"446",2446,0.77,0.83,4,231,2,0,0,"management","medium","exist"
"447",2447,0.61,0.51,3,156,3,1,0,"marketing","medium","exist"
"448",2448,0.48,0.9,4,201,4,0,0,"marketing","medium","exist"
"449",2449,0.25,0.69,3,187,4,0,0,"marketing","medium","exist"
"450",2450,0.91,0.7,3,132,4,0,0,"sales","medium","exist"
"451",2451,0.72,0.58,5,147,3,1,0,"sales","medium","exist"
"452",2452,0.77,0.71,4,223,3,0,0,"sales","high","exist"
"453",2453,0.41,0.4,2,194,2,0,0,"sales","low","exist"
"454",2454,0.51,0.49,4,234,2,0,0,"sales","medium","exist"
"455",2455,0.72,0.79,3,149,3,0,0,"sales","medium","exist"
"456",2456,0.47,0.57,3,162,3,1,0,"sales","medium","exist"
"457",2457,0.53,0.67,4,238,2,0,0,"sales","medium","exist"
"458",2458,0.65,0.52,5,149,3,0,0,"sales","low","exist"
"459",2459,0.18,0.75,4,170,5,0,0,"sales","low","exist"
"460",2460,0.61,0.48,3,250,2,0,0,"sales","low","exist"
"461",2461,0.86,0.72,4,167,2,0,0,"sales","low","exist"
"462",2462,0.14,0.77,4,166,5,0,0,"sales","low","exist"
"463",2463,0.63,0.8,3,205,2,0,0,"sales","low","exist"
"464",2464,0.79,0.57,3,250,3,0,0,"sales","low","exist"
"465",2465,0.78,0.97,4,142,3,0,0,"sales","low","exist"
"466",2466,0.14,0.52,4,217,6,0,0,"sales","low","exist"
"467",2467,0.85,0.54,3,139,3,0,0,"sales","low","exist"
"468",2468,0.85,0.75,4,139,3,0,0,"sales","low","exist"
"469",2469,0.91,0.76,5,152,3,0,0,"accounting","low","exist"
"470",2470,0.76,0.74,3,224,2,0,0,"accounting","low","exist"
"471",2471,0.62,0.72,5,180,3,0,0,"accounting","low","exist"
"472",2472,0.53,0.69,4,216,2,0,0,"hr","low","exist"
"473",2473,0.97,0.63,3,133,3,0,0,"hr","low","exist"
"474",2474,0.48,0.53,4,271,3,0,0,"hr","low","exist"
"475",2475,0.5,0.55,4,148,3,1,0,"hr","low","exist"
"476",2476,0.32,0.42,2,99,4,0,0,"technical","low","exist"
"477",2477,0.58,0.77,4,196,2,1,0,"technical","low","exist"
"478",2478,0.81,0.83,3,196,2,0,0,"technical","low","exist"
"479",2479,0.48,0.84,4,228,3,0,0,"technical","medium","exist"
"480",2480,0.96,0.88,4,165,2,0,0,"technical","medium","exist"
"481",2481,0.56,0.9,3,235,2,0,0,"technical","medium","exist"
"482",2482,0.63,0.96,4,167,2,0,0,"technical","medium","exist"
"483",2483,0.21,0.5,5,255,5,0,0,"technical","medium","exist"
"484",2484,0.94,0.78,3,184,3,1,0,"technical","medium","exist"
"485",2485,0.94,0.89,4,239,3,0,0,"technical","medium","exist"
"486",2486,0.96,0.54,3,153,2,0,0,"technical","medium","exist"
"487",2487,0.49,0.5,4,187,5,1,0,"support","medium","exist"
"488",2488,0.82,0.68,2,285,2,0,0,"support","medium","exist"
"489",2489,0.6,0.5,3,274,3,0,0,"support","medium","exist"
"490",2490,0.76,0.5,3,156,3,1,0,"support","medium","exist"
"491",2491,0.69,0.64,5,265,2,1,0,"support","high","exist"
"492",2492,1,0.94,4,144,3,0,0,"support","low","exist"
"493",2493,0.62,0.66,4,143,3,0,0,"support","medium","exist"
"494",2494,0.4,0.99,4,214,6,1,0,"support","medium","exist"
"495",2495,0.94,0.91,3,163,3,0,0,"support","medium","exist"
"496",2496,0.76,0.84,4,236,4,0,0,"support","medium","exist"
"497",2497,0.58,0.69,3,146,4,0,0,"support","low","exist"
"498",2498,0.85,0.78,4,106,2,0,0,"technical","low","exist"
"499",2499,0.45,0.52,2,105,3,0,0,"technical","low","exist"
"500",2500,0.13,0.67,3,181,4,0,0,"technical","low","exist"
"501",2501,0.24,0.5,5,174,4,0,0,"management","low","exist"
"502",2502,0.64,0.69,3,207,2,1,0,"IT","low","exist"
"503",2503,0.63,0.61,6,118,2,0,0,"IT","low","exist"
"504",2504,0.61,0.99,4,251,2,0,0,"IT","low","exist"
"505",2505,0.71,0.99,2,136,3,0,0,"IT","low","exist"
"506",2506,0.9,0.89,5,249,3,1,0,"IT","low","exist"
"507",2507,0.17,0.76,4,171,5,0,0,"product_mng","low","exist"
"508",2508,0.93,0.97,3,256,2,1,0,"product_mng","low","exist"
"509",2509,0.83,0.89,5,141,3,1,0,"product_mng","low","exist"
"510",2510,0.58,0.75,4,186,2,0,0,"product_mng","low","exist"
"511",2511,0.76,0.5,3,258,3,0,0,"IT","low","exist"
"512",2512,0.5,0.78,3,228,2,0,0,"RandD","low","exist"
"513",2513,0.22,0.81,5,205,4,0,0,"RandD","low","exist"
"514",2514,0.9,0.88,4,174,3,0,0,"RandD","low","exist"
"515",2515,0.7,0.63,3,155,4,1,0,"RandD","low","exist"
"516",2516,0.73,0.85,5,245,3,0,0,"RandD","low","exist"
"517",2517,0.84,0.87,3,271,3,0,0,"marketing","low","exist"
"518",2518,0.55,0.63,5,184,4,0,0,"marketing","medium","exist"
"519",2519,0.63,0.98,4,175,2,0,0,"sales","medium","exist"
"520",2520,0.51,0.92,3,224,3,0,0,"accounting","medium","exist"
"521",2521,0.81,0.76,4,177,3,0,0,"support","medium","exist"
"522",2522,0.8,0.96,4,268,3,0,0,"technical","medium","exist"
"523",2523,0.99,0.97,4,208,3,0,0,"management","medium","exist"
"524",2524,0.9,0.87,5,219,2,0,0,"marketing","medium","exist"
"525",2525,0.65,0.67,5,128,5,0,0,"marketing","medium","exist"
"526",2526,0.75,0.75,3,273,3,0,0,"marketing","medium","exist"
"527",2527,0.62,0.49,4,218,4,0,0,"sales","medium","exist"
"528",2528,0.61,0.63,5,230,3,0,0,"sales","medium","exist"
"529",2529,0.24,0.6,4,195,5,0,0,"sales","medium","exist"
"530",2530,0.71,0.63,3,254,3,1,0,"sales","high","exist"
"531",2531,0.49,0.8,2,275,2,0,0,"sales","low","exist"
"532",2532,0.44,0.66,3,162,2,0,0,"sales","medium","exist"
"533",2533,0.75,0.87,4,193,3,0,0,"sales","medium","exist"
"534",2534,0.74,0.84,3,239,4,0,0,"sales","medium","exist"
"535",2535,0.62,0.87,5,149,3,0,0,"sales","medium","exist"
"536",2536,0.51,0.58,3,155,3,0,0,"sales","low","exist"
"537",2537,0.61,0.59,5,271,2,0,0,"sales","low","exist"
"538",2538,0.56,0.49,5,163,3,0,0,"sales","low","exist"
"539",2539,0.79,0.76,3,160,3,0,0,"sales","low","exist"
"540",2540,0.68,0.75,6,274,5,0,0,"sales","low","exist"
"541",2541,0.9,0.84,2,199,3,0,0,"sales","low","exist"
"542",2542,0.83,0.93,5,241,3,0,0,"sales","low","exist"
"543",2543,0.94,0.82,3,187,3,0,0,"sales","low","exist"
"544",2544,0.21,0.65,5,223,3,1,0,"sales","low","exist"
"545",2545,0.58,0.87,3,268,2,0,0,"sales","low","exist"
"546",2546,0.52,0.38,6,169,3,0,0,"accounting","low","exist"
"547",2547,0.18,0.67,5,285,5,0,0,"accounting","low","exist"
"548",2548,0.94,0.91,5,254,3,0,0,"accounting","low","exist"
"549",2549,0.69,0.5,3,208,4,0,0,"hr","low","exist"
"550",2550,0.65,0.83,4,218,3,0,0,"hr","low","exist"
"551",2551,0.46,0.62,2,187,3,0,0,"hr","low","exist"
"552",2552,0.72,0.62,4,256,3,0,0,"hr","low","exist"
"553",2553,0.3,0.37,6,278,3,0,0,"technical","low","exist"
"554",2554,0.51,0.51,4,204,2,0,0,"technical","low","exist"
"555",2555,0.43,0.75,3,108,2,0,0,"technical","low","exist"
"556",2556,0.56,0.94,3,226,2,0,0,"technical","low","exist"
"557",2557,0.63,0.91,4,246,3,0,0,"technical","medium","exist"
"558",2558,0.61,0.55,5,260,3,0,0,"technical","medium","exist"
"559",2559,0.53,0.73,4,248,2,0,0,"technical","medium","exist"
"560",2560,0.87,0.75,3,132,3,0,0,"technical","medium","exist"
"561",2561,0.68,0.7,4,185,4,0,0,"technical","medium","exist"
"562",2562,0.78,0.84,3,269,2,0,0,"technical","medium","exist"
"563",2563,0.49,0.95,4,156,2,0,0,"technical","medium","exist"
"564",2564,0.96,0.81,3,212,3,0,0,"support","medium","exist"
"565",2565,0.83,0.74,3,221,2,0,0,"support","medium","exist"
"566",2566,0.48,0.67,5,273,3,0,0,"support","medium","exist"
"567",2567,0.63,0.86,4,271,3,1,0,"support","medium","exist"
"568",2568,0.87,0.38,4,183,5,0,0,"support","medium","exist"
"569",2569,0.21,0.9,4,271,6,0,0,"support","high","exist"
"570",2570,0.79,0.58,5,165,3,0,0,"support","low","exist"
"571",2571,0.8,0.96,3,257,5,0,0,"support","medium","exist"
"572",2572,0.78,0.82,4,143,3,0,0,"support","medium","exist"
"573",2573,0.67,0.65,5,156,2,0,0,"support","medium","exist"
"574",2574,0.67,0.71,3,190,3,1,0,"support","medium","exist"
"575",2575,0.26,0.67,2,242,6,0,0,"technical","low","exist"
"576",2576,0.89,0.83,5,267,4,0,0,"technical","low","exist"
"577",2577,0.7,0.53,4,152,3,0,0,"technical","low","exist"
"578",2578,0.51,0.48,5,136,4,0,0,"management","low","exist"
"579",2579,0.53,0.88,3,157,3,0,0,"IT","low","exist"
"580",2580,0.76,0.51,4,281,3,0,0,"IT","low","exist"
"581",2581,0.86,0.93,5,208,2,0,0,"IT","low","exist"
"582",2582,0.63,0.96,5,152,3,0,0,"IT","low","exist"
"583",2583,0.58,0.86,5,271,3,0,0,"IT","low","exist"
"584",2584,0.58,0.83,4,163,3,1,0,"product_mng","low","exist"
"585",2585,0.9,0.82,4,136,3,0,0,"product_mng","low","exist"
"586",2586,0.79,0.57,4,233,2,0,0,"product_mng","low","exist"
"587",2587,0.8,0.74,4,221,4,0,0,"product_mng","low","exist"
"588",2588,0.53,0.65,2,189,2,1,0,"IT","low","exist"
"589",2589,0.52,0.84,2,226,3,0,0,"RandD","low","exist"
"590",2590,0.82,0.59,5,201,3,0,0,"RandD","low","exist"
"591",2591,0.68,0.9,2,133,4,0,0,"RandD","low","exist"
"592",2592,0.21,0.61,3,173,2,0,0,"RandD","low","exist"
"593",2593,0.81,0.5,4,152,3,1,0,"RandD","low","exist"
"594",2594,0.57,0.9,3,256,4,0,0,"RandD","low","exist"
"595",2595,0.99,0.72,3,119,2,1,0,"marketing","low","exist"
"596",2596,0.9,1,4,207,3,0,0,"sales","medium","exist"
"597",2597,0.76,0.64,3,189,3,0,0,"accounting","medium","exist"
"598",2598,0.56,0.92,4,172,2,0,0,"support","medium","exist"
"599",2599,0.5,0.93,6,150,3,1,0,"technical","medium","exist"
"600",2600,0.48,0.89,5,179,3,0,0,"management","medium","exist"
"601",2601,0.99,0.97,3,257,2,0,0,"marketing","medium","exist"
"602",2602,0.76,0.8,5,229,2,0,0,"marketing","medium","exist"
"603",2603,0.93,0.97,4,227,3,0,0,"marketing","medium","exist"
"604",2604,0.99,0.78,4,140,3,0,0,"sales","medium","exist"
"605",2605,0.85,0.78,4,251,3,0,0,"sales","medium","exist"
"606",2606,0.63,0.95,4,137,3,0,0,"sales","medium","exist"
"607",2607,0.63,0.78,3,153,3,1,0,"sales","medium","exist"
"608",2608,0.5,0.65,5,242,3,0,0,"sales","high","exist"
"609",2609,0.52,0.57,3,150,3,0,0,"sales","low","exist"
"610",2610,0.63,0.99,3,247,3,0,0,"sales","medium","exist"
"611",2611,0.78,0.5,4,212,2,0,0,"sales","medium","exist"
"612",2612,0.98,0.53,3,234,3,0,0,"sales","medium","exist"
"613",2613,0.14,1,5,174,5,0,0,"sales","medium","exist"
"614",2614,0.7,0.9,3,225,2,0,0,"sales","low","exist"
"615",2615,0.88,0.6,4,224,2,0,0,"sales","low","exist"
"616",2616,0.72,0.62,3,270,4,0,0,"sales","low","exist"
"617",2617,0.88,0.51,4,139,3,0,0,"sales","low","exist"
"618",2618,0.71,0.51,3,248,4,0,0,"sales","low","exist"
"619",2619,0.6,0.85,3,172,2,0,0,"sales","low","exist"
"620",2620,0.88,0.86,4,224,3,1,0,"sales","low","exist"
"621",2621,0.55,0.72,5,232,4,0,0,"sales","low","exist"
"622",2622,0.85,0.55,4,260,2,1,0,"sales","low","exist"
"623",2623,0.84,0.51,2,117,4,0,0,"accounting","low","exist"
"624",2624,0.91,0.61,4,243,2,1,0,"accounting","low","exist"
"625",2625,0.82,0.62,4,202,2,0,0,"accounting","low","exist"
"626",2626,0.6,0.91,2,168,4,0,0,"hr","low","exist"
"627",2627,0.89,0.71,5,194,3,0,0,"hr","low","exist"
"628",2628,0.6,0.97,4,219,4,1,0,"hr","low","exist"
"629",2629,0.64,0.52,4,207,3,0,0,"hr","low","exist"
"630",2630,0.93,0.88,4,177,3,0,0,"technical","low","exist"
"631",2631,0.81,0.99,3,239,2,1,0,"technical","low","exist"
"632",2632,0.31,0.49,4,165,3,1,0,"technical","low","exist"
"633",2633,0.68,0.69,4,225,2,0,0,"technical","low","exist"
"634",2634,0.78,0.59,3,212,2,0,0,"technical","low","exist"
"635",2635,0.44,0.42,4,159,4,0,0,"technical","medium","exist"
"636",2636,0.64,0.93,4,233,2,1,0,"technical","medium","exist"
"637",2637,0.81,0.63,4,108,6,0,0,"technical","medium","exist"
"638",2638,0.5,0.49,3,214,3,0,0,"technical","medium","exist"
"639",2639,0.69,0.61,5,229,3,0,0,"technical","medium","exist"
"640",2640,0.77,0.75,4,223,3,0,0,"technical","medium","exist"
"641",2641,0.69,0.56,4,178,3,0,0,"support","medium","exist"
"642",2642,0.87,0.68,4,246,2,0,0,"support","medium","exist"
"643",2643,0.85,0.91,4,145,3,0,0,"support","medium","exist"
"644",2644,0.83,0.83,4,224,4,0,0,"support","medium","exist"
"645",2645,0.68,0.51,3,259,3,0,0,"support","medium","exist"
"646",2646,0.78,0.65,4,207,2,0,0,"support","medium","exist"
"647",2647,0.78,0.89,3,253,3,0,0,"support","high","exist"
"648",2648,0.93,0.68,4,196,2,1,0,"support","low","exist"
"649",2649,0.54,0.75,3,240,3,0,0,"support","medium","exist"
"650",2650,0.76,0.56,3,255,3,0,0,"support","medium","exist"
"651",2651,0.4,0.72,3,139,2,0,0,"support","medium","exist"
"652",2652,0.73,0.81,3,168,2,0,0,"technical","medium","exist"
"653",2653,0.86,0.98,5,233,3,0,0,"technical","low","exist"
"654",2654,0.38,0.68,5,211,6,0,0,"technical","low","exist"
"655",2655,0.71,0.48,5,114,3,0,0,"management","low","exist"
"656",2656,0.58,0.97,5,202,2,0,0,"IT","low","exist"
"657",2657,0.67,0.59,3,177,3,1,0,"IT","low","exist"
"658",2658,0.55,0.76,4,233,4,0,0,"IT","low","exist"
"659",2659,0.76,0.98,2,111,2,0,0,"IT","low","exist"
"660",2660,0.7,0.82,3,178,3,0,0,"IT","low","exist"
"661",2661,0.66,0.46,4,204,4,0,0,"product_mng","low","exist"
"662",2662,0.96,0.72,3,272,3,0,0,"product_mng","low","exist"
"663",2663,0.6,0.77,4,157,4,0,0,"product_mng","low","exist"
"664",2664,0.54,0.94,5,229,3,1,0,"product_mng","low","exist"
"665",2665,0.85,0.9,5,202,3,0,0,"IT","low","exist"
"666",2666,0.96,0.84,3,264,3,0,0,"RandD","low","exist"
"667",2667,0.86,0.62,3,256,3,1,0,"RandD","low","exist"
"668",2668,0.53,0.87,3,151,2,0,0,"RandD","low","exist"
"669",2669,0.91,0.95,3,251,3,0,0,"RandD","low","exist"
"670",2670,0.33,0.7,5,271,4,0,0,"RandD","low","exist"
"671",2671,0.75,0.73,4,274,2,0,0,"RandD","low","exist"
"672",2672,0.97,0.8,3,169,3,0,0,"marketing","low","exist"
"673",2673,0.68,0.51,4,176,4,1,0,"sales","low","exist"
"674",2674,0.68,0.7,5,168,2,0,0,"accounting","medium","exist"
"675",2675,0.57,0.87,4,171,2,0,0,"support","medium","exist"
"676",2676,0.87,0.9,4,214,3,0,0,"technical","medium","exist"
"677",2677,0.5,0.91,5,224,2,1,0,"management","medium","exist"
"678",2678,0.76,0.59,3,191,4,0,0,"marketing","medium","exist"
"679",2679,0.79,0.61,5,96,4,0,0,"marketing","medium","exist"
"680",2680,0.17,0.9,6,217,6,1,0,"marketing","medium","exist"
"681",2681,0.6,0.62,4,135,2,1,0,"sales","medium","exist"
"682",2682,0.89,0.67,3,226,3,0,0,"sales","medium","exist"
"683",2683,0.69,0.87,3,202,2,0,0,"sales","medium","exist"
"684",2684,0.68,0.85,2,180,6,0,0,"sales","medium","exist"
"685",2685,0.61,0.87,5,174,4,0,0,"sales","medium","exist"
"686",2686,0.63,0.5,3,140,2,0,0,"sales","high","exist"
"687",2687,0.5,0.96,4,147,3,0,0,"sales","low","exist"
"688",2688,0.49,0.74,2,263,3,1,0,"sales","medium","exist"
"689",2689,0.83,0.55,5,261,5,0,0,"sales","medium","exist"
"690",2690,0.59,0.71,2,176,2,1,0,"sales","medium","exist"
"691",2691,0.75,0.93,2,98,5,0,0,"sales","medium","exist"
"692",2692,0.66,0.48,3,192,3,1,0,"sales","low","exist"
"693",2693,0.68,0.51,4,157,3,0,0,"sales","low","exist"
"694",2694,0.73,0.58,5,230,3,0,0,"sales","low","exist"
"695",2695,0.98,0.53,4,192,2,1,0,"sales","low","exist"
"696",2696,0.86,0.65,3,161,3,0,0,"sales","low","exist"
"697",2697,0.5,0.55,3,176,3,0,0,"sales","low","exist"
"698",2698,0.76,0.76,3,216,3,0,0,"sales","low","exist"
"699",2699,0.3,0.47,4,176,2,0,0,"sales","low","exist"
"700",2700,0.3,0.86,3,276,5,1,0,"accounting","low","exist"
"701",2701,0.64,0.59,3,174,3,1,0,"accounting","low","exist"
"702",2702,0.59,0.75,3,106,2,0,0,"accounting","low","exist"
"703",2703,0.85,0.63,4,154,3,0,0,"hr","low","exist"
"704",2704,0.76,0.93,3,271,5,0,0,"hr","low","exist"
"705",2705,0.63,0.5,5,246,2,0,0,"hr","low","exist"
"706",2706,0.65,0.86,4,264,2,1,0,"hr","low","exist"
"707",2707,0.43,0.68,3,197,2,0,0,"technical","low","exist"
"708",2708,0.83,0.56,4,165,2,0,0,"technical","low","exist"
"709",2709,0.49,0.77,4,218,2,0,0,"technical","low","exist"
"710",2710,0.67,0.73,3,203,2,0,0,"technical","low","exist"
"711",2711,0.9,0.47,2,107,6,1,0,"technical","low","exist"
"712",2712,0.83,0.96,3,179,2,0,0,"technical","low","exist"
"713",2713,0.92,0.84,5,264,3,0,0,"technical","medium","exist"
"714",2714,0.83,0.7,5,154,3,0,0,"technical","medium","exist"
"715",2715,0.64,0.55,4,167,3,1,0,"technical","medium","exist"
"716",2716,0.93,0.97,4,158,3,1,0,"technical","medium","exist"
"717",2717,0.6,0.87,4,227,3,1,0,"technical","medium","exist"
"718",2718,0.74,0.69,3,230,2,0,0,"support","medium","exist"
"719",2719,0.56,0.75,5,143,5,0,0,"support","medium","exist"
"720",2720,0.61,0.77,4,142,3,0,0,"support","medium","exist"
"721",2721,0.63,0.62,4,184,4,0,0,"support","medium","exist"
"722",2722,0.24,0.62,5,169,4,0,0,"support","medium","exist"
"723",2723,0.17,0.56,5,218,4,1,0,"support","medium","exist"
"724",2724,0.46,0.64,2,121,3,0,0,"support","medium","exist"
"725",2725,0.68,0.48,4,251,4,0,0,"support","high","exist"
"726",2726,0.68,0.6,2,192,6,0,0,"support","low","exist"
"727",2727,0.16,0.71,6,227,5,0,0,"support","medium","exist"
"728",2728,0.15,0.56,6,140,5,0,0,"support","medium","exist"
"729",2729,0.55,0.49,3,152,2,0,0,"technical","medium","exist"
"730",2730,0.72,0.66,4,202,4,0,0,"technical","medium","exist"
"731",2731,0.91,0.89,2,219,4,0,0,"technical","low","exist"
"732",2732,0.3,0.91,4,248,4,0,0,"management","low","exist"
"733",2733,0.56,0.68,5,203,2,0,0,"IT","low","exist"
"734",2734,0.94,0.94,3,255,3,0,0,"IT","low","exist"
"735",2735,0.82,0.63,5,177,3,0,0,"IT","low","exist"
"736",2736,0.66,0.86,5,185,3,0,0,"IT","low","exist"
"737",2737,0.74,0.64,4,101,6,1,0,"IT","low","exist"
"738",2738,0.63,0.5,3,246,3,0,0,"product_mng","low","exist"
"739",2739,0.65,0.42,6,220,2,0,0,"product_mng","low","exist"
"740",2740,0.56,0.81,3,145,2,0,0,"product_mng","low","exist"
"741",2741,0.32,0.73,6,194,5,0,0,"product_mng","low","exist"
"742",2742,0.8,0.9,4,241,2,0,0,"IT","low","exist"
"743",2743,0.34,0.87,6,175,4,0,0,"RandD","low","exist"
"744",2744,0.62,0.71,5,149,2,0,0,"RandD","low","exist"
"745",2745,0.5,0.86,3,253,2,0,0,"RandD","low","exist"
"746",2746,0.58,0.98,5,218,3,0,0,"RandD","low","exist"
"747",2747,0.94,0.9,2,263,3,0,0,"RandD","low","exist"
"748",2748,0.67,0.99,4,247,3,1,0,"RandD","low","exist"
"749",2749,0.2,0.74,6,148,4,0,0,"marketing","low","exist"
"750",2750,0.91,0.59,5,162,2,0,0,"sales","low","exist"
"751",2751,0.91,0.67,2,255,4,0,0,"accounting","low","exist"
"752",2752,0.78,0.87,3,191,3,1,0,"support","medium","exist"
"753",2753,0.82,0.55,3,217,4,1,0,"technical","medium","exist"
"754",2754,0.54,0.96,3,201,3,0,0,"management","medium","exist"
"755",2755,0.53,0.81,3,253,3,0,0,"marketing","medium","exist"
"756",2756,0.47,0.55,4,122,5,1,0,"marketing","medium","exist"
"757",2757,0.87,0.5,3,269,3,0,0,"marketing","medium","exist"
"758",2758,0.5,0.68,4,161,3,0,0,"sales","medium","exist"
"759",2759,0.59,0.83,3,156,2,0,0,"sales","medium","exist"
"760",2760,0.89,0.69,3,173,3,0,0,"sales","medium","exist"
"761",2761,0.54,0.49,4,152,3,1,0,"sales","medium","exist"
"762",2762,0.62,0.85,3,145,3,0,0,"sales","medium","exist"
"763",2763,0.91,0.85,3,248,3,0,0,"sales","medium","exist"
"764",2764,0.84,0.99,2,184,2,0,0,"sales","high","exist"
"765",2765,0.69,0.65,4,232,2,1,0,"sales","low","exist"
"766",2766,0.76,0.63,3,162,2,0,0,"sales","medium","exist"
"767",2767,0.8,0.54,4,269,3,0,0,"sales","medium","exist"
"768",2768,0.4,0.47,5,108,3,0,0,"sales","medium","exist"
"769",2769,0.8,0.99,3,248,3,1,0,"sales","medium","exist"
"770",2770,0.76,0.4,2,122,5,0,0,"sales","low","exist"
"771",2771,0.55,0.9,4,273,2,1,0,"sales","low","exist"
"772",2772,0.98,0.63,3,285,6,0,0,"sales","low","exist"
"773",2773,0.54,0.56,4,227,3,0,0,"sales","low","exist"
"774",2774,0.63,0.56,4,248,2,1,0,"sales","low","exist"
"775",2775,0.88,0.63,3,257,3,0,0,"sales","low","exist"
"776",2776,0.5,0.95,5,194,3,0,0,"sales","low","exist"
"777",2777,0.52,0.72,3,253,3,0,0,"accounting","low","exist"
"778",2778,0.89,0.95,4,141,3,0,0,"accounting","low","exist"
"779",2779,0.55,0.9,4,199,3,0,0,"accounting","low","exist"
"780",2780,0.51,0.81,3,143,2,0,0,"hr","low","exist"
"781",2781,0.35,0.52,5,244,3,0,0,"hr","low","exist"
"782",2782,0.54,0.71,5,173,2,0,0,"hr","low","exist"
"783",2783,0.72,0.84,4,186,3,0,0,"hr","low","exist"
"784",2784,0.61,0.93,2,247,3,0,0,"technical","low","exist"
"785",2785,0.17,0.93,3,218,4,0,0,"technical","low","exist"
"786",2786,0.71,0.88,3,140,2,0,0,"technical","low","exist"
"787",2787,0.88,0.52,4,166,2,0,0,"technical","low","exist"
"788",2788,0.48,1,3,216,2,0,0,"technical","low","exist"
"789",2789,0.16,0.97,6,235,3,0,0,"technical","low","exist"
"790",2790,0.62,0.72,3,188,3,0,0,"technical","low","exist"
"791",2791,0.59,0.47,3,143,2,0,0,"technical","medium","exist"
"792",2792,0.14,0.9,4,198,4,0,0,"technical","medium","exist"
"793",2793,0.96,0.92,4,148,3,0,0,"technical","medium","exist"
"794",2794,0.96,0.42,6,101,4,0,0,"technical","medium","exist"
"795",2795,0.13,0.89,4,249,6,1,0,"support","medium","exist"
"796",2796,0.64,0.61,5,249,3,1,0,"support","medium","exist"
"797",2797,0.64,0.67,5,198,2,1,0,"support","medium","exist"
"798",2798,0.57,0.72,3,202,3,1,0,"support","medium","exist"
"799",2799,0.49,1,3,176,3,0,0,"support","medium","exist"
"800",2800,0.89,0.79,4,133,2,0,0,"support","medium","exist"
"801",2801,0.94,0.75,5,238,2,0,0,"support","medium","exist"
"802",2802,0.51,0.58,2,181,4,0,0,"support","medium","exist"
"803",2803,0.8,0.85,5,242,3,0,0,"support","high","exist"
"804",2804,0.74,0.51,4,185,2,1,0,"support","low","exist"
"805",2805,0.66,0.85,4,237,3,1,0,"support","medium","exist"
"806",2806,0.66,0.99,5,244,3,0,0,"technical","medium","exist"
"807",2807,0.59,0.62,3,178,4,0,0,"technical","medium","exist"
"808",2808,0.91,0.57,3,164,3,0,0,"technical","medium","exist"
"809",2809,0.83,0.98,5,189,4,1,0,"management","low","exist"
"810",2810,0.5,0.91,3,212,2,0,0,"IT","low","exist"
"811",2811,0.69,0.97,4,233,3,0,0,"IT","low","exist"
"812",2812,0.87,0.91,5,268,3,0,0,"IT","low","exist"
"813",2813,0.37,0.43,2,155,2,0,0,"IT","low","exist"
"814",2814,0.9,0.98,4,257,3,0,0,"IT","low","exist"
"815",2815,0.68,0.41,4,254,5,0,0,"product_mng","low","exist"
"816",2816,0.93,0.63,4,143,3,1,0,"product_mng","low","exist"
"817",2817,0.95,0.45,3,225,2,0,0,"product_mng","low","exist"
"818",2818,0.99,1,4,223,2,1,0,"product_mng","low","exist"
"819",2819,0.64,0.9,2,101,6,0,0,"IT","low","exist"
"820",2820,0.96,0.37,2,159,6,0,0,"RandD","low","exist"
"821",2821,0.92,0.54,5,141,2,0,0,"RandD","low","exist"
"822",2822,0.22,0.52,5,147,5,1,0,"RandD","low","exist"
"823",2823,0.82,0.99,5,252,3,1,0,"RandD","low","exist"
"824",2824,0.75,0.89,3,196,3,0,0,"RandD","low","exist"
"825",2825,0.2,0.89,6,244,3,0,0,"RandD","low","exist"
"826",2826,0.64,0.73,3,142,3,0,0,"marketing","low","exist"
"827",2827,0.62,0.9,4,155,4,0,0,"sales","low","exist"
"828",2828,0.73,0.59,3,219,2,0,0,"accounting","low","exist"
"829",2829,0.52,0.51,3,213,4,0,0,"support","low","exist"
"830",2830,0.63,0.67,5,263,3,0,0,"technical","medium","exist"
"831",2831,0.84,0.92,4,274,3,0,0,"management","medium","exist"
"832",2832,0.49,0.96,3,140,3,0,0,"marketing","medium","exist"
"833",2833,0.54,0.78,4,176,2,0,0,"marketing","medium","exist"
"834",2834,0.52,0.78,4,206,2,0,0,"marketing","medium","exist"
"835",2835,0.66,0.63,6,223,6,0,0,"sales","medium","exist"
"836",2836,0.73,0.41,2,231,6,1,0,"sales","medium","exist"
"837",2837,0.54,0.64,3,250,3,0,0,"sales","medium","exist"
"838",2838,0.72,0.68,5,266,4,0,0,"sales","medium","exist"
"839",2839,0.75,0.64,4,247,3,1,0,"sales","medium","exist"
"840",2840,0.77,0.57,3,189,2,0,0,"sales","medium","exist"
"841",2841,0.42,0.94,5,227,5,0,0,"sales","medium","exist"
"842",2842,0.13,0.69,4,127,4,0,0,"sales","high","exist"
"843",2843,0.73,0.88,5,204,5,0,0,"sales","low","exist"
"844",2844,0.5,0.95,5,137,3,0,0,"sales","medium","exist"
"845",2845,0.92,0.62,4,265,3,1,0,"sales","medium","exist"
"846",2846,0.73,0.66,3,135,2,0,0,"sales","medium","exist"
"847",2847,0.74,0.38,2,126,3,0,0,"sales","medium","exist"
"848",2848,0.76,0.78,3,189,2,0,0,"sales","low","exist"
"849",2849,0.53,0.92,3,207,4,1,0,"sales","low","exist"
"850",2850,0.65,0.72,3,134,3,0,0,"sales","low","exist"
"851",2851,0.91,0.85,4,203,2,0,0,"sales","low","exist"
"852",2852,0.69,0.76,5,222,3,1,0,"sales","low","exist"
"853",2853,0.56,0.66,3,232,2,0,0,"sales","low","exist"
"854",2854,0.55,0.81,4,267,5,0,0,"accounting","low","exist"
"855",2855,0.74,0.5,5,131,3,0,0,"accounting","low","exist"
"856",2856,0.86,0.86,3,155,4,0,0,"accounting","low","exist"
"857",2857,0.82,0.74,3,232,3,0,0,"hr","low","exist"
"858",2858,0.35,0.8,3,137,5,0,0,"hr","low","exist"
"859",2859,0.93,0.99,4,136,4,0,0,"hr","low","exist"
"860",2860,0.55,0.77,3,237,2,0,0,"hr","low","exist"
"861",2861,0.99,0.68,4,190,3,0,0,"technical","low","exist"
"862",2862,0.91,0.89,4,144,3,1,0,"technical","low","exist"
"863",2863,0.24,0.65,6,194,3,0,0,"technical","low","exist"
"864",2864,0.77,0.67,3,186,2,0,0,"technical","low","exist"
"865",2865,0.64,0.66,3,218,3,0,0,"technical","low","exist"
"866",2866,0.58,0.76,5,260,2,1,0,"technical","low","exist"
"867",2867,0.65,0.99,4,200,4,0,0,"technical","low","exist"
"868",2868,0.44,0.68,3,140,3,0,0,"technical","low","exist"
"869",2869,0.59,0.75,2,156,3,0,0,"technical","medium","exist"
"870",2870,0.99,0.56,3,193,3,1,0,"technical","medium","exist"
"871",2871,0.75,0.79,4,145,3,0,0,"technical","medium","exist"
"872",2872,0.77,0.49,4,217,2,0,0,"support","medium","exist"
"873",2873,0.85,0.64,4,162,3,0,0,"support","medium","exist"
"874",2874,0.77,0.93,5,182,4,0,0,"support","medium","exist"
"875",2875,0.54,0.95,3,221,3,0,0,"support","medium","exist"
"876",2876,0.69,0.82,4,208,2,0,0,"support","medium","exist"
"877",2877,0.66,0.65,5,161,3,0,0,"support","medium","exist"
"878",2878,0.51,0.65,4,269,3,0,0,"support","medium","exist"
"879",2879,0.74,0.59,4,155,3,0,0,"support","medium","exist"
"880",2880,0.55,0.72,3,110,3,0,0,"support","medium","exist"
"881",2881,0.65,0.84,3,154,3,0,0,"support","high","exist"
"882",2882,0.2,0.77,6,213,4,0,0,"support","low","exist"
"883",2883,0.92,0.94,5,248,3,0,0,"technical","medium","exist"
"884",2884,0.57,0.6,3,202,3,0,0,"technical","medium","exist"
"885",2885,0.75,0.78,2,251,6,0,0,"technical","medium","exist"
"886",2886,0.68,0.84,3,239,2,0,0,"management","medium","exist"
"887",2887,0.97,0.7,3,203,3,0,0,"IT","low","exist"
"888",2888,0.79,0.48,4,184,5,1,0,"IT","low","exist"
"889",2889,0.66,0.75,4,203,3,1,0,"IT","low","exist"
"890",2890,0.96,0.69,3,214,2,1,0,"IT","low","exist"
"891",2891,0.73,0.69,4,161,3,0,0,"IT","low","exist"
"892",2892,0.29,0.58,5,234,2,0,0,"product_mng","low","exist"
"893",2893,0.58,0.56,3,151,2,0,0,"product_mng","low","exist"
"894",2894,0.72,0.58,4,149,3,0,0,"product_mng","low","exist"
"895",2895,0.94,0.87,4,240,3,0,0,"product_mng","low","exist"
"896",2896,0.48,0.56,5,140,2,0,0,"IT","low","exist"
"897",2897,0.6,0.99,3,187,2,0,0,"RandD","low","exist"
"898",2898,0.97,0.58,5,156,2,1,0,"RandD","low","exist"
"899",2899,0.74,0.41,4,250,4,0,0,"RandD","low","exist"
"900",2900,0.97,0.61,3,165,2,0,0,"RandD","low","exist"
"901",2901,0.88,0.67,5,260,3,1,0,"RandD","low","exist"
"902",2902,0.5,0.7,3,274,3,0,0,"marketing","low","exist"
"903",2903,0.93,0.98,4,160,3,0,0,"sales","low","exist"
"904",2904,0.3,0.7,5,280,4,1,0,"accounting","low","exist"
"905",2905,0.69,0.53,3,142,3,0,0,"support","low","exist"
"906",2906,0.69,0.9,2,155,2,0,0,"technical","low","exist"
"907",2907,0.53,0.67,4,167,2,0,0,"management","low","exist"
"908",2908,0.32,0.8,3,263,3,0,0,"marketing","medium","exist"
"909",2909,0.73,0.75,3,259,4,0,0,"marketing","medium","exist"
"910",2910,0.77,0.61,4,223,3,0,0,"marketing","medium","exist"
"911",2911,0.59,0.81,6,123,5,0,0,"sales","medium","exist"
"912",2912,0.19,0.51,5,226,3,0,0,"sales","medium","exist"
"913",2913,0.78,0.95,3,270,2,0,0,"sales","medium","exist"
"914",2914,0.84,0.74,3,139,3,0,0,"sales","medium","exist"
"915",2915,0.65,0.77,5,241,2,0,0,"sales","medium","exist"
"916",2916,0.38,0.43,2,160,6,0,0,"sales","medium","exist"
"917",2917,0.12,0.47,3,258,5,0,0,"sales","medium","exist"
"918",2918,0.74,0.81,5,106,5,0,0,"sales","medium","exist"
"919",2919,0.67,0.82,4,171,2,0,0,"sales","medium","exist"
"920",2920,0.5,0.79,3,186,3,0,0,"sales","high","exist"
"921",2921,0.99,0.39,6,214,5,1,0,"sales","low","exist"
"922",2922,0.79,0.89,4,240,3,0,0,"sales","medium","exist"
"923",2923,0.72,0.51,4,164,3,0,0,"sales","medium","exist"
"924",2924,0.83,0.57,4,232,3,0,0,"sales","medium","exist"
"925",2925,0.69,0.55,5,242,2,0,0,"sales","medium","exist"
"926",2926,0.5,0.89,5,222,3,0,0,"sales","low","exist"
"927",2927,0.82,0.84,3,139,2,1,0,"sales","low","exist"
"928",2928,0.68,0.56,4,272,3,0,0,"sales","low","exist"
"929",2929,0.82,0.69,4,262,2,0,0,"sales","low","exist"
"930",2930,0.32,0.81,2,249,3,0,0,"accounting","low","exist"
"931",2931,0.93,0.86,4,219,3,0,0,"accounting","low","exist"
"932",2932,0.42,0.73,4,208,5,0,0,"accounting","low","exist"
"933",2933,0.22,0.44,3,166,6,0,0,"hr","low","exist"
"934",2934,0.56,0.88,3,174,3,0,0,"hr","low","exist"
"935",2935,0.77,0.75,4,225,3,0,0,"hr","low","exist"
"936",2936,0.29,0.48,2,116,6,1,0,"hr","low","exist"
"937",2937,0.97,0.65,3,219,2,0,0,"technical","low","exist"
"938",2938,0.91,0.7,4,196,2,0,0,"technical","low","exist"
"939",2939,0.52,0.67,4,210,3,1,0,"technical","low","exist"
"940",2940,0.54,0.64,2,219,3,0,0,"technical","low","exist"
"941",2941,0.54,0.98,3,197,3,0,0,"technical","low","exist"
"942",2942,0.67,0.52,2,102,6,0,0,"technical","low","exist"
"943",2943,0.72,0.85,3,186,4,0,0,"technical","low","exist"
"944",2944,0.68,0.51,4,224,2,0,0,"technical","low","exist"
"945",2945,0.65,0.98,3,283,2,1,0,"technical","low","exist"
"946",2946,0.72,0.98,5,197,4,0,0,"technical","low","exist"
"947",2947,0.51,0.79,5,267,3,0,0,"technical","medium","exist"
"948",2948,0.8,0.58,4,172,3,0,0,"support","medium","exist"
"949",2949,0.83,0.93,4,261,2,0,0,"support","medium","exist"
"950",2950,0.15,0.86,3,204,4,0,0,"support","medium","exist"
"951",2951,0.5,0.73,4,237,2,0,0,"support","medium","exist"
"952",2952,0.8,0.55,2,212,3,0,0,"support","medium","exist"
"953",2953,0.96,0.62,4,217,2,0,0,"support","medium","exist"
"954",2954,0.67,0.7,5,159,3,1,0,"support","medium","exist"
"955",2955,0.98,0.96,5,139,3,0,0,"support","medium","exist"
"956",2956,0.88,0.59,5,230,3,0,0,"support","medium","exist"
"957",2957,0.85,0.79,3,157,4,0,0,"support","medium","exist"
"958",2958,0.75,0.7,5,269,3,0,0,"support","medium","exist"
"959",2959,0.38,0.77,2,170,3,0,0,"technical","high","exist"
"960",2960,0.55,0.82,2,197,4,0,0,"technical","low","exist"
"961",2961,0.63,0.89,4,246,3,0,0,"technical","medium","exist"
"962",2962,0.78,0.51,4,278,3,0,0,"management","medium","exist"
"963",2963,0.99,0.84,5,138,2,0,0,"IT","medium","exist"
"964",2964,0.72,0.87,3,238,3,0,0,"IT","medium","exist"
"965",2965,0.14,0.83,5,175,6,1,0,"IT","low","exist"
"966",2966,0.81,0.67,4,216,3,0,0,"IT","low","exist"
"967",2967,0.73,0.86,4,196,4,1,0,"IT","low","exist"
"968",2968,0.58,0.8,5,187,3,1,0,"product_mng","low","exist"
"969",2969,0.24,0.85,4,155,5,0,0,"product_mng","low","exist"
"970",2970,0.31,0.86,3,205,5,0,0,"product_mng","low","exist"
"971",2971,0.74,0.63,3,230,2,0,0,"product_mng","low","exist"
"972",2972,0.86,0.69,5,157,3,0,0,"IT","low","exist"
"973",2973,0.22,0.8,4,287,4,0,0,"RandD","low","exist"
"974",2974,0.66,0.7,4,161,3,0,0,"RandD","low","exist"
"975",2975,0.21,0.76,5,239,2,0,0,"RandD","low","exist"
"976",2976,0.95,0.61,3,267,2,0,0,"RandD","low","exist"
"977",2977,0.24,0.55,5,208,5,0,0,"RandD","low","exist"
"978",2978,0.66,0.95,3,133,3,0,0,"marketing","low","exist"
"979",2979,0.88,0.86,3,187,3,0,0,"marketing","low","exist"
"980",2980,0.67,0.61,4,140,2,0,0,"sales","low","exist"
"981",2981,0.75,0.58,4,270,3,0,0,"accounting","low","exist"
"982",2982,0.93,0.48,3,147,3,0,0,"support","low","exist"
"983",2983,0.64,0.71,3,181,2,0,0,"technical","low","exist"
"984",2984,0.51,0.53,3,156,3,0,0,"management","low","exist"
"985",2985,0.98,0.5,4,207,3,0,0,"marketing","low","exist"
"986",2986,0.72,0.63,4,241,4,1,0,"marketing","medium","exist"
"987",2987,0.51,0.75,4,154,3,0,0,"marketing","medium","exist"
"988",2988,0.54,0.58,4,206,3,0,0,"sales","medium","exist"
"989",2989,0.99,0.76,4,204,2,0,0,"sales","medium","exist"
"990",2990,0.44,0.9,4,117,3,0,0,"sales","medium","exist"
"991",2991,0.74,0.48,5,144,3,0,0,"sales","medium","exist"
"992",2992,0.9,0.77,3,156,3,0,0,"sales","medium","exist"
"993",2993,0.86,0.58,4,211,4,0,0,"sales","medium","exist"
"994",2994,0.66,0.52,3,149,4,1,0,"sales","medium","exist"
"995",2995,0.64,0.96,4,152,5,0,0,"sales","medium","exist"
"996",2996,0.5,0.59,4,192,2,0,0,"sales","medium","exist"
"997",2997,0.88,0.68,4,274,4,0,0,"sales","medium","exist"
"998",2998,0.72,0.47,5,168,6,0,0,"sales","high","exist"
"999",2999,0.53,0.53,4,205,3,0,0,"sales","low","exist"
"1000",3000,0.83,0.77,3,228,3,0,0,"sales","medium","exist"
"1001",3001,0.24,0.52,4,228,5,0,0,"sales","medium","exist"
"1002",3002,0.66,0.75,5,227,3,1,0,"sales","medium","exist"
"1003",3003,0.43,0.63,3,156,3,0,0,"sales","medium","exist"
"1004",3004,0.75,0.66,5,177,2,0,0,"sales","low","exist"
"1005",3005,0.42,0.89,6,188,5,1,0,"sales","low","exist"
"1006",3006,0.54,0.74,3,185,4,0,0,"sales","low","exist"
"1007",3007,0.84,0.85,3,153,4,0,0,"accounting","low","exist"
"1008",3008,0.95,0.79,4,174,3,0,0,"accounting","low","exist"
"1009",3009,0.6,0.61,4,209,3,0,0,"accounting","low","exist"
"1010",3010,0.95,0.71,3,251,2,1,0,"hr","low","exist"
"1011",3011,0.62,0.89,3,153,3,1,0,"hr","low","exist"
"1012",3012,0.89,0.73,3,210,2,0,0,"hr","low","exist"
"1013",3013,0.73,0.93,5,167,3,0,0,"hr","low","exist"
"1014",3014,0.86,0.94,3,151,2,0,0,"technical","low","exist"
"1015",3015,0.76,0.73,3,158,2,0,0,"technical","low","exist"
"1016",3016,0.91,0.76,3,116,5,0,0,"technical","low","exist"
"1017",3017,1,0.81,5,178,2,0,0,"technical","low","exist"
"1018",3018,0.98,0.78,4,155,3,1,0,"technical","low","exist"
"1019",3019,0.65,0.89,3,151,2,0,0,"technical","low","exist"
"1020",3020,0.62,0.79,4,216,2,1,0,"technical","low","exist"
"1021",3021,0.83,0.82,5,179,3,0,0,"technical","low","exist"
"1022",3022,0.75,1,4,135,4,0,0,"technical","low","exist"
"1023",3023,0.82,0.63,4,232,4,0,0,"technical","low","exist"
"1024",3024,0.69,0.68,4,168,3,0,0,"technical","low","exist"
"1025",3025,0.41,0.96,6,171,5,1,0,"support","medium","exist"
"1026",3026,0.52,0.64,5,258,2,0,0,"support","medium","exist"
"1027",3027,0.74,0.86,3,221,2,0,0,"support","medium","exist"
"1028",3028,0.33,0.96,5,97,3,0,0,"support","medium","exist"
"1029",3029,0.8,0.69,3,164,3,0,0,"support","medium","exist"
"1030",3030,0.82,0.89,4,237,3,0,0,"support","medium","exist"
"1031",3031,0.59,0.65,5,161,2,0,0,"support","medium","exist"
"1032",3032,0.98,0.8,4,134,2,1,0,"support","medium","exist"
"1033",3033,0.93,0.94,4,188,3,0,0,"support","medium","exist"
"1034",3034,0.49,0.95,4,181,3,0,0,"support","medium","exist"
"1035",3035,0.6,0.94,4,160,2,0,0,"support","medium","exist"
"1036",3036,0.34,0.82,6,197,5,1,0,"technical","medium","exist"
"1037",3037,0.71,0.77,3,145,3,0,0,"technical","high","exist"
"1038",3038,0.6,0.64,5,221,2,0,0,"technical","low","exist"
"1039",3039,0.12,0.78,6,260,5,0,0,"management","medium","exist"
"1040",3040,0.16,0.87,3,99,5,1,0,"IT","medium","exist"
"1041",3041,0.57,0.61,3,243,3,0,0,"IT","medium","exist"
"1042",3042,0.72,0.8,5,244,3,0,0,"IT","medium","exist"
"1043",3043,0.91,0.55,4,179,4,0,0,"IT","low","exist"
"1044",3044,0.95,0.49,4,146,6,1,0,"IT","low","exist"
"1045",3045,0.71,0.9,3,262,2,1,0,"product_mng","low","exist"
"1046",3046,0.9,0.69,4,174,2,0,0,"product_mng","low","exist"
"1047",3047,0.66,0.81,4,148,4,0,0,"product_mng","low","exist"
"1048",3048,0.48,0.59,5,235,3,0,0,"product_mng","low","exist"
"1049",3049,0.82,0.82,5,285,2,1,0,"IT","low","exist"
"1050",3050,0.83,0.79,4,143,3,0,0,"RandD","low","exist"
"1051",3051,0.85,0.82,6,141,5,0,0,"RandD","low","exist"
"1052",3052,0.84,0.47,3,125,4,0,0,"RandD","low","exist"
"1053",3053,0.99,0.51,4,232,3,0,0,"RandD","low","exist"
"1054",3054,0.54,0.72,3,172,2,0,0,"RandD","low","exist"
"1055",3055,0.64,0.42,5,283,5,0,0,"marketing","low","exist"
"1056",3056,0.67,0.68,3,189,3,0,0,"sales","low","exist"
"1057",3057,0.48,0.54,2,144,3,0,0,"accounting","low","exist"
"1058",3058,0.58,0.77,4,145,3,1,0,"support","low","exist"
"1059",3059,0.54,0.59,3,200,3,0,0,"technical","low","exist"
"1060",3060,0.25,0.65,3,264,4,0,0,"management","low","exist"
"1061",3061,0.9,0.53,3,215,3,0,0,"marketing","low","exist"
"1062",3062,0.48,0.39,4,272,3,0,0,"marketing","low","exist"
"1063",3063,0.76,0.9,5,142,3,0,0,"marketing","low","exist"
"1064",3064,0.72,0.53,5,240,2,0,0,"sales","medium","exist"
"1065",3065,0.95,0.66,4,168,2,0,0,"sales","medium","exist"
"1066",3066,0.73,0.55,4,171,4,0,0,"sales","medium","exist"
"1067",3067,0.93,0.7,3,159,2,0,0,"sales","medium","exist"
"1068",3068,0.89,0.61,3,175,4,0,0,"sales","medium","exist"
"1069",3069,0.7,0.97,4,244,3,0,0,"sales","medium","exist"
"1070",3070,0.98,0.57,3,198,3,0,0,"sales","medium","exist"
"1071",3071,0.72,0.65,5,151,3,0,0,"sales","medium","exist"
"1072",3072,0.49,0.69,2,188,4,0,0,"sales","medium","exist"
"1073",3073,0.15,0.85,3,199,2,0,0,"sales","medium","exist"
"1074",3074,0.57,0.96,4,257,3,0,0,"sales","medium","exist"
"1075",3075,0.21,0.81,4,144,4,0,0,"sales","medium","exist"
"1076",3076,0.46,0.57,4,275,3,0,0,"sales","high","exist"
"1077",3077,0.56,0.52,3,243,3,0,0,"sales","low","exist"
"1078",3078,0.81,0.66,3,181,2,1,0,"sales","medium","exist"
"1079",3079,0.93,0.59,5,172,3,0,0,"sales","medium","exist"
"1080",3080,0.82,0.97,3,244,5,0,0,"sales","medium","exist"
"1081",3081,0.76,0.51,4,242,3,0,0,"sales","medium","exist"
"1082",3082,0.97,0.81,3,249,2,0,0,"sales","low","exist"
"1083",3083,0.38,0.81,5,128,3,1,0,"accounting","low","exist"
"1084",3084,0.46,0.49,3,213,3,0,0,"accounting","low","exist"
"1085",3085,0.34,0.57,4,152,3,1,0,"accounting","low","exist"
"1086",3086,0.63,0.76,4,245,3,0,0,"hr","low","exist"
"1087",3087,0.57,0.56,4,113,3,0,0,"hr","low","exist"
"1088",3088,0.17,0.76,4,280,5,0,0,"hr","low","exist"
"1089",3089,0.74,0.67,3,273,3,1,0,"hr","low","exist"
"1090",3090,0.59,0.56,4,221,3,1,0,"technical","low","exist"
"1091",3091,0.49,0.61,5,133,3,0,0,"technical","low","exist"
"1092",3092,0.49,0.58,3,136,4,1,0,"technical","low","exist"
"1093",3093,0.61,0.71,4,243,3,1,0,"technical","low","exist"
"1094",3094,0.81,0.79,5,135,3,0,0,"technical","low","exist"
"1095",3095,0.74,0.63,3,231,3,0,0,"technical","low","exist"
"1096",3096,0.91,0.98,3,259,4,0,0,"technical","low","exist"
"1097",3097,0.71,0.66,3,238,2,0,0,"technical","low","exist"
"1098",3098,0.73,0.71,3,210,3,0,0,"technical","low","exist"
"1099",3099,0.44,0.4,3,120,6,0,0,"technical","low","exist"
"1100",3100,0.6,0.56,2,203,4,0,0,"technical","low","exist"
"1101",3101,0.73,0.88,4,148,2,0,0,"support","low","exist"
"1102",3102,0.8,0.54,4,258,3,0,0,"support","low","exist"
"1103",3103,0.97,0.5,3,225,2,0,0,"support","medium","exist"
"1104",3104,0.99,0.75,4,208,2,0,0,"support","medium","exist"
"1105",3105,0.96,0.82,4,274,3,0,0,"support","medium","exist"
"1106",3106,0.24,0.7,5,147,6,1,0,"support","medium","exist"
"1107",3107,0.45,0.39,2,167,3,0,0,"support","medium","exist"
"1108",3108,0.74,0.96,4,154,4,0,0,"support","medium","exist"
"1109",3109,0.6,0.98,4,195,3,0,0,"support","medium","exist"
"1110",3110,0.67,0.56,3,237,4,0,0,"support","medium","exist"
"1111",3111,0.57,0.99,4,222,2,0,0,"support","medium","exist"
"1112",3112,0.87,0.71,5,145,4,0,0,"technical","medium","exist"
"1113",3113,0.25,0.83,3,257,5,1,0,"technical","medium","exist"
"1114",3114,0.98,0.84,3,286,4,0,0,"technical","medium","exist"
"1115",3115,0.3,0.64,2,137,3,0,0,"management","high","exist"
"1116",3116,0.21,0.52,5,130,2,0,0,"IT","low","exist"
"1117",3117,0.56,0.7,3,214,2,0,0,"IT","medium","exist"
"1118",3118,0.75,0.96,3,138,2,0,0,"IT","medium","exist"
"1119",3119,0.5,0.77,3,166,3,0,0,"IT","medium","exist"
"1120",3120,0.61,0.92,4,159,5,0,0,"IT","medium","exist"
"1121",3121,0.83,0.59,5,160,4,0,0,"product_mng","low","exist"
"1122",3122,0.66,0.76,3,155,4,1,0,"product_mng","low","exist"
"1123",3123,0.84,0.68,3,231,3,0,0,"product_mng","low","exist"
"1124",3124,0.87,0.57,4,227,3,0,0,"product_mng","low","exist"
"1125",3125,0.48,0.37,3,181,2,0,0,"IT","low","exist"
"1126",3126,0.84,0.79,4,222,3,0,0,"RandD","low","exist"
"1127",3127,0.49,0.71,3,196,3,0,0,"RandD","low","exist"
"1128",3128,0.67,0.93,3,206,3,0,0,"RandD","low","exist"
"1129",3129,0.12,0.93,6,257,6,1,0,"RandD","low","exist"
"1130",3130,0.99,0.67,5,153,2,0,0,"RandD","low","exist"
"1131",3131,0.17,0.59,5,250,5,0,0,"marketing","low","exist"
"1132",3132,0.58,0.66,3,250,5,0,0,"sales","low","exist"
"1133",3133,0.5,0.73,3,148,3,0,0,"accounting","low","exist"
"1134",3134,0.35,0.69,3,141,2,1,0,"support","low","exist"
"1135",3135,0.93,0.95,6,147,3,0,0,"technical","low","exist"
"1136",3136,0.73,0.87,3,142,3,0,0,"management","low","exist"
"1137",3137,0.91,0.54,3,210,2,0,0,"marketing","low","exist"
"1138",3138,0.72,0.66,3,152,2,0,0,"marketing","low","exist"
"1139",3139,0.51,0.39,3,149,3,0,0,"marketing","low","exist"
"1140",3140,0.55,0.92,3,198,3,0,0,"sales","low","exist"
"1141",3141,0.66,0.76,3,139,5,0,0,"sales","low","exist"
"1142",3142,0.84,0.41,6,255,6,1,0,"sales","medium","exist"
"1143",3143,0.81,0.8,4,229,2,0,0,"sales","medium","exist"
"1144",3144,0.81,0.69,5,134,2,0,0,"sales","medium","exist"
"1145",3145,0.5,0.75,5,255,3,0,0,"sales","medium","exist"
"1146",3146,0.78,0.68,5,189,3,0,0,"sales","medium","exist"
"1147",3147,0.76,0.74,3,183,3,0,0,"sales","medium","exist"
"1148",3148,0.49,0.71,3,154,2,0,0,"sales","medium","exist"
"1149",3149,0.99,0.61,3,167,3,0,0,"sales","medium","exist"
"1150",3150,0.73,0.48,4,139,5,0,0,"sales","medium","exist"
"1151",3151,0.88,0.74,5,245,2,0,0,"sales","medium","exist"
"1152",3152,0.79,0.91,4,200,3,0,0,"sales","medium","exist"
"1153",3153,0.83,0.98,3,159,2,0,0,"sales","medium","exist"
"1154",3154,0.21,0.44,4,163,6,0,0,"sales","high","exist"
"1155",3155,0.87,0.52,3,158,2,1,0,"sales","low","exist"
"1156",3156,1,0.89,3,194,3,0,0,"sales","medium","exist"
"1157",3157,0.49,0.98,3,267,3,1,0,"sales","medium","exist"
"1158",3158,0.51,0.63,3,183,2,0,0,"sales","medium","exist"
"1159",3159,0.63,0.64,3,174,2,0,0,"accounting","medium","exist"
"1160",3160,0.91,0.63,4,240,3,0,0,"accounting","low","exist"
"1161",3161,0.54,0.5,2,123,4,1,0,"accounting","low","exist"
"1162",3162,1,0.59,4,174,3,0,0,"hr","low","exist"
"1163",3163,0.64,0.91,5,246,3,0,0,"hr","low","exist"
"1164",3164,0.65,0.96,5,173,2,0,0,"hr","low","exist"
"1165",3165,0.15,0.93,4,185,5,0,0,"hr","low","exist"
"1166",3166,0.81,0.83,4,259,3,1,0,"technical","low","exist"
"1167",3167,0.61,0.83,3,112,4,1,0,"technical","low","exist"
"1168",3168,0.86,0.55,5,219,2,0,0,"technical","medium","exist"
"1169",3169,0.71,0.62,3,258,2,0,0,"technical","medium","exist"
"1170",3170,0.72,0.82,5,287,3,0,0,"technical","medium","exist"
"1171",3171,0.84,0.37,5,186,2,0,0,"technical","medium","exist"
"1172",3172,0.38,0.74,3,159,4,0,0,"technical","medium","exist"
"1173",3173,0.75,0.56,4,230,3,0,0,"technical","medium","exist"
"1174",3174,0.93,0.77,5,106,5,0,0,"technical","medium","exist"
"1175",3175,0.71,0.64,4,189,3,0,0,"technical","medium","exist"
"1176",3176,0.75,0.96,3,252,3,0,0,"technical","medium","exist"
"1177",3177,0.56,0.68,4,220,2,0,0,"support","medium","exist"
"1178",3178,0.57,0.82,5,218,3,0,0,"support","medium","exist"
"1179",3179,0.63,0.83,4,145,4,0,0,"support","medium","exist"
"1180",3180,0.59,0.91,4,142,3,1,0,"support","medium","exist"
"1181",3181,0.77,0.62,3,218,2,0,0,"support","medium","exist"
"1182",3182,0.65,0.7,4,157,4,0,0,"support","medium","exist"
"1183",3183,0.84,0.49,4,178,3,0,0,"support","medium","exist"
"1184",3184,0.9,0.45,4,241,6,0,0,"support","medium","exist"
"1185",3185,0.6,0.83,3,230,3,0,0,"support","medium","exist"
"1186",3186,0.9,0.74,5,249,3,0,0,"support","medium","exist"
"1187",3187,0.94,0.7,5,147,2,0,0,"support","medium","exist"
"1188",3188,0.56,0.9,4,115,3,0,0,"technical","medium","exist"
"1189",3189,0.95,0.86,5,143,3,0,0,"technical","medium","exist"
"1190",3190,0.97,0.85,4,219,3,0,0,"technical","medium","exist"
"1191",3191,0.55,0.63,4,218,2,0,0,"management","medium","exist"
"1192",3192,0.79,0.6,5,235,2,0,0,"IT","medium","exist"
"1193",3193,0.49,0.76,5,237,3,0,0,"IT","high","exist"
"1194",3194,0.49,0.58,5,186,2,0,0,"IT","high","exist"
"1195",3195,0.57,0.65,5,177,2,0,0,"IT","high","exist"
"1196",3196,0.89,0.81,4,228,4,0,0,"IT","high","exist"
"1197",3197,0.66,0.59,3,204,3,0,0,"product_mng","high","exist"
"1198",3198,0.94,0.77,5,210,3,0,0,"product_mng","high","exist"
"1199",3199,0.98,0.95,4,250,2,1,0,"product_mng","high","exist"
"1200",3200,0.18,0.52,5,185,6,0,0,"product_mng","high","exist"
"1201",3201,0.57,0.73,3,146,3,0,0,"IT","high","exist"
"1202",3202,0.67,0.55,3,217,2,0,0,"RandD","high","exist"
"1203",3203,0.12,0.61,6,172,6,0,0,"RandD","high","exist"
"1204",3204,0.48,0.95,3,184,2,0,0,"RandD","high","exist"
"1205",3205,0.61,0.97,3,148,3,0,0,"RandD","low","exist"
"1206",3206,0.23,0.52,5,236,4,0,0,"RandD","low","exist"
"1207",3207,0.4,0.38,3,280,2,0,0,"marketing","low","exist"
"1208",3208,0.57,0.6,3,218,3,0,0,"sales","low","exist"
"1209",3209,0.95,0.98,5,155,3,0,0,"accounting","low","exist"
"1210",3210,0.93,0.66,4,242,4,0,0,"support","low","exist"
"1211",3211,0.7,0.88,3,166,5,0,0,"technical","low","exist"
"1212",3212,0.58,0.9,4,175,3,1,0,"management","low","exist"
"1213",3213,0.52,0.95,5,234,3,0,0,"marketing","low","exist"
"1214",3214,0.98,0.88,5,232,3,0,0,"marketing","low","exist"
"1215",3215,0.93,0.94,4,156,3,1,0,"marketing","low","exist"
"1216",3216,0.34,0.63,5,248,3,0,0,"sales","low","exist"
"1217",3217,0.87,0.75,4,218,2,0,0,"sales","low","exist"
"1218",3218,0.52,0.96,5,251,2,1,0,"sales","low","exist"
"1219",3219,0.58,0.91,4,173,4,0,0,"sales","low","exist"
"1220",3220,0.65,0.51,4,157,3,1,0,"sales","medium","exist"
"1221",3221,0.74,0.59,3,274,3,0,0,"sales","medium","exist"
"1222",3222,0.63,0.7,5,182,3,0,0,"sales","medium","exist"
"1223",3223,0.74,0.74,4,233,2,0,0,"sales","medium","exist"
"1224",3224,0.65,1,4,249,3,0,0,"sales","medium","exist"
"1225",3225,0.48,0.94,3,162,3,1,0,"sales","medium","exist"
"1226",3226,0.84,0.75,3,184,3,0,0,"sales","medium","exist"
"1227",3227,0.6,0.62,3,135,2,0,0,"sales","medium","exist"
"1228",3228,0.56,0.57,3,143,2,0,0,"sales","medium","exist"
"1229",3229,0.13,0.8,5,203,5,0,0,"sales","medium","exist"
"1230",3230,0.83,0.51,5,143,4,0,0,"sales","medium","exist"
"1231",3231,0.91,0.42,2,142,3,1,0,"sales","medium","exist"
"1232",3232,0.97,0.97,5,171,2,0,0,"sales","high","exist"
"1233",3233,0.9,0.96,3,223,4,0,0,"sales","high","exist"
"1234",3234,0.57,0.87,4,148,3,0,0,"sales","high","exist"
"1235",3235,0.84,0.79,6,140,2,0,0,"accounting","high","exist"
"1236",3236,0.84,0.74,4,226,2,0,0,"accounting","high","exist"
"1237",3237,0.17,0.93,5,183,5,0,0,"accounting","high","exist"
"1238",3238,0.97,0.86,5,135,3,0,0,"hr","high","exist"
"1239",3239,0.94,0.66,3,236,2,0,0,"hr","high","exist"
"1240",3240,0.83,0.61,5,257,2,0,0,"hr","low","exist"
"1241",3241,0.91,0.73,3,155,3,0,0,"hr","low","exist"
"1242",3242,0.9,0.76,2,211,4,0,0,"technical","low","exist"
"1243",3243,0.95,0.86,3,207,2,0,0,"technical","low","exist"
"1244",3244,0.69,0.95,3,126,6,0,0,"technical","low","exist"
"1245",3245,0.49,0.98,3,267,2,0,0,"technical","low","exist"
"1246",3246,0.45,0.37,6,226,2,0,0,"technical","low","exist"
"1247",3247,0.21,0.9,2,239,2,0,0,"technical","low","exist"
"1248",3248,0.67,0.61,3,202,2,0,0,"technical","medium","exist"
"1249",3249,0.76,0.62,3,150,2,1,0,"technical","medium","exist"
"1250",3250,0.19,0.78,5,156,6,0,0,"technical","medium","exist"
"1251",3251,0.52,0.73,2,233,3,0,0,"technical","medium","exist"
"1252",3252,0.66,0.59,5,262,2,0,0,"technical","medium","exist"
"1253",3253,0.95,0.67,3,183,3,0,0,"support","medium","exist"
"1254",3254,0.95,0.78,4,225,2,0,0,"support","medium","exist"
"1255",3255,0.57,0.54,5,216,3,0,0,"support","medium","exist"
"1256",3256,0.48,0.57,5,227,3,0,0,"support","medium","exist"
"1257",3257,0.95,0.5,4,242,2,0,0,"support","medium","exist"
"1258",3258,0.7,0.67,4,224,3,0,0,"support","medium","exist"
"1259",3259,0.48,0.61,3,223,3,1,0,"support","medium","exist"
"1260",3260,0.62,0.58,3,202,2,0,0,"support","medium","exist"
"1261",3261,0.58,0.76,3,220,3,0,0,"support","medium","exist"
"1262",3262,1,0.87,4,129,5,0,0,"support","medium","exist"
"1263",3263,0.79,0.65,2,193,5,0,0,"support","medium","exist"
"1264",3264,0.58,0.73,3,165,2,0,0,"technical","medium","exist"
"1265",3265,0.59,0.79,4,209,2,0,0,"technical","medium","exist"
"1266",3266,0.66,0.8,4,183,2,1,0,"technical","medium","exist"
"1267",3267,0.71,0.59,4,138,3,0,0,"management","medium","exist"
"1268",3268,0.9,0.74,5,152,3,0,0,"IT","medium","exist"
"1269",3269,0.74,0.63,4,170,2,0,0,"IT","medium","exist"
"1270",3270,0.66,0.93,5,185,3,0,0,"IT","medium","exist"
"1271",3271,0.92,0.53,2,249,2,0,0,"IT","high","exist"
"1272",3272,0.55,0.51,4,155,3,0,0,"IT","low","exist"
"1273",3273,0.51,0.43,3,204,4,0,0,"product_mng","medium","exist"
"1274",3274,0.49,0.52,5,188,4,0,0,"product_mng","medium","exist"
"1275",3275,0.88,0.52,5,264,4,0,0,"product_mng","medium","exist"
"1276",3276,0.6,0.8,4,146,2,0,0,"product_mng","medium","exist"
"1277",3277,0.93,0.65,4,212,4,0,0,"IT","medium","exist"
"1278",3278,0.86,0.59,3,215,3,0,0,"RandD","medium","exist"
"1279",3279,0.78,0.98,4,239,3,0,0,"RandD","medium","exist"
"1280",3280,0.69,0.55,3,188,3,0,0,"RandD","medium","exist"
"1281",3281,0.84,0.51,4,259,3,0,0,"RandD","medium","exist"
"1282",3282,0.48,0.62,3,200,3,1,0,"RandD","medium","exist"
"1283",3283,0.63,0.86,3,245,2,0,0,"marketing","low","exist"
"1284",3284,0.54,0.61,3,182,2,0,0,"sales","low","exist"
"1285",3285,0.85,0.53,4,181,2,0,0,"accounting","low","exist"
"1286",3286,0.51,0.52,3,164,3,0,0,"support","low","exist"
"1287",3287,0.88,0.86,5,257,3,0,0,"technical","low","exist"
"1288",3288,0.87,0.93,3,178,3,0,0,"management","low","exist"
"1289",3289,0.54,0.5,4,224,3,0,0,"marketing","low","exist"
"1290",3290,0.96,0.67,5,170,3,0,0,"marketing","high","exist"
"1291",3291,0.58,0.75,4,233,2,0,0,"marketing","low","exist"
"1292",3292,0.21,0.57,5,239,3,0,0,"sales","high","exist"
"1293",3293,0.5,0.56,5,185,2,0,0,"sales","high","exist"
"1294",3294,0.52,0.54,4,184,3,0,0,"sales","low","exist"
"1295",3295,0.5,0.7,3,188,2,0,0,"sales","low","exist"
"1296",3296,0.74,0.86,3,186,3,1,0,"sales","high","exist"
"1297",3297,0.69,0.63,3,226,3,0,0,"sales","low","exist"
"1298",3298,0.61,0.74,2,143,6,0,0,"sales","medium","exist"
"1299",3299,0.5,0.82,3,213,3,0,0,"sales","high","exist"
"1300",3300,0.79,0.53,2,217,2,0,0,"sales","medium","exist"
"1301",3301,0.73,0.68,5,190,2,0,0,"sales","medium","exist"
"1302",3302,0.49,0.69,2,147,2,0,0,"sales","medium","exist"
"1303",3303,0.7,0.77,2,235,3,1,0,"sales","medium","exist"
"1304",3304,0.27,0.62,6,136,3,0,0,"sales","high","exist"
"1305",3305,0.8,0.54,5,261,2,0,0,"sales","medium","exist"
"1306",3306,0.45,0.6,6,176,4,0,0,"sales","medium","exist"
"1307",3307,0.63,0.64,4,212,4,0,0,"sales","medium","exist"
"1308",3308,0.76,0.52,2,148,3,0,0,"sales","high","exist"
"1309",3309,0.42,0.74,6,218,6,0,0,"sales","medium","exist"
"1310",3310,0.41,0.87,6,262,6,0,0,"sales","high","exist"
"1311",3311,0.74,0.46,6,145,3,0,0,"accounting","low","exist"
"1312",3312,0.82,0.75,3,230,4,1,0,"accounting","medium","exist"
"1313",3313,0.82,0.65,4,210,2,0,0,"accounting","medium","exist"
"1314",3314,0.53,0.73,4,227,3,0,0,"hr","medium","exist"
"1315",3315,0.71,0.77,4,142,2,0,0,"hr","medium","exist"
"1316",3316,0.5,0.9,4,171,2,0,0,"hr","low","exist"
"1317",3317,0.68,0.89,3,241,3,0,0,"hr","low","exist"
"1318",3318,0.68,0.99,3,247,2,1,0,"technical","low","exist"
"1319",3319,0.48,0.86,5,246,3,0,0,"technical","low","exist"
"1320",3320,0.59,0.72,3,188,3,1,0,"technical","low","exist"
"1321",3321,0.21,0.7,3,104,2,0,0,"technical","low","exist"
"1322",3322,0.99,0.73,5,197,4,1,0,"technical","low","exist"
"1323",3323,1,0.88,4,191,4,0,0,"technical","low","exist"
"1324",3324,0.84,0.51,4,173,2,0,0,"technical","low","exist"
"1325",3325,0.86,0.82,2,207,4,1,0,"technical","low","exist"
"1326",3326,0.52,0.54,5,247,4,0,0,"technical","high","exist"
"1327",3327,0.94,0.97,5,197,5,1,0,"technical","low","exist"
"1328",3328,0.55,0.81,3,242,2,0,0,"technical","low","exist"
"1329",3329,0.79,0.59,3,145,2,0,0,"support","low","exist"
"1330",3330,0.91,0.9,4,174,3,0,0,"support","low","exist"
"1331",3331,0.82,0.99,3,195,2,0,0,"support","high","exist"
"1332",3332,0.84,0.94,4,270,3,1,0,"support","low","exist"
"1333",3333,0.72,0.68,4,238,3,0,0,"support","low","exist"
"1334",3334,0.68,0.85,4,156,2,0,0,"support","low","exist"
"1335",3335,0.99,0.96,5,261,3,0,0,"support","high","exist"
"1336",3336,0.74,0.85,5,135,2,0,0,"support","low","exist"
"1337",3337,0.8,0.41,6,185,4,0,0,"support","medium","exist"
"1338",3338,0.55,0.55,3,197,3,0,0,"support","high","exist"
"1339",3339,0.56,0.95,3,241,3,0,0,"support","medium","exist"
"1340",3340,0.88,0.98,3,263,3,1,0,"technical","high","exist"
"1341",3341,0.86,0.49,4,251,2,0,0,"technical","medium","exist"
"1342",3342,0.64,0.95,3,218,2,0,0,"technical","medium","exist"
"1343",3343,0.82,0.58,4,183,3,0,0,"management","medium","exist"
"1344",3344,0.66,0.8,4,252,3,0,0,"IT","medium","exist"
"1345",3345,0.64,0.95,3,98,3,0,0,"IT","medium","exist"
"1346",3346,0.17,0.78,6,284,4,0,0,"IT","medium","exist"
"1347",3347,0.75,0.68,4,220,2,0,0,"IT","medium","exist"
"1348",3348,0.45,0.39,4,275,3,0,0,"IT","medium","exist"
"1349",3349,0.99,0.9,4,164,3,0,0,"product_mng","high","exist"
"1350",3350,0.93,0.67,5,237,4,0,0,"product_mng","low","exist"
"1351",3351,0.88,0.8,3,133,3,0,0,"product_mng","medium","exist"
"1352",3352,0.62,0.52,5,256,4,0,0,"product_mng","medium","exist"
"1353",3353,0.94,0.88,4,270,3,0,0,"IT","medium","exist"
"1354",3354,0.59,0.92,4,196,2,0,0,"RandD","medium","exist"
"1355",3355,0.93,0.89,4,209,2,0,0,"RandD","medium","exist"
"1356",3356,0.71,0.39,3,261,3,0,0,"RandD","medium","exist"
"1357",3357,0.6,0.6,3,221,2,0,0,"RandD","medium","exist"
"1358",3358,0.71,0.69,4,180,3,1,0,"RandD","medium","exist"
"1359",3359,0.91,0.57,4,145,3,0,0,"marketing","medium","exist"
"1360",3360,0.78,0.96,4,111,2,0,0,"sales","high","exist"
"1361",3361,0.52,0.63,4,232,3,0,0,"accounting","low","exist"
"1362",3362,0.99,0.7,5,195,3,0,0,"support","low","exist"
"1363",3363,0.86,0.51,4,211,2,1,0,"technical","low","exist"
"1364",3364,0.74,0.68,4,194,2,0,0,"management","high","exist"
"1365",3365,0.15,0.84,4,275,5,0,0,"marketing","low","exist"
"1366",3366,0.57,0.54,3,140,3,0,0,"marketing","low","exist"
"1367",3367,0.67,0.6,3,157,3,0,0,"marketing","low","exist"
"1368",3368,0.8,0.93,4,258,3,0,0,"sales","high","exist"
"1369",3369,0.83,0.82,5,273,4,0,0,"sales","low","exist"
"1370",3370,0.69,0.53,3,135,2,0,0,"sales","low","exist"
"1371",3371,0.64,0.83,3,188,4,0,0,"sales","low","exist"
"1372",3372,0.95,0.52,5,222,2,0,0,"sales","low","exist"
"1373",3373,0.97,0.52,4,231,3,0,0,"sales","low","exist"
"1374",3374,0.85,0.86,3,255,2,0,0,"sales","low","exist"
"1375",3375,0.72,0.51,3,254,2,0,0,"sales","low","exist"
"1376",3376,0.91,0.85,3,244,3,1,0,"sales","medium","exist"
"1377",3377,0.81,0.83,3,132,4,0,0,"sales","medium","exist"
"1378",3378,0.13,0.67,5,234,6,0,0,"sales","medium","exist"
"1379",3379,0.95,0.81,4,272,3,0,0,"sales","medium","exist"
"1380",3380,0.82,0.54,4,191,3,0,0,"sales","medium","exist"
"1381",3381,0.63,0.93,4,238,4,0,0,"sales","medium","exist"
"1382",3382,0.55,0.69,3,225,2,0,0,"sales","medium","exist"
"1383",3383,0.83,0.95,3,211,2,0,0,"sales","medium","exist"
"1384",3384,0.54,0.98,3,217,3,0,0,"sales","medium","exist"
"1385",3385,0.89,0.91,5,156,4,0,0,"sales","medium","exist"
"1386",3386,0.79,0.55,3,147,3,0,0,"sales","medium","exist"
"1387",3387,0.56,0.73,4,134,3,0,0,"accounting","medium","exist"
"1388",3388,0.89,0.7,4,235,2,1,0,"accounting","high","exist"
"1389",3389,1,0.66,4,238,4,0,0,"accounting","low","exist"
"1390",3390,0.43,0.72,3,258,4,0,0,"hr","medium","exist"
"1391",3391,0.71,0.55,5,224,3,0,0,"hr","medium","exist"
"1392",3392,0.98,0.59,4,149,2,0,0,"hr","medium","exist"
"1393",3393,0.81,0.7,4,219,2,1,0,"hr","medium","exist"
"1394",3394,0.71,0.53,3,186,2,0,0,"technical","low","exist"
"1395",3395,0.99,0.6,4,259,4,0,0,"technical","low","exist"
"1396",3396,0.34,0.62,3,119,2,0,0,"technical","low","exist"
"1397",3397,0.58,0.91,3,223,2,0,0,"technical","low","exist"
"1398",3398,0.77,0.49,4,184,3,0,0,"technical","low","exist"
"1399",3399,0.64,0.5,3,238,4,0,0,"technical","low","exist"
"1400",3400,0.13,1,4,157,5,1,0,"technical","low","exist"
"1401",3401,0.98,0.6,2,249,3,0,0,"technical","low","exist"
"1402",3402,0.72,0.84,4,195,2,0,0,"technical","low","exist"
"1403",3403,0.55,0.79,3,162,2,0,0,"technical","low","exist"
"1404",3404,0.62,0.91,4,231,3,0,0,"technical","low","exist"
"1405",3405,0.74,0.91,2,264,3,0,0,"support","low","exist"
"1406",3406,0.69,0.96,3,210,3,0,0,"support","low","exist"
"1407",3407,0.6,0.63,4,192,3,0,0,"support","low","exist"
"1408",3408,0.54,0.43,3,192,2,0,0,"support","low","exist"
"1409",3409,0.56,0.75,4,249,3,0,0,"support","low","exist"
"1410",3410,0.58,0.86,5,206,3,0,0,"support","low","exist"
"1411",3411,0.21,0.66,4,165,5,0,0,"support","low","exist"
"1412",3412,0.52,0.85,5,183,3,0,0,"support","low","exist"
"1413",3413,0.98,0.7,4,253,2,0,0,"support","low","exist"
"1414",3414,0.85,0.97,3,256,3,0,0,"support","low","exist"
"1415",3415,0.62,0.88,2,263,4,0,0,"support","medium","exist"
"1416",3416,0.63,0.61,3,234,3,1,0,"technical","medium","exist"
"1417",3417,0.84,0.97,6,223,2,0,0,"technical","medium","exist"
"1418",3418,0.71,0.69,2,172,3,0,0,"technical","medium","exist"
"1419",3419,0.36,0.55,6,226,6,0,0,"management","medium","exist"
"1420",3420,0.79,0.56,6,205,4,1,0,"IT","medium","exist"
"1421",3421,0.59,0.55,4,235,2,0,0,"IT","medium","exist"
"1422",3422,0.5,0.53,4,263,3,0,0,"IT","medium","exist"
"1423",3423,0.88,0.66,4,271,3,1,0,"IT","medium","exist"
"1424",3424,0.65,0.69,6,131,5,0,0,"IT","medium","exist"
"1425",3425,0.71,0.56,4,238,4,0,0,"product_mng","medium","exist"
"1426",3426,0.89,0.54,3,214,2,0,0,"product_mng","medium","exist"
"1427",3427,0.83,0.84,3,267,3,0,0,"product_mng","high","exist"
"1428",3428,0.73,0.53,3,184,2,0,0,"product_mng","low","exist"
"1429",3429,0.71,0.67,3,264,3,0,0,"IT","medium","exist"
"1430",3430,0.53,0.66,3,109,2,0,0,"RandD","medium","exist"
"1431",3431,0.74,0.7,3,253,4,1,0,"RandD","medium","exist"
"1432",3432,0.6,0.52,4,207,2,1,0,"RandD","medium","exist"
"1433",3433,0.47,0.6,4,263,5,0,0,"RandD","low","exist"
"1434",3434,0.88,0.88,3,214,2,1,0,"RandD","low","exist"
"1435",3435,0.62,0.67,2,153,3,0,0,"marketing","low","exist"
"1436",3436,0.24,0.48,5,123,5,1,0,"sales","low","exist"
"1437",3437,0.15,0.36,5,159,2,0,0,"accounting","low","exist"
"1438",3438,0.92,0.82,3,185,3,0,0,"support","low","exist"
"1439",3439,0.74,0.7,4,167,3,1,0,"technical","low","exist"
"1440",3440,0.86,0.77,3,271,3,0,0,"management","low","exist"
"1441",3441,0.96,0.61,3,140,3,0,0,"marketing","low","exist"
"1442",3442,0.54,0.6,4,141,3,0,0,"marketing","low","exist"
"1443",3443,0.99,0.87,4,190,2,0,0,"marketing","low","exist"
"1444",3444,0.72,0.73,5,134,2,0,0,"sales","low","exist"
"1445",3445,0.96,0.75,5,176,4,0,0,"sales","low","exist"
"1446",3446,0.73,0.73,4,194,2,0,0,"sales","low","exist"
"1447",3447,0.67,0.44,5,212,4,0,0,"sales","low","exist"
"1448",3448,0.58,0.72,5,234,3,1,0,"sales","low","exist"
"1449",3449,0.9,0.99,4,190,2,0,0,"sales","low","exist"
"1450",3450,0.99,0.37,5,155,3,0,0,"sales","low","exist"
"1451",3451,0.89,0.58,4,232,2,0,0,"sales","low","exist"
"1452",3452,0.72,0.95,4,216,3,0,0,"sales","low","exist"
"1453",3453,0.77,0.8,3,207,3,0,0,"sales","low","exist"
"1454",3454,0.8,0.94,4,200,4,0,0,"sales","medium","exist"
"1455",3455,0.89,0.86,3,178,4,0,0,"sales","medium","exist"
"1456",3456,0.59,0.81,4,173,2,0,0,"sales","medium","exist"
"1457",3457,0.24,0.87,5,268,5,0,0,"sales","medium","exist"
"1458",3458,0.94,0.59,6,212,2,0,0,"sales","medium","exist"
"1459",3459,0.74,0.5,4,216,3,0,0,"sales","medium","exist"
"1460",3460,0.63,0.62,5,212,6,0,0,"sales","medium","exist"
"1461",3461,0.63,0.59,3,155,3,1,0,"sales","medium","exist"
"1462",3462,0.77,0.65,4,164,2,0,0,"sales","medium","exist"
"1463",3463,0.87,0.74,3,209,3,0,0,"accounting","medium","exist"
"1464",3464,0.79,0.61,4,136,3,0,0,"accounting","medium","exist"
"1465",3465,0.54,0.84,4,223,3,0,0,"accounting","medium","exist"
"1466",3466,0.59,0.83,4,239,4,0,0,"hr","high","exist"
"1467",3467,0.86,0.87,3,205,4,0,0,"hr","low","exist"
"1468",3468,0.98,0.68,3,203,3,0,0,"hr","medium","exist"
"1469",3469,1,0.79,5,152,4,0,0,"hr","medium","exist"
"1470",3470,0.9,0.79,4,236,3,1,0,"technical","medium","exist"
"1471",3471,0.34,0.6,4,114,3,0,0,"technical","medium","exist"
"1472",3472,0.95,0.93,3,209,2,0,0,"technical","low","exist"
"1473",3473,0.79,0.48,4,178,2,0,0,"technical","low","exist"
"1474",3474,0.13,0.91,4,175,5,0,0,"technical","low","exist"
"1475",3475,0.72,0.69,3,270,4,0,0,"technical","low","exist"
"1476",3476,0.63,0.54,3,145,2,1,0,"technical","low","exist"
"1477",3477,0.81,0.76,5,134,2,0,0,"technical","low","exist"
"1478",3478,0.71,0.75,4,272,4,0,0,"technical","low","exist"
"1479",3479,0.49,0.76,3,208,2,0,0,"technical","low","exist"
"1480",3480,0.59,0.65,4,219,3,1,0,"technical","low","exist"
"1481",3481,0.87,0.59,5,154,3,0,0,"support","low","exist"
"1482",3482,0.66,0.6,3,179,4,0,0,"support","low","exist"
"1483",3483,0.78,0.92,5,137,2,0,0,"support","low","exist"
"1484",3484,0.65,0.7,4,211,2,0,0,"support","low","exist"
"1485",3485,0.8,0.97,5,271,4,0,0,"support","low","exist"
"1486",3486,0.56,0.75,4,192,2,0,0,"support","low","exist"
"1487",3487,0.85,0.67,4,228,4,0,0,"support","low","exist"
"1488",3488,0.14,0.46,2,267,6,1,0,"support","low","exist"
"1489",3489,0.74,0.62,4,272,2,0,0,"support","low","exist"
"1490",3490,0.86,0.93,2,260,2,0,0,"support","low","exist"
"1491",3491,0.71,0.96,4,161,3,0,0,"support","low","exist"
"1492",3492,0.83,0.99,4,226,2,0,0,"technical","low","exist"
"1493",3493,0.52,0.49,4,249,4,1,0,"technical","medium","exist"
"1494",3494,0.63,0.8,3,243,3,0,0,"technical","medium","exist"
"1495",3495,0.92,0.93,4,247,2,0,0,"management","medium","exist"
"1496",3496,0.66,0.87,3,186,2,0,0,"IT","medium","exist"
"1497",3497,0.77,0.7,3,155,3,0,0,"IT","medium","exist"
"1498",3498,0.74,0.97,2,135,4,0,0,"IT","medium","exist"
"1499",3499,0.81,0.76,5,198,4,0,0,"IT","medium","exist"
"1500",3500,0.67,0.55,3,175,2,0,0,"IT","medium","exist"
"1501",3501,0.75,0.92,3,192,3,0,0,"product_mng","medium","exist"
"1502",3502,0.65,0.36,2,282,3,0,0,"product_mng","medium","exist"
"1503",3503,0.81,0.6,4,179,3,0,0,"product_mng","medium","exist"
"1504",3504,0.57,0.77,4,245,3,0,0,"product_mng","medium","exist"
"1505",3505,0.89,0.66,4,235,3,0,0,"IT","high","exist"
"1506",3506,0.5,0.56,4,266,2,0,0,"RandD","low","exist"
"1507",3507,0.21,0.42,4,269,4,1,0,"RandD","medium","exist"
"1508",3508,0.79,0.67,3,264,2,0,0,"RandD","medium","exist"
"1509",3509,0.66,0.57,3,161,2,1,0,"RandD","medium","exist"
"1510",3510,0.82,0.95,3,203,3,0,0,"RandD","medium","exist"
"1511",3511,0.85,0.85,4,258,2,0,0,"marketing","low","exist"
"1512",3512,0.72,0.96,3,143,3,0,0,"sales","low","exist"
"1513",3513,0.52,0.92,3,214,3,0,0,"accounting","low","exist"
"1514",3514,0.5,0.62,4,166,3,1,0,"support","low","exist"
"1515",3515,0.52,0.82,3,192,3,0,0,"technical","low","exist"
"1516",3516,0.13,0.87,3,185,4,1,0,"management","low","exist"
"1517",3517,0.87,0.98,4,173,3,0,0,"marketing","low","exist"
"1518",3518,0.53,0.98,2,163,3,0,0,"marketing","low","exist"
"1519",3519,0.15,0.76,5,277,4,0,0,"marketing","low","exist"
"1520",3520,0.64,0.86,5,274,2,1,0,"sales","low","exist"
"1521",3521,0.52,0.44,3,119,2,0,0,"sales","low","exist"
"1522",3522,0.79,0.67,4,267,3,0,0,"sales","low","exist"
"1523",3523,0.74,0.89,3,160,2,0,0,"sales","low","exist"
"1524",3524,0.81,0.84,4,198,2,0,0,"sales","low","exist"
"1525",3525,0.94,0.6,3,193,3,0,0,"sales","low","exist"
"1526",3526,0.67,0.84,6,250,2,0,0,"sales","low","exist"
"1527",3527,0.93,0.84,3,237,4,1,0,"sales","low","exist"
"1528",3528,0.52,0.96,4,170,2,0,0,"sales","low","exist"
"1529",3529,0.48,0.74,2,157,2,1,0,"sales","medium","exist"
"1530",3530,0.57,0.92,6,267,4,0,0,"sales","medium","exist"
"1531",3531,0.71,0.77,4,192,4,0,0,"sales","medium","exist"
"1532",3532,0.9,0.74,4,218,2,1,0,"sales","medium","exist"
"1533",3533,0.58,0.61,4,210,3,0,0,"sales","medium","exist"
"1534",3534,0.66,0.48,4,229,4,0,0,"sales","medium","exist"
"1535",3535,0.59,0.66,3,186,3,0,0,"sales","medium","exist"
"1536",3536,0.96,0.5,3,234,2,1,0,"sales","medium","exist"
"1537",3537,0.13,0.91,2,149,5,0,0,"sales","medium","exist"
"1538",3538,0.93,0.92,3,205,3,0,0,"sales","medium","exist"
"1539",3539,0.54,0.72,3,148,4,0,0,"accounting","medium","exist"
"1540",3540,0.54,0.77,5,270,3,0,0,"accounting","medium","exist"
"1541",3541,0.87,0.62,3,176,3,0,0,"accounting","high","exist"
"1542",3542,0.79,0.91,3,226,2,0,0,"hr","low","exist"
"1543",3543,0.83,0.57,3,232,4,0,0,"hr","medium","exist"
"1544",3544,0.73,0.6,3,137,3,0,0,"hr","medium","exist"
"1545",3545,0.29,0.42,6,253,3,0,0,"hr","medium","exist"
"1546",3546,0.8,0.7,6,231,4,0,0,"technical","medium","exist"
"1547",3547,0.29,0.81,3,189,6,0,0,"technical","low","exist"
"1548",3548,0.43,0.85,6,168,2,0,0,"technical","low","exist"
"1549",3549,0.99,0.93,3,261,3,0,0,"technical","low","exist"
"1550",3550,0.86,0.51,4,152,3,0,0,"technical","low","exist"
"1551",3551,0.85,0.78,5,263,2,1,0,"technical","low","exist"
"1552",3552,0.66,0.68,3,258,3,0,0,"technical","low","exist"
"1553",3553,0.57,0.99,3,205,3,0,0,"technical","low","exist"
"1554",3554,0.31,0.64,6,183,2,1,0,"technical","low","exist"
"1555",3555,0.88,0.88,3,153,3,0,0,"technical","low","exist"
"1556",3556,0.72,0.72,5,194,4,0,0,"technical","low","exist"
"1557",3557,0.75,0.66,4,202,2,0,0,"support","low","exist"
"1558",3558,0.82,0.71,4,164,4,0,0,"support","low","exist"
"1559",3559,0.82,0.67,3,213,2,0,0,"support","low","exist"
"1560",3560,0.15,0.52,4,265,6,0,0,"support","low","exist"
"1561",3561,0.16,0.88,6,218,3,0,0,"support","low","exist"
"1562",3562,0.95,0.4,5,286,4,0,0,"support","low","exist"
"1563",3563,0.87,0.73,3,172,3,1,0,"support","low","exist"
"1564",3564,0.85,0.89,4,251,2,0,0,"support","low","exist"
"1565",3565,0.58,0.6,3,110,3,1,0,"support","low","exist"
"1566",3566,0.63,0.86,3,269,4,0,0,"support","low","exist"
"1567",3567,0.69,0.68,3,151,3,1,0,"support","low","exist"
"1568",3568,0.53,0.85,5,161,2,0,0,"technical","medium","exist"
"1569",3569,0.96,0.71,4,210,3,0,0,"technical","medium","exist"
"1570",3570,0.73,0.83,2,167,3,0,0,"technical","medium","exist"
"1571",3571,0.5,0.6,3,270,3,1,0,"management","medium","exist"
"1572",3572,0.81,0.83,3,133,3,1,0,"IT","medium","exist"
"1573",3573,0.82,0.59,3,249,3,0,0,"IT","medium","exist"
"1574",3574,0.76,0.72,4,266,3,0,0,"IT","medium","exist"
"1575",3575,0.27,0.82,4,276,2,0,0,"IT","medium","exist"
"1576",3576,0.64,0.38,6,215,3,0,0,"IT","medium","exist"
"1577",3577,0.57,0.9,3,262,2,0,0,"product_mng","medium","exist"
"1578",3578,0.49,0.84,5,193,3,0,0,"product_mng","medium","exist"
"1579",3579,0.66,0.92,2,102,4,0,0,"product_mng","medium","exist"
"1580",3580,0.71,0.36,4,278,4,1,0,"product_mng","high","exist"
"1581",3581,0.61,0.63,3,250,4,0,0,"IT","low","exist"
"1582",3582,0.52,0.89,5,193,4,0,0,"RandD","medium","exist"
"1583",3583,0.62,0.91,3,133,3,0,0,"RandD","medium","exist"
"1584",3584,0.98,0.65,4,216,4,0,0,"RandD","medium","exist"
"1585",3585,0.67,0.56,5,174,2,1,0,"RandD","medium","exist"
"1586",3586,0.85,0.62,2,280,4,0,0,"RandD","low","exist"
"1587",3587,0.84,0.85,4,246,2,0,0,"marketing","low","exist"
"1588",3588,1,0.53,3,142,3,0,0,"sales","low","exist"
"1589",3589,0.72,0.76,4,144,3,1,0,"accounting","low","exist"
"1590",3590,0.66,0.75,6,100,4,1,0,"support","low","exist"
"1591",3591,0.64,0.71,5,212,2,0,0,"technical","low","exist"
"1592",3592,1,0.76,5,201,3,0,0,"management","low","exist"
"1593",3593,0.75,0.59,5,206,2,0,0,"marketing","low","exist"
"1594",3594,0.81,0.77,4,197,3,0,0,"marketing","low","exist"
"1595",3595,0.31,0.98,5,232,5,0,0,"marketing","low","exist"
"1596",3596,0.32,0.97,6,272,2,0,0,"sales","low","exist"
"1597",3597,0.48,0.5,4,173,3,0,0,"sales","low","exist"
"1598",3598,0.62,0.42,2,124,2,0,0,"sales","low","exist"
"1599",3599,0.77,0.86,5,282,4,0,0,"sales","low","exist"
"1600",3600,0.76,0.8,2,219,4,1,0,"sales","low","exist"
"1601",3601,0.68,0.41,3,106,6,0,0,"sales","low","exist"
"1602",3602,0.72,0.79,4,192,4,0,0,"sales","low","exist"
"1603",3603,0.87,0.91,5,190,2,0,0,"sales","low","exist"
"1604",3604,0.59,0.57,2,156,2,0,0,"sales","low","exist"
"1605",3605,0.7,0.64,3,180,2,0,0,"sales","low","exist"
"1606",3606,0.73,0.81,4,245,2,0,0,"sales","low","exist"
"1607",3607,0.58,0.62,3,169,2,0,0,"sales","medium","exist"
"1608",3608,0.64,0.66,3,188,4,0,0,"sales","medium","exist"
"1609",3609,0.89,0.66,5,224,2,0,0,"sales","medium","exist"
"1610",3610,0.95,0.71,3,263,2,1,0,"sales","medium","exist"
"1611",3611,0.84,0.9,3,262,2,0,0,"sales","medium","exist"
"1612",3612,0.6,0.99,4,225,3,0,0,"sales","medium","exist"
"1613",3613,0.71,0.63,5,224,3,1,0,"sales","medium","exist"
"1614",3614,0.6,0.95,3,266,3,0,0,"sales","medium","exist"
"1615",3615,0.91,0.68,3,218,3,1,0,"accounting","medium","exist"
"1616",3616,0.75,1,3,102,3,0,0,"accounting","medium","exist"
"1617",3617,0.7,0.67,4,181,3,0,0,"accounting","medium","exist"
"1618",3618,0.23,0.64,5,150,5,0,0,"hr","medium","exist"
"1619",3619,0.73,0.83,4,270,2,0,0,"hr","high","exist"
"1620",3620,0.99,0.49,4,270,2,0,0,"hr","low","exist"
"1621",3621,0.62,0.68,5,265,3,0,0,"hr","medium","exist"
"1622",3622,0.98,0.89,4,169,3,0,0,"technical","medium","exist"
"1623",3623,0.61,0.94,3,224,4,0,0,"technical","medium","exist"
"1624",3624,0.52,0.92,3,150,2,1,0,"technical","medium","exist"
"1625",3625,0.65,0.66,5,239,4,0,0,"technical","low","exist"
"1626",3626,0.59,0.9,3,245,3,1,0,"technical","low","exist"
"1627",3627,0.71,0.8,3,182,2,1,0,"technical","low","exist"
"1628",3628,0.18,0.7,5,182,4,0,0,"technical","low","exist"
"1629",3629,0.21,0.93,4,243,4,0,0,"technical","low","exist"
"1630",3630,0.15,0.67,6,209,5,0,0,"technical","low","exist"
"1631",3631,0.97,0.53,4,195,4,0,0,"technical","low","exist"
"1632",3632,0.39,0.48,3,190,2,0,0,"technical","low","exist"
"1633",3633,0.35,0.66,2,140,2,0,0,"support","medium","exist"
"1634",3634,0.88,0.58,4,147,4,0,0,"support","medium","exist"
"1635",3635,0.58,0.97,3,265,3,0,0,"support","medium","exist"
"1636",3636,0.61,0.78,3,238,2,0,0,"support","medium","exist"
"1637",3637,0.59,0.98,3,155,3,0,0,"support","medium","exist"
"1638",3638,0.55,0.41,4,132,5,1,0,"support","medium","exist"
"1639",3639,0.52,0.8,5,257,3,0,0,"support","medium","exist"
"1640",3640,0.71,0.77,2,255,3,0,0,"support","medium","exist"
"1641",3641,0.63,0.82,5,229,2,0,0,"support","medium","exist"
"1642",3642,0.17,0.84,5,216,4,0,0,"support","medium","exist"
"1643",3643,0.95,0.49,4,269,4,0,0,"support","medium","exist"
"1644",3644,1,0.73,3,205,2,0,0,"technical","medium","exist"
"1645",3645,0.73,0.96,2,151,3,0,0,"technical","medium","exist"
"1646",3646,0.8,0.64,4,246,3,1,0,"technical","medium","exist"
"1647",3647,0.63,0.8,4,256,4,0,0,"management","medium","exist"
"1648",3648,0.95,0.59,4,235,3,0,0,"IT","medium","exist"
"1649",3649,0.75,0.83,4,229,2,0,0,"IT","medium","exist"
"1650",3650,0.64,0.72,5,158,3,0,0,"IT","medium","exist"
"1651",3651,0.72,1,3,197,3,0,0,"IT","medium","exist"
"1652",3652,0.72,0.53,3,151,4,0,0,"IT","medium","exist"
"1653",3653,0.85,0.63,3,167,3,0,0,"product_mng","medium","exist"
"1654",3654,0.24,0.94,4,146,4,0,0,"product_mng","medium","exist"
"1655",3655,0.69,0.53,4,281,4,0,0,"product_mng","medium","exist"
"1656",3656,0.69,0.7,3,212,2,0,0,"product_mng","medium","exist"
"1657",3657,0.87,0.59,5,261,3,0,0,"IT","medium","exist"
"1658",3658,0.78,0.91,3,238,2,0,0,"RandD","high","exist"
"1659",3659,0.76,0.74,4,235,3,0,0,"RandD","high","exist"
"1660",3660,0.35,0.7,3,107,3,0,0,"RandD","high","exist"
"1661",3661,0.88,0.59,6,243,4,0,0,"RandD","high","exist"
"1662",3662,0.78,0.79,5,233,3,1,0,"RandD","high","exist"
"1663",3663,0.36,0.42,3,206,5,0,0,"marketing","high","exist"
"1664",3664,0.24,0.7,5,172,4,0,0,"sales","high","exist"
"1665",3665,0.55,0.82,3,248,2,0,0,"accounting","high","exist"
"1666",3666,0.91,0.53,3,147,4,1,0,"support","high","exist"
"1667",3667,0.66,0.61,5,259,3,0,0,"technical","high","exist"
"1668",3668,0.57,0.82,3,267,3,0,0,"management","high","exist"
"1669",3669,0.64,0.64,3,263,3,0,0,"marketing","high","exist"
"1670",3670,0.63,0.98,3,160,4,1,0,"marketing","low","exist"
"1671",3671,0.49,0.92,5,176,3,0,0,"marketing","low","exist"
"1672",3672,0.7,0.7,3,224,3,0,0,"sales","low","exist"
"1673",3673,0.3,0.71,3,155,2,0,0,"sales","low","exist"
"1674",3674,0.51,0.52,3,132,3,0,0,"sales","low","exist"
"1675",3675,0.71,0.87,5,173,3,0,0,"sales","low","exist"
"1676",3676,0.64,0.67,3,165,3,0,0,"sales","low","exist"
"1677",3677,0.67,0.66,6,272,3,0,0,"sales","low","exist"
"1678",3678,0.83,0.94,5,156,3,1,0,"sales","low","exist"
"1679",3679,0.92,0.96,3,154,2,1,0,"sales","low","exist"
"1680",3680,0.84,0.53,3,237,3,1,0,"sales","low","exist"
"1681",3681,0.97,0.75,2,271,6,0,0,"sales","low","exist"
"1682",3682,0.19,0.91,6,152,3,0,0,"sales","low","exist"
"1683",3683,0.86,0.51,4,152,3,1,0,"sales","low","exist"
"1684",3684,0.71,0.69,3,172,3,0,0,"sales","low","exist"
"1685",3685,0.76,0.56,4,214,2,1,0,"sales","medium","exist"
"1686",3686,0.68,0.88,5,201,3,0,0,"sales","medium","exist"
"1687",3687,0.61,0.99,2,275,3,1,0,"sales","medium","exist"
"1688",3688,0.85,0.79,3,156,3,0,0,"sales","medium","exist"
"1689",3689,0.84,0.89,3,234,2,0,0,"sales","medium","exist"
"1690",3690,0.62,0.63,2,123,2,0,0,"sales","medium","exist"
"1691",3691,0.77,0.76,3,201,4,0,0,"accounting","medium","exist"
"1692",3692,0.79,0.93,5,160,4,1,0,"accounting","medium","exist"
"1693",3693,0.97,0.5,3,173,2,0,0,"accounting","medium","exist"
"1694",3694,0.59,0.61,4,141,2,1,0,"hr","medium","exist"
"1695",3695,0.93,0.62,3,231,6,0,0,"hr","medium","exist"
"1696",3696,0.85,0.54,4,174,2,0,0,"hr","medium","exist"
"1697",3697,0.7,0.57,4,211,3,0,0,"hr","high","exist"
"1698",3698,0.54,0.91,3,249,3,0,0,"technical","high","exist"
"1699",3699,0.42,0.86,2,108,5,0,0,"technical","high","exist"
"1700",3700,0.96,0.75,4,165,3,0,0,"technical","high","exist"
"1701",3701,0.58,0.8,3,181,4,0,0,"technical","high","exist"
"1702",3702,0.61,0.59,3,237,2,0,0,"technical","high","exist"
"1703",3703,0.62,0.97,5,180,2,0,0,"technical","high","exist"
"1704",3704,0.68,0.7,3,209,3,0,0,"technical","high","exist"
"1705",3705,0.6,0.73,3,191,5,1,0,"technical","low","exist"
"1706",3706,0.7,0.91,4,181,2,1,0,"technical","low","exist"
"1707",3707,0.65,0.55,4,250,2,0,0,"technical","low","exist"
"1708",3708,0.48,0.59,2,179,2,0,0,"technical","low","exist"
"1709",3709,0.75,0.73,3,225,4,0,0,"support","low","exist"
"1710",3710,0.69,0.78,5,267,2,1,0,"support","low","exist"
"1711",3711,0.34,0.46,2,169,2,1,0,"support","low","exist"
"1712",3712,0.59,0.49,4,164,3,0,0,"support","low","exist"
"1713",3713,0.94,0.95,4,155,3,0,0,"support","medium","exist"
"1714",3714,0.98,0.77,5,223,2,1,0,"support","medium","exist"
"1715",3715,0.72,0.66,3,160,3,0,0,"support","medium","exist"
"1716",3716,0.79,0.7,4,268,2,0,0,"support","medium","exist"
"1717",3717,0.54,0.61,4,151,4,1,0,"support","medium","exist"
"1718",3718,0.73,0.95,3,156,2,0,0,"support","medium","exist"
"1719",3719,0.82,0.58,4,136,3,0,0,"support","medium","exist"
"1720",3720,0.57,0.45,3,189,2,0,0,"technical","medium","exist"
"1721",3721,0.98,0.93,5,203,2,0,0,"technical","medium","exist"
"1722",3722,0.91,0.65,5,174,2,0,0,"technical","medium","exist"
"1723",3723,0.72,0.91,3,239,2,0,0,"management","medium","exist"
"1724",3724,0.48,0.73,5,152,6,0,0,"IT","medium","exist"
"1725",3725,0.57,0.86,4,268,6,0,0,"IT","medium","exist"
"1726",3726,0.86,0.96,4,146,3,0,0,"IT","medium","exist"
"1727",3727,0.63,0.85,3,232,4,0,0,"IT","medium","exist"
"1728",3728,0.81,0.52,3,255,5,0,0,"IT","medium","exist"
"1729",3729,0.21,0.37,4,129,4,0,0,"product_mng","medium","exist"
"1730",3730,0.9,0.66,3,261,3,1,0,"product_mng","medium","exist"
"1731",3731,0.79,0.78,3,101,2,1,0,"product_mng","medium","exist"
"1732",3732,0.95,0.49,4,229,3,0,0,"product_mng","medium","exist"
"1733",3733,0.29,0.43,3,162,4,0,0,"IT","medium","exist"
"1734",3734,0.81,0.52,4,270,3,0,0,"RandD","medium","exist"
"1735",3735,0.21,0.56,6,277,5,0,0,"RandD","medium","exist"
"1736",3736,0.7,0.83,5,261,4,0,0,"RandD","high","exist"
"1737",3737,0.66,0.64,4,204,2,0,0,"RandD","low","exist"
"1738",3738,0.16,0.86,2,215,2,0,0,"RandD","medium","exist"
"1739",3739,0.72,0.8,3,219,3,0,0,"marketing","medium","exist"
"1740",3740,0.51,0.5,3,147,4,0,0,"sales","medium","exist"
"1741",3741,0.59,0.63,5,243,2,0,0,"accounting","medium","exist"
"1742",3742,0.66,0.4,2,155,5,1,0,"support","medium","exist"
"1743",3743,0.69,0.84,3,253,2,0,0,"technical","medium","exist"
"1744",3744,0.69,0.94,4,235,3,0,0,"management","medium","exist"
"1745",3745,0.89,0.79,5,257,3,0,0,"marketing","medium","exist"
"1746",3746,0.52,0.56,4,225,2,0,0,"marketing","medium","exist"
"1747",3747,0.91,0.91,3,167,3,0,0,"marketing","medium","exist"
"1748",3748,0.96,0.53,5,224,4,0,0,"sales","low","exist"
"1749",3749,0.8,0.58,5,127,3,0,0,"sales","low","exist"
"1750",3750,0.55,0.77,3,256,3,1,0,"sales","low","exist"
"1751",3751,0.93,0.63,4,233,2,0,0,"sales","low","exist"
"1752",3752,0.93,0.86,4,169,4,0,0,"sales","low","exist"
"1753",3753,0.54,0.48,3,152,2,0,0,"sales","low","exist"
"1754",3754,0.48,0.76,5,236,2,0,0,"sales","low","exist"
"1755",3755,0.19,0.99,3,154,2,0,0,"sales","high","exist"
"1756",3756,0.95,0.71,3,223,2,0,0,"sales","low","exist"
"1757",3757,0.96,0.81,4,211,3,0,0,"sales","high","exist"
"1758",3758,0.63,0.89,4,192,2,0,0,"sales","high","exist"
"1759",3759,0.81,0.8,4,227,2,1,0,"sales","low","exist"
"1760",3760,0.5,0.88,4,265,3,1,0,"sales","low","exist"
"1761",3761,0.76,0.72,5,228,2,0,0,"sales","high","exist"
"1762",3762,0.84,0.49,4,152,3,0,0,"sales","low","exist"
"1763",3763,0.2,0.95,5,169,5,0,0,"sales","medium","exist"
"1764",3764,0.78,0.92,3,169,3,0,0,"sales","high","exist"
"1765",3765,0.8,0.68,4,157,3,0,0,"sales","medium","exist"
"1766",3766,0.94,0.57,4,251,2,1,0,"sales","medium","exist"
"1767",3767,0.44,0.74,5,253,2,0,0,"accounting","medium","exist"
"1768",3768,0.92,0.85,3,155,2,0,0,"accounting","medium","exist"
"1769",3769,0.54,0.8,3,232,2,0,0,"accounting","high","exist"
"1770",3770,0.56,0.56,4,195,2,0,0,"hr","medium","exist"
"1771",3771,0.66,0.69,4,198,3,1,0,"hr","medium","exist"
"1772",3772,0.8,0.91,3,246,3,0,0,"hr","medium","exist"
"1773",3773,0.6,0.81,2,214,3,0,0,"hr","high","exist"
"1774",3774,0.73,0.73,5,249,3,0,0,"technical","medium","exist"
"1775",3775,0.55,0.74,3,211,3,1,0,"technical","high","exist"
"1776",3776,0.7,0.71,5,269,3,0,0,"technical","low","exist"
"1777",3777,0.53,0.68,4,214,3,0,0,"technical","medium","exist"
"1778",3778,0.9,0.94,4,231,3,0,0,"technical","medium","exist"
"1779",3779,0.8,0.78,3,175,3,0,0,"technical","medium","exist"
"1780",3780,0.51,0.6,5,175,3,0,0,"technical","medium","exist"
"1781",3781,0.86,0.96,5,238,5,0,0,"technical","low","exist"
"1782",3782,0.63,0.79,4,228,4,0,0,"technical","low","exist"
"1783",3783,0.83,0.93,3,220,4,0,0,"technical","low","exist"
"1784",3784,0.85,0.55,4,233,2,1,0,"technical","low","exist"
"1785",3785,0.8,0.57,3,225,4,0,0,"support","low","exist"
"1786",3786,0.79,0.97,5,187,3,0,0,"support","low","exist"
"1787",3787,0.37,0.71,3,117,3,0,0,"support","low","exist"
"1788",3788,0.85,0.9,3,184,3,1,0,"support","low","exist"
"1789",3789,0.76,0.56,3,216,3,0,0,"support","low","exist"
"1790",3790,0.99,0.62,4,140,2,1,0,"support","low","exist"
"1791",3791,0.55,0.63,3,201,2,1,0,"support","high","exist"
"1792",3792,0.69,0.69,4,167,3,1,0,"support","low","exist"
"1793",3793,0.68,1,5,203,2,0,0,"support","low","exist"
"1794",3794,0.69,0.9,5,231,2,1,0,"support","low","exist"
"1795",3795,0.65,0.7,3,141,4,0,0,"support","low","exist"
"1796",3796,0.17,0.88,6,226,3,1,0,"technical","high","exist"
"1797",3797,0.48,0.65,3,199,2,0,0,"technical","low","exist"
"1798",3798,0.65,0.59,4,192,3,0,0,"technical","low","exist"
"1799",3799,0.57,0.49,4,247,2,0,0,"management","low","exist"
"1800",3800,0.8,0.67,3,164,3,0,0,"IT","high","exist"
"1801",3801,0.8,0.6,5,234,2,0,0,"IT","low","exist"
"1802",3802,0.5,0.6,2,177,3,1,0,"IT","medium","exist"
"1803",3803,0.95,0.87,3,208,2,0,0,"IT","high","exist"
"1804",3804,0.69,0.93,3,233,3,0,0,"IT","medium","exist"
"1805",3805,0.74,0.57,4,172,3,0,0,"product_mng","high","exist"
"1806",3806,0.68,0.59,3,141,3,0,0,"product_mng","medium","exist"
"1807",3807,0.68,0.55,3,213,3,0,0,"product_mng","medium","exist"
"1808",3808,0.65,0.37,5,128,3,1,0,"product_mng","medium","exist"
"1809",3809,0.85,0.92,4,151,3,1,0,"IT","medium","exist"
"1810",3810,0.49,0.48,4,247,3,0,0,"RandD","medium","exist"
"1811",3811,0.19,0.8,6,143,4,0,0,"RandD","medium","exist"
"1812",3812,0.95,0.61,3,164,3,0,0,"RandD","medium","exist"
"1813",3813,0.63,0.62,3,183,3,0,0,"RandD","medium","exist"
"1814",3814,0.83,0.8,4,274,3,0,0,"RandD","high","exist"
"1815",3815,0.62,0.78,4,261,2,0,0,"marketing","low","exist"
"1816",3816,0.74,0.98,5,200,3,0,0,"sales","medium","exist"
"1817",3817,0.7,0.87,5,177,2,0,0,"accounting","medium","exist"
"1818",3818,0.28,0.38,5,139,4,0,0,"support","medium","exist"
"1819",3819,0.39,0.56,6,228,5,0,0,"technical","medium","exist"
"1820",3820,0.62,0.63,3,230,2,0,0,"management","medium","exist"
"1821",3821,0.79,0.54,5,212,4,0,0,"marketing","medium","exist"
"1822",3822,0.87,0.74,4,265,4,0,0,"marketing","medium","exist"
"1823",3823,0.29,0.75,3,124,4,0,0,"marketing","medium","exist"
"1824",3824,0.37,0.88,4,155,3,0,0,"sales","medium","exist"
"1825",3825,0.68,0.55,4,215,4,0,0,"sales","high","exist"
"1826",3826,0.9,0.51,4,214,2,0,0,"sales","low","exist"
"1827",3827,0.97,0.65,3,181,3,0,0,"sales","low","exist"
"1828",3828,0.6,0.77,4,229,2,0,0,"sales","low","exist"
"1829",3829,0.58,0.79,5,262,2,0,0,"sales","high","exist"
"1830",3830,0.83,0.86,3,165,3,0,0,"sales","low","exist"
"1831",3831,0.83,0.73,5,136,3,0,0,"sales","low","exist"
"1832",3832,0.94,0.71,4,151,2,1,0,"sales","low","exist"
"1833",3833,0.69,0.9,5,226,4,0,0,"sales","high","exist"
"1834",3834,0.81,0.81,5,215,2,0,0,"sales","low","exist"
"1835",3835,0.19,0.5,6,143,5,1,0,"sales","low","exist"
"1836",3836,0.18,0.64,3,223,4,1,0,"sales","low","exist"
"1837",3837,0.58,0.63,4,271,3,0,0,"sales","low","exist"
"1838",3838,0.76,0.65,3,220,3,0,0,"sales","low","exist"
"1839",3839,0.26,0.47,6,182,4,0,0,"sales","low","exist"
"1840",3840,0.78,0.87,3,190,3,0,0,"sales","low","exist"
"1841",3841,0.71,0.56,3,198,3,0,0,"sales","medium","exist"
"1842",3842,0.76,0.61,3,141,2,0,0,"sales","medium","exist"
"1843",3843,0.44,0.81,3,248,2,0,0,"accounting","medium","exist"
"1844",3844,0.26,0.69,2,168,5,0,0,"accounting","medium","exist"
"1845",3845,0.75,0.57,5,162,3,0,0,"accounting","medium","exist"
"1846",3846,0.64,0.9,4,211,3,0,0,"hr","medium","exist"
"1847",3847,0.79,0.71,4,271,3,0,0,"hr","medium","exist"
"1848",3848,0.96,0.61,3,271,2,0,0,"hr","medium","exist"
"1849",3849,0.63,1,5,244,2,0,0,"hr","medium","exist"
"1850",3850,0.65,0.55,5,261,3,1,0,"technical","medium","exist"
"1851",3851,0.76,0.81,3,225,3,0,0,"technical","medium","exist"
"1852",3852,0.26,0.9,4,266,5,0,0,"technical","medium","exist"
"1853",3853,0.84,0.73,3,146,5,1,0,"technical","high","exist"
"1854",3854,0.74,0.77,3,233,3,0,0,"technical","low","exist"
"1855",3855,0.53,0.45,4,180,3,0,0,"technical","medium","exist"
"1856",3856,0.7,0.56,5,208,3,0,0,"technical","medium","exist"
"1857",3857,0.62,0.69,4,165,4,1,0,"technical","medium","exist"
"1858",3858,0.71,0.68,4,221,4,0,0,"technical","medium","exist"
"1859",3859,0.5,0.65,3,215,2,0,0,"technical","low","exist"
"1860",3860,0.53,0.5,6,264,6,0,0,"technical","low","exist"
"1861",3861,0.88,0.89,2,160,4,0,0,"support","low","exist"
"1862",3862,0.79,0.83,2,192,3,0,0,"support","low","exist"
"1863",3863,0.69,0.89,5,201,2,0,0,"support","low","exist"
"1864",3864,0.57,0.96,4,264,2,0,0,"support","low","exist"
"1865",3865,0.68,0.8,4,186,3,1,0,"support","low","exist"
"1866",3866,0.6,0.82,4,272,3,0,0,"support","low","exist"
"1867",3867,0.7,0.9,4,216,3,0,0,"support","low","exist"
"1868",3868,0.71,0.76,4,142,2,1,0,"support","low","exist"
"1869",3869,0.49,0.51,4,133,3,0,0,"support","low","exist"
"1870",3870,0.74,0.7,4,260,2,0,0,"support","low","exist"
"1871",3871,0.2,0.93,4,101,5,0,0,"support","low","exist"
"1872",3872,0.57,0.5,4,230,2,0,0,"technical","low","exist"
"1873",3873,0.83,0.48,3,242,4,0,0,"technical","low","exist"
"1874",3874,0.22,0.64,3,193,2,0,0,"technical","low","exist"
"1875",3875,0.67,0.57,2,234,4,0,0,"management","low","exist"
"1876",3876,0.54,0.84,3,132,3,0,0,"IT","low","exist"
"1877",3877,0.54,0.53,4,183,3,0,0,"IT","low","exist"
"1878",3878,0.64,0.61,4,191,4,0,0,"IT","low","exist"
"1879",3879,0.23,0.84,5,140,4,0,0,"IT","low","exist"
"1880",3880,0.88,0.87,5,257,3,0,0,"IT","medium","exist"
"1881",3881,0.83,0.54,4,219,3,0,0,"product_mng","medium","exist"
"1882",3882,0.88,0.67,5,141,2,0,0,"product_mng","medium","exist"
"1883",3883,0.9,0.85,5,162,2,0,0,"product_mng","medium","exist"
"1884",3884,0.49,0.61,3,181,3,0,0,"product_mng","medium","exist"
"1885",3885,0.55,0.49,3,191,3,0,0,"IT","medium","exist"
"1886",3886,0.92,0.69,4,149,6,1,0,"RandD","medium","exist"
"1887",3887,0.72,0.67,3,164,4,0,0,"RandD","medium","exist"
"1888",3888,0.63,0.5,4,235,3,0,0,"RandD","medium","exist"
"1889",3889,0.98,0.66,3,199,3,0,0,"RandD","medium","exist"
"1890",3890,0.76,0.52,3,248,3,0,0,"RandD","medium","exist"
"1891",3891,0.42,0.56,2,103,3,1,0,"marketing","medium","exist"
"1892",3892,0.58,0.65,4,248,4,0,0,"marketing","high","exist"
"1893",3893,0.85,0.82,3,153,3,1,0,"sales","low","exist"
"1894",3894,0.67,0.95,4,241,4,0,0,"accounting","medium","exist"
"1895",3895,0.58,1,4,258,3,0,0,"support","medium","exist"
"1896",3896,0.84,0.76,5,172,5,1,0,"technical","medium","exist"
"1897",3897,0.56,0.68,3,149,2,0,0,"management","medium","exist"
"1898",3898,0.63,0.83,4,217,3,0,0,"marketing","low","exist"
"1899",3899,0.51,0.92,3,243,3,0,0,"marketing","low","exist"
"1900",3900,0.95,0.81,5,120,3,0,0,"marketing","low","exist"
"1901",3901,0.85,0.55,3,137,3,1,0,"sales","low","exist"
"1902",3902,0.73,0.78,5,150,3,0,0,"sales","low","exist"
"1903",3903,0.2,0.49,5,199,5,0,0,"sales","low","exist"
"1904",3904,0.78,0.79,3,203,2,0,0,"sales","low","exist"
"1905",3905,0.83,0.8,3,273,3,0,0,"sales","low","exist"
"1906",3906,0.87,0.56,2,214,3,1,0,"sales","low","exist"
"1907",3907,0.98,0.99,5,141,3,0,0,"sales","low","exist"
"1908",3908,1,0.81,5,243,3,0,0,"sales","low","exist"
"1909",3909,0.7,0.55,4,159,3,0,0,"sales","low","exist"
"1910",3910,0.62,0.88,5,146,3,0,0,"sales","low","exist"
"1911",3911,0.67,0.72,4,159,2,0,0,"sales","low","exist"
"1912",3912,0.86,0.96,4,196,3,0,0,"sales","low","exist"
"1913",3913,0.44,0.57,5,183,2,1,0,"sales","low","exist"
"1914",3914,0.19,0.86,4,192,4,0,0,"sales","low","exist"
"1915",3915,0.76,0.83,4,246,2,0,0,"sales","low","exist"
"1916",3916,0.89,0.93,5,191,4,0,0,"sales","low","exist"
"1917",3917,0.25,0.97,3,158,3,0,0,"sales","low","exist"
"1918",3918,0.68,1,4,167,2,0,0,"sales","low","exist"
"1919",3919,0.38,0.5,2,187,2,1,0,"sales","medium","exist"
"1920",3920,0.64,0.95,4,199,4,0,0,"accounting","medium","exist"
"1921",3921,0.76,0.62,4,157,3,0,0,"accounting","medium","exist"
"1922",3922,0.26,0.45,2,157,3,0,0,"accounting","medium","exist"
"1923",3923,0.69,0.67,3,178,2,0,0,"hr","medium","exist"
"1924",3924,0.98,0.66,3,154,2,0,0,"hr","medium","exist"
"1925",3925,0.86,0.83,5,208,2,1,0,"hr","medium","exist"
"1926",3926,0.14,0.68,4,273,5,1,0,"hr","medium","exist"
"1927",3927,0.58,0.63,4,159,3,0,0,"technical","medium","exist"
"1928",3928,0.63,0.97,4,234,2,0,0,"technical","medium","exist"
"1929",3929,0.7,0.91,5,139,3,1,0,"technical","medium","exist"
"1930",3930,0.73,0.62,4,210,4,0,0,"technical","medium","exist"
"1931",3931,0.89,0.52,3,164,3,0,0,"technical","high","exist"
"1932",3932,0.17,0.61,3,241,5,1,0,"technical","low","exist"
"1933",3933,0.86,0.73,5,259,2,0,0,"technical","medium","exist"
"1934",3934,0.59,0.73,3,159,3,1,0,"technical","medium","exist"
"1935",3935,0.22,0.51,4,131,2,0,0,"technical","medium","exist"
"1936",3936,0.55,0.57,3,266,2,0,0,"technical","medium","exist"
"1937",3937,0.74,0.6,3,153,2,0,0,"technical","low","exist"
"1938",3938,0.55,0.54,3,253,2,0,0,"support","low","exist"
"1939",3939,0.67,0.94,4,141,2,1,0,"support","low","exist"
"1940",3940,0.64,0.8,3,199,3,0,0,"support","low","exist"
"1941",3941,0.58,0.71,3,205,3,0,0,"support","low","exist"
"1942",3942,0.9,0.6,4,252,3,0,0,"support","low","exist"
"1943",3943,0.89,0.9,4,153,3,0,0,"support","low","exist"
"1944",3944,0.74,0.37,2,171,4,0,0,"support","low","exist"
"1945",3945,0.78,0.91,5,150,4,0,0,"support","low","exist"
"1946",3946,0.97,0.53,4,247,3,0,0,"support","low","exist"
"1947",3947,0.52,0.92,4,150,2,1,0,"support","low","exist"
"1948",3948,0.99,0.86,4,206,2,0,0,"support","low","exist"
"1949",3949,0.76,0.44,4,192,4,1,0,"technical","low","exist"
"1950",3950,0.69,0.96,5,164,3,0,0,"technical","low","exist"
"1951",3951,0.59,0.69,3,186,3,0,0,"technical","low","exist"
"1952",3952,0.95,0.63,3,249,3,0,0,"management","low","exist"
"1953",3953,0.69,0.81,3,214,2,0,0,"IT","low","exist"
"1954",3954,0.76,0.75,4,193,4,0,0,"IT","low","exist"
"1955",3955,0.7,0.84,2,114,4,0,0,"IT","low","exist"
"1956",3956,0.87,0.6,2,122,2,0,0,"IT","low","exist"
"1957",3957,0.44,0.55,3,170,3,1,0,"IT","low","exist"
"1958",3958,0.54,0.91,3,151,3,0,0,"product_mng","medium","exist"
"1959",3959,0.55,0.75,3,156,3,0,0,"product_mng","medium","exist"
"1960",3960,0.81,0.75,5,170,3,1,0,"product_mng","medium","exist"
"1961",3961,0.5,0.55,3,188,2,0,0,"product_mng","medium","exist"
"1962",3962,0.93,0.74,4,201,3,1,0,"IT","medium","exist"
"1963",3963,0.64,0.5,6,254,6,0,0,"RandD","medium","exist"
"1964",3964,0.57,0.78,3,206,4,0,0,"RandD","medium","exist"
"1965",3965,0.95,0.74,4,216,3,0,0,"RandD","medium","exist"
"1966",3966,0.86,0.67,5,200,3,0,0,"RandD","medium","exist"
"1967",3967,0.82,0.58,4,202,2,0,0,"RandD","medium","exist"
"1968",3968,0.18,0.79,4,217,4,0,0,"RandD","medium","exist"
"1969",3969,0.58,0.7,5,151,4,0,0,"marketing","medium","exist"
"1970",3970,0.57,0.56,3,224,2,0,0,"sales","high","exist"
"1971",3971,0.94,0.83,5,148,3,0,0,"accounting","low","exist"
"1972",3972,0.2,0.97,6,224,5,1,0,"support","medium","exist"
"1973",3973,0.73,0.75,2,243,5,1,0,"technical","medium","exist"
"1974",3974,0.94,0.8,4,238,3,1,0,"management","medium","exist"
"1975",3975,0.99,0.61,3,254,3,0,0,"marketing","medium","exist"
"1976",3976,0.22,0.49,4,218,4,0,0,"marketing","low","exist"
"1977",3977,0.67,0.66,3,237,3,0,0,"marketing","low","exist"
"1978",3978,0.49,0.67,4,185,2,0,0,"sales","low","exist"
"1979",3979,0.78,0.95,4,184,3,0,0,"sales","low","exist"
"1980",3980,0.15,0.74,2,176,3,1,0,"sales","low","exist"
"1981",3981,0.91,0.89,4,260,3,0,0,"sales","low","exist"
"1982",3982,0.7,0.78,4,254,4,0,0,"sales","low","exist"
"1983",3983,0.6,0.95,3,221,3,0,0,"sales","low","exist"
"1984",3984,0.59,0.57,5,257,2,0,0,"sales","low","exist"
"1985",3985,0.59,0.93,3,265,3,1,0,"sales","low","exist"
"1986",3986,0.55,0.64,3,138,3,1,0,"sales","low","exist"
"1987",3987,0.42,0.75,3,163,2,1,0,"sales","low","exist"
"1988",3988,0.57,0.81,3,144,3,0,0,"sales","low","exist"
"1989",3989,0.72,0.51,3,146,2,1,0,"sales","low","exist"
"1990",3990,0.83,0.81,3,242,6,0,0,"sales","low","exist"
"1991",3991,0.89,0.93,4,249,3,0,0,"sales","low","exist"
"1992",3992,0.55,0.59,3,245,3,0,0,"sales","low","exist"
"1993",3993,0.53,0.75,4,201,2,0,0,"sales","low","exist"
"1994",3994,0.74,0.59,4,177,4,1,0,"sales","medium","exist"
"1995",3995,0.63,0.98,4,160,2,0,0,"sales","medium","exist"
"1996",3996,0.63,0.4,6,177,2,1,0,"sales","medium","exist"
"1997",3997,0.61,0.97,4,153,3,0,0,"accounting","medium","exist"
"1998",3998,0.76,0.79,3,197,5,0,0,"accounting","medium","exist"
"1999",3999,0.67,0.82,4,246,4,0,0,"accounting","medium","exist"
"2000",4000,0.51,0.95,4,258,3,0,0,"hr","medium","exist"
"2001",4001,0.67,0.55,3,137,3,0,0,"hr","medium","exist"
"2002",4002,0.16,0.94,3,178,4,0,0,"hr","medium","exist"
"2003",4003,0.34,0.85,4,123,4,0,0,"hr","medium","exist"
"2004",4004,0.58,0.73,5,162,3,1,0,"technical","medium","exist"
"2005",4005,0.5,0.71,5,210,5,0,0,"technical","medium","exist"
"2006",4006,0.56,0.48,4,197,3,1,0,"technical","high","exist"
"2007",4007,0.56,0.98,3,220,3,0,0,"technical","low","exist"
"2008",4008,0.74,0.71,4,133,2,0,0,"technical","medium","exist"
"2009",4009,0.69,0.99,3,198,2,0,0,"technical","medium","exist"
"2010",4010,0.15,0.81,4,160,5,0,0,"technical","medium","exist"
"2011",4011,0.58,0.67,2,180,2,0,0,"technical","medium","exist"
"2012",4012,0.8,0.79,3,217,3,0,0,"technical","low","exist"
"2013",4013,0.5,0.57,3,223,3,0,0,"technical","low","exist"
"2014",4014,0.66,0.92,4,229,2,0,0,"technical","low","exist"
"2015",4015,0.81,0.78,4,159,3,0,0,"support","low","exist"
"2016",4016,0.14,0.68,5,253,5,0,0,"support","low","exist"
"2017",4017,0.92,0.85,3,235,2,0,0,"support","low","exist"
"2018",4018,0.69,0.91,4,186,3,0,0,"support","low","exist"
"2019",4019,0.66,0.99,4,263,2,0,0,"support","low","exist"
"2020",4020,0.85,0.39,4,239,4,0,0,"support","low","exist"
"2021",4021,0.83,0.69,4,151,2,0,0,"support","low","exist"
"2022",4022,1,0.49,3,106,4,0,0,"support","low","exist"
"2023",4023,0.68,0.65,3,183,4,0,0,"support","low","exist"
"2024",4024,0.26,0.97,3,125,2,0,0,"support","low","exist"
"2025",4025,0.35,0.41,3,157,2,0,0,"support","low","exist"
"2026",4026,0.12,0.58,3,241,2,0,0,"technical","low","exist"
"2027",4027,0.91,0.93,4,184,3,0,0,"technical","low","exist"
"2028",4028,0.65,0.67,4,144,3,0,0,"technical","low","exist"
"2029",4029,0.78,0.72,3,255,4,1,0,"management","low","exist"
"2030",4030,0.61,0.82,3,261,4,0,0,"IT","low","exist"
"2031",4031,0.64,0.51,4,207,2,0,0,"IT","low","exist"
"2032",4032,0.58,0.73,4,205,4,0,0,"IT","low","exist"
"2033",4033,0.63,0.76,4,217,2,1,0,"IT","medium","exist"
"2034",4034,0.94,0.6,4,215,2,1,0,"IT","medium","exist"
"2035",4035,0.26,0.81,5,139,4,0,0,"product_mng","medium","exist"
"2036",4036,0.59,0.69,3,146,3,0,0,"product_mng","medium","exist"
"2037",4037,0.78,0.8,4,175,3,0,0,"product_mng","medium","exist"
"2038",4038,0.86,0.64,5,188,3,0,0,"product_mng","medium","exist"
"2039",4039,0.79,0.73,5,155,3,0,0,"IT","medium","exist"
"2040",4040,0.89,0.73,4,171,4,0,0,"RandD","medium","exist"
"2041",4041,0.34,0.99,4,184,3,0,0,"RandD","medium","exist"
"2042",4042,0.29,0.77,3,164,6,0,0,"RandD","medium","exist"
"2043",4043,0.71,0.89,3,244,4,0,0,"RandD","medium","exist"
"2044",4044,0.93,0.58,4,222,2,0,0,"RandD","medium","exist"
"2045",4045,0.94,0.76,4,231,3,1,0,"RandD","high","exist"
"2046",4046,0.91,0.72,3,213,3,0,0,"marketing","low","exist"
"2047",4047,0.71,0.96,4,144,4,0,0,"sales","medium","exist"
"2048",4048,0.67,0.81,3,221,4,1,0,"accounting","medium","exist"
"2049",4049,0.54,0.42,3,138,3,1,0,"support","medium","exist"
"2050",4050,0.89,0.58,3,183,4,0,0,"technical","medium","exist"
"2051",4051,0.94,0.91,3,250,3,1,0,"management","low","exist"
"2052",4052,0.15,0.89,3,219,6,0,0,"marketing","low","exist"
"2053",4053,0.45,0.92,6,164,4,1,0,"marketing","low","exist"
"2054",4054,0.92,0.67,4,161,4,1,0,"marketing","low","exist"
"2055",4055,0.63,0.56,4,274,5,1,0,"sales","low","exist"
"2056",4056,0.8,0.66,4,181,3,1,0,"sales","low","exist"
"2057",4057,0.45,0.72,3,148,2,0,0,"sales","low","exist"
"2058",4058,0.72,0.54,3,226,2,1,0,"sales","low","exist"
"2059",4059,0.84,0.36,6,256,5,0,0,"sales","low","exist"
"2060",4060,0.61,0.51,3,141,2,0,0,"sales","low","exist"
"2061",4061,0.63,0.88,3,177,3,0,0,"sales","low","exist"
"2062",4062,0.49,0.54,4,173,2,0,0,"sales","low","exist"
"2063",4063,0.68,0.51,2,196,3,0,0,"sales","low","exist"
"2064",4064,0.64,0.85,5,179,2,0,0,"sales","low","exist"
"2065",4065,0.61,0.64,4,201,3,0,0,"sales","low","exist"
"2066",4066,0.83,0.55,5,247,3,0,0,"sales","low","exist"
"2067",4067,0.94,0.51,5,238,2,0,0,"sales","low","exist"
"2068",4068,0.27,0.66,3,133,6,0,0,"sales","low","exist"
"2069",4069,0.6,0.49,2,194,4,0,0,"sales","low","exist"
"2070",4070,0.83,0.69,4,232,2,0,0,"sales","low","exist"
"2071",4071,0.53,0.75,3,232,3,0,0,"sales","low","exist"
"2072",4072,0.79,0.97,4,204,3,0,0,"sales","medium","exist"
"2073",4073,0.96,0.49,4,252,3,1,0,"sales","medium","exist"
"2074",4074,0.5,0.56,3,164,3,0,0,"accounting","medium","exist"
"2075",4075,0.68,0.81,5,266,2,0,0,"accounting","medium","exist"
"2076",4076,0.24,0.54,4,226,4,1,0,"accounting","medium","exist"
"2077",4077,0.8,0.8,4,252,6,0,0,"hr","medium","exist"
"2078",4078,0.62,0.74,4,155,3,0,0,"hr","medium","exist"
"2079",4079,1,0.85,3,202,2,0,0,"hr","medium","exist"
"2080",4080,0.64,0.75,3,214,3,0,0,"hr","medium","exist"
"2081",4081,0.51,0.55,4,267,3,0,0,"technical","medium","exist"
"2082",4082,0.74,0.5,4,265,3,0,0,"technical","medium","exist"
"2083",4083,0.49,0.99,3,221,2,1,0,"technical","medium","exist"
"2084",4084,0.96,0.83,4,132,6,0,0,"technical","high","exist"
"2085",4085,0.59,0.62,4,227,4,1,0,"technical","low","exist"
"2086",4086,0.84,0.57,4,234,3,0,0,"technical","medium","exist"
"2087",4087,0.16,0.84,3,238,6,0,0,"technical","medium","exist"
"2088",4088,0.62,0.75,3,149,2,1,0,"technical","medium","exist"
"2089",4089,0.79,0.67,4,152,3,0,0,"technical","medium","exist"
"2090",4090,0.53,0.83,4,249,3,1,0,"technical","low","exist"
"2091",4091,0.17,0.48,6,270,5,0,0,"technical","low","exist"
"2092",4092,0.57,0.76,3,200,2,0,0,"support","low","exist"
"2093",4093,0.65,0.82,4,207,2,0,0,"support","low","exist"
"2094",4094,0.69,0.74,4,266,2,1,0,"support","low","exist"
"2095",4095,0.3,0.8,6,250,3,0,0,"support","low","exist"
"2096",4096,0.99,0.92,3,220,3,0,0,"support","low","exist"
"2097",4097,0.5,0.69,4,177,2,0,0,"support","low","exist"
"2098",4098,0.54,0.96,3,147,2,0,0,"support","medium","exist"
"2099",4099,0.69,0.57,4,227,4,0,0,"support","medium","exist"
"2100",4100,0.5,0.85,4,272,2,0,0,"support","medium","exist"
"2101",4101,0.66,0.73,3,244,3,0,0,"support","medium","exist"
"2102",4102,1,0.93,5,182,3,1,0,"support","medium","exist"
"2103",4103,0.15,0.61,3,146,6,1,0,"technical","medium","exist"
"2104",4104,0.31,0.54,5,259,5,1,0,"technical","medium","exist"
"2105",4105,0.65,0.49,4,184,4,0,0,"technical","medium","exist"
"2106",4106,0.72,0.69,4,149,2,0,0,"management","medium","exist"
"2107",4107,0.84,0.85,4,185,3,1,0,"IT","medium","exist"
"2108",4108,0.55,0.8,3,254,2,0,0,"IT","medium","exist"
"2109",4109,0.6,0.58,4,249,3,0,0,"IT","medium","exist"
"2110",4110,0.84,0.48,4,269,2,0,0,"IT","medium","exist"
"2111",4111,0.68,0.87,3,168,3,0,0,"IT","medium","exist"
"2112",4112,0.13,1,6,225,3,0,0,"product_mng","medium","exist"
"2113",4113,0.88,0.98,3,160,2,0,0,"product_mng","medium","exist"
"2114",4114,0.64,0.57,5,149,3,1,0,"product_mng","medium","exist"
"2115",4115,0.19,0.95,5,193,3,0,0,"product_mng","medium","exist"
"2116",4116,0.83,0.88,5,171,3,1,0,"IT","medium","exist"
"2117",4117,0.17,0.41,4,232,3,1,0,"RandD","medium","exist"
"2118",4118,0.44,0.74,4,250,6,0,0,"RandD","medium","exist"
"2119",4119,0.52,0.55,4,243,2,0,0,"RandD","medium","exist"
"2120",4120,0.66,0.62,4,250,2,0,0,"RandD","medium","exist"
"2121",4121,0.96,0.55,4,153,3,0,0,"RandD","medium","exist"
"2122",4122,0.19,0.5,5,126,3,1,0,"RandD","medium","exist"
"2123",4123,0.86,1,3,192,3,0,0,"marketing","high","exist"
"2124",4124,0.65,0.57,3,137,4,1,0,"sales","high","exist"
"2125",4125,0.61,0.97,3,199,3,1,0,"accounting","high","exist"
"2126",4126,0.53,0.96,3,181,3,1,0,"support","high","exist"
"2127",4127,0.14,0.76,5,260,6,0,0,"technical","high","exist"
"2128",4128,0.74,1,4,199,4,0,0,"management","high","exist"
"2129",4129,0.47,0.62,6,171,5,1,0,"marketing","high","exist"
"2130",4130,0.6,0.56,4,260,3,0,0,"marketing","high","exist"
"2131",4131,0.81,0.58,3,243,4,0,0,"marketing","high","exist"
"2132",4132,0.95,0.59,2,154,4,0,0,"sales","high","exist"
"2133",4133,0.98,0.8,2,201,2,0,0,"sales","high","exist"
"2134",4134,0.52,0.8,5,234,3,0,0,"sales","high","exist"
"2135",4135,1,0.97,3,216,3,1,0,"sales","low","exist"
"2136",4136,0.16,0.74,5,205,4,1,0,"sales","low","exist"
"2137",4137,0.19,0.83,3,225,4,0,0,"sales","low","exist"
"2138",4138,0.94,0.87,3,229,3,0,0,"sales","low","exist"
"2139",4139,0.58,0.43,6,132,3,0,0,"sales","low","exist"
"2140",4140,0.92,0.69,4,180,3,0,0,"sales","low","exist"
"2141",4141,0.65,0.8,4,164,4,0,0,"sales","low","exist"
"2142",4142,0.74,0.74,5,175,3,0,0,"sales","low","exist"
"2143",4143,0.6,0.53,5,103,2,0,0,"sales","low","exist"
"2144",4144,1,1,5,142,4,0,0,"sales","low","exist"
"2145",4145,0.81,0.96,4,212,3,0,0,"sales","low","exist"
"2146",4146,0.54,0.67,2,129,3,1,0,"sales","low","exist"
"2147",4147,0.18,0.81,5,140,4,0,0,"sales","low","exist"
"2148",4148,0.82,0.88,4,149,2,0,0,"sales","low","exist"
"2149",4149,0.9,0.96,2,258,2,0,0,"sales","low","exist"
"2150",4150,0.62,0.62,4,136,2,0,0,"sales","medium","exist"
"2151",4151,0.77,0.76,2,216,3,1,0,"accounting","medium","exist"
"2152",4152,0.58,0.86,3,151,2,0,0,"accounting","medium","exist"
"2153",4153,0.27,0.47,5,217,6,0,0,"accounting","medium","exist"
"2154",4154,0.78,0.72,4,219,3,0,0,"hr","medium","exist"
"2155",4155,0.98,0.66,3,150,3,0,0,"hr","medium","exist"
"2156",4156,0.75,0.5,4,98,4,0,0,"hr","medium","exist"
"2157",4157,0.74,0.67,5,140,3,0,0,"hr","medium","exist"
"2158",4158,0.9,0.99,3,259,3,1,0,"technical","medium","exist"
"2159",4159,0.73,0.71,5,215,6,1,0,"technical","medium","exist"
"2160",4160,0.9,0.62,4,258,3,0,0,"technical","medium","exist"
"2161",4161,0.68,0.91,4,162,3,1,0,"technical","medium","exist"
"2162",4162,0.9,0.74,3,260,4,0,0,"technical","high","exist"
"2163",4163,0.64,0.93,5,198,2,0,0,"technical","high","exist"
"2164",4164,0.63,0.9,2,242,3,1,0,"technical","high","exist"
"2165",4165,0.87,0.89,4,219,4,0,0,"technical","high","exist"
"2166",4166,0.6,0.55,4,204,3,0,0,"technical","high","exist"
"2167",4167,0.7,0.74,3,157,3,0,0,"technical","high","exist"
"2168",4168,0.15,0.94,5,244,6,0,0,"technical","high","exist"
"2169",4169,0.45,0.75,3,122,3,0,0,"support","high","exist"
"2170",4170,0.43,0.9,5,282,5,0,0,"support","low","exist"
"2171",4171,0.92,0.46,5,195,5,0,0,"support","low","exist"
"2172",4172,0.72,0.81,4,134,2,0,0,"support","low","exist"
"2173",4173,0.94,0.84,3,210,2,0,0,"support","low","exist"
"2174",4174,0.74,0.52,3,170,3,0,0,"support","low","exist"
"2175",4175,0.48,0.71,2,140,3,1,0,"support","low","exist"
"2176",4176,0.84,0.76,5,215,2,1,0,"support","low","exist"
"2177",4177,0.53,0.86,3,150,3,0,0,"support","low","exist"
"2178",4178,0.45,0.88,5,268,3,1,0,"support","medium","exist"
"2179",4179,0.95,0.67,4,270,4,0,0,"support","medium","exist"
"2180",4180,0.95,0.58,3,163,3,0,0,"technical","medium","exist"
"2181",4181,0.7,0.85,3,188,2,0,0,"technical","medium","exist"
"2182",4182,0.19,0.93,3,110,4,0,0,"technical","medium","exist"
"2183",4183,0.74,0.91,5,135,3,0,0,"management","medium","exist"
"2184",4184,0.54,0.59,4,228,3,0,0,"IT","medium","exist"
"2185",4185,0.5,0.69,5,172,2,0,0,"IT","medium","exist"
"2186",4186,0.95,0.72,3,168,5,0,0,"IT","medium","exist"
"2187",4187,0.69,0.69,5,160,3,0,0,"IT","medium","exist"
"2188",4188,0.24,0.74,5,282,3,0,0,"IT","medium","exist"
"2189",4189,0.96,0.7,5,184,2,1,0,"product_mng","medium","exist"
"2190",4190,0.61,0.5,4,217,4,1,0,"product_mng","medium","exist"
"2191",4191,0.76,0.53,6,281,3,0,0,"product_mng","medium","exist"
"2192",4192,0.61,0.64,3,245,2,0,0,"product_mng","medium","exist"
"2193",4193,0.37,0.48,2,285,5,0,0,"IT","medium","exist"
"2194",4194,0.81,0.9,4,165,3,0,0,"RandD","medium","exist"
"2195",4195,0.59,0.9,3,146,3,0,0,"RandD","medium","exist"
"2196",4196,0.73,0.82,3,133,3,0,0,"RandD","medium","exist"
"2197",4197,0.87,0.57,2,181,6,0,0,"RandD","medium","exist"
"2198",4198,0.67,0.66,6,152,3,0,0,"RandD","medium","exist"
"2199",4199,0.96,0.78,2,284,3,0,0,"RandD","medium","exist"
"2200",4200,0.98,0.51,3,157,3,0,0,"marketing","medium","exist"
"2201",4201,0.5,0.58,4,165,2,0,0,"sales","high","exist"
"2202",4202,0.49,0.71,3,255,2,1,0,"accounting","low","exist"
"2203",4203,0.13,0.65,4,194,4,0,0,"support","medium","exist"
"2204",4204,0.57,0.68,3,102,6,0,0,"technical","medium","exist"
"2205",4205,0.51,0.94,4,247,2,0,0,"management","medium","exist"
"2206",4206,0.99,0.69,3,206,2,0,0,"marketing","medium","exist"
"2207",4207,0.8,0.77,3,171,2,0,0,"marketing","medium","exist"
"2208",4208,0.56,0.39,5,263,4,0,0,"marketing","medium","exist"
"2209",4209,0.96,0.84,4,272,4,1,0,"sales","medium","exist"
"2210",4210,0.96,0.5,3,158,2,1,0,"sales","medium","exist"
"2211",4211,0.87,0.53,6,216,6,1,0,"sales","medium","exist"
"2212",4212,0.61,0.9,5,113,3,0,0,"sales","medium","exist"
"2213",4213,0.18,0.6,3,140,4,0,0,"sales","low","exist"
"2214",4214,0.24,0.77,5,178,5,0,0,"sales","low","exist"
"2215",4215,0.86,0.48,4,153,3,0,0,"sales","low","exist"
"2216",4216,0.25,0.62,6,271,5,0,0,"sales","low","exist"
"2217",4217,0.25,0.82,3,269,5,0,0,"sales","low","exist"
"2218",4218,0.63,0.55,3,160,3,0,0,"sales","low","exist"
"2219",4219,0.89,0.68,4,258,2,1,0,"sales","low","exist"
"2220",4220,0.87,0.82,4,251,3,0,0,"sales","high","exist"
"2221",4221,0.87,0.78,3,170,2,0,0,"sales","low","exist"
"2222",4222,0.71,0.99,4,238,4,1,0,"sales","high","exist"
"2223",4223,0.98,0.87,4,152,2,0,0,"sales","high","exist"
"2224",4224,0.16,0.51,3,262,6,0,0,"sales","low","exist"
"2225",4225,0.83,0.68,4,273,2,0,0,"sales","low","exist"
"2226",4226,0.65,0.77,3,233,2,0,0,"sales","high","exist"
"2227",4227,0.95,0.64,3,262,3,0,0,"sales","low","exist"
"2228",4228,0.67,0.67,3,183,3,0,0,"accounting","medium","exist"
"2229",4229,0.53,0.88,3,150,3,1,0,"accounting","high","exist"
"2230",4230,0.74,0.94,3,132,3,0,0,"accounting","medium","exist"
"2231",4231,0.77,0.79,3,200,4,0,0,"hr","medium","exist"
"2232",4232,0.13,0.72,4,148,5,0,0,"hr","medium","exist"
"2233",4233,0.82,0.92,3,187,3,1,0,"hr","medium","exist"
"2234",4234,0.88,0.65,4,224,2,0,0,"hr","high","exist"
"2235",4235,0.89,0.5,4,147,2,0,0,"technical","medium","exist"
"2236",4236,0.85,0.51,3,153,4,0,0,"technical","medium","exist"
"2237",4237,0.93,0.87,5,154,2,0,0,"technical","medium","exist"
"2238",4238,0.62,0.7,5,270,3,0,0,"technical","high","exist"
"2239",4239,0.58,0.96,3,202,2,0,0,"technical","medium","exist"
"2240",4240,0.98,0.5,4,264,3,0,0,"technical","high","exist"
"2241",4241,0.52,0.45,2,145,3,0,0,"technical","low","exist"
"2242",4242,0.76,0.49,3,138,2,1,0,"technical","medium","exist"
"2243",4243,0.73,0.91,3,238,2,0,0,"technical","medium","exist"
"2244",4244,0.74,0.64,4,147,5,0,0,"technical","medium","exist"
"2245",4245,0.49,0.48,3,190,2,0,0,"technical","medium","exist"
"2246",4246,0.91,0.97,4,183,3,0,0,"support","low","exist"
"2247",4247,0.74,0.92,5,255,3,0,0,"support","low","exist"
"2248",4248,0.86,0.82,4,252,4,0,0,"support","low","exist"
"2249",4249,0.52,0.47,6,141,5,1,0,"support","low","exist"
"2250",4250,0.95,0.81,3,260,2,0,0,"support","low","exist"
"2251",4251,0.65,0.98,3,136,3,0,0,"support","low","exist"
"2252",4252,0.7,0.56,4,182,2,0,0,"support","low","exist"
"2253",4253,0.14,0.66,6,142,5,0,0,"support","low","exist"
"2254",4254,0.92,0.53,2,261,4,0,0,"support","low","exist"
"2255",4255,0.54,0.6,3,209,2,0,0,"support","low","exist"
"2256",4256,0.76,0.95,2,156,4,0,0,"support","high","exist"
"2257",4257,0.78,0.66,4,214,2,0,0,"technical","low","exist"
"2258",4258,0.85,0.86,4,250,3,0,0,"technical","low","exist"
"2259",4259,0.78,0.8,4,197,3,1,0,"technical","low","exist"
"2260",4260,0.55,0.72,3,149,3,0,0,"management","low","exist"
"2261",4261,0.68,0.61,5,271,2,0,0,"IT","high","exist"
"2262",4262,0.91,0.68,4,187,2,0,0,"IT","low","exist"
"2263",4263,0.76,0.56,3,154,2,0,0,"IT","low","exist"
"2264",4264,0.15,0.67,4,264,3,0,0,"IT","low","exist"
"2265",4265,0.95,0.6,2,144,3,0,0,"IT","high","exist"
"2266",4266,0.74,0.37,6,200,3,0,0,"product_mng","low","exist"
"2267",4267,0.92,0.56,5,197,2,1,0,"product_mng","medium","exist"
"2268",4268,0.64,0.89,3,175,3,1,0,"product_mng","high","exist"
"2269",4269,0.96,0.9,3,161,3,0,0,"product_mng","medium","exist"
"2270",4270,0.21,0.72,3,245,6,0,0,"IT","high","exist"
"2271",4271,0.85,0.67,3,167,4,0,0,"RandD","medium","exist"
"2272",4272,0.52,0.86,5,168,3,0,0,"RandD","medium","exist"
"2273",4273,0.54,0.81,5,261,6,0,0,"RandD","medium","exist"
"2274",4274,0.56,0.81,5,274,4,0,0,"RandD","medium","exist"
"2275",4275,0.59,0.79,4,181,3,0,0,"RandD","medium","exist"
"2276",4276,0.88,0.58,5,266,3,1,0,"marketing","medium","exist"
"2277",4277,0.7,0.84,4,184,3,0,0,"sales","medium","exist"
"2278",4278,0.95,0.58,3,193,2,0,0,"accounting","medium","exist"
"2279",4279,0.83,0.88,3,217,2,1,0,"support","high","exist"
"2280",4280,0.76,0.62,4,197,3,0,0,"technical","low","exist"
"2281",4281,0.82,0.92,5,252,3,0,0,"management","medium","exist"
"2282",4282,0.55,0.97,4,136,3,0,0,"marketing","medium","exist"
"2283",4283,0.89,0.55,5,194,4,0,0,"marketing","medium","exist"
"2284",4284,0.66,0.94,4,190,3,0,0,"marketing","medium","exist"
"2285",4285,0.41,0.61,3,185,2,1,0,"sales","medium","exist"
"2286",4286,0.83,0.77,3,232,2,1,0,"sales","medium","exist"
"2287",4287,0.88,0.72,3,200,2,1,0,"sales","medium","exist"
"2288",4288,0.56,0.66,5,155,2,0,0,"sales","medium","exist"
"2289",4289,0.53,0.72,3,228,3,0,0,"sales","medium","exist"
"2290",4290,0.8,0.41,3,188,4,0,0,"sales","high","exist"
"2291",4291,0.95,0.78,4,245,3,0,0,"sales","low","exist"
"2292",4292,0.9,0.88,4,205,2,0,0,"sales","low","exist"
"2293",4293,0.5,0.97,4,269,3,0,0,"sales","low","exist"
"2294",4294,0.4,0.59,3,207,6,0,0,"sales","high","exist"
"2295",4295,0.52,0.85,3,250,4,0,0,"sales","low","exist"
"2296",4296,0.51,0.5,5,251,3,1,0,"sales","low","exist"
"2297",4297,0.84,0.62,5,217,3,0,0,"sales","low","exist"
"2298",4298,0.78,0.98,5,252,3,0,0,"sales","high","exist"
"2299",4299,0.91,0.71,2,159,3,1,0,"sales","low","exist"
"2300",4300,0.59,0.71,5,169,2,0,0,"sales","low","exist"
"2301",4301,0.69,0.97,5,105,4,0,0,"sales","low","exist"
"2302",4302,0.87,0.93,4,268,2,0,0,"sales","low","exist"
"2303",4303,0.66,1,5,145,2,0,0,"sales","low","exist"
"2304",4304,0.5,0.54,4,165,3,0,0,"accounting","low","exist"
"2305",4305,0.95,0.75,3,253,4,0,0,"accounting","low","exist"
"2306",4306,0.77,0.64,3,254,3,0,0,"accounting","medium","exist"
"2307",4307,0.66,0.5,3,218,2,0,0,"hr","medium","exist"
"2308",4308,0.61,0.98,3,138,5,1,0,"hr","medium","exist"
"2309",4309,0.7,0.87,5,239,3,0,0,"hr","medium","exist"
"2310",4310,0.89,0.71,5,182,2,1,0,"hr","medium","exist"
"2311",4311,0.6,0.78,4,152,3,1,0,"technical","medium","exist"
"2312",4312,0.62,0.51,3,222,3,1,0,"technical","medium","exist"
"2313",4313,0.59,0.9,2,219,2,1,0,"technical","medium","exist"
"2314",4314,0.69,0.59,4,266,4,0,0,"technical","medium","exist"
"2315",4315,0.36,0.94,3,113,5,1,0,"technical","medium","exist"
"2316",4316,0.59,0.93,4,146,3,0,0,"technical","medium","exist"
"2317",4317,0.5,0.51,5,238,4,0,0,"technical","medium","exist"
"2318",4318,0.53,0.99,3,188,3,0,0,"technical","high","exist"
"2319",4319,0.84,0.9,4,237,2,0,0,"technical","low","exist"
"2320",4320,0.51,0.95,4,201,3,0,0,"technical","medium","exist"
"2321",4321,0.53,0.56,5,188,3,0,0,"technical","medium","exist"
"2322",4322,0.98,0.63,3,137,3,0,0,"support","medium","exist"
"2323",4323,0.95,0.81,4,263,3,0,0,"support","medium","exist"
"2324",4324,0.13,0.76,5,217,3,0,0,"support","low","exist"
"2325",4325,0.5,0.64,4,213,4,0,0,"support","low","exist"
"2326",4326,0.93,0.76,3,244,3,0,0,"support","low","exist"
"2327",4327,0.5,0.87,3,252,2,0,0,"support","low","exist"
"2328",4328,0.66,0.84,4,168,2,0,0,"support","low","exist"
"2329",4329,0.89,0.79,4,212,3,0,0,"support","low","exist"
"2330",4330,0.46,0.43,4,258,3,0,0,"support","low","exist"
"2331",4331,0.67,0.6,4,210,3,0,0,"support","low","exist"
"2332",4332,0.96,0.9,4,246,3,0,0,"support","low","exist"
"2333",4333,0.98,0.76,4,221,3,1,0,"technical","low","exist"
"2334",4334,0.67,0.57,5,215,4,0,0,"technical","low","exist"
"2335",4335,0.72,0.72,4,175,4,0,0,"technical","low","exist"
"2336",4336,0.65,0.49,5,221,2,0,0,"management","low","exist"
"2337",4337,0.72,0.67,5,219,3,0,0,"IT","low","exist"
"2338",4338,0.78,0.58,3,154,3,0,0,"IT","low","exist"
"2339",4339,0.93,0.56,3,266,2,0,0,"IT","low","exist"
"2340",4340,0.73,0.85,5,224,4,0,0,"IT","low","exist"
"2341",4341,0.2,0.57,4,269,5,1,0,"IT","low","exist"
"2342",4342,0.47,0.5,4,159,2,0,0,"product_mng","low","exist"
"2343",4343,0.56,0.62,4,247,3,0,0,"product_mng","low","exist"
"2344",4344,0.72,0.86,4,137,2,0,0,"product_mng","low","exist"
"2345",4345,0.91,0.56,4,251,4,0,0,"product_mng","medium","exist"
"2346",4346,0.79,0.49,5,210,3,0,0,"IT","medium","exist"
"2347",4347,0.53,0.75,2,123,4,0,0,"RandD","medium","exist"
"2348",4348,0.14,0.51,4,253,6,0,0,"RandD","medium","exist"
"2349",4349,0.78,0.92,5,242,4,0,0,"RandD","medium","exist"
"2350",4350,0.52,0.74,3,238,3,0,0,"RandD","medium","exist"
"2351",4351,0.94,0.68,5,151,2,0,0,"RandD","medium","exist"
"2352",4352,0.83,0.8,4,158,4,0,0,"marketing","medium","exist"
"2353",4353,0.44,0.68,2,120,2,0,0,"marketing","medium","exist"
"2354",4354,0.72,0.74,3,187,2,0,0,"sales","medium","exist"
"2355",4355,0.84,0.58,3,140,3,0,0,"accounting","medium","exist"
"2356",4356,0.76,0.99,3,164,2,0,0,"support","medium","exist"
"2357",4357,0.92,0.94,5,253,2,0,0,"technical","high","exist"
"2358",4358,0.58,0.93,5,235,2,0,0,"management","low","exist"
"2359",4359,0.85,0.81,4,189,4,0,0,"marketing","medium","exist"
"2360",4360,0.74,0.51,4,246,3,1,0,"marketing","medium","exist"
"2361",4361,0.74,0.87,4,258,2,0,0,"marketing","medium","exist"
"2362",4362,0.72,0.8,3,158,3,0,0,"sales","medium","exist"
"2363",4363,0.76,0.98,4,173,3,1,0,"sales","low","exist"
"2364",4364,0.85,0.72,3,201,3,0,0,"sales","low","exist"
"2365",4365,0.82,0.49,5,185,3,0,0,"sales","low","exist"
"2366",4366,0.6,0.93,5,270,3,1,0,"sales","low","exist"
"2367",4367,0.39,0.64,3,152,2,0,0,"sales","low","exist"
"2368",4368,0.83,0.53,3,211,4,1,0,"sales","low","exist"
"2369",4369,0.7,0.92,3,162,3,1,0,"sales","low","exist"
"2370",4370,0.84,0.97,4,253,2,1,0,"sales","low","exist"
"2371",4371,0.9,0.77,4,236,3,0,0,"sales","low","exist"
"2372",4372,0.18,0.84,4,231,5,0,0,"sales","low","exist"
"2373",4373,0.52,0.71,2,231,2,0,0,"sales","low","exist"
"2374",4374,0.87,0.51,4,233,3,0,0,"sales","low","exist"
"2375",4375,0.6,0.79,5,217,2,0,0,"sales","low","exist"
"2376",4376,0.72,0.93,4,174,2,0,0,"sales","low","exist"
"2377",4377,0.48,0.64,4,227,3,0,0,"sales","low","exist"
"2378",4378,0.75,0.53,3,222,2,0,0,"sales","low","exist"
"2379",4379,0.85,0.55,2,207,6,0,0,"sales","low","exist"
"2380",4380,0.39,0.41,6,115,3,0,0,"sales","low","exist"
"2381",4381,0.61,0.68,3,254,2,0,0,"accounting","low","exist"
"2382",4382,0.6,0.61,4,170,3,0,0,"accounting","low","exist"
"2383",4383,0.57,0.64,2,206,3,0,0,"accounting","low","exist"
"2384",4384,0.19,0.68,5,149,3,0,0,"hr","medium","exist"
"2385",4385,0.48,0.77,3,197,3,0,0,"hr","medium","exist"
"2386",4386,0.69,0.75,4,261,3,0,0,"hr","medium","exist"
"2387",4387,0.7,0.55,4,256,4,0,0,"hr","medium","exist"
"2388",4388,0.65,0.92,3,219,3,0,0,"technical","medium","exist"
"2389",4389,0.81,0.96,4,143,3,1,0,"technical","medium","exist"
"2390",4390,0.85,0.95,4,171,3,0,0,"technical","medium","exist"
"2391",4391,0.93,0.76,4,207,2,0,0,"technical","medium","exist"
"2392",4392,0.72,0.94,4,235,3,0,0,"technical","medium","exist"
"2393",4393,0.49,0.55,2,173,3,0,0,"technical","medium","exist"
"2394",4394,0.94,0.47,5,131,6,0,0,"technical","medium","exist"
"2395",4395,0.81,0.84,4,143,2,0,0,"technical","medium","exist"
"2396",4396,0.92,0.78,5,193,4,0,0,"technical","high","exist"
"2397",4397,0.97,0.82,3,135,2,0,0,"technical","low","exist"
"2398",4398,0.98,0.61,4,265,2,1,0,"technical","medium","exist"
"2399",4399,0.44,0.45,3,133,2,1,0,"support","medium","exist"
"2400",4400,0.72,0.87,4,184,4,0,0,"support","medium","exist"
"2401",4401,0.8,0.73,5,135,3,0,0,"support","medium","exist"
"2402",4402,0.6,0.72,4,267,4,0,0,"support","low","exist"
"2403",4403,0.89,0.65,4,184,3,0,0,"support","low","exist"
"2404",4404,0.69,0.57,5,227,2,0,0,"support","low","exist"
"2405",4405,0.54,0.81,4,257,2,0,0,"support","low","exist"
"2406",4406,0.97,0.75,5,235,3,1,0,"support","low","exist"
"2407",4407,0.37,0.4,4,161,2,0,0,"support","low","exist"
"2408",4408,0.73,0.52,4,215,3,0,0,"support","low","exist"
"2409",4409,0.57,0.69,4,146,3,0,0,"support","low","exist"
"2410",4410,0.86,0.66,4,191,2,0,0,"technical","low","exist"
"2411",4411,0.83,0.85,4,261,3,0,0,"technical","low","exist"
"2412",4412,0.19,0.52,5,200,5,0,0,"technical","low","exist"
"2413",4413,0.55,0.93,4,239,4,0,0,"management","low","exist"
"2414",4414,0.6,0.65,4,160,2,0,0,"IT","low","exist"
"2415",4415,0.62,0.71,3,177,3,1,0,"IT","low","exist"
"2416",4416,0.65,0.55,5,215,2,0,0,"IT","low","exist"
"2417",4417,0.4,0.48,4,97,2,0,0,"IT","low","exist"
"2418",4418,0.82,0.78,5,231,2,1,0,"IT","low","exist"
"2419",4419,0.75,0.69,2,257,2,1,0,"product_mng","low","exist"
"2420",4420,0.83,0.53,3,232,3,1,0,"product_mng","low","exist"
"2421",4421,0.77,0.78,4,212,3,0,0,"product_mng","low","exist"
"2422",4422,0.59,0.98,4,198,3,0,0,"product_mng","low","exist"
"2423",4423,0.8,0.79,5,240,4,0,0,"IT","medium","exist"
"2424",4424,0.84,0.55,3,242,3,0,0,"RandD","medium","exist"
"2425",4425,0.88,0.71,4,250,3,0,0,"RandD","medium","exist"
"2426",4426,0.37,0.9,3,229,6,0,0,"RandD","medium","exist"
"2427",4427,0.88,0.66,4,133,2,0,0,"RandD","medium","exist"
"2428",4428,0.69,0.85,3,200,2,0,0,"RandD","medium","exist"
"2429",4429,0.54,0.49,2,155,2,1,0,"marketing","medium","exist"
"2430",4430,0.76,0.57,5,139,4,0,0,"sales","medium","exist"
"2431",4431,0.51,0.58,4,272,3,0,0,"accounting","medium","exist"
"2432",4432,0.16,0.52,3,100,4,0,0,"support","medium","exist"
"2433",4433,0.65,0.6,5,160,3,0,0,"technical","medium","exist"
"2434",4434,0.54,0.81,5,195,4,0,0,"management","medium","exist"
"2435",4435,0.66,0.68,4,152,3,0,0,"marketing","high","exist"
"2436",4436,0.95,0.65,5,182,3,0,0,"marketing","low","exist"
"2437",4437,0.83,0.57,3,137,2,0,0,"marketing","medium","exist"
"2438",4438,0.98,0.84,4,182,3,0,0,"sales","medium","exist"
"2439",4439,0.72,0.72,3,217,2,0,0,"sales","medium","exist"
"2440",4440,0.71,0.62,4,192,4,0,0,"sales","medium","exist"
"2441",4441,0.36,0.65,3,116,2,1,0,"sales","low","exist"
"2442",4442,0.55,0.65,4,170,4,0,0,"sales","low","exist"
"2443",4443,0.64,0.52,3,223,3,0,0,"sales","low","exist"
"2444",4444,0.62,0.57,3,174,2,0,0,"sales","low","exist"
"2445",4445,1,0.95,3,193,6,0,0,"sales","low","exist"
"2446",4446,0.91,0.91,6,167,2,1,0,"sales","low","exist"
"2447",4447,0.81,0.67,4,225,3,0,0,"sales","low","exist"
"2448",4448,0.71,0.92,4,248,3,1,0,"sales","low","exist"
"2449",4449,0.97,0.74,3,104,5,0,0,"sales","low","exist"
"2450",4450,0.75,0.86,6,148,4,0,0,"sales","low","exist"
"2451",4451,0.68,1,3,148,4,1,0,"sales","low","exist"
"2452",4452,0.87,0.68,5,187,3,0,0,"sales","low","exist"
"2453",4453,0.96,0.57,3,151,2,0,0,"sales","low","exist"
"2454",4454,0.41,0.86,6,215,5,0,0,"sales","low","exist"
"2455",4455,0.81,0.61,4,142,4,0,0,"sales","low","exist"
"2456",4456,0.68,1,6,258,5,0,0,"sales","low","exist"
"2457",4457,0.63,0.56,4,228,3,0,0,"accounting","low","exist"
"2458",4458,0.6,0.84,2,221,3,0,0,"accounting","low","exist"
"2459",4459,0.66,0.97,3,263,3,0,0,"accounting","medium","exist"
"2460",4460,0.69,0.82,4,273,4,0,0,"hr","medium","exist"
"2461",4461,0.23,0.37,3,247,4,0,0,"hr","medium","exist"
"2462",4462,0.52,0.63,3,225,3,0,0,"hr","medium","exist"
"2463",4463,0.65,0.86,4,269,2,1,0,"hr","medium","exist"
"2464",4464,0.78,0.4,6,174,3,0,0,"technical","medium","exist"
"2465",4465,0.6,0.65,5,201,2,0,0,"technical","medium","exist"
"2466",4466,0.9,0.62,3,254,3,0,0,"technical","medium","exist"
"2467",4467,0.59,0.95,5,254,2,0,0,"technical","medium","exist"
"2468",4468,0.89,0.78,3,185,2,1,0,"technical","medium","exist"
"2469",4469,0.96,0.85,4,200,4,0,0,"technical","medium","exist"
"2470",4470,0.68,0.76,3,190,3,1,0,"technical","medium","exist"
"2471",4471,0.52,0.72,2,160,2,0,0,"technical","high","exist"
"2472",4472,0.68,0.94,3,264,2,0,0,"technical","low","exist"
"2473",4473,0.6,0.59,3,172,3,0,0,"technical","medium","exist"
"2474",4474,0.28,0.83,5,279,4,0,0,"technical","medium","exist"
"2475",4475,0.98,0.54,5,260,3,0,0,"support","medium","exist"
"2476",4476,0.67,0.52,4,262,2,0,0,"support","medium","exist"
"2477",4477,0.86,0.6,5,134,2,1,0,"support","low","exist"
"2478",4478,0.83,0.9,3,195,3,0,0,"support","low","exist"
"2479",4479,0.79,0.86,3,139,3,0,0,"support","low","exist"
"2480",4480,0.54,0.86,4,205,5,0,0,"support","low","exist"
"2481",4481,0.51,0.86,5,212,2,0,0,"support","low","exist"
"2482",4482,0.75,0.65,4,148,2,0,0,"support","low","exist"
"2483",4483,0.51,0.67,4,228,3,0,0,"support","low","exist"
"2484",4484,0.73,0.74,5,230,3,0,0,"support","low","exist"
"2485",4485,0.17,0.4,4,286,3,0,0,"support","low","exist"
"2486",4486,0.88,0.59,4,142,3,0,0,"technical","low","exist"
"2487",4487,0.76,0.79,3,161,3,0,0,"technical","low","exist"
"2488",4488,0.29,0.77,3,152,3,1,0,"technical","low","exist"
"2489",4489,0.62,0.97,4,266,3,0,0,"management","low","exist"
"2490",4490,0.5,0.85,2,143,3,0,0,"IT","low","exist"
"2491",4491,0.16,0.65,5,165,3,0,0,"IT","low","exist"
"2492",4492,0.18,0.99,4,204,6,0,0,"IT","low","exist"
"2493",4493,0.86,0.68,4,170,2,0,0,"IT","low","exist"
"2494",4494,0.68,0.52,5,203,3,0,0,"IT","low","exist"
"2495",4495,0.68,0.85,3,199,3,0,0,"product_mng","low","exist"
"2496",4496,0.89,0.78,4,223,3,0,0,"product_mng","low","exist"
"2497",4497,0.86,0.88,4,220,2,0,0,"product_mng","low","exist"
"2498",4498,0.43,0.41,4,190,3,0,0,"product_mng","medium","exist"
"2499",4499,0.85,0.58,4,143,6,1,0,"IT","medium","exist"
"2500",4500,0.94,0.8,5,111,4,0,1,"RandD","medium","exist"
"2501",4501,0.58,0.71,4,145,3,1,1,"RandD","medium","exist"
"2502",4502,0.43,0.6,3,138,3,0,1,"RandD","medium","exist"
"2503",4503,0.72,0.49,3,203,3,0,1,"RandD","medium","exist"
"2504",4504,0.95,0.96,5,175,3,1,1,"RandD","medium","exist"
"2505",4505,0.35,0.67,3,119,3,0,1,"marketing","medium","exist"
"2506",4506,0.77,0.79,3,157,3,0,1,"sales","medium","exist"
"2507",4507,0.74,0.7,5,135,2,0,1,"accounting","medium","exist"
"2508",4508,0.5,0.6,4,200,2,0,1,"support","medium","exist"
"2509",4509,0.87,0.56,4,121,2,0,1,"technical","medium","exist"
"2510",4510,0.55,0.48,3,162,3,0,1,"management","high","exist"
"2511",4511,0.8,0.65,3,135,3,0,1,"marketing","low","exist"
"2512",4512,0.49,0.52,4,183,2,0,1,"marketing","medium","exist"
"2513",4513,0.54,0.92,4,228,3,0,1,"marketing","medium","exist"
"2514",4514,0.21,0.87,4,148,5,0,0,"sales","medium","exist"
"2515",4515,0.77,0.77,3,219,2,0,0,"sales","medium","exist"
"2516",4516,0.53,0.73,4,147,3,0,0,"sales","low","exist"
"2517",4517,0.89,0.49,5,165,6,0,0,"sales","low","exist"
"2518",4518,0.25,0.59,4,218,3,1,0,"sales","low","exist"
"2519",4519,0.53,0.59,3,231,2,0,0,"sales","low","exist"
"2520",4520,0.44,0.45,2,124,3,0,0,"sales","low","exist"
"2521",4521,0.7,0.44,2,131,2,0,0,"sales","low","exist"
"2522",4522,0.54,0.76,3,199,4,0,0,"sales","low","exist"
"2523",4523,0.9,0.56,3,220,3,0,0,"sales","low","exist"
"2524",4524,0.69,0.8,5,215,3,0,0,"sales","low","exist"
"2525",4525,0.78,0.59,4,180,3,0,0,"sales","low","exist"
"2526",4526,0.91,0.49,6,272,6,0,0,"sales","low","exist"
"2527",4527,0.92,0.61,2,228,2,0,0,"sales","low","exist"
"2528",4528,0.96,0.88,3,168,2,0,0,"sales","low","exist"
"2529",4529,0.8,0.77,4,205,2,0,0,"sales","low","exist"
"2530",4530,0.52,0.67,3,244,3,0,0,"sales","low","exist"
"2531",4531,0.52,0.97,3,185,2,0,0,"sales","low","exist"
"2532",4532,0.93,0.96,5,135,3,1,0,"sales","low","exist"
"2533",4533,0.89,0.9,4,160,2,0,0,"accounting","low","exist"
"2534",4534,0.83,0.61,4,262,4,0,0,"accounting","low","exist"
"2535",4535,0.7,0.84,6,225,6,0,0,"accounting","low","exist"
"2536",4536,0.89,0.74,4,174,2,0,0,"hr","low","exist"
"2537",4537,0.21,0.37,6,266,3,0,1,"hr","medium","exist"
"2538",4538,0.67,0.78,4,241,4,0,1,"hr","medium","exist"
"2539",4539,0.73,0.97,3,245,2,0,1,"hr","medium","exist"
"2540",4540,0.85,0.51,3,261,4,0,1,"technical","medium","exist"
"2541",4541,0.54,0.85,4,157,3,0,1,"technical","medium","exist"
"2542",4542,0.74,0.94,3,286,6,0,1,"technical","medium","exist"
"2543",4543,0.71,0.65,4,263,2,1,1,"technical","medium","exist"
"2544",4544,0.76,0.52,2,229,3,0,1,"technical","medium","exist"
"2545",4545,0.85,0.5,4,159,2,0,1,"technical","medium","exist"
"2546",4546,0.82,0.89,4,196,3,0,1,"technical","medium","exist"
"2547",4547,0.71,0.79,4,133,3,0,1,"technical","medium","exist"
"2548",4548,0.79,0.65,5,228,3,0,1,"technical","medium","exist"
"2549",4549,0.86,0.56,4,247,3,0,1,"technical","high","exist"
"2550",4550,0.9,0.78,5,215,3,0,1,"technical","low","exist"
"2551",4551,0.65,0.8,6,233,3,1,1,"support","medium","exist"
"2552",4552,0.53,0.74,5,237,2,0,1,"support","medium","exist"
"2553",4553,0.51,0.91,4,269,5,0,1,"support","medium","exist"
"2554",4554,1,0.76,6,246,3,1,1,"support","medium","exist"
"2555",4555,0.92,0.82,4,168,3,0,1,"support","low","exist"
"2556",4556,1,0.59,3,168,3,0,1,"support","low","exist"
"2557",4557,0.23,0.67,4,163,5,0,1,"support","low","exist"
"2558",4558,0.52,0.77,3,142,2,0,1,"support","low","exist"
"2559",4559,0.8,0.83,4,183,2,1,1,"support","low","exist"
"2560",4560,0.59,0.72,4,264,3,0,0,"support","low","exist"
"2561",4561,0.75,0.55,2,184,2,0,0,"support","low","exist"
"2562",4562,0.99,0.58,4,138,3,0,0,"technical","low","exist"
"2563",4563,0.57,0.76,3,195,3,0,0,"technical","medium","exist"
"2564",4564,0.87,0.95,4,251,3,0,0,"technical","medium","exist"
"2565",4565,0.52,0.47,3,108,5,0,0,"management","medium","exist"
"2566",4566,0.57,0.9,3,144,2,0,0,"IT","medium","exist"
"2567",4567,0.32,0.77,6,112,4,0,0,"IT","medium","exist"
"2568",4568,0.66,0.81,2,201,3,0,0,"IT","medium","exist"
"2569",4569,0.53,0.8,4,204,3,0,0,"IT","medium","exist"
"2570",4570,0.55,0.62,5,226,3,0,0,"IT","medium","exist"
"2571",4571,0.66,0.9,3,217,4,0,0,"product_mng","medium","exist"
"2572",4572,0.69,0.92,4,149,2,0,0,"product_mng","medium","exist"
"2573",4573,0.67,0.99,5,237,3,0,0,"product_mng","medium","exist"
"2574",4574,0.75,0.37,3,256,3,0,0,"product_mng","medium","exist"
"2575",4575,0.7,0.98,4,194,2,0,0,"IT","medium","exist"
"2576",4576,0.71,0.74,3,202,2,0,0,"RandD","medium","exist"
"2577",4577,0.97,0.6,5,133,2,0,0,"RandD","medium","exist"
"2578",4578,0.5,0.48,3,246,3,0,0,"RandD","medium","exist"
"2579",4579,0.34,0.92,3,206,3,0,0,"RandD","medium","exist"
"2580",4580,0.67,0.74,3,204,3,0,0,"RandD","medium","exist"
"2581",4581,0.78,0.98,4,157,3,0,0,"marketing","medium","exist"
"2582",4582,0.52,0.91,4,187,2,0,0,"sales","medium","exist"
"2583",4583,0.91,0.51,2,211,2,0,0,"accounting","medium","exist"
"2584",4584,0.79,0.92,3,204,3,0,0,"support","medium","exist"
"2585",4585,0.83,0.53,5,182,2,1,0,"technical","medium","exist"
"2586",4586,0.74,0.76,3,244,3,0,0,"management","medium","exist"
"2587",4587,0.77,0.97,3,184,3,1,0,"marketing","medium","exist"
"2588",4588,0.49,0.71,2,117,3,0,0,"marketing","high","exist"
"2589",4589,0.94,0.89,3,230,3,1,0,"marketing","high","exist"
"2590",4590,0.99,0.91,3,241,2,0,0,"sales","high","exist"
"2591",4591,0.69,0.84,3,227,3,1,0,"sales","high","exist"
"2592",4592,0.88,0.59,5,147,4,0,0,"sales","high","exist"
"2593",4593,0.89,0.9,3,240,3,0,0,"sales","high","exist"
"2594",4594,0.8,0.58,4,238,2,0,0,"sales","high","exist"
"2595",4595,0.22,0.89,4,262,4,0,0,"sales","high","exist"
"2596",4596,0.87,0.83,4,215,2,0,0,"sales","high","exist"
"2597",4597,0.66,0.66,3,211,4,0,0,"sales","high","exist"
"2598",4598,0.9,0.68,3,156,3,1,0,"sales","high","exist"
"2599",4599,0.95,0.54,4,180,5,0,0,"sales","high","exist"
"2600",4600,0.49,0.56,4,260,6,0,0,"sales","low","exist"
"2601",4601,0.71,0.68,4,228,2,0,0,"sales","low","exist"
"2602",4602,0.2,0.96,2,249,3,0,0,"sales","low","exist"
"2603",4603,0.95,0.64,3,242,5,1,0,"sales","low","exist"
"2604",4604,0.68,0.8,2,254,2,1,0,"sales","low","exist"
"2605",4605,0.93,0.68,3,272,2,1,0,"sales","low","exist"
"2606",4606,0.85,0.57,5,210,3,0,0,"sales","low","exist"
"2607",4607,0.82,0.68,3,140,2,0,0,"sales","low","exist"
"2608",4608,0.87,0.42,5,252,5,0,0,"sales","low","exist"
"2609",4609,0.88,0.84,3,266,3,0,0,"accounting","low","exist"
"2610",4610,0.98,0.79,4,138,3,0,0,"accounting","low","exist"
"2611",4611,0.89,0.98,3,220,3,0,0,"accounting","low","exist"
"2612",4612,0.92,0.51,5,214,3,0,0,"hr","low","exist"
"2613",4613,0.48,0.6,2,121,5,0,0,"hr","low","exist"
"2614",4614,0.67,0.49,4,195,3,0,0,"hr","low","exist"
"2615",4615,0.84,0.55,3,135,3,0,0,"hr","medium","exist"
"2616",4616,0.97,0.78,4,261,2,0,0,"technical","medium","exist"
"2617",4617,0.65,0.53,5,205,3,1,0,"technical","medium","exist"
"2618",4618,0.79,0.87,5,240,2,1,0,"technical","medium","exist"
"2619",4619,0.75,0.75,2,141,4,0,0,"technical","medium","exist"
"2620",4620,0.69,0.64,4,200,3,0,0,"technical","medium","exist"
"2621",4621,0.85,0.6,4,257,3,0,0,"technical","medium","exist"
"2622",4622,0.34,0.41,2,141,3,0,0,"technical","medium","exist"
"2623",4623,0.97,0.68,3,185,2,0,0,"technical","medium","exist"
"2624",4624,0.39,0.99,6,235,6,0,0,"technical","medium","exist"
"2625",4625,0.54,0.81,4,264,4,0,0,"technical","medium","exist"
"2626",4626,0.78,0.67,4,260,3,0,0,"technical","medium","exist"
"2627",4627,0.49,0.79,4,158,3,0,0,"support","high","exist"
"2628",4628,0.17,0.83,6,195,5,0,0,"support","high","exist"
"2629",4629,0.98,0.81,3,180,2,1,0,"support","high","exist"
"2630",4630,0.9,1,2,114,5,0,0,"support","high","exist"
"2631",4631,0.24,0.89,5,228,4,1,0,"support","high","exist"
"2632",4632,0.92,0.79,5,243,3,1,0,"support","high","exist"
"2633",4633,0.36,0.72,3,179,3,0,0,"support","high","exist"
"2634",4634,0.19,0.76,2,158,3,0,0,"support","high","exist"
"2635",4635,0.75,0.76,4,254,4,0,0,"support","low","exist"
"2636",4636,0.91,0.81,3,220,3,1,0,"support","low","exist"
"2637",4637,0.72,0.95,5,171,3,0,0,"support","low","exist"
"2638",4638,0.62,0.64,4,142,2,0,0,"technical","low","exist"
"2639",4639,0.97,0.75,3,241,2,0,0,"technical","low","exist"
"2640",4640,0.69,0.64,3,275,3,0,0,"technical","low","exist"
"2641",4641,0.98,0.56,3,157,2,0,0,"management","low","exist"
"2642",4642,0.62,0.53,5,169,3,0,0,"IT","low","exist"
"2643",4643,0.59,0.82,2,233,2,0,0,"IT","medium","exist"
"2644",4644,0.73,0.98,4,160,3,0,0,"IT","medium","exist"
"2645",4645,0.71,0.79,3,189,3,0,0,"IT","medium","exist"
"2646",4646,0.85,0.58,4,273,4,0,0,"IT","medium","exist"
"2647",4647,0.8,0.94,4,141,3,1,0,"product_mng","medium","exist"
"2648",4648,0.48,0.38,3,134,3,0,0,"product_mng","medium","exist"
"2649",4649,0.64,0.4,3,248,3,0,0,"product_mng","medium","exist"
"2650",4650,0.19,0.64,6,222,5,0,0,"product_mng","medium","exist"
"2651",4651,0.82,0.69,5,219,3,0,0,"IT","medium","exist"
"2652",4652,0.99,0.96,4,202,3,0,0,"RandD","medium","exist"
"2653",4653,0.45,0.58,3,200,2,0,0,"RandD","medium","exist"
"2654",4654,0.89,0.89,3,138,2,0,0,"RandD","medium","exist"
"2655",4655,0.75,0.78,4,158,5,1,0,"RandD","medium","exist"
"2656",4656,0.8,0.86,3,136,2,0,0,"RandD","medium","exist"
"2657",4657,0.78,0.49,4,222,2,0,0,"marketing","medium","exist"
"2658",4658,0.95,0.76,3,236,2,1,0,"sales","medium","exist"
"2659",4659,0.68,0.67,4,135,2,0,0,"accounting","medium","exist"
"2660",4660,0.82,0.97,5,235,3,0,0,"support","medium","exist"
"2661",4661,0.9,0.69,4,274,2,0,0,"technical","medium","exist"
"2662",4662,0.59,0.99,3,235,2,0,0,"management","medium","exist"
"2663",4663,0.66,0.57,3,169,4,1,0,"marketing","medium","exist"
"2664",4664,0.63,0.56,5,286,4,0,0,"marketing","medium","exist"
"2665",4665,0.73,0.78,3,166,2,0,0,"marketing","medium","exist"
"2666",4666,0.63,0.93,3,236,4,0,0,"sales","high","exist"
"2667",4667,0.87,0.62,5,197,2,0,0,"sales","low","exist"
"2668",4668,0.62,0.87,6,169,2,1,0,"sales","medium","exist"
"2669",4669,0.53,0.9,3,210,3,0,0,"sales","medium","exist"
"2670",4670,0.94,0.83,4,156,2,0,0,"sales","medium","exist"
"2671",4671,0.94,0.55,5,231,2,0,0,"sales","medium","exist"
"2672",4672,0.55,0.64,3,101,2,1,0,"sales","medium","exist"
"2673",4673,0.58,0.72,4,259,3,0,0,"sales","medium","exist"
"2674",4674,0.98,0.88,5,203,5,1,0,"sales","medium","exist"
"2675",4675,0.74,0.76,5,255,4,0,0,"sales","medium","exist"
"2676",4676,0.65,0.48,3,131,3,1,0,"sales","medium","exist"
"2677",4677,0.97,0.79,2,272,2,0,0,"sales","medium","exist"
"2678",4678,0.58,0.75,4,253,2,0,0,"sales","low","exist"
"2679",4679,0.49,0.77,2,254,2,0,0,"sales","low","exist"
"2680",4680,0.69,0.98,3,214,2,0,0,"sales","low","exist"
"2681",4681,0.55,0.49,5,195,3,0,0,"sales","low","exist"
"2682",4682,0.65,0.89,4,240,3,0,0,"sales","low","exist"
"2683",4683,0.87,0.49,4,149,2,0,0,"sales","low","exist"
"2684",4684,0.12,0.7,4,276,4,0,0,"sales","low","exist"
"2685",4685,0.65,0.84,4,247,4,0,0,"accounting","high","exist"
"2686",4686,0.23,0.66,4,212,4,1,0,"accounting","low","exist"
"2687",4687,0.62,0.77,4,256,3,0,0,"accounting","high","exist"
"2688",4688,0.86,0.72,4,178,2,0,0,"hr","high","exist"
"2689",4689,0.85,0.6,4,255,4,0,0,"hr","low","exist"
"2690",4690,0.74,0.76,3,224,2,0,0,"hr","low","exist"
"2691",4691,0.53,0.76,2,204,4,0,0,"hr","high","exist"
"2692",4692,0.99,0.44,6,104,6,0,0,"technical","low","exist"
"2693",4693,0.48,0.81,5,113,3,0,0,"technical","medium","exist"
"2694",4694,0.72,0.74,3,243,2,0,0,"technical","high","exist"
"2695",4695,0.76,0.72,3,242,5,1,0,"technical","medium","exist"
"2696",4696,0.41,0.7,4,177,3,0,0,"technical","medium","exist"
"2697",4697,0.85,0.88,3,235,2,0,0,"technical","medium","exist"
"2698",4698,0.62,0.49,4,175,3,0,0,"technical","medium","exist"
"2699",4699,0.16,0.78,4,280,6,0,0,"technical","high","exist"
"2700",4700,0.58,0.61,3,205,2,0,0,"technical","medium","exist"
"2701",4701,0.73,0.95,4,243,3,0,0,"technical","medium","exist"
"2702",4702,0.28,0.76,3,150,2,0,0,"technical","medium","exist"
"2703",4703,0.77,0.61,4,178,2,1,0,"support","high","exist"
"2704",4704,0.85,0.86,3,231,3,0,0,"support","medium","exist"
"2705",4705,0.56,0.76,3,134,2,0,0,"support","high","exist"
"2706",4706,0.81,1,5,143,2,0,0,"support","low","exist"
"2707",4707,0.83,0.87,3,156,3,0,0,"support","medium","exist"
"2708",4708,0.49,0.48,4,249,3,0,0,"support","medium","exist"
"2709",4709,0.81,0.97,3,247,3,0,0,"support","medium","exist"
"2710",4710,0.56,0.92,5,222,4,0,0,"support","medium","exist"
"2711",4711,0.77,0.83,3,160,3,0,0,"support","low","exist"
"2712",4712,0.73,0.5,4,224,4,1,0,"support","low","exist"
"2713",4713,0.95,0.72,4,235,2,0,0,"support","low","exist"
"2714",4714,0.69,0.68,5,167,3,0,0,"technical","low","exist"
"2715",4715,0.79,0.89,3,104,4,0,0,"technical","low","exist"
"2716",4716,0.63,0.57,5,160,2,0,0,"technical","low","exist"
"2717",4717,0.8,0.79,4,168,3,0,0,"management","low","exist"
"2718",4718,0.64,0.61,2,153,2,0,0,"IT","low","exist"
"2719",4719,0.92,0.9,4,249,3,0,0,"IT","low","exist"
"2720",4720,0.96,0.75,4,177,2,0,0,"IT","low","exist"
"2721",4721,0.56,0.85,3,225,3,0,0,"IT","high","exist"
"2722",4722,0.37,0.61,3,186,2,0,0,"IT","low","exist"
"2723",4723,0.5,0.82,3,133,2,1,0,"product_mng","low","exist"
"2724",4724,0.49,0.58,4,213,4,1,0,"product_mng","low","exist"
"2725",4725,1,0.73,3,245,2,1,0,"product_mng","low","exist"
"2726",4726,0.82,0.75,5,160,3,1,0,"product_mng","high","exist"
"2727",4727,0.52,0.54,4,212,3,0,0,"IT","low","exist"
"2728",4728,0.76,0.96,4,135,3,1,1,"RandD","low","exist"
"2729",4729,0.2,0.53,3,149,4,0,1,"RandD","low","exist"
"2730",4730,0.6,0.9,4,178,3,0,1,"RandD","high","exist"
"2731",4731,0.69,0.9,6,224,4,0,1,"RandD","low","exist"
"2732",4732,0.93,0.51,3,196,2,0,1,"RandD","medium","exist"
"2733",4733,0.7,0.64,4,178,3,1,1,"marketing","high","exist"
"2734",4734,0.56,0.54,4,191,2,1,1,"sales","medium","exist"
"2735",4735,0.97,0.61,4,167,3,0,1,"accounting","high","exist"
"2736",4736,0.24,0.65,6,275,5,1,1,"support","medium","exist"
"2737",4737,0.83,0.91,3,168,3,0,1,"technical","medium","exist"
"2738",4738,0.55,0.85,3,152,2,0,1,"management","medium","exist"
"2739",4739,0.68,0.99,3,263,3,1,1,"marketing","medium","exist"
"2740",4740,0.48,0.53,3,113,4,0,0,"marketing","medium","exist"
"2741",4741,0.75,0.95,3,253,3,0,0,"marketing","medium","exist"
"2742",4742,0.61,0.5,4,271,3,1,0,"sales","medium","exist"
"2743",4743,0.5,0.74,4,165,2,1,0,"sales","medium","exist"
"2744",4744,0.78,0.54,5,257,3,0,0,"sales","high","exist"
"2745",4745,0.61,0.68,4,244,3,0,0,"sales","low","exist"
"2746",4746,0.48,0.5,4,179,3,0,0,"sales","medium","exist"
"2747",4747,0.93,0.92,3,248,2,0,0,"sales","medium","exist"
"2748",4748,0.78,0.58,3,224,2,0,0,"sales","medium","exist"
"2749",4749,0.92,0.99,5,236,4,0,0,"sales","medium","exist"
"2750",4750,0.71,0.98,5,213,3,0,0,"sales","medium","exist"
"2751",4751,0.15,0.42,4,238,3,0,0,"sales","medium","exist"
"2752",4752,0.14,0.83,5,153,5,1,0,"sales","medium","exist"
"2753",4753,0.56,0.72,4,247,2,0,0,"sales","medium","exist"
"2754",4754,1,0.84,3,154,3,0,0,"sales","medium","exist"
"2755",4755,0.77,0.82,3,147,4,0,0,"sales","high","exist"
"2756",4756,0.86,0.66,3,150,2,0,0,"sales","low","exist"
"2757",4757,0.88,0.95,3,137,3,0,0,"sales","low","exist"
"2758",4758,0.85,0.84,5,179,3,0,0,"sales","low","exist"
"2759",4759,0.95,0.56,5,194,2,0,0,"sales","high","exist"
"2760",4760,0.65,0.65,4,224,3,0,0,"sales","low","exist"
"2761",4761,0.7,0.55,3,253,2,1,0,"accounting","low","exist"
"2762",4762,0.6,0.55,3,177,3,1,0,"accounting","low","exist"
"2763",4763,0.84,0.83,3,215,3,0,0,"accounting","high","exist"
"2764",4764,0.23,0.59,5,245,5,0,0,"hr","low","exist"
"2765",4765,0.89,0.75,3,134,3,0,0,"hr","low","exist"
"2766",4766,0.98,0.91,4,273,2,0,0,"hr","low","exist"
"2767",4767,0.88,0.83,4,163,3,0,0,"hr","low","exist"
"2768",4768,0.87,0.52,3,152,4,0,0,"technical","low","exist"
"2769",4769,0.93,0.96,3,268,4,0,0,"technical","low","exist"
"2770",4770,0.13,0.95,5,149,2,0,0,"technical","low","exist"
"2771",4771,0.99,0.56,6,128,4,0,0,"technical","medium","exist"
"2772",4772,0.52,0.51,3,218,2,1,0,"technical","medium","exist"
"2773",4773,0.58,0.98,3,146,3,0,0,"technical","medium","exist"
"2774",4774,0.85,0.57,5,190,2,1,0,"technical","medium","exist"
"2775",4775,0.41,0.59,2,182,3,0,0,"technical","medium","exist"
"2776",4776,1,0.8,5,162,3,1,0,"technical","medium","exist"
"2777",4777,0.63,0.64,3,243,3,0,0,"technical","medium","exist"
"2778",4778,0.63,0.58,4,141,3,1,0,"technical","medium","exist"
"2779",4779,0.63,0.9,3,142,3,0,0,"support","medium","exist"
"2780",4780,0.59,0.62,3,203,4,0,0,"support","medium","exist"
"2781",4781,0.88,0.77,4,168,4,0,0,"support","medium","exist"
"2782",4782,0.72,0.7,3,149,3,0,0,"support","medium","exist"
"2783",4783,0.67,0.81,4,168,2,0,0,"support","high","exist"
"2784",4784,0.41,0.54,2,190,4,0,0,"support","low","exist"
"2785",4785,0.3,0.68,3,229,6,0,0,"support","medium","exist"
"2786",4786,0.83,0.84,3,249,2,0,0,"support","medium","exist"
"2787",4787,0.73,0.93,4,162,2,1,0,"support","medium","exist"
"2788",4788,0.87,0.9,4,163,2,0,0,"support","medium","exist"
"2789",4789,0.93,0.74,2,169,4,0,0,"support","low","exist"
"2790",4790,0.97,0.91,4,257,3,1,0,"technical","low","exist"
"2791",4791,0.7,0.54,4,150,4,0,0,"technical","low","exist"
"2792",4792,0.66,0.95,4,183,3,0,0,"technical","low","exist"
"2793",4793,0.62,0.66,3,208,3,0,0,"management","low","exist"
"2794",4794,0.56,0.52,4,158,2,0,0,"IT","low","exist"
"2795",4795,0.32,0.72,2,240,5,0,0,"IT","low","exist"
"2796",4796,0.55,0.81,5,251,3,0,0,"IT","low","exist"
"2797",4797,0.69,0.91,5,205,2,0,0,"IT","low","exist"
"2798",4798,0.91,0.63,4,226,3,1,0,"IT","low","exist"
"2799",4799,0.33,0.82,5,249,6,0,0,"product_mng","low","exist"
"2800",4800,0.37,0.74,2,197,3,0,0,"product_mng","low","exist"
"2801",4801,0.95,0.57,5,216,3,0,0,"product_mng","low","exist"
"2802",4802,0.17,0.91,4,260,5,0,0,"product_mng","low","exist"
"2803",4803,0.95,0.53,4,263,3,0,0,"IT","low","exist"
"2804",4804,0.27,0.69,2,177,5,0,0,"RandD","low","exist"
"2805",4805,0.91,0.95,4,171,3,0,0,"RandD","low","exist"
"2806",4806,0.49,0.61,3,148,2,1,0,"RandD","low","exist"
"2807",4807,0.6,0.56,4,138,3,0,0,"RandD","low","exist"
"2808",4808,0.52,0.57,4,183,3,0,0,"RandD","low","exist"
"2809",4809,0.54,0.65,3,202,3,0,0,"marketing","low","exist"
"2810",4810,0.86,0.53,4,160,2,1,0,"sales","medium","exist"
"2811",4811,0.78,0.87,4,264,3,0,0,"accounting","medium","exist"
"2812",4812,0.6,0.81,4,245,3,1,0,"support","medium","exist"
"2813",4813,0.15,0.91,2,207,3,0,0,"technical","medium","exist"
"2814",4814,0.72,0.92,3,225,3,0,0,"management","medium","exist"
"2815",4815,0.94,0.85,4,236,2,0,0,"marketing","medium","exist"
"2816",4816,0.92,0.56,4,170,3,0,0,"marketing","medium","exist"
"2817",4817,0.6,0.88,3,261,3,1,0,"marketing","medium","exist"
"2818",4818,0.41,0.68,4,273,3,0,0,"sales","medium","exist"
"2819",4819,0.5,0.43,3,184,2,0,0,"sales","medium","exist"
"2820",4820,0.8,0.91,3,202,2,0,0,"sales","medium","exist"
"2821",4821,0.67,0.83,4,195,3,0,0,"sales","medium","exist"
"2822",4822,0.71,0.88,4,266,3,0,0,"sales","high","exist"
"2823",4823,0.66,0.85,3,266,5,0,0,"sales","low","exist"
"2824",4824,0.77,0.74,5,263,2,0,0,"sales","medium","exist"
"2825",4825,0.62,0.54,3,142,2,0,0,"sales","medium","exist"
"2826",4826,0.95,0.53,4,162,3,0,0,"sales","medium","exist"
"2827",4827,0.89,0.75,5,258,2,0,0,"sales","medium","exist"
"2828",4828,0.74,0.83,4,170,2,0,0,"sales","low","exist"
"2829",4829,0.19,0.8,4,249,5,0,0,"sales","low","exist"
"2830",4830,0.83,0.77,3,171,3,0,0,"sales","low","exist"
"2831",4831,0.53,0.64,2,177,4,0,0,"sales","low","exist"
"2832",4832,0.98,0.75,5,188,2,0,0,"sales","low","exist"
"2833",4833,0.74,0.99,5,146,2,0,0,"sales","low","exist"
"2834",4834,0.22,0.88,5,154,5,0,0,"sales","low","exist"
"2835",4835,0.76,0.68,4,204,3,1,0,"sales","low","exist"
"2836",4836,0.89,0.91,5,224,3,1,0,"sales","low","exist"
"2837",4837,0.5,0.84,3,156,4,0,0,"accounting","low","exist"
"2838",4838,0.17,0.82,6,259,4,0,0,"accounting","low","exist"
"2839",4839,0.46,0.38,6,165,3,0,0,"accounting","low","exist"
"2840",4840,0.68,0.78,3,264,3,0,0,"hr","low","exist"
"2841",4841,0.77,0.86,4,215,2,0,0,"hr","low","exist"
"2842",4842,0.68,0.83,3,133,3,0,0,"hr","low","exist"
"2843",4843,0.15,0.7,4,220,4,0,0,"hr","low","exist"
"2844",4844,0.7,0.98,4,176,5,0,0,"technical","low","exist"
"2845",4845,0.66,0.96,4,103,2,0,0,"technical","low","exist"
"2846",4846,0.54,0.52,5,155,3,0,0,"technical","low","exist"
"2847",4847,0.57,0.57,4,141,3,0,0,"technical","low","exist"
"2848",4848,0.78,0.58,3,150,3,1,0,"technical","low","exist"
"2849",4849,0.14,0.83,5,171,6,0,0,"technical","medium","exist"
"2850",4850,0.73,0.86,4,179,3,0,0,"technical","medium","exist"
"2851",4851,0.65,0.97,4,145,2,0,0,"technical","medium","exist"
"2852",4852,0.31,0.59,3,176,3,0,0,"technical","medium","exist"
"2853",4853,0.77,0.55,2,172,2,0,0,"technical","medium","exist"
"2854",4854,0.68,0.85,3,243,4,0,0,"technical","medium","exist"
"2855",4855,0.79,0.69,4,209,2,0,0,"support","medium","exist"
"2856",4856,0.92,0.62,4,196,3,0,0,"support","medium","exist"
"2857",4857,0.77,0.96,6,225,4,0,0,"support","medium","exist"
"2858",4858,0.48,0.89,3,261,2,0,0,"support","medium","exist"
"2859",4859,0.63,0.66,4,157,2,0,0,"support","medium","exist"
"2860",4860,0.92,0.49,5,259,3,1,0,"support","medium","exist"
"2861",4861,0.5,0.85,4,224,6,0,0,"support","high","exist"
"2862",4862,0.52,0.91,5,193,2,0,0,"support","low","exist"
"2863",4863,0.73,0.79,4,157,3,0,0,"support","medium","exist"
"2864",4864,0.99,0.87,4,223,3,0,0,"support","medium","exist"
"2865",4865,0.91,0.99,3,188,3,1,0,"support","medium","exist"
"2866",4866,0.85,0.79,3,217,2,0,0,"technical","medium","exist"
"2867",4867,0.95,0.69,4,207,2,1,0,"technical","low","exist"
"2868",4868,0.67,0.85,3,153,4,0,0,"technical","low","exist"
"2869",4869,0.86,0.55,3,269,2,0,0,"management","low","exist"
"2870",4870,0.71,0.54,4,198,3,0,0,"IT","low","exist"
"2871",4871,0.99,0.95,4,102,5,0,0,"IT","low","exist"
"2872",4872,0.57,0.61,3,167,2,1,0,"IT","low","exist"
"2873",4873,0.98,0.72,3,252,2,0,0,"IT","low","exist"
"2874",4874,0.62,0.58,3,192,2,0,0,"IT","low","exist"
"2875",4875,0.74,0.79,5,237,4,0,0,"product_mng","low","exist"
"2876",4876,0.7,0.6,4,158,3,0,0,"product_mng","low","exist"
"2877",4877,0.8,0.93,3,260,3,0,0,"product_mng","low","exist"
"2878",4878,0.65,0.69,4,153,3,0,0,"product_mng","low","exist"
"2879",4879,0.53,0.52,3,233,4,0,0,"IT","low","exist"
"2880",4880,0.43,0.62,2,180,3,0,0,"RandD","low","exist"
"2881",4881,0.59,0.65,4,163,3,1,0,"RandD","low","exist"
"2882",4882,0.16,0.96,6,211,6,1,0,"RandD","low","exist"
"2883",4883,0.84,0.8,3,151,3,1,0,"RandD","low","exist"
"2884",4884,0.78,0.95,3,249,4,0,0,"RandD","low","exist"
"2885",4885,0.66,0.91,5,199,3,1,0,"marketing","low","exist"
"2886",4886,0.7,0.74,4,247,2,0,0,"sales","low","exist"
"2887",4887,0.73,0.63,4,174,3,0,0,"accounting","low","exist"
"2888",4888,0.65,0.88,4,268,4,0,0,"support","medium","exist"
"2889",4889,0.79,0.59,5,197,4,0,0,"technical","medium","exist"
"2890",4890,0.57,0.68,4,154,3,1,0,"management","medium","exist"
"2891",4891,0.24,0.58,5,279,4,0,0,"marketing","medium","exist"
"2892",4892,0.95,0.78,3,204,2,1,0,"marketing","medium","exist"
"2893",4893,0.38,0.54,2,112,3,0,0,"marketing","medium","exist"
"2894",4894,0.9,0.78,4,261,2,1,0,"sales","medium","exist"
"2895",4895,0.5,0.4,3,180,4,0,0,"sales","medium","exist"
"2896",4896,0.68,0.61,3,261,5,0,0,"sales","medium","exist"
"2897",4897,0.5,0.78,6,138,3,0,0,"sales","medium","exist"
"2898",4898,0.85,0.81,4,164,4,0,0,"sales","medium","exist"
"2899",4899,0.95,0.52,3,144,3,0,0,"sales","medium","exist"
"2900",4900,0.92,0.92,3,244,2,0,0,"sales","high","exist"
"2901",4901,0.83,0.87,5,233,3,0,0,"sales","low","exist"
"2902",4902,0.9,0.78,4,225,2,1,0,"sales","medium","exist"
"2903",4903,0.21,0.77,6,215,4,0,0,"sales","medium","exist"
"2904",4904,0.94,0.86,3,223,4,0,0,"sales","medium","exist"
"2905",4905,0.7,0.85,4,232,3,0,0,"sales","medium","exist"
"2906",4906,0.54,0.76,3,157,4,0,0,"sales","low","exist"
"2907",4907,0.77,0.65,4,268,3,0,0,"sales","low","exist"
"2908",4908,0.62,0.49,3,158,2,0,0,"sales","low","exist"
"2909",4909,0.93,0.55,5,222,2,0,0,"sales","low","exist"
"2910",4910,0.81,0.86,3,210,3,0,0,"sales","low","exist"
"2911",4911,0.99,0.79,4,133,2,0,0,"sales","low","exist"
"2912",4912,0.78,0.49,3,224,3,0,0,"sales","low","exist"
"2913",4913,0.66,0.63,5,264,5,0,0,"accounting","low","exist"
"2914",4914,0.9,0.72,5,237,2,0,0,"accounting","low","exist"
"2915",4915,0.74,0.53,5,141,2,0,0,"accounting","low","exist"
"2916",4916,0.65,0.78,4,238,5,1,0,"hr","low","exist"
"2917",4917,0.99,0.52,4,167,3,0,0,"hr","low","exist"
"2918",4918,0.83,0.72,4,161,3,0,0,"hr","low","exist"
"2919",4919,0.6,0.82,4,194,3,0,0,"hr","low","exist"
"2920",4920,0.55,0.93,3,217,3,1,0,"technical","low","exist"
"2921",4921,0.96,0.71,3,170,3,0,0,"technical","low","exist"
"2922",4922,0.83,0.94,4,243,3,0,0,"technical","low","exist"
"2923",4923,0.95,0.7,4,267,3,1,0,"technical","low","exist"
"2924",4924,0.77,0.88,2,169,3,0,0,"technical","medium","exist"
"2925",4925,0.83,0.95,3,255,3,0,0,"technical","medium","exist"
"2926",4926,0.87,0.54,4,211,3,0,0,"technical","medium","exist"
"2927",4927,0.69,0.49,3,198,4,0,0,"technical","medium","exist"
"2928",4928,0.67,0.58,3,246,3,0,0,"technical","medium","exist"
"2929",4929,0.55,0.49,3,146,2,0,0,"technical","medium","exist"
"2930",4930,0.55,0.82,4,134,6,0,0,"technical","medium","exist"
"2931",4931,0.39,0.48,3,169,3,0,0,"support","medium","exist"
"2932",4932,0.51,0.93,5,232,3,0,0,"support","medium","exist"
"2933",4933,0.39,0.38,2,106,3,1,0,"support","medium","exist"
"2934",4934,0.96,0.93,4,260,3,0,0,"support","medium","exist"
"2935",4935,0.68,0.81,3,232,2,0,0,"support","medium","exist"
"2936",4936,0.67,0.71,4,173,3,0,0,"support","high","exist"
"2937",4937,0.68,0.44,5,152,5,0,0,"support","low","exist"
"2938",4938,0.56,0.58,3,173,3,0,0,"support","medium","exist"
"2939",4939,0.9,0.7,3,274,3,0,0,"support","medium","exist"
"2940",4940,0.69,0.59,3,233,3,0,0,"support","medium","exist"
"2941",4941,0.99,0.71,4,232,3,0,0,"support","medium","exist"
"2942",4942,0.42,0.59,3,156,2,0,0,"technical","low","exist"
"2943",4943,0.28,0.51,3,124,3,0,0,"technical","low","exist"
"2944",4944,0.55,0.65,3,207,3,0,0,"technical","low","exist"
"2945",4945,0.91,0.53,3,273,6,0,0,"management","low","exist"
"2946",4946,0.53,0.98,3,219,4,0,0,"IT","low","exist"
"2947",4947,0.87,0.74,4,207,3,0,0,"IT","low","exist"
"2948",4948,0.57,0.6,4,248,4,0,0,"IT","low","exist"
"2949",4949,0.59,0.77,3,169,3,0,0,"IT","low","exist"
"2950",4950,0.76,0.89,4,181,3,0,0,"IT","low","exist"
"2951",4951,0.59,0.42,3,196,3,0,0,"product_mng","low","exist"
"2952",4952,0.5,0.54,3,254,2,0,0,"product_mng","low","exist"
"2953",4953,0.55,0.55,4,191,4,0,0,"product_mng","low","exist"
"2954",4954,0.92,0.53,3,238,2,0,0,"product_mng","low","exist"
"2955",4955,0.8,0.51,5,196,3,0,0,"IT","low","exist"
"2956",4956,0.93,0.66,4,228,3,0,0,"RandD","low","exist"
"2957",4957,0.67,0.57,4,165,3,0,0,"RandD","low","exist"
"2958",4958,0.78,0.55,3,144,2,0,0,"RandD","low","exist"
"2959",4959,0.61,0.7,4,243,3,0,0,"RandD","low","exist"
"2960",4960,0.74,0.84,3,206,3,0,0,"RandD","low","exist"
"2961",4961,0.5,0.49,3,180,3,1,0,"marketing","low","exist"
"2962",4962,0.84,0.96,3,161,2,1,0,"sales","low","exist"
"2963",4963,0.89,0.55,4,196,2,0,0,"accounting","medium","exist"
"2964",4964,0.77,0.89,5,152,3,0,0,"support","medium","exist"
"2965",4965,0.64,0.71,3,231,4,0,0,"technical","medium","exist"
"2966",4966,0.77,0.89,2,215,5,1,0,"management","medium","exist"
"2967",4967,0.74,0.58,4,233,4,0,0,"marketing","medium","exist"
"2968",4968,0.88,0.96,4,155,2,0,0,"marketing","medium","exist"
"2969",4969,0.88,0.96,5,182,4,1,0,"marketing","medium","exist"
"2970",4970,0.89,0.88,3,165,4,0,0,"sales","medium","exist"
"2971",4971,0.74,0.59,2,257,4,1,0,"sales","medium","exist"
"2972",4972,0.63,0.74,4,155,2,0,0,"sales","medium","exist"
"2973",4973,0.63,0.8,4,243,2,0,0,"sales","medium","exist"
"2974",4974,0.68,0.92,2,184,4,0,0,"sales","medium","exist"
"2975",4975,0.14,0.81,4,138,3,1,0,"sales","high","exist"
"2976",4976,0.86,0.94,5,209,4,0,0,"sales","low","exist"
"2977",4977,0.73,0.53,3,205,2,0,0,"sales","medium","exist"
"2978",4978,0.57,0.56,3,191,3,0,0,"sales","medium","exist"
"2979",4979,0.97,0.75,5,270,3,1,0,"sales","medium","exist"
"2980",4980,0.67,0.36,4,97,4,0,0,"sales","medium","exist"
"2981",4981,0.89,0.74,4,174,2,0,0,"sales","low","exist"
"2982",4982,0.8,0.96,5,124,3,0,0,"sales","low","exist"
"2983",4983,0.3,0.51,2,178,3,0,0,"sales","low","exist"
"2984",4984,0.14,0.73,5,266,6,0,0,"sales","low","exist"
"2985",4985,0.91,0.8,4,181,3,0,0,"sales","low","exist"
"2986",4986,0.49,0.81,4,233,3,0,0,"sales","low","exist"
"2987",4987,0.57,0.68,3,254,4,0,0,"sales","low","exist"
"2988",4988,0.59,0.62,3,219,3,0,0,"sales","low","exist"
"2989",4989,0.5,0.7,5,166,2,0,0,"accounting","low","exist"
"2990",4990,0.69,0.97,3,158,2,0,0,"accounting","low","exist"
"2991",4991,0.81,0.68,3,151,3,0,0,"accounting","low","exist"
"2992",4992,0.79,0.82,3,98,3,1,0,"hr","low","exist"
"2993",4993,0.55,0.91,4,187,4,1,0,"hr","low","exist"
"2994",4994,0.92,0.62,4,266,2,0,0,"hr","low","exist"
"2995",4995,0.94,0.59,5,250,3,0,0,"hr","low","exist"
"2996",4996,0.67,0.55,5,193,3,0,0,"technical","low","exist"
"2997",4997,0.53,0.92,4,223,3,1,0,"technical","low","exist"
"2998",4998,0.77,0.59,5,189,5,0,0,"technical","low","exist"
"2999",4999,0.57,0.82,5,138,3,0,0,"technical","low","exist"
"3000",5000,0.64,0.97,4,268,2,0,0,"technical","low","exist"
"3001",5001,0.35,1,6,186,2,0,0,"technical","low","exist"
"3002",5002,0.66,0.71,3,136,3,1,0,"technical","medium","exist"
"3003",5003,0.59,0.84,4,245,3,0,0,"technical","medium","exist"
"3004",5004,0.49,0.93,4,184,3,0,0,"technical","medium","exist"
"3005",5005,0.91,0.99,5,152,3,0,0,"technical","medium","exist"
"3006",5006,0.12,0.6,2,194,4,0,0,"technical","medium","exist"
"3007",5007,0.74,0.68,3,242,5,1,0,"support","medium","exist"
"3008",5008,0.84,0.94,4,246,2,1,0,"support","medium","exist"
"3009",5009,0.51,0.99,4,211,3,0,0,"support","medium","exist"
"3010",5010,0.94,0.71,4,189,3,0,0,"support","medium","exist"
"3011",5011,0.74,0.66,3,254,2,0,0,"support","medium","exist"
"3012",5012,0.52,0.54,5,239,3,0,0,"support","medium","exist"
"3013",5013,0.31,0.92,4,133,6,0,0,"support","medium","exist"
"3014",5014,0.72,0.59,3,255,2,0,0,"support","high","exist"
"3015",5015,0.92,1,3,212,2,0,0,"support","low","exist"
"3016",5016,0.56,0.64,3,270,3,0,0,"support","medium","exist"
"3017",5017,0.76,0.45,5,177,6,0,0,"support","medium","exist"
"3018",5018,0.59,0.9,4,261,4,0,0,"technical","medium","exist"
"3019",5019,0.5,0.74,3,220,3,0,0,"technical","medium","exist"
"3020",5020,0.88,0.72,2,144,4,1,0,"technical","low","exist"
"3021",5021,0.86,0.49,4,274,2,0,0,"management","low","exist"
"3022",5022,0.66,0.99,4,195,3,0,0,"IT","low","exist"
"3023",5023,0.7,0.69,4,158,3,0,0,"IT","low","exist"
"3024",5024,0.98,0.93,5,145,4,0,0,"IT","low","exist"
"3025",5025,0.61,0.73,3,165,3,1,0,"IT","low","exist"
"3026",5026,0.57,0.66,4,270,2,0,0,"IT","low","exist"
"3027",5027,0.84,0.91,5,208,3,0,0,"product_mng","low","exist"
"3028",5028,0.76,0.4,2,245,5,0,0,"product_mng","medium","exist"
"3029",5029,0.64,0.99,4,180,4,0,0,"product_mng","medium","exist"
"3030",5030,0.87,0.7,5,225,3,0,0,"product_mng","medium","exist"
"3031",5031,0.62,0.69,3,261,2,0,0,"IT","medium","exist"
"3032",5032,0.16,0.99,3,213,6,1,1,"RandD","medium","exist"
"3033",5033,0.83,0.87,3,230,3,0,1,"RandD","medium","exist"
"3034",5034,0.36,0.59,2,198,2,0,1,"RandD","medium","exist"
"3035",5035,0.47,0.51,6,190,5,0,1,"RandD","medium","exist"
"3036",5036,0.54,0.51,4,137,4,0,1,"RandD","medium","exist"
"3037",5037,0.83,0.83,3,186,3,1,1,"marketing","medium","exist"
"3038",5038,0.96,0.68,4,137,2,0,1,"sales","medium","exist"
"3039",5039,0.91,0.74,5,192,3,0,1,"accounting","medium","exist"
"3040",5040,0.56,0.59,4,164,3,0,1,"support","medium","exist"
"3041",5041,0.73,0.66,6,195,3,0,1,"technical","medium","exist"
"3042",5042,0.97,0.63,4,151,3,0,1,"management","medium","exist"
"3043",5043,0.75,0.74,5,231,3,0,1,"marketing","medium","exist"
"3044",5044,0.49,0.76,3,257,2,1,0,"marketing","medium","exist"
"3045",5045,0.57,0.94,4,257,3,0,0,"marketing","medium","exist"
"3046",5046,0.41,0.58,5,274,2,1,0,"sales","medium","exist"
"3047",5047,0.53,0.7,3,138,2,0,0,"sales","medium","exist"
"3048",5048,0.93,0.6,4,184,3,0,0,"sales","medium","exist"
"3049",5049,0.58,0.9,3,151,3,0,0,"sales","medium","exist"
"3050",5050,0.6,0.54,3,265,3,1,0,"sales","medium","exist"
"3051",5051,0.74,0.8,4,241,2,0,0,"sales","medium","exist"
"3052",5052,0.62,0.52,3,148,3,0,0,"sales","medium","exist"
"3053",5053,0.7,0.76,5,165,3,0,0,"sales","medium","exist"
"3054",5054,0.93,0.75,3,243,2,0,0,"sales","medium","exist"
"3055",5055,0.75,0.9,4,197,2,0,0,"sales","medium","exist"
"3056",5056,0.95,0.48,5,214,6,1,1,"sales","medium","exist"
"3057",5057,0.43,0.98,4,164,3,0,1,"sales","medium","exist"
"3058",5058,0.77,0.58,4,243,4,1,1,"sales","medium","exist"
"3059",5059,0.67,1,4,145,3,1,1,"sales","medium","exist"
"3060",5060,0.51,0.72,4,163,4,0,1,"sales","medium","exist"
"3061",5061,0.94,0.53,5,257,2,0,1,"sales","medium","exist"
"3062",5062,0.9,0.85,3,253,3,0,0,"sales","medium","exist"
"3063",5063,0.8,0.78,4,234,3,0,0,"sales","medium","exist"
"3064",5064,0.34,0.89,5,266,6,0,0,"sales","medium","exist"
"3065",5065,0.45,0.53,3,181,4,1,0,"accounting","low","exist"
"3066",5066,0.97,0.66,4,193,4,0,0,"accounting","low","exist"
"3067",5067,0.5,0.48,3,163,4,0,0,"accounting","low","exist"
"3068",5068,0.89,0.62,5,144,2,0,0,"hr","low","exist"
"3069",5069,0.76,0.5,4,245,3,0,0,"hr","low","exist"
"3070",5070,0.66,0.84,4,197,2,0,0,"hr","low","exist"
"3071",5071,0.74,0.63,3,180,2,0,0,"hr","low","exist"
"3072",5072,0.69,0.74,4,237,3,0,0,"technical","low","exist"
"3073",5073,0.59,0.57,2,170,3,0,0,"technical","low","exist"
"3074",5074,1,0.85,3,150,3,0,0,"technical","low","exist"
"3075",5075,0.61,0.75,2,100,4,0,0,"technical","low","exist"
"3076",5076,0.98,0.42,5,226,3,0,0,"technical","low","exist"
"3077",5077,0.59,0.71,5,222,3,0,0,"technical","low","exist"
"3078",5078,0.22,0.69,4,182,6,0,0,"technical","low","exist"
"3079",5079,0.71,0.95,3,150,2,0,0,"technical","low","exist"
"3080",5080,0.86,0.53,4,244,3,0,0,"technical","medium","exist"
"3081",5081,0.65,0.59,5,271,3,0,0,"technical","medium","exist"
"3082",5082,0.93,0.67,5,167,3,0,0,"technical","medium","exist"
"3083",5083,0.49,0.69,2,128,2,0,0,"support","medium","exist"
"3084",5084,0.78,0.77,3,149,4,1,0,"support","medium","exist"
"3085",5085,0.62,0.7,4,141,4,0,0,"support","medium","exist"
"3086",5086,0.72,0.63,3,149,2,0,0,"support","medium","exist"
"3087",5087,0.7,0.56,4,107,6,0,0,"support","medium","exist"
"3088",5088,0.54,0.93,5,189,2,0,0,"support","medium","exist"
"3089",5089,0.61,0.95,4,169,4,0,0,"support","medium","exist"
"3090",5090,0.84,0.95,4,208,3,1,0,"support","medium","exist"
"3091",5091,0.8,0.58,3,197,3,0,0,"support","medium","exist"
"3092",5092,0.58,0.5,4,225,3,0,0,"support","high","exist"
"3093",5093,0.97,0.87,2,175,6,0,0,"support","high","exist"
"3094",5094,0.92,0.55,3,172,2,0,0,"technical","high","exist"
"3095",5095,0.96,0.51,3,237,4,0,0,"technical","high","exist"
"3096",5096,0.73,0.87,4,155,3,1,0,"technical","high","exist"
"3097",5097,0.73,0.71,4,148,3,0,0,"management","high","exist"
"3098",5098,0.53,0.83,4,246,3,0,0,"IT","high","exist"
"3099",5099,0.17,0.82,5,193,5,0,0,"IT","high","exist"
"3100",5100,0.75,0.76,5,175,4,0,0,"IT","low","exist"
"3101",5101,0.76,0.44,3,121,4,0,0,"IT","low","exist"
"3102",5102,0.76,0.77,4,223,3,0,0,"IT","low","exist"
"3103",5103,0.92,0.55,3,259,3,0,0,"product_mng","low","exist"
"3104",5104,0.82,0.88,4,171,2,0,0,"product_mng","low","exist"
"3105",5105,0.38,0.5,2,170,3,0,0,"product_mng","low","exist"
"3106",5106,0.49,0.72,4,246,3,0,0,"product_mng","low","exist"
"3107",5107,0.53,0.8,3,175,3,0,0,"IT","low","exist"
"3108",5108,0.56,0.59,3,185,2,0,0,"RandD","medium","exist"
"3109",5109,0.69,0.98,3,168,2,0,0,"RandD","medium","exist"
"3110",5110,0.62,0.99,4,171,3,0,0,"RandD","medium","exist"
"3111",5111,0.71,0.76,3,201,2,0,0,"RandD","medium","exist"
"3112",5112,0.52,1,4,148,3,0,0,"RandD","medium","exist"
"3113",5113,0.2,0.53,6,189,4,0,0,"marketing","medium","exist"
"3114",5114,0.93,0.61,3,166,3,0,0,"sales","medium","exist"
"3115",5115,0.74,0.81,4,150,2,0,0,"accounting","medium","exist"
"3116",5116,0.78,0.45,3,253,6,0,1,"support","medium","exist"
"3117",5117,0.85,0.79,3,243,2,0,1,"technical","medium","exist"
"3118",5118,0.79,0.56,5,250,2,1,1,"management","medium","exist"
"3119",5119,0.92,0.91,3,228,2,0,1,"marketing","medium","exist"
"3120",5120,0.58,0.97,3,186,3,0,1,"marketing","medium","exist"
"3121",5121,0.68,0.72,3,213,3,0,1,"marketing","medium","exist"
"3122",5122,0.9,0.67,4,233,3,0,1,"sales","medium","exist"
"3123",5123,0.67,0.71,5,265,2,0,1,"sales","medium","exist"
"3124",5124,0.79,0.73,4,226,3,0,0,"sales","medium","exist"
"3125",5125,0.23,0.48,5,221,6,0,0,"sales","medium","exist"
"3126",5126,0.98,0.99,3,253,4,0,0,"sales","medium","exist"
"3127",5127,0.8,0.75,3,134,4,0,0,"sales","medium","exist"
"3128",5128,0.77,0.84,3,188,4,0,0,"sales","medium","exist"
"3129",5129,1,0.91,3,160,4,0,0,"sales","medium","exist"
"3130",5130,0.6,0.92,4,164,4,0,0,"sales","medium","exist"
"3131",5131,0.49,0.54,6,214,3,0,0,"sales","high","exist"
"3132",5132,0.91,0.99,5,228,4,1,0,"sales","low","exist"
"3133",5133,0.97,0.52,5,149,3,1,0,"sales","medium","exist"
"3134",5134,0.71,0.76,3,175,2,0,0,"sales","medium","exist"
"3135",5135,0.62,0.91,3,195,3,0,0,"sales","medium","exist"
"3136",5136,0.61,0.92,3,222,4,0,0,"sales","medium","exist"
"3137",5137,0.21,0.6,5,249,4,0,0,"sales","medium","exist"
"3138",5138,0.64,0.97,2,226,3,1,0,"sales","medium","exist"
"3139",5139,0.61,0.65,2,117,2,1,0,"sales","medium","exist"
"3140",5140,0.58,0.75,4,255,3,0,0,"sales","medium","exist"
"3141",5141,0.41,0.9,6,155,2,0,0,"accounting","medium","exist"
"3142",5142,0.98,0.73,5,185,3,0,0,"accounting","medium","exist"
"3143",5143,0.5,0.88,4,275,5,0,0,"accounting","low","exist"
"3144",5144,0.98,0.61,3,226,2,0,0,"hr","low","exist"
"3145",5145,0.4,0.85,4,198,2,0,0,"hr","low","exist"
"3146",5146,0.63,0.92,2,198,2,0,0,"hr","low","exist"
"3147",5147,0.75,0.53,4,251,3,0,0,"hr","low","exist"
"3148",5148,0.82,0.84,3,237,2,0,0,"technical","low","exist"
"3149",5149,0.55,0.62,5,197,2,0,0,"technical","low","exist"
"3150",5150,0.44,0.36,2,136,3,0,0,"technical","high","exist"
"3151",5151,0.92,0.88,3,184,3,1,0,"technical","low","exist"
"3152",5152,0.57,0.56,2,159,3,0,0,"technical","high","exist"
"3153",5153,0.73,0.86,4,133,3,0,0,"technical","high","exist"
"3154",5154,0.82,0.92,5,198,3,0,0,"technical","low","exist"
"3155",5155,0.54,0.75,3,260,2,0,0,"technical","low","exist"
"3156",5156,0.64,0.95,3,154,4,0,0,"technical","high","exist"
"3157",5157,0.99,0.76,4,185,5,0,0,"technical","low","exist"
"3158",5158,0.19,0.92,5,193,6,0,0,"technical","medium","exist"
"3159",5159,0.86,0.96,4,167,3,0,0,"support","high","exist"
"3160",5160,0.65,0.66,5,165,3,0,0,"support","medium","exist"
"3161",5161,0.52,0.81,3,253,2,0,0,"support","medium","exist"
"3162",5162,0.85,0.49,4,142,3,0,0,"support","medium","exist"
"3163",5163,0.61,0.64,5,186,4,0,0,"support","medium","exist"
"3164",5164,0.77,0.57,4,203,3,1,0,"support","high","exist"
"3165",5165,0.54,0.94,4,217,2,0,0,"support","medium","exist"
"3166",5166,0.76,0.74,4,187,3,0,0,"support","medium","exist"
"3167",5167,0.79,0.9,3,152,4,0,0,"support","medium","exist"
"3168",5168,0.89,0.93,5,150,2,0,0,"support","high","exist"
"3169",5169,0.6,0.8,3,191,2,0,0,"support","medium","exist"
"3170",5170,0.51,0.58,4,140,3,0,0,"technical","high","exist"
"3171",5171,0.2,0.72,5,123,2,1,0,"technical","low","exist"
"3172",5172,0.93,0.6,5,170,2,0,0,"technical","medium","exist"
"3173",5173,0.77,0.54,3,227,4,0,0,"management","medium","exist"
"3174",5174,0.8,0.87,4,220,2,0,0,"IT","medium","exist"
"3175",5175,0.8,0.97,5,258,3,0,0,"IT","medium","exist"
"3176",5176,0.62,0.92,5,149,3,0,0,"IT","low","exist"
"3177",5177,0.79,0.72,4,192,3,0,0,"IT","low","exist"
"3178",5178,0.88,0.73,5,267,3,0,0,"IT","low","exist"
"3179",5179,0.96,0.73,5,169,3,1,0,"product_mng","low","exist"
"3180",5180,0.34,0.69,2,178,3,0,0,"product_mng","low","exist"
"3181",5181,0.34,0.65,2,165,4,0,0,"product_mng","low","exist"
"3182",5182,0.88,0.85,4,231,3,0,0,"product_mng","low","exist"
"3183",5183,0.66,0.61,3,260,3,0,0,"IT","low","exist"
"3184",5184,0.55,0.71,4,181,2,1,0,"RandD","low","exist"
"3185",5185,0.59,0.51,3,243,2,0,0,"RandD","low","exist"
"3186",5186,0.62,0.73,4,191,3,0,0,"RandD","high","exist"
"3187",5187,0.78,0.93,3,200,3,0,0,"RandD","low","exist"
"3188",5188,0.73,0.75,5,265,3,0,0,"RandD","low","exist"
"3189",5189,0.71,0.94,4,246,3,0,0,"marketing","low","exist"
"3190",5190,0.97,0.86,3,187,2,0,0,"sales","low","exist"
"3191",5191,0.21,0.74,5,141,4,0,0,"accounting","high","exist"
"3192",5192,0.52,0.96,4,246,3,0,0,"support","low","exist"
"3193",5193,0.73,0.88,4,236,3,1,0,"technical","low","exist"
"3194",5194,0.74,0.83,3,170,3,0,0,"management","low","exist"
"3195",5195,0.26,0.71,6,189,2,0,0,"marketing","high","exist"
"3196",5196,0.52,0.78,4,237,3,0,0,"marketing","low","exist"
"3197",5197,0.69,0.54,4,180,3,0,0,"marketing","medium","exist"
"3198",5198,0.79,0.59,2,157,6,0,0,"sales","high","exist"
"3199",5199,0.93,0.62,4,258,3,0,0,"sales","medium","exist"
"3200",5200,0.34,0.87,4,283,2,0,0,"sales","high","exist"
"3201",5201,0.77,0.52,4,216,3,0,0,"sales","medium","exist"
"3202",5202,0.36,0.73,3,187,3,0,0,"sales","medium","exist"
"3203",5203,0.93,0.58,3,215,3,0,0,"sales","medium","exist"
"3204",5204,0.7,0.58,3,211,3,0,0,"sales","medium","exist"
"3205",5205,0.51,0.49,4,182,2,0,0,"sales","medium","exist"
"3206",5206,0.83,0.78,3,165,3,1,0,"sales","medium","exist"
"3207",5207,0.89,0.89,4,265,2,0,0,"sales","medium","exist"
"3208",5208,0.94,0.59,3,137,2,0,0,"sales","medium","exist"
"3209",5209,0.8,0.55,4,269,3,0,0,"sales","high","exist"
"3210",5210,0.74,0.66,3,177,2,0,0,"sales","low","exist"
"3211",5211,0.5,0.91,3,240,2,0,0,"sales","medium","exist"
"3212",5212,0.54,0.84,4,174,2,0,0,"sales","medium","exist"
"3213",5213,0.5,0.54,3,134,3,0,0,"sales","medium","exist"
"3214",5214,0.17,0.91,2,271,4,0,0,"sales","medium","exist"
"3215",5215,0.57,0.53,5,216,2,1,0,"sales","medium","exist"
"3216",5216,0.8,0.51,4,213,3,0,0,"sales","medium","exist"
"3217",5217,0.45,0.64,5,133,4,0,0,"accounting","medium","exist"
"3218",5218,0.87,0.5,4,267,2,1,0,"accounting","medium","exist"
"3219",5219,0.98,0.64,3,263,4,0,0,"accounting","medium","exist"
"3220",5220,0.55,0.8,4,260,3,0,0,"hr","high","exist"
"3221",5221,0.53,0.5,4,185,3,0,0,"hr","low","exist"
"3222",5222,0.75,0.48,2,284,6,0,0,"hr","low","exist"
"3223",5223,0.96,0.59,3,229,3,0,0,"hr","low","exist"
"3224",5224,0.71,0.97,3,189,3,1,0,"technical","high","exist"
"3225",5225,0.7,0.63,3,209,3,0,0,"technical","low","exist"
"3226",5226,0.33,0.94,4,166,6,0,0,"technical","low","exist"
"3227",5227,0.93,0.94,3,183,2,0,0,"technical","low","exist"
"3228",5228,0.64,0.65,3,181,2,0,0,"technical","high","exist"
"3229",5229,0.27,0.45,3,239,4,0,0,"technical","low","exist"
"3230",5230,0.99,0.99,3,158,2,0,0,"technical","low","exist"
"3231",5231,0.81,0.62,3,187,3,0,0,"technical","low","exist"
"3232",5232,0.6,0.91,4,236,3,0,0,"technical","low","exist"
"3233",5233,0.32,0.4,6,162,5,0,0,"technical","low","exist"
"3234",5234,0.48,0.68,4,163,2,1,0,"technical","low","exist"
"3235",5235,0.87,0.51,4,173,3,0,0,"support","low","exist"
"3236",5236,0.91,0.79,5,273,4,1,0,"support","medium","exist"
"3237",5237,0.24,0.89,5,142,4,0,0,"support","medium","exist"
"3238",5238,0.66,0.56,4,141,4,0,0,"support","medium","exist"
"3239",5239,0.94,0.59,3,234,2,0,0,"support","medium","exist"
"3240",5240,0.93,0.97,5,255,4,0,0,"support","medium","exist"
"3241",5241,0.19,0.55,5,148,4,0,0,"support","medium","exist"
"3242",5242,0.88,0.45,5,274,2,1,0,"support","medium","exist"
"3243",5243,0.76,0.47,3,223,2,0,0,"support","medium","exist"
"3244",5244,0.67,0.64,5,248,3,0,0,"support","medium","exist"
"3245",5245,0.54,0.5,4,146,2,0,0,"support","medium","exist"
"3246",5246,0.53,0.95,2,101,5,0,0,"technical","medium","exist"
"3247",5247,0.67,0.92,4,265,4,0,0,"technical","medium","exist"
"3248",5248,0.5,0.38,5,175,4,0,0,"technical","high","exist"
"3249",5249,0.49,0.76,4,202,3,0,0,"management","low","exist"
"3250",5250,0.82,0.71,3,160,3,0,0,"IT","medium","exist"
"3251",5251,0.86,0.71,3,235,3,0,0,"IT","medium","exist"
"3252",5252,0.5,0.5,4,267,3,0,0,"IT","medium","exist"
"3253",5253,0.82,0.6,3,261,2,1,0,"IT","medium","exist"
"3254",5254,0.95,0.78,2,148,2,0,0,"IT","low","exist"
"3255",5255,0.64,0.87,3,239,4,0,0,"product_mng","low","exist"
"3256",5256,0.91,0.5,3,178,2,0,0,"product_mng","low","exist"
"3257",5257,0.79,0.75,2,221,3,0,0,"product_mng","low","exist"
"3258",5258,0.83,0.56,4,269,3,0,0,"product_mng","low","exist"
"3259",5259,0.66,0.6,3,262,2,1,0,"IT","low","exist"
"3260",5260,0.92,0.8,4,263,4,0,0,"RandD","low","exist"
"3261",5261,0.59,0.47,5,191,3,0,0,"RandD","low","exist"
"3262",5262,0.6,0.83,2,189,2,0,0,"RandD","low","exist"
"3263",5263,0.68,0.97,5,207,4,1,0,"RandD","low","exist"
"3264",5264,0.58,0.73,3,265,6,0,0,"RandD","low","exist"
"3265",5265,0.93,0.77,5,224,2,0,0,"marketing","low","exist"
"3266",5266,0.66,0.5,3,229,3,0,0,"marketing","low","exist"
"3267",5267,0.8,0.99,3,158,3,0,0,"sales","low","exist"
"3268",5268,0.28,0.79,5,202,5,0,0,"accounting","low","exist"
"3269",5269,0.84,0.59,4,216,2,0,0,"support","low","exist"
"3270",5270,0.86,0.58,4,220,5,0,0,"technical","low","exist"
"3271",5271,0.46,0.45,2,172,2,1,0,"management","low","exist"
"3272",5272,0.94,0.92,3,187,2,0,0,"marketing","low","exist"
"3273",5273,0.8,0.76,3,270,2,1,0,"marketing","low","exist"
"3274",5274,0.13,0.63,6,219,6,0,0,"marketing","low","exist"
"3275",5275,0.95,0.73,3,243,3,1,0,"sales","medium","exist"
"3276",5276,0.93,0.88,4,261,4,0,0,"sales","medium","exist"
"3277",5277,0.86,0.81,4,179,3,0,0,"sales","medium","exist"
"3278",5278,0.67,0.93,5,133,2,0,0,"sales","medium","exist"
"3279",5279,0.73,0.6,4,224,3,0,0,"sales","medium","exist"
"3280",5280,0.62,0.92,4,198,2,0,0,"sales","medium","exist"
"3281",5281,0.53,0.81,5,135,2,0,0,"sales","medium","exist"
"3282",5282,0.68,0.68,3,143,3,0,0,"sales","medium","exist"
"3283",5283,0.69,0.55,4,234,2,0,0,"sales","medium","exist"
"3284",5284,0.66,0.92,3,177,3,0,0,"sales","medium","exist"
"3285",5285,0.98,0.56,5,180,3,0,0,"sales","medium","exist"
"3286",5286,0.57,0.39,3,193,6,0,0,"sales","medium","exist"
"3287",5287,0.64,0.78,5,148,4,0,0,"sales","high","exist"
"3288",5288,0.71,0.58,3,194,4,0,0,"sales","low","exist"
"3289",5289,0.94,0.7,3,271,4,0,0,"sales","medium","exist"
"3290",5290,0.8,0.85,3,135,2,0,0,"sales","medium","exist"
"3291",5291,0.59,0.94,4,136,2,0,0,"sales","medium","exist"
"3292",5292,0.95,0.7,6,243,3,0,0,"sales","medium","exist"
"3293",5293,1,0.39,2,210,5,0,0,"sales","low","exist"
"3294",5294,0.53,0.59,3,163,4,0,0,"accounting","low","exist"
"3295",5295,0.35,0.59,5,268,3,0,0,"accounting","low","exist"
"3296",5296,0.73,0.66,3,244,3,0,0,"accounting","low","exist"
"3297",5297,0.89,0.63,4,164,3,0,0,"hr","low","exist"
"3298",5298,0.21,0.93,4,260,3,0,0,"hr","low","exist"
"3299",5299,0.21,0.85,5,153,3,0,0,"hr","low","exist"
"3300",5300,0.6,0.83,4,216,2,1,0,"hr","low","exist"
"3301",5301,0.94,0.69,2,198,3,0,0,"technical","low","exist"
"3302",5302,0.92,0.68,4,196,3,1,0,"technical","low","exist"
"3303",5303,0.92,0.78,3,218,3,0,0,"technical","low","exist"
"3304",5304,0.71,0.98,5,167,3,0,0,"technical","low","exist"
"3305",5305,0.69,0.83,4,264,3,0,0,"technical","low","exist"
"3306",5306,0.26,0.51,2,284,2,0,0,"technical","low","exist"
"3307",5307,0.21,0.78,4,218,6,0,0,"technical","low","exist"
"3308",5308,0.36,0.42,2,192,2,0,0,"technical","low","exist"
"3309",5309,0.81,0.92,5,255,4,1,0,"technical","low","exist"
"3310",5310,0.54,0.88,3,251,2,0,0,"technical","low","exist"
"3311",5311,0.63,0.87,5,248,2,0,0,"technical","low","exist"
"3312",5312,0.86,0.75,5,157,4,0,0,"support","low","exist"
"3313",5313,0.8,0.79,5,240,2,0,0,"support","low","exist"
"3314",5314,0.55,0.58,5,262,3,0,0,"support","medium","exist"
"3315",5315,0.18,0.6,3,130,2,1,0,"support","medium","exist"
"3316",5316,0.88,0.98,3,152,3,0,0,"support","medium","exist"
"3317",5317,0.65,0.86,4,256,2,0,0,"support","medium","exist"
"3318",5318,0.99,1,3,139,2,0,0,"support","medium","exist"
"3319",5319,0.88,0.93,4,195,2,0,0,"support","medium","exist"
"3320",5320,0.67,0.59,3,205,5,0,0,"support","medium","exist"
"3321",5321,0.53,0.59,4,265,2,0,0,"support","medium","exist"
"3322",5322,0.83,0.61,5,246,3,0,0,"support","medium","exist"
"3323",5323,0.36,0.71,3,100,3,0,0,"technical","medium","exist"
"3324",5324,0.62,0.64,5,150,3,1,0,"technical","medium","exist"
"3325",5325,0.72,0.67,4,147,2,0,0,"technical","medium","exist"
"3326",5326,0.79,0.54,4,244,3,0,0,"management","high","exist"
"3327",5327,1,0.87,4,256,3,0,0,"IT","low","exist"
"3328",5328,0.65,0.52,4,266,3,1,0,"IT","medium","exist"
"3329",5329,0.84,0.91,3,199,4,0,0,"IT","medium","exist"
"3330",5330,0.81,0.59,2,236,3,0,0,"IT","medium","exist"
"3331",5331,0.59,0.51,3,203,2,1,0,"IT","medium","exist"
"3332",5332,0.78,0.53,3,156,3,0,0,"product_mng","low","exist"
"3333",5333,0.22,0.52,5,109,4,0,0,"product_mng","low","exist"
"3334",5334,0.96,0.98,5,248,3,0,0,"product_mng","low","exist"
"3335",5335,0.85,0.8,4,254,2,0,0,"product_mng","low","exist"
"3336",5336,0.12,0.73,6,166,3,0,0,"IT","low","exist"
"3337",5337,0.6,0.68,4,264,2,1,0,"RandD","low","exist"
"3338",5338,0.93,0.84,5,266,3,0,0,"RandD","low","exist"
"3339",5339,0.73,0.86,4,138,2,0,0,"RandD","low","exist"
"3340",5340,0.7,0.66,3,151,2,0,0,"RandD","low","exist"
"3341",5341,0.18,0.59,4,132,3,0,0,"RandD","low","exist"
"3342",5342,0.81,0.6,4,133,3,0,0,"RandD","low","exist"
"3343",5343,0.28,0.9,4,275,6,0,0,"marketing","low","exist"
"3344",5344,0.74,0.79,3,275,3,0,0,"sales","low","exist"
"3345",5345,0.5,0.74,4,272,5,0,0,"accounting","low","exist"
"3346",5346,0.83,0.85,4,201,2,1,0,"support","low","exist"
"3347",5347,0.55,0.66,3,164,2,0,0,"technical","low","exist"
"3348",5348,0.77,0.94,4,224,2,0,0,"management","low","exist"
"3349",5349,0.92,0.58,4,201,2,0,0,"marketing","low","exist"
"3350",5350,0.59,0.89,5,169,2,1,0,"marketing","low","exist"
"3351",5351,0.45,0.72,4,149,3,0,0,"marketing","low","exist"
"3352",5352,0.76,0.97,3,271,3,0,0,"sales","low","exist"
"3353",5353,0.89,0.69,4,137,3,0,0,"sales","medium","exist"
"3354",5354,0.73,0.5,3,208,2,0,0,"sales","medium","exist"
"3355",5355,0.65,0.7,3,231,3,0,0,"sales","medium","exist"
"3356",5356,0.14,0.96,3,196,5,1,0,"sales","medium","exist"
"3357",5357,0.3,0.47,2,159,4,0,0,"sales","medium","exist"
"3358",5358,0.53,0.82,5,184,3,0,0,"sales","medium","exist"
"3359",5359,0.66,0.89,3,257,3,0,0,"sales","medium","exist"
"3360",5360,0.84,0.59,3,234,2,0,0,"sales","medium","exist"
"3361",5361,0.74,0.97,3,239,4,1,0,"sales","medium","exist"
"3362",5362,0.56,0.4,2,255,3,0,0,"sales","medium","exist"
"3363",5363,0.42,0.47,4,146,3,1,0,"sales","medium","exist"
"3364",5364,0.29,0.8,5,103,6,0,0,"sales","medium","exist"
"3365",5365,0.54,0.72,5,206,4,0,0,"sales","high","exist"
"3366",5366,0.8,0.52,3,253,2,1,0,"sales","low","exist"
"3367",5367,0.89,0.93,4,245,4,0,0,"sales","medium","exist"
"3368",5368,0.92,0.58,3,261,3,1,0,"sales","medium","exist"
"3369",5369,0.87,0.68,4,217,3,0,0,"sales","medium","exist"
"3370",5370,0.76,0.82,4,172,3,1,0,"sales","medium","exist"
"3371",5371,0.64,0.61,3,221,3,0,0,"accounting","low","exist"
"3372",5372,0.83,0.57,2,246,5,1,0,"accounting","low","exist"
"3373",5373,0.55,0.6,3,145,4,0,0,"accounting","low","exist"
"3374",5374,0.83,0.7,5,168,3,0,0,"hr","low","exist"
"3375",5375,0.58,0.62,5,184,3,0,0,"hr","low","exist"
"3376",5376,0.67,0.97,4,186,3,0,0,"hr","low","exist"
"3377",5377,0.65,0.57,3,238,3,0,0,"hr","low","exist"
"3378",5378,0.89,0.95,5,203,3,0,0,"technical","low","exist"
"3379",5379,0.84,0.5,5,195,3,0,0,"technical","low","exist"
"3380",5380,0.5,0.7,5,264,2,0,0,"technical","low","exist"
"3381",5381,0.7,0.51,3,256,3,0,0,"technical","low","exist"
"3382",5382,0.79,0.83,5,268,3,0,0,"technical","low","exist"
"3383",5383,0.19,0.72,6,243,6,1,0,"technical","low","exist"
"3384",5384,0.89,0.5,4,136,2,1,0,"technical","low","exist"
"3385",5385,0.36,0.6,2,136,6,0,0,"technical","low","exist"
"3386",5386,0.62,0.66,5,165,3,0,0,"technical","low","exist"
"3387",5387,0.84,0.93,6,166,4,0,0,"technical","low","exist"
"3388",5388,0.65,0.87,4,267,2,0,0,"technical","low","exist"
"3389",5389,0.65,0.7,4,233,3,0,0,"support","medium","exist"
"3390",5390,0.87,0.92,3,141,2,0,0,"support","medium","exist"
"3391",5391,0.66,0.73,5,249,2,0,0,"support","medium","exist"
"3392",5392,0.83,0.9,3,102,4,0,0,"support","medium","exist"
"3393",5393,0.89,0.63,3,268,3,0,0,"support","medium","exist"
"3394",5394,0.91,0.97,4,139,3,0,0,"support","medium","exist"
"3395",5395,0.91,0.56,3,168,2,0,0,"support","medium","exist"
"3396",5396,0.83,0.5,4,259,2,0,0,"support","medium","exist"
"3397",5397,0.87,0.82,4,248,2,0,0,"support","medium","exist"
"3398",5398,0.62,0.79,3,274,3,0,0,"support","medium","exist"
"3399",5399,0.54,1,3,169,2,0,0,"support","medium","exist"
"3400",5400,0.84,0.53,5,190,3,0,0,"technical","medium","exist"
"3401",5401,0.33,0.82,2,114,5,0,0,"technical","high","exist"
"3402",5402,0.79,0.58,4,191,6,0,0,"technical","low","exist"
"3403",5403,0.31,0.41,2,263,3,0,0,"management","medium","exist"
"3404",5404,0.68,0.81,3,166,2,0,0,"IT","medium","exist"
"3405",5405,0.52,0.7,4,247,5,0,0,"IT","medium","exist"
"3406",5406,0.54,0.64,3,203,4,0,0,"IT","medium","exist"
"3407",5407,0.73,0.78,4,181,4,0,0,"IT","low","exist"
"3408",5408,0.49,0.74,3,229,3,1,0,"IT","low","exist"
"3409",5409,0.37,0.67,2,159,2,0,0,"product_mng","low","exist"
"3410",5410,0.53,0.84,3,151,3,0,0,"product_mng","low","exist"
"3411",5411,0.58,0.75,4,222,3,1,0,"product_mng","low","exist"
"3412",5412,0.2,0.51,2,163,2,0,0,"product_mng","low","exist"
"3413",5413,0.91,0.6,4,163,5,0,0,"IT","low","exist"
"3414",5414,0.53,0.78,2,138,2,0,0,"RandD","low","exist"
"3415",5415,0.99,0.72,4,136,3,0,0,"RandD","low","exist"
"3416",5416,0.97,0.87,3,207,4,0,0,"RandD","low","exist"
"3417",5417,0.18,0.93,3,245,4,1,0,"RandD","low","exist"
"3418",5418,0.83,0.93,6,130,5,0,0,"RandD","low","exist"
"3419",5419,0.49,0.47,4,285,3,0,0,"RandD","low","exist"
"3420",5420,0.74,0.93,3,204,4,0,0,"marketing","low","exist"
"3421",5421,0.7,0.6,3,183,3,0,0,"sales","low","exist"
"3422",5422,0.97,0.91,3,246,2,0,0,"accounting","low","exist"
"3423",5423,0.92,0.91,3,250,4,0,0,"support","low","exist"
"3424",5424,0.94,0.7,3,176,3,0,0,"technical","low","exist"
"3425",5425,1,0.98,3,177,2,0,0,"management","low","exist"
"3426",5426,0.5,0.51,3,169,4,0,0,"marketing","low","exist"
"3427",5427,0.77,0.89,3,142,3,0,0,"marketing","low","exist"
"3428",5428,0.68,0.71,5,135,4,1,0,"marketing","medium","exist"
"3429",5429,0.57,0.43,3,167,3,0,0,"sales","medium","exist"
"3430",5430,0.57,0.61,5,191,4,0,0,"sales","medium","exist"
"3431",5431,0.48,0.97,3,224,6,0,0,"sales","medium","exist"
"3432",5432,0.7,0.95,5,234,6,1,0,"sales","medium","exist"
"3433",5433,0.68,0.43,3,161,2,1,0,"sales","medium","exist"
"3434",5434,0.62,0.68,3,124,3,0,0,"sales","medium","exist"
"3435",5435,0.61,0.51,4,242,3,0,0,"sales","medium","exist"
"3436",5436,0.83,0.77,2,186,2,0,0,"sales","medium","exist"
"3437",5437,0.99,0.8,5,254,5,0,0,"sales","medium","exist"
"3438",5438,0.58,0.72,4,170,2,0,0,"sales","medium","exist"
"3439",5439,0.93,0.83,4,225,2,0,0,"sales","medium","exist"
"3440",5440,0.66,0.5,4,263,3,0,0,"sales","high","exist"
"3441",5441,0.52,0.98,4,148,3,0,0,"sales","low","exist"
"3442",5442,0.5,0.6,5,216,3,0,0,"sales","medium","exist"
"3443",5443,0.16,0.7,5,257,4,0,0,"sales","medium","exist"
"3444",5444,0.62,0.74,4,173,2,0,0,"sales","medium","exist"
"3445",5445,0.49,0.49,6,188,3,0,0,"sales","medium","exist"
"3446",5446,0.56,0.91,4,188,2,1,0,"sales","low","exist"
"3447",5447,0.96,0.59,4,108,6,0,0,"sales","low","exist"
"3448",5448,0.5,0.75,5,179,3,0,0,"accounting","low","exist"
"3449",5449,0.99,0.99,4,195,2,0,0,"accounting","low","exist"
"3450",5450,0.54,0.51,3,265,3,0,0,"accounting","low","exist"
"3451",5451,0.52,0.9,4,285,2,0,0,"hr","low","exist"
"3452",5452,0.81,0.99,5,202,4,0,0,"hr","low","exist"
"3453",5453,0.5,0.73,4,271,2,0,0,"hr","low","exist"
"3454",5454,0.51,0.88,3,202,4,0,0,"hr","low","exist"
"3455",5455,0.41,0.47,6,171,2,0,0,"technical","low","exist"
"3456",5456,0.62,0.72,2,180,2,1,0,"technical","low","exist"
"3457",5457,0.56,0.68,3,269,3,1,0,"technical","low","exist"
"3458",5458,0.96,0.75,3,231,3,0,0,"technical","low","exist"
"3459",5459,0.58,0.64,2,249,2,0,0,"technical","low","exist"
"3460",5460,0.66,0.75,3,228,2,0,0,"technical","low","exist"
"3461",5461,0.56,0.75,2,264,2,0,0,"technical","low","exist"
"3462",5462,0.56,0.93,4,210,3,1,0,"technical","low","exist"
"3463",5463,0.67,0.91,3,256,3,0,0,"technical","low","exist"
"3464",5464,0.72,0.71,5,137,3,0,0,"technical","low","exist"
"3465",5465,0.59,0.79,4,272,3,0,0,"technical","low","exist"
"3466",5466,0.95,0.55,5,185,2,0,0,"support","low","exist"
"3467",5467,1,0.93,3,264,4,0,0,"support","medium","exist"
"3468",5468,0.56,0.64,3,238,3,0,0,"support","medium","exist"
"3469",5469,0.52,0.49,4,98,3,0,0,"support","medium","exist"
"3470",5470,0.88,0.9,4,248,2,0,0,"support","medium","exist"
"3471",5471,0.58,0.84,4,271,2,0,0,"support","medium","exist"
"3472",5472,0.86,0.92,3,180,2,0,0,"support","medium","exist"
"3473",5473,0.19,0.64,5,181,4,1,0,"support","medium","exist"
"3474",5474,0.6,0.6,4,182,3,0,0,"support","medium","exist"
"3475",5475,0.82,0.87,3,204,4,0,0,"support","medium","exist"
"3476",5476,0.64,0.75,4,170,3,0,0,"support","medium","exist"
"3477",5477,0.83,0.67,4,139,3,0,0,"technical","medium","exist"
"3478",5478,0.57,0.75,3,159,2,0,0,"technical","medium","exist"
"3479",5479,0.98,0.92,3,254,3,0,0,"technical","high","exist"
"3480",5480,0.54,0.69,4,168,2,0,0,"management","low","exist"
"3481",5481,0.72,0.66,3,256,2,0,0,"IT","medium","exist"
"3482",5482,0.89,0.87,4,209,2,0,0,"IT","medium","exist"
"3483",5483,0.41,0.57,3,193,3,1,0,"IT","medium","exist"
"3484",5484,0.93,0.62,4,142,2,0,0,"IT","medium","exist"
"3485",5485,0.9,0.9,3,274,3,0,0,"IT","low","exist"
"3486",5486,0.38,0.59,4,276,2,0,0,"product_mng","low","exist"
"3487",5487,0.52,0.88,4,155,3,1,0,"product_mng","low","exist"
"3488",5488,0.99,0.72,3,220,2,1,0,"product_mng","low","exist"
"3489",5489,0.69,0.74,2,271,2,0,0,"product_mng","low","exist"
"3490",5490,0.76,0.76,5,175,3,0,0,"IT","low","exist"
"3491",5491,0.42,0.46,3,128,2,1,0,"RandD","low","exist"
"3492",5492,0.78,0.9,4,104,4,0,0,"RandD","low","exist"
"3493",5493,0.37,0.46,3,173,6,0,0,"RandD","medium","exist"
"3494",5494,0.89,0.39,6,190,3,1,0,"RandD","medium","exist"
"3495",5495,0.93,0.49,5,167,3,0,0,"RandD","medium","exist"
"3496",5496,0.98,0.56,3,187,3,0,0,"RandD","medium","exist"
"3497",5497,0.65,0.56,3,259,3,0,0,"marketing","medium","exist"
"3498",5498,0.3,0.61,3,138,5,0,0,"sales","medium","exist"
"3499",5499,0.97,1,5,251,2,0,0,"accounting","medium","exist"
"3500",5500,0.84,0.49,5,189,2,0,0,"support","medium","exist"
"3501",5501,0.76,0.76,4,149,3,0,0,"technical","medium","exist"
"3502",5502,0.5,0.74,4,246,3,0,0,"management","medium","exist"
"3503",5503,0.48,0.61,3,146,3,0,0,"marketing","medium","exist"
"3504",5504,0.56,0.63,4,204,4,0,0,"marketing","medium","exist"
"3505",5505,0.99,0.77,4,184,3,0,0,"marketing","medium","exist"
"3506",5506,0.65,0.94,4,174,3,0,0,"sales","medium","exist"
"3507",5507,0.92,0.81,3,196,2,0,0,"sales","medium","exist"
"3508",5508,0.88,0.76,3,223,3,0,0,"sales","medium","exist"
"3509",5509,0.99,0.86,3,198,3,0,0,"sales","medium","exist"
"3510",5510,0.96,0.93,5,141,2,1,0,"sales","medium","exist"
"3511",5511,0.55,0.85,4,273,2,0,0,"sales","medium","exist"
"3512",5512,0.71,0.94,4,209,3,0,0,"sales","medium","exist"
"3513",5513,0.72,0.68,3,135,4,0,0,"sales","medium","exist"
"3514",5514,0.23,0.5,5,100,3,0,0,"sales","medium","exist"
"3515",5515,0.78,0.61,3,193,3,0,0,"sales","medium","exist"
"3516",5516,0.82,0.61,3,229,2,0,0,"sales","medium","exist"
"3517",5517,0.49,0.74,4,104,4,0,0,"sales","medium","exist"
"3518",5518,0.96,0.82,4,201,2,0,0,"sales","high","exist"
"3519",5519,0.5,0.78,3,206,3,1,1,"sales","high","exist"
"3520",5520,0.98,0.57,5,141,3,0,1,"sales","high","exist"
"3521",5521,0.85,0.57,4,150,3,0,1,"sales","high","exist"
"3522",5522,0.72,0.75,3,166,3,0,1,"sales","high","exist"
"3523",5523,0.78,0.83,4,252,2,0,1,"sales","high","exist"
"3524",5524,0.62,0.43,2,106,2,0,1,"sales","high","exist"
"3525",5525,0.64,0.38,5,171,6,1,1,"accounting","high","exist"
"3526",5526,0.24,0.5,4,232,3,0,1,"accounting","high","exist"
"3527",5527,0.84,0.78,5,172,2,0,1,"accounting","high","exist"
"3528",5528,0.61,0.61,4,239,2,0,1,"hr","high","exist"
"3529",5529,0.79,0.71,4,222,3,0,1,"hr","high","exist"
"3530",5530,0.86,0.77,3,152,3,0,1,"hr","low","exist"
"3531",5531,0.7,0.54,3,198,3,1,1,"hr","low","exist"
"3532",5532,0.53,0.76,5,143,4,0,1,"technical","low","exist"
"3533",5533,0.58,0.88,3,157,4,0,1,"technical","low","exist"
"3534",5534,0.45,0.55,5,268,2,0,0,"technical","low","exist"
"3535",5535,0.86,0.87,4,183,3,0,0,"technical","low","exist"
"3536",5536,0.95,0.81,4,238,2,0,0,"technical","low","exist"
"3537",5537,0.51,0.84,4,214,2,0,0,"technical","low","exist"
"3538",5538,0.35,0.41,6,244,3,0,0,"technical","low","exist"
"3539",5539,0.99,0.57,3,221,3,0,0,"technical","low","exist"
"3540",5540,0.73,0.49,4,200,2,1,0,"technical","low","exist"
"3541",5541,0.44,0.48,2,226,3,0,0,"technical","low","exist"
"3542",5542,0.43,0.74,4,121,5,1,0,"technical","low","exist"
"3543",5543,0.81,0.77,5,249,2,0,0,"support","low","exist"
"3544",5544,0.77,0.83,3,204,3,0,0,"support","low","exist"
"3545",5545,0.52,0.86,5,256,3,0,0,"support","medium","exist"
"3546",5546,0.21,0.92,2,211,2,0,0,"support","medium","exist"
"3547",5547,0.88,0.93,3,162,3,0,0,"support","medium","exist"
"3548",5548,0.48,0.8,5,235,2,0,0,"support","medium","exist"
"3549",5549,0.21,0.63,5,226,3,0,0,"support","medium","exist"
"3550",5550,0.81,0.53,4,242,3,0,0,"support","medium","exist"
"3551",5551,0.38,0.77,3,173,5,0,0,"support","medium","exist"
"3552",5552,0.67,0.77,5,167,2,0,0,"support","medium","exist"
"3553",5553,0.87,0.94,4,256,2,0,0,"support","medium","exist"
"3554",5554,0.85,0.41,2,229,6,0,0,"technical","medium","exist"
"3555",5555,0.52,0.9,5,176,3,0,0,"technical","medium","exist"
"3556",5556,0.9,0.95,3,133,4,0,0,"technical","medium","exist"
"3557",5557,0.85,0.56,5,203,3,0,0,"management","high","exist"
"3558",5558,0.77,0.52,3,210,3,1,0,"IT","high","exist"
"3559",5559,0.61,0.97,4,198,2,0,0,"IT","high","exist"
"3560",5560,0.74,0.54,3,175,3,1,0,"IT","high","exist"
"3561",5561,0.56,0.85,5,245,3,0,0,"IT","high","exist"
"3562",5562,0.28,0.97,4,102,3,0,0,"IT","high","exist"
"3563",5563,0.86,0.68,2,192,3,0,0,"product_mng","high","exist"
"3564",5564,0.63,0.78,4,160,2,0,0,"product_mng","high","exist"
"3565",5565,0.85,0.96,3,211,2,0,0,"product_mng","low","exist"
"3566",5566,0.84,0.84,6,261,5,0,0,"product_mng","low","exist"
"3567",5567,0.98,0.6,4,191,3,0,0,"IT","low","exist"
"3568",5568,0.51,0.78,5,225,4,0,0,"RandD","low","exist"
"3569",5569,0.71,0.85,4,157,2,0,0,"RandD","low","exist"
"3570",5570,0.88,0.69,3,248,4,0,0,"RandD","low","exist"
"3571",5571,0.16,0.81,2,159,6,1,0,"RandD","low","exist"
"3572",5572,0.98,0.86,4,254,2,1,0,"RandD","low","exist"
"3573",5573,0.81,0.76,3,203,3,0,0,"RandD","medium","exist"
"3574",5574,0.17,0.79,2,126,5,0,0,"marketing","medium","exist"
"3575",5575,0.22,0.65,6,212,4,0,0,"sales","medium","exist"
"3576",5576,0.67,0.69,5,225,3,0,0,"accounting","medium","exist"
"3577",5577,0.72,0.83,5,193,2,0,0,"support","medium","exist"
"3578",5578,0.67,0.91,3,147,3,0,0,"technical","medium","exist"
"3579",5579,0.47,0.55,2,156,2,0,0,"management","medium","exist"
"3580",5580,0.51,0.75,3,234,2,1,0,"marketing","medium","exist"
"3581",5581,0.88,0.71,5,246,3,0,0,"marketing","medium","exist"
"3582",5582,0.48,0.94,4,231,4,0,0,"marketing","medium","exist"
"3583",5583,0.66,0.99,4,209,3,0,0,"sales","medium","exist"
"3584",5584,0.58,0.5,3,144,3,0,0,"sales","medium","exist"
"3585",5585,0.23,0.96,2,234,4,0,0,"sales","medium","exist"
"3586",5586,0.86,0.77,5,230,2,0,0,"sales","medium","exist"
"3587",5587,0.81,0.99,2,156,5,0,0,"sales","medium","exist"
"3588",5588,0.75,0.54,6,138,4,1,0,"sales","medium","exist"
"3589",5589,0.49,0.89,2,233,4,1,0,"sales","medium","exist"
"3590",5590,0.31,0.5,3,262,5,0,0,"sales","medium","exist"
"3591",5591,0.83,0.75,6,215,5,0,0,"sales","medium","exist"
"3592",5592,0.7,0.55,4,227,3,0,0,"sales","medium","exist"
"3593",5593,0.49,0.99,3,199,3,0,0,"sales","medium","exist"
"3594",5594,0.57,0.92,3,238,2,0,0,"sales","medium","exist"
"3595",5595,0.37,0.45,6,100,5,1,0,"sales","medium","exist"
"3596",5596,0.69,0.75,3,179,2,1,0,"sales","high","exist"
"3597",5597,0.62,0.98,4,107,2,0,0,"sales","low","exist"
"3598",5598,0.5,0.68,4,274,4,0,0,"sales","medium","exist"
"3599",5599,0.81,0.73,4,272,2,0,0,"sales","medium","exist"
"3600",5600,0.2,0.41,6,264,3,0,0,"sales","medium","exist"
"3601",5601,0.22,0.58,2,255,5,0,0,"sales","medium","exist"
"3602",5602,0.63,0.79,5,215,2,1,0,"accounting","medium","exist"
"3603",5603,0.68,0.53,3,156,4,0,0,"accounting","medium","exist"
"3604",5604,0.52,0.49,3,146,2,1,0,"accounting","medium","exist"
"3605",5605,0.22,0.52,6,217,6,0,0,"hr","medium","exist"
"3606",5606,0.51,0.82,3,206,4,0,0,"hr","medium","exist"
"3607",5607,0.66,0.92,4,239,3,0,0,"hr","medium","exist"
"3608",5608,0.26,0.37,2,232,3,1,0,"hr","low","exist"
"3609",5609,0.42,0.4,3,160,2,0,0,"technical","low","exist"
"3610",5610,0.86,0.77,5,237,3,1,0,"technical","low","exist"
"3611",5611,0.52,0.68,3,162,4,1,0,"technical","low","exist"
"3612",5612,0.95,0.64,3,138,4,1,0,"technical","low","exist"
"3613",5613,0.63,0.94,2,228,2,1,0,"technical","low","exist"
"3614",5614,1,0.54,3,151,2,1,0,"technical","low","exist"
"3615",5615,0.54,0.58,3,169,2,1,0,"technical","high","exist"
"3616",5616,0.9,0.7,3,147,4,0,0,"technical","low","exist"
"3617",5617,0.49,0.99,6,205,5,0,0,"technical","high","exist"
"3618",5618,0.81,0.6,3,140,2,0,0,"technical","high","exist"
"3619",5619,0.5,0.66,4,150,4,0,0,"technical","low","exist"
"3620",5620,0.7,0.88,4,191,3,1,0,"support","low","exist"
"3621",5621,0.5,0.85,4,150,2,0,0,"support","high","exist"
"3622",5622,0.98,0.66,2,255,3,0,0,"support","low","exist"
"3623",5623,0.86,0.51,3,230,3,0,0,"support","medium","exist"
"3624",5624,0.93,0.77,3,202,5,0,0,"support","high","exist"
"3625",5625,0.62,0.75,3,180,3,0,0,"support","medium","exist"
"3626",5626,0.64,0.57,3,179,3,0,0,"support","medium","exist"
"3627",5627,0.66,0.94,4,198,3,1,0,"support","medium","exist"
"3628",5628,0.65,0.86,4,267,2,0,0,"support","medium","exist"
"3629",5629,0.89,0.84,3,166,2,0,0,"support","high","exist"
"3630",5630,0.77,0.58,4,162,2,0,0,"support","medium","exist"
"3631",5631,0.4,0.36,4,128,4,0,0,"technical","medium","exist"
"3632",5632,0.36,0.44,4,114,4,0,0,"technical","medium","exist"
"3633",5633,0.3,0.48,2,104,2,0,0,"technical","high","exist"
"3634",5634,0.9,0.64,4,139,3,1,0,"management","medium","exist"
"3635",5635,0.23,0.49,5,214,5,0,0,"IT","high","exist"
"3636",5636,0.24,0.79,2,175,5,0,0,"IT","low","exist"
"3637",5637,0.98,0.92,4,175,2,0,0,"IT","medium","exist"
"3638",5638,0.49,0.48,2,186,2,0,0,"IT","medium","exist"
"3639",5639,0.23,0.48,3,139,4,0,0,"IT","medium","exist"
"3640",5640,0.79,0.71,3,202,3,0,0,"product_mng","medium","exist"
"3641",5641,0.21,0.76,4,165,6,1,0,"product_mng","low","exist"
"3642",5642,0.38,0.92,5,238,2,0,0,"product_mng","low","exist"
"3643",5643,0.17,0.59,4,179,4,0,0,"product_mng","low","exist"
"3644",5644,0.56,0.69,5,239,2,1,0,"IT","low","exist"
"3645",5645,0.97,0.7,5,195,2,1,0,"RandD","low","exist"
"3646",5646,0.22,0.78,6,206,6,0,0,"RandD","low","exist"
"3647",5647,0.84,0.88,3,194,3,0,0,"RandD","low","exist"
"3648",5648,0.64,0.63,5,105,5,1,0,"RandD","low","exist"
"3649",5649,0.78,0.69,5,256,3,0,0,"RandD","low","exist"
"3650",5650,0.23,0.4,6,110,4,1,0,"marketing","low","exist"
"3651",5651,0.99,0.82,6,185,4,1,0,"sales","high","exist"
"3652",5652,0.15,0.76,4,255,6,0,0,"accounting","low","exist"
"3653",5653,0.24,0.96,3,174,6,0,0,"support","low","exist"
"3654",5654,0.84,0.71,4,273,3,0,0,"technical","low","exist"
"3655",5655,0.82,0.58,2,248,6,0,0,"management","low","exist"
"3656",5656,0.17,0.86,3,286,6,0,0,"marketing","high","exist"
"3657",5657,0.72,0.71,5,248,2,0,0,"marketing","low","exist"
"3658",5658,0.86,0.91,3,234,3,1,0,"marketing","low","exist"
"3659",5659,0.75,0.55,3,162,3,0,0,"sales","low","exist"
"3660",5660,0.93,0.82,5,272,3,0,0,"sales","high","exist"
"3661",5661,0.75,0.52,3,260,3,1,0,"sales","low","exist"
"3662",5662,0.45,0.55,3,151,3,0,0,"sales","medium","exist"
"3663",5663,0.44,0.87,2,140,4,0,0,"sales","high","exist"
"3664",5664,0.55,0.9,5,237,3,0,0,"sales","medium","exist"
"3665",5665,0.78,0.56,5,252,2,0,0,"sales","high","exist"
"3666",5666,0.5,0.52,4,178,3,0,0,"sales","medium","exist"
"3667",5667,0.96,0.66,4,268,3,0,0,"sales","medium","exist"
"3668",5668,0.72,0.53,5,244,4,0,0,"sales","medium","exist"
"3669",5669,0.77,0.55,3,225,3,0,0,"sales","medium","exist"
"3670",5670,0.89,0.94,5,223,3,0,0,"sales","medium","exist"
"3671",5671,0.58,0.79,4,149,6,0,0,"sales","medium","exist"
"3672",5672,0.75,0.96,5,190,3,0,0,"sales","medium","exist"
"3673",5673,0.77,0.8,4,167,3,0,0,"sales","medium","exist"
"3674",5674,0.76,0.87,4,161,3,0,0,"sales","high","exist"
"3675",5675,0.87,0.76,4,218,2,0,0,"sales","low","exist"
"3676",5676,0.95,0.74,3,212,3,0,0,"sales","medium","exist"
"3677",5677,0.73,0.54,3,150,3,0,0,"sales","medium","exist"
"3678",5678,0.2,0.56,5,181,6,0,0,"accounting","medium","exist"
"3679",5679,0.55,0.43,3,120,6,1,0,"accounting","medium","exist"
"3680",5680,0.21,0.53,3,229,5,0,0,"accounting","medium","exist"
"3681",5681,0.91,0.74,3,139,5,1,0,"hr","medium","exist"
"3682",5682,0.61,0.87,4,151,3,0,0,"hr","medium","exist"
"3683",5683,0.89,0.59,4,230,3,0,0,"hr","medium","exist"
"3684",5684,0.65,0.76,4,193,2,0,0,"hr","medium","exist"
"3685",5685,0.7,0.48,4,229,2,0,0,"technical","high","exist"
"3686",5686,0.79,0.95,3,222,4,0,0,"technical","low","exist"
"3687",5687,0.99,0.67,3,200,2,0,0,"technical","low","exist"
"3688",5688,0.52,0.77,4,134,4,0,0,"technical","low","exist"
"3689",5689,0.71,0.97,3,219,3,0,0,"technical","high","exist"
"3690",5690,0.21,0.58,5,197,4,0,0,"technical","low","exist"
"3691",5691,0.4,0.62,3,283,5,0,0,"technical","low","exist"
"3692",5692,0.74,0.75,4,149,3,0,0,"technical","low","exist"
"3693",5693,0.79,0.6,4,161,3,0,0,"technical","high","exist"
"3694",5694,0.88,0.58,5,264,3,0,0,"technical","low","exist"
"3695",5695,0.89,0.93,4,137,2,0,0,"technical","low","exist"
"3696",5696,0.61,0.72,3,144,2,0,0,"support","low","exist"
"3697",5697,0.48,0.54,4,105,5,1,0,"support","low","exist"
"3698",5698,0.81,0.98,6,196,2,0,0,"support","low","exist"
"3699",5699,0.71,0.74,3,250,3,0,0,"support","low","exist"
"3700",5700,0.92,0.53,3,253,3,0,0,"support","low","exist"
"3701",5701,0.99,0.71,4,199,4,0,0,"support","medium","exist"
"3702",5702,0.74,0.55,6,130,2,0,0,"support","medium","exist"
"3703",5703,1,0.94,3,257,4,0,0,"support","medium","exist"
"3704",5704,0.81,0.55,3,127,4,0,0,"support","medium","exist"
"3705",5705,0.59,0.7,2,153,2,0,0,"support","medium","exist"
"3706",5706,0.9,0.58,5,260,2,0,0,"support","medium","exist"
"3707",5707,0.98,0.9,4,247,2,0,0,"technical","medium","exist"
"3708",5708,0.56,0.55,3,250,4,0,0,"technical","medium","exist"
"3709",5709,0.86,0.89,4,136,4,0,0,"technical","medium","exist"
"3710",5710,0.82,0.59,3,210,3,0,0,"management","medium","exist"
"3711",5711,0.94,0.53,4,183,3,0,0,"IT","medium","exist"
"3712",5712,0.68,0.96,4,255,3,0,0,"IT","medium","exist"
"3713",5713,0.81,0.69,5,109,2,0,0,"IT","high","exist"
"3714",5714,0.59,0.59,3,173,3,0,0,"IT","low","exist"
"3715",5715,0.54,0.82,4,266,2,0,0,"IT","medium","exist"
"3716",5716,0.77,0.87,5,257,2,0,0,"product_mng","medium","exist"
"3717",5717,0.62,0.61,6,103,4,0,0,"product_mng","medium","exist"
"3718",5718,0.58,0.57,5,105,6,0,0,"product_mng","medium","exist"
"3719",5719,0.63,0.84,3,269,2,0,0,"product_mng","low","exist"
"3720",5720,0.78,1,4,154,2,0,0,"IT","low","exist"
"3721",5721,0.82,0.78,5,232,3,0,0,"RandD","low","exist"
"3722",5722,0.73,0.86,3,215,4,0,0,"RandD","low","exist"
"3723",5723,0.53,0.74,4,272,2,1,0,"RandD","low","exist"
"3724",5724,0.88,0.62,4,221,2,0,0,"RandD","low","exist"
"3725",5725,0.65,0.6,4,200,4,0,0,"RandD","low","exist"
"3726",5726,0.57,0.61,5,254,5,0,0,"marketing","low","exist"
"3727",5727,0.93,0.76,5,187,3,0,0,"marketing","low","exist"
"3728",5728,0.83,0.64,2,192,2,0,0,"sales","low","exist"
"3729",5729,0.73,0.45,5,232,4,0,0,"accounting","low","exist"
"3730",5730,0.78,0.67,4,221,3,1,0,"support","low","exist"
"3731",5731,0.9,0.62,3,233,5,1,0,"technical","low","exist"
"3732",5732,0.59,0.66,3,166,3,1,0,"management","low","exist"
"3733",5733,0.67,0.89,2,173,3,0,0,"marketing","low","exist"
"3734",5734,0.59,0.51,4,184,2,0,0,"marketing","low","exist"
"3735",5735,0.53,0.54,4,257,3,1,0,"marketing","low","exist"
"3736",5736,0.56,0.73,4,226,2,0,0,"sales","low","exist"
"3737",5737,0.72,0.89,3,221,3,0,0,"sales","low","exist"
"3738",5738,0.81,0.49,2,205,5,1,0,"sales","low","exist"
"3739",5739,0.54,0.68,3,158,3,0,0,"sales","low","exist"
"3740",5740,0.91,0.87,3,199,3,0,0,"sales","medium","exist"
"3741",5741,0.51,0.96,3,192,3,0,0,"sales","medium","exist"
"3742",5742,0.59,0.39,4,190,5,0,0,"sales","medium","exist"
"3743",5743,0.64,0.86,5,222,3,0,0,"sales","medium","exist"
"3744",5744,0.95,0.68,5,225,3,0,0,"sales","medium","exist"
"3745",5745,0.75,0.69,3,274,2,0,0,"sales","medium","exist"
"3746",5746,0.44,0.38,3,197,2,0,0,"sales","medium","exist"
"3747",5747,0.55,0.6,4,176,3,0,0,"sales","medium","exist"
"3748",5748,0.6,0.81,3,226,2,0,0,"sales","medium","exist"
"3749",5749,0.84,0.58,5,186,2,0,0,"sales","medium","exist"
"3750",5750,0.49,0.65,3,226,3,0,0,"sales","medium","exist"
"3751",5751,0.75,0.71,4,209,3,0,0,"sales","medium","exist"
"3752",5752,0.35,0.81,5,182,5,0,0,"sales","high","exist"
"3753",5753,0.68,0.78,3,232,3,0,0,"sales","low","exist"
"3754",5754,0.52,0.53,2,286,3,0,0,"sales","medium","exist"
"3755",5755,0.78,0.57,3,177,3,0,0,"accounting","medium","exist"
"3756",5756,0.44,0.92,6,268,4,1,0,"accounting","medium","exist"
"3757",5757,0.18,0.86,5,267,4,0,0,"accounting","medium","exist"
"3758",5758,0.37,0.52,4,211,4,0,0,"hr","low","exist"
"3759",5759,0.71,0.76,3,246,3,0,0,"hr","low","exist"
"3760",5760,0.55,0.83,5,220,3,0,0,"hr","low","exist"
"3761",5761,0.98,0.78,3,197,2,0,0,"hr","low","exist"
"3762",5762,0.88,0.53,3,188,3,0,0,"technical","low","exist"
"3763",5763,0.79,0.9,5,212,5,0,0,"technical","low","exist"
"3764",5764,0.96,0.66,3,230,3,0,0,"technical","low","exist"
"3765",5765,0.3,0.55,6,178,2,0,0,"technical","low","exist"
"3766",5766,0.59,0.9,4,226,2,0,0,"technical","low","exist"
"3767",5767,0.72,0.55,4,202,3,0,0,"technical","low","exist"
"3768",5768,0.59,0.87,4,191,2,0,0,"technical","low","exist"
"3769",5769,0.93,0.68,2,150,3,0,0,"technical","low","exist"
"3770",5770,0.49,0.86,5,235,5,0,0,"technical","low","exist"
"3771",5771,0.73,0.95,3,258,3,0,0,"technical","low","exist"
"3772",5772,0.53,0.6,5,247,3,0,0,"technical","low","exist"
"3773",5773,0.77,0.83,6,271,3,0,0,"support","low","exist"
"3774",5774,0.45,0.62,6,129,5,0,0,"support","low","exist"
"3775",5775,0.95,0.78,5,246,3,0,0,"support","low","exist"
"3776",5776,0.86,0.69,5,157,4,0,0,"support","low","exist"
"3777",5777,0.59,0.58,4,233,4,0,0,"support","low","exist"
"3778",5778,0.95,0.63,4,153,3,0,0,"support","low","exist"
"3779",5779,0.7,0.92,4,142,2,1,0,"support","medium","exist"
"3780",5780,0.56,0.64,5,241,3,1,0,"support","medium","exist"
"3781",5781,0.5,0.92,3,186,2,0,0,"support","medium","exist"
"3782",5782,0.76,0.92,4,154,3,0,0,"support","medium","exist"
"3783",5783,0.85,0.77,5,263,3,0,0,"support","medium","exist"
"3784",5784,0.98,1,5,150,3,0,0,"technical","medium","exist"
"3785",5785,0.65,0.4,2,277,2,0,0,"technical","medium","exist"
"3786",5786,0.44,0.97,4,240,5,1,0,"technical","medium","exist"
"3787",5787,0.55,0.97,3,222,2,0,0,"management","medium","exist"
"3788",5788,0.16,0.8,4,140,5,1,0,"IT","medium","exist"
"3789",5789,0.16,0.9,6,213,2,0,0,"IT","medium","exist"
"3790",5790,0.75,1,4,272,4,1,0,"IT","medium","exist"
"3791",5791,0.59,0.57,4,261,2,0,0,"IT","high","exist"
"3792",5792,0.48,0.87,3,236,2,0,0,"IT","low","exist"
"3793",5793,0.18,0.68,6,154,5,0,0,"product_mng","medium","exist"
"3794",5794,0.8,0.72,3,271,2,0,0,"product_mng","medium","exist"
"3795",5795,0.8,0.88,3,154,2,0,0,"product_mng","medium","exist"
"3796",5796,0.15,0.52,4,207,4,0,0,"product_mng","medium","exist"
"3797",5797,0.62,0.86,4,181,2,0,0,"IT","low","exist"
"3798",5798,0.21,0.99,6,165,4,1,0,"RandD","low","exist"
"3799",5799,0.9,0.82,3,203,2,0,0,"RandD","low","exist"
"3800",5800,0.51,1,4,197,2,0,0,"RandD","low","exist"
"3801",5801,0.99,0.9,4,177,3,0,0,"RandD","low","exist"
"3802",5802,0.71,0.49,4,273,2,1,0,"RandD","low","exist"
"3803",5803,0.89,0.93,4,141,2,0,0,"marketing","low","exist"
"3804",5804,0.74,0.67,4,158,3,0,0,"sales","low","exist"
"3805",5805,0.84,0.85,3,243,2,0,0,"accounting","low","exist"
"3806",5806,0.4,0.64,3,188,3,0,0,"support","low","exist"
"3807",5807,1,0.71,4,216,2,0,0,"technical","low","exist"
"3808",5808,0.48,0.51,5,286,3,0,0,"management","low","exist"
"3809",5809,0.99,0.6,3,262,2,0,0,"marketing","low","exist"
"3810",5810,0.73,0.81,5,173,3,0,0,"marketing","low","exist"
"3811",5811,0.84,0.91,3,247,4,0,0,"marketing","low","exist"
"3812",5812,0.55,0.7,3,237,4,0,0,"sales","low","exist"
"3813",5813,0.44,0.99,5,119,2,0,0,"sales","low","exist"
"3814",5814,0.95,0.67,4,227,3,0,0,"sales","low","exist"
"3815",5815,0.76,0.65,4,195,3,0,0,"sales","low","exist"
"3816",5816,0.94,0.7,6,217,5,0,0,"sales","low","exist"
"3817",5817,0.85,0.5,4,267,3,0,0,"sales","low","exist"
"3818",5818,0.57,0.62,3,154,2,0,0,"sales","medium","exist"
"3819",5819,0.67,0.49,5,161,2,0,0,"sales","medium","exist"
"3820",5820,0.7,0.67,3,179,3,1,0,"sales","medium","exist"
"3821",5821,0.67,0.55,4,214,3,1,0,"sales","medium","exist"
"3822",5822,0.72,0.84,3,167,3,0,0,"sales","medium","exist"
"3823",5823,0.71,0.53,6,203,3,0,0,"sales","medium","exist"
"3824",5824,0.51,0.8,4,231,3,0,0,"sales","medium","exist"
"3825",5825,0.98,0.65,4,263,2,1,0,"sales","medium","exist"
"3826",5826,0.52,0.83,2,227,4,0,0,"sales","medium","exist"
"3827",5827,0.21,0.9,4,235,4,0,0,"sales","medium","exist"
"3828",5828,0.43,0.93,6,127,3,1,0,"sales","medium","exist"
"3829",5829,0.91,0.62,4,158,3,1,0,"sales","medium","exist"
"3830",5830,0.74,0.85,4,105,5,0,0,"sales","high","exist"
"3831",5831,0.34,0.81,3,257,5,0,0,"accounting","low","exist"
"3832",5832,0.28,0.46,4,260,2,0,0,"accounting","medium","exist"
"3833",5833,0.7,0.79,6,145,3,0,0,"accounting","medium","exist"
"3834",5834,0.53,0.59,2,201,3,1,0,"hr","medium","exist"
"3835",5835,0.97,0.51,4,241,4,0,0,"hr","medium","exist"
"3836",5836,0.96,0.59,3,214,2,0,0,"hr","low","exist"
"3837",5837,0.74,0.53,4,166,3,0,0,"hr","low","exist"
"3838",5838,0.79,0.86,4,173,4,0,0,"technical","low","exist"
"3839",5839,0.61,0.47,4,181,5,0,0,"technical","low","exist"
"3840",5840,0.36,0.4,4,114,4,1,0,"technical","low","exist"
"3841",5841,0.15,0.91,5,267,4,0,0,"technical","low","exist"
"3842",5842,0.61,0.5,4,216,2,0,0,"technical","low","exist"
"3843",5843,0.59,0.94,4,265,3,0,0,"technical","low","exist"
"3844",5844,0.58,0.77,5,272,2,1,0,"technical","low","exist"
"3845",5845,0.49,0.92,4,229,2,0,0,"technical","low","exist"
"3846",5846,0.92,0.96,5,174,3,1,0,"technical","low","exist"
"3847",5847,0.72,0.92,3,264,3,0,0,"technical","low","exist"
"3848",5848,0.77,0.85,5,221,5,0,0,"technical","low","exist"
"3849",5849,0.6,0.57,3,202,3,0,0,"support","low","exist"
"3850",5850,0.21,0.4,3,262,3,0,0,"support","low","exist"
"3851",5851,0.83,0.75,3,150,3,0,0,"support","low","exist"
"3852",5852,0.71,0.95,3,251,3,0,0,"support","low","exist"
"3853",5853,0.94,0.46,2,230,2,1,0,"support","low","exist"
"3854",5854,0.59,0.99,3,185,2,0,0,"support","medium","exist"
"3855",5855,0.59,0.59,4,216,2,1,0,"support","medium","exist"
"3856",5856,0.99,0.68,3,181,3,1,0,"support","medium","exist"
"3857",5857,0.64,0.7,5,140,4,0,0,"support","medium","exist"
"3858",5858,0.54,0.5,4,160,3,0,0,"support","medium","exist"
"3859",5859,0.78,0.63,3,192,2,0,0,"support","medium","exist"
"3860",5860,0.7,0.79,6,257,4,0,0,"technical","medium","exist"
"3861",5861,0.9,0.62,5,236,6,0,0,"technical","medium","exist"
"3862",5862,0.14,0.74,6,160,5,0,0,"technical","medium","exist"
"3863",5863,0.33,0.69,3,125,3,0,0,"management","medium","exist"
"3864",5864,0.73,0.53,4,139,2,0,0,"IT","medium","exist"
"3865",5865,0.8,0.87,4,217,3,0,0,"IT","medium","exist"
"3866",5866,0.17,0.91,6,246,5,0,0,"IT","high","exist"
"3867",5867,0.34,0.91,4,284,4,0,0,"IT","low","exist"
"3868",5868,0.61,0.9,3,263,3,0,0,"IT","medium","exist"
"3869",5869,0.18,0.95,4,241,6,0,0,"product_mng","medium","exist"
"3870",5870,0.72,0.94,3,258,3,0,0,"product_mng","medium","exist"
"3871",5871,0.32,0.79,4,136,3,0,0,"product_mng","medium","exist"
"3872",5872,0.85,0.81,2,223,3,1,0,"product_mng","low","exist"
"3873",5873,0.85,0.74,5,170,4,0,0,"IT","low","exist"
"3874",5874,0.8,0.81,4,194,3,1,0,"RandD","low","exist"
"3875",5875,0.36,0.82,4,218,5,0,0,"RandD","low","exist"
"3876",5876,0.8,0.99,6,178,5,0,0,"RandD","low","exist"
"3877",5877,0.55,0.9,3,181,3,1,0,"RandD","low","exist"
"3878",5878,0.69,0.56,3,183,4,1,0,"RandD","low","exist"
"3879",5879,0.71,0.61,2,198,2,1,0,"marketing","low","exist"
"3880",5880,0.74,0.56,3,203,3,0,0,"sales","low","exist"
"3881",5881,0.76,0.89,5,204,3,0,0,"accounting","low","exist"
"3882",5882,0.81,0.62,3,257,3,0,0,"support","low","exist"
"3883",5883,0.59,1,4,169,2,0,0,"technical","low","exist"
"3884",5884,0.97,0.69,4,203,2,0,0,"management","low","exist"
"3885",5885,0.98,0.74,4,260,2,1,0,"marketing","low","exist"
"3886",5886,0.96,0.87,5,202,2,0,0,"marketing","low","exist"
"3887",5887,0.82,0.63,4,199,2,0,0,"marketing","low","exist"
"3888",5888,0.97,0.93,2,270,4,0,0,"sales","low","exist"
"3889",5889,0.74,0.51,5,258,2,0,0,"sales","low","exist"
"3890",5890,0.14,0.52,4,108,6,0,0,"sales","low","exist"
"3891",5891,0.3,0.67,3,232,3,0,0,"sales","low","exist"
"3892",5892,0.74,0.89,4,149,2,0,0,"sales","low","exist"
"3893",5893,0.85,0.48,4,214,3,0,0,"sales","medium","exist"
"3894",5894,0.69,0.65,4,136,2,0,0,"sales","medium","exist"
"3895",5895,0.6,0.95,4,164,4,0,0,"sales","medium","exist"
"3896",5896,0.53,0.85,3,236,6,0,0,"sales","medium","exist"
"3897",5897,0.94,0.88,3,270,3,0,0,"sales","medium","exist"
"3898",5898,0.57,0.63,5,156,4,0,0,"sales","medium","exist"
"3899",5899,0.2,0.73,3,250,5,0,0,"sales","medium","exist"
"3900",5900,0.82,0.92,4,196,3,0,0,"sales","medium","exist"
"3901",5901,0.62,0.92,5,169,2,0,0,"sales","medium","exist"
"3902",5902,0.88,0.59,2,144,3,0,0,"sales","medium","exist"
"3903",5903,0.82,0.62,4,160,3,0,0,"sales","medium","exist"
"3904",5904,0.62,0.91,3,142,6,1,0,"sales","medium","exist"
"3905",5905,0.74,0.48,5,165,2,0,0,"sales","high","exist"
"3906",5906,0.91,0.66,4,163,3,0,0,"sales","low","exist"
"3907",5907,0.7,0.96,3,263,3,0,0,"accounting","medium","exist"
"3908",5908,0.84,0.9,3,178,2,0,0,"accounting","medium","exist"
"3909",5909,0.35,0.57,3,109,3,0,0,"accounting","medium","exist"
"3910",5910,0.28,0.83,4,206,5,0,0,"hr","medium","exist"
"3911",5911,0.37,0.37,3,168,3,0,0,"hr","low","exist"
"3912",5912,0.75,0.5,4,155,2,1,0,"hr","low","exist"
"3913",5913,0.34,0.6,4,154,2,0,0,"hr","low","exist"
"3914",5914,0.55,0.5,4,179,3,0,0,"technical","low","exist"
"3915",5915,0.97,0.92,3,168,3,0,0,"technical","low","exist"
"3916",5916,0.91,0.57,3,158,3,0,0,"technical","low","exist"
"3917",5917,0.48,0.63,3,180,2,1,0,"technical","low","exist"
"3918",5918,0.53,0.71,4,227,3,0,0,"technical","low","exist"
"3919",5919,0.84,0.67,3,139,2,0,0,"technical","low","exist"
"3920",5920,0.31,0.69,3,120,3,0,0,"technical","low","exist"
"3921",5921,0.81,0.62,4,255,4,1,0,"technical","low","exist"
"3922",5922,0.78,0.95,5,273,2,0,0,"technical","low","exist"
"3923",5923,0.64,0.68,3,272,3,0,0,"technical","low","exist"
"3924",5924,0.41,0.77,4,231,6,0,0,"technical","low","exist"
"3925",5925,0.74,0.81,5,281,3,1,0,"support","low","exist"
"3926",5926,0.89,0.86,3,208,3,0,0,"support","low","exist"
"3927",5927,0.26,0.43,4,215,4,1,0,"support","low","exist"
"3928",5928,0.72,0.39,5,111,5,0,0,"support","low","exist"
"3929",5929,0.84,0.74,2,168,3,0,0,"support","low","exist"
"3930",5930,0.52,0.8,2,144,4,0,0,"support","low","exist"
"3931",5931,0.65,0.95,3,266,3,1,0,"support","low","exist"
"3932",5932,0.66,0.56,3,169,2,1,0,"support","medium","exist"
"3933",5933,0.86,0.63,4,162,2,0,0,"support","medium","exist"
"3934",5934,0.91,0.9,3,243,3,0,0,"support","medium","exist"
"3935",5935,0.84,0.6,3,186,3,1,0,"support","medium","exist"
"3936",5936,0.87,0.57,4,231,4,0,0,"technical","medium","exist"
"3937",5937,0.57,0.54,4,167,3,0,0,"technical","medium","exist"
"3938",5938,0.68,0.5,3,139,3,0,0,"technical","medium","exist"
"3939",5939,1,0.59,5,182,3,1,0,"management","medium","exist"
"3940",5940,0.86,0.74,4,261,2,0,0,"IT","medium","exist"
"3941",5941,0.7,0.99,4,248,3,0,0,"IT","medium","exist"
"3942",5942,0.28,0.7,2,164,4,0,0,"IT","medium","exist"
"3943",5943,0.84,0.9,3,230,3,0,0,"IT","medium","exist"
"3944",5944,0.68,0.92,3,226,2,0,0,"IT","high","exist"
"3945",5945,0.45,0.6,2,98,3,0,0,"product_mng","low","exist"
"3946",5946,0.37,0.74,5,117,3,0,0,"product_mng","medium","exist"
"3947",5947,0.98,0.84,4,200,2,0,0,"product_mng","medium","exist"
"3948",5948,0.67,0.57,3,206,3,1,0,"product_mng","medium","exist"
"3949",5949,0.74,0.83,4,142,3,0,0,"IT","medium","exist"
"3950",5950,0.48,0.46,2,174,3,0,0,"RandD","low","exist"
"3951",5951,0.22,0.63,5,284,6,0,0,"RandD","low","exist"
"3952",5952,0.14,0.79,5,163,6,0,0,"RandD","low","exist"
"3953",5953,0.93,0.92,5,189,2,0,0,"RandD","low","exist"
"3954",5954,0.83,0.54,4,189,4,0,0,"RandD","low","exist"
"3955",5955,0.94,0.79,3,256,3,0,0,"marketing","low","exist"
"3956",5956,0.7,0.98,3,215,2,0,0,"sales","low","exist"
"3957",5957,0.74,0.86,4,221,2,1,0,"accounting","low","exist"
"3958",5958,0.83,0.85,4,263,3,0,0,"support","medium","exist"
"3959",5959,0.97,0.61,3,208,3,0,0,"technical","medium","exist"
"3960",5960,0.61,0.71,3,216,4,0,0,"management","medium","exist"
"3961",5961,0.77,0.71,2,242,2,0,0,"marketing","medium","exist"
"3962",5962,0.66,0.73,2,135,6,0,0,"marketing","medium","exist"
"3963",5963,0.92,0.99,3,190,3,0,0,"marketing","medium","exist"
"3964",5964,0.62,0.55,3,108,2,1,0,"sales","medium","exist"
"3965",5965,0.15,0.67,6,195,2,0,0,"sales","medium","exist"
"3966",5966,0.82,0.68,3,160,4,0,0,"sales","medium","exist"
"3967",5967,0.7,0.48,5,273,2,0,0,"sales","medium","exist"
"3968",5968,0.18,0.39,2,177,6,0,0,"sales","medium","exist"
"3969",5969,0.99,0.59,3,163,2,0,0,"sales","medium","exist"
"3970",5970,0.22,0.9,4,106,2,0,0,"sales","medium","exist"
"3971",5971,0.61,0.83,5,236,2,0,0,"sales","medium","exist"
"3972",5972,0.78,0.91,3,132,2,0,0,"sales","medium","exist"
"3973",5973,0.84,0.61,3,253,2,0,0,"sales","medium","exist"
"3974",5974,0.87,0.74,4,151,4,0,0,"sales","medium","exist"
"3975",5975,0.73,0.9,4,266,3,0,0,"sales","medium","exist"
"3976",5976,0.7,0.86,3,141,2,1,0,"sales","medium","exist"
"3977",5977,0.98,0.71,5,217,3,0,0,"sales","medium","exist"
"3978",5978,0.85,0.49,3,258,3,0,0,"sales","medium","exist"
"3979",5979,0.56,0.83,5,275,2,0,0,"sales","medium","exist"
"3980",5980,0.48,0.62,4,210,2,0,0,"sales","medium","exist"
"3981",5981,0.65,0.7,3,243,3,0,0,"sales","medium","exist"
"3982",5982,0.84,0.59,3,234,3,1,0,"sales","medium","exist"
"3983",5983,0.17,0.73,4,274,3,0,0,"accounting","high","exist"
"3984",5984,0.84,0.61,4,261,2,0,0,"accounting","high","exist"
"3985",5985,0.96,0.59,3,158,3,1,0,"accounting","high","exist"
"3986",5986,0.62,0.96,5,251,2,0,0,"hr","high","exist"
"3987",5987,0.57,0.7,3,158,3,0,0,"hr","high","exist"
"3988",5988,0.98,0.87,3,246,3,0,0,"hr","high","exist"
"3989",5989,0.72,0.99,4,227,3,0,0,"hr","high","exist"
"3990",5990,0.43,0.46,4,169,5,0,0,"technical","high","exist"
"3991",5991,0.68,0.57,5,187,4,0,0,"technical","high","exist"
"3992",5992,0.69,0.86,4,238,3,0,0,"technical","high","exist"
"3993",5993,0.91,0.66,4,139,3,0,0,"technical","high","exist"
"3994",5994,0.42,0.37,2,284,3,0,0,"technical","high","exist"
"3995",5995,0.8,0.99,4,255,5,1,0,"technical","low","exist"
"3996",5996,0.79,0.57,5,230,2,0,0,"technical","low","exist"
"3997",5997,1,0.94,3,272,3,0,0,"technical","low","exist"
"3998",5998,0.63,0.75,4,155,3,0,0,"technical","low","exist"
"3999",5999,0.61,0.51,6,163,6,0,0,"technical","low","exist"
"4000",6000,0.78,0.98,4,260,3,0,0,"technical","low","exist"
"4001",6001,0.72,0.96,5,223,3,1,0,"support","low","exist"
"4002",6002,0.64,0.51,4,247,2,1,0,"support","low","exist"
"4003",6003,0.79,0.86,3,126,5,0,0,"support","low","exist"
"4004",6004,0.64,0.55,3,147,2,0,0,"support","low","exist"
"4005",6005,0.82,0.88,4,259,3,0,0,"support","low","exist"
"4006",6006,0.51,0.86,4,196,2,0,0,"support","low","exist"
"4007",6007,0.18,0.51,6,227,2,0,0,"support","low","exist"
"4008",6008,0.67,0.58,5,161,3,1,0,"support","low","exist"
"4009",6009,0.65,0.85,3,213,2,0,0,"support","low","exist"
"4010",6010,0.7,0.8,4,183,2,0,0,"support","medium","exist"
"4011",6011,0.59,0.59,3,194,2,1,0,"support","medium","exist"
"4012",6012,0.56,0.76,3,237,3,0,0,"technical","medium","exist"
"4013",6013,0.17,0.94,5,273,4,0,0,"technical","medium","exist"
"4014",6014,0.8,0.89,2,166,3,0,0,"technical","medium","exist"
"4015",6015,0.91,0.62,5,169,4,0,0,"management","medium","exist"
"4016",6016,0.51,0.54,3,154,3,1,0,"IT","medium","exist"
"4017",6017,0.76,0.59,3,201,6,1,0,"IT","medium","exist"
"4018",6018,0.82,0.59,3,178,2,0,0,"IT","medium","exist"
"4019",6019,0.44,0.66,3,161,3,0,0,"IT","medium","exist"
"4020",6020,0.5,0.48,4,269,3,0,0,"IT","medium","exist"
"4021",6021,0.54,0.49,3,203,3,1,0,"product_mng","medium","exist"
"4022",6022,0.56,0.63,4,271,2,1,0,"product_mng","high","exist"
"4023",6023,0.77,0.66,6,181,4,0,0,"product_mng","high","exist"
"4024",6024,0.39,0.38,4,135,2,0,0,"product_mng","high","exist"
"4025",6025,0.52,0.62,3,275,2,0,0,"IT","high","exist"
"4026",6026,0.63,0.91,3,252,2,0,0,"RandD","high","exist"
"4027",6027,0.49,0.46,2,129,2,0,0,"RandD","high","exist"
"4028",6028,0.2,0.47,4,230,4,0,0,"RandD","high","exist"
"4029",6029,0.21,0.94,3,287,5,0,0,"RandD","high","exist"
"4030",6030,0.85,0.98,5,156,2,0,0,"RandD","low","exist"
"4031",6031,0.54,0.82,2,279,3,1,0,"marketing","low","exist"
"4032",6032,0.23,0.88,5,156,4,0,0,"sales","low","exist"
"4033",6033,0.65,0.96,3,168,2,0,0,"accounting","low","exist"
"4034",6034,0.19,0.85,6,259,3,1,0,"support","low","exist"
"4035",6035,0.76,0.58,4,188,3,0,0,"technical","low","exist"
"4036",6036,0.83,0.8,4,149,3,0,0,"management","low","exist"
"4037",6037,0.97,0.47,3,157,4,0,0,"marketing","low","exist"
"4038",6038,0.67,1,3,201,4,0,0,"marketing","medium","exist"
"4039",6039,0.53,0.62,3,185,3,0,0,"marketing","medium","exist"
"4040",6040,0.34,0.71,2,160,3,1,0,"sales","medium","exist"
"4041",6041,0.58,0.48,5,251,3,0,0,"sales","medium","exist"
"4042",6042,0.96,0.68,5,145,3,1,0,"sales","medium","exist"
"4043",6043,0.72,0.76,3,269,3,1,0,"sales","medium","exist"
"4044",6044,0.58,0.62,3,213,2,0,0,"sales","medium","exist"
"4045",6045,0.39,0.67,6,276,6,0,0,"sales","medium","exist"
"4046",6046,0.24,0.57,5,232,3,0,0,"sales","medium","exist"
"4047",6047,0.64,0.73,4,184,3,0,0,"sales","medium","exist"
"4048",6048,0.98,0.55,3,260,3,0,0,"sales","medium","exist"
"4049",6049,0.64,0.99,3,214,2,0,0,"sales","medium","exist"
"4050",6050,0.56,0.41,2,194,2,0,0,"sales","medium","exist"
"4051",6051,0.53,0.74,5,181,2,0,0,"sales","medium","exist"
"4052",6052,0.62,0.57,4,215,3,0,0,"sales","medium","exist"
"4053",6053,0.85,0.69,3,194,4,0,0,"sales","medium","exist"
"4054",6054,0.76,0.85,4,190,3,0,0,"sales","medium","exist"
"4055",6055,0.69,0.5,3,260,4,0,0,"sales","medium","exist"
"4056",6056,0.35,0.67,2,171,3,0,0,"sales","medium","exist"
"4057",6057,0.54,0.47,2,193,4,0,0,"sales","medium","exist"
"4058",6058,0.63,0.49,3,252,3,0,0,"sales","medium","exist"
"4059",6059,0.58,0.58,5,171,2,0,0,"accounting","medium","exist"
"4060",6060,0.7,0.93,3,185,4,0,0,"accounting","medium","exist"
"4061",6061,0.48,0.51,4,152,4,0,0,"accounting","high","exist"
"4062",6062,0.59,0.92,4,183,2,0,0,"hr","low","exist"
"4063",6063,0.96,0.8,4,145,2,0,0,"hr","medium","exist"
"4064",6064,0.99,0.77,3,190,4,0,0,"hr","medium","exist"
"4065",6065,0.73,0.59,4,214,5,0,0,"hr","medium","exist"
"4066",6066,0.7,0.73,2,139,2,0,0,"technical","medium","exist"
"4067",6067,0.85,0.88,5,236,4,0,0,"technical","medium","exist"
"4068",6068,0.66,0.61,3,156,3,1,0,"technical","medium","exist"
"4069",6069,0.94,0.97,2,221,2,0,0,"technical","medium","exist"
"4070",6070,0.54,0.64,6,278,2,0,0,"technical","medium","exist"
"4071",6071,0.78,0.47,4,129,2,0,0,"technical","medium","exist"
"4072",6072,0.64,0.85,3,213,4,0,0,"technical","medium","exist"
"4073",6073,0.68,0.56,3,146,3,0,0,"technical","low","exist"
"4074",6074,0.92,0.84,4,159,3,0,0,"technical","low","exist"
"4075",6075,0.72,0.73,3,198,2,0,0,"technical","low","exist"
"4076",6076,0.78,0.74,6,251,4,0,0,"technical","low","exist"
"4077",6077,0.35,0.54,2,124,3,0,0,"support","low","exist"
"4078",6078,0.97,0.77,5,223,2,0,0,"support","low","exist"
"4079",6079,0.57,0.65,3,163,2,0,0,"support","low","exist"
"4080",6080,0.9,0.66,4,242,3,0,0,"support","high","exist"
"4081",6081,0.31,0.61,4,97,2,0,0,"support","low","exist"
"4082",6082,0.17,0.5,4,267,6,0,0,"support","high","exist"
"4083",6083,0.8,0.4,5,199,4,1,0,"support","high","exist"
"4084",6084,0.19,0.76,3,107,5,0,0,"support","low","exist"
"4085",6085,0.57,0.65,5,144,3,1,0,"support","low","exist"
"4086",6086,0.22,0.96,3,213,3,0,0,"support","high","exist"
"4087",6087,0.15,0.9,5,284,4,0,0,"support","low","exist"
"4088",6088,0.62,0.67,5,259,3,0,0,"technical","medium","exist"
"4089",6089,0.61,0.41,3,103,2,0,0,"technical","high","exist"
"4090",6090,0.87,0.81,5,236,3,0,0,"technical","medium","exist"
"4091",6091,0.54,0.75,4,199,2,0,0,"management","medium","exist"
"4092",6092,0.71,0.54,3,201,2,0,0,"IT","medium","exist"
"4093",6093,0.66,0.67,3,123,4,0,0,"IT","medium","exist"
"4094",6094,0.7,0.68,4,143,5,0,0,"IT","high","exist"
"4095",6095,0.53,0.5,5,159,2,0,0,"IT","medium","exist"
"4096",6096,0.92,0.54,5,203,3,0,0,"IT","medium","exist"
"4097",6097,0.93,0.73,4,168,2,0,0,"product_mng","medium","exist"
"4098",6098,0.62,0.7,5,180,4,0,0,"product_mng","high","exist"
"4099",6099,0.65,0.53,5,142,3,0,0,"product_mng","medium","exist"
"4100",6100,0.87,0.98,4,266,2,1,0,"product_mng","high","exist"
"4101",6101,0.97,0.89,5,265,2,0,0,"IT","low","exist"
"4102",6102,0.76,0.77,5,257,3,0,0,"RandD","medium","exist"
"4103",6103,0.96,0.55,4,234,4,0,0,"RandD","medium","exist"
"4104",6104,1,0.8,3,223,3,0,0,"RandD","medium","exist"
"4105",6105,0.99,0.85,5,261,4,0,0,"RandD","medium","exist"
"4106",6106,0.67,0.84,4,197,2,0,0,"RandD","low","exist"
"4107",6107,0.61,0.52,4,171,2,0,0,"marketing","low","exist"
"4108",6108,0.62,0.92,3,228,2,0,0,"sales","low","exist"
"4109",6109,0.62,0.79,3,141,3,0,0,"accounting","low","exist"
"4110",6110,0.97,0.76,3,147,3,1,0,"support","low","exist"
"4111",6111,0.86,0.56,5,237,3,0,0,"technical","low","exist"
"4112",6112,0.15,0.44,3,199,2,0,0,"management","low","exist"
"4113",6113,0.14,0.95,4,144,5,0,0,"marketing","low","exist"
"4114",6114,0.7,0.98,4,146,3,0,0,"marketing","low","exist"
"4115",6115,0.95,0.7,4,139,3,0,0,"marketing","low","exist"
"4116",6116,0.63,0.86,4,169,2,0,0,"sales","high","exist"
"4117",6117,0.45,0.75,4,169,2,0,0,"sales","low","exist"
"4118",6118,0.9,0.6,3,268,3,0,0,"sales","low","exist"
"4119",6119,0.15,0.87,4,194,4,0,0,"sales","low","exist"
"4120",6120,0.75,0.86,3,249,3,0,0,"sales","low","exist"
"4121",6121,0.14,0.52,4,122,2,0,0,"sales","high","exist"
"4122",6122,0.5,0.94,5,176,4,0,0,"sales","low","exist"
"4123",6123,0.45,0.45,4,168,2,0,0,"sales","low","exist"
"4124",6124,0.86,0.92,3,260,2,0,0,"sales","low","exist"
"4125",6125,0.52,0.62,3,179,3,0,0,"sales","high","exist"
"4126",6126,0.79,0.48,5,200,3,0,0,"sales","low","exist"
"4127",6127,0.47,0.56,4,165,3,0,0,"sales","medium","exist"
"4128",6128,0.76,0.64,4,144,2,0,0,"sales","high","exist"
"4129",6129,0.52,0.72,4,186,2,0,0,"sales","medium","exist"
"4130",6130,0.84,0.54,4,156,4,0,0,"sales","high","exist"
"4131",6131,0.5,0.7,4,162,2,0,0,"sales","medium","exist"
"4132",6132,0.52,0.63,3,269,2,0,0,"sales","medium","exist"
"4133",6133,0.76,0.37,3,127,4,0,0,"sales","medium","exist"
"4134",6134,0.59,0.58,2,267,3,0,0,"sales","medium","exist"
"4135",6135,0.65,0.79,4,196,2,0,0,"accounting","medium","exist"
"4136",6136,0.68,0.83,3,144,2,0,0,"accounting","medium","exist"
"4137",6137,0.52,0.72,2,247,4,0,0,"accounting","medium","exist"
"4138",6138,0.92,0.5,5,258,3,0,0,"hr","medium","exist"
"4139",6139,0.53,0.84,4,219,2,0,0,"hr","high","exist"
"4140",6140,0.5,0.95,2,208,2,0,0,"hr","low","exist"
"4141",6141,0.98,0.77,4,184,3,0,0,"hr","medium","exist"
"4142",6142,0.85,0.6,5,178,2,0,0,"technical","medium","exist"
"4143",6143,0.49,0.83,4,194,3,0,0,"technical","medium","exist"
"4144",6144,0.52,0.73,4,245,4,0,0,"technical","medium","exist"
"4145",6145,0.96,0.77,3,193,3,0,0,"technical","medium","exist"
"4146",6146,0.86,0.85,3,254,3,0,0,"technical","medium","exist"
"4147",6147,0.35,0.59,3,281,2,0,0,"technical","medium","exist"
"4148",6148,0.99,0.97,5,229,2,0,0,"technical","medium","exist"
"4149",6149,0.52,0.92,4,112,2,0,0,"technical","medium","exist"
"4150",6150,0.75,0.91,4,243,3,0,0,"technical","high","exist"
"4151",6151,0.67,0.66,3,151,3,0,0,"technical","low","exist"
"4152",6152,0.49,0.37,4,216,4,0,0,"technical","low","exist"
"4153",6153,0.51,0.62,3,110,3,0,0,"support","low","exist"
"4154",6154,0.65,0.6,3,142,2,0,0,"support","high","exist"
"4155",6155,0.73,0.8,4,251,2,1,0,"support","low","exist"
"4156",6156,0.46,0.75,6,276,6,0,0,"support","low","exist"
"4157",6157,0.94,0.82,4,159,2,1,0,"support","low","exist"
"4158",6158,0.53,0.69,4,257,4,0,0,"support","high","exist"
"4159",6159,0.6,0.79,5,154,2,0,0,"support","low","exist"
"4160",6160,0.63,0.97,5,146,3,0,0,"support","low","exist"
"4161",6161,0.75,0.77,4,204,2,0,0,"support","low","exist"
"4162",6162,0.69,0.53,4,156,3,0,0,"support","low","exist"
"4163",6163,0.81,0.5,4,170,4,0,0,"support","low","exist"
"4164",6164,0.74,0.84,3,239,3,0,0,"technical","low","exist"
"4165",6165,0.72,0.55,4,145,3,0,0,"technical","low","exist"
"4166",6166,0.27,0.39,4,193,4,0,0,"technical","medium","exist"
"4167",6167,0.86,0.74,2,178,3,0,0,"management","medium","exist"
"4168",6168,0.5,0.59,3,260,3,0,0,"IT","medium","exist"
"4169",6169,0.82,0.5,3,198,4,0,0,"IT","medium","exist"
"4170",6170,0.73,0.51,4,249,5,0,0,"IT","medium","exist"
"4171",6171,0.7,0.72,4,202,3,0,0,"IT","medium","exist"
"4172",6172,0.9,0.72,4,143,3,0,0,"IT","medium","exist"
"4173",6173,0.72,0.95,2,178,4,1,0,"product_mng","medium","exist"
"4174",6174,0.63,0.85,3,151,4,1,0,"product_mng","medium","exist"
"4175",6175,0.84,0.99,4,134,3,0,0,"product_mng","medium","exist"
"4176",6176,0.98,0.92,5,221,3,1,0,"product_mng","medium","exist"
"4177",6177,0.41,0.48,6,165,4,0,0,"IT","medium","exist"
"4178",6178,0.72,0.58,4,255,2,0,0,"RandD","high","exist"
"4179",6179,0.87,0.89,3,140,2,1,0,"RandD","low","exist"
"4180",6180,0.63,0.71,5,141,2,0,0,"RandD","medium","exist"
"4181",6181,0.6,0.96,4,99,6,0,0,"RandD","medium","exist"
"4182",6182,0.58,0.79,5,197,3,0,0,"RandD","medium","exist"
"4183",6183,0.64,0.52,3,240,3,0,0,"marketing","medium","exist"
"4184",6184,0.74,0.62,3,216,3,1,0,"sales","low","exist"
"4185",6185,0.93,0.7,5,206,4,0,0,"accounting","low","exist"
"4186",6186,0.74,0.75,4,257,3,0,0,"support","low","exist"
"4187",6187,0.98,0.6,4,160,3,0,0,"technical","low","exist"
"4188",6188,0.87,0.82,5,138,3,1,0,"management","low","exist"
"4189",6189,0.76,0.99,3,216,3,0,0,"marketing","low","exist"
"4190",6190,0.15,0.91,6,281,3,0,0,"marketing","low","exist"
"4191",6191,0.18,0.57,6,238,6,1,0,"marketing","low","exist"
"4192",6192,1,0.67,3,199,4,0,0,"sales","low","exist"
"4193",6193,0.98,0.63,3,135,3,0,0,"sales","low","exist"
"4194",6194,0.73,0.97,3,165,2,0,0,"sales","low","exist"
"4195",6195,0.67,0.72,3,180,3,0,0,"sales","low","exist"
"4196",6196,0.9,0.74,3,227,3,0,0,"sales","low","exist"
"4197",6197,0.54,0.53,3,251,2,0,0,"sales","low","exist"
"4198",6198,0.15,0.39,5,229,4,0,0,"sales","low","exist"
"4199",6199,0.58,0.54,4,199,2,0,0,"sales","low","exist"
"4200",6200,0.81,0.51,4,271,2,0,0,"sales","low","exist"
"4201",6201,0.17,0.51,5,221,3,0,0,"sales","low","exist"
"4202",6202,0.68,0.73,4,251,3,0,0,"sales","low","exist"
"4203",6203,0.68,0.49,4,153,4,0,0,"sales","low","exist"
"4204",6204,0.7,0.93,4,241,3,0,0,"sales","low","exist"
"4205",6205,0.49,0.68,4,201,4,0,0,"sales","medium","exist"
"4206",6206,0.55,0.96,4,267,3,0,0,"sales","medium","exist"
"4207",6207,0.48,0.84,3,146,2,1,0,"sales","medium","exist"
"4208",6208,0.63,0.98,4,210,3,0,0,"sales","medium","exist"
"4209",6209,0.83,0.69,4,233,2,0,0,"sales","medium","exist"
"4210",6210,0.48,0.87,3,221,2,0,0,"sales","medium","exist"
"4211",6211,0.98,0.96,5,183,3,1,0,"accounting","medium","exist"
"4212",6212,0.57,0.72,4,221,3,0,0,"accounting","medium","exist"
"4213",6213,0.72,0.66,3,167,3,0,0,"accounting","medium","exist"
"4214",6214,0.9,0.8,4,240,3,0,0,"hr","medium","exist"
"4215",6215,0.64,0.59,3,200,2,1,0,"hr","medium","exist"
"4216",6216,0.55,0.98,2,144,2,0,0,"hr","medium","exist"
"4217",6217,0.56,0.59,5,209,2,1,0,"hr","high","exist"
"4218",6218,0.8,0.55,3,206,2,0,0,"technical","low","exist"
"4219",6219,0.65,0.76,3,111,5,0,0,"technical","medium","exist"
"4220",6220,0.75,0.78,3,241,3,0,0,"technical","medium","exist"
"4221",6221,0.69,0.79,3,207,3,0,0,"technical","medium","exist"
"4222",6222,0.91,0.76,3,197,3,0,0,"technical","medium","exist"
"4223",6223,0.78,0.63,5,200,2,0,0,"technical","low","exist"
"4224",6224,0.71,0.68,4,242,4,0,0,"technical","low","exist"
"4225",6225,0.79,0.96,4,180,3,0,0,"technical","low","exist"
"4226",6226,0.86,0.72,4,173,3,0,0,"technical","low","exist"
"4227",6227,0.87,0.82,3,224,3,0,0,"technical","low","exist"
"4228",6228,0.76,0.99,2,183,2,0,0,"technical","low","exist"
"4229",6229,0.76,0.8,4,226,5,0,0,"support","low","exist"
"4230",6230,0.74,0.66,3,257,3,0,0,"support","low","exist"
"4231",6231,0.56,0.81,3,165,4,0,0,"support","low","exist"
"4232",6232,0.54,0.91,3,142,2,0,0,"support","low","exist"
"4233",6233,0.84,0.79,4,258,4,0,0,"support","low","exist"
"4234",6234,0.55,0.69,5,193,2,0,0,"support","low","exist"
"4235",6235,0.69,0.51,3,176,2,0,0,"support","low","exist"
"4236",6236,0.79,0.88,4,188,3,0,0,"support","low","exist"
"4237",6237,0.21,0.38,3,275,5,0,0,"support","low","exist"
"4238",6238,0.57,0.58,3,132,3,0,0,"support","low","exist"
"4239",6239,0.89,0.95,3,246,4,0,0,"support","low","exist"
"4240",6240,0.72,0.98,3,181,4,0,0,"technical","low","exist"
"4241",6241,0.56,0.58,5,266,3,0,0,"technical","low","exist"
"4242",6242,0.84,0.68,4,151,2,0,0,"technical","low","exist"
"4243",6243,0.94,0.76,3,257,4,1,0,"management","low","exist"
"4244",6244,0.29,0.88,6,183,4,0,0,"IT","medium","exist"
"4245",6245,0.54,0.93,3,124,5,0,0,"IT","medium","exist"
"4246",6246,0.93,0.73,4,153,2,1,0,"IT","medium","exist"
"4247",6247,0.8,0.68,4,199,2,0,0,"IT","medium","exist"
"4248",6248,1,0.73,5,142,4,0,0,"IT","medium","exist"
"4249",6249,0.89,0.56,4,159,3,0,0,"product_mng","medium","exist"
"4250",6250,0.6,0.78,6,211,4,1,0,"product_mng","medium","exist"
"4251",6251,0.49,0.94,5,136,3,0,0,"product_mng","medium","exist"
"4252",6252,0.65,0.75,4,153,2,0,0,"product_mng","medium","exist"
"4253",6253,0.6,0.71,5,263,2,1,0,"IT","medium","exist"
"4254",6254,0.51,1,3,168,3,1,0,"RandD","medium","exist"
"4255",6255,0.74,0.89,4,234,3,1,0,"RandD","medium","exist"
"4256",6256,0.57,0.42,4,154,5,0,0,"RandD","high","exist"
"4257",6257,0.82,0.84,5,173,2,0,0,"RandD","low","exist"
"4258",6258,0.19,0.63,5,206,6,0,0,"RandD","medium","exist"
"4259",6259,0.5,0.64,4,208,2,0,0,"marketing","medium","exist"
"4260",6260,0.91,0.68,4,178,3,1,0,"sales","medium","exist"
"4261",6261,0.19,0.86,4,198,6,0,0,"accounting","medium","exist"
"4262",6262,0.94,0.84,4,220,3,0,0,"support","low","exist"
"4263",6263,0.88,0.67,4,226,2,0,0,"technical","low","exist"
"4264",6264,0.9,0.87,4,231,5,0,0,"management","low","exist"
"4265",6265,0.49,0.96,2,206,2,0,0,"marketing","low","exist"
"4266",6266,0.99,0.55,4,179,4,0,0,"marketing","low","exist"
"4267",6267,0.72,0.81,4,200,2,0,0,"marketing","low","exist"
"4268",6268,0.66,0.69,5,202,6,0,0,"sales","low","exist"
"4269",6269,0.96,0.51,4,237,3,0,0,"sales","low","exist"
"4270",6270,0.49,0.69,4,270,3,0,0,"sales","low","exist"
"4271",6271,0.73,0.49,3,168,2,1,0,"sales","low","exist"
"4272",6272,0.48,0.98,5,132,4,0,0,"sales","low","exist"
"4273",6273,0.57,0.78,3,162,2,0,0,"sales","low","exist"
"4274",6274,0.63,0.82,3,269,3,0,0,"sales","low","exist"
"4275",6275,0.46,0.87,5,254,5,1,0,"sales","low","exist"
"4276",6276,0.64,0.5,3,261,2,0,0,"sales","low","exist"
"4277",6277,0.56,0.73,4,148,3,0,0,"sales","low","exist"
"4278",6278,0.97,0.75,5,228,4,0,0,"sales","low","exist"
"4279",6279,0.69,0.68,3,138,3,0,0,"sales","low","exist"
"4280",6280,0.23,0.97,4,200,3,0,0,"sales","low","exist"
"4281",6281,0.77,0.48,4,258,4,1,0,"sales","low","exist"
"4282",6282,0.76,0.57,4,266,3,0,0,"sales","low","exist"
"4283",6283,0.98,0.66,3,204,2,0,0,"sales","medium","exist"
"4284",6284,0.92,0.77,3,236,3,0,0,"sales","medium","exist"
"4285",6285,0.63,0.67,4,149,3,1,0,"sales","medium","exist"
"4286",6286,0.91,0.69,5,240,3,0,0,"sales","medium","exist"
"4287",6287,0.4,0.67,3,115,3,0,0,"accounting","medium","exist"
"4288",6288,0.82,0.62,4,267,3,1,0,"accounting","medium","exist"
"4289",6289,0.81,0.88,4,149,3,0,0,"accounting","medium","exist"
"4290",6290,0.61,0.69,3,224,3,0,0,"hr","medium","exist"
"4291",6291,0.3,0.57,2,158,2,0,0,"hr","medium","exist"
"4292",6292,0.59,0.72,2,107,3,0,0,"hr","medium","exist"
"4293",6293,0.2,0.56,3,217,5,0,0,"hr","medium","exist"
"4294",6294,0.75,0.56,2,212,2,0,0,"technical","medium","exist"
"4295",6295,0.59,0.79,3,270,3,0,0,"technical","high","exist"
"4296",6296,0.63,0.53,4,243,2,0,0,"technical","low","exist"
"4297",6297,0.77,0.68,5,162,4,1,0,"technical","medium","exist"
"4298",6298,0.82,0.6,5,232,2,0,0,"technical","medium","exist"
"4299",6299,0.6,0.85,5,187,4,0,0,"technical","medium","exist"
"4300",6300,0.83,0.72,3,259,2,0,0,"technical","medium","exist"
"4301",6301,0.67,0.6,4,209,2,0,0,"technical","low","exist"
"4302",6302,0.84,0.56,5,97,6,0,0,"technical","low","exist"
"4303",6303,0.68,0.79,5,139,4,0,0,"technical","low","exist"
"4304",6304,0.74,0.92,4,258,3,1,0,"technical","low","exist"
"4305",6305,0.63,0.64,3,208,2,1,0,"support","low","exist"
"4306",6306,0.88,0.9,2,233,3,0,0,"support","low","exist"
"4307",6307,1,0.81,3,168,4,0,0,"support","low","exist"
"4308",6308,0.7,0.87,4,252,2,1,0,"support","low","exist"
"4309",6309,0.5,0.71,5,171,4,0,0,"support","low","exist"
"4310",6310,0.94,0.66,5,219,3,0,0,"support","low","exist"
"4311",6311,0.67,0.54,3,213,4,0,0,"support","low","exist"
"4312",6312,0.8,0.88,5,199,4,0,0,"support","low","exist"
"4313",6313,0.7,0.88,4,245,2,0,0,"support","low","exist"
"4314",6314,0.24,0.73,4,273,5,0,0,"support","low","exist"
"4315",6315,0.98,1,4,202,3,0,0,"support","low","exist"
"4316",6316,0.76,0.65,4,240,3,0,0,"technical","low","exist"
"4317",6317,0.78,0.5,4,155,4,0,0,"technical","low","exist"
"4318",6318,0.42,0.91,3,209,2,0,0,"technical","low","exist"
"4319",6319,0.83,0.64,4,210,3,0,0,"management","medium","exist"
"4320",6320,0.98,0.81,4,266,6,0,0,"IT","medium","exist"
"4321",6321,0.64,0.81,2,226,2,0,0,"IT","medium","exist"
"4322",6322,0.67,0.75,4,133,2,0,0,"IT","medium","exist"
"4323",6323,0.26,0.39,3,99,4,0,0,"IT","medium","exist"
"4324",6324,0.97,0.64,3,237,3,0,0,"IT","medium","exist"
"4325",6325,0.48,0.6,4,230,3,0,0,"product_mng","medium","exist"
"4326",6326,0.84,0.55,4,149,2,0,0,"product_mng","medium","exist"
"4327",6327,0.71,0.74,4,206,4,0,0,"product_mng","medium","exist"
"4328",6328,1,0.84,3,185,3,0,0,"product_mng","medium","exist"
"4329",6329,0.6,0.76,5,269,2,0,0,"IT","medium","exist"
"4330",6330,0.95,0.37,6,233,5,0,0,"RandD","medium","exist"
"4331",6331,0.56,0.56,3,162,2,0,0,"RandD","high","exist"
"4332",6332,0.75,0.49,2,173,3,1,0,"RandD","low","exist"
"4333",6333,0.98,0.53,6,253,4,0,0,"RandD","medium","exist"
"4334",6334,0.7,0.72,5,134,3,0,0,"RandD","medium","exist"
"4335",6335,0.65,0.92,4,133,3,0,0,"marketing","medium","exist"
"4336",6336,0.8,0.81,5,143,2,0,0,"sales","medium","exist"
"4337",6337,0.49,0.78,3,264,4,0,0,"accounting","low","exist"
"4338",6338,0.32,0.73,3,180,6,0,0,"support","low","exist"
"4339",6339,0.88,0.54,3,235,2,1,0,"technical","low","exist"
"4340",6340,0.8,0.97,4,232,2,0,0,"management","low","exist"
"4341",6341,0.73,0.74,4,181,4,0,0,"marketing","low","exist"
"4342",6342,0.72,0.58,3,198,2,0,0,"marketing","low","exist"
"4343",6343,0.58,0.78,5,211,3,1,0,"marketing","low","exist"
"4344",6344,0.66,0.96,4,216,3,0,0,"sales","low","exist"
"4345",6345,0.63,0.79,3,197,3,1,0,"sales","low","exist"
"4346",6346,0.69,0.56,2,214,2,0,0,"sales","low","exist"
"4347",6347,0.49,0.59,3,185,3,0,0,"sales","low","exist"
"4348",6348,0.6,0.45,3,173,2,0,0,"sales","low","exist"
"4349",6349,0.5,0.63,4,229,4,0,0,"sales","low","exist"
"4350",6350,0.34,0.81,4,116,3,1,0,"sales","low","exist"
"4351",6351,0.79,0.6,5,223,3,0,0,"sales","low","exist"
"4352",6352,0.98,0.68,4,154,3,0,0,"sales","low","exist"
"4353",6353,0.96,0.97,5,240,2,0,0,"sales","low","exist"
"4354",6354,0.88,0.89,3,139,2,0,0,"sales","low","exist"
"4355",6355,0.6,0.61,2,275,2,0,0,"sales","low","exist"
"4356",6356,0.62,0.73,3,158,3,1,0,"sales","low","exist"
"4357",6357,0.4,0.72,3,204,4,1,0,"sales","low","exist"
"4358",6358,0.16,0.82,5,121,2,0,0,"sales","medium","exist"
"4359",6359,0.81,0.98,5,243,6,0,0,"sales","medium","exist"
"4360",6360,0.69,0.69,4,195,2,0,0,"sales","medium","exist"
"4361",6361,0.66,0.51,5,149,3,0,0,"sales","medium","exist"
"4362",6362,0.66,0.62,5,214,2,0,0,"sales","medium","exist"
"4363",6363,0.61,0.53,4,266,3,0,0,"accounting","medium","exist"
"4364",6364,0.99,0.77,5,222,2,0,0,"accounting","medium","exist"
"4365",6365,0.73,0.54,3,283,6,0,0,"accounting","medium","exist"
"4366",6366,0.63,0.72,2,161,5,0,0,"hr","medium","exist"
"4367",6367,0.6,0.91,3,157,4,0,0,"hr","medium","exist"
"4368",6368,0.44,0.44,3,126,2,0,0,"hr","medium","exist"
"4369",6369,0.64,0.95,3,131,6,0,0,"hr","medium","exist"
"4370",6370,0.91,0.81,4,139,2,0,0,"technical","high","exist"
"4371",6371,0.55,0.59,4,230,3,0,0,"technical","low","exist"
"4372",6372,0.7,0.73,4,240,2,1,0,"technical","medium","exist"
"4373",6373,0.37,0.59,3,134,3,0,0,"technical","medium","exist"
"4374",6374,0.16,0.56,6,196,3,0,0,"technical","medium","exist"
"4375",6375,0.69,0.97,5,240,4,0,0,"technical","medium","exist"
"4376",6376,0.96,0.47,2,233,5,1,0,"technical","low","exist"
"4377",6377,0.62,0.91,5,267,3,0,0,"technical","low","exist"
"4378",6378,0.94,0.45,2,211,3,0,0,"technical","low","exist"
"4379",6379,0.67,0.83,3,260,3,0,0,"technical","low","exist"
"4380",6380,0.89,0.57,3,113,3,0,0,"technical","low","exist"
"4381",6381,0.6,0.63,5,267,3,0,0,"support","low","exist"
"4382",6382,0.89,0.62,5,196,3,0,0,"support","low","exist"
"4383",6383,0.6,0.89,3,232,2,0,0,"support","low","exist"
"4384",6384,0.93,0.95,2,156,3,1,0,"support","low","exist"
"4385",6385,0.38,0.78,4,159,5,0,0,"support","low","exist"
"4386",6386,0.62,0.57,3,223,3,1,0,"support","low","exist"
"4387",6387,0.86,0.86,4,197,3,1,0,"support","low","exist"
"4388",6388,0.61,0.62,2,192,2,1,0,"support","low","exist"
"4389",6389,0.77,0.64,4,192,3,0,0,"support","low","exist"
"4390",6390,0.85,0.73,4,174,3,0,0,"support","low","exist"
"4391",6391,0.94,0.62,4,191,3,0,0,"support","low","exist"
"4392",6392,0.59,0.59,4,270,2,0,0,"technical","low","exist"
"4393",6393,0.9,0.92,3,139,3,0,0,"technical","low","exist"
"4394",6394,0.86,0.65,4,243,2,0,0,"technical","low","exist"
"4395",6395,0.72,0.7,3,238,2,0,0,"management","low","exist"
"4396",6396,0.84,0.52,5,189,2,1,0,"IT","low","exist"
"4397",6397,0.64,0.5,4,189,3,0,0,"IT","medium","exist"
"4398",6398,0.81,0.75,4,206,3,0,0,"IT","medium","exist"
"4399",6399,0.66,0.7,5,254,2,0,0,"IT","medium","exist"
"4400",6400,0.75,0.55,3,253,6,0,0,"IT","medium","exist"
"4401",6401,0.54,0.67,3,243,2,0,0,"product_mng","medium","exist"
"4402",6402,0.98,0.76,3,224,2,0,0,"product_mng","medium","exist"
"4403",6403,0.8,0.85,4,139,2,0,0,"product_mng","medium","exist"
"4404",6404,0.68,0.7,5,270,3,0,0,"product_mng","medium","exist"
"4405",6405,0.54,0.57,3,257,2,0,0,"IT","medium","exist"
"4406",6406,0.88,0.84,4,170,2,0,0,"RandD","medium","exist"
"4407",6407,0.71,0.62,3,222,3,0,0,"RandD","medium","exist"
"4408",6408,0.77,0.58,2,247,2,1,0,"RandD","medium","exist"
"4409",6409,0.6,0.6,4,201,3,0,0,"RandD","high","exist"
"4410",6410,0.53,0.75,4,263,5,0,0,"RandD","low","exist"
"4411",6411,0.85,0.5,3,168,2,1,0,"marketing","medium","exist"
"4412",6412,0.59,0.75,4,190,2,0,0,"sales","medium","exist"
"4413",6413,0.23,0.77,4,140,5,0,0,"accounting","medium","exist"
"4414",6414,0.7,0.6,3,224,3,0,0,"support","medium","exist"
"4415",6415,0.92,0.49,5,145,3,0,0,"technical","low","exist"
"4416",6416,0.95,0.37,4,285,3,0,0,"management","low","exist"
"4417",6417,0.39,0.43,3,154,3,0,0,"marketing","low","exist"
"4418",6418,0.85,0.8,4,200,2,0,0,"marketing","low","exist"
"4419",6419,0.98,0.7,3,255,2,1,0,"marketing","low","exist"
"4420",6420,0.15,0.48,6,204,6,1,0,"sales","low","exist"
"4421",6421,0.68,0.54,3,270,4,1,0,"sales","low","exist"
"4422",6422,0.24,0.91,5,177,5,0,0,"sales","low","exist"
"4423",6423,0.77,0.59,4,140,3,0,0,"sales","medium","exist"
"4424",6424,0.36,0.69,3,165,3,0,0,"sales","medium","exist"
"4425",6425,0.5,0.89,3,187,4,1,0,"sales","medium","exist"
"4426",6426,0.2,0.98,4,166,4,1,0,"sales","medium","exist"
"4427",6427,0.86,0.56,5,141,2,0,0,"sales","medium","exist"
"4428",6428,0.84,0.63,4,135,2,0,0,"sales","medium","exist"
"4429",6429,0.9,0.98,5,148,4,0,0,"sales","medium","exist"
"4430",6430,0.4,0.51,3,120,6,0,0,"sales","medium","exist"
"4431",6431,0.63,0.62,3,141,3,0,0,"sales","medium","exist"
"4432",6432,0.73,0.95,3,222,4,0,0,"sales","medium","exist"
"4433",6433,0.25,0.67,2,136,6,0,0,"sales","medium","exist"
"4434",6434,0.7,0.57,3,180,2,0,0,"sales","medium","exist"
"4435",6435,0.6,0.97,3,187,3,1,0,"sales","medium","exist"
"4436",6436,0.99,0.66,3,202,3,0,0,"sales","medium","exist"
"4437",6437,0.29,0.86,3,251,4,0,0,"sales","medium","exist"
"4438",6438,0.53,0.63,4,259,2,0,0,"sales","medium","exist"
"4439",6439,0.83,0.76,3,262,2,0,0,"accounting","medium","exist"
"4440",6440,0.18,0.92,5,251,4,0,0,"accounting","medium","exist"
"4441",6441,0.14,0.74,4,117,5,0,0,"accounting","medium","exist"
"4442",6442,0.5,0.66,4,155,2,0,0,"hr","medium","exist"
"4443",6443,0.36,0.89,3,197,6,0,0,"hr","medium","exist"
"4444",6444,0.34,0.56,3,139,2,0,0,"hr","medium","exist"
"4445",6445,0.51,0.75,4,175,2,0,0,"hr","medium","exist"
"4446",6446,0.5,0.52,5,137,3,1,0,"technical","medium","exist"
"4447",6447,0.69,0.93,3,228,4,0,0,"technical","medium","exist"
"4448",6448,0.52,0.51,2,232,4,0,0,"technical","high","exist"
"4449",6449,0.32,0.5,2,143,3,0,0,"technical","high","exist"
"4450",6450,0.88,0.99,3,190,5,0,0,"technical","high","exist"
"4451",6451,0.6,0.87,3,196,3,1,0,"technical","high","exist"
"4452",6452,0.72,0.8,3,213,3,0,0,"technical","high","exist"
"4453",6453,0.65,0.9,3,200,3,0,0,"technical","high","exist"
"4454",6454,0.82,0.78,5,166,2,0,0,"technical","high","exist"
"4455",6455,0.67,0.92,5,258,3,0,0,"technical","high","exist"
"4456",6456,0.44,0.87,5,104,4,0,0,"technical","high","exist"
"4457",6457,0.52,0.85,5,173,2,0,0,"support","high","exist"
"4458",6458,0.54,0.51,2,176,3,0,0,"support","high","exist"
"4459",6459,0.67,0.76,5,181,3,0,0,"support","high","exist"
"4460",6460,0.16,0.64,6,143,5,1,0,"support","low","exist"
"4461",6461,0.81,0.73,4,186,3,0,0,"support","low","exist"
"4462",6462,0.77,0.85,3,136,3,0,0,"support","low","exist"
"4463",6463,0.84,0.99,4,219,2,0,0,"support","low","exist"
"4464",6464,0.56,0.56,5,229,4,1,0,"support","low","exist"
"4465",6465,0.67,0.97,5,239,3,0,0,"support","low","exist"
"4466",6466,0.65,0.7,4,182,2,0,0,"support","low","exist"
"4467",6467,0.39,0.57,2,132,3,0,0,"support","low","exist"
"4468",6468,0.77,0.75,3,272,3,0,0,"technical","low","exist"
"4469",6469,0.41,0.96,5,167,3,1,0,"technical","low","exist"
"4470",6470,0.59,0.67,3,180,2,0,0,"technical","low","exist"
"4471",6471,0.14,0.72,6,100,5,0,0,"management","low","exist"
"4472",6472,0.6,0.82,4,134,2,0,0,"IT","low","exist"
"4473",6473,0.14,0.98,6,221,5,0,0,"IT","low","exist"
"4474",6474,0.88,0.8,3,166,2,0,0,"IT","low","exist"
"4475",6475,0.6,0.91,4,214,2,0,0,"IT","medium","exist"
"4476",6476,1,0.49,4,227,3,1,0,"IT","medium","exist"
"4477",6477,0.56,0.98,4,207,3,0,0,"product_mng","medium","exist"
"4478",6478,0.72,0.54,3,286,6,0,0,"product_mng","medium","exist"
"4479",6479,0.59,0.83,3,240,3,1,0,"product_mng","medium","exist"
"4480",6480,0.74,0.75,4,111,4,1,0,"product_mng","medium","exist"
"4481",6481,0.52,0.69,4,164,2,0,0,"IT","medium","exist"
"4482",6482,0.77,0.74,2,187,6,0,0,"RandD","medium","exist"
"4483",6483,0.48,0.81,4,248,2,0,0,"RandD","medium","exist"
"4484",6484,0.99,0.56,5,210,2,0,0,"RandD","medium","exist"
"4485",6485,0.23,0.78,4,163,6,0,0,"RandD","medium","exist"
"4486",6486,0.63,1,5,241,4,0,0,"RandD","medium","exist"
"4487",6487,0.51,0.83,3,136,3,0,0,"marketing","high","exist"
"4488",6488,0.54,0.55,4,208,5,0,0,"sales","high","exist"
"4489",6489,0.53,0.73,5,174,3,0,0,"accounting","high","exist"
"4490",6490,0.72,0.84,4,250,3,0,0,"support","high","exist"
"4491",6491,0.57,0.61,2,189,2,1,0,"technical","high","exist"
"4492",6492,0.81,0.77,3,204,3,0,0,"management","high","exist"
"4493",6493,0.64,0.57,4,217,3,0,0,"marketing","high","exist"
"4494",6494,0.77,0.57,5,162,3,0,0,"marketing","high","exist"
"4495",6495,0.83,0.55,3,257,2,0,0,"marketing","low","exist"
"4496",6496,0.6,0.71,3,195,2,0,0,"sales","low","exist"
"4497",6497,0.86,0.87,5,156,4,0,0,"sales","low","exist"
"4498",6498,0.5,0.55,2,128,3,0,0,"sales","low","exist"
"4499",6499,0.6,0.75,5,233,3,1,0,"sales","low","exist"
"4500",6500,0.85,0.73,4,260,4,0,0,"sales","low","exist"
"4501",6501,0.4,0.87,5,250,4,0,0,"sales","low","exist"
"4502",6502,0.38,0.79,5,176,3,0,0,"sales","low","exist"
"4503",6503,0.96,0.59,6,133,5,0,0,"sales","medium","exist"
"4504",6504,0.59,0.57,4,197,2,0,0,"sales","medium","exist"
"4505",6505,0.56,0.5,3,156,3,1,0,"sales","medium","exist"
"4506",6506,0.84,0.96,3,162,3,0,0,"sales","medium","exist"
"4507",6507,0.94,0.99,3,207,2,0,0,"sales","medium","exist"
"4508",6508,0.72,0.63,3,223,2,1,0,"sales","medium","exist"
"4509",6509,0.82,0.7,3,149,2,0,0,"sales","medium","exist"
"4510",6510,1,0.95,3,275,3,1,0,"sales","medium","exist"
"4511",6511,0.62,0.77,3,271,2,0,0,"sales","medium","exist"
"4512",6512,0.76,0.89,3,273,2,1,0,"sales","medium","exist"
"4513",6513,0.23,0.74,5,219,4,0,0,"sales","medium","exist"
"4514",6514,0.7,0.99,5,135,4,0,0,"sales","medium","exist"
"4515",6515,0.71,0.88,3,158,3,0,0,"accounting","medium","exist"
"4516",6516,0.32,0.37,3,167,3,0,0,"accounting","medium","exist"
"4517",6517,0.69,0.67,4,274,4,0,0,"accounting","medium","exist"
"4518",6518,0.66,0.59,3,145,3,0,0,"hr","medium","exist"
"4519",6519,0.7,0.67,6,233,6,0,0,"hr","medium","exist"
"4520",6520,0.91,0.76,3,159,3,0,0,"hr","medium","exist"
"4521",6521,0.2,0.7,4,221,5,0,0,"hr","medium","exist"
"4522",6522,0.72,0.83,3,132,2,0,0,"technical","medium","exist"
"4523",6523,0.74,0.93,5,140,3,0,0,"technical","medium","exist"
"4524",6524,0.53,0.79,3,206,3,0,0,"technical","medium","exist"
"4525",6525,0.99,0.92,4,229,2,0,0,"technical","medium","exist"
"4526",6526,0.75,0.85,4,272,4,0,0,"technical","high","exist"
"4527",6527,0.86,0.78,4,164,2,0,0,"technical","low","exist"
"4528",6528,0.93,0.86,4,145,3,0,0,"technical","medium","exist"
"4529",6529,0.81,0.95,3,212,3,0,0,"technical","medium","exist"
"4530",6530,0.49,0.67,3,254,2,1,0,"technical","medium","exist"
"4531",6531,0.97,0.72,5,260,4,0,0,"technical","medium","exist"
"4532",6532,0.62,0.89,3,201,3,0,0,"technical","medium","exist"
"4533",6533,0.75,0.79,4,176,4,1,0,"support","medium","exist"
"4534",6534,0.87,0.58,3,140,3,1,0,"support","medium","exist"
"4535",6535,0.93,0.65,5,201,3,0,0,"support","medium","exist"
"4536",6536,0.74,0.68,3,206,2,1,0,"support","medium","exist"
"4537",6537,0.68,0.57,3,197,3,1,0,"support","medium","exist"
"4538",6538,0.49,0.63,3,245,4,0,0,"support","low","exist"
"4539",6539,0.75,0.84,3,145,3,1,0,"support","low","exist"
"4540",6540,0.95,0.58,4,131,5,0,0,"support","low","exist"
"4541",6541,0.95,0.75,5,235,3,1,0,"support","low","exist"
"4542",6542,0.67,0.65,5,242,3,0,0,"support","low","exist"
"4543",6543,0.71,0.6,2,251,3,0,0,"support","low","exist"
"4544",6544,0.81,0.73,4,258,2,0,0,"technical","low","exist"
"4545",6545,0.79,0.36,3,114,3,0,0,"technical","high","exist"
"4546",6546,0.57,0.52,3,143,2,0,0,"technical","low","exist"
"4547",6547,0.85,0.65,3,187,2,0,0,"management","high","exist"
"4548",6548,0.15,0.78,5,255,3,0,0,"IT","high","exist"
"4549",6549,0.48,0.87,3,267,2,0,0,"IT","low","exist"
"4550",6550,0.92,0.91,4,247,3,0,0,"IT","low","exist"
"4551",6551,0.73,0.67,4,153,3,0,0,"IT","high","exist"
"4552",6552,0.68,0.71,3,237,3,0,0,"IT","low","exist"
"4553",6553,0.88,0.55,5,182,3,0,0,"product_mng","medium","exist"
"4554",6554,0.8,0.55,4,144,5,1,0,"product_mng","high","exist"
"4555",6555,0.66,0.9,3,176,2,1,0,"product_mng","medium","exist"
"4556",6556,0.19,0.8,5,203,6,0,0,"product_mng","medium","exist"
"4557",6557,0.3,0.53,3,148,4,0,0,"IT","medium","exist"
"4558",6558,0.8,0.8,3,175,2,0,0,"RandD","medium","exist"
"4559",6559,0.66,0.84,3,211,4,0,0,"RandD","high","exist"
"4560",6560,0.22,0.67,6,175,5,0,0,"RandD","medium","exist"
"4561",6561,0.57,0.59,4,206,3,0,0,"RandD","medium","exist"
"4562",6562,0.83,0.73,4,262,2,0,0,"RandD","medium","exist"
"4563",6563,0.57,0.5,4,177,2,0,0,"marketing","high","exist"
"4564",6564,0.78,0.54,3,134,3,0,0,"sales","medium","exist"
"4565",6565,0.88,0.89,2,201,3,0,0,"accounting","high","exist"
"4566",6566,0.48,0.69,3,105,3,0,0,"support","low","exist"
"4567",6567,0.91,0.82,4,259,3,0,0,"technical","medium","exist"
"4568",6568,0.3,0.97,3,171,4,1,0,"management","medium","exist"
"4569",6569,0.63,0.98,4,228,3,0,0,"marketing","medium","exist"
"4570",6570,0.62,0.36,2,137,4,1,0,"marketing","medium","exist"
"4571",6571,0.74,0.72,5,196,3,0,0,"marketing","low","exist"
"4572",6572,0.5,0.53,3,207,4,0,0,"sales","low","exist"
"4573",6573,0.36,0.74,6,280,3,0,0,"sales","low","exist"
"4574",6574,0.57,0.65,4,162,3,1,0,"sales","low","exist"
"4575",6575,0.73,0.55,4,267,3,0,0,"sales","low","exist"
"4576",6576,0.77,0.67,5,207,2,0,0,"sales","low","exist"
"4577",6577,0.86,0.5,4,196,2,0,0,"sales","low","exist"
"4578",6578,0.24,0.55,6,231,4,0,0,"sales","low","exist"
"4579",6579,0.83,0.62,4,242,3,0,0,"sales","low","exist"
"4580",6580,0.72,0.63,4,207,3,0,0,"sales","low","exist"
"4581",6581,0.52,0.82,4,206,2,1,0,"sales","high","exist"
"4582",6582,0.99,0.54,4,236,4,0,0,"sales","low","exist"
"4583",6583,0.15,0.68,5,246,2,0,0,"sales","low","exist"
"4584",6584,0.79,0.94,3,204,2,1,0,"sales","low","exist"
"4585",6585,0.19,0.91,3,268,4,0,0,"sales","low","exist"
"4586",6586,0.59,0.62,3,212,2,1,0,"sales","high","exist"
"4587",6587,0.15,0.74,6,178,3,0,0,"sales","low","exist"
"4588",6588,0.75,0.72,5,260,3,0,0,"sales","low","exist"
"4589",6589,0.47,0.71,2,241,4,0,0,"sales","low","exist"
"4590",6590,0.64,0.64,3,234,3,1,0,"sales","high","exist"
"4591",6591,0.66,0.76,3,207,2,0,0,"accounting","low","exist"
"4592",6592,0.8,0.91,5,242,3,1,0,"accounting","medium","exist"
"4593",6593,0.82,0.52,5,225,2,0,0,"accounting","high","exist"
"4594",6594,0.8,0.98,3,161,3,0,0,"hr","medium","exist"
"4595",6595,0.98,0.98,4,259,2,0,0,"hr","high","exist"
"4596",6596,0.95,0.96,3,250,3,0,0,"hr","medium","exist"
"4597",6597,0.88,0.61,3,236,3,0,0,"hr","medium","exist"
"4598",6598,0.9,0.97,4,239,4,0,0,"technical","medium","exist"
"4599",6599,0.66,0.83,4,266,3,1,0,"technical","medium","exist"
"4600",6600,0.99,0.62,5,133,3,0,0,"technical","medium","exist"
"4601",6601,0.86,0.95,5,275,2,0,0,"technical","medium","exist"
"4602",6602,0.96,0.95,5,189,2,0,0,"technical","medium","exist"
"4603",6603,1,0.63,5,171,3,0,0,"technical","medium","exist"
"4604",6604,0.46,0.38,6,140,3,0,0,"technical","high","exist"
"4605",6605,0.19,0.85,6,116,3,0,0,"technical","low","exist"
"4606",6606,0.73,0.6,3,145,6,1,0,"technical","medium","exist"
"4607",6607,0.63,0.5,4,167,3,1,0,"technical","medium","exist"
"4608",6608,0.68,0.89,4,227,3,0,0,"technical","medium","exist"
"4609",6609,0.78,0.96,4,245,3,0,0,"support","medium","exist"
"4610",6610,0.79,0.56,4,132,3,0,0,"support","medium","exist"
"4611",6611,0.86,0.99,3,254,2,0,0,"support","medium","exist"
"4612",6612,0.98,0.53,4,166,2,0,0,"support","medium","exist"
"4613",6613,0.89,0.79,2,208,3,1,0,"support","medium","exist"
"4614",6614,0.86,0.87,3,197,4,1,0,"support","medium","exist"
"4615",6615,0.65,0.83,4,263,3,1,0,"support","high","exist"
"4616",6616,0.52,0.98,4,272,2,0,0,"support","low","exist"
"4617",6617,0.54,0.65,3,147,3,1,0,"support","low","exist"
"4618",6618,0.68,0.73,4,197,3,0,0,"support","low","exist"
"4619",6619,0.88,0.65,3,268,2,0,0,"support","high","exist"
"4620",6620,0.75,0.85,4,181,3,0,0,"technical","low","exist"
"4621",6621,0.17,0.93,6,192,4,0,0,"technical","low","exist"
"4622",6622,0.92,0.83,5,270,3,0,0,"technical","low","exist"
"4623",6623,0.79,0.66,3,183,3,0,0,"management","high","exist"
"4624",6624,0.52,0.66,5,184,3,0,0,"IT","low","exist"
"4625",6625,0.95,0.73,4,238,3,1,0,"IT","low","exist"
"4626",6626,0.72,0.49,4,148,2,0,0,"IT","low","exist"
"4627",6627,0.4,0.41,4,127,3,0,0,"IT","low","exist"
"4628",6628,0.61,0.59,3,162,2,1,0,"IT","low","exist"
"4629",6629,0.49,0.97,4,166,5,0,0,"product_mng","low","exist"
"4630",6630,0.32,0.55,4,283,4,0,0,"product_mng","low","exist"
"4631",6631,0.82,0.77,3,108,6,0,0,"product_mng","medium","exist"
"4632",6632,0.9,0.79,3,154,2,0,0,"product_mng","medium","exist"
"4633",6633,0.95,0.74,3,139,2,0,0,"IT","medium","exist"
"4634",6634,0.55,0.6,5,271,3,1,0,"RandD","medium","exist"
"4635",6635,0.91,0.78,4,153,3,0,0,"RandD","medium","exist"
"4636",6636,0.45,0.41,3,216,6,1,0,"RandD","medium","exist"
"4637",6637,0.65,0.6,3,218,4,0,0,"RandD","medium","exist"
"4638",6638,0.88,0.89,3,171,2,0,0,"RandD","medium","exist"
"4639",6639,0.78,0.76,3,238,3,0,0,"marketing","medium","exist"
"4640",6640,0.77,0.71,3,134,3,0,0,"marketing","medium","exist"
"4641",6641,0.32,0.75,3,255,4,0,0,"sales","medium","exist"
"4642",6642,0.98,0.38,4,140,6,0,0,"accounting","medium","exist"
"4643",6643,0.51,0.76,2,239,3,0,0,"support","high","exist"
"4644",6644,0.81,0.59,4,187,2,0,0,"technical","low","exist"
"4645",6645,0.64,0.9,5,279,5,0,0,"management","medium","exist"
"4646",6646,0.73,0.94,4,213,3,1,0,"marketing","medium","exist"
"4647",6647,0.49,0.56,5,202,2,0,0,"marketing","medium","exist"
"4648",6648,0.84,0.54,3,265,2,0,0,"marketing","medium","exist"
"4649",6649,0.81,0.86,4,249,3,0,0,"sales","low","exist"
"4650",6650,0.77,0.5,4,281,2,0,0,"sales","low","exist"
"4651",6651,0.24,0.83,3,208,5,0,0,"sales","low","exist"
"4652",6652,0.77,0.78,3,165,2,0,0,"sales","low","exist"
"4653",6653,0.13,0.88,3,146,5,0,0,"sales","low","exist"
"4654",6654,0.94,0.66,4,230,2,0,0,"sales","low","exist"
"4655",6655,0.61,0.82,3,209,2,0,0,"sales","low","exist"
"4656",6656,0.95,0.49,4,178,2,0,0,"sales","low","exist"
"4657",6657,0.22,0.92,6,220,4,1,0,"sales","low","exist"
"4658",6658,0.65,0.56,3,142,3,0,0,"sales","low","exist"
"4659",6659,0.95,0.67,3,153,2,0,0,"sales","low","exist"
"4660",6660,0.98,0.62,5,254,5,1,0,"sales","low","exist"
"4661",6661,0.88,0.72,3,193,4,0,0,"sales","low","exist"
"4662",6662,0.94,0.69,3,248,3,0,0,"sales","low","exist"
"4663",6663,0.62,0.75,4,216,2,0,0,"sales","low","exist"
"4664",6664,0.81,0.96,3,226,3,0,0,"sales","low","exist"
"4665",6665,0.56,0.51,4,140,2,0,0,"sales","low","exist"
"4666",6666,0.52,0.86,6,103,4,0,0,"sales","low","exist"
"4667",6667,0.88,0.57,4,185,2,0,0,"sales","low","exist"
"4668",6668,0.56,0.92,5,160,3,0,0,"accounting","low","exist"
"4669",6669,0.36,0.63,3,130,5,0,0,"accounting","low","exist"
"4670",6670,0.56,0.85,5,230,3,0,0,"accounting","medium","exist"
"4671",6671,0.89,0.46,4,248,5,1,0,"hr","medium","exist"
"4672",6672,0.95,0.42,3,189,2,1,0,"hr","medium","exist"
"4673",6673,0.48,0.52,3,280,2,0,0,"hr","medium","exist"
"4674",6674,0.75,0.75,4,266,3,0,0,"hr","medium","exist"
"4675",6675,0.65,0.54,4,260,3,0,0,"technical","medium","exist"
"4676",6676,0.4,0.86,2,264,5,0,0,"technical","medium","exist"
"4677",6677,0.52,0.53,5,182,2,0,0,"technical","medium","exist"
"4678",6678,0.52,0.68,4,233,2,0,0,"technical","medium","exist"
"4679",6679,0.68,0.49,3,230,3,0,0,"technical","medium","exist"
"4680",6680,0.72,0.61,5,170,3,0,0,"technical","medium","exist"
"4681",6681,0.78,0.72,4,258,3,0,0,"technical","medium","exist"
"4682",6682,0.54,0.9,3,164,3,0,0,"technical","high","exist"
"4683",6683,0.18,0.46,4,249,4,0,0,"technical","low","exist"
"4684",6684,0.51,0.5,2,235,2,0,0,"technical","medium","exist"
"4685",6685,0.63,0.86,6,206,6,0,0,"technical","medium","exist"
"4686",6686,0.83,0.86,4,139,3,0,0,"support","medium","exist"
"4687",6687,0.91,0.82,3,145,3,1,0,"support","medium","exist"
"4688",6688,0.79,0.66,4,139,4,0,0,"support","low","exist"
"4689",6689,0.52,0.95,3,171,3,1,0,"support","low","exist"
"4690",6690,0.83,0.94,3,160,3,0,0,"support","low","exist"
"4691",6691,0.92,0.74,3,137,2,0,0,"support","low","exist"
"4692",6692,0.14,0.72,4,254,4,0,0,"support","low","exist"
"4693",6693,0.8,0.38,3,215,6,0,0,"support","low","exist"
"4694",6694,0.79,0.72,3,216,4,0,0,"support","low","exist"
"4695",6695,0.86,0.6,3,229,2,1,0,"support","low","exist"
"4696",6696,0.95,0.47,6,215,4,0,0,"support","low","exist"
"4697",6697,0.77,0.9,4,163,3,0,0,"technical","low","exist"
"4698",6698,0.55,0.72,4,273,3,0,0,"technical","low","exist"
"4699",6699,0.42,0.91,2,176,3,0,0,"technical","low","exist"
"4700",6700,0.79,0.86,5,270,2,0,0,"management","low","exist"
"4701",6701,0.41,0.48,3,182,2,0,0,"IT","low","exist"
"4702",6702,0.66,0.72,4,223,4,0,0,"IT","low","exist"
"4703",6703,1,0.65,4,237,3,0,0,"IT","low","exist"
"4704",6704,0.87,0.74,5,248,2,0,0,"IT","low","exist"
"4705",6705,0.51,0.99,3,233,4,0,0,"IT","low","exist"
"4706",6706,0.63,0.79,2,206,5,0,0,"product_mng","low","exist"
"4707",6707,0.86,0.86,4,227,2,0,0,"product_mng","low","exist"
"4708",6708,0.4,0.98,4,154,3,0,0,"product_mng","low","exist"
"4709",6709,0.79,0.97,6,113,2,1,0,"product_mng","medium","exist"
"4710",6710,0.7,0.9,4,254,3,0,0,"IT","medium","exist"
"4711",6711,0.49,0.91,5,231,3,0,0,"RandD","medium","exist"
"4712",6712,0.76,0.62,4,190,3,0,0,"RandD","medium","exist"
"4713",6713,0.89,0.52,3,190,3,0,0,"RandD","medium","exist"
"4714",6714,0.83,0.86,3,179,2,0,0,"RandD","medium","exist"
"4715",6715,0.19,0.69,4,269,6,0,0,"RandD","medium","exist"
"4716",6716,0.68,0.67,3,228,2,0,0,"RandD","medium","exist"
"4717",6717,0.62,0.68,4,251,4,0,0,"marketing","medium","exist"
"4718",6718,0.87,0.49,2,251,3,0,0,"sales","medium","exist"
"4719",6719,0.66,0.75,4,200,4,0,0,"accounting","medium","exist"
"4720",6720,0.37,0.41,2,146,2,0,0,"support","medium","exist"
"4721",6721,0.57,0.49,3,159,4,1,0,"technical","high","exist"
"4722",6722,0.66,0.81,5,135,2,0,0,"management","low","exist"
"4723",6723,0.63,0.88,5,260,3,0,0,"marketing","medium","exist"
"4724",6724,0.65,0.96,5,226,2,1,0,"marketing","medium","exist"
"4725",6725,0.33,0.85,2,127,3,0,0,"marketing","medium","exist"
"4726",6726,0.66,0.57,6,278,3,0,0,"sales","medium","exist"
"4727",6727,0.87,0.95,3,242,5,0,0,"sales","low","exist"
"4728",6728,0.85,0.85,3,182,3,0,0,"sales","low","exist"
"4729",6729,0.49,0.51,2,182,3,0,0,"sales","low","exist"
"4730",6730,0.87,0.8,4,197,3,0,0,"sales","low","exist"
"4731",6731,0.17,0.49,4,286,5,1,0,"sales","low","exist"
"4732",6732,0.55,0.46,4,226,5,0,0,"sales","low","exist"
"4733",6733,0.91,0.71,5,156,3,0,0,"sales","low","exist"
"4734",6734,0.96,0.62,5,185,3,0,0,"sales","low","exist"
"4735",6735,0.53,0.5,3,231,3,0,0,"sales","low","exist"
"4736",6736,0.25,0.59,4,166,5,0,0,"sales","low","exist"
"4737",6737,0.98,0.57,3,229,3,0,0,"sales","low","exist"
"4738",6738,0.83,0.36,4,242,3,0,0,"sales","low","exist"
"4739",6739,0.71,0.83,5,206,3,0,0,"sales","low","exist"
"4740",6740,0.74,0.77,4,206,3,0,0,"sales","low","exist"
"4741",6741,0.56,0.7,4,135,2,0,0,"sales","low","exist"
"4742",6742,0.23,0.9,5,234,3,1,0,"sales","low","exist"
"4743",6743,0.35,0.64,4,147,2,0,0,"sales","low","exist"
"4744",6744,0.48,0.98,4,174,3,0,0,"sales","low","exist"
"4745",6745,0.83,0.74,3,259,3,1,0,"accounting","low","exist"
"4746",6746,0.73,0.87,3,227,4,0,0,"accounting","low","exist"
"4747",6747,0.85,0.97,4,104,5,0,0,"accounting","low","exist"
"4748",6748,0.8,0.95,3,247,3,0,0,"hr","medium","exist"
"4749",6749,0.98,0.74,4,139,3,0,0,"hr","medium","exist"
"4750",6750,0.96,0.85,3,186,2,0,0,"hr","medium","exist"
"4751",6751,0.67,0.75,3,194,3,0,0,"hr","medium","exist"
"4752",6752,0.58,0.91,3,124,2,0,0,"technical","medium","exist"
"4753",6753,0.83,0.86,3,273,3,0,0,"technical","medium","exist"
"4754",6754,0.9,0.57,4,186,4,0,0,"technical","medium","exist"
"4755",6755,0.89,0.66,4,252,3,0,0,"technical","medium","exist"
"4756",6756,0.99,0.92,3,154,3,0,0,"technical","medium","exist"
"4757",6757,0.89,0.5,4,238,3,0,0,"technical","medium","exist"
"4758",6758,0.79,0.5,4,151,3,1,0,"technical","medium","exist"
"4759",6759,0.64,0.41,3,231,6,0,0,"technical","medium","exist"
"4760",6760,0.22,0.57,5,174,6,0,0,"technical","high","exist"
"4761",6761,0.94,0.6,5,278,2,0,0,"technical","low","exist"
"4762",6762,0.56,0.97,3,270,3,1,0,"technical","medium","exist"
"4763",6763,0.85,0.8,4,158,3,0,0,"support","medium","exist"
"4764",6764,0.8,0.62,3,191,3,0,0,"support","medium","exist"
"4765",6765,0.86,0.53,3,163,3,0,0,"support","medium","exist"
"4766",6766,0.96,1,5,152,4,0,0,"support","low","exist"
"4767",6767,0.51,0.61,4,251,2,1,0,"support","low","exist"
"4768",6768,0.73,0.95,3,149,2,0,0,"support","low","exist"
"4769",6769,0.31,0.75,4,220,3,0,0,"support","low","exist"
"4770",6770,0.62,0.51,4,175,3,0,0,"support","low","exist"
"4771",6771,0.55,0.91,3,179,3,1,0,"support","low","exist"
"4772",6772,0.51,0.8,4,257,2,0,0,"support","low","exist"
"4773",6773,0.54,0.54,3,196,3,0,0,"support","low","exist"
"4774",6774,0.65,0.95,3,190,3,0,0,"technical","low","exist"
"4775",6775,0.65,0.75,4,270,2,0,0,"technical","low","exist"
"4776",6776,0.9,0.64,5,226,2,0,0,"technical","low","exist"
"4777",6777,0.55,0.71,3,211,2,0,0,"management","low","exist"
"4778",6778,0.59,0.89,3,192,2,0,0,"IT","low","exist"
"4779",6779,0.34,0.67,5,96,2,1,0,"IT","low","exist"
"4780",6780,0.31,0.92,5,197,5,0,0,"IT","low","exist"
"4781",6781,0.83,0.71,3,243,2,1,0,"IT","low","exist"
"4782",6782,0.8,0.73,3,168,2,0,0,"IT","low","exist"
"4783",6783,0.66,0.85,5,271,4,0,0,"product_mng","low","exist"
"4784",6784,0.98,0.39,5,158,4,1,0,"product_mng","medium","exist"
"4785",6785,0.89,0.52,4,243,5,0,0,"product_mng","medium","exist"
"4786",6786,0.64,0.94,3,148,2,0,0,"product_mng","medium","exist"
"4787",6787,0.95,0.68,3,165,3,0,0,"IT","medium","exist"
"4788",6788,0.96,0.85,5,171,2,0,0,"RandD","medium","exist"
"4789",6789,0.96,0.82,5,164,2,0,0,"RandD","medium","exist"
"4790",6790,0.63,0.81,4,265,2,0,0,"RandD","medium","exist"
"4791",6791,0.83,0.71,4,196,2,0,0,"RandD","medium","exist"
"4792",6792,0.61,0.72,4,182,2,1,0,"RandD","medium","exist"
"4793",6793,0.89,0.66,3,272,3,0,0,"RandD","medium","exist"
"4794",6794,0.67,0.63,3,241,3,0,0,"marketing","medium","exist"
"4795",6795,0.61,1,5,139,2,0,0,"sales","medium","exist"
"4796",6796,0.58,0.77,3,180,2,0,0,"accounting","high","exist"
"4797",6797,0.56,0.76,4,206,2,1,0,"support","low","exist"
"4798",6798,0.13,0.49,6,227,4,0,0,"technical","medium","exist"
"4799",6799,0.39,1,5,204,5,1,0,"management","medium","exist"
"4800",6800,0.94,0.48,4,218,3,0,0,"marketing","medium","exist"
"4801",6801,0.63,0.61,3,205,2,0,0,"marketing","medium","exist"
"4802",6802,0.75,0.63,4,261,3,1,0,"marketing","low","exist"
"4803",6803,0.7,0.83,3,159,3,0,0,"sales","low","exist"
"4804",6804,0.28,0.83,4,162,3,0,0,"sales","low","exist"
"4805",6805,0.77,0.42,4,98,2,0,0,"sales","low","exist"
"4806",6806,0.79,0.64,4,263,2,0,0,"sales","low","exist"
"4807",6807,0.51,0.46,3,176,3,0,0,"sales","low","exist"
"4808",6808,0.96,0.99,4,233,3,0,0,"sales","low","exist"
"4809",6809,0.72,0.99,4,156,2,1,0,"sales","low","exist"
"4810",6810,0.97,1,3,198,2,0,0,"sales","low","exist"
"4811",6811,0.55,0.9,4,191,3,0,0,"sales","low","exist"
"4812",6812,0.32,0.45,2,188,3,0,0,"sales","low","exist"
"4813",6813,0.78,0.65,3,157,2,0,0,"sales","low","exist"
"4814",6814,0.17,0.57,5,286,3,0,0,"sales","low","exist"
"4815",6815,0.88,0.5,4,216,2,0,0,"sales","low","exist"
"4816",6816,0.97,0.5,3,188,3,1,0,"sales","low","exist"
"4817",6817,0.74,0.86,5,153,2,0,0,"sales","low","exist"
"4818",6818,0.26,0.45,5,187,2,0,0,"sales","low","exist"
"4819",6819,0.87,0.92,4,141,3,0,0,"sales","low","exist"
"4820",6820,0.29,0.47,5,139,4,0,0,"sales","low","exist"
"4821",6821,0.91,0.95,3,189,2,0,0,"sales","low","exist"
"4822",6822,0.71,0.77,3,193,3,1,0,"accounting","low","exist"
"4823",6823,0.6,0.63,3,182,3,0,0,"accounting","medium","exist"
"4824",6824,0.5,0.61,4,135,3,0,0,"accounting","medium","exist"
"4825",6825,0.49,0.85,4,238,2,0,0,"hr","medium","exist"
"4826",6826,0.53,0.92,3,199,2,0,0,"hr","medium","exist"
"4827",6827,0.42,0.38,2,115,3,0,0,"hr","medium","exist"
"4828",6828,0.53,0.82,3,133,3,0,0,"hr","medium","exist"
"4829",6829,0.34,0.62,4,158,2,0,0,"technical","medium","exist"
"4830",6830,0.68,0.51,5,158,3,0,0,"technical","medium","exist"
"4831",6831,0.56,0.77,5,238,4,0,0,"technical","medium","exist"
"4832",6832,0.72,0.71,3,242,2,0,0,"technical","medium","exist"
"4833",6833,0.76,0.55,4,250,3,0,0,"technical","medium","exist"
"4834",6834,0.87,0.57,4,175,2,0,0,"technical","medium","exist"
"4835",6835,0.97,0.63,3,270,4,1,0,"technical","high","exist"
"4836",6836,0.8,0.62,3,171,3,0,0,"technical","low","exist"
"4837",6837,0.67,0.81,5,175,6,1,0,"technical","medium","exist"
"4838",6838,0.6,0.97,5,145,2,0,0,"technical","medium","exist"
"4839",6839,0.88,0.5,3,170,3,0,0,"technical","medium","exist"
"4840",6840,0.64,0.74,3,267,2,0,0,"support","medium","exist"
"4841",6841,0.85,0.7,3,188,2,0,0,"support","low","exist"
"4842",6842,0.9,0.48,3,213,3,0,0,"support","low","exist"
"4843",6843,0.76,0.84,5,249,3,0,0,"support","low","exist"
"4844",6844,0.55,0.66,3,134,3,0,0,"support","low","exist"
"4845",6845,0.76,0.77,5,234,3,0,0,"support","low","exist"
"4846",6846,0.87,0.72,3,201,3,0,0,"support","low","exist"
"4847",6847,0.8,0.82,3,178,3,0,0,"support","low","exist"
"4848",6848,0.54,0.68,4,183,2,0,0,"support","low","exist"
"4849",6849,0.84,0.91,4,207,3,0,0,"support","low","exist"
"4850",6850,0.85,0.64,4,147,3,0,0,"support","low","exist"
"4851",6851,0.95,0.49,3,188,3,0,0,"technical","low","exist"
"4852",6852,0.48,0.56,3,229,3,0,0,"technical","low","exist"
"4853",6853,0.53,0.77,2,271,2,0,0,"technical","low","exist"
"4854",6854,0.8,0.82,4,175,2,0,0,"management","low","exist"
"4855",6855,0.4,0.46,2,109,3,0,0,"IT","low","exist"
"4856",6856,0.76,0.69,4,253,3,0,0,"IT","low","exist"
"4857",6857,0.99,0.64,3,174,4,0,0,"IT","low","exist"
"4858",6858,0.49,0.64,3,142,4,0,0,"IT","low","exist"
"4859",6859,0.94,0.71,3,175,3,0,0,"IT","low","exist"
"4860",6860,0.54,0.73,4,266,3,0,0,"product_mng","low","exist"
"4861",6861,0.13,0.93,4,253,5,1,0,"product_mng","low","exist"
"4862",6862,0.91,0.84,3,237,2,0,0,"product_mng","medium","exist"
"4863",6863,0.73,0.56,3,215,3,0,0,"product_mng","medium","exist"
"4864",6864,0.65,0.97,3,171,3,0,0,"IT","medium","exist"
"4865",6865,0.23,0.51,6,194,4,1,0,"RandD","medium","exist"
"4866",6866,0.23,0.88,5,238,6,0,0,"RandD","medium","exist"
"4867",6867,0.89,0.51,3,249,3,0,0,"RandD","medium","exist"
"4868",6868,0.81,0.8,3,183,2,1,0,"RandD","medium","exist"
"4869",6869,0.51,0.74,3,271,4,0,0,"RandD","medium","exist"
"4870",6870,0.35,0.81,6,256,3,0,0,"RandD","medium","exist"
"4871",6871,0.49,0.66,3,169,3,0,0,"marketing","medium","exist"
"4872",6872,0.51,0.8,3,254,2,0,0,"sales","medium","exist"
"4873",6873,0.66,0.86,4,112,6,0,0,"accounting","medium","exist"
"4874",6874,0.74,0.96,5,222,2,0,0,"support","high","exist"
"4875",6875,0.57,0.96,4,177,2,0,0,"technical","low","exist"
"4876",6876,0.8,0.74,5,181,4,1,0,"management","medium","exist"
"4877",6877,0.79,0.84,4,144,2,1,0,"marketing","medium","exist"
"4878",6878,0.74,0.94,4,255,4,0,0,"marketing","medium","exist"
"4879",6879,0.74,0.8,3,219,2,0,0,"marketing","medium","exist"
"4880",6880,0.4,1,6,206,2,0,0,"sales","low","exist"
"4881",6881,0.14,0.71,2,155,3,0,0,"sales","low","exist"
"4882",6882,0.83,0.87,5,248,3,0,0,"sales","low","exist"
"4883",6883,0.76,0.52,4,259,2,0,0,"sales","low","exist"
"4884",6884,0.8,0.8,3,271,4,0,0,"sales","low","exist"
"4885",6885,0.95,0.38,2,103,3,0,0,"sales","low","exist"
"4886",6886,0.88,0.76,4,159,4,1,0,"sales","low","exist"
"4887",6887,0.92,0.85,4,184,3,0,0,"sales","low","exist"
"4888",6888,0.16,0.88,4,201,6,0,0,"sales","medium","exist"
"4889",6889,0.7,0.63,3,157,4,0,0,"sales","medium","exist"
"4890",6890,0.71,0.93,3,287,5,0,0,"sales","medium","exist"
"4891",6891,0.52,0.82,2,242,3,0,0,"sales","medium","exist"
"4892",6892,0.49,0.58,5,246,3,1,0,"sales","medium","exist"
"4893",6893,0.5,0.57,3,219,3,0,0,"sales","medium","exist"
"4894",6894,0.86,0.94,3,212,3,0,0,"sales","medium","exist"
"4895",6895,0.49,0.99,5,262,2,1,0,"sales","medium","exist"
"4896",6896,0.69,0.91,4,128,3,1,0,"sales","medium","exist"
"4897",6897,0.96,1,3,231,6,0,0,"sales","medium","exist"
"4898",6898,0.87,0.54,3,260,2,0,0,"sales","medium","exist"
"4899",6899,0.36,0.4,3,160,3,0,0,"accounting","medium","exist"
"4900",6900,0.86,1,3,166,3,1,0,"accounting","medium","exist"
"4901",6901,0.79,0.74,4,222,2,0,0,"accounting","medium","exist"
"4902",6902,1,0.52,4,171,4,0,0,"hr","medium","exist"
"4903",6903,0.88,0.88,3,220,4,0,0,"hr","medium","exist"
"4904",6904,0.49,0.65,4,176,3,1,0,"hr","medium","exist"
"4905",6905,0.52,0.62,3,160,2,0,0,"hr","medium","exist"
"4906",6906,0.76,0.78,3,162,3,0,0,"technical","medium","exist"
"4907",6907,0.69,0.91,3,167,2,0,0,"technical","medium","exist"
"4908",6908,0.69,0.81,5,217,2,0,0,"technical","medium","exist"
"4909",6909,0.75,0.58,3,159,3,0,0,"technical","medium","exist"
"4910",6910,0.47,0.47,4,191,3,0,0,"technical","medium","exist"
"4911",6911,0.88,1,3,125,3,0,0,"technical","medium","exist"
"4912",6912,0.49,0.43,5,210,4,1,0,"technical","medium","exist"
"4913",6913,0.92,0.67,4,241,3,0,0,"technical","high","exist"
"4914",6914,0.24,0.48,4,145,3,1,0,"technical","high","exist"
"4915",6915,0.69,1,5,237,3,0,0,"technical","high","exist"
"4916",6916,0.81,0.57,4,213,4,0,0,"technical","high","exist"
"4917",6917,0.61,0.48,4,257,2,0,0,"support","high","exist"
"4918",6918,0.75,0.86,6,114,4,0,0,"support","high","exist"
"4919",6919,0.69,0.86,4,214,2,0,0,"support","high","exist"
"4920",6920,0.53,0.49,3,191,3,0,0,"support","high","exist"
"4921",6921,0.93,0.96,4,223,3,1,0,"support","high","exist"
"4922",6922,0.15,0.67,5,249,5,0,0,"support","high","exist"
"4923",6923,0.48,0.41,5,286,3,0,0,"support","high","exist"
"4924",6924,0.67,0.73,4,251,3,0,0,"support","high","exist"
"4925",6925,0.36,0.93,3,162,5,0,0,"support","low","exist"
"4926",6926,0.35,0.54,3,138,4,0,0,"support","low","exist"
"4927",6927,0.65,0.62,4,235,3,0,0,"support","low","exist"
"4928",6928,0.8,0.5,4,125,3,0,0,"technical","low","exist"
"4929",6929,0.97,0.96,5,210,6,1,0,"technical","low","exist"
"4930",6930,0.67,0.64,4,136,3,0,0,"technical","low","exist"
"4931",6931,0.58,0.78,3,223,3,0,0,"management","low","exist"
"4932",6932,0.61,0.67,3,188,5,0,0,"IT","low","exist"
"4933",6933,0.97,0.66,4,214,2,1,0,"IT","low","exist"
"4934",6934,0.87,0.97,4,160,3,0,0,"IT","low","exist"
"4935",6935,0.8,0.71,4,200,2,0,0,"IT","low","exist"
"4936",6936,0.91,0.55,3,223,3,0,0,"IT","low","exist"
"4937",6937,0.63,0.73,3,272,2,0,0,"product_mng","low","exist"
"4938",6938,0.79,0.96,4,170,2,0,0,"product_mng","low","exist"
"4939",6939,0.89,0.57,2,235,3,1,0,"product_mng","low","exist"
"4940",6940,1,0.87,3,274,3,0,0,"product_mng","medium","exist"
"4941",6941,0.6,0.73,5,203,2,0,0,"IT","medium","exist"
"4942",6942,0.7,0.8,4,236,2,0,0,"RandD","medium","exist"
"4943",6943,0.79,0.81,4,203,3,0,0,"RandD","medium","exist"
"4944",6944,0.88,0.72,4,249,3,1,0,"RandD","medium","exist"
"4945",6945,0.87,0.48,4,133,2,0,0,"RandD","medium","exist"
"4946",6946,0.52,0.58,3,203,2,0,0,"RandD","medium","exist"
"4947",6947,0.59,0.75,3,168,3,0,0,"RandD","medium","exist"
"4948",6948,0.64,0.75,4,172,4,0,0,"marketing","medium","exist"
"4949",6949,0.81,0.83,3,177,2,0,0,"sales","medium","exist"
"4950",6950,0.87,0.57,3,149,2,0,0,"accounting","medium","exist"
"4951",6951,0.74,0.61,3,231,2,0,0,"support","medium","exist"
"4952",6952,0.73,0.89,3,226,3,1,0,"technical","high","exist"
"4953",6953,0.97,0.58,4,187,4,1,0,"management","high","exist"
"4954",6954,0.54,0.81,3,145,2,0,0,"marketing","high","exist"
"4955",6955,0.59,0.55,4,138,2,0,0,"marketing","high","exist"
"4956",6956,0.99,0.95,3,153,4,0,0,"marketing","high","exist"
"4957",6957,0.79,0.75,4,168,3,0,0,"sales","high","exist"
"4958",6958,0.96,0.37,3,111,2,0,0,"sales","high","exist"
"4959",6959,0.54,0.67,3,154,2,0,0,"sales","high","exist"
"4960",6960,0.79,0.84,4,171,3,0,0,"sales","low","exist"
"4961",6961,0.64,0.79,3,253,2,0,0,"sales","low","exist"
"4962",6962,0.65,0.53,4,160,3,0,0,"sales","low","exist"
"4963",6963,0.87,0.86,3,196,4,0,0,"sales","low","exist"
"4964",6964,0.7,0.59,4,178,3,0,0,"sales","low","exist"
"4965",6965,0.89,0.81,3,268,3,0,0,"sales","low","exist"
"4966",6966,0.61,0.58,6,146,3,0,0,"sales","low","exist"
"4967",6967,0.9,0.49,4,185,2,0,0,"sales","low","exist"
"4968",6968,0.49,0.54,4,247,3,1,0,"sales","medium","exist"
"4969",6969,0.85,0.97,4,210,2,0,0,"sales","medium","exist"
"4970",6970,0.54,0.58,3,234,3,0,0,"sales","medium","exist"
"4971",6971,0.64,0.57,4,271,2,0,0,"sales","medium","exist"
"4972",6972,0.81,0.77,5,102,5,0,0,"sales","medium","exist"
"4973",6973,0.49,0.66,3,163,3,0,0,"sales","medium","exist"
"4974",6974,0.58,0.57,3,144,4,1,0,"sales","medium","exist"
"4975",6975,0.62,0.49,3,172,3,0,0,"sales","medium","exist"
"4976",6976,0.8,0.84,3,203,3,1,0,"accounting","medium","exist"
"4977",6977,0.64,0.64,3,192,3,1,0,"accounting","medium","exist"
"4978",6978,0.81,0.86,5,159,2,0,0,"accounting","medium","exist"
"4979",6979,0.8,0.74,3,159,2,0,0,"hr","medium","exist"
"4980",6980,0.92,0.81,4,206,2,0,0,"hr","medium","exist"
"4981",6981,0.66,0.98,4,225,2,1,0,"hr","medium","exist"
"4982",6982,0.79,0.89,3,252,2,0,0,"hr","medium","exist"
"4983",6983,0.74,0.54,6,113,3,0,0,"technical","medium","exist"
"4984",6984,0.79,0.74,3,238,2,0,0,"technical","medium","exist"
"4985",6985,0.87,0.94,3,217,3,0,0,"technical","medium","exist"
"4986",6986,0.49,0.57,4,145,2,0,0,"technical","medium","exist"
"4987",6987,0.3,0.44,5,128,4,1,0,"technical","medium","exist"
"4988",6988,0.85,0.89,4,177,3,0,0,"technical","medium","exist"
"4989",6989,0.61,0.97,4,256,4,0,0,"technical","medium","exist"
"4990",6990,0.68,0.55,3,182,3,1,0,"technical","medium","exist"
"4991",6991,0.67,0.67,4,226,2,0,0,"technical","high","exist"
"4992",6992,0.63,0.73,5,168,3,0,0,"technical","low","exist"
"4993",6993,0.63,0.94,4,145,3,0,0,"technical","medium","exist"
"4994",6994,0.5,0.88,4,172,4,0,0,"support","medium","exist"
"4995",6995,0.7,0.55,4,233,2,0,0,"support","medium","exist"
"4996",6996,0.18,0.46,5,202,4,0,0,"support","medium","exist"
"4997",6997,0.77,0.55,5,255,2,0,0,"support","medium","exist"
"4998",6998,0.78,0.61,3,257,3,0,0,"support","medium","exist"
"4999",6999,0.54,0.77,3,185,3,0,0,"support","medium","exist"
"5000",7000,0.9,0.69,3,231,4,0,0,"support","medium","exist"
"5001",7001,0.56,0.76,3,207,2,0,0,"support","medium","exist"
"5002",7002,0.63,0.81,3,215,3,0,0,"support","medium","exist"
"5003",7003,0.68,0.75,5,243,3,1,0,"support","low","exist"
"5004",7004,0.96,0.54,3,198,3,0,0,"support","low","exist"
"5005",7005,0.85,0.87,6,232,6,0,0,"technical","low","exist"
"5006",7006,0.82,0.66,4,150,3,0,0,"technical","low","exist"
"5007",7007,0.44,0.39,2,188,3,0,0,"technical","low","exist"
"5008",7008,0.86,0.97,4,155,3,0,0,"management","low","exist"
"5009",7009,0.56,0.68,3,109,3,0,0,"IT","low","exist"
"5010",7010,0.69,0.94,3,170,3,0,0,"IT","high","exist"
"5011",7011,0.91,0.85,5,214,2,0,0,"IT","low","exist"
"5012",7012,0.99,0.94,3,244,3,0,0,"IT","high","exist"
"5013",7013,0.76,0.84,5,137,4,0,0,"IT","high","exist"
"5014",7014,0.63,0.67,5,250,2,0,0,"product_mng","low","exist"
"5015",7015,0.21,0.62,4,247,3,1,0,"product_mng","low","exist"
"5016",7016,0.63,0.43,2,222,4,0,0,"product_mng","high","exist"
"5017",7017,0.58,0.51,2,100,2,0,0,"product_mng","low","exist"
"5018",7018,0.52,0.84,4,212,3,0,0,"IT","medium","exist"
"5019",7019,0.89,0.64,3,184,5,0,0,"RandD","high","exist"
"5020",7020,0.81,0.81,4,177,3,0,0,"RandD","medium","exist"
"5021",7021,0.62,0.73,3,138,3,0,0,"RandD","medium","exist"
"5022",7022,0.83,0.5,4,167,3,1,0,"RandD","medium","exist"
"5023",7023,0.85,0.99,3,201,3,0,0,"RandD","medium","exist"
"5024",7024,0.52,0.61,5,162,3,0,0,"marketing","high","exist"
"5025",7025,0.57,0.97,5,126,5,0,0,"sales","medium","exist"
"5026",7026,0.93,1,4,145,3,0,0,"accounting","medium","exist"
"5027",7027,0.78,0.89,3,211,3,0,0,"support","medium","exist"
"5028",7028,0.65,0.59,3,167,2,0,0,"technical","high","exist"
"5029",7029,0.42,0.74,5,256,6,0,0,"management","medium","exist"
"5030",7030,0.22,0.61,6,237,5,0,0,"marketing","high","exist"
"5031",7031,0.71,0.96,5,135,4,1,0,"marketing","low","exist"
"5032",7032,0.44,0.68,5,209,4,0,0,"marketing","medium","exist"
"5033",7033,0.6,0.52,4,190,3,0,0,"sales","medium","exist"
"5034",7034,0.68,0.61,3,134,4,0,0,"sales","medium","exist"
"5035",7035,0.53,0.41,2,148,2,1,0,"sales","medium","exist"
"5036",7036,0.8,0.82,4,202,3,0,0,"sales","low","exist"
"5037",7037,0.97,0.82,4,176,2,0,0,"sales","low","exist"
"5038",7038,0.47,0.47,2,221,6,0,0,"sales","low","exist"
"5039",7039,0.96,0.93,3,156,3,0,0,"sales","low","exist"
"5040",7040,0.81,0.45,6,98,3,0,0,"sales","low","exist"
"5041",7041,0.86,0.65,4,134,4,0,0,"sales","low","exist"
"5042",7042,0.59,0.82,4,203,4,1,0,"sales","low","exist"
"5043",7043,0.53,0.97,3,189,3,0,0,"sales","low","exist"
"5044",7044,0.57,0.86,3,258,2,0,0,"sales","low","exist"
"5045",7045,0.7,0.48,4,237,3,0,0,"sales","low","exist"
"5046",7046,0.58,0.59,4,224,3,0,0,"sales","high","exist"
"5047",7047,0.43,0.86,5,125,3,1,0,"sales","low","exist"
"5048",7048,0.92,0.82,4,207,4,0,0,"sales","low","exist"
"5049",7049,0.24,0.7,5,194,3,0,0,"sales","low","exist"
"5050",7050,0.67,0.52,3,273,3,0,0,"sales","low","exist"
"5051",7051,0.68,0.84,3,209,3,0,0,"sales","high","exist"
"5052",7052,0.54,0.75,3,181,3,0,0,"accounting","low","exist"
"5053",7053,0.73,0.63,3,172,4,1,0,"accounting","low","exist"
"5054",7054,0.59,0.41,4,139,3,0,0,"accounting","low","exist"
"5055",7055,0.22,0.64,6,260,4,0,0,"hr","high","exist"
"5056",7056,0.49,0.83,3,168,4,1,0,"hr","low","exist"
"5057",7057,0.91,1,6,242,3,1,0,"hr","medium","exist"
"5058",7058,0.18,0.97,4,206,3,0,0,"hr","high","exist"
"5059",7059,0.71,0.41,5,107,4,0,0,"technical","medium","exist"
"5060",7060,0.56,0.66,5,216,2,0,0,"technical","high","exist"
"5061",7061,0.84,0.62,4,152,3,0,0,"technical","medium","exist"
"5062",7062,0.59,0.49,5,122,5,0,0,"technical","medium","exist"
"5063",7063,0.88,0.62,4,138,3,0,0,"technical","medium","exist"
"5064",7064,0.8,0.52,3,182,2,0,0,"technical","medium","exist"
"5065",7065,0.53,0.63,3,205,2,0,0,"technical","medium","exist"
"5066",7066,0.53,0.83,3,267,4,0,0,"technical","medium","exist"
"5067",7067,0.3,0.67,3,150,2,0,0,"technical","medium","exist"
"5068",7068,0.91,0.7,4,134,2,0,0,"technical","medium","exist"
"5069",7069,0.32,0.66,5,116,5,1,0,"technical","high","exist"
"5070",7070,0.73,0.87,3,181,3,0,0,"support","low","exist"
"5071",7071,0.87,0.54,3,268,3,0,0,"support","medium","exist"
"5072",7072,0.57,0.73,3,129,3,0,0,"support","medium","exist"
"5073",7073,0.62,0.94,3,151,4,0,0,"support","medium","exist"
"5074",7074,0.55,0.91,3,243,4,0,0,"support","medium","exist"
"5075",7075,0.93,0.57,5,143,2,0,0,"support","medium","exist"
"5076",7076,0.3,0.47,6,156,2,1,0,"support","medium","exist"
"5077",7077,0.57,0.7,3,210,2,0,0,"support","medium","exist"
"5078",7078,0.9,0.85,4,279,6,0,0,"support","medium","exist"
"5079",7079,0.83,0.79,4,270,3,1,0,"support","medium","exist"
"5080",7080,0.38,0.64,5,160,3,0,0,"support","high","exist"
"5081",7081,0.97,0.95,4,173,2,0,0,"technical","low","exist"
"5082",7082,0.7,1,4,261,3,0,0,"technical","low","exist"
"5083",7083,0.26,0.73,4,178,6,0,0,"technical","low","exist"
"5084",7084,0.58,0.58,3,122,3,0,0,"management","high","exist"
"5085",7085,0.69,0.57,5,227,4,0,0,"IT","low","exist"
"5086",7086,0.88,0.6,2,168,3,0,0,"IT","low","exist"
"5087",7087,0.57,0.91,4,252,4,0,0,"IT","low","exist"
"5088",7088,0.94,0.8,5,170,4,0,0,"IT","high","exist"
"5089",7089,0.94,0.58,3,135,3,0,0,"IT","low","exist"
"5090",7090,0.46,0.49,5,286,5,0,0,"product_mng","low","exist"
"5091",7091,0.49,0.57,2,213,3,1,0,"product_mng","low","exist"
"5092",7092,0.96,1,5,148,3,0,0,"product_mng","low","exist"
"5093",7093,0.29,0.95,5,117,4,0,0,"product_mng","low","exist"
"5094",7094,0.94,0.69,3,164,2,0,0,"IT","low","exist"
"5095",7095,0.56,0.64,3,262,2,1,0,"RandD","low","exist"
"5096",7096,0.18,0.49,5,250,5,1,0,"RandD","medium","exist"
"5097",7097,0.84,0.83,4,222,3,0,0,"RandD","medium","exist"
"5098",7098,0.58,0.96,3,192,4,0,0,"RandD","medium","exist"
"5099",7099,0.21,0.6,5,151,6,0,0,"RandD","medium","exist"
"5100",7100,0.59,0.53,4,216,2,0,0,"marketing","medium","exist"
"5101",7101,0.66,0.65,3,234,2,0,0,"marketing","medium","exist"
"5102",7102,0.58,0.82,4,268,3,0,0,"sales","medium","exist"
"5103",7103,0.66,0.49,4,194,5,0,0,"accounting","medium","exist"
"5104",7104,0.56,0.78,3,200,2,0,0,"support","medium","exist"
"5105",7105,0.92,0.78,3,194,3,0,0,"technical","medium","exist"
"5106",7106,0.56,0.69,3,176,3,0,0,"management","medium","exist"
"5107",7107,0.57,0.59,4,158,3,0,0,"marketing","medium","exist"
"5108",7108,0.99,0.79,3,271,4,0,0,"marketing","high","exist"
"5109",7109,0.76,0.93,4,187,4,0,0,"marketing","low","exist"
"5110",7110,0.78,0.91,4,202,2,0,0,"sales","medium","exist"
"5111",7111,0.99,0.48,5,202,2,0,0,"sales","medium","exist"
"5112",7112,0.71,0.95,6,204,3,1,0,"sales","medium","exist"
"5113",7113,0.51,0.96,4,204,3,1,0,"sales","medium","exist"
"5114",7114,0.88,0.82,3,244,3,0,0,"sales","low","exist"
"5115",7115,0.96,0.83,3,234,3,0,0,"sales","low","exist"
"5116",7116,0.9,0.64,4,217,3,0,0,"sales","low","exist"
"5117",7117,0.77,0.51,4,142,4,0,0,"sales","low","exist"
"5118",7118,0.95,0.5,4,186,3,1,0,"sales","low","exist"
"5119",7119,0.85,0.67,3,267,2,1,0,"sales","low","exist"
"5120",7120,0.46,0.79,2,108,3,0,0,"sales","low","exist"
"5121",7121,0.57,0.95,3,274,4,0,0,"sales","low","exist"
"5122",7122,0.93,1,3,148,4,0,0,"sales","low","exist"
"5123",7123,0.78,0.68,5,168,3,0,0,"sales","low","exist"
"5124",7124,0.68,1,4,185,2,1,0,"sales","low","exist"
"5125",7125,0.83,0.78,3,257,2,0,0,"sales","low","exist"
"5126",7126,0.56,0.51,5,256,4,1,0,"sales","low","exist"
"5127",7127,0.93,0.78,2,188,2,0,0,"sales","low","exist"
"5128",7128,0.13,0.53,6,173,4,0,0,"sales","low","exist"
"5129",7129,0.71,0.99,5,208,2,1,0,"accounting","low","exist"
"5130",7130,0.98,0.74,4,202,3,0,0,"accounting","low","exist"
"5131",7131,0.83,0.82,3,134,3,0,0,"accounting","low","exist"
"5132",7132,0.78,0.65,3,154,2,0,0,"hr","low","exist"
"5133",7133,0.35,0.58,3,103,3,0,0,"hr","low","exist"
"5134",7134,0.67,0.55,4,256,6,0,0,"hr","low","exist"
"5135",7135,0.86,0.88,4,274,3,0,0,"hr","medium","exist"
"5136",7136,0.33,0.61,2,163,3,0,0,"technical","medium","exist"
"5137",7137,0.3,0.86,6,232,3,0,0,"technical","medium","exist"
"5138",7138,0.75,0.63,4,268,3,0,0,"technical","medium","exist"
"5139",7139,0.8,0.98,3,209,3,0,0,"technical","medium","exist"
"5140",7140,0.98,0.53,5,238,3,0,0,"technical","medium","exist"
"5141",7141,0.72,0.48,3,155,2,0,0,"technical","medium","exist"
"5142",7142,0.82,0.52,5,270,3,0,0,"technical","medium","exist"
"5143",7143,0.91,0.59,3,134,2,1,0,"technical","medium","exist"
"5144",7144,0.84,0.78,3,221,3,1,0,"technical","medium","exist"
"5145",7145,0.95,0.74,4,258,3,0,0,"technical","medium","exist"
"5146",7146,0.53,0.51,6,272,5,0,0,"technical","medium","exist"
"5147",7147,0.5,0.5,4,184,3,0,0,"support","high","exist"
"5148",7148,0.36,0.95,6,276,2,0,0,"support","low","exist"
"5149",7149,0.33,0.38,4,186,3,0,0,"support","medium","exist"
"5150",7150,0.38,0.47,3,189,5,0,0,"support","medium","exist"
"5151",7151,0.7,0.9,3,224,3,0,0,"support","medium","exist"
"5152",7152,0.44,0.45,6,237,6,0,0,"support","medium","exist"
"5153",7153,0.32,0.66,3,144,2,0,0,"support","low","exist"
"5154",7154,0.63,0.93,6,171,3,1,0,"support","low","exist"
"5155",7155,0.56,0.54,3,232,2,0,0,"support","low","exist"
"5156",7156,0.56,0.78,4,193,2,1,0,"support","low","exist"
"5157",7157,0.81,0.78,3,166,2,0,0,"support","low","exist"
"5158",7158,0.89,0.75,3,167,2,0,0,"technical","low","exist"
"5159",7159,0.63,0.87,2,101,3,0,0,"technical","low","exist"
"5160",7160,0.64,0.66,5,266,3,0,0,"technical","low","exist"
"5161",7161,0.46,0.53,3,135,2,0,0,"management","low","exist"
"5162",7162,0.76,0.56,4,137,3,0,0,"IT","low","exist"
"5163",7163,0.99,0.71,3,191,3,0,0,"IT","low","exist"
"5164",7164,0.85,0.76,4,262,2,0,0,"IT","low","exist"
"5165",7165,0.78,0.99,3,174,3,0,0,"IT","low","exist"
"5166",7166,0.91,0.56,4,241,2,0,0,"IT","low","exist"
"5167",7167,0.16,0.57,5,144,4,1,0,"product_mng","low","exist"
"5168",7168,0.71,0.57,3,218,3,0,0,"product_mng","low","exist"
"5169",7169,0.92,0.68,5,210,2,0,0,"product_mng","low","exist"
"5170",7170,0.21,0.98,6,208,5,1,0,"product_mng","low","exist"
"5171",7171,0.74,0.6,3,232,3,0,0,"IT","low","exist"
"5172",7172,0.76,0.6,3,140,2,0,0,"RandD","low","exist"
"5173",7173,0.62,0.95,3,189,4,0,0,"RandD","low","exist"
"5174",7174,1,0.61,5,264,3,0,0,"RandD","medium","exist"
"5175",7175,0.67,0.54,5,157,2,0,0,"RandD","medium","exist"
"5176",7176,0.81,0.87,4,161,2,0,0,"RandD","medium","exist"
"5177",7177,0.84,0.69,4,149,3,0,0,"marketing","medium","exist"
"5178",7178,0.84,0.99,3,144,4,0,0,"sales","medium","exist"
"5179",7179,0.97,0.97,4,242,2,0,0,"accounting","medium","exist"
"5180",7180,0.7,0.5,6,214,5,0,0,"support","medium","exist"
"5181",7181,0.52,0.74,4,174,3,0,0,"technical","medium","exist"
"5182",7182,0.46,0.88,5,169,3,0,0,"management","medium","exist"
"5183",7183,1,0.87,4,268,2,0,0,"marketing","medium","exist"
"5184",7184,0.91,0.58,3,257,3,0,0,"marketing","medium","exist"
"5185",7185,0.16,0.69,4,187,5,0,0,"marketing","medium","exist"
"5186",7186,0.58,0.62,5,270,2,0,0,"sales","high","exist"
"5187",7187,0.75,0.61,5,173,4,0,0,"sales","low","exist"
"5188",7188,0.96,0.62,6,193,4,0,0,"sales","medium","exist"
"5189",7189,0.92,0.78,4,212,2,0,0,"sales","medium","exist"
"5190",7190,0.35,0.63,3,156,3,0,0,"sales","medium","exist"
"5191",7191,0.56,0.96,3,244,3,0,0,"sales","medium","exist"
"5192",7192,0.27,0.96,3,255,4,0,0,"sales","low","exist"
"5193",7193,0.66,0.72,5,152,3,1,0,"sales","low","exist"
"5194",7194,0.66,0.98,4,163,3,1,0,"sales","low","exist"
"5195",7195,0.98,0.69,3,150,2,0,0,"sales","low","exist"
"5196",7196,0.51,0.58,4,169,2,0,0,"sales","low","exist"
"5197",7197,0.51,0.83,3,133,3,0,0,"sales","low","exist"
"5198",7198,0.53,0.94,4,202,3,1,0,"sales","low","exist"
"5199",7199,0.69,0.7,4,169,2,0,0,"sales","low","exist"
"5200",7200,0.66,0.74,4,270,2,1,0,"sales","low","exist"
"5201",7201,0.89,0.76,3,251,2,1,0,"sales","low","exist"
"5202",7202,0.74,0.64,3,267,5,0,0,"sales","low","exist"
"5203",7203,0.82,0.75,4,224,3,0,0,"sales","low","exist"
"5204",7204,0.66,0.9,3,250,2,0,0,"sales","low","exist"
"5205",7205,0.59,0.97,3,258,2,0,0,"accounting","low","exist"
"5206",7206,0.13,0.65,2,209,5,0,0,"accounting","low","exist"
"5207",7207,0.68,0.74,4,215,3,0,0,"accounting","low","exist"
"5208",7208,0.5,0.81,3,183,3,0,0,"hr","low","exist"
"5209",7209,0.6,0.82,3,143,3,0,0,"hr","low","exist"
"5210",7210,0.87,0.98,3,174,3,0,0,"hr","low","exist"
"5211",7211,0.51,0.89,6,170,4,0,0,"hr","low","exist"
"5212",7212,0.78,0.63,3,202,2,0,0,"technical","low","exist"
"5213",7213,0.66,0.96,4,160,2,0,0,"technical","medium","exist"
"5214",7214,0.72,0.73,5,211,2,0,0,"technical","medium","exist"
"5215",7215,0.57,0.98,3,236,3,1,0,"technical","medium","exist"
"5216",7216,0.5,0.49,4,236,3,0,0,"technical","medium","exist"
"5217",7217,0.72,0.62,4,252,2,1,0,"technical","medium","exist"
"5218",7218,0.41,0.48,3,155,2,1,0,"technical","medium","exist"
"5219",7219,0.55,0.65,5,138,2,0,0,"technical","medium","exist"
"5220",7220,0.49,0.94,4,195,3,1,0,"technical","medium","exist"
"5221",7221,0.8,0.94,3,150,3,0,0,"technical","medium","exist"
"5222",7222,0.78,0.51,3,172,3,0,0,"technical","medium","exist"
"5223",7223,0.69,0.56,3,240,2,0,0,"support","medium","exist"
"5224",7224,0.83,0.98,3,229,6,0,0,"support","medium","exist"
"5225",7225,0.89,0.73,3,169,3,0,0,"support","high","exist"
"5226",7226,0.94,0.82,3,246,3,0,0,"support","low","exist"
"5227",7227,0.51,0.53,4,260,2,1,0,"support","medium","exist"
"5228",7228,0.89,0.9,4,101,6,0,0,"support","medium","exist"
"5229",7229,0.99,0.69,3,190,3,0,0,"support","medium","exist"
"5230",7230,0.79,0.66,3,154,4,0,0,"support","medium","exist"
"5231",7231,0.98,0.97,4,196,4,0,0,"support","low","exist"
"5232",7232,0.98,0.97,3,209,3,0,0,"support","low","exist"
"5233",7233,0.97,0.67,4,223,3,0,0,"support","low","exist"
"5234",7234,0.71,0.71,4,221,3,0,0,"technical","low","exist"
"5235",7235,0.49,0.6,4,141,3,0,0,"technical","low","exist"
"5236",7236,0.72,0.71,3,135,3,0,0,"technical","low","exist"
"5237",7237,0.58,0.61,2,191,3,1,0,"management","low","exist"
"5238",7238,0.65,1,4,195,3,0,0,"IT","low","exist"
"5239",7239,0.18,0.55,5,217,4,0,0,"IT","low","exist"
"5240",7240,0.83,0.99,4,184,3,0,0,"IT","low","exist"
"5241",7241,0.2,0.76,5,188,3,0,0,"IT","low","exist"
"5242",7242,0.96,0.93,6,240,6,0,0,"IT","low","exist"
"5243",7243,0.59,0.69,4,226,3,0,0,"product_mng","low","exist"
"5244",7244,0.97,0.99,3,196,3,0,0,"product_mng","low","exist"
"5245",7245,0.14,0.99,6,251,4,0,0,"product_mng","low","exist"
"5246",7246,0.75,0.96,4,150,2,0,0,"product_mng","low","exist"
"5247",7247,0.71,0.63,3,249,3,0,0,"IT","low","exist"
"5248",7248,0.84,0.52,4,251,3,0,0,"RandD","low","exist"
"5249",7249,0.57,0.75,5,252,3,0,0,"RandD","medium","exist"
"5250",7250,0.46,0.55,5,261,5,0,0,"RandD","medium","exist"
"5251",7251,0.77,0.94,4,225,2,0,0,"RandD","medium","exist"
"5252",7252,0.44,0.65,2,151,3,0,0,"RandD","medium","exist"
"5253",7253,0.68,0.59,4,147,2,0,0,"marketing","medium","exist"
"5254",7254,0.94,0.58,4,159,3,0,0,"sales","medium","exist"
"5255",7255,0.73,0.91,4,241,2,1,0,"accounting","medium","exist"
"5256",7256,0.51,0.5,5,176,5,0,0,"support","medium","exist"
"5257",7257,0.93,0.87,4,218,4,0,0,"technical","medium","exist"
"5258",7258,0.74,1,4,219,3,0,0,"management","medium","exist"
"5259",7259,0.82,0.9,3,227,3,0,0,"marketing","medium","exist"
"5260",7260,0.86,0.91,4,182,2,0,0,"marketing","medium","exist"
"5261",7261,0.99,0.86,4,196,2,1,0,"marketing","high","exist"
"5262",7262,0.58,0.86,4,257,3,0,0,"sales","low","exist"
"5263",7263,0.96,0.6,5,182,5,0,0,"sales","medium","exist"
"5264",7264,0.72,0.67,4,192,3,0,0,"sales","medium","exist"
"5265",7265,0.23,0.94,4,142,4,0,0,"sales","medium","exist"
"5266",7266,0.99,0.79,4,172,2,0,0,"sales","medium","exist"
"5267",7267,0.95,0.58,4,188,3,0,0,"sales","low","exist"
"5268",7268,0.75,0.55,5,281,3,1,0,"sales","low","exist"
"5269",7269,0.95,0.54,4,255,2,0,0,"sales","low","exist"
"5270",7270,0.97,0.84,3,223,3,0,0,"sales","low","exist"
"5271",7271,0.98,0.86,2,219,4,0,0,"sales","low","exist"
"5272",7272,0.79,0.98,3,195,2,0,0,"sales","low","exist"
"5273",7273,0.54,0.91,2,156,3,0,0,"sales","low","exist"
"5274",7274,0.51,0.51,5,259,4,0,0,"sales","low","exist"
"5275",7275,0.83,0.91,4,266,3,1,0,"sales","low","exist"
"5276",7276,0.6,0.7,3,147,2,0,0,"sales","low","exist"
"5277",7277,0.58,0.83,4,207,3,0,0,"sales","low","exist"
"5278",7278,0.55,0.68,3,185,2,0,0,"sales","low","exist"
"5279",7279,0.5,0.64,5,195,2,0,0,"sales","low","exist"
"5280",7280,0.46,0.41,6,148,4,0,0,"sales","low","exist"
"5281",7281,0.61,0.82,3,157,2,0,0,"accounting","low","exist"
"5282",7282,0.91,0.98,4,146,3,0,0,"accounting","low","exist"
"5283",7283,0.5,0.94,3,262,4,0,0,"accounting","low","exist"
"5284",7284,0.75,0.82,3,169,3,0,0,"hr","low","exist"
"5285",7285,0.74,0.87,3,192,3,1,0,"hr","low","exist"
"5286",7286,0.62,0.53,4,147,2,0,0,"hr","low","exist"
"5287",7287,0.87,0.76,5,254,2,1,0,"hr","low","exist"
"5288",7288,0.13,0.72,3,244,4,0,0,"technical","medium","exist"
"5289",7289,0.71,0.43,2,100,6,0,0,"technical","medium","exist"
"5290",7290,0.7,0.9,3,173,2,0,0,"technical","medium","exist"
"5291",7291,0.32,0.87,2,197,2,1,0,"technical","medium","exist"
"5292",7292,0.84,0.72,3,256,4,0,0,"technical","medium","exist"
"5293",7293,0.79,0.87,4,253,2,0,0,"technical","medium","exist"
"5294",7294,0.97,0.64,4,152,2,0,0,"technical","medium","exist"
"5295",7295,0.76,0.58,5,136,3,0,0,"technical","medium","exist"
"5296",7296,0.97,0.63,3,141,3,0,0,"technical","medium","exist"
"5297",7297,0.53,0.4,5,212,3,1,0,"technical","medium","exist"
"5298",7298,0.61,0.57,4,144,3,0,0,"technical","medium","exist"
"5299",7299,0.94,0.89,2,118,4,0,0,"support","medium","exist"
"5300",7300,0.52,0.79,5,265,3,1,0,"support","high","exist"
"5301",7301,0.91,0.67,3,143,3,0,0,"support","low","exist"
"5302",7302,0.52,0.63,3,230,2,0,0,"support","medium","exist"
"5303",7303,0.59,0.68,5,243,2,0,0,"support","medium","exist"
"5304",7304,0.61,0.71,3,152,4,1,0,"support","medium","exist"
"5305",7305,0.78,0.78,3,252,3,0,0,"support","medium","exist"
"5306",7306,0.44,0.67,3,113,2,0,0,"support","low","exist"
"5307",7307,0.8,0.97,4,259,2,0,0,"support","low","exist"
"5308",7308,0.54,0.6,4,139,5,0,0,"support","low","exist"
"5309",7309,0.96,0.91,4,228,3,1,0,"support","low","exist"
"5310",7310,0.98,0.49,4,214,3,0,0,"technical","low","exist"
"5311",7311,0.83,0.91,4,210,4,0,0,"technical","low","exist"
"5312",7312,0.64,0.89,4,146,3,0,0,"technical","low","exist"
"5313",7313,0.51,0.78,3,155,2,0,0,"management","low","exist"
"5314",7314,0.31,0.42,2,169,5,0,0,"IT","low","exist"
"5315",7315,0.53,0.68,3,258,3,0,0,"IT","low","exist"
"5316",7316,0.81,0.53,3,258,2,0,0,"IT","low","exist"
"5317",7317,0.17,0.85,3,168,4,0,0,"IT","low","exist"
"5318",7318,0.72,0.98,3,211,2,0,0,"IT","low","exist"
"5319",7319,0.49,0.49,2,245,3,0,0,"product_mng","low","exist"
"5320",7320,0.81,0.95,3,204,2,0,0,"product_mng","low","exist"
"5321",7321,0.75,0.98,2,161,3,0,0,"product_mng","low","exist"
"5322",7322,0.74,0.73,3,267,3,0,0,"product_mng","low","exist"
"5323",7323,0.82,0.73,3,183,3,0,0,"IT","low","exist"
"5324",7324,0.36,0.4,2,105,3,0,0,"RandD","low","exist"
"5325",7325,0.89,0.55,3,260,2,0,0,"RandD","low","exist"
"5326",7326,0.78,0.87,3,183,4,0,0,"RandD","low","exist"
"5327",7327,0.81,0.56,4,262,3,0,0,"RandD","medium","exist"
"5328",7328,0.61,0.78,4,244,4,0,0,"RandD","medium","exist"
"5329",7329,0.23,0.96,4,242,6,0,0,"marketing","medium","exist"
"5330",7330,0.73,1,4,146,3,0,0,"sales","medium","exist"
"5331",7331,0.4,0.65,4,252,6,0,0,"accounting","medium","exist"
"5332",7332,0.99,0.63,5,229,2,0,0,"support","medium","exist"
"5333",7333,0.62,0.54,4,170,3,0,0,"technical","medium","exist"
"5334",7334,0.61,0.93,3,250,4,0,0,"management","medium","exist"
"5335",7335,0.9,0.98,2,243,3,0,0,"marketing","medium","exist"
"5336",7336,0.93,0.67,4,135,3,1,0,"marketing","medium","exist"
"5337",7337,0.52,0.75,4,266,3,0,0,"marketing","medium","exist"
"5338",7338,0.77,0.72,4,223,3,0,0,"sales","medium","exist"
"5339",7339,0.59,0.76,4,234,3,0,0,"sales","high","exist"
"5340",7340,0.51,0.59,4,187,3,0,0,"sales","low","exist"
"5341",7341,0.67,0.95,3,229,3,0,0,"sales","medium","exist"
"5342",7342,0.95,0.65,3,155,2,1,0,"sales","medium","exist"
"5343",7343,0.75,0.76,3,246,3,0,0,"sales","medium","exist"
"5344",7344,0.54,0.61,3,152,3,0,0,"sales","medium","exist"
"5345",7345,0.45,0.71,2,172,2,0,0,"sales","low","exist"
"5346",7346,0.66,0.66,4,255,5,0,0,"sales","low","exist"
"5347",7347,0.36,0.69,3,98,2,0,0,"sales","low","exist"
"5348",7348,0.3,0.47,6,141,6,0,0,"sales","low","exist"
"5349",7349,0.61,0.63,4,146,4,1,0,"sales","low","exist"
"5350",7350,0.71,0.7,4,213,3,0,0,"sales","low","exist"
"5351",7351,0.6,0.99,4,160,3,0,0,"sales","low","exist"
"5352",7352,0.19,0.61,3,272,4,0,0,"sales","low","exist"
"5353",7353,0.91,1,4,125,4,0,0,"sales","medium","exist"
"5354",7354,0.98,0.69,3,152,2,0,0,"sales","medium","exist"
"5355",7355,0.9,0.78,3,162,2,0,0,"sales","medium","exist"
"5356",7356,0.73,0.94,3,251,6,0,0,"sales","medium","exist"
"5357",7357,0.52,0.56,3,225,3,0,0,"accounting","medium","exist"
"5358",7358,0.77,0.56,3,236,3,1,0,"accounting","medium","exist"
"5359",7359,0.98,0.62,3,203,2,0,0,"accounting","medium","exist"
"5360",7360,0.79,0.5,4,252,3,1,0,"hr","medium","exist"
"5361",7361,0.73,0.91,3,135,2,0,0,"hr","medium","exist"
"5362",7362,0.97,0.95,3,257,2,0,0,"hr","medium","exist"
"5363",7363,0.38,0.6,5,145,5,0,0,"hr","medium","exist"
"5364",7364,0.59,0.48,5,267,3,0,0,"technical","medium","exist"
"5365",7365,0.73,0.79,4,208,5,0,0,"technical","medium","exist"
"5366",7366,0.84,0.53,4,206,3,0,0,"technical","medium","exist"
"5367",7367,0.61,0.59,4,247,2,0,0,"technical","medium","exist"
"5368",7368,0.79,0.78,2,228,2,0,0,"technical","medium","exist"
"5369",7369,0.73,0.91,4,248,2,1,0,"technical","medium","exist"
"5370",7370,0.22,0.9,4,209,5,0,0,"technical","medium","exist"
"5371",7371,0.84,0.52,5,171,3,0,0,"technical","medium","exist"
"5372",7372,0.21,0.85,6,221,5,0,0,"technical","medium","exist"
"5373",7373,0.44,0.69,2,173,2,0,0,"technical","medium","exist"
"5374",7374,0.2,0.52,5,218,5,0,0,"technical","medium","exist"
"5375",7375,0.51,0.86,4,223,3,1,0,"support","medium","exist"
"5376",7376,0.55,0.98,3,169,2,0,0,"support","medium","exist"
"5377",7377,0.24,0.38,6,109,2,0,0,"support","medium","exist"
"5378",7378,0.65,0.77,4,273,2,0,0,"support","high","exist"
"5379",7379,0.44,0.42,3,178,3,0,0,"support","high","exist"
"5380",7380,0.98,0.67,4,189,4,0,0,"support","high","exist"
"5381",7381,0.69,0.8,5,203,2,1,0,"support","high","exist"
"5382",7382,0.71,0.56,3,177,4,0,0,"support","high","exist"
"5383",7383,0.54,0.71,5,253,2,1,0,"support","high","exist"
"5384",7384,0.77,0.98,3,273,3,0,0,"support","high","exist"
"5385",7385,0.53,0.43,2,139,3,0,0,"support","high","exist"
"5386",7386,0.64,0.72,3,185,2,1,0,"technical","high","exist"
"5387",7387,0.69,0.59,5,182,4,0,0,"technical","high","exist"
"5388",7388,0.93,0.71,5,270,2,0,0,"technical","high","exist"
"5389",7389,0.58,0.65,3,139,4,0,0,"management","high","exist"
"5390",7390,0.33,0.46,5,261,6,1,0,"IT","low","exist"
"5391",7391,0.95,0.57,3,238,3,0,0,"IT","low","exist"
"5392",7392,0.65,0.9,3,241,3,0,0,"IT","low","exist"
"5393",7393,0.9,0.7,3,223,2,0,0,"IT","low","exist"
"5394",7394,0.59,0.8,3,258,3,1,0,"IT","low","exist"
"5395",7395,0.88,0.55,4,205,4,0,0,"product_mng","low","exist"
"5396",7396,0.63,0.83,4,243,4,0,0,"product_mng","low","exist"
"5397",7397,0.53,0.61,4,198,2,0,0,"product_mng","low","exist"
"5398",7398,0.63,0.64,4,178,3,0,0,"product_mng","low","exist"
"5399",7399,0.96,0.76,4,158,3,0,0,"IT","low","exist"
"5400",7400,0.7,0.73,3,194,2,0,0,"RandD","low","exist"
"5401",7401,0.73,0.36,4,253,2,1,0,"RandD","low","exist"
"5402",7402,0.94,0.8,4,228,2,0,0,"RandD","low","exist"
"5403",7403,0.82,0.58,5,227,3,0,0,"RandD","low","exist"
"5404",7404,0.44,0.63,3,162,2,0,0,"RandD","low","exist"
"5405",7405,0.58,0.9,5,257,3,0,0,"marketing","medium","exist"
"5406",7406,0.55,0.97,2,140,2,0,0,"sales","medium","exist"
"5407",7407,0.92,0.84,3,164,2,0,0,"accounting","medium","exist"
"5408",7408,0.91,0.59,4,177,4,0,0,"support","medium","exist"
"5409",7409,0.69,0.61,4,260,4,0,0,"technical","medium","exist"
"5410",7410,0.23,0.7,4,233,2,0,0,"management","medium","exist"
"5411",7411,0.21,0.81,4,227,5,0,0,"marketing","medium","exist"
"5412",7412,0.51,0.6,4,140,3,0,0,"marketing","medium","exist"
"5413",7413,0.73,0.74,3,254,4,1,0,"marketing","medium","exist"
"5414",7414,0.65,0.67,3,245,3,0,0,"sales","medium","exist"
"5415",7415,0.64,0.48,2,157,2,0,0,"sales","medium","exist"
"5416",7416,0.77,0.49,3,265,3,0,0,"sales","medium","exist"
"5417",7417,0.71,0.79,4,261,3,0,0,"sales","high","exist"
"5418",7418,0.2,0.38,6,212,6,0,0,"sales","high","exist"
"5419",7419,0.99,0.57,4,216,3,0,0,"sales","high","exist"
"5420",7420,0.77,0.57,4,238,3,0,0,"sales","high","exist"
"5421",7421,0.8,0.56,2,204,3,0,0,"sales","high","exist"
"5422",7422,0.97,0.5,4,216,2,0,0,"sales","high","exist"
"5423",7423,0.89,0.53,4,208,3,0,0,"sales","high","exist"
"5424",7424,0.97,0.7,4,218,2,0,0,"sales","high","exist"
"5425",7425,0.23,0.99,5,176,4,1,0,"sales","low","exist"
"5426",7426,0.6,0.75,4,144,2,0,0,"sales","low","exist"
"5427",7427,0.52,0.63,5,241,3,0,0,"sales","low","exist"
"5428",7428,0.86,0.63,3,271,2,0,0,"sales","low","exist"
"5429",7429,0.86,0.95,4,184,3,0,0,"sales","low","exist"
"5430",7430,0.76,0.58,3,262,2,0,0,"sales","low","exist"
"5431",7431,0.79,0.77,6,233,6,0,0,"sales","low","exist"
"5432",7432,0.35,0.52,3,155,3,0,0,"sales","low","exist"
"5433",7433,1,0.97,5,141,2,0,0,"accounting","medium","exist"
"5434",7434,0.2,0.8,6,251,5,0,0,"accounting","medium","exist"
"5435",7435,0.57,0.62,5,141,3,0,0,"accounting","medium","exist"
"5436",7436,0.23,0.46,4,274,5,1,0,"hr","medium","exist"
"5437",7437,0.82,0.97,3,160,2,0,0,"hr","medium","exist"
"5438",7438,0.98,0.8,3,166,2,0,0,"hr","medium","exist"
"5439",7439,0.52,0.7,4,219,3,0,0,"hr","medium","exist"
"5440",7440,0.96,0.61,4,158,6,0,0,"technical","medium","exist"
"5441",7441,0.69,0.64,4,190,4,0,0,"technical","medium","exist"
"5442",7442,0.92,0.77,5,191,2,0,0,"technical","medium","exist"
"5443",7443,0.91,0.43,4,117,5,1,0,"technical","medium","exist"
"5444",7444,0.85,0.96,4,240,6,0,0,"technical","medium","exist"
"5445",7445,0.91,0.77,4,239,2,0,0,"technical","medium","exist"
"5446",7446,0.79,0.55,4,145,3,0,0,"technical","medium","exist"
"5447",7447,0.74,0.95,3,157,4,0,0,"technical","medium","exist"
"5448",7448,0.73,0.72,3,166,3,0,0,"technical","medium","exist"
"5449",7449,0.55,0.98,4,137,2,0,0,"technical","medium","exist"
"5450",7450,0.79,0.97,5,208,4,1,0,"technical","medium","exist"
"5451",7451,0.53,0.51,4,174,2,0,0,"support","medium","exist"
"5452",7452,0.7,0.6,3,267,3,0,0,"support","medium","exist"
"5453",7453,0.74,0.56,3,125,6,0,0,"support","medium","exist"
"5454",7454,0.95,0.76,4,220,3,0,0,"support","medium","exist"
"5455",7455,0.49,0.57,4,141,3,0,0,"support","medium","exist"
"5456",7456,0.79,0.9,5,146,3,1,0,"support","high","exist"
"5457",7457,0.99,0.86,3,166,2,0,0,"support","low","exist"
"5458",7458,0.56,0.79,4,197,2,0,0,"support","medium","exist"
"5459",7459,0.7,0.79,4,240,2,0,0,"support","medium","exist"
"5460",7460,0.93,0.65,4,258,3,0,0,"support","medium","exist"
"5461",7461,0.46,0.66,6,229,3,0,0,"support","medium","exist"
"5462",7462,0.24,0.61,5,252,4,0,0,"technical","medium","exist"
"5463",7463,0.32,0.41,3,138,3,1,0,"technical","medium","exist"
"5464",7464,0.5,0.78,4,208,3,1,0,"technical","medium","exist"
"5465",7465,0.58,0.72,3,113,3,1,0,"management","medium","exist"
"5466",7466,0.83,0.81,4,209,4,0,0,"IT","medium","exist"
"5467",7467,0.57,0.42,2,248,4,0,0,"IT","medium","exist"
"5468",7468,0.51,0.83,5,161,3,0,0,"IT","low","exist"
"5469",7469,0.65,0.96,2,246,2,1,0,"IT","low","exist"
"5470",7470,0.52,0.41,3,283,3,0,0,"IT","low","exist"
"5471",7471,0.77,0.7,3,145,2,0,0,"product_mng","low","exist"
"5472",7472,0.42,0.77,3,270,3,0,0,"product_mng","low","exist"
"5473",7473,0.68,0.79,4,273,4,0,0,"product_mng","low","exist"
"5474",7474,0.83,0.92,4,187,6,1,0,"product_mng","low","exist"
"5475",7475,0.66,0.63,3,166,3,0,0,"IT","high","exist"
"5476",7476,0.75,0.57,3,158,2,1,0,"RandD","low","exist"
"5477",7477,0.65,0.48,4,229,3,0,0,"RandD","high","exist"
"5478",7478,0.49,0.6,3,191,3,1,0,"RandD","high","exist"
"5479",7479,0.77,0.96,3,232,2,1,0,"RandD","low","exist"
"5480",7480,0.65,0.97,3,198,3,0,0,"RandD","low","exist"
"5481",7481,0.65,0.49,5,238,4,0,0,"marketing","high","exist"
"5482",7482,0.44,0.58,2,157,2,0,0,"sales","low","exist"
"5483",7483,0.61,0.72,4,134,2,0,0,"accounting","medium","exist"
"5484",7484,0.98,0.89,3,150,3,0,0,"support","high","exist"
"5485",7485,0.68,0.88,5,256,2,0,0,"technical","medium","exist"
"5486",7486,0.58,0.5,3,208,3,0,0,"management","medium","exist"
"5487",7487,0.81,0.92,3,136,3,0,0,"marketing","medium","exist"
"5488",7488,0.76,0.5,4,136,3,0,0,"marketing","medium","exist"
"5489",7489,0.14,0.93,4,180,4,0,0,"marketing","high","exist"
"5490",7490,0.49,0.91,3,227,3,0,0,"sales","medium","exist"
"5491",7491,0.97,0.78,5,156,3,0,0,"sales","medium","exist"
"5492",7492,0.91,0.6,4,133,4,1,0,"sales","medium","exist"
"5493",7493,0.15,0.98,2,96,2,0,0,"sales","high","exist"
"5494",7494,0.82,0.63,3,171,3,0,0,"sales","medium","exist"
"5495",7495,0.67,0.87,3,177,4,0,0,"sales","high","exist"
"5496",7496,0.5,0.96,4,274,3,0,0,"sales","low","exist"
"5497",7497,0.57,0.39,2,145,3,0,0,"sales","medium","exist"
"5498",7498,0.99,0.94,5,221,2,0,0,"sales","medium","exist"
"5499",7499,0.97,0.94,3,202,2,0,0,"sales","medium","exist"
"5500",7500,0.93,0.58,5,238,2,0,0,"sales","medium","exist"
"5501",7501,0.62,0.6,4,170,2,0,0,"sales","low","exist"
"5502",7502,0.62,0.51,4,208,2,1,0,"sales","low","exist"
"5503",7503,0.96,0.61,4,199,3,0,0,"sales","low","exist"
"5504",7504,0.98,0.96,4,253,3,0,0,"sales","low","exist"
"5505",7505,0.52,0.57,4,239,3,0,0,"sales","low","exist"
"5506",7506,0.56,0.77,5,279,4,0,0,"sales","low","exist"
"5507",7507,0.14,0.41,6,114,3,0,0,"sales","low","exist"
"5508",7508,0.29,0.38,6,105,5,0,0,"sales","low","exist"
"5509",7509,0.76,0.81,4,193,3,1,0,"accounting","low","exist"
"5510",7510,0.39,0.58,3,152,3,1,0,"accounting","low","exist"
"5511",7511,0.96,0.72,4,228,2,0,0,"accounting","high","exist"
"5512",7512,0.84,0.93,3,242,4,0,0,"hr","low","exist"
"5513",7513,0.81,0.62,4,197,3,0,0,"hr","low","exist"
"5514",7514,0.51,0.51,5,222,4,0,0,"hr","low","exist"
"5515",7515,0.87,0.75,3,222,3,0,0,"hr","low","exist"
"5516",7516,0.94,0.77,5,233,4,0,0,"technical","high","exist"
"5517",7517,0.69,0.97,4,264,3,0,0,"technical","low","exist"
"5518",7518,0.44,0.53,3,132,3,1,0,"technical","low","exist"
"5519",7519,0.85,0.55,5,182,3,0,0,"technical","low","exist"
"5520",7520,0.18,0.86,6,264,3,0,0,"technical","high","exist"
"5521",7521,0.91,0.74,6,253,2,0,0,"technical","low","exist"
"5522",7522,0.81,0.83,3,193,3,0,0,"technical","medium","exist"
"5523",7523,0.82,0.59,5,143,2,1,0,"technical","high","exist"
"5524",7524,0.48,0.79,3,180,2,0,0,"technical","medium","exist"
"5525",7525,0.92,0.84,3,220,3,1,0,"technical","high","exist"
"5526",7526,0.94,0.88,5,150,4,0,0,"technical","medium","exist"
"5527",7527,1,0.56,3,182,3,0,0,"support","medium","exist"
"5528",7528,0.96,0.91,3,257,3,0,0,"support","medium","exist"
"5529",7529,0.24,0.74,3,269,4,1,0,"support","medium","exist"
"5530",7530,0.62,0.89,5,243,3,0,0,"support","medium","exist"
"5531",7531,0.55,0.76,4,257,3,0,0,"support","medium","exist"
"5532",7532,0.82,0.52,5,233,2,0,0,"support","medium","exist"
"5533",7533,0.62,0.56,4,267,4,0,0,"support","medium","exist"
"5534",7534,0.61,0.69,4,160,2,1,0,"support","high","exist"
"5535",7535,0.72,0.52,3,143,4,1,0,"support","low","exist"
"5536",7536,0.45,0.76,4,143,2,1,0,"support","medium","exist"
"5537",7537,0.51,0.93,3,162,4,0,0,"support","medium","exist"
"5538",7538,0.42,0.53,3,181,5,0,0,"technical","medium","exist"
"5539",7539,0.69,0.64,3,286,3,0,0,"technical","medium","exist"
"5540",7540,0.61,0.66,2,111,3,0,0,"technical","medium","exist"
"5541",7541,0.5,0.98,5,177,4,0,0,"management","medium","exist"
"5542",7542,0.25,0.68,4,279,5,1,0,"IT","medium","exist"
"5543",7543,0.88,0.89,4,135,3,0,0,"IT","medium","exist"
"5544",7544,0.81,0.66,3,160,2,0,0,"IT","medium","exist"
"5545",7545,0.75,0.77,3,178,4,0,0,"IT","high","exist"
"5546",7546,0.77,0.8,3,147,3,0,0,"IT","low","exist"
"5547",7547,0.55,0.72,3,204,2,0,0,"product_mng","low","exist"
"5548",7548,0.7,0.73,5,151,2,0,0,"product_mng","low","exist"
"5549",7549,0.96,0.78,3,209,2,0,0,"product_mng","high","exist"
"5550",7550,0.18,0.73,6,225,4,0,0,"product_mng","low","exist"
"5551",7551,0.22,0.62,6,142,3,0,0,"IT","low","exist"
"5552",7552,0.95,0.49,3,158,2,0,0,"RandD","low","exist"
"5553",7553,0.37,0.71,2,139,4,0,0,"RandD","high","exist"
"5554",7554,0.84,0.45,3,263,2,0,0,"RandD","low","exist"
"5555",7555,0.8,0.68,3,160,3,0,0,"RandD","low","exist"
"5556",7556,0.57,0.55,2,173,2,0,0,"RandD","low","exist"
"5557",7557,0.98,0.63,3,169,2,0,0,"marketing","low","exist"
"5558",7558,0.95,0.62,3,161,3,0,0,"sales","low","exist"
"5559",7559,0.8,0.65,4,172,3,1,0,"accounting","low","exist"
"5560",7560,0.52,0.7,3,257,3,0,0,"support","low","exist"
"5561",7561,0.31,0.62,2,139,3,0,0,"technical","medium","exist"
"5562",7562,0.71,0.59,5,245,2,0,0,"management","medium","exist"
"5563",7563,0.71,0.85,3,260,3,0,0,"marketing","medium","exist"
"5564",7564,0.5,0.96,5,229,4,0,0,"marketing","medium","exist"
"5565",7565,0.95,0.9,2,129,5,0,0,"marketing","medium","exist"
"5566",7566,0.95,0.77,3,184,4,0,0,"sales","medium","exist"
"5567",7567,0.65,0.85,4,204,3,0,0,"sales","medium","exist"
"5568",7568,0.94,0.72,3,152,2,1,0,"sales","medium","exist"
"5569",7569,0.72,0.85,4,142,3,0,0,"sales","medium","exist"
"5570",7570,0.94,0.79,4,136,2,0,0,"sales","medium","exist"
"5571",7571,0.79,0.94,4,216,4,0,0,"sales","medium","exist"
"5572",7572,0.6,0.58,3,201,3,0,0,"sales","medium","exist"
"5573",7573,0.62,0.76,4,163,3,0,0,"sales","high","exist"
"5574",7574,0.94,0.74,4,224,5,0,0,"sales","low","exist"
"5575",7575,0.24,0.5,4,209,3,0,0,"sales","medium","exist"
"5576",7576,0.17,0.71,5,257,4,1,0,"sales","medium","exist"
"5577",7577,0.66,0.83,4,234,4,0,0,"sales","medium","exist"
"5578",7578,0.65,0.56,3,221,2,0,0,"sales","medium","exist"
"5579",7579,0.51,0.62,2,186,2,0,0,"sales","low","exist"
"5580",7580,0.41,0.75,4,199,3,0,0,"sales","low","exist"
"5581",7581,0.98,0.99,3,235,3,0,0,"sales","low","exist"
"5582",7582,0.96,0.55,5,211,2,0,0,"sales","low","exist"
"5583",7583,0.55,0.97,4,136,4,0,0,"sales","low","exist"
"5584",7584,0.99,0.71,4,155,3,0,0,"sales","low","exist"
"5585",7585,0.51,0.98,4,269,3,0,0,"accounting","low","exist"
"5586",7586,0.74,0.9,3,285,3,0,0,"accounting","low","exist"
"5587",7587,0.81,0.87,5,241,3,0,0,"accounting","low","exist"
"5588",7588,0.51,0.87,3,180,4,0,0,"hr","low","exist"
"5589",7589,0.53,0.55,5,224,2,1,0,"hr","low","exist"
"5590",7590,0.67,0.48,6,107,2,1,0,"hr","low","exist"
"5591",7591,0.68,0.64,2,167,2,0,0,"hr","low","exist"
"5592",7592,0.69,0.63,3,137,3,0,0,"technical","low","exist"
"5593",7593,0.71,0.65,4,239,3,0,0,"technical","low","exist"
"5594",7594,0.64,0.56,3,239,3,0,0,"technical","low","exist"
"5595",7595,0.62,0.58,3,148,2,1,0,"technical","low","exist"
"5596",7596,0.81,0.5,4,231,3,1,0,"technical","low","exist"
"5597",7597,0.84,0.54,4,179,2,0,0,"technical","low","exist"
"5598",7598,1,0.67,3,181,2,0,0,"technical","low","exist"
"5599",7599,0.72,0.73,5,184,4,1,0,"technical","low","exist"
"5600",7600,0.57,0.67,3,207,2,0,0,"technical","medium","exist"
"5601",7601,0.73,0.99,4,152,4,0,0,"technical","medium","exist"
"5602",7602,0.91,0.59,4,133,4,0,0,"technical","medium","exist"
"5603",7603,0.98,0.85,4,178,3,0,0,"support","medium","exist"
"5604",7604,0.58,0.95,4,173,3,0,0,"support","medium","exist"
"5605",7605,0.73,0.52,2,113,5,1,0,"support","medium","exist"
"5606",7606,0.96,0.95,3,236,2,0,0,"support","medium","exist"
"5607",7607,0.57,0.98,3,188,5,0,0,"support","medium","exist"
"5608",7608,0.77,0.73,3,269,2,0,0,"support","medium","exist"
"5609",7609,0.3,0.85,2,203,3,0,0,"support","medium","exist"
"5610",7610,0.85,0.75,3,214,3,0,0,"support","medium","exist"
"5611",7611,0.49,0.83,2,185,6,0,0,"support","medium","exist"
"5612",7612,0.77,0.43,4,265,6,0,0,"support","high","exist"
"5613",7613,1,0.99,4,184,4,0,0,"support","low","exist"
"5614",7614,0.85,0.74,3,157,3,0,0,"technical","medium","exist"
"5615",7615,0.87,0.75,3,258,3,0,0,"technical","medium","exist"
"5616",7616,0.9,0.79,3,222,6,1,0,"technical","medium","exist"
"5617",7617,0.71,0.8,5,248,4,0,0,"management","medium","exist"
"5618",7618,0.59,0.56,5,162,4,0,0,"IT","low","exist"
"5619",7619,0.85,0.74,3,250,3,1,0,"IT","low","exist"
"5620",7620,0.72,0.82,4,231,3,0,0,"IT","low","exist"
"5621",7621,0.73,0.65,3,165,3,0,0,"IT","low","exist"
"5622",7622,0.9,0.54,3,272,2,0,0,"IT","low","exist"
"5623",7623,0.59,0.65,4,177,2,0,0,"product_mng","low","exist"
"5624",7624,0.52,0.9,3,133,3,0,0,"product_mng","low","exist"
"5625",7625,0.85,0.49,4,159,3,0,0,"product_mng","low","exist"
"5626",7626,0.35,0.4,3,130,3,0,0,"product_mng","low","exist"
"5627",7627,0.7,0.68,3,185,4,0,0,"IT","low","exist"
"5628",7628,0.58,0.86,3,182,3,0,0,"RandD","low","exist"
"5629",7629,0.89,0.5,2,238,4,0,0,"RandD","low","exist"
"5630",7630,0.54,0.63,3,211,3,0,0,"RandD","low","exist"
"5631",7631,0.55,0.89,4,209,3,0,0,"RandD","low","exist"
"5632",7632,0.77,0.62,5,190,3,0,0,"RandD","low","exist"
"5633",7633,0.55,0.61,4,272,4,1,0,"marketing","low","exist"
"5634",7634,0.6,0.77,3,202,3,0,0,"sales","low","exist"
"5635",7635,0.75,0.9,4,185,3,0,0,"accounting","low","exist"
"5636",7636,0.57,0.88,3,176,3,0,0,"support","low","exist"
"5637",7637,0.69,0.94,4,239,3,0,0,"technical","low","exist"
"5638",7638,0.87,0.98,4,238,3,1,0,"management","low","exist"
"5639",7639,0.69,0.36,5,269,6,1,0,"marketing","medium","exist"
"5640",7640,0.58,0.92,3,232,5,0,0,"marketing","medium","exist"
"5641",7641,0.87,0.64,2,148,6,1,0,"marketing","medium","exist"
"5642",7642,0.71,0.77,3,149,2,0,0,"sales","medium","exist"
"5643",7643,0.74,0.78,4,203,2,0,0,"sales","medium","exist"
"5644",7644,0.75,0.53,5,235,3,0,0,"sales","medium","exist"
"5645",7645,0.5,0.54,2,269,2,1,0,"sales","medium","exist"
"5646",7646,0.59,0.86,4,260,2,0,0,"sales","medium","exist"
"5647",7647,0.81,0.84,3,216,3,0,0,"sales","medium","exist"
"5648",7648,0.34,0.55,3,136,2,1,0,"sales","medium","exist"
"5649",7649,0.53,0.87,5,158,3,0,0,"sales","medium","exist"
"5650",7650,0.94,0.85,4,180,4,0,0,"sales","medium","exist"
"5651",7651,0.76,0.77,5,133,2,0,0,"sales","high","exist"
"5652",7652,0.2,0.58,5,199,4,0,0,"sales","low","exist"
"5653",7653,0.97,0.67,3,169,2,1,0,"sales","medium","exist"
"5654",7654,0.96,0.72,3,195,2,0,0,"sales","medium","exist"
"5655",7655,0.81,0.5,5,205,3,0,0,"sales","medium","exist"
"5656",7656,0.2,0.48,4,156,4,1,0,"sales","medium","exist"
"5657",7657,0.53,0.71,3,125,2,1,0,"sales","low","exist"
"5658",7658,0.75,0.76,3,171,4,0,0,"sales","low","exist"
"5659",7659,0.55,0.91,4,199,3,0,0,"sales","low","exist"
"5660",7660,0.58,0.65,5,187,2,0,0,"sales","low","exist"
"5661",7661,0.99,0.64,4,218,4,0,0,"accounting","low","exist"
"5662",7662,0.96,0.86,4,268,3,0,0,"accounting","low","exist"
"5663",7663,0.82,0.92,3,257,2,0,0,"accounting","low","exist"
"5664",7664,0.88,0.77,4,224,5,1,0,"hr","low","exist"
"5665",7665,0.78,0.97,4,221,4,0,0,"hr","low","exist"
"5666",7666,0.46,0.47,6,101,5,0,0,"hr","low","exist"
"5667",7667,0.88,0.59,3,224,2,0,0,"hr","low","exist"
"5668",7668,0.91,0.55,3,223,4,1,0,"technical","low","exist"
"5669",7669,0.6,0.68,4,271,4,0,0,"technical","low","exist"
"5670",7670,0.82,0.51,3,210,2,0,0,"technical","low","exist"
"5671",7671,0.67,0.56,4,241,3,0,0,"technical","low","exist"
"5672",7672,0.55,0.61,3,209,3,0,0,"technical","low","exist"
"5673",7673,0.73,0.62,5,186,4,0,0,"technical","low","exist"
"5674",7674,0.59,0.68,4,273,2,0,0,"technical","low","exist"
"5675",7675,0.4,0.65,6,172,2,1,0,"technical","low","exist"
"5676",7676,0.56,0.99,3,209,2,0,0,"technical","low","exist"
"5677",7677,0.87,0.57,4,175,2,1,0,"technical","low","exist"
"5678",7678,0.5,0.53,5,239,3,0,0,"technical","medium","exist"
"5679",7679,0.98,0.79,4,231,4,0,0,"support","medium","exist"
"5680",7680,0.71,0.96,4,131,3,0,0,"support","medium","exist"
"5681",7681,0.72,0.89,4,217,3,0,0,"support","medium","exist"
"5682",7682,0.5,0.83,4,242,2,0,0,"support","medium","exist"
"5683",7683,0.89,0.56,3,224,2,0,0,"support","medium","exist"
"5684",7684,0.56,0.68,3,208,3,1,0,"support","medium","exist"
"5685",7685,0.32,0.55,4,167,5,0,0,"support","medium","exist"
"5686",7686,0.96,0.88,5,269,2,0,0,"support","medium","exist"
"5687",7687,0.67,0.92,4,156,2,0,0,"support","medium","exist"
"5688",7688,0.26,0.7,3,238,6,0,0,"support","medium","exist"
"5689",7689,0.51,0.9,5,193,4,0,0,"support","medium","exist"
"5690",7690,0.16,0.78,4,196,5,0,0,"technical","high","exist"
"5691",7691,0.77,0.71,5,233,2,0,0,"technical","low","exist"
"5692",7692,0.67,0.52,2,152,5,0,0,"technical","medium","exist"
"5693",7693,0.36,0.77,4,252,2,0,0,"management","medium","exist"
"5694",7694,0.69,0.82,3,262,5,1,0,"IT","medium","exist"
"5695",7695,0.72,0.76,3,261,4,0,0,"IT","medium","exist"
"5696",7696,0.72,0.81,4,144,2,0,0,"IT","low","exist"
"5697",7697,0.88,0.95,4,234,3,0,0,"IT","low","exist"
"5698",7698,0.91,0.55,2,234,2,0,0,"IT","low","exist"
"5699",7699,0.96,0.6,4,170,2,1,0,"product_mng","low","exist"
"5700",7700,0.49,0.8,3,238,4,0,0,"product_mng","low","exist"
"5701",7701,0.59,0.97,5,242,3,0,0,"product_mng","low","exist"
"5702",7702,0.8,0.87,4,209,3,0,0,"product_mng","low","exist"
"5703",7703,0.91,0.67,4,206,3,0,0,"IT","low","exist"
"5704",7704,0.18,0.79,4,240,5,0,0,"RandD","low","exist"
"5705",7705,0.94,0.58,5,215,2,0,0,"RandD","low","exist"
"5706",7706,0.44,0.61,3,147,4,0,0,"RandD","low","exist"
"5707",7707,0.96,0.59,2,265,2,0,0,"RandD","low","exist"
"5708",7708,0.55,0.97,4,162,3,0,0,"RandD","low","exist"
"5709",7709,0.99,0.54,4,239,3,0,0,"marketing","low","exist"
"5710",7710,0.75,0.88,3,224,2,0,0,"sales","low","exist"
"5711",7711,0.66,0.78,4,256,3,0,0,"accounting","low","exist"
"5712",7712,0.96,0.57,3,263,3,0,0,"support","low","exist"
"5713",7713,0.6,0.86,6,272,4,0,0,"technical","low","exist"
"5714",7714,0.64,0.78,4,159,4,0,0,"management","medium","exist"
"5715",7715,0.85,0.8,4,219,2,0,0,"marketing","medium","exist"
"5716",7716,0.3,0.53,3,210,6,0,0,"marketing","medium","exist"
"5717",7717,0.74,0.95,4,237,3,1,0,"marketing","medium","exist"
"5718",7718,0.14,0.8,5,226,3,0,0,"sales","medium","exist"
"5719",7719,0.93,0.76,3,212,3,0,0,"sales","medium","exist"
"5720",7720,0.75,0.48,3,250,5,0,0,"sales","medium","exist"
"5721",7721,0.58,0.63,4,171,3,0,0,"sales","medium","exist"
"5722",7722,0.59,0.6,3,149,4,0,0,"sales","medium","exist"
"5723",7723,0.2,0.9,5,228,6,0,0,"sales","medium","exist"
"5724",7724,1,0.84,3,215,2,0,0,"sales","medium","exist"
"5725",7725,0.51,0.51,3,272,3,0,0,"sales","medium","exist"
"5726",7726,0.16,0.72,4,192,6,0,0,"sales","high","exist"
"5727",7727,0.77,0.56,5,226,4,0,0,"sales","low","exist"
"5728",7728,0.61,0.47,2,149,3,0,0,"sales","medium","exist"
"5729",7729,0.73,0.51,3,244,2,0,0,"sales","medium","exist"
"5730",7730,0.52,0.85,4,193,3,0,0,"sales","medium","exist"
"5731",7731,0.13,0.72,4,247,3,0,0,"sales","medium","exist"
"5732",7732,0.73,0.62,3,181,3,0,0,"sales","low","exist"
"5733",7733,0.39,0.68,2,137,3,1,0,"sales","low","exist"
"5734",7734,0.92,0.8,3,211,4,0,0,"sales","low","exist"
"5735",7735,0.34,0.78,5,137,4,0,0,"sales","low","exist"
"5736",7736,0.94,0.51,4,229,2,1,0,"sales","low","exist"
"5737",7737,0.82,0.65,4,168,3,0,0,"accounting","low","exist"
"5738",7738,0.26,0.69,4,180,3,1,0,"accounting","low","exist"
"5739",7739,0.78,0.53,4,177,2,0,0,"accounting","low","exist"
"5740",7740,0.61,0.95,4,191,2,0,0,"hr","low","exist"
"5741",7741,0.5,0.53,3,191,2,1,0,"hr","low","exist"
"5742",7742,0.52,0.96,4,125,3,0,0,"hr","low","exist"
"5743",7743,0.89,0.79,4,152,3,0,0,"hr","low","exist"
"5744",7744,0.85,0.52,4,174,2,0,0,"technical","low","exist"
"5745",7745,0.62,0.86,4,135,3,0,0,"technical","low","exist"
"5746",7746,0.38,0.67,2,117,3,0,0,"technical","low","exist"
"5747",7747,0.55,0.49,2,180,5,1,0,"technical","low","exist"
"5748",7748,0.83,0.84,4,146,3,0,0,"technical","low","exist"
"5749",7749,0.62,0.65,3,249,3,0,0,"technical","low","exist"
"5750",7750,0.6,0.54,3,136,3,1,0,"technical","low","exist"
"5751",7751,0.62,0.5,4,198,3,1,0,"technical","low","exist"
"5752",7752,0.23,0.88,5,201,3,0,0,"technical","low","exist"
"5753",7753,0.13,0.74,6,132,4,1,0,"technical","medium","exist"
"5754",7754,0.96,0.63,4,142,4,0,0,"technical","medium","exist"
"5755",7755,0.5,0.74,5,256,3,1,0,"support","medium","exist"
"5756",7756,0.66,0.72,3,135,2,0,0,"support","medium","exist"
"5757",7757,0.61,0.72,4,209,2,0,0,"support","medium","exist"
"5758",7758,0.45,0.48,5,287,5,0,0,"support","medium","exist"
"5759",7759,0.5,0.95,4,222,3,1,0,"support","medium","exist"
"5760",7760,0.75,0.82,3,227,2,0,0,"support","medium","exist"
"5761",7761,0.88,0.5,4,162,2,0,0,"support","medium","exist"
"5762",7762,0.49,0.79,5,206,2,0,0,"support","medium","exist"
"5763",7763,0.82,0.87,5,273,6,0,0,"support","medium","exist"
"5764",7764,0.92,0.65,4,135,3,0,0,"support","medium","exist"
"5765",7765,0.4,0.85,5,99,2,1,0,"support","high","exist"
"5766",7766,0.36,0.61,4,166,4,0,0,"technical","low","exist"
"5767",7767,0.8,0.99,5,187,3,1,0,"technical","medium","exist"
"5768",7768,0.68,0.65,4,134,3,0,0,"technical","medium","exist"
"5769",7769,0.54,0.45,4,137,3,1,0,"management","medium","exist"
"5770",7770,0.73,0.69,3,175,2,0,0,"IT","medium","exist"
"5771",7771,0.64,0.49,5,188,2,0,0,"IT","low","exist"
"5772",7772,0.12,0.39,5,161,4,0,0,"IT","low","exist"
"5773",7773,0.6,0.7,4,145,6,0,0,"IT","low","exist"
"5774",7774,0.36,0.62,4,111,6,0,0,"IT","low","exist"
"5775",7775,0.63,0.76,3,176,2,0,0,"product_mng","low","exist"
"5776",7776,0.67,0.94,2,192,3,0,0,"product_mng","low","exist"
"5777",7777,0.83,0.9,3,179,2,1,0,"product_mng","low","exist"
"5778",7778,0.48,0.9,4,224,3,0,0,"product_mng","low","exist"
"5779",7779,0.89,0.56,4,241,5,0,0,"IT","low","exist"
"5780",7780,0.71,0.96,3,201,3,0,0,"RandD","low","exist"
"5781",7781,0.31,0.59,4,138,2,0,0,"RandD","low","exist"
"5782",7782,0.89,0.84,5,168,2,0,0,"RandD","low","exist"
"5783",7783,0.38,0.51,2,120,3,0,0,"RandD","low","exist"
"5784",7784,0.88,0.92,3,179,3,0,0,"RandD","low","exist"
"5785",7785,0.64,0.85,3,250,3,0,0,"marketing","low","exist"
"5786",7786,0.65,0.74,4,237,3,1,0,"sales","low","exist"
"5787",7787,0.65,0.81,4,192,3,0,0,"accounting","low","exist"
"5788",7788,0.54,0.97,4,258,3,0,0,"support","low","exist"
"5789",7789,0.69,0.76,4,257,4,0,0,"technical","low","exist"
"5790",7790,0.77,0.78,2,271,3,0,0,"management","low","exist"
"5791",7791,0.28,0.66,3,184,2,0,0,"marketing","low","exist"
"5792",7792,0.33,0.4,6,214,6,0,0,"marketing","medium","exist"
"5793",7793,0.83,0.68,4,198,3,0,0,"marketing","medium","exist"
"5794",7794,0.89,0.73,3,274,2,0,0,"sales","medium","exist"
"5795",7795,0.76,0.6,5,279,2,0,0,"sales","medium","exist"
"5796",7796,0.83,0.64,5,272,2,0,0,"sales","medium","exist"
"5797",7797,0.57,0.85,4,152,3,0,0,"sales","medium","exist"
"5798",7798,0.61,0.89,2,287,4,0,0,"sales","medium","exist"
"5799",7799,0.96,0.89,2,239,6,1,0,"sales","medium","exist"
"5800",7800,0.17,0.5,4,274,6,0,0,"sales","medium","exist"
"5801",7801,0.61,0.6,4,121,2,0,0,"sales","medium","exist"
"5802",7802,0.73,0.74,5,199,2,0,0,"sales","medium","exist"
"5803",7803,0.3,0.39,3,175,3,0,0,"sales","medium","exist"
"5804",7804,0.42,0.67,2,115,3,0,0,"sales","high","exist"
"5805",7805,0.65,0.75,3,194,4,1,0,"sales","low","exist"
"5806",7806,0.17,0.45,2,119,3,0,0,"sales","medium","exist"
"5807",7807,0.12,0.82,5,162,4,1,0,"sales","medium","exist"
"5808",7808,0.7,0.78,5,264,2,0,0,"sales","medium","exist"
"5809",7809,0.87,0.88,3,179,2,0,0,"sales","medium","exist"
"5810",7810,0.43,0.44,5,213,3,0,0,"sales","low","exist"
"5811",7811,0.84,0.65,3,269,2,0,0,"sales","low","exist"
"5812",7812,0.94,0.55,3,160,3,0,0,"sales","low","exist"
"5813",7813,0.89,0.76,4,133,2,0,0,"accounting","low","exist"
"5814",7814,0.69,0.75,5,201,3,0,0,"accounting","low","exist"
"5815",7815,0.18,0.99,4,160,5,0,0,"accounting","low","exist"
"5816",7816,0.98,0.69,3,274,3,0,0,"hr","low","exist"
"5817",7817,0.3,0.88,5,245,4,0,0,"hr","low","exist"
"5818",7818,0.51,0.53,3,237,3,0,0,"hr","medium","exist"
"5819",7819,0.76,0.9,3,279,6,0,0,"hr","medium","exist"
"5820",7820,0.67,0.96,4,207,3,0,0,"technical","medium","exist"
"5821",7821,0.12,0.84,4,218,6,0,0,"technical","medium","exist"
"5822",7822,0.42,0.41,5,240,2,0,0,"technical","medium","exist"
"5823",7823,0.69,0.76,3,153,3,0,0,"technical","medium","exist"
"5824",7824,0.63,0.96,3,144,2,0,0,"technical","medium","exist"
"5825",7825,0.66,0.62,2,159,3,1,0,"technical","medium","exist"
"5826",7826,0.5,0.5,3,237,2,0,0,"technical","medium","exist"
"5827",7827,0.67,0.82,5,148,2,1,0,"technical","medium","exist"
"5828",7828,0.59,0.62,4,205,3,0,0,"technical","medium","exist"
"5829",7829,0.75,0.78,2,264,3,1,0,"technical","medium","exist"
"5830",7830,0.88,0.89,5,189,2,0,0,"technical","medium","exist"
"5831",7831,0.99,0.75,3,243,3,0,0,"support","medium","exist"
"5832",7832,0.44,0.42,2,199,2,0,0,"support","medium","exist"
"5833",7833,0.65,0.78,4,205,2,0,0,"support","medium","exist"
"5834",7834,0.88,0.63,3,184,4,0,0,"support","medium","exist"
"5835",7835,0.58,0.79,2,274,4,0,0,"support","medium","exist"
"5836",7836,0.99,0.9,4,181,3,0,0,"support","medium","exist"
"5837",7837,0.76,0.71,3,205,2,0,0,"support","medium","exist"
"5838",7838,0.14,0.98,5,172,5,0,0,"support","medium","exist"
"5839",7839,0.57,0.86,3,164,2,0,0,"support","medium","exist"
"5840",7840,0.52,0.82,3,138,2,1,0,"support","medium","exist"
"5841",7841,0.71,0.55,5,262,3,0,0,"support","medium","exist"
"5842",7842,0.27,0.75,5,264,3,0,0,"technical","medium","exist"
"5843",7843,0.49,0.52,3,225,2,0,0,"technical","high","exist"
"5844",7844,0.59,0.5,3,199,2,0,0,"technical","high","exist"
"5845",7845,0.24,0.65,6,210,5,0,0,"management","high","exist"
"5846",7846,0.18,0.87,6,226,4,0,0,"IT","high","exist"
"5847",7847,0.73,0.62,5,134,3,0,0,"IT","high","exist"
"5848",7848,0.78,0.63,6,111,6,0,0,"IT","high","exist"
"5849",7849,0.94,0.73,5,142,2,1,0,"IT","high","exist"
"5850",7850,0.86,0.53,3,213,3,0,0,"IT","high","exist"
"5851",7851,0.97,0.88,4,139,2,0,0,"product_mng","high","exist"
"5852",7852,0.49,0.54,3,145,3,0,0,"product_mng","high","exist"
"5853",7853,0.82,0.48,4,149,6,0,0,"product_mng","high","exist"
"5854",7854,0.86,0.64,4,147,2,1,0,"product_mng","high","exist"
"5855",7855,0.3,0.39,3,193,3,1,0,"IT","low","exist"
"5856",7856,0.7,0.38,4,270,2,0,0,"RandD","low","exist"
"5857",7857,0.8,0.78,5,266,3,0,0,"RandD","low","exist"
"5858",7858,0.36,0.63,2,278,4,0,0,"RandD","low","exist"
"5859",7859,0.4,0.61,3,165,2,0,0,"RandD","low","exist"
"5860",7860,0.8,0.58,4,175,2,0,0,"RandD","low","exist"
"5861",7861,0.98,0.73,4,140,4,0,0,"marketing","low","exist"
"5862",7862,0.92,0.67,3,149,3,0,0,"sales","low","exist"
"5863",7863,0.68,1,3,205,4,0,0,"accounting","low","exist"
"5864",7864,1,0.59,3,253,3,0,0,"support","low","exist"
"5865",7865,0.8,0.54,3,222,4,0,0,"technical","low","exist"
"5866",7866,0.85,0.69,4,216,4,0,0,"management","low","exist"
"5867",7867,0.69,0.6,3,139,2,0,0,"marketing","low","exist"
"5868",7868,0.57,0.52,4,252,2,0,0,"marketing","low","exist"
"5869",7869,0.33,0.72,2,173,2,0,0,"marketing","low","exist"
"5870",7870,0.19,0.48,6,178,3,1,0,"sales","medium","exist"
"5871",7871,0.5,0.63,3,160,2,0,0,"sales","medium","exist"
"5872",7872,0.52,0.88,3,261,4,0,0,"sales","medium","exist"
"5873",7873,0.13,0.52,6,188,3,0,0,"sales","medium","exist"
"5874",7874,0.18,0.73,4,219,5,0,0,"sales","medium","exist"
"5875",7875,0.86,0.64,4,263,3,0,0,"sales","medium","exist"
"5876",7876,0.86,0.59,4,165,3,0,0,"sales","medium","exist"
"5877",7877,0.16,0.76,6,218,6,0,0,"sales","medium","exist"
"5878",7878,0.43,0.46,2,239,3,1,0,"sales","medium","exist"
"5879",7879,0.79,0.63,3,212,2,1,0,"sales","medium","exist"
"5880",7880,0.51,0.67,4,133,3,0,0,"sales","medium","exist"
"5881",7881,0.83,0.55,5,250,3,1,0,"sales","medium","exist"
"5882",7882,0.61,0.45,3,114,3,0,0,"sales","high","exist"
"5883",7883,0.65,0.57,3,168,2,0,0,"sales","high","exist"
"5884",7884,0.95,0.83,4,252,2,0,0,"sales","high","exist"
"5885",7885,0.63,0.64,5,189,4,0,0,"sales","high","exist"
"5886",7886,0.72,0.6,3,265,2,1,0,"sales","high","exist"
"5887",7887,0.8,0.57,3,176,3,0,0,"sales","high","exist"
"5888",7888,0.7,0.57,4,150,4,0,0,"sales","high","exist"
"5889",7889,0.45,0.79,5,97,6,1,0,"accounting","high","exist"
"5890",7890,0.64,0.85,4,265,2,0,0,"accounting","low","exist"
"5891",7891,0.94,0.48,4,260,4,0,0,"accounting","low","exist"
"5892",7892,0.57,0.76,4,164,2,0,0,"hr","low","exist"
"5893",7893,0.35,0.56,4,142,2,0,0,"hr","low","exist"
"5894",7894,0.75,0.81,2,247,4,0,0,"hr","low","exist"
"5895",7895,0.7,0.92,5,182,3,0,0,"hr","low","exist"
"5896",7896,0.7,0.47,2,238,3,0,0,"technical","low","exist"
"5897",7897,0.61,0.62,3,191,3,0,0,"technical","low","exist"
"5898",7898,0.78,0.87,4,178,3,0,0,"technical","medium","exist"
"5899",7899,0.97,0.77,4,208,2,0,0,"technical","medium","exist"
"5900",7900,0.51,0.76,3,256,2,0,0,"technical","medium","exist"
"5901",7901,0.56,0.71,5,243,3,0,0,"technical","medium","exist"
"5902",7902,0.87,0.55,3,233,3,0,0,"technical","medium","exist"
"5903",7903,0.64,0.78,5,200,3,0,0,"technical","medium","exist"
"5904",7904,0.6,0.85,2,226,2,1,0,"technical","medium","exist"
"5905",7905,0.9,0.76,2,150,2,0,0,"technical","medium","exist"
"5906",7906,0.54,0.62,2,141,2,0,0,"technical","medium","exist"
"5907",7907,0.23,0.8,5,139,3,0,0,"support","medium","exist"
"5908",7908,0.8,0.81,3,199,4,1,0,"support","medium","exist"
"5909",7909,0.23,0.78,4,154,6,1,0,"support","medium","exist"
"5910",7910,0.81,0.51,3,247,2,0,0,"support","medium","exist"
"5911",7911,0.35,0.6,5,239,5,0,0,"support","medium","exist"
"5912",7912,0.67,0.8,4,137,2,0,0,"support","medium","exist"
"5913",7913,0.46,0.6,4,119,5,0,0,"support","medium","exist"
"5914",7914,0.84,0.98,4,134,5,1,0,"support","medium","exist"
"5915",7915,0.92,0.79,3,243,4,0,0,"support","medium","exist"
"5916",7916,0.75,0.93,5,210,3,0,0,"support","medium","exist"
"5917",7917,0.7,0.57,4,265,2,0,0,"support","medium","exist"
"5918",7918,0.7,0.75,4,204,3,0,0,"technical","medium","exist"
"5919",7919,0.9,0.81,6,273,5,0,0,"technical","medium","exist"
"5920",7920,0.8,1,3,177,2,0,0,"technical","medium","exist"
"5921",7921,0.5,0.65,5,285,6,0,0,"management","high","exist"
"5922",7922,0.84,0.72,4,222,2,0,0,"IT","low","exist"
"5923",7923,0.48,0.94,3,185,2,1,0,"IT","medium","exist"
"5924",7924,0.98,0.87,5,151,6,0,0,"IT","medium","exist"
"5925",7925,0.64,0.96,3,109,4,0,0,"IT","medium","exist"
"5926",7926,0.58,0.53,4,192,4,0,0,"IT","medium","exist"
"5927",7927,0.66,0.89,4,139,3,0,0,"product_mng","medium","exist"
"5928",7928,0.76,0.98,4,191,2,0,0,"product_mng","medium","exist"
"5929",7929,0.32,0.42,6,114,3,0,0,"product_mng","medium","exist"
"5930",7930,0.49,0.87,3,212,2,0,0,"product_mng","medium","exist"
"5931",7931,0.81,0.51,3,162,2,0,0,"IT","medium","exist"
"5932",7932,0.42,0.48,5,191,5,0,0,"RandD","medium","exist"
"5933",7933,0.17,0.85,3,234,3,0,0,"RandD","low","exist"
"5934",7934,0.49,0.59,4,265,3,0,0,"RandD","low","exist"
"5935",7935,0.34,0.69,6,283,2,0,0,"RandD","low","exist"
"5936",7936,0.86,0.81,3,232,4,0,0,"RandD","low","exist"
"5937",7937,0.51,0.71,4,208,3,1,0,"marketing","low","exist"
"5938",7938,0.49,0.99,4,258,3,1,0,"sales","low","exist"
"5939",7939,0.49,0.63,3,175,2,0,0,"accounting","low","exist"
"5940",7940,0.51,0.59,3,238,3,0,0,"support","high","exist"
"5941",7941,0.64,0.52,3,166,3,0,0,"technical","low","exist"
"5942",7942,0.62,0.85,4,225,2,0,0,"management","high","exist"
"5943",7943,0.81,0.52,4,221,6,0,0,"marketing","high","exist"
"5944",7944,0.95,0.57,2,263,3,0,0,"marketing","low","exist"
"5945",7945,0.88,0.66,4,218,4,1,0,"marketing","low","exist"
"5946",7946,0.87,0.68,5,236,4,0,0,"sales","high","exist"
"5947",7947,0.73,0.68,5,133,2,0,0,"sales","low","exist"
"5948",7948,0.98,0.73,2,237,3,0,0,"sales","medium","exist"
"5949",7949,0.77,0.48,3,204,6,0,0,"sales","high","exist"
"5950",7950,0.76,0.99,3,166,3,0,0,"sales","medium","exist"
"5951",7951,0.21,0.93,4,189,2,1,0,"sales","medium","exist"
"5952",7952,0.72,0.63,4,251,3,0,0,"sales","medium","exist"
"5953",7953,0.41,0.56,2,121,2,0,0,"sales","medium","exist"
"5954",7954,0.9,0.56,3,149,2,0,0,"sales","high","exist"
"5955",7955,0.96,0.9,5,198,2,0,0,"sales","medium","exist"
"5956",7956,0.61,0.48,4,163,3,0,0,"sales","medium","exist"
"5957",7957,0.6,0.8,4,222,2,0,0,"sales","medium","exist"
"5958",7958,0.5,0.76,2,107,4,0,0,"sales","high","exist"
"5959",7959,0.79,0.61,4,162,2,0,0,"sales","medium","exist"
"5960",7960,0.32,0.69,6,192,3,0,0,"sales","high","exist"
"5961",7961,0.85,0.59,5,236,3,0,0,"sales","low","exist"
"5962",7962,0.9,0.62,5,225,2,0,0,"sales","medium","exist"
"5963",7963,0.74,0.54,4,167,2,0,0,"sales","medium","exist"
"5964",7964,0.21,0.76,6,219,4,1,0,"sales","medium","exist"
"5965",7965,0.91,0.61,3,255,3,0,0,"accounting","medium","exist"
"5966",7966,0.9,0.66,5,137,2,0,0,"accounting","low","exist"
"5967",7967,0.74,0.99,4,193,3,0,0,"accounting","low","exist"
"5968",7968,0.76,0.75,3,239,2,0,0,"hr","low","exist"
"5969",7969,0.45,0.61,5,179,5,0,0,"hr","low","exist"
"5970",7970,0.73,0.63,3,205,2,1,0,"hr","low","exist"
"5971",7971,0.6,0.73,3,140,5,1,0,"hr","low","exist"
"5972",7972,0.8,0.77,5,256,2,0,0,"technical","low","exist"
"5973",7973,0.53,0.7,4,243,3,0,0,"technical","low","exist"
"5974",7974,0.97,0.63,5,163,3,0,0,"technical","low","exist"
"5975",7975,0.64,0.99,3,167,2,0,0,"technical","low","exist"
"5976",7976,0.92,0.59,4,190,5,0,0,"technical","high","exist"
"5977",7977,0.6,0.57,5,145,3,1,0,"technical","low","exist"
"5978",7978,1,0.6,4,265,4,0,0,"technical","low","exist"
"5979",7979,0.69,0.63,4,272,2,0,0,"technical","low","exist"
"5980",7980,0.53,0.45,5,140,5,0,0,"technical","low","exist"
"5981",7981,0.63,0.58,4,236,2,0,0,"technical","high","exist"
"5982",7982,0.57,0.89,4,255,3,1,0,"technical","low","exist"
"5983",7983,0.79,0.45,5,131,6,0,0,"support","low","exist"
"5984",7984,0.68,0.92,4,209,6,0,0,"support","low","exist"
"5985",7985,0.56,0.61,3,250,2,0,0,"support","high","exist"
"5986",7986,0.48,0.51,3,201,2,1,0,"support","low","exist"
"5987",7987,0.59,0.67,4,271,2,0,0,"support","medium","exist"
"5988",7988,0.34,0.76,6,237,5,0,0,"support","high","exist"
"5989",7989,0.98,0.87,3,239,4,0,0,"support","medium","exist"
"5990",7990,0.36,0.45,2,135,3,1,0,"support","high","exist"
"5991",7991,0.6,0.58,2,182,2,0,0,"support","medium","exist"
"5992",7992,0.24,0.54,6,193,4,0,0,"support","medium","exist"
"5993",7993,0.67,0.72,4,192,3,1,0,"support","medium","exist"
"5994",7994,0.17,0.6,5,144,6,0,0,"technical","medium","exist"
"5995",7995,0.57,0.58,3,251,3,0,0,"technical","medium","exist"
"5996",7996,0.7,0.85,3,161,2,0,0,"technical","medium","exist"
"5997",7997,0.73,0.62,3,171,3,0,0,"management","medium","exist"
"5998",7998,0.61,0.86,4,153,5,0,0,"IT","medium","exist"
"5999",7999,0.95,0.96,4,161,2,0,0,"IT","high","exist"
"6000",8000,0.85,0.55,3,226,4,0,0,"IT","low","exist"
"6001",8001,0.72,0.9,3,193,3,0,0,"IT","medium","exist"
"6002",8002,0.84,0.66,4,204,3,0,0,"IT","medium","exist"
"6003",8003,0.57,0.47,2,158,2,0,0,"product_mng","medium","exist"
"6004",8004,0.69,0.69,3,236,4,0,0,"product_mng","medium","exist"
"6005",8005,0.57,0.68,4,191,3,0,0,"product_mng","medium","exist"
"6006",8006,0.52,0.59,6,104,4,0,0,"product_mng","medium","exist"
"6007",8007,0.56,0.55,3,245,2,0,0,"IT","medium","exist"
"6008",8008,0.75,0.74,3,186,3,1,0,"RandD","medium","exist"
"6009",8009,0.98,0.75,5,168,4,1,0,"RandD","medium","exist"
"6010",8010,0.48,0.55,4,262,3,1,0,"RandD","high","exist"
"6011",8011,0.35,0.67,2,116,3,0,0,"RandD","low","exist"
"6012",8012,0.66,0.93,4,187,2,0,0,"RandD","low","exist"
"6013",8013,0.79,0.9,5,184,3,0,0,"marketing","low","exist"
"6014",8014,0.86,0.53,4,155,3,0,0,"marketing","high","exist"
"6015",8015,0.88,1,5,190,2,0,0,"sales","low","exist"
"6016",8016,0.83,0.64,3,242,3,0,0,"accounting","low","exist"
"6017",8017,0.8,0.64,5,204,2,0,0,"support","low","exist"
"6018",8018,0.64,0.69,4,232,3,0,0,"technical","high","exist"
"6019",8019,0.72,0.59,4,245,2,0,0,"management","low","exist"
"6020",8020,0.67,0.61,3,251,2,0,0,"marketing","low","exist"
"6021",8021,0.75,0.7,4,179,2,0,0,"marketing","low","exist"
"6022",8022,0.65,0.95,4,153,2,0,0,"marketing","low","exist"
"6023",8023,0.98,0.98,5,210,6,0,0,"sales","low","exist"
"6024",8024,0.68,0.8,2,257,2,1,0,"sales","low","exist"
"6025",8025,0.52,0.37,3,137,4,0,0,"sales","low","exist"
"6026",8026,0.68,0.93,4,179,2,0,0,"sales","medium","exist"
"6027",8027,0.92,0.74,4,213,3,0,0,"sales","medium","exist"
"6028",8028,0.86,0.79,3,106,6,1,0,"sales","medium","exist"
"6029",8029,0.5,0.99,4,272,2,0,0,"sales","medium","exist"
"6030",8030,0.35,0.37,4,153,2,0,0,"sales","medium","exist"
"6031",8031,0.76,0.97,5,172,3,0,0,"sales","medium","exist"
"6032",8032,0.66,0.95,4,224,2,0,0,"sales","medium","exist"
"6033",8033,0.58,0.71,3,230,3,1,0,"sales","medium","exist"
"6034",8034,0.55,0.97,4,222,4,0,0,"sales","medium","exist"
"6035",8035,0.9,0.53,3,270,3,0,0,"sales","medium","exist"
"6036",8036,0.75,0.71,4,205,3,0,0,"sales","medium","exist"
"6037",8037,0.9,0.63,4,134,3,1,0,"sales","medium","exist"
"6038",8038,0.14,0.54,5,275,4,1,0,"sales","high","exist"
"6039",8039,0.86,0.57,4,183,3,0,0,"sales","low","exist"
"6040",8040,0.56,0.84,4,143,4,0,0,"sales","medium","exist"
"6041",8041,0.54,0.63,5,259,2,0,0,"sales","medium","exist"
"6042",8042,0.36,0.8,5,186,4,0,0,"accounting","medium","exist"
"6043",8043,0.82,0.59,3,155,3,0,0,"accounting","medium","exist"
"6044",8044,0.49,0.42,2,266,3,0,0,"accounting","low","exist"
"6045",8045,0.77,0.97,3,215,2,0,0,"hr","low","exist"
"6046",8046,0.64,0.83,4,179,3,0,0,"hr","low","exist"
"6047",8047,0.93,0.53,3,217,3,0,0,"hr","low","exist"
"6048",8048,0.96,0.94,5,235,3,0,0,"hr","low","exist"
"6049",8049,0.6,0.97,3,164,2,0,0,"technical","low","exist"
"6050",8050,0.58,0.55,3,178,5,0,0,"technical","low","exist"
"6051",8051,0.52,0.49,5,170,3,0,0,"technical","low","exist"
"6052",8052,0.74,0.79,5,241,3,0,0,"technical","low","exist"
"6053",8053,0.64,0.99,4,222,2,0,0,"technical","low","exist"
"6054",8054,0.93,0.59,3,233,3,0,0,"technical","low","exist"
"6055",8055,0.76,0.49,5,243,3,0,0,"technical","low","exist"
"6056",8056,0.5,0.54,4,175,2,0,0,"technical","low","exist"
"6057",8057,0.79,0.55,4,266,3,0,0,"technical","low","exist"
"6058",8058,0.97,0.95,5,201,2,0,0,"technical","low","exist"
"6059",8059,0.6,0.49,5,259,4,0,0,"technical","low","exist"
"6060",8060,0.84,0.73,5,105,5,0,0,"support","low","exist"
"6061",8061,0.75,0.99,5,189,3,0,0,"support","low","exist"
"6062",8062,0.24,0.91,4,232,2,0,0,"support","low","exist"
"6063",8063,0.24,0.91,5,258,6,0,0,"support","low","exist"
"6064",8064,0.51,0.58,5,173,3,0,0,"support","low","exist"
"6065",8065,0.5,0.88,4,147,3,0,0,"support","medium","exist"
"6066",8066,0.66,0.36,3,256,6,0,0,"support","medium","exist"
"6067",8067,0.97,0.77,3,206,4,0,0,"support","medium","exist"
"6068",8068,0.61,0.5,4,175,3,0,0,"support","medium","exist"
"6069",8069,0.77,0.49,4,274,2,1,0,"support","medium","exist"
"6070",8070,0.74,0.64,3,229,3,0,0,"support","medium","exist"
"6071",8071,0.92,0.98,4,161,3,0,0,"technical","medium","exist"
"6072",8072,0.78,0.99,2,188,2,0,0,"technical","medium","exist"
"6073",8073,0.56,0.57,3,205,3,0,0,"technical","medium","exist"
"6074",8074,0.82,0.63,3,246,3,0,0,"management","medium","exist"
"6075",8075,0.26,0.98,5,161,5,0,0,"IT","medium","exist"
"6076",8076,0.69,0.85,5,246,3,0,0,"IT","medium","exist"
"6077",8077,0.78,0.83,4,158,3,0,0,"IT","high","exist"
"6078",8078,0.67,0.86,3,175,3,0,0,"IT","low","exist"
"6079",8079,0.77,0.91,5,268,3,1,0,"IT","medium","exist"
"6080",8080,0.8,0.63,4,211,2,0,0,"product_mng","medium","exist"
"6081",8081,0.51,0.51,3,274,2,0,0,"product_mng","medium","exist"
"6082",8082,0.77,0.52,4,241,3,0,0,"product_mng","medium","exist"
"6083",8083,0.65,0.71,3,170,2,0,0,"product_mng","low","exist"
"6084",8084,0.58,0.53,3,287,5,0,0,"IT","low","exist"
"6085",8085,0.67,0.39,2,235,6,0,0,"RandD","low","exist"
"6086",8086,0.33,0.39,3,98,3,1,0,"RandD","low","exist"
"6087",8087,0.78,0.66,6,105,5,1,0,"RandD","low","exist"
"6088",8088,0.58,0.83,3,226,3,0,0,"RandD","low","exist"
"6089",8089,0.63,0.59,4,171,4,0,0,"RandD","low","exist"
"6090",8090,0.63,0.51,4,153,4,0,0,"RandD","low","exist"
"6091",8091,0.59,0.55,3,183,4,0,0,"marketing","low","exist"
"6092",8092,0.6,0.9,5,139,3,0,0,"sales","low","exist"
"6093",8093,0.93,0.9,5,210,3,0,0,"accounting","low","exist"
"6094",8094,0.78,0.77,2,177,4,0,0,"support","low","exist"
"6095",8095,0.65,0.6,3,148,2,0,0,"technical","low","exist"
"6096",8096,1,0.61,4,198,2,0,0,"management","low","exist"
"6097",8097,0.96,1,3,137,4,1,0,"marketing","low","exist"
"6098",8098,0.54,0.97,5,233,3,1,0,"marketing","low","exist"
"6099",8099,0.98,0.69,2,204,4,0,0,"marketing","low","exist"
"6100",8100,0.34,0.59,2,164,2,0,0,"sales","low","exist"
"6101",8101,0.71,0.53,5,162,2,0,0,"sales","low","exist"
"6102",8102,0.64,0.64,3,180,2,1,0,"sales","low","exist"
"6103",8103,0.71,0.93,2,199,2,0,0,"sales","low","exist"
"6104",8104,0.58,0.63,4,190,2,0,0,"sales","medium","exist"
"6105",8105,0.87,0.96,4,151,3,1,0,"sales","medium","exist"
"6106",8106,0.58,0.85,4,162,3,0,0,"sales","medium","exist"
"6107",8107,0.87,0.67,3,139,2,0,0,"sales","medium","exist"
"6108",8108,0.72,0.86,3,231,3,1,0,"sales","medium","exist"
"6109",8109,0.67,0.83,5,269,2,0,0,"sales","medium","exist"
"6110",8110,0.53,0.97,4,249,3,0,0,"sales","medium","exist"
"6111",8111,0.78,0.61,3,148,2,0,0,"sales","medium","exist"
"6112",8112,0.19,0.63,4,233,5,0,0,"sales","medium","exist"
"6113",8113,1,0.88,4,240,4,0,0,"sales","medium","exist"
"6114",8114,0.75,0.75,5,229,2,1,0,"sales","medium","exist"
"6115",8115,0.29,0.66,3,256,2,1,0,"sales","medium","exist"
"6116",8116,0.37,0.7,2,188,2,0,0,"sales","high","exist"
"6117",8117,0.78,0.5,3,167,2,0,0,"sales","low","exist"
"6118",8118,0.24,0.64,5,190,4,0,0,"sales","medium","exist"
"6119",8119,0.49,0.7,4,168,3,0,0,"accounting","medium","exist"
"6120",8120,0.18,0.64,6,154,5,0,0,"accounting","medium","exist"
"6121",8121,0.76,0.85,4,135,3,0,0,"accounting","medium","exist"
"6122",8122,0.5,0.97,4,217,3,0,0,"hr","low","exist"
"6123",8123,0.82,0.94,3,253,2,0,0,"hr","low","exist"
"6124",8124,0.97,0.94,3,180,3,0,0,"hr","low","exist"
"6125",8125,0.72,0.9,4,225,2,0,0,"hr","low","exist"
"6126",8126,0.98,0.64,4,134,3,1,0,"technical","low","exist"
"6127",8127,0.76,0.73,3,192,2,0,0,"technical","low","exist"
"6128",8128,0.72,0.88,3,224,3,0,0,"technical","low","exist"
"6129",8129,0.96,0.91,3,260,5,0,0,"technical","low","exist"
"6130",8130,0.62,0.78,3,178,3,1,0,"technical","low","exist"
"6131",8131,0.25,0.98,4,166,5,1,0,"technical","low","exist"
"6132",8132,0.82,0.56,5,180,3,0,0,"technical","low","exist"
"6133",8133,0.59,0.9,3,189,2,0,0,"technical","low","exist"
"6134",8134,0.94,0.73,3,154,3,0,0,"technical","low","exist"
"6135",8135,0.72,0.88,3,236,3,0,0,"technical","low","exist"
"6136",8136,0.53,0.78,5,198,3,0,0,"technical","low","exist"
"6137",8137,0.67,0.83,3,148,3,0,0,"support","low","exist"
"6138",8138,0.99,0.52,4,205,2,0,0,"support","low","exist"
"6139",8139,0.64,0.53,4,133,3,0,0,"support","low","exist"
"6140",8140,0.61,0.57,4,160,3,1,0,"support","low","exist"
"6141",8141,0.89,0.85,4,201,2,1,0,"support","low","exist"
"6142",8142,0.61,0.7,5,157,4,0,0,"support","low","exist"
"6143",8143,0.9,0.74,3,260,2,0,0,"support","medium","exist"
"6144",8144,0.96,0.51,5,152,3,0,0,"support","medium","exist"
"6145",8145,0.62,0.55,4,218,3,0,0,"support","medium","exist"
"6146",8146,0.89,0.57,3,252,2,0,0,"support","medium","exist"
"6147",8147,0.52,0.67,4,216,3,0,0,"support","medium","exist"
"6148",8148,0.66,0.99,3,183,2,0,0,"technical","medium","exist"
"6149",8149,0.96,0.6,5,269,2,0,0,"technical","medium","exist"
"6150",8150,0.95,0.89,5,132,4,0,0,"technical","medium","exist"
"6151",8151,0.75,0.98,4,170,4,0,0,"management","medium","exist"
"6152",8152,0.39,0.87,5,257,5,1,0,"IT","medium","exist"
"6153",8153,0.93,0.69,3,138,2,0,0,"IT","medium","exist"
"6154",8154,0.44,0.54,3,115,3,0,0,"IT","medium","exist"
"6155",8155,0.9,0.67,3,165,2,0,0,"IT","high","exist"
"6156",8156,0.75,0.81,3,214,3,0,0,"IT","low","exist"
"6157",8157,0.45,0.75,2,246,2,0,0,"product_mng","medium","exist"
"6158",8158,0.42,0.6,2,188,3,0,0,"product_mng","medium","exist"
"6159",8159,0.99,0.82,3,255,2,0,0,"product_mng","medium","exist"
"6160",8160,0.89,0.91,4,190,2,0,0,"product_mng","medium","exist"
"6161",8161,0.96,0.9,4,164,4,0,0,"IT","low","exist"
"6162",8162,0.5,0.46,3,165,3,0,0,"RandD","low","exist"
"6163",8163,0.59,0.59,3,141,3,0,0,"RandD","low","exist"
"6164",8164,0.57,0.69,3,154,2,0,0,"RandD","low","exist"
"6165",8165,1,0.87,3,165,2,0,0,"RandD","low","exist"
"6166",8166,0.6,0.59,5,266,2,0,0,"RandD","low","exist"
"6167",8167,0.21,0.85,6,235,6,0,0,"RandD","low","exist"
"6168",8168,0.63,0.83,4,159,2,0,0,"marketing","low","exist"
"6169",8169,0.8,0.82,3,218,3,0,0,"sales","low","exist"
"6170",8170,0.51,0.96,3,149,4,0,0,"accounting","low","exist"
"6171",8171,0.89,0.96,5,239,3,0,0,"support","low","exist"
"6172",8172,0.83,0.58,4,225,3,0,0,"technical","low","exist"
"6173",8173,0.77,0.74,6,247,3,0,0,"management","low","exist"
"6174",8174,0.79,0.99,4,183,2,0,0,"marketing","low","exist"
"6175",8175,0.63,0.85,5,214,2,0,0,"marketing","low","exist"
"6176",8176,0.68,0.48,5,113,2,0,0,"marketing","low","exist"
"6177",8177,0.74,0.69,4,244,2,0,0,"sales","low","exist"
"6178",8178,0.49,0.67,6,286,4,0,0,"sales","low","exist"
"6179",8179,0.46,0.55,3,139,2,0,0,"sales","medium","exist"
"6180",8180,0.9,0.91,5,176,3,0,0,"sales","medium","exist"
"6181",8181,0.7,0.67,5,136,3,0,0,"sales","medium","exist"
"6182",8182,0.84,0.71,4,222,2,0,0,"sales","medium","exist"
"6183",8183,0.89,0.77,4,269,4,0,0,"sales","medium","exist"
"6184",8184,0.59,0.87,4,183,2,0,0,"sales","medium","exist"
"6185",8185,0.57,0.72,3,206,3,0,0,"sales","medium","exist"
"6186",8186,0.53,0.49,3,158,3,0,0,"sales","medium","exist"
"6187",8187,0.83,0.89,4,136,3,0,0,"sales","medium","exist"
"6188",8188,0.51,0.66,4,182,2,0,0,"sales","medium","exist"
"6189",8189,0.78,0.61,4,268,3,0,0,"sales","medium","exist"
"6190",8190,0.52,0.69,3,144,3,0,0,"sales","medium","exist"
"6191",8191,0.42,0.5,5,286,4,0,0,"sales","high","exist"
"6192",8192,0.61,0.38,2,268,3,0,0,"sales","low","exist"
"6193",8193,0.85,1,3,255,3,0,0,"sales","medium","exist"
"6194",8194,0.17,0.85,6,245,5,0,0,"sales","medium","exist"
"6195",8195,0.79,0.52,3,134,2,1,0,"sales","medium","exist"
"6196",8196,0.56,0.98,3,251,3,1,0,"accounting","medium","exist"
"6197",8197,0.5,0.73,5,165,2,0,0,"accounting","low","exist"
"6198",8198,0.51,0.53,3,223,2,1,0,"accounting","low","exist"
"6199",8199,0.77,0.67,4,225,4,0,0,"hr","low","exist"
"6200",8200,0.84,0.9,3,196,3,1,0,"hr","low","exist"
"6201",8201,0.21,0.49,3,253,3,0,0,"hr","low","exist"
"6202",8202,0.65,0.57,5,222,3,0,0,"hr","low","exist"
"6203",8203,0.95,0.87,4,135,3,0,0,"technical","low","exist"
"6204",8204,0.8,0.75,4,217,2,0,0,"technical","low","exist"
"6205",8205,0.77,0.85,5,192,2,0,0,"technical","low","exist"
"6206",8206,0.57,0.7,3,172,3,0,0,"technical","low","exist"
"6207",8207,0.92,0.55,4,183,3,0,0,"technical","low","exist"
"6208",8208,1,0.71,5,186,2,0,0,"technical","low","exist"
"6209",8209,0.85,0.67,4,163,3,0,0,"technical","low","exist"
"6210",8210,0.57,0.8,4,262,3,0,0,"technical","low","exist"
"6211",8211,0.66,0.68,3,202,3,0,0,"technical","low","exist"
"6212",8212,0.85,0.8,4,248,3,0,0,"technical","low","exist"
"6213",8213,0.99,0.5,5,214,2,0,0,"technical","low","exist"
"6214",8214,0.91,0.82,4,260,4,0,0,"support","low","exist"
"6215",8215,0.96,0.97,4,260,3,0,0,"support","low","exist"
"6216",8216,0.49,0.52,4,251,2,0,0,"support","low","exist"
"6217",8217,0.39,0.85,5,179,5,0,0,"support","low","exist"
"6218",8218,0.87,0.74,4,178,2,1,0,"support","medium","exist"
"6219",8219,0.19,0.85,6,210,4,0,0,"support","medium","exist"
"6220",8220,0.9,0.83,3,273,4,0,0,"support","medium","exist"
"6221",8221,0.5,0.5,5,166,2,0,0,"support","medium","exist"
"6222",8222,0.7,0.9,5,246,2,0,0,"support","medium","exist"
"6223",8223,0.52,0.55,5,192,3,0,0,"support","medium","exist"
"6224",8224,0.71,0.69,3,274,3,0,0,"support","medium","exist"
"6225",8225,0.4,0.41,3,232,3,0,0,"technical","medium","exist"
"6226",8226,0.96,0.53,3,158,4,0,0,"technical","medium","exist"
"6227",8227,0.86,0.92,5,137,3,0,0,"technical","medium","exist"
"6228",8228,0.68,0.85,3,209,2,0,0,"management","medium","exist"
"6229",8229,0.56,0.64,3,206,2,0,0,"IT","medium","exist"
"6230",8230,0.65,0.56,3,230,2,0,0,"IT","high","exist"
"6231",8231,0.98,0.61,5,239,3,0,0,"IT","low","exist"
"6232",8232,0.18,0.51,5,159,6,0,0,"IT","medium","exist"
"6233",8233,0.66,0.65,4,244,2,0,0,"IT","medium","exist"
"6234",8234,0.14,0.51,5,259,5,0,0,"product_mng","medium","exist"
"6235",8235,0.94,0.8,5,245,3,1,0,"product_mng","medium","exist"
"6236",8236,0.56,1,3,141,2,1,0,"product_mng","low","exist"
"6237",8237,0.56,0.8,5,202,4,0,0,"product_mng","low","exist"
"6238",8238,0.59,0.89,5,143,3,1,0,"IT","low","exist"
"6239",8239,0.63,0.62,4,286,5,1,0,"RandD","low","exist"
"6240",8240,0.97,0.88,5,173,3,0,0,"RandD","low","exist"
"6241",8241,0.76,0.7,5,195,3,0,0,"RandD","low","exist"
"6242",8242,0.85,0.58,4,167,4,0,0,"RandD","low","exist"
"6243",8243,0.23,0.73,5,197,4,1,0,"RandD","low","exist"
"6244",8244,0.68,0.62,3,255,5,0,0,"RandD","low","exist"
"6245",8245,0.71,0.73,3,274,3,0,0,"marketing","low","exist"
"6246",8246,0.5,0.59,3,192,2,0,0,"sales","low","exist"
"6247",8247,0.61,0.7,3,225,3,0,0,"accounting","low","exist"
"6248",8248,0.99,0.65,3,209,2,1,0,"support","low","exist"
"6249",8249,0.97,0.86,5,222,3,0,0,"technical","low","exist"
"6250",8250,0.82,0.71,5,208,2,0,0,"management","low","exist"
"6251",8251,0.72,0.68,5,162,5,0,0,"marketing","low","exist"
"6252",8252,0.53,0.74,3,135,2,0,0,"marketing","low","exist"
"6253",8253,0.55,0.87,4,200,3,0,0,"marketing","low","exist"
"6254",8254,0.52,0.53,4,159,4,0,0,"sales","low","exist"
"6255",8255,0.8,0.81,5,156,2,0,0,"sales","low","exist"
"6256",8256,0.51,0.95,4,169,3,1,0,"sales","low","exist"
"6257",8257,0.66,0.65,4,154,3,0,0,"sales","medium","exist"
"6258",8258,0.56,0.43,2,169,3,0,0,"sales","medium","exist"
"6259",8259,0.5,0.84,3,233,3,1,0,"sales","medium","exist"
"6260",8260,0.94,0.78,3,218,2,1,0,"sales","medium","exist"
"6261",8261,0.42,0.8,4,279,6,0,0,"sales","medium","exist"
"6262",8262,0.6,0.61,3,195,3,0,0,"sales","medium","exist"
"6263",8263,0.55,0.71,4,223,3,0,0,"sales","medium","exist"
"6264",8264,0.76,0.72,3,275,4,1,0,"sales","medium","exist"
"6265",8265,0.84,0.74,3,234,3,1,0,"sales","medium","exist"
"6266",8266,0.33,0.62,4,113,6,0,0,"sales","medium","exist"
"6267",8267,0.61,0.95,3,133,5,0,0,"sales","medium","exist"
"6268",8268,0.91,0.93,5,158,4,0,0,"sales","medium","exist"
"6269",8269,0.73,0.74,4,214,3,0,0,"sales","high","exist"
"6270",8270,0.87,0.67,4,272,4,0,0,"sales","low","exist"
"6271",8271,0.38,0.42,2,127,4,0,0,"sales","medium","exist"
"6272",8272,0.8,0.51,4,141,3,1,0,"sales","medium","exist"
"6273",8273,0.69,0.8,5,263,3,1,0,"accounting","medium","exist"
"6274",8274,0.99,0.92,5,174,5,0,0,"accounting","medium","exist"
"6275",8275,0.92,0.76,5,246,2,1,0,"accounting","low","exist"
"6276",8276,0.6,0.88,3,201,2,0,0,"hr","low","exist"
"6277",8277,0.89,0.93,3,181,3,0,0,"hr","low","exist"
"6278",8278,0.91,0.93,3,238,2,0,0,"hr","low","exist"
"6279",8279,0.35,0.52,3,167,2,0,0,"hr","low","exist"
"6280",8280,0.88,0.68,5,224,2,0,0,"technical","low","exist"
"6281",8281,0.66,0.69,3,182,3,1,0,"technical","low","exist"
"6282",8282,0.21,0.55,4,189,2,0,0,"technical","low","exist"
"6283",8283,0.78,0.64,3,169,2,1,0,"technical","medium","exist"
"6284",8284,0.21,0.96,4,287,5,0,0,"technical","medium","exist"
"6285",8285,0.64,0.94,3,150,2,0,0,"technical","medium","exist"
"6286",8286,0.68,0.95,4,146,2,0,0,"technical","medium","exist"
"6287",8287,0.99,0.87,4,162,4,0,0,"technical","medium","exist"
"6288",8288,0.85,0.55,4,158,5,0,0,"technical","medium","exist"
"6289",8289,0.86,0.51,3,185,2,0,0,"technical","medium","exist"
"6290",8290,0.89,0.98,3,214,3,0,0,"technical","medium","exist"
"6291",8291,0.49,0.85,4,200,3,0,0,"support","medium","exist"
"6292",8292,0.76,0.97,4,219,2,0,0,"support","medium","exist"
"6293",8293,0.79,0.87,3,218,3,0,0,"support","medium","exist"
"6294",8294,0.89,0.64,4,237,2,0,0,"support","medium","exist"
"6295",8295,0.34,0.51,3,105,3,0,0,"support","medium","exist"
"6296",8296,0.81,0.92,3,251,3,1,0,"support","medium","exist"
"6297",8297,0.96,0.7,3,227,2,0,0,"support","medium","exist"
"6298",8298,0.7,0.87,3,158,2,0,0,"support","medium","exist"
"6299",8299,0.92,0.61,4,252,2,0,0,"support","medium","exist"
"6300",8300,0.5,0.76,4,198,3,0,0,"support","medium","exist"
"6301",8301,0.75,0.72,2,192,3,0,0,"support","medium","exist"
"6302",8302,0.42,0.38,2,139,4,0,0,"technical","medium","exist"
"6303",8303,0.29,0.4,6,205,3,0,0,"technical","medium","exist"
"6304",8304,0.91,0.48,3,224,3,0,0,"technical","medium","exist"
"6305",8305,0.55,0.97,4,267,4,0,0,"management","medium","exist"
"6306",8306,0.57,0.81,4,200,3,1,0,"IT","medium","exist"
"6307",8307,0.27,0.48,3,97,6,0,0,"IT","medium","exist"
"6308",8308,0.7,0.43,6,253,3,0,0,"IT","high","exist"
"6309",8309,0.63,0.68,4,191,2,0,0,"IT","high","exist"
"6310",8310,0.97,0.63,5,199,2,1,0,"IT","high","exist"
"6311",8311,0.28,0.52,3,127,4,0,0,"product_mng","high","exist"
"6312",8312,0.7,0.6,3,187,2,0,0,"product_mng","high","exist"
"6313",8313,0.83,0.51,4,215,3,0,0,"product_mng","high","exist"
"6314",8314,0.22,0.76,4,176,6,1,0,"product_mng","high","exist"
"6315",8315,0.55,0.47,3,194,2,0,0,"IT","high","exist"
"6316",8316,0.33,0.77,3,216,3,0,0,"RandD","high","exist"
"6317",8317,0.5,0.78,4,185,3,0,0,"RandD","high","exist"
"6318",8318,0.93,0.88,5,140,3,0,0,"RandD","high","exist"
"6319",8319,0.77,0.66,3,260,4,0,0,"RandD","high","exist"
"6320",8320,0.93,0.97,5,137,4,0,0,"RandD","low","exist"
"6321",8321,0.72,1,4,151,2,0,0,"RandD","low","exist"
"6322",8322,0.78,0.53,3,152,2,0,0,"marketing","low","exist"
"6323",8323,0.55,0.75,4,166,2,0,0,"sales","low","exist"
"6324",8324,0.39,0.86,3,261,2,0,0,"accounting","low","exist"
"6325",8325,0.67,0.78,3,235,3,0,0,"support","low","exist"
"6326",8326,0.61,0.89,3,201,2,0,0,"technical","low","exist"
"6327",8327,0.6,0.69,6,250,5,1,0,"management","low","exist"
"6328",8328,0.48,0.64,4,146,2,0,0,"marketing","low","exist"
"6329",8329,0.75,0.84,4,195,3,0,0,"marketing","low","exist"
"6330",8330,0.87,0.58,4,259,3,0,0,"marketing","low","exist"
"6331",8331,0.51,0.54,4,166,4,1,0,"sales","low","exist"
"6332",8332,0.63,0.9,4,188,4,1,0,"sales","low","exist"
"6333",8333,0.6,0.57,3,203,2,0,0,"sales","low","exist"
"6334",8334,0.7,0.99,3,167,3,0,0,"sales","low","exist"
"6335",8335,0.5,0.99,2,258,3,1,0,"sales","medium","exist"
"6336",8336,0.59,0.51,2,126,3,0,0,"sales","medium","exist"
"6337",8337,0.52,0.39,6,246,4,0,0,"sales","medium","exist"
"6338",8338,0.55,0.49,3,205,3,0,0,"sales","medium","exist"
"6339",8339,0.81,0.62,5,201,3,1,0,"sales","medium","exist"
"6340",8340,0.94,0.98,4,197,3,0,0,"sales","medium","exist"
"6341",8341,0.98,0.61,3,272,3,0,0,"sales","medium","exist"
"6342",8342,0.83,0.84,4,206,2,0,0,"sales","medium","exist"
"6343",8343,0.93,0.62,3,184,3,0,0,"sales","medium","exist"
"6344",8344,0.99,0.54,3,199,2,0,0,"sales","medium","exist"
"6345",8345,0.55,0.57,4,220,3,0,0,"sales","medium","exist"
"6346",8346,0.96,0.83,3,233,3,0,0,"sales","medium","exist"
"6347",8347,0.28,0.77,3,221,3,0,0,"sales","high","exist"
"6348",8348,0.97,0.6,6,168,5,1,0,"sales","high","exist"
"6349",8349,0.8,0.78,3,251,3,0,0,"sales","high","exist"
"6350",8350,0.75,0.55,2,188,3,0,0,"accounting","high","exist"
"6351",8351,0.89,0.88,3,203,3,0,0,"accounting","high","exist"
"6352",8352,0.6,0.76,5,168,2,1,0,"accounting","high","exist"
"6353",8353,0.73,0.98,3,227,2,1,0,"hr","high","exist"
"6354",8354,0.88,0.75,4,159,2,0,0,"hr","high","exist"
"6355",8355,0.5,0.7,3,159,3,0,0,"hr","low","exist"
"6356",8356,0.53,0.78,5,275,5,0,0,"hr","low","exist"
"6357",8357,0.95,0.43,6,283,2,0,0,"technical","low","exist"
"6358",8358,0.94,0.53,5,169,3,0,0,"technical","low","exist"
"6359",8359,0.49,0.8,3,227,4,1,0,"technical","low","exist"
"6360",8360,0.59,0.57,3,147,4,0,0,"technical","low","exist"
"6361",8361,0.51,0.91,3,227,2,0,0,"technical","low","exist"
"6362",8362,0.66,0.66,4,166,3,0,0,"technical","low","exist"
"6363",8363,0.76,0.94,4,168,6,0,0,"technical","medium","exist"
"6364",8364,0.12,0.59,3,229,6,0,0,"technical","medium","exist"
"6365",8365,0.84,0.65,3,134,3,0,0,"technical","medium","exist"
"6366",8366,0.94,0.81,3,196,3,0,0,"technical","medium","exist"
"6367",8367,0.63,0.84,4,181,3,0,0,"technical","medium","exist"
"6368",8368,0.79,0.99,4,177,3,1,0,"support","medium","exist"
"6369",8369,0.85,0.68,3,272,2,1,0,"support","medium","exist"
"6370",8370,0.74,0.52,3,213,3,0,0,"support","medium","exist"
"6371",8371,0.23,0.75,6,220,3,0,0,"support","medium","exist"
"6372",8372,0.62,0.51,4,274,2,0,0,"support","medium","exist"
"6373",8373,0.36,0.56,6,242,6,0,0,"support","medium","exist"
"6374",8374,0.7,0.83,4,182,3,0,0,"support","medium","exist"
"6375",8375,0.57,0.75,5,172,4,0,0,"support","medium","exist"
"6376",8376,0.83,0.99,3,226,3,0,0,"support","medium","exist"
"6377",8377,0.71,0.96,3,132,2,0,0,"support","medium","exist"
"6378",8378,0.23,0.72,6,121,3,0,0,"support","medium","exist"
"6379",8379,0.59,0.69,4,207,2,0,0,"technical","medium","exist"
"6380",8380,0.69,0.61,2,141,3,0,0,"technical","medium","exist"
"6381",8381,0.63,0.81,5,189,3,0,0,"technical","medium","exist"
"6382",8382,0.9,0.59,6,269,4,1,0,"management","medium","exist"
"6383",8383,0.31,0.57,4,200,4,0,0,"IT","medium","exist"
"6384",8384,0.92,0.62,3,199,2,0,0,"IT","medium","exist"
"6385",8385,0.96,0.87,4,213,3,0,0,"IT","medium","exist"
"6386",8386,0.66,0.51,6,105,4,0,0,"IT","high","exist"
"6387",8387,0.48,0.97,4,141,2,0,0,"IT","low","exist"
"6388",8388,0.15,0.55,3,255,3,1,0,"product_mng","medium","exist"
"6389",8389,0.59,0.79,3,217,4,0,0,"product_mng","medium","exist"
"6390",8390,0.66,0.85,6,165,5,0,0,"product_mng","medium","exist"
"6391",8391,0.69,0.92,5,220,2,0,0,"product_mng","medium","exist"
"6392",8392,0.65,0.79,4,241,4,0,0,"IT","medium","exist"
"6393",8393,0.58,0.94,5,274,3,0,0,"RandD","medium","exist"
"6394",8394,0.72,0.57,4,224,4,0,0,"RandD","medium","exist"
"6395",8395,0.65,0.99,5,240,5,0,0,"RandD","medium","exist"
"6396",8396,0.63,0.77,5,210,3,0,0,"RandD","medium","exist"
"6397",8397,0.55,0.87,3,215,2,0,0,"RandD","medium","exist"
"6398",8398,0.74,0.56,4,254,2,0,0,"marketing","low","exist"
"6399",8399,0.58,0.84,4,150,4,1,0,"sales","low","exist"
"6400",8400,0.71,0.72,4,177,3,0,0,"accounting","low","exist"
"6401",8401,0.83,0.37,5,101,4,1,0,"support","low","exist"
"6402",8402,0.63,0.52,3,183,2,0,0,"technical","low","exist"
"6403",8403,0.56,0.61,3,224,3,0,0,"management","low","exist"
"6404",8404,0.88,0.55,3,263,3,0,0,"marketing","low","exist"
"6405",8405,0.82,0.55,3,207,2,0,0,"marketing","high","exist"
"6406",8406,0.69,0.72,3,243,3,0,0,"marketing","low","exist"
"6407",8407,0.57,0.54,3,157,4,1,0,"sales","high","exist"
"6408",8408,0.75,0.69,3,242,3,0,0,"sales","high","exist"
"6409",8409,0.6,0.98,4,265,2,0,0,"sales","low","exist"
"6410",8410,0.96,0.92,3,196,4,0,0,"sales","low","exist"
"6411",8411,0.75,0.67,4,135,2,0,0,"sales","high","exist"
"6412",8412,1,0.61,6,270,3,0,0,"sales","low","exist"
"6413",8413,0.92,0.97,4,201,2,0,0,"sales","medium","exist"
"6414",8414,0.84,0.93,5,225,4,0,0,"sales","high","exist"
"6415",8415,0.82,0.77,4,205,3,0,0,"sales","medium","exist"
"6416",8416,0.74,0.42,3,131,3,0,0,"sales","medium","exist"
"6417",8417,0.21,0.39,2,118,4,0,0,"sales","medium","exist"
"6418",8418,0.62,0.64,5,187,3,0,0,"sales","medium","exist"
"6419",8419,0.54,0.48,3,275,2,0,0,"sales","high","exist"
"6420",8420,0.55,0.97,5,125,4,0,0,"sales","medium","exist"
"6421",8421,0.84,0.55,4,270,3,1,0,"sales","medium","exist"
"6422",8422,0.61,0.56,2,123,2,0,0,"sales","medium","exist"
"6423",8423,0.64,0.53,3,281,3,0,0,"sales","high","exist"
"6424",8424,0.92,0.51,3,223,2,0,0,"sales","medium","exist"
"6425",8425,0.86,0.87,3,268,2,0,0,"sales","high","exist"
"6426",8426,0.6,0.74,4,174,3,0,0,"accounting","low","exist"
"6427",8427,0.86,0.92,3,162,3,1,0,"accounting","medium","exist"
"6428",8428,0.55,0.51,3,192,3,0,0,"accounting","medium","exist"
"6429",8429,0.54,0.58,4,178,3,0,0,"hr","medium","exist"
"6430",8430,0.49,0.9,3,250,2,0,0,"hr","medium","exist"
"6431",8431,0.98,0.72,3,262,4,0,0,"hr","low","exist"
"6432",8432,0.55,0.55,5,194,3,1,0,"hr","low","exist"
"6433",8433,0.64,0.5,3,146,3,0,0,"technical","low","exist"
"6434",8434,0.54,0.53,4,245,2,0,0,"technical","low","exist"
"6435",8435,0.58,0.45,3,131,2,0,0,"technical","low","exist"
"6436",8436,0.57,0.37,3,108,4,0,0,"technical","low","exist"
"6437",8437,0.65,0.64,5,206,3,0,0,"technical","low","exist"
"6438",8438,0.6,0.4,3,146,4,1,0,"technical","low","exist"
"6439",8439,0.59,0.45,2,171,2,0,0,"technical","low","exist"
"6440",8440,0.77,0.5,4,173,2,1,0,"technical","low","exist"
"6441",8441,0.55,0.49,5,240,3,0,0,"technical","high","exist"
"6442",8442,0.5,0.6,4,199,2,0,0,"technical","low","exist"
"6443",8443,0.43,0.77,3,237,3,1,0,"technical","low","exist"
"6444",8444,0.58,0.84,3,258,4,0,0,"support","low","exist"
"6445",8445,0.66,0.68,4,269,3,1,0,"support","low","exist"
"6446",8446,0.7,0.8,5,245,4,0,0,"support","high","exist"
"6447",8447,0.82,0.54,4,164,3,0,0,"support","low","exist"
"6448",8448,0.49,0.49,4,256,3,1,0,"support","low","exist"
"6449",8449,0.99,0.79,4,213,3,0,0,"support","low","exist"
"6450",8450,0.96,0.73,3,193,3,1,0,"support","high","exist"
"6451",8451,0.7,0.57,3,179,2,0,0,"support","low","exist"
"6452",8452,0.22,0.89,6,278,5,1,0,"support","medium","exist"
"6453",8453,0.91,0.52,3,256,2,0,0,"support","high","exist"
"6454",8454,0.18,0.76,5,173,4,0,0,"support","medium","exist"
"6455",8455,0.84,0.68,4,179,3,0,0,"technical","high","exist"
"6456",8456,0.66,0.38,4,145,5,0,0,"technical","medium","exist"
"6457",8457,0.49,0.65,3,168,4,0,0,"technical","medium","exist"
"6458",8458,0.88,0.89,4,213,3,0,0,"management","medium","exist"
"6459",8459,0.69,0.91,6,150,5,0,0,"IT","medium","exist"
"6460",8460,0.83,0.75,3,262,3,0,0,"IT","medium","exist"
"6461",8461,0.56,0.84,4,149,4,1,0,"IT","medium","exist"
"6462",8462,0.95,0.77,5,139,2,0,0,"IT","medium","exist"
"6463",8463,0.56,1,3,272,2,0,0,"IT","medium","exist"
"6464",8464,0.93,0.73,3,252,4,0,0,"product_mng","high","exist"
"6465",8465,0.84,0.52,3,232,4,0,0,"product_mng","low","exist"
"6466",8466,0.84,0.48,3,266,2,0,0,"product_mng","medium","exist"
"6467",8467,0.52,0.65,4,264,3,0,0,"product_mng","medium","exist"
"6468",8468,0.98,0.8,4,142,2,0,0,"IT","medium","exist"
"6469",8469,0.66,0.64,5,208,4,0,0,"RandD","medium","exist"
"6470",8470,0.92,0.49,5,178,2,1,0,"RandD","medium","exist"
"6471",8471,0.71,0.8,5,192,3,0,0,"RandD","medium","exist"
"6472",8472,0.65,0.92,4,242,2,0,0,"RandD","medium","exist"
"6473",8473,0.23,0.47,4,277,5,0,0,"RandD","medium","exist"
"6474",8474,0.71,0.97,3,173,2,1,0,"marketing","medium","exist"
"6475",8475,0.21,0.65,4,276,6,0,0,"marketing","high","exist"
"6476",8476,0.7,0.72,2,189,3,0,0,"sales","low","exist"
"6477",8477,0.9,0.5,4,139,2,0,0,"accounting","low","exist"
"6478",8478,0.6,0.52,5,140,3,0,0,"support","low","exist"
"6479",8479,0.58,0.63,5,191,3,1,0,"technical","high","exist"
"6480",8480,0.73,0.72,5,178,2,0,0,"management","low","exist"
"6481",8481,0.56,0.67,4,184,3,0,0,"marketing","low","exist"
"6482",8482,0.97,0.57,3,144,3,0,0,"marketing","low","exist"
"6483",8483,0.92,0.91,3,160,2,0,0,"marketing","high","exist"
"6484",8484,0.77,0.68,3,225,2,0,0,"sales","low","exist"
"6485",8485,0.97,0.81,5,266,2,0,0,"sales","low","exist"
"6486",8486,0.7,0.69,5,154,2,0,0,"sales","low","exist"
"6487",8487,0.78,0.82,4,142,2,1,0,"sales","low","exist"
"6488",8488,0.77,0.87,3,207,4,1,0,"sales","low","exist"
"6489",8489,0.66,0.53,4,162,3,0,0,"sales","low","exist"
"6490",8490,0.25,0.98,6,287,5,1,0,"sales","low","exist"
"6491",8491,0.89,0.87,2,270,6,1,0,"sales","medium","exist"
"6492",8492,0.15,0.66,5,160,4,1,0,"sales","medium","exist"
"6493",8493,0.26,0.91,6,113,2,0,0,"sales","medium","exist"
"6494",8494,0.74,0.58,4,178,4,0,0,"sales","medium","exist"
"6495",8495,0.52,0.83,3,153,2,0,0,"sales","medium","exist"
"6496",8496,0.95,0.62,4,255,2,0,0,"sales","medium","exist"
"6497",8497,0.66,0.82,4,257,3,1,0,"sales","medium","exist"
"6498",8498,0.79,0.66,4,243,3,0,0,"sales","medium","exist"
"6499",8499,0.98,0.94,3,179,3,0,0,"sales","medium","exist"
"6500",8500,0.4,0.37,3,123,2,0,0,"sales","medium","exist"
"6501",8501,1,0.68,3,132,2,0,0,"sales","medium","exist"
"6502",8502,0.71,0.79,3,134,3,0,0,"sales","medium","exist"
"6503",8503,0.48,0.45,3,277,2,1,0,"accounting","high","exist"
"6504",8504,0.76,1,5,265,2,0,0,"accounting","low","exist"
"6505",8505,0.61,0.62,4,269,4,0,0,"accounting","medium","exist"
"6506",8506,0.74,0.9,4,156,4,0,0,"hr","medium","exist"
"6507",8507,0.24,0.94,6,237,5,0,0,"hr","medium","exist"
"6508",8508,0.79,0.97,3,271,2,0,0,"hr","medium","exist"
"6509",8509,0.75,0.98,3,206,2,0,0,"hr","low","exist"
"6510",8510,0.6,0.98,4,192,3,0,0,"technical","low","exist"
"6511",8511,0.72,0.95,4,230,3,0,0,"technical","low","exist"
"6512",8512,1,0.6,4,261,3,0,0,"technical","low","exist"
"6513",8513,0.55,0.88,3,173,3,1,0,"technical","low","exist"
"6514",8514,0.3,0.98,2,109,4,1,0,"technical","low","exist"
"6515",8515,0.89,0.59,3,247,4,0,0,"technical","low","exist"
"6516",8516,0.84,0.84,5,163,3,0,0,"technical","low","exist"
"6517",8517,0.67,0.64,4,149,4,0,0,"technical","low","exist"
"6518",8518,0.15,0.48,6,218,6,0,0,"technical","low","exist"
"6519",8519,0.59,0.75,4,194,2,0,0,"technical","low","exist"
"6520",8520,0.5,0.59,4,157,2,0,0,"technical","low","exist"
"6521",8521,0.23,0.68,5,244,3,0,0,"support","low","exist"
"6522",8522,0.95,0.58,5,169,2,0,0,"support","low","exist"
"6523",8523,0.31,0.53,2,146,3,1,0,"support","low","exist"
"6524",8524,0.47,0.55,5,207,3,0,0,"support","low","exist"
"6525",8525,0.26,0.95,3,195,5,0,0,"support","low","exist"
"6526",8526,0.55,0.64,6,148,4,0,0,"support","low","exist"
"6527",8527,0.89,0.58,3,272,2,0,0,"support","low","exist"
"6528",8528,0.88,0.68,3,185,2,0,0,"support","low","exist"
"6529",8529,0.98,0.62,5,260,2,1,0,"support","low","exist"
"6530",8530,0.96,0.48,3,182,2,1,0,"support","medium","exist"
"6531",8531,0.85,0.65,3,195,3,0,0,"support","medium","exist"
"6532",8532,0.96,0.85,3,168,3,0,0,"technical","medium","exist"
"6533",8533,0.85,0.88,3,198,4,1,0,"technical","medium","exist"
"6534",8534,0.59,0.93,5,172,2,0,0,"technical","medium","exist"
"6535",8535,0.51,0.5,4,216,2,1,0,"management","medium","exist"
"6536",8536,0.5,0.75,3,232,2,0,0,"IT","medium","exist"
"6537",8537,0.53,0.59,3,148,3,0,0,"IT","medium","exist"
"6538",8538,0.44,0.83,4,210,2,0,0,"IT","medium","exist"
"6539",8539,0.99,0.55,3,197,2,0,0,"IT","medium","exist"
"6540",8540,0.73,0.83,4,241,3,0,0,"IT","medium","exist"
"6541",8541,0.51,0.71,5,154,2,0,0,"product_mng","medium","exist"
"6542",8542,0.5,0.84,3,259,2,0,0,"product_mng","high","exist"
"6543",8543,0.52,0.76,4,106,2,1,0,"product_mng","low","exist"
"6544",8544,0.74,0.74,5,262,2,0,0,"product_mng","medium","exist"
"6545",8545,0.69,0.89,2,202,2,0,0,"IT","medium","exist"
"6546",8546,0.22,0.65,5,174,5,1,0,"RandD","medium","exist"
"6547",8547,0.49,0.89,4,240,2,0,0,"RandD","medium","exist"
"6548",8548,0.7,0.57,5,247,3,0,0,"RandD","low","exist"
"6549",8549,0.68,0.63,4,148,3,0,0,"RandD","low","exist"
"6550",8550,0.66,0.84,5,187,2,1,0,"RandD","low","exist"
"6551",8551,0.99,0.58,4,183,3,0,0,"marketing","low","exist"
"6552",8552,0.88,0.59,4,240,2,0,0,"sales","low","exist"
"6553",8553,0.2,0.54,4,149,3,0,0,"accounting","low","exist"
"6554",8554,0.56,0.44,2,130,3,0,0,"support","low","exist"
"6555",8555,0.68,0.85,4,203,2,0,0,"technical","low","exist"
"6556",8556,0.85,0.6,3,218,3,0,0,"management","low","exist"
"6557",8557,0.95,0.95,4,204,3,1,0,"marketing","low","exist"
"6558",8558,0.6,0.77,4,163,3,1,0,"marketing","low","exist"
"6559",8559,0.61,0.53,4,183,3,0,0,"marketing","low","exist"
"6560",8560,0.55,0.55,4,211,4,0,0,"sales","low","exist"
"6561",8561,0.64,0.78,5,156,5,1,0,"sales","low","exist"
"6562",8562,0.64,0.6,3,196,3,0,0,"sales","low","exist"
"6563",8563,0.87,0.54,4,162,2,0,0,"sales","low","exist"
"6564",8564,0.2,0.9,3,218,4,0,0,"sales","low","exist"
"6565",8565,0.99,0.64,4,135,2,1,0,"sales","low","exist"
"6566",8566,0.96,0.7,2,273,3,0,0,"sales","low","exist"
"6567",8567,0.53,0.65,3,241,3,0,0,"sales","low","exist"
"6568",8568,0.7,0.39,6,285,4,0,0,"sales","low","exist"
"6569",8569,0.68,0.61,6,236,3,0,0,"sales","medium","exist"
"6570",8570,0.96,0.48,4,222,3,0,0,"sales","medium","exist"
"6571",8571,0.64,0.64,4,242,3,0,0,"sales","medium","exist"
"6572",8572,0.86,0.65,5,166,3,0,0,"sales","medium","exist"
"6573",8573,0.87,0.84,3,172,3,0,0,"sales","medium","exist"
"6574",8574,0.53,0.56,4,249,2,0,0,"sales","medium","exist"
"6575",8575,0.72,0.98,4,180,2,0,0,"sales","medium","exist"
"6576",8576,0.83,0.59,4,197,4,0,0,"sales","medium","exist"
"6577",8577,0.97,0.54,5,185,2,0,0,"sales","medium","exist"
"6578",8578,0.92,0.76,3,171,2,0,0,"sales","medium","exist"
"6579",8579,0.82,0.95,6,191,6,0,0,"accounting","medium","exist"
"6580",8580,0.59,0.56,4,250,2,0,0,"accounting","medium","exist"
"6581",8581,0.84,0.95,5,199,3,0,0,"accounting","high","exist"
"6582",8582,0.71,0.84,3,139,2,0,0,"hr","low","exist"
"6583",8583,0.49,0.98,3,224,3,0,0,"hr","medium","exist"
"6584",8584,0.78,0.61,3,227,3,0,0,"hr","medium","exist"
"6585",8585,0.84,0.81,4,198,2,0,0,"hr","medium","exist"
"6586",8586,0.85,0.96,5,165,5,0,0,"technical","medium","exist"
"6587",8587,0.87,0.93,4,199,3,0,0,"technical","low","exist"
"6588",8588,0.94,0.84,5,203,3,0,0,"technical","low","exist"
"6589",8589,0.82,0.97,4,243,3,1,0,"technical","low","exist"
"6590",8590,0.78,0.78,3,135,3,0,0,"technical","low","exist"
"6591",8591,0.47,0.55,4,100,4,1,0,"technical","low","exist"
"6592",8592,0.5,0.48,2,150,3,1,0,"technical","low","exist"
"6593",8593,0.75,0.82,4,252,3,0,0,"technical","low","exist"
"6594",8594,0.36,0.39,3,98,3,0,0,"technical","low","exist"
"6595",8595,0.91,0.61,3,262,3,0,0,"technical","low","exist"
"6596",8596,0.87,0.68,3,257,3,0,0,"technical","low","exist"
"6597",8597,0.97,0.94,3,160,3,0,0,"support","low","exist"
"6598",8598,0.71,0.65,3,190,3,0,0,"support","low","exist"
"6599",8599,0.83,0.65,3,231,2,0,0,"support","low","exist"
"6600",8600,0.42,0.51,3,190,4,0,0,"support","low","exist"
"6601",8601,0.53,0.51,4,181,3,0,0,"support","low","exist"
"6602",8602,0.56,0.88,4,273,3,0,0,"support","low","exist"
"6603",8603,0.26,0.7,5,214,6,1,0,"support","low","exist"
"6604",8604,0.53,0.49,4,192,2,0,0,"support","low","exist"
"6605",8605,0.99,0.73,4,224,2,0,0,"support","low","exist"
"6606",8606,0.48,0.43,3,96,3,0,0,"support","low","exist"
"6607",8607,0.91,0.5,3,276,4,0,0,"support","low","exist"
"6608",8608,0.76,0.79,3,162,2,1,0,"technical","medium","exist"
"6609",8609,0.67,0.8,4,190,4,0,0,"technical","medium","exist"
"6610",8610,0.58,0.6,4,147,3,0,0,"technical","medium","exist"
"6611",8611,0.57,0.78,4,143,3,0,0,"management","medium","exist"
"6612",8612,0.55,0.57,5,280,6,1,0,"IT","medium","exist"
"6613",8613,0.79,0.49,3,137,2,0,0,"IT","medium","exist"
"6614",8614,0.48,0.98,3,259,6,0,0,"IT","medium","exist"
"6615",8615,0.68,0.69,4,176,3,1,0,"IT","medium","exist"
"6616",8616,0.19,0.64,5,231,4,1,0,"IT","medium","exist"
"6617",8617,0.99,0.48,3,104,3,0,0,"product_mng","medium","exist"
"6618",8618,0.3,0.76,5,224,2,0,0,"product_mng","medium","exist"
"6619",8619,0.81,0.85,4,202,3,1,0,"product_mng","medium","exist"
"6620",8620,0.58,0.74,4,180,3,0,0,"product_mng","high","exist"
"6621",8621,0.74,0.61,3,228,2,1,0,"IT","low","exist"
"6622",8622,0.59,0.74,5,165,2,0,0,"RandD","medium","exist"
"6623",8623,0.46,0.63,2,177,6,0,0,"RandD","medium","exist"
"6624",8624,0.58,0.43,3,194,2,1,0,"RandD","medium","exist"
"6625",8625,0.77,0.95,3,192,4,1,0,"RandD","medium","exist"
"6626",8626,0.79,0.77,4,171,2,0,0,"RandD","low","exist"
"6627",8627,0.51,0.95,3,187,2,0,0,"marketing","low","exist"
"6628",8628,0.7,0.58,3,205,3,0,0,"sales","low","exist"
"6629",8629,0.84,0.73,5,230,4,1,0,"accounting","low","exist"
"6630",8630,0.19,0.9,5,172,2,0,0,"support","low","exist"
"6631",8631,0.9,0.52,4,167,3,1,0,"technical","low","exist"
"6632",8632,0.19,0.91,5,145,3,0,0,"management","low","exist"
"6633",8633,0.96,0.53,3,166,3,0,0,"marketing","low","exist"
"6634",8634,0.87,1,3,148,3,0,0,"marketing","low","exist"
"6635",8635,0.5,0.89,5,223,3,0,0,"marketing","low","exist"
"6636",8636,0.88,0.58,2,123,4,0,0,"sales","low","exist"
"6637",8637,0.55,0.99,3,158,3,0,0,"sales","low","exist"
"6638",8638,0.89,0.86,3,223,2,0,0,"sales","low","exist"
"6639",8639,0.58,0.69,3,252,3,0,0,"sales","low","exist"
"6640",8640,0.58,0.96,5,143,2,0,0,"sales","low","exist"
"6641",8641,0.34,0.88,5,131,6,0,0,"sales","low","exist"
"6642",8642,0.54,0.65,5,206,4,1,0,"sales","low","exist"
"6643",8643,0.59,0.54,4,210,3,0,0,"sales","low","exist"
"6644",8644,0.88,0.96,4,262,3,0,0,"sales","medium","exist"
"6645",8645,0.72,0.69,4,147,3,0,0,"sales","medium","exist"
"6646",8646,0.79,0.75,4,259,3,0,0,"sales","medium","exist"
"6647",8647,0.51,0.73,4,174,3,0,0,"sales","medium","exist"
"6648",8648,0.84,0.84,3,150,4,0,0,"sales","medium","exist"
"6649",8649,0.95,0.67,4,219,2,0,0,"sales","medium","exist"
"6650",8650,0.58,0.88,5,178,4,0,0,"sales","medium","exist"
"6651",8651,0.69,0.98,3,269,3,1,0,"sales","medium","exist"
"6652",8652,0.17,0.64,6,205,5,1,0,"sales","medium","exist"
"6653",8653,0.81,0.72,3,232,3,1,0,"sales","medium","exist"
"6654",8654,0.41,0.5,3,193,3,0,0,"sales","medium","exist"
"6655",8655,0.12,0.42,3,110,2,0,0,"accounting","medium","exist"
"6656",8656,0.71,0.6,4,208,3,0,0,"accounting","high","exist"
"6657",8657,0.32,0.69,5,157,4,0,0,"accounting","low","exist"
"6658",8658,0.83,0.98,5,187,4,0,0,"hr","medium","exist"
"6659",8659,0.74,0.92,4,226,3,0,0,"hr","medium","exist"
"6660",8660,0.67,0.85,4,266,3,0,0,"hr","medium","exist"
"6661",8661,0.85,0.56,3,159,3,0,0,"hr","medium","exist"
"6662",8662,0.49,0.75,4,259,3,1,0,"technical","low","exist"
"6663",8663,0.7,0.74,4,150,3,1,0,"technical","low","exist"
"6664",8664,0.44,0.58,4,152,3,0,0,"technical","low","exist"
"6665",8665,0.5,0.87,5,245,2,0,0,"technical","low","exist"
"6666",8666,0.63,0.74,5,227,2,0,0,"technical","low","exist"
"6667",8667,0.87,0.77,4,261,3,0,0,"technical","low","exist"
"6668",8668,0.82,0.53,4,162,3,1,0,"technical","low","exist"
"6669",8669,0.97,0.89,4,193,3,0,0,"technical","low","exist"
"6670",8670,0.9,0.81,4,144,2,0,0,"technical","low","exist"
"6671",8671,0.41,0.5,6,151,2,0,0,"technical","low","exist"
"6672",8672,0.58,0.94,4,225,2,0,0,"technical","low","exist"
"6673",8673,0.77,0.5,5,170,2,0,0,"support","low","exist"
"6674",8674,0.89,0.75,4,246,3,1,0,"support","low","exist"
"6675",8675,0.64,0.72,4,254,3,0,0,"support","low","exist"
"6676",8676,0.31,0.79,2,193,4,0,0,"support","low","exist"
"6677",8677,0.6,0.88,4,175,3,0,0,"support","low","exist"
"6678",8678,0.2,1,3,123,4,0,0,"support","low","exist"
"6679",8679,0.13,0.6,3,178,5,0,0,"support","low","exist"
"6680",8680,0.95,0.9,3,259,2,0,0,"support","low","exist"
"6681",8681,0.15,0.96,5,201,6,0,0,"support","low","exist"
"6682",8682,0.22,0.98,4,185,3,0,0,"support","low","exist"
"6683",8683,0.33,0.51,2,166,3,0,0,"support","medium","exist"
"6684",8684,0.23,0.96,4,213,4,0,0,"technical","medium","exist"
"6685",8685,0.85,0.79,4,138,2,0,0,"technical","medium","exist"
"6686",8686,0.79,0.57,3,168,2,0,0,"technical","medium","exist"
"6687",8687,0.6,0.6,4,197,3,0,0,"management","medium","exist"
"6688",8688,0.89,0.74,5,220,3,0,0,"IT","medium","exist"
"6689",8689,0.65,0.92,3,101,3,1,0,"IT","medium","exist"
"6690",8690,0.61,0.7,4,175,3,1,0,"IT","medium","exist"
"6691",8691,0.4,0.79,5,181,5,0,0,"IT","medium","exist"
"6692",8692,0.49,0.57,3,157,3,0,0,"IT","medium","exist"
"6693",8693,0.95,0.75,3,247,2,0,0,"product_mng","medium","exist"
"6694",8694,0.85,1,5,244,2,0,0,"product_mng","medium","exist"
"6695",8695,0.24,0.39,4,152,5,0,0,"product_mng","high","exist"
"6696",8696,0.85,0.99,5,176,4,0,0,"product_mng","low","exist"
"6697",8697,0.99,0.98,5,241,2,0,0,"IT","medium","exist"
"6698",8698,0.49,0.49,4,240,2,0,0,"RandD","medium","exist"
"6699",8699,0.56,0.73,3,226,3,0,0,"RandD","medium","exist"
"6700",8700,0.65,0.66,6,240,4,0,0,"RandD","medium","exist"
"6701",8701,0.62,0.68,3,253,5,1,0,"RandD","low","exist"
"6702",8702,0.78,0.68,4,174,3,1,0,"RandD","low","exist"
"6703",8703,0.54,0.7,3,213,2,0,0,"marketing","low","exist"
"6704",8704,0.61,0.77,4,195,2,0,0,"sales","low","exist"
"6705",8705,0.49,0.99,6,230,4,0,0,"accounting","low","exist"
"6706",8706,0.29,0.85,2,248,6,1,0,"support","low","exist"
"6707",8707,0.64,0.79,4,274,2,1,0,"technical","low","exist"
"6708",8708,0.93,0.94,4,217,2,0,0,"management","low","exist"
"6709",8709,0.16,0.66,6,229,6,0,0,"marketing","low","exist"
"6710",8710,0.68,0.85,5,173,3,0,0,"marketing","low","exist"
"6711",8711,0.71,0.8,2,146,4,0,0,"marketing","low","exist"
"6712",8712,0.62,0.82,5,151,5,0,0,"sales","low","exist"
"6713",8713,0.74,0.75,2,137,3,1,0,"sales","low","exist"
"6714",8714,0.81,0.5,3,198,3,0,0,"sales","low","exist"
"6715",8715,0.2,0.82,4,190,5,0,0,"sales","low","exist"
"6716",8716,0.51,0.91,4,206,3,0,0,"sales","low","exist"
"6717",8717,0.55,0.99,4,238,3,0,0,"sales","low","exist"
"6718",8718,0.45,0.41,3,193,2,1,0,"sales","low","exist"
"6719",8719,0.91,0.61,4,176,3,0,0,"sales","low","exist"
"6720",8720,0.73,0.59,6,121,5,0,0,"sales","low","exist"
"6721",8721,0.98,0.88,4,145,2,0,0,"sales","low","exist"
"6722",8722,0.62,0.65,4,212,3,1,0,"sales","medium","exist"
"6723",8723,0.57,0.62,3,198,4,0,0,"sales","medium","exist"
"6724",8724,0.99,0.57,3,189,4,1,0,"sales","medium","exist"
"6725",8725,0.82,0.68,2,200,3,0,0,"sales","medium","exist"
"6726",8726,0.24,0.81,4,217,5,0,0,"sales","medium","exist"
"6727",8727,0.84,0.73,5,245,3,0,0,"sales","medium","exist"
"6728",8728,0.9,0.55,3,260,3,0,0,"sales","medium","exist"
"6729",8729,0.13,0.73,5,206,5,0,0,"sales","medium","exist"
"6730",8730,0.6,0.67,3,249,2,0,0,"sales","medium","exist"
"6731",8731,0.72,0.87,4,154,2,1,0,"accounting","medium","exist"
"6732",8732,0.68,0.61,4,147,3,0,0,"accounting","medium","exist"
"6733",8733,0.51,0.72,3,148,2,0,0,"accounting","medium","exist"
"6734",8734,0.74,0.58,3,220,2,0,0,"hr","high","exist"
"6735",8735,0.86,0.73,3,241,3,0,0,"hr","low","exist"
"6736",8736,0.85,0.51,3,242,3,0,0,"hr","medium","exist"
"6737",8737,0.63,0.85,2,156,3,1,0,"hr","medium","exist"
"6738",8738,0.74,0.87,3,155,3,0,0,"technical","medium","exist"
"6739",8739,0.6,0.5,3,211,3,0,0,"technical","medium","exist"
"6740",8740,0.69,0.82,4,137,2,1,0,"technical","low","exist"
"6741",8741,0.56,0.96,2,269,2,0,0,"technical","low","exist"
"6742",8742,0.5,0.67,2,142,3,0,0,"technical","low","exist"
"6743",8743,0.84,0.5,5,267,2,0,0,"technical","low","exist"
"6744",8744,0.93,0.48,5,134,6,0,0,"technical","low","exist"
"6745",8745,0.12,0.5,5,287,4,0,0,"technical","low","exist"
"6746",8746,0.52,0.58,4,134,3,0,0,"technical","low","exist"
"6747",8747,0.6,0.54,3,185,2,0,0,"technical","low","exist"
"6748",8748,0.71,1,3,181,4,0,0,"technical","medium","exist"
"6749",8749,0.21,0.81,5,169,4,0,0,"support","medium","exist"
"6750",8750,0.15,0.84,3,201,6,0,0,"support","medium","exist"
"6751",8751,0.38,0.55,2,215,6,0,0,"support","medium","exist"
"6752",8752,0.27,0.86,3,222,5,0,0,"support","medium","exist"
"6753",8753,0.86,0.64,4,137,2,0,0,"support","medium","exist"
"6754",8754,0.17,0.52,6,176,5,0,0,"support","medium","exist"
"6755",8755,0.66,0.69,3,257,2,0,0,"support","medium","exist"
"6756",8756,0.95,0.51,3,224,4,0,0,"support","medium","exist"
"6757",8757,0.59,0.92,5,226,3,0,0,"support","medium","exist"
"6758",8758,0.49,0.61,5,196,3,0,0,"support","medium","exist"
"6759",8759,0.9,0.88,5,256,4,0,0,"support","medium","exist"
"6760",8760,0.98,0.81,3,153,4,0,0,"technical","medium","exist"
"6761",8761,0.52,1,4,221,3,0,0,"technical","medium","exist"
"6762",8762,0.12,0.95,3,236,3,0,0,"technical","medium","exist"
"6763",8763,0.91,0.67,5,137,3,0,0,"management","medium","exist"
"6764",8764,0.99,0.62,4,256,2,0,0,"IT","medium","exist"
"6765",8765,0.49,0.8,4,161,2,0,0,"IT","medium","exist"
"6766",8766,0.92,0.51,4,167,3,1,0,"IT","medium","exist"
"6767",8767,0.21,0.84,3,194,2,0,0,"IT","medium","exist"
"6768",8768,0.89,0.9,3,231,3,0,0,"IT","medium","exist"
"6769",8769,0.84,0.81,4,152,2,1,0,"product_mng","medium","exist"
"6770",8770,0.72,0.68,3,150,3,1,0,"product_mng","medium","exist"
"6771",8771,0.57,0.46,3,207,3,0,0,"product_mng","medium","exist"
"6772",8772,0.9,0.69,4,172,3,0,0,"product_mng","medium","exist"
"6773",8773,0.59,0.75,2,273,2,0,0,"IT","high","exist"
"6774",8774,0.97,0.69,4,134,3,0,0,"RandD","high","exist"
"6775",8775,0.56,0.85,3,109,2,0,0,"RandD","high","exist"
"6776",8776,0.78,0.59,4,124,3,1,0,"RandD","high","exist"
"6777",8777,0.64,0.72,4,253,4,0,0,"RandD","high","exist"
"6778",8778,0.58,0.9,5,224,3,0,0,"RandD","high","exist"
"6779",8779,0.68,0.58,3,217,2,0,0,"marketing","high","exist"
"6780",8780,0.82,0.73,3,148,4,0,0,"sales","high","exist"
"6781",8781,0.83,0.78,5,240,3,0,0,"accounting","high","exist"
"6782",8782,0.49,0.49,2,226,3,0,0,"support","high","exist"
"6783",8783,0.57,0.95,4,176,3,0,0,"technical","high","exist"
"6784",8784,0.66,0.93,4,248,3,0,0,"management","high","exist"
"6785",8785,0.78,0.6,2,206,2,0,0,"marketing","low","exist"
"6786",8786,0.55,0.8,3,192,3,1,0,"marketing","low","exist"
"6787",8787,0.98,0.62,3,140,4,0,0,"marketing","low","exist"
"6788",8788,0.89,0.51,4,141,3,0,0,"sales","low","exist"
"6789",8789,0.67,0.83,3,220,3,0,0,"sales","low","exist"
"6790",8790,1,0.49,4,140,3,0,0,"sales","low","exist"
"6791",8791,0.67,0.44,4,194,2,1,0,"sales","low","exist"
"6792",8792,0.2,0.98,2,228,3,0,0,"sales","low","exist"
"6793",8793,0.71,0.87,4,238,3,0,0,"sales","low","exist"
"6794",8794,0.65,0.91,3,207,3,0,0,"sales","low","exist"
"6795",8795,0.82,0.82,4,164,2,0,0,"sales","low","exist"
"6796",8796,0.48,0.89,3,224,3,0,0,"sales","low","exist"
"6797",8797,0.96,0.9,4,201,3,0,0,"sales","low","exist"
"6798",8798,0.52,0.63,3,171,2,0,0,"sales","low","exist"
"6799",8799,0.24,0.78,5,131,5,0,0,"sales","low","exist"
"6800",8800,0.92,0.95,6,239,4,0,0,"sales","medium","exist"
"6801",8801,0.66,0.89,3,202,3,1,0,"sales","medium","exist"
"6802",8802,0.93,0.68,3,137,3,1,0,"sales","medium","exist"
"6803",8803,0.77,0.59,4,153,3,0,0,"sales","medium","exist"
"6804",8804,0.6,0.48,4,219,4,1,0,"sales","medium","exist"
"6805",8805,0.78,0.49,3,194,3,1,0,"sales","medium","exist"
"6806",8806,0.6,0.53,4,228,3,0,0,"sales","medium","exist"
"6807",8807,0.31,1,4,177,5,0,0,"accounting","medium","exist"
"6808",8808,0.49,0.68,3,181,3,0,0,"accounting","medium","exist"
"6809",8809,0.33,0.95,4,280,3,0,0,"accounting","medium","exist"
"6810",8810,0.76,0.91,3,133,2,0,0,"hr","medium","exist"
"6811",8811,0.65,0.63,3,237,3,0,0,"hr","medium","exist"
"6812",8812,0.88,0.75,5,152,3,0,0,"hr","high","exist"
"6813",8813,0.52,0.92,5,280,6,1,0,"hr","high","exist"
"6814",8814,0.48,0.5,6,253,4,0,0,"technical","high","exist"
"6815",8815,0.2,0.59,5,105,4,0,0,"technical","high","exist"
"6816",8816,0.93,0.84,3,159,3,0,0,"technical","high","exist"
"6817",8817,0.55,0.92,4,257,2,0,0,"technical","high","exist"
"6818",8818,0.73,0.64,3,202,4,0,0,"technical","high","exist"
"6819",8819,0.57,0.56,3,241,3,0,0,"technical","high","exist"
"6820",8820,0.63,0.8,3,267,3,0,0,"technical","low","exist"
"6821",8821,0.23,0.88,4,175,6,0,0,"technical","low","exist"
"6822",8822,0.93,0.53,3,257,2,1,0,"technical","low","exist"
"6823",8823,0.78,0.86,4,240,3,0,0,"technical","low","exist"
"6824",8824,0.75,0.73,5,181,3,1,0,"technical","low","exist"
"6825",8825,0.61,0.82,3,271,3,0,0,"support","low","exist"
"6826",8826,0.36,0.97,5,151,3,0,0,"support","low","exist"
"6827",8827,0.59,0.67,2,168,3,0,0,"support","low","exist"
"6828",8828,0.78,0.63,4,265,3,0,0,"support","medium","exist"
"6829",8829,0.93,0.53,5,204,2,0,0,"support","medium","exist"
"6830",8830,0.67,0.72,4,223,3,0,0,"support","medium","exist"
"6831",8831,0.52,0.63,4,136,2,0,0,"support","medium","exist"
"6832",8832,0.69,0.95,5,184,2,0,0,"support","medium","exist"
"6833",8833,0.25,0.8,5,186,4,1,0,"support","medium","exist"
"6834",8834,0.4,0.43,3,128,3,0,0,"support","medium","exist"
"6835",8835,0.98,0.83,5,211,3,0,0,"support","medium","exist"
"6836",8836,0.92,0.89,4,236,4,1,0,"technical","medium","exist"
"6837",8837,0.57,0.98,3,214,2,0,0,"technical","medium","exist"
"6838",8838,0.81,0.52,4,274,3,0,0,"technical","medium","exist"
"6839",8839,0.56,0.67,5,165,3,1,0,"management","medium","exist"
"6840",8840,0.86,0.71,5,235,4,0,0,"IT","medium","exist"
"6841",8841,0.74,0.9,4,189,2,0,0,"IT","medium","exist"
"6842",8842,0.57,0.61,3,112,5,0,0,"IT","medium","exist"
"6843",8843,0.9,0.64,3,163,3,0,0,"IT","medium","exist"
"6844",8844,0.8,0.57,3,162,2,0,0,"IT","medium","exist"
"6845",8845,0.22,0.8,4,149,5,0,0,"product_mng","medium","exist"
"6846",8846,0.73,0.84,4,238,2,0,0,"product_mng","medium","exist"
"6847",8847,0.48,0.47,3,160,3,0,0,"product_mng","medium","exist"
"6848",8848,0.52,0.94,3,263,3,0,0,"product_mng","medium","exist"
"6849",8849,0.53,0.71,4,271,3,0,0,"IT","medium","exist"
"6850",8850,0.97,0.48,4,221,3,0,0,"RandD","medium","exist"
"6851",8851,0.97,0.54,3,255,2,1,0,"RandD","high","exist"
"6852",8852,0.54,0.88,4,170,4,0,0,"RandD","low","exist"
"6853",8853,0.99,0.7,4,190,4,1,0,"RandD","medium","exist"
"6854",8854,0.79,0.76,4,216,4,0,0,"RandD","medium","exist"
"6855",8855,0.71,0.54,3,249,3,0,0,"marketing","medium","exist"
"6856",8856,0.82,0.76,3,174,3,0,0,"sales","medium","exist"
"6857",8857,0.6,0.7,4,265,4,1,0,"accounting","medium","exist"
"6858",8858,0.17,0.88,2,206,4,0,0,"support","medium","exist"
"6859",8859,0.73,0.6,4,222,3,0,0,"technical","medium","exist"
"6860",8860,0.69,0.54,5,152,3,1,0,"management","medium","exist"
"6861",8861,0.86,0.61,4,221,2,0,0,"marketing","medium","exist"
"6862",8862,0.67,0.55,5,239,2,0,0,"marketing","medium","exist"
"6863",8863,0.25,0.96,6,217,4,0,0,"marketing","low","exist"
"6864",8864,0.65,0.66,3,164,2,0,0,"sales","low","exist"
"6865",8865,0.81,0.56,3,142,3,0,0,"sales","low","exist"
"6866",8866,0.58,0.53,4,181,3,1,0,"sales","low","exist"
"6867",8867,0.14,0.57,4,207,5,0,0,"sales","low","exist"
"6868",8868,0.15,0.37,2,167,3,0,0,"sales","low","exist"
"6869",8869,0.98,0.51,3,243,2,0,0,"sales","low","exist"
"6870",8870,0.91,0.5,4,231,3,0,0,"sales","high","exist"
"6871",8871,0.86,0.71,4,250,3,1,0,"sales","low","exist"
"6872",8872,0.56,0.63,3,145,2,0,0,"sales","high","exist"
"6873",8873,0.58,0.77,4,190,6,0,0,"sales","high","exist"
"6874",8874,0.54,0.64,2,128,2,0,0,"sales","low","exist"
"6875",8875,0.59,0.99,5,254,3,1,0,"sales","low","exist"
"6876",8876,0.92,0.88,3,145,4,1,0,"sales","high","exist"
"6877",8877,0.82,0.8,4,246,3,0,0,"sales","low","exist"
"6878",8878,0.86,0.68,5,246,2,0,0,"sales","medium","exist"
"6879",8879,0.66,0.77,5,236,3,0,0,"sales","high","exist"
"6880",8880,0.85,0.66,3,234,3,0,0,"sales","medium","exist"
"6881",8881,0.8,0.6,3,247,2,0,0,"sales","medium","exist"
"6882",8882,0.99,0.61,3,154,3,0,0,"sales","medium","exist"
"6883",8883,0.25,0.45,3,228,5,0,0,"accounting","medium","exist"
"6884",8884,0.93,0.99,4,209,3,1,0,"accounting","high","exist"
"6885",8885,0.5,0.54,5,173,2,0,0,"accounting","medium","exist"
"6886",8886,0.68,0.71,4,206,2,0,0,"hr","medium","exist"
"6887",8887,0.62,0.87,3,151,2,1,0,"hr","medium","exist"
"6888",8888,0.99,0.54,4,196,4,1,0,"hr","high","exist"
"6889",8889,0.93,0.52,3,229,2,1,0,"hr","medium","exist"
"6890",8890,0.2,0.75,3,235,4,0,0,"technical","high","exist"
"6891",8891,0.58,0.61,4,200,3,0,0,"technical","low","exist"
"6892",8892,0.94,0.76,4,261,6,0,0,"technical","medium","exist"
"6893",8893,0.18,0.54,4,165,3,0,0,"technical","medium","exist"
"6894",8894,0.18,0.62,3,165,4,0,0,"technical","medium","exist"
"6895",8895,0.7,0.74,5,255,2,0,0,"technical","medium","exist"
"6896",8896,0.93,0.92,5,185,5,0,0,"technical","low","exist"
"6897",8897,0.5,0.76,4,229,3,1,0,"technical","low","exist"
"6898",8898,0.54,0.71,3,153,3,0,0,"technical","low","exist"
"6899",8899,0.74,0.63,4,238,2,0,0,"technical","low","exist"
"6900",8900,0.66,0.67,3,199,2,0,0,"technical","low","exist"
"6901",8901,0.61,0.87,3,185,2,1,0,"support","low","exist"
"6902",8902,0.74,0.98,3,196,6,1,0,"support","low","exist"
"6903",8903,0.48,0.51,4,201,4,0,0,"support","low","exist"
"6904",8904,0.65,0.84,3,189,2,1,0,"support","low","exist"
"6905",8905,0.94,0.49,2,250,5,0,0,"support","low","exist"
"6906",8906,0.91,0.79,4,254,2,0,0,"support","high","exist"
"6907",8907,0.87,0.65,3,212,3,1,0,"support","low","exist"
"6908",8908,0.23,0.79,5,196,5,1,0,"support","low","exist"
"6909",8909,0.4,0.73,4,146,3,0,0,"support","low","exist"
"6910",8910,0.68,0.85,3,250,3,0,0,"support","low","exist"
"6911",8911,0.95,0.88,3,266,2,1,0,"support","high","exist"
"6912",8912,0.63,0.96,4,133,2,0,0,"technical","low","exist"
"6913",8913,0.47,0.53,4,181,3,0,0,"technical","low","exist"
"6914",8914,0.2,0.5,6,282,6,1,0,"technical","low","exist"
"6915",8915,0.72,0.84,2,173,2,1,0,"management","high","exist"
"6916",8916,0.56,0.57,5,237,2,0,0,"IT","low","exist"
"6917",8917,0.7,0.74,3,202,2,0,0,"IT","medium","exist"
"6918",8918,0.59,0.82,3,162,2,0,0,"IT","high","exist"
"6919",8919,0.78,0.96,3,248,3,0,0,"IT","medium","exist"
"6920",8920,0.62,0.64,3,165,3,0,0,"IT","high","exist"
"6921",8921,0.71,0.61,2,216,2,0,0,"product_mng","medium","exist"
"6922",8922,0.72,0.45,4,143,6,0,0,"product_mng","medium","exist"
"6923",8923,0.76,0.77,3,254,3,0,0,"product_mng","medium","exist"
"6924",8924,0.83,0.56,3,186,3,0,0,"product_mng","medium","exist"
"6925",8925,0.92,0.99,4,245,4,0,0,"IT","medium","exist"
"6926",8926,0.67,0.77,3,157,3,0,0,"RandD","medium","exist"
"6927",8927,0.56,0.45,3,184,3,0,0,"RandD","medium","exist"
"6928",8928,0.91,0.63,4,210,3,0,0,"RandD","medium","exist"
"6929",8929,0.56,0.86,4,137,2,1,0,"RandD","high","exist"
"6930",8930,0.72,0.95,3,145,2,0,0,"RandD","low","exist"
"6931",8931,0.56,0.86,4,181,3,0,0,"marketing","medium","exist"
"6932",8932,0.92,0.56,3,174,3,0,0,"sales","medium","exist"
"6933",8933,0.74,0.88,5,183,3,1,0,"accounting","medium","exist"
"6934",8934,0.88,0.84,4,171,4,1,0,"support","medium","exist"
"6935",8935,0.69,0.72,2,190,2,0,0,"technical","medium","exist"
"6936",8936,0.87,0.78,4,142,3,0,0,"management","medium","exist"
"6937",8937,0.98,0.5,3,198,3,0,0,"marketing","medium","exist"
"6938",8938,0.9,0.61,3,185,3,0,0,"marketing","medium","exist"
"6939",8939,0.49,0.87,4,171,3,1,0,"marketing","medium","exist"
"6940",8940,0.78,0.57,4,264,3,0,0,"sales","high","exist"
"6941",8941,0.58,0.98,3,175,3,0,0,"sales","low","exist"
"6942",8942,0.91,0.88,5,210,2,1,0,"sales","low","exist"
"6943",8943,0.92,0.75,4,212,3,0,0,"sales","low","exist"
"6944",8944,0.36,0.66,4,97,2,0,0,"sales","high","exist"
"6945",8945,0.55,0.53,4,214,3,0,0,"sales","low","exist"
"6946",8946,0.95,0.96,4,244,3,0,0,"sales","low","exist"
"6947",8947,0.5,0.67,3,246,3,0,0,"sales","low","exist"
"6948",8948,0.42,0.73,3,115,6,0,0,"sales","high","exist"
"6949",8949,0.75,0.68,3,237,5,0,0,"sales","low","exist"
"6950",8950,0.88,0.7,4,146,4,0,0,"sales","low","exist"
"6951",8951,0.53,0.63,5,159,4,0,0,"sales","low","exist"
"6952",8952,0.84,0.4,4,246,3,0,0,"sales","low","exist"
"6953",8953,0.49,0.93,3,226,3,0,0,"sales","low","exist"
"6954",8954,0.71,0.91,3,261,3,0,0,"sales","low","exist"
"6955",8955,0.83,0.64,4,242,2,0,0,"sales","low","exist"
"6956",8956,0.88,0.93,4,177,3,0,0,"sales","medium","exist"
"6957",8957,0.87,0.53,4,144,3,0,0,"sales","medium","exist"
"6958",8958,0.43,0.82,2,221,5,0,0,"sales","medium","exist"
"6959",8959,0.8,0.9,5,265,3,0,0,"accounting","medium","exist"
"6960",8960,0.32,0.67,5,224,4,1,0,"accounting","medium","exist"
"6961",8961,0.77,0.56,3,167,4,0,0,"accounting","medium","exist"
"6962",8962,0.97,0.77,3,245,3,0,0,"hr","medium","exist"
"6963",8963,0.98,0.63,4,232,2,0,0,"hr","medium","exist"
"6964",8964,0.62,0.64,5,229,2,0,0,"hr","medium","exist"
"6965",8965,0.53,0.94,4,128,6,0,0,"hr","medium","exist"
"6966",8966,0.93,0.49,3,211,2,0,0,"technical","medium","exist"
"6967",8967,0.51,0.91,4,194,2,0,0,"technical","medium","exist"
"6968",8968,0.76,0.76,4,214,3,0,0,"technical","high","exist"
"6969",8969,0.69,0.89,3,216,4,0,0,"technical","low","exist"
"6970",8970,0.58,0.6,4,222,3,0,0,"technical","medium","exist"
"6971",8971,0.98,0.77,4,144,4,0,0,"technical","medium","exist"
"6972",8972,0.58,0.54,3,287,6,0,0,"technical","medium","exist"
"6973",8973,0.57,0.97,4,224,4,0,0,"technical","medium","exist"
"6974",8974,0.84,0.79,4,157,4,0,0,"technical","low","exist"
"6975",8975,0.15,0.67,5,216,6,0,0,"technical","low","exist"
"6976",8976,0.88,0.72,5,181,4,0,0,"technical","low","exist"
"6977",8977,0.69,0.99,3,133,3,0,0,"support","low","exist"
"6978",8978,0.56,0.84,5,154,2,1,0,"support","low","exist"
"6979",8979,0.49,0.58,3,265,3,0,0,"support","low","exist"
"6980",8980,0.4,0.45,4,113,3,0,0,"support","low","exist"
"6981",8981,0.67,0.36,3,280,4,0,0,"support","low","exist"
"6982",8982,0.79,0.5,3,213,3,1,0,"support","low","exist"
"6983",8983,0.47,0.44,5,255,5,1,0,"support","low","exist"
"6984",8984,0.82,0.54,3,243,4,0,0,"support","low","exist"
"6985",8985,0.82,0.87,3,206,2,0,0,"support","low","exist"
"6986",8986,0.63,0.57,5,149,3,0,0,"support","low","exist"
"6987",8987,0.91,0.53,2,273,3,0,0,"support","low","exist"
"6988",8988,0.89,1,4,226,2,1,0,"technical","low","exist"
"6989",8989,0.96,0.93,3,238,2,0,0,"technical","low","exist"
"6990",8990,0.83,0.72,2,226,3,0,0,"technical","low","exist"
"6991",8991,0.75,0.92,3,199,3,1,0,"management","low","exist"
"6992",8992,0.75,0.82,5,202,3,1,0,"IT","low","exist"
"6993",8993,0.41,0.69,2,152,4,1,0,"IT","low","exist"
"6994",8994,0.96,0.94,3,167,3,0,0,"IT","low","exist"
"6995",8995,0.58,0.79,4,130,3,0,0,"IT","medium","exist"
"6996",8996,0.74,0.89,3,229,3,0,0,"IT","medium","exist"
"6997",8997,0.78,0.74,4,261,3,1,0,"product_mng","medium","exist"
"6998",8998,0.5,0.72,3,182,2,1,0,"product_mng","medium","exist"
"6999",8999,1,0.52,4,198,3,0,0,"product_mng","medium","exist"
"7000",9000,0.85,0.91,3,244,3,0,0,"product_mng","medium","exist"
"7001",9001,0.82,0.89,4,275,3,0,0,"IT","medium","exist"
"7002",9002,0.19,0.81,5,245,5,0,0,"RandD","medium","exist"
"7003",9003,0.9,0.9,3,147,3,1,0,"RandD","medium","exist"
"7004",9004,0.59,1,4,275,3,0,0,"RandD","medium","exist"
"7005",9005,0.53,0.46,2,167,2,0,0,"RandD","medium","exist"
"7006",9006,0.57,0.5,5,149,5,1,0,"RandD","medium","exist"
"7007",9007,0.85,0.99,4,233,2,0,0,"marketing","high","exist"
"7008",9008,0.64,0.67,5,167,2,0,0,"sales","low","exist"
"7009",9009,0.57,0.54,3,159,3,1,0,"accounting","medium","exist"
"7010",9010,0.86,0.85,2,195,4,0,0,"support","medium","exist"
"7011",9011,0.6,0.7,5,229,2,0,0,"technical","medium","exist"
"7012",9012,0.17,0.76,4,199,5,0,0,"management","medium","exist"
"7013",9013,0.54,0.63,3,174,3,0,0,"marketing","low","exist"
"7014",9014,0.35,0.78,5,275,4,0,0,"marketing","low","exist"
"7015",9015,0.92,0.77,5,217,4,0,0,"marketing","low","exist"
"7016",9016,0.66,1,4,192,2,0,0,"sales","low","exist"
"7017",9017,0.83,0.9,4,195,3,0,0,"sales","low","exist"
"7018",9018,0.89,0.86,3,261,4,0,0,"sales","low","exist"
"7019",9019,0.94,0.61,4,199,3,0,0,"sales","low","exist"
"7020",9020,0.24,0.85,4,160,5,0,0,"sales","low","exist"
"7021",9021,0.69,0.8,3,177,4,0,0,"sales","low","exist"
"7022",9022,0.45,0.46,3,179,2,1,0,"sales","low","exist"
"7023",9023,0.78,0.93,4,161,3,0,0,"sales","low","exist"
"7024",9024,0.91,0.38,5,279,5,0,0,"sales","low","exist"
"7025",9025,0.63,0.65,4,246,6,1,0,"sales","low","exist"
"7026",9026,0.71,0.8,4,199,2,0,0,"sales","low","exist"
"7027",9027,0.73,0.69,3,161,3,0,0,"sales","low","exist"
"7028",9028,0.69,0.52,5,219,3,0,0,"sales","low","exist"
"7029",9029,0.52,0.57,5,162,3,0,0,"sales","low","exist"
"7030",9030,0.78,0.66,4,258,3,0,0,"sales","low","exist"
"7031",9031,0.94,0.69,3,269,3,0,0,"sales","low","exist"
"7032",9032,0.55,0.73,4,201,3,0,0,"sales","low","exist"
"7033",9033,0.43,0.38,2,278,3,1,0,"sales","low","exist"
"7034",9034,0.77,0.66,3,147,2,0,0,"sales","medium","exist"
"7035",9035,0.59,0.8,5,247,3,0,0,"accounting","medium","exist"
"7036",9036,0.65,0.54,4,191,4,0,0,"accounting","medium","exist"
"7037",9037,0.82,0.37,2,280,3,0,0,"accounting","medium","exist"
"7038",9038,0.31,0.72,2,191,3,0,0,"hr","medium","exist"
"7039",9039,0.84,0.65,4,264,2,0,0,"hr","medium","exist"
"7040",9040,0.15,0.4,3,236,5,0,0,"hr","medium","exist"
"7041",9041,0.64,0.52,4,271,2,1,0,"hr","medium","exist"
"7042",9042,0.48,0.63,5,129,5,0,0,"technical","medium","exist"
"7043",9043,0.82,0.58,4,249,5,0,0,"technical","medium","exist"
"7044",9044,0.99,0.54,3,188,3,0,0,"technical","medium","exist"
"7045",9045,0.8,0.52,3,147,3,1,0,"technical","medium","exist"
"7046",9046,0.94,0.92,3,273,3,0,0,"technical","high","exist"
"7047",9047,0.94,0.81,4,237,3,1,0,"technical","low","exist"
"7048",9048,0.77,0.79,3,273,2,0,0,"technical","medium","exist"
"7049",9049,0.48,0.54,3,190,3,0,0,"technical","medium","exist"
"7050",9050,0.62,0.68,3,226,3,0,0,"technical","medium","exist"
"7051",9051,0.61,0.9,4,216,3,0,0,"technical","medium","exist"
"7052",9052,0.27,0.6,6,205,5,1,0,"technical","low","exist"
"7053",9053,0.89,0.65,3,208,2,0,0,"support","low","exist"
"7054",9054,0.58,0.81,4,266,2,0,0,"support","low","exist"
"7055",9055,0.64,0.77,3,249,2,1,0,"support","low","exist"
"7056",9056,0.73,0.88,5,134,2,1,0,"support","low","exist"
"7057",9057,0.74,0.85,2,189,3,0,0,"support","low","exist"
"7058",9058,0.75,0.82,4,143,2,0,0,"support","low","exist"
"7059",9059,0.78,0.84,4,173,3,0,0,"support","low","exist"
"7060",9060,0.18,0.95,6,248,3,0,0,"support","low","exist"
"7061",9061,0.8,0.84,3,186,6,0,0,"support","low","exist"
"7062",9062,0.89,0.64,5,191,3,0,0,"support","low","exist"
"7063",9063,0.84,0.5,3,227,2,0,0,"support","low","exist"
"7064",9064,0.64,0.38,2,269,5,0,0,"technical","low","exist"
"7065",9065,0.53,0.82,3,254,3,1,0,"technical","low","exist"
"7066",9066,0.15,0.66,4,180,4,0,0,"technical","low","exist"
"7067",9067,0.66,0.62,3,144,3,0,0,"management","low","exist"
"7068",9068,0.49,0.78,5,137,3,1,0,"IT","low","exist"
"7069",9069,0.78,0.72,3,223,4,0,0,"IT","low","exist"
"7070",9070,0.39,0.75,5,286,5,0,0,"IT","low","exist"
"7071",9071,0.9,0.83,3,151,3,0,0,"IT","low","exist"
"7072",9072,0.96,0.74,5,244,2,1,0,"IT","low","exist"
"7073",9073,0.63,0.81,4,216,4,0,0,"product_mng","medium","exist"
"7074",9074,0.63,0.74,4,173,3,0,0,"product_mng","medium","exist"
"7075",9075,0.89,0.81,3,186,3,0,0,"product_mng","medium","exist"
"7076",9076,0.93,0.57,2,205,4,1,0,"product_mng","medium","exist"
"7077",9077,0.87,0.59,4,202,3,0,0,"IT","medium","exist"
"7078",9078,0.56,0.53,3,189,3,0,0,"RandD","medium","exist"
"7079",9079,0.97,0.55,4,181,5,1,0,"RandD","medium","exist"
"7080",9080,0.61,0.51,3,207,3,0,0,"RandD","medium","exist"
"7081",9081,0.73,0.46,4,240,4,1,0,"RandD","medium","exist"
"7082",9082,0.61,0.69,2,164,2,0,0,"RandD","medium","exist"
"7083",9083,0.99,0.71,4,212,2,0,0,"marketing","medium","exist"
"7084",9084,0.57,0.75,4,151,2,0,0,"sales","medium","exist"
"7085",9085,0.74,0.96,4,197,3,0,0,"accounting","high","exist"
"7086",9086,0.86,0.61,5,265,3,0,0,"support","low","exist"
"7087",9087,0.68,0.72,4,274,3,0,0,"technical","medium","exist"
"7088",9088,0.66,0.63,3,201,4,0,0,"management","medium","exist"
"7089",9089,0.86,0.89,3,250,2,0,0,"marketing","medium","exist"
"7090",9090,0.85,0.78,3,165,4,0,0,"marketing","medium","exist"
"7091",9091,0.98,0.53,5,186,3,0,0,"marketing","low","exist"
"7092",9092,0.14,0.73,5,273,4,1,0,"sales","low","exist"
"7093",9093,0.2,0.54,5,162,6,0,0,"sales","low","exist"
"7094",9094,0.9,0.97,3,141,3,0,0,"sales","low","exist"
"7095",9095,0.51,0.96,5,268,4,0,0,"sales","low","exist"
"7096",9096,0.63,0.77,3,176,2,1,0,"sales","low","exist"
"7097",9097,0.83,0.88,3,223,3,1,0,"sales","low","exist"
"7098",9098,0.67,0.72,4,218,2,1,0,"sales","low","exist"
"7099",9099,0.96,0.52,4,228,3,0,0,"sales","low","exist"
"7100",9100,0.69,0.75,3,204,3,0,0,"sales","low","exist"
"7101",9101,0.69,0.9,4,148,2,0,0,"sales","low","exist"
"7102",9102,0.64,0.94,3,221,2,0,0,"sales","low","exist"
"7103",9103,0.62,0.48,4,271,3,0,0,"sales","low","exist"
"7104",9104,0.55,0.75,3,191,3,0,0,"sales","low","exist"
"7105",9105,0.98,0.51,4,223,2,0,0,"sales","low","exist"
"7106",9106,0.83,0.78,5,250,2,1,0,"sales","low","exist"
"7107",9107,0.73,0.77,3,230,2,0,0,"sales","low","exist"
"7108",9108,0.58,0.86,3,226,2,0,0,"sales","low","exist"
"7109",9109,0.52,0.67,4,182,3,0,0,"sales","medium","exist"
"7110",9110,0.91,0.7,3,195,3,0,0,"sales","medium","exist"
"7111",9111,0.72,0.64,3,231,2,0,0,"accounting","medium","exist"
"7112",9112,0.7,0.74,3,224,3,0,0,"accounting","medium","exist"
"7113",9113,0.86,0.92,4,229,4,0,0,"accounting","medium","exist"
"7114",9114,0.82,0.57,2,158,3,1,0,"hr","medium","exist"
"7115",9115,0.83,0.78,4,242,3,1,0,"hr","medium","exist"
"7116",9116,0.99,0.64,3,183,3,0,0,"hr","medium","exist"
"7117",9117,0.88,0.58,5,213,4,0,0,"hr","medium","exist"
"7118",9118,0.68,0.74,4,263,2,1,0,"technical","medium","exist"
"7119",9119,0.9,0.49,3,237,2,0,0,"technical","medium","exist"
"7120",9120,0.59,0.67,6,126,3,0,0,"technical","medium","exist"
"7121",9121,0.76,0.71,6,168,2,0,0,"technical","high","exist"
"7122",9122,0.23,0.63,5,151,4,1,0,"technical","low","exist"
"7123",9123,0.8,0.85,4,239,3,0,0,"technical","medium","exist"
"7124",9124,0.62,0.49,4,174,3,0,0,"technical","medium","exist"
"7125",9125,0.28,0.46,5,277,6,0,0,"technical","medium","exist"
"7126",9126,0.81,0.97,3,133,3,0,0,"technical","medium","exist"
"7127",9127,0.64,0.91,4,150,3,0,0,"technical","low","exist"
"7128",9128,0.76,0.6,5,244,3,1,0,"technical","low","exist"
"7129",9129,0.79,0.87,3,232,2,0,0,"support","low","exist"
"7130",9130,0.72,0.91,3,267,2,0,0,"support","low","exist"
"7131",9131,0.22,0.59,5,162,2,0,0,"support","low","exist"
"7132",9132,0.18,0.73,5,228,5,0,0,"support","low","exist"
"7133",9133,0.91,0.49,2,180,3,0,0,"support","low","exist"
"7134",9134,0.69,0.63,2,252,3,0,0,"support","low","exist"
"7135",9135,0.91,0.66,5,212,3,0,0,"support","low","exist"
"7136",9136,0.67,0.84,4,224,3,0,0,"support","low","exist"
"7137",9137,0.98,0.62,2,240,3,1,0,"support","low","exist"
"7138",9138,0.69,0.62,4,183,4,0,0,"support","low","exist"
"7139",9139,0.96,0.74,5,160,5,1,0,"support","low","exist"
"7140",9140,0.69,0.68,4,225,3,0,0,"technical","low","exist"
"7141",9141,0.65,0.68,3,268,2,1,0,"technical","low","exist"
"7142",9142,0.7,0.75,3,221,3,0,0,"technical","low","exist"
"7143",9143,0.48,0.94,3,173,2,0,0,"management","low","exist"
"7144",9144,0.48,0.51,4,103,4,0,0,"IT","low","exist"
"7145",9145,0.16,0.89,4,196,3,1,0,"IT","low","exist"
"7146",9146,0.72,0.97,3,239,3,0,0,"IT","low","exist"
"7147",9147,0.91,0.71,3,171,2,0,0,"IT","low","exist"
"7148",9148,0.74,0.54,3,243,3,0,0,"IT","medium","exist"
"7149",9149,0.56,0.56,2,153,2,0,0,"product_mng","medium","exist"
"7150",9150,0.56,0.41,6,142,3,0,0,"product_mng","medium","exist"
"7151",9151,0.88,0.55,5,168,2,0,0,"product_mng","medium","exist"
"7152",9152,0.86,0.9,5,180,4,0,0,"product_mng","medium","exist"
"7153",9153,0.66,0.84,4,186,3,0,0,"IT","medium","exist"
"7154",9154,0.41,0.45,3,236,2,0,0,"RandD","medium","exist"
"7155",9155,0.68,0.83,5,267,3,0,0,"RandD","medium","exist"
"7156",9156,0.59,0.47,3,129,2,0,0,"RandD","medium","exist"
"7157",9157,0.52,0.78,3,181,3,0,0,"RandD","medium","exist"
"7158",9158,0.3,0.54,2,99,2,0,0,"RandD","medium","exist"
"7159",9159,0.44,0.67,5,170,3,1,0,"marketing","medium","exist"
"7160",9160,0.75,0.64,3,195,3,0,0,"sales","high","exist"
"7161",9161,0.23,0.94,4,149,6,0,0,"accounting","low","exist"
"7162",9162,0.34,0.46,6,132,2,0,0,"support","medium","exist"
"7163",9163,0.52,0.59,3,164,3,0,0,"technical","medium","exist"
"7164",9164,0.79,0.83,4,250,2,0,0,"management","medium","exist"
"7165",9165,0.5,0.77,3,204,2,0,0,"marketing","medium","exist"
"7166",9166,0.89,0.65,3,210,3,0,0,"marketing","low","exist"
"7167",9167,0.84,0.52,6,98,3,0,0,"marketing","low","exist"
"7168",9168,0.26,0.47,3,241,4,0,0,"sales","low","exist"
"7169",9169,0.57,0.96,5,203,4,0,0,"sales","low","exist"
"7170",9170,0.14,0.99,3,257,4,1,0,"sales","low","exist"
"7171",9171,0.94,0.62,5,201,2,0,0,"sales","low","exist"
"7172",9172,0.3,0.58,2,124,3,0,0,"sales","low","exist"
"7173",9173,0.29,0.43,6,175,3,0,0,"sales","low","exist"
"7174",9174,0.82,0.75,3,161,3,0,0,"sales","low","exist"
"7175",9175,0.62,0.75,4,183,4,1,0,"sales","low","exist"
"7176",9176,0.64,0.99,5,262,5,0,0,"sales","low","exist"
"7177",9177,0.17,0.52,4,184,4,0,0,"sales","low","exist"
"7178",9178,0.75,0.56,3,207,3,0,0,"sales","low","exist"
"7179",9179,0.49,0.73,4,185,3,0,0,"sales","low","exist"
"7180",9180,0.84,0.58,4,180,2,0,0,"sales","low","exist"
"7181",9181,0.48,0.96,4,224,2,1,0,"sales","low","exist"
"7182",9182,0.54,0.53,3,184,3,0,0,"sales","low","exist"
"7183",9183,0.76,0.99,5,252,3,1,0,"sales","low","exist"
"7184",9184,0.77,0.84,4,196,3,0,0,"sales","low","exist"
"7185",9185,0.95,0.97,4,203,2,0,0,"sales","low","exist"
"7186",9186,0.72,0.83,4,181,3,0,0,"sales","low","exist"
"7187",9187,0.74,0.67,4,148,3,0,0,"accounting","medium","exist"
"7188",9188,0.9,0.55,4,211,3,0,0,"accounting","medium","exist"
"7189",9189,0.67,0.55,3,246,3,0,0,"accounting","medium","exist"
"7190",9190,0.97,0.55,4,258,3,0,0,"hr","medium","exist"
"7191",9191,0.55,0.59,3,231,4,0,0,"hr","medium","exist"
"7192",9192,0.32,0.95,2,184,5,0,0,"hr","medium","exist"
"7193",9193,0.4,0.42,3,146,2,1,0,"hr","medium","exist"
"7194",9194,0.66,0.54,2,136,2,0,0,"technical","low","exist"
"7195",9195,0.7,0.77,4,266,2,0,0,"technical","low","exist"
"7196",9196,0.69,0.89,2,220,3,0,0,"technical","low","exist"
"7197",9197,0.72,0.57,2,248,2,0,0,"technical","low","exist"
"7198",9198,0.21,0.65,3,183,3,0,0,"technical","low","exist"
"7199",9199,0.91,0.9,3,169,3,0,0,"technical","low","exist"
"7200",9200,0.72,0.71,3,132,2,1,0,"technical","low","exist"
"7201",9201,0.96,0.72,3,197,3,0,0,"technical","low","exist"
"7202",9202,1,0.89,4,152,3,0,0,"technical","low","exist"
"7203",9203,0.63,0.51,3,126,6,0,0,"technical","low","exist"
"7204",9204,0.24,0.74,6,106,5,0,0,"technical","low","exist"
"7205",9205,0.44,0.38,4,128,2,0,0,"support","low","exist"
"7206",9206,0.92,0.57,3,191,3,0,0,"support","low","exist"
"7207",9207,0.51,0.51,4,189,3,0,0,"support","low","exist"
"7208",9208,0.77,0.71,5,141,3,0,0,"support","low","exist"
"7209",9209,0.8,0.97,4,220,3,0,0,"support","low","exist"
"7210",9210,0.84,0.46,5,118,3,0,0,"support","low","exist"
"7211",9211,0.91,0.88,5,223,3,0,0,"support","low","exist"
"7212",9212,0.64,0.61,3,263,3,0,0,"support","low","exist"
"7213",9213,0.15,0.59,5,209,4,1,0,"support","medium","exist"
"7214",9214,0.74,0.58,4,193,3,0,0,"support","medium","exist"
"7215",9215,0.94,0.78,4,211,3,0,0,"support","medium","exist"
"7216",9216,0.57,0.58,3,192,3,0,0,"technical","medium","exist"
"7217",9217,0.92,0.63,5,156,3,0,0,"technical","medium","exist"
"7218",9218,0.76,0.54,5,278,2,1,0,"technical","medium","exist"
"7219",9219,0.73,0.92,3,199,3,1,0,"management","medium","exist"
"7220",9220,0.24,0.6,2,194,6,0,0,"IT","medium","exist"
"7221",9221,0.42,0.47,2,125,4,0,0,"IT","medium","exist"
"7222",9222,0.92,0.82,4,96,4,0,0,"IT","medium","exist"
"7223",9223,0.92,0.94,3,234,2,1,0,"IT","medium","exist"
"7224",9224,0.68,0.55,6,181,3,0,0,"IT","medium","exist"
"7225",9225,0.49,0.86,4,246,2,0,0,"product_mng","medium","exist"
"7226",9226,0.57,0.98,3,171,3,0,0,"product_mng","medium","exist"
"7227",9227,0.3,0.66,3,198,2,0,0,"product_mng","medium","exist"
"7228",9228,0.17,0.81,5,280,4,0,0,"product_mng","medium","exist"
"7229",9229,0.91,0.49,3,267,3,0,0,"IT","medium","exist"
"7230",9230,0.83,0.91,3,251,2,0,0,"RandD","medium","exist"
"7231",9231,0.87,0.76,5,182,3,0,0,"RandD","medium","exist"
"7232",9232,0.71,0.8,4,157,3,0,0,"RandD","medium","exist"
"7233",9233,0.88,0.5,3,206,2,0,0,"RandD","medium","exist"
"7234",9234,0.63,0.94,3,237,3,1,0,"RandD","medium","exist"
"7235",9235,0.99,0.58,2,166,3,0,0,"marketing","medium","exist"
"7236",9236,0.99,0.81,4,229,2,1,0,"sales","medium","exist"
"7237",9237,0.77,0.53,5,256,3,0,0,"accounting","medium","exist"
"7238",9238,0.64,0.69,5,114,6,0,0,"support","medium","exist"
"7239",9239,0.61,1,5,243,2,0,0,"technical","medium","exist"
"7240",9240,0.37,0.82,3,199,5,0,0,"management","medium","exist"
"7241",9241,0.19,1,4,188,4,1,0,"marketing","medium","exist"
"7242",9242,0.96,0.87,4,187,2,0,0,"marketing","medium","exist"
"7243",9243,0.8,0.62,4,216,2,0,0,"marketing","medium","exist"
"7244",9244,0.14,0.63,6,215,5,0,0,"sales","low","exist"
"7245",9245,0.15,0.69,6,213,6,0,0,"sales","low","exist"
"7246",9246,0.52,0.82,4,198,4,0,0,"sales","low","exist"
"7247",9247,0.27,0.55,5,121,3,1,0,"sales","low","exist"
"7248",9248,0.97,0.96,3,212,3,0,0,"sales","low","exist"
"7249",9249,0.52,0.93,2,271,3,0,0,"sales","low","exist"
"7250",9250,0.98,0.89,3,186,2,0,0,"sales","low","exist"
"7251",9251,0.96,0.95,4,265,2,0,0,"sales","low","exist"
"7252",9252,0.28,0.92,3,151,3,0,0,"sales","low","exist"
"7253",9253,0.65,0.55,5,206,3,0,0,"sales","low","exist"
"7254",9254,0.59,0.63,4,153,3,1,0,"sales","low","exist"
"7255",9255,0.64,0.48,4,267,2,1,0,"sales","low","exist"
"7256",9256,0.71,0.48,3,161,3,0,0,"sales","low","exist"
"7257",9257,0.83,0.84,2,149,3,0,0,"sales","low","exist"
"7258",9258,0.95,0.94,2,269,4,1,0,"sales","low","exist"
"7259",9259,0.73,0.49,3,248,3,0,0,"sales","low","exist"
"7260",9260,0.81,0.75,4,243,2,0,0,"sales","low","exist"
"7261",9261,0.71,0.44,2,207,4,0,0,"sales","low","exist"
"7262",9262,0.8,0.56,6,111,6,0,0,"sales","low","exist"
"7263",9263,0.85,0.53,3,226,2,0,0,"accounting","low","exist"
"7264",9264,0.41,0.7,2,151,3,1,0,"accounting","low","exist"
"7265",9265,0.51,0.84,4,224,2,0,0,"accounting","medium","exist"
"7266",9266,0.49,0.57,4,146,4,0,0,"hr","medium","exist"
"7267",9267,0.76,0.55,4,163,2,1,0,"hr","medium","exist"
"7268",9268,0.57,0.69,4,255,3,0,0,"hr","medium","exist"
"7269",9269,0.54,0.48,6,196,2,0,0,"hr","medium","exist"
"7270",9270,0.68,0.74,3,227,2,0,0,"technical","medium","exist"
"7271",9271,0.7,0.5,3,251,2,0,0,"technical","medium","exist"
"7272",9272,0.77,0.87,4,209,3,0,0,"technical","medium","exist"
"7273",9273,0.95,0.51,3,254,4,0,0,"technical","medium","exist"
"7274",9274,0.5,0.64,3,249,2,1,0,"technical","low","exist"
"7275",9275,0.99,0.53,4,131,3,1,0,"technical","low","exist"
"7276",9276,0.94,0.51,5,142,2,0,0,"technical","low","exist"
"7277",9277,0.83,0.66,3,239,3,0,0,"technical","low","exist"
"7278",9278,0.64,0.81,3,225,3,1,0,"technical","low","exist"
"7279",9279,0.16,0.73,6,170,3,0,0,"technical","low","exist"
"7280",9280,0.83,0.71,3,254,2,0,0,"technical","low","exist"
"7281",9281,0.93,0.73,4,156,2,0,0,"support","low","exist"
"7282",9282,0.32,0.64,3,151,3,0,0,"support","low","exist"
"7283",9283,0.12,0.9,3,200,3,0,0,"support","low","exist"
"7284",9284,0.5,0.5,3,184,3,1,0,"support","low","exist"
"7285",9285,0.57,0.74,3,257,2,0,0,"support","low","exist"
"7286",9286,0.25,0.75,5,194,5,1,0,"support","low","exist"
"7287",9287,0.98,0.56,3,139,2,1,0,"support","low","exist"
"7288",9288,0.81,0.51,3,273,2,1,0,"support","low","exist"
"7289",9289,0.94,0.63,5,261,3,0,0,"support","low","exist"
"7290",9290,0.83,0.57,3,135,3,1,0,"support","low","exist"
"7291",9291,0.77,0.4,4,207,5,0,0,"support","low","exist"
"7292",9292,0.57,0.65,4,265,3,0,0,"technical","low","exist"
"7293",9293,0.18,0.96,5,208,6,0,0,"technical","medium","exist"
"7294",9294,0.67,0.71,4,159,2,1,0,"technical","medium","exist"
"7295",9295,0.35,0.47,4,151,6,0,0,"management","medium","exist"
"7296",9296,0.78,0.44,3,97,4,0,0,"IT","medium","exist"
"7297",9297,0.72,0.79,4,154,3,0,0,"IT","medium","exist"
"7298",9298,0.9,0.58,3,264,3,0,0,"IT","medium","exist"
"7299",9299,0.58,0.49,3,135,2,0,0,"IT","medium","exist"
"7300",9300,0.64,0.56,3,238,2,0,0,"IT","medium","exist"
"7301",9301,0.91,0.79,4,166,3,0,0,"product_mng","medium","exist"
"7302",9302,0.59,0.51,3,156,3,0,0,"product_mng","medium","exist"
"7303",9303,0.76,0.8,3,202,3,0,0,"product_mng","medium","exist"
"7304",9304,0.76,0.85,3,204,2,1,0,"product_mng","medium","exist"
"7305",9305,0.51,0.69,3,135,3,0,0,"IT","medium","exist"
"7306",9306,0.54,0.55,4,252,3,0,0,"RandD","medium","exist"
"7307",9307,0.67,0.93,5,254,3,1,0,"RandD","medium","exist"
"7308",9308,0.68,0.44,5,165,3,0,0,"RandD","medium","exist"
"7309",9309,0.97,0.58,3,200,2,0,0,"RandD","medium","exist"
"7310",9310,0.5,0.74,3,155,3,0,0,"RandD","medium","exist"
"7311",9311,0.81,0.52,3,162,3,0,0,"marketing","medium","exist"
"7312",9312,0.77,0.73,4,159,3,1,0,"sales","medium","exist"
"7313",9313,0.59,0.75,4,266,3,0,0,"accounting","medium","exist"
"7314",9314,1,0.96,4,155,3,0,0,"support","medium","exist"
"7315",9315,0.74,0.95,5,170,4,0,0,"technical","medium","exist"
"7316",9316,0.91,0.52,4,172,4,1,0,"management","high","exist"
"7317",9317,0.77,0.65,3,187,3,0,0,"marketing","low","exist"
"7318",9318,0.79,0.98,4,185,2,0,0,"marketing","medium","exist"
"7319",9319,0.82,0.51,5,232,3,0,0,"marketing","medium","exist"
"7320",9320,0.89,0.96,5,260,3,0,0,"sales","low","exist"
"7321",9321,0.83,0.62,4,218,3,0,0,"sales","low","exist"
"7322",9322,0.72,0.7,4,217,3,0,0,"sales","low","exist"
"7323",9323,0.7,0.74,3,212,3,0,0,"sales","low","exist"
"7324",9324,1,0.89,3,189,3,0,0,"sales","low","exist"
"7325",9325,0.57,0.66,4,158,2,0,0,"sales","low","exist"
"7326",9326,0.55,0.54,5,168,2,0,0,"sales","low","exist"
"7327",9327,0.47,0.7,4,134,3,0,0,"sales","low","exist"
"7328",9328,0.95,0.77,4,213,3,1,0,"sales","low","exist"
"7329",9329,0.29,0.57,5,149,3,0,0,"sales","low","exist"
"7330",9330,0.71,0.5,3,201,2,0,0,"sales","low","exist"
"7331",9331,0.89,0.68,4,146,3,0,0,"sales","low","exist"
"7332",9332,0.81,0.97,4,212,2,0,0,"sales","low","exist"
"7333",9333,0.72,0.64,4,140,2,0,0,"sales","low","exist"
"7334",9334,1,0.85,4,156,3,0,0,"sales","low","exist"
"7335",9335,0.79,0.49,4,163,3,0,0,"sales","high","exist"
"7336",9336,0.69,0.84,3,154,2,0,0,"sales","low","exist"
"7337",9337,0.97,0.66,4,218,3,0,0,"sales","high","exist"
"7338",9338,0.61,0.59,3,157,2,0,0,"sales","high","exist"
"7339",9339,0.71,0.89,3,222,3,0,0,"accounting","low","exist"
"7340",9340,0.96,0.76,4,152,3,1,0,"accounting","low","exist"
"7341",9341,0.77,0.73,5,263,2,0,0,"accounting","high","exist"
"7342",9342,0.57,0.99,3,231,3,0,0,"hr","low","exist"
"7343",9343,0.92,0.76,4,258,2,0,0,"hr","medium","exist"
"7344",9344,0.99,0.92,5,213,2,0,0,"hr","high","exist"
"7345",9345,0.86,0.73,3,159,3,0,0,"hr","medium","exist"
"7346",9346,0.78,0.66,4,156,3,1,0,"technical","low","exist"
"7347",9347,0.85,0.66,3,235,3,0,0,"technical","low","exist"
"7348",9348,0.38,0.6,4,190,2,0,0,"technical","low","exist"
"7349",9349,0.63,0.93,4,238,2,0,0,"technical","low","exist"
"7350",9350,0.66,0.72,4,137,3,0,0,"technical","low","exist"
"7351",9351,0.19,0.79,5,171,3,1,0,"technical","low","exist"
"7352",9352,0.63,0.59,4,249,2,0,0,"technical","low","exist"
"7353",9353,0.32,0.74,6,205,3,0,0,"technical","low","exist"
"7354",9354,0.73,0.55,3,149,3,0,0,"technical","low","exist"
"7355",9355,0.75,0.89,4,139,3,0,0,"technical","low","exist"
"7356",9356,0.7,0.66,4,168,3,0,0,"technical","low","exist"
"7357",9357,0.77,0.61,4,181,2,0,0,"support","low","exist"
"7358",9358,0.83,0.8,4,150,3,0,0,"support","low","exist"
"7359",9359,0.75,0.49,4,246,3,0,0,"support","low","exist"
"7360",9360,0.97,0.54,3,271,3,0,0,"support","medium","exist"
"7361",9361,0.75,0.55,5,204,3,0,0,"support","low","exist"
"7362",9362,0.66,0.84,3,170,4,0,0,"support","low","exist"
"7363",9363,0.56,0.49,3,208,3,0,0,"support","low","exist"
"7364",9364,0.77,0.98,2,226,3,0,0,"support","low","exist"
"7365",9365,0.82,0.81,3,149,3,0,0,"support","low","exist"
"7366",9366,0.85,0.59,2,264,2,0,0,"support","low","exist"
"7367",9367,0.49,0.79,5,177,2,0,0,"support","low","exist"
"7368",9368,0.24,0.87,4,262,3,0,0,"technical","low","exist"
"7369",9369,0.32,0.74,3,211,3,0,0,"technical","low","exist"
"7370",9370,0.77,0.51,4,141,3,1,0,"technical","low","exist"
"7371",9371,0.77,0.83,5,197,4,0,0,"management","high","exist"
"7372",9372,0.93,0.87,3,154,3,1,0,"IT","low","exist"
"7373",9373,0.22,0.74,5,178,5,0,0,"IT","low","exist"
"7374",9374,0.24,0.89,5,169,4,0,0,"IT","low","exist"
"7375",9375,0.99,0.99,3,228,4,0,0,"IT","low","exist"
"7376",9376,0.61,0.5,3,231,3,0,0,"IT","high","exist"
"7377",9377,0.6,0.91,4,185,3,1,0,"product_mng","low","exist"
"7378",9378,0.79,0.7,3,195,2,0,0,"product_mng","low","exist"
"7379",9379,0.94,0.62,3,147,3,0,0,"product_mng","low","exist"
"7380",9380,0.18,0.85,5,192,3,0,0,"product_mng","high","exist"
"7381",9381,0.51,0.73,5,241,3,0,0,"IT","low","exist"
"7382",9382,0.55,0.92,3,151,3,0,0,"RandD","medium","exist"
"7383",9383,0.73,0.74,3,221,3,0,0,"RandD","high","exist"
"7384",9384,0.41,0.63,5,263,3,0,0,"RandD","medium","exist"
"7385",9385,0.88,0.66,3,178,3,0,0,"RandD","high","exist"
"7386",9386,0.23,0.56,5,169,5,0,0,"RandD","medium","exist"
"7387",9387,0.78,0.56,3,271,4,0,0,"marketing","medium","exist"
"7388",9388,0.34,0.69,3,155,3,0,0,"marketing","medium","exist"
"7389",9389,0.51,0.41,2,164,4,0,0,"sales","medium","exist"
"7390",9390,0.8,0.86,3,226,2,0,0,"accounting","medium","exist"
"7391",9391,0.66,0.57,4,220,2,1,0,"support","medium","exist"
"7392",9392,0.62,0.63,5,153,6,0,0,"technical","medium","exist"
"7393",9393,0.5,0.97,2,252,4,0,0,"management","medium","exist"
"7394",9394,0.96,0.94,3,182,3,0,0,"marketing","high","exist"
"7395",9395,0.5,0.84,3,150,2,0,0,"marketing","low","exist"
"7396",9396,0.73,0.69,6,273,4,1,0,"marketing","medium","exist"
"7397",9397,0.47,0.39,6,215,5,0,0,"sales","medium","exist"
"7398",9398,0.49,0.83,3,172,2,0,0,"sales","medium","exist"
"7399",9399,0.92,0.62,3,264,2,0,0,"sales","medium","exist"
"7400",9400,0.24,0.39,5,158,2,0,0,"sales","medium","exist"
"7401",9401,0.61,0.58,4,142,4,0,0,"sales","medium","exist"
"7402",9402,0.83,0.89,4,137,3,0,0,"sales","medium","exist"
"7403",9403,0.88,0.66,4,275,3,0,0,"sales","medium","exist"
"7404",9404,0.61,0.55,3,245,3,0,0,"sales","medium","exist"
"7405",9405,0.68,0.54,4,165,4,0,0,"sales","high","exist"
"7406",9406,0.51,0.7,4,142,4,1,0,"sales","low","exist"
"7407",9407,0.88,0.58,4,215,2,0,0,"sales","low","exist"
"7408",9408,0.94,0.84,5,240,3,0,0,"sales","low","exist"
"7409",9409,0.58,0.88,4,255,3,0,0,"sales","high","exist"
"7410",9410,0.63,0.98,4,265,3,0,0,"sales","low","exist"
"7411",9411,0.81,0.49,4,285,4,0,0,"sales","low","exist"
"7412",9412,0.61,0.86,3,238,2,0,0,"sales","low","exist"
"7413",9413,0.65,0.63,3,137,3,0,0,"sales","high","exist"
"7414",9414,0.67,0.63,3,270,5,0,0,"sales","low","exist"
"7415",9415,0.64,0.62,4,145,3,1,0,"sales","low","exist"
"7416",9416,0.25,0.76,6,182,3,0,0,"accounting","low","exist"
"7417",9417,0.13,0.62,3,264,6,0,0,"accounting","low","exist"
"7418",9418,0.14,0.89,3,212,6,0,0,"accounting","low","exist"
"7419",9419,0.74,0.51,5,198,3,0,0,"hr","low","exist"
"7420",9420,0.8,0.81,5,200,3,0,0,"hr","low","exist"
"7421",9421,0.5,0.56,3,263,4,0,0,"hr","medium","exist"
"7422",9422,0.69,0.75,4,249,4,0,0,"hr","medium","exist"
"7423",9423,0.91,0.53,4,212,3,0,0,"technical","low","exist"
"7424",9424,0.8,0.51,3,159,3,0,0,"technical","low","exist"
"7425",9425,0.93,0.52,3,181,3,0,0,"technical","low","exist"
"7426",9426,0.57,0.99,3,100,4,1,0,"technical","low","exist"
"7427",9427,0.51,0.58,2,218,2,1,0,"technical","low","exist"
"7428",9428,0.98,0.54,4,178,3,0,0,"technical","low","exist"
"7429",9429,0.85,0.83,4,219,3,0,0,"technical","low","exist"
"7430",9430,0.73,0.56,5,239,3,0,0,"technical","low","exist"
"7431",9431,0.97,0.9,3,255,2,0,0,"technical","low","exist"
"7432",9432,0.52,0.61,4,163,2,0,0,"technical","low","exist"
"7433",9433,0.31,0.38,3,173,2,0,0,"technical","low","exist"
"7434",9434,0.49,0.77,3,147,3,0,0,"support","low","exist"
"7435",9435,0.81,0.44,4,166,4,1,0,"support","low","exist"
"7436",9436,0.52,0.8,5,209,2,0,0,"support","low","exist"
"7437",9437,0.69,0.56,5,271,3,0,0,"support","low","exist"
"7438",9438,0.7,0.74,3,253,4,1,0,"support","low","exist"
"7439",9439,0.65,0.85,4,233,2,0,0,"support","low","exist"
"7440",9440,0.54,0.71,2,194,2,0,0,"support","low","exist"
"7441",9441,0.57,0.49,2,237,2,0,0,"support","low","exist"
"7442",9442,0.78,0.9,4,238,2,0,0,"support","low","exist"
"7443",9443,0.99,0.92,4,212,3,0,0,"support","low","exist"
"7444",9444,0.57,0.83,5,189,3,0,0,"support","low","exist"
"7445",9445,0.33,0.58,3,115,3,0,0,"technical","low","exist"
"7446",9446,0.97,0.58,4,159,3,0,0,"technical","low","exist"
"7447",9447,0.95,0.58,5,133,3,0,0,"technical","low","exist"
"7448",9448,0.69,0.83,5,225,3,0,0,"management","low","exist"
"7449",9449,0.97,0.91,2,112,5,0,0,"IT","low","exist"
"7450",9450,0.4,0.59,3,111,4,1,0,"IT","low","exist"
"7451",9451,0.67,0.71,4,178,6,0,0,"IT","low","exist"
"7452",9452,0.96,0.58,5,178,3,0,0,"IT","low","exist"
"7453",9453,0.49,0.95,2,181,4,1,0,"IT","low","exist"
"7454",9454,0.56,0.66,3,139,2,0,0,"product_mng","low","exist"
"7455",9455,0.99,0.78,2,177,4,0,0,"product_mng","low","exist"
"7456",9456,0.49,0.88,4,270,3,0,0,"product_mng","low","exist"
"7457",9457,0.53,0.69,4,135,3,0,0,"product_mng","low","exist"
"7458",9458,0.75,0.5,4,166,4,0,0,"IT","low","exist"
"7459",9459,0.51,0.89,3,230,4,0,0,"marketing","low","exist"
"7460",9460,0.65,0.9,3,163,3,1,0,"marketing","medium","exist"
"7461",9461,0.45,0.66,2,236,3,0,0,"marketing","medium","exist"
"7462",9462,0.98,0.91,3,264,4,1,0,"marketing","medium","exist"
"7463",9463,0.9,0.74,3,185,2,0,0,"marketing","medium","exist"
"7464",9464,0.37,0.62,4,253,2,0,0,"marketing","medium","exist"
"7465",9465,0.52,0.99,4,253,3,0,0,"marketing","medium","exist"
"7466",9466,0.96,0.78,3,135,3,0,0,"sales","medium","exist"
"7467",9467,0.99,0.7,2,182,4,0,0,"accounting","medium","exist"
"7468",9468,0.66,0.56,5,202,3,0,0,"support","medium","exist"
"7469",9469,0.84,0.54,5,186,2,0,0,"technical","medium","exist"
"7470",9470,0.16,0.87,5,163,3,0,0,"management","medium","exist"
"7471",9471,0.75,0.59,3,242,3,0,0,"marketing","medium","exist"
"7472",9472,0.52,0.74,3,160,2,0,0,"marketing","high","exist"
"7473",9473,0.86,0.86,3,173,3,0,0,"marketing","low","exist"
"7474",9474,0.75,0.53,3,154,2,0,0,"sales","medium","exist"
"7475",9475,0.73,0.99,3,160,3,0,0,"sales","medium","exist"
"7476",9476,0.98,0.84,3,139,2,0,0,"sales","medium","exist"
"7477",9477,0.8,0.84,3,251,3,0,0,"sales","medium","exist"
"7478",9478,0.18,0.48,4,176,4,1,0,"sales","low","exist"
"7479",9479,0.37,0.72,2,163,3,0,0,"sales","low","exist"
"7480",9480,0.97,0.86,3,257,4,0,0,"sales","low","exist"
"7481",9481,0.56,0.68,4,159,3,0,0,"sales","low","exist"
"7482",9482,0.32,0.65,2,183,3,0,0,"sales","low","exist"
"7483",9483,0.63,0.88,4,260,2,0,0,"sales","low","exist"
"7484",9484,0.36,0.78,6,151,3,0,0,"sales","low","exist"
"7485",9485,0.75,0.49,4,246,3,1,0,"sales","low","exist"
"7486",9486,0.42,0.86,3,160,4,1,0,"sales","low","exist"
"7487",9487,0.96,0.66,3,155,2,0,0,"sales","low","exist"
"7488",9488,0.62,0.78,5,250,6,0,0,"sales","low","exist"
"7489",9489,0.78,0.96,2,174,3,0,0,"sales","low","exist"
"7490",9490,0.93,0.89,3,262,2,0,0,"sales","low","exist"
"7491",9491,0.93,0.87,4,257,2,0,0,"sales","low","exist"
"7492",9492,0.45,0.42,4,140,2,0,0,"sales","low","exist"
"7493",9493,0.44,0.56,3,123,3,0,0,"accounting","medium","exist"
"7494",9494,0.57,0.55,3,264,2,0,0,"accounting","medium","exist"
"7495",9495,0.77,0.51,2,254,5,0,0,"accounting","medium","exist"
"7496",9496,0.6,0.98,4,205,4,0,0,"hr","medium","exist"
"7497",9497,0.25,0.94,6,199,4,0,0,"hr","medium","exist"
"7498",9498,0.59,0.43,3,171,3,0,0,"hr","medium","exist"
"7499",9499,0.29,0.57,5,98,5,0,0,"hr","medium","exist"
"7500",9500,0.5,0.95,3,166,4,1,0,"technical","low","exist"
"7501",9501,0.91,0.94,4,264,4,0,0,"technical","low","exist"
"7502",9502,0.78,0.65,3,176,2,0,0,"technical","low","exist"
"7503",9503,0.73,0.76,2,166,3,0,0,"technical","low","exist"
"7504",9504,0.51,0.59,4,169,3,0,0,"technical","low","exist"
"7505",9505,0.65,0.82,4,257,3,0,0,"technical","low","exist"
"7506",9506,0.25,0.87,3,265,4,0,0,"technical","low","exist"
"7507",9507,0.5,0.63,5,167,2,0,0,"technical","low","exist"
"7508",9508,0.53,0.58,4,134,2,0,0,"technical","low","exist"
"7509",9509,0.57,0.76,2,176,3,0,0,"technical","low","exist"
"7510",9510,0.77,0.91,5,274,3,0,0,"technical","low","exist"
"7511",9511,0.94,0.77,3,201,3,0,0,"support","low","exist"
"7512",9512,0.5,0.53,3,121,4,0,0,"support","low","exist"
"7513",9513,0.47,0.57,3,97,4,0,0,"support","low","exist"
"7514",9514,0.92,0.54,4,217,4,0,0,"support","low","exist"
"7515",9515,0.9,0.87,3,220,3,0,0,"support","low","exist"
"7516",9516,0.54,0.46,2,98,4,0,0,"support","low","exist"
"7517",9517,0.58,0.97,5,265,3,0,0,"support","low","exist"
"7518",9518,0.95,0.87,3,201,3,0,0,"support","low","exist"
"7519",9519,0.52,0.71,3,151,3,0,0,"support","low","exist"
"7520",9520,0.83,0.51,4,199,3,0,0,"support","low","exist"
"7521",9521,0.54,0.92,4,175,3,0,0,"support","low","exist"
"7522",9522,0.8,0.85,5,253,3,1,0,"technical","low","exist"
"7523",9523,0.52,0.57,3,183,2,0,0,"technical","low","exist"
"7524",9524,0.83,0.8,5,223,3,0,0,"technical","low","exist"
"7525",9525,0.74,0.55,5,168,4,0,0,"management","low","exist"
"7526",9526,0.87,0.71,5,244,2,0,0,"IT","low","exist"
"7527",9527,0.45,0.87,2,268,4,1,0,"IT","low","exist"
"7528",9528,0.72,0.72,4,218,4,0,0,"IT","low","exist"
"7529",9529,0.27,0.85,2,277,6,1,0,"IT","low","exist"
"7530",9530,0.51,0.88,5,225,2,1,0,"IT","low","exist"
"7531",9531,0.55,0.55,4,257,3,0,0,"product_mng","low","exist"
"7532",9532,0.89,0.69,4,170,3,0,0,"product_mng","low","exist"
"7533",9533,0.85,0.86,3,179,3,0,0,"product_mng","low","exist"
"7534",9534,0.29,0.85,4,211,2,0,0,"product_mng","low","exist"
"7535",9535,0.96,0.5,3,217,2,1,0,"IT","low","exist"
"7536",9536,0.9,0.68,3,135,3,0,0,"RandD","low","exist"
"7537",9537,0.28,0.94,6,167,3,1,0,"RandD","low","exist"
"7538",9538,0.93,0.98,4,189,3,0,0,"RandD","medium","exist"
"7539",9539,0.51,0.57,3,162,3,1,0,"RandD","medium","exist"
"7540",9540,0.97,0.76,3,193,3,0,0,"RandD","medium","exist"
"7541",9541,0.71,0.55,4,273,3,1,0,"RandD","medium","exist"
"7542",9542,0.52,0.69,6,138,5,0,0,"marketing","medium","exist"
"7543",9543,0.87,0.84,4,237,3,1,0,"sales","medium","exist"
"7544",9544,0.78,0.61,5,260,2,0,0,"accounting","medium","exist"
"7545",9545,0.57,0.82,3,149,5,1,0,"support","medium","exist"
"7546",9546,0.34,0.49,4,149,3,0,0,"technical","medium","exist"
"7547",9547,0.95,0.95,4,137,4,0,0,"management","medium","exist"
"7548",9548,0.72,0.73,5,167,3,0,0,"marketing","medium","exist"
"7549",9549,0.61,0.37,4,165,6,0,0,"marketing","medium","exist"
"7550",9550,0.39,0.39,2,131,2,0,0,"marketing","high","exist"
"7551",9551,0.72,0.59,5,138,2,0,0,"sales","low","exist"
"7552",9552,0.86,0.91,4,234,3,1,0,"sales","medium","exist"
"7553",9553,0.69,0.67,4,141,3,0,0,"sales","medium","exist"
"7554",9554,0.5,0.65,4,266,3,1,0,"sales","medium","exist"
"7555",9555,0.62,0.68,3,134,2,0,0,"sales","medium","exist"
"7556",9556,0.76,0.55,4,147,3,0,0,"sales","low","exist"
"7557",9557,0.97,0.88,4,237,4,0,0,"sales","low","exist"
"7558",9558,0.78,0.57,5,114,4,0,0,"sales","low","exist"
"7559",9559,0.81,0.89,4,166,2,1,0,"sales","low","exist"
"7560",9560,0.15,0.95,4,173,5,1,0,"sales","low","exist"
"7561",9561,0.72,0.5,3,205,3,1,0,"sales","low","exist"
"7562",9562,0.8,0.5,3,219,3,0,0,"sales","low","exist"
"7563",9563,0.76,0.74,3,173,2,0,0,"sales","low","exist"
"7564",9564,0.19,0.73,4,231,3,0,0,"sales","low","exist"
"7565",9565,0.75,0.75,5,133,2,0,0,"sales","low","exist"
"7566",9566,0.94,0.49,4,220,3,0,0,"sales","low","exist"
"7567",9567,0.93,0.59,5,158,3,0,0,"sales","low","exist"
"7568",9568,0.96,0.92,3,182,4,0,0,"sales","low","exist"
"7569",9569,0.14,0.57,6,275,5,0,0,"sales","low","exist"
"7570",9570,0.75,0.71,3,237,3,1,0,"accounting","low","exist"
"7571",9571,0.6,0.59,5,146,4,0,0,"accounting","low","exist"
"7572",9572,0.65,0.48,4,144,3,0,0,"accounting","low","exist"
"7573",9573,0.59,0.79,2,195,3,0,0,"hr","low","exist"
"7574",9574,0.93,0.78,5,191,4,0,0,"hr","medium","exist"
"7575",9575,0.5,1,3,149,2,0,0,"hr","medium","exist"
"7576",9576,0.62,0.55,4,137,3,1,0,"hr","medium","exist"
"7577",9577,0.24,0.58,3,184,5,1,0,"technical","medium","exist"
"7578",9578,0.66,0.87,4,139,3,0,0,"technical","medium","exist"
"7579",9579,0.55,0.95,4,249,4,0,0,"technical","medium","exist"
"7580",9580,0.91,0.66,3,168,3,0,0,"technical","medium","exist"
"7581",9581,0.59,0.51,2,145,5,0,0,"technical","medium","exist"
"7582",9582,0.74,0.54,5,221,3,0,0,"technical","medium","exist"
"7583",9583,0.43,0.51,2,123,3,0,0,"technical","medium","exist"
"7584",9584,0.85,0.99,6,153,6,0,0,"technical","medium","exist"
"7585",9585,0.46,0.54,3,183,4,0,0,"technical","medium","exist"
"7586",9586,0.48,0.56,4,271,3,0,0,"technical","high","exist"
"7587",9587,0.96,1,4,167,2,1,0,"technical","low","exist"
"7588",9588,0.55,0.9,4,177,3,0,0,"support","medium","exist"
"7589",9589,0.82,0.74,3,256,3,1,0,"support","medium","exist"
"7590",9590,0.24,0.65,3,143,4,0,0,"support","medium","exist"
"7591",9591,0.69,0.71,3,241,3,0,0,"support","medium","exist"
"7592",9592,0.29,0.68,4,210,3,0,0,"support","low","exist"
"7593",9593,0.53,0.7,4,155,5,1,0,"support","low","exist"
"7594",9594,0.65,0.77,2,248,3,0,0,"support","low","exist"
"7595",9595,0.57,0.53,3,162,3,0,0,"support","low","exist"
"7596",9596,0.6,0.5,4,137,3,0,0,"support","low","exist"
"7597",9597,0.79,0.55,5,242,2,0,0,"support","low","exist"
"7598",9598,0.41,0.5,6,257,6,1,0,"support","low","exist"
"7599",9599,0.79,0.72,5,245,3,1,0,"technical","low","exist"
"7600",9600,0.5,0.8,3,234,3,0,0,"technical","low","exist"
"7601",9601,0.8,0.76,4,135,2,0,0,"technical","low","exist"
"7602",9602,0.61,0.79,5,269,2,0,0,"management","low","exist"
"7603",9603,0.99,0.68,4,238,3,0,0,"IT","low","exist"
"7604",9604,0.77,0.86,3,101,5,0,0,"IT","low","exist"
"7605",9605,0.7,0.52,5,200,2,1,0,"IT","low","exist"
"7606",9606,0.55,0.87,3,241,4,0,0,"IT","low","exist"
"7607",9607,0.87,0.63,3,143,3,1,0,"IT","low","exist"
"7608",9608,0.97,0.6,3,169,2,1,0,"product_mng","low","exist"
"7609",9609,0.56,0.99,4,270,2,0,0,"product_mng","low","exist"
"7610",9610,0.99,0.81,4,246,3,1,0,"product_mng","low","exist"
"7611",9611,0.57,0.66,4,151,2,0,0,"product_mng","low","exist"
"7612",9612,1,0.84,3,227,3,0,0,"IT","low","exist"
"7613",9613,0.97,0.74,3,134,3,1,0,"marketing","high","exist"
"7614",9614,0.81,0.54,4,155,4,0,0,"accounting","high","exist"
"7615",9615,0.76,0.48,5,173,3,0,0,"accounting","high","exist"
"7616",9616,0.95,0.55,5,134,3,0,0,"IT","medium","exist"
"7617",9617,0.81,0.65,3,195,2,0,0,"IT","medium","exist"
"7618",9618,0.8,0.65,3,264,4,0,0,"management","high","exist"
"7619",9619,0.72,0.57,3,203,2,1,0,"marketing","medium","exist"
"7620",9620,0.68,0.65,3,243,2,0,0,"sales","medium","exist"
"7621",9621,0.21,0.61,6,159,5,1,0,"accounting","medium","exist"
"7622",9622,0.46,0.4,3,145,2,0,0,"support","medium","exist"
"7623",9623,0.8,0.7,3,238,2,0,0,"technical","medium","exist"
"7624",9624,0.57,0.64,4,151,2,0,0,"management","medium","exist"
"7625",9625,0.58,0.57,5,205,2,0,0,"marketing","high","exist"
"7626",9626,0.91,1,4,211,3,0,0,"marketing","low","exist"
"7627",9627,0.63,0.67,5,169,2,0,0,"marketing","medium","exist"
"7628",9628,0.95,0.86,2,263,4,0,0,"sales","medium","exist"
"7629",9629,0.87,0.67,5,143,3,0,0,"sales","medium","exist"
"7630",9630,0.22,0.53,5,160,4,1,0,"sales","medium","exist"
"7631",9631,0.95,0.65,5,142,2,1,0,"sales","low","exist"
"7632",9632,0.18,0.5,4,169,4,0,0,"sales","low","exist"
"7633",9633,0.87,0.63,5,214,3,0,0,"sales","low","exist"
"7634",9634,0.23,0.84,5,131,5,0,0,"sales","low","exist"
"7635",9635,0.93,0.69,3,213,4,0,0,"sales","low","exist"
"7636",9636,0.58,0.75,4,244,4,0,0,"sales","low","exist"
"7637",9637,0.68,0.55,4,169,3,1,0,"sales","low","exist"
"7638",9638,0.76,0.71,4,156,3,0,0,"sales","low","exist"
"7639",9639,0.68,0.84,5,161,3,0,0,"sales","low","exist"
"7640",9640,0.99,0.47,3,152,5,0,0,"sales","low","exist"
"7641",9641,0.64,0.55,3,201,2,0,0,"sales","low","exist"
"7642",9642,0.61,0.83,5,269,5,1,0,"sales","low","exist"
"7643",9643,0.51,0.5,5,242,2,0,0,"sales","low","exist"
"7644",9644,0.69,0.66,3,113,4,0,0,"sales","low","exist"
"7645",9645,0.85,0.6,3,251,2,0,0,"sales","low","exist"
"7646",9646,0.55,0.89,6,99,3,0,0,"sales","low","exist"
"7647",9647,0.56,0.89,4,263,3,0,0,"accounting","low","exist"
"7648",9648,0.69,0.68,4,214,4,0,0,"accounting","low","exist"
"7649",9649,0.61,0.46,4,172,3,0,0,"accounting","low","exist"
"7650",9650,0.47,0.65,4,172,2,0,0,"hr","low","exist"
"7651",9651,0.58,0.79,4,196,3,1,0,"hr","low","exist"
"7652",9652,0.16,0.56,5,152,5,0,0,"hr","medium","exist"
"7653",9653,0.53,0.64,2,109,3,0,0,"hr","medium","exist"
"7654",9654,0.82,0.82,5,193,4,0,0,"technical","medium","exist"
"7655",9655,0.68,0.61,4,227,3,0,0,"technical","medium","exist"
"7656",9656,0.6,0.72,3,181,2,0,0,"technical","medium","exist"
"7657",9657,0.93,0.44,5,190,5,0,0,"technical","medium","exist"
"7658",9658,0.58,0.49,2,107,3,0,0,"technical","medium","exist"
"7659",9659,0.61,0.96,4,161,3,1,0,"technical","medium","exist"
"7660",9660,0.74,0.71,4,243,3,0,0,"technical","medium","exist"
"7661",9661,0.88,0.91,3,157,2,0,0,"technical","medium","exist"
"7662",9662,0.94,0.8,6,147,3,0,0,"technical","medium","exist"
"7663",9663,0.44,0.46,3,121,3,0,0,"technical","medium","exist"
"7664",9664,0.73,0.52,3,274,2,0,0,"technical","high","exist"
"7665",9665,0.9,0.68,4,204,4,1,0,"support","low","exist"
"7666",9666,0.97,0.49,3,199,2,0,0,"support","medium","exist"
"7667",9667,0.86,0.96,5,246,3,0,0,"support","medium","exist"
"7668",9668,0.81,0.98,3,141,3,0,0,"support","medium","exist"
"7669",9669,0.24,0.76,6,213,4,0,0,"support","medium","exist"
"7670",9670,0.92,0.97,4,199,3,0,0,"support","low","exist"
"7671",9671,0.34,0.62,2,257,6,1,0,"support","low","exist"
"7672",9672,0.95,0.53,4,143,3,0,0,"support","low","exist"
"7673",9673,0.94,0.81,3,150,2,0,0,"support","low","exist"
"7674",9674,0.54,0.82,3,284,2,1,0,"support","low","exist"
"7675",9675,0.87,0.57,3,149,3,0,0,"support","low","exist"
"7676",9676,0.54,0.74,4,160,6,0,0,"technical","low","exist"
"7677",9677,0.75,0.49,4,208,4,0,0,"technical","low","exist"
"7678",9678,0.88,1,3,248,3,0,0,"technical","medium","exist"
"7679",9679,0.78,0.86,3,210,3,0,0,"management","medium","exist"
"7680",9680,0.88,0.71,5,219,2,0,0,"IT","medium","exist"
"7681",9681,0.51,0.94,4,155,3,0,0,"IT","medium","exist"
"7682",9682,0.31,0.7,4,182,3,0,0,"IT","medium","exist"
"7683",9683,0.22,1,6,244,3,0,0,"IT","medium","exist"
"7684",9684,0.56,0.83,5,157,3,0,0,"IT","medium","exist"
"7685",9685,0.38,0.63,2,182,2,0,0,"product_mng","medium","exist"
"7686",9686,0.56,0.47,5,185,4,0,0,"product_mng","medium","exist"
"7687",9687,0.55,0.9,4,206,3,0,0,"product_mng","medium","exist"
"7688",9688,0.74,0.99,4,156,3,0,0,"product_mng","medium","exist"
"7689",9689,0.64,0.92,5,211,2,1,0,"IT","medium","exist"
"7690",9690,0.69,0.91,6,247,6,0,0,"RandD","medium","exist"
"7691",9691,0.99,0.54,3,247,3,0,0,"RandD","medium","exist"
"7692",9692,0.66,0.75,4,235,3,0,0,"RandD","medium","exist"
"7693",9693,0.79,0.93,5,169,3,0,0,"RandD","medium","exist"
"7694",9694,0.92,0.54,3,246,3,0,0,"RandD","medium","exist"
"7695",9695,0.84,0.49,3,172,4,0,0,"RandD","medium","exist"
"7696",9696,0.31,0.59,4,218,5,0,0,"marketing","medium","exist"
"7697",9697,0.34,0.52,6,265,6,0,0,"sales","medium","exist"
"7698",9698,0.88,0.96,5,173,4,0,0,"accounting","medium","exist"
"7699",9699,0.8,0.84,3,195,3,0,0,"support","medium","exist"
"7700",9700,0.75,0.92,3,160,3,1,0,"technical","medium","exist"
"7701",9701,0.71,0.7,4,237,4,0,0,"management","medium","exist"
"7702",9702,0.66,0.49,3,206,2,0,0,"marketing","medium","exist"
"7703",9703,0.54,0.72,6,222,5,0,0,"marketing","high","exist"
"7704",9704,0.47,0.4,3,113,3,0,0,"marketing","high","exist"
"7705",9705,0.87,0.79,3,244,3,0,0,"sales","high","exist"
"7706",9706,0.9,0.52,3,162,2,0,0,"sales","high","exist"
"7707",9707,0.51,0.63,3,234,2,0,0,"sales","high","exist"
"7708",9708,0.62,0.71,4,168,2,0,0,"sales","high","exist"
"7709",9709,0.47,0.43,3,120,3,0,0,"sales","high","exist"
"7710",9710,0.59,0.94,5,274,2,0,0,"sales","high","exist"
"7711",9711,0.87,0.62,4,202,3,0,0,"sales","high","exist"
"7712",9712,0.59,0.97,3,209,3,0,0,"sales","high","exist"
"7713",9713,0.87,0.71,6,224,6,0,0,"sales","high","exist"
"7714",9714,0.89,0.93,3,168,2,0,0,"sales","high","exist"
"7715",9715,0.73,0.68,4,227,3,0,0,"sales","low","exist"
"7716",9716,0.79,0.98,3,217,3,0,0,"sales","low","exist"
"7717",9717,0.8,0.74,2,205,3,1,0,"sales","low","exist"
"7718",9718,0.73,0.89,5,223,3,0,0,"sales","low","exist"
"7719",9719,0.96,0.9,3,175,3,1,0,"sales","low","exist"
"7720",9720,0.66,0.96,3,175,2,0,0,"sales","low","exist"
"7721",9721,0.53,0.97,4,254,3,0,0,"sales","low","exist"
"7722",9722,0.86,0.56,5,215,2,0,0,"sales","low","exist"
"7723",9723,0.92,0.86,3,166,4,0,0,"sales","low","exist"
"7724",9724,0.31,0.95,5,205,3,0,0,"accounting","low","exist"
"7725",9725,0.69,0.73,4,233,2,0,0,"accounting","low","exist"
"7726",9726,0.7,0.83,3,189,3,0,0,"accounting","low","exist"
"7727",9727,0.63,0.74,4,202,3,0,0,"hr","low","exist"
"7728",9728,0.24,0.53,6,165,5,0,0,"hr","low","exist"
"7729",9729,0.13,0.94,4,213,5,1,0,"hr","low","exist"
"7730",9730,0.47,0.41,2,140,3,0,0,"hr","medium","exist"
"7731",9731,0.53,0.58,4,251,2,0,0,"technical","medium","exist"
"7732",9732,0.96,0.96,5,243,3,0,0,"technical","medium","exist"
"7733",9733,0.92,0.88,4,111,4,0,0,"technical","medium","exist"
"7734",9734,0.56,0.59,3,178,2,0,0,"technical","medium","exist"
"7735",9735,0.51,0.88,5,230,4,0,0,"technical","medium","exist"
"7736",9736,0.22,0.85,6,172,6,1,0,"technical","medium","exist"
"7737",9737,0.83,0.92,3,268,2,1,0,"technical","medium","exist"
"7738",9738,0.85,0.48,2,208,4,0,0,"technical","medium","exist"
"7739",9739,0.85,0.92,3,188,4,0,0,"technical","medium","exist"
"7740",9740,0.85,0.9,5,247,2,0,0,"technical","medium","exist"
"7741",9741,0.73,0.82,4,205,4,0,0,"technical","medium","exist"
"7742",9742,0.18,0.52,3,213,4,0,0,"support","high","exist"
"7743",9743,0.63,0.87,4,145,3,1,0,"support","high","exist"
"7744",9744,0.5,0.48,2,106,2,0,0,"support","high","exist"
"7745",9745,0.63,0.47,3,180,3,1,0,"support","high","exist"
"7746",9746,0.15,0.95,4,251,5,0,0,"support","high","exist"
"7747",9747,0.96,0.69,4,156,5,0,0,"support","high","exist"
"7748",9748,0.96,0.91,5,179,2,0,0,"support","high","exist"
"7749",9749,0.31,0.51,4,229,6,0,0,"support","high","exist"
"7750",9750,0.59,0.49,4,149,4,0,0,"support","low","exist"
"7751",9751,0.75,0.98,4,198,3,0,0,"support","low","exist"
"7752",9752,0.96,0.51,3,241,6,1,0,"support","low","exist"
"7753",9753,0.69,0.67,4,156,3,0,0,"technical","low","exist"
"7754",9754,0.92,0.89,3,220,2,0,0,"technical","low","exist"
"7755",9755,0.96,0.82,5,185,3,0,0,"technical","low","exist"
"7756",9756,0.67,0.7,4,222,3,0,0,"management","low","exist"
"7757",9757,0.49,0.56,3,221,3,1,0,"IT","low","exist"
"7758",9758,0.85,0.65,4,280,3,1,0,"IT","medium","exist"
"7759",9759,0.85,0.53,3,250,3,0,0,"IT","medium","exist"
"7760",9760,0.91,0.77,4,167,3,1,0,"IT","medium","exist"
"7761",9761,0.7,0.48,4,238,3,0,0,"IT","medium","exist"
"7762",9762,0.98,0.99,4,132,2,0,0,"product_mng","medium","exist"
"7763",9763,0.48,0.48,2,245,5,0,0,"product_mng","medium","exist"
"7764",9764,0.39,0.6,2,161,2,0,0,"product_mng","medium","exist"
"7765",9765,0.66,0.89,3,242,3,0,0,"product_mng","medium","exist"
"7766",9766,0.6,0.61,3,104,5,0,0,"IT","medium","exist"
"7767",9767,0.88,0.9,4,152,4,0,0,"RandD","medium","exist"
"7768",9768,0.85,0.83,3,226,2,0,0,"RandD","medium","exist"
"7769",9769,0.76,0.81,3,175,3,0,0,"RandD","medium","exist"
"7770",9770,1,0.67,5,241,4,0,0,"RandD","medium","exist"
"7771",9771,0.79,0.74,2,158,3,0,0,"RandD","medium","exist"
"7772",9772,0.63,0.52,5,226,4,0,0,"marketing","medium","exist"
"7773",9773,0.5,0.83,2,220,2,1,0,"sales","medium","exist"
"7774",9774,0.83,0.74,4,233,3,0,0,"accounting","medium","exist"
"7775",9775,0.71,0.81,3,141,3,0,0,"support","medium","exist"
"7776",9776,0.94,0.87,4,157,3,0,0,"technical","medium","exist"
"7777",9777,0.56,0.57,2,112,4,0,0,"management","medium","exist"
"7778",9778,0.78,0.71,4,216,2,0,0,"marketing","medium","exist"
"7779",9779,0.34,0.46,5,131,3,0,0,"marketing","medium","exist"
"7780",9780,0.62,0.67,3,212,3,0,0,"marketing","medium","exist"
"7781",9781,0.82,0.74,3,163,2,0,0,"sales","high","exist"
"7782",9782,0.42,0.5,2,151,3,0,0,"sales","low","exist"
"7783",9783,0.51,0.79,3,137,3,0,0,"sales","medium","exist"
"7784",9784,0.63,0.78,2,158,5,1,0,"sales","medium","exist"
"7785",9785,0.43,0.81,3,102,3,0,0,"sales","medium","exist"
"7786",9786,0.5,0.49,5,256,3,0,0,"sales","medium","exist"
"7787",9787,0.81,0.87,4,203,2,0,0,"sales","medium","exist"
"7788",9788,0.63,0.7,5,177,2,1,0,"sales","medium","exist"
"7789",9789,0.86,0.7,4,197,3,0,0,"sales","medium","exist"
"7790",9790,0.92,0.91,3,202,2,0,0,"sales","medium","exist"
"7791",9791,0.72,0.78,3,229,2,0,0,"sales","medium","exist"
"7792",9792,0.78,0.63,4,181,2,0,0,"sales","medium","exist"
"7793",9793,0.76,0.65,3,254,2,0,0,"sales","low","exist"
"7794",9794,0.84,0.63,2,162,3,0,0,"sales","low","exist"
"7795",9795,0.78,0.54,4,102,3,0,0,"sales","low","exist"
"7796",9796,0.57,0.59,4,197,3,0,0,"sales","low","exist"
"7797",9797,0.15,0.42,3,98,3,0,0,"sales","low","exist"
"7798",9798,0.69,0.77,3,232,2,0,0,"sales","low","exist"
"7799",9799,0.73,0.6,3,252,4,1,0,"sales","low","exist"
"7800",9800,0.96,0.54,5,161,3,1,0,"accounting","high","exist"
"7801",9801,0.91,0.78,4,169,4,0,0,"accounting","low","exist"
"7802",9802,0.58,0.97,2,216,3,0,0,"accounting","high","exist"
"7803",9803,0.84,0.56,3,266,3,0,0,"hr","high","exist"
"7804",9804,0.51,0.58,3,141,3,0,0,"hr","low","exist"
"7805",9805,0.71,0.95,4,249,3,0,0,"hr","low","exist"
"7806",9806,0.63,0.82,5,268,3,0,0,"hr","high","exist"
"7807",9807,0.66,0.51,3,192,3,0,0,"technical","low","exist"
"7808",9808,0.5,0.8,6,201,5,0,0,"technical","medium","exist"
"7809",9809,0.56,0.89,3,163,3,1,0,"technical","high","exist"
"7810",9810,0.57,0.46,3,167,4,0,0,"technical","medium","exist"
"7811",9811,0.7,0.65,5,202,3,1,0,"technical","medium","exist"
"7812",9812,0.84,0.62,5,245,4,0,0,"technical","medium","exist"
"7813",9813,0.33,0.59,3,243,2,0,0,"technical","medium","exist"
"7814",9814,0.64,0.94,3,204,2,0,0,"technical","high","exist"
"7815",9815,0.93,0.54,4,239,2,0,0,"technical","medium","exist"
"7816",9816,1,0.58,4,229,2,1,0,"technical","medium","exist"
"7817",9817,0.91,0.49,3,213,4,0,0,"technical","medium","exist"
"7818",9818,0.56,0.59,5,254,4,0,0,"support","high","exist"
"7819",9819,0.62,0.52,6,253,4,0,0,"support","medium","exist"
"7820",9820,0.98,0.68,4,253,3,0,0,"support","high","exist"
"7821",9821,0.96,0.85,5,211,4,0,0,"support","low","exist"
"7822",9822,0.61,0.99,5,98,2,0,0,"support","medium","exist"
"7823",9823,0.92,0.66,4,133,3,0,0,"support","medium","exist"
"7824",9824,0.58,0.67,5,265,3,0,0,"support","medium","exist"
"7825",9825,0.47,0.49,2,112,3,1,0,"support","medium","exist"
"7826",9826,0.87,0.7,3,224,3,0,0,"support","low","exist"
"7827",9827,0.8,0.64,5,180,2,0,0,"support","low","exist"
"7828",9828,0.54,0.53,3,203,2,0,0,"support","low","exist"
"7829",9829,0.14,0.83,6,275,6,0,0,"technical","low","exist"
"7830",9830,0.98,0.76,5,168,3,0,0,"technical","low","exist"
"7831",9831,0.13,0.58,4,203,4,0,0,"technical","low","exist"
"7832",9832,0.64,0.81,3,209,3,0,0,"management","low","exist"
"7833",9833,0.91,0.75,3,166,2,0,0,"IT","low","exist"
"7834",9834,0.24,0.71,3,187,5,0,0,"IT","low","exist"
"7835",9835,0.34,0.42,6,287,5,1,0,"IT","low","exist"
"7836",9836,0.51,0.85,2,248,4,0,0,"IT","high","exist"
"7837",9837,0.91,0.7,3,193,2,0,0,"IT","low","exist"
"7838",9838,0.86,0.9,4,162,3,0,0,"product_mng","low","exist"
"7839",9839,1,0.61,3,188,4,0,0,"product_mng","low","exist"
"7840",9840,0.37,0.41,6,101,3,0,0,"product_mng","low","exist"
"7841",9841,0.46,0.73,6,256,4,0,0,"product_mng","high","exist"
"7842",9842,0.86,0.8,5,134,2,0,0,"IT","low","exist"
"7843",9843,0.36,0.68,2,126,4,0,0,"marketing","high","exist"
"7844",9844,0.52,0.93,2,243,3,0,0,"accounting","high","exist"
"7845",9845,0.51,0.73,3,205,4,0,0,"accounting","high","exist"
"7846",9846,0.69,0.94,5,259,2,0,0,"IT","medium","exist"
"7847",9847,0.67,0.5,5,219,3,0,0,"IT","medium","exist"
"7848",9848,0.6,0.99,5,161,3,0,0,"management","high","exist"
"7849",9849,0.71,0.57,3,207,3,0,0,"marketing","medium","exist"
"7850",9850,0.65,0.79,3,201,3,1,0,"sales","high","exist"
"7851",9851,0.48,0.92,3,234,3,0,0,"accounting","medium","exist"
"7852",9852,0.67,0.58,4,158,3,0,0,"support","medium","exist"
"7853",9853,0.68,0.63,5,185,4,0,0,"technical","medium","exist"
"7854",9854,0.74,0.85,3,176,3,0,0,"management","medium","exist"
"7855",9855,0.9,0.77,5,163,3,0,0,"marketing","medium","exist"
"7856",9856,0.67,0.83,3,171,3,1,0,"marketing","medium","exist"
"7857",9857,0.64,0.66,5,163,4,0,0,"marketing","medium","exist"
"7858",9858,0.54,0.87,4,163,3,0,0,"sales","medium","exist"
"7859",9859,0.6,0.73,2,180,2,0,0,"sales","high","exist"
"7860",9860,0.72,0.67,3,243,3,0,0,"sales","low","exist"
"7861",9861,0.97,0.49,4,213,2,0,0,"sales","medium","exist"
"7862",9862,0.99,0.89,3,273,2,0,0,"sales","medium","exist"
"7863",9863,0.75,0.93,4,195,3,0,0,"sales","medium","exist"
"7864",9864,0.84,0.98,4,246,3,0,0,"sales","medium","exist"
"7865",9865,0.76,0.5,3,196,3,1,0,"sales","medium","exist"
"7866",9866,0.96,0.51,5,205,2,1,0,"sales","medium","exist"
"7867",9867,0.12,0.81,4,287,6,0,0,"sales","medium","exist"
"7868",9868,0.54,0.79,3,211,3,0,0,"sales","medium","exist"
"7869",9869,0.69,0.98,3,261,4,0,0,"sales","medium","exist"
"7870",9870,0.77,0.71,5,204,3,0,0,"sales","high","exist"
"7871",9871,0.96,0.86,2,163,3,0,0,"sales","low","exist"
"7872",9872,0.53,0.62,4,162,2,0,0,"sales","low","exist"
"7873",9873,0.54,0.72,4,259,2,1,0,"sales","low","exist"
"7874",9874,0.89,0.64,4,151,5,0,0,"sales","high","exist"
"7875",9875,0.52,0.84,2,266,2,0,0,"sales","low","exist"
"7876",9876,0.29,0.65,5,110,5,1,0,"sales","low","exist"
"7877",9877,0.93,0.6,4,271,3,0,0,"accounting","low","exist"
"7878",9878,0.71,0.68,4,208,2,0,0,"accounting","high","exist"
"7879",9879,0.23,0.5,2,150,6,0,0,"accounting","low","exist"
"7880",9880,0.89,0.96,3,122,4,0,0,"hr","low","exist"
"7881",9881,0.51,0.5,4,246,3,0,0,"hr","low","exist"
"7882",9882,0.27,0.64,2,188,3,0,0,"hr","low","exist"
"7883",9883,0.9,0.53,3,167,3,0,0,"hr","low","exist"
"7884",9884,0.88,0.57,4,261,4,0,0,"technical","low","exist"
"7885",9885,0.91,0.83,4,235,3,0,0,"technical","low","exist"
"7886",9886,0.65,0.63,4,199,2,0,0,"technical","medium","exist"
"7887",9887,0.68,0.5,4,166,4,1,0,"technical","medium","exist"
"7888",9888,0.58,0.63,4,272,3,0,0,"technical","medium","exist"
"7889",9889,0.68,0.62,3,158,3,0,0,"technical","medium","exist"
"7890",9890,0.59,0.76,3,264,3,0,0,"technical","medium","exist"
"7891",9891,0.56,0.57,5,274,3,0,0,"technical","medium","exist"
"7892",9892,0.74,0.44,5,169,3,1,0,"technical","medium","exist"
"7893",9893,0.5,0.91,4,148,2,0,0,"technical","medium","exist"
"7894",9894,0.85,0.65,4,162,2,0,0,"technical","medium","exist"
"7895",9895,0.57,0.48,5,221,3,0,0,"support","medium","exist"
"7896",9896,0.89,0.58,3,167,3,0,0,"support","medium","exist"
"7897",9897,0.76,0.66,5,206,2,0,0,"support","medium","exist"
"7898",9898,0.96,0.7,3,169,3,0,0,"support","high","exist"
"7899",9899,0.81,0.68,4,179,3,0,0,"support","low","exist"
"7900",9900,0.79,0.85,4,100,6,0,0,"support","medium","exist"
"7901",9901,0.63,0.66,3,177,2,1,0,"support","medium","exist"
"7902",9902,0.92,0.82,5,252,3,0,0,"support","medium","exist"
"7903",9903,0.77,0.74,4,202,4,0,0,"support","medium","exist"
"7904",9904,0.73,0.87,4,263,2,0,0,"support","low","exist"
"7905",9905,0.74,0.98,4,160,2,0,0,"support","low","exist"
"7906",9906,0.8,0.74,5,229,3,1,0,"technical","low","exist"
"7907",9907,0.82,0.85,5,195,4,0,0,"technical","low","exist"
"7908",9908,0.48,0.81,3,212,2,0,0,"technical","low","exist"
"7909",9909,0.79,0.54,6,190,4,0,0,"management","low","exist"
"7910",9910,0.87,0.41,3,219,3,1,0,"IT","low","exist"
"7911",9911,0.96,0.88,2,193,2,0,0,"IT","low","exist"
"7912",9912,0.96,0.58,5,197,4,1,0,"IT","low","exist"
"7913",9913,0.69,0.66,3,206,2,0,0,"IT","low","exist"
"7914",9914,0.42,0.58,2,140,3,0,0,"IT","low","exist"
"7915",9915,0.7,0.76,3,173,2,0,0,"product_mng","low","exist"
"7916",9916,0.97,0.76,6,142,2,0,0,"product_mng","low","exist"
"7917",9917,0.6,0.59,3,237,4,1,0,"product_mng","low","exist"
"7918",9918,0.63,0.63,5,252,2,0,0,"product_mng","low","exist"
"7919",9919,0.65,0.82,4,196,2,0,0,"IT","low","exist"
"7920",9920,0.85,0.81,3,205,3,0,0,"marketing","high","exist"
"7921",9921,0.54,0.83,3,201,3,0,0,"accounting","high","exist"
"7922",9922,0.23,0.74,5,120,4,0,0,"accounting","high","exist"
"7923",9923,0.95,0.73,2,187,2,0,0,"IT","medium","exist"
"7924",9924,1,0.51,5,274,4,0,0,"IT","medium","exist"
"7925",9925,0.77,0.93,3,227,3,0,0,"management","high","exist"
"7926",9926,0.8,0.53,3,245,3,0,0,"sales","medium","exist"
"7927",9927,0.88,0.56,4,243,4,0,0,"accounting","medium","exist"
"7928",9928,0.73,0.68,3,132,2,0,0,"support","medium","exist"
"7929",9929,0.54,0.9,3,206,3,0,0,"technical","medium","exist"
"7930",9930,0.92,0.58,5,205,2,1,0,"management","medium","exist"
"7931",9931,0.14,0.88,3,162,4,0,0,"marketing","medium","exist"
"7932",9932,0.65,0.79,5,266,3,0,0,"marketing","medium","exist"
"7933",9933,0.17,0.89,5,261,5,0,0,"marketing","medium","exist"
"7934",9934,0.18,0.67,5,209,4,0,0,"sales","medium","exist"
"7935",9935,0.58,0.5,5,184,4,0,0,"sales","medium","exist"
"7936",9936,0.63,0.67,4,229,3,0,0,"sales","medium","exist"
"7937",9937,0.68,0.81,3,180,2,1,0,"sales","high","exist"
"7938",9938,0.91,0.98,5,135,3,0,0,"sales","low","exist"
"7939",9939,0.95,0.94,3,174,3,0,0,"sales","medium","exist"
"7940",9940,0.89,0.76,2,278,2,0,0,"sales","medium","exist"
"7941",9941,0.76,0.76,3,197,2,0,0,"sales","medium","exist"
"7942",9942,0.96,0.72,3,157,3,0,0,"sales","medium","exist"
"7943",9943,0.78,0.63,4,156,3,0,0,"sales","low","exist"
"7944",9944,0.98,0.9,3,186,4,0,0,"sales","low","exist"
"7945",9945,0.76,0.42,3,217,2,0,0,"sales","low","exist"
"7946",9946,0.63,0.49,5,192,2,0,0,"sales","low","exist"
"7947",9947,0.39,0.37,3,127,3,0,0,"sales","low","exist"
"7948",9948,0.91,0.67,3,257,2,0,0,"sales","low","exist"
"7949",9949,0.8,0.8,4,229,4,0,0,"sales","low","exist"
"7950",9950,0.89,0.64,4,274,2,1,0,"sales","low","exist"
"7951",9951,0.75,0.41,5,196,4,0,0,"sales","low","exist"
"7952",9952,0.94,0.85,3,137,3,0,0,"sales","low","exist"
"7953",9953,0.5,0.75,4,239,2,0,0,"accounting","low","exist"
"7954",9954,0.75,0.95,4,177,3,0,0,"accounting","low","exist"
"7955",9955,0.84,0.78,5,164,3,0,0,"accounting","low","exist"
"7956",9956,0.55,0.81,5,229,3,0,0,"hr","low","exist"
"7957",9957,0.59,0.82,3,149,3,0,0,"hr","low","exist"
"7958",9958,0.58,0.43,3,224,6,0,0,"hr","low","exist"
"7959",9959,0.91,0.59,5,179,3,0,0,"hr","low","exist"
"7960",9960,0.43,0.92,5,151,4,0,0,"technical","low","exist"
"7961",9961,0.51,0.79,5,222,2,0,0,"technical","low","exist"
"7962",9962,0.81,0.96,4,219,2,0,0,"technical","low","exist"
"7963",9963,0.72,0.39,3,257,3,0,0,"technical","low","exist"
"7964",9964,0.89,0.99,4,258,3,1,0,"technical","medium","exist"
"7965",9965,0.61,0.74,5,185,2,1,0,"technical","medium","exist"
"7966",9966,0.57,0.7,3,248,2,0,0,"technical","medium","exist"
"7967",9967,0.74,0.82,5,154,2,0,0,"technical","medium","exist"
"7968",9968,0.87,0.64,3,187,2,0,0,"technical","medium","exist"
"7969",9969,0.58,0.62,3,182,3,0,0,"technical","medium","exist"
"7970",9970,0.63,0.59,3,189,3,0,0,"technical","medium","exist"
"7971",9971,0.89,0.85,4,195,4,0,0,"support","medium","exist"
"7972",9972,0.49,0.74,2,154,3,0,0,"support","medium","exist"
"7973",9973,0.59,0.59,4,244,3,0,0,"support","medium","exist"
"7974",9974,0.71,0.99,3,228,2,0,0,"support","medium","exist"
"7975",9975,0.58,0.62,3,218,3,0,0,"support","medium","exist"
"7976",9976,0.84,0.61,5,202,3,0,0,"support","high","exist"
"7977",9977,0.92,0.48,4,208,3,0,0,"support","low","exist"
"7978",9978,0.91,0.59,3,266,2,0,0,"support","medium","exist"
"7979",9979,0.92,0.78,4,177,2,0,0,"support","medium","exist"
"7980",9980,0.95,0.65,3,183,3,0,0,"support","medium","exist"
"7981",9981,0.53,0.62,4,201,3,0,0,"support","medium","exist"
"7982",9982,0.89,0.89,5,179,2,0,0,"technical","low","exist"
"7983",9983,0.81,0.84,3,198,2,0,0,"technical","low","exist"
"7984",9984,0.78,0.67,5,209,3,0,0,"technical","low","exist"
"7985",9985,0.66,0.48,3,203,4,1,0,"management","low","exist"
"7986",9986,0.37,0.71,6,266,5,0,0,"IT","low","exist"
"7987",9987,0.55,0.84,4,200,4,0,0,"IT","low","exist"
"7988",9988,0.79,0.88,3,195,4,0,0,"IT","low","exist"
"7989",9989,0.89,0.83,5,269,3,0,0,"IT","low","exist"
"7990",9990,0.54,0.76,5,226,2,0,0,"IT","low","exist"
"7991",9991,0.74,0.8,4,200,4,0,0,"product_mng","low","exist"
"7992",9992,0.7,0.47,2,176,5,0,0,"product_mng","low","exist"
"7993",9993,0.37,0.85,2,185,3,0,0,"product_mng","low","exist"
"7994",9994,0.84,0.71,3,179,2,0,0,"product_mng","low","exist"
"7995",9995,0.55,0.58,5,208,3,0,0,"IT","low","exist"
"7996",9996,0.93,0.79,5,241,4,0,0,"marketing","high","exist"
"7997",9997,0.97,0.55,4,166,3,1,0,"accounting","high","exist"
"7998",9998,0.64,0.53,3,216,3,0,0,"accounting","high","exist"
"7999",9999,0.62,0.64,4,185,2,0,0,"IT","medium","exist"
"8000",10000,0.26,0.91,5,183,6,0,0,"IT","medium","exist"
"8001",10001,0.93,0.49,4,255,2,0,0,"management","high","exist"
"8002",10002,0.27,0.61,3,213,6,0,0,"sales","low","exist"
"8003",10003,0.9,0.63,4,173,3,0,0,"accounting","medium","exist"
"8004",10004,0.16,0.7,6,246,4,0,0,"support","medium","exist"
"8005",10005,0.75,0.63,3,148,4,0,0,"technical","medium","exist"
"8006",10006,0.72,0.74,2,238,3,0,0,"management","medium","exist"
"8007",10007,0.68,0.51,3,185,3,0,0,"marketing","medium","exist"
"8008",10008,0.13,0.77,4,201,5,0,0,"marketing","medium","exist"
"8009",10009,0.96,0.92,3,150,2,0,0,"marketing","medium","exist"
"8010",10010,0.71,0.72,4,137,3,0,0,"sales","medium","exist"
"8011",10011,0.85,0.66,5,189,3,0,0,"sales","medium","exist"
"8012",10012,0.87,0.91,3,229,3,1,0,"sales","medium","exist"
"8013",10013,0.86,0.93,3,199,3,1,0,"sales","medium","exist"
"8014",10014,0.49,0.85,3,250,2,0,0,"sales","medium","exist"
"8015",10015,0.99,0.59,5,263,3,0,0,"sales","high","exist"
"8016",10016,0.75,0.95,3,268,3,1,0,"sales","low","exist"
"8017",10017,0.61,0.64,3,187,2,0,0,"sales","medium","exist"
"8018",10018,0.89,0.84,6,196,4,0,0,"sales","medium","exist"
"8019",10019,0.77,0.7,4,232,3,0,0,"sales","medium","exist"
"8020",10020,0.7,0.79,3,226,4,0,0,"sales","medium","exist"
"8021",10021,0.5,0.58,4,96,3,0,0,"sales","low","exist"
"8022",10022,0.61,1,4,133,4,0,0,"sales","low","exist"
"8023",10023,0.98,0.53,4,214,2,0,0,"sales","low","exist"
"8024",10024,0.61,0.77,4,252,2,0,0,"sales","low","exist"
"8025",10025,0.85,0.56,3,199,4,0,0,"sales","low","exist"
"8026",10026,0.42,0.85,3,150,3,0,0,"sales","low","exist"
"8027",10027,0.56,0.75,4,141,3,0,0,"sales","low","exist"
"8028",10028,0.88,0.8,4,239,3,0,0,"sales","low","exist"
"8029",10029,0.92,0.69,3,139,2,0,0,"accounting","low","exist"
"8030",10030,0.85,0.77,3,146,4,0,0,"accounting","low","exist"
"8031",10031,0.66,0.74,4,179,3,0,0,"accounting","low","exist"
"8032",10032,0.82,0.93,3,160,3,0,0,"hr","low","exist"
"8033",10033,0.14,0.58,6,205,3,0,0,"hr","low","exist"
"8034",10034,0.6,0.98,5,213,3,0,0,"hr","low","exist"
"8035",10035,0.92,0.65,4,260,2,0,0,"hr","low","exist"
"8036",10036,0.51,0.72,3,242,3,0,0,"technical","low","exist"
"8037",10037,0.85,0.46,3,123,5,1,0,"technical","low","exist"
"8038",10038,0.43,0.84,2,285,5,0,0,"technical","low","exist"
"8039",10039,0.98,0.56,3,103,5,0,0,"technical","medium","exist"
"8040",10040,0.84,0.55,5,264,3,0,0,"technical","medium","exist"
"8041",10041,0.7,0.52,3,227,2,0,0,"technical","medium","exist"
"8042",10042,0.82,0.82,5,267,2,0,0,"technical","medium","exist"
"8043",10043,0.94,0.67,3,142,3,0,0,"technical","medium","exist"
"8044",10044,0.55,0.79,3,254,3,1,0,"technical","medium","exist"
"8045",10045,0.98,0.5,3,251,3,0,0,"technical","medium","exist"
"8046",10046,0.78,0.63,4,158,3,0,0,"technical","medium","exist"
"8047",10047,0.99,0.77,5,160,3,0,0,"support","medium","exist"
"8048",10048,0.74,0.58,4,215,3,1,0,"support","medium","exist"
"8049",10049,0.5,0.74,4,237,2,1,0,"support","medium","exist"
"8050",10050,0.25,0.8,5,237,6,0,0,"support","medium","exist"
"8051",10051,0.49,0.55,4,268,4,0,0,"support","high","exist"
"8052",10052,0.63,0.74,4,234,3,0,0,"support","low","exist"
"8053",10053,0.68,0.73,3,250,2,0,0,"support","medium","exist"
"8054",10054,0.62,0.54,4,212,4,0,0,"support","medium","exist"
"8055",10055,0.89,0.52,4,189,3,0,0,"support","medium","exist"
"8056",10056,0.31,0.37,2,104,3,1,0,"support","medium","exist"
"8057",10057,0.89,0.61,3,211,2,0,0,"support","low","exist"
"8058",10058,0.72,0.65,3,109,5,1,0,"technical","low","exist"
"8059",10059,0.84,0.75,2,168,2,0,0,"technical","low","exist"
"8060",10060,0.88,0.71,3,184,3,0,0,"technical","low","exist"
"8061",10061,0.74,1,4,242,2,0,0,"management","low","exist"
"8062",10062,0.96,0.95,6,215,4,0,0,"IT","low","exist"
"8063",10063,0.82,0.89,5,182,3,0,0,"IT","low","exist"
"8064",10064,0.7,0.64,5,260,3,0,0,"IT","low","exist"
"8065",10065,0.61,0.84,4,265,2,0,0,"IT","low","exist"
"8066",10066,0.65,0.83,5,182,2,1,0,"IT","low","exist"
"8067",10067,0.77,0.64,3,191,3,1,0,"product_mng","low","exist"
"8068",10068,0.81,0.77,5,167,4,0,0,"product_mng","low","exist"
"8069",10069,0.87,0.66,3,270,2,0,0,"product_mng","low","exist"
"8070",10070,0.96,0.73,4,273,2,0,0,"product_mng","low","exist"
"8071",10071,0.48,0.7,3,251,3,0,0,"IT","low","exist"
"8072",10072,0.78,0.96,3,217,3,0,0,"marketing","high","exist"
"8073",10073,0.75,1,4,222,2,1,0,"accounting","high","exist"
"8074",10074,0.23,0.87,5,258,4,1,0,"accounting","high","exist"
"8075",10075,0.85,0.76,3,197,5,0,0,"IT","medium","exist"
"8076",10076,0.67,0.56,3,193,2,1,0,"IT","medium","exist"
"8077",10077,0.71,0.81,2,182,3,0,0,"management","high","exist"
"8078",10078,0.72,0.7,3,163,3,1,0,"sales","medium","exist"
"8079",10079,0.8,0.77,4,224,2,0,0,"accounting","medium","exist"
"8080",10080,0.64,0.86,4,143,2,0,0,"support","medium","exist"
"8081",10081,0.54,0.42,6,218,3,0,0,"technical","medium","exist"
"8082",10082,0.73,0.67,3,208,4,0,0,"management","medium","exist"
"8083",10083,0.73,1,2,229,3,0,0,"marketing","medium","exist"
"8084",10084,0.55,0.62,5,184,4,0,0,"marketing","medium","exist"
"8085",10085,0.63,0.41,3,180,5,1,0,"marketing","medium","exist"
"8086",10086,0.15,0.8,5,121,5,0,0,"sales","medium","exist"
"8087",10087,0.6,0.5,5,203,3,0,0,"sales","medium","exist"
"8088",10088,0.38,0.51,3,151,2,0,0,"sales","medium","exist"
"8089",10089,0.81,0.77,4,239,3,0,0,"sales","medium","exist"
"8090",10090,0.75,0.53,3,166,3,0,0,"sales","high","exist"
"8091",10091,0.52,0.92,3,268,3,0,0,"sales","low","exist"
"8092",10092,0.51,1,5,196,4,0,0,"sales","medium","exist"
"8093",10093,0.66,0.62,4,241,3,0,0,"sales","medium","exist"
"8094",10094,0.8,0.87,5,251,3,0,0,"sales","medium","exist"
"8095",10095,0.85,0.69,3,263,3,0,0,"sales","medium","exist"
"8096",10096,0.77,0.73,3,224,2,1,0,"sales","low","exist"
"8097",10097,0.29,0.4,4,138,4,0,0,"sales","low","exist"
"8098",10098,0.15,0.67,6,167,6,0,0,"sales","low","exist"
"8099",10099,0.73,0.83,5,266,5,0,0,"sales","low","exist"
"8100",10100,0.55,0.74,2,116,3,0,0,"sales","low","exist"
"8101",10101,0.2,0.69,2,160,4,0,0,"sales","low","exist"
"8102",10102,0.56,0.68,3,144,3,1,0,"sales","low","exist"
"8103",10103,0.55,0.54,3,190,3,0,0,"sales","low","exist"
"8104",10104,0.9,0.49,6,175,4,0,0,"sales","low","exist"
"8105",10105,0.73,0.55,3,206,4,0,0,"accounting","low","exist"
"8106",10106,0.48,0.99,5,180,2,0,0,"accounting","low","exist"
"8107",10107,0.64,0.74,4,157,4,0,0,"accounting","low","exist"
"8108",10108,0.95,0.75,4,203,3,0,0,"hr","low","exist"
"8109",10109,0.82,0.66,4,238,3,1,0,"hr","low","exist"
"8110",10110,0.95,0.65,3,273,4,0,0,"hr","low","exist"
"8111",10111,0.92,0.9,4,179,3,0,0,"hr","low","exist"
"8112",10112,0.22,0.84,3,131,5,1,0,"technical","low","exist"
"8113",10113,0.17,0.77,4,151,6,0,0,"technical","low","exist"
"8114",10114,0.51,0.55,3,261,3,0,0,"technical","low","exist"
"8115",10115,0.67,0.64,3,203,2,1,0,"technical","low","exist"
"8116",10116,0.6,0.66,5,143,2,0,0,"technical","low","exist"
"8117",10117,0.99,0.55,3,97,6,1,0,"technical","medium","exist"
"8118",10118,0.35,0.71,6,204,4,1,0,"technical","medium","exist"
"8119",10119,0.13,0.72,4,154,4,0,0,"technical","medium","exist"
"8120",10120,0.97,0.93,2,160,6,0,0,"technical","medium","exist"
"8121",10121,0.49,0.61,4,232,3,1,0,"technical","medium","exist"
"8122",10122,0.62,0.71,3,255,2,0,0,"technical","medium","exist"
"8123",10123,0.35,0.54,3,128,3,0,0,"support","medium","exist"
"8124",10124,0.81,0.79,4,222,3,0,0,"support","medium","exist"
"8125",10125,0.57,0.75,5,171,4,0,0,"support","medium","exist"
"8126",10126,1,0.66,4,173,2,0,0,"support","medium","exist"
"8127",10127,0.93,0.71,4,272,2,0,0,"support","medium","exist"
"8128",10128,0.89,0.85,3,166,3,0,0,"support","medium","exist"
"8129",10129,0.64,0.61,4,143,2,1,0,"support","high","exist"
"8130",10130,0.54,0.95,4,149,3,0,0,"support","low","exist"
"8131",10131,0.52,0.85,4,257,2,0,0,"support","medium","exist"
"8132",10132,0.12,0.65,5,262,6,0,0,"support","medium","exist"
"8133",10133,0.14,0.49,4,115,4,0,0,"support","medium","exist"
"8134",10134,0.57,0.54,4,142,4,0,0,"technical","medium","exist"
"8135",10135,0.57,0.64,4,144,4,0,0,"technical","low","exist"
"8136",10136,1,0.56,5,247,3,0,0,"technical","low","exist"
"8137",10137,0.94,0.58,4,216,3,0,0,"management","low","exist"
"8138",10138,0.93,0.48,3,276,3,0,0,"IT","low","exist"
"8139",10139,0.91,0.88,5,123,5,0,0,"IT","low","exist"
"8140",10140,0.85,0.77,4,264,3,0,0,"IT","low","exist"
"8141",10141,0.8,0.98,3,189,6,0,0,"IT","low","exist"
"8142",10142,0.68,0.69,3,148,2,0,0,"IT","low","exist"
"8143",10143,0.91,0.6,5,150,3,0,0,"product_mng","medium","exist"
"8144",10144,0.93,0.9,3,172,3,0,0,"product_mng","medium","exist"
"8145",10145,0.81,0.68,3,236,2,0,0,"product_mng","medium","exist"
"8146",10146,0.51,0.74,4,151,3,0,0,"product_mng","medium","exist"
"8147",10147,0.49,0.52,3,168,3,0,0,"IT","medium","exist"
"8148",10148,0.55,0.55,5,256,3,0,0,"RandD","medium","exist"
"8149",10149,0.17,0.51,6,213,3,0,0,"RandD","medium","exist"
"8150",10150,0.8,0.79,4,148,3,0,0,"RandD","medium","exist"
"8151",10151,0.61,0.67,3,266,3,0,0,"RandD","medium","exist"
"8152",10152,0.59,0.73,3,195,2,0,0,"RandD","medium","exist"
"8153",10153,0.67,0.77,4,242,3,0,0,"marketing","medium","exist"
"8154",10154,0.96,0.81,4,183,3,0,0,"sales","medium","exist"
"8155",10155,0.72,0.66,3,134,3,0,0,"accounting","medium","exist"
"8156",10156,0.72,0.76,4,189,2,0,0,"support","medium","exist"
"8157",10157,0.99,0.61,5,196,3,0,0,"technical","medium","exist"
"8158",10158,0.22,0.61,4,150,6,0,0,"management","medium","exist"
"8159",10159,0.32,0.52,4,191,2,0,0,"marketing","medium","exist"
"8160",10160,0.64,0.86,4,248,6,0,0,"marketing","medium","exist"
"8161",10161,0.9,0.49,3,256,2,0,0,"marketing","medium","exist"
"8162",10162,0.86,0.9,3,158,2,0,0,"sales","medium","exist"
"8163",10163,0.67,0.76,2,210,2,0,0,"sales","medium","exist"
"8164",10164,0.9,0.59,3,247,2,0,0,"sales","medium","exist"
"8165",10165,0.52,0.8,3,156,3,0,0,"sales","medium","exist"
"8166",10166,0.57,0.89,3,202,2,0,0,"sales","medium","exist"
"8167",10167,0.55,0.83,3,157,2,0,0,"sales","medium","exist"
"8168",10168,0.2,0.83,4,258,4,0,0,"sales","high","exist"
"8169",10169,0.89,0.66,4,176,3,0,0,"sales","high","exist"
"8170",10170,0.15,0.56,4,214,5,0,0,"sales","high","exist"
"8171",10171,0.8,0.6,3,212,3,0,0,"sales","high","exist"
"8172",10172,0.55,0.48,4,271,6,1,0,"sales","high","exist"
"8173",10173,0.53,0.64,5,281,4,0,0,"sales","high","exist"
"8174",10174,0.62,0.77,3,204,4,0,0,"sales","high","exist"
"8175",10175,1,0.58,3,112,2,0,0,"sales","high","exist"
"8176",10176,0.31,0.75,3,120,4,0,0,"sales","high","exist"
"8177",10177,0.62,0.51,5,134,3,0,0,"sales","high","exist"
"8178",10178,0.73,0.61,5,222,3,0,0,"sales","high","exist"
"8179",10179,0.52,0.61,3,203,3,0,0,"sales","high","exist"
"8180",10180,0.33,0.65,2,239,5,0,0,"sales","low","exist"
"8181",10181,0.88,0.5,3,142,3,0,0,"accounting","low","exist"
"8182",10182,0.65,0.54,2,177,3,0,0,"accounting","low","exist"
"8183",10183,0.91,0.7,6,201,2,1,0,"accounting","low","exist"
"8184",10184,0.83,0.91,3,196,4,0,0,"hr","low","exist"
"8185",10185,0.2,0.87,3,140,6,1,0,"hr","low","exist"
"8186",10186,0.96,0.8,5,195,4,1,0,"hr","low","exist"
"8187",10187,0.75,0.89,5,154,4,0,0,"hr","low","exist"
"8188",10188,0.93,0.57,3,141,2,0,0,"technical","low","exist"
"8189",10189,0.87,0.49,4,213,3,0,0,"technical","low","exist"
"8190",10190,0.94,0.58,5,222,3,0,0,"technical","low","exist"
"8191",10191,0.85,0.72,3,150,2,1,0,"technical","low","exist"
"8192",10192,0.63,0.5,4,172,2,0,0,"technical","low","exist"
"8193",10193,0.78,0.63,5,261,3,0,0,"technical","low","exist"
"8194",10194,0.87,0.92,2,248,3,0,0,"technical","low","exist"
"8195",10195,0.77,0.76,5,149,2,0,0,"technical","medium","exist"
"8196",10196,1,0.61,5,178,3,0,0,"technical","medium","exist"
"8197",10197,0.93,0.81,3,212,3,0,0,"technical","medium","exist"
"8198",10198,0.5,0.4,2,108,2,0,0,"technical","medium","exist"
"8199",10199,0.9,0.66,4,160,2,1,0,"support","medium","exist"
"8200",10200,0.61,0.56,2,160,3,0,0,"support","medium","exist"
"8201",10201,0.57,0.97,5,196,2,0,0,"support","medium","exist"
"8202",10202,0.43,0.5,4,121,5,1,0,"support","medium","exist"
"8203",10203,0.6,0.56,5,268,2,0,0,"support","medium","exist"
"8204",10204,0.56,0.92,3,232,2,0,0,"support","medium","exist"
"8205",10205,0.57,0.62,5,263,2,0,0,"support","medium","exist"
"8206",10206,0.56,0.82,3,208,2,0,0,"support","medium","exist"
"8207",10207,0.64,0.9,6,143,5,0,0,"support","high","exist"
"8208",10208,0.53,0.56,5,236,4,1,0,"support","high","exist"
"8209",10209,0.19,0.6,5,198,4,0,0,"support","high","exist"
"8210",10210,0.5,0.8,4,261,3,0,0,"technical","high","exist"
"8211",10211,0.86,0.97,4,258,3,0,0,"technical","high","exist"
"8212",10212,0.92,0.66,3,230,3,0,0,"technical","high","exist"
"8213",10213,0.82,0.97,3,137,3,0,0,"management","high","exist"
"8214",10214,0.54,0.51,5,258,3,0,0,"IT","high","exist"
"8215",10215,0.23,0.51,5,139,6,0,0,"IT","low","exist"
"8216",10216,0.65,0.71,4,186,2,0,0,"IT","low","exist"
"8217",10217,0.99,0.98,4,259,3,0,0,"IT","low","exist"
"8218",10218,0.54,0.59,4,202,3,0,0,"IT","low","exist"
"8219",10219,0.99,0.68,4,235,3,0,0,"product_mng","low","exist"
"8220",10220,0.76,0.89,4,224,2,0,0,"product_mng","low","exist"
"8221",10221,0.57,0.54,4,210,3,0,0,"product_mng","low","exist"
"8222",10222,0.53,0.75,4,240,4,0,0,"product_mng","low","exist"
"8223",10223,0.86,0.55,5,149,4,0,0,"IT","medium","exist"
"8224",10224,0.97,0.96,4,250,6,0,0,"RandD","medium","exist"
"8225",10225,0.13,0.76,5,171,5,0,0,"RandD","medium","exist"
"8226",10226,0.73,0.89,3,139,3,0,0,"RandD","medium","exist"
"8227",10227,0.62,0.95,4,132,3,0,0,"RandD","medium","exist"
"8228",10228,0.59,0.37,3,125,2,0,0,"RandD","medium","exist"
"8229",10229,0.63,0.7,6,141,3,1,0,"marketing","medium","exist"
"8230",10230,0.64,0.52,3,269,2,0,0,"sales","medium","exist"
"8231",10231,0.5,0.85,5,249,3,1,0,"accounting","medium","exist"
"8232",10232,0.58,0.89,3,256,2,0,0,"support","medium","exist"
"8233",10233,0.32,0.87,4,179,4,0,0,"technical","medium","exist"
"8234",10234,0.72,0.67,5,210,2,0,0,"management","medium","exist"
"8235",10235,0.61,0.74,3,160,3,0,0,"marketing","medium","exist"
"8236",10236,0.97,0.55,2,267,4,0,0,"marketing","medium","exist"
"8237",10237,0.87,0.64,2,169,3,1,0,"marketing","medium","exist"
"8238",10238,0.88,0.81,4,235,6,0,0,"sales","medium","exist"
"8239",10239,0.52,0.99,3,136,3,0,0,"sales","medium","exist"
"8240",10240,0.95,0.81,5,210,4,0,0,"sales","medium","exist"
"8241",10241,0.96,0.62,5,230,2,0,0,"sales","medium","exist"
"8242",10242,0.98,0.58,5,186,3,0,0,"sales","medium","exist"
"8243",10243,0.51,0.51,2,271,3,0,0,"sales","medium","exist"
"8244",10244,1,0.63,2,105,2,0,0,"sales","medium","exist"
"8245",10245,0.97,0.67,2,147,2,0,0,"sales","medium","exist"
"8246",10246,0.79,0.56,4,177,3,0,0,"sales","high","exist"
"8247",10247,0.64,0.45,3,135,6,0,0,"sales","low","exist"
"8248",10248,0.84,0.76,5,243,3,0,0,"sales","medium","exist"
"8249",10249,0.94,0.57,3,166,4,0,0,"sales","medium","exist"
"8250",10250,0.7,0.79,4,194,3,0,0,"sales","medium","exist"
"8251",10251,0.64,1,4,201,2,0,0,"sales","medium","exist"
"8252",10252,0.56,0.88,4,248,2,0,0,"sales","medium","exist"
"8253",10253,0.32,0.81,5,111,4,0,0,"sales","medium","exist"
"8254",10254,0.75,0.72,5,174,3,0,0,"sales","medium","exist"
"8255",10255,0.78,0.58,3,241,3,1,0,"sales","medium","exist"
"8256",10256,0.7,0.49,4,173,3,0,0,"sales","medium","exist"
"8257",10257,0.21,0.39,6,132,5,0,0,"accounting","medium","exist"
"8258",10258,0.64,0.96,3,274,3,0,0,"accounting","low","exist"
"8259",10259,0.54,0.52,3,115,3,0,0,"accounting","low","exist"
"8260",10260,0.79,0.98,4,170,3,0,0,"hr","low","exist"
"8261",10261,0.91,0.58,3,172,3,0,0,"hr","low","exist"
"8262",10262,0.76,0.73,4,148,2,1,0,"hr","low","exist"
"8263",10263,0.77,0.95,3,246,3,0,0,"hr","low","exist"
"8264",10264,0.92,0.88,4,151,3,0,0,"technical","low","exist"
"8265",10265,0.53,0.57,5,141,3,1,0,"technical","high","exist"
"8266",10266,0.44,0.52,3,269,4,0,0,"technical","low","exist"
"8267",10267,0.54,0.52,5,170,4,0,0,"technical","high","exist"
"8268",10268,0.93,0.5,2,135,3,0,0,"technical","high","exist"
"8269",10269,0.67,0.68,4,254,3,0,0,"technical","low","exist"
"8270",10270,0.66,0.99,3,228,2,0,0,"technical","low","exist"
"8271",10271,0.7,0.6,3,266,2,0,0,"technical","high","exist"
"8272",10272,0.79,0.57,4,152,3,0,0,"technical","low","exist"
"8273",10273,0.5,0.75,5,178,6,0,0,"technical","medium","exist"
"8274",10274,1,0.75,3,237,3,0,0,"technical","high","exist"
"8275",10275,0.61,0.52,5,255,3,0,0,"support","medium","exist"
"8276",10276,0.72,0.5,4,245,3,0,0,"support","medium","exist"
"8277",10277,0.78,0.95,3,155,3,1,0,"support","medium","exist"
"8278",10278,0.87,0.84,5,216,3,1,0,"support","medium","exist"
"8279",10279,0.57,0.58,3,251,2,0,0,"support","high","exist"
"8280",10280,0.85,0.96,2,260,3,0,0,"support","medium","exist"
"8281",10281,0.83,0.67,5,132,2,1,0,"support","medium","exist"
"8282",10282,0.4,0.37,3,169,3,1,0,"support","medium","exist"
"8283",10283,0.91,0.69,4,259,3,0,0,"support","high","exist"
"8284",10284,0.48,0.98,3,257,2,0,0,"support","medium","exist"
"8285",10285,0.94,0.58,5,190,3,1,0,"support","high","exist"
"8286",10286,0.65,0.76,4,171,4,0,0,"technical","low","exist"
"8287",10287,0.54,0.49,4,190,3,0,0,"technical","medium","exist"
"8288",10288,0.76,0.81,5,270,4,0,0,"technical","medium","exist"
"8289",10289,0.88,0.59,4,227,3,0,0,"management","medium","exist"
"8290",10290,0.9,0.55,3,195,3,0,0,"IT","medium","exist"
"8291",10291,0.64,0.75,3,169,3,0,0,"IT","low","exist"
"8292",10292,0.74,0.75,4,169,4,0,0,"IT","low","exist"
"8293",10293,0.45,0.54,2,184,2,0,0,"IT","low","exist"
"8294",10294,0.61,0.62,3,240,3,0,0,"IT","low","exist"
"8295",10295,0.16,0.97,6,282,4,0,0,"product_mng","low","exist"
"8296",10296,0.67,0.74,3,226,3,0,0,"product_mng","low","exist"
"8297",10297,0.74,0.74,2,254,3,0,0,"product_mng","low","exist"
"8298",10298,0.53,0.57,4,250,3,1,0,"product_mng","low","exist"
"8299",10299,0.75,0.98,3,143,2,0,0,"IT","low","exist"
"8300",10300,0.76,0.98,4,258,3,0,0,"RandD","low","exist"
"8301",10301,0.72,0.72,5,265,3,0,0,"RandD","high","exist"
"8302",10302,0.65,0.54,6,181,4,0,0,"RandD","low","exist"
"8303",10303,0.69,0.66,4,178,3,0,0,"RandD","low","exist"
"8304",10304,0.7,0.74,3,194,3,0,0,"RandD","low","exist"
"8305",10305,0.66,0.84,4,253,4,0,0,"marketing","low","exist"
"8306",10306,0.13,0.48,3,210,3,0,0,"sales","high","exist"
"8307",10307,0.67,0.53,3,264,2,0,0,"accounting","low","exist"
"8308",10308,0.99,0.7,3,219,3,0,0,"support","low","exist"
"8309",10309,0.51,0.86,3,198,2,0,0,"technical","low","exist"
"8310",10310,0.61,0.7,4,161,3,1,0,"management","high","exist"
"8311",10311,0.9,0.6,3,255,4,1,0,"marketing","low","exist"
"8312",10312,0.61,0.62,4,233,3,0,0,"marketing","medium","exist"
"8313",10313,0.15,0.89,3,251,4,0,0,"marketing","high","exist"
"8314",10314,0.53,0.85,5,268,3,1,0,"sales","medium","exist"
"8315",10315,0.41,0.48,3,135,3,0,0,"sales","high","exist"
"8316",10316,0.9,0.64,4,201,2,1,0,"sales","medium","exist"
"8317",10317,0.67,0.9,5,171,3,0,0,"sales","medium","exist"
"8318",10318,0.22,0.7,4,225,4,0,0,"sales","medium","exist"
"8319",10319,0.35,0.56,3,144,3,0,0,"sales","medium","exist"
"8320",10320,0.66,0.96,4,185,2,1,0,"sales","medium","exist"
"8321",10321,0.63,0.82,5,275,3,1,0,"sales","medium","exist"
"8322",10322,0.89,0.67,3,269,3,0,0,"sales","medium","exist"
"8323",10323,0.88,0.75,4,201,2,0,0,"sales","medium","exist"
"8324",10324,0.73,0.6,4,166,3,0,0,"sales","high","exist"
"8325",10325,1,0.83,4,280,4,1,0,"sales","low","exist"
"8326",10326,0.99,0.89,4,254,3,1,0,"sales","medium","exist"
"8327",10327,0.12,0.84,5,230,4,0,0,"sales","medium","exist"
"8328",10328,0.64,0.43,5,269,3,0,0,"sales","medium","exist"
"8329",10329,0.65,0.72,3,248,4,0,0,"sales","medium","exist"
"8330",10330,0.56,0.57,4,161,3,0,0,"sales","medium","exist"
"8331",10331,0.88,0.62,4,237,2,0,0,"sales","medium","exist"
"8332",10332,0.54,0.68,3,144,5,0,0,"sales","medium","exist"
"8333",10333,0.77,0.8,2,255,2,1,0,"accounting","medium","exist"
"8334",10334,0.66,0.67,5,148,3,0,0,"accounting","medium","exist"
"8335",10335,0.54,0.65,3,185,2,0,0,"accounting","high","exist"
"8336",10336,0.14,0.43,2,238,3,0,0,"hr","low","exist"
"8337",10337,0.85,0.69,5,273,3,1,0,"hr","low","exist"
"8338",10338,0.9,0.66,3,256,4,0,0,"hr","low","exist"
"8339",10339,0.81,0.79,4,177,2,0,0,"hr","high","exist"
"8340",10340,0.14,0.76,5,215,4,0,0,"technical","low","exist"
"8341",10341,0.94,0.96,4,270,3,1,0,"technical","low","exist"
"8342",10342,0.69,0.82,4,272,2,0,0,"technical","low","exist"
"8343",10343,0.66,0.67,4,268,2,0,0,"technical","high","exist"
"8344",10344,0.75,0.61,4,272,2,0,0,"technical","low","exist"
"8345",10345,0.53,0.61,4,182,3,0,0,"technical","low","exist"
"8346",10346,0.91,0.82,6,280,3,0,0,"technical","low","exist"
"8347",10347,0.93,0.61,4,205,3,0,0,"technical","low","exist"
"8348",10348,0.89,0.91,3,264,3,1,0,"technical","low","exist"
"8349",10349,0.84,0.79,2,150,3,0,0,"technical","low","exist"
"8350",10350,0.94,0.86,3,150,2,0,0,"technical","low","exist"
"8351",10351,1,0.86,4,195,4,0,0,"support","medium","exist"
"8352",10352,0.79,0.68,5,272,3,0,0,"support","medium","exist"
"8353",10353,0.62,0.61,3,171,3,0,0,"support","medium","exist"
"8354",10354,0.45,0.43,4,253,4,0,0,"support","medium","exist"
"8355",10355,0.54,0.48,3,158,2,0,0,"support","medium","exist"
"8356",10356,0.13,0.97,3,156,2,0,0,"support","medium","exist"
"8357",10357,0.99,0.73,3,181,2,0,0,"support","medium","exist"
"8358",10358,0.54,0.75,4,249,4,0,0,"support","medium","exist"
"8359",10359,0.52,0.38,3,132,4,0,0,"support","medium","exist"
"8360",10360,0.24,0.65,4,248,3,0,0,"support","medium","exist"
"8361",10361,0.12,0.7,5,277,3,0,0,"support","medium","exist"
"8362",10362,0.52,0.96,6,166,5,1,0,"technical","medium","exist"
"8363",10363,0.44,0.63,3,193,3,0,0,"technical","high","exist"
"8364",10364,0.81,0.53,3,148,3,0,0,"technical","low","exist"
"8365",10365,0.25,0.64,4,226,5,0,0,"management","medium","exist"
"8366",10366,0.63,0.91,3,233,4,0,0,"IT","medium","exist"
"8367",10367,0.61,0.46,3,171,3,0,0,"IT","medium","exist"
"8368",10368,0.51,0.56,4,157,3,0,0,"IT","medium","exist"
"8369",10369,0.66,0.54,5,191,2,0,0,"IT","low","exist"
"8370",10370,0.86,0.59,4,189,2,0,0,"IT","low","exist"
"8371",10371,0.98,0.89,5,181,3,0,0,"product_mng","low","exist"
"8372",10372,0.99,0.37,6,219,6,0,0,"product_mng","low","exist"
"8373",10373,0.78,0.91,3,166,2,1,0,"product_mng","low","exist"
"8374",10374,0.84,0.53,2,275,3,0,0,"product_mng","low","exist"
"8375",10375,0.17,0.59,6,160,2,0,0,"IT","low","exist"
"8376",10376,0.48,0.72,4,186,3,0,0,"RandD","low","exist"
"8377",10377,0.63,0.66,3,256,4,1,0,"RandD","low","exist"
"8378",10378,0.58,0.67,3,156,2,0,0,"RandD","low","exist"
"8379",10379,0.7,0.48,5,99,4,0,0,"RandD","low","exist"
"8380",10380,0.61,0.85,4,273,3,0,0,"RandD","low","exist"
"8381",10381,0.91,0.81,4,135,2,0,0,"marketing","low","exist"
"8382",10382,0.34,0.82,6,202,3,0,0,"sales","low","exist"
"8383",10383,0.56,0.49,4,256,3,0,0,"accounting","low","exist"
"8384",10384,0.93,0.81,3,143,3,0,0,"support","low","exist"
"8385",10385,0.56,0.81,4,216,2,0,0,"technical","low","exist"
"8386",10386,0.99,0.5,4,173,3,0,0,"management","low","exist"
"8387",10387,0.77,0.83,4,154,3,0,0,"marketing","low","exist"
"8388",10388,0.76,0.61,4,172,2,0,0,"marketing","low","exist"
"8389",10389,0.65,0.65,5,180,2,0,0,"marketing","low","exist"
"8390",10390,0.5,0.76,3,174,3,0,0,"sales","medium","exist"
"8391",10391,0.59,0.61,3,210,2,0,0,"sales","medium","exist"
"8392",10392,0.68,0.58,4,186,2,0,0,"sales","medium","exist"
"8393",10393,0.85,0.82,5,184,3,0,0,"sales","medium","exist"
"8394",10394,0.83,0.77,3,260,2,0,0,"sales","medium","exist"
"8395",10395,0.7,0.58,4,207,2,1,0,"sales","medium","exist"
"8396",10396,0.16,0.76,6,210,5,0,0,"sales","medium","exist"
"8397",10397,0.66,0.95,5,206,2,0,0,"sales","medium","exist"
"8398",10398,0.81,0.84,4,173,4,0,0,"sales","medium","exist"
"8399",10399,0.96,0.74,5,194,4,1,0,"sales","medium","exist"
"8400",10400,0.66,0.54,5,203,2,0,0,"sales","medium","exist"
"8401",10401,0.83,0.53,3,186,4,0,0,"sales","medium","exist"
"8402",10402,0.99,0.9,4,175,3,1,0,"sales","high","exist"
"8403",10403,0.99,0.83,4,274,2,0,0,"sales","low","exist"
"8404",10404,0.67,0.78,4,193,3,0,0,"sales","medium","exist"
"8405",10405,0.54,0.61,2,264,3,0,0,"sales","medium","exist"
"8406",10406,0.22,0.69,3,212,3,0,0,"sales","medium","exist"
"8407",10407,0.25,0.82,5,244,5,1,0,"sales","medium","exist"
"8408",10408,0.73,0.98,4,216,2,0,0,"sales","low","exist"
"8409",10409,1,0.88,4,252,4,0,0,"accounting","low","exist"
"8410",10410,0.4,0.58,3,135,3,0,0,"accounting","low","exist"
"8411",10411,0.45,0.5,5,99,4,1,0,"accounting","low","exist"
"8412",10412,0.61,0.81,5,136,3,0,0,"hr","low","exist"
"8413",10413,0.81,0.64,6,176,5,0,0,"hr","low","exist"
"8414",10414,0.61,0.76,4,135,2,0,0,"hr","low","exist"
"8415",10415,0.57,0.94,3,230,2,0,0,"hr","low","exist"
"8416",10416,0.9,0.65,4,221,3,0,0,"technical","low","exist"
"8417",10417,0.43,0.82,4,138,5,0,0,"technical","low","exist"
"8418",10418,0.99,0.98,4,169,3,1,0,"technical","low","exist"
"8419",10419,0.62,0.49,4,174,3,0,0,"technical","low","exist"
"8420",10420,0.63,0.65,3,162,2,0,0,"technical","low","exist"
"8421",10421,0.89,0.99,4,274,4,0,0,"technical","low","exist"
"8422",10422,0.61,0.84,3,206,2,0,0,"technical","low","exist"
"8423",10423,0.62,0.89,4,254,3,1,0,"technical","low","exist"
"8424",10424,0.86,0.61,4,181,4,0,0,"technical","low","exist"
"8425",10425,0.75,0.62,5,144,3,0,0,"technical","low","exist"
"8426",10426,0.63,0.54,4,147,4,0,0,"technical","low","exist"
"8427",10427,0.69,0.8,3,212,4,0,0,"support","low","exist"
"8428",10428,0.71,0.76,3,134,3,1,0,"support","low","exist"
"8429",10429,0.63,0.95,4,134,3,0,0,"support","medium","exist"
"8430",10430,0.89,0.7,3,256,4,0,0,"support","medium","exist"
"8431",10431,0.71,0.36,2,132,5,0,0,"support","medium","exist"
"8432",10432,0.88,0.82,4,109,2,0,0,"support","medium","exist"
"8433",10433,0.73,0.52,4,141,3,0,0,"support","medium","exist"
"8434",10434,0.52,0.83,4,180,2,0,0,"support","medium","exist"
"8435",10435,0.77,0.65,2,162,4,1,0,"support","medium","exist"
"8436",10436,0.94,0.48,4,143,2,0,0,"support","medium","exist"
"8437",10437,0.99,0.87,5,211,2,0,0,"support","medium","exist"
"8438",10438,0.89,0.56,4,225,4,0,0,"technical","medium","exist"
"8439",10439,0.53,0.52,2,135,4,0,0,"technical","medium","exist"
"8440",10440,0.23,0.64,3,228,4,1,0,"technical","medium","exist"
"8441",10441,0.87,0.73,5,111,4,0,0,"management","high","exist"
"8442",10442,0.21,0.69,3,144,6,1,0,"IT","low","exist"
"8443",10443,0.71,0.51,4,202,3,0,0,"IT","medium","exist"
"8444",10444,0.75,0.71,5,147,4,1,0,"IT","medium","exist"
"8445",10445,0.63,0.89,3,239,3,0,0,"IT","medium","exist"
"8446",10446,0.55,0.4,5,219,4,0,0,"IT","medium","exist"
"8447",10447,0.93,0.55,3,134,3,1,0,"product_mng","low","exist"
"8448",10448,0.53,0.89,3,167,2,0,0,"product_mng","low","exist"
"8449",10449,0.94,0.89,4,192,2,0,0,"product_mng","low","exist"
"8450",10450,0.46,0.82,2,189,5,0,0,"product_mng","low","exist"
"8451",10451,0.59,0.53,4,213,2,0,0,"IT","low","exist"
"8452",10452,0.75,0.56,5,231,2,0,0,"RandD","low","exist"
"8453",10453,0.76,0.63,3,198,3,0,0,"RandD","low","exist"
"8454",10454,0.96,0.89,3,163,2,0,0,"RandD","low","exist"
"8455",10455,0.55,0.93,4,251,4,1,0,"RandD","low","exist"
"8456",10456,0.52,0.82,3,170,3,0,0,"RandD","low","exist"
"8457",10457,0.55,0.5,5,231,3,0,0,"marketing","low","exist"
"8458",10458,0.52,0.98,4,165,2,0,0,"sales","low","exist"
"8459",10459,0.49,0.5,5,183,3,0,0,"accounting","low","exist"
"8460",10460,0.49,0.89,3,213,2,0,0,"support","low","exist"
"8461",10461,1,0.89,3,230,3,1,0,"technical","low","exist"
"8462",10462,0.97,0.62,3,167,3,0,0,"management","low","exist"
"8463",10463,0.97,0.89,3,264,3,0,0,"marketing","low","exist"
"8464",10464,0.21,0.43,2,249,3,0,0,"marketing","low","exist"
"8465",10465,0.24,0.7,6,153,5,1,0,"marketing","low","exist"
"8466",10466,0.76,0.79,3,111,2,0,0,"sales","low","exist"
"8467",10467,0.78,0.6,3,232,2,0,0,"sales","low","exist"
"8468",10468,0.59,0.52,6,190,4,0,0,"sales","medium","exist"
"8469",10469,0.54,0.71,3,145,3,1,0,"sales","medium","exist"
"8470",10470,0.34,0.69,2,193,3,0,0,"sales","medium","exist"
"8471",10471,0.91,0.82,3,183,2,0,0,"sales","medium","exist"
"8472",10472,0.49,0.61,3,240,3,0,0,"sales","medium","exist"
"8473",10473,0.71,1,5,210,3,0,0,"sales","medium","exist"
"8474",10474,0.64,0.72,4,152,3,1,0,"sales","medium","exist"
"8475",10475,0.6,0.61,4,140,4,0,0,"sales","medium","exist"
"8476",10476,0.91,0.66,3,208,4,0,0,"sales","medium","exist"
"8477",10477,0.92,0.6,3,198,2,0,0,"sales","medium","exist"
"8478",10478,0.91,0.52,3,178,3,0,0,"sales","medium","exist"
"8479",10479,0.88,0.77,3,279,3,1,0,"sales","medium","exist"
"8480",10480,0.86,0.82,3,263,6,0,0,"sales","high","exist"
"8481",10481,0.81,0.54,3,215,4,1,0,"sales","low","exist"
"8482",10482,0.84,0.73,3,181,3,1,0,"sales","medium","exist"
"8483",10483,0.56,0.55,2,270,3,0,0,"sales","medium","exist"
"8484",10484,0.6,0.52,3,236,3,0,0,"sales","medium","exist"
"8485",10485,0.71,0.87,3,271,2,0,0,"accounting","medium","exist"
"8486",10486,0.62,0.79,5,172,3,0,0,"accounting","low","exist"
"8487",10487,0.73,0.65,3,145,2,0,0,"accounting","low","exist"
"8488",10488,0.56,0.69,5,198,3,0,0,"hr","low","exist"
"8489",10489,0.6,0.74,3,175,3,1,0,"hr","low","exist"
"8490",10490,0.55,0.64,4,260,3,0,0,"hr","low","exist"
"8491",10491,0.5,0.7,4,135,3,0,0,"hr","low","exist"
"8492",10492,0.42,0.73,3,108,4,0,0,"technical","low","exist"
"8493",10493,0.51,0.94,5,260,4,0,0,"technical","low","exist"
"8494",10494,0.66,0.94,4,176,2,1,0,"technical","low","exist"
"8495",10495,0.79,0.67,5,222,4,0,0,"technical","low","exist"
"8496",10496,0.3,0.66,4,119,3,0,0,"technical","low","exist"
"8497",10497,0.57,1,3,241,4,0,0,"technical","low","exist"
"8498",10498,0.74,0.93,6,225,4,0,0,"technical","low","exist"
"8499",10499,0.98,0.56,5,188,3,0,0,"technical","low","exist"
"8500",10500,0.17,0.73,4,188,5,0,0,"technical","low","exist"
"8501",10501,0.62,0.77,3,225,4,1,0,"technical","low","exist"
"8502",10502,0.32,0.4,2,132,3,0,0,"technical","low","exist"
"8503",10503,0.58,0.91,5,185,2,0,0,"support","low","exist"
"8504",10504,0.59,0.9,4,173,3,0,0,"support","medium","exist"
"8505",10505,0.59,0.55,3,179,3,0,0,"support","medium","exist"
"8506",10506,0.8,0.58,4,189,2,0,0,"support","medium","exist"
"8507",10507,0.84,0.85,5,246,3,0,0,"support","medium","exist"
"8508",10508,0.54,0.76,2,166,4,0,0,"support","medium","exist"
"8509",10509,0.51,0.98,4,245,3,0,0,"support","medium","exist"
"8510",10510,0.66,0.56,2,104,3,1,0,"support","medium","exist"
"8511",10511,0.37,0.52,4,151,2,0,0,"support","medium","exist"
"8512",10512,0.49,0.63,4,213,3,0,0,"support","medium","exist"
"8513",10513,0.88,0.71,5,255,3,0,0,"support","medium","exist"
"8514",10514,0.66,0.9,4,268,3,0,0,"technical","medium","exist"
"8515",10515,0.25,0.53,4,160,4,0,0,"technical","medium","exist"
"8516",10516,0.49,0.52,4,267,2,0,0,"technical","high","exist"
"8517",10517,0.87,0.77,3,190,3,0,0,"management","low","exist"
"8518",10518,0.54,0.95,5,255,2,0,0,"IT","medium","exist"
"8519",10519,0.24,0.95,3,168,4,0,0,"IT","medium","exist"
"8520",10520,0.65,0.74,4,228,2,1,0,"IT","medium","exist"
"8521",10521,0.58,0.87,4,181,3,0,0,"IT","medium","exist"
"8522",10522,0.77,0.54,5,252,2,0,0,"IT","low","exist"
"8523",10523,0.86,0.63,4,244,3,0,0,"product_mng","low","exist"
"8524",10524,0.62,0.69,3,207,4,0,0,"product_mng","low","exist"
"8525",10525,0.56,0.48,3,134,3,0,0,"product_mng","low","exist"
"8526",10526,0.75,0.53,3,244,2,0,0,"product_mng","low","exist"
"8527",10527,0.8,0.96,4,160,4,0,0,"IT","low","exist"
"8528",10528,0.56,0.93,4,260,4,0,0,"RandD","low","exist"
"8529",10529,0.83,0.6,4,170,3,0,0,"RandD","low","exist"
"8530",10530,0.51,0.98,4,171,2,0,0,"RandD","low","exist"
"8531",10531,0.82,0.9,4,232,3,0,0,"RandD","low","exist"
"8532",10532,0.81,0.91,3,184,3,0,0,"RandD","low","exist"
"8533",10533,0.52,0.64,4,268,3,0,0,"marketing","low","exist"
"8534",10534,0.79,0.56,4,248,3,0,0,"sales","low","exist"
"8535",10535,0.83,0.5,5,274,3,0,0,"accounting","low","exist"
"8536",10536,0.97,0.81,3,145,3,0,0,"support","low","exist"
"8537",10537,0.61,0.88,5,134,4,0,0,"technical","low","exist"
"8538",10538,0.84,0.66,3,114,6,1,0,"management","low","exist"
"8539",10539,0.9,1,4,218,2,0,0,"marketing","low","exist"
"8540",10540,0.82,0.77,4,152,2,1,0,"marketing","low","exist"
"8541",10541,0.69,0.76,5,174,3,0,0,"marketing","low","exist"
"8542",10542,0.18,0.73,6,231,4,0,0,"sales","low","exist"
"8543",10543,0.33,1,2,210,3,1,0,"sales","medium","exist"
"8544",10544,0.15,0.92,5,164,3,0,0,"sales","medium","exist"
"8545",10545,0.61,0.78,4,198,3,0,0,"sales","medium","exist"
"8546",10546,0.92,0.55,4,220,2,0,0,"sales","medium","exist"
"8547",10547,0.13,0.61,6,283,5,0,0,"sales","medium","exist"
"8548",10548,0.18,0.48,4,240,4,0,0,"sales","medium","exist"
"8549",10549,0.27,0.85,5,142,6,0,0,"sales","medium","exist"
"8550",10550,0.66,0.61,4,263,4,0,0,"sales","medium","exist"
"8551",10551,0.21,0.81,5,142,4,0,0,"sales","medium","exist"
"8552",10552,0.92,0.9,4,203,4,0,0,"sales","medium","exist"
"8553",10553,0.97,0.5,3,266,3,1,0,"sales","medium","exist"
"8554",10554,0.97,0.7,3,253,3,0,0,"sales","medium","exist"
"8555",10555,0.64,0.61,4,136,3,0,0,"sales","high","exist"
"8556",10556,0.75,0.9,3,140,3,0,0,"sales","low","exist"
"8557",10557,0.9,0.76,4,252,4,0,0,"sales","medium","exist"
"8558",10558,0.81,0.75,5,101,5,0,0,"sales","medium","exist"
"8559",10559,0.99,0.72,3,163,3,0,0,"sales","medium","exist"
"8560",10560,0.49,0.5,5,170,2,0,0,"sales","medium","exist"
"8561",10561,0.92,0.4,2,238,3,0,0,"accounting","low","exist"
"8562",10562,0.74,0.56,4,190,3,1,0,"accounting","low","exist"
"8563",10563,0.37,0.37,5,173,2,0,0,"accounting","low","exist"
"8564",10564,0.67,0.61,4,145,4,0,0,"hr","low","exist"
"8565",10565,0.74,0.89,5,182,2,0,0,"hr","low","exist"
"8566",10566,0.85,0.64,4,188,3,0,0,"hr","low","exist"
"8567",10567,0.72,0.71,3,133,2,0,0,"hr","low","exist"
"8568",10568,0.75,0.71,4,155,4,0,0,"technical","low","exist"
"8569",10569,0.91,0.4,6,153,3,0,0,"technical","low","exist"
"8570",10570,0.84,0.62,4,138,3,0,0,"technical","low","exist"
"8571",10571,0.64,0.51,4,177,3,0,0,"technical","low","exist"
"8572",10572,0.15,0.91,6,98,6,1,0,"technical","low","exist"
"8573",10573,0.66,0.66,3,225,3,0,0,"technical","low","exist"
"8574",10574,0.2,0.69,6,236,4,0,0,"technical","low","exist"
"8575",10575,0.97,0.78,3,268,3,1,0,"technical","low","exist"
"8576",10576,0.59,0.73,2,230,3,0,0,"technical","low","exist"
"8577",10577,0.88,0.6,4,162,2,0,0,"technical","low","exist"
"8578",10578,0.16,0.73,4,197,2,0,0,"technical","low","exist"
"8579",10579,0.61,0.96,3,247,3,0,0,"support","low","exist"
"8580",10580,0.52,0.79,4,234,3,0,0,"support","low","exist"
"8581",10581,0.82,0.49,4,276,4,0,0,"support","low","exist"
"8582",10582,0.75,0.94,5,217,2,0,0,"support","medium","exist"
"8583",10583,0.62,0.5,4,156,2,0,0,"support","medium","exist"
"8584",10584,0.91,0.88,3,189,2,0,0,"support","medium","exist"
"8585",10585,0.61,0.98,2,238,4,0,0,"support","medium","exist"
"8586",10586,0.79,0.77,3,201,6,1,0,"support","medium","exist"
"8587",10587,0.9,0.93,4,263,3,1,0,"support","medium","exist"
"8588",10588,0.75,0.83,3,146,3,0,0,"support","medium","exist"
"8589",10589,0.81,0.64,4,213,3,0,0,"support","medium","exist"
"8590",10590,0.59,0.88,3,159,2,0,0,"technical","medium","exist"
"8591",10591,0.56,0.83,3,236,3,1,0,"technical","medium","exist"
"8592",10592,0.98,0.79,5,257,4,0,0,"technical","medium","exist"
"8593",10593,0.59,0.72,4,168,4,0,0,"management","medium","exist"
"8594",10594,0.61,0.67,4,151,3,0,0,"IT","high","exist"
"8595",10595,0.78,0.7,4,139,3,0,0,"IT","low","exist"
"8596",10596,0.55,0.93,5,196,3,0,0,"IT","medium","exist"
"8597",10597,0.2,0.97,4,237,5,0,0,"IT","medium","exist"
"8598",10598,0.79,0.44,2,236,3,0,0,"IT","medium","exist"
"8599",10599,0.52,0.98,4,265,3,0,0,"product_mng","medium","exist"
"8600",10600,0.97,0.52,4,207,3,0,0,"product_mng","low","exist"
"8601",10601,0.63,0.94,4,219,3,0,0,"product_mng","low","exist"
"8602",10602,0.85,0.99,3,208,2,0,0,"product_mng","low","exist"
"8603",10603,0.59,0.74,3,240,3,0,0,"IT","low","exist"
"8604",10604,0.64,0.6,3,135,3,0,0,"RandD","low","exist"
"8605",10605,0.8,0.67,3,236,3,1,0,"RandD","low","exist"
"8606",10606,0.61,0.75,3,140,3,0,0,"RandD","low","exist"
"8607",10607,0.87,0.61,3,162,2,0,0,"RandD","low","exist"
"8608",10608,0.75,0.59,3,117,3,1,0,"RandD","medium","exist"
"8609",10609,0.96,0.51,4,225,3,0,0,"marketing","medium","exist"
"8610",10610,0.75,0.92,3,211,3,0,0,"sales","medium","exist"
"8611",10611,0.19,0.58,4,173,5,0,0,"accounting","medium","exist"
"8612",10612,0.52,0.97,4,170,3,0,0,"support","medium","exist"
"8613",10613,0.6,0.6,3,242,3,0,0,"technical","medium","exist"
"8614",10614,0.9,0.81,4,175,3,0,0,"management","medium","exist"
"8615",10615,0.89,0.92,3,195,2,0,0,"marketing","medium","exist"
"8616",10616,0.54,0.93,4,184,2,1,0,"marketing","medium","exist"
"8617",10617,0.99,0.55,3,170,3,0,0,"marketing","medium","exist"
"8618",10618,0.66,0.56,4,185,3,0,0,"sales","medium","exist"
"8619",10619,0.92,0.64,4,259,2,0,0,"sales","medium","exist"
"8620",10620,0.19,0.72,4,102,3,0,0,"sales","medium","exist"
"8621",10621,0.39,0.37,5,156,4,0,0,"sales","medium","exist"
"8622",10622,0.41,0.68,3,191,4,0,0,"sales","medium","exist"
"8623",10623,0.6,0.49,3,239,2,0,0,"sales","medium","exist"
"8624",10624,0.95,0.54,4,235,4,0,0,"sales","medium","exist"
"8625",10625,0.51,0.87,2,130,4,0,0,"sales","medium","exist"
"8626",10626,0.54,0.74,2,166,3,0,0,"sales","medium","exist"
"8627",10627,0.16,0.54,5,206,5,0,0,"sales","medium","exist"
"8628",10628,0.98,0.77,3,191,2,0,0,"sales","medium","exist"
"8629",10629,0.65,0.75,3,214,3,0,0,"sales","medium","exist"
"8630",10630,0.38,0.5,3,196,3,0,0,"sales","medium","exist"
"8631",10631,0.95,0.71,4,151,4,0,0,"sales","medium","exist"
"8632",10632,0.6,0.62,5,165,2,0,0,"sales","medium","exist"
"8633",10633,0.78,0.91,3,177,2,0,0,"sales","high","exist"
"8634",10634,0.19,0.63,6,241,6,0,0,"sales","high","exist"
"8635",10635,0.56,0.99,4,230,3,0,0,"sales","high","exist"
"8636",10636,0.21,0.71,4,270,2,0,0,"sales","high","exist"
"8637",10637,0.83,0.71,3,234,4,0,0,"accounting","high","exist"
"8638",10638,0.5,0.64,3,257,2,1,0,"accounting","high","exist"
"8639",10639,0.74,0.87,5,264,3,0,0,"accounting","high","exist"
"8640",10640,0.75,0.83,4,133,4,0,0,"hr","high","exist"
"8641",10641,0.85,0.66,4,155,4,0,0,"hr","high","exist"
"8642",10642,0.93,0.59,3,202,2,0,0,"hr","high","exist"
"8643",10643,0.76,0.7,3,136,2,0,0,"hr","high","exist"
"8644",10644,0.91,0.78,3,269,3,1,0,"technical","high","exist"
"8645",10645,0.22,0.54,6,169,4,0,0,"technical","low","exist"
"8646",10646,0.78,0.52,5,192,3,1,0,"technical","low","exist"
"8647",10647,0.53,0.8,4,241,3,1,0,"technical","low","exist"
"8648",10648,0.58,0.69,4,165,3,0,0,"technical","low","exist"
"8649",10649,0.99,0.81,3,183,2,0,0,"technical","low","exist"
"8650",10650,0.62,0.64,4,163,3,0,0,"technical","low","exist"
"8651",10651,0.59,0.69,3,162,3,0,0,"technical","low","exist"
"8652",10652,0.13,0.76,5,219,4,0,0,"technical","low","exist"
"8653",10653,0.19,0.63,4,278,6,0,0,"technical","low","exist"
"8654",10654,0.94,0.99,2,273,4,0,0,"technical","low","exist"
"8655",10655,0.53,0.96,4,272,2,0,0,"support","low","exist"
"8656",10656,0.96,0.85,5,168,2,0,0,"support","low","exist"
"8657",10657,0.62,0.87,4,221,3,1,0,"support","low","exist"
"8658",10658,0.81,0.86,4,213,3,0,0,"support","low","exist"
"8659",10659,0.63,0.78,4,275,3,0,0,"support","low","exist"
"8660",10660,0.92,0.68,5,177,4,0,0,"support","medium","exist"
"8661",10661,0.83,0.74,4,249,2,0,0,"support","medium","exist"
"8662",10662,0.49,0.37,5,246,3,0,0,"support","medium","exist"
"8663",10663,0.8,0.66,4,223,3,0,0,"support","medium","exist"
"8664",10664,0.54,0.76,4,244,2,0,0,"support","medium","exist"
"8665",10665,0.37,0.72,3,169,2,1,0,"support","medium","exist"
"8666",10666,0.93,0.56,5,140,3,0,0,"technical","medium","exist"
"8667",10667,0.88,0.99,5,253,2,0,0,"technical","medium","exist"
"8668",10668,0.79,0.87,3,194,2,0,0,"technical","medium","exist"
"8669",10669,0.65,0.88,4,173,3,0,0,"management","medium","exist"
"8670",10670,0.72,0.7,4,172,3,0,0,"IT","medium","exist"
"8671",10671,0.58,0.49,3,167,3,0,0,"IT","medium","exist"
"8672",10672,0.37,0.51,2,153,3,0,0,"IT","high","exist"
"8673",10673,0.87,0.97,4,243,3,0,0,"IT","high","exist"
"8674",10674,0.63,0.72,6,163,4,0,0,"IT","high","exist"
"8675",10675,0.72,0.79,3,221,3,0,0,"product_mng","high","exist"
"8676",10676,0.36,0.55,3,191,3,0,0,"product_mng","high","exist"
"8677",10677,0.96,0.7,4,272,3,0,0,"product_mng","high","exist"
"8678",10678,0.52,0.37,2,118,2,0,0,"product_mng","high","exist"
"8679",10679,0.16,0.83,5,173,4,0,0,"IT","high","exist"
"8680",10680,0.63,0.55,4,200,3,1,0,"RandD","low","exist"
"8681",10681,0.92,0.76,5,132,3,1,0,"RandD","low","exist"
"8682",10682,0.82,0.49,4,180,2,0,0,"RandD","low","exist"
"8683",10683,0.18,0.54,4,145,5,0,0,"RandD","low","exist"
"8684",10684,0.73,0.48,4,139,2,0,0,"RandD","low","exist"
"8685",10685,0.44,0.61,5,230,6,0,0,"marketing","low","exist"
"8686",10686,0.73,0.62,4,247,4,0,0,"sales","low","exist"
"8687",10687,0.62,0.95,4,140,2,0,0,"accounting","low","exist"
"8688",10688,0.94,0.8,4,266,3,1,0,"support","medium","exist"
"8689",10689,0.76,0.74,4,261,3,0,0,"technical","medium","exist"
"8690",10690,0.89,0.49,4,275,3,0,0,"management","medium","exist"
"8691",10691,0.9,0.88,5,254,2,0,0,"marketing","medium","exist"
"8692",10692,1,0.93,5,231,2,0,0,"marketing","medium","exist"
"8693",10693,0.71,0.9,3,138,3,0,0,"marketing","medium","exist"
"8694",10694,0.73,0.97,4,163,3,0,0,"sales","medium","exist"
"8695",10695,0.97,0.9,5,262,3,0,0,"sales","medium","exist"
"8696",10696,0.6,0.59,4,201,3,0,0,"sales","medium","exist"
"8697",10697,0.82,0.67,3,229,3,0,0,"sales","medium","exist"
"8698",10698,0.95,0.48,4,228,2,0,0,"sales","medium","exist"
"8699",10699,0.88,0.65,5,228,3,0,0,"sales","medium","exist"
"8700",10700,0.79,0.49,3,273,3,0,0,"sales","medium","exist"
"8701",10701,0.52,0.96,4,171,2,0,0,"sales","medium","exist"
"8702",10702,0.22,0.61,3,148,5,0,0,"sales","medium","exist"
"8703",10703,0.59,0.96,5,211,3,0,0,"sales","medium","exist"
"8704",10704,0.84,0.64,2,211,3,0,0,"sales","medium","exist"
"8705",10705,0.54,0.41,3,175,3,0,0,"sales","medium","exist"
"8706",10706,1,0.86,4,245,4,0,0,"sales","medium","exist"
"8707",10707,0.93,0.59,3,273,2,1,0,"sales","medium","exist"
"8708",10708,0.96,0.55,3,225,4,1,0,"sales","medium","exist"
"8709",10709,0.56,0.41,5,152,3,0,0,"sales","medium","exist"
"8710",10710,0.49,0.66,5,194,3,0,0,"sales","medium","exist"
"8711",10711,0.89,0.51,4,185,3,1,0,"sales","high","exist"
"8712",10712,0.57,0.91,3,193,2,0,0,"sales","low","exist"
"8713",10713,0.96,0.64,3,166,2,0,0,"accounting","medium","exist"
"8714",10714,0.65,0.89,5,223,3,1,0,"accounting","medium","exist"
"8715",10715,0.14,0.66,5,281,4,1,0,"accounting","medium","exist"
"8716",10716,0.64,0.49,3,241,3,0,0,"hr","medium","exist"
"8717",10717,0.98,0.91,3,165,2,1,0,"hr","medium","exist"
"8718",10718,0.71,0.59,4,143,2,0,0,"hr","medium","exist"
"8719",10719,0.96,0.49,5,137,3,0,0,"hr","medium","exist"
"8720",10720,0.9,0.57,4,185,3,1,0,"technical","medium","exist"
"8721",10721,0.52,0.96,3,271,3,1,0,"technical","medium","exist"
"8722",10722,0.78,0.98,4,207,2,1,0,"technical","medium","exist"
"8723",10723,0.62,0.69,4,184,3,0,0,"technical","low","exist"
"8724",10724,0.6,0.8,4,253,2,0,0,"technical","low","exist"
"8725",10725,0.82,0.62,3,152,6,0,0,"technical","low","exist"
"8726",10726,0.52,0.55,3,225,2,0,0,"technical","low","exist"
"8727",10727,0.13,0.84,5,189,5,0,0,"technical","low","exist"
"8728",10728,0.97,0.93,3,153,2,0,0,"technical","low","exist"
"8729",10729,0.63,0.9,4,245,3,0,0,"technical","low","exist"
"8730",10730,0.68,0.78,5,233,3,0,0,"technical","high","exist"
"8731",10731,0.74,0.83,4,210,3,0,0,"support","low","exist"
"8732",10732,0.89,0.57,4,176,4,0,0,"support","high","exist"
"8733",10733,0.28,0.95,5,191,3,0,0,"support","high","exist"
"8734",10734,0.61,0.9,3,224,3,0,0,"support","low","exist"
"8735",10735,0.67,0.49,3,185,3,0,0,"support","low","exist"
"8736",10736,0.86,0.64,3,245,4,0,0,"support","high","exist"
"8737",10737,0.87,0.93,3,173,2,0,0,"support","low","exist"
"8738",10738,0.7,0.95,4,231,3,0,0,"support","medium","exist"
"8739",10739,0.68,0.84,3,270,3,0,0,"support","high","exist"
"8740",10740,0.69,0.75,5,196,3,0,0,"support","medium","exist"
"8741",10741,0.97,0.83,3,238,2,0,0,"support","medium","exist"
"8742",10742,0.62,0.89,4,261,2,0,0,"technical","medium","exist"
"8743",10743,0.55,0.87,3,201,2,0,0,"technical","medium","exist"
"8744",10744,0.61,0.73,3,252,3,0,0,"technical","high","exist"
"8745",10745,0.15,0.81,3,191,5,0,0,"management","medium","exist"
"8746",10746,0.84,0.86,3,199,3,0,0,"IT","medium","exist"
"8747",10747,0.87,0.64,5,234,2,1,0,"IT","medium","exist"
"8748",10748,0.93,0.86,4,192,4,0,0,"IT","high","exist"
"8749",10749,0.14,0.73,6,237,5,0,0,"IT","medium","exist"
"8750",10750,0.96,0.7,3,207,3,0,0,"IT","high","exist"
"8751",10751,0.41,0.63,2,145,2,0,0,"product_mng","low","exist"
"8752",10752,0.84,0.96,6,155,5,0,0,"product_mng","medium","exist"
"8753",10753,0.94,0.69,5,145,2,0,0,"product_mng","medium","exist"
"8754",10754,0.6,0.86,6,247,6,0,0,"product_mng","medium","exist"
"8755",10755,0.7,0.73,4,182,3,0,0,"IT","medium","exist"
"8756",10756,0.29,0.91,4,183,4,0,0,"RandD","low","exist"
"8757",10757,0.31,0.51,2,146,3,0,0,"RandD","low","exist"
"8758",10758,0.73,0.99,3,241,3,0,0,"RandD","low","exist"
"8759",10759,0.51,0.52,5,261,3,1,0,"RandD","low","exist"
"8760",10760,0.58,0.77,4,140,3,0,0,"RandD","low","exist"
"8761",10761,0.59,0.97,3,257,3,0,0,"marketing","low","exist"
"8762",10762,0.95,0.9,3,186,2,0,0,"marketing","low","exist"
"8763",10763,0.84,0.93,3,159,3,0,0,"sales","low","exist"
"8764",10764,0.28,0.37,3,164,4,1,0,"accounting","low","exist"
"8765",10765,0.94,0.52,4,217,6,1,0,"support","low","exist"
"8766",10766,0.49,0.59,4,137,4,0,0,"technical","high","exist"
"8767",10767,0.72,0.5,4,164,2,1,0,"management","low","exist"
"8768",10768,0.19,0.85,5,249,3,0,0,"marketing","low","exist"
"8769",10769,0.83,0.95,3,264,2,0,0,"marketing","low","exist"
"8770",10770,0.79,0.92,4,208,2,1,0,"marketing","low","exist"
"8771",10771,0.72,0.61,3,175,3,0,0,"sales","high","exist"
"8772",10772,0.97,0.74,4,209,2,0,0,"sales","low","exist"
"8773",10773,0.92,0.83,4,268,4,0,0,"sales","low","exist"
"8774",10774,0.95,0.53,3,264,3,0,0,"sales","low","exist"
"8775",10775,0.76,0.64,4,234,2,0,0,"sales","high","exist"
"8776",10776,0.24,0.62,5,199,4,0,0,"sales","low","exist"
"8777",10777,0.89,0.99,4,205,2,0,1,"sales","medium","exist"
"8778",10778,0.69,0.63,5,140,4,0,1,"sales","high","exist"
"8779",10779,0.92,0.98,3,257,3,0,1,"sales","medium","exist"
"8780",10780,0.79,0.61,4,227,2,0,1,"sales","high","exist"
"8781",10781,0.87,0.94,4,189,3,0,1,"sales","medium","exist"
"8782",10782,0.89,0.88,5,241,2,1,0,"sales","medium","exist"
"8783",10783,0.75,0.77,5,199,4,0,0,"sales","medium","exist"
"8784",10784,0.78,0.6,4,206,3,0,0,"sales","medium","exist"
"8785",10785,0.13,0.62,5,268,3,0,0,"sales","medium","exist"
"8786",10786,0.94,0.86,3,221,3,1,0,"sales","medium","exist"
"8787",10787,0.94,0.88,4,262,2,0,0,"sales","medium","exist"
"8788",10788,0.67,0.6,5,253,6,0,0,"sales","medium","exist"
"8789",10789,0.6,0.73,5,241,3,0,0,"sales","high","exist"
"8790",10790,0.62,0.94,4,252,4,0,0,"accounting","low","exist"
"8791",10791,0.38,0.52,2,171,3,0,0,"accounting","medium","exist"
"8792",10792,0.8,0.77,4,194,3,0,0,"accounting","medium","exist"
"8793",10793,0.61,0.42,3,104,2,0,0,"hr","medium","exist"
"8794",10794,0.61,0.56,4,176,3,0,0,"hr","medium","exist"
"8795",10795,0.66,0.8,4,192,3,0,0,"hr","medium","exist"
"8796",10796,0.56,0.74,3,154,2,0,0,"hr","medium","exist"
"8797",10797,1,0.55,4,186,4,1,0,"technical","medium","exist"
"8798",10798,0.73,0.86,3,200,4,0,0,"technical","medium","exist"
"8799",10799,0.6,0.66,4,132,4,0,0,"technical","medium","exist"
"8800",10800,0.78,0.59,5,236,3,0,0,"technical","high","exist"
"8801",10801,0.48,0.53,3,211,4,0,0,"technical","low","exist"
"8802",10802,0.9,0.77,4,273,2,0,0,"technical","low","exist"
"8803",10803,0.16,0.76,4,223,4,0,0,"technical","low","exist"
"8804",10804,0.5,0.75,3,204,2,0,0,"technical","high","exist"
"8805",10805,0.66,0.65,3,196,3,1,0,"technical","low","exist"
"8806",10806,0.44,0.37,2,219,2,0,0,"technical","low","exist"
"8807",10807,0.95,0.67,4,261,3,0,0,"technical","low","exist"
"8808",10808,0.9,0.65,3,254,2,0,0,"support","high","exist"
"8809",10809,0.27,0.48,4,185,2,0,0,"support","low","exist"
"8810",10810,0.51,0.74,6,98,3,0,0,"support","low","exist"
"8811",10811,0.68,0.76,3,260,4,0,0,"support","low","exist"
"8812",10812,0.97,0.93,5,137,2,1,0,"support","low","exist"
"8813",10813,0.91,0.75,4,159,3,1,0,"support","low","exist"
"8814",10814,0.76,0.88,5,265,4,0,0,"support","low","exist"
"8815",10815,0.88,0.61,4,177,4,1,0,"support","low","exist"
"8816",10816,0.83,0.73,4,247,2,0,0,"support","medium","exist"
"8817",10817,0.78,0.54,3,161,3,0,0,"support","medium","exist"
"8818",10818,0.52,0.38,2,103,3,0,0,"support","medium","exist"
"8819",10819,0.63,0.49,4,151,3,0,0,"technical","medium","exist"
"8820",10820,0.9,0.74,3,193,3,0,0,"technical","medium","exist"
"8821",10821,0.48,0.58,3,194,3,0,0,"technical","medium","exist"
"8822",10822,0.7,0.6,5,208,3,0,0,"management","medium","exist"
"8823",10823,0.68,0.66,4,229,3,0,0,"IT","medium","exist"
"8824",10824,0.7,0.87,3,166,2,0,0,"IT","medium","exist"
"8825",10825,0.77,0.5,3,141,3,0,0,"IT","medium","exist"
"8826",10826,0.73,0.93,3,249,2,0,0,"IT","medium","exist"
"8827",10827,0.87,0.48,4,264,3,0,0,"IT","medium","exist"
"8828",10828,0.65,0.98,3,252,2,0,0,"product_mng","high","exist"
"8829",10829,0.62,0.7,2,134,3,0,0,"product_mng","low","exist"
"8830",10830,0.53,0.51,3,274,2,1,0,"product_mng","medium","exist"
"8831",10831,0.59,0.39,5,200,4,0,0,"product_mng","medium","exist"
"8832",10832,0.87,0.72,2,154,3,0,0,"IT","medium","exist"
"8833",10833,0.47,0.53,3,111,4,0,0,"RandD","medium","exist"
"8834",10834,0.96,0.81,3,247,3,0,0,"RandD","low","exist"
"8835",10835,0.79,0.74,3,169,3,0,0,"RandD","low","exist"
"8836",10836,0.84,0.6,3,250,3,1,0,"RandD","low","exist"
"8837",10837,0.68,0.49,3,178,3,1,0,"RandD","low","exist"
"8838",10838,0.86,0.66,4,251,3,0,0,"RandD","low","exist"
"8839",10839,0.73,0.98,5,272,2,0,0,"marketing","low","exist"
"8840",10840,0.9,0.67,2,229,4,0,0,"sales","low","exist"
"8841",10841,0.63,0.64,3,180,3,0,0,"accounting","low","exist"
"8842",10842,0.71,0.72,3,271,2,0,0,"support","low","exist"
"8843",10843,0.71,0.68,5,226,3,0,0,"technical","low","exist"
"8844",10844,0.95,0.62,4,150,2,0,0,"management","low","exist"
"8845",10845,0.51,0.86,4,260,3,1,0,"marketing","low","exist"
"8846",10846,0.77,0.91,4,161,3,0,0,"marketing","low","exist"
"8847",10847,0.48,0.51,3,136,3,0,0,"marketing","low","exist"
"8848",10848,0.93,0.91,2,238,2,1,0,"sales","low","exist"
"8849",10849,0.83,0.86,4,98,4,0,0,"sales","low","exist"
"8850",10850,0.61,0.73,5,156,4,0,0,"sales","low","exist"
"8851",10851,0.97,0.89,4,248,2,0,0,"sales","low","exist"
"8852",10852,0.5,0.81,3,170,2,0,0,"sales","low","exist"
"8853",10853,0.84,0.54,3,245,3,0,0,"sales","low","exist"
"8854",10854,0.58,0.38,4,203,5,0,0,"sales","low","exist"
"8855",10855,0.59,0.72,3,182,3,0,0,"sales","medium","exist"
"8856",10856,0.77,0.83,3,175,3,0,1,"sales","medium","exist"
"8857",10857,0.78,0.4,4,145,5,1,1,"sales","medium","exist"
"8858",10858,0.6,0.96,4,220,3,1,1,"sales","medium","exist"
"8859",10859,0.53,0.77,4,259,2,1,1,"sales","medium","exist"
"8860",10860,0.73,0.69,3,228,2,0,1,"sales","medium","exist"
"8861",10861,0.76,0.94,3,189,3,0,0,"sales","medium","exist"
"8862",10862,0.12,0.61,6,257,3,0,0,"sales","medium","exist"
"8863",10863,0.2,0.98,3,180,6,0,0,"sales","medium","exist"
"8864",10864,0.5,0.77,4,180,3,0,0,"sales","medium","exist"
"8865",10865,0.79,0.65,5,215,2,1,0,"sales","medium","exist"
"8866",10866,0.96,0.68,3,132,2,0,0,"sales","medium","exist"
"8867",10867,0.26,0.69,5,213,2,0,0,"accounting","high","exist"
"8868",10868,0.8,0.72,4,173,3,0,0,"accounting","low","exist"
"8869",10869,0.43,0.71,3,186,2,0,0,"accounting","medium","exist"
"8870",10870,0.87,0.71,4,157,2,0,0,"hr","medium","exist"
"8871",10871,0.63,0.75,4,175,4,0,0,"hr","medium","exist"
"8872",10872,0.58,0.48,3,135,3,1,0,"hr","medium","exist"
"8873",10873,0.2,0.42,4,256,5,0,0,"hr","low","exist"
"8874",10874,0.62,0.71,4,268,3,0,0,"technical","low","exist"
"8875",10875,0.91,0.94,5,159,3,0,0,"technical","low","exist"
"8876",10876,0.66,0.91,3,191,4,0,0,"technical","low","exist"
"8877",10877,0.53,0.81,3,275,2,0,0,"technical","low","exist"
"8878",10878,0.52,0.98,5,217,2,1,0,"technical","low","exist"
"8879",10879,1,0.88,6,201,4,0,0,"technical","low","exist"
"8880",10880,0.73,0.67,4,205,3,0,0,"technical","low","exist"
"8881",10881,0.65,0.67,3,240,2,1,0,"technical","low","exist"
"8882",10882,0.5,0.95,5,137,3,0,0,"technical","low","exist"
"8883",10883,0.94,0.59,4,241,2,0,0,"technical","low","exist"
"8884",10884,0.48,0.86,5,198,4,0,0,"technical","low","exist"
"8885",10885,0.67,0.87,5,254,2,0,0,"support","low","exist"
"8886",10886,0.73,0.94,4,262,3,0,0,"support","low","exist"
"8887",10887,0.63,0.71,4,244,2,0,0,"support","low","exist"
"8888",10888,0.84,0.84,4,266,3,0,0,"support","low","exist"
"8889",10889,0.2,0.94,6,191,5,0,0,"support","low","exist"
"8890",10890,0.76,0.57,3,148,3,1,0,"support","low","exist"
"8891",10891,0.55,0.54,3,233,2,0,0,"support","low","exist"
"8892",10892,0.8,0.55,4,178,2,1,0,"support","low","exist"
"8893",10893,0.64,0.91,3,165,3,1,0,"support","low","exist"
"8894",10894,0.59,0.97,5,179,6,0,0,"support","medium","exist"
"8895",10895,0.92,0.98,3,149,3,0,0,"support","medium","exist"
"8896",10896,0.75,0.76,3,269,2,1,0,"technical","medium","exist"
"8897",10897,0.69,0.74,5,227,2,0,0,"technical","medium","exist"
"8898",10898,0.82,0.93,3,247,3,0,0,"technical","medium","exist"
"8899",10899,0.88,0.85,3,220,3,0,0,"management","medium","exist"
"8900",10900,0.89,0.91,3,233,2,0,0,"IT","medium","exist"
"8901",10901,1,0.79,5,171,5,0,0,"IT","medium","exist"
"8902",10902,0.66,0.91,4,234,2,1,0,"IT","medium","exist"
"8903",10903,0.76,0.92,3,176,2,0,0,"IT","medium","exist"
"8904",10904,0.8,0.62,5,190,4,1,0,"IT","medium","exist"
"8905",10905,0.58,0.86,4,168,2,0,0,"product_mng","medium","exist"
"8906",10906,0.73,0.93,3,205,3,0,0,"product_mng","high","exist"
"8907",10907,1,0.73,5,189,3,1,0,"product_mng","low","exist"
"8908",10908,0.18,0.9,4,282,4,0,0,"product_mng","medium","exist"
"8909",10909,0.47,0.46,2,152,2,0,0,"IT","medium","exist"
"8910",10910,0.92,0.64,4,217,4,0,0,"RandD","medium","exist"
"8911",10911,0.51,0.5,4,130,3,0,0,"RandD","medium","exist"
"8912",10912,0.81,0.62,4,153,4,0,0,"RandD","low","exist"
"8913",10913,0.52,0.57,3,270,3,0,0,"RandD","low","exist"
"8914",10914,0.95,0.96,3,220,3,0,0,"RandD","low","exist"
"8915",10915,0.93,0.64,4,253,3,0,0,"RandD","low","exist"
"8916",10916,0.98,0.67,4,209,6,0,0,"marketing","low","exist"
"8917",10917,0.79,0.79,4,231,2,0,0,"sales","low","exist"
"8918",10918,0.99,0.73,4,240,4,0,0,"accounting","low","exist"
"8919",10919,0.64,0.9,5,266,3,0,0,"support","low","exist"
"8920",10920,0.54,0.44,3,153,2,0,0,"technical","low","exist"
"8921",10921,0.79,0.59,4,187,2,0,0,"management","low","exist"
"8922",10922,0.55,0.98,4,185,2,1,0,"marketing","low","exist"
"8923",10923,0.18,0.81,4,147,4,0,0,"marketing","low","exist"
"8924",10924,0.56,0.81,4,188,3,1,0,"marketing","low","exist"
"8925",10925,0.92,0.67,2,252,2,0,0,"sales","low","exist"
"8926",10926,0.99,0.75,4,163,2,0,0,"sales","low","exist"
"8927",10927,0.77,0.85,4,189,2,0,0,"sales","low","exist"
"8928",10928,0.49,0.52,3,156,2,0,0,"sales","low","exist"
"8929",10929,0.98,0.58,3,183,3,0,0,"sales","low","exist"
"8930",10930,0.18,0.54,6,209,5,1,0,"sales","low","exist"
"8931",10931,0.8,0.82,4,271,4,0,0,"sales","low","exist"
"8932",10932,0.81,0.77,5,251,2,0,0,"sales","low","exist"
"8933",10933,0.13,0.61,5,198,5,0,0,"sales","medium","exist"
"8934",10934,0.58,0.97,3,274,4,1,1,"sales","medium","exist"
"8935",10935,0.75,0.63,4,209,3,0,1,"sales","medium","exist"
"8936",10936,0.8,0.94,4,271,4,0,1,"sales","medium","exist"
"8937",10937,0.78,0.6,4,143,2,0,1,"sales","medium","exist"
"8938",10938,0.92,0.6,5,236,3,1,1,"sales","medium","exist"
"8939",10939,0.85,0.98,5,222,3,0,1,"sales","medium","exist"
"8940",10940,0.52,0.63,3,233,3,0,1,"sales","medium","exist"
"8941",10941,0.95,0.84,3,270,3,1,1,"sales","medium","exist"
"8942",10942,0.81,0.92,5,258,3,0,1,"sales","medium","exist"
"8943",10943,0.16,0.82,6,202,4,1,1,"sales","medium","exist"
"8944",10944,0.91,0.74,3,150,2,0,0,"accounting","medium","exist"
"8945",10945,0.62,0.51,4,193,3,0,0,"accounting","high","exist"
"8946",10946,0.24,0.42,5,210,5,0,0,"accounting","low","exist"
"8947",10947,0.88,0.51,3,208,3,0,0,"hr","medium","exist"
"8948",10948,0.94,0.73,3,196,3,0,0,"hr","medium","exist"
"8949",10949,0.76,0.79,5,187,4,0,0,"hr","medium","exist"
"8950",10950,0.49,0.67,3,140,2,0,0,"hr","medium","exist"
"8951",10951,0.93,0.9,4,256,4,0,0,"technical","low","exist"
"8952",10952,0.92,0.66,4,113,3,0,0,"technical","low","exist"
"8953",10953,0.19,0.94,4,196,5,0,0,"technical","low","exist"
"8954",10954,0.66,0.76,3,170,3,0,0,"technical","low","exist"
"8955",10955,0.16,0.94,4,261,6,0,0,"technical","low","exist"
"8956",10956,0.83,0.99,5,132,3,0,0,"technical","low","exist"
"8957",10957,0.69,0.53,3,153,3,0,0,"technical","low","exist"
"8958",10958,0.82,0.53,3,147,3,1,0,"technical","low","exist"
"8959",10959,0.88,0.72,5,244,2,0,0,"technical","low","exist"
"8960",10960,0.31,0.42,4,108,4,0,0,"technical","low","exist"
"8961",10961,0.83,0.49,4,218,2,0,0,"technical","low","exist"
"8962",10962,0.94,0.52,5,133,3,0,0,"support","low","exist"
"8963",10963,0.65,0.79,5,233,3,0,0,"support","low","exist"
"8964",10964,0.6,0.6,4,147,3,0,0,"support","low","exist"
"8965",10965,0.52,0.43,3,176,3,0,0,"support","low","exist"
"8966",10966,0.66,0.89,4,169,4,0,0,"support","low","exist"
"8967",10967,0.87,0.87,4,144,3,0,0,"support","low","exist"
"8968",10968,0.2,0.99,5,151,3,1,0,"support","low","exist"
"8969",10969,0.63,0.91,4,252,3,1,0,"support","medium","exist"
"8970",10970,0.69,0.98,4,180,3,0,0,"support","medium","exist"
"8971",10971,0.48,0.61,3,251,3,0,0,"support","medium","exist"
"8972",10972,0.8,0.8,4,263,4,0,0,"support","medium","exist"
"8973",10973,0.89,0.74,5,260,6,0,0,"technical","medium","exist"
"8974",10974,0.67,0.63,3,227,3,0,0,"technical","medium","exist"
"8975",10975,0.37,0.86,6,260,3,0,0,"technical","medium","exist"
"8976",10976,0.93,0.61,5,158,3,0,0,"management","medium","exist"
"8977",10977,0.69,0.52,3,186,3,0,0,"IT","medium","exist"
"8978",10978,0.16,0.61,4,171,6,0,0,"IT","medium","exist"
"8979",10979,0.81,0.55,3,199,2,1,0,"IT","medium","exist"
"8980",10980,0.97,0.63,5,258,2,0,0,"IT","medium","exist"
"8981",10981,0.77,0.59,4,273,2,0,0,"IT","high","exist"
"8982",10982,0.75,0.78,2,259,3,0,0,"product_mng","low","exist"
"8983",10983,0.88,0.82,3,265,3,0,0,"product_mng","medium","exist"
"8984",10984,0.43,0.51,5,168,4,0,0,"product_mng","medium","exist"
"8985",10985,0.99,0.99,4,163,4,0,0,"product_mng","medium","exist"
"8986",10986,0.59,0.65,5,265,3,0,0,"IT","medium","exist"
"8987",10987,0.89,0.71,4,190,3,0,0,"RandD","low","exist"
"8988",10988,0.54,0.73,3,157,3,0,0,"RandD","low","exist"
"8989",10989,0.32,0.86,4,266,4,0,0,"RandD","low","exist"
"8990",10990,0.17,0.55,6,240,6,0,0,"RandD","low","exist"
"8991",10991,0.78,0.55,3,143,3,0,0,"RandD","low","exist"
"8992",10992,0.73,0.68,3,121,5,0,0,"RandD","low","exist"
"8993",10993,0.65,0.76,2,170,5,0,0,"IT","low","exist"
"8994",10994,0.8,0.71,4,161,4,0,0,"IT","low","exist"
"8995",10995,0.61,0.86,3,239,3,0,0,"IT","low","exist"
"8996",10996,0.67,0.49,3,224,3,0,0,"IT","low","exist"
"8997",10997,0.63,0.57,3,242,3,0,0,"product_mng","low","exist"
"8998",10998,0.51,0.58,4,140,2,1,0,"product_mng","low","exist"
"8999",10999,0.82,0.59,5,170,3,0,0,"product_mng","low","exist"
"9000",11000,0.79,0.67,5,156,2,0,0,"product_mng","low","exist"
"9001",11001,0.49,0.6,2,113,5,0,0,"IT","low","exist"
"9002",11002,0.7,0.59,3,138,3,0,0,"RandD","low","exist"
"9003",11003,0.13,0.5,3,137,5,0,0,"RandD","low","exist"
"9004",11004,0.83,0.52,5,217,3,0,0,"RandD","low","exist"
"9005",11005,0.83,0.91,3,155,3,0,0,"RandD","low","exist"
"9006",11006,0.19,0.83,5,280,4,0,0,"RandD","low","exist"
"9007",11007,0.8,0.81,5,248,2,1,0,"RandD","low","exist"
"9008",11008,0.49,0.67,2,190,8,0,0,"marketing","medium","exist"
"9009",11009,0.92,0.99,3,176,8,0,0,"sales","medium","exist"
"9010",11010,0.81,0.55,4,217,8,0,0,"accounting","medium","exist"
"9011",11011,0.62,0.91,3,269,8,0,0,"support","medium","exist"
"9012",11012,0.21,0.7,3,238,8,0,0,"technical","medium","exist"
"9013",11013,0.95,0.74,5,243,6,0,0,"management","medium","exist"
"9014",11014,0.51,0.8,4,198,6,0,0,"marketing","medium","exist"
"9015",11015,0.52,0.89,3,188,6,0,0,"marketing","medium","exist"
"9016",11016,0.64,0.56,3,257,6,0,0,"marketing","medium","exist"
"9017",11017,0.62,0.79,4,268,6,0,0,"sales","medium","exist"
"9018",11018,0.73,0.88,5,233,4,1,0,"sales","medium","exist"
"9019",11019,0.32,0.86,4,214,5,0,0,"sales","medium","exist"
"9020",11020,0.78,0.96,2,285,3,0,0,"sales","high","exist"
"9021",11021,0.65,0.91,4,224,2,1,0,"sales","low","exist"
"9022",11022,0.56,0.92,4,224,3,0,0,"sales","medium","exist"
"9023",11023,0.96,0.89,3,142,4,0,0,"sales","medium","exist"
"9024",11024,0.79,0.82,4,220,3,0,0,"sales","medium","exist"
"9025",11025,0.66,0.58,4,244,3,0,0,"sales","medium","exist"
"9026",11026,0.67,0.68,4,171,3,0,0,"sales","low","exist"
"9027",11027,0.86,0.82,4,274,2,1,0,"sales","low","exist"
"9028",11028,0.57,0.72,4,214,2,1,0,"sales","low","exist"
"9029",11029,0.86,0.87,5,171,2,0,0,"sales","low","exist"
"9030",11030,0.52,0.59,5,150,2,0,0,"sales","low","exist"
"9031",11031,0.73,0.61,4,260,2,1,0,"sales","low","exist"
"9032",11032,0.78,0.63,5,259,3,0,0,"sales","low","exist"
"9033",11033,0.95,0.63,3,153,2,0,0,"sales","low","exist"
"9034",11034,0.75,0.61,3,263,3,0,0,"sales","low","exist"
"9035",11035,0.83,0.52,2,149,2,1,0,"sales","low","exist"
"9036",11036,0.48,1,4,261,3,0,0,"accounting","low","exist"
"9037",11037,0.3,0.58,2,189,4,1,0,"accounting","low","exist"
"9038",11038,0.72,0.85,5,237,4,0,0,"accounting","low","exist"
"9039",11039,0.61,0.52,3,224,3,0,0,"hr","low","exist"
"9040",11040,0.31,0.87,6,240,3,1,0,"hr","low","exist"
"9041",11041,0.62,0.81,3,245,2,1,0,"hr","low","exist"
"9042",11042,0.48,0.49,3,268,3,0,0,"hr","low","exist"
"9043",11043,0.97,0.89,4,208,2,1,0,"technical","low","exist"
"9044",11044,0.61,0.83,4,153,2,0,0,"technical","low","exist"
"9045",11045,0.93,0.99,3,169,3,0,0,"technical","low","exist"
"9046",11046,0.89,0.39,5,218,2,0,0,"technical","low","exist"
"9047",11047,0.95,0.9,3,155,3,0,0,"technical","medium","exist"
"9048",11048,0.36,0.44,5,155,3,0,0,"technical","medium","exist"
"9049",11049,0.29,0.39,6,105,6,0,0,"technical","medium","exist"
"9050",11050,0.65,0.83,4,251,2,0,0,"technical","medium","exist"
"9051",11051,0.72,0.54,4,219,2,0,0,"technical","medium","exist"
"9052",11052,0.51,0.56,4,198,2,1,0,"technical","medium","exist"
"9053",11053,0.54,0.53,4,158,2,0,0,"technical","medium","exist"
"9054",11054,0.66,0.58,3,157,2,0,0,"support","medium","exist"
"9055",11055,0.59,0.54,4,178,2,0,0,"support","medium","exist"
"9056",11056,0.45,0.48,3,145,2,0,0,"support","medium","exist"
"9057",11057,0.15,0.91,5,230,3,0,0,"support","medium","exist"
"9058",11058,0.95,0.53,3,174,3,0,0,"support","medium","exist"
"9059",11059,0.49,0.59,5,140,3,0,0,"support","high","exist"
"9060",11060,0.68,0.97,3,174,2,0,0,"support","low","exist"
"9061",11061,0.7,0.76,4,173,2,0,0,"support","medium","exist"
"9062",11062,0.9,0.73,2,203,4,0,0,"support","medium","exist"
"9063",11063,0.94,0.95,5,170,3,0,0,"support","medium","exist"
"9064",11064,0.8,0.86,3,203,3,0,0,"support","medium","exist"
"9065",11065,0.59,0.53,5,169,3,0,0,"technical","low","exist"
"9066",11066,0.43,0.96,3,109,6,0,0,"technical","low","exist"
"9067",11067,0.7,0.54,5,263,3,0,0,"technical","low","exist"
"9068",11068,0.51,0.62,4,185,3,0,0,"management","low","exist"
"9069",11069,0.12,0.49,4,191,5,0,0,"IT","low","exist"
"9070",11070,0.14,0.56,5,259,4,1,0,"IT","low","exist"
"9071",11071,0.86,0.91,4,253,3,0,0,"IT","low","exist"
"9072",11072,0.97,0.5,3,216,3,0,0,"IT","low","exist"
"9073",11073,1,0.86,2,264,3,0,0,"IT","medium","exist"
"9074",11074,0.49,0.63,3,181,3,1,0,"product_mng","medium","exist"
"9075",11075,0.9,0.93,3,209,3,0,0,"product_mng","medium","exist"
"9076",11076,0.82,0.89,4,239,2,0,0,"product_mng","medium","exist"
"9077",11077,0.59,0.48,3,197,3,0,0,"product_mng","medium","exist"
"9078",11078,0.97,0.57,4,150,2,0,0,"IT","medium","exist"
"9079",11079,0.69,0.88,3,164,10,0,0,"management","medium","exist"
"9080",11080,0.73,0.84,3,216,8,0,0,"management","medium","exist"
"9081",11081,0.48,0.74,2,271,8,1,0,"management","medium","exist"
"9082",11082,0.94,0.49,4,176,8,0,0,"management","medium","exist"
"9083",11083,0.74,0.73,3,156,8,0,0,"management","medium","exist"
"9084",11084,0.65,0.63,4,143,8,0,0,"management","medium","exist"
"9085",11085,0.93,0.94,4,233,6,0,0,"IT","medium","exist"
"9086",11086,0.57,0.67,3,138,6,1,0,"IT","medium","exist"
"9087",11087,0.9,0.49,3,259,6,0,0,"IT","medium","exist"
"9088",11088,0.55,0.86,4,169,6,0,0,"IT","medium","exist"
"9089",11089,0.59,0.73,3,172,6,0,0,"product_mng","medium","exist"
"9090",11090,0.72,0.98,4,156,3,0,0,"product_mng","medium","exist"
"9091",11091,0.87,0.52,4,140,3,0,0,"product_mng","medium","exist"
"9092",11092,0.86,0.82,4,212,2,0,0,"product_mng","medium","exist"
"9093",11093,0.61,0.5,4,269,3,0,0,"IT","medium","exist"
"9094",11094,0.45,0.63,5,111,5,0,0,"management","medium","exist"
"9095",11095,0.51,0.63,4,198,2,0,0,"management","medium","exist"
"9096",11096,0.87,0.92,4,263,3,0,0,"management","medium","exist"
"9097",11097,0.29,0.38,5,191,5,0,0,"management","medium","exist"
"9098",11098,0.57,0.64,3,188,3,0,0,"management","medium","exist"
"9099",11099,0.69,0.83,4,252,3,0,0,"management","medium","exist"
"9100",11100,0.61,0.9,2,142,3,0,0,"marketing","high","exist"
"9101",11101,0.96,0.85,4,247,3,0,0,"sales","high","exist"
"9102",11102,0.16,0.61,6,269,2,0,0,"accounting","high","exist"
"9103",11103,0.96,0.82,4,244,3,0,0,"support","high","exist"
"9104",11104,0.77,0.81,4,164,3,0,0,"technical","high","exist"
"9105",11105,0.85,0.87,6,232,5,0,0,"management","high","exist"
"9106",11106,0.37,0.49,3,177,3,0,0,"marketing","high","exist"
"9107",11107,0.68,0.65,3,173,3,1,0,"marketing","high","exist"
"9108",11108,0.87,0.6,5,165,2,1,0,"marketing","high","exist"
"9109",11109,0.95,0.8,3,225,2,0,0,"sales","high","exist"
"9110",11110,0.84,0.63,3,121,3,1,0,"sales","low","exist"
"9111",11111,0.44,0.51,2,219,4,0,0,"sales","low","exist"
"9112",11112,0.94,0.73,4,204,2,0,0,"sales","low","exist"
"9113",11113,0.85,0.94,5,235,4,0,0,"sales","low","exist"
"9114",11114,0.75,0.51,2,215,2,1,0,"sales","low","exist"
"9115",11115,0.76,0.67,5,243,3,0,0,"sales","low","exist"
"9116",11116,0.13,0.97,4,162,6,0,0,"sales","low","exist"
"9117",11117,0.6,0.79,4,262,3,0,0,"sales","low","exist"
"9118",11118,0.45,0.55,4,206,2,0,0,"sales","low","exist"
"9119",11119,0.49,1,2,125,4,1,0,"sales","low","exist"
"9120",11120,0.19,0.36,3,167,5,0,0,"sales","low","exist"
"9121",11121,0.68,0.89,5,218,5,0,0,"sales","low","exist"
"9122",11122,0.53,0.91,5,181,3,0,0,"sales","low","exist"
"9123",11123,1,0.77,5,269,3,0,0,"sales","low","exist"
"9124",11124,0.99,0.86,3,167,2,0,0,"sales","low","exist"
"9125",11125,0.29,0.75,6,271,10,0,0,"sales","medium","exist"
"9126",11126,0.54,0.83,4,201,8,1,0,"sales","medium","exist"
"9127",11127,0.25,0.9,6,229,8,0,0,"sales","medium","exist"
"9128",11128,0.71,0.76,4,148,8,0,0,"accounting","medium","exist"
"9129",11129,0.96,0.84,3,147,8,0,0,"accounting","medium","exist"
"9130",11130,0.8,0.9,4,211,8,0,0,"accounting","medium","exist"
"9131",11131,0.82,0.87,5,145,6,0,0,"hr","medium","exist"
"9132",11132,0.19,0.97,6,269,6,0,0,"hr","medium","exist"
"9133",11133,0.43,0.74,4,129,6,0,0,"hr","medium","exist"
"9134",11134,0.62,0.84,3,270,6,0,0,"hr","medium","exist"
"9135",11135,0.75,0.85,3,250,6,0,0,"technical","medium","exist"
"9136",11136,0.56,0.48,5,192,2,1,0,"technical","medium","exist"
"9137",11137,0.88,0.91,4,233,4,0,0,"technical","high","exist"
"9138",11138,0.63,0.57,4,192,3,0,0,"technical","high","exist"
"9139",11139,0.75,0.93,3,247,2,0,0,"technical","high","exist"
"9140",11140,0.74,1,4,192,4,0,0,"technical","high","exist"
"9141",11141,0.55,0.68,3,178,3,1,0,"technical","high","exist"
"9142",11142,0.87,0.55,4,197,3,0,0,"technical","high","exist"
"9143",11143,0.13,0.9,5,264,6,0,0,"technical","high","exist"
"9144",11144,0.33,0.64,2,274,3,1,0,"technical","high","exist"
"9145",11145,0.89,0.97,4,147,2,0,0,"technical","low","exist"
"9146",11146,0.56,0.94,3,154,3,1,0,"support","low","exist"
"9147",11147,0.95,0.61,3,224,2,1,0,"support","low","exist"
"9148",11148,0.57,0.59,4,250,2,0,0,"support","low","exist"
"9149",11149,0.72,0.53,3,179,3,0,0,"support","low","exist"
"9150",11150,0.28,0.44,4,170,2,0,0,"support","low","exist"
"9151",11151,0.54,0.61,4,118,5,0,0,"support","low","exist"
"9152",11152,0.54,0.95,4,256,3,0,0,"support","low","exist"
"9153",11153,0.99,0.8,3,209,2,0,0,"support","medium","exist"
"9154",11154,0.37,0.69,2,146,3,0,0,"support","medium","exist"
"9155",11155,0.77,0.87,3,275,4,1,0,"support","medium","exist"
"9156",11156,0.7,0.88,4,180,2,0,0,"support","medium","exist"
"9157",11157,0.8,0.74,3,228,3,0,0,"technical","medium","exist"
"9158",11158,0.52,0.63,3,204,3,0,0,"technical","medium","exist"
"9159",11159,0.69,0.55,3,172,2,0,0,"technical","medium","exist"
"9160",11160,0.6,0.62,5,274,3,0,0,"management","medium","exist"
"9161",11161,0.74,0.64,3,136,2,0,0,"IT","medium","exist"
"9162",11162,0.69,0.82,4,252,3,1,0,"IT","medium","exist"
"9163",11163,0.78,0.89,4,137,3,0,0,"IT","medium","exist"
"9164",11164,0.77,0.75,4,191,3,0,0,"IT","medium","exist"
"9165",11165,0.91,0.68,4,132,4,0,0,"IT","medium","exist"
"9166",11166,0.54,0.68,6,249,5,0,0,"product_mng","medium","exist"
"9167",11167,0.48,0.77,6,274,6,0,0,"product_mng","medium","exist"
"9168",11168,0.55,0.96,3,194,3,0,0,"product_mng","medium","exist"
"9169",11169,0.17,0.36,6,191,2,0,0,"product_mng","medium","exist"
"9170",11170,0.77,0.83,5,216,4,0,0,"IT","medium","exist"
"9171",11171,0.93,0.98,3,241,3,0,0,"IT","medium","exist"
"9172",11172,0.65,0.91,4,243,5,1,0,"IT","medium","exist"
"9173",11173,0.67,0.52,4,207,3,0,0,"IT","medium","exist"
"9174",11174,0.95,0.88,3,199,3,0,0,"IT","medium","exist"
"9175",11175,0.61,0.97,6,286,4,0,0,"product_mng","medium","exist"
"9176",11176,0.57,0.39,4,132,3,0,0,"product_mng","high","exist"
"9177",11177,0.65,1,4,229,4,0,0,"product_mng","low","exist"
"9178",11178,0.85,0.81,4,260,3,0,0,"product_mng","medium","exist"
"9179",11179,0.61,0.96,3,214,2,0,0,"IT","medium","exist"
"9180",11180,0.65,0.9,6,217,4,1,1,"RandD","medium","exist"
"9181",11181,0.92,0.93,4,225,2,0,1,"RandD","medium","exist"
"9182",11182,0.37,0.41,2,113,3,0,1,"RandD","medium","exist"
"9183",11183,0.48,0.77,5,250,2,0,1,"RandD","medium","exist"
"9184",11184,0.82,0.91,5,271,2,0,1,"RandD","medium","exist"
"9185",11185,0.84,0.75,4,135,3,0,1,"RandD","medium","exist"
"9186",11186,0.57,0.46,2,100,6,1,1,"marketing","medium","exist"
"9187",11187,0.8,0.75,4,224,3,0,1,"sales","medium","exist"
"9188",11188,0.49,0.91,4,134,4,0,0,"accounting","low","exist"
"9189",11189,0.79,0.82,5,158,2,0,0,"support","low","exist"
"9190",11190,0.48,0.67,3,183,2,0,0,"technical","low","exist"
"9191",11191,0.28,0.89,4,97,6,0,0,"management","low","exist"
"9192",11192,0.47,0.56,4,226,3,0,0,"marketing","low","exist"
"9193",11193,0.91,0.6,4,235,4,1,0,"marketing","low","exist"
"9194",11194,0.75,0.6,4,186,10,1,0,"marketing","low","exist"
"9195",11195,0.61,0.89,3,242,10,0,0,"sales","high","exist"
"9196",11196,0.47,0.79,3,284,10,0,0,"sales","low","exist"
"9197",11197,0.22,0.7,2,274,10,0,0,"sales","high","exist"
"9198",11198,0.5,0.48,4,130,10,0,0,"sales","high","exist"
"9199",11199,0.56,0.87,3,146,10,0,0,"sales","low","exist"
"9200",11200,0.84,0.85,4,207,10,0,0,"sales","low","exist"
"9201",11201,0.69,0.72,4,210,2,1,0,"sales","high","exist"
"9202",11202,0.53,0.64,3,143,2,0,0,"sales","low","exist"
"9203",11203,0.17,0.57,4,116,3,0,0,"sales","medium","exist"
"9204",11204,0.48,0.71,2,162,3,1,0,"sales","high","exist"
"9205",11205,0.94,0.51,3,242,3,0,0,"sales","medium","exist"
"9206",11206,0.77,0.89,4,153,7,0,0,"sales","medium","exist"
"9207",11207,1,0.72,5,194,7,1,0,"sales","medium","exist"
"9208",11208,0.49,0.65,4,233,7,0,0,"sales","medium","exist"
"9209",11209,0.93,0.73,4,283,7,0,0,"sales","high","exist"
"9210",11210,0.38,0.43,3,188,7,0,0,"sales","medium","exist"
"9211",11211,0.6,0.54,4,182,6,0,0,"sales","medium","exist"
"9212",11212,0.5,0.82,2,286,6,0,0,"sales","medium","exist"
"9213",11213,0.97,0.55,5,212,6,0,0,"sales","high","exist"
"9214",11214,0.93,0.95,5,176,6,0,1,"accounting","medium","exist"
"9215",11215,0.5,1,5,264,8,0,1,"accounting","high","exist"
"9216",11216,0.52,0.84,3,261,8,0,1,"accounting","low","exist"
"9217",11217,0.5,0.71,4,163,8,0,1,"hr","medium","exist"
"9218",11218,0.55,0.4,3,139,8,0,1,"hr","medium","exist"
"9219",11219,0.95,0.84,3,261,8,1,1,"hr","medium","exist"
"9220",11220,0.48,0.42,2,275,6,1,1,"hr","medium","exist"
"9221",11221,0.51,0.39,5,132,6,1,1,"technical","low","exist"
"9222",11222,0.96,0.48,3,202,6,0,0,"technical","low","exist"
"9223",11223,0.97,0.84,4,177,6,0,0,"technical","low","exist"
"9224",11224,0.97,0.66,5,234,6,0,0,"technical","low","exist"
"9225",11225,0.71,0.54,4,188,6,0,0,"technical","low","exist"
"9226",11226,0.82,0.49,5,203,6,0,0,"technical","low","exist"
"9227",11227,0.57,1,4,227,10,0,0,"technical","low","exist"
"9228",11228,0.48,0.93,3,150,10,0,0,"technical","low","exist"
"9229",11229,0.71,0.64,3,267,3,0,0,"technical","low","exist"
"9230",11230,0.63,0.61,5,186,10,0,0,"technical","low","exist"
"9231",11231,0.99,0.84,4,142,10,0,0,"technical","high","exist"
"9232",11232,0.79,0.83,3,126,10,1,0,"support","low","exist"
"9233",11233,0.65,0.85,4,201,10,0,0,"support","low","exist"
"9234",11234,0.7,0.85,4,142,2,0,0,"support","low","exist"
"9235",11235,0.99,0.94,4,167,4,0,0,"support","low","exist"
"9236",11236,0.65,0.62,4,258,2,0,0,"support","high","exist"
"9237",11237,0.92,0.85,3,207,2,0,0,"support","low","exist"
"9238",11238,0.24,0.5,4,282,4,1,0,"support","low","exist"
"9239",11239,0.39,0.89,3,188,5,0,0,"support","low","exist"
"9240",11240,0.82,0.85,3,214,2,0,0,"support","high","exist"
"9241",11241,0.78,0.89,4,272,2,0,0,"support","low","exist"
"9242",11242,0.62,0.79,3,259,3,0,0,"support","medium","exist"
"9243",11243,0.6,0.61,5,191,2,1,0,"technical","high","exist"
"9244",11244,0.49,0.57,3,192,3,0,0,"technical","medium","exist"
"9245",11245,0.82,0.82,3,164,3,0,0,"technical","high","exist"
"9246",11246,0.48,0.81,4,149,2,0,0,"management","medium","exist"
"9247",11247,0.69,0.56,4,149,3,0,0,"IT","medium","exist"
"9248",11248,0.4,0.89,2,165,3,0,0,"IT","medium","exist"
"9249",11249,0.72,0.8,3,222,3,0,0,"IT","medium","exist"
"9250",11250,0.75,0.84,5,222,3,1,0,"IT","medium","exist"
"9251",11251,0.5,0.77,3,265,3,0,0,"IT","medium","exist"
"9252",11252,0.78,0.5,5,247,4,0,0,"product_mng","medium","exist"
"9253",11253,0.76,0.45,4,147,2,0,0,"product_mng","medium","exist"
"9254",11254,0.94,0.52,3,273,3,0,0,"product_mng","high","exist"
"9255",11255,0.24,0.94,6,144,4,0,0,"product_mng","low","exist"
"9256",11256,0.99,0.66,3,181,2,0,0,"IT","medium","exist"
"9257",11257,0.67,0.64,3,198,2,1,0,"management","medium","exist"
"9258",11258,0.76,0.57,5,255,4,0,0,"management","medium","exist"
"9259",11259,0.76,0.77,4,169,10,0,0,"management","medium","exist"
"9260",11260,0.55,0.64,4,201,10,1,0,"management","medium","exist"
"9261",11261,0.74,0.6,4,274,10,1,0,"management","medium","exist"
"9262",11262,0.81,0.85,4,134,10,1,0,"management","medium","exist"
"9263",11263,0.98,0.67,3,190,10,0,0,"IT","medium","exist"
"9264",11264,0.98,0.98,4,170,10,0,0,"IT","medium","exist"
"9265",11265,0.58,0.91,3,154,10,0,0,"product_mng","high","exist"
"9266",11266,0.18,0.75,3,142,2,0,0,"product_mng","low","exist"
"9267",11267,0.57,0.67,5,235,2,0,0,"product_mng","low","exist"
"9268",11268,0.7,0.62,3,110,3,0,0,"product_mng","low","exist"
"9269",11269,0.49,0.77,3,211,3,0,0,"IT","high","exist"
"9270",11270,0.7,0.56,4,214,3,0,1,"management","medium","exist"
"9271",11271,0.16,0.93,5,210,7,0,1,"management","medium","exist"
"9272",11272,0.58,0.59,3,207,7,0,1,"management","medium","exist"
"9273",11273,0.66,0.57,4,161,7,0,1,"management","medium","exist"
"9274",11274,0.51,0.55,2,102,7,0,1,"management","medium","exist"
"9275",11275,0.48,0.84,4,186,7,0,1,"management","medium","exist"
"9276",11276,0.56,0.71,3,211,6,0,1,"marketing","low","exist"
"9277",11277,0.81,0.62,3,240,6,0,1,"sales","low","exist"
"9278",11278,0.57,0.7,4,237,6,0,0,"accounting","low","exist"
"9279",11279,0.66,0.53,3,164,6,0,0,"support","low","exist"
"9280",11280,0.22,0.91,6,222,8,0,0,"technical","low","exist"
"9281",11281,0.96,0.71,3,205,8,1,0,"management","medium","exist"
"9282",11282,0.87,0.88,4,140,8,0,0,"marketing","medium","exist"
"9283",11283,0.61,0.42,2,103,8,0,0,"marketing","medium","exist"
"9284",11284,0.66,0.85,3,178,8,1,0,"marketing","medium","exist"
"9285",11285,0.9,0.51,4,137,6,0,0,"sales","medium","exist"
"9286",11286,0.64,0.67,3,143,6,0,0,"sales","medium","exist"
"9287",11287,0.76,0.82,4,170,6,0,0,"sales","medium","exist"
"9288",11288,0.97,0.41,5,135,6,0,0,"sales","medium","exist"
"9289",11289,0.69,0.76,3,174,6,0,0,"sales","medium","exist"
"9290",11290,0.98,0.55,3,166,6,1,0,"sales","medium","exist"
"9291",11291,0.18,0.61,5,174,6,0,0,"sales","medium","exist"
"9292",11292,0.62,0.91,3,251,10,0,0,"sales","medium","exist"
"9293",11293,0.29,0.37,6,187,10,1,0,"sales","high","exist"
"9294",11294,0.87,0.48,5,170,3,0,0,"sales","low","exist"
"9295",11295,0.91,0.64,3,241,10,0,0,"sales","medium","exist"
"9296",11296,0.53,0.79,3,221,10,1,0,"sales","medium","exist"
"9297",11297,0.69,0.73,4,257,10,1,0,"sales","medium","exist"
"9298",11298,0.14,0.58,4,275,10,0,0,"sales","medium","exist"
"9299",11299,0.7,0.77,4,245,2,0,0,"sales","low","exist"
"9300",11300,0.77,0.75,6,246,6,0,0,"sales","low","exist"
"9301",11301,0.77,0.76,6,263,6,0,0,"sales","low","exist"
"9302",11302,0.76,0.99,3,133,4,0,0,"sales","low","exist"
"9303",11303,0.66,0.49,4,157,3,0,0,"sales","low","exist"
"9304",11304,0.5,0.95,3,198,4,0,0,"accounting","low","exist"
"9305",11305,0.57,0.9,5,145,3,0,0,"accounting","low","exist"
"9306",11306,0.97,0.62,6,118,2,0,0,"accounting","low","exist"
"9307",11307,0.26,0.99,5,184,5,0,0,"hr","low","exist"
"9308",11308,0.72,0.62,3,243,2,1,0,"hr","low","exist"
"9309",11309,0.83,0.93,3,247,2,0,0,"hr","low","exist"
"9310",11310,0.55,0.4,3,158,3,0,0,"hr","low","exist"
"9311",11311,0.77,0.74,5,243,2,0,0,"technical","low","exist"
"9312",11312,0.24,0.63,4,203,5,0,0,"technical","low","exist"
"9313",11313,0.8,0.96,3,161,3,0,0,"technical","low","exist"
"9314",11314,0.5,0.59,4,214,3,1,0,"technical","low","exist"
"9315",11315,0.66,0.59,4,179,3,0,0,"technical","low","exist"
"9316",11316,0.66,0.77,4,188,2,0,0,"technical","low","exist"
"9317",11317,0.66,0.81,3,174,3,0,0,"technical","low","exist"
"9318",11318,0.96,0.83,3,177,4,0,0,"technical","low","exist"
"9319",11319,0.75,0.94,5,194,4,0,0,"technical","low","exist"
"9320",11320,0.78,0.77,3,244,2,0,0,"technical","medium","exist"
"9321",11321,0.57,0.82,4,269,2,0,0,"technical","medium","exist"
"9322",11322,0.78,0.68,2,159,3,1,0,"support","medium","exist"
"9323",11323,0.57,0.88,4,140,2,0,0,"support","medium","exist"
"9324",11324,0.84,0.56,5,224,2,0,0,"support","medium","exist"
"9325",11325,0.23,0.94,5,242,4,0,0,"support","medium","exist"
"9326",11326,0.53,0.37,3,180,3,0,0,"support","medium","exist"
"9327",11327,0.82,0.71,3,150,3,0,0,"support","medium","exist"
"9328",11328,0.59,0.64,5,269,3,0,0,"support","medium","exist"
"9329",11329,0.5,0.52,2,178,2,0,0,"support","medium","exist"
"9330",11330,1,0.74,2,187,3,0,0,"support","medium","exist"
"9331",11331,0.94,0.61,3,140,2,0,0,"support","medium","exist"
"9332",11332,0.86,0.61,4,193,2,0,0,"support","high","exist"
"9333",11333,0.73,0.49,4,243,2,0,0,"technical","low","exist"
"9334",11334,0.49,0.94,3,144,3,1,0,"technical","medium","exist"
"9335",11335,0.79,0.73,2,147,2,0,0,"technical","medium","exist"
"9336",11336,0.83,0.5,6,165,3,0,0,"management","medium","exist"
"9337",11337,0.85,0.67,3,176,2,0,0,"IT","medium","exist"
"9338",11338,0.65,0.37,3,170,6,0,0,"IT","low","exist"
"9339",11339,0.94,0.65,4,213,2,1,0,"IT","low","exist"
"9340",11340,0.76,0.81,4,242,2,0,0,"IT","low","exist"
"9341",11341,0.77,0.54,4,139,3,1,0,"IT","low","exist"
"9342",11342,0.77,0.91,4,239,3,1,0,"product_mng","low","exist"
"9343",11343,0.59,0.64,5,123,2,0,0,"product_mng","low","exist"
"9344",11344,0.69,0.9,3,185,4,0,0,"product_mng","low","exist"
"9345",11345,0.51,0.85,4,186,2,0,0,"product_mng","low","exist"
"9346",11346,0.8,0.67,3,178,3,0,0,"IT","low","exist"
"9347",11347,0.98,0.7,3,153,10,0,0,"management","high","exist"
"9348",11348,0.69,0.72,4,185,10,1,0,"management","high","exist"
"9349",11349,0.14,0.76,4,142,10,0,0,"management","high","exist"
"9350",11350,0.95,0.9,4,221,10,1,0,"management","high","exist"
"9351",11351,0.53,0.61,3,148,10,0,0,"management","high","exist"
"9352",11352,0.64,0.52,5,258,10,1,0,"management","high","exist"
"9353",11353,0.51,0.73,4,229,3,0,0,"sales","low","exist"
"9354",11354,0.36,0.73,2,111,2,0,0,"sales","low","exist"
"9355",11355,0.62,0.97,2,271,3,0,0,"sales","low","exist"
"9356",11356,0.98,0.58,4,133,3,0,0,"sales","low","exist"
"9357",11357,0.53,0.7,4,223,3,0,0,"sales","low","exist"
"9358",11358,0.8,0.95,4,272,2,0,0,"sales","low","exist"
"9359",11359,0.73,0.77,3,233,3,0,0,"sales","medium","exist"
"9360",11360,0.82,0.8,3,162,3,0,0,"sales","medium","exist"
"9361",11361,0.62,0.75,5,165,4,0,0,"sales","medium","exist"
"9362",11362,0.87,0.48,5,242,3,0,0,"sales","medium","exist"
"9363",11363,0.43,0.65,4,124,2,0,0,"sales","medium","exist"
"9364",11364,0.57,0.6,2,163,3,0,0,"sales","medium","exist"
"9365",11365,0.91,0.77,3,144,3,0,0,"sales","medium","exist"
"9366",11366,0.75,0.59,5,149,4,0,0,"sales","medium","exist"
"9367",11367,0.76,0.8,5,217,2,0,0,"sales","medium","exist"
"9368",11368,0.85,0.49,4,139,2,0,0,"sales","medium","exist"
"9369",11369,0.56,0.67,3,270,2,0,0,"sales","medium","exist"
"9370",11370,0.86,0.84,3,177,3,0,0,"sales","medium","exist"
"9371",11371,0.21,0.43,5,175,2,1,0,"sales","high","exist"
"9372",11372,0.94,0.59,3,151,2,0,0,"sales","low","exist"
"9373",11373,0.98,0.74,3,185,3,0,0,"sales","medium","exist"
"9374",11374,0.42,0.45,3,227,3,0,0,"sales","medium","exist"
"9375",11375,0.98,0.89,4,218,2,0,0,"sales","medium","exist"
"9376",11376,1,0.93,5,167,3,0,0,"sales","medium","exist"
"9377",11377,0.95,0.52,3,183,2,1,0,"sales","low","exist"
"9378",11378,0.95,0.5,4,259,3,0,0,"sales","low","exist"
"9379",11379,0.68,0.53,3,138,2,1,0,"sales","low","exist"
"9380",11380,0.64,0.38,5,122,4,0,0,"sales","low","exist"
"9381",11381,0.24,0.62,6,225,6,0,0,"sales","low","exist"
"9382",11382,0.37,0.72,3,121,2,0,0,"sales","low","exist"
"9383",11383,0.67,0.4,4,274,3,0,0,"sales","low","exist"
"9384",11384,0.86,0.89,4,153,4,0,0,"sales","low","exist"
"9385",11385,0.43,0.38,3,119,2,0,0,"sales","low","exist"
"9386",11386,0.67,0.67,4,141,2,1,0,"sales","low","exist"
"9387",11387,0.92,0.6,4,161,3,0,0,"IT","low","exist"
"9388",11388,0.43,0.46,2,186,2,0,0,"product_mng","low","exist"
"9389",11389,0.52,0.8,3,252,4,0,0,"product_mng","low","exist"
"9390",11390,0.16,0.42,3,182,3,1,0,"product_mng","low","exist"
"9391",11391,0.49,0.6,4,264,2,1,0,"product_mng","low","exist"
"9392",11392,0.37,0.63,4,167,3,0,0,"IT","low","exist"
"9393",11393,0.98,0.68,5,171,3,0,0,"management","high","exist"
"9394",11394,0.33,0.97,5,130,4,0,0,"management","high","exist"
"9395",11395,0.14,0.79,5,271,4,0,0,"management","high","exist"
"9396",11396,0.54,0.79,5,249,3,1,0,"management","high","exist"
"9397",11397,0.63,0.48,4,180,4,0,0,"management","high","exist"
"9398",11398,0.55,0.69,4,220,3,1,0,"management","high","exist"
"9399",11399,0.84,0.53,3,210,4,1,0,"marketing","medium","exist"
"9400",11400,0.51,0.97,4,258,2,0,0,"sales","medium","exist"
"9401",11401,0.15,0.75,3,150,4,0,1,"accounting","medium","exist"
"9402",11402,0.97,0.79,5,259,3,0,1,"support","medium","exist"
"9403",11403,0.67,0.69,3,231,3,0,1,"technical","medium","exist"
"9404",11404,0.48,0.67,4,220,3,0,1,"management","medium","exist"
"9405",11405,0.69,0.58,4,149,3,0,1,"marketing","medium","exist"
"9406",11406,0.6,0.62,3,238,4,0,1,"marketing","medium","exist"
"9407",11407,0.82,0.71,2,209,5,0,1,"marketing","medium","exist"
"9408",11408,0.86,0.95,4,149,3,0,1,"sales","medium","exist"
"9409",11409,0.69,0.59,4,264,3,0,0,"sales","medium","exist"
"9410",11410,0.87,0.87,5,207,2,0,0,"sales","high","exist"
"9411",11411,0.17,0.78,3,239,6,0,0,"sales","low","exist"
"9412",11412,0.94,0.51,6,239,5,0,0,"sales","medium","exist"
"9413",11413,0.5,1,4,258,3,0,0,"sales","medium","exist"
"9414",11414,0.16,0.72,3,203,3,0,0,"sales","medium","exist"
"9415",11415,0.89,0.99,2,258,3,0,0,"sales","medium","exist"
"9416",11416,0.69,0.51,3,257,3,1,0,"IT","low","exist"
"9417",11417,0.5,0.51,5,134,3,0,0,"product_mng","low","exist"
"9418",11418,0.16,0.46,6,240,2,0,0,"product_mng","low","exist"
"9419",11419,0.75,0.99,2,237,5,1,0,"product_mng","low","exist"
"9420",11420,0.64,0.66,5,157,2,0,0,"product_mng","low","exist"
"9421",11421,0.78,0.43,4,275,3,0,0,"IT","low","exist"
"9422",11422,0.81,0.74,2,228,3,0,0,"management","high","exist"
"9423",11423,0.55,0.58,3,254,2,0,0,"management","high","exist"
"9424",11424,0.53,0.53,4,257,2,0,0,"management","high","exist"
"9425",11425,0.69,0.73,3,231,2,1,0,"management","high","exist"
"9426",11426,0.8,0.53,3,217,3,0,0,"management","high","exist"
"9427",11427,0.77,0.98,3,286,6,0,0,"management","high","exist"
"9428",11428,0.84,0.8,4,236,3,0,0,"marketing","low","exist"
"9429",11429,0.64,0.55,4,215,2,0,0,"sales","low","exist"
"9430",11430,0.78,0.57,4,157,3,0,0,"accounting","low","exist"
"9431",11431,0.67,0.7,3,149,3,0,0,"support","low","exist"
"9432",11432,0.81,0.77,3,221,2,0,0,"technical","low","exist"
"9433",11433,0.91,0.82,4,238,2,0,0,"management","low","exist"
"9434",11434,0.75,0.89,6,250,2,0,0,"marketing","medium","exist"
"9435",11435,0.78,0.96,3,202,4,1,0,"marketing","medium","exist"
"9436",11436,0.54,0.52,4,173,2,0,0,"marketing","medium","exist"
"9437",11437,0.77,0.71,5,250,3,1,0,"sales","medium","exist"
"9438",11438,0.89,0.63,4,270,3,1,0,"sales","medium","exist"
"9439",11439,0.16,0.98,3,232,5,0,0,"sales","medium","exist"
"9440",11440,0.77,0.99,4,260,3,0,0,"sales","medium","exist"
"9441",11441,0.69,0.48,5,232,4,0,0,"sales","medium","exist"
"9442",11442,0.61,0.81,4,134,3,0,0,"sales","medium","exist"
"9443",11443,0.59,0.81,4,189,3,0,0,"sales","medium","exist"
"9444",11444,0.58,0.8,4,113,3,0,0,"IT","medium","exist"
"9445",11445,0.88,0.67,5,264,3,0,0,"product_mng","medium","exist"
"9446",11446,0.51,0.63,5,260,2,0,0,"product_mng","high","exist"
"9447",11447,0.31,0.7,3,132,3,0,0,"product_mng","low","exist"
"9448",11448,0.52,0.52,4,168,3,0,0,"product_mng","medium","exist"
"9449",11449,0.57,0.46,3,186,3,1,0,"IT","medium","exist"
"9450",11450,0.5,0.77,3,267,2,0,0,"management","high","exist"
"9451",11451,0.74,0.63,3,180,3,0,0,"management","high","exist"
"9452",11452,0.74,0.77,3,211,3,0,0,"management","high","exist"
"9453",11453,0.82,0.51,2,268,2,0,0,"management","high","exist"
"9454",11454,0.74,0.71,3,206,3,0,0,"management","high","exist"
"9455",11455,0.2,0.59,6,113,3,0,0,"management","high","exist"
"9456",11456,0.63,0.48,4,179,3,0,0,"marketing","low","exist"
"9457",11457,0.19,0.8,6,157,6,1,0,"sales","low","exist"
"9458",11458,0.4,0.62,4,127,5,0,0,"accounting","low","exist"
"9459",11459,0.71,0.37,2,179,5,0,1,"support","low","exist"
"9460",11460,0.84,0.73,4,197,3,0,1,"technical","low","exist"
"9461",11461,0.59,0.84,4,251,4,1,1,"management","low","exist"
"9462",11462,0.57,0.85,4,250,3,1,1,"marketing","low","exist"
"9463",11463,0.81,0.61,2,176,5,0,1,"marketing","low","exist"
"9464",11464,0.8,0.7,4,246,3,0,1,"marketing","low","exist"
"9465",11465,0.49,0.66,3,155,3,0,1,"sales","low","exist"
"9466",11466,0.55,0.64,3,178,2,0,1,"sales","low","exist"
"9467",11467,0.68,0.4,3,213,5,1,1,"sales","low","exist"
"9468",11468,0.55,0.67,3,150,2,0,1,"sales","low","exist"
"9469",11469,0.59,0.62,3,166,2,0,0,"sales","low","exist"
"9470",11470,0.91,0.8,5,169,4,0,0,"sales","low","exist"
"9471",11471,0.48,0.9,4,208,3,0,0,"sales","low","exist"
"9472",11472,0.84,0.66,3,209,2,0,0,"sales","low","exist"
"9473",11473,0.73,0.54,4,167,3,0,0,"IT","medium","exist"
"9474",11474,0.28,0.59,6,281,3,0,0,"product_mng","medium","exist"
"9475",11475,0.77,0.65,3,156,4,0,0,"product_mng","medium","exist"
"9476",11476,0.67,0.65,3,265,3,0,0,"product_mng","medium","exist"
"9477",11477,0.5,0.53,3,142,3,1,0,"product_mng","medium","exist"
"9478",11478,0.32,0.47,3,143,4,0,0,"IT","medium","exist"
"9479",11479,0.57,0.78,3,134,3,0,0,"RandD","medium","exist"
"9480",11480,0.51,0.8,5,268,3,0,0,"RandD","medium","exist"
"9481",11481,0.61,0.6,3,255,2,0,0,"RandD","medium","exist"
"9482",11482,0.83,0.73,4,157,2,0,0,"RandD","medium","exist"
"9483",11483,0.87,0.97,5,151,3,0,0,"RandD","medium","exist"
"9484",11484,0.7,0.63,3,157,2,0,0,"RandD","medium","exist"
"9485",11485,0.78,0.65,3,139,3,0,0,"marketing","high","exist"
"9486",11486,0.71,0.53,4,196,2,1,0,"sales","low","exist"
"9487",11487,0.68,0.99,3,159,2,0,0,"accounting","medium","exist"
"9488",11488,0.75,0.53,4,224,4,1,0,"support","medium","exist"
"9489",11489,0.7,0.53,3,215,7,1,0,"technical","medium","exist"
"9490",11490,0.59,0.94,5,157,7,1,0,"management","medium","exist"
"9491",11491,0.64,0.87,4,157,7,0,0,"marketing","low","exist"
"9492",11492,0.61,0.88,5,146,7,1,0,"marketing","low","exist"
"9493",11493,0.77,0.49,2,286,7,0,0,"marketing","low","exist"
"9494",11494,0.51,0.64,3,203,3,0,0,"sales","low","exist"
"9495",11495,0.49,0.49,3,168,7,0,0,"sales","low","exist"
"9496",11496,0.77,0.75,3,170,7,0,0,"sales","low","exist"
"9497",11497,0.31,0.86,3,266,7,0,0,"sales","low","exist"
"9498",11498,0.54,0.76,3,183,3,0,0,"sales","low","exist"
"9499",11499,0.56,0.66,4,264,3,0,0,"sales","low","exist"
"9500",11500,0.65,0.77,4,205,3,0,0,"sales","low","exist"
"9501",11501,0.49,0.36,2,192,3,0,0,"sales","low","exist"
"9502",11502,0.82,0.79,3,176,3,0,0,"technical","low","exist"
"9503",11503,0.6,0.52,3,183,2,0,0,"support","low","exist"
"9504",11504,0.64,0.63,3,156,6,1,0,"support","low","exist"
"9505",11505,0.7,0.68,3,150,3,0,0,"support","low","exist"
"9506",11506,0.65,0.89,4,204,8,1,0,"support","low","exist"
"9507",11507,0.69,0.78,5,131,8,0,0,"support","low","exist"
"9508",11508,0.93,0.74,5,248,8,1,0,"support","low","exist"
"9509",11509,0.55,0.52,4,168,8,0,0,"support","low","exist"
"9510",11510,0.75,0.87,4,146,8,1,0,"support","low","exist"
"9511",11511,0.47,0.43,4,246,3,0,0,"support","low","exist"
"9512",11512,0.72,0.88,5,216,10,1,0,"support","medium","exist"
"9513",11513,0.59,0.92,3,203,10,0,0,"support","medium","exist"
"9514",11514,0.98,0.49,3,199,10,0,0,"technical","medium","exist"
"9515",11515,0.39,0.52,2,102,8,0,0,"technical","medium","exist"
"9516",11516,0.93,0.87,4,139,8,0,0,"technical","medium","exist"
"9517",11517,0.71,0.97,5,208,8,1,0,"management","medium","exist"
"9518",11518,0.49,0.54,4,215,4,0,0,"IT","medium","exist"
"9519",11519,0.63,0.93,4,233,3,0,0,"IT","medium","exist"
"9520",11520,0.45,0.64,3,169,10,0,0,"IT","medium","exist"
"9521",11521,0.77,0.64,3,190,10,1,0,"IT","medium","exist"
"9522",11522,0.77,0.63,4,236,7,0,0,"IT","medium","exist"
"9523",11523,0.5,0.92,4,266,7,0,0,"product_mng","medium","exist"
"9524",11524,0.45,0.42,4,156,7,0,0,"product_mng","high","exist"
"9525",11525,0.81,0.47,4,153,7,0,0,"product_mng","low","exist"
"9526",11526,0.83,0.67,3,175,3,0,0,"product_mng","medium","exist"
"9527",11527,0.47,0.76,6,174,10,0,0,"IT","medium","exist"
"9528",11528,0.25,0.89,4,154,10,0,0,"management","high","exist"
"9529",11529,0.89,0.55,5,251,10,0,0,"management","high","exist"
"9530",11530,0.97,0.57,3,164,10,0,0,"management","high","exist"
"9531",11531,0.6,0.65,2,225,10,0,0,"management","high","exist"
"9532",11532,0.67,0.72,2,134,10,0,0,"management","high","exist"
"9533",11533,0.89,0.77,3,144,3,0,0,"management","high","exist"
"9534",11534,0.6,0.91,5,211,3,0,0,"sales","low","exist"
"9535",11535,0.64,0.79,4,139,3,0,0,"sales","low","exist"
"9536",11536,0.57,0.66,3,268,3,0,0,"sales","low","exist"
"9537",11537,0.56,0.98,5,171,3,1,0,"sales","low","exist"
"9538",11538,0.6,0.9,4,260,2,0,0,"sales","medium","exist"
"9539",11539,0.17,0.66,6,224,3,0,0,"sales","medium","exist"
"9540",11540,0.74,0.49,4,233,3,0,0,"sales","medium","exist"
"9541",11541,0.44,0.41,3,125,7,0,0,"sales","medium","exist"
"9542",11542,0.51,0.89,4,233,7,0,0,"sales","medium","exist"
"9543",11543,0.86,0.57,3,162,7,0,0,"sales","medium","exist"
"9544",11544,0.96,0.48,4,198,7,0,0,"sales","medium","exist"
"9545",11545,0.87,0.82,4,198,7,0,0,"sales","medium","exist"
"9546",11546,0.58,0.79,3,243,3,1,0,"sales","medium","exist"
"9547",11547,0.24,0.56,4,281,7,0,0,"sales","medium","exist"
"9548",11548,0.42,0.8,4,259,7,1,0,"sales","medium","exist"
"9549",11549,0.65,0.94,4,184,7,0,0,"sales","medium","exist"
"9550",11550,0.73,0.92,6,189,3,1,0,"sales","medium","exist"
"9551",11551,0.63,0.6,4,258,3,0,0,"sales","medium","exist"
"9552",11552,0.95,0.48,4,225,3,0,0,"sales","medium","exist"
"9553",11553,0.52,0.83,5,145,3,0,0,"sales","medium","exist"
"9554",11554,0.96,0.55,3,164,3,0,0,"sales","medium","exist"
"9555",11555,0.66,0.51,4,254,2,0,0,"sales","medium","exist"
"9556",11556,0.98,0.44,4,154,6,1,0,"sales","medium","exist"
"9557",11557,0.56,0.79,5,248,3,0,0,"sales","medium","exist"
"9558",11558,0.97,0.54,3,154,8,1,0,"sales","medium","exist"
"9559",11559,0.72,0.92,3,242,8,0,0,"sales","medium","exist"
"9560",11560,0.74,0.78,4,194,8,0,0,"sales","medium","exist"
"9561",11561,0.2,0.6,5,261,8,0,0,"sales","medium","exist"
"9562",11562,0.73,0.56,3,245,8,0,0,"sales","medium","exist"
"9563",11563,0.76,0.79,3,247,3,0,0,"sales","low","exist"
"9564",11564,0.65,0.54,4,147,10,0,0,"sales","low","exist"
"9565",11565,0.66,0.5,3,139,10,1,0,"sales","low","exist"
"9566",11566,0.96,0.97,6,137,10,0,0,"sales","low","exist"
"9567",11567,0.57,0.55,4,177,8,0,0,"sales","low","exist"
"9568",11568,0.61,0.82,4,184,8,0,0,"IT","low","exist"
"9569",11569,0.57,0.69,3,212,8,0,0,"product_mng","low","exist"
"9570",11570,0.59,0.47,3,159,4,0,0,"product_mng","low","exist"
"9571",11571,0.92,0.68,4,178,3,0,0,"product_mng","low","exist"
"9572",11572,0.79,0.56,3,149,10,0,0,"product_mng","low","exist"
"9573",11573,0.95,0.66,4,223,10,0,0,"IT","low","exist"
"9574",11574,0.24,0.81,6,263,7,0,0,"management","high","exist"
"9575",11575,0.49,0.52,4,161,7,0,0,"management","high","exist"
"9576",11576,0.49,0.68,3,192,7,0,0,"management","high","exist"
"9577",11577,0.97,0.51,5,215,7,0,0,"management","high","exist"
"9578",11578,0.55,0.78,4,261,3,0,0,"management","high","exist"
"9579",11579,0.76,0.56,5,222,10,0,0,"management","high","exist"
"9580",11580,0.53,0.99,3,223,10,0,0,"marketing","low","exist"
"9581",11581,0.51,0.86,3,182,10,0,0,"sales","low","exist"
"9582",11582,0.57,0.93,2,204,10,0,0,"accounting","low","exist"
"9583",11583,0.58,0.91,3,195,10,0,0,"support","low","exist"
"9584",11584,0.6,0.98,4,146,10,0,0,"technical","low","exist"
"9585",11585,0.65,0.74,4,233,4,1,0,"management","low","exist"
"9586",11586,0.91,0.75,2,147,3,0,0,"marketing","low","exist"
"9587",11587,0.65,0.55,3,156,5,0,0,"marketing","low","exist"
"9588",11588,0.18,0.49,3,240,2,1,0,"marketing","low","exist"
"9589",11589,0.66,0.9,4,177,7,0,0,"sales","low","exist"
"9590",11590,0.78,0.8,3,135,7,0,0,"sales","medium","exist"
"9591",11591,0.82,0.65,5,178,7,1,0,"sales","medium","exist"
"9592",11592,0.54,0.64,3,190,7,0,0,"sales","medium","exist"
"9593",11593,0.95,0.84,3,240,7,0,0,"sales","medium","exist"
"9594",11594,0.65,0.85,4,172,3,0,0,"sales","medium","exist"
"9595",11595,0.83,0.55,3,271,7,0,0,"sales","medium","exist"
"9596",11596,0.15,0.6,5,188,7,0,0,"sales","medium","exist"
"9597",11597,0.59,0.59,4,197,7,0,0,"IT","medium","exist"
"9598",11598,0.99,0.94,5,151,3,0,0,"product_mng","medium","exist"
"9599",11599,0.76,0.72,3,263,3,0,0,"product_mng","medium","exist"
"9600",11600,0.64,0.67,2,223,3,0,0,"product_mng","medium","exist"
"9601",11601,0.95,0.75,4,151,3,0,0,"product_mng","medium","exist"
"9602",11602,0.53,0.66,3,191,3,0,0,"IT","high","exist"
"9603",11603,0.59,0.5,2,162,2,0,0,"management","high","exist"
"9604",11604,0.69,0.86,5,195,6,0,0,"management","high","exist"
"9605",11605,0.5,0.49,4,222,3,0,0,"management","high","exist"
"9606",11606,0.89,0.96,3,179,8,0,0,"management","high","exist"
"9607",11607,0.56,0.39,3,106,8,0,0,"management","high","exist"
"9608",11608,0.77,0.68,3,214,8,1,0,"management","high","exist"
"9609",11609,0.15,0.75,3,259,8,1,0,"marketing","high","exist"
"9610",11610,0.88,0.58,3,145,8,0,0,"sales","low","exist"
"9611",11611,0.89,0.86,4,153,3,0,0,"accounting","low","exist"
"9612",11612,0.65,0.52,2,117,10,1,0,"support","low","exist"
"9613",11613,0.58,0.99,3,207,10,0,0,"technical","low","exist"
"9614",11614,0.56,0.85,3,265,10,1,0,"management","low","exist"
"9615",11615,0.25,0.72,5,279,8,0,0,"marketing","low","exist"
"9616",11616,0.87,0.89,4,225,8,0,0,"marketing","low","exist"
"9617",11617,0.62,0.4,3,158,8,1,0,"marketing","low","exist"
"9618",11618,0.72,0.75,4,211,4,0,0,"sales","medium","exist"
"9619",11619,0.49,0.94,4,175,3,0,0,"sales","medium","exist"
"9620",11620,0.57,0.91,4,224,10,0,0,"sales","medium","exist"
"9621",11621,0.63,0.65,3,190,10,0,0,"sales","medium","exist"
"9622",11622,0.91,0.63,5,240,7,0,0,"sales","medium","exist"
"9623",11623,0.7,0.68,5,225,7,0,0,"sales","medium","exist"
"9624",11624,0.66,0.95,5,192,7,0,0,"sales","medium","exist"
"9625",11625,0.77,0.48,5,262,7,0,0,"IT","medium","exist"
"9626",11626,0.68,0.97,3,250,3,1,0,"product_mng","medium","exist"
"9627",11627,0.34,0.46,3,155,10,0,0,"product_mng","medium","exist"
"9628",11628,0.97,0.64,4,238,10,1,0,"product_mng","medium","exist"
"9629",11629,0.57,0.75,4,249,10,0,0,"product_mng","medium","exist"
"9630",11630,0.66,0.65,3,272,10,0,0,"IT","medium","exist"
"9631",11631,0.68,0.67,4,162,10,0,0,"management","high","exist"
"9632",11632,0.49,0.78,4,254,10,0,0,"management","high","exist"
"9633",11633,0.72,0.66,4,184,3,0,0,"management","high","exist"
"9634",11634,0.77,0.89,4,269,10,0,0,"management","high","exist"
"9635",11635,0.77,0.73,3,201,10,0,0,"management","high","exist"
"9636",11636,0.59,0.73,4,247,10,0,0,"management","high","exist"
"9637",11637,0.41,0.67,6,221,10,0,0,"marketing","medium","exist"
"9638",11638,0.94,0.64,5,247,10,0,0,"sales","medium","exist"
"9639",11639,0.91,0.61,4,135,10,0,0,"accounting","medium","exist"
"9640",11640,0.7,0.84,3,260,4,1,0,"support","medium","exist"
"9641",11641,0.51,0.52,3,188,3,0,0,"technical","high","exist"
"9642",11642,0.22,0.7,4,159,5,0,0,"management","low","exist"
"9643",11643,0.69,0.65,3,153,2,0,0,"marketing","medium","exist"
"9644",11644,0.2,0.68,5,167,7,0,0,"marketing","medium","exist"
"9645",11645,0.9,0.85,3,158,7,0,0,"marketing","medium","exist"
"9646",11646,0.76,0.85,3,180,7,0,0,"sales","medium","exist"
"9647",11647,0.88,0.51,3,211,7,0,0,"sales","medium","exist"
"9648",11648,0.31,0.63,4,104,7,1,0,"sales","medium","exist"
"9649",11649,0.17,0.66,6,174,3,0,0,"sales","medium","exist"
"9650",11650,0.91,0.77,3,195,7,0,0,"sales","medium","exist"
"9651",11651,0.97,0.38,5,211,7,1,0,"sales","medium","exist"
"9652",11652,0.61,0.77,5,232,7,0,0,"sales","medium","exist"
"9653",11653,0.74,0.67,5,216,3,0,0,"sales","low","exist"
"9654",11654,0.65,0.82,5,265,3,0,0,"IT","low","exist"
"9655",11655,0.87,0.73,5,184,3,0,0,"product_mng","low","exist"
"9656",11656,0.56,0.71,5,244,3,0,0,"product_mng","low","exist"
"9657",11657,0.78,0.69,4,202,3,0,0,"product_mng","low","exist"
"9658",11658,0.73,0.57,3,146,2,0,0,"product_mng","low","exist"
"9659",11659,0.66,0.78,4,161,6,0,0,"IT","low","exist"
"9660",11660,0.15,0.81,5,280,3,1,0,"RandD","high","exist"
"9661",11661,0.52,0.69,5,208,8,1,0,"RandD","low","exist"
"9662",11662,0.44,0.66,6,134,8,0,0,"RandD","high","exist"
"9663",11663,0.7,0.7,3,162,8,0,0,"RandD","high","exist"
"9664",11664,0.63,0.52,5,209,8,1,0,"RandD","low","exist"
"9665",11665,0.89,0.59,3,265,8,0,0,"RandD","low","exist"
"9666",11666,0.96,0.85,4,173,3,0,0,"marketing","high","exist"
"9667",11667,0.98,0.99,4,261,10,1,0,"sales","low","exist"
"9668",11668,0.62,0.82,3,204,10,0,0,"accounting","medium","exist"
"9669",11669,0.62,0.73,3,144,10,1,0,"support","high","exist"
"9670",11670,0.69,0.43,5,113,8,0,0,"technical","medium","exist"
"9671",11671,0.5,0.91,4,144,8,0,0,"management","medium","exist"
"9672",11672,0.71,0.93,5,140,8,0,0,"marketing","medium","exist"
"9673",11673,0.5,0.68,3,245,4,0,0,"marketing","medium","exist"
"9674",11674,0.93,0.6,3,188,3,0,0,"marketing","high","exist"
"9675",11675,0.95,0.77,5,199,10,1,0,"sales","medium","exist"
"9676",11676,0.17,0.61,6,154,10,1,0,"sales","medium","exist"
"9677",11677,0.92,0.68,4,138,7,0,0,"sales","medium","exist"
"9678",11678,0.64,0.48,3,147,7,0,0,"sales","high","exist"
"9679",11679,0.27,0.42,6,173,7,0,0,"sales","medium","exist"
"9680",11680,0.66,0.87,3,223,7,0,0,"sales","high","exist"
"9681",11681,0.59,0.69,3,200,3,0,0,"sales","low","exist"
"9682",11682,0.93,0.98,4,189,10,0,0,"sales","medium","exist"
"9683",11683,0.58,0.67,5,133,10,0,0,"technical","medium","exist"
"9684",11684,0.96,0.6,3,160,10,0,0,"support","medium","exist"
"9685",11685,0.69,0.85,3,153,10,0,0,"support","medium","exist"
"9686",11686,0.41,0.38,4,142,10,1,0,"support","low","exist"
"9687",11687,0.36,0.41,3,167,10,0,0,"support","low","exist"
"9688",11688,0.71,0.78,4,227,2,0,0,"support","low","exist"
"9689",11689,0.94,0.9,4,144,4,0,0,"support","low","exist"
"9690",11690,0.51,0.76,4,140,3,0,0,"support","low","exist"
"9691",11691,0.83,0.48,4,220,3,1,0,"support","low","exist"
"9692",11692,0.22,0.62,3,180,3,0,0,"support","low","exist"
"9693",11693,0.66,0.89,4,173,4,0,0,"support","low","exist"
"9694",11694,0.14,0.58,3,179,5,0,0,"support","low","exist"
"9695",11695,0.16,0.96,5,137,5,1,0,"technical","low","exist"
"9696",11696,0.81,0.78,3,165,3,0,0,"technical","high","exist"
"9697",11697,0.73,0.94,3,177,3,0,0,"technical","low","exist"
"9698",11698,0.7,0.58,5,168,3,0,0,"management","low","exist"
"9699",11699,0.62,0.73,3,245,4,0,0,"IT","low","exist"
"9700",11700,0.5,0.83,5,258,2,0,0,"IT","low","exist"
"9701",11701,0.7,0.88,3,159,2,0,0,"IT","high","exist"
"9702",11702,0.53,0.73,3,163,3,1,0,"IT","low","exist"
"9703",11703,0.87,0.9,3,174,2,0,0,"IT","low","exist"
"9704",11704,0.59,0.6,3,214,2,1,0,"product_mng","low","exist"
"9705",11705,0.94,0.67,4,191,3,1,0,"product_mng","high","exist"
"9706",11706,0.2,0.53,5,272,5,0,0,"product_mng","low","exist"
"9707",11707,0.42,0.44,3,183,2,0,0,"product_mng","medium","exist"
"9708",11708,0.43,0.66,4,135,2,0,0,"IT","high","exist"
"9709",11709,0.43,0.76,6,154,2,0,0,"management","high","exist"
"9710",11710,0.77,0.86,5,238,3,0,0,"management","high","exist"
"9711",11711,0.76,0.98,2,235,3,0,0,"management","high","exist"
"9712",11712,0.82,0.9,3,215,4,0,0,"management","high","exist"
"9713",11713,0.75,0.66,5,234,2,0,0,"management","high","exist"
"9714",11714,0.63,0.98,4,187,3,0,0,"management","high","exist"
"9715",11715,0.51,0.75,3,133,3,0,0,"sales","medium","exist"
"9716",11716,0.23,0.7,3,123,5,0,0,"sales","medium","exist"
"9717",11717,0.77,0.58,4,202,3,0,0,"sales","medium","exist"
"9718",11718,0.54,0.63,4,140,3,0,0,"sales","medium","exist"
"9719",11719,0.63,0.85,4,182,3,1,0,"sales","high","exist"
"9720",11720,0.55,0.45,3,179,2,1,0,"sales","low","exist"
"9721",11721,0.31,0.63,3,150,3,1,0,"sales","medium","exist"
"9722",11722,0.98,0.74,4,151,3,0,0,"sales","medium","exist"
"9723",11723,0.16,0.95,6,117,7,0,0,"sales","medium","exist"
"9724",11724,0.54,0.78,3,156,7,0,0,"sales","medium","exist"
"9725",11725,0.73,0.48,3,211,7,0,0,"sales","medium","exist"
"9726",11726,0.16,0.63,6,286,7,0,1,"sales","medium","exist"
"9727",11727,0.57,0.82,5,233,7,0,1,"sales","medium","exist"
"9728",11728,0.88,0.88,4,222,3,1,1,"sales","medium","exist"
"9729",11729,0.95,0.81,4,258,7,0,1,"sales","medium","exist"
"9730",11730,0.93,0.7,5,231,7,0,1,"sales","high","exist"
"9731",11731,0.91,0.58,3,220,7,0,1,"sales","low","exist"
"9732",11732,0.77,0.82,4,134,3,0,1,"sales","low","exist"
"9733",11733,0.24,0.94,6,141,3,1,0,"sales","low","exist"
"9734",11734,0.74,0.74,5,160,3,1,0,"sales","high","exist"
"9735",11735,0.53,0.59,4,259,3,0,0,"sales","low","exist"
"9736",11736,0.89,0.77,5,232,3,0,0,"sales","low","exist"
"9737",11737,0.23,0.77,5,272,2,0,0,"sales","low","exist"
"9738",11738,0.69,0.66,3,215,6,0,0,"sales","high","exist"
"9739",11739,0.52,0.72,4,222,3,0,0,"sales","low","exist"
"9740",11740,0.9,0.58,3,244,8,0,0,"sales","low","exist"
"9741",11741,0.92,0.63,5,224,8,1,0,"sales","low","exist"
"9742",11742,0.72,0.59,5,200,8,1,0,"sales","low","exist"
"9743",11743,0.92,0.61,4,143,8,0,0,"sales","low","exist"
"9744",11744,0.79,0.86,5,238,8,0,0,"sales","low","exist"
"9745",11745,0.48,0.89,4,145,3,0,0,"sales","low","exist"
"9746",11746,0.27,0.76,4,108,10,0,0,"sales","medium","exist"
"9747",11747,0.67,0.49,3,247,10,0,0,"sales","medium","exist"
"9748",11748,0.48,0.7,3,213,10,0,0,"sales","medium","exist"
"9749",11749,0.99,0.6,4,209,8,1,0,"IT","medium","exist"
"9750",11750,0.49,0.88,4,240,8,0,0,"product_mng","medium","exist"
"9751",11751,0.39,0.45,3,100,8,1,0,"product_mng","medium","exist"
"9752",11752,0.99,0.92,4,265,4,1,0,"product_mng","medium","exist"
"9753",11753,0.78,0.57,3,209,3,0,0,"product_mng","medium","exist"
"9754",11754,0.5,0.73,3,154,10,0,0,"IT","medium","exist"
"9755",11755,0.61,0.79,5,230,10,0,0,"management","high","exist"
"9756",11756,0.88,0.6,4,208,7,0,1,"management","high","exist"
"9757",11757,0.44,0.44,2,141,7,1,1,"management","high","exist"
"9758",11758,0.73,0.78,3,262,7,1,1,"management","high","exist"
"9759",11759,0.58,0.84,4,206,7,0,1,"management","high","exist"
"9760",11760,0.74,0.98,3,166,3,1,1,"management","high","exist"
"9761",11761,0.32,0.48,4,117,10,0,1,"marketing","medium","exist"
"9762",11762,0.88,0.83,4,273,10,0,0,"sales","medium","exist"
"9763",11763,0.81,0.9,4,270,10,0,0,"accounting","medium","exist"
"9764",11764,0.59,0.92,3,138,10,0,0,"support","low","exist"
"9765",11765,0.79,0.65,3,235,10,0,0,"technical","low","exist"
"9766",11766,0.92,0.64,4,190,10,1,0,"management","low","exist"
"9767",11767,0.76,0.85,3,192,3,0,0,"marketing","low","exist"
"9768",11768,0.91,0.65,5,214,3,1,0,"marketing","low","exist"
"9769",11769,0.8,0.84,4,242,2,1,0,"marketing","low","exist"
"9770",11770,0.73,0.72,4,233,2,0,0,"sales","low","exist"
"9771",11771,0.88,0.53,3,218,4,0,0,"sales","low","exist"
"9772",11772,0.65,0.4,5,125,4,0,0,"sales","low","exist"
"9773",11773,0.84,0.5,4,178,2,0,0,"sales","low","exist"
"9774",11774,0.93,0.5,5,272,3,0,0,"sales","low","exist"
"9775",11775,0.64,0.6,3,265,4,0,0,"sales","low","exist"
"9776",11776,0.66,0.72,4,271,3,0,0,"sales","low","exist"
"9777",11777,0.76,0.56,3,179,3,1,0,"sales","low","exist"
"9778",11778,0.34,0.72,3,118,4,0,0,"IT","low","exist"
"9779",11779,0.48,0.8,4,196,5,0,0,"product_mng","low","exist"
"9780",11780,0.79,0.61,4,173,2,0,0,"product_mng","low","exist"
"9781",11781,0.82,0.67,4,156,3,0,0,"product_mng","low","exist"
"9782",11782,0.51,0.71,2,180,3,0,0,"product_mng","low","exist"
"9783",11783,0.84,0.78,4,263,3,0,0,"IT","low","exist"
"9784",11784,0.66,0.79,5,134,3,0,0,"management","high","exist"
"9785",11785,0.72,0.88,3,189,3,1,0,"management","high","exist"
"9786",11786,0.53,0.91,4,167,4,0,0,"management","high","exist"
"9787",11787,0.81,0.8,5,132,2,1,0,"management","high","exist"
"9788",11788,0.58,0.9,3,209,2,0,0,"management","high","exist"
"9789",11789,0.82,0.56,2,227,5,1,0,"management","high","exist"
"9790",11790,0.72,0.99,4,239,3,0,0,"marketing","medium","exist"
"9791",11791,0.9,0.54,4,172,4,0,0,"sales","medium","exist"
"9792",11792,0.98,0.91,3,188,4,0,0,"accounting","medium","exist"
"9793",11793,0.56,0.74,3,265,3,0,0,"support","medium","exist"
"9794",11794,0.77,0.82,3,153,3,1,0,"technical","medium","exist"
"9795",11795,0.61,0.89,4,242,2,0,0,"management","medium","exist"
"9796",11796,0.97,0.61,4,262,3,1,1,"marketing","medium","exist"
"9797",11797,0.64,0.55,3,246,2,0,1,"marketing","high","exist"
"9798",11798,0.99,0.97,4,211,2,0,1,"marketing","low","exist"
"9799",11799,0.61,0.42,3,145,4,0,1,"sales","medium","exist"
"9800",11800,0.72,0.71,4,256,3,0,1,"sales","medium","exist"
"9801",11801,0.67,0.91,2,245,2,1,1,"sales","medium","exist"
"9802",11802,0.9,0.56,3,151,3,0,0,"sales","medium","exist"
"9803",11803,0.9,0.73,4,245,2,0,0,"sales","low","exist"
"9804",11804,0.63,0.61,4,171,3,0,0,"sales","low","exist"
"9805",11805,0.64,0.88,3,252,2,0,0,"sales","low","exist"
"9806",11806,0.44,0.65,3,175,2,0,0,"IT","low","exist"
"9807",11807,0.64,0.94,4,210,3,0,0,"product_mng","low","exist"
"9808",11808,0.65,0.95,2,180,2,0,0,"product_mng","low","exist"
"9809",11809,0.69,0.9,5,103,5,0,0,"product_mng","low","exist"
"9810",11810,0.93,0.51,4,110,3,0,0,"product_mng","low","exist"
"9811",11811,0.73,0.9,5,184,3,0,0,"IT","low","exist"
"9812",11812,0.83,0.6,4,161,2,0,0,"management","high","exist"
"9813",11813,0.82,0.49,5,210,3,0,0,"management","high","exist"
"9814",11814,0.39,0.91,2,212,2,0,0,"management","high","exist"
"9815",11815,0.53,0.47,6,106,6,0,0,"management","high","exist"
"9816",11816,0.88,0.81,4,179,3,0,0,"management","high","exist"
"9817",11817,0.8,0.6,5,217,3,0,0,"management","high","exist"
"9818",11818,0.68,0.79,4,184,3,0,0,"marketing","low","exist"
"9819",11819,0.66,0.6,3,210,2,0,0,"sales","low","exist"
"9820",11820,0.61,0.61,4,246,2,0,0,"accounting","low","exist"
"9821",11821,0.74,0.55,4,262,3,0,0,"support","low","exist"
"9822",11822,0.61,0.83,4,245,3,1,1,"technical","low","exist"
"9823",11823,0.57,0.99,3,222,3,1,1,"management","low","exist"
"9824",11824,0.68,0.54,4,146,4,0,1,"marketing","medium","exist"
"9825",11825,0.75,0.79,4,263,3,0,1,"marketing","medium","exist"
"9826",11826,0.29,0.57,5,134,2,0,1,"marketing","medium","exist"
"9827",11827,0.81,0.81,5,250,4,0,1,"sales","medium","exist"
"9828",11828,0.53,0.68,4,173,3,0,0,"sales","medium","exist"
"9829",11829,0.42,0.96,6,173,3,0,0,"sales","medium","exist"
"9830",11830,0.64,0.67,4,252,3,1,0,"sales","medium","exist"
"9831",11831,0.63,0.5,2,230,4,0,0,"sales","medium","exist"
"9832",11832,0.81,0.81,4,212,2,1,0,"sales","medium","exist"
"9833",11833,0.71,0.66,5,187,2,0,0,"sales","medium","exist"
"9834",11834,0.7,0.83,5,241,2,0,0,"sales","medium","exist"
"9835",11835,0.53,1,3,164,4,0,0,"IT","medium","exist"
"9836",11836,0.16,0.93,6,218,6,1,0,"product_mng","high","exist"
"9837",11837,0.17,0.55,4,194,3,0,0,"product_mng","low","exist"
"9838",11838,0.7,0.95,3,158,2,0,0,"product_mng","medium","exist"
"9839",11839,0.43,0.88,2,149,4,0,0,"product_mng","medium","exist"
"9840",11840,0.49,0.62,4,161,2,0,0,"IT","medium","exist"
"9841",11841,0.5,0.9,5,226,2,0,0,"management","high","exist"
"9842",11842,0.57,0.59,2,111,2,0,0,"management","high","exist"
"9843",11843,0.68,0.75,4,258,3,0,0,"management","high","exist"
"9844",11844,0.62,0.61,3,266,2,0,0,"management","high","exist"
"9845",11845,0.69,0.75,3,253,3,0,0,"management","high","exist"
"9846",11846,0.63,0.7,5,160,3,0,0,"management","high","exist"
"9847",11847,0.76,0.62,5,230,3,1,0,"marketing","low","exist"
"9848",11848,0.62,0.76,5,198,3,0,0,"sales","low","exist"
"9849",11849,0.82,0.69,3,250,3,0,0,"accounting","low","exist"
"9850",11850,0.2,0.7,4,225,5,0,0,"support","low","exist"
"9851",11851,0.16,0.7,3,178,3,1,0,"technical","low","exist"
"9852",11852,0.2,0.78,4,196,3,0,0,"management","low","exist"
"9853",11853,0.53,0.51,4,240,2,0,0,"marketing","low","exist"
"9854",11854,0.71,0.63,3,204,3,0,0,"marketing","low","exist"
"9855",11855,0.7,0.93,3,250,3,0,0,"marketing","low","exist"
"9856",11856,0.92,0.94,2,224,2,0,0,"sales","low","exist"
"9857",11857,0.81,0.92,4,268,3,0,0,"sales","low","exist"
"9858",11858,0.79,0.62,5,167,3,0,0,"sales","low","exist"
"9859",11859,0.53,0.64,3,168,3,0,0,"sales","low","exist"
"9860",11860,0.51,0.56,4,168,2,0,0,"sales","low","exist"
"9861",11861,0.78,0.9,5,158,3,0,0,"sales","low","exist"
"9862",11862,0.5,0.91,3,240,3,0,0,"sales","low","exist"
"9863",11863,0.92,1,4,261,4,0,0,"sales","medium","exist"
"9864",11864,0.59,0.54,4,176,2,0,0,"technical","medium","exist"
"9865",11865,0.77,0.55,3,217,3,0,0,"support","medium","exist"
"9866",11866,0.74,0.87,5,224,2,0,0,"support","medium","exist"
"9867",11867,0.67,0.97,4,196,3,0,0,"support","medium","exist"
"9868",11868,0.56,0.59,3,223,3,0,0,"support","medium","exist"
"9869",11869,0.84,0.44,5,131,5,1,0,"support","medium","exist"
"9870",11870,0.53,0.77,2,167,2,0,0,"support","medium","exist"
"9871",11871,0.86,0.71,5,273,3,0,0,"support","medium","exist"
"9872",11872,0.77,0.68,3,98,3,0,0,"support","medium","exist"
"9873",11873,0.97,0.94,4,253,3,0,0,"support","medium","exist"
"9874",11874,0.69,0.87,5,174,3,0,0,"support","medium","exist"
"9875",11875,0.73,0.9,3,274,2,0,0,"support","high","exist"
"9876",11876,0.42,0.47,6,174,5,0,0,"technical","low","exist"
"9877",11877,0.4,0.47,5,173,5,0,0,"technical","medium","exist"
"9878",11878,0.33,0.41,2,198,4,0,0,"technical","medium","exist"
"9879",11879,0.68,0.66,3,238,2,0,0,"management","medium","exist"
"9880",11880,0.78,0.8,3,256,2,0,0,"IT","medium","exist"
"9881",11881,0.92,0.6,3,179,2,0,0,"IT","low","exist"
"9882",11882,0.66,0.66,4,273,4,1,0,"IT","low","exist"
"9883",11883,0.6,0.45,3,104,4,0,0,"IT","low","exist"
"9884",11884,0.86,0.83,4,208,3,1,0,"IT","low","exist"
"9885",11885,0.61,0.49,3,275,2,0,0,"product_mng","low","exist"
"9886",11886,0.71,0.68,3,231,2,1,0,"product_mng","low","exist"
"9887",11887,0.86,0.62,4,186,3,0,0,"product_mng","low","exist"
"9888",11888,0.96,0.59,3,241,3,0,0,"product_mng","low","exist"
"9889",11889,0.7,0.54,3,194,2,1,0,"IT","low","exist"
"9890",11890,0.38,0.49,4,196,3,0,1,"management","high","exist"
"9891",11891,0.39,0.75,6,185,3,0,1,"management","high","exist"
"9892",11892,0.49,0.4,2,148,2,1,1,"management","high","exist"
"9893",11893,0.78,0.62,4,150,3,0,1,"management","high","exist"
"9894",11894,0.74,0.79,5,121,5,0,1,"management","high","exist"
"9895",11895,0.82,0.76,4,266,3,0,1,"management","high","exist"
"9896",11896,0.6,0.42,2,109,6,0,0,"sales","low","exist"
"9897",11897,0.72,0.99,3,143,4,0,0,"sales","low","exist"
"9898",11898,0.73,0.97,3,174,3,0,0,"sales","low","exist"
"9899",11899,0.89,0.55,4,159,2,0,0,"sales","medium","exist"
"9900",11900,0.74,0.94,4,157,2,0,0,"sales","medium","exist"
"9901",11901,0.83,0.57,2,222,2,0,0,"sales","medium","exist"
"9902",11902,0.24,0.88,5,199,5,1,0,"sales","medium","exist"
"9903",11903,0.93,0.89,3,255,7,1,0,"sales","medium","exist"
"9904",11904,0.96,0.62,4,253,7,0,0,"sales","medium","exist"
"9905",11905,0.16,0.68,5,149,7,1,0,"sales","medium","exist"
"9906",11906,0.21,0.85,6,285,7,0,0,"sales","medium","exist"
"9907",11907,0.69,0.54,3,164,7,0,0,"sales","medium","exist"
"9908",11908,0.66,0.96,3,243,3,1,0,"sales","medium","exist"
"9909",11909,0.67,0.39,2,207,7,0,0,"sales","medium","exist"
"9910",11910,0.85,0.58,4,186,7,0,0,"sales","medium","exist"
"9911",11911,0.37,0.92,4,211,7,0,0,"sales","high","exist"
"9912",11912,0.64,0.64,2,190,3,0,0,"sales","low","exist"
"9913",11913,0.91,0.82,2,241,3,0,0,"sales","medium","exist"
"9914",11914,0.96,0.68,3,206,3,0,0,"sales","medium","exist"
"9915",11915,0.74,0.7,4,273,3,0,0,"sales","medium","exist"
"9916",11916,0.94,0.99,2,157,3,0,0,"sales","medium","exist"
"9917",11917,0.37,0.72,3,183,2,0,0,"sales","low","exist"
"9918",11918,0.92,0.85,3,151,6,1,0,"sales","low","exist"
"9919",11919,0.86,0.72,3,217,3,0,0,"sales","low","exist"
"9920",11920,0.66,0.49,5,235,8,1,0,"sales","low","exist"
"9921",11921,0.19,0.61,4,127,8,0,0,"sales","low","exist"
"9922",11922,0.65,0.61,5,167,8,0,0,"sales","low","exist"
"9923",11923,0.92,0.44,3,260,8,0,0,"sales","low","exist"
"9924",11924,0.83,0.8,3,240,8,0,0,"sales","low","exist"
"9925",11925,0.94,0.82,4,187,3,0,0,"sales","low","exist"
"9926",11926,0.42,0.69,3,126,2,0,0,"sales","low","exist"
"9927",11927,0.78,0.53,6,168,3,0,0,"sales","low","exist"
"9928",11928,0.58,0.76,4,197,5,0,0,"sales","low","exist"
"9929",11929,0.5,0.64,2,170,8,0,0,"sales","low","exist"
"9930",11930,0.82,0.76,3,219,8,1,0,"IT","low","exist"
"9931",11931,0.97,0.92,6,137,8,1,0,"product_mng","low","exist"
"9932",11932,0.8,0.93,3,225,4,0,0,"product_mng","low","exist"
"9933",11933,0.82,0.84,3,194,3,0,0,"product_mng","low","exist"
"9934",11934,0.95,0.99,5,251,4,0,0,"product_mng","low","exist"
"9935",11935,0.88,0.51,5,195,4,0,0,"IT","low","exist"
"9936",11936,0.5,0.86,3,180,7,0,1,"management","high","exist"
"9937",11937,0.53,0.8,2,225,7,1,1,"management","high","exist"
"9938",11938,0.82,0.74,3,229,7,0,1,"management","high","exist"
"9939",11939,0.15,0.74,6,144,7,0,1,"management","high","exist"
"9940",11940,0.92,0.7,3,129,3,0,1,"management","high","exist"
"9941",11941,0.53,0.74,3,172,10,0,1,"management","high","exist"
"9942",11942,0.58,1,4,220,10,0,0,"marketing","medium","exist"
"9943",11943,0.88,0.74,3,273,10,0,0,"sales","medium","exist"
"9944",11944,0.85,0.72,3,245,10,0,0,"accounting","medium","exist"
"9945",11945,0.99,0.68,5,264,10,1,0,"support","medium","exist"
"9946",11946,0.94,0.73,3,268,10,0,0,"technical","medium","exist"
"9947",11947,0.63,0.94,3,172,3,0,0,"management","medium","exist"
"9948",11948,0.85,0.9,3,245,3,0,0,"marketing","medium","exist"
"9949",11949,0.95,0.66,5,192,3,0,0,"marketing","medium","exist"
"9950",11950,0.71,0.66,3,268,4,0,0,"marketing","high","exist"
"9951",11951,0.49,0.88,4,244,3,0,0,"sales","low","exist"
"9952",11952,0.71,0.69,4,222,4,0,0,"sales","medium","exist"
"9953",11953,0.52,0.62,5,239,2,0,0,"sales","medium","exist"
"9954",11954,0.48,0.72,3,143,4,0,0,"sales","medium","exist"
"9955",11955,0.82,0.79,3,160,3,0,0,"sales","medium","exist"
"9956",11956,0.83,0.76,2,255,7,0,0,"sales","low","exist"
"9957",11957,0.85,0.87,4,152,7,0,0,"sales","low","exist"
"9958",11958,0.57,0.64,4,226,7,0,0,"sales","low","exist"
"9959",11959,0.16,0.63,5,266,7,0,0,"IT","low","exist"
"9960",11960,0.85,0.64,5,256,7,0,0,"product_mng","low","exist"
"9961",11961,0.82,0.67,3,198,3,1,0,"product_mng","low","exist"
"9962",11962,0.9,0.89,4,254,7,0,0,"product_mng","low","exist"
"9963",11963,0.92,0.64,2,104,7,0,0,"product_mng","low","exist"
"9964",11964,0.9,0.48,4,136,7,0,0,"IT","low","exist"
"9965",11965,0.82,0.8,5,205,3,0,0,"IT","low","exist"
"9966",11966,0.84,0.81,4,236,3,1,0,"IT","low","exist"
"9967",11967,0.92,0.65,3,176,3,0,0,"IT","low","exist"
"9968",11968,0.82,0.82,3,148,3,0,0,"IT","low","exist"
"9969",11969,0.8,0.8,4,146,3,1,0,"IT","low","exist"
"9970",11970,0.6,0.85,3,242,2,0,0,"IT","low","exist"
"9971",11971,0.14,0.38,5,115,6,1,0,"marketing","high","exist"
"9972",11972,0.85,0.89,4,150,3,0,0,"accounting","high","exist"
"9973",11973,0.55,0.81,3,239,8,0,0,"accounting","high","exist"
"9974",11974,0.49,0.71,4,178,8,0,0,"IT","medium","exist"
"9975",11975,0.82,0.58,5,263,8,0,0,"IT","medium","exist"
"9976",11976,0.59,0.77,3,272,8,0,0,"management","high","exist"
"9977",11977,0.9,0.82,3,133,8,0,0,"marketing","medium","exist"
"9978",11978,0.62,0.72,3,149,3,1,0,"marketing","medium","exist"
"9979",11979,0.61,0.68,3,193,2,0,0,"marketing","medium","exist"
"9980",11980,0.52,0.55,5,174,3,1,0,"sales","medium","exist"
"9981",11981,0.79,0.87,4,223,5,0,0,"sales","medium","exist"
"9982",11982,0.49,0.89,4,201,8,0,0,"sales","medium","exist"
"9983",11983,0.73,0.67,2,139,8,0,0,"sales","medium","exist"
"9984",11984,0.67,0.49,5,241,8,0,0,"sales","medium","exist"
"9985",11985,0.52,0.61,4,187,4,1,0,"sales","medium","exist"
"9986",11986,0.72,0.64,4,192,3,0,0,"sales","medium","exist"
"9987",11987,0.48,0.5,5,142,4,0,0,"IT","medium","exist"
"9988",11988,0.19,0.79,4,229,4,0,0,"product_mng","medium","exist"
"9989",11989,0.49,0.49,3,104,7,0,0,"product_mng","high","exist"
"9990",11990,0.9,0.76,3,255,7,0,0,"product_mng","low","exist"
"9991",11991,0.49,0.49,4,212,7,0,0,"product_mng","medium","exist"
"9992",11992,0.6,0.53,2,235,7,0,0,"IT","medium","exist"
"9993",11993,0.62,0.85,3,237,3,1,0,"IT","medium","exist"
"9994",11994,0.64,0.5,4,253,10,0,1,"management","high","exist"
"9995",11995,0.22,0.94,3,193,10,0,1,"management","high","exist"
"9996",11996,0.9,0.55,3,259,10,1,1,"management","high","exist"
"9997",11997,0.74,0.95,5,266,10,0,1,"management","high","exist"
"9998",11998,0.85,0.54,3,185,10,0,1,"management","high","exist"
"9999",11999,0.33,0.65,3,172,10,0,1,"marketing","high","exist"
"10000",12000,0.5,0.73,4,180,3,0,0,"IT","low","exist"
"10001",12784,0.66,0.66,3,225,3,0,0,"technical","low","exist"
"10002",12785,0.2,0.69,6,236,4,0,0,"technical","low","exist"
"10003",12786,0.97,0.78,3,268,3,1,0,"technical","low","exist"
"10004",12787,0.59,0.73,2,230,3,0,0,"technical","low","exist"
"10005",12788,0.88,0.6,4,162,2,0,0,"technical","low","exist"
"10006",12789,0.16,0.73,4,197,2,0,0,"technical","low","exist"
"10007",12790,0.61,0.96,3,247,3,0,0,"support","low","exist"
"10008",12791,0.52,0.79,4,234,3,0,0,"support","low","exist"
"10009",12792,0.82,0.49,4,276,4,0,0,"support","low","exist"
"10010",12793,0.75,0.94,5,217,2,0,0,"support","medium","exist"
"10011",12794,0.62,0.5,4,156,2,0,0,"support","medium","exist"
"10012",12795,0.91,0.88,3,189,2,0,0,"support","medium","exist"
"10013",12796,0.61,0.98,2,238,4,0,0,"support","medium","exist"
"10014",12797,0.79,0.77,3,201,6,1,0,"support","medium","exist"
"10015",12798,0.9,0.93,4,263,3,1,0,"support","medium","exist"
"10016",12799,0.75,0.83,3,146,3,0,0,"support","medium","exist"
"10017",12800,0.81,0.64,4,213,3,0,0,"support","medium","exist"
"10018",12801,0.59,0.88,3,159,2,0,0,"technical","medium","exist"
"10019",12802,0.56,0.83,3,236,3,1,0,"technical","medium","exist"
"10020",12803,0.98,0.79,5,257,4,0,0,"technical","medium","exist"
"10021",12804,0.59,0.72,4,168,4,0,0,"management","medium","exist"
"10022",12805,0.61,0.67,4,151,3,0,0,"IT","high","exist"
"10023",12806,0.78,0.7,4,139,3,0,0,"IT","low","exist"
"10024",12807,0.55,0.93,5,196,3,0,0,"IT","medium","exist"
"10025",12808,0.2,0.97,4,237,5,0,0,"IT","medium","exist"
"10026",12809,0.79,0.44,2,236,3,0,0,"IT","medium","exist"
"10027",12810,0.52,0.98,4,265,3,0,0,"product_mng","medium","exist"
"10028",12811,0.97,0.52,4,207,3,0,0,"product_mng","low","exist"
"10029",12812,0.63,0.94,4,219,3,0,0,"product_mng","low","exist"
"10030",12813,0.85,0.99,3,208,2,0,0,"product_mng","low","exist"
"10031",12814,0.59,0.74,3,240,3,0,0,"IT","low","exist"
"10032",12815,0.64,0.6,3,135,3,0,0,"RandD","low","exist"
"10033",12816,0.8,0.67,3,236,3,1,0,"RandD","low","exist"
"10034",12817,0.61,0.75,3,140,3,0,0,"RandD","low","exist"
"10035",12818,0.87,0.61,3,162,2,0,0,"RandD","low","exist"
"10036",12819,0.75,0.59,3,117,3,1,0,"RandD","medium","exist"
"10037",12820,0.96,0.51,4,225,3,0,0,"marketing","medium","exist"
"10038",12821,0.75,0.92,3,211,3,0,0,"sales","medium","exist"
"10039",12822,0.19,0.58,4,173,5,0,0,"accounting","medium","exist"
"10040",12823,0.52,0.97,4,170,3,0,0,"support","medium","exist"
"10041",12824,0.6,0.6,3,242,3,0,0,"technical","medium","exist"
"10042",12825,0.9,0.81,4,175,3,0,0,"management","medium","exist"
"10043",12826,0.89,0.92,3,195,2,0,0,"marketing","medium","exist"
"10044",12827,0.54,0.93,4,184,2,1,0,"marketing","medium","exist"
"10045",12828,0.99,0.55,3,170,3,0,0,"marketing","medium","exist"
"10046",12829,0.66,0.56,4,185,3,0,0,"sales","medium","exist"
"10047",12830,0.92,0.64,4,259,2,0,0,"sales","medium","exist"
"10048",12831,0.19,0.72,4,102,3,0,0,"sales","medium","exist"
"10049",12832,0.39,0.37,5,156,4,0,0,"sales","medium","exist"
"10050",12833,0.41,0.68,3,191,4,0,0,"sales","medium","exist"
"10051",12834,0.6,0.49,3,239,2,0,0,"sales","medium","exist"
"10052",12835,0.95,0.54,4,235,4,0,0,"sales","medium","exist"
"10053",12836,0.51,0.87,2,130,4,0,0,"sales","medium","exist"
"10054",12837,0.54,0.74,2,166,3,0,0,"sales","medium","exist"
"10055",12838,0.16,0.54,5,206,5,0,0,"sales","medium","exist"
"10056",12839,0.98,0.77,3,191,2,0,0,"sales","medium","exist"
"10057",12840,0.65,0.75,3,214,3,0,0,"sales","medium","exist"
"10058",12841,0.38,0.5,3,196,3,0,0,"sales","medium","exist"
"10059",12842,0.95,0.71,4,151,4,0,0,"sales","medium","exist"
"10060",12843,0.6,0.62,5,165,2,0,0,"sales","medium","exist"
"10061",12844,0.78,0.91,3,177,2,0,0,"sales","high","exist"
"10062",12845,0.19,0.63,6,241,6,0,0,"sales","high","exist"
"10063",12846,0.56,0.99,4,230,3,0,0,"sales","high","exist"
"10064",12847,0.21,0.71,4,270,2,0,0,"sales","high","exist"
"10065",12848,0.83,0.71,3,234,4,0,0,"accounting","high","exist"
"10066",12849,0.5,0.64,3,257,2,1,0,"accounting","high","exist"
"10067",12850,0.74,0.87,5,264,3,0,0,"accounting","high","exist"
"10068",12851,0.75,0.83,4,133,4,0,0,"hr","high","exist"
"10069",12852,0.85,0.66,4,155,4,0,0,"hr","high","exist"
"10070",12853,0.93,0.59,3,202,2,0,0,"hr","high","exist"
"10071",12854,0.76,0.7,3,136,2,0,0,"hr","high","exist"
"10072",12855,0.91,0.78,3,269,3,1,0,"technical","high","exist"
"10073",12856,0.22,0.54,6,169,4,0,0,"technical","low","exist"
"10074",12857,0.78,0.52,5,192,3,1,0,"technical","low","exist"
"10075",12858,0.53,0.8,4,241,3,1,0,"technical","low","exist"
"10076",12859,0.58,0.69,4,165,3,0,0,"technical","low","exist"
"10077",12860,0.99,0.81,3,183,2,0,0,"technical","low","exist"
"10078",12861,0.62,0.64,4,163,3,0,0,"technical","low","exist"
"10079",12862,0.59,0.69,3,162,3,0,0,"technical","low","exist"
"10080",12863,0.13,0.76,5,219,4,0,0,"technical","low","exist"
"10081",12864,0.19,0.63,4,278,6,0,0,"technical","low","exist"
"10082",12865,0.94,0.99,2,273,4,0,0,"technical","low","exist"
"10083",12866,0.53,0.96,4,272,2,0,0,"support","low","exist"
"10084",12867,0.96,0.85,5,168,2,0,0,"support","low","exist"
"10085",12868,0.62,0.87,4,221,3,1,0,"support","low","exist"
"10086",12869,0.81,0.86,4,213,3,0,0,"support","low","exist"
"10087",12870,0.63,0.78,4,275,3,0,0,"support","low","exist"
"10088",12871,0.92,0.68,5,177,4,0,0,"support","medium","exist"
"10089",12872,0.83,0.74,4,249,2,0,0,"support","medium","exist"
"10090",12873,0.49,0.37,5,246,3,0,0,"support","medium","exist"
"10091",12874,0.8,0.66,4,223,3,0,0,"support","medium","exist"
"10092",12875,0.54,0.76,4,244,2,0,0,"support","medium","exist"
"10093",12876,0.37,0.72,3,169,2,1,0,"support","medium","exist"
"10094",12877,0.93,0.56,5,140,3,0,0,"technical","medium","exist"
"10095",12878,0.88,0.99,5,253,2,0,0,"technical","medium","exist"
"10096",12879,0.79,0.87,3,194,2,0,0,"technical","medium","exist"
"10097",12880,0.65,0.88,4,173,3,0,0,"management","medium","exist"
"10098",12881,0.72,0.7,4,172,3,0,0,"IT","medium","exist"
"10099",12882,0.58,0.49,3,167,3,0,0,"IT","medium","exist"
"10100",12883,0.37,0.51,2,153,3,0,0,"IT","high","exist"
"10101",12884,0.87,0.97,4,243,3,0,0,"IT","high","exist"
"10102",12885,0.63,0.72,6,163,4,0,0,"IT","high","exist"
"10103",12886,0.72,0.79,3,221,3,0,0,"product_mng","high","exist"
"10104",12887,0.36,0.55,3,191,3,0,0,"product_mng","high","exist"
"10105",12888,0.96,0.7,4,272,3,0,0,"product_mng","high","exist"
"10106",12889,0.52,0.37,2,118,2,0,0,"product_mng","high","exist"
"10107",12890,0.16,0.83,5,173,4,0,0,"IT","high","exist"
"10108",12891,0.63,0.55,4,200,3,1,0,"RandD","low","exist"
"10109",12892,0.92,0.76,5,132,3,1,0,"RandD","low","exist"
"10110",12893,0.82,0.49,4,180,2,0,0,"RandD","low","exist"
"10111",12894,0.18,0.54,4,145,5,0,0,"RandD","low","exist"
"10112",12895,0.73,0.48,4,139,2,0,0,"RandD","low","exist"
"10113",12896,0.44,0.61,5,230,6,0,0,"marketing","low","exist"
"10114",12897,0.73,0.62,4,247,4,0,0,"sales","low","exist"
"10115",12898,0.62,0.95,4,140,2,0,0,"accounting","low","exist"
"10116",12899,0.94,0.8,4,266,3,1,0,"support","medium","exist"
"10117",12900,0.76,0.74,4,261,3,0,0,"technical","medium","exist"
"10118",12901,0.89,0.49,4,275,3,0,0,"management","medium","exist"
"10119",12902,0.9,0.88,5,254,2,0,0,"marketing","medium","exist"
"10120",12903,1,0.93,5,231,2,0,0,"marketing","medium","exist"
"10121",12904,0.71,0.9,3,138,3,0,0,"marketing","medium","exist"
"10122",12905,0.73,0.97,4,163,3,0,0,"sales","medium","exist"
"10123",12906,0.97,0.9,5,262,3,0,0,"sales","medium","exist"
"10124",12907,0.6,0.59,4,201,3,0,0,"sales","medium","exist"
"10125",12908,0.82,0.67,3,229,3,0,0,"sales","medium","exist"
"10126",12909,0.95,0.48,4,228,2,0,0,"sales","medium","exist"
"10127",12910,0.88,0.65,5,228,3,0,0,"sales","medium","exist"
"10128",12911,0.79,0.49,3,273,3,0,0,"sales","medium","exist"
"10129",12912,0.52,0.96,4,171,2,0,0,"sales","medium","exist"
"10130",12913,0.22,0.61,3,148,5,0,0,"sales","medium","exist"
"10131",12914,0.59,0.96,5,211,3,0,0,"sales","medium","exist"
"10132",12915,0.84,0.64,2,211,3,0,0,"sales","medium","exist"
"10133",12916,0.54,0.41,3,175,3,0,0,"sales","medium","exist"
"10134",12917,1,0.86,4,245,4,0,0,"sales","medium","exist"
"10135",12918,0.93,0.59,3,273,2,1,0,"sales","medium","exist"
"10136",12919,0.96,0.55,3,225,4,1,0,"sales","medium","exist"
"10137",12920,0.56,0.41,5,152,3,0,0,"sales","medium","exist"
"10138",12921,0.49,0.66,5,194,3,0,0,"sales","medium","exist"
"10139",12922,0.89,0.51,4,185,3,1,0,"sales","high","exist"
"10140",12923,0.57,0.91,3,193,2,0,0,"sales","low","exist"
"10141",12924,0.96,0.64,3,166,2,0,0,"accounting","medium","exist"
"10142",12925,0.65,0.89,5,223,3,1,0,"accounting","medium","exist"
"10143",12926,0.14,0.66,5,281,4,1,0,"accounting","medium","exist"
"10144",12927,0.64,0.49,3,241,3,0,0,"hr","medium","exist"
"10145",12928,0.98,0.91,3,165,2,1,0,"hr","medium","exist"
"10146",12929,0.71,0.59,4,143,2,0,0,"hr","medium","exist"
"10147",12930,0.96,0.49,5,137,3,0,0,"hr","medium","exist"
"10148",12931,0.9,0.57,4,185,3,1,0,"technical","medium","exist"
"10149",12932,0.52,0.96,3,271,3,1,0,"technical","medium","exist"
"10150",12933,0.78,0.98,4,207,2,1,0,"technical","medium","exist"
"10151",12934,0.62,0.69,4,184,3,0,0,"technical","low","exist"
"10152",12935,0.6,0.8,4,253,2,0,0,"technical","low","exist"
"10153",12936,0.82,0.62,3,152,6,0,0,"technical","low","exist"
"10154",12937,0.52,0.55,3,225,2,0,0,"technical","low","exist"
"10155",12938,0.13,0.84,5,189,5,0,0,"technical","low","exist"
"10156",12939,0.97,0.93,3,153,2,0,0,"technical","low","exist"
"10157",12940,0.63,0.9,4,245,3,0,0,"technical","low","exist"
"10158",12941,0.68,0.78,5,233,3,0,0,"technical","high","exist"
"10159",12942,0.74,0.83,4,210,3,0,0,"support","low","exist"
"10160",12943,0.89,0.57,4,176,4,0,0,"support","high","exist"
"10161",12944,0.28,0.95,5,191,3,0,0,"support","high","exist"
"10162",12945,0.61,0.9,3,224,3,0,0,"support","low","exist"
"10163",12946,0.67,0.49,3,185,3,0,0,"support","low","exist"
"10164",12947,0.86,0.64,3,245,4,0,0,"support","high","exist"
"10165",12948,0.87,0.93,3,173,2,0,0,"support","low","exist"
"10166",12949,0.7,0.95,4,231,3,0,0,"support","medium","exist"
"10167",12950,0.68,0.84,3,270,3,0,0,"support","high","exist"
"10168",12951,0.69,0.75,5,196,3,0,0,"support","medium","exist"
"10169",12952,0.97,0.83,3,238,2,0,0,"support","medium","exist"
"10170",12953,0.62,0.89,4,261,2,0,0,"technical","medium","exist"
"10171",12954,0.55,0.87,3,201,2,0,0,"technical","medium","exist"
"10172",12955,0.61,0.73,3,252,3,0,0,"technical","high","exist"
"10173",12956,0.15,0.81,3,191,5,0,0,"management","medium","exist"
"10174",12957,0.84,0.86,3,199,3,0,0,"IT","medium","exist"
"10175",12958,0.87,0.64,5,234,2,1,0,"IT","medium","exist"
"10176",12959,0.93,0.86,4,192,4,0,0,"IT","high","exist"
"10177",12960,0.14,0.73,6,237,5,0,0,"IT","medium","exist"
"10178",12961,0.96,0.7,3,207,3,0,0,"IT","high","exist"
"10179",12962,0.41,0.63,2,145,2,0,0,"product_mng","low","exist"
"10180",12963,0.84,0.96,6,155,5,0,0,"product_mng","medium","exist"
"10181",12964,0.94,0.69,5,145,2,0,0,"product_mng","medium","exist"
"10182",12965,0.6,0.86,6,247,6,0,0,"product_mng","medium","exist"
"10183",12966,0.7,0.73,4,182,3,0,0,"IT","medium","exist"
"10184",12967,0.29,0.91,4,183,4,0,0,"RandD","low","exist"
"10185",12968,0.31,0.51,2,146,3,0,0,"RandD","low","exist"
"10186",12969,0.73,0.99,3,241,3,0,0,"RandD","low","exist"
"10187",12970,0.51,0.52,5,261,3,1,0,"RandD","low","exist"
"10188",12971,0.58,0.77,4,140,3,0,0,"RandD","low","exist"
"10189",12972,0.59,0.97,3,257,3,0,0,"marketing","low","exist"
"10190",12973,0.95,0.9,3,186,2,0,0,"marketing","low","exist"
"10191",12974,0.84,0.93,3,159,3,0,0,"sales","low","exist"
"10192",12975,0.28,0.37,3,164,4,1,0,"accounting","low","exist"
"10193",12976,0.94,0.52,4,217,6,1,0,"support","low","exist"
"10194",12977,0.49,0.59,4,137,4,0,0,"technical","high","exist"
"10195",12978,0.72,0.5,4,164,2,1,0,"management","low","exist"
"10196",12979,0.19,0.85,5,249,3,0,0,"marketing","low","exist"
"10197",12980,0.83,0.95,3,264,2,0,0,"marketing","low","exist"
"10198",12981,0.79,0.92,4,208,2,1,0,"marketing","low","exist"
"10199",12982,0.72,0.61,3,175,3,0,0,"sales","high","exist"
"10200",12983,0.97,0.74,4,209,2,0,0,"sales","low","exist"
"10201",12984,0.92,0.83,4,268,4,0,0,"sales","low","exist"
"10202",12985,0.95,0.53,3,264,3,0,0,"sales","low","exist"
"10203",12986,0.76,0.64,4,234,2,0,0,"sales","high","exist"
"10204",12987,0.24,0.62,5,199,4,0,0,"sales","low","exist"
"10205",12988,0.89,0.99,4,205,2,0,1,"sales","medium","exist"
"10206",12989,0.69,0.63,5,140,4,0,1,"sales","high","exist"
"10207",12990,0.92,0.98,3,257,3,0,1,"sales","medium","exist"
"10208",12991,0.79,0.61,4,227,2,0,1,"sales","high","exist"
"10209",12992,0.87,0.94,4,189,3,0,1,"sales","medium","exist"
"10210",12993,0.89,0.88,5,241,2,1,0,"sales","medium","exist"
"10211",12994,0.75,0.77,5,199,4,0,0,"sales","medium","exist"
"10212",12995,0.78,0.6,4,206,3,0,0,"sales","medium","exist"
"10213",12996,0.13,0.62,5,268,3,0,0,"sales","medium","exist"
"10214",12997,0.94,0.86,3,221,3,1,0,"sales","medium","exist"
"10215",12998,0.94,0.88,4,262,2,0,0,"sales","medium","exist"
"10216",12999,0.67,0.6,5,253,6,0,0,"sales","medium","exist"
"10217",13000,0.6,0.73,5,241,3,0,0,"sales","high","exist"
"10218",13001,0.62,0.94,4,252,4,0,0,"accounting","low","exist"
"10219",13002,0.38,0.52,2,171,3,0,0,"accounting","medium","exist"
"10220",13003,0.8,0.77,4,194,3,0,0,"accounting","medium","exist"
"10221",13004,0.61,0.42,3,104,2,0,0,"hr","medium","exist"
"10222",13005,0.61,0.56,4,176,3,0,0,"hr","medium","exist"
"10223",13006,0.66,0.8,4,192,3,0,0,"hr","medium","exist"
"10224",13007,0.56,0.74,3,154,2,0,0,"hr","medium","exist"
"10225",13008,1,0.55,4,186,4,1,0,"technical","medium","exist"
"10226",13009,0.73,0.86,3,200,4,0,0,"technical","medium","exist"
"10227",13010,0.6,0.66,4,132,4,0,0,"technical","medium","exist"
"10228",13011,0.78,0.59,5,236,3,0,0,"technical","high","exist"
"10229",13012,0.48,0.53,3,211,4,0,0,"technical","low","exist"
"10230",13013,0.9,0.77,4,273,2,0,0,"technical","low","exist"
"10231",13014,0.16,0.76,4,223,4,0,0,"technical","low","exist"
"10232",13015,0.5,0.75,3,204,2,0,0,"technical","high","exist"
"10233",13016,0.66,0.65,3,196,3,1,0,"technical","low","exist"
"10234",13017,0.44,0.37,2,219,2,0,0,"technical","low","exist"
"10235",13018,0.95,0.67,4,261,3,0,0,"technical","low","exist"
"10236",13019,0.9,0.65,3,254,2,0,0,"support","high","exist"
"10237",13020,0.27,0.48,4,185,2,0,0,"support","low","exist"
"10238",13021,0.51,0.74,6,98,3,0,0,"support","low","exist"
"10239",13022,0.68,0.76,3,260,4,0,0,"support","low","exist"
"10240",13023,0.97,0.93,5,137,2,1,0,"support","low","exist"
"10241",13024,0.91,0.75,4,159,3,1,0,"support","low","exist"
"10242",13025,0.76,0.88,5,265,4,0,0,"support","low","exist"
"10243",13026,0.88,0.61,4,177,4,1,0,"support","low","exist"
"10244",13027,0.83,0.73,4,247,2,0,0,"support","medium","exist"
"10245",13028,0.78,0.54,3,161,3,0,0,"support","medium","exist"
"10246",13029,0.52,0.38,2,103,3,0,0,"support","medium","exist"
"10247",13030,0.63,0.49,4,151,3,0,0,"technical","medium","exist"
"10248",13031,0.9,0.74,3,193,3,0,0,"technical","medium","exist"
"10249",13032,0.48,0.58,3,194,3,0,0,"technical","medium","exist"
"10250",13033,0.7,0.6,5,208,3,0,0,"management","medium","exist"
"10251",13034,0.68,0.66,4,229,3,0,0,"IT","medium","exist"
"10252",13035,0.7,0.87,3,166,2,0,0,"IT","medium","exist"
"10253",13036,0.77,0.5,3,141,3,0,0,"IT","medium","exist"
"10254",13037,0.73,0.93,3,249,2,0,0,"IT","medium","exist"
"10255",13038,0.87,0.48,4,264,3,0,0,"IT","medium","exist"
"10256",13039,0.65,0.98,3,252,2,0,0,"product_mng","high","exist"
"10257",13040,0.62,0.7,2,134,3,0,0,"product_mng","low","exist"
"10258",13041,0.53,0.51,3,274,2,1,0,"product_mng","medium","exist"
"10259",13042,0.59,0.39,5,200,4,0,0,"product_mng","medium","exist"
"10260",13043,0.87,0.72,2,154,3,0,0,"IT","medium","exist"
"10261",13044,0.47,0.53,3,111,4,0,0,"RandD","medium","exist"
"10262",13045,0.96,0.81,3,247,3,0,0,"RandD","low","exist"
"10263",13046,0.79,0.74,3,169,3,0,0,"RandD","low","exist"
"10264",13047,0.84,0.6,3,250,3,1,0,"RandD","low","exist"
"10265",13048,0.68,0.49,3,178,3,1,0,"RandD","low","exist"
"10266",13049,0.86,0.66,4,251,3,0,0,"RandD","low","exist"
"10267",13050,0.73,0.98,5,272,2,0,0,"marketing","low","exist"
"10268",13051,0.9,0.67,2,229,4,0,0,"sales","low","exist"
"10269",13052,0.63,0.64,3,180,3,0,0,"accounting","low","exist"
"10270",13053,0.71,0.72,3,271,2,0,0,"support","low","exist"
"10271",13054,0.71,0.68,5,226,3,0,0,"technical","low","exist"
"10272",13055,0.95,0.62,4,150,2,0,0,"management","low","exist"
"10273",13056,0.51,0.86,4,260,3,1,0,"marketing","low","exist"
"10274",13057,0.77,0.91,4,161,3,0,0,"marketing","low","exist"
"10275",13058,0.48,0.51,3,136,3,0,0,"marketing","low","exist"
"10276",13059,0.93,0.91,2,238,2,1,0,"sales","low","exist"
"10277",13060,0.83,0.86,4,98,4,0,0,"sales","low","exist"
"10278",13061,0.61,0.73,5,156,4,0,0,"sales","low","exist"
"10279",13062,0.97,0.89,4,248,2,0,0,"sales","low","exist"
"10280",13063,0.5,0.81,3,170,2,0,0,"sales","low","exist"
"10281",13064,0.84,0.54,3,245,3,0,0,"sales","low","exist"
"10282",13065,0.58,0.38,4,203,5,0,0,"sales","low","exist"
"10283",13066,0.59,0.72,3,182,3,0,0,"sales","medium","exist"
"10284",13067,0.77,0.83,3,175,3,0,1,"sales","medium","exist"
"10285",13068,0.78,0.4,4,145,5,1,1,"sales","medium","exist"
"10286",13069,0.6,0.96,4,220,3,1,1,"sales","medium","exist"
"10287",13070,0.53,0.77,4,259,2,1,1,"sales","medium","exist"
"10288",13071,0.73,0.69,3,228,2,0,1,"sales","medium","exist"
"10289",13072,0.76,0.94,3,189,3,0,0,"sales","medium","exist"
"10290",13073,0.12,0.61,6,257,3,0,0,"sales","medium","exist"
"10291",13074,0.2,0.98,3,180,6,0,0,"sales","medium","exist"
"10292",13075,0.5,0.77,4,180,3,0,0,"sales","medium","exist"
"10293",13076,0.79,0.65,5,215,2,1,0,"sales","medium","exist"
"10294",13077,0.96,0.68,3,132,2,0,0,"sales","medium","exist"
"10295",13078,0.26,0.69,5,213,2,0,0,"accounting","high","exist"
"10296",13079,0.8,0.72,4,173,3,0,0,"accounting","low","exist"
"10297",13080,0.43,0.71,3,186,2,0,0,"accounting","medium","exist"
"10298",13081,0.87,0.71,4,157,2,0,0,"hr","medium","exist"
"10299",13082,0.63,0.75,4,175,4,0,0,"hr","medium","exist"
"10300",13083,0.58,0.48,3,135,3,1,0,"hr","medium","exist"
"10301",13084,0.2,0.42,4,256,5,0,0,"hr","low","exist"
"10302",13085,0.62,0.71,4,268,3,0,0,"technical","low","exist"
"10303",13086,0.91,0.94,5,159,3,0,0,"technical","low","exist"
"10304",13087,0.66,0.91,3,191,4,0,0,"technical","low","exist"
"10305",13088,0.53,0.81,3,275,2,0,0,"technical","low","exist"
"10306",13089,0.52,0.98,5,217,2,1,0,"technical","low","exist"
"10307",13090,1,0.88,6,201,4,0,0,"technical","low","exist"
"10308",13091,0.73,0.67,4,205,3,0,0,"technical","low","exist"
"10309",13092,0.65,0.67,3,240,2,1,0,"technical","low","exist"
"10310",13093,0.5,0.95,5,137,3,0,0,"technical","low","exist"
"10311",13094,0.94,0.59,4,241,2,0,0,"technical","low","exist"
"10312",13095,0.48,0.86,5,198,4,0,0,"technical","low","exist"
"10313",13096,0.67,0.87,5,254,2,0,0,"support","low","exist"
"10314",13097,0.73,0.94,4,262,3,0,0,"support","low","exist"
"10315",13098,0.63,0.71,4,244,2,0,0,"support","low","exist"
"10316",13099,0.84,0.84,4,266,3,0,0,"support","low","exist"
"10317",13100,0.2,0.94,6,191,5,0,0,"support","low","exist"
"10318",13101,0.76,0.57,3,148,3,1,0,"support","low","exist"
"10319",13102,0.55,0.54,3,233,2,0,0,"support","low","exist"
"10320",13103,0.8,0.55,4,178,2,1,0,"support","low","exist"
"10321",13104,0.64,0.91,3,165,3,1,0,"support","low","exist"
"10322",13105,0.59,0.97,5,179,6,0,0,"support","medium","exist"
"10323",13106,0.92,0.98,3,149,3,0,0,"support","medium","exist"
"10324",13107,0.75,0.76,3,269,2,1,0,"technical","medium","exist"
"10325",13108,0.69,0.74,5,227,2,0,0,"technical","medium","exist"
"10326",13109,0.82,0.93,3,247,3,0,0,"technical","medium","exist"
"10327",13110,0.88,0.85,3,220,3,0,0,"management","medium","exist"
"10328",13111,0.89,0.91,3,233,2,0,0,"IT","medium","exist"
"10329",13112,1,0.79,5,171,5,0,0,"IT","medium","exist"
"10330",13113,0.66,0.91,4,234,2,1,0,"IT","medium","exist"
"10331",13114,0.76,0.92,3,176,2,0,0,"IT","medium","exist"
"10332",13115,0.8,0.62,5,190,4,1,0,"IT","medium","exist"
"10333",13116,0.58,0.86,4,168,2,0,0,"product_mng","medium","exist"
"10334",13117,0.73,0.93,3,205,3,0,0,"product_mng","high","exist"
"10335",13118,1,0.73,5,189,3,1,0,"product_mng","low","exist"
"10336",13119,0.18,0.9,4,282,4,0,0,"product_mng","medium","exist"
"10337",13120,0.47,0.46,2,152,2,0,0,"IT","medium","exist"
"10338",13121,0.92,0.64,4,217,4,0,0,"RandD","medium","exist"
"10339",13122,0.51,0.5,4,130,3,0,0,"RandD","medium","exist"
"10340",13123,0.81,0.62,4,153,4,0,0,"RandD","low","exist"
"10341",13124,0.52,0.57,3,270,3,0,0,"RandD","low","exist"
"10342",13125,0.95,0.96,3,220,3,0,0,"RandD","low","exist"
"10343",13126,0.93,0.64,4,253,3,0,0,"RandD","low","exist"
"10344",13127,0.98,0.67,4,209,6,0,0,"marketing","low","exist"
"10345",13128,0.79,0.79,4,231,2,0,0,"sales","low","exist"
"10346",13129,0.99,0.73,4,240,4,0,0,"accounting","low","exist"
"10347",13130,0.64,0.9,5,266,3,0,0,"support","low","exist"
"10348",13131,0.54,0.44,3,153,2,0,0,"technical","low","exist"
"10349",13132,0.79,0.59,4,187,2,0,0,"management","low","exist"
"10350",13133,0.55,0.98,4,185,2,1,0,"marketing","low","exist"
"10351",13134,0.18,0.81,4,147,4,0,0,"marketing","low","exist"
"10352",13135,0.56,0.81,4,188,3,1,0,"marketing","low","exist"
"10353",13136,0.92,0.67,2,252,2,0,0,"sales","low","exist"
"10354",13137,0.99,0.75,4,163,2,0,0,"sales","low","exist"
"10355",13138,0.77,0.85,4,189,2,0,0,"sales","low","exist"
"10356",13139,0.49,0.52,3,156,2,0,0,"sales","low","exist"
"10357",13140,0.98,0.58,3,183,3,0,0,"sales","low","exist"
"10358",13141,0.18,0.54,6,209,5,1,0,"sales","low","exist"
"10359",13142,0.8,0.82,4,271,4,0,0,"sales","low","exist"
"10360",13143,0.81,0.77,5,251,2,0,0,"sales","low","exist"
"10361",13144,0.13,0.61,5,198,5,0,0,"sales","medium","exist"
"10362",13145,0.58,0.97,3,274,4,1,1,"sales","medium","exist"
"10363",13146,0.75,0.63,4,209,3,0,1,"sales","medium","exist"
"10364",13147,0.8,0.94,4,271,4,0,1,"sales","medium","exist"
"10365",13148,0.78,0.6,4,143,2,0,1,"sales","medium","exist"
"10366",13149,0.92,0.6,5,236,3,1,1,"sales","medium","exist"
"10367",13150,0.85,0.98,5,222,3,0,1,"sales","medium","exist"
"10368",13151,0.52,0.63,3,233,3,0,1,"sales","medium","exist"
"10369",13152,0.95,0.84,3,270,3,1,1,"sales","medium","exist"
"10370",13153,0.81,0.92,5,258,3,0,1,"sales","medium","exist"
"10371",13154,0.16,0.82,6,202,4,1,1,"sales","medium","exist"
"10372",13155,0.91,0.74,3,150,2,0,0,"accounting","medium","exist"
"10373",13156,0.62,0.51,4,193,3,0,0,"accounting","high","exist"
"10374",13157,0.24,0.42,5,210,5,0,0,"accounting","low","exist"
"10375",13158,0.88,0.51,3,208,3,0,0,"hr","medium","exist"
"10376",13159,0.94,0.73,3,196,3,0,0,"hr","medium","exist"
"10377",13160,0.76,0.79,5,187,4,0,0,"hr","medium","exist"
"10378",13161,0.49,0.67,3,140,2,0,0,"hr","medium","exist"
"10379",13162,0.93,0.9,4,256,4,0,0,"technical","low","exist"
"10380",13163,0.92,0.66,4,113,3,0,0,"technical","low","exist"
"10381",13164,0.19,0.94,4,196,5,0,0,"technical","low","exist"
"10382",13165,0.66,0.76,3,170,3,0,0,"technical","low","exist"
"10383",13166,0.16,0.94,4,261,6,0,0,"technical","low","exist"
"10384",13167,0.83,0.99,5,132,3,0,0,"technical","low","exist"
"10385",13168,0.69,0.53,3,153,3,0,0,"technical","low","exist"
"10386",13169,0.82,0.53,3,147,3,1,0,"technical","low","exist"
"10387",13170,0.88,0.72,5,244,2,0,0,"technical","low","exist"
"10388",13171,0.31,0.42,4,108,4,0,0,"technical","low","exist"
"10389",13172,0.83,0.49,4,218,2,0,0,"technical","low","exist"
"10390",13173,0.94,0.52,5,133,3,0,0,"support","low","exist"
"10391",13174,0.65,0.79,5,233,3,0,0,"support","low","exist"
"10392",13175,0.6,0.6,4,147,3,0,0,"support","low","exist"
"10393",13176,0.52,0.43,3,176,3,0,0,"support","low","exist"
"10394",13177,0.66,0.89,4,169,4,0,0,"support","low","exist"
"10395",13178,0.87,0.87,4,144,3,0,0,"support","low","exist"
"10396",13179,0.2,0.99,5,151,3,1,0,"support","low","exist"
"10397",13180,0.63,0.91,4,252,3,1,0,"support","medium","exist"
"10398",13181,0.69,0.98,4,180,3,0,0,"support","medium","exist"
"10399",13182,0.48,0.61,3,251,3,0,0,"support","medium","exist"
"10400",13183,0.8,0.8,4,263,4,0,0,"support","medium","exist"
"10401",13184,0.89,0.74,5,260,6,0,0,"technical","medium","exist"
"10402",13185,0.67,0.63,3,227,3,0,0,"technical","medium","exist"
"10403",13186,0.37,0.86,6,260,3,0,0,"technical","medium","exist"
"10404",13187,0.93,0.61,5,158,3,0,0,"management","medium","exist"
"10405",13188,0.69,0.52,3,186,3,0,0,"IT","medium","exist"
"10406",13189,0.16,0.61,4,171,6,0,0,"IT","medium","exist"
"10407",13190,0.81,0.55,3,199,2,1,0,"IT","medium","exist"
"10408",13191,0.97,0.63,5,258,2,0,0,"IT","medium","exist"
"10409",13192,0.77,0.59,4,273,2,0,0,"IT","high","exist"
"10410",13193,0.75,0.78,2,259,3,0,0,"product_mng","low","exist"
"10411",13194,0.88,0.82,3,265,3,0,0,"product_mng","medium","exist"
"10412",13195,0.43,0.51,5,168,4,0,0,"product_mng","medium","exist"
"10413",13196,0.99,0.99,4,163,4,0,0,"product_mng","medium","exist"
"10414",13197,0.59,0.65,5,265,3,0,0,"IT","medium","exist"
"10415",13198,0.89,0.71,4,190,3,0,0,"RandD","low","exist"
"10416",13199,0.54,0.73,3,157,3,0,0,"RandD","low","exist"
"10417",13200,0.32,0.86,4,266,4,0,0,"RandD","low","exist"
"10418",13201,0.17,0.55,6,240,6,0,0,"RandD","low","exist"
"10419",13202,0.78,0.55,3,143,3,0,0,"RandD","low","exist"
"10420",13203,0.73,0.68,3,121,5,0,0,"RandD","low","exist"
"10421",13204,0.65,0.76,2,170,5,0,0,"IT","low","exist"
"10422",13205,0.8,0.71,4,161,4,0,0,"IT","low","exist"
"10423",13206,0.61,0.86,3,239,3,0,0,"IT","low","exist"
"10424",13207,0.67,0.49,3,224,3,0,0,"IT","low","exist"
"10425",13208,0.63,0.57,3,242,3,0,0,"product_mng","low","exist"
"10426",13209,0.51,0.58,4,140,2,1,0,"product_mng","low","exist"
"10427",13210,0.82,0.59,5,170,3,0,0,"product_mng","low","exist"
"10428",13211,0.79,0.67,5,156,2,0,0,"product_mng","low","exist"
"10429",13212,0.49,0.6,2,113,5,0,0,"IT","low","exist"
"10430",13213,0.7,0.59,3,138,3,0,0,"RandD","low","exist"
"10431",13214,0.13,0.5,3,137,5,0,0,"RandD","low","exist"
"10432",13215,0.83,0.52,5,217,3,0,0,"RandD","low","exist"
"10433",13216,0.83,0.91,3,155,3,0,0,"RandD","low","exist"
"10434",13217,0.19,0.83,5,280,4,0,0,"RandD","low","exist"
"10435",13218,0.8,0.81,5,248,2,1,0,"RandD","low","exist"
"10436",13219,0.49,0.67,2,190,8,0,0,"marketing","medium","exist"
"10437",13220,0.92,0.99,3,176,8,0,0,"sales","medium","exist"
"10438",13221,0.81,0.55,4,217,8,0,0,"accounting","medium","exist"
"10439",13222,0.62,0.91,3,269,8,0,0,"support","medium","exist"
"10440",13223,0.21,0.7,3,238,8,0,0,"technical","medium","exist"
"10441",13224,0.95,0.74,5,243,6,0,0,"management","medium","exist"
"10442",13225,0.51,0.8,4,198,6,0,0,"marketing","medium","exist"
"10443",13226,0.52,0.89,3,188,6,0,0,"marketing","medium","exist"
"10444",13227,0.64,0.56,3,257,6,0,0,"marketing","medium","exist"
"10445",13228,0.62,0.79,4,268,6,0,0,"sales","medium","exist"
"10446",13229,0.73,0.88,5,233,4,1,0,"sales","medium","exist"
"10447",13230,0.32,0.86,4,214,5,0,0,"sales","medium","exist"
"10448",13231,0.78,0.96,2,285,3,0,0,"sales","high","exist"
"10449",13232,0.65,0.91,4,224,2,1,0,"sales","low","exist"
"10450",13233,0.56,0.92,4,224,3,0,0,"sales","medium","exist"
"10451",13234,0.96,0.89,3,142,4,0,0,"sales","medium","exist"
"10452",13235,0.79,0.82,4,220,3,0,0,"sales","medium","exist"
"10453",13236,0.66,0.58,4,244,3,0,0,"sales","medium","exist"
"10454",13237,0.67,0.68,4,171,3,0,0,"sales","low","exist"
"10455",13238,0.86,0.82,4,274,2,1,0,"sales","low","exist"
"10456",13239,0.57,0.72,4,214,2,1,0,"sales","low","exist"
"10457",13240,0.86,0.87,5,171,2,0,0,"sales","low","exist"
"10458",13241,0.52,0.59,5,150,2,0,0,"sales","low","exist"
"10459",13242,0.73,0.61,4,260,2,1,0,"sales","low","exist"
"10460",13243,0.78,0.63,5,259,3,0,0,"sales","low","exist"
"10461",13244,0.95,0.63,3,153,2,0,0,"sales","low","exist"
"10462",13245,0.75,0.61,3,263,3,0,0,"sales","low","exist"
"10463",13246,0.83,0.52,2,149,2,1,0,"sales","low","exist"
"10464",13247,0.48,1,4,261,3,0,0,"accounting","low","exist"
"10465",13248,0.3,0.58,2,189,4,1,0,"accounting","low","exist"
"10466",13249,0.72,0.85,5,237,4,0,0,"accounting","low","exist"
"10467",13250,0.61,0.52,3,224,3,0,0,"hr","low","exist"
"10468",13251,0.31,0.87,6,240,3,1,0,"hr","low","exist"
"10469",13252,0.62,0.81,3,245,2,1,0,"hr","low","exist"
"10470",13253,0.48,0.49,3,268,3,0,0,"hr","low","exist"
"10471",13254,0.97,0.89,4,208,2,1,0,"technical","low","exist"
"10472",13255,0.61,0.83,4,153,2,0,0,"technical","low","exist"
"10473",13256,0.93,0.99,3,169,3,0,0,"technical","low","exist"
"10474",13257,0.89,0.39,5,218,2,0,0,"technical","low","exist"
"10475",13258,0.95,0.9,3,155,3,0,0,"technical","medium","exist"
"10476",13259,0.36,0.44,5,155,3,0,0,"technical","medium","exist"
"10477",13260,0.29,0.39,6,105,6,0,0,"technical","medium","exist"
"10478",13261,0.65,0.83,4,251,2,0,0,"technical","medium","exist"
"10479",13262,0.72,0.54,4,219,2,0,0,"technical","medium","exist"
"10480",13263,0.51,0.56,4,198,2,1,0,"technical","medium","exist"
"10481",13264,0.54,0.53,4,158,2,0,0,"technical","medium","exist"
"10482",13265,0.66,0.58,3,157,2,0,0,"support","medium","exist"
"10483",13266,0.59,0.54,4,178,2,0,0,"support","medium","exist"
"10484",13267,0.45,0.48,3,145,2,0,0,"support","medium","exist"
"10485",13268,0.15,0.91,5,230,3,0,0,"support","medium","exist"
"10486",13269,0.95,0.53,3,174,3,0,0,"support","medium","exist"
"10487",13270,0.49,0.59,5,140,3,0,0,"support","high","exist"
"10488",13271,0.68,0.97,3,174,2,0,0,"support","low","exist"
"10489",13272,0.7,0.76,4,173,2,0,0,"support","medium","exist"
"10490",13273,0.9,0.73,2,203,4,0,0,"support","medium","exist"
"10491",13274,0.94,0.95,5,170,3,0,0,"support","medium","exist"
"10492",13275,0.8,0.86,3,203,3,0,0,"support","medium","exist"
"10493",13276,0.59,0.53,5,169,3,0,0,"technical","low","exist"
"10494",13277,0.43,0.96,3,109,6,0,0,"technical","low","exist"
"10495",13278,0.7,0.54,5,263,3,0,0,"technical","low","exist"
"10496",13279,0.51,0.62,4,185,3,0,0,"management","low","exist"
"10497",13280,0.12,0.49,4,191,5,0,0,"IT","low","exist"
"10498",13281,0.14,0.56,5,259,4,1,0,"IT","low","exist"
"10499",13282,0.86,0.91,4,253,3,0,0,"IT","low","exist"
"10500",13283,0.97,0.5,3,216,3,0,0,"IT","low","exist"
"10501",13284,1,0.86,2,264,3,0,0,"IT","medium","exist"
"10502",13285,0.49,0.63,3,181,3,1,0,"product_mng","medium","exist"
"10503",13286,0.9,0.93,3,209,3,0,0,"product_mng","medium","exist"
"10504",13287,0.82,0.89,4,239,2,0,0,"product_mng","medium","exist"
"10505",13288,0.59,0.48,3,197,3,0,0,"product_mng","medium","exist"
"10506",13289,0.97,0.57,4,150,2,0,0,"IT","medium","exist"
"10507",13290,0.69,0.88,3,164,10,0,0,"management","medium","exist"
"10508",13291,0.73,0.84,3,216,8,0,0,"management","medium","exist"
"10509",13292,0.48,0.74,2,271,8,1,0,"management","medium","exist"
"10510",13293,0.94,0.49,4,176,8,0,0,"management","medium","exist"
"10511",13294,0.74,0.73,3,156,8,0,0,"management","medium","exist"
"10512",13295,0.65,0.63,4,143,8,0,0,"management","medium","exist"
"10513",13296,0.93,0.94,4,233,6,0,0,"IT","medium","exist"
"10514",13297,0.57,0.67,3,138,6,1,0,"IT","medium","exist"
"10515",13298,0.9,0.49,3,259,6,0,0,"IT","medium","exist"
"10516",13299,0.55,0.86,4,169,6,0,0,"IT","medium","exist"
"10517",13300,0.59,0.73,3,172,6,0,0,"product_mng","medium","exist"
"10518",13301,0.72,0.98,4,156,3,0,0,"product_mng","medium","exist"
"10519",13302,0.87,0.52,4,140,3,0,0,"product_mng","medium","exist"
"10520",13303,0.86,0.82,4,212,2,0,0,"product_mng","medium","exist"
"10521",13304,0.61,0.5,4,269,3,0,0,"IT","medium","exist"
"10522",13305,0.45,0.63,5,111,5,0,0,"management","medium","exist"
"10523",13306,0.51,0.63,4,198,2,0,0,"management","medium","exist"
"10524",13307,0.87,0.92,4,263,3,0,0,"management","medium","exist"
"10525",13308,0.29,0.38,5,191,5,0,0,"management","medium","exist"
"10526",13309,0.57,0.64,3,188,3,0,0,"management","medium","exist"
"10527",13310,0.69,0.83,4,252,3,0,0,"management","medium","exist"
"10528",13311,0.61,0.9,2,142,3,0,0,"marketing","high","exist"
"10529",13312,0.96,0.85,4,247,3,0,0,"sales","high","exist"
"10530",13313,0.16,0.61,6,269,2,0,0,"accounting","high","exist"
"10531",13314,0.96,0.82,4,244,3,0,0,"support","high","exist"
"10532",13315,0.77,0.81,4,164,3,0,0,"technical","high","exist"
"10533",13316,0.85,0.87,6,232,5,0,0,"management","high","exist"
"10534",13317,0.37,0.49,3,177,3,0,0,"marketing","high","exist"
"10535",13318,0.68,0.65,3,173,3,1,0,"marketing","high","exist"
"10536",13319,0.87,0.6,5,165,2,1,0,"marketing","high","exist"
"10537",13320,0.95,0.8,3,225,2,0,0,"sales","high","exist"
"10538",13321,0.84,0.63,3,121,3,1,0,"sales","low","exist"
"10539",13322,0.44,0.51,2,219,4,0,0,"sales","low","exist"
"10540",13323,0.94,0.73,4,204,2,0,0,"sales","low","exist"
"10541",13324,0.85,0.94,5,235,4,0,0,"sales","low","exist"
"10542",13325,0.75,0.51,2,215,2,1,0,"sales","low","exist"
"10543",13326,0.76,0.67,5,243,3,0,0,"sales","low","exist"
"10544",13327,0.13,0.97,4,162,6,0,0,"sales","low","exist"
"10545",13328,0.6,0.79,4,262,3,0,0,"sales","low","exist"
"10546",13329,0.45,0.55,4,206,2,0,0,"sales","low","exist"
"10547",13330,0.49,1,2,125,4,1,0,"sales","low","exist"
"10548",13331,0.19,0.36,3,167,5,0,0,"sales","low","exist"
"10549",13332,0.68,0.89,5,218,5,0,0,"sales","low","exist"
"10550",13333,0.53,0.91,5,181,3,0,0,"sales","low","exist"
"10551",13334,1,0.77,5,269,3,0,0,"sales","low","exist"
"10552",13335,0.99,0.86,3,167,2,0,0,"sales","low","exist"
"10553",13336,0.29,0.75,6,271,10,0,0,"sales","medium","exist"
"10554",13337,0.54,0.83,4,201,8,1,0,"sales","medium","exist"
"10555",13338,0.25,0.9,6,229,8,0,0,"sales","medium","exist"
"10556",13339,0.71,0.76,4,148,8,0,0,"accounting","medium","exist"
"10557",13340,0.96,0.84,3,147,8,0,0,"accounting","medium","exist"
"10558",13341,0.8,0.9,4,211,8,0,0,"accounting","medium","exist"
"10559",13342,0.82,0.87,5,145,6,0,0,"hr","medium","exist"
"10560",13343,0.19,0.97,6,269,6,0,0,"hr","medium","exist"
"10561",13344,0.43,0.74,4,129,6,0,0,"hr","medium","exist"
"10562",13345,0.62,0.84,3,270,6,0,0,"hr","medium","exist"
"10563",13346,0.75,0.85,3,250,6,0,0,"technical","medium","exist"
"10564",13347,0.56,0.48,5,192,2,1,0,"technical","medium","exist"
"10565",13348,0.88,0.91,4,233,4,0,0,"technical","high","exist"
"10566",13349,0.63,0.57,4,192,3,0,0,"technical","high","exist"
"10567",13350,0.75,0.93,3,247,2,0,0,"technical","high","exist"
"10568",13351,0.74,1,4,192,4,0,0,"technical","high","exist"
"10569",13352,0.55,0.68,3,178,3,1,0,"technical","high","exist"
"10570",13353,0.87,0.55,4,197,3,0,0,"technical","high","exist"
"10571",13354,0.13,0.9,5,264,6,0,0,"technical","high","exist"
"10572",13355,0.33,0.64,2,274,3,1,0,"technical","high","exist"
"10573",13356,0.89,0.97,4,147,2,0,0,"technical","low","exist"
"10574",13357,0.56,0.94,3,154,3,1,0,"support","low","exist"
"10575",13358,0.95,0.61,3,224,2,1,0,"support","low","exist"
"10576",13359,0.57,0.59,4,250,2,0,0,"support","low","exist"
"10577",13360,0.72,0.53,3,179,3,0,0,"support","low","exist"
"10578",13361,0.28,0.44,4,170,2,0,0,"support","low","exist"
"10579",13362,0.54,0.61,4,118,5,0,0,"support","low","exist"
"10580",13363,0.54,0.95,4,256,3,0,0,"support","low","exist"
"10581",13364,0.99,0.8,3,209,2,0,0,"support","medium","exist"
"10582",13365,0.37,0.69,2,146,3,0,0,"support","medium","exist"
"10583",13366,0.77,0.87,3,275,4,1,0,"support","medium","exist"
"10584",13367,0.7,0.88,4,180,2,0,0,"support","medium","exist"
"10585",13368,0.8,0.74,3,228,3,0,0,"technical","medium","exist"
"10586",13369,0.52,0.63,3,204,3,0,0,"technical","medium","exist"
"10587",13370,0.69,0.55,3,172,2,0,0,"technical","medium","exist"
"10588",13371,0.6,0.62,5,274,3,0,0,"management","medium","exist"
"10589",13372,0.74,0.64,3,136,2,0,0,"IT","medium","exist"
"10590",13373,0.69,0.82,4,252,3,1,0,"IT","medium","exist"
"10591",13374,0.78,0.89,4,137,3,0,0,"IT","medium","exist"
"10592",13375,0.77,0.75,4,191,3,0,0,"IT","medium","exist"
"10593",13376,0.91,0.68,4,132,4,0,0,"IT","medium","exist"
"10594",13377,0.54,0.68,6,249,5,0,0,"product_mng","medium","exist"
"10595",13378,0.48,0.77,6,274,6,0,0,"product_mng","medium","exist"
"10596",13379,0.55,0.96,3,194,3,0,0,"product_mng","medium","exist"
"10597",13380,0.17,0.36,6,191,2,0,0,"product_mng","medium","exist"
"10598",13381,0.77,0.83,5,216,4,0,0,"IT","medium","exist"
"10599",13382,0.93,0.98,3,241,3,0,0,"IT","medium","exist"
"10600",13383,0.65,0.91,4,243,5,1,0,"IT","medium","exist"
"10601",13384,0.67,0.52,4,207,3,0,0,"IT","medium","exist"
"10602",13385,0.95,0.88,3,199,3,0,0,"IT","medium","exist"
"10603",13386,0.61,0.97,6,286,4,0,0,"product_mng","medium","exist"
"10604",13387,0.57,0.39,4,132,3,0,0,"product_mng","high","exist"
"10605",13388,0.65,1,4,229,4,0,0,"product_mng","low","exist"
"10606",13389,0.85,0.81,4,260,3,0,0,"product_mng","medium","exist"
"10607",13390,0.61,0.96,3,214,2,0,0,"IT","medium","exist"
"10608",13391,0.65,0.9,6,217,4,1,1,"RandD","medium","exist"
"10609",13392,0.92,0.93,4,225,2,0,1,"RandD","medium","exist"
"10610",13393,0.37,0.41,2,113,3,0,1,"RandD","medium","exist"
"10611",13394,0.48,0.77,5,250,2,0,1,"RandD","medium","exist"
"10612",13395,0.82,0.91,5,271,2,0,1,"RandD","medium","exist"
"10613",13396,0.84,0.75,4,135,3,0,1,"RandD","medium","exist"
"10614",13397,0.57,0.46,2,100,6,1,1,"marketing","medium","exist"
"10615",13398,0.8,0.75,4,224,3,0,1,"sales","medium","exist"
"10616",13399,0.49,0.91,4,134,4,0,0,"accounting","low","exist"
"10617",13400,0.79,0.82,5,158,2,0,0,"support","low","exist"
"10618",13401,0.48,0.67,3,183,2,0,0,"technical","low","exist"
"10619",13402,0.28,0.89,4,97,6,0,0,"management","low","exist"
"10620",13403,0.47,0.56,4,226,3,0,0,"marketing","low","exist"
"10621",13404,0.91,0.6,4,235,4,1,0,"marketing","low","exist"
"10622",13405,0.75,0.6,4,186,10,1,0,"marketing","low","exist"
"10623",13406,0.61,0.89,3,242,10,0,0,"sales","high","exist"
"10624",13407,0.47,0.79,3,284,10,0,0,"sales","low","exist"
"10625",13408,0.22,0.7,2,274,10,0,0,"sales","high","exist"
"10626",13409,0.5,0.48,4,130,10,0,0,"sales","high","exist"
"10627",13410,0.56,0.87,3,146,10,0,0,"sales","low","exist"
"10628",13411,0.84,0.85,4,207,10,0,0,"sales","low","exist"
"10629",13412,0.69,0.72,4,210,2,1,0,"sales","high","exist"
"10630",13413,0.53,0.64,3,143,2,0,0,"sales","low","exist"
"10631",13414,0.17,0.57,4,116,3,0,0,"sales","medium","exist"
"10632",13415,0.48,0.71,2,162,3,1,0,"sales","high","exist"
"10633",13416,0.94,0.51,3,242,3,0,0,"sales","medium","exist"
"10634",13417,0.77,0.89,4,153,7,0,0,"sales","medium","exist"
"10635",13418,1,0.72,5,194,7,1,0,"sales","medium","exist"
"10636",13419,0.49,0.65,4,233,7,0,0,"sales","medium","exist"
"10637",13420,0.93,0.73,4,283,7,0,0,"sales","high","exist"
"10638",13421,0.38,0.43,3,188,7,0,0,"sales","medium","exist"
"10639",13422,0.6,0.54,4,182,6,0,0,"sales","medium","exist"
"10640",13423,0.5,0.82,2,286,6,0,0,"sales","medium","exist"
"10641",13424,0.97,0.55,5,212,6,0,0,"sales","high","exist"
"10642",13425,0.93,0.95,5,176,6,0,1,"accounting","medium","exist"
"10643",13426,0.5,1,5,264,8,0,1,"accounting","high","exist"
"10644",13427,0.52,0.84,3,261,8,0,1,"accounting","low","exist"
"10645",13428,0.5,0.71,4,163,8,0,1,"hr","medium","exist"
"10646",13429,0.55,0.4,3,139,8,0,1,"hr","medium","exist"
"10647",13430,0.95,0.84,3,261,8,1,1,"hr","medium","exist"
"10648",13431,0.48,0.42,2,275,6,1,1,"hr","medium","exist"
"10649",13432,0.51,0.39,5,132,6,1,1,"technical","low","exist"
"10650",13433,0.96,0.48,3,202,6,0,0,"technical","low","exist"
"10651",13434,0.97,0.84,4,177,6,0,0,"technical","low","exist"
"10652",13435,0.97,0.66,5,234,6,0,0,"technical","low","exist"
"10653",13436,0.71,0.54,4,188,6,0,0,"technical","low","exist"
"10654",13437,0.82,0.49,5,203,6,0,0,"technical","low","exist"
"10655",13438,0.57,1,4,227,10,0,0,"technical","low","exist"
"10656",13439,0.48,0.93,3,150,10,0,0,"technical","low","exist"
"10657",13440,0.71,0.64,3,267,3,0,0,"technical","low","exist"
"10658",13441,0.63,0.61,5,186,10,0,0,"technical","low","exist"
"10659",13442,0.99,0.84,4,142,10,0,0,"technical","high","exist"
"10660",13443,0.79,0.83,3,126,10,1,0,"support","low","exist"
"10661",13444,0.65,0.85,4,201,10,0,0,"support","low","exist"
"10662",13445,0.7,0.85,4,142,2,0,0,"support","low","exist"
"10663",13446,0.99,0.94,4,167,4,0,0,"support","low","exist"
"10664",13447,0.65,0.62,4,258,2,0,0,"support","high","exist"
"10665",13448,0.92,0.85,3,207,2,0,0,"support","low","exist"
"10666",13449,0.24,0.5,4,282,4,1,0,"support","low","exist"
"10667",13450,0.39,0.89,3,188,5,0,0,"support","low","exist"
"10668",13451,0.82,0.85,3,214,2,0,0,"support","high","exist"
"10669",13452,0.78,0.89,4,272,2,0,0,"support","low","exist"
"10670",13453,0.62,0.79,3,259,3,0,0,"support","medium","exist"
"10671",13454,0.6,0.61,5,191,2,1,0,"technical","high","exist"
"10672",13455,0.49,0.57,3,192,3,0,0,"technical","medium","exist"
"10673",13456,0.82,0.82,3,164,3,0,0,"technical","high","exist"
"10674",13457,0.48,0.81,4,149,2,0,0,"management","medium","exist"
"10675",13458,0.69,0.56,4,149,3,0,0,"IT","medium","exist"
"10676",13459,0.4,0.89,2,165,3,0,0,"IT","medium","exist"
"10677",13460,0.72,0.8,3,222,3,0,0,"IT","medium","exist"
"10678",13461,0.75,0.84,5,222,3,1,0,"IT","medium","exist"
"10679",13462,0.5,0.77,3,265,3,0,0,"IT","medium","exist"
"10680",13463,0.78,0.5,5,247,4,0,0,"product_mng","medium","exist"
"10681",13464,0.76,0.45,4,147,2,0,0,"product_mng","medium","exist"
"10682",13465,0.94,0.52,3,273,3,0,0,"product_mng","high","exist"
"10683",13466,0.24,0.94,6,144,4,0,0,"product_mng","low","exist"
"10684",13467,0.99,0.66,3,181,2,0,0,"IT","medium","exist"
"10685",13468,0.67,0.64,3,198,2,1,0,"management","medium","exist"
"10686",13469,0.76,0.57,5,255,4,0,0,"management","medium","exist"
"10687",13470,0.76,0.77,4,169,10,0,0,"management","medium","exist"
"10688",13471,0.55,0.64,4,201,10,1,0,"management","medium","exist"
"10689",13472,0.74,0.6,4,274,10,1,0,"management","medium","exist"
"10690",13473,0.81,0.85,4,134,10,1,0,"management","medium","exist"
"10691",13474,0.98,0.67,3,190,10,0,0,"IT","medium","exist"
"10692",13475,0.98,0.98,4,170,10,0,0,"IT","medium","exist"
"10693",13476,0.58,0.91,3,154,10,0,0,"product_mng","high","exist"
"10694",13477,0.18,0.75,3,142,2,0,0,"product_mng","low","exist"
"10695",13478,0.57,0.67,5,235,2,0,0,"product_mng","low","exist"
"10696",13479,0.7,0.62,3,110,3,0,0,"product_mng","low","exist"
"10697",13480,0.49,0.77,3,211,3,0,0,"IT","high","exist"
"10698",13481,0.7,0.56,4,214,3,0,1,"management","medium","exist"
"10699",13482,0.16,0.93,5,210,7,0,1,"management","medium","exist"
"10700",13483,0.58,0.59,3,207,7,0,1,"management","medium","exist"
"10701",13484,0.66,0.57,4,161,7,0,1,"management","medium","exist"
"10702",13485,0.51,0.55,2,102,7,0,1,"management","medium","exist"
"10703",13486,0.48,0.84,4,186,7,0,1,"management","medium","exist"
"10704",13487,0.56,0.71,3,211,6,0,1,"marketing","low","exist"
"10705",13488,0.81,0.62,3,240,6,0,1,"sales","low","exist"
"10706",13489,0.57,0.7,4,237,6,0,0,"accounting","low","exist"
"10707",13490,0.66,0.53,3,164,6,0,0,"support","low","exist"
"10708",13491,0.22,0.91,6,222,8,0,0,"technical","low","exist"
"10709",13492,0.96,0.71,3,205,8,1,0,"management","medium","exist"
"10710",13493,0.87,0.88,4,140,8,0,0,"marketing","medium","exist"
"10711",13494,0.61,0.42,2,103,8,0,0,"marketing","medium","exist"
"10712",13495,0.66,0.85,3,178,8,1,0,"marketing","medium","exist"
"10713",13496,0.9,0.51,4,137,6,0,0,"sales","medium","exist"
"10714",13497,0.64,0.67,3,143,6,0,0,"sales","medium","exist"
"10715",13498,0.76,0.82,4,170,6,0,0,"sales","medium","exist"
"10716",13499,0.97,0.41,5,135,6,0,0,"sales","medium","exist"
"10717",13500,0.69,0.76,3,174,6,0,0,"sales","medium","exist"
"10718",13501,0.98,0.55,3,166,6,1,0,"sales","medium","exist"
"10719",13502,0.18,0.61,5,174,6,0,0,"sales","medium","exist"
"10720",13503,0.62,0.91,3,251,10,0,0,"sales","medium","exist"
"10721",13504,0.29,0.37,6,187,10,1,0,"sales","high","exist"
"10722",13505,0.87,0.48,5,170,3,0,0,"sales","low","exist"
"10723",13506,0.91,0.64,3,241,10,0,0,"sales","medium","exist"
"10724",13507,0.53,0.79,3,221,10,1,0,"sales","medium","exist"
"10725",13508,0.69,0.73,4,257,10,1,0,"sales","medium","exist"
"10726",13509,0.14,0.58,4,275,10,0,0,"sales","medium","exist"
"10727",13510,0.7,0.77,4,245,2,0,0,"sales","low","exist"
"10728",13511,0.77,0.75,6,246,6,0,0,"sales","low","exist"
"10729",13512,0.77,0.76,6,263,6,0,0,"sales","low","exist"
"10730",13513,0.76,0.99,3,133,4,0,0,"sales","low","exist"
"10731",13514,0.66,0.49,4,157,3,0,0,"sales","low","exist"
"10732",13515,0.5,0.95,3,198,4,0,0,"accounting","low","exist"
"10733",13516,0.57,0.9,5,145,3,0,0,"accounting","low","exist"
"10734",13517,0.97,0.62,6,118,2,0,0,"accounting","low","exist"
"10735",13518,0.26,0.99,5,184,5,0,0,"hr","low","exist"
"10736",13519,0.72,0.62,3,243,2,1,0,"hr","low","exist"
"10737",13520,0.83,0.93,3,247,2,0,0,"hr","low","exist"
"10738",13521,0.55,0.4,3,158,3,0,0,"hr","low","exist"
"10739",13522,0.77,0.74,5,243,2,0,0,"technical","low","exist"
"10740",13523,0.24,0.63,4,203,5,0,0,"technical","low","exist"
"10741",13524,0.8,0.96,3,161,3,0,0,"technical","low","exist"
"10742",13525,0.5,0.59,4,214,3,1,0,"technical","low","exist"
"10743",13526,0.66,0.59,4,179,3,0,0,"technical","low","exist"
"10744",13527,0.66,0.77,4,188,2,0,0,"technical","low","exist"
"10745",13528,0.66,0.81,3,174,3,0,0,"technical","low","exist"
"10746",13529,0.96,0.83,3,177,4,0,0,"technical","low","exist"
"10747",13530,0.75,0.94,5,194,4,0,0,"technical","low","exist"
"10748",13531,0.78,0.77,3,244,2,0,0,"technical","medium","exist"
"10749",13532,0.57,0.82,4,269,2,0,0,"technical","medium","exist"
"10750",13533,0.78,0.68,2,159,3,1,0,"support","medium","exist"
"10751",13534,0.57,0.88,4,140,2,0,0,"support","medium","exist"
"10752",13535,0.84,0.56,5,224,2,0,0,"support","medium","exist"
"10753",13536,0.23,0.94,5,242,4,0,0,"support","medium","exist"
"10754",13537,0.53,0.37,3,180,3,0,0,"support","medium","exist"
"10755",13538,0.82,0.71,3,150,3,0,0,"support","medium","exist"
"10756",13539,0.59,0.64,5,269,3,0,0,"support","medium","exist"
"10757",13540,0.5,0.52,2,178,2,0,0,"support","medium","exist"
"10758",13541,1,0.74,2,187,3,0,0,"support","medium","exist"
"10759",13542,0.94,0.61,3,140,2,0,0,"support","medium","exist"
"10760",13543,0.86,0.61,4,193,2,0,0,"support","high","exist"
"10761",13544,0.73,0.49,4,243,2,0,0,"technical","low","exist"
"10762",13545,0.49,0.94,3,144,3,1,0,"technical","medium","exist"
"10763",13546,0.79,0.73,2,147,2,0,0,"technical","medium","exist"
"10764",13547,0.83,0.5,6,165,3,0,0,"management","medium","exist"
"10765",13548,0.85,0.67,3,176,2,0,0,"IT","medium","exist"
"10766",13549,0.65,0.37,3,170,6,0,0,"IT","low","exist"
"10767",13550,0.94,0.65,4,213,2,1,0,"IT","low","exist"
"10768",13551,0.76,0.81,4,242,2,0,0,"IT","low","exist"
"10769",13552,0.77,0.54,4,139,3,1,0,"IT","low","exist"
"10770",13553,0.77,0.91,4,239,3,1,0,"product_mng","low","exist"
"10771",13554,0.59,0.64,5,123,2,0,0,"product_mng","low","exist"
"10772",13555,0.69,0.9,3,185,4,0,0,"product_mng","low","exist"
"10773",13556,0.51,0.85,4,186,2,0,0,"product_mng","low","exist"
"10774",13557,0.8,0.67,3,178,3,0,0,"IT","low","exist"
"10775",13558,0.98,0.7,3,153,10,0,0,"management","high","exist"
"10776",13559,0.69,0.72,4,185,10,1,0,"management","high","exist"
"10777",13560,0.14,0.76,4,142,10,0,0,"management","high","exist"
"10778",13561,0.95,0.9,4,221,10,1,0,"management","high","exist"
"10779",13562,0.53,0.61,3,148,10,0,0,"management","high","exist"
"10780",13563,0.64,0.52,5,258,10,1,0,"management","high","exist"
"10781",13564,0.51,0.73,4,229,3,0,0,"sales","low","exist"
"10782",13565,0.36,0.73,2,111,2,0,0,"sales","low","exist"
"10783",13566,0.62,0.97,2,271,3,0,0,"sales","low","exist"
"10784",13567,0.98,0.58,4,133,3,0,0,"sales","low","exist"
"10785",13568,0.53,0.7,4,223,3,0,0,"sales","low","exist"
"10786",13569,0.8,0.95,4,272,2,0,0,"sales","low","exist"
"10787",13570,0.73,0.77,3,233,3,0,0,"sales","medium","exist"
"10788",13571,0.82,0.8,3,162,3,0,0,"sales","medium","exist"
"10789",13572,0.62,0.75,5,165,4,0,0,"sales","medium","exist"
"10790",13573,0.87,0.48,5,242,3,0,0,"sales","medium","exist"
"10791",13574,0.43,0.65,4,124,2,0,0,"sales","medium","exist"
"10792",13575,0.57,0.6,2,163,3,0,0,"sales","medium","exist"
"10793",13576,0.91,0.77,3,144,3,0,0,"sales","medium","exist"
"10794",13577,0.75,0.59,5,149,4,0,0,"sales","medium","exist"
"10795",13578,0.76,0.8,5,217,2,0,0,"sales","medium","exist"
"10796",13579,0.85,0.49,4,139,2,0,0,"sales","medium","exist"
"10797",13580,0.56,0.67,3,270,2,0,0,"sales","medium","exist"
"10798",13581,0.86,0.84,3,177,3,0,0,"sales","medium","exist"
"10799",13582,0.21,0.43,5,175,2,1,0,"sales","high","exist"
"10800",13583,0.94,0.59,3,151,2,0,0,"sales","low","exist"
"10801",13584,0.98,0.74,3,185,3,0,0,"sales","medium","exist"
"10802",13585,0.42,0.45,3,227,3,0,0,"sales","medium","exist"
"10803",13586,0.98,0.89,4,218,2,0,0,"sales","medium","exist"
"10804",13587,1,0.93,5,167,3,0,0,"sales","medium","exist"
"10805",13588,0.95,0.52,3,183,2,1,0,"sales","low","exist"
"10806",13589,0.95,0.5,4,259,3,0,0,"sales","low","exist"
"10807",13590,0.68,0.53,3,138,2,1,0,"sales","low","exist"
"10808",13591,0.64,0.38,5,122,4,0,0,"sales","low","exist"
"10809",13592,0.24,0.62,6,225,6,0,0,"sales","low","exist"
"10810",13593,0.37,0.72,3,121,2,0,0,"sales","low","exist"
"10811",13594,0.67,0.4,4,274,3,0,0,"sales","low","exist"
"10812",13595,0.86,0.89,4,153,4,0,0,"sales","low","exist"
"10813",13596,0.43,0.38,3,119,2,0,0,"sales","low","exist"
"10814",13597,0.67,0.67,4,141,2,1,0,"sales","low","exist"
"10815",13598,0.92,0.6,4,161,3,0,0,"IT","low","exist"
"10816",13599,0.43,0.46,2,186,2,0,0,"product_mng","low","exist"
"10817",13600,0.52,0.8,3,252,4,0,0,"product_mng","low","exist"
"10818",13601,0.16,0.42,3,182,3,1,0,"product_mng","low","exist"
"10819",13602,0.49,0.6,4,264,2,1,0,"product_mng","low","exist"
"10820",13603,0.37,0.63,4,167,3,0,0,"IT","low","exist"
"10821",13604,0.98,0.68,5,171,3,0,0,"management","high","exist"
"10822",13605,0.33,0.97,5,130,4,0,0,"management","high","exist"
"10823",13606,0.14,0.79,5,271,4,0,0,"management","high","exist"
"10824",13607,0.54,0.79,5,249,3,1,0,"management","high","exist"
"10825",13608,0.63,0.48,4,180,4,0,0,"management","high","exist"
"10826",13609,0.55,0.69,4,220,3,1,0,"management","high","exist"
"10827",13610,0.84,0.53,3,210,4,1,0,"marketing","medium","exist"
"10828",13611,0.51,0.97,4,258,2,0,0,"sales","medium","exist"
"10829",13612,0.15,0.75,3,150,4,0,1,"accounting","medium","exist"
"10830",13613,0.97,0.79,5,259,3,0,1,"support","medium","exist"
"10831",13614,0.67,0.69,3,231,3,0,1,"technical","medium","exist"
"10832",13615,0.48,0.67,4,220,3,0,1,"management","medium","exist"
"10833",13616,0.69,0.58,4,149,3,0,1,"marketing","medium","exist"
"10834",13617,0.6,0.62,3,238,4,0,1,"marketing","medium","exist"
"10835",13618,0.82,0.71,2,209,5,0,1,"marketing","medium","exist"
"10836",13619,0.86,0.95,4,149,3,0,1,"sales","medium","exist"
"10837",13620,0.69,0.59,4,264,3,0,0,"sales","medium","exist"
"10838",13621,0.87,0.87,5,207,2,0,0,"sales","high","exist"
"10839",13622,0.17,0.78,3,239,6,0,0,"sales","low","exist"
"10840",13623,0.94,0.51,6,239,5,0,0,"sales","medium","exist"
"10841",13624,0.5,1,4,258,3,0,0,"sales","medium","exist"
"10842",13625,0.16,0.72,3,203,3,0,0,"sales","medium","exist"
"10843",13626,0.89,0.99,2,258,3,0,0,"sales","medium","exist"
"10844",13627,0.69,0.51,3,257,3,1,0,"IT","low","exist"
"10845",13628,0.5,0.51,5,134,3,0,0,"product_mng","low","exist"
"10846",13629,0.16,0.46,6,240,2,0,0,"product_mng","low","exist"
"10847",13630,0.75,0.99,2,237,5,1,0,"product_mng","low","exist"
"10848",13631,0.64,0.66,5,157,2,0,0,"product_mng","low","exist"
"10849",13632,0.78,0.43,4,275,3,0,0,"IT","low","exist"
"10850",13633,0.81,0.74,2,228,3,0,0,"management","high","exist"
"10851",13634,0.55,0.58,3,254,2,0,0,"management","high","exist"
"10852",13635,0.53,0.53,4,257,2,0,0,"management","high","exist"
"10853",13636,0.69,0.73,3,231,2,1,0,"management","high","exist"
"10854",13637,0.8,0.53,3,217,3,0,0,"management","high","exist"
"10855",13638,0.77,0.98,3,286,6,0,0,"management","high","exist"
"10856",13639,0.84,0.8,4,236,3,0,0,"marketing","low","exist"
"10857",13640,0.64,0.55,4,215,2,0,0,"sales","low","exist"
"10858",13641,0.78,0.57,4,157,3,0,0,"accounting","low","exist"
"10859",13642,0.67,0.7,3,149,3,0,0,"support","low","exist"
"10860",13643,0.81,0.77,3,221,2,0,0,"technical","low","exist"
"10861",13644,0.91,0.82,4,238,2,0,0,"management","low","exist"
"10862",13645,0.75,0.89,6,250,2,0,0,"marketing","medium","exist"
"10863",13646,0.78,0.96,3,202,4,1,0,"marketing","medium","exist"
"10864",13647,0.54,0.52,4,173,2,0,0,"marketing","medium","exist"
"10865",13648,0.77,0.71,5,250,3,1,0,"sales","medium","exist"
"10866",13649,0.89,0.63,4,270,3,1,0,"sales","medium","exist"
"10867",13650,0.16,0.98,3,232,5,0,0,"sales","medium","exist"
"10868",13651,0.77,0.99,4,260,3,0,0,"sales","medium","exist"
"10869",13652,0.69,0.48,5,232,4,0,0,"sales","medium","exist"
"10870",13653,0.61,0.81,4,134,3,0,0,"sales","medium","exist"
"10871",13654,0.59,0.81,4,189,3,0,0,"sales","medium","exist"
"10872",13655,0.58,0.8,4,113,3,0,0,"IT","medium","exist"
"10873",13656,0.88,0.67,5,264,3,0,0,"product_mng","medium","exist"
"10874",13657,0.51,0.63,5,260,2,0,0,"product_mng","high","exist"
"10875",13658,0.31,0.7,3,132,3,0,0,"product_mng","low","exist"
"10876",13659,0.52,0.52,4,168,3,0,0,"product_mng","medium","exist"
"10877",13660,0.57,0.46,3,186,3,1,0,"IT","medium","exist"
"10878",13661,0.5,0.77,3,267,2,0,0,"management","high","exist"
"10879",13662,0.74,0.63,3,180,3,0,0,"management","high","exist"
"10880",13663,0.74,0.77,3,211,3,0,0,"management","high","exist"
"10881",13664,0.82,0.51,2,268,2,0,0,"management","high","exist"
"10882",13665,0.74,0.71,3,206,3,0,0,"management","high","exist"
"10883",13666,0.2,0.59,6,113,3,0,0,"management","high","exist"
"10884",13667,0.63,0.48,4,179,3,0,0,"marketing","low","exist"
"10885",13668,0.19,0.8,6,157,6,1,0,"sales","low","exist"
"10886",13669,0.4,0.62,4,127,5,0,0,"accounting","low","exist"
"10887",13670,0.71,0.37,2,179,5,0,1,"support","low","exist"
"10888",13671,0.84,0.73,4,197,3,0,1,"technical","low","exist"
"10889",13672,0.59,0.84,4,251,4,1,1,"management","low","exist"
"10890",13673,0.57,0.85,4,250,3,1,1,"marketing","low","exist"
"10891",13674,0.81,0.61,2,176,5,0,1,"marketing","low","exist"
"10892",13675,0.8,0.7,4,246,3,0,1,"marketing","low","exist"
"10893",13676,0.49,0.66,3,155,3,0,1,"sales","low","exist"
"10894",13677,0.55,0.64,3,178,2,0,1,"sales","low","exist"
"10895",13678,0.68,0.4,3,213,5,1,1,"sales","low","exist"
"10896",13679,0.55,0.67,3,150,2,0,1,"sales","low","exist"
"10897",13680,0.59,0.62,3,166,2,0,0,"sales","low","exist"
"10898",13681,0.91,0.8,5,169,4,0,0,"sales","low","exist"
"10899",13682,0.48,0.9,4,208,3,0,0,"sales","low","exist"
"10900",13683,0.84,0.66,3,209,2,0,0,"sales","low","exist"
"10901",13684,0.73,0.54,4,167,3,0,0,"IT","medium","exist"
"10902",13685,0.28,0.59,6,281,3,0,0,"product_mng","medium","exist"
"10903",13686,0.77,0.65,3,156,4,0,0,"product_mng","medium","exist"
"10904",13687,0.67,0.65,3,265,3,0,0,"product_mng","medium","exist"
"10905",13688,0.5,0.53,3,142,3,1,0,"product_mng","medium","exist"
"10906",13689,0.32,0.47,3,143,4,0,0,"IT","medium","exist"
"10907",13690,0.57,0.78,3,134,3,0,0,"RandD","medium","exist"
"10908",13691,0.51,0.8,5,268,3,0,0,"RandD","medium","exist"
"10909",13692,0.61,0.6,3,255,2,0,0,"RandD","medium","exist"
"10910",13693,0.83,0.73,4,157,2,0,0,"RandD","medium","exist"
"10911",13694,0.87,0.97,5,151,3,0,0,"RandD","medium","exist"
"10912",13695,0.7,0.63,3,157,2,0,0,"RandD","medium","exist"
"10913",13696,0.78,0.65,3,139,3,0,0,"marketing","high","exist"
"10914",13697,0.71,0.53,4,196,2,1,0,"sales","low","exist"
"10915",13698,0.68,0.99,3,159,2,0,0,"accounting","medium","exist"
"10916",13699,0.75,0.53,4,224,4,1,0,"support","medium","exist"
"10917",13700,0.7,0.53,3,215,7,1,0,"technical","medium","exist"
"10918",13701,0.59,0.94,5,157,7,1,0,"management","medium","exist"
"10919",13702,0.64,0.87,4,157,7,0,0,"marketing","low","exist"
"10920",13703,0.61,0.88,5,146,7,1,0,"marketing","low","exist"
"10921",13704,0.77,0.49,2,286,7,0,0,"marketing","low","exist"
"10922",13705,0.51,0.64,3,203,3,0,0,"sales","low","exist"
"10923",13706,0.49,0.49,3,168,7,0,0,"sales","low","exist"
"10924",13707,0.77,0.75,3,170,7,0,0,"sales","low","exist"
"10925",13708,0.31,0.86,3,266,7,0,0,"sales","low","exist"
"10926",13709,0.54,0.76,3,183,3,0,0,"sales","low","exist"
"10927",13710,0.56,0.66,4,264,3,0,0,"sales","low","exist"
"10928",13711,0.65,0.77,4,205,3,0,0,"sales","low","exist"
"10929",13712,0.49,0.36,2,192,3,0,0,"sales","low","exist"
"10930",13713,0.82,0.79,3,176,3,0,0,"technical","low","exist"
"10931",13714,0.6,0.52,3,183,2,0,0,"support","low","exist"
"10932",13715,0.64,0.63,3,156,6,1,0,"support","low","exist"
"10933",13716,0.7,0.68,3,150,3,0,0,"support","low","exist"
"10934",13717,0.65,0.89,4,204,8,1,0,"support","low","exist"
"10935",13718,0.69,0.78,5,131,8,0,0,"support","low","exist"
"10936",13719,0.93,0.74,5,248,8,1,0,"support","low","exist"
"10937",13720,0.55,0.52,4,168,8,0,0,"support","low","exist"
"10938",13721,0.75,0.87,4,146,8,1,0,"support","low","exist"
"10939",13722,0.47,0.43,4,246,3,0,0,"support","low","exist"
"10940",13723,0.72,0.88,5,216,10,1,0,"support","medium","exist"
"10941",13724,0.59,0.92,3,203,10,0,0,"support","medium","exist"
"10942",13725,0.98,0.49,3,199,10,0,0,"technical","medium","exist"
"10943",13726,0.39,0.52,2,102,8,0,0,"technical","medium","exist"
"10944",13727,0.93,0.87,4,139,8,0,0,"technical","medium","exist"
"10945",13728,0.71,0.97,5,208,8,1,0,"management","medium","exist"
"10946",13729,0.49,0.54,4,215,4,0,0,"IT","medium","exist"
"10947",13730,0.63,0.93,4,233,3,0,0,"IT","medium","exist"
"10948",13731,0.45,0.64,3,169,10,0,0,"IT","medium","exist"
"10949",13732,0.77,0.64,3,190,10,1,0,"IT","medium","exist"
"10950",13733,0.77,0.63,4,236,7,0,0,"IT","medium","exist"
"10951",13734,0.5,0.92,4,266,7,0,0,"product_mng","medium","exist"
"10952",13735,0.45,0.42,4,156,7,0,0,"product_mng","high","exist"
"10953",13736,0.81,0.47,4,153,7,0,0,"product_mng","low","exist"
"10954",13737,0.83,0.67,3,175,3,0,0,"product_mng","medium","exist"
"10955",13738,0.47,0.76,6,174,10,0,0,"IT","medium","exist"
"10956",13739,0.25,0.89,4,154,10,0,0,"management","high","exist"
"10957",13740,0.89,0.55,5,251,10,0,0,"management","high","exist"
"10958",13741,0.97,0.57,3,164,10,0,0,"management","high","exist"
"10959",13742,0.6,0.65,2,225,10,0,0,"management","high","exist"
"10960",13743,0.67,0.72,2,134,10,0,0,"management","high","exist"
"10961",13744,0.89,0.77,3,144,3,0,0,"management","high","exist"
"10962",13745,0.6,0.91,5,211,3,0,0,"sales","low","exist"
"10963",13746,0.64,0.79,4,139,3,0,0,"sales","low","exist"
"10964",13747,0.57,0.66,3,268,3,0,0,"sales","low","exist"
"10965",13748,0.56,0.98,5,171,3,1,0,"sales","low","exist"
"10966",13749,0.6,0.9,4,260,2,0,0,"sales","medium","exist"
"10967",13750,0.17,0.66,6,224,3,0,0,"sales","medium","exist"
"10968",13751,0.74,0.49,4,233,3,0,0,"sales","medium","exist"
"10969",13752,0.44,0.41,3,125,7,0,0,"sales","medium","exist"
"10970",13753,0.51,0.89,4,233,7,0,0,"sales","medium","exist"
"10971",13754,0.86,0.57,3,162,7,0,0,"sales","medium","exist"
"10972",13755,0.96,0.48,4,198,7,0,0,"sales","medium","exist"
"10973",13756,0.87,0.82,4,198,7,0,0,"sales","medium","exist"
"10974",13757,0.58,0.79,3,243,3,1,0,"sales","medium","exist"
"10975",13758,0.24,0.56,4,281,7,0,0,"sales","medium","exist"
"10976",13759,0.42,0.8,4,259,7,1,0,"sales","medium","exist"
"10977",13760,0.65,0.94,4,184,7,0,0,"sales","medium","exist"
"10978",13761,0.73,0.92,6,189,3,1,0,"sales","medium","exist"
"10979",13762,0.63,0.6,4,258,3,0,0,"sales","medium","exist"
"10980",13763,0.95,0.48,4,225,3,0,0,"sales","medium","exist"
"10981",13764,0.52,0.83,5,145,3,0,0,"sales","medium","exist"
"10982",13765,0.96,0.55,3,164,3,0,0,"sales","medium","exist"
"10983",13766,0.66,0.51,4,254,2,0,0,"sales","medium","exist"
"10984",13767,0.98,0.44,4,154,6,1,0,"sales","medium","exist"
"10985",13768,0.56,0.79,5,248,3,0,0,"sales","medium","exist"
"10986",13769,0.97,0.54,3,154,8,1,0,"sales","medium","exist"
"10987",13770,0.72,0.92,3,242,8,0,0,"sales","medium","exist"
"10988",13771,0.74,0.78,4,194,8,0,0,"sales","medium","exist"
"10989",13772,0.2,0.6,5,261,8,0,0,"sales","medium","exist"
"10990",13773,0.73,0.56,3,245,8,0,0,"sales","medium","exist"
"10991",13774,0.76,0.79,3,247,3,0,0,"sales","low","exist"
"10992",13775,0.65,0.54,4,147,10,0,0,"sales","low","exist"
"10993",13776,0.66,0.5,3,139,10,1,0,"sales","low","exist"
"10994",13777,0.96,0.97,6,137,10,0,0,"sales","low","exist"
"10995",13778,0.57,0.55,4,177,8,0,0,"sales","low","exist"
"10996",13779,0.61,0.82,4,184,8,0,0,"IT","low","exist"
"10997",13780,0.57,0.69,3,212,8,0,0,"product_mng","low","exist"
"10998",13781,0.59,0.47,3,159,4,0,0,"product_mng","low","exist"
"10999",13782,0.92,0.68,4,178,3,0,0,"product_mng","low","exist"
"11000",13783,0.79,0.56,3,149,10,0,0,"product_mng","low","exist"
"11001",13784,0.95,0.66,4,223,10,0,0,"IT","low","exist"
"11002",13785,0.24,0.81,6,263,7,0,0,"management","high","exist"
"11003",13786,0.49,0.52,4,161,7,0,0,"management","high","exist"
"11004",13787,0.49,0.68,3,192,7,0,0,"management","high","exist"
"11005",13788,0.97,0.51,5,215,7,0,0,"management","high","exist"
"11006",13789,0.55,0.78,4,261,3,0,0,"management","high","exist"
"11007",13790,0.76,0.56,5,222,10,0,0,"management","high","exist"
"11008",13791,0.53,0.99,3,223,10,0,0,"marketing","low","exist"
"11009",13792,0.51,0.86,3,182,10,0,0,"sales","low","exist"
"11010",13793,0.57,0.93,2,204,10,0,0,"accounting","low","exist"
"11011",13794,0.58,0.91,3,195,10,0,0,"support","low","exist"
"11012",13795,0.6,0.98,4,146,10,0,0,"technical","low","exist"
"11013",13796,0.65,0.74,4,233,4,1,0,"management","low","exist"
"11014",13797,0.91,0.75,2,147,3,0,0,"marketing","low","exist"
"11015",13798,0.65,0.55,3,156,5,0,0,"marketing","low","exist"
"11016",13799,0.18,0.49,3,240,2,1,0,"marketing","low","exist"
"11017",13800,0.66,0.9,4,177,7,0,0,"sales","low","exist"
"11018",13801,0.78,0.8,3,135,7,0,0,"sales","medium","exist"
"11019",13802,0.82,0.65,5,178,7,1,0,"sales","medium","exist"
"11020",13803,0.54,0.64,3,190,7,0,0,"sales","medium","exist"
"11021",13804,0.95,0.84,3,240,7,0,0,"sales","medium","exist"
"11022",13805,0.65,0.85,4,172,3,0,0,"sales","medium","exist"
"11023",13806,0.83,0.55,3,271,7,0,0,"sales","medium","exist"
"11024",13807,0.15,0.6,5,188,7,0,0,"sales","medium","exist"
"11025",13808,0.59,0.59,4,197,7,0,0,"IT","medium","exist"
"11026",13809,0.99,0.94,5,151,3,0,0,"product_mng","medium","exist"
"11027",13810,0.76,0.72,3,263,3,0,0,"product_mng","medium","exist"
"11028",13811,0.64,0.67,2,223,3,0,0,"product_mng","medium","exist"
"11029",13812,0.95,0.75,4,151,3,0,0,"product_mng","medium","exist"
"11030",13813,0.53,0.66,3,191,3,0,0,"IT","high","exist"
"11031",13814,0.59,0.5,2,162,2,0,0,"management","high","exist"
"11032",13815,0.69,0.86,5,195,6,0,0,"management","high","exist"
"11033",13816,0.5,0.49,4,222,3,0,0,"management","high","exist"
"11034",13817,0.89,0.96,3,179,8,0,0,"management","high","exist"
"11035",13818,0.56,0.39,3,106,8,0,0,"management","high","exist"
"11036",13819,0.77,0.68,3,214,8,1,0,"management","high","exist"
"11037",13820,0.15,0.75,3,259,8,1,0,"marketing","high","exist"
"11038",13821,0.88,0.58,3,145,8,0,0,"sales","low","exist"
"11039",13822,0.89,0.86,4,153,3,0,0,"accounting","low","exist"
"11040",13823,0.65,0.52,2,117,10,1,0,"support","low","exist"
"11041",13824,0.58,0.99,3,207,10,0,0,"technical","low","exist"
"11042",13825,0.56,0.85,3,265,10,1,0,"management","low","exist"
"11043",13826,0.25,0.72,5,279,8,0,0,"marketing","low","exist"
"11044",13827,0.87,0.89,4,225,8,0,0,"marketing","low","exist"
"11045",13828,0.62,0.4,3,158,8,1,0,"marketing","low","exist"
"11046",13829,0.72,0.75,4,211,4,0,0,"sales","medium","exist"
"11047",13830,0.49,0.94,4,175,3,0,0,"sales","medium","exist"
"11048",13831,0.57,0.91,4,224,10,0,0,"sales","medium","exist"
"11049",13832,0.63,0.65,3,190,10,0,0,"sales","medium","exist"
"11050",13833,0.91,0.63,5,240,7,0,0,"sales","medium","exist"
"11051",13834,0.7,0.68,5,225,7,0,0,"sales","medium","exist"
"11052",13835,0.66,0.95,5,192,7,0,0,"sales","medium","exist"
"11053",13836,0.77,0.48,5,262,7,0,0,"IT","medium","exist"
"11054",13837,0.68,0.97,3,250,3,1,0,"product_mng","medium","exist"
"11055",13838,0.34,0.46,3,155,10,0,0,"product_mng","medium","exist"
"11056",13839,0.97,0.64,4,238,10,1,0,"product_mng","medium","exist"
"11057",13840,0.57,0.75,4,249,10,0,0,"product_mng","medium","exist"
"11058",13841,0.66,0.65,3,272,10,0,0,"IT","medium","exist"
"11059",13842,0.68,0.67,4,162,10,0,0,"management","high","exist"
"11060",13843,0.49,0.78,4,254,10,0,0,"management","high","exist"
"11061",13844,0.72,0.66,4,184,3,0,0,"management","high","exist"
"11062",13845,0.77,0.89,4,269,10,0,0,"management","high","exist"
"11063",13846,0.77,0.73,3,201,10,0,0,"management","high","exist"
"11064",13847,0.59,0.73,4,247,10,0,0,"management","high","exist"
"11065",13848,0.41,0.67,6,221,10,0,0,"marketing","medium","exist"
"11066",13849,0.94,0.64,5,247,10,0,0,"sales","medium","exist"
"11067",13850,0.91,0.61,4,135,10,0,0,"accounting","medium","exist"
"11068",13851,0.7,0.84,3,260,4,1,0,"support","medium","exist"
"11069",13852,0.51,0.52,3,188,3,0,0,"technical","high","exist"
"11070",13853,0.22,0.7,4,159,5,0,0,"management","low","exist"
"11071",13854,0.69,0.65,3,153,2,0,0,"marketing","medium","exist"
"11072",13855,0.2,0.68,5,167,7,0,0,"marketing","medium","exist"
"11073",13856,0.9,0.85,3,158,7,0,0,"marketing","medium","exist"
"11074",13857,0.76,0.85,3,180,7,0,0,"sales","medium","exist"
"11075",13858,0.88,0.51,3,211,7,0,0,"sales","medium","exist"
"11076",13859,0.31,0.63,4,104,7,1,0,"sales","medium","exist"
"11077",13860,0.17,0.66,6,174,3,0,0,"sales","medium","exist"
"11078",13861,0.91,0.77,3,195,7,0,0,"sales","medium","exist"
"11079",13862,0.97,0.38,5,211,7,1,0,"sales","medium","exist"
"11080",13863,0.61,0.77,5,232,7,0,0,"sales","medium","exist"
"11081",13864,0.74,0.67,5,216,3,0,0,"sales","low","exist"
"11082",13865,0.65,0.82,5,265,3,0,0,"IT","low","exist"
"11083",13866,0.87,0.73,5,184,3,0,0,"product_mng","low","exist"
"11084",13867,0.56,0.71,5,244,3,0,0,"product_mng","low","exist"
"11085",13868,0.78,0.69,4,202,3,0,0,"product_mng","low","exist"
"11086",13869,0.73,0.57,3,146,2,0,0,"product_mng","low","exist"
"11087",13870,0.66,0.78,4,161,6,0,0,"IT","low","exist"
"11088",13871,0.15,0.81,5,280,3,1,0,"RandD","high","exist"
"11089",13872,0.52,0.69,5,208,8,1,0,"RandD","low","exist"
"11090",13873,0.44,0.66,6,134,8,0,0,"RandD","high","exist"
"11091",13874,0.7,0.7,3,162,8,0,0,"RandD","high","exist"
"11092",13875,0.63,0.52,5,209,8,1,0,"RandD","low","exist"
"11093",13876,0.89,0.59,3,265,8,0,0,"RandD","low","exist"
"11094",13877,0.96,0.85,4,173,3,0,0,"marketing","high","exist"
"11095",13878,0.98,0.99,4,261,10,1,0,"sales","low","exist"
"11096",13879,0.62,0.82,3,204,10,0,0,"accounting","medium","exist"
"11097",13880,0.62,0.73,3,144,10,1,0,"support","high","exist"
"11098",13881,0.69,0.43,5,113,8,0,0,"technical","medium","exist"
"11099",13882,0.5,0.91,4,144,8,0,0,"management","medium","exist"
"11100",13883,0.71,0.93,5,140,8,0,0,"marketing","medium","exist"
"11101",13884,0.5,0.68,3,245,4,0,0,"marketing","medium","exist"
"11102",13885,0.93,0.6,3,188,3,0,0,"marketing","high","exist"
"11103",13886,0.95,0.77,5,199,10,1,0,"sales","medium","exist"
"11104",13887,0.17,0.61,6,154,10,1,0,"sales","medium","exist"
"11105",13888,0.92,0.68,4,138,7,0,0,"sales","medium","exist"
"11106",13889,0.64,0.48,3,147,7,0,0,"sales","high","exist"
"11107",13890,0.27,0.42,6,173,7,0,0,"sales","medium","exist"
"11108",13891,0.66,0.87,3,223,7,0,0,"sales","high","exist"
"11109",13892,0.59,0.69,3,200,3,0,0,"sales","low","exist"
"11110",13893,0.93,0.98,4,189,10,0,0,"sales","medium","exist"
"11111",13894,0.58,0.67,5,133,10,0,0,"technical","medium","exist"
"11112",13895,0.96,0.6,3,160,10,0,0,"support","medium","exist"
"11113",13896,0.69,0.85,3,153,10,0,0,"support","medium","exist"
"11114",13897,0.41,0.38,4,142,10,1,0,"support","low","exist"
"11115",13898,0.36,0.41,3,167,10,0,0,"support","low","exist"
"11116",13899,0.71,0.78,4,227,2,0,0,"support","low","exist"
"11117",13900,0.94,0.9,4,144,4,0,0,"support","low","exist"
"11118",13901,0.51,0.76,4,140,3,0,0,"support","low","exist"
"11119",13902,0.83,0.48,4,220,3,1,0,"support","low","exist"
"11120",13903,0.22,0.62,3,180,3,0,0,"support","low","exist"
"11121",13904,0.66,0.89,4,173,4,0,0,"support","low","exist"
"11122",13905,0.14,0.58,3,179,5,0,0,"support","low","exist"
"11123",13906,0.16,0.96,5,137,5,1,0,"technical","low","exist"
"11124",13907,0.81,0.78,3,165,3,0,0,"technical","high","exist"
"11125",13908,0.73,0.94,3,177,3,0,0,"technical","low","exist"
"11126",13909,0.7,0.58,5,168,3,0,0,"management","low","exist"
"11127",13910,0.62,0.73,3,245,4,0,0,"IT","low","exist"
"11128",13911,0.5,0.83,5,258,2,0,0,"IT","low","exist"
"11129",13912,0.7,0.88,3,159,2,0,0,"IT","high","exist"
"11130",13913,0.53,0.73,3,163,3,1,0,"IT","low","exist"
"11131",13914,0.87,0.9,3,174,2,0,0,"IT","low","exist"
"11132",13915,0.59,0.6,3,214,2,1,0,"product_mng","low","exist"
"11133",13916,0.94,0.67,4,191,3,1,0,"product_mng","high","exist"
"11134",13917,0.2,0.53,5,272,5,0,0,"product_mng","low","exist"
"11135",13918,0.42,0.44,3,183,2,0,0,"product_mng","medium","exist"
"11136",13919,0.43,0.66,4,135,2,0,0,"IT","high","exist"
"11137",13920,0.43,0.76,6,154,2,0,0,"management","high","exist"
"11138",13921,0.77,0.86,5,238,3,0,0,"management","high","exist"
"11139",13922,0.76,0.98,2,235,3,0,0,"management","high","exist"
"11140",13923,0.82,0.9,3,215,4,0,0,"management","high","exist"
"11141",13924,0.75,0.66,5,234,2,0,0,"management","high","exist"
"11142",13925,0.63,0.98,4,187,3,0,0,"management","high","exist"
"11143",13926,0.51,0.75,3,133,3,0,0,"sales","medium","exist"
"11144",13927,0.23,0.7,3,123,5,0,0,"sales","medium","exist"
"11145",13928,0.77,0.58,4,202,3,0,0,"sales","medium","exist"
"11146",13929,0.54,0.63,4,140,3,0,0,"sales","medium","exist"
"11147",13930,0.63,0.85,4,182,3,1,0,"sales","high","exist"
"11148",13931,0.55,0.45,3,179,2,1,0,"sales","low","exist"
"11149",13932,0.31,0.63,3,150,3,1,0,"sales","medium","exist"
"11150",13933,0.98,0.74,4,151,3,0,0,"sales","medium","exist"
"11151",13934,0.16,0.95,6,117,7,0,0,"sales","medium","exist"
"11152",13935,0.54,0.78,3,156,7,0,0,"sales","medium","exist"
"11153",13936,0.73,0.48,3,211,7,0,0,"sales","medium","exist"
"11154",13937,0.16,0.63,6,286,7,0,1,"sales","medium","exist"
"11155",13938,0.57,0.82,5,233,7,0,1,"sales","medium","exist"
"11156",13939,0.88,0.88,4,222,3,1,1,"sales","medium","exist"
"11157",13940,0.95,0.81,4,258,7,0,1,"sales","medium","exist"
"11158",13941,0.93,0.7,5,231,7,0,1,"sales","high","exist"
"11159",13942,0.91,0.58,3,220,7,0,1,"sales","low","exist"
"11160",13943,0.77,0.82,4,134,3,0,1,"sales","low","exist"
"11161",13944,0.24,0.94,6,141,3,1,0,"sales","low","exist"
"11162",13945,0.74,0.74,5,160,3,1,0,"sales","high","exist"
"11163",13946,0.53,0.59,4,259,3,0,0,"sales","low","exist"
"11164",13947,0.89,0.77,5,232,3,0,0,"sales","low","exist"
"11165",13948,0.23,0.77,5,272,2,0,0,"sales","low","exist"
"11166",13949,0.69,0.66,3,215,6,0,0,"sales","high","exist"
"11167",13950,0.52,0.72,4,222,3,0,0,"sales","low","exist"
"11168",13951,0.9,0.58,3,244,8,0,0,"sales","low","exist"
"11169",13952,0.92,0.63,5,224,8,1,0,"sales","low","exist"
"11170",13953,0.72,0.59,5,200,8,1,0,"sales","low","exist"
"11171",13954,0.92,0.61,4,143,8,0,0,"sales","low","exist"
"11172",13955,0.79,0.86,5,238,8,0,0,"sales","low","exist"
"11173",13956,0.48,0.89,4,145,3,0,0,"sales","low","exist"
"11174",13957,0.27,0.76,4,108,10,0,0,"sales","medium","exist"
"11175",13958,0.67,0.49,3,247,10,0,0,"sales","medium","exist"
"11176",13959,0.48,0.7,3,213,10,0,0,"sales","medium","exist"
"11177",13960,0.99,0.6,4,209,8,1,0,"IT","medium","exist"
"11178",13961,0.49,0.88,4,240,8,0,0,"product_mng","medium","exist"
"11179",13962,0.39,0.45,3,100,8,1,0,"product_mng","medium","exist"
"11180",13963,0.99,0.92,4,265,4,1,0,"product_mng","medium","exist"
"11181",13964,0.78,0.57,3,209,3,0,0,"product_mng","medium","exist"
"11182",13965,0.5,0.73,3,154,10,0,0,"IT","medium","exist"
"11183",13966,0.61,0.79,5,230,10,0,0,"management","high","exist"
"11184",13967,0.88,0.6,4,208,7,0,1,"management","high","exist"
"11185",13968,0.44,0.44,2,141,7,1,1,"management","high","exist"
"11186",13969,0.73,0.78,3,262,7,1,1,"management","high","exist"
"11187",13970,0.58,0.84,4,206,7,0,1,"management","high","exist"
"11188",13971,0.74,0.98,3,166,3,1,1,"management","high","exist"
"11189",13972,0.32,0.48,4,117,10,0,1,"marketing","medium","exist"
"11190",13973,0.88,0.83,4,273,10,0,0,"sales","medium","exist"
"11191",13974,0.81,0.9,4,270,10,0,0,"accounting","medium","exist"
"11192",13975,0.59,0.92,3,138,10,0,0,"support","low","exist"
"11193",13976,0.79,0.65,3,235,10,0,0,"technical","low","exist"
"11194",13977,0.92,0.64,4,190,10,1,0,"management","low","exist"
"11195",13978,0.76,0.85,3,192,3,0,0,"marketing","low","exist"
"11196",13979,0.91,0.65,5,214,3,1,0,"marketing","low","exist"
"11197",13980,0.8,0.84,4,242,2,1,0,"marketing","low","exist"
"11198",13981,0.73,0.72,4,233,2,0,0,"sales","low","exist"
"11199",13982,0.88,0.53,3,218,4,0,0,"sales","low","exist"
"11200",13983,0.65,0.4,5,125,4,0,0,"sales","low","exist"
"11201",13984,0.84,0.5,4,178,2,0,0,"sales","low","exist"
"11202",13985,0.93,0.5,5,272,3,0,0,"sales","low","exist"
"11203",13986,0.64,0.6,3,265,4,0,0,"sales","low","exist"
"11204",13987,0.66,0.72,4,271,3,0,0,"sales","low","exist"
"11205",13988,0.76,0.56,3,179,3,1,0,"sales","low","exist"
"11206",13989,0.34,0.72,3,118,4,0,0,"IT","low","exist"
"11207",13990,0.48,0.8,4,196,5,0,0,"product_mng","low","exist"
"11208",13991,0.79,0.61,4,173,2,0,0,"product_mng","low","exist"
"11209",13992,0.82,0.67,4,156,3,0,0,"product_mng","low","exist"
"11210",13993,0.51,0.71,2,180,3,0,0,"product_mng","low","exist"
"11211",13994,0.84,0.78,4,263,3,0,0,"IT","low","exist"
"11212",13995,0.66,0.79,5,134,3,0,0,"management","high","exist"
"11213",13996,0.72,0.88,3,189,3,1,0,"management","high","exist"
"11214",13997,0.53,0.91,4,167,4,0,0,"management","high","exist"
"11215",13998,0.81,0.8,5,132,2,1,0,"management","high","exist"
"11216",13999,0.58,0.9,3,209,2,0,0,"management","high","exist"
"11217",14000,0.82,0.56,2,227,5,1,0,"management","high","exist"
"11218",14001,0.72,0.99,4,239,3,0,0,"marketing","medium","exist"
"11219",14002,0.9,0.54,4,172,4,0,0,"sales","medium","exist"
"11220",14003,0.98,0.91,3,188,4,0,0,"accounting","medium","exist"
"11221",14004,0.56,0.74,3,265,3,0,0,"support","medium","exist"
"11222",14005,0.77,0.82,3,153,3,1,0,"technical","medium","exist"
"11223",14006,0.61,0.89,4,242,2,0,0,"management","medium","exist"
"11224",14007,0.97,0.61,4,262,3,1,1,"marketing","medium","exist"
"11225",14008,0.64,0.55,3,246,2,0,1,"marketing","high","exist"
"11226",14009,0.99,0.97,4,211,2,0,1,"marketing","low","exist"
"11227",14010,0.61,0.42,3,145,4,0,1,"sales","medium","exist"
"11228",14011,0.72,0.71,4,256,3,0,1,"sales","medium","exist"
"11229",14012,0.67,0.91,2,245,2,1,1,"sales","medium","exist"
"11230",14013,0.9,0.56,3,151,3,0,0,"sales","medium","exist"
"11231",14014,0.9,0.73,4,245,2,0,0,"sales","low","exist"
"11232",14015,0.63,0.61,4,171,3,0,0,"sales","low","exist"
"11233",14016,0.64,0.88,3,252,2,0,0,"sales","low","exist"
"11234",14017,0.44,0.65,3,175,2,0,0,"IT","low","exist"
"11235",14018,0.64,0.94,4,210,3,0,0,"product_mng","low","exist"
"11236",14019,0.65,0.95,2,180,2,0,0,"product_mng","low","exist"
"11237",14020,0.69,0.9,5,103,5,0,0,"product_mng","low","exist"
"11238",14021,0.93,0.51,4,110,3,0,0,"product_mng","low","exist"
"11239",14022,0.73,0.9,5,184,3,0,0,"IT","low","exist"
"11240",14023,0.83,0.6,4,161,2,0,0,"management","high","exist"
"11241",14024,0.82,0.49,5,210,3,0,0,"management","high","exist"
"11242",14025,0.39,0.91,2,212,2,0,0,"management","high","exist"
"11243",14026,0.53,0.47,6,106,6,0,0,"management","high","exist"
"11244",14027,0.88,0.81,4,179,3,0,0,"management","high","exist"
"11245",14028,0.8,0.6,5,217,3,0,0,"management","high","exist"
"11246",14029,0.68,0.79,4,184,3,0,0,"marketing","low","exist"
"11247",14030,0.66,0.6,3,210,2,0,0,"sales","low","exist"
"11248",14031,0.61,0.61,4,246,2,0,0,"accounting","low","exist"
"11249",14032,0.74,0.55,4,262,3,0,0,"support","low","exist"
"11250",14033,0.61,0.83,4,245,3,1,1,"technical","low","exist"
"11251",14034,0.57,0.99,3,222,3,1,1,"management","low","exist"
"11252",14035,0.68,0.54,4,146,4,0,1,"marketing","medium","exist"
"11253",14036,0.75,0.79,4,263,3,0,1,"marketing","medium","exist"
"11254",14037,0.29,0.57,5,134,2,0,1,"marketing","medium","exist"
"11255",14038,0.81,0.81,5,250,4,0,1,"sales","medium","exist"
"11256",14039,0.53,0.68,4,173,3,0,0,"sales","medium","exist"
"11257",14040,0.42,0.96,6,173,3,0,0,"sales","medium","exist"
"11258",14041,0.64,0.67,4,252,3,1,0,"sales","medium","exist"
"11259",14042,0.63,0.5,2,230,4,0,0,"sales","medium","exist"
"11260",14043,0.81,0.81,4,212,2,1,0,"sales","medium","exist"
"11261",14044,0.71,0.66,5,187,2,0,0,"sales","medium","exist"
"11262",14045,0.7,0.83,5,241,2,0,0,"sales","medium","exist"
"11263",14046,0.53,1,3,164,4,0,0,"IT","medium","exist"
"11264",14047,0.16,0.93,6,218,6,1,0,"product_mng","high","exist"
"11265",14048,0.17,0.55,4,194,3,0,0,"product_mng","low","exist"
"11266",14049,0.7,0.95,3,158,2,0,0,"product_mng","medium","exist"
"11267",14050,0.43,0.88,2,149,4,0,0,"product_mng","medium","exist"
"11268",14051,0.49,0.62,4,161,2,0,0,"IT","medium","exist"
"11269",14052,0.5,0.9,5,226,2,0,0,"management","high","exist"
"11270",14053,0.57,0.59,2,111,2,0,0,"management","high","exist"
"11271",14054,0.68,0.75,4,258,3,0,0,"management","high","exist"
"11272",14055,0.62,0.61,3,266,2,0,0,"management","high","exist"
"11273",14056,0.69,0.75,3,253,3,0,0,"management","high","exist"
"11274",14057,0.63,0.7,5,160,3,0,0,"management","high","exist"
"11275",14058,0.76,0.62,5,230,3,1,0,"marketing","low","exist"
"11276",14059,0.62,0.76,5,198,3,0,0,"sales","low","exist"
"11277",14060,0.82,0.69,3,250,3,0,0,"accounting","low","exist"
"11278",14061,0.2,0.7,4,225,5,0,0,"support","low","exist"
"11279",14062,0.16,0.7,3,178,3,1,0,"technical","low","exist"
"11280",14063,0.2,0.78,4,196,3,0,0,"management","low","exist"
"11281",14064,0.53,0.51,4,240,2,0,0,"marketing","low","exist"
"11282",14065,0.71,0.63,3,204,3,0,0,"marketing","low","exist"
"11283",14066,0.7,0.93,3,250,3,0,0,"marketing","low","exist"
"11284",14067,0.92,0.94,2,224,2,0,0,"sales","low","exist"
"11285",14068,0.81,0.92,4,268,3,0,0,"sales","low","exist"
"11286",14069,0.79,0.62,5,167,3,0,0,"sales","low","exist"
"11287",14070,0.53,0.64,3,168,3,0,0,"sales","low","exist"
"11288",14071,0.51,0.56,4,168,2,0,0,"sales","low","exist"
"11289",14072,0.78,0.9,5,158,3,0,0,"sales","low","exist"
"11290",14073,0.5,0.91,3,240,3,0,0,"sales","low","exist"
"11291",14074,0.92,1,4,261,4,0,0,"sales","medium","exist"
"11292",14075,0.59,0.54,4,176,2,0,0,"technical","medium","exist"
"11293",14076,0.77,0.55,3,217,3,0,0,"support","medium","exist"
"11294",14077,0.74,0.87,5,224,2,0,0,"support","medium","exist"
"11295",14078,0.67,0.97,4,196,3,0,0,"support","medium","exist"
"11296",14079,0.56,0.59,3,223,3,0,0,"support","medium","exist"
"11297",14080,0.84,0.44,5,131,5,1,0,"support","medium","exist"
"11298",14081,0.53,0.77,2,167,2,0,0,"support","medium","exist"
"11299",14082,0.86,0.71,5,273,3,0,0,"support","medium","exist"
"11300",14083,0.77,0.68,3,98,3,0,0,"support","medium","exist"
"11301",14084,0.97,0.94,4,253,3,0,0,"support","medium","exist"
"11302",14085,0.69,0.87,5,174,3,0,0,"support","medium","exist"
"11303",14086,0.73,0.9,3,274,2,0,0,"support","high","exist"
"11304",14087,0.42,0.47,6,174,5,0,0,"technical","low","exist"
"11305",14088,0.4,0.47,5,173,5,0,0,"technical","medium","exist"
"11306",14089,0.33,0.41,2,198,4,0,0,"technical","medium","exist"
"11307",14090,0.68,0.66,3,238,2,0,0,"management","medium","exist"
"11308",14091,0.78,0.8,3,256,2,0,0,"IT","medium","exist"
"11309",14092,0.92,0.6,3,179,2,0,0,"IT","low","exist"
"11310",14093,0.66,0.66,4,273,4,1,0,"IT","low","exist"
"11311",14094,0.6,0.45,3,104,4,0,0,"IT","low","exist"
"11312",14095,0.86,0.83,4,208,3,1,0,"IT","low","exist"
"11313",14096,0.61,0.49,3,275,2,0,0,"product_mng","low","exist"
"11314",14097,0.71,0.68,3,231,2,1,0,"product_mng","low","exist"
"11315",14098,0.86,0.62,4,186,3,0,0,"product_mng","low","exist"
"11316",14099,0.96,0.59,3,241,3,0,0,"product_mng","low","exist"
"11317",14100,0.7,0.54,3,194,2,1,0,"IT","low","exist"
"11318",14101,0.38,0.49,4,196,3,0,1,"management","high","exist"
"11319",14102,0.39,0.75,6,185,3,0,1,"management","high","exist"
"11320",14103,0.49,0.4,2,148,2,1,1,"management","high","exist"
"11321",14104,0.78,0.62,4,150,3,0,1,"management","high","exist"
"11322",14105,0.74,0.79,5,121,5,0,1,"management","high","exist"
"11323",14106,0.82,0.76,4,266,3,0,1,"management","high","exist"
"11324",14107,0.6,0.42,2,109,6,0,0,"sales","low","exist"
"11325",14108,0.72,0.99,3,143,4,0,0,"sales","low","exist"
"11326",14109,0.73,0.97,3,174,3,0,0,"sales","low","exist"
"11327",14110,0.89,0.55,4,159,2,0,0,"sales","medium","exist"
"11328",14111,0.74,0.94,4,157,2,0,0,"sales","medium","exist"
"11329",14112,0.83,0.57,2,222,2,0,0,"sales","medium","exist"
"11330",14113,0.24,0.88,5,199,5,1,0,"sales","medium","exist"
"11331",14114,0.93,0.89,3,255,7,1,0,"sales","medium","exist"
"11332",14115,0.96,0.62,4,253,7,0,0,"sales","medium","exist"
"11333",14116,0.16,0.68,5,149,7,1,0,"sales","medium","exist"
"11334",14117,0.21,0.85,6,285,7,0,0,"sales","medium","exist"
"11335",14118,0.69,0.54,3,164,7,0,0,"sales","medium","exist"
"11336",14119,0.66,0.96,3,243,3,1,0,"sales","medium","exist"
"11337",14120,0.67,0.39,2,207,7,0,0,"sales","medium","exist"
"11338",14121,0.85,0.58,4,186,7,0,0,"sales","medium","exist"
"11339",14122,0.37,0.92,4,211,7,0,0,"sales","high","exist"
"11340",14123,0.64,0.64,2,190,3,0,0,"sales","low","exist"
"11341",14124,0.91,0.82,2,241,3,0,0,"sales","medium","exist"
"11342",14125,0.96,0.68,3,206,3,0,0,"sales","medium","exist"
"11343",14126,0.74,0.7,4,273,3,0,0,"sales","medium","exist"
"11344",14127,0.94,0.99,2,157,3,0,0,"sales","medium","exist"
"11345",14128,0.37,0.72,3,183,2,0,0,"sales","low","exist"
"11346",14129,0.92,0.85,3,151,6,1,0,"sales","low","exist"
"11347",14130,0.86,0.72,3,217,3,0,0,"sales","low","exist"
"11348",14131,0.66,0.49,5,235,8,1,0,"sales","low","exist"
"11349",14132,0.19,0.61,4,127,8,0,0,"sales","low","exist"
"11350",14133,0.65,0.61,5,167,8,0,0,"sales","low","exist"
"11351",14134,0.92,0.44,3,260,8,0,0,"sales","low","exist"
"11352",14135,0.83,0.8,3,240,8,0,0,"sales","low","exist"
"11353",14136,0.94,0.82,4,187,3,0,0,"sales","low","exist"
"11354",14137,0.42,0.69,3,126,2,0,0,"sales","low","exist"
"11355",14138,0.78,0.53,6,168,3,0,0,"sales","low","exist"
"11356",14139,0.58,0.76,4,197,5,0,0,"sales","low","exist"
"11357",14140,0.5,0.64,2,170,8,0,0,"sales","low","exist"
"11358",14141,0.82,0.76,3,219,8,1,0,"IT","low","exist"
"11359",14142,0.97,0.92,6,137,8,1,0,"product_mng","low","exist"
"11360",14143,0.8,0.93,3,225,4,0,0,"product_mng","low","exist"
"11361",14144,0.82,0.84,3,194,3,0,0,"product_mng","low","exist"
"11362",14145,0.95,0.99,5,251,4,0,0,"product_mng","low","exist"
"11363",14146,0.88,0.51,5,195,4,0,0,"IT","low","exist"
"11364",14147,0.5,0.86,3,180,7,0,1,"management","high","exist"
"11365",14148,0.53,0.8,2,225,7,1,1,"management","high","exist"
"11366",14149,0.82,0.74,3,229,7,0,1,"management","high","exist"
"11367",14150,0.15,0.74,6,144,7,0,1,"management","high","exist"
"11368",14151,0.92,0.7,3,129,3,0,1,"management","high","exist"
"11369",14152,0.53,0.74,3,172,10,0,1,"management","high","exist"
"11370",14153,0.58,1,4,220,10,0,0,"marketing","medium","exist"
"11371",14154,0.88,0.74,3,273,10,0,0,"sales","medium","exist"
"11372",14155,0.85,0.72,3,245,10,0,0,"accounting","medium","exist"
"11373",14156,0.99,0.68,5,264,10,1,0,"support","medium","exist"
"11374",14157,0.94,0.73,3,268,10,0,0,"technical","medium","exist"
"11375",14158,0.63,0.94,3,172,3,0,0,"management","medium","exist"
"11376",14159,0.85,0.9,3,245,3,0,0,"marketing","medium","exist"
"11377",14160,0.95,0.66,5,192,3,0,0,"marketing","medium","exist"
"11378",14161,0.71,0.66,3,268,4,0,0,"marketing","high","exist"
"11379",14162,0.49,0.88,4,244,3,0,0,"sales","low","exist"
"11380",14163,0.71,0.69,4,222,4,0,0,"sales","medium","exist"
"11381",14164,0.52,0.62,5,239,2,0,0,"sales","medium","exist"
"11382",14165,0.48,0.72,3,143,4,0,0,"sales","medium","exist"
"11383",14166,0.82,0.79,3,160,3,0,0,"sales","medium","exist"
"11384",14167,0.83,0.76,2,255,7,0,0,"sales","low","exist"
"11385",14168,0.85,0.87,4,152,7,0,0,"sales","low","exist"
"11386",14169,0.57,0.64,4,226,7,0,0,"sales","low","exist"
"11387",14170,0.16,0.63,5,266,7,0,0,"IT","low","exist"
"11388",14171,0.85,0.64,5,256,7,0,0,"product_mng","low","exist"
"11389",14172,0.82,0.67,3,198,3,1,0,"product_mng","low","exist"
"11390",14173,0.9,0.89,4,254,7,0,0,"product_mng","low","exist"
"11391",14174,0.92,0.64,2,104,7,0,0,"product_mng","low","exist"
"11392",14175,0.9,0.48,4,136,7,0,0,"IT","low","exist"
"11393",14176,0.82,0.8,5,205,3,0,0,"IT","low","exist"
"11394",14177,0.84,0.81,4,236,3,1,0,"IT","low","exist"
"11395",14178,0.92,0.65,3,176,3,0,0,"IT","low","exist"
"11396",14179,0.82,0.82,3,148,3,0,0,"IT","low","exist"
"11397",14180,0.8,0.8,4,146,3,1,0,"IT","low","exist"
"11398",14181,0.6,0.85,3,242,2,0,0,"IT","low","exist"
"11399",14182,0.14,0.38,5,115,6,1,0,"marketing","high","exist"
"11400",14183,0.85,0.89,4,150,3,0,0,"accounting","high","exist"
"11401",14184,0.55,0.81,3,239,8,0,0,"accounting","high","exist"
"11402",14185,0.49,0.71,4,178,8,0,0,"IT","medium","exist"
"11403",14186,0.82,0.58,5,263,8,0,0,"IT","medium","exist"
"11404",14187,0.59,0.77,3,272,8,0,0,"management","high","exist"
"11405",14188,0.9,0.82,3,133,8,0,0,"marketing","medium","exist"
"11406",14189,0.62,0.72,3,149,3,1,0,"marketing","medium","exist"
"11407",14190,0.61,0.68,3,193,2,0,0,"marketing","medium","exist"
"11408",14191,0.52,0.55,5,174,3,1,0,"sales","medium","exist"
"11409",14192,0.79,0.87,4,223,5,0,0,"sales","medium","exist"
"11410",14193,0.49,0.89,4,201,8,0,0,"sales","medium","exist"
"11411",14194,0.73,0.67,2,139,8,0,0,"sales","medium","exist"
"11412",14195,0.67,0.49,5,241,8,0,0,"sales","medium","exist"
"11413",14196,0.52,0.61,4,187,4,1,0,"sales","medium","exist"
"11414",14197,0.72,0.64,4,192,3,0,0,"sales","medium","exist"
"11415",14198,0.48,0.5,5,142,4,0,0,"IT","medium","exist"
"11416",14199,0.19,0.79,4,229,4,0,0,"product_mng","medium","exist"
"11417",14200,0.49,0.49,3,104,7,0,0,"product_mng","high","exist"
"11418",14201,0.9,0.76,3,255,7,0,0,"product_mng","low","exist"
"11419",14202,0.49,0.49,4,212,7,0,0,"product_mng","medium","exist"
"11420",14203,0.6,0.53,2,235,7,0,0,"IT","medium","exist"
"11421",14204,0.62,0.85,3,237,3,1,0,"IT","medium","exist"
"11422",14205,0.64,0.5,4,253,10,0,1,"management","high","exist"
"11423",14206,0.22,0.94,3,193,10,0,1,"management","high","exist"
"11424",14207,0.9,0.55,3,259,10,1,1,"management","high","exist"
"11425",14208,0.74,0.95,5,266,10,0,1,"management","high","exist"
"11426",14209,0.85,0.54,3,185,10,0,1,"management","high","exist"
"11427",14210,0.33,0.65,3,172,10,0,1,"marketing","high","exist"
"11428",14211,0.5,0.73,4,180,3,0,0,"IT","low","exist"
"11429",1,0.38,0.53,2,157,3,0,0,"sales","low","left"
"11430",2,0.8,0.86,5,262,6,0,0,"sales","medium","left"
"11431",3,0.11,0.88,7,272,4,0,0,"sales","medium","left"
"11432",4,0.72,0.87,5,223,5,0,0,"sales","low","left"
"11433",5,0.37,0.52,2,159,3,0,0,"sales","low","left"
"11434",6,0.41,0.5,2,153,3,0,0,"sales","low","left"
"11435",7,0.1,0.77,6,247,4,0,0,"sales","low","left"
"11436",8,0.92,0.85,5,259,5,0,0,"sales","low","left"
"11437",9,0.89,1,5,224,5,0,0,"sales","low","left"
"11438",10,0.42,0.53,2,142,3,0,0,"sales","low","left"
"11439",11,0.45,0.54,2,135,3,0,0,"sales","low","left"
"11440",12,0.11,0.81,6,305,4,0,0,"sales","low","left"
"11441",13,0.84,0.92,4,234,5,0,0,"sales","low","left"
"11442",14,0.41,0.55,2,148,3,0,0,"sales","low","left"
"11443",15,0.36,0.56,2,137,3,0,0,"sales","low","left"
"11444",16,0.38,0.54,2,143,3,0,0,"sales","low","left"
"11445",17,0.45,0.47,2,160,3,0,0,"sales","low","left"
"11446",18,0.78,0.99,4,255,6,0,0,"sales","low","left"
"11447",19,0.45,0.51,2,160,3,1,1,"sales","low","left"
"11448",20,0.76,0.89,5,262,5,0,0,"sales","low","left"
"11449",21,0.11,0.83,6,282,4,0,0,"sales","low","left"
"11450",22,0.38,0.55,2,147,3,0,0,"sales","low","left"
"11451",23,0.09,0.95,6,304,4,0,0,"sales","low","left"
"11452",24,0.46,0.57,2,139,3,0,0,"sales","low","left"
"11453",25,0.4,0.53,2,158,3,0,0,"sales","low","left"
"11454",26,0.89,0.92,5,242,5,0,0,"sales","low","left"
"11455",27,0.82,0.87,4,239,5,0,0,"sales","low","left"
"11456",28,0.4,0.49,2,135,3,0,0,"sales","low","left"
"11457",29,0.41,0.46,2,128,3,0,0,"accounting","low","left"
"11458",30,0.38,0.5,2,132,3,0,0,"accounting","low","left"
"11459",31,0.09,0.62,6,294,4,0,0,"accounting","low","left"
"11460",32,0.45,0.57,2,134,3,0,0,"hr","low","left"
"11461",33,0.4,0.51,2,145,3,0,0,"hr","low","left"
"11462",34,0.45,0.55,2,140,3,0,0,"hr","low","left"
"11463",35,0.84,0.87,4,246,6,0,0,"hr","low","left"
"11464",36,0.1,0.94,6,255,4,0,0,"technical","low","left"
"11465",37,0.38,0.46,2,137,3,0,0,"technical","low","left"
"11466",38,0.45,0.5,2,126,3,0,0,"technical","low","left"
"11467",39,0.11,0.89,6,306,4,0,0,"technical","low","left"
"11468",40,0.41,0.54,2,152,3,0,0,"technical","low","left"
"11469",41,0.87,0.88,5,269,5,0,0,"technical","low","left"
"11470",42,0.45,0.48,2,158,3,0,0,"technical","low","left"
"11471",43,0.4,0.46,2,127,3,0,0,"technical","low","left"
"11472",44,0.1,0.8,7,281,4,0,0,"technical","low","left"
"11473",45,0.09,0.89,6,276,4,0,0,"technical","low","left"
"11474",46,0.84,0.74,3,182,4,0,0,"technical","low","left"
"11475",47,0.4,0.55,2,147,3,0,0,"support","low","left"
"11476",48,0.57,0.7,3,273,6,0,0,"support","low","left"
"11477",49,0.4,0.54,2,148,3,0,0,"support","low","left"
"11478",50,0.43,0.47,2,147,3,0,0,"support","low","left"
"11479",51,0.13,0.78,6,152,2,0,0,"support","low","left"
"11480",52,0.44,0.55,2,135,3,0,0,"support","low","left"
"11481",53,0.38,0.55,2,134,3,0,0,"support","low","left"
"11482",54,0.39,0.54,2,132,3,0,0,"support","low","left"
"11483",55,0.1,0.92,7,307,4,0,0,"support","low","left"
"11484",56,0.37,0.46,2,140,3,0,0,"support","low","left"
"11485",57,0.11,0.94,7,255,4,0,0,"support","low","left"
"11486",58,0.1,0.81,6,309,4,0,0,"technical","low","left"
"11487",59,0.38,0.54,2,128,3,0,0,"technical","low","left"
"11488",60,0.85,1,4,225,5,0,0,"technical","low","left"
"11489",61,0.85,0.91,5,226,5,0,0,"management","medium","left"
"11490",62,0.11,0.93,7,308,4,0,0,"IT","medium","left"
"11491",63,0.1,0.95,6,244,5,0,0,"IT","medium","left"
"11492",64,0.36,0.56,2,132,3,0,0,"IT","medium","left"
"11493",65,0.11,0.94,6,286,4,0,0,"IT","medium","left"
"11494",66,0.81,0.7,6,161,4,0,0,"IT","medium","left"
"11495",67,0.43,0.54,2,153,3,0,0,"product_mng","medium","left"
"11496",68,0.9,0.98,4,264,6,0,0,"product_mng","medium","left"
"11497",69,0.76,0.86,5,223,5,1,0,"product_mng","medium","left"
"11498",70,0.43,0.5,2,135,3,0,0,"product_mng","medium","left"
"11499",71,0.74,0.99,2,277,3,0,0,"IT","medium","left"
"11500",72,0.09,0.77,5,275,4,0,0,"product_mng","medium","left"
"11501",73,0.45,0.49,2,149,3,0,0,"product_mng","high","left"
"11502",74,0.09,0.87,7,295,4,0,0,"product_mng","low","left"
"11503",75,0.11,0.97,6,277,4,0,0,"product_mng","medium","left"
"11504",76,0.11,0.79,7,306,4,0,0,"product_mng","medium","left"
"11505",77,0.1,0.83,6,295,4,0,0,"product_mng","medium","left"
"11506",78,0.4,0.54,2,137,3,0,0,"marketing","medium","left"
"11507",79,0.43,0.56,2,157,3,0,0,"sales","low","left"
"11508",80,0.39,0.56,2,142,3,0,0,"accounting","low","left"
"11509",81,0.45,0.54,2,140,3,0,0,"support","low","left"
"11510",82,0.38,0.49,2,151,3,0,0,"technical","low","left"
"11511",83,0.79,0.59,4,139,3,0,1,"management","low","left"
"11512",84,0.84,0.85,4,249,6,0,0,"marketing","low","left"
"11513",85,0.11,0.77,6,291,4,0,0,"marketing","low","left"
"11514",86,0.11,0.87,6,305,4,0,0,"marketing","low","left"
"11515",87,0.17,0.84,5,232,3,0,0,"sales","low","left"
"11516",88,0.44,0.45,2,132,3,0,0,"sales","low","left"
"11517",89,0.37,0.57,2,130,3,0,0,"sales","low","left"
"11518",90,0.1,0.79,6,291,4,0,0,"sales","low","left"
"11519",91,0.4,0.5,2,130,3,0,0,"sales","low","left"
"11520",92,0.89,1,5,246,5,0,0,"sales","low","left"
"11521",93,0.42,0.48,2,143,3,0,0,"sales","low","left"
"11522",94,0.46,0.55,2,129,3,0,0,"sales","low","left"
"11523",95,0.09,0.83,6,255,4,0,0,"sales","low","left"
"11524",96,0.37,0.51,2,155,3,0,0,"sales","low","left"
"11525",97,0.1,0.77,6,265,4,0,0,"sales","low","left"
"11526",98,0.1,0.84,6,279,4,0,0,"sales","low","left"
"11527",99,0.11,0.97,6,284,4,0,0,"sales","low","left"
"11528",100,0.9,1,5,221,6,0,0,"sales","medium","left"
"11529",101,0.38,0.52,2,154,3,0,0,"sales","medium","left"
"11530",102,0.36,0.52,2,147,3,0,0,"sales","medium","left"
"11531",103,0.42,0.46,2,150,3,0,0,"sales","medium","left"
"11532",104,0.09,0.94,7,267,4,0,0,"sales","medium","left"
"11533",105,0.43,0.52,2,158,3,0,0,"sales","medium","left"
"11534",106,0.24,0.46,7,224,5,0,0,"accounting","medium","left"
"11535",107,0.91,1,4,257,5,0,0,"accounting","medium","left"
"11536",108,0.44,0.5,2,148,3,0,0,"accounting","medium","left"
"11537",109,0.71,0.87,3,177,4,0,0,"hr","medium","left"
"11538",110,0.4,0.49,2,155,3,0,0,"hr","medium","left"
"11539",111,0.43,0.47,2,144,3,0,0,"hr","medium","left"
"11540",112,0.09,0.85,6,289,4,0,0,"hr","high","left"
"11541",113,0.43,0.52,2,160,3,0,0,"technical","low","left"
"11542",114,0.9,0.96,4,258,5,0,0,"technical","medium","left"
"11543",115,0.84,1,5,234,5,0,0,"technical","medium","left"
"11544",116,0.37,0.48,2,137,3,0,0,"technical","medium","left"
"11545",117,0.86,0.68,5,263,2,0,0,"technical","medium","left"
"11546",118,0.11,0.84,6,251,4,0,0,"technical","low","left"
"11547",119,0.37,0.57,2,133,3,0,0,"technical","low","left"
"11548",120,0.4,0.46,2,132,3,0,0,"technical","low","left"
"11549",121,0.14,0.62,4,158,4,1,0,"technical","low","left"
"11550",122,0.4,0.46,2,135,3,0,0,"technical","low","left"
"11551",123,0.75,1,4,216,6,0,0,"technical","low","left"
"11552",124,0.11,0.84,6,300,5,1,0,"support","low","left"
"11553",125,0.46,0.49,2,138,3,0,0,"support","low","left"
"11554",126,0.11,0.92,6,260,4,0,0,"support","low","left"
"11555",127,0.38,0.49,2,132,3,0,0,"support","low","left"
"11556",128,0.7,0.89,3,183,5,0,0,"support","low","left"
"11557",129,0.09,0.82,6,250,4,0,0,"support","low","left"
"11558",130,0.37,0.45,2,151,3,0,0,"support","low","left"
"11559",131,0.1,0.83,6,292,4,0,0,"support","low","left"
"11560",132,0.38,0.57,2,140,3,0,0,"support","low","left"
"11561",133,0.9,1,5,221,5,0,0,"support","low","left"
"11562",134,0.44,0.51,2,138,3,0,0,"support","low","left"
"11563",135,0.36,0.5,2,132,3,0,0,"technical","low","left"
"11564",136,0.31,0.84,7,133,5,0,0,"technical","low","left"
"11565",137,0.1,0.84,6,283,4,1,0,"technical","low","left"
"11566",138,0.42,0.48,2,129,3,0,0,"management","low","left"
"11567",139,0.74,1,4,249,5,0,0,"IT","low","left"
"11568",140,0.73,0.87,5,257,5,0,0,"IT","low","left"
"11569",141,0.09,0.96,6,245,4,0,0,"IT","low","left"
"11570",142,0.45,0.53,2,155,3,0,0,"IT","low","left"
"11571",143,0.11,0.8,6,256,4,0,0,"IT","low","left"
"11572",144,0.37,0.47,2,152,3,0,0,"product_mng","low","left"
"11573",145,0.84,0.99,4,267,5,0,0,"product_mng","low","left"
"11574",146,0.41,0.46,2,151,3,0,0,"product_mng","low","left"
"11575",147,0.76,0.92,4,239,5,0,0,"product_mng","low","left"
"11576",148,0.11,0.87,6,306,4,0,0,"IT","low","left"
"11577",149,0.84,0.88,4,263,5,1,0,"marketing","low","left"
"11578",150,0.39,0.5,2,147,3,0,0,"marketing","low","left"
"11579",151,0.11,0.91,6,278,4,0,0,"marketing","low","left"
"11580",152,0.45,0.56,2,154,3,0,0,"marketing","low","left"
"11581",153,0.37,0.52,2,143,3,0,0,"marketing","low","left"
"11582",154,0.4,0.52,2,155,3,0,0,"marketing","low","left"
"11583",155,0.39,0.48,2,160,3,0,0,"sales","low","left"
"11584",156,0.11,0.8,6,304,4,0,0,"accounting","low","left"
"11585",157,0.83,1,5,240,5,0,0,"support","low","left"
"11586",158,0.11,0.92,6,305,4,0,0,"technical","low","left"
"11587",159,0.39,0.5,2,136,3,0,0,"management","low","left"
"11588",160,0.45,0.45,2,132,3,0,0,"marketing","low","left"
"11589",161,0.1,0.95,7,301,4,0,0,"marketing","low","left"
"11590",162,0.9,0.98,5,243,6,0,0,"marketing","low","left"
"11591",163,0.45,0.51,2,147,3,0,0,"sales","low","left"
"11592",164,0.79,0.89,5,239,5,0,0,"sales","low","left"
"11593",165,0.9,0.99,5,260,5,0,0,"sales","low","left"
"11594",166,0.11,0.84,7,296,4,0,0,"sales","low","left"
"11595",167,0.43,0.55,2,129,3,0,0,"sales","low","left"
"11596",168,0.31,0.54,5,132,5,0,0,"sales","low","left"
"11597",169,0.32,0.5,2,135,5,0,0,"sales","low","left"
"11598",170,0.45,0.57,2,158,3,0,0,"sales","low","left"
"11599",171,0.81,0.99,4,259,5,0,0,"sales","low","left"
"11600",172,0.41,0.46,2,160,3,0,1,"sales","low","left"
"11601",173,0.11,0.78,7,278,4,0,0,"sales","low","left"
"11602",174,0.1,0.88,6,284,4,0,0,"sales","low","left"
"11603",175,0.7,0.53,2,274,4,0,0,"sales","low","left"
"11604",176,0.54,0.74,4,164,2,0,0,"sales","low","left"
"11605",177,0.41,0.48,2,148,3,0,0,"sales","low","left"
"11606",178,0.38,0.5,2,140,3,0,0,"sales","medium","left"
"11607",179,0.37,0.51,2,127,3,0,0,"sales","medium","left"
"11608",180,0.11,0.85,6,308,5,0,0,"sales","medium","left"
"11609",181,0.4,0.47,2,146,3,0,0,"sales","medium","left"
"11610",182,0.1,0.84,6,261,4,0,0,"accounting","medium","left"
"11611",183,0.89,0.99,5,257,5,0,0,"accounting","medium","left"
"11612",184,0.11,0.8,6,285,4,0,0,"accounting","medium","left"
"11613",185,0.36,0.55,2,141,3,0,0,"hr","medium","left"
"11614",186,0.4,0.46,2,127,3,0,0,"hr","medium","left"
"11615",187,0.09,0.85,6,297,4,0,0,"hr","medium","left"
"11616",188,0.4,0.46,2,143,3,0,0,"hr","medium","left"
"11617",189,0.37,0.55,2,152,3,0,0,"technical","medium","left"
"11618",190,0.44,0.51,2,156,3,0,0,"technical","high","left"
"11619",191,0.09,0.8,7,283,5,0,0,"technical","low","left"
"11620",192,0.92,0.87,4,226,6,1,0,"technical","medium","left"
"11621",193,0.74,0.91,4,232,5,0,0,"technical","medium","left"
"11622",194,0.09,0.82,6,249,4,0,0,"technical","medium","left"
"11623",195,0.89,0.95,4,275,5,0,0,"technical","medium","left"
"11624",196,0.09,0.8,6,304,4,0,0,"technical","low","left"
"11625",197,0.27,0.54,7,278,3,0,0,"technical","low","left"
"11626",198,0.1,0.91,6,287,4,0,0,"technical","low","left"
"11627",199,0.1,0.89,7,285,4,0,0,"technical","low","left"
"11628",200,0.77,0.94,5,226,6,0,0,"support","low","left"
"11629",201,0.9,0.82,5,259,5,0,0,"support","low","left"
"11630",202,0.39,0.5,2,135,3,0,0,"support","low","left"
"11631",203,0.76,1,5,219,5,0,0,"support","low","left"
"11632",204,0.1,0.93,6,256,4,0,0,"support","low","left"
"11633",205,0.87,0.9,5,254,6,0,0,"support","low","left"
"11634",206,0.38,0.5,2,153,3,0,0,"support","low","left"
"11635",207,0.77,0.99,5,228,5,0,0,"support","low","left"
"11636",208,0.78,0.87,4,228,5,0,0,"support","low","left"
"11637",209,0.44,0.5,2,128,3,0,0,"support","low","left"
"11638",210,0.38,0.52,2,153,3,0,0,"support","low","left"
"11639",211,0.43,0.46,2,156,3,0,0,"technical","low","left"
"11640",212,0.39,0.5,4,294,3,0,0,"technical","low","left"
"11641",213,0.88,1,5,219,5,0,0,"technical","low","left"
"11642",214,0.45,0.46,2,153,3,0,0,"management","low","left"
"11643",215,0.4,0.53,2,151,3,0,0,"IT","low","left"
"11644",216,0.36,0.51,2,155,3,0,0,"IT","low","left"
"11645",217,0.36,0.48,2,158,3,0,0,"IT","low","left"
"11646",218,0.9,0.98,5,245,5,0,0,"IT","low","left"
"11647",219,0.43,0.53,2,131,3,0,0,"IT","low","left"
"11648",220,0.89,0.87,5,225,5,0,0,"product_mng","low","left"
"11649",221,0.1,0.84,6,286,4,0,0,"product_mng","low","left"
"11650",222,0.37,0.5,2,135,3,0,0,"product_mng","low","left"
"11651",223,0.37,0.51,2,153,3,0,0,"product_mng","low","left"
"11652",224,0.87,0.9,5,252,5,0,0,"IT","low","left"
"11653",225,0.4,0.56,2,149,3,0,0,"accounting","low","left"
"11654",226,0.9,0.97,4,258,5,0,0,"accounting","low","left"
"11655",227,0.37,0.46,2,158,3,0,0,"hr","low","left"
"11656",228,0.44,0.54,2,149,3,0,0,"hr","low","left"
"11657",229,0.85,0.95,5,236,5,0,0,"hr","low","left"
"11658",230,0.78,0.98,5,239,6,0,0,"marketing","low","left"
"11659",231,0.42,0.47,2,159,3,0,0,"marketing","low","left"
"11660",232,0.92,0.99,5,255,6,0,0,"sales","low","left"
"11661",233,0.11,0.83,6,244,4,0,0,"accounting","low","left"
"11662",234,0.42,0.56,2,134,3,0,0,"support","low","left"
"11663",235,0.48,0.57,4,270,4,0,0,"technical","low","left"
"11664",236,0.83,0.85,4,255,5,0,0,"management","low","left"
"11665",237,0.4,0.53,2,151,3,0,0,"marketing","low","left"
"11666",238,0.43,0.45,2,135,3,0,0,"marketing","low","left"
"11667",239,0.43,0.53,2,146,3,0,0,"marketing","low","left"
"11668",240,0.1,0.97,7,254,4,0,0,"sales","low","left"
"11669",241,0.1,0.87,7,289,4,0,0,"sales","low","left"
"11670",242,0.37,0.46,2,156,3,0,0,"sales","low","left"
"11671",243,0.38,0.53,2,156,3,0,0,"sales","low","left"
"11672",244,0.4,0.5,2,128,3,0,0,"sales","low","left"
"11673",245,0.89,0.86,5,275,5,0,0,"sales","low","left"
"11674",246,0.45,0.46,2,155,3,0,0,"sales","low","left"
"11675",247,0.37,0.48,2,159,3,0,0,"sales","low","left"
"11676",248,0.46,0.49,2,148,3,0,0,"sales","low","left"
"11677",249,0.87,0.91,4,228,5,0,0,"sales","low","left"
"11678",250,0.11,0.84,6,298,4,0,0,"sales","low","left"
"11679",251,0.79,0.87,5,261,5,0,0,"sales","low","left"
"11680",252,0.79,0.92,5,254,6,0,0,"sales","low","left"
"11681",253,0.19,0.59,7,192,3,0,0,"sales","low","left"
"11682",254,0.87,0.98,4,248,5,0,0,"sales","low","left"
"11683",255,0.6,0.92,2,258,5,0,0,"sales","low","left"
"11684",256,0.44,0.45,2,156,3,0,0,"sales","medium","left"
"11685",257,0.11,0.81,6,266,4,1,0,"sales","medium","left"
"11686",258,0.42,0.54,2,156,3,0,0,"sales","medium","left"
"11687",259,0.88,0.88,5,232,5,1,0,"accounting","medium","left"
"11688",260,0.11,0.84,6,287,4,0,0,"accounting","medium","left"
"11689",261,0.46,0.46,2,154,3,0,0,"accounting","medium","left"
"11690",262,0.82,0.97,5,263,5,0,0,"hr","medium","left"
"11691",263,0.44,0.56,2,131,3,0,0,"hr","medium","left"
"11692",264,0.11,0.78,6,260,4,0,0,"hr","medium","left"
"11693",265,0.42,0.5,2,139,3,0,0,"hr","medium","left"
"11694",266,0.84,0.93,4,251,5,0,0,"technical","medium","left"
"11695",267,0.11,0.95,6,286,4,0,0,"technical","medium","left"
"11696",268,0.45,0.53,2,129,3,0,0,"technical","high","left"
"11697",269,0.38,0.56,2,156,3,0,0,"technical","low","left"
"11698",270,0.38,0.86,6,139,6,0,0,"technical","medium","left"
"11699",271,0.44,0.51,2,127,3,0,0,"technical","medium","left"
"11700",272,0.11,0.84,6,251,4,0,0,"technical","medium","left"
"11701",273,0.81,0.93,5,270,5,0,0,"technical","medium","left"
"11702",274,0.09,0.96,6,296,4,0,0,"technical","low","left"
"11703",275,0.11,0.9,6,254,4,0,0,"technical","low","left"
"11704",276,0.81,0.95,5,238,6,0,0,"technical","low","left"
"11705",277,0.1,0.97,6,267,4,1,0,"support","low","left"
"11706",278,0.74,0.89,5,229,6,0,0,"support","low","left"
"11707",279,0.09,0.78,6,254,4,0,0,"support","low","left"
"11708",280,0.82,0.81,4,233,4,1,0,"support","low","left"
"11709",281,0.1,0.98,6,268,4,0,0,"support","low","left"
"11710",282,0.27,0.56,3,301,3,0,0,"support","low","left"
"11711",283,0.83,0.92,5,267,6,0,0,"support","low","left"
"11712",284,0.1,0.93,6,289,4,1,0,"support","low","left"
"11713",285,0.38,0.47,2,144,3,0,0,"support","low","left"
"11714",286,0.4,0.56,2,148,3,0,0,"support","low","left"
"11715",287,0.11,0.83,6,306,4,0,0,"support","low","left"
"11716",288,0.11,0.79,6,292,4,0,1,"technical","low","left"
"11717",289,0.82,0.91,5,232,5,0,0,"technical","low","left"
"11718",290,0.36,0.48,2,137,3,0,0,"technical","low","left"
"11719",291,0.4,0.46,2,128,3,0,0,"management","low","left"
"11720",292,0.87,0.84,5,231,5,0,0,"IT","low","left"
"11721",293,0.41,0.49,2,146,3,0,0,"IT","low","left"
"11722",294,0.11,0.91,6,308,4,1,0,"IT","low","left"
"11723",295,0.1,0.93,6,253,4,0,0,"IT","medium","left"
"11724",296,0.38,0.51,2,146,3,0,0,"IT","medium","left"
"11725",297,0.39,0.55,2,156,3,0,0,"product_mng","medium","left"
"11726",298,0.4,0.52,2,147,3,0,0,"product_mng","medium","left"
"11727",299,0.45,0.48,2,136,3,0,0,"product_mng","medium","left"
"11728",300,0.74,0.84,5,249,5,0,0,"product_mng","medium","left"
"11729",301,0.45,0.55,2,151,3,0,0,"IT","medium","left"
"11730",302,0.12,1,3,278,4,0,0,"RandD","medium","left"
"11731",303,0.1,0.77,7,250,5,0,0,"RandD","medium","left"
"11732",304,0.37,0.55,2,127,3,0,0,"RandD","medium","left"
"11733",305,0.89,0.87,5,255,5,0,0,"RandD","medium","left"
"11734",306,0.45,0.47,2,135,3,0,0,"RandD","medium","left"
"11735",307,0.37,0.46,2,149,3,0,0,"marketing","high","left"
"11736",308,0.11,0.81,5,287,4,0,0,"sales","low","left"
"11737",309,0.41,0.48,2,145,3,0,0,"accounting","medium","left"
"11738",310,0.1,0.94,6,285,4,0,0,"support","medium","left"
"11739",311,0.1,0.93,7,305,4,0,0,"technical","medium","left"
"11740",312,0.11,0.95,7,300,4,0,0,"management","medium","left"
"11741",313,0.4,0.54,2,139,3,0,0,"marketing","low","left"
"11742",314,0.41,0.49,2,130,3,0,0,"marketing","low","left"
"11743",315,0.1,0.81,6,268,4,0,0,"marketing","low","left"
"11744",316,0.73,0.86,4,245,6,0,0,"sales","low","left"
"11745",317,0.43,0.47,2,135,3,0,0,"sales","low","left"
"11746",318,0.37,0.46,2,153,3,0,0,"sales","low","left"
"11747",319,0.11,0.94,6,276,4,0,0,"sales","low","left"
"11748",320,0.4,0.46,2,130,3,0,0,"sales","low","left"
"11749",321,0.41,0.54,2,153,3,1,0,"sales","low","left"
"11750",322,0.82,0.84,5,244,5,0,0,"sales","low","left"
"11751",323,0.61,0.47,2,253,3,0,0,"sales","low","left"
"11752",324,0.11,0.91,7,287,4,0,0,"sales","low","left"
"11753",325,0.37,0.45,2,131,3,0,0,"sales","low","left"
"11754",326,0.41,0.52,2,135,3,0,0,"sales","low","left"
"11755",327,0.37,0.52,2,157,3,0,0,"sales","low","left"
"11756",328,0.88,0.99,5,262,6,0,0,"sales","low","left"
"11757",329,0.1,0.85,6,266,4,0,0,"sales","low","left"
"11758",330,0.44,0.48,2,148,3,0,0,"sales","low","left"
"11759",331,0.38,0.57,2,140,3,0,0,"sales","low","left"
"11760",332,0.11,0.85,7,302,4,0,0,"sales","low","left"
"11761",333,0.09,0.98,6,271,4,0,0,"sales","low","left"
"11762",334,0.45,0.52,2,145,3,0,0,"sales","medium","left"
"11763",335,0.1,0.81,6,290,4,0,0,"accounting","medium","left"
"11764",336,0.45,0.47,2,151,3,0,0,"accounting","medium","left"
"11765",337,0.77,0.87,5,266,5,0,0,"accounting","medium","left"
"11766",338,0.44,0.51,2,140,3,0,0,"hr","medium","left"
"11767",339,0.39,0.5,2,142,3,0,0,"hr","medium","left"
"11768",340,0.1,0.91,6,246,4,0,0,"hr","medium","left"
"11769",341,0.09,0.89,7,308,5,0,0,"hr","medium","left"
"11770",342,0.37,0.47,2,141,3,0,0,"technical","medium","left"
"11771",343,0.9,1,5,232,5,0,0,"technical","medium","left"
"11772",344,0.41,0.56,2,143,3,0,0,"technical","medium","left"
"11773",345,0.37,0.52,2,155,3,0,0,"technical","medium","left"
"11774",346,0.1,0.86,6,278,4,0,0,"technical","high","left"
"11775",347,0.81,1,4,253,5,0,0,"technical","low","left"
"11776",348,0.11,0.8,6,282,4,0,0,"technical","medium","left"
"11777",349,0.11,0.84,7,264,4,0,0,"technical","medium","left"
"11778",350,0.4,0.46,2,149,3,0,0,"technical","medium","left"
"11779",351,0.09,0.8,6,304,5,0,0,"technical","medium","left"
"11780",352,0.48,0.93,3,219,6,0,0,"technical","low","left"
"11781",353,0.91,0.91,4,262,6,0,0,"support","low","left"
"11782",354,0.43,0.57,2,135,3,0,0,"support","low","left"
"11783",355,0.33,0.88,6,219,5,0,0,"support","low","left"
"11784",356,0.41,0.57,2,136,3,0,0,"support","low","left"
"11785",357,0.41,0.55,2,154,3,0,0,"support","low","left"
"11786",358,0.37,0.54,2,149,3,0,0,"support","low","left"
"11787",359,0.31,0.62,6,135,5,0,0,"support","low","left"
"11788",360,0.09,0.91,6,275,4,0,0,"support","low","left"
"11789",361,0.1,0.87,6,290,4,0,0,"support","low","left"
"11790",362,0.76,0.9,4,263,5,0,0,"support","low","left"
"11791",363,0.41,0.54,2,145,3,0,0,"support","low","left"
"11792",364,0.72,0.96,5,267,5,0,0,"technical","low","left"
"11793",365,0.4,0.5,2,141,3,1,0,"technical","low","left"
"11794",366,0.91,0.87,4,235,5,0,0,"technical","low","left"
"11795",367,0.1,0.83,6,258,4,0,0,"management","low","left"
"11796",368,0.4,0.56,2,131,3,0,0,"IT","low","left"
"11797",369,0.82,0.86,5,243,5,0,0,"IT","low","left"
"11798",370,0.1,0.82,6,266,4,0,0,"IT","low","left"
"11799",371,0.37,0.45,2,142,3,0,0,"IT","low","left"
"11800",372,0.36,0.51,2,135,3,0,0,"IT","low","left"
"11801",373,0.39,0.48,2,141,3,0,0,"product_mng","medium","left"
"11802",374,0.36,0.57,2,142,3,0,0,"product_mng","medium","left"
"11803",375,0.86,0.84,5,254,5,0,0,"product_mng","medium","left"
"11804",376,0.73,0.99,5,262,5,0,0,"product_mng","medium","left"
"11805",377,0.56,0.71,4,296,2,0,0,"IT","medium","left"
"11806",378,0.44,0.56,2,158,3,0,0,"accounting","medium","left"
"11807",379,0.31,0.56,4,238,2,0,0,"accounting","medium","left"
"11808",380,0.77,0.93,4,231,5,0,0,"hr","medium","left"
"11809",381,0.44,0.45,2,156,3,0,0,"hr","medium","left"
"11810",382,0.38,0.46,2,145,3,0,0,"hr","medium","left"
"11811",383,0.45,0.48,2,144,3,0,0,"marketing","medium","left"
"11812",384,0.38,0.51,2,159,3,0,0,"sales","medium","left"
"11813",385,0.36,0.48,2,156,3,0,0,"accounting","high","left"
"11814",386,0.75,0.9,5,256,5,0,0,"support","low","left"
"11815",387,0.1,0.93,6,298,4,0,0,"technical","medium","left"
"11816",388,0.1,0.97,6,247,4,0,0,"management","medium","left"
"11817",389,0.45,0.5,2,157,3,0,0,"marketing","medium","left"
"11818",390,0.42,0.57,2,154,3,1,0,"marketing","medium","left"
"11819",391,0.78,1,4,253,5,0,0,"marketing","low","left"
"11820",392,0.45,0.55,2,148,3,0,0,"sales","low","left"
"11821",393,0.84,1,4,261,5,0,0,"sales","low","left"
"11822",394,0.11,0.93,6,282,4,0,0,"sales","low","left"
"11823",395,0.42,0.56,2,133,3,0,0,"sales","low","left"
"11824",396,0.45,0.46,2,128,3,0,0,"sales","low","left"
"11825",397,0.46,0.57,2,139,3,0,0,"sales","low","left"
"11826",398,0.09,0.79,6,293,5,0,0,"sales","low","left"
"11827",399,0.87,0.83,4,265,6,0,0,"sales","low","left"
"11828",400,0.1,0.87,6,250,4,0,0,"sales","low","left"
"11829",401,0.91,1,5,251,6,0,0,"sales","low","left"
"11830",402,0.76,0.92,4,246,5,0,0,"sales","low","left"
"11831",403,0.74,1,5,275,5,0,0,"sales","low","left"
"11832",404,0.92,0.93,5,240,5,0,0,"sales","low","left"
"11833",405,0.76,0.87,5,245,5,0,0,"sales","low","left"
"11834",406,0.47,0.5,4,254,4,0,0,"sales","low","left"
"11835",407,0.73,0.99,5,241,5,0,0,"sales","low","left"
"11836",408,0.09,0.94,6,257,4,0,0,"sales","low","left"
"11837",409,0.91,0.92,4,246,5,0,0,"sales","low","left"
"11838",410,0.82,0.98,4,233,5,0,0,"sales","low","left"
"11839",411,0.28,0.45,6,218,4,0,0,"accounting","low","left"
"11840",412,0.84,0.99,4,262,6,0,0,"accounting","medium","left"
"11841",413,0.45,0.53,2,138,3,0,0,"accounting","medium","left"
"11842",414,0.45,0.54,2,142,3,0,0,"hr","medium","left"
"11843",415,0.91,0.97,5,233,5,0,0,"hr","medium","left"
"11844",416,0.42,0.48,2,155,3,0,0,"hr","medium","left"
"11845",417,0.82,1,4,229,6,0,0,"hr","medium","left"
"11846",418,0.11,0.9,6,264,4,0,0,"technical","medium","left"
"11847",419,0.42,0.53,3,199,4,0,0,"technical","medium","left"
"11848",420,0.82,0.85,4,223,5,0,0,"technical","medium","left"
"11849",421,0.09,0.96,6,268,4,0,0,"technical","medium","left"
"11850",422,0.1,0.94,6,287,4,0,0,"technical","medium","left"
"11851",423,0.86,1,5,257,5,0,0,"technical","medium","left"
"11852",424,0.4,0.46,2,143,3,0,0,"technical","high","left"
"11853",425,0.45,0.46,2,130,3,0,0,"technical","low","left"
"11854",426,0.42,0.51,2,136,3,0,0,"technical","medium","left"
"11855",427,0.74,0.92,4,261,5,0,0,"technical","medium","left"
"11856",428,0.55,0.6,3,180,4,0,0,"technical","medium","left"
"11857",429,0.37,0.45,2,126,3,0,0,"support","medium","left"
"11858",430,0.41,0.52,2,127,3,1,0,"support","low","left"
"11859",431,0.89,0.65,5,195,6,0,0,"support","low","left"
"11860",432,0.41,0.57,2,160,3,0,0,"support","low","left"
"11861",433,0.44,0.51,2,150,3,0,0,"support","low","left"
"11862",434,0.87,0.84,4,264,6,0,0,"support","low","left"
"11863",435,0.1,0.84,6,309,4,0,0,"support","low","left"
"11864",436,0.41,0.47,2,135,3,0,0,"support","low","left"
"11865",437,0.11,0.85,6,261,4,0,0,"support","low","left"
"11866",438,0.43,0.53,2,160,3,0,0,"support","low","left"
"11867",439,0.77,0.9,4,237,5,0,0,"support","low","left"
"11868",440,0.41,0.52,2,136,3,0,0,"technical","low","left"
"11869",441,0.41,0.48,2,139,3,0,0,"technical","low","left"
"11870",442,0.36,0.78,2,151,4,0,0,"technical","low","left"
"11871",443,0.77,1,5,229,5,0,0,"management","low","left"
"11872",444,0.81,0.98,5,245,5,0,0,"IT","low","left"
"11873",445,0.39,0.54,2,127,3,0,0,"IT","low","left"
"11874",446,0.09,0.94,6,283,5,0,0,"IT","low","left"
"11875",447,0.44,0.46,2,143,3,0,0,"IT","low","left"
"11876",448,0.1,0.84,5,298,4,0,0,"IT","low","left"
"11877",449,0.36,0.48,2,159,3,0,0,"product_mng","low","left"
"11878",450,0.81,0.92,5,239,5,0,0,"product_mng","low","left"
"11879",451,0.81,0.9,4,226,5,0,0,"product_mng","medium","left"
"11880",452,0.85,0.98,5,248,5,0,0,"product_mng","medium","left"
"11881",453,0.1,0.87,6,286,4,0,0,"IT","medium","left"
"11882",454,0.37,0.54,2,145,3,0,0,"RandD","medium","left"
"11883",455,0.09,0.97,7,254,4,1,0,"RandD","medium","left"
"11884",456,0.44,0.53,2,127,3,0,0,"RandD","medium","left"
"11885",457,0.86,0.93,5,223,5,0,0,"RandD","medium","left"
"11886",458,0.77,1,4,255,5,0,0,"RandD","medium","left"
"11887",459,0.41,0.48,2,136,3,0,0,"marketing","medium","left"
"11888",460,0.4,0.48,2,137,3,0,0,"sales","medium","left"
"11889",461,0.43,0.49,2,135,3,0,0,"accounting","medium","left"
"11890",462,0.43,0.5,2,137,3,0,0,"support","medium","left"
"11891",463,0.8,0.53,3,255,5,0,0,"technical","high","left"
"11892",464,0.8,0.85,4,273,5,0,0,"management","low","left"
"11893",465,0.82,0.98,5,234,5,0,0,"marketing","medium","left"
"11894",466,0.37,0.54,2,152,3,0,0,"marketing","medium","left"
"11895",467,0.37,0.48,2,134,3,0,0,"marketing","medium","left"
"11896",468,0.09,0.95,6,292,4,0,0,"sales","medium","left"
"11897",469,0.9,0.92,5,245,5,0,0,"sales","low","left"
"11898",470,0.41,0.52,2,159,3,0,0,"sales","low","left"
"11899",471,0.1,0.85,6,260,4,0,0,"sales","low","left"
"11900",472,0.44,0.53,2,149,3,0,0,"sales","low","left"
"11901",473,0.89,0.85,5,266,5,0,0,"sales","low","left"
"11902",474,0.42,0.56,2,149,3,0,0,"sales","low","left"
"11903",475,0.87,1,5,242,5,0,0,"sales","low","left"
"11904",476,0.45,0.57,2,134,3,0,0,"sales","low","left"
"11905",477,0.11,0.87,5,271,4,0,0,"sales","low","left"
"11906",478,0.09,0.79,6,275,4,0,0,"sales","low","left"
"11907",479,0.76,0.83,5,227,5,0,0,"sales","low","left"
"11908",480,0.11,0.96,7,277,5,0,0,"sales","low","left"
"11909",481,0.37,0.49,2,151,3,0,0,"sales","low","left"
"11910",482,0.1,0.79,6,274,4,0,0,"sales","low","left"
"11911",483,0.77,0.87,4,242,6,0,0,"sales","low","left"
"11912",484,0.42,0.54,2,143,3,1,0,"sales","low","left"
"11913",485,0.38,0.52,2,145,3,0,0,"sales","low","left"
"11914",486,0.32,0.95,5,172,2,0,0,"sales","low","left"
"11915",487,0.38,0.49,2,135,3,0,0,"accounting","low","left"
"11916",488,0.19,1,4,192,4,0,0,"accounting","low","left"
"11917",489,0.1,0.83,7,276,4,0,0,"accounting","low","left"
"11918",490,0.76,0.88,4,206,4,0,0,"hr","medium","left"
"11919",491,0.53,0.56,4,281,6,0,0,"hr","medium","left"
"11920",492,0.39,0.51,2,151,3,0,0,"hr","medium","left"
"11921",493,0.11,0.83,6,244,4,0,0,"hr","medium","left"
"11922",494,0.1,0.94,6,309,4,0,0,"technical","medium","left"
"11923",495,0.84,1,5,218,5,0,0,"technical","medium","left"
"11924",496,0.82,0.99,4,263,6,0,0,"technical","medium","left"
"11925",497,0.1,0.82,6,244,4,0,0,"technical","medium","left"
"11926",498,0.59,0.49,7,263,4,0,0,"technical","medium","left"
"11927",499,0.44,0.48,2,143,3,0,0,"technical","medium","left"
"11928",500,0.89,0.95,2,181,5,0,0,"technical","medium","left"
"11929",501,0.91,0.84,5,265,5,0,0,"technical","medium","left"
"11930",502,0.66,0.57,5,161,5,0,0,"technical","high","left"
"11931",503,0.11,0.87,7,282,5,0,0,"technical","low","left"
"11932",504,0.43,0.51,2,155,3,0,0,"technical","medium","left"
"11933",505,0.78,0.83,4,217,6,0,0,"support","medium","left"
"11934",506,0.11,0.97,6,289,5,0,0,"support","medium","left"
"11935",507,0.83,0.98,4,259,5,0,0,"support","medium","left"
"11936",508,0.39,0.54,2,158,3,0,0,"support","low","left"
"11937",509,0.38,0.55,2,158,3,0,0,"support","low","left"
"11938",510,0.37,0.57,2,155,3,0,0,"support","low","left"
"11939",511,0.44,0.48,2,146,3,0,0,"support","low","left"
"11940",512,0.53,0.85,2,164,5,0,0,"support","low","left"
"11941",513,0.09,0.96,6,259,4,0,0,"support","low","left"
"11942",514,0.11,0.89,6,293,4,0,0,"support","low","left"
"11943",515,0.83,0.96,5,275,5,0,0,"support","low","left"
"11944",516,0.88,1,5,219,6,1,0,"technical","low","left"
"11945",517,0.1,0.89,6,247,4,0,0,"technical","low","left"
"11946",518,0.09,0.86,7,309,4,0,0,"technical","low","left"
"11947",519,0.44,0.54,2,151,3,0,0,"management","low","left"
"11948",520,0.39,0.51,2,129,3,0,0,"IT","low","left"
"11949",521,0.87,0.94,4,274,5,0,0,"IT","low","left"
"11950",522,0.74,0.99,4,233,5,0,0,"IT","low","left"
"11951",523,0.1,0.95,7,289,4,0,0,"IT","low","left"
"11952",524,0.74,0.82,4,239,6,0,0,"IT","low","left"
"11953",525,0.75,0.99,5,221,5,0,0,"product_mng","low","left"
"11954",526,0.41,0.56,2,150,3,0,0,"product_mng","low","left"
"11955",527,0.41,0.45,2,144,3,1,0,"product_mng","low","left"
"11956",528,0.09,0.9,7,289,4,0,0,"product_mng","low","left"
"11957",529,0.09,0.8,6,301,5,0,0,"IT","medium","left"
"11958",530,0.39,0.57,2,145,3,0,0,"accounting","medium","left"
"11959",531,0.4,0.56,2,137,3,0,0,"accounting","medium","left"
"11960",532,0.37,0.54,2,131,3,1,0,"hr","medium","left"
"11961",533,0.1,0.84,6,246,4,0,0,"hr","medium","left"
"11962",534,0.43,0.51,2,136,3,0,0,"hr","medium","left"
"11963",535,0.75,0.85,5,240,6,1,0,"marketing","medium","left"
"11964",536,0.37,0.56,2,156,3,0,0,"sales","medium","left"
"11965",537,0.11,0.85,6,305,4,0,0,"accounting","medium","left"
"11966",538,0.45,0.45,2,154,3,1,0,"support","medium","left"
"11967",539,0.87,1,5,261,5,1,0,"technical","medium","left"
"11968",540,0.11,0.94,7,244,4,0,0,"management","medium","left"
"11969",541,0.45,0.54,2,129,3,0,0,"marketing","high","left"
"11970",542,0.81,0.87,4,254,5,0,0,"marketing","low","left"
"11971",543,0.77,0.91,5,236,5,0,0,"marketing","medium","left"
"11972",544,0.89,0.92,5,237,5,0,0,"sales","medium","left"
"11973",545,0.43,0.49,2,135,3,0,0,"sales","medium","left"
"11974",546,0.78,1,5,236,5,0,0,"sales","medium","left"
"11975",547,0.37,0.47,2,149,3,0,0,"sales","low","left"
"11976",548,0.37,0.5,2,141,3,0,0,"sales","low","left"
"11977",549,0.85,0.82,4,270,5,0,0,"sales","low","left"
"11978",550,0.41,0.47,2,138,3,0,0,"sales","low","left"
"11979",551,0.11,0.96,6,298,4,0,0,"sales","low","left"
"11980",552,0.75,0.99,5,254,5,0,0,"sales","low","left"
"11981",553,0.82,0.85,5,248,5,0,0,"sales","low","left"
"11982",554,0.79,1,5,257,6,0,0,"sales","low","left"
"11983",555,0.43,0.53,2,150,3,0,0,"sales","low","left"
"11984",556,0.1,0.9,7,281,4,0,0,"sales","low","left"
"11985",557,0.46,0.48,2,141,3,1,0,"sales","low","left"
"11986",558,0.43,0.57,2,157,3,0,0,"sales","low","left"
"11987",559,0.43,0.55,2,136,3,0,0,"sales","low","left"
"11988",560,0.11,0.8,7,296,4,0,0,"sales","low","left"
"11989",561,0.09,0.86,6,279,4,0,0,"sales","low","left"
"11990",562,0.37,0.53,2,131,3,0,0,"sales","low","left"
"11991",563,0.4,0.57,2,160,3,0,0,"accounting","low","left"
"11992",564,0.1,0.77,7,291,4,0,0,"accounting","low","left"
"11993",565,0.41,0.53,2,157,3,0,0,"accounting","low","left"
"11994",566,0.79,0.58,3,294,4,0,0,"hr","low","left"
"11995",567,0.11,0.79,7,310,4,0,0,"hr","low","left"
"11996",568,0.1,0.97,6,282,4,0,0,"hr","medium","left"
"11997",569,0.44,0.51,2,134,3,0,0,"hr","medium","left"
"11998",570,0.25,0.46,4,214,4,0,0,"technical","medium","left"
"11999",571,0.44,0.52,2,137,3,0,0,"technical","medium","left"
"12000",572,0.73,1,4,252,5,0,0,"technical","medium","left"
"12001",573,0.75,0.97,5,243,6,0,0,"technical","medium","left"
"12002",574,0.36,0.47,2,148,3,0,0,"technical","medium","left"
"12003",575,0.37,0.49,2,151,3,0,0,"technical","medium","left"
"12004",576,0.39,0.49,2,129,3,0,0,"technical","medium","left"
"12005",577,0.48,0.78,2,198,2,0,0,"technical","medium","left"
"12006",578,0.57,0.72,4,275,6,0,0,"technical","medium","left"
"12007",579,0.9,0.96,5,243,5,0,0,"technical","medium","left"
"12008",580,0.39,0.55,2,159,3,0,0,"technical","high","left"
"12009",581,0.44,0.51,2,145,3,0,0,"support","low","left"
"12010",582,0.81,0.88,5,242,5,0,0,"support","medium","left"
"12011",583,0.74,0.87,5,242,5,0,0,"support","medium","left"
"12012",584,0.44,0.56,2,145,3,0,0,"support","medium","left"
"12013",585,0.41,0.56,2,154,3,0,1,"support","medium","left"
"12014",586,0.4,0.51,2,139,3,0,0,"support","low","left"
"12015",587,0.46,0.57,2,152,3,0,0,"support","low","left"
"12016",588,0.8,0.83,2,211,3,0,0,"support","low","left"
"12017",589,0.87,0.9,5,258,5,0,0,"support","low","left"
"12018",590,0.39,0.54,2,155,3,0,0,"support","low","left"
"12019",591,0.38,0.55,2,148,3,0,0,"support","low","left"
"12020",592,0.66,0.67,2,255,3,0,0,"technical","low","left"
"12021",593,0.1,0.8,6,264,4,0,0,"technical","low","left"
"12022",594,0.37,0.54,2,132,3,0,0,"technical","low","left"
"12023",595,0.1,0.77,6,255,4,0,0,"management","low","left"
"12024",596,0.09,0.87,5,263,4,0,0,"IT","low","left"
"12025",597,0.86,0.84,5,222,5,0,0,"IT","low","left"
"12026",598,0.11,0.9,6,263,4,0,0,"IT","low","left"
"12027",599,0.37,0.46,2,157,3,0,0,"IT","low","left"
"12028",600,0.11,0.92,7,307,4,0,0,"IT","low","left"
"12029",601,0.77,0.98,5,259,6,0,0,"product_mng","low","left"
"12030",602,0.84,0.94,5,222,6,0,0,"product_mng","low","left"
"12031",603,0.1,0.84,7,250,4,0,0,"product_mng","low","left"
"12032",604,0.83,0.9,5,245,5,0,0,"product_mng","low","left"
"12033",605,0.11,0.79,6,292,4,0,0,"IT","low","left"
"12034",606,0.86,0.92,5,252,5,0,0,"RandD","low","left"
"12035",607,0.38,0.56,2,161,3,0,0,"RandD","medium","left"
"12036",608,0.11,0.88,5,250,4,0,0,"RandD","medium","left"
"12037",609,0.45,0.49,2,134,3,0,0,"RandD","medium","left"
"12038",610,0.1,0.85,7,279,4,0,0,"RandD","medium","left"
"12039",611,0.09,0.95,7,256,4,0,0,"marketing","medium","left"
"12040",612,0.39,0.53,2,127,3,0,0,"sales","medium","left"
"12041",613,0.37,0.47,2,138,3,1,0,"accounting","medium","left"
"12042",614,0.81,0.97,5,243,5,0,0,"support","medium","left"
"12043",615,0.09,0.9,7,296,4,0,0,"technical","medium","left"
"12044",616,0.1,0.88,7,267,4,0,0,"management","medium","left"
"12045",617,0.39,0.49,2,144,3,0,0,"marketing","medium","left"
"12046",618,0.83,0.95,4,251,5,0,0,"marketing","medium","left"
"12047",619,0.45,0.57,2,148,3,0,0,"marketing","high","left"
"12048",620,0.43,0.51,2,141,3,0,0,"sales","low","left"
"12049",621,0.8,0.75,3,268,2,0,0,"sales","medium","left"
"12050",622,0.1,0.86,6,247,4,0,0,"sales","medium","left"
"12051",623,0.1,0.55,2,247,4,0,0,"sales","medium","left"
"12052",624,0.36,0.52,2,146,3,0,0,"sales","medium","left"
"12053",625,0.38,0.5,2,140,3,0,0,"sales","low","left"
"12054",626,0.78,0.98,5,263,6,0,0,"sales","low","left"
"12055",627,0.44,0.49,2,145,3,0,0,"sales","low","left"
"12056",628,0.41,0.46,2,156,3,1,0,"sales","low","left"
"12057",629,0.72,0.85,5,244,6,0,0,"sales","low","left"
"12058",630,0.46,0.54,2,144,3,0,0,"sales","low","left"
"12059",631,0.1,0.9,7,286,4,0,0,"sales","low","left"
"12060",632,0.34,0.67,4,141,2,0,0,"sales","low","left"
"12061",633,0.11,0.89,6,260,5,0,0,"sales","low","left"
"12062",634,0.38,0.56,2,154,3,0,0,"sales","low","left"
"12063",635,0.82,0.92,5,225,5,0,0,"sales","low","left"
"12064",636,0.39,0.57,2,127,3,0,0,"sales","low","left"
"12065",637,0.44,0.53,2,140,3,0,0,"sales","low","left"
"12066",638,0.43,0.52,2,147,3,0,0,"sales","low","left"
"12067",639,0.84,0.83,4,227,5,0,0,"accounting","low","left"
"12068",640,0.43,0.48,2,153,3,0,0,"accounting","low","left"
"12069",641,0.37,0.52,2,128,3,0,0,"accounting","low","left"
"12070",642,0.74,0.97,4,228,5,0,0,"hr","low","left"
"12071",643,0.73,0.97,5,235,5,0,0,"hr","low","left"
"12072",644,0.37,0.47,2,148,3,0,0,"hr","low","left"
"12073",645,0.58,0.62,4,238,3,0,0,"hr","low","left"
"12074",646,0.4,0.54,2,141,3,0,0,"technical","medium","left"
"12075",647,0.51,0.83,5,249,4,0,0,"technical","medium","left"
"12076",648,0.46,0.5,2,151,3,0,0,"technical","medium","left"
"12077",649,0.45,0.54,2,129,3,0,0,"technical","medium","left"
"12078",650,0.46,0.5,2,156,3,0,0,"technical","medium","left"
"12079",651,0.39,0.45,2,134,3,0,0,"technical","medium","left"
"12080",652,0.09,0.88,6,269,4,0,0,"technical","medium","left"
"12081",653,0.09,0.77,6,290,4,0,0,"technical","medium","left"
"12082",654,0.37,0.51,2,132,3,0,0,"technical","medium","left"
"12083",655,0.1,0.89,7,308,4,0,0,"technical","medium","left"
"12084",656,0.77,1,4,232,5,0,0,"technical","medium","left"
"12085",657,0.79,0.86,5,235,5,0,0,"support","medium","left"
"12086",658,0.43,0.55,2,130,3,0,0,"support","high","left"
"12087",659,0.38,0.53,2,146,3,0,0,"support","low","left"
"12088",660,0.77,0.91,5,221,6,0,0,"support","medium","left"
"12089",661,0.44,0.5,2,130,3,0,0,"support","medium","left"
"12090",662,0.39,0.46,2,136,3,0,0,"support","medium","left"
"12091",663,0.78,0.89,5,274,6,0,0,"support","medium","left"
"12092",664,0.1,0.79,6,256,5,0,0,"support","low","left"
"12093",665,0.1,0.77,5,276,4,0,0,"support","low","left"
"12094",666,0.75,0.85,5,267,5,0,0,"support","low","left"
"12095",667,0.46,0.62,6,213,3,0,0,"support","low","left"
"12096",668,0.91,0.97,4,274,6,0,0,"technical","low","left"
"12097",669,0.1,0.92,6,258,4,0,0,"technical","low","left"
"12098",670,0.72,0.6,3,153,5,0,0,"technical","low","left"
"12099",671,0.11,0.95,6,245,4,0,0,"management","low","left"
"12100",672,0.11,0.94,6,264,4,0,0,"IT","low","left"
"12101",673,0.46,0.57,2,154,3,0,0,"IT","low","left"
"12102",674,0.37,0.46,2,149,3,0,0,"IT","low","left"
"12103",675,0.46,0.5,2,157,3,0,0,"IT","low","left"
"12104",676,0.43,0.57,2,127,3,0,0,"IT","low","left"
"12105",677,0.11,0.82,6,270,4,0,0,"product_mng","low","left"
"12106",678,0.73,0.89,5,236,6,0,0,"product_mng","low","left"
"12107",679,0.43,0.47,2,158,3,0,0,"product_mng","low","left"
"12108",680,0.86,1,5,229,5,0,0,"product_mng","low","left"
"12109",681,0.1,0.83,6,269,4,0,0,"IT","low","left"
"12110",682,0.4,0.49,2,128,3,0,0,"sales","low","left"
"12111",683,0.11,0.87,7,278,4,0,0,"sales","low","left"
"12112",684,0.86,0.98,3,158,5,0,0,"sales","low","left"
"12113",685,0.42,1,3,202,3,0,0,"sales","medium","left"
"12114",686,0.79,0.84,4,240,5,0,0,"sales","medium","left"
"12115",687,0.1,0.96,7,255,4,0,0,"marketing","medium","left"
"12116",688,0.09,0.92,7,254,4,0,0,"sales","medium","left"
"12117",689,0.09,0.82,6,257,4,0,0,"accounting","medium","left"
"12118",690,0.87,1,4,228,5,0,0,"support","medium","left"
"12119",691,0.36,0.49,2,145,3,0,0,"technical","medium","left"
"12120",692,0.42,0.75,3,218,4,0,0,"management","medium","left"
"12121",693,0.84,0.86,5,268,5,0,0,"marketing","medium","left"
"12122",694,0.1,0.83,6,278,4,0,0,"marketing","medium","left"
"12123",695,0.78,0.71,3,249,5,0,0,"marketing","medium","left"
"12124",696,0.35,0.99,3,236,4,0,0,"sales","medium","left"
"12125",697,0.1,0.81,7,291,4,0,0,"sales","high","left"
"12126",698,0.11,0.8,6,306,4,0,0,"sales","low","left"
"12127",699,0.43,0.48,2,135,3,0,0,"sales","medium","left"
"12128",700,0.38,0.45,2,156,3,0,0,"sales","medium","left"
"12129",701,0.46,0.54,2,143,3,0,0,"sales","medium","left"
"12130",702,0.89,0.82,4,243,5,0,0,"sales","medium","left"
"12131",703,0.45,0.5,2,147,3,0,0,"sales","low","left"
"12132",704,0.44,0.53,2,159,3,0,0,"sales","low","left"
"12133",705,0.74,0.54,5,216,3,0,0,"sales","low","left"
"12134",706,0.45,0.54,2,152,3,0,0,"sales","low","left"
"12135",707,0.79,0.93,4,226,5,0,0,"sales","low","left"
"12136",708,0.79,0.91,5,271,5,0,0,"sales","low","left"
"12137",709,0.11,0.87,6,255,4,0,0,"sales","low","left"
"12138",710,0.42,0.48,2,140,3,0,0,"sales","low","left"
"12139",711,0.64,0.9,6,252,2,0,0,"sales","low","left"
"12140",712,0.4,0.55,2,159,3,0,0,"sales","low","left"
"12141",713,0.84,0.98,5,270,5,0,0,"sales","low","left"
"12142",714,0.73,0.92,5,232,5,0,0,"sales","low","left"
"12143",715,0.4,0.51,2,144,3,0,0,"accounting","low","left"
"12144",716,0.36,0.45,2,127,3,0,0,"accounting","low","left"
"12145",717,0.43,0.47,2,131,3,0,0,"accounting","low","left"
"12146",718,0.11,0.78,6,243,4,0,0,"hr","low","left"
"12147",719,0.91,1,5,244,6,0,0,"hr","low","left"
"12148",720,0.8,1,5,260,5,0,0,"hr","low","left"
"12149",721,0.42,0.49,2,139,3,0,0,"hr","low","left"
"12150",722,0.31,0.87,4,184,3,0,0,"technical","low","left"
"12151",723,0.44,0.47,2,130,3,0,0,"technical","low","left"
"12152",724,0.38,0.54,2,135,3,0,0,"technical","medium","left"
"12153",725,0.45,0.56,2,146,3,0,0,"technical","medium","left"
"12154",726,0.43,0.46,2,149,3,0,0,"technical","medium","left"
"12155",727,0.45,0.46,2,153,3,1,0,"technical","medium","left"
"12156",728,0.43,0.57,2,160,3,0,0,"technical","medium","left"
"12157",729,0.43,0.49,2,160,3,0,0,"technical","medium","left"
"12158",730,0.09,0.83,6,282,4,0,0,"technical","medium","left"
"12159",731,0.43,0.47,2,128,3,0,0,"technical","medium","left"
"12160",732,0.79,0.94,4,232,5,0,0,"technical","medium","left"
"12161",733,0.85,0.58,3,226,2,0,0,"support","medium","left"
"12162",734,0.38,0.45,2,129,3,0,0,"support","medium","left"
"12163",735,0.11,0.92,7,255,4,0,0,"support","medium","left"
"12164",736,0.83,0.99,5,258,5,0,0,"support","high","left"
"12165",737,0.81,0.91,4,229,5,0,0,"support","low","left"
"12166",738,0.42,0.56,2,143,3,0,0,"support","medium","left"
"12167",739,0.11,0.87,6,257,4,0,0,"support","medium","left"
"12168",740,0.11,0.85,7,275,4,0,0,"support","medium","left"
"12169",741,0.1,0.89,7,291,4,0,0,"support","medium","left"
"12170",742,0.5,0.54,5,153,4,0,0,"support","low","left"
"12171",743,0.44,0.49,2,154,3,0,0,"support","low","left"
"12172",744,0.11,0.9,6,301,4,0,0,"technical","low","left"
"12173",745,0.39,0.52,2,134,3,0,0,"technical","low","left"
"12174",746,0.11,0.78,6,245,4,0,0,"technical","low","left"
"12175",747,0.36,0.5,2,132,3,0,0,"management","low","left"
"12176",748,0.43,0.51,2,130,3,0,0,"IT","low","left"
"12177",749,0.4,0.5,2,127,3,0,0,"IT","low","left"
"12178",750,0.86,0.84,4,246,6,0,0,"IT","low","left"
"12179",751,0.38,0.49,2,145,3,0,0,"IT","low","left"
"12180",752,0.46,0.45,2,138,3,0,1,"IT","low","left"
"12181",753,0.37,0.57,2,129,3,0,0,"product_mng","low","left"
"12182",754,0.43,0.52,2,150,3,0,0,"product_mng","low","left"
"12183",755,0.66,0.93,5,253,5,0,0,"product_mng","low","left"
"12184",756,0.37,0.48,2,160,3,0,0,"product_mng","low","left"
"12185",757,0.77,0.92,5,235,5,0,0,"IT","low","left"
"12186",758,0.38,0.55,2,151,3,0,0,"sales","low","left"
"12187",759,0.39,0.54,2,127,3,0,0,"sales","low","left"
"12188",760,0.41,0.55,2,151,3,0,0,"sales","low","left"
"12189",761,0.1,0.9,7,290,4,0,0,"sales","low","left"
"12190",762,0.09,0.93,6,249,4,0,0,"sales","low","left"
"12191",763,0.41,0.47,2,131,3,0,0,"marketing","medium","left"
"12192",764,0.39,0.46,2,159,3,0,0,"sales","medium","left"
"12193",765,0.83,0.99,4,223,5,0,0,"accounting","medium","left"
"12194",766,0.09,0.87,3,214,2,0,0,"support","medium","left"
"12195",767,0.75,0.81,5,227,5,0,0,"technical","medium","left"
"12196",768,0.44,0.54,2,127,3,0,0,"management","medium","left"
"12197",769,0.1,0.84,6,293,5,0,0,"marketing","medium","left"
"12198",770,0.42,0.46,2,141,3,0,0,"marketing","medium","left"
"12199",771,0.1,0.83,6,300,4,0,0,"marketing","medium","left"
"12200",772,0.1,0.86,6,309,4,0,0,"sales","medium","left"
"12201",773,0.31,0.77,4,149,3,0,0,"sales","medium","left"
"12202",774,0.42,0.54,2,159,3,0,0,"sales","medium","left"
"12203",775,0.38,0.5,2,152,3,0,0,"sales","high","left"
"12204",776,0.39,0.57,2,158,3,0,0,"sales","low","left"
"12205",777,0.1,0.97,6,254,5,0,0,"sales","medium","left"
"12206",778,0.11,0.93,6,294,4,0,0,"sales","medium","left"
"12207",779,0.1,0.92,7,269,4,0,0,"sales","medium","left"
"12208",780,0.11,0.9,7,247,4,0,0,"sales","medium","left"
"12209",781,0.44,0.65,3,271,4,0,0,"sales","low","left"
"12210",782,0.91,0.96,4,232,5,0,0,"sales","low","left"
"12211",783,0.72,1,4,245,5,0,0,"sales","low","left"
"12212",784,0.36,0.46,2,132,3,0,0,"sales","low","left"
"12213",785,0.44,0.57,2,131,3,0,0,"sales","low","left"
"12214",786,0.85,0.99,5,248,5,0,0,"sales","low","left"
"12215",787,0.78,0.93,5,225,5,0,0,"sales","low","left"
"12216",788,0.39,0.46,2,156,3,0,0,"sales","low","left"
"12217",789,0.78,0.81,3,222,2,0,0,"sales","low","left"
"12218",790,0.1,0.92,6,243,4,1,0,"sales","low","left"
"12219",791,0.23,0.99,4,204,4,1,0,"accounting","low","left"
"12220",792,0.11,0.87,6,301,4,0,0,"accounting","low","left"
"12221",793,0.9,0.83,5,259,5,0,0,"accounting","low","left"
"12222",794,0.91,0.89,4,247,5,0,0,"hr","low","left"
"12223",795,0.11,0.79,7,295,4,0,0,"hr","low","left"
"12224",796,0.43,0.54,2,150,3,0,0,"hr","low","left"
"12225",797,0.45,0.49,2,151,3,0,0,"hr","low","left"
"12226",798,0.11,0.91,5,291,4,0,0,"technical","low","left"
"12227",799,0.11,0.93,6,253,4,1,0,"technical","low","left"
"12228",800,0.43,0.5,2,161,3,0,0,"technical","low","left"
"12229",801,0.91,0.97,4,251,6,0,0,"technical","low","left"
"12230",802,0.43,0.55,2,153,3,0,0,"technical","medium","left"
"12231",803,0.85,0.82,5,264,6,0,0,"technical","medium","left"
"12232",804,0.1,0.77,6,310,4,0,0,"technical","medium","left"
"12233",805,0.81,0.95,5,266,5,0,0,"technical","medium","left"
"12234",806,0.36,0.62,4,237,2,0,0,"technical","medium","left"
"12235",807,0.45,0.54,2,138,3,0,0,"technical","medium","left"
"12236",808,0.86,1,5,227,5,0,0,"technical","medium","left"
"12237",809,0.71,1,4,300,5,0,0,"support","medium","left"
"12238",810,0.11,0.97,7,310,4,0,0,"support","medium","left"
"12239",811,0.84,0.93,5,236,5,0,0,"support","medium","left"
"12240",812,0.09,0.97,7,288,4,0,0,"support","medium","left"
"12241",813,0.38,0.49,2,127,3,0,0,"support","medium","left"
"12242",814,0.15,0.55,6,139,4,0,0,"support","high","left"
"12243",815,0.1,0.92,7,253,4,1,0,"support","low","left"
"12244",816,0.8,0.97,4,218,5,1,0,"support","medium","left"
"12245",817,0.84,0.97,5,251,5,0,0,"support","medium","left"
"12246",818,0.11,0.87,6,264,4,0,0,"support","medium","left"
"12247",819,0.89,0.79,3,149,2,0,0,"support","medium","left"
"12248",820,0.45,0.51,2,138,3,0,0,"technical","low","left"
"12249",821,0.11,0.93,7,284,4,0,0,"technical","low","left"
"12250",822,0.74,0.93,5,244,5,0,0,"technical","low","left"
"12251",823,0.41,0.5,2,128,3,0,0,"management","low","left"
"12252",824,0.44,0.53,2,154,3,0,0,"IT","low","left"
"12253",825,0.37,0.56,2,138,3,0,0,"IT","low","left"
"12254",826,0.11,0.86,6,308,4,0,0,"IT","low","left"
"12255",827,0.1,0.93,6,269,4,0,0,"IT","low","left"
"12256",828,0.7,0.74,6,136,3,0,0,"IT","low","left"
"12257",829,0.59,1,2,160,5,0,0,"product_mng","low","left"
"12258",830,0.38,0.53,2,138,3,0,0,"product_mng","low","left"
"12259",831,0.72,0.95,4,220,5,0,0,"product_mng","low","left"
"12260",832,0.73,1,5,274,5,0,0,"product_mng","low","left"
"12261",833,0.39,0.48,2,161,3,0,0,"IT","low","left"
"12262",834,0.89,0.82,5,224,6,0,0,"RandD","low","left"
"12263",835,0.89,1,4,260,5,0,0,"RandD","low","left"
"12264",836,0.11,0.78,6,300,4,1,0,"RandD","low","left"
"12265",837,0.43,0.56,2,133,3,0,0,"RandD","low","left"
"12266",838,0.09,0.93,6,308,4,0,0,"RandD","low","left"
"12267",839,0.81,0.9,5,238,6,0,0,"marketing","low","left"
"12268",840,0.37,0.53,2,126,3,0,0,"sales","low","left"
"12269",841,0.36,0.56,2,138,3,0,0,"accounting","medium","left"
"12270",842,0.11,0.85,6,299,4,0,0,"support","medium","left"
"12271",843,0.1,0.85,6,254,4,0,0,"technical","medium","left"
"12272",844,0.66,0.47,7,156,2,0,0,"management","medium","left"
"12273",845,0.39,0.47,2,152,3,0,0,"marketing","medium","left"
"12274",846,0.44,0.51,2,146,3,0,0,"marketing","medium","left"
"12275",847,0.1,0.84,6,253,4,0,0,"marketing","medium","left"
"12276",848,0.79,0.94,5,227,6,0,0,"sales","medium","left"
"12277",849,0.1,0.81,6,301,4,1,0,"sales","medium","left"
"12278",850,0.54,0.94,6,294,3,0,0,"sales","medium","left"
"12279",851,0.37,0.47,2,151,3,0,0,"sales","medium","left"
"12280",852,0.37,0.57,2,128,3,0,0,"sales","medium","left"
"12281",853,0.82,0.89,5,217,5,0,0,"sales","high","left"
"12282",854,0.45,0.52,2,160,3,0,0,"sales","low","left"
"12283",855,0.79,0.9,5,263,5,0,0,"sales","medium","left"
"12284",856,0.42,0.56,2,156,3,0,0,"sales","medium","left"
"12285",857,0.1,0.85,6,273,4,0,0,"sales","medium","left"
"12286",858,0.11,0.78,6,303,4,0,0,"sales","medium","left"
"12287",859,0.74,1,4,253,5,0,0,"sales","low","left"
"12288",860,0.1,0.93,6,270,4,0,0,"sales","low","left"
"12289",861,0.79,1,4,218,5,0,0,"sales","low","left"
"12290",862,0.43,0.48,2,144,3,0,0,"sales","low","left"
"12291",863,0.41,0.47,2,154,3,0,0,"sales","low","left"
"12292",864,0.39,0.55,2,146,3,0,0,"sales","low","left"
"12293",865,0.1,0.94,6,260,4,0,0,"sales","low","left"
"12294",866,0.82,0.85,5,218,5,0,0,"sales","low","left"
"12295",867,0.41,0.46,2,128,3,0,0,"accounting","low","left"
"12296",868,0.42,0.56,2,128,3,0,0,"accounting","low","left"
"12297",869,0.74,0.88,4,248,6,0,0,"accounting","low","left"
"12298",870,0.38,0.57,2,152,3,1,0,"hr","low","left"
"12299",871,0.39,0.56,2,126,3,0,0,"hr","low","left"
"12300",872,0.87,0.94,4,260,5,0,0,"hr","low","left"
"12301",873,0.1,0.9,5,263,4,0,0,"hr","low","left"
"12302",874,0.78,1,5,220,5,0,0,"technical","low","left"
"12303",875,0.14,0.73,7,282,5,0,0,"technical","low","left"
"12304",876,0.11,0.94,6,277,5,0,0,"technical","low","left"
"12305",877,0.91,0.94,5,257,5,0,0,"technical","low","left"
"12306",878,0.49,0.63,6,265,3,0,0,"technical","low","left"
"12307",879,0.38,0.47,2,143,3,0,0,"technical","low","left"
"12308",880,0.82,0.97,5,263,5,0,0,"technical","medium","left"
"12309",881,0.38,0.88,3,154,4,0,0,"technical","medium","left"
"12310",882,0.89,1,5,253,5,0,0,"technical","medium","left"
"12311",883,0.11,0.79,6,294,4,0,0,"technical","medium","left"
"12312",884,0.37,0.51,2,128,3,0,0,"technical","medium","left"
"12313",885,0.38,0.5,2,153,3,0,0,"support","medium","left"
"12314",886,0.78,0.87,5,256,5,0,0,"support","medium","left"
"12315",887,0.41,0.51,2,127,3,0,0,"support","medium","left"
"12316",888,0.41,0.51,2,137,3,0,0,"support","medium","left"
"12317",889,0.11,0.83,6,295,4,0,0,"support","medium","left"
"12318",890,0.11,0.79,6,281,4,0,0,"support","medium","left"
"12319",891,0.43,0.57,2,131,3,1,0,"support","medium","left"
"12320",892,0.75,0.86,5,237,5,0,0,"support","high","left"
"12321",893,0.74,0.99,4,276,5,0,0,"support","low","left"
"12322",894,0.85,0.85,5,267,5,0,0,"support","medium","left"
"12323",895,0.73,0.92,5,266,5,0,0,"support","medium","left"
"12324",896,0.1,0.79,6,294,4,0,0,"technical","medium","left"
"12325",897,0.44,0.56,2,134,3,0,0,"technical","medium","left"
"12326",898,0.3,0.56,3,309,4,1,0,"technical","low","left"
"12327",899,0.11,0.77,7,273,4,0,0,"management","low","left"
"12328",900,0.84,0.83,5,238,5,0,0,"IT","low","left"
"12329",901,0.78,0.94,5,271,6,0,0,"IT","low","left"
"12330",902,0.43,0.53,2,145,3,0,0,"IT","low","left"
"12331",903,0.36,0.55,2,152,3,0,0,"IT","low","left"
"12332",904,0.43,0.47,2,128,3,0,0,"IT","low","left"
"12333",905,0.45,0.46,2,142,3,0,0,"product_mng","low","left"
"12334",906,0.76,0.93,5,238,5,0,0,"product_mng","low","left"
"12335",907,0.1,0.78,7,286,4,0,0,"product_mng","low","left"
"12336",908,0.09,0.86,6,291,4,0,0,"product_mng","low","left"
"12337",909,0.92,1,5,259,5,0,0,"IT","low","left"
"12338",910,0.92,0.9,5,248,5,0,0,"sales","low","left"
"12339",911,0.79,0.98,4,271,5,0,0,"sales","low","left"
"12340",912,0.43,0.51,2,140,3,0,0,"sales","low","left"
"12341",913,0.8,0.95,4,274,5,0,0,"sales","low","left"
"12342",914,0.44,0.49,2,127,3,1,0,"sales","low","left"
"12343",915,0.89,0.87,5,275,6,0,0,"marketing","low","left"
"12344",916,0.48,0.88,3,239,3,0,0,"sales","low","left"
"12345",917,0.11,0.82,6,304,4,1,0,"accounting","low","left"
"12346",918,0.38,0.55,2,145,3,0,0,"support","low","left"
"12347",919,0.11,0.85,6,259,4,0,0,"technical","medium","left"
"12348",920,0.82,0.86,4,264,5,0,0,"management","medium","left"
"12349",921,0.37,0.45,2,160,3,0,0,"marketing","medium","left"
"12350",922,0.4,0.48,2,138,3,0,0,"marketing","medium","left"
"12351",923,0.43,0.47,2,137,3,0,0,"marketing","medium","left"
"12352",924,0.44,0.5,2,156,3,0,0,"sales","medium","left"
"12353",925,0.42,0.56,2,147,3,0,0,"sales","medium","left"
"12354",926,0.11,0.8,7,243,4,0,0,"sales","medium","left"
"12355",927,0.78,0.87,4,236,5,0,0,"sales","medium","left"
"12356",928,0.46,0.86,2,212,4,0,0,"sales","medium","left"
"12357",929,0.77,0.91,5,261,6,0,0,"sales","medium","left"
"12358",930,0.83,0.82,4,243,5,0,0,"sales","medium","left"
"12359",931,0.32,0.58,5,271,5,0,0,"sales","high","left"
"12360",932,0.9,0.92,5,154,4,0,0,"sales","low","left"
"12361",933,0.42,0.52,2,151,3,0,0,"sales","medium","left"
"12362",934,0.1,0.96,6,254,4,1,0,"sales","medium","left"
"12363",935,0.1,0.91,6,285,4,0,0,"sales","medium","left"
"12364",936,0.44,0.49,2,130,3,0,0,"sales","medium","left"
"12365",937,0.1,0.95,7,301,4,0,0,"sales","low","left"
"12366",938,0.11,0.8,6,286,4,0,0,"sales","low","left"
"12367",939,0.1,0.89,6,246,4,0,0,"sales","low","left"
"12368",940,0.39,0.47,2,135,3,0,0,"sales","low","left"
"12369",941,0.92,0.92,4,245,5,0,0,"sales","low","left"
"12370",942,0.43,0.56,2,136,3,0,0,"sales","low","left"
"12371",943,0.11,0.89,6,301,4,0,0,"accounting","low","left"
"12372",944,0.81,1,5,235,5,0,0,"accounting","low","left"
"12373",945,0.11,0.85,7,272,4,0,0,"accounting","low","left"
"12374",946,0.87,1,5,274,5,0,0,"hr","low","left"
"12375",947,0.37,0.46,2,131,3,0,0,"hr","low","left"
"12376",948,0.39,0.56,2,135,3,0,0,"hr","low","left"
"12377",949,0.61,0.86,4,196,4,0,0,"hr","low","left"
"12378",950,0.11,0.95,6,285,4,0,0,"technical","low","left"
"12379",951,0.09,0.9,7,289,5,0,0,"technical","low","left"
"12380",952,0.36,0.52,2,157,3,0,0,"technical","low","left"
"12381",953,0.09,0.94,6,308,4,0,0,"technical","low","left"
"12382",954,0.41,0.71,4,301,5,0,0,"technical","low","left"
"12383",955,0.4,0.46,2,131,3,0,0,"technical","low","left"
"12384",956,0.1,0.91,6,262,5,1,0,"technical","low","left"
"12385",957,0.46,0.53,2,143,3,1,0,"technical","low","left"
"12386",958,0.39,0.57,2,133,3,0,0,"technical","medium","left"
"12387",959,0.41,0.5,2,153,3,0,0,"technical","medium","left"
"12388",960,0.1,0.94,7,281,4,0,0,"technical","medium","left"
"12389",961,0.39,0.51,2,132,3,0,0,"support","medium","left"
"12390",962,0.73,0.83,5,270,5,1,0,"support","medium","left"
"12391",963,0.41,0.45,2,150,3,0,0,"support","medium","left"
"12392",964,0.37,0.51,2,140,3,0,0,"support","medium","left"
"12393",965,0.38,0.5,2,150,3,1,0,"support","medium","left"
"12394",966,0.8,0.63,5,180,5,0,0,"support","medium","left"
"12395",967,0.09,0.85,5,281,4,0,0,"support","medium","left"
"12396",968,0.85,0.92,4,275,5,0,0,"support","medium","left"
"12397",969,0.42,0.54,2,130,3,0,0,"support","medium","left"
"12398",970,0.41,0.48,2,130,3,0,0,"support","high","left"
"12399",971,0.38,0.46,2,147,3,0,0,"support","low","left"
"12400",972,0.72,1,5,264,5,0,0,"technical","medium","left"
"12401",973,0.11,0.74,6,290,5,0,0,"technical","medium","left"
"12402",974,0.37,0.47,2,150,3,0,0,"technical","medium","left"
"12403",975,0.1,0.81,6,304,4,0,0,"management","medium","left"
"12404",976,0.36,0.54,2,136,3,0,0,"IT","low","left"
"12405",977,0.92,0.94,5,307,5,0,0,"IT","low","left"
"12406",978,0.11,0.87,5,303,4,0,0,"IT","low","left"
"12407",979,0.39,0.56,2,156,3,0,0,"IT","low","left"
"12408",980,0.11,0.95,6,271,4,0,0,"IT","low","left"
"12409",981,0.45,0.45,2,140,3,0,0,"product_mng","low","left"
"12410",982,0.44,0.55,2,130,3,1,0,"product_mng","low","left"
"12411",983,0.85,0.97,4,266,6,0,0,"product_mng","low","left"
"12412",984,0.43,0.52,2,139,3,0,0,"product_mng","low","left"
"12413",985,0.75,0.86,5,260,5,0,0,"IT","low","left"
"12414",986,0.11,0.55,2,137,3,1,0,"RandD","low","left"
"12415",987,0.36,0.5,2,158,3,0,0,"RandD","low","left"
"12416",988,0.1,0.79,6,249,4,0,0,"RandD","low","left"
"12417",989,0.74,0.89,5,259,5,0,0,"RandD","low","left"
"12418",990,0.4,0.46,2,144,3,0,0,"RandD","low","left"
"12419",991,0.09,0.77,6,244,4,0,0,"marketing","low","left"
"12420",992,0.76,0.91,4,219,5,0,0,"sales","low","left"
"12421",993,0.45,0.57,2,151,3,0,0,"accounting","low","left"
"12422",994,0.84,0.88,4,269,5,0,0,"support","low","left"
"12423",995,0.38,0.45,2,127,3,0,0,"technical","low","left"
"12424",996,0.38,0.46,2,144,3,0,0,"management","low","left"
"12425",997,0.38,0.54,2,157,3,0,0,"marketing","medium","left"
"12426",998,0.86,0.94,5,224,5,0,0,"marketing","medium","left"
"12427",999,0.37,0.46,2,155,3,0,0,"marketing","medium","left"
"12428",1000,0.37,0.5,2,131,3,0,0,"sales","medium","left"
"12429",1001,0.87,1,4,258,5,1,1,"sales","medium","left"
"12430",1002,0.11,0.85,6,267,4,0,0,"sales","medium","left"
"12431",1003,0.42,0.5,2,141,3,0,0,"sales","medium","left"
"12432",1004,0.43,0.48,2,160,3,0,0,"sales","medium","left"
"12433",1005,0.09,0.8,6,247,4,0,0,"sales","medium","left"
"12434",1006,0.54,0.56,4,260,3,0,0,"sales","medium","left"
"12435",1007,0.4,0.47,2,151,3,0,0,"sales","medium","left"
"12436",1008,0.36,0.52,2,137,3,1,0,"sales","medium","left"
"12437",1009,0.87,0.9,4,256,5,0,0,"sales","high","left"
"12438",1010,0.75,0.88,4,239,5,0,0,"sales","low","left"
"12439",1011,0.43,0.53,2,152,3,0,0,"sales","medium","left"
"12440",1012,0.43,0.47,2,149,3,0,0,"sales","medium","left"
"12441",1013,0.1,0.87,6,284,4,0,0,"sales","medium","left"
"12442",1014,0.11,0.78,7,248,4,0,0,"sales","medium","left"
"12443",1015,0.44,0.53,2,156,3,0,0,"sales","low","left"
"12444",1016,0.39,0.48,2,138,3,0,0,"sales","low","left"
"12445",1017,0.4,0.55,2,155,3,0,0,"sales","low","left"
"12446",1018,0.92,0.87,4,229,6,0,0,"sales","low","left"
"12447",1019,0.36,0.47,2,136,3,0,0,"accounting","low","left"
"12448",1020,0.86,0.95,4,241,5,0,0,"accounting","low","left"
"12449",1021,0.74,0.87,5,258,5,0,0,"accounting","low","left"
"12450",1022,0.8,0.95,3,146,5,0,0,"hr","low","left"
"12451",1023,0.36,0.48,2,145,3,0,0,"hr","low","left"
"12452",1024,0.42,0.57,2,159,3,0,0,"hr","low","left"
"12453",1025,0.42,0.47,2,129,3,0,0,"hr","low","left"
"12454",1026,0.4,0.45,2,142,3,0,0,"technical","low","left"
"12455",1027,0.46,0.53,2,129,3,0,0,"technical","low","left"
"12456",1028,0.09,0.9,6,287,4,0,0,"technical","low","left"
"12457",1029,0.88,0.89,4,275,5,0,0,"technical","low","left"
"12458",1030,0.1,0.82,6,272,4,0,0,"technical","low","left"
"12459",1031,0.1,0.97,6,307,4,0,0,"technical","low","left"
"12460",1032,0.11,0.93,4,295,3,0,0,"technical","low","left"
"12461",1033,0.84,0.88,5,237,5,0,0,"technical","low","left"
"12462",1034,0.42,0.56,2,158,3,0,0,"technical","low","left"
"12463",1035,0.1,0.86,6,266,4,1,0,"technical","low","left"
"12464",1036,0.1,0.95,6,256,4,0,0,"technical","medium","left"
"12465",1037,0.46,0.54,2,158,3,0,0,"support","medium","left"
"12466",1038,0.09,0.97,7,268,4,0,0,"support","medium","left"
"12467",1039,0.89,1,4,237,5,0,0,"support","medium","left"
"12468",1040,0.82,1,4,273,6,0,0,"support","medium","left"
"12469",1041,0.11,0.89,6,309,4,0,0,"support","medium","left"
"12470",1042,0.81,0.84,5,258,5,0,0,"support","medium","left"
"12471",1043,0.81,0.94,5,233,6,0,0,"support","medium","left"
"12472",1044,0.77,1,4,249,6,0,0,"support","medium","left"
"12473",1045,0.63,0.94,3,179,2,0,0,"support","medium","left"
"12474",1046,0.4,0.57,2,128,3,0,0,"support","medium","left"
"12475",1047,0.86,1,4,250,6,0,0,"support","medium","left"
"12476",1048,0.37,0.49,2,151,3,0,0,"technical","high","left"
"12477",1049,0.44,0.5,2,132,3,0,0,"technical","low","left"
"12478",1050,0.74,0.89,5,232,6,0,0,"technical","medium","left"
"12479",1051,0.79,1,4,229,5,1,0,"management","medium","left"
"12480",1052,0.09,0.92,6,261,4,0,0,"IT","medium","left"
"12481",1053,0.37,0.48,2,129,3,0,0,"IT","medium","left"
"12482",1054,0.09,0.78,6,244,4,0,0,"IT","low","left"
"12483",1055,0.09,0.84,6,258,4,0,0,"IT","low","left"
"12484",1056,0.1,0.8,7,292,5,0,0,"IT","low","left"
"12485",1057,0.45,0.56,2,143,3,0,0,"product_mng","low","left"
"12486",1058,0.4,0.51,2,136,3,0,0,"product_mng","low","left"
"12487",1059,0.31,0.95,6,235,5,0,0,"product_mng","low","left"
"12488",1060,0.7,0.93,2,310,3,0,0,"product_mng","low","left"
"12489",1061,0.36,0.48,2,152,3,0,0,"IT","low","left"
"12490",1062,0.34,0.97,6,157,5,1,0,"RandD","low","left"
"12491",1063,0.87,0.83,5,267,5,0,0,"RandD","low","left"
"12492",1064,0.11,0.91,6,302,4,0,0,"RandD","low","left"
"12493",1065,0.47,0.81,4,133,3,0,0,"RandD","low","left"
"12494",1066,0.43,0.52,2,142,3,0,0,"RandD","low","left"
"12495",1067,0.43,0.47,2,129,3,0,0,"marketing","low","left"
"12496",1068,0.45,0.57,2,136,3,0,0,"sales","low","left"
"12497",1069,0.74,1,5,223,5,0,0,"accounting","low","left"
"12498",1070,0.1,0.89,6,244,4,1,0,"support","low","left"
"12499",1071,0.78,0.87,5,243,5,0,0,"technical","low","left"
"12500",1072,0.9,0.95,5,275,3,0,0,"management","low","left"
"12501",1073,0.53,0.95,6,205,4,0,0,"marketing","low","left"
"12502",1074,0.45,0.54,2,154,3,0,0,"marketing","low","left"
"12503",1075,0.86,0.83,4,270,5,0,0,"marketing","medium","left"
"12504",1076,0.74,0.89,4,267,5,0,0,"sales","medium","left"
"12505",1077,0.09,0.79,6,276,4,0,0,"sales","medium","left"
"12506",1078,0.8,0.95,5,244,5,0,0,"sales","medium","left"
"12507",1079,0.46,0.53,2,128,3,0,0,"sales","medium","left"
"12508",1080,0.86,0.95,5,269,6,0,0,"sales","medium","left"
"12509",1081,0.86,0.95,4,238,5,0,0,"sales","medium","left"
"12510",1082,0.9,0.91,4,269,5,0,0,"sales","medium","left"
"12511",1083,0.87,0.88,5,231,6,0,0,"sales","medium","left"
"12512",1084,0.83,0.94,4,267,5,0,0,"sales","medium","left"
"12513",1085,0.77,0.83,4,245,5,0,0,"sales","medium","left"
"12514",1086,0.77,1,4,272,5,0,0,"sales","medium","left"
"12515",1087,0.9,0.86,5,254,5,0,0,"sales","high","left"
"12516",1088,0.73,0.92,4,273,5,0,0,"sales","low","left"
"12517",1089,0.91,0.9,4,250,5,0,0,"sales","medium","left"
"12518",1090,0.36,0.53,2,133,3,0,0,"sales","medium","left"
"12519",1091,0.38,0.54,2,150,3,0,0,"sales","medium","left"
"12520",1092,0.44,0.46,2,157,3,0,0,"sales","medium","left"
"12521",1093,0.73,1,5,230,5,0,0,"sales","low","left"
"12522",1094,0.89,0.91,5,260,5,0,0,"sales","low","left"
"12523",1095,0.11,0.77,6,275,4,0,0,"accounting","low","left"
"12524",1096,0.1,0.77,7,308,4,0,0,"accounting","low","left"
"12525",1097,0.37,0.46,2,129,3,0,0,"accounting","low","left"
"12526",1098,0.38,0.48,2,134,3,0,0,"hr","low","left"
"12527",1099,0.42,0.48,2,132,3,0,0,"hr","low","left"
"12528",1100,0.44,0.46,2,153,3,0,0,"hr","low","left"
"12529",1101,0.11,0.83,6,262,4,0,0,"hr","low","left"
"12530",1102,0.09,0.97,7,262,4,0,0,"technical","low","left"
"12531",1103,0.43,0.47,2,130,3,0,0,"technical","low","left"
"12532",1104,0.38,0.52,2,156,3,0,0,"technical","low","left"
"12533",1105,0.11,0.89,6,254,4,0,0,"technical","low","left"
"12534",1106,0.37,0.49,2,130,3,0,0,"technical","low","left"
"12535",1107,0.44,0.57,2,139,3,0,0,"technical","low","left"
"12536",1108,0.72,0.82,5,269,5,0,0,"technical","low","left"
"12537",1109,0.1,0.91,7,297,4,0,0,"technical","low","left"
"12538",1110,0.73,0.86,5,249,5,0,0,"technical","low","left"
"12539",1111,0.09,0.82,7,267,4,0,0,"technical","low","left"
"12540",1112,0.44,0.46,2,149,3,0,0,"technical","low","left"
"12541",1113,0.09,0.82,6,251,4,0,0,"support","low","left"
"12542",1114,0.1,0.87,6,306,4,0,0,"support","medium","left"
"12543",1115,0.11,0.86,6,279,4,0,0,"support","medium","left"
"12544",1116,0.42,0.46,2,131,3,0,0,"support","medium","left"
"12545",1117,0.09,0.85,6,260,4,0,0,"support","medium","left"
"12546",1118,0.72,0.88,5,249,5,0,0,"support","medium","left"
"12547",1119,0.75,0.97,4,245,5,0,0,"support","medium","left"
"12548",1120,0.44,0.5,2,138,3,0,0,"support","medium","left"
"12549",1121,0.11,0.91,6,278,4,0,0,"support","medium","left"
"12550",1122,0.38,0.47,2,147,3,0,0,"support","medium","left"
"12551",1123,0.39,0.57,2,131,3,0,0,"support","medium","left"
"12552",1124,0.1,0.9,7,301,4,0,0,"technical","medium","left"
"12553",1125,0.43,0.52,2,141,3,0,0,"technical","medium","left"
"12554",1126,0.39,0.57,2,158,3,0,0,"technical","high","left"
"12555",1127,0.88,0.87,4,235,6,0,0,"management","low","left"
"12556",1128,0.1,0.85,7,261,4,0,0,"IT","medium","left"
"12557",1129,0.1,0.89,5,270,4,0,0,"IT","medium","left"
"12558",1130,0.11,0.93,6,290,4,0,0,"IT","medium","left"
"12559",1131,0.37,0.47,2,149,3,0,0,"IT","medium","left"
"12560",1132,0.37,0.48,2,160,3,0,0,"IT","low","left"
"12561",1133,0.77,0.87,4,150,4,0,0,"product_mng","low","left"
"12562",1134,0.91,0.94,5,218,6,0,0,"product_mng","low","left"
"12563",1135,0.46,0.51,2,155,3,0,0,"product_mng","low","left"
"12564",1136,0.11,0.87,6,291,4,0,0,"product_mng","low","left"
"12565",1137,0.86,0.91,5,265,5,0,0,"IT","low","left"
"12566",1138,0.87,0.88,5,262,6,0,0,"sales","low","left"
"12567",1139,0.09,0.92,6,303,5,0,0,"sales","low","left"
"12568",1140,0.42,0.46,2,132,3,0,0,"sales","low","left"
"12569",1141,0.82,0.83,4,245,5,1,0,"sales","low","left"
"12570",1142,0.46,0.48,2,129,3,0,0,"sales","low","left"
"12571",1143,0.88,1,5,226,6,0,0,"marketing","low","left"
"12572",1144,0.1,0.91,6,286,4,0,0,"marketing","low","left"
"12573",1145,0.43,0.45,2,140,3,0,0,"sales","low","left"
"12574",1146,0.37,0.49,2,153,3,0,0,"accounting","low","left"
"12575",1147,0.8,0.95,5,217,5,0,0,"support","low","left"
"12576",1148,0.83,0.95,5,258,5,0,0,"technical","low","left"
"12577",1149,0.39,0.57,2,156,3,0,0,"management","low","left"
"12578",1150,0.77,0.92,5,255,5,0,0,"marketing","low","left"
"12579",1151,0.43,0.46,2,129,3,0,0,"marketing","low","left"
"12580",1152,0.79,0.96,4,234,5,0,0,"marketing","low","left"
"12581",1153,0.39,0.55,2,152,3,1,0,"sales","medium","left"
"12582",1154,0.1,0.88,7,300,4,0,0,"sales","medium","left"
"12583",1155,0.39,0.53,2,131,3,0,0,"sales","medium","left"
"12584",1156,0.11,0.89,6,301,4,0,0,"sales","medium","left"
"12585",1157,0.4,0.51,2,156,3,0,0,"sales","medium","left"
"12586",1158,0.42,0.52,2,141,3,1,0,"sales","medium","left"
"12587",1159,0.57,0.85,4,219,2,1,0,"sales","medium","left"
"12588",1160,0.11,0.95,7,269,5,0,0,"sales","medium","left"
"12589",1161,0.36,0.73,4,276,2,0,0,"sales","medium","left"
"12590",1162,0.11,0.94,7,302,4,0,0,"sales","medium","left"
"12591",1163,0.35,0.8,6,281,2,0,0,"sales","medium","left"
"12592",1164,0.78,0.99,5,241,5,0,0,"sales","medium","left"
"12593",1165,0.11,0.93,7,288,4,0,0,"sales","high","left"
"12594",1166,0.1,0.96,6,303,4,0,0,"sales","low","left"
"12595",1167,0.42,0.54,2,135,3,0,0,"sales","medium","left"
"12596",1168,0.43,0.5,2,127,3,1,0,"sales","medium","left"
"12597",1169,0.79,0.84,5,245,5,0,0,"sales","medium","left"
"12598",1170,0.45,0.45,2,145,3,0,0,"sales","medium","left"
"12599",1171,0.09,0.91,6,248,4,0,0,"sales","low","left"
"12600",1172,0.11,0.91,6,302,4,0,0,"accounting","low","left"
"12601",1173,0.45,0.49,2,144,3,0,0,"accounting","low","left"
"12602",1174,0.11,0.91,6,272,4,0,0,"accounting","low","left"
"12603",1175,0.09,0.8,6,294,4,0,0,"hr","low","left"
"12604",1176,0.78,0.71,4,296,3,0,0,"hr","low","left"
"12605",1177,0.38,0.5,2,151,3,1,0,"hr","low","left"
"12606",1178,0.82,0.82,4,249,5,0,0,"hr","low","left"
"12607",1179,0.85,0.89,4,221,5,0,0,"technical","low","left"
"12608",1180,0.45,0.46,2,146,3,0,0,"technical","low","left"
"12609",1181,0.77,0.89,5,243,5,0,0,"technical","low","left"
"12610",1182,0.39,0.5,2,127,3,0,0,"technical","low","left"
"12611",1183,0.91,0.9,4,245,5,0,0,"technical","low","left"
"12612",1184,0.11,0.77,6,264,4,0,0,"technical","low","left"
"12613",1185,0.46,0.45,2,143,3,0,0,"technical","low","left"
"12614",1186,0.43,0.49,2,135,3,0,0,"technical","low","left"
"12615",1187,0.11,0.96,6,262,4,1,0,"technical","low","left"
"12616",1188,0.1,0.93,6,299,4,0,0,"technical","low","left"
"12617",1189,0.09,0.8,5,279,4,0,0,"technical","low","left"
"12618",1190,0.36,0.51,2,155,3,0,0,"support","low","left"
"12619",1191,0.11,0.89,6,264,4,0,0,"support","low","left"
"12620",1192,0.09,0.77,6,256,5,0,0,"support","medium","left"
"12621",1193,0.44,0.51,2,129,3,0,0,"support","medium","left"
"12622",1194,0.73,0.97,5,217,6,0,0,"support","medium","left"
"12623",1195,0.21,0.58,7,203,5,0,0,"support","medium","left"
"12624",1196,0.8,0.85,4,264,5,0,0,"support","medium","left"
"12625",1197,0.37,0.55,2,159,3,0,0,"support","medium","left"
"12626",1198,0.79,0.96,5,218,5,0,0,"support","medium","left"
"12627",1199,0.09,0.8,6,298,4,0,0,"support","medium","left"
"12628",1200,0.75,0.74,6,134,3,0,0,"support","medium","left"
"12629",1201,0.83,1,5,263,5,0,0,"technical","medium","left"
"12630",1202,0.1,0.77,5,252,4,0,0,"technical","medium","left"
"12631",1203,0.44,0.55,2,136,3,0,0,"technical","medium","left"
"12632",1204,0.42,0.97,6,259,4,0,0,"management","high","left"
"12633",1205,0.43,0.56,2,158,3,0,0,"IT","low","left"
"12634",1206,0.09,0.84,7,307,4,0,0,"IT","medium","left"
"12635",1207,0.44,0.53,2,152,3,0,0,"IT","medium","left"
"12636",1208,0.81,0.98,5,237,5,0,0,"IT","medium","left"
"12637",1209,0.1,0.79,7,284,4,0,0,"IT","medium","left"
"12638",1210,0.1,0.93,6,243,4,0,0,"product_mng","low","left"
"12639",1211,0.11,0.83,6,268,4,0,0,"product_mng","low","left"
"12640",1212,0.09,0.77,6,244,4,0,0,"product_mng","low","left"
"12641",1213,0.75,0.83,5,262,5,0,0,"product_mng","low","left"
"12642",1214,0.38,0.55,2,134,3,0,0,"IT","low","left"
"12643",1215,0.09,0.87,7,278,4,0,0,"IT","low","left"
"12644",1216,0.74,0.97,5,238,5,0,0,"IT","low","left"
"12645",1217,0.44,0.56,2,127,3,0,0,"IT","low","left"
"12646",1218,0.76,0.95,4,259,5,0,0,"RandD","low","left"
"12647",1219,0.42,0.56,2,146,3,0,0,"RandD","low","left"
"12648",1220,0.75,1,4,243,5,0,0,"RandD","low","left"
"12649",1221,0.36,0.52,2,137,3,0,0,"marketing","low","left"
"12650",1222,0.75,0.93,5,229,5,0,0,"sales","low","left"
"12651",1223,0.4,0.46,2,134,3,0,0,"accounting","low","left"
"12652",1224,0.75,0.89,4,228,5,0,0,"support","low","left"
"12653",1225,0.09,0.84,6,301,4,0,0,"technical","low","left"
"12654",1226,0.39,0.46,2,127,3,0,0,"management","low","left"
"12655",1227,0.4,0.48,2,142,3,0,0,"marketing","low","left"
"12656",1228,0.39,0.54,2,131,3,0,0,"marketing","low","left"
"12657",1229,0.1,0.85,7,310,5,0,0,"marketing","low","left"
"12658",1230,0.42,0.55,2,148,3,0,0,"sales","low","left"
"12659",1231,0.37,0.52,2,143,3,0,0,"sales","medium","left"
"12660",1232,0.11,0.98,6,250,4,0,0,"sales","medium","left"
"12661",1233,0.09,0.88,7,265,4,0,0,"sales","medium","left"
"12662",1234,0.41,0.54,2,152,3,0,0,"sales","medium","left"
"12663",1235,0.42,0.49,2,145,3,0,0,"sales","medium","left"
"12664",1236,0.4,0.49,2,140,3,0,0,"sales","medium","left"
"12665",1237,0.36,0.47,2,129,3,0,0,"sales","medium","left"
"12666",1238,0.74,0.9,4,226,5,0,0,"sales","medium","left"
"12667",1239,0.66,1,5,269,5,0,0,"sales","medium","left"
"12668",1240,0.38,0.47,2,152,3,0,0,"sales","medium","left"
"12669",1241,0.43,0.51,2,132,3,0,0,"sales","medium","left"
"12670",1242,0.43,0.53,2,148,3,0,0,"sales","medium","left"
"12671",1243,0.1,0.85,6,297,5,0,0,"sales","high","left"
"12672",1244,0.82,0.85,4,274,5,0,0,"sales","low","left"
"12673",1245,0.1,0.77,6,280,4,0,0,"sales","medium","left"
"12674",1246,0.1,0.93,6,288,4,0,0,"sales","medium","left"
"12675",1247,0.43,0.49,2,155,3,0,0,"sales","medium","left"
"12676",1248,0.09,0.94,7,247,4,0,0,"sales","medium","left"
"12677",1249,0.41,0.54,2,138,3,0,0,"accounting","low","left"
"12678",1250,0.1,0.82,7,284,4,0,0,"accounting","low","left"
"12679",1251,0.88,0.92,4,225,5,0,0,"accounting","low","left"
"12680",1252,0.43,0.57,2,151,3,1,0,"hr","low","left"
"12681",1253,0.42,0.5,2,155,3,0,0,"hr","low","left"
"12682",1254,0.85,1,4,234,5,0,0,"hr","low","left"
"12683",1255,0.38,0.49,2,144,3,0,0,"hr","low","left"
"12684",1256,0.39,0.47,2,142,3,0,0,"technical","low","left"
"12685",1257,0.41,0.48,2,126,3,0,0,"technical","low","left"
"12686",1258,0.88,0.92,4,233,6,0,0,"technical","low","left"
"12687",1259,0.78,0.96,4,241,5,0,0,"technical","low","left"
"12688",1260,0.45,0.48,2,138,3,0,0,"technical","low","left"
"12689",1261,0.09,0.95,6,260,4,1,0,"technical","low","left"
"12690",1262,0.44,0.56,2,145,3,0,0,"technical","low","left"
"12691",1263,0.11,0.84,6,252,4,0,0,"technical","low","left"
"12692",1264,0.36,0.51,2,143,3,0,0,"technical","low","left"
"12693",1265,0.86,0.98,4,270,5,0,0,"technical","low","left"
"12694",1266,0.1,0.92,6,285,4,0,0,"technical","low","left"
"12695",1267,0.45,0.53,2,149,3,0,0,"support","low","left"
"12696",1268,0.42,0.53,2,158,3,0,0,"support","low","left"
"12697",1269,0.36,0.55,2,134,3,0,0,"support","low","left"
"12698",1270,0.45,0.55,2,129,3,0,0,"support","medium","left"
"12699",1271,0.38,0.57,2,131,3,0,0,"support","medium","left"
"12700",1272,0.11,0.97,6,288,4,0,0,"support","medium","left"
"12701",1273,0.45,0.46,2,142,3,0,0,"support","medium","left"
"12702",1274,0.87,0.95,5,227,5,0,0,"support","medium","left"
"12703",1275,0.45,0.53,2,131,3,0,0,"support","medium","left"
"12704",1276,0.1,0.83,6,283,5,0,0,"support","medium","left"
"12705",1277,0.44,0.54,2,139,3,0,0,"support","medium","left"
"12706",1278,0.78,1,4,267,5,0,0,"technical","medium","left"
"12707",1279,0.38,0.56,2,148,3,0,0,"technical","medium","left"
"12708",1280,0.85,0.84,5,272,6,0,0,"technical","medium","left"
"12709",1281,0.36,0.48,2,148,3,1,0,"management","medium","left"
"12710",1282,0.75,0.88,5,270,5,0,0,"IT","high","left"
"12711",1283,0.81,0.81,4,218,5,1,0,"IT","low","left"
"12712",1284,0.4,0.55,2,150,3,0,0,"IT","medium","left"
"12713",1285,0.83,0.83,5,260,5,0,0,"IT","medium","left"
"12714",1286,0.41,0.52,2,127,3,0,0,"IT","medium","left"
"12715",1287,0.42,0.57,2,134,3,0,0,"product_mng","medium","left"
"12716",1288,0.09,0.83,7,258,4,0,0,"product_mng","low","left"
"12717",1289,0.87,0.81,5,304,5,0,0,"product_mng","low","left"
"12718",1290,0.43,0.56,6,149,4,0,0,"product_mng","low","left"
"12719",1291,0.39,0.51,2,139,3,0,0,"IT","low","left"
"12720",1292,0.1,0.9,6,272,5,0,0,"RandD","low","left"
"12721",1293,0.41,0.52,2,132,3,0,0,"RandD","low","left"
"12722",1294,0.72,1,2,240,2,0,0,"RandD","low","left"
"12723",1295,0.44,0.55,2,137,3,0,0,"RandD","low","left"
"12724",1296,0.38,0.5,2,139,3,0,0,"RandD","low","left"
"12725",1297,0.46,0.52,2,148,3,0,0,"RandD","low","left"
"12726",1298,0.4,0.49,2,149,3,0,0,"marketing","low","left"
"12727",1299,0.45,0.45,2,131,3,0,0,"sales","low","left"
"12728",1300,0.89,0.89,5,262,5,0,0,"accounting","low","left"
"12729",1301,0.1,0.97,7,284,4,0,0,"support","low","left"
"12730",1302,0.46,0.48,2,161,3,0,0,"technical","low","left"
"12731",1303,0.09,0.78,7,290,4,0,0,"management","low","left"
"12732",1304,0.45,0.57,2,149,3,0,0,"marketing","low","left"
"12733",1305,0.89,0.98,4,242,6,0,0,"marketing","low","left"
"12734",1306,0.62,0.77,5,227,4,0,0,"marketing","low","left"
"12735",1307,0.11,0.93,6,276,4,0,0,"sales","low","left"
"12736",1308,0.44,0.5,2,135,3,0,0,"sales","low","left"
"12737",1309,0.09,0.94,6,266,4,0,0,"sales","medium","left"
"12738",1310,0.56,0.75,5,236,2,0,0,"sales","medium","left"
"12739",1311,0.77,0.89,4,270,5,0,0,"sales","medium","left"
"12740",1312,0.39,0.49,2,146,3,0,0,"sales","medium","left"
"12741",1313,0.1,0.92,5,272,4,0,0,"sales","medium","left"
"12742",1314,0.72,0.85,5,246,5,0,0,"sales","medium","left"
"12743",1315,0.4,0.52,2,136,3,0,0,"sales","medium","left"
"12744",1316,0.11,0.81,6,260,4,0,0,"sales","medium","left"
"12745",1317,0.88,1,5,247,5,0,0,"sales","medium","left"
"12746",1318,0.37,0.51,2,127,3,0,0,"sales","medium","left"
"12747",1319,0.11,0.96,6,267,4,0,0,"sales","medium","left"
"12748",1320,0.4,0.52,2,143,3,0,0,"sales","medium","left"
"12749",1321,0.1,0.86,6,306,4,0,0,"sales","high","left"
"12750",1322,0.62,0.89,3,131,4,0,0,"sales","low","left"
"12751",1323,0.78,0.86,4,249,5,0,0,"sales","medium","left"
"12752",1324,0.37,0.52,2,144,3,0,0,"sales","medium","left"
"12753",1325,0.76,0.82,4,254,5,1,0,"sales","medium","left"
"12754",1326,0.42,0.53,2,131,3,0,0,"accounting","medium","left"
"12755",1327,0.1,0.77,6,272,4,0,0,"accounting","low","left"
"12756",1328,0.42,0.47,2,157,3,0,0,"accounting","low","left"
"12757",1329,0.1,0.96,7,301,4,0,0,"hr","low","left"
"12758",1330,0.1,0.81,6,252,4,0,0,"hr","low","left"
"12759",1331,0.42,0.47,2,130,3,0,0,"hr","low","left"
"12760",1332,0.09,0.86,6,297,4,0,0,"hr","low","left"
"12761",1333,0.1,0.8,6,248,4,0,0,"technical","low","left"
"12762",1334,0.11,0.89,7,257,4,0,0,"technical","low","left"
"12763",1335,0.44,0.53,2,147,3,0,0,"technical","low","left"
"12764",1336,0.87,0.9,3,307,5,0,0,"technical","low","left"
"12765",1337,0.44,0.46,2,154,3,0,0,"technical","low","left"
"12766",1338,0.41,0.56,2,143,3,0,0,"technical","low","left"
"12767",1339,0.51,0.79,4,134,3,0,0,"technical","low","left"
"12768",1340,0.89,0.96,5,221,5,0,0,"technical","low","left"
"12769",1341,0.1,0.96,6,275,4,0,0,"technical","low","left"
"12770",1342,0.9,0.94,5,247,5,0,0,"technical","low","left"
"12771",1343,0.36,0.55,2,131,3,0,0,"technical","low","left"
"12772",1344,0.44,0.54,2,150,3,0,0,"support","low","left"
"12773",1345,0.39,0.57,2,150,3,0,0,"support","low","left"
"12774",1346,0.38,1,5,137,4,0,0,"support","low","left"
"12775",1347,0.73,0.95,4,223,6,0,0,"support","low","left"
"12776",1348,0.1,0.96,6,292,4,0,0,"support","medium","left"
"12777",1349,0.89,0.83,5,130,4,1,0,"support","medium","left"
"12778",1350,0.87,0.85,5,221,6,0,0,"support","medium","left"
"12779",1351,0.84,0.89,4,245,5,1,0,"support","medium","left"
"12780",1352,0.11,0.77,6,274,4,0,0,"support","medium","left"
"12781",1353,0.74,1,5,248,5,0,0,"support","medium","left"
"12782",1354,0.79,0.97,4,243,5,0,0,"support","medium","left"
"12783",1355,0.1,0.92,7,273,4,0,0,"technical","medium","left"
"12784",1356,0.83,0.95,5,221,5,0,0,"technical","medium","left"
"12785",1357,0.1,0.79,6,271,4,0,0,"technical","medium","left"
"12786",1358,0.45,0.5,2,157,3,0,0,"management","medium","left"
"12787",1359,0.29,0.48,2,249,4,0,0,"IT","medium","left"
"12788",1360,0.46,0.46,2,145,3,0,0,"IT","high","left"
"12789",1361,0.11,0.83,6,262,5,0,0,"IT","low","left"
"12790",1362,0.75,0.89,5,272,5,0,0,"IT","medium","left"
"12791",1363,0.4,0.5,2,129,3,0,0,"IT","medium","left"
"12792",1364,0.37,0.46,2,134,3,0,0,"product_mng","medium","left"
"12793",1365,0.41,0.52,2,147,3,0,0,"product_mng","medium","left"
"12794",1366,0.1,0.83,6,293,4,0,0,"product_mng","low","left"
"12795",1367,0.39,0.52,2,129,3,0,0,"product_mng","low","left"
"12796",1368,0.4,0.45,2,140,3,0,0,"IT","low","left"
"12797",1369,0.41,0.52,2,132,3,0,0,"RandD","low","left"
"12798",1370,0.11,0.89,6,284,4,0,0,"RandD","low","left"
"12799",1371,0.74,0.99,5,263,5,0,0,"RandD","low","left"
"12800",1372,0.42,0.46,2,143,3,0,0,"RandD","low","left"
"12801",1373,0.88,0.88,4,265,5,0,0,"RandD","low","left"
"12802",1374,0.37,0.53,2,147,3,0,0,"RandD","low","left"
"12803",1375,0.78,0.81,4,253,5,0,0,"marketing","low","left"
"12804",1376,0.46,0.5,2,141,3,0,0,"sales","low","left"
"12805",1377,0.74,1,5,222,6,0,0,"accounting","low","left"
"12806",1378,0.11,0.86,6,273,4,1,0,"support","low","left"
"12807",1379,0.8,0.87,5,240,6,0,0,"technical","low","left"
"12808",1380,0.37,0.48,2,154,3,0,0,"management","low","left"
"12809",1381,0.87,0.92,4,253,6,0,0,"marketing","low","left"
"12810",1382,0.9,0.84,5,221,5,0,0,"marketing","low","left"
"12811",1383,0.1,0.88,6,263,4,0,0,"marketing","low","left"
"12812",1384,0.43,0.46,2,145,3,0,0,"sales","low","left"
"12813",1385,0.11,0.91,6,279,4,0,0,"sales","low","left"
"12814",1386,0.4,0.46,2,146,3,0,0,"sales","low","left"
"12815",1387,0.09,0.93,7,270,4,0,0,"sales","medium","left"
"12816",1388,0.4,0.5,2,135,3,0,0,"sales","medium","left"
"12817",1389,0.14,0.7,5,236,3,0,0,"sales","medium","left"
"12818",1390,0.4,0.49,2,151,3,0,0,"sales","medium","left"
"12819",1391,0.11,0.79,6,244,4,1,0,"sales","medium","left"
"12820",1392,0.72,1,4,169,3,0,0,"sales","medium","left"
"12821",1393,0.39,0.57,2,157,3,0,0,"sales","medium","left"
"12822",1394,0.82,0.93,4,246,5,0,0,"sales","medium","left"
"12823",1395,0.41,0.52,2,142,3,0,0,"sales","medium","left"
"12824",1396,0.45,0.51,2,156,3,0,0,"sales","medium","left"
"12825",1397,0.2,0.7,6,281,5,0,0,"sales","medium","left"
"12826",1398,0.43,0.53,2,146,3,0,0,"sales","medium","left"
"12827",1399,0.39,0.55,2,156,3,0,0,"sales","high","left"
"12828",1400,0.11,0.86,6,299,4,1,0,"sales","low","left"
"12829",1401,0.45,0.47,2,136,3,0,0,"sales","medium","left"
"12830",1402,0.11,0.87,6,272,4,0,0,"sales","medium","left"
"12831",1403,0.84,0.86,5,240,5,0,0,"accounting","medium","left"
"12832",1404,0.86,0.96,5,245,6,0,0,"accounting","medium","left"
"12833",1405,0.79,0.93,5,269,5,0,0,"accounting","low","left"
"12834",1406,0.39,0.57,2,130,3,0,0,"hr","low","left"
"12835",1407,0.15,0.62,4,257,3,0,0,"hr","low","left"
"12836",1408,0.81,1,4,241,5,0,0,"hr","low","left"
"12837",1409,0.39,0.53,2,136,3,0,0,"hr","low","left"
"12838",1410,0.92,0.94,4,219,5,0,0,"technical","low","left"
"12839",1411,0.9,0.98,5,271,5,0,0,"technical","low","left"
"12840",1412,0.32,0.6,2,280,4,0,0,"technical","low","left"
"12841",1413,0.46,0.46,2,140,3,0,0,"technical","low","left"
"12842",1414,0.83,0.98,4,254,5,0,0,"technical","low","left"
"12843",1415,0.39,0.47,2,131,3,0,0,"technical","low","left"
"12844",1416,0.2,0.9,6,138,3,0,0,"technical","low","left"
"12845",1417,0.11,0.85,6,295,4,0,0,"technical","low","left"
"12846",1418,0.11,0.96,6,301,5,0,0,"technical","low","left"
"12847",1419,0.1,0.95,7,296,4,0,0,"technical","low","left"
"12848",1420,0.75,0.87,5,246,5,0,0,"technical","low","left"
"12849",1421,0.44,0.57,2,145,3,0,0,"support","low","left"
"12850",1422,0.86,0.93,5,241,5,1,0,"support","low","left"
"12851",1423,0.1,0.82,6,269,4,0,0,"support","low","left"
"12852",1424,0.39,0.49,2,146,3,0,0,"support","low","left"
"12853",1425,0.45,0.48,2,149,3,0,0,"support","low","left"
"12854",1426,0.1,0.94,7,287,4,0,0,"support","medium","left"
"12855",1427,0.36,0.55,2,138,3,0,0,"support","medium","left"
"12856",1428,0.57,0.61,4,158,5,0,0,"support","medium","left"
"12857",1429,0.09,0.87,6,266,4,0,0,"support","medium","left"
"12858",1430,0.87,0.91,4,255,5,0,0,"support","medium","left"
"12859",1431,0.43,0.52,2,156,3,0,0,"support","medium","left"
"12860",1432,0.36,0.5,2,147,3,0,0,"technical","medium","left"
"12861",1433,0.91,0.99,5,265,5,1,0,"technical","medium","left"
"12862",1434,0.41,0.48,2,136,3,0,0,"technical","medium","left"
"12863",1435,0.37,0.52,2,140,3,0,0,"management","medium","left"
"12864",1436,0.43,0.45,2,146,3,0,0,"IT","medium","left"
"12865",1437,0.43,0.57,2,142,3,0,0,"IT","medium","left"
"12866",1438,0.4,0.53,2,155,3,0,0,"IT","high","left"
"12867",1439,0.1,0.89,7,285,4,0,0,"IT","low","left"
"12868",1440,0.76,0.99,4,253,5,1,0,"IT","medium","left"
"12869",1441,0.82,0.93,4,248,5,0,0,"product_mng","medium","left"
"12870",1442,0.11,0.83,7,255,5,0,0,"product_mng","medium","left"
"12871",1443,0.43,0.52,2,154,3,1,0,"product_mng","medium","left"
"12872",1444,0.11,0.88,7,305,4,0,0,"product_mng","low","left"
"12873",1445,0.41,0.48,2,141,3,0,0,"IT","low","left"
"12874",1446,0.73,0.87,5,252,5,0,0,"RandD","low","left"
"12875",1447,0.37,0.57,2,157,3,0,0,"RandD","low","left"
"12876",1448,0.11,0.89,6,250,4,0,0,"RandD","low","left"
"12877",1449,0.46,0.52,2,131,3,0,0,"RandD","low","left"
"12878",1450,0.41,0.5,2,149,3,0,0,"RandD","low","left"
"12879",1451,0.78,0.78,4,260,5,0,0,"RandD","low","left"
"12880",1452,0.78,0.86,5,260,6,0,0,"marketing","low","left"
"12881",1453,0.72,0.86,5,251,5,0,0,"sales","low","left"
"12882",1454,0.63,0.83,6,242,5,0,0,"accounting","low","left"
"12883",1455,0.55,1,6,136,3,0,0,"support","low","left"
"12884",1456,0.45,0.55,2,155,3,0,0,"technical","low","left"
"12885",1457,0.39,0.51,2,155,3,0,0,"management","low","left"
"12886",1458,0.1,0.81,6,248,4,0,0,"marketing","low","left"
"12887",1459,0.4,0.5,2,136,3,0,0,"marketing","low","left"
"12888",1460,0.39,0.54,2,133,3,0,0,"marketing","low","left"
"12889",1461,0.78,0.45,4,128,2,0,0,"sales","low","left"
"12890",1462,0.42,0.53,2,142,3,0,0,"sales","low","left"
"12891",1463,0.39,0.54,2,149,3,0,0,"sales","low","left"
"12892",1464,0.46,0.57,2,145,3,0,0,"sales","low","left"
"12893",1465,0.1,0.92,6,279,4,0,0,"sales","medium","left"
"12894",1466,0.45,0.47,2,146,3,0,0,"sales","medium","left"
"12895",1467,0.1,0.83,6,264,4,1,0,"sales","medium","left"
"12896",1468,0.1,0.89,7,272,4,0,0,"sales","medium","left"
"12897",1469,0.78,0.86,5,256,5,0,0,"sales","medium","left"
"12898",1470,0.4,0.65,2,296,5,0,0,"sales","medium","left"
"12899",1471,0.45,0.51,2,155,3,1,0,"sales","medium","left"
"12900",1472,0.39,0.56,2,130,3,0,0,"sales","medium","left"
"12901",1473,0.43,0.48,2,157,3,0,0,"sales","medium","left"
"12902",1474,0.09,0.96,6,245,4,0,0,"sales","medium","left"
"12903",1475,0.79,0.86,5,226,5,0,0,"sales","medium","left"
"12904",1476,0.44,0.47,2,156,3,1,0,"sales","medium","left"
"12905",1477,0.79,0.95,5,228,5,0,0,"sales","high","left"
"12906",1478,0.38,0.46,2,155,3,0,0,"sales","low","left"
"12907",1479,0.36,0.56,2,159,3,0,0,"sales","medium","left"
"12908",1480,0.39,0.57,2,142,3,0,0,"accounting","medium","left"
"12909",1481,0.09,0.93,6,271,4,0,0,"accounting","medium","left"
"12910",1482,0.16,0.65,4,277,5,0,0,"accounting","medium","left"
"12911",1483,0.09,0.77,6,310,4,0,0,"hr","low","left"
"12912",1484,0.11,0.87,6,254,4,0,0,"hr","low","left"
"12913",1485,0.44,0.56,2,142,3,0,0,"hr","low","left"
"12914",1486,0.11,0.88,7,253,4,0,0,"hr","low","left"
"12915",1487,0.11,0.97,6,260,4,0,0,"technical","low","left"
"12916",1488,0.45,0.52,2,147,3,0,0,"technical","low","left"
"12917",1489,0.1,0.96,7,288,4,0,0,"technical","low","left"
"12918",1490,0.4,0.46,2,160,3,0,0,"technical","low","left"
"12919",1491,0.38,0.55,2,130,3,0,0,"technical","low","left"
"12920",1492,0.39,0.56,2,133,3,0,0,"technical","low","left"
"12921",1493,0.38,0.55,2,160,3,0,0,"technical","low","left"
"12922",1494,0.38,0.45,2,151,3,0,0,"technical","low","left"
"12923",1495,0.17,0.75,3,188,4,0,0,"technical","low","left"
"12924",1496,0.79,0.84,5,247,5,0,0,"technical","low","left"
"12925",1497,0.1,0.85,7,259,4,0,0,"technical","low","left"
"12926",1498,0.37,0.48,2,129,3,0,0,"support","low","left"
"12927",1499,0.42,0.49,2,152,3,0,0,"support","low","left"
"12928",1500,0.44,0.48,2,128,3,0,0,"support","low","left"
"12929",1501,0.11,0.83,6,253,4,0,0,"support","low","left"
"12930",1502,0.39,0.48,2,151,3,0,0,"support","low","left"
"12931",1503,0.1,0.83,5,271,5,0,0,"support","low","left"
"12932",1504,0.4,0.46,2,155,3,0,0,"support","medium","left"
"12933",1505,0.86,0.91,5,245,5,0,0,"support","medium","left"
"12934",1506,0.37,0.46,2,157,3,0,0,"support","medium","left"
"12935",1507,0.4,0.51,2,160,3,0,0,"support","medium","left"
"12936",1508,0.43,0.48,2,149,3,0,0,"support","medium","left"
"12937",1509,0.88,0.88,5,248,5,0,0,"technical","medium","left"
"12938",1510,0.44,0.52,2,128,3,0,0,"technical","medium","left"
"12939",1511,0.87,1,4,224,5,0,0,"technical","medium","left"
"12940",1512,0.4,0.46,2,156,3,0,0,"management","medium","left"
"12941",1513,0.45,0.52,2,143,3,0,0,"IT","medium","left"
"12942",1514,0.84,0.93,4,250,5,0,0,"IT","medium","left"
"12943",1515,0.1,0.68,3,179,3,0,0,"IT","medium","left"
"12944",1516,0.72,0.99,5,257,5,0,0,"IT","high","left"
"12945",1517,0.4,0.5,2,127,3,0,0,"IT","low","left"
"12946",1518,0.45,0.5,2,145,3,0,0,"product_mng","medium","left"
"12947",1519,0.89,0.98,5,274,5,0,0,"product_mng","medium","left"
"12948",1520,0.4,0.55,2,131,3,0,0,"product_mng","medium","left"
"12949",1521,0.38,0.53,2,136,3,0,0,"product_mng","medium","left"
"12950",1522,0.38,0.47,2,140,3,1,0,"IT","low","left"
"12951",1523,0.1,0.82,7,265,4,0,0,"RandD","low","left"
"12952",1524,0.42,0.48,2,148,3,1,0,"RandD","low","left"
"12953",1525,0.74,0.86,5,262,5,0,0,"RandD","low","left"
"12954",1526,0.1,0.8,6,261,4,0,0,"RandD","low","left"
"12955",1527,0.82,0.9,5,248,5,0,0,"RandD","low","left"
"12956",1528,0.38,0.55,2,131,3,0,0,"marketing","low","left"
"12957",1529,0.41,0.56,2,160,3,0,0,"sales","low","left"
"12958",1530,0.4,0.47,2,152,3,0,0,"accounting","low","left"
"12959",1531,0.83,0.98,4,249,5,0,0,"support","low","left"
"12960",1532,0.36,0.57,2,144,3,0,0,"technical","low","left"
"12961",1533,0.75,0.98,4,245,5,0,0,"management","low","left"
"12962",1534,0.44,0.53,2,146,3,0,0,"marketing","low","left"
"12963",1535,0.1,0.94,6,297,5,0,0,"marketing","low","left"
"12964",1536,0.39,0.52,2,148,3,1,0,"marketing","low","left"
"12965",1537,0.09,0.84,7,260,4,0,0,"sales","low","left"
"12966",1538,0.4,0.57,2,152,3,0,0,"sales","low","left"
"12967",1539,0.41,0.54,2,135,3,0,0,"sales","low","left"
"12968",1540,0.83,0.92,4,235,5,0,0,"sales","low","left"
"12969",1541,0.42,0.47,2,145,3,0,0,"sales","low","left"
"12970",1542,0.61,0.46,5,220,4,0,0,"sales","low","left"
"12971",1543,0.44,0.52,2,128,3,0,0,"sales","medium","left"
"12972",1544,0.77,0.81,5,237,5,0,0,"sales","medium","left"
"12973",1545,0.81,0.95,4,275,5,0,0,"sales","medium","left"
"12974",1546,0.1,0.78,6,310,4,0,0,"sales","medium","left"
"12975",1547,0.73,0.84,5,251,6,0,0,"sales","medium","left"
"12976",1548,0.09,0.83,6,250,4,0,0,"sales","medium","left"
"12977",1549,0.9,0.99,4,259,6,0,0,"sales","medium","left"
"12978",1550,0.88,1,4,259,5,0,0,"sales","medium","left"
"12979",1551,0.09,0.87,7,305,4,0,0,"sales","medium","left"
"12980",1552,0.4,0.56,2,130,3,1,0,"sales","medium","left"
"12981",1553,0.17,0.55,6,250,5,0,0,"sales","medium","left"
"12982",1554,0.82,0.82,5,220,6,0,0,"sales","medium","left"
"12983",1555,0.9,0.83,4,266,5,0,0,"sales","high","left"
"12984",1556,0.83,0.92,4,255,5,0,0,"accounting","low","left"
"12985",1557,0.44,0.5,2,154,3,0,0,"accounting","medium","left"
"12986",1558,0.5,0.85,4,138,3,0,0,"accounting","medium","left"
"12987",1559,0.14,0.61,6,291,5,0,0,"hr","medium","left"
"12988",1560,0.77,0.82,4,217,5,0,0,"hr","medium","left"
"12989",1561,0.09,0.83,6,286,4,0,0,"hr","low","left"
"12990",1562,0.11,0.88,6,290,4,0,0,"hr","low","left"
"12991",1563,0.44,0.57,2,148,3,0,0,"technical","low","left"
"12992",1564,0.43,0.52,2,146,3,0,0,"technical","low","left"
"12993",1565,0.12,0.8,5,136,2,0,0,"technical","low","left"
"12994",1566,0.1,0.86,7,261,5,0,0,"technical","low","left"
"12995",1567,0.1,0.96,6,274,4,0,0,"technical","low","left"
"12996",1568,0.4,0.48,2,132,3,0,0,"technical","low","left"
"12997",1569,0.41,0.47,2,145,3,0,0,"technical","low","left"
"12998",1570,0.37,0.48,2,153,3,0,0,"technical","low","left"
"12999",1571,0.4,0.48,2,139,3,0,0,"technical","low","left"
"13000",1572,0.74,0.96,4,231,5,0,0,"technical","low","left"
"13001",1573,0.39,0.48,2,154,3,0,0,"technical","low","left"
"13002",1574,0.11,0.9,5,307,4,0,0,"support","low","left"
"13003",1575,0.36,0.51,2,129,3,0,0,"support","low","left"
"13004",1576,0.8,0.83,5,275,5,0,0,"support","low","left"
"13005",1577,0.6,0.85,3,250,2,0,0,"support","low","left"
"13006",1578,0.36,0.54,2,158,3,0,0,"support","low","left"
"13007",1579,0.1,0.96,6,310,5,0,0,"support","low","left"
"13008",1580,0.65,0.86,2,181,2,0,0,"support","low","left"
"13009",1581,0.49,0.73,4,244,3,0,0,"support","low","left"
"13010",1582,0.73,0.96,5,256,6,1,0,"support","medium","left"
"13011",1583,0.11,0.8,6,259,5,0,0,"support","medium","left"
"13012",1584,0.73,0.91,4,247,5,0,0,"support","medium","left"
"13013",1585,0.43,0.46,2,129,3,0,0,"technical","medium","left"
"13014",1586,0.73,0.93,5,229,5,0,0,"technical","medium","left"
"13015",1587,0.76,0.85,5,236,6,0,0,"technical","medium","left"
"13016",1588,0.09,0.96,6,281,4,0,0,"management","medium","left"
"13017",1589,0.9,0.81,5,264,5,0,0,"IT","medium","left"
"13018",1590,0.1,0.81,6,308,4,0,0,"IT","medium","left"
"13019",1591,0.43,0.48,2,147,3,0,0,"IT","medium","left"
"13020",1592,0.41,0.55,2,159,3,0,0,"IT","medium","left"
"13021",1593,0.41,0.57,2,154,3,0,0,"IT","medium","left"
"13022",1594,0.1,0.87,6,307,4,0,0,"product_mng","high","left"
"13023",1595,0.4,0.46,2,132,3,0,0,"product_mng","low","left"
"13024",1596,0.4,0.53,2,152,3,0,0,"product_mng","medium","left"
"13025",1597,0.36,0.48,5,310,3,0,0,"product_mng","medium","left"
"13026",1598,0.83,0.95,5,230,5,0,0,"IT","medium","left"
"13027",1599,0.83,0.94,5,273,5,0,0,"RandD","medium","left"
"13028",1600,0.41,0.51,2,144,3,0,0,"RandD","low","left"
"13029",1601,0.11,0.93,7,296,4,0,0,"RandD","low","left"
"13030",1602,0.68,0.62,5,198,5,1,0,"RandD","low","left"
"13031",1603,0.43,0.53,2,157,3,0,0,"RandD","low","left"
"13032",1604,0.44,0.51,2,145,3,0,0,"marketing","low","left"
"13033",1605,0.87,0.94,5,219,5,0,0,"marketing","low","left"
"13034",1606,0.43,0.54,2,153,3,0,0,"sales","low","left"
"13035",1607,0.89,0.48,3,178,5,0,0,"accounting","low","left"
"13036",1608,0.83,0.88,5,239,5,0,0,"support","low","left"
"13037",1609,0.11,0.87,6,278,5,0,0,"technical","low","left"
"13038",1610,0.85,1,6,260,3,1,0,"management","low","left"
"13039",1611,0.89,0.97,4,264,5,0,0,"marketing","low","left"
"13040",1612,0.09,0.92,7,301,4,0,0,"marketing","low","left"
"13041",1613,0.43,0.55,4,134,3,0,0,"marketing","low","left"
"13042",1614,0.42,0.46,2,147,3,0,0,"sales","low","left"
"13043",1615,0.43,0.54,2,130,3,0,0,"sales","low","left"
"13044",1616,0.1,0.93,6,307,4,0,0,"sales","low","left"
"13045",1617,0.37,0.46,2,156,3,0,0,"sales","low","left"
"13046",1618,0.76,0.98,4,237,5,0,0,"sales","low","left"
"13047",1619,0.88,0.89,4,254,5,0,0,"sales","low","left"
"13048",1620,0.39,0.48,2,151,3,0,0,"sales","low","left"
"13049",1621,0.45,0.54,2,131,3,0,0,"sales","medium","left"
"13050",1622,0.91,0.95,5,241,5,0,0,"sales","medium","left"
"13051",1623,0.86,0.85,5,267,5,0,0,"sales","medium","left"
"13052",1624,0.39,0.53,2,153,3,0,0,"sales","medium","left"
"13053",1625,0.89,1,4,217,5,0,0,"sales","medium","left"
"13054",1626,0.11,0.86,6,254,4,0,0,"sales","medium","left"
"13055",1627,0.1,0.87,6,265,5,0,0,"sales","medium","left"
"13056",1628,0.38,0.57,2,146,3,0,0,"sales","medium","left"
"13057",1629,0.4,0.54,2,156,3,0,0,"sales","medium","left"
"13058",1630,0.86,0.97,5,269,5,0,0,"sales","medium","left"
"13059",1631,0.1,0.86,6,288,4,0,0,"sales","medium","left"
"13060",1632,0.1,0.85,6,283,4,0,0,"sales","medium","left"
"13061",1633,0.42,0.5,2,128,3,0,0,"accounting","high","left"
"13062",1634,0.36,0.46,2,130,3,0,0,"accounting","low","left"
"13063",1635,0.39,0.48,2,127,3,0,0,"accounting","medium","left"
"13064",1636,0.43,0.47,2,137,3,0,0,"hr","medium","left"
"13065",1637,0.36,0.49,2,133,3,0,0,"hr","medium","left"
"13066",1638,0.09,0.91,6,275,4,0,0,"hr","medium","left"
"13067",1639,0.42,0.55,2,146,3,0,0,"hr","low","left"
"13068",1640,0.42,0.46,2,135,3,0,0,"technical","low","left"
"13069",1641,0.91,0.89,5,217,5,0,0,"technical","low","left"
"13070",1642,0.41,0.56,2,154,3,0,0,"technical","low","left"
"13071",1643,0.11,0.78,6,247,4,0,0,"technical","low","left"
"13072",1644,0.09,0.83,6,295,5,0,0,"technical","low","left"
"13073",1645,0.83,1,5,224,5,0,0,"technical","low","left"
"13074",1646,0.11,0.78,6,281,4,0,0,"technical","low","left"
"13075",1647,0.1,0.93,7,258,4,0,0,"technical","low","left"
"13076",1648,0.42,0.55,2,150,3,0,0,"technical","low","left"
"13077",1649,0.1,0.97,7,282,4,0,0,"technical","low","left"
"13078",1650,0.38,0.51,2,138,3,0,0,"technical","low","left"
"13079",1651,0.77,0.98,4,238,6,0,0,"support","low","left"
"13080",1652,0.11,0.85,7,244,4,0,0,"support","low","left"
"13081",1653,0.87,0.97,5,250,6,0,0,"support","low","left"
"13082",1654,0.1,0.88,7,282,4,0,0,"support","low","left"
"13083",1655,0.1,0.89,7,253,4,0,0,"support","low","left"
"13084",1656,0.09,0.9,6,256,4,0,0,"support","low","left"
"13085",1657,0.84,0.85,5,260,5,0,0,"support","low","left"
"13086",1658,0.11,0.86,6,245,4,0,0,"support","low","left"
"13087",1659,0.81,0.97,5,230,5,0,0,"support","low","left"
"13088",1660,0.77,0.85,5,276,5,0,0,"support","medium","left"
"13089",1661,0.42,0.47,2,137,3,0,0,"support","medium","left"
"13090",1662,0.36,0.56,2,140,3,0,0,"technical","medium","left"
"13091",1663,0.81,0.83,5,269,6,0,0,"technical","medium","left"
"13092",1664,0.37,0.46,2,130,3,1,0,"technical","medium","left"
"13093",1665,0.1,0.96,6,264,4,0,0,"management","medium","left"
"13094",1666,0.14,0.55,6,175,5,0,0,"IT","medium","left"
"13095",1667,0.41,0.51,2,159,3,0,0,"IT","medium","left"
"13096",1668,0.44,0.55,2,128,3,0,1,"IT","medium","left"
"13097",1669,0.82,0.94,5,232,5,1,0,"IT","medium","left"
"13098",1670,0.67,0.54,3,166,5,0,0,"IT","medium","left"
"13099",1671,0.44,0.57,2,141,3,0,0,"product_mng","medium","left"
"13100",1672,0.42,0.54,2,143,3,0,0,"product_mng","high","left"
"13101",1673,0.84,0.83,4,239,5,0,0,"product_mng","low","left"
"13102",1674,0.86,1,4,232,5,0,0,"product_mng","medium","left"
"13103",1675,0.56,0.86,5,252,2,0,0,"IT","medium","left"
"13104",1676,0.09,0.93,6,255,4,0,0,"RandD","medium","left"
"13105",1677,0.1,0.81,7,270,4,0,0,"RandD","medium","left"
"13106",1678,0.39,0.54,2,149,3,0,0,"RandD","low","left"
"13107",1679,0.75,0.89,5,276,5,0,0,"RandD","low","left"
"13108",1680,0.43,0.55,2,159,3,0,0,"RandD","low","left"
"13109",1681,0.09,0.96,7,274,5,0,0,"marketing","low","left"
"13110",1682,0.83,0.94,4,264,5,0,0,"sales","low","left"
"13111",1683,0.59,1,3,156,4,0,0,"accounting","low","left"
"13112",1684,0.44,0.54,2,135,3,0,0,"support","low","left"
"13113",1685,0.38,0.49,2,128,3,0,0,"technical","low","left"
"13114",1686,0.76,0.98,5,242,5,0,0,"management","low","left"
"13115",1687,0.22,0.86,4,293,3,0,0,"marketing","low","left"
"13116",1688,0.4,0.46,2,141,3,0,0,"marketing","low","left"
"13117",1689,0.41,0.48,2,155,3,0,0,"marketing","low","left"
"13118",1690,0.38,0.51,2,141,3,0,0,"sales","low","left"
"13119",1691,0.45,0.68,4,212,4,1,0,"sales","low","left"
"13120",1692,0.39,0.56,2,160,3,0,0,"sales","low","left"
"13121",1693,0.45,0.47,2,150,3,0,0,"sales","low","left"
"13122",1694,0.42,0.53,2,132,3,1,0,"sales","low","left"
"13123",1695,0.1,0.87,6,248,4,0,0,"sales","low","left"
"13124",1696,0.78,0.96,5,272,5,0,0,"sales","low","left"
"13125",1697,0.39,0.56,2,160,3,0,0,"sales","low","left"
"13126",1698,0.1,0.87,7,299,4,0,0,"sales","low","left"
"13127",1699,0.1,0.8,6,292,4,0,0,"sales","medium","left"
"13128",1700,0.43,0.46,2,126,3,0,0,"sales","medium","left"
"13129",1701,0.38,0.45,2,132,3,0,0,"sales","medium","left"
"13130",1702,0.09,0.77,6,282,5,0,0,"sales","medium","left"
"13131",1703,0.39,0.53,2,141,3,0,0,"sales","medium","left"
"13132",1704,0.73,0.99,6,206,5,0,0,"sales","medium","left"
"13133",1705,0.38,0.49,2,140,3,0,0,"sales","medium","left"
"13134",1706,0.1,0.91,6,255,4,0,0,"sales","medium","left"
"13135",1707,0.41,0.54,2,133,3,0,0,"sales","medium","left"
"13136",1708,0.43,0.51,2,131,3,0,0,"sales","medium","left"
"13137",1709,0.79,0.96,4,257,5,0,0,"accounting","medium","left"
"13138",1710,0.1,0.81,6,269,4,0,0,"accounting","medium","left"
"13139",1711,0.11,0.97,6,254,4,0,0,"accounting","high","left"
"13140",1712,0.42,0.5,2,143,3,0,0,"hr","low","left"
"13141",1713,0.36,0.51,2,157,3,0,0,"hr","medium","left"
"13142",1714,0.63,0.93,5,163,3,0,0,"hr","medium","left"
"13143",1715,0.41,0.56,2,133,3,0,0,"hr","medium","left"
"13144",1716,0.36,0.46,2,157,3,0,0,"technical","medium","left"
"13145",1717,0.1,0.9,6,301,5,0,0,"technical","low","left"
"13146",1718,0.11,0.96,6,310,4,0,0,"technical","low","left"
"13147",1719,0.44,0.54,2,133,3,0,0,"technical","low","left"
"13148",1720,0.77,0.96,5,249,6,0,0,"technical","low","left"
"13149",1721,0.91,1,4,251,6,0,0,"technical","low","left"
"13150",1722,0.26,0.46,2,242,3,0,0,"technical","low","left"
"13151",1723,0.81,0.93,5,265,6,0,0,"technical","low","left"
"13152",1724,0.11,0.87,6,280,4,0,0,"technical","low","left"
"13153",1725,0.92,0.89,4,241,5,0,0,"technical","low","left"
"13154",1726,0.1,0.86,5,253,4,0,0,"technical","low","left"
"13155",1727,0.45,0.51,2,137,3,0,0,"support","low","left"
"13156",1728,0.11,0.94,6,266,4,0,0,"support","low","left"
"13157",1729,0.23,0.7,5,168,4,0,0,"support","low","left"
"13158",1730,0.86,0.95,4,270,5,0,0,"support","low","left"
"13159",1731,0.44,0.55,2,141,3,0,0,"support","low","left"
"13160",1732,0.41,0.56,2,133,3,0,0,"support","low","left"
"13161",1733,0.84,0.97,5,256,5,0,0,"support","low","left"
"13162",1734,0.42,0.52,2,160,3,0,0,"support","low","left"
"13163",1735,0.11,0.88,7,275,4,0,0,"support","low","left"
"13164",1736,0.38,0.46,2,160,3,0,0,"support","low","left"
"13165",1737,0.11,0.96,7,244,4,0,0,"support","low","left"
"13166",1738,0.1,0.83,6,271,4,0,0,"technical","medium","left"
"13167",1739,0.86,0.88,5,268,5,0,0,"technical","medium","left"
"13168",1740,0.91,1,4,253,5,1,0,"technical","medium","left"
"13169",1741,0.37,0.53,2,140,3,0,0,"management","medium","left"
"13170",1742,0.46,0.5,2,146,3,0,0,"IT","medium","left"
"13171",1743,0.1,0.89,6,259,5,0,0,"IT","medium","left"
"13172",1744,0.37,0.46,2,127,3,0,0,"IT","medium","left"
"13173",1745,0.4,0.48,2,161,3,0,0,"IT","medium","left"
"13174",1746,0.09,0.78,6,260,4,0,0,"IT","medium","left"
"13175",1747,0.11,0.89,6,272,4,0,0,"product_mng","medium","left"
"13176",1748,0.39,0.48,2,159,3,0,0,"product_mng","medium","left"
"13177",1749,0.89,0.96,4,219,6,0,0,"product_mng","medium","left"
"13178",1750,0.09,0.91,6,243,4,0,0,"product_mng","high","left"
"13179",1751,0.88,0.97,4,255,5,1,0,"IT","low","left"
"13180",1752,0.11,0.9,7,245,4,0,0,"RandD","medium","left"
"13181",1753,0.1,0.95,6,264,5,0,0,"RandD","medium","left"
"13182",1754,0.91,1,4,245,6,0,0,"RandD","medium","left"
"13183",1755,0.44,0.52,2,137,3,0,0,"RandD","medium","left"
"13184",1756,0.63,0.76,2,157,4,0,0,"RandD","low","left"
"13185",1757,0.1,0.87,7,247,4,0,0,"marketing","low","left"
"13186",1758,0.36,0.51,2,144,3,0,0,"sales","low","left"
"13187",1759,0.45,0.51,2,149,3,0,0,"accounting","low","left"
"13188",1760,0.73,1,5,253,6,0,0,"support","low","left"
"13189",1761,0.37,0.55,2,140,3,0,0,"technical","low","left"
"13190",1762,0.09,0.85,7,307,4,0,0,"management","low","left"
"13191",1763,0.41,0.71,3,205,4,0,0,"marketing","low","left"
"13192",1764,0.72,1,5,234,5,0,0,"marketing","low","left"
"13193",1765,0.36,0.54,2,127,3,0,0,"marketing","low","left"
"13194",1766,0.9,1,4,229,5,0,0,"sales","low","left"
"13195",1767,0.44,0.56,2,141,3,0,0,"sales","low","left"
"13196",1768,0.78,0.95,4,260,5,0,0,"sales","low","left"
"13197",1769,0.37,0.52,2,141,3,0,0,"sales","low","left"
"13198",1770,0.4,0.47,2,144,3,1,0,"sales","low","left"
"13199",1771,0.84,1,5,250,5,0,0,"sales","low","left"
"13200",1772,0.09,0.86,6,245,4,0,0,"sales","low","left"
"13201",1773,0.83,0.93,4,269,5,0,0,"sales","low","left"
"13202",1774,0.11,0.87,6,273,4,0,0,"sales","low","left"
"13203",1775,0.37,0.5,2,142,3,0,0,"sales","low","left"
"13204",1776,0.09,0.93,6,273,5,0,0,"sales","low","left"
"13205",1777,0.43,0.47,2,248,2,0,0,"sales","medium","left"
"13206",1778,0.39,0.56,2,147,3,0,0,"sales","medium","left"
"13207",1779,0.85,0.9,2,168,2,0,0,"sales","medium","left"
"13208",1780,0.38,0.52,2,128,3,0,0,"sales","medium","left"
"13209",1781,0.76,0.84,5,227,5,1,0,"sales","medium","left"
"13210",1782,0.44,0.51,2,135,3,0,0,"sales","medium","left"
"13211",1783,0.73,1,4,268,5,0,0,"sales","medium","left"
"13212",1784,0.43,0.53,2,136,3,0,0,"sales","medium","left"
"13213",1785,0.43,0.51,2,149,3,1,0,"accounting","medium","left"
"13214",1786,0.09,0.96,7,264,4,0,0,"accounting","medium","left"
"13215",1787,0.43,0.53,2,143,3,0,0,"accounting","medium","left"
"13216",1788,0.45,0.57,2,138,3,0,0,"hr","medium","left"
"13217",1789,0.42,0.48,2,146,3,0,0,"hr","high","left"
"13218",1790,0.41,0.46,2,150,3,1,0,"hr","low","left"
"13219",1791,0.44,0.55,2,156,3,0,0,"hr","medium","left"
"13220",1792,0.09,0.92,7,245,4,0,0,"technical","medium","left"
"13221",1793,0.41,0.51,2,156,3,0,0,"technical","medium","left"
"13222",1794,0.43,0.51,2,143,3,0,0,"technical","medium","left"
"13223",1795,0.38,0.51,2,159,3,0,0,"technical","low","left"
"13224",1796,0.85,0.96,4,217,5,0,0,"technical","low","left"
"13225",1797,0.88,0.91,4,234,6,0,0,"technical","low","left"
"13226",1798,0.44,0.46,2,138,3,0,0,"technical","low","left"
"13227",1799,0.11,0.92,7,265,4,0,0,"technical","low","left"
"13228",1800,0.38,0.5,2,145,3,0,0,"technical","low","left"
"13229",1801,0.09,0.78,6,263,4,0,0,"technical","low","left"
"13230",1802,0.11,0.79,6,264,4,0,0,"technical","low","left"
"13231",1803,0.11,0.88,6,253,4,0,0,"support","low","left"
"13232",1804,0.44,0.48,2,155,3,0,0,"support","low","left"
"13233",1805,0.38,0.51,2,137,3,0,0,"support","low","left"
"13234",1806,0.1,0.87,6,254,5,0,0,"support","low","left"
"13235",1807,0.45,0.57,2,143,3,0,0,"support","low","left"
"13236",1808,0.11,0.94,7,280,5,0,0,"support","low","left"
"13237",1809,0.36,0.48,2,136,3,0,0,"support","low","left"
"13238",1810,0.72,0.95,5,271,5,0,0,"support","low","left"
"13239",1811,0.43,0.48,2,157,3,0,0,"support","low","left"
"13240",1812,0.45,0.5,2,150,3,0,0,"support","low","left"
"13241",1813,0.4,0.53,2,127,3,0,0,"support","low","left"
"13242",1814,0.1,0.81,6,271,4,0,0,"technical","low","left"
"13243",1815,0.83,0.93,5,257,5,0,0,"technical","low","left"
"13244",1816,0.11,0.8,7,305,4,0,0,"technical","medium","left"
"13245",1817,0.43,0.5,2,152,3,0,0,"management","medium","left"
"13246",1818,0.38,0.5,2,144,3,0,0,"IT","medium","left"
"13247",1819,0.83,1,5,269,5,0,0,"IT","medium","left"
"13248",1820,0.11,0.82,7,285,4,0,0,"IT","medium","left"
"13249",1821,0.43,0.52,2,136,3,0,0,"IT","medium","left"
"13250",1822,0.11,0.88,6,294,4,0,0,"IT","medium","left"
"13251",1823,0.43,0.46,2,157,3,0,0,"product_mng","medium","left"
"13252",1824,0.1,0.89,6,280,4,0,0,"product_mng","medium","left"
"13253",1825,0.44,0.51,2,152,3,0,0,"product_mng","medium","left"
"13254",1826,0.82,0.91,5,276,6,0,0,"product_mng","medium","left"
"13255",1827,0.1,0.86,6,247,4,0,0,"IT","medium","left"
"13256",1828,0.1,0.95,5,286,4,0,0,"RandD","high","left"
"13257",1829,0.3,0.89,5,257,5,0,0,"RandD","low","left"
"13258",1830,0.1,0.93,6,258,4,0,0,"RandD","medium","left"
"13259",1831,0.39,0.5,2,151,3,0,0,"RandD","medium","left"
"13260",1832,0.14,0.47,4,175,2,0,0,"RandD","medium","left"
"13261",1833,0.82,0.92,4,252,5,0,0,"marketing","medium","left"
"13262",1834,0.1,0.85,6,266,4,0,0,"sales","low","left"
"13263",1835,0.09,0.9,6,295,4,0,0,"accounting","low","left"
"13264",1836,0.54,0.83,6,165,6,0,0,"support","low","left"
"13265",1837,0.61,0.58,2,264,4,0,0,"technical","low","left"
"13266",1838,0.1,0.79,6,275,4,0,0,"management","low","left"
"13267",1839,0.1,0.9,6,299,4,0,0,"marketing","low","left"
"13268",1840,0.36,0.49,2,147,3,0,0,"marketing","low","left"
"13269",1841,0.1,0.97,7,306,4,0,0,"marketing","low","left"
"13270",1842,0.84,1,5,242,5,0,0,"sales","low","left"
"13271",1843,0.38,0.51,2,159,3,0,0,"sales","low","left"
"13272",1844,0.41,0.49,2,147,3,0,0,"sales","low","left"
"13273",1845,0.37,0.51,2,154,3,1,0,"sales","low","left"
"13274",1846,0.43,0.56,2,129,3,0,0,"sales","low","left"
"13275",1847,0.46,0.53,2,161,3,0,0,"sales","low","left"
"13276",1848,0.09,0.84,6,269,4,0,0,"sales","low","left"
"13277",1849,0.78,0.86,5,274,5,0,0,"sales","low","left"
"13278",1850,0.45,0.53,2,159,3,0,0,"sales","low","left"
"13279",1851,0.42,0.47,2,135,3,0,0,"sales","low","left"
"13280",1852,0.46,0.53,2,147,3,0,0,"sales","low","left"
"13281",1853,0.39,0.49,2,142,3,0,0,"sales","low","left"
"13282",1854,0.36,0.51,2,130,3,0,0,"sales","low","left"
"13283",1855,0.43,0.53,2,147,3,0,0,"sales","medium","left"
"13284",1856,0.85,0.87,5,246,5,1,0,"sales","medium","left"
"13285",1857,0.11,0.92,6,281,4,0,0,"sales","medium","left"
"13286",1858,0.11,0.9,6,253,4,0,0,"sales","medium","left"
"13287",1859,0.38,0.47,2,128,3,0,0,"sales","medium","left"
"13288",1860,0.43,0.57,2,129,3,0,0,"sales","medium","left"
"13289",1861,0.75,1,5,223,6,0,0,"accounting","medium","left"
"13290",1862,0.11,0.92,6,269,4,0,0,"accounting","medium","left"
"13291",1863,0.1,0.9,7,269,4,0,0,"accounting","medium","left"
"13292",1864,0.1,0.81,7,244,5,0,0,"hr","medium","left"
"13293",1865,0.37,0.5,2,154,3,0,0,"hr","medium","left"
"13294",1866,0.11,0.93,5,140,5,0,0,"hr","medium","left"
"13295",1867,0.45,0.46,2,159,3,0,0,"hr","high","left"
"13296",1868,0.44,0.48,2,158,3,0,0,"technical","low","left"
"13297",1869,0.44,0.56,2,133,3,0,0,"technical","medium","left"
"13298",1870,0.11,0.77,6,247,4,0,0,"technical","medium","left"
"13299",1871,0.79,0.93,5,268,5,0,0,"technical","medium","left"
"13300",1872,0.8,0.9,5,267,5,0,0,"technical","medium","left"
"13301",1873,0.1,0.87,7,251,5,0,0,"technical","low","left"
"13302",1874,0.09,0.93,6,279,4,0,0,"technical","low","left"
"13303",1875,0.7,0.84,6,161,4,0,0,"technical","low","left"
"13304",1876,0.72,0.84,4,256,5,0,0,"technical","low","left"
"13305",1877,0.11,0.8,6,304,4,0,0,"technical","low","left"
"13306",1878,0.39,0.51,2,137,3,0,0,"technical","low","left"
"13307",1879,0.4,0.49,2,144,3,0,0,"support","low","left"
"13308",1880,0.43,0.54,2,142,3,0,0,"support","low","left"
"13309",1881,0.76,0.87,5,262,5,0,0,"support","low","left"
"13310",1882,0.4,0.48,2,142,3,0,0,"support","low","left"
"13311",1883,0.09,0.89,6,282,4,0,0,"support","low","left"
"13312",1884,0.37,0.54,2,157,3,0,0,"support","low","left"
"13313",1885,0.87,0.91,5,228,5,0,0,"support","low","left"
"13314",1886,0.1,0.86,6,283,4,0,0,"support","low","left"
"13315",1887,0.11,0.86,6,286,4,0,0,"support","low","left"
"13316",1888,0.43,0.5,2,148,3,0,0,"support","low","left"
"13317",1889,0.1,0.81,6,245,4,0,0,"support","low","left"
"13318",1890,0.11,0.95,6,279,4,0,0,"technical","low","left"
"13319",1891,0.85,0.87,5,245,5,0,0,"technical","low","left"
"13320",1892,0.37,0.49,2,138,3,0,0,"technical","low","left"
"13321",1893,0.44,0.52,2,141,3,0,0,"management","low","left"
"13322",1894,0.1,0.83,7,302,5,0,0,"IT","medium","left"
"13323",1895,0.11,0.89,6,268,4,0,0,"IT","medium","left"
"13324",1896,0.87,0.88,5,240,5,0,0,"IT","medium","left"
"13325",1897,0.39,0.49,2,127,3,0,0,"IT","medium","left"
"13326",1898,0.1,0.94,7,264,4,0,0,"IT","medium","left"
"13327",1899,0.44,0.53,2,155,3,0,0,"product_mng","medium","left"
"13328",1900,0.4,0.49,2,143,3,0,0,"product_mng","medium","left"
"13329",1901,0.76,0.98,5,217,6,0,0,"product_mng","medium","left"
"13330",1902,0.46,0.55,2,147,3,0,0,"product_mng","medium","left"
"13331",1903,0.9,0.92,4,271,5,0,0,"IT","medium","left"
"13332",1904,0.85,0.87,4,273,5,0,0,"RandD","medium","left"
"13333",1905,0.1,0.78,5,285,4,1,0,"RandD","medium","left"
"13334",1906,0.43,0.49,2,131,3,0,0,"RandD","high","left"
"13335",1907,0.2,0.5,5,135,6,0,0,"RandD","low","left"
"13336",1908,0.81,0.92,5,239,5,0,0,"RandD","medium","left"
"13337",1909,0.83,0.85,5,237,5,0,0,"marketing","medium","left"
"13338",1910,0.14,0.75,4,277,5,1,0,"sales","medium","left"
"13339",1911,0.1,0.84,5,303,5,0,0,"accounting","medium","left"
"13340",1912,0.91,0.98,4,242,6,0,0,"support","low","left"
"13341",1913,0.37,0.57,2,158,3,0,0,"technical","low","left"
"13342",1914,0.42,0.57,2,147,3,1,0,"management","low","left"
"13343",1915,0.39,0.68,2,282,5,0,0,"marketing","low","left"
"13344",1916,0.39,0.54,2,154,3,0,0,"marketing","low","left"
"13345",1917,0.44,0.52,2,149,3,0,0,"marketing","low","left"
"13346",1918,0.37,0.45,2,149,3,0,0,"sales","low","left"
"13347",1919,0.39,0.53,2,146,3,0,0,"sales","low","left"
"13348",1920,0.72,0.94,4,258,5,0,0,"sales","low","left"
"13349",1921,0.37,0.49,2,148,3,0,0,"sales","low","left"
"13350",1922,0.82,0.94,5,236,5,0,0,"sales","low","left"
"13351",1923,0.42,0.52,2,134,3,0,0,"sales","low","left"
"13352",1924,0.59,1,2,155,5,0,0,"sales","low","left"
"13353",1925,0.82,0.86,5,257,5,0,0,"sales","low","left"
"13354",1926,0.73,0.97,6,189,2,0,0,"sales","low","left"
"13355",1927,0.78,0.66,3,164,3,0,0,"sales","low","left"
"13356",1928,0.09,0.95,6,271,4,0,0,"sales","low","left"
"13357",1929,0.1,0.97,6,280,4,0,0,"sales","low","left"
"13358",1930,0.45,0.46,2,149,3,0,0,"sales","low","left"
"13359",1931,0.83,0.81,5,219,5,0,0,"sales","low","left"
"13360",1932,0.43,0.51,2,128,3,0,0,"sales","low","left"
"13361",1933,0.4,0.47,2,128,3,0,0,"sales","medium","left"
"13362",1934,0.43,0.46,2,157,3,0,0,"sales","medium","left"
"13363",1935,0.78,0.93,4,225,5,0,0,"sales","medium","left"
"13364",1936,0.39,0.45,2,140,3,0,0,"sales","medium","left"
"13365",1937,0.11,0.97,6,310,4,0,0,"accounting","medium","left"
"13366",1938,0.36,0.52,2,143,3,0,0,"accounting","medium","left"
"13367",1939,0.36,0.54,2,153,3,0,0,"accounting","medium","left"
"13368",1940,0.1,0.79,7,310,4,0,0,"hr","medium","left"
"13369",1941,0.4,0.47,2,136,3,0,0,"hr","medium","left"
"13370",1942,0.81,0.85,4,251,6,0,0,"hr","medium","left"
"13371",1943,0.4,0.47,2,144,3,0,0,"hr","medium","left"
"13372",1944,0.09,0.93,6,296,4,0,0,"technical","medium","left"
"13373",1945,0.76,0.89,5,238,5,0,0,"technical","high","left"
"13374",1946,0.73,0.93,5,162,4,0,0,"technical","low","left"
"13375",1947,0.38,0.49,2,137,3,0,0,"technical","medium","left"
"13376",1948,0.72,0.84,5,257,5,0,0,"technical","medium","left"
"13377",1949,0.4,0.56,2,148,3,0,0,"technical","medium","left"
"13378",1950,0.91,0.99,5,254,5,0,0,"technical","medium","left"
"13379",1951,0.85,0.85,4,247,6,0,0,"technical","low","left"
"13380",1952,0.9,0.7,5,206,4,0,0,"technical","low","left"
"13381",1953,0.46,0.55,2,145,3,0,0,"technical","low","left"
"13382",1954,0.43,0.57,2,159,3,1,0,"technical","low","left"
"13383",1955,0.89,0.88,5,228,5,1,0,"support","low","left"
"13384",1956,0.09,0.81,6,257,4,0,0,"support","low","left"
"13385",1957,0.4,0.48,2,155,3,0,0,"support","low","left"
"13386",1958,0.76,0.83,6,293,6,0,0,"support","low","left"
"13387",1959,0.4,0.57,2,151,3,0,0,"support","low","left"
"13388",1960,0.37,0.48,2,160,3,0,0,"support","low","left"
"13389",1961,0.37,0.53,2,143,3,0,0,"support","low","left"
"13390",1962,0.11,0.96,6,280,4,0,0,"support","low","left"
"13391",1963,0.37,0.52,2,158,3,0,0,"support","low","left"
"13392",1964,0.09,0.89,7,310,4,0,0,"support","low","left"
"13393",1965,0.88,0.86,5,258,5,0,0,"support","low","left"
"13394",1966,0.84,0.94,5,262,5,0,0,"technical","low","left"
"13395",1967,0.1,0.98,6,265,4,0,0,"technical","low","left"
"13396",1968,0.41,0.47,2,143,3,1,0,"technical","low","left"
"13397",1969,0.84,0.91,5,232,6,0,0,"management","low","left"
"13398",1970,0.41,0.55,2,161,3,0,0,"IT","low","left"
"13399",1971,0.53,0.76,5,132,6,0,0,"IT","low","left"
"13400",1972,0.42,0.47,2,139,3,1,0,"IT","medium","left"
"13401",1973,0.36,0.5,2,131,3,0,0,"IT","medium","left"
"13402",1974,0.38,0.52,2,161,3,0,0,"IT","medium","left"
"13403",1975,0.36,0.48,2,152,3,0,0,"product_mng","medium","left"
"13404",1976,0.46,0.54,2,138,3,0,0,"product_mng","medium","left"
"13405",1977,0.37,0.47,2,159,3,1,0,"product_mng","medium","left"
"13406",1978,0.42,0.49,2,153,3,0,0,"product_mng","medium","left"
"13407",1979,0.44,0.56,2,156,3,0,0,"IT","medium","left"
"13408",1980,0.92,0.82,5,265,5,0,0,"RandD","medium","left"
"13409",1981,0.1,0.79,6,301,5,0,0,"RandD","medium","left"
"13410",1982,0.76,1,4,220,6,0,0,"RandD","medium","left"
"13411",1983,0.11,0.79,6,247,4,0,0,"RandD","medium","left"
"13412",1984,0.43,0.48,2,136,3,0,0,"RandD","high","left"
"13413",1985,0.4,0.49,2,160,3,0,0,"marketing","low","left"
"13414",1986,0.11,0.84,7,310,4,0,0,"sales","medium","left"
"13415",1987,0.84,0.82,5,240,5,0,0,"accounting","medium","left"
"13416",1988,0.84,0.84,5,238,5,0,0,"support","medium","left"
"13417",1989,0.51,0.6,7,243,5,0,0,"technical","medium","left"
"13418",1990,0.66,0.91,5,248,4,0,0,"management","low","left"
"13419",1991,0.42,0.56,2,137,3,0,0,"marketing","low","left"
"13420",1992,0.38,0.49,2,155,3,0,0,"marketing","low","left"
"13421",1993,0.15,0.63,7,229,3,0,0,"marketing","low","left"
"13422",1994,0.38,0.53,2,140,3,0,0,"sales","low","left"
"13423",1995,0.43,0.54,2,156,3,0,0,"sales","low","left"
"13424",1996,0.37,0.57,2,147,3,0,0,"sales","low","left"
"13425",1997,0.11,0.92,7,293,4,0,0,"sales","low","left"
"13426",1998,0.41,0.53,2,157,3,0,0,"sales","low","left"
"13427",1999,0.84,0.96,4,247,5,0,0,"sales","low","left"
"13428",2000,0.4,0.51,2,148,3,0,0,"sales","low","left"
"13429",12001,0.38,0.53,2,157,3,0,0,"sales","low","left"
"13430",12002,0.8,0.86,5,262,6,0,0,"sales","medium","left"
"13431",12003,0.11,0.88,7,272,4,0,0,"sales","medium","left"
"13432",12004,0.72,0.87,5,223,5,0,0,"sales","low","left"
"13433",12005,0.37,0.52,2,159,3,0,0,"sales","low","left"
"13434",12006,0.41,0.5,2,153,3,0,0,"sales","low","left"
"13435",12007,0.1,0.77,6,247,4,0,0,"sales","low","left"
"13436",12008,0.92,0.85,5,259,5,0,0,"sales","low","left"
"13437",12009,0.89,1,5,224,5,0,0,"sales","low","left"
"13438",12010,0.42,0.53,2,142,3,0,0,"sales","low","left"
"13439",12011,0.45,0.54,2,135,3,0,0,"sales","low","left"
"13440",12012,0.11,0.81,6,305,4,0,0,"sales","low","left"
"13441",12013,0.84,0.92,4,234,5,0,0,"sales","low","left"
"13442",12014,0.41,0.55,2,148,3,0,0,"sales","low","left"
"13443",12015,0.36,0.56,2,137,3,0,0,"sales","low","left"
"13444",12016,0.38,0.54,2,143,3,0,0,"sales","low","left"
"13445",12017,0.45,0.47,2,160,3,0,0,"sales","low","left"
"13446",12018,0.78,0.99,4,255,6,0,0,"sales","low","left"
"13447",12019,0.45,0.51,2,160,3,1,1,"sales","low","left"
"13448",12020,0.76,0.89,5,262,5,0,0,"sales","low","left"
"13449",12021,0.11,0.83,6,282,4,0,0,"sales","low","left"
"13450",12022,0.38,0.55,2,147,3,0,0,"sales","low","left"
"13451",12023,0.09,0.95,6,304,4,0,0,"sales","low","left"
"13452",12024,0.46,0.57,2,139,3,0,0,"sales","low","left"
"13453",12025,0.4,0.53,2,158,3,0,0,"sales","low","left"
"13454",12026,0.89,0.92,5,242,5,0,0,"sales","low","left"
"13455",12027,0.82,0.87,4,239,5,0,0,"sales","low","left"
"13456",12028,0.4,0.49,2,135,3,0,0,"sales","low","left"
"13457",12029,0.41,0.46,2,128,3,0,0,"accounting","low","left"
"13458",12030,0.38,0.5,2,132,3,0,0,"accounting","low","left"
"13459",12031,0.09,0.62,6,294,4,0,0,"accounting","low","left"
"13460",12032,0.45,0.57,2,134,3,0,0,"hr","low","left"
"13461",12033,0.4,0.51,2,145,3,0,0,"hr","low","left"
"13462",12034,0.45,0.55,2,140,3,0,0,"hr","low","left"
"13463",12035,0.84,0.87,4,246,6,0,0,"hr","low","left"
"13464",12036,0.1,0.94,6,255,4,0,0,"technical","low","left"
"13465",12037,0.38,0.46,2,137,3,0,0,"technical","low","left"
"13466",12038,0.45,0.5,2,126,3,0,0,"technical","low","left"
"13467",12039,0.11,0.89,6,306,4,0,0,"technical","low","left"
"13468",12040,0.41,0.54,2,152,3,0,0,"technical","low","left"
"13469",12041,0.87,0.88,5,269,5,0,0,"technical","low","left"
"13470",12042,0.45,0.48,2,158,3,0,0,"technical","low","left"
"13471",12043,0.4,0.46,2,127,3,0,0,"technical","low","left"
"13472",12044,0.1,0.8,7,281,4,0,0,"technical","low","left"
"13473",12045,0.09,0.89,6,276,4,0,0,"technical","low","left"
"13474",12046,0.84,0.74,3,182,4,0,0,"technical","low","left"
"13475",12047,0.4,0.55,2,147,3,0,0,"support","low","left"
"13476",12048,0.57,0.7,3,273,6,0,0,"support","low","left"
"13477",12049,0.4,0.54,2,148,3,0,0,"support","low","left"
"13478",12050,0.43,0.47,2,147,3,0,0,"support","low","left"
"13479",12051,0.13,0.78,6,152,2,0,0,"support","low","left"
"13480",12052,0.44,0.55,2,135,3,0,0,"support","low","left"
"13481",12053,0.38,0.55,2,134,3,0,0,"support","low","left"
"13482",12054,0.39,0.54,2,132,3,0,0,"support","low","left"
"13483",12055,0.1,0.92,7,307,4,0,0,"support","low","left"
"13484",12056,0.37,0.46,2,140,3,0,0,"support","low","left"
"13485",12057,0.11,0.94,7,255,4,0,0,"support","low","left"
"13486",12058,0.1,0.81,6,309,4,0,0,"technical","low","left"
"13487",12059,0.38,0.54,2,128,3,0,0,"technical","low","left"
"13488",12060,0.85,1,4,225,5,0,0,"technical","low","left"
"13489",12061,0.85,0.91,5,226,5,0,0,"management","medium","left"
"13490",12062,0.11,0.93,7,308,4,0,0,"IT","medium","left"
"13491",12063,0.1,0.95,6,244,5,0,0,"IT","medium","left"
"13492",12064,0.36,0.56,2,132,3,0,0,"IT","medium","left"
"13493",12065,0.11,0.94,6,286,4,0,0,"IT","medium","left"
"13494",12066,0.81,0.7,6,161,4,0,0,"IT","medium","left"
"13495",12067,0.43,0.54,2,153,3,0,0,"product_mng","medium","left"
"13496",12068,0.9,0.98,4,264,6,0,0,"product_mng","medium","left"
"13497",12069,0.76,0.86,5,223,5,1,0,"product_mng","medium","left"
"13498",12070,0.43,0.5,2,135,3,0,0,"product_mng","medium","left"
"13499",12071,0.74,0.99,2,277,3,0,0,"IT","medium","left"
"13500",12072,0.09,0.77,5,275,4,0,0,"product_mng","medium","left"
"13501",12073,0.45,0.49,2,149,3,0,0,"product_mng","high","left"
"13502",12074,0.09,0.87,7,295,4,0,0,"product_mng","low","left"
"13503",12075,0.11,0.97,6,277,4,0,0,"product_mng","medium","left"
"13504",12076,0.11,0.79,7,306,4,0,0,"product_mng","medium","left"
"13505",12077,0.1,0.83,6,295,4,0,0,"product_mng","medium","left"
"13506",12078,0.4,0.54,2,137,3,0,0,"marketing","medium","left"
"13507",12079,0.43,0.56,2,157,3,0,0,"sales","low","left"
"13508",12080,0.39,0.56,2,142,3,0,0,"accounting","low","left"
"13509",12081,0.45,0.54,2,140,3,0,0,"support","low","left"
"13510",12082,0.38,0.49,2,151,3,0,0,"technical","low","left"
"13511",12083,0.79,0.59,4,139,3,0,1,"management","low","left"
"13512",12084,0.84,0.85,4,249,6,0,0,"marketing","low","left"
"13513",12085,0.11,0.77,6,291,4,0,0,"marketing","low","left"
"13514",12086,0.11,0.87,6,305,4,0,0,"marketing","low","left"
"13515",12087,0.17,0.84,5,232,3,0,0,"sales","low","left"
"13516",12088,0.44,0.45,2,132,3,0,0,"sales","low","left"
"13517",12089,0.37,0.57,2,130,3,0,0,"sales","low","left"
"13518",12090,0.1,0.79,6,291,4,0,0,"sales","low","left"
"13519",12091,0.4,0.5,2,130,3,0,0,"sales","low","left"
"13520",12092,0.89,1,5,246,5,0,0,"sales","low","left"
"13521",12093,0.42,0.48,2,143,3,0,0,"sales","low","left"
"13522",12094,0.46,0.55,2,129,3,0,0,"sales","low","left"
"13523",12095,0.09,0.83,6,255,4,0,0,"sales","low","left"
"13524",12096,0.37,0.51,2,155,3,0,0,"sales","low","left"
"13525",12097,0.1,0.77,6,265,4,0,0,"sales","low","left"
"13526",12098,0.1,0.84,6,279,4,0,0,"sales","low","left"
"13527",12099,0.11,0.97,6,284,4,0,0,"sales","low","left"
"13528",12100,0.9,1,5,221,6,0,0,"sales","medium","left"
"13529",12101,0.38,0.52,2,154,3,0,0,"sales","medium","left"
"13530",12102,0.36,0.52,2,147,3,0,0,"sales","medium","left"
"13531",12103,0.42,0.46,2,150,3,0,0,"sales","medium","left"
"13532",12104,0.09,0.94,7,267,4,0,0,"sales","medium","left"
"13533",12105,0.43,0.52,2,158,3,0,0,"sales","medium","left"
"13534",12106,0.24,0.46,7,224,5,0,0,"accounting","medium","left"
"13535",12107,0.91,1,4,257,5,0,0,"accounting","medium","left"
"13536",12108,0.44,0.5,2,148,3,0,0,"accounting","medium","left"
"13537",12109,0.71,0.87,3,177,4,0,0,"hr","medium","left"
"13538",12110,0.4,0.49,2,155,3,0,0,"hr","medium","left"
"13539",12111,0.43,0.47,2,144,3,0,0,"hr","medium","left"
"13540",12112,0.09,0.85,6,289,4,0,0,"hr","high","left"
"13541",12113,0.43,0.52,2,160,3,0,0,"technical","low","left"
"13542",12114,0.9,0.96,4,258,5,0,0,"technical","medium","left"
"13543",12115,0.84,1,5,234,5,0,0,"technical","medium","left"
"13544",12116,0.37,0.48,2,137,3,0,0,"technical","medium","left"
"13545",12117,0.86,0.68,5,263,2,0,0,"technical","medium","left"
"13546",12118,0.11,0.84,6,251,4,0,0,"technical","low","left"
"13547",12119,0.37,0.57,2,133,3,0,0,"technical","low","left"
"13548",12120,0.4,0.46,2,132,3,0,0,"technical","low","left"
"13549",12121,0.14,0.62,4,158,4,1,0,"technical","low","left"
"13550",12122,0.4,0.46,2,135,3,0,0,"technical","low","left"
"13551",12123,0.75,1,4,216,6,0,0,"technical","low","left"
"13552",12124,0.11,0.84,6,300,5,1,0,"support","low","left"
"13553",12125,0.46,0.49,2,138,3,0,0,"support","low","left"
"13554",12126,0.11,0.92,6,260,4,0,0,"support","low","left"
"13555",12127,0.38,0.49,2,132,3,0,0,"support","low","left"
"13556",12128,0.7,0.89,3,183,5,0,0,"support","low","left"
"13557",12129,0.09,0.82,6,250,4,0,0,"support","low","left"
"13558",12130,0.37,0.45,2,151,3,0,0,"support","low","left"
"13559",12131,0.1,0.83,6,292,4,0,0,"support","low","left"
"13560",12132,0.38,0.57,2,140,3,0,0,"support","low","left"
"13561",12133,0.9,1,5,221,5,0,0,"support","low","left"
"13562",12134,0.44,0.51,2,138,3,0,0,"support","low","left"
"13563",12135,0.36,0.5,2,132,3,0,0,"technical","low","left"
"13564",12136,0.31,0.84,7,133,5,0,0,"technical","low","left"
"13565",12137,0.1,0.84,6,283,4,1,0,"technical","low","left"
"13566",12138,0.42,0.48,2,129,3,0,0,"management","low","left"
"13567",12139,0.74,1,4,249,5,0,0,"IT","low","left"
"13568",12140,0.73,0.87,5,257,5,0,0,"IT","low","left"
"13569",12141,0.09,0.96,6,245,4,0,0,"IT","low","left"
"13570",12142,0.45,0.53,2,155,3,0,0,"IT","low","left"
"13571",12143,0.11,0.8,6,256,4,0,0,"IT","low","left"
"13572",12144,0.37,0.47,2,152,3,0,0,"product_mng","low","left"
"13573",12145,0.84,0.99,4,267,5,0,0,"product_mng","low","left"
"13574",12146,0.41,0.46,2,151,3,0,0,"product_mng","low","left"
"13575",12147,0.76,0.92,4,239,5,0,0,"product_mng","low","left"
"13576",12148,0.11,0.87,6,306,4,0,0,"IT","low","left"
"13577",12149,0.84,0.88,4,263,5,1,0,"marketing","low","left"
"13578",12150,0.39,0.5,2,147,3,0,0,"marketing","low","left"
"13579",12151,0.11,0.91,6,278,4,0,0,"marketing","low","left"
"13580",12152,0.45,0.56,2,154,3,0,0,"marketing","low","left"
"13581",12153,0.37,0.52,2,143,3,0,0,"marketing","low","left"
"13582",12154,0.4,0.52,2,155,3,0,0,"marketing","low","left"
"13583",12155,0.39,0.48,2,160,3,0,0,"sales","low","left"
"13584",12156,0.11,0.8,6,304,4,0,0,"accounting","low","left"
"13585",12157,0.83,1,5,240,5,0,0,"support","low","left"
"13586",12158,0.11,0.92,6,305,4,0,0,"technical","low","left"
"13587",12159,0.39,0.5,2,136,3,0,0,"management","low","left"
"13588",12160,0.45,0.45,2,132,3,0,0,"marketing","low","left"
"13589",12161,0.1,0.95,7,301,4,0,0,"marketing","low","left"
"13590",12162,0.9,0.98,5,243,6,0,0,"marketing","low","left"
"13591",12163,0.45,0.51,2,147,3,0,0,"sales","low","left"
"13592",12164,0.79,0.89,5,239,5,0,0,"sales","low","left"
"13593",12165,0.9,0.99,5,260,5,0,0,"sales","low","left"
"13594",12166,0.11,0.84,7,296,4,0,0,"sales","low","left"
"13595",12167,0.43,0.55,2,129,3,0,0,"sales","low","left"
"13596",12168,0.31,0.54,5,132,5,0,0,"sales","low","left"
"13597",12169,0.32,0.5,2,135,5,0,0,"sales","low","left"
"13598",12170,0.45,0.57,2,158,3,0,0,"sales","low","left"
"13599",12171,0.81,0.99,4,259,5,0,0,"sales","low","left"
"13600",12172,0.41,0.46,2,160,3,0,1,"sales","low","left"
"13601",12173,0.11,0.78,7,278,4,0,0,"sales","low","left"
"13602",12174,0.1,0.88,6,284,4,0,0,"sales","low","left"
"13603",12175,0.7,0.53,2,274,4,0,0,"sales","low","left"
"13604",12176,0.54,0.74,4,164,2,0,0,"sales","low","left"
"13605",12177,0.41,0.48,2,148,3,0,0,"sales","low","left"
"13606",12178,0.38,0.5,2,140,3,0,0,"sales","medium","left"
"13607",12179,0.37,0.51,2,127,3,0,0,"sales","medium","left"
"13608",12180,0.11,0.85,6,308,5,0,0,"sales","medium","left"
"13609",12181,0.4,0.47,2,146,3,0,0,"sales","medium","left"
"13610",12182,0.1,0.84,6,261,4,0,0,"accounting","medium","left"
"13611",12183,0.89,0.99,5,257,5,0,0,"accounting","medium","left"
"13612",12184,0.11,0.8,6,285,4,0,0,"accounting","medium","left"
"13613",12185,0.36,0.55,2,141,3,0,0,"hr","medium","left"
"13614",12186,0.4,0.46,2,127,3,0,0,"hr","medium","left"
"13615",12187,0.09,0.85,6,297,4,0,0,"hr","medium","left"
"13616",12188,0.4,0.46,2,143,3,0,0,"hr","medium","left"
"13617",12189,0.37,0.55,2,152,3,0,0,"technical","medium","left"
"13618",12190,0.44,0.51,2,156,3,0,0,"technical","high","left"
"13619",12191,0.09,0.8,7,283,5,0,0,"technical","low","left"
"13620",12192,0.92,0.87,4,226,6,1,0,"technical","medium","left"
"13621",12193,0.74,0.91,4,232,5,0,0,"technical","medium","left"
"13622",12194,0.09,0.82,6,249,4,0,0,"technical","medium","left"
"13623",12195,0.89,0.95,4,275,5,0,0,"technical","medium","left"
"13624",12196,0.09,0.8,6,304,4,0,0,"technical","low","left"
"13625",12197,0.27,0.54,7,278,3,0,0,"technical","low","left"
"13626",12198,0.1,0.91,6,287,4,0,0,"technical","low","left"
"13627",12199,0.1,0.89,7,285,4,0,0,"technical","low","left"
"13628",12200,0.77,0.94,5,226,6,0,0,"support","low","left"
"13629",12201,0.9,0.82,5,259,5,0,0,"support","low","left"
"13630",12202,0.39,0.5,2,135,3,0,0,"support","low","left"
"13631",12203,0.76,1,5,219,5,0,0,"support","low","left"
"13632",12204,0.1,0.93,6,256,4,0,0,"support","low","left"
"13633",12205,0.87,0.9,5,254,6,0,0,"support","low","left"
"13634",12206,0.38,0.5,2,153,3,0,0,"support","low","left"
"13635",12207,0.77,0.99,5,228,5,0,0,"support","low","left"
"13636",12208,0.78,0.87,4,228,5,0,0,"support","low","left"
"13637",12209,0.44,0.5,2,128,3,0,0,"support","low","left"
"13638",12210,0.38,0.52,2,153,3,0,0,"support","low","left"
"13639",12211,0.43,0.46,2,156,3,0,0,"technical","low","left"
"13640",12212,0.39,0.5,4,294,3,0,0,"technical","low","left"
"13641",12213,0.88,1,5,219,5,0,0,"technical","low","left"
"13642",12214,0.45,0.46,2,153,3,0,0,"management","low","left"
"13643",12215,0.4,0.53,2,151,3,0,0,"IT","low","left"
"13644",12216,0.36,0.51,2,155,3,0,0,"IT","low","left"
"13645",12217,0.36,0.48,2,158,3,0,0,"IT","low","left"
"13646",12218,0.9,0.98,5,245,5,0,0,"IT","low","left"
"13647",12219,0.43,0.53,2,131,3,0,0,"IT","low","left"
"13648",12220,0.89,0.87,5,225,5,0,0,"product_mng","low","left"
"13649",12221,0.1,0.84,6,286,4,0,0,"product_mng","low","left"
"13650",12222,0.37,0.5,2,135,3,0,0,"product_mng","low","left"
"13651",12223,0.37,0.51,2,153,3,0,0,"product_mng","low","left"
"13652",12224,0.87,0.9,5,252,5,0,0,"IT","low","left"
"13653",12225,0.4,0.56,2,149,3,0,0,"accounting","low","left"
"13654",12226,0.9,0.97,4,258,5,0,0,"accounting","low","left"
"13655",12227,0.37,0.46,2,158,3,0,0,"hr","low","left"
"13656",12228,0.44,0.54,2,149,3,0,0,"hr","low","left"
"13657",12229,0.85,0.95,5,236,5,0,0,"hr","low","left"
"13658",12230,0.78,0.98,5,239,6,0,0,"marketing","low","left"
"13659",12231,0.42,0.47,2,159,3,0,0,"marketing","low","left"
"13660",12232,0.92,0.99,5,255,6,0,0,"sales","low","left"
"13661",12233,0.11,0.83,6,244,4,0,0,"accounting","low","left"
"13662",12234,0.42,0.56,2,134,3,0,0,"support","low","left"
"13663",12235,0.48,0.57,4,270,4,0,0,"technical","low","left"
"13664",12236,0.83,0.85,4,255,5,0,0,"management","low","left"
"13665",12237,0.4,0.53,2,151,3,0,0,"marketing","low","left"
"13666",12238,0.43,0.45,2,135,3,0,0,"marketing","low","left"
"13667",12239,0.43,0.53,2,146,3,0,0,"marketing","low","left"
"13668",12240,0.1,0.97,7,254,4,0,0,"sales","low","left"
"13669",12241,0.1,0.87,7,289,4,0,0,"sales","low","left"
"13670",12242,0.37,0.46,2,156,3,0,0,"sales","low","left"
"13671",12243,0.38,0.53,2,156,3,0,0,"sales","low","left"
"13672",12244,0.4,0.5,2,128,3,0,0,"sales","low","left"
"13673",12245,0.89,0.86,5,275,5,0,0,"sales","low","left"
"13674",12246,0.45,0.46,2,155,3,0,0,"sales","low","left"
"13675",12247,0.37,0.48,2,159,3,0,0,"sales","low","left"
"13676",12248,0.46,0.49,2,148,3,0,0,"sales","low","left"
"13677",12249,0.87,0.91,4,228,5,0,0,"sales","low","left"
"13678",12250,0.11,0.84,6,298,4,0,0,"sales","low","left"
"13679",12251,0.79,0.87,5,261,5,0,0,"sales","low","left"
"13680",12252,0.79,0.92,5,254,6,0,0,"sales","low","left"
"13681",12253,0.19,0.59,7,192,3,0,0,"sales","low","left"
"13682",12254,0.87,0.98,4,248,5,0,0,"sales","low","left"
"13683",12255,0.6,0.92,2,258,5,0,0,"sales","low","left"
"13684",12256,0.44,0.45,2,156,3,0,0,"sales","medium","left"
"13685",12257,0.11,0.81,6,266,4,1,0,"sales","medium","left"
"13686",12258,0.42,0.54,2,156,3,0,0,"sales","medium","left"
"13687",12259,0.88,0.88,5,232,5,1,0,"accounting","medium","left"
"13688",12260,0.11,0.84,6,287,4,0,0,"accounting","medium","left"
"13689",12261,0.46,0.46,2,154,3,0,0,"accounting","medium","left"
"13690",12262,0.82,0.97,5,263,5,0,0,"hr","medium","left"
"13691",12263,0.44,0.56,2,131,3,0,0,"hr","medium","left"
"13692",12264,0.11,0.78,6,260,4,0,0,"hr","medium","left"
"13693",12265,0.42,0.5,2,139,3,0,0,"hr","medium","left"
"13694",12266,0.84,0.93,4,251,5,0,0,"technical","medium","left"
"13695",12267,0.11,0.95,6,286,4,0,0,"technical","medium","left"
"13696",12268,0.45,0.53,2,129,3,0,0,"technical","high","left"
"13697",12269,0.38,0.56,2,156,3,0,0,"technical","low","left"
"13698",12270,0.38,0.86,6,139,6,0,0,"technical","medium","left"
"13699",12271,0.44,0.51,2,127,3,0,0,"technical","medium","left"
"13700",12272,0.11,0.84,6,251,4,0,0,"technical","medium","left"
"13701",12273,0.81,0.93,5,270,5,0,0,"technical","medium","left"
"13702",12274,0.09,0.96,6,296,4,0,0,"technical","low","left"
"13703",12275,0.11,0.9,6,254,4,0,0,"technical","low","left"
"13704",12276,0.81,0.95,5,238,6,0,0,"technical","low","left"
"13705",12277,0.1,0.97,6,267,4,1,0,"support","low","left"
"13706",12278,0.74,0.89,5,229,6,0,0,"support","low","left"
"13707",12279,0.09,0.78,6,254,4,0,0,"support","low","left"
"13708",12280,0.82,0.81,4,233,4,1,0,"support","low","left"
"13709",12281,0.1,0.98,6,268,4,0,0,"support","low","left"
"13710",12282,0.27,0.56,3,301,3,0,0,"support","low","left"
"13711",12283,0.83,0.92,5,267,6,0,0,"support","low","left"
"13712",12284,0.1,0.93,6,289,4,1,0,"support","low","left"
"13713",12285,0.38,0.47,2,144,3,0,0,"support","low","left"
"13714",12286,0.4,0.56,2,148,3,0,0,"support","low","left"
"13715",12287,0.11,0.83,6,306,4,0,0,"support","low","left"
"13716",12288,0.11,0.79,6,292,4,0,1,"technical","low","left"
"13717",12289,0.82,0.91,5,232,5,0,0,"technical","low","left"
"13718",12290,0.36,0.48,2,137,3,0,0,"technical","low","left"
"13719",12291,0.4,0.46,2,128,3,0,0,"management","low","left"
"13720",12292,0.87,0.84,5,231,5,0,0,"IT","low","left"
"13721",12293,0.41,0.49,2,146,3,0,0,"IT","low","left"
"13722",12294,0.11,0.91,6,308,4,1,0,"IT","low","left"
"13723",12295,0.1,0.93,6,253,4,0,0,"IT","medium","left"
"13724",12296,0.38,0.51,2,146,3,0,0,"IT","medium","left"
"13725",12297,0.39,0.55,2,156,3,0,0,"product_mng","medium","left"
"13726",12298,0.4,0.52,2,147,3,0,0,"product_mng","medium","left"
"13727",12299,0.45,0.48,2,136,3,0,0,"product_mng","medium","left"
"13728",12300,0.74,0.84,5,249,5,0,0,"product_mng","medium","left"
"13729",12301,0.45,0.55,2,151,3,0,0,"IT","medium","left"
"13730",12302,0.12,1,3,278,4,0,0,"RandD","medium","left"
"13731",12303,0.1,0.77,7,250,5,0,0,"RandD","medium","left"
"13732",12304,0.37,0.55,2,127,3,0,0,"RandD","medium","left"
"13733",12305,0.89,0.87,5,255,5,0,0,"RandD","medium","left"
"13734",12306,0.45,0.47,2,135,3,0,0,"RandD","medium","left"
"13735",12307,0.37,0.46,2,149,3,0,0,"marketing","high","left"
"13736",12308,0.11,0.81,5,287,4,0,0,"sales","low","left"
"13737",12309,0.41,0.48,2,145,3,0,0,"accounting","medium","left"
"13738",12310,0.1,0.94,6,285,4,0,0,"support","medium","left"
"13739",12311,0.1,0.93,7,305,4,0,0,"technical","medium","left"
"13740",12312,0.11,0.95,7,300,4,0,0,"management","medium","left"
"13741",12313,0.4,0.54,2,139,3,0,0,"marketing","low","left"
"13742",12314,0.41,0.49,2,130,3,0,0,"marketing","low","left"
"13743",12315,0.1,0.81,6,268,4,0,0,"marketing","low","left"
"13744",12316,0.73,0.86,4,245,6,0,0,"sales","low","left"
"13745",12317,0.43,0.47,2,135,3,0,0,"sales","low","left"
"13746",12318,0.37,0.46,2,153,3,0,0,"sales","low","left"
"13747",12319,0.11,0.94,6,276,4,0,0,"sales","low","left"
"13748",12320,0.4,0.46,2,130,3,0,0,"sales","low","left"
"13749",12321,0.41,0.54,2,153,3,1,0,"sales","low","left"
"13750",12322,0.82,0.84,5,244,5,0,0,"sales","low","left"
"13751",12323,0.61,0.47,2,253,3,0,0,"sales","low","left"
"13752",12324,0.11,0.91,7,287,4,0,0,"sales","low","left"
"13753",12325,0.37,0.45,2,131,3,0,0,"sales","low","left"
"13754",12326,0.41,0.52,2,135,3,0,0,"sales","low","left"
"13755",12327,0.37,0.52,2,157,3,0,0,"sales","low","left"
"13756",12328,0.88,0.99,5,262,6,0,0,"sales","low","left"
"13757",12329,0.1,0.85,6,266,4,0,0,"sales","low","left"
"13758",12330,0.44,0.48,2,148,3,0,0,"sales","low","left"
"13759",12331,0.38,0.57,2,140,3,0,0,"sales","low","left"
"13760",12332,0.11,0.85,7,302,4,0,0,"sales","low","left"
"13761",12333,0.09,0.98,6,271,4,0,0,"sales","low","left"
"13762",12334,0.45,0.52,2,145,3,0,0,"sales","medium","left"
"13763",12335,0.1,0.81,6,290,4,0,0,"accounting","medium","left"
"13764",12336,0.45,0.47,2,151,3,0,0,"accounting","medium","left"
"13765",12337,0.77,0.87,5,266,5,0,0,"accounting","medium","left"
"13766",12338,0.44,0.51,2,140,3,0,0,"hr","medium","left"
"13767",12339,0.39,0.5,2,142,3,0,0,"hr","medium","left"
"13768",12340,0.1,0.91,6,246,4,0,0,"hr","medium","left"
"13769",12341,0.09,0.89,7,308,5,0,0,"hr","medium","left"
"13770",12342,0.37,0.47,2,141,3,0,0,"technical","medium","left"
"13771",12343,0.9,1,5,232,5,0,0,"technical","medium","left"
"13772",12344,0.41,0.56,2,143,3,0,0,"technical","medium","left"
"13773",12345,0.37,0.52,2,155,3,0,0,"technical","medium","left"
"13774",12346,0.1,0.86,6,278,4,0,0,"technical","high","left"
"13775",12347,0.81,1,4,253,5,0,0,"technical","low","left"
"13776",12348,0.11,0.8,6,282,4,0,0,"technical","medium","left"
"13777",12349,0.11,0.84,7,264,4,0,0,"technical","medium","left"
"13778",12350,0.4,0.46,2,149,3,0,0,"technical","medium","left"
"13779",12351,0.09,0.8,6,304,5,0,0,"technical","medium","left"
"13780",12352,0.48,0.93,3,219,6,0,0,"technical","low","left"
"13781",12353,0.91,0.91,4,262,6,0,0,"support","low","left"
"13782",12354,0.43,0.57,2,135,3,0,0,"support","low","left"
"13783",12355,0.33,0.88,6,219,5,0,0,"support","low","left"
"13784",12356,0.41,0.57,2,136,3,0,0,"support","low","left"
"13785",12357,0.41,0.55,2,154,3,0,0,"support","low","left"
"13786",12358,0.37,0.54,2,149,3,0,0,"support","low","left"
"13787",12359,0.31,0.62,6,135,5,0,0,"support","low","left"
"13788",12360,0.09,0.91,6,275,4,0,0,"support","low","left"
"13789",12361,0.1,0.87,6,290,4,0,0,"support","low","left"
"13790",12362,0.76,0.9,4,263,5,0,0,"support","low","left"
"13791",12363,0.41,0.54,2,145,3,0,0,"support","low","left"
"13792",12364,0.72,0.96,5,267,5,0,0,"technical","low","left"
"13793",12365,0.4,0.5,2,141,3,1,0,"technical","low","left"
"13794",12366,0.91,0.87,4,235,5,0,0,"technical","low","left"
"13795",12367,0.1,0.83,6,258,4,0,0,"management","low","left"
"13796",12368,0.4,0.56,2,131,3,0,0,"IT","low","left"
"13797",12369,0.82,0.86,5,243,5,0,0,"IT","low","left"
"13798",12370,0.1,0.82,6,266,4,0,0,"IT","low","left"
"13799",12371,0.37,0.45,2,142,3,0,0,"IT","low","left"
"13800",12372,0.36,0.51,2,135,3,0,0,"IT","low","left"
"13801",12373,0.39,0.48,2,141,3,0,0,"product_mng","medium","left"
"13802",12374,0.36,0.57,2,142,3,0,0,"product_mng","medium","left"
"13803",12375,0.86,0.84,5,254,5,0,0,"product_mng","medium","left"
"13804",12376,0.73,0.99,5,262,5,0,0,"product_mng","medium","left"
"13805",12377,0.56,0.71,4,296,2,0,0,"IT","medium","left"
"13806",12378,0.44,0.56,2,158,3,0,0,"accounting","medium","left"
"13807",12379,0.31,0.56,4,238,2,0,0,"accounting","medium","left"
"13808",12380,0.77,0.93,4,231,5,0,0,"hr","medium","left"
"13809",12381,0.44,0.45,2,156,3,0,0,"hr","medium","left"
"13810",12382,0.38,0.46,2,145,3,0,0,"hr","medium","left"
"13811",12383,0.45,0.48,2,144,3,0,0,"marketing","medium","left"
"13812",12384,0.38,0.51,2,159,3,0,0,"sales","medium","left"
"13813",12385,0.36,0.48,2,156,3,0,0,"accounting","high","left"
"13814",12386,0.75,0.9,5,256,5,0,0,"support","low","left"
"13815",12387,0.1,0.93,6,298,4,0,0,"technical","medium","left"
"13816",12388,0.1,0.97,6,247,4,0,0,"management","medium","left"
"13817",12389,0.45,0.5,2,157,3,0,0,"marketing","medium","left"
"13818",12390,0.42,0.57,2,154,3,1,0,"marketing","medium","left"
"13819",12391,0.78,1,4,253,5,0,0,"marketing","low","left"
"13820",12392,0.45,0.55,2,148,3,0,0,"sales","low","left"
"13821",12393,0.84,1,4,261,5,0,0,"sales","low","left"
"13822",12394,0.11,0.93,6,282,4,0,0,"sales","low","left"
"13823",12395,0.42,0.56,2,133,3,0,0,"sales","low","left"
"13824",12396,0.45,0.46,2,128,3,0,0,"sales","low","left"
"13825",12397,0.46,0.57,2,139,3,0,0,"sales","low","left"
"13826",12398,0.09,0.79,6,293,5,0,0,"sales","low","left"
"13827",12399,0.87,0.83,4,265,6,0,0,"sales","low","left"
"13828",12400,0.1,0.87,6,250,4,0,0,"sales","low","left"
"13829",12401,0.91,1,5,251,6,0,0,"sales","low","left"
"13830",12402,0.76,0.92,4,246,5,0,0,"sales","low","left"
"13831",12403,0.74,1,5,275,5,0,0,"sales","low","left"
"13832",12404,0.92,0.93,5,240,5,0,0,"sales","low","left"
"13833",12405,0.76,0.87,5,245,5,0,0,"sales","low","left"
"13834",12406,0.47,0.5,4,254,4,0,0,"sales","low","left"
"13835",12407,0.73,0.99,5,241,5,0,0,"sales","low","left"
"13836",12408,0.09,0.94,6,257,4,0,0,"sales","low","left"
"13837",12409,0.91,0.92,4,246,5,0,0,"sales","low","left"
"13838",12410,0.82,0.98,4,233,5,0,0,"sales","low","left"
"13839",12411,0.28,0.45,6,218,4,0,0,"accounting","low","left"
"13840",12412,0.84,0.99,4,262,6,0,0,"accounting","medium","left"
"13841",12413,0.45,0.53,2,138,3,0,0,"accounting","medium","left"
"13842",12414,0.45,0.54,2,142,3,0,0,"hr","medium","left"
"13843",12415,0.91,0.97,5,233,5,0,0,"hr","medium","left"
"13844",12416,0.42,0.48,2,155,3,0,0,"hr","medium","left"
"13845",12417,0.82,1,4,229,6,0,0,"hr","medium","left"
"13846",12418,0.11,0.9,6,264,4,0,0,"technical","medium","left"
"13847",12419,0.42,0.53,3,199,4,0,0,"technical","medium","left"
"13848",12420,0.82,0.85,4,223,5,0,0,"technical","medium","left"
"13849",12421,0.09,0.96,6,268,4,0,0,"technical","medium","left"
"13850",12422,0.1,0.94,6,287,4,0,0,"technical","medium","left"
"13851",12423,0.86,1,5,257,5,0,0,"technical","medium","left"
"13852",12424,0.4,0.46,2,143,3,0,0,"technical","high","left"
"13853",12425,0.45,0.46,2,130,3,0,0,"technical","low","left"
"13854",12426,0.42,0.51,2,136,3,0,0,"technical","medium","left"
"13855",12427,0.74,0.92,4,261,5,0,0,"technical","medium","left"
"13856",12428,0.55,0.6,3,180,4,0,0,"technical","medium","left"
"13857",12429,0.37,0.45,2,126,3,0,0,"support","medium","left"
"13858",12430,0.41,0.52,2,127,3,1,0,"support","low","left"
"13859",12431,0.89,0.65,5,195,6,0,0,"support","low","left"
"13860",12432,0.41,0.57,2,160,3,0,0,"support","low","left"
"13861",12433,0.44,0.51,2,150,3,0,0,"support","low","left"
"13862",12434,0.87,0.84,4,264,6,0,0,"support","low","left"
"13863",12435,0.1,0.84,6,309,4,0,0,"support","low","left"
"13864",12436,0.41,0.47,2,135,3,0,0,"support","low","left"
"13865",12437,0.11,0.85,6,261,4,0,0,"support","low","left"
"13866",12438,0.43,0.53,2,160,3,0,0,"support","low","left"
"13867",12439,0.77,0.9,4,237,5,0,0,"support","low","left"
"13868",12440,0.41,0.52,2,136,3,0,0,"technical","low","left"
"13869",12441,0.41,0.48,2,139,3,0,0,"technical","low","left"
"13870",12442,0.36,0.78,2,151,4,0,0,"technical","low","left"
"13871",12443,0.77,1,5,229,5,0,0,"management","low","left"
"13872",12444,0.81,0.98,5,245,5,0,0,"IT","low","left"
"13873",12445,0.39,0.54,2,127,3,0,0,"IT","low","left"
"13874",12446,0.09,0.94,6,283,5,0,0,"IT","low","left"
"13875",12447,0.44,0.46,2,143,3,0,0,"IT","low","left"
"13876",12448,0.1,0.84,5,298,4,0,0,"IT","low","left"
"13877",12449,0.36,0.48,2,159,3,0,0,"product_mng","low","left"
"13878",12450,0.81,0.92,5,239,5,0,0,"product_mng","low","left"
"13879",12451,0.81,0.9,4,226,5,0,0,"product_mng","medium","left"
"13880",12452,0.85,0.98,5,248,5,0,0,"product_mng","medium","left"
"13881",12453,0.1,0.87,6,286,4,0,0,"IT","medium","left"
"13882",12454,0.37,0.54,2,145,3,0,0,"RandD","medium","left"
"13883",12455,0.09,0.97,7,254,4,1,0,"RandD","medium","left"
"13884",12456,0.44,0.53,2,127,3,0,0,"RandD","medium","left"
"13885",12457,0.86,0.93,5,223,5,0,0,"RandD","medium","left"
"13886",12458,0.77,1,4,255,5,0,0,"RandD","medium","left"
"13887",12459,0.41,0.48,2,136,3,0,0,"marketing","medium","left"
"13888",12460,0.4,0.48,2,137,3,0,0,"sales","medium","left"
"13889",12461,0.43,0.49,2,135,3,0,0,"accounting","medium","left"
"13890",12462,0.43,0.5,2,137,3,0,0,"support","medium","left"
"13891",12463,0.8,0.53,3,255,5,0,0,"technical","high","left"
"13892",12464,0.8,0.85,4,273,5,0,0,"management","low","left"
"13893",12465,0.82,0.98,5,234,5,0,0,"marketing","medium","left"
"13894",12466,0.37,0.54,2,152,3,0,0,"marketing","medium","left"
"13895",12467,0.37,0.48,2,134,3,0,0,"marketing","medium","left"
"13896",12468,0.09,0.95,6,292,4,0,0,"sales","medium","left"
"13897",12469,0.9,0.92,5,245,5,0,0,"sales","low","left"
"13898",12470,0.41,0.52,2,159,3,0,0,"sales","low","left"
"13899",12471,0.1,0.85,6,260,4,0,0,"sales","low","left"
"13900",12472,0.44,0.53,2,149,3,0,0,"sales","low","left"
"13901",12473,0.89,0.85,5,266,5,0,0,"sales","low","left"
"13902",12474,0.42,0.56,2,149,3,0,0,"sales","low","left"
"13903",12475,0.87,1,5,242,5,0,0,"sales","low","left"
"13904",12476,0.45,0.57,2,134,3,0,0,"sales","low","left"
"13905",12477,0.11,0.87,5,271,4,0,0,"sales","low","left"
"13906",12478,0.09,0.79,6,275,4,0,0,"sales","low","left"
"13907",12479,0.76,0.83,5,227,5,0,0,"sales","low","left"
"13908",12480,0.11,0.96,7,277,5,0,0,"sales","low","left"
"13909",12481,0.37,0.49,2,151,3,0,0,"sales","low","left"
"13910",12482,0.1,0.79,6,274,4,0,0,"sales","low","left"
"13911",12483,0.77,0.87,4,242,6,0,0,"sales","low","left"
"13912",12484,0.42,0.54,2,143,3,1,0,"sales","low","left"
"13913",12485,0.38,0.52,2,145,3,0,0,"sales","low","left"
"13914",12486,0.32,0.95,5,172,2,0,0,"sales","low","left"
"13915",12487,0.38,0.49,2,135,3,0,0,"accounting","low","left"
"13916",12488,0.19,1,4,192,4,0,0,"accounting","low","left"
"13917",12489,0.1,0.83,7,276,4,0,0,"accounting","low","left"
"13918",12490,0.76,0.88,4,206,4,0,0,"hr","medium","left"
"13919",12491,0.53,0.56,4,281,6,0,0,"hr","medium","left"
"13920",12492,0.39,0.51,2,151,3,0,0,"hr","medium","left"
"13921",12493,0.11,0.83,6,244,4,0,0,"hr","medium","left"
"13922",12494,0.1,0.94,6,309,4,0,0,"technical","medium","left"
"13923",12495,0.84,1,5,218,5,0,0,"technical","medium","left"
"13924",12496,0.82,0.99,4,263,6,0,0,"technical","medium","left"
"13925",12497,0.1,0.82,6,244,4,0,0,"technical","medium","left"
"13926",12498,0.59,0.49,7,263,4,0,0,"technical","medium","left"
"13927",12499,0.44,0.48,2,143,3,0,0,"technical","medium","left"
"13928",12500,0.89,0.95,2,181,5,0,0,"technical","medium","left"
"13929",12501,0.91,0.84,5,265,5,0,0,"technical","medium","left"
"13930",12502,0.66,0.57,5,161,5,0,0,"technical","high","left"
"13931",12503,0.11,0.87,7,282,5,0,0,"technical","low","left"
"13932",12504,0.43,0.51,2,155,3,0,0,"technical","medium","left"
"13933",12505,0.78,0.83,4,217,6,0,0,"support","medium","left"
"13934",12506,0.11,0.97,6,289,5,0,0,"support","medium","left"
"13935",12507,0.83,0.98,4,259,5,0,0,"support","medium","left"
"13936",12508,0.39,0.54,2,158,3,0,0,"support","low","left"
"13937",12509,0.38,0.55,2,158,3,0,0,"support","low","left"
"13938",12510,0.37,0.57,2,155,3,0,0,"support","low","left"
"13939",12511,0.44,0.48,2,146,3,0,0,"support","low","left"
"13940",12512,0.53,0.85,2,164,5,0,0,"support","low","left"
"13941",12513,0.09,0.96,6,259,4,0,0,"support","low","left"
"13942",12514,0.11,0.89,6,293,4,0,0,"support","low","left"
"13943",12515,0.83,0.96,5,275,5,0,0,"support","low","left"
"13944",12516,0.88,1,5,219,6,1,0,"technical","low","left"
"13945",12517,0.1,0.89,6,247,4,0,0,"technical","low","left"
"13946",12518,0.09,0.86,7,309,4,0,0,"technical","low","left"
"13947",12519,0.44,0.54,2,151,3,0,0,"management","low","left"
"13948",12520,0.39,0.51,2,129,3,0,0,"IT","low","left"
"13949",12521,0.87,0.94,4,274,5,0,0,"IT","low","left"
"13950",12522,0.74,0.99,4,233,5,0,0,"IT","low","left"
"13951",12523,0.1,0.95,7,289,4,0,0,"IT","low","left"
"13952",12524,0.74,0.82,4,239,6,0,0,"IT","low","left"
"13953",12525,0.75,0.99,5,221,5,0,0,"product_mng","low","left"
"13954",12526,0.41,0.56,2,150,3,0,0,"product_mng","low","left"
"13955",12527,0.41,0.45,2,144,3,1,0,"product_mng","low","left"
"13956",12528,0.09,0.9,7,289,4,0,0,"product_mng","low","left"
"13957",12529,0.09,0.8,6,301,5,0,0,"IT","medium","left"
"13958",12530,0.39,0.57,2,145,3,0,0,"accounting","medium","left"
"13959",12531,0.4,0.56,2,137,3,0,0,"accounting","medium","left"
"13960",12532,0.37,0.54,2,131,3,1,0,"hr","medium","left"
"13961",12533,0.1,0.84,6,246,4,0,0,"hr","medium","left"
"13962",12534,0.43,0.51,2,136,3,0,0,"hr","medium","left"
"13963",12535,0.75,0.85,5,240,6,1,0,"marketing","medium","left"
"13964",12536,0.37,0.56,2,156,3,0,0,"sales","medium","left"
"13965",12537,0.11,0.85,6,305,4,0,0,"accounting","medium","left"
"13966",12538,0.45,0.45,2,154,3,1,0,"support","medium","left"
"13967",12539,0.87,1,5,261,5,1,0,"technical","medium","left"
"13968",12540,0.11,0.94,7,244,4,0,0,"management","medium","left"
"13969",12541,0.45,0.54,2,129,3,0,0,"marketing","high","left"
"13970",12542,0.81,0.87,4,254,5,0,0,"marketing","low","left"
"13971",12543,0.77,0.91,5,236,5,0,0,"marketing","medium","left"
"13972",12544,0.89,0.92,5,237,5,0,0,"sales","medium","left"
"13973",12545,0.43,0.49,2,135,3,0,0,"sales","medium","left"
"13974",12546,0.78,1,5,236,5,0,0,"sales","medium","left"
"13975",12547,0.37,0.47,2,149,3,0,0,"sales","low","left"
"13976",12548,0.37,0.5,2,141,3,0,0,"sales","low","left"
"13977",12549,0.85,0.82,4,270,5,0,0,"sales","low","left"
"13978",12550,0.41,0.47,2,138,3,0,0,"sales","low","left"
"13979",12551,0.11,0.96,6,298,4,0,0,"sales","low","left"
"13980",12552,0.75,0.99,5,254,5,0,0,"sales","low","left"
"13981",12553,0.82,0.85,5,248,5,0,0,"sales","low","left"
"13982",12554,0.79,1,5,257,6,0,0,"sales","low","left"
"13983",12555,0.43,0.53,2,150,3,0,0,"sales","low","left"
"13984",12556,0.1,0.9,7,281,4,0,0,"sales","low","left"
"13985",12557,0.46,0.48,2,141,3,1,0,"sales","low","left"
"13986",12558,0.43,0.57,2,157,3,0,0,"sales","low","left"
"13987",12559,0.43,0.55,2,136,3,0,0,"sales","low","left"
"13988",12560,0.11,0.8,7,296,4,0,0,"sales","low","left"
"13989",12561,0.09,0.86,6,279,4,0,0,"sales","low","left"
"13990",12562,0.37,0.53,2,131,3,0,0,"sales","low","left"
"13991",12563,0.4,0.57,2,160,3,0,0,"accounting","low","left"
"13992",12564,0.1,0.77,7,291,4,0,0,"accounting","low","left"
"13993",12565,0.41,0.53,2,157,3,0,0,"accounting","low","left"
"13994",12566,0.79,0.58,3,294,4,0,0,"hr","low","left"
"13995",12567,0.11,0.79,7,310,4,0,0,"hr","low","left"
"13996",12568,0.1,0.97,6,282,4,0,0,"hr","medium","left"
"13997",12569,0.44,0.51,2,134,3,0,0,"hr","medium","left"
"13998",12570,0.25,0.46,4,214,4,0,0,"technical","medium","left"
"13999",12571,0.44,0.52,2,137,3,0,0,"technical","medium","left"
"14000",12572,0.73,1,4,252,5,0,0,"technical","medium","left"
"14001",12573,0.75,0.97,5,243,6,0,0,"technical","medium","left"
"14002",12574,0.36,0.47,2,148,3,0,0,"technical","medium","left"
"14003",12575,0.37,0.49,2,151,3,0,0,"technical","medium","left"
"14004",12576,0.39,0.49,2,129,3,0,0,"technical","medium","left"
"14005",12577,0.48,0.78,2,198,2,0,0,"technical","medium","left"
"14006",12578,0.57,0.72,4,275,6,0,0,"technical","medium","left"
"14007",12579,0.9,0.96,5,243,5,0,0,"technical","medium","left"
"14008",12580,0.39,0.55,2,159,3,0,0,"technical","high","left"
"14009",12581,0.44,0.51,2,145,3,0,0,"support","low","left"
"14010",12582,0.81,0.88,5,242,5,0,0,"support","medium","left"
"14011",12583,0.74,0.87,5,242,5,0,0,"support","medium","left"
"14012",12584,0.44,0.56,2,145,3,0,0,"support","medium","left"
"14013",12585,0.41,0.56,2,154,3,0,1,"support","medium","left"
"14014",12586,0.4,0.51,2,139,3,0,0,"support","low","left"
"14015",12587,0.46,0.57,2,152,3,0,0,"support","low","left"
"14016",12588,0.8,0.83,2,211,3,0,0,"support","low","left"
"14017",12589,0.87,0.9,5,258,5,0,0,"support","low","left"
"14018",12590,0.39,0.54,2,155,3,0,0,"support","low","left"
"14019",12591,0.38,0.55,2,148,3,0,0,"support","low","left"
"14020",12592,0.66,0.67,2,255,3,0,0,"technical","low","left"
"14021",12593,0.1,0.8,6,264,4,0,0,"technical","low","left"
"14022",12594,0.37,0.54,2,132,3,0,0,"technical","low","left"
"14023",12595,0.1,0.77,6,255,4,0,0,"management","low","left"
"14024",12596,0.09,0.87,5,263,4,0,0,"IT","low","left"
"14025",12597,0.86,0.84,5,222,5,0,0,"IT","low","left"
"14026",12598,0.11,0.9,6,263,4,0,0,"IT","low","left"
"14027",12599,0.37,0.46,2,157,3,0,0,"IT","low","left"
"14028",12600,0.11,0.92,7,307,4,0,0,"IT","low","left"
"14029",12601,0.77,0.98,5,259,6,0,0,"product_mng","low","left"
"14030",12602,0.84,0.94,5,222,6,0,0,"product_mng","low","left"
"14031",12603,0.1,0.84,7,250,4,0,0,"product_mng","low","left"
"14032",12604,0.83,0.9,5,245,5,0,0,"product_mng","low","left"
"14033",12605,0.11,0.79,6,292,4,0,0,"IT","low","left"
"14034",12606,0.86,0.92,5,252,5,0,0,"RandD","low","left"
"14035",12607,0.38,0.56,2,161,3,0,0,"RandD","medium","left"
"14036",12608,0.11,0.88,5,250,4,0,0,"RandD","medium","left"
"14037",12609,0.45,0.49,2,134,3,0,0,"RandD","medium","left"
"14038",12610,0.1,0.85,7,279,4,0,0,"RandD","medium","left"
"14039",12611,0.09,0.95,7,256,4,0,0,"marketing","medium","left"
"14040",12612,0.39,0.53,2,127,3,0,0,"sales","medium","left"
"14041",12613,0.37,0.47,2,138,3,1,0,"accounting","medium","left"
"14042",12614,0.81,0.97,5,243,5,0,0,"support","medium","left"
"14043",12615,0.09,0.9,7,296,4,0,0,"technical","medium","left"
"14044",12616,0.1,0.88,7,267,4,0,0,"management","medium","left"
"14045",12617,0.39,0.49,2,144,3,0,0,"marketing","medium","left"
"14046",12618,0.83,0.95,4,251,5,0,0,"marketing","medium","left"
"14047",12619,0.45,0.57,2,148,3,0,0,"marketing","high","left"
"14048",12620,0.43,0.51,2,141,3,0,0,"sales","low","left"
"14049",12621,0.8,0.75,3,268,2,0,0,"sales","medium","left"
"14050",12622,0.1,0.86,6,247,4,0,0,"sales","medium","left"
"14051",12623,0.1,0.55,2,247,4,0,0,"sales","medium","left"
"14052",12624,0.36,0.52,2,146,3,0,0,"sales","medium","left"
"14053",12625,0.38,0.5,2,140,3,0,0,"sales","low","left"
"14054",12626,0.78,0.98,5,263,6,0,0,"sales","low","left"
"14055",12627,0.44,0.49,2,145,3,0,0,"sales","low","left"
"14056",12628,0.41,0.46,2,156,3,1,0,"sales","low","left"
"14057",12629,0.72,0.85,5,244,6,0,0,"sales","low","left"
"14058",12630,0.46,0.54,2,144,3,0,0,"sales","low","left"
"14059",12631,0.1,0.9,7,286,4,0,0,"sales","low","left"
"14060",12632,0.34,0.67,4,141,2,0,0,"sales","low","left"
"14061",12633,0.11,0.89,6,260,5,0,0,"sales","low","left"
"14062",12634,0.38,0.56,2,154,3,0,0,"sales","low","left"
"14063",12635,0.82,0.92,5,225,5,0,0,"sales","low","left"
"14064",12636,0.39,0.57,2,127,3,0,0,"sales","low","left"
"14065",12637,0.44,0.53,2,140,3,0,0,"sales","low","left"
"14066",12638,0.43,0.52,2,147,3,0,0,"sales","low","left"
"14067",12639,0.84,0.83,4,227,5,0,0,"accounting","low","left"
"14068",12640,0.43,0.48,2,153,3,0,0,"accounting","low","left"
"14069",12641,0.37,0.52,2,128,3,0,0,"accounting","low","left"
"14070",12642,0.74,0.97,4,228,5,0,0,"hr","low","left"
"14071",12643,0.73,0.97,5,235,5,0,0,"hr","low","left"
"14072",12644,0.37,0.47,2,148,3,0,0,"hr","low","left"
"14073",12645,0.58,0.62,4,238,3,0,0,"hr","low","left"
"14074",12646,0.4,0.54,2,141,3,0,0,"technical","medium","left"
"14075",12647,0.51,0.83,5,249,4,0,0,"technical","medium","left"
"14076",12648,0.46,0.5,2,151,3,0,0,"technical","medium","left"
"14077",12649,0.45,0.54,2,129,3,0,0,"technical","medium","left"
"14078",12650,0.46,0.5,2,156,3,0,0,"technical","medium","left"
"14079",12651,0.39,0.45,2,134,3,0,0,"technical","medium","left"
"14080",12652,0.09,0.88,6,269,4,0,0,"technical","medium","left"
"14081",12653,0.09,0.77,6,290,4,0,0,"technical","medium","left"
"14082",12654,0.37,0.51,2,132,3,0,0,"technical","medium","left"
"14083",12655,0.1,0.89,7,308,4,0,0,"technical","medium","left"
"14084",12656,0.77,1,4,232,5,0,0,"technical","medium","left"
"14085",12657,0.79,0.86,5,235,5,0,0,"support","medium","left"
"14086",12658,0.43,0.55,2,130,3,0,0,"support","high","left"
"14087",12659,0.38,0.53,2,146,3,0,0,"support","low","left"
"14088",12660,0.77,0.91,5,221,6,0,0,"support","medium","left"
"14089",12661,0.44,0.5,2,130,3,0,0,"support","medium","left"
"14090",12662,0.39,0.46,2,136,3,0,0,"support","medium","left"
"14091",12663,0.78,0.89,5,274,6,0,0,"support","medium","left"
"14092",12664,0.1,0.79,6,256,5,0,0,"support","low","left"
"14093",12665,0.1,0.77,5,276,4,0,0,"support","low","left"
"14094",12666,0.75,0.85,5,267,5,0,0,"support","low","left"
"14095",12667,0.46,0.62,6,213,3,0,0,"support","low","left"
"14096",12668,0.91,0.97,4,274,6,0,0,"technical","low","left"
"14097",12669,0.1,0.92,6,258,4,0,0,"technical","low","left"
"14098",12670,0.72,0.6,3,153,5,0,0,"technical","low","left"
"14099",12671,0.11,0.95,6,245,4,0,0,"management","low","left"
"14100",12672,0.11,0.94,6,264,4,0,0,"IT","low","left"
"14101",12673,0.46,0.57,2,154,3,0,0,"IT","low","left"
"14102",12674,0.37,0.46,2,149,3,0,0,"IT","low","left"
"14103",12675,0.46,0.5,2,157,3,0,0,"IT","low","left"
"14104",12676,0.43,0.57,2,127,3,0,0,"IT","low","left"
"14105",12677,0.11,0.82,6,270,4,0,0,"product_mng","low","left"
"14106",12678,0.73,0.89,5,236,6,0,0,"product_mng","low","left"
"14107",12679,0.43,0.47,2,158,3,0,0,"product_mng","low","left"
"14108",12680,0.86,1,5,229,5,0,0,"product_mng","low","left"
"14109",12681,0.1,0.83,6,269,4,0,0,"IT","low","left"
"14110",12682,0.4,0.49,2,128,3,0,0,"sales","low","left"
"14111",12683,0.11,0.87,7,278,4,0,0,"sales","low","left"
"14112",12684,0.86,0.98,3,158,5,0,0,"sales","low","left"
"14113",12685,0.42,1,3,202,3,0,0,"sales","medium","left"
"14114",12686,0.79,0.84,4,240,5,0,0,"sales","medium","left"
"14115",12687,0.1,0.96,7,255,4,0,0,"marketing","medium","left"
"14116",12688,0.09,0.92,7,254,4,0,0,"sales","medium","left"
"14117",12689,0.09,0.82,6,257,4,0,0,"accounting","medium","left"
"14118",12690,0.87,1,4,228,5,0,0,"support","medium","left"
"14119",12691,0.36,0.49,2,145,3,0,0,"technical","medium","left"
"14120",12692,0.42,0.75,3,218,4,0,0,"management","medium","left"
"14121",12693,0.84,0.86,5,268,5,0,0,"marketing","medium","left"
"14122",12694,0.1,0.83,6,278,4,0,0,"marketing","medium","left"
"14123",12695,0.78,0.71,3,249,5,0,0,"marketing","medium","left"
"14124",12696,0.35,0.99,3,236,4,0,0,"sales","medium","left"
"14125",12697,0.1,0.81,7,291,4,0,0,"sales","high","left"
"14126",12698,0.11,0.8,6,306,4,0,0,"sales","low","left"
"14127",12699,0.43,0.48,2,135,3,0,0,"sales","medium","left"
"14128",12700,0.38,0.45,2,156,3,0,0,"sales","medium","left"
"14129",12701,0.46,0.54,2,143,3,0,0,"sales","medium","left"
"14130",12702,0.89,0.82,4,243,5,0,0,"sales","medium","left"
"14131",12703,0.45,0.5,2,147,3,0,0,"sales","low","left"
"14132",12704,0.44,0.53,2,159,3,0,0,"sales","low","left"
"14133",12705,0.74,0.54,5,216,3,0,0,"sales","low","left"
"14134",12706,0.45,0.54,2,152,3,0,0,"sales","low","left"
"14135",12707,0.79,0.93,4,226,5,0,0,"sales","low","left"
"14136",12708,0.79,0.91,5,271,5,0,0,"sales","low","left"
"14137",12709,0.11,0.87,6,255,4,0,0,"sales","low","left"
"14138",12710,0.42,0.48,2,140,3,0,0,"sales","low","left"
"14139",12711,0.64,0.9,6,252,2,0,0,"sales","low","left"
"14140",12712,0.4,0.55,2,159,3,0,0,"sales","low","left"
"14141",12713,0.84,0.98,5,270,5,0,0,"sales","low","left"
"14142",12714,0.73,0.92,5,232,5,0,0,"sales","low","left"
"14143",12715,0.4,0.51,2,144,3,0,0,"accounting","low","left"
"14144",12716,0.36,0.45,2,127,3,0,0,"accounting","low","left"
"14145",12717,0.43,0.47,2,131,3,0,0,"accounting","low","left"
"14146",12718,0.11,0.78,6,243,4,0,0,"hr","low","left"
"14147",12719,0.91,1,5,244,6,0,0,"hr","low","left"
"14148",12720,0.8,1,5,260,5,0,0,"hr","low","left"
"14149",12721,0.42,0.49,2,139,3,0,0,"hr","low","left"
"14150",12722,0.31,0.87,4,184,3,0,0,"technical","low","left"
"14151",12723,0.44,0.47,2,130,3,0,0,"technical","low","left"
"14152",12724,0.38,0.54,2,135,3,0,0,"technical","medium","left"
"14153",12725,0.45,0.56,2,146,3,0,0,"technical","medium","left"
"14154",12726,0.43,0.46,2,149,3,0,0,"technical","medium","left"
"14155",12727,0.45,0.46,2,153,3,1,0,"technical","medium","left"
"14156",12728,0.43,0.57,2,160,3,0,0,"technical","medium","left"
"14157",12729,0.43,0.49,2,160,3,0,0,"technical","medium","left"
"14158",12730,0.09,0.83,6,282,4,0,0,"technical","medium","left"
"14159",12731,0.43,0.47,2,128,3,0,0,"technical","medium","left"
"14160",12732,0.79,0.94,4,232,5,0,0,"technical","medium","left"
"14161",12733,0.85,0.58,3,226,2,0,0,"support","medium","left"
"14162",12734,0.38,0.45,2,129,3,0,0,"support","medium","left"
"14163",12735,0.11,0.92,7,255,4,0,0,"support","medium","left"
"14164",12736,0.83,0.99,5,258,5,0,0,"support","high","left"
"14165",12737,0.81,0.91,4,229,5,0,0,"support","low","left"
"14166",12738,0.42,0.56,2,143,3,0,0,"support","medium","left"
"14167",12739,0.11,0.87,6,257,4,0,0,"support","medium","left"
"14168",12740,0.11,0.85,7,275,4,0,0,"support","medium","left"
"14169",12741,0.1,0.89,7,291,4,0,0,"support","medium","left"
"14170",12742,0.5,0.54,5,153,4,0,0,"support","low","left"
"14171",12743,0.44,0.49,2,154,3,0,0,"support","low","left"
"14172",12744,0.11,0.9,6,301,4,0,0,"technical","low","left"
"14173",12745,0.39,0.52,2,134,3,0,0,"technical","low","left"
"14174",12746,0.11,0.78,6,245,4,0,0,"technical","low","left"
"14175",12747,0.36,0.5,2,132,3,0,0,"management","low","left"
"14176",12748,0.43,0.51,2,130,3,0,0,"IT","low","left"
"14177",12749,0.4,0.5,2,127,3,0,0,"IT","low","left"
"14178",12750,0.86,0.84,4,246,6,0,0,"IT","low","left"
"14179",12751,0.38,0.49,2,145,3,0,0,"IT","low","left"
"14180",12752,0.46,0.45,2,138,3,0,1,"IT","low","left"
"14181",12753,0.37,0.57,2,129,3,0,0,"product_mng","low","left"
"14182",12754,0.43,0.52,2,150,3,0,0,"product_mng","low","left"
"14183",12755,0.66,0.93,5,253,5,0,0,"product_mng","low","left"
"14184",12756,0.37,0.48,2,160,3,0,0,"product_mng","low","left"
"14185",12757,0.77,0.92,5,235,5,0,0,"IT","low","left"
"14186",12758,0.38,0.55,2,151,3,0,0,"sales","low","left"
"14187",12759,0.39,0.54,2,127,3,0,0,"sales","low","left"
"14188",12760,0.41,0.55,2,151,3,0,0,"sales","low","left"
"14189",12761,0.1,0.9,7,290,4,0,0,"sales","low","left"
"14190",12762,0.09,0.93,6,249,4,0,0,"sales","low","left"
"14191",12763,0.41,0.47,2,131,3,0,0,"marketing","medium","left"
"14192",12764,0.39,0.46,2,159,3,0,0,"sales","medium","left"
"14193",12765,0.83,0.99,4,223,5,0,0,"accounting","medium","left"
"14194",12766,0.09,0.87,3,214,2,0,0,"support","medium","left"
"14195",12767,0.75,0.81,5,227,5,0,0,"technical","medium","left"
"14196",12768,0.44,0.54,2,127,3,0,0,"management","medium","left"
"14197",12769,0.1,0.84,6,293,5,0,0,"marketing","medium","left"
"14198",12770,0.42,0.46,2,141,3,0,0,"marketing","medium","left"
"14199",12771,0.1,0.83,6,300,4,0,0,"marketing","medium","left"
"14200",12772,0.1,0.86,6,309,4,0,0,"sales","medium","left"
"14201",12773,0.31,0.77,4,149,3,0,0,"sales","medium","left"
"14202",12774,0.42,0.54,2,159,3,0,0,"sales","medium","left"
"14203",12775,0.38,0.5,2,152,3,0,0,"sales","high","left"
"14204",12776,0.39,0.57,2,158,3,0,0,"sales","low","left"
"14205",12777,0.1,0.97,6,254,5,0,0,"sales","medium","left"
"14206",12778,0.11,0.93,6,294,4,0,0,"sales","medium","left"
"14207",12779,0.1,0.92,7,269,4,0,0,"sales","medium","left"
"14208",12780,0.11,0.9,7,247,4,0,0,"sales","medium","left"
"14209",12781,0.44,0.65,3,271,4,0,0,"sales","low","left"
"14210",12782,0.91,0.96,4,232,5,0,0,"sales","low","left"
"14211",12783,0.72,1,4,245,5,0,0,"sales","low","left"
"14212",14212,0.38,0.53,2,157,3,0,0,"sales","low","left"
"14213",14213,0.8,0.86,5,262,6,0,0,"sales","medium","left"
"14214",14214,0.11,0.88,7,272,4,0,0,"sales","medium","left"
"14215",14215,0.72,0.87,5,223,5,0,0,"sales","low","left"
"14216",14216,0.37,0.52,2,159,3,0,0,"sales","low","left"
"14217",14217,0.41,0.5,2,153,3,0,0,"sales","low","left"
"14218",14218,0.1,0.77,6,247,4,0,0,"sales","low","left"
"14219",14219,0.92,0.85,5,259,5,0,0,"sales","low","left"
"14220",14220,0.89,1,5,224,5,0,0,"sales","low","left"
"14221",14221,0.42,0.53,2,142,3,0,0,"sales","low","left"
"14222",14222,0.45,0.54,2,135,3,0,0,"sales","low","left"
"14223",14223,0.11,0.81,6,305,4,0,0,"sales","low","left"
"14224",14224,0.84,0.92,4,234,5,0,0,"sales","low","left"
"14225",14225,0.41,0.55,2,148,3,0,0,"sales","low","left"
"14226",14226,0.36,0.56,2,137,3,0,0,"sales","low","left"
"14227",14227,0.38,0.54,2,143,3,0,0,"sales","low","left"
"14228",14228,0.45,0.47,2,160,3,0,0,"sales","low","left"
"14229",14229,0.78,0.99,4,255,6,0,0,"sales","low","left"
"14230",14230,0.45,0.51,2,160,3,1,1,"sales","low","left"
"14231",14231,0.76,0.89,5,262,5,0,0,"sales","low","left"
"14232",14232,0.11,0.83,6,282,4,0,0,"sales","low","left"
"14233",14233,0.38,0.55,2,147,3,0,0,"sales","low","left"
"14234",14234,0.09,0.95,6,304,4,0,0,"sales","low","left"
"14235",14235,0.46,0.57,2,139,3,0,0,"sales","low","left"
"14236",14236,0.4,0.53,2,158,3,0,0,"sales","low","left"
"14237",14237,0.89,0.92,5,242,5,0,0,"sales","low","left"
"14238",14238,0.82,0.87,4,239,5,0,0,"sales","low","left"
"14239",14239,0.4,0.49,2,135,3,0,0,"sales","low","left"
"14240",14240,0.41,0.46,2,128,3,0,0,"accounting","low","left"
"14241",14241,0.38,0.5,2,132,3,0,0,"accounting","low","left"
"14242",14242,0.09,0.62,6,294,4,0,0,"accounting","low","left"
"14243",14243,0.45,0.57,2,134,3,0,0,"hr","low","left"
"14244",14244,0.4,0.51,2,145,3,0,0,"hr","low","left"
"14245",14245,0.45,0.55,2,140,3,0,0,"hr","low","left"
"14246",14246,0.84,0.87,4,246,6,0,0,"hr","low","left"
"14247",14247,0.1,0.94,6,255,4,0,0,"technical","low","left"
"14248",14248,0.38,0.46,2,137,3,0,0,"technical","low","left"
"14249",14249,0.45,0.5,2,126,3,0,0,"technical","low","left"
"14250",14250,0.11,0.89,6,306,4,0,0,"technical","low","left"
"14251",14251,0.41,0.54,2,152,3,0,0,"technical","low","left"
"14252",14252,0.87,0.88,5,269,5,0,0,"technical","low","left"
"14253",14253,0.45,0.48,2,158,3,0,0,"technical","low","left"
"14254",14254,0.4,0.46,2,127,3,0,0,"technical","low","left"
"14255",14255,0.1,0.8,7,281,4,0,0,"technical","low","left"
"14256",14256,0.09,0.89,6,276,4,0,0,"technical","low","left"
"14257",14257,0.84,0.74,3,182,4,0,0,"technical","low","left"
"14258",14258,0.4,0.55,2,147,3,0,0,"support","low","left"
"14259",14259,0.57,0.7,3,273,6,0,0,"support","low","left"
"14260",14260,0.4,0.54,2,148,3,0,0,"support","low","left"
"14261",14261,0.43,0.47,2,147,3,0,0,"support","low","left"
"14262",14262,0.13,0.78,6,152,2,0,0,"support","low","left"
"14263",14263,0.44,0.55,2,135,3,0,0,"support","low","left"
"14264",14264,0.38,0.55,2,134,3,0,0,"support","low","left"
"14265",14265,0.39,0.54,2,132,3,0,0,"support","low","left"
"14266",14266,0.1,0.92,7,307,4,0,0,"support","low","left"
"14267",14267,0.37,0.46,2,140,3,0,0,"support","low","left"
"14268",14268,0.11,0.94,7,255,4,0,0,"support","low","left"
"14269",14269,0.1,0.81,6,309,4,0,0,"technical","low","left"
"14270",14270,0.38,0.54,2,128,3,0,0,"technical","low","left"
"14271",14271,0.85,1,4,225,5,0,0,"technical","low","left"
"14272",14272,0.85,0.91,5,226,5,0,0,"management","medium","left"
"14273",14273,0.11,0.93,7,308,4,0,0,"IT","medium","left"
"14274",14274,0.1,0.95,6,244,5,0,0,"IT","medium","left"
"14275",14275,0.36,0.56,2,132,3,0,0,"IT","medium","left"
"14276",14276,0.11,0.94,6,286,4,0,0,"IT","medium","left"
"14277",14277,0.81,0.7,6,161,4,0,0,"IT","medium","left"
"14278",14278,0.43,0.54,2,153,3,0,0,"product_mng","medium","left"
"14279",14279,0.9,0.98,4,264,6,0,0,"product_mng","medium","left"
"14280",14280,0.76,0.86,5,223,5,1,0,"product_mng","medium","left"
"14281",14281,0.43,0.5,2,135,3,0,0,"product_mng","medium","left"
"14282",14282,0.74,0.99,2,277,3,0,0,"IT","medium","left"
"14283",14283,0.09,0.77,5,275,4,0,0,"product_mng","medium","left"
"14284",14284,0.45,0.49,2,149,3,0,0,"product_mng","high","left"
"14285",14285,0.09,0.87,7,295,4,0,0,"product_mng","low","left"
"14286",14286,0.11,0.97,6,277,4,0,0,"product_mng","medium","left"
"14287",14287,0.11,0.79,7,306,4,0,0,"product_mng","medium","left"
"14288",14288,0.1,0.83,6,295,4,0,0,"product_mng","medium","left"
"14289",14289,0.4,0.54,2,137,3,0,0,"marketing","medium","left"
"14290",14290,0.43,0.56,2,157,3,0,0,"sales","low","left"
"14291",14291,0.39,0.56,2,142,3,0,0,"accounting","low","left"
"14292",14292,0.45,0.54,2,140,3,0,0,"support","low","left"
"14293",14293,0.38,0.49,2,151,3,0,0,"technical","low","left"
"14294",14294,0.79,0.59,4,139,3,0,1,"management","low","left"
"14295",14295,0.84,0.85,4,249,6,0,0,"marketing","low","left"
"14296",14296,0.11,0.77,6,291,4,0,0,"marketing","low","left"
"14297",14297,0.11,0.87,6,305,4,0,0,"marketing","low","left"
"14298",14298,0.17,0.84,5,232,3,0,0,"sales","low","left"
"14299",14299,0.44,0.45,2,132,3,0,0,"sales","low","left"
"14300",14300,0.37,0.57,2,130,3,0,0,"sales","low","left"
"14301",14301,0.1,0.79,6,291,4,0,0,"sales","low","left"
"14302",14302,0.4,0.5,2,130,3,0,0,"sales","low","left"
"14303",14303,0.89,1,5,246,5,0,0,"sales","low","left"
"14304",14304,0.42,0.48,2,143,3,0,0,"sales","low","left"
"14305",14305,0.46,0.55,2,129,3,0,0,"sales","low","left"
"14306",14306,0.09,0.83,6,255,4,0,0,"sales","low","left"
"14307",14307,0.37,0.51,2,155,3,0,0,"sales","low","left"
"14308",14308,0.1,0.77,6,265,4,0,0,"sales","low","left"
"14309",14309,0.1,0.84,6,279,4,0,0,"sales","low","left"
"14310",14310,0.11,0.97,6,284,4,0,0,"sales","low","left"
"14311",14311,0.9,1,5,221,6,0,0,"sales","medium","left"
"14312",14312,0.38,0.52,2,154,3,0,0,"sales","medium","left"
"14313",14313,0.36,0.52,2,147,3,0,0,"sales","medium","left"
"14314",14314,0.42,0.46,2,150,3,0,0,"sales","medium","left"
"14315",14315,0.09,0.94,7,267,4,0,0,"sales","medium","left"
"14316",14316,0.43,0.52,2,158,3,0,0,"sales","medium","left"
"14317",14317,0.24,0.46,7,224,5,0,0,"accounting","medium","left"
"14318",14318,0.91,1,4,257,5,0,0,"accounting","medium","left"
"14319",14319,0.44,0.5,2,148,3,0,0,"accounting","medium","left"
"14320",14320,0.71,0.87,3,177,4,0,0,"hr","medium","left"
"14321",14321,0.4,0.49,2,155,3,0,0,"hr","medium","left"
"14322",14322,0.43,0.47,2,144,3,0,0,"hr","medium","left"
"14323",14323,0.09,0.85,6,289,4,0,0,"hr","high","left"
"14324",14324,0.43,0.52,2,160,3,0,0,"technical","low","left"
"14325",14325,0.9,0.96,4,258,5,0,0,"technical","medium","left"
"14326",14326,0.84,1,5,234,5,0,0,"technical","medium","left"
"14327",14327,0.37,0.48,2,137,3,0,0,"technical","medium","left"
"14328",14328,0.86,0.68,5,263,2,0,0,"technical","medium","left"
"14329",14329,0.11,0.84,6,251,4,0,0,"technical","low","left"
"14330",14330,0.37,0.57,2,133,3,0,0,"technical","low","left"
"14331",14331,0.4,0.46,2,132,3,0,0,"technical","low","left"
"14332",14332,0.14,0.62,4,158,4,1,0,"technical","low","left"
"14333",14333,0.4,0.46,2,135,3,0,0,"technical","low","left"
"14334",14334,0.75,1,4,216,6,0,0,"technical","low","left"
"14335",14335,0.11,0.84,6,300,5,1,0,"support","low","left"
"14336",14336,0.46,0.49,2,138,3,0,0,"support","low","left"
"14337",14337,0.11,0.92,6,260,4,0,0,"support","low","left"
"14338",14338,0.38,0.49,2,132,3,0,0,"support","low","left"
"14339",14339,0.7,0.89,3,183,5,0,0,"support","low","left"
"14340",14340,0.09,0.82,6,250,4,0,0,"support","low","left"
"14341",14341,0.37,0.45,2,151,3,0,0,"support","low","left"
"14342",14342,0.1,0.83,6,292,4,0,0,"support","low","left"
"14343",14343,0.38,0.57,2,140,3,0,0,"support","low","left"
"14344",14344,0.9,1,5,221,5,0,0,"support","low","left"
"14345",14345,0.44,0.51,2,138,3,0,0,"support","low","left"
"14346",14346,0.36,0.5,2,132,3,0,0,"technical","low","left"
"14347",14347,0.31,0.84,7,133,5,0,0,"technical","low","left"
"14348",14348,0.1,0.84,6,283,4,1,0,"technical","low","left"
"14349",14349,0.42,0.48,2,129,3,0,0,"management","low","left"
"14350",14350,0.74,1,4,249,5,0,0,"IT","low","left"
"14351",14351,0.73,0.87,5,257,5,0,0,"IT","low","left"
"14352",14352,0.09,0.96,6,245,4,0,0,"IT","low","left"
"14353",14353,0.45,0.53,2,155,3,0,0,"IT","low","left"
"14354",14354,0.11,0.8,6,256,4,0,0,"IT","low","left"
"14355",14355,0.37,0.47,2,152,3,0,0,"product_mng","low","left"
"14356",14356,0.84,0.99,4,267,5,0,0,"product_mng","low","left"
"14357",14357,0.41,0.46,2,151,3,0,0,"product_mng","low","left"
"14358",14358,0.76,0.92,4,239,5,0,0,"product_mng","low","left"
"14359",14359,0.11,0.87,6,306,4,0,0,"IT","low","left"
"14360",14360,0.84,0.88,4,263,5,1,0,"marketing","low","left"
"14361",14361,0.39,0.5,2,147,3,0,0,"marketing","low","left"
"14362",14362,0.11,0.91,6,278,4,0,0,"marketing","low","left"
"14363",14363,0.45,0.56,2,154,3,0,0,"marketing","low","left"
"14364",14364,0.37,0.52,2,143,3,0,0,"marketing","low","left"
"14365",14365,0.4,0.52,2,155,3,0,0,"marketing","low","left"
"14366",14366,0.39,0.48,2,160,3,0,0,"sales","low","left"
"14367",14367,0.11,0.8,6,304,4,0,0,"accounting","low","left"
"14368",14368,0.83,1,5,240,5,0,0,"support","low","left"
"14369",14369,0.11,0.92,6,305,4,0,0,"technical","low","left"
"14370",14370,0.39,0.5,2,136,3,0,0,"management","low","left"
"14371",14371,0.45,0.45,2,132,3,0,0,"marketing","low","left"
"14372",14372,0.1,0.95,7,301,4,0,0,"marketing","low","left"
"14373",14373,0.9,0.98,5,243,6,0,0,"marketing","low","left"
"14374",14374,0.45,0.51,2,147,3,0,0,"sales","low","left"
"14375",14375,0.79,0.89,5,239,5,0,0,"sales","low","left"
"14376",14376,0.9,0.99,5,260,5,0,0,"sales","low","left"
"14377",14377,0.11,0.84,7,296,4,0,0,"sales","low","left"
"14378",14378,0.43,0.55,2,129,3,0,0,"sales","low","left"
"14379",14379,0.31,0.54,5,132,5,0,0,"sales","low","left"
"14380",14380,0.32,0.5,2,135,5,0,0,"sales","low","left"
"14381",14381,0.45,0.57,2,158,3,0,0,"sales","low","left"
"14382",14382,0.81,0.99,4,259,5,0,0,"sales","low","left"
"14383",14383,0.41,0.46,2,160,3,0,1,"sales","low","left"
"14384",14384,0.11,0.78,7,278,4,0,0,"sales","low","left"
"14385",14385,0.1,0.88,6,284,4,0,0,"sales","low","left"
"14386",14386,0.7,0.53,2,274,4,0,0,"sales","low","left"
"14387",14387,0.54,0.74,4,164,2,0,0,"sales","low","left"
"14388",14388,0.41,0.48,2,148,3,0,0,"sales","low","left"
"14389",14389,0.38,0.5,2,140,3,0,0,"sales","medium","left"
"14390",14390,0.37,0.51,2,127,3,0,0,"sales","medium","left"
"14391",14391,0.11,0.85,6,308,5,0,0,"sales","medium","left"
"14392",14392,0.4,0.47,2,146,3,0,0,"sales","medium","left"
"14393",14393,0.1,0.84,6,261,4,0,0,"accounting","medium","left"
"14394",14394,0.89,0.99,5,257,5,0,0,"accounting","medium","left"
"14395",14395,0.11,0.8,6,285,4,0,0,"accounting","medium","left"
"14396",14396,0.36,0.55,2,141,3,0,0,"hr","medium","left"
"14397",14397,0.4,0.46,2,127,3,0,0,"hr","medium","left"
"14398",14398,0.09,0.85,6,297,4,0,0,"hr","medium","left"
"14399",14399,0.4,0.46,2,143,3,0,0,"hr","medium","left"
"14400",14400,0.37,0.55,2,152,3,0,0,"technical","medium","left"
"14401",14401,0.44,0.51,2,156,3,0,0,"technical","high","left"
"14402",14402,0.09,0.8,7,283,5,0,0,"technical","low","left"
"14403",14403,0.92,0.87,4,226,6,1,0,"technical","medium","left"
"14404",14404,0.74,0.91,4,232,5,0,0,"technical","medium","left"
"14405",14405,0.09,0.82,6,249,4,0,0,"technical","medium","left"
"14406",14406,0.89,0.95,4,275,5,0,0,"technical","medium","left"
"14407",14407,0.09,0.8,6,304,4,0,0,"technical","low","left"
"14408",14408,0.27,0.54,7,278,3,0,0,"technical","low","left"
"14409",14409,0.1,0.91,6,287,4,0,0,"technical","low","left"
"14410",14410,0.1,0.89,7,285,4,0,0,"technical","low","left"
"14411",14411,0.77,0.94,5,226,6,0,0,"support","low","left"
"14412",14412,0.9,0.82,5,259,5,0,0,"support","low","left"
"14413",14413,0.39,0.5,2,135,3,0,0,"support","low","left"
"14414",14414,0.76,1,5,219,5,0,0,"support","low","left"
"14415",14415,0.1,0.93,6,256,4,0,0,"support","low","left"
"14416",14416,0.87,0.9,5,254,6,0,0,"support","low","left"
"14417",14417,0.38,0.5,2,153,3,0,0,"support","low","left"
"14418",14418,0.77,0.99,5,228,5,0,0,"support","low","left"
"14419",14419,0.78,0.87,4,228,5,0,0,"support","low","left"
"14420",14420,0.44,0.5,2,128,3,0,0,"support","low","left"
"14421",14421,0.38,0.52,2,153,3,0,0,"support","low","left"
"14422",14422,0.43,0.46,2,156,3,0,0,"technical","low","left"
"14423",14423,0.39,0.5,4,294,3,0,0,"technical","low","left"
"14424",14424,0.88,1,5,219,5,0,0,"technical","low","left"
"14425",14425,0.45,0.46,2,153,3,0,0,"management","low","left"
"14426",14426,0.4,0.53,2,151,3,0,0,"IT","low","left"
"14427",14427,0.36,0.51,2,155,3,0,0,"IT","low","left"
"14428",14428,0.36,0.48,2,158,3,0,0,"IT","low","left"
"14429",14429,0.9,0.98,5,245,5,0,0,"IT","low","left"
"14430",14430,0.43,0.53,2,131,3,0,0,"IT","low","left"
"14431",14431,0.89,0.87,5,225,5,0,0,"product_mng","low","left"
"14432",14432,0.1,0.84,6,286,4,0,0,"product_mng","low","left"
"14433",14433,0.37,0.5,2,135,3,0,0,"product_mng","low","left"
"14434",14434,0.37,0.51,2,153,3,0,0,"product_mng","low","left"
"14435",14435,0.87,0.9,5,252,5,0,0,"IT","low","left"
"14436",14436,0.4,0.56,2,149,3,0,0,"accounting","low","left"
"14437",14437,0.9,0.97,4,258,5,0,0,"accounting","low","left"
"14438",14438,0.37,0.46,2,158,3,0,0,"hr","low","left"
"14439",14439,0.44,0.54,2,149,3,0,0,"hr","low","left"
"14440",14440,0.85,0.95,5,236,5,0,0,"hr","low","left"
"14441",14441,0.78,0.98,5,239,6,0,0,"marketing","low","left"
"14442",14442,0.42,0.47,2,159,3,0,0,"marketing","low","left"
"14443",14443,0.92,0.99,5,255,6,0,0,"sales","low","left"
"14444",14444,0.11,0.83,6,244,4,0,0,"accounting","low","left"
"14445",14445,0.42,0.56,2,134,3,0,0,"support","low","left"
"14446",14446,0.48,0.57,4,270,4,0,0,"technical","low","left"
"14447",14447,0.83,0.85,4,255,5,0,0,"management","low","left"
"14448",14448,0.4,0.53,2,151,3,0,0,"marketing","low","left"
"14449",14449,0.43,0.45,2,135,3,0,0,"marketing","low","left"
"14450",14450,0.43,0.53,2,146,3,0,0,"marketing","low","left"
"14451",14451,0.1,0.97,7,254,4,0,0,"sales","low","left"
"14452",14452,0.1,0.87,7,289,4,0,0,"sales","low","left"
"14453",14453,0.37,0.46,2,156,3,0,0,"sales","low","left"
"14454",14454,0.38,0.53,2,156,3,0,0,"sales","low","left"
"14455",14455,0.4,0.5,2,128,3,0,0,"sales","low","left"
"14456",14456,0.89,0.86,5,275,5,0,0,"sales","low","left"
"14457",14457,0.45,0.46,2,155,3,0,0,"sales","low","left"
"14458",14458,0.37,0.48,2,159,3,0,0,"sales","low","left"
"14459",14459,0.46,0.49,2,148,3,0,0,"sales","low","left"
"14460",14460,0.87,0.91,4,228,5,0,0,"sales","low","left"
"14461",14461,0.11,0.84,6,298,4,0,0,"sales","low","left"
"14462",14462,0.79,0.87,5,261,5,0,0,"sales","low","left"
"14463",14463,0.79,0.92,5,254,6,0,0,"sales","low","left"
"14464",14464,0.19,0.59,7,192,3,0,0,"sales","low","left"
"14465",14465,0.87,0.98,4,248,5,0,0,"sales","low","left"
"14466",14466,0.6,0.92,2,258,5,0,0,"sales","low","left"
"14467",14467,0.44,0.45,2,156,3,0,0,"sales","medium","left"
"14468",14468,0.11,0.81,6,266,4,1,0,"sales","medium","left"
"14469",14469,0.42,0.54,2,156,3,0,0,"sales","medium","left"
"14470",14470,0.88,0.88,5,232,5,1,0,"accounting","medium","left"
"14471",14471,0.11,0.84,6,287,4,0,0,"accounting","medium","left"
"14472",14472,0.46,0.46,2,154,3,0,0,"accounting","medium","left"
"14473",14473,0.82,0.97,5,263,5,0,0,"hr","medium","left"
"14474",14474,0.44,0.56,2,131,3,0,0,"hr","medium","left"
"14475",14475,0.11,0.78,6,260,4,0,0,"hr","medium","left"
"14476",14476,0.42,0.5,2,139,3,0,0,"hr","medium","left"
"14477",14477,0.84,0.93,4,251,5,0,0,"technical","medium","left"
"14478",14478,0.11,0.95,6,286,4,0,0,"technical","medium","left"
"14479",14479,0.45,0.53,2,129,3,0,0,"technical","high","left"
"14480",14480,0.38,0.56,2,156,3,0,0,"technical","low","left"
"14481",14481,0.38,0.86,6,139,6,0,0,"technical","medium","left"
"14482",14482,0.44,0.51,2,127,3,0,0,"technical","medium","left"
"14483",14483,0.11,0.84,6,251,4,0,0,"technical","medium","left"
"14484",14484,0.81,0.93,5,270,5,0,0,"technical","medium","left"
"14485",14485,0.09,0.96,6,296,4,0,0,"technical","low","left"
"14486",14486,0.11,0.9,6,254,4,0,0,"technical","low","left"
"14487",14487,0.81,0.95,5,238,6,0,0,"technical","low","left"
"14488",14488,0.1,0.97,6,267,4,1,0,"support","low","left"
"14489",14489,0.74,0.89,5,229,6,0,0,"support","low","left"
"14490",14490,0.09,0.78,6,254,4,0,0,"support","low","left"
"14491",14491,0.82,0.81,4,233,4,1,0,"support","low","left"
"14492",14492,0.1,0.98,6,268,4,0,0,"support","low","left"
"14493",14493,0.27,0.56,3,301,3,0,0,"support","low","left"
"14494",14494,0.83,0.92,5,267,6,0,0,"support","low","left"
"14495",14495,0.1,0.93,6,289,4,1,0,"support","low","left"
"14496",14496,0.38,0.47,2,144,3,0,0,"support","low","left"
"14497",14497,0.4,0.56,2,148,3,0,0,"support","low","left"
"14498",14498,0.11,0.83,6,306,4,0,0,"support","low","left"
"14499",14499,0.11,0.79,6,292,4,0,1,"technical","low","left"
"14500",14500,0.82,0.91,5,232,5,0,0,"technical","low","left"
"14501",14501,0.36,0.48,2,137,3,0,0,"technical","low","left"
"14502",14502,0.4,0.46,2,128,3,0,0,"management","low","left"
"14503",14503,0.87,0.84,5,231,5,0,0,"IT","low","left"
"14504",14504,0.41,0.49,2,146,3,0,0,"IT","low","left"
"14505",14505,0.11,0.91,6,308,4,1,0,"IT","low","left"
"14506",14506,0.1,0.93,6,253,4,0,0,"IT","medium","left"
"14507",14507,0.38,0.51,2,146,3,0,0,"IT","medium","left"
"14508",14508,0.39,0.55,2,156,3,0,0,"product_mng","medium","left"
"14509",14509,0.4,0.52,2,147,3,0,0,"product_mng","medium","left"
"14510",14510,0.45,0.48,2,136,3,0,0,"product_mng","medium","left"
"14511",14511,0.74,0.84,5,249,5,0,0,"product_mng","medium","left"
"14512",14512,0.45,0.55,2,151,3,0,0,"IT","medium","left"
"14513",14513,0.12,1,3,278,4,0,0,"RandD","medium","left"
"14514",14514,0.1,0.77,7,250,5,0,0,"RandD","medium","left"
"14515",14515,0.37,0.55,2,127,3,0,0,"RandD","medium","left"
"14516",14516,0.89,0.87,5,255,5,0,0,"RandD","medium","left"
"14517",14517,0.45,0.47,2,135,3,0,0,"RandD","medium","left"
"14518",14518,0.37,0.46,2,149,3,0,0,"marketing","high","left"
"14519",14519,0.11,0.81,5,287,4,0,0,"sales","low","left"
"14520",14520,0.41,0.48,2,145,3,0,0,"accounting","medium","left"
"14521",14521,0.1,0.94,6,285,4,0,0,"support","medium","left"
"14522",14522,0.1,0.93,7,305,4,0,0,"technical","medium","left"
"14523",14523,0.11,0.95,7,300,4,0,0,"management","medium","left"
"14524",14524,0.4,0.54,2,139,3,0,0,"marketing","low","left"
"14525",14525,0.41,0.49,2,130,3,0,0,"marketing","low","left"
"14526",14526,0.1,0.81,6,268,4,0,0,"marketing","low","left"
"14527",14527,0.73,0.86,4,245,6,0,0,"sales","low","left"
"14528",14528,0.43,0.47,2,135,3,0,0,"sales","low","left"
"14529",14529,0.37,0.46,2,153,3,0,0,"sales","low","left"
"14530",14530,0.11,0.94,6,276,4,0,0,"sales","low","left"
"14531",14531,0.4,0.46,2,130,3,0,0,"sales","low","left"
"14532",14532,0.41,0.54,2,153,3,1,0,"sales","low","left"
"14533",14533,0.82,0.84,5,244,5,0,0,"sales","low","left"
"14534",14534,0.61,0.47,2,253,3,0,0,"sales","low","left"
"14535",14535,0.11,0.91,7,287,4,0,0,"sales","low","left"
"14536",14536,0.37,0.45,2,131,3,0,0,"sales","low","left"
"14537",14537,0.41,0.52,2,135,3,0,0,"sales","low","left"
"14538",14538,0.37,0.52,2,157,3,0,0,"sales","low","left"
"14539",14539,0.88,0.99,5,262,6,0,0,"sales","low","left"
"14540",14540,0.1,0.85,6,266,4,0,0,"sales","low","left"
"14541",14541,0.44,0.48,2,148,3,0,0,"sales","low","left"
"14542",14542,0.38,0.57,2,140,3,0,0,"sales","low","left"
"14543",14543,0.11,0.85,7,302,4,0,0,"sales","low","left"
"14544",14544,0.09,0.98,6,271,4,0,0,"sales","low","left"
"14545",14545,0.45,0.52,2,145,3,0,0,"sales","medium","left"
"14546",14546,0.1,0.81,6,290,4,0,0,"accounting","medium","left"
"14547",14547,0.45,0.47,2,151,3,0,0,"accounting","medium","left"
"14548",14548,0.77,0.87,5,266,5,0,0,"accounting","medium","left"
"14549",14549,0.44,0.51,2,140,3,0,0,"hr","medium","left"
"14550",14550,0.39,0.5,2,142,3,0,0,"hr","medium","left"
"14551",14551,0.1,0.91,6,246,4,0,0,"hr","medium","left"
"14552",14552,0.09,0.89,7,308,5,0,0,"hr","medium","left"
"14553",14553,0.37,0.47,2,141,3,0,0,"technical","medium","left"
"14554",14554,0.9,1,5,232,5,0,0,"technical","medium","left"
"14555",14555,0.41,0.56,2,143,3,0,0,"technical","medium","left"
"14556",14556,0.37,0.52,2,155,3,0,0,"technical","medium","left"
"14557",14557,0.1,0.86,6,278,4,0,0,"technical","high","left"
"14558",14558,0.81,1,4,253,5,0,0,"technical","low","left"
"14559",14559,0.11,0.8,6,282,4,0,0,"technical","medium","left"
"14560",14560,0.11,0.84,7,264,4,0,0,"technical","medium","left"
"14561",14561,0.4,0.46,2,149,3,0,0,"technical","medium","left"
"14562",14562,0.09,0.8,6,304,5,0,0,"technical","medium","left"
"14563",14563,0.48,0.93,3,219,6,0,0,"technical","low","left"
"14564",14564,0.91,0.91,4,262,6,0,0,"support","low","left"
"14565",14565,0.43,0.57,2,135,3,0,0,"support","low","left"
"14566",14566,0.33,0.88,6,219,5,0,0,"support","low","left"
"14567",14567,0.41,0.57,2,136,3,0,0,"support","low","left"
"14568",14568,0.41,0.55,2,154,3,0,0,"support","low","left"
"14569",14569,0.37,0.54,2,149,3,0,0,"support","low","left"
"14570",14570,0.31,0.62,6,135,5,0,0,"support","low","left"
"14571",14571,0.09,0.91,6,275,4,0,0,"support","low","left"
"14572",14572,0.1,0.87,6,290,4,0,0,"support","low","left"
"14573",14573,0.76,0.9,4,263,5,0,0,"support","low","left"
"14574",14574,0.41,0.54,2,145,3,0,0,"support","low","left"
"14575",14575,0.72,0.96,5,267,5,0,0,"technical","low","left"
"14576",14576,0.4,0.5,2,141,3,1,0,"technical","low","left"
"14577",14577,0.91,0.87,4,235,5,0,0,"technical","low","left"
"14578",14578,0.1,0.83,6,258,4,0,0,"management","low","left"
"14579",14579,0.4,0.56,2,131,3,0,0,"IT","low","left"
"14580",14580,0.82,0.86,5,243,5,0,0,"IT","low","left"
"14581",14581,0.1,0.82,6,266,4,0,0,"IT","low","left"
"14582",14582,0.37,0.45,2,142,3,0,0,"IT","low","left"
"14583",14583,0.36,0.51,2,135,3,0,0,"IT","low","left"
"14584",14584,0.39,0.48,2,141,3,0,0,"product_mng","medium","left"
"14585",14585,0.36,0.57,2,142,3,0,0,"product_mng","medium","left"
"14586",14586,0.86,0.84,5,254,5,0,0,"product_mng","medium","left"
"14587",14587,0.73,0.99,5,262,5,0,0,"product_mng","medium","left"
"14588",14588,0.56,0.71,4,296,2,0,0,"IT","medium","left"
"14589",14589,0.44,0.56,2,158,3,0,0,"accounting","medium","left"
"14590",14590,0.31,0.56,4,238,2,0,0,"accounting","medium","left"
"14591",14591,0.77,0.93,4,231,5,0,0,"hr","medium","left"
"14592",14592,0.44,0.45,2,156,3,0,0,"hr","medium","left"
"14593",14593,0.38,0.46,2,145,3,0,0,"hr","medium","left"
"14594",14594,0.45,0.48,2,144,3,0,0,"marketing","medium","left"
"14595",14595,0.38,0.51,2,159,3,0,0,"sales","medium","left"
"14596",14596,0.36,0.48,2,156,3,0,0,"accounting","high","left"
"14597",14597,0.75,0.9,5,256,5,0,0,"support","low","left"
"14598",14598,0.1,0.93,6,298,4,0,0,"technical","medium","left"
"14599",14599,0.1,0.97,6,247,4,0,0,"management","medium","left"
"14600",14600,0.45,0.5,2,157,3,0,0,"marketing","medium","left"
"14601",14601,0.42,0.57,2,154,3,1,0,"marketing","medium","left"
"14602",14602,0.78,1,4,253,5,0,0,"marketing","low","left"
"14603",14603,0.45,0.55,2,148,3,0,0,"sales","low","left"
"14604",14604,0.84,1,4,261,5,0,0,"sales","low","left"
"14605",14605,0.11,0.93,6,282,4,0,0,"sales","low","left"
"14606",14606,0.42,0.56,2,133,3,0,0,"sales","low","left"
"14607",14607,0.45,0.46,2,128,3,0,0,"sales","low","left"
"14608",14608,0.46,0.57,2,139,3,0,0,"sales","low","left"
"14609",14609,0.09,0.79,6,293,5,0,0,"sales","low","left"
"14610",14610,0.87,0.83,4,265,6,0,0,"sales","low","left"
"14611",14611,0.1,0.87,6,250,4,0,0,"sales","low","left"
"14612",14612,0.91,1,5,251,6,0,0,"sales","low","left"
"14613",14613,0.76,0.92,4,246,5,0,0,"sales","low","left"
"14614",14614,0.74,1,5,275,5,0,0,"sales","low","left"
"14615",14615,0.92,0.93,5,240,5,0,0,"sales","low","left"
"14616",14616,0.76,0.87,5,245,5,0,0,"sales","low","left"
"14617",14617,0.47,0.5,4,254,4,0,0,"sales","low","left"
"14618",14618,0.73,0.99,5,241,5,0,0,"sales","low","left"
"14619",14619,0.09,0.94,6,257,4,0,0,"sales","low","left"
"14620",14620,0.91,0.92,4,246,5,0,0,"sales","low","left"
"14621",14621,0.82,0.98,4,233,5,0,0,"sales","low","left"
"14622",14622,0.28,0.45,6,218,4,0,0,"accounting","low","left"
"14623",14623,0.84,0.99,4,262,6,0,0,"accounting","medium","left"
"14624",14624,0.45,0.53,2,138,3,0,0,"accounting","medium","left"
"14625",14625,0.45,0.54,2,142,3,0,0,"hr","medium","left"
"14626",14626,0.91,0.97,5,233,5,0,0,"hr","medium","left"
"14627",14627,0.42,0.48,2,155,3,0,0,"hr","medium","left"
"14628",14628,0.82,1,4,229,6,0,0,"hr","medium","left"
"14629",14629,0.11,0.9,6,264,4,0,0,"technical","medium","left"
"14630",14630,0.42,0.53,3,199,4,0,0,"technical","medium","left"
"14631",14631,0.82,0.85,4,223,5,0,0,"technical","medium","left"
"14632",14632,0.09,0.96,6,268,4,0,0,"technical","medium","left"
"14633",14633,0.1,0.94,6,287,4,0,0,"technical","medium","left"
"14634",14634,0.86,1,5,257,5,0,0,"technical","medium","left"
"14635",14635,0.4,0.46,2,143,3,0,0,"technical","high","left"
"14636",14636,0.45,0.46,2,130,3,0,0,"technical","low","left"
"14637",14637,0.42,0.51,2,136,3,0,0,"technical","medium","left"
"14638",14638,0.74,0.92,4,261,5,0,0,"technical","medium","left"
"14639",14639,0.55,0.6,3,180,4,0,0,"technical","medium","left"
"14640",14640,0.37,0.45,2,126,3,0,0,"support","medium","left"
"14641",14641,0.41,0.52,2,127,3,1,0,"support","low","left"
"14642",14642,0.89,0.65,5,195,6,0,0,"support","low","left"
"14643",14643,0.41,0.57,2,160,3,0,0,"support","low","left"
"14644",14644,0.44,0.51,2,150,3,0,0,"support","low","left"
"14645",14645,0.87,0.84,4,264,6,0,0,"support","low","left"
"14646",14646,0.1,0.84,6,309,4,0,0,"support","low","left"
"14647",14647,0.41,0.47,2,135,3,0,0,"support","low","left"
"14648",14648,0.11,0.85,6,261,4,0,0,"support","low","left"
"14649",14649,0.43,0.53,2,160,3,0,0,"support","low","left"
"14650",14650,0.77,0.9,4,237,5,0,0,"support","low","left"
"14651",14651,0.41,0.52,2,136,3,0,0,"technical","low","left"
"14652",14652,0.41,0.48,2,139,3,0,0,"technical","low","left"
"14653",14653,0.36,0.78,2,151,4,0,0,"technical","low","left"
"14654",14654,0.77,1,5,229,5,0,0,"management","low","left"
"14655",14655,0.81,0.98,5,245,5,0,0,"IT","low","left"
"14656",14656,0.39,0.54,2,127,3,0,0,"IT","low","left"
"14657",14657,0.09,0.94,6,283,5,0,0,"IT","low","left"
"14658",14658,0.44,0.46,2,143,3,0,0,"IT","low","left"
"14659",14659,0.1,0.84,5,298,4,0,0,"IT","low","left"
"14660",14660,0.36,0.48,2,159,3,0,0,"product_mng","low","left"
"14661",14661,0.81,0.92,5,239,5,0,0,"product_mng","low","left"
"14662",14662,0.81,0.9,4,226,5,0,0,"product_mng","medium","left"
"14663",14663,0.85,0.98,5,248,5,0,0,"product_mng","medium","left"
"14664",14664,0.1,0.87,6,286,4,0,0,"IT","medium","left"
"14665",14665,0.37,0.54,2,145,3,0,0,"RandD","medium","left"
"14666",14666,0.09,0.97,7,254,4,1,0,"RandD","medium","left"
"14667",14667,0.44,0.53,2,127,3,0,0,"RandD","medium","left"
"14668",14668,0.86,0.93,5,223,5,0,0,"RandD","medium","left"
"14669",14669,0.77,1,4,255,5,0,0,"RandD","medium","left"
"14670",14670,0.41,0.48,2,136,3,0,0,"marketing","medium","left"
"14671",14671,0.4,0.48,2,137,3,0,0,"sales","medium","left"
"14672",14672,0.43,0.49,2,135,3,0,0,"accounting","medium","left"
"14673",14673,0.43,0.5,2,137,3,0,0,"support","medium","left"
"14674",14674,0.8,0.53,3,255,5,0,0,"technical","high","left"
"14675",14675,0.8,0.85,4,273,5,0,0,"management","low","left"
"14676",14676,0.82,0.98,5,234,5,0,0,"marketing","medium","left"
"14677",14677,0.37,0.54,2,152,3,0,0,"marketing","medium","left"
"14678",14678,0.37,0.48,2,134,3,0,0,"marketing","medium","left"
"14679",14679,0.09,0.95,6,292,4,0,0,"sales","medium","left"
"14680",14680,0.9,0.92,5,245,5,0,0,"sales","low","left"
"14681",14681,0.41,0.52,2,159,3,0,0,"sales","low","left"
"14682",14682,0.1,0.85,6,260,4,0,0,"sales","low","left"
"14683",14683,0.44,0.53,2,149,3,0,0,"sales","low","left"
"14684",14684,0.89,0.85,5,266,5,0,0,"sales","low","left"
"14685",14685,0.42,0.56,2,149,3,0,0,"sales","low","left"
"14686",14686,0.87,1,5,242,5,0,0,"sales","low","left"
"14687",14687,0.45,0.57,2,134,3,0,0,"sales","low","left"
"14688",14688,0.11,0.87,5,271,4,0,0,"sales","low","left"
"14689",14689,0.09,0.79,6,275,4,0,0,"sales","low","left"
"14690",14690,0.76,0.83,5,227,5,0,0,"sales","low","left"
"14691",14691,0.11,0.96,7,277,5,0,0,"sales","low","left"
"14692",14692,0.37,0.49,2,151,3,0,0,"sales","low","left"
"14693",14693,0.1,0.79,6,274,4,0,0,"sales","low","left"
"14694",14694,0.77,0.87,4,242,6,0,0,"sales","low","left"
"14695",14695,0.42,0.54,2,143,3,1,0,"sales","low","left"
"14696",14696,0.38,0.52,2,145,3,0,0,"sales","low","left"
"14697",14697,0.32,0.95,5,172,2,0,0,"sales","low","left"
"14698",14698,0.38,0.49,2,135,3,0,0,"accounting","low","left"
"14699",14699,0.19,1,4,192,4,0,0,"accounting","low","left"
"14700",14700,0.1,0.83,7,276,4,0,0,"accounting","low","left"
"14701",14701,0.76,0.88,4,206,4,0,0,"hr","medium","left"
"14702",14702,0.53,0.56,4,281,6,0,0,"hr","medium","left"
"14703",14703,0.39,0.51,2,151,3,0,0,"hr","medium","left"
"14704",14704,0.11,0.83,6,244,4,0,0,"hr","medium","left"
"14705",14705,0.1,0.94,6,309,4,0,0,"technical","medium","left"
"14706",14706,0.84,1,5,218,5,0,0,"technical","medium","left"
"14707",14707,0.82,0.99,4,263,6,0,0,"technical","medium","left"
"14708",14708,0.1,0.82,6,244,4,0,0,"technical","medium","left"
"14709",14709,0.59,0.49,7,263,4,0,0,"technical","medium","left"
"14710",14710,0.44,0.48,2,143,3,0,0,"technical","medium","left"
"14711",14711,0.89,0.95,2,181,5,0,0,"technical","medium","left"
"14712",14712,0.91,0.84,5,265,5,0,0,"technical","medium","left"
"14713",14713,0.66,0.57,5,161,5,0,0,"technical","high","left"
"14714",14714,0.11,0.87,7,282,5,0,0,"technical","low","left"
"14715",14715,0.43,0.51,2,155,3,0,0,"technical","medium","left"
"14716",14716,0.78,0.83,4,217,6,0,0,"support","medium","left"
"14717",14717,0.11,0.97,6,289,5,0,0,"support","medium","left"
"14718",14718,0.83,0.98,4,259,5,0,0,"support","medium","left"
"14719",14719,0.39,0.54,2,158,3,0,0,"support","low","left"
"14720",14720,0.38,0.55,2,158,3,0,0,"support","low","left"
"14721",14721,0.37,0.57,2,155,3,0,0,"support","low","left"
"14722",14722,0.44,0.48,2,146,3,0,0,"support","low","left"
"14723",14723,0.53,0.85,2,164,5,0,0,"support","low","left"
"14724",14724,0.09,0.96,6,259,4,0,0,"support","low","left"
"14725",14725,0.11,0.89,6,293,4,0,0,"support","low","left"
"14726",14726,0.83,0.96,5,275,5,0,0,"support","low","left"
"14727",14727,0.88,1,5,219,6,1,0,"technical","low","left"
"14728",14728,0.1,0.89,6,247,4,0,0,"technical","low","left"
"14729",14729,0.09,0.86,7,309,4,0,0,"technical","low","left"
"14730",14730,0.44,0.54,2,151,3,0,0,"management","low","left"
"14731",14731,0.39,0.51,2,129,3,0,0,"IT","low","left"
"14732",14732,0.87,0.94,4,274,5,0,0,"IT","low","left"
"14733",14733,0.74,0.99,4,233,5,0,0,"IT","low","left"
"14734",14734,0.1,0.95,7,289,4,0,0,"IT","low","left"
"14735",14735,0.74,0.82,4,239,6,0,0,"IT","low","left"
"14736",14736,0.75,0.99,5,221,5,0,0,"product_mng","low","left"
"14737",14737,0.41,0.56,2,150,3,0,0,"product_mng","low","left"
"14738",14738,0.41,0.45,2,144,3,1,0,"product_mng","low","left"
"14739",14739,0.09,0.9,7,289,4,0,0,"product_mng","low","left"
"14740",14740,0.09,0.8,6,301,5,0,0,"IT","medium","left"
"14741",14741,0.39,0.57,2,145,3,0,0,"accounting","medium","left"
"14742",14742,0.4,0.56,2,137,3,0,0,"accounting","medium","left"
"14743",14743,0.37,0.54,2,131,3,1,0,"hr","medium","left"
"14744",14744,0.1,0.84,6,246,4,0,0,"hr","medium","left"
"14745",14745,0.43,0.51,2,136,3,0,0,"hr","medium","left"
"14746",14746,0.75,0.85,5,240,6,1,0,"marketing","medium","left"
"14747",14747,0.37,0.56,2,156,3,0,0,"sales","medium","left"
"14748",14748,0.11,0.85,6,305,4,0,0,"accounting","medium","left"
"14749",14749,0.45,0.45,2,154,3,1,0,"support","medium","left"
"14750",14750,0.87,1,5,261,5,1,0,"technical","medium","left"
"14751",14751,0.11,0.94,7,244,4,0,0,"management","medium","left"
"14752",14752,0.45,0.54,2,129,3,0,0,"marketing","high","left"
"14753",14753,0.81,0.87,4,254,5,0,0,"marketing","low","left"
"14754",14754,0.77,0.91,5,236,5,0,0,"marketing","medium","left"
"14755",14755,0.89,0.92,5,237,5,0,0,"sales","medium","left"
"14756",14756,0.43,0.49,2,135,3,0,0,"sales","medium","left"
"14757",14757,0.78,1,5,236,5,0,0,"sales","medium","left"
"14758",14758,0.37,0.47,2,149,3,0,0,"sales","low","left"
"14759",14759,0.37,0.5,2,141,3,0,0,"sales","low","left"
"14760",14760,0.85,0.82,4,270,5,0,0,"sales","low","left"
"14761",14761,0.41,0.47,2,138,3,0,0,"sales","low","left"
"14762",14762,0.11,0.96,6,298,4,0,0,"sales","low","left"
"14763",14763,0.75,0.99,5,254,5,0,0,"sales","low","left"
"14764",14764,0.82,0.85,5,248,5,0,0,"sales","low","left"
"14765",14765,0.79,1,5,257,6,0,0,"sales","low","left"
"14766",14766,0.43,0.53,2,150,3,0,0,"sales","low","left"
"14767",14767,0.1,0.9,7,281,4,0,0,"sales","low","left"
"14768",14768,0.46,0.48,2,141,3,1,0,"sales","low","left"
"14769",14769,0.43,0.57,2,157,3,0,0,"sales","low","left"
"14770",14770,0.43,0.55,2,136,3,0,0,"sales","low","left"
"14771",14771,0.11,0.8,7,296,4,0,0,"sales","low","left"
"14772",14772,0.09,0.86,6,279,4,0,0,"sales","low","left"
"14773",14773,0.37,0.53,2,131,3,0,0,"sales","low","left"
"14774",14774,0.4,0.57,2,160,3,0,0,"accounting","low","left"
"14775",14775,0.1,0.77,7,291,4,0,0,"accounting","low","left"
"14776",14776,0.41,0.53,2,157,3,0,0,"accounting","low","left"
"14777",14777,0.79,0.58,3,294,4,0,0,"hr","low","left"
"14778",14778,0.11,0.79,7,310,4,0,0,"hr","low","left"
"14779",14779,0.1,0.97,6,282,4,0,0,"hr","medium","left"
"14780",14780,0.44,0.51,2,134,3,0,0,"hr","medium","left"
"14781",14781,0.25,0.46,4,214,4,0,0,"technical","medium","left"
"14782",14782,0.44,0.52,2,137,3,0,0,"technical","medium","left"
"14783",14783,0.73,1,4,252,5,0,0,"technical","medium","left"
"14784",14784,0.75,0.97,5,243,6,0,0,"technical","medium","left"
"14785",14785,0.36,0.47,2,148,3,0,0,"technical","medium","left"
"14786",14786,0.37,0.49,2,151,3,0,0,"technical","medium","left"
"14787",14787,0.39,0.49,2,129,3,0,0,"technical","medium","left"
"14788",14788,0.48,0.78,2,198,2,0,0,"technical","medium","left"
"14789",14789,0.57,0.72,4,275,6,0,0,"technical","medium","left"
"14790",14790,0.9,0.96,5,243,5,0,0,"technical","medium","left"
"14791",14791,0.39,0.55,2,159,3,0,0,"technical","high","left"
"14792",14792,0.44,0.51,2,145,3,0,0,"support","low","left"
"14793",14793,0.81,0.88,5,242,5,0,0,"support","medium","left"
"14794",14794,0.74,0.87,5,242,5,0,0,"support","medium","left"
"14795",14795,0.44,0.56,2,145,3,0,0,"support","medium","left"
"14796",14796,0.41,0.56,2,154,3,0,1,"support","medium","left"
"14797",14797,0.4,0.51,2,139,3,0,0,"support","low","left"
"14798",14798,0.46,0.57,2,152,3,0,0,"support","low","left"
"14799",14799,0.8,0.83,2,211,3,0,0,"support","low","left"
"14800",14800,0.87,0.9,5,258,5,0,0,"support","low","left"
"14801",14801,0.39,0.54,2,155,3,0,0,"support","low","left"
"14802",14802,0.38,0.55,2,148,3,0,0,"support","low","left"
"14803",14803,0.66,0.67,2,255,3,0,0,"technical","low","left"
"14804",14804,0.1,0.8,6,264,4,0,0,"technical","low","left"
"14805",14805,0.37,0.54,2,132,3,0,0,"technical","low","left"
"14806",14806,0.1,0.77,6,255,4,0,0,"management","low","left"
"14807",14807,0.09,0.87,5,263,4,0,0,"IT","low","left"
"14808",14808,0.86,0.84,5,222,5,0,0,"IT","low","left"
"14809",14809,0.11,0.9,6,263,4,0,0,"IT","low","left"
"14810",14810,0.37,0.46,2,157,3,0,0,"IT","low","left"
"14811",14811,0.11,0.92,7,307,4,0,0,"IT","low","left"
"14812",14812,0.77,0.98,5,259,6,0,0,"product_mng","low","left"
"14813",14813,0.84,0.94,5,222,6,0,0,"product_mng","low","left"
"14814",14814,0.1,0.84,7,250,4,0,0,"product_mng","low","left"
"14815",14815,0.83,0.9,5,245,5,0,0,"product_mng","low","left"
"14816",14816,0.11,0.79,6,292,4,0,0,"IT","low","left"
"14817",14817,0.86,0.92,5,252,5,0,0,"RandD","low","left"
"14818",14818,0.38,0.56,2,161,3,0,0,"RandD","medium","left"
"14819",14819,0.11,0.88,5,250,4,0,0,"RandD","medium","left"
"14820",14820,0.45,0.49,2,134,3,0,0,"RandD","medium","left"
"14821",14821,0.1,0.85,7,279,4,0,0,"RandD","medium","left"
"14822",14822,0.09,0.95,7,256,4,0,0,"marketing","medium","left"
"14823",14823,0.39,0.53,2,127,3,0,0,"sales","medium","left"
"14824",14824,0.37,0.47,2,138,3,1,0,"accounting","medium","left"
"14825",14825,0.81,0.97,5,243,5,0,0,"support","medium","left"
"14826",14826,0.09,0.9,7,296,4,0,0,"technical","medium","left"
"14827",14827,0.1,0.88,7,267,4,0,0,"management","medium","left"
"14828",14828,0.39,0.49,2,144,3,0,0,"marketing","medium","left"
"14829",14829,0.83,0.95,4,251,5,0,0,"marketing","medium","left"
"14830",14830,0.45,0.57,2,148,3,0,0,"marketing","high","left"
"14831",14831,0.43,0.51,2,141,3,0,0,"sales","low","left"
"14832",14832,0.8,0.75,3,268,2,0,0,"sales","medium","left"
"14833",14833,0.1,0.86,6,247,4,0,0,"sales","medium","left"
"14834",14834,0.1,0.55,2,247,4,0,0,"sales","medium","left"
"14835",14835,0.36,0.52,2,146,3,0,0,"sales","medium","left"
"14836",14836,0.38,0.5,2,140,3,0,0,"sales","low","left"
"14837",14837,0.78,0.98,5,263,6,0,0,"sales","low","left"
"14838",14838,0.44,0.49,2,145,3,0,0,"sales","low","left"
"14839",14839,0.41,0.46,2,156,3,1,0,"sales","low","left"
"14840",14840,0.72,0.85,5,244,6,0,0,"sales","low","left"
"14841",14841,0.46,0.54,2,144,3,0,0,"sales","low","left"
"14842",14842,0.1,0.9,7,286,4,0,0,"sales","low","left"
"14843",14843,0.34,0.67,4,141,2,0,0,"sales","low","left"
"14844",14844,0.11,0.89,6,260,5,0,0,"sales","low","left"
"14845",14845,0.38,0.56,2,154,3,0,0,"sales","low","left"
"14846",14846,0.82,0.92,5,225,5,0,0,"sales","low","left"
"14847",14847,0.39,0.57,2,127,3,0,0,"sales","low","left"
"14848",14848,0.44,0.53,2,140,3,0,0,"sales","low","left"
"14849",14849,0.43,0.52,2,147,3,0,0,"sales","low","left"
"14850",14850,0.84,0.83,4,227,5,0,0,"accounting","low","left"
"14851",14851,0.43,0.48,2,153,3,0,0,"accounting","low","left"
"14852",14852,0.37,0.52,2,128,3,0,0,"accounting","low","left"
"14853",14853,0.74,0.97,4,228,5,0,0,"hr","low","left"
"14854",14854,0.73,0.97,5,235,5,0,0,"hr","low","left"
"14855",14855,0.37,0.47,2,148,3,0,0,"hr","low","left"
"14856",14856,0.58,0.62,4,238,3,0,0,"hr","low","left"
"14857",14857,0.4,0.54,2,141,3,0,0,"technical","medium","left"
"14858",14858,0.51,0.83,5,249,4,0,0,"technical","medium","left"
"14859",14859,0.46,0.5,2,151,3,0,0,"technical","medium","left"
"14860",14860,0.45,0.54,2,129,3,0,0,"technical","medium","left"
"14861",14861,0.46,0.5,2,156,3,0,0,"technical","medium","left"
"14862",14862,0.39,0.45,2,134,3,0,0,"technical","medium","left"
"14863",14863,0.09,0.88,6,269,4,0,0,"technical","medium","left"
"14864",14864,0.09,0.77,6,290,4,0,0,"technical","medium","left"
"14865",14865,0.37,0.51,2,132,3,0,0,"technical","medium","left"
"14866",14866,0.1,0.89,7,308,4,0,0,"technical","medium","left"
"14867",14867,0.77,1,4,232,5,0,0,"technical","medium","left"
"14868",14868,0.79,0.86,5,235,5,0,0,"support","medium","left"
"14869",14869,0.43,0.55,2,130,3,0,0,"support","high","left"
"14870",14870,0.38,0.53,2,146,3,0,0,"support","low","left"
"14871",14871,0.77,0.91,5,221,6,0,0,"support","medium","left"
"14872",14872,0.44,0.5,2,130,3,0,0,"support","medium","left"
"14873",14873,0.39,0.46,2,136,3,0,0,"support","medium","left"
"14874",14874,0.1,0.79,6,275,4,0,0,"management","low","left"
"14875",14875,0.1,0.9,6,299,4,0,0,"marketing","low","left"
"14876",14876,0.36,0.49,2,147,3,0,0,"marketing","low","left"
"14877",14877,0.1,0.97,7,306,4,0,0,"marketing","low","left"
"14878",14878,0.84,1,5,242,5,0,0,"sales","low","left"
"14879",14879,0.38,0.51,2,159,3,0,0,"sales","low","left"
"14880",14880,0.41,0.49,2,147,3,0,0,"sales","low","left"
"14881",14881,0.37,0.51,2,154,3,1,0,"sales","low","left"
"14882",14882,0.43,0.56,2,129,3,0,0,"sales","low","left"
"14883",14883,0.46,0.53,2,161,3,0,0,"sales","low","left"
"14884",14884,0.09,0.84,6,269,4,0,0,"sales","low","left"
"14885",14885,0.78,0.86,5,274,5,0,0,"sales","low","left"
"14886",14886,0.45,0.53,2,159,3,0,0,"sales","low","left"
"14887",14887,0.42,0.47,2,135,3,0,0,"sales","low","left"
"14888",14888,0.46,0.53,2,147,3,0,0,"sales","low","left"
"14889",14889,0.39,0.49,2,142,3,0,0,"sales","low","left"
"14890",14890,0.36,0.51,2,130,3,0,0,"sales","low","left"
"14891",14891,0.43,0.53,2,147,3,0,0,"sales","medium","left"
"14892",14892,0.85,0.87,5,246,5,1,0,"sales","medium","left"
"14893",14893,0.11,0.92,6,281,4,0,0,"sales","medium","left"
"14894",14894,0.11,0.9,6,253,4,0,0,"sales","medium","left"
"14895",14895,0.38,0.47,2,128,3,0,0,"sales","medium","left"
"14896",14896,0.43,0.57,2,129,3,0,0,"sales","medium","left"
"14897",14897,0.75,1,5,223,6,0,0,"accounting","medium","left"
"14898",14898,0.11,0.92,6,269,4,0,0,"accounting","medium","left"
"14899",14899,0.1,0.9,7,269,4,0,0,"accounting","medium","left"
"14900",14900,0.1,0.81,7,244,5,0,0,"hr","medium","left"
"14901",14901,0.37,0.5,2,154,3,0,0,"hr","medium","left"
"14902",14902,0.11,0.93,5,140,5,0,0,"hr","medium","left"
"14903",14903,0.45,0.46,2,159,3,0,0,"hr","high","left"
"14904",14904,0.44,0.48,2,158,3,0,0,"technical","low","left"
"14905",14905,0.44,0.56,2,133,3,0,0,"technical","medium","left"
"14906",14906,0.11,0.77,6,247,4,0,0,"technical","medium","left"
"14907",14907,0.79,0.93,5,268,5,0,0,"technical","medium","left"
"14908",14908,0.8,0.9,5,267,5,0,0,"technical","medium","left"
"14909",14909,0.1,0.87,7,251,5,0,0,"technical","low","left"
"14910",14910,0.09,0.93,6,279,4,0,0,"technical","low","left"
"14911",14911,0.7,0.84,6,161,4,0,0,"technical","low","left"
"14912",14912,0.72,0.84,4,256,5,0,0,"technical","low","left"
"14913",14913,0.11,0.8,6,304,4,0,0,"technical","low","left"
"14914",14914,0.39,0.51,2,137,3,0,0,"technical","low","left"
"14915",14915,0.4,0.49,2,144,3,0,0,"support","low","left"
"14916",14916,0.43,0.54,2,142,3,0,0,"support","low","left"
"14917",14917,0.76,0.87,5,262,5,0,0,"support","low","left"
"14918",14918,0.4,0.48,2,142,3,0,0,"support","low","left"
"14919",14919,0.09,0.89,6,282,4,0,0,"support","low","left"
"14920",14920,0.37,0.54,2,157,3,0,0,"support","low","left"
"14921",14921,0.87,0.91,5,228,5,0,0,"support","low","left"
"14922",14922,0.1,0.86,6,283,4,0,0,"support","low","left"
"14923",14923,0.11,0.86,6,286,4,0,0,"support","low","left"
"14924",14924,0.43,0.5,2,148,3,0,0,"support","low","left"
"14925",14925,0.1,0.81,6,245,4,0,0,"support","low","left"
"14926",14926,0.11,0.95,6,279,4,0,0,"technical","low","left"
"14927",14927,0.85,0.87,5,245,5,0,0,"technical","low","left"
"14928",14928,0.37,0.49,2,138,3,0,0,"technical","low","left"
"14929",14929,0.44,0.52,2,141,3,0,0,"management","low","left"
"14930",14930,0.1,0.83,7,302,5,0,0,"IT","medium","left"
"14931",14931,0.11,0.89,6,268,4,0,0,"IT","medium","left"
"14932",14932,0.87,0.88,5,240,5,0,0,"IT","medium","left"
"14933",14933,0.39,0.49,2,127,3,0,0,"IT","medium","left"
"14934",14934,0.1,0.94,7,264,4,0,0,"IT","medium","left"
"14935",14935,0.44,0.53,2,155,3,0,0,"product_mng","medium","left"
"14936",14936,0.4,0.49,2,143,3,0,0,"product_mng","medium","left"
"14937",14937,0.76,0.98,5,217,6,0,0,"product_mng","medium","left"
"14938",14938,0.46,0.55,2,147,3,0,0,"product_mng","medium","left"
"14939",14939,0.9,0.92,4,271,5,0,0,"IT","medium","left"
"14940",14940,0.85,0.87,4,273,5,0,0,"RandD","medium","left"
"14941",14941,0.1,0.78,5,285,4,1,0,"RandD","medium","left"
"14942",14942,0.43,0.49,2,131,3,0,0,"RandD","high","left"
"14943",14943,0.2,0.5,5,135,6,0,0,"RandD","low","left"
"14944",14944,0.81,0.92,5,239,5,0,0,"RandD","medium","left"
"14945",14945,0.83,0.85,5,237,5,0,0,"marketing","medium","left"
"14946",14946,0.14,0.75,4,277,5,1,0,"sales","medium","left"
"14947",14947,0.1,0.84,5,303,5,0,0,"accounting","medium","left"
"14948",14948,0.91,0.98,4,242,6,0,0,"support","low","left"
"14949",14949,0.37,0.57,2,158,3,0,0,"technical","low","left"
"14950",14950,0.42,0.57,2,147,3,1,0,"management","low","left"
"14951",14951,0.39,0.68,2,282,5,0,0,"marketing","low","left"
"14952",14952,0.39,0.54,2,154,3,0,0,"marketing","low","left"
"14953",14953,0.44,0.52,2,149,3,0,0,"marketing","low","left"
"14954",14954,0.37,0.45,2,149,3,0,0,"sales","low","left"
"14955",14955,0.39,0.53,2,146,3,0,0,"sales","low","left"
"14956",14956,0.72,0.94,4,258,5,0,0,"sales","low","left"
"14957",14957,0.37,0.49,2,148,3,0,0,"sales","low","left"
"14958",14958,0.82,0.94,5,236,5,0,0,"sales","low","left"
"14959",14959,0.42,0.52,2,134,3,0,0,"sales","low","left"
"14960",14960,0.59,1,2,155,5,0,0,"sales","low","left"
"14961",14961,0.82,0.86,5,257,5,0,0,"sales","low","left"
"14962",14962,0.73,0.97,6,189,2,0,0,"sales","low","left"
"14963",14963,0.78,0.66,3,164,3,0,0,"sales","low","left"
"14964",14964,0.09,0.95,6,271,4,0,0,"sales","low","left"
"14965",14965,0.1,0.97,6,280,4,0,0,"sales","low","left"
"14966",14966,0.45,0.46,2,149,3,0,0,"sales","low","left"
"14967",14967,0.83,0.81,5,219,5,0,0,"sales","low","left"
"14968",14968,0.43,0.51,2,128,3,0,0,"sales","low","left"
"14969",14969,0.4,0.47,2,128,3,0,0,"sales","medium","left"
"14970",14970,0.43,0.46,2,157,3,0,0,"sales","medium","left"
"14971",14971,0.78,0.93,4,225,5,0,0,"sales","medium","left"
"14972",14972,0.39,0.45,2,140,3,0,0,"sales","medium","left"
"14973",14973,0.11,0.97,6,310,4,0,0,"accounting","medium","left"
"14974",14974,0.36,0.52,2,143,3,0,0,"accounting","medium","left"
"14975",14975,0.36,0.54,2,153,3,0,0,"accounting","medium","left"
"14976",14976,0.1,0.79,7,310,4,0,0,"hr","medium","left"
"14977",14977,0.4,0.47,2,136,3,0,0,"hr","medium","left"
"14978",14978,0.81,0.85,4,251,6,0,0,"hr","medium","left"
"14979",14979,0.4,0.47,2,144,3,0,0,"hr","medium","left"
"14980",14980,0.09,0.93,6,296,4,0,0,"technical","medium","left"
"14981",14981,0.76,0.89,5,238,5,0,0,"technical","high","left"
"14982",14982,0.73,0.93,5,162,4,0,0,"technical","low","left"
"14983",14983,0.38,0.49,2,137,3,0,0,"technical","medium","left"
"14984",14984,0.72,0.84,5,257,5,0,0,"technical","medium","left"
"14985",14985,0.4,0.56,2,148,3,0,0,"technical","medium","left"
"14986",14986,0.91,0.99,5,254,5,0,0,"technical","medium","left"
"14987",14987,0.85,0.85,4,247,6,0,0,"technical","low","left"
"14988",14988,0.9,0.7,5,206,4,0,0,"technical","low","left"
"14989",14989,0.46,0.55,2,145,3,0,0,"technical","low","left"
"14990",14990,0.43,0.57,2,159,3,1,0,"technical","low","left"
"14991",14991,0.89,0.88,5,228,5,1,0,"support","low","left"
"14992",14992,0.09,0.81,6,257,4,0,0,"support","low","left"
"14993",14993,0.4,0.48,2,155,3,0,0,"support","low","left"
"14994",14994,0.76,0.83,6,293,6,0,0,"support","low","left"
"14995",14995,0.4,0.57,2,151,3,0,0,"support","low","left"
"14996",14996,0.37,0.48,2,160,3,0,0,"support","low","left"
"14997",14997,0.37,0.53,2,143,3,0,0,"support","low","left"
"14998",14998,0.11,0.96,6,280,4,0,0,"support","low","left"
"14999",14999,0.37,0.52,2,158,3,0,0,"support","low","left"


================================================
FILE: data/bakery.csv
================================================
daywk,weekday,sdate,cakes,pies,cookies,smoothies,coffee,promotion
Tuesday,2,01-01-13,79,46,518,60,233,none
Wednesday,3,02-01-13,91,50,539,161,427,none
Thursday,4,03-01-13,47,60,222,166,347,none
Friday,5,04-01-13,89,64,734,153,358,none
Saturday,6,05-01-13,112,73,764,240,392,none
Sunday,7,06-01-13,89,57,922,259,510,none
Monday,1,07-01-13,70,50,476,120,334,none
Tuesday,2,08-01-13,70,48,496,222,316,none
Wednesday,3,09-01-13,59,37,587,181,156,none
Thursday,4,10-01-13,71,36,488,178,298,none
Friday,5,11-01-13,74,50,645,100,490,none
Saturday,6,12-01-13,119,71,438,162,416,none
Sunday,7,13-01-13,90,51,568,137,434,none
Monday,1,14-01-13,96,48,585,194,573,promotion
Tuesday,2,15-01-13,62,56,536,112,287,none
Wednesday,3,16-01-13,48,33,336,151,420,none
Thursday,4,17-01-13,58,67,404,166,376,none
Friday,5,18-01-13,74,74,533,181,456,none
Saturday,6,19-01-13,94,45,470,208,584,none
Sunday,7,20-01-13,97,54,395,231,411,none
Monday,1,21-01-13,79,36,431,159,308,none
Tuesday,2,22-01-13,67,39,276,204,382,none
Wednesday,3,23-01-13,81,43,387,184,295,none
Thursday,4,24-01-13,71,42,456,114,365,none
Friday,5,25-01-13,95,58,647,113,362,none
Saturday,6,26-01-13,95,16,597,178,425,none
Sunday,7,27-01-13,93,66,470,192,355,none
Monday,1,28-01-13,60,46,590,203,255,none
Tuesday,2,29-01-13,79,38,535,141,287,none
Wednesday,3,30-01-13,80,40,397,204,382,none
Thursday,4,31-01-13,77,45,508,181,503,none
Friday,5,01-02-13,71,59,731,236,419,none
Saturday,6,02-02-13,89,62,602,213,329,none
Sunday,7,03-02-13,90,59,536,224,627,none
Monday,1,04-02-13,81,49,518,127,429,none
Tuesday,2,05-02-13,93,54,331,171,328,none
Wednesday,3,06-02-13,65,34,406,146,435,none
Thursday,4,07-02-13,61,50,430,160,376,none
Friday,5,08-02-13,112,70,601,267,581,none
Saturday,6,09-02-13,103,58,411,141,488,none
Sunday,7,10-02-13,113,60,440,187,387,none
Monday,1,11-02-13,63,43,334,209,236,none
Tuesday,2,12-02-13,72,49,424,184,460,none
Wednesday,3,13-02-13,93,61,599,218,388,promotion
Thursday,4,14-02-13,63,53,371,118,226,none
Friday,5,15-02-13,67,50,730,195,523,promotion
Saturday,6,16-02-13,66,71,820,225,568,none
Sunday,7,17-02-13,83,69,611,125,535,none
Monday,1,18-02-13,84,56,549,179,320,promotion
Tuesday,2,19-02-13,77,43,405,206,310,none
Wednesday,3,20-02-13,69,59,431,202,509,none
Thursday,4,21-02-13,94,45,632,260,200,promotion
Friday,5,22-02-13,93,44,689,159,420,none
Saturday,6,23-02-13,70,70,612,244,258,none
Sunday,7,24-02-13,58,51,599,203,331,none
Monday,1,25-02-13,63,57,502,143,330,none
Tuesday,2,26-02-13,68,34,411,99,282,none
Wednesday,3,27-02-13,66,56,551,159,445,none
Thursday,4,28-02-13,48,50,470,165,248,none
Friday,5,01-03-13,73,56,622,119,355,none
Saturday,6,02-03-13,120,84,597,247,506,promotion
Sunday,7,03-03-13,122,52,679,139,506,promotion
Monday,1,04-03-13,55,42,352,156,297,none
Tuesday,2,05-03-13,84,46,513,161,251,none
Wednesday,3,06-03-13,75,52,365,100,357,none
Thursday,4,07-03-13,44,45,392,126,317,none
Friday,5,08-03-13,87,49,653,241,455,none
Saturday,6,09-03-13,128,48,674,168,630,none
Sunday,7,10-03-13,103,59,762,206,481,none
Monday,1,11-03-13,62,54,356,137,231,none
Tuesday,2,12-03-13,92,50,605,171,388,none
Wednesday,3,13-03-13,83,52,527,150,485,none
Thursday,4,14-03-13,78,38,439,92,285,none
Friday,5,15-03-13,67,47,639,246,444,none
Saturday,6,16-03-13,116,51,557,291,381,none
Sunday,7,17-03-13,117,57,718,269,440,promotion
Monday,1,18-03-13,57,45,520,140,186,none
Tuesday,2,19-03-13,71,30,343,130,255,none
Wednesday,3,20-03-13,110,43,261,129,304,none
Thursday,4,21-03-13,84,37,420,179,462,none
Friday,5,22-03-13,81,44,515,224,483,none
Saturday,6,23-03-13,108,68,793,235,375,none
Sunday,7,24-03-13,87,46,585,281,455,none
Monday,1,25-03-13,80,46,308,117,374,none
Tuesday,2,26-03-13,61,37,440,196,322,none
Wednesday,3,27-03-13,95,23,529,142,231,none
Thursday,4,28-03-13,61,42,359,68,352,none
Friday,5,29-03-13,78,41,528,91,460,none
Saturday,6,30-03-13,88,33,817,271,225,none
Sunday,7,31-03-13,78,77,515,172,300,none
Monday,1,01-04-13,75,37,411,166,335,none
Tuesday,2,02-04-13,73,41,417,68,262,none
Wednesday,3,03-04-13,76,54,497,215,348,none
Thursday,4,04-04-13,83,46,417,191,500,none
Friday,5,05-04-13,101,72,519,289,439,none
Saturday,6,06-04-13,94,74,552,247,333,none
Sunday,7,07-04-13,89,53,487,253,435,none
Monday,1,08-04-13,95,54,594,147,282,promotion
Tuesday,2,09-04-13,64,63,284,147,254,none
Wednesday,3,10-04-13,75,36,522,149,370,none
Thursday,4,11-04-13,88,39,320,212,263,none
Friday,5,12-04-13,106,69,445,119,433,none
Saturday,6,13-04-13,101,74,653,215,444,none
Sunday,7,14-04-13,89,52,633,261,518,none
Monday,1,15-04-13,76,44,491,140,300,none
Tuesday,2,16-04-13,74,44,599,164,396,none
Wednesday,3,17-04-13,67,52,410,231,295,none
Thursday,4,18-04-13,87,38,564,305,339,none
Friday,5,19-04-13,107,45,609,288,412,none
Saturday,6,20-04-13,94,75,632,272,444,none
Sunday,7,21-04-13,77,53,606,257,313,none
Monday,1,22-04-13,73,36,360,180,475,none
Tuesday,2,23-04-13,51,47,631,147,253,none
Wednesday,3,24-04-13,72,44,460,232,391,none
Thursday,4,25-04-13,59,43,285,162,292,none
Friday,5,26-04-13,94,78,685,245,621,none
Saturday,6,27-04-13,79,68,400,252,385,none
Sunday,7,28-04-13,64,61,757,216,301,none
Monday,1,29-04-13,87,43,405,143,309,none
Tuesday,2,30-04-13,72,40,403,188,294,none
Wednesday,3,01-05-13,71,61,467,178,273,none
Thursday,4,02-05-13,57,49,418,251,295,none
Friday,5,03-05-13,100,44,434,205,467,none
Saturday,6,04-05-13,84,98,622,346,366,none
Sunday,7,05-05-13,140,41,635,226,372,none
Monday,1,06-05-13,83,46,588,159,357,none
Tuesday,2,07-05-13,118,45,461,201,183,none
Wednesday,3,08-05-13,73,43,463,277,257,none
Thursday,4,09-05-13,77,54,552,254,111,none
Friday,5,10-05-13,95,45,530,248,491,none
Saturday,6,11-05-13,90,52,431,303,525,none
Sunday,7,12-05-13,96,58,395,362,388,none
Monday,1,13-05-13,90,42,391,193,273,none
Tuesday,2,14-05-13,53,52,432,297,248,none
Wednesday,3,15-05-13,79,40,581,182,408,none
Thursday,4,16-05-13,66,55,525,195,248,none
Friday,5,17-05-13,82,50,740,272,276,none
Saturday,6,18-05-13,115,60,710,405,353,none
Sunday,7,19-05-13,61,41,433,300,326,none
Monday,1,20-05-13,96,45,394,293,516,none
Tuesday,2,21-05-13,64,48,363,194,287,none
Wednesday,3,22-05-13,77,68,611,145,506,promotion
Thursday,4,23-05-13,80,44,359,153,321,none
Friday,5,24-05-13,93,57,415,194,424,none
Saturday,6,25-05-13,112,61,568,377,565,none
Sunday,7,26-05-13,100,60,595,257,452,none
Monday,1,27-05-13,81,47,504,167,418,none
Tuesday,2,28-05-13,79,59,460,231,283,none
Wednesday,3,29-05-13,66,54,521,254,475,none
Thursday,4,30-05-13,45,59,547,173,419,none
Friday,5,31-05-13,82,59,305,98,294,none
Saturday,6,01-06-13,93,64,631,297,394,none
Sunday,7,02-06-13,102,59,612,322,456,none
Monday,1,03-06-13,45,36,417,313,282,none
Tuesday,2,04-06-13,58,50,385,198,263,none
Wednesday,3,05-06-13,95,43,373,185,438,none
Thursday,4,06-06-13,61,30,513,235,311,none
Friday,5,07-06-13,56,66,572,423,326,none
Saturday,6,08-06-13,77,76,601,379,617,none
Sunday,7,09-06-13,77,66,579,327,315,none
Monday,1,10-06-13,83,43,502,112,330,none
Tuesday,2,11-06-13,83,56,609,323,422,promotion
Wednesday,3,12-06-13,69,32,543,258,312,none
Thursday,4,13-06-13,61,52,322,289,294,none
Friday,5,14-06-13,76,85,534,385,353,none
Saturday,6,15-06-13,136,66,844,293,398,none
Sunday,7,16-06-13,75,65,604,219,398,none
Monday,1,17-06-13,89,40,452,211,321,none
Tuesday,2,18-06-13,43,40,458,299,344,none
Wednesday,3,19-06-13,81,38,489,202,225,none
Thursday,4,20-06-13,61,42,455,237,365,none
Friday,5,21-06-13,92,68,488,269,436,promotion
Saturday,6,22-06-13,117,75,477,352,424,none
Sunday,7,23-06-13,86,79,681,292,432,none
Monday,1,24-06-13,78,61,430,212,381,none
Tuesday,2,25-06-13,81,61,440,251,327,none
Wednesday,3,26-06-13,78,51,303,265,195,none
Thursday,4,27-06-13,79,43,429,234,469,none
Friday,5,28-06-13,98,67,632,184,495,none
Saturday,6,29-06-13,80,62,791,204,402,none
Sunday,7,30-06-13,113,82,590,147,489,promotion
Monday,1,01-07-13,76,52,504,347,366,none
Tuesday,2,02-07-13,80,46,546,160,291,none
Wednesday,3,03-07-13,55,47,369,226,359,none
Thursday,4,04-07-13,77,51,393,247,394,none
Friday,5,05-07-13,113,81,848,437,327,promotion
Saturday,6,06-07-13,90,58,550,353,357,none
Sunday,7,07-07-13,98,53,885,244,369,none
Monday,1,08-07-13,60,53,452,279,238,none
Tuesday,2,09-07-13,68,37,562,281,412,none
Wednesday,3,10-07-13,70,55,483,227,506,none
Thursday,4,11-07-13,76,29,393,316,445,none
Friday,5,12-07-13,99,52,572,224,327,none
Saturday,6,13-07-13,93,75,580,295,563,none
Sunday,7,14-07-13,123,71,561,389,566,promotion
Monday,1,15-07-13,77,55,564,172,308,none
Tuesday,2,16-07-13,79,41,379,199,302,none
Wednesday,3,17-07-13,82,40,411,239,236,none
Thursday,4,18-07-13,85,56,448,221,412,none
Friday,5,19-07-13,90,77,611,411,442,none
Saturday,6,20-07-13,65,56,613,303,446,none
Sunday,7,21-07-13,114,66,816,149,409,none
Monday,1,22-07-13,96,54,311,135,365,none
Tuesday,2,23-07-13,80,62,268,284,434,none
Wednesday,3,24-07-13,67,43,629,265,399,none
Thursday,4,25-07-13,76,52,556,265,421,none
Friday,5,26-07-13,101,52,640,224,494,none
Saturday,6,27-07-13,136,76,621,511,751,promotion
Sunday,7,28-07-13,93,48,523,339,381,none
Monday,1,29-07-13,83,61,469,345,402,none
Tuesday,2,30-07-13,77,46,453,250,420,none
Wednesday,3,31-07-13,70,41,506,198,227,none
Thursday,4,01-08-13,74,44,520,268,358,none
Friday,5,02-08-13,96,40,507,307,289,none
Saturday,6,03-08-13,78,47,785,277,548,none
Sunday,7,04-08-13,32,58,478,307,366,none
Monday,1,05-08-13,47,41,391,239,432,none
Tuesday,2,06-08-13,58,50,307,277,284,none
Wednesday,3,07-08-13,67,56,437,198,214,none
Thursday,4,08-08-13,77,62,462,389,418,promotion
Friday,5,09-08-13,90,54,586,186,270,none
Saturday,6,10-08-13,123,81,576,291,653,none
Sunday,7,11-08-13,81,58,752,317,371,none
Monday,1,12-08-13,73,29,309,257,278,none
Tuesday,2,13-08-13,85,44,503,192,302,none
Wednesday,3,14-08-13,63,48,361,263,273,none
Thursday,4,15-08-13,66,57,608,260,433,promotion
Friday,5,16-08-13,86,94,274,351,447,none
Saturday,6,17-08-13,87,65,650,439,349,none
Sunday,7,18-08-13,78,79,639,294,570,none
Monday,1,19-08-13,67,43,370,258,272,none
Tuesday,2,20-08-13,60,51,386,179,409,none
Wednesday,3,21-08-13,95,72,491,326,356,promotion
Thursday,4,22-08-13,88,41,551,223,431,none
Friday,5,23-08-13,68,61,548,191,424,none
Saturday,6,24-08-13,92,54,505,349,359,none
Sunday,7,25-08-13,72,46,522,426,259,none
Monday,1,26-08-13,75,45,366,301,359,none
Tuesday,2,27-08-13,73,48,617,271,180,promotion
Wednesday,3,28-08-13,62,54,507,261,349,promotion
Thursday,4,29-08-13,52,35,420,183,345,none
Friday,5,30-08-13,50,61,427,282,386,none
Saturday,6,31-08-13,118,55,547,173,519,none
Sunday,7,01-09-13,108,46,549,316,576,none
Monday,1,02-09-13,66,32,435,162,339,none
Tuesday,2,03-09-13,56,47,693,214,208,promotion
Wednesday,3,04-09-13,65,46,371,219,428,none
Thursday,4,05-09-13,42,51,522,231,239,none
Friday,5,06-09-13,84,63,336,300,256,none
Saturday,6,07-09-13,79,51,691,357,405,none
Sunday,7,08-09-13,88,41,633,177,340,none
Monday,1,09-09-13,64,49,539,249,331,none
Tuesday,2,10-09-13,93,47,402,138,275,none
Wednesday,3,11-09-13,73,59,460,234,315,none
Thursday,4,12-09-13,80,42,603,274,198,promotion
Friday,5,13-09-13,95,50,626,187,589,none
Saturday,6,14-09-13,89,59,725,184,394,none
Sunday,7,15-09-13,98,79,359,316,415,promotion
Monday,1,16-09-13,67,48,459,247,341,none
Tuesday,2,17-09-13,82,40,364,203,258,none
Wednesday,3,18-09-13,70,48,310,230,305,none
Thursday,4,19-09-13,75,32,509,216,257,none
Friday,5,20-09-13,91,46,387,237,477,none
Saturday,6,21-09-13,109,69,460,199,353,none
Sunday,7,22-09-13,91,53,665,161,209,none
Monday,1,23-09-13,59,42,647,216,394,promotion
Tuesday,2,24-09-13,69,47,437,208,267,none
Wednesday,3,25-09-13,81,52,247,197,359,none
Thursday,4,26-09-13,81,53,397,200,448,none
Friday,5,27-09-13,69,46,598,157,329,none
Saturday,6,28-09-13,68,44,702,330,475,none
Sunday,7,29-09-13,89,57,506,207,374,none
Monday,1,30-09-13,94,46,348,237,438,none
Tuesday,2,01-10-13,79,49,504,87,392,none
Wednesday,3,02-10-13,71,50,528,232,300,none
Thursday,4,03-10-13,75,66,433,342,288,none
Friday,5,04-10-13,98,35,743,167,413,none
Saturday,6,05-10-13,104,91,777,191,534,none
Sunday,7,06-10-13,110,53,796,217,378,none
Monday,1,07-10-13,59,37,551,153,339,none
Tuesday,2,08-10-13,58,41,633,192,375,none
Wednesday,3,09-10-13,86,58,507,172,253,none
Thursday,4,10-10-13,53,51,539,159,470,none
Friday,5,11-10-13,76,48,675,207,319,none
Saturday,6,12-10-13,102,69,624,397,432,promotion
Sunday,7,13-10-13,92,75,726,175,441,none
Monday,1,14-10-13,75,39,480,141,378,none
Tuesday,2,15-10-13,85,60,601,166,394,none
Wednesday,3,16-10-13,60,51,497,191,389,none
Thursday,4,17-10-13,99,41,453,191,349,none
Friday,5,18-10-13,101,57,452,176,325,none
Saturday,6,19-10-13,117,98,688,238,373,promotion
Sunday,7,20-10-13,100,81,630,325,448,promotion
Monday,1,21-10-13,51,50,648,216,324,none
Tuesday,2,22-10-13,84,39,596,213,412,promotion
Wednesday,3,23-10-13,73,46,304,264,348,none
Thursday,4,24-10-13,70,49,514,50,228,none
Friday,5,25-10-13,99,57,413,184,432,none
Saturday,6,26-10-13,102,71,564,213,399,none
Sunday,7,27-10-13,63,42,701,261,462,none
Monday,1,28-10-13,59,42,497,153,204,none
Tuesday,2,29-10-13,66,24,456,232,273,none
Wednesday,3,30-10-13,74,42,444,115,308,none
Thursday,4,31-10-13,66,43,443,221,305,none
Friday,5,01-11-13,48,79,512,167,446,none
Saturday,6,02-11-13,73,55,884,243,343,none
Sunday,7,03-11-13,106,62,348,199,380,none
Monday,1,04-11-13,80,57,485,111,443,none
Tuesday,2,05-11-13,84,38,665,182,363,none
Wednesday,3,06-11-13,67,46,730,169,328,none
Thursday,4,07-11-13,100,41,636,151,478,promotion
Friday,5,08-11-13,113,80,543,230,410,none
Saturday,6,09-11-13,114,67,674,206,545,none
Sunday,7,10-11-13,96,40,568,203,382,none
Monday,1,11-11-13,93,51,446,180,364,none
Tuesday,2,12-11-13,101,34,703,154,346,none
Wednesday,3,13-11-13,79,52,512,123,394,none
Thursday,4,14-11-13,72,53,497,167,302,none
Friday,5,15-11-13,96,51,803,160,365,none
Saturday,6,16-11-13,112,63,855,166,554,none
Sunday,7,17-11-13,121,58,694,175,405,none
Monday,1,18-11-13,73,44,526,197,423,none
Tuesday,2,19-11-13,77,49,513,72,355,none
Wednesday,3,20-11-13,77,41,388,99,289,none
Thursday,4,21-11-13,79,52,443,149,221,none
Friday,5,22-11-13,87,44,692,169,439,none
Saturday,6,23-11-13,141,88,645,340,771,promotion
Sunday,7,24-11-13,82,48,575,203,446,none
Monday,1,25-11-13,64,52,430,200,451,promotion
Tuesday,2,26-11-13,73,58,489,152,275,none
Wednesday,3,27-11-13,85,65,538,111,426,promotion
Thursday,4,28-11-13,81,57,409,104,403,none
Friday,5,29-11-13,97,54,647,182,412,none
Saturday,6,30-11-13,116,51,794,183,399,none
Sunday,7,01-12-13,81,29,694,220,367,none
Monday,1,02-12-13,92,46,358,126,404,none
Tuesday,2,03-12-13,78,41,342,154,428,none
Wednesday,3,04-12-13,71,51,407,155,195,none
Thursday,4,05-12-13,59,61,360,164,336,none
Friday,5,06-12-13,98,56,722,311,441,none
Saturday,6,07-12-13,99,71,684,202,217,none
Sunday,7,08-12-13,84,61,534,178,561,none
Monday,1,09-12-13,86,52,425,103,317,none
Tuesday,2,10-12-13,65,54,557,163,446,promotion
Wednesday,3,11-12-13,67,36,418,148,373,none
Thursday,4,12-12-13,76,53,476,186,346,none
Friday,5,13-12-13,103,66,538,201,391,none
Saturday,6,14-12-13,72,61,681,214,449,none
Sunday,7,15-12-13,82,61,515,259,529,none
Monday,1,16-12-13,84,46,322,148,318,none
Tuesday,2,17-12-13,65,43,487,195,300,none
Wednesday,3,18-12-13,66,51,478,181,317,none
Thursday,4,19-12-13,93,57,704,224,564,promotion
Friday,5,20-12-13,90,62,628,237,408,none
Saturday,6,21-12-13,86,65,613,147,310,none
Sunday,7,22-12-13,100,50,532,175,535,none
Monday,1,23-12-13,58,27,329,133,341,none
Tuesday,2,24-12-13,84,49,476,231,357,none
Wednesday,3,25-12-13,84,63,591,98,286,promotion
Thursday,4,26-12-13,76,47,455,131,337,none
Friday,5,27-12-13,96,50,270,149,384,none
Saturday,6,28-12-13,101,76,658,206,518,none
Sunday,7,29-12-13,114,43,514,180,493,none
Monday,1,30-12-13,85,51,438,157,390,none
Tuesday,2,31-12-13,71,51,506,181,363,none
Wednesday,3,01-01-14,85,52,475,146,331,none
Thursday,4,02-01-14,56,47,461,129,409,none
Friday,5,03-01-14,86,71,752,266,536,none
Saturday,6,04-01-14,51,79,354,282,512,none
Sunday,7,05-01-14,75,64,466,205,288,none
Monday,1,06-01-14,84,62,475,177,379,none
Tuesday,2,07-01-14,84,42,408,126,445,none
Wednesday,3,08-01-14,85,31,653,178,387,none
Thursday,4,09-01-14,78,56,620,203,326,promotion
Friday,5,10-01-14,51,51,661,128,375,none
Saturday,6,11-01-14,122,85,644,235,719,promotion
Sunday,7,12-01-14,123,65,556,187,405,none
Monday,1,13-01-14,71,43,412,70,478,none
Tuesday,2,14-01-14,80,46,375,198,302,none
Wednesday,3,15-01-14,73,33,671,167,513,promotion
Thursday,4,16-01-14,62,38,508,98,332,none
Friday,5,17-01-14,87,52,525,185,406,none
Saturday,6,18-01-14,88,71,661,184,609,none
Sunday,7,19-01-14,95,53,441,152,456,none
Monday,1,20-01-14,89,39,542,174,491,none
Tuesday,2,21-01-14,56,39,393,148,328,none
Wednesday,3,22-01-14,111,51,461,180,218,promotion
Thursday,4,23-01-14,63,43,426,172,459,none
Friday,5,24-01-14,85,62,576,150,466,none
Saturday,6,25-01-14,101,43,714,216,419,none
Sunday,7,26-01-14,93,84,407,238,382,none
Monday,1,27-01-14,94,54,524,224,372,none
Tuesday,2,28-01-14,78,43,425,163,327,none
Wednesday,3,29-01-14,100,50,487,190,343,none
Thursday,4,30-01-14,77,62,459,181,443,none
Friday,5,31-01-14,70,62,500,267,505,none
Saturday,6,01-02-14,98,54,754,186,603,none
Sunday,7,02-02-14,92,70,572,209,506,none
Monday,1,03-02-14,80,71,555,166,284,none
Tuesday,2,04-02-14,58,41,554,176,263,none
Wednesday,3,05-02-14,90,49,407,75,443,none
Thursday,4,06-02-14,79,49,517,102,320,none
Friday,5,07-02-14,73,44,397,136,507,none
Saturday,6,08-02-14,87,72,752,180,568,none
Sunday,7,09-02-14,94,68,546,214,582,none
Monday,1,10-02-14,77,37,462,136,356,none
Tuesday,2,11-02-14,60,57,422,160,376,none
Wednesday,3,12-02-14,99,50,548,204,365,promotion
Thursday,4,13-02-14,63,44,484,128,397,none
Friday,5,14-02-14,89,77,660,176,398,none
Saturday,6,15-02-14,121,63,690,125,493,none
Sunday,7,16-02-14,106,66,340,142,374,none
Monday,1,17-02-14,50,53,495,129,268,promotion
Tuesday,2,18-02-14,47,34,564,173,219,none
Wednesday,3,19-02-14,63,51,442,193,397,none
Thursday,4,20-02-14,77,51,324,151,392,none
Friday,5,21-02-14,111,40,510,205,493,none
Saturday,6,22-02-14,67,73,288,200,561,none
Sunday,7,23-02-14,116,45,458,205,506,none
Monday,1,24-02-14,80,58,680,178,330,none
Tuesday,2,25-02-14,90,50,490,205,399,none
Wednesday,3,26-02-14,79,39,368,165,282,none
Thursday,4,27-02-14,87,24,525,117,428,none
Friday,5,28-02-14,90,51,464,240,495,none
Saturday,6,01-03-14,93,86,684,95,405,none
Sunday,7,02-03-14,87,57,818,217,507,none
Monday,1,03-03-14,97,59,561,167,500,promotion
Tuesday,2,04-03-14,70,38,509,174,361,none
Wednesday,3,05-03-14,72,64,687,178,401,promotion
Thursday,4,06-03-14,69,66,330,162,320,none
Friday,5,07-03-14,113,64,679,137,320,none
Saturday,6,08-03-14,100,64,707,253,368,none
Sunday,7,09-03-14,80,64,593,187,299,none
Monday,1,10-03-14,94,54,505,177,526,promotion
Tuesday,2,11-03-14,78,46,324,126,471,none
Wednesday,3,12-03-14,47,65,562,123,280,none
Thursday,4,13-03-14,96,71,746,162,468,promotion
Friday,5,14-03-14,89,59,776,276,516,none
Saturday,6,15-03-14,72,88,596,209,299,none
Sunday,7,16-03-14,75,60,534,227,366,none
Monday,1,17-03-14,96,38,579,147,486,promotion
Tuesday,2,18-03-14,75,44,552,108,254,none
Wednesday,3,19-03-14,73,46,651,178,448,none
Thursday,4,20-03-14,56,29,414,169,453,none
Friday,5,21-03-14,74,40,752,165,471,none
Saturday,6,22-03-14,116,53,760,243,375,none
Sunday,7,23-03-14,80,61,691,246,492,none
Monday,1,24-03-14,78,58,479,144,245,none
Tuesday,2,25-03-14,59,51,582,197,362,none
Wednesday,3,26-03-14,58,45,519,206,412,none
Thursday,4,27-03-14,80,55,461,194,286,none
Friday,5,28-03-14,122,45,561,201,548,none
Saturday,6,29-03-14,98,42,581,186,434,none
Sunday,7,30-03-14,97,60,519,127,397,promotion
Monday,1,31-03-14,74,53,446,206,346,none
Tuesday,2,01-04-14,85,38,420,203,352,none
Wednesday,3,02-04-14,76,57,378,186,262,none
Thursday,4,03-04-14,74,53,530,164,382,none
Friday,5,04-04-14,108,66,522,224,465,none
Saturday,6,05-04-14,109,57,698,286,499,none
Sunday,7,06-04-14,96,46,535,257,531,none
Monday,1,07-04-14,78,54,541,202,440,none
Tuesday,2,08-04-14,61,41,435,162,205,none
Wednesday,3,09-04-14,52,34,437,219,348,none
Thursday,4,10-04-14,88,43,529,182,306,none
Friday,5,11-04-14,107,51,603,199,519,none
Saturday,6,12-04-14,94,53,520,181,592,none
Sunday,7,13-04-14,123,42,624,256,288,none
Monday,1,14-04-14,90,58,398,175,258,none
Tuesday,2,15-04-14,40,48,597,247,214,none
Wednesday,3,16-04-14,86,28,516,203,307,none
Thursday,4,17-04-14,89,57,397,183,409,none
Friday,5,18-04-14,80,59,548,218,224,none
Saturday,6,19-04-14,109,66,499,291,577,none
Sunday,7,20-04-14,86,46,545,265,519,none
Monday,1,21-04-14,85,65,610,115,410,none
Tuesday,2,22-04-14,71,56,448,320,135,none
Wednesday,3,23-04-14,100,35,530,215,339,none
Thursday,4,24-04-14,83,46,421,210,338,none
Friday,5,25-04-14,99,44,424,177,567,none
Saturday,6,26-04-14,110,37,603,311,633,none
Sunday,7,27-04-14,125,44,761,147,344,none
Monday,1,28-04-14,73,59,529,173,297,none
Tuesday,2,29-04-14,85,44,456,143,438,none
Wednesday,3,30-04-14,82,31,488,93,396,none
Thursday,4,01-05-14,65,52,552,270,379,promotion
Friday,5,02-05-14,112,66,523,136,324,none
Saturday,6,03-05-14,122,49,690,449,279,none
Sunday,7,04-05-14,86,36,670,334,275,none
Monday,1,05-05-14,75,43,460,202,174,none
Tuesday,2,06-05-14,98,26,456,205,175,none
Wednesday,3,07-05-14,73,57,529,302,271,none
Thursday,4,08-05-14,77,39,397,288,401,none
Friday,5,09-05-14,90,64,559,249,371,none
Saturday,6,10-05-14,150,77,718,271,505,none
Sunday,7,11-05-14,81,67,629,184,377,none
Monday,1,12-05-14,77,31,401,120,421,none
Tuesday,2,13-05-14,105,44,484,196,338,none
Wednesday,3,14-05-14,101,40,686,231,322,promotion
Thursday,4,15-05-14,87,45,545,128,488,promotion
Friday,5,16-05-14,90,47,250,235,243,none
Saturday,6,17-05-14,94,66,649,357,562,promotion
Sunday,7,18-05-14,106,60,466,267,458,none
Monday,1,19-05-14,70,67,275,260,308,none
Tuesday,2,20-05-14,86,45,364,243,446,none
Wednesday,3,21-05-14,78,59,627,313,411,promotion
Thursday,4,22-05-14,83,54,447,244,295,none
Friday,5,23-05-14,106,50,837,159,370,none
Saturday,6,24-05-14,118,41,296,145,346,none
Sunday,7,25-05-14,97,59,556,255,240,none
Monday,1,26-05-14,106,28,606,181,345,none
Tuesday,2,27-05-14,75,48,480,145,418,none
Wednesday,3,28-05-14,82,46,484,279,444,promotion
Thursday,4,29-05-14,77,42,568,150,232,none
Friday,5,30-05-14,107,54,659,280,420,promotion
Saturday,6,31-05-14,90,75,738,314,312,promotion
Sunday,7,01-06-14,114,42,706,325,494,none
Monday,1,02-06-14,45,34,374,303,326,none
Tuesday,2,03-06-14,85,48,451,323,431,none
Wednesday,3,04-06-14,52,54,551,323,290,none
Thursday,4,05-06-14,90,56,504,271,401,none
Friday,5,06-06-14,112,86,804,440,357,none
Saturday,6,07-06-14,92,70,602,281,460,none
Sunday,7,08-06-14,93,59,517,341,400,none
Monday,1,09-06-14,79,31,445,210,448,none
Tuesday,2,10-06-14,58,34,526,288,458,none
Wednesday,3,11-06-14,103,43,449,288,443,none
Thursday,4,12-06-14,75,50,360,264,371,none
Friday,5,13-06-14,100,51,552,302,497,none
Saturday,6,14-06-14,106,95,896,213,555,promotion
Sunday,7,15-06-14,115,66,512,318,536,none
Monday,1,16-06-14,68,33,453,218,359,none
Tuesday,2,17-06-14,89,39,404,333,340,none
Wednesday,3,18-06-14,89,59,311,237,329,none
Thursday,4,19-06-14,83,48,403,181,494,none
Friday,5,20-06-14,108,79,575,347,283,none
Saturday,6,21-06-14,57,66,794,400,446,none
Sunday,7,22-06-14,139,75,693,320,487,none
Monday,1,23-06-14,52,47,430,212,291,none
Tuesday,2,24-06-14,62,46,447,196,379,none
Wednesday,3,25-06-14,91,61,514,244,302,none
Thursday,4,26-06-14,82,37,375,207,380,none
Friday,5,27-06-14,84,50,376,318,470,none
Saturday,6,28-06-14,88,66,640,253,405,none
Sunday,7,29-06-14,99,62,568,288,497,none
Monday,1,30-06-14,79,51,398,285,342,none
Tuesday,2,01-07-14,88,32,502,197,310,none
Wednesday,3,02-07-14,59,51,471,339,338,none
Thursday,4,03-07-14,61,32,493,225,484,none
Friday,5,04-07-14,81,66,590,386,483,none
Saturday,6,05-07-14,124,56,693,393,219,none
Sunday,7,06-07-14,107,69,1152,420,499,promotion
Monday,1,07-07-14,52,52,526,247,246,none
Tuesday,2,08-07-14,69,36,490,336,448,none
Wednesday,3,09-07-14,96,31,481,125,252,promotion
Thursday,4,10-07-14,89,72,609,252,402,promotion
Friday,5,11-07-14,54,60,468,195,532,none
Saturday,6,12-07-14,133,38,535,322,521,none
Sunday,7,13-07-14,105,48,634,265,324,none
Monday,1,14-07-14,63,49,537,378,330,none
Tuesday,2,15-07-14,104,34,541,370,314,none
Wednesday,3,16-07-14,81,45,338,243,352,none
Thursday,4,17-07-14,42,38,396,317,395,none
Friday,5,18-07-14,116,66,538,335,334,none
Saturday,6,19-07-14,82,56,555,407,490,none
Sunday,7,20-07-14,103,57,590,344,562,none
Monday,1,21-07-14,85,57,490,238,257,none
Tuesday,2,22-07-14,58,43,435,224,398,none
Wednesday,3,23-07-14,63,48,479,311,388,none
Thursday,4,24-07-14,84,53,449,281,316,none
Friday,5,25-07-14,119,56,743,298,135,none
Saturday,6,26-07-14,107,54,438,191,458,none
Sunday,7,27-07-14,122,69,662,298,500,none
Monday,1,28-07-14,61,45,406,305,396,none
Tuesday,2,29-07-14,77,49,392,244,316,none
Wednesday,3,30-07-14,59,50,554,172,440,none
Thursday,4,31-07-14,90,68,561,305,272,promotion
Friday,5,01-08-14,89,59,671,290,421,none
Saturday,6,02-08-14,74,47,707,465,406,none
Sunday,7,03-08-14,87,53,451,412,561,none
Monday,1,04-08-14,85,59,435,262,428,none
Tuesday,2,05-08-14,96,52,533,254,539,promotion
Wednesday,3,06-08-14,80,35,295,299,428,none
Thursday,4,07-08-14,64,31,477,146,443,none
Friday,5,08-08-14,80,90,559,468,486,promotion
Saturday,6,09-08-14,105,73,789,336,504,none
Sunday,7,10-08-14,115,54,714,235,441,none
Monday,1,11-08-14,95,49,548,304,308,none
Tuesday,2,12-08-14,81,32,353,200,347,none
Wednesday,3,13-08-14,73,63,445,274,317,none
Thursday,4,14-08-14,95,56,401,237,416,none
Friday,5,15-08-14,100,67,483,328,475,none
Saturday,6,16-08-14,98,47,655,460,578,promotion
Sunday,7,17-08-14,102,49,559,247,450,none
Monday,1,18-08-14,85,55,571,209,249,none
Tuesday,2,19-08-14,79,51,530,280,334,none
Wednesday,3,20-08-14,84,64,482,146,294,none
Thursday,4,21-08-14,88,26,429,186,435,none
Friday,5,22-08-14,92,32,578,278,478,none
Saturday,6,23-08-14,127,78,701,311,418,none
Sunday,7,24-08-14,126,86,560,551,546,promotion
Monday,1,25-08-14,78,52,623,327,445,none
Tuesday,2,26-08-14,69,68,502,212,499,none
Wednesday,3,27-08-14,90,40,490,333,393,none
Thursday,4,28-08-14,86,52,552,277,365,none
Friday,5,29-08-14,99,45,576,353,442,promotion
Saturday,6,30-08-14,74,59,886,250,615,none
Sunday,7,31-08-14,137,58,698,385,581,promotion
Monday,1,01-09-14,90,52,351,221,229,none
Tuesday,2,02-09-14,108,71,555,212,322,promotion
Wednesday,3,03-09-14,82,46,634,133,333,none
Thursday,4,04-09-14,82,34,445,211,214,none
Friday,5,05-09-14,124,82,759,204,426,none
Saturday,6,06-09-14,90,80,465,312,355,none
Sunday,7,07-09-14,85,42,511,298,303,none
Monday,1,08-09-14,70,40,467,140,219,none
Tuesday,2,09-09-14,94,40,681,210,458,none
Wednesday,3,10-09-14,51,46,570,172,347,none
Thursday,4,11-09-14,96,52,294,173,214,none
Friday,5,12-09-14,105,60,788,157,416,none
Saturday,6,13-09-14,90,95,422,189,443,none
Sunday,7,14-09-14,72,62,576,206,486,none
Monday,1,15-09-14,86,29,575,288,335,none
Tuesday,2,16-09-14,99,37,397,200,360,none
Wednesday,3,17-09-14,68,55,395,198,442,none
Thursday,4,18-09-14,43,48,441,251,420,none
Friday,5,19-09-14,104,68,697,306,334,none
Saturday,6,20-09-14,142,87,555,230,638,promotion
Sunday,7,21-09-14,113,54,656,216,601,none
Monday,1,22-09-14,68,63,494,256,422,none
Tuesday,2,23-09-14,67,60,370,270,374,none
Wednesday,3,24-09-14,96,43,590,248,281,none
Thursday,4,25-09-14,95,34,501,180,526,none
Friday,5,26-09-14,102,53,514,279,338,none
Saturday,6,27-09-14,99,49,769,275,537,none
Sunday,7,28-09-14,81,64,697,138,293,none
Monday,1,29-09-14,106,43,600,161,435,none
Tuesday,2,30-09-14,66,40,626,125,361,none
Wednesday,3,01-10-14,87,48,440,193,306,none
Thursday,4,02-10-14,76,29,535,240,402,none
Friday,5,03-10-14,96,71,631,267,274,none
Saturday,6,04-10-14,102,77,444,230,687,none
Sunday,7,05-10-14,113,53,689,272,617,none
Monday,1,06-10-14,84,61,585,148,397,none
Tuesday,2,07-10-14,75,51,482,176,241,none
Wednesday,3,08-10-14,74,22,376,184,461,none
Thursday,4,09-10-14,62,65,625,206,253,none
Friday,5,10-10-14,86,63,514,225,329,none
Saturday,6,11-10-14,85,58,540,269,491,none
Sunday,7,12-10-14,80,76,385,223,233,none
Monday,1,13-10-14,63,52,637,209,317,none
Tuesday,2,14-10-14,65,49,590,147,366,none
Wednesday,3,15-10-14,70,45,532,259,346,none
Thursday,4,16-10-14,103,28,569,314,433,none
Friday,5,17-10-14,117,45,527,128,567,none
Saturday,6,18-10-14,134,84,941,196,461,none
Sunday,7,19-10-14,79,63,658,196,459,promotion
Monday,1,20-10-14,106,42,584,141,303,none
Tuesday,2,21-10-14,90,44,508,219,233,none
Wednesday,3,22-10-14,78,53,403,243,442,none
Thursday,4,23-10-14,82,46,754,202,376,none
Friday,5,24-10-14,110,78,403,223,543,none
Saturday,6,25-10-14,95,72,582,253,323,none
Sunday,7,26-10-14,93,69,554,271,343,none
Monday,1,27-10-14,74,46,488,194,347,none
Tuesday,2,28-10-14,100,50,528,120,411,none
Wednesday,3,29-10-14,59,37,290,123,301,none
Thursday,4,30-10-14,82,41,414,235,482,none
Friday,5,31-10-14,89,82,552,234,426,none
Saturday,6,01-11-14,119,71,840,233,758,none
Sunday,7,02-11-14,123,68,860,174,427,none
Monday,1,03-11-14,97,38,377,119,433,none
Tuesday,2,04-11-14,74,58,358,165,214,none
Wednesday,3,05-11-14,49,49,613,175,306,none
Thursday,4,06-11-14,97,39,636,109,316,none
Friday,5,07-11-14,76,69,614,209,521,none
Saturday,6,08-11-14,124,78,803,256,404,none
Sunday,7,09-11-14,59,64,722,182,329,none
Monday,1,10-11-14,75,54,456,120,253,none
Tuesday,2,11-11-14,72,33,473,147,295,promotion
Wednesday,3,12-11-14,97,62,489,155,210,none
Thursday,4,13-11-14,87,38,425,166,298,none
Friday,5,14-11-14,96,71,602,217,376,none
Saturday,6,15-11-14,125,88,744,267,500,none
Sunday,7,16-11-14,87,89,649,193,391,none
Monday,1,17-11-14,69,44,549,151,289,none
Tuesday,2,18-11-14,84,47,497,164,214,none
Wednesday,3,19-11-14,90,33,584,106,279,none
Thursday,4,20-11-14,62,46,512,85,222,none
Friday,5,21-11-14,66,64,720,147,535,none
Saturday,6,22-11-14,115,66,745,316,494,none
Sunday,7,23-11-14,69,53,594,144,591,none
Monday,1,24-11-14,93,46,465,186,435,promotion
Tuesday,2,25-11-14,77,49,432,151,356,none
Wednesday,3,26-11-14,102,61,558,132,331,none
Thursday,4,27-11-14,63,52,561,92,278,none
Friday,5,28-11-14,87,55,601,201,471,none
Saturday,6,29-11-14,93,56,746,135,332,none
Sunday,7,30-11-14,77,53,401,190,518,none
Monday,1,01-12-14,92,56,629,104,409,none
Tuesday,2,02-12-14,83,55,464,128,396,none
Wednesday,3,03-12-14,71,46,465,142,236,none
Thursday,4,04-12-14,52,56,385,201,328,none
Friday,5,05-12-14,98,63,582,198,444,none
Saturday,6,06-12-14,114,52,657,277,392,none
Sunday,7,07-12-14,87,50,685,129,488,none
Monday,1,08-12-14,70,54,577,149,228,none
Tuesday,2,09-12-14,110,68,716,239,444,promotion
Wednesday,3,10-12-14,86,49,533,220,345,none
Thursday,4,11-12-14,55,63,566,239,306,none
Friday,5,12-12-14,106,64,686,96,410,none
Saturday,6,13-12-14,143,93,594,342,639,none
Sunday,7,14-12-14,100,67,707,243,277,none
Monday,1,15-12-14,101,39,423,167,305,none
Tuesday,2,16-12-14,76,63,581,202,347,none
Wednesday,3,17-12-14,38,40,489,96,433,none
Thursday,4,18-12-14,90,52,387,129,378,none
Friday,5,19-12-14,80,68,532,236,441,none
Saturday,6,20-12-14,140,62,657,292,473,none
Sunday,7,21-12-14,68,49,391,148,563,none
Monday,1,22-12-14,73,71,381,171,368,none
Tuesday,2,23-12-14,71,43,419,155,352,none
Wednesday,3,24-12-14,55,28,647,206,305,none
Thursday,4,25-12-14,90,51,650,115,216,none
Friday,5,26-12-14,88,67,663,182,375,none
Saturday,6,27-12-14,113,74,600,207,528,none
Sunday,7,28-12-14,88,82,601,156,427,none
Monday,1,29-12-14,115,48,555,252,338,promotion
Tuesday,2,30-12-14,84,43,555,160,339,none
Wednesday,3,31-12-14,87,64,337,151,322,none
Thursday,4,01-01-15,88,39,281,155,407,none
Friday,5,02-01-15,97,35,575,131,420,none
Saturday,6,03-01-15,104,48,742,175,536,none
Sunday,7,04-01-15,85,76,394,196,530,none
Monday,1,05-01-15,87,52,335,116,362,none
Tuesday,2,06-01-15,82,43,458,146,295,none
Wednesday,3,07-01-15,96,49,400,173,246,none
Thursday,4,08-01-15,94,43,570,185,445,none
Friday,5,09-01-15,90,54,526,186,400,none
Saturday,6,10-01-15,111,64,641,198,570,none
Sunday,7,11-01-15,109,67,614,163,451,none
Monday,1,12-01-15,76,51,410,166,411,none
Tuesday,2,13-01-15,75,50,484,143,408,none
Wednesday,3,14-01-15,68,41,596,132,345,none
Thursday,4,15-01-15,81,41,491,167,282,none
Friday,5,16-01-15,110,38,668,148,476,none
Saturday,6,17-01-15,129,66,759,190,309,none
Sunday,7,18-01-15,79,58,580,208,284,none
Monday,1,19-01-15,76,41,381,125,410,none
Tuesday,2,20-01-15,60,40,322,111,294,none
Wednesday,3,21-01-15,87,38,559,119,375,none
Thursday,4,22-01-15,80,52,466,154,315,none
Friday,5,23-01-15,98,73,778,187,417,none
Saturday,6,24-01-15,92,56,702,215,448,none
Sunday,7,25-01-15,109,75,325,193,511,none
Monday,1,26-01-15,58,63,443,188,346,none
Tuesday,2,27-01-15,62,35,423,169,319,none
Wednesday,3,28-01-15,83,39,373,173,411,none
Thursday,4,29-01-15,51,57,568,100,463,none
Friday,5,30-01-15,74,48,629,185,421,none
Saturday,6,31-01-15,116,59,595,184,434,none
Sunday,7,01-02-15,107,72,412,175,424,none
Monday,1,02-02-15,63,40,513,165,375,none
Tuesday,2,03-02-15,86,37,308,138,312,none
Wednesday,3,04-02-15,87,61,521,167,337,none
Thursday,4,05-02-15,72,42,578,132,448,none
Friday,5,06-02-15,89,77,883,189,356,none
Saturday,6,07-02-15,110,67,713,219,397,none
Sunday,7,08-02-15,90,53,845,205,319,none
Monday,1,09-02-15,96,43,499,179,229,none
Tuesday,2,10-02-15,53,54,565,240,435,none
Wednesday,3,11-02-15,71,50,463,150,429,none
Thursday,4,12-02-15,107,67,576,119,376,promotion
Friday,5,13-02-15,89,41,594,205,428,none
Saturday,6,14-02-15,105,64,764,208,712,none
Sunday,7,15-02-15,117,54,387,166,303,none
Monday,1,16-02-15,86,54,525,151,392,none
Tuesday,2,17-02-15,84,43,629,140,406,none
Wednesday,3,18-02-15,88,46,530,119,387,none
Thursday,4,19-02-15,102,52,460,216,486,none
Friday,5,20-02-15,108,70,684,170,518,promotion
Saturday,6,21-02-15,146,56,634,180,393,none
Sunday,7,22-02-15,75,31,459,124,453,none
Monday,1,23-02-15,104,35,552,114,334,none
Tuesday,2,24-02-15,83,53,538,145,358,promotion
Wednesday,3,25-02-15,55,45,621,191,257,none
Thursday,4,26-02-15,102,53,365,190,333,none
Friday,5,27-02-15,101,77,584,229,335,none
Saturday,6,28-02-15,115,73,670,225,389,none
Sunday,7,01-03-15,100,54,648,217,522,none
Monday,1,02-03-15,116,47,492,193,247,none
Tuesday,2,03-03-15,37,41,419,199,453,none
Wednesday,3,04-03-15,85,47,393,166,241,none
Thursday,4,05-03-15,61,54,568,123,342,none
Friday,5,06-03-15,124,66,674,235,492,none
Saturday,6,07-03-15,101,84,581,221,539,none
Sunday,7,08-03-15,77,77,614,237,208,none
Monday,1,09-03-15,98,43,451,165,426,promotion
Tuesday,2,10-03-15,83,49,508,145,448,none
Wednesday,3,11-03-15,51,48,397,127,317,none
Thursday,4,12-03-15,70,54,492,171,597,none
Friday,5,13-03-15,78,49,463,150,244,none
Saturday,6,14-03-15,119,89,751,153,412,none
Sunday,7,15-03-15,77,48,475,212,458,none
Monday,1,16-03-15,78,54,597,297,380,promotion
Tuesday,2,17-03-15,52,77,448,213,397,promotion
Wednesday,3,18-03-15,71,57,470,230,164,none
Thursday,4,19-03-15,88,43,530,194,416,none
Friday,5,20-03-15,108,89,585,250,558,none
Saturday,6,21-03-15,94,72,696,276,319,none
Sunday,7,22-03-15,97,46,708,174,389,none
Monday,1,23-03-15,54,45,380,161,330,none
Tuesday,2,24-03-15,67,48,435,138,272,none
Wednesday,3,25-03-15,67,63,348,199,251,none
Thursday,4,26-03-15,78,56,523,118,367,none
Friday,5,27-03-15,94,66,726,149,608,none
Saturday,6,28-03-15,109,64,686,244,609,none
Sunday,7,29-03-15,127,56,257,236,446,none
Monday,1,30-03-15,93,55,547,234,427,promotion
Tuesday,2,31-03-15,81,39,361,122,429,none
Wednesday,3,01-04-15,74,41,457,297,259,none
Thursday,4,02-04-15,73,40,482,199,359,none
Friday,5,03-04-15,88,41,744,222,584,none
Saturday,6,04-04-15,84,36,711,326,481,none
Sunday,7,05-04-15,71,53,541,294,564,none
Monday,1,06-04-15,48,33,584,211,232,none
Tuesday,2,07-04-15,92,44,576,212,441,none
Wednesday,3,08-04-15,74,71,427,194,520,none
Thursday,4,09-04-15,96,54,521,140,316,none
Friday,5,10-04-15,94,72,324,239,385,none
Saturday,6,11-04-15,87,38,701,291,503,none
Sunday,7,12-04-15,86,61,658,183,540,none
Monday,1,13-04-15,48,44,485,196,269,none
Tuesday,2,14-04-15,67,50,575,145,309,none
Wednesday,3,15-04-15,70,55,354,104,212,none
Thursday,4,16-04-15,73,48,616,188,357,none
Friday,5,17-04-15,98,68,657,243,431,none
Saturday,6,18-04-15,110,72,760,330,431,none
Sunday,7,19-04-15,112,53,481,231,553,none
Monday,1,20-04-15,64,48,559,206,454,none
Tuesday,2,21-04-15,86,47,362,277,484,none
Wednesday,3,22-04-15,84,65,511,200,342,none
Thursday,4,23-04-15,83,57,682,301,308,none
Friday,5,24-04-15,137,68,517,240,398,none
Saturday,6,25-04-15,85,67,705,175,407,none
Sunday,7,26-04-15,112,66,465,237,393,none
Monday,1,27-04-15,71,69,435,162,280,none
Tuesday,2,28-04-15,90,52,444,160,291,none
Wednesday,3,29-04-15,81,62,488,253,542,none
Thursday,4,30-04-15,77,39,328,252,275,none
Friday,5,01-05-15,85,47,571,290,533,none
Saturday,6,02-05-15,111,58,760,171,451,none
Sunday,7,03-05-15,84,58,691,326,553,promotion
Monday,1,04-05-15,77,37,320,244,262,none
Tuesday,2,05-05-15,90,48,556,223,412,none
Wednesday,3,06-05-15,83,51,501,183,341,none
Thursday,4,07-05-15,97,44,699,153,290,none
Friday,5,08-05-15,96,45,744,313,480,none
Saturday,6,09-05-15,113,58,917,351,323,none
Sunday,7,10-05-15,51,52,678,330,268,none
Monday,1,11-05-15,92,50,487,282,615,promotion
Tuesday,2,12-05-15,107,45,444,287,286,none
Wednesday,3,13-05-15,59,51,598,262,177,none
Thursday,4,14-05-15,107,64,642,221,395,none
Friday,5,15-05-15,138,60,704,174,160,none
Saturday,6,16-05-15,103,66,487,283,543,none
Sunday,7,17-05-15,108,65,277,310,251,none
Monday,1,18-05-15,67,51,300,249,460,none
Tuesday,2,19-05-15,73,57,340,236,413,none
Wednesday,3,20-05-15,74,44,423,65,278,none
Thursday,4,21-05-15,54,22,457,266,303,none
Friday,5,22-05-15,108,57,665,334,303,none
Saturday,6,23-05-15,165,69,736,312,550,promotion
Sunday,7,24-05-15,82,68,768,185,570,none
Monday,1,25-05-15,102,61,569,159,366,promotion
Tuesday,2,26-05-15,91,49,391,97,261,none
Wednesday,3,27-05-15,94,30,543,219,506,none
Thursday,4,28-05-15,90,50,546,212,380,none
Friday,5,29-05-15,110,65,759,263,489,none
Saturday,6,30-05-15,99,54,716,419,429,none
Sunday,7,31-05-15,81,56,525,233,621,none
Monday,1,01-06-15,56,29,398,336,261,none
Tuesday,2,02-06-15,58,37,428,259,510,none
Wednesday,3,03-06-15,63,39,539,244,350,none
Thursday,4,04-06-15,88,43,623,316,314,none
Friday,5,05-06-15,115,75,750,290,367,none
Saturday,6,06-06-15,89,87,684,434,459,none
Sunday,7,07-06-15,118,55,563,181,462,none
Monday,1,08-06-15,97,53,394,316,386,none
Tuesday,2,09-06-15,115,39,553,326,499,promotion
Wednesday,3,10-06-15,55,52,453,241,300,none
Thursday,4,11-06-15,89,59,515,281,373,none
Friday,5,12-06-15,95,63,625,274,370,none
Saturday,6,13-06-15,118,42,638,260,634,none
Sunday,7,14-06-15,113,67,477,227,349,none
Monday,1,15-06-15,82,54,548,276,369,none
Tuesday,2,16-06-15,65,37,622,351,228,promotion
Wednesday,3,17-06-15,81,61,548,381,440,none
Thursday,4,18-06-15,104,49,519,271,323,none
Friday,5,19-06-15,115,57,621,331,521,none
Saturday,6,20-06-15,136,61,600,377,480,none
Sunday,7,21-06-15,118,67,595,241,349,none
Monday,1,22-06-15,59,61,551,249,318,none
Tuesday,2,23-06-15,80,54,505,276,395,none
Wednesday,3,24-06-15,101,59,469,186,201,none
Thursday,4,25-06-15,76,35,501,157,325,none
Friday,5,26-06-15,90,64,602,317,434,none
Saturday,6,27-06-15,106,41,390,251,451,none
Sunday,7,28-06-15,116,64,364,232,365,none
Monday,1,29-06-15,85,46,395,283,291,none
Tuesday,2,30-06-15,65,62,442,361,329,none
Wednesday,3,01-07-15,85,25,397,256,394,none
Thursday,4,02-07-15,76,39,415,368,233,none
Friday,5,03-07-15,114,61,514,269,476,none
Saturday,6,04-07-15,121,58,814,162,306,none
Sunday,7,05-07-15,126,59,606,337,555,promotion
Monday,1,06-07-15,74,45,699,290,359,none
Tuesday,2,07-07-15,73,57,645,238,368,none
Wednesday,3,08-07-15,79,56,521,252,344,none
Thursday,4,09-07-15,81,50,383,274,378,none
Friday,5,10-07-15,74,66,564,421,621,none
Saturday,6,11-07-15,126,64,868,477,505,promotion
Sunday,7,12-07-15,107,38,640,354,482,none
Monday,1,13-07-15,97,44,366,233,313,none
Tuesday,2,14-07-15,91,69,525,308,167,none
Wednesday,3,15-07-15,67,36,373,258,402,none
Thursday,4,16-07-15,61,42,581,232,491,none
Friday,5,17-07-15,114,60,627,387,734,promotion
Saturday,6,18-07-15,105,33,635,214,516,none
Sunday,7,19-07-15,113,72,688,302,397,none
Monday,1,20-07-15,97,46,388,205,388,none
Tuesday,2,21-07-15,65,42,473,220,469,none
Wednesday,3,22-07-15,94,44,475,232,514,none
Thursday,4,23-07-15,65,54,487,265,287,none
Friday,5,24-07-15,130,56,678,301,543,none
Saturday,6,25-07-15,114,73,623,361,534,none
Sunday,7,26-07-15,82,61,526,514,386,none
Monday,1,27-07-15,92,48,615,211,344,none
Tuesday,2,28-07-15,51,50,493,307,338,none
Wednesday,3,29-07-15,94,69,473,386,378,promotion
Thursday,4,30-07-15,87,43,334,229,441,none
Friday,5,31-07-15,47,55,538,293,272,none
Saturday,6,01-08-15,109,81,538,417,556,none
Sunday,7,02-08-15,77,54,340,356,251,none
Monday,1,03-08-15,69,59,595,261,309,promotion
Tuesday,2,04-08-15,67,48,612,200,466,none
Wednesday,3,05-08-15,69,47,544,320,438,none
Thursday,4,06-08-15,103,61,460,212,375,none
Friday,5,07-08-15,86,46,749,416,404,promotion
Saturday,6,08-08-15,61,67,662,326,417,none
Sunday,7,09-08-15,114,73,526,357,403,none
Monday,1,10-08-15,43,46,419,259,311,none
Tuesday,2,11-08-15,86,33,380,308,446,none
Wednesday,3,12-08-15,57,39,398,161,356,none
Thursday,4,13-08-15,74,45,539,246,504,none
Friday,5,14-08-15,122,56,496,241,422,none
Saturday,6,15-08-15,119,59,470,249,401,none
Sunday,7,16-08-15,95,57,570,248,406,none
Monday,1,17-08-15,100,55,643,105,470,promotion
Tuesday,2,18-08-15,91,56,656,295,428,none
Wednesday,3,19-08-15,64,48,702,239,225,none
Thursday,4,20-08-15,81,53,534,221,308,none
Friday,5,21-08-15,100,76,787,275,277,none
Saturday,6,22-08-15,71,69,913,272,404,none
Sunday,7,23-08-15,88,85,846,265,607,none
Monday,1,24-08-15,74,53,588,261,323,promotion
Tuesday,2,25-08-15,103,58,474,382,526,none
Wednesday,3,26-08-15,66,37,669,316,442,promotion
Thursday,4,27-08-15,87,51,381,304,348,none
Friday,5,28-08-15,81,67,657,441,432,promotion
Saturday,6,29-08-15,144,65,695,378,611,none
Sunday,7,30-08-15,147,74,692,335,343,none
Monday,1,31-08-15,84,57,484,208,430,none
Tuesday,2,01-09-15,78,37,415,272,386,none
Wednesday,3,02-09-15,91,50,577,159,403,none
Thursday,4,03-09-15,72,65,536,131,390,none
Friday,5,04-09-15,92,42,686,333,409,none
Saturday,6,05-09-15,104,74,619,191,545,none
Sunday,7,06-09-15,116,51,785,277,525,none
Monday,1,07-09-15,66,47,593,134,316,none
Tuesday,2,08-09-15,54,48,584,235,230,none
Wednesday,3,09-09-15,71,29,576,211,304,none
Thursday,4,10-09-15,101,38,656,124,500,none
Friday,5,11-09-15,132,61,640,228,254,none
Saturday,6,12-09-15,115,72,777,299,334,none
Sunday,7,13-09-15,108,66,762,251,509,none
Monday,1,14-09-15,76,38,543,139,417,none
Tuesday,2,15-09-15,129,42,746,243,463,promotion
Wednesday,3,16-09-15,70,36,434,182,206,none
Thursday,4,17-09-15,81,59,399,222,460,none
Friday,5,18-09-15,109,29,621,176,453,none
Saturday,6,19-09-15,117,77,818,377,495,promotion
Sunday,7,20-09-15,88,76,651,244,451,none
Monday,1,21-09-15,153,62,545,258,528,promotion
Tuesday,2,22-09-15,78,46,510,202,410,none
Wednesday,3,23-09-15,79,53,618,250,351,none
Thursday,4,24-09-15,101,57,532,205,368,none
Friday,5,25-09-15,98,51,445,143,547,none
Saturday,6,26-09-15,94,79,977,315,557,none
Sunday,7,27-09-15,109,62,718,140,431,none
Monday,1,28-09-15,70,44,462,217,272,none
Tuesday,2,29-09-15,64,44,369,143,260,none
Wednesday,3,30-09-15,77,63,636,174,253,none
Thursday,4,01-10-15,86,44,602,267,436,none
Friday,5,02-10-15,138,71,635,260,387,promotion
Saturday,6,03-10-15,113,63,966,268,634,none
Sunday,7,04-10-15,85,68,682,208,450,none
Monday,1,05-10-15,71,39,653,262,424,none
Tuesday,2,06-10-15,69,49,275,150,456,none
Wednesday,3,07-10-15,81,40,849,243,348,none
Thursday,4,08-10-15,70,32,534,230,246,none
Friday,5,09-10-15,92,91,719,216,685,promotion
Saturday,6,10-10-15,105,71,567,338,475,none
Sunday,7,11-10-15,141,38,515,290,453,none
Monday,1,12-10-15,50,33,470,234,372,none
Tuesday,2,13-10-15,61,44,539,228,354,none
Wednesday,3,14-10-15,115,45,537,176,448,promotion
Thursday,4,15-10-15,82,42,549,192,492,none
Friday,5,16-10-15,124,84,573,261,463,none
Saturday,6,17-10-15,70,76,566,349,686,none
Sunday,7,18-10-15,119,82,584,269,543,none
Monday,1,19-10-15,69,37,589,187,298,none
Tuesday,2,20-10-15,67,38,650,161,337,none
Wednesday,3,21-10-15,90,47,386,203,316,none
Thursday,4,22-10-15,94,64,506,235,316,none
Friday,5,23-10-15,108,45,826,275,361,none
Saturday,6,24-10-15,63,82,689,275,370,none
Sunday,7,25-10-15,86,87,465,280,528,none
Monday,1,26-10-15,91,62,386,110,379,none
Tuesday,2,27-10-15,97,44,310,137,287,none
Wednesday,3,28-10-15,69,54,358,176,531,none
Thursday,4,29-10-15,93,61,581,73,303,none
Friday,5,30-10-15,91,77,348,258,345,none
Saturday,6,31-10-15,135,52,441,221,340,none
Sunday,7,01-11-15,88,80,981,123,417,promotion
Monday,1,02-11-15,74,39,462,149,286,none
Tuesday,2,03-11-15,81,48,403,138,419,none
Wednesday,3,04-11-15,82,37,452,160,234,none
Thursday,4,05-11-15,73,38,514,223,344,none
Friday,5,06-11-15,83,57,533,245,469,none
Saturday,6,07-11-15,105,73,612,166,333,none
Sunday,7,08-11-15,75,62,646,189,473,promotion
Monday,1,09-11-15,118,46,523,247,336,promotion
Tuesday,2,10-11-15,85,66,713,172,404,none
Wednesday,3,11-11-15,96,38,628,157,237,none
Thursday,4,12-11-15,92,52,485,142,271,none
Friday,5,13-11-15,96,48,614,169,265,none
Saturday,6,14-11-15,132,35,792,147,580,none
Sunday,7,15-11-15,115,32,529,83,425,none
Monday,1,16-11-15,96,66,472,172,480,promotion
Tuesday,2,17-11-15,95,45,699,165,572,none
Wednesday,3,18-11-15,59,59,375,180,436,none
Thursday,4,19-11-15,86,63,384,115,369,none
Friday,5,20-11-15,89,73,490,224,570,none
Saturday,6,21-11-15,113,74,799,308,470,none
Sunday,7,22-11-15,105,44,564,225,269,none
Monday,1,23-11-15,88,40,285,179,318,none
Tuesday,2,24-11-15,74,41,355,124,332,none
Wednesday,3,25-11-15,75,52,467,176,461,none
Thursday,4,26-11-15,97,53,554,122,344,none
Friday,5,27-11-15,83,67,559,191,391,none
Saturday,6,28-11-15,113,86,596,197,389,none
Sunday,7,29-11-15,104,63,532,196,626,none
Monday,1,30-11-15,82,54,329,145,659,none
Tuesday,2,01-12-15,85,52,361,148,368,none
Wednesday,3,02-12-15,59,61,398,156,358,none
Thursday,4,03-12-15,87,47,345,165,439,none
Friday,5,04-12-15,109,61,678,192,624,none
Saturday,6,05-12-15,115,51,535,156,547,none
Sunday,7,06-12-15,109,45,734,199,453,promotion
Monday,1,07-12-15,129,72,846,185,384,promotion
Tuesday,2,08-12-15,79,48,467,188,317,none
Wednesday,3,09-12-15,85,51,562,134,434,none
Thursday,4,10-12-15,99,48,728,164,445,none
Friday,5,11-12-15,156,70,907,274,504,promotion
Saturday,6,12-12-15,53,58,590,262,423,none
Sunday,7,13-12-15,78,77,707,185,490,none
Monday,1,14-12-15,72,70,404,198,380,none
Tuesday,2,15-12-15,107,60,444,174,272,none
Wednesday,3,16-12-15,77,48,598,155,419,none
Thursday,4,17-12-15,65,54,638,107,341,none
Friday,5,18-12-15,103,63,402,154,327,none
Saturday,6,19-12-15,148,76,589,168,439,none
Sunday,7,20-12-15,106,62,657,151,477,none
Monday,1,21-12-15,73,46,570,156,238,none
Tuesday,2,22-12-15,103,53,420,134,420,none
Wednesday,3,23-12-15,72,44,505,156,375,none
Thursday,4,24-12-15,87,46,564,237,405,none
Friday,5,25-12-15,90,37,838,240,581,none
Saturday,6,26-12-15,113,46,445,255,488,none
Sunday,7,27-12-15,136,49,700,265,483,none
Monday,1,28-12-15,77,45,605,69,408,none
Tuesday,2,29-12-15,93,56,508,185,452,none
Wednesday,3,30-12-15,62,28,458,141,356,none
Thursday,4,31-12-15,65,56,491,190,446,none

================================================
FILE: data/bank.csv
================================================
"age";"job";"marital";"education";"default";"balance";"housing";"loan";"contact";"day";"month";"duration";"campaign";"pdays";"previous";"poutcome";"y"
30;"unemployed";"married";"primary";"no";1787;"no";"no";"cellular";19;"oct";79;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";4789;"yes";"yes";"cellular";11;"may";220;1;339;4;"failure";"no"
35;"management";"single";"tertiary";"no";1350;"yes";"no";"cellular";16;"apr";185;1;330;1;"failure";"no"
30;"management";"married";"tertiary";"no";1476;"yes";"yes";"unknown";3;"jun";199;4;-1;0;"unknown";"no"
59;"blue-collar";"married";"secondary";"no";0;"yes";"no";"unknown";5;"may";226;1;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";747;"no";"no";"cellular";23;"feb";141;2;176;3;"failure";"no"
36;"self-employed";"married";"tertiary";"no";307;"yes";"no";"cellular";14;"may";341;1;330;2;"other";"no"
39;"technician";"married";"secondary";"no";147;"yes";"no";"cellular";6;"may";151;2;-1;0;"unknown";"no"
41;"entrepreneur";"married";"tertiary";"no";221;"yes";"no";"unknown";14;"may";57;2;-1;0;"unknown";"no"
43;"services";"married";"primary";"no";-88;"yes";"yes";"cellular";17;"apr";313;1;147;2;"failure";"no"
39;"services";"married";"secondary";"no";9374;"yes";"no";"unknown";20;"may";273;1;-1;0;"unknown";"no"
43;"admin.";"married";"secondary";"no";264;"yes";"no";"cellular";17;"apr";113;2;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";1109;"no";"no";"cellular";13;"aug";328;2;-1;0;"unknown";"no"
20;"student";"single";"secondary";"no";502;"no";"no";"cellular";30;"apr";261;1;-1;0;"unknown";"yes"
31;"blue-collar";"married";"secondary";"no";360;"yes";"yes";"cellular";29;"jan";89;1;241;1;"failure";"no"
40;"management";"married";"tertiary";"no";194;"no";"yes";"cellular";29;"aug";189;2;-1;0;"unknown";"no"
56;"technician";"married";"secondary";"no";4073;"no";"no";"cellular";27;"aug";239;5;-1;0;"unknown";"no"
37;"admin.";"single";"tertiary";"no";2317;"yes";"no";"cellular";20;"apr";114;1;152;2;"failure";"no"
25;"blue-collar";"single";"primary";"no";-221;"yes";"no";"unknown";23;"may";250;1;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";132;"no";"no";"cellular";7;"jul";148;1;152;1;"other";"no"
38;"management";"divorced";"unknown";"no";0;"yes";"no";"cellular";18;"nov";96;2;-1;0;"unknown";"no"
42;"management";"divorced";"tertiary";"no";16;"no";"no";"cellular";19;"nov";140;3;-1;0;"unknown";"no"
44;"services";"single";"secondary";"no";106;"no";"no";"unknown";12;"jun";109;2;-1;0;"unknown";"no"
44;"entrepreneur";"married";"secondary";"no";93;"no";"no";"cellular";7;"jul";125;2;-1;0;"unknown";"no"
26;"housemaid";"married";"tertiary";"no";543;"no";"no";"cellular";30;"jan";169;3;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";5883;"no";"no";"cellular";20;"nov";182;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";627;"yes";"no";"unknown";5;"may";247;1;-1;0;"unknown";"no"
67;"retired";"married";"unknown";"no";696;"no";"no";"telephone";17;"aug";119;1;105;2;"failure";"no"
56;"self-employed";"married";"secondary";"no";784;"no";"yes";"cellular";30;"jul";149;2;-1;0;"unknown";"no"
53;"admin.";"married";"secondary";"no";105;"no";"yes";"cellular";21;"aug";74;2;-1;0;"unknown";"no"
68;"retired";"divorced";"secondary";"no";4189;"no";"no";"telephone";14;"jul";897;2;-1;0;"unknown";"yes"
31;"technician";"married";"secondary";"no";171;"no";"no";"cellular";27;"aug";81;3;-1;0;"unknown";"no"
59;"management";"married";"secondary";"no";42;"no";"no";"cellular";21;"nov";40;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";2536;"yes";"no";"cellular";26;"aug";958;6;-1;0;"unknown";"yes"
49;"technician";"married";"tertiary";"no";1235;"no";"no";"cellular";13;"aug";354;3;-1;0;"unknown";"yes"
42;"admin.";"divorced";"secondary";"no";1811;"yes";"no";"unknown";14;"may";150;1;-1;0;"unknown";"no"
78;"retired";"divorced";"primary";"no";229;"no";"no";"telephone";22;"oct";97;1;-1;0;"unknown";"yes"
32;"blue-collar";"married";"secondary";"no";2089;"yes";"no";"cellular";14;"nov";132;1;-1;0;"unknown";"yes"
33;"management";"married";"secondary";"no";3935;"yes";"no";"cellular";6;"may";765;1;342;2;"failure";"yes"
23;"services";"single";"tertiary";"no";363;"yes";"no";"unknown";30;"may";16;18;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";11971;"yes";"no";"unknown";17;"nov";609;2;101;3;"failure";"no"
36;"management";"single";"tertiary";"no";553;"no";"no";"cellular";11;"aug";106;2;-1;0;"unknown";"no"
52;"blue-collar";"married";"secondary";"no";1117;"yes";"no";"cellular";13;"may";365;1;-1;0;"unknown";"no"
32;"technician";"married";"tertiary";"no";396;"yes";"no";"cellular";13;"may";205;3;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";2204;"yes";"no";"cellular";21;"nov";11;4;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";872;"yes";"no";"unknown";7;"may";105;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";145;"no";"no";"telephone";2;"feb";59;3;5;2;"other";"no"
26;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";21;"may";425;1;-1;0;"unknown";"no"
32;"entrepreneur";"single";"primary";"yes";-849;"yes";"yes";"cellular";4;"feb";204;1;-1;0;"unknown";"no"
61;"admin.";"married";"unknown";"no";4629;"yes";"no";"cellular";27;"jan";181;1;92;1;"success";"yes"
45;"blue-collar";"divorced";"primary";"no";844;"no";"no";"unknown";5;"jun";1018;3;-1;0;"unknown";"yes"
37;"technician";"single";"secondary";"no";228;"yes";"no";"cellular";20;"aug";1740;2;-1;0;"unknown";"no"
38;"technician";"single";"secondary";"no";50;"no";"no";"cellular";29;"aug";98;2;-1;0;"unknown";"no"
34;"technician";"married";"tertiary";"no";1539;"yes";"no";"cellular";15;"jun";441;1;56;1;"other";"yes"
53;"blue-collar";"married";"secondary";"no";2231;"yes";"no";"cellular";14;"jul";272;3;-1;0;"unknown";"no"
48;"admin.";"married";"secondary";"no";3064;"yes";"no";"telephone";15;"jul";159;2;-1;0;"unknown";"no"
57;"management";"married";"secondary";"no";82;"no";"yes";"telephone";4;"feb";140;1;-1;0;"unknown";"no"
33;"self-employed";"single";"tertiary";"no";2155;"no";"no";"cellular";17;"nov";295;1;-1;0;"unknown";"no"
36;"retired";"single";"secondary";"no";101;"yes";"yes";"unknown";26;"may";314;1;-1;0;"unknown";"no"
54;"technician";"divorced";"secondary";"no";784;"yes";"yes";"unknown";15;"may";579;1;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";-516;"no";"yes";"telephone";8;"jul";554;3;-1;0;"unknown";"no"
63;"retired";"married";"secondary";"no";415;"yes";"no";"cellular";7;"oct";323;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";5887;"no";"no";"cellular";13;"aug";227;2;-1;0;"unknown";"no"
48;"services";"married";"secondary";"no";1355;"no";"no";"cellular";4;"jul";134;2;-1;0;"unknown";"no"
56;"admin.";"married";"secondary";"no";16873;"no";"no";"cellular";7;"oct";223;1;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"secondary";"no";203;"yes";"no";"cellular";8;"may";134;1;170;5;"failure";"no"
31;"services";"married";"secondary";"no";338;"yes";"no";"cellular";28;"jan";155;1;-1;0;"unknown";"no"
29;"technician";"married";"secondary";"no";444;"yes";"no";"cellular";21;"jul";130;4;-1;0;"unknown";"no"
41;"management";"married";"secondary";"no";0;"no";"yes";"cellular";7;"jul";630;3;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";360;"no";"no";"cellular";19;"nov";164;2;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";0;"no";"no";"cellular";16;"jul";268;2;182;3;"success";"yes"
42;"entrepreneur";"divorced";"tertiary";"yes";2;"yes";"no";"unknown";5;"may";380;1;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";6248;"yes";"no";"unknown";9;"may";154;2;-1;0;"unknown";"no"
41;"services";"married";"tertiary";"no";412;"yes";"no";"unknown";3;"jun";154;5;-1;0;"unknown";"no"
56;"retired";"married";"secondary";"no";344;"yes";"no";"unknown";15;"may";221;1;-1;0;"unknown";"no"
54;"management";"divorced";"tertiary";"no";3222;"no";"no";"cellular";14;"aug";67;2;-1;0;"unknown";"no"
41;"blue-collar";"divorced";"secondary";"no";174;"yes";"no";"cellular";14;"may";367;2;297;1;"other";"no"
40;"technician";"single";"secondary";"no";591;"no";"yes";"cellular";14;"aug";87;6;-1;0;"unknown";"no"
50;"blue-collar";"divorced";"primary";"no";388;"no";"no";"cellular";5;"feb";701;1;-1;0;"unknown";"no"
40;"unemployed";"married";"secondary";"no";219;"yes";"no";"cellular";17;"nov";204;2;196;1;"failure";"no"
27;"admin.";"divorced";"secondary";"no";451;"yes";"no";"cellular";16;"jul";652;1;-1;0;"unknown";"yes"
60;"retired";"married";"primary";"no";5;"no";"no";"cellular";26;"aug";63;2;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";177;"yes";"yes";"cellular";6;"may";150;1;-1;0;"unknown";"no"
52;"services";"married";"secondary";"no";657;"no";"no";"telephone";7;"jul";398;2;460;2;"failure";"yes"
37;"management";"married";"tertiary";"no";1315;"no";"no";"cellular";30;"jun";224;1;-1;0;"unknown";"yes"
51;"blue-collar";"married";"primary";"no";1466;"yes";"no";"unknown";7;"may";406;2;-1;0;"unknown";"no"
41;"technician";"single";"secondary";"no";879;"no";"yes";"cellular";7;"jul";60;4;-1;0;"unknown";"no"
35;"blue-collar";"single";"primary";"no";293;"yes";"no";"unknown";30;"may";521;2;-1;0;"unknown";"no"
38;"admin.";"married";"secondary";"no";424;"yes";"no";"cellular";17;"apr";279;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";1831;"yes";"no";"unknown";20;"may";203;2;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";111;"no";"no";"cellular";18;"nov";201;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";455;"yes";"no";"unknown";20;"jun";372;3;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";-195;"yes";"no";"cellular";18;"may";391;1;-1;0;"unknown";"yes"
32;"management";"single";"tertiary";"no";3616;"no";"no";"cellular";14;"aug";165;2;-1;0;"unknown";"no"
57;"blue-collar";"married";"secondary";"no";14093;"no";"no";"cellular";11;"aug";231;5;-1;0;"unknown";"no"
41;"unknown";"single";"tertiary";"no";1567;"no";"no";"cellular";1;"jul";291;2;-1;0;"unknown";"no"
41;"admin.";"married";"tertiary";"no";5426;"yes";"no";"unknown";14;"may";149;2;-1;0;"unknown";"no"
30;"admin.";"single";"tertiary";"no";261;"no";"no";"cellular";19;"oct";233;1;137;20;"failure";"no"
36;"blue-collar";"divorced";"secondary";"no";2843;"no";"no";"cellular";12;"feb";473;1;182;1;"success";"no"
31;"unemployed";"single";"primary";"no";406;"no";"no";"cellular";4;"feb";736;1;-1;0;"unknown";"yes"
36;"blue-collar";"married";"secondary";"no";0;"yes";"no";"unknown";6;"may";337;1;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";493;"yes";"no";"cellular";11;"may";553;1;367;7;"failure";"no"
49;"technician";"married";"secondary";"no";5996;"no";"no";"telephone";21;"nov";345;2;-1;0;"unknown";"no"
57;"services";"single";"primary";"no";3777;"yes";"no";"telephone";13;"may";65;2;-1;0;"unknown";"no"
34;"management";"single";"secondary";"no";524;"no";"no";"unknown";13;"aug";9;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";574;"yes";"no";"cellular";14;"apr";259;2;145;3;"failure";"no"
37;"blue-collar";"married";"secondary";"no";427;"yes";"yes";"unknown";9;"jun";371;3;-1;0;"unknown";"no"
33;"self-employed";"married";"tertiary";"no";483;"yes";"no";"cellular";3;"feb";280;2;-1;0;"unknown";"no"
56;"unemployed";"married";"primary";"no";3391;"no";"no";"cellular";21;"apr";243;1;-1;0;"unknown";"yes"
27;"housemaid";"married";"primary";"no";0;"yes";"no";"cellular";23;"jul";435;3;-1;0;"unknown";"no"
21;"student";"single";"secondary";"no";2488;"no";"no";"cellular";30;"jun";258;6;169;3;"success";"yes"
58;"management";"married";"tertiary";"no";1517;"yes";"no";"telephone";29;"jul";7;5;-1;0;"unknown";"no"
32;"self-employed";"married";"secondary";"no";217;"yes";"yes";"cellular";15;"jul";317;5;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";22;"no";"no";"cellular";30;"jan";76;2;207;1;"failure";"no"
25;"student";"single";"secondary";"no";331;"no";"no";"telephone";26;"aug";170;4;-1;0;"unknown";"yes"
25;"technician";"single";"secondary";"no";505;"no";"yes";"cellular";17;"nov";386;2;-1;0;"unknown";"yes"
30;"services";"married";"secondary";"no";1808;"yes";"no";"cellular";18;"may";83;2;-1;0;"unknown";"no"
22;"admin.";"single";"secondary";"no";4111;"no";"yes";"cellular";19;"aug";65;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";52;"no";"no";"cellular";13;"aug";69;1;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";25;"no";"no";"unknown";17;"jun";564;2;-1;0;"unknown";"no"
31;"admin.";"divorced";"secondary";"no";1890;"yes";"no";"cellular";21;"jul";588;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";419;"no";"no";"cellular";18;"aug";113;4;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";2693;"yes";"no";"unknown";21;"may";16;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";-231;"no";"yes";"cellular";15;"jul";779;2;-1;0;"unknown";"no"
51;"management";"single";"tertiary";"yes";-55;"yes";"no";"cellular";11;"may";281;2;266;6;"failure";"no"
34;"self-employed";"single";"tertiary";"no";462;"no";"no";"cellular";21;"aug";1877;3;-1;0;"unknown";"yes"
24;"unemployed";"single";"secondary";"no";119;"no";"no";"unknown";15;"sep";51;1;-1;0;"unknown";"no"
45;"technician";"single";"secondary";"no";586;"no";"yes";"cellular";28;"jul";32;3;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";228;"no";"no";"telephone";5;"feb";176;1;288;3;"failure";"no"
77;"retired";"divorced";"tertiary";"no";4659;"no";"no";"cellular";14;"apr";161;1;-1;0;"unknown";"yes"
41;"blue-collar";"married";"primary";"no";168;"yes";"no";"cellular";18;"nov";187;3;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";26;"may";24;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"unknown";"yes";-715;"yes";"no";"unknown";30;"may";67;3;-1;0;"unknown";"no"
48;"admin.";"married";"unknown";"no";0;"yes";"no";"cellular";8;"may";85;1;168;2;"failure";"no"
27;"technician";"single";"secondary";"no";769;"yes";"no";"unknown";9;"may";317;2;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";0;"no";"no";"cellular";28;"aug";236;10;-1;0;"unknown";"no"
29;"admin.";"single";"secondary";"no";428;"yes";"yes";"cellular";7;"may";54;1;345;2;"failure";"no"
34;"management";"single";"tertiary";"no";0;"no";"no";"cellular";5;"feb";71;1;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";-970;"yes";"no";"cellular";4;"feb";489;1;-1;0;"unknown";"yes"
60;"management";"married";"unknown";"no";6313;"no";"no";"unknown";19;"jun";39;2;-1;0;"unknown";"no"
32;"management";"divorced";"tertiary";"no";948;"no";"no";"cellular";12;"aug";204;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";22;"jul";455;2;-1;0;"unknown";"no"
56;"services";"divorced";"unknown";"no";1877;"yes";"no";"unknown";30;"may";86;1;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";874;"yes";"no";"cellular";18;"nov";190;2;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";323;"yes";"yes";"unknown";8;"may";280;2;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";106;"no";"yes";"cellular";11;"aug";588;2;-1;0;"unknown";"no"
56;"retired";"married";"primary";"no";1906;"no";"yes";"unknown";19;"jun";45;9;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";1152;"yes";"no";"cellular";21;"jul";168;5;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";179;"yes";"no";"cellular";29;"jan";194;4;436;1;"other";"no"
37;"technician";"single";"unknown";"no";391;"yes";"no";"unknown";8;"may";103;3;-1;0;"unknown";"no"
41;"entrepreneur";"married";"unknown";"no";89;"yes";"no";"unknown";6;"may";333;2;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";442;"yes";"yes";"cellular";29;"jul";102;3;-1;0;"unknown";"no"
45;"unemployed";"divorced";"primary";"yes";-249;"yes";"yes";"unknown";1;"jul";92;1;-1;0;"unknown";"no"
55;"entrepreneur";"married";"secondary";"no";8104;"no";"no";"cellular";6;"feb";213;2;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"no";501;"no";"no";"unknown";26;"may";289;1;-1;0;"unknown";"no"
35;"technician";"divorced";"secondary";"no";308;"no";"no";"cellular";22;"aug";77;2;-1;0;"unknown";"no"
55;"admin.";"married";"secondary";"no";602;"no";"no";"cellular";1;"jun";324;3;90;2;"success";"yes"
55;"technician";"married";"secondary";"no";273;"yes";"no";"cellular";29;"jan";84;3;183;3;"failure";"no"
27;"self-employed";"single";"secondary";"no";50;"no";"no";"cellular";29;"jan";168;1;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";1641;"yes";"no";"cellular";17;"apr";380;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"tertiary";"no";20;"no";"no";"unknown";4;"oct";10;1;-1;0;"unknown";"no"
25;"admin.";"single";"secondary";"no";4590;"yes";"no";"unknown";28;"may";199;2;-1;0;"unknown";"no"
41;"admin.";"single";"secondary";"no";-256;"no";"yes";"unknown";9;"may";35;3;-1;0;"unknown";"no"
43;"services";"married";"secondary";"no";978;"yes";"yes";"unknown";26;"may";82;2;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";80;"no";"no";"cellular";20;"oct";676;2;146;2;"failure";"yes"
31;"admin.";"single";"secondary";"no";2104;"yes";"no";"unknown";3;"jun";455;1;-1;0;"unknown";"no"
78;"housemaid";"married";"secondary";"no";499;"no";"no";"telephone";16;"mar";80;4;-1;0;"unknown";"no"
45;"blue-collar";"divorced";"secondary";"no";1269;"yes";"no";"cellular";29;"jan";549;2;-1;0;"unknown";"no"
48;"technician";"married";"tertiary";"no";310;"no";"no";"unknown";6;"may";135;1;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";1143;"yes";"yes";"cellular";10;"jul";412;1;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";2980;"no";"no";"unknown";20;"jun";101;3;-1;0;"unknown";"no"
37;"management";"single";"tertiary";"no";37;"no";"no";"cellular";28;"jan";253;2;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";462;"yes";"yes";"cellular";6;"apr";272;1;335;4;"other";"no"
32;"admin.";"married";"secondary";"no";1031;"yes";"no";"cellular";17;"jul";84;1;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";9009;"no";"no";"cellular";20;"nov";135;2;-1;0;"unknown";"no"
43;"admin.";"married";"secondary";"no";-465;"yes";"yes";"cellular";23;"jul";166;1;-1;0;"unknown";"no"
44;"admin.";"married";"secondary";"no";5181;"yes";"yes";"cellular";31;"jul";18;7;-1;0;"unknown";"no"
37;"admin.";"married";"secondary";"no";176;"yes";"no";"cellular";5;"may";147;2;347;1;"failure";"no"
37;"unknown";"married";"unknown";"no";1699;"no";"no";"unknown";9;"jun";63;1;-1;0;"unknown";"no"
37;"admin.";"married";"tertiary";"no";0;"yes";"no";"cellular";17;"nov";65;2;119;1;"failure";"no"
28;"technician";"single";"tertiary";"no";172;"yes";"yes";"cellular";2;"feb";194;2;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";0;"no";"yes";"cellular";11;"jul";333;2;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";6979;"yes";"no";"cellular";15;"may";14;5;-1;0;"unknown";"no"
40;"admin.";"divorced";"secondary";"no";6;"no";"no";"unknown";11;"jun";140;1;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";-62;"yes";"no";"unknown";16;"jun";109;3;-1;0;"unknown";"no"
34;"self-employed";"married";"tertiary";"no";1972;"yes";"no";"cellular";23;"jul";61;2;-1;0;"unknown";"no"
34;"admin.";"married";"tertiary";"no";899;"yes";"no";"telephone";19;"nov";377;3;7;4;"success";"no"
37;"services";"single";"secondary";"no";21;"yes";"no";"cellular";2;"feb";96;1;271;2;"other";"no"
36;"technician";"married";"secondary";"no";23;"yes";"no";"cellular";8;"may";152;2;347;1;"failure";"no"
56;"retired";"married";"secondary";"no";-1206;"yes";"no";"cellular";15;"jun";382;2;-1;0;"unknown";"yes"
49;"blue-collar";"married";"primary";"no";0;"yes";"yes";"telephone";23;"jul";97;6;-1;0;"unknown";"no"
33;"self-employed";"married";"secondary";"no";215;"yes";"no";"cellular";29;"jul";543;12;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";1372;"no";"no";"cellular";20;"nov";240;2;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";1981;"no";"no";"unknown";17;"jun";48;2;-1;0;"unknown";"no"
23;"student";"single";"secondary";"no";9216;"no";"no";"cellular";5;"jun";471;2;-1;0;"unknown";"no"
57;"retired";"divorced";"secondary";"no";39;"yes";"no";"cellular";30;"jul";354;3;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";5563;"yes";"no";"unknown";30;"may";435;2;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";1;"no";"yes";"cellular";21;"nov";152;2;-1;0;"unknown";"no"
42;"technician";"married";"tertiary";"no";782;"no";"no";"cellular";3;"apr";285;1;-1;0;"unknown";"no"
75;"retired";"married";"secondary";"no";3771;"no";"no";"telephone";15;"apr";185;1;181;2;"success";"yes"
34;"technician";"single";"tertiary";"no";992;"yes";"no";"cellular";4;"may";301;1;88;2;"success";"no"
48;"technician";"married";"secondary";"no";1526;"no";"no";"cellular";6;"aug";273;2;-1;0;"unknown";"no"
35;"technician";"single";"tertiary";"no";2658;"yes";"no";"cellular";19;"aug";768;2;141;3;"failure";"yes"
39;"management";"divorced";"tertiary";"no";1315;"yes";"no";"cellular";19;"nov";1337;4;126;1;"failure";"no"
29;"blue-collar";"single";"tertiary";"no";455;"no";"no";"telephone";21;"apr";403;1;-1;0;"unknown";"yes"
45;"technician";"single";"tertiary";"no";0;"yes";"no";"cellular";18;"nov";139;2;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";2030;"yes";"yes";"cellular";9;"jul";196;1;-1;0;"unknown";"no"
29;"technician";"married";"secondary";"no";1599;"yes";"no";"cellular";8;"jul";115;1;61;1;"failure";"no"
38;"services";"single";"secondary";"no";-196;"yes";"no";"cellular";2;"feb";60;1;271;1;"failure";"no"
56;"retired";"married";"secondary";"no";1333;"no";"no";"cellular";28;"aug";17;14;-1;0;"unknown";"no"
36;"technician";"single";"secondary";"no";9149;"yes";"no";"cellular";13;"aug";141;3;-1;0;"unknown";"no"
25;"blue-collar";"single";"secondary";"no";-350;"yes";"no";"unknown";23;"jun";95;3;-1;0;"unknown";"no"
38;"technician";"married";"tertiary";"no";432;"yes";"no";"cellular";15;"may";10;2;373;1;"other";"no"
57;"self-employed";"married";"secondary";"no";11494;"no";"no";"cellular";19;"nov";198;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"yes";-253;"no";"no";"cellular";12;"aug";220;4;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";372;"yes";"no";"cellular";3;"aug";654;2;-1;0;"unknown";"yes"
45;"technician";"married";"secondary";"no";1410;"no";"no";"cellular";11;"aug";71;2;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";975;"yes";"no";"cellular";13;"may";256;2;351;2;"failure";"no"
49;"blue-collar";"married";"primary";"no";305;"yes";"yes";"telephone";10;"jul";834;10;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";400;"no";"no";"cellular";21;"aug";151;3;-1;0;"unknown";"no"
53;"retired";"married";"tertiary";"no";-22;"yes";"no";"cellular";31;"jul";20;3;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";4030;"yes";"no";"unknown";26;"may";178;5;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";365;"no";"no";"unknown";26;"may";59;2;-1;0;"unknown";"no"
41;"self-employed";"married";"primary";"no";204;"no";"no";"cellular";13;"aug";111;7;-1;0;"unknown";"no"
54;"management";"married";"primary";"no";1691;"yes";"no";"cellular";20;"nov";186;1;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";406;"yes";"no";"unknown";6;"may";227;1;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";-204;"no";"no";"cellular";15;"may";297;3;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";359;"yes";"no";"cellular";20;"apr";210;1;242;1;"other";"no"
36;"blue-collar";"married";"primary";"no";123;"yes";"no";"cellular";18;"may";9;1;-1;0;"unknown";"no"
35;"technician";"married";"tertiary";"no";0;"yes";"yes";"cellular";23;"sep";112;1;62;6;"other";"no"
52;"unknown";"married";"secondary";"no";142;"no";"no";"cellular";25;"aug";112;6;-1;0;"unknown";"no"
40;"entrepreneur";"married";"tertiary";"no";3571;"no";"no";"unknown";9;"jun";222;3;-1;0;"unknown";"no"
44;"technician";"single";"secondary";"no";33;"yes";"no";"cellular";13;"may";195;3;351;10;"failure";"no"
45;"blue-collar";"single";"secondary";"no";0;"yes";"no";"cellular";26;"jul";768;2;91;5;"success";"no"
30;"student";"single";"secondary";"no";3096;"no";"no";"cellular";26;"jan";123;1;-1;0;"unknown";"no"
48;"technician";"divorced";"secondary";"yes";20;"no";"no";"cellular";27;"aug";145;3;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";3576;"yes";"no";"cellular";2;"apr";124;1;308;2;"other";"no"
57;"technician";"married";"unknown";"no";206;"yes";"no";"unknown";5;"may";216;3;-1;0;"unknown";"no"
57;"housemaid";"married";"primary";"no";7082;"no";"no";"unknown";6;"jun";69;3;-1;0;"unknown";"no"
51;"unemployed";"married";"tertiary";"no";1634;"yes";"no";"cellular";22;"jul";168;4;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";1;"yes";"no";"cellular";22;"jul";483;7;-1;0;"unknown";"no"
50;"self-employed";"married";"tertiary";"no";4012;"no";"no";"cellular";19;"nov";690;3;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";3762;"no";"no";"cellular";28;"aug";125;12;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";-247;"yes";"yes";"unknown";4;"jun";344;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";606;"yes";"no";"unknown";14;"may";673;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";28;"jan";60;1;250;1;"other";"no"
42;"management";"married";"secondary";"no";-189;"yes";"no";"unknown";12;"may";247;2;-1;0;"unknown";"no"
32;"unemployed";"single";"secondary";"no";0;"no";"no";"cellular";27;"may";123;2;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";484;"yes";"no";"cellular";11;"jul";144;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";0;"no";"yes";"cellular";8;"may";9;2;172;5;"failure";"no"
31;"management";"married";"tertiary";"no";395;"yes";"no";"cellular";9;"feb";155;3;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";2349;"no";"no";"cellular";14;"aug";134;5;-1;0;"unknown";"no"
55;"blue-collar";"divorced";"primary";"no";410;"no";"yes";"cellular";11;"jul";221;2;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";3401;"yes";"no";"cellular";3;"feb";84;3;265;2;"failure";"no"
46;"blue-collar";"married";"primary";"no";222;"no";"no";"cellular";8;"aug";71;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"secondary";"no";989;"yes";"yes";"unknown";23;"may";246;4;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";415;"no";"yes";"cellular";23;"jul";361;2;-1;0;"unknown";"no"
28;"unemployed";"single";"secondary";"no";16;"no";"no";"cellular";12;"aug";119;4;-1;0;"unknown";"no"
42;"technician";"divorced";"tertiary";"no";7190;"yes";"no";"cellular";29;"jul";375;2;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";482;"no";"yes";"cellular";10;"jul";1097;3;-1;0;"unknown";"yes"
54;"housemaid";"married";"secondary";"no";209;"yes";"yes";"cellular";25;"jul";97;1;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";0;"no";"no";"cellular";26;"aug";181;2;-1;0;"unknown";"no"
39;"technician";"single";"tertiary";"no";5803;"no";"no";"cellular";2;"feb";83;3;-1;0;"unknown";"no"
53;"entrepreneur";"married";"tertiary";"no";624;"no";"yes";"cellular";21;"jul";180;4;-1;0;"unknown";"no"
58;"technician";"married";"tertiary";"no";883;"no";"no";"cellular";18;"aug";543;1;78;2;"failure";"no"
40;"self-employed";"married";"secondary";"no";1516;"yes";"yes";"unknown";23;"may";373;1;-1;0;"unknown";"no"
29;"student";"single";"secondary";"no";872;"yes";"no";"unknown";7;"may";230;1;-1;0;"unknown";"no"
33;"technician";"married";"tertiary";"no";-988;"yes";"yes";"cellular";15;"jul";83;3;-1;0;"unknown";"no"
38;"admin.";"single";"secondary";"no";902;"yes";"no";"telephone";31;"jul";58;13;-1;0;"unknown";"no"
56;"services";"divorced";"secondary";"no";1007;"no";"no";"cellular";29;"jul";88;6;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";0;"yes";"no";"cellular";2;"jun";297;1;28;3;"failure";"no"
24;"blue-collar";"single";"secondary";"no";174;"yes";"yes";"cellular";18;"may";487;1;-1;0;"unknown";"yes"
46;"management";"married";"secondary";"no";12186;"no";"no";"unknown";20;"jun";29;3;-1;0;"unknown";"no"
35;"self-employed";"married";"secondary";"no";524;"yes";"no";"cellular";21;"nov";484;4;-1;0;"unknown";"no"
35;"blue-collar";"single";"tertiary";"no";0;"yes";"no";"cellular";21;"jul";262;4;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";4;"yes";"no";"unknown";15;"may";644;1;-1;0;"unknown";"no"
35;"housemaid";"married";"tertiary";"no";11219;"no";"no";"cellular";12;"aug";699;2;79;1;"failure";"no"
38;"technician";"single";"tertiary";"no";0;"no";"no";"cellular";28;"aug";247;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";5776;"no";"no";"cellular";18;"nov";49;3;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"primary";"no";37;"yes";"no";"cellular";13;"may";341;1;-1;0;"unknown";"no"
59;"self-employed";"married";"tertiary";"no";593;"no";"no";"unknown";13;"may";64;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";1148;"yes";"no";"cellular";20;"apr";121;1;-1;0;"unknown";"no"
37;"blue-collar";"single";"secondary";"no";3561;"yes";"no";"unknown";2;"jun";98;1;-1;0;"unknown";"no"
42;"admin.";"divorced";"secondary";"no";63;"no";"no";"unknown";16;"may";88;6;-1;0;"unknown";"no"
40;"student";"married";"secondary";"no";260;"no";"no";"unknown";20;"jun";197;6;-1;0;"unknown";"no"
28;"technician";"single";"tertiary";"no";2269;"yes";"no";"unknown";9;"may";331;1;-1;0;"unknown";"no"
47;"admin.";"married";"secondary";"no";1060;"yes";"no";"unknown";6;"jun";83;3;-1;0;"unknown";"no"
27;"self-employed";"married";"secondary";"no";-220;"yes";"no";"unknown";26;"may";138;2;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";49;"yes";"no";"cellular";17;"jul";312;5;-1;0;"unknown";"no"
34;"admin.";"married";"tertiary";"no";69;"no";"yes";"cellular";4;"aug";120;3;1;6;"success";"yes"
50;"blue-collar";"married";"primary";"no";4108;"yes";"no";"cellular";7;"aug";526;2;-1;0;"unknown";"yes"
47;"blue-collar";"married";"secondary";"no";480;"yes";"no";"unknown";28;"may";211;2;-1;0;"unknown";"no"
53;"services";"married";"secondary";"no";4994;"no";"no";"cellular";18;"aug";62;6;-1;0;"unknown";"no"
53;"technician";"divorced";"secondary";"no";0;"yes";"yes";"cellular";15;"jul";135;5;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";3681;"no";"no";"cellular";14;"aug";101;2;-1;0;"unknown";"no"
49;"entrepreneur";"divorced";"unknown";"yes";-701;"yes";"no";"cellular";30;"jul";988;2;-1;0;"unknown";"yes"
29;"management";"married";"tertiary";"no";451;"no";"no";"cellular";28;"jan";451;1;-1;0;"unknown";"no"
45;"blue-collar";"divorced";"primary";"no";-311;"yes";"no";"unknown";23;"may";1030;1;-1;0;"unknown";"yes"
38;"entrepreneur";"married";"secondary";"no";593;"yes";"yes";"cellular";24;"jul";1484;24;-1;0;"unknown";"yes"
32;"blue-collar";"married";"secondary";"no";759;"yes";"no";"cellular";9;"apr";317;5;-1;0;"unknown";"yes"
70;"retired";"divorced";"primary";"no";4531;"no";"no";"cellular";18;"may";445;1;-1;0;"unknown";"no"
65;"retired";"divorced";"primary";"no";1840;"no";"no";"cellular";1;"jun";383;2;188;5;"failure";"no"
56;"blue-collar";"married";"secondary";"no";345;"yes";"no";"telephone";22;"jul";605;1;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";1287;"yes";"no";"cellular";14;"jul";330;2;-1;0;"unknown";"no"
56;"management";"married";"unknown";"no";353;"no";"yes";"cellular";25;"jul";171;2;-1;0;"unknown";"no"
56;"technician";"married";"secondary";"no";205;"no";"yes";"cellular";23;"jul";442;2;-1;0;"unknown";"no"
49;"entrepreneur";"married";"secondary";"no";687;"yes";"no";"cellular";17;"apr";772;1;-1;0;"unknown";"yes"
38;"unemployed";"married";"primary";"no";1147;"yes";"yes";"unknown";8;"may";249;5;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";4286;"yes";"no";"unknown";9;"may";357;1;-1;0;"unknown";"no"
38;"admin.";"married";"tertiary";"no";605;"no";"no";"cellular";18;"nov";147;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";8545;"yes";"yes";"cellular";6;"may";199;7;167;1;"failure";"no"
55;"management";"married";"unknown";"no";559;"no";"no";"cellular";28;"jan";271;2;-1;0;"unknown";"no"
43;"blue-collar";"single";"primary";"no";2;"yes";"no";"cellular";30;"jul";783;3;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";2;"no";"yes";"cellular";20;"aug";472;2;-1;0;"unknown";"no"
25;"admin.";"single";"tertiary";"no";760;"yes";"no";"cellular";27;"may";223;3;89;2;"failure";"yes"
30;"blue-collar";"married";"primary";"no";1317;"yes";"no";"unknown";21;"may";395;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";182;"no";"no";"cellular";4;"feb";56;1;-1;0;"unknown";"no"
50;"admin.";"married";"primary";"no";276;"no";"yes";"unknown";17;"jun";641;6;-1;0;"unknown";"no"
35;"entrepreneur";"married";"secondary";"no";2971;"no";"no";"cellular";4;"may";429;4;164;2;"failure";"yes"
26;"unemployed";"single";"secondary";"no";1064;"no";"no";"unknown";31;"may";113;1;-1;0;"unknown";"no"
45;"admin.";"married";"unknown";"no";1783;"no";"no";"unknown";20;"jun";11;11;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";-67;"no";"no";"cellular";2;"feb";157;1;-1;0;"unknown";"no"
60;"self-employed";"single";"tertiary";"no";179;"no";"yes";"cellular";29;"jul";79;4;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";1199;"no";"no";"cellular";4;"aug";178;1;1;1;"success";"yes"
47;"blue-collar";"married";"primary";"no";214;"yes";"yes";"unknown";9;"jun";168;1;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";6699;"no";"no";"cellular";9;"nov";425;1;462;1;"failure";"no"
51;"unemployed";"married";"secondary";"no";1760;"no";"yes";"cellular";19;"nov";162;1;-1;0;"unknown";"no"
35;"entrepreneur";"single";"tertiary";"no";145;"yes";"no";"cellular";18;"jul";799;2;-1;0;"unknown";"yes"
35;"unemployed";"married";"secondary";"no";978;"no";"no";"cellular";29;"dec";1370;3;209;3;"success";"yes"
48;"admin.";"single";"secondary";"no";1150;"yes";"no";"unknown";28;"may";357;1;-1;0;"unknown";"no"
37;"admin.";"married";"secondary";"no";121;"no";"no";"cellular";22;"jul";22;6;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";828;"yes";"no";"cellular";16;"apr";215;3;321;3;"other";"no"
34;"admin.";"married";"secondary";"no";244;"yes";"no";"cellular";31;"jul";56;4;-1;0;"unknown";"no"
32;"services";"married";"primary";"no";228;"yes";"no";"cellular";21;"jul";1017;6;-1;0;"unknown";"yes"
30;"management";"single";"tertiary";"no";424;"no";"no";"cellular";16;"apr";298;4;-1;0;"unknown";"yes"
47;"management";"divorced";"tertiary";"no";447;"no";"yes";"unknown";7;"may";126;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";-195;"no";"no";"unknown";20;"sep";9;1;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";1410;"yes";"no";"cellular";5;"feb";8;1;254;5;"failure";"no"
63;"retired";"married";"primary";"no";1084;"no";"no";"cellular";14;"oct";201;1;183;1;"success";"no"
51;"self-employed";"divorced";"primary";"no";10924;"no";"no";"cellular";6;"may";106;2;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";832;"yes";"yes";"cellular";21;"nov";164;1;176;1;"failure";"no"
56;"retired";"married";"tertiary";"no";478;"no";"no";"cellular";5;"feb";97;1;-1;0;"unknown";"no"
60;"management";"single";"tertiary";"no";0;"no";"yes";"cellular";23;"jul";151;1;-1;0;"unknown";"no"
41;"self-employed";"single";"tertiary";"no";3950;"yes";"no";"unknown";30;"may";96;2;-1;0;"unknown";"no"
37;"management";"single";"unknown";"no";504;"yes";"no";"cellular";16;"apr";555;1;-1;0;"unknown";"no"
36;"blue-collar";"single";"secondary";"no";219;"yes";"yes";"cellular";11;"may";270;2;347;1;"failure";"no"
57;"management";"married";"tertiary";"no";1602;"no";"no";"cellular";29;"jul";339;3;-1;0;"unknown";"no"
55;"management";"married";"primary";"no";2587;"no";"no";"cellular";4;"sep";328;2;94;2;"success";"yes"
44;"admin.";"married";"secondary";"no";0;"yes";"yes";"unknown";16;"may";159;1;-1;0;"unknown";"no"
47;"self-employed";"married";"tertiary";"no";2303;"yes";"no";"cellular";6;"may";323;2;364;1;"failure";"no"
31;"blue-collar";"married";"secondary";"no";1660;"yes";"no";"cellular";20;"apr";342;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"yes";32;"yes";"no";"cellular";21;"nov";180;1;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";278;"no";"yes";"cellular";28;"jul";60;3;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";4;"no";"no";"cellular";17;"nov";223;1;96;2;"failure";"no"
29;"management";"married";"tertiary";"no";318;"yes";"no";"cellular";18;"may";1434;2;356;4;"failure";"no"
30;"services";"single";"secondary";"no";209;"yes";"no";"unknown";6;"may";30;2;-1;0;"unknown";"no"
46;"services";"married";"secondary";"no";271;"yes";"no";"unknown";16;"may";134;4;-1;0;"unknown";"no"
27;"management";"single";"unknown";"no";3196;"no";"no";"cellular";9;"feb";10;2;-1;0;"unknown";"no"
48;"admin.";"divorced";"secondary";"no";4099;"no";"no";"cellular";2;"apr";397;2;-1;0;"unknown";"yes"
26;"student";"single";"secondary";"no";2572;"yes";"no";"cellular";5;"may";620;1;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";339;"no";"no";"cellular";21;"nov";6;6;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";0;"yes";"yes";"cellular";7;"may";209;2;-1;0;"unknown";"no"
50;"housemaid";"married";"primary";"no";395;"yes";"no";"unknown";15;"may";419;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";2039;"yes";"no";"cellular";18;"nov";283;2;-1;0;"unknown";"no"
45;"management";"married";"tertiary";"no";5450;"no";"no";"cellular";8;"aug";188;2;-1;0;"unknown";"no"
23;"student";"single";"secondary";"no";780;"no";"no";"cellular";16;"sep";267;1;-1;0;"unknown";"no"
38;"technician";"single";"secondary";"no";0;"yes";"no";"cellular";7;"apr";196;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";8436;"yes";"no";"cellular";17;"apr";245;1;149;2;"failure";"no"
60;"technician";"single";"primary";"no";11262;"no";"no";"cellular";26;"aug";323;1;-1;0;"unknown";"yes"
30;"entrepreneur";"married";"secondary";"no";946;"yes";"no";"cellular";19;"nov";1065;1;183;1;"other";"no"
58;"housemaid";"married";"tertiary";"no";497;"no";"no";"unknown";11;"jun";138;5;-1;0;"unknown";"no"
60;"retired";"married";"primary";"no";55;"no";"no";"cellular";4;"jun";207;1;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";-140;"no";"no";"unknown";25;"jun";456;3;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";-379;"yes";"no";"cellular";5;"aug";261;1;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";-396;"yes";"no";"cellular";11;"may";80;1;308;4;"failure";"no"
30;"services";"single";"secondary";"no";0;"yes";"no";"cellular";18;"may";131;6;363;9;"failure";"no"
40;"management";"married";"primary";"no";733;"yes";"no";"unknown";6;"jun";94;7;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";393;"yes";"no";"cellular";14;"may";567;3;275;4;"failure";"yes"
53;"technician";"divorced";"primary";"no";7290;"yes";"no";"unknown";30;"may";153;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";468;"yes";"no";"cellular";13;"may";109;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";30;"apr";53;1;325;1;"failure";"no"
39;"blue-collar";"married";"primary";"no";3705;"yes";"no";"unknown";16;"jun";77;1;-1;0;"unknown";"no"
59;"retired";"married";"tertiary";"no";9676;"no";"no";"telephone";27;"mar";234;2;-1;0;"unknown";"yes"
37;"services";"married";"secondary";"no";1328;"yes";"no";"telephone";13;"may";121;2;341;4;"failure";"no"
41;"blue-collar";"married";"primary";"no";8;"yes";"no";"cellular";15;"jul";155;2;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";61;"no";"no";"cellular";17;"apr";108;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";6613;"no";"no";"unknown";17;"jun";159;3;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";302;"yes";"no";"unknown";26;"may";208;3;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";1005;"no";"no";"cellular";12;"may";215;1;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";109;"yes";"no";"unknown";6;"may";597;3;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";1240;"yes";"no";"cellular";4;"feb";505;2;260;1;"other";"no"
28;"blue-collar";"single";"primary";"no";0;"yes";"yes";"unknown";3;"jun";380;4;-1;0;"unknown";"no"
64;"retired";"married";"unknown";"no";2923;"no";"no";"cellular";12;"mar";120;1;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";0;"no";"no";"cellular";27;"aug";106;4;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";43;"yes";"no";"cellular";14;"may";332;2;358;2;"failure";"no"
41;"technician";"married";"secondary";"no";288;"yes";"no";"unknown";15;"may";221;4;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";613;"yes";"no";"cellular";4;"jun";80;1;-1;0;"unknown";"no"
37;"technician";"single";"tertiary";"no";6101;"no";"no";"cellular";4;"may";60;5;87;1;"success";"no"
38;"blue-collar";"married";"primary";"no";168;"yes";"yes";"telephone";13;"may";212;3;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";7685;"yes";"no";"cellular";14;"apr";493;3;-1;0;"unknown";"no"
35;"technician";"divorced";"tertiary";"no";4515;"yes";"no";"cellular";19;"nov";98;2;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";517;"no";"yes";"cellular";8;"jul";681;1;-1;0;"unknown";"yes"
27;"blue-collar";"single";"secondary";"no";148;"no";"no";"cellular";22;"jul";203;1;-1;0;"unknown";"no"
48;"retired";"married";"primary";"yes";-285;"no";"yes";"cellular";19;"nov";157;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";887;"no";"no";"unknown";16;"jun";287;1;-1;0;"unknown";"no"
45;"self-employed";"married";"secondary";"no";3098;"yes";"no";"cellular";7;"aug";159;2;-1;0;"unknown";"no"
41;"technician";"married";"tertiary";"no";744;"yes";"no";"unknown";14;"may";125;1;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"no";130;"no";"no";"cellular";8;"jul";202;2;-1;0;"unknown";"no"
44;"management";"married";"secondary";"no";438;"yes";"no";"telephone";8;"may";37;3;303;1;"failure";"no"
54;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";28;"jan";161;1;98;2;"failure";"yes"
49;"management";"married";"tertiary";"no";322;"no";"no";"cellular";6;"aug";72;4;-1;0;"unknown";"no"
34;"blue-collar";"single";"secondary";"no";186;"no";"no";"cellular";2;"feb";342;1;-1;0;"unknown";"no"
75;"retired";"divorced";"tertiary";"no";3810;"yes";"no";"cellular";16;"nov";262;1;183;1;"failure";"yes"
42;"management";"married";"tertiary";"no";0;"no";"no";"cellular";13;"aug";325;2;-1;0;"unknown";"no"
53;"blue-collar";"married";"secondary";"no";4380;"no";"no";"telephone";19;"aug";223;7;-1;0;"unknown";"no"
49;"management";"divorced";"tertiary";"no";755;"no";"no";"cellular";31;"jul";1212;4;-1;0;"unknown";"yes"
31;"services";"single";"secondary";"no";2070;"no";"no";"cellular";29;"aug";319;2;-1;0;"unknown";"no"
45;"blue-collar";"single";"primary";"no";999;"no";"no";"cellular";29;"jan";182;2;-1;0;"unknown";"no"
39;"self-employed";"married";"secondary";"no";167;"yes";"yes";"cellular";17;"jul";330;1;-1;0;"unknown";"no"
46;"technician";"married";"secondary";"no";1322;"yes";"yes";"cellular";19;"nov";514;1;-1;0;"unknown";"no"
42;"admin.";"divorced";"secondary";"no";1076;"yes";"no";"unknown";14;"may";213;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";502;"yes";"no";"cellular";7;"may";551;1;351;1;"failure";"no"
26;"technician";"single";"tertiary";"no";1216;"yes";"no";"cellular";16;"apr";472;2;-1;0;"unknown";"no"
37;"management";"single";"tertiary";"no";876;"yes";"no";"cellular";15;"apr";140;4;327;2;"failure";"no"
36;"services";"single";"secondary";"no";342;"yes";"no";"unknown";7;"may";142;1;-1;0;"unknown";"no"
29;"blue-collar";"single";"primary";"no";950;"yes";"no";"unknown";4;"jun";135;3;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";973;"yes";"yes";"unknown";23;"may";293;1;-1;0;"unknown";"no"
26;"technician";"married";"tertiary";"no";3825;"yes";"no";"unknown";13;"may";107;1;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";2366;"yes";"no";"unknown";20;"may";127;2;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";542;"yes";"no";"cellular";20;"nov";1816;1;-1;0;"unknown";"no"
53;"management";"married";"primary";"no";995;"no";"no";"cellular";4;"feb";200;3;-1;0;"unknown";"no"
29;"management";"married";"tertiary";"no";20;"yes";"no";"cellular";12;"may";418;1;-1;0;"unknown";"no"
34;"technician";"single";"tertiary";"no";228;"yes";"no";"cellular";22;"jul";387;4;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";130;"no";"no";"cellular";5;"may";156;1;337;3;"failure";"no"
56;"management";"married";"tertiary";"no";217;"no";"yes";"cellular";21;"jul";121;2;-1;0;"unknown";"no"
55;"entrepreneur";"divorced";"tertiary";"no";0;"no";"no";"unknown";6;"jun";108;2;-1;0;"unknown";"no"
31;"self-employed";"married";"tertiary";"no";259;"yes";"no";"cellular";7;"may";47;1;-1;0;"unknown";"no"
27;"admin.";"single";"secondary";"no";619;"yes";"no";"cellular";20;"apr";16;5;322;4;"other";"no"
36;"management";"married";"secondary";"no";644;"yes";"no";"cellular";18;"nov";54;2;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";22;"yes";"no";"cellular";6;"aug";265;2;-1;0;"unknown";"no"
37;"admin.";"divorced";"tertiary";"no";4303;"yes";"no";"unknown";23;"may";168;1;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";239;"yes";"no";"cellular";7;"may";115;3;-1;0;"unknown";"no"
42;"technician";"single";"secondary";"no";275;"no";"no";"cellular";26;"aug";31;8;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";1221;"yes";"no";"telephone";25;"jul";279;4;-1;0;"unknown";"yes"
62;"retired";"married";"secondary";"no";816;"no";"no";"cellular";1;"sep";28;2;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";1222;"yes";"no";"cellular";20;"apr";369;1;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";2363;"yes";"no";"unknown";13;"may";247;1;-1;0;"unknown";"no"
66;"retired";"married";"tertiary";"no";2262;"no";"no";"cellular";22;"apr";155;1;-1;0;"unknown";"no"
47;"admin.";"married";"secondary";"no";2;"no";"no";"cellular";10;"jul";89;1;-1;0;"unknown";"no"
39;"entrepreneur";"married";"tertiary";"no";2;"yes";"no";"cellular";17;"nov";180;1;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";4508;"yes";"no";"cellular";18;"nov";854;1;-1;0;"unknown";"yes"
35;"admin.";"divorced";"secondary";"no";-402;"yes";"no";"cellular";17;"apr";46;1;-1;0;"unknown";"no"
27;"technician";"married";"secondary";"no";1577;"yes";"yes";"unknown";28;"may";266;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";34;"yes";"no";"cellular";5;"may";321;2;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";795;"no";"no";"cellular";28;"aug";99;24;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";117;"yes";"no";"unknown";9;"may";147;1;-1;0;"unknown";"no"
34;"self-employed";"single";"tertiary";"no";5462;"no";"no";"cellular";30;"apr";430;1;-1;0;"unknown";"yes"
25;"services";"single";"secondary";"no";402;"no";"yes";"cellular";7;"jul";264;1;-1;0;"unknown";"no"
40;"technician";"single";"secondary";"no";641;"no";"no";"cellular";16;"jul";156;6;-1;0;"unknown";"no"
43;"management";"divorced";"tertiary";"no";36;"no";"no";"cellular";26;"may";118;1;102;2;"failure";"no"
55;"unemployed";"married";"tertiary";"no";383;"no";"no";"unknown";6;"may";343;3;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";283;"yes";"no";"cellular";7;"aug";226;3;-1;0;"unknown";"no"
45;"management";"divorced";"tertiary";"no";0;"no";"no";"unknown";1;"jun";5;1;-1;0;"unknown";"no"
35;"blue-collar";"divorced";"secondary";"no";3102;"yes";"no";"cellular";20;"nov";138;1;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";889;"yes";"no";"cellular";10;"sep";185;1;99;5;"success";"yes"
59;"housemaid";"married";"secondary";"no";0;"no";"no";"telephone";3;"feb";722;1;99;4;"failure";"no"
34;"self-employed";"married";"secondary";"no";-370;"yes";"no";"unknown";21;"may";748;1;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";-606;"yes";"no";"unknown";26;"may";142;1;-1;0;"unknown";"no"
40;"management";"single";"secondary";"no";10378;"yes";"no";"cellular";17;"nov";523;3;-1;0;"unknown";"no"
36;"blue-collar";"single";"primary";"no";1313;"yes";"no";"unknown";20;"jun";8;10;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";8969;"yes";"no";"cellular";17;"nov";153;1;-1;0;"unknown";"no"
55;"self-employed";"married";"secondary";"no";-196;"yes";"no";"unknown";20;"may";210;2;-1;0;"unknown";"no"
38;"entrepreneur";"married";"secondary";"yes";15;"yes";"no";"cellular";10;"jul";121;1;-1;0;"unknown";"no"
61;"admin.";"married";"secondary";"no";4608;"no";"no";"cellular";12;"mar";212;1;-1;0;"unknown";"yes"
41;"management";"married";"tertiary";"no";666;"yes";"no";"unknown";21;"may";253;3;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";452;"yes";"no";"unknown";13;"may";139;4;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";149;"yes";"no";"unknown";9;"may";71;1;-1;0;"unknown";"no"
77;"retired";"married";"tertiary";"no";7802;"no";"no";"telephone";4;"may";421;1;92;2;"success";"yes"
35;"services";"married";"secondary";"no";1453;"yes";"no";"unknown";28;"may";199;1;-1;0;"unknown";"no"
54;"blue-collar";"married";"secondary";"no";1480;"yes";"no";"unknown";14;"may";108;1;-1;0;"unknown";"no"
37;"management";"single";"unknown";"no";715;"yes";"no";"unknown";20;"may";247;2;-1;0;"unknown";"no"
56;"management";"single";"secondary";"no";237;"yes";"no";"telephone";9;"jul";236;4;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";694;"yes";"no";"cellular";4;"feb";97;4;196;5;"failure";"no"
40;"management";"married";"tertiary";"no";0;"no";"no";"cellular";14;"jul";109;8;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";55;"yes";"no";"cellular";2;"jun";112;2;-1;0;"unknown";"no"
55;"housemaid";"married";"primary";"no";136;"yes";"no";"unknown";19;"jun";15;10;-1;0;"unknown";"no"
55;"services";"divorced";"secondary";"yes";-404;"yes";"no";"cellular";22;"jul";502;3;-1;0;"unknown";"no"
52;"blue-collar";"married";"primary";"no";779;"yes";"no";"unknown";16;"may";30;3;-1;0;"unknown";"no"
55;"blue-collar";"single";"primary";"no";1187;"yes";"no";"unknown";2;"jun";193;2;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";641;"yes";"no";"cellular";13;"aug";347;2;-1;0;"unknown";"no"
52;"blue-collar";"married";"unknown";"no";826;"yes";"no";"unknown";15;"may";468;2;-1;0;"unknown";"no"
33;"services";"divorced";"secondary";"no";991;"yes";"no";"unknown";27;"may";148;1;-1;0;"unknown";"no"
53;"admin.";"married";"secondary";"no";1796;"no";"no";"telephone";16;"mar";295;1;91;2;"success";"yes"
46;"admin.";"married";"secondary";"no";22;"no";"no";"cellular";26;"aug";388;6;-1;0;"unknown";"yes"
44;"technician";"single";"secondary";"no";244;"yes";"no";"cellular";12;"aug";1735;4;-1;0;"unknown";"yes"
48;"management";"divorced";"tertiary";"no";1328;"yes";"no";"unknown";6;"may";339;1;-1;0;"unknown";"no"
50;"housemaid";"married";"secondary";"no";7317;"yes";"no";"cellular";13;"may";172;1;370;1;"failure";"no"
34;"technician";"single";"secondary";"no";4943;"yes";"yes";"cellular";19;"nov";117;2;-1;0;"unknown";"no"
38;"technician";"single";"secondary";"no";258;"no";"yes";"unknown";20;"jun";587;2;-1;0;"unknown";"no"
42;"management";"single";"tertiary";"no";515;"yes";"no";"cellular";19;"nov";501;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";12607;"no";"no";"cellular";14;"aug";203;5;84;1;"failure";"no"
58;"admin.";"married";"secondary";"no";2;"yes";"yes";"telephone";25;"jul";282;9;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";13;"no";"no";"cellular";28;"aug";110;5;-1;0;"unknown";"no"
19;"student";"single";"primary";"no";103;"no";"no";"cellular";10;"jul";104;2;-1;0;"unknown";"yes"
41;"management";"married";"tertiary";"no";3872;"no";"no";"cellular";15;"oct";231;1;-1;0;"unknown";"no"
31;"unemployed";"married";"secondary";"no";296;"yes";"no";"unknown";20;"may";378;3;-1;0;"unknown";"no"
52;"self-employed";"single";"primary";"no";217;"no";"yes";"cellular";15;"jul";193;6;-1;0;"unknown";"no"
42;"self-employed";"divorced";"tertiary";"no";360;"yes";"no";"unknown";24;"jun";110;5;-1;0;"unknown";"no"
41;"blue-collar";"single";"secondary";"no";428;"yes";"no";"cellular";12;"may";92;1;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";315;"no";"no";"cellular";23;"jun";1407;1;-1;0;"unknown";"yes"
38;"blue-collar";"married";"secondary";"no";121;"yes";"yes";"cellular";6;"feb";168;1;212;2;"failure";"no"
32;"management";"married";"tertiary";"no";820;"yes";"no";"cellular";17;"jul";738;3;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";195;"yes";"no";"cellular";18;"feb";220;1;63;5;"failure";"yes"
48;"management";"divorced";"tertiary";"no";108;"yes";"no";"unknown";27;"may";89;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";-381;"yes";"no";"cellular";31;"jul";30;29;-1;0;"unknown";"no"
45;"unemployed";"divorced";"primary";"no";757;"no";"no";"cellular";20;"apr";103;1;325;4;"failure";"no"
34;"technician";"married";"tertiary";"no";691;"yes";"yes";"unknown";2;"jun";70;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";1925;"yes";"no";"cellular";20;"apr";904;2;-1;0;"unknown";"yes"
34;"management";"married";"tertiary";"no";902;"yes";"no";"cellular";14;"may";159;1;367;2;"other";"no"
61;"retired";"married";"secondary";"no";5207;"no";"no";"cellular";11;"sep";336;1;81;2;"other";"no"
54;"services";"married";"secondary";"no";0;"no";"no";"unknown";19;"jun";220;1;-1;0;"unknown";"no"
50;"entrepreneur";"married";"secondary";"no";665;"no";"no";"telephone";17;"nov";238;1;-1;0;"unknown";"no"
43;"admin.";"divorced";"secondary";"no";738;"yes";"no";"cellular";21;"nov";585;4;191;4;"failure";"no"
47;"blue-collar";"married";"primary";"no";233;"no";"no";"cellular";8;"aug";293;3;-1;0;"unknown";"no"
45;"services";"married";"secondary";"no";-839;"yes";"no";"unknown";28;"may";68;1;-1;0;"unknown";"no"
57;"retired";"married";"primary";"no";5799;"no";"no";"unknown";17;"jun";97;1;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";1033;"yes";"no";"unknown";9;"may";1713;1;-1;0;"unknown";"no"
49;"unemployed";"divorced";"secondary";"no";66;"no";"no";"cellular";9;"jul";218;3;-1;0;"unknown";"no"
43;"services";"married";"primary";"no";2567;"yes";"no";"cellular";14;"may";109;1;360;2;"failure";"no"
40;"management";"married";"tertiary";"no";1584;"yes";"no";"telephone";21;"nov";661;1;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";182;"yes";"no";"cellular";6;"may";145;1;-1;0;"unknown";"no"
58;"management";"married";"primary";"no";76;"yes";"no";"telephone";18;"may";54;4;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";663;"yes";"no";"cellular";20;"apr";111;1;-1;0;"unknown";"no"
46;"technician";"divorced";"secondary";"no";3455;"yes";"no";"unknown";14;"may";566;2;-1;0;"unknown";"no"
29;"blue-collar";"married";"secondary";"no";15;"no";"no";"cellular";3;"jun";136;1;-1;0;"unknown";"no"
52;"blue-collar";"married";"primary";"no";1054;"no";"no";"cellular";4;"aug";273;1;-1;0;"unknown";"no"
45;"management";"married";"tertiary";"no";1529;"no";"no";"cellular";30;"jun";160;1;-1;0;"unknown";"no"
26;"management";"single";"secondary";"no";63;"no";"no";"cellular";28;"jul";76;4;-1;0;"unknown";"no"
40;"technician";"married";"tertiary";"no";0;"no";"no";"cellular";19;"aug";44;1;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";920;"yes";"yes";"unknown";27;"may";239;1;-1;0;"unknown";"no"
47;"admin.";"single";"secondary";"no";3696;"no";"no";"cellular";12;"jul";250;2;181;4;"success";"yes"
27;"technician";"single";"unknown";"no";59;"no";"no";"cellular";15;"jul";792;4;-1;0;"unknown";"no"
27;"services";"married";"secondary";"no";0;"yes";"no";"cellular";23;"jul";198;13;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";157;"yes";"no";"unknown";6;"may";73;1;-1;0;"unknown";"no"
49;"admin.";"married";"secondary";"no";2465;"no";"no";"cellular";20;"aug";157;3;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";24;"jul";90;6;-1;0;"unknown";"no"
47;"self-employed";"married";"secondary";"yes";2;"no";"no";"cellular";5;"feb";236;2;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";0;"no";"no";"cellular";16;"jul";118;5;-1;0;"unknown";"no"
39;"management";"single";"tertiary";"no";12437;"no";"no";"telephone";18;"nov";40;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";378;"yes";"no";"cellular";17;"apr";346;1;-1;0;"unknown";"no"
54;"blue-collar";"married";"primary";"no";211;"no";"no";"unknown";19;"jun";200;2;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";-228;"yes";"no";"cellular";20;"apr";187;3;332;3;"other";"no"
63;"retired";"married";"secondary";"no";2747;"no";"no";"cellular";11;"sep";113;1;80;1;"failure";"no"
42;"self-employed";"married";"tertiary";"no";1932;"yes";"no";"unknown";16;"may";192;32;-1;0;"unknown";"no"
43;"technician";"single";"secondary";"no";11269;"no";"no";"unknown";29;"may";92;1;-1;0;"unknown";"no"
37;"admin.";"married";"secondary";"no";1314;"yes";"no";"cellular";8;"may";682;2;85;7;"success";"no"
55;"blue-collar";"married";"primary";"no";2275;"no";"no";"cellular";25;"aug";139;9;-1;0;"unknown";"no"
43;"management";"single";"tertiary";"no";1304;"no";"no";"cellular";6;"aug";501;2;-1;0;"unknown";"yes"
41;"blue-collar";"married";"secondary";"yes";720;"no";"yes";"cellular";24;"jul";651;1;-1;0;"unknown";"yes"
47;"technician";"divorced";"secondary";"no";2914;"yes";"no";"cellular";18;"aug";405;13;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";1464;"yes";"no";"unknown";23;"may";350;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";11555;"no";"no";"cellular";8;"apr";125;2;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";-302;"yes";"no";"cellular";13;"may";36;2;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";2497;"yes";"no";"cellular";21;"nov";35;1;176;1;"failure";"no"
35;"blue-collar";"married";"secondary";"no";-339;"yes";"no";"unknown";8;"may";389;2;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";56;"yes";"yes";"cellular";29;"jan";194;1;247;1;"failure";"no"
26;"self-employed";"single";"tertiary";"no";211;"no";"no";"cellular";29;"jan";168;3;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";3611;"yes";"no";"unknown";4;"jun";347;3;-1;0;"unknown";"no"
59;"unemployed";"married";"primary";"no";0;"no";"no";"cellular";30;"jan";3025;2;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";459;"no";"no";"cellular";16;"mar";83;13;-1;0;"unknown";"no"
46;"entrepreneur";"married";"unknown";"no";242;"yes";"no";"unknown";9;"may";332;2;-1;0;"unknown";"no"
50;"self-employed";"married";"secondary";"no";1138;"no";"no";"cellular";14;"aug";219;10;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";1319;"yes";"no";"cellular";16;"apr";427;1;150;1;"failure";"no"
81;"retired";"married";"secondary";"no";1;"no";"no";"cellular";19;"aug";65;5;-1;0;"unknown";"no"
35;"services";"divorced";"secondary";"no";67;"yes";"no";"cellular";5;"may";192;2;-1;0;"unknown";"no"
58;"retired";"married";"unknown";"no";937;"no";"no";"cellular";5;"aug";155;1;-1;0;"unknown";"no"
44;"services";"married";"secondary";"no";0;"yes";"yes";"cellular";11;"may";161;1;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";-558;"yes";"no";"cellular";21;"jul";533;1;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";171;"no";"no";"cellular";4;"feb";19;3;175;2;"other";"no"
33;"technician";"single";"tertiary";"no";696;"no";"yes";"cellular";5;"feb";193;1;176;8;"failure";"no"
29;"management";"married";"tertiary";"no";361;"yes";"yes";"cellular";28;"jul";819;3;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";227;"no";"no";"cellular";30;"apr";278;1;-1;0;"unknown";"yes"
31;"admin.";"married";"secondary";"no";236;"yes";"yes";"telephone";4;"may";211;3;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";-53;"no";"no";"cellular";29;"jan";617;2;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";192;"yes";"no";"cellular";23;"jul";61;2;-1;0;"unknown";"no"
43;"management";"divorced";"tertiary";"no";38;"yes";"no";"cellular";25;"jul";197;6;-1;0;"unknown";"no"
32;"self-employed";"single";"secondary";"no";518;"yes";"no";"cellular";18;"may";34;7;-1;0;"unknown";"no"
36;"self-employed";"married";"tertiary";"no";16;"yes";"yes";"cellular";18;"jul";95;4;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";-256;"yes";"no";"unknown";3;"jun";147;2;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";105;"no";"yes";"cellular";4;"jul";668;2;-1;0;"unknown";"no"
45;"self-employed";"married";"tertiary";"no";334;"no";"no";"unknown";5;"jun";87;2;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"no";1048;"no";"yes";"cellular";21;"jul";405;1;-1;0;"unknown";"no"
41;"services";"married";"secondary";"no";108;"yes";"yes";"unknown";13;"may";83;7;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";660;"no";"no";"cellular";22;"jun";115;1;-1;0;"unknown";"no"
37;"unemployed";"single";"tertiary";"yes";0;"yes";"no";"cellular";10;"jul";108;1;-1;0;"unknown";"no"
28;"entrepreneur";"married";"unknown";"no";1034;"no";"no";"cellular";29;"jan";113;1;-1;0;"unknown";"no"
47;"housemaid";"married";"primary";"no";201;"yes";"no";"cellular";7;"aug";58;2;382;1;"failure";"no"
37;"housemaid";"married";"primary";"no";189;"no";"no";"cellular";8;"aug";148;4;-1;0;"unknown";"no"
60;"retired";"divorced";"unknown";"no";-49;"yes";"no";"cellular";29;"jan";188;1;261;2;"failure";"no"
43;"technician";"divorced";"secondary";"no";1413;"yes";"no";"telephone";28;"jul";75;3;-1;0;"unknown";"no"
58;"retired";"divorced";"tertiary";"no";264;"yes";"yes";"cellular";8;"jul";523;3;-1;0;"unknown";"no"
52;"entrepreneur";"married";"tertiary";"no";54;"yes";"yes";"telephone";18;"nov";103;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";12519;"yes";"no";"cellular";17;"apr";146;2;147;4;"success";"no"
32;"services";"single";"secondary";"no";2881;"no";"no";"cellular";2;"jul";356;1;-1;0;"unknown";"yes"
42;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";17;"jul";71;1;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";88;"yes";"yes";"cellular";8;"aug";189;2;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";1020;"yes";"no";"unknown";28;"may";251;1;-1;0;"unknown";"no"
36;"technician";"divorced";"tertiary";"no";119;"no";"no";"cellular";23;"jul";127;8;-1;0;"unknown";"no"
51;"unemployed";"single";"tertiary";"no";1594;"no";"no";"telephone";4;"feb";197;1;-1;0;"unknown";"no"
46;"technician";"married";"tertiary";"no";342;"yes";"yes";"cellular";5;"may";84;1;-1;0;"unknown";"no"
50;"services";"single";"secondary";"no";72;"no";"no";"unknown";8;"may";141;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";14;"yes";"no";"unknown";2;"jun";171;1;-1;0;"unknown";"no"
26;"entrepreneur";"single";"tertiary";"no";81;"no";"no";"cellular";21;"jul";262;3;-1;0;"unknown";"no"
41;"self-employed";"married";"tertiary";"no";231;"no";"no";"cellular";7;"aug";352;2;-1;0;"unknown";"yes"
39;"technician";"married";"tertiary";"no";1161;"yes";"no";"cellular";9;"apr";236;1;336;1;"failure";"no"
31;"blue-collar";"married";"secondary";"no";-45;"yes";"no";"unknown";2;"jun";184;1;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";231;"yes";"yes";"cellular";25;"jul";568;2;-1;0;"unknown";"no"
31;"management";"single";"secondary";"no";36;"no";"no";"cellular";27;"aug";105;2;-1;0;"unknown";"no"
30;"technician";"married";"tertiary";"no";1570;"yes";"no";"cellular";26;"may";260;1;-1;0;"unknown";"no"
35;"technician";"married";"tertiary";"no";7050;"no";"no";"cellular";11;"aug";222;3;182;4;"success";"yes"
33;"blue-collar";"married";"primary";"no";3704;"yes";"no";"unknown";6;"jun";447;2;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";6619;"yes";"no";"cellular";21;"aug";239;8;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";-84;"yes";"no";"cellular";11;"may";426;1;-1;0;"unknown";"no"
40;"management";"single";"tertiary";"no";1509;"no";"no";"cellular";12;"may";333;1;58;3;"failure";"no"
31;"self-employed";"married";"tertiary";"no";1134;"no";"no";"cellular";6;"feb";174;2;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";146;"yes";"yes";"unknown";15;"may";284;2;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";9449;"yes";"no";"cellular";8;"may";428;1;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"no";374;"yes";"yes";"cellular";16;"jul";236;1;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";88;"no";"no";"cellular";29;"aug";150;2;-1;0;"unknown";"no"
59;"management";"married";"tertiary";"no";2764;"no";"no";"cellular";24;"dec";237;1;206;2;"failure";"no"
30;"management";"single";"tertiary";"no";2;"yes";"yes";"cellular";25;"jul";1031;8;-1;0;"unknown";"yes"
33;"technician";"married";"tertiary";"no";8023;"no";"no";"cellular";30;"nov";107;1;105;3;"failure";"no"
52;"blue-collar";"married";"secondary";"no";289;"yes";"no";"unknown";21;"may";45;13;-1;0;"unknown";"no"
83;"retired";"married";"secondary";"no";0;"no";"no";"cellular";18;"mar";140;10;-1;0;"unknown";"no"
58;"self-employed";"divorced";"tertiary";"no";1382;"no";"no";"unknown";9;"jun";700;1;-1;0;"unknown";"yes"
33;"management";"divorced";"tertiary";"yes";280;"no";"yes";"cellular";18;"nov";155;1;-1;0;"unknown";"no"
41;"blue-collar";"divorced";"primary";"no";2195;"yes";"no";"unknown";3;"jun";211;5;-1;0;"unknown";"no"
31;"self-employed";"married";"secondary";"no";33;"no";"no";"cellular";6;"aug";165;4;-1;0;"unknown";"no"
35;"blue-collar";"divorced";"secondary";"no";275;"no";"yes";"cellular";10;"jul";144;5;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";703;"yes";"no";"unknown";5;"jun";590;3;-1;0;"unknown";"yes"
57;"admin.";"divorced";"unknown";"no";887;"no";"no";"unknown";20;"jun";43;8;-1;0;"unknown";"no"
56;"unknown";"married";"unknown";"no";0;"no";"no";"unknown";20;"jun";471;2;-1;0;"unknown";"no"
53;"admin.";"single";"secondary";"no";1178;"yes";"no";"unknown";15;"may";295;2;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";2763;"yes";"no";"unknown";2;"jun";526;2;-1;0;"unknown";"yes"
32;"entrepreneur";"single";"tertiary";"no";890;"yes";"yes";"cellular";28;"aug";27;10;-1;0;"unknown";"no"
46;"self-employed";"married";"secondary";"no";548;"no";"no";"cellular";12;"aug";1181;4;-1;0;"unknown";"yes"
56;"technician";"married";"tertiary";"no";272;"no";"no";"cellular";26;"aug";103;2;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";6269;"yes";"no";"telephone";18;"nov";249;3;112;2;"failure";"no"
29;"entrepreneur";"married";"secondary";"no";178;"yes";"no";"cellular";5;"may";231;4;-1;0;"unknown";"no"
60;"management";"married";"primary";"no";252;"yes";"yes";"cellular";13;"apr";123;2;147;3;"failure";"no"
33;"housemaid";"single";"tertiary";"no";23663;"yes";"no";"cellular";16;"apr";199;2;146;2;"failure";"no"
27;"entrepreneur";"single";"tertiary";"no";81;"no";"no";"cellular";5;"feb";197;1;199;3;"failure";"no"
50;"self-employed";"divorced";"tertiary";"no";59;"yes";"no";"unknown";28;"may";67;2;-1;0;"unknown";"no"
45;"services";"married";"secondary";"no";1343;"no";"yes";"cellular";8;"jul";220;1;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";225;"no";"no";"cellular";7;"aug";122;6;-1;0;"unknown";"no"
59;"retired";"married";"primary";"no";0;"yes";"no";"unknown";26;"may";104;7;-1;0;"unknown";"no"
58;"retired";"divorced";"primary";"no";2538;"yes";"no";"cellular";31;"jul";307;6;-1;0;"unknown";"no"
64;"retired";"married";"primary";"no";43;"no";"no";"cellular";11;"mar";117;1;91;1;"success";"no"
47;"technician";"married";"secondary";"no";57;"no";"no";"cellular";5;"may";253;1;342;1;"success";"no"
40;"management";"married";"tertiary";"no";-117;"yes";"no";"cellular";17;"jul";770;2;-1;0;"unknown";"no"
36;"technician";"divorced";"secondary";"no";666;"yes";"yes";"cellular";3;"apr";190;1;133;1;"other";"no"
28;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";16;"jul";767;5;-1;0;"unknown";"yes"
38;"technician";"single";"tertiary";"no";25;"yes";"no";"cellular";1;"jun";232;2;-1;0;"unknown";"yes"
53;"entrepreneur";"married";"primary";"no";1034;"yes";"no";"cellular";19;"nov";854;2;170;3;"other";"no"
46;"blue-collar";"single";"secondary";"no";324;"yes";"no";"unknown";30;"may";293;6;-1;0;"unknown";"no"
54;"technician";"married";"secondary";"no";0;"no";"no";"telephone";16;"nov";986;1;94;8;"failure";"no"
32;"management";"married";"tertiary";"no";0;"no";"no";"cellular";19;"nov";66;1;-1;0;"unknown";"no"
55;"management";"married";"primary";"no";621;"no";"yes";"cellular";2;"feb";114;1;208;1;"failure";"no"
55;"retired";"divorced";"secondary";"no";138;"no";"no";"cellular";17;"jul";205;1;-1;0;"unknown";"no"
56;"technician";"married";"secondary";"no";4063;"no";"yes";"telephone";28;"jul";31;4;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";11084;"no";"no";"unknown";11;"jun";113;1;-1;0;"unknown";"no"
44;"technician";"married";"secondary";"no";310;"yes";"no";"cellular";6;"feb";200;1;253;1;"failure";"no"
32;"entrepreneur";"married";"unknown";"no";73;"yes";"no";"cellular";21;"nov";158;1;135;1;"failure";"no"
33;"management";"married";"tertiary";"no";1808;"yes";"no";"unknown";4;"jun";99;4;-1;0;"unknown";"no"
41;"unemployed";"divorced";"secondary";"no";1281;"no";"no";"cellular";31;"jul";45;3;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";64;"yes";"no";"cellular";12;"may";239;4;278;2;"other";"no"
58;"management";"single";"tertiary";"no";2;"yes";"no";"telephone";11;"may";168;3;-1;0;"unknown";"no"
52;"blue-collar";"married";"secondary";"no";60;"no";"yes";"cellular";26;"aug";196;2;-1;0;"unknown";"no"
49;"services";"single";"secondary";"no";133;"no";"no";"unknown";11;"jun";319;6;-1;0;"unknown";"no"
44;"housemaid";"married";"primary";"no";3706;"no";"no";"unknown";17;"jun";209;3;-1;0;"unknown";"no"
28;"admin.";"single";"unknown";"no";174;"no";"no";"cellular";28;"sep";184;1;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";251;"no";"yes";"cellular";29;"aug";156;2;-1;0;"unknown";"no"
37;"self-employed";"married";"secondary";"no";4153;"yes";"no";"cellular";31;"jul";35;16;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";985;"yes";"no";"cellular";19;"feb";88;2;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";168;"no";"no";"cellular";1;"jun";347;3;140;2;"success";"yes"
46;"services";"married";"secondary";"no";1550;"yes";"no";"cellular";16;"apr";126;2;149;8;"failure";"no"
47;"management";"married";"tertiary";"no";7708;"yes";"no";"cellular";21;"nov";157;5;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";-12;"yes";"no";"unknown";12;"may";212;5;-1;0;"unknown";"no"
80;"management";"married";"primary";"no";6483;"no";"no";"telephone";19;"oct";123;1;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";60;"yes";"yes";"unknown";7;"may";306;5;-1;0;"unknown";"no"
55;"technician";"single";"secondary";"no";3339;"yes";"no";"unknown";2;"jun";63;1;-1;0;"unknown";"no"
22;"student";"single";"tertiary";"no";1161;"no";"yes";"cellular";16;"jun";119;1;-1;0;"unknown";"no"
33;"blue-collar";"single";"primary";"no";599;"yes";"no";"unknown";21;"may";29;1;-1;0;"unknown";"no"
53;"entrepreneur";"married";"unknown";"no";28;"no";"no";"cellular";31;"jul";117;2;-1;0;"unknown";"no"
49;"management";"married";"tertiary";"no";1430;"no";"no";"cellular";18;"nov";231;2;-1;0;"unknown";"no"
51;"retired";"married";"secondary";"no";35;"no";"yes";"cellular";8;"jul";123;4;-1;0;"unknown";"no"
41;"self-employed";"married";"tertiary";"no";6000;"yes";"no";"cellular";18;"nov";265;1;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";0;"yes";"no";"cellular";15;"jul";144;1;-1;0;"unknown";"no"
36;"blue-collar";"single";"secondary";"no";340;"no";"yes";"unknown";16;"jun";559;3;-1;0;"unknown";"no"
35;"entrepreneur";"married";"tertiary";"no";0;"no";"no";"cellular";5;"may";74;1;168;4;"failure";"no"
29;"services";"married";"secondary";"no";3;"yes";"no";"cellular";10;"jul";183;1;-1;0;"unknown";"no"
40;"technician";"single";"tertiary";"no";445;"yes";"no";"cellular";24;"jul";631;1;-1;0;"unknown";"no"
32;"management";"divorced";"tertiary";"no";874;"no";"no";"cellular";12;"aug";1282;6;-1;0;"unknown";"yes"
48;"blue-collar";"married";"secondary";"no";705;"no";"no";"unknown";13;"may";108;2;-1;0;"unknown";"no"
59;"retired";"married";"unknown";"no";1033;"no";"no";"unknown";11;"jun";1199;1;-1;0;"unknown";"yes"
35;"self-employed";"divorced";"tertiary";"no";1354;"yes";"no";"unknown";23;"may";736;2;-1;0;"unknown";"yes"
33;"technician";"single";"tertiary";"no";123;"no";"no";"cellular";19;"aug";264;1;-1;0;"unknown";"no"
55;"retired";"married";"primary";"no";8894;"no";"no";"unknown";11;"jun";262;1;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";429;"yes";"no";"unknown";13;"may";244;1;-1;0;"unknown";"no"
42;"housemaid";"married";"primary";"no";14752;"no";"no";"unknown";19;"may";107;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";67;"no";"no";"unknown";24;"jun";9;1;-1;0;"unknown";"no"
35;"housemaid";"divorced";"secondary";"no";39;"no";"no";"cellular";31;"jul";149;2;-1;0;"unknown";"no"
28;"technician";"single";"tertiary";"no";102;"no";"no";"cellular";28;"jan";321;2;-1;0;"unknown";"no"
25;"student";"single";"secondary";"no";1790;"no";"no";"cellular";17;"apr";224;1;322;3;"failure";"no"
56;"management";"single";"tertiary";"no";15311;"no";"no";"cellular";29;"apr";157;6;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";-635;"no";"no";"cellular";25;"jul";250;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";115;"no";"no";"cellular";11;"aug";209;4;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";10786;"no";"no";"unknown";20;"jun";111;3;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";498;"yes";"no";"cellular";8;"may";55;2;298;4;"failure";"no"
39;"blue-collar";"married";"secondary";"no";1045;"yes";"yes";"cellular";9;"jul";290;13;-1;0;"unknown";"no"
48;"entrepreneur";"married";"tertiary";"no";810;"no";"no";"unknown";6;"jun";383;1;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";-162;"yes";"no";"cellular";18;"may";380;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";55;"no";"no";"cellular";2;"feb";124;3;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";586;"yes";"yes";"unknown";8;"may";224;2;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";455;"no";"no";"cellular";29;"jan";240;1;182;8;"other";"no"
31;"management";"married";"tertiary";"no";1010;"yes";"no";"cellular";15;"may";385;3;364;9;"other";"no"
37;"management";"single";"tertiary";"no";509;"yes";"no";"cellular";6;"may";133;1;-1;0;"unknown";"no"
29;"student";"single";"unknown";"no";2929;"no";"no";"cellular";11;"aug";91;1;-1;0;"unknown";"no"
57;"technician";"married";"secondary";"no";3071;"yes";"yes";"cellular";7;"may";63;3;-1;0;"unknown";"no"
47;"management";"single";"tertiary";"no";153;"yes";"no";"cellular";18;"nov";192;2;-1;0;"unknown";"no"
38;"self-employed";"single";"secondary";"no";1146;"yes";"no";"unknown";21;"may";91;2;-1;0;"unknown";"no"
48;"blue-collar";"married";"primary";"no";1092;"no";"yes";"cellular";18;"nov";79;1;172;8;"failure";"no"
50;"management";"married";"tertiary";"no";3463;"no";"yes";"cellular";28;"oct";96;1;-1;0;"unknown";"yes"
33;"admin.";"married";"secondary";"no";1158;"yes";"no";"cellular";20;"apr";25;2;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";26;"yes";"no";"cellular";7;"may";227;2;-1;0;"unknown";"no"
30;"services";"single";"secondary";"no";566;"no";"no";"cellular";5;"feb";110;1;-1;0;"unknown";"no"
52;"technician";"divorced";"secondary";"no";204;"no";"no";"cellular";7;"aug";134;2;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";480;"no";"no";"cellular";20;"oct";213;1;-1;0;"unknown";"yes"
36;"technician";"single";"secondary";"no";1855;"yes";"no";"cellular";4;"feb";75;1;273;1;"failure";"no"
46;"services";"married";"secondary";"no";1034;"no";"no";"cellular";8;"aug";109;2;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";3250;"no";"no";"cellular";20;"aug";162;3;-1;0;"unknown";"no"
28;"management";"single";"secondary";"no";1633;"no";"no";"cellular";5;"jun";275;1;-1;0;"unknown";"yes"
46;"services";"married";"secondary";"no";0;"yes";"no";"cellular";22;"jun";154;1;124;1;"other";"no"
55;"retired";"married";"primary";"no";3334;"no";"no";"cellular";14;"aug";632;4;-1;0;"unknown";"yes"
33;"management";"married";"tertiary";"no";4020;"yes";"yes";"cellular";15;"apr";251;1;-1;0;"unknown";"no"
53;"management";"married";"secondary";"no";66;"no";"yes";"telephone";8;"jul";141;1;-1;0;"unknown";"no"
41;"technician";"single";"secondary";"no";-41;"yes";"yes";"cellular";3;"apr";154;1;-1;0;"unknown";"no"
60;"blue-collar";"married";"secondary";"no";322;"yes";"no";"cellular";7;"may";63;1;-1;0;"unknown";"no"
35;"entrepreneur";"married";"primary";"no";2643;"no";"yes";"cellular";8;"jul";259;1;-1;0;"unknown";"no"
43;"blue-collar";"divorced";"primary";"no";49;"yes";"no";"unknown";9;"may";100;1;-1;0;"unknown";"no"
57;"admin.";"divorced";"primary";"no";1;"no";"no";"unknown";15;"may";17;10;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";3098;"yes";"no";"telephone";21;"nov";192;9;-1;0;"unknown";"no"
31;"blue-collar";"married";"primary";"no";352;"yes";"no";"unknown";19;"may";97;9;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";65;"no";"no";"cellular";25;"aug";41;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";1949;"yes";"no";"unknown";2;"jun";200;3;-1;0;"unknown";"no"
32;"admin.";"single";"secondary";"no";739;"yes";"no";"cellular";20;"nov";91;3;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";1167;"yes";"yes";"cellular";1;"jun";264;1;-1;0;"unknown";"yes"
50;"admin.";"married";"secondary";"no";231;"yes";"no";"cellular";21;"nov";397;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";1136;"yes";"no";"unknown";21;"may";86;1;-1;0;"unknown";"no"
42;"entrepreneur";"married";"tertiary";"no";233;"no";"no";"cellular";5;"feb";446;1;-1;0;"unknown";"no"
59;"admin.";"married";"secondary";"no";817;"no";"no";"cellular";9;"apr";304;1;-1;0;"unknown";"no"
27;"blue-collar";"single";"primary";"no";1128;"yes";"no";"unknown";30;"jun";88;4;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";1831;"yes";"no";"cellular";20;"apr";86;2;-1;0;"unknown";"no"
35;"technician";"single";"secondary";"no";450;"yes";"no";"cellular";13;"may";63;1;-1;0;"unknown";"no"
28;"services";"married";"secondary";"no";0;"yes";"no";"cellular";5;"may";335;2;281;8;"failure";"no"
37;"management";"divorced";"secondary";"no";414;"no";"no";"cellular";29;"jan";293;2;162;4;"failure";"no"
57;"management";"married";"primary";"no";1052;"yes";"no";"unknown";13;"may";227;4;-1;0;"unknown";"no"
42;"admin.";"single";"secondary";"no";1022;"yes";"no";"unknown";5;"may";146;2;-1;0;"unknown";"no"
55;"admin.";"married";"primary";"no";343;"yes";"no";"cellular";14;"jul";276;3;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";1944;"yes";"no";"cellular";15;"apr";238;3;323;2;"failure";"no"
26;"blue-collar";"single";"secondary";"no";-32;"yes";"yes";"cellular";31;"jul";14;6;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";1781;"yes";"no";"unknown";19;"may";144;2;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";350;"no";"no";"cellular";8;"jul";367;1;-1;0;"unknown";"no"
33;"unemployed";"married";"secondary";"no";2;"yes";"no";"cellular";16;"jul";132;1;-1;0;"unknown";"no"
24;"blue-collar";"single";"primary";"no";111;"yes";"yes";"unknown";27;"may";42;4;-1;0;"unknown";"no"
37;"management";"divorced";"tertiary";"no";168;"yes";"no";"unknown";5;"jun";227;1;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";3;"no";"no";"telephone";18;"may";40;8;349;6;"failure";"no"
30;"technician";"single";"secondary";"no";102;"yes";"no";"unknown";7;"may";614;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";15;"may";270;1;-1;0;"unknown";"no"
55;"unknown";"married";"primary";"no";159;"no";"no";"unknown";4;"sep";15;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";1134;"yes";"no";"unknown";23;"may";304;2;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";352;"yes";"no";"cellular";11;"may";79;3;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";865;"yes";"no";"cellular";3;"jun";101;1;-1;0;"unknown";"no"
59;"blue-collar";"married";"primary";"no";179;"yes";"no";"unknown";5;"may";55;3;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";5;"yes";"no";"unknown";19;"may";205;3;-1;0;"unknown";"no"
23;"student";"married";"tertiary";"yes";-230;"yes";"no";"cellular";18;"may";259;2;-1;0;"unknown";"no"
51;"management";"divorced";"tertiary";"no";0;"no";"no";"cellular";13;"aug";60;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";3924;"yes";"no";"cellular";20;"apr";122;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";764;"yes";"no";"cellular";14;"jul";557;5;-1;0;"unknown";"no"
37;"blue-collar";"divorced";"primary";"no";511;"yes";"no";"telephone";11;"may";42;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";4348;"yes";"no";"cellular";28;"aug";373;8;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";3016;"yes";"no";"cellular";14;"jul";80;3;-1;0;"unknown";"no"
26;"student";"single";"secondary";"no";279;"no";"no";"cellular";7;"may";239;3;-1;0;"unknown";"no"
49;"management";"divorced";"tertiary";"no";1405;"no";"no";"unknown";8;"jun";29;1;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";908;"yes";"no";"unknown";20;"jun";1663;1;-1;0;"unknown";"yes"
30;"management";"single";"tertiary";"no";162;"yes";"no";"unknown";20;"may";186;4;-1;0;"unknown";"no"
38;"services";"divorced";"secondary";"no";792;"no";"no";"unknown";6;"jun";430;1;-1;0;"unknown";"no"
58;"management";"married";"secondary";"no";111;"no";"no";"telephone";24;"mar";58;2;-1;0;"unknown";"no"
34;"student";"single";"unknown";"no";1281;"no";"no";"cellular";25;"aug";279;1;117;1;"failure";"no"
53;"services";"divorced";"unknown";"no";1012;"yes";"no";"cellular";28;"jul";332;2;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";2881;"no";"no";"cellular";5;"aug";510;2;2;5;"other";"no"
30;"blue-collar";"single";"secondary";"no";445;"yes";"no";"cellular";16;"apr";218;1;330;2;"failure";"no"
40;"management";"married";"secondary";"no";643;"yes";"no";"cellular";17;"apr";74;2;256;1;"failure";"no"
57;"housemaid";"single";"secondary";"no";254;"no";"no";"cellular";8;"oct";676;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";-1212;"yes";"no";"cellular";15;"may";1259;2;333;18;"failure";"no"
41;"technician";"married";"secondary";"no";2125;"yes";"yes";"cellular";21;"nov";285;3;116;4;"failure";"no"
53;"technician";"divorced";"unknown";"no";629;"yes";"no";"cellular";8;"may";298;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";-202;"yes";"yes";"cellular";30;"jul";225;4;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";271;"yes";"no";"cellular";13;"may";80;2;268;2;"other";"no"
34;"management";"married";"tertiary";"no";703;"yes";"no";"cellular";14;"apr";342;1;-1;0;"unknown";"yes"
46;"blue-collar";"married";"secondary";"no";-216;"yes";"no";"cellular";28;"jul";404;3;-1;0;"unknown";"no"
45;"services";"divorced";"secondary";"no";88;"no";"yes";"cellular";7;"jul";332;1;-1;0;"unknown";"no"
51;"admin.";"single";"tertiary";"no";16;"yes";"no";"cellular";30;"jan";158;1;-1;0;"unknown";"no"
31;"management";"married";"secondary";"no";466;"yes";"no";"cellular";14;"may";164;1;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";-75;"yes";"no";"cellular";4;"feb";57;1;247;1;"other";"no"
32;"blue-collar";"married";"secondary";"no";456;"no";"no";"cellular";12;"may";121;1;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";77;"yes";"no";"cellular";7;"may";56;1;-1;0;"unknown";"no"
37;"services";"single";"secondary";"no";-244;"no";"no";"unknown";3;"jul";178;1;-1;0;"unknown";"no"
27;"blue-collar";"single";"primary";"no";1209;"no";"no";"cellular";29;"jan";283;1;-1;0;"unknown";"no"
27;"services";"married";"secondary";"no";58;"yes";"no";"cellular";7;"may";113;1;337;2;"other";"no"
50;"management";"married";"secondary";"yes";421;"no";"no";"cellular";20;"nov";22;5;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";112;"yes";"no";"cellular";2;"feb";159;2;271;1;"failure";"no"
40;"self-employed";"married";"tertiary";"no";13669;"no";"no";"cellular";15;"oct";138;1;136;8;"other";"no"
43;"management";"married";"tertiary";"no";1429;"yes";"no";"cellular";19;"nov";1015;1;198;2;"other";"yes"
39;"technician";"married";"tertiary";"no";794;"no";"no";"telephone";2;"feb";182;1;-1;0;"unknown";"no"
31;"entrepreneur";"single";"tertiary";"no";1957;"no";"no";"cellular";4;"feb";112;3;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";454;"yes";"no";"cellular";10;"jul";114;1;-1;0;"unknown";"no"
32;"blue-collar";"single";"primary";"no";882;"no";"yes";"cellular";29;"jul";80;8;-1;0;"unknown";"no"
47;"unemployed";"married";"primary";"no";168;"yes";"no";"telephone";30;"jan";66;1;241;1;"other";"no"
35;"management";"single";"tertiary";"no";714;"yes";"no";"cellular";26;"aug";135;22;-1;0;"unknown";"no"
42;"management";"single";"tertiary";"no";0;"no";"no";"cellular";7;"sep";99;1;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";1996;"no";"no";"unknown";5;"jun";761;2;-1;0;"unknown";"yes"
59;"retired";"married";"secondary";"no";163;"no";"no";"cellular";24;"jul";464;1;-1;0;"unknown";"no"
38;"admin.";"married";"secondary";"no";3190;"yes";"no";"unknown";20;"jun";20;2;-1;0;"unknown";"no"
50;"unemployed";"married";"secondary";"no";1772;"no";"no";"cellular";29;"jul";206;4;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";-151;"no";"no";"cellular";8;"jul";283;2;-1;0;"unknown";"no"
55;"housemaid";"married";"primary";"no";16;"no";"no";"unknown";20;"jun";64;1;-1;0;"unknown";"no"
39;"services";"single";"secondary";"no";7066;"yes";"no";"cellular";15;"may";667;3;357;3;"other";"yes"
31;"technician";"single";"tertiary";"no";454;"no";"no";"cellular";4;"feb";73;3;-1;0;"unknown";"no"
37;"entrepreneur";"married";"tertiary";"no";4798;"yes";"no";"unknown";20;"may";156;2;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"yes";-298;"yes";"no";"unknown";3;"jun";559;7;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";439;"no";"no";"cellular";20;"aug";143;2;-1;0;"unknown";"no"
40;"services";"divorced";"secondary";"no";1927;"yes";"no";"unknown";15;"may";62;1;-1;0;"unknown";"no"
59;"management";"single";"secondary";"no";671;"yes";"yes";"cellular";29;"jan";219;2;259;5;"failure";"no"
40;"services";"single";"secondary";"no";65;"yes";"no";"cellular";15;"may";15;3;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";-932;"yes";"no";"cellular";16;"apr";567;1;-1;0;"unknown";"no"
38;"technician";"single";"secondary";"no";4366;"yes";"no";"cellular";29;"jul";188;2;-1;0;"unknown";"no"
54;"retired";"married";"primary";"no";0;"yes";"no";"cellular";11;"jul";159;2;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";4900;"yes";"no";"telephone";31;"jul";121;2;-1;0;"unknown";"no"
55;"housemaid";"divorced";"primary";"no";976;"no";"no";"cellular";28;"jun";345;1;94;3;"other";"yes"
33;"management";"married";"tertiary";"no";148;"no";"no";"unknown";20;"may";717;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";5;"no";"no";"cellular";18;"aug";71;3;-1;0;"unknown";"no"
36;"management";"married";"secondary";"no";-148;"no";"no";"cellular";18;"nov";144;2;-1;0;"unknown";"no"
48;"technician";"divorced";"tertiary";"no";113;"no";"yes";"cellular";22;"jul";38;1;-1;0;"unknown";"no"
42;"management";"married";"secondary";"no";-105;"yes";"no";"cellular";28;"aug";60;2;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";624;"no";"no";"cellular";17;"sep";336;2;183;2;"success";"yes"
49;"technician";"married";"secondary";"yes";-1680;"yes";"yes";"unknown";27;"jun";92;4;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";-461;"no";"no";"cellular";11;"may";254;2;353;1;"failure";"no"
34;"technician";"single";"tertiary";"no";144;"yes";"yes";"cellular";14;"oct";104;1;85;19;"failure";"no"
51;"entrepreneur";"divorced";"tertiary";"no";4;"yes";"yes";"cellular";20;"apr";74;5;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";309;"no";"no";"cellular";19;"aug";156;2;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";866;"no";"no";"cellular";29;"jan";146;2;-1;0;"unknown";"no"
41;"admin.";"single";"secondary";"no";1020;"yes";"no";"unknown";19;"may";882;1;-1;0;"unknown";"yes"
49;"self-employed";"married";"primary";"no";489;"yes";"no";"unknown";27;"may";84;4;-1;0;"unknown";"no"
43;"management";"married";"primary";"no";8167;"yes";"no";"unknown";19;"may";114;2;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";-255;"no";"yes";"unknown";2;"jul";957;3;-1;0;"unknown";"no"
54;"retired";"married";"secondary";"no";3671;"no";"no";"unknown";11;"jun";67;3;-1;0;"unknown";"no"
33;"blue-collar";"divorced";"primary";"no";636;"yes";"no";"cellular";9;"jul";211;11;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";141;"yes";"no";"cellular";19;"aug";299;2;-1;0;"unknown";"no"
56;"technician";"married";"secondary";"no";150;"yes";"yes";"cellular";11;"may";102;6;349;1;"failure";"no"
57;"technician";"married";"secondary";"no";839;"no";"yes";"unknown";5;"may";225;1;-1;0;"unknown";"no"
31;"housemaid";"single";"primary";"no";26965;"no";"no";"cellular";21;"apr";654;2;-1;0;"unknown";"yes"
33;"technician";"single";"secondary";"no";716;"yes";"no";"cellular";14;"may";234;2;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";2485;"no";"no";"telephone";24;"jul";48;6;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";15030;"yes";"no";"cellular";13;"may";167;1;174;7;"other";"no"
37;"admin.";"married";"secondary";"no";11303;"no";"no";"cellular";26;"may";500;2;-1;0;"unknown";"no"
33;"entrepreneur";"married";"tertiary";"no";595;"no";"no";"cellular";7;"aug";22;2;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";19358;"no";"no";"cellular";19;"nov";258;2;-1;0;"unknown";"no"
31;"self-employed";"single";"secondary";"no";203;"no";"yes";"cellular";19;"nov";177;1;-1;0;"unknown";"no"
28;"technician";"single";"tertiary";"no";183;"yes";"yes";"unknown";28;"may";62;2;-1;0;"unknown";"no"
26;"admin.";"single";"secondary";"no";255;"no";"no";"cellular";30;"apr";457;1;80;1;"success";"yes"
31;"technician";"single";"secondary";"no";102;"yes";"no";"telephone";17;"apr";460;2;345;2;"failure";"no"
50;"housemaid";"divorced";"primary";"no";94;"yes";"no";"cellular";22;"jul";184;4;-1;0;"unknown";"no"
45;"entrepreneur";"married";"primary";"no";58;"yes";"no";"telephone";13;"may";119;3;371;3;"failure";"no"
48;"management";"married";"primary";"no";606;"no";"no";"cellular";5;"mar";77;6;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";0;"no";"yes";"cellular";23;"jul";107;1;-1;0;"unknown";"no"
60;"technician";"married";"primary";"no";65;"yes";"no";"unknown";9;"may";1028;2;-1;0;"unknown";"yes"
41;"blue-collar";"married";"secondary";"no";338;"yes";"no";"unknown";14;"may";87;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";757;"yes";"no";"unknown";9;"may";315;4;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";9269;"yes";"yes";"cellular";19;"nov";107;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";1206;"no";"yes";"telephone";16;"jul";201;5;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";882;"no";"yes";"cellular";29;"jan";94;1;205;2;"failure";"no"
27;"student";"single";"tertiary";"no";5291;"no";"no";"cellular";12;"nov";192;2;92;2;"failure";"yes"
49;"management";"single";"secondary";"no";61;"yes";"no";"cellular";29;"jan";185;2;246;1;"failure";"no"
37;"technician";"married";"tertiary";"no";0;"yes";"no";"cellular";17;"nov";77;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";1407;"yes";"no";"cellular";6;"may";142;3;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"yes";25;"no";"no";"cellular";2;"feb";381;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";1354;"yes";"yes";"cellular";17;"nov";222;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";793;"yes";"no";"cellular";17;"apr";106;1;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"unknown";"no";10287;"yes";"no";"unknown";29;"may";117;1;-1;0;"unknown";"no"
33;"technician";"single";"secondary";"no";5235;"no";"no";"cellular";7;"aug";67;3;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";3;"yes";"no";"cellular";6;"may";45;2;169;2;"failure";"no"
58;"technician";"single";"secondary";"no";409;"no";"yes";"cellular";14;"jul";643;2;-1;0;"unknown";"no"
34;"admin.";"married";"tertiary";"no";35;"yes";"no";"cellular";15;"oct";76;1;69;2;"failure";"no"
45;"management";"married";"tertiary";"no";-459;"no";"no";"cellular";21;"aug";508;6;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";198;"no";"no";"cellular";5;"jul";128;2;-1;0;"unknown";"yes"
31;"blue-collar";"married";"primary";"no";99;"yes";"no";"cellular";17;"apr";193;2;322;1;"other";"no"
40;"management";"married";"tertiary";"no";60;"no";"no";"unknown";16;"jun";172;6;-1;0;"unknown";"no"
30;"services";"single";"secondary";"no";3485;"no";"no";"cellular";2;"feb";142;2;-1;0;"unknown";"no"
34;"entrepreneur";"married";"secondary";"no";-139;"yes";"yes";"unknown";27;"may";492;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";952;"no";"yes";"unknown";12;"jun";140;1;-1;0;"unknown";"no"
59;"retired";"married";"primary";"no";1427;"no";"no";"unknown";18;"jun";140;2;-1;0;"unknown";"no"
43;"technician";"single";"tertiary";"no";1017;"yes";"no";"unknown";19;"may";257;2;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";-29;"no";"no";"cellular";29;"jan";41;2;266;2;"failure";"no"
33;"technician";"divorced";"secondary";"no";9;"no";"no";"cellular";21;"aug";91;2;-1;0;"unknown";"no"
33;"management";"married";"secondary";"no";4040;"yes";"no";"cellular";20;"apr";132;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";13;"no";"no";"unknown";3;"jun";284;1;-1;0;"unknown";"no"
32;"self-employed";"single";"secondary";"no";1259;"no";"no";"cellular";25;"jul";184;2;-1;0;"unknown";"no"
25;"admin.";"married";"secondary";"no";18;"yes";"no";"cellular";18;"jul";225;1;-1;0;"unknown";"no"
25;"blue-collar";"married";"secondary";"no";-76;"yes";"no";"unknown";12;"may";241;3;-1;0;"unknown";"no"
38;"technician";"single";"tertiary";"no";221;"no";"no";"cellular";20;"aug";79;3;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";136;"yes";"no";"cellular";22;"apr";156;1;-1;0;"unknown";"no"
67;"blue-collar";"married";"secondary";"no";16353;"no";"no";"cellular";27;"oct";223;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";-63;"yes";"no";"unknown";30;"may";198;1;-1;0;"unknown";"no"
45;"housemaid";"married";"primary";"no";7353;"no";"no";"cellular";13;"aug";536;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";511;"yes";"no";"unknown";21;"may";304;2;-1;0;"unknown";"no"
38;"admin.";"married";"primary";"no";3913;"yes";"no";"cellular";20;"apr";196;2;315;2;"failure";"no"
32;"technician";"divorced";"secondary";"no";1097;"yes";"no";"unknown";12;"may";298;2;-1;0;"unknown";"no"
54;"retired";"divorced";"tertiary";"no";6102;"yes";"yes";"unknown";21;"may";86;6;-1;0;"unknown";"no"
51;"admin.";"divorced";"secondary";"no";340;"yes";"no";"unknown";8;"may";186;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";-119;"yes";"no";"cellular";22;"jul";167;3;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";2993;"yes";"no";"cellular";22;"jul";24;12;-1;0;"unknown";"no"
39;"management";"divorced";"tertiary";"no";3575;"no";"yes";"cellular";18;"aug";74;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";361;"yes";"no";"unknown";11;"jun";601;1;-1;0;"unknown";"no"
39;"management";"married";"secondary";"no";1072;"yes";"no";"unknown";27;"may";1168;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";2904;"no";"no";"cellular";20;"apr";277;2;-1;0;"unknown";"no"
50;"entrepreneur";"divorced";"tertiary";"no";1790;"no";"yes";"cellular";6;"feb";748;3;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";86;"yes";"no";"unknown";19;"may";364;1;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";1633;"yes";"no";"unknown";19;"may";229;3;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";8312;"no";"no";"cellular";29;"aug";43;13;-1;0;"unknown";"no"
34;"blue-collar";"divorced";"unknown";"no";172;"no";"no";"cellular";15;"jun";177;1;-1;0;"unknown";"yes"
34;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";7;"aug";262;1;99;2;"success";"yes"
36;"management";"married";"tertiary";"no";222;"yes";"no";"cellular";6;"may";317;1;169;2;"failure";"no"
40;"technician";"married";"secondary";"no";109;"no";"no";"unknown";20;"jun";402;22;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";1451;"no";"no";"cellular";1;"jun";314;1;-1;0;"unknown";"yes"
41;"technician";"married";"secondary";"no";1066;"no";"no";"cellular";11;"feb";109;3;-1;0;"unknown";"yes"
52;"management";"married";"tertiary";"no";3163;"no";"no";"cellular";25;"aug";56;6;-1;0;"unknown";"no"
40;"services";"single";"secondary";"no";888;"yes";"no";"unknown";12;"may";169;2;-1;0;"unknown";"no"
48;"blue-collar";"single";"unknown";"no";428;"yes";"no";"cellular";18;"may";127;1;-1;0;"unknown";"no"
33;"management";"divorced";"secondary";"no";106;"no";"no";"cellular";20;"aug";175;3;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";0;"no";"no";"cellular";12;"aug";56;2;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";979;"no";"yes";"cellular";8;"jul";123;1;-1;0;"unknown";"no"
51;"admin.";"married";"secondary";"no";2662;"no";"no";"telephone";28;"jan";148;2;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";84;"no";"no";"cellular";5;"feb";231;1;-1;0;"unknown";"no"
58;"retired";"divorced";"unknown";"no";5920;"yes";"no";"cellular";23;"jul";255;1;-1;0;"unknown";"no"
39;"management";"single";"tertiary";"no";816;"yes";"no";"cellular";27;"aug";37;3;-1;0;"unknown";"no"
27;"unemployed";"single";"secondary";"no";3276;"no";"no";"cellular";5;"aug";106;1;110;2;"other";"no"
55;"management";"divorced";"unknown";"no";2;"yes";"no";"unknown";19;"may";95;1;-1;0;"unknown";"no"
35;"technician";"single";"secondary";"no";-51;"no";"no";"unknown";2;"jul";134;2;-1;0;"unknown";"no"
37;"technician";"single";"tertiary";"no";3315;"yes";"no";"cellular";9;"apr";820;2;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";196;"yes";"yes";"cellular";24;"feb";116;1;273;3;"failure";"no"
41;"blue-collar";"married";"secondary";"no";552;"yes";"no";"cellular";12;"may";463;1;-1;0;"unknown";"no"
52;"admin.";"married";"secondary";"no";2347;"no";"yes";"cellular";12;"aug";603;3;-1;0;"unknown";"no"
34;"blue-collar";"single";"secondary";"no";3723;"yes";"no";"unknown";27;"may";47;5;-1;0;"unknown";"no"
53;"management";"divorced";"primary";"no";255;"no";"no";"cellular";19;"nov";126;1;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";3143;"no";"no";"cellular";29;"jun";421;2;-1;0;"unknown";"yes"
47;"self-employed";"married";"secondary";"no";0;"no";"no";"unknown";26;"jun";174;8;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";6411;"yes";"yes";"cellular";30;"jan";144;1;266;2;"failure";"no"
33;"management";"single";"tertiary";"no";1909;"no";"no";"cellular";8;"aug";191;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";3873;"no";"no";"telephone";21;"apr";222;1;-1;0;"unknown";"no"
58;"self-employed";"married";"primary";"no";1013;"yes";"no";"cellular";11;"aug";206;1;461;1;"failure";"yes"
44;"admin.";"married";"secondary";"no";262;"yes";"no";"cellular";9;"jul";74;1;-1;0;"unknown";"no"
25;"services";"single";"secondary";"no";3217;"yes";"no";"unknown";28;"may";24;1;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";5436;"yes";"no";"cellular";6;"may";7;11;351;1;"failure";"no"
41;"blue-collar";"married";"primary";"no";2067;"yes";"no";"cellular";15;"may";493;7;-1;0;"unknown";"no"
34;"blue-collar";"single";"secondary";"no";215;"no";"no";"cellular";3;"mar";350;3;-1;0;"unknown";"yes"
36;"services";"divorced";"secondary";"no";1594;"no";"no";"unknown";6;"jun";250;3;-1;0;"unknown";"no"
44;"self-employed";"married";"tertiary";"no";134;"no";"no";"cellular";28;"aug";86;4;-1;0;"unknown";"no"
34;"entrepreneur";"divorced";"tertiary";"no";262;"no";"no";"cellular";20;"nov";371;1;-1;0;"unknown";"no"
56;"blue-collar";"divorced";"primary";"no";3021;"no";"no";"unknown";9;"jun";419;2;-1;0;"unknown";"no"
43;"management";"divorced";"tertiary";"no";388;"yes";"no";"unknown";8;"may";2087;2;-1;0;"unknown";"yes"
28;"blue-collar";"single";"secondary";"no";3412;"yes";"no";"cellular";15;"may";22;4;-1;0;"unknown";"no"
54;"blue-collar";"married";"secondary";"no";1291;"yes";"no";"unknown";5;"may";266;1;-1;0;"unknown";"no"
37;"technician";"divorced";"secondary";"no";79;"no";"no";"cellular";21;"jul";191;1;-1;0;"unknown";"no"
59;"self-employed";"married";"tertiary";"no";2013;"no";"no";"cellular";19;"feb";222;4;94;2;"success";"yes"
58;"blue-collar";"divorced";"secondary";"no";543;"no";"yes";"telephone";17;"jul";264;3;-1;0;"unknown";"no"
56;"management";"divorced";"tertiary";"no";1016;"yes";"no";"unknown";9;"jun";184;5;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";604;"no";"yes";"cellular";28;"may";95;3;-1;0;"unknown";"no"
42;"management";"single";"tertiary";"no";525;"no";"no";"cellular";13;"aug";127;2;-1;0;"unknown";"no"
52;"retired";"divorced";"primary";"no";353;"no";"no";"cellular";31;"jul";395;4;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";964;"yes";"no";"cellular";8;"apr";174;1;-1;0;"unknown";"no"
37;"admin.";"married";"tertiary";"no";3913;"yes";"no";"cellular";27;"apr";124;1;-1;0;"unknown";"yes"
39;"blue-collar";"married";"primary";"no";879;"yes";"no";"unknown";26;"may";102;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";314;"yes";"no";"cellular";17;"apr";279;1;-1;0;"unknown";"no"
59;"housemaid";"married";"primary";"no";0;"no";"no";"cellular";27;"aug";76;11;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";1370;"yes";"no";"cellular";11;"may";472;2;-1;0;"unknown";"no"
58;"management";"married";"secondary";"no";37;"no";"no";"cellular";18;"aug";84;11;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";673;"yes";"yes";"cellular";20;"nov";271;1;184;1;"failure";"no"
40;"services";"married";"secondary";"no";-342;"yes";"no";"unknown";26;"may";754;1;-1;0;"unknown";"no"
20;"student";"single";"secondary";"no";291;"no";"no";"telephone";11;"may";172;5;371;5;"failure";"no"
34;"technician";"married";"secondary";"no";209;"yes";"yes";"cellular";8;"apr";375;2;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";438;"yes";"no";"cellular";12;"aug";130;2;-1;0;"unknown";"no"
39;"admin.";"single";"secondary";"no";1242;"yes";"no";"unknown";12;"may";303;3;-1;0;"unknown";"no"
34;"entrepreneur";"married";"tertiary";"no";355;"yes";"no";"unknown";16;"may";261;1;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";204;"yes";"yes";"cellular";17;"apr";229;3;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";10758;"yes";"no";"cellular";1;"jun";288;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";5;"yes";"no";"cellular";18;"nov";219;3;-1;0;"unknown";"no"
45;"technician";"single";"secondary";"no";410;"yes";"no";"unknown";30;"may";891;4;-1;0;"unknown";"yes"
36;"management";"married";"tertiary";"no";605;"yes";"no";"unknown";26;"may";204;1;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";4;"no";"no";"unknown";20;"jun";19;6;-1;0;"unknown";"no"
23;"student";"single";"secondary";"no";8494;"no";"no";"cellular";25;"aug";158;2;-1;0;"unknown";"no"
42;"entrepreneur";"married";"primary";"no";198;"yes";"no";"unknown";8;"may";314;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";841;"yes";"no";"unknown";19;"may";215;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";8;"no";"no";"cellular";25;"aug";169;4;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";470;"yes";"no";"cellular";31;"jul";101;4;-1;0;"unknown";"no"
43;"retired";"married";"secondary";"no";520;"yes";"no";"unknown";20;"may";15;13;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";439;"yes";"no";"unknown";21;"may";260;5;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";-454;"yes";"no";"cellular";14;"may";558;1;-1;0;"unknown";"yes"
55;"retired";"married";"secondary";"no";466;"no";"no";"unknown";17;"jun";287;4;-1;0;"unknown";"no"
53;"retired";"married";"secondary";"no";665;"yes";"no";"cellular";22;"apr";371;2;-1;0;"unknown";"yes"
42;"technician";"married";"secondary";"no";994;"yes";"no";"cellular";11;"aug";149;1;78;5;"failure";"yes"
45;"admin.";"married";"secondary";"no";5346;"yes";"yes";"unknown";21;"oct";187;1;-1;0;"unknown";"yes"
31;"blue-collar";"married";"secondary";"no";1243;"yes";"no";"unknown";21;"may";32;1;-1;0;"unknown";"no"
32;"admin.";"married";"tertiary";"no";0;"yes";"no";"unknown";5;"may";138;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";2357;"yes";"no";"unknown";17;"jun";228;2;-1;0;"unknown";"no"
56;"retired";"married";"secondary";"no";1044;"no";"no";"telephone";3;"jul";353;2;-1;0;"unknown";"yes"
46;"admin.";"married";"secondary";"no";1069;"yes";"no";"unknown";8;"may";45;2;-1;0;"unknown";"no"
41;"housemaid";"single";"secondary";"no";231;"no";"no";"cellular";9;"jul";76;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";0;"yes";"yes";"unknown";6;"may";144;2;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";266;"yes";"no";"cellular";19;"aug";168;2;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";-70;"yes";"no";"cellular";30;"jan";140;1;2;3;"other";"no"
49;"retired";"single";"primary";"no";25824;"no";"no";"unknown";17;"jun";94;1;-1;0;"unknown";"no"
46;"management";"single";"primary";"no";538;"no";"no";"unknown";17;"jun";118;3;-1;0;"unknown";"no"
29;"technician";"single";"secondary";"no";534;"yes";"no";"cellular";18;"may";382;1;-1;0;"unknown";"no"
36;"admin.";"married";"unknown";"no";2323;"no";"no";"cellular";19;"nov";111;1;-1;0;"unknown";"no"
38;"admin.";"married";"secondary";"no";1852;"yes";"no";"cellular";15;"may";298;2;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";2647;"yes";"no";"unknown";30;"may";121;1;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";2767;"yes";"no";"unknown";15;"may";317;7;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";176;"yes";"yes";"unknown";5;"may";42;1;-1;0;"unknown";"no"
42;"management";"single";"secondary";"no";9720;"no";"no";"cellular";5;"aug";81;2;-1;0;"unknown";"no"
55;"blue-collar";"divorced";"secondary";"no";1613;"yes";"no";"cellular";3;"apr";296;1;270;3;"failure";"yes"
39;"admin.";"divorced";"secondary";"no";2903;"yes";"yes";"telephone";25;"jul";259;2;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";81;"yes";"no";"cellular";6;"aug";121;6;112;3;"other";"no"
41;"blue-collar";"married";"secondary";"no";780;"yes";"no";"cellular";15;"may";432;3;-1;0;"unknown";"no"
44;"unemployed";"married";"tertiary";"no";178;"no";"no";"cellular";8;"aug";126;2;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";299;"no";"no";"cellular";16;"jul";120;4;-1;0;"unknown";"no"
35;"retired";"married";"primary";"no";285;"yes";"no";"cellular";29;"jan";136;1;-1;0;"unknown";"no"
27;"blue-collar";"married";"secondary";"no";872;"yes";"no";"cellular";17;"apr";1130;3;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";1337;"no";"no";"cellular";17;"dec";330;2;127;3;"success";"yes"
25;"services";"married";"secondary";"no";72;"yes";"no";"cellular";23;"jul";305;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";0;"no";"no";"unknown";20;"jun";332;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";-388;"yes";"no";"unknown";9;"may";412;1;-1;0;"unknown";"no"
30;"admin.";"married";"secondary";"no";11;"yes";"no";"cellular";11;"may";157;1;-1;0;"unknown";"no"
47;"blue-collar";"divorced";"secondary";"yes";-535;"yes";"no";"cellular";30;"jul";71;2;-1;0;"unknown";"no"
41;"services";"divorced";"secondary";"no";787;"yes";"no";"cellular";8;"may";297;2;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";3343;"no";"no";"cellular";18;"nov";307;1;-1;0;"unknown";"no"
63;"entrepreneur";"married";"secondary";"no";3904;"no";"no";"telephone";4;"aug";250;2;187;2;"success";"yes"
23;"management";"single";"tertiary";"no";736;"yes";"no";"cellular";30;"apr";119;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";1390;"no";"no";"cellular";7;"aug";116;2;-1;0;"unknown";"no"
57;"retired";"married";"primary";"no";808;"no";"no";"telephone";7;"jul";274;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";1147;"no";"no";"cellular";5;"aug";389;1;64;2;"success";"yes"
49;"services";"married";"secondary";"no";1122;"no";"no";"cellular";4;"aug";143;1;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";-568;"yes";"yes";"unknown";28;"may";860;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";1165;"yes";"no";"unknown";21;"may";420;2;-1;0;"unknown";"no"
53;"services";"divorced";"secondary";"no";-905;"yes";"no";"unknown";28;"may";213;1;-1;0;"unknown";"no"
30;"unemployed";"married";"tertiary";"no";0;"yes";"no";"cellular";18;"nov";756;1;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";740;"no";"no";"cellular";7;"aug";259;3;-1;0;"unknown";"no"
44;"management";"married";"secondary";"no";8262;"yes";"no";"cellular";23;"jul";201;16;-1;0;"unknown";"no"
39;"admin.";"single";"secondary";"no";14;"yes";"yes";"unknown";13;"may";19;9;-1;0;"unknown";"no"
45;"technician";"single";"unknown";"no";7108;"yes";"yes";"cellular";18;"nov";53;1;172;3;"failure";"no"
36;"blue-collar";"married";"secondary";"no";1049;"yes";"no";"cellular";14;"may";224;4;-1;0;"unknown";"yes"
38;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";13;"may";57;5;349;1;"other";"no"
36;"housemaid";"married";"secondary";"no";419;"yes";"no";"unknown";30;"may";251;1;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";726;"yes";"no";"cellular";16;"apr";39;1;342;3;"failure";"no"
28;"technician";"divorced";"tertiary";"no";594;"yes";"yes";"unknown";18;"jun";54;3;-1;0;"unknown";"no"
26;"unemployed";"married";"tertiary";"no";454;"yes";"yes";"unknown";23;"may";28;18;-1;0;"unknown";"no"
54;"unknown";"married";"secondary";"no";2206;"no";"no";"cellular";12;"nov";104;1;99;2;"other";"yes"
38;"admin.";"married";"tertiary";"no";1988;"no";"no";"cellular";27;"mar";164;2;130;2;"failure";"yes"
32;"technician";"single";"tertiary";"no";0;"no";"no";"cellular";7;"aug";268;2;-1;0;"unknown";"no"
50;"admin.";"divorced";"secondary";"no";202;"no";"yes";"cellular";17;"nov";148;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";667;"yes";"no";"unknown";7;"may";228;3;-1;0;"unknown";"no"
48;"admin.";"married";"secondary";"no";112;"no";"no";"cellular";18;"aug";139;5;-1;0;"unknown";"no"
57;"housemaid";"married";"primary";"no";50;"no";"no";"cellular";18;"aug";89;3;-1;0;"unknown";"no"
64;"housemaid";"married";"primary";"no";768;"no";"no";"cellular";15;"jul";250;2;182;1;"success";"no"
26;"services";"divorced";"secondary";"no";74;"yes";"no";"unknown";23;"may";106;6;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";661;"yes";"no";"cellular";18;"jul";968;1;-1;0;"unknown";"yes"
50;"self-employed";"married";"secondary";"no";3025;"no";"yes";"cellular";6;"aug";168;3;-1;0;"unknown";"no"
47;"self-employed";"married";"tertiary";"no";751;"no";"no";"cellular";24;"jul";408;14;-1;0;"unknown";"no"
39;"management";"single";"tertiary";"no";416;"no";"no";"cellular";23;"oct";56;4;-1;0;"unknown";"no"
52;"services";"married";"primary";"no";14;"no";"yes";"cellular";28;"jul";55;5;-1;0;"unknown";"no"
31;"blue-collar";"single";"primary";"no";-502;"yes";"no";"unknown";29;"may";76;4;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";0;"no";"no";"cellular";11;"aug";72;5;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";599;"yes";"no";"cellular";13;"aug";99;3;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";-218;"yes";"no";"cellular";27;"aug";13;15;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";1012;"no";"no";"cellular";14;"apr";178;1;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";300;"no";"no";"unknown";9;"jun";287;1;-1;0;"unknown";"no"
46;"technician";"married";"tertiary";"no";217;"yes";"no";"cellular";14;"may";141;1;346;2;"other";"no"
45;"management";"divorced";"tertiary";"no";2096;"no";"no";"cellular";21;"nov";127;1;100;2;"failure";"no"
37;"technician";"single";"secondary";"no";0;"no";"no";"cellular";4;"aug";69;2;-1;0;"unknown";"no"
58;"housemaid";"married";"primary";"no";5278;"yes";"no";"unknown";4;"jun";763;4;-1;0;"unknown";"no"
59;"management";"married";"tertiary";"no";1831;"no";"no";"cellular";17;"feb";122;1;92;1;"failure";"no"
28;"management";"married";"tertiary";"no";1480;"yes";"no";"cellular";18;"may";143;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";7289;"yes";"no";"cellular";2;"feb";316;3;-1;0;"unknown";"no"
55;"blue-collar";"married";"unknown";"no";1665;"no";"no";"telephone";30;"jul";89;2;-1;0;"unknown";"no"
37;"blue-collar";"single";"primary";"no";851;"yes";"no";"cellular";15;"may";151;1;352;1;"failure";"no"
41;"management";"married";"tertiary";"no";2885;"no";"no";"cellular";19;"nov";40;1;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";137;"no";"yes";"unknown";4;"oct";5;1;808;12;"failure";"no"
34;"blue-collar";"single";"secondary";"no";1239;"yes";"no";"cellular";18;"jul";90;1;-1;0;"unknown";"no"
43;"management";"single";"tertiary";"no";146;"no";"no";"cellular";21;"nov";50;1;113;5;"other";"no"
59;"technician";"married";"secondary";"no";1;"no";"no";"cellular";15;"apr";159;2;378;3;"failure";"yes"
50;"blue-collar";"divorced";"primary";"no";-10;"no";"yes";"cellular";10;"jul";369;6;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";480;"no";"no";"cellular";22;"oct";344;2;182;8;"other";"yes"
35;"technician";"married";"secondary";"no";2789;"yes";"no";"cellular";4;"sep";256;2;94;1;"failure";"no"
50;"technician";"single";"secondary";"no";3897;"no";"no";"cellular";26;"oct";298;1;-1;0;"unknown";"no"
36;"technician";"divorced";"secondary";"no";487;"no";"yes";"cellular";11;"aug";268;3;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";5110;"yes";"no";"cellular";13;"apr";231;1;-1;0;"unknown";"no"
28;"management";"married";"tertiary";"no";318;"yes";"no";"unknown";27;"may";4;4;-1;0;"unknown";"no"
42;"technician";"single";"tertiary";"no";-411;"yes";"no";"cellular";12;"may";78;5;292;3;"failure";"no"
60;"retired";"married";"secondary";"no";4820;"no";"no";"unknown";5;"jun";419;1;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";4937;"yes";"no";"telephone";13;"may";421;3;-1;0;"unknown";"no"
48;"entrepreneur";"married";"secondary";"no";725;"no";"yes";"cellular";10;"jul";105;1;-1;0;"unknown";"no"
51;"entrepreneur";"married";"tertiary";"no";5403;"no";"no";"unknown";6;"jun";42;1;-1;0;"unknown";"no"
32;"technician";"married";"tertiary";"no";4089;"no";"no";"cellular";26;"may";114;7;287;3;"failure";"no"
35;"technician";"married";"tertiary";"no";188;"no";"no";"cellular";26;"aug";286;2;-1;0;"unknown";"no"
37;"technician";"married";"unknown";"no";189;"no";"no";"cellular";1;"oct";238;1;107;2;"success";"yes"
38;"blue-collar";"divorced";"secondary";"no";2998;"yes";"no";"unknown";11;"jun";91;1;-1;0;"unknown";"no"
77;"retired";"married";"secondary";"no";610;"no";"no";"cellular";13;"aug";766;3;-1;0;"unknown";"no"
32;"management";"single";"unknown";"no";787;"no";"no";"cellular";28;"jan";92;1;-1;0;"unknown";"no"
43;"technician";"divorced";"secondary";"no";167;"no";"no";"cellular";4;"aug";211;3;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";243;"yes";"no";"cellular";7;"may";160;2;360;3;"failure";"no"
29;"management";"married";"tertiary";"no";877;"no";"no";"cellular";3;"feb";166;3;-1;0;"unknown";"no"
32;"admin.";"single";"secondary";"no";2817;"yes";"no";"cellular";21;"nov";195;1;-1;0;"unknown";"no"
41;"management";"divorced";"tertiary";"no";4567;"yes";"yes";"cellular";16;"jul";106;1;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";127;"yes";"yes";"cellular";23;"jul";85;3;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";176;"no";"no";"cellular";24;"jul";78;1;-1;0;"unknown";"no"
57;"retired";"married";"tertiary";"no";0;"yes";"yes";"unknown";28;"may";648;1;-1;0;"unknown";"yes"
36;"services";"single";"secondary";"no";1482;"yes";"no";"cellular";18;"nov";177;2;174;1;"other";"no"
42;"blue-collar";"married";"secondary";"no";3052;"yes";"no";"unknown";27;"may";32;1;-1;0;"unknown";"no"
35;"technician";"divorced";"secondary";"no";56;"no";"no";"cellular";29;"aug";24;12;-1;0;"unknown";"no"
52;"entrepreneur";"divorced";"primary";"no";278;"yes";"no";"unknown";7;"may";688;2;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";3875;"yes";"no";"unknown";27;"may";21;5;-1;0;"unknown";"no"
46;"technician";"married";"secondary";"no";-337;"yes";"no";"unknown";27;"may";112;1;-1;0;"unknown";"no"
60;"management";"divorced";"tertiary";"no";366;"no";"no";"cellular";3;"jun";593;1;-1;0;"unknown";"yes"
57;"housemaid";"married";"primary";"no";501;"yes";"yes";"cellular";7;"aug";59;2;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";998;"yes";"no";"cellular";29;"jan";121;3;-1;0;"unknown";"no"
59;"self-employed";"married";"primary";"no";0;"yes";"no";"telephone";28;"jul";97;2;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";246;"no";"no";"cellular";11;"aug";86;2;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";-182;"yes";"yes";"cellular";14;"may";10;10;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";446;"no";"no";"cellular";23;"jun";205;3;-1;0;"unknown";"no"
59;"retired";"married";"secondary";"no";1173;"no";"no";"unknown";9;"jun";77;2;-1;0;"unknown";"no"
36;"technician";"divorced";"secondary";"no";5957;"yes";"no";"cellular";18;"nov";138;2;-1;0;"unknown";"no"
44;"admin.";"married";"secondary";"no";205;"no";"no";"cellular";3;"nov";289;1;-1;0;"unknown";"yes"
50;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";4;"aug";86;4;-1;0;"unknown";"no"
41;"management";"divorced";"tertiary";"no";369;"yes";"no";"telephone";22;"aug";9;3;-1;0;"unknown";"no"
47;"admin.";"married";"secondary";"no";1934;"no";"yes";"cellular";14;"may";225;1;364;1;"failure";"no"
32;"technician";"single";"tertiary";"no";259;"no";"no";"cellular";22;"jul";407;4;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";284;"yes";"yes";"unknown";3;"jun";1030;6;-1;0;"unknown";"no"
55;"blue-collar";"married";"unknown";"no";23;"no";"no";"cellular";13;"aug";123;2;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";1373;"no";"no";"cellular";22;"oct";119;4;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";62;"yes";"no";"cellular";18;"may";175;1;293;5;"failure";"no"
38;"blue-collar";"divorced";"secondary";"no";157;"no";"no";"unknown";9;"jun";299;5;-1;0;"unknown";"no"
29;"unemployed";"single";"tertiary";"no";991;"no";"no";"cellular";5;"feb";563;1;-1;0;"unknown";"no"
55;"services";"married";"secondary";"no";186;"yes";"yes";"unknown";9;"may";241;1;-1;0;"unknown";"no"
35;"unemployed";"married";"secondary";"no";977;"yes";"no";"cellular";4;"may";52;4;91;1;"other";"no"
40;"self-employed";"single";"tertiary";"no";2893;"no";"no";"cellular";2;"jun";76;3;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";1897;"yes";"yes";"cellular";19;"nov";441;1;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";5514;"no";"no";"cellular";22;"apr";319;1;182;1;"success";"yes"
33;"blue-collar";"married";"secondary";"no";889;"yes";"no";"unknown";18;"sep";13;1;373;6;"success";"no"
33;"management";"married";"tertiary";"no";273;"yes";"no";"unknown";16;"may";803;1;-1;0;"unknown";"yes"
37;"admin.";"married";"secondary";"no";1193;"no";"no";"cellular";12;"may";396;2;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";2729;"no";"no";"unknown";18;"jun";188;1;-1;0;"unknown";"no"
36;"admin.";"single";"secondary";"no";0;"no";"no";"cellular";20;"apr";637;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";230;"yes";"no";"unknown";5;"jun";175;7;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";2729;"yes";"no";"cellular";6;"apr";945;1;139;1;"success";"no"
35;"management";"married";"tertiary";"no";317;"yes";"no";"unknown";23;"may";162;3;-1;0;"unknown";"no"
60;"housemaid";"married";"primary";"no";517;"no";"no";"unknown";12;"jun";1178;3;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";-470;"yes";"no";"cellular";8;"may";506;2;170;3;"failure";"no"
28;"self-employed";"single";"tertiary";"no";4579;"no";"no";"cellular";12;"jan";409;2;-1;0;"unknown";"yes"
27;"blue-collar";"divorced";"secondary";"no";1;"yes";"no";"cellular";14;"jul";116;3;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";5106;"no";"no";"cellular";30;"apr";244;1;-1;0;"unknown";"yes"
31;"technician";"married";"tertiary";"no";636;"yes";"no";"cellular";4;"may";352;4;-1;0;"unknown";"yes"
34;"services";"married";"secondary";"no";1076;"no";"no";"cellular";12;"may";152;1;182;6;"success";"yes"
38;"management";"married";"tertiary";"no";126;"yes";"no";"unknown";21;"may";164;2;-1;0;"unknown";"no"
32;"self-employed";"married";"secondary";"no";-33;"yes";"no";"unknown";28;"may";234;3;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";430;"no";"no";"cellular";27;"aug";154;4;-1;0;"unknown";"no"
33;"admin.";"married";"secondary";"no";-6;"yes";"no";"unknown";7;"may";566;2;-1;0;"unknown";"no"
71;"technician";"married";"tertiary";"no";383;"no";"no";"cellular";27;"oct";327;2;183;3;"success";"no"
27;"technician";"married";"secondary";"no";130;"yes";"no";"cellular";7;"may";265;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";2569;"no";"no";"unknown";9;"jun";172;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";201;"no";"no";"cellular";22;"jul";257;2;-1;0;"unknown";"no"
54;"retired";"divorced";"tertiary";"no";0;"no";"no";"unknown";13;"may";618;1;-1;0;"unknown";"yes"
30;"blue-collar";"married";"secondary";"no";6;"yes";"no";"cellular";28;"jan";241;3;-1;0;"unknown";"no"
35;"entrepreneur";"single";"secondary";"no";475;"no";"yes";"cellular";21;"jul";936;4;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";706;"yes";"no";"cellular";7;"apr";329;1;322;1;"failure";"no"
36;"management";"married";"secondary";"no";1825;"yes";"no";"cellular";14;"may";24;1;346;1;"failure";"no"
59;"self-employed";"married";"tertiary";"no";185;"no";"no";"cellular";22;"jun";177;5;138;1;"failure";"no"
72;"blue-collar";"married";"primary";"no";4415;"no";"no";"telephone";13;"oct";120;1;-1;0;"unknown";"no"
64;"management";"married";"tertiary";"no";679;"no";"no";"telephone";22;"apr";135;3;-1;0;"unknown";"no"
53;"technician";"divorced";"secondary";"no";320;"no";"no";"cellular";9;"jul";175;1;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";2988;"yes";"no";"cellular";25;"may";179;1;-1;0;"unknown";"no"
58;"self-employed";"married";"secondary";"no";1407;"yes";"no";"unknown";30;"may";58;1;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";1270;"no";"no";"cellular";17;"apr";342;2;-1;0;"unknown";"no"
41;"services";"divorced";"secondary";"no";654;"yes";"no";"cellular";13;"may";139;2;-1;0;"unknown";"no"
48;"retired";"married";"primary";"no";5173;"yes";"no";"unknown";18;"jun";88;8;-1;0;"unknown";"no"
44;"entrepreneur";"married";"tertiary";"no";3463;"yes";"yes";"cellular";28;"aug";210;3;-1;0;"unknown";"no"
39;"blue-collar";"married";"unknown";"no";0;"no";"no";"cellular";2;"feb";731;1;193;3;"failure";"no"
61;"management";"married";"tertiary";"no";6016;"no";"no";"cellular";2;"feb";670;3;-1;0;"unknown";"yes"
36;"entrepreneur";"married";"primary";"no";177;"yes";"no";"unknown";12;"may";63;5;-1;0;"unknown";"no"
28;"management";"married";"tertiary";"no";3694;"no";"yes";"cellular";8;"jul";70;2;-1;0;"unknown";"no"
58;"retired";"single";"secondary";"no";1640;"no";"yes";"cellular";4;"feb";72;6;274;4;"failure";"no"
60;"admin.";"divorced";"secondary";"no";174;"yes";"no";"cellular";15;"may";241;1;364;1;"failure";"no"
66;"admin.";"single";"secondary";"no";10177;"no";"no";"cellular";5;"may";272;4;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";490;"no";"no";"cellular";28;"aug";207;8;98;1;"failure";"yes"
49;"self-employed";"married";"secondary";"no";2576;"yes";"no";"cellular";15;"may";64;6;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";0;"no";"no";"unknown";12;"jun";543;2;-1;0;"unknown";"no"
23;"technician";"single";"secondary";"no";598;"no";"no";"cellular";30;"apr";166;1;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";15459;"no";"no";"cellular";26;"may";245;3;97;7;"success";"no"
44;"blue-collar";"married";"primary";"no";271;"yes";"yes";"unknown";9;"may";94;1;-1;0;"unknown";"no"
54;"unknown";"married";"unknown";"no";2508;"no";"no";"unknown";20;"jun";34;8;-1;0;"unknown";"no"
47;"blue-collar";"married";"primary";"no";320;"no";"no";"telephone";30;"jul";203;2;-1;0;"unknown";"no"
33;"admin.";"married";"tertiary";"no";640;"no";"no";"cellular";24;"jul";318;1;-1;0;"unknown";"no"
30;"self-employed";"married";"secondary";"no";1772;"yes";"no";"cellular";13;"apr";158;4;-1;0;"unknown";"no"
27;"admin.";"single";"secondary";"no";15;"no";"yes";"cellular";21;"jul";177;1;-1;0;"unknown";"no"
49;"management";"divorced";"tertiary";"no";2370;"yes";"yes";"unknown";17;"nov";56;1;103;2;"other";"no"
27;"services";"single";"secondary";"no";201;"yes";"no";"unknown";4;"jun";172;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"secondary";"no";160;"yes";"no";"cellular";7;"may";136;1;359;1;"failure";"no"
32;"admin.";"divorced";"secondary";"yes";5;"yes";"yes";"cellular";18;"may";82;3;363;2;"other";"no"
30;"blue-collar";"single";"secondary";"no";75;"yes";"no";"unknown";16;"may";123;1;-1;0;"unknown";"no"
33;"admin.";"married";"secondary";"no";4902;"yes";"no";"cellular";20;"nov";110;1;-1;0;"unknown";"no"
27;"blue-collar";"single";"primary";"no";-79;"yes";"no";"unknown";30;"may";385;5;-1;0;"unknown";"no"
75;"retired";"divorced";"secondary";"no";1341;"no";"no";"cellular";18;"dec";415;7;-1;0;"unknown";"yes"
37;"services";"divorced";"secondary";"no";490;"yes";"no";"cellular";2;"feb";73;1;-1;0;"unknown";"no"
36;"technician";"single";"secondary";"no";1521;"yes";"no";"unknown";16;"jun";243;2;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";131;"yes";"no";"unknown";9;"may";137;2;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";8;"yes";"no";"unknown";23;"may";55;4;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";-632;"yes";"yes";"cellular";20;"apr";18;5;-1;0;"unknown";"no"
32;"admin.";"single";"secondary";"no";116;"no";"yes";"cellular";16;"jul";255;2;-1;0;"unknown";"no"
35;"admin.";"married";"primary";"no";189;"yes";"no";"cellular";11;"may";95;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"primary";"no";2;"yes";"yes";"cellular";10;"jul";112;1;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";4229;"yes";"yes";"cellular";5;"may";205;2;-1;0;"unknown";"no"
27;"services";"married";"secondary";"no";54;"yes";"no";"cellular";18;"may";97;1;370;1;"failure";"no"
22;"student";"single";"unknown";"no";549;"no";"no";"cellular";2;"sep";154;1;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";298;"yes";"no";"cellular";13;"may";135;2;370;2;"failure";"no"
69;"retired";"married";"primary";"no";579;"no";"no";"cellular";14;"aug";230;1;-1;0;"unknown";"no"
37;"entrepreneur";"single";"tertiary";"no";1467;"yes";"yes";"cellular";17;"nov";349;1;-1;0;"unknown";"no"
45;"admin.";"single";"secondary";"no";0;"yes";"no";"cellular";8;"may";38;1;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";109;"yes";"no";"cellular";22;"jul";268;1;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";-11;"yes";"no";"cellular";11;"may";94;4;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";90;"yes";"no";"cellular";28;"jul";263;2;-1;0;"unknown";"no"
60;"blue-collar";"married";"primary";"no";493;"no";"no";"cellular";18;"aug";671;4;-1;0;"unknown";"yes"
33;"management";"divorced";"secondary";"no";1897;"yes";"yes";"unknown";2;"jun";197;1;-1;0;"unknown";"no"
44;"admin.";"married";"secondary";"no";2403;"yes";"no";"cellular";28;"jan";180;2;-1;0;"unknown";"no"
38;"entrepreneur";"married";"secondary";"no";868;"no";"no";"cellular";20;"nov";452;2;-1;0;"unknown";"no"
54;"entrepreneur";"married";"secondary";"no";653;"yes";"no";"unknown";15;"may";352;1;-1;0;"unknown";"no"
32;"unemployed";"divorced";"primary";"no";0;"no";"no";"cellular";15;"jul";175;4;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";0;"yes";"yes";"cellular";19;"nov";163;2;-1;0;"unknown";"no"
55;"services";"divorced";"secondary";"no";218;"yes";"no";"unknown";29;"may";200;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";5050;"no";"yes";"unknown";16;"jun";75;7;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";741;"yes";"no";"cellular";16;"apr";227;1;-1;0;"unknown";"no"
37;"services";"single";"secondary";"no";1050;"yes";"no";"cellular";3;"feb";586;3;-1;0;"unknown";"no"
33;"services";"single";"secondary";"no";-56;"no";"yes";"cellular";10;"jul";119;5;-1;0;"unknown";"no"
30;"blue-collar";"married";"primary";"no";1;"yes";"yes";"telephone";22;"jul";80;2;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";-98;"yes";"yes";"cellular";12;"may";112;2;330;3;"other";"no"
49;"technician";"married";"secondary";"no";4667;"yes";"no";"unknown";19;"may";130;2;-1;0;"unknown";"no"
54;"unemployed";"married";"primary";"no";541;"yes";"no";"cellular";3;"feb";237;3;-1;0;"unknown";"no"
31;"unemployed";"married";"tertiary";"no";7744;"no";"no";"cellular";26;"aug";377;8;-1;0;"unknown";"no"
54;"unknown";"married";"unknown";"no";895;"no";"no";"cellular";7;"oct";335;1;-1;0;"unknown";"yes"
36;"unemployed";"married";"secondary";"no";-129;"yes";"no";"cellular";29;"jul";121;3;-1;0;"unknown";"no"
30;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";15;"jul";127;5;-1;0;"unknown";"no"
27;"technician";"single";"secondary";"no";158;"no";"no";"unknown";1;"jul";650;2;-1;0;"unknown";"yes"
30;"admin.";"married";"secondary";"no";122;"yes";"no";"unknown";16;"may";107;1;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";673;"yes";"no";"unknown";20;"may";610;2;-1;0;"unknown";"no"
35;"technician";"divorced";"secondary";"no";0;"no";"no";"cellular";25;"aug";124;4;-1;0;"unknown";"no"
33;"services";"divorced";"secondary";"no";411;"yes";"no";"unknown";8;"may";216;1;-1;0;"unknown";"no"
43;"blue-collar";"divorced";"primary";"no";-97;"yes";"yes";"cellular";31;"jul";747;4;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";-217;"yes";"no";"cellular";11;"may";313;1;349;3;"other";"no"
41;"management";"divorced";"tertiary";"no";5037;"no";"no";"cellular";23;"apr";252;9;185;5;"other";"yes"
46;"admin.";"divorced";"secondary";"no";2232;"no";"no";"cellular";13;"feb";121;1;-1;0;"unknown";"yes"
54;"blue-collar";"married";"secondary";"no";-932;"yes";"no";"unknown";7;"may";799;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";913;"yes";"no";"cellular";23;"jun";883;1;674;4;"failure";"no"
30;"technician";"single";"tertiary";"no";236;"yes";"no";"unknown";16;"may";197;1;-1;0;"unknown";"no"
40;"entrepreneur";"married";"primary";"no";0;"yes";"no";"cellular";11;"jul";271;2;-1;0;"unknown";"no"
30;"blue-collar";"married";"primary";"no";2581;"no";"no";"unknown";12;"may";221;2;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";639;"no";"yes";"cellular";14;"jul";243;3;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";849;"yes";"no";"unknown";15;"may";72;1;-1;0;"unknown";"no"
54;"services";"married";"secondary";"no";386;"yes";"no";"cellular";2;"aug";155;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";1218;"yes";"no";"telephone";28;"jan";70;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";2603;"yes";"no";"cellular";11;"feb";280;4;-1;0;"unknown";"yes"
42;"blue-collar";"married";"secondary";"no";-921;"yes";"no";"unknown";26;"may";115;2;-1;0;"unknown";"no"
48;"admin.";"married";"primary";"no";607;"no";"no";"unknown";20;"jun";29;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";79;"yes";"no";"cellular";14;"may";406;2;-1;0;"unknown";"no"
55;"technician";"divorced";"secondary";"no";61;"yes";"yes";"cellular";25;"aug";113;3;-1;0;"unknown";"no"
49;"technician";"married";"secondary";"no";1091;"no";"no";"telephone";30;"jul";47;2;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";743;"no";"no";"unknown";5;"jun";36;2;-1;0;"unknown";"no"
47;"entrepreneur";"married";"unknown";"no";209;"yes";"no";"unknown";6;"may";135;2;-1;0;"unknown";"no"
24;"management";"single";"tertiary";"no";172;"no";"no";"cellular";3;"jul";67;2;-1;0;"unknown";"no"
58;"entrepreneur";"married";"secondary";"no";2;"no";"no";"unknown";16;"jun";684;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";41;"no";"no";"cellular";21;"aug";686;5;-1;0;"unknown";"yes"
25;"student";"single";"secondary";"no";1116;"yes";"no";"cellular";2;"mar";170;3;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";-55;"no";"no";"unknown";17;"jun";150;3;-1;0;"unknown";"no"
59;"retired";"divorced";"secondary";"no";5845;"no";"no";"cellular";14;"may";1060;2;-1;0;"unknown";"yes"
30;"management";"single";"tertiary";"no";329;"yes";"no";"unknown";6;"jun";122;16;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";494;"yes";"no";"unknown";26;"may";724;1;-1;0;"unknown";"no"
42;"management";"married";"unknown";"no";1519;"no";"no";"cellular";19;"oct";230;1;92;1;"success";"yes"
37;"entrepreneur";"married";"tertiary";"no";2685;"no";"no";"unknown";20;"jun";187;2;-1;0;"unknown";"no"
44;"blue-collar";"married";"primary";"no";-14;"yes";"yes";"unknown";19;"may";179;4;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";5673;"no";"yes";"cellular";4;"aug";357;3;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";1459;"no";"no";"unknown";5;"jun";82;1;-1;0;"unknown";"no"
48;"technician";"married";"tertiary";"no";0;"no";"no";"telephone";26;"aug";176;15;-1;0;"unknown";"no"
31;"management";"married";"secondary";"no";625;"yes";"no";"cellular";7;"apr";424;1;138;1;"failure";"no"
37;"student";"married";"unknown";"no";0;"no";"no";"unknown";18;"jun";115;1;-1;0;"unknown";"no"
51;"services";"married";"secondary";"no";867;"yes";"no";"cellular";3;"feb";177;2;211;3;"failure";"no"
80;"retired";"married";"secondary";"no";8304;"no";"no";"telephone";9;"jun";712;1;64;12;"failure";"yes"
32;"management";"single";"tertiary";"no";151;"yes";"no";"unknown";6;"may";118;1;-1;0;"unknown";"no"
48;"services";"married";"primary";"yes";-583;"yes";"no";"unknown";2;"jun";25;7;-1;0;"unknown";"no"
49;"blue-collar";"married";"tertiary";"no";1784;"no";"no";"unknown";12;"jun";753;1;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";3134;"yes";"no";"cellular";17;"jul";124;1;-1;0;"unknown";"no"
27;"self-employed";"single";"secondary";"no";49;"yes";"no";"cellular";30;"jan";208;2;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";-346;"no";"no";"cellular";13;"aug";136;4;-1;0;"unknown";"no"
49;"management";"married";"secondary";"no";1623;"yes";"no";"cellular";19;"nov";1081;2;-1;0;"unknown";"yes"
48;"management";"married";"tertiary";"no";60;"no";"no";"telephone";7;"may";376;2;339;7;"failure";"no"
40;"services";"married";"secondary";"no";17;"no";"no";"cellular";4;"jun";78;1;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";2008;"yes";"no";"cellular";18;"nov";354;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";91;"yes";"no";"cellular";6;"may";29;1;356;2;"failure";"no"
41;"entrepreneur";"married";"tertiary";"no";171;"yes";"no";"cellular";19;"nov";209;1;191;4;"failure";"no"
28;"management";"single";"tertiary";"no";1027;"yes";"no";"cellular";14;"may";433;1;-1;0;"unknown";"no"
51;"services";"married";"unknown";"no";5801;"yes";"no";"unknown";29;"may";147;2;-1;0;"unknown";"no"
33;"blue-collar";"divorced";"primary";"no";142;"yes";"no";"telephone";11;"may";41;2;300;5;"failure";"no"
26;"management";"single";"tertiary";"no";205;"no";"no";"unknown";26;"may";111;1;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";852;"no";"no";"cellular";14;"apr";108;2;-1;0;"unknown";"no"
27;"blue-collar";"married";"secondary";"yes";-584;"yes";"no";"cellular";7;"may";411;2;-1;0;"unknown";"no"
42;"self-employed";"married";"secondary";"no";97;"no";"no";"cellular";10;"jul";108;2;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";204;"yes";"no";"cellular";3;"jun";224;2;-1;0;"unknown";"yes"
31;"technician";"single";"secondary";"no";628;"yes";"no";"unknown";12;"may";1083;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";311;"no";"no";"unknown";12;"may";757;2;-1;0;"unknown";"yes"
39;"blue-collar";"married";"primary";"no";1564;"no";"no";"unknown";17;"jun";57;1;-1;0;"unknown";"no"
26;"blue-collar";"single";"secondary";"no";856;"yes";"yes";"cellular";30;"jul";95;5;-1;0;"unknown";"no"
40;"management";"single";"tertiary";"no";1509;"no";"no";"cellular";15;"mar";144;2;124;1;"failure";"no"
52;"services";"married";"secondary";"no";-888;"no";"no";"cellular";16;"jul";135;1;-1;0;"unknown";"no"
40;"technician";"single";"tertiary";"no";226;"no";"no";"cellular";15;"jan";176;2;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";127;"no";"yes";"cellular";7;"aug";524;5;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";413;"yes";"no";"unknown";13;"may";65;1;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";710;"yes";"yes";"cellular";6;"may";653;1;334;2;"success";"yes"
47;"technician";"married";"secondary";"no";1233;"yes";"no";"unknown";11;"sep";91;1;100;2;"other";"yes"
28;"management";"single";"secondary";"no";171;"no";"no";"cellular";10;"mar";93;1;-1;0;"unknown";"yes"
40;"management";"divorced";"tertiary";"no";69;"yes";"no";"cellular";6;"may";503;2;280;2;"failure";"no"
34;"admin.";"married";"secondary";"no";1499;"yes";"no";"cellular";13;"apr";186;1;327;2;"failure";"no"
29;"self-employed";"married";"tertiary";"no";751;"yes";"no";"unknown";23;"may";121;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";61;"no";"yes";"cellular";19;"aug";452;2;-1;0;"unknown";"no"
83;"retired";"married";"primary";"no";425;"no";"no";"telephone";22;"jun";883;1;140;1;"failure";"yes"
55;"services";"divorced";"primary";"no";2923;"yes";"no";"unknown";15;"may";104;2;-1;0;"unknown";"no"
43;"management";"divorced";"tertiary";"no";99;"no";"no";"cellular";29;"jan";24;3;-1;0;"unknown";"no"
23;"student";"single";"secondary";"no";1021;"no";"no";"telephone";19;"jun";79;9;-1;0;"unknown";"no"
44;"technician";"married";"tertiary";"no";1146;"no";"no";"cellular";4;"jul";178;2;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";781;"yes";"no";"cellular";15;"may";74;2;-1;0;"unknown";"no"
29;"admin.";"single";"secondary";"no";1448;"no";"no";"cellular";10;"mar";217;1;184;7;"other";"no"
30;"management";"single";"tertiary";"no";604;"no";"yes";"cellular";2;"feb";475;1;-1;0;"unknown";"no"
29;"admin.";"single";"secondary";"no";-194;"yes";"no";"cellular";7;"may";127;1;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";0;"no";"no";"cellular";11;"mar";101;1;-1;0;"unknown";"no"
35;"housemaid";"married";"primary";"no";0;"yes";"no";"cellular";14;"jul";340;3;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";1014;"yes";"no";"cellular";23;"jul";158;4;-1;0;"unknown";"no"
27;"admin.";"single";"tertiary";"no";73;"no";"no";"cellular";29;"jan";242;1;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";2597;"yes";"no";"unknown";16;"may";323;1;-1;0;"unknown";"no"
53;"self-employed";"married";"tertiary";"no";2627;"yes";"yes";"unknown";16;"jun";18;30;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";1906;"yes";"no";"cellular";14;"jul";68;3;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";-319;"yes";"yes";"cellular";9;"jul";134;4;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";-296;"yes";"no";"cellular";14;"may";113;3;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";169;"yes";"no";"cellular";18;"aug";43;4;-1;0;"unknown";"no"
37;"self-employed";"single";"tertiary";"no";556;"no";"no";"unknown";17;"jun";200;2;-1;0;"unknown";"no"
65;"retired";"divorced";"unknown";"no";300;"no";"no";"cellular";2;"aug";245;2;-1;0;"unknown";"no"
53;"technician";"divorced";"secondary";"no";751;"yes";"no";"unknown";16;"may";343;4;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";126;"no";"no";"cellular";3;"mar";229;1;-1;0;"unknown";"no"
33;"technician";"divorced";"tertiary";"no";-386;"yes";"no";"unknown";23;"may";530;1;-1;0;"unknown";"no"
56;"blue-collar";"married";"primary";"no";379;"no";"no";"cellular";13;"aug";58;5;-1;0;"unknown";"no"
45;"self-employed";"married";"secondary";"no";706;"no";"no";"cellular";30;"apr";78;2;176;1;"success";"no"
39;"technician";"married";"secondary";"no";9121;"no";"no";"unknown";5;"jun";147;1;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";1466;"yes";"no";"cellular";25;"jul";40;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";18;"nov";618;2;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";951;"yes";"no";"cellular";20;"apr";23;4;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";368;"no";"no";"cellular";20;"aug";293;1;90;4;"other";"yes"
41;"blue-collar";"married";"secondary";"no";121;"yes";"no";"cellular";13;"may";16;6;176;5;"other";"no"
59;"services";"married";"secondary";"no";204;"no";"no";"cellular";23;"jul";286;2;-1;0;"unknown";"no"
24;"admin.";"married";"secondary";"no";299;"yes";"no";"cellular";6;"may";209;1;321;1;"failure";"no"
34;"management";"married";"tertiary";"no";4170;"yes";"no";"unknown";21;"may";935;1;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";-335;"yes";"no";"unknown";27;"may";260;1;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";3165;"yes";"no";"unknown";16;"jun";298;4;-1;0;"unknown";"no"
50;"services";"married";"secondary";"no";254;"yes";"no";"cellular";15;"apr";397;2;337;1;"failure";"no"
25;"management";"single";"tertiary";"no";553;"yes";"no";"cellular";17;"sep";113;4;479;1;"failure";"no"
51;"services";"divorced";"secondary";"no";518;"yes";"no";"telephone";17;"nov";76;1;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";85;"no";"yes";"cellular";1;"jun";141;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"unknown";"no";603;"yes";"no";"unknown";23;"may";773;2;-1;0;"unknown";"no"
21;"services";"single";"secondary";"no";361;"no";"no";"telephone";5;"jun";329;1;95;1;"other";"no"
52;"blue-collar";"married";"secondary";"no";-98;"yes";"no";"unknown";7;"may";423;1;-1;0;"unknown";"no"
55;"retired";"married";"primary";"no";4424;"no";"no";"cellular";5;"aug";67;4;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";2610;"yes";"yes";"unknown";2;"jun";626;1;-1;0;"unknown";"no"
38;"technician";"married";"tertiary";"no";3234;"no";"no";"cellular";18;"nov";578;2;-1;0;"unknown";"yes"
32;"blue-collar";"married";"secondary";"no";687;"yes";"no";"unknown";9;"may";61;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";631;"yes";"no";"unknown";23;"may";248;4;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";341;"no";"yes";"cellular";29;"jan";136;1;262;3;"failure";"no"
33;"technician";"single";"tertiary";"no";316;"no";"no";"cellular";21;"aug";126;3;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";2;"no";"no";"telephone";28;"jan";653;3;-1;0;"unknown";"no"
44;"management";"married";"unknown";"no";415;"yes";"no";"telephone";14;"may";7;7;-1;0;"unknown";"no"
48;"management";"divorced";"tertiary";"no";507;"yes";"no";"cellular";9;"apr";528;1;-1;0;"unknown";"no"
29;"blue-collar";"married";"secondary";"no";912;"yes";"no";"cellular";13;"may";785;1;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";0;"no";"no";"cellular";9;"jul";243;5;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";37;"yes";"no";"cellular";13;"may";24;2;362;1;"other";"no"
44;"admin.";"single";"secondary";"no";2524;"no";"no";"unknown";19;"jun";82;1;-1;0;"unknown";"no"
36;"unemployed";"single";"tertiary";"no";9019;"yes";"no";"unknown";11;"jun";952;3;-1;0;"unknown";"no"
49;"blue-collar";"divorced";"unknown";"no";39;"yes";"no";"cellular";13;"may";27;5;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";0;"yes";"no";"unknown";5;"may";143;1;-1;0;"unknown";"no"
49;"technician";"divorced";"secondary";"no";265;"yes";"no";"unknown";12;"may";274;6;-1;0;"unknown";"no"
35;"blue-collar";"single";"secondary";"no";0;"yes";"no";"cellular";16;"apr";1174;1;345;4;"other";"yes"
57;"admin.";"divorced";"unknown";"no";4012;"no";"no";"cellular";20;"nov";915;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";1520;"yes";"no";"cellular";5;"feb";115;1;-1;0;"unknown";"no"
36;"blue-collar";"divorced";"secondary";"no";-140;"yes";"no";"unknown";26;"may";222;2;-1;0;"unknown";"no"
75;"retired";"divorced";"primary";"no";852;"no";"no";"cellular";15;"mar";341;1;-1;0;"unknown";"no"
38;"blue-collar";"single";"secondary";"no";578;"yes";"no";"unknown";21;"may";112;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"yes";0;"yes";"no";"unknown";6;"may";64;2;-1;0;"unknown";"no"
38;"services";"single";"secondary";"no";2253;"yes";"no";"cellular";13;"may";937;3;-1;0;"unknown";"yes"
34;"management";"single";"tertiary";"no";1171;"yes";"no";"cellular";10;"jul";129;1;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";452;"yes";"no";"cellular";11;"may";59;1;-1;0;"unknown";"no"
28;"admin.";"married";"secondary";"no";-97;"yes";"no";"cellular";29;"jan";79;1;225;2;"success";"no"
79;"retired";"married";"secondary";"no";4738;"no";"no";"telephone";9;"jul";151;5;-1;0;"unknown";"no"
51;"admin.";"divorced";"secondary";"no";467;"yes";"no";"unknown";19;"may";83;1;-1;0;"unknown";"no"
32;"student";"single";"tertiary";"no";0;"no";"no";"cellular";6;"aug";101;5;3;6;"other";"no"
36;"management";"married";"tertiary";"no";495;"no";"no";"cellular";5;"aug";157;3;97;2;"success";"yes"
38;"entrepreneur";"married";"secondary";"no";157;"no";"yes";"cellular";15;"may";57;11;366;1;"other";"no"
49;"self-employed";"married";"secondary";"no";358;"yes";"yes";"unknown";13;"may";176;3;-1;0;"unknown";"no"
42;"services";"married";"secondary";"no";292;"yes";"no";"cellular";11;"may";222;3;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";7343;"yes";"no";"unknown";2;"jun";217;4;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";-563;"yes";"no";"unknown";26;"may";252;1;-1;0;"unknown";"no"
59;"retired";"single";"secondary";"no";351;"yes";"no";"unknown";27;"may";1063;5;-1;0;"unknown";"yes"
35;"unknown";"married";"primary";"no";365;"no";"no";"cellular";29;"aug";41;2;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";13494;"yes";"no";"unknown";20;"jun";27;25;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";846;"yes";"no";"cellular";17;"apr";758;2;-1;0;"unknown";"no"
64;"retired";"married";"secondary";"no";1574;"no";"no";"telephone";29;"jul";301;3;-1;0;"unknown";"yes"
34;"entrepreneur";"married";"tertiary";"no";-28;"yes";"no";"cellular";16;"jul";574;1;-1;0;"unknown";"no"
77;"retired";"married";"tertiary";"no";0;"no";"no";"cellular";16;"oct";847;1;60;1;"success";"yes"
57;"retired";"married";"primary";"no";2927;"no";"yes";"telephone";30;"jul";78;5;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";501;"yes";"yes";"unknown";27;"may";514;1;-1;0;"unknown";"no"
56;"housemaid";"divorced";"primary";"yes";1238;"no";"no";"unknown";5;"jun";1558;1;-1;0;"unknown";"yes"
49;"unknown";"married";"unknown";"no";301;"no";"no";"cellular";14;"aug";789;2;-1;0;"unknown";"no"
73;"retired";"married";"primary";"no";1388;"no";"no";"cellular";20;"jul";146;3;190;3;"success";"no"
39;"technician";"single";"secondary";"no";102;"yes";"no";"cellular";14;"aug";272;2;-1;0;"unknown";"no"
57;"technician";"divorced";"primary";"no";13;"yes";"no";"cellular";8;"may";323;2;368;2;"failure";"no"
32;"self-employed";"married";"secondary";"no";413;"yes";"no";"cellular";11;"jul";712;2;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";4665;"yes";"no";"unknown";21;"may";860;2;-1;0;"unknown";"yes"
45;"admin.";"married";"secondary";"no";638;"yes";"no";"cellular";20;"aug";75;9;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";101;"yes";"no";"unknown";19;"may";191;4;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1224;"yes";"yes";"cellular";24;"jul";1441;2;-1;0;"unknown";"yes"
49;"self-employed";"married";"secondary";"no";1103;"no";"no";"cellular";12;"aug";428;2;-1;0;"unknown";"no"
29;"services";"married";"secondary";"no";3748;"no";"no";"cellular";28;"may";322;3;-1;0;"unknown";"no"
55;"technician";"married";"secondary";"no";955;"yes";"no";"cellular";29;"jan";104;1;-1;0;"unknown";"no"
30;"admin.";"married";"secondary";"no";518;"yes";"no";"cellular";8;"may";127;1;-1;0;"unknown";"no"
45;"services";"single";"primary";"no";1893;"yes";"no";"unknown";3;"jun";1259;6;-1;0;"unknown";"yes"
39;"technician";"married";"tertiary";"no";14;"no";"no";"cellular";28;"jan";114;2;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";-423;"yes";"yes";"unknown";13;"may";106;1;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";3696;"no";"no";"cellular";19;"nov";305;1;182;3;"failure";"no"
36;"management";"married";"tertiary";"no";2987;"yes";"no";"cellular";12;"aug";307;1;-1;0;"unknown";"yes"
31;"technician";"single";"secondary";"no";433;"no";"no";"cellular";11;"aug";1504;2;-1;0;"unknown";"no"
36;"technician";"married";"unknown";"no";756;"no";"no";"cellular";11;"jul";396;3;-1;0;"unknown";"no"
38;"technician";"single";"tertiary";"no";0;"yes";"no";"cellular";1;"jun";71;1;-1;0;"unknown";"no"
33;"unknown";"married";"secondary";"no";2727;"no";"no";"cellular";6;"aug";205;3;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";93;"yes";"no";"unknown";13;"may";134;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";1766;"no";"yes";"cellular";5;"jun";166;2;-1;0;"unknown";"yes"
39;"services";"married";"secondary";"no";124;"yes";"no";"cellular";31;"jul";81;5;-1;0;"unknown";"no"
33;"admin.";"married";"tertiary";"no";69;"no";"yes";"cellular";4;"may";77;2;-1;0;"unknown";"no"
53;"services";"divorced";"secondary";"no";0;"no";"no";"cellular";12;"mar";80;2;-1;0;"unknown";"yes"
38;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";13;"may";537;5;303;2;"failure";"no"
59;"technician";"married";"secondary";"no";259;"no";"no";"cellular";15;"jun";415;1;89;1;"success";"yes"
55;"blue-collar";"married";"primary";"no";4103;"no";"no";"cellular";19;"nov";291;1;-1;0;"unknown";"no"
38;"technician";"single";"tertiary";"no";744;"yes";"no";"cellular";14;"may";283;1;288;12;"other";"no"
48;"services";"married";"secondary";"no";116;"yes";"no";"telephone";20;"apr";70;4;-1;0;"unknown";"no"
53;"blue-collar";"married";"secondary";"no";70;"yes";"no";"unknown";5;"may";611;2;-1;0;"unknown";"no"
44;"admin.";"divorced";"secondary";"no";432;"no";"no";"cellular";9;"feb";132;3;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";1172;"no";"no";"unknown";18;"jun";26;1;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";9;"yes";"no";"cellular";11;"may";262;3;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";428;"yes";"no";"unknown";21;"may";272;1;-1;0;"unknown";"no"
35;"technician";"single";"tertiary";"no";7546;"yes";"no";"cellular";28;"dec";137;1;122;3;"failure";"no"
31;"blue-collar";"married";"primary";"no";620;"no";"no";"cellular";30;"mar";95;1;-1;0;"unknown";"no"
35;"self-employed";"married";"tertiary";"no";8647;"no";"no";"cellular";19;"nov";140;2;-1;0;"unknown";"no"
40;"management";"single";"secondary";"no";192;"yes";"no";"unknown";16;"may";71;11;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";7;"may";502;1;7;2;"failure";"no"
43;"technician";"single";"tertiary";"no";27733;"yes";"no";"unknown";3;"jun";164;7;-1;0;"unknown";"no"
44;"entrepreneur";"married";"primary";"no";276;"yes";"yes";"unknown";15;"may";219;1;-1;0;"unknown";"no"
29;"technician";"single";"secondary";"no";113;"no";"no";"cellular";12;"feb";328;2;183;4;"success";"yes"
40;"technician";"married";"secondary";"no";782;"yes";"no";"unknown";14;"may";224;2;-1;0;"unknown";"no"
33;"entrepreneur";"married";"primary";"no";82;"yes";"no";"cellular";18;"nov";63;2;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";1008;"no";"no";"cellular";29;"aug";24;13;-1;0;"unknown";"no"
27;"unknown";"single";"tertiary";"no";6421;"no";"no";"telephone";28;"jan";147;4;-1;0;"unknown";"no"
26;"admin.";"single";"secondary";"no";443;"no";"no";"cellular";30;"jul";84;3;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";351;"yes";"no";"cellular";9;"jul";175;1;-1;0;"unknown";"no"
34;"technician";"married";"tertiary";"no";5060;"yes";"no";"cellular";21;"aug";12;21;-1;0;"unknown";"no"
39;"technician";"single";"tertiary";"no";-41;"no";"yes";"cellular";25;"aug";40;15;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";-124;"no";"no";"cellular";18;"jul";217;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";79;"yes";"yes";"cellular";23;"jul";235;11;-1;0;"unknown";"no"
44;"unemployed";"single";"secondary";"no";3945;"no";"no";"cellular";28;"jan";255;1;-1;0;"unknown";"no"
55;"retired";"married";"tertiary";"no";197;"no";"yes";"cellular";8;"jul";256;1;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"primary";"no";837;"no";"no";"telephone";30;"jul";41;2;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";-94;"no";"yes";"unknown";25;"jun";75;1;-1;0;"unknown";"no"
30;"services";"married";"secondary";"no";1743;"yes";"no";"cellular";5;"feb";192;4;-1;0;"unknown";"no"
46;"technician";"married";"secondary";"no";57;"no";"no";"unknown";28;"may";796;1;-1;0;"unknown";"yes"
49;"admin.";"married";"secondary";"no";635;"no";"no";"cellular";25;"aug";220;3;-1;0;"unknown";"no"
56;"retired";"married";"secondary";"no";-113;"yes";"yes";"cellular";12;"aug";614;2;-1;0;"unknown";"no"
47;"admin.";"divorced";"secondary";"no";225;"no";"no";"cellular";28;"aug";1126;4;-1;0;"unknown";"yes"
30;"admin.";"married";"secondary";"no";124;"no";"no";"telephone";16;"jun";161;2;-1;0;"unknown";"no"
36;"entrepreneur";"single";"secondary";"no";475;"no";"no";"cellular";25;"jul";141;3;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";-7;"no";"yes";"telephone";25;"jul";697;1;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"yes";375;"no";"no";"unknown";9;"jun";223;2;-1;0;"unknown";"no"
42;"admin.";"divorced";"secondary";"no";936;"no";"no";"cellular";30;"jan";931;1;-1;0;"unknown";"yes"
45;"technician";"married";"secondary";"no";1415;"yes";"no";"cellular";17;"nov";483;1;116;1;"failure";"no"
34;"unemployed";"single";"secondary";"no";0;"no";"no";"cellular";17;"nov";63;2;-1;0;"unknown";"no"
27;"admin.";"single";"secondary";"no";3638;"no";"no";"cellular";11;"jul";271;5;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";-99;"yes";"no";"cellular";15;"may";144;5;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";167;"yes";"yes";"cellular";8;"aug";79;3;-1;0;"unknown";"no"
46;"housemaid";"married";"primary";"no";19;"yes";"no";"telephone";21;"jul";171;1;-1;0;"unknown";"no"
29;"self-employed";"married";"tertiary";"no";242;"yes";"no";"cellular";18;"jul";73;4;-1;0;"unknown";"no"
53;"unknown";"married";"unknown";"no";13;"no";"no";"telephone";28;"jan";69;1;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";286;"no";"no";"cellular";20;"apr";60;1;343;4;"failure";"no"
49;"unemployed";"married";"secondary";"no";769;"yes";"no";"unknown";14;"may";257;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";93;"no";"no";"cellular";4;"may";198;2;88;1;"other";"no"
38;"blue-collar";"married";"secondary";"no";1663;"yes";"no";"cellular";13;"apr";217;3;327;1;"failure";"no"
39;"entrepreneur";"married";"primary";"no";238;"yes";"yes";"cellular";19;"nov";134;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";2178;"yes";"no";"cellular";26;"may";41;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";792;"yes";"yes";"cellular";19;"nov";611;2;175;12;"other";"no"
35;"blue-collar";"single";"unknown";"no";871;"yes";"no";"unknown";6;"jun";216;3;-1;0;"unknown";"no"
60;"admin.";"married";"secondary";"no";1025;"no";"no";"cellular";6;"aug";388;8;-1;0;"unknown";"no"
57;"technician";"divorced";"secondary";"no";63;"yes";"no";"unknown";5;"may";242;1;-1;0;"unknown";"no"
31;"blue-collar";"single";"secondary";"no";16;"no";"no";"cellular";7;"jul";387;1;-1;0;"unknown";"no"
29;"management";"single";"secondary";"no";417;"no";"no";"cellular";7;"oct";90;1;-1;0;"unknown";"no"
41;"services";"married";"secondary";"no";3992;"yes";"no";"telephone";20;"nov";281;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";2749;"yes";"no";"cellular";17;"nov";332;1;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";133;"no";"no";"cellular";4;"may";218;1;-1;0;"unknown";"yes"
32;"blue-collar";"single";"secondary";"no";1721;"yes";"no";"unknown";21;"may";650;2;-1;0;"unknown";"no"
54;"management";"divorced";"tertiary";"no";2171;"yes";"yes";"telephone";18;"nov";1034;1;-1;0;"unknown";"yes"
37;"services";"married";"secondary";"no";4760;"yes";"no";"cellular";8;"may";182;2;169;2;"failure";"no"
32;"technician";"single";"secondary";"no";2979;"no";"no";"cellular";25;"may";156;1;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";690;"yes";"no";"cellular";6;"aug";171;3;-1;0;"unknown";"no"
50;"blue-collar";"divorced";"secondary";"no";203;"yes";"no";"telephone";19;"nov";265;1;127;4;"other";"no"
34;"blue-collar";"married";"secondary";"no";322;"yes";"no";"cellular";20;"apr";10;3;-1;0;"unknown";"no"
31;"admin.";"divorced";"secondary";"no";1210;"no";"no";"cellular";16;"jul";350;1;-1;0;"unknown";"no"
43;"self-employed";"married";"secondary";"no";634;"yes";"no";"cellular";17;"nov";65;1;131;1;"failure";"no"
49;"entrepreneur";"married";"secondary";"no";273;"no";"no";"cellular";23;"jul";58;1;-1;0;"unknown";"no"
22;"student";"single";"unknown";"no";47;"no";"no";"cellular";3;"jul";69;3;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";1100;"no";"yes";"cellular";29;"jan";127;2;-1;0;"unknown";"no"
56;"admin.";"married";"secondary";"no";0;"yes";"no";"telephone";19;"nov";154;2;-1;0;"unknown";"no"
53;"blue-collar";"married";"secondary";"no";0;"no";"no";"telephone";31;"jul";36;4;-1;0;"unknown";"no"
54;"entrepreneur";"married";"tertiary";"no";1544;"no";"yes";"cellular";5;"feb";85;7;196;3;"failure";"no"
32;"services";"single";"unknown";"no";6145;"yes";"no";"cellular";14;"may";362;1;287;13;"failure";"no"
41;"self-employed";"married";"secondary";"no";28;"yes";"no";"cellular";11;"may";49;1;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";1012;"yes";"no";"cellular";6;"may";410;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";245;"yes";"yes";"cellular";8;"may";253;3;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";1998;"no";"no";"cellular";19;"nov";570;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";3990;"yes";"no";"cellular";24;"jul";633;7;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";1328;"yes";"no";"unknown";6;"jun";82;4;-1;0;"unknown";"no"
34;"entrepreneur";"married";"tertiary";"no";412;"yes";"yes";"cellular";8;"jul";164;1;-1;0;"unknown";"no"
55;"management";"married";"primary";"no";229;"no";"yes";"unknown";9;"jun";175;3;-1;0;"unknown";"no"
28;"student";"single";"tertiary";"no";1562;"no";"no";"cellular";15;"feb";659;4;-1;0;"unknown";"yes"
29;"admin.";"single";"secondary";"no";3432;"yes";"no";"cellular";17;"apr";139;2;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";144;"yes";"no";"unknown";4;"jul";43;11;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";1339;"yes";"no";"cellular";22;"aug";343;5;-1;0;"unknown";"no"
54;"services";"married";"unknown";"no";386;"no";"yes";"cellular";19;"nov";74;1;152;1;"success";"no"
43;"blue-collar";"married";"primary";"no";7727;"yes";"no";"unknown";15;"may";302;8;-1;0;"unknown";"no"
55;"entrepreneur";"married";"secondary";"no";204;"no";"no";"cellular";14;"jul";455;13;-1;0;"unknown";"no"
28;"blue-collar";"divorced";"unknown";"no";101;"yes";"no";"unknown";28;"may";126;4;-1;0;"unknown";"no"
45;"unemployed";"divorced";"secondary";"no";728;"yes";"yes";"cellular";18;"nov";107;1;-1;0;"unknown";"no"
45;"entrepreneur";"married";"primary";"no";2418;"yes";"no";"unknown";14;"may";120;1;-1;0;"unknown";"no"
51;"management";"married";"tertiary";"no";2934;"yes";"no";"cellular";18;"nov";727;4;-1;0;"unknown";"no"
42;"technician";"single";"secondary";"no";0;"yes";"no";"unknown";7;"may";275;4;-1;0;"unknown";"no"
43;"unknown";"single";"unknown";"no";181;"no";"no";"telephone";28;"jan";41;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";176;"no";"no";"cellular";18;"aug";61;4;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";49;"yes";"no";"cellular";15;"may";232;2;365;5;"other";"no"
37;"blue-collar";"married";"secondary";"no";2023;"yes";"no";"cellular";10;"jul";214;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";372;"yes";"no";"telephone";11;"may";21;1;-1;0;"unknown";"no"
42;"management";"divorced";"unknown";"no";7702;"no";"no";"cellular";16;"feb";138;2;-1;0;"unknown";"yes"
34;"blue-collar";"single";"primary";"no";464;"yes";"no";"cellular";18;"may";173;1;299;2;"other";"no"
48;"management";"married";"secondary";"no";117;"yes";"no";"cellular";16;"apr";635;1;-1;0;"unknown";"no"
47;"admin.";"married";"secondary";"no";104;"yes";"yes";"cellular";14;"jul";77;2;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";1041;"yes";"no";"unknown";12;"jun";73;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";539;"no";"no";"cellular";18;"jan";540;2;115;3;"failure";"no"
38;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";3;"jun";179;2;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";406;"yes";"no";"cellular";17;"jul";119;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";2558;"yes";"no";"telephone";20;"nov";107;1;167;1;"other";"no"
26;"blue-collar";"married";"secondary";"no";484;"yes";"no";"unknown";19;"may";48;2;-1;0;"unknown";"no"
30;"services";"single";"tertiary";"no";3516;"yes";"no";"unknown";29;"may";447;4;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";5639;"yes";"no";"cellular";17;"jul";430;2;-1;0;"unknown";"no"
42;"management";"single";"tertiary";"no";100;"no";"no";"cellular";7;"aug";64;2;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";1306;"yes";"no";"unknown";28;"may";217;5;-1;0;"unknown";"no"
37;"management";"single";"tertiary";"no";815;"yes";"no";"cellular";7;"may";77;1;-1;0;"unknown";"no"
26;"blue-collar";"married";"secondary";"no";245;"yes";"no";"unknown";26;"may";16;11;-1;0;"unknown";"no"
54;"blue-collar";"married";"unknown";"no";191;"no";"no";"cellular";4;"aug";753;2;-1;0;"unknown";"no"
70;"retired";"married";"secondary";"no";2815;"no";"no";"cellular";26;"apr";125;2;182;1;"failure";"no"
29;"self-employed";"single";"secondary";"no";3672;"yes";"yes";"unknown";28;"may";1210;1;-1;0;"unknown";"no"
50;"services";"married";"secondary";"no";1572;"yes";"no";"cellular";9;"jul";185;1;-1;0;"unknown";"no"
60;"unemployed";"divorced";"secondary";"no";385;"yes";"no";"cellular";4;"feb";80;1;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";29;"no";"no";"cellular";19;"aug";156;2;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";84;"no";"yes";"cellular";29;"jul";403;3;-1;0;"unknown";"no"
34;"self-employed";"single";"tertiary";"no";876;"yes";"no";"cellular";17;"nov";145;2;-1;0;"unknown";"no"
48;"services";"single";"primary";"no";1499;"yes";"no";"cellular";12;"may";17;7;-1;0;"unknown";"no"
25;"student";"married";"tertiary";"no";0;"yes";"no";"cellular";9;"jul";160;1;-1;0;"unknown";"no"
32;"management";"married";"secondary";"no";6217;"yes";"yes";"cellular";18;"nov";486;2;181;2;"failure";"no"
32;"blue-collar";"married";"primary";"no";329;"yes";"no";"cellular";3;"feb";321;1;-1;0;"unknown";"no"
47;"admin.";"divorced";"secondary";"no";162;"yes";"no";"unknown";9;"may";203;4;-1;0;"unknown";"no"
32;"technician";"married";"unknown";"no";14533;"no";"no";"cellular";31;"dec";646;3;198;2;"success";"no"
49;"management";"single";"tertiary";"no";151;"yes";"no";"cellular";8;"jul";97;3;-1;0;"unknown";"yes"
53;"services";"married";"secondary";"no";-2;"yes";"yes";"cellular";16;"apr";144;2;-1;0;"unknown";"no"
41;"blue-collar";"single";"unknown";"no";4684;"no";"no";"unknown";20;"jun";30;6;-1;0;"unknown";"no"
47;"management";"divorced";"tertiary";"no";1149;"no";"yes";"cellular";15;"jul";82;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";11;"yes";"no";"unknown";13;"may";326;4;-1;0;"unknown";"no"
33;"management";"single";"secondary";"no";8004;"no";"no";"cellular";19;"nov";414;3;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";1019;"yes";"no";"cellular";16;"apr";285;5;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";1297;"no";"no";"cellular";11;"aug";716;3;-1;0;"unknown";"yes"
29;"management";"divorced";"tertiary";"no";415;"yes";"yes";"unknown";28;"may";449;4;-1;0;"unknown";"no"
58;"self-employed";"married";"primary";"no";671;"yes";"no";"unknown";17;"jun";16;3;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";2378;"yes";"no";"unknown";2;"jun";65;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";417;"yes";"no";"cellular";18;"nov";138;1;-1;0;"unknown";"no"
60;"retired";"married";"primary";"no";71;"no";"no";"cellular";10;"jul";121;3;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";1188;"no";"no";"cellular";27;"may";140;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";8139;"yes";"no";"cellular";18;"aug";256;6;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";249;"yes";"no";"cellular";31;"jul";61;4;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";689;"yes";"no";"cellular";14;"jul";367;2;-1;0;"unknown";"no"
55;"self-employed";"married";"secondary";"no";203;"yes";"no";"telephone";14;"may";9;5;174;12;"other";"no"
45;"blue-collar";"single";"primary";"yes";-183;"no";"yes";"cellular";15;"jul";395;4;-1;0;"unknown";"no"
47;"blue-collar";"married";"primary";"no";3451;"no";"yes";"cellular";21;"nov";125;2;-1;0;"unknown";"no"
58;"technician";"married";"secondary";"no";116;"no";"no";"unknown";16;"jun";580;3;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";1746;"yes";"no";"cellular";7;"apr";15;1;316;1;"failure";"no"
55;"technician";"married";"secondary";"no";0;"yes";"no";"unknown";30;"may";119;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";897;"yes";"no";"unknown";20;"may";26;2;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";821;"yes";"yes";"cellular";17;"nov";224;2;180;1;"failure";"no"
42;"blue-collar";"married";"secondary";"no";130;"yes";"no";"unknown";29;"may";285;1;-1;0;"unknown";"no"
32;"management";"divorced";"tertiary";"no";1;"yes";"no";"unknown";8;"may";399;4;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";8;"no";"no";"cellular";30;"jul";88;3;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";55;"yes";"no";"cellular";6;"aug";185;4;-1;0;"unknown";"no"
59;"blue-collar";"married";"primary";"no";699;"no";"no";"cellular";28;"jan";98;1;154;6;"failure";"no"
42;"unemployed";"divorced";"secondary";"no";759;"no";"no";"cellular";28;"jan";97;1;169;3;"failure";"no"
33;"blue-collar";"married";"secondary";"no";687;"yes";"no";"cellular";14;"may";72;2;370;1;"failure";"no"
53;"retired";"married";"secondary";"no";4968;"no";"no";"cellular";6;"may";109;1;87;2;"failure";"no"
43;"blue-collar";"married";"primary";"no";1211;"yes";"no";"unknown";6;"may";208;3;-1;0;"unknown";"no"
39;"self-employed";"single";"tertiary";"no";426;"no";"no";"unknown";18;"jun";1029;1;-1;0;"unknown";"yes"
27;"entrepreneur";"single";"secondary";"no";376;"yes";"yes";"cellular";5;"may";273;1;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";12;"yes";"no";"unknown";23;"may";130;1;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";23;"no";"no";"cellular";26;"feb";590;2;-1;0;"unknown";"no"
33;"admin.";"married";"secondary";"no";353;"yes";"no";"cellular";19;"nov";107;2;-1;0;"unknown";"no"
29;"services";"divorced";"secondary";"no";330;"yes";"no";"unknown";12;"may";88;2;-1;0;"unknown";"no"
33;"management";"divorced";"tertiary";"no";0;"no";"no";"cellular";13;"aug";305;2;-1;0;"unknown";"no"
41;"entrepreneur";"married";"secondary";"no";353;"no";"no";"cellular";19;"nov";90;2;-1;0;"unknown";"no"
38;"technician";"single";"tertiary";"no";2273;"yes";"no";"cellular";17;"apr";222;1;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";9;"jul";127;1;-1;0;"unknown";"no"
58;"unemployed";"married";"tertiary";"no";744;"no";"no";"telephone";28;"jan";38;1;-1;0;"unknown";"no"
37;"services";"single";"secondary";"no";608;"no";"no";"cellular";14;"may";380;3;-1;0;"unknown";"no"
36;"admin.";"divorced";"secondary";"no";356;"yes";"no";"cellular";7;"apr";244;3;313;1;"failure";"no"
45;"blue-collar";"married";"primary";"no";1291;"yes";"no";"cellular";28;"aug";189;17;-1;0;"unknown";"no"
23;"technician";"single";"secondary";"no";-306;"yes";"no";"unknown";4;"jun";217;2;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";306;"no";"no";"cellular";28;"jul";388;3;181;1;"success";"yes"
44;"blue-collar";"married";"primary";"no";-312;"yes";"no";"unknown";20;"jun";22;3;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";493;"no";"no";"cellular";13;"aug";289;2;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";71;"yes";"no";"cellular";21;"jul";221;8;-1;0;"unknown";"no"
38;"admin.";"married";"tertiary";"no";669;"yes";"yes";"cellular";31;"jul";129;2;-1;0;"unknown";"no"
47;"blue-collar";"married";"primary";"no";556;"yes";"no";"cellular";4;"feb";147;2;264;4;"other";"no"
34;"services";"single";"secondary";"no";84;"yes";"no";"unknown";23;"may";299;1;-1;0;"unknown";"no"
52;"services";"married";"secondary";"no";108;"yes";"no";"unknown";15;"may";543;12;-1;0;"unknown";"no"
45;"admin.";"single";"secondary";"no";-11;"yes";"no";"cellular";5;"may";247;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"primary";"no";1164;"yes";"no";"unknown";8;"may";166;2;-1;0;"unknown";"no"
52;"blue-collar";"divorced";"primary";"no";-191;"yes";"no";"unknown";15;"may";755;1;-1;0;"unknown";"yes"
37;"blue-collar";"married";"secondary";"no";132;"no";"no";"unknown";27;"may";336;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"secondary";"no";2237;"yes";"no";"unknown";29;"may";619;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";4761;"yes";"no";"cellular";5;"may";159;2;350;2;"failure";"no"
33;"blue-collar";"married";"secondary";"no";-346;"yes";"yes";"unknown";23;"may";29;3;-1;0;"unknown";"no"
28;"unemployed";"married";"secondary";"no";655;"yes";"no";"unknown";4;"jun";606;2;-1;0;"unknown";"no"
66;"management";"married";"tertiary";"no";1048;"yes";"no";"cellular";23;"jun";971;2;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";0;"yes";"no";"unknown";28;"may";231;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";624;"yes";"no";"unknown";7;"may";163;1;-1;0;"unknown";"no"
47;"services";"single";"unknown";"no";1784;"no";"no";"cellular";19;"oct";385;2;-1;0;"unknown";"yes"
36;"admin.";"single";"secondary";"no";1281;"no";"no";"telephone";24;"jul";119;2;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";569;"no";"yes";"cellular";29;"jan";348;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";1666;"yes";"no";"unknown";31;"mar";30;1;-1;0;"unknown";"no"
40;"admin.";"married";"secondary";"no";168;"yes";"no";"cellular";7;"may";249;2;169;1;"failure";"no"
33;"self-employed";"married";"tertiary";"no";263;"no";"no";"cellular";4;"may";119;2;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";-405;"yes";"no";"cellular";8;"may";177;1;-1;0;"unknown";"no"
38;"self-employed";"married";"secondary";"no";279;"yes";"no";"unknown";28;"may";110;5;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";0;"no";"no";"cellular";18;"aug";594;4;-1;0;"unknown";"yes"
65;"unknown";"married";"unknown";"no";300;"no";"no";"cellular";12;"jan";105;1;-1;0;"unknown";"no"
34;"unemployed";"single";"tertiary";"no";415;"no";"no";"cellular";12;"aug";123;1;-1;0;"unknown";"no"
34;"admin.";"single";"primary";"no";0;"yes";"yes";"cellular";6;"may";172;4;-1;0;"unknown";"no"
42;"blue-collar";"single";"primary";"no";1322;"yes";"yes";"cellular";8;"jul";178;1;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";1062;"yes";"yes";"cellular";6;"apr";230;1;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";302;"yes";"no";"unknown";20;"jun";89;3;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";809;"yes";"no";"unknown";15;"may";118;4;-1;0;"unknown";"no"
38;"housemaid";"married";"secondary";"no";-278;"yes";"no";"cellular";28;"jul";143;2;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";3238;"yes";"no";"unknown";19;"may";7;1;-1;0;"unknown";"no"
46;"admin.";"single";"secondary";"no";751;"yes";"no";"unknown";13;"may";104;2;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"primary";"no";-28;"no";"yes";"unknown";2;"jul";220;2;-1;0;"unknown";"no"
42;"management";"divorced";"tertiary";"no";0;"no";"no";"unknown";20;"jun";85;2;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";2442;"no";"no";"cellular";16;"feb";91;3;-1;0;"unknown";"yes"
58;"unemployed";"married";"tertiary";"no";1798;"yes";"no";"cellular";5;"feb";19;8;-1;0;"unknown";"no"
43;"admin.";"married";"secondary";"no";132;"no";"no";"cellular";19;"aug";574;1;84;3;"success";"yes"
36;"management";"single";"tertiary";"no";260;"yes";"no";"cellular";7;"may";49;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";7;"yes";"yes";"cellular";2;"feb";171;2;193;2;"other";"no"
31;"management";"single";"tertiary";"no";3283;"no";"no";"cellular";2;"feb";258;1;73;1;"failure";"yes"
49;"technician";"married";"unknown";"no";323;"yes";"no";"cellular";18;"nov";41;2;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";2960;"yes";"no";"cellular";20;"nov";83;1;-1;0;"unknown";"no"
29;"unemployed";"single";"secondary";"no";486;"no";"no";"telephone";30;"apr";95;1;-1;0;"unknown";"no"
51;"retired";"divorced";"secondary";"no";343;"yes";"no";"unknown";30;"may";78;2;-1;0;"unknown";"no"
40;"unemployed";"divorced";"secondary";"no";507;"yes";"no";"cellular";2;"feb";134;1;266;7;"failure";"no"
35;"management";"single";"tertiary";"no";5958;"yes";"no";"cellular";22;"mar";215;1;-1;0;"unknown";"yes"
25;"student";"single";"secondary";"no";304;"no";"no";"cellular";26;"may";226;2;-1;0;"unknown";"yes"
32;"blue-collar";"single";"secondary";"no";118;"yes";"no";"cellular";17;"jul";1275;1;-1;0;"unknown";"yes"
30;"self-employed";"married";"secondary";"no";123;"no";"yes";"unknown";17;"jul";66;1;85;5;"other";"no"
38;"management";"married";"tertiary";"no";76;"yes";"no";"cellular";7;"jul";196;3;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";70;"yes";"no";"cellular";27;"mar";229;2;-1;0;"unknown";"yes"
43;"unemployed";"married";"primary";"no";960;"yes";"yes";"unknown";15;"may";241;2;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";2185;"no";"no";"cellular";20;"nov";354;2;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";309;"yes";"no";"cellular";11;"may";471;3;-1;0;"unknown";"no"
56;"blue-collar";"married";"secondary";"no";2190;"no";"yes";"cellular";30;"jan";90;2;232;1;"failure";"no"
57;"technician";"married";"primary";"no";0;"no";"no";"unknown";5;"jun";58;8;-1;0;"unknown";"no"
38;"admin.";"married";"secondary";"no";3732;"yes";"no";"unknown";21;"may";379;2;-1;0;"unknown";"no"
51;"technician";"married";"primary";"no";1550;"no";"no";"unknown";20;"jun";12;5;-1;0;"unknown";"no"
35;"admin.";"single";"tertiary";"no";62;"no";"yes";"cellular";30;"apr";67;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";704;"no";"no";"unknown";18;"jun";165;7;-1;0;"unknown";"no"
48;"management";"married";"secondary";"no";4415;"yes";"no";"cellular";8;"apr";1032;3;141;7;"failure";"yes"
30;"blue-collar";"single";"secondary";"yes";4;"yes";"no";"unknown";23;"may";84;3;-1;0;"unknown";"no"
60;"retired";"married";"secondary";"no";262;"no";"no";"cellular";18;"aug";393;4;-1;0;"unknown";"no"
56;"management";"married";"secondary";"no";238;"yes";"no";"cellular";19;"nov";808;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";97;"yes";"no";"unknown";7;"may";923;3;-1;0;"unknown";"no"
38;"technician";"divorced";"secondary";"no";410;"no";"no";"cellular";11;"aug";63;6;-1;0;"unknown";"no"
20;"student";"single";"secondary";"no";1191;"no";"no";"cellular";12;"feb";274;1;-1;0;"unknown";"no"
59;"retired";"married";"primary";"no";-47;"yes";"no";"cellular";27;"aug";84;4;-1;0;"unknown";"no"
34;"technician";"married";"tertiary";"no";197;"no";"no";"cellular";20;"aug";78;3;-1;0;"unknown";"yes"
36;"blue-collar";"married";"unknown";"no";6;"no";"no";"unknown";20;"jun";41;7;-1;0;"unknown";"no"
53;"admin.";"single";"secondary";"no";2398;"yes";"no";"cellular";2;"aug";317;1;180;2;"failure";"yes"
51;"management";"married";"tertiary";"no";630;"yes";"no";"cellular";5;"may";138;1;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";235;"no";"no";"cellular";27;"aug";88;4;-1;0;"unknown";"no"
48;"management";"married";"primary";"no";1910;"yes";"no";"unknown";29;"may";413;2;-1;0;"unknown";"no"
56;"management";"married";"tertiary";"no";3172;"yes";"no";"cellular";5;"feb";139;1;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";872;"no";"no";"cellular";8;"aug";82;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";407;"yes";"no";"cellular";20;"apr";12;6;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";100;"yes";"no";"cellular";11;"aug";221;1;461;1;"success";"yes"
59;"housemaid";"married";"secondary";"no";345;"no";"no";"unknown";9;"jun";38;1;-1;0;"unknown";"no"
44;"management";"divorced";"tertiary";"no";2058;"no";"yes";"cellular";21;"nov";661;1;-1;0;"unknown";"no"
49;"housemaid";"married";"primary";"no";889;"no";"no";"telephone";7;"jul";388;1;-1;0;"unknown";"yes"
35;"admin.";"married";"tertiary";"no";0;"yes";"no";"cellular";28;"jul";151;7;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";51;"no";"no";"cellular";25;"aug";140;8;-1;0;"unknown";"no"
55;"retired";"married";"primary";"no";150;"no";"no";"cellular";8;"jul";124;1;-1;0;"unknown";"no"
51;"self-employed";"married";"secondary";"yes";34;"no";"yes";"cellular";7;"jul";200;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";795;"no";"no";"cellular";6;"feb";101;3;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";282;"no";"yes";"cellular";7;"jul";261;12;-1;0;"unknown";"no"
51;"management";"single";"tertiary";"no";2139;"no";"no";"cellular";17;"nov";176;2;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";-469;"yes";"no";"unknown";16;"jun";209;12;-1;0;"unknown";"no"
49;"admin.";"married";"secondary";"no";197;"yes";"no";"cellular";13;"apr";223;1;182;2;"failure";"yes"
40;"technician";"single";"tertiary";"no";825;"yes";"no";"cellular";20;"apr";38;1;335;1;"other";"no"
47;"housemaid";"single";"tertiary";"no";3232;"no";"no";"telephone";6;"oct";541;1;204;3;"failure";"no"
48;"management";"divorced";"secondary";"no";1598;"yes";"no";"unknown";29;"may";84;1;-1;0;"unknown";"no"
47;"services";"married";"tertiary";"no";871;"no";"no";"cellular";22;"aug";602;9;-1;0;"unknown";"no"
56;"services";"married";"secondary";"no";83;"no";"no";"cellular";27;"aug";26;11;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";1445;"no";"no";"cellular";26;"apr";328;2;143;2;"success";"yes"
38;"technician";"married";"secondary";"no";226;"yes";"no";"unknown";30;"may";762;3;-1;0;"unknown";"yes"
32;"management";"married";"tertiary";"no";-55;"yes";"no";"cellular";26;"aug";107;4;-1;0;"unknown";"no"
42;"admin.";"single";"secondary";"no";243;"yes";"no";"unknown";14;"may";46;5;-1;0;"unknown";"no"
30;"blue-collar";"married";"primary";"no";-336;"no";"no";"cellular";10;"jul";128;6;-1;0;"unknown";"no"
26;"admin.";"single";"tertiary";"no";3672;"yes";"no";"unknown";29;"may";360;5;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";0;"yes";"no";"cellular";9;"nov";524;1;96;5;"success";"yes"
35;"technician";"married";"secondary";"no";251;"yes";"yes";"unknown";1;"jul";128;3;-1;0;"unknown";"no"
35;"self-employed";"single";"tertiary";"no";44;"no";"no";"cellular";7;"sep";310;2;96;2;"other";"no"
57;"blue-collar";"married";"secondary";"no";2887;"no";"no";"cellular";21;"aug";819;10;-1;0;"unknown";"yes"
29;"admin.";"single";"secondary";"no";1992;"no";"no";"cellular";8;"jul";202;1;87;2;"failure";"no"
32;"blue-collar";"single";"secondary";"no";2;"no";"no";"unknown";9;"may";144;2;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";1415;"yes";"no";"cellular";24;"jul";311;1;-1;0;"unknown";"no"
51;"management";"single";"tertiary";"no";297;"yes";"no";"cellular";15;"may";415;4;375;1;"failure";"yes"
31;"technician";"single";"secondary";"no";1094;"yes";"no";"unknown";12;"jun";93;8;-1;0;"unknown";"no"
50;"self-employed";"married";"unknown";"no";-84;"yes";"no";"unknown";6;"may";101;1;-1;0;"unknown";"no"
38;"blue-collar";"single";"secondary";"no";-1;"yes";"no";"unknown";7;"may";72;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";347;"yes";"no";"cellular";22;"jul";282;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";1253;"yes";"no";"unknown";19;"may";210;2;-1;0;"unknown";"no"
37;"unemployed";"married";"unknown";"no";4565;"no";"no";"cellular";4;"sep";369;1;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";781;"no";"no";"cellular";9;"apr";125;6;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";7124;"no";"no";"unknown";16;"jun";301;6;-1;0;"unknown";"no"
43;"management";"married";"secondary";"no";17555;"no";"no";"cellular";26;"aug";14;14;-1;0;"unknown";"no"
43;"management";"married";"unknown";"no";5172;"yes";"no";"unknown";29;"may";57;3;-1;0;"unknown";"no"
56;"management";"married";"tertiary";"no";15520;"no";"no";"cellular";18;"nov";420;1;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";13711;"yes";"no";"cellular";14;"may";638;1;175;3;"success";"no"
58;"management";"divorced";"tertiary";"no";1533;"no";"no";"unknown";15;"may";137;2;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";286;"no";"no";"cellular";22;"aug";557;7;-1;0;"unknown";"no"
31;"blue-collar";"single";"secondary";"no";-219;"yes";"no";"unknown";30;"may";66;2;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";20;"may";206;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";810;"yes";"no";"unknown";14;"may";19;14;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";300;"no";"no";"cellular";6;"feb";166;2;-1;0;"unknown";"no"
37;"services";"single";"secondary";"no";1175;"yes";"no";"cellular";15;"may";102;1;333;2;"failure";"no"
39;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";2;"jun";46;2;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";113;"yes";"no";"unknown";4;"jun";126;1;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";641;"yes";"no";"unknown";13;"may";161;1;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";1477;"yes";"no";"cellular";8;"jul";355;1;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";0;"yes";"no";"unknown";14;"may";40;2;-1;0;"unknown";"no"
54;"services";"married";"primary";"no";8;"yes";"no";"cellular";14;"may";143;1;-1;0;"unknown";"no"
39;"admin.";"divorced";"secondary";"no";399;"yes";"yes";"cellular";14;"aug";133;2;-1;0;"unknown";"no"
56;"admin.";"divorced";"secondary";"yes";-435;"yes";"yes";"unknown";9;"may";185;2;-1;0;"unknown";"no"
35;"admin.";"single";"secondary";"no";2830;"no";"no";"unknown";20;"jun";22;12;-1;0;"unknown";"no"
34;"management";"married";"secondary";"no";15;"no";"no";"cellular";25;"aug";184;7;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";125;"no";"no";"cellular";22;"aug";78;2;-1;0;"unknown";"no"
38;"technician";"married";"tertiary";"no";45;"no";"no";"cellular";22;"jul";125;4;-1;0;"unknown";"no"
41;"technician";"married";"tertiary";"no";13;"no";"no";"unknown";5;"jun";189;2;-1;0;"unknown";"no"
44;"entrepreneur";"married";"tertiary";"no";0;"yes";"no";"unknown";9;"jul";300;1;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";579;"yes";"no";"unknown";28;"may";396;1;-1;0;"unknown";"no"
30;"services";"single";"secondary";"no";432;"yes";"no";"unknown";20;"may";340;3;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";1987;"yes";"no";"telephone";20;"nov";55;2;-1;0;"unknown";"no"
34;"management";"married";"secondary";"no";857;"yes";"no";"cellular";22;"aug";238;6;-1;0;"unknown";"no"
70;"retired";"married";"tertiary";"no";15834;"no";"no";"cellular";5;"apr";172;3;186;1;"failure";"no"
45;"management";"married";"tertiary";"no";3370;"no";"no";"unknown";12;"jun";36;3;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";6290;"no";"no";"cellular";20;"nov";303;2;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";808;"no";"no";"cellular";14;"jul";417;3;-1;0;"unknown";"no"
40;"services";"married";"unknown";"no";4075;"yes";"no";"unknown";16;"jun";162;3;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";373;"yes";"no";"cellular";14;"may";10;9;344;1;"failure";"no"
44;"admin.";"single";"secondary";"no";-101;"yes";"no";"cellular";10;"jul";99;1;-1;0;"unknown";"no"
49;"services";"divorced";"secondary";"no";3250;"no";"no";"unknown";16;"jun";32;3;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";1375;"yes";"no";"cellular";17;"nov";308;3;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";180;"no";"no";"cellular";16;"jun";298;3;-1;0;"unknown";"no"
58;"technician";"married";"secondary";"no";175;"no";"no";"cellular";12;"aug";92;4;-1;0;"unknown";"no"
52;"services";"married";"secondary";"no";381;"no";"yes";"cellular";25;"aug";288;12;-1;0;"unknown";"no"
27;"blue-collar";"married";"secondary";"no";2171;"yes";"no";"cellular";16;"apr";130;1;323;1;"other";"no"
48;"technician";"married";"secondary";"no";340;"yes";"no";"cellular";28;"jan";255;2;-1;0;"unknown";"no"
33;"technician";"divorced";"secondary";"yes";10;"yes";"yes";"cellular";18;"nov";167;5;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";4958;"yes";"no";"cellular";17;"nov";64;3;150;5;"other";"no"
51;"housemaid";"married";"unknown";"no";21244;"no";"yes";"cellular";4;"aug";166;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";156;"no";"no";"cellular";13;"aug";657;7;-1;0;"unknown";"yes"
35;"technician";"divorced";"secondary";"no";473;"yes";"yes";"unknown";26;"may";434;4;-1;0;"unknown";"no"
37;"admin.";"single";"secondary";"no";781;"no";"no";"cellular";20;"aug";75;1;-1;0;"unknown";"no"
60;"entrepreneur";"married";"primary";"no";0;"no";"no";"telephone";23;"jul";40;1;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";1819;"no";"no";"cellular";5;"jun";398;2;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";4374;"yes";"no";"cellular";21;"nov";163;2;113;3;"failure";"no"
34;"entrepreneur";"married";"secondary";"no";456;"yes";"yes";"cellular";28;"aug";137;8;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";253;"yes";"no";"unknown";4;"jun";257;3;-1;0;"unknown";"no"
36;"services";"single";"secondary";"no";774;"yes";"no";"cellular";3;"feb";231;2;259;1;"failure";"no"
32;"admin.";"single";"tertiary";"no";-59;"no";"yes";"unknown";4;"jul";192;1;-1;0;"unknown";"no"
40;"services";"divorced";"secondary";"no";-81;"no";"yes";"cellular";23;"jul";186;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";6;"may";226;1;363;3;"other";"no"
57;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";21;"nov";67;1;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";-3;"no";"no";"cellular";28;"aug";205;2;-1;0;"unknown";"no"
31;"blue-collar";"single";"primary";"no";2488;"yes";"no";"cellular";20;"apr";14;1;-1;0;"unknown";"no"
39;"technician";"divorced";"tertiary";"yes";3;"no";"no";"cellular";6;"may";488;1;-1;0;"unknown";"yes"
35;"management";"married";"secondary";"no";839;"yes";"no";"unknown";23;"may";50;3;-1;0;"unknown";"no"
32;"blue-collar";"married";"primary";"no";103;"no";"no";"cellular";23;"jul";312;1;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";316;"yes";"no";"cellular";21;"jul";163;3;-1;0;"unknown";"no"
41;"technician";"single";"secondary";"no";1680;"yes";"no";"unknown";2;"jun";76;2;-1;0;"unknown";"no"
59;"retired";"divorced";"primary";"no";7179;"no";"no";"cellular";5;"feb";150;1;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";247;"yes";"no";"cellular";17;"apr";244;2;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";8295;"no";"no";"cellular";5;"jun";207;1;-1;0;"unknown";"yes"
27;"services";"single";"secondary";"no";261;"yes";"yes";"cellular";18;"may";113;1;-1;0;"unknown";"no"
51;"self-employed";"married";"secondary";"no";95;"yes";"no";"cellular";23;"jul";1309;4;-1;0;"unknown";"yes"
33;"technician";"single";"secondary";"no";291;"no";"no";"cellular";25;"aug";72;6;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";2240;"yes";"no";"cellular";19;"nov";551;1;183;1;"failure";"no"
52;"entrepreneur";"married";"tertiary";"no";0;"no";"no";"unknown";19;"jun";163;3;-1;0;"unknown";"no"
54;"management";"divorced";"tertiary";"no";1295;"yes";"no";"cellular";21;"jul";179;10;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";324;"yes";"no";"cellular";13;"may";217;2;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";669;"yes";"no";"unknown";2;"jun";88;1;-1;0;"unknown";"no"
39;"blue-collar";"single";"secondary";"no";4143;"yes";"yes";"unknown";2;"jun";21;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";2646;"no";"no";"cellular";14;"apr";142;1;-1;0;"unknown";"yes"
41;"technician";"single";"secondary";"no";618;"no";"no";"cellular";5;"feb";1056;1;-1;0;"unknown";"no"
48;"management";"divorced";"tertiary";"no";541;"yes";"no";"cellular";14;"may";72;1;363;2;"other";"no"
38;"services";"married";"secondary";"no";434;"yes";"no";"unknown";20;"may";411;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";622;"yes";"no";"unknown";21;"may";145;1;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";8;"may";80;2;-1;0;"unknown";"no"
48;"management";"married";"secondary";"no";99;"yes";"no";"unknown";28;"may";344;1;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";290;"yes";"no";"cellular";18;"may";128;1;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";1230;"yes";"no";"cellular";30;"apr";412;1;169;1;"success";"yes"
32;"admin.";"single";"secondary";"no";45;"yes";"yes";"cellular";14;"may";908;3;344;7;"other";"no"
32;"admin.";"married";"secondary";"no";923;"yes";"no";"cellular";17;"apr";819;4;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";950;"no";"no";"cellular";5;"aug";210;2;-1;0;"unknown";"no"
86;"retired";"married";"secondary";"no";1503;"no";"no";"telephone";18;"mar";165;3;101;1;"other";"no"
47;"technician";"married";"secondary";"no";65;"no";"no";"cellular";28;"aug";118;8;-1;0;"unknown";"no"
42;"management";"married";"primary";"no";-679;"yes";"yes";"cellular";6;"may";159;1;343;2;"failure";"no"
44;"management";"married";"tertiary";"no";773;"no";"yes";"cellular";9;"jul";618;1;-1;0;"unknown";"no"
25;"student";"single";"unknown";"no";10788;"no";"no";"cellular";23;"dec";102;2;210;2;"other";"no"
34;"services";"divorced";"secondary";"no";991;"yes";"no";"cellular";6;"may";89;1;344;1;"failure";"no"
34;"admin.";"married";"secondary";"no";247;"no";"no";"cellular";8;"oct";401;2;-1;0;"unknown";"yes"
53;"blue-collar";"married";"primary";"no";4143;"no";"no";"cellular";15;"jul";200;3;-1;0;"unknown";"no"
42;"blue-collar";"married";"unknown";"no";-406;"yes";"no";"cellular";30;"jul";152;5;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";180;"no";"no";"cellular";14;"jul";203;1;183;2;"failure";"no"
26;"blue-collar";"single";"primary";"no";-887;"yes";"no";"cellular";18;"may";827;2;-1;0;"unknown";"yes"
40;"services";"married";"secondary";"no";4157;"yes";"no";"unknown";15;"may";735;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";1386;"yes";"yes";"unknown";28;"may";185;6;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";2211;"no";"no";"unknown";8;"may";149;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";-495;"no";"yes";"cellular";29;"jul";303;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";27359;"yes";"no";"unknown";3;"jun";71;2;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";440;"yes";"no";"cellular";6;"may";445;2;345;2;"failure";"no"
32;"blue-collar";"single";"secondary";"no";135;"yes";"no";"cellular";18;"may";232;2;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"no";-735;"yes";"no";"cellular";30;"jan";144;5;248;2;"failure";"no"
36;"technician";"married";"tertiary";"no";60;"no";"no";"cellular";19;"aug";209;2;-1;0;"unknown";"no"
39;"technician";"divorced";"secondary";"no";902;"yes";"no";"cellular";10;"feb";691;2;180;5;"failure";"yes"
33;"management";"married";"unknown";"no";18347;"yes";"no";"unknown";23;"may";415;1;-1;0;"unknown";"no"
34;"blue-collar";"single";"secondary";"no";-72;"yes";"no";"cellular";7;"jul";273;5;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";1205;"no";"no";"telephone";8;"sep";376;2;-1;0;"unknown";"yes"
46;"technician";"married";"secondary";"no";499;"yes";"no";"cellular";31;"jul";37;10;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";656;"yes";"yes";"cellular";20;"apr";148;2;-1;0;"unknown";"no"
27;"services";"married";"secondary";"no";100;"no";"no";"cellular";16;"jul";57;2;-1;0;"unknown";"no"
35;"technician";"divorced";"secondary";"no";69;"yes";"no";"unknown";8;"may";461;3;-1;0;"unknown";"no"
53;"technician";"married";"unknown";"no";619;"no";"no";"unknown";17;"jun";198;6;-1;0;"unknown";"no"
29;"services";"single";"secondary";"no";192;"no";"no";"cellular";30;"jan";327;4;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";0;"no";"no";"cellular";29;"aug";669;6;-1;0;"unknown";"no"
49;"management";"married";"tertiary";"no";87;"no";"no";"cellular";29;"jan";165;1;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";3228;"no";"no";"cellular";19;"aug";58;2;-1;0;"unknown";"no"
40;"management";"single";"tertiary";"no";1361;"no";"no";"cellular";29;"jan";1473;1;-1;0;"unknown";"yes"
19;"student";"single";"unknown";"no";0;"no";"no";"cellular";11;"feb";123;3;-1;0;"unknown";"no"
30;"student";"single";"tertiary";"no";34;"yes";"no";"unknown";6;"may";289;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";318;"no";"yes";"cellular";25;"jul";239;5;-1;0;"unknown";"no"
60;"retired";"married";"secondary";"no";197;"no";"no";"cellular";7;"oct";1386;1;-1;0;"unknown";"no"
28;"student";"single";"tertiary";"no";0;"no";"no";"cellular";28;"sep";294;2;105;7;"other";"yes"
46;"management";"married";"tertiary";"no";273;"yes";"no";"cellular";18;"mar";910;2;184;4;"success";"yes"
49;"blue-collar";"divorced";"primary";"no";15;"yes";"no";"unknown";27;"may";550;7;-1;0;"unknown";"no"
59;"management";"divorced";"tertiary";"no";7813;"yes";"no";"cellular";21;"nov";75;1;-1;0;"unknown";"no"
44;"technician";"single";"secondary";"no";-27;"yes";"yes";"unknown";14;"may";100;3;-1;0;"unknown";"no"
33;"technician";"married";"tertiary";"no";75;"no";"no";"cellular";21;"jul";204;4;-1;0;"unknown";"no"
54;"retired";"married";"secondary";"no";1139;"yes";"no";"cellular";14;"may";155;1;177;7;"failure";"no"
26;"blue-collar";"married";"secondary";"no";115;"yes";"no";"unknown";4;"jul";46;3;-1;0;"unknown";"no"
54;"blue-collar";"married";"secondary";"no";0;"no";"no";"telephone";16;"apr";1366;1;150;1;"failure";"no"
48;"housemaid";"married";"primary";"no";7195;"no";"no";"cellular";21;"nov";609;2;-1;0;"unknown";"yes"
30;"services";"single";"tertiary";"no";-477;"no";"yes";"cellular";21;"aug";1532;2;-1;0;"unknown";"yes"
29;"management";"single";"tertiary";"no";1026;"no";"no";"cellular";12;"nov";226;1;92;1;"other";"yes"
51;"entrepreneur";"married";"tertiary";"no";3921;"yes";"no";"cellular";5;"may";168;1;-1;0;"unknown";"no"
53;"unemployed";"married";"primary";"no";980;"yes";"no";"cellular";3;"dec";352;1;-1;0;"unknown";"yes"
43;"blue-collar";"married";"secondary";"no";-179;"yes";"no";"cellular";30;"jul";335;6;-1;0;"unknown";"no"
34;"admin.";"married";"tertiary";"no";2552;"yes";"yes";"cellular";25;"sep";74;1;-1;0;"unknown";"no"
40;"entrepreneur";"married";"tertiary";"no";1729;"yes";"no";"cellular";12;"may";85;1;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";434;"yes";"no";"unknown";5;"may";117;1;-1;0;"unknown";"no"
33;"admin.";"single";"secondary";"no";-300;"yes";"yes";"cellular";25;"jul";184;1;-1;0;"unknown";"no"
66;"retired";"married";"secondary";"no";4785;"no";"no";"cellular";13;"aug";72;3;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";-329;"yes";"no";"unknown";7;"may";171;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";0;"yes";"no";"telephone";22;"jul";44;8;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";391;"no";"no";"cellular";13;"aug";336;2;-1;0;"unknown";"no"
71;"retired";"married";"primary";"no";4657;"no";"no";"cellular";16;"feb";93;5;-1;0;"unknown";"yes"
22;"student";"single";"primary";"no";3472;"no";"no";"cellular";20;"oct";332;1;183;3;"failure";"yes"
46;"unemployed";"single";"tertiary";"no";705;"no";"no";"cellular";4;"feb";398;1;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";255;"yes";"yes";"unknown";21;"may";86;2;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";4365;"no";"yes";"cellular";9;"feb";45;2;-1;0;"unknown";"no"
32;"management";"married";"secondary";"no";350;"yes";"no";"unknown";28;"may";110;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";474;"yes";"yes";"telephone";9;"feb";152;3;221;1;"failure";"no"
27;"technician";"single";"secondary";"no";489;"yes";"no";"cellular";30;"jul";356;4;-1;0;"unknown";"yes"
46;"entrepreneur";"married";"secondary";"no";757;"no";"no";"unknown";16;"jun";382;3;-1;0;"unknown";"no"
37;"management";"single";"tertiary";"no";695;"no";"no";"cellular";28;"jan";233;1;-1;0;"unknown";"no"
30;"unemployed";"single";"secondary";"yes";-770;"yes";"no";"unknown";28;"may";87;1;-1;0;"unknown";"no"
44;"self-employed";"divorced";"secondary";"no";80;"yes";"yes";"unknown";14;"may";387;2;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";315;"yes";"no";"cellular";30;"jun";103;1;-1;0;"unknown";"no"
27;"student";"single";"secondary";"no";213;"no";"no";"cellular";27;"mar";378;1;-1;0;"unknown";"yes"
57;"housemaid";"married";"primary";"no";0;"no";"no";"unknown";9;"jun";257;2;-1;0;"unknown";"no"
66;"management";"married";"tertiary";"no";2169;"no";"no";"cellular";7;"jul";282;1;-1;0;"unknown";"no"
40;"admin.";"divorced";"secondary";"no";212;"no";"no";"unknown";7;"jan";176;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";333;"yes";"no";"cellular";14;"may";24;6;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";4404;"yes";"no";"cellular";20;"nov";766;3;-1;0;"unknown";"no"
42;"services";"divorced";"secondary";"no";257;"yes";"no";"cellular";12;"may";955;2;-1;0;"unknown";"yes"
47;"unemployed";"married";"secondary";"no";3188;"yes";"no";"cellular";3;"feb";426;3;-1;0;"unknown";"no"
27;"technician";"single";"tertiary";"no";3083;"yes";"no";"unknown";29;"may";244;2;-1;0;"unknown";"no"
78;"retired";"married";"tertiary";"no";226;"no";"no";"telephone";6;"nov";136;1;-1;0;"unknown";"no"
50;"services";"married";"primary";"no";1260;"no";"no";"cellular";18;"nov";172;1;-1;0;"unknown";"no"
46;"entrepreneur";"single";"tertiary";"no";1410;"yes";"no";"cellular";19;"nov";93;1;189;8;"failure";"no"
41;"admin.";"divorced";"secondary";"no";6046;"yes";"yes";"telephone";15;"mar";300;6;182;2;"success";"yes"
36;"admin.";"single";"tertiary";"no";980;"no";"no";"cellular";11;"nov";118;4;104;7;"failure";"no"
49;"retired";"married";"primary";"no";-37;"no";"no";"unknown";4;"jul";191;2;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";-154;"yes";"yes";"unknown";27;"may";107;2;-1;0;"unknown";"no"
77;"retired";"married";"primary";"no";680;"no";"no";"telephone";24;"feb";513;2;89;7;"failure";"no"
30;"technician";"single";"tertiary";"no";799;"yes";"yes";"unknown";6;"jun";91;18;-1;0;"unknown";"no"
43;"management";"single";"secondary";"yes";-1164;"yes";"yes";"cellular";18;"jul";76;4;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";2523;"yes";"no";"unknown";29;"may";1236;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";98;"yes";"no";"unknown";2;"jun";193;5;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";3419;"yes";"no";"unknown";20;"jun";324;5;-1;0;"unknown";"no"
25;"unemployed";"single";"tertiary";"no";343;"no";"no";"cellular";1;"jun";377;1;-1;0;"unknown";"yes"
49;"management";"married";"tertiary";"no";852;"yes";"no";"cellular";20;"nov";408;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";50;"yes";"no";"cellular";7;"may";246;2;-1;0;"unknown";"no"
40;"management";"single";"tertiary";"no";0;"no";"no";"cellular";26;"aug";58;7;-1;0;"unknown";"no"
44;"blue-collar";"married";"primary";"no";10;"yes";"no";"cellular";17;"jul";196;1;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";950;"yes";"yes";"cellular";20;"apr";219;4;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";9;"jul";197;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";175;"yes";"no";"cellular";7;"aug";257;2;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";-259;"yes";"no";"cellular";29;"jan";381;1;247;2;"failure";"no"
32;"management";"married";"secondary";"no";6217;"yes";"yes";"unknown";21;"may";486;2;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";-1310;"yes";"yes";"unknown";28;"may";162;1;-1;0;"unknown";"no"
59;"blue-collar";"married";"unknown";"no";139;"no";"no";"unknown";5;"jun";153;1;-1;0;"unknown";"no"
44;"technician";"single";"secondary";"no";7138;"no";"no";"cellular";8;"aug";809;2;-1;0;"unknown";"yes"
50;"admin.";"divorced";"secondary";"no";1111;"yes";"no";"cellular";17;"apr";50;1;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";113;"no";"no";"cellular";22;"jul";39;17;-1;0;"unknown";"no"
55;"management";"divorced";"tertiary";"no";-612;"no";"no";"cellular";26;"aug";131;2;-1;0;"unknown";"no"
54;"blue-collar";"married";"unknown";"no";310;"yes";"no";"telephone";28;"jan";526;4;258;3;"other";"no"
31;"unemployed";"single";"secondary";"no";-186;"yes";"no";"unknown";26;"may";172;3;-1;0;"unknown";"no"
56;"admin.";"married";"unknown";"no";2038;"no";"no";"cellular";8;"feb";405;1;305;1;"success";"no"
51;"self-employed";"married";"tertiary";"no";833;"no";"no";"cellular";31;"jul";77;3;-1;0;"unknown";"no"
61;"blue-collar";"married";"primary";"no";625;"yes";"no";"unknown";19;"may";158;2;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";105;"yes";"no";"cellular";7;"may";146;2;171;3;"failure";"no"
56;"blue-collar";"married";"primary";"no";551;"no";"no";"unknown";29;"may";27;1;-1;0;"unknown";"no"
29;"student";"single";"unknown";"no";145;"no";"no";"cellular";14;"aug";105;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";618;"yes";"no";"unknown";3;"jun";119;4;-1;0;"unknown";"no"
35;"services";"single";"secondary";"no";1063;"yes";"yes";"cellular";16;"apr";327;1;-1;0;"unknown";"no"
56;"admin.";"divorced";"secondary";"no";985;"no";"yes";"cellular";18;"aug";150;2;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";2926;"yes";"yes";"cellular";19;"nov";482;3;120;2;"failure";"no"
31;"admin.";"single";"secondary";"no";-449;"yes";"no";"unknown";28;"may";81;2;-1;0;"unknown";"no"
37;"entrepreneur";"divorced";"tertiary";"no";66;"no";"no";"cellular";18;"nov";1164;2;-1;0;"unknown";"yes"
43;"admin.";"married";"secondary";"no";0;"yes";"no";"cellular";17;"jul";674;3;-1;0;"unknown";"no"
40;"management";"married";"unknown";"no";419;"yes";"no";"cellular";10;"jul";347;1;-1;0;"unknown";"no"
44;"management";"married";"primary";"no";4758;"yes";"no";"cellular";22;"jul";712;6;-1;0;"unknown";"yes"
38;"admin.";"single";"secondary";"no";-23;"no";"no";"cellular";20;"nov";48;2;-1;0;"unknown";"no"
58;"management";"divorced";"tertiary";"no";1573;"yes";"yes";"cellular";17;"nov";279;1;-1;0;"unknown";"no"
24;"blue-collar";"single";"secondary";"no";77;"yes";"no";"unknown";16;"may";236;2;-1;0;"unknown";"no"
53;"services";"married";"secondary";"no";1378;"yes";"no";"cellular";18;"jul";91;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";-73;"yes";"yes";"cellular";18;"may";15;8;-1;0;"unknown";"no"
28;"blue-collar";"married";"primary";"no";60;"yes";"no";"cellular";6;"may";161;1;337;1;"failure";"no"
45;"blue-collar";"married";"primary";"no";137;"yes";"yes";"unknown";16;"may";187;19;-1;0;"unknown";"no"
32;"admin.";"single";"tertiary";"no";1583;"yes";"no";"cellular";19;"nov";100;1;182;1;"failure";"no"
46;"admin.";"married";"secondary";"no";-121;"yes";"yes";"cellular";8;"jul";214;4;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";1065;"yes";"yes";"cellular";17;"apr";406;1;317;1;"failure";"no"
51;"unemployed";"married";"secondary";"no";-13;"yes";"no";"cellular";13;"may";361;1;364;1;"failure";"no"
51;"blue-collar";"married";"primary";"no";1869;"no";"no";"unknown";17;"jun";30;3;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";4787;"yes";"no";"unknown";4;"jun";99;2;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"yes";-183;"yes";"no";"unknown";14;"may";202;2;-1;0;"unknown";"no"
28;"blue-collar";"married";"primary";"no";702;"yes";"no";"unknown";26;"jun";318;2;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";1494;"no";"no";"cellular";13;"aug";275;12;-1;0;"unknown";"no"
31;"blue-collar";"married";"primary";"no";0;"yes";"yes";"unknown";12;"may";59;4;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";46;"no";"no";"cellular";29;"aug";70;4;-1;0;"unknown";"no"
31;"blue-collar";"single";"secondary";"no";609;"yes";"no";"cellular";14;"apr";1126;1;335;1;"failure";"no"
74;"retired";"divorced";"secondary";"no";4079;"no";"no";"cellular";19;"oct";343;3;-1;0;"unknown";"yes"
40;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";23;"feb";228;2;182;6;"failure";"yes"
43;"technician";"married";"secondary";"no";7876;"yes";"no";"cellular";11;"aug";209;2;-1;0;"unknown";"no"
34;"technician";"single";"tertiary";"no";925;"yes";"no";"cellular";13;"apr";709;1;-1;0;"unknown";"yes"
54;"management";"divorced";"tertiary";"no";7010;"no";"no";"cellular";11;"mar";275;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";42;"yes";"no";"cellular";12;"may";51;2;342;3;"failure";"no"
33;"blue-collar";"married";"secondary";"no";285;"no";"yes";"unknown";3;"jul";267;1;-1;0;"unknown";"no"
41;"technician";"married";"tertiary";"no";0;"no";"no";"unknown";20;"jun";95;1;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";3170;"no";"no";"cellular";28;"oct";246;2;-1;0;"unknown";"no"
48;"admin.";"married";"secondary";"no";8;"no";"yes";"cellular";7;"jul";46;1;-1;0;"unknown";"no"
42;"self-employed";"divorced";"tertiary";"no";216;"yes";"no";"cellular";8;"may";144;3;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";2975;"no";"no";"cellular";8;"aug";249;2;-1;0;"unknown";"no"
25;"admin.";"married";"secondary";"no";894;"yes";"no";"unknown";28;"may";328;1;-1;0;"unknown";"no"
38;"blue-collar";"single";"primary";"no";490;"yes";"yes";"unknown";25;"aug";17;1;-1;0;"unknown";"no"
45;"entrepreneur";"married";"secondary";"no";245;"yes";"no";"unknown";14;"may";126;3;-1;0;"unknown";"no"
59;"management";"divorced";"tertiary";"no";0;"yes";"no";"cellular";31;"jul";52;8;-1;0;"unknown";"no"
47;"management";"married";"unknown";"no";150;"no";"no";"unknown";9;"jun";52;1;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";4;"yes";"no";"cellular";16;"jul";285;5;-1;0;"unknown";"no"
59;"management";"married";"primary";"no";1727;"no";"no";"telephone";25;"may";500;4;358;2;"failure";"yes"
41;"blue-collar";"divorced";"primary";"no";664;"no";"yes";"cellular";15;"jul";955;5;-1;0;"unknown";"yes"
42;"technician";"married";"secondary";"no";473;"yes";"no";"cellular";7;"jul";216;5;-1;0;"unknown";"no"
51;"technician";"divorced";"tertiary";"no";0;"no";"no";"cellular";28;"aug";84;7;-1;0;"unknown";"no"
45;"management";"married";"tertiary";"no";-192;"yes";"no";"cellular";6;"may";138;3;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";1942;"yes";"yes";"cellular";21;"nov";436;2;178;3;"other";"yes"
33;"blue-collar";"single";"secondary";"no";5137;"yes";"no";"cellular";6;"may";123;1;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";13044;"no";"no";"cellular";18;"aug";36;4;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";1053;"no";"no";"unknown";16;"jun";405;3;-1;0;"unknown";"no"
73;"retired";"married";"primary";"no";154;"no";"no";"cellular";15;"oct";103;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";625;"yes";"no";"unknown";30;"sep";175;1;-1;0;"unknown";"no"
52;"unemployed";"married";"primary";"no";255;"no";"yes";"cellular";10;"jul";374;3;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";529;"yes";"no";"unknown";4;"jun";129;4;-1;0;"unknown";"no"
56;"management";"married";"tertiary";"no";318;"no";"yes";"telephone";9;"jul";246;3;-1;0;"unknown";"no"
21;"services";"single";"secondary";"no";1903;"yes";"no";"unknown";29;"may";107;2;-1;0;"unknown";"no"
43;"unknown";"married";"tertiary";"no";1937;"no";"no";"unknown";19;"jun";385;1;-1;0;"unknown";"no"
54;"management";"married";"primary";"no";3859;"no";"yes";"cellular";20;"nov";104;3;-1;0;"unknown";"no"
59;"technician";"married";"primary";"no";4198;"no";"no";"cellular";14;"jan";309;1;91;10;"success";"yes"
52;"blue-collar";"married";"primary";"no";230;"yes";"no";"cellular";6;"may";198;2;170;2;"failure";"no"
31;"admin.";"single";"secondary";"no";747;"yes";"no";"unknown";26;"may";543;2;-1;0;"unknown";"yes"
53;"admin.";"married";"secondary";"no";263;"no";"yes";"cellular";18;"jul";143;7;-1;0;"unknown";"no"
24;"technician";"single";"secondary";"no";-46;"yes";"no";"unknown";19;"may";23;1;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"no";0;"no";"yes";"unknown";30;"jun";104;2;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";568;"yes";"no";"cellular";2;"sep";378;2;386;1;"success";"yes"
57;"technician";"married";"secondary";"no";1469;"yes";"no";"cellular";17;"nov";364;1;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";18;"no";"yes";"cellular";25;"jul";363;6;-1;0;"unknown";"no"
50;"management";"divorced";"tertiary";"no";201;"yes";"no";"cellular";24;"jul";248;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";1412;"yes";"yes";"unknown";27;"may";157;2;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";-93;"yes";"no";"unknown";26;"may";402;2;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";-331;"yes";"no";"unknown";23;"may";203;5;-1;0;"unknown";"no"
29;"blue-collar";"married";"unknown";"no";486;"yes";"no";"cellular";6;"may";422;1;363;1;"failure";"no"
39;"blue-collar";"married";"primary";"no";694;"yes";"no";"cellular";21;"nov";44;1;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";2806;"yes";"no";"unknown";30;"may";358;4;-1;0;"unknown";"no"
38;"unemployed";"single";"tertiary";"no";0;"no";"no";"cellular";6;"feb";39;2;-1;0;"unknown";"no"
56;"management";"divorced";"tertiary";"no";2959;"yes";"no";"telephone";18;"may";325;1;-1;0;"unknown";"no"
41;"housemaid";"divorced";"primary";"no";22;"yes";"no";"cellular";18;"nov";238;3;126;4;"other";"no"
47;"technician";"married";"secondary";"no";1167;"yes";"no";"cellular";30;"apr";968;1;87;5;"failure";"yes"
38;"management";"single";"tertiary";"no";35;"yes";"no";"cellular";17;"apr";156;1;273;3;"failure";"no"
80;"retired";"married";"secondary";"no";8304;"no";"no";"telephone";6;"apr";681;1;118;11;"success";"no"
37;"services";"married";"secondary";"no";4760;"yes";"no";"cellular";20;"nov";187;2;-1;0;"unknown";"no"
44;"management";"single";"tertiary";"no";-1313;"yes";"yes";"cellular";15;"may";104;10;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";697;"no";"no";"cellular";6;"feb";860;2;-1;0;"unknown";"yes"
53;"technician";"married";"secondary";"no";230;"no";"yes";"cellular";13;"aug";112;2;-1;0;"unknown";"no"
69;"retired";"married";"tertiary";"no";473;"no";"no";"cellular";7;"jul";112;1;78;1;"failure";"no"
46;"technician";"married";"secondary";"no";1427;"yes";"no";"unknown";14;"may";32;3;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";201;"yes";"no";"cellular";17;"mar";325;2;-1;0;"unknown";"yes"
31;"self-employed";"single";"secondary";"no";1086;"no";"no";"cellular";20;"nov";410;1;-1;0;"unknown";"no"
38;"management";"divorced";"tertiary";"no";4335;"yes";"no";"cellular";20;"nov";640;1;-1;0;"unknown";"no"
34;"services";"divorced";"secondary";"no";44;"no";"no";"unknown";9;"jun";118;1;-1;0;"unknown";"no"
48;"unknown";"married";"unknown";"no";286;"no";"no";"cellular";30;"jul";166;2;-1;0;"unknown";"no"
46;"blue-collar";"divorced";"primary";"no";216;"no";"no";"cellular";2;"feb";173;1;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";214;"yes";"no";"unknown";28;"may";21;24;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";343;"yes";"no";"unknown";14;"may";89;7;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";-77;"yes";"no";"cellular";28;"jul";151;3;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";1046;"no";"no";"unknown";19;"jun";112;4;-1;0;"unknown";"no"
27;"student";"single";"secondary";"no";1566;"no";"no";"cellular";30;"jan";164;5;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";592;"no";"no";"telephone";29;"jan";145;2;164;1;"other";"no"
38;"management";"single";"tertiary";"no";104;"yes";"no";"cellular";6;"may";606;3;-1;0;"unknown";"yes"
48;"blue-collar";"married";"secondary";"no";0;"yes";"no";"unknown";26;"may";105;1;-1;0;"unknown";"no"
54;"services";"married";"secondary";"no";-22;"no";"yes";"cellular";9;"jul";85;1;-1;0;"unknown";"no"
51;"services";"married";"secondary";"no";0;"no";"no";"cellular";18;"jul";439;3;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";72;"no";"no";"unknown";29;"may";278;1;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";100;"no";"no";"cellular";13;"aug";205;4;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";319;"yes";"no";"cellular";16;"jul";221;1;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";9339;"yes";"no";"telephone";18;"nov";52;3;172;3;"other";"no"
52;"management";"divorced";"tertiary";"no";50;"yes";"no";"unknown";12;"jun";84;3;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"yes";32;"yes";"no";"cellular";15;"may";476;3;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";6922;"no";"no";"cellular";20;"nov";371;1;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";633;"yes";"no";"cellular";17;"apr";128;3;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";4265;"yes";"no";"unknown";13;"may";220;2;-1;0;"unknown";"no"
46;"entrepreneur";"married";"tertiary";"no";2262;"no";"no";"telephone";14;"jul";44;14;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";1970;"yes";"no";"unknown";8;"jun";30;1;404;2;"success";"no"
38;"self-employed";"divorced";"tertiary";"no";1513;"no";"no";"cellular";7;"may";330;1;342;1;"failure";"no"
34;"entrepreneur";"married";"secondary";"no";4659;"yes";"no";"unknown";30;"aug";28;1;-1;0;"unknown";"no"
47;"entrepreneur";"married";"primary";"no";668;"no";"no";"cellular";12;"may";908;1;-1;0;"unknown";"no"
35;"management";"divorced";"tertiary";"no";649;"no";"no";"cellular";16;"apr";177;1;-1;0;"unknown";"no"
46;"blue-collar";"divorced";"primary";"no";452;"yes";"no";"unknown";9;"may";113;4;-1;0;"unknown";"no"
27;"admin.";"single";"secondary";"no";-145;"yes";"no";"telephone";15;"may";289;8;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";-498;"yes";"no";"cellular";21;"jul";357;1;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";0;"no";"no";"cellular";28;"aug";38;16;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";22;"yes";"no";"cellular";7;"aug";414;4;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";2304;"yes";"no";"cellular";20;"nov";257;2;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";1412;"yes";"no";"unknown";19;"aug";480;1;-1;0;"unknown";"yes"
42;"housemaid";"married";"primary";"no";145;"no";"no";"cellular";20;"aug";252;2;-1;0;"unknown";"no"
33;"management";"single";"primary";"no";4897;"yes";"no";"unknown";20;"may";151;1;-1;0;"unknown";"no"
40;"housemaid";"married";"primary";"no";1832;"no";"no";"cellular";6;"feb";213;4;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";3726;"no";"yes";"cellular";27;"aug";119;12;-1;0;"unknown";"no"
31;"unemployed";"married";"tertiary";"no";591;"no";"no";"cellular";21;"nov";161;2;-1;0;"unknown";"no"
62;"retired";"divorced";"tertiary";"no";0;"no";"no";"cellular";13;"nov";227;2;-1;0;"unknown";"yes"
24;"student";"single";"secondary";"no";508;"yes";"no";"telephone";18;"may";12;3;374;1;"other";"no"
48;"unemployed";"married";"secondary";"no";817;"no";"no";"cellular";14;"jan";517;1;282;4;"success";"yes"
41;"technician";"divorced";"secondary";"no";348;"yes";"no";"cellular";7;"may";225;2;275;2;"failure";"no"
43;"management";"married";"tertiary";"no";291;"yes";"no";"unknown";9;"may";150;2;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";3554;"no";"yes";"cellular";8;"jul";89;1;-1;0;"unknown";"no"
54;"blue-collar";"married";"primary";"no";781;"yes";"no";"unknown";9;"may";103;2;-1;0;"unknown";"no"
32;"technician";"divorced";"tertiary";"no";267;"no";"no";"cellular";26;"may";128;1;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";1327;"yes";"no";"cellular";20;"nov";277;1;-1;0;"unknown";"no"
41;"admin.";"married";"primary";"no";-306;"yes";"no";"unknown";15;"may";500;1;-1;0;"unknown";"yes"
31;"blue-collar";"single";"primary";"no";864;"yes";"no";"telephone";14;"may";113;2;-1;0;"unknown";"no"
35;"housemaid";"married";"tertiary";"no";1265;"yes";"no";"cellular";15;"may";326;5;179;4;"failure";"yes"
26;"admin.";"married";"secondary";"no";0;"no";"no";"unknown";27;"may";755;1;-1;0;"unknown";"no"
52;"technician";"married";"tertiary";"no";214;"no";"no";"cellular";29;"aug";161;3;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";425;"yes";"yes";"cellular";17;"nov";100;1;-1;0;"unknown";"no"
57;"housemaid";"married";"tertiary";"no";3;"no";"no";"cellular";4;"aug";142;2;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";8;"may";214;2;-1;0;"unknown";"no"
59;"blue-collar";"married";"primary";"no";0;"no";"no";"cellular";18;"nov";167;2;-1;0;"unknown";"no"
27;"technician";"single";"secondary";"no";282;"no";"yes";"cellular";9;"jul";676;5;-1;0;"unknown";"no"
42;"entrepreneur";"married";"tertiary";"no";0;"no";"no";"cellular";9;"jul";236;1;-1;0;"unknown";"no"
47;"housemaid";"single";"tertiary";"no";3727;"no";"no";"unknown";12;"jun";993;6;-1;0;"unknown";"yes"
33;"blue-collar";"married";"secondary";"no";0;"yes";"yes";"cellular";28;"jul";83;12;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";864;"yes";"no";"cellular";6;"may";100;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";4089;"yes";"no";"cellular";2;"feb";289;2;174;4;"other";"yes"
29;"blue-collar";"married";"secondary";"no";-478;"no";"yes";"cellular";8;"jul";528;2;-1;0;"unknown";"no"
38;"unemployed";"married";"primary";"no";-363;"no";"no";"unknown";12;"jun";340;2;-1;0;"unknown";"no"
35;"self-employed";"married";"secondary";"no";8883;"no";"no";"cellular";21;"nov";33;3;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";1970;"no";"no";"telephone";17;"apr";284;3;-1;0;"unknown";"no"
62;"management";"married";"tertiary";"no";1859;"no";"no";"telephone";6;"aug";245;3;-1;0;"unknown";"yes"
32;"technician";"single";"tertiary";"no";1059;"yes";"no";"cellular";17;"apr";115;1;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";388;"yes";"no";"unknown";5;"may";730;2;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"primary";"no";602;"yes";"no";"cellular";11;"may";347;1;284;2;"failure";"no"
29;"services";"single";"secondary";"no";0;"no";"no";"cellular";17;"jul";58;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";255;"yes";"no";"cellular";17;"jul";180;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";63;"yes";"no";"unknown";2;"jun";56;8;-1;0;"unknown";"no"
34;"self-employed";"married";"primary";"no";712;"yes";"no";"unknown";2;"jun";326;3;-1;0;"unknown";"no"
29;"services";"married";"secondary";"no";57;"yes";"no";"telephone";21;"jul";342;9;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"no";523;"no";"yes";"cellular";2;"feb";49;6;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";422;"no";"no";"cellular";12;"aug";110;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";386;"no";"no";"cellular";12;"nov";253;1;92;4;"failure";"yes"
60;"management";"married";"tertiary";"no";3387;"no";"no";"cellular";14;"aug";636;2;-1;0;"unknown";"no"
51;"technician";"married";"primary";"no";260;"no";"no";"cellular";19;"aug";137;2;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";-1;"no";"no";"cellular";7;"jul";173;1;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";1490;"yes";"no";"cellular";18;"jul";227;2;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";497;"yes";"yes";"unknown";19;"may";88;5;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";1707;"yes";"no";"unknown";2;"jun";426;3;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";826;"yes";"no";"telephone";18;"nov";195;3;112;8;"other";"no"
45;"blue-collar";"married";"primary";"no";907;"no";"no";"cellular";18;"aug";230;8;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";1836;"yes";"no";"cellular";12;"may";750;2;-1;0;"unknown";"yes"
29;"entrepreneur";"single";"secondary";"no";404;"no";"no";"cellular";30;"jan";413;3;-1;0;"unknown";"no"
52;"management";"divorced";"tertiary";"no";907;"yes";"no";"unknown";4;"jun";104;2;-1;0;"unknown";"no"
26;"technician";"married";"secondary";"no";18;"yes";"yes";"cellular";5;"may";138;1;-1;0;"unknown";"no"
31;"blue-collar";"single";"primary";"no";-495;"yes";"no";"unknown";29;"may";334;2;-1;0;"unknown";"no"
55;"services";"married";"unknown";"no";1210;"yes";"no";"unknown";30;"may";868;1;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";819;"yes";"no";"telephone";14;"jul";102;14;-1;0;"unknown";"no"
56;"management";"divorced";"tertiary";"no";442;"no";"no";"unknown";28;"may";77;3;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";-46;"yes";"no";"cellular";1;"sep";199;1;92;14;"success";"yes"
27;"student";"single";"secondary";"no";671;"yes";"no";"unknown";7;"may";332;2;-1;0;"unknown";"no"
30;"technician";"married";"secondary";"no";371;"no";"no";"cellular";7;"aug";124;2;-1;0;"unknown";"no"
51;"technician";"married";"tertiary";"no";-3;"no";"no";"cellular";25;"aug";91;9;-1;0;"unknown";"no"
33;"management";"married";"secondary";"no";11;"yes";"no";"cellular";4;"may";104;3;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";33;"yes";"no";"cellular";18;"may";125;1;368;4;"failure";"no"
43;"entrepreneur";"married";"secondary";"no";104;"yes";"no";"cellular";18;"jul";635;3;-1;0;"unknown";"yes"
46;"blue-collar";"married";"primary";"no";174;"no";"yes";"cellular";15;"jul";90;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";3107;"yes";"yes";"cellular";25;"jul";310;1;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";1438;"yes";"no";"cellular";6;"may";207;1;351;2;"failure";"no"
39;"blue-collar";"married";"secondary";"no";1367;"no";"no";"unknown";12;"jun";43;1;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";-11;"no";"no";"telephone";16;"jul";126;4;-1;0;"unknown";"no"
46;"admin.";"single";"secondary";"no";1693;"yes";"no";"cellular";25;"jan";137;1;97;1;"success";"no"
43;"blue-collar";"married";"primary";"no";778;"yes";"yes";"unknown";15;"may";284;5;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";837;"no";"no";"cellular";28;"aug";68;6;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";363;"no";"no";"cellular";18;"nov";67;1;-1;0;"unknown";"no"
26;"admin.";"single";"secondary";"no";236;"yes";"yes";"cellular";18;"may";8;2;-1;0;"unknown";"no"
39;"admin.";"single";"unknown";"no";0;"yes";"no";"cellular";11;"may";62;1;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";-50;"no";"yes";"cellular";7;"jul";175;1;-1;0;"unknown";"no"
23;"entrepreneur";"single";"primary";"no";4;"yes";"no";"unknown";13;"may";395;2;-1;0;"unknown";"yes"
54;"management";"divorced";"tertiary";"no";26306;"yes";"no";"cellular";11;"feb";27;1;84;3;"failure";"no"
38;"admin.";"married";"secondary";"no";46;"yes";"yes";"unknown";6;"may";195;1;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";575;"no";"yes";"cellular";4;"feb";254;2;-1;0;"unknown";"no"
35;"admin.";"divorced";"secondary";"no";148;"no";"yes";"cellular";8;"jul";199;1;-1;0;"unknown";"no"
52;"services";"married";"secondary";"no";431;"no";"no";"cellular";26;"aug";73;17;-1;0;"unknown";"no"
25;"technician";"single";"tertiary";"no";777;"yes";"no";"unknown";14;"may";351;1;-1;0;"unknown";"no"
29;"management";"married";"tertiary";"no";199;"yes";"yes";"unknown";7;"may";1689;4;-1;0;"unknown";"yes"
35;"technician";"single";"tertiary";"no";3552;"no";"no";"cellular";21;"nov";267;1;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";47;"no";"no";"unknown";5;"may";234;2;-1;0;"unknown";"no"
41;"admin.";"divorced";"secondary";"no";3823;"no";"no";"cellular";26;"apr";234;10;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";138;"no";"no";"cellular";20;"aug";94;4;-1;0;"unknown";"no"
29;"admin.";"single";"secondary";"no";1117;"yes";"no";"unknown";8;"may";77;2;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";1325;"yes";"no";"cellular";15;"may";242;1;-1;0;"unknown";"no"
59;"retired";"married";"secondary";"no";0;"no";"no";"cellular";18;"jul";247;4;-1;0;"unknown";"no"
34;"entrepreneur";"married";"tertiary";"no";4343;"no";"no";"unknown";9;"jun";85;4;-1;0;"unknown";"no"
55;"management";"divorced";"tertiary";"no";1598;"no";"no";"cellular";22;"aug";607;4;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";3301;"yes";"no";"cellular";17;"jun";485;2;227;4;"failure";"yes"
24;"student";"single";"secondary";"no";822;"no";"no";"cellular";27;"oct";184;1;91;2;"failure";"yes"
42;"technician";"divorced";"secondary";"no";-280;"yes";"no";"unknown";26;"may";258;1;-1;0;"unknown";"no"
34;"management";"single";"unknown";"no";1534;"yes";"no";"cellular";16;"apr";156;1;147;7;"other";"no"
34;"admin.";"married";"secondary";"no";1671;"no";"no";"unknown";21;"may";160;1;-1;0;"unknown";"no"
51;"self-employed";"married";"tertiary";"no";2007;"no";"no";"unknown";16;"jun";1021;1;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";31;"jul";5;25;-1;0;"unknown";"no"
44;"technician";"married";"secondary";"no";2776;"yes";"no";"cellular";27;"jan";701;1;-1;0;"unknown";"yes"
33;"blue-collar";"married";"secondary";"no";139;"yes";"yes";"cellular";18;"may";732;2;291;5;"failure";"yes"
34;"management";"married";"tertiary";"no";573;"no";"no";"cellular";10;"aug";122;1;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";475;"yes";"no";"unknown";7;"may";559;1;-1;0;"unknown";"no"
42;"blue-collar";"divorced";"secondary";"no";47;"yes";"yes";"unknown";8;"may";141;1;-1;0;"unknown";"no"
46;"management";"single";"tertiary";"no";2154;"yes";"no";"cellular";21;"jul";87;4;-1;0;"unknown";"no"
31;"self-employed";"single";"tertiary";"no";96;"no";"no";"cellular";5;"feb";577;1;-1;0;"unknown";"no"
39;"entrepreneur";"married";"tertiary";"no";83;"yes";"no";"cellular";11;"jul";193;2;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";6766;"no";"no";"cellular";28;"aug";210;5;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";3973;"no";"no";"cellular";6;"aug";732;2;-1;0;"unknown";"no"
42;"services";"divorced";"secondary";"no";372;"yes";"yes";"unknown";6;"may";121;2;-1;0;"unknown";"no"
48;"blue-collar";"single";"secondary";"no";-552;"no";"yes";"cellular";21;"nov";150;2;-1;0;"unknown";"no"
30;"self-employed";"single";"tertiary";"no";48;"no";"no";"cellular";21;"nov";140;1;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";7696;"yes";"no";"cellular";20;"nov";386;2;-1;0;"unknown";"no"
24;"services";"married";"secondary";"no";4500;"yes";"no";"cellular";11;"may";733;2;-1;0;"unknown";"yes"
34;"technician";"married";"secondary";"no";1;"yes";"no";"cellular";19;"nov";108;1;-1;0;"unknown";"no"
46;"management";"single";"tertiary";"no";46;"no";"yes";"cellular";17;"nov";80;1;152;1;"failure";"no"
32;"admin.";"divorced";"secondary";"yes";-18;"yes";"no";"unknown";12;"may";155;3;-1;0;"unknown";"no"
46;"services";"married";"secondary";"no";451;"yes";"yes";"cellular";13;"may";389;4;-1;0;"unknown";"no"
26;"self-employed";"married";"tertiary";"no";479;"yes";"no";"cellular";9;"feb";133;3;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";21;"nov";328;6;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";1130;"yes";"no";"cellular";23;"jul";119;2;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";389;"yes";"no";"unknown";16;"jun";87;6;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";10;"no";"yes";"cellular";18;"nov";162;1;173;3;"failure";"no"
32;"services";"married";"secondary";"no";401;"yes";"no";"unknown";15;"may";294;2;-1;0;"unknown";"no"
58;"housemaid";"single";"tertiary";"no";145;"yes";"yes";"cellular";16;"jul";128;2;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";115;"yes";"no";"cellular";6;"may";108;1;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";89;"yes";"yes";"cellular";6;"feb";172;2;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";94;"yes";"yes";"unknown";14;"may";208;5;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";1438;"yes";"no";"unknown";20;"may";212;2;-1;0;"unknown";"no"
49;"unemployed";"divorced";"tertiary";"no";780;"no";"no";"cellular";8;"nov";148;1;871;2;"failure";"no"
50;"management";"married";"tertiary";"no";207;"yes";"no";"cellular";4;"feb";99;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";238;"no";"no";"cellular";3;"feb";185;1;92;1;"success";"yes"
45;"blue-collar";"married";"primary";"no";2433;"no";"no";"cellular";6;"aug";133;2;-1;0;"unknown";"no"
30;"housemaid";"single";"secondary";"no";1924;"no";"no";"cellular";6;"feb";170;1;-1;0;"unknown";"no"
48;"unemployed";"married";"tertiary";"no";-471;"yes";"no";"unknown";19;"may";668;7;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";1531;"yes";"no";"cellular";8;"may";81;3;170;1;"failure";"no"
41;"management";"single";"secondary";"no";764;"no";"no";"cellular";12;"jun";230;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"secondary";"no";383;"no";"no";"unknown";5;"may";287;1;-1;0;"unknown";"no"
70;"retired";"married";"primary";"no";3782;"no";"no";"cellular";16;"mar";618;1;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";326;"yes";"no";"cellular";5;"may";788;1;-1;0;"unknown";"yes"
47;"services";"divorced";"secondary";"no";691;"no";"no";"cellular";6;"feb";285;6;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";0;"no";"no";"cellular";21;"aug";163;2;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";99;"no";"no";"cellular";8;"jul";159;1;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";502;"yes";"no";"cellular";12;"may";197;2;370;3;"failure";"no"
51;"management";"married";"tertiary";"no";-15;"no";"no";"cellular";25;"jul";212;2;-1;0;"unknown";"no"
45;"management";"married";"tertiary";"no";1929;"yes";"no";"unknown";29;"may";44;15;-1;0;"unknown";"no"
55;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";23;"sep";180;2;238;3;"success";"no"
30;"blue-collar";"single";"primary";"no";185;"no";"no";"unknown";26;"jun";11;1;-1;0;"unknown";"no"
33;"management";"single";"secondary";"no";-424;"yes";"no";"unknown";26;"may";863;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";0;"yes";"no";"unknown";7;"may";388;7;-1;0;"unknown";"no"
59;"retired";"married";"secondary";"no";319;"yes";"no";"unknown";5;"may";143;3;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";171;"yes";"no";"cellular";7;"may";191;1;294;1;"failure";"no"
47;"technician";"divorced";"tertiary";"no";-1148;"yes";"yes";"unknown";27;"may";62;2;-1;0;"unknown";"no"
44;"blue-collar";"married";"primary";"no";-11;"yes";"no";"unknown";9;"may";13;1;-1;0;"unknown";"no"
35;"admin.";"single";"secondary";"no";0;"yes";"no";"cellular";13;"may";1073;1;-1;0;"unknown";"yes"
45;"management";"married";"tertiary";"no";-206;"yes";"no";"cellular";6;"may";337;2;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";704;"yes";"yes";"telephone";31;"jul";88;10;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";1931;"no";"no";"cellular";2;"jun";167;1;298;10;"other";"no"
34;"technician";"single";"secondary";"no";943;"no";"no";"cellular";29;"aug";96;4;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";327;"yes";"no";"cellular";13;"apr";537;3;-1;0;"unknown";"no"
29;"management";"single";"secondary";"no";-105;"yes";"no";"unknown";27;"may";5;3;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";983;"no";"no";"cellular";5;"aug";139;1;-1;0;"unknown";"no"
44;"blue-collar";"single";"primary";"no";787;"yes";"no";"unknown";30;"may";570;2;-1;0;"unknown";"no"
45;"entrepreneur";"married";"tertiary";"no";0;"yes";"no";"cellular";29;"jul";100;3;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";3060;"yes";"no";"unknown";15;"may";253;2;-1;0;"unknown";"no"
44;"services";"married";"secondary";"no";5780;"yes";"no";"cellular";1;"apr";525;2;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";790;"no";"no";"cellular";20;"aug";696;3;-1;0;"unknown";"no"
38;"unemployed";"married";"tertiary";"no";1449;"no";"no";"unknown";26;"may";535;6;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";1412;"no";"no";"cellular";6;"aug";131;3;-1;0;"unknown";"no"
21;"student";"single";"secondary";"no";681;"no";"no";"unknown";20;"aug";6;1;-1;0;"unknown";"no"
38;"entrepreneur";"married";"tertiary";"no";228;"yes";"no";"cellular";5;"feb";105;1;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";0;"no";"no";"unknown";19;"jun";370;1;-1;0;"unknown";"no"
45;"technician";"single";"tertiary";"no";2087;"no";"no";"cellular";29;"aug";29;14;-1;0;"unknown";"no"
34;"entrepreneur";"married";"secondary";"no";2;"yes";"yes";"cellular";5;"may";184;1;167;6;"failure";"no"
48;"technician";"married";"secondary";"no";197;"no";"yes";"cellular";3;"feb";47;2;222;3;"other";"no"
34;"services";"married";"secondary";"no";0;"no";"yes";"unknown";2;"jul";193;4;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";1905;"yes";"no";"cellular";16;"jul";709;2;-1;0;"unknown";"yes"
35;"technician";"married";"tertiary";"no";118;"no";"no";"cellular";6;"aug";358;2;-1;0;"unknown";"no"
39;"entrepreneur";"married";"tertiary";"no";2644;"yes";"no";"unknown";20;"may";253;1;-1;0;"unknown";"no"
36;"self-employed";"married";"tertiary";"no";16430;"yes";"no";"unknown";6;"jun";197;3;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";13;"no";"no";"cellular";22;"aug";94;2;-1;0;"unknown";"no"
57;"services";"divorced";"secondary";"no";14;"no";"yes";"cellular";22;"jul";445;2;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";769;"yes";"no";"cellular";15;"may";153;1;371;2;"failure";"no"
43;"admin.";"married";"unknown";"no";3485;"no";"no";"unknown";18;"jun";113;1;-1;0;"unknown";"no"
57;"management";"married";"secondary";"no";139;"no";"no";"cellular";17;"aug";80;1;-1;0;"unknown";"no"
34;"services";"divorced";"secondary";"no";202;"yes";"no";"unknown";15;"may";318;1;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";6;"yes";"no";"cellular";27;"jan";100;1;435;1;"failure";"no"
47;"management";"divorced";"tertiary";"no";2515;"no";"no";"unknown";20;"jun";40;3;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";5195;"yes";"no";"unknown";2;"jun";55;25;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";2098;"no";"no";"cellular";20;"nov";67;1;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";320;"no";"no";"cellular";7;"aug";76;2;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";3431;"yes";"no";"unknown";16;"may";73;1;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";50;"yes";"no";"unknown";20;"may";89;2;-1;0;"unknown";"no"
51;"housemaid";"divorced";"tertiary";"no";370;"yes";"no";"cellular";19;"aug";109;3;-1;0;"unknown";"no"
54;"blue-collar";"divorced";"primary";"no";-182;"no";"no";"cellular";9;"jul";98;1;-1;0;"unknown";"no"
52;"self-employed";"single";"tertiary";"no";1623;"yes";"no";"unknown";5;"jun";21;17;-1;0;"unknown";"no"
43;"entrepreneur";"married";"tertiary";"no";13342;"yes";"no";"cellular";18;"nov";465;1;-1;0;"unknown";"no"
58;"retired";"single";"primary";"no";6570;"no";"no";"cellular";21;"jul";106;3;-1;0;"unknown";"no"
53;"admin.";"married";"secondary";"no";225;"yes";"no";"cellular";20;"apr";304;1;340;1;"failure";"no"
44;"management";"married";"secondary";"no";-665;"no";"no";"cellular";30;"jul";342;3;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";0;"no";"no";"unknown";16;"may";16;25;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";54;"yes";"no";"unknown";28;"may";543;2;-1;0;"unknown";"yes"
35;"blue-collar";"married";"primary";"no";46;"no";"no";"cellular";8;"jul";338;3;-1;0;"unknown";"no"
40;"self-employed";"married";"secondary";"no";2;"no";"no";"cellular";22;"aug";197;5;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";103;"yes";"yes";"unknown";8;"may";956;2;-1;0;"unknown";"no"
38;"admin.";"single";"secondary";"no";883;"yes";"no";"cellular";12;"may";95;1;327;9;"other";"no"
32;"services";"married";"secondary";"no";55;"yes";"no";"unknown";15;"may";546;9;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";8238;"yes";"yes";"unknown";30;"may";124;4;-1;0;"unknown";"no"
25;"blue-collar";"single";"secondary";"no";913;"yes";"no";"cellular";7;"jul";113;4;-1;0;"unknown";"no"
38;"blue-collar";"divorced";"secondary";"no";595;"yes";"no";"cellular";7;"may";245;3;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";7;"yes";"no";"cellular";22;"jul";188;2;-1;0;"unknown";"no"
37;"technician";"single";"tertiary";"no";1661;"no";"no";"cellular";19;"aug";199;5;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";102;"yes";"no";"cellular";5;"apr";470;7;426;3;"other";"yes"
36;"services";"married";"secondary";"no";338;"no";"no";"unknown";14;"may";90;1;-1;0;"unknown";"no"
33;"management";"married";"secondary";"no";3132;"yes";"no";"unknown";23;"may";350;5;-1;0;"unknown";"no"
33;"admin.";"single";"tertiary";"no";490;"yes";"no";"cellular";7;"may";31;1;363;2;"failure";"no"
35;"management";"married";"tertiary";"no";2682;"yes";"no";"cellular";9;"jul";156;1;-1;0;"unknown";"no"
24;"services";"married";"tertiary";"no";0;"yes";"no";"unknown";27;"may";299;1;-1;0;"unknown";"no"
29;"unemployed";"married";"secondary";"no";596;"no";"no";"cellular";30;"jan";510;2;-1;0;"unknown";"no"
54;"blue-collar";"married";"secondary";"no";2059;"yes";"no";"cellular";25;"aug";177;4;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";132;"no";"no";"cellular";13;"oct";123;2;-1;0;"unknown";"no"
34;"management";"married";"secondary";"no";0;"no";"no";"cellular";2;"feb";270;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";385;"yes";"no";"unknown";16;"may";225;4;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";223;"no";"no";"cellular";28;"jan";63;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";3;"jun";247;13;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";512;"yes";"yes";"cellular";30;"jan";206;5;239;3;"failure";"no"
36;"entrepreneur";"married";"tertiary";"no";1169;"yes";"no";"unknown";6;"may";168;2;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";71;"yes";"no";"unknown";21;"may";836;12;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";940;"no";"no";"cellular";8;"aug";131;2;-1;0;"unknown";"no"
25;"technician";"single";"secondary";"no";-80;"yes";"yes";"unknown";3;"jun";89;1;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";0;"yes";"no";"cellular";29;"aug";116;4;-1;0;"unknown";"no"
52;"unknown";"married";"unknown";"no";1317;"no";"no";"unknown";12;"jun";58;1;-1;0;"unknown";"no"
39;"admin.";"married";"secondary";"no";179;"no";"no";"unknown";6;"sep";36;1;-1;0;"unknown";"no"
34;"management";"married";"secondary";"no";1007;"yes";"no";"cellular";21;"jul";240;2;-1;0;"unknown";"no"
34;"management";"single";"secondary";"no";0;"no";"no";"cellular";22;"aug";132;4;-1;0;"unknown";"no"
30;"services";"married";"secondary";"no";1286;"yes";"yes";"cellular";17;"jul";544;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";804;"yes";"yes";"unknown";8;"may";266;1;-1;0;"unknown";"no"
30;"student";"single";"secondary";"no";4;"no";"no";"cellular";4;"feb";443;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";0;"yes";"no";"cellular";10;"jul";324;1;-1;0;"unknown";"no"
36;"services";"single";"secondary";"no";417;"yes";"no";"cellular";6;"feb";15;10;207;3;"failure";"no"
48;"blue-collar";"married";"primary";"no";1678;"no";"no";"cellular";28;"jan";263;2;-1;0;"unknown";"no"
37;"services";"divorced";"secondary";"no";532;"yes";"no";"unknown";11;"jun";199;1;-1;0;"unknown";"no"
58;"management";"married";"secondary";"no";407;"yes";"no";"cellular";29;"jul";145;2;-1;0;"unknown";"no"
56;"management";"divorced";"tertiary";"no";659;"no";"yes";"cellular";2;"jun";56;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";393;"yes";"no";"cellular";5;"may";11;9;-1;0;"unknown";"no"
33;"admin.";"divorced";"secondary";"no";661;"yes";"no";"cellular";18;"nov";411;1;173;2;"other";"no"
26;"services";"single";"secondary";"no";0;"no";"no";"cellular";29;"jul";165;23;-1;0;"unknown";"no"
40;"management";"single";"unknown";"no";838;"yes";"no";"unknown";12;"may";619;3;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";195;"yes";"no";"cellular";17;"apr";203;1;-1;0;"unknown";"no"
45;"entrepreneur";"married";"secondary";"no";68;"yes";"no";"unknown";7;"may";254;1;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";-242;"yes";"no";"cellular";13;"aug";1149;6;-1;0;"unknown";"yes"
35;"management";"divorced";"tertiary";"no";321;"yes";"no";"unknown";9;"may";189;1;-1;0;"unknown";"no"
43;"services";"single";"secondary";"no";3403;"yes";"no";"cellular";12;"nov";707;2;97;2;"success";"yes"
28;"blue-collar";"married";"secondary";"no";8;"yes";"no";"cellular";15;"may";196;1;-1;0;"unknown";"no"
32;"blue-collar";"divorced";"secondary";"no";739;"yes";"no";"unknown";9;"may";171;1;-1;0;"unknown";"no"
26;"unemployed";"single";"secondary";"no";622;"no";"no";"cellular";28;"jan";1451;2;-1;0;"unknown";"yes"
35;"services";"married";"secondary";"no";152;"yes";"no";"unknown";2;"jun";563;1;-1;0;"unknown";"yes"
35;"technician";"single";"secondary";"no";2;"no";"no";"cellular";11;"aug";239;4;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";1439;"yes";"no";"telephone";28;"jul";73;6;-1;0;"unknown";"no"
42;"blue-collar";"divorced";"secondary";"no";1491;"yes";"no";"cellular";6;"may";203;2;334;2;"failure";"no"
32;"technician";"single";"secondary";"no";6402;"yes";"no";"cellular";6;"may";158;1;167;1;"success";"no"
30;"unemployed";"married";"secondary";"no";1210;"no";"no";"cellular";9;"jul";100;4;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";122;"no";"yes";"cellular";8;"jul";160;1;-1;0;"unknown";"no"
30;"student";"single";"secondary";"no";0;"yes";"no";"cellular";19;"nov";661;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";62;"yes";"no";"cellular";14;"may";166;1;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";3333;"yes";"yes";"cellular";29;"jul";114;2;-1;0;"unknown";"no"
33;"technician";"married";"tertiary";"no";16992;"yes";"no";"unknown";29;"may";58;1;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";23;"yes";"no";"telephone";31;"jul";25;8;-1;0;"unknown";"no"
35;"admin.";"married";"secondary";"no";0;"yes";"no";"cellular";7;"may";121;2;-1;0;"unknown";"no"
53;"technician";"married";"tertiary";"no";6072;"no";"yes";"unknown";16;"jun";226;4;-1;0;"unknown";"no"
24;"retired";"single";"secondary";"no";366;"no";"yes";"unknown";16;"may";234;2;-1;0;"unknown";"no"
54;"management";"divorced";"tertiary";"no";0;"no";"yes";"cellular";10;"jul";88;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1343;"yes";"yes";"unknown";21;"may";85;1;-1;0;"unknown";"no"
41;"entrepreneur";"married";"secondary";"no";2105;"yes";"no";"cellular";18;"nov";134;1;-1;0;"unknown";"no"
44;"unknown";"married";"secondary";"no";2319;"no";"no";"cellular";4;"aug";251;3;-1;0;"unknown";"no"
35;"services";"single";"secondary";"no";35;"yes";"no";"cellular";14;"may";152;1;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";877;"no";"no";"cellular";13;"aug";202;1;83;5;"failure";"no"
43;"blue-collar";"single";"primary";"no";30;"yes";"no";"unknown";30;"may";379;17;-1;0;"unknown";"no"
51;"entrepreneur";"married";"secondary";"yes";-2082;"no";"yes";"cellular";28;"jul";123;6;-1;0;"unknown";"no"
60;"retired";"married";"secondary";"no";89;"no";"no";"cellular";30;"apr";156;2;-1;0;"unknown";"no"
43;"self-employed";"married";"secondary";"no";104;"no";"no";"unknown";17;"jun";332;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1750;"yes";"no";"telephone";14;"may";90;3;-1;0;"unknown";"no"
56;"management";"married";"primary";"no";772;"yes";"no";"telephone";20;"nov";291;1;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";1048;"yes";"no";"telephone";6;"feb";23;2;-1;0;"unknown";"no"
52;"entrepreneur";"married";"primary";"no";592;"no";"no";"cellular";21;"jul";122;2;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";0;"yes";"yes";"cellular";26;"jul";105;2;172;1;"failure";"no"
36;"services";"married";"secondary";"no";3064;"yes";"no";"telephone";11;"may";1143;4;-1;0;"unknown";"no"
40;"services";"married";"primary";"no";33;"no";"no";"unknown";26;"may";70;3;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";8;"may";264;2;-1;0;"unknown";"no"
32;"admin.";"single";"secondary";"no";1649;"no";"no";"cellular";27;"may";204;2;-1;0;"unknown";"no"
31;"housemaid";"single";"tertiary";"no";287;"yes";"no";"cellular";19;"nov";182;1;-1;0;"unknown";"no"
41;"technician";"divorced";"secondary";"no";0;"yes";"no";"cellular";17;"nov";386;2;-1;0;"unknown";"no"
58;"housemaid";"married";"primary";"no";419;"no";"yes";"unknown";5;"jun";295;3;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";257;"yes";"no";"cellular";12;"may";86;2;-1;0;"unknown";"no"
52;"technician";"married";"secondary";"no";118;"no";"no";"cellular";28;"aug";16;20;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";251;"no";"no";"cellular";20;"aug";413;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";1731;"yes";"yes";"unknown";29;"may";323;3;-1;0;"unknown";"no"
62;"management";"married";"tertiary";"no";0;"no";"no";"cellular";13;"oct";78;3;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";23;"no";"no";"cellular";25;"aug";121;3;-1;0;"unknown";"no"
41;"blue-collar";"single";"secondary";"yes";-386;"no";"yes";"cellular";20;"nov";477;1;-1;0;"unknown";"yes"
34;"self-employed";"married";"tertiary";"no";5498;"no";"no";"cellular";19;"nov";265;1;-1;0;"unknown";"no"
44;"management";"single";"tertiary";"no";5063;"no";"no";"cellular";27;"oct";418;2;-1;0;"unknown";"yes"
42;"blue-collar";"married";"primary";"no";7041;"yes";"no";"cellular";28;"jul";386;4;-1;0;"unknown";"no"
37;"entrepreneur";"single";"secondary";"no";654;"no";"no";"unknown";19;"may";594;2;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";1890;"no";"no";"cellular";8;"jul";161;1;-1;0;"unknown";"no"
32;"unemployed";"married";"secondary";"no";2400;"yes";"yes";"cellular";5;"feb";450;3;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";519;"yes";"no";"cellular";25;"aug";217;6;-1;0;"unknown";"no"
39;"technician";"single";"tertiary";"no";1190;"no";"no";"cellular";8;"aug";136;1;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";229;"yes";"no";"unknown";9;"may";322;1;-1;0;"unknown";"no"
26;"student";"single";"secondary";"no";203;"no";"no";"cellular";16;"nov";104;1;196;1;"failure";"no"
52;"technician";"married";"secondary";"no";1818;"no";"yes";"cellular";17;"nov";189;2;111;4;"failure";"no"
42;"unemployed";"married";"secondary";"no";1289;"no";"no";"cellular";15;"jul";250;4;415;2;"failure";"yes"
33;"technician";"divorced";"tertiary";"no";39;"no";"no";"cellular";19;"aug";123;5;-1;0;"unknown";"no"
73;"retired";"married";"primary";"no";796;"no";"no";"cellular";23;"oct";427;1;182;2;"failure";"yes"
35;"admin.";"single";"secondary";"no";57;"no";"yes";"cellular";8;"jul";632;2;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";1548;"yes";"no";"cellular";13;"apr";101;3;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";117;"no";"no";"cellular";24;"aug";467;2;88;4;"other";"yes"
37;"admin.";"single";"secondary";"no";0;"yes";"no";"cellular";16;"apr";640;2;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";691;"yes";"yes";"cellular";16;"apr";517;1;255;1;"failure";"no"
28;"blue-collar";"married";"secondary";"no";64;"yes";"no";"unknown";13;"may";160;3;-1;0;"unknown";"no"
49;"technician";"divorced";"tertiary";"no";0;"yes";"no";"cellular";12;"jun";177;3;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";957;"yes";"no";"unknown";21;"may";206;1;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";118;"yes";"no";"unknown";7;"may";208;2;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";218;"yes";"no";"cellular";12;"apr";169;1;-1;0;"unknown";"no"
35;"management";"married";"secondary";"no";724;"yes";"no";"cellular";14;"jul";287;2;415;1;"failure";"yes"
35;"housemaid";"married";"tertiary";"no";20;"yes";"yes";"cellular";17;"jul";505;3;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";-28;"yes";"no";"cellular";18;"may";284;2;371;1;"failure";"no"
46;"blue-collar";"married";"primary";"no";665;"no";"no";"cellular";30;"jan";89;4;235;2;"failure";"no"
25;"student";"single";"tertiary";"no";5;"no";"no";"cellular";29;"sep";138;1;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";925;"no";"no";"cellular";14;"dec";321;3;189;4;"success";"yes"
42;"blue-collar";"married";"primary";"no";2519;"yes";"no";"unknown";15;"may";262;4;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";3927;"no";"no";"cellular";13;"oct";61;1;-1;0;"unknown";"no"
42;"management";"divorced";"tertiary";"no";401;"yes";"no";"cellular";24;"jul";217;9;-1;0;"unknown";"no"
29;"management";"married";"tertiary";"no";581;"yes";"yes";"cellular";14;"jul";192;2;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";1327;"no";"no";"cellular";29;"oct";174;2;-1;0;"unknown";"no"
31;"unemployed";"married";"secondary";"no";3242;"no";"no";"unknown";25;"jun";292;2;-1;0;"unknown";"no"
59;"technician";"married";"primary";"no";3163;"yes";"no";"unknown";29;"may";240;2;-1;0;"unknown";"no"
50;"unemployed";"divorced";"secondary";"no";32;"no";"no";"cellular";4;"aug";282;2;-1;0;"unknown";"no"
47;"admin.";"married";"secondary";"no";310;"yes";"yes";"telephone";14;"aug";128;6;-1;0;"unknown";"no"
46;"housemaid";"married";"primary";"no";0;"no";"no";"telephone";2;"feb";161;1;-1;0;"unknown";"no"
26;"blue-collar";"single";"primary";"no";155;"yes";"no";"cellular";29;"jan";291;1;244;4;"failure";"no"
31;"services";"single";"secondary";"no";11386;"yes";"no";"cellular";20;"nov";356;1;-1;0;"unknown";"no"
30;"services";"married";"tertiary";"no";1380;"yes";"no";"unknown";21;"may";294;2;-1;0;"unknown";"no"
47;"blue-collar";"single";"unknown";"no";2443;"no";"no";"unknown";18;"jun";422;1;-1;0;"unknown";"no"
23;"technician";"single";"tertiary";"no";1129;"yes";"no";"unknown";28;"may";806;1;-1;0;"unknown";"yes"
44;"unemployed";"single";"primary";"no";4232;"no";"no";"unknown";16;"jun";227;1;-1;0;"unknown";"no"
42;"self-employed";"married";"secondary";"no";5564;"no";"no";"cellular";17;"nov";560;2;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";-402;"yes";"no";"cellular";17;"apr";135;1;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";1323;"yes";"no";"unknown";3;"jun";361;1;-1;0;"unknown";"no"
57;"retired";"married";"primary";"no";1623;"yes";"yes";"cellular";18;"jul";246;1;-1;0;"unknown";"no"
38;"technician";"married";"tertiary";"no";1042;"yes";"no";"cellular";4;"feb";419;2;-1;0;"unknown";"no"
22;"student";"single";"secondary";"no";2488;"no";"no";"cellular";8;"nov";449;2;38;10;"other";"yes"
54;"admin.";"married";"secondary";"no";865;"yes";"no";"cellular";17;"nov";71;1;-1;0;"unknown";"no"
32;"technician";"married";"tertiary";"no";-1053;"yes";"no";"cellular";21;"jul";357;3;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";1667;"yes";"no";"cellular";18;"nov";164;2;179;3;"failure";"no"
59;"admin.";"divorced";"secondary";"no";975;"yes";"no";"unknown";15;"may";263;4;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";2571;"no";"no";"unknown";18;"jun";146;2;-1;0;"unknown";"no"
53;"unknown";"married";"primary";"no";732;"no";"no";"cellular";27;"oct";759;2;-1;0;"unknown";"yes"
50;"admin.";"divorced";"secondary";"no";769;"no";"no";"cellular";28;"jul";72;2;363;4;"failure";"no"
34;"technician";"married";"secondary";"no";-136;"yes";"yes";"cellular";12;"aug";131;2;-1;0;"unknown";"no"
35;"admin.";"divorced";"secondary";"no";3636;"no";"yes";"cellular";21;"nov";808;2;-1;0;"unknown";"yes"
30;"management";"married";"tertiary";"no";931;"no";"no";"cellular";17;"jul";1183;1;-1;0;"unknown";"no"
62;"housemaid";"married";"secondary";"no";565;"no";"no";"cellular";4;"sep";123;3;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";271;"yes";"no";"unknown";29;"may";483;2;-1;0;"unknown";"no"
26;"self-employed";"single";"tertiary";"no";1153;"no";"no";"cellular";4;"may";238;3;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";-73;"yes";"no";"cellular";11;"jul";90;1;-1;0;"unknown";"no"
50;"unknown";"married";"unknown";"no";558;"no";"no";"cellular";9;"jul";464;1;683;6;"failure";"yes"
31;"unknown";"divorced";"tertiary";"no";137;"no";"no";"cellular";22;"aug";385;9;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";700;"yes";"no";"unknown";23;"may";240;2;-1;0;"unknown";"no"
55;"retired";"married";"primary";"no";-966;"no";"yes";"cellular";14;"may";104;1;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";96;"no";"no";"cellular";29;"aug";109;2;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";4007;"no";"no";"cellular";30;"nov";598;4;-1;0;"unknown";"no"
50;"management";"single";"tertiary";"no";567;"yes";"no";"unknown";16;"may";274;8;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";40;"no";"no";"cellular";7;"aug";123;2;-1;0;"unknown";"no"
47;"admin.";"divorced";"secondary";"no";136;"no";"yes";"cellular";23;"jul";70;1;-1;0;"unknown";"no"
38;"self-employed";"single";"tertiary";"no";917;"yes";"no";"unknown";23;"may";260;1;-1;0;"unknown";"no"
46;"management";"single";"tertiary";"no";593;"yes";"no";"cellular";29;"jan";190;3;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";505;"yes";"no";"unknown";14;"may";521;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";2542;"yes";"no";"cellular";14;"may";228;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";5355;"no";"no";"telephone";17;"feb";466;4;-1;0;"unknown";"yes"
38;"student";"single";"tertiary";"no";807;"no";"no";"cellular";27;"feb";74;2;-1;0;"unknown";"no"
59;"retired";"married";"secondary";"no";9976;"yes";"no";"unknown";29;"may";57;2;-1;0;"unknown";"no"
32;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";30;"jan";119;3;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";540;"no";"no";"cellular";19;"nov";124;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";280;"yes";"yes";"cellular";31;"jul";65;50;-1;0;"unknown";"no"
56;"management";"divorced";"tertiary";"no";-59;"no";"yes";"cellular";28;"jul";127;6;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"yes";-517;"yes";"no";"cellular";14;"jul";172;3;-1;0;"unknown";"no"
42;"unemployed";"single";"secondary";"no";2613;"yes";"no";"cellular";30;"jul";174;6;-1;0;"unknown";"no"
42;"student";"married";"unknown";"no";-7;"no";"no";"telephone";31;"jul";41;6;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";30;"may";232;3;-1;0;"unknown";"no"
53;"retired";"divorced";"secondary";"yes";-200;"no";"yes";"unknown";6;"jun";161;1;-1;0;"unknown";"no"
67;"retired";"married";"primary";"no";2301;"no";"no";"cellular";16;"apr";124;1;-1;0;"unknown";"no"
53;"housemaid";"married";"primary";"no";104;"no";"yes";"unknown";5;"jun";701;1;-1;0;"unknown";"yes"
33;"blue-collar";"married";"primary";"no";191;"yes";"no";"unknown";9;"may";162;2;-1;0;"unknown";"no"
31;"technician";"single";"unknown";"no";694;"yes";"no";"unknown";6;"jun";431;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";212;"yes";"no";"cellular";18;"jul";191;3;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";1882;"yes";"no";"cellular";11;"aug";249;5;-1;0;"unknown";"no"
48;"blue-collar";"married";"unknown";"no";131;"yes";"no";"unknown";6;"may";189;1;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";175;"no";"no";"cellular";25;"aug";167;4;-1;0;"unknown";"no"
43;"technician";"single";"tertiary";"yes";0;"no";"no";"cellular";29;"aug";73;4;-1;0;"unknown";"no"
38;"management";"divorced";"tertiary";"no";4425;"yes";"no";"unknown";30;"may";162;1;-1;0;"unknown";"no"
46;"unemployed";"married";"secondary";"no";3533;"yes";"no";"cellular";12;"aug";235;2;-1;0;"unknown";"no"
59;"blue-collar";"divorced";"secondary";"no";3573;"no";"no";"cellular";15;"may";44;1;-1;0;"unknown";"no"
44;"management";"divorced";"tertiary";"no";476;"yes";"yes";"unknown";26;"may";127;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";1257;"yes";"no";"cellular";20;"apr";93;3;329;7;"failure";"no"
55;"blue-collar";"married";"unknown";"no";23;"no";"no";"cellular";30;"jan";143;1;2;3;"other";"no"
39;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";16;"apr";81;2;-1;0;"unknown";"no"
36;"services";"single";"secondary";"no";398;"yes";"yes";"unknown";17;"jun";77;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";745;"yes";"no";"unknown";23;"may";115;4;-1;0;"unknown";"no"
48;"management";"single";"tertiary";"no";8106;"no";"no";"unknown";17;"nov";125;1;111;3;"failure";"no"
33;"management";"single";"tertiary";"no";0;"no";"no";"cellular";19;"aug";53;4;-1;0;"unknown";"no"
67;"retired";"married";"unknown";"no";696;"no";"no";"telephone";4;"may";77;2;-1;0;"unknown";"no"
48;"unemployed";"divorced";"secondary";"no";1266;"yes";"no";"telephone";3;"feb";45;1;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";2416;"no";"no";"cellular";11;"aug";74;2;-1;0;"unknown";"no"
31;"housemaid";"married";"primary";"no";25;"no";"no";"cellular";21;"aug";169;5;-1;0;"unknown";"no"
30;"student";"single";"tertiary";"no";7513;"no";"no";"cellular";11;"aug";269;2;-1;0;"unknown";"no"
56;"entrepreneur";"divorced";"primary";"no";29;"no";"no";"cellular";29;"jan";52;2;227;1;"failure";"no"
26;"blue-collar";"married";"primary";"no";5116;"yes";"no";"cellular";22;"jul";246;3;-1;0;"unknown";"no"
44;"management";"married";"secondary";"no";-1746;"yes";"no";"unknown";20;"jun";218;1;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";14;"no";"no";"cellular";13;"nov";167;1;92;1;"success";"yes"
31;"management";"married";"tertiary";"no";4148;"yes";"no";"cellular";21;"nov";316;3;99;9;"other";"yes"
47;"self-employed";"married";"unknown";"no";14;"yes";"no";"cellular";15;"may";142;1;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";105;"yes";"no";"cellular";21;"nov";75;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";767;"no";"yes";"cellular";2;"feb";204;1;-1;0;"unknown";"no"
56;"admin.";"divorced";"primary";"no";0;"no";"yes";"cellular";10;"jul";542;1;-1;0;"unknown";"no"
43;"management";"single";"secondary";"no";-228;"yes";"yes";"unknown";16;"may";122;2;-1;0;"unknown";"no"
42;"services";"married";"secondary";"no";508;"yes";"no";"unknown";30;"may";70;1;-1;0;"unknown";"no"
60;"management";"married";"secondary";"no";725;"yes";"no";"cellular";8;"jul";266;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"unknown";"no";41;"yes";"no";"cellular";6;"may";34;9;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";184;"no";"no";"cellular";22;"jul";203;4;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";251;"no";"yes";"unknown";6;"jun";85;3;-1;0;"unknown";"no"
38;"management";"divorced";"secondary";"no";-17;"no";"no";"cellular";21;"nov";11;4;-1;0;"unknown";"no"
23;"services";"married";"secondary";"no";-19;"yes";"no";"unknown";20;"jun";13;10;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";1064;"yes";"no";"cellular";13;"may";562;1;-1;0;"unknown";"yes"
36;"blue-collar";"married";"secondary";"no";777;"yes";"no";"cellular";6;"may";185;3;-1;0;"unknown";"no"
49;"unemployed";"married";"tertiary";"no";377;"no";"no";"cellular";22;"may";158;1;59;2;"failure";"no"
34;"admin.";"divorced";"secondary";"no";479;"yes";"no";"cellular";12;"may";515;1;350;5;"failure";"yes"
40;"technician";"divorced";"secondary";"no";-375;"yes";"no";"cellular";13;"may";60;5;-1;0;"unknown";"no"
72;"retired";"married";"primary";"no";796;"no";"no";"cellular";24;"apr";173;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";17;"yes";"no";"unknown";16;"may";353;4;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";1071;"yes";"no";"cellular";11;"may";143;3;357;1;"failure";"no"
35;"admin.";"married";"secondary";"no";0;"yes";"no";"cellular";20;"apr";18;5;-1;0;"unknown";"no"
46;"admin.";"divorced";"secondary";"no";7378;"no";"no";"cellular";18;"nov";466;1;-1;0;"unknown";"no"
55;"admin.";"married";"secondary";"no";865;"yes";"no";"cellular";17;"apr";85;1;151;1;"failure";"no"
50;"technician";"divorced";"secondary";"no";-701;"yes";"yes";"cellular";24;"jul";148;1;-1;0;"unknown";"no"
23;"blue-collar";"single";"unknown";"no";817;"yes";"no";"cellular";18;"may";123;1;-1;0;"unknown";"no"
34;"student";"married";"unknown";"no";1387;"no";"no";"cellular";30;"apr";121;1;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";0;"yes";"yes";"unknown";29;"may";245;3;-1;0;"unknown";"no"
61;"retired";"married";"secondary";"no";2917;"no";"no";"cellular";6;"oct";215;1;-1;0;"unknown";"yes"
53;"technician";"married";"secondary";"no";4541;"yes";"no";"unknown";15;"may";190;6;-1;0;"unknown";"no"
57;"technician";"married";"secondary";"no";0;"no";"no";"unknown";13;"may";44;1;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";4287;"no";"no";"cellular";30;"apr";171;1;-1;0;"unknown";"no"
25;"admin.";"married";"secondary";"no";242;"yes";"no";"cellular";30;"jul";62;12;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";171;"yes";"yes";"unknown";20;"may";172;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";259;"yes";"no";"unknown";23;"may";78;10;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";827;"yes";"no";"unknown";15;"may";319;5;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";469;"no";"yes";"cellular";2;"feb";111;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";2226;"yes";"no";"unknown";6;"may";158;1;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";0;"no";"no";"cellular";29;"aug";82;12;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";477;"yes";"no";"unknown";20;"may";128;6;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";42;"yes";"no";"unknown";8;"may";102;4;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";775;"yes";"no";"unknown";16;"jun";124;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";9;"yes";"no";"cellular";17;"apr";334;2;-1;0;"unknown";"no"
52;"technician";"married";"tertiary";"no";2557;"no";"no";"cellular";28;"jan";134;5;204;2;"success";"yes"
31;"management";"married";"tertiary";"no";2395;"no";"no";"cellular";27;"apr";218;1;-1;0;"unknown";"no"
60;"management";"married";"primary";"no";4895;"no";"no";"unknown";9;"jun";313;1;-1;0;"unknown";"no"
35;"blue-collar";"single";"secondary";"no";376;"yes";"yes";"unknown";6;"may";207;3;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";389;"yes";"no";"unknown";6;"may";262;1;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";134;"no";"no";"cellular";19;"aug";134;2;-1;0;"unknown";"no"
45;"entrepreneur";"divorced";"tertiary";"no";1273;"yes";"yes";"unknown";11;"jun";197;3;-1;0;"unknown";"no"
59;"services";"married";"secondary";"no";0;"yes";"no";"cellular";12;"may";214;3;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";2892;"yes";"yes";"cellular";30;"jul";1165;7;-1;0;"unknown";"no"
33;"technician";"divorced";"secondary";"no";0;"yes";"no";"cellular";7;"may";177;2;-1;0;"unknown";"no"
46;"management";"single";"secondary";"no";295;"yes";"no";"unknown";23;"may";141;6;-1;0;"unknown";"no"
48;"admin.";"married";"secondary";"no";292;"yes";"yes";"cellular";29;"jul";164;6;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";85;"no";"no";"cellular";16;"jul";151;1;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";4;"no";"no";"cellular";4;"feb";145;2;260;4;"failure";"no"
35;"blue-collar";"divorced";"primary";"no";-587;"yes";"no";"unknown";26;"may";111;1;-1;0;"unknown";"no"
36;"blue-collar";"single";"primary";"no";367;"no";"no";"cellular";2;"feb";217;1;221;1;"failure";"no"
52;"management";"married";"tertiary";"no";-1224;"yes";"no";"cellular";7;"may";253;4;-1;0;"unknown";"no"
45;"management";"divorced";"tertiary";"no";959;"no";"no";"unknown";18;"jun";74;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";0;"no";"no";"cellular";20;"aug";263;2;-1;0;"unknown";"no"
25;"blue-collar";"married";"primary";"yes";-589;"yes";"no";"unknown";3;"jun";233;1;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";-8;"no";"no";"cellular";13;"aug";402;2;-1;0;"unknown";"no"
57;"management";"married";"unknown";"no";0;"no";"no";"cellular";30;"sep";585;1;-1;0;"unknown";"no"
48;"management";"married";"primary";"no";247;"yes";"yes";"unknown";14;"may";145;1;-1;0;"unknown";"no"
48;"technician";"married";"unknown";"no";5543;"no";"no";"unknown";16;"jun";143;5;-1;0;"unknown";"no"
32;"blue-collar";"married";"primary";"no";642;"no";"yes";"cellular";8;"jul";524;1;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";11;"no";"no";"cellular";11;"aug";270;1;-1;0;"unknown";"no"
57;"management";"married";"primary";"no";-226;"yes";"no";"unknown";4;"jun";272;1;-1;0;"unknown";"no"
31;"entrepreneur";"married";"tertiary";"no";5191;"no";"no";"cellular";20;"nov";187;2;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";-320;"no";"no";"cellular";18;"may";268;2;-1;0;"unknown";"no"
53;"entrepreneur";"married";"tertiary";"no";2289;"yes";"yes";"cellular";17;"nov";183;1;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";725;"yes";"no";"cellular";27;"feb";123;3;99;8;"failure";"no"
32;"technician";"single";"tertiary";"no";705;"yes";"no";"cellular";20;"apr";20;4;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";776;"yes";"no";"cellular";6;"feb";232;4;253;1;"failure";"no"
35;"unemployed";"married";"tertiary";"no";816;"yes";"no";"cellular";19;"nov";547;7;-1;0;"unknown";"no"
41;"services";"married";"secondary";"no";171;"no";"no";"cellular";21;"jul";71;9;-1;0;"unknown";"no"
54;"services";"married";"secondary";"no";812;"no";"no";"cellular";31;"jul";113;6;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";823;"no";"no";"cellular";8;"aug";780;4;-1;0;"unknown";"yes"
28;"technician";"single";"secondary";"no";1453;"yes";"no";"cellular";12;"may";246;1;343;1;"other";"no"
24;"blue-collar";"single";"unknown";"no";431;"yes";"no";"unknown";3;"jun";108;12;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";1009;"yes";"no";"cellular";29;"jan";253;1;-1;0;"unknown";"no"
32;"self-employed";"married";"primary";"no";286;"yes";"no";"cellular";6;"aug";175;1;-1;0;"unknown";"no"
53;"entrepreneur";"married";"tertiary";"no";22370;"yes";"no";"unknown";15;"may";106;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";2111;"yes";"no";"cellular";7;"may";86;2;-1;0;"unknown";"no"
48;"blue-collar";"married";"primary";"no";5366;"yes";"no";"unknown";21;"may";248;3;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";181;"yes";"yes";"telephone";28;"jul";265;2;-1;0;"unknown";"no"
31;"blue-collar";"divorced";"secondary";"no";-452;"yes";"no";"unknown";26;"may";182;5;-1;0;"unknown";"no"
56;"unemployed";"divorced";"primary";"no";1770;"no";"no";"cellular";5;"feb";916;3;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";0;"no";"no";"cellular";4;"aug";506;1;-1;0;"unknown";"no"
37;"admin.";"single";"secondary";"no";-296;"yes";"no";"cellular";20;"apr";81;1;-1;0;"unknown";"no"
59;"management";"married";"primary";"no";2358;"yes";"no";"cellular";17;"apr";30;1;-1;0;"unknown";"no"
50;"management";"single";"tertiary";"no";1200;"no";"no";"cellular";14;"aug";904;3;-1;0;"unknown";"yes"
39;"housemaid";"single";"primary";"no";109;"yes";"no";"unknown";6;"may";699;3;-1;0;"unknown";"no"
30;"technician";"married";"secondary";"no";616;"yes";"no";"cellular";6;"may";19;1;-1;0;"unknown";"no"
47;"services";"divorced";"secondary";"no";4906;"yes";"no";"unknown";14;"may";389;1;-1;0;"unknown";"no"
44;"self-employed";"married";"secondary";"no";313;"no";"no";"cellular";6;"aug";211;4;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";290;"yes";"no";"cellular";16;"apr";283;1;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";1240;"no";"no";"cellular";12;"aug";474;2;-1;0;"unknown";"no"
27;"admin.";"single";"tertiary";"no";3672;"yes";"no";"cellular";15;"may";157;2;351;5;"failure";"no"
73;"retired";"married";"secondary";"no";19;"no";"no";"cellular";5;"nov";509;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";143;"yes";"no";"unknown";7;"may";279;1;-1;0;"unknown";"no"
56;"retired";"divorced";"secondary";"no";212;"no";"yes";"telephone";17;"jul";27;1;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";9;"no";"no";"cellular";11;"aug";386;4;-1;0;"unknown";"yes"
33;"blue-collar";"married";"primary";"no";3074;"no";"no";"cellular";18;"nov";679;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";1734;"yes";"no";"unknown";2;"jun";169;1;-1;0;"unknown";"no"
52;"management";"married";"tertiary";"no";575;"no";"no";"unknown";3;"jun";315;1;-1;0;"unknown";"no"
51;"blue-collar";"married";"secondary";"no";397;"yes";"no";"cellular";20;"aug";293;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";640;"yes";"no";"cellular";3;"feb";439;3;-1;0;"unknown";"no"
54;"services";"married";"secondary";"no";158;"yes";"no";"unknown";19;"may";198;1;-1;0;"unknown";"no"
44;"management";"married";"secondary";"no";2984;"yes";"no";"unknown";15;"may";770;2;-1;0;"unknown";"no"
50;"entrepreneur";"married";"secondary";"no";1011;"yes";"no";"telephone";30;"jul";340;2;-1;0;"unknown";"no"
45;"blue-collar";"single";"secondary";"no";0;"yes";"no";"cellular";26;"apr";1472;1;192;4;"failure";"yes"
36;"management";"married";"tertiary";"no";1133;"yes";"no";"cellular";4;"aug";293;1;181;2;"other";"yes"
42;"technician";"married";"secondary";"no";4945;"yes";"no";"cellular";2;"mar";311;2;-1;0;"unknown";"yes"
37;"technician";"married";"secondary";"no";712;"yes";"no";"cellular";18;"nov";291;2;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";-427;"yes";"yes";"unknown";21;"may";965;2;-1;0;"unknown";"no"
56;"admin.";"married";"secondary";"no";2146;"no";"yes";"unknown";9;"jun";594;1;-1;0;"unknown";"no"
59;"admin.";"married";"secondary";"no";92;"yes";"no";"cellular";12;"may";139;2;350;1;"failure";"no"
32;"unemployed";"married";"secondary";"no";10;"yes";"no";"unknown";6;"may";501;4;-1;0;"unknown";"no"
57;"retired";"married";"primary";"no";1238;"yes";"no";"cellular";15;"jul";274;1;-1;0;"unknown";"no"
49;"admin.";"single";"secondary";"no";981;"yes";"no";"telephone";21;"nov";10;3;-1;0;"unknown";"no"
56;"entrepreneur";"married";"unknown";"no";575;"no";"no";"unknown";20;"jun";18;2;-1;0;"unknown";"no"
48;"admin.";"divorced";"unknown";"no";-70;"no";"no";"cellular";14;"may";243;1;-1;0;"unknown";"no"
27;"student";"single";"tertiary";"no";3733;"yes";"no";"cellular";22;"may";198;1;98;6;"success";"yes"
31;"management";"single";"tertiary";"no";574;"yes";"no";"cellular";16;"jul";410;2;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";43;"no";"no";"unknown";19;"may";190;1;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";428;"no";"no";"unknown";28;"may";121;1;-1;0;"unknown";"no"
58;"housemaid";"married";"primary";"no";0;"yes";"no";"telephone";9;"jul";148;1;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";20;"apr";239;2;259;2;"failure";"no"
32;"management";"single";"tertiary";"no";474;"yes";"no";"cellular";7;"aug";90;3;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";-540;"no";"no";"unknown";6;"jun";29;1;-1;0;"unknown";"no"
35;"self-employed";"married";"tertiary";"no";560;"yes";"no";"cellular";2;"apr";58;1;136;1;"failure";"no"
31;"management";"married";"tertiary";"no";325;"yes";"no";"cellular";18;"jul";1139;2;-1;0;"unknown";"yes"
29;"management";"single";"tertiary";"no";437;"no";"no";"cellular";5;"aug";504;1;92;1;"success";"yes"
48;"blue-collar";"married";"primary";"no";574;"no";"no";"cellular";5;"aug";348;1;-1;0;"unknown";"no"
60;"retired";"married";"primary";"no";0;"no";"no";"telephone";30;"jul";130;3;-1;0;"unknown";"no"
74;"retired";"married";"secondary";"no";935;"no";"no";"cellular";26;"jan";100;1;104;1;"other";"no"
30;"services";"married";"secondary";"no";699;"yes";"no";"unknown";29;"may";544;1;-1;0;"unknown";"no"
52;"admin.";"single";"primary";"no";0;"no";"no";"unknown";19;"may";182;11;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";-220;"yes";"no";"cellular";2;"apr";90;1;-1;0;"unknown";"no"
29;"services";"married";"secondary";"no";11417;"yes";"no";"unknown";28;"may";272;2;-1;0;"unknown";"no"
33;"housemaid";"married";"primary";"no";212;"yes";"no";"cellular";11;"jul";260;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";67;"yes";"yes";"unknown";8;"may";672;3;-1;0;"unknown";"no"
45;"admin.";"divorced";"secondary";"no";0;"no";"no";"cellular";29;"oct";264;1;-1;0;"unknown";"yes"
31;"blue-collar";"married";"secondary";"no";0;"no";"yes";"telephone";15;"jul";275;2;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";1673;"no";"no";"cellular";23;"oct";76;1;-1;0;"unknown";"no"
27;"services";"married";"secondary";"no";-1202;"yes";"no";"unknown";4;"jun";749;2;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";4213;"no";"no";"cellular";25;"aug";142;4;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";0;"no";"no";"cellular";26;"aug";454;2;-1;0;"unknown";"no"
57;"blue-collar";"married";"secondary";"no";1521;"yes";"yes";"cellular";18;"may";102;2;178;5;"failure";"no"
34;"management";"married";"tertiary";"no";1310;"no";"no";"telephone";30;"jan";521;3;158;7;"other";"no"
40;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";17;"nov";57;1;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";399;"no";"no";"cellular";9;"apr";504;1;-1;0;"unknown";"no"
46;"technician";"married";"tertiary";"no";405;"no";"no";"cellular";11;"aug";138;3;-1;0;"unknown";"no"
55;"management";"divorced";"tertiary";"no";9756;"yes";"no";"cellular";5;"jun";103;2;95;1;"other";"yes"
25;"management";"single";"tertiary";"no";2506;"no";"no";"cellular";17;"feb";485;1;-1;0;"unknown";"yes"
34;"management";"married";"tertiary";"no";697;"no";"no";"cellular";5;"aug";166;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";403;"yes";"no";"cellular";8;"may";318;2;338;1;"success";"no"
64;"retired";"divorced";"tertiary";"no";890;"no";"no";"cellular";3;"jul";531;3;-1;0;"unknown";"no"
45;"technician";"divorced";"secondary";"no";809;"yes";"no";"cellular";13;"may";155;2;363;4;"failure";"no"
57;"housemaid";"married";"secondary";"no";158;"yes";"no";"telephone";10;"jul";73;1;-1;0;"unknown";"no"
21;"student";"single";"unknown";"no";137;"yes";"no";"unknown";12;"may";198;3;-1;0;"unknown";"no"
58;"housemaid";"married";"secondary";"no";2705;"no";"no";"telephone";14;"aug";137;1;-1;0;"unknown";"no"
41;"blue-collar";"single";"secondary";"no";75;"yes";"no";"unknown";1;"jul";479;9;-1;0;"unknown";"yes"
41;"management";"married";"unknown";"no";2457;"yes";"no";"unknown";9;"may";285;1;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";541;"yes";"no";"cellular";8;"may";98;1;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";287;"yes";"no";"cellular";15;"may";199;2;274;3;"other";"no"
32;"management";"married";"tertiary";"no";273;"yes";"yes";"cellular";18;"jul";97;3;-1;0;"unknown";"no"
44;"services";"divorced";"secondary";"no";0;"yes";"no";"unknown";4;"jun";389;5;-1;0;"unknown";"no"
47;"unknown";"married";"unknown";"no";677;"no";"no";"cellular";7;"aug";384;1;99;4;"success";"no"
41;"self-employed";"married";"primary";"no";340;"no";"no";"unknown";6;"jun";37;3;-1;0;"unknown";"no"
54;"technician";"married";"tertiary";"no";98;"no";"no";"cellular";8;"aug";420;4;-1;0;"unknown";"no"
47;"technician";"divorced";"tertiary";"no";477;"yes";"no";"telephone";7;"may";973;1;366;1;"failure";"no"
32;"management";"single";"tertiary";"no";2629;"no";"no";"cellular";28;"aug";77;4;-1;0;"unknown";"no"
59;"housemaid";"married";"tertiary";"no";0;"no";"yes";"cellular";29;"jul";376;2;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";2325;"no";"no";"cellular";2;"feb";125;1;-1;0;"unknown";"yes"
48;"services";"married";"secondary";"yes";-20;"no";"no";"cellular";20;"aug";142;2;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";230;"yes";"no";"cellular";18;"may";442;1;266;8;"failure";"yes"
46;"management";"divorced";"secondary";"no";-852;"no";"yes";"cellular";29;"aug";728;2;-1;0;"unknown";"no"
31;"blue-collar";"single";"primary";"no";-280;"no";"no";"unknown";21;"may";281;3;-1;0;"unknown";"no"
53;"blue-collar";"married";"secondary";"no";25;"no";"no";"cellular";22;"aug";528;2;-1;0;"unknown";"yes"
34;"technician";"single";"tertiary";"no";9851;"yes";"no";"cellular";20;"nov";128;5;-1;0;"unknown";"no"
48;"services";"single";"unknown";"no";5095;"yes";"no";"unknown";16;"jun";71;1;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";687;"no";"no";"cellular";20;"aug";161;4;-1;0;"unknown";"no"
31;"management";"married";"secondary";"no";1368;"yes";"no";"cellular";12;"aug";129;4;-1;0;"unknown";"no"
27;"blue-collar";"single";"primary";"no";-179;"yes";"no";"cellular";15;"may";54;2;-1;0;"unknown";"no"
60;"admin.";"married";"secondary";"no";-396;"yes";"no";"cellular";11;"may";443;1;350;1;"failure";"no"
34;"blue-collar";"single";"tertiary";"no";1011;"yes";"no";"unknown";6;"may";136;1;-1;0;"unknown";"no"
52;"self-employed";"married";"secondary";"no";9;"no";"yes";"unknown";6;"jun";44;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";1159;"yes";"yes";"cellular";15;"may";1181;9;-1;0;"unknown";"no"
45;"technician";"married";"primary";"no";-322;"no";"no";"unknown";9;"jun";225;2;-1;0;"unknown";"no"
54;"technician";"divorced";"secondary";"no";1114;"yes";"no";"cellular";30;"jan";81;1;-1;0;"unknown";"no"
52;"services";"married";"secondary";"no";2072;"no";"no";"cellular";10;"jul";224;1;-1;0;"unknown";"no"
47;"entrepreneur";"married";"tertiary";"no";459;"yes";"no";"cellular";29;"jul";189;4;-1;0;"unknown";"no"
54;"unknown";"married";"secondary";"no";1157;"no";"no";"cellular";26;"may";98;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";8309;"yes";"yes";"cellular";19;"nov";50;1;-1;0;"unknown";"no"
39;"technician";"single";"tertiary";"no";1043;"no";"no";"cellular";18;"aug";195;10;-1;0;"unknown";"no"
46;"technician";"married";"secondary";"no";2098;"no";"no";"cellular";7;"aug";110;2;-1;0;"unknown";"no"
26;"unemployed";"single";"secondary";"no";461;"no";"no";"cellular";28;"jan";214;1;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";61;"yes";"no";"unknown";19;"may";182;8;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";1203;"yes";"no";"cellular";17;"apr";239;2;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";1067;"no";"yes";"cellular";12;"aug";179;6;-1;0;"unknown";"no"
57;"unemployed";"married";"primary";"no";0;"no";"no";"unknown";9;"jun";540;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";1657;"yes";"no";"cellular";18;"may";566;2;356;4;"failure";"no"
47;"technician";"married";"secondary";"no";1219;"no";"no";"cellular";18;"aug";697;4;-1;0;"unknown";"yes"
27;"technician";"married";"secondary";"no";7;"yes";"no";"unknown";15;"may";98;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";-839;"yes";"yes";"cellular";16;"jul";1018;2;-1;0;"unknown";"yes"
39;"technician";"married";"secondary";"no";994;"no";"no";"cellular";5;"feb";221;1;241;1;"failure";"no"
36;"entrepreneur";"married";"tertiary";"no";6;"no";"no";"telephone";30;"oct";246;1;-1;0;"unknown";"no"
42;"technician";"single";"secondary";"no";0;"no";"no";"cellular";21;"aug";656;2;-1;0;"unknown";"no"
23;"blue-collar";"single";"primary";"no";8627;"yes";"no";"unknown";3;"jun";281;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";133;"yes";"no";"unknown";8;"may";65;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";247;"yes";"yes";"unknown";26;"may";193;5;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";1114;"no";"no";"cellular";17;"nov";283;1;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";1592;"no";"yes";"unknown";20;"jun";78;4;-1;0;"unknown";"no"
59;"retired";"married";"unknown";"no";0;"no";"no";"unknown";8;"may";425;6;-1;0;"unknown";"no"
57;"admin.";"married";"tertiary";"no";46;"no";"no";"cellular";7;"aug";238;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";1221;"yes";"no";"cellular";6;"may";182;1;90;8;"failure";"no"
61;"management";"married";"secondary";"no";985;"no";"no";"cellular";18;"aug";998;6;-1;0;"unknown";"yes"
35;"blue-collar";"divorced";"secondary";"no";0;"yes";"no";"unknown";8;"may";477;1;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";520;"no";"no";"cellular";4;"nov";158;1;-1;0;"unknown";"no"
57;"services";"married";"tertiary";"no";10;"no";"no";"telephone";8;"jul";320;3;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";2157;"yes";"no";"cellular";13;"may";149;1;364;2;"failure";"no"
53;"retired";"married";"secondary";"no";6095;"yes";"yes";"cellular";14;"may";410;5;-1;0;"unknown";"no"
63;"retired";"married";"tertiary";"no";133;"yes";"no";"cellular";13;"feb";104;2;-1;0;"unknown";"no"
47;"self-employed";"married";"secondary";"no";1121;"no";"no";"unknown";20;"jun";454;6;-1;0;"unknown";"no"
54;"services";"married";"secondary";"no";88;"yes";"no";"unknown";13;"may";190;1;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";19;"aug";145;10;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";-328;"yes";"no";"telephone";2;"feb";318;2;-1;0;"unknown";"no"
58;"unemployed";"married";"secondary";"no";610;"yes";"no";"telephone";29;"jul";246;2;-1;0;"unknown";"no"
48;"management";"single";"tertiary";"no";414;"no";"no";"cellular";21;"jul";761;2;-1;0;"unknown";"no"
35;"management";"divorced";"tertiary";"no";0;"yes";"no";"telephone";29;"oct";171;1;-1;0;"unknown";"yes"
37;"self-employed";"married";"tertiary";"no";0;"no";"no";"unknown";6;"jun";143;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";685;"yes";"no";"unknown";6;"jun";968;1;-1;0;"unknown";"no"
37;"management";"married";"primary";"no";22856;"no";"no";"cellular";2;"jul";154;1;388;1;"failure";"no"
25;"services";"single";"secondary";"no";-352;"no";"no";"cellular";15;"jul";246;1;-1;0;"unknown";"no"
58;"blue-collar";"married";"secondary";"no";602;"yes";"no";"cellular";13;"may";201;1;-1;0;"unknown";"no"
52;"management";"married";"secondary";"no";-588;"yes";"no";"cellular";22;"jul";81;4;-1;0;"unknown";"no"
19;"student";"single";"secondary";"no";302;"no";"no";"cellular";16;"jul";205;1;-1;0;"unknown";"yes"
59;"blue-collar";"married";"secondary";"no";5314;"yes";"no";"cellular";5;"may";200;3;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";64;"yes";"yes";"unknown";8;"may";210;2;-1;0;"unknown";"no"
34;"technician";"single";"tertiary";"no";632;"no";"no";"cellular";22;"aug";215;10;-1;0;"unknown";"no"
35;"technician";"single";"secondary";"no";5731;"yes";"no";"cellular";4;"may";164;2;167;4;"other";"no"
41;"technician";"married";"secondary";"no";902;"no";"no";"unknown";16;"jun";35;6;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";6307;"yes";"no";"unknown";28;"may";615;1;-1;0;"unknown";"no"
53;"management";"divorced";"secondary";"no";112;"no";"yes";"cellular";11;"aug";246;3;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";0;"no";"yes";"telephone";9;"jul";42;1;-1;0;"unknown";"no"
33;"admin.";"single";"tertiary";"no";621;"no";"no";"cellular";8;"jul";217;1;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";435;"yes";"no";"unknown";26;"may";210;1;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";782;"yes";"no";"unknown";26;"may";116;4;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";478;"yes";"no";"unknown";3;"jun";200;1;-1;0;"unknown";"no"
54;"services";"married";"unknown";"no";7864;"yes";"no";"unknown";15;"may";145;4;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";1910;"yes";"no";"unknown";5;"jun";41;1;-1;0;"unknown";"no"
36;"technician";"divorced";"tertiary";"no";1174;"yes";"no";"cellular";18;"nov";192;2;-1;0;"unknown";"no"
56;"admin.";"married";"secondary";"no";1270;"yes";"no";"cellular";17;"nov";80;2;165;2;"other";"no"
30;"management";"single";"tertiary";"no";18;"no";"no";"unknown";20;"may";121;11;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";141;"yes";"no";"unknown";23;"may";106;3;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";2886;"no";"no";"cellular";20;"nov";230;1;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";2715;"yes";"no";"unknown";29;"may";125;2;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";1100;"yes";"yes";"cellular";11;"aug";67;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";516;"yes";"yes";"unknown";2;"jun";26;1;-1;0;"unknown";"no"
37;"services";"married";"secondary";"no";1;"no";"no";"unknown";15;"may";395;2;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";775;"no";"no";"cellular";29;"jan";603;2;-1;0;"unknown";"no"
35;"unknown";"married";"primary";"no";634;"no";"no";"cellular";7;"jul";185;2;-1;0;"unknown";"no"
53;"blue-collar";"single";"secondary";"no";5715;"no";"no";"cellular";28;"jan";287;2;-1;0;"unknown";"no"
60;"admin.";"married";"unknown";"no";1088;"yes";"no";"cellular";26;"may";149;1;-1;0;"unknown";"no"
40;"entrepreneur";"married";"secondary";"no";1532;"yes";"yes";"cellular";18;"nov";173;7;-1;0;"unknown";"no"
57;"retired";"married";"primary";"no";75;"no";"no";"cellular";28;"aug";118;2;-1;0;"unknown";"no"
29;"technician";"single";"secondary";"no";103;"no";"no";"unknown";19;"may";314;1;-1;0;"unknown";"no"
44;"blue-collar";"married";"primary";"no";134;"yes";"no";"cellular";7;"may";378;1;-1;0;"unknown";"no"
41;"admin.";"single";"secondary";"no";748;"no";"no";"telephone";21;"nov";94;7;130;3;"other";"no"
47;"technician";"married";"secondary";"no";1990;"yes";"yes";"cellular";25;"aug";108;4;-1;0;"unknown";"no"
43;"technician";"married";"tertiary";"no";192;"no";"no";"cellular";5;"aug";203;2;-1;0;"unknown";"no"
37;"technician";"single";"secondary";"no";267;"no";"no";"cellular";13;"may";121;1;-1;0;"unknown";"no"
34;"technician";"single";"tertiary";"no";586;"yes";"no";"unknown";16;"may";489;2;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";648;"yes";"yes";"unknown";8;"may";287;1;-1;0;"unknown";"no"
57;"entrepreneur";"divorced";"secondary";"yes";25;"yes";"no";"cellular";11;"may";652;1;370;4;"other";"no"
26;"admin.";"single";"secondary";"no";96;"yes";"no";"cellular";13;"may";612;2;348;3;"other";"no"
34;"management";"married";"tertiary";"no";1116;"yes";"no";"cellular";25;"jul";53;5;-1;0;"unknown";"no"
46;"technician";"divorced";"secondary";"no";-90;"no";"yes";"telephone";17;"oct";270;1;-1;0;"unknown";"yes"
38;"entrepreneur";"married";"tertiary";"no";170;"yes";"no";"cellular";18;"may";442;1;-1;0;"unknown";"yes"
37;"technician";"single";"secondary";"no";3665;"no";"no";"cellular";12;"aug";664;3;-1;0;"unknown";"yes"
28;"management";"single";"tertiary";"no";2380;"no";"no";"cellular";14;"sep";394;2;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";179;"no";"no";"unknown";9;"jun";230;3;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";763;"yes";"no";"cellular";11;"may";877;7;300;2;"other";"yes"
49;"services";"married";"secondary";"no";320;"no";"no";"telephone";9;"feb";1971;4;-1;0;"unknown";"yes"
38;"technician";"married";"secondary";"no";1960;"yes";"no";"cellular";21;"aug";161;2;-1;0;"unknown";"no"
27;"blue-collar";"married";"secondary";"no";818;"yes";"no";"unknown";3;"jun";188;5;-1;0;"unknown";"no"
30;"admin.";"married";"secondary";"no";2678;"yes";"no";"cellular";18;"may";23;5;-1;0;"unknown";"no"
38;"admin.";"married";"tertiary";"no";1980;"no";"no";"cellular";5;"aug";176;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";1582;"no";"no";"telephone";3;"feb";26;2;197;1;"failure";"no"
38;"retired";"single";"unknown";"no";62;"no";"no";"unknown";19;"jun";212;1;-1;0;"unknown";"no"
59;"housemaid";"married";"primary";"no";235;"no";"no";"cellular";13;"aug";119;2;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";489;"yes";"no";"cellular";7;"aug";205;5;-1;0;"unknown";"no"
29;"self-employed";"married";"tertiary";"no";3931;"yes";"no";"unknown";3;"jun";73;4;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";699;"yes";"yes";"cellular";20;"nov";1258;2;122;2;"other";"yes"
33;"technician";"married";"secondary";"no";58;"yes";"no";"telephone";29;"aug";57;2;-1;0;"unknown";"no"
54;"housemaid";"divorced";"secondary";"no";1327;"no";"no";"unknown";5;"jun";115;2;-1;0;"unknown";"no"
55;"services";"married";"secondary";"no";557;"no";"no";"unknown";19;"jun";58;1;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";741;"yes";"no";"cellular";11;"aug";238;1;-1;0;"unknown";"no"
48;"management";"married";"primary";"no";3230;"yes";"no";"telephone";20;"nov";207;3;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";1439;"yes";"no";"cellular";3;"jun";178;2;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";120;"yes";"no";"unknown";14;"may";20;11;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";1058;"no";"no";"cellular";11;"mar";188;3;-1;0;"unknown";"yes"
47;"blue-collar";"married";"primary";"no";881;"yes";"no";"cellular";14;"may";143;1;370;2;"failure";"no"
38;"technician";"married";"secondary";"no";6728;"no";"no";"cellular";25;"nov";232;1;96;4;"success";"yes"
80;"retired";"married";"secondary";"no";462;"no";"no";"cellular";7;"dec";127;2;-1;0;"unknown";"no"
40;"self-employed";"married";"secondary";"no";167;"yes";"yes";"cellular";8;"may";380;4;295;1;"failure";"no"
43;"entrepreneur";"married";"tertiary";"no";1574;"yes";"no";"cellular";19;"nov";167;3;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";1625;"no";"no";"cellular";22;"may";796;1;109;1;"failure";"yes"
39;"management";"married";"tertiary";"no";562;"no";"no";"cellular";13;"sep";303;2;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";9305;"yes";"no";"cellular";17;"apr";407;2;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";35;"no";"yes";"cellular";16;"jul";229;1;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";0;"no";"no";"cellular";11;"aug";108;2;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";2486;"yes";"no";"cellular";25;"may";265;1;-1;0;"unknown";"no"
25;"services";"single";"secondary";"no";368;"no";"no";"telephone";25;"jul";168;1;-1;0;"unknown";"no"
33;"management";"single";"secondary";"no";1558;"yes";"no";"cellular";17;"apr";97;1;-1;0;"unknown";"no"
23;"blue-collar";"single";"primary";"no";8627;"yes";"no";"cellular";15;"may";169;1;346;1;"failure";"no"
31;"management";"single";"tertiary";"no";2590;"no";"no";"cellular";23;"sep";103;2;484;2;"other";"no"
36;"management";"married";"tertiary";"no";0;"yes";"yes";"unknown";3;"jun";599;3;-1;0;"unknown";"no"
44;"technician";"single";"tertiary";"no";900;"yes";"no";"cellular";29;"jan";322;2;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";385;"yes";"no";"unknown";21;"may";304;1;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";1649;"yes";"no";"cellular";12;"may";52;3;-1;0;"unknown";"no"
51;"housemaid";"divorced";"secondary";"no";9228;"no";"no";"cellular";18;"jul";655;4;-1;0;"unknown";"yes"
36;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";9;"feb";101;2;-1;0;"unknown";"no"
27;"unemployed";"single";"tertiary";"no";1;"no";"no";"cellular";3;"feb";65;2;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";771;"yes";"no";"cellular";15;"may";173;4;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";280;"yes";"no";"telephone";14;"jul";162;3;-1;0;"unknown";"no"
59;"retired";"married";"primary";"no";46;"no";"no";"cellular";14;"aug";78;2;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";2431;"yes";"no";"cellular";14;"may";646;5;-1;0;"unknown";"yes"
42;"retired";"single";"tertiary";"no";308;"no";"no";"cellular";30;"jan";114;2;-1;0;"unknown";"no"
35;"management";"divorced";"tertiary";"no";160;"yes";"no";"unknown";4;"jun";141;6;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";555;"yes";"no";"cellular";15;"may";133;4;-1;0;"unknown";"no"
29;"technician";"single";"secondary";"no";778;"yes";"no";"unknown";6;"jun";1994;2;-1;0;"unknown";"no"
37;"blue-collar";"single";"secondary";"no";48;"no";"no";"cellular";6;"may";140;2;-1;0;"unknown";"no"
49;"admin.";"married";"primary";"no";169;"yes";"no";"cellular";15;"may";82;3;-1;0;"unknown";"no"
51;"services";"divorced";"secondary";"no";12;"yes";"no";"cellular";17;"nov";150;1;-1;0;"unknown";"no"
61;"housemaid";"married";"primary";"no";848;"no";"no";"cellular";27;"apr";605;1;187;1;"other";"no"
43;"management";"married";"secondary";"no";481;"yes";"no";"telephone";18;"nov";164;1;110;2;"other";"no"
42;"services";"married";"tertiary";"no";12223;"yes";"no";"cellular";19;"nov";36;1;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";-254;"yes";"yes";"unknown";27;"may";290;2;-1;0;"unknown";"no"
46;"self-employed";"married";"tertiary";"no";1148;"no";"no";"telephone";29;"jul";104;2;-1;0;"unknown";"no"
23;"admin.";"single";"secondary";"no";5;"no";"no";"unknown";12;"may";413;1;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";50;"yes";"no";"cellular";12;"aug";153;1;-1;0;"unknown";"no"
43;"blue-collar";"divorced";"primary";"no";92;"yes";"no";"unknown";15;"may";237;2;-1;0;"unknown";"no"
35;"technician";"single";"tertiary";"no";470;"yes";"no";"cellular";25;"jul";743;13;-1;0;"unknown";"yes"
38;"technician";"married";"secondary";"no";0;"no";"no";"cellular";28;"aug";82;4;-1;0;"unknown";"no"
56;"unemployed";"divorced";"primary";"no";109;"no";"no";"cellular";15;"jul";924;4;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";440;"yes";"no";"unknown";17;"jun";117;3;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";0;"no";"no";"cellular";5;"feb";550;4;-1;0;"unknown";"yes"
28;"services";"single";"secondary";"no";2685;"no";"yes";"unknown";24;"aug";5;1;-1;0;"unknown";"no"
26;"admin.";"divorced";"secondary";"yes";-3;"yes";"no";"unknown";13;"may";181;5;-1;0;"unknown";"no"
49;"management";"married";"tertiary";"no";7007;"no";"no";"cellular";17;"nov";325;2;-1;0;"unknown";"no"
33;"admin.";"married";"secondary";"no";220;"yes";"no";"unknown";27;"may";345;1;-1;0;"unknown";"no"
80;"retired";"married";"primary";"no";1548;"no";"no";"cellular";6;"aug";331;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";326;"yes";"yes";"cellular";7;"jul";112;1;-1;0;"unknown";"no"
42;"technician";"divorced";"secondary";"no";0;"yes";"no";"unknown";30;"may";115;6;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";123;"no";"no";"cellular";20;"aug";381;3;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";697;"no";"no";"cellular";7;"aug";119;1;99;3;"failure";"no"
37;"services";"single";"tertiary";"no";223;"yes";"no";"unknown";28;"may";25;1;-1;0;"unknown";"no"
32;"student";"married";"secondary";"no";1694;"yes";"no";"cellular";28;"jan";929;1;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";4446;"yes";"no";"unknown";14;"may";429;1;-1;0;"unknown";"no"
39;"management";"married";"tertiary";"no";894;"yes";"no";"cellular";20;"apr";491;1;326;1;"other";"no"
31;"technician";"divorced";"secondary";"no";202;"yes";"no";"cellular";18;"jul";146;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";0;"no";"no";"cellular";21;"aug";191;2;-1;0;"unknown";"no"
44;"technician";"married";"tertiary";"no";1707;"no";"no";"cellular";8;"aug";406;3;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";13658;"yes";"no";"telephone";9;"dec";220;2;-1;0;"unknown";"no"
39;"admin.";"married";"secondary";"no";406;"no";"no";"cellular";30;"jul";253;5;-1;0;"unknown";"no"
55;"services";"married";"secondary";"no";96;"no";"no";"telephone";30;"jul";340;2;-1;0;"unknown";"no"
34;"housemaid";"married";"secondary";"no";0;"yes";"no";"unknown";30;"oct";154;1;-1;0;"unknown";"yes"
39;"technician";"married";"tertiary";"no";-276;"no";"yes";"cellular";11;"aug";121;4;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";413;"no";"no";"cellular";27;"aug";104;5;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";655;"yes";"no";"cellular";5;"feb";127;2;258;4;"failure";"no"
56;"retired";"divorced";"primary";"no";694;"no";"no";"cellular";29;"jan";144;1;102;1;"other";"yes"
32;"admin.";"single";"secondary";"no";10218;"yes";"no";"cellular";19;"nov";149;2;-1;0;"unknown";"no"
41;"technician";"single";"tertiary";"no";1646;"yes";"no";"cellular";4;"feb";261;1;184;6;"success";"yes"
29;"admin.";"single";"tertiary";"no";14;"no";"no";"cellular";29;"sep";87;1;120;1;"failure";"no"
44;"management";"married";"tertiary";"no";7635;"no";"no";"cellular";21;"aug";719;5;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";9;"yes";"no";"unknown";27;"may";292;7;-1;0;"unknown";"no"
43;"management";"single";"secondary";"no";1029;"yes";"no";"unknown";2;"jun";145;1;-1;0;"unknown";"no"
26;"admin.";"married";"tertiary";"no";2855;"no";"no";"cellular";30;"mar";52;3;-1;0;"unknown";"no"
39;"admin.";"married";"secondary";"no";260;"yes";"no";"cellular";17;"apr";146;1;281;1;"failure";"no"
49;"blue-collar";"married";"secondary";"no";2039;"yes";"no";"cellular";6;"may";451;1;169;2;"failure";"yes"
52;"blue-collar";"divorced";"primary";"no";-97;"yes";"yes";"unknown";9;"may";167;4;-1;0;"unknown";"no"
33;"blue-collar";"married";"tertiary";"no";1465;"no";"no";"cellular";19;"aug";98;4;-1;0;"unknown";"no"
30;"blue-collar";"married";"secondary";"no";125;"yes";"no";"unknown";29;"may";238;2;-1;0;"unknown";"no"
32;"technician";"single";"tertiary";"no";930;"no";"no";"cellular";27;"aug";85;7;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";30;"jan";323;4;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";73;"no";"no";"unknown";17;"jun";116;1;-1;0;"unknown";"no"
46;"retired";"married";"primary";"no";-35;"yes";"no";"cellular";7;"may";304;3;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";1351;"no";"no";"cellular";10;"dec";267;1;-1;0;"unknown";"no"
39;"services";"married";"primary";"no";1601;"yes";"no";"unknown";23;"may";201;2;-1;0;"unknown";"no"
41;"services";"married";"unknown";"no";88;"yes";"no";"cellular";11;"may";105;1;336;2;"failure";"no"
38;"services";"married";"secondary";"no";435;"yes";"no";"unknown";23;"may";193;2;-1;0;"unknown";"no"
50;"unemployed";"divorced";"secondary";"no";717;"yes";"no";"cellular";30;"jan";113;1;-1;0;"unknown";"no"
56;"services";"married";"secondary";"no";705;"yes";"no";"unknown";29;"may";151;2;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";1553;"no";"no";"cellular";20;"aug";200;2;-1;0;"unknown";"no"
33;"blue-collar";"divorced";"secondary";"no";-131;"yes";"no";"unknown";16;"may";145;1;-1;0;"unknown";"no"
61;"technician";"married";"secondary";"no";1191;"no";"no";"cellular";21;"may";214;4;-1;0;"unknown";"yes"
36;"admin.";"single";"secondary";"no";47;"no";"yes";"cellular";9;"jul";36;6;-1;0;"unknown";"no"
34;"technician";"married";"tertiary";"no";544;"yes";"yes";"cellular";18;"nov";58;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";688;"yes";"no";"unknown";6;"may";174;2;-1;0;"unknown";"no"
45;"management";"married";"tertiary";"no";242;"no";"no";"cellular";30;"apr";587;1;-1;0;"unknown";"yes"
27;"student";"single";"tertiary";"no";118;"no";"no";"cellular";25;"may";112;1;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";-388;"yes";"no";"cellular";14;"jul";175;2;-1;0;"unknown";"no"
40;"self-employed";"married";"tertiary";"no";1603;"no";"no";"unknown";17;"jun";279;1;-1;0;"unknown";"no"
36;"management";"single";"secondary";"no";1511;"yes";"no";"cellular";16;"nov";270;1;-1;0;"unknown";"yes"
46;"blue-collar";"married";"primary";"no";147;"yes";"no";"cellular";6;"may";116;2;169;3;"failure";"no"
37;"entrepreneur";"married";"tertiary";"no";836;"yes";"no";"unknown";27;"may";129;2;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";55;"yes";"no";"cellular";13;"may";38;2;369;4;"failure";"no"
37;"admin.";"married";"secondary";"no";1123;"no";"yes";"unknown";28;"may";138;1;-1;0;"unknown";"no"
22;"student";"single";"tertiary";"no";691;"no";"no";"cellular";19;"aug";338;1;-1;0;"unknown";"yes"
51;"services";"married";"secondary";"no";0;"no";"yes";"unknown";26;"jun";565;3;-1;0;"unknown";"no"
49;"technician";"married";"secondary";"no";12531;"no";"no";"cellular";13;"aug";77;8;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";552;"yes";"yes";"cellular";19;"nov";314;2;124;1;"failure";"no"
50;"housemaid";"married";"primary";"no";0;"no";"no";"unknown";20;"jun";179;2;-1;0;"unknown";"no"
31;"services";"married";"tertiary";"no";309;"yes";"yes";"unknown";7;"may";294;1;-1;0;"unknown";"no"
47;"services";"single";"secondary";"no";33;"yes";"no";"cellular";5;"may";298;1;-1;0;"unknown";"no"
59;"blue-collar";"married";"secondary";"no";681;"yes";"no";"telephone";14;"apr";427;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";1787;"yes";"no";"cellular";12;"may";87;4;357;4;"failure";"no"
71;"retired";"married";"tertiary";"no";14220;"yes";"no";"cellular";9;"sep";397;1;-1;0;"unknown";"yes"
31;"management";"single";"tertiary";"no";12569;"no";"no";"cellular";9;"sep";344;1;295;1;"failure";"yes"
41;"blue-collar";"married";"primary";"no";4;"no";"no";"cellular";23;"jul";74;5;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";3587;"yes";"no";"unknown";30;"may";148;4;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";254;"no";"no";"unknown";6;"jun";161;3;-1;0;"unknown";"no"
46;"blue-collar";"divorced";"primary";"no";1712;"no";"no";"unknown";19;"jun";110;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";4855;"yes";"no";"cellular";10;"aug";1529;1;397;1;"failure";"no"
47;"technician";"married";"tertiary";"no";2408;"no";"no";"cellular";12;"aug";236;1;104;1;"success";"no"
35;"management";"single";"tertiary";"no";252;"yes";"no";"cellular";8;"aug";89;2;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";346;"yes";"no";"cellular";17;"apr";390;1;316;1;"failure";"no"
56;"admin.";"married";"secondary";"no";45;"no";"no";"unknown";5;"may";1467;1;-1;0;"unknown";"yes"
46;"admin.";"married";"secondary";"no";556;"yes";"yes";"unknown";15;"may";646;3;-1;0;"unknown";"yes"
44;"management";"divorced";"tertiary";"no";-710;"yes";"no";"cellular";10;"jul";183;1;-1;0;"unknown";"no"
48;"technician";"single";"tertiary";"no";-551;"yes";"no";"cellular";11;"may";168;2;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";56;"yes";"no";"unknown";23;"may";66;1;-1;0;"unknown";"no"
38;"entrepreneur";"married";"tertiary";"no";1514;"no";"yes";"cellular";21;"nov";160;3;120;3;"failure";"no"
34;"management";"single";"tertiary";"no";178;"yes";"no";"cellular";15;"may";14;2;-1;0;"unknown";"no"
56;"technician";"married";"secondary";"no";359;"yes";"no";"unknown";6;"jun";220;4;-1;0;"unknown";"no"
55;"retired";"divorced";"secondary";"no";1580;"no";"yes";"unknown";19;"jun";1007;1;-1;0;"unknown";"yes"
44;"blue-collar";"divorced";"secondary";"no";221;"no";"no";"cellular";30;"jul";149;13;-1;0;"unknown";"no"
38;"technician";"married";"tertiary";"no";1161;"yes";"no";"unknown";8;"may";665;1;-1;0;"unknown";"no"
53;"retired";"married";"secondary";"no";1278;"yes";"no";"telephone";15;"jul";174;3;-1;0;"unknown";"no"
41;"admin.";"single";"tertiary";"no";982;"no";"no";"cellular";23;"jul";105;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";16;"no";"no";"cellular";13;"aug";990;1;-1;0;"unknown";"yes"
34;"management";"single";"tertiary";"no";1;"no";"no";"cellular";19;"nov";582;1;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";128;"yes";"no";"cellular";12;"may";136;1;-1;0;"unknown";"no"
38;"entrepreneur";"single";"secondary";"no";2543;"no";"no";"cellular";10;"aug";223;1;414;4;"failure";"yes"
42;"entrepreneur";"married";"tertiary";"no";42045;"no";"no";"cellular";8;"aug";205;2;-1;0;"unknown";"no"
56;"blue-collar";"married";"primary";"no";828;"yes";"no";"cellular";14;"may";214;1;349;1;"failure";"no"
37;"management";"divorced";"tertiary";"no";0;"yes";"no";"cellular";11;"may";217;6;-1;0;"unknown";"no"
47;"housemaid";"married";"secondary";"no";3940;"yes";"no";"cellular";9;"nov";171;2;96;5;"success";"no"
29;"blue-collar";"married";"secondary";"no";125;"yes";"no";"unknown";13;"may";50;1;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";140;"yes";"no";"unknown";5;"jun";65;3;-1;0;"unknown";"no"
43;"technician";"single";"secondary";"no";593;"yes";"no";"unknown";5;"may";55;1;-1;0;"unknown";"no"
38;"self-employed";"married";"secondary";"no";746;"no";"no";"unknown";20;"jun";256;3;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";589;"yes";"yes";"unknown";21;"may";151;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";2343;"no";"no";"cellular";18;"jul";226;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";294;"yes";"no";"cellular";23;"jul";97;5;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";644;"no";"yes";"telephone";22;"jul";92;1;-1;0;"unknown";"no"
43;"management";"married";"secondary";"no";-26;"yes";"no";"unknown";16;"may";343;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"no";3354;"yes";"no";"cellular";14;"may";493;5;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";23;"no";"no";"cellular";20;"aug";216;3;-1;0;"unknown";"no"
37;"admin.";"married";"secondary";"no";7;"yes";"no";"cellular";27;"may";224;7;105;1;"other";"no"
60;"admin.";"married";"secondary";"no";3735;"no";"no";"cellular";22;"dec";190;1;188;9;"success";"yes"
51;"self-employed";"married";"unknown";"no";2094;"no";"no";"cellular";8;"jun";458;6;-1;0;"unknown";"yes"
44;"blue-collar";"married";"secondary";"no";508;"yes";"no";"cellular";20;"apr";222;1;154;1;"failure";"no"
48;"management";"married";"unknown";"no";0;"yes";"no";"cellular";8;"may";612;1;344;1;"other";"no"
57;"blue-collar";"married";"primary";"no";93;"yes";"no";"telephone";17;"apr";137;1;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";0;"yes";"no";"unknown";7;"may";104;2;-1;0;"unknown";"no"
50;"services";"married";"secondary";"no";26394;"no";"no";"cellular";25;"aug";206;4;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";-11;"yes";"no";"cellular";30;"jul";172;28;-1;0;"unknown";"no"
49;"technician";"married";"tertiary";"no";523;"yes";"no";"cellular";3;"mar";152;2;-1;0;"unknown";"yes"
49;"technician";"married";"secondary";"yes";-74;"yes";"no";"cellular";19;"nov";114;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";0;"no";"no";"cellular";11;"jul";89;1;-1;0;"unknown";"no"
39;"technician";"single";"secondary";"no";122;"yes";"no";"unknown";2;"jun";158;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";351;"no";"yes";"cellular";14;"jul";326;11;-1;0;"unknown";"no"
57;"self-employed";"married";"secondary";"no";35;"no";"yes";"unknown";26;"jun";215;3;-1;0;"unknown";"no"
37;"technician";"divorced";"secondary";"no";2610;"yes";"no";"cellular";13;"may";53;1;-1;0;"unknown";"no"
25;"services";"single";"secondary";"no";64;"no";"no";"cellular";21;"jul";71;11;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";60;"no";"no";"cellular";8;"may";487;2;367;2;"other";"no"
37;"blue-collar";"married";"secondary";"no";342;"yes";"no";"cellular";12;"may";340;2;175;1;"failure";"no"
36;"blue-collar";"single";"tertiary";"no";1830;"no";"no";"cellular";14;"oct";183;1;91;11;"failure";"no"
49;"management";"divorced";"tertiary";"no";1904;"no";"yes";"cellular";17;"nov";131;2;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";16957;"yes";"no";"telephone";29;"jan";208;1;247;2;"failure";"no"
39;"management";"divorced";"tertiary";"no";26;"no";"no";"unknown";18;"jun";311;5;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";0;"no";"no";"cellular";10;"nov";147;1;96;8;"success";"yes"
34;"admin.";"married";"secondary";"no";79;"yes";"no";"unknown";23;"may";83;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";2303;"yes";"no";"unknown";17;"jun";775;3;-1;0;"unknown";"yes"
53;"blue-collar";"married";"secondary";"no";65;"no";"no";"cellular";28;"aug";25;18;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";332;"yes";"no";"cellular";23;"jul";497;5;-1;0;"unknown";"no"
32;"technician";"divorced";"secondary";"no";-399;"yes";"no";"unknown";2;"jun";180;1;-1;0;"unknown";"no"
50;"technician";"divorced";"secondary";"no";-54;"yes";"yes";"cellular";13;"may";698;2;292;1;"failure";"yes"
29;"services";"married";"primary";"no";-102;"no";"no";"unknown";4;"jul";134;1;-1;0;"unknown";"no"
55;"unemployed";"married";"tertiary";"no";4809;"yes";"no";"cellular";21;"nov";223;2;186;3;"other";"no"
73;"retired";"married";"primary";"no";279;"no";"no";"cellular";28;"jan";399;3;-1;0;"unknown";"yes"
52;"technician";"married";"secondary";"no";11;"yes";"no";"unknown";13;"may";420;1;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";2201;"yes";"no";"unknown";15;"may";233;3;-1;0;"unknown";"no"
63;"retired";"married";"primary";"no";8044;"no";"no";"cellular";12;"nov";702;1;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";2374;"yes";"no";"cellular";9;"nov";161;1;209;2;"failure";"yes"
32;"admin.";"single";"secondary";"no";92;"yes";"no";"cellular";18;"may";110;1;315;2;"failure";"no"
49;"housemaid";"married";"tertiary";"no";443;"no";"no";"cellular";8;"aug";27;6;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";1397;"yes";"no";"telephone";12;"may";58;2;334;1;"failure";"no"
53;"entrepreneur";"married";"unknown";"no";7;"no";"no";"cellular";20;"nov";68;1;-1;0;"unknown";"no"
53;"technician";"married";"tertiary";"no";3177;"no";"no";"cellular";8;"aug";92;2;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";232;"no";"yes";"cellular";15;"may";45;1;-1;0;"unknown";"no"
35;"management";"single";"secondary";"no";81;"yes";"no";"cellular";29;"jan";79;1;-1;0;"unknown";"no"
35;"services";"single";"secondary";"no";414;"yes";"no";"cellular";20;"apr";11;5;319;2;"other";"no"
25;"student";"single";"tertiary";"no";1433;"yes";"no";"cellular";15;"may";520;1;-1;0;"unknown";"no"
31;"technician";"single";"unknown";"no";187;"no";"no";"cellular";17;"jul";86;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";0;"no";"no";"cellular";28;"aug";16;16;-1;0;"unknown";"no"
40;"housemaid";"married";"secondary";"no";4610;"no";"no";"cellular";14;"aug";154;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";1429;"yes";"no";"unknown";14;"may";91;1;-1;0;"unknown";"no"
30;"services";"single";"secondary";"no";334;"no";"no";"cellular";29;"jan";389;2;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";2;"no";"yes";"cellular";21;"jul";71;15;-1;0;"unknown";"no"
23;"services";"single";"secondary";"no";500;"yes";"no";"unknown";4;"jun";111;3;-1;0;"unknown";"no"
29;"technician";"married";"tertiary";"no";43;"yes";"no";"unknown";20;"may";68;2;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";4030;"yes";"no";"cellular";19;"nov";75;2;177;5;"failure";"no"
56;"housemaid";"married";"primary";"no";306;"no";"yes";"cellular";8;"jul";212;1;-1;0;"unknown";"no"
52;"admin.";"married";"secondary";"no";0;"no";"no";"cellular";22;"apr";91;2;275;1;"failure";"no"
55;"retired";"married";"secondary";"no";168;"no";"no";"cellular";19;"aug";79;2;-1;0;"unknown";"no"
43;"entrepreneur";"married";"tertiary";"no";210;"no";"no";"cellular";29;"jul";646;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";518;"yes";"no";"cellular";11;"may";206;1;-1;0;"unknown";"no"
43;"management";"single";"tertiary";"no";1696;"yes";"no";"cellular";26;"aug";257;1;474;3;"failure";"yes"
55;"services";"married";"secondary";"no";-31;"no";"yes";"cellular";13;"aug";81;2;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";856;"yes";"no";"cellular";20;"apr";470;1;150;5;"other";"no"
34;"management";"single";"tertiary";"no";318;"yes";"no";"unknown";9;"may";133;2;-1;0;"unknown";"no"
43;"self-employed";"married";"tertiary";"no";64;"no";"no";"unknown";6;"jun";230;2;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";800;"yes";"no";"cellular";19;"nov";76;6;-1;0;"unknown";"no"
39;"technician";"single";"unknown";"no";75;"no";"no";"unknown";6;"jun";26;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";347;"yes";"no";"cellular";6;"feb";154;2;197;6;"failure";"no"
55;"management";"married";"tertiary";"no";2366;"yes";"yes";"cellular";30;"jul";94;4;-1;0;"unknown";"no"
67;"retired";"married";"tertiary";"no";701;"no";"no";"cellular";6;"aug";254;2;-1;0;"unknown";"yes"
34;"entrepreneur";"married";"tertiary";"no";475;"yes";"yes";"cellular";9;"jul";1156;1;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";3285;"no";"no";"cellular";31;"jul";248;5;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";925;"no";"no";"cellular";20;"nov";884;3;-1;0;"unknown";"no"
45;"entrepreneur";"married";"secondary";"no";128;"no";"yes";"cellular";18;"nov";275;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";1587;"yes";"no";"cellular";28;"jul";200;2;-1;0;"unknown";"no"
59;"technician";"divorced";"secondary";"no";879;"yes";"no";"unknown";26;"may";228;1;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";478;"no";"no";"cellular";30;"apr";475;2;-1;0;"unknown";"yes"
52;"technician";"divorced";"secondary";"no";280;"no";"no";"cellular";14;"jul";393;5;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";1868;"no";"no";"cellular";8;"aug";156;4;-1;0;"unknown";"no"
41;"unemployed";"married";"primary";"no";819;"no";"no";"cellular";9;"feb";244;3;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";4646;"yes";"no";"unknown";13;"may";118;1;-1;0;"unknown";"no"
60;"self-employed";"single";"secondary";"no";27;"no";"no";"cellular";22;"aug";40;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";736;"yes";"no";"unknown";23;"may";700;3;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";224;"yes";"no";"cellular";2;"feb";97;3;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";2544;"no";"no";"cellular";15;"jul";252;1;184;3;"success";"yes"
53;"blue-collar";"married";"secondary";"no";427;"no";"yes";"cellular";14;"aug";360;4;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";2764;"no";"no";"cellular";17;"nov";235;1;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";376;"yes";"no";"unknown";7;"may";1521;1;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";523;"yes";"no";"cellular";6;"may";105;4;366;2;"failure";"no"
53;"entrepreneur";"married";"tertiary";"no";71;"no";"yes";"cellular";29;"jan";86;1;-1;0;"unknown";"no"
56;"retired";"married";"secondary";"no";3948;"no";"no";"cellular";3;"apr";484;1;135;3;"failure";"no"
52;"services";"single";"secondary";"no";1810;"no";"no";"telephone";19;"nov";117;1;-1;0;"unknown";"no"
38;"technician";"married";"secondary";"no";994;"no";"no";"unknown";9;"jun";119;1;-1;0;"unknown";"no"
37;"services";"married";"primary";"no";52;"yes";"no";"unknown";7;"may";359;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";11;"no";"no";"telephone";30;"jul";109;8;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"yes";0;"no";"no";"unknown";18;"jun";236;1;-1;0;"unknown";"no"
66;"retired";"married";"primary";"no";747;"no";"no";"telephone";28;"jan";203;3;93;1;"failure";"no"
44;"blue-collar";"married";"primary";"no";2657;"yes";"no";"unknown";20;"jun";202;2;-1;0;"unknown";"no"
61;"retired";"married";"secondary";"no";313;"no";"yes";"unknown";3;"jul";42;1;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";61;"no";"yes";"unknown";5;"may";21;1;-1;0;"unknown";"no"
33;"admin.";"single";"secondary";"no";2557;"yes";"no";"cellular";18;"may";186;1;-1;0;"unknown";"no"
30;"services";"divorced";"secondary";"no";0;"yes";"no";"cellular";13;"may";172;1;-1;0;"unknown";"no"
27;"services";"divorced";"secondary";"no";10;"yes";"no";"cellular";22;"jul";527;1;-1;0;"unknown";"no"
37;"services";"married";"secondary";"no";658;"yes";"no";"telephone";23;"jul";175;1;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";1679;"yes";"no";"unknown";20;"jun";21;18;-1;0;"unknown";"no"
35;"management";"single";"tertiary";"no";0;"no";"yes";"cellular";14;"aug";370;4;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";841;"yes";"no";"unknown";29;"may";54;2;-1;0;"unknown";"no"
39;"management";"married";"secondary";"no";0;"yes";"no";"cellular";27;"aug";63;3;-1;0;"unknown";"no"
35;"management";"married";"secondary";"no";1160;"yes";"no";"unknown";27;"may";116;3;-1;0;"unknown";"no"
36;"admin.";"divorced";"secondary";"no";-37;"yes";"no";"unknown";27;"may";151;1;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";94;"yes";"no";"cellular";16;"apr";300;1;-1;0;"unknown";"no"
39;"self-employed";"married";"tertiary";"no";585;"yes";"no";"cellular";18;"nov";48;1;181;3;"failure";"no"
39;"self-employed";"married";"secondary";"no";235;"no";"no";"unknown";9;"jun";217;1;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";2145;"yes";"no";"cellular";18;"may";727;2;-1;0;"unknown";"yes"
32;"services";"married";"secondary";"no";1464;"yes";"no";"unknown";3;"jun";193;1;-1;0;"unknown";"no"
28;"technician";"married";"secondary";"no";232;"yes";"yes";"cellular";29;"jan";72;1;261;1;"failure";"no"
47;"technician";"married";"primary";"no";187;"yes";"no";"unknown";9;"may";165;1;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";12;"no";"no";"cellular";27;"aug";345;10;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";318;"yes";"no";"unknown";27;"may";206;2;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";405;"no";"yes";"cellular";8;"aug";994;2;-1;0;"unknown";"yes"
56;"management";"married";"tertiary";"no";334;"yes";"no";"unknown";15;"may";127;2;-1;0;"unknown";"no"
38;"admin.";"married";"primary";"no";0;"yes";"no";"unknown";2;"jun";29;32;-1;0;"unknown";"no"
52;"blue-collar";"married";"primary";"no";2;"no";"no";"unknown";9;"jun";70;3;-1;0;"unknown";"no"
46;"technician";"married";"secondary";"no";966;"yes";"yes";"cellular";19;"aug";40;2;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"yes";195;"no";"yes";"telephone";30;"jul";185;4;-1;0;"unknown";"no"
42;"blue-collar";"divorced";"unknown";"no";0;"no";"no";"cellular";7;"jul";64;2;-1;0;"unknown";"no"
57;"unemployed";"married";"secondary";"no";0;"no";"no";"unknown";19;"jun";97;1;-1;0;"unknown";"no"
51;"housemaid";"married";"secondary";"no";260;"no";"no";"cellular";27;"aug";316;5;-1;0;"unknown";"no"
53;"self-employed";"married";"tertiary";"no";451;"yes";"no";"cellular";2;"feb";125;1;249;2;"failure";"no"
37;"blue-collar";"married";"primary";"no";3242;"yes";"no";"unknown";21;"may";233;1;-1;0;"unknown";"no"
47;"self-employed";"divorced";"secondary";"no";8515;"yes";"no";"unknown";20;"nov";14;7;112;4;"other";"no"
48;"management";"married";"tertiary";"no";1808;"yes";"no";"cellular";26;"feb";245;1;-1;0;"unknown";"yes"
39;"blue-collar";"married";"primary";"no";20;"yes";"no";"unknown";7;"may";109;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";3068;"yes";"yes";"cellular";11;"may";429;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";23;"no";"no";"cellular";26;"aug";133;11;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";1076;"no";"no";"cellular";8;"aug";70;2;-1;0;"unknown";"no"
61;"retired";"married";"primary";"no";1060;"yes";"no";"unknown";13;"may";118;1;-1;0;"unknown";"no"
32;"admin.";"single";"secondary";"no";678;"yes";"no";"cellular";4;"feb";1579;1;246;4;"failure";"yes"
35;"blue-collar";"single";"primary";"no";1147;"yes";"yes";"telephone";7;"may";29;1;-1;0;"unknown";"no"
54;"blue-collar";"married";"secondary";"no";-630;"no";"no";"unknown";23;"may";173;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";459;"no";"no";"cellular";28;"aug";13;14;-1;0;"unknown";"no"
45;"technician";"divorced";"secondary";"no";976;"yes";"no";"cellular";17;"apr";183;1;338;2;"failure";"no"
32;"housemaid";"married";"tertiary";"no";357;"yes";"no";"cellular";18;"nov";175;2;-1;0;"unknown";"no"
52;"unemployed";"married";"secondary";"no";-9;"no";"no";"unknown";9;"jun";123;1;-1;0;"unknown";"no"
36;"admin.";"single";"secondary";"no";1927;"yes";"no";"cellular";18;"nov";288;1;137;1;"other";"no"
31;"services";"married";"secondary";"no";500;"yes";"no";"unknown";6;"jun";19;18;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";9146;"no";"no";"cellular";13;"aug";136;4;-1;0;"unknown";"no"
43;"entrepreneur";"married";"secondary";"no";59;"no";"no";"unknown";13;"may";437;2;-1;0;"unknown";"no"
47;"housemaid";"married";"primary";"no";0;"no";"no";"cellular";10;"aug";1225;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";328;"yes";"no";"unknown";21;"may";526;8;-1;0;"unknown";"no"
34;"management";"divorced";"secondary";"no";291;"no";"no";"cellular";7;"aug";91;2;-1;0;"unknown";"no"
55;"technician";"single";"tertiary";"no";-703;"yes";"no";"cellular";8;"may";123;3;-1;0;"unknown";"no"
23;"admin.";"single";"secondary";"no";589;"yes";"no";"cellular";6;"may";62;1;-1;0;"unknown";"no"
75;"blue-collar";"married";"secondary";"no";6053;"no";"no";"cellular";11;"feb";865;2;190;1;"failure";"no"
58;"admin.";"married";"secondary";"no";1142;"no";"no";"telephone";4;"feb";82;3;-1;0;"unknown";"no"
35;"admin.";"married";"secondary";"no";725;"yes";"no";"cellular";20;"nov";219;7;133;1;"failure";"no"
27;"management";"married";"tertiary";"no";802;"no";"no";"cellular";22;"sep";569;1;-1;0;"unknown";"yes"
24;"technician";"married";"secondary";"no";556;"yes";"no";"cellular";18;"aug";460;2;-1;0;"unknown";"yes"
48;"admin.";"married";"secondary";"no";191;"yes";"no";"cellular";5;"feb";238;3;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";373;"no";"no";"cellular";22;"aug";346;5;-1;0;"unknown";"no"
57;"technician";"married";"secondary";"no";6164;"no";"no";"cellular";19;"nov";161;2;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";37;"no";"no";"cellular";28;"aug";51;9;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";1156;"yes";"no";"telephone";20;"apr";37;1;-1;0;"unknown";"no"
50;"entrepreneur";"married";"secondary";"no";806;"yes";"yes";"cellular";7;"may";141;8;297;2;"other";"no"
59;"admin.";"married";"secondary";"no";2381;"no";"no";"cellular";30;"apr";173;2;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";-314;"yes";"no";"unknown";28;"may";66;2;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";285;"yes";"yes";"unknown";6;"may";209;1;-1;0;"unknown";"no"
28;"blue-collar";"single";"primary";"no";1199;"yes";"no";"cellular";30;"jul";1011;20;-1;0;"unknown";"no"
55;"management";"divorced";"tertiary";"no";481;"yes";"no";"telephone";8;"may";147;2;298;3;"failure";"no"
33;"technician";"single";"tertiary";"no";137;"no";"no";"cellular";25;"aug";88;4;-1;0;"unknown";"no"
36;"blue-collar";"single";"secondary";"no";168;"yes";"no";"telephone";12;"may";52;1;-1;0;"unknown";"no"
38;"admin.";"divorced";"secondary";"no";156;"yes";"no";"unknown";17;"jun";544;3;-1;0;"unknown";"no"
28;"admin.";"single";"secondary";"no";4745;"no";"no";"telephone";12;"may";360;1;181;5;"success";"yes"
32;"management";"single";"tertiary";"no";11797;"yes";"no";"cellular";11;"aug";95;2;-1;0;"unknown";"no"
35;"admin.";"married";"secondary";"no";786;"no";"yes";"cellular";25;"jul";322;1;-1;0;"unknown";"no"
58;"technician";"married";"primary";"no";4015;"no";"no";"unknown";20;"jun";106;3;-1;0;"unknown";"no"
30;"services";"married";"secondary";"no";0;"yes";"no";"unknown";9;"may";20;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";331;"yes";"no";"cellular";14;"may";178;7;365;5;"other";"no"
36;"blue-collar";"married";"secondary";"no";1554;"yes";"no";"unknown";12;"may";325;4;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";132;"yes";"yes";"unknown";19;"may";343;2;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";5086;"no";"no";"cellular";25;"may";213;4;-1;0;"unknown";"yes"
42;"admin.";"married";"secondary";"no";529;"yes";"yes";"cellular";19;"nov";193;3;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";4824;"no";"yes";"cellular";18;"jul";168;7;-1;0;"unknown";"no"
63;"retired";"married";"secondary";"no";474;"no";"no";"cellular";25;"jan";423;1;-1;0;"unknown";"yes"
38;"admin.";"divorced";"tertiary";"no";86;"yes";"no";"unknown";6;"may";218;1;-1;0;"unknown";"no"
54;"blue-collar";"married";"primary";"no";2281;"yes";"no";"unknown";20;"jun";158;1;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";186;"yes";"no";"cellular";17;"apr";433;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";137;"no";"no";"unknown";20;"jun";252;3;-1;0;"unknown";"no"
37;"technician";"married";"tertiary";"no";6968;"no";"no";"cellular";1;"jun";175;1;-1;0;"unknown";"no"
76;"retired";"divorced";"primary";"no";86;"no";"no";"cellular";12;"aug";192;1;-1;0;"unknown";"no"
28;"management";"single";"unknown";"no";81;"no";"yes";"cellular";8;"jul";158;1;-1;0;"unknown";"no"
33;"services";"married";"primary";"no";6904;"no";"no";"telephone";20;"nov";114;2;-1;0;"unknown";"no"
46;"self-employed";"married";"tertiary";"no";137;"yes";"yes";"unknown";5;"may";246;1;-1;0;"unknown";"no"
30;"student";"single";"secondary";"no";25;"no";"no";"telephone";30;"apr";44;1;-1;0;"unknown";"no"
60;"management";"married";"tertiary";"no";759;"no";"no";"cellular";14;"aug";136;2;-1;0;"unknown";"no"
56;"housemaid";"married";"tertiary";"no";1922;"yes";"no";"cellular";24;"jul";247;2;-1;0;"unknown";"no"
24;"student";"single";"unknown";"no";3298;"yes";"no";"unknown";28;"may";227;1;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";764;"no";"no";"cellular";12;"aug";219;4;-1;0;"unknown";"no"
79;"retired";"married";"primary";"no";8556;"no";"no";"telephone";18;"jan";326;2;272;2;"success";"yes"
32;"blue-collar";"married";"secondary";"no";3514;"yes";"no";"unknown";20;"may";226;2;-1;0;"unknown";"no"
33;"services";"married";"primary";"no";0;"yes";"no";"cellular";18;"jul";351;2;-1;0;"unknown";"no"
77;"retired";"married";"primary";"no";680;"no";"no";"telephone";27;"nov";341;4;94;3;"failure";"no"
60;"housemaid";"married";"primary";"no";0;"no";"yes";"telephone";22;"jul";124;1;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";2819;"no";"no";"cellular";7;"aug";123;3;-1;0;"unknown";"no"
30;"blue-collar";"married";"primary";"no";-518;"yes";"yes";"unknown";28;"may";244;6;-1;0;"unknown";"no"
50;"services";"married";"secondary";"no";60;"no";"yes";"cellular";27;"aug";151;8;-1;0;"unknown";"no"
50;"management";"married";"secondary";"no";732;"no";"no";"cellular";7;"apr";736;7;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";2707;"yes";"no";"telephone";16;"jul";109;1;-1;0;"unknown";"no"
41;"technician";"married";"tertiary";"no";177;"yes";"no";"telephone";11;"jul";117;2;-1;0;"unknown";"no"
49;"admin.";"married";"secondary";"no";13229;"no";"yes";"telephone";8;"jul";77;2;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";-204;"yes";"yes";"unknown";6;"jun";42;1;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";990;"yes";"no";"cellular";6;"feb";252;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";648;"yes";"no";"unknown";3;"jun";179;1;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";50;"no";"no";"cellular";11;"aug";106;3;-1;0;"unknown";"no"
49;"entrepreneur";"married";"secondary";"no";1916;"no";"yes";"telephone";14;"jul";281;3;-1;0;"unknown";"no"
72;"retired";"married";"primary";"no";763;"no";"no";"cellular";24;"apr";384;2;-1;0;"unknown";"yes"
34;"blue-collar";"married";"secondary";"no";470;"yes";"yes";"cellular";20;"apr";217;1;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";0;"no";"yes";"cellular";19;"aug";144;4;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";5739;"yes";"yes";"cellular";21;"nov";374;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";0;"no";"no";"unknown";14;"may";298;1;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";-375;"no";"no";"cellular";30;"jul";814;2;-1;0;"unknown";"yes"
25;"unemployed";"married";"secondary";"no";157;"yes";"no";"cellular";28;"jul";33;31;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";1518;"yes";"no";"cellular";20;"nov";128;2;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";1343;"no";"no";"cellular";5;"aug";129;4;-1;0;"unknown";"no"
39;"management";"single";"tertiary";"yes";-401;"yes";"no";"cellular";19;"aug";274;4;-1;0;"unknown";"no"
41;"management";"married";"primary";"no";5310;"yes";"no";"cellular";17;"nov";984;1;-1;0;"unknown";"yes"
32;"blue-collar";"single";"secondary";"no";2285;"yes";"no";"unknown";14;"may";413;2;-1;0;"unknown";"no"
29;"admin.";"married";"secondary";"no";22171;"yes";"no";"cellular";18;"may";44;1;355;3;"failure";"no"
56;"management";"married";"tertiary";"no";856;"yes";"yes";"cellular";4;"feb";70;4;7;7;"other";"no"
19;"student";"single";"unknown";"no";1169;"no";"no";"cellular";6;"feb";463;18;-1;0;"unknown";"no"
39;"management";"single";"tertiary";"no";3797;"no";"no";"cellular";2;"jun";96;2;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";53;"yes";"no";"cellular";18;"nov";276;1;195;1;"failure";"no"
43;"blue-collar";"married";"primary";"no";2519;"yes";"no";"cellular";16;"apr";257;2;146;9;"other";"no"
32;"entrepreneur";"married";"primary";"no";795;"no";"no";"cellular";8;"aug";131;3;-1;0;"unknown";"no"
53;"admin.";"divorced";"secondary";"no";0;"yes";"yes";"cellular";30;"apr";586;2;-1;0;"unknown";"no"
52;"blue-collar";"married";"secondary";"no";2087;"yes";"no";"cellular";31;"aug";267;1;474;1;"failure";"yes"
43;"technician";"single";"secondary";"no";34;"no";"no";"telephone";30;"jul";29;1;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";5;"no";"no";"unknown";9;"jun";65;6;-1;0;"unknown";"no"
27;"technician";"single";"secondary";"no";15;"yes";"no";"cellular";25;"jul";67;3;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";390;"no";"no";"cellular";28;"oct";190;1;82;1;"success";"no"
51;"management";"married";"unknown";"no";712;"no";"no";"cellular";13;"aug";287;2;-1;0;"unknown";"no"
39;"admin.";"single";"tertiary";"no";2600;"yes";"no";"unknown";17;"jun";77;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";-241;"yes";"yes";"unknown";27;"may";169;1;-1;0;"unknown";"no"
50;"blue-collar";"divorced";"primary";"no";1545;"yes";"no";"cellular";20;"nov";25;1;-1;0;"unknown";"no"
59;"retired";"divorced";"secondary";"no";1026;"no";"no";"unknown";5;"nov";669;1;-1;0;"unknown";"yes"
58;"housemaid";"married";"primary";"no";435;"yes";"no";"telephone";28;"jul";122;6;-1;0;"unknown";"no"
54;"technician";"married";"tertiary";"no";876;"no";"no";"cellular";27;"oct";269;3;541;3;"success";"yes"
60;"blue-collar";"married";"secondary";"no";1801;"no";"no";"telephone";29;"oct";167;1;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";2278;"yes";"yes";"cellular";13;"may";244;1;-1;0;"unknown";"no"
39;"blue-collar";"married";"unknown";"no";157;"yes";"no";"unknown";30;"may";99;6;-1;0;"unknown";"no"
40;"admin.";"married";"secondary";"no";1327;"yes";"yes";"unknown";26;"may";76;2;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";284;"no";"yes";"telephone";14;"jul";252;4;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";495;"no";"no";"cellular";12;"mar";213;3;182;6;"success";"yes"
31;"admin.";"single";"secondary";"no";1588;"yes";"no";"unknown";3;"jun";14;1;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";0;"yes";"no";"telephone";28;"jul";121;2;-1;0;"unknown";"no"
52;"management";"married";"secondary";"no";428;"yes";"no";"unknown";30;"may";153;1;-1;0;"unknown";"no"
45;"admin.";"married";"primary";"no";542;"no";"no";"cellular";11;"may";8;5;350;3;"other";"no"
32;"blue-collar";"married";"primary";"no";66;"no";"no";"cellular";19;"nov";244;1;-1;0;"unknown";"no"
58;"blue-collar";"divorced";"secondary";"no";314;"no";"no";"unknown";25;"jun";67;2;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";24;"yes";"no";"cellular";8;"apr";169;1;231;2;"failure";"no"
36;"management";"single";"tertiary";"no";613;"no";"no";"cellular";7;"aug";86;2;-1;0;"unknown";"no"
55;"retired";"married";"secondary";"no";1467;"no";"yes";"cellular";29;"jul";277;2;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";192;"yes";"no";"cellular";13;"may";154;5;176;1;"other";"no"
49;"management";"married";"secondary";"no";321;"no";"no";"unknown";9;"jun";135;3;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";788;"yes";"no";"cellular";17;"apr";392;1;-1;0;"unknown";"no"
37;"blue-collar";"divorced";"secondary";"no";48;"yes";"no";"unknown";8;"may";133;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";262;"no";"no";"cellular";2;"jul";96;2;109;4;"success";"no"
36;"blue-collar";"married";"secondary";"no";4438;"yes";"no";"unknown";20;"may";446;1;-1;0;"unknown";"yes"
44;"services";"single";"secondary";"no";5045;"yes";"no";"cellular";21;"nov";20;6;175;15;"other";"no"
32;"blue-collar";"single";"tertiary";"no";3014;"yes";"no";"cellular";7;"may";291;2;-1;0;"unknown";"no"
56;"management";"married";"tertiary";"no";21664;"no";"no";"unknown";17;"jun";214;8;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";5878;"no";"no";"cellular";19;"aug";159;2;-1;0;"unknown";"no"
35;"entrepreneur";"married";"tertiary";"no";2723;"yes";"yes";"cellular";8;"aug";275;4;-1;0;"unknown";"no"
44;"management";"married";"secondary";"no";576;"yes";"no";"unknown";8;"may";128;3;-1;0;"unknown";"no"
59;"entrepreneur";"married";"primary";"no";0;"no";"no";"telephone";19;"nov";207;2;153;1;"failure";"no"
31;"blue-collar";"single";"tertiary";"no";2197;"yes";"no";"unknown";23;"may";146;3;-1;0;"unknown";"no"
60;"blue-collar";"married";"unknown";"no";104;"yes";"no";"unknown";5;"may";22;1;-1;0;"unknown";"no"
33;"technician";"single";"secondary";"no";1279;"yes";"no";"cellular";18;"may";239;2;349;3;"success";"no"
45;"admin.";"married";"secondary";"no";295;"yes";"no";"cellular";23;"jul";92;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";2128;"yes";"no";"unknown";19;"may";149;4;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";367;"no";"no";"telephone";16;"nov";107;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";1477;"no";"no";"cellular";7;"aug";71;2;-1;0;"unknown";"no"
30;"blue-collar";"single";"primary";"no";71;"yes";"no";"unknown";19;"may";122;6;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";127;"yes";"no";"cellular";18;"may";16;6;368;1;"other";"no"
59;"unemployed";"divorced";"secondary";"no";2921;"no";"no";"telephone";3;"feb";251;1;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";16264;"no";"no";"telephone";17;"nov";215;3;-1;0;"unknown";"no"
49;"retired";"married";"primary";"no";1672;"no";"no";"cellular";17;"jul";128;3;-1;0;"unknown";"no"
48;"technician";"married";"tertiary";"no";9207;"no";"no";"cellular";18;"aug";445;3;-1;0;"unknown";"no"
28;"technician";"single";"tertiary";"no";356;"no";"no";"cellular";2;"mar";76;2;-1;0;"unknown";"no"
59;"management";"divorced";"primary";"no";133;"yes";"no";"cellular";9;"jul";206;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";1217;"yes";"yes";"cellular";18;"nov";155;1;-1;0;"unknown";"no"
32;"technician";"divorced";"secondary";"no";202;"yes";"no";"cellular";4;"feb";429;1;201;1;"failure";"no"
58;"blue-collar";"married";"secondary";"no";590;"no";"yes";"unknown";5;"jun";133;1;-1;0;"unknown";"no"
28;"self-employed";"single";"tertiary";"no";442;"no";"no";"cellular";19;"jun";474;3;95;3;"other";"yes"
27;"technician";"single";"tertiary";"no";808;"yes";"no";"cellular";15;"may";578;1;364;1;"other";"no"
38;"management";"single";"tertiary";"no";427;"yes";"no";"cellular";6;"may";17;3;169;2;"failure";"no"
53;"technician";"married";"tertiary";"no";431;"yes";"no";"unknown";11;"jun";74;1;-1;0;"unknown";"no"
60;"services";"married";"tertiary";"no";4256;"yes";"no";"cellular";16;"aug";239;3;761;1;"other";"yes"
33;"management";"divorced";"tertiary";"no";0;"yes";"yes";"cellular";19;"aug";275;8;-1;0;"unknown";"no"
52;"services";"divorced";"secondary";"no";238;"yes";"no";"cellular";6;"may";101;1;362;5;"other";"no"
64;"retired";"married";"tertiary";"no";846;"no";"no";"cellular";11;"feb";671;1;181;3;"failure";"yes"
40;"blue-collar";"single";"secondary";"no";3;"no";"no";"cellular";19;"nov";998;1;-1;0;"unknown";"yes"
52;"admin.";"married";"secondary";"no";693;"no";"no";"unknown";12;"jun";271;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";0;"yes";"no";"unknown";15;"may";133;3;-1;0;"unknown";"no"
49;"management";"married";"secondary";"no";362;"yes";"no";"unknown";4;"jun";84;2;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";618;"yes";"no";"cellular";7;"may";281;1;-1;0;"unknown";"no"
29;"technician";"single";"tertiary";"no";2893;"yes";"no";"unknown";3;"jun";250;1;-1;0;"unknown";"no"
87;"retired";"married";"primary";"no";230;"no";"no";"cellular";30;"oct";144;1;-1;0;"unknown";"yes"
37;"management";"divorced";"secondary";"no";1287;"yes";"no";"cellular";14;"may";432;1;-1;0;"unknown";"no"
36;"services";"married";"primary";"no";179;"no";"no";"unknown";9;"jun";411;1;-1;0;"unknown";"no"
35;"self-employed";"married";"tertiary";"no";980;"no";"no";"cellular";2;"aug";428;2;336;6;"failure";"no"
33;"admin.";"married";"tertiary";"no";79;"yes";"no";"cellular";22;"oct";249;2;335;2;"failure";"yes"
60;"admin.";"married";"secondary";"no";3043;"no";"no";"cellular";19;"aug";235;3;-1;0;"unknown";"no"
25;"management";"single";"tertiary";"no";808;"no";"no";"cellular";18;"sep";267;2;114;2;"failure";"yes"
69;"retired";"married";"secondary";"no";745;"no";"no";"telephone";9;"sep";595;1;-1;0;"unknown";"no"
41;"student";"married";"secondary";"no";1309;"no";"no";"unknown";20;"jun";28;4;-1;0;"unknown";"no"
48;"management";"single";"secondary";"no";290;"yes";"no";"cellular";21;"jul";257;2;-1;0;"unknown";"no"
35;"blue-collar";"divorced";"primary";"no";1792;"yes";"no";"cellular";31;"jul";630;6;-1;0;"unknown";"yes"
30;"blue-collar";"single";"secondary";"no";178;"yes";"no";"unknown";20;"may";747;5;-1;0;"unknown";"no"
59;"management";"married";"tertiary";"no";0;"no";"no";"cellular";27;"aug";87;4;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";537;"no";"no";"unknown";29;"may";187;3;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";416;"yes";"no";"unknown";13;"may";452;2;-1;0;"unknown";"no"
60;"blue-collar";"married";"primary";"no";1472;"no";"no";"unknown";20;"jun";36;4;-1;0;"unknown";"no"
36;"management";"married";"primary";"no";3953;"yes";"no";"cellular";19;"nov";277;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";112;"yes";"no";"cellular";5;"may";118;1;-1;0;"unknown";"no"
34;"management";"divorced";"tertiary";"no";13204;"yes";"yes";"cellular";20;"nov";197;2;-1;0;"unknown";"no"
58;"blue-collar";"married";"secondary";"no";0;"no";"yes";"cellular";14;"apr";101;5;-1;0;"unknown";"no"
53;"management";"married";"unknown";"no";2240;"yes";"no";"cellular";2;"jun";282;2;385;1;"success";"yes"
31;"management";"married";"tertiary";"no";22546;"yes";"no";"cellular";14;"may";8;6;267;4;"failure";"no"
33;"admin.";"married";"secondary";"no";0;"yes";"no";"telephone";17;"jul";1448;7;-1;0;"unknown";"yes"
46;"unemployed";"divorced";"primary";"no";23;"no";"no";"cellular";21;"nov";344;1;-1;0;"unknown";"no"
29;"blue-collar";"married";"secondary";"no";228;"yes";"no";"cellular";11;"may";12;8;342;9;"failure";"no"
37;"self-employed";"married";"secondary";"no";215;"yes";"no";"unknown";6;"jun";61;1;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";23;"yes";"no";"unknown";20;"may";91;6;-1;0;"unknown";"no"
34;"management";"married";"secondary";"no";293;"yes";"no";"unknown";21;"may";162;3;-1;0;"unknown";"no"
34;"blue-collar";"divorced";"secondary";"no";0;"no";"yes";"unknown";21;"may";339;1;-1;0;"unknown";"no"
49;"entrepreneur";"married";"primary";"no";199;"no";"no";"unknown";13;"may";113;2;-1;0;"unknown";"no"
53;"admin.";"married";"secondary";"no";459;"no";"no";"cellular";5;"apr";558;1;-1;0;"unknown";"yes"
42;"unemployed";"married";"secondary";"no";8131;"yes";"no";"unknown";30;"may";69;1;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";-378;"yes";"yes";"cellular";13;"may";541;1;295;6;"failure";"no"
30;"blue-collar";"single";"primary";"no";395;"no";"no";"cellular";21;"nov";429;1;-1;0;"unknown";"no"
31;"technician";"divorced";"secondary";"no";10787;"yes";"no";"unknown";4;"jun";135;2;-1;0;"unknown";"no"
55;"admin.";"married";"secondary";"no";865;"yes";"no";"cellular";27;"oct";96;1;111;3;"failure";"no"
36;"management";"married";"tertiary";"no";340;"yes";"no";"cellular";20;"apr";68;2;-1;0;"unknown";"no"
31;"blue-collar";"single";"secondary";"no";1882;"no";"no";"cellular";7;"may";529;2;-1;0;"unknown";"no"
26;"management";"married";"tertiary";"no";265;"no";"no";"cellular";30;"jan";650;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";389;"yes";"no";"cellular";7;"dec";69;1;185;2;"failure";"no"
49;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";29;"jul";71;7;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";384;"yes";"no";"cellular";12;"may";30;7;-1;0;"unknown";"no"
47;"management";"divorced";"tertiary";"no";94;"yes";"no";"cellular";31;"jul";9;19;-1;0;"unknown";"no"
61;"management";"married";"secondary";"no";1471;"no";"no";"cellular";12;"aug";331;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";937;"yes";"no";"unknown";12;"jun";66;1;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";500;"yes";"no";"cellular";20;"nov";26;6;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";25;"yes";"no";"unknown";6;"may";266;3;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";365;"no";"no";"cellular";21;"nov";168;2;-1;0;"unknown";"no"
25;"unemployed";"single";"secondary";"no";34;"no";"yes";"unknown";4;"jul";316;1;-1;0;"unknown";"no"
79;"retired";"married";"primary";"no";429;"no";"no";"telephone";8;"jul";1006;2;-1;0;"unknown";"yes"
53;"technician";"married";"primary";"no";6839;"yes";"no";"cellular";19;"nov";77;1;-1;0;"unknown";"no"
21;"student";"single";"secondary";"no";6;"no";"no";"unknown";9;"may";622;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";467;"yes";"no";"unknown";21;"may";494;2;-1;0;"unknown";"no"
40;"self-employed";"divorced";"secondary";"no";1876;"no";"yes";"unknown";20;"jun";269;1;-1;0;"unknown";"no"
34;"entrepreneur";"married";"secondary";"no";1847;"yes";"no";"cellular";9;"apr";543;3;321;6;"failure";"no"
52;"unknown";"married";"primary";"no";247;"no";"no";"cellular";29;"jul";268;6;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";897;"no";"no";"cellular";5;"jun";397;1;-1;0;"unknown";"yes"
57;"services";"divorced";"secondary";"no";4664;"no";"no";"cellular";21;"nov";22;4;114;1;"failure";"no"
51;"self-employed";"divorced";"secondary";"no";1291;"yes";"no";"unknown";3;"jun";51;2;-1;0;"unknown";"no"
31;"student";"single";"tertiary";"no";64;"no";"no";"cellular";15;"sep";208;1;105;2;"success";"no"
49;"unemployed";"married";"tertiary";"no";-471;"yes";"no";"cellular";20;"nov";77;3;185;7;"failure";"no"
32;"management";"single";"tertiary";"no";1160;"yes";"yes";"unknown";11;"jun";450;2;-1;0;"unknown";"no"
28;"admin.";"single";"tertiary";"no";14;"no";"no";"cellular";1;"jun";201;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";26;"yes";"no";"cellular";17;"apr";342;3;332;1;"other";"no"
50;"blue-collar";"married";"unknown";"no";452;"no";"no";"cellular";20;"aug";181;2;-1;0;"unknown";"no"
42;"blue-collar";"single";"secondary";"no";382;"yes";"no";"cellular";13;"may";294;1;-1;0;"unknown";"no"
45;"admin.";"married";"unknown";"no";2033;"no";"no";"cellular";30;"nov";123;1;186;4;"failure";"no"
34;"unemployed";"married";"secondary";"no";0;"yes";"no";"cellular";13;"may";172;1;-1;0;"unknown";"no"
56;"admin.";"divorced";"unknown";"no";789;"yes";"no";"cellular";8;"may";1022;1;360;2;"failure";"no"
33;"blue-collar";"single";"secondary";"no";142;"yes";"no";"cellular";14;"may";123;1;360;3;"other";"no"
34;"admin.";"married";"secondary";"no";645;"yes";"no";"unknown";19;"may";420;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"unknown";"no";897;"yes";"no";"cellular";13;"apr";119;2;280;2;"failure";"no"
37;"blue-collar";"married";"secondary";"no";103;"yes";"no";"cellular";12;"may";153;1;-1;0;"unknown";"no"
35;"admin.";"single";"secondary";"no";847;"yes";"no";"cellular";11;"may";85;2;-1;0;"unknown";"no"
38;"unknown";"single";"tertiary";"no";0;"no";"no";"cellular";11;"sep";260;3;95;3;"success";"yes"
32;"blue-collar";"married";"secondary";"no";517;"yes";"no";"unknown";5;"may";528;1;-1;0;"unknown";"no"
52;"technician";"single";"tertiary";"no";1308;"yes";"no";"unknown";29;"may";361;1;-1;0;"unknown";"no"
49;"services";"married";"secondary";"no";66;"yes";"yes";"cellular";21;"nov";59;4;-1;0;"unknown";"no"
46;"management";"divorced";"secondary";"no";361;"yes";"no";"unknown";19;"may";86;7;-1;0;"unknown";"no"
42;"entrepreneur";"married";"primary";"yes";66;"yes";"yes";"cellular";6;"feb";139;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"unknown";"no";70;"yes";"no";"unknown";8;"may";219;4;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";180;"yes";"no";"telephone";15;"may";45;2;-1;0;"unknown";"no"
49;"technician";"married";"tertiary";"no";279;"yes";"no";"unknown";19;"may";168;4;-1;0;"unknown";"no"
46;"services";"married";"secondary";"no";4;"no";"no";"cellular";30;"apr";268;1;261;5;"other";"yes"
40;"entrepreneur";"married";"secondary";"no";1416;"yes";"no";"unknown";14;"may";210;2;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";465;"yes";"no";"unknown";20;"may";160;1;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";998;"yes";"no";"unknown";14;"may";1044;2;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";550;"no";"yes";"cellular";9;"jul";601;1;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";1285;"yes";"no";"unknown";27;"may";89;1;-1;0;"unknown";"no"
32;"services";"married";"tertiary";"no";4908;"no";"yes";"cellular";13;"may";491;2;-1;0;"unknown";"no"
32;"unemployed";"single";"tertiary";"no";336;"yes";"no";"unknown";8;"may";139;1;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";131;"no";"no";"unknown";3;"jun";267;3;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";348;"no";"yes";"cellular";15;"jul";88;3;-1;0;"unknown";"no"
25;"self-employed";"single";"tertiary";"no";453;"yes";"no";"cellular";17;"jul";134;1;-1;0;"unknown";"no"
55;"admin.";"married";"secondary";"no";1102;"yes";"no";"unknown";15;"may";126;2;-1;0;"unknown";"no"
24;"admin.";"single";"secondary";"no";926;"yes";"yes";"cellular";15;"may";191;1;-1;0;"unknown";"no"
40;"management";"divorced";"tertiary";"no";502;"yes";"no";"cellular";3;"feb";267;4;270;3;"other";"no"
38;"management";"single";"tertiary";"no";8669;"yes";"no";"telephone";20;"apr";5;3;150;6;"failure";"no"
48;"blue-collar";"single";"primary";"no";316;"no";"no";"unknown";5;"jun";560;2;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";9324;"yes";"no";"cellular";30;"mar";138;8;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";0;"no";"no";"cellular";19;"aug";188;4;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";72;"yes";"yes";"cellular";15;"may";262;2;352;2;"failure";"no"
33;"self-employed";"single";"tertiary";"no";61;"no";"no";"cellular";19;"aug";836;3;-1;0;"unknown";"yes"
38;"technician";"single";"secondary";"no";173;"no";"no";"cellular";29;"aug";150;4;-1;0;"unknown";"no"
26;"services";"single";"secondary";"yes";-162;"yes";"no";"cellular";15;"may";44;2;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";47;"no";"yes";"cellular";31;"jul";11;17;-1;0;"unknown";"no"
55;"services";"divorced";"tertiary";"no";1983;"yes";"no";"telephone";19;"nov";123;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";326;"yes";"yes";"unknown";15;"may";150;2;-1;0;"unknown";"no"
60;"admin.";"married";"secondary";"no";2552;"no";"yes";"cellular";30;"jan";255;2;2;4;"other";"no"
36;"management";"single";"tertiary";"no";1283;"no";"no";"cellular";18;"aug";92;5;-1;0;"unknown";"no"
65;"housemaid";"married";"primary";"no";2179;"yes";"no";"cellular";4;"sep";112;7;-1;0;"unknown";"no"
39;"self-employed";"married";"tertiary";"no";1047;"no";"no";"unknown";6;"jun";554;1;-1;0;"unknown";"yes"
52;"blue-collar";"married";"primary";"no";36;"no";"no";"cellular";13;"aug";109;2;-1;0;"unknown";"no"
55;"technician";"married";"secondary";"yes";-308;"no";"no";"cellular";2;"feb";781;1;-1;0;"unknown";"yes"
26;"management";"single";"tertiary";"no";1177;"no";"no";"cellular";19;"oct";116;1;-1;0;"unknown";"no"
40;"self-employed";"married";"primary";"no";2077;"yes";"no";"cellular";13;"may";363;1;173;24;"other";"no"
52;"entrepreneur";"married";"secondary";"no";0;"no";"no";"cellular";7;"aug";82;2;-1;0;"unknown";"no"
29;"self-employed";"married";"tertiary";"no";1;"no";"no";"cellular";18;"jul";167;3;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";7735;"yes";"no";"cellular";20;"nov";220;1;190;1;"failure";"no"
30;"technician";"single";"tertiary";"no";1922;"yes";"no";"cellular";18;"may";546;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";153;"no";"no";"cellular";23;"jul";1124;2;-1;0;"unknown";"yes"
26;"blue-collar";"married";"secondary";"no";2770;"yes";"no";"unknown";8;"may";141;1;-1;0;"unknown";"no"
58;"management";"married";"secondary";"no";139;"no";"no";"cellular";27;"may";188;1;161;2;"other";"no"
29;"management";"single";"tertiary";"no";21;"yes";"no";"cellular";14;"may";107;4;371;3;"failure";"no"
48;"technician";"married";"secondary";"no";178;"yes";"no";"unknown";17;"jun";320;4;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";444;"yes";"no";"cellular";15;"apr";244;3;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";209;"yes";"no";"cellular";22;"jul";319;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"unknown";"no";3096;"no";"no";"unknown";16;"jun";147;2;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";5320;"no";"no";"cellular";3;"mar";114;6;467;5;"other";"no"
32;"services";"married";"secondary";"no";4885;"yes";"yes";"cellular";18;"nov";400;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";18;"nov";79;3;-1;0;"unknown";"no"
48;"unemployed";"married";"primary";"no";5462;"no";"no";"unknown";6;"jun";29;4;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";6767;"yes";"no";"cellular";20;"nov";97;2;-1;0;"unknown";"no"
42;"technician";"divorced";"secondary";"no";2974;"yes";"no";"unknown";21;"may";187;5;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";427;"no";"no";"unknown";14;"may";201;1;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";8876;"no";"no";"cellular";2;"feb";280;1;75;1;"failure";"no"
53;"management";"married";"tertiary";"no";146;"no";"no";"unknown";30;"may";150;1;-1;0;"unknown";"no"
59;"retired";"divorced";"secondary";"no";319;"no";"no";"cellular";24;"jul";129;1;-1;0;"unknown";"no"
28;"unemployed";"single";"secondary";"no";304;"no";"no";"cellular";14;"jun";516;1;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";389;"yes";"no";"unknown";16;"jun";127;2;-1;0;"unknown";"no"
50;"management";"single";"primary";"no";766;"no";"no";"unknown";17;"jun";174;2;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";0;"yes";"no";"cellular";29;"jul";82;5;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";3107;"yes";"no";"unknown";3;"jun";1516;2;-1;0;"unknown";"no"
59;"admin.";"divorced";"secondary";"no";174;"yes";"no";"unknown";16;"may";204;1;-1;0;"unknown";"no"
33;"technician";"divorced";"secondary";"yes";-305;"no";"yes";"unknown";9;"may";109;1;-1;0;"unknown";"no"
35;"self-employed";"married";"secondary";"no";670;"no";"no";"cellular";9;"jul";378;1;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";6164;"yes";"no";"unknown";27;"may";152;11;-1;0;"unknown";"no"
71;"retired";"married";"secondary";"no";2850;"no";"no";"cellular";17;"feb";185;1;-1;0;"unknown";"yes"
30;"services";"single";"primary";"no";79;"yes";"no";"cellular";15;"may";317;1;-1;0;"unknown";"no"
24;"services";"married";"secondary";"yes";110;"yes";"no";"unknown";23;"may";27;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";5039;"no";"no";"cellular";20;"nov";205;6;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";360;"yes";"no";"telephone";19;"nov";46;1;182;2;"failure";"no"
52;"admin.";"married";"secondary";"no";199;"no";"no";"cellular";28;"jan";978;2;189;1;"other";"no"
33;"self-employed";"single";"secondary";"no";238;"yes";"yes";"cellular";5;"feb";45;5;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";5092;"yes";"no";"unknown";17;"jun";145;6;-1;0;"unknown";"no"
41;"admin.";"divorced";"secondary";"no";1088;"yes";"no";"cellular";28;"jan";138;1;-1;0;"unknown";"no"
42;"management";"divorced";"tertiary";"no";221;"yes";"no";"unknown";7;"may";720;2;-1;0;"unknown";"no"
38;"blue-collar";"divorced";"secondary";"no";889;"no";"yes";"unknown";18;"jun";362;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"primary";"no";750;"yes";"no";"cellular";18;"may";90;6;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";0;"yes";"yes";"unknown";16;"may";214;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";1291;"yes";"no";"cellular";14;"may";142;3;259;17;"failure";"no"
33;"technician";"married";"secondary";"no";1844;"yes";"no";"unknown";29;"may";241;3;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";89;"yes";"yes";"telephone";9;"feb";148;6;-1;0;"unknown";"no"
36;"admin.";"married";"secondary";"no";4280;"no";"no";"unknown";19;"jun";227;6;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";0;"no";"no";"cellular";21;"aug";126;2;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";625;"yes";"no";"unknown";28;"may";410;1;-1;0;"unknown";"no"
55;"technician";"married";"unknown";"no";574;"yes";"no";"cellular";29;"jan";243;2;-1;0;"unknown";"no"
58;"management";"married";"primary";"no";320;"yes";"no";"unknown";30;"may";171;2;-1;0;"unknown";"no"
25;"technician";"single";"tertiary";"no";468;"yes";"no";"unknown";15;"may";147;1;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";440;"yes";"no";"unknown";12;"jun";91;1;-1;0;"unknown";"no"
40;"unemployed";"married";"secondary";"no";0;"yes";"no";"cellular";19;"nov";332;1;183;1;"failure";"no"
59;"retired";"married";"secondary";"no";5706;"yes";"no";"cellular";25;"jul";471;2;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";7780;"no";"no";"telephone";4;"may";364;1;355;1;"other";"no"
30;"management";"married";"secondary";"no";-559;"yes";"yes";"cellular";7;"may";98;1;167;2;"failure";"no"
25;"entrepreneur";"single";"tertiary";"no";16874;"yes";"yes";"cellular";15;"may";117;2;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";1987;"no";"no";"telephone";14;"jul";185;4;-1;0;"unknown";"no"
39;"entrepreneur";"married";"unknown";"no";98;"yes";"no";"telephone";15;"may";59;3;367;1;"other";"no"
40;"self-employed";"married";"tertiary";"no";642;"no";"no";"cellular";18;"nov";147;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";526;"yes";"yes";"unknown";23;"may";553;2;-1;0;"unknown";"no"
66;"management";"single";"tertiary";"no";6483;"no";"no";"cellular";13;"aug";275;1;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";2306;"no";"no";"telephone";6;"aug";239;4;-1;0;"unknown";"yes"
37;"blue-collar";"married";"secondary";"no";954;"yes";"no";"unknown";2;"jun";402;9;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";-679;"yes";"yes";"cellular";7;"may";172;1;93;1;"failure";"no"
50;"blue-collar";"married";"primary";"no";-1;"no";"no";"cellular";19;"aug";195;2;-1;0;"unknown";"no"
76;"retired";"married";"primary";"no";2590;"no";"no";"telephone";9;"feb";68;12;-1;0;"unknown";"no"
58;"management";"single";"tertiary";"no";331;"no";"yes";"cellular";28;"jul";82;3;-1;0;"unknown";"no"
42;"admin.";"married";"secondary";"no";4209;"yes";"no";"unknown";5;"jun";57;6;-1;0;"unknown";"no"
58;"services";"divorced";"secondary";"no";462;"no";"no";"cellular";13;"nov";495;1;93;1;"success";"yes"
28;"self-employed";"single";"tertiary";"no";0;"yes";"no";"cellular";21;"jul";479;1;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";0;"no";"no";"cellular";29;"aug";141;2;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";151;"no";"no";"cellular";19;"aug";82;4;-1;0;"unknown";"no"
32;"management";"divorced";"tertiary";"no";883;"yes";"no";"cellular";10;"dec";798;1;185;5;"other";"yes"
27;"services";"single";"secondary";"no";266;"no";"yes";"cellular";7;"jul";232;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";0;"no";"no";"cellular";26;"aug";60;5;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";491;"yes";"no";"unknown";8;"may";103;3;-1;0;"unknown";"no"
54;"management";"married";"tertiary";"no";3;"no";"yes";"cellular";8;"jul";443;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";198;"yes";"no";"unknown";9;"may";400;2;-1;0;"unknown";"no"
52;"housemaid";"married";"secondary";"no";14363;"no";"no";"unknown";5;"jun";132;1;-1;0;"unknown";"no"
34;"technician";"divorced";"secondary";"no";0;"yes";"yes";"cellular";18;"aug";374;16;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";136;"no";"no";"cellular";25;"aug";363;7;-1;0;"unknown";"yes"
32;"management";"single";"tertiary";"no";28;"no";"no";"cellular";8;"aug";111;2;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";335;"no";"no";"cellular";29;"aug";130;4;-1;0;"unknown";"no"
51;"housemaid";"married";"unknown";"no";174;"no";"no";"telephone";29;"jul";308;3;-1;0;"unknown";"no"
39;"unemployed";"married";"secondary";"no";270;"yes";"no";"unknown";3;"jun";116;1;-1;0;"unknown";"no"
44;"management";"single";"tertiary";"no";1206;"no";"no";"cellular";9;"jul";160;2;-1;0;"unknown";"no"
49;"housemaid";"divorced";"primary";"no";198;"yes";"no";"unknown";13;"may";205;3;-1;0;"unknown";"no"
25;"technician";"married";"tertiary";"no";914;"yes";"no";"cellular";12;"may";185;2;-1;0;"unknown";"no"
39;"self-employed";"married";"secondary";"no";31;"yes";"no";"cellular";20;"nov";61;2;181;4;"failure";"no"
54;"technician";"married";"secondary";"no";0;"no";"no";"telephone";14;"aug";159;2;106;6;"success";"no"
32;"management";"single";"tertiary";"no";347;"yes";"no";"cellular";18;"may";213;1;264;6;"failure";"no"
36;"management";"married";"tertiary";"no";860;"yes";"no";"unknown";20;"may";294;15;-1;0;"unknown";"no"
54;"technician";"married";"secondary";"no";89;"yes";"no";"cellular";10;"jul";140;3;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";0;"yes";"yes";"cellular";28;"jan";71;1;223;9;"other";"no"
45;"services";"married";"secondary";"no";0;"no";"yes";"cellular";8;"jul";47;7;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";68;"yes";"no";"cellular";16;"apr";570;2;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";-149;"yes";"no";"cellular";14;"jul";287;2;-1;0;"unknown";"no"
33;"services";"divorced";"secondary";"no";2411;"yes";"no";"cellular";23;"oct";130;1;161;1;"failure";"yes"
46;"management";"divorced";"tertiary";"no";225;"yes";"no";"cellular";14;"may";201;1;345;1;"other";"no"
43;"unknown";"single";"tertiary";"no";2731;"no";"no";"cellular";21;"nov";11;7;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";112;"yes";"no";"cellular";4;"nov";70;5;85;5;"other";"no"
44;"management";"divorced";"tertiary";"no";0;"no";"no";"cellular";22;"aug";54;2;-1;0;"unknown";"no"
42;"services";"married";"secondary";"no";3620;"yes";"no";"unknown";27;"may";22;16;-1;0;"unknown";"no"
26;"blue-collar";"single";"secondary";"no";179;"no";"no";"cellular";30;"jul";103;3;-1;0;"unknown";"no"
47;"admin.";"single";"secondary";"no";1544;"yes";"no";"cellular";6;"apr";104;1;312;1;"failure";"no"
48;"blue-collar";"married";"primary";"no";199;"no";"no";"telephone";18;"aug";29;2;-1;0;"unknown";"no"
60;"blue-collar";"married";"unknown";"no";231;"yes";"no";"unknown";12;"may";57;2;-1;0;"unknown";"no"
27;"admin.";"divorced";"secondary";"no";76;"yes";"no";"unknown";4;"jun";236;1;-1;0;"unknown";"no"
30;"unemployed";"single";"tertiary";"no";302;"no";"no";"cellular";11;"feb";528;3;-1;0;"unknown";"yes"
28;"admin.";"single";"secondary";"no";189;"yes";"no";"cellular";31;"jul";68;2;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";565;"no";"no";"telephone";7;"jul";876;1;-1;0;"unknown";"yes"
37;"entrepreneur";"married";"tertiary";"no";310;"no";"no";"cellular";1;"sep";76;1;-1;0;"unknown";"no"
36;"services";"married";"secondary";"no";1746;"yes";"no";"unknown";26;"may";99;1;-1;0;"unknown";"no"
39;"technician";"married";"secondary";"no";6;"yes";"no";"cellular";18;"may";122;3;357;4;"failure";"no"
52;"unemployed";"married";"primary";"no";-110;"no";"yes";"cellular";29;"jan";141;1;-1;0;"unknown";"no"
55;"retired";"married";"secondary";"no";2749;"yes";"yes";"unknown";29;"may";58;2;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";6495;"no";"no";"cellular";30;"mar";78;3;-1;0;"unknown";"no"
36;"blue-collar";"single";"secondary";"no";35;"yes";"no";"unknown";3;"jun";435;3;-1;0;"unknown";"no"
39;"admin.";"married";"secondary";"no";-650;"no";"no";"cellular";15;"may";123;2;364;1;"failure";"no"
47;"blue-collar";"married";"primary";"no";292;"no";"yes";"telephone";28;"jul";210;3;-1;0;"unknown";"no"
42;"housemaid";"single";"primary";"no";5774;"yes";"no";"unknown";15;"may";54;2;-1;0;"unknown";"no"
42;"management";"married";"secondary";"no";2416;"yes";"no";"unknown";20;"may";138;4;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";35;"no";"no";"cellular";29;"jul";336;7;-1;0;"unknown";"no"
68;"retired";"married";"secondary";"no";19317;"yes";"no";"cellular";4;"aug";249;1;-1;0;"unknown";"yes"
43;"technician";"married";"secondary";"no";50;"no";"no";"unknown";28;"may";63;1;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";-674;"yes";"no";"unknown";5;"may";257;1;-1;0;"unknown";"no"
49;"technician";"married";"tertiary";"no";3726;"no";"no";"cellular";5;"aug";875;2;-1;0;"unknown";"yes"
57;"retired";"married";"tertiary";"no";1180;"yes";"yes";"unknown";14;"may";191;2;-1;0;"unknown";"no"
61;"self-employed";"divorced";"tertiary";"no";6610;"no";"no";"cellular";24;"feb";481;2;-1;0;"unknown";"yes"
36;"admin.";"single";"secondary";"no";319;"yes";"no";"cellular";18;"may";120;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";1545;"yes";"yes";"cellular";13;"may";207;1;-1;0;"unknown";"no"
51;"technician";"married";"tertiary";"no";1315;"no";"no";"telephone";5;"jun";303;2;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";424;"no";"no";"cellular";21;"aug";93;2;-1;0;"unknown";"no"
56;"blue-collar";"married";"primary";"no";151;"no";"no";"cellular";3;"feb";128;1;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";-970;"no";"yes";"cellular";21;"aug";194;2;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";-762;"yes";"yes";"unknown";30;"may";408;2;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";181;"yes";"no";"cellular";31;"jul";5;3;-1;0;"unknown";"no"
31;"blue-collar";"married";"unknown";"no";-120;"yes";"no";"cellular";17;"jul";231;2;-1;0;"unknown";"no"
39;"admin.";"divorced";"secondary";"no";1022;"yes";"no";"unknown";20;"jun";20;3;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";78;"yes";"no";"cellular";14;"jan";218;3;161;9;"other";"no"
39;"technician";"married";"tertiary";"no";209;"no";"no";"unknown";23;"jul";36;2;-1;0;"unknown";"no"
26;"admin.";"married";"secondary";"no";2469;"no";"no";"cellular";16;"jul";136;8;-1;0;"unknown";"no"
55;"technician";"married";"secondary";"no";0;"no";"no";"cellular";19;"nov";336;1;-1;0;"unknown";"no"
43;"blue-collar";"divorced";"primary";"no";328;"no";"yes";"cellular";8;"jul";192;1;-1;0;"unknown";"no"
34;"housemaid";"married";"tertiary";"no";0;"no";"no";"cellular";26;"may";141;1;188;1;"failure";"no"
51;"self-employed";"married";"primary";"no";307;"yes";"yes";"unknown";17;"jun";65;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1384;"yes";"no";"cellular";30;"jan";203;2;2;3;"other";"no"
32;"management";"married";"tertiary";"no";0;"no";"no";"cellular";26;"aug";104;9;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";719;"yes";"no";"unknown";14;"may";230;1;-1;0;"unknown";"no"
44;"management";"divorced";"tertiary";"no";0;"no";"no";"cellular";10;"aug";71;1;-1;0;"unknown";"no"
38;"entrepreneur";"married";"tertiary";"no";6704;"yes";"no";"unknown";21;"may";205;2;-1;0;"unknown";"no"
47;"self-employed";"married";"tertiary";"no";4543;"yes";"no";"unknown";14;"may";162;1;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";27;"may";145;2;-1;0;"unknown";"no"
30;"management";"single";"tertiary";"no";149;"yes";"no";"unknown";3;"jun";220;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";-1400;"yes";"no";"telephone";6;"may";309;3;355;4;"failure";"no"
33;"entrepreneur";"married";"secondary";"no";-118;"yes";"yes";"unknown";27;"may";421;3;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";2160;"no";"no";"cellular";8;"sep";261;1;98;1;"success";"yes"
38;"management";"married";"tertiary";"no";3452;"no";"no";"cellular";13;"aug";132;2;-1;0;"unknown";"no"
27;"technician";"single";"secondary";"no";842;"no";"no";"cellular";30;"jul";273;1;91;1;"other";"no"
38;"management";"married";"unknown";"no";3576;"no";"no";"cellular";25;"nov";349;2;183;1;"success";"yes"
41;"unemployed";"married";"tertiary";"no";1666;"no";"no";"unknown";9;"jun";291;3;-1;0;"unknown";"no"
29;"technician";"married";"secondary";"no";0;"yes";"no";"cellular";28;"jul";83;6;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";0;"yes";"no";"unknown";20;"may";793;2;-1;0;"unknown";"no"
42;"blue-collar";"single";"primary";"no";1628;"yes";"no";"cellular";5;"feb";119;1;195;8;"success";"no"
47;"services";"married";"secondary";"no";51;"yes";"yes";"unknown";16;"may";312;1;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"yes";447;"no";"no";"unknown";14;"may";103;6;-1;0;"unknown";"no"
34;"admin.";"married";"secondary";"no";396;"yes";"no";"cellular";16;"jul";101;3;-1;0;"unknown";"no"
45;"housemaid";"married";"primary";"no";4994;"no";"no";"unknown";20;"jun";48;1;-1;0;"unknown";"no"
43;"admin.";"married";"secondary";"no";3215;"yes";"no";"cellular";21;"jul";228;3;-1;0;"unknown";"no"
59;"blue-collar";"married";"primary";"no";2814;"no";"no";"unknown";20;"jun";81;3;-1;0;"unknown";"no"
50;"technician";"married";"secondary";"no";0;"no";"no";"cellular";7;"jul";152;2;-1;0;"unknown";"no"
39;"blue-collar";"married";"secondary";"no";293;"yes";"no";"unknown";23;"may";160;3;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";474;"yes";"no";"unknown";27;"may";59;1;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";16178;"yes";"no";"cellular";21;"nov";297;1;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";0;"no";"no";"cellular";22;"aug";147;2;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";88;"no";"no";"cellular";4;"may";143;1;-1;0;"unknown";"no"
29;"services";"married";"secondary";"no";308;"yes";"no";"unknown";28;"may";75;1;-1;0;"unknown";"no"
70;"retired";"married";"secondary";"no";1164;"no";"no";"telephone";14;"may";338;3;185;2;"failure";"no"
44;"management";"married";"tertiary";"no";13893;"no";"no";"unknown";11;"jun";29;2;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";0;"yes";"no";"unknown";14;"may";159;2;-1;0;"unknown";"no"
36;"technician";"divorced";"secondary";"no";-435;"yes";"no";"unknown";2;"jun";85;8;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";389;"yes";"no";"cellular";11;"may";15;7;-1;0;"unknown";"no"
50;"management";"divorced";"tertiary";"no";356;"no";"no";"cellular";8;"may";198;2;358;5;"other";"no"
49;"technician";"married";"secondary";"no";2718;"no";"no";"unknown";19;"may";114;2;-1;0;"unknown";"no"
71;"retired";"divorced";"tertiary";"no";261;"no";"no";"cellular";12;"aug";1117;2;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";757;"no";"no";"cellular";3;"feb";343;2;-1;0;"unknown";"no"
45;"management";"single";"tertiary";"no";16;"no";"no";"cellular";31;"jul";59;2;-1;0;"unknown";"no"
42;"blue-collar";"married";"secondary";"no";-422;"no";"no";"cellular";9;"jul";31;1;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";248;"no";"no";"cellular";16;"jul";212;2;-1;0;"unknown";"no"
47;"technician";"married";"primary";"no";145;"yes";"no";"unknown";7;"may";157;6;-1;0;"unknown";"no"
60;"retired";"divorced";"secondary";"no";598;"no";"yes";"cellular";8;"jul";96;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";949;"yes";"no";"unknown";27;"may";245;2;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";614;"no";"no";"cellular";11;"aug";72;4;-1;0;"unknown";"no"
33;"blue-collar";"married";"primary";"no";1209;"yes";"no";"cellular";16;"apr";700;1;-1;0;"unknown";"no"
50;"self-employed";"married";"tertiary";"no";202;"yes";"no";"cellular";15;"apr";107;1;148;4;"failure";"no"
51;"blue-collar";"married";"secondary";"no";1840;"yes";"no";"cellular";18;"aug";312;3;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";114;"yes";"yes";"cellular";4;"jun";153;1;309;3;"other";"no"
56;"services";"married";"secondary";"no";-229;"no";"yes";"cellular";23;"jul";55;10;-1;0;"unknown";"no"
29;"services";"married";"secondary";"no";182;"yes";"no";"telephone";13;"may";38;3;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";401;"no";"no";"cellular";14;"aug";96;3;-1;0;"unknown";"no"
44;"technician";"divorced";"unknown";"no";759;"yes";"no";"unknown";23;"may";397;1;-1;0;"unknown";"no"
55;"retired";"married";"secondary";"no";4194;"no";"yes";"telephone";30;"jul";208;3;-1;0;"unknown";"no"
35;"student";"single";"unknown";"no";0;"no";"no";"telephone";28;"jan";122;3;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";629;"yes";"yes";"cellular";29;"dec";109;1;246;1;"failure";"no"
54;"retired";"married";"secondary";"no";153;"yes";"yes";"unknown";26;"may";74;1;-1;0;"unknown";"no"
32;"management";"married";"tertiary";"no";553;"yes";"no";"cellular";22;"jul";72;1;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";1057;"no";"no";"cellular";5;"feb";383;2;-1;0;"unknown";"no"
60;"retired";"married";"secondary";"no";78;"yes";"no";"unknown";12;"may";113;3;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";-28;"no";"yes";"unknown";4;"jul";400;1;-1;0;"unknown";"no"
57;"management";"married";"tertiary";"no";929;"no";"no";"cellular";19;"aug";387;8;-1;0;"unknown";"no"
34;"blue-collar";"married";"primary";"no";428;"yes";"no";"cellular";18;"may";59;2;-1;0;"unknown";"no"
27;"student";"single";"secondary";"no";268;"yes";"no";"cellular";13;"may";528;1;358;2;"failure";"no"
52;"management";"single";"tertiary";"no";83;"no";"no";"cellular";26;"aug";1223;6;-1;0;"unknown";"yes"
45;"admin.";"divorced";"unknown";"no";998;"yes";"no";"telephone";30;"jul";38;2;-1;0;"unknown";"no"
58;"admin.";"single";"secondary";"no";2223;"no";"no";"cellular";4;"dec";1139;2;114;1;"success";"no"
39;"blue-collar";"single";"primary";"no";6;"yes";"no";"cellular";19;"nov";286;2;-1;0;"unknown";"no"
48;"blue-collar";"single";"secondary";"no";312;"yes";"yes";"cellular";3;"feb";369;2;-1;0;"unknown";"no"
36;"management";"single";"secondary";"no";0;"no";"no";"cellular";13;"aug";333;5;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";691;"yes";"no";"cellular";8;"may";198;1;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";771;"yes";"no";"cellular";26;"aug";106;8;-1;0;"unknown";"no"
30;"self-employed";"single";"secondary";"no";192;"no";"no";"cellular";22;"jul";423;1;-1;0;"unknown";"no"
57;"retired";"married";"tertiary";"no";0;"no";"no";"cellular";12;"aug";116;4;-1;0;"unknown";"no"
29;"admin.";"single";"secondary";"no";1070;"yes";"no";"unknown";19;"may";30;1;357;1;"other";"yes"
29;"services";"divorced";"secondary";"no";84;"yes";"no";"cellular";13;"apr";1101;2;-1;0;"unknown";"yes"
30;"management";"single";"tertiary";"no";2;"no";"no";"cellular";16;"jun";102;1;85;1;"failure";"no"
44;"management";"married";"tertiary";"no";1242;"yes";"no";"cellular";6;"aug";384;6;-1;0;"unknown";"no"
33;"student";"single";"unknown";"no";5969;"no";"no";"cellular";4;"sep";156;2;127;8;"failure";"no"
56;"admin.";"divorced";"secondary";"no";3337;"yes";"no";"telephone";17;"nov";99;3;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";1347;"no";"no";"telephone";18;"jun";280;6;92;5;"other";"yes"
52;"technician";"married";"secondary";"no";64;"no";"no";"telephone";29;"aug";20;2;-1;0;"unknown";"no"
32;"management";"single";"secondary";"no";249;"yes";"no";"unknown";2;"jun";258;1;-1;0;"unknown";"no"
36;"technician";"single";"secondary";"no";0;"no";"no";"cellular";29;"aug";85;6;-1;0;"unknown";"no"
26;"blue-collar";"married";"secondary";"no";128;"no";"no";"unknown";4;"jul";355;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";196;"yes";"no";"unknown";5;"jun";66;3;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";351;"yes";"no";"cellular";18;"jul";467;1;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";5533;"yes";"no";"cellular";7;"may";582;2;283;4;"failure";"no"
32;"services";"married";"secondary";"no";38;"yes";"no";"cellular";15;"may";744;1;-1;0;"unknown";"yes"
30;"management";"single";"tertiary";"yes";35;"no";"no";"cellular";30;"jul";185;4;-1;0;"unknown";"no"
27;"technician";"single";"secondary";"no";235;"yes";"no";"cellular";12;"may";13;5;-1;0;"unknown";"no"
42;"admin.";"single";"secondary";"no";30;"yes";"no";"cellular";4;"feb";98;1;266;3;"failure";"no"
33;"blue-collar";"single";"primary";"no";92;"yes";"no";"unknown";9;"may";127;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"secondary";"no";67;"no";"no";"unknown";3;"jun";45;2;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";4022;"yes";"no";"cellular";29;"jan";244;3;-1;0;"unknown";"no"
36;"entrepreneur";"married";"tertiary";"no";3057;"no";"no";"unknown";16;"jun";2769;4;-1;0;"unknown";"yes"
39;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";12;"may";561;4;369;3;"failure";"no"
33;"management";"single";"tertiary";"no";349;"no";"no";"cellular";22;"aug";182;1;-1;0;"unknown";"no"
47;"services";"married";"secondary";"no";2322;"no";"no";"cellular";22;"aug";618;1;-1;0;"unknown";"no"
49;"entrepreneur";"married";"secondary";"no";167;"yes";"yes";"unknown";6;"may";198;3;-1;0;"unknown";"no"
46;"management";"divorced";"secondary";"no";558;"no";"no";"cellular";19;"aug";126;6;-1;0;"unknown";"no"
31;"services";"married";"secondary";"no";-230;"yes";"yes";"cellular";16;"jul";715;1;-1;0;"unknown";"no"
43;"unemployed";"divorced";"secondary";"no";4094;"yes";"no";"cellular";21;"nov";63;1;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"yes";239;"yes";"no";"unknown";21;"may";412;1;-1;0;"unknown";"yes"
43;"management";"divorced";"tertiary";"no";497;"no";"no";"cellular";13;"may";66;1;363;22;"failure";"no"
42;"blue-collar";"married";"secondary";"no";179;"yes";"no";"cellular";17;"nov";96;1;-1;0;"unknown";"no"
30;"admin.";"single";"tertiary";"no";305;"no";"no";"cellular";30;"apr";633;1;85;2;"success";"yes"
33;"management";"single";"tertiary";"no";-67;"yes";"no";"unknown";5;"may";364;1;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";4539;"no";"no";"unknown";30;"may";298;3;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";770;"yes";"no";"cellular";17;"apr";341;1;-1;0;"unknown";"no"
38;"unemployed";"married";"secondary";"no";-673;"yes";"no";"unknown";26;"may";364;1;-1;0;"unknown";"no"
29;"technician";"married";"tertiary";"no";858;"yes";"no";"cellular";12;"jun";234;7;330;1;"failure";"no"
80;"housemaid";"married";"primary";"no";0;"no";"no";"cellular";23;"feb";639;1;189;1;"failure";"yes"
38;"management";"single";"tertiary";"no";5244;"no";"no";"cellular";8;"aug";310;4;-1;0;"unknown";"no"
36;"blue-collar";"divorced";"primary";"no";117;"yes";"no";"unknown";2;"jun";123;4;-1;0;"unknown";"no"
38;"self-employed";"married";"tertiary";"no";846;"no";"no";"cellular";2;"jun";253;1;-1;0;"unknown";"no"
44;"management";"married";"secondary";"yes";0;"yes";"no";"cellular";11;"jul";96;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1331;"no";"no";"cellular";30;"apr";297;1;86;3;"success";"yes"
31;"services";"single";"secondary";"no";31;"yes";"yes";"unknown";26;"may";501;2;-1;0;"unknown";"no"
57;"admin.";"divorced";"primary";"no";179;"yes";"no";"cellular";15;"may";67;1;368;2;"failure";"no"
37;"blue-collar";"married";"primary";"no";543;"yes";"no";"unknown";21;"may";281;1;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";253;"yes";"yes";"cellular";5;"may";475;2;-1;0;"unknown";"no"
60;"retired";"married";"primary";"no";71188;"no";"no";"cellular";6;"oct";205;1;-1;0;"unknown";"no"
51;"management";"married";"tertiary";"no";5108;"yes";"no";"cellular";8;"oct";272;1;102;8;"other";"no"
48;"blue-collar";"married";"secondary";"no";1738;"yes";"no";"cellular";15;"apr";349;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";-97;"yes";"no";"cellular";3;"feb";216;4;271;5;"failure";"no"
36;"blue-collar";"married";"secondary";"no";199;"yes";"no";"cellular";9;"jul";183;1;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";489;"yes";"no";"cellular";8;"may";130;1;172;2;"other";"no"
31;"admin.";"married";"primary";"no";355;"yes";"yes";"unknown";4;"jun";61;6;-1;0;"unknown";"no"
49;"technician";"single";"secondary";"no";-97;"yes";"no";"unknown";6;"jun";92;2;-1;0;"unknown";"no"
35;"management";"divorced";"tertiary";"no";473;"no";"yes";"cellular";5;"aug";37;2;-1;0;"unknown";"no"
44;"entrepreneur";"married";"secondary";"no";6649;"yes";"no";"cellular";19;"nov";354;2;-1;0;"unknown";"no"
34;"self-employed";"married";"tertiary";"no";263;"no";"no";"cellular";4;"may";203;1;78;8;"other";"no"
46;"blue-collar";"married";"secondary";"no";781;"no";"no";"unknown";13;"may";152;1;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";1032;"yes";"no";"unknown";2;"jun";41;5;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";119;"no";"no";"cellular";4;"may";20;5;-1;0;"unknown";"no"
35;"management";"divorced";"tertiary";"no";434;"yes";"no";"cellular";11;"may";277;1;271;2;"success";"no"
51;"admin.";"married";"secondary";"no";2967;"no";"no";"unknown";17;"jun";83;2;-1;0;"unknown";"no"
57;"retired";"divorced";"secondary";"no";-331;"yes";"no";"unknown";6;"may";531;1;-1;0;"unknown";"no"
59;"management";"married";"tertiary";"no";1585;"no";"no";"telephone";28;"aug";91;5;-1;0;"unknown";"no"
40;"services";"divorced";"unknown";"no";4384;"yes";"no";"unknown";5;"may";315;1;-1;0;"unknown";"no"
49;"management";"single";"tertiary";"no";588;"no";"no";"cellular";28;"aug";70;9;-1;0;"unknown";"no"
51;"management";"married";"secondary";"no";3180;"yes";"no";"cellular";9;"jul";384;1;-1;0;"unknown";"no"
35;"admin.";"married";"secondary";"yes";0;"no";"yes";"unknown";4;"jul";105;3;-1;0;"unknown";"no"
29;"self-employed";"married";"secondary";"no";-76;"yes";"no";"unknown";27;"may";305;2;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";152;"yes";"no";"cellular";25;"may";158;2;-1;0;"unknown";"yes"
37;"housemaid";"divorced";"secondary";"no";1040;"yes";"no";"cellular";13;"may";199;1;173;2;"failure";"no"
49;"unemployed";"married";"primary";"no";-384;"yes";"no";"unknown";27;"may";25;1;-1;0;"unknown";"no"
45;"technician";"married";"unknown";"no";473;"yes";"no";"unknown";27;"may";147;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";475;"yes";"no";"cellular";29;"jul";538;8;-1;0;"unknown";"no"
53;"technician";"married";"secondary";"no";0;"no";"no";"cellular";22;"aug";77;9;-1;0;"unknown";"no"
42;"entrepreneur";"married";"primary";"no";149;"no";"no";"cellular";20;"aug";94;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";3285;"yes";"no";"unknown";13;"may";1721;2;-1;0;"unknown";"yes"
52;"retired";"married";"secondary";"no";424;"no";"no";"cellular";19;"nov";143;1;154;3;"failure";"no"
34;"blue-collar";"single";"secondary";"no";79;"no";"yes";"cellular";20;"nov";104;1;-1;0;"unknown";"no"
34;"technician";"divorced";"tertiary";"no";674;"yes";"no";"cellular";7;"aug";587;2;-1;0;"unknown";"yes"
27;"services";"married";"secondary";"no";0;"yes";"no";"cellular";22;"jul";1608;13;-1;0;"unknown";"yes"
43;"unemployed";"married";"tertiary";"no";58;"yes";"no";"cellular";9;"feb";48;4;-1;0;"unknown";"no"
41;"technician";"married";"tertiary";"no";532;"yes";"no";"cellular";9;"jul";74;1;-1;0;"unknown";"no"
72;"retired";"married";"secondary";"no";4130;"no";"no";"cellular";12;"nov";427;1;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";422;"yes";"no";"cellular";14;"may";73;1;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";437;"yes";"no";"unknown";3;"jun";14;21;-1;0;"unknown";"no"
36;"blue-collar";"divorced";"primary";"no";-308;"yes";"no";"cellular";12;"may";725;1;-1;0;"unknown";"yes"
51;"housemaid";"married";"primary";"no";30;"yes";"no";"cellular";14;"jul";333;2;-1;0;"unknown";"no"
37;"self-employed";"married";"tertiary";"no";137;"no";"no";"cellular";13;"aug";244;6;-1;0;"unknown";"no"
42;"admin.";"single";"secondary";"no";-247;"yes";"yes";"telephone";21;"oct";519;1;166;1;"other";"yes"
26;"admin.";"single";"secondary";"no";321;"yes";"no";"cellular";22;"jul";242;7;-1;0;"unknown";"no"
23;"services";"single";"secondary";"no";250;"yes";"no";"cellular";18;"may";222;1;-1;0;"unknown";"no"
50;"management";"married";"tertiary";"no";-470;"yes";"no";"cellular";19;"nov";130;2;160;1;"failure";"no"
37;"blue-collar";"married";"secondary";"no";140;"yes";"yes";"cellular";7;"may";490;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";2793;"no";"no";"cellular";20;"nov";258;2;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";410;"no";"no";"cellular";29;"jan";337;2;-1;0;"unknown";"no"
79;"retired";"divorced";"unknown";"no";2628;"no";"no";"telephone";8;"jul";220;7;450;2;"failure";"no"
39;"technician";"married";"secondary";"no";116;"no";"no";"cellular";24;"aug";79;2;94;2;"success";"no"
47;"services";"married";"secondary";"no";0;"yes";"no";"telephone";9;"jul";250;1;-1;0;"unknown";"no"
44;"blue-collar";"married";"secondary";"no";878;"yes";"no";"cellular";2;"jun";130;1;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";2944;"no";"no";"cellular";18;"aug";882;8;-1;0;"unknown";"yes"
33;"blue-collar";"single";"secondary";"no";3975;"yes";"yes";"cellular";17;"apr";515;1;150;1;"other";"no"
42;"services";"married";"secondary";"no";96;"yes";"no";"cellular";5;"may";238;3;340;2;"failure";"no"
35;"technician";"single";"tertiary";"no";2288;"no";"no";"telephone";28;"may";32;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";472;"yes";"no";"unknown";13;"may";907;2;-1;0;"unknown";"no"
58;"services";"married";"primary";"no";65;"no";"no";"unknown";27;"jun";162;1;-1;0;"unknown";"no"
46;"management";"married";"tertiary";"no";0;"no";"no";"unknown";7;"may";70;2;-1;0;"unknown";"no"
41;"admin.";"single";"secondary";"no";400;"yes";"no";"unknown";4;"jun";99;6;-1;0;"unknown";"no"
58;"technician";"married";"secondary";"no";7495;"yes";"no";"unknown";13;"may";325;1;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";137;"no";"yes";"cellular";18;"jul";305;12;-1;0;"unknown";"no"
31;"unemployed";"married";"secondary";"no";24;"yes";"no";"cellular";19;"nov";631;1;-1;0;"unknown";"no"
55;"technician";"divorced";"secondary";"no";3450;"no";"no";"cellular";10;"feb";169;1;183;1;"failure";"yes"
38;"technician";"single";"secondary";"no";311;"yes";"no";"cellular";28;"may";61;2;-1;0;"unknown";"no"
55;"housemaid";"married";"secondary";"no";139;"no";"no";"cellular";22;"aug";256;2;-1;0;"unknown";"no"
47;"management";"single";"tertiary";"no";0;"no";"no";"unknown";11;"jun";58;3;-1;0;"unknown";"no"
51;"housemaid";"married";"primary";"no";492;"yes";"no";"unknown";20;"may";142;3;-1;0;"unknown";"no"
46;"management";"single";"tertiary";"no";334;"yes";"no";"telephone";4;"may";36;6;90;2;"other";"no"
61;"management";"married";"tertiary";"no";997;"no";"no";"cellular";10;"mar";106;1;-1;0;"unknown";"yes"
47;"management";"single";"tertiary";"no";440;"no";"no";"cellular";21;"aug";71;4;-1;0;"unknown";"no"
34;"technician";"married";"secondary";"no";47;"no";"no";"cellular";25;"aug";132;6;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";1298;"yes";"no";"unknown";20;"may";120;1;-1;0;"unknown";"no"
41;"management";"divorced";"tertiary";"no";3355;"no";"no";"cellular";14;"oct";181;2;500;1;"failure";"no"
36;"blue-collar";"divorced";"secondary";"no";-11;"no";"no";"unknown";5;"jun";152;2;-1;0;"unknown";"no"
26;"blue-collar";"single";"secondary";"no";396;"yes";"no";"unknown";28;"may";302;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";8;"no";"no";"cellular";23;"apr";93;3;-1;0;"unknown";"yes"
36;"blue-collar";"married";"secondary";"no";1409;"no";"no";"unknown";17;"jun";235;2;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";170;"no";"no";"cellular";15;"jun";129;1;-1;0;"unknown";"yes"
58;"management";"married";"primary";"no";2769;"yes";"no";"cellular";11;"jul";94;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";2320;"yes";"no";"cellular";7;"apr";310;1;-1;0;"unknown";"no"
61;"retired";"married";"secondary";"no";76;"yes";"no";"cellular";15;"jul";195;7;-1;0;"unknown";"no"
28;"technician";"single";"secondary";"no";2034;"yes";"no";"unknown";21;"may";196;1;-1;0;"unknown";"no"
49;"blue-collar";"single";"primary";"no";3018;"yes";"no";"unknown";15;"may";93;1;-1;0;"unknown";"no"
74;"retired";"married";"secondary";"no";921;"no";"no";"telephone";17;"apr";394;2;-1;0;"unknown";"yes"
53;"blue-collar";"married";"secondary";"no";483;"yes";"no";"cellular";3;"feb";288;3;-1;0;"unknown";"no"
46;"self-employed";"married";"secondary";"no";390;"yes";"no";"cellular";31;"jul";208;2;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";0;"no";"no";"cellular";27;"oct";508;1;-1;0;"unknown";"yes"
42;"blue-collar";"single";"primary";"no";92;"no";"no";"cellular";30;"jul";86;2;-1;0;"unknown";"no"
56;"services";"married";"secondary";"no";22196;"yes";"no";"cellular";21;"nov";189;1;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";2;"no";"no";"unknown";29;"may";249;2;-1;0;"unknown";"no"
49;"housemaid";"married";"primary";"no";249;"yes";"no";"cellular";21;"nov";450;1;-1;0;"unknown";"no"
43;"unemployed";"single";"unknown";"no";3274;"yes";"no";"cellular";22;"may";680;1;95;1;"failure";"no"
39;"services";"married";"secondary";"no";5731;"yes";"no";"unknown";20;"may";175;2;-1;0;"unknown";"no"
66;"retired";"married";"primary";"no";206;"no";"no";"cellular";9;"feb";479;1;-1;0;"unknown";"yes"
30;"technician";"single";"tertiary";"no";2325;"no";"no";"cellular";5;"apr";270;3;241;4;"other";"no"
38;"management";"single";"tertiary";"no";0;"no";"no";"cellular";24;"jul";164;1;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";1664;"yes";"no";"cellular";20;"apr";158;1;182;1;"success";"no"
32;"technician";"married";"secondary";"no";3333;"no";"no";"cellular";8;"aug";50;4;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";7020;"no";"no";"cellular";6;"aug";80;2;-1;0;"unknown";"no"
34;"admin.";"married";"tertiary";"no";37;"no";"no";"cellular";6;"feb";45;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";28;"no";"no";"cellular";29;"jan";220;2;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";1670;"yes";"no";"unknown";18;"jun";161;2;-1;0;"unknown";"no"
36;"entrepreneur";"married";"secondary";"no";134;"no";"no";"unknown";20;"may";98;1;-1;0;"unknown";"no"
52;"retired";"married";"secondary";"no";603;"no";"yes";"unknown";9;"jun";79;1;-1;0;"unknown";"no"
46;"admin.";"married";"primary";"no";276;"yes";"yes";"unknown";6;"may";157;2;-1;0;"unknown";"no"
25;"technician";"single";"secondary";"no";2157;"yes";"no";"cellular";21;"jul";339;1;-1;0;"unknown";"no"
52;"blue-collar";"married";"secondary";"no";161;"yes";"no";"unknown";13;"may";274;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";3658;"yes";"no";"unknown";14;"may";977;1;-1;0;"unknown";"no"
26;"management";"divorced";"tertiary";"yes";-402;"no";"yes";"unknown";12;"may";127;2;-1;0;"unknown";"no"
26;"blue-collar";"single";"primary";"no";3342;"yes";"no";"unknown";4;"jun";329;4;-1;0;"unknown";"no"
35;"self-employed";"single";"secondary";"no";1732;"no";"no";"telephone";21;"nov";1441;3;-1;0;"unknown";"no"
40;"admin.";"single";"secondary";"no";263;"yes";"no";"unknown";6;"may";231;1;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";623;"yes";"no";"cellular";13;"may";96;2;349;5;"failure";"no"
32;"admin.";"married";"secondary";"no";3540;"yes";"no";"cellular";18;"may";213;1;178;1;"failure";"no"
33;"admin.";"married";"secondary";"no";95;"yes";"no";"cellular";30;"apr";335;1;-1;0;"unknown";"yes"
53;"services";"divorced";"secondary";"no";765;"yes";"no";"cellular";14;"may";959;3;174;7;"other";"yes"
46;"technician";"divorced";"secondary";"no";-824;"yes";"no";"cellular";7;"aug";429;3;-1;0;"unknown";"yes"
28;"management";"single";"tertiary";"no";5549;"no";"no";"cellular";28;"jul";383;6;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";191;"yes";"no";"unknown";2;"jun";40;1;-1;0;"unknown";"no"
44;"blue-collar";"married";"primary";"no";420;"yes";"no";"unknown";7;"may";110;2;-1;0;"unknown";"no"
35;"admin.";"married";"secondary";"no";0;"no";"no";"unknown";7;"may";326;1;-1;0;"unknown";"no"
50;"technician";"divorced";"unknown";"no";2706;"yes";"no";"unknown";28;"may";179;3;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"yes";4;"yes";"yes";"cellular";25;"aug";132;8;-1;0;"unknown";"no"
51;"admin.";"married";"secondary";"no";423;"yes";"yes";"cellular";16;"oct";95;1;-1;0;"unknown";"no"
51;"management";"married";"tertiary";"no";-892;"yes";"yes";"cellular";14;"aug";248;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"unknown";"no";-120;"yes";"no";"cellular";3;"feb";100;3;201;2;"failure";"no"
41;"admin.";"divorced";"secondary";"no";-365;"yes";"no";"unknown";28;"may";226;10;-1;0;"unknown";"no"
57;"technician";"married";"tertiary";"no";27069;"no";"yes";"unknown";20;"jun";174;3;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";8038;"yes";"no";"cellular";20;"apr";29;3;152;1;"failure";"no"
47;"technician";"married";"secondary";"no";-938;"yes";"no";"unknown";28;"may";176;2;-1;0;"unknown";"no"
47;"admin.";"divorced";"secondary";"no";1139;"yes";"no";"cellular";11;"jul";106;2;-1;0;"unknown";"no"
36;"self-employed";"married";"secondary";"no";189;"yes";"no";"cellular";19;"nov";136;1;120;1;"failure";"no"
29;"housemaid";"single";"tertiary";"no";19;"no";"no";"cellular";5;"feb";110;2;64;2;"success";"yes"
39;"technician";"married";"secondary";"no";3663;"no";"no";"cellular";11;"aug";230;3;-1;0;"unknown";"no"
53;"blue-collar";"single";"unknown";"no";0;"no";"no";"unknown";12;"may";140;7;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"yes";-614;"yes";"no";"unknown";26;"may";118;2;-1;0;"unknown";"no"
51;"technician";"divorced";"secondary";"no";2323;"yes";"yes";"cellular";18;"aug";151;10;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";425;"yes";"no";"telephone";9;"jul";693;2;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";5122;"no";"no";"cellular";13;"aug";31;1;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";608;"no";"no";"cellular";26;"aug";125;2;-1;0;"unknown";"no"
50;"unknown";"married";"primary";"no";341;"yes";"yes";"cellular";20;"apr";670;4;340;2;"success";"yes"
39;"admin.";"married";"secondary";"no";607;"yes";"no";"cellular";12;"may";454;2;364;3;"failure";"yes"
31;"blue-collar";"married";"secondary";"no";1495;"yes";"yes";"unknown";23;"may";159;3;-1;0;"unknown";"no"
49;"admin.";"married";"secondary";"no";381;"no";"no";"cellular";5;"aug";504;2;-1;0;"unknown";"no"
33;"unemployed";"divorced";"secondary";"no";385;"no";"no";"telephone";27;"aug";29;28;-1;0;"unknown";"no"
26;"management";"single";"tertiary";"no";1274;"yes";"no";"cellular";1;"jun";190;1;84;1;"failure";"no"
27;"blue-collar";"married";"secondary";"no";362;"yes";"no";"unknown";14;"may";332;2;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";377;"yes";"no";"telephone";3;"feb";143;4;272;1;"failure";"no"
26;"admin.";"single";"secondary";"no";802;"no";"no";"cellular";30;"jan";77;1;2;2;"other";"no"
45;"admin.";"married";"secondary";"no";194;"no";"no";"cellular";31;"jul";517;4;-1;0;"unknown";"no"
54;"technician";"married";"secondary";"no";-315;"no";"yes";"cellular";10;"jul";2029;1;-1;0;"unknown";"yes"
49;"technician";"married";"tertiary";"no";1843;"yes";"no";"cellular";25;"may";269;1;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";-9;"no";"no";"cellular";14;"aug";457;2;-1;0;"unknown";"no"
42;"housemaid";"married";"primary";"no";83;"no";"yes";"cellular";7;"aug";184;1;-1;0;"unknown";"no"
50;"entrepreneur";"married";"tertiary";"no";433;"yes";"no";"cellular";18;"may";47;4;-1;0;"unknown";"no"
37;"admin.";"married";"secondary";"no";-107;"yes";"no";"unknown";28;"may";158;2;-1;0;"unknown";"no"
54;"blue-collar";"married";"primary";"no";4503;"no";"no";"telephone";21;"nov";168;3;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";51;"yes";"no";"cellular";11;"may";50;1;-1;0;"unknown";"no"
50;"services";"married";"secondary";"no";961;"no";"yes";"cellular";14;"aug";757;4;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"no";49;"yes";"yes";"cellular";25;"aug";93;4;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";3470;"no";"no";"cellular";11;"aug";285;2;-1;0;"unknown";"no"
49;"technician";"married";"secondary";"no";-147;"yes";"no";"cellular";20;"apr";390;1;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";0;"yes";"no";"cellular";18;"may";146;2;-1;0;"unknown";"no"
28;"blue-collar";"single";"secondary";"no";0;"yes";"no";"unknown";28;"may";386;1;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";172;"no";"no";"cellular";19;"aug";145;7;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";-407;"no";"yes";"cellular";7;"jul";230;1;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";649;"no";"no";"cellular";16;"jul";237;2;91;1;"failure";"no"
49;"blue-collar";"single";"primary";"no";1040;"no";"no";"cellular";8;"jul";474;2;-1;0;"unknown";"no"
30;"unemployed";"married";"secondary";"no";142;"yes";"no";"unknown";20;"may";350;4;-1;0;"unknown";"no"
29;"services";"single";"secondary";"no";209;"yes";"no";"cellular";28;"jul";487;7;-1;0;"unknown";"no"
50;"management";"married";"unknown";"no";3304;"no";"no";"unknown";5;"jun";1009;2;-1;0;"unknown";"no"
26;"student";"single";"tertiary";"no";294;"no";"no";"cellular";5;"mar";87;1;91;2;"success";"yes"
27;"housemaid";"married";"secondary";"no";4848;"yes";"yes";"telephone";23;"jul";82;3;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";1670;"yes";"no";"cellular";18;"may";183;1;-1;0;"unknown";"no"
62;"unknown";"married";"secondary";"no";367;"no";"no";"telephone";2;"mar";139;5;-1;0;"unknown";"no"
34;"blue-collar";"single";"secondary";"no";13683;"yes";"no";"unknown";3;"jun";115;3;-1;0;"unknown";"no"
25;"admin.";"single";"tertiary";"no";1420;"no";"no";"cellular";8;"apr";124;2;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";340;"yes";"no";"unknown";3;"jun";633;28;-1;0;"unknown";"no"
44;"technician";"single";"secondary";"no";1679;"no";"no";"telephone";20;"oct";718;1;-1;0;"unknown";"yes"
38;"management";"married";"tertiary";"no";1198;"yes";"no";"cellular";2;"feb";63;1;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";169;"no";"no";"cellular";14;"aug";180;1;106;2;"failure";"no"
36;"self-employed";"married";"tertiary";"no";506;"no";"no";"cellular";25;"aug";47;1;120;1;"failure";"no"
41;"entrepreneur";"married";"secondary";"no";1249;"yes";"no";"cellular";9;"jul";391;1;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";678;"no";"yes";"unknown";4;"jul";85;3;-1;0;"unknown";"no"
27;"management";"single";"tertiary";"no";212;"yes";"no";"cellular";15;"may";373;1;-1;0;"unknown";"no"
37;"technician";"divorced";"secondary";"no";23;"no";"no";"cellular";20;"aug";289;2;-1;0;"unknown";"no"
47;"management";"single";"tertiary";"no";4402;"no";"no";"telephone";10;"dec";805;1;190;1;"other";"yes"
31;"services";"divorced";"primary";"no";459;"yes";"no";"cellular";7;"may";623;1;293;1;"success";"yes"
39;"self-employed";"married";"secondary";"no";4722;"yes";"no";"unknown";17;"jun";483;1;-1;0;"unknown";"no"
35;"admin.";"divorced";"tertiary";"no";1555;"yes";"no";"unknown";9;"may";26;2;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";61;"yes";"no";"unknown";7;"may";112;3;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";0;"yes";"no";"cellular";17;"nov";213;2;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";3706;"yes";"no";"cellular";20;"nov";129;1;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";1177;"no";"no";"cellular";15;"apr";330;1;311;4;"success";"yes"
53;"management";"married";"primary";"no";2096;"no";"no";"unknown";9;"jun";104;1;-1;0;"unknown";"no"
59;"blue-collar";"married";"primary";"no";748;"no";"no";"unknown";29;"may";184;1;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";3357;"no";"no";"cellular";21;"jul";120;3;-1;0;"unknown";"no"
30;"admin.";"single";"secondary";"no";3809;"yes";"no";"unknown";3;"jun";159;4;-1;0;"unknown";"no"
52;"admin.";"married";"secondary";"no";395;"yes";"no";"unknown";15;"may";391;3;-1;0;"unknown";"no"
55;"blue-collar";"married";"secondary";"no";1011;"yes";"no";"cellular";12;"aug";112;2;-1;0;"unknown";"no"
56;"blue-collar";"married";"primary";"no";420;"yes";"no";"unknown";9;"may";76;1;-1;0;"unknown";"no"
32;"admin.";"married";"secondary";"no";76;"yes";"no";"unknown";21;"may";189;2;-1;0;"unknown";"no"
24;"blue-collar";"married";"secondary";"no";0;"no";"no";"unknown";28;"may";94;1;-1;0;"unknown";"no"
51;"blue-collar";"married";"primary";"no";844;"yes";"no";"unknown";29;"may";184;3;-1;0;"unknown";"no"
36;"management";"married";"tertiary";"no";1318;"yes";"no";"cellular";7;"aug";100;2;-1;0;"unknown";"no"
36;"technician";"married";"tertiary";"no";167;"yes";"no";"cellular";28;"jan";57;1;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";2187;"yes";"no";"cellular";17;"nov";525;3;-1;0;"unknown";"yes"
49;"admin.";"married";"secondary";"no";14440;"yes";"no";"cellular";21;"nov";60;1;-1;0;"unknown";"no"
48;"management";"married";"secondary";"no";2294;"yes";"no";"unknown";26;"may";251;11;-1;0;"unknown";"no"
36;"management";"married";"secondary";"no";80;"yes";"no";"unknown";30;"may";251;3;-1;0;"unknown";"no"
48;"management";"married";"primary";"no";2537;"no";"no";"cellular";26;"aug";219;6;-1;0;"unknown";"no"
30;"technician";"married";"tertiary";"no";569;"no";"no";"cellular";27;"aug";976;12;-1;0;"unknown";"yes"
27;"management";"single";"tertiary";"yes";0;"no";"yes";"cellular";8;"jul";46;1;-1;0;"unknown";"no"
38;"entrepreneur";"married";"tertiary";"no";0;"yes";"no";"cellular";18;"nov";185;3;-1;0;"unknown";"no"
36;"technician";"single";"secondary";"yes";12;"no";"no";"cellular";12;"aug";587;2;-1;0;"unknown";"yes"
45;"technician";"married";"secondary";"no";49;"no";"no";"cellular";29;"jul";65;2;-1;0;"unknown";"no"
44;"housemaid";"married";"tertiary";"no";460;"no";"no";"unknown";17;"jun";491;1;-1;0;"unknown";"no"
42;"technician";"married";"secondary";"no";613;"yes";"no";"cellular";11;"may";432;2;-1;0;"unknown";"no"
37;"entrepreneur";"married";"tertiary";"no";7944;"no";"no";"cellular";21;"nov";102;1;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";1143;"yes";"no";"cellular";17;"apr";264;1;317;1;"failure";"no"
57;"retired";"married";"primary";"no";-464;"yes";"no";"cellular";4;"feb";75;1;190;6;"failure";"no"
44;"services";"married";"secondary";"no";1989;"yes";"yes";"cellular";20;"apr";106;2;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";17;"yes";"yes";"cellular";17;"nov";81;1;-1;0;"unknown";"no"
56;"retired";"married";"primary";"no";-1;"no";"no";"cellular";27;"aug";89;23;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";1698;"yes";"no";"unknown";6;"jun";95;2;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";5306;"yes";"no";"unknown";15;"may";322;1;-1;0;"unknown";"no"
54;"housemaid";"married";"unknown";"no";0;"no";"no";"cellular";14;"apr";94;5;145;3;"other";"no"
51;"retired";"divorced";"secondary";"no";1679;"no";"no";"cellular";12;"aug";417;1;365;9;"other";"yes"
47;"admin.";"married";"secondary";"no";3676;"no";"no";"cellular";18;"oct";567;1;95;3;"success";"yes"
25;"management";"married";"tertiary";"no";-242;"yes";"yes";"cellular";18;"may";600;4;332;4;"other";"yes"
39;"services";"single";"secondary";"no";471;"no";"no";"unknown";15;"oct";5;1;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";8902;"yes";"no";"cellular";14;"aug";464;5;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";1704;"no";"no";"cellular";1;"sep";222;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";3050;"yes";"no";"cellular";13;"may";469;1;-1;0;"unknown";"yes"
53;"housemaid";"married";"primary";"no";2581;"no";"no";"cellular";15;"apr";99;1;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";162;"yes";"no";"unknown";14;"may";297;2;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";243;"yes";"no";"cellular";20;"apr";41;3;-1;0;"unknown";"no"
24;"student";"single";"secondary";"no";1847;"no";"no";"cellular";9;"dec";390;1;-1;0;"unknown";"no"
58;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";29;"may";535;2;-1;0;"unknown";"no"
49;"management";"married";"primary";"no";3371;"no";"no";"cellular";11;"nov";269;2;-1;0;"unknown";"yes"
45;"services";"single";"secondary";"no";1757;"yes";"no";"cellular";20;"apr";1010;3;326;1;"other";"no"
55;"management";"married";"tertiary";"no";171;"yes";"no";"cellular";8;"aug";73;6;-1;0;"unknown";"no"
42;"entrepreneur";"married";"tertiary";"no";157;"yes";"yes";"unknown";7;"may";650;1;-1;0;"unknown";"no"
28;"admin.";"married";"secondary";"no";934;"yes";"no";"unknown";4;"jul";53;2;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";-19;"no";"yes";"unknown";4;"jul";270;1;-1;0;"unknown";"no"
51;"management";"single";"tertiary";"yes";-55;"yes";"no";"cellular";18;"aug";119;6;-1;0;"unknown";"no"
38;"housemaid";"married";"primary";"no";21;"no";"no";"cellular";26;"aug";634;2;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";14;"no";"no";"unknown";14;"may";72;2;-1;0;"unknown";"no"
50;"technician";"single";"secondary";"no";246;"yes";"no";"cellular";17;"nov";270;1;123;2;"failure";"no"
44;"blue-collar";"married";"primary";"no";887;"yes";"no";"cellular";12;"may";165;2;363;1;"failure";"no"
40;"management";"married";"tertiary";"no";51;"no";"no";"cellular";19;"aug";164;2;-1;0;"unknown";"no"
29;"services";"single";"secondary";"no";-287;"yes";"no";"cellular";18;"may";196;1;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";0;"yes";"no";"cellular";14;"jul";78;4;-1;0;"unknown";"no"
44;"self-employed";"single";"tertiary";"no";2734;"yes";"no";"cellular";12;"may";897;4;-1;0;"unknown";"yes"
50;"entrepreneur";"married";"tertiary";"no";52;"no";"no";"cellular";31;"jul";22;8;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";2594;"yes";"no";"cellular";21;"nov";161;3;-1;0;"unknown";"no"
54;"retired";"divorced";"primary";"no";22;"no";"no";"unknown";20;"jun";23;4;-1;0;"unknown";"no"
34;"technician";"married";"tertiary";"no";1611;"yes";"no";"cellular";11;"may";490;2;-1;0;"unknown";"no"
34;"admin.";"single";"tertiary";"no";18;"yes";"yes";"cellular";30;"apr";359;1;-1;0;"unknown";"yes"
52;"blue-collar";"married";"primary";"no";1331;"yes";"no";"cellular";29;"jul";213;2;-1;0;"unknown";"no"
60;"unknown";"married";"unknown";"no";7337;"no";"no";"cellular";4;"aug";122;1;-1;0;"unknown";"no"
39;"management";"single";"secondary";"no";835;"yes";"no";"unknown";29;"may";587;4;-1;0;"unknown";"no"
58;"retired";"married";"primary";"no";5092;"no";"no";"unknown";11;"jun";199;6;-1;0;"unknown";"no"
41;"technician";"divorced";"secondary";"no";52;"no";"no";"cellular";20;"aug";261;2;-1;0;"unknown";"no"
48;"technician";"married";"secondary";"no";10655;"no";"no";"telephone";31;"jul";5;3;-1;0;"unknown";"no"
58;"management";"married";"tertiary";"no";1831;"no";"no";"cellular";17;"nov";1531;1;-1;0;"unknown";"no"
40;"self-employed";"married";"primary";"no";92;"yes";"no";"unknown";13;"may";92;1;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";586;"yes";"yes";"cellular";6;"may";179;2;-1;0;"unknown";"no"
52;"blue-collar";"divorced";"primary";"no";1758;"yes";"no";"unknown";15;"may";41;1;-1;0;"unknown";"no"
50;"self-employed";"married";"secondary";"no";2061;"yes";"yes";"cellular";29;"jan";197;2;244;3;"failure";"no"
25;"technician";"single";"secondary";"no";959;"no";"no";"cellular";9;"jul";144;5;-1;0;"unknown";"no"
54;"retired";"married";"primary";"no";208;"no";"no";"unknown";12;"may";255;1;-1;0;"unknown";"no"
34;"services";"married";"secondary";"no";-315;"yes";"no";"unknown";8;"may";56;2;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"yes";-13;"yes";"no";"cellular";15;"may";20;6;291;2;"other";"no"
37;"admin.";"married";"tertiary";"no";801;"no";"no";"cellular";11;"aug";331;7;-1;0;"unknown";"no"
66;"retired";"married";"tertiary";"no";1948;"no";"no";"cellular";28;"jan";216;1;91;4;"success";"yes"
32;"management";"single";"tertiary";"no";751;"yes";"no";"unknown";8;"may";64;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"secondary";"no";784;"yes";"yes";"unknown";3;"jun";34;1;-1;0;"unknown";"no"
43;"blue-collar";"single";"secondary";"no";24;"no";"no";"cellular";12;"may";68;1;363;1;"failure";"no"
41;"management";"married";"tertiary";"no";72;"yes";"no";"unknown";7;"may";764;3;-1;0;"unknown";"no"
40;"technician";"divorced";"tertiary";"no";1871;"yes";"no";"cellular";17;"nov";64;1;-1;0;"unknown";"no"
44;"services";"single";"secondary";"no";1378;"yes";"no";"unknown";8;"may";257;2;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";188;"yes";"no";"cellular";12;"may";58;3;347;5;"failure";"no"
30;"unknown";"single";"tertiary";"no";6836;"no";"no";"cellular";27;"feb";30;3;-1;0;"unknown";"no"
32;"services";"married";"secondary";"no";61;"yes";"no";"cellular";8;"jul";529;5;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";-967;"yes";"no";"telephone";15;"may";8;7;-1;0;"unknown";"no"
31;"blue-collar";"single";"tertiary";"no";462;"yes";"yes";"unknown";19;"may";147;1;-1;0;"unknown";"no"
49;"unknown";"married";"secondary";"no";0;"no";"no";"cellular";28;"aug";173;2;-1;0;"unknown";"no"
31;"admin.";"single";"secondary";"no";5527;"yes";"no";"unknown";21;"may";132;1;-1;0;"unknown";"no"
41;"admin.";"married";"tertiary";"no";1287;"no";"no";"unknown";16;"jun";286;1;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";17361;"no";"no";"unknown";18;"jun";476;1;-1;0;"unknown";"no"
54;"entrepreneur";"married";"unknown";"no";1956;"no";"no";"cellular";19;"nov";221;5;-1;0;"unknown";"no"
37;"blue-collar";"divorced";"secondary";"no";0;"yes";"no";"cellular";21;"apr";146;4;-1;0;"unknown";"yes"
34;"management";"married";"secondary";"no";-294;"yes";"no";"unknown";27;"may";532;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";275;"no";"no";"cellular";25;"aug";69;6;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";724;"yes";"no";"cellular";17;"nov";157;2;172;1;"failure";"no"
44;"services";"married";"secondary";"no";27;"no";"no";"cellular";25;"may";485;1;-1;0;"unknown";"no"
53;"admin.";"divorced";"secondary";"no";26;"yes";"no";"cellular";7;"may";56;1;359;1;"failure";"no"
36;"technician";"married";"secondary";"no";191;"no";"no";"cellular";31;"aug";69;1;-1;0;"unknown";"no"
58;"technician";"divorced";"secondary";"no";-123;"no";"no";"cellular";5;"aug";131;2;-1;0;"unknown";"no"
26;"student";"single";"secondary";"no";-147;"no";"no";"unknown";4;"jun";95;2;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";179;"no";"no";"cellular";19;"aug";294;3;-1;0;"unknown";"no"
55;"blue-collar";"married";"primary";"no";1086;"yes";"no";"cellular";6;"may";146;1;272;2;"failure";"no"
55;"blue-collar";"married";"secondary";"no";471;"yes";"no";"unknown";30;"may";58;2;-1;0;"unknown";"no"
34;"entrepreneur";"married";"tertiary";"no";105;"yes";"no";"unknown";28;"may";249;2;-1;0;"unknown";"no"
41;"entrepreneur";"divorced";"secondary";"no";1588;"yes";"yes";"unknown";20;"jun";10;8;-1;0;"unknown";"no"
38;"housemaid";"divorced";"secondary";"no";70;"no";"no";"cellular";27;"oct";255;3;148;1;"success";"yes"
28;"blue-collar";"single";"secondary";"no";2700;"no";"no";"cellular";3;"feb";337;4;-1;0;"unknown";"no"
41;"technician";"married";"secondary";"no";1602;"yes";"no";"cellular";2;"jun";142;1;-1;0;"unknown";"no"
30;"admin.";"married";"tertiary";"no";0;"yes";"no";"cellular";24;"jul";209;2;-1;0;"unknown";"no"
39;"technician";"married";"unknown";"no";839;"no";"no";"unknown";30;"may";825;2;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";21515;"yes";"no";"unknown";5;"jun";87;1;-1;0;"unknown";"no"
36;"technician";"married";"secondary";"no";5902;"yes";"no";"unknown";23;"may";219;4;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";547;"yes";"yes";"unknown";21;"may";143;1;-1;0;"unknown";"no"
59;"self-employed";"married";"tertiary";"no";3800;"no";"yes";"cellular";9;"jul";70;1;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";261;"yes";"no";"unknown";27;"may";371;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";0;"yes";"yes";"cellular";4;"feb";539;8;204;5;"failure";"no"
42;"management";"married";"secondary";"yes";-34;"no";"no";"cellular";5;"feb";176;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"unknown";"no";9328;"yes";"no";"cellular";5;"may";725;3;-1;0;"unknown";"yes"
31;"admin.";"married";"secondary";"no";5;"yes";"yes";"cellular";16;"jul";101;7;-1;0;"unknown";"no"
43;"unemployed";"married";"secondary";"no";3529;"no";"no";"cellular";5;"feb";169;2;-1;0;"unknown";"no"
48;"blue-collar";"single";"tertiary";"no";2330;"yes";"no";"unknown";4;"jun";15;1;-1;0;"unknown";"no"
41;"blue-collar";"married";"secondary";"no";1334;"yes";"no";"cellular";20;"apr";234;5;-1;0;"unknown";"no"
32;"self-employed";"married";"tertiary";"no";3601;"no";"no";"cellular";22;"aug";171;15;-1;0;"unknown";"no"
45;"technician";"married";"tertiary";"no";265;"no";"no";"cellular";28;"aug";14;3;-1;0;"unknown";"no"
52;"admin.";"single";"unknown";"no";2227;"no";"no";"cellular";24;"feb";242;2;-1;0;"unknown";"yes"
37;"self-employed";"married";"secondary";"no";487;"no";"yes";"cellular";27;"aug";155;15;-1;0;"unknown";"no"
38;"blue-collar";"divorced";"secondary";"no";0;"yes";"no";"cellular";11;"may";580;1;173;1;"failure";"no"
36;"services";"single";"secondary";"no";174;"yes";"no";"unknown";7;"may";223;2;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";14412;"no";"no";"cellular";18;"nov";297;1;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";3288;"no";"no";"cellular";8;"aug";124;2;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";57;"no";"no";"cellular";2;"feb";465;2;-1;0;"unknown";"no"
47;"technician";"married";"secondary";"no";2246;"yes";"no";"cellular";10;"jul";330;1;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";4441;"yes";"no";"cellular";20;"nov";153;2;183;2;"failure";"no"
42;"management";"divorced";"tertiary";"no";0;"no";"no";"unknown";19;"may";131;2;-1;0;"unknown";"no"
71;"unknown";"married";"tertiary";"no";353;"no";"no";"cellular";27;"jan";208;1;93;2;"failure";"no"
54;"blue-collar";"married";"secondary";"no";448;"yes";"yes";"cellular";5;"aug";98;1;-1;0;"unknown";"no"
53;"entrepreneur";"married";"primary";"no";27;"yes";"no";"telephone";10;"sep";230;3;-1;0;"unknown";"yes"
33;"blue-collar";"divorced";"secondary";"no";474;"no";"no";"cellular";22;"jul";400;2;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";1185;"yes";"no";"cellular";30;"apr";284;1;339;1;"failure";"no"
39;"services";"divorced";"secondary";"no";282;"yes";"no";"cellular";17;"jul";243;1;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";2;"yes";"no";"cellular";15;"may";16;9;309;1;"failure";"no"
34;"technician";"single";"secondary";"no";60;"no";"no";"cellular";7;"aug";274;2;-1;0;"unknown";"no"
39;"self-employed";"single";"tertiary";"no";3972;"no";"no";"cellular";30;"jul";122;3;-1;0;"unknown";"no"
75;"retired";"married";"secondary";"no";26452;"no";"no";"telephone";15;"jul";219;2;-1;0;"unknown";"no"
33;"admin.";"divorced";"secondary";"no";818;"yes";"yes";"cellular";16;"apr";301;3;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";9374;"yes";"no";"cellular";21;"nov";7;8;185;1;"failure";"no"
37;"technician";"married";"unknown";"no";47;"no";"no";"unknown";19;"jun";374;3;-1;0;"unknown";"no"
69;"retired";"married";"primary";"no";279;"no";"no";"telephone";12;"aug";26;1;-1;0;"unknown";"no"
52;"unemployed";"married";"primary";"no";20;"yes";"no";"telephone";28;"jan";172;1;-1;0;"unknown";"no"
31;"admin.";"divorced";"secondary";"no";360;"no";"no";"cellular";15;"may";332;1;297;2;"other";"no"
28;"technician";"single";"secondary";"no";1532;"no";"no";"cellular";22;"may";253;1;107;5;"failure";"no"
29;"management";"single";"tertiary";"no";1673;"no";"no";"cellular";16;"aug";200;2;159;3;"other";"no"
46;"services";"married";"secondary";"no";2420;"no";"no";"cellular";19;"nov";405;2;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";100;"yes";"yes";"unknown";27;"may";132;2;-1;0;"unknown";"no"
41;"admin.";"married";"secondary";"no";0;"no";"no";"unknown";20;"jun";89;1;-1;0;"unknown";"no"
42;"technician";"divorced";"secondary";"no";1533;"no";"yes";"cellular";5;"aug";86;1;-1;0;"unknown";"no"
34;"admin.";"single";"secondary";"no";296;"no";"no";"cellular";7;"jul";147;4;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";2788;"yes";"no";"unknown";6;"jun";369;2;-1;0;"unknown";"yes"
43;"management";"single";"secondary";"no";109;"yes";"no";"unknown";27;"may";64;3;-1;0;"unknown";"no"
32;"technician";"married";"secondary";"no";485;"no";"no";"cellular";26;"aug";98;14;-1;0;"unknown";"no"
33;"technician";"single";"tertiary";"no";125;"no";"no";"telephone";29;"aug";66;2;-1;0;"unknown";"no"
70;"retired";"married";"secondary";"no";616;"no";"no";"cellular";27;"apr";149;2;182;1;"failure";"no"
36;"services";"married";"secondary";"no";3064;"yes";"no";"telephone";26;"aug";240;3;107;4;"failure";"no"
53;"technician";"married";"secondary";"no";206;"no";"no";"cellular";26;"aug";87;6;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";6900;"no";"no";"cellular";21;"nov";816;1;-1;0;"unknown";"no"
45;"services";"divorced";"secondary";"no";-179;"no";"no";"unknown";3;"jul";155;2;-1;0;"unknown";"no"
31;"services";"single";"secondary";"no";481;"yes";"no";"cellular";20;"nov";106;2;183;2;"failure";"no"
28;"admin.";"married";"secondary";"no";12;"yes";"no";"cellular";30;"jan";73;3;256;3;"other";"no"
45;"blue-collar";"married";"primary";"no";0;"no";"no";"unknown";23;"may";574;5;-1;0;"unknown";"no"
51;"management";"married";"tertiary";"no";176;"yes";"yes";"unknown";1;"jun";8;1;687;2;"failure";"no"
53;"management";"married";"primary";"no";4086;"no";"no";"unknown";20;"jun";14;3;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";257;"yes";"no";"unknown";16;"may";731;4;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";53;"yes";"no";"cellular";7;"apr";208;1;140;1;"failure";"no"
31;"blue-collar";"divorced";"secondary";"no";6;"yes";"no";"cellular";29;"jan";821;1;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";562;"no";"no";"cellular";21;"aug";137;4;-1;0;"unknown";"no"
53;"entrepreneur";"married";"secondary";"no";-118;"yes";"no";"unknown";9;"may";101;3;-1;0;"unknown";"no"
40;"blue-collar";"married";"primary";"no";2805;"yes";"no";"cellular";14;"may";173;6;353;23;"failure";"no"
26;"management";"single";"secondary";"no";565;"yes";"no";"unknown";6;"jun";582;11;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";0;"yes";"no";"cellular";21;"jul";676;1;-1;0;"unknown";"no"
51;"management";"married";"tertiary";"no";1612;"no";"no";"cellular";16;"mar";384;1;182;5;"failure";"no"
45;"unemployed";"married";"primary";"no";180;"yes";"yes";"unknown";27;"may";62;2;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";21;"jul";295;3;-1;0;"unknown";"no"
36;"management";"single";"tertiary";"no";876;"yes";"no";"unknown";23;"may";241;2;-1;0;"unknown";"no"
57;"admin.";"married";"secondary";"no";2137;"no";"no";"telephone";8;"jul";132;7;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";260;"yes";"no";"telephone";31;"jul";175;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"yes";-53;"yes";"no";"cellular";16;"apr";648;1;272;1;"other";"yes"
30;"management";"married";"tertiary";"no";944;"no";"no";"cellular";29;"oct";251;2;-1;0;"unknown";"yes"
36;"technician";"married";"secondary";"no";2394;"no";"no";"cellular";14;"aug";184;2;-1;0;"unknown";"no"
43;"technician";"married";"secondary";"no";297;"yes";"no";"cellular";14;"oct";119;3;-1;0;"unknown";"no"
69;"retired";"single";"tertiary";"no";2144;"no";"no";"cellular";29;"jul";417;1;184;4;"success";"yes"
31;"technician";"single";"tertiary";"no";969;"no";"no";"cellular";15;"jun";95;1;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";0;"no";"no";"cellular";14;"jul";125;2;-1;0;"unknown";"no"
26;"entrepreneur";"married";"tertiary";"no";37;"yes";"yes";"cellular";6;"may";207;1;363;2;"failure";"no"
46;"technician";"married";"secondary";"no";410;"no";"yes";"cellular";21;"nov";162;5;-1;0;"unknown";"no"
30;"technician";"married";"secondary";"no";1211;"yes";"no";"cellular";18;"may";176;1;-1;0;"unknown";"no"
33;"management";"single";"tertiary";"no";71;"no";"no";"cellular";22;"aug";107;6;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";436;"yes";"no";"cellular";17;"apr";28;1;345;1;"failure";"no"
48;"blue-collar";"married";"primary";"no";559;"yes";"no";"unknown";15;"may";1231;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1170;"no";"no";"cellular";20;"aug";200;4;-1;0;"unknown";"no"
46;"management";"divorced";"tertiary";"no";25;"no";"no";"unknown";26;"may";103;2;-1;0;"unknown";"no"
40;"services";"married";"secondary";"no";1325;"no";"no";"unknown";11;"jun";150;2;-1;0;"unknown";"no"
39;"entrepreneur";"married";"secondary";"no";0;"no";"no";"cellular";21;"nov";108;7;-1;0;"unknown";"no"
33;"technician";"single";"secondary";"no";647;"yes";"yes";"cellular";4;"feb";124;1;273;1;"other";"no"
50;"blue-collar";"married";"primary";"no";2142;"yes";"no";"cellular";29;"jan";309;2;224;1;"other";"no"
84;"retired";"divorced";"primary";"no";639;"no";"no";"telephone";18;"may";353;3;-1;0;"unknown";"yes"
40;"blue-collar";"married";"primary";"no";177;"yes";"yes";"telephone";21;"jul";742;4;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";236;"no";"no";"cellular";4;"aug";200;1;-1;0;"unknown";"no"
40;"services";"divorced";"secondary";"no";1959;"yes";"no";"unknown";30;"may";177;1;-1;0;"unknown";"no"
46;"admin.";"married";"primary";"no";96;"no";"no";"telephone";4;"feb";151;2;-1;0;"unknown";"no"
43;"self-employed";"single";"tertiary";"no";0;"no";"no";"unknown";19;"jun";147;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";250;"yes";"no";"unknown";29;"may";193;6;-1;0;"unknown";"no"
35;"technician";"single";"secondary";"no";126;"no";"no";"cellular";22;"aug";112;2;-1;0;"unknown";"no"
57;"retired";"married";"unknown";"no";1621;"yes";"no";"unknown";19;"jun";108;1;-1;0;"unknown";"no"
25;"student";"single";"secondary";"no";8;"no";"no";"cellular";22;"apr";107;2;-1;0;"unknown";"yes"
32;"self-employed";"married";"tertiary";"no";189;"yes";"no";"unknown";6;"jun";106;2;-1;0;"unknown";"no"
52;"retired";"single";"secondary";"no";316;"no";"no";"cellular";12;"may";463;3;-1;0;"unknown";"no"
31;"self-employed";"single";"tertiary";"yes";-282;"yes";"no";"cellular";20;"nov";131;3;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";-308;"yes";"no";"cellular";30;"apr";140;2;-1;0;"unknown";"no"
42;"technician";"divorced";"secondary";"no";248;"no";"no";"cellular";19;"aug";163;4;-1;0;"unknown";"no"
47;"blue-collar";"divorced";"primary";"no";126;"yes";"no";"unknown";3;"jun";2456;2;-1;0;"unknown";"yes"
50;"self-employed";"divorced";"tertiary";"no";13;"yes";"no";"cellular";20;"apr";76;1;-1;0;"unknown";"yes"
30;"technician";"married";"secondary";"no";-393;"yes";"yes";"cellular";16;"jul";435;1;-1;0;"unknown";"yes"
33;"services";"single";"secondary";"no";358;"yes";"no";"cellular";7;"may";302;1;356;3;"failure";"no"
56;"technician";"single";"tertiary";"no";917;"no";"no";"unknown";9;"jun";306;1;-1;0;"unknown";"no"
34;"admin.";"divorced";"secondary";"no";1268;"yes";"no";"cellular";15;"may";259;3;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";-163;"yes";"yes";"cellular";8;"aug";502;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";473;"yes";"no";"cellular";11;"may";72;2;361;1;"other";"no"
34;"management";"married";"tertiary";"no";4831;"yes";"no";"cellular";4;"feb";265;1;-1;0;"unknown";"no"
37;"technician";"married";"secondary";"no";1063;"yes";"no";"cellular";31;"jul";413;2;-1;0;"unknown";"no"
30;"management";"married";"secondary";"no";0;"yes";"no";"cellular";12;"may";756;3;-1;0;"unknown";"no"
29;"blue-collar";"married";"secondary";"no";424;"yes";"yes";"unknown";14;"may";241;2;-1;0;"unknown";"no"
56;"unemployed";"divorced";"primary";"no";802;"no";"no";"telephone";3;"feb";258;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";430;"yes";"no";"cellular";25;"jul";137;16;-1;0;"unknown";"no"
59;"unknown";"married";"unknown";"no";3940;"no";"no";"unknown";5;"jun";48;2;-1;0;"unknown";"no"
36;"housemaid";"married";"secondary";"no";2880;"no";"no";"unknown";12;"jun";283;1;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"secondary";"no";1069;"yes";"no";"unknown";30;"may";247;2;-1;0;"unknown";"no"
54;"self-employed";"divorced";"primary";"no";4382;"yes";"no";"telephone";18;"may";193;4;-1;0;"unknown";"no"
37;"management";"married";"tertiary";"no";0;"no";"no";"cellular";11;"mar";129;4;107;2;"failure";"no"
32;"technician";"married";"tertiary";"no";3939;"no";"no";"cellular";8;"aug";209;2;-1;0;"unknown";"no"
42;"self-employed";"married";"tertiary";"no";753;"no";"no";"unknown";17;"jun";31;1;-1;0;"unknown";"no"
29;"blue-collar";"married";"secondary";"no";358;"no";"no";"cellular";21;"jul";88;5;-1;0;"unknown";"no"
52;"blue-collar";"married";"primary";"no";1141;"yes";"no";"cellular";12;"may";22;5;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";1259;"no";"no";"unknown";20;"may";234;5;-1;0;"unknown";"no"
30;"services";"married";"secondary";"no";227;"no";"yes";"unknown";3;"jun";23;1;-1;0;"unknown";"no"
55;"entrepreneur";"divorced";"secondary";"no";538;"yes";"no";"cellular";7;"aug";353;2;-1;0;"unknown";"yes"
49;"management";"married";"tertiary";"no";92;"yes";"no";"unknown";6;"may";221;2;-1;0;"unknown";"no"
33;"technician";"married";"tertiary";"no";8023;"no";"no";"cellular";25;"feb";196;1;87;4;"failure";"no"
32;"services";"single";"secondary";"no";2478;"no";"yes";"cellular";20;"nov";395;1;-1;0;"unknown";"no"
21;"student";"single";"secondary";"no";6844;"no";"no";"cellular";14;"aug";126;3;127;7;"other";"no"
42;"technician";"married";"secondary";"no";440;"yes";"no";"cellular";3;"feb";13;5;-1;0;"unknown";"no"
56;"self-employed";"married";"secondary";"no";800;"no";"yes";"cellular";9;"jul";170;1;-1;0;"unknown";"no"
39;"unknown";"single";"secondary";"no";914;"no";"no";"unknown";5;"jun";20;1;-1;0;"unknown";"no"
30;"entrepreneur";"single";"secondary";"no";638;"no";"no";"cellular";14;"jul";358;6;-1;0;"unknown";"no"
55;"retired";"married";"secondary";"no";159;"no";"no";"cellular";28;"aug";159;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";483;"yes";"no";"cellular";12;"may";225;3;-1;0;"unknown";"no"
53;"unemployed";"married";"secondary";"no";94;"no";"no";"cellular";10;"aug";493;1;181;14;"success";"yes"
57;"retired";"married";"secondary";"no";1044;"no";"no";"telephone";11;"jan";585;1;192;2;"success";"yes"
39;"self-employed";"single";"unknown";"no";0;"no";"no";"unknown";20;"jun";13;11;-1;0;"unknown";"no"
56;"technician";"divorced";"tertiary";"no";401;"yes";"no";"telephone";20;"nov";26;1;-1;0;"unknown";"no"
29;"services";"single";"unknown";"no";33;"no";"no";"unknown";1;"jul";161;1;-1;0;"unknown";"no"
38;"blue-collar";"married";"primary";"no";506;"yes";"yes";"unknown";12;"jun";122;1;-1;0;"unknown";"no"
59;"management";"married";"tertiary";"no";1881;"no";"no";"cellular";19;"nov";456;2;-1;0;"unknown";"no"
42;"technician";"single";"unknown";"no";344;"yes";"no";"unknown";3;"jun";26;7;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";0;"yes";"no";"unknown";12;"may";51;9;-1;0;"unknown";"no"
31;"admin.";"married";"secondary";"no";3;"yes";"no";"cellular";28;"jul";37;12;-1;0;"unknown";"no"
35;"services";"divorced";"secondary";"no";221;"yes";"no";"cellular";14;"may";150;1;360;1;"failure";"no"
50;"self-employed";"married";"secondary";"no";36;"no";"no";"cellular";13;"aug";104;10;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";969;"yes";"no";"cellular";13;"may";721;1;-1;0;"unknown";"no"
36;"services";"single";"secondary";"no";760;"no";"no";"unknown";20;"jun";9;13;-1;0;"unknown";"no"
25;"admin.";"single";"tertiary";"no";220;"no";"no";"cellular";26;"may";68;1;-1;0;"unknown";"no"
42;"management";"single";"tertiary";"no";10907;"no";"no";"cellular";4;"may";74;3;-1;0;"unknown";"no"
27;"student";"single";"tertiary";"no";-148;"yes";"no";"cellular";11;"sep";146;3;224;1;"other";"no"
32;"blue-collar";"married";"primary";"no";415;"yes";"no";"unknown";2;"jun";777;4;-1;0;"unknown";"yes"
57;"management";"married";"tertiary";"no";1341;"yes";"no";"unknown";8;"may";339;2;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";865;"yes";"no";"cellular";13;"may";78;2;-1;0;"unknown";"no"
34;"blue-collar";"divorced";"secondary";"no";-566;"yes";"no";"unknown";26;"may";248;2;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";210;"yes";"yes";"unknown";20;"may";201;1;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";1275;"yes";"no";"unknown";20;"jun";18;9;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";579;"no";"no";"cellular";26;"aug";83;4;-1;0;"unknown";"no"
60;"technician";"single";"tertiary";"no";2934;"no";"no";"cellular";8;"aug";233;2;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";6637;"yes";"no";"cellular";17;"apr";174;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";271;"no";"no";"cellular";18;"nov";89;1;-1;0;"unknown";"no"
52;"technician";"married";"tertiary";"no";0;"no";"no";"cellular";9;"aug";145;3;94;4;"success";"no"
36;"services";"married";"secondary";"no";472;"yes";"no";"unknown";19;"jun";1017;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"primary";"no";487;"yes";"no";"unknown";7;"may";548;2;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";699;"yes";"no";"cellular";11;"aug";213;4;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";150;"no";"no";"cellular";5;"feb";210;1;-1;0;"unknown";"no"
31;"management";"single";"tertiary";"no";1130;"yes";"no";"cellular";17;"apr";224;3;74;4;"failure";"no"
29;"management";"single";"secondary";"no";584;"yes";"no";"unknown";16;"jun";541;19;-1;0;"unknown";"no"
48;"services";"divorced";"secondary";"no";3186;"no";"yes";"cellular";9;"apr";104;1;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";133;"no";"no";"cellular";15;"apr";171;1;-1;0;"unknown";"yes"
30;"housemaid";"single";"tertiary";"no";868;"no";"no";"cellular";7;"aug";387;2;-1;0;"unknown";"no"
48;"blue-collar";"married";"secondary";"no";480;"yes";"no";"cellular";15;"may";830;1;352;2;"failure";"yes"
51;"blue-collar";"married";"unknown";"no";1883;"yes";"no";"unknown";5;"may";57;1;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";606;"no";"no";"cellular";20;"apr";97;1;-1;0;"unknown";"yes"
35;"blue-collar";"married";"secondary";"no";603;"yes";"no";"cellular";17;"apr";474;2;-1;0;"unknown";"no"
49;"management";"single";"primary";"no";548;"no";"no";"telephone";31;"jul";115;6;-1;0;"unknown";"no"
36;"unemployed";"married";"secondary";"no";-872;"yes";"yes";"cellular";20;"nov";153;1;183;1;"failure";"no"
37;"blue-collar";"divorced";"secondary";"no";33;"yes";"no";"cellular";20;"nov";106;2;167;1;"failure";"no"
37;"management";"married";"tertiary";"no";1689;"yes";"no";"unknown";29;"may";645;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"primary";"no";902;"no";"no";"unknown";9;"jun";210;2;-1;0;"unknown";"no"
44;"services";"divorced";"secondary";"no";0;"yes";"yes";"unknown";13;"may";97;1;-1;0;"unknown";"no"
40;"student";"married";"secondary";"no";260;"no";"no";"cellular";19;"nov";52;1;152;6;"failure";"no"
57;"technician";"married";"secondary";"no";589;"yes";"no";"cellular";2;"feb";223;1;76;4;"failure";"no"
39;"technician";"married";"secondary";"no";626;"yes";"no";"cellular";11;"may";126;2;-1;0;"unknown";"no"
38;"services";"married";"secondary";"no";0;"no";"yes";"cellular";16;"jul";1473;6;-1;0;"unknown";"no"
51;"blue-collar";"divorced";"secondary";"no";78;"yes";"no";"unknown";27;"may";723;2;-1;0;"unknown";"no"
27;"blue-collar";"single";"secondary";"no";3792;"no";"no";"cellular";22;"apr";279;2;-1;0;"unknown";"yes"
38;"blue-collar";"single";"tertiary";"no";14058;"no";"no";"telephone";17;"jul";293;20;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";-522;"yes";"yes";"cellular";5;"may";670;2;286;6;"failure";"yes"
36;"student";"single";"unknown";"no";386;"no";"no";"unknown";18;"jun";359;2;-1;0;"unknown";"no"
37;"unemployed";"married";"secondary";"no";1978;"no";"no";"cellular";9;"sep";404;1;182;1;"success";"yes"
53;"admin.";"married";"secondary";"no";459;"no";"no";"cellular";14;"oct";317;6;192;1;"success";"no"
38;"unemployed";"married";"tertiary";"no";2812;"yes";"no";"cellular";30;"apr";830;1;-1;0;"unknown";"yes"
48;"management";"married";"tertiary";"no";5057;"no";"yes";"cellular";19;"nov";37;1;-1;0;"unknown";"no"
26;"housemaid";"married";"secondary";"no";-759;"yes";"no";"cellular";11;"jul";191;1;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";-416;"yes";"no";"unknown";16;"may";767;2;-1;0;"unknown";"yes"
47;"technician";"married";"unknown";"no";2106;"yes";"no";"unknown";6;"may";168;1;-1;0;"unknown";"no"
45;"management";"divorced";"tertiary";"no";220;"yes";"no";"unknown";18;"jun";89;4;-1;0;"unknown";"no"
32;"blue-collar";"divorced";"secondary";"no";0;"yes";"no";"unknown";2;"jun";746;2;-1;0;"unknown";"no"
69;"retired";"married";"unknown";"no";426;"no";"no";"telephone";9;"mar";225;1;90;1;"success";"yes"
65;"unknown";"married";"unknown";"no";4717;"no";"no";"telephone";6;"aug";256;1;-1;0;"unknown";"yes"
37;"blue-collar";"married";"secondary";"no";395;"yes";"no";"unknown";6;"may";219;2;-1;0;"unknown";"no"
45;"blue-collar";"divorced";"primary";"no";0;"yes";"no";"cellular";21;"jul";211;2;-1;0;"unknown";"no"
36;"blue-collar";"married";"secondary";"no";-206;"yes";"no";"unknown";8;"may";177;2;-1;0;"unknown";"no"
62;"self-employed";"divorced";"tertiary";"no";6;"yes";"no";"cellular";13;"oct";216;1;183;4;"success";"yes"
37;"management";"married";"tertiary";"no";4039;"no";"yes";"cellular";25;"jul";106;2;-1;0;"unknown";"no"
39;"services";"divorced";"secondary";"no";687;"yes";"no";"cellular";9;"jul";869;1;-1;0;"unknown";"yes"
38;"technician";"single";"secondary";"no";-242;"yes";"no";"cellular";18;"may";1173;3;278;6;"success";"yes"
31;"management";"married";"tertiary";"no";582;"yes";"no";"cellular";15;"jul";58;2;-1;0;"unknown";"no"
58;"blue-collar";"divorced";"primary";"no";8218;"yes";"no";"cellular";31;"jul";124;10;-1;0;"unknown";"no"
44;"management";"single";"tertiary";"no";1424;"yes";"no";"cellular";8;"aug";201;2;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";92;"yes";"no";"unknown";16;"may";688;1;-1;0;"unknown";"yes"
33;"technician";"single";"secondary";"no";2085;"no";"no";"cellular";9;"feb";501;2;-1;0;"unknown";"yes"
36;"technician";"single";"tertiary";"no";197;"no";"no";"cellular";30;"apr";260;1;-1;0;"unknown";"yes"
31;"technician";"single";"secondary";"no";2305;"no";"no";"cellular";4;"aug";139;1;-1;0;"unknown";"no"
45;"admin.";"married";"secondary";"no";-547;"yes";"no";"unknown";3;"jun";214;2;-1;0;"unknown";"no"
30;"technician";"single";"tertiary";"no";411;"no";"no";"cellular";21;"apr";142;1;-1;0;"unknown";"no"
34;"blue-collar";"married";"secondary";"no";584;"yes";"no";"cellular";18;"jul";154;2;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";997;"yes";"yes";"cellular";21;"nov";81;1;-1;0;"unknown";"no"
39;"admin.";"divorced";"secondary";"no";83;"yes";"no";"cellular";30;"jul";69;6;-1;0;"unknown";"no"
41;"management";"married";"tertiary";"no";6563;"no";"no";"cellular";27;"aug";136;8;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";0;"yes";"no";"cellular";31;"jul";99;13;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";0;"no";"no";"cellular";21;"nov";9;6;-1;0;"unknown";"no"
36;"blue-collar";"married";"primary";"no";398;"yes";"no";"unknown";12;"jun";278;1;-1;0;"unknown";"no"
35;"admin.";"married";"tertiary";"no";1145;"yes";"no";"cellular";2;"feb";82;2;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";2;"yes";"no";"cellular";11;"sep";100;2;-1;0;"unknown";"yes"
33;"management";"single";"tertiary";"no";201;"yes";"no";"cellular";19;"nov";171;1;183;1;"failure";"no"
49;"entrepreneur";"married";"primary";"no";563;"no";"no";"unknown";19;"jun";84;1;-1;0;"unknown";"no"
35;"management";"married";"tertiary";"no";3161;"no";"no";"cellular";8;"aug";433;2;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";0;"no";"yes";"cellular";15;"jul";624;6;-1;0;"unknown";"no"
42;"management";"married";"secondary";"no";1331;"no";"no";"cellular";23;"jul";698;2;-1;0;"unknown";"no"
34;"housemaid";"married";"primary";"no";2929;"no";"no";"cellular";14;"aug";518;2;-1;0;"unknown";"yes"
32;"services";"married";"secondary";"yes";-220;"yes";"yes";"cellular";25;"jul";123;2;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";5980;"yes";"no";"unknown";28;"may";317;1;-1;0;"unknown";"no"
32;"services";"single";"secondary";"no";48;"yes";"no";"cellular";20;"apr";8;3;-1;0;"unknown";"no"
50;"housemaid";"single";"unknown";"no";0;"no";"no";"telephone";28;"jul";118;8;-1;0;"unknown";"no"
58;"retired";"married";"secondary";"no";3382;"no";"yes";"cellular";28;"may";294;2;309;2;"failure";"no"
59;"technician";"married";"secondary";"no";2308;"no";"no";"cellular";30;"apr";815;1;87;1;"other";"yes"
25;"student";"married";"tertiary";"no";10;"yes";"no";"cellular";21;"jul";857;4;-1;0;"unknown";"yes"
57;"services";"married";"secondary";"no";162;"yes";"no";"unknown";5;"may";174;1;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";1073;"yes";"no";"cellular";5;"may";1130;4;-1;0;"unknown";"no"
26;"services";"single";"secondary";"no";1700;"yes";"no";"cellular";7;"may";13;5;-1;0;"unknown";"no"
50;"services";"single";"primary";"no";148;"yes";"no";"unknown";7;"may";198;2;-1;0;"unknown";"no"
39;"services";"married";"secondary";"no";0;"yes";"no";"cellular";8;"may";503;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";185;"yes";"no";"cellular";29;"jan";249;3;-1;0;"unknown";"no"
37;"management";"divorced";"tertiary";"no";896;"no";"yes";"cellular";14;"aug";343;2;-1;0;"unknown";"no"
47;"blue-collar";"divorced";"primary";"no";1279;"yes";"no";"cellular";6;"feb";479;2;-1;0;"unknown";"no"
50;"technician";"married";"tertiary";"no";199;"yes";"yes";"cellular";3;"feb";116;2;253;1;"failure";"no"
40;"management";"married";"secondary";"no";99;"no";"no";"cellular";9;"feb";15;5;-1;0;"unknown";"no"
35;"blue-collar";"married";"primary";"no";378;"yes";"no";"unknown";16;"may";427;3;-1;0;"unknown";"no"
53;"management";"married";"tertiary";"no";15;"no";"no";"cellular";20;"aug";921;2;-1;0;"unknown";"yes"
35;"admin.";"married";"secondary";"no";147;"yes";"yes";"cellular";29;"jan";41;1;184;7;"failure";"no"
54;"technician";"married";"secondary";"no";2225;"no";"yes";"cellular";13;"aug";73;2;-1;0;"unknown";"no"
35;"admin.";"single";"secondary";"no";1810;"yes";"no";"cellular";25;"may";65;2;-1;0;"unknown";"no"
31;"housemaid";"married";"unknown";"yes";-6;"no";"yes";"telephone";7;"jul";94;2;-1;0;"unknown";"no"
46;"technician";"divorced";"secondary";"no";435;"yes";"no";"unknown";15;"may";192;3;-1;0;"unknown";"no"
52;"management";"married";"secondary";"no";335;"no";"no";"cellular";21;"oct";411;1;182;1;"failure";"yes"
34;"admin.";"divorced";"secondary";"no";-251;"no";"no";"cellular";18;"jul";641;1;-1;0;"unknown";"yes"
46;"services";"divorced";"secondary";"no";5;"yes";"yes";"cellular";29;"jan";305;2;-1;0;"unknown";"no"
54;"self-employed";"married";"secondary";"no";641;"yes";"no";"cellular";17;"apr";472;1;148;2;"failure";"no"
37;"admin.";"married";"tertiary";"no";0;"yes";"no";"cellular";21;"jul";115;1;-1;0;"unknown";"no"
39;"admin.";"married";"tertiary";"no";722;"no";"no";"telephone";23;"apr";141;1;-1;0;"unknown";"no"
50;"technician";"married";"tertiary";"no";3337;"yes";"yes";"telephone";31;"jul";24;14;-1;0;"unknown";"no"
44;"technician";"divorced";"secondary";"no";0;"no";"no";"cellular";28;"jul";264;5;-1;0;"unknown";"no"
56;"admin.";"married";"secondary";"no";1504;"yes";"no";"unknown";29;"may";66;2;-1;0;"unknown";"no"
40;"services";"married";"tertiary";"no";35;"no";"no";"cellular";3;"jul";318;4;317;3;"failure";"no"
36;"services";"married";"secondary";"no";895;"yes";"yes";"unknown";4;"jun";622;1;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";752;"yes";"no";"cellular";11;"aug";104;4;-1;0;"unknown";"no"
33;"blue-collar";"single";"secondary";"no";1925;"yes";"no";"cellular";2;"mar";76;2;-1;0;"unknown";"no"
42;"services";"married";"secondary";"no";-246;"yes";"no";"unknown";7;"may";149;1;-1;0;"unknown";"no"
30;"self-employed";"single";"tertiary";"no";901;"yes";"yes";"unknown";16;"may";601;1;-1;0;"unknown";"no"
33;"admin.";"single";"secondary";"no";894;"no";"no";"cellular";5;"feb";509;2;-1;0;"unknown";"no"
26;"blue-collar";"single";"secondary";"no";592;"yes";"yes";"unknown";27;"may";174;1;-1;0;"unknown";"no"
26;"admin.";"single";"secondary";"no";3529;"no";"yes";"cellular";7;"sep";57;2;-1;0;"unknown";"no"
37;"services";"married";"secondary";"no";0;"yes";"yes";"cellular";31;"jul";187;2;-1;0;"unknown";"no"
59;"retired";"married";"primary";"no";0;"no";"no";"cellular";5;"jun";493;2;-1;0;"unknown";"yes"
49;"blue-collar";"married";"primary";"no";371;"yes";"no";"cellular";31;"jul";18;44;-1;0;"unknown";"no"
33;"blue-collar";"single";"primary";"no";1;"yes";"no";"cellular";20;"apr";257;1;-1;0;"unknown";"no"
46;"technician";"single";"secondary";"no";834;"yes";"no";"cellular";11;"jul";196;2;-1;0;"unknown";"no"
55;"entrepreneur";"married";"primary";"no";298;"yes";"no";"unknown";8;"may";144;3;-1;0;"unknown";"no"
49;"services";"married";"tertiary";"no";306;"yes";"no";"cellular";18;"aug";285;7;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";143;"yes";"no";"unknown";16;"may";627;1;-1;0;"unknown";"no"
27;"admin.";"married";"secondary";"no";186;"yes";"no";"unknown";27;"may";136;2;-1;0;"unknown";"no"
44;"technician";"married";"secondary";"no";5597;"no";"no";"unknown";20;"jun";27;3;-1;0;"unknown";"no"
40;"services";"single";"secondary";"no";1547;"yes";"no";"cellular";12;"may";88;2;173;1;"success";"no"
36;"management";"married";"tertiary";"no";1852;"no";"no";"unknown";8;"may";362;1;-1;0;"unknown";"no"
46;"admin.";"single";"secondary";"no";1790;"yes";"no";"unknown";5;"jun";214;5;-1;0;"unknown";"no"
30;"services";"single";"secondary";"no";20;"yes";"no";"cellular";20;"apr";404;6;340;1;"failure";"no"
35;"admin.";"married";"secondary";"no";8652;"yes";"no";"unknown";24;"jun";103;6;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";1626;"no";"no";"cellular";7;"may";356;2;286;3;"failure";"no"
37;"admin.";"married";"secondary";"no";749;"no";"no";"cellular";21;"apr";219;1;-1;0;"unknown";"no"
49;"blue-collar";"married";"unknown";"no";51;"yes";"no";"unknown";7;"may";984;1;-1;0;"unknown";"no"
27;"services";"single";"secondary";"yes";-1;"no";"no";"cellular";22;"jul";60;2;-1;0;"unknown";"no"
32;"blue-collar";"married";"secondary";"no";-32;"yes";"no";"cellular";12;"may";145;1;-1;0;"unknown";"no"
70;"retired";"divorced";"secondary";"no";6850;"no";"no";"telephone";5;"mar";252;3;183;1;"failure";"no"
22;"technician";"single";"secondary";"no";333;"no";"yes";"telephone";12;"nov";129;1;-1;0;"unknown";"no"
58;"blue-collar";"married";"secondary";"no";-27;"no";"yes";"telephone";31;"jul";77;13;-1;0;"unknown";"no"
33;"blue-collar";"married";"secondary";"no";932;"yes";"no";"unknown";21;"may";226;1;-1;0;"unknown";"no"
83;"retired";"divorced";"primary";"no";0;"no";"no";"telephone";31;"may";664;1;77;3;"success";"no"
50;"management";"divorced";"tertiary";"no";100;"yes";"yes";"cellular";13;"may";157;1;299;3;"failure";"no"
53;"blue-collar";"married";"secondary";"no";-626;"yes";"no";"cellular";9;"jul";205;1;-1;0;"unknown";"no"
37;"management";"single";"tertiary";"no";9;"no";"no";"cellular";26;"aug";484;2;-1;0;"unknown";"no"
55;"services";"divorced";"primary";"no";653;"no";"no";"unknown";6;"jun";800;1;-1;0;"unknown";"no"
49;"admin.";"married";"secondary";"no";3608;"yes";"no";"unknown";13;"nov";231;1;-1;0;"unknown";"yes"
41;"blue-collar";"married";"primary";"no";293;"yes";"yes";"cellular";7;"may";102;1;-1;0;"unknown";"no"
40;"management";"single";"tertiary";"no";36;"yes";"no";"unknown";27;"may";240;3;-1;0;"unknown";"no"
48;"management";"married";"tertiary";"no";263;"yes";"no";"unknown";5;"may";350;2;-1;0;"unknown";"no"
27;"student";"single";"secondary";"no";8366;"no";"no";"cellular";7;"may";458;2;349;1;"other";"no"
30;"management";"single";"tertiary";"no";2186;"yes";"no";"unknown";3;"jun";366;1;-1;0;"unknown";"no"
37;"entrepreneur";"single";"secondary";"no";20453;"yes";"no";"telephone";4;"may";115;1;-1;0;"unknown";"no"
37;"services";"single";"secondary";"no";105;"no";"yes";"unknown";17;"jun";260;4;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";440;"yes";"no";"unknown";5;"jun";327;2;-1;0;"unknown";"no"
73;"retired";"married";"unknown";"no";519;"no";"no";"telephone";16;"oct";434;1;57;1;"failure";"yes"
50;"management";"married";"tertiary";"no";19447;"yes";"yes";"cellular";21;"nov";166;1;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";225;"no";"no";"cellular";18;"jul";171;7;-1;0;"unknown";"no"
47;"blue-collar";"married";"secondary";"no";2548;"yes";"no";"cellular";11;"may";577;1;368;1;"other";"no"
33;"technician";"divorced";"secondary";"no";0;"no";"no";"cellular";21;"aug";224;8;-1;0;"unknown";"no"
50;"blue-collar";"married";"unknown";"no";2284;"yes";"no";"telephone";31;"jul";1088;17;-1;0;"unknown";"yes"
51;"blue-collar";"married";"primary";"no";40;"yes";"no";"cellular";13;"may";79;1;370;1;"failure";"no"
42;"housemaid";"divorced";"tertiary";"no";364;"no";"no";"cellular";18;"nov";52;1;-1;0;"unknown";"no"
34;"technician";"single";"secondary";"no";174;"no";"no";"telephone";11;"may";259;4;-1;0;"unknown";"no"
44;"technician";"married";"secondary";"no";10888;"yes";"no";"cellular";5;"aug";163;1;-1;0;"unknown";"no"
32;"admin.";"married";"secondary";"no";0;"yes";"yes";"cellular";17;"nov";159;2;195;2;"failure";"no"
60;"retired";"married";"tertiary";"no";225;"no";"no";"cellular";19;"aug";54;2;-1;0;"unknown";"no"
60;"retired";"divorced";"tertiary";"no";2131;"no";"no";"cellular";10;"jul";421;6;-1;0;"unknown";"no"
29;"blue-collar";"single";"secondary";"no";-20;"no";"yes";"cellular";21;"jul";574;1;-1;0;"unknown";"yes"
39;"management";"single";"secondary";"no";36;"no";"no";"cellular";29;"aug";70;2;-1;0;"unknown";"no"
47;"blue-collar";"married";"primary";"no";2642;"no";"no";"cellular";13;"aug";310;2;-1;0;"unknown";"no"
30;"admin.";"married";"tertiary";"no";400;"yes";"no";"cellular";7;"may";223;1;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";1214;"no";"no";"cellular";3;"feb";39;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";-292;"yes";"no";"cellular";19;"aug";181;6;-1;0;"unknown";"no"
51;"admin.";"married";"secondary";"no";1415;"yes";"no";"unknown";2;"jun";151;3;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";213;"yes";"no";"unknown";16;"jun";270;15;-1;0;"unknown";"no"
47;"admin.";"single";"secondary";"no";2540;"no";"no";"cellular";18;"nov";110;5;-1;0;"unknown";"no"
46;"housemaid";"single";"tertiary";"no";3232;"no";"no";"telephone";15;"jun";94;2;-1;0;"unknown";"no"
33;"management";"married";"tertiary";"no";296;"no";"no";"cellular";16;"jul";90;2;-1;0;"unknown";"no"
30;"management";"married";"tertiary";"no";42;"no";"no";"cellular";25;"aug";115;6;-1;0;"unknown";"no"
40;"technician";"married";"secondary";"no";697;"no";"no";"cellular";24;"jun";220;2;219;2;"other";"no"
38;"blue-collar";"single";"secondary";"no";13156;"yes";"no";"cellular";17;"apr";291;4;331;1;"failure";"no"
63;"retired";"divorced";"secondary";"no";1490;"no";"no";"cellular";8;"jun";506;1;-1;0;"unknown";"yes"
33;"management";"married";"tertiary";"no";150;"no";"no";"cellular";21;"aug";151;2;-1;0;"unknown";"no"
59;"retired";"married";"secondary";"no";0;"no";"no";"unknown";5;"jun";812;1;-1;0;"unknown";"yes"
29;"management";"single";"tertiary";"no";4210;"yes";"no";"cellular";18;"may";119;1;-1;0;"unknown";"no"
54;"services";"divorced";"secondary";"no";1012;"yes";"no";"cellular";12;"may";33;3;175;2;"other";"no"
57;"technician";"married";"secondary";"no";16063;"yes";"no";"unknown";30;"may";352;3;-1;0;"unknown";"no"
57;"blue-collar";"married";"primary";"no";5431;"yes";"yes";"unknown";6;"may";383;1;-1;0;"unknown";"no"
28;"services";"single";"secondary";"no";317;"yes";"no";"unknown";16;"may";21;3;-1;0;"unknown";"no"
30;"technician";"single";"secondary";"no";4787;"yes";"no";"cellular";18;"nov";266;1;167;2;"failure";"no"
28;"blue-collar";"married";"secondary";"no";225;"yes";"yes";"unknown";7;"may";866;2;-1;0;"unknown";"no"
45;"services";"divorced";"primary";"no";3854;"yes";"no";"unknown";20;"jun";10;3;-1;0;"unknown";"no"
29;"services";"divorced";"secondary";"no";92;"yes";"no";"cellular";7;"may";240;1;-1;0;"unknown";"no"
42;"blue-collar";"married";"primary";"no";-10;"yes";"no";"unknown";9;"may";291;4;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";-617;"yes";"no";"unknown";2;"jun";115;2;-1;0;"unknown";"no"
59;"housemaid";"divorced";"primary";"no";195;"no";"no";"unknown";5;"may";366;2;-1;0;"unknown";"no"
41;"blue-collar";"married";"primary";"no";1172;"yes";"no";"unknown";15;"may";153;1;-1;0;"unknown";"no"
57;"housemaid";"married";"secondary";"no";0;"no";"no";"cellular";22;"aug";197;2;-1;0;"unknown";"no"
43;"services";"divorced";"secondary";"no";0;"no";"no";"cellular";31;"jul";106;4;-1;0;"unknown";"no"
30;"services";"divorced";"secondary";"no";-94;"yes";"no";"cellular";14;"jul";41;2;-1;0;"unknown";"no"
26;"admin.";"single";"secondary";"no";609;"no";"no";"telephone";28;"jan";112;1;-1;0;"unknown";"no"
52;"management";"single";"tertiary";"no";4;"no";"yes";"cellular";28;"jul";30;9;-1;0;"unknown";"no"
32;"entrepreneur";"divorced";"secondary";"no";1121;"yes";"yes";"cellular";16;"apr";211;3;-1;0;"unknown";"no"
39;"management";"divorced";"tertiary";"no";355;"yes";"no";"cellular";25;"aug";313;5;-1;0;"unknown";"no"
31;"blue-collar";"married";"secondary";"no";3653;"yes";"yes";"cellular";21;"nov";252;1;168;4;"failure";"no"
83;"retired";"divorced";"primary";"no";1097;"no";"no";"telephone";5;"mar";181;1;-1;0;"unknown";"yes"
34;"management";"married";"tertiary";"no";436;"no";"yes";"cellular";28;"jul";118;4;-1;0;"unknown";"no"
40;"blue-collar";"married";"secondary";"no";4795;"yes";"no";"cellular";17;"apr";57;1;-1;0;"unknown";"no"
39;"self-employed";"married";"secondary";"no";266;"no";"no";"cellular";11;"may";102;2;-1;0;"unknown";"no"
50;"unemployed";"married";"secondary";"no";3357;"no";"no";"cellular";9;"jun";239;4;-1;0;"unknown";"yes"
30;"technician";"married";"tertiary";"no";101;"yes";"yes";"cellular";7;"jul";187;3;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";10910;"yes";"no";"cellular";15;"may";66;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";1568;"no";"no";"unknown";21;"may";206;6;-1;0;"unknown";"no"
41;"retired";"single";"primary";"no";0;"no";"no";"cellular";8;"may";239;1;368;2;"failure";"no"
35;"blue-collar";"married";"secondary";"no";305;"yes";"no";"cellular";18;"may";7;7;367;25;"failure";"no"
57;"retired";"married";"tertiary";"no";31;"no";"no";"cellular";19;"aug";260;2;-1;0;"unknown";"no"
29;"entrepreneur";"married";"secondary";"no";452;"yes";"no";"unknown";27;"may";347;1;-1;0;"unknown";"no"
48;"admin.";"married";"secondary";"no";5;"yes";"no";"unknown";7;"may";697;2;-1;0;"unknown";"no"
38;"entrepreneur";"divorced";"tertiary";"no";7929;"no";"no";"cellular";8;"jul";182;3;-1;0;"unknown";"no"
41;"entrepreneur";"married";"primary";"no";39;"yes";"no";"cellular";21;"nov";549;2;-1;0;"unknown";"yes"
39;"housemaid";"married";"primary";"no";267;"no";"no";"unknown";9;"jun";1151;5;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";4945;"no";"no";"cellular";11;"aug";445;1;-1;0;"unknown";"no"
51;"technician";"married";"secondary";"no";463;"yes";"no";"unknown";13;"may";213;5;-1;0;"unknown";"no"
35;"unemployed";"single";"secondary";"no";2116;"yes";"no";"cellular";13;"may";409;2;-1;0;"unknown";"no"
45;"admin.";"divorced";"secondary";"no";59;"yes";"no";"cellular";24;"jul";873;8;-1;0;"unknown";"yes"
28;"blue-collar";"married";"secondary";"no";131;"yes";"no";"unknown";12;"may";147;5;-1;0;"unknown";"no"
57;"retired";"married";"secondary";"no";107;"no";"no";"cellular";14;"jul";85;2;-1;0;"unknown";"no"
36;"unemployed";"married";"secondary";"no";-35;"no";"no";"cellular";30;"jan";339;3;-1;0;"unknown";"no"
49;"technician";"married";"primary";"no";230;"yes";"no";"unknown";8;"may";96;1;-1;0;"unknown";"no"
34;"management";"single";"tertiary";"no";2679;"yes";"no";"cellular";7;"may";195;1;-1;0;"unknown";"no"
65;"retired";"married";"primary";"no";493;"no";"no";"cellular";16;"oct";622;1;182;1;"failure";"no"
26;"services";"single";"secondary";"no";256;"yes";"no";"cellular";4;"may";101;2;-1;0;"unknown";"no"
52;"blue-collar";"married";"primary";"no";-133;"no";"no";"unknown";5;"jun";96;2;-1;0;"unknown";"no"
32;"blue-collar";"single";"secondary";"no";811;"yes";"no";"cellular";14;"may";563;1;-1;0;"unknown";"yes"
33;"unemployed";"married";"tertiary";"no";302;"yes";"yes";"cellular";16;"apr";670;1;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";3529;"yes";"no";"cellular";4;"aug";190;3;-1;0;"unknown";"no"
26;"technician";"single";"tertiary";"no";75;"yes";"yes";"unknown";19;"may";351;1;-1;0;"unknown";"no"
45;"technician";"married";"secondary";"no";999;"yes";"no";"cellular";20;"nov";215;6;188;2;"failure";"no"
30;"management";"single";"tertiary";"no";7845;"yes";"no";"unknown";3;"jun";59;1;-1;0;"unknown";"no"
37;"services";"divorced";"primary";"no";260;"yes";"no";"cellular";17;"apr";446;2;-1;0;"unknown";"no"
45;"management";"single";"tertiary";"no";2048;"yes";"no";"cellular";18;"oct";310;1;804;1;"failure";"yes"
34;"management";"married";"tertiary";"no";1557;"yes";"yes";"unknown";13;"may";213;1;-1;0;"unknown";"no"
35;"services";"married";"secondary";"no";505;"yes";"yes";"unknown";27;"may";371;2;-1;0;"unknown";"no"
41;"admin.";"married";"tertiary";"no";1536;"no";"no";"cellular";4;"may";54;2;-1;0;"unknown";"no"
42;"self-employed";"married";"tertiary";"no";0;"no";"no";"cellular";8;"aug";118;6;-1;0;"unknown";"no"
48;"blue-collar";"married";"unknown";"no";439;"yes";"no";"cellular";2;"feb";212;1;249;2;"other";"no"
45;"unknown";"married";"primary";"no";44;"no";"no";"unknown";11;"jun";81;1;-1;0;"unknown";"no"
47;"management";"married";"tertiary";"no";295;"yes";"no";"cellular";5;"aug";157;1;-1;0;"unknown";"no"
37;"blue-collar";"married";"primary";"no";190;"yes";"yes";"unknown";8;"may";194;1;-1;0;"unknown";"no"
32;"management";"married";"secondary";"no";3935;"yes";"no";"unknown";29;"may";129;2;-1;0;"unknown";"no"
40;"management";"married";"tertiary";"no";-17;"yes";"yes";"cellular";11;"may";474;1;256;1;"success";"yes"
35;"technician";"single";"secondary";"no";175;"yes";"no";"cellular";29;"jan";219;1;246;1;"failure";"no"
31;"technician";"single";"secondary";"no";4888;"yes";"no";"telephone";5;"may";475;4;166;2;"failure";"no"
36;"technician";"single";"tertiary";"no";40;"no";"no";"cellular";26;"aug";359;3;-1;0;"unknown";"no"
44;"management";"married";"tertiary";"no";4460;"no";"no";"telephone";31;"jul";168;2;-1;0;"unknown";"no"
52;"admin.";"divorced";"secondary";"no";98;"no";"yes";"unknown";19;"jun";148;1;-1;0;"unknown";"no"
28;"student";"single";"secondary";"no";330;"no";"no";"telephone";2;"feb";197;1;-1;0;"unknown";"no"
45;"blue-collar";"married";"primary";"no";13117;"no";"no";"unknown";11;"jun";42;2;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";12877;"no";"no";"unknown";17;"jun";118;3;-1;0;"unknown";"no"
39;"blue-collar";"married";"primary";"no";1506;"yes";"no";"cellular";11;"may";299;2;-1;0;"unknown";"no"
28;"management";"single";"tertiary";"no";0;"no";"no";"telephone";27;"may";198;2;-1;0;"unknown";"no"
31;"technician";"single";"secondary";"no";775;"no";"no";"cellular";2;"feb";361;1;-1;0;"unknown";"no"
46;"blue-collar";"married";"primary";"no";258;"yes";"no";"unknown";27;"may";217;1;-1;0;"unknown";"no"
22;"student";"single";"secondary";"no";246;"yes";"no";"telephone";12;"may";25;1;-1;0;"unknown";"no"
49;"blue-collar";"single";"primary";"no";2146;"yes";"no";"cellular";7;"may";1516;2;353;2;"other";"yes"
44;"unemployed";"married";"secondary";"no";304;"yes";"no";"unknown";12;"may";129;2;-1;0;"unknown";"no"
31;"self-employed";"single";"secondary";"no";360;"yes";"no";"unknown";26;"may";592;4;-1;0;"unknown";"no"
53;"blue-collar";"married";"primary";"no";3316;"yes";"no";"unknown";23;"may";331;6;-1;0;"unknown";"no"
55;"management";"married";"tertiary";"no";6880;"no";"no";"cellular";8;"aug";353;2;-1;0;"unknown";"no"
35;"technician";"married";"secondary";"no";2189;"no";"no";"cellular";19;"nov";256;3;-1;0;"unknown";"no"
35;"blue-collar";"married";"secondary";"no";5304;"yes";"no";"cellular";20;"nov";340;1;183;2;"failure";"no"
37;"management";"single";"tertiary";"no";102;"yes";"no";"cellular";21;"aug";101;2;-1;0;"unknown";"no"
50;"blue-collar";"married";"secondary";"no";181;"yes";"no";"cellular";17;"apr";599;2;-1;0;"unknown";"no"
37;"management";"married";"primary";"no";3953;"yes";"no";"cellular";6;"apr";610;2;138;1;"failure";"no"
37;"blue-collar";"single";"primary";"no";6969;"yes";"no";"unknown";20;"may";412;1;-1;0;"unknown";"no"
29;"management";"single";"tertiary";"no";572;"no";"no";"cellular";16;"dec";314;1;195;1;"success";"yes"
31;"admin.";"single";"secondary";"no";223;"yes";"no";"cellular";17;"apr";508;1;315;11;"success";"no"
40;"technician";"married";"secondary";"no";-396;"yes";"no";"unknown";26;"may";127;1;-1;0;"unknown";"no"
47;"services";"divorced";"secondary";"no";164;"no";"no";"unknown";5;"may";212;1;-1;0;"unknown";"no"
55;"self-employed";"married";"secondary";"no";2678;"no";"yes";"cellular";18;"aug";151;12;-1;0;"unknown";"no"
33;"technician";"single";"secondary";"no";920;"yes";"no";"cellular";21;"nov";58;1;122;4;"failure";"no"
53;"services";"divorced";"secondary";"no";4554;"no";"no";"cellular";5;"feb";8;6;-1;0;"unknown";"no"
38;"management";"single";"tertiary";"no";623;"no";"no";"cellular";28;"jun";297;4;144;4;"other";"yes"
61;"management";"married";"secondary";"no";967;"no";"no";"cellular";20;"aug";180;1;-1;0;"unknown";"no"
43;"unemployed";"divorced";"secondary";"no";1577;"yes";"no";"cellular";19;"nov";87;1;-1;0;"unknown";"no"
42;"housemaid";"married";"primary";"no";1;"yes";"no";"telephone";28;"jul";59;5;-1;0;"unknown";"no"
36;"management";"divorced";"tertiary";"no";0;"no";"no";"cellular";28;"aug";81;4;-1;0;"unknown";"no"
31;"technician";"single";"tertiary";"no";1166;"yes";"no";"unknown";3;"jun";207;1;-1;0;"unknown";"no"
59;"management";"married";"unknown";"no";3534;"no";"no";"cellular";21;"nov";216;4;-1;0;"unknown";"no"
29;"blue-collar";"married";"primary";"no";200;"yes";"no";"unknown";30;"may";322;2;-1;0;"unknown";"no"
33;"technician";"married";"secondary";"no";4790;"yes";"no";"cellular";20;"apr";137;1;272;2;"failure";"no"
44;"blue-collar";"married";"primary";"no";-237;"yes";"no";"telephone";10;"jul";73;1;-1;0;"unknown";"no"
43;"blue-collar";"married";"secondary";"no";1335;"yes";"no";"unknown";6;"may";371;2;-1;0;"unknown";"no"
42;"management";"married";"tertiary";"no";603;"yes";"no";"cellular";5;"nov";298;5;-1;0;"unknown";"no"
32;"management";"single";"tertiary";"no";577;"no";"no";"cellular";7;"aug";101;2;-1;0;"unknown";"no"
43;"management";"married";"tertiary";"no";0;"yes";"no";"cellular";17;"nov";110;1;-1;0;"unknown";"no"
39;"admin.";"single";"secondary";"no";163;"yes";"no";"cellular";30;"jan";107;2;-1;0;"unknown";"no"
23;"blue-collar";"married";"primary";"no";1158;"yes";"no";"cellular";16;"apr";743;1;-1;0;"unknown";"no"
43;"services";"married";"secondary";"no";642;"no";"no";"cellular";31;"jul";644;2;-1;0;"unknown";"no"
46;"admin.";"married";"secondary";"no";466;"yes";"no";"cellular";29;"jan";169;1;-1;0;"unknown";"no"
31;"technician";"married";"secondary";"no";119;"yes";"no";"cellular";4;"feb";380;1;-1;0;"unknown";"no"
38;"management";"married";"tertiary";"no";3436;"no";"no";"cellular";21;"nov";663;4;-1;0;"unknown";"yes"
53;"blue-collar";"married";"primary";"no";238;"yes";"no";"cellular";11;"may";238;2;361;5;"failure";"no"
37;"blue-collar";"married";"primary";"no";378;"yes";"no";"unknown";9;"may";514;1;-1;0;"unknown";"no"
49;"entrepreneur";"divorced";"secondary";"no";32;"no";"no";"cellular";5;"feb";128;1;-1;0;"unknown";"no"
31;"unemployed";"single";"secondary";"no";315;"no";"no";"cellular";30;"jan";130;1;2;1;"other";"no"
45;"management";"married";"tertiary";"no";6945;"no";"no";"cellular";5;"aug";131;5;356;3;"failure";"no"
58;"admin.";"married";"secondary";"no";3496;"yes";"no";"unknown";2;"jun";111;4;-1;0;"unknown";"no"
35;"blue-collar";"single";"secondary";"no";0;"yes";"no";"cellular";16;"apr";169;1;-1;0;"unknown";"no"
32;"technician";"single";"secondary";"no";309;"yes";"yes";"cellular";16;"apr";346;1;234;3;"failure";"no"
28;"technician";"single";"tertiary";"no";0;"yes";"no";"unknown";4;"jun";205;6;-1;0;"unknown";"no"
26;"technician";"single";"secondary";"no";668;"yes";"no";"unknown";28;"may";576;3;-1;0;"unknown";"yes"
48;"management";"married";"tertiary";"no";1175;"yes";"no";"telephone";18;"nov";1476;3;-1;0;"unknown";"no"
30;"blue-collar";"single";"secondary";"no";363;"no";"no";"cellular";28;"jul";171;3;-1;0;"unknown";"no"
31;"entrepreneur";"single";"tertiary";"no";38;"no";"no";"cellular";20;"nov";185;2;-1;0;"unknown";"no"
31;"management";"married";"tertiary";"no";1183;"yes";"no";"unknown";27;"may";676;6;-1;0;"unknown";"no"
45;"blue-collar";"divorced";"primary";"no";942;"no";"no";"cellular";21;"nov";362;1;-1;0;"unknown";"no"
38;"admin.";"married";"secondary";"no";4196;"yes";"no";"cellular";12;"may";193;2;-1;0;"unknown";"no"
34;"management";"married";"tertiary";"no";297;"yes";"no";"cellular";26;"aug";63;4;-1;0;"unknown";"no"
42;"services";"married";"secondary";"no";-91;"yes";"yes";"cellular";5;"feb";43;1;-1;0;"unknown";"no"
60;"self-employed";"married";"primary";"no";362;"no";"yes";"cellular";29;"jul";816;6;-1;0;"unknown";"yes"
42;"blue-collar";"single";"secondary";"no";1080;"yes";"yes";"cellular";13;"may";951;3;370;4;"failure";"yes"
32;"admin.";"single";"secondary";"no";620;"yes";"no";"unknown";26;"may";1234;3;-1;0;"unknown";"yes"
42;"unemployed";"divorced";"tertiary";"no";-166;"no";"no";"cellular";29;"aug";85;4;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";288;"yes";"no";"cellular";17;"apr";306;1;-1;0;"unknown";"no"
42;"admin.";"married";"unknown";"no";642;"yes";"yes";"unknown";16;"may";509;2;-1;0;"unknown";"no"
51;"technician";"married";"tertiary";"no";2506;"no";"no";"cellular";30;"nov";210;3;-1;0;"unknown";"no"
36;"technician";"divorced";"secondary";"no";566;"yes";"no";"unknown";20;"may";129;2;-1;0;"unknown";"no"
46;"blue-collar";"married";"secondary";"no";668;"yes";"no";"unknown";15;"may";1263;2;-1;0;"unknown";"yes"
40;"blue-collar";"married";"secondary";"no";1100;"yes";"no";"unknown";29;"may";660;2;-1;0;"unknown";"no"
49;"blue-collar";"married";"secondary";"no";322;"no";"no";"cellular";14;"aug";356;2;-1;0;"unknown";"no"
38;"blue-collar";"married";"secondary";"no";1205;"yes";"no";"cellular";20;"apr";45;4;153;1;"failure";"no"
32;"services";"single";"secondary";"no";473;"yes";"no";"cellular";7;"jul";624;5;-1;0;"unknown";"no"
33;"services";"married";"secondary";"no";-333;"yes";"no";"cellular";30;"jul";329;5;-1;0;"unknown";"no"
57;"self-employed";"married";"tertiary";"yes";-3313;"yes";"yes";"unknown";9;"may";153;1;-1;0;"unknown";"no"
57;"technician";"married";"secondary";"no";295;"no";"no";"cellular";19;"aug";151;11;-1;0;"unknown";"no"
28;"blue-collar";"married";"secondary";"no";1137;"no";"no";"cellular";6;"feb";129;4;211;3;"other";"no"
44;"entrepreneur";"single";"tertiary";"no";1136;"yes";"yes";"cellular";3;"apr";345;2;249;7;"other";"no"


================================================
FILE: data/binary.csv
================================================
admit,gre,gpa,rank
0,380,3.61,3
1,660,3.67,3
1,800,4,1
1,640,3.19,4
0,520,2.93,4
1,760,3,2
1,560,2.98,1
0,400,3.08,2
1,540,3.39,3
0,700,3.92,2
0,800,4,4
0,440,3.22,1
1,760,4,1
0,700,3.08,2
1,700,4,1
0,480,3.44,3
0,780,3.87,4
0,360,2.56,3
0,800,3.75,2
1,540,3.81,1
0,500,3.17,3
1,660,3.63,2
0,600,2.82,4
0,680,3.19,4
1,760,3.35,2
1,800,3.66,1
1,620,3.61,1
1,520,3.74,4
1,780,3.22,2
0,520,3.29,1
0,540,3.78,4
0,760,3.35,3
0,600,3.4,3
1,800,4,3
0,360,3.14,1
0,400,3.05,2
0,580,3.25,1
0,520,2.9,3
1,500,3.13,2
1,520,2.68,3
0,560,2.42,2
1,580,3.32,2
1,600,3.15,2
0,500,3.31,3
0,700,2.94,2
1,460,3.45,3
1,580,3.46,2
0,500,2.97,4
0,440,2.48,4
0,400,3.35,3
0,640,3.86,3
0,440,3.13,4
0,740,3.37,4
1,680,3.27,2
0,660,3.34,3
1,740,4,3
0,560,3.19,3
0,380,2.94,3
0,400,3.65,2
0,600,2.82,4
1,620,3.18,2
0,560,3.32,4
0,640,3.67,3
1,680,3.85,3
0,580,4,3
0,600,3.59,2
0,740,3.62,4
0,620,3.3,1
0,580,3.69,1
0,800,3.73,1
0,640,4,3
0,300,2.92,4
0,480,3.39,4
0,580,4,2
0,720,3.45,4
0,720,4,3
0,560,3.36,3
1,800,4,3
0,540,3.12,1
1,620,4,1
0,700,2.9,4
0,620,3.07,2
0,500,2.71,2
0,380,2.91,4
1,500,3.6,3
0,520,2.98,2
0,600,3.32,2
0,600,3.48,2
0,700,3.28,1
1,660,4,2
0,700,3.83,2
1,720,3.64,1
0,800,3.9,2
0,580,2.93,2
1,660,3.44,2
0,660,3.33,2
0,640,3.52,4
0,480,3.57,2
0,700,2.88,2
0,400,3.31,3
0,340,3.15,3
0,580,3.57,3
0,380,3.33,4
0,540,3.94,3
1,660,3.95,2
1,740,2.97,2
1,700,3.56,1
0,480,3.13,2
0,400,2.93,3
0,480,3.45,2
0,680,3.08,4
0,420,3.41,4
0,360,3,3
0,600,3.22,1
0,720,3.84,3
0,620,3.99,3
1,440,3.45,2
0,700,3.72,2
1,800,3.7,1
0,340,2.92,3
1,520,3.74,2
1,480,2.67,2
0,520,2.85,3
0,500,2.98,3
0,720,3.88,3
0,540,3.38,4
1,600,3.54,1
0,740,3.74,4
0,540,3.19,2
0,460,3.15,4
1,620,3.17,2
0,640,2.79,2
0,580,3.4,2
0,500,3.08,3
0,560,2.95,2
0,500,3.57,3
0,560,3.33,4
0,700,4,3
0,620,3.4,2
1,600,3.58,1
0,640,3.93,2
1,700,3.52,4
0,620,3.94,4
0,580,3.4,3
0,580,3.4,4
0,380,3.43,3
0,480,3.4,2
0,560,2.71,3
1,480,2.91,1
0,740,3.31,1
1,800,3.74,1
0,400,3.38,2
1,640,3.94,2
0,580,3.46,3
0,620,3.69,3
1,580,2.86,4
0,560,2.52,2
1,480,3.58,1
0,660,3.49,2
0,700,3.82,3
0,600,3.13,2
0,640,3.5,2
1,700,3.56,2
0,520,2.73,2
0,580,3.3,2
0,700,4,1
0,440,3.24,4
0,720,3.77,3
0,500,4,3
0,600,3.62,3
0,400,3.51,3
0,540,2.81,3
0,680,3.48,3
1,800,3.43,2
0,500,3.53,4
1,620,3.37,2
0,520,2.62,2
1,620,3.23,3
0,620,3.33,3
0,300,3.01,3
0,620,3.78,3
0,500,3.88,4
0,700,4,2
1,540,3.84,2
0,500,2.79,4
0,800,3.6,2
0,560,3.61,3
0,580,2.88,2
0,560,3.07,2
0,500,3.35,2
1,640,2.94,2
0,800,3.54,3
0,640,3.76,3
0,380,3.59,4
1,600,3.47,2
0,560,3.59,2
0,660,3.07,3
1,400,3.23,4
0,600,3.63,3
0,580,3.77,4
0,800,3.31,3
1,580,3.2,2
1,700,4,1
0,420,3.92,4
1,600,3.89,1
1,780,3.8,3
0,740,3.54,1
1,640,3.63,1
0,540,3.16,3
0,580,3.5,2
0,740,3.34,4
0,580,3.02,2
0,460,2.87,2
0,640,3.38,3
1,600,3.56,2
1,660,2.91,3
0,340,2.9,1
1,460,3.64,1
0,460,2.98,1
1,560,3.59,2
0,540,3.28,3
0,680,3.99,3
1,480,3.02,1
0,800,3.47,3
0,800,2.9,2
1,720,3.5,3
0,620,3.58,2
0,540,3.02,4
0,480,3.43,2
1,720,3.42,2
0,580,3.29,4
0,600,3.28,3
0,380,3.38,2
0,420,2.67,3
1,800,3.53,1
0,620,3.05,2
1,660,3.49,2
0,480,4,2
0,500,2.86,4
0,700,3.45,3
0,440,2.76,2
1,520,3.81,1
1,680,2.96,3
0,620,3.22,2
0,540,3.04,1
0,800,3.91,3
0,680,3.34,2
0,440,3.17,2
0,680,3.64,3
0,640,3.73,3
0,660,3.31,4
0,620,3.21,4
1,520,4,2
1,540,3.55,4
1,740,3.52,4
0,640,3.35,3
1,520,3.3,2
1,620,3.95,3
0,520,3.51,2
0,640,3.81,2
0,680,3.11,2
0,440,3.15,2
1,520,3.19,3
1,620,3.95,3
1,520,3.9,3
0,380,3.34,3
0,560,3.24,4
1,600,3.64,3
1,680,3.46,2
0,500,2.81,3
1,640,3.95,2
0,540,3.33,3
1,680,3.67,2
0,660,3.32,1
0,520,3.12,2
1,600,2.98,2
0,460,3.77,3
1,580,3.58,1
1,680,3,4
1,660,3.14,2
0,660,3.94,2
0,360,3.27,3
0,660,3.45,4
0,520,3.1,4
1,440,3.39,2
0,600,3.31,4
1,800,3.22,1
1,660,3.7,4
0,800,3.15,4
0,420,2.26,4
1,620,3.45,2
0,800,2.78,2
0,680,3.7,2
0,800,3.97,1
0,480,2.55,1
0,520,3.25,3
0,560,3.16,1
0,460,3.07,2
0,540,3.5,2
0,720,3.4,3
0,640,3.3,2
1,660,3.6,3
1,400,3.15,2
1,680,3.98,2
0,220,2.83,3
0,580,3.46,4
1,540,3.17,1
0,580,3.51,2
0,540,3.13,2
0,440,2.98,3
0,560,4,3
0,660,3.67,2
0,660,3.77,3
1,520,3.65,4
0,540,3.46,4
1,300,2.84,2
1,340,3,2
1,780,3.63,4
1,480,3.71,4
0,540,3.28,1
0,460,3.14,3
0,460,3.58,2
0,500,3.01,4
0,420,2.69,2
0,520,2.7,3
0,680,3.9,1
0,680,3.31,2
1,560,3.48,2
0,580,3.34,2
0,500,2.93,4
0,740,4,3
0,660,3.59,3
0,420,2.96,1
0,560,3.43,3
1,460,3.64,3
1,620,3.71,1
0,520,3.15,3
0,620,3.09,4
0,540,3.2,1
1,660,3.47,3
0,500,3.23,4
1,560,2.65,3
0,500,3.95,4
0,580,3.06,2
0,520,3.35,3
0,500,3.03,3
0,600,3.35,2
0,580,3.8,2
0,400,3.36,2
0,620,2.85,2
1,780,4,2
0,620,3.43,3
1,580,3.12,3
0,700,3.52,2
1,540,3.78,2
1,760,2.81,1
0,700,3.27,2
0,720,3.31,1
1,560,3.69,3
0,720,3.94,3
1,520,4,1
1,540,3.49,1
0,680,3.14,2
0,460,3.44,2
1,560,3.36,1
0,480,2.78,3
0,460,2.93,3
0,620,3.63,3
0,580,4,1
0,800,3.89,2
1,540,3.77,2
1,680,3.76,3
1,680,2.42,1
1,620,3.37,1
0,560,3.78,2
0,560,3.49,4
0,620,3.63,2
1,800,4,2
0,640,3.12,3
0,540,2.7,2
0,700,3.65,2
1,540,3.49,2
0,540,3.51,2
0,660,4,1
1,480,2.62,2
0,420,3.02,1
1,740,3.86,2
0,580,3.36,2
0,640,3.17,2
0,640,3.51,2
1,800,3.05,2
1,660,3.88,2
1,600,3.38,3
1,620,3.75,2
1,460,3.99,3
0,620,4,2
0,560,3.04,3
0,460,2.63,2
0,700,3.65,2
0,600,3.89,3


================================================
FILE: data/bitsgoa.csv
================================================
"","rollno","name","batch","gender","marks1","marks2"
"1","1","student-1","2017","F",67.8770578477587,55.0688420923589
"2","2","student-2","2018","M",54.8621482649326,60.9294088753698
"3","3","student-3","2016","F",60.0205542168559,54.574850971531
"4","4","student-4","2018","M",58.9832743621393,57.1309909530326
"5","5","student-5","2016","F",57.250728121683,62.1399942357651
"6","6","student-6","2017","F",67.087609408716,67.6794686374909
"7","7","student-7","2018","F",66.6110677250014,46.2953817344146
"8","8","student-8","2017","M",70.7122799284521,54.3588545801549
"9","9","student-9","2016","F",64.8062177437439,40.6808724871144
"10","10","student-10","2016","F",56.7738338494698,74.0692359260511
"11","11","student-11","2016","M",55.803277491975,62.9192370187829
"12","12","student-12","2017","M",61.0508174537004,75.7753297612989
"13","13","student-13","2018","M",60.8649133180452,82.4924662528674
"14","14","student-14","2018","F",62.2617538002879,39.7578373926294
"15","15","student-15","2017","F",77.2714385374742,59.0114536333426
"16","16","student-16","2017","F",62.0056599542774,44.1017514585545
"17","17","student-17","2016","M",66.9174950171247,60.0481160031432
"18","18","student-18","2018","M",71.5262134335681,61.2915809908696
"19","19","student-19","2018","F",63.2556774776407,52.501319801273
"20","20","student-20","2017","F",61.4200955707872,41.9441659521386
"21","21","student-21","2016","M",66.8192582130148,58.684027129478
"22","22","student-22","2016","M",77.6007942762418,46.8205682800491
"23","23","student-23","2018","M",67.9523824669946,51.4264431628717
"24","24","student-24","2018","F",60.2123252584406,69.3833750717121
"25","25","student-25","2017","M",69.5395882754565,41.6205894298385
"26","26","student-26","2018","M",68.0342137316606,46.7833776685463
"27","27","student-27","2016","M",59.6274031068599,61.0965125936565
"28","28","student-28","2016","M",72.5863552063259,52.3530420727807
"29","29","student-29","2018","F",55.7749131590701,60.2432477219161
"30","30","student-30","2018","F",53.2864374316539,55.3196451138849


================================================
FILE: data/cclogr.csv
================================================
"","default","student","balance","income"
"1","No","No",729.526495207286,44361.6250742669
"2","No","Yes",817.180406555498,12106.1347003149
"3","No","No",1073.54916401173,31767.1389473999
"4","No","No",529.250604745278,35704.4939350781
"5","No","No",785.655882930501,38463.4958787229
"6","No","Yes",919.5885304745,7491.55857182746
"7","No","No",825.513330517201,24905.2265775235
"8","No","Yes",808.667504297266,17600.4513435869
"9","No","No",1161.05785403132,37468.5292875375
"10","No","No",0,29275.2682931678
"11","No","Yes",0,21871.0730892776
"12","No","Yes",1220.58375300377,13268.5622209261
"13","No","No",237.045113977472,28251.6953449631
"14","No","No",606.742343302464,44994.5558492173
"15","No","No",1112.96840063305,23810.174050036
"16","No","No",286.232560139224,45042.4130358855
"17","No","No",0,50265.3123535985
"18","No","Yes",527.540184074571,17636.5396172936
"19","No","No",485.936864171729,61566.106117801
"20","No","No",1095.0727352874,26464.631389229
"21","No","No",228.952549642572,50500.1821977441
"22","No","No",954.261792773545,32457.5090750754
"23","No","No",1055.95660472904,51317.8830823874
"24","No","No",641.984388848021,30466.1032574814
"25","No","No",773.211724534176,34353.3143050038
"26","No","No",855.008522525692,25211.3321614539
"27","No","No",642.999738515429,41473.5118010187
"28","No","No",1454.86327248404,32189.0949523178
"29","No","No",615.704276560405,39376.3946187014
"30","No","Yes",1119.56935332865,16556.0702053511
"31","No","No",494.816228807326,54384.7828361386
"32","No","Yes",448.880656256039,15799.4704097956
"33","No","Yes",584.90489468245,22429.9350462168
"34","No","No",913.587172608928,46907.2254028623
"35","No","Yes",1423.93891664088,22634.4880875812
"36","No","Yes",1499.72465655973,13190.6527164103
"37","No","No",692.034167065432,47806.6117728963
"38","No","No",351.45347152301,35087.4886480858
"39","No","No",742.627620785597,37864.8239388952
"40","No","No",653.118400123388,39489.5947028613
"41","No","No",872.138679538477,41787.5672689012
"42","No","No",837.262622754171,51471.7720946377
"43","No","No",1151.63056608265,42917.4695636282
"44","No","Yes",220.55560838644,16872.9475557814
"45","No","Yes",1690.23441038701,19052.5722246955
"46","No","No",408.772914305185,54206.9392120179
"47","No","No",1238.61032124112,50066.6805336538
"48","No","No",1228.30835035336,37408.5038685076
"49","No","No",820.919204146184,47746.5420731016
"50","No","No",857.4851177765,31688.3459738919
"51","No","No",563.618177182362,42641.253266646
"52","No","Yes",1282.97253359578,13120.6364696027
"53","No","Yes",1505.78267482884,26557.1414458808
"54","No","Yes",904.040259066813,16882.3006056535
"55","No","No",0,49956.580571906
"56","No","Yes",1294.4973466731,10464.3207319231
"57","No","Yes",1275.55063272364,15887.4684867774
"58","No","No",1536.59460097252,48766.9074575615
"59","No","No",1332.52264386695,39143.1370690178
"60","No","No",492.079767815411,33379.0946028395
"61","No","No",766.234379303894,46478.2942569524
"62","No","No",690.127246199993,63432.9842544674
"63","No","No",0,32481.5400461151
"64","No","No",1480.65530926266,36866.1570686848
"65","No","No",989.055596975304,45344.2513202648
"66","No","No",1302.34183516856,33695.4262332636
"67","No","No",1044.55234328472,54696.7659816226
"68","No","No",0,43658.2277239957
"69","No","No",866.02843636799,38363.4216347118
"70","No","No",690.800792354134,48140.471335995
"71","No","No",597.757142017061,31577.6151255677
"72","No","No",429.485402306276,51311.218101023
"73","No","No",1398.15620148326,39644.9421931896
"74","No","Yes",1578.06409853994,19886.4939515168
"75","No","No",857.069053134242,26655.6889653755
"76","No","Yes",752.459945958806,16211.2757881826
"77","No","Yes",774.738426963927,15193.733135525
"78","No","No",728.373251302587,45131.718264691
"79","No","No",76.9912905299071,28392.0934122846
"80","No","No",196.456913136183,41346.78591324
"81","No","Yes",948.747918388616,14297.6190485513
"82","No","No",431.920885304176,39369.8815631008
"83","No","No",461.63818223378,46221.2114948234
"84","No","No",572.015760230599,33046.3129575033
"85","No","No",335.568144733179,51258.4442299621
"86","No","No",510.23759315638,60397.7186486948
"87","No","Yes",1005.16130555577,24038.6520433118
"88","No","Yes",162.454451086564,13241.7520600759
"89","No","No",932.533155682331,50537.4656484602
"90","No","Yes",893.330467612275,11905.6817925484
"91","No","No",1332.78256352571,44495.1627629054
"92","No","No",1404.38306945215,35043.1060286742
"93","No","Yes",1148.96513915926,16578.1921184729
"94","No","No",368.223425720545,57596.8258431935
"95","No","No",449.466628769273,45950.6667238686
"96","No","No",820.017112578423,51584.6573183834
"97","No","Yes",619.751868575593,15750.6220834222
"98","No","No",1047.71812400961,46416.9709942419
"99","No","No",243.84132832576,47193.8881309706
"100","No","No",186.500386931495,45430.5502710943
"101","No","No",1422.01848757576,38224.0315214972
"102","No","No",383.798659005674,61425.286388114
"103","No","No",1159.43509608859,45802.1049386909
"104","No","No",882.99191255185,17181.7379663818
"105","No","No",1463.23289161086,65457.8541875824
"106","No","Yes",1722.35584628315,19311.4229408044
"107","No","Yes",773.921861257735,14558.9121383835
"108","No","No",958.169418468988,32816.2847956415
"109","No","No",1207.53273014951,68037.5751261777
"110","No","No",1056.6108304963,30546.6010343774
"111","No","No",117.642483931776,46550.2672705963
"112","No","No",596.964119237004,58088.3604512649
"113","No","No",0,47729.6257581971
"114","No","Yes",541.662755324457,16335.7034406021
"115","No","No",1119.17830341806,45020.0191217013
"116","No","Yes",810.320936469835,20498.66581411
"117","No","No",271.55143774701,32850.1201448079
"118","No","No",1114.40323650428,47714.0221084582
"119","No","No",867.030828053565,33540.9799848111
"120","No","Yes",1176.7894016772,17632.712103849
"121","No","No",610.227818661398,42465.3629640684
"122","No","No",272.615661715535,55449.3386406402
"123","No","No",449.735085836859,49643.4264416677
"124","No","Yes",827.767209400324,10673.5262253922
"125","No","No",430.000099449901,33925.2195976742
"126","No","No",0,31825.7649719215
"127","No","Yes",1557.34463668228,18404.594189222
"128","No","No",928.236966261476,33722.1589415677
"129","No","No",1373.45016328929,49101.7982141897
"130","No","Yes",951.072907499984,18601.5332374944
"131","No","Yes",1292.21086850542,23065.850975377
"132","No","No",582.188700706555,24760.7957330564
"133","No","Yes",339.424638280913,19307.98029025
"134","No","No",409.982324608766,44641.090118396
"135","No","No",1216.45150955433,53639.8468273843
"136","No","No",598.461392123585,44124.2681146436
"137","Yes","Yes",1486.99812160021,17854.3970280731
"138","No","No",943.796338713762,59976.8398676444
"139","No","No",0,49525.7450208494
"140","No","Yes",996.276086766592,20883.2407676238
"141","No","Yes",748.301340361255,11248.6707527348
"142","No","No",324.737921782032,15411.5185672943
"143","No","Yes",575.382158489508,16005.6813190057
"144","No","No",441.738284926253,36012.2358964554
"145","No","No",1188.64217955294,39526.5612652646
"146","No","No",95.1476801904055,51371.1999147598
"147","No","No",1015.61483748402,43218.794375243
"148","No","No",1258.56739272567,44931.6749177461
"149","No","Yes",1178.24441352846,7750.28922055921
"150","No","No",731.532656254407,43956.0600839728
"151","No","No",350.088792139872,44455.1995150356
"152","No","No",418.848345433837,36901.7833281202
"153","No","No",533.229465181374,39393.5424772748
"154","No","No",95.296974128368,32359.3890284286
"155","No","Yes",1453.00593207303,15933.0021109819
"156","No","No",352.644414031465,39404.7443664762
"157","No","Yes",736.2389354625,14107.9939653376
"158","No","No",1306.4845722752,41224.6250242432
"159","No","No",1149.14562717899,45194.7946620784
"160","No","No",703.226890721538,57679.2791425179
"161","No","Yes",1360.80043541238,18864.3084886679
"162","No","No",31.4450805447328,53287.3503013149
"163","No","No",988.214458069386,48570.3634566327
"164","No","No",360.019073584363,23927.5466133912
"165","No","No",647.483528619273,33226.2775446785
"166","No","Yes",0,9816.78943755756
"167","No","No",445.241819216304,31974.6245068978
"168","No","Yes",878.542064501041,17496.7414821956
"169","No","Yes",335.834118576172,21759.8702696952
"170","No","No",666.608881434336,30438.4688266267
"171","No","No",813.876647019864,40737.4597766098
"172","No","No",836.300071623191,54607.1787395438
"173","No","No",967.48268368178,33055.1102723298
"174","Yes","Yes",2205.79952114829,14271.492252976
"175","No","Yes",1258.18521914431,11672.2220033397
"176","No","No",1239.80169908455,40107.1722104231
"177","No","Yes",0,13760.2984556873
"178","No","Yes",927.887732263607,22473.3796109537
"179","No","Yes",1390.03078251892,21299.0992265335
"180","No","No",410.497704767954,53471.2136303255
"181","No","No",73.0765421846567,30681.7375351468
"182","No","No",790.59775924737,45303.1033141261
"183","No","No",789.49509698382,32579.9002059474
"184","No","No",791.360422078743,46319.2361190854
"185","No","No",479.042378990356,37255.8803342804
"186","No","No",888.613272550212,28899.7917513637
"187","No","No",1284.38636050636,42831.9546193997
"188","No","Yes",712.276351280275,23341.9502302432
"189","No","Yes",1290.62016864921,18408.0490819608
"190","No","No",126.362685399057,44280.9297885654
"191","No","No",779.376257695651,61158.8851121863
"192","No","No",697.490957463777,49476.759599281
"193","No","No",606.974613548306,43610.0330173371
"194","No","Yes",1802.90333355739,21411.4239490249
"195","No","No",900.085332597499,41276.5631894987
"196","No","No",966.280287968092,33440.9792997775
"197","No","No",671.061359639164,35689.1118739108
"198","No","No",171.559115959856,23505.9927713232
"199","No","No",1482.48030006315,45260.6115977848
"200","No","No",640.662751732473,46464.5659322762
"201","No","Yes",706.161027541986,18078.3517921729
"202","Yes","Yes",1774.69422305776,20359.5060855671
"203","No","No",916.49931692309,25768.4074467213
"204","No","No",1271.2708123604,36053.5029503061
"205","No","No",110.572909811983,47339.1087511931
"206","No","No",1747.25888152959,37819.2890860198
"207","Yes","No",1889.59918976304,48956.1715885681
"208","No","No",27.2013946505765,21051.8401259701
"209","No","No",746.229775057391,35159.8502288788
"210","Yes","Yes",1899.3906258472,20655.2000032624
"211","No","Yes",813.994565525349,20391.5289481713
"212","No","No",499.457815094636,38899.1604610096
"213","No","No",853.16133166086,36272.4517835911
"214","No","No",474.727319088288,32064.3351057974
"215","No","No",1235.87611345417,35838.7770153588
"216","No","No",620.675152595598,44213.770101262
"217","No","No",220.028569764934,37880.0296471597
"218","No","Yes",1304.91429931829,18074.0754152021
"219","No","Yes",1608.15255984335,22358.0467020111
"220","No","No",256.471227559815,37657.0559167999
"221","No","Yes",1153.60499815384,19224.9752298419
"222","No","No",771.895874508519,31170.0852836766
"223","No","Yes",1099.63042217728,17064.0428279584
"224","No","No",782.883029663905,40357.1334266019
"225","No","No",444.264906175272,35924.3387929274
"226","No","No",504.02837873803,24780.9612369205
"227","No","No",1762.50340198816,42124.6600673934
"228","No","Yes",850.096116596461,18618.9879075019
"229","No","No",1412.23872130317,40785.0597694212
"230","No","No",715.496829588669,47482.5653767305
"231","No","No",1363.52859942133,55713.0416079129
"232","No","No",493.241889799387,42495.2030840663
"233","No","No",1354.75253720358,33375.1316903778
"234","No","Yes",571.293911015155,15416.2693546858
"235","No","No",956.15135218345,44086.7077342314
"236","No","No",964.820252985381,34390.7460350566
"237","No","No",552.634366024658,50513.1642384347
"238","No","No",338.266201647092,37767.239862893
"239","No","No",709.123351356216,44041.6300941084
"240","No","No",978.978200410458,57839.0537343736
"241","No","No",371.010127817542,15702.7070830359
"242","Yes","Yes",1572.85648082363,14930.1783306833
"243","No","No",0,40150.217981059
"244","Yes","No",1964.47687225275,39054.5891443139
"245","No","No",548.037783442488,33605.6494686572
"246","No","No",1257.05522168594,34045.0088247242
"247","No","No",1189.91805697419,38016.8939028578
"248","No","No",687.683759014873,39373.5797462415
"249","No","No",845.698965838534,39899.5126808393
"250","No","Yes",957.68483377014,19676.3456491723
"251","No","No",233.516846267051,43326.105379399
"252","No","Yes",1126.94874392123,14298.2645686729
"253","No","Yes",488.855176548209,15159.4948475337
"254","No","No",767.903559780495,44323.5796638006
"255","No","No",561.925799124395,31388.5056454162
"256","No","Yes",1092.27001277246,17454.6018395308
"257","No","No",71.7886830035824,31965.7447145836
"258","No","Yes",1839.14368196222,13625.3979303665
"259","No","Yes",876.000777906746,22474.5740898236
"260","No","No",554.107811535592,30357.0259771625
"261","No","No",891.317898050276,43945.0416541409
"262","No","No",464.836333095802,27324.8996870729
"263","No","Yes",563.542661100162,24082.0393441031
"264","Yes","No",1530.35315714612,30003.8171511159
"265","No","No",990.567026186295,41690.2526225589
"266","No","No",590.399786498762,42769.2038801219
"267","No","Yes",715.164109574279,21153.5110081716
"268","No","No",1540.37772667771,41852.0784358238
"269","No","No",1243.32651840828,41639.6803816334
"270","No","Yes",235.328499908876,20909.7047538096
"271","No","No",528.254323170427,49394.340774526
"272","No","No",592.25389562288,30731.201603129
"273","No","No",705.204971986239,42683.187243286
"274","No","No",456.515643990607,48195.7758370754
"275","No","Yes",470.717574652806,18052.1049990983
"276","No","Yes",1503.07512985524,10600.4135403235
"277","No","No",1362.96559540383,44217.9160352291
"278","No","No",908.77157368794,45032.8459851152
"279","No","Yes",1096.82027214515,16148.0600066064
"280","No","No",369.221942475653,47835.0910453142
"281","No","No",673.399899046348,50256.2902118721
"282","No","No",1038.50000752153,51173.1413763774
"283","No","No",0,28762.9237797484
"284","No","No",527.194086573308,32431.1182596039
"285","No","No",817.469336816105,32974.4273524307
"286","No","Yes",1549.04275451742,19702.293205826
"287","No","Yes",566.713789351631,11853.9531668182
"288","No","No",1496.05646487591,24935.9187420962
"289","No","No",862.656656416651,42844.8340023417
"290","No","No",1259.14941789257,31263.641081672
"291","No","No",1025.03883303162,43510.0496378576
"292","No","Yes",703.277787102737,14793.7426766893
"293","No","Yes",1082.39149734589,15175.3612149216
"294","No","No",910.449023533628,44054.9164232696
"295","No","Yes",996.543862160796,16995.6604049481
"296","No","No",353.778226008973,58514.3180764772
"297","No","Yes",782.71082750063,13300.2974683438
"298","No","No",1716.59541824424,51056.8682867977
"299","No","Yes",502.898638357521,15153.965762645
"300","No","No",1033.46846850523,59203.9676630271
"301","No","No",0,34925.3654101008
"302","No","Yes",1087.08349936833,19263.7695817024
"303","No","No",1047.24424881394,43899.7592451193
"304","No","No",1056.92735252411,38367.8679465264
"305","No","Yes",979.224424632862,26010.8878031666
"306","No","Yes",847.818706253068,15978.633625651
"307","No","No",781.77128476306,50958.7905135708
"308","No","Yes",402.488886825952,15904.0559208087
"309","No","No",204.740181714577,17534.2905392742
"310","No","Yes",2022.67464324227,18336.74316845
"311","No","No",748.937063784024,44186.6177602638
"312","No","No",838.275687752419,35917.4906102388
"313","No","No",386.016049290846,44694.0309570293
"314","No","No",812.399296525647,49623.1009820914
"315","No","Yes",1809.92656439359,6985.13594539539
"316","No","No",0,30360.5489438194
"317","No","Yes",1328.18380411907,24688.4737209337
"318","No","Yes",1464.93604882699,26716.6863746825
"319","No","No",1200.56513764739,39718.6926569602
"320","No","No",989.686419964429,27856.5770606696
"321","No","No",870.734579925775,35539.2702158304
"322","No","Yes",1200.84739001594,19849.8314129485
"323","No","Yes",1428.83204736141,23204.6814610386
"324","No","No",0,33951.2887259244
"325","No","Yes",359.458866537597,21783.2694913357
"326","No","No",265.683949786655,40862.7540056257
"327","No","No",1027.77053169201,44406.83691434
"328","No","No",729.047433497618,35042.1329969977
"329","No","No",1042.80491991389,25867.1477168844
"330","No","No",230.230587898767,37875.614823143
"331","No","No",367.466012183201,30611.2722706897
"332","No","Yes",737.70535394131,20863.7996686999
"333","No","No",18.2612853635445,30349.460422935
"334","No","No",1257.76020889191,30834.7713682736
"335","No","No",455.521513206303,40907.3983537652
"336","No","No",56.9675603504705,35458.4815614207
"337","No","Yes",723.931985299722,12213.0371368412
"338","No","No",612.383157677443,41379.7678082606
"339","No","Yes",1220.1776056907,15308.1270712607
"340","No","No",1222.34769171407,32363.3710505847
"341","No","No",1165.49113814696,47684.2194497489
"342","Yes","No",1642.81999663825,46856.947039092
"343","No","No",944.173560017781,56088.6837995041
"344","No","No",749.608003900673,41423.9411731786
"345","No","No",370.375186777666,31231.0078371514
"346","Yes","No",1991.64912011299,42133.3731759574
"347","No","No",773.915226576855,44562.3381169342
"348","No","Yes",817.249811262394,20480.88705606
"349","No","No",1060.71621967666,50260.4643659169
"350","Yes","No",1550.44926441315,56273.5136083638
"351","No","Yes",454.466962212937,15092.439307714
"352","No","Yes",1256.23932541513,17784.5589437522
"353","No","No",1484.39539297524,36731.3086949029
"354","No","Yes",1050.47732793251,12661.6116309912
"355","No","No",986.40061892638,45430.4722089423
"356","No","No",717.341926951688,44266.3765401792
"357","No","No",692.143222681582,47038.0903196941
"358","Yes","No",1328.89272521776,34710.0623657107
"359","No","No",673.468191056081,32930.0693612804
"360","No","No",218.639297043088,48991.3953793342
"361","No","No",1218.16696582374,43954.2774856976
"362","No","Yes",13.5168025503509,19322.5450270524
"363","No","Yes",531.478699850065,21726.558972624
"364","No","No",165.53815169523,33436.4557377411
"365","No","Yes",1351.84972233302,25890.5039884998
"366","No","No",107.282083049617,54143.9627498165
"367","No","No",947.193571380436,35604.117192781
"368","No","Yes",434.560259226125,14812.6452403767
"369","No","No",1020.06724048864,37458.8792492482
"370","No","No",662.305318022324,31597.8976165624
"371","No","Yes",754.420612920964,20767.6830202616
"372","No","No",289.489575693364,44412.8951033074
"373","No","No",198.038409229701,36270.9381202214
"374","No","Yes",516.060491676575,16615.0219627607
"375","No","No",697.563808989791,41755.3981466578
"376","No","Yes",502.324783467779,16947.4229346552
"377","No","Yes",1459.68708304516,15575.419233378
"378","No","No",716.998722689295,34418.2602228491
"379","No","Yes",1037.97270501782,18324.0300568905
"380","No","No",673.016117196098,29735.9615970911
"381","No","No",371.921784701252,41099.7724418194
"382","No","No",89.1349579572228,55604.6075315396
"383","No","No",1273.28018105912,40895.2827703241
"384","No","No",600.723302967373,18680.516769028
"385","No","No",905.038164636789,40711.7988982471
"386","No","No",1061.44156214448,54437.0103173027
"387","No","No",1672.63924830477,46334.7877633441
"388","No","Yes",883.702645600325,19750.0940028726
"389","No","No",373.780330004006,53936.2417769944
"390","No","No",1287.79518693788,52382.6647043692
"391","No","No",1229.74257147896,48739.9273906526
"392","No","Yes",510.613846954321,22457.0517475138
"393","No","No",775.630168716812,27205.8573938823
"394","No","No",1152.98288688657,50206.5769341591
"395","No","Yes",1303.66258970167,7986.00033583452
"396","No","No",1675.27885173207,28244.5034750868
"397","No","No",398.277733044632,30001.7950602906
"398","No","Yes",739.901759396798,25023.8738564122
"399","No","No",226.417696514023,32018.7136305149
"400","No","No",1217.32171213419,25173.6196080491
"401","No","No",639.820558249208,29902.0019867792
"402","No","Yes",883.166976880327,12717.4922312992
"403","No","No",2.2876109529974,38186.5242727915
"404","No","No",419.637347665869,55967.9931672653
"405","No","Yes",1544.14024986831,11102.5347437309
"406","No","No",1101.01501795199,32153.0135926701
"407","Yes","No",1700.59991348658,30488.9834104565
"408","No","No",311.82458686811,48301.0596601046
"409","No","No",442.830222252675,55074.1308290001
"410","No","No",728.81496006121,17909.5810715927
"411","No","No",278.877902520137,37188.6683650846
"412","No","No",758.702327534491,36625.9895523394
"413","No","No",620.339679439371,22803.7410338396
"414","No","Yes",1890.16741147529,18402.4648845956
"415","No","No",0,28951.4518951572
"416","No","Yes",1329.19884221594,23184.0155485336
"417","No","No",1075.86377237476,44128.6282564037
"418","No","No",618.527509222818,54956.9905446621
"419","No","No",1111.0865758202,27466.8936820066
"420","No","No",864.039743518541,45881.9302056307
"421","No","No",895.229809614619,48512.4556411759
"422","No","Yes",954.030433688608,21908.5271562436
"423","No","No",947.634932740896,46813.5163080957
"424","No","No",267.303727744013,23117.9286733782
"425","No","Yes",1366.8057832287,23913.2088013083
"426","No","No",624.731883509283,38325.1737553638
"427","No","No",13.67938266968,25221.6376687793
"428","No","No",785.866485650099,44778.0157756766
"429","No","Yes",1353.09384569505,18933.4961949702
"430","No","No",480.32126391483,25553.2628515188
"431","No","No",0,29507.3132469218
"432","No","Yes",1221.97124290595,17916.8560894888
"433","No","Yes",1184.93652302344,25108.756711813
"434","No","Yes",478.572055604816,27512.3448997584
"435","No","No",221.690461893355,34786.1013515581
"436","No","No",578.387621687668,41054.1386619277
"437","No","Yes",1361.45843240672,19576.9571319919
"438","No","Yes",977.848095602787,19731.9609897188
"439","No","No",1184.42524488153,36887.439966855
"440","Yes","Yes",1118.70103943078,21848.4429000063
"441","Yes","No",1119.09724476071,37224.5678135129
"442","No","No",776.349979307366,31329.4848506225
"443","No","No",1322.29709108436,51792.4023687088
"444","No","No",878.026457591946,27438.585115052
"445","No","No",1268.37951467727,16921.1808790391
"446","No","No",0,32611.3730692625
"447","No","No",0,37262.5740938171
"448","No","Yes",366.786957314063,25833.0630989749
"449","No","No",747.561036377875,39898.7690858912
"450","No","No",1787.285143726,37499.0935479677
"451","No","Yes",1929.44698025845,14995.4921684901
"452","No","No",987.14289095702,27961.3196085338
"453","No","Yes",856.357368420521,20758.2146891725
"454","No","No",1101.14970709405,18153.8443165945
"455","No","No",704.184519980701,31515.9816788054
"456","No","No",555.623436676053,40837.7316360849
"457","No","No",1073.49826059844,44764.1105476201
"458","No","No",1126.2230230127,32842.6523719603
"459","No","Yes",649.773318564214,15708.2963812027
"460","No","No",977.657620997298,43743.4967298485
"461","No","No",1049.88996420613,42637.5110638279
"462","No","Yes",1383.5663330639,15764.345875927
"463","No","Yes",1095.46573957961,14065.1675680038
"464","No","No",1089.3717909992,30224.5961304622
"465","No","No",605.220968306144,21792.3215199717
"466","No","No",1033.88539901665,49262.0453292257
"467","No","No",1604.72055375478,38186.9850695393
"468","No","No",826.327025939312,24679.715044052
"469","No","Yes",1176.14682328504,16670.6807862768
"470","No","Yes",1275.02319938737,19937.0980337404
"471","No","Yes",1290.28314005644,23538.9441422913
"472","No","No",1133.47419931933,51414.4959558752
"473","No","No",837.412566855277,37400.4928845625
"474","No","Yes",379.082166688415,18280.3670338005
"475","No","Yes",1134.16627849803,12453.3635924034
"476","No","No",0,39120.0847898987
"477","No","No",1491.17480280474,59394.3830781267
"478","No","Yes",1695.35958565634,9582.94189712211
"479","No","No",705.57332763872,54589.2398684094
"480","No","No",239.193366265676,33743.2265524886
"481","No","Yes",1183.71064142778,12428.6145139245
"482","No","No",898.761838912064,38267.08157841
"483","No","Yes",1524.29040044934,11719.5934790985
"484","No","No",777.023737418038,35115.0185512798
"485","No","No",1394.79581919528,26074.9343643672
"486","No","Yes",0,20476.1557245306
"487","No","No",62.5715751601826,47946.7670938155
"488","Yes","No",1981.45181538436,28127.8954733126
"489","No","No",514.054034777825,50053.261925801
"490","No","No",722.788038958828,53714.186065152
"491","No","No",152.866304156592,33716.8640608
"492","No","No",293.184772853638,35390.2875967122
"493","No","No",0,38613.6204965308
"494","No","Yes",645.36685722913,17878.3643114477
"495","No","Yes",1515.15283305045,26046.7720831097
"496","No","Yes",767.439505481761,18846.4892212338
"497","No","Yes",491.879747053414,21715.226298099
"498","No","No",0,41389.4352722328
"499","No","No",0,34589.4880601879
"500","No","No",509.155164105994,40132.6592071943
"501","No","Yes",965.587370085486,16440.0996975194
"502","No","Yes",1868.54007230747,20489.5981077627
"503","No","No",403.49720580419,41803.7781625719
"504","No","No",593.14362506876,37670.9350241925
"505","No","No",0,44547.8655465918
"506","No","No",742.290015884451,39367.1933081303
"507","No","Yes",1110.10045653192,13013.6105672136
"508","No","No",771.551295297819,49440.4012944686
"509","No","No",624.338891497583,42492.7530686958
"510","No","Yes",499.957229779348,22884.9525653222
"511","No","No",1088.63752037344,60650.0361323175
"512","No","No",1556.49192796768,49669.6688731681
"513","No","Yes",797.084335731408,23558.6830823662
"514","No","No",415.99250259786,38215.6780429903
"515","No","Yes",538.232681166002,26833.0396297828
"516","No","Yes",1181.93470239963,21086.0820079429
"517","No","Yes",836.020688627074,9271.78992423107
"518","No","No",104.443215077792,46928.0054911712
"519","No","No",564.209478577945,53196.6230194221
"520","No","Yes",804.53940215468,25091.4607662506
"521","No","No",1028.95364023327,21218.3035199452
"522","No","No",748.746583667659,8983.85690177194
"523","No","No",66.6320807349912,28735.9741149412
"524","No","No",452.802228898258,48034.2377125974
"525","No","Yes",798.460510113667,27724.0657528508
"526","No","Yes",1941.90292814168,23467.126966094
"527","No","No",1446.10101191515,22996.4323020653
"528","No","No",761.064098290519,61580.034125346
"529","No","No",1350.17272835748,43147.4555837922
"530","No","No",380.950200559961,36943.3619940607
"531","No","Yes",1602.84960709395,15906.466797439
"532","No","No",147.61011548848,37588.5626099252
"533","No","No",0,36283.0801271054
"534","No","Yes",733.072512376335,18818.1673820965
"535","No","Yes",1082.74797626804,26096.3370339348
"536","No","No",779.657300310476,40804.4757033746
"537","No","No",994.158712170624,65254.0757961504
"538","No","No",769.181374128886,50070.7815461683
"539","No","Yes",684.098519096228,4985.1691133645
"540","No","No",442.56330169432,27898.4653945517
"541","Yes","No",1717.07159258608,38408.8909167269
"542","No","No",0,36302.528437156
"543","No","No",1092.34586485398,44717.0125770421
"544","No","No",497.33495411576,35146.7987733695
"545","No","Yes",1149.68065499451,16907.7007441301
"546","Yes","No",1465.21016364328,58699.9831975197
"547","No","No",565.317604157056,41789.6110016585
"548","No","Yes",922.137890502439,12224.1851701983
"549","No","Yes",175.59938060484,11510.0578678315
"550","No","No",1024.94681888114,49675.5604190352
"551","No","No",596.885432303659,54091.624759736
"552","No","No",750.410484208866,53084.9074806631
"553","No","Yes",790.870027432839,14183.9700258003
"554","No","No",112.327872497541,42386.0485550432
"555","No","Yes",738.394487315734,16093.2845506875
"556","No","No",1157.85556068872,54419.8085690598
"557","No","No",1213.24314925522,45149.516318112
"558","No","No",1339.6262041855,41656.0028929824
"559","No","No",588.312430203572,56520.9625106958
"560","No","No",721.808132499821,56375.7219879443
"561","No","No",680.820718973428,43843.4741174494
"562","No","Yes",815.406570873487,12071.7624998357
"563","No","No",1042.50711204107,37170.1090492838
"564","No","Yes",663.249868122172,20454.6165493286
"565","No","Yes",1478.61854506445,18026.4710989649
"566","No","No",337.756753994278,33655.5724347316
"567","No","Yes",1273.55071961651,23126.6361991141
"568","No","No",114.179725877987,49562.989265344
"569","No","No",0,52295.6009297642
"570","No","Yes",1516.31768923235,26377.5934939198
"571","No","Yes",898.114673108613,24164.7511007409
"572","No","No",790.590582117334,48219.5944665748
"573","No","No",0,29072.7214938039
"574","No","No",1273.24483033059,49136.4741958684
"575","No","No",1279.2004483305,36063.114108959
"576","No","Yes",732.719380888972,14568.7474096201
"577","Yes","No",1763.57908789461,46227.0745421357
"578","No","Yes",1280.41844842492,16310.9055269133
"579","No","No",1529.93799536515,36912.8973015701
"580","No","Yes",345.467636476389,18251.4945592886
"581","No","No",197.989403751685,33723.9857591643
"582","Yes","Yes",1770.96944052649,15975.5371965119
"583","No","Yes",639.371943541069,17037.0547998481
"584","No","No",238.814643857072,23526.7109062151
"585","No","No",390.555843076737,45338.357214774
"586","No","Yes",830.171511106982,19476.5875790804
"587","No","Yes",1378.87607564381,21613.578470166
"588","No","No",242.403449315825,37364.4230927587
"589","No","No",1236.54749355223,36211.4903618167
"590","No","No",1063.77493669311,34914.2752545523
"591","No","No",126.923178973913,33819.1433220656
"592","No","No",758.465501261611,38018.1456896637
"593","No","No",908.931458644613,68758.8783988945
"594","No","No",558.518342410987,46297.4254108936
"595","No","No",1819.24233339211,36969.5525152008
"596","No","Yes",1110.68174400549,19327.5750379268
"597","No","No",723.243374669993,43459.0118542978
"598","No","No",972.031864031495,18510.946033198
"599","No","No",696.014430649643,40741.4202403335
"600","No","Yes",1178.24890887526,29362.604609009
"601","No","Yes",329.558510877062,19371.8120916883
"602","No","Yes",792.529238472827,14546.30176771
"603","No","No",1246.03775769599,33691.7213091022
"604","No","No",591.760776481539,41670.257382541
"605","No","No",244.081138800791,24854.2498236435
"606","No","No",1038.54836006039,49944.8651506496
"607","No","No",679.074586796153,49488.5639815129
"608","No","No",1061.415836002,40585.2216446978
"609","No","Yes",1268.24069091931,11914.5782684287
"610","No","Yes",955.533685819795,14736.7208791128
"611","No","No",1115.82062964092,27882.8092643982
"612","No","Yes",601.163260610188,22029.1935171555
"613","No","No",1097.60003894511,52286.4318229537
"614","No","No",1042.78307264667,45982.9663185287
"615","No","Yes",1321.44305286527,8624.11068874281
"616","No","No",1052.39326016205,37637.6593074495
"617","No","No",687.951701062434,27604.0792714302
"618","No","Yes",566.460512626349,24828.2659835652
"619","No","No",769.414224037938,35071.1665353159
"620","No","No",955.1353421032,26372.985815295
"621","No","No",643.409526464766,28660.1401685022
"622","No","No",0,42745.3020131405
"623","No","No",437.636062596235,45384.0474502375
"624","No","No",1278.86564161404,34673.1709932026
"625","No","No",1639.39057196627,30624.7759284918
"626","No","No",891.643737265774,36470.8283401861
"627","No","Yes",316.459048313239,18813.9402088352
"628","No","No",622.929507387178,48874.5557680461
"629","No","No",107.277526758955,42287.3028620524
"630","No","Yes",990.543215773955,12398.4906038395
"631","No","No",0,45659.9957026185
"632","No","No",0,33119.9553306198
"633","No","No",591.21848728877,35287.8245843759
"634","No","Yes",928.060078265644,21121.8448328186
"635","No","No",304.599223898782,40785.9891775391
"636","No","No",235.939497686815,31336.5101185978
"637","No","Yes",1108.05351768099,10853.2309395985
"638","No","No",687.306396976996,24563.7576757258
"639","No","No",60.1860361263316,39864.8635503901
"640","No","No",0,34648.9725974223
"641","No","No",272.893257145115,58730.5728615017
"642","Yes","No",1531.71645931978,43930.4000955678
"643","No","No",949.608648513314,47702.5737457984
"644","No","No",561.916178455117,35080.5792438221
"645","No","No",468.132522196994,25231.3462344957
"646","No","Yes",993.108169991847,12215.9468218066
"647","No","No",0,31314.6950884232
"648","No","Yes",759.478369178202,12710.7209232412
"649","No","No",1340.78720929653,36440.2283216228
"650","No","Yes",1521.46339550225,15210.4740694568
"651","No","No",133.743344349705,30457.0163661004
"652","Yes","No",780.172569171513,51656.8740637741
"653","No","No",839.915458349589,22884.793017069
"654","No","No",913.505872937903,36670.8542230351
"655","No","No",924.881441457044,35712.6379057533
"656","No","No",1197.83150461167,54652.3092984374
"657","No","No",49.2096658960132,30451.152858766
"658","No","No",970.716096585793,48387.648617342
"659","No","No",783.057400540258,54082.4240845222
"660","No","Yes",465.062888613853,20379.0620895269
"661","No","No",0,39337.7499765406
"662","No","Yes",732.264267594484,21986.0454536505
"663","No","No",576.091261344071,37679.6985892523
"664","No","Yes",588.991056252216,16309.8754485617
"665","No","No",863.140717831164,43779.4778545985
"666","No","No",857.575733973881,49675.3703182707
"667","No","No",361.501731061125,33674.8902393376
"668","No","No",891.155427066673,36023.0850073461
"669","No","No",918.122205965447,44738.5560304555
"670","No","No",1335.02801828106,21150.8559447406
"671","No","No",421.40084342513,39873.5217445341
"672","No","Yes",743.636144477128,18883.9715059268
"673","No","No",773.740372463441,41872.4947626065
"674","No","No",731.3934721696,45251.8819209678
"675","No","No",964.188922685005,36330.3945986058
"676","No","Yes",350.015184264179,17657.1726575117
"677","No","No",1206.66136205449,37125.5913512782
"678","No","No",1051.94757326739,52888.1850963327
"679","No","No",454.760370677831,45572.172484486
"680","No","No",932.665307535504,42758.6007035183
"681","No","No",535.745846468796,36259.4637552901
"682","No","No",241.80525216874,41186.1828072136
"683","No","No",1026.50670980239,42042.1679417363
"684","No","Yes",187.679885571478,18278.833967134
"685","No","No",197.538321479143,58646.9278047098
"686","No","No",585.708088259361,43288.4003826895
"687","No","Yes",1947.02240116176,12147.0464091645
"688","No","No",829.547597492268,39717.5376088077
"689","No","No",196.9384595398,35488.4488835876
"690","No","No",591.863619018556,31438.7760126708
"691","No","No",0,39742.1054579553
"692","No","Yes",1008.29161867619,11939.2952561454
"693","No","Yes",1052.22747774062,17410.751592587
"694","No","No",331.680526736089,49756.1710110556
"695","No","No",913.107203974466,29414.6521751635
"696","No","Yes",561.391612168585,21747.2631759341
"697","No","No",246.955462978737,47692.8942495176
"698","No","No",578.978610085742,46304.717476204
"699","No","No",557.80877712832,62352.8460745643
"700","No","Yes",788.251751590529,10418.1828574391
"701","No","No",1060.80742927021,39174.0563607053
"702","No","Yes",1247.9070287109,19816.720143272
"703","No","No",1184.36072325231,34259.9901986231
"704","No","No",1024.57823828863,33071.5499188853
"705","No","No",1080.29165556776,43367.8995668736
"706","No","No",540.279044842846,26267.1563495095
"707","No","No",1448.83546537495,33835.7362563617
"708","No","Yes",561.19403528401,27421.1112560912
"709","No","No",1191.48030658909,30040.5721479464
"710","No","No",565.138987531877,38202.5839366837
"711","No","No",482.98219522045,35845.192831506
"712","No","No",950.350093348717,37486.2495546276
"713","No","Yes",350.702913455224,21235.360416199
"714","Yes","Yes",1551.02346886055,19027.5086325602
"715","No","No",981.596275086756,37747.9133548875
"716","No","No",1442.12980525117,10921.6150280099
"717","No","No",1060.33586008787,56607.2539731991
"718","No","No",1154.89079229674,61794.3463333657
"719","No","Yes",544.139769261479,20056.828543898
"720","No","No",1137.17545422725,27588.9333054472
"721","No","No",414.084049490222,47811.4189244222
"722","No","Yes",436.008314418274,17504.44777329
"723","No","No",987.814799701723,25809.9802798683
"724","No","No",679.39184176547,46603.5464098654
"725","No","No",1377.77200718577,51633.329550876
"726","No","No",856.811960459733,32437.1318348945
"727","No","No",1036.67683328589,44923.8027469326
"728","No","No",0,46826.8044671654
"729","No","No",961.472070662985,51936.7598875077
"730","No","No",275.988091260141,35622.829451765
"731","No","No",426.69354209974,30769.3447724704
"732","No","No",290.251908152609,46214.1592342021
"733","No","No",934.969703457723,42325.7499854437
"734","No","Yes",668.991155587469,26342.0484612914
"735","No","Yes",2004.72756831,27136.5379770062
"736","No","No",1312.86572975147,29938.2594627799
"737","No","Yes",662.273556493038,15092.0510234018
"738","No","No",524.639805989354,36120.8922605785
"739","No","Yes",1212.58956759787,21058.3486627819
"740","No","No",961.315769351981,30290.8065048843
"741","Yes","Yes",1504.29017813787,13965.1860445333
"742","No","Yes",613.656318023126,25040.4884531578
"743","No","Yes",1233.34360465673,13402.0732850411
"744","No","No",1221.38595934501,36961.3151242225
"745","No","No",1217.05680668466,46256.7804872945
"746","No","No",628.757698443741,43205.1752975326
"747","No","Yes",819.097316108521,15957.9436405491
"748","No","Yes",843.493455075409,12710.0257138998
"749","No","No",1173.55981500254,29141.3657347634
"750","No","Yes",1195.59028306582,13329.5964048166
"751","No","No",1202.88312357915,12288.127075016
"752","No","No",24.8718234850631,29316.9703343848
"753","No","No",853.241421584688,29484.0523801776
"754","No","No",398.775705283568,40223.9098079387
"755","No","Yes",1521.17239555469,18149.8861866188
"756","No","Yes",1160.22179270323,15941.0508838215
"757","No","No",985.614062736995,49948.4671119046
"758","No","No",583.820719256707,38215.9647632209
"759","No","Yes",638.691982603121,18148.3017050128
"760","No","No",384.997719463751,25380.7034543934
"761","No","No",1050.76423069077,31558.8984246114
"762","Yes","Yes",1871.93838694355,18077.4870927503
"763","No","No",793.761717609342,35157.7399755377
"764","No","Yes",591.660651332961,21790.5024250406
"765","No","No",419.476441266895,24001.5121969332
"766","No","Yes",514.283890206455,18185.4796974861
"767","No","No",1027.89503525082,21551.6110897047
"768","No","Yes",1029.68154856023,15977.3211389052
"769","No","Yes",953.626323812467,18363.0684974424
"770","No","Yes",1463.33776514964,11579.1594522765
"771","No","No",1172.45949910584,34690.1382789868
"772","No","Yes",548.272863944776,12048.8228922671
"773","No","No",1752.88378856299,48250.1046209779
"774","No","Yes",555.920680160548,23909.7064937556
"775","No","Yes",1560.93175219903,13621.5691623243
"776","No","Yes",839.883556561924,16883.3388582069
"777","No","Yes",1112.8179784831,13634.9076283835
"778","No","No",1309.2537724595,43278.0592842939
"779","No","No",940.590874822012,41560.4709935342
"780","No","No",1019.24897718592,41195.3701168568
"781","No","No",433.669019529031,32904.6977154916
"782","No","No",1015.11542504267,42050.4665242275
"783","No","No",75.1776630661825,52765.9782830813
"784","No","No",1050.31637646534,39585.6242243462
"785","No","No",48.9118568414431,35155.1724669932
"786","No","No",1721.64777991975,48236.1263334984
"787","No","No",1607.35144043076,48700.8480467206
"788","No","No",1180.44754170552,43914.4198194427
"789","No","No",609.877134574805,40875.9465364492
"790","No","Yes",1054.2002082358,19440.9731795923
"791","No","Yes",793.820465702134,19074.0317333481
"792","No","No",879.675617944934,30021.3833465031
"793","No","No",1237.64742197945,29151.2346616317
"794","No","Yes",1412.764358307,18234.4592165626
"795","No","Yes",1174.71648266832,13025.7694481722
"796","No","No",363.344803781613,44325.7888525566
"797","No","No",919.485776297976,44928.1846393809
"798","No","Yes",1195.0596696449,21648.6566808064
"799","No","Yes",794.137787843789,15721.7996591962
"800","No","No",10.1888185108063,33776.8254558982
"801","No","No",0,31083.2214565069
"802","No","Yes",498.505979218863,16967.6197633608
"803","No","Yes",1227.16110689129,18459.4186146975
"804","Yes","No",1902.61299103773,53394.0762256759
"805","No","Yes",1059.20463689881,11742.3798595281
"806","No","Yes",450.305390754806,21177.6110782599
"807","No","No",418.539870435286,55002.7334074783
"808","No","No",1070.48067666244,32939.3667112495
"809","No","No",1306.77057329409,56640.6340336077
"810","No","Yes",1315.3372979029,8431.17500361516
"811","No","No",74.5334251957576,19146.2456270511
"812","No","No",66.152330488706,38927.124508577
"813","No","No",56.4207116526935,54217.3382458634
"814","No","No",1056.59687510996,25123.8596166639
"815","No","No",259.237577825768,51355.3746230815
"816","No","Yes",1143.69257803243,23500.9012839272
"817","No","Yes",0,15682.0685039174
"818","No","No",877.793933789175,30239.7207155808
"819","No","No",919.077555018396,45315.5574507726
"820","No","No",94.9324758740624,27212.9512378545
"821","No","No",333.374188920034,29413.1489918422
"822","No","No",197.910716505068,30971.1972485545
"823","No","No",624.91008800743,41047.5557873222
"824","No","No",138.252251807784,54406.5022458143
"825","No","No",787.551771667647,46185.5520006902
"826","No","No",1030.92756438288,36271.3345629976
"827","No","No",1049.74764261543,30159.2098936227
"828","No","No",0,35738.9875659081
"829","No","Yes",1130.05678744088,18467.1021942011
"830","No","No",795.541223505837,51303.1401368965
"831","No","Yes",1503.59623846768,18710.6144823399
"832","No","No",436.135012404984,46071.6605240224
"833","No","No",800.705593268976,36314.4363313898
"834","Yes","Yes",1881.04995231902,16580.4505635578
"835","No","Yes",1445.45802967522,18632.6160618408
"836","No","No",1166.10023562566,23994.4039068604
"837","No","No",163.747103724139,38970.0289045853
"838","No","No",0,46353.8021213475
"839","No","No",1322.05293700543,47814.1742032386
"840","No","No",1283.52325340241,61525.6961773731
"841","No","No",1000.89843370124,43059.0486011163
"842","No","No",249.860323038141,47602.1164993638
"843","No","No",500.222178363597,38991.0124810993
"844","No","No",568.094485122303,36374.4339230205
"845","No","Yes",1047.08515382753,13714.2793843271
"846","No","Yes",186.668374310028,15976.6378608407
"847","No","No",782.778332002182,39224.4092738836
"848","No","No",388.548350469936,18007.8329600719
"849","No","Yes",842.572226289192,17279.5218205669
"850","No","No",556.11563127995,44197.9084625663
"851","No","No",1234.57861793258,48112.4805359665
"852","No","No",412.894341852175,42123.2172146197
"853","No","No",683.357393752549,29269.3335200448
"854","No","No",872.226171550897,48101.6116884956
"855","No","No",415.765428516259,24740.7535995478
"856","No","Yes",1518.01880894666,16740.4430837437
"857","No","No",254.39445522627,44612.1475667156
"858","No","No",1507.24919460052,24057.5179445895
"859","No","No",900.567503074961,40848.6917935743
"860","No","No",595.145133604105,46011.9466316937
"861","No","Yes",163.939823069243,21083.0045099261
"862","No","No",389.613251859694,43472.6742993942
"863","No","Yes",1334.97118024022,14834.8642935984
"864","No","Yes",1961.72865697375,17864.0992535126
"865","No","No",1173.1614932717,36439.6787266044
"866","No","No",1102.41824206016,23218.0838040839
"867","No","No",1228.90791879055,46411.8200587014
"868","Yes","No",1505.83147454065,29525.7494012187
"869","No","Yes",1217.62391318403,21160.4293008005
"870","No","Yes",843.159090993044,12042.8942197634
"871","No","No",1004.53290350781,59403.9300095678
"872","No","No",294.062944441265,57500.1110544571
"873","No","No",484.72347948412,24569.5672907264
"874","No","No",775.556138836832,34586.253337608
"875","No","Yes",1339.31276875945,21031.8598306849
"876","No","No",801.855034413267,39156.6274892218
"877","No","No",551.887100316107,37573.1413923906
"878","No","Yes",522.381180598609,23440.0642225885
"879","No","Yes",1237.45472270608,20660.4693787312
"880","No","No",320.005916922744,57563.4892343032
"881","No","No",960.670030602657,30093.727535682
"882","No","No",272.019517715797,29773.6093193606
"883","No","No",927.165918219897,33781.8151051117
"884","No","No",75.7952593119642,55189.6859931147
"885","No","No",1285.85138073458,37635.9540815737
"886","No","No",1471.77507287232,32478.0439109335
"887","No","Yes",808.014719128948,14485.4684384242
"888","No","No",747.353555712602,42900.0249481755
"889","No","No",327.795731659084,23865.3280982629
"890","No","Yes",851.341791940435,18786.5803421911
"891","No","No",110.854365105375,27370.30436569
"892","No","No",541.755569348689,57322.1050225554
"893","No","No",0,36269.292539222
"894","No","No",1201.36026411294,51740.8900511035
"895","No","No",57.21983790346,18982.5557145634
"896","No","Yes",785.093484581621,32945.8280664548
"897","No","Yes",383.639664344123,17445.182442159
"898","No","No",1107.3041567002,52765.0586636951
"899","No","No",396.985985441859,55454.6310403682
"900","No","No",0,44807.3782736911
"901","No","No",924.241534804409,45207.3876257005
"902","No","No",602.493724895076,33202.5343827786
"903","No","Yes",828.739825030737,17962.3110875424
"904","No","Yes",1.61117557184241,20837.335463841
"905","No","Yes",894.005789144511,18002.3251896023
"906","No","No",1234.47647873186,31313.3745754932
"907","No","No",406.013980421047,42642.2373730984
"908","No","Yes",1051.99844854114,12376.0959820386
"909","No","Yes",1085.83807964848,18890.2280817727
"910","No","No",1656.17327163556,41133.1268472153
"911","No","Yes",242.652608865188,20426.7939710666
"912","No","Yes",602.306495490726,12927.7128023964
"913","No","No",187.597302610554,59660.997035609
"914","No","No",331.297081896645,34497.7426835848
"915","No","No",911.387795135943,57131.3203683842
"916","No","No",391.408200513922,39761.5618426607
"917","No","No",1450.34918369889,33957.1819605723
"918","No","No",819.916988526305,28960.1128050344
"919","No","Yes",1590.17641257216,18666.4122118689
"920","No","Yes",1256.61487000808,22493.5012611123
"921","Yes","Yes",1889.33211032708,22652.1096342004
"922","No","No",743.685374931591,10774.9719084207
"923","No","No",343.194499373316,29555.9054677704
"924","No","No",534.973154698066,44079.3328679291
"925","No","No",1207.69790606359,34857.5403138442
"926","No","No",564.813897226514,39829.4168314528
"927","No","Yes",495.365263297121,22938.136865064
"928","No","No",902.513823067702,38647.5218924775
"929","No","Yes",1741.27335406342,18544.0516544313
"930","No","No",1166.64262489824,52700.1096932984
"931","No","Yes",631.159770000693,23245.0707596437
"932","No","Yes",1076.97796559172,14668.3807126095
"933","Yes","No",1243.55402519945,37634.3463986484
"934","No","No",2113.01902301561,34747.7557770979
"935","No","No",1296.06099706844,43313.2829877461
"936","No","No",1088.6740772095,47147.7259452981
"937","No","No",824.731704075054,42313.5157494728
"938","No","No",684.294841367277,37011.1832764975
"939","No","No",37.3648864980893,26221.5179003895
"940","No","Yes",878.149005483414,18414.5700678425
"941","No","No",904.241522236051,40683.460345652
"942","No","Yes",942.042214953173,22181.3945457544
"943","No","Yes",957.492536316181,16323.3647607655
"944","No","No",285.290875789697,17606.6359250896
"945","No","Yes",1468.38274775613,19846.0647530616
"946","No","No",322.713019502604,27668.5760951812
"947","No","No",414.134063133659,40877.2264536574
"948","No","No",1105.0028119368,36812.7060929345
"949","No","No",0.445756768543902,31934.8379751055
"950","No","No",619.701399647492,13902.1790020542
"951","No","No",293.869320215957,56676.8017535974
"952","No","Yes",637.382728273361,19704.9194208198
"953","No","No",1109.5286172344,69325.0798211788
"954","No","No",1223.11294353625,52903.4613674735
"955","No","Yes",1183.69295023871,13081.661063285
"956","No","No",569.790872578607,54335.2240998272
"957","No","No",625.531922916042,46026.4879077479
"958","No","No",833.655299315209,16908.7780990483
"959","No","No",866.045922597997,44827.2658786343
"960","No","Yes",945.479849222592,17279.2868116854
"961","No","No",1554.8455452416,43901.8053189526
"962","No","No",575.768345325989,51927.7467980249
"963","No","No",1141.60368724373,38722.9611910965
"964","No","No",1146.64022020839,45376.5564836309
"965","No","No",0,34305.9186818322
"966","No","No",434.714771292189,35454.7554164715
"967","No","No",1026.13443029103,39135.6369619437
"968","No","Yes",1302.79720625578,29252.3614341099
"969","No","No",690.70587061269,59561.9052001087
"970","No","No",574.451632934148,40727.642926059
"971","No","No",0,43346.6222644447
"972","No","Yes",251.306023741879,13058.6066581806
"973","No","Yes",1252.11395420994,16490.7069325506
"974","No","No",207.895206863744,52249.3123410668
"975","Yes","No",1753.08438927503,48965.3469660968
"976","No","Yes",1279.93522684716,21107.5202436778
"977","No","No",862.876513688263,36461.9011295762
"978","No","No",1143.54140485283,33394.9791032222
"979","No","No",1253.18164046029,71238.550598183
"980","No","No",624.083972234424,25557.6396710092
"981","No","Yes",1027.86106974252,14322.0883601209
"982","Yes","No",1964.01468409879,50553.5345217328
"983","No","No",752.426109073027,32539.645010165
"984","No","No",953.929817672466,32640.1440477816
"985","No","No",773.204212695115,66749.4334388805
"986","No","No",432.791088457237,52238.087436574
"987","No","No",797.734016170854,31616.8002356646
"988","No","No",240.841552778801,56089.1542511444
"989","No","No",662.315857397632,31815.3465553473
"990","No","Yes",1056.36554989132,15432.3959887695
"991","No","No",741.08759609685,43015.5391532467
"992","No","No",0,42284.6821746769
"993","No","No",981.954489805675,41123.2124696344
"994","No","No",1569.83566642918,42781.3833042045
"995","No","No",136.505521337915,45596.1078294951
"996","No","Yes",731.951715263318,18117.4279105891
"997","No","No",717.810767816132,32040.9312602139
"998","No","Yes",1005.17622287668,18262.17614318
"999","No","No",561.928360945712,31192.9127239783
"1000","Yes","No",2033.19178970717,44998.2874372569
"1001","No","No",638.817455651788,46704.7380027047
"1002","No","Yes",1104.94968692492,11528.9996637775
"1003","No","No",0,29514.0336126113
"1004","No","No",633.766672475066,34290.6049187037
"1005","No","Yes",1526.02513350733,20894.0766566208
"1006","No","No",522.766271539235,43026.3291277584
"1007","No","No",853.103127554178,47381.7242321925
"1008","No","No",1225.22515222291,39338.8621179726
"1009","No","Yes",1559.62786903645,22047.7816148887
"1010","No","No",260.339935694837,34932.4937627442
"1011","No","No",1320.94324512886,50505.3668955538
"1012","No","No",1443.64838984541,45089.3895443578
"1013","No","No",791.638233418398,30303.6067766383
"1014","No","No",922.666469149122,33445.4896891145
"1015","No","No",80.7528636417692,35887.5463607598
"1016","No","No",193.358538633313,34728.0214273877
"1017","No","No",939.098501835431,45519.0189767343
"1018","No","No",1336.80301473215,30787.1557165826
"1019","Yes","No",1488.77956191303,49803.2930787071
"1020","No","Yes",375.196746931796,13709.2053280026
"1021","No","Yes",773.381163196626,18978.1884504424
"1022","No","No",581.423169475544,44600.6627973171
"1023","No","No",461.050249375609,43147.7720272572
"1024","Yes","Yes",1424.55932328854,25398.1974394264
"1025","No","Yes",1139.39621359668,17139.8480911334
"1026","No","No",1032.20326972472,41673.7341538879
"1027","No","No",96.641838693463,44556.2194186218
"1028","No","No",338.779408193535,42678.7131911716
"1029","No","Yes",1331.42546161804,13793.185177665
"1030","No","No",0,34479.623487361
"1031","No","No",616.253970000918,42436.6864423497
"1032","No","No",599.800862543391,30146.635742307
"1033","No","Yes",1466.56289000134,20904.4478767522
"1034","No","No",593.303890859047,49925.323917144
"1035","No","No",786.98126003688,49432.9527599407
"1036","No","No",926.985326670123,48753.3128742021
"1037","No","No",1175.42043326533,37462.6433294708
"1038","No","Yes",1697.74966709098,23295.8432768538
"1039","No","No",575.514383348284,54203.1902133298
"1040","No","No",1386.30244368266,54404.4816929543
"1041","No","Yes",1222.24509239018,23152.7230957415
"1042","No","No",1556.19419401156,26905.5807480981
"1043","No","No",1103.9218798007,52821.534851342
"1044","Yes","No",1496.07211037409,40214.6208266244
"1045","No","Yes",975.642874013062,25920.3219107255
"1046","No","Yes",781.174257050636,23403.0637701792
"1047","No","Yes",940.43546629748,18939.8427921635
"1048","No","No",375.329620037707,32531.2157555247
"1049","No","No",627.876133290845,51770.4583502486
"1050","No","No",1304.38320880201,49371.9569059475
"1051","No","No",1496.23236999032,37534.3292363065
"1052","No","Yes",1005.59382873394,15851.4826456384
"1053","No","No",828.857754277826,46060.8529335004
"1054","No","Yes",1144.35851962952,18903.3784424655
"1055","No","No",1386.19194850385,42537.9907928054
"1056","No","Yes",1321.80520597311,11453.6115299764
"1057","No","No",1121.15125107007,35765.7064949288
"1058","No","No",547.863342238693,45966.3510607549
"1059","No","No",813.997341222043,42461.561421278
"1060","No","No",75.1839852575265,52166.0059677808
"1061","No","No",598.219297785381,28621.1254234809
"1062","No","Yes",63.0363232718853,12308.0183210287
"1063","No","No",0,48642.2545051056
"1064","No","No",1553.70330850943,35929.1635489322
"1065","No","No",813.716093928425,46419.9970823921
"1066","No","No",1386.17675310876,42875.0060232127
"1067","No","No",933.767315398992,43693.5211629212
"1068","No","Yes",1240.0956001901,20134.6472923928
"1069","No","No",0,33781.6563086993
"1070","No","No",613.581862403872,30833.8020564507
"1071","No","No",253.516795606147,53125.483453833
"1072","No","Yes",947.885845436684,17094.4788899247
"1073","No","Yes",112.811909496946,16100.5708324214
"1074","No","Yes",827.041305351259,18892.8212923273
"1075","No","No",484.679498992764,43900.0915627634
"1076","No","Yes",271.249704929045,25157.7314155335
"1077","No","No",1018.24890629919,52509.7424393596
"1078","No","No",0,43730.7666522244
"1079","No","Yes",0,16421.4899199996
"1080","No","No",1101.80371492885,36555.4627038954
"1081","No","Yes",1471.89782186442,18913.0382150486
"1082","No","No",1057.71318049474,40707.6187725798
"1083","No","No",352.550537246057,17626.1671331159
"1084","No","No",1029.81930810508,50635.8908134497
"1085","No","No",1308.66309413507,35964.7919280857
"1086","No","Yes",1581.79048948189,10537.5298371772
"1087","No","No",918.118901852202,53710.2153023883
"1088","No","No",637.80029105974,40323.5590650437
"1089","No","No",369.041787994189,38468.2446481102
"1090","No","Yes",985.092698575156,18499.9615427609
"1091","No","No",306.956926626839,32428.8129068856
"1092","No","No",1205.91323877975,43715.4137557695
"1093","No","No",1079.50296284952,40504.9154530742
"1094","No","No",555.28848862916,40046.4219820768
"1095","No","No",905.011694432408,39293.9825127873
"1096","No","Yes",1348.95607421149,20870.2679822065
"1097","No","Yes",1461.83324856434,19252.2372905694
"1098","No","No",443.446992281183,29811.3634534199
"1099","Yes","No",2024.10501794555,51508.8688750637
"1100","No","No",1516.55115236998,39368.1418726725
"1101","No","Yes",520.55764649278,18256.2883915625
"1102","No","No",850.548098661389,44501.9150376188
"1103","No","No",926.489673303745,49919.9672941973
"1104","No","Yes",1275.82422479209,9611.96315721431
"1105","No","No",249.598383222232,20684.2374148176
"1106","No","Yes",1564.47141053711,19372.821596214
"1107","No","No",443.175564513603,37639.9824345739
"1108","No","No",323.546908292229,33991.9911077722
"1109","No","Yes",512.320202471464,24949.6216077124
"1110","No","Yes",524.760633586772,14154.9134748808
"1111","No","No",741.420460867951,38660.9996466846
"1112","No","Yes",1994.04918807544,14305.1114657784
"1113","No","No",1175.71519291045,24000.8949377252
"1114","No","No",284.048775672597,41243.8547877224
"1115","No","No",565.505384485186,39109.3096711426
"1116","No","No",47.7456654427857,31150.3746922421
"1117","No","No",776.954865809858,45083.3202024572
"1118","No","No",353.739097129679,65526.8097376763
"1119","No","No",76.1322492185705,53594.4200078911
"1120","No","No",839.603597582824,45994.6447324797
"1121","No","Yes",754.484022622877,22425.5354752613
"1122","No","Yes",833.120773570663,23630.2736656378
"1123","No","No",277.294917912513,27548.9502982988
"1124","No","No",1594.69718916083,37091.8738266472
"1125","No","No",0,40393.475426742
"1126","No","Yes",1281.61709121705,14236.0919939822
"1127","No","No",1469.70378608501,40174.9503542028
"1128","No","No",0,38165.6883141482
"1129","No","No",891.403240954195,46611.7316362549
"1130","No","No",493.764909913585,51518.0552452664
"1131","No","Yes",884.61401155588,24306.6503533345
"1132","No","No",0,32582.7455647636
"1133","No","No",1028.52038633802,47944.6009631919
"1134","No","Yes",425.831740346561,13275.9144055105
"1135","No","No",1132.35806231732,52489.7642194785
"1136","No","No",965.273803867657,46218.4605577078
"1137","Yes","No",2499.01674959731,51504.2939603673
"1138","No","Yes",1435.18126392821,11346.841867294
"1139","No","Yes",1391.19635951909,8637.96330726594
"1140","No","Yes",1804.41336882629,17665.5490483662
"1141","No","Yes",321.795067191554,19229.2258698321
"1142","No","No",1506.66982857751,41931.7044839488
"1143","Yes","Yes",1402.2675157905,12104.3156125042
"1144","Yes","No",1379.43071716928,38881.9693488013
"1145","No","No",1232.97872910186,57182.3321169239
"1146","No","No",134.8580207926,35755.8666236451
"1147","No","Yes",422.91401738152,18787.3370277614
"1148","No","No",1373.56421329756,21785.2527545924
"1149","No","No",661.425887551556,55591.5570250899
"1150","No","No",1427.71842109148,27425.6749546043
"1151","No","No",938.637862081905,39459.7670351625
"1152","No","No",608.428994066855,56483.0568967942
"1153","No","No",1477.53239379449,46965.1382601229
"1154","No","Yes",1470.58874098652,17876.2926926974
"1155","No","Yes",1488.59885868055,20739.6267863319
"1156","No","No",0,49608.0061692772
"1157","No","Yes",328.038334167857,19566.267236924
"1158","No","Yes",1333.80612782554,21087.3235241636
"1159","No","No",764.534716932002,40593.2854222443
"1160","No","No",110.326678985078,52106.2049030669
"1161","Yes","Yes",2502.68493124851,14947.5197523987
"1162","No","No",604.315515354891,36592.7568387284
"1163","No","Yes",1595.28815784271,22645.0400572771
"1164","No","No",0,50076.263573802
"1165","No","No",440.637650067582,50902.1361446325
"1166","No","Yes",876.91862459295,13372.5129190972
"1167","No","No",1366.89031408419,35566.9563710813
"1168","No","No",1129.97593669534,32795.0463387124
"1169","No","No",1077.02082435363,56862.0669379978
"1170","No","No",1411.07424052845,26040.0045902552
"1171","No","No",0,45788.4921149217
"1172","No","No",861.652948042994,27355.8050223478
"1173","No","No",1185.6611151702,56483.5372041058
"1174","No","No",901.55391841873,57100.417673064
"1175","No","Yes",773.730215692355,14841.9896203264
"1176","No","No",1387.97338494722,30716.643061226
"1177","No","No",1207.69472622489,37355.9443441242
"1178","No","Yes",455.716437749994,12587.4014518905
"1179","No","Yes",550.89504879092,19110.0744657258
"1180","No","Yes",1484.80542343076,22965.1766792838
"1181","No","Yes",441.6261267835,15261.7080321273
"1182","No","No",810.412075206354,28056.7839140013
"1183","No","No",1137.03989872874,49810.8999928717
"1184","No","Yes",1402.40771590191,13870.9696838977
"1185","No","No",1541.81280611611,29374.9288818287
"1186","No","No",898.980725863394,63041.2247822201
"1187","No","Yes",700.331159311357,18702.9559953232
"1188","No","Yes",895.458063540101,23795.5514988932
"1189","No","No",770.132191937447,48489.3786594128
"1190","No","No",841.951730995958,53779.0182507663
"1191","No","No",44.3136199316928,30449.2501938471
"1192","No","No",377.300360293832,42257.4931884345
"1193","No","No",264.801150517454,49216.2481525872
"1194","No","No",522.021639127425,37341.8987786128
"1195","No","No",1681.91600493709,41866.5580569381
"1196","No","No",1069.53936754679,55421.5527948288
"1197","No","No",375.700949263496,37925.974621095
"1198","No","Yes",670.991525972821,19835.9690488338
"1199","No","No",318.912568249803,49305.553165678
"1200","No","Yes",955.120217585801,25314.8530876177
"1201","No","No",1123.79343887029,24901.9963402933
"1202","No","No",993.580878716165,34836.2527191327
"1203","No","No",278.805260305244,54559.11666453
"1204","No","No",1553.28960393601,44150.16208824
"1205","No","No",0,34522.8855582819
"1206","No","No",483.59130114851,36497.468463815
"1207","No","Yes",311.530549144093,18534.4307209132
"1208","No","No",536.253861565682,32994.1513632918
"1209","No","No",127.707918266324,27483.8950029773
"1210","Yes","Yes",1507.33394777159,23898.8782347949
"1211","No","No",1351.03593016271,40946.6055213149
"1212","No","No",723.198986574556,42751.7652446347
"1213","No","Yes",1041.20279873936,22618.4241475367
"1214","No","No",1102.11010931046,34246.6949101404
"1215","No","Yes",420.370264310909,16419.7129717032
"1216","Yes","No",1278.40730029171,36675.6068828001
"1217","No","No",542.469658032295,52741.8106709888
"1218","No","No",401.829042222614,47175.6145225579
"1219","No","No",506.505871615376,51114.319721702
"1220","No","No",1160.49960409805,23498.3002028493
"1221","No","No",1277.79591275967,56605.5988561131
"1222","No","Yes",1200.62447093622,18973.9076818191
"1223","No","Yes",1418.58607157426,11556.6953127023
"1224","No","Yes",480.339703591062,14592.8404029742
"1225","No","No",370.530018801453,31139.7218358579
"1226","No","No",737.685240540124,51138.2654555752
"1227","No","No",501.921445987957,41366.6778934933
"1228","No","No",1682.20122361406,30441.5548461711
"1229","No","Yes",813.452791611745,21073.9888335265
"1230","No","No",683.826251435648,46937.622870695
"1231","No","Yes",1036.60044401754,15197.248514096
"1232","No","Yes",471.143388673042,24945.0377452165
"1233","No","No",336.419640443448,39707.0423260875
"1234","No","Yes",443.723043916105,13136.8304234267
"1235","No","No",967.138888417836,35269.1229673177
"1236","No","No",549.275969561753,15706.2150942701
"1237","No","No",1601.1600560556,17974.7411792535
"1238","No","No",902.137819758817,59695.2904641564
"1239","No","No",0,37553.5510529754
"1240","No","No",1433.18354789508,41744.3150767776
"1241","No","Yes",502.797735462042,13376.0533426289
"1242","No","No",433.452818738995,35858.9302041276
"1243","No","No",406.426718303055,36750.4989762499
"1244","No","No",935.481004968962,56610.8155695667
"1245","No","Yes",1545.83064077307,12160.842279892
"1246","No","No",93.9405627936824,19811.3305427787
"1247","No","Yes",192.411154711618,24050.371721417
"1248","No","No",716.920227461434,24015.519124564
"1249","No","Yes",1157.13953794831,21222.4155415026
"1250","No","No",72.1697402833453,39560.6349779677
"1251","No","Yes",1417.26825793396,20464.0257371703
"1252","No","No",1195.48395634087,38452.6413385831
"1253","No","No",559.118650256646,55007.2651000453
"1254","No","No",0,37499.6892716672
"1255","No","No",359.225740891036,25243.0984801316
"1256","Yes","Yes",2123.36921697603,23836.4642624001
"1257","No","No",1316.54238433391,20353.4988319531
"1258","No","No",1172.30088990896,34513.6108491013
"1259","No","No",1453.08363720655,36828.3271922962
"1260","No","Yes",323.957086736561,12327.2439903559
"1261","No","No",1152.55066350681,27346.0306093646
"1262","No","No",1333.99816556628,57662.121925967
"1263","No","No",709.658699114718,37992.2129471808
"1264","No","No",731.309122160637,33126.8497707675
"1265","No","Yes",279.315574362792,19742.0370612017
"1266","No","No",0,17059.3683209395
"1267","No","Yes",630.704463000824,16501.8090037026
"1268","No","No",543.037129679086,31268.0951621908
"1269","No","No",618.881501992201,27906.5896181767
"1270","No","Yes",1053.16173509751,25222.978843404
"1271","No","Yes",0,19622.5771706294
"1272","No","Yes",1762.35218285289,17032.3423516869
"1273","No","No",0,43943.2449143298
"1274","No","No",469.151974452928,30366.7195981448
"1275","No","No",699.762558773795,32881.3045147277
"1276","No","No",446.142725206317,42611.5835477328
"1277","No","No",571.179947622194,39682.8028691115
"1278","No","No",952.33482134642,44864.1548648431
"1279","No","No",584.667022756139,55682.4667032247
"1280","No","No",174.367865868658,47750.1206074812
"1281","No","Yes",671.801700876134,15299.2598850673
"1282","No","No",1339.55551031668,53585.7613313739
"1283","No","No",0,49232.7065464366
"1284","No","Yes",1579.07097721235,21101.2046126527
"1285","No","No",917.323540859649,40193.8380967199
"1286","No","No",251.428221532138,25962.0591912929
"1287","No","No",998.362061052119,38385.2913078491
"1288","No","No",828.232510551025,42246.2173550012
"1289","No","Yes",1500.283089198,16943.0290244786
"1290","No","No",1142.63797607011,26731.0903622272
"1291","No","Yes",1415.19882255962,16737.5187114242
"1292","No","No",198.730917929493,33512.9303722799
"1293","No","No",341.769250192687,41662.7426108218
"1294","No","Yes",146.735363398003,12716.2128314385
"1295","No","No",1576.30691570179,30547.7999713877
"1296","No","No",1830.47154712685,24053.4769206631
"1297","No","No",368.800728626843,34526.0353689162
"1298","No","Yes",1026.57628435045,23636.5527691491
"1299","No","No",0,36225.5072520619
"1300","No","No",241.336031072717,40122.4016171763
"1301","No","No",1272.05389130072,44895.5933005043
"1302","No","No",361.200702749123,49395.0267414522
"1303","No","No",1464.00991554481,32023.5955155257
"1304","No","Yes",815.077408427175,18445.6596956189
"1305","No","Yes",1026.12824734079,16430.6411248679
"1306","No","No",630.725559473879,30466.3785151142
"1307","No","No",1624.12643171564,16054.3042106769
"1308","No","No",744.73263686552,44965.0464148846
"1309","No","No",1439.29677868912,36170.5983674823
"1310","No","Yes",1491.05099828582,13033.1227421395
"1311","No","Yes",0,13334.2401303582
"1312","No","No",1162.69979639329,27206.6618497453
"1313","No","No",1463.5931611141,34064.878717232
"1314","No","Yes",1309.69754331363,15536.2387640855
"1315","No","No",920.647422272782,37936.0891353303
"1316","No","Yes",6.31886443929102,18912.6140608781
"1317","No","No",128.991161897018,56946.4364049684
"1318","No","Yes",1323.28906291317,21149.3136871236
"1319","No","No",383.215769348354,15233.3163469465
"1320","No","No",267.380973008781,32265.1189106581
"1321","No","No",429.074698048258,48109.8082506231
"1322","No","No",711.003890959046,51035.7457701793
"1323","No","Yes",309.524147859907,12135.038134265
"1324","No","Yes",1317.9262579595,14070.1670640815
"1325","No","No",427.332842684436,55503.0736319648
"1326","No","No",872.856965749605,31471.5453894656
"1327","No","No",1105.55617273125,38886.0358064885
"1328","No","No",678.018921587752,59416.7788647884
"1329","No","Yes",1164.63072986992,17929.6529845722
"1330","No","No",214.901626088699,33904.5777170432
"1331","No","No",721.586707765586,50952.4848214391
"1332","No","Yes",224.338769023462,18282.2165953956
"1333","No","Yes",1199.23777449457,13809.3504939622
"1334","No","No",580.054584423838,37278.2793128897
"1335","No","No",821.493601873292,34739.3948123132
"1336","No","No",602.862931618408,37849.1342401996
"1337","No","Yes",377.767603673887,14021.5888992056
"1338","No","No",727.632557992218,38431.1580605875
"1339","No","Yes",600.430859835173,18538.3017141838
"1340","No","No",993.59167708779,26682.3285685202
"1341","No","No",1096.58703732555,47235.4531253372
"1342","No","No",1603.33339741999,43474.6376048989
"1343","No","No",1146.97142806638,30637.2972983416
"1344","No","No",1342.17534039196,58049.9940384293
"1345","No","Yes",1663.68701355121,19847.6986056198
"1346","No","No",1005.24796646966,41308.2548453845
"1347","No","No",962.894927346411,25063.4929775853
"1348","No","Yes",1092.62065648598,27128.1672477681
"1349","No","No",919.343346352876,43287.9023052747
"1350","No","Yes",176.840578245143,20021.6933876891
"1351","No","No",460.604465458861,38818.181635265
"1352","No","Yes",782.544846300914,20593.5266878077
"1353","No","No",613.742933850307,56951.3596957531
"1354","No","No",362.103498334539,39658.9504493537
"1355","No","No",861.681923700847,49672.3471809124
"1356","No","Yes",936.473489576073,19389.4800308699
"1357","No","No",1222.7370415634,68564.9906810762
"1358","No","No",794.48678716163,44145.2158474446
"1359","No","No",0,13573.5623986809
"1360","Yes","No",2220.96620112365,40725.0962073577
"1361","No","No",523.216245168787,38063.4016721751
"1362","Yes","No",1907.37731125308,42346.8284633201
"1363","No","Yes",1809.46346847556,18804.7238584061
"1364","No","No",1392.26131534944,32283.3883773109
"1365","No","No",1349.46645574713,35870.0195124443
"1366","No","No",621.736250817544,51595.7517158554
"1367","No","Yes",1273.58198146604,18475.1923845938
"1368","No","No",636.240520317224,17355.7575002454
"1369","No","No",680.451887147092,33192.401795686
"1370","No","No",836.849866321647,27936.5189207732
"1371","No","Yes",1115.10674045042,19169.0715836009
"1372","No","Yes",799.74334079071,16147.5766201027
"1373","No","Yes",996.386878813313,20070.8139205822
"1374","No","No",1632.80804546263,33455.901109151
"1375","No","No",439.837480329247,24452.6465959395
"1376","No","No",276.745313404546,50523.6778878214
"1377","No","No",1715.31506279254,22824.0976154195
"1378","No","No",848.693635853365,29042.5593990651
"1379","No","Yes",323.503357397226,19144.815716939
"1380","No","No",793.612232394447,24973.2084760282
"1381","No","No",612.078447476017,39443.9574108415
"1382","No","Yes",1418.42900414221,14907.0700138967
"1383","No","Yes",1466.93943905615,19139.6296258492
"1384","No","Yes",440.790055424108,19896.029670371
"1385","No","No",0,53432.615491462
"1386","No","No",885.180221984712,46571.7716949208
"1387","No","No",635.969705377164,42451.6143388121
"1388","No","No",664.816301656802,32894.8040660421
"1389","No","No",1100.23877937825,34281.2551656353
"1390","No","Yes",1606.32036631954,13422.3071172582
"1391","No","Yes",1704.42785736529,20892.3141234442
"1392","No","No",1295.99163272121,39799.9975427976
"1393","No","No",468.412123970836,22943.1256871183
"1394","No","No",303.783211829015,44745.1195456996
"1395","No","No",971.709025015626,33774.8919114669
"1396","Yes","No",1758.42094743883,49787.4565722879
"1397","No","Yes",1334.70111864638,24621.9630513517
"1398","No","No",982.699211306085,40194.4653802215
"1399","No","No",52.0388862844743,14680.1000481072
"1400","No","No",401.503539737404,25010.8000911639
"1401","No","No",1456.84937772931,40186.5308796148
"1402","No","No",772.924577096729,42678.3880806454
"1403","No","Yes",1844.36171830721,14238.1132465561
"1404","No","Yes",1015.51139664236,21362.93892147
"1405","No","No",988.678026917533,31210.2432280646
"1406","No","No",1072.74410646127,45704.8395694107
"1407","No","No",1576.90067152779,39835.3487223896
"1408","No","No",840.105158203194,33976.7075446593
"1409","No","Yes",1210.43892922803,19022.1922700507
"1410","No","Yes",714.549612788089,13431.8416509003
"1411","No","Yes",607.094029022965,16373.9344560572
"1412","No","No",413.36352252749,43071.186309797
"1413","No","No",1184.21201473742,34990.5806073176
"1414","No","No",273.602274094738,27996.5771021176
"1415","No","Yes",871.815219474198,9788.57126016834
"1416","No","No",1724.5568166256,46397.4523058011
"1417","No","Yes",1107.40037979683,15661.5797305601
"1418","No","Yes",1161.40734492091,18420.2988894148
"1419","No","Yes",107.602508351688,17941.7391726895
"1420","No","No",136.422035556422,42719.8940623756
"1421","No","No",682.035983958937,30288.9418047374
"1422","No","Yes",791.530359130689,18342.7373544151
"1423","No","No",457.869074702161,43134.5480105639
"1424","No","No",1514.9632356217,36403.0361445491
"1425","No","No",816.093887178331,37763.0365071823
"1426","No","No",1241.95424288944,41952.1157720972
"1427","No","No",932.20254612697,49214.7051207012
"1428","No","No",608.108446611742,46962.7870124299
"1429","No","Yes",627.593246606189,15423.2076281931
"1430","No","Yes",593.055241241436,22786.9167069869
"1431","No","No",187.310192572034,31140.7467172041
"1432","No","No",736.475778783959,39538.3307952149
"1433","No","Yes",838.081158903366,23698.707252675
"1434","No","No",511.515996186748,43737.1818050442
"1435","No","No",355.359646122681,65798.9200344516
"1436","No","No",1138.39668633473,24098.3488630096
"1437","No","No",828.004955429911,53806.3162782398
"1438","No","No",1218.55300529569,51224.9173051827
"1439","No","No",316.012130893778,34250.521603289
"1440","No","Yes",480.469765302404,14866.6122509044
"1441","No","No",152.50300740836,39502.8367966927
"1442","No","No",1129.31646567778,46689.9793958435
"1443","No","Yes",887.290639981827,14958.5514708638
"1444","No","No",938.436913679829,34184.9428780471
"1445","No","No",1076.66543906847,43972.6938555403
"1446","Yes","No",1575.48781838196,35735.4550144135
"1447","No","No",148.145980665246,43104.3860859918
"1448","Yes","No",1865.63577947078,49604.8821305309
"1449","No","Yes",633.769188903703,14109.8218115815
"1450","No","Yes",0,23422.1372192108
"1451","No","No",922.817168040893,50800.3590145358
"1452","No","No",4.71214169670725,28521.3668849029
"1453","No","No",1198.17592683547,42968.63988353
"1454","No","No",1726.61869814519,43814.9612698479
"1455","No","No",1075.21972115915,30485.0597420452
"1456","No","Yes",883.923543700637,14740.1386756169
"1457","No","No",1099.29298509598,47625.3054566206
"1458","No","No",948.965133073432,45456.928800392
"1459","No","Yes",814.630075346124,22948.1577580283
"1460","No","No",687.29293871283,35989.2276496801
"1461","No","No",693.671589365794,32578.6063075364
"1462","No","No",814.843118419675,28526.2168292417
"1463","No","No",1031.97460083075,59555.8658103409
"1464","No","No",1319.48357858413,62054.9863940438
"1465","No","No",1366.02602189242,51551.1083201808
"1466","No","No",1033.45504262437,55487.219887062
"1467","No","No",0,50231.4473361649
"1468","No","No",857.582361058953,54372.8941872866
"1469","No","No",1156.28955808283,33959.7385286484
"1470","No","No",1713.26358997434,34005.8614037709
"1471","No","Yes",902.980719695453,15076.3385432134
"1472","No","Yes",212.042182883039,16179.3077540229
"1473","No","No",632.803980291224,51149.4252527408
"1474","No","Yes",378.021115432097,19571.3923039925
"1475","No","No",999.538016798513,43934.8562610525
"1476","No","No",970.852545776176,49043.7823235489
"1477","No","No",1319.1876132958,35466.2418626747
"1478","No","No",286.329963629257,59576.650317901
"1479","No","No",830.571875567958,39723.7686575355
"1480","No","Yes",657.532135554775,19395.7389272898
"1481","No","No",786.641008454562,57704.644609212
"1482","No","No",111.061910509757,40675.3273778653
"1483","No","No",298.236583687474,27838.6841232142
"1484","No","Yes",819.858672611375,19802.4610662658
"1485","Yes","No",1790.67498344072,44607.4290190773
"1486","No","No",883.471999596354,44207.3330334248
"1487","No","Yes",384.831328093516,17844.5175144789
"1488","Yes","No",1567.61070445614,38785.3576829858
"1489","No","Yes",927.772723605775,18148.0692848799
"1490","No","No",796.326145394374,28616.7090415275
"1491","No","No",652.553316643973,32438.0580993505
"1492","No","No",315.012298389242,44690.4977413746
"1493","No","No",1609.79745262808,38756.4546866537
"1494","No","No",851.796275058559,57950.7717530083
"1495","No","No",460.207866888242,44527.2727547891
"1496","No","No",1203.82896359988,41484.9394268072
"1497","Yes","No",2074.80758856856,38988.8592451984
"1498","No","No",1243.70832217009,37926.1052449778
"1499","No","Yes",1373.03783495249,19457.6092989472
"1500","No","No",449.412686268649,52892.250152274
"1501","No","No",126.558594574605,69541.9485970211
"1502","No","No",387.236142873437,24243.9444577883
"1503","Yes","Yes",2332.8782541994,11770.2341236926
"1504","No","No",1359.43849254375,24411.1347390653
"1505","No","No",469.33171312718,58360.3891158194
"1506","No","No",111.294953864346,40086.6901221233
"1507","No","No",1443.87516538514,54355.7261440114
"1508","No","No",396.820315984991,51560.0281005464
"1509","No","Yes",146.151144830101,15160.4894735416
"1510","No","No",607.200005842074,36615.8294402393
"1511","No","No",1708.14996698855,46416.9455838446
"1512","No","No",956.533336957818,53702.5415622221
"1513","No","Yes",676.613270637706,16148.1863737952
"1514","No","Yes",238.31810694883,17194.1541417353
"1515","No","Yes",0,15967.6142121715
"1516","No","Yes",740.737951241186,13065.5659358939
"1517","No","Yes",933.711693272187,11810.5545699631
"1518","No","No",860.034364598748,33740.699107592
"1519","No","No",1032.94082520166,31152.1406858513
"1520","No","No",0,36949.9498905999
"1521","No","No",835.273499671236,51471.1429480796
"1522","No","No",188.408755938627,35073.5146010696
"1523","No","No",506.324983096079,43631.2965258365
"1524","No","No",496.751812161108,35955.6392777941
"1525","No","Yes",739.179456441967,15819.7305666793
"1526","No","Yes",916.536936849813,20130.9152577
"1527","No","No",1559.75214620523,37227.2688180877
"1528","No","No",392.2395398963,44983.2017971733
"1529","No","No",1065.82556021692,39718.9492489258
"1530","No","No",523.708897901411,48091.7445309658
"1531","No","No",789.564771038807,39199.029533928
"1532","No","No",805.195304832195,38222.2552310845
"1533","No","No",1255.54337529249,45142.27359399
"1534","No","No",654.808846012063,44255.7671952925
"1535","No","No",716.764910805379,51528.498658684
"1536","No","No",686.072308570088,32318.4222408101
"1537","No","No",1530.37001706297,29479.022449285
"1538","No","No",879.373824284674,29697.1464939606
"1539","No","No",738.327804251468,37273.1711484488
"1540","No","No",1647.426763853,49156.1593053497
"1541","No","Yes",503.216622570078,21371.2463239397
"1542","No","No",215.025329445692,40399.4099427169
"1543","No","Yes",820.656300958554,24820.0482387008
"1544","No","No",990.674804194908,56141.1137562027
"1545","No","Yes",914.106496229866,15546.7836705556
"1546","No","Yes",1021.01161324493,8970.91203629163
"1547","No","No",1143.43137940289,35773.4018459501
"1548","No","Yes",763.397388910862,15224.2634633417
"1549","Yes","No",1532.32629971463,42152.3617128869
"1550","No","No",1860.74114848111,39551.0358268744
"1551","No","No",1101.29631071735,27781.452389348
"1552","No","No",541.996802547945,43873.289405485
"1553","No","Yes",696.583526934606,2981.27954754111
"1554","No","No",1536.23227623546,30635.571790594
"1555","No","No",1333.19731299735,33782.9038472638
"1556","No","Yes",869.50920096313,18056.4547776168
"1557","No","No",1048.55213986057,40916.124546005
"1558","No","No",12.0792665336124,32670.4196401503
"1559","No","No",699.382581738009,34276.5925049358
"1560","No","No",855.42637484694,48653.5825586135
"1561","No","Yes",562.304198182497,20164.3110069354
"1562","No","No",62.1700497191049,28660.7475084726
"1563","No","No",977.889750130773,45005.2564239161
"1564","No","No",1685.45062340964,60890.3647952767
"1565","No","No",1257.79532040336,21679.4904991617
"1566","No","No",671.784325023815,53374.9540011256
"1567","No","No",63.0974703902123,38493.0872467354
"1568","No","No",686.384431931289,28953.2703261944
"1569","No","No",582.944764026331,40368.5438191646
"1570","No","No",808.625197210798,32316.3727394222
"1571","No","No",1077.9803067516,17705.2189572525
"1572","No","Yes",1716.08977388041,22755.2619249036
"1573","No","No",417.727581328508,31206.2904522443
"1574","No","No",1300.99516598655,42493.9555486844
"1575","No","No",896.678115981988,46328.5441977357
"1576","No","No",136.828320028455,13585.9974978034
"1577","No","No",29.1750990816449,38871.4786871997
"1578","No","No",282.909562251236,35445.9186115451
"1579","No","No",858.542630394262,19071.8484313316
"1580","No","No",864.047198400084,27690.1135354669
"1581","No","No",1225.97500773502,42203.2081629059
"1582","No","No",696.428764268445,38028.2997175117
"1583","No","Yes",1840.217986795,26480.7192667426
"1584","No","Yes",1227.48527263112,16717.6205876885
"1585","No","No",1716.67513437773,39543.2910788717
"1586","No","No",330.62242766305,11732.2513825039
"1587","No","Yes",915.656813630785,17710.8547048787
"1588","No","No",133.097030783518,34578.9968471276
"1589","No","No",420.611586995114,33293.4668646698
"1590","No","No",1058.05879623197,27196.2585787855
"1591","Yes","No",1972.16681967648,34362.6350079998
"1592","No","No",790.238985859862,43646.1431322213
"1593","No","No",131.624713698702,40158.7089794291
"1594","No","No",0,37468.4807659043
"1595","No","Yes",106.250607422263,17638.7997330987
"1596","No","Yes",712.253706637239,21407.0436171613
"1597","No","No",906.223225781145,44626.942672054
"1598","No","No",127.409903185881,49735.6416370995
"1599","No","No",1698.07191560776,48595.7046456565
"1600","No","Yes",1448.03560597594,18989.3970469034
"1601","No","No",711.439451354052,40507.8212380723
"1602","No","No",326.628956809194,52696.7251843216
"1603","No","No",352.94387653612,59372.7118679629
"1604","No","Yes",1247.06723758895,15522.5841453256
"1605","No","No",439.068967048103,47899.169678067
"1606","No","No",1030.21005766399,40711.453172514
"1607","No","No",0,33168.0580487243
"1608","No","No",1113.27652891078,51457.5390581311
"1609","No","No",1341.0777774559,40199.8414148092
"1610","Yes","Yes",2269.94696613409,18021.105947569
"1611","No","No",1384.26492431692,27737.6085213274
"1612","No","No",0,32724.506367132
"1613","No","No",745.813204823963,42762.4749919349
"1614","No","No",909.633623933358,41915.5206551351
"1615","No","No",196.374008280193,57397.277202827
"1616","No","No",866.174668793343,41365.4563803086
"1617","No","Yes",1460.74766730585,24960.3593807279
"1618","No","No",760.776700641741,44851.560260433
"1619","No","Yes",1165.28321495274,10354.9376205022
"1620","No","No",1085.4255718051,39274.8338712352
"1621","No","Yes",724.385576002922,18641.4984152384
"1622","No","No",114.884475066108,27365.4222002411
"1623","No","No",794.587568399101,52507.4796276615
"1624","No","No",1093.25661935165,28811.2734917492
"1625","No","No",943.937833482203,48198.4006094561
"1626","Yes","No",1861.06087104511,55671.723664137
"1627","No","No",900.838759144024,33130.7093374951
"1628","No","Yes",1682.79614211699,23344.0739391453
"1629","No","No",196.19541561495,26612.5603018635
"1630","No","No",1181.12374855228,29181.8390049907
"1631","No","Yes",1101.15808973692,13142.1814879757
"1632","No","Yes",1506.86047253713,14914.8233564104
"1633","No","Yes",1213.84055423107,17374.0639458298
"1634","No","Yes",547.246325087015,17445.901718035
"1635","No","No",1708.57407859584,34890.717129216
"1636","No","No",248.041482421002,36257.161447609
"1637","No","No",386.265071247349,45776.2066773424
"1638","No","No",527.463274362782,35708.1692547216
"1639","No","No",1546.84250109648,51161.9794953012
"1640","No","No",0,47214.433595293
"1641","No","No",0,55781.2048436798
"1642","No","No",955.466433781886,47520.1755792009
"1643","No","Yes",750.86203452227,16932.3277128853
"1644","No","Yes",1255.7481347252,12209.5770680825
"1645","No","No",818.060883412421,35902.4718016752
"1646","No","Yes",1469.36666818125,11337.4229891593
"1647","No","No",331.720734726363,46853.7458503906
"1648","Yes","No",1456.54605630224,51508.5743439271
"1649","No","Yes",1290.85429311542,19532.7315313487
"1650","No","Yes",711.030495814378,12685.8248596782
"1651","No","No",734.317202778122,33240.0390316634
"1652","No","No",329.218897389854,48133.8244215813
"1653","No","No",1231.50621054989,35068.7907373395
"1654","No","No",847.05414804777,28539.1079012539
"1655","No","No",811.428911459262,55119.8969566434
"1656","No","Yes",1656.8575982207,15359.4365128175
"1657","No","Yes",1547.15324755822,14344.2895934615
"1658","No","Yes",590.718725969,13041.1984597878
"1659","No","Yes",893.966466246846,15092.7760309073
"1660","No","No",0,34066.4381145502
"1661","No","No",800.154679994502,51730.704612224
"1662","No","Yes",685.522535103167,12361.8633973803
"1663","No","No",1262.75744462161,68579.1046648238
"1664","No","No",770.115026382123,49856.6901226882
"1665","No","No",1181.00035898971,35123.7400666379
"1666","No","Yes",527.653897972175,17819.9653389802
"1667","No","No",441.489919754581,39209.9001280774
"1668","No","No",921.974413411946,38340.5688927588
"1669","No","Yes",875.362352530899,14637.1769330155
"1670","No","No",1033.12115530997,66304.7738246534
"1671","No","No",1046.74354286894,40822.4474126399
"1672","No","Yes",1319.70644850247,23373.9311983134
"1673","No","Yes",698.512205127449,26768.9131247588
"1674","No","No",570.981491394889,47021.0067172901
"1675","No","No",842.541774744879,33897.9265106513
"1676","No","No",0,47256.0621146315
"1677","No","No",850.384888087638,44547.4855112922
"1678","No","Yes",963.187906369737,12799.5093789997
"1679","No","No",1136.33113177792,42149.8689946234
"1680","No","Yes",1945.49048287582,14941.3179564765
"1681","No","Yes",1442.54360101309,13049.3193822184
"1682","No","No",135.629015517193,39925.7001694146
"1683","No","No",423.876946748039,36783.0514316689
"1684","No","No",299.779595327805,28917.5378107748
"1685","No","Yes",701.539370559622,17607.4578217575
"1686","No","No",283.207206967033,39263.3920225272
"1687","No","Yes",1357.84973416896,11402.5406341367
"1688","No","Yes",1327.86416271289,11020.9064258435
"1689","No","Yes",946.449982295599,15179.2120481767
"1690","No","No",663.505282228982,53102.245512031
"1691","No","No",115.840084017037,47519.8497166199
"1692","No","No",800.486211245275,37715.8363282901
"1693","No","No",640.06389375316,52413.8589700978
"1694","No","No",877.283013558901,27405.1354982312
"1695","No","Yes",1382.08769153044,15552.2520534633
"1696","No","No",751.394538882795,50910.2456238742
"1697","No","No",0,39893.2719259536
"1698","No","No",1120.7032386567,29613.8875832103
"1699","No","Yes",1312.05143098981,28483.1355092216
"1700","No","No",389.055327796639,22581.6236528053
"1701","No","No",861.690446191552,58029.640234209
"1702","No","No",1079.39600547751,38864.1448016021
"1703","Yes","No",1893.28979213479,31821.7255806098
"1704","No","No",337.495903885671,55853.265132957
"1705","No","No",1142.3517057945,45580.5748105396
"1706","No","Yes",847.056485301184,13741.3270672054
"1707","No","Yes",1575.23811614421,24469.4913979243
"1708","No","Yes",403.347053465851,19690.3455102708
"1709","No","Yes",566.52311940044,25398.3983784735
"1710","Yes","Yes",2009.6857436175,25694.4961577195
"1711","No","No",954.598167759023,50139.0929900909
"1712","No","No",1057.9404153634,45514.6725264215
"1713","No","No",381.3874762368,28265.0860891034
"1714","No","No",793.186855645959,35555.7802801686
"1715","No","No",1078.91038460747,54419.7865521959
"1716","No","Yes",1311.0984831085,21189.2384030857
"1717","No","No",38.2955762452304,37186.5515932779
"1718","No","No",756.216699632025,36513.921210849
"1719","No","No",56.8693777172818,56245.065400061
"1720","No","No",1189.7480394451,30315.8851349596
"1721","No","No",1337.39087740802,51507.5009172858
"1722","No","Yes",1255.49921454235,16703.0173676496
"1723","No","No",1211.86792636394,42777.5823449863
"1724","No","No",318.877270818323,50069.02432659
"1725","No","No",336.070545184453,38595.9115460888
"1726","No","No",934.490492585725,31043.4083271013
"1727","No","No",812.488297873969,42721.3006969775
"1728","No","No",1184.68390385796,37937.9526452613
"1729","No","Yes",1737.60759331587,7153.53966613685
"1730","No","No",94.1207207052075,36303.9415847774
"1731","No","No",994.674438200426,33002.3390513655
"1732","No","No",0,21809.2185090883
"1733","No","No",1006.46882979404,44563.7708044782
"1734","No","No",331.922818528942,47779.9265626849
"1735","No","No",967.135255397538,23278.6659099311
"1736","No","No",132.158429036917,26878.4721288802
"1737","No","No",813.740992900912,30975.2140864352
"1738","No","Yes",0,15986.2419692976
"1739","No","No",529.264958998472,38814.6066989842
"1740","No","No",743.055931704974,40944.5638044847
"1741","No","No",27.7011326957247,45540.9438952868
"1742","No","No",1299.92466935923,44214.5409068333
"1743","No","No",906.541762633329,27074.7747433343
"1744","No","No",1451.17947567421,53556.5521032807
"1745","No","No",1027.56653131669,48558.0872833985
"1746","No","No",1047.2274214374,40974.728128351
"1747","No","No",1452.60329285459,28989.0439548126
"1748","No","Yes",1190.54411198787,20291.1719762324
"1749","Yes","No",1858.9045150328,35525.2139266658
"1750","No","Yes",1152.52022436875,12699.405867675
"1751","No","No",831.595186595008,37372.8282736507
"1752","No","No",922.696676206001,34622.6193419817
"1753","No","Yes",469.456415113981,13944.8933994208
"1754","No","No",474.829219137666,41288.8291033446
"1755","No","Yes",1485.27211754258,10300.8554419372
"1756","No","Yes",943.347555917682,19977.8917657018
"1757","No","No",1789.46451178686,27427.0045664285
"1758","No","No",659.54319765296,21835.1717983776
"1759","No","Yes",1319.53400499073,24032.5441186774
"1760","No","Yes",514.368265849261,18934.6834675869
"1761","No","No",1244.46519780121,40036.0080793479
"1762","No","No",946.810050734945,41237.7692527975
"1763","No","Yes",535.686020138973,11019.606930876
"1764","No","No",682.818458764429,33026.0037380434
"1765","No","No",1190.4859598224,38553.5747928755
"1766","No","No",171.001189215614,42749.130710704
"1767","No","Yes",1238.01661579529,26854.3662753424
"1768","No","No",1037.15769934664,36034.5783662925
"1769","No","Yes",464.835310415625,19121.2286476846
"1770","No","No",124.699031959162,65211.0742137628
"1771","No","No",667.203060826678,44682.8315566199
"1772","No","No",1111.06099784139,50301.4756839878
"1773","No","No",1142.39653992749,33740.4429785247
"1774","No","No",778.830165590655,44257.3310325373
"1775","No","No",1412.0247547105,47727.5646264883
"1776","No","No",986.08181602082,52009.3996458339
"1777","No","No",1251.78401940822,32721.1440442512
"1778","No","No",149.024488439011,33398.4623670415
"1779","No","No",465.08553459872,43480.5684862969
"1780","No","No",1031.12679572637,41022.095640174
"1781","Yes","No",1478.12406941257,31515.3444926478
"1782","No","No",789.00070567225,35798.472637862
"1783","No","Yes",790.295940300018,14086.4483687159
"1784","No","Yes",1123.59504743967,16082.9057091627
"1785","No","No",1370.10949573634,40101.3791314575
"1786","No","No",396.513587196819,41969.7467736577
"1787","No","No",682.078365531623,30736.8876521614
"1788","No","No",338.735981024486,44923.0455293594
"1789","No","Yes",1056.32947590818,16000.8417743231
"1790","No","No",927.211780951994,44368.8830946909
"1791","No","No",634.696104527899,32594.6922349326
"1792","No","No",833.533112973416,40526.3056193295
"1793","No","Yes",1345.2306269047,18518.7156890476
"1794","No","No",550.898966923896,45347.781616879
"1795","No","No",495.708954178561,24845.4329744805
"1796","No","No",661.42615221656,22610.3102990663
"1797","No","No",136.199589610616,20935.6191133423
"1798","No","No",480.192585338536,26084.4071365904
"1799","No","No",230.868924844914,32798.7825914845
"1800","No","No",93.5701986962354,42930.7791819229
"1801","No","Yes",508.824851676069,22663.7098340226
"1802","No","Yes",970.515946612273,16584.8282890372
"1803","No","No",1324.29667077346,46875.3942656965
"1804","No","No",932.963846810656,32105.4419240328
"1805","No","No",0,31418.3691364503
"1806","No","Yes",984.76316184824,17039.9733179552
"1807","No","No",725.120500372107,47549.2007503581
"1808","No","No",246.823809671181,40953.3427649492
"1809","No","No",1051.32109144088,23859.4318006112
"1810","No","Yes",926.892612598001,19445.9371721
"1811","No","Yes",1024.02488477689,18545.8996437402
"1812","No","No",699.84202422219,41985.2576732019
"1813","No","Yes",1036.32351410449,14108.4545196219
"1814","No","No",321.108806748075,36275.1428853987
"1815","No","No",0,41152.1739500131
"1816","No","Yes",1299.18732366924,19878.7473498519
"1817","No","No",763.375309314187,40978.1411815488
"1818","No","Yes",808.742771858343,9775.58139591036
"1819","No","No",808.507503648157,34543.0188012876
"1820","No","Yes",677.552825772912,20745.0153256246
"1821","No","Yes",904.008926715623,13996.501895776
"1822","No","Yes",102.166771629378,26266.3628194819
"1823","No","No",1218.34651457605,43930.9898912831
"1824","No","Yes",774.413837130444,12289.8643730739
"1825","No","No",1309.56437410882,47432.432781126
"1826","No","No",921.998990759072,31553.7340487163
"1827","No","No",629.696208455544,60230.2190055931
"1828","No","No",77.5029386309678,31050.1558214859
"1829","Yes","No",1772.85548374805,42080.1068361856
"1830","No","No",960.237692052783,53418.510829153
"1831","No","No",664.652987898369,56677.6214123668
"1832","No","No",109.918733972492,33419.0068888307
"1833","Yes","Yes",1790.36053654332,14306.823602407
"1834","No","No",1425.80162855037,34015.8498706349
"1835","No","Yes",1167.62012409765,23502.2128231377
"1836","No","No",891.192146223565,35862.1673027478
"1837","No","Yes",1124.25931173712,23168.9234464214
"1838","No","No",1796.11437554937,31862.5752894627
"1839","No","No",237.976185648148,38229.5178649747
"1840","No","No",1353.26492393312,43888.8830574219
"1841","No","Yes",1176.55006037624,18662.5988285908
"1842","No","Yes",1229.44149773733,12158.0446578658
"1843","No","No",270.548125020502,34258.8312272797
"1844","No","Yes",722.055420122016,14661.5245624088
"1845","Yes","No",1170.19897414894,46692.1047752222
"1846","No","No",840.136505158277,38738.1771531566
"1847","No","Yes",873.641237177001,21810.6119901713
"1848","No","No",434.243340354206,57146.4261041008
"1849","No","No",605.274447356026,58469.4754932954
"1850","No","No",428.842161894841,36980.1392701934
"1851","No","No",1662.76219163808,36691.562186102
"1852","No","No",682.712411748219,33175.1830451957
"1853","No","No",598.513776952532,42584.1208108417
"1854","No","No",1088.25476515341,32617.5934807198
"1855","No","No",911.052232910622,38629.5183603014
"1856","No","No",538.160022756727,44654.0931815619
"1857","No","No",721.743821817129,21162.7427156506
"1858","No","Yes",438.517367100739,17400.3908541523
"1859","No","Yes",516.865449012954,20179.3460581977
"1860","No","No",811.171202855366,42185.4435714864
"1861","No","No",775.981834559193,68179.761202979
"1862","No","No",285.400001355113,39398.5653544955
"1863","Yes","No",1554.81631872328,26430.4894049186
"1864","No","No",1057.01521227957,54652.7103326948
"1865","No","Yes",673.812427068436,17672.2879836527
"1866","No","No",689.687362383195,24085.4348274782
"1867","No","Yes",1400.34458861735,14768.6962199807
"1868","No","No",601.417290539683,41490.6158444197
"1869","No","Yes",754.859558740815,18996.8367038408
"1870","No","No",639.665261488891,49977.2949555232
"1871","No","No",765.772445061049,35122.0273798104
"1872","No","No",220.368957650688,24019.3105599941
"1873","No","No",47.3026675420401,38293.3111425555
"1874","No","Yes",780.722398716924,23819.7887922
"1875","No","No",168.813495469025,57262.7545045714
"1876","No","No",1557.64095143419,46030.2620885468
"1877","Yes","No",1891.10961430646,34448.693854621
"1878","No","Yes",688.747548362728,26662.376604636
"1879","No","No",452.007877066517,39990.7656923565
"1880","No","No",574.49119284902,31632.6242446603
"1881","No","No",572.166138659987,43807.2190457683
"1882","No","No",946.770746033435,26618.5227609799
"1883","No","No",523.908694764035,43928.6379332755
"1884","No","No",1146.35838527664,44840.0249597264
"1885","No","No",1646.61391882233,39347.4567996525
"1886","No","No",1216.20230943934,43656.1288917427
"1887","No","No",1184.89962505962,53896.9022479321
"1888","No","No",1416.06555486279,40371.2845887418
"1889","No","No",431.942741359302,46658.013814119
"1890","No","Yes",521.033066390733,16887.1603924733
"1891","No","Yes",806.996111210473,15964.9969756192
"1892","No","No",775.44392753938,33781.7810651214
"1893","No","No",942.279908319669,24683.3808068886
"1894","No","Yes",1261.25713821857,26085.0761176399
"1895","No","No",509.370892230809,39546.4726439018
"1896","No","No",1389.3864522944,39707.9895454689
"1897","Yes","Yes",1836.16176454549,18944.8078756839
"1898","No","No",196.482990431978,41972.9849545641
"1899","No","No",1348.88886606099,40548.7527125412
"1900","No","No",661.602025255988,43157.1192790073
"1901","No","No",718.986722837885,21044.6738318112
"1902","No","No",451.129324126637,39849.7132394904
"1903","No","Yes",114.418241597804,12540.907277886
"1904","No","Yes",889.01231271351,10382.9368009385
"1905","No","No",1046.13532436582,47218.8167896895
"1906","No","Yes",1263.87483062068,18520.063670075
"1907","No","No",1109.03463427706,29759.6363034254
"1908","No","No",698.405381166292,33265.4358443004
"1909","No","No",1328.96712552825,51721.3716111438
"1910","No","Yes",1199.80924416626,19538.0274856053
"1911","No","No",1261.77845265055,26767.9232836531
"1912","No","No",605.365694250338,39002.2296432859
"1913","No","No",550.639886158318,35648.3906286712
"1914","No","No",894.275095526371,36513.5391982464
"1915","No","No",1163.53457380034,46173.768243076
"1916","No","Yes",1061.75508628024,17502.8196418475
"1917","No","Yes",1204.54963030766,19058.9845429835
"1918","No","No",1666.41562382804,18916.3748629881
"1919","No","No",412.902060866997,49060.9237393665
"1920","No","No",595.065973398186,29842.8860499033
"1921","No","Yes",836.005768320167,7404.71584303711
"1922","No","No",178.154515698657,28785.9620969575
"1923","No","Yes",627.645320776991,17868.4448460212
"1924","No","No",1004.25509531634,36895.865549294
"1925","No","No",1195.39956829296,34333.9299147998
"1926","No","No",515.635635549008,51072.8470169857
"1927","No","No",1242.11037786126,37980.7583661376
"1928","No","Yes",153.261039737442,14027.7907404296
"1929","No","No",1086.74028592913,42451.9930812468
"1930","No","No",280.959049718201,29998.8573033458
"1931","No","No",955.881414291977,48657.7903659122
"1932","No","No",1093.28225760166,18844.2503915266
"1933","No","No",734.087945443028,22342.5503467431
"1934","No","No",793.829387731878,45089.5520625048
"1935","No","Yes",410.496187222397,25838.1973641363
"1936","No","Yes",677.653196612127,18930.077199437
"1937","No","No",814.553874491948,39148.5583732843
"1938","No","No",800.177345639351,43547.3981682596
"1939","No","Yes",300.795504329199,19673.6740583538
"1940","Yes","Yes",1748.68084923477,13715.4243783026
"1941","No","No",0,54901.8222134325
"1942","Yes","No",1807.68449070323,42308.9544537047
"1943","No","No",833.070562943636,36330.2620852814
"1944","No","No",794.464327017522,32431.8205514509
"1945","No","No",631.598011335177,46841.1109643927
"1946","No","No",108.249452612372,45264.5647937895
"1947","No","No",1040.39643430957,41395.2882512831
"1948","No","Yes",1207.05248263756,24747.8899692653
"1949","No","No",67.2304390711129,45751.4479321279
"1950","No","No",438.39890382946,40576.7421222242
"1951","No","No",708.579794983589,21728.8201971829
"1952","No","No",775.40221164083,41857.5780281973
"1953","No","No",0,49459.275484274
"1954","No","No",610.506735467534,52124.2335736703
"1955","No","No",845.343264468077,34216.7543637819
"1956","No","No",181.91977853135,37725.2473553696
"1957","No","No",272.987840705252,63566.3659949776
"1958","No","No",805.096332709731,40236.8996303352
"1959","No","No",226.855704750183,33014.8933279067
"1960","No","No",0,39576.4346515849
"1961","No","No",691.928828791926,23957.7148776853
"1962","No","No",1674.19782994054,54658.0741462208
"1963","No","Yes",715.717417435997,27285.3332362387
"1964","No","No",339.433429654687,44889.8749067007
"1965","No","Yes",767.337131679801,21979.6465311961
"1966","No","No",1088.48809632531,38171.3720733572
"1967","No","No",28.5673908447128,35414.5782763459
"1968","No","Yes",792.577561599726,21347.2734497751
"1969","No","Yes",1093.11502043624,22593.0816087832
"1970","No","No",408.489246623899,35984.561683511
"1971","No","No",1214.83096837004,38224.9620526713
"1972","No","Yes",850.159886593427,26764.2924616864
"1973","No","Yes",1078.662962939,25945.1789642873
"1974","No","No",403.64645291367,55458.8912456237
"1975","No","No",1378.16988733139,48152.9878524834
"1976","No","No",306.995202580848,43504.3052364636
"1977","No","No",1569.38083521793,42414.6253557756
"1978","No","Yes",1377.26968341296,17077.2421060231
"1979","No","Yes",656.379056976585,26578.8835572058
"1980","No","Yes",1654.63273450854,23687.357206599
"1981","No","No",702.084751460991,47811.758073426
"1982","No","No",1110.060615566,40236.5014860719
"1983","No","Yes",718.211753993742,18788.7481217329
"1984","No","No",737.150431835345,50544.7841921839
"1985","No","No",672.380047621219,40236.8732028806
"1986","No","No",1298.00276254287,42219.9112378061
"1987","No","No",489.399374070078,37338.5829188421
"1988","No","Yes",636.104269012361,20763.8278588407
"1989","No","Yes",1406.79982672379,16686.6280102287
"1990","No","No",997.80984304604,50287.8737409593
"1991","No","No",0,49792.7572585251
"1992","No","Yes",691.224742286061,16872.15171466
"1993","No","No",485.868909720813,14213.7055827875
"1994","No","No",730.179675305981,42685.1846676899
"1995","No","No",152.119512563396,41847.2318400977
"1996","No","Yes",1105.92557104881,13126.214243143
"1997","No","No",0,47592.7281367208
"1998","No","No",815.737668044725,48788.1033402742
"1999","No","No",568.629894009905,28122.5940878453
"2000","No","No",589.944419374562,24854.6045830214
"2001","No","No",1139.56716632629,36363.6248039883
"2002","No","Yes",344.522896814282,21341.6538375363
"2003","Yes","No",2005.57512847055,36636.0085937313
"2004","No","No",1391.03380861176,51881.8186725178
"2005","No","No",1338.88459679214,32500.5848740456
"2006","No","No",78.7825536317007,37605.452512539
"2007","No","No",764.645179619002,49100.9483808579
"2008","No","No",602.616877680922,56017.1275374002
"2009","No","No",834.690059477593,42468.9136646455
"2010","No","No",1391.51659069133,30152.8038725424
"2011","Yes","No",1823.7514257788,53526.3564109476
"2012","No","No",1010.07075927463,39456.908335945
"2013","No","Yes",1069.80034045604,14736.1469974806
"2014","No","No",1116.06577105079,37074.0613666501
"2015","No","No",428.402283947888,36340.651070882
"2016","No","No",147.488575172857,44482.2687786577
"2017","No","Yes",689.519195003873,15600.0473661363
"2018","No","No",939.420716799608,55360.0331224343
"2019","No","Yes",591.140265303186,19937.0225685188
"2020","No","No",1139.26436423453,41869.3186673096
"2021","No","No",500.64925119635,31353.7500167738
"2022","No","No",1109.40861565878,64213.1924852685
"2023","No","Yes",733.928448994375,28105.6061988492
"2024","No","No",687.956792216651,40354.2160422405
"2025","No","No",118.598450931479,35974.7640052522
"2026","No","No",0,42009.0418247155
"2027","No","No",634.865043829464,49681.2245331545
"2028","No","No",615.471254254668,38161.6557035216
"2029","No","No",457.957743633761,36211.1949861006
"2030","No","Yes",854.149364662585,12843.4365131166
"2031","No","No",770.954517823288,42673.9931495883
"2032","No","Yes",0,24283.3359823875
"2033","No","No",58.4438977974733,44838.6949893381
"2034","Yes","Yes",1893.6643342683,21526.3506270682
"2035","No","No",768.043633927125,35951.1246696067
"2036","No","No",336.242417261439,46279.5258632712
"2037","No","No",852.971339880667,42773.7714603003
"2038","No","Yes",1300.70792413699,13675.520182646
"2039","No","No",743.516076473323,33764.571812105
"2040","No","No",293.452357466707,38528.4473640524
"2041","No","Yes",1472.94846140665,23877.5926590672
"2042","No","No",756.160130857701,52778.0536252184
"2043","No","No",465.495600172177,42358.161541564
"2044","No","Yes",1041.43874269112,24847.8812033725
"2045","No","No",709.738287402227,46363.3436703512
"2046","No","No",427.117689978097,34193.1609253433
"2047","No","No",612.225776174973,60323.3572672294
"2048","No","No",1322.15285429724,47892.8899845687
"2049","No","No",673.747681225898,25013.0631402561
"2050","Yes","No",1135.04734947211,48982.225852038
"2051","No","Yes",509.612940118272,21928.9109904009
"2052","No","No",113.669024032811,38513.3322894258
"2053","No","Yes",560.431764396497,24511.2102139238
"2054","No","No",1163.73601016216,32467.7480420105
"2055","No","No",133.508914180283,49516.3408273916
"2056","No","No",897.420265657772,36296.7481384119
"2057","No","Yes",793.562784376054,13446.0562906254
"2058","No","No",942.786853642612,27736.6451197432
"2059","No","No",953.807008362411,40908.4061180661
"2060","No","No",814.243678946983,29631.1100659498
"2061","No","No",1387.70446873637,26863.1723272568
"2062","No","Yes",601.345719044377,15897.1379986349
"2063","No","No",656.52270125347,34229.3741032096
"2064","No","Yes",1790.54409245898,21938.2092322296
"2065","Yes","Yes",1925.98279481192,17763.3503033729
"2066","No","No",0,16834.8027078641
"2067","No","No",310.118642459262,31445.7710846884
"2068","No","Yes",956.720391942074,14464.8073831659
"2069","No","No",205.173197849377,47284.2535051627
"2070","No","Yes",1572.80377803434,24557.352113832
"2071","No","Yes",1455.68418242228,14822.8330565636
"2072","No","Yes",1011.45881429469,28316.7576084851
"2073","No","No",413.599005794662,31466.220592534
"2074","No","Yes",0,19281.7146572359
"2075","No","No",377.193845357772,41901.8473663191
"2076","No","No",0,51480.3873512327
"2077","No","Yes",0,13407.758588235
"2078","No","Yes",527.056085247217,22986.2838213203
"2079","No","Yes",1470.33083426297,15752.6817921671
"2080","No","No",776.56835738045,54662.5160004175
"2081","No","No",1096.20366837014,41016.2182273767
"2082","No","No",130.45291025962,46094.077150219
"2083","No","No",1331.60730925679,27665.6754702124
"2084","No","Yes",598.081831318484,12385.8240760546
"2085","No","No",177.010865081754,53545.0151134357
"2086","No","No",937.033052836217,27174.5267042589
"2087","No","No",0,46581.3617831305
"2088","No","Yes",970.21073142428,19245.1981577568
"2089","No","No",268.87949151699,48006.1500438521
"2090","No","Yes",708.977828284692,15968.9165569585
"2091","No","No",596.009182737349,32983.5435039705
"2092","Yes","Yes",1944.677458621,13026.0467334358
"2093","No","Yes",1805.2930711196,10405.4266603484
"2094","No","No",405.38804384492,28493.3507218984
"2095","No","No",849.129011101001,26970.736094989
"2096","No","No",988.407715272309,32458.7693705721
"2097","Yes","Yes",2261.84816166666,20030.1651194096
"2098","No","No",1221.59976389318,40865.3128875121
"2099","No","No",1057.35640003951,45099.0915945878
"2100","No","Yes",292.300020907307,17321.2271219559
"2101","No","Yes",940.079732079506,18581.5771825384
"2102","No","No",368.196933199287,32107.6787202062
"2103","No","No",703.386008388494,38419.4138282152
"2104","No","No",1018.22125956717,39893.3028681844
"2105","No","No",240.225612056097,25042.4017278854
"2106","No","Yes",446.634847450068,18309.7907377691
"2107","No","No",1277.79338062019,35620.1560022537
"2108","No","No",519.584026350788,30546.8643738174
"2109","No","No",933.521200054954,40378.4231241276
"2110","No","Yes",1074.57787055668,18574.3537335753
"2111","No","Yes",1697.75398883114,19021.0677000564
"2112","No","No",1240.66470702238,46148.1974680897
"2113","No","No",573.584802642977,54427.9211476451
"2114","No","No",486.057013965658,54794.3921961543
"2115","No","No",455.348416682872,41378.9971241894
"2116","No","No",1110.43993045955,39311.5345181127
"2117","No","No",1225.06555248783,49090.3693115935
"2118","No","No",286.258242830821,28838.3213087935
"2119","No","No",1444.63047690274,40364.5558777602
"2120","No","No",1031.04458789228,38199.0467920706
"2121","No","Yes",382.003276268327,19951.7217195845
"2122","No","No",1337.3132659055,41529.5520507843
"2123","No","No",463.90181290855,39626.7694268247
"2124","No","No",948.777978664131,24094.7864892109
"2125","No","No",626.516141890442,49207.001302772
"2126","No","No",350.0715602255,45354.74618708
"2127","Yes","Yes",1492.96342101535,11054.0684385758
"2128","No","No",847.635895494919,33199.3753811158
"2129","No","No",241.94073994952,47843.0797377543
"2130","No","No",616.538677802606,31925.1832420121
"2131","No","No",733.414845283055,36724.8933064774
"2132","No","No",1100.97288154077,50237.2578180061
"2133","No","No",665.627833705603,55093.3823987094
"2134","No","No",174.935484980574,55044.3700745005
"2135","No","No",124.227296928522,51265.5461992287
"2136","No","No",344.545009104846,38463.6852600914
"2137","No","No",954.948270304332,42963.6617335514
"2138","No","No",1629.32593561974,44911.6435301241
"2139","No","No",913.04491900285,31841.6349702873
"2140","No","No",1306.8430233515,49036.741695979
"2141","No","Yes",2308.89323579289,19110.2664124636
"2142","No","No",1042.42038966929,58234.766470505
"2143","No","No",1264.87562644701,44359.0606151527
"2144","No","Yes",1348.64545582785,21789.3822247358
"2145","No","No",1256.89228843842,23100.0094995806
"2146","No","No",1046.63155340262,45717.6280506608
"2147","No","No",0,22535.506356251
"2148","No","No",432.655426473308,22986.9317533642
"2149","No","No",563.150191515129,43028.597200306
"2150","No","No",721.093433921572,41411.143680846
"2151","No","No",741.59275976993,24393.3159518231
"2152","No","No",1071.30534740383,19529.0351866058
"2153","No","Yes",1589.63859843754,22541.8920327552
"2154","No","No",1194.59757867202,38222.5061057634
"2155","No","No",269.587918794174,38650.3460881726
"2156","No","Yes",455.599026268747,20549.1348293718
"2157","No","Yes",795.540791546717,10257.6724866771
"2158","No","No",595.368804329898,31418.8156662782
"2159","No","Yes",539.834793088502,13119.6858549153
"2160","Yes","No",1322.72397902781,23229.9091337731
"2161","No","No",1267.29579128671,36784.8369937151
"2162","No","No",1091.9997376942,34704.512288981
"2163","No","Yes",951.785550568645,14354.6348784919
"2164","No","Yes",559.498302110046,16994.5786720969
"2165","No","Yes",1222.14132688581,9080.14570014628
"2166","No","Yes",1026.10449504049,20991.3964273735
"2167","No","No",1140.59420875618,25116.6682879217
"2168","No","No",556.4448382508,39707.1087851566
"2169","No","Yes",306.610770383547,11511.8842202514
"2170","No","No",1271.25098586546,42533.2676642295
"2171","No","No",333.07270829892,61508.7565557497
"2172","No","No",1174.51950600219,23942.2057745183
"2173","No","No",473.961439125699,35133.0704073127
"2174","Yes","Yes",1724.96323223373,22170.6873405632
"2175","No","Yes",1056.77065098729,14194.6589987093
"2176","No","No",841.734663004714,38023.3307435185
"2177","No","No",590.878172644642,52977.6419781171
"2178","No","Yes",1070.66636054645,19312.8406016709
"2179","No","No",914.972379535741,52871.324365823
"2180","No","No",1130.51289893728,35071.6733795272
"2181","Yes","No",1706.95684088777,21738.7963336109
"2182","No","No",839.325506864637,43909.0039557966
"2183","No","Yes",330.936107888082,21272.3073820023
"2184","No","Yes",762.585019280848,24738.1184475607
"2185","No","No",986.818165877003,32386.162358721
"2186","No","No",916.073000088086,42201.8205982355
"2187","No","No",1263.67678751621,25695.4813712979
"2188","No","No",0,42118.1966584012
"2189","No","No",441.008786395518,47707.1251761512
"2190","No","No",349.054411370492,46698.0274257906
"2191","No","No",423.985471704096,37979.7853256312
"2192","No","Yes",1295.02971264941,21717.4695488552
"2193","No","No",681.724475489414,31153.6375421398
"2194","No","No",1153.02130969969,20586.2781092411
"2195","No","No",1470.34259270138,26136.7436879453
"2196","No","Yes",1040.87331114945,12177.0389584814
"2197","No","No",1007.26867943193,28245.3200752387
"2198","No","No",667.144583247857,44863.0689857382
"2199","No","Yes",634.679970187067,17023.6828647141
"2200","No","No",298.704516620129,39599.6615793386
"2201","No","No",909.48421999673,48358.4409666082
"2202","Yes","Yes",1737.53781487332,20974.0345941776
"2203","No","No",203.263817193147,46518.1324112585
"2204","No","Yes",0,19750.3962215709
"2205","No","Yes",1889.67831898356,14756.9000181625
"2206","Yes","No",1558.46347122157,41648.326172772
"2207","No","No",1201.92753899842,45382.6135660511
"2208","No","No",1051.60440723879,47529.2173130498
"2209","No","No",641.525383215407,18350.7334185609
"2210","No","No",883.907073044784,41668.1062021513
"2211","No","Yes",953.809047021419,18241.7414386977
"2212","No","Yes",1168.89554505706,14970.870086781
"2213","No","No",431.330740107029,58551.8932582891
"2214","No","Yes",1291.91526301142,17910.0646052095
"2215","No","Yes",335.357947876117,5118.74555143655
"2216","No","No",30.8244500988488,35960.6060192327
"2217","No","Yes",1119.23757441914,15214.1156270435
"2218","Yes","Yes",1812.07999802736,17962.2701593326
"2219","No","No",385.690838730115,44650.7829612035
"2220","No","Yes",651.550004936349,17743.0656159922
"2221","No","No",1717.49502509778,38791.0576377771
"2222","No","Yes",1181.51322144909,11784.8022592774
"2223","No","No",745.279514716451,34704.6202469011
"2224","No","Yes",1453.30974668049,15183.7292181496
"2225","No","Yes",1333.09747140116,29363.9717414648
"2226","No","Yes",571.886359139704,15632.7692772291
"2227","No","No",494.653229366309,40377.6683594854
"2228","No","No",163.582608502788,58220.780465229
"2229","No","Yes",1371.46837154538,24248.5519615584
"2230","No","No",1129.20265874509,40688.6355917437
"2231","No","No",0,53663.166680695
"2232","No","No",84.8565488441197,53760.4652568516
"2233","No","No",681.451239454484,48892.5246942349
"2234","No","Yes",1366.47753375554,11268.7743330454
"2235","No","No",82.0884565037652,27357.5999305025
"2236","No","No",602.996226080387,33888.4091879832
"2237","No","No",176.32319984023,54798.6987534016
"2238","No","Yes",713.799156468317,20676.9051858148
"2239","No","No",137.724616656813,44747.3276836455
"2240","No","No",159.97347308844,30567.8013137362
"2241","Yes","Yes",1956.92390589788,15574.389976252
"2242","No","No",183.576382191855,51149.281572877
"2243","No","No",975.335484892047,50751.0385898927
"2244","No","No",0,44216.9293776847
"2245","No","No",424.285797016623,35675.7411016182
"2246","No","No",1477.66920681625,42011.8079623361
"2247","No","Yes",1211.36364809966,14186.3886650946
"2248","No","No",903.549304730362,46971.5621697052
"2249","No","Yes",1076.23358719821,18096.7014811134
"2250","No","No",988.667817971184,28486.3397522489
"2251","No","No",1311.08880317964,32797.989915748
"2252","No","No",0,40056.692657077
"2253","No","No",289.329908276397,45559.1763926521
"2254","No","No",22.5371752321505,59536.2756235063
"2255","No","Yes",287.607413255777,24440.2251953526
"2256","No","No",25.8283712126898,22720.0945425665
"2257","No","Yes",725.79336805305,23202.9639603002
"2258","No","Yes",1137.02831791176,25117.7629744123
"2259","No","No",1335.115552174,30115.1549636595
"2260","No","No",1193.67697332195,34360.9095002687
"2261","No","Yes",0,18162.7704916096
"2262","No","No",1047.72339011776,45181.748595089
"2263","No","No",0,48271.8704439342
"2264","No","No",327.322508076445,29816.3803207051
"2265","No","No",771.682916735053,38854.2424835777
"2266","No","Yes",873.678652391843,17655.7100333686
"2267","No","No",974.907789283596,36763.1459689516
"2268","No","No",309.823995175971,28139.6258485682
"2269","No","No",286.852326895067,18402.1647545271
"2270","No","No",509.852253653762,49345.4256379167
"2271","No","Yes",840.338690093895,21097.3703069157
"2272","No","No",223.686918061528,23513.295384302
"2273","No","No",1407.22457088516,22806.8482142243
"2274","No","No",1178.98262547138,45545.1684231506
"2275","No","No",1298.48481522487,39143.2919242425
"2276","No","No",627.353353332945,37616.465943813
"2277","No","No",907.169775895606,27631.5677957721
"2278","No","No",872.151841558757,40524.3387449511
"2279","No","No",620.320355649478,52536.7766460997
"2280","No","No",380.33310801284,30890.0736042066
"2281","No","No",1073.16853283521,51668.9608305471
"2282","No","No",883.233753978824,32310.7450219943
"2283","No","Yes",1558.21783126298,7364.83007811734
"2284","Yes","No",2023.73360278694,32094.6278057806
"2285","No","Yes",803.96110476315,13172.8705078849
"2286","No","No",1060.20470687507,27870.1553890068
"2287","No","No",869.854539572928,33589.1395470743
"2288","No","Yes",1306.4830309386,13984.7852630212
"2289","No","No",1597.89874127005,36524.7162417731
"2290","No","No",1351.96246519761,43630.0369164341
"2291","No","No",0,29475.3813765944
"2292","No","No",442.590924942444,38947.3543696336
"2293","No","Yes",963.201198699628,15938.0660973046
"2294","No","No",1063.79708720936,41157.3571458083
"2295","No","No",214.411562735884,42758.8128206941
"2296","No","No",1258.57639880193,33571.144518039
"2297","No","No",506.627150894389,49068.8708652295
"2298","No","No",38.4077852191765,38702.1016770795
"2299","No","No",1237.5474004851,37387.1541539934
"2300","No","No",0,47437.5122368664
"2301","No","No",555.665290924347,46584.7305012296
"2302","No","No",747.937523947625,44030.4614912561
"2303","No","Yes",1000.62176785607,17223.637965174
"2304","No","No",773.672947531567,40427.7419253366
"2305","No","No",744.766199664744,35067.6437229118
"2306","No","No",953.12336416549,51257.1438956213
"2307","No","No",998.375769500884,37816.843904052
"2308","No","Yes",1098.95568535445,21025.9274564034
"2309","No","No",521.687353906343,48700.1530798727
"2310","No","No",82.7303897500606,27627.2240812824
"2311","No","No",219.115003152167,31490.5334213201
"2312","No","No",276.379828600186,38721.8679676236
"2313","No","Yes",1180.36381739222,24063.3127448186
"2314","No","No",620.918596651177,39099.3076254565
"2315","No","No",414.857562442064,38030.8246165394
"2316","No","No",1389.89055624727,48689.7303942425
"2317","No","Yes",703.011900414414,21411.9300665588
"2318","No","No",493.549422828412,52241.9221585187
"2319","No","Yes",1351.41154301245,14003.4757244327
"2320","No","Yes",461.782764768532,17609.8305189709
"2321","No","No",143.102261880534,44846.9010094286
"2322","No","No",1280.00323371245,30274.3355978527
"2323","No","Yes",1764.68272962341,17995.7286127654
"2324","Yes","No",1648.4740168088,55548.2541750171
"2325","No","No",0,37409.00299116
"2326","No","No",610.5977515706,22635.066474244
"2327","No","No",355.148585726055,55634.3306483916
"2328","No","Yes",472.511100780113,19903.892050653
"2329","No","No",786.013911427482,52693.9437515261
"2330","No","No",772.732074090217,47161.2767003098
"2331","No","No",751.297218621075,25013.6140745111
"2332","No","No",1433.71078041798,42798.5655127619
"2333","No","No",1037.0085986933,36622.7922300473
"2334","No","Yes",1827.13494992165,19616.0097315428
"2335","No","No",764.505938856141,37869.2142885917
"2336","No","No",589.542016743032,28237.8206768537
"2337","No","No",754.127489894913,41341.8026042076
"2338","No","No",911.926441504846,41249.2817354287
"2339","No","No",311.918278951811,39694.4837314036
"2340","No","Yes",933.287231176741,20694.5877844271
"2341","Yes","No",1374.47471172714,35805.708860939
"2342","No","Yes",322.311954337769,10056.1105453176
"2343","No","Yes",703.83876552012,19318.0593335456
"2344","No","Yes",534.692906824283,18729.5662404161
"2345","No","No",0,26626.4858812158
"2346","No","No",770.519898519213,44509.5753879851
"2347","No","No",1015.72588136653,31820.9412716706
"2348","No","No",421.146499621363,61655.3480462195
"2349","No","No",1125.09140231743,33564.0755500554
"2350","No","No",857.380821963549,28755.1807565094
"2351","No","No",363.941462516251,46155.5392200076
"2352","No","No",1157.77970145716,32219.8317281535
"2353","No","No",993.30255103886,28501.8560064325
"2354","No","Yes",403.459427433934,15333.8762884491
"2355","Yes","Yes",2134.93448768395,20330.8648725049
"2356","No","Yes",1234.94271600877,26919.1159560062
"2357","No","Yes",805.104251682653,22174.0288924726
"2358","No","No",0,41933.0957701916
"2359","No","Yes",157.51404250204,24506.1868262113
"2360","No","No",257.897657222952,39477.0318565651
"2361","No","No",1056.91506315657,53080.5863449038
"2362","No","No",0,48290.8540329372
"2363","No","Yes",1730.2910812987,19446.6757215789
"2364","No","No",694.500710655688,14150.9143775224
"2365","No","No",1546.76052584633,32928.6361255712
"2366","No","No",428.665305286794,45570.7909478986
"2367","No","Yes",1764.5323087933,18148.5240778253
"2368","No","Yes",1181.9796970175,17223.3672279335
"2369","No","No",943.132389752471,30178.7340616488
"2370","No","No",0,41234.8917498709
"2371","No","No",1087.1937668964,54961.2884808736
"2372","No","No",14.8209530535119,32320.2655222242
"2373","No","No",0,47207.0507207127
"2374","No","No",836.010590425218,40178.9346993862
"2375","No","No",1494.52113519744,42280.4078513644
"2376","No","Yes",1318.52895693248,15821.1523474123
"2377","No","No",431.127915131931,37772.8047134803
"2378","No","No",1335.47357265421,45847.3821106777
"2379","No","No",1141.44217585929,27914.4022527693
"2380","No","No",795.119291607454,38710.3813279163
"2381","No","No",1005.4440884865,40615.342195768
"2382","No","No",481.24016359761,49010.7555057344
"2383","No","No",427.340142965323,27260.7354645627
"2384","No","Yes",1157.5939252283,17996.9033276586
"2385","No","No",890.213022886436,43643.5823952575
"2386","No","Yes",1321.32915896267,20302.2194675724
"2387","No","No",1167.94362182515,57256.0074063843
"2388","No","Yes",1197.17095276732,10834.1113799846
"2389","No","Yes",922.885355540815,22988.8426517771
"2390","No","No",834.711894553582,28553.4718813516
"2391","No","Yes",1264.58694064668,14649.1550227355
"2392","No","No",1603.66522270643,25802.303467605
"2393","No","No",838.276067699318,55082.317526616
"2394","No","Yes",850.677279125118,21251.639505372
"2395","No","Yes",1508.30771296294,17645.3050237888
"2396","No","No",1272.48057873786,34570.5706624876
"2397","No","No",981.839796266037,23636.9763420578
"2398","No","No",487.695020918891,46480.0359695332
"2399","No","No",1046.41667284717,47598.3073685701
"2400","No","No",1533.91725712455,42888.6410509943
"2401","No","No",709.953565164241,37302.6221316962
"2402","No","Yes",1610.0158576143,19649.4844285022
"2403","No","Yes",645.68853045309,14211.147136718
"2404","No","No",377.713995836784,46729.3329914261
"2405","No","No",994.990169963546,39315.9558909818
"2406","No","No",1240.66574426481,57303.7919348134
"2407","No","No",736.590612372767,37031.484769493
"2408","No","No",1201.23061835405,46478.1513183908
"2409","No","Yes",647.367388243078,16897.7896582685
"2410","No","No",1161.23993389444,52064.4713529493
"2411","No","Yes",1521.91273780211,21370.7938938827
"2412","No","No",805.520628314094,43383.7065437576
"2413","No","No",1155.39954792162,34539.5643783574
"2414","No","No",613.111142058477,38632.130636444
"2415","No","No",623.957676295728,46188.294958031
"2416","No","No",1000.90104000868,46469.1025160643
"2417","No","Yes",807.748827444968,18760.3451969824
"2418","No","No",1156.04020757226,38295.5712385133
"2419","No","No",637.283463985274,32885.7019929855
"2420","No","Yes",1241.69098684,20105.0679995001
"2421","No","No",741.193062241411,31104.0044472485
"2422","No","No",1169.83411318443,39386.3700254072
"2423","No","No",0,35821.7865291597
"2424","No","No",1088.30319967893,33673.3606828863
"2425","No","Yes",0,22187.9662133554
"2426","No","No",608.948580101663,26463.3747616096
"2427","No","Yes",431.30278922563,15606.2458412751
"2428","No","No",512.653437908393,39631.695229879
"2429","No","Yes",1241.6164198854,12058.5976188622
"2430","No","No",0,53058.2552072079
"2431","No","No",561.433746424985,37884.9611039777
"2432","No","No",245.244283858514,35132.2211702208
"2433","No","No",1081.06366330651,34138.3237431471
"2434","No","No",492.326454777319,42317.2424218171
"2435","No","No",1349.57175041655,50645.2905655432
"2436","No","Yes",1150.43167977495,11666.9897505353
"2437","No","No",357.865880853581,42499.0322877445
"2438","No","No",331.105276484828,45104.8151964321
"2439","No","Yes",1457.67336933605,20042.8639700766
"2440","No","Yes",852.3139524994,15956.6488436805
"2441","No","No",1378.94051426054,43998.1414025864
"2442","No","Yes",1005.5215077975,22128.0802015919
"2443","No","No",1420.99345352703,34126.421687852
"2444","No","No",570.295541157143,30615.3305485379
"2445","No","Yes",1228.9903728544,17304.6435043052
"2446","No","Yes",1142.93525655529,20942.0033383042
"2447","No","No",759.345814829436,32066.7067252726
"2448","No","No",846.68351168539,41651.2155769265
"2449","Yes","No",2133.46420858019,28237.8823408005
"2450","No","No",354.574368417241,30954.4766757732
"2451","No","No",0,46450.966041528
"2452","No","No",1051.40173289117,30072.749307419
"2453","No","No",0,41371.661191841
"2454","No","No",887.468137060003,30563.7702579257
"2455","No","No",688.109344546568,20098.5934559042
"2456","No","No",1257.34063003991,40810.172766323
"2457","No","No",1175.81763466725,41447.5848149282
"2458","No","No",0,34838.4392651422
"2459","No","No",608.551738816828,22555.5085942346
"2460","No","No",0,32608.4888384632
"2461","Yes","Yes",2110.55685791454,19345.1047348054
"2462","No","No",696.900167306679,38713.3170563461
"2463","No","No",559.468583346205,61187.8105696928
"2464","No","No",715.392921772967,49473.3763755855
"2465","No","Yes",2026.86363126394,20469.9294870885
"2466","No","Yes",1187.35889119047,24267.3964798931
"2467","No","No",785.790670116465,42887.5927249087
"2468","No","No",845.920369752003,42799.4395264286
"2469","No","Yes",1032.67081461066,13901.1189667766
"2470","No","No",861.272739167302,38111.0328679332
"2471","No","No",1601.87794976206,27885.1635505525
"2472","No","No",955.927022070949,10836.7235170759
"2473","No","Yes",877.879803531887,18217.6136511989
"2474","No","No",1043.75826449574,31523.6868953253
"2475","No","Yes",1080.0715265152,11239.5311629715
"2476","No","No",589.217895377853,47722.1701143386
"2477","No","Yes",769.531243654557,23048.5406115574
"2478","No","No",951.047531896824,33326.5871976552
"2479","No","No",1189.15893364362,36223.8705642312
"2480","No","No",1709.34428538326,35290.5231107356
"2481","No","Yes",1402.55392633496,16607.5642547431
"2482","No","No",250.879583544009,51662.2278223809
"2483","No","No",681.417622702994,26426.2292056102
"2484","No","No",1333.24865929666,39196.663006234
"2485","No","No",858.76112539063,29150.3440134128
"2486","No","Yes",992.776244853594,14204.6721465368
"2487","No","No",265.302283874262,32276.4629373611
"2488","No","No",754.477577636107,31592.1998927786
"2489","No","Yes",584.081228595012,24440.020350386
"2490","No","No",1175.23337747506,47684.4611258042
"2491","No","No",1388.66414913095,31021.5500811071
"2492","No","No",410.09926501352,37328.4550202437
"2493","No","No",59.1324986388108,34162.2352560831
"2494","No","Yes",1939.71236045898,13836.9860660018
"2495","No","No",886.149057557468,24466.1253855442
"2496","No","No",770.999401867146,40990.6214980283
"2497","No","Yes",306.410927063284,13594.1882331104
"2498","No","No",1022.10163765589,37095.2159409159
"2499","No","No",847.984035292872,24950.5339243402
"2500","No","No",1596.22780752234,42993.5765262923
"2501","No","Yes",1635.22294194078,8531.03808542902
"2502","No","No",0,45217.850656957
"2503","No","No",1306.79691602462,48799.5119069181
"2504","No","No",932.952369124233,35858.1891507658
"2505","No","Yes",1052.87514170912,13486.1469401636
"2506","No","Yes",1304.63434628265,13496.8798714941
"2507","No","Yes",2052.1334632792,13130.6536036871
"2508","No","No",1216.72561587454,52377.8489000255
"2509","No","No",526.470864997169,44241.565615351
"2510","No","No",153.447936134918,36813.1987057434
"2511","No","No",905.641505720143,47271.3491216911
"2512","No","No",1068.87395709384,41690.0486707805
"2513","No","No",0,30049.1002617945
"2514","No","Yes",992.440019012244,14207.2683434664
"2515","No","No",964.602563113006,36676.5716524275
"2516","No","No",493.635426428972,43524.2497780804
"2517","No","No",835.6564355114,44234.1703860437
"2518","No","No",480.093086854764,57068.1304585653
"2519","No","No",637.601870028651,52347.7620234725
"2520","No","Yes",2134.01562679742,17897.6466128748
"2521","No","No",1475.20722935075,42243.1774847016
"2522","No","No",887.343288415293,55751.7208997423
"2523","No","No",1376.30991124464,43234.1399246767
"2524","No","No",1304.70087909965,45162.6510434416
"2525","No","No",225.506104019464,54534.7097197206
"2526","No","No",1261.26821856746,39984.5295894226
"2527","No","Yes",30.5836717154543,16246.5730543365
"2528","No","Yes",437.035235755779,16261.2075730818
"2529","No","No",922.637402707839,52205.5068740832
"2530","No","No",412.746814904254,44650.0784287271
"2531","No","No",693.10134494208,54492.7479580071
"2532","No","Yes",915.4626481639,27283.3198293763
"2533","No","Yes",1322.49281185069,22930.4450001465
"2534","No","No",1610.08698766662,40498.9981074819
"2535","No","No",1113.1342567811,27110.6030086741
"2536","No","No",0,47803.2583706419
"2537","No","No",1163.14638522091,44424.8259635543
"2538","No","No",924.295347531613,26397.5972914876
"2539","Yes","Yes",1983.23447461812,25687.9297474383
"2540","No","No",725.70058680128,49337.4023462493
"2541","No","Yes",1030.49615530291,16662.7315670032
"2542","No","Yes",1038.9770199786,13739.7192828713
"2543","No","Yes",292.85741080938,15220.9358723481
"2544","No","Yes",495.113911291502,14881.840617552
"2545","No","No",659.04041078169,47942.2498219449
"2546","No","No",291.968139689217,48511.6030793504
"2547","No","No",471.356517447274,45573.3081141684
"2548","No","No",855.822381005313,56219.8088366298
"2549","No","No",918.681779425597,48160.5242871647
"2550","No","No",1251.32255013337,37881.820957954
"2551","Yes","No",1066.88408412325,44918.4123168182
"2552","No","Yes",1427.12403694347,12553.88673131
"2553","No","Yes",1263.88005849002,12418.8076474375
"2554","No","Yes",1128.1414929102,10159.241880994
"2555","No","No",694.398583061349,36570.4254410152
"2556","No","Yes",805.398004854441,17902.5791933665
"2557","No","No",519.517295548749,39704.6929625122
"2558","No","Yes",1051.29716078292,16299.6117503271
"2559","No","No",421.896902214972,37323.7594127167
"2560","No","Yes",622.413720314637,13072.0279621729
"2561","No","Yes",1005.26424821409,21394.1240085659
"2562","No","Yes",537.492542862479,9421.71485785827
"2563","No","Yes",189.998714191062,24716.9561292745
"2564","No","No",309.862823258618,56100.0544139902
"2565","No","No",969.445759217257,33411.8010856992
"2566","No","Yes",775.589232604776,17030.5788535646
"2567","No","No",1296.47473703462,48682.373504953
"2568","No","No",1037.15551645908,38490.3848881263
"2569","No","No",978.652180274857,25742.1197305596
"2570","No","No",965.046132081327,47987.3197478297
"2571","No","Yes",816.042798135129,17894.7937734054
"2572","No","Yes",716.185750858655,7997.62294361932
"2573","No","No",1130.60888109548,43936.6464094879
"2574","No","No",383.70490110482,41311.2439523668
"2575","No","No",1008.15452173895,20326.5921826228
"2576","No","No",652.817454280779,41161.8573542243
"2577","No","Yes",1232.79473656528,16279.1854012298
"2578","No","No",803.224401043935,52869.8078080478
"2579","No","Yes",1443.43169552882,22693.6577546513
"2580","No","Yes",1288.44855995707,21216.9625737112
"2581","No","No",0,43193.232149062
"2582","No","Yes",1053.33264063015,10212.5023579687
"2583","No","Yes",744.787902383805,22343.0973893148
"2584","No","No",412.687613169049,33923.4580434937
"2585","No","No",943.402037124016,35281.728486957
"2586","No","No",1192.97402833567,39504.476023622
"2587","No","Yes",1071.51158264737,11082.0628991517
"2588","No","No",1493.27046658619,41005.2174763553
"2589","No","Yes",1132.83772066986,15703.0252007474
"2590","No","Yes",597.484040633687,14796.7496702089
"2591","No","Yes",792.975486057324,17346.1709244642
"2592","No","No",1376.07540071899,22317.5655920813
"2593","No","No",1060.01692111738,39757.438166238
"2594","No","No",532.326738059543,30851.2777692275
"2595","No","No",0,28711.7571298473
"2596","No","Yes",894.125809153853,24670.1188959719
"2597","No","No",906.436388161149,37309.6818073641
"2598","No","No",0,28727.1201628575
"2599","No","No",506.425913944997,35333.1822214085
"2600","No","Yes",943.501721088937,14123.7035658802
"2601","No","Yes",0,14874.8002114834
"2602","No","No",442.489564867692,54996.8427500424
"2603","No","Yes",380.963307708165,20117.8300344603
"2604","No","No",961.999353015986,37073.1923811729
"2605","Yes","No",1789.09339109987,48331.1268575393
"2606","No","No",76.4760889556027,27283.6917439672
"2607","No","No",1067.13902561623,53995.7304950264
"2608","No","No",0,39592.4369317594
"2609","No","No",253.131982153796,40973.6465438182
"2610","No","No",1056.92834752857,55409.4595968406
"2611","No","No",179.70007051145,37727.5351161306
"2612","No","Yes",990.935762651983,18197.2287289779
"2613","No","No",693.753718271082,36723.4487731456
"2614","No","No",965.276817800471,37705.2432205848
"2615","No","No",300.876206227772,51423.6946425968
"2616","No","No",1265.04493206661,47297.7227040899
"2617","No","No",558.670513820666,40335.6109906479
"2618","No","No",1183.98331144049,53599.7455269133
"2619","No","Yes",1165.84020910987,20495.1586007315
"2620","No","Yes",1542.19122652944,17265.9186145271
"2621","No","Yes",587.767759147185,22669.4243298994
"2622","No","No",0,31005.0357580094
"2623","No","No",389.976694553251,27329.9793137832
"2624","No","No",647.075900997426,55522.6239251505
"2625","No","No",70.9831629934718,40012.5329121316
"2626","No","Yes",115.545355510686,12670.0276198346
"2627","No","No",90.9334471934815,42451.73557619
"2628","No","No",1230.42425584871,51716.7399008311
"2629","No","No",201.558399425731,27924.8465630726
"2630","No","Yes",1562.84615505181,10353.9039107506
"2631","No","Yes",622.895209146374,11019.8775588539
"2632","No","Yes",1142.12695524822,9184.73276156983
"2633","No","Yes",1796.26763981163,21126.1772070552
"2634","No","No",947.423970124183,38505.7838671204
"2635","No","No",419.440579482408,34332.6613136933
"2636","No","No",1660.82770182402,53754.2007216096
"2637","No","No",391.240461740877,24972.2459136159
"2638","No","No",613.430936470434,34745.0074718263
"2639","No","Yes",1024.10880694689,18667.9256525825
"2640","No","Yes",0,25976.0651146896
"2641","No","No",1252.95833769345,33876.1890302511
"2642","No","No",708.837481761085,55675.7598635713
"2643","No","No",266.647086073168,16515.3084266927
"2644","No","Yes",1339.3636140539,19496.1652150139
"2645","No","No",781.595335858498,49457.1052970342
"2646","No","Yes",1021.37011086602,19032.5090112019
"2647","No","No",948.494278459002,40739.859930577
"2648","No","Yes",1312.93377963415,20208.5446086433
"2649","No","No",0,50861.7888483642
"2650","No","No",0,24566.4833655163
"2651","No","No",0,30922.6042126623
"2652","No","Yes",859.336963698517,13993.6545371706
"2653","No","No",646.157667991942,45696.8316119872
"2654","Yes","Yes",1707.91463425405,10591.7174194295
"2655","No","No",133.673454962256,28492.4003038224
"2656","No","Yes",80.5956758170269,16145.6376951065
"2657","No","Yes",631.600847713155,17845.0741549791
"2658","No","No",1236.6179140601,40668.7253963171
"2659","No","No",742.583373337698,40085.4399499287
"2660","No","Yes",97.0314627755507,12731.7575532721
"2661","No","No",1054.22211036239,50900.2425566065
"2662","No","No",959.600287013541,29850.2120699576
"2663","No","No",1258.85227738491,33683.1817331799
"2664","No","No",443.246466812385,36547.1610411452
"2665","No","No",1605.10241527746,26126.3294382767
"2666","No","No",0,25964.8443397551
"2667","No","No",832.645975118982,25106.3747819931
"2668","No","Yes",443.537028835006,19632.1754217151
"2669","No","No",817.431769304621,40088.050514941
"2670","No","No",881.663799741016,43725.9975260941
"2671","No","No",1154.09427380224,48400.2411526907
"2672","No","Yes",961.575563726496,9681.49389260076
"2673","No","Yes",516.121959538247,17538.4616181002
"2674","No","Yes",1357.41186805469,22572.7901256335
"2675","No","No",1036.03059447073,35261.3557800662
"2676","No","Yes",900.609341667945,17802.9272603154
"2677","No","No",1516.32602496867,46393.2645380991
"2678","No","Yes",1015.20453493839,22208.3289188997
"2679","No","No",232.105743961613,40368.9568536783
"2680","No","Yes",1082.28413297939,19451.8162821095
"2681","No","No",682.439660780583,17992.9711781875
"2682","No","Yes",1218.1388796678,18144.9848326959
"2683","No","No",802.413142185247,47444.4928019833
"2684","No","No",143.854898263964,38014.873488625
"2685","No","No",1087.46579379215,32908.5271228366
"2686","No","Yes",895.753955582569,14753.2973335429
"2687","No","No",660.285496472662,58956.5131752064
"2688","No","Yes",630.973489225016,19087.1747800951
"2689","No","Yes",1648.96602929082,15402.4820810586
"2690","No","Yes",975.95559735843,18246.8157222368
"2691","No","No",297.005378251911,22955.082122953
"2692","No","No",0,47328.2329138298
"2693","No","No",0,32629.9448499908
"2694","No","Yes",789.214894854669,18231.8302550712
"2695","Yes","No",1804.03647479983,31318.2960261534
"2696","No","No",1266.11386715158,29698.5162713578
"2697","No","Yes",135.187643441951,20414.4121733742
"2698","No","No",28.8137434583155,36941.0634015026
"2699","No","Yes",0,15347.9142879003
"2700","No","No",750.698996449432,49150.3458527716
"2701","No","No",805.305116879864,57386.1287972175
"2702","No","No",1093.80300483916,29573.2382295888
"2703","No","No",483.965271303409,44335.9581121448
"2704","No","Yes",1534.3948836384,19592.1093866605
"2705","No","No",1349.18729128375,48013.6902006639
"2706","No","No",158.570442748481,20575.4650114134
"2707","No","Yes",172.462019863184,25850.38312555
"2708","No","No",1280.76286092074,53675.5675472481
"2709","No","Yes",213.173275202052,22811.4569780822
"2710","No","No",0,32248.5365682336
"2711","No","Yes",704.013648569298,13903.4557309642
"2712","No","No",708.813976162634,38295.2338064477
"2713","No","No",257.396383130285,49477.7856417044
"2714","No","Yes",1188.7442962229,22867.0705856512
"2715","No","No",0,62886.709228091
"2716","No","No",1321.70389084265,45581.7947809336
"2717","No","Yes",882.581967513754,17749.9720524962
"2718","No","Yes",1418.13414828413,11964.4250856384
"2719","No","No",1245.42220532725,36897.7801091249
"2720","No","No",339.529268529111,58747.3877347941
"2721","No","Yes",824.547830415245,29088.1113478079
"2722","No","No",241.442690317856,30171.624493137
"2723","No","No",1510.79325032928,47684.2394572224
"2724","No","No",396.641249971247,52174.9542732655
"2725","No","Yes",1633.69780348788,15786.3173190985
"2726","No","No",650.418341067727,47122.0724707326
"2727","No","Yes",1382.43823059019,15680.6104930059
"2728","No","No",613.480294534845,24367.861421199
"2729","No","No",1424.35121841429,37845.9992477028
"2730","No","Yes",914.108175564389,19053.0058906904
"2731","No","Yes",802.008930409247,15469.6526246998
"2732","No","Yes",539.674902980334,18852.2738479108
"2733","No","No",337.575999886826,52192.274717818
"2734","No","Yes",813.494837592495,16681.3502966219
"2735","No","No",477.882121951292,39834.0308074596
"2736","No","No",342.191602086332,32154.7535517213
"2737","No","No",840.365781019741,45316.8354075388
"2738","No","Yes",865.565592160238,16643.2409619884
"2739","No","No",645.591636551786,44402.1062312719
"2740","No","No",1755.38891023201,35031.532001505
"2741","No","No",498.749450828636,51690.0634671793
"2742","No","No",587.781081680209,30859.6363376973
"2743","No","Yes",824.616593532163,10062.5759326033
"2744","No","No",0,32660.7252649811
"2745","No","Yes",880.803636009252,20436.4390294741
"2746","No","No",917.122134744439,48227.8193683666
"2747","No","Yes",789.717540631928,19280.5442609264
"2748","No","No",763.855624656873,52938.0354701239
"2749","No","Yes",1062.16650954245,17383.2585454709
"2750","No","No",944.332567427518,44444.1491764787
"2751","No","No",877.501422954718,25587.7439274116
"2752","No","No",1344.10340586866,41417.3459241838
"2753","Yes","Yes",1543.09958172153,18304.6505704658
"2754","No","No",380.317450057093,38864.5841504209
"2755","No","Yes",1176.89330253283,23634.8157312548
"2756","No","No",733.199914922477,34524.9188645623
"2757","No","No",348.997028765627,48942.025780663
"2758","No","Yes",1380.08752574482,19245.8326918502
"2759","No","No",943.165719446864,40369.698030948
"2760","No","No",1248.88584911739,51897.394908379
"2761","No","No",713.223492012257,51107.8600950232
"2762","No","No",834.235608163585,36721.8622064862
"2763","No","No",0,26200.0384004908
"2764","No","No",1558.8838618394,37489.722114493
"2765","No","No",1362.18661906241,37736.7496039702
"2766","No","No",1581.52150477331,45729.8880504535
"2767","No","No",324.854971443152,39752.3541911937
"2768","No","No",1112.73574005439,47527.1255757851
"2769","No","No",379.598726050867,32578.4358262251
"2770","No","No",1549.0981513814,26239.7818673704
"2771","No","No",1489.41591455079,46732.7678074658
"2772","No","No",548.206363841178,41647.7271645225
"2773","No","No",561.968472973583,37637.0050338339
"2774","No","No",595.486701367495,34470.4180269734
"2775","No","No",267.513968799493,27364.8654758574
"2776","No","Yes",1793.30318771576,21034.879651448
"2777","No","Yes",1773.15354152212,10177.8667369628
"2778","No","No",618.860372699188,37333.3338338768
"2779","No","No",205.643808098518,37766.4936916137
"2780","No","No",1011.43154284565,36364.7538519546
"2781","No","No",0,38130.6456699094
"2782","No","Yes",1154.43210536761,15221.1723898836
"2783","No","No",292.515980695165,43705.3790386263
"2784","No","Yes",1106.94627728915,19233.7738901492
"2785","Yes","No",1665.95462052155,30070.1352632412
"2786","No","Yes",968.671100957994,22212.7410345934
"2787","No","No",797.249369291247,39928.5881786116
"2788","No","Yes",636.897822720102,18620.2389516182
"2789","No","Yes",1011.33484841619,14101.2902997406
"2790","No","No",791.035222304402,47303.676639153
"2791","No","No",942.474749546844,36965.045572252
"2792","No","No",706.974277907863,46165.1545334877
"2793","No","No",767.434812504422,55990.8746075411
"2794","No","No",568.843668259643,37074.54081369
"2795","No","Yes",1271.56295706236,18999.6168066482
"2796","No","No",938.939141726121,44280.7863513009
"2797","No","No",1012.19419884963,34631.7060500926
"2798","No","No",1355.49323059172,18935.5115098086
"2799","No","No",912.048721941633,43486.9570806418
"2800","No","No",289.598834250938,25724.7706367641
"2801","Yes","Yes",2035.8615565476,14435.8431942214
"2802","No","Yes",1602.17988580192,17858.0679077375
"2803","No","Yes",0,20067.8097636582
"2804","No","Yes",1202.39070517802,14036.620879558
"2805","No","No",554.707652869949,28164.1985522538
"2806","No","Yes",229.988723491338,2541.20081429887
"2807","No","No",768.979148022833,35503.9089588381
"2808","No","Yes",1211.04014613146,16898.1294674003
"2809","No","No",0,35648.3811626748
"2810","No","No",0,27537.7353522315
"2811","No","Yes",859.936651054092,15966.7171268937
"2812","No","No",0,47556.3012519448
"2813","No","Yes",1471.53839065508,14959.6618849533
"2814","No","Yes",582.873867246958,17730.5511020257
"2815","No","Yes",463.119339198825,25858.2817451931
"2816","No","No",986.154130259504,48940.4900300522
"2817","No","No",534.74605449766,29685.1292285638
"2818","No","No",1402.69052668977,39656.6321983598
"2819","No","No",967.74800102954,46278.0745202707
"2820","No","No",884.022517779638,36889.4282456394
"2821","No","Yes",455.699938269667,19080.8089865564
"2822","No","No",0,66915.5717874548
"2823","No","No",1141.2382104748,37917.6645392021
"2824","No","No",365.235879827926,21358.0000548145
"2825","No","No",38.3759245804088,33579.3562250616
"2826","No","Yes",731.970611270272,13593.140510988
"2827","No","No",1006.71978598059,47784.3184738
"2828","No","No",1107.80115591006,34561.9495390561
"2829","No","No",777.530808732216,45673.6739067692
"2830","No","No",1501.42608729105,35398.9571179789
"2831","No","Yes",862.060038480201,12085.4530819151
"2832","No","Yes",12.1864202972865,19910.9689671724
"2833","No","No",0,30208.0464441746
"2834","No","No",1820.32548997904,31309.9984840595
"2835","No","No",576.068992874936,40179.8300541546
"2836","No","No",837.752621293608,31017.9011357947
"2837","No","No",399.79388495453,34875.3323287297
"2838","No","Yes",578.755098896162,20909.8353696313
"2839","No","No",1106.54087450672,46565.059953601
"2840","No","No",390.745611388115,38442.2960634912
"2841","No","Yes",274.194627807201,15421.5516574415
"2842","No","No",1476.87764199904,42271.3334081723
"2843","No","No",578.460742369529,20306.9360124815
"2844","No","No",1064.14771330635,28192.1258830284
"2845","No","No",1598.30341726465,46404.6646948089
"2846","No","No",863.451829577627,33529.8806538019
"2847","No","No",1157.75985725331,32574.5028988198
"2848","No","Yes",406.38604907433,24086.9524874473
"2849","No","No",309.244487695008,44202.707504713
"2850","No","No",943.951244268014,28353.6300145366
"2851","No","No",1206.79193715819,42916.7013947578
"2852","No","Yes",1448.06045753637,21028.2867502157
"2853","No","No",1166.59398929301,26977.6227486722
"2854","No","No",800.30867198517,61281.3008105003
"2855","No","No",714.926000080201,31032.869700076
"2856","No","No",471.255083069548,28417.7419907703
"2857","No","No",662.164445094238,30390.0600699129
"2858","No","No",770.431961924154,53398.3147279233
"2859","No","No",597.326869569873,55496.7771129435
"2860","No","No",140.853022519257,48183.2767398541
"2861","No","No",1035.55294399822,29423.2320285923
"2862","No","Yes",784.10878118083,11560.567698562
"2863","No","Yes",442.027425025125,17072.3366388213
"2864","No","No",888.081431985558,54072.6570198483
"2865","No","No",704.202908307045,44775.122006996
"2866","No","Yes",1016.45061642843,15099.5302012588
"2867","No","No",1036.46072800599,42914.9816724656
"2868","No","Yes",1060.22003413917,22795.9429883192
"2869","No","No",1243.92805989896,38075.4382991816
"2870","No","Yes",1291.97734580897,26571.8745602489
"2871","No","No",69.2524758053386,20222.8662388676
"2872","No","No",196.389285525812,29932.6620347112
"2873","No","Yes",709.027102883811,18305.7345223333
"2874","No","No",1034.14996239185,52106.8387037871
"2875","No","No",868.006030057611,50549.5334619527
"2876","No","No",1236.00699323763,48800.3568478614
"2877","No","No",12.9123495546218,46282.2577329848
"2878","No","Yes",826.453636513914,21342.7030297359
"2879","No","Yes",703.990009364016,20752.1754356717
"2880","No","Yes",817.121438400311,13152.0341263612
"2881","No","No",1210.01463106309,45066.1332443431
"2882","No","Yes",766.406247421834,22296.9836996044
"2883","No","No",1643.65405150903,33980.8258311547
"2884","No","No",1053.01477816353,54009.6319447306
"2885","No","No",240.077434521457,41508.9556964546
"2886","No","No",62.6529485075978,44440.7054833724
"2887","No","Yes",924.233203921663,11194.6688781448
"2888","No","Yes",944.0710358903,26027.6876516962
"2889","No","No",295.785442761409,51837.752325646
"2890","Yes","No",2085.58697818353,35657.2256698962
"2891","No","No",474.355263765365,58068.622244121
"2892","No","No",504.723352582326,54297.1136556321
"2893","No","No",292.384144390823,33961.1004508478
"2894","No","No",593.28046779734,40227.1438816191
"2895","No","No",76.0640883415747,50054.7623469691
"2896","No","Yes",1270.09281045792,16809.0064517963
"2897","No","No",877.343686869075,40689.4785866351
"2898","No","Yes",932.560942548355,20980.8055046157
"2899","No","No",279.963388337914,30262.1171373507
"2900","No","No",1155.1750058879,40398.3993465472
"2901","No","No",0,45431.7575917119
"2902","No","Yes",1216.76756165806,23378.5728235637
"2903","No","Yes",0,15274.4690275405
"2904","No","Yes",728.549399239302,22891.9293782851
"2905","No","No",0,48365.5775942221
"2906","No","Yes",643.565267155745,21323.0494420305
"2907","No","No",973.082364343837,27289.2713224843
"2908","No","Yes",1177.95742476219,14385.1670694871
"2909","No","No",196.987111415453,41314.993615811
"2910","No","No",1526.26866859685,44845.1362915324
"2911","No","No",441.396084563066,56060.2036101667
"2912","No","No",1004.64177302613,56533.3130669828
"2913","No","No",1258.06953382511,36645.1405748822
"2914","No","No",1563.99341883986,37119.6019665666
"2915","No","No",367.35157626488,29905.5278559754
"2916","No","Yes",1222.76804081128,18276.4340649165
"2917","No","Yes",1582.20281304722,19682.8724899139
"2918","No","No",647.13028302857,32725.3866528516
"2919","No","No",0,33146.4953386028
"2920","No","Yes",1585.33302945842,15079.4889372439
"2921","No","Yes",837.972336155771,25773.18264869
"2922","No","Yes",686.782149406402,10543.5563164881
"2923","No","No",947.842158985084,37891.0370436422
"2924","No","Yes",1205.0550079097,18101.6763143196
"2925","No","No",1254.13105462545,37456.2619909468
"2926","No","No",949.123846295031,50528.5338652233
"2927","No","No",530.246019980846,27610.7030900379
"2928","No","No",833.689970256381,54558.1401094237
"2929","No","No",638.465310882778,36173.982445364
"2930","Yes","Yes",2387.31486743364,28296.9147184635
"2931","No","Yes",839.390890223131,21383.2311743407
"2932","No","No",294.634280951069,63285.0251126079
"2933","No","No",1178.1794376938,34720.93615732
"2934","No","No",1149.72013584049,57890.5159582794
"2935","No","No",909.764267678658,42468.2782209453
"2936","No","Yes",1391.50802679448,18539.5903551186
"2937","No","Yes",1029.24963218458,18018.3583136311
"2938","No","No",1504.5969399062,30773.7428447844
"2939","No","No",1276.4892999898,35122.8536147464
"2940","No","No",874.915042538445,25023.3230927001
"2941","No","No",1104.68946321199,51516.3362893771
"2942","No","No",0,47056.1991877335
"2943","No","No",698.80044395676,37466.0202992199
"2944","No","No",1055.41086822615,33945.1427605779
"2945","No","Yes",131.28461051854,14491.8440664949
"2946","No","No",795.903785966359,44181.671863611
"2947","No","No",611.954296807175,39291.2037327881
"2948","No","No",1670.42300806805,41409.7711489966
"2949","No","No",1423.14954532056,33591.2315394355
"2950","No","No",770.641939176912,29623.4712882976
"2951","No","No",1315.46960886839,29808.3522278599
"2952","No","No",599.204105037746,53218.1108551285
"2953","No","No",584.356741021442,34128.7669919245
"2954","No","No",27.2718784920017,51659.8784114258
"2955","No","No",65.7275950839411,32332.1826526434
"2956","No","No",1338.46111617072,23909.5469466814
"2957","No","Yes",1120.42296847456,16151.2528468194
"2958","No","No",726.02934382868,41996.1244144328
"2959","No","No",1353.5882835195,44372.7939185397
"2960","No","No",531.744210691984,38122.1923999718
"2961","No","No",1824.64138514207,36762.8088401801
"2962","No","Yes",894.465468093504,14035.4991381188
"2963","No","Yes",1275.53856458026,16445.6247238911
"2964","No","No",550.624394138456,19106.2657896555
"2965","No","Yes",270.391026233552,13158.4422264499
"2966","No","No",651.998604093421,35266.1284410747
"2967","No","No",718.351102130362,50765.8926332739
"2968","No","No",1008.94653988106,31235.782923348
"2969","No","Yes",1510.74781182427,13390.9200005461
"2970","No","No",1393.36828331237,38371.9774624643
"2971","No","No",586.588274391792,45519.8377116616
"2972","No","No",793.41392669019,45873.1799719756
"2973","No","No",285.910169131953,43263.6306402332
"2974","No","Yes",550.381055833744,20322.7686347979
"2975","No","Yes",716.491995031388,16785.38532485
"2976","No","No",383.686466865314,39946.4849685718
"2977","No","No",252.333309120169,38431.8186913772
"2978","No","No",1164.10427048393,40167.0233128899
"2979","No","No",1080.18577692773,38398.8660985364
"2980","No","No",906.478544181794,47101.990627242
"2981","No","No",457.800098565629,26567.2410810939
"2982","No","No",1215.86735927273,42762.1218348043
"2983","No","No",217.361520631417,45083.1113673867
"2984","No","Yes",1076.72770461126,12502.6449158541
"2985","No","Yes",58.0660620767269,23639.233623289
"2986","No","No",1615.22500095208,55219.5200109582
"2987","No","Yes",1119.00178869124,22811.393799216
"2988","No","No",671.958067158232,17621.4538490348
"2989","No","No",634.21792638252,38672.7496473852
"2990","No","Yes",831.818536264928,16164.4846069406
"2991","No","Yes",373.062516538222,22018.9821001083
"2992","No","No",776.454876211341,58481.5439128062
"2993","No","Yes",1031.30393864174,12195.3798286488
"2994","No","No",910.67241583492,43795.5817003373
"2995","No","No",1010.80906498234,42163.4535969445
"2996","No","No",1090.35035030747,48280.9463176417
"2997","No","No",1008.55034292713,53801.615635285
"2998","No","No",0,50127.1804741922
"2999","No","Yes",1824.36800350788,20074.2810967841
"3000","No","No",730.164418478993,44411.6782657154
"3001","No","No",991.131782568509,30941.5485184932
"3002","No","No",1356.51666465842,35030.6350247283
"3003","No","No",576.635576855956,41699.2097914708
"3004","No","No",1334.5541912103,43385.821809676
"3005","No","No",1487.66180770108,47624.3796002969
"3006","No","No",778.693492520055,48551.9710763615
"3007","No","No",444.196382313779,37599.4201866653
"3008","No","No",1023.3621601843,34632.1026922622
"3009","No","Yes",153.400095790985,17840.3848795582
"3010","No","No",661.760616380205,47169.3568472448
"3011","No","No",312.614706540541,34918.6129080311
"3012","No","No",645.372564533029,30102.0355391677
"3013","No","No",0,49010.2733191107
"3014","No","No",779.614723353116,22975.493022604
"3015","No","Yes",569.498790806108,19755.5211422929
"3016","No","No",1287.76589516641,38507.119385618
"3017","No","No",1177.81506956381,32294.5456510691
"3018","No","No",795.368557078411,25704.646642625
"3019","No","No",877.039196960132,24902.735490804
"3020","No","No",823.991988281088,36701.5301668566
"3021","No","No",1019.21535840238,52089.6446283697
"3022","No","No",942.737482135501,38908.8811192003
"3023","No","No",6.78786175914354,40145.3390915312
"3024","No","No",0,55158.1512404129
"3025","No","Yes",1420.20420508849,22790.7958314379
"3026","No","Yes",1254.66258256834,18189.5207628528
"3027","No","No",0,37796.4034569112
"3028","No","No",1140.07189936954,33605.3779326314
"3029","No","Yes",747.958511235591,19869.3901776741
"3030","No","No",1598.60255266893,48717.2631496507
"3031","No","No",1226.37071722573,21426.3069842744
"3032","No","No",929.819934850511,50611.9859546811
"3033","No","No",807.405738044712,43853.8842526713
"3034","No","No",345.364941188499,56965.7021104141
"3035","No","No",982.450944888935,42907.0261908208
"3036","No","Yes",1067.67076675223,14485.9484894986
"3037","No","No",742.540614206706,39017.9503227464
"3038","No","No",1060.17844742674,30948.2902664065
"3039","No","No",523.753367762277,33767.0507141976
"3040","No","No",697.982915044228,29799.3191983562
"3041","Yes","Yes",1500.89453315482,15802.2328831476
"3042","No","Yes",1682.89763103161,14378.8906007666
"3043","No","Yes",1264.04207020703,6466.51340820681
"3044","No","No",1352.46233583861,34247.7800247361
"3045","No","No",376.878916634948,50671.3227510852
"3046","No","No",629.561281534804,37255.3650881247
"3047","No","Yes",709.414229834248,11355.1127102972
"3048","No","No",541.346278833227,42332.2462776755
"3049","No","No",421.172381915514,42721.4777704317
"3050","No","No",500.804219905074,47910.9638496034
"3051","No","No",0,41663.3401134605
"3052","No","No",1089.36587309619,43360.160154297
"3053","No","No",603.847482534768,56468.1222446001
"3054","Yes","No",1165.53985274392,50341.5739037884
"3055","No","No",1457.62260167023,43155.3626996992
"3056","No","No",390.055026370177,27108.2136591038
"3057","No","No",584.14121640762,35517.2291107955
"3058","No","No",901.012282940652,13498.1265266133
"3059","No","Yes",1380.09673715717,14142.0754374369
"3060","No","No",1146.06768909789,39902.4418921895
"3061","No","No",1058.02596863124,43327.026105494
"3062","No","No",474.37966209093,35980.7527880856
"3063","No","Yes",969.143518900583,22425.4057574307
"3064","No","Yes",1616.73887826784,12557.8228818817
"3065","No","No",1185.02865067439,33298.500399848
"3066","No","No",472.633228937598,41257.3860294191
"3067","No","No",1257.62512281906,30964.234583001
"3068","No","No",640.918438752274,37306.6939870991
"3069","No","No",105.744247655831,42442.8473498049
"3070","No","No",672.426792683469,25947.2454089183
"3071","No","No",568.66644703703,45186.9759350042
"3072","No","Yes",368.19829877759,26670.0121129722
"3073","No","No",1546.54850637671,57266.8295893597
"3074","No","No",230.220394093858,28930.2545205789
"3075","No","No",815.054702367188,34200.1833534919
"3076","No","Yes",164.305511667688,17995.858258227
"3077","No","Yes",1785.79751592117,15291.6707951933
"3078","No","No",661.052297809298,27873.637911952
"3079","No","Yes",979.877154915478,13522.4215063984
"3080","No","Yes",778.249905207304,23805.9105658439
"3081","No","Yes",190.619278273277,21744.5093170033
"3082","No","No",523.813716991703,45284.1893484952
"3083","No","No",175.698868756298,40044.4703210203
"3084","No","No",977.947923151752,38368.4841700357
"3085","No","No",837.695359207891,44260.7644148961
"3086","No","Yes",1436.32081999306,27658.3866683395
"3087","No","No",638.229500996192,53812.7978817314
"3088","No","No",714.011659055547,48631.3643616269
"3089","No","No",738.253247663666,36086.0921345944
"3090","No","No",507.526534856239,58027.7709224602
"3091","No","Yes",1033.83349441518,15818.0613960182
"3092","No","Yes",19.7394609228467,20514.1713659729
"3093","No","No",434.704864003897,48898.3701163411
"3094","No","No",0,45334.8393330862
"3095","No","No",629.764071852454,53069.3251510713
"3096","No","Yes",522.767758901133,26900.2807227485
"3097","No","Yes",294.65648754174,11681.4623321504
"3098","No","Yes",469.425632910345,14647.0022693022
"3099","No","No",838.437038320412,40903.4806861138
"3100","No","No",1407.6395145697,52746.1331466994
"3101","No","Yes",1417.22549877778,16053.9742636275
"3102","No","No",363.994064622591,38110.4431111926
"3103","No","Yes",128.2518222274,17076.5736044951
"3104","Yes","Yes",1894.1682010356,28321.6845474436
"3105","No","Yes",1624.79564181453,10623.5974508246
"3106","No","No",645.949270764739,46406.3379836008
"3107","No","No",684.08490485463,38184.5683849571
"3108","No","No",1168.2845384156,46904.6038233881
"3109","No","No",766.124054874063,51614.0562036621
"3110","No","Yes",738.268601159608,14063.0754606611
"3111","No","No",525.89033827246,45530.5091218435
"3112","No","No",631.253506690788,38877.5703324556
"3113","No","No",0,65943.7839407987
"3114","No","No",913.913965747442,53809.3303582319
"3115","No","No",578.752566335341,60498.9254643735
"3116","No","No",1503.84995075327,43888.3966632069
"3117","No","Yes",555.265796927511,11581.2206625316
"3118","Yes","Yes",1289.24621049482,13624.5452584786
"3119","No","No",545.566510669189,35714.6511274284
"3120","No","No",1112.3489922615,33468.8221845045
"3121","No","No",564.907448112708,35820.8856846426
"3122","No","No",591.841407435347,38879.5098790241
"3123","No","Yes",908.122811892835,22084.2716490223
"3124","Yes","Yes",2169.1961867561,18195.266900905
"3125","No","Yes",1108.10536566553,15337.3022718717
"3126","No","No",0,27167.2603910132
"3127","No","No",957.59190285973,35330.8553531479
"3128","No","No",448.039968335434,29537.3102493442
"3129","No","No",1188.07287547976,40704.7072215123
"3130","No","Yes",1223.88581431865,19520.0090003742
"3131","No","Yes",959.967042003212,18413.863852335
"3132","No","No",734.721836402583,35010.0258726036
"3133","No","No",0,47628.4118282353
"3134","No","No",1423.28425807833,48914.197080844
"3135","No","No",217.163501322414,26975.491809377
"3136","No","No",672.045138944368,36705.3404589011
"3137","No","No",830.765389790969,45958.9842240714
"3138","No","No",652.528178921526,58750.4959950061
"3139","No","No",533.989148195491,64930.2397951394
"3140","No","Yes",1626.63407931271,17721.6300644139
"3141","No","No",1382.12097668256,30395.6882250574
"3142","No","No",0,38663.2005143247
"3143","No","No",1165.68915192015,38110.3647750537
"3144","No","No",1190.42050704867,34198.0513266133
"3145","No","No",435.214657082187,39505.412457212
"3146","No","Yes",1382.42254388129,17166.0425659832
"3147","No","No",1265.95483303592,27233.5630600525
"3148","No","No",1303.62613153881,40010.4651612535
"3149","No","Yes",240.101366708209,21142.0419014152
"3150","No","Yes",326.438624759239,21109.4318148152
"3151","No","No",562.801930706545,41934.5973333166
"3152","No","No",531.486360558459,28836.1926228196
"3153","No","Yes",594.544379822067,17875.3390701558
"3154","No","No",745.733892852885,60180.5611339407
"3155","No","Yes",1218.63890457214,29899.8068497328
"3156","No","No",266.460481236813,37622.0537634591
"3157","Yes","No",1899.54684151896,46076.2371099545
"3158","No","Yes",717.601673035206,13729.05176324
"3159","No","No",507.334595801347,56075.1028426007
"3160","No","Yes",1422.77458643495,14044.5334985769
"3161","No","No",1253.69961429402,37645.7999594114
"3162","No","No",1301.65076544526,38918.9941683265
"3163","Yes","Yes",2415.31699391853,17429.5033745137
"3164","No","Yes",1367.01387474484,11293.4692594247
"3165","No","No",449.051598436776,31624.9056780779
"3166","No","No",505.69787294247,31971.312911759
"3167","No","No",1150.45878459287,54982.7518342462
"3168","No","No",981.492213375106,30385.0690772959
"3169","No","Yes",612.266052400423,16270.8356417841
"3170","No","Yes",1122.29238597316,20351.3360667593
"3171","No","No",59.7970712775649,36000.0726853645
"3172","No","Yes",1353.01184468869,26207.6862791211
"3173","No","No",1232.23352673705,50731.8642626049
"3174","No","No",924.013153395013,28803.774692854
"3175","No","No",517.824545856159,65501.6207632461
"3176","No","Yes",1795.7168696683,12547.8290854169
"3177","No","No",983.560560031475,26050.1300463799
"3178","No","Yes",990.354260295393,18998.0907794504
"3179","No","No",1529.51700578982,52681.7066584365
"3180","No","No",272.87312934717,44249.6786805901
"3181","No","Yes",636.206615613261,19468.4338686626
"3182","Yes","No",1751.34708758191,38381.5857951998
"3183","No","No",721.920379651685,46269.8372071629
"3184","No","No",511.417511107721,25768.2854912986
"3185","No","No",563.687202812715,41354.3501614923
"3186","No","No",1110.72092233933,47221.4476200208
"3187","No","No",403.034376540736,48874.151164611
"3188","No","No",430.090423291077,29246.3770929262
"3189","No","No",278.473984331957,33187.7689316935
"3190","Yes","No",2228.47228298224,27438.3489877222
"3191","No","No",1201.78261651821,30099.1623728274
"3192","No","No",490.798404423966,44686.1031484568
"3193","No","Yes",792.855972361212,8699.5983434718
"3194","No","No",870.662370949312,37180.0676510813
"3195","No","Yes",1149.20983594409,11337.0592459497
"3196","No","No",493.533675003618,49735.5676052008
"3197","No","Yes",874.34837577951,10845.0188294157
"3198","No","No",1097.94754012716,23224.1278255065
"3199","No","No",976.149122427934,29998.3630717934
"3200","No","No",1373.72537886877,43124.2131477427
"3201","No","Yes",1282.76284131274,22360.1963285237
"3202","No","No",636.728473821258,45649.1708111331
"3203","No","No",1823.23192186717,24744.8540564407
"3204","No","No",605.863800626186,34515.1956633224
"3205","No","No",0,27888.8260319906
"3206","No","No",491.520310598524,37128.7743004734
"3207","No","No",248.362396685326,37868.5597638725
"3208","No","No",1036.50374625816,37151.410641444
"3209","No","No",1130.89393293249,35235.3656766103
"3210","No","Yes",1048.53898279114,10392.0641103791
"3211","No","No",1251.88850207924,50174.3290696742
"3212","Yes","No",1578.84749623035,61220.1331459947
"3213","No","No",0,40341.3388308665
"3214","No","No",1531.9629579638,33595.4327847968
"3215","No","No",935.465250260437,30909.9309111235
"3216","No","Yes",1056.18106719726,16873.5117589506
"3217","No","Yes",1060.7768268,21430.7912243482
"3218","No","No",253.850878596446,41472.12196196
"3219","No","No",323.123255734341,36119.4288231761
"3220","No","No",1051.22456471934,31749.6390256998
"3221","No","Yes",925.947058262368,14258.2674251906
"3222","No","Yes",831.241934194458,13702.9999252162
"3223","No","No",280.836868259055,56567.0083341455
"3224","No","Yes",1092.34254978767,20465.1538178253
"3225","No","No",702.276627024546,49708.9552518842
"3226","No","No",663.095887703947,32756.2599089523
"3227","No","No",224.742276443815,25386.994874052
"3228","No","No",1108.68051381324,60725.5268353645
"3229","No","Yes",0,20071.8216934198
"3230","No","No",352.798060721095,36751.9778574127
"3231","No","Yes",1390.18542567836,21735.246943326
"3232","No","No",599.647361025925,32471.216238284
"3233","No","No",514.545492306024,45763.8998309111
"3234","No","Yes",484.894316676272,15883.6644313447
"3235","No","No",278.720441419798,49231.1908001463
"3236","No","No",661.055733669173,35806.0515057641
"3237","No","No",1110.29633959695,27501.122647951
"3238","No","No",1306.25756759,29376.4413748146
"3239","Yes","Yes",1966.06296646795,17735.7788026984
"3240","No","No",588.564771289966,33224.4447530513
"3241","No","No",0,42869.1588324281
"3242","No","No",1289.60223717482,35164.8897588596
"3243","No","No",1054.2784985631,50795.2066162593
"3244","No","No",0,43513.1146613051
"3245","No","No",897.807241768884,37899.6677887156
"3246","No","Yes",673.785590697002,13344.8569533159
"3247","No","No",636.335023462698,49487.600894741
"3248","No","No",412.071614629222,48347.2969819857
"3249","Yes","No",1898.32349723464,61011.2159467102
"3250","No","No",1606.40367507054,27382.3253889581
"3251","No","No",0,41040.195117173
"3252","No","Yes",0,20157.2512618026
"3253","No","No",486.384423514895,53051.72367742
"3254","No","No",1738.11083484111,38821.6262992155
"3255","No","No",0,62746.8367910018
"3256","No","No",355.936626583037,34730.8855238897
"3257","No","No",0,24818.358625646
"3258","No","Yes",1925.98745137576,20441.674444239
"3259","No","No",819.111800228691,55716.5487287266
"3260","No","Yes",588.75670700404,24015.9606718244
"3261","No","No",975.571998093294,28804.0002507826
"3262","No","No",127.161925515578,40727.5549957016
"3263","No","No",713.436813413471,48176.4282203172
"3264","No","No",737.723042521754,48994.8128507247
"3265","No","Yes",687.064419498525,14059.4576259625
"3266","No","No",444.514583625639,41957.7449421118
"3267","No","No",1061.2842367146,32839.344860116
"3268","No","No",1425.49558151611,37189.0251363891
"3269","No","Yes",638.26193248667,21282.6510921882
"3270","No","Yes",1101.44496742737,20936.9269183621
"3271","No","No",888.244587469896,41489.1912488471
"3272","No","No",951.208724793444,20616.0807025014
"3273","No","Yes",1152.09664394135,15852.4234470929
"3274","No","No",883.160300099528,27125.9069556656
"3275","No","No",1126.98801117244,47378.0191766657
"3276","No","No",742.821230246206,34353.4879054632
"3277","No","Yes",1532.11664528117,19462.1544099914
"3278","No","Yes",266.768630098069,28452.5982743193
"3279","No","Yes",310.430599701327,16479.9923887754
"3280","No","No",1702.30135742104,46589.0626235188
"3281","No","No",649.782495505476,39655.3981843522
"3282","No","No",648.321454451411,45392.2840271357
"3283","No","No",995.274151938156,24782.8767574669
"3284","No","No",330.074882386637,45757.3755755937
"3285","No","No",1358.57343347494,20700.7060831009
"3286","Yes","No",1166.79852718668,30367.6108561366
"3287","No","No",671.669572518594,26142.9116090434
"3288","No","Yes",484.085483620726,12045.3104191307
"3289","No","No",1404.62755268013,47712.1840700714
"3290","No","No",617.001386972161,54489.7438606909
"3291","No","Yes",964.624889710123,21436.5555923373
"3292","No","No",466.579142984644,24719.3955181875
"3293","No","No",618.801159013855,25927.5715647528
"3294","No","No",1310.95236456225,28978.4641182962
"3295","No","No",123.927463494326,28311.8309042242
"3296","No","No",786.264219192566,45304.793357761
"3297","No","No",748.21058944285,46981.9317616208
"3298","Yes","Yes",2124.48923886948,12651.0743188128
"3299","No","Yes",326.391243387214,16988.923303541
"3300","No","Yes",474.882782464273,14986.6432917397
"3301","No","No",59.9658271032766,24889.2545892544
"3302","No","No",1611.33388185999,47894.2766516584
"3303","No","No",545.605211623034,27236.5581442037
"3304","No","No",0,46590.4406360683
"3305","No","No",1249.35413897066,52232.2511884741
"3306","No","Yes",587.874754114786,18448.100273857
"3307","Yes","No",1302.73474174655,43680.065235685
"3308","No","Yes",908.37274540593,27416.6243495981
"3309","No","Yes",1252.13301937038,13860.9350309585
"3310","No","No",277.168651892652,37177.0360936892
"3311","No","No",623.574809063757,57483.5042108883
"3312","No","No",943.199390152837,41765.142501212
"3313","No","Yes",1815.44523079173,18919.4169686979
"3314","No","Yes",927.424650391502,20498.527653012
"3315","Yes","Yes",2008.03298475156,18601.4030905943
"3316","No","No",1028.23523510955,31297.9619581896
"3317","No","No",408.236016977623,51131.5132648769
"3318","No","No",1355.43684723649,42630.6686555469
"3319","No","Yes",376.770798828235,16042.2389216328
"3320","No","No",856.47915734974,69124.2684951903
"3321","No","No",1110.74370452239,43991.9201313875
"3322","No","No",437.814278970858,27855.7549698386
"3323","No","No",1013.70540675709,60574.8058732982
"3324","Yes","No",1922.82904619158,56202.5761963902
"3325","No","No",1066.79279362836,49629.9030054647
"3326","No","No",465.553280059934,33998.8475783305
"3327","No","No",899.483312099838,36361.9780794584
"3328","No","No",592.800666683193,34355.9095454688
"3329","No","No",164.001019441243,42695.3979153921
"3330","No","No",0,62975.1287390269
"3331","No","Yes",1335.1313235085,17044.3653970318
"3332","No","Yes",1549.61638029724,11927.5319540726
"3333","No","No",1441.07069568953,33852.6775970728
"3334","No","No",1157.65206769237,39080.2635290685
"3335","No","No",899.462006521363,36335.219517661
"3336","No","No",331.16703204898,44752.2292920748
"3337","No","No",439.201791769032,42132.013626294
"3338","No","No",489.717690887527,27043.2305590416
"3339","No","No",652.993679181062,37143.5812264389
"3340","No","No",206.820878289734,49221.1485186867
"3341","No","Yes",932.487404912021,14950.0947349898
"3342","No","No",1098.23245141508,42331.8910833343
"3343","No","Yes",1959.59551983283,19677.9357288247
"3344","No","Yes",1706.95376649647,20804.6561208467
"3345","No","No",1255.88371142839,55971.1854749681
"3346","No","No",537.954408771502,54481.3080034763
"3347","No","No",349.161806174377,38844.3498392063
"3348","No","No",167.606098803978,20150.902761931
"3349","No","No",1612.46531468496,42281.2311402749
"3350","No","No",551.254052411868,48007.9918075426
"3351","No","Yes",1521.49668224023,16830.5651365535
"3352","No","No",826.960124205594,61365.4443242382
"3353","No","No",770.081934570081,38010.2611136634
"3354","No","Yes",1109.42549735877,10973.0882494351
"3355","No","No",559.324027305301,53542.4127190244
"3356","No","Yes",390.278197919582,9050.00278108054
"3357","No","No",1567.02386232976,42258.7733461712
"3358","No","No",170.260236584301,62708.1620175011
"3359","No","No",528.850203578729,46222.2889572449
"3360","No","No",0,49471.749397732
"3361","No","Yes",1000.41580660284,14071.0085362555
"3362","No","No",538.748646421516,34834.4457069503
"3363","No","No",435.402804256198,35168.7270884563
"3364","No","No",110.660530711258,64525.9378993549
"3365","No","No",153.563343023163,49405.2609827479
"3366","No","Yes",856.624728089322,19056.8678387124
"3367","No","Yes",934.252048089287,20747.2122273177
"3368","No","No",783.12296011937,36514.6901415882
"3369","No","Yes",1271.11249459206,17451.041186393
"3370","No","No",910.759822660294,33660.7189686808
"3371","No","No",748.652080396294,40612.2157591974
"3372","No","No",668.942259766387,42028.923839609
"3373","No","Yes",1061.93204093717,18589.6214456992
"3374","No","No",766.008842060671,45155.0972106875
"3375","No","Yes",1396.23460288284,15837.2533759543
"3376","No","No",265.435891352397,34040.5484925118
"3377","Yes","No",2080.93724738167,34494.1712062838
"3378","No","No",324.691264321875,47103.4578190388
"3379","No","Yes",1135.01076743719,24005.0051043532
"3380","Yes","No",1731.6807661524,56228.9216823573
"3381","No","Yes",1133.23296556513,16749.4614462397
"3382","No","No",782.648690118751,37497.0870021449
"3383","No","Yes",745.565474645239,12896.6328980992
"3384","No","No",1444.36123709977,51154.5081209419
"3385","No","Yes",1340.52919980707,19318.8404293024
"3386","Yes","No",1903.66721921081,41173.5039918827
"3387","No","No",317.903573559349,42209.4873423349
"3388","No","No",901.317747909679,43242.61208683
"3389","No","No",527.983482263253,39950.9585214959
"3390","No","Yes",915.653755753437,16888.8124249196
"3391","No","No",1143.55035363774,42694.8039243586
"3392","Yes","No",1488.55877463986,22256.8636914601
"3393","No","No",1024.23028360918,27104.8579400196
"3394","No","No",619.903822341423,29217.3523431864
"3395","No","No",1007.24865564428,34893.1376858329
"3396","No","No",0,42577.8145154607
"3397","No","No",1342.04759431789,51442.5655820133
"3398","No","Yes",1478.1697847446,22564.5848836263
"3399","No","No",149.357779532702,52310.4115265425
"3400","No","No",279.724275200053,52008.4158082558
"3401","No","No",869.8087826652,49312.5064893276
"3402","No","No",1493.28785220094,56364.2548943371
"3403","No","No",1127.8226863357,43344.8942878488
"3404","No","No",924.692705395402,38676.9704373565
"3405","No","No",311.507988004406,43379.6346768697
"3406","No","No",696.51006679722,28610.1946592679
"3407","No","Yes",295.633452061397,25452.4758534912
"3408","No","No",464.762682024611,17862.2268684626
"3409","No","Yes",750.41267450035,17291.5268864259
"3410","No","No",890.407653226291,52943.3834046946
"3411","No","No",0,37455.1988754218
"3412","No","Yes",340.697865960545,12351.8259611963
"3413","No","No",724.243805795046,43399.7182823954
"3414","No","No",979.855046191761,40374.051564876
"3415","No","No",33.4570264675971,30510.0384248079
"3416","No","No",1203.73617853155,40969.0823467377
"3417","No","No",735.730856697404,23225.1771045656
"3418","No","Yes",884.918846585518,27208.7712745881
"3419","No","No",825.8499756766,48216.7118880664
"3420","No","Yes",1734.58583985537,14165.2788642707
"3421","No","Yes",670.493492932823,16513.0442606905
"3422","No","No",874.847285610237,36709.1083061063
"3423","No","No",0,40021.3553584359
"3424","No","No",827.350473625965,43833.4237008983
"3425","No","No",1508.84137777861,31739.2719200746
"3426","No","Yes",0,19095.8303695336
"3427","No","Yes",914.515478973011,7525.5842563445
"3428","No","No",302.961244468736,51030.4395324823
"3429","No","Yes",275.485893220682,13448.3341939049
"3430","No","No",589.558094262681,42335.4470346994
"3431","No","No",1120.26745836208,45153.0400679357
"3432","No","Yes",1416.9661563736,11104.5663246846
"3433","No","No",1270.17667773508,44185.3577147632
"3434","No","No",848.234486270745,35186.5774991595
"3435","No","No",560.96807384839,43254.715410826
"3436","No","No",619.615504777482,45463.3009029534
"3437","No","No",1197.57977470106,18449.020776497
"3438","Yes","No",1585.72940149394,52274.8601245548
"3439","No","No",478.363326131214,28262.8681327123
"3440","No","No",1144.91247877907,41370.5165808812
"3441","No","No",778.751922851176,58860.3807631449
"3442","No","Yes",1675.42214090055,13703.881091161
"3443","No","No",680.769773484551,56759.2345948152
"3444","No","Yes",1113.57797796538,17593.5298518795
"3445","No","No",339.351397521402,38097.016679235
"3446","No","No",908.871252095821,55612.1723587685
"3447","No","No",480.787554964629,11193.4156989659
"3448","No","Yes",874.583020719088,17378.2872736105
"3449","No","No",0,42515.1628277468
"3450","No","No",1353.5222027697,52048.5041194701
"3451","No","No",1073.95003337493,28845.7420333549
"3452","No","Yes",1402.28776899962,13276.2490843018
"3453","No","No",759.148003414857,24473.1065999823
"3454","No","No",4.1094981453831,38326.1983601291
"3455","No","No",194.554390070911,38794.1459081932
"3456","No","Yes",597.599614481473,16526.6773312497
"3457","No","Yes",1067.78111469819,18641.7243063209
"3458","No","No",379.291013494282,43096.4898085055
"3459","No","Yes",583.599749188687,15179.6861261427
"3460","No","No",465.522214212983,30792.8835943144
"3461","No","No",1144.12274664919,33624.941426599
"3462","No","Yes",1025.08757276608,29019.3262183216
"3463","No","No",203.958043684028,63638.1873187928
"3464","No","Yes",439.581726628727,17823.501942841
"3465","No","No",0,40574.4207852576
"3466","No","No",720.857568601496,42753.7216938114
"3467","No","No",1385.79514000934,44983.296558965
"3468","No","No",1127.50757701848,64618.742136042
"3469","No","No",808.000167811353,36076.0798002245
"3470","No","Yes",280.24815366878,25307.3770794565
"3471","No","No",378.723020307566,15738.3318359457
"3472","No","Yes",885.516846437988,21211.3259040908
"3473","No","No",0,37482.4857282659
"3474","No","Yes",1006.9422455803,19065.6927865183
"3475","No","No",490.645063024137,37998.7143516608
"3476","No","Yes",141.026549625642,11314.3917806231
"3477","No","No",761.885541249903,46421.7640748476
"3478","No","No",1787.60381985453,33312.8066836098
"3479","No","No",964.541305769544,33908.0513820675
"3480","No","No",931.576984186363,52703.7912231804
"3481","No","No",10.0565726041083,37673.3165455752
"3482","No","Yes",1971.66323615651,22040.2627413006
"3483","No","Yes",1171.51991582291,17642.2677538646
"3484","No","No",784.38369199232,44927.4957037727
"3485","No","No",503.498537295314,20476.0729395675
"3486","No","Yes",295.480714529083,17324.0416254492
"3487","Yes","Yes",1475.05743781716,20210.2726246288
"3488","No","No",950.47136951895,38990.1738558434
"3489","No","Yes",1083.70236158974,17522.7306868447
"3490","No","Yes",1334.66626447237,18064.7783445647
"3491","Yes","No",1332.38617766582,53517.350489625
"3492","No","No",314.548049421055,43440.6040612716
"3493","No","Yes",683.189896759167,12367.6971268167
"3494","No","Yes",1087.41275534669,17060.5942899899
"3495","No","No",443.634783424809,46611.0206328516
"3496","No","No",426.55288027594,34191.2952216818
"3497","No","No",532.984740760252,55847.2859530335
"3498","No","Yes",1378.21733852018,20119.8291955569
"3499","No","No",470.035913442893,29309.127258716
"3500","No","Yes",470.309392705283,10062.0836038357
"3501","No","No",98.3966175462976,25308.5899305255
"3502","No","No",400.059110376214,30160.2889819035
"3503","No","No",1265.72586791133,20816.9533260862
"3504","No","Yes",700.335171683785,15905.2126952829
"3505","No","No",1266.77263593222,47991.0501265717
"3506","No","No",116.353735409607,30765.3518002466
"3507","No","Yes",1023.63853479624,15738.1600121976
"3508","No","No",823.664169396695,24533.2031356683
"3509","No","Yes",1328.67153101328,13389.4337862024
"3510","No","No",1032.18517772079,46541.6494690502
"3511","No","Yes",1093.63905841129,14790.7691896556
"3512","No","No",587.764496004288,39618.8173562607
"3513","No","No",1379.18431417485,48254.600009129
"3514","No","Yes",444.025308381459,23691.9832171524
"3515","No","Yes",597.602487061121,21296.6123042634
"3516","No","No",738.194410175601,35134.2653548643
"3517","No","No",563.491173125737,47142.4498544045
"3518","No","Yes",1518.71564552176,23877.819849633
"3519","No","No",1610.71968064752,30098.8239905995
"3520","No","Yes",542.389693840344,14294.5950802359
"3521","No","No",586.169070351128,31466.8513376787
"3522","No","Yes",648.8853641449,24457.3640797038
"3523","No","Yes",913.046455922211,17139.5796008871
"3524","No","No",1650.89601880151,43478.128206742
"3525","No","No",758.506112361259,37485.9081337746
"3526","No","No",1955.55735559179,45507.9115047977
"3527","No","No",446.964465927462,27626.6025690877
"3528","No","No",874.941620919224,39807.6795515543
"3529","No","Yes",1124.56737590151,28936.9062201587
"3530","No","No",0,29427.9148424807
"3531","No","No",184.569141530728,44021.3021640857
"3532","No","No",1133.79978343124,39860.1632176141
"3533","No","Yes",1148.92313944526,19717.4262918616
"3534","No","No",1040.2724722088,35639.5543014442
"3535","No","No",753.96082861166,21507.9949174421
"3536","No","No",818.496293230639,34903.6775480158
"3537","No","Yes",1228.65581630413,17460.6421607778
"3538","No","Yes",16.4148836028645,24291.3494232028
"3539","No","No",763.900776015518,64967.6404491418
"3540","No","Yes",1667.30882700937,15614.8209145114
"3541","No","No",637.491879262073,48942.6121416591
"3542","No","No",911.132057880504,31265.2177134849
"3543","No","No",125.053844055462,50059.5471192676
"3544","No","Yes",548.278823221224,15308.599994734
"3545","No","No",795.034869917366,34169.669733554
"3546","No","No",649.159045714336,37015.2749917907
"3547","No","No",0,51758.4102919257
"3548","No","No",292.334382785833,41036.1292563615
"3549","No","No",1285.99180262279,31068.1225273061
"3550","No","No",845.854302307457,44480.6553247618
"3551","No","Yes",1660.11179791869,26838.933054139
"3552","No","No",610.56863853285,36145.0170221682
"3553","No","Yes",1137.42108492749,16177.2729087737
"3554","No","Yes",1115.76856218029,11496.5418079491
"3555","No","No",302.164289781916,28805.0626731863
"3556","No","No",1074.14335684724,35054.5844246288
"3557","No","No",510.96017599863,32151.8489790563
"3558","No","No",974.410565287029,43861.8019235383
"3559","No","No",46.1832884430346,42533.7153588048
"3560","No","No",1731.60452777077,58075.9436880706
"3561","No","No",1429.39337850864,31387.700328731
"3562","No","No",503.513808814529,62115.1050819044
"3563","Yes","Yes",2179.22142805877,15302.7765041428
"3564","No","No",18.788280579833,24699.0968352417
"3565","No","No",1047.13968862834,46007.9806068582
"3566","No","Yes",1049.6035740461,11137.861038864
"3567","No","No",1372.93582176137,35467.7576566948
"3568","No","No",958.326632217788,28145.8424785305
"3569","No","No",826.638592854712,47358.5490213203
"3570","No","No",1005.1630821859,42127.9944739671
"3571","No","No",1132.86248470996,25402.4710043271
"3572","No","No",618.610523972339,45154.7478427691
"3573","No","No",1073.82542088911,47935.0053222894
"3574","No","No",1108.80099679987,39676.554561882
"3575","No","No",583.712000758947,42590.1241396148
"3576","No","No",835.533443337154,23160.5786180678
"3577","No","No",698.673829200129,29580.0691003383
"3578","No","No",929.576189948997,40442.0631153395
"3579","No","No",916.597958538644,41432.4344975866
"3580","No","No",1324.19876915942,10892.3291550933
"3581","No","Yes",1219.37667694921,11361.8238642259
"3582","No","No",265.295848438513,45828.0188499132
"3583","No","Yes",1255.42583518715,19885.7611482268
"3584","No","No",1524.43670328131,18878.5544825791
"3585","No","No",326.397293438249,30156.3554233718
"3586","No","No",1821.00315181145,54051.9823381261
"3587","No","Yes",1357.42346575736,25733.3093953223
"3588","No","No",1187.47380064447,53318.9933606233
"3589","No","Yes",925.904860674285,20952.5861354429
"3590","No","No",1451.78500633505,27853.9677827104
"3591","No","No",32.0680789944308,38788.6604713797
"3592","No","No",685.197586468114,36015.4188571331
"3593","No","No",1071.89915017718,54791.8055670014
"3594","No","No",874.490251234789,50078.4594220413
"3595","No","No",926.309641202025,38093.9775189344
"3596","No","No",1235.64080883847,53171.2968821341
"3597","No","Yes",1155.50466685911,24840.7617468418
"3598","Yes","No",1360.38578198715,28992.0018194355
"3599","No","No",234.995776461112,40394.4210479398
"3600","No","No",854.691243314712,26672.1817076696
"3601","No","Yes",1405.65892678336,19701.7554335566
"3602","No","Yes",1161.02617651119,13913.4419563153
"3603","No","No",329.347689299132,20708.9877221068
"3604","No","No",1541.3208022226,32508.3839000633
"3605","No","No",1456.65245644587,59361.9183453124
"3606","No","No",0,24565.5716463348
"3607","No","No",83.0803727287823,45442.8298623253
"3608","No","No",95.8981854984619,31437.4232324923
"3609","No","No",674.804716962235,47653.6159785354
"3610","No","No",917.78398027012,44466.8850284853
"3611","No","Yes",759.803326380694,14217.6988810763
"3612","No","No",379.063067543713,14792.5118362139
"3613","Yes","Yes",2004.3940010495,18860.8466453611
"3614","No","No",1469.60383564639,43436.9520748192
"3615","No","No",200.274808373529,36926.3975468474
"3616","No","No",843.429659446319,39512.0690455385
"3617","No","No",676.763350597062,34400.8608868938
"3618","No","No",46.0920895774312,42025.762307949
"3619","No","No",542.842025366022,48897.7525440451
"3620","No","No",89.9098954243769,30004.4707967452
"3621","No","No",908.184442812385,37319.6225749996
"3622","No","No",0,46306.936285731
"3623","No","Yes",1326.84839242118,20790.2539652011
"3624","No","Yes",993.8172665905,12531.0255005456
"3625","No","Yes",1084.38132390172,20881.4565211616
"3626","No","No",1197.71153968387,30377.2500292142
"3627","No","No",1378.29706776945,43411.6619224584
"3628","No","No",182.741994878604,32359.2092126212
"3629","No","No",529.650306882188,35102.2751291454
"3630","No","Yes",1632.35436117305,15763.584571284
"3631","No","Yes",1119.20252561696,20619.5097530825
"3632","No","No",787.136817634983,45772.6140322452
"3633","No","Yes",969.784159054437,21412.6009801252
"3634","No","No",1256.98265019019,38083.130364008
"3635","No","No",1015.04542705688,31026.4130144171
"3636","No","Yes",1197.60895727108,17452.0000360637
"3637","No","Yes",847.458025642289,16926.3500223677
"3638","No","No",598.438635556875,52896.0596979918
"3639","No","Yes",983.092276546234,17001.6491859906
"3640","No","No",1296.26742330524,45226.121016122
"3641","No","No",412.397141967932,30206.9588196402
"3642","Yes","No",1504.75537803833,26183.2453525162
"3643","No","Yes",367.363419274672,16295.5624749992
"3644","No","No",0,27889.3047001377
"3645","No","Yes",894.725490593895,19335.6340358215
"3646","No","No",75.8341255842853,42075.0478357006
"3647","No","No",642.53521756118,34893.785940483
"3648","No","Yes",1777.28455467843,14567.8139813121
"3649","No","No",370.033287888415,44507.211314214
"3650","No","No",653.373456959241,52276.5854009763
"3651","No","No",793.912163386316,48907.6838409864
"3652","No","No",917.075277826575,19003.7749764239
"3653","No","No",509.778439256327,38614.4131337821
"3654","No","No",947.813789455262,42094.4701536065
"3655","No","Yes",860.835399899637,19514.763516061
"3656","No","No",302.227048591714,35232.4604410526
"3657","No","No",891.887379296637,35062.5381865466
"3658","No","No",1377.55875173049,38364.6791518545
"3659","No","Yes",1222.95846367801,15174.9724609139
"3660","No","No",378.970424423049,39492.0119537958
"3661","No","No",0,41523.752477371
"3662","No","Yes",1103.91300124722,28552.5373346619
"3663","No","Yes",704.472521881142,21140.275640308
"3664","No","Yes",1402.31056982512,21566.5568755441
"3665","No","Yes",68.3766078184741,10996.0878573815
"3666","No","No",409.596072288602,30627.5029402373
"3667","No","Yes",655.700944684446,19915.2328521123
"3668","No","Yes",654.814668194886,14504.3964074862
"3669","No","No",1255.92012470085,43929.1432181938
"3670","No","No",1217.96088773575,33443.3064093113
"3671","No","No",691.893198507127,35562.6093686611
"3672","No","Yes",548.13628878805,19501.3410678754
"3673","No","No",437.546069429019,31537.1269325829
"3674","No","No",1067.84243863848,70700.6478404576
"3675","No","No",521.439778127422,31981.8581197516
"3676","No","Yes",1348.9953202389,25805.8099647288
"3677","No","No",674.66370031704,41798.3916232607
"3678","No","No",516.359041795444,25891.7596483203
"3679","No","Yes",1470.33856265862,19647.1423253636
"3680","No","No",859.007910133498,43101.8738068424
"3681","No","No",1114.088014707,59200.1369964562
"3682","No","No",773.326806663148,51787.7894414781
"3683","No","Yes",803.968975047329,16553.7728259018
"3684","No","No",376.292564774471,43940.0480641767
"3685","No","No",829.729677070215,36164.1541822576
"3686","No","No",299.890633267322,34920.1815445572
"3687","No","Yes",148.657094714831,15645.7771213044
"3688","No","No",705.113585993753,47752.1592298761
"3689","No","No",467.864135290801,43308.6965719809
"3690","No","No",382.094286204579,38348.2861872948
"3691","No","No",450.309011613408,46172.7250884025
"3692","No","No",379.914592855663,39064.0485206065
"3693","No","Yes",151.771401058765,18659.0709439774
"3694","No","No",1535.22447265136,35282.2280185965
"3695","No","No",1596.87342986645,34655.8800325605
"3696","No","No",778.060154635799,52545.578049973
"3697","No","No",107.706671906495,42199.9676256284
"3698","No","Yes",961.797820619253,13389.0694050288
"3699","No","Yes",235.249789340508,13360.9251942154
"3700","No","No",935.33815565705,57139.3048612283
"3701","No","No",0,34563.0458822401
"3702","No","No",10.3068907225734,37590.3095819752
"3703","No","Yes",2370.46361152636,24251.9587219194
"3704","No","No",1447.02228057939,37322.710221796
"3705","No","No",829.023319092933,25161.8985145042
"3706","No","No",1474.06154429605,47040.8085875203
"3707","No","No",913.638326726234,50351.1551950528
"3708","No","No",1028.88633806886,34257.632772859
"3709","No","No",563.89172073513,54147.7083859983
"3710","No","Yes",747.821682807119,19330.3084037704
"3711","No","Yes",811.539940902829,13828.9686562225
"3712","No","Yes",797.373647480833,23438.1458035913
"3713","No","No",701.918156574897,53151.7607278083
"3714","No","No",1195.35982220074,42107.3640000123
"3715","Yes","No",1672.50606273916,66466.460890915
"3716","No","No",889.901199990565,20805.6140385332
"3717","No","Yes",579.721304609121,18555.7525862376
"3718","No","No",1143.20045945329,28900.7277879119
"3719","Yes","Yes",1237.62186571379,14862.7054597298
"3720","No","No",740.957080483265,48683.2203818321
"3721","No","Yes",1470.47458641012,18621.170430494
"3722","No","No",507.678616315415,45839.1341797568
"3723","No","Yes",452.302355609914,19922.6401247417
"3724","No","No",431.904928118842,30115.152829135
"3725","No","No",388.935465259897,36932.8101191799
"3726","No","Yes",647.260114248229,18905.8538390556
"3727","No","No",1069.13030064378,42361.0467518263
"3728","No","No",517.988518060339,45495.0054253039
"3729","No","No",996.251183302201,23720.9874695717
"3730","No","No",995.122330640652,47286.8309114811
"3731","No","No",0,55069.8122112149
"3732","No","No",480.571142147903,36192.0903233484
"3733","No","No",413.858824635309,42453.6818753791
"3734","No","No",730.072003864106,47074.2289180681
"3735","No","No",11.307783333379,43520.4888003279
"3736","No","Yes",1507.89245127672,14632.2576606953
"3737","No","Yes",1129.11390918636,13580.2569642632
"3738","No","No",1159.93657960376,46789.8806265537
"3739","No","No",817.982440262695,40658.5899130617
"3740","No","Yes",901.175243522745,16745.066848549
"3741","No","Yes",1778.12151243631,17060.9274523287
"3742","No","No",1036.75668615923,34912.7894575472
"3743","No","No",199.136148704383,49292.5456549724
"3744","No","Yes",1295.69055098517,23191.2365261002
"3745","No","Yes",468.257661766054,6365.56457589519
"3746","No","No",688.105491060263,27080.7425441104
"3747","No","No",202.321208104165,37188.5693842123
"3748","No","No",621.003443540267,37249.4364352527
"3749","No","No",963.332481084664,30100.7622696918
"3750","No","No",1106.69701234151,51879.7184800111
"3751","No","No",1391.40768882587,30427.2025804345
"3752","No","No",198.594790541604,58598.6676423446
"3753","No","No",90.6826966305595,37914.4013067847
"3754","No","No",460.873865041046,49243.4447853166
"3755","No","No",1182.44483185605,34784.7881285648
"3756","No","No",748.434422248033,43215.8297292764
"3757","No","Yes",1727.55459145414,11608.128446401
"3758","No","No",831.47822217826,39079.7884540231
"3759","No","No",655.629150055933,40257.9329916046
"3760","No","Yes",787.1367086129,22434.9764528939
"3761","No","Yes",760.895350124073,20070.1480040007
"3762","No","No",0,44387.5749015598
"3763","No","No",523.388442648939,53115.599362511
"3764","No","No",481.591003405382,35817.7612925548
"3765","No","No",0,26275.2565388957
"3766","No","Yes",1414.6577991223,17954.8525455556
"3767","No","Yes",1103.21677644164,22410.9082953532
"3768","No","No",948.696452799542,38247.913228973
"3769","No","Yes",725.572419455187,9019.54551235871
"3770","No","No",1727.34900141241,40791.1429214149
"3771","No","No",891.607304157422,43925.9644223325
"3772","No","No",667.290493003104,66538.3442219194
"3773","No","Yes",1060.26371026219,15661.409003229
"3774","No","Yes",830.860575104084,18259.6642152219
"3775","No","No",319.174576691641,44410.5315566714
"3776","No","No",1147.27448398082,44871.5494446034
"3777","No","No",664.511520418358,47878.8338334518
"3778","No","No",1066.89951236104,35567.7620740215
"3779","No","Yes",374.976767394742,19806.5190903651
"3780","No","No",549.191342739887,43688.6804186368
"3781","No","Yes",943.581370801553,17730.919262011
"3782","No","No",111.285039767861,42292.2977730561
"3783","No","No",504.739525342174,59560.1796602225
"3784","No","No",798.477351064392,32921.633935552
"3785","Yes","No",2008.45859591035,35145.0476213865
"3786","No","No",965.926572220327,29219.0707750405
"3787","No","No",1768.13997797286,30762.1052139087
"3788","No","No",435.055376918744,32198.0248784528
"3789","No","Yes",685.631944540025,15892.0925406445
"3790","No","Yes",745.230621260668,12877.5095009707
"3791","No","No",161.759487374323,43676.0574090646
"3792","No","No",0,32558.4750927352
"3793","No","No",1389.28646709021,38373.0145244978
"3794","No","No",802.092675362193,45052.5804474234
"3795","No","Yes",335.82263545205,16123.1133521949
"3796","No","No",1031.78625745329,39260.0062189948
"3797","No","No",893.241644741964,43816.2254733528
"3798","No","No",1007.27799464763,45124.0404355969
"3799","No","No",786.802308362249,38023.6148140319
"3800","No","Yes",1814.55889529126,24177.2725239766
"3801","No","No",116.218875620068,50109.0234146974
"3802","No","No",839.924094203699,47347.6767077934
"3803","No","Yes",824.691701826297,15158.6247871037
"3804","No","No",239.384478789428,25485.17338129
"3805","No","No",933.751152075546,52319.1535099304
"3806","No","No",459.387665326059,37630.6133189642
"3807","No","Yes",870.023992705307,15955.8851517715
"3808","No","No",232.207550415873,11151.0476161126
"3809","No","No",150.78384068053,32106.4836371526
"3810","No","No",1215.17047346831,55230.648185661
"3811","No","No",1435.36009542623,33728.8923192302
"3812","No","No",338.06832186229,47340.0266909586
"3813","No","Yes",1056.94739892769,20304.0981203317
"3814","No","No",194.173416620166,28743.1643692778
"3815","No","No",298.509689604326,50229.7797437753
"3816","No","Yes",938.339328985524,21911.7272119656
"3817","No","No",1297.39243050355,47095.2215664773
"3818","No","No",690.228382764494,56958.599355471
"3819","No","No",1013.90643057987,34166.8264938287
"3820","No","No",1213.7941049861,48796.8492780349
"3821","No","No",0,39269.8359156486
"3822","No","Yes",1399.95025661838,11010.2124515938
"3823","No","No",590.999730015612,50246.7155358911
"3824","No","No",1438.83369098514,35532.3993174811
"3825","No","No",438.234685195282,48859.2610204599
"3826","No","No",783.001515615034,37428.2200371324
"3827","No","No",1864.94640342503,48678.4151933007
"3828","No","No",208.050443946045,46840.5192698469
"3829","No","No",621.165135100387,32168.5528989598
"3830","No","No",994.199964316657,52689.5394318756
"3831","No","No",674.640158540427,42463.5249695872
"3832","No","No",537.587186451237,46953.3768092795
"3833","No","No",690.340942195294,29924.229458693
"3834","No","Yes",1369.40827588944,18144.3502642041
"3835","No","No",654.277589031942,37706.5948741313
"3836","No","No",798.031313993959,29934.1432291843
"3837","No","No",763.224892920809,48170.3599207953
"3838","No","No",1663.61634756329,52975.2636119246
"3839","No","No",988.393947106499,49804.2837531124
"3840","No","No",1419.63189161462,40295.2687995018
"3841","No","Yes",1196.041821814,19665.8291971639
"3842","No","No",618.051384938394,16944.3444933857
"3843","No","No",1028.18124945831,32249.8456721917
"3844","No","No",523.501099352472,39168.1742529548
"3845","No","No",517.45962093638,39543.5871031994
"3846","No","Yes",764.562997415372,5082.99263135122
"3847","No","No",206.948846369348,53058.561408706
"3848","No","No",847.981506567761,43023.5850115808
"3849","No","No",0,40540.6944115759
"3850","No","No",631.747132163768,37936.0434327245
"3851","No","No",1014.59910437962,51438.7101991499
"3852","No","No",157.280028213278,41415.3960305557
"3853","No","No",525.485199814085,43737.904263688
"3854","No","No",962.948614604359,28660.9305693435
"3855","No","No",594.106133663565,24069.2130253837
"3856","Yes","Yes",2321.88222078406,21331.3147812222
"3857","No","Yes",1128.78015595712,17231.808467394
"3858","No","Yes",2118.80057421913,18791.8520844699
"3859","No","No",290.564873898186,13239.8728352638
"3860","No","No",1244.39224372204,33880.2992197885
"3861","No","No",1177.91726793043,49061.7229070424
"3862","No","No",0,44225.6158550561
"3863","No","Yes",492.657068547904,10154.1562624726
"3864","No","No",0,62689.0330317791
"3865","No","No",530.251298561418,39311.3257110561
"3866","No","No",0,45104.0940277016
"3867","No","No",1028.17461084237,51228.3021076555
"3868","No","Yes",488.238551481836,33003.4229511569
"3869","No","No",4.45893829925035,46970.4964206441
"3870","No","No",1636.31615275065,35814.7919837632
"3871","No","No",107.921849415466,33764.2020744309
"3872","No","Yes",129.184357002533,20570.8541996302
"3873","No","No",956.985400588607,38285.7478995927
"3874","No","Yes",823.064063126566,17254.3984155632
"3875","No","No",458.998467641761,33163.5453487024
"3876","No","Yes",867.90467888042,18949.0273423076
"3877","No","Yes",117.809441288368,18752.8302016095
"3878","No","No",474.666105515736,42058.777310287
"3879","No","Yes",1180.52914588773,19139.3002329259
"3880","No","No",315.850608427301,47328.9827476038
"3881","No","Yes",552.204087340281,22554.5785225528
"3882","Yes","No",1342.26287404933,35691.6373626218
"3883","No","No",407.28975596518,45376.2359070935
"3884","No","No",1624.95646496252,47574.5597163584
"3885","No","Yes",548.454729888128,21672.5398863769
"3886","No","No",908.8882984423,42205.2064968203
"3887","No","Yes",969.898684944156,17940.1191271848
"3888","No","Yes",966.35083804263,11776.5291469195
"3889","No","No",628.566971300707,37617.1191790581
"3890","No","Yes",882.97742492169,8833.66859574522
"3891","No","No",824.995490317263,34277.5258971332
"3892","No","No",762.912837088917,41617.8139540496
"3893","No","No",1246.22952508902,49479.5768167893
"3894","No","No",60.9607616734327,41548.1172690808
"3895","No","No",1377.97881570218,34683.6124468226
"3896","No","No",1215.25254590985,43793.1158439609
"3897","No","No",494.901917766186,13980.3611783447
"3898","No","No",839.873755369443,39158.2375495066
"3899","No","No",982.381607125144,29157.2922710833
"3900","No","Yes",927.516587606127,4143.1188436957
"3901","No","No",433.857819332761,30581.5828674348
"3902","No","No",1213.03372043657,17726.67010762
"3903","No","No",316.004350795276,26518.4626373009
"3904","No","Yes",973.903110195755,21590.0349041124
"3905","No","No",0,25603.5332583784
"3906","No","No",713.75110679484,37230.0737161792
"3907","No","No",997.465434734612,22061.7998101081
"3908","No","No",1326.06486447881,34945.8009455043
"3909","No","Yes",1135.36118371592,24314.0368794181
"3910","No","No",0,50805.0365860861
"3911","No","No",43.8964732139597,48543.5990899145
"3912","No","No",666.370176975337,50105.267599429
"3913","No","Yes",0,18426.2607220242
"3914","Yes","Yes",2334.12355928646,19335.8892870067
"3915","No","No",303.791175031742,28525.4017296092
"3916","No","No",634.495047320779,35131.3015268333
"3917","No","Yes",853.818357435337,18750.7506332069
"3918","No","No",747.455393381215,43898.7506313104
"3919","No","Yes",1120.03027338121,19170.4583479794
"3920","No","No",1290.19154200367,36898.9287227477
"3921","No","No",1348.78501842297,33488.4129166473
"3922","Yes","No",1632.89411755965,44326.5848389279
"3923","No","No",693.630693268542,32594.6637582436
"3924","No","No",467.583088541574,43435.8366137129
"3925","No","No",618.125338151726,27868.7938862439
"3926","No","Yes",1217.6673550098,20977.2351863501
"3927","No","No",872.07135762222,36004.6448067571
"3928","No","No",0,28659.4883821409
"3929","No","No",894.73448421392,37903.5264631725
"3930","No","Yes",361.610466866982,21685.6922327124
"3931","No","Yes",937.197434022924,18411.8244296162
"3932","No","No",0,55760.5019383757
"3933","No","No",339.531864697194,31092.7576531692
"3934","No","No",423.714550584752,32640.2940725967
"3935","No","Yes",1529.06170478383,17358.0429765912
"3936","No","No",894.114487522128,47681.6228289047
"3937","No","No",760.720823811111,40854.469068475
"3938","No","No",883.016251146865,37961.1559197859
"3939","No","Yes",1322.58549706765,15304.2603945019
"3940","No","No",2036.99318923915,31384.1300026505
"3941","No","No",751.39991725403,54993.4078424962
"3942","No","No",578.836277403294,25522.5199803339
"3943","No","No",829.281651229759,58070.1125623816
"3944","No","No",1160.06126524024,51895.3040377882
"3945","No","No",1197.77188104285,44047.691182016
"3946","No","No",417.592210757132,48035.6096423654
"3947","No","No",777.824448114388,43910.9885619203
"3948","No","No",625.746198837327,29644.2083028025
"3949","No","No",1382.77577383148,36232.3193301436
"3950","No","No",986.305802570999,25342.3047937036
"3951","No","No",937.738325490594,24932.7211302843
"3952","No","No",0,44525.8493492165
"3953","No","No",131.576672964284,51106.9948502149
"3954","No","Yes",1096.18460643552,15535.5486634707
"3955","No","No",499.173919333681,44773.5753282789
"3956","No","No",1236.21871976753,37884.5660973391
"3957","No","No",637.01454099823,27828.1375979546
"3958","Yes","No",2147.3125781201,58271.3908258596
"3959","No","No",516.216569301407,42446.0411816577
"3960","No","Yes",113.276475965853,21068.1640255051
"3961","No","No",352.327702835245,40716.1748054209
"3962","No","No",918.014298027406,26798.0714113008
"3963","No","Yes",1378.91311651019,14329.7712342385
"3964","No","Yes",482.545258293181,21369.2750915314
"3965","No","No",813.200651231514,49477.5115079057
"3966","No","Yes",1330.48518064805,13765.4413250231
"3967","No","No",1373.53303261699,56878.3350569795
"3968","No","Yes",1411.60736458353,16677.3069228501
"3969","No","No",313.805085904673,37720.7620953016
"3970","Yes","Yes",1803.94456724079,29100.8192252135
"3971","No","No",964.524655704574,40814.6529077642
"3972","No","No",1139.37070195429,42759.3825328246
"3973","No","Yes",1009.43108895395,21904.6927706133
"3974","No","No",615.465388230695,25865.1806189451
"3975","No","No",673.698051443921,41891.4259686795
"3976","No","Yes",1288.55195777107,24918.4150782296
"3977","No","Yes",2388.17400939421,7832.13564354245
"3978","No","No",0,34545.09712324
"3979","No","Yes",1119.38660954773,20011.4688698159
"3980","No","No",1352.70344251148,35743.0039605073
"3981","No","Yes",886.348540111273,17534.4670783535
"3982","No","Yes",1468.79521972382,25038.8165029696
"3983","No","No",727.373492884909,46329.0277506726
"3984","No","No",593.433837737072,36995.8215420113
"3985","No","Yes",1168.34064050981,13580.9466378274
"3986","No","No",541.784474964924,56100.1054254979
"3987","No","No",1055.41478885429,29967.173376934
"3988","No","No",399.723590496681,43243.6660984663
"3989","No","Yes",552.877704238618,14320.8597947813
"3990","No","No",1189.75213362091,50838.5172180485
"3991","No","No",698.001477300336,38135.5587008128
"3992","No","No",431.536055178104,46941.0589440984
"3993","No","No",851.103567490966,53433.5741701953
"3994","No","No",1457.71605432132,37453.8397748468
"3995","No","Yes",992.280250534139,13958.291477701
"3996","No","No",387.76134166606,40063.7276405824
"3997","No","No",794.176212819499,43335.9850414375
"3998","No","No",412.25489056191,37088.2123302523
"3999","No","Yes",1163.45251217975,27308.6909253501
"4000","No","Yes",880.281524012368,20177.4002932926
"4001","No","No",752.2218482128,48764.0227335012
"4002","No","No",40.0931001292735,33278.3651877443
"4003","No","Yes",1242.5214000392,18331.0035192795
"4004","No","Yes",1671.81369058885,20074.5093287591
"4005","No","No",1097.03908336192,40021.3530152455
"4006","No","No",918.74734351281,28327.5503602205
"4007","No","No",1185.28892689186,39926.6069284213
"4008","No","No",997.477342703065,44748.7919391108
"4009","No","Yes",741.442367736876,15499.9193212486
"4010","No","Yes",1234.24991259327,12268.3649559429
"4011","No","No",0,52585.685868057
"4012","No","Yes",985.262251090303,30295.6309587895
"4013","No","No",1023.19846694708,40866.0188373617
"4014","No","No",423.430473770691,46000.7966775868
"4015","No","Yes",1007.1394839918,17217.5050571061
"4016","No","No",893.355267878727,49960.3061635294
"4017","No","No",1008.3574622206,39610.6253746006
"4018","No","No",691.99675702089,45428.539318492
"4019","No","No",410.236803912654,36502.3785672846
"4020","No","Yes",1215.38361034015,15994.0410236354
"4021","No","No",599.03497050462,49813.1266974055
"4022","No","No",355.251657024056,50160.7031110918
"4023","No","No",869.898435189339,33540.0495193781
"4024","No","No",1786.46395168459,54207.1677946309
"4025","No","Yes",1221.81375636032,12603.3028574264
"4026","No","No",0,39164.0223455767
"4027","No","No",1621.24537469273,38590.8980916654
"4028","No","No",1121.49658624578,39395.8993577758
"4029","No","No",736.637502910077,55187.9198175268
"4030","No","No",1739.53592448521,51623.2875371972
"4031","No","No",1478.6214441698,41986.0477290777
"4032","No","No",903.564452484125,35296.4479016564
"4033","No","No",739.652879192582,48283.1870188822
"4034","No","No",228.955854756653,36783.0052497006
"4035","No","No",1313.55952545877,39989.0090717833
"4036","No","No",903.925863947006,27395.796144282
"4037","No","No",667.659830354829,42078.6848638463
"4038","No","No",539.887946834072,33856.4540327504
"4039","No","No",1877.1148086508,43442.3250059442
"4040","No","No",739.859747736702,30853.2930851045
"4041","No","Yes",408.562974447758,18471.7743266692
"4042","No","Yes",482.325150884949,30588.0302395231
"4043","No","No",1097.81775295012,43065.6187271322
"4044","No","No",1580.86672734265,36513.6167167876
"4045","No","Yes",920.231114913882,16088.2080999153
"4046","No","No",425.05374539019,37351.3555153373
"4047","No","No",0,53273.3416623972
"4048","No","Yes",928.182275856265,16968.8675232024
"4049","No","No",617.348763535131,38106.7883339974
"4050","Yes","No",1673.48634915562,49310.3329074721
"4051","No","No",1023.05155185734,19354.3979175702
"4052","No","No",1459.56140152943,44334.6169669311
"4053","No","No",1235.77751655543,35140.4587965845
"4054","No","No",621.97816785707,43472.2334593568
"4055","No","No",280.944386688864,50404.0329743684
"4056","No","No",326.652236024477,45562.5742373376
"4057","No","No",902.762304956933,52528.2374798269
"4058","No","Yes",672.693221527889,13795.9627457166
"4059","No","Yes",710.779996529605,17625.9675228698
"4060","No","No",901.208441163058,48855.4508386826
"4061","Yes","Yes",2216.01766888986,20911.695635345
"4062","No","No",1210.59299920567,40182.7068208555
"4063","No","No",828.970738675171,33423.936365316
"4064","No","Yes",533.492796417447,14707.2958101747
"4065","No","Yes",768.823046886614,22138.2159011172
"4066","No","No",746.670330655761,40354.558473576
"4067","No","Yes",730.739495206666,7299.52009926392
"4068","No","Yes",1308.02458812072,17353.8604211182
"4069","No","No",481.261049635246,33500.5652988707
"4070","No","Yes",1409.98910236248,21569.8907676641
"4071","No","No",524.607913831661,40663.0434148193
"4072","No","Yes",1122.13790030182,13929.4736725959
"4073","No","No",1105.87881627602,42110.4244036918
"4074","Yes","No",1319.81645438772,36548.8183522009
"4075","No","Yes",823.736672842706,19041.8183804585
"4076","No","No",172.975785497621,47060.6314248485
"4077","No","Yes",1497.36480871339,10576.3414807282
"4078","Yes","Yes",1819.94093603444,19393.4829690793
"4079","No","No",389.395881134109,20200.3799415541
"4080","Yes","Yes",2047.41700651498,13678.508032147
"4081","No","No",909.865553606509,44879.2775838025
"4082","No","No",686.163294807317,33190.4271290868
"4083","No","Yes",0,20565.777654669
"4084","No","Yes",1676.09768352054,25687.914394485
"4085","No","No",971.960011035032,33543.3349269627
"4086","No","No",887.594631980958,59416.1597583341
"4087","No","No",1312.84770615108,64402.6089073751
"4088","No","No",1808.72265816,30020.6429000601
"4089","No","No",1170.74346404427,49832.9514015609
"4090","No","No",439.352384001982,44620.2651488029
"4091","No","No",103.04234182744,45798.6746990815
"4092","No","No",1473.89756364907,32860.1009924404
"4093","No","No",780.709251488092,37023.1171524826
"4094","No","No",1144.24200204036,28018.9955741467
"4095","No","No",1148.50933615601,37392.1678382689
"4096","No","No",667.412163656887,31150.0265366089
"4097","No","No",1013.78519429173,50268.9923132871
"4098","No","Yes",1418.38864963031,12788.719027696
"4099","No","Yes",1345.28829718908,23169.4041300909
"4100","No","Yes",1493.10606637006,14295.6647405799
"4101","No","No",828.265303729576,36241.6601297718
"4102","No","Yes",208.89691523043,17966.3611423564
"4103","No","No",567.294084420402,39841.2283492398
"4104","No","No",686.869875998055,32359.3374551019
"4105","No","Yes",1926.28900807515,12283.408513263
"4106","No","No",814.286311995171,36613.7070884905
"4107","No","No",1569.51821551732,43024.9706832812
"4108","No","No",0,42035.338983851
"4109","No","Yes",0,13226.1549646145
"4110","No","Yes",1828.61432512578,20225.8061141141
"4111","No","No",777.646062948943,50869.7286119432
"4112","Yes","No",1644.69662278627,61474.9344461318
"4113","No","No",295.128657603893,36539.0848250222
"4114","No","No",781.123714097136,22327.6759781473
"4115","No","No",673.926656787536,39508.6241247705
"4116","No","No",343.249590531263,58411.6775372388
"4117","No","Yes",1302.3403089258,16292.2671406477
"4118","No","No",225.81966007063,54124.2060414619
"4119","No","Yes",1456.85805376701,14530.3112001678
"4120","No","No",955.090754255521,52224.4927939227
"4121","No","No",777.023000126856,47369.116830613
"4122","No","No",0,23616.5390057379
"4123","No","No",527.252016259759,49280.3009402279
"4124","No","No",29.848982881347,36972.934138124
"4125","No","No",125.103948532388,16157.2183071314
"4126","No","Yes",1095.86882248829,16127.5246644441
"4127","No","No",0,41194.0713915091
"4128","No","No",973.998849623091,48095.0679714769
"4129","No","No",1288.25268199635,42226.4620096718
"4130","No","No",269.289219595483,33846.508638631
"4131","No","No",1105.89023752596,35450.7272653153
"4132","No","No",360.892385237613,37581.7918044689
"4133","No","No",933.104365993473,24113.9172855589
"4134","No","No",1184.21049484192,38409.5355114041
"4135","No","Yes",1850.61214561206,20409.0380471898
"4136","No","Yes",1517.80416119472,12958.6509187697
"4137","No","No",969.55040776643,41301.9715355252
"4138","No","No",683.809045509462,41004.6590152701
"4139","No","No",915.075205885282,23682.9133227548
"4140","No","No",0,47895.8049172029
"4141","No","No",606.581004829702,32497.4488581538
"4142","No","No",689.919452126868,37695.7914454234
"4143","No","No",561.622858899832,38894.5508767785
"4144","No","No",303.517760977296,43791.1054184975
"4145","Yes","No",1823.40410518463,14664.1381225121
"4146","No","Yes",1698.29181559885,11764.862109723
"4147","No","Yes",1355.31321601212,11070.8549739706
"4148","No","Yes",1009.91157599377,18001.8645008404
"4149","No","No",887.903298925522,29642.1440308044
"4150","No","No",308.533535836126,39121.3640608267
"4151","No","No",1512.25631384363,35652.7678584774
"4152","No","No",0,39858.1272281271
"4153","No","No",1274.60300516704,55206.7023324083
"4154","No","Yes",1795.21984596123,7112.40849869462
"4155","No","No",848.997262632983,44379.7533473671
"4156","No","No",588.7801860656,58764.3637567687
"4157","No","No",551.174351603282,24975.0270616299
"4158","No","Yes",1084.04408433742,13680.1132986044
"4159","No","No",723.415718628873,35938.1397752091
"4160","Yes","No",698.56735192862,31754.5234215337
"4161","No","No",910.739618245199,40222.7125016071
"4162","No","Yes",1581.06593572758,17331.3258296657
"4163","No","No",948.689701645866,39671.5338752441
"4164","No","No",199.174565816466,21753.3569623462
"4165","No","Yes",1759.41232517457,11556.1845280469
"4166","No","No",780.658964465254,29691.4942717387
"4167","No","Yes",1770.79681540763,18837.4201675506
"4168","Yes","Yes",2182.60434354643,20780.69245487
"4169","No","No",696.012758739235,35920.6620176138
"4170","No","No",563.917725899879,47839.6172580637
"4171","No","No",1041.07152319365,41485.6237181921
"4172","No","No",293.65537518518,38893.6045913333
"4173","No","Yes",1489.04815023232,17934.4933667081
"4174","No","No",237.489889757362,44614.7163235577
"4175","No","No",1266.79602128258,34652.1088830282
"4176","No","No",1059.86582999024,33567.8450260249
"4177","No","Yes",704.292704155334,13472.4831922635
"4178","No","No",0,45701.085159879
"4179","No","No",0,47846.9565733864
"4180","No","No",499.622780567795,42052.8701176574
"4181","No","No",728.818859467603,33662.0709670495
"4182","No","No",1238.10517698086,37544.9421289734
"4183","No","No",776.899874606161,37685.4493016399
"4184","No","No",202.070432925658,28814.1753058697
"4185","No","No",625.399024910973,52074.9682238148
"4186","No","No",1089.30217845927,28232.1412814794
"4187","No","No",992.963525690813,45540.4456802251
"4188","No","No",352.746535946012,41548.8829454378
"4189","No","No",416.747666598944,69386.9015897979
"4190","No","No",937.923175402473,31664.0669007571
"4191","No","Yes",15.1537927269189,13430.4158449196
"4192","No","No",1128.67359368677,42566.2975475258
"4193","No","Yes",1924.55977546273,8097.91797528429
"4194","No","No",211.075501758336,50506.220185253
"4195","No","No",23.5291262860873,42814.8185641646
"4196","No","No",891.394821935661,43535.9399390918
"4197","No","No",707.107463519103,18675.9172804639
"4198","No","Yes",1018.09933818812,18239.2817032363
"4199","No","No",1404.09915555023,28764.4967062106
"4200","No","No",817.085888361365,55892.7261036349
"4201","No","Yes",908.848633759071,20332.3910564198
"4202","No","No",1216.19515610407,47561.712762495
"4203","No","No",176.904045793469,43150.2685305518
"4204","No","No",401.708352972404,51338.2160136299
"4205","No","No",1214.21119185954,45778.0267076337
"4206","No","Yes",1835.56411176438,17807.4495185463
"4207","No","No",66.7727220597995,43189.6645286797
"4208","No","No",1343.18588302872,30960.616025598
"4209","No","No",1309.40574622583,34469.9801719868
"4210","No","Yes",463.091661446481,21248.6529351879
"4211","Yes","No",1672.0314827914,50327.3677566251
"4212","No","No",357.744559308234,45516.7761149121
"4213","No","No",705.095045797165,38297.8549493873
"4214","No","Yes",453.643969731847,9770.00643655795
"4215","No","Yes",1799.14934936528,20215.6238235123
"4216","No","No",748.138164599069,21153.482463354
"4217","No","No",650.089355226512,46496.5101577786
"4218","No","Yes",341.485593332755,21278.8261394043
"4219","No","No",1476.47893269427,50701.8453154219
"4220","No","No",1134.34447245866,41748.1834479526
"4221","No","Yes",852.35866725484,20948.9708785949
"4222","No","Yes",930.663262975502,22919.9971704174
"4223","No","No",1027.01757050938,47046.0123584044
"4224","No","No",1494.25411867558,48133.9384431848
"4225","Yes","No",1396.78918130163,40371.1247388957
"4226","No","No",44.0593988387884,50765.6982779735
"4227","No","No",938.280180319813,33341.9631911132
"4228","No","No",776.964679227001,36704.0519013828
"4229","No","Yes",1589.64963269922,15262.4216192198
"4230","No","Yes",1247.55632093348,16410.8903685008
"4231","No","No",0,30143.9637125602
"4232","Yes","Yes",2291.61768844883,20837.2094471977
"4233","No","No",676.959581088393,33013.8247572839
"4234","No","No",135.998235596591,59369.6484070611
"4235","No","No",630.608540429953,52491.5586737334
"4236","No","No",315.953257508048,38938.975748331
"4237","No","Yes",564.738466990779,15411.1915898003
"4238","No","No",663.244157339197,25728.8684447605
"4239","No","No",72.1232567768386,35374.8645729716
"4240","No","No",491.036498048265,37835.6132114576
"4241","No","No",1331.89567629233,38903.1979786692
"4242","No","No",755.339662904859,37027.2545972807
"4243","No","No",684.186023658561,45400.0173568738
"4244","No","Yes",407.340440417818,25376.7286324237
"4245","No","No",0,37727.4635794782
"4246","No","No",130.31596934149,44559.8172904068
"4247","No","No",1224.45449345277,46811.6143935601
"4248","No","No",1525.35393018537,52675.7637044107
"4249","No","No",618.998439866302,17122.347555865
"4250","No","No",353.638334210687,43145.179661493
"4251","No","No",422.644529182164,58345.0145187153
"4252","No","No",25.7362691691972,28163.6929703336
"4253","No","No",697.62139806607,48469.8814036304
"4254","No","No",462.138386747959,44546.2371893247
"4255","No","No",599.66771994222,36584.0762392672
"4256","No","No",1251.23590590048,43191.3120886386
"4257","No","No",1432.29846516717,31250.5829812295
"4258","No","Yes",975.915404092626,12102.8178865863
"4259","No","Yes",445.929060495343,10629.618349193
"4260","No","No",694.412430789019,47705.8712772338
"4261","No","Yes",320.012202336553,14704.4776901877
"4262","No","No",1102.30203229907,38872.1299228802
"4263","No","No",0,39250.0955243705
"4264","No","No",165.973008628943,28439.5879563403
"4265","No","No",1258.87880589312,40858.86844416
"4266","No","No",1372.76199336205,30219.1500946263
"4267","No","No",1464.02896199018,43700.1426258785
"4268","No","Yes",1594.09316720836,14135.211282255
"4269","No","No",0,31741.0456239313
"4270","No","No",1595.91003269695,55712.2937441276
"4271","No","No",901.176073691345,55064.6364886004
"4272","No","No",789.750797188921,39022.3532458469
"4273","No","No",1203.20583932405,43809.2599519992
"4274","No","Yes",1399.33805345892,22736.2190720193
"4275","No","No",1125.87648806806,49359.3427235076
"4276","No","No",236.338466236238,39260.060190791
"4277","No","No",218.146314461255,27166.6975416027
"4278","No","No",413.57655127627,49424.3103755304
"4279","No","No",469.053980293215,25680.1749280984
"4280","No","No",923.992516417842,27818.2468659303
"4281","No","No",182.628059937346,31623.9364457733
"4282","No","No",478.401744719427,30338.3568611308
"4283","No","No",918.546151295038,53331.2784279661
"4284","No","Yes",873.930546923689,29870.747365702
"4285","No","No",748.704835614302,48036.0791048472
"4286","No","No",146.818190779362,48280.709929901
"4287","No","No",1266.64098896732,41930.586270236
"4288","No","Yes",883.881831762394,14888.518114146
"4289","No","Yes",1353.981431586,17431.6053548792
"4290","No","Yes",1777.74467095913,24820.8562683475
"4291","No","No",695.493321681063,48899.3431818239
"4292","No","No",0,33313.3110724284
"4293","No","No",377.726631320199,49730.7045824799
"4294","No","No",975.737181991567,39453.2263082774
"4295","No","Yes",918.590687000593,14936.7081866305
"4296","No","No",1313.50037821727,31145.7083503014
"4297","No","No",1302.39985423502,15468.3447408757
"4298","No","No",1246.62613832251,41930.155003598
"4299","No","No",198.385707995449,37085.6037122098
"4300","No","No",395.607773854553,27750.8214956836
"4301","No","Yes",638.065584784846,17922.4034914657
"4302","No","No",677.20483918057,52080.523362498
"4303","No","No",681.575749997487,29980.3305443007
"4304","No","No",1114.94577466771,45832.9483249515
"4305","No","No",558.178484419266,12740.8646435052
"4306","No","No",531.477978521723,35314.071037428
"4307","No","Yes",1080.96999628813,23760.7588832121
"4308","No","No",150.770544592663,31216.7858646369
"4309","Yes","Yes",1665.70832014732,21024.2716484296
"4310","No","No",1086.19348901131,52761.4130388312
"4311","No","No",0,41435.4379394559
"4312","No","No",1253.92349841602,34304.7669082517
"4313","No","No",100.463116063705,24687.8530666445
"4314","No","No",961.1524668748,51179.4601199922
"4315","No","Yes",1055.86105389087,9045.24162581889
"4316","No","No",89.4101021957102,24086.6011429412
"4317","No","No",918.950347839629,47761.4027126413
"4318","No","No",995.387047631767,44217.6856384302
"4319","No","No",1774.23308862536,42955.8279672847
"4320","No","No",701.69143244907,35335.7826320459
"4321","No","No",1177.92084256329,30169.2958145926
"4322","No","No",1308.2672570288,43611.4615642246
"4323","No","No",1114.53687054082,35044.7298212792
"4324","No","Yes",815.548669141047,16908.139756724
"4325","No","Yes",1010.50678353427,6786.38808971369
"4326","No","No",752.985919181889,45523.4967931178
"4327","No","Yes",729.189718410075,18329.7208714106
"4328","No","No",1095.47177939067,53581.1567122445
"4329","No","No",355.17017607474,30440.8951928132
"4330","No","No",1308.98796693688,31777.2018815539
"4331","No","No",0,40257.795499868
"4332","No","No",464.064104923968,16670.1528109478
"4333","No","No",570.916398504619,46569.1677265188
"4334","No","Yes",1045.33881349941,29601.4039439579
"4335","No","No",0,39134.9319187105
"4336","No","No",642.256231412638,29223.9953446317
"4337","No","No",486.815972242664,25117.6438190184
"4338","No","No",1082.11982831446,43256.4005642526
"4339","No","Yes",1224.63687161053,25717.0706415941
"4340","No","No",953.147356270312,29328.0391608131
"4341","No","No",1323.17521403561,52019.3499661481
"4342","No","Yes",139.19286882554,24734.0829253905
"4343","No","No",856.881451045322,32449.3113343807
"4344","No","No",311.889288737823,46116.1315257271
"4345","No","No",0,49959.141007874
"4346","No","No",498.854154668701,47736.2367906901
"4347","No","Yes",465.871073366709,14519.4733831391
"4348","No","No",892.820269988203,45379.8442652294
"4349","No","Yes",456.240694517499,17657.6088569363
"4350","No","No",557.26171195281,45040.9444083377
"4351","No","No",0,28094.1570910795
"4352","No","No",138.620739346357,50249.4172431944
"4353","No","No",241.163150062669,36581.2591238761
"4354","No","No",763.177239332049,42481.4987501749
"4355","No","Yes",1195.81381121066,19906.7314933236
"4356","No","No",130.996930290863,54366.8027607212
"4357","No","No",1253.0413172963,32362.6525065495
"4358","No","No",1496.09804230128,44309.902897034
"4359","No","Yes",1071.95523702871,19813.996969574
"4360","No","Yes",724.904735454183,16643.1269450404
"4361","No","Yes",194.185012052313,20889.852002779
"4362","No","No",1441.74335861583,45037.1676788742
"4363","No","Yes",1678.38200097825,8154.23977389186
"4364","No","Yes",2128.43414823871,22664.219843924
"4365","No","Yes",1393.46442103117,15573.7931129306
"4366","No","Yes",1437.83036292863,23062.4948694903
"4367","No","No",1257.9581072609,38532.1921927497
"4368","No","Yes",1329.3983510722,15971.5732370007
"4369","No","No",605.12443216774,29769.9783500417
"4370","No","No",601.081665167516,45607.3468645804
"4371","No","No",1164.51340619208,29874.4887927038
"4372","Yes","No",2054.45252745952,37366.7650507786
"4373","No","No",701.632841263145,43340.9202996197
"4374","No","No",673.092439896694,27894.0939871045
"4375","No","Yes",1262.92591939434,16935.1931085721
"4376","No","No",357.996305460536,30217.0212871579
"4377","No","Yes",1052.59812294326,24418.2139473269
"4378","No","No",1234.51247251861,50293.4335358584
"4379","No","No",1153.80219423116,37552.6216193783
"4380","No","No",1375.29736012851,35810.9142097506
"4381","No","No",249.103685164101,51051.3357610077
"4382","No","No",874.031434043843,39664.9361264591
"4383","No","No",768.158723384486,23991.0412896025
"4384","No","Yes",1236.25642303914,16389.8638086909
"4385","No","Yes",641.220757059698,23265.6923595285
"4386","No","Yes",825.522881718494,14539.1620062194
"4387","No","No",931.041170135349,42225.3671429443
"4388","No","No",650.075529479081,32560.7610419904
"4389","No","Yes",0,17577.9963356867
"4390","No","No",682.604508896646,57553.9765752877
"4391","No","No",1207.10500154702,50010.8298274342
"4392","No","Yes",840.220967633549,18103.7355199788
"4393","No","No",894.627905765509,39027.6302767467
"4394","No","No",711.525816057206,35468.3821539389
"4395","No","No",1635.21756895271,47669.7419597268
"4396","No","No",740.869370680082,46541.2111680628
"4397","No","No",773.483075895744,38647.3706700462
"4398","No","No",1142.32631903636,45173.3745996018
"4399","No","No",894.168319992039,42212.4458263685
"4400","No","Yes",14.0338457182518,13846.8010395538
"4401","No","No",537.040332892497,21364.3104965004
"4402","No","No",1092.5243064854,19759.1226373765
"4403","No","No",541.071380843328,29807.405551667
"4404","No","No",1049.00411801587,34088.1697894671
"4405","No","No",463.932561210409,37842.0477815607
"4406","No","No",214.272690395661,56032.6475812564
"4407","No","Yes",997.762482254186,13799.5447379368
"4408","No","Yes",938.684221979389,18454.3990337024
"4409","Yes","No",1350.60326099354,21541.2987938108
"4410","No","Yes",935.757199246756,5386.17648259929
"4411","No","No",819.048549167453,61602.6366710843
"4412","No","Yes",1669.09103762141,16830.7529473416
"4413","No","No",395.851856906679,33708.5923827756
"4414","No","Yes",810.542406900765,17975.1571288115
"4415","No","Yes",62.2371283591804,20267.4999277937
"4416","No","No",1133.11258529704,23110.6536499869
"4417","No","No",748.487595522024,36453.6451213344
"4418","Yes","Yes",1337.23518702176,32761.0534040074
"4419","No","No",1138.43098430744,36133.6105343391
"4420","No","Yes",1247.45553179616,15382.6096545468
"4421","No","Yes",875.979710502071,17286.6944919904
"4422","No","No",980.763938624199,43650.5344586779
"4423","No","No",238.981506549414,30412.7020181739
"4424","No","No",185.010114509294,40874.8328514801
"4425","No","Yes",478.961863795831,19911.5102678277
"4426","No","No",145.476590146266,41196.4202020265
"4427","No","No",339.95904709248,36132.805687911
"4428","No","No",303.903742328566,43836.7047325122
"4429","No","No",536.214278066008,47956.5245563522
"4430","No","Yes",1219.58145404792,12434.04183884
"4431","Yes","Yes",1926.3715830091,17012.4381440591
"4432","No","Yes",460.202332018861,14782.4587719904
"4433","No","No",825.287865652256,46147.955752768
"4434","No","No",31.2565457414114,44970.7680127096
"4435","No","No",535.584814611588,51971.7056291761
"4436","No","No",667.711628636986,30803.4446563457
"4437","No","No",694.326759084331,42041.4565572933
"4438","No","No",81.422936426937,51044.407811267
"4439","No","No",1324.4929494963,28691.8366142527
"4440","No","Yes",1587.13295655322,25819.4853929207
"4441","No","No",775.022944702917,39137.0767283306
"4442","No","No",0,46542.9852774578
"4443","No","Yes",819.727712621639,10175.3099539293
"4444","No","No",1416.22439665098,31303.1633146522
"4445","No","No",1005.2938512555,34010.2782138331
"4446","No","Yes",1419.36036165965,20118.5581992659
"4447","No","No",230.006655926868,44232.1384762055
"4448","No","Yes",1559.45787021154,20707.3186080696
"4449","No","No",672.708232643736,47532.5876838394
"4450","No","Yes",457.235746879721,5296.71092118226
"4451","No","No",968.458967843151,45299.0008313587
"4452","No","No",257.546332188622,29967.2042826122
"4453","No","Yes",1130.54463982657,20862.9044853653
"4454","No","No",486.576050197581,27687.3411659989
"4455","No","No",207.303809187677,49999.4957354912
"4456","No","No",110.915120844136,58185.5598391222
"4457","No","No",678.485146409376,30196.0456896206
"4458","No","Yes",515.149996523628,10214.705259205
"4459","No","Yes",837.911885761804,20984.1790586289
"4460","No","No",709.617220538757,34624.8357471742
"4461","No","Yes",745.282471593171,17803.7573638302
"4462","No","Yes",1288.39370512832,22919.6377281931
"4463","No","Yes",819.742345092291,20792.8309579563
"4464","Yes","No",1708.65972028717,38203.5094749589
"4465","No","No",1061.61360190645,38250.7777458906
"4466","No","No",372.741249859074,35570.2117813337
"4467","No","No",883.867885324978,42866.3839541323
"4468","No","No",0,41864.2591327518
"4469","No","No",657.687617719088,26454.5857525237
"4470","No","No",221.023882200551,42087.0822309886
"4471","No","Yes",1009.89600417223,9804.66603916733
"4472","No","No",174.412944777163,40282.4875461437
"4473","No","No",358.848341230842,44486.906473652
"4474","No","No",421.759624929585,35522.6659777317
"4475","No","Yes",1295.85184404715,10987.265192511
"4476","No","No",1005.10095136541,36275.6487886561
"4477","No","No",268.961482796412,40733.4679825431
"4478","Yes","Yes",1807.66621807195,18166.3226526991
"4479","No","No",1237.86134424222,33816.1815718186
"4480","No","Yes",700.169039463254,28160.6422651881
"4481","No","Yes",1236.67843724152,16298.5681470607
"4482","No","No",354.702587096373,29893.039733018
"4483","No","No",657.021951179148,47044.3335267286
"4484","No","No",1444.92250041891,54599.1577636358
"4485","No","No",1456.41125420584,30332.9859888347
"4486","No","Yes",501.140260845364,19467.5376482681
"4487","No","Yes",1155.22217512644,18442.34092635
"4488","No","No",804.469753698074,46869.3201682876
"4489","No","Yes",1473.63371289165,17816.6208536902
"4490","No","No",1345.38059193616,30175.94166664
"4491","No","Yes",545.612477437577,13020.9591436908
"4492","No","No",1134.27984820202,37658.2309555963
"4493","No","No",212.36136845208,32283.405306208
"4494","No","No",424.735234854806,22327.4342029864
"4495","No","No",658.463644874415,39242.7554158439
"4496","No","No",1023.79588473631,42335.7371898711
"4497","No","No",815.566907409863,31752.0771532132
"4498","No","Yes",953.421739992227,14062.053538906
"4499","No","No",905.645634729108,35876.7956914194
"4500","No","No",623.09205398354,42035.4526196687
"4501","No","No",287.200151889446,45952.728923726
"4502","No","Yes",1310.79350272579,16742.9525237844
"4503","No","Yes",1509.66340166383,16777.5623438888
"4504","No","No",1616.10873432918,33046.1879353576
"4505","No","No",710.159309928127,32649.6198725826
"4506","No","No",1213.65867211341,45662.6466052607
"4507","No","No",1004.11682846699,52822.404383106
"4508","No","No",36.3584399548112,44603.4273332462
"4509","No","No",1349.19233180838,51189.1122680341
"4510","Yes","No",1854.6041538508,38406.3564635228
"4511","No","No",126.153204675651,33964.0506128594
"4512","No","Yes",987.154095590613,13002.4855508597
"4513","No","Yes",1127.99393381003,15186.8686920989
"4514","Yes","No",1436.31154943473,18810.8041351982
"4515","No","No",0,41564.7698338846
"4516","No","Yes",1068.86074173834,20804.3786766452
"4517","No","No",0,62128.6666318456
"4518","No","Yes",568.975552395383,17291.8146908178
"4519","No","No",1608.74546942757,41572.2736975715
"4520","No","No",885.004067297585,48753.5599562476
"4521","No","Yes",886.951017327419,21792.6097936256
"4522","No","No",1358.13247178913,49903.5970805788
"4523","No","No",215.75271922503,22254.5891732126
"4524","No","Yes",410.46674874912,25574.4383224847
"4525","No","No",1146.61402886155,40870.9013277309
"4526","No","Yes",1262.39329360308,24797.6075720524
"4527","No","No",778.474666113432,39360.1640871243
"4528","No","No",444.304074050634,43424.9349887183
"4529","No","No",826.953672491255,16124.6256836141
"4530","No","No",204.694595970694,42076.5761239639
"4531","No","No",819.530733510064,38456.6150377655
"4532","No","No",1386.29696434561,31739.513240888
"4533","No","No",698.90327627991,28682.9574197461
"4534","No","No",756.433468623078,33296.5671206744
"4535","No","No",739.901842518832,47349.5319726392
"4536","No","No",220.229117680588,37451.0176128561
"4537","No","Yes",458.967737070662,18444.2978455002
"4538","No","No",0,36584.7569962462
"4539","No","No",102.258372426939,23476.7453563298
"4540","No","No",215.068134966975,47815.5996963503
"4541","No","No",906.081359888139,45720.0130427207
"4542","No","No",735.927000954023,18373.4864963009
"4543","No","No",944.321716916994,34546.116805141
"4544","No","No",1071.00736936624,40758.0020422819
"4545","No","No",1345.45173685592,43322.1930190477
"4546","No","Yes",250.173010019935,17439.6292696405
"4547","No","No",265.729004541498,60182.6771885936
"4548","No","No",1006.05398774745,27002.0658104182
"4549","No","Yes",1670.12689461037,14746.8926097897
"4550","No","Yes",291.962364907345,7105.29202471072
"4551","No","No",381.52096933305,31750.7773240363
"4552","No","Yes",851.729729640048,14726.3760194068
"4553","No","No",1375.04922403625,42557.689089595
"4554","No","No",816.73372053828,40949.3644365051
"4555","No","No",819.018615016962,49111.3915717458
"4556","No","Yes",1026.15356049033,25393.7687970803
"4557","No","No",853.438145266577,58757.6813645568
"4558","No","Yes",1200.37661268053,10817.5619567491
"4559","No","No",1037.13480288192,43368.2233731399
"4560","No","No",0,28363.7136990121
"4561","No","No",357.138146216191,35623.4652668674
"4562","No","Yes",970.426089456089,20569.640036535
"4563","No","No",812.347981166699,33654.6774179645
"4564","No","No",91.94732281422,44337.4059033946
"4565","No","No",1396.03791633922,46378.7305083936
"4566","No","No",72.6500816609296,50786.979185169
"4567","No","No",1168.0925556287,27823.4542224725
"4568","No","Yes",1098.45404586514,16837.2103236566
"4569","Yes","No",1549.89467343683,47916.2948560542
"4570","No","No",1039.09837389666,26217.132054761
"4571","No","No",309.838214997499,54945.5816755763
"4572","No","No",1477.52861102329,36709.6684407159
"4573","No","No",79.5389496655106,34417.3757217671
"4574","No","No",1219.34788226544,34310.2919397433
"4575","No","No",812.588032381551,43980.1399685787
"4576","No","No",557.374968867559,35067.2662366586
"4577","No","No",1688.31455669806,32723.4516291513
"4578","No","No",1137.79115713306,21103.4296209784
"4579","No","No",856.282959899419,40394.0495771435
"4580","No","No",1126.4104826892,35125.6777238733
"4581","No","No",541.021814319389,29519.8801523274
"4582","No","No",682.879609670442,43142.0545225125
"4583","No","No",538.949887636749,35634.2028032149
"4584","No","Yes",1305.80969861959,28929.3847360892
"4585","No","No",735.018855874434,25305.2539541423
"4586","No","Yes",344.3089096317,14256.2417485038
"4587","No","No",1060.69581236718,36920.682410632
"4588","No","No",848.676390931552,38742.0597937341
"4589","No","Yes",1245.26750709863,16772.6193726086
"4590","Yes","No",1563.53519785347,51704.9199686192
"4591","Yes","Yes",1433.16763517475,18087.6863730016
"4592","No","Yes",1238.58950959484,10514.8286406744
"4593","No","No",625.615086363204,53655.2704755254
"4594","No","Yes",1681.57246482441,20059.6747908348
"4595","Yes","Yes",1681.48150579852,10155.3239877604
"4596","No","No",602.638147132055,70021.6484355306
"4597","No","No",681.213002165506,37287.2297831665
"4598","No","Yes",966.026981387918,27960.371891395
"4599","No","Yes",539.160036521217,17824.2607149352
"4600","No","No",921.304294606852,25350.4002048286
"4601","No","Yes",1325.34692274166,21487.6237029563
"4602","No","No",0,46714.558115548
"4603","No","Yes",900.274608175963,22986.6136751281
"4604","No","No",695.370830129287,31779.3966695702
"4605","No","Yes",1270.99640350538,15069.0487071799
"4606","No","No",1388.56113942232,54935.9412044927
"4607","No","No",635.741695957086,21297.1198992919
"4608","No","No",484.515768741921,39775.2498897027
"4609","No","No",1185.09627093982,42969.7180869225
"4610","No","No",656.980282929656,32225.2952878255
"4611","No","No",1084.92565413113,36908.6884305228
"4612","No","No",1309.55944948148,36851.2691176579
"4613","No","No",847.530654151052,40982.4197962145
"4614","No","No",0,35600.1493662398
"4615","No","No",78.1680828433628,56254.1753065052
"4616","No","Yes",251.97174455438,11757.9632656835
"4617","No","No",871.231398400026,31770.6239820177
"4618","No","Yes",918.758791227891,14304.5973617949
"4619","No","No",0,31774.6475362394
"4620","No","No",450.057359138237,36121.2373912058
"4621","No","Yes",1079.37288016418,14223.2668124756
"4622","No","Yes",320.230481928856,14185.2746317243
"4623","No","No",799.370027923619,34293.7275086072
"4624","No","No",1206.59215458569,25646.0822038978
"4625","No","No",0,39830.0916182418
"4626","No","No",603.661670476063,44663.7361170603
"4627","No","No",672.062804619824,33850.9788397769
"4628","No","Yes",959.222353292648,16082.4476346076
"4629","No","No",670.471905106349,24265.5868972486
"4630","No","No",587.259459765496,33732.2162304135
"4631","No","Yes",602.307676282023,11019.5869460741
"4632","No","Yes",937.606059288741,19962.1980442127
"4633","No","No",1502.75774867973,53129.7830394396
"4634","No","No",1063.15567471295,34642.0647569343
"4635","No","No",511.613751416205,36796.7947847658
"4636","No","No",926.969872799966,61937.5696692316
"4637","No","Yes",1004.36463665711,13422.3794608111
"4638","No","No",367.668095917734,50025.944580281
"4639","No","Yes",256.707070719681,22166.7943931341
"4640","No","Yes",1777.68565079089,21573.9000171012
"4641","No","No",717.742833830408,49347.021805396
"4642","No","Yes",532.382135295296,17704.8143787361
"4643","No","Yes",1346.67950656337,16104.8949657536
"4644","No","No",334.466419532525,40156.2444927105
"4645","No","No",0,36179.8268590675
"4646","No","Yes",774.498460230419,10333.8005475059
"4647","No","No",779.259990627242,38387.721274812
"4648","No","No",881.939111911127,62222.9807017551
"4649","No","No",1563.23020536396,31126.3267807912
"4650","No","No",991.060968479028,37597.3230643757
"4651","No","No",430.651094177701,38372.021246678
"4652","No","No",806.377684881681,40454.0678792373
"4653","No","No",438.766636101515,32762.1862301046
"4654","No","Yes",1457.85884469899,17365.5667158128
"4655","No","No",366.204888151025,30631.9631128473
"4656","No","No",707.646931639431,35268.0174326897
"4657","No","Yes",244.382157810974,19773.0724565957
"4658","No","No",903.615716442037,20444.4241231633
"4659","No","Yes",1031.49888597034,24636.3002154852
"4660","No","No",914.378279892509,40659.1259603737
"4661","No","No",1047.25164984966,39222.0588862653
"4662","No","No",884.494356569448,30520.3532717113
"4663","No","No",434.778826535929,39099.0918533728
"4664","No","Yes",36.8440382391803,16942.3053373994
"4665","No","No",189.362232508863,36706.8119673594
"4666","No","No",366.321281302656,40904.7636333436
"4667","No","Yes",1092.4645722139,14659.7180687514
"4668","No","No",732.734288956684,39024.1492327605
"4669","No","No",594.440525597425,28723.3418554818
"4670","No","Yes",604.592700002911,14896.5918697721
"4671","No","No",1732.84360466407,30322.8693052295
"4672","No","No",577.433332439527,43585.0441430742
"4673","No","No",0,38626.4229206895
"4674","No","No",54.675220865283,32279.5684271435
"4675","No","No",75.9517639373938,60826.9940271173
"4676","No","No",550.532712518723,27223.85786997
"4677","No","Yes",1343.8744841052,17280.4386325809
"4678","No","No",1350.42884688991,26450.2917378784
"4679","No","No",866.337987458604,35141.4276434822
"4680","No","No",6.0718923507153,46930.5751609952
"4681","No","No",501.345178098676,45368.2311760233
"4682","Yes","No",1488.46992484111,36457.2232810267
"4683","No","No",1462.78673740114,60296.3983414094
"4684","Yes","Yes",1567.67928171741,19172.6794504669
"4685","No","No",771.789346888835,42139.0702685072
"4686","No","Yes",1534.96839986598,17084.5811173852
"4687","No","Yes",801.0497163256,14679.8334438848
"4688","No","No",1724.36913158985,32610.8103326017
"4689","No","No",0,50258.5389376857
"4690","No","Yes",1277.37438672437,19675.1778370989
"4691","No","No",786.987434157094,33289.8508246553
"4692","No","Yes",231.770725857912,13702.4709168115
"4693","No","No",173.959460514891,53965.0512087132
"4694","No","No",1184.92742226796,40958.6439892978
"4695","No","No",1308.20671058333,32215.5333744215
"4696","No","No",203.300081077181,53055.6944283309
"4697","No","No",1092.09883401769,32122.0511847396
"4698","Yes","Yes",1902.96956487606,12464.3030428074
"4699","No","Yes",1004.43827309183,16456.2772500216
"4700","No","No",366.701551503356,60456.1177682285
"4701","No","Yes",1148.73388988892,23812.6269938105
"4702","No","No",0,33272.1521467807
"4703","No","No",476.235271127698,37627.3500232473
"4704","No","Yes",477.782945771117,22753.5954724521
"4705","No","Yes",970.832756037516,19096.8894116814
"4706","No","No",1268.59397286501,42012.8965861893
"4707","No","No",657.531112911779,40807.2813020757
"4708","No","No",0,47447.7965740125
"4709","Yes","No",1661.75264500024,21871.4629698414
"4710","Yes","No",2075.18875447378,40882.4942205655
"4711","No","No",915.771563906364,24243.1801621741
"4712","No","Yes",1048.27476281908,20659.1448600909
"4713","No","No",987.566250569473,49542.4042328822
"4714","No","No",535.135342889862,44448.6070341664
"4715","No","No",1057.93260126087,26628.440742067
"4716","No","No",1023.14792054594,43698.8166919161
"4717","No","No",749.038516136778,53711.4604878896
"4718","No","Yes",1143.32901606626,17367.9255273604
"4719","No","Yes",1269.22650557239,18552.4241402812
"4720","No","No",0,35282.5672911043
"4721","No","Yes",723.371847127379,17623.5856688343
"4722","No","No",1000.37678190467,40272.3020222245
"4723","No","Yes",1069.69305831626,10722.2494639199
"4724","No","Yes",1391.33500191617,18309.4209980289
"4725","No","No",1075.82417073247,22982.2266330705
"4726","No","No",548.645307903485,38094.4261696886
"4727","Yes","Yes",2109.99939482269,17774.5252912207
"4728","No","Yes",649.360116506354,10524.3261012953
"4729","No","No",863.138440981706,58119.716604212
"4730","No","No",861.644850689235,39272.0090424491
"4731","No","No",567.48919433319,22304.5739463089
"4732","No","Yes",642.536489791415,14763.3771815958
"4733","No","Yes",1045.6574660296,23335.0463297184
"4734","No","No",738.144128251302,43693.0663883118
"4735","No","Yes",0,11537.8932286442
"4736","No","Yes",1137.95416230334,22110.9557563921
"4737","No","No",462.674628272181,50229.5858853097
"4738","No","No",1006.20156056956,42689.2736872236
"4739","No","No",584.193242183484,36676.7891499965
"4740","No","No",1314.765204662,30067.4106473223
"4741","No","No",0,30236.4563342521
"4742","No","Yes",1005.70700620661,10887.4998722589
"4743","No","No",113.571264398777,32803.8326475959
"4744","No","No",967.739852296794,26210.7921411168
"4745","No","No",0,45486.4586774752
"4746","No","No",651.325134914705,51583.2502025242
"4747","No","No",1035.31564464554,53066.1048806134
"4748","No","Yes",58.9719451002576,20914.9310231373
"4749","No","No",0,60480.5779714553
"4750","No","Yes",1190.02172815921,16240.7675716638
"4751","No","No",37.9925363825407,48739.835381004
"4752","No","No",652.266673982479,48639.4414824399
"4753","No","No",1529.68785747477,40977.413884081
"4754","No","No",626.928556799094,42139.1844588824
"4755","No","Yes",756.995609419385,26674.3907819419
"4756","No","Yes",496.796907366618,21058.9615915699
"4757","No","Yes",396.348073964702,19541.0460996816
"4758","No","No",1590.64164138129,36242.4969956276
"4759","No","Yes",1331.98056755563,20672.3395409647
"4760","No","No",36.0879903528757,37015.5573565846
"4761","No","No",338.476753874062,32666.5125332143
"4762","No","Yes",992.348320272267,22324.995595221
"4763","No","Yes",1123.34001168521,20785.6402268094
"4764","No","No",743.623145225239,37333.5741226045
"4765","No","No",1111.69151735154,43659.0705877284
"4766","No","No",969.656018252646,40142.1341458025
"4767","No","No",974.507818365927,25346.7782137892
"4768","No","Yes",285.649618597346,26874.7545848903
"4769","No","No",596.620026917752,30764.6620547444
"4770","No","No",1417.91211322906,38015.1663619642
"4771","No","Yes",1055.01732721534,24188.0497493234
"4772","No","No",1001.22197077828,44801.4010017881
"4773","No","No",771.441004544643,44810.299357671
"4774","No","Yes",672.568078513351,22788.3673814709
"4775","Yes","No",1511.61095196469,53506.9449260287
"4776","No","No",601.689646743678,31522.7848464765
"4777","No","Yes",1316.65207634709,12165.7289983026
"4778","No","No",122.094308759452,46609.3974801976
"4779","No","No",840.015018103735,51350.6376713073
"4780","No","Yes",1765.89393783248,18053.4997231119
"4781","No","Yes",1752.73614962439,15596.8914294528
"4782","No","No",1147.97807477466,40515.690457161
"4783","No","No",1839.32220772717,38104.0911062601
"4784","No","No",1208.39512763795,32430.182774582
"4785","No","No",952.127530094181,49137.3956883078
"4786","No","No",1251.1510345903,45205.2458041651
"4787","No","No",215.541422559435,55278.4326522823
"4788","No","No",1294.4571219953,40768.4510534547
"4789","No","No",463.085599132038,46760.4958851049
"4790","No","No",815.244183095315,26231.4654068668
"4791","No","No",854.511278704168,34463.7651086686
"4792","No","No",1062.05871081173,35781.125486661
"4793","No","No",1035.16023564515,67450.6880740411
"4794","No","No",880.39651795853,60206.0865523297
"4795","No","No",847.432632019156,37303.5743998862
"4796","No","No",529.189078465686,50195.1130066062
"4797","No","No",1163.01689294047,38902.7636192958
"4798","No","No",570.024684333736,40987.062331077
"4799","No","No",0,29254.8682343059
"4800","No","Yes",673.473688920932,20926.4985495467
"4801","No","No",1728.335976312,33488.3043629755
"4802","No","No",1285.82859393833,43507.4660616916
"4803","No","Yes",1511.89419713912,22794.0710515534
"4804","No","No",676.684640060302,24989.8336879992
"4805","No","No",1010.51628568877,36298.9949767778
"4806","No","Yes",613.156883050935,17665.4777002854
"4807","No","Yes",1134.29609388583,18991.8212122874
"4808","No","No",295.843230952289,30602.2668901059
"4809","No","No",206.542522622756,14909.3670998149
"4810","No","Yes",1194.88454948711,18943.404107102
"4811","No","No",1551.09273901932,44596.7459450461
"4812","No","No",1210.17603584528,29460.1915964576
"4813","No","No",407.860744514451,47826.2137672175
"4814","No","No",1561.93370103165,56614.5215842107
"4815","No","No",602.545629733876,42798.0787516512
"4816","No","Yes",640.364464045535,14938.7665747018
"4817","No","No",927.66918169688,39934.14447599
"4818","No","Yes",1436.67638543656,14418.1660192643
"4819","No","No",552.551318491268,48267.6733934583
"4820","No","No",839.221856390201,43213.6941068639
"4821","No","Yes",1248.95418862755,14024.9049826448
"4822","No","No",929.925922127169,34535.4552697912
"4823","No","No",1198.83280693131,40275.0895006703
"4824","No","No",479.546200741212,24805.9234039658
"4825","No","No",1523.37267411625,44603.3263715189
"4826","No","No",990.806914855646,44966.2067311637
"4827","No","No",890.565507661517,14327.9907812727
"4828","No","No",678.466828551987,40803.5749599981
"4829","No","No",1462.22317250061,29574.2345717155
"4830","No","No",0,48781.4789584836
"4831","No","Yes",490.321797387267,24403.076672713
"4832","No","Yes",2216.32975271824,24737.0817608225
"4833","No","No",1336.36324569324,22245.5125152231
"4834","No","No",355.060596425255,36888.834322938
"4835","No","Yes",1094.52167851979,15617.5214126772
"4836","No","Yes",1969.40774822746,23674.376304138
"4837","No","No",1274.15968500757,41943.6775238178
"4838","No","No",224.170029650401,33542.3081803635
"4839","No","Yes",781.452305613836,18301.5837988415
"4840","No","No",1360.95293058479,46419.8169938933
"4841","No","No",210.797282960451,34468.8991617379
"4842","No","No",671.732605646625,26431.2725981968
"4843","No","No",275.063482035166,28266.1719807673
"4844","No","No",306.18951612478,30587.0067789107
"4845","No","No",293.614966998029,35945.6602591222
"4846","No","No",394.412944764813,52725.1599391522
"4847","No","No",1002.03064144933,49319.3386450314
"4848","No","No",357.517194944239,29711.3596849902
"4849","No","No",959.063053220935,55089.9902579629
"4850","No","No",734.335829432546,49128.5377547577
"4851","No","No",325.331214705521,32961.6570345964
"4852","No","No",910.482750696698,47298.7206352739
"4853","No","No",496.773960094507,38316.1354372489
"4854","No","No",416.632531990628,53256.279631146
"4855","No","Yes",1525.17230577148,21427.0936211979
"4856","No","No",124.754171373223,45763.7921971983
"4857","No","No",0,52069.2946661817
"4858","No","No",854.913441784921,39190.2956730676
"4859","No","No",796.173999850849,27278.3968655676
"4860","No","No",1044.86316119444,41726.2336321003
"4861","No","No",1690.66807668525,55412.7594728702
"4862","No","No",935.712944122396,34067.0542216434
"4863","No","No",306.459195932147,36812.6260466299
"4864","No","No",698.440564304252,21903.0380990784
"4865","No","No",973.990820697017,45941.0220132698
"4866","No","No",455.01778226024,45611.4764507006
"4867","No","Yes",1133.69962699868,26119.3046838437
"4868","No","No",598.193073483425,42734.4396915184
"4869","No","No",615.477640984647,45792.8999573859
"4870","No","No",1270.13090566539,47817.556620848
"4871","No","No",956.92261305132,50834.4083382596
"4872","No","Yes",1095.07681187651,18087.7198650968
"4873","No","No",143.56302092038,54411.2621205176
"4874","No","Yes",604.055091635501,21187.7105609043
"4875","No","No",0,38954.1074974879
"4876","No","No",0,37825.2214149259
"4877","No","Yes",1179.99452466386,18954.3937931283
"4878","No","No",337.651368245182,36787.019606017
"4879","No","No",855.596783860998,40412.7256889939
"4880","No","No",437.577738904493,55720.9729622054
"4881","No","Yes",596.806527835537,17397.3452404381
"4882","No","Yes",1725.38695367817,19915.9294906171
"4883","No","No",619.540641629038,39049.5004190243
"4884","No","No",627.987739442838,56266.1534015339
"4885","No","No",186.523520052956,36717.5145751435
"4886","No","Yes",1093.02752981373,12179.0381928815
"4887","No","No",684.270463762744,42437.914761452
"4888","No","No",963.092668712372,33110.3041089651
"4889","No","No",190.503430147143,53690.5912303308
"4890","No","No",68.3059319848821,52323.9021376562
"4891","No","No",1336.96983981619,33148.7686484431
"4892","No","No",372.42585488596,53695.2963822028
"4893","No","No",1095.02824134231,36282.4745747899
"4894","No","No",813.133978036295,59309.4766826029
"4895","No","No",31.7041228690579,42191.6591895568
"4896","No","No",655.832041740591,37267.5828058215
"4897","No","No",1340.24042928874,37464.3187635305
"4898","No","No",881.256511405969,41728.8460965129
"4899","No","No",684.715044622936,27951.1972343005
"4900","No","No",1158.37980436503,61054.7639322688
"4901","No","No",945.268218836474,38738.535388431
"4902","No","Yes",465.583628699224,15625.6335294608
"4903","No","No",999.63485520902,51385.6997288261
"4904","No","No",1143.68079469267,48044.8970095031
"4905","No","Yes",669.380788171137,16805.1937165814
"4906","No","Yes",838.970942885318,21407.4524046558
"4907","No","Yes",1384.73759704675,23083.6670866922
"4908","No","Yes",1401.0731910563,21811.2682494468
"4909","No","No",1591.1935826083,37501.4868906071
"4910","No","No",54.0505937218859,46662.1128147156
"4911","No","No",862.91971023389,36774.2159052201
"4912","No","No",712.207720692241,29546.4898791291
"4913","No","No",908.926548419013,40757.094123221
"4914","No","No",779.076437925571,31710.9740519862
"4915","No","Yes",99.3825624494822,16725.2450704367
"4916","No","No",1081.00133671618,32344.5785843183
"4917","No","No",1124.24913483266,27521.1515772183
"4918","No","No",923.406566904925,33122.2200360473
"4919","No","No",1096.21846957418,30653.2627505315
"4920","No","No",999.667805904389,51420.9767106412
"4921","No","No",0,16601.6352758397
"4922","No","Yes",517.821959362896,12673.9060745038
"4923","No","No",1461.88746288921,38559.0734798993
"4924","No","No",699.053865774975,41722.8060676529
"4925","No","Yes",1241.06189825831,4664.56504744167
"4926","No","No",1380.74980362947,52664.8765861706
"4927","No","No",561.964606426332,40555.4075956276
"4928","No","No",1122.74695829233,47140.8290039082
"4929","No","Yes",736.244056555543,13756.5455214776
"4930","No","No",643.481154792509,45925.8866630799
"4931","No","No",1141.15793778151,44333.2106253742
"4932","No","Yes",1273.89113671131,20104.4301645593
"4933","No","Yes",1306.0465368104,20776.3886514723
"4934","No","Yes",840.988909202537,15406.2074109607
"4935","No","No",289.245438541484,46550.5255056293
"4936","No","No",758.134285088564,33220.5754915223
"4937","No","No",930.716938556975,46501.2757116981
"4938","Yes","Yes",2177.15086891132,17659.7478222343
"4939","No","No",0,24892.9156877399
"4940","No","Yes",565.830058840306,21042.2277169228
"4941","No","No",1076.12658440012,23632.5203007461
"4942","No","Yes",1031.86992982705,18668.4835488198
"4943","No","Yes",768.403741780636,15417.8415373175
"4944","No","Yes",690.421049211989,19273.7323863817
"4945","No","No",469.844241251744,51308.3185057706
"4946","No","No",617.860025366679,50177.7733818704
"4947","No","Yes",1779.04969869116,15689.7766299802
"4948","No","No",0,47669.7040770385
"4949","Yes","No",1928.28028339196,35492.1282298837
"4950","No","No",1123.7192596589,56217.6848959766
"4951","No","No",794.646108436353,41033.5877136228
"4952","No","Yes",1428.0668825016,19818.2916600493
"4953","Yes","No",1028.76720660767,40346.8332744194
"4954","No","No",1006.20297747562,50501.7615442059
"4955","No","No",691.751713492646,45420.9691166199
"4956","No","No",524.83815013125,41268.4236470728
"4957","No","No",493.914160754547,37409.1839297761
"4958","No","No",1520.98047817005,37510.539355771
"4959","No","No",665.039756552143,47062.2530853035
"4960","No","Yes",1520.44210105793,18462.4306583934
"4961","No","No",1094.78047257691,34190.8765220756
"4962","No","No",735.091040828818,44933.3231505618
"4963","No","No",681.69357639072,33327.1130348719
"4964","No","No",376.034543992985,50748.2717350015
"4965","Yes","No",2037.94335373876,43016.0721799483
"4966","No","Yes",1630.19958945524,14232.66153338
"4967","No","Yes",0,21881.7059101601
"4968","No","No",184.427531874368,36731.6269503081
"4969","No","No",349.660665623366,39391.3223138929
"4970","No","No",673.555270265584,49169.7285398627
"4971","No","No",82.7245249090897,42048.4447973563
"4972","No","No",1247.12060523244,50539.9074463094
"4973","No","Yes",640.639543022389,29236.6301917974
"4974","No","No",273.446972354708,52492.7498232478
"4975","No","No",0,35377.140726393
"4976","No","No",803.831186814095,33417.7724119945
"4977","No","No",1248.37574903962,37469.8646245304
"4978","No","No",0,40348.314179772
"4979","No","Yes",1307.20497291736,19381.5413197222
"4980","No","No",0,45793.3930350652
"4981","No","No",1186.09870513382,50353.9254295505
"4982","No","Yes",1037.57301799006,18769.5790241645
"4983","Yes","Yes",1878.00114593831,17473.1839905825
"4984","No","No",736.234836930656,36313.6335527216
"4985","No","No",260.16217537031,33551.7152965123
"4986","No","Yes",1103.68178180144,14225.7213396247
"4987","No","Yes",886.059316803902,10378.6422946741
"4988","No","No",221.163782753085,43072.8942477535
"4989","No","No",1098.05775086164,29402.8731375652
"4990","No","No",0,38686.6752901228
"4991","No","No",826.949810434257,46946.0524494613
"4992","No","No",569.96772151171,38982.0174276273
"4993","No","No",1316.05741075183,45308.5638370534
"4994","No","No",86.7255186935218,51257.5834623667
"4995","No","No",1458.83532494993,24850.584777006
"4996","No","No",1243.45134821097,41184.8989068281
"4997","No","No",790.755760017408,41909.1076701414
"4998","No","No",1997.172809798,50273.6010347617
"4999","No","No",960.912667754959,29304.4577217283
"5000","No","No",646.856743672621,28836.7571332658
"5001","No","Yes",287.64948929053,15441.3667468103
"5002","No","No",630.665336901894,56266.7584991141
"5003","No","No",610.500866970475,28664.0426596326
"5004","No","No",1224.2294141262,34490.2250037052
"5005","No","No",326.873676115074,48756.0275802893
"5006","No","Yes",325.282819884072,15644.241969999
"5007","No","Yes",623.757859297093,19191.9669165633
"5008","No","No",1394.47677556729,44092.5486778967
"5009","No","No",929.085467738706,35571.7766510459
"5010","No","No",887.743410952946,43478.8851944059
"5011","No","No",0,26598.6432244359
"5012","No","Yes",127.222763146235,9801.50016731735
"5013","No","No",986.358895012314,41688.0643432576
"5014","No","Yes",1430.32550175549,20846.9633160869
"5015","Yes","No",1026.35885461087,56182.1299303767
"5016","No","No",556.017498447854,33705.0786503377
"5017","No","No",1018.43623649899,53962.2776889911
"5018","No","Yes",1083.71139106704,17040.5193960842
"5019","No","No",1602.8069430866,34700.2659635459
"5020","No","No",921.347854114992,47610.9551969613
"5021","No","Yes",769.449761812267,21717.9437219793
"5022","No","Yes",289.834142708942,13914.6037195669
"5023","No","No",955.339164891699,46310.1075238865
"5024","No","No",387.786699789214,38745.3611515006
"5025","No","Yes",1340.96638891561,18725.9542084895
"5026","No","No",1252.89403929371,35587.7235974753
"5027","No","No",435.393159422012,32907.3324671792
"5028","No","No",491.048506081094,37219.5210759241
"5029","No","No",1112.50930363905,44298.3856437862
"5030","No","No",828.98020983988,55696.8036988707
"5031","No","No",568.352003557277,38963.9592439543
"5032","No","Yes",672.410410996218,16508.4250338839
"5033","No","No",931.784107242425,37280.4101469294
"5034","No","Yes",982.773097327345,24145.529432349
"5035","No","Yes",686.528758338996,25779.7833400234
"5036","No","No",1069.52691083267,34694.0007489652
"5037","No","No",863.954854648355,17159.1633825291
"5038","No","Yes",976.451484674357,10541.5702251666
"5039","Yes","No",1586.50219904833,48925.9333773086
"5040","No","No",544.126248458484,49900.5840191049
"5041","No","No",198.929141819723,50463.775492981
"5042","No","No",1026.19409513964,35685.4837341184
"5043","No","No",1317.5648641479,40731.7639670574
"5044","No","No",0,33322.4823586788
"5045","No","No",358.613776928252,47688.8047213017
"5046","No","Yes",1511.37287983489,14982.5985069438
"5047","No","No",359.916678749368,52248.1180712292
"5048","No","Yes",529.343106295732,17245.8006101566
"5049","No","Yes",888.564819034569,21820.217178924
"5050","Yes","No",1954.33329923565,35067.5349242276
"5051","No","No",131.747075963338,36947.7786180191
"5052","No","No",518.899020411541,46192.5543655143
"5053","No","Yes",1593.32749210594,11570.7286227592
"5054","Yes","Yes",1473.03456108025,18108.9616707213
"5055","No","No",1527.92479896565,44640.7447447419
"5056","No","No",1349.90847931826,48103.0307778647
"5057","No","No",371.881863400765,46682.5469471478
"5058","No","No",758.021077838799,55278.5919447949
"5059","No","No",1306.83203413692,57561.4112609049
"5060","No","No",605.847239898752,58892.9054842696
"5061","No","No",1085.24381139608,21805.7923070268
"5062","No","Yes",1582.23460960239,20214.6895370087
"5063","No","No",1078.48346006262,40944.2697839083
"5064","No","No",711.663817704793,35741.2021602845
"5065","No","No",1204.45918317075,39418.7070865023
"5066","No","Yes",885.209895707751,18693.7271194525
"5067","No","No",1795.88371131615,25878.51931005
"5068","No","No",230.916226613247,36139.4105825641
"5069","No","Yes",1038.55559796008,17055.7950391374
"5070","No","No",731.477128185087,18843.2713163752
"5071","No","No",1734.22804374841,37621.6331723075
"5072","No","No",640.520349799475,30327.6077411327
"5073","No","No",218.11485408738,38766.1076269185
"5074","No","No",912.390423465777,26857.1718991427
"5075","No","No",726.04214847851,42272.4378377617
"5076","No","No",408.619017832787,35622.5983889609
"5077","No","No",309.740932405142,33333.5647205627
"5078","No","No",1392.6193716631,54027.1994679767
"5079","No","Yes",587.044939922979,15939.8195167005
"5080","No","No",898.272656314256,48233.875497513
"5081","No","No",325.841005644733,37392.681507636
"5082","No","No",896.027135886118,30452.0368389113
"5083","No","No",803.764577734824,48013.6101491714
"5084","No","No",509.195633409241,46368.3940729585
"5085","No","Yes",1031.01773195343,16568.6330047446
"5086","No","No",782.413394401973,34830.6262855281
"5087","No","No",1168.37748585253,42388.1210151284
"5088","No","No",561.623001826995,36266.8058793083
"5089","No","No",850.518457429395,35683.8322872263
"5090","No","No",752.510218476464,18960.365575285
"5091","No","No",245.505961457986,31307.2436442818
"5092","Yes","No",1733.82467870178,26330.5117293113
"5093","No","No",590.666032001301,59574.2177200191
"5094","No","Yes",711.582822595621,17780.7598281387
"5095","No","Yes",831.845343849951,19797.3565884764
"5096","No","No",1344.71128149314,26215.3918353377
"5097","No","No",224.691145051763,55040.6127338996
"5098","No","No",361.742665529608,54071.3405585766
"5099","No","No",1322.81096388371,48016.6173357397
"5100","No","No",1043.37439662006,28079.2626836385
"5101","No","No",1475.51848523651,48653.6837529258
"5102","No","No",812.083886904557,41821.2997786732
"5103","No","Yes",287.792362307828,13060.0085705572
"5104","No","No",775.309789469854,38721.2458944943
"5105","No","No",344.624077138957,43246.5779895621
"5106","No","Yes",372.430810388496,17243.0493024185
"5107","No","Yes",2088.52910874916,18078.3291678441
"5108","No","No",946.398166907214,30770.0667301801
"5109","No","No",675.376726291362,36921.5759561219
"5110","No","No",1012.76845381661,36406.2410883707
"5111","No","Yes",1011.93720941388,18509.2971336598
"5112","No","No",452.372650854573,59429.1231408102
"5113","No","No",1092.49125891644,30654.685256551
"5114","No","No",71.5776897613994,61588.2545022497
"5115","No","No",1319.04171817484,57768.899984811
"5116","No","No",266.277758688327,41204.2949910119
"5117","No","No",370.963648959728,25321.3528225484
"5118","No","No",848.724426358956,41585.9525525882
"5119","No","No",507.396131594456,35353.4055306309
"5120","No","Yes",267.32782909653,19698.7138804734
"5121","No","No",420.899331867238,39764.7024313117
"5122","No","No",129.596448519056,52169.2315710792
"5123","No","No",327.240437287516,40061.6245140304
"5124","No","No",678.07110557078,63791.8242046779
"5125","No","Yes",199.127092682206,19913.602502446
"5126","No","No",1215.6982445086,33598.9707553906
"5127","No","No",460.681505823092,37129.2708182323
"5128","No","Yes",1300.72043313613,9266.52973132816
"5129","No","No",534.568707980078,37281.0402106883
"5130","No","No",1308.52917152034,57190.8748728432
"5131","No","No",507.065032012892,42753.1919155811
"5132","No","No",197.327265397348,24562.119655863
"5133","No","Yes",819.834292040018,16412.9970215885
"5134","No","No",0,57003.5937659082
"5135","No","No",1489.78033181431,30296.6104370113
"5136","No","No",581.520818602129,53100.5724783833
"5137","No","Yes",286.291564637705,19553.6356059805
"5138","No","No",371.865220141344,45949.9142067639
"5139","No","Yes",992.780221091888,16585.3242820471
"5140","No","Yes",758.13852355797,23758.5776345545
"5141","No","No",1174.19490858526,35533.4845189991
"5142","No","Yes",1196.09826443678,23851.2213915256
"5143","No","No",613.231402434956,34664.5713252985
"5144","No","No",968.972340284544,33763.1902797405
"5145","No","Yes",1151.29871640683,14384.7670811016
"5146","No","No",848.702739852829,35192.8388667608
"5147","No","Yes",989.090770148173,20262.0219405694
"5148","No","No",92.7406629973356,40722.5906489062
"5149","No","No",1654.93417536157,33179.7839229244
"5150","No","No",212.497928709852,53619.4757063793
"5151","No","No",73.8932348303483,44308.9054028973
"5152","No","No",692.15523399331,37592.2608516004
"5153","No","No",957.583962212343,40315.7728375368
"5154","No","No",69.023877541276,41566.5820708201
"5155","No","No",1394.12544339822,42921.017077944
"5156","No","No",1317.86646809989,36610.8861107082
"5157","No","No",602.096395581271,53553.7224709536
"5158","No","No",430.11822118801,41105.2876346529
"5159","No","No",211.983028197665,33448.2447835816
"5160","No","No",544.143145637753,33861.5539040239
"5161","No","Yes",943.843064280149,16701.7830932761
"5162","Yes","No",1412.19244842734,37283.0081014143
"5163","No","No",1416.44476989919,33099.4968843458
"5164","No","No",193.283164669805,37376.6497130654
"5165","No","No",857.23644841428,67124.0570892684
"5166","No","No",1132.07848219228,29715.3049436
"5167","No","No",1489.3570273597,37758.4128863958
"5168","No","No",1527.35932473709,33923.5287652524
"5169","No","Yes",1427.04938367718,18268.5268315544
"5170","No","No",964.863037287274,43744.9833544683
"5171","No","No",326.15092990097,32919.9710526922
"5172","No","No",866.299668540652,65931.951109459
"5173","No","No",626.160154224477,34007.9083467236
"5174","No","No",836.192864603416,43275.1452255696
"5175","No","No",826.558020541498,44371.747340566
"5176","No","No",102.122522558306,33300.1875216945
"5177","No","No",1609.29703093561,52752.9684251831
"5178","No","Yes",140.455621890314,11179.4978592832
"5179","No","No",1147.98069460388,20766.3950725174
"5180","No","No",855.292565736026,35749.0327861947
"5181","No","No",1682.20185616143,36366.9912912394
"5182","No","No",689.820773571356,30304.7803009091
"5183","No","No",1706.04630524722,32900.7114384092
"5184","No","No",479.521909132235,43743.6223126641
"5185","No","Yes",765.363356845999,11976.408494525
"5186","No","No",894.329102865568,45250.7303099136
"5187","No","No",993.647092771748,26768.9566762714
"5188","No","Yes",378.49251967355,18659.7721270352
"5189","No","Yes",576.065007536312,13536.6090365199
"5190","Yes","Yes",2113.62976138136,21100.7217870678
"5191","No","No",542.6962061198,40996.2123286467
"5192","No","Yes",493.629546126545,20500.2126317106
"5193","No","No",0,29322.6313944581
"5194","No","No",1185.5812526407,47782.4971306183
"5195","No","Yes",1719.87111940058,22431.8580010025
"5196","No","No",495.750069071721,44361.3911905098
"5197","No","No",533.894154434264,33785.7461367448
"5198","No","No",150.142705477658,38610.3773141685
"5199","No","Yes",1298.90149315481,14975.264926979
"5200","No","No",957.238959913911,57698.4082232021
"5201","No","No",836.933291572557,51031.9398320695
"5202","No","Yes",1400.70282406811,23083.2601589174
"5203","No","No",857.329141983597,25742.1737665286
"5204","No","No",876.765555277728,49291.1665644759
"5205","No","No",979.316714853783,27534.9121409756
"5206","No","Yes",491.704320814421,8352.20898846706
"5207","No","No",1621.7116428388,37970.4740221371
"5208","No","No",643.61550941031,44251.0158829684
"5209","No","Yes",343.798638052777,19971.7762095045
"5210","Yes","No",1711.16909322993,18579.1024704064
"5211","No","No",1170.02740630312,33332.6257830801
"5212","No","Yes",638.204399721592,14641.383733897
"5213","No","Yes",1457.83941086875,18496.9320583648
"5214","No","No",383.860154972825,31749.6784240199
"5215","No","No",618.031682935868,44419.5476702595
"5216","No","No",565.218299871113,29015.7240563929
"5217","No","No",290.145958733314,32862.5552897116
"5218","No","Yes",576.944278840372,22800.8670501164
"5219","No","No",633.127115560167,54034.3550059018
"5220","No","Yes",1350.62900231003,11854.9844393708
"5221","No","Yes",893.558271644587,23033.8122137051
"5222","No","Yes",895.318965804225,20869.355939084
"5223","No","No",1470.57249106757,44490.321073276
"5224","No","No",789.256511246828,43835.3402744718
"5225","No","No",218.769004560376,52074.6627185221
"5226","No","Yes",531.693802744842,15794.5816061376
"5227","No","Yes",1268.53785480677,21336.8901013062
"5228","No","No",0,34040.3129149758
"5229","No","No",1046.56286488318,26555.4889385252
"5230","No","No",389.992175159101,29032.1126978627
"5231","No","No",642.234627903716,35800.0838167837
"5232","No","No",391.989952172087,35012.8696092424
"5233","No","No",1483.68901084985,58310.5123479193
"5234","No","Yes",438.57477088502,17439.1149542308
"5235","No","No",897.861693678463,47557.5240974682
"5236","No","No",0,41693.3858354334
"5237","No","Yes",776.639070176992,15338.8886029457
"5238","No","No",112.470708913449,34320.0261502942
"5239","No","No",634.477775997833,56726.1200096328
"5240","No","No",791.94749610165,47886.5232680271
"5241","No","No",1088.28530583846,43134.5314123793
"5242","No","Yes",2018.35853570806,15472.8495805739
"5243","No","No",1470.6352731309,40080.214746584
"5244","No","Yes",611.208896633182,19733.2518009617
"5245","No","No",1117.08002839593,42388.4155540623
"5246","No","No",1589.99091896711,42268.2798533658
"5247","No","No",0,57898.4151586563
"5248","No","No",636.238668216172,39172.3633653161
"5249","No","Yes",714.397171134248,17412.7179760367
"5250","No","Yes",946.779603881376,12881.4021094055
"5251","No","No",108.056775837302,41840.3258124129
"5252","No","No",816.888352751253,52492.3459447917
"5253","No","No",5.88381627957153,45840.4724566522
"5254","No","No",761.75063431318,13889.1325349332
"5255","No","Yes",1173.7813429519,15823.8054768027
"5256","No","No",1418.47537255722,38034.1444323705
"5257","No","Yes",864.357126766229,10484.7705038946
"5258","No","Yes",1101.16009199422,14307.0010845492
"5259","No","No",0,46615.6980277052
"5260","No","No",0,43782.2558593837
"5261","No","No",582.317221802386,29239.8460732045
"5262","No","No",369.050487595412,38418.8665205537
"5263","No","No",966.051877458136,10174.7298015834
"5264","No","No",399.445615081295,54329.9229320772
"5265","No","No",51.8884865930952,41839.1733870635
"5266","No","No",460.879415522297,40046.0293272785
"5267","No","No",1139.0127276922,36958.5780108845
"5268","No","No",707.573102936183,48790.4022479433
"5269","No","Yes",1034.60315139143,18614.037496336
"5270","No","No",118.869800099736,42823.5719634177
"5271","No","Yes",461.004340791417,15554.0297650627
"5272","No","No",1057.27499998475,42731.876409648
"5273","No","No",1035.48588062782,41714.3737690575
"5274","No","No",1010.27951299617,34188.9846488206
"5275","No","No",0,41199.7993550432
"5276","No","No",656.023725381919,36349.436988707
"5277","No","No",539.137500807565,23561.4305215043
"5278","No","No",1377.46206134276,49406.072701575
"5279","No","Yes",1322.14599591033,21272.5759430238
"5280","No","Yes",1393.58949068025,23261.8632426465
"5281","No","No",1324.0139835537,45114.740311117
"5282","No","No",1020.52955729319,35652.6634579727
"5283","No","No",2033.54023491605,41629.2372883968
"5284","No","No",888.642912599117,19942.6028703941
"5285","No","No",1350.1162674817,39555.962310795
"5286","Yes","Yes",1936.06186248878,13377.8288411616
"5287","No","Yes",1406.91736397007,16427.1007755423
"5288","No","No",282.838124549013,29478.1380100909
"5289","No","No",601.082571361275,25319.3660998709
"5290","No","Yes",1639.42660841751,14682.7766583761
"5291","No","Yes",48.528095875895,16647.3061614762
"5292","No","No",324.504531784576,51139.8768545561
"5293","No","Yes",886.401507936787,13995.8895892463
"5294","No","No",967.441573778468,51674.4193685904
"5295","No","No",711.737607680277,45419.8819421071
"5296","No","No",1547.8807897846,50638.3395897593
"5297","No","Yes",1200.59444464954,13772.9891155444
"5298","No","No",0,45201.1287123812
"5299","No","No",1454.98158871843,43821.2213471973
"5300","No","No",355.442338612803,46147.517874115
"5301","No","No",0,46927.7844526087
"5302","No","No",0,49784.3363547474
"5303","No","No",282.865313074855,39961.0398249001
"5304","No","No",303.7151176961,29048.0352380344
"5305","No","No",157.763001571446,36206.2856905703
"5306","No","No",300.000466508024,41189.4522010561
"5307","No","No",463.982072534495,43330.5524368375
"5308","No","No",1654.23495626913,39580.1297092869
"5309","No","No",762.57449743267,45526.8947859781
"5310","No","Yes",1887.88171576879,21901.1824969382
"5311","No","No",1206.26349180131,34621.7733090617
"5312","No","Yes",247.653103614015,20444.7524326315
"5313","No","No",576.796119948856,39209.8328678459
"5314","No","Yes",1214.40830614039,18055.2384673481
"5315","No","No",987.745528266879,40243.6200175724
"5316","No","Yes",1257.01612997428,19247.9006122535
"5317","No","No",524.085618869391,40319.1181371203
"5318","No","Yes",1236.20709180902,23364.0486787434
"5319","No","No",1008.67500060109,68610.4120555314
"5320","No","Yes",1474.77960459732,17382.4673857923
"5321","No","No",772.375314624076,36997.3367352005
"5322","No","No",701.552593711794,39526.5771906576
"5323","No","No",0,28798.0399729688
"5324","No","Yes",1165.89551137885,14969.287247901
"5325","No","No",0,28226.557953656
"5326","No","No",1429.22104820795,40564.8863059473
"5327","No","Yes",806.669235669924,17359.6747587048
"5328","No","No",528.089315636181,46389.3406847376
"5329","No","No",963.428148153574,60084.1264776155
"5330","No","Yes",764.527166698458,22578.6356703964
"5331","No","No",573.699136954666,49465.7548491151
"5332","No","No",1410.36924005266,62395.0381452745
"5333","No","No",816.741027053221,47272.3336067607
"5334","No","Yes",1220.52491385296,12195.3619458105
"5335","No","No",245.438753254707,54133.0383062382
"5336","No","Yes",1083.91128186265,8584.07757718566
"5337","No","No",485.348806539971,14676.2955127899
"5338","No","No",677.758794018199,50309.0647156314
"5339","No","No",286.660874697687,34499.5326305991
"5340","No","Yes",843.920206689305,20754.0241650763
"5341","No","No",831.836619855653,41247.6422019852
"5342","No","No",1024.32637691556,33832.4896292671
"5343","No","No",1277.12309805961,42472.9082664883
"5344","No","Yes",1221.94391099058,19127.3502338688
"5345","No","No",967.249281733551,27663.1340686837
"5346","No","Yes",1556.90441828681,19272.2364832438
"5347","No","Yes",419.729554950423,24679.7314674036
"5348","No","No",276.164884319754,53625.7140947421
"5349","No","Yes",1407.49727226474,14688.3889037995
"5350","No","No",1041.08802327575,39515.8430210841
"5351","No","No",182.325444181601,39703.2688512737
"5352","No","No",162.218486671005,41381.2707079487
"5353","No","No",405.273980579651,52571.1841278696
"5354","No","No",887.158361413145,51519.1264793758
"5355","Yes","No",1844.88383884583,35508.6754221796
"5356","No","No",792.981525937216,37549.9499998242
"5357","No","Yes",942.140104921355,23835.3854718109
"5358","No","No",938.785896813178,14524.3576239936
"5359","No","No",18.904202687713,35590.2325848995
"5360","No","Yes",599.470983151448,18575.4084167931
"5361","No","Yes",679.809546964661,19263.1233225777
"5362","No","No",601.26329258483,24400.0962522342
"5363","No","No",132.081039256955,51694.7395595321
"5364","No","No",743.032968860053,25186.4259047665
"5365","No","No",728.186897542766,52515.067548616
"5366","No","No",467.24756371474,33644.2578423146
"5367","No","Yes",1104.93334843928,17377.4981465423
"5368","No","Yes",310.198181760548,14672.9256971296
"5369","No","No",1575.71270470809,45957.2447176986
"5370","No","Yes",1370.24893741273,13358.27420233
"5371","No","No",1593.43280272516,73554.2334951972
"5372","No","No",463.002217743353,42297.457797763
"5373","No","Yes",786.106023872725,20954.4515707624
"5374","No","Yes",731.511801077057,26505.3445248465
"5375","No","Yes",468.273219312504,27054.8938154627
"5376","No","No",219.145709509397,37993.0436785143
"5377","No","Yes",846.959069993446,20659.8532659399
"5378","No","No",702.15926284292,57468.4044412075
"5379","No","Yes",1381.64443433899,11059.5680660885
"5380","No","No",1455.50583675848,33455.0496944581
"5381","No","No",1357.90971764829,43377.1121805759
"5382","No","No",1032.62707913177,23349.8301266316
"5383","No","No",798.85963667668,48336.1293083261
"5384","No","Yes",117.903081769161,14649.8580553618
"5385","No","No",234.743603957668,27499.9536219838
"5386","No","No",890.266872175803,36631.498422027
"5387","No","Yes",518.478839254005,18635.5097072673
"5388","No","No",1124.49595976431,31739.563746405
"5389","No","No",1468.78805535161,38521.8701999955
"5390","No","No",938.106178830587,13405.2109419603
"5391","No","No",722.029162469719,26984.2562874375
"5392","No","No",610.641708045754,42899.8481269583
"5393","No","No",407.771300691402,18555.3235405547
"5394","No","No",1057.22881723348,58133.5278357482
"5395","No","No",1262.40451593839,57158.7550202353
"5396","No","Yes",1082.67454484205,22417.5561475327
"5397","Yes","No",1969.94292393429,29415.7532920711
"5398","No","No",760.099642771661,45921.3431313597
"5399","No","No",929.396254308671,23133.6598112204
"5400","No","No",1177.2495984995,35419.6103097622
"5401","No","No",309.27068081804,42226.65131333
"5402","No","No",981.943933832087,38483.5318863506
"5403","No","No",200.162803965695,24280.1301574474
"5404","No","No",1630.48300985988,54323.4228868628
"5405","No","No",947.235416304317,28310.4937788445
"5406","No","No",537.396354192918,40828.140116245
"5407","No","Yes",1292.56878393393,14859.2400849317
"5408","No","No",356.827645393509,39444.8253488201
"5409","No","No",500.155117007218,34437.7068935173
"5410","No","No",1345.70698005887,40802.8865154653
"5411","No","Yes",1091.13975599585,19990.8353295959
"5412","No","No",1025.52762476694,22741.3913036463
"5413","No","No",403.240024857581,42993.4364142112
"5414","No","No",699.342672690461,36957.6805692933
"5415","No","No",1445.80516373713,15666.0574301554
"5416","No","Yes",963.899257788196,16561.255635469
"5417","No","No",542.765601644382,52625.4157104306
"5418","No","No",766.929094869356,25376.1037623839
"5419","No","No",808.429127594511,29212.9047289716
"5420","No","No",736.536729918891,46416.1829197087
"5421","No","No",1076.13982317578,31845.9515280532
"5422","No","Yes",731.314859465698,11510.1514733817
"5423","No","No",236.003705939958,38202.5138392406
"5424","No","No",1382.4549524961,40394.3086001964
"5425","No","Yes",915.571546852198,22586.3382776513
"5426","No","No",176.596945808558,48957.7563824849
"5427","No","Yes",639.99962630239,19900.0064038998
"5428","No","Yes",950.678008585236,20435.8178098611
"5429","No","No",1320.71111520739,29105.4424418385
"5430","No","Yes",1203.02939896646,15952.4520239732
"5431","No","No",1335.93525388814,19482.2062817827
"5432","Yes","Yes",1647.28224769539,16154.4622803728
"5433","No","Yes",1434.38646448511,15761.6996158335
"5434","No","No",307.476238460687,36120.9685897979
"5435","No","Yes",1277.89489991076,20649.9299868452
"5436","No","No",998.393069937923,46051.9228596918
"5437","No","No",561.343258988322,44403.9942871378
"5438","No","Yes",1577.08358059243,15230.8206004836
"5439","No","Yes",412.071444309261,12588.9711331098
"5440","Yes","Yes",1758.40657080375,14272.2737755857
"5441","No","Yes",251.468193783358,19373.8880179322
"5442","No","No",1353.13173451443,40164.0485885543
"5443","No","No",10.1944566929947,32641.9325638431
"5444","No","No",933.554734219053,33633.1486889292
"5445","No","Yes",1692.75253804708,23743.8419510997
"5446","No","No",663.250479154119,36276.8639525225
"5447","No","No",51.5304014162657,36851.9128724154
"5448","No","No",797.742647053305,53398.9285871911
"5449","No","No",0,50243.9311704645
"5450","No","No",1217.07276191295,62764.0976635433
"5451","No","No",0,44980.2930390375
"5452","No","No",741.844666024469,45808.5402807401
"5453","No","No",455.184490479099,43513.3506271492
"5454","No","Yes",1788.14082161796,26228.7104423056
"5455","No","No",1253.77341200631,28153.0140567459
"5456","No","No",918.09337371899,44112.8101652491
"5457","No","No",0,30241.9485766869
"5458","No","Yes",699.173401299605,17572.8310644687
"5459","No","No",171.996376988763,33505.4934304586
"5460","No","No",1326.34885429976,46587.0802885257
"5461","No","No",1275.49530305434,40026.2859538126
"5462","Yes","Yes",2247.42188932705,17926.7230141293
"5463","No","No",993.77211910269,52645.6630948018
"5464","No","No",997.379504092706,34824.848061927
"5465","No","Yes",652.372669135781,12139.0571198897
"5466","No","No",1124.88618865227,41989.0341969489
"5467","No","No",1608.81844748478,36721.1034831342
"5468","No","Yes",776.606472977211,4755.25219033558
"5469","No","No",845.020724913514,33895.2378498713
"5470","No","No",570.054698668476,42157.8077120892
"5471","No","No",186.438134380559,13325.1213335376
"5472","No","No",0,48672.9560136099
"5473","No","No",1429.48871595734,31995.4879988516
"5474","No","No",295.056674121521,39479.7516839202
"5475","No","No",896.581276619335,54840.0578653019
"5476","No","Yes",1562.53232379506,13754.3340702828
"5477","No","No",889.308741715182,33036.2447896186
"5478","No","Yes",1805.68295452897,20727.6402234923
"5479","No","No",1334.1059277835,43704.4308597779
"5480","No","No",527.940223666282,56820.8207444954
"5481","No","No",1282.07694622826,52753.0765682445
"5482","No","No",932.828729648173,48165.1802367937
"5483","No","Yes",1850.88824722219,21575.7633259386
"5484","No","No",455.190784198115,36488.1578499807
"5485","No","No",748.286644782209,45626.9945465569
"5486","No","No",874.478742268915,33485.5205119318
"5487","No","Yes",538.706536221086,16350.3601824157
"5488","No","No",841.85172718326,48615.0145715462
"5489","No","No",1814.16819486658,28322.8391895754
"5490","No","Yes",365.704408735642,15953.9756084283
"5491","No","No",401.180757478873,39686.6759484968
"5492","No","No",2096.13639137234,49992.5298141708
"5493","No","No",1429.87855917136,33452.5875602965
"5494","No","Yes",1737.34771498223,19202.7101911288
"5495","No","No",1043.39033839845,45309.9489669253
"5496","No","Yes",1155.45654811422,22194.4778758481
"5497","No","Yes",931.649113036332,19179.4467878978
"5498","No","No",146.755460850544,49925.0693858343
"5499","No","No",1406.9476517096,27667.8360308734
"5500","No","Yes",705.896825598943,18447.8760934385
"5501","No","No",641.803940740381,32628.6412619614
"5502","No","No",379.603563484232,42862.4888881861
"5503","No","No",706.22679193318,33920.4569663211
"5504","No","No",577.131996919527,43616.0962322882
"5505","No","No",90.99981606327,52387.591991635
"5506","No","No",549.39904175625,48424.4043821238
"5507","Yes","Yes",1102.43498209896,17391.779646823
"5508","No","Yes",1228.33879593452,18129.6940624009
"5509","No","Yes",974.014384870528,20108.1035841497
"5510","No","No",263.656061763757,50782.8322029761
"5511","No","No",847.069147369421,35581.2901156659
"5512","No","No",835.191809113554,39977.1314704097
"5513","No","No",757.907711197946,37088.8395732868
"5514","No","Yes",1013.66576572018,13821.0900819589
"5515","No","Yes",643.577000917672,28765.910525223
"5516","No","Yes",757.272763497851,18876.0891573188
"5517","No","No",1248.27894291764,41100.6261742015
"5518","No","No",699.285850186167,41475.9820952634
"5519","No","No",866.402631163086,18586.7271653018
"5520","No","No",697.666382564536,35402.2679567815
"5521","No","No",899.096892824399,39630.6387900137
"5522","No","No",674.204629620179,46481.9526798699
"5523","No","No",902.92395856054,33778.4032313443
"5524","No","No",1591.75580588687,46259.6599752908
"5525","No","No",484.217358639711,30178.5752399951
"5526","No","No",751.68728655516,51327.3172739106
"5527","No","No",604.194947116334,38292.7690977453
"5528","No","No",640.633469892213,46075.4150579683
"5529","No","Yes",782.445165134074,23447.2133813526
"5530","No","No",1026.95519673516,40307.0110217945
"5531","No","Yes",862.902253834498,16157.8665298407
"5532","No","Yes",780.512125824943,14813.3415822723
"5533","No","No",878.009385167249,15262.9351149015
"5534","No","No",481.862028269873,32928.2390042737
"5535","No","Yes",1579.99003223015,13274.7315023903
"5536","No","No",1.67402590271342,23001.6670760201
"5537","No","No",514.849025301181,37656.8478724252
"5538","No","No",2087.67874085049,44997.3643500816
"5539","No","Yes",973.514710211862,20770.4840331544
"5540","No","No",528.872496895311,45235.4971935422
"5541","No","No",1131.41243362462,37663.2268693235
"5542","No","No",955.343370548383,40368.5978855312
"5543","No","No",759.032260453678,45774.3835405276
"5544","No","No",1023.85241371526,31492.9983652979
"5545","No","No",1096.07775578352,30374.8347206174
"5546","No","No",829.32969070991,48734.1661690429
"5547","No","No",792.356871704534,39911.4272279602
"5548","No","No",882.264534452783,18379.5148607438
"5549","No","Yes",733.847089936321,16400.1283445279
"5550","No","No",553.519436208433,45385.3113268622
"5551","No","Yes",822.959594920215,8918.70253904663
"5552","No","No",493.95999044986,34621.7576420746
"5553","No","No",88.2540567051822,43927.315885611
"5554","No","No",0,45077.5745780792
"5555","No","No",611.825734885616,21716.5343596837
"5556","No","Yes",1096.69213583028,20856.5876398187
"5557","No","No",191.611070550308,35119.5813835898
"5558","No","No",447.958161182748,54044.8231061654
"5559","No","No",1149.68918788583,39974.5630184873
"5560","No","Yes",463.300004723583,16416.6127293146
"5561","No","No",1347.63559514332,58953.0926684651
"5562","No","Yes",0,16979.8930728145
"5563","No","No",643.010847393226,27735.1582360746
"5564","No","Yes",365.222955835046,15375.1517219432
"5565","No","No",289.681569442895,45991.0443210763
"5566","No","No",578.290656572117,48044.3844904488
"5567","No","No",1186.93272905935,51742.5764367357
"5568","No","No",426.007283754863,50874.5651795002
"5569","No","No",1115.73877693947,48125.6467552087
"5570","No","Yes",932.695814190911,18743.3137407493
"5571","No","Yes",1048.48759625782,22935.5954509348
"5572","No","No",768.372947015316,44405.3087453
"5573","No","No",785.494708435686,50683.1541835067
"5574","No","Yes",865.205866068633,22081.5816392531
"5575","Yes","No",1801.80118346806,24152.264834226
"5576","No","No",950.341274781584,64396.1653447148
"5577","No","No",787.042934912837,46266.9414512335
"5578","No","No",982.839988770985,32419.665047656
"5579","No","No",1175.38957718705,35339.556671872
"5580","No","Yes",887.284872180981,13132.901347241
"5581","No","No",801.994436947096,46439.0035534273
"5582","No","No",1096.24673322037,42685.1072902999
"5583","No","No",628.855331566381,40490.4260532201
"5584","No","Yes",567.722580913533,14892.3244451469
"5585","No","Yes",843.008786630283,22037.4704117827
"5586","No","Yes",1217.30987516687,10520.0430746812
"5587","No","No",0,10593.9212460847
"5588","No","No",1186.77795118128,38581.9278558539
"5589","No","No",439.043893107684,40148.108054317
"5590","No","Yes",778.999353313474,10489.5735268384
"5591","No","Yes",742.549323468977,24505.0193460592
"5592","No","Yes",763.337623826175,15849.8639600086
"5593","No","No",408.116029993621,39388.3175611827
"5594","No","No",1071.25835436445,36869.0525759518
"5595","No","No",1373.57560129775,22182.4165874166
"5596","No","No",52.9180891783735,30506.358853652
"5597","No","Yes",1140.45436292959,18991.886035461
"5598","No","No",1162.29922732475,47513.124771207
"5599","No","Yes",1528.49437007321,22664.3493517211
"5600","No","No",991.961167212548,49910.0968835837
"5601","No","No",592.432321380633,32283.2641204669
"5602","No","No",848.327608233813,30472.7179252633
"5603","No","No",309.517982126985,35293.1935706219
"5604","No","Yes",425.844645376775,15324.758004218
"5605","No","Yes",1268.48611351102,19862.0444216306
"5606","No","Yes",898.631950753244,15293.8740494594
"5607","No","No",274.956946474187,24102.3915140994
"5608","No","No",399.443127979133,45360.9322533118
"5609","No","Yes",850.386406238716,6389.07056944882
"5610","No","No",92.9000889701402,43955.8813664077
"5611","No","No",854.023334564867,32107.3083419182
"5612","No","No",0,29721.3418700634
"5613","No","No",1094.81283301666,32936.0586617393
"5614","No","No",680.164459380056,25391.6330751958
"5615","No","No",1231.35189432337,29867.8764736882
"5616","No","No",0,38228.2601678094
"5617","No","No",830.046094160683,51847.1229335732
"5618","No","No",1267.00144444768,32752.1937409219
"5619","No","No",1623.69027195188,36747.9039783266
"5620","No","No",302.719017710888,45260.3977308049
"5621","No","No",439.961958832399,28215.4788428897
"5622","No","Yes",1016.82547814025,18703.5552303283
"5623","No","No",1421.20476385362,47423.7917724389
"5624","No","No",479.677151780228,40995.0362099409
"5625","No","Yes",925.896916667239,12384.2042299631
"5626","No","Yes",1192.70215939554,21597.7516651387
"5627","No","No",1649.91703023619,36091.3847479288
"5628","No","No",808.999102581178,26379.8866963464
"5629","No","No",752.056294701375,49741.4315184812
"5630","No","No",727.190776306582,27282.1983550083
"5631","No","No",845.607309992116,31633.0872543091
"5632","No","No",445.661897901178,46816.105113725
"5633","No","No",1459.04496635786,49753.8049162963
"5634","No","Yes",988.641609584159,22085.4508197692
"5635","No","No",796.991208463392,25159.554855822
"5636","No","No",903.459131606741,33640.0996762864
"5637","No","No",1163.08390408128,45488.7505586529
"5638","No","No",1217.21052749133,33865.050577167
"5639","No","Yes",740.228677226658,23469.3875040857
"5640","No","No",1259.45052417311,28881.7252478321
"5641","No","No",501.391685956458,42856.1899982353
"5642","No","No",808.611888576478,48650.4338778726
"5643","No","No",413.888271616934,53142.0239048614
"5644","No","Yes",666.498230169417,11951.8987902488
"5645","No","Yes",412.160788336474,17679.5217697791
"5646","No","Yes",372.652495532665,22308.0412831276
"5647","No","No",1235.0766543147,48687.5571560364
"5648","No","No",853.246221589931,41581.195336223
"5649","No","No",1911.66851672829,52802.0888231585
"5650","No","No",357.969460728933,57012.5508813006
"5651","No","Yes",698.157981667433,19582.8161575693
"5652","No","No",918.461091220465,32468.4507371825
"5653","Yes","No",1741.91491543247,35067.4251226861
"5654","No","No",577.49347668941,39015.4168357708
"5655","No","Yes",1415.68199444299,21856.3209297024
"5656","No","Yes",1524.48683341545,15845.8712481288
"5657","No","Yes",1171.44140128616,19213.385726928
"5658","No","Yes",396.801484079162,26061.7642503507
"5659","No","No",1236.15812440683,19682.7109013115
"5660","No","No",1053.40549467371,52921.8537919159
"5661","No","No",1207.04062486695,42171.9889675781
"5662","No","No",528.961945199657,27065.7052750894
"5663","No","No",662.703254958649,48652.1699429375
"5664","No","No",517.228507618777,40248.5860722947
"5665","No","No",938.846004261055,40914.2282285304
"5666","Yes","No",1823.63655922363,44260.1563740071
"5667","No","No",638.721987169513,48085.707914938
"5668","No","No",929.022614418526,23889.6800602367
"5669","No","Yes",282.248706317179,19809.0986748885
"5670","No","Yes",1429.60932761342,14827.8945358154
"5671","No","Yes",672.358163240043,24495.0376192773
"5672","No","No",752.466763910773,39896.1093744995
"5673","No","No",471.346126989889,30337.1411809513
"5674","No","No",270.585758872418,44958.6312672761
"5675","Yes","No",2075.32789240119,43817.4994165192
"5676","No","No",96.8038990504192,31371.7260760776
"5677","No","Yes",568.294950972513,13286.3504957018
"5678","No","Yes",990.880561930152,21164.6639904912
"5679","No","No",284.560972136525,45542.1738019605
"5680","No","No",983.837340767148,28980.1020215873
"5681","No","No",924.437340522378,40987.4861505338
"5682","No","No",857.15319686911,31252.6513536423
"5683","No","No",195.022335186118,44222.9787399682
"5684","No","No",1046.00899599925,45573.3837714878
"5685","No","Yes",933.350405092987,15557.6325634422
"5686","No","No",0,43646.9117171667
"5687","No","No",691.892090491284,32512.195389798
"5688","No","Yes",1097.96962125845,16275.6836356714
"5689","No","No",0,45374.9971794388
"5690","No","Yes",402.909163047921,22752.3269494562
"5691","No","No",1230.9031608653,44303.1979964794
"5692","No","No",959.482038937641,31357.4463317624
"5693","No","No",630.229125536699,46713.6304386072
"5694","No","Yes",1353.49302986961,16727.6637244271
"5695","No","Yes",603.575190027631,15477.3538789755
"5696","No","No",830.250090634854,35377.8570634333
"5697","No","No",858.179964537275,30892.7357962171
"5698","No","No",380.173508308381,37395.718934378
"5699","No","Yes",1005.81173310083,12112.6574158034
"5700","No","No",1027.82530711925,33089.4672946505
"5701","No","No",456.003327034018,29898.0050635925
"5702","No","No",1043.09783017499,30516.2627004716
"5703","No","No",586.968885382876,50317.6528901077
"5704","No","No",271.942208218099,27790.0692786973
"5705","No","No",419.729051721571,37444.5360607249
"5706","No","Yes",597.112380190364,12660.8145668556
"5707","No","No",342.957369597552,43688.5348603431
"5708","No","No",728.278954840128,30028.180114333
"5709","No","Yes",513.836763185943,9879.11522086725
"5710","No","No",795.958609109475,22570.4833957793
"5711","No","No",1.97669165657703,51672.3606746991
"5712","No","No",1355.64121959561,36671.6598743079
"5713","No","Yes",319.26267613224,24881.3856596421
"5714","No","No",1719.16924073123,57866.0587647857
"5715","No","Yes",1191.08557367385,20895.408187878
"5716","No","No",984.43916305411,25294.8679947978
"5717","No","No",947.795904132801,45284.2059483097
"5718","No","No",949.038713528626,47760.4001660739
"5719","No","Yes",219.728619473391,18401.9824159211
"5720","No","No",1465.74393129421,25521.269892488
"5721","No","Yes",645.338826892833,20122.2022949879
"5722","No","Yes",856.564480509726,23199.9770243004
"5723","No","Yes",912.832537265206,19467.9793084482
"5724","No","Yes",774.347691273785,15147.4050875774
"5725","No","Yes",1376.34002229257,17101.4277855388
"5726","No","No",699.68038610832,50267.8498703097
"5727","No","Yes",903.458650208612,17254.9607045282
"5728","No","Yes",1150.54718578729,23705.9534246888
"5729","No","No",751.338737032109,42736.4281010473
"5730","No","No",556.077320959279,25371.2137682282
"5731","No","No",751.392240487586,29875.0468943881
"5732","No","No",425.986919625979,39772.7524115106
"5733","No","No",297.680814254857,26586.5338259687
"5734","No","No",507.246293687735,46106.2333111186
"5735","No","No",865.697003766891,33541.0463795618
"5736","No","Yes",1265.52737698449,14672.2561827333
"5737","No","No",564.411306490337,39643.536293479
"5738","No","No",621.041219289815,50804.6753093259
"5739","No","No",1205.5906901595,25920.860620437
"5740","No","No",1943.9323001153,24193.6089530073
"5741","No","No",415.288751920261,35790.1308927711
"5742","No","Yes",1169.42044435962,19879.2481698692
"5743","No","No",685.636883981982,49260.6253734599
"5744","No","No",781.919019378319,54925.5077888557
"5745","No","No",651.37432949101,44648.6964111729
"5746","No","No",490.251032848557,49222.4913819642
"5747","No","No",976.289542388024,35909.438285819
"5748","No","No",1476.8381231555,41154.8817495442
"5749","No","No",752.948233887715,62329.1213278647
"5750","No","No",511.605906978194,29914.1762585761
"5751","No","Yes",1384.37213177951,21059.6048699065
"5752","No","Yes",1031.6704462058,22440.6219784314
"5753","No","Yes",1491.50749266748,23636.1648057493
"5754","No","No",672.236354138002,46336.0582746324
"5755","No","Yes",644.413563981559,23319.6491325713
"5756","No","No",539.593429120106,9950.22944716287
"5757","No","Yes",0,17648.3555520933
"5758","No","No",919.812637213681,44316.3583269836
"5759","No","No",1249.87279356488,50418.0445177431
"5760","No","No",539.805316131412,35924.6100061739
"5761","No","No",1162.11326015823,49255.1443250688
"5762","No","No",944.450297862103,47463.3335845437
"5763","No","No",0,29675.0403940132
"5764","No","Yes",1440.52879490447,20882.0173151698
"5765","No","Yes",1558.47191874284,17470.4766997472
"5766","No","No",854.140622660478,52239.8002409183
"5767","No","No",468.549457486006,53273.9190207264
"5768","No","No",902.497409853233,56947.863738876
"5769","No","No",897.285608502453,66547.9153478775
"5770","No","Yes",422.189410133187,14366.5690535026
"5771","No","No",194.575966814186,44543.7810045915
"5772","No","Yes",487.51140380731,17713.5886677689
"5773","No","No",1558.8604674365,42112.3133015424
"5774","No","No",630.028950845409,43547.5421743949
"5775","No","No",1056.65462971505,30711.8612088361
"5776","No","No",1712.47003698299,40395.3463717659
"5777","No","No",25.6029427284892,35464.2479757401
"5778","No","No",1384.85057128336,40131.4938945229
"5779","No","No",961.365708967295,42619.0777329367
"5780","No","No",229.214973977971,68145.1337293181
"5781","No","No",778.791564992489,53219.5273540955
"5782","No","No",653.436222212024,41440.9285587421
"5783","Yes","No",961.488850067884,27717.6149963238
"5784","No","No",1084.4575635668,33080.9653776216
"5785","No","Yes",869.667755818248,20285.2517778161
"5786","No","No",818.334937015477,27390.8369123046
"5787","No","No",416.861181771703,39672.5172956473
"5788","No","No",250.835244898776,58139.0215907988
"5789","No","No",0,48411.0768955359
"5790","No","Yes",481.588375402497,19623.4605356064
"5791","No","Yes",1506.91151259556,23375.6459315861
"5792","No","No",1100.00893133463,34358.0746761477
"5793","No","No",220.666752812166,64467.7333980933
"5794","No","Yes",789.283587775891,20370.1737142088
"5795","No","No",41.3591758437337,47351.8335386886
"5796","No","No",382.962697242854,43645.9477878306
"5797","No","No",567.91705140977,28875.5218052449
"5798","No","No",244.509086414582,33751.2450670081
"5799","No","Yes",815.341565034172,26064.3656179441
"5800","No","No",529.072681274052,43914.6714302252
"5801","No","No",736.418557671665,48660.1510831738
"5802","No","Yes",0,17744.9299653708
"5803","No","Yes",1260.34116519144,11696.677762784
"5804","No","Yes",708.213553028619,12092.3144674758
"5805","No","Yes",99.9976112314506,4376.81033732814
"5806","No","No",1143.30818005697,48630.0338419905
"5807","No","Yes",892.383860904144,17841.5226997873
"5808","No","No",1250.0472445382,50883.7144156979
"5809","No","No",415.655366739528,43389.6607477004
"5810","No","Yes",1058.86738423105,16059.8327513806
"5811","No","No",857.699664613559,43162.6736510032
"5812","No","No",1429.76720500634,37255.911102443
"5813","No","Yes",322.266857038367,19231.9390865947
"5814","No","No",572.186729626896,35926.3762376315
"5815","No","No",1438.75847303818,24061.6475966988
"5816","No","Yes",1742.88693796721,20344.428006051
"5817","No","No",325.267593810214,39431.5354092195
"5818","No","No",1091.41855088011,43091.1568580348
"5819","No","No",1252.40952932279,53237.103541455
"5820","No","No",607.431269534736,34541.3020043844
"5821","Yes","No",1258.76479390195,26331.3723972254
"5822","No","No",1026.70270669984,33915.0488366579
"5823","No","Yes",590.566232554882,9143.39643269774
"5824","No","Yes",1218.1703464705,18918.3771488751
"5825","No","No",260.618801935897,42125.1486747
"5826","No","Yes",1498.07059121614,13274.6305622079
"5827","No","No",909.946875783543,53430.8954262706
"5828","No","Yes",1167.11058272521,18676.569773133
"5829","No","No",923.685583086262,38403.0106218174
"5830","No","Yes",1275.1970778934,15793.9091712164
"5831","No","No",709.257519701069,23249.9369139183
"5832","No","No",682.988012158536,41679.7597909339
"5833","No","No",1355.961899173,25208.0488594895
"5834","No","Yes",1815.88929401806,15490.0282669227
"5835","No","No",698.291724729687,43876.1618752754
"5836","No","No",189.129858226454,28827.2130879046
"5837","No","No",1418.70284028208,35619.966731296
"5838","No","No",670.422582334202,53666.1923387936
"5839","No","No",206.016345165762,38550.3766690572
"5840","No","No",1616.96212541135,47504.664497325
"5841","No","No",995.529599688077,24126.6103607743
"5842","No","No",591.036836257336,30834.7117877026
"5843","No","No",1294.90323367953,52338.2214607824
"5844","No","Yes",1259.37281261196,22361.0547379156
"5845","No","Yes",1313.35267047883,17437.6158261673
"5846","No","No",0,24461.8544604009
"5847","No","Yes",769.479121723841,27515.5709281393
"5848","No","Yes",571.577121800137,15325.0791014608
"5849","No","No",756.087910765568,49163.2233042613
"5850","No","No",183.428692215826,39020.2288570725
"5851","No","No",1054.80558912853,52290.3406700021
"5852","No","No",1386.20772607892,44536.4873031943
"5853","No","No",596.187984922023,45929.4491787607
"5854","No","Yes",1029.91697478065,16691.8032876491
"5855","No","No",417.538455374446,31730.866601925
"5856","No","No",1218.42139073093,40449.3117121187
"5857","No","No",210.78078813108,46326.7611216619
"5858","Yes","No",1178.15885831725,44437.2021385296
"5859","No","No",997.625113692672,30670.9471528594
"5860","No","No",637.902180966316,48425.7226516901
"5861","No","No",0,54245.1197952075
"5862","No","No",350.051851978195,48411.9866835366
"5863","No","No",1517.91895836893,49400.1708378773
"5864","No","No",190.641105320772,31463.4199838288
"5865","No","No",971.583673643437,41278.4190266194
"5866","No","Yes",925.39067815206,16427.5710095756
"5867","No","No",1637.83992155682,34107.080564485
"5868","No","No",224.592472478657,27246.0452995502
"5869","No","No",255.117030975662,55980.074042527
"5870","No","No",1201.0165477298,41585.8320478656
"5871","No","No",1075.5701114884,51370.2097044905
"5872","No","No",438.420240631268,29899.8221569397
"5873","No","Yes",1246.4371783918,15756.7182461179
"5874","No","Yes",1655.11286003131,17269.7989092957
"5875","No","No",1063.81680159536,38457.2492873684
"5876","No","No",1626.56379859686,43592.8405019296
"5877","No","No",409.404019995777,48746.4343503696
"5878","No","No",1018.67301772637,38421.8109111405
"5879","No","Yes",1090.89188238703,20421.9577177004
"5880","No","No",324.885413967638,36555.3997483081
"5881","No","Yes",1085.15296585933,20054.8984973707
"5882","No","Yes",869.772061046401,18724.4036763584
"5883","No","Yes",608.840648304347,24523.8540972795
"5884","No","Yes",1042.97256454135,9595.58757061018
"5885","No","Yes",1276.66776193299,19073.1085231485
"5886","No","No",928.094090319006,55894.6620242872
"5887","No","No",1592.76390391801,35084.0931718366
"5888","No","No",0,39653.9232059936
"5889","No","No",340.999798019847,48058.3831830102
"5890","No","No",81.5318651256417,40847.8113111642
"5891","Yes","Yes",2145.60767410022,23516.1913448152
"5892","No","No",654.416009758667,47244.7041815369
"5893","No","Yes",1155.78057709463,15146.8964638526
"5894","No","No",1745.31352843476,49090.1285571159
"5895","No","No",534.784891919657,46138.4317949854
"5896","No","No",584.837411673331,39553.1714989784
"5897","No","No",1365.55685085965,38511.3198025482
"5898","No","Yes",946.36714979165,12532.6575241334
"5899","No","No",263.190295009475,34112.4719691253
"5900","No","Yes",1090.92505943976,18935.8182197473
"5901","No","No",835.189928006226,44678.8317508059
"5902","No","No",1265.53598576055,35133.2172929742
"5903","No","No",357.50662154887,66404.6831116793
"5904","No","Yes",564.005440905646,16502.3373713175
"5905","No","No",817.927709730839,59431.8391841725
"5906","No","No",1366.27862101812,41192.462650331
"5907","No","No",836.343137128427,34559.1584045926
"5908","No","No",0,27367.7826593374
"5909","No","Yes",780.227918143533,25228.7107258463
"5910","No","No",995.877319235738,52860.5849428525
"5911","No","No",0,22827.184490446
"5912","No","No",554.344586281001,31028.9241956699
"5913","No","No",1204.57884130054,39583.1205263355
"5914","No","No",1053.3758785391,39612.6719417459
"5915","No","No",821.498846966475,39318.4893922058
"5916","No","Yes",470.912347174972,24074.3646500836
"5917","No","Yes",1534.85373461482,21678.3509653313
"5918","No","No",1177.97686676911,50811.7594557063
"5919","No","Yes",1151.89951661119,15097.5123322887
"5920","No","Yes",906.43906910328,24662.2907204924
"5921","No","Yes",1513.54243669697,13246.3752793563
"5922","No","No",740.885186560821,34196.067455362
"5923","No","No",1176.4145117071,57318.7019687111
"5924","No","No",208.295079052557,37295.8019809341
"5925","No","Yes",375.30630481794,19342.158087761
"5926","No","Yes",953.130545313744,18057.7864868779
"5927","No","No",280.955900327091,37690.0334766458
"5928","No","No",399.129471557993,38004.1049833837
"5929","No","No",1229.85812166833,58399.4966673188
"5930","No","No",1740.86410659557,46458.6061241292
"5931","No","Yes",795.139553543434,24606.1542900494
"5932","No","No",584.816776146095,47449.6242381957
"5933","No","No",1140.74820920772,39017.0776173997
"5934","No","No",1202.31672214754,49914.5220391356
"5935","No","Yes",476.720465848488,10516.8697175559
"5936","No","No",1059.87227059407,38698.6727333414
"5937","No","No",1367.10860567106,36427.4297417666
"5938","No","Yes",1604.77765926804,23999.760339155
"5939","No","Yes",1055.68253715521,25109.5084708156
"5940","No","No",367.611687180574,44046.0175141989
"5941","No","Yes",0,18032.0737811624
"5942","No","No",543.805741425591,31512.6105904963
"5943","No","No",192.627462934508,38042.0183883803
"5944","No","No",1184.83995305363,34527.6867960494
"5945","No","Yes",1510.77723291864,19491.6940654638
"5946","No","No",801.639909419777,37441.7921162931
"5947","No","No",631.030835762073,44525.8899911047
"5948","No","No",279.116875728491,30144.4325045929
"5949","No","No",674.03256185589,54832.6657286731
"5950","No","Yes",957.630204213306,15018.9136483864
"5951","No","No",274.424220630795,31292.6274526086
"5952","No","No",1044.44989668679,43374.0041002429
"5953","No","No",319.807606999716,45202.6354001605
"5954","No","Yes",429.222462997544,19442.0290889131
"5955","No","No",835.585023630256,41032.3713069298
"5956","No","No",2.78892034879289,54838.8055107672
"5957","No","Yes",867.441644756525,16396.6823596118
"5958","No","Yes",271.730925652373,14215.0928835875
"5959","No","No",404.194652122229,41668.419578518
"5960","No","No",1572.81110764373,18218.4743623389
"5961","No","No",730.633559272148,42300.1468678425
"5962","No","No",0,56610.2364993919
"5963","No","No",1046.16855901415,46078.9823084735
"5964","No","No",514.871773828457,50339.1917982163
"5965","No","Yes",1181.58600890881,22142.0521488123
"5966","No","No",0,45235.2908693099
"5967","No","No",272.219927772865,47757.8244082396
"5968","No","No",1175.12552612742,33284.2060671339
"5969","No","No",812.889626440805,48563.7997585162
"5970","No","No",571.041501274187,40389.4463334741
"5971","No","Yes",1041.14735129673,12488.7319281208
"5972","No","No",334.019494911054,40916.0332464492
"5973","No","No",1402.60119405591,37273.593072847
"5974","No","No",1569.8703239927,55938.7662199414
"5975","No","Yes",1035.9661070392,30531.6131073667
"5976","No","No",1376.11324068477,63073.4451350521
"5977","No","Yes",668.642228957829,20240.9549635787
"5978","No","No",0,36249.1749894424
"5979","No","No",1112.65568371353,35159.2154610084
"5980","No","No",718.579877417863,12571.6893560988
"5981","No","No",641.550367704605,45574.4995780144
"5982","Yes","Yes",2125.79220209824,19539.1486085893
"5983","No","Yes",814.35410278798,17973.5091609266
"5984","No","No",904.033628607277,18898.3113286727
"5985","No","No",795.49732640836,25336.3268493182
"5986","No","Yes",1753.59814404144,21130.9185850811
"5987","No","No",284.395697403294,60840.8098445297
"5988","No","Yes",1300.88969344098,14156.6456712982
"5989","No","No",1237.84339138252,31880.1755862105
"5990","No","No",563.466595350795,31151.1870860483
"5991","No","Yes",6.72786973540246,21695.7027559026
"5992","No","No",661.528186240096,58519.8597929747
"5993","No","No",630.714844593192,53546.5706034489
"5994","No","No",721.141686063012,40458.9542078904
"5995","No","Yes",360.009160165101,18487.4332542325
"5996","No","No",751.030985242178,45581.7188592217
"5997","No","No",572.4682971281,52797.4293765822
"5998","No","Yes",242.462988762061,23413.3595887971
"5999","No","No",199.454789588577,57455.2101645723
"6000","No","No",477.566817124151,16978.2711270042
"6001","No","Yes",1184.72503438385,19797.7701544466
"6002","No","Yes",604.758740181076,19369.3492279276
"6003","No","No",384.331069368267,34232.0692319686
"6004","No","No",1399.57356126492,23577.7645096764
"6005","No","No",328.778147645303,48626.356737746
"6006","No","No",271.059866110878,30465.3075728577
"6007","No","Yes",145.002121469021,19927.0920983813
"6008","No","Yes",344.154111624342,20439.6881075385
"6009","No","No",985.75273950769,22829.5879790936
"6010","No","Yes",1002.41924969378,17797.3437053801
"6011","No","Yes",1004.84432345119,20249.2074251626
"6012","No","No",483.415851611021,53873.5126536571
"6013","No","No",1034.60116239922,41252.7283291139
"6014","No","Yes",854.626200243518,16322.7225871478
"6015","No","No",796.836729677501,29397.884089772
"6016","No","No",535.696856517735,55828.0817743105
"6017","No","Yes",1070.63926115023,14833.0291829764
"6018","No","No",0,30524.7560124986
"6019","No","No",1333.90120569319,23261.8465412135
"6020","Yes","No",1114.83218093412,39776.9989569363
"6021","No","Yes",793.248091585398,24314.682390475
"6022","No","No",433.93529004808,26888.0873419002
"6023","No","Yes",1004.27364640269,17069.5235700115
"6024","No","Yes",1112.70118907868,20901.2137448942
"6025","No","No",298.775139374477,33255.838852479
"6026","No","No",796.846346195829,20078.7892262371
"6027","No","Yes",969.397162515276,17686.5619959655
"6028","No","No",1075.11834569501,47566.7840330557
"6029","No","Yes",0,16451.9423947393
"6030","No","Yes",44.6555662288123,26346.8119312976
"6031","No","Yes",1000.74058586802,14689.4946797589
"6032","No","No",1311.99931279821,26377.9739545637
"6033","Yes","Yes",2086.53616450154,17893.7213718312
"6034","No","No",697.046859382139,38743.0931764454
"6035","No","No",1240.6642503436,30938.537452736
"6036","No","Yes",726.764145685598,16079.6367519751
"6037","No","No",633.50418717187,40571.7665185333
"6038","No","Yes",386.156432013818,22527.647218635
"6039","No","No",0,34701.1959628584
"6040","No","No",808.593585430211,41222.3516738451
"6041","No","No",1384.39245361744,35499.526931748
"6042","No","No",713.067543966655,44413.7513911328
"6043","No","No",590.676532830074,26673.8475991813
"6044","No","No",294.84465334931,41591.7799629843
"6045","No","No",208.007706163141,42399.5295644159
"6046","No","No",220.449327881615,33213.3249240332
"6047","No","No",773.172691038837,49308.1648913188
"6048","No","No",1248.47705838491,37204.0735235058
"6049","No","No",634.186610299309,32255.989236893
"6050","No","No",678.186893655929,55542.9696290547
"6051","No","No",1252.62149720678,27295.5179274614
"6052","No","Yes",1297.2650427955,16864.4348878162
"6053","No","No",0,38870.4967984093
"6054","No","No",1190.76129184854,27257.1225311302
"6055","No","No",196.515561979567,44226.6317697564
"6056","No","No",619.559206544415,33476.6678580755
"6057","No","No",438.625084914629,41454.7212837096
"6058","No","No",676.09886006944,38571.4137503099
"6059","No","No",589.914146029848,49945.3890058123
"6060","No","No",1068.14635014194,27859.8644498139
"6061","No","No",1210.41612201718,45113.1625230447
"6062","No","Yes",390.356473829136,9114.27528946381
"6063","No","No",1505.70716693071,35786.3271594838
"6064","No","No",490.055975362601,35895.3539099007
"6065","No","No",123.286068118428,47467.9589538027
"6066","No","No",563.286411224409,51720.911526537
"6067","No","No",409.160368105623,45055.8974731354
"6068","No","Yes",781.680396250219,21342.4787054192
"6069","No","No",870.30272557068,24263.1274357931
"6070","No","No",509.15601107729,47444.1514545912
"6071","No","No",1056.66248436439,39255.763309381
"6072","No","No",394.990214436483,41574.8335065642
"6073","No","Yes",1592.00464654421,19982.2900564444
"6074","No","No",0,49250.4641066052
"6075","No","No",1276.40327757431,52215.1340325795
"6076","Yes","No",2413.31944892365,38540.5727051179
"6077","No","No",210.123259392863,58413.1036704192
"6078","Yes","No",2187.22484631191,42205.1230478132
"6079","No","No",1159.84194470986,43385.0190429097
"6080","No","No",1339.60317733156,46442.3537684526
"6081","No","Yes",793.347450948707,11495.3931419745
"6082","No","Yes",163.503391345992,17545.7266406611
"6083","No","No",867.629482747378,43091.9359878415
"6084","No","Yes",1231.02580419572,13363.9780519247
"6085","No","No",312.41548868663,47118.9994332504
"6086","No","Yes",758.448011241565,20936.0686445542
"6087","No","Yes",1035.82346778548,13959.4204534083
"6088","No","No",763.735279612754,44125.7187251985
"6089","No","Yes",728.543556143868,12475.4607349102
"6090","No","No",1066.29655789445,42247.7785501089
"6091","No","No",338.725271742496,37017.1014891509
"6092","No","No",264.942490599027,39317.5739109735
"6093","No","No",1167.3256809004,43337.1300586773
"6094","No","No",553.742886176071,39653.9830884822
"6095","No","No",485.792841695534,38281.3147946812
"6096","No","No",485.944814840486,46447.4052764736
"6097","No","No",690.438303078773,66807.9362979989
"6098","Yes","No",1588.52620557925,38014.5688450189
"6099","No","No",460.43019079267,56002.2231998379
"6100","No","No",275.660214988249,31259.9781998673
"6101","No","No",336.907119558385,41907.7161462936
"6102","No","No",307.693118992996,42220.853917674
"6103","No","Yes",1431.07769446686,20576.7799488487
"6104","No","Yes",787.618249414904,11928.555234286
"6105","No","Yes",1660.40401324683,18861.1546603918
"6106","No","No",0,41482.7016300727
"6107","No","No",1744.93580277786,43978.1123466617
"6108","No","Yes",758.309662741287,12662.1260038181
"6109","No","No",1173.46246609367,48396.5048279567
"6110","No","No",1335.54141772911,53354.8492094829
"6111","No","Yes",1434.84916401448,17459.0312731554
"6112","No","No",591.066666018387,43869.3158967986
"6113","No","No",862.7765105382,49444.7846508243
"6114","No","Yes",1233.25001064076,16076.854261266
"6115","No","Yes",911.786777701097,20928.3924409517
"6116","No","No",0,43066.267558787
"6117","No","No",286.928183997805,50618.7226076452
"6118","No","No",377.534491905743,28045.5951152414
"6119","No","No",811.792488461646,36874.6186267191
"6120","No","Yes",1856.60559557309,17845.3368472939
"6121","No","No",607.837992236717,33737.1754916676
"6122","Yes","Yes",2182.34895374804,22037.8596945121
"6123","No","Yes",1406.56767107532,23050.8882693756
"6124","No","Yes",662.090603124755,16310.7945058364
"6125","No","No",777.01624823921,52797.9390479479
"6126","No","No",0,47236.9737748412
"6127","No","Yes",769.711499590641,11262.6385471944
"6128","No","No",970.184702115234,38784.4591549757
"6129","No","No",537.926697481975,45615.1956649841
"6130","No","No",768.154883649884,39701.9349136446
"6131","No","No",1150.04092324427,19536.1603742206
"6132","No","No",902.557289065848,32765.3881295506
"6133","No","No",442.827579810392,42004.1967261282
"6134","No","No",100.866844377552,31389.4585657072
"6135","No","Yes",904.0563158125,23188.7151417704
"6136","No","No",1396.26372250623,24379.0715403844
"6137","No","No",1001.33174458437,47640.7370028943
"6138","Yes","No",1631.61561720793,38906.956406913
"6139","No","No",790.740517947027,45900.2973580157
"6140","No","No",1097.39309399029,46807.0816795955
"6141","No","No",0,37598.8627860684
"6142","No","Yes",1262.83538473139,12406.9989476438
"6143","No","No",738.19292856769,54491.1426665823
"6144","No","No",655.267748369062,28642.5825660655
"6145","No","No",0,32944.9958224265
"6146","No","No",638.540333212731,20607.4593037699
"6147","No","No",570.930887565477,46840.7212579065
"6148","No","No",1056.44366196118,31268.7370023024
"6149","No","No",1522.85515471843,35805.2559666713
"6150","No","No",977.321555533534,35296.2814099745
"6151","No","No",743.56501357471,28893.876006198
"6152","No","No",352.033277916786,38259.224093326
"6153","No","No",318.209460807772,48975.3674286855
"6154","No","Yes",674.850939233073,15798.1345957042
"6155","No","No",268.954349521987,55892.7271050541
"6156","No","Yes",1023.20547852868,17694.9655968053
"6157","No","No",718.156916820057,42172.0052137546
"6158","No","No",843.290805162947,41692.4222129256
"6159","No","No",866.729785017612,36452.7221234765
"6160","No","No",449.949321999232,38987.0248623351
"6161","No","No",459.101423002025,45043.6664005583
"6162","No","Yes",760.219529926462,17657.9855125586
"6163","No","No",1160.82763199827,26022.9525905551
"6164","No","No",1317.51298657233,49285.5454683772
"6165","No","Yes",561.781867072271,18470.2826118409
"6166","No","Yes",176.533834591692,15175.5860363194
"6167","No","Yes",1059.23634056817,12601.4712132607
"6168","No","No",526.798489619543,28491.4805026881
"6169","No","No",20.9645038465402,62862.7518268173
"6170","No","No",528.942463459578,42134.2706302658
"6171","No","No",1561.53353016525,54594.8467076871
"6172","No","No",963.722811166049,47605.7685862013
"6173","No","Yes",1227.36527335002,12128.1481729045
"6174","No","No",1209.377358572,35071.3166163516
"6175","No","Yes",671.175868767628,17564.9547548174
"6176","No","No",275.624646694949,35761.3165021919
"6177","No","No",776.1888729587,34140.9803931269
"6178","No","Yes",699.303115031174,19789.5306648714
"6179","No","Yes",606.12513463701,16957.6487635412
"6180","No","Yes",1231.21292303876,20000.062545376
"6181","Yes","No",1408.43808460249,48012.9135707117
"6182","No","No",338.49547066901,34332.8432410069
"6183","No","No",1380.48149032194,24856.6430609977
"6184","No","No",1316.42181029894,45806.9015129082
"6185","No","No",1046.18204494041,48752.1317619521
"6186","No","Yes",904.684255009856,17281.1191657024
"6187","No","No",1281.5206480373,37665.2939400411
"6188","No","No",0,46597.4950794085
"6189","No","Yes",501.111644711926,22393.2730800367
"6190","No","No",1085.95015026206,36532.718758
"6191","No","No",351.945107392988,48726.0476710277
"6192","Yes","No",1803.1702590052,36192.6302248573
"6193","No","No",666.622349038193,44931.9397555623
"6194","No","No",154.703583999015,33162.1932088175
"6195","No","No",360.089179178127,44448.9158031897
"6196","No","No",105.441003838571,35291.4633497544
"6197","No","No",492.428292481969,25848.9885016535
"6198","No","Yes",1232.42246851404,24093.2964372921
"6199","No","No",1154.39852948099,20457.2207787254
"6200","No","Yes",1113.32696440837,14579.6569774387
"6201","No","Yes",699.69803744324,22590.6679750258
"6202","No","Yes",1001.78568340883,24771.726937438
"6203","No","Yes",1217.22694663176,16449.9989993373
"6204","No","No",1156.93399834045,45325.3409651328
"6205","No","Yes",1637.4507138022,18766.8551780145
"6206","No","No",824.241432581919,53320.2829354298
"6207","No","No",662.342897180105,49885.0191183303
"6208","No","No",617.640764160629,37604.9335638557
"6209","No","No",271.451911186232,34863.1041958902
"6210","No","Yes",688.181241721348,18755.9532326181
"6211","No","No",797.709165567355,39677.6972051307
"6212","No","No",464.404331084908,53215.931577276
"6213","No","Yes",976.20221752797,23191.7707041583
"6214","No","No",1430.76614709359,45672.5199668785
"6215","No","Yes",1208.14448240103,17721.2936623594
"6216","No","No",0,41960.174498434
"6217","No","Yes",402.358799457341,15256.4841845409
"6218","No","Yes",824.514377910164,15309.2762101205
"6219","No","Yes",1060.87882369475,11041.3314995672
"6220","No","No",310.495919327847,47427.3290910962
"6221","No","No",691.41849858598,40232.6566576251
"6222","No","No",23.1522666943621,42016.8990932486
"6223","No","Yes",913.276157564341,20446.2030006723
"6224","No","No",970.816438216424,35835.3550804302
"6225","No","No",1148.44753760901,39662.1224082796
"6226","No","No",1638.68704646198,38441.1580318724
"6227","No","Yes",0,19134.4447504644
"6228","No","Yes",1448.95020726173,28273.8353885733
"6229","No","Yes",1286.83975634999,17464.9935281955
"6230","No","No",762.073599588594,48268.5913873023
"6231","No","No",977.576527456252,51074.2127413645
"6232","No","No",368.768052364588,45647.297609605
"6233","No","No",267.959338826255,46134.6226561867
"6234","No","Yes",503.156388314512,18728.2969360048
"6235","No","No",0,48523.3355536444
"6236","No","No",282.602877141145,56118.322399718
"6237","No","No",748.057043338722,47775.1808226916
"6238","No","No",999.654013569972,27564.688706363
"6239","No","No",494.660496038613,47035.7546779674
"6240","No","No",1022.68238813533,22588.5554727516
"6241","No","Yes",1135.21787066304,19062.5968717997
"6242","No","No",707.802330263558,40140.8004546232
"6243","Yes","Yes",1954.32168919689,17137.4737238985
"6244","No","Yes",1282.35040946049,7230.03047089224
"6245","No","No",808.616921547397,23519.672554832
"6246","No","Yes",1219.9337508547,21765.3821156194
"6247","No","No",481.880421732302,44137.0795000152
"6248","No","No",409.411749149658,53624.4577497651
"6249","No","No",1157.27734673627,31369.4919406711
"6250","Yes","No",1424.93289000674,27668.952235473
"6251","No","No",0,58825.6151260292
"6252","No","No",687.069130737925,34434.2182340633
"6253","No","No",1435.6629330318,31507.0892769087
"6254","No","No",188.544102040965,49697.2317876452
"6255","No","No",1084.3874989557,34558.6157778435
"6256","No","No",644.618590793448,37452.0672064142
"6257","No","No",1345.56313071406,38602.1129816912
"6258","No","Yes",733.719463533471,27165.4799804705
"6259","No","Yes",744.178014844323,20773.627399272
"6260","No","No",0,29542.0253992459
"6261","No","Yes",1395.96188164562,23717.8511138612
"6262","Yes","No",1920.24233205636,57242.9834069871
"6263","No","No",538.413267908729,40773.9414587936
"6264","No","Yes",385.69826231499,18352.4362518163
"6265","No","Yes",1350.7575768851,19692.9187842779
"6266","No","No",1567.76385602067,39201.2418579226
"6267","No","No",753.686888348247,42230.6477729661
"6268","No","No",72.371953370087,28717.9406215258
"6269","No","Yes",675.036024406725,21115.7931230146
"6270","No","Yes",980.164350015296,13713.5022348193
"6271","No","Yes",910.383604650027,19163.8069881897
"6272","No","Yes",1262.88429915506,14340.5079413575
"6273","No","No",621.312719186294,39372.0771909379
"6274","No","No",643.402638638703,50208.8237204257
"6275","No","Yes",728.259291664541,13438.9855560242
"6276","No","No",459.260227693732,59731.2887824528
"6277","No","No",289.616664242634,49066.8336532824
"6278","No","Yes",784.746614098042,11642.9222904291
"6279","No","Yes",712.594773206876,18441.0261223747
"6280","No","No",1096.00360127447,52030.9726046463
"6281","No","Yes",435.485531143428,19899.7545673387
"6282","No","No",688.022470132904,29019.8178882188
"6283","No","No",599.428358613756,36654.6481747663
"6284","No","No",68.7562944774306,45811.7429439996
"6285","No","Yes",765.136998638814,14290.2676305606
"6286","No","No",1273.12457069577,57688.5680465556
"6287","No","No",1078.62106094141,46060.0847661993
"6288","No","No",1677.34895638258,44972.7098631219
"6289","No","No",531.756183939059,42365.0736708439
"6290","No","Yes",631.172612415915,11075.8940578867
"6291","No","No",1081.47853357271,34072.6438141387
"6292","No","No",368.515345585694,60953.8247890952
"6293","No","No",343.726999112545,38054.5553979042
"6294","No","No",1715.28268889019,60137.6336767813
"6295","No","No",450.335579768253,20080.1021192825
"6296","No","No",740.580773422977,20717.544444772
"6297","No","No",0,26237.4743420025
"6298","No","No",475.869038714081,35621.6911156828
"6299","No","Yes",1383.74505860674,14633.9858800545
"6300","No","Yes",406.482384904556,22708.9010795616
"6301","No","No",857.261565698386,34406.6108232183
"6302","No","Yes",1637.23520108736,16751.1697505978
"6303","No","No",1094.31587152691,34854.3423889239
"6304","No","No",1213.12707351588,47848.0479031211
"6305","No","Yes",1074.74640640823,22729.3086500762
"6306","No","Yes",1010.21263339552,13469.8666480292
"6307","No","No",416.128286440287,21960.6245554699
"6308","No","Yes",675.841346528661,17801.474355562
"6309","No","No",0,36338.848496024
"6310","No","No",867.304173246052,29541.0431470904
"6311","No","No",843.907303418097,23956.519367605
"6312","No","No",766.430430038519,39078.7969995895
"6313","No","No",1286.53723250416,46337.1238933082
"6314","No","No",1142.93515351666,34401.6671117773
"6315","No","Yes",925.411287617391,15120.3356225576
"6316","No","Yes",1110.45382300899,14965.4373853847
"6317","No","Yes",938.605975477252,24392.1040357114
"6318","No","No",321.039742945537,37026.5871638077
"6319","No","No",943.387640676355,39415.5956472554
"6320","No","No",511.598770167744,54002.5116638967
"6321","No","No",743.849598629398,24303.4676265731
"6322","No","No",1089.57706066055,46890.2154037835
"6323","No","No",0,46275.3634688309
"6324","No","No",0,31511.6574646955
"6325","No","No",1282.32124736986,37437.7109632329
"6326","No","No",1373.7967923188,36888.9284103698
"6327","No","No",1228.33429993148,43577.66949173
"6328","No","No",1536.78831191053,34734.8638835725
"6329","No","No",735.470434133364,53312.5305272226
"6330","No","Yes",1142.42760501705,26114.4031521738
"6331","No","No",1490.71507559311,36417.400041399
"6332","No","No",880.032832524916,60203.8815636414
"6333","No","No",2.84301484022319,39923.8192960501
"6334","Yes","Yes",2066.69560264995,10470.6359999562
"6335","Yes","No",2343.79751269872,51095.2939288164
"6336","No","No",929.745481690635,36071.5242639506
"6337","No","Yes",764.627790674236,26188.0694950508
"6338","No","No",504.604495228821,31870.0182915052
"6339","No","No",685.206941440502,40555.7998847453
"6340","No","No",473.02745797478,43016.8607304856
"6341","No","No",1302.55349925073,32235.0524502403
"6342","No","No",435.004134228777,30828.8603927349
"6343","No","No",601.838500949302,37697.3700511627
"6344","No","No",456.763657699437,50160.6336955449
"6345","No","No",1065.04291804197,37747.1040516635
"6346","No","No",1153.33189611141,37936.0870765089
"6347","No","No",59.6330014772174,35709.7384629771
"6348","No","Yes",1106.43067393635,16960.6941248389
"6349","No","Yes",1106.45352459194,14840.9381345027
"6350","No","Yes",385.430994048668,21619.6634121321
"6351","No","No",0,45025.5116482779
"6352","No","No",727.957482040349,53606.9731408304
"6353","No","No",1028.77760890511,36276.0851393196
"6354","No","Yes",1035.91668862827,10607.9061350408
"6355","No","No",974.597718681195,25316.8587476378
"6356","No","No",772.809219890657,39944.6072003905
"6357","No","No",1014.9739793686,41993.6406134555
"6358","No","Yes",0,10276.2561072754
"6359","No","Yes",1403.78934069962,22893.9605150922
"6360","No","No",580.423341333844,25926.2350880841
"6361","No","No",499.550744378248,45954.6712416404
"6362","No","Yes",748.236205405347,11613.2229514115
"6363","No","No",568.093969521188,31392.0669487111
"6364","No","No",1366.72577354255,35425.51207701
"6365","No","No",511.678894076063,32693.8508798088
"6366","No","Yes",678.217875305686,19979.6939940884
"6367","No","Yes",1692.42056037406,7761.34290395257
"6368","No","No",631.804597881122,32752.5192557897
"6369","No","No",585.050054612907,34297.5652717648
"6370","No","No",1173.49079160122,44134.744873303
"6371","No","No",544.237427620852,43306.6211365794
"6372","Yes","Yes",1985.32173867563,22248.4026595649
"6373","No","No",1011.06090429807,38001.7121026909
"6374","No","No",1231.214516945,48156.3575590725
"6375","No","No",875.84248302776,45524.5321991767
"6376","No","No",1516.06538379219,59194.2392055884
"6377","No","No",563.121261146761,30226.4827235198
"6378","No","Yes",1244.56692703869,21717.4758713767
"6379","No","Yes",1215.6434680202,16403.6857647769
"6380","No","No",1077.7471273438,30133.2576289141
"6381","No","Yes",1393.91636806336,16658.483076576
"6382","No","No",803.570067410546,45353.970680412
"6383","No","No",916.749237784122,58469.9335612685
"6384","No","No",147.049333558096,52074.5106972395
"6385","No","No",734.47278062031,46805.5530163825
"6386","No","Yes",996.205915012754,13647.5357449778
"6387","No","No",1883.20020660212,47438.2557139199
"6388","No","No",1271.11569766438,40578.3161858595
"6389","No","No",173.733418051803,32480.5339803496
"6390","No","No",1004.18766453742,37132.657213994
"6391","No","Yes",2033.3624960493,18549.7453853583
"6392","No","No",269.357861809914,40883.1854707986
"6393","No","No",441.701436036663,44227.9102669818
"6394","No","No",1471.60862883275,61385.5998656021
"6395","No","Yes",1133.82200395797,16602.0280192795
"6396","No","No",843.352661071678,51736.9102504954
"6397","No","No",250.468332543304,42309.0828207204
"6398","No","No",1569.72827330174,30604.2864482531
"6399","No","Yes",995.347111840736,11690.7412581951
"6400","Yes","No",1572.34558970181,37895.1730685155
"6401","No","No",1570.4161790221,49501.8103764281
"6402","No","No",645.931623040968,39695.6272589398
"6403","No","No",1001.85301122289,32748.6309370085
"6404","No","Yes",905.231295487767,15699.8984589623
"6405","No","Yes",859.379689793499,19287.5036985077
"6406","No","No",1670.85028486118,54442.2661966056
"6407","No","No",391.256627667898,33651.4055616119
"6408","No","No",670.008248424474,35919.7967694236
"6409","No","No",1036.05383453276,44869.4601898453
"6410","No","No",481.989491304848,19240.3142866129
"6411","No","Yes",542.769255622058,17664.8814338304
"6412","No","Yes",887.288924292299,17409.0311196404
"6413","No","Yes",1465.84955404493,17678.7363065959
"6414","No","Yes",1545.63745986469,13959.9936735773
"6415","No","No",262.791336186532,28974.7505490968
"6416","No","Yes",793.796375781413,21172.4041224321
"6417","No","No",1192.76615832583,45902.5075546804
"6418","No","Yes",1120.32087759081,29045.4067978248
"6419","No","Yes",514.199497794009,21482.0348084397
"6420","No","No",710.760427503805,53050.4853756102
"6421","No","No",1176.24327053548,27956.7828970299
"6422","Yes","No",1805.55958800801,40119.7878293954
"6423","No","No",124.828459521634,39478.3000749473
"6424","No","No",1056.9932389288,45489.8966758987
"6425","No","Yes",904.994670662576,19108.7423327689
"6426","No","No",1402.12013840063,41275.6454675378
"6427","No","No",7.06953029158558,48711.6374862113
"6428","No","Yes",733.78339042547,16124.3542488896
"6429","No","No",612.960653042669,43392.887089339
"6430","No","No",998.704699011465,46697.7777728682
"6431","No","No",1078.45700052818,33001.1265148157
"6432","No","No",638.476386396818,53034.552332322
"6433","No","Yes",890.048104543381,15156.9191332201
"6434","No","No",815.682132476711,53817.2797514384
"6435","No","No",1276.98264992429,40611.3486878798
"6436","No","No",671.185770842318,44120.6417390268
"6437","No","No",818.28434663854,48490.8121880235
"6438","No","No",1371.33089893175,35820.629832872
"6439","No","No",897.495756050913,47816.4116102516
"6440","No","Yes",699.696422430527,17318.8818697219
"6441","No","No",848.940738136621,35534.9838909539
"6442","No","No",0,27297.9095207254
"6443","No","Yes",1660.86106385367,13694.8564537132
"6444","No","No",1475.50384683721,44769.1221419027
"6445","No","No",0,22431.4535442587
"6446","No","No",1186.96435587922,33439.8363961669
"6447","No","No",844.403802606626,36854.6363431765
"6448","No","Yes",1540.30882373607,15241.0158797215
"6449","No","No",679.707580876161,29534.8214632962
"6450","Yes","No",1650.57800035387,31514.2596520501
"6451","No","Yes",614.783859495222,18487.2337118371
"6452","No","No",1379.67804381773,31766.2935985596
"6453","No","Yes",770.105538487904,15284.719925954
"6454","No","No",1209.54028191565,40805.7776315861
"6455","No","Yes",1380.96981860135,13633.4334735927
"6456","No","No",1349.10644291826,28340.4373630662
"6457","No","Yes",1051.39385283445,23520.4259953959
"6458","No","Yes",428.425822056004,15446.9580283448
"6459","No","No",1714.18602954936,52779.0888169049
"6460","No","Yes",1647.80128271056,15392.2129768749
"6461","No","No",482.975818834569,47924.6766405041
"6462","Yes","No",2124.67131340206,44520.000102649
"6463","No","No",297.838221600525,34549.4626310993
"6464","No","No",1201.34804245462,35858.4042876616
"6465","No","No",811.241947619258,26448.3284969801
"6466","No","No",0,36201.5388732693
"6467","No","No",585.804176768659,30143.3285117119
"6468","No","No",511.32185498375,38510.1609106431
"6469","No","No",1249.19417416597,45586.8902165017
"6470","No","No",48.9655502639223,27069.7397840305
"6471","No","No",863.574709912629,32889.3839613994
"6472","No","No",1579.35946543667,50216.5716279253
"6473","No","No",800.609332848504,50324.5549267962
"6474","No","Yes",1356.53469066146,19480.5437729765
"6475","Yes","Yes",1704.57852928341,16887.4051366611
"6476","No","Yes",528.585688721324,19621.6292186254
"6477","No","Yes",1390.50766599778,15885.5107827365
"6478","No","Yes",333.541196216677,27321.5578347645
"6479","No","No",1569.556298596,36306.0194414557
"6480","No","No",1509.78088703495,43650.4185057626
"6481","No","No",961.241554483775,31647.287484501
"6482","No","No",1443.50744678497,47205.348659789
"6483","No","No",0,37605.379393724
"6484","No","No",895.395951814918,48939.7356573301
"6485","No","No",1355.8668429275,45537.9549160757
"6486","No","Yes",960.11398559513,24326.060273482
"6487","No","No",1164.47640009829,36997.7189763332
"6488","No","No",535.182915811552,37042.7664904696
"6489","No","Yes",1424.77292741312,21852.9298809929
"6490","No","No",773.172491507927,50069.2088216444
"6491","No","No",685.192569602747,33185.010437872
"6492","No","No",343.768806093232,51626.1356931331
"6493","No","Yes",1513.54156843839,23556.6905037062
"6494","No","No",415.003634136448,37859.31445796
"6495","No","No",1034.36018039369,25111.2125512162
"6496","No","No",1115.8960083931,21646.3059481466
"6497","No","Yes",748.746430922286,17440.626550084
"6498","No","No",291.408728923275,54408.788976338
"6499","No","Yes",453.929798646848,12665.3069954235
"6500","No","No",889.565857036579,49941.0886297848
"6501","No","No",345.190624410366,48111.4462184115
"6502","No","No",393.828577983662,28362.6246287932
"6503","No","Yes",991.093996282783,16842.4361428305
"6504","No","No",4.46645782066093,45157.9096157594
"6505","No","No",660.05891868537,39163.3272651966
"6506","No","Yes",1168.86840328253,18113.3437663792
"6507","No","No",528.867388165435,38881.4762648934
"6508","No","No",89.3340476687288,36515.9815919035
"6509","No","Yes",196.776728724821,15858.3533012716
"6510","No","No",1637.34775868048,37676.6631174408
"6511","No","No",574.955357679402,33517.2110282314
"6512","No","No",969.134944037313,29705.0413235056
"6513","No","Yes",1049.241310546,23332.4838409643
"6514","No","No",772.531269674791,23222.7397654429
"6515","No","No",1304.06828488441,26219.3231448631
"6516","No","No",875.875269355093,37160.9607959084
"6517","No","No",587.074529854938,34975.9764731986
"6518","No","Yes",495.136132955298,15519.4851235698
"6519","No","No",131.633998250593,42028.0085987333
"6520","No","No",1333.18212763679,19396.0318800155
"6521","Yes","No",1819.24909460129,49621.0561715013
"6522","No","No",953.816075507603,45281.3698500808
"6523","No","No",327.038582491764,51348.9372686679
"6524","No","No",989.636309999908,27318.9437058364
"6525","No","No",1109.22148329882,56680.1375308278
"6526","No","Yes",0,9321.4637343151
"6527","No","Yes",635.394657997528,18344.0754556225
"6528","No","No",100.021825035447,48377.6412807732
"6529","No","No",264.96805412597,36842.8226614232
"6530","No","No",465.845663824603,52714.0888633024
"6531","No","No",1227.26180035355,49697.0461841938
"6532","No","No",1378.67764270611,30862.9963586514
"6533","No","No",715.656721728525,23266.1356879781
"6534","No","Yes",0,21719.4548323269
"6535","No","No",1335.23028139805,43660.3917144764
"6536","No","No",1576.90364668831,35117.8053539428
"6537","No","Yes",1232.59035337314,22108.5803668768
"6538","No","No",1613.71411366105,47151.6097573017
"6539","No","No",763.743166120878,59856.7966744683
"6540","No","No",741.430158989983,32951.1793551857
"6541","No","No",325.586549913805,45809.9842404834
"6542","Yes","No",1797.76701894827,47068.724605617
"6543","No","No",899.902700389718,47093.73837724
"6544","Yes","No",1605.21458639037,26149.1482230276
"6545","No","No",0,42879.3969382104
"6546","No","No",886.689023472008,50507.9308095184
"6547","No","No",1008.29063504735,30801.0897521694
"6548","No","Yes",1034.50445305921,19105.6282177488
"6549","No","Yes",1125.73450731592,23410.0994120351
"6550","No","Yes",1070.95073407284,12707.580411669
"6551","No","No",920.408497828846,40536.6424788207
"6552","No","Yes",734.733164890242,10816.9497658311
"6553","No","No",1701.68848691138,38567.2047054257
"6554","No","No",1453.31617301335,29830.0567407011
"6555","No","No",294.024346115685,49988.1473141347
"6556","No","No",1302.47913157629,43154.3929272714
"6557","No","No",1135.18798440953,47075.0323003011
"6558","No","No",1123.63313874114,17292.3031718193
"6559","No","Yes",483.109799304421,15067.313708392
"6560","No","No",1669.5026869634,30259.256369494
"6561","No","Yes",1051.37688911756,13271.4646920284
"6562","No","Yes",778.340916811529,23737.2667567406
"6563","No","No",37.9908485136482,43787.7818950852
"6564","No","No",599.879669777376,51733.8251737169
"6565","No","Yes",1037.64286692044,22889.4889815833
"6566","No","No",1187.46360760233,37092.5904084287
"6567","No","No",1287.20589549305,37135.3639398797
"6568","No","No",599.380380137431,58909.3442631081
"6569","No","No",278.078455799143,33811.6692142213
"6570","No","No",536.700808563883,35786.5148166
"6571","No","No",626.514786193039,55155.0778632325
"6572","No","No",0,54298.8596097499
"6573","No","No",885.510006686116,42683.4797282413
"6574","No","Yes",1314.84172413687,15965.4150053432
"6575","No","No",580.164720972546,31879.138933856
"6576","No","No",1144.53051107846,40998.9273365533
"6577","No","No",0,55435.4305474685
"6578","No","No",321.308581363967,33136.3364817528
"6579","No","No",776.395873647769,49705.8161799169
"6580","No","No",519.308691599167,42861.9326596232
"6581","No","No",892.281071819882,40492.1005817823
"6582","No","Yes",945.0549544733,21095.5381789028
"6583","No","No",840.236847473966,24801.368106669
"6584","No","No",660.533671837818,31480.0408165192
"6585","No","No",213.010073963225,38950.6000660758
"6586","No","No",506.824141742222,50493.0952625075
"6587","No","No",884.660671550052,35324.0202172327
"6588","No","Yes",296.462756021594,20138.2469854034
"6589","No","Yes",700.075337653754,14828.5505925092
"6590","No","No",927.468684967434,44308.2823244709
"6591","No","Yes",1172.27349764421,22792.6216909739
"6592","No","No",841.526249578981,40239.677398712
"6593","No","No",49.682397301772,47026.0385452057
"6594","No","Yes",903.916652133078,20246.3932304422
"6595","No","No",600.18270085246,50578.0542344249
"6596","No","Yes",1749.12218401084,21359.1315466029
"6597","Yes","Yes",1893.02300430521,16853.3295653838
"6598","No","Yes",1636.51972469254,17533.3422009671
"6599","No","Yes",1533.91193640526,16453.8830816312
"6600","No","Yes",1523.72257079709,15708.9577283378
"6601","No","Yes",1156.62020011113,11102.5681462402
"6602","Yes","Yes",2168.45319610867,24648.9799157642
"6603","No","No",456.830738069141,39629.5808401885
"6604","No","No",1616.16858639682,39654.6188645202
"6605","No","Yes",1311.86009883369,15565.1976959325
"6606","No","Yes",437.068295607682,21638.9478395241
"6607","No","No",683.114615595568,31615.3655535259
"6608","No","Yes",1327.28243902948,15819.3593199174
"6609","No","No",797.630942096371,54042.3622475245
"6610","No","No",635.707607612644,26084.6960491472
"6611","No","Yes",1312.88531954721,16221.2028921882
"6612","No","Yes",944.8328467186,23323.8227309825
"6613","Yes","No",1838.87136892563,32129.3398189179
"6614","No","No",267.372318799335,41183.6574862839
"6615","No","Yes",723.540414655993,28289.2359115583
"6616","No","No",610.052170853123,35855.3879303433
"6617","No","No",1861.31769729946,46653.5887355559
"6618","No","No",0,34882.2285462766
"6619","No","No",1577.97254360463,50430.2398100676
"6620","No","Yes",954.150390455917,15969.8199667989
"6621","No","No",204.39619956638,38979.6238800119
"6622","No","Yes",1109.46821724848,21663.1324734238
"6623","No","No",771.224877649241,47146.4395098562
"6624","No","No",376.350443987224,32168.4421591768
"6625","No","Yes",1523.88009546054,19342.983519581
"6626","No","Yes",1178.21173671124,12077.0132631664
"6627","No","Yes",1457.57538055378,19668.1577588603
"6628","No","Yes",1292.44731610947,8231.03742327335
"6629","No","No",1233.50222154788,23031.6008932731
"6630","No","No",542.140575136042,22394.1911044491
"6631","No","No",1381.81926933794,48394.289618696
"6632","No","No",1157.76811279803,49960.7652059971
"6633","No","No",744.493874512598,24506.6781957733
"6634","No","No",1387.49054933013,40993.5953389797
"6635","Yes","No",1434.12771574145,47311.5304210632
"6636","No","No",1483.53656327982,34156.3742030791
"6637","No","No",1340.89319243269,31959.9655971505
"6638","No","No",972.094401559996,26231.1459131138
"6639","No","No",1511.87938711542,36381.2376497741
"6640","No","Yes",1111.99772246377,20549.6203542259
"6641","No","Yes",1555.5633519974,15957.3288311695
"6642","No","No",1727.36514642417,41707.4773032752
"6643","No","No",25.7358488954937,49089.6040759974
"6644","Yes","Yes",1895.33493298048,16394.5081086404
"6645","No","Yes",417.478256980257,17787.9478232724
"6646","No","No",247.19905719341,37377.3578246637
"6647","No","No",91.3876980949715,44091.8760022769
"6648","No","Yes",617.089796003652,22801.6639674736
"6649","No","Yes",793.333785080227,10487.9301783322
"6650","No","Yes",1035.7209657486,20393.2700245789
"6651","No","No",826.981750400875,35426.1807962439
"6652","No","No",1734.45156392034,31578.0049503928
"6653","No","No",1069.6220603923,40299.3199102539
"6654","No","No",626.838428236661,41334.1835259785
"6655","No","No",932.056966292719,45505.3072242837
"6656","No","No",1551.02773138271,31768.0306081718
"6657","No","Yes",315.090268398632,22551.891246075
"6658","No","No",1059.35160533058,41179.0100155982
"6659","No","No",976.704047909637,59291.7225668652
"6660","No","Yes",562.558936899144,11524.8103500998
"6661","No","Yes",310.020277059122,16382.1529296615
"6662","No","No",936.9447650212,40240.4971080424
"6663","No","No",0,43033.4922461325
"6664","No","Yes",431.197047291067,20301.4936695647
"6665","No","No",1411.54871811982,42956.6313940264
"6666","No","Yes",1183.45281522434,16189.2883933786
"6667","No","Yes",1849.60366586347,16966.8859214148
"6668","No","No",391.482079303432,45821.6332279707
"6669","No","No",10.8849413073991,32866.4452384084
"6670","No","Yes",697.248632586975,25730.9175826238
"6671","No","No",175.054336957999,22644.9958781972
"6672","No","Yes",1094.74057904676,19638.265428074
"6673","No","Yes",134.624041122727,19464.2987072675
"6674","No","No",1069.74833184278,32441.2819775573
"6675","No","No",1165.81259867426,47005.5732858795
"6676","No","No",321.218102077606,43257.3534881096
"6677","No","No",178.275032128603,35615.9281065657
"6678","No","Yes",953.85848181184,18121.1133574643
"6679","No","Yes",1335.81270700321,12843.4480324123
"6680","No","Yes",853.594325403116,25373.4621853561
"6681","No","No",639.62567466641,38660.9232245854
"6682","No","No",1051.84774862331,37160.2733032415
"6683","No","No",335.107155766791,36374.2143825641
"6684","No","No",1193.76279235836,34909.3088814725
"6685","No","No",915.203083538807,31422.9455472119
"6686","No","No",426.087027438672,15498.8694742336
"6687","Yes","Yes",1805.6183529996,19168.0299741275
"6688","No","No",717.478956364503,37810.4721333394
"6689","No","No",520.947287347575,41039.2824540712
"6690","No","No",1034.89243123867,34539.8457594936
"6691","No","Yes",1022.59833632716,11739.0959841144
"6692","No","No",617.591440461858,43871.6979585579
"6693","No","Yes",1322.16229345805,16984.1982641472
"6694","No","No",476.591394942606,33760.4583097893
"6695","No","No",1101.09122392197,43035.5620761571
"6696","No","Yes",1019.57911236953,15833.790650411
"6697","No","No",329.44827264028,43242.8753661793
"6698","No","Yes",949.23382481874,25040.7123388485
"6699","No","Yes",1230.71462791196,18581.2746126331
"6700","No","No",1252.65305823313,35382.1403253839
"6701","No","No",0,47019.6632264432
"6702","No","No",642.269304833237,37752.3845900199
"6703","No","No",512.51352584874,44178.8405036654
"6704","No","No",1114.90155183715,38155.3683870141
"6705","No","Yes",1742.34653491169,10778.1548681166
"6706","No","No",763.022312459412,34931.3482868207
"6707","No","No",878.44473088973,47722.8103498075
"6708","No","Yes",936.229550899064,14217.401520315
"6709","No","No",585.237716368989,40585.7465430587
"6710","No","No",347.614099566677,40138.0778024224
"6711","No","No",607.898642294942,26117.6651716242
"6712","No","Yes",1251.08575644472,21968.2699917387
"6713","No","No",316.536434918891,39995.3188027128
"6714","No","No",336.797040313976,42880.464298445
"6715","No","Yes",871.066795757733,13144.3655839827
"6716","No","No",1187.90468623557,43873.9829119949
"6717","No","No",86.0127899647048,43467.5026537496
"6718","No","No",737.041709018461,27879.6568461805
"6719","No","Yes",1419.87072143754,10401.0466649939
"6720","No","Yes",402.503365602264,12255.8707416565
"6721","No","No",191.215000735492,41921.6828541407
"6722","No","Yes",1836.78008641535,15422.2321631999
"6723","No","Yes",1273.1862477934,20429.6213674886
"6724","No","No",1037.60063686564,40415.8229822359
"6725","No","No",827.542885547743,39655.6770086561
"6726","No","No",915.913590234859,34934.4823935399
"6727","No","Yes",212.708644106102,13155.6720563754
"6728","No","No",941.168179641529,58991.8615093822
"6729","No","No",487.416894339608,34439.9033140515
"6730","No","No",1889.52477548295,32509.6162569272
"6731","No","No",691.202279432214,38948.1053558833
"6732","No","Yes",502.645821222757,14425.0841680532
"6733","No","No",140.018575657783,52099.1162994549
"6734","No","No",0,36484.5941490833
"6735","No","No",519.461494214481,44498.015589091
"6736","No","Yes",1031.86979767225,19322.5523521758
"6737","No","No",939.66939011627,47112.7653034597
"6738","No","No",0,44902.9293469253
"6739","No","No",1093.68490238496,39684.451930144
"6740","No","Yes",1623.33660466742,14338.4087813252
"6741","No","No",851.204010639394,44905.3253123763
"6742","No","No",1119.7026481001,33510.3571772555
"6743","No","No",0,32720.571704904
"6744","No","Yes",744.914266466495,15711.716417211
"6745","No","Yes",303.345868403883,15220.599320804
"6746","No","No",1522.7559269208,29165.4431869562
"6747","No","No",361.367609935872,36533.7103584246
"6748","No","No",1062.72792746379,48041.6493951147
"6749","No","Yes",1344.0946787466,22012.2931469098
"6750","No","No",387.731918536029,55163.7969407479
"6751","No","No",860.028242922584,56687.1435385628
"6752","No","No",1637.35795482487,46629.9469681977
"6753","No","No",975.198512299475,30573.258054574
"6754","No","No",487.797479695295,21418.6946785754
"6755","No","No",0,49626.4156245673
"6756","Yes","No",1562.45458493467,43067.3337360648
"6757","No","No",1399.12616301261,26505.0201256997
"6758","No","Yes",542.064065970151,9135.88677403038
"6759","No","No",901.075726458657,38613.4266514959
"6760","No","No",295.870568858272,19054.0304980425
"6761","No","No",90.5614102875277,50939.5211535323
"6762","No","No",476.149931465101,58837.7231108606
"6763","No","No",382.885062371549,42620.5302295829
"6764","No","No",1013.96374594514,49653.797125496
"6765","No","Yes",932.685798904273,15774.7010629533
"6766","No","No",360.574948754149,42217.3829194421
"6767","No","No",727.963608364032,40258.7017158036
"6768","No","No",917.952756407793,31380.2331709961
"6769","No","No",1567.16535665761,38255.0505677174
"6770","No","No",1006.68048810668,33348.5753598313
"6771","No","No",1278.93266550967,42894.7652950297
"6772","No","No",0,56493.4860935359
"6773","No","No",1107.48786714994,23530.6227254858
"6774","No","Yes",1719.56685067072,18696.2987495256
"6775","No","No",1066.17466172765,33911.5648735603
"6776","No","No",0,30172.8105945502
"6777","No","Yes",1296.10352692953,15709.4557934374
"6778","No","No",720.244598512222,38493.4919321852
"6779","No","No",574.638223310543,41914.4695159586
"6780","No","No",241.430088847648,34507.7759344293
"6781","No","No",345.14214305995,41943.7728369267
"6782","No","No",1341.70344157685,53122.0689582977
"6783","Yes","No",1847.79137287982,30202.8462381974
"6784","No","Yes",1497.06776735759,9030.40256351934
"6785","No","No",503.391240045086,29187.4441155392
"6786","No","No",1119.57544932473,42280.8090301403
"6787","No","No",1172.41517190972,58067.1298181025
"6788","No","Yes",1594.38458640784,21514.6027977762
"6789","No","No",566.443852583261,36039.3940739969
"6790","No","No",542.470159190065,39037.8010123156
"6791","No","No",1086.42093928535,38656.6631307308
"6792","No","Yes",995.03943010905,8750.55879965906
"6793","No","Yes",1200.87597553929,13833.9821981928
"6794","No","No",0,56895.9619836109
"6795","Yes","No",1508.64924310288,37111.3089039706
"6796","No","No",1170.54794573518,52358.8009264251
"6797","No","No",1356.74383618414,54489.9387774168
"6798","No","No",850.03641056271,31522.2346514117
"6799","No","No",1184.14038273806,25196.7617863616
"6800","No","No",902.713664465989,23847.3922912817
"6801","No","Yes",1597.89091404172,24006.180390084
"6802","No","No",1079.07612251215,36636.2800459142
"6803","No","Yes",1503.57844528679,18776.5338455694
"6804","Yes","Yes",1570.6587796556,16239.1531737462
"6805","No","No",0,41655.6822607587
"6806","No","Yes",735.578364822891,13976.6859106159
"6807","No","No",923.346231764488,44670.8642404417
"6808","No","Yes",1269.67822272096,13998.3067322622
"6809","No","No",706.481931471293,48339.8789934832
"6810","No","No",1146.8753977636,64644.9052937777
"6811","No","Yes",1749.54960903014,20078.3618751111
"6812","No","Yes",263.491319996353,25363.5952468832
"6813","No","No",468.869908473501,44285.3201124122
"6814","No","No",443.913087817629,35241.9929724056
"6815","No","No",375.720573035678,35564.4102724882
"6816","No","No",772.822997669751,25556.7571651335
"6817","No","No",750.139537230604,17993.8204877237
"6818","No","No",414.804258718943,37825.3721833992
"6819","No","No",626.175227742675,33930.9750471622
"6820","No","No",645.201209110071,44324.2243635148
"6821","No","Yes",1287.28723524741,13215.9702541318
"6822","No","No",1391.26115350664,51169.3622077771
"6823","No","Yes",601.804689792233,16266.3025981603
"6824","No","No",1061.31087443623,33942.6197454918
"6825","No","No",272.750468578767,42646.7048890104
"6826","No","Yes",1421.74012892227,11134.9396021509
"6827","No","No",1065.62680637814,36015.6875836991
"6828","No","No",1167.03505894924,34467.5428636393
"6829","No","No",949.129084899619,28411.6564861553
"6830","No","Yes",565.988728443035,18645.9366490187
"6831","Yes","No",1914.10696202772,55573.7017123721
"6832","No","No",515.942805113736,50678.3369443292
"6833","No","No",1347.46071020221,32720.4457444376
"6834","No","No",124.305910805526,44464.6358474661
"6835","No","No",1007.22029633501,42183.0897289732
"6836","No","No",218.337959915025,44179.9465035539
"6837","No","No",1344.55595046163,34841.260517708
"6838","No","No",1063.21952649285,27887.2505285566
"6839","No","Yes",624.543165674628,14820.4671927587
"6840","No","No",559.832011277457,39144.4768374331
"6841","No","No",341.386840479662,29326.3392356403
"6842","No","No",574.164492504123,44072.0607972807
"6843","No","Yes",734.898256761058,11868.0742124963
"6844","No","No",875.295334786835,46041.7562780251
"6845","No","No",366.090802610492,47096.3780661141
"6846","No","Yes",1009.99387200895,19870.5365805021
"6847","No","No",221.922490582919,32988.5713577033
"6848","Yes","Yes",1957.12029492724,18805.9521289077
"6849","No","Yes",1050.4312909571,19246.3330925914
"6850","No","No",612.324988948466,55326.4128432861
"6851","Yes","No",1456.96393044029,49054.1938883123
"6852","No","Yes",1821.52391746074,15802.3892930249
"6853","No","Yes",770.328029480587,29059.1446947617
"6854","No","Yes",1269.90627395329,771.967729386357
"6855","No","Yes",1923.38194071368,12940.2278324251
"6856","No","No",890.759497161851,20257.3799905086
"6857","No","No",1341.05478800605,33778.1620359403
"6858","No","No",888.265039364919,37745.605173638
"6859","No","Yes",1189.31395853398,19545.7271170543
"6860","No","No",1820.54049444127,46251.4486884001
"6861","No","No",481.840936169969,36377.6474336846
"6862","No","No",432.092064833132,56732.7372801469
"6863","No","No",683.997177817983,36578.1846500597
"6864","No","No",354.701953160098,44412.1939231786
"6865","No","Yes",669.737237276828,17109.630480395
"6866","No","Yes",713.92287437803,26049.9756416507
"6867","No","No",597.041608734639,31713.0976939754
"6868","No","Yes",1306.41687939066,18996.3503217393
"6869","No","No",1991.13551404059,30809.6745673939
"6870","No","Yes",717.632894780562,19590.9710043964
"6871","No","No",390.606676891254,38083.5473003731
"6872","No","Yes",1862.53865898232,23839.5725230292
"6873","Yes","No",1143.68050317136,37751.0173411394
"6874","No","No",264.372356972425,45778.308946636
"6875","No","No",216.846533279374,37160.9410951088
"6876","No","No",686.892928589707,51120.8299797229
"6877","No","No",1642.19231860975,24444.3121751467
"6878","No","Yes",0,15587.9006038082
"6879","No","Yes",1218.64921197662,19206.1334637425
"6880","No","No",1970.85976085399,37905.2526333646
"6881","No","Yes",1151.73331728929,23149.5474930787
"6882","No","No",539.077720825741,36275.4557232692
"6883","Yes","Yes",2287.17384201509,18692.1443107782
"6884","No","No",1218.24580162308,56816.8089387227
"6885","No","No",700.453136926996,54661.1150697389
"6886","No","No",1167.23167850084,30648.4287991214
"6887","No","No",1492.61502806146,46174.6979911956
"6888","No","No",666.394794790436,34912.33670141
"6889","No","Yes",636.898809515342,23005.575945443
"6890","No","No",420.72702302223,37831.5700769464
"6891","No","No",879.052649120858,30633.8419389473
"6892","No","No",376.241161424901,25821.1610609312
"6893","No","No",961.40245406434,25478.8183475055
"6894","No","No",500.72854111181,33406.0541135306
"6895","No","Yes",369.679932317278,24834.4175810052
"6896","No","No",719.93804397567,31031.2193961697
"6897","No","No",1168.34700459466,39275.1935417815
"6898","No","No",600.538096885772,39260.3821217756
"6899","No","No",0,18593.9147350522
"6900","No","No",252.944896791095,23443.4164575638
"6901","No","No",676.430888753692,53378.9885244964
"6902","No","Yes",1066.82218828697,26348.3036410949
"6903","No","Yes",1004.65057736924,13869.7520966379
"6904","No","No",1010.42114145537,46217.8095555543
"6905","No","No",1260.93129441239,27697.338251997
"6906","No","No",402.632427287244,58828.7922472825
"6907","No","Yes",919.47548466323,6100.73320179588
"6908","No","No",1004.65381639424,42729.1822272495
"6909","No","No",614.538566272609,33811.9876207301
"6910","No","No",1358.10977879143,50662.8927850888
"6911","No","No",160.524878725024,29675.8354904945
"6912","No","Yes",1190.18882924144,15860.3266837987
"6913","No","No",262.599260446498,48762.2835507908
"6914","No","No",647.918024055962,35494.4852868572
"6915","No","No",1574.651282455,30559.5136956957
"6916","No","Yes",1658.60225711853,14821.0045610976
"6917","No","No",4.81490868174683,28714.1353764479
"6918","No","No",1220.31228233318,36190.9936096812
"6919","No","No",802.857102212522,45806.1991738207
"6920","No","No",81.8744894682283,35818.2280012137
"6921","No","Yes",70.7336120060838,17466.1389631533
"6922","No","Yes",962.905796409212,22795.0324982253
"6923","No","No",926.694301047722,44233.6461669561
"6924","Yes","Yes",1233.44589515108,12586.4781909623
"6925","No","Yes",644.111717476592,9778.219986444
"6926","No","Yes",1338.659659247,8078.17604672449
"6927","No","No",740.386785882644,46877.960742233
"6928","No","Yes",901.186502571389,19204.7016202932
"6929","No","No",975.388741777061,45597.9376922718
"6930","No","No",298.71961515065,49585.8712690845
"6931","No","No",591.498013908176,29507.4870633153
"6932","No","No",177.014401129228,37553.8018121917
"6933","No","No",292.27197689876,60582.2262136544
"6934","No","No",625.191833063067,32826.409651775
"6935","No","No",546.986650105736,37513.7003808368
"6936","No","No",727.675332278311,23515.6108852627
"6937","No","Yes",531.976408238036,21357.6321613268
"6938","No","No",311.212767452465,51257.8742890846
"6939","No","Yes",703.063069636357,17692.6583930126
"6940","No","No",1239.68306767989,52241.4556649147
"6941","No","No",810.278839424482,51283.147864078
"6942","No","No",303.482691590285,21381.314486889
"6943","No","No",1372.03570594285,36188.2397816099
"6944","No","No",1146.3755626233,37053.8284571923
"6945","No","Yes",730.962752416666,20097.4009356218
"6946","No","No",807.219216290949,42138.6530736505
"6947","No","No",450.40551878583,34206.2736478661
"6948","No","Yes",467.912738885715,25424.4100657609
"6949","No","Yes",684.891035475141,17582.9855418272
"6950","No","No",1013.46926055685,38280.9170083462
"6951","No","No",676.652720204,31741.4310589349
"6952","No","No",287.966887449483,37409.9777560406
"6953","No","No",816.648909601169,52163.7815488925
"6954","No","No",1118.78983433131,39111.792335154
"6955","No","No",532.153609268785,43789.3351854842
"6956","No","Yes",1364.2326842718,18423.0476618707
"6957","No","Yes",1464.69087058547,16542.0450715198
"6958","No","Yes",997.642404039563,27054.7274125397
"6959","No","No",335.340082541427,41942.7525113637
"6960","No","No",1149.01295796219,35310.129547081
"6961","No","Yes",905.565799186285,24032.8005473796
"6962","No","No",649.673269527654,32276.4291404655
"6963","No","No",380.189188166333,40689.9204355211
"6964","No","No",0,48303.8715137401
"6965","No","Yes",485.434317495701,21278.3408079929
"6966","No","No",649.841985476221,54693.1517919876
"6967","No","No",698.572453391445,44949.2690840763
"6968","No","Yes",1328.95366930756,20666.7028058968
"6969","No","No",609.477689904886,41798.2923453603
"6970","Yes","No",1723.18721476173,43143.1644908776
"6971","No","No",0,36581.8038993462
"6972","No","No",594.601261246347,36190.7563884601
"6973","No","Yes",875.712944121739,18696.9535767098
"6974","No","No",949.314976716357,50404.2787609121
"6975","No","No",252.376727131597,39723.4837311651
"6976","No","No",1162.56797548573,21753.5283456455
"6977","No","Yes",1244.35956262629,19232.0474392262
"6978","No","No",578.495565421087,46030.8812771014
"6979","No","No",443.900952937401,50768.4448744471
"6980","No","No",1316.0892527487,41314.1695220835
"6981","No","No",904.424080886182,56062.0165312447
"6982","No","Yes",1037.9745946763,15580.7688790303
"6983","No","No",842.985664608997,42682.1420130652
"6984","No","Yes",1258.5287351543,10920.9645851391
"6985","No","Yes",297.390738058888,19383.7911466926
"6986","Yes","Yes",1553.34965666218,18091.9437361812
"6987","No","Yes",684.935381218755,21908.5715872975
"6988","No","No",1265.50126447533,32910.3974372661
"6989","No","No",362.611151080774,39296.0779934241
"6990","No","No",1532.91620917717,22080.9217712123
"6991","No","Yes",1341.6157387998,26319.0155875505
"6992","No","Yes",1086.16110772543,23576.7867298481
"6993","No","No",496.001762649659,32818.157065104
"6994","No","No",1597.05208352263,50827.8075383476
"6995","No","Yes",2092.4585301161,14514.7699586373
"6996","No","Yes",1019.98429310496,23795.4224757891
"6997","No","No",765.233878264981,29096.4172613222
"6998","No","No",928.964969607818,46709.4802764204
"6999","No","Yes",1787.20193794362,17553.607248986
"7000","No","No",628.136803682396,38436.1914515465
"7001","No","No",351.82039602293,30513.5179302546
"7002","No","Yes",265.503922870572,23775.4355664208
"7003","No","Yes",1886.5505699102,6467.14966324312
"7004","No","No",57.647049217624,38218.1844732497
"7005","No","No",933.827036002857,57377.4327774162
"7006","No","Yes",996.232072600701,10707.9426639873
"7007","No","Yes",1033.05628347808,15453.0337037444
"7008","No","Yes",824.518400683749,20689.7242231995
"7009","No","No",358.647641719878,56331.7787681505
"7010","No","No",1393.36946267428,43439.6740696835
"7011","No","No",332.413825874447,37129.1511776559
"7012","No","No",1215.08894988552,39002.4969645977
"7013","No","Yes",1793.78940473659,22740.5485627044
"7014","No","No",874.174254976681,33821.9060145171
"7015","Yes","Yes",1893.94295526224,12399.1247374186
"7016","No","No",1119.6134259786,32213.2290285762
"7017","No","No",234.699320726285,28098.893254432
"7018","No","Yes",874.017163849367,24635.3110317565
"7019","No","No",1283.228886582,47514.9933659047
"7020","No","Yes",1326.55808330628,25853.1425435955
"7021","No","No",1263.79824190405,47203.8202849677
"7022","No","Yes",1145.27901777596,12906.2041393171
"7023","No","No",1118.85173810187,30558.3838568659
"7024","No","No",753.83229649878,46957.2818757947
"7025","No","No",1039.23790961497,50653.9137882032
"7026","No","Yes",1947.07267886716,13157.9565570619
"7027","No","No",864.777442376434,32120.2941224717
"7028","No","No",1165.85183799472,48855.5094451628
"7029","No","No",1101.43879639113,39045.9939051909
"7030","No","No",438.516508307876,36838.8820335319
"7031","No","No",750.720008151587,47876.8958940807
"7032","No","Yes",743.415446250591,19610.1799781656
"7033","No","No",1371.3510156933,34955.4078971076
"7034","No","No",673.384203313401,34891.5427807964
"7035","No","No",1208.22057225773,36402.7452329903
"7036","No","Yes",483.420566632934,12596.9340146603
"7037","No","No",1394.07975394572,27179.4287768602
"7038","No","Yes",1405.31717376017,17933.2370088231
"7039","No","No",736.392672850291,32026.963957527
"7040","No","Yes",1167.34909040544,16356.9305659673
"7041","No","No",1031.86993245697,46725.3740229681
"7042","No","Yes",0,17189.3713959353
"7043","No","No",0,51226.7616106643
"7044","No","No",1334.9507305498,51086.4446460526
"7045","No","Yes",997.936283119464,25788.3631535607
"7046","No","No",419.444569831827,47155.6922177728
"7047","Yes","Yes",1806.92184567205,11506.2177502211
"7048","No","No",474.495395966397,43522.4623780293
"7049","No","Yes",531.358419832627,11420.5686950673
"7050","No","No",880.581024417902,49547.1760151448
"7051","No","Yes",1491.16610501992,18171.6222280094
"7052","No","No",976.902807317222,32837.1655487296
"7053","No","Yes",642.382285890744,13563.4632874001
"7054","No","Yes",1670.82999110515,11385.8802715792
"7055","No","No",1285.37127902736,39925.203396523
"7056","No","No",199.594525689419,27579.3533932148
"7057","No","No",66.5131134234801,29670.6988318902
"7058","No","No",577.531956379148,22111.8669113285
"7059","No","No",1142.61630705206,42053.5704731481
"7060","No","No",767.139212107638,49171.978177546
"7061","No","Yes",851.338372167527,22829.0247831648
"7062","No","No",683.942140800258,35232.8351834229
"7063","No","No",485.44043108254,38656.4546259537
"7064","No","No",0,49772.9747466163
"7065","No","Yes",915.147661540853,21545.7173616191
"7066","No","No",1143.52956232014,38663.2286266236
"7067","No","No",365.242329763141,27027.4080555126
"7068","No","No",661.104187133466,42945.5702924694
"7069","No","No",964.766019325237,49646.8065727842
"7070","No","No",1044.78688912355,41148.5400571297
"7071","No","No",201.80740022199,71878.7726357675
"7072","No","No",655.662247318421,54844.2047262558
"7073","No","No",10.9057992239271,48181.6891524716
"7074","No","No",528.40443472107,41066.7319025751
"7075","No","No",728.356058470814,32388.6157596306
"7076","No","No",938.386763627777,18594.127976644
"7077","No","Yes",359.936375470556,26500.2087376331
"7078","No","No",1185.1493653956,32467.4177408487
"7079","No","Yes",811.046695390703,21263.0991404914
"7080","No","Yes",1089.83145380546,15056.0778671339
"7081","No","Yes",288.318814270766,18356.5652468838
"7082","No","No",351.959107430178,29281.0307109066
"7083","No","No",977.19550210948,58658.7501507598
"7084","No","No",919.67240486341,40573.179445659
"7085","No","No",800.243337878895,19051.3239981764
"7086","No","No",771.825496903894,40520.8094002804
"7087","No","No",585.869313047934,67100.6852055329
"7088","No","No",1077.81237937944,26518.0802779105
"7089","No","No",907.306758410301,28461.2937369726
"7090","No","No",838.017911018859,42232.9415102544
"7091","No","No",1006.93493132388,25733.347810803
"7092","No","No",712.412048874675,42352.0914911586
"7093","No","No",508.453244165149,31966.400879379
"7094","Yes","No",1476.50978220195,42984.4128511994
"7095","No","No",1224.47123850969,37088.0532034538
"7096","No","No",636.309343984964,50040.4197931458
"7097","No","No",1609.69881514507,60123.6273023235
"7098","No","Yes",885.212531955794,11774.4165700229
"7099","No","Yes",137.039372229701,10994.794100075
"7100","No","Yes",988.89562982093,19449.1791846268
"7101","No","Yes",563.290133206154,22323.8335480741
"7102","No","No",309.782353880228,33671.5626141523
"7103","No","Yes",821.257288150696,17347.4394608849
"7104","No","No",434.189904174905,42205.2648924287
"7105","Yes","Yes",1321.53537905713,23735.1598892282
"7106","No","No",549.853837247715,50084.3895096604
"7107","No","No",1170.01902164463,48445.2168744962
"7108","No","No",1238.37188710574,42321.5271544434
"7109","No","Yes",0,15698.9812337332
"7110","No","No",803.946256032651,42286.2878154554
"7111","No","Yes",1456.00596108476,15069.8828890755
"7112","No","No",954.330590043764,27180.5459647451
"7113","No","No",1224.93071142997,44002.654530336
"7114","No","No",796.390802627468,47600.4945153565
"7115","No","No",461.026219252558,15755.647226184
"7116","No","No",468.285776829294,23336.030005624
"7117","No","No",321.012762881255,30275.5418828184
"7118","No","No",1653.43470936577,44965.0323623953
"7119","No","No",1190.5362188905,41630.2828343243
"7120","No","No",178.047346838311,35401.7089664284
"7121","No","Yes",1447.85678228622,24669.8420252258
"7122","No","No",0,56043.9102166148
"7123","No","No",282.30768016583,38099.1433107002
"7124","No","No",28.7388025495519,41809.2041766343
"7125","No","Yes",1279.31680609639,17837.0345506092
"7126","No","No",0,36061.7007635649
"7127","No","No",1470.34814886117,49018.8018455176
"7128","No","Yes",405.568290094582,20523.9123131832
"7129","No","Yes",509.172294671135,19962.8178766147
"7130","No","No",1207.15084223977,29875.3790445721
"7131","No","No",546.37505990967,37524.4343166153
"7132","No","No",86.9186579694452,34638.903904325
"7133","No","No",418.823888877566,56320.6338597974
"7134","No","No",0,56412.0075265771
"7135","No","No",41.6514375126486,47522.0236650279
"7136","No","No",906.745807676788,38881.0110720928
"7137","No","Yes",1499.85634865852,25156.7364373756
"7138","No","No",1561.63131934766,32952.8407694823
"7139","No","Yes",1615.50480202178,19566.1579588502
"7140","No","No",766.096021086797,41435.591897162
"7141","Yes","Yes",1807.70584411186,21849.4816313093
"7142","No","No",594.916782631428,43152.5332365085
"7143","No","No",1458.67564680684,57786.3231230033
"7144","No","No",901.288508994425,44594.3480307008
"7145","No","No",32.99359889857,31644.7706159164
"7146","No","No",1050.2685247574,59401.1956734579
"7147","No","No",1329.09676232805,60321.6132109193
"7148","No","No",410.935624537375,39076.2728228248
"7149","No","No",1591.82828968015,27668.4417692371
"7150","No","No",561.989745355229,40446.0087799328
"7151","No","Yes",592.170002642002,18684.2356401468
"7152","No","No",624.696396360594,30763.4651232863
"7153","No","No",1433.74049679502,39445.0840889239
"7154","No","Yes",635.362156102893,13912.2185315797
"7155","No","No",982.968718072735,50700.1738622413
"7156","No","No",877.415879371153,22997.9450275693
"7157","No","No",433.429970678414,49209.2563988888
"7158","No","No",951.313044194811,25540.8159627897
"7159","No","No",205.006264369155,36554.6265286998
"7160","No","Yes",1214.7833466882,11789.449842704
"7161","No","No",974.897205649927,26921.3658786339
"7162","No","Yes",470.107181330254,16014.1133107149
"7163","No","No",0,30684.7305500516
"7164","No","No",1666.22422994917,34918.432526347
"7165","No","No",798.659894709359,17463.0213544847
"7166","No","No",286.267421594117,47960.4382132519
"7167","No","No",908.572629442983,48426.9274352118
"7168","No","No",78.7464414361228,41335.2624810457
"7169","No","Yes",1051.42596467127,6583.87443801092
"7170","No","No",1301.15829550987,28673.6459846632
"7171","No","Yes",1506.69167525026,20680.1826452459
"7172","No","No",0,29901.3994159427
"7173","No","Yes",0,15759.9151700449
"7174","No","No",182.890963170823,21286.8499922913
"7175","No","Yes",1468.96935473959,15977.6625508514
"7176","No","No",1291.24433825788,42098.4789481663
"7177","No","No",608.958315930244,24975.6464069233
"7178","No","No",713.720610164183,17753.767315476
"7179","No","No",1446.64670417993,28951.7273811312
"7180","No","Yes",889.292843041319,14273.2937150958
"7181","No","No",1219.63445995773,47936.306775547
"7182","No","Yes",533.007580306485,20556.4171504616
"7183","No","No",1024.14382177552,45585.9687697656
"7184","No","Yes",510.296040090535,19682.5608310299
"7185","No","No",797.347588529402,35177.4099946928
"7186","No","Yes",1686.60887402386,22175.1316000617
"7187","No","No",530.547329206817,44627.5626446297
"7188","No","No",1332.53107136617,36974.6082048979
"7189","No","No",1401.34680959927,31403.0654839952
"7190","No","No",586.811387065307,40353.3504780337
"7191","No","No",628.419545661226,34756.2476400261
"7192","No","Yes",1193.30792924147,16430.01599162
"7193","No","Yes",666.424383991103,19177.6793706387
"7194","No","Yes",1017.66711978071,13553.09142624
"7195","No","No",648.34461282168,18843.606232431
"7196","No","No",952.138822970475,28642.3637962168
"7197","No","Yes",1492.06585380683,11615.9834652312
"7198","No","Yes",171.598895068837,24809.1145254887
"7199","No","Yes",1231.28109427322,15716.5322596172
"7200","Yes","No",1833.64654831254,43539.7843334378
"7201","No","Yes",633.170443606051,23694.3252385599
"7202","No","No",655.20772780495,30593.1475751348
"7203","No","No",331.407086136963,20588.1718710621
"7204","No","No",1213.36264204807,33527.4888071951
"7205","No","No",881.649854860828,50114.7005356449
"7206","No","Yes",1799.361875875,14250.199392948
"7207","No","No",1165.93650402873,42252.2627098774
"7208","No","Yes",758.673477864191,24166.7623021281
"7209","No","No",269.290141295066,43778.7328806282
"7210","No","Yes",1043.88098357702,27380.4700856437
"7211","No","No",893.114550360715,43511.6521648528
"7212","No","Yes",728.467137692598,15958.7470001168
"7213","No","No",413.752193061782,44803.3602792581
"7214","No","No",97.2648758399586,37468.1802348357
"7215","No","No",765.046486164449,44870.6842914765
"7216","No","Yes",1054.48505359921,10997.4040673927
"7217","No","Yes",649.530745027353,13408.6629471632
"7218","No","No",742.38409994638,34265.3623052066
"7219","No","No",1016.4637019272,49635.1048515898
"7220","No","No",636.304309844044,52011.0692162142
"7221","No","No",976.428533159154,31897.566033739
"7222","No","No",1197.81805113426,37487.6505399685
"7223","No","No",483.454879073622,44281.3465718496
"7224","No","No",1239.49238918487,40312.2302286101
"7225","No","No",942.387354970361,34685.8679609533
"7226","No","No",712.131245668675,48835.9928900325
"7227","No","No",214.981790411236,46693.9377394772
"7228","No","No",558.338857384781,38284.6755343357
"7229","No","Yes",1134.5597310429,20964.2127023783
"7230","No","No",523.647651791954,37590.8199380363
"7231","No","No",593.160786354557,46475.03132161
"7232","No","Yes",868.712519648453,13025.5249765279
"7233","No","No",607.721136462867,41791.8877368967
"7234","No","No",1059.80410067008,64315.0262055869
"7235","No","Yes",0,29106.5837352519
"7236","No","No",1141.7438167848,45455.1630728873
"7237","No","No",1127.25955558426,47545.8468044751
"7238","No","No",456.915217880585,42506.8631535506
"7239","No","Yes",385.397521456814,13739.1661351729
"7240","No","No",705.996771687683,53423.149800625
"7241","No","No",742.562223947455,48207.6150768854
"7242","No","Yes",1310.39973378749,24267.777757484
"7243","No","No",670.982468145979,55003.0913231936
"7244","No","No",441.725465566661,39307.5040375216
"7245","No","No",1075.71843059287,38020.7235915033
"7246","No","Yes",675.240691942867,18827.781887281
"7247","Yes","No",1377.68001967136,41435.2694991584
"7248","No","No",302.399908922779,32048.7421630936
"7249","No","No",910.230777507496,46274.3436016861
"7250","No","No",812.52823846174,58338.925779377
"7251","No","Yes",1298.51626554485,18755.7676358519
"7252","No","No",679.827666865443,43692.5363856043
"7253","No","No",1187.28392841327,56503.5729486472
"7254","No","Yes",813.185316292918,24823.7991709967
"7255","No","Yes",893.708471146775,16399.778520564
"7256","No","Yes",894.657334725516,23656.8418118625
"7257","No","Yes",0,18495.7798208196
"7258","No","Yes",1179.16120481058,16923.402492625
"7259","No","No",1067.3790343619,56291.602012056
"7260","No","Yes",1291.60714177539,16114.0242798708
"7261","No","No",119.47192587933,47761.1499926396
"7262","No","No",343.72428475339,53705.4596253535
"7263","No","No",1699.30157218096,29196.081852041
"7264","No","No",1558.59698527099,38880.4448720673
"7265","No","Yes",1455.50462316194,13555.3356922271
"7266","No","No",1079.37274393436,66134.7178932348
"7267","No","No",508.153059619727,52772.2614547636
"7268","No","No",965.012755605124,32549.5515102086
"7269","No","No",553.649019153505,47021.4918151893
"7270","No","Yes",920.778967083772,14194.8691528619
"7271","No","No",637.338107076551,49657.3942823564
"7272","No","No",744.44020589283,40280.2669976661
"7273","No","No",1529.1526518707,43826.2912683102
"7274","No","No",755.356991705821,53091.5152898842
"7275","No","No",69.5617975429155,49074.2475961957
"7276","No","Yes",1567.21198212064,15877.7864757816
"7277","No","No",429.352532991566,17208.7140267255
"7278","No","No",1348.30355448988,39165.6591737614
"7279","No","No",1068.10797157273,37863.4478466063
"7280","No","No",1074.79140055745,25715.1774543536
"7281","No","No",395.884601970322,34555.8269729204
"7282","No","No",1047.13677049326,46567.9250408872
"7283","No","Yes",1518.62963981646,18925.4900066106
"7284","No","No",964.046253454033,46501.9112011749
"7285","No","Yes",1099.54290396258,25314.8449400213
"7286","No","No",737.176958218147,54160.8578903793
"7287","No","No",587.753816447594,38885.6592059653
"7288","No","No",1177.08837749571,29949.497842595
"7289","No","Yes",1182.00008160582,18102.833748716
"7290","No","No",351.288800597847,30345.6989047543
"7291","No","No",658.023632747905,30391.6317592047
"7292","No","Yes",9.5140328909888,24581.9771065443
"7293","No","No",515.58028071252,43509.0439815997
"7294","No","No",312.127697020277,37743.5081225188
"7295","No","No",820.691852133479,28064.7322000798
"7296","No","No",1498.56503628008,39948.9779866269
"7297","No","No",1090.54796975797,20027.7563910929
"7298","No","Yes",717.339189620971,18382.187193365
"7299","No","No",631.596908174781,30517.2294190789
"7300","No","No",1118.92980278403,49988.0913273555
"7301","No","Yes",496.516313708831,12862.0265626775
"7302","No","Yes",524.173510127699,8272.09032088846
"7303","No","No",1021.22009993951,47650.0025308944
"7304","No","No",749.883065470362,28580.921921531
"7305","No","No",181.724438911984,36858.0586734834
"7306","No","No",1555.83618423158,30110.9602923828
"7307","No","No",895.746675008825,35531.8370865647
"7308","No","Yes",400.904548807575,20912.2886482642
"7309","No","No",540.237503974576,32931.8753767927
"7310","No","No",403.810034348588,40535.1661684496
"7311","No","Yes",1218.54316488104,14063.1168892943
"7312","No","Yes",1252.34417144086,17621.5295944567
"7313","No","Yes",384.590932449319,21364.2300923962
"7314","No","No",319.030066005898,27401.1167743525
"7315","No","Yes",850.839296206177,19413.2039354365
"7316","No","No",1072.82262116598,54527.9792844031
"7317","No","No",1637.31945384267,43364.9561247752
"7318","No","Yes",600.956313060787,21613.9136094683
"7319","No","No",1014.72519283073,40223.9927344649
"7320","No","No",1311.45127913014,47805.7978452956
"7321","No","Yes",1084.54298048584,17410.8465778844
"7322","No","Yes",1302.0867477865,21949.2769081235
"7323","No","Yes",329.785853938403,16476.9869115811
"7324","No","No",625.846305544569,34245.8428734237
"7325","No","Yes",777.64273092137,18190.645274987
"7326","No","Yes",1161.88110072523,13499.8301473532
"7327","No","No",138.588586671783,38437.9918872461
"7328","No","Yes",740.057881955935,24710.119000646
"7329","No","No",824.987976975462,42408.8955441622
"7330","No","No",1292.16016111276,39387.6882367056
"7331","No","No",528.060825176608,34016.8887465416
"7332","No","No",708.851823990675,36322.3491334553
"7333","No","Yes",1066.19424074312,16736.4338383772
"7334","No","No",1072.57277447481,38483.4617230819
"7335","No","Yes",1315.47627172776,27959.6434664187
"7336","Yes","Yes",1988.86974718816,17762.8659115926
"7337","No","No",416.229309671986,35580.5556887531
"7338","Yes","No",1721.43491831519,41999.7033384974
"7339","No","Yes",1706.76043724784,21127.4824181988
"7340","No","No",707.443669728332,42656.6491442273
"7341","No","No",249.112128350614,46332.3667284741
"7342","No","No",446.861677111716,49645.3457108433
"7343","No","No",764.806803042124,33127.1559867552
"7344","No","Yes",824.123364807797,25108.7662912811
"7345","No","Yes",786.915038133869,12881.6893294211
"7346","No","No",308.006275895233,37361.7751439722
"7347","No","No",1733.89536118172,47872.6209803203
"7348","No","No",996.852510824787,49954.7040795591
"7349","No","No",0,52632.3298935436
"7350","No","Yes",1070.33209932377,20053.0285334227
"7351","No","No",0,39329.8081786913
"7352","No","No",1250.67965337158,37012.4075750985
"7353","No","No",1753.91514623481,43855.833129686
"7354","No","Yes",425.419972827313,18109.3206913529
"7355","No","No",1056.8244199283,37994.9695479256
"7356","No","Yes",1546.5818321879,14981.1686681869
"7357","No","No",330.324289217389,55734.0457402734
"7358","No","No",1080.15556406203,35471.9867773415
"7359","No","No",691.168358366468,42342.3506993609
"7360","No","No",584.223460529395,38683.3748761287
"7361","No","Yes",681.972289898639,19925.4443917639
"7362","No","No",999.485645451799,37919.0212559565
"7363","Yes","No",1757.73849947833,51991.463494111
"7364","No","No",1185.73682691834,24265.4359173425
"7365","No","Yes",1770.52557033015,17447.1269195648
"7366","No","No",149.822550028042,33948.8662896245
"7367","No","No",0,36708.3573405233
"7368","No","No",771.016021909898,47614.7381580489
"7369","No","No",0,56982.5059208913
"7370","No","No",607.094632221471,27145.6375410312
"7371","No","No",306.658097609939,30135.6287972626
"7372","No","Yes",364.826699712477,22122.5210796027
"7373","No","No",814.693521955203,39020.8976875159
"7374","No","No",1656.03804743546,29816.0501663427
"7375","No","No",125.937700100074,44260.4818181622
"7376","No","Yes",1766.64977358033,15423.4372844809
"7377","No","No",1593.83791898813,30570.1610817938
"7378","No","No",0,30131.9758084807
"7379","No","Yes",210.897922722621,19075.2069630191
"7380","No","No",449.141606354352,40992.3027259711
"7381","No","No",1125.30209345745,33408.6603429212
"7382","No","No",1529.14313952535,43125.1232811978
"7383","No","No",483.63752662047,47517.6550035722
"7384","No","No",570.983412418201,40580.9091764566
"7385","No","Yes",989.054283529197,17276.4439014947
"7386","No","No",896.734422758778,21627.4525178203
"7387","No","Yes",1533.89898571274,10488.4126010726
"7388","No","No",1357.6932506726,25389.8307107958
"7389","No","No",40.3011123429584,33656.7165255831
"7390","No","Yes",1014.89112912922,15253.3115972714
"7391","No","Yes",594.310593322279,14159.6417849044
"7392","No","No",1038.32287099795,45057.6420464299
"7393","No","No",1423.38440157414,48315.00730867
"7394","No","No",601.777083186907,15298.2131863943
"7395","No","No",947.597792145413,34869.1268512489
"7396","No","No",296.355437034889,24625.8852207068
"7397","No","No",633.850429789732,29213.3758259483
"7398","No","Yes",1154.44636742935,14301.1571179952
"7399","No","No",504.615359459887,56623.0102899515
"7400","No","Yes",1176.93452516159,19829.2264598045
"7401","No","No",1001.32023805225,41063.1973471399
"7402","No","Yes",1352.29282619523,23660.6548262496
"7403","No","Yes",1060.06007196524,18617.2181382756
"7404","No","No",1371.70327519923,48298.544535983
"7405","No","No",1015.96911317644,37952.964284075
"7406","No","No",1171.41159010859,52278.2442048295
"7407","No","No",1021.28360782199,50389.6207372374
"7408","No","No",1743.09923810045,31438.5919691711
"7409","No","No",84.3492471707539,59022.176608549
"7410","No","No",879.417151715171,35228.1105632839
"7411","No","No",820.945303026583,34064.6736772751
"7412","No","No",1085.89836833237,29771.0645570651
"7413","No","No",648.364868099607,40276.5718280912
"7414","No","No",0,27614.4992681141
"7415","No","Yes",313.396031622244,18071.688034514
"7416","No","No",477.550762096018,24273.4186375882
"7417","No","No",403.268308454751,38629.9474215703
"7418","No","Yes",864.947986054747,8624.22071922716
"7419","No","No",502.025954193766,31574.7125087496
"7420","No","No",971.814888689421,35583.1905392444
"7421","No","No",0,49606.9952337978
"7422","No","Yes",823.471373200441,12331.9162287798
"7423","No","No",969.806305448881,60396.7891910946
"7424","No","Yes",881.265405002496,15275.4205798952
"7425","No","No",1315.19148915858,42694.97506713
"7426","No","No",1149.65995633683,34635.1377011897
"7427","No","No",943.566247081687,31377.9428154229
"7428","No","No",270.072593432952,36833.6451384243
"7429","Yes","No",1392.59975316796,37620.2413146324
"7430","No","No",0,30836.4405061652
"7431","No","No",939.642406135128,38395.61380003
"7432","No","Yes",838.978435279789,21176.479172625
"7433","No","No",142.614404811541,34695.1158879329
"7434","No","No",1493.03389710926,30848.619107257
"7435","No","No",1193.40655438006,55017.462587952
"7436","No","No",0,19572.3001100385
"7437","No","Yes",1078.51425918891,20073.3552404101
"7438","Yes","Yes",2461.50697867784,11878.5570446708
"7439","No","No",614.140621191737,17472.9148915626
"7440","No","No",872.122709141156,43154.7199595567
"7441","No","No",880.047280737316,45092.5924026719
"7442","No","No",433.400112593774,25085.0701182121
"7443","No","No",618.127037783515,29836.2856549674
"7444","No","No",1115.15431349235,38623.5482005718
"7445","No","Yes",327.419883103167,14938.9443523853
"7446","No","Yes",888.718674812637,15037.6082952694
"7447","No","Yes",995.915054636554,24897.1732454987
"7448","No","No",1219.39010265135,42549.9135790507
"7449","No","Yes",984.3611537241,16577.8319329448
"7450","No","No",980.397103132796,41753.0759839079
"7451","No","No",422.451641728953,34797.7965440488
"7452","No","Yes",311.321863971391,22648.7634451749
"7453","No","Yes",1019.64775514386,2702.98233149687
"7454","No","Yes",1250.97282891881,19275.2540337784
"7455","No","No",1042.69918861583,10693.6215983792
"7456","No","No",436.575627385809,22389.4633927745
"7457","No","No",698.074827953718,37027.8160711082
"7458","No","No",296.881830650885,43136.5616829479
"7459","No","Yes",610.185368803368,19010.1257851595
"7460","No","No",0,38686.5814275621
"7461","No","Yes",1230.9575079814,20573.7863074106
"7462","No","Yes",712.026704476097,17273.1158606978
"7463","No","No",772.40563332837,39023.4916397536
"7464","No","No",175.167760833609,53019.4230127665
"7465","No","Yes",124.871271082982,20886.4961947608
"7466","No","No",423.337696771481,60170.5744657902
"7467","No","No",364.773883586177,31857.1807342331
"7468","No","Yes",851.975904788836,14170.9223069456
"7469","No","Yes",1619.71415137897,21781.9405624911
"7470","No","No",1200.94675835261,47636.8596254039
"7471","No","No",765.132036910884,39600.4846206278
"7472","No","No",1343.95492193688,26365.526253543
"7473","No","Yes",1669.76311196123,23741.5105983345
"7474","No","No",266.759321008082,25864.9390291814
"7475","No","Yes",1039.90941904259,21212.6826814127
"7476","No","No",246.756413859954,37138.3862992113
"7477","No","No",1282.56757095112,36558.8379206386
"7478","No","Yes",1070.32046448364,12756.76045352
"7479","No","No",766.512524415546,43426.8308000715
"7480","No","No",1102.92430902942,57828.1787773859
"7481","No","No",924.959329762828,26332.2096245083
"7482","No","Yes",1034.90239115771,15945.1398517742
"7483","No","No",1807.68866700157,40731.5164105235
"7484","No","No",520.654612793611,39914.919047822
"7485","No","No",1018.76996929855,35813.4207428702
"7486","No","No",1281.67517177822,41481.3210021225
"7487","Yes","No",1621.57578487284,36577.8462027349
"7488","No","No",0,40772.9268631337
"7489","No","Yes",1848.82661771773,18128.5695920069
"7490","Yes","Yes",2117.12061387647,12143.474803771
"7491","No","No",1002.14412432149,44445.5272721442
"7492","No","No",551.185871800838,36603.9753357363
"7493","No","Yes",149.15410001691,22802.8196789176
"7494","No","Yes",1360.86563270173,22310.9295094468
"7495","No","No",1136.14843693437,38474.6136937729
"7496","No","No",717.667389813139,27956.2735197124
"7497","No","Yes",651.548784488851,23374.832966803
"7498","No","Yes",604.574350110705,16153.4142865339
"7499","No","No",759.090406452521,41891.8575903576
"7500","No","No",1204.95781100677,24788.7678660894
"7501","No","No",796.257260821885,33760.5182633628
"7502","No","No",506.462320775495,36596.1267387874
"7503","No","No",160.012403610926,48121.1810184217
"7504","No","No",339.691180585286,49109.3613833469
"7505","No","No",0,35288.3350799733
"7506","No","Yes",1344.7093588173,17318.839902875
"7507","No","No",368.086167001206,51601.3608445693
"7508","No","No",1005.129339452,43501.6964850784
"7509","No","No",712.863665600652,47454.7444273651
"7510","No","No",384.527884132362,25950.2675640907
"7511","No","No",512.656466934724,43846.9797077064
"7512","No","No",0,37112.597798942
"7513","No","Yes",721.517319658893,18310.9455223602
"7514","No","Yes",1458.89346152272,21186.5629553544
"7515","No","No",388.609229128308,23040.4417429292
"7516","No","No",516.109644702662,48303.8010364758
"7517","No","No",575.754615672545,26757.5793449716
"7518","No","No",0,26100.7519107615
"7519","No","No",1440.39242035855,46113.2684523111
"7520","No","Yes",1136.40075609126,24962.8490701275
"7521","No","No",326.519432025491,20310.452477616
"7522","No","No",837.209149819569,38032.9563034442
"7523","No","No",1324.84258038352,41590.3351912124
"7524","No","No",244.935778985894,27369.0105220281
"7525","Yes","Yes",1875.45889462485,17820.4633288357
"7526","No","No",0,30287.490340717
"7527","No","No",1793.29061177025,45117.3819107052
"7528","No","Yes",1874.49450092902,19592.0698667164
"7529","No","No",1036.98559901712,46388.6245271626
"7530","No","No",91.415284347161,46468.7879829995
"7531","No","No",438.812399733576,39390.7255279315
"7532","No","Yes",1371.09386785115,16204.7689613384
"7533","No","Yes",950.776787199094,17711.7417569151
"7534","No","No",370.000934879272,39286.6136623794
"7535","No","No",688.905761536255,43606.9237368143
"7536","No","No",1452.32898016853,37691.7701917003
"7537","No","No",641.908417067159,45107.4316859664
"7538","No","No",618.139164308692,50146.4292488553
"7539","No","No",516.449766323607,41922.8884877092
"7540","No","Yes",720.435239898748,24582.8806904421
"7541","No","Yes",485.01489072382,23261.3309456867
"7542","No","No",929.755400626855,30908.8981998424
"7543","No","No",591.787159925885,46381.2372516318
"7544","No","Yes",753.383573607443,12258.6045124657
"7545","No","No",1255.42594165203,17917.7947894635
"7546","No","No",694.538762632041,42855.5653098464
"7547","No","No",431.159893985935,51727.5403221504
"7548","No","Yes",23.2060517124803,22196.6533688783
"7549","No","Yes",584.57623277459,16639.6558412378
"7550","No","No",877.029457220268,35054.9488981605
"7551","No","No",1300.29692368306,51338.8574052067
"7552","No","No",1049.92742742092,25445.9200724683
"7553","Yes","No",1099.05770130181,42590.8944537008
"7554","No","No",763.77376432623,60938.6640992445
"7555","No","No",506.776514229109,40625.4874731359
"7556","No","No",41.279727992759,52066.1473455983
"7557","No","No",789.639900431882,44073.2466932269
"7558","No","Yes",867.470764391778,11905.6789549914
"7559","No","No",1352.2909303135,33077.0888162943
"7560","No","Yes",0,20217.0909125425
"7561","No","No",337.874909315449,30090.5305102133
"7562","No","No",660.244841965786,40885.8469473715
"7563","No","No",1144.48179310641,45548.3803818706
"7564","No","Yes",1075.09276443643,7031.29455616636
"7565","No","No",535.897241673312,43917.568696246
"7566","No","Yes",1170.22462428213,8747.47272040443
"7567","No","No",922.124106932605,48137.3716728773
"7568","No","No",1064.08717221108,34677.9317899044
"7569","No","Yes",1294.28570216638,17444.6035649087
"7570","No","No",845.736704633298,34676.2642308225
"7571","No","No",212.647740871301,29592.1708509053
"7572","No","No",501.286648883935,36154.6526910388
"7573","No","No",463.045455797693,35621.0803502897
"7574","No","Yes",722.227333114557,18487.0405647803
"7575","No","No",724.205592217337,36489.6905470156
"7576","No","No",209.042788867425,40075.9841483956
"7577","No","Yes",1108.62409883895,18761.7671044817
"7578","No","No",0,60203.5960308717
"7579","No","No",960.068059922622,23482.835612708
"7580","No","No",685.3419782018,51856.890980685
"7581","No","No",1446.60460617167,45552.0098993525
"7582","No","No",1187.65668385132,46731.554866363
"7583","No","No",1073.02581933369,39553.831261169
"7584","No","No",1608.68334961791,40078.2204348079
"7585","No","No",1160.13322168939,32978.6595972612
"7586","No","No",621.149260807172,15546.7689826685
"7587","No","Yes",1367.42963096758,23617.612359145
"7588","No","Yes",909.10199369612,7283.76314467767
"7589","No","No",524.416793070104,41535.324655475
"7590","No","No",654.493618548728,12696.803246099
"7591","No","Yes",1010.57612407862,20982.9703055547
"7592","No","Yes",130.147710478838,15174.6729794389
"7593","No","No",150.663617880078,42800.6969182602
"7594","No","No",584.838347580316,41447.4135332999
"7595","No","Yes",771.295014017138,15594.0118532611
"7596","No","No",1363.48166419022,35359.7540473893
"7597","No","Yes",414.80645089261,18832.0342148101
"7598","No","Yes",1647.00457279337,16614.8041663474
"7599","No","Yes",562.842591176323,19669.7693367743
"7600","No","No",843.700911167162,50891.6733167055
"7601","No","No",455.37633600577,36590.5503622636
"7602","No","No",459.423921256549,49958.756800464
"7603","No","No",967.72062448087,31641.6763377917
"7604","No","No",84.3444663188471,42333.533628084
"7605","No","No",530.603502829406,59740.2099741483
"7606","No","No",1369.92596745005,49089.8196259944
"7607","No","No",692.156208075448,39199.4465014916
"7608","No","Yes",564.638739024053,24342.0307911737
"7609","No","Yes",1251.18680552354,11229.6412228286
"7610","No","No",378.445644033206,30562.0955174959
"7611","No","Yes",0,24000.3302320209
"7612","No","No",0,37361.5408049442
"7613","No","No",820.48501722613,35951.1199800878
"7614","No","Yes",246.867027277152,16209.5366803905
"7615","No","No",1490.13748475784,35882.7586735584
"7616","No","No",421.038871967722,17137.1681277715
"7617","No","No",1147.57189675942,32012.7653479457
"7618","No","Yes",1087.91774442624,18053.0838952554
"7619","No","No",295.150455976312,48387.4724479823
"7620","No","No",543.794274359592,51964.7512239709
"7621","No","Yes",1002.44392127105,14152.6694871327
"7622","No","No",809.478458031615,51608.5424596493
"7623","No","No",659.75081505078,50536.9009715526
"7624","No","Yes",1627.77461253757,23516.6390314813
"7625","No","No",751.631732836333,50562.4986069294
"7626","No","Yes",625.772022490437,23122.6110304004
"7627","No","Yes",486.224823282763,20289.6747200262
"7628","No","No",1302.13421827805,64016.7603980853
"7629","No","Yes",1233.73552347869,15480.2077785454
"7630","No","Yes",1220.27693189676,11573.8484490094
"7631","No","No",982.514587249152,62140.9254615139
"7632","No","Yes",306.041314131214,13190.8322602958
"7633","No","Yes",806.699910999563,19269.6816815998
"7634","No","No",789.326869307377,36183.6566367513
"7635","No","Yes",1481.57398076178,23170.5647926284
"7636","No","No",1228.78852913204,59100.8511498722
"7637","No","No",464.9391825004,58247.1215740083
"7638","No","No",958.047432763855,15052.4457238653
"7639","No","Yes",0,21294.1484759847
"7640","No","No",1523.9302766086,58551.3595877966
"7641","No","No",0,50633.7925605149
"7642","No","No",387.75527193235,34086.3870439287
"7643","No","No",1194.20572499595,45588.180687979
"7644","No","Yes",1430.82481135904,10766.9377730224
"7645","No","Yes",1251.99489098262,18922.0343634547
"7646","No","Yes",207.925970320052,19078.8919047644
"7647","No","No",1596.38595214977,34190.1897212476
"7648","No","No",0,33769.6048655454
"7649","No","No",1213.35295480262,39344.1240263279
"7650","No","No",407.263424219925,40524.3386075399
"7651","No","No",786.585649124404,43014.2207503651
"7652","No","Yes",695.476439342878,14030.3605474387
"7653","No","Yes",1113.23688519268,19139.2664321704
"7654","No","Yes",717.205265524902,20627.8189153679
"7655","No","No",374.926190630489,39167.9726848544
"7656","No","Yes",270.599297436056,15728.9107072974
"7657","No","Yes",1334.78080792518,20468.4408385118
"7658","No","No",280.614830515579,35494.5978623224
"7659","No","No",143.917847054434,41626.6876952005
"7660","No","No",1027.05457933546,44268.9587280937
"7661","No","No",127.54538940606,33070.4990606837
"7662","Yes","No",1809.40986352205,33453.406538286
"7663","No","No",1626.39108717515,61438.0944057847
"7664","No","No",628.87553620734,48380.5108388692
"7665","No","Yes",385.350358534473,22395.8823581039
"7666","No","No",753.999942823528,29020.7290572193
"7667","No","No",650.2493011075,33475.7442375698
"7668","No","No",232.763666039282,58488.7902999066
"7669","No","No",1005.62300888315,43471.7803207004
"7670","No","No",82.4275698236085,29761.5445256896
"7671","No","Yes",41.3855163829771,18664.9010060066
"7672","No","No",510.657513331156,25855.917022987
"7673","No","No",1588.91625121915,54043.0062999103
"7674","No","Yes",1127.43595508475,23495.5655168774
"7675","No","Yes",1242.72294339113,23037.4137184069
"7676","No","No",1730.11045772474,48766.8009059003
"7677","No","No",915.462438962574,33887.763638343
"7678","No","No",65.3329838593119,31992.2422205879
"7679","No","No",180.171498916997,21403.0777065766
"7680","No","No",989.262577822306,48055.4122370631
"7681","No","No",314.985176814067,51112.9378660257
"7682","No","No",607.27706010457,36487.990989239
"7683","No","Yes",724.437846238017,15835.6275220604
"7684","No","No",832.217401259712,44053.2560569607
"7685","No","Yes",794.146806723789,12519.7683906824
"7686","No","No",1806.71487708391,39082.7860963992
"7687","No","No",394.501839643007,29642.9102659475
"7688","No","No",108.003864382255,44764.2888968549
"7689","No","No",806.550181313343,27622.9041506617
"7690","No","No",845.077745416634,38410.8435707644
"7691","No","No",659.808946789516,39712.958918746
"7692","No","No",1248.83064907828,39317.9041803876
"7693","No","No",852.522041028705,34237.6456678607
"7694","No","No",878.927937083077,47228.3409313744
"7695","No","Yes",819.912190542397,16585.0280991299
"7696","No","No",0,34213.4915205813
"7697","No","No",620.681596802363,34549.0864666961
"7698","No","No",917.856226992842,49353.9506343612
"7699","No","No",539.441908788564,26164.8446811129
"7700","No","No",747.139341956808,42318.9332825577
"7701","No","No",864.460772629318,44429.0667539874
"7702","No","Yes",391.998226720825,9995.04163332249
"7703","No","No",151.260770326673,57657.2822222499
"7704","No","No",873.125525094058,34352.7003173698
"7705","No","No",28.7897760191421,55369.201386131
"7706","No","No",228.511851114921,57232.2685587695
"7707","No","No",1190.13932322518,27734.7389829589
"7708","No","Yes",1845.97643893727,12107.7418248053
"7709","No","No",107.154768462625,42776.3018691308
"7710","No","No",2058.78478074305,55021.5518021545
"7711","No","No",914.637312628131,46492.6488301171
"7712","No","No",799.73270458257,45702.4016718804
"7713","No","No",890.248108001647,34679.4827616344
"7714","No","No",1184.49229812903,43228.8058978192
"7715","No","No",1143.12286444744,42672.129149678
"7716","No","No",718.743118187293,31371.7810801086
"7717","No","No",847.654914338946,43515.3022044232
"7718","No","No",1067.27320063911,50647.8724040193
"7719","No","No",791.542583674003,32574.3412705245
"7720","No","No",826.355312408359,37241.440631236
"7721","No","Yes",652.755692307818,23492.3232419236
"7722","No","No",827.634736669455,36439.6609398738
"7723","No","Yes",704.095463378673,16509.5229344982
"7724","No","No",773.593731357215,29661.4635320764
"7725","No","No",402.129014682231,42978.3936060898
"7726","No","No",411.537626865253,30065.4443474788
"7727","No","No",340.964399114303,27262.0579810414
"7728","No","Yes",1021.56825009016,14500.6291712537
"7729","No","No",1323.64068113702,30517.2082497801
"7730","No","Yes",1362.6444485929,18205.2317816041
"7731","No","No",347.460916522461,24735.5506928776
"7732","Yes","No",1610.48401491748,35589.7338305398
"7733","No","No",1313.83680422269,40843.0997786338
"7734","No","Yes",705.390544240798,19125.6849820595
"7735","No","No",705.735232894335,45280.8192290912
"7736","No","No",0,38968.4157913305
"7737","No","Yes",0,23735.4085420558
"7738","No","No",1562.66985789502,52422.7802153993
"7739","No","Yes",655.611221042021,19039.1682730003
"7740","No","Yes",780.254075842619,20758.4712409247
"7741","No","No",946.137573571247,40038.683105794
"7742","No","No",1402.51244469243,46207.7798397669
"7743","No","No",321.588332058415,52711.6173635883
"7744","No","No",1824.29374770111,22471.4457240322
"7745","No","No",0,23562.2906124825
"7746","No","No",602.195012175499,26768.9174174437
"7747","No","No",314.45919529541,40016.4759256171
"7748","No","No",1223.71401172324,64440.2175262069
"7749","No","No",487.11688827906,55459.4529950443
"7750","No","No",324.851543385128,52838.8493294787
"7751","No","No",98.2290556803614,48070.1593162269
"7752","No","No",0,18063.8883588351
"7753","No","Yes",345.799607124424,15967.9908471267
"7754","No","No",869.308119274179,32571.1692558153
"7755","No","No",437.555256719357,37212.2693602431
"7756","No","No",323.595054114491,34129.6560741734
"7757","No","No",1286.90555743471,45569.0675184161
"7758","No","Yes",940.47935428257,20578.8072250756
"7759","No","No",1385.30347695259,35045.438734077
"7760","No","No",765.818663634114,33216.3531980412
"7761","No","No",599.009724958459,53004.5854640233
"7762","No","No",1153.3292507774,17598.4340274502
"7763","No","No",352.278325535015,34359.8412383948
"7764","No","No",226.217345575457,59680.0022588775
"7765","No","Yes",754.968696018072,30340.120404255
"7766","No","No",1713.53785147756,43899.0842902538
"7767","No","No",969.538309805488,33293.7947247849
"7768","No","No",1024.61298649142,34401.017743276
"7769","No","No",838.653016857442,25272.4860672589
"7770","No","No",1072.03377011872,47029.260367657
"7771","No","Yes",686.694378860884,18551.5300581174
"7772","No","No",715.544075669576,50127.9556316689
"7773","No","No",1130.77214546715,40314.2715443769
"7774","No","No",1286.63632041227,44825.8658404379
"7775","No","No",937.501193105071,33266.5194383876
"7776","No","Yes",1881.25652109686,14992.3867271917
"7777","No","No",135.953345751202,49372.8359844109
"7778","No","Yes",1471.92392898896,19774.9238785274
"7779","No","No",773.171566359335,37877.6045199555
"7780","No","No",552.672566101431,50688.4963275297
"7781","No","No",1074.08765319171,35873.2286380915
"7782","No","Yes",137.431468636565,22783.4209710542
"7783","No","Yes",523.831492714496,12062.3943636302
"7784","No","No",724.537790180162,33254.5284949545
"7785","Yes","No",2040.59017105597,50930.9107859245
"7786","No","Yes",1415.89080238048,22823.6278584034
"7787","No","No",174.483634673712,54992.9893047717
"7788","No","No",805.314488077273,36864.6892862884
"7789","No","Yes",1008.03312956121,13547.0785381477
"7790","No","Yes",0,21665.5268097879
"7791","No","No",1078.41038955072,44474.420294412
"7792","No","No",768.694006876275,32373.771051203
"7793","No","No",391.189784489765,42212.0303371115
"7794","No","No",0,46910.7978946537
"7795","No","Yes",1318.57773531948,18042.0176142666
"7796","No","No",955.449340796982,40698.9761263581
"7797","No","No",1425.53485976141,33666.1042566101
"7798","No","Yes",1196.49194120137,14916.4446906476
"7799","Yes","No",2038.89185057887,58606.4862432055
"7800","No","Yes",1047.99823953348,15846.655228066
"7801","No","No",995.346099927345,46702.239073216
"7802","No","No",1657.25590018684,29945.5660173299
"7803","No","Yes",396.582633428736,18244.166018854
"7804","No","Yes",594.558985346466,13180.5021134281
"7805","No","No",931.710876495276,50210.0694668726
"7806","No","No",1318.80706574655,44778.0460012765
"7807","No","No",24.2635999898018,36070.138850872
"7808","No","No",713.673397858288,48914.5602035217
"7809","No","No",891.751685184564,53044.8356060182
"7810","No","No",1794.41157502235,44037.8475163971
"7811","No","Yes",1412.08996685442,17049.6600099038
"7812","Yes","No",2024.82021156467,64135.4310819166
"7813","No","No",399.597296046437,42578.0459414764
"7814","Yes","No",1598.02083082886,39163.3610556876
"7815","No","No",1045.31135178129,31754.023779871
"7816","Yes","Yes",2578.46902158917,25706.6477744655
"7817","No","No",1113.2891420998,42292.4901451926
"7818","No","Yes",645.118687643084,16097.2170185444
"7819","No","Yes",861.104127009331,15873.6128609255
"7820","No","Yes",408.218352153679,21432.7264338213
"7821","No","Yes",794.973101379144,21590.9618652424
"7822","No","No",1477.63795612057,36546.8231488484
"7823","No","No",634.399979828231,33377.360386157
"7824","No","No",798.082090735915,38080.9623405107
"7825","No","Yes",0,19919.5634409027
"7826","No","Yes",980.101705745882,16381.2031300222
"7827","No","No",831.245423092704,27706.9538518972
"7828","Yes","Yes",2083.22837647615,20103.602739227
"7829","No","Yes",1436.07482635181,21511.4578059897
"7830","No","No",1467.22519103062,28845.0222016802
"7831","No","No",469.438477180174,37879.2565232606
"7832","No","No",1166.71587666917,31032.3859736364
"7833","No","No",844.041292318579,32089.6246466696
"7834","No","Yes",1538.97375070282,15757.9335417675
"7835","No","No",562.701116936926,39067.7515107504
"7836","No","No",1112.11088540741,29229.5316824489
"7837","No","No",246.39376404033,33647.993225282
"7838","No","No",562.723868379586,60196.1826087633
"7839","No","No",916.569239827395,42486.3989116241
"7840","No","No",618.5663093699,28477.2795325714
"7841","No","Yes",1604.6137382887,15467.2189070394
"7842","No","No",847.614963857933,42308.052316687
"7843","No","No",304.482793486527,52543.8282903446
"7844","No","No",778.819743345405,31986.5712070236
"7845","No","Yes",1464.39479015494,13968.5080055771
"7846","No","No",221.47998570854,37782.9970839142
"7847","No","No",592.045181611353,39064.994480207
"7848","No","No",1010.61245522492,34055.5715925025
"7849","No","No",1194.45646352906,49316.9977584631
"7850","No","No",844.312566858413,52237.7254846561
"7851","No","Yes",179.503977524429,19020.2959614411
"7852","No","No",355.930112598893,32120.3172410799
"7853","No","Yes",1776.72897362065,21280.8766053934
"7854","No","No",27.3018826937546,25050.827087992
"7855","No","Yes",631.019886409912,20753.5181088166
"7856","No","No",187.977684707438,49989.3074311213
"7857","No","Yes",1511.43409836329,11793.1617767047
"7858","No","No",1008.47823148071,22094.7480844484
"7859","No","No",820.493047331982,43829.6950758028
"7860","No","No",112.033309975348,40057.0774440998
"7861","No","Yes",1675.65876432611,20773.1332965924
"7862","No","Yes",592.893197700487,14833.289667755
"7863","No","No",492.39892684554,36004.3787656855
"7864","No","No",364.746192677668,46096.1601457535
"7865","No","No",1025.32734772889,44194.5137131782
"7866","No","No",612.657277408042,38635.364437286
"7867","No","No",1456.63098450781,38676.5456644415
"7868","No","Yes",1762.59824620554,13099.5888853228
"7869","No","Yes",180.095142403637,27351.4288203371
"7870","Yes","Yes",1721.97302703926,15179.7493287096
"7871","No","No",0,30822.454186489
"7872","No","No",1029.78686137899,69547.4369460224
"7873","No","No",268.443287008073,55294.9511030761
"7874","No","No",1711.43208391272,39019.6896454184
"7875","No","No",0,36886.9862572698
"7876","No","Yes",1295.81806531379,22241.7034671195
"7877","No","No",1722.12591993846,21623.0364539985
"7878","No","Yes",560.381753116869,23546.3963352205
"7879","No","No",676.572023475436,41328.4276238236
"7880","No","No",518.93699412848,28231.0120735046
"7881","No","No",626.404687195669,18130.3220987571
"7882","No","No",524.380176840498,38020.6399233986
"7883","No","No",0,33932.8527518265
"7884","No","No",517.499185500047,60790.9083158423
"7885","No","No",563.369693580382,22083.9085966864
"7886","No","No",865.886543679469,59740.7325304862
"7887","No","Yes",618.119217335142,24698.8272377675
"7888","No","No",1428.81282966821,41417.0536914817
"7889","No","No",1195.62870012121,34701.8024887858
"7890","No","No",218.014100689461,29211.398463684
"7891","No","No",1434.13804791172,38038.6038911651
"7892","No","No",506.267392227237,48843.0018072785
"7893","No","No",1322.36785444532,34622.6862457067
"7894","No","No",584.818827844497,49317.8348463311
"7895","No","No",815.072837026635,35017.8533639568
"7896","No","No",0,50956.67560531
"7897","No","Yes",489.927554476048,21445.5300432689
"7898","No","No",644.88983010905,42211.3598012121
"7899","No","No",1151.20364449996,36468.7244958993
"7900","No","No",1440.43396156724,27532.6535733937
"7901","No","Yes",858.215383079938,12588.108122033
"7902","No","No",0,29021.7452781286
"7903","No","No",0,54053.903924382
"7904","Yes","Yes",1402.20572671052,15964.2091821951
"7905","No","Yes",1171.7247120782,23505.1976209285
"7906","No","No",662.302632070322,44664.005908681
"7907","No","Yes",1142.73479164867,12893.9590863901
"7908","No","No",785.430997498407,37465.3600970998
"7909","No","No",705.548610503539,32899.6067177725
"7910","No","Yes",1713.31247126829,17432.2735890031
"7911","No","No",861.642116047731,38982.3915157176
"7912","No","No",398.427319929336,37094.5678677176
"7913","No","Yes",703.685970450978,16617.5700884556
"7914","No","No",366.260111383432,52360.9128805627
"7915","No","No",1054.1159630688,41781.7285800243
"7916","No","No",189.732742353513,53266.3383188017
"7917","No","No",944.204246156795,41883.4142988308
"7918","No","No",0,36969.1717526428
"7919","No","No",499.913435722248,28352.5533921151
"7920","No","No",866.31097695602,59647.4402882698
"7921","No","No",761.988490630495,39172.9452351635
"7922","No","No",1150.99981758188,50099.2936231523
"7923","No","No",53.4370529548792,51027.0683584044
"7924","No","No",319.767426854798,36057.4011097469
"7925","No","No",0,35116.5909600078
"7926","No","No",222.105456229842,41217.3493585232
"7927","No","Yes",1336.99863968121,21309.2510819392
"7928","No","Yes",1397.50486310496,26604.5570945362
"7929","No","No",794.584066954045,37520.2533303122
"7930","No","Yes",883.157341326026,18213.0759618371
"7931","No","No",1168.1673080252,17198.9835593493
"7932","No","No",257.447025110164,38275.9533677335
"7933","No","Yes",1273.48851311202,17105.8670470047
"7934","No","Yes",1934.51114219071,17084.048577191
"7935","No","Yes",1704.01419282432,17986.7635790881
"7936","No","Yes",220.891418767173,25667.8659407236
"7937","No","No",323.305124093072,51306.2686232584
"7938","No","No",969.404142604547,31476.5166150492
"7939","No","No",385.185120450238,50316.8015163965
"7940","No","No",995.40840887124,40907.4969532705
"7941","No","No",758.392131608197,40751.4074796687
"7942","No","Yes",652.867587670394,23515.1313179576
"7943","No","Yes",903.176837327325,15810.5741940783
"7944","No","No",533.429586263046,24857.9065324464
"7945","No","Yes",1151.85630836281,23250.384513708
"7946","No","No",1271.32854424032,43878.8133733466
"7947","No","No",136.118440276158,44757.692802711
"7948","No","Yes",1171.95044240375,15850.7772560773
"7949","No","No",512.660945594458,49989.5995789838
"7950","No","No",172.157592295485,28370.8659088461
"7951","No","No",1256.22265846487,42125.9373293965
"7952","No","No",835.837183227152,57505.9653004257
"7953","No","No",1026.63361568078,61064.3875769551
"7954","No","Yes",1144.27482876516,16002.3554700591
"7955","No","Yes",682.954298780229,15728.0479104477
"7956","No","No",797.672816440294,45377.7233416886
"7957","No","No",97.3269353362984,62335.1968533621
"7958","No","Yes",1436.32931848388,7761.31894307372
"7959","No","Yes",1325.624925599,15880.4471490978
"7960","No","No",1462.69493022106,50508.7464409683
"7961","No","No",191.913012578999,55659.4732930998
"7962","No","No",854.884279854547,44173.8277325458
"7963","No","No",864.343796306282,34598.4343942247
"7964","No","No",476.567195634253,46953.4182817587
"7965","No","No",1155.68038885258,50955.1065346111
"7966","No","No",1127.70019314671,32068.1465386873
"7967","No","No",0,62192.9956472797
"7968","No","No",312.916985128483,31328.0435013798
"7969","No","Yes",467.894660421885,16371.6977475365
"7970","No","Yes",1055.59777321371,16810.5595800266
"7971","No","Yes",989.235474989736,13609.631284009
"7972","No","No",245.70672370182,40462.5538115373
"7973","No","No",585.752613204564,43627.3279236822
"7974","No","No",610.854306313866,39175.0805291355
"7975","No","No",258.036510671323,40138.8417326327
"7976","No","No",1092.99815937252,37351.3396590005
"7977","No","No",1387.87328681612,27145.7032793199
"7978","No","No",833.084193233188,49704.5119584713
"7979","No","No",1184.87086972763,41802.944332633
"7980","No","Yes",916.794393751044,17152.1579637038
"7981","No","Yes",1427.33757806663,22909.430467279
"7982","No","Yes",1413.27442745588,19064.6221920762
"7983","No","Yes",1592.54963490252,18024.2817202795
"7984","No","No",0,26941.7977424554
"7985","No","No",1050.20409704366,34803.5337637529
"7986","No","No",1511.17770618893,35899.2558430202
"7987","No","No",894.681739327321,37232.293745958
"7988","No","Yes",781.613729542008,12195.9699546127
"7989","No","No",0,43311.6406962292
"7990","No","No",1094.88661521626,38342.715005316
"7991","No","No",0,59266.9608687076
"7992","No","Yes",875.670060915039,18687.0451725029
"7993","No","No",81.9180917096782,40936.906439517
"7994","No","No",371.181858124564,34679.9607338813
"7995","No","Yes",1438.05549698093,19200.5018262535
"7996","No","No",674.598956464598,67278.9484413768
"7997","No","No",1097.7625478144,42228.9704754292
"7998","No","No",593.060286904382,47787.5959226368
"7999","No","No",1167.10118913466,35952.1936607058
"8000","No","No",1114.826925896,33729.3898552217
"8001","No","No",304.146597789837,23866.9358580166
"8002","No","No",353.63113130111,40686.7601478226
"8003","No","No",1269.42033524182,39010.1481869142
"8004","No","Yes",397.54248845196,22710.8657401321
"8005","No","No",607.166270936521,49547.4658155602
"8006","No","No",1667.48036480455,47991.5850452853
"8007","No","No",956.311350604852,42971.901112669
"8008","No","No",668.3980286378,38637.4338847191
"8009","No","No",290.679874013809,37603.9721703408
"8010","No","Yes",826.727455791863,18776.3012421982
"8011","No","No",1102.11145915496,48079.1253033663
"8012","No","No",348.658867896976,36519.0901860126
"8013","No","Yes",991.232829247188,8926.799451933
"8014","No","No",1224.23578236207,32751.6969190413
"8015","No","No",146.365856827095,44666.9323010869
"8016","No","Yes",697.135579980937,18377.1497139722
"8017","No","No",956.446909552778,40740.3291117901
"8018","No","Yes",1184.90943155283,13276.221116225
"8019","No","No",1702.23668300415,36530.4736214361
"8020","No","No",0,36850.9711754768
"8021","No","No",965.459761747828,25045.7255420055
"8022","No","No",331.164338742849,25922.4167354017
"8023","No","No",1307.09366867241,35425.6099922775
"8024","No","No",1637.03755674049,45027.538161563
"8025","No","Yes",1091.93210326853,11703.3805642246
"8026","No","Yes",1499.19013015969,17560.3718351052
"8027","No","Yes",770.290543708965,22114.7182592628
"8028","No","Yes",1147.35490966558,19188.9082733679
"8029","No","No",1037.27416271053,28437.7724528546
"8030","No","No",626.973938404489,50447.7741503172
"8031","No","No",847.720792941679,55260.3498094267
"8032","No","Yes",1199.59268578389,16454.749842006
"8033","No","No",1288.28509541537,54666.3774464645
"8034","No","No",675.225387145285,33700.2126144862
"8035","No","No",541.44096207966,32502.6968388091
"8036","No","Yes",1165.00739121577,18389.0790407725
"8037","No","No",1042.79193502536,26141.0059458739
"8038","No","No",989.217092749214,42813.375110046
"8039","No","No",586.602608270933,27798.1683237731
"8040","No","No",931.591055383966,33302.240019542
"8041","No","Yes",978.53856684773,16514.3249498654
"8042","No","No",958.936828886891,47468.6697094555
"8043","No","No",384.532041382923,36019.4103983883
"8044","No","No",645.075136365722,43244.5237618794
"8045","No","No",424.760272309022,29502.4035832783
"8046","No","No",612.551875753766,33036.2413164312
"8047","No","No",1381.44222217693,47470.0187655353
"8048","No","No",0,45560.9128917675
"8049","No","No",1096.02610874365,23954.2179245426
"8050","No","No",945.685449516158,38714.5182938666
"8051","No","No",722.768006648193,20962.1064483564
"8052","No","Yes",887.880819486513,20563.1471269038
"8053","No","Yes",1049.95328779119,19724.8326135229
"8054","No","No",728.041685121268,29188.9427360122
"8055","No","No",1114.90403246296,40084.0014692393
"8056","No","Yes",1451.30354178896,14185.1379196466
"8057","No","Yes",629.213189509353,16007.6929485013
"8058","No","No",630.230741457689,49059.2627644256
"8059","No","Yes",1372.97803245667,10776.4442846822
"8060","No","Yes",746.863836108701,13904.4276434159
"8061","No","Yes",1400.54583563652,16519.0832437927
"8062","No","No",971.35143511577,46533.8564831326
"8063","No","No",637.082386355173,49097.9006748914
"8064","No","No",1778.42910924859,34591.2382681188
"8065","No","Yes",678.205083642297,12787.0905671847
"8066","No","No",1094.77298187378,46617.8825725774
"8067","No","Yes",879.852004959113,23996.7401964865
"8068","No","No",1449.32434566807,38720.3815836494
"8069","No","No",322.00162519978,43774.4421094367
"8070","No","No",1430.57687000073,44104.9589155541
"8071","No","Yes",1199.3855860032,18884.1274708906
"8072","No","No",168.367517347414,47684.5262213006
"8073","No","Yes",1143.3982045504,15997.1711895633
"8074","No","No",416.906490149379,38104.7732254315
"8075","No","No",1352.61366887416,42194.3866996432
"8076","No","No",1431.74197311519,37793.2873225102
"8077","No","No",481.305040751321,35747.7451040985
"8078","No","No",774.073176557921,46082.0432177328
"8079","No","No",0,34749.7935898833
"8080","No","Yes",1186.89100186271,14256.7295542742
"8081","No","Yes",978.359335975962,16679.8939738249
"8082","No","No",1409.11216032403,30626.2239874826
"8083","No","No",1491.47928224386,54835.7700509163
"8084","No","Yes",1228.48496561983,18937.2069449315
"8085","No","No",1553.47898510568,44425.890616496
"8086","No","No",1200.04162184687,56081.0802320515
"8087","No","No",984.860839214177,45435.4971157382
"8088","No","No",558.320540180268,38014.7600990968
"8089","No","Yes",778.853871913263,16007.4569754174
"8090","No","No",163.353170816826,45183.1451245658
"8091","No","Yes",574.818229191728,16479.5388337621
"8092","No","No",1149.93685609785,39231.6504810631
"8093","No","No",774.53807406404,22691.8824439653
"8094","No","No",866.772499634281,30400.5605118558
"8095","No","No",325.859608024347,36682.2825686063
"8096","No","Yes",879.627008263598,14898.3114661048
"8097","No","No",160.50306078312,19132.6948604593
"8098","No","No",905.719002527264,38210.7337742896
"8099","No","Yes",649.931060017559,13475.0195145782
"8100","No","No",196.912925664956,35737.6036276591
"8101","No","No",488.566318858869,25407.2159165707
"8102","No","No",449.274316089442,13124.5631922754
"8103","No","No",824.802432209204,40957.5222660038
"8104","No","Yes",600.602171819777,18630.3791308478
"8105","No","No",912.065530843446,62142.0610606175
"8106","No","No",619.139261665316,41281.9809640348
"8107","No","No",225.364695124737,60275.8833588615
"8108","No","No",369.430953350466,63536.7751705256
"8109","No","No",745.666146610358,54891.4339618085
"8110","No","Yes",782.352280928641,22692.6699880665
"8111","No","Yes",1051.24716182541,23200.9709928678
"8112","No","Yes",613.601284003546,9506.46030537807
"8113","No","No",863.618400665702,30397.1872633216
"8114","No","No",943.086028421649,29622.6077434668
"8115","No","Yes",1102.63126154414,10671.9235752487
"8116","No","Yes",1175.13333149015,15444.9269201034
"8117","No","No",491.449369823872,51484.245489143
"8118","No","Yes",1131.08556788632,21829.0823944909
"8119","No","Yes",717.371845383157,16319.1169756245
"8120","No","Yes",517.300565516777,17068.3388808094
"8121","Yes","Yes",2038.54315996182,12181.902584317
"8122","No","No",868.494476319032,22176.2762497007
"8123","No","No",111.416890994626,33328.2458835673
"8124","No","Yes",1587.56439786646,13080.6887489066
"8125","No","Yes",640.998430228533,21680.097686865
"8126","No","Yes",1928.59211959305,17570.1950770634
"8127","No","Yes",759.678794164084,18871.4017881629
"8128","No","No",1306.35396408392,35652.7619798141
"8129","No","No",908.174289735733,46791.0363559916
"8130","No","No",871.67741216338,28182.116302446
"8131","No","No",0,38878.1745666488
"8132","No","No",0,53302.2130321841
"8133","No","Yes",947.525154569841,24073.0909458376
"8134","No","No",222.880275629056,40849.9209013436
"8135","No","No",1237.53437819226,30967.5887589274
"8136","No","No",1318.71911205766,36822.9249600433
"8137","No","Yes",1030.05211219446,9760.39956109943
"8138","No","No",925.706239813761,62764.8842873843
"8139","No","No",425.779790910006,42131.5359555794
"8140","No","Yes",1901.6537550579,21323.1632661849
"8141","Yes","No",1940.3716198907,41162.9336560565
"8142","No","Yes",227.31723199312,24282.2285628356
"8143","No","No",836.574229518366,51238.5809287878
"8144","No","Yes",981.997998407385,20147.5118507633
"8145","No","No",1186.79623136733,52689.2501384527
"8146","No","Yes",315.978748024358,13186.1176938327
"8147","No","Yes",768.447241174727,21497.5288874548
"8148","No","Yes",969.90946533819,13962.3324283703
"8149","No","No",1175.19147310583,50921.2562794578
"8150","No","Yes",1684.2753411013,20344.1782031723
"8151","No","No",1120.62319369579,48571.2600711802
"8152","No","No",784.470053468051,52617.207089956
"8153","No","No",604.980002579675,38885.3103196378
"8154","No","No",517.978901464771,41755.3439780178
"8155","No","No",270.264088797442,41978.0478846199
"8156","No","No",825.600966046992,33986.6973244399
"8157","No","No",623.517380541497,51677.3454162215
"8158","No","No",529.508360526441,38328.6126282346
"8159","No","Yes",1014.76966176828,12239.8838074693
"8160","No","No",288.534653798734,44081.0348138192
"8161","No","No",500.25272381345,17104.0542524538
"8162","No","No",565.675982283623,37299.0792940605
"8163","No","No",1005.33716823962,34297.8977552662
"8164","No","No",852.393368457823,46320.8548881979
"8165","No","No",613.150113768004,34286.112453527
"8166","No","No",905.565725913233,52997.7310413533
"8167","No","No",1086.18172881883,52507.5972870699
"8168","No","No",1057.17812713189,35807.0248552158
"8169","No","No",252.899460581594,27454.6189545124
"8170","No","Yes",1401.28270335388,16683.0530020691
"8171","No","No",1529.13125892736,36572.0256735791
"8172","No","Yes",947.453711010131,21945.7930360235
"8173","No","No",0,33917.5941246225
"8174","No","Yes",1312.90720253482,15495.0265880783
"8175","No","No",1269.2905749073,43581.1988403355
"8176","No","Yes",20.2580938434145,21530.3315137875
"8177","No","No",0,30818.5119478012
"8178","No","No",35.3229997480005,53407.2696317298
"8179","No","No",897.312690752823,31465.9647696669
"8180","No","No",1043.55235783125,38393.6118953888
"8181","No","No",1165.32396595771,33433.8289153912
"8182","No","No",1299.47321012077,55165.4729773302
"8183","No","No",675.776528968365,21758.0228077015
"8184","No","Yes",1202.38503020909,20550.6377000894
"8185","No","No",1190.15983100714,30746.3769711559
"8186","No","Yes",535.010596224683,13909.3915908113
"8187","No","No",194.193554789385,43150.501726601
"8188","No","No",430.620319374712,46182.6649756
"8189","No","No",1047.92709078908,59765.5996686006
"8190","No","No",220.791699313198,26453.2892952264
"8191","Yes","Yes",1493.96853901776,24126.029785681
"8192","No","Yes",856.655399246759,15523.1029435474
"8193","No","Yes",1635.50002360542,10596.5268944023
"8194","No","No",666.613888740094,49215.0245344022
"8195","Yes","No",1562.83791774789,49303.0094684327
"8196","No","No",255.888371780844,48833.2287713088
"8197","No","No",602.361701994454,53800.5472092154
"8198","No","No",1599.31469461935,28825.4745687684
"8199","No","No",150.94951081572,36923.5521943078
"8200","No","No",1388.81659047311,35460.8012411956
"8201","No","No",87.9486517918008,47924.6979109095
"8202","No","Yes",1935.82770125724,17969.7985313908
"8203","No","Yes",1289.3159628111,7677.89639750202
"8204","No","No",871.987212227636,45496.9481609842
"8205","No","No",1333.69998094298,44361.6208714047
"8206","No","No",181.179344587582,37380.1443180605
"8207","No","No",791.182225188319,48206.731750984
"8208","No","No",69.177023653734,24556.0217815517
"8209","No","No",940.894483512081,54839.1236462734
"8210","No","Yes",346.215577878245,17866.5280381284
"8211","No","No",214.84623495229,39629.2001796429
"8212","No","Yes",676.831307875609,16998.6517153961
"8213","No","No",595.996994273606,37417.137823355
"8214","No","Yes",999.391696394237,23688.6489687946
"8215","No","No",1447.04307363001,22433.6367726481
"8216","No","No",464.098477616815,44769.8648263187
"8217","No","No",872.63990775056,29749.170678731
"8218","No","No",381.704099186758,25011.1033931994
"8219","No","Yes",866.49676067859,24262.6938973924
"8220","No","Yes",1243.34410193846,23634.7485750949
"8221","No","No",1184.30802110083,37600.0753409499
"8222","No","Yes",757.407102750296,25309.77323254
"8223","No","No",939.258489668574,56225.8207299037
"8224","No","No",560.770233567651,61472.8586941428
"8225","No","No",971.278833313912,26853.3009009738
"8226","No","No",518.680792277915,30151.3304613229
"8227","No","No",834.263678916678,28022.8368210329
"8228","No","Yes",545.585579909597,19454.3257965573
"8229","No","No",1057.35087177407,39651.123507722
"8230","No","Yes",720.516450875564,20222.9489909686
"8231","No","No",1285.51797563905,37442.6425889869
"8232","No","Yes",406.339769411729,30088.4425077181
"8233","No","No",1023.89203173754,33392.7097834514
"8234","No","No",356.244118146921,21319.0859608547
"8235","No","No",657.088818826134,31724.6285500371
"8236","No","Yes",1168.54641431588,25802.2764766761
"8237","No","No",344.88224152182,26227.3292753008
"8238","No","Yes",535.335989397998,24268.8284962213
"8239","No","Yes",1222.9665870908,21373.3500951251
"8240","No","Yes",663.602728097061,21436.5216376398
"8241","No","Yes",591.537144690117,20961.4652602437
"8242","No","Yes",941.718008883302,23362.9771743543
"8243","No","No",434.544943739374,35620.0652976999
"8244","Yes","No",1804.19050453849,23096.0843640887
"8245","No","No",0,34227.947139365
"8246","No","No",0,28356.2519409008
"8247","No","No",586.470977472847,52203.8589273578
"8248","No","No",707.43614919,42407.7542299089
"8249","No","No",1491.6405870679,61096.4921750445
"8250","No","Yes",556.182234259286,21650.8299183564
"8251","No","No",683.14754196625,44439.5287386404
"8252","No","Yes",671.131730723303,11857.4776351159
"8253","No","No",1020.19775584997,43592.8341359861
"8254","No","Yes",1183.06770190887,13210.8776348588
"8255","No","Yes",1127.53613107086,19322.9696557329
"8256","No","No",1545.98280486837,32691.9238251651
"8257","No","No",1114.3977298144,44195.5445481969
"8258","No","No",843.497244001002,41917.319765103
"8259","No","No",1047.67337025525,34413.1959130021
"8260","No","Yes",810.540945659107,17252.2542514058
"8261","No","Yes",501.110562487269,26425.6980101533
"8262","No","Yes",1031.63836091527,21432.147052673
"8263","No","No",360.215031490771,44082.1871282945
"8264","No","No",1234.83167806921,40155.4514111614
"8265","Yes","No",2236.76421456131,37113.8830697644
"8266","No","No",750.840531468744,32034.7121252064
"8267","No","No",231.968340323912,42465.9608187879
"8268","No","No",1616.50019408524,41825.4039844618
"8269","No","No",981.799329814043,49644.9187081982
"8270","No","No",538.019925084371,27284.5108594155
"8271","No","No",136.30734254125,48799.6549055704
"8272","No","No",614.940983622697,30905.479817434
"8273","No","Yes",824.040741788241,11659.1932751954
"8274","No","No",393.061267139273,44149.388311577
"8275","No","Yes",759.397130569606,18693.1879014694
"8276","No","Yes",1743.79883736231,17541.0028662017
"8277","No","Yes",621.170106310103,17503.6899747362
"8278","No","No",1360.54717336406,54705.1269778723
"8279","No","No",253.521881501858,37506.2067343011
"8280","No","No",1392.33267765748,48709.5292601184
"8281","No","No",1508.25085941898,43525.0527725642
"8282","No","Yes",1823.86706045805,19447.9665572514
"8283","No","No",0,39423.5125227997
"8284","No","No",728.673461012883,36557.2348355276
"8285","No","No",1471.19873782547,56477.1308610149
"8286","No","No",967.256762447018,32201.6340844054
"8287","No","No",640.705982383149,38792.3571871445
"8288","No","Yes",559.566410763486,15720.3824613913
"8289","No","Yes",1258.11453317171,20010.576269278
"8290","No","No",1126.88769620057,32770.5790184123
"8291","No","No",1341.26314052855,46263.8850122052
"8292","No","No",751.101930768505,50290.1441115538
"8293","No","No",325.927636632494,57313.2824969147
"8294","No","Yes",905.901945054864,20285.2251283281
"8295","No","No",699.604275562255,49035.2474604604
"8296","No","No",996.445905646337,51393.7887153379
"8297","No","Yes",1034.1702134313,23825.0285135993
"8298","No","Yes",1464.63624150705,14763.5626843165
"8299","No","No",280.724169013609,40382.5992622225
"8300","No","No",725.867685728056,41792.6729233945
"8301","No","Yes",1162.59092159727,15419.1105209588
"8302","No","Yes",193.719762500207,18002.547648728
"8303","No","No",743.18182701654,50250.9190142536
"8304","No","No",1403.83983418344,43225.0418957429
"8305","No","No",460.139804417708,52708.5598496177
"8306","No","No",1218.21988712889,36290.16200667
"8307","No","No",903.987020156951,51705.2716003957
"8308","No","Yes",1473.69593100509,19759.5018682479
"8309","No","No",525.515682022251,50763.9840052729
"8310","Yes","Yes",1874.82261243745,14957.8163342872
"8311","No","No",700.622387844543,42023.8887828787
"8312","No","Yes",1164.66696609579,23297.1578875709
"8313","No","Yes",1803.76634928102,23201.826982472
"8314","No","Yes",451.711523066804,22483.0958101843
"8315","No","No",1101.98446046958,36231.8538227436
"8316","No","Yes",1343.77712401475,28198.005353463
"8317","No","No",606.435876464573,38897.0794077548
"8318","No","No",0,45380.6567630619
"8319","No","Yes",0,20963.3831145759
"8320","No","No",1140.82015178859,36495.0553896058
"8321","No","No",1597.64740187317,42515.0888693793
"8322","No","Yes",521.841479256212,10316.3367412718
"8323","No","Yes",837.521493456654,13215.7858131515
"8324","No","No",390.310315291063,42322.246697405
"8325","No","Yes",806.481972726929,22127.0817878431
"8326","No","No",890.266646268634,38376.0906470453
"8327","No","Yes",237.383029626622,21376.6832351471
"8328","No","Yes",808.396167027043,11804.0344760529
"8329","No","No",694.390945705502,46128.8647070701
"8330","No","No",492.25394545489,42143.1984404939
"8331","No","Yes",460.595414827766,12939.009089927
"8332","No","Yes",838.459954530528,19805.8752348908
"8333","No","No",675.842504719532,38173.9257538527
"8334","No","No",625.419874645033,47406.2407650345
"8335","No","No",45.4762817817571,27599.0723332859
"8336","No","No",474.769235479092,30799.949385159
"8337","No","No",1024.82365673274,46113.6471170703
"8338","No","No",696.107576993413,40325.38480824
"8339","No","Yes",953.533658439997,25820.5874829041
"8340","No","No",1143.85523050669,50690.2576998117
"8341","No","Yes",683.441300405235,22595.4539568037
"8342","No","No",644.604409116029,30276.5707302591
"8343","Yes","Yes",2034.67471782133,17133.9659619014
"8344","No","Yes",1345.49162351815,12235.3876699232
"8345","No","Yes",1307.0870915147,18082.5548896089
"8346","No","No",705.500524263981,52559.0312585917
"8347","No","No",0,47725.5215295292
"8348","No","No",501.169619687987,35664.8053896805
"8349","No","No",991.520923604118,30775.5207643452
"8350","No","Yes",956.692234248303,19594.5490428608
"8351","No","No",353.649992387444,30726.2856927119
"8352","No","No",838.924060067205,47864.4189213508
"8353","No","Yes",353.652760924622,20531.4965369275
"8354","No","Yes",751.243140185971,16475.0275586338
"8355","No","No",991.158408318519,42767.6902792807
"8356","No","Yes",1096.4896471406,17856.265031594
"8357","Yes","No",1655.77559694702,25639.7935387017
"8358","No","No",0,35577.425283317
"8359","No","Yes",485.653177783496,16099.9179399102
"8360","No","No",850.466593719231,34367.6765291129
"8361","No","Yes",0,17695.8401686948
"8362","No","Yes",766.635537082227,21718.8136181928
"8363","No","No",309.179162515638,31232.0708902559
"8364","No","No",244.662374521967,29275.115419093
"8365","Yes","Yes",1013.21688638161,19651.2617936432
"8366","No","No",357.592156699681,48565.0177083166
"8367","No","Yes",1908.24200791737,17642.6266276482
"8368","No","No",1449.13570769994,40495.4721803858
"8369","No","No",464.41630119677,38236.740022089
"8370","No","No",1491.70754528024,40637.4479829066
"8371","No","No",256.89418684711,39019.8757442
"8372","No","No",0,35025.9713946151
"8373","No","No",0,23910.1396582202
"8374","No","No",817.621089103321,43705.822710524
"8375","No","Yes",888.306320762449,22901.6339371219
"8376","No","No",1240.75460457603,51511.2975985728
"8377","No","No",828.644161650737,10948.504497517
"8378","No","Yes",959.854227591364,18481.7526600941
"8379","No","No",1281.44884880957,48837.3787603345
"8380","No","Yes",1577.67543060735,11951.4786137937
"8381","No","Yes",1444.11426844796,21441.0121279953
"8382","No","Yes",647.670287929141,13618.3477949365
"8383","No","No",720.164918693873,43654.2303440958
"8384","No","No",64.1547933628275,47969.0051087279
"8385","No","Yes",546.028664099578,21615.2690793228
"8386","No","Yes",1102.12727684037,14009.9429940502
"8387","No","No",1294.52356958022,28001.0490731826
"8388","No","No",341.322290452253,33808.3361791774
"8389","No","No",399.390381379442,17348.7842322496
"8390","No","No",710.394633535282,31483.3472045777
"8391","No","No",1467.57338371779,26697.7858985394
"8392","No","Yes",282.831518827634,13371.055021089
"8393","No","No",544.22518162658,46149.5102207517
"8394","No","No",1585.86037254851,42709.4918931997
"8395","No","No",0,28784.4692369344
"8396","No","No",1033.42627176237,43594.7623211524
"8397","No","No",1131.05364603221,38834.2362457054
"8398","No","No",544.287295321035,24477.4719149196
"8399","No","No",1116.72035656284,33716.1775223316
"8400","Yes","No",2155.28898610335,34787.2526819072
"8401","No","No",732.056646560392,39433.4806360451
"8402","No","No",591.636269770793,33852.3096699306
"8403","No","No",0,63251.307626935
"8404","No","No",453.849958087348,27865.2847981077
"8405","No","No",170.070917002394,27005.5771185451
"8406","No","No",916.729177155089,37212.8085297821
"8407","No","No",611.337290220736,38389.2825323233
"8408","No","No",258.629672515951,33327.9377775435
"8409","No","Yes",357.595665269024,18141.0028164559
"8410","No","No",813.173887123882,49274.3828394648
"8411","Yes","No",1743.06470981777,22308.1257490576
"8412","No","No",801.802711515901,46182.2570982408
"8413","No","No",674.736614723598,26060.4860985104
"8414","No","No",430.490648307719,39921.1369084698
"8415","No","No",1352.99553198552,34089.0331773621
"8416","No","Yes",1040.88021196313,19427.8142430555
"8417","No","No",1305.77469775412,34412.116373967
"8418","No","No",1294.76170642516,37712.3868971499
"8419","No","Yes",583.295482029807,23694.3673679543
"8420","No","Yes",1409.2991781164,21508.5015068054
"8421","No","No",886.216628786159,40486.8594433597
"8422","No","No",515.619799710376,39638.6396270428
"8423","No","No",1866.30465255816,58710.8876485058
"8424","No","No",657.705720718074,41224.7968784511
"8425","No","No",270.224332861831,49128.0371444139
"8426","No","No",953.391788924619,20749.338158676
"8427","No","No",630.220778875387,24919.1838767956
"8428","Yes","Yes",2024.66016984007,9663.7881589584
"8429","No","Yes",497.056988344806,23119.1350274858
"8430","No","No",48.7255629593075,37957.9021382577
"8431","No","No",440.024542832313,49358.4257857338
"8432","No","No",1217.94865980061,43996.0139956854
"8433","No","Yes",1203.98225964262,21524.318420608
"8434","No","Yes",1493.87356174054,15626.6594167928
"8435","No","No",0,48025.6168546343
"8436","No","No",1204.95434803363,47167.9384961006
"8437","No","No",945.669717469501,39928.2438966955
"8438","No","Yes",807.595571807103,16926.3733350784
"8439","No","No",514.381527331564,20632.2681224495
"8440","No","No",1493.3574345851,51510.1480660087
"8441","No","No",738.221429789569,25184.4673054809
"8442","No","Yes",451.91246146881,22316.2613245872
"8443","No","No",997.027882271094,35591.3872732176
"8444","No","No",1082.07439363356,30627.4616912529
"8445","No","Yes",1001.34888620142,19314.9223285455
"8446","No","No",1058.62751384414,49380.0784348946
"8447","No","No",245.346492518079,42703.0186661609
"8448","No","No",579.381824881827,32296.2634047504
"8449","No","No",1180.93571396495,35352.1517992282
"8450","No","No",969.083028484442,45192.338963931
"8451","No","No",0,44561.3254511423
"8452","No","Yes",714.175499668216,19995.8563982326
"8453","No","No",0,36379.5188526604
"8454","No","Yes",100.850759138746,17315.1301918906
"8455","No","No",1856.53087551349,33498.8345204157
"8456","No","No",620.45730931752,41059.0965018736
"8457","Yes","No",1815.79252000645,31632.5838611298
"8458","No","No",1508.14501433939,31101.0812174785
"8459","No","No",1170.91697571401,38712.4200991916
"8460","Yes","No",2063.57193445013,37372.7584928128
"8461","No","Yes",284.841577198801,20477.837227372
"8462","No","No",878.446109878267,29561.7830760682
"8463","Yes","Yes",1740.76830587972,18161.2440790232
"8464","Yes","No",1994.39506620033,44794.3375042287
"8465","No","No",1290.17921206669,42813.975170851
"8466","No","No",441.336983219759,55970.9233586644
"8467","No","Yes",1790.41372522763,22953.4310754149
"8468","No","No",821.250479687844,68721.9744687402
"8469","No","No",341.040378604856,40255.4675973271
"8470","No","No",216.961691805065,31742.9944511373
"8471","No","No",48.237989993603,37536.3614328607
"8472","No","No",501.360657821036,49897.300730356
"8473","No","No",267.416624954798,24154.5097950922
"8474","No","No",623.618428429323,54008.1958257334
"8475","No","No",752.739378189563,38671.7859860491
"8476","No","No",0,38665.8702365134
"8477","No","No",413.114594729778,63793.5576339873
"8478","No","No",895.439223193743,26138.1012162755
"8479","No","Yes",127.663375125104,13942.2441190945
"8480","Yes","No",1361.73579057918,50322.355320138
"8481","No","No",592.611517621491,55124.8644992906
"8482","No","Yes",947.850996952402,22047.9205558962
"8483","No","No",784.171574845199,47322.6891083565
"8484","No","No",952.311152143376,21990.091473372
"8485","No","No",448.263531441352,26350.871022339
"8486","No","No",316.124008088576,37322.9315631556
"8487","No","Yes",1246.11719840856,9846.70264448697
"8488","No","No",680.706468644222,42316.4031121769
"8489","No","No",1208.30437571947,38461.0465350321
"8490","Yes","No",1292.43021067816,50990.5771675202
"8491","No","No",663.610198685482,48089.520547403
"8492","No","No",435.592200214182,38830.5180173373
"8493","No","No",579.193281037698,36181.8127643826
"8494","No","Yes",819.031064689632,22092.7223037395
"8495","No","No",603.020399834637,51229.7884696056
"8496","Yes","Yes",2654.32257628018,21930.3888786977
"8497","No","Yes",0,23274.9761587336
"8498","No","Yes",1487.04354464178,21412.3515514101
"8499","No","No",1466.77219186985,46875.0835420014
"8500","No","No",1168.4885668002,40615.5666922786
"8501","No","Yes",592.296945792535,19744.9783929469
"8502","No","Yes",1255.97389096594,21757.6763903522
"8503","No","No",389.792381033256,53685.4731538122
"8504","No","No",604.304327008842,38758.5455337612
"8505","No","No",940.392399703689,33874.8145278687
"8506","No","No",329.518976607022,38150.6349302136
"8507","No","No",1465.02703244964,18600.2135039367
"8508","No","Yes",1817.28007187911,20751.4324171627
"8509","No","Yes",1055.06379764618,16174.2063787243
"8510","No","Yes",536.010217846914,23587.4940781989
"8511","No","No",817.474328116273,41994.9253402325
"8512","No","No",1244.56356571126,36157.8184658576
"8513","No","No",983.127571332577,19985.6014307873
"8514","No","No",644.800255330071,32298.3085674304
"8515","No","No",896.981467925666,35755.7484517946
"8516","No","No",527.271574424434,41573.9819083321
"8517","No","No",0,51122.5156157189
"8518","No","Yes",1427.94053080774,12666.2607692499
"8519","No","Yes",1262.48076570285,15636.2573617591
"8520","No","No",1283.27848260299,46335.8360623501
"8521","No","No",489.51991369456,22750.6921764142
"8522","No","No",887.201436107651,41641.4535720123
"8523","No","No",328.013011130292,38500.3995970506
"8524","No","Yes",1521.49494912154,18018.9043408893
"8525","No","No",326.038112595005,25498.0135270893
"8526","No","No",1816.9735015648,29897.1818126619
"8527","No","No",1534.00700450546,35566.2670692778
"8528","No","Yes",1657.11385858028,10852.2931297992
"8529","No","No",1270.4569758694,36092.5870995613
"8530","No","No",560.514619197812,25254.0416554866
"8531","No","No",0,28490.7714643949
"8532","No","No",697.932758212992,35706.6613545057
"8533","No","No",1420.50748026314,37437.7379047098
"8534","No","No",695.669009847766,36977.6401878797
"8535","No","Yes",212.821368527709,24515.9754650541
"8536","No","Yes",929.411129986556,23236.7517512913
"8537","No","No",1676.11479875847,25317.1713937274
"8538","No","No",571.959941777441,24932.0059234333
"8539","No","No",355.016331184177,36327.8770094896
"8540","No","Yes",1315.42144462588,21650.4151619057
"8541","No","Yes",1006.99410894368,17834.0641906451
"8542","No","No",332.674169599933,22938.5931481233
"8543","Yes","No",1622.4990392531,39228.5414258989
"8544","No","No",0,26394.5453626774
"8545","No","No",1131.60205423585,19893.7723062072
"8546","No","No",0,44981.150289516
"8547","No","No",1493.74224806877,32742.8582115942
"8548","No","No",47.4234792771126,29567.857431916
"8549","No","No",153.712633619584,54331.5297705247
"8550","No","No",458.997386968815,21165.866479197
"8551","No","Yes",1260.82817647004,22372.287502262
"8552","No","Yes",1080.47766220473,18741.3168628509
"8553","No","No",1814.10564871295,32977.9086247262
"8554","No","Yes",1739.83781216118,21593.0128492377
"8555","No","No",0,50496.4737233152
"8556","No","No",983.490679181841,20828.5306523573
"8557","No","No",0,28176.1188629536
"8558","No","Yes",138.906309174776,13005.8951509307
"8559","No","No",870.347965939696,42397.0998165847
"8560","No","Yes",584.486401116587,14017.3905494261
"8561","No","Yes",1508.70177606082,25338.2646859801
"8562","No","Yes",1282.4749516367,25304.5864378799
"8563","No","No",521.736727978673,28401.4974561068
"8564","No","No",1150.67771161838,38342.2890564805
"8565","No","Yes",1010.84859095171,16428.7545549193
"8566","No","Yes",1746.29737190901,24536.5538729105
"8567","No","No",1382.26899258445,19023.9962118209
"8568","No","No",499.818601940851,31335.9607609844
"8569","No","No",339.676804208343,43308.965897573
"8570","No","No",253.630102218173,50236.5051828946
"8571","No","No",690.185731263381,32761.2272146714
"8572","No","No",743.052553428231,33556.0164881628
"8573","No","No",736.49552163315,49119.3478190345
"8574","No","Yes",584.518096158552,19624.5886598667
"8575","Yes","Yes",1276.68508261978,19282.4359237479
"8576","No","No",399.877160707318,47291.0158892892
"8577","No","No",182.238720004123,45937.9768756614
"8578","No","No",743.666169687606,37542.5803390161
"8579","No","No",731.101627213473,26655.4147068459
"8580","No","No",615.726657355146,38687.1152204681
"8581","No","No",499.149148884078,56343.6104353604
"8582","No","No",785.033516931214,45598.9153979293
"8583","No","Yes",969.25234104125,16864.0434885401
"8584","No","No",841.490063864523,43371.9244400238
"8585","No","No",181.171482584218,35175.9035445688
"8586","No","No",811.596366615229,38323.7238673524
"8587","No","No",1625.63987632474,65026.7389252459
"8588","No","No",962.223837965554,43974.0568690248
"8589","No","No",617.025054245827,33369.0893641112
"8590","No","Yes",478.923674934818,19730.1851629865
"8591","No","No",1017.74972070304,30227.8632254641
"8592","No","No",708.500161609795,35086.9049249202
"8593","No","No",423.407947289886,40844.3415096871
"8594","No","No",0,29775.1588729276
"8595","No","Yes",935.693635042886,25362.3218840663
"8596","No","No",0,51719.3748711348
"8597","No","Yes",1757.84545885824,17426.5197120225
"8598","No","No",733.5453865337,30638.2161878522
"8599","No","Yes",1050.40938603661,16746.0628214843
"8600","No","No",25.8003766684075,44091.687910866
"8601","No","No",646.081240203633,36686.9594469989
"8602","No","No",695.471767186167,25544.8920404563
"8603","No","No",536.517494230315,28629.4636706794
"8604","No","No",542.277143335525,52447.2193378773
"8605","No","No",811.769946136174,46076.3022364223
"8606","No","Yes",911.760824480021,21082.5527948271
"8607","No","No",621.303498905369,50474.9790084357
"8608","No","No",549.393127695549,39131.4948805194
"8609","No","No",929.297154195484,42503.4097188356
"8610","No","No",154.663920437736,44714.3716756576
"8611","No","No",36.2350412345537,49075.9815581156
"8612","No","No",718.595253590074,39157.7899326726
"8613","No","No",379.100360643884,31350.9286329779
"8614","No","No",904.335711193638,30890.745549857
"8615","No","No",1028.96434239191,18701.4095944644
"8616","No","No",343.493829781314,67930.3436152597
"8617","No","No",375.79931917431,21297.5846156923
"8618","No","Yes",502.261086940031,13572.748909077
"8619","No","Yes",1231.55882465424,15727.6696283886
"8620","No","No",456.871512841706,54894.1644540352
"8621","No","Yes",1112.12991899495,22709.4334546068
"8622","No","No",1063.71286098607,36475.0979894526
"8623","No","Yes",895.823244916992,19825.6298997185
"8624","No","No",1407.09214245668,43677.9764174713
"8625","No","No",418.498344050518,29856.471677401
"8626","No","No",684.90629721516,14756.9983525477
"8627","No","No",650.04611001589,46137.9329673966
"8628","No","No",495.421154240144,26740.7690090816
"8629","No","No",698.905129961079,27001.5538993092
"8630","No","No",847.772967201651,42193.5975640784
"8631","No","No",1185.15973428525,35793.3485205462
"8632","No","No",1315.609131464,54833.0555196068
"8633","No","No",0,32809.33478523
"8634","No","No",629.087770988306,32296.9316451165
"8635","No","No",326.670018453137,50562.4903339961
"8636","No","No",0,35775.3169310198
"8637","No","No",949.282372434899,26960.7427591373
"8638","No","No",1077.73308347226,50334.8541187515
"8639","No","No",1256.18672994804,31706.1785617479
"8640","No","No",1356.72983609052,37664.0373894486
"8641","No","No",564.128292140862,37695.7753708914
"8642","No","No",793.970969480621,41931.585345531
"8643","No","Yes",412.388636174531,9892.1275493933
"8644","No","Yes",810.668587280614,19925.7437697109
"8645","No","Yes",1273.83577633193,15593.6416810446
"8646","No","No",1386.66847806246,56325.4493583427
"8647","No","Yes",574.673144256394,22814.3464399736
"8648","No","Yes",1614.14163797244,11334.2924739847
"8649","No","No",0,45642.7619356526
"8650","No","No",949.996706817807,32676.2307500825
"8651","No","Yes",618.756752341743,18297.3277705301
"8652","No","No",1408.17611903654,47496.1643104349
"8653","No","No",465.919878364343,41133.9485633097
"8654","No","No",952.927819302997,44894.4429254273
"8655","No","Yes",17.6095775951027,13739.7546026196
"8656","No","Yes",989.884597421335,23416.5004655854
"8657","No","Yes",1817.17117640169,24601.0374851681
"8658","No","No",746.836510472922,41294.4522465602
"8659","No","No",549.06492953427,58539.4142705508
"8660","No","Yes",993.049538227269,20538.5457418214
"8661","No","Yes",1447.4181082241,16658.4240877415
"8662","No","Yes",598.218194371973,19794.5764098749
"8663","No","Yes",79.9899964022965,14390.8131164386
"8664","No","No",641.254587415807,31306.5348373144
"8665","No","Yes",562.837698264136,25068.0464477888
"8666","No","Yes",448.243210690723,23540.8910274387
"8667","No","Yes",1134.69657246644,20068.0185456427
"8668","No","No",1649.21160693666,42148.7438795368
"8669","No","Yes",817.676646707753,22339.8854145467
"8670","No","Yes",948.151302609926,18107.3571528728
"8671","No","Yes",1180.01808623234,19871.2067440711
"8672","No","No",761.187658836634,54681.8283898569
"8673","No","No",420.495831855675,58264.205816867
"8674","No","Yes",1841.40853381388,18000.7046803395
"8675","No","No",102.721441131555,13719.3568745031
"8676","No","No",265.592845889327,24833.3415942315
"8677","No","No",1558.86107533,45255.9671130598
"8678","No","Yes",220.659533399654,20148.5797584961
"8679","No","No",325.870131459589,35015.2689694775
"8680","No","No",1272.47738589833,42671.3185857913
"8681","No","No",906.231097084323,46715.2346835251
"8682","No","No",1070.81004817041,35979.4701445554
"8683","No","Yes",0,15572.0212631125
"8684","No","No",722.133486767882,22669.2398040671
"8685","No","No",1453.29287212607,43370.7578850906
"8686","No","No",1207.2878520836,33671.4595004888
"8687","No","No",1118.2199230073,32632.9130726689
"8688","No","No",1281.92157268812,56778.4153893202
"8689","No","Yes",878.998393552351,12054.8590388342
"8690","No","No",735.103337670526,51403.7159698486
"8691","No","No",914.341082341985,43460.1663790825
"8692","No","No",4.15111164507675,22722.2220379254
"8693","No","No",1063.59232923721,38057.3335707839
"8694","No","No",331.842827689507,37264.6948218815
"8695","No","No",838.578904753819,43501.2575307341
"8696","No","No",418.299642765707,49670.02198153
"8697","No","No",538.942731686188,23289.2804604069
"8698","No","Yes",2133.74548094099,19675.5075861463
"8699","No","No",1651.32255274413,34024.6636828514
"8700","No","Yes",1152.97327187599,13036.0667091531
"8701","No","Yes",1065.25635541797,16658.9472446304
"8702","No","No",486.652639922064,30838.629697034
"8703","No","Yes",770.826666509719,18896.728996789
"8704","No","No",1202.46348882287,42818.6431073491
"8705","No","No",323.800101436117,34576.4920335218
"8706","Yes","No",2062.87062844915,30839.1806730601
"8707","No","No",1058.28433162142,39314.5134702092
"8708","No","No",796.025053310521,38443.8964134579
"8709","No","Yes",1379.7700166326,15509.4093982645
"8710","No","No",1201.3324891043,23969.451819827
"8711","No","No",1114.11600743864,40359.9672061569
"8712","No","No",821.607102285366,50689.1932164429
"8713","No","No",1079.96576515199,43215.8058047755
"8714","No","Yes",662.99536303253,12008.3357418696
"8715","No","Yes",1343.70154908718,27168.4082179624
"8716","No","Yes",553.455736792548,21358.4206646296
"8717","No","No",472.95405502977,63759.787033535
"8718","No","Yes",1806.55167228604,17648.1976000877
"8719","No","Yes",855.226995923742,15348.7066607031
"8720","No","Yes",790.55432859871,20568.8272424665
"8721","No","No",1380.14403201419,48087.825053096
"8722","No","No",1522.53080154876,30765.5637963114
"8723","No","Yes",967.34904728104,14725.1409118618
"8724","No","Yes",886.221140404153,12796.0589938103
"8725","Yes","No",1323.62814220734,18820.7949690603
"8726","No","No",1351.32542269803,46663.3922059486
"8727","No","No",294.01879031285,59128.5034044914
"8728","No","No",1756.05071636763,52497.5856036506
"8729","No","No",439.496364012163,46784.9448129273
"8730","No","No",725.78329190466,35503.6442079735
"8731","No","No",1049.94676843874,43172.3271897794
"8732","No","No",1138.62885942419,36978.4593789249
"8733","No","No",568.224254556575,39873.2428466328
"8734","No","No",0,43160.324200842
"8735","No","No",572.665857566113,42285.2852956933
"8736","No","No",1270.39317811661,43032.2715865563
"8737","No","No",1444.43210450129,19153.3427287452
"8738","Yes","No",1926.59908212714,59224.4527589955
"8739","No","No",101.993455724981,27909.6310390187
"8740","No","No",547.174974932957,36969.0032256196
"8741","No","No",907.633103990479,28577.0965501992
"8742","No","No",282.889217701565,42132.0738127017
"8743","No","Yes",811.965045172706,12853.175607259
"8744","No","Yes",1305.10445258064,20573.5538687732
"8745","No","Yes",533.916542918411,21390.6866179289
"8746","No","Yes",1420.59613950384,20197.8115347314
"8747","No","No",89.3129240969444,39915.4535908417
"8748","No","No",839.040920045443,36665.7014593259
"8749","No","No",522.15345763795,40717.1625219394
"8750","No","No",488.721126253656,46503.1661460333
"8751","No","Yes",983.268502993472,24554.2715630512
"8752","No","No",442.665900584843,43991.8123694379
"8753","No","Yes",977.589372049767,17637.7341978841
"8754","No","Yes",317.267999810114,17801.5372223518
"8755","No","No",1033.75665823613,21837.2428327308
"8756","No","No",629.320183234782,52089.633050086
"8757","No","No",1328.33410346743,39877.6124571625
"8758","No","Yes",1486.37729960117,19164.3503996306
"8759","No","No",760.55329349048,19957.8391361009
"8760","No","Yes",1204.24147023262,19302.90147641
"8761","No","Yes",1367.78651452727,12449.27903567
"8762","No","No",226.299225371108,34305.2560781237
"8763","No","No",902.416395242314,37905.6314758056
"8764","No","No",462.004289552745,33157.3621976191
"8765","No","No",1072.37936473568,19958.54399237
"8766","No","No",424.048189994703,25081.84300618
"8767","No","No",846.131294772518,29812.0877039202
"8768","No","Yes",951.58853631512,12163.1848463548
"8769","No","No",1079.82329871977,44390.7160538533
"8770","No","Yes",1395.66898658783,21374.5921195464
"8771","No","No",828.889545195886,52560.907351965
"8772","No","No",829.884262065395,40749.7874334028
"8773","No","No",1626.16941874389,32827.7028585804
"8774","No","No",1057.63317363302,37229.8073120709
"8775","No","No",729.225990859972,35633.5029167277
"8776","No","Yes",154.875531554762,20685.4574218412
"8777","No","No",1583.096100745,15350.4560462834
"8778","No","No",0,40766.6953402928
"8779","No","No",528.569204946604,42249.3569999467
"8780","No","No",809.104673981283,24820.5255893773
"8781","No","Yes",132.104558748117,20065.722478815
"8782","No","Yes",2149.33681165388,15093.827527253
"8783","No","No",497.725785169606,26591.113170886
"8784","No","No",489.165560680408,26875.7237397548
"8785","No","Yes",596.539256038559,9518.83233207234
"8786","No","Yes",1883.06545604736,23989.8924556863
"8787","No","Yes",748.059228265943,17362.8074609483
"8788","No","No",1046.91812133872,42376.5021708491
"8789","No","No",310.130223883326,37697.2201903282
"8790","No","Yes",1065.52107615044,21129.2344637769
"8791","No","Yes",1097.06748078784,20564.1257982162
"8792","No","No",1154.85867839604,57640.9169618088
"8793","Yes","No",2049.0298451449,52568.9088071495
"8794","No","No",1398.65246735208,34194.082402249
"8795","No","No",130.346822483144,53852.755086508
"8796","No","No",1400.16318688457,52343.4771775422
"8797","No","No",0,51740.4215992712
"8798","No","Yes",552.33423955957,21262.5663587934
"8799","No","No",0,58233.020960533
"8800","No","No",246.636545293022,18953.671937326
"8801","No","No",0,38249.9823756967
"8802","No","Yes",655.510126293906,15771.4924298364
"8803","No","Yes",100.355432954224,13940.6026640982
"8804","No","Yes",1103.00356549214,25385.6019398336
"8805","No","Yes",928.233411504668,20932.6920591533
"8806","No","No",509.317798586812,18221.1465584724
"8807","No","Yes",420.257063909017,24703.9980491056
"8808","No","No",307.730562590318,48123.5510489485
"8809","No","No",733.076086748384,40056.1003653385
"8810","No","No",851.564659931381,33211.1755013218
"8811","No","No",1300.33718168884,47580.5438257053
"8812","No","Yes",203.966127021897,16433.6742949772
"8813","No","Yes",1557.52557142796,17132.312357933
"8814","No","No",0,41359.8916092437
"8815","No","No",1136.45503135114,47721.2737069072
"8816","No","No",986.181543809418,34780.9543914093
"8817","No","No",452.827107725658,62912.4443393412
"8818","No","No",775.493559468194,46025.8708003111
"8819","No","Yes",926.078163581303,17660.8072407267
"8820","No","No",1074.99126730894,27834.7900360085
"8821","No","No",844.465479105882,44518.3952707398
"8822","No","No",391.558358025563,45822.5243565918
"8823","No","Yes",1283.02855570716,15638.0065909407
"8824","No","Yes",1343.24351654883,7032.06000893064
"8825","No","Yes",1049.42003607627,15815.3439749118
"8826","No","Yes",728.985838205656,17360.5229950272
"8827","No","Yes",1203.10325643309,19967.863098697
"8828","No","No",221.334856461027,31764.2029103442
"8829","No","No",149.223218755774,36550.3334635728
"8830","No","No",474.628374825756,28182.9755685504
"8831","No","Yes",962.466536438663,14606.0900592801
"8832","No","No",1166.21318553678,36324.6788608866
"8833","Yes","Yes",2207.59905386637,19780.7635185799
"8834","No","Yes",1244.92351098597,24291.4079834424
"8835","No","No",913.453599210553,52793.1802484307
"8836","Yes","No",1315.55876543897,35456.6960512827
"8837","No","No",80.7149257730499,37651.567826129
"8838","No","Yes",745.221141236584,24689.5184815316
"8839","No","No",1505.0940829015,43036.1211524722
"8840","No","Yes",903.438811968714,13028.1508626046
"8841","No","No",250.988820359623,33568.5315548661
"8842","No","No",857.150742319289,34691.3221784726
"8843","No","No",92.9756885589492,49608.5864392816
"8844","No","No",1363.31037880418,35387.6989212415
"8845","No","No",146.897152742157,41872.1725190737
"8846","No","Yes",2014.49764757381,15728.93807223
"8847","No","No",1112.46852397583,43201.5204912199
"8848","No","Yes",237.163586366623,25219.9830590923
"8849","No","No",1018.3667900265,47723.3267841425
"8850","No","No",150.302385684606,39730.1650335799
"8851","No","No",1074.12404031907,47517.8669242321
"8852","No","No",0,30098.4215418435
"8853","No","Yes",1205.94504053624,12950.81642991
"8854","No","Yes",713.246393380936,20549.851899935
"8855","No","No",1351.85296584556,40177.95896223
"8856","No","No",254.728057897549,53696.832200196
"8857","No","Yes",842.255987408822,23310.2288610289
"8858","No","No",390.980823542815,47083.4314972953
"8859","No","Yes",639.46830362075,12323.3498411588
"8860","No","Yes",514.334987256277,27443.9752498741
"8861","No","No",791.663341774579,48127.6261396151
"8862","No","Yes",1204.91790442105,11339.1567691282
"8863","No","No",1000.52543821309,53680.9837392223
"8864","No","Yes",810.308012417556,16248.4555455672
"8865","No","No",797.865393271633,43717.0684112059
"8866","No","No",0,46461.3201193472
"8867","No","No",990.144772287608,48040.6018919919
"8868","No","Yes",284.050551961391,15352.3597814414
"8869","No","No",1080.32128118414,48226.7297027404
"8870","No","No",1433.99232687817,19864.975375759
"8871","No","No",792.974067741037,28674.6706260091
"8872","No","No",1351.95743719203,24136.0719476857
"8873","No","No",528.676844819959,39925.1847361413
"8874","No","No",692.052822388672,39586.8490171988
"8875","No","No",1085.52023487308,36746.3203519003
"8876","No","No",567.704178336162,50643.3162179576
"8877","No","No",1190.06130329303,41965.8799496593
"8878","No","Yes",289.356947353925,15715.5006522284
"8879","No","No",1440.1329269194,28370.1043239688
"8880","No","No",373.178886417864,30826.0750245479
"8881","No","No",733.20056907891,38592.0420222931
"8882","No","Yes",1095.61200214885,10450.7448786067
"8883","No","No",1117.62970369796,61940.0157774647
"8884","No","No",505.015128111594,43658.813144044
"8885","No","Yes",230.159168685849,24776.6931484107
"8886","No","No",586.775650400868,32622.3761384089
"8887","No","Yes",513.797677681162,15540.4543835634
"8888","No","Yes",1212.75666202829,13722.900826077
"8889","No","No",578.483646602543,45066.2073302758
"8890","No","No",1229.52030155885,59755.0301243891
"8891","No","Yes",1829.9099412937,21018.9601164828
"8892","No","Yes",973.264479997202,16984.6107557205
"8893","No","No",690.640342447775,26989.3943527933
"8894","No","No",781.667785915406,49427.5177211641
"8895","No","No",652.202761375539,36348.2962358233
"8896","No","No",762.923197677572,50293.8722536696
"8897","No","Yes",843.642431501666,17992.3171675151
"8898","No","Yes",1316.21098311729,16327.6259820021
"8899","No","Yes",559.903182577809,9357.75924827898
"8900","No","No",355.030065741944,52926.7777630445
"8901","No","No",1635.20290200127,31541.094039298
"8902","No","No",696.895693166017,63952.3134935061
"8903","No","No",444.042702686403,47857.7146122207
"8904","No","No",1585.52508909154,49011.4156672029
"8905","No","No",1322.0809830351,48906.5183241643
"8906","No","No",941.741632288605,34559.6465423569
"8907","No","Yes",1426.01417736484,20779.6789340972
"8908","Yes","Yes",1359.21394743248,25548.4874099867
"8909","No","No",1504.56938589151,36697.4019859209
"8910","No","No",48.2656403155554,32081.0922978199
"8911","No","No",0,47393.328688244
"8912","No","Yes",802.369267071566,18999.5252923259
"8913","No","No",600.93909025164,43846.2088931824
"8914","No","No",1440.02571265889,41814.8038417079
"8915","No","Yes",1184.20444313768,23235.6567863856
"8916","No","No",31.8620653736333,33702.6511297977
"8917","No","No",580.379056126581,55724.2712260263
"8918","No","Yes",591.986469895437,8815.1763760225
"8919","No","No",915.592009370862,37720.1872737065
"8920","Yes","No",2006.98617301263,50033.5331529481
"8921","No","Yes",1216.58625032905,18140.6239934368
"8922","No","No",970.925552198971,46051.8683748365
"8923","No","No",664.71894351335,36998.5660140661
"8924","No","No",873.879608564925,46203.9095502441
"8925","No","No",444.281400129866,56881.2065664973
"8926","No","No",641.760448114412,44857.7012299345
"8927","No","Yes",1106.32369416819,21202.0300750189
"8928","No","No",616.554649797188,46107.2079296282
"8929","No","No",1164.36214026539,38759.7305513767
"8930","No","No",828.662811758189,28427.6702930321
"8931","No","No",23.2983318114218,37975.5389792909
"8932","No","No",717.434420182684,45267.819845936
"8933","No","No",167.584156691883,49087.2556946302
"8934","No","No",821.853292644843,35358.3927357629
"8935","No","No",769.666701899912,39401.4750601507
"8936","No","No",0,37343.4168074676
"8937","No","No",1038.70026585785,33783.7142156398
"8938","No","Yes",1050.15133269262,16963.8126910891
"8939","No","Yes",927.632100647308,20640.7741049258
"8940","No","No",1014.72262870562,38780.7897165122
"8941","No","Yes",1391.4415971628,11685.6454285004
"8942","No","No",290.483067019993,47724.2397601074
"8943","No","No",656.434943665867,26551.6009022426
"8944","No","No",1456.64293691918,24089.0953411859
"8945","No","Yes",1240.72401222539,19702.7994991218
"8946","No","No",636.606045459298,31870.5514728832
"8947","No","No",1665.03697302705,48295.5251505377
"8948","No","No",825.955944652207,31661.6259861064
"8949","No","No",0,61608.5320455831
"8950","No","Yes",1152.12441898794,18644.4000439626
"8951","No","Yes",711.039875304138,22000.1065648767
"8952","No","No",1221.0831226742,29370.5849131354
"8953","No","No",781.291702589044,36744.5682634056
"8954","No","No",0,41671.2094610296
"8955","No","No",919.300136080993,33866.3633768088
"8956","No","No",155.989916940972,50135.8501709004
"8957","No","No",0,42616.6015727837
"8958","No","Yes",1520.85164475355,20044.0814993756
"8959","No","No",407.321538164032,38285.4149141094
"8960","No","No",555.22995118937,22788.9680825098
"8961","No","No",705.197176762044,35864.0117216156
"8962","No","Yes",916.12897063954,28839.5837329101
"8963","No","No",1083.28118704441,45260.4166799581
"8964","No","Yes",771.133261396283,20655.0710742505
"8965","No","No",1700.29497614814,53844.5789864386
"8966","No","No",679.266804773665,36694.7502626291
"8967","No","No",762.991490694281,44162.3628946132
"8968","No","Yes",1196.53699597576,25651.5363814897
"8969","No","No",627.383759585527,47489.2830811426
"8970","No","Yes",1175.79905681463,18178.0819336875
"8971","No","No",660.200571229794,33598.089137525
"8972","No","Yes",822.625324265157,10923.3816767271
"8973","No","No",842.407539122614,38161.7386616171
"8974","No","No",1248.39968478018,41555.8109876492
"8975","No","Yes",1405.87665380559,27071.322496909
"8976","No","No",419.702000130701,44989.7628489879
"8977","No","Yes",91.733946503422,19153.7284337776
"8978","No","No",157.003453234473,50131.8788806077
"8979","No","No",666.529950650783,51876.4896525719
"8980","No","No",806.781241079411,55735.5491959437
"8981","No","No",0,44754.2488808886
"8982","No","No",1378.26643610381,40228.9341678732
"8983","No","No",354.216390971718,25427.0544326248
"8984","No","No",596.182688889079,55591.0589187562
"8985","No","No",102.469862910735,44600.4319739208
"8986","No","No",637.216999432894,48461.9094371498
"8987","No","No",1274.79655026368,18044.8231222898
"8988","No","No",403.041481443931,29200.9837620227
"8989","No","No",619.515173902886,53747.5491352023
"8990","No","No",1111.69076640993,31059.4626439165
"8991","No","No",1235.48576796347,29992.185950678
"8992","No","No",850.385568380253,45530.3770572617
"8993","Yes","Yes",2352.05494931522,24067.5481040571
"8994","No","No",1518.35251457509,53971.7305926683
"8995","No","No",0,47935.283197733
"8996","No","No",541.944001798071,37640.3002604157
"8997","No","Yes",1401.19360905062,17688.8564659358
"8998","No","No",329.465684346918,46028.0154528353
"8999","No","No",530.093820522838,21736.8098186961
"9000","No","No",0,27455.9089977047
"9001","No","No",1112.06413995964,27694.7212677386
"9002","No","No",291.076141258525,23479.6134347452
"9003","No","No",1133.4149388708,36456.9490261566
"9004","No","Yes",488.957012076425,15912.3045797469
"9005","No","No",926.547899439048,25363.0146355545
"9006","No","No",783.797563315914,33151.5672432225
"9007","No","Yes",246.081818997006,20046.7724578198
"9008","No","Yes",860.917730636065,11575.7021870812
"9009","No","No",1838.19486106974,32308.024036755
"9010","No","Yes",918.828888145091,16110.9904627923
"9011","No","No",1040.10566508529,16905.4579370983
"9012","No","No",951.620225963668,41689.5683729734
"9013","No","No",749.114739643679,46478.1051575446
"9014","No","No",70.1854776566071,36216.9635264652
"9015","No","No",1002.35121821511,43048.1699151943
"9016","No","No",948.705393027503,14095.809610144
"9017","No","No",983.619015796229,24666.6828328998
"9018","No","Yes",0,16156.0291216807
"9019","No","Yes",1209.3841983712,12830.8328351056
"9020","No","No",527.172784913709,28931.1282763333
"9021","No","No",671.765480006205,40907.8081017102
"9022","No","No",807.29453070076,49367.7759269551
"9023","No","Yes",1189.4647404608,17287.6392061415
"9024","No","No",612.10792157668,31367.7130801548
"9025","No","No",1.98073444076283,42380.8748504793
"9026","No","No",507.907526435418,45727.76852889
"9027","No","No",1678.11846572952,41813.8923436118
"9028","No","No",0,51054.5690234529
"9029","No","Yes",378.672174850814,14328.3118855115
"9030","No","Yes",1202.76071083541,20973.2596532462
"9031","No","Yes",1038.54099708928,21839.720028992
"9032","No","No",333.180057472427,29400.4778472657
"9033","No","No",1322.2517605997,43802.6272391063
"9034","No","Yes",1579.21699595418,15111.9144799438
"9035","No","No",852.530776892318,46662.7679457832
"9036","No","No",1407.86920793967,43033.9359309394
"9037","No","Yes",0,17791.4332277217
"9038","No","No",350.196085511402,46893.7319861286
"9039","No","Yes",1276.35083901532,27732.2688220081
"9040","No","No",17.0316800105643,24492.2134816103
"9041","Yes","No",1288.40699655441,44253.3087353926
"9042","No","No",861.36386043585,47969.6772642797
"9043","No","No",1209.75664132667,40605.8134050359
"9044","No","No",1067.77922869907,28077.2546815944
"9045","No","No",831.587329996423,32203.2718441375
"9046","Yes","No",1747.98366856951,18966.10957371
"9047","No","No",0,58211.5360729805
"9048","No","Yes",1177.32765461107,13737.0503356651
"9049","No","No",0,50559.470747056
"9050","No","No",943.467289283768,26388.6171578782
"9051","No","No",61.1833446196181,37604.2178046417
"9052","No","No",1436.64766913975,38571.3280595237
"9053","No","No",343.643178302344,43483.7379647757
"9054","No","No",1018.38287102808,30846.8209363716
"9055","No","Yes",1173.09812006685,23811.0003015511
"9056","No","No",979.851049135932,44869.0469120209
"9057","No","No",444.604796363456,51584.3363563541
"9058","No","No",282.042433702886,50501.0772381175
"9059","No","Yes",1420.02298162135,26424.5390754184
"9060","No","No",654.661360097169,32400.4814786393
"9061","No","No",1388.56348925402,21151.2631286477
"9062","No","Yes",0,13621.8111912884
"9063","No","No",0.0238162970920257,37981.7806257266
"9064","No","Yes",1132.08405700115,15675.5344367768
"9065","No","No",1330.48109685153,50459.0468967413
"9066","No","No",815.174023270193,37946.9813996175
"9067","No","No",1163.32404039928,38323.0844014907
"9068","No","No",769.230636542265,49590.0665811938
"9069","No","No",698.078872879415,38960.2671895006
"9070","No","No",0,41239.0205103957
"9071","Yes","Yes",1789.47747479376,17667.7822202713
"9072","No","No",1124.966526782,50701.727536775
"9073","No","No",1012.28620807044,60697.6336154775
"9074","No","No",0,31722.3296748607
"9075","No","No",481.77780560423,34478.9529015915
"9076","No","Yes",1383.56982356533,17245.0286854431
"9077","No","No",856.770451438768,42662.8019755055
"9078","No","No",994.011888922971,52188.7752690302
"9079","No","No",203.328123506147,40490.1449892228
"9080","Yes","No",1856.91471650834,33445.6168691596
"9081","No","No",566.872719168326,67018.4196136116
"9082","No","No",1428.72525803425,51633.4328536076
"9083","No","No",1339.19996299738,44257.9352999586
"9084","No","Yes",215.63389776376,23221.6193715472
"9085","Yes","No",1125.6565618153,34758.1237796202
"9086","No","No",514.859436188812,57241.7030807118
"9087","No","Yes",564.802762069897,21314.7794434341
"9088","No","Yes",705.883064516133,11121.6993090859
"9089","No","No",390.021724300271,39029.8570729439
"9090","No","No",690.601097770566,43511.3372747162
"9091","No","Yes",1596.70105479512,21581.272259309
"9092","No","No",1578.46371134843,51413.8402653719
"9093","No","Yes",1363.99295684132,18140.3610309984
"9094","No","No",819.326374631826,22406.0549367066
"9095","No","No",0,44712.2316847198
"9096","No","No",870.020567920588,40535.3101192342
"9097","No","No",629.271891437048,35695.0886113163
"9098","No","No",217.51241222814,39616.2602452715
"9099","No","Yes",1702.98170171568,25203.4567626209
"9100","No","No",86.5788882292029,31893.2233621957
"9101","No","Yes",967.1689244161,27077.6267654492
"9102","No","Yes",329.764331286904,17762.7738754145
"9103","No","No",594.364428159126,24273.5624820861
"9104","No","No",51.0723020671674,38764.3946808286
"9105","No","No",345.393868141835,27012.753881309
"9106","No","Yes",740.577850671659,17700.0720673343
"9107","No","No",1128.83347504188,45043.9356174808
"9108","No","No",1549.2633061346,43240.459025584
"9109","No","Yes",472.024537068754,13685.4701306953
"9110","No","No",466.109768644457,26598.1213495462
"9111","No","No",633.7831602792,37672.1580967363
"9112","No","No",959.021460172678,46133.1342729851
"9113","No","No",1634.99668519232,36995.7511638982
"9114","No","No",155.993061347122,37599.7679668851
"9115","No","No",1060.97292028918,37410.3763831821
"9116","No","No",862.150195723777,59240.8450311379
"9117","No","Yes",380.90510662748,22042.5700144763
"9118","No","No",184.530473857988,35058.3406548568
"9119","No","No",1690.3873495135,46228.3200574542
"9120","No","No",406.998521872771,41505.4060514131
"9121","No","No",626.168125274639,35196.0782332706
"9122","No","No",1915.33206600636,21361.048134107
"9123","No","Yes",1017.36096639883,21702.1831030868
"9124","No","No",669.36147892363,46007.0052657782
"9125","No","No",574.469877633891,46696.1863447367
"9126","No","Yes",256.325670466206,15627.662788709
"9127","No","Yes",1365.79119035916,22301.7060630287
"9128","No","No",1194.64523564299,35622.3282129261
"9129","No","No",286.301054522557,30026.0707568402
"9130","No","Yes",1260.49400898862,8549.2531756823
"9131","No","No",659.783085264805,42054.9149889584
"9132","No","Yes",583.774223240858,12240.6605181135
"9133","No","No",1024.81947679618,40380.2979636242
"9134","No","No",1045.42688593122,50639.0600933807
"9135","No","No",522.774408905781,37481.6821850974
"9136","No","No",212.93178251117,46843.9124938965
"9137","No","No",0,54609.728556779
"9138","No","No",316.56387222423,30293.9633320726
"9139","No","No",625.017328733068,58911.6086513796
"9140","No","Yes",789.377266158372,21140.5088670155
"9141","No","No",454.488146311927,39608.3228717815
"9142","No","No",1293.29760636786,65128.4045281209
"9143","No","No",964.090413444914,48405.8431092384
"9144","No","No",316.017511904727,39959.9300030972
"9145","No","No",826.022722200688,53874.3719444383
"9146","No","No",667.492041321912,24613.6755190923
"9147","No","Yes",950.477292877919,17932.1250861819
"9148","No","No",0,54659.1631878342
"9149","No","No",650.290138457368,44358.6516229275
"9150","No","No",448.881996836721,38426.2593436877
"9151","No","No",1326.41236983709,35158.1903317436
"9152","No","No",1322.09397673286,51845.1803956487
"9153","No","No",575.137656473335,25669.6971678928
"9154","No","No",392.47718191513,20674.6728093574
"9155","No","Yes",716.048096702279,15149.8757737644
"9156","No","No",358.923951537742,50989.7441954935
"9157","No","No",946.892055516687,9364.84978102991
"9158","No","No",184.887298302054,49716.0204052985
"9159","No","No",600.820085751327,49727.4899119465
"9160","No","No",965.150483568005,34783.006878247
"9161","No","No",937.037055360113,42169.0433987106
"9162","No","No",1225.92264757412,43718.6949293441
"9163","No","No",581.313104571568,32454.1101531922
"9164","No","No",62.1109338524258,51206.2925195464
"9165","No","No",1653.55187635321,23137.5612152665
"9166","No","Yes",994.090738839954,17401.8472379021
"9167","No","Yes",1080.75973269095,16900.3906039523
"9168","No","No",663.663792792779,51135.0862626239
"9169","No","No",0,44464.2972936873
"9170","No","No",11.1380700574389,52195.9205188668
"9171","No","Yes",1403.1635255755,28207.9316716435
"9172","No","No",1005.52542938779,34587.2436322169
"9173","No","Yes",831.304495716088,17572.3179345351
"9174","No","No",154.212463380725,29153.4515783616
"9175","No","No",497.881905243542,55543.6246875475
"9176","No","Yes",601.891220583823,16948.6132290621
"9177","No","No",832.344535599559,51737.371047564
"9178","No","No",1557.55901598546,36742.1885265389
"9179","No","No",705.338209861416,23952.5445114994
"9180","Yes","No",1234.10202131484,43764.8686376452
"9181","No","Yes",816.443608151587,19141.7302065139
"9182","No","No",768.37858481136,43325.9703901599
"9183","No","Yes",1725.33924356463,18268.1953665361
"9184","No","No",1004.12540732619,53397.3919952222
"9185","No","Yes",952.887131441381,15082.6771111652
"9186","No","No",1965.32935816705,31804.6342196168
"9187","No","No",930.081301597559,21575.4238472606
"9188","No","No",499.646323784874,69342.6724803793
"9189","No","No",1140.1401141,41775.5181102699
"9190","No","No",518.369905831186,27527.2348024601
"9191","No","No",1167.88915820962,35969.0235564662
"9192","No","No",1402.24271716909,32742.2706120643
"9193","No","No",0,31363.6495017099
"9194","No","No",42.1499932320341,31812.8915318739
"9195","No","No",989.017791693055,35129.0917367816
"9196","No","No",10.2314853105521,27237.3807737476
"9197","No","Yes",1298.51180862112,20491.7703437282
"9198","No","No",987.250044908775,50045.7631787153
"9199","No","No",920.574314458369,40527.9870563083
"9200","No","No",534.055544790253,30948.806749184
"9201","No","Yes",1249.76647351391,16251.2847001414
"9202","No","Yes",732.429941772304,26433.105443857
"9203","No","No",655.199872371663,38192.5303918194
"9204","No","No",224.209109065902,51199.6416695462
"9205","No","No",635.808710980531,40497.3816214593
"9206","No","Yes",1384.41537314232,13707.0415964143
"9207","No","No",979.105937917122,46319.9119942633
"9208","No","Yes",1463.82811094,15657.4344832514
"9209","No","No",1302.7837778175,50709.5086274927
"9210","No","Yes",50.951486258891,16251.6110559006
"9211","No","No",1235.18768402375,50865.52407028
"9212","No","Yes",1047.22562427263,18069.2523486806
"9213","No","Yes",1032.62578929059,21122.9570145721
"9214","No","Yes",1285.95639658624,24106.5802664105
"9215","No","No",1265.12773929921,49385.5218570693
"9216","No","No",1032.51300052367,42495.4626119106
"9217","No","No",566.367097183945,39033.3249938756
"9218","No","No",521.800206899137,22209.0552074691
"9219","No","Yes",313.213361441899,28399.3508567594
"9220","No","Yes",0,18251.7256240426
"9221","No","Yes",982.800355690071,20312.210964034
"9222","No","No",430.469060274272,47462.0709857981
"9223","No","Yes",1255.84888805629,22022.5765757422
"9224","No","No",1082.1694961193,54099.9195245639
"9225","No","No",450.327485622248,52446.4100002697
"9226","No","No",814.655042444551,42520.0936476942
"9227","No","Yes",1068.45464067538,13638.7026931268
"9228","No","No",549.168989264142,64952.6126393417
"9229","No","No",928.409668215284,41482.4435346174
"9230","No","No",1476.76264263876,54844.519341711
"9231","No","Yes",785.854959727702,19188.9142292579
"9232","No","No",1493.61037995993,38744.2282579575
"9233","No","No",1074.01591044855,50491.2605127071
"9234","No","No",693.358479926231,46466.2993893935
"9235","No","No",848.605387082035,22749.6476762886
"9236","No","No",1233.71187441207,72461.3013920211
"9237","No","No",1264.13238499978,35385.1168046383
"9238","No","No",1474.63803590209,39872.2277658541
"9239","No","No",700.244848184181,25919.0393463935
"9240","No","No",803.975566261737,41429.2482535756
"9241","No","No",120.843053016424,36205.8777610819
"9242","No","No",1032.40348383143,38691.7939259647
"9243","No","No",563.992089929205,60222.3676486086
"9244","No","No",377.363641801591,37442.8426514135
"9245","No","No",0,26493.2426781175
"9246","No","Yes",688.697976264174,18794.4897248302
"9247","No","Yes",583.248474622869,17353.8385772636
"9248","No","No",1509.87113078024,41246.5870119825
"9249","No","No",43.1435004219103,34316.8901483245
"9250","No","No",860.751294274713,36605.231775094
"9251","No","No",204.030246673872,51108.0449636601
"9252","No","No",1059.24438703765,59865.0282342774
"9253","No","No",1207.74122415822,33416.6065697538
"9254","No","No",615.821849382944,34590.6835797159
"9255","No","No",1018.56812990717,34103.8795198849
"9256","No","No",1436.67072419004,46755.2808284924
"9257","Yes","No",1800.64173306343,48708.9599348881
"9258","No","No",0,56943.909437874
"9259","No","Yes",1006.9691579167,10364.8185337407
"9260","No","No",868.72989169156,40902.4296102798
"9261","No","No",1068.57703503957,26315.0470909899
"9262","No","No",838.849150459366,34358.8666722966
"9263","No","No",1278.42997211826,25590.6565248632
"9264","No","No",264.016096356061,45987.473909461
"9265","No","No",1168.57073487805,47561.0980956441
"9266","No","Yes",1600.62553366906,24223.2514087651
"9267","No","No",456.138752190196,43943.4958888918
"9268","No","Yes",1578.28955492315,12778.5984954933
"9269","No","Yes",1205.31003148042,19889.5707482555
"9270","No","Yes",1115.54335974595,22552.7130871241
"9271","No","Yes",950.488155976941,13299.4869550647
"9272","Yes","No",1547.99544475031,37524.2535271978
"9273","No","No",256.438984796291,40227.3820841249
"9274","No","Yes",1367.85595504108,12489.814247912
"9275","No","No",400.888873152191,53900.664457035
"9276","No","Yes",1020.58903292674,13094.9457323397
"9277","No","No",596.429706719568,12135.5415583733
"9278","No","No",289.531496978857,36841.998173589
"9279","No","No",775.733982696868,37022.003238302
"9280","No","No",895.323834867129,43554.0743998906
"9281","No","No",1798.77891710501,57191.6322372635
"9282","No","No",1112.45500095933,40576.1902203327
"9283","No","Yes",1225.34597052574,5524.3747895647
"9284","No","No",927.615950846859,40618.1106498673
"9285","No","No",252.106953682049,38494.654686523
"9286","No","Yes",1128.35620851187,15669.096533398
"9287","No","No",424.495933817511,23983.7731637878
"9288","No","Yes",1084.44653079612,26713.7971950018
"9289","No","No",1360.39857054015,29868.3111925025
"9290","No","Yes",1009.10925113012,8868.65674492432
"9291","No","No",0,29097.5448355388
"9292","No","No",336.503548704535,40626.994174489
"9293","No","No",537.140426047159,52513.6672206072
"9294","No","No",600.134860988896,39395.9783993341
"9295","No","No",938.090134362438,46370.8212570407
"9296","Yes","No",2095.11481689194,44647.5864831921
"9297","No","No",1176.7915053022,30579.2492243849
"9298","No","No",1099.624909713,48202.2148811926
"9299","No","No",417.646983046744,54009.4508784744
"9300","No","No",1175.59495320808,19030.1394436242
"9301","No","No",1245.08629467938,40290.5038943139
"9302","No","Yes",1381.27451157699,23903.8545527795
"9303","No","No",1068.9868881856,37171.6514118965
"9304","No","No",635.761848193517,49414.7265627533
"9305","No","No",347.601510839327,49332.9942041319
"9306","No","No",1579.90936267244,45406.5115513117
"9307","No","No",288.178064347863,36514.6759218012
"9308","No","Yes",1626.90142107237,11702.2834962136
"9309","No","Yes",846.171959465112,19577.4271953577
"9310","No","No",1395.60265575127,54148.8485683706
"9311","No","Yes",339.695098161915,27387.2624791806
"9312","No","Yes",1634.26972580171,18036.6305852339
"9313","No","No",75.3986906473086,50551.0364578898
"9314","No","Yes",375.918849493335,20740.0761917877
"9315","No","Yes",770.408789116406,1498.22727449612
"9316","No","No",347.371078226378,18488.1473162035
"9317","No","No",771.173706094358,45956.7873065643
"9318","No","No",1272.07680514996,21503.1953515833
"9319","No","Yes",985.240889672317,17263.756508129
"9320","No","No",751.354391933644,27179.7616531196
"9321","No","No",829.689664731405,35594.2239070406
"9322","No","No",910.087841290055,25183.7682534076
"9323","No","No",712.936992421474,20810.3189465256
"9324","No","No",977.515331107566,33690.6493109395
"9325","No","No",1379.28702192603,57843.8867791593
"9326","No","No",1322.96904167252,51956.2918282558
"9327","No","No",460.23443941905,47305.2160400713
"9328","No","Yes",596.056247366741,16084.9077085452
"9329","Yes","Yes",1538.0156030863,21356.9371123679
"9330","No","No",471.918842996091,60656.0507877414
"9331","No","No",1186.88519892898,27494.329371648
"9332","No","Yes",541.33470653612,21245.3206345669
"9333","No","Yes",1286.355522567,20029.8912328088
"9334","No","Yes",1723.21605596598,23278.9957793958
"9335","No","No",288.807949617577,30470.7070130486
"9336","No","Yes",1595.90680015678,23308.1362848463
"9337","No","No",1064.97510630953,45737.7812288087
"9338","No","Yes",1941.0540620836,14756.2114753991
"9339","No","Yes",889.642251418017,16701.3693468808
"9340","No","No",604.839524336024,27419.4252219695
"9341","No","No",1003.6014265304,24978.084721436
"9342","No","Yes",1464.35589747508,21826.4414843116
"9343","No","No",322.288698122369,19420.3997838148
"9344","No","No",639.867034351065,34879.5585105014
"9345","No","No",687.156829276007,26902.7025896597
"9346","No","Yes",978.236903521764,15073.7878502656
"9347","No","No",337.175668241883,35040.9416198665
"9348","No","No",880.75085172512,34604.6793905292
"9349","No","No",1285.52803809771,39530.7596773893
"9350","No","No",939.69867396675,52383.2752163082
"9351","No","Yes",729.812198863064,9871.92421926215
"9352","No","No",0,62160.2862201972
"9353","No","Yes",1471.03353995012,12665.2418905555
"9354","No","Yes",816.965766002804,13932.2099389238
"9355","No","Yes",1323.39565781292,23072.5499188748
"9356","No","Yes",383.823400970598,11777.5376921697
"9357","No","No",1634.73155470246,40868.3321227274
"9358","No","No",846.393292818549,38352.5651402481
"9359","No","No",364.663051203468,10239.9724854423
"9360","No","Yes",833.614495245054,11078.3902844208
"9361","No","Yes",1867.55226831937,16650.0417627663
"9362","No","No",823.609775829112,34856.5260704175
"9363","No","No",1255.34997743235,23297.4749578769
"9364","No","No",1024.4083050747,45202.2655386312
"9365","No","No",0,36922.8679525624
"9366","No","Yes",1447.97079301048,16421.4554957761
"9367","No","Yes",1057.59936601373,21570.7527878446
"9368","No","Yes",1027.13290130047,16259.380550917
"9369","No","Yes",1212.73704115468,18090.4779152969
"9370","Yes","No",1815.17411191986,23648.4135102395
"9371","No","Yes",167.637914781781,16771.5670281056
"9372","No","No",272.526141718933,32687.1095927352
"9373","No","No",0,39462.6882917635
"9374","No","No",1387.28864569868,29712.7724259701
"9375","No","No",624.547832745273,48345.3366279687
"9376","No","No",262.995114778805,17822.6133515974
"9377","No","No",986.760089723137,44605.4451140349
"9378","No","No",0,43930.9036889936
"9379","No","Yes",884.311899856719,17542.9196731794
"9380","No","No",929.830153708422,45178.0530481921
"9381","No","No",751.843861266577,52663.2929945444
"9382","No","Yes",0,18875.7716745482
"9383","No","Yes",879.710515261613,17863.8422294102
"9384","No","Yes",1313.97624829786,20030.4287054934
"9385","No","No",319.131596884487,34296.7741208314
"9386","No","No",996.181984627573,57972.5044444709
"9387","No","No",1123.43472291482,45366.811408952
"9388","No","No",110.555931121184,32419.1507927822
"9389","No","No",581.960758402304,31654.5589159892
"9390","No","No",982.226465463733,38152.6966116717
"9391","No","No",874.617420386964,37143.6448678682
"9392","No","No",650.007041287637,41427.8773217528
"9393","No","No",1452.68442788761,43418.8290856032
"9394","No","Yes",1443.86042588629,19146.2914155916
"9395","No","Yes",0,13911.4412816929
"9396","No","No",737.348779490127,50264.7601726517
"9397","No","No",106.191037697253,41514.7584689918
"9398","No","Yes",1273.75233922209,19635.6615017973
"9399","No","No",863.046051003096,50667.8767561549
"9400","No","No",1504.68256832061,38223.2459784659
"9401","No","Yes",1182.67904454398,17287.7484677075
"9402","No","No",853.060422763735,39553.3656783293
"9403","No","Yes",160.887137472449,19686.7441982352
"9404","No","No",1118.56597660815,46578.4655109694
"9405","No","Yes",271.669900090936,14080.7540730871
"9406","No","No",584.090255400821,24580.8568389694
"9407","No","No",911.931544151435,40503.003979245
"9408","No","No",745.86661817674,54677.6833488426
"9409","No","Yes",1291.07585166573,17136.5133355865
"9410","No","No",572.88950065498,43381.9152726882
"9411","No","No",445.829287617145,42343.8006824259
"9412","No","No",865.400213478165,53798.8192148106
"9413","No","No",886.777432995006,43410.381538287
"9414","No","Yes",451.695309013678,11460.4346982733
"9415","No","No",558.321406990805,42767.3247588271
"9416","No","No",1602.00389348608,41827.7012514993
"9417","No","No",503.499285174721,36521.4325133663
"9418","No","No",521.90282712011,47032.5993473814
"9419","No","No",1214.54965376974,21630.0378315175
"9420","No","Yes",596.205213621811,22324.4684122297
"9421","No","No",926.166134018374,41501.7233827868
"9422","No","No",522.644337059267,35893.2877978631
"9423","No","No",986.264752865454,38664.7898161626
"9424","No","Yes",885.923577632711,22365.8549815303
"9425","No","No",562.314178399933,63794.5420691952
"9426","No","No",503.301857053805,37749.5207774081
"9427","No","No",1375.38495384183,47099.2068738094
"9428","No","Yes",1089.40525917146,19053.5890648439
"9429","No","No",770.486334076718,25445.8776820274
"9430","No","No",797.316656880086,35034.8069148208
"9431","No","Yes",389.213241765338,22907.7674644876
"9432","No","No",589.391036933054,38255.5370311758
"9433","Yes","No",2073.7828464432,52335.5505993235
"9434","No","No",1128.41146297844,51778.1798537095
"9435","No","No",1142.66970009878,40823.3797085521
"9436","No","Yes",1007.53782731167,16814.2955002652
"9437","No","Yes",789.099153486285,18653.6328463855
"9438","Yes","No",961.732661954925,27600.4162967222
"9439","No","Yes",1264.64988701795,23852.642260934
"9440","No","Yes",1627.45984040875,19630.1613011152
"9441","No","Yes",979.065326799836,17666.3870981162
"9442","No","No",768.810302366349,47003.4405822046
"9443","No","No",714.512491266083,48115.5046955907
"9444","No","No",223.356345671541,51662.5972987814
"9445","No","No",1659.29219094815,36641.7272339608
"9446","No","No",1062.28400958965,50894.1036458965
"9447","No","No",38.6791212371011,30961.7422399322
"9448","No","No",1216.98145158854,37967.6880750024
"9449","Yes","No",1825.97059923332,39407.9170346961
"9450","No","Yes",293.519570874315,10592.8466920888
"9451","No","No",1588.87635591145,45093.4459240335
"9452","No","No",1926.2468757744,46535.201990519
"9453","No","No",892.919519826276,9299.86818569379
"9454","No","Yes",1583.41803094344,23434.7087125009
"9455","No","Yes",968.953961017051,13867.5243122361
"9456","No","No",258.25525911809,38309.9880459355
"9457","No","Yes",492.245941753357,23689.7895020492
"9458","No","No",1240.57389071057,35598.887273363
"9459","Yes","No",1658.96768927915,38204.1226198861
"9460","No","Yes",877.156229246366,22046.5385541872
"9461","No","Yes",0,20877.8877962384
"9462","No","Yes",1041.31044163521,12078.7776128838
"9463","No","No",1806.91653062691,36306.8176295486
"9464","No","No",0,28705.2025005847
"9465","No","No",1500.59400368875,27354.5002462612
"9466","No","No",1329.91138843467,50560.7622488765
"9467","No","No",913.886573498561,15220.503772624
"9468","No","No",898.566828456017,37919.7506774816
"9469","No","No",0,47231.2730755509
"9470","No","No",457.601501646266,49908.5974364961
"9471","No","No",882.087963611419,43549.9904046431
"9472","No","No",602.622894902464,25376.4500993846
"9473","No","No",513.396271437243,19021.6422758867
"9474","No","No",880.717005225234,35579.2426234722
"9475","No","No",815.691294664279,48646.541509537
"9476","No","No",1295.61334588531,39717.736012915
"9477","No","No",1740.63412327172,48311.4395809251
"9478","Yes","No",1506.19184823604,47061.3945398275
"9479","No","No",906.416188038229,49815.6511359632
"9480","No","No",1278.01755344051,41691.1955153279
"9481","No","No",45.8224265782371,32525.63843899
"9482","No","No",629.605587398253,50113.8207069077
"9483","No","No",270.587863722704,33490.0052711357
"9484","No","No",1486.0548096624,34705.3612203416
"9485","No","Yes",830.651798095757,14420.6226252465
"9486","Yes","Yes",1719.25565479764,15752.0080061962
"9487","No","No",935.636598446705,21730.8612688672
"9488","No","Yes",482.13520417464,17459.0041908228
"9489","No","No",335.824542648486,29402.7675584317
"9490","No","No",870.412379656213,17685.4482369216
"9491","No","No",527.940359109328,47963.1790421382
"9492","No","No",1104.30662323205,40006.133313516
"9493","No","Yes",1220.93883078966,6744.04242461348
"9494","No","No",1338.53245663217,35072.3032579073
"9495","No","Yes",1129.23429282609,22689.6135887033
"9496","No","No",915.518055990193,42331.0216310488
"9497","No","No",833.957706602402,34174.6453235057
"9498","No","No",381.566519897813,49114.8926501904
"9499","No","No",218.589806829151,34512.6334707916
"9500","No","No",1156.99452812612,53450.2735865097
"9501","Yes","Yes",2046.63951494124,17910.6344721428
"9502","No","Yes",1368.38301833943,16449.7249385124
"9503","No","Yes",1302.51631796853,18210.8064208001
"9504","No","No",1175.28913922627,35830.045567112
"9505","No","Yes",256.367784428102,18200.8804470926
"9506","No","No",908.998794729013,46174.4710581454
"9507","No","Yes",1107.00241299216,30618.6203455371
"9508","No","No",618.460731226128,49385.7880266266
"9509","No","No",932.872998040885,61192.8971316666
"9510","No","No",547.717727410382,50619.3610620419
"9511","Yes","Yes",1726.47960966875,13654.6049381595
"9512","No","No",581.003569445871,52917.7294357654
"9513","No","No",356.613167951343,44473.3964918855
"9514","No","No",644.863070527484,38794.6761434136
"9515","No","Yes",737.082247582862,24637.617488291
"9516","Yes","No",1666.1138341785,25054.7678239285
"9517","No","Yes",1215.47001413849,20456.4382893475
"9518","No","Yes",1002.77528438556,11604.2877499049
"9519","No","No",363.229330063018,51821.4268263811
"9520","No","No",0,45178.5916919899
"9521","No","No",29.6164764973496,26710.009566346
"9522","No","Yes",1036.11739842406,15067.748240504
"9523","Yes","No",959.159213374077,59435.2345810762
"9524","No","No",390.530294876658,57525.2664771593
"9525","No","No",451.076051642189,38348.4903195754
"9526","No","No",1412.99670449889,53295.8729914521
"9527","No","No",216.528262219422,28859.6946187879
"9528","No","Yes",1027.07059027312,24346.9397452388
"9529","No","No",143.754709337909,48250.3075662067
"9530","No","Yes",887.656422160429,16993.2876040408
"9531","No","No",1199.98026561327,38251.1825246834
"9532","No","Yes",1075.17907339542,21569.3705840819
"9533","No","No",932.602433217053,44714.4841039603
"9534","No","Yes",1434.58997748353,16613.4314534251
"9535","No","No",444.586300271673,51386.2831024757
"9536","No","No",853.212216590271,31607.3933989458
"9537","No","Yes",973.047858215706,23033.8503613238
"9538","No","Yes",102.283071552971,20671.0472107307
"9539","Yes","No",652.397134412879,46155.0438680055
"9540","No","No",132.73090182934,33370.8343428413
"9541","No","No",1128.47074465602,49540.702085553
"9542","No","No",1315.22523886647,52556.1538329754
"9543","No","Yes",1025.85824127717,21561.8889323212
"9544","No","No",785.786657440919,48179.3828770989
"9545","No","Yes",1775.18153723891,20385.8735233901
"9546","No","No",362.055049681232,50906.4812600996
"9547","No","No",1154.57891449163,61089.4308700936
"9548","No","No",18.294581765502,43247.9034246982
"9549","No","No",0,37517.2464147507
"9550","No","Yes",1740.87005025992,17478.0312948152
"9551","No","Yes",1228.99390086462,18810.3843581736
"9552","No","Yes",1669.72370904243,20301.3159786935
"9553","No","No",1584.56594315008,45447.1870281177
"9554","No","No",751.122224356379,37304.1989940684
"9555","No","No",1063.33626057666,39301.2818118726
"9556","No","No",710.136461015427,46308.2696465604
"9557","No","No",1063.72450300539,26045.9283574804
"9558","No","No",162.784651965433,33935.4913310022
"9559","No","No",615.001394580548,40236.5335580854
"9560","No","No",1011.36719214186,28172.9153660159
"9561","No","Yes",1658.37357455808,22010.4856755611
"9562","No","No",1490.98304584582,39596.0574087303
"9563","No","Yes",1630.48855467074,16310.5340605392
"9564","No","No",781.274957828979,53178.926801699
"9565","No","No",476.301211791106,25774.4876162707
"9566","No","No",892.445059906566,28187.540727095
"9567","No","Yes",1671.52320759969,9981.89701353152
"9568","No","No",813.86094063887,33357.8088167162
"9569","No","No",1280.23019022832,23647.3332028812
"9570","No","No",564.394853544301,30380.6400043349
"9571","No","No",525.871381443789,8017.63859108991
"9572","No","No",837.328407015019,61950.8030010727
"9573","No","Yes",1847.46964656061,19388.6893093459
"9574","No","Yes",717.759666195472,19567.702770302
"9575","No","Yes",1935.50421960499,9431.2938713916
"9576","No","No",808.494793730309,56204.6630143615
"9577","No","No",218.739421958108,53259.7623227317
"9578","No","Yes",118.201492870089,15472.655985471
"9579","No","No",988.172046073138,49057.0419217536
"9580","No","Yes",673.327247930574,14431.4594866873
"9581","No","Yes",616.312018469622,14663.5638803911
"9582","No","Yes",475.853204682757,19993.7559366484
"9583","No","No",221.546755897456,43668.392321756
"9584","No","No",959.172025652896,38310.086011965
"9585","No","No",575.674057777466,36958.7524117206
"9586","No","No",1017.65591423765,37241.790991973
"9587","No","No",1763.84563955144,36686.3483703886
"9588","No","No",883.390659909625,38408.9767061792
"9589","No","No",830.234100005215,36523.4811843369
"9590","No","No",163.261027090534,40600.4154666133
"9591","No","Yes",2.99446199667136,18710.2647984133
"9592","No","No",51.1859856979337,39385.7596831095
"9593","No","No",806.216663210777,35259.3046445798
"9594","No","No",1186.48588492632,50371.7254602767
"9595","No","Yes",357.951290354411,14338.5287369149
"9596","No","Yes",1590.8230986799,21784.1131156343
"9597","No","Yes",822.090169266773,20906.2178401806
"9598","No","No",1082.8154177681,41355.9855919617
"9599","No","No",779.477523952232,34089.7970086937
"9600","No","No",4.6794144400302,44571.7062209609
"9601","No","No",58.7512052743834,26939.0399063082
"9602","No","Yes",966.481828288842,23116.5802065553
"9603","No","No",683.921880812288,37383.2326530231
"9604","No","Yes",0,18235.6743746751
"9605","No","No",1139.02030927059,24573.920953365
"9606","No","No",1293.45968181568,48039.9364466138
"9607","No","No",304.544338825751,47217.8722103104
"9608","No","No",1215.1489392085,51804.9939548461
"9609","No","Yes",305.787956878261,16594.5278409234
"9610","No","No",693.717332635451,40148.8711102766
"9611","Yes","Yes",1502.18673866603,18603.495531833
"9612","No","No",1645.42317956837,36839.3962080674
"9613","No","No",279.670673019681,58566.8842182417
"9614","No","No",1267.48521068266,40034.5667342325
"9615","No","No",456.373018755051,47633.7854435193
"9616","No","No",962.134281546038,44696.2905307889
"9617","No","Yes",698.9785363366,25103.0094533339
"9618","No","No",619.029259422622,42868.5896075151
"9619","No","No",870.971725740693,39653.3767206921
"9620","No","No",217.738226298202,47657.3062761409
"9621","No","No",713.326063707486,44605.3511864782
"9622","No","Yes",306.433812567408,13934.5183209416
"9623","No","No",1314.89538557468,33706.5593609176
"9624","No","No",1057.6796686199,39708.0333375873
"9625","No","Yes",625.601199796924,16134.2115079184
"9626","No","No",1302.65558132293,28978.5573129088
"9627","No","No",1408.35147419162,33697.3459632332
"9628","No","Yes",926.569431024724,19243.406446276
"9629","No","No",1018.40963958971,25394.2546280925
"9630","No","No",0,40737.866812646
"9631","No","No",724.887324252241,27800.9523774513
"9632","No","Yes",325.283631228391,15962.7098774277
"9633","No","No",445.717373373907,25171.8603546071
"9634","No","No",1367.44276590447,45919.5922447472
"9635","No","No",1353.91874089042,39879.1590476355
"9636","No","No",1555.96807003044,34417.8730826113
"9637","No","No",1267.16580008473,51197.7995950094
"9638","No","Yes",640.819040858052,19055.9165274273
"9639","No","No",658.053709086965,41882.5806543853
"9640","No","Yes",808.833786517671,18791.5595576156
"9641","No","No",819.11260539853,50082.8887115636
"9642","No","Yes",1365.02282368458,23281.320499788
"9643","No","No",396.844085106912,32656.5228381594
"9644","No","No",421.698130449243,32290.3259596544
"9645","No","No",969.358837365413,46026.7193738543
"9646","No","Yes",1490.07545066509,15910.8018539424
"9647","No","Yes",1079.0779493071,17966.7693767183
"9648","No","No",809.455689909933,39077.4328082427
"9649","No","Yes",641.893645617273,16400.3647500988
"9650","No","No",872.32089314313,44407.7901606273
"9651","No","Yes",1004.63015294009,9643.36615066435
"9652","No","Yes",1170.91153622666,19473.6919261194
"9653","No","No",1433.64610048881,44617.7387007948
"9654","No","No",579.280219432576,50813.5092488359
"9655","Yes","No",2128.79599194797,42096.5023677566
"9656","No","Yes",842.910746273251,14462.2764563912
"9657","No","No",4.10094016965502,42174.1797420201
"9658","No","No",218.379741369937,36044.8150648758
"9659","No","Yes",717.875483900523,23406.8197691114
"9660","No","Yes",1146.8349874397,14336.4285457286
"9661","No","No",968.515851521507,41714.9898391776
"9662","No","No",1124.5871562623,45622.9423367253
"9663","No","No",419.000799287843,37186.789402739
"9664","No","Yes",320.725569726674,12589.7675596142
"9665","No","No",426.764149765367,38495.7980226005
"9666","No","No",949.177591550543,36557.9615912885
"9667","No","No",968.736619525114,17352.410331845
"9668","No","No",1188.96516821723,42286.488174869
"9669","No","Yes",1436.82998948372,22925.4699177569
"9670","No","Yes",843.017896577615,12268.9335211883
"9671","No","No",1410.95991508544,58310.255932724
"9672","No","Yes",598.733312937679,23118.5310986333
"9673","No","No",72.9283697000853,13300.7695476218
"9674","No","No",1347.78084965639,29345.9582133348
"9675","No","No",0,41160.622506494
"9676","No","Yes",1280.84806618713,20575.2450146805
"9677","No","No",1088.57477295941,43883.1492462512
"9678","No","No",1111.95814288827,47254.6895873957
"9679","No","No",1124.14174377155,46052.1816302638
"9680","No","No",0,36337.3521663594
"9681","No","Yes",711.851231569769,15132.3935899747
"9682","No","No",548.839275833041,44909.0661956785
"9683","No","No",385.250183291053,55268.6605512573
"9684","No","No",1132.17490347674,56152.4290396227
"9685","No","No",399.118375316584,48889.9890284738
"9686","No","Yes",810.102225230511,15102.32418046
"9687","No","No",1036.77474609973,46239.0732170886
"9688","No","No",927.76611806029,38197.3735404727
"9689","No","No",999.826090658299,43661.1417440736
"9690","No","No",649.269775479187,31039.0020043034
"9691","No","No",1391.31830146058,48414.9057421767
"9692","No","No",1058.56369974681,38917.8309339491
"9693","No","No",415.541737898413,44147.2206538574
"9694","No","No",771.137416855989,42418.3680140177
"9695","No","No",727.974547043257,20456.156388785
"9696","No","No",239.327165000461,49328.2092582724
"9697","No","Yes",1219.76034968629,17208.2088611799
"9698","No","No",953.936465024584,41250.4780369892
"9699","No","No",143.375320218317,17435.096380549
"9700","No","Yes",1669.27810558434,13771.3933964254
"9701","No","Yes",719.554053545902,17195.7445380271
"9702","No","No",295.3071501902,45776.8432775556
"9703","No","No",804.499658822933,51014.8240369344
"9704","No","No",619.368815439907,45886.0785009503
"9705","No","No",1157.11692920969,38867.0636275315
"9706","No","No",287.843200948489,46617.0633958336
"9707","No","Yes",1070.7521199751,11383.4426471097
"9708","No","No",452.367912290375,29254.8710158075
"9709","No","No",492.352311836963,39305.2270474498
"9710","No","Yes",1033.04184648414,18958.3150837176
"9711","No","No",1191.610584117,49004.8361762043
"9712","No","Yes",1463.37845118945,14380.6253065591
"9713","No","No",152.440267006946,23366.5174413879
"9714","No","No",182.370459994264,45507.9336965974
"9715","No","No",732.137066876115,42693.9402612385
"9716","No","No",1916.08850756454,23316.5702484103
"9717","No","Yes",419.710782172972,18073.0018129069
"9718","No","No",1418.55617364355,33960.5677357044
"9719","No","Yes",811.688675857525,20016.7525348576
"9720","No","No",214.236495085322,38720.8107572671
"9721","No","Yes",799.159267652907,19547.4131831191
"9722","No","No",831.456141881062,58476.7392311882
"9723","No","Yes",713.559839270764,24324.1439422418
"9724","No","Yes",870.773035482323,16446.354825625
"9725","No","No",301.319402807109,51539.9523173009
"9726","No","Yes",1235.96648680657,21706.6303125178
"9727","No","No",572.004055050836,42484.5705940718
"9728","No","No",545.993291219821,52619.6217723053
"9729","No","Yes",1407.04012421074,25589.7427593565
"9730","No","No",1171.16331315331,27338.0027086173
"9731","No","No",322.982327526016,25267.3976162142
"9732","No","No",497.540672262589,43898.4284977249
"9733","No","No",92.1021951040721,22726.4131778057
"9734","No","No",395.475507300842,45084.105615353
"9735","No","Yes",184.925246905994,11126.2661759336
"9736","No","No",209.674450616566,35688.1358061838
"9737","No","No",376.185498059799,31373.3563929914
"9738","No","No",1492.18965381051,39004.5029437998
"9739","No","No",905.531751819024,43066.0600596803
"9740","No","Yes",627.66812838312,18539.564145443
"9741","No","Yes",1224.52065302574,22327.4078004255
"9742","No","No",667.372311156338,35694.4287751412
"9743","No","No",278.703161386556,30113.8533896619
"9744","No","No",1389.80307501266,48868.0931533735
"9745","No","No",1060.72884144048,49396.7719405986
"9746","No","Yes",1080.5689215192,20439.852989353
"9747","No","Yes",912.559428715564,14262.1284978301
"9748","No","No",534.432226361313,34129.4754090997
"9749","No","No",280.461678558834,26212.9076178057
"9750","No","Yes",801.721578482952,18698.4092841064
"9751","No","No",1125.15873643759,37864.3259790881
"9752","No","Yes",1569.30275654962,13711.492401417
"9753","No","No",755.460443248137,33460.9262064348
"9754","No","Yes",860.828676826597,27186.5000242093
"9755","No","No",1233.61892565538,49448.1710013837
"9756","No","No",699.568498285891,48773.819553427
"9757","No","No",914.881075502437,39789.0554443783
"9758","No","No",1650.03210342038,49481.7130266447
"9759","No","No",366.594361337791,33708.7860682045
"9760","No","No",400.646928884124,37173.4847875624
"9761","No","No",0,37538.0450518034
"9762","No","No",1902.14989954198,35008.6661733839
"9763","No","No",982.631024138588,60388.0198980962
"9764","No","No",717.969637240982,43826.8788845714
"9765","No","No",874.45120069409,24707.490254048
"9766","No","No",1577.03483745032,33319.3124684899
"9767","No","Yes",1700.57236567333,20844.719155924
"9768","No","No",425.819908128894,14540.9046427866
"9769","No","No",667.656103587994,52946.3650042158
"9770","No","No",0,48112.4782746703
"9771","No","Yes",764.807163405562,18347.3113707265
"9772","No","No",1030.19129899136,34250.8745410279
"9773","No","No",426.28639637922,38585.5490904503
"9774","No","No",0,43599.4532930336
"9775","No","No",1039.20725473548,40028.9041967729
"9776","No","No",603.253138096093,50214.8553012712
"9777","No","Yes",1446.64376016459,8797.28391539218
"9778","No","No",1396.20751479794,39023.4817075264
"9779","No","No",297.243033683459,39081.9228975074
"9780","No","No",613.977076452465,34752.0876379183
"9781","No","No",828.840863016667,39984.2734678397
"9782","No","No",675.114951936145,58672.0516489082
"9783","No","Yes",1670.46730713431,15838.3145696838
"9784","Yes","Yes",1530.55147909759,13003.9292044954
"9785","No","Yes",356.19874792355,21466.2070771506
"9786","No","No",284.169210304994,37463.6175129986
"9787","Yes","No",1698.82614249543,27374.8875156932
"9788","No","Yes",0,20195.0026594829
"9789","No","Yes",1038.88674842652,10414.5543533648
"9790","No","No",392.566454848147,45469.7222771505
"9791","No","No",23.3571065489047,56686.9156223935
"9792","No","Yes",1503.9727209424,14661.8318983141
"9793","No","Yes",799.51645086393,23024.8527528002
"9794","No","No",1534.68321023724,48592.3338269502
"9795","No","Yes",655.449164552291,18265.5954293392
"9796","No","Yes",939.704886735988,11331.4325243357
"9797","Yes","Yes",1867.30856911342,24720.4805591524
"9798","No","No",399.901820436333,40863.1150523706
"9799","No","No",381.258558545531,55896.2338543989
"9800","No","No",1509.91566408553,24427.7685054556
"9801","No","No",1209.65202808003,58174.9210563944
"9802","No","No",1359.80213917798,46235.2353636695
"9803","No","No",1267.55073982519,10233.8797549679
"9804","No","No",890.149840963937,38350.2924207536
"9805","No","No",1508.74901177888,30587.3439815421
"9806","No","No",1260.15486906425,35733.4658537
"9807","No","No",0,33220.9851811449
"9808","No","No",67.3884622916294,48792.1174388257
"9809","No","No",1132.64780262819,33677.781447611
"9810","No","No",0,22074.4936909088
"9811","No","No",861.590053056554,32901.010461517
"9812","No","No",796.298975514892,37923.3936187679
"9813","No","No",1356.15922208888,42262.9300706052
"9814","Yes","No",1975.65302824061,38221.8397449546
"9815","No","No",663.663633754263,43951.2530570445
"9816","No","No",0,39043.2774348674
"9817","No","No",398.023124263748,33056.6711872941
"9818","No","No",304.077953300737,47196.0764675098
"9819","No","No",1292.67276322286,52541.6492484632
"9820","No","No",1664.09565193609,54087.6410964728
"9821","No","No",1069.71985668762,34538.9974168635
"9822","No","Yes",618.624121259494,20836.7930537286
"9823","No","Yes",1609.13605855122,16373.9453593471
"9824","No","Yes",1538.44930304403,15991.2210227014
"9825","No","No",734.253410791397,56253.8102626269
"9826","No","No",931.957796048969,40913.4349678136
"9827","No","Yes",0,17557.9633365884
"9828","No","No",524.862618447016,54738.5066498565
"9829","No","No",1135.49329308145,37962.9829034037
"9830","No","No",1115.96125304207,30620.2684728371
"9831","No","No",157.66047507126,42125.7336109977
"9832","No","Yes",11.3955682154315,19387.920643909
"9833","No","Yes",434.839281135161,16775.3204827761
"9834","No","Yes",863.321267047228,20425.8513260722
"9835","No","Yes",822.886668672738,22376.226052396
"9836","No","Yes",1203.94690391731,20904.8381765433
"9837","No","No",901.613561622198,35283.8956355289
"9838","No","No",1000.76456080856,36709.083782756
"9839","No","Yes",715.341171028252,21309.1405400513
"9840","No","No",325.091275054021,41257.5607449296
"9841","No","Yes",948.880409167519,8503.60256314103
"9842","No","No",751.340141034422,68883.3952663083
"9843","No","No",1484.77769480138,32479.0899645699
"9844","No","No",1253.86047741196,41767.3970567096
"9845","No","No",1262.16841196536,29545.5002108332
"9846","No","Yes",46.0102253175322,13950.0869473289
"9847","No","No",0,39469.9289877503
"9848","No","Yes",906.123815340083,19275.8001435789
"9849","No","No",273.63978799535,21911.6338748215
"9850","No","No",1037.76652304171,53823.2298198052
"9851","No","Yes",508.050299741586,14925.9611545816
"9852","No","No",543.971897115436,34390.5025931627
"9853","No","Yes",1219.97548042256,15039.5536205707
"9854","No","Yes",697.171326380402,22254.0619647508
"9855","No","Yes",1066.54575990381,15553.0884143881
"9856","No","No",761.553724640498,46836.6722671501
"9857","Yes","No",1973.82214669185,27340.0122687502
"9858","No","No",1021.27960003743,44718.4077235003
"9859","No","No",783.087084312125,36917.1031687996
"9860","No","No",1354.33294605204,29590.8288796286
"9861","No","No",677.708824050348,31158.468211245
"9862","No","No",317.855557759746,63021.7479456945
"9863","No","No",141.128483677956,41300.7220664453
"9864","No","No",759.230268223227,48560.1362364964
"9865","No","No",1133.9457958626,47188.9477836487
"9866","No","No",0,43593.953602744
"9867","No","No",599.718846289024,48085.8843441802
"9868","No","Yes",529.273442611172,20358.8336346446
"9869","No","No",962.144360844805,60455.463133907
"9870","No","No",675.02273307249,44480.0130544315
"9871","No","Yes",1038.62008149838,12312.8959635019
"9872","No","Yes",656.808385859257,19017.4277962265
"9873","No","No",1103.72380706592,39532.9075065434
"9874","No","No",2391.0077393729,50302.9095568637
"9875","No","No",440.487028918919,39395.9601410214
"9876","No","No",1070.86403155186,36017.0029644353
"9877","No","No",1275.20685173645,58961.7330359858
"9878","No","No",763.039976306975,35277.9017788768
"9879","No","No",781.904404455422,65617.6449283193
"9880","No","No",853.704015364221,46291.5811377682
"9881","No","No",323.034915715153,33673.8096869927
"9882","No","No",980.037168872519,31977.5633320135
"9883","No","No",1077.00753980888,33071.4685262081
"9884","Yes","No",1335.61287132382,37595.3442741023
"9885","No","No",438.080376882359,44737.658451196
"9886","No","No",955.420645475078,45575.4209895681
"9887","No","Yes",923.018822925402,19136.6149186515
"9888","No","No",728.212549683954,50403.5592512078
"9889","No","No",259.506285168662,40405.3591974712
"9890","No","Yes",606.951443394452,26718.8715454547
"9891","No","Yes",512.449777710332,24192.8889910366
"9892","No","No",0,43969.2150996534
"9893","No","No",663.675171211673,38338.205726286
"9894","Yes","No",2288.40808192017,52043.5690521956
"9895","No","No",0,34836.3406992442
"9896","No","No",1693.64257792408,26995.6933911804
"9897","No","Yes",906.033700467737,22658.7633289403
"9898","No","No",763.534729752426,49201.4346992107
"9899","No","No",1218.86498579543,45593.8602149062
"9900","No","No",496.375478407846,33782.5371965622
"9901","No","No",1695.38723798363,26623.7681519036
"9902","No","Yes",1128.76872801296,17718.941736732
"9903","No","No",978.239289996625,43410.3633154939
"9904","No","No",1084.90992326231,15981.6890852726
"9905","No","No",1248.88790288262,31960.4212982045
"9906","No","No",1071.24143217021,58134.5550658653
"9907","No","No",834.319787658759,33687.7504000506
"9908","No","No",1500.57210625523,39891.8640984595
"9909","No","Yes",958.874768969235,25282.2410536958
"9910","No","No",981.062638685672,54020.434153568
"9911","No","No",1017.78825221517,29828.3303377842
"9912","No","Yes",1106.10720094121,9610.50342319676
"9913","Yes","No",2148.89845431192,44309.9171725874
"9914","No","Yes",1113.75259775529,17810.6730641504
"9915","No","Yes",1882.84179031769,15968.0365405299
"9916","No","No",1189.24118445074,29889.6022774471
"9917","No","No",1092.42595724569,43578.6469767627
"9918","No","No",748.487023875035,40727.1415628446
"9919","No","No",631.512865795982,35053.3471968094
"9920","No","No",477.907292410447,34318.9831975541
"9921","No","No",1128.7915629401,29722.1726627668
"9922","Yes","Yes",1627.89832265731,17546.9970160528
"9923","No","No",625.870177750823,53756.0440632584
"9924","No","No",751.366627619233,31574.4211611568
"9925","No","No",1074.5145384901,41545.6835352068
"9926","No","Yes",501.058247569098,23090.9971921175
"9927","No","No",876.474860305473,39499.2768099668
"9928","No","Yes",580.351481416073,17624.1589132222
"9929","No","No",560.297082198125,28419.3543789813
"9930","No","Yes",364.173414588991,21487.644097097
"9931","No","No",898.89572800124,41268.312610849
"9932","No","Yes",705.930232147799,19891.3037808484
"9933","No","No",846.419381894194,36115.7620857935
"9934","No","No",813.364070539993,44707.6770312609
"9935","No","No",528.882015990961,44538.5514600982
"9936","No","No",1486.20106810863,51021.6143568027
"9937","No","No",696.465751345071,37599.6201297167
"9938","No","Yes",1775.58170109373,13735.1908717439
"9939","No","No",319.60236937232,53135.3670426105
"9940","No","No",879.035415033513,52945.8189085451
"9941","No","Yes",421.957264818512,21744.9742361818
"9942","No","No",449.312927967573,47494.4324934018
"9943","No","No",611.179743925803,38712.3942626891
"9944","No","No",461.75210433955,37617.0436674515
"9945","No","Yes",1478.60087988669,13375.0157233804
"9946","No","No",1105.46742506561,32371.4011676102
"9947","No","Yes",734.459199314855,17619.4871763725
"9948","No","No",583.307443674091,37118.8306627309
"9949","No","No",456.710814525515,21892.177211324
"9950","Yes","No",1750.25315038805,51578.9401632488
"9951","No","No",879.624128238925,33682.1861575423
"9952","Yes","No",1515.60623850741,48688.5120858599
"9953","No","No",13.9444746256728,31071.6342873547
"9954","No","Yes",776.544280000297,14229.7281237904
"9955","No","Yes",865.906315848438,18064.243987294
"9956","No","Yes",692.356317768419,18689.0521414683
"9957","No","No",415.76825935316,38425.7594997553
"9958","No","No",1765.9908949866,47642.4221201503
"9959","No","No",1391.03387680895,53255.0211240433
"9960","No","No",0,53946.2336524856
"9961","No","Yes",826.741212842474,18856.9000299931
"9962","No","No",896.721924827569,48703.4129743348
"9963","No","Yes",1635.17512227273,13518.9306215118
"9964","No","No",453.648232351194,32178.6221698724
"9965","No","No",789.551178485739,30777.8389985845
"9966","No","Yes",905.142369281084,13485.5142357004
"9967","No","No",1439.11879366717,20187.7876921826
"9968","No","No",1092.53077482295,43482.7882015413
"9969","No","Yes",871.750774173528,14247.9468496142
"9970","No","No",991.335394429677,44445.5789158122
"9971","No","Yes",1294.50040763618,25687.3260499738
"9972","No","Yes",180.620128036497,20975.5604951023
"9973","No","No",755.432800695292,14455.8653647921
"9974","No","No",876.119027014223,37668.3667878908
"9975","No","Yes",933.332024817394,26051.3983197067
"9976","No","No",908.315933521783,21287.9424874118
"9977","No","No",218.417559204658,25401.1331213124
"9978","No","Yes",915.439827443333,16624.3391107753
"9979","Yes","No",2202.46239490796,47287.2571079624
"9980","No","No",173.249171660872,30697.2450619626
"9981","No","Yes",770.01574072709,13684.7899518481
"9982","No","No",739.418017847965,40656.9514480558
"9983","No","No",623.526118935649,59441.3099813585
"9984","No","No",506.625453519083,49861.0034106156
"9985","No","No",875.241640429435,52861.7441970002
"9986","No","No",842.949429303309,39957.1278550817
"9987","No","Yes",401.332673531641,15332.0178326495
"9988","No","No",1092.90658305201,45479.4669852726
"9989","No","No",0,41740.6865972451
"9990","No","Yes",999.281111981561,20013.3506439163
"9991","No","No",372.379238540558,25374.8990852495
"9992","No","No",658.799558170067,54802.078221336
"9993","No","No",1111.64731685838,45490.6824625512
"9994","No","No",938.83624142578,56633.4487439326
"9995","No","Yes",172.412987480205,14955.9416889885
"9996","No","No",711.555020492185,52992.3789139665
"9997","No","No",757.962918447896,19660.721767987
"9998","No","No",845.411989217448,58636.1569838071
"9999","No","No",1569.00905338372,36669.1123645833
"10000","No","Yes",200.922182634797,16862.9523209407


================================================
FILE: data/clscredit.csv
================================================
agegp,income,student,creditrating,buypc
Youth,High,No,Fair,No
Youth,High,No,Excellent,No
Middle ,High,No,Fair,Yes
Senior,Medium,No,Fair,Yes
Senior,Low,Yes,Fair,Yes
Middle ,Low,Yes,Excellent,No
Youth,Low,Yes,Excellent,Yes
Youth,Low,Yes,Excellent,Yes
Senior,Medium,No,Fair,No
Youth,Low,Yes,Fair,Yes
Middle ,Medium,Yes,Fair,Yes
Middle ,Medium,Yes,Excellent,Yes
Middle ,Medium,No,Excellent,Yes
Senior,Medium,No,Excellent,No

================================================
FILE: data/clsplay.csv
================================================
day,outlook,temperature,humidity,wind,play
D1,Sunny,Hot,High,Weak,No
D2,Sunny,Hot,High,Strong,No
D3,Overcast,Hot,High,Weak,Yes
D4,Rain,Mild,High,Weak,Yes
D5,Rain,Cool,Normal,Weak,Yes
D6,Rain,Cool,Normal,Strong,No
D7,Overcast,Cool,Normal,Strong,Yes
D8,Sunny,Mild,High,Weak,No
D9,Sunny,Cool,Normal,Weak,Yes
D10,Rain,Mild,Normal,Weak,Yes
D11,Sunny,Mild,Normal,Strong,Yes
D12,Overcast,Mild,High,Strong,Yes
D13,Overcast,Hot,Normal,Weak,Yes
D14,Rain,Mild,High,Strong,No

================================================
FILE: data/clust_custseg.csv
================================================
Channel,Region,Fresh,Milk,Grocery,Frozen,Detergents_Paper,Delicassen
2,3,12669,9656,7561,214,2674,1338
2,3,7057,9810,9568,1762,3293,1776
2,3,6353,8808,7684,2405,3516,7844
1,3,13265,1196,4221,6404,507,1788
2,3,22615,5410,7198,3915,1777,5185
2,3,9413,8259,5126,666,1795,1451
2,3,12126,3199,6975,480,3140,545
2,3,7579,4956,9426,1669,3321,2566
1,3,5963,3648,6192,425,1716,750
2,3,6006,11093,18881,1159,7425,2098
2,3,3366,5403,12974,4400,5977,1744
2,3,13146,1124,4523,1420,549,497
2,3,31714,12319,11757,287,3881,2931
2,3,21217,6208,14982,3095,6707,602
2,3,24653,9465,12091,294,5058,2168
1,3,10253,1114,3821,397,964,412
2,3,1020,8816,12121,134,4508,1080
1,3,5876,6157,2933,839,370,4478
2,3,18601,6327,10099,2205,2767,3181
1,3,7780,2495,9464,669,2518,501
2,3,17546,4519,4602,1066,2259,2124
1,3,5567,871,2010,3383,375,569
1,3,31276,1917,4469,9408,2381,4334
2,3,26373,36423,22019,5154,4337,16523
2,3,22647,9776,13792,2915,4482,5778
2,3,16165,4230,7595,201,4003,57
1,3,9898,961,2861,3151,242,833
1,3,14276,803,3045,485,100,518
2,3,4113,20484,25957,1158,8604,5206
1,3,43088,2100,2609,1200,1107,823
1,3,18815,3610,11107,1148,2134,2963
1,3,2612,4339,3133,2088,820,985
1,3,21632,1318,2886,266,918,405
1,3,29729,4786,7326,6130,361,1083
1,3,1502,1979,2262,425,483,395
2,3,688,5491,11091,833,4239,436
1,3,29955,4362,5428,1729,862,4626
2,3,15168,10556,12477,1920,6506,714
2,3,4591,15729,16709,33,6956,433
1,3,56159,555,902,10002,212,2916
1,3,24025,4332,4757,9510,1145,5864
1,3,19176,3065,5956,2033,2575,2802
2,3,10850,7555,14961,188,6899,46
2,3,630,11095,23998,787,9529,72
2,3,9670,7027,10471,541,4618,65
2,3,5181,22044,21531,1740,7353,4985
2,3,3103,14069,21955,1668,6792,1452
2,3,44466,54259,55571,7782,24171,6465
2,3,11519,6152,10868,584,5121,1476
2,3,4967,21412,28921,1798,13583,1163
1,3,6269,1095,1980,3860,609,2162
1,3,3347,4051,6996,239,1538,301
2,3,40721,3916,5876,532,2587,1278
2,3,491,10473,11532,744,5611,224
1,3,27329,1449,1947,2436,204,1333
1,3,5264,3683,5005,1057,2024,1130
2,3,4098,29892,26866,2616,17740,1340
2,3,5417,9933,10487,38,7572,1282
1,3,13779,1970,1648,596,227,436
1,3,6137,5360,8040,129,3084,1603
2,3,8590,3045,7854,96,4095,225
2,3,35942,38369,59598,3254,26701,2017
2,3,7823,6245,6544,4154,4074,964
2,3,9396,11601,15775,2896,7677,1295
1,3,4760,1227,3250,3724,1247,1145
2,3,85,20959,45828,36,24231,1423
1,3,9,1534,7417,175,3468,27
2,3,19913,6759,13462,1256,5141,834
1,3,2446,7260,3993,5870,788,3095
1,3,8352,2820,1293,779,656,144
1,3,16705,2037,3202,10643,116,1365
1,3,18291,1266,21042,5373,4173,14472
1,3,4420,5139,2661,8872,1321,181
2,3,19899,5332,8713,8132,764,648
2,3,8190,6343,9794,1285,1901,1780
1,3,20398,1137,3,4407,3,975
1,3,717,3587,6532,7530,529,894
2,3,12205,12697,28540,869,12034,1009
1,3,10766,1175,2067,2096,301,167
1,3,1640,3259,3655,868,1202,1653
1,3,7005,829,3009,430,610,529
2,3,219,9540,14403,283,7818,156
2,3,10362,9232,11009,737,3537,2342
1,3,20874,1563,1783,2320,550,772
2,3,11867,3327,4814,1178,3837,120
2,3,16117,46197,92780,1026,40827,2944
2,3,22925,73498,32114,987,20070,903
1,3,43265,5025,8117,6312,1579,14351
1,3,7864,542,4042,9735,165,46
1,3,24904,3836,5330,3443,454,3178
1,3,11405,596,1638,3347,69,360
1,3,12754,2762,2530,8693,627,1117
2,3,9198,27472,32034,3232,18906,5130
1,3,11314,3090,2062,35009,71,2698
2,3,5626,12220,11323,206,5038,244
1,3,3,2920,6252,440,223,709
2,3,23,2616,8118,145,3874,217
1,3,403,254,610,774,54,63
1,3,503,112,778,895,56,132
1,3,9658,2182,1909,5639,215,323
2,3,11594,7779,12144,3252,8035,3029
2,3,1420,10810,16267,1593,6766,1838
2,3,2932,6459,7677,2561,4573,1386
1,3,56082,3504,8906,18028,1480,2498
1,3,14100,2132,3445,1336,1491,548
1,3,15587,1014,3970,910,139,1378
2,3,1454,6337,10704,133,6830,1831
2,3,8797,10646,14886,2471,8969,1438
2,3,1531,8397,6981,247,2505,1236
2,3,1406,16729,28986,673,836,3
1,3,11818,1648,1694,2276,169,1647
2,3,12579,11114,17569,805,6457,1519
1,3,19046,2770,2469,8853,483,2708
1,3,14438,2295,1733,3220,585,1561
1,3,18044,1080,2000,2555,118,1266
1,3,11134,793,2988,2715,276,610
1,3,11173,2521,3355,1517,310,222
1,3,6990,3880,5380,1647,319,1160
1,3,20049,1891,2362,5343,411,933
1,3,8258,2344,2147,3896,266,635
1,3,17160,1200,3412,2417,174,1136
1,3,4020,3234,1498,2395,264,255
1,3,12212,201,245,1991,25,860
2,3,11170,10769,8814,2194,1976,143
1,3,36050,1642,2961,4787,500,1621
1,3,76237,3473,7102,16538,778,918
1,3,19219,1840,1658,8195,349,483
2,3,21465,7243,10685,880,2386,2749
1,3,140,8847,3823,142,1062,3
1,3,42312,926,1510,1718,410,1819
1,3,7149,2428,699,6316,395,911
1,3,2101,589,314,346,70,310
1,3,14903,2032,2479,576,955,328
1,3,9434,1042,1235,436,256,396
1,3,7388,1882,2174,720,47,537
1,3,6300,1289,2591,1170,199,326
1,3,4625,8579,7030,4575,2447,1542
1,3,3087,8080,8282,661,721,36
1,3,13537,4257,5034,155,249,3271
1,3,5387,4979,3343,825,637,929
1,3,17623,4280,7305,2279,960,2616
1,3,30379,13252,5189,321,51,1450
1,3,37036,7152,8253,2995,20,3
1,3,10405,1596,1096,8425,399,318
1,3,18827,3677,1988,118,516,201
2,3,22039,8384,34792,42,12591,4430
1,3,7769,1936,2177,926,73,520
1,3,9203,3373,2707,1286,1082,526
1,3,5924,584,542,4052,283,434
1,3,31812,1433,1651,800,113,1440
1,3,16225,1825,1765,853,170,1067
1,3,1289,3328,2022,531,255,1774
1,3,18840,1371,3135,3001,352,184
1,3,3463,9250,2368,779,302,1627
1,3,622,55,137,75,7,8
2,3,1989,10690,19460,233,11577,2153
2,3,3830,5291,14855,317,6694,3182
1,3,17773,1366,2474,3378,811,418
2,3,2861,6570,9618,930,4004,1682
2,3,355,7704,14682,398,8077,303
2,3,1725,3651,12822,824,4424,2157
1,3,12434,540,283,1092,3,2233
1,3,15177,2024,3810,2665,232,610
2,3,5531,15726,26870,2367,13726,446
2,3,5224,7603,8584,2540,3674,238
2,3,15615,12653,19858,4425,7108,2379
2,3,4822,6721,9170,993,4973,3637
1,3,2926,3195,3268,405,1680,693
1,3,5809,735,803,1393,79,429
1,3,5414,717,2155,2399,69,750
2,3,260,8675,13430,1116,7015,323
2,3,200,25862,19816,651,8773,6250
1,3,955,5479,6536,333,2840,707
2,3,514,7677,19805,937,9836,716
1,3,286,1208,5241,2515,153,1442
2,3,2343,7845,11874,52,4196,1697
1,3,45640,6958,6536,7368,1532,230
1,3,12759,7330,4533,1752,20,2631
1,3,11002,7075,4945,1152,120,395
1,3,3157,4888,2500,4477,273,2165
1,3,12356,6036,8887,402,1382,2794
1,3,112151,29627,18148,16745,4948,8550
1,3,694,8533,10518,443,6907,156
1,3,36847,43950,20170,36534,239,47943
1,3,327,918,4710,74,334,11
1,3,8170,6448,1139,2181,58,247
1,3,3009,521,854,3470,949,727
1,3,2438,8002,9819,6269,3459,3
2,3,8040,7639,11687,2758,6839,404
2,3,834,11577,11522,275,4027,1856
1,3,16936,6250,1981,7332,118,64
1,3,13624,295,1381,890,43,84
1,3,5509,1461,2251,547,187,409
2,3,180,3485,20292,959,5618,666
1,3,7107,1012,2974,806,355,1142
1,3,17023,5139,5230,7888,330,1755
1,1,30624,7209,4897,18711,763,2876
2,1,2427,7097,10391,1127,4314,1468
1,1,11686,2154,6824,3527,592,697
1,1,9670,2280,2112,520,402,347
2,1,3067,13240,23127,3941,9959,731
2,1,4484,14399,24708,3549,14235,1681
1,1,25203,11487,9490,5065,284,6854
1,1,583,685,2216,469,954,18
1,1,1956,891,5226,1383,5,1328
2,1,1107,11711,23596,955,9265,710
1,1,6373,780,950,878,288,285
2,1,2541,4737,6089,2946,5316,120
1,1,1537,3748,5838,1859,3381,806
2,1,5550,12729,16767,864,12420,797
1,1,18567,1895,1393,1801,244,2100
2,1,12119,28326,39694,4736,19410,2870
1,1,7291,1012,2062,1291,240,1775
1,1,3317,6602,6861,1329,3961,1215
2,1,2362,6551,11364,913,5957,791
1,1,2806,10765,15538,1374,5828,2388
2,1,2532,16599,36486,179,13308,674
1,1,18044,1475,2046,2532,130,1158
2,1,18,7504,15205,1285,4797,6372
1,1,4155,367,1390,2306,86,130
1,1,14755,899,1382,1765,56,749
1,1,5396,7503,10646,91,4167,239
1,1,5041,1115,2856,7496,256,375
2,1,2790,2527,5265,5612,788,1360
1,1,7274,659,1499,784,70,659
1,1,12680,3243,4157,660,761,786
2,1,20782,5921,9212,1759,2568,1553
1,1,4042,2204,1563,2286,263,689
1,1,1869,577,572,950,4762,203
1,1,8656,2746,2501,6845,694,980
2,1,11072,5989,5615,8321,955,2137
1,1,2344,10678,3828,1439,1566,490
1,1,25962,1780,3838,638,284,834
1,1,964,4984,3316,937,409,7
1,1,15603,2703,3833,4260,325,2563
1,1,1838,6380,2824,1218,1216,295
1,1,8635,820,3047,2312,415,225
1,1,18692,3838,593,4634,28,1215
1,1,7363,475,585,1112,72,216
1,1,47493,2567,3779,5243,828,2253
1,1,22096,3575,7041,11422,343,2564
1,1,24929,1801,2475,2216,412,1047
1,1,18226,659,2914,3752,586,578
1,1,11210,3576,5119,561,1682,2398
1,1,6202,7775,10817,1183,3143,1970
2,1,3062,6154,13916,230,8933,2784
1,1,8885,2428,1777,1777,430,610
1,1,13569,346,489,2077,44,659
1,1,15671,5279,2406,559,562,572
1,1,8040,3795,2070,6340,918,291
1,1,3191,1993,1799,1730,234,710
2,1,6134,23133,33586,6746,18594,5121
1,1,6623,1860,4740,7683,205,1693
1,1,29526,7961,16966,432,363,1391
1,1,10379,17972,4748,4686,1547,3265
1,1,31614,489,1495,3242,111,615
1,1,11092,5008,5249,453,392,373
1,1,8475,1931,1883,5004,3593,987
1,1,56083,4563,2124,6422,730,3321
1,1,53205,4959,7336,3012,967,818
1,1,9193,4885,2157,327,780,548
1,1,7858,1110,1094,6818,49,287
1,1,23257,1372,1677,982,429,655
1,1,2153,1115,6684,4324,2894,411
2,1,1073,9679,15445,61,5980,1265
1,1,5909,23527,13699,10155,830,3636
2,1,572,9763,22182,2221,4882,2563
1,1,20893,1222,2576,3975,737,3628
2,1,11908,8053,19847,1069,6374,698
1,1,15218,258,1138,2516,333,204
1,1,4720,1032,975,5500,197,56
1,1,2083,5007,1563,1120,147,1550
1,1,514,8323,6869,529,93,1040
1,3,36817,3045,1493,4802,210,1824
1,3,894,1703,1841,744,759,1153
1,3,680,1610,223,862,96,379
1,3,27901,3749,6964,4479,603,2503
1,3,9061,829,683,16919,621,139
1,3,11693,2317,2543,5845,274,1409
2,3,17360,6200,9694,1293,3620,1721
1,3,3366,2884,2431,977,167,1104
2,3,12238,7108,6235,1093,2328,2079
1,3,49063,3965,4252,5970,1041,1404
1,3,25767,3613,2013,10303,314,1384
1,3,68951,4411,12609,8692,751,2406
1,3,40254,640,3600,1042,436,18
1,3,7149,2247,1242,1619,1226,128
1,3,15354,2102,2828,8366,386,1027
1,3,16260,594,1296,848,445,258
1,3,42786,286,471,1388,32,22
1,3,2708,2160,2642,502,965,1522
1,3,6022,3354,3261,2507,212,686
1,3,2838,3086,4329,3838,825,1060
2,2,3996,11103,12469,902,5952,741
1,2,21273,2013,6550,909,811,1854
2,2,7588,1897,5234,417,2208,254
1,2,19087,1304,3643,3045,710,898
2,2,8090,3199,6986,1455,3712,531
2,2,6758,4560,9965,934,4538,1037
1,2,444,879,2060,264,290,259
2,2,16448,6243,6360,824,2662,2005
2,2,5283,13316,20399,1809,8752,172
2,2,2886,5302,9785,364,6236,555
2,2,2599,3688,13829,492,10069,59
2,2,161,7460,24773,617,11783,2410
2,2,243,12939,8852,799,3909,211
2,2,6468,12867,21570,1840,7558,1543
1,2,17327,2374,2842,1149,351,925
1,2,6987,1020,3007,416,257,656
2,2,918,20655,13567,1465,6846,806
1,2,7034,1492,2405,12569,299,1117
1,2,29635,2335,8280,3046,371,117
2,2,2137,3737,19172,1274,17120,142
1,2,9784,925,2405,4447,183,297
1,2,10617,1795,7647,1483,857,1233
2,2,1479,14982,11924,662,3891,3508
1,2,7127,1375,2201,2679,83,1059
1,2,1182,3088,6114,978,821,1637
1,2,11800,2713,3558,2121,706,51
2,2,9759,25071,17645,1128,12408,1625
1,2,1774,3696,2280,514,275,834
1,2,9155,1897,5167,2714,228,1113
1,2,15881,713,3315,3703,1470,229
1,2,13360,944,11593,915,1679,573
1,2,25977,3587,2464,2369,140,1092
1,2,32717,16784,13626,60869,1272,5609
1,2,4414,1610,1431,3498,387,834
1,2,542,899,1664,414,88,522
1,2,16933,2209,3389,7849,210,1534
1,2,5113,1486,4583,5127,492,739
1,2,9790,1786,5109,3570,182,1043
2,2,11223,14881,26839,1234,9606,1102
1,2,22321,3216,1447,2208,178,2602
2,2,8565,4980,67298,131,38102,1215
2,2,16823,928,2743,11559,332,3486
2,2,27082,6817,10790,1365,4111,2139
1,2,13970,1511,1330,650,146,778
1,2,9351,1347,2611,8170,442,868
1,2,3,333,7021,15601,15,550
1,2,2617,1188,5332,9584,573,1942
2,3,381,4025,9670,388,7271,1371
2,3,2320,5763,11238,767,5162,2158
1,3,255,5758,5923,349,4595,1328
2,3,1689,6964,26316,1456,15469,37
1,3,3043,1172,1763,2234,217,379
1,3,1198,2602,8335,402,3843,303
2,3,2771,6939,15541,2693,6600,1115
2,3,27380,7184,12311,2809,4621,1022
1,3,3428,2380,2028,1341,1184,665
2,3,5981,14641,20521,2005,12218,445
1,3,3521,1099,1997,1796,173,995
2,3,1210,10044,22294,1741,12638,3137
1,3,608,1106,1533,830,90,195
2,3,117,6264,21203,228,8682,1111
1,3,14039,7393,2548,6386,1333,2341
1,3,190,727,2012,245,184,127
1,3,22686,134,218,3157,9,548
2,3,37,1275,22272,137,6747,110
1,3,759,18664,1660,6114,536,4100
1,3,796,5878,2109,340,232,776
1,3,19746,2872,2006,2601,468,503
1,3,4734,607,864,1206,159,405
1,3,2121,1601,2453,560,179,712
1,3,4627,997,4438,191,1335,314
1,3,2615,873,1524,1103,514,468
2,3,4692,6128,8025,1619,4515,3105
1,3,9561,2217,1664,1173,222,447
1,3,3477,894,534,1457,252,342
1,3,22335,1196,2406,2046,101,558
1,3,6211,337,683,1089,41,296
2,3,39679,3944,4955,1364,523,2235
1,3,20105,1887,1939,8164,716,790
1,3,3884,3801,1641,876,397,4829
2,3,15076,6257,7398,1504,1916,3113
1,3,6338,2256,1668,1492,311,686
1,3,5841,1450,1162,597,476,70
2,3,3136,8630,13586,5641,4666,1426
1,3,38793,3154,2648,1034,96,1242
1,3,3225,3294,1902,282,68,1114
2,3,4048,5164,10391,130,813,179
1,3,28257,944,2146,3881,600,270
1,3,17770,4591,1617,9927,246,532
1,3,34454,7435,8469,2540,1711,2893
1,3,1821,1364,3450,4006,397,361
1,3,10683,21858,15400,3635,282,5120
1,3,11635,922,1614,2583,192,1068
1,3,1206,3620,2857,1945,353,967
1,3,20918,1916,1573,1960,231,961
1,3,9785,848,1172,1677,200,406
1,3,9385,1530,1422,3019,227,684
1,3,3352,1181,1328,5502,311,1000
1,3,2647,2761,2313,907,95,1827
1,3,518,4180,3600,659,122,654
1,3,23632,6730,3842,8620,385,819
1,3,12377,865,3204,1398,149,452
1,3,9602,1316,1263,2921,841,290
2,3,4515,11991,9345,2644,3378,2213
1,3,11535,1666,1428,6838,64,743
1,3,11442,1032,582,5390,74,247
1,3,9612,577,935,1601,469,375
1,3,4446,906,1238,3576,153,1014
1,3,27167,2801,2128,13223,92,1902
1,3,26539,4753,5091,220,10,340
1,3,25606,11006,4604,127,632,288
1,3,18073,4613,3444,4324,914,715
1,3,6884,1046,1167,2069,593,378
1,3,25066,5010,5026,9806,1092,960
2,3,7362,12844,18683,2854,7883,553
2,3,8257,3880,6407,1646,2730,344
1,3,8708,3634,6100,2349,2123,5137
1,3,6633,2096,4563,1389,1860,1892
1,3,2126,3289,3281,1535,235,4365
1,3,97,3605,12400,98,2970,62
1,3,4983,4859,6633,17866,912,2435
1,3,5969,1990,3417,5679,1135,290
2,3,7842,6046,8552,1691,3540,1874
2,3,4389,10940,10908,848,6728,993
1,3,5065,5499,11055,364,3485,1063
2,3,660,8494,18622,133,6740,776
1,3,8861,3783,2223,633,1580,1521
1,3,4456,5266,13227,25,6818,1393
2,3,17063,4847,9053,1031,3415,1784
1,3,26400,1377,4172,830,948,1218
2,3,17565,3686,4657,1059,1803,668
2,3,16980,2884,12232,874,3213,249
1,3,11243,2408,2593,15348,108,1886
1,3,13134,9347,14316,3141,5079,1894
1,3,31012,16687,5429,15082,439,1163
1,3,3047,5970,4910,2198,850,317
1,3,8607,1750,3580,47,84,2501
1,3,3097,4230,16483,575,241,2080
1,3,8533,5506,5160,13486,1377,1498
1,3,21117,1162,4754,269,1328,395
1,3,1982,3218,1493,1541,356,1449
1,3,16731,3922,7994,688,2371,838
1,3,29703,12051,16027,13135,182,2204
1,3,39228,1431,764,4510,93,2346
2,3,14531,15488,30243,437,14841,1867
1,3,10290,1981,2232,1038,168,2125
1,3,2787,1698,2510,65,477,52


================================================
FILE: data/dar1.csv
================================================
java,cpp,dbms,dwm,cbnst,os,grade
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,No,Yes,No,No,Fail
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,No,Yes,Yes,Pass
Yes,Yes,Yes,Yes,No,No,Pass
No,No,No,No,No,Yes,Fail
Yes,Yes,Yes,Yes,No,No,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
No,Yes,No,Yes,No,No,Fail
No,No,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,No,Yes,Yes,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
No,No,No,No,No,No,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,No,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,Yes,No,No,No,Fail
No,Yes,Yes,Yes,No,No,Pass
No,No,Yes,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,Yes,Yes,Pass
No,No,No,No,Yes,No,Fail
No,No,Yes,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,No,No,Yes,No,No,Fail
No,Yes,No,No,No,No,Fail
No,No,No,Yes,Yes,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,Yes,No,No,No,Fail
No,No,No,Yes,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,No,No,No,Yes,Fail

================================================
FILE: data/dar1w.csv
================================================
"java","cpp","dbms","dwm","cbnst","os","grade"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","No","No","Yes","No","No","Fail"
"No","Yes","No","No","No","No","Fail"
"Yes","Yes","Yes","No","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","No","No","Pass"
"No","No","No","No","No","Yes","Fail"
"Yes","Yes","Yes","Yes","No","No","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","Yes","No","No","No","No","Fail"
"No","Yes","No","Yes","No","No","Fail"
"No","No","No","No","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","No","Yes","Yes","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","Yes","No","No","No","No","Fail"
"No","No","No","No","No","No","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","No","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","No","Yes","No","No","No","Fail"
"No","Yes","Yes","Yes","No","No","Pass"
"No","No","Yes","No","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","Yes","No","No","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","Yes","No","No","Yes","Yes","Pass"
"No","No","No","No","Yes","No","Fail"
"No","No","Yes","No","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","Yes","No","No","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","No","No","Yes","No","No","Fail"
"No","Yes","No","No","No","No","Fail"
"No","No","No","Yes","Yes","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","No","Yes","No","No","No","Fail"
"No","No","No","Yes","No","No","Fail"
"Yes","Yes","Yes","Yes","Yes","Yes","Pass"
"No","No","No","No","No","Yes","Fail"


================================================
FILE: data/dar1w.csv.arff
================================================
@relation dar1w

@attribute java {Yes,No}
@attribute cpp {Yes,No}
@attribute dbms {Yes,No}
@attribute dwm {Yes,No}
@attribute cbnst {Yes,No}
@attribute os {Yes,No}
@attribute grade {Pass,Fail}

@data
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,No,Yes,No,No,Fail
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,No,Yes,Yes,Pass
Yes,Yes,Yes,Yes,No,No,Pass
No,No,No,No,No,Yes,Fail
Yes,Yes,Yes,Yes,No,No,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
No,Yes,No,Yes,No,No,Fail
No,No,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,No,Yes,Yes,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
No,No,No,No,No,No,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,No,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,Yes,No,No,No,Fail
No,Yes,Yes,Yes,No,No,Pass
No,No,Yes,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,Yes,Yes,Pass
No,No,No,No,Yes,No,Fail
No,No,Yes,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,Yes,No,No,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,No,No,Yes,No,No,Fail
No,Yes,No,No,No,No,Fail
No,No,No,Yes,Yes,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,Yes,No,No,No,Fail
No,No,No,Yes,No,No,Fail
Yes,Yes,Yes,Yes,Yes,Yes,Pass
No,No,No,No,No,Yes,Fail


================================================
FILE: data/dar2.csv
================================================
studentid,comnskills,englishspk,acadperf,relocwilling,eyecontact,extraco,gender,select
s01,1,1,1,1,1,1,1,1
s02,1,1,1,1,1,1,1,1
s03,0,0,0,0,0,0,0,0
s04,0,1,0,0,0,0,0,0
s05,1,1,1,0,1,1,1,1
s06,1,1,1,1,0,0,0,0
s07,0,0,0,0,0,0,0,0
s08,1,1,1,1,0,0,0,1
s09,1,1,1,1,1,1,1,1
s10,0,1,0,0,0,0,0,0
s11,0,1,0,1,0,0,0,0
s12,0,0,0,0,0,0,0,0
s13,1,1,1,1,1,1,1,1
s14,1,1,0,1,1,0,0,0
s15,1,1,1,0,1,0,0,1
s16,1,1,0,0,1,1,1,1
s17,1,0,0,1,1,1,1,0
s18,0,0,0,0,1,1,0,0
s19,0,0,0,0,0,0,1,0
s20,1,0,1,1,1,1,1,1
s21,1,1,0,1,1,1,1,0
s22,1,1,1,0,1,1,1,1
s23,1,1,1,1,0,0,1,1
s24,1,1,1,1,1,1,0,0
s25,0,0,0,1,0,0,0,0
s26,0,1,1,1,0,0,0,0
s27,0,0,1,0,0,0,0,0
s28,1,1,1,1,0,1,1,1
s29,1,1,1,1,1,0,1,1
s30,1,1,1,1,0,1,1,1
s31,1,1,1,0,1,1,1,1
s32,1,0,0,1,1,1,1,0
s33,0,0,0,0,0,0,1,0
s34,1,1,1,1,1,1,1,1
s35,1,1,1,1,1,1,1,1
s36,0,1,0,0,1,1,1,0
s37,0,0,0,0,0,0,0,0
s38,0,0,0,0,0,0,0,0
s39,1,1,0,1,1,1,1,1
s40,0,0,0,0,0,0,0,0
s41,1,0,1,1,1,1,1,1
s42,1,0,0,0,0,0,0,0
s43,0,0,0,0,1,0,0,0
s44,0,1,0,0,0,0,1,0
s45,1,1,0,1,1,1,1,1
s46,1,1,1,0,1,0,1,0
s47,0,0,0,0,0,0,0,0
s48,0,0,1,0,0,1,0,0
s49,0,1,1,0,1,1,1,0
s50,0,0,0,0,0,1,0,0

================================================
FILE: data/dar3.csv
================================================
bigdata,mobcomp,cloudcomp,patternrecogn,adbms,crypto
Yes,Yes,Yes,,,
Yes,,,,Yes,Yes
,Yes,Yes,Yes,,
,Yes,Yes,Yes,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
,Yes,Yes,Yes,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
,Yes,,Yes,Yes,
,Yes,,Yes,Yes,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,,Yes,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
,,,Yes,Yes,Yes
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,,,,Yes
Yes,,,,Yes,Yes
,,,Yes,Yes,Yes
Yes,Yes,Yes,,,
,Yes,Yes,Yes,,
,Yes,Yes,Yes,,
Yes,Yes,Yes,,,
Yes,Yes,,,,Yes
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
,,,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
,Yes,,,Yes,Yes
,,,,,
,,,,,
Yes,Yes,Yes,,,
,,,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,Yes,Yes,Yes
Yes,Yes,Yes,Yes,Yes,Yes
,Yes,Yes,Yes,,
,Yes,Yes,Yes,,
Yes,Yes,Yes,,,
Yes,Yes,Yes,,,

================================================
FILE: data/dar3a.csv
================================================
"s01,bigdata,mobcomp,cloudcomp"
"s02,mobcomp,patternrecogn,adbms"
"s03,mobcomp,patternrecogn,adbms"
"s04,bigdata,mobcomp,cloudcomp"
"s05,bigdata,mobcomp,cloudcomp"
"s06,bigdata,mobcomp,patternrecogn"
"s07,bigdata,mobcomp,cloudcomp"
"s08,bigdata,mobcomp,cloudcomp"
"s09,bigdata,mobcomp,cloudcomp"
"s10,patternrecogn,adbms,crypto"
"s11,bigdata,mobcomp,cloudcomp"
"s12,bigdata,adbms,crypto"
"s13,bigdata,mobcomp,cloudcomp"
"s14,bigdata,mobcomp,cloudcomp"
"s15,bigdata,mobcomp,crypto"
"s16,bigdata,adbms,crypto"
"s17,patternrecogn,adbms,crypto"
"s18,bigdata,mobcomp,cloudcomp"
"s19,mobcomp,cloudcomp,patternrecogn"
"s20,mobcomp,cloudcomp,patternrecogn"
"s21,bigdata,mobcomp,cloudcomp"
"s22,bigdata,mobcomp,crypto"
"s23,mobcomp,cloudcomp,patternrecogn"
"s24,bigdata,mobcomp,cloudcomp"
"s25,bigdata,mobcomp,cloudcomp"
"s26,bigdata,mobcomp,cloudcomp"
"s27,cloudcomp,patternrecogn,adbms"
"s28,bigdata,mobcomp,cloudcomp"
"s29,bigdata,mobcomp,cloudcomp"
"s30,mobcomp,adbms,crypto"
"s31,cloudcomp,patternrecogn,adbms"
"s32,cloudcomp,patternrecogn,adbms"
"s33,bigdata,mobcomp,cloudcomp"
"s34,mobcomp,cloudcomp,patternrecogn"
"s35,cloudcomp,patternrecogn,adbms"
"s36,bigdata,mobcomp,cloudcomp"
"s37,bigdata,mobcomp,cloudcomp"
"s38,bigdata,mobcomp,cloudcomp"
"s39,bigdata,mobcomp,cloudcomp"
"s40,bigdata,mobcomp,cloudcomp"
"s41,bigdata,mobcomp,crypto"
"s42,mobcomp,cloudcomp,patternrecogn"
"s43,mobcomp,cloudcomp,patternrecogn"
"s44,bigdata,mobcomp,cloudcomp"
"s45,bigdata,mobcomp,cloudcomp"
"s46,bigdata,mobcomp,cloudcomp"
"s47,bigdata,mobcomp,cloudcomp"
"s48,mobcomp,cloudcomp,patternrecogn"
"s49,bigdata,mobcomp,cloudcomp"
"s50,bigdata,mobcomp,cloudcomp"

================================================
FILE: data/dar3b.csv
================================================
studentch,elective1,elective2,elective3,elective4,elective5,elective6
c01,1,1,1,1,1,1
c02,1,1,1,1,1,1
c03,0,0,0,0,0,0
c04,0,1,0,0,0,0
c05,1,1,1,0,1,1
c06,1,1,1,1,0,0
c07,0,0,0,0,0,0
c08,1,1,1,1,0,0
c09,1,1,1,1,1,1
c10,0,1,0,0,0,0
c11,0,1,0,1,0,0
c12,0,0,0,0,0,0
c13,1,1,1,1,1,1
c14,1,1,0,1,1,0
c15,1,1,1,1,1,1
c16,1,1,1,1,1,1
c17,1,1,1,1,1,1
c18,0,0,0,0,0,0
c19,0,0,0,0,0,0
c20,1,1,1,1,1,1
c21,1,1,1,1,1,1
c22,1,1,1,1,1,1
c23,1,1,1,1,1,1
c24,1,1,1,1,1,1
c25,0,0,0,0,0,0
c26,0,1,1,1,0,0
c27,0,0,1,0,0,0
c28,1,1,1,1,1,1
c29,1,1,1,1,1,1
c30,1,1,1,1,1,1
c31,1,1,1,1,1,1
c32,1,1,1,1,1,1
c33,0,0,0,0,0,0
c34,1,1,1,1,1,1
c35,1,1,1,1,1,1
c36,0,1,0,0,1,1
c37,0,0,0,0,0,0
c38,0,0,0,0,0,0
c39,1,1,1,1,1,1
c40,0,0,0,0,0,0
c41,1,1,1,1,1,1
c42,1,0,0,0,0,0
c43,0,0,0,0,0,0
c44,0,0,0,0,0,0
c45,1,1,1,1,1,1
c46,1,1,1,1,1,1
c47,0,0,0,0,0,0
c48,0,0,0,0,0,0
c49,1,1,1,1,1,1
c50,0,0,0,0,0,0

================================================
FILE: data/data1.R
================================================
# Importing and Sorting Data
train = read.csv('data/train.csv')
test = read.csv('data/test.csv')
str(train) # has purchase coln extra
str(test)
summary(train)
summary(test)
head(train)
head(train)
y_train = train$Purchase
x_train = train$Occupation
x_test = test$Occupation



================================================
FILE: data/data4cluster2.csv
================================================
"","Cust_id","Age","Annual_Income_Lacs","Twitter User","Number of Vehicles","Gender","Married","Daily hours of work","Affluent_Lifestyle","No of Dependents","Urban_residence","Social","Outstation_Travel_last_year"
"1",9400,15,1,1,4,0,1,12,1,6,1,1,1
"2",8418,15,1.7,1,3,0,1,12,1,4,1,1,4
"3",10559,15,1.8,1,2,0,1,12,1,3,1,1,4
"4",9924,15,1.8,1,2,0,1,15,1,6,1,1,6
"5",9823,15,2.1,1,5,0,1,13,1,3,1,1,1
"6",8995,15,2.1,1,1,0,1,12,1,5,1,1,3
"7",10472,15,2.3,1,4,0,1,9,1,7,1,1,7
"8",2480,15,2.7,1,4,0,1,12,0,5,1,1,2
"9",6744,15,2.8,1,1,0,1,9,1,1,1,1,3
"10",9571,15,3.8,1,2,0,1,10,1,2,1,1,4
"11",1422,15,3.8,1,3,0,1,14,0,8,1,1,0
"12",7839,15,4,1,4,0,1,9,1,6,1,1,1
"13",9375,15,4.9,1,2,0,1,14,1,4,1,1,1
"14",1457,15,4.9,1,2,0,1,12,0,6,1,1,2
"15",1351,15,5,1,1,0,1,10,0,3,1,1,4
"16",1454,15,5.1,1,2,0,1,12,0,3,1,1,0
"17",9624,15,5.5,1,3,0,1,12,1,3,1,1,2
"18",8314,15,5.6,1,4,0,1,12,1,5,1,1,0
"19",6074,15,5.9,1,3,0,1,12,1,8,1,1,5
"20",6750,15,5.9,1,5,0,1,15,1,1,1,1,5
"21",10808,15,6.2,1,4,0,1,14,1,3,1,1,3
"22",4600,15,6.3,1,1,0,1,15,0,8,1,1,3
"23",4599,15,6.4,1,5,0,1,10,0,7,1,1,1
"24",4252,15,6.5,1,3,0,1,10,0,2,1,1,6
"25",8096,15,6.5,1,4,0,1,11,1,5,1,1,6
"26",1834,15,6.6,1,4,0,1,10,0,5,1,1,0
"27",9393,15,6.8,1,5,0,1,14,1,5,1,1,1
"28",8120,15,8.2,1,3,0,1,11,1,7,1,1,0
"29",7414,15,8.7,1,3,0,1,12,1,7,1,1,7
"30",8894,15,8.7,1,3,0,1,11,1,7,1,1,6
"31",64,15,8.9,1,2,0,1,15,0,1,1,1,5
"32",1981,15,9.1,1,2,0,1,11,0,2,1,1,1
"33",1892,15,9.3,1,2,0,1,12,0,8,1,1,5
"34",295,15,9.4,1,4,0,1,11,0,4,1,1,1
"35",1402,15,9.8,1,4,0,1,12,0,3,1,1,1
"36",490,15,10.1,1,4,0,1,12,0,7,1,1,10
"37",4071,15,10.3,1,5,0,1,14,0,6,1,1,1
"38",9132,15,10.7,1,2,0,1,13,1,6,1,1,7
"39",8642,15,11.5,1,4,0,1,15,1,4,1,1,7
"40",2113,15,11.6,1,2,0,1,11,0,8,1,1,1
"41",10273,15,11.9,1,3,0,1,12,1,3,1,1,1
"42",3134,15,11.9,1,5,0,1,12,0,2,1,1,3
"43",5000,15,12.2,1,4,0,1,11,0,3,1,1,3
"44",3961,15,12.2,1,2,0,1,10,0,6,1,1,3
"45",3621,15,12.6,1,3,0,1,14,0,6,1,1,0
"46",6450,15,12.7,1,1,0,1,12,1,2,1,1,8
"47",691,15,12.9,1,4,0,1,11,0,5,1,1,3
"48",4838,15,12.9,1,5,0,1,12,0,3,1,1,4
"49",3850,15,13.4,1,3,0,1,14,0,2,1,1,1
"50",4818,15,14,1,4,0,1,14,0,6,1,1,9
"51",1272,15,14.5,1,4,0,1,12,0,3,1,1,2
"52",6530,15,14.6,1,3,0,1,9,1,6,1,1,10
"53",4512,15,15.1,1,3,0,1,13,0,6,1,1,4
"54",3595,15,15.1,1,2,0,1,11,0,1,1,1,0
"55",5026,15,15.9,1,1,0,1,10,0,3,1,1,7
"56",4539,15,16.1,1,1,0,1,13,0,5,1,1,3
"57",7422,15,16.2,1,4,0,1,10,1,8,1,1,10
"58",6960,15,16.4,1,1,0,1,9,1,1,1,1,9
"59",1677,15,16.7,1,1,0,1,13,0,2,1,1,0
"60",1411,15,16.8,1,1,0,1,12,0,1,1,1,4
"61",10816,15,16.8,1,3,0,1,14,1,6,1,1,8
"62",3090,15,16.9,1,5,0,1,10,0,8,1,1,6
"63",642,15,17.5,1,3,0,1,10,0,2,1,1,10
"64",8174,15,17.7,1,5,0,1,9,1,2,1,1,0
"65",9297,15,18,1,2,0,1,12,1,7,1,1,1
"66",3548,15,18.3,1,5,0,1,15,0,1,1,1,3
"67",5848,15,19,1,3,0,1,13,1,4,1,1,1
"68",3370,15,19.1,1,2,0,1,12,0,1,1,1,7
"69",7932,15,19.4,1,1,0,1,11,1,3,1,1,8
"70",9477,15,19.6,1,5,0,1,10,1,2,1,1,0
"71",2332,15,20,1,5,0,1,13,0,7,1,1,2
"72",10404,15,21.1,1,3,0,1,13,1,1,1,1,7
"73",7979,15,21.3,1,5,0,1,9,1,2,1,1,3
"74",10624,15,21.5,1,4,0,1,9,1,7,1,1,7
"75",3254,15,22,1,3,0,1,14,0,6,1,1,1
"76",4632,15,22.1,1,5,0,1,12,0,2,1,1,1
"77",8384,15,22.5,1,3,0,1,12,1,4,1,1,9
"78",1340,15,22.9,1,3,0,1,13,0,7,1,1,3
"79",2684,15,23.4,1,1,0,1,12,0,1,1,1,0
"80",3111,15,23.6,1,3,0,1,15,0,3,1,1,0
"81",4425,15,23.8,1,2,0,1,14,0,6,1,1,2
"82",3044,15,24.1,1,3,0,1,10,0,5,1,1,2
"83",634,15,24.1,1,4,0,1,9,0,6,1,1,2
"84",10275,15,24.2,1,4,0,1,13,1,3,1,1,2
"85",1067,15,24.4,1,5,0,1,15,0,7,1,1,1
"86",7983,15,24.4,1,3,0,1,10,1,1,1,1,4
"87",4337,15,24.6,1,1,0,1,12,0,4,1,1,1
"88",3977,15,25.3,1,1,0,1,12,0,8,1,1,4
"89",10846,15,26.4,1,4,0,1,13,1,1,1,1,8
"90",4335,15,26.5,1,2,0,1,10,0,8,1,1,3
"91",3832,15,26.7,1,3,0,1,13,0,3,1,1,8
"92",10798,15,26.9,1,5,0,1,13,1,5,1,1,9
"93",3741,15,27.1,1,3,0,1,12,0,1,1,1,2
"94",7886,15,27.1,1,5,0,1,9,1,2,1,1,8
"95",10857,15,27.6,1,2,0,1,12,1,4,1,1,1
"96",7995,15,27.6,1,1,0,1,12,1,1,1,1,3
"97",1936,15,28.8,1,2,0,1,9,0,4,1,1,7
"98",2786,15,28.8,1,5,0,1,9,0,8,1,1,0
"99",8650,15,29.1,1,1,0,1,10,1,2,1,1,5
"100",10263,15,29.2,1,3,0,1,12,1,5,1,1,4
"101",2813,15,29.2,1,5,0,1,12,0,4,1,1,4
"102",7380,15,29.4,1,5,0,1,12,1,6,1,1,6
"103",1202,15,29.5,1,4,0,1,15,0,1,1,1,2
"104",10544,15,29.6,1,5,0,1,11,1,3,1,1,5
"105",3778,15,29.7,1,5,0,1,12,0,3,1,1,3
"106",9618,15,29.8,1,3,0,1,14,1,4,1,1,9
"107",1828,15,29.9,1,2,0,1,9,0,1,1,1,5
"108",8906,15,29.9,1,5,0,1,13,1,5,1,1,7
"109",3779,16,0.2,1,1,0,1,12,0,3,1,1,0
"110",278,16,0.6,1,1,0,1,13,0,3,1,1,4
"111",3959,16,0.8,1,1,0,1,12,0,8,1,1,3
"112",2930,16,0.8,1,5,0,1,12,0,5,1,1,1
"113",7715,16,0.8,1,5,0,1,14,1,3,1,1,0
"114",3252,16,1,1,4,0,1,15,0,4,1,1,3
"115",5869,16,1.2,1,2,0,1,9,1,4,1,1,1
"116",4666,16,1.6,1,3,0,1,12,0,4,1,1,3
"117",7625,16,1.7,1,3,0,1,15,1,3,1,1,4
"118",1094,16,1.8,1,5,0,1,15,0,6,1,1,1
"119",1502,16,1.9,1,4,0,1,10,0,1,1,1,6
"120",2398,16,1.9,1,5,0,1,10,0,1,1,1,4
"121",8129,16,2.1,1,2,0,1,9,1,4,1,1,3
"122",8972,16,2.2,1,2,0,1,15,1,2,1,1,9
"123",4776,16,2.3,1,5,0,1,11,0,3,1,1,0
"124",6652,16,2.3,1,4,0,1,13,1,8,1,1,2
"125",10744,16,2.3,1,3,0,1,12,1,7,1,1,8
"126",7234,16,2.4,1,4,0,1,12,1,8,1,1,5
"127",9596,16,2.7,1,1,0,1,14,1,2,1,1,6
"128",1756,16,2.8,1,5,0,1,12,0,8,1,1,1
"129",9960,16,2.8,1,4,0,1,12,1,8,1,1,1
"130",2310,16,3.1,1,3,0,1,9,0,7,1,1,2
"131",8782,16,3.3,1,4,0,1,12,1,6,1,1,7
"132",10022,16,3.8,1,1,0,1,15,1,1,1,1,6
"133",2564,16,4.2,1,3,0,1,9,0,5,1,1,6
"134",5834,16,4.3,1,5,0,1,10,1,8,1,1,2
"135",8488,16,4.7,1,2,0,1,10,1,5,1,1,10
"136",8813,16,5.4,1,1,0,1,9,1,4,1,1,3
"137",4394,16,6,1,2,0,1,13,0,5,1,1,5
"138",1966,16,6.3,1,5,0,1,15,0,2,1,1,0
"139",7026,16,6.6,1,3,0,1,10,1,4,1,1,10
"140",4362,16,6.7,1,1,0,1,12,0,5,1,1,4
"141",8297,16,6.9,1,4,0,1,14,1,2,1,1,0
"142",2521,16,7.1,1,2,0,1,10,0,5,1,1,3
"143",9002,16,7.3,1,1,0,1,10,1,4,1,1,9
"144",10485,16,7.4,1,1,0,1,15,1,3,1,1,1
"145",9948,16,7.4,1,1,0,1,12,1,3,1,1,2
"146",4992,16,7.5,1,5,0,1,12,0,6,1,1,1
"147",6664,16,7.8,1,3,0,1,15,1,2,1,1,10
"148",392,16,7.9,1,4,0,1,12,0,5,1,1,7
"149",5920,16,8.1,1,5,0,1,12,1,1,1,1,10
"150",10442,16,8.3,1,2,0,1,15,1,3,1,1,2
"151",8246,16,8.4,1,3,0,1,12,1,3,1,1,0
"152",7037,16,8.8,1,5,0,1,9,1,8,1,1,2
"153",838,16,9.3,1,4,0,1,13,0,3,1,1,4
"154",8803,16,9.8,1,5,0,1,15,1,8,1,1,4
"155",4654,16,10,1,3,0,1,13,0,7,1,1,4
"156",5123,16,10.3,1,1,0,1,12,0,1,1,1,3
"157",4885,16,10.4,1,1,0,1,11,0,7,1,1,1
"158",3736,16,10.7,1,1,0,1,14,0,2,1,1,1
"159",10118,16,11.4,1,3,0,1,13,1,7,1,1,1
"160",8254,16,11.8,1,4,0,1,12,1,2,1,1,3
"161",5790,16,11.9,1,5,0,1,11,1,8,1,1,8
"162",2356,16,12.1,1,5,0,1,12,0,5,1,1,5
"163",1450,16,12.2,1,2,0,1,14,0,7,1,1,8
"164",8216,16,12.2,1,5,0,1,10,1,5,1,1,5
"165",1241,16,12.7,1,4,0,1,11,0,6,1,1,2
"166",6774,16,13.2,1,2,0,1,13,1,4,1,1,9
"167",5964,16,13.6,1,5,0,1,12,1,8,1,1,2
"168",8068,16,13.6,1,2,0,1,15,1,4,1,1,5
"169",3360,16,14,1,2,0,1,10,0,1,1,1,5
"170",4379,16,14,1,3,0,1,12,0,5,1,1,1
"171",6964,16,15.2,1,2,0,1,10,1,6,1,1,0
"172",8048,16,15.3,1,5,0,1,13,1,5,1,1,6
"173",10326,16,15.7,1,2,0,1,12,1,6,1,1,1
"174",3846,16,16,1,3,0,1,15,0,2,1,1,2
"175",8808,16,16.3,1,5,0,1,12,1,4,1,1,5
"176",8102,16,16.5,1,3,0,1,14,1,6,1,1,8
"177",2134,16,17,1,1,0,1,13,0,7,1,1,3
"178",4963,16,17.2,1,2,0,1,12,0,2,1,1,0
"179",2196,16,18.2,1,5,0,1,12,0,4,1,1,7
"180",9381,16,18.2,1,4,0,1,10,1,3,1,1,0
"181",6577,16,18.9,1,1,0,1,14,1,5,1,1,4
"182",4916,16,19,1,3,0,1,12,0,6,1,1,8
"183",9092,16,19.2,1,3,0,1,15,1,8,1,1,9
"184",5078,16,19.4,1,2,0,1,14,0,3,1,1,1
"185",8829,16,19.5,1,3,0,1,14,1,6,1,1,3
"186",3812,16,19.7,1,5,0,1,12,0,1,1,1,0
"187",2756,16,19.8,1,4,0,1,10,0,1,1,1,2
"188",5068,16,19.8,1,5,0,1,13,0,1,1,1,1
"189",6483,16,20.1,1,5,0,1,15,1,4,1,1,4
"190",2576,16,20.3,1,4,0,1,11,0,2,1,1,2
"191",1925,16,20.5,1,5,0,1,12,0,3,1,1,2
"192",1020,16,20.7,1,5,0,1,11,0,8,1,1,3
"193",7535,16,21.1,1,1,0,1,10,1,7,1,1,2
"194",2699,16,21.6,1,3,0,1,12,0,3,1,1,1
"195",8428,16,21.6,1,3,0,1,11,1,1,1,1,9
"196",10730,16,21.7,1,5,0,1,15,1,8,1,1,2
"197",9076,16,22.3,1,1,0,1,11,1,6,1,1,7
"198",6331,16,22.5,1,5,0,1,11,1,8,1,1,2
"199",5161,16,22.9,1,3,0,1,14,0,4,1,1,4
"200",7758,16,23.1,1,3,0,1,9,1,8,1,1,9
"201",1657,16,23.3,1,4,0,1,14,0,6,1,1,1
"202",8713,16,24.1,1,5,0,1,15,1,4,1,1,4
"203",2728,16,24.1,1,2,0,1,12,0,4,1,1,7
"204",3877,16,24.5,1,3,0,1,9,0,4,1,1,3
"205",394,16,24.5,1,3,0,1,9,0,2,1,1,0
"206",1484,16,24.7,1,2,0,1,14,0,7,1,1,3
"207",5917,16,24.9,1,1,0,1,10,1,8,1,1,2
"208",993,16,25.2,1,2,0,1,12,0,7,1,1,2
"209",7174,16,25.2,1,3,0,1,9,1,1,1,1,0
"210",3057,16,25.2,1,2,0,1,9,0,2,1,1,2
"211",2696,16,25.2,1,3,0,1,12,0,7,1,1,2
"212",7188,16,25.4,1,5,0,1,15,1,3,1,1,4
"213",3923,16,25.5,1,4,0,1,14,0,7,1,1,2
"214",452,16,25.6,1,4,0,1,14,0,7,1,1,9
"215",6394,16,26,1,2,0,1,13,1,5,1,1,7
"216",8049,16,26.1,1,1,0,1,13,1,2,1,1,4
"217",3428,16,26.2,1,1,0,1,12,0,7,1,1,10
"218",828,16,26.2,1,3,0,1,13,0,7,1,1,1
"219",4353,16,26.3,1,1,0,1,10,0,2,1,1,4
"220",5168,16,26.7,1,3,0,1,10,0,4,1,1,1
"221",972,16,26.9,1,2,0,1,15,0,8,1,1,8
"222",3120,16,27.4,1,4,0,1,9,0,8,1,1,4
"223",8470,16,27.4,1,1,0,1,13,1,6,1,1,4
"224",3522,16,27.5,1,3,0,1,12,0,1,1,1,8
"225",7789,16,27.7,1,2,0,1,15,1,5,1,1,4
"226",3584,16,27.8,1,3,0,1,9,0,6,1,1,9
"227",2000,16,27.9,1,1,0,1,13,0,3,1,1,1
"228",3065,16,28.1,1,5,0,1,12,0,2,1,1,4
"229",2898,16,28.5,1,4,0,1,15,0,7,1,1,3
"230",3602,16,28.6,1,3,0,1,12,0,5,1,1,8
"231",3996,16,29,1,1,0,1,13,0,6,1,1,8
"232",7572,16,29.3,1,5,0,1,12,1,1,1,1,6
"233",3413,17,0.3,1,2,0,1,10,0,2,1,1,2
"234",6359,17,0.4,1,2,0,1,11,1,1,1,1,0
"235",7160,17,0.8,1,3,0,1,12,1,1,1,1,8
"236",7388,17,1.3,1,5,0,1,12,1,2,1,1,9
"237",4810,17,1.5,1,2,0,1,9,0,5,1,1,8
"238",7619,17,2.9,1,1,0,1,9,1,2,1,1,4
"239",8550,17,3.1,1,3,0,1,9,1,6,1,1,10
"240",9848,17,3.1,1,1,0,1,13,1,2,1,1,3
"241",10558,17,3.4,1,2,0,1,13,1,2,1,1,4
"242",3461,17,3.9,1,2,0,1,11,0,5,1,1,0
"243",7309,17,4,1,4,0,1,9,1,4,1,1,3
"244",10836,17,4.1,1,4,0,1,12,1,3,1,1,1
"245",4938,17,4.3,1,2,0,1,10,0,5,1,1,5
"246",4791,17,5.1,1,2,0,1,11,0,4,1,1,2
"247",4442,17,5.3,1,5,0,1,12,0,7,1,1,8
"248",737,17,5.3,1,1,0,1,15,0,6,1,1,1
"249",9900,17,6,1,3,0,1,11,1,7,1,1,3
"250",4168,17,6.3,1,2,0,1,12,0,4,1,1,1
"251",5908,17,7.1,1,3,0,1,12,1,1,1,1,1
"252",9862,17,7.2,1,2,0,1,10,1,7,1,1,4
"253",3394,17,7.9,1,2,0,1,11,0,5,1,1,4
"254",2581,17,8,1,5,0,1,13,0,5,1,1,3
"255",1220,17,8,1,2,0,1,11,0,6,1,1,2
"256",4464,17,8,1,4,0,1,15,0,4,1,1,8
"257",1022,17,8.8,1,2,0,1,13,0,6,1,1,4
"258",4166,17,9.4,1,2,0,1,10,0,7,1,1,2
"259",8564,17,9.4,1,1,0,1,12,1,5,1,1,7
"260",3754,17,9.7,1,1,0,1,14,0,2,1,1,5
"261",9295,17,9.8,1,2,0,1,12,1,3,1,1,2
"262",2673,17,10.1,1,1,0,1,12,0,7,1,1,3
"263",3814,17,10.2,1,1,0,1,13,0,2,1,1,6
"264",9190,17,10.2,1,1,0,1,12,1,7,1,1,9
"265",4952,17,10.4,1,2,0,1,10,0,7,1,1,5
"266",6669,17,10.6,1,5,0,1,12,1,7,1,1,1
"267",3300,17,10.6,1,1,0,1,10,0,1,1,1,7
"268",8518,17,10.9,1,4,0,1,12,1,8,1,1,1
"269",5878,17,10.9,1,3,0,1,11,1,6,1,1,7
"270",5006,17,11.5,1,5,0,1,14,0,8,1,1,1
"271",3082,17,11.8,1,3,0,1,12,0,8,1,1,0
"272",4188,17,12.3,1,1,0,1,12,0,8,1,1,9
"273",1324,17,12.5,1,1,0,1,12,0,1,1,1,7
"274",3242,17,12.6,1,5,0,1,14,0,3,1,1,9
"275",5736,17,12.8,1,4,0,1,14,1,4,1,1,9
"276",2905,17,12.8,1,2,0,1,14,0,5,1,1,3
"277",103,17,12.9,1,1,0,1,11,0,2,1,1,0
"278",2296,17,14.2,1,1,0,1,13,0,2,1,1,1
"279",4470,17,15.3,1,5,0,1,10,0,6,1,1,2
"280",536,17,15.9,1,3,0,1,9,0,7,1,1,9
"281",8790,17,16.1,1,5,0,1,9,1,2,1,1,5
"282",10637,17,16.1,1,5,0,1,10,1,3,1,1,4
"283",10055,17,16.2,1,2,0,1,9,1,1,1,1,1
"284",2698,17,17,1,2,0,1,14,0,7,1,1,3
"285",1119,17,17.3,1,2,0,1,14,0,7,1,1,2
"286",4752,17,17.4,1,5,0,1,14,0,4,1,1,6
"287",9018,17,17.5,1,3,0,1,12,1,2,1,1,8
"288",10178,17,18.1,1,4,0,1,11,1,5,1,1,5
"289",128,17,18.4,1,1,0,1,11,0,7,1,1,10
"290",4767,17,19,1,5,0,1,11,0,6,1,1,4
"291",8036,17,19.4,1,1,0,1,15,1,1,1,1,0
"292",10338,17,19.4,1,4,0,1,12,1,4,1,1,8
"293",10074,17,19.6,1,5,0,1,10,1,5,1,1,2
"294",6738,17,19.8,1,5,0,1,9,1,1,1,1,10
"295",4367,17,20.5,1,3,0,1,13,0,2,1,1,4
"296",6160,17,20.5,1,3,0,1,13,1,7,1,1,7
"297",6258,17,21.8,1,4,0,1,12,1,2,1,1,3
"298",5130,17,22.1,1,5,0,1,9,0,7,1,1,0
"299",2420,17,22.5,1,1,0,1,9,0,7,1,1,5
"300",8358,17,22.7,1,3,0,1,11,1,1,1,1,1
"301",5108,17,23,1,5,0,1,12,0,2,1,1,10
"302",108,17,23.1,1,2,0,1,11,0,1,1,1,1
"303",2231,17,23.1,1,1,0,1,11,0,3,1,1,2
"304",6491,17,23.3,1,2,0,1,14,1,6,1,1,0
"305",3219,17,24.4,1,5,0,1,12,0,2,1,1,0
"306",650,17,24.5,1,4,0,1,12,0,1,1,1,6
"307",9237,17,24.9,1,5,0,1,14,1,3,1,1,4
"308",9612,17,25.1,1,2,0,1,11,1,7,1,1,7
"309",4295,17,25.4,1,1,0,1,14,0,3,1,1,4
"310",5231,17,25.5,1,3,0,1,9,0,6,1,1,3
"311",2111,17,26.2,1,4,0,1,9,0,7,1,1,4
"312",5571,17,26.5,1,5,0,1,9,1,6,1,1,4
"313",10443,17,27.3,1,1,0,1,10,1,5,1,1,0
"314",4035,17,27.4,1,1,0,1,13,0,5,1,1,3
"315",900,17,27.6,1,2,0,1,14,0,2,1,1,0
"316",1368,17,27.7,1,5,0,1,9,0,4,1,1,8
"317",3104,17,27.7,1,4,0,1,9,0,4,1,1,4
"318",5538,17,27.8,1,3,0,1,11,1,3,1,1,10
"319",6043,17,28.3,1,5,0,1,9,1,8,1,1,1
"320",2523,17,28.3,1,4,0,1,13,0,7,1,1,4
"321",3358,17,28.4,1,4,0,1,9,0,1,1,1,7
"322",8574,17,28.4,1,5,0,1,9,1,2,1,1,4
"323",6634,17,28.7,1,2,0,1,9,1,4,1,1,0
"324",2580,17,29.1,1,1,0,1,10,0,7,1,1,0
"325",1399,17,29.2,1,2,0,1,9,0,5,1,1,1
"326",7344,17,29.4,1,5,0,1,12,1,5,1,1,3
"327",7468,17,29.7,1,4,0,1,11,1,8,1,1,1
"328",2634,18,0.2,1,1,0,1,12,0,7,1,1,6
"329",3549,18,1.1,1,2,0,1,15,0,2,1,1,0
"330",124,18,1.2,1,5,0,1,15,0,6,1,1,8
"331",5866,18,1.3,1,3,0,1,14,1,7,1,1,8
"332",8708,18,1.4,1,3,0,1,11,1,3,1,1,8
"333",9247,18,1.7,1,3,0,1,13,1,7,1,1,4
"334",7741,18,1.9,1,2,0,1,15,1,2,1,1,0
"335",9207,18,2.5,1,1,0,1,12,1,2,1,1,1
"336",6977,18,2.6,1,4,0,1,15,1,8,1,1,1
"337",9084,18,3.2,1,3,0,1,12,1,5,1,1,0
"338",10513,18,3.4,1,2,0,1,14,1,8,1,1,2
"339",7452,18,3.5,1,4,0,1,10,1,7,1,1,4
"340",8037,18,3.9,1,2,0,1,9,1,3,1,1,3
"341",3894,18,3.9,1,1,0,1,13,0,3,1,1,0
"342",1976,18,4,1,4,0,1,12,0,6,1,1,4
"343",3144,18,4.3,1,1,0,1,14,0,6,1,1,6
"344",9887,18,4.6,1,4,0,1,9,1,4,1,1,4
"345",1335,18,4.7,1,4,0,1,13,0,7,1,1,1
"346",10494,18,4.8,1,3,0,1,11,1,5,1,1,0
"347",8099,18,4.9,1,1,0,1,10,1,3,1,1,1
"348",6171,18,5,1,5,0,1,13,1,8,1,1,2
"349",2808,18,5,1,5,0,1,12,0,6,1,1,2
"350",3604,18,5.1,1,4,0,1,9,0,8,1,1,2
"351",5671,18,5.2,1,5,0,1,12,1,5,1,1,0
"352",8346,18,5.7,1,2,0,1,10,1,6,1,1,5
"353",6913,18,6.2,1,3,0,1,12,1,1,1,1,2
"354",8085,18,6.2,1,1,0,1,9,1,4,1,1,0
"355",1236,18,6.3,1,1,0,1,9,0,7,1,1,0
"356",4730,18,6.4,1,3,0,1,14,0,6,1,1,2
"357",8964,18,6.6,1,4,0,1,10,1,7,1,1,9
"358",1919,18,6.9,1,3,0,1,13,0,4,1,1,4
"359",7838,18,7.3,1,2,0,1,12,1,6,1,1,1
"360",8878,18,8,1,4,0,1,9,1,7,1,1,8
"361",1590,18,8.1,1,4,0,1,12,0,8,1,1,10
"362",5348,18,8.4,1,3,0,1,14,0,6,1,1,2
"363",9233,18,8.8,1,3,0,1,12,1,3,1,1,2
"364",4490,18,9.7,1,2,0,1,10,0,2,1,1,10
"365",7454,18,10,1,1,0,1,10,1,1,1,1,3
"366",7763,18,10.2,1,1,0,1,12,1,1,1,1,2
"367",5140,18,10.3,1,4,0,1,15,0,6,1,1,0
"368",10477,18,10.5,1,3,0,1,14,1,1,1,1,1
"369",5686,18,11.2,1,4,0,1,12,1,6,1,1,8
"370",7946,18,11.2,1,2,0,1,13,1,5,1,1,3
"371",486,18,11.6,1,5,0,1,12,0,5,1,1,1
"372",7639,18,11.8,1,3,0,1,11,1,5,1,1,1
"373",2461,18,12,1,2,0,1,12,0,4,1,1,3
"374",646,18,12,1,5,0,1,15,0,1,1,1,2
"375",5216,18,12.2,1,4,0,1,14,0,7,1,1,8
"376",5874,18,12.4,1,1,0,1,11,1,8,1,1,0
"377",976,18,12.5,1,3,0,1,10,0,6,1,1,2
"378",8705,18,12.7,1,2,0,1,11,1,7,1,1,3
"379",6787,18,12.8,1,1,0,1,9,1,7,1,1,0
"380",10662,18,12.9,1,3,0,1,11,1,7,1,1,9
"381",9726,18,13.2,1,5,0,1,15,1,7,1,1,0
"382",527,18,13.3,1,5,0,1,10,0,4,1,1,1
"383",8976,18,13.6,1,5,0,1,11,1,3,1,1,1
"384",872,18,14,1,5,0,1,15,0,8,1,1,2
"385",6970,18,14.1,1,2,0,1,14,1,6,1,1,2
"386",2214,18,14.3,1,3,0,1,11,0,8,1,1,6
"387",2248,18,14.6,1,2,0,1,9,0,6,1,1,2
"388",8637,18,14.7,1,5,0,1,9,1,4,1,1,3
"389",4034,18,15.1,1,1,0,1,15,0,6,1,1,0
"390",6765,18,15.5,1,5,0,1,12,1,6,1,1,4
"391",864,18,16,1,5,0,1,12,0,3,1,1,8
"392",2110,18,16.4,1,3,0,1,13,0,3,1,1,1
"393",9073,18,16.5,1,3,0,1,15,1,3,1,1,2
"394",8656,18,16.6,1,3,0,1,15,1,2,1,1,8
"395",8486,18,17,1,2,0,1,15,1,4,1,1,8
"396",10859,18,17.4,1,5,0,1,11,1,1,1,1,2
"397",2304,18,17.7,1,4,0,1,10,0,7,1,1,8
"398",3490,18,17.9,1,1,0,1,14,0,5,1,1,4
"399",10703,18,18.1,1,5,0,1,10,1,5,1,1,2
"400",10452,18,18.2,1,3,0,1,12,1,7,1,1,4
"401",4494,18,18.3,1,3,0,1,9,0,2,1,1,6
"402",118,18,18.7,1,4,0,1,9,0,8,1,1,6
"403",977,18,19.5,1,2,0,1,11,0,2,1,1,1
"404",8882,18,19.5,1,4,0,1,9,1,5,1,1,7
"405",2940,18,19.6,1,2,0,1,9,0,6,1,1,0
"406",5624,18,19.6,1,4,0,1,13,1,7,1,1,3
"407",3492,18,19.7,1,1,0,1,15,0,1,1,1,6
"408",8280,18,19.7,1,2,0,1,13,1,3,1,1,9
"409",10194,18,20.3,1,5,0,1,12,1,8,1,1,7
"410",3938,18,20.7,1,2,0,1,12,0,8,1,1,7
"411",4332,18,21,1,4,0,1,12,0,1,1,1,6
"412",8580,18,21.1,1,4,0,1,15,1,2,1,1,2
"413",5673,18,21.4,1,5,0,1,14,1,5,1,1,1
"414",9797,18,22.1,1,1,0,1,15,1,2,1,1,3
"415",9331,18,22.3,1,4,0,1,15,1,6,1,1,0
"416",5574,18,22.9,1,4,0,1,13,1,5,1,1,5
"417",2084,18,23.1,1,5,0,1,11,0,1,1,1,10
"418",436,18,23.4,1,2,0,1,12,0,2,1,1,8
"419",1133,18,23.6,1,2,0,1,14,0,4,1,1,3
"420",1371,18,23.6,1,2,0,1,12,0,4,1,1,0
"421",2732,18,23.9,1,3,0,1,12,0,3,1,1,8
"422",6434,18,23.9,1,2,0,1,15,1,6,1,1,8
"423",6528,18,23.9,1,5,0,1,14,1,2,1,1,8
"424",844,18,24,1,1,0,1,12,0,8,1,1,10
"425",4032,18,24.2,1,3,0,1,14,0,3,1,1,6
"426",1712,18,24.2,1,4,0,1,11,0,3,1,1,1
"427",4899,18,24.8,1,3,0,1,14,0,7,1,1,2
"428",7456,18,24.9,1,4,0,1,9,1,4,1,1,1
"429",9060,18,25.2,1,5,0,1,12,1,3,1,1,4
"430",628,18,25.4,1,2,0,1,13,0,3,1,1,0
"431",1458,18,25.4,1,1,0,1,13,0,6,1,1,3
"432",10779,18,25.7,1,5,0,1,14,1,7,1,1,1
"433",8874,18,25.8,1,2,0,1,13,1,2,1,1,0
"434",1126,18,26,1,5,0,1,10,0,6,1,1,1
"435",1134,18,26.1,1,5,0,1,14,0,8,1,1,1
"436",4737,18,26.1,1,5,0,1,12,0,8,1,1,4
"437",1380,18,26.7,1,5,0,1,10,0,8,1,1,7
"438",8930,18,27.3,1,4,0,1,9,1,4,1,1,2
"439",7927,18,27.3,1,2,0,1,15,1,1,1,1,3
"440",7080,18,27.7,1,2,0,1,11,1,5,1,1,7
"441",5665,18,28,1,3,0,1,15,1,8,1,1,2
"442",7972,18,28.2,1,3,0,1,14,1,7,1,1,5
"443",9007,18,28.3,1,2,0,1,14,1,4,1,1,1
"444",1346,18,28.6,1,5,0,1,12,0,7,1,1,0
"445",8695,18,29.7,1,4,0,1,14,1,6,1,1,2
"446",9536,18,29.9,1,1,0,1,9,1,7,1,1,1
"447",3866,19,0.2,1,3,0,1,12,0,1,1,1,0
"448",9480,19,0.3,1,3,0,1,9,1,5,1,1,7
"449",4482,19,0.7,1,3,0,1,14,0,7,1,1,4
"450",2002,19,0.7,1,2,0,1,9,0,8,1,1,8
"451",1824,19,0.9,1,4,0,1,15,0,5,1,1,7
"452",878,19,1.1,1,1,0,1,9,0,8,1,1,5
"453",5258,19,1.5,1,3,0,1,12,0,5,1,1,0
"454",10689,19,1.6,1,1,0,1,9,1,6,1,1,4
"455",4718,19,1.9,1,4,0,1,15,0,4,1,1,1
"456",10623,19,1.9,1,4,0,1,12,1,7,1,1,1
"457",3498,19,1.9,1,3,0,1,12,0,6,1,1,4
"458",8609,19,2,1,1,0,1,14,1,7,1,1,0
"459",7486,19,2.6,1,1,0,1,9,1,3,1,1,4
"460",2518,19,2.7,1,2,0,1,14,0,4,1,1,10
"461",438,19,3.4,1,5,0,1,15,0,2,1,1,3
"462",7351,19,3.7,1,1,0,1,12,1,3,1,1,3
"463",4658,19,3.7,1,5,0,1,11,0,7,1,1,9
"464",338,19,3.9,1,2,0,1,11,0,2,1,1,6
"465",10421,19,4.4,1,5,0,1,12,1,2,1,1,2
"466",7192,19,5.1,1,5,0,1,10,1,3,1,1,7
"467",7480,19,5.8,1,4,0,1,13,1,7,1,1,9
"468",928,19,5.9,1,3,0,1,13,0,1,1,1,7
"469",7958,19,5.9,1,5,0,1,15,1,5,1,1,0
"470",9817,19,5.9,1,5,0,1,12,1,1,1,1,4
"471",5553,19,6.1,1,4,0,1,9,1,4,1,1,0
"472",5558,19,6.2,1,5,0,1,11,1,1,1,1,9
"473",7218,19,6.9,1,4,0,1,13,1,4,1,1,10
"474",9826,19,7.6,1,1,0,1,9,1,2,1,1,2
"475",3907,19,7.6,1,4,0,1,11,0,3,1,1,2
"476",4159,19,7.6,1,5,0,1,15,0,4,1,1,2
"477",3690,19,8,1,1,0,1,9,0,1,1,1,2
"478",5486,19,8.7,1,5,0,1,13,1,1,1,1,7
"479",1390,19,8.7,1,5,0,1,13,0,4,1,1,2
"480",7987,19,8.9,1,4,0,1,15,1,3,1,1,0
"481",6811,19,8.9,1,5,0,1,9,1,7,1,1,3
"482",2068,19,9.2,1,4,0,1,12,0,2,1,1,10
"483",2760,19,9.2,1,1,0,1,13,0,2,1,1,6
"484",1870,19,9.7,1,2,0,1,10,0,3,1,1,7
"485",8114,19,9.8,1,3,0,1,14,1,6,1,1,1
"486",4475,19,10.1,1,5,0,1,12,0,1,1,1,3
"487",6618,19,10.6,1,4,0,1,10,1,7,1,1,4
"488",2275,19,10.8,1,2,0,1,10,0,4,1,1,0
"489",4706,19,11.3,1,5,0,1,12,0,5,1,1,5
"490",10427,19,11.4,1,4,0,1,12,1,7,1,1,0
"491",9644,19,11.4,1,3,0,1,15,1,3,1,1,2
"492",4039,19,11.5,1,4,0,1,11,0,7,1,1,2
"493",2344,19,11.9,1,3,0,1,11,0,6,1,1,10
"494",1928,19,12.1,1,3,0,1,10,0,5,1,1,3
"495",6466,19,12.1,1,5,0,1,12,1,5,1,1,7
"496",8547,19,12.1,1,1,0,1,10,1,6,1,1,2
"497",9356,19,12.5,1,3,0,1,9,1,2,1,1,2
"498",5870,19,12.8,1,2,0,1,15,1,4,1,1,10
"499",5264,19,12.8,1,5,0,1,11,0,2,1,1,8
"500",10378,19,12.9,1,3,0,1,11,1,4,1,1,10
"501",8020,19,13,1,3,0,1,14,1,3,1,1,7
"502",6876,19,13.2,1,5,0,1,10,1,7,1,1,10
"503",2776,19,13.6,1,1,0,1,15,0,1,1,1,1
"504",2583,19,14,1,4,0,1,9,0,7,1,1,0
"505",6714,19,15.1,1,3,0,1,10,1,7,1,1,3
"506",7941,19,15.2,1,3,0,1,10,1,5,1,1,3
"507",8514,19,15.4,1,2,0,1,12,1,1,1,1,10
"508",9198,19,15.5,1,3,0,1,11,1,1,1,1,2
"509",4219,19,16.1,1,3,0,1,13,0,4,1,1,3
"510",1666,19,16.3,1,4,0,1,14,0,1,1,1,4
"511",4110,19,16.3,1,4,0,1,12,0,7,1,1,2
"512",1816,19,16.8,1,1,0,1,11,0,6,1,1,7
"513",9072,19,16.9,1,1,0,1,10,1,1,1,1,7
"514",9043,19,17,1,4,0,1,13,1,4,1,1,4
"515",3268,19,17.5,1,4,0,1,14,0,1,1,1,10
"516",9763,19,17.6,1,4,0,1,12,1,3,1,1,3
"517",556,19,17.6,1,3,0,1,14,0,4,1,1,4
"518",9812,19,17.6,1,5,0,1,12,1,3,1,1,6
"519",6543,19,18.2,1,4,0,1,12,1,8,1,1,1
"520",4057,19,18.6,1,5,0,1,12,0,8,1,1,3
"521",8577,19,18.7,1,4,0,1,15,1,2,1,1,1
"522",575,19,18.7,1,5,0,1,11,0,7,1,1,3
"523",9066,19,19.1,1,5,0,1,11,1,2,1,1,9
"524",635,19,19.4,1,1,0,1,12,0,3,1,1,4
"525",6681,19,19.4,1,3,0,1,12,1,6,1,1,2
"526",636,19,19.6,1,4,0,1,15,0,5,1,1,0
"527",3018,19,19.8,1,1,0,1,11,0,7,1,1,8
"528",2818,19,19.9,1,3,0,1,14,0,4,1,1,7
"529",6343,19,20,1,4,0,1,11,1,5,1,1,3
"530",9042,19,20.1,1,4,0,1,10,1,4,1,1,0
"531",5300,19,20.1,1,1,0,1,12,0,3,1,1,3
"532",2140,19,20.8,1,4,0,1,14,0,5,1,1,7
"533",3620,19,20.9,1,2,0,1,15,0,2,1,1,2
"534",2545,19,20.9,1,1,0,1,13,0,4,1,1,2
"535",2862,19,21.1,1,5,0,1,13,0,6,1,1,9
"536",6832,19,21.7,1,4,0,1,12,1,3,1,1,0
"537",1836,19,22.3,1,3,0,1,12,0,8,1,1,10
"538",2434,19,22.5,1,1,0,1,13,0,2,1,1,7
"539",6237,19,22.6,1,4,0,1,10,1,3,1,1,3
"540",2096,19,22.7,1,4,0,1,10,0,7,1,1,8
"541",9397,19,23,1,2,0,1,12,1,5,1,1,0
"542",3478,19,23.2,1,1,0,1,11,0,6,1,1,2
"543",8160,19,23.2,1,5,0,1,12,1,5,1,1,8
"544",3407,19,24.2,1,3,0,1,15,0,2,1,1,1
"545",10231,19,24.2,1,1,0,1,14,1,8,1,1,0
"546",10286,19,24.2,1,3,0,1,10,1,6,1,1,6
"547",8732,19,24.7,1,2,0,1,11,1,1,1,1,5
"548",8673,19,24.8,1,4,0,1,12,1,5,1,1,3
"549",6976,19,25,1,3,0,1,12,1,8,1,1,10
"550",6860,19,25.2,1,2,0,1,14,1,8,1,1,5
"551",7584,19,25.7,1,3,0,1,12,1,2,1,1,1
"552",412,19,25.7,1,4,0,1,12,0,7,1,1,2
"553",3529,19,25.9,1,1,0,1,11,0,4,1,1,0
"554",3889,19,25.9,1,3,0,1,9,0,4,1,1,4
"555",1291,19,26.2,1,2,0,1,11,0,1,1,1,3
"556",6812,19,26.3,1,2,0,1,15,1,3,1,1,1
"557",5388,19,26.5,1,5,0,1,15,0,7,1,1,1
"558",3611,19,26.7,1,5,0,1,13,0,6,1,1,2
"559",6802,19,27.3,1,3,0,1,12,1,6,1,1,10
"560",6170,19,27.3,1,4,0,1,10,1,8,1,1,9
"561",7328,19,27.5,1,2,0,1,12,1,2,1,1,3
"562",9436,19,27.6,1,4,0,1,10,1,5,1,1,3
"563",1964,19,27.9,1,5,0,1,10,0,6,1,1,2
"564",9370,19,28.2,1,1,0,1,13,1,1,1,1,0
"565",6795,19,28.9,1,1,0,1,12,1,2,1,1,4
"566",6826,19,29.2,1,2,0,1,9,1,8,1,1,6
"567",4568,19,29.5,1,5,0,1,11,0,4,1,1,9
"568",7412,20,0.1,1,2,0,1,12,1,6,1,1,4
"569",6886,20,0.4,1,4,0,1,11,1,4,1,1,1
"570",5838,20,0.4,1,3,0,1,15,1,1,1,1,5
"571",5191,20,0.4,1,1,0,1,12,0,1,1,1,0
"572",1746,20,0.4,1,1,0,1,14,0,2,1,1,6
"573",462,20,1.3,1,2,0,1,9,0,8,1,1,5
"574",4114,20,1.4,1,4,0,1,14,0,1,1,1,6
"575",7236,20,1.5,1,1,0,1,11,1,2,1,1,2
"576",6214,20,2,1,2,0,1,9,1,7,1,1,9
"577",4686,20,2.4,1,2,0,1,9,0,7,1,1,1
"578",5007,20,2.6,1,5,0,1,12,0,6,1,1,0
"579",6220,20,2.7,1,4,0,1,10,1,2,1,1,0
"580",5580,20,2.8,1,4,0,1,13,1,2,1,1,7
"581",1580,20,3.5,1,3,0,1,11,0,7,1,1,8
"582",3644,20,4.2,1,5,0,1,13,0,1,1,1,7
"583",8965,20,4.2,1,2,0,1,12,1,6,1,1,1
"584",1406,20,4.5,1,2,0,1,15,0,1,1,1,4
"585",9254,20,4.6,1,4,0,1,14,1,2,1,1,0
"586",2046,20,5.1,1,5,0,1,14,0,6,1,1,3
"587",8192,20,5.4,1,3,0,1,13,1,4,1,1,6
"588",8142,20,5.6,1,4,0,1,11,1,4,1,1,4
"589",2069,20,5.7,1,2,0,1,13,0,4,1,1,1
"590",9156,20,6,1,3,0,1,12,1,5,1,1,0
"591",494,20,6,1,1,0,1,11,0,8,1,1,3
"592",10189,20,6,1,3,0,1,11,1,4,1,1,1
"593",5901,20,6.2,1,4,0,1,14,1,4,1,1,2
"594",3950,20,6.3,1,5,0,1,10,0,8,1,1,4
"595",8794,20,6.3,1,1,0,1,10,1,1,1,1,7
"596",2795,20,6.7,1,3,0,1,12,0,2,1,1,3
"597",4028,20,6.9,1,4,0,1,11,0,5,1,1,1
"598",6048,20,7.1,1,4,0,1,14,1,6,1,1,3
"599",3140,20,7.1,1,3,0,1,11,0,6,1,1,0
"600",2603,20,7.1,1,5,0,1,11,0,7,1,1,2
"601",3656,20,7.2,1,3,0,1,11,0,5,1,1,3
"602",2744,20,7.2,1,2,0,1,15,0,1,1,1,0
"603",780,20,7.4,1,1,0,1,9,0,4,1,1,0
"604",10635,20,7.7,1,3,0,1,15,1,8,1,1,2
"605",6056,20,7.9,1,2,0,1,9,1,6,1,1,4
"606",9544,20,8,1,5,0,1,14,1,4,1,1,8
"607",46,20,8,1,4,0,1,11,0,4,1,1,1
"608",1751,20,8,1,4,0,1,15,0,5,1,1,0
"609",5456,20,8.4,1,3,0,1,9,1,3,1,1,3
"610",860,20,8.7,1,3,0,1,11,0,3,1,1,10
"611",417,20,9,1,4,0,1,10,0,7,1,1,1
"612",9856,20,9.3,1,4,0,1,14,1,5,1,1,4
"613",7536,20,9.7,1,4,0,1,15,1,8,1,1,8
"614",5351,20,10,1,2,0,1,12,0,3,1,1,1
"615",10698,20,10.2,1,3,0,1,13,1,5,1,1,10
"616",5692,20,10.2,1,3,0,1,11,1,3,1,1,2
"617",5112,20,10.9,1,4,0,1,10,0,6,1,1,3
"618",1108,20,10.9,1,4,0,1,14,0,2,1,1,0
"619",1461,20,11,1,3,0,1,15,0,5,1,1,2
"620",2120,20,11.1,1,3,0,1,15,0,4,1,1,1
"621",7386,20,11.5,1,3,0,1,12,1,7,1,1,6
"622",2906,20,11.8,1,2,0,1,12,0,2,1,1,9
"623",6312,20,11.9,1,1,0,1,12,1,6,1,1,5
"624",920,20,12.1,1,2,0,1,12,0,3,1,1,6
"625",468,20,12.7,1,2,0,1,11,0,6,1,1,1
"626",7124,20,12.9,1,1,0,1,12,1,1,1,1,0
"627",9771,20,13.1,1,5,0,1,9,1,4,1,1,1
"628",8405,20,13.4,1,5,0,1,10,1,4,1,1,2
"629",1975,20,13.4,1,4,0,1,13,0,7,1,1,3
"630",416,20,14,1,2,0,1,15,0,4,1,1,4
"631",7764,20,14.5,1,5,0,1,14,1,7,1,1,3
"632",1161,20,14.6,1,2,0,1,15,0,3,1,1,4
"633",3998,20,14.9,1,4,0,1,9,0,5,1,1,4
"634",4393,20,15,1,3,0,1,10,0,4,1,1,3
"635",10153,20,15.2,1,1,0,1,10,1,2,1,1,0
"636",10370,20,15.3,1,4,0,1,11,1,6,1,1,0
"637",4065,20,15.4,1,5,0,1,15,0,2,1,1,3
"638",9105,20,15.6,1,4,0,1,12,1,3,1,1,1
"639",6349,20,15.8,1,4,0,1,14,1,6,1,1,4
"640",4136,20,15.8,1,1,0,1,15,0,1,1,1,8
"641",4826,20,15.9,1,1,0,1,10,0,2,1,1,2
"642",6907,20,16,1,1,0,1,14,1,8,1,1,2
"643",8988,20,16.3,1,2,0,1,13,1,2,1,1,9
"644",5278,20,16.4,1,5,0,1,15,0,1,1,1,3
"645",8365,20,16.4,1,5,0,1,10,1,1,1,1,2
"646",7791,20,17,1,2,0,1,14,1,8,1,1,1
"647",5954,20,17,1,4,0,1,9,1,5,1,1,9
"648",4298,20,17.5,1,3,0,1,10,0,2,1,1,3
"649",9894,20,17.6,1,4,0,1,14,1,1,1,1,6
"650",49,20,18,1,2,0,1,10,0,3,1,1,3
"651",7006,20,18.1,1,1,0,1,12,1,8,1,1,5
"652",3308,20,18.7,1,5,0,1,10,0,7,1,1,10
"653",9606,20,18.8,1,5,0,1,12,1,6,1,1,3
"654",3166,20,18.9,1,3,0,1,9,0,1,1,1,3
"655",5209,20,19.2,1,3,0,1,11,0,1,1,1,4
"656",6636,20,19.2,1,4,0,1,9,1,7,1,1,3
"657",7620,20,19.3,1,3,0,1,13,1,1,1,1,9
"658",7183,20,19.6,1,1,0,1,12,1,5,1,1,1
"659",6965,20,19.6,1,1,0,1,14,1,7,1,1,4
"660",1917,20,19.8,1,1,0,1,15,0,5,1,1,2
"661",5226,20,20.1,1,1,0,1,11,0,5,1,1,4
"662",3784,20,20.2,1,4,0,1,10,0,4,1,1,5
"663",7420,20,20.6,1,5,0,1,15,1,8,1,1,3
"664",5428,20,20.6,1,2,0,1,13,1,7,1,1,2
"665",6404,20,21.3,1,2,0,1,14,1,2,1,1,8
"666",5262,20,21.5,1,4,0,1,9,0,6,1,1,0
"667",2051,20,21.8,1,1,0,1,9,0,8,1,1,3
"668",5041,20,22,1,1,0,1,15,0,8,1,1,4
"669",7172,20,22.8,1,1,0,1,12,1,4,1,1,10
"670",3486,20,23.7,1,4,0,1,12,0,5,1,1,2
"671",6499,20,23.7,1,1,0,1,9,1,7,1,1,3
"672",2415,20,24.1,1,5,0,1,10,0,1,1,1,0
"673",10298,20,24.2,1,5,0,1,11,1,1,1,1,10
"674",8079,20,24.4,1,2,0,1,12,1,4,1,1,4
"675",7126,20,24.6,1,4,0,1,12,1,5,1,1,9
"676",601,20,24.7,1,2,0,1,12,0,1,1,1,4
"677",6628,20,24.8,1,4,0,1,9,1,7,1,1,3
"678",1101,20,25.1,1,4,0,1,15,0,7,1,1,1
"679",3367,20,25.7,1,1,0,1,12,0,1,1,1,0
"680",7474,20,25.7,1,5,0,1,14,1,5,1,1,1
"681",5426,20,25.8,1,3,0,1,13,1,2,1,1,2
"682",3576,20,26.2,1,4,0,1,15,0,4,1,1,9
"683",10414,20,26.3,1,5,0,1,12,1,1,1,1,0
"684",7649,20,26.5,1,5,0,1,11,1,2,1,1,3
"685",9374,20,26.7,1,2,0,1,13,1,8,1,1,10
"686",6544,20,27.2,1,2,0,1,15,1,5,1,1,7
"687",6656,20,27.5,1,3,0,1,12,1,1,1,1,0
"688",5516,20,27.5,1,3,0,1,15,1,3,1,1,4
"689",5554,20,27.6,1,5,0,1,9,1,4,1,1,7
"690",6658,20,27.6,1,2,0,1,10,1,3,1,1,6
"691",1578,20,27.8,1,3,0,1,10,0,3,1,1,4
"692",6956,20,27.9,1,5,0,1,12,1,2,1,1,8
"693",8668,20,28.6,1,4,0,1,11,1,4,1,1,7
"694",3724,20,28.7,1,1,0,1,12,0,4,1,1,4
"695",6057,20,28.7,1,5,0,1,15,1,4,1,1,0
"696",9011,20,28.8,1,4,0,1,9,1,2,1,1,4
"697",351,20,28.9,1,1,0,1,11,0,7,1,1,3
"698",10436,20,29,1,2,0,1,12,1,3,1,1,8
"699",2094,20,29,1,2,0,1,12,0,8,1,1,7
"700",9358,20,29.4,1,3,0,1,12,1,5,1,1,1
"701",6144,20,29.6,1,3,0,1,11,1,3,1,1,8
"702",5856,21,0.3,1,1,0,1,13,1,1,1,1,5
"703",8044,21,1.5,1,4,0,1,11,1,8,1,1,0
"704",716,21,1.5,1,2,0,1,11,0,4,1,1,9
"705",1378,21,1.7,1,2,0,1,12,0,6,1,1,4
"706",6234,21,2.7,1,5,0,1,10,1,3,1,1,7
"707",9758,21,2.8,1,3,0,1,12,1,5,1,1,5
"708",524,21,2.9,1,3,0,1,15,0,3,1,1,8
"709",7733,21,3,1,3,0,1,15,1,4,1,1,2
"710",1010,21,3.8,1,1,0,1,10,0,7,1,1,4
"711",10820,21,4,1,3,0,1,15,1,1,1,1,8
"712",3336,21,4.2,1,5,0,1,9,0,4,1,1,2
"713",3294,21,4.7,1,2,0,1,15,0,6,1,1,3
"714",4451,21,5.8,1,1,0,1,11,0,6,1,1,2
"715",8370,21,6,1,5,0,1,15,1,5,1,1,3
"716",8467,21,6.1,1,5,0,1,10,1,4,1,1,3
"717",8553,21,6.5,1,4,0,1,10,1,5,1,1,1
"718",3642,21,6.8,1,1,0,1,9,0,3,1,1,3
"719",1604,21,6.9,1,5,0,1,15,0,4,1,1,3
"720",7549,21,7.1,1,4,0,1,11,1,3,1,1,1
"721",2312,21,7.7,1,1,0,1,10,0,2,1,1,6
"722",1418,21,8.1,1,5,0,1,9,0,6,1,1,1
"723",3354,21,8.4,1,1,0,1,14,0,7,1,1,10
"724",3862,21,8.4,1,5,0,1,9,0,4,1,1,5
"725",4989,21,8.9,1,1,0,1,10,0,7,1,1,1
"726",2426,21,8.9,1,5,0,1,13,0,8,1,1,9
"727",10198,21,9,1,3,0,1,13,1,5,1,1,4
"728",4581,21,9.5,1,1,0,1,15,0,1,1,1,2
"729",1877,21,9.7,1,4,0,1,12,0,4,1,1,4
"730",221,21,9.7,1,5,0,1,11,0,6,1,1,4
"731",582,21,9.9,1,3,0,1,15,0,7,1,1,5
"732",10040,21,10,1,4,0,1,13,1,3,1,1,4
"733",764,21,10.3,1,4,0,1,15,0,8,1,1,5
"734",3546,21,10.4,1,2,0,1,15,0,5,1,1,7
"735",4402,21,11.1,1,2,0,1,14,0,1,1,1,7
"736",2916,21,11.5,1,3,0,1,11,0,8,1,1,8
"737",63,21,11.7,1,1,0,1,14,0,7,1,1,1
"738",5636,21,11.8,1,5,0,1,13,1,2,1,1,6
"739",6453,21,12,1,4,0,1,13,1,4,1,1,3
"740",1058,21,12,1,5,0,1,12,0,1,1,1,10
"741",630,21,12.4,1,2,0,1,10,0,8,1,1,5
"742",3852,21,12.8,1,2,0,1,11,0,8,1,1,7
"743",10480,21,13.1,1,4,0,1,14,1,5,1,1,0
"744",6261,21,13.2,1,1,0,1,12,1,6,1,1,1
"745",3315,21,13.2,1,4,0,1,11,0,4,1,1,3
"746",903,21,13.2,1,5,0,1,14,0,5,1,1,0
"747",10848,21,13.2,1,5,0,1,13,1,8,1,1,1
"748",3234,21,13.2,1,3,0,1,11,0,5,1,1,7
"749",6019,21,13.4,1,2,0,1,12,1,3,1,1,4
"750",9852,21,14.1,1,2,0,1,10,1,1,1,1,1
"751",3859,21,14.1,1,5,0,1,15,0,7,1,1,2
"752",10529,21,14.2,1,2,0,1,12,1,2,1,1,0
"753",6244,21,14.3,1,5,0,1,9,1,7,1,1,8
"754",10464,21,14.6,1,2,0,1,9,1,8,1,1,0
"755",5025,21,14.9,1,5,0,1,9,0,2,1,1,1
"756",3960,21,15,1,5,0,1,10,0,8,1,1,1
"757",5948,21,15,1,1,0,1,13,1,3,1,1,5
"758",1938,21,15.4,1,4,0,1,12,0,3,1,1,0
"759",6897,21,16.1,1,5,0,1,9,1,5,1,1,1
"760",3266,21,16.2,1,5,0,1,13,0,3,1,1,3
"761",881,21,16.2,1,1,0,1,11,0,4,1,1,3
"762",3982,21,16.4,1,3,0,1,14,0,5,1,1,3
"763",9888,21,16.9,1,3,0,1,13,1,1,1,1,4
"764",9984,21,18,1,3,0,1,14,1,3,1,1,3
"765",1278,21,18,1,2,0,1,10,0,1,1,1,9
"766",7493,21,18.1,1,5,0,1,10,1,7,1,1,4
"767",4721,21,18.6,1,5,0,1,12,0,4,1,1,1
"768",10428,21,18.8,1,2,0,1,10,1,7,1,1,6
"769",1556,21,19.8,1,2,0,1,14,0,8,1,1,3
"770",948,21,20.5,1,2,0,1,12,0,8,1,1,10
"771",8406,21,20.7,1,2,0,1,12,1,6,1,1,10
"772",3715,21,20.8,1,1,0,1,14,0,3,1,1,3
"773",5478,21,21.1,1,5,0,1,12,1,8,1,1,1
"774",3100,21,21.2,1,1,0,1,12,0,3,1,1,6
"775",2032,21,21.7,1,2,0,1,15,0,5,1,1,1
"776",10358,21,22,1,3,0,1,13,1,8,1,1,0
"777",414,21,22.2,1,5,0,1,12,0,2,1,1,6
"778",7125,21,22.2,1,5,0,1,12,1,6,1,1,0
"779",7319,21,22.3,1,3,0,1,10,1,6,1,1,0
"780",4009,21,22.7,1,4,0,1,9,0,3,1,1,1
"781",9678,21,22.8,1,5,0,1,15,1,4,1,1,9
"782",10743,21,23.4,1,3,0,1,14,1,1,1,1,4
"783",9886,21,23.4,1,2,0,1,14,1,3,1,1,10
"784",6566,21,23.6,1,5,0,1,9,1,4,1,1,7
"785",2658,21,23.7,1,3,0,1,13,0,2,1,1,5
"786",10420,21,24,1,3,0,1,11,1,1,1,1,2
"787",1546,21,24.1,1,5,0,1,10,0,4,1,1,5
"788",1525,21,24.2,1,1,0,1,12,0,4,1,1,3
"789",3264,21,24.5,1,1,0,1,15,0,1,1,1,10
"790",3845,21,24.8,1,5,0,1,9,0,1,1,1,2
"791",4462,21,24.9,1,4,0,1,14,0,6,1,1,2
"792",10226,21,24.9,1,4,0,1,10,1,4,1,1,8
"793",6760,21,25.7,1,3,0,1,9,1,7,1,1,9
"794",8400,21,25.8,1,3,0,1,12,1,2,1,1,4
"795",4164,21,26,1,1,0,1,9,0,1,1,1,6
"796",10641,21,26.2,1,2,0,1,14,1,6,1,1,1
"797",8733,21,26.6,1,4,0,1,11,1,7,1,1,3
"798",6274,21,26.7,1,2,0,1,11,1,8,1,1,2
"799",6556,21,26.8,1,4,0,1,12,1,6,1,1,2
"800",8776,21,27,1,1,0,1,14,1,3,1,1,2
"801",1639,21,27.2,1,2,0,1,14,0,3,1,1,1
"802",8088,21,27.3,1,2,0,1,12,1,2,1,1,6
"803",968,21,27.5,1,1,0,1,13,0,7,1,1,9
"804",3365,21,27.7,1,3,0,1,14,0,5,1,1,4
"805",9562,21,28.7,1,2,0,1,12,1,5,1,1,4
"806",7228,21,28.8,1,5,0,1,14,1,1,1,1,3
"807",2396,21,29.2,1,2,0,1,12,0,1,1,1,4
"808",9988,21,29.2,1,1,0,1,12,1,6,1,1,5
"809",4852,21,29.8,1,1,0,1,14,0,5,1,1,9
"810",1949,21,29.8,1,5,0,1,13,0,3,1,1,2
"811",2330,21,30,1,3,0,1,9,0,3,1,1,6
"812",9857,22,0.2,1,5,0,1,15,1,8,1,1,1
"813",6708,22,0.9,1,5,0,1,10,1,2,1,1,5
"814",2778,22,1,1,4,0,1,15,0,5,1,1,7
"815",35,22,1.3,1,1,0,1,12,0,8,1,1,0
"816",2811,22,1.5,1,2,0,1,12,0,5,1,1,2
"817",10258,22,1.9,1,2,0,1,14,1,8,1,1,4
"818",10061,22,2.8,1,3,0,1,12,1,8,1,1,1
"819",8361,22,3.3,1,4,0,1,14,1,2,1,1,4
"820",1649,22,3.7,1,5,0,1,12,0,3,1,1,0
"821",1972,22,3.7,1,2,0,1,12,0,7,1,1,4
"822",9884,22,3.9,1,5,0,1,11,1,6,1,1,1
"823",2715,22,4,1,2,0,1,12,0,7,1,1,1
"824",7312,22,4.4,1,4,0,1,12,1,6,1,1,10
"825",9635,22,4.7,1,4,0,1,12,1,4,1,1,1
"826",7634,22,4.7,1,2,0,1,15,1,6,1,1,2
"827",6722,22,4.7,1,4,0,1,12,1,5,1,1,7
"828",2021,22,4.9,1,3,0,1,14,0,2,1,1,3
"829",9031,22,4.9,1,3,0,1,10,1,4,1,1,0
"830",8235,22,5.1,1,3,0,1,13,1,2,1,1,2
"831",8340,22,5.4,1,5,0,1,11,1,6,1,1,2
"832",4649,22,5.4,1,1,0,1,12,0,8,1,1,3
"833",752,22,5.4,1,2,0,1,15,0,7,1,1,2
"834",8666,22,5.4,1,5,0,1,14,1,7,1,1,6
"835",3089,22,6.4,1,5,0,1,13,0,3,1,1,1
"836",1153,22,7.2,1,4,0,1,12,0,3,1,1,0
"837",6408,22,7.6,1,3,0,1,9,1,8,1,1,8
"838",4342,22,7.7,1,2,0,1,12,0,5,1,1,3
"839",6006,22,8.6,1,1,0,1,15,1,5,1,1,6
"840",10408,22,8.7,1,2,0,1,11,1,5,1,1,9
"841",6818,22,8.7,1,5,0,1,9,1,5,1,1,2
"842",6366,22,8.8,1,2,0,1,12,1,2,1,1,8
"843",5439,22,8.8,1,1,0,1,10,1,3,1,1,1
"844",6323,22,9.7,1,3,0,1,12,1,1,1,1,0
"845",3372,22,10.1,1,5,0,1,12,0,5,1,1,0
"846",5620,22,10.5,1,5,0,1,12,1,4,1,1,3
"847",4029,22,10.6,1,5,0,1,14,0,4,1,1,1
"848",8716,22,10.9,1,4,0,1,12,1,3,1,1,2
"849",1315,22,10.9,1,2,0,1,14,0,2,1,1,0
"850",6428,22,10.9,1,1,0,1,12,1,2,1,1,7
"851",1392,22,11.1,1,2,0,1,15,0,1,1,1,5
"852",66,22,11.6,1,5,0,1,15,0,6,1,1,9
"853",2104,22,11.7,1,3,0,1,14,0,4,1,1,1
"854",3430,22,12,1,4,0,1,12,0,8,1,1,7
"855",8039,22,12.3,1,5,0,1,12,1,1,1,1,1
"856",10481,22,12.7,1,5,0,1,10,1,5,1,1,1
"857",8670,22,13,1,2,0,1,14,1,5,1,1,1
"858",4312,22,13.7,1,2,0,1,11,0,2,1,1,6
"859",680,22,13.9,1,3,0,1,11,0,4,1,1,6
"860",294,22,14.1,1,3,0,1,9,0,2,1,1,0
"861",4303,22,14.4,1,1,0,1,11,0,4,1,1,4
"862",6979,22,14.5,1,1,0,1,10,1,3,1,1,0
"863",2472,22,15.3,1,2,0,1,13,0,4,1,1,5
"864",1009,22,15.4,1,5,0,1,12,0,5,1,1,0
"865",9616,22,15.5,1,3,0,1,11,1,8,1,1,7
"866",7194,22,15.9,1,4,0,1,15,1,3,1,1,6
"867",5364,22,15.9,1,5,0,1,12,0,7,1,1,2
"868",7890,22,16.6,1,5,0,1,11,1,6,1,1,10
"869",8720,22,16.8,1,5,0,1,15,1,1,1,1,1
"870",9188,22,16.8,1,5,0,1,12,1,2,1,1,9
"871",4060,22,17.2,1,1,0,1,9,0,3,1,1,4
"872",3709,22,17.3,1,2,0,1,10,0,6,1,1,0
"873",6840,22,18,1,5,0,1,12,1,1,1,1,0
"874",5732,22,18.3,1,3,0,1,10,1,3,1,1,1
"875",375,22,18.5,1,2,0,1,15,0,1,1,1,2
"876",6384,22,18.7,1,2,0,1,14,1,6,1,1,9
"877",4685,22,18.7,1,5,0,1,14,0,3,1,1,4
"878",7692,22,18.9,1,5,0,1,14,1,1,1,1,6
"879",5524,22,19.9,1,5,0,1,13,1,5,1,1,3
"880",8680,22,20.1,1,4,0,1,14,1,3,1,1,10
"881",5709,22,20.7,1,3,0,1,12,1,4,1,1,1
"882",5462,22,21.2,1,5,0,1,11,1,4,1,1,1
"883",5824,22,21.6,1,2,0,1,14,1,8,1,1,7
"884",6344,22,21.6,1,2,0,1,14,1,2,1,1,5
"885",2997,22,22.1,1,2,0,1,13,0,7,1,1,0
"886",2455,22,22.3,1,5,0,1,14,0,7,1,1,0
"887",4378,22,22.3,1,5,0,1,10,0,5,1,1,10
"888",2616,22,22.4,1,2,0,1,15,0,3,1,1,4
"889",5064,22,23.5,1,3,0,1,15,0,4,1,1,5
"890",8443,22,23.5,1,3,0,1,14,1,5,1,1,0
"891",4120,22,23.6,1,5,0,1,9,0,3,1,1,10
"892",1948,22,23.6,1,3,0,1,15,0,1,1,1,7
"893",5138,22,24,1,4,0,1,15,0,2,1,1,2
"894",504,22,24.7,1,1,0,1,12,0,6,1,1,5
"895",9611,22,24.9,1,4,0,1,11,1,1,1,1,1
"896",7750,22,25,1,1,0,1,10,1,2,1,1,5
"897",9008,22,25.5,1,5,0,1,9,1,8,1,1,6
"898",3145,22,25.6,1,5,0,1,12,0,4,1,1,0
"899",6548,22,26.2,1,3,0,1,15,1,6,1,1,6
"900",10045,22,26.7,1,4,0,1,10,1,6,1,1,4
"901",2270,22,27.1,1,1,0,1,11,0,8,1,1,7
"902",3587,22,27.6,1,5,0,1,15,0,8,1,1,0
"903",8084,22,27.6,1,4,0,1,12,1,1,1,1,8
"904",5135,22,28.2,1,5,0,1,11,0,2,1,1,4
"905",1308,22,28.8,1,4,0,1,10,0,5,1,1,10
"906",3174,22,29,1,3,0,1,12,0,3,1,1,8
"907",10815,22,29.5,1,4,0,1,12,1,7,1,1,0
"908",8402,23,0.3,1,1,0,1,13,1,5,1,1,8
"909",6239,23,0.5,1,4,0,1,13,1,1,1,1,1
"910",4947,23,0.8,1,1,0,1,9,0,6,1,1,4
"911",110,23,1.2,1,1,0,1,10,0,3,1,1,0
"912",6586,23,1.3,1,5,0,1,14,1,7,1,1,0
"913",1514,23,2.1,1,4,0,1,13,0,1,1,1,3
"914",5469,23,2.5,1,5,0,1,15,1,5,1,1,3
"915",5711,23,2.8,1,2,0,1,15,1,1,1,1,2
"916",1684,23,3.1,1,1,0,1,9,0,7,1,1,3
"917",6254,23,3.4,1,1,0,1,14,1,2,1,1,0
"918",8110,23,3.5,1,2,0,1,12,1,6,1,1,10
"919",517,23,3.6,1,4,0,1,12,0,7,1,1,2
"920",1182,23,3.8,1,1,0,1,11,0,2,1,1,2
"921",10554,23,4,1,5,0,1,15,1,5,1,1,7
"922",5953,23,4.5,1,2,0,1,12,1,2,1,1,0
"923",2913,23,4.7,1,5,0,1,13,0,8,1,1,3
"924",9633,23,5,1,3,0,1,12,1,4,1,1,0
"925",883,23,5.1,1,4,0,1,15,0,7,1,1,1
"926",10292,23,5.2,1,5,0,1,15,1,1,1,1,8
"927",4976,23,5.5,1,2,0,1,12,0,8,1,1,0
"928",8132,23,5.8,1,5,0,1,12,1,4,1,1,6
"929",939,23,6,1,1,0,1,13,0,5,1,1,3
"930",2948,23,6.3,1,1,0,1,14,0,5,1,1,0
"931",6742,23,6.4,1,3,0,1,14,1,4,1,1,0
"932",9283,23,6.5,1,3,0,1,10,1,5,1,1,2
"933",2822,23,6.5,1,3,0,1,10,0,4,1,1,9
"934",9012,23,6.8,1,2,0,1,12,1,3,1,1,5
"935",2633,23,7,1,2,0,1,14,0,8,1,1,0
"936",6351,23,7,1,1,0,1,10,1,2,1,1,2
"937",3042,23,7.3,1,4,0,1,10,0,6,1,1,7
"938",5432,23,7.5,1,4,0,1,13,1,6,1,1,4
"939",2334,23,8.4,1,2,0,1,13,0,4,1,1,8
"940",1784,23,8.5,1,2,0,1,15,0,3,1,1,2
"941",380,23,9,1,1,0,1,12,0,4,1,1,6
"942",3831,23,9.1,1,5,0,1,11,0,3,1,1,3
"943",2655,23,9.1,1,4,0,1,10,0,2,1,1,4
"944",8204,23,9.3,1,5,0,1,11,1,2,1,1,7
"945",1200,23,9.7,1,4,0,1,9,0,6,1,1,8
"946",3114,23,9.8,1,1,0,1,9,0,7,1,1,4
"947",3720,23,9.8,1,3,0,1,15,0,7,1,1,6
"948",5228,23,10,1,3,0,1,13,0,4,1,1,1
"949",4894,23,10.5,1,4,0,1,10,0,7,1,1,10
"950",1488,23,10.7,1,4,0,1,9,0,1,1,1,9
"951",9126,23,11.4,1,1,0,1,14,1,8,1,1,2
"952",8188,23,11.7,1,2,0,1,12,1,2,1,1,9
"953",2589,23,11.8,1,2,0,1,14,0,7,1,1,4
"954",9366,23,11.8,1,2,0,1,12,1,8,1,1,2
"955",4646,23,11.9,1,5,0,1,11,0,1,1,1,2
"956",4116,23,12,1,3,0,1,12,0,7,1,1,0
"957",2984,23,12.5,1,5,0,1,9,0,7,1,1,7
"958",6054,23,13.2,1,4,0,1,15,1,3,1,1,5
"959",1967,23,13.3,1,5,0,1,9,0,7,1,1,4
"960",1791,23,13.6,1,1,0,1,15,0,1,1,1,4
"961",800,23,13.7,1,1,0,1,15,0,2,1,1,8
"962",946,23,13.8,1,2,0,1,11,0,6,1,1,4
"963",8244,23,13.9,1,4,0,1,14,1,7,1,1,6
"964",5904,23,13.9,1,5,0,1,14,1,5,1,1,3
"965",2618,23,14.2,1,3,0,1,15,0,4,1,1,9
"966",612,23,14.3,1,5,0,1,15,0,2,1,1,10
"967",4106,23,14.4,1,5,0,1,15,0,6,1,1,5
"968",2719,23,14.6,1,1,0,1,12,0,1,1,1,2
"969",5600,23,14.8,1,2,0,1,15,1,8,1,1,6
"970",8797,23,15.1,1,3,0,1,12,1,6,1,1,4
"971",9832,23,15.1,1,3,0,1,10,1,4,1,1,9
"972",9062,23,15.9,1,3,0,1,12,1,6,1,1,1
"973",884,23,15.9,1,4,0,1,11,0,7,1,1,6
"974",9218,23,16.2,1,3,0,1,12,1,7,1,1,10
"975",7782,23,17,1,1,0,1,14,1,2,1,1,2
"976",792,23,17.1,1,4,0,1,14,0,5,1,1,5
"977",5994,23,17.2,1,2,0,1,10,1,8,1,1,2
"978",1019,23,17.6,1,1,0,1,10,0,7,1,1,2
"979",7083,23,17.8,1,5,0,1,14,1,3,1,1,1
"980",9225,23,17.9,1,4,0,1,9,1,6,1,1,2
"981",7277,23,18.1,1,2,0,1,11,1,7,1,1,4
"982",2490,23,18.3,1,4,0,1,15,0,6,1,1,5
"983",7254,23,18.7,1,3,0,1,14,1,3,1,1,10
"984",1096,23,18.8,1,5,0,1,13,0,8,1,1,2
"985",4444,23,19,1,3,0,1,12,0,3,1,1,7
"986",5446,23,19.1,1,5,0,1,10,1,1,1,1,2
"987",9291,23,19.1,1,2,0,1,12,1,7,1,1,1
"988",8456,23,19.2,1,2,0,1,11,1,8,1,1,7
"989",8097,23,19.3,1,2,0,1,14,1,3,1,1,3
"990",2646,23,19.4,1,4,0,1,9,0,7,1,1,3
"991",4970,23,19.6,1,4,0,1,14,0,2,1,1,1
"992",7988,23,20,1,5,0,1,11,1,3,1,1,10
"993",579,23,20.5,1,5,0,1,14,0,1,1,1,1
"994",3350,23,20.6,1,2,0,1,13,0,8,1,1,7
"995",1252,23,20.7,1,4,0,1,12,0,4,1,1,10
"996",9284,23,20.7,1,2,0,1,11,1,6,1,1,8
"997",5380,23,21.5,1,4,0,1,14,0,1,1,1,9
"998",5652,23,21.6,1,3,0,1,12,1,8,1,1,6
"999",1296,23,21.8,1,2,0,1,12,0,7,1,1,5
"1000",2348,23,22.5,1,3,0,1,11,0,3,1,1,10
"1001",4582,23,22.8,1,2,0,1,10,0,5,1,1,4
"1002",234,23,22.8,1,3,0,1,15,0,1,1,1,4
"1003",7831,23,24.3,1,2,0,1,10,1,8,1,1,1
"1004",3148,23,24.6,1,1,0,1,12,0,2,1,1,0
"1005",5939,23,24.7,1,5,0,1,15,1,2,1,1,3
"1006",9330,23,24.9,1,3,0,1,12,1,2,1,1,8
"1007",6158,23,25.3,1,1,0,1,14,1,8,1,1,9
"1008",6034,23,25.7,1,5,0,1,13,1,6,1,1,1
"1009",4414,23,26,1,3,0,1,11,0,2,1,1,2
"1010",4671,23,26.1,1,3,0,1,12,0,8,1,1,2
"1011",6108,23,26.2,1,5,0,1,13,1,4,1,1,9
"1012",10196,23,26.4,1,2,0,1,12,1,5,1,1,0
"1013",8892,23,27.1,1,1,0,1,13,1,3,1,1,6
"1014",4524,23,27.2,1,4,0,1,15,0,5,1,1,7
"1015",268,23,27.3,1,4,0,1,15,0,7,1,1,7
"1016",10218,23,27.5,1,2,0,1,12,1,4,1,1,9
"1017",10264,23,27.7,1,2,0,1,10,1,7,1,1,9
"1018",3666,23,27.8,1,4,0,1,9,0,5,1,1,6
"1019",154,23,28.2,1,2,0,1,12,0,4,1,1,8
"1020",9700,23,28.8,1,5,0,1,11,1,5,1,1,8
"1021",4200,23,28.8,1,1,0,1,12,0,2,1,1,6
"1022",8522,23,29.3,1,3,0,1,15,1,8,1,1,0
"1023",7537,23,29.4,1,2,0,1,15,1,5,1,1,2
"1024",2607,23,29.5,1,4,0,1,12,0,2,1,1,1
"1025",7719,23,29.6,1,4,0,1,9,1,7,1,1,1
"1026",1031,23,29.7,1,4,0,1,9,0,5,1,1,4
"1027",4656,24,0.2,1,5,0,1,15,0,1,1,1,9
"1028",531,24,0.4,1,1,0,1,12,0,8,1,1,3
"1029",4189,24,1.3,1,3,0,1,12,0,8,1,1,3
"1030",5466,24,2.2,1,5,0,1,12,1,1,1,1,3
"1031",1699,24,2.8,1,3,0,1,9,0,4,1,1,2
"1032",1199,24,3,1,3,0,1,15,0,8,1,1,3
"1033",4688,24,3.1,1,4,0,1,9,0,5,1,1,8
"1034",9324,24,3.2,1,4,0,1,10,1,6,1,1,1
"1035",6322,24,4,1,4,0,1,14,1,8,1,1,8
"1036",7310,24,4.9,1,1,0,1,14,1,4,1,1,7
"1037",5105,24,5.2,1,4,0,1,11,0,4,1,1,0
"1038",1046,24,5.4,1,3,0,1,11,0,7,1,1,0
"1039",8008,24,5.8,1,1,0,1,15,1,1,1,1,2
"1040",7278,24,6.4,1,2,0,1,11,1,5,1,1,6
"1041",9488,24,7,1,1,0,1,15,1,3,1,1,2
"1042",5766,24,7.5,1,2,0,1,12,1,5,1,1,3
"1043",7826,24,7.9,1,2,0,1,12,1,7,1,1,1
"1044",6106,24,8,1,2,0,1,12,1,1,1,1,1
"1045",7506,24,9.1,1,4,0,1,9,1,3,1,1,5
"1046",1510,24,9.6,1,4,0,1,12,0,6,1,1,2
"1047",6858,24,9.6,1,1,0,1,9,1,1,1,1,1
"1048",5517,24,10,1,3,0,1,11,1,4,1,1,4
"1049",4559,24,10.1,1,2,0,1,12,0,8,1,1,4
"1050",5722,24,10.5,1,5,0,1,12,1,6,1,1,3
"1051",2876,24,10.5,1,5,0,1,12,0,6,1,1,9
"1052",1270,24,10.5,1,4,0,1,12,0,5,1,1,0
"1053",9773,24,10.6,1,2,0,1,9,1,8,1,1,3
"1054",8681,24,11.1,1,4,0,1,13,1,4,1,1,4
"1055",9696,24,11.3,1,1,0,1,11,1,6,1,1,1
"1056",5200,24,12.4,1,4,0,1,12,0,7,1,1,2
"1057",2710,24,12.8,1,3,0,1,12,0,4,1,1,2
"1058",8938,24,12.9,1,4,0,1,13,1,2,1,1,2
"1059",7909,24,13.3,1,2,0,1,11,1,1,1,1,1
"1060",1362,24,13.4,1,4,0,1,15,0,7,1,1,4
"1061",7306,24,13.6,1,1,0,1,10,1,4,1,1,4
"1062",7518,24,14,1,5,0,1,12,1,6,1,1,7
"1063",4888,24,14.3,1,2,0,1,15,0,4,1,1,8
"1064",8040,24,14.9,1,1,0,1,13,1,2,1,1,10
"1065",8311,24,15,1,1,0,0,15,1,1,1,1,5
"1066",8219,24,15,1,2,0,0,15,1,2,1,1,7
"1067",247,24,15.1,1,5,0,0,10,0,1,1,1,5
"1068",8299,24,15.1,1,3,0,0,13,1,1,1,1,6
"1069",919,24,15.1,1,1,0,0,13,0,2,1,1,8
"1070",2185,24,15.1,1,5,0,0,9,0,2,1,1,7
"1071",905,24,15.2,1,4,0,0,11,0,0,1,1,9
"1072",3381,24,15.2,1,2,0,0,14,0,2,1,1,7
"1073",4291,24,15.2,1,5,0,0,15,0,2,1,1,5
"1074",8363,24,15.3,1,4,0,1,13,1,5,1,1,0
"1075",5799,24,15.3,1,4,0,0,11,1,0,1,1,10
"1076",8665,24,15.3,1,2,0,0,9,1,0,1,1,5
"1077",2691,24,15.3,1,4,0,0,9,0,2,1,1,8
"1078",10371,24,15.3,1,3,0,0,13,1,1,1,1,6
"1079",69,24,15.3,1,2,0,0,15,0,2,1,1,9
"1080",2197,24,15.3,1,2,0,0,15,0,0,1,1,5
"1081",1641,24,15.3,1,1,0,0,12,0,0,1,1,6
"1082",989,24,15.3,1,2,0,0,14,0,0,1,1,6
"1083",6581,24,15.3,1,2,0,0,13,1,1,1,1,7
"1084",8436,24,15.4,1,3,0,1,10,1,4,1,1,6
"1085",2251,24,15.4,1,3,0,0,15,0,0,1,1,7
"1086",10677,24,15.4,1,1,0,0,9,1,1,1,1,7
"1087",5997,24,15.5,1,4,0,0,12,1,2,1,1,7
"1088",7007,24,15.5,1,2,0,0,15,1,1,1,1,8
"1089",10215,24,15.6,1,1,0,0,15,1,2,1,1,8
"1090",571,24,15.6,1,5,0,0,13,0,1,1,1,7
"1091",10563,24,15.6,1,1,0,0,13,1,0,1,1,6
"1092",7947,24,15.7,1,4,0,0,14,1,2,1,1,6
"1093",3235,24,15.7,1,5,0,0,14,0,1,1,1,5
"1094",9519,24,15.7,1,2,0,0,9,1,2,1,1,10
"1095",8411,24,15.8,1,5,0,0,9,1,1,1,1,7
"1096",2163,24,15.8,1,4,0,0,14,0,1,1,1,5
"1097",10339,24,15.8,1,1,0,0,15,1,2,1,1,6
"1098",2247,24,15.8,1,2,0,0,9,0,0,1,1,7
"1099",7695,24,15.8,1,5,0,0,9,1,0,1,1,10
"1100",5225,24,15.9,1,5,0,0,12,0,1,1,1,5
"1101",9561,24,15.9,1,1,0,0,14,1,2,1,1,8
"1102",1627,24,15.9,1,5,0,0,10,0,0,1,1,5
"1103",8975,24,15.9,1,2,0,0,9,1,2,1,1,8
"1104",10817,24,15.9,1,4,0,0,9,1,2,1,1,5
"1105",4905,24,16,1,2,0,0,14,0,2,1,1,7
"1106",7133,24,16,1,2,0,0,14,1,2,1,1,7
"1107",8757,24,16,1,2,0,0,15,1,2,1,1,9
"1108",6875,24,16,1,2,0,0,9,1,0,1,1,6
"1109",3969,24,16,1,1,0,0,14,0,1,1,1,5
"1110",2955,24,16.1,1,1,0,0,9,0,0,1,1,6
"1111",157,24,16.1,1,1,0,0,12,0,1,1,1,6
"1112",7345,24,16.1,1,5,0,0,10,1,1,1,1,5
"1113",6097,24,16.1,1,1,0,0,13,1,1,1,1,7
"1114",7783,24,16.1,1,5,0,0,9,1,1,1,1,9
"1115",7333,24,16.1,1,5,0,0,11,1,0,1,1,6
"1116",2423,24,16.2,1,1,0,0,12,0,1,1,1,5
"1117",795,24,16.2,1,4,0,0,11,0,1,1,1,5
"1118",1177,24,16.3,1,2,0,1,14,0,1,1,1,2
"1119",4261,24,16.3,1,3,0,0,13,0,1,1,1,5
"1120",6287,24,16.3,1,1,0,0,10,1,1,1,1,10
"1121",4103,24,16.4,1,5,0,0,12,0,2,1,1,7
"1122",6519,24,16.4,1,2,0,0,10,1,1,1,1,6
"1123",9935,24,16.4,1,2,0,0,15,1,0,1,1,5
"1124",10699,24,16.4,1,1,0,0,9,1,0,1,1,8
"1125",1607,24,16.5,1,2,0,0,9,0,1,1,1,8
"1126",7473,24,16.5,1,5,0,0,10,1,0,1,1,9
"1127",3713,24,16.6,1,3,0,0,12,0,0,1,1,6
"1128",10573,24,16.6,1,1,0,0,9,1,1,1,1,5
"1129",1929,24,16.6,1,4,0,0,12,0,0,1,1,5
"1130",1933,24,16.6,1,5,0,0,14,0,1,1,1,6
"1131",8625,24,16.6,1,1,0,0,14,1,0,1,1,9
"1132",431,24,16.6,1,2,0,0,12,0,1,1,1,9
"1133",8141,24,16.6,1,4,0,0,14,1,1,1,1,9
"1134",9205,24,16.6,1,2,0,0,9,1,0,1,1,6
"1135",5355,24,16.6,1,3,0,0,10,0,0,1,1,8
"1136",3355,24,16.6,1,1,0,0,15,0,0,1,1,6
"1137",1678,24,16.7,1,1,0,1,12,0,3,1,1,10
"1138",2617,24,16.7,1,3,0,0,15,0,1,1,1,9
"1139",9061,24,16.7,1,4,0,0,11,1,1,1,1,10
"1140",1879,24,16.7,1,2,0,0,13,0,2,1,1,6
"1141",6175,24,16.8,1,2,0,0,13,1,2,1,1,5
"1142",8885,24,16.8,1,1,0,0,10,1,1,1,1,9
"1143",1564,24,16.8,1,5,0,1,14,0,4,1,1,0
"1144",5637,24,16.8,1,5,0,0,9,1,1,1,1,9
"1145",9759,24,16.9,1,1,0,0,15,1,2,1,1,8
"1146",8799,24,16.9,1,4,0,1,12,1,2,1,1,3
"1147",8769,24,16.9,1,1,0,0,14,1,2,1,1,6
"1148",1862,24,16.9,1,1,0,1,14,0,1,1,1,7
"1149",3761,24,16.9,1,5,0,0,12,0,0,1,1,8
"1150",10683,24,17,1,3,0,0,14,1,0,1,1,5
"1151",4007,24,17,1,4,0,0,12,0,2,1,1,9
"1152",8171,24,17,1,1,0,1,12,1,7,1,1,1
"1153",6857,24,17,1,2,0,0,11,1,0,1,1,10
"1154",2081,24,17,1,4,0,0,12,0,2,1,1,8
"1155",203,24,17.1,1,1,0,0,10,0,1,1,1,9
"1156",6937,24,17.1,1,1,0,0,10,1,1,1,1,5
"1157",7058,24,17.1,1,5,0,1,12,1,5,1,1,1
"1158",4675,24,17.2,1,4,0,0,12,0,1,1,1,5
"1159",5787,24,17.2,1,4,0,0,13,1,1,1,1,8
"1160",5321,24,17.2,1,2,0,0,12,0,2,1,1,6
"1161",4569,24,17.3,1,5,0,0,12,0,0,1,1,10
"1162",4069,24,17.3,1,3,0,0,13,0,0,1,1,6
"1163",8273,24,17.3,1,3,0,0,10,1,0,1,1,5
"1164",6737,24,17.4,1,3,0,0,15,1,1,1,1,8
"1165",7559,24,17.4,1,5,0,0,14,1,0,1,1,5
"1166",1011,24,17.4,1,4,0,0,12,0,0,1,1,7
"1167",1121,24,17.4,1,1,0,0,10,0,1,1,1,5
"1168",9917,24,17.4,1,1,0,0,11,1,1,1,1,6
"1169",10467,24,17.5,1,3,0,0,14,1,1,1,1,8
"1170",9733,24,17.5,1,4,0,0,12,1,2,1,1,9
"1171",1427,24,17.5,1,2,0,0,12,0,1,1,1,9
"1172",8557,24,17.6,1,5,0,0,10,1,1,1,1,10
"1173",10567,24,17.6,1,4,0,0,13,1,1,1,1,6
"1174",5401,24,17.6,1,4,0,0,12,0,2,1,1,8
"1175",4239,24,17.6,1,2,0,0,10,0,0,1,1,7
"1176",1021,24,17.6,1,1,0,0,14,0,2,1,1,8
"1177",10687,24,17.6,1,3,0,0,11,1,0,1,1,5
"1178",10173,24,17.7,1,3,0,0,13,1,1,1,1,5
"1179",917,24,17.8,1,2,0,0,14,0,2,1,1,8
"1180",7235,24,17.8,1,4,0,0,12,1,0,1,1,10
"1181",10063,24,17.8,1,5,0,0,11,1,1,1,1,8
"1182",6631,24,17.9,1,3,0,0,13,1,1,1,1,5
"1183",8253,24,17.9,1,5,0,0,11,1,1,1,1,10
"1184",999,24,17.9,1,5,0,0,10,0,2,1,1,5
"1185",1935,24,17.9,1,2,0,0,11,0,2,1,1,10
"1186",7759,24,17.9,1,1,0,0,12,1,1,1,1,10
"1187",2917,24,17.9,1,1,0,0,14,0,2,1,1,7
"1188",9145,24,17.9,1,1,0,0,11,1,2,1,1,7
"1189",4595,24,17.9,1,3,0,0,13,0,1,1,1,6
"1190",583,24,17.9,1,2,0,0,12,0,0,1,1,8
"1191",10255,24,17.9,1,5,0,0,12,1,1,1,1,6
"1192",5111,24,17.9,1,3,0,0,11,0,2,1,1,6
"1193",4679,24,18,1,5,0,0,11,0,0,1,1,8
"1194",2857,24,18,1,5,0,0,9,0,2,1,1,7
"1195",10713,24,18,1,5,0,0,11,1,0,1,1,5
"1196",7211,24,18,1,3,0,0,12,1,0,1,1,7
"1197",4479,24,18.1,1,1,0,0,10,0,0,1,1,8
"1198",229,24,18.1,1,3,0,0,15,0,0,1,1,7
"1199",3851,24,18.1,1,2,0,0,12,0,1,1,1,8
"1200",6093,24,18.1,1,3,0,0,12,1,2,1,1,9
"1201",2761,24,18.1,1,5,0,0,15,0,0,1,1,8
"1202",1349,24,18.1,1,1,0,0,9,0,2,1,1,8
"1203",7677,24,18.1,1,2,0,0,9,1,0,1,1,7
"1204",3605,24,18.2,1,2,0,0,10,0,1,1,1,9
"1205",6063,24,18.2,1,2,0,0,15,1,2,1,1,9
"1206",9881,24,18.2,1,3,0,0,11,1,2,1,1,9
"1207",2772,24,18.2,1,4,0,1,10,0,3,1,1,4
"1208",3085,24,18.2,1,3,0,0,13,0,2,1,1,7
"1209",5763,24,18.2,1,3,0,0,14,1,1,1,1,6
"1210",2128,24,18.3,1,5,0,1,10,0,1,1,1,8
"1211",2615,24,18.3,1,5,0,0,10,0,0,1,1,7
"1212",2283,24,18.3,1,4,0,0,11,0,0,1,1,7
"1213",6127,24,18.3,1,4,0,0,12,1,2,1,1,7
"1214",1405,24,18.3,1,2,0,0,12,0,2,1,1,10
"1215",4719,24,18.3,1,1,0,0,9,0,0,1,1,5
"1216",7786,24,18.3,1,3,0,1,11,1,4,1,1,9
"1217",6341,24,18.3,1,1,0,0,12,1,0,1,1,6
"1218",2514,24,18.4,1,4,0,1,13,0,3,1,1,1
"1219",6251,24,18.4,1,2,0,0,14,1,2,1,1,5
"1220",3107,24,18.4,1,4,0,0,13,0,0,1,1,10
"1221",1621,24,18.5,1,1,0,0,9,0,2,1,1,5
"1222",1745,24,18.5,1,1,0,0,12,0,2,1,1,7
"1223",6357,24,18.5,1,2,0,0,14,1,2,1,1,9
"1224",6881,24,18.5,1,1,0,0,11,1,2,1,1,8
"1225",2747,24,18.5,1,2,0,0,12,0,0,1,1,10
"1226",7407,24,18.6,1,3,0,0,12,1,1,1,1,7
"1227",4933,24,18.6,1,3,0,0,12,0,0,1,1,10
"1228",1221,24,18.6,1,3,0,0,12,0,0,1,1,5
"1229",6645,24,18.6,1,2,0,0,11,1,2,1,1,7
"1230",7427,24,18.7,1,3,0,0,9,1,0,1,1,10
"1231",2191,24,18.7,1,4,0,0,13,0,2,1,1,5
"1232",6073,24,18.7,1,4,0,0,12,1,0,1,1,7
"1233",637,24,18.8,1,1,0,0,10,0,1,1,1,9
"1234",7159,24,18.8,1,3,0,0,12,1,2,1,1,9
"1235",1681,24,18.8,1,4,0,0,10,0,0,1,1,9
"1236",3083,24,18.8,1,5,0,0,14,0,0,1,1,7
"1237",5261,24,18.8,1,3,0,0,13,0,2,1,1,9
"1238",9385,24,18.8,1,3,0,0,12,1,2,1,1,6
"1239",1739,24,18.8,1,3,0,0,15,0,1,1,1,8
"1240",8959,24,18.8,1,2,0,0,13,1,2,1,1,10
"1241",4971,24,18.8,1,2,0,0,15,0,2,1,1,5
"1242",10737,24,18.8,1,3,0,0,11,1,2,1,1,5
"1243",10813,24,18.9,1,3,0,0,12,1,1,1,1,6
"1244",1027,24,18.9,1,3,0,0,9,0,1,1,1,9
"1245",3071,24,18.9,1,2,0,0,15,0,2,1,1,6
"1246",10778,24,18.9,1,3,0,1,11,1,3,1,1,1
"1247",2481,24,19,1,2,0,0,14,0,1,1,1,8
"1248",6541,24,19,1,3,0,0,14,1,0,1,1,8
"1249",783,24,19,1,2,0,0,10,0,1,1,1,9
"1250",5633,24,19,1,1,0,0,12,1,1,1,1,9
"1251",10679,24,19.1,1,4,0,0,12,1,2,1,1,5
"1252",4973,24,19.1,1,2,0,0,12,0,1,1,1,8
"1253",1343,24,19.1,1,5,0,0,11,0,1,1,1,7
"1254",1137,24,19.2,1,1,0,0,9,0,0,1,1,5
"1255",10389,24,19.2,1,1,0,0,14,1,2,1,1,7
"1256",5483,24,19.2,1,1,0,0,11,1,0,1,1,7
"1257",1375,24,19.2,1,3,0,0,11,0,1,1,1,10
"1258",2121,24,19.2,1,2,0,0,13,0,2,1,1,5
"1259",7491,24,19.2,1,2,0,0,12,1,1,1,1,10
"1260",1219,24,19.2,1,4,0,0,9,0,0,1,1,9
"1261",6383,24,19.3,1,1,0,0,9,1,0,1,1,9
"1262",205,24,19.3,1,3,0,0,11,0,2,1,1,6
"1263",3550,24,19.3,1,5,0,1,9,0,3,1,1,1
"1264",6887,24,19.3,1,1,0,0,12,1,2,1,1,8
"1265",9989,24,19.3,1,1,0,0,12,1,2,1,1,10
"1266",523,24,19.3,1,5,0,1,14,0,6,1,1,2
"1267",1183,24,19.4,1,1,0,0,15,0,1,1,1,7
"1268",2263,24,19.4,1,2,0,0,12,0,1,1,1,10
"1269",10745,24,19.4,1,5,0,0,12,1,0,1,1,10
"1270",2327,24,19.5,1,1,0,0,14,0,1,1,1,9
"1271",10095,24,19.5,1,1,0,0,9,1,0,1,1,7
"1272",5837,24,19.6,1,1,0,0,12,1,0,1,1,10
"1273",10043,24,19.6,1,3,0,0,15,1,2,1,1,8
"1274",10611,24,19.6,1,2,0,0,13,1,2,1,1,8
"1275",7571,24,19.7,1,2,0,0,14,1,0,1,1,9
"1276",10241,24,19.7,1,4,0,0,9,1,2,1,1,10
"1277",2085,24,19.7,1,4,0,0,9,0,0,1,1,9
"1278",4031,24,19.7,1,4,0,0,12,0,0,1,1,6
"1279",2447,24,19.8,1,5,0,0,13,0,0,1,1,6
"1280",8289,24,19.8,1,2,0,0,11,1,2,1,1,7
"1281",10553,24,19.8,1,3,0,0,12,1,1,1,1,6
"1282",1029,24,19.8,1,1,0,0,15,0,0,1,1,8
"1283",5909,24,19.9,1,1,0,0,10,1,1,1,1,10
"1284",9549,24,19.9,1,1,0,0,15,1,1,1,1,8
"1285",7221,24,19.9,1,5,0,0,13,1,0,1,1,9
"1286",10607,24,20,1,5,0,0,12,1,2,1,1,5
"1287",1173,24,20,1,1,0,0,14,0,1,1,1,8
"1288",7269,24,20,1,2,0,0,11,1,0,1,1,9
"1289",6741,24,20,1,5,0,1,12,1,3,1,1,4
"1290",6585,24,20,1,2,0,0,10,1,2,1,1,10
"1291",243,24,20.1,1,1,0,0,11,0,2,1,1,10
"1292",8067,24,20.1,1,5,0,0,12,1,0,1,1,7
"1293",1617,24,20.2,1,5,0,0,10,0,1,1,1,9
"1294",6273,24,20.2,1,4,0,0,9,1,2,1,1,7
"1295",1593,24,20.2,1,1,0,0,12,0,1,1,1,9
"1296",7215,24,20.2,1,1,0,0,12,1,1,1,1,10
"1297",8531,24,20.2,1,2,0,0,12,1,1,1,1,5
"1298",10765,24,20.3,1,1,0,0,12,1,2,1,1,10
"1299",911,24,20.3,1,1,0,0,15,0,2,1,1,6
"1300",1409,24,20.3,1,2,0,0,14,0,2,1,1,9
"1301",9827,24,20.4,1,4,0,0,10,1,2,1,1,6
"1302",8387,24,20.4,1,1,0,0,14,1,1,1,1,9
"1303",9365,24,20.4,1,5,0,0,15,1,1,1,1,7
"1304",6935,24,20.5,1,3,0,0,15,1,0,1,1,5
"1305",3095,24,20.5,1,1,0,0,10,0,0,1,1,8
"1306",2625,24,20.5,1,3,0,0,9,0,1,1,1,5
"1307",9545,24,20.5,1,1,0,0,10,1,1,1,1,5
"1308",4525,24,20.5,1,1,0,0,10,0,1,1,1,8
"1309",4847,24,20.5,1,2,0,0,12,0,2,1,1,7
"1310",6807,24,20.5,1,2,0,0,13,1,2,1,1,6
"1311",7603,24,20.6,1,4,0,0,9,1,1,1,1,10
"1312",1169,24,20.6,1,4,0,0,13,0,0,1,1,8
"1313",10233,24,20.6,1,1,0,0,12,1,2,1,1,6
"1314",9113,24,20.6,1,1,0,0,13,1,1,1,1,7
"1315",7661,24,20.7,1,5,0,0,14,1,0,1,1,5
"1316",3455,24,20.7,1,2,0,0,10,0,1,1,1,8
"1317",8879,24,20.7,1,5,0,0,13,1,2,1,1,8
"1318",9599,24,20.8,1,5,0,0,12,1,2,1,1,10
"1319",1851,24,20.8,1,5,0,0,11,0,0,1,1,8
"1320",1671,24,20.9,1,5,0,0,10,0,0,1,1,7
"1321",1053,24,20.9,1,3,0,0,13,0,0,1,1,5
"1322",4771,24,20.9,1,4,0,0,9,0,1,1,1,6
"1323",10811,24,21,1,5,0,0,14,1,2,1,1,5
"1324",6143,24,21,1,4,0,0,14,1,1,1,1,7
"1325",8517,24,21,1,3,0,0,9,1,1,1,1,6
"1326",10818,24,21,1,3,0,1,14,1,7,1,1,2
"1327",2311,24,21,1,3,0,0,11,0,1,1,1,5
"1328",5727,24,21.1,1,1,0,0,9,1,0,1,1,6
"1329",9021,24,21.1,1,5,0,0,9,1,0,1,1,7
"1330",3356,24,21.2,1,2,0,1,11,0,5,1,1,3
"1331",935,24,21.2,1,5,0,0,12,0,0,1,1,8
"1332",9507,24,21.2,1,3,0,0,14,1,1,1,1,7
"1333",5277,24,21.2,1,3,0,0,11,0,1,1,1,8
"1334",5681,24,21.2,1,5,0,0,9,1,1,1,1,6
"1335",6233,24,21.2,1,5,0,0,14,1,1,1,1,6
"1336",4123,24,21.2,1,2,0,0,11,0,0,1,1,9
"1337",389,24,21.2,1,5,0,0,14,0,2,1,1,10
"1338",6574,24,21.3,1,1,0,1,12,1,4,1,1,0
"1339",8131,24,21.3,1,2,0,0,13,1,0,1,1,5
"1340",5503,24,21.3,1,1,0,0,15,1,1,1,1,5
"1341",8491,24,21.3,1,1,0,0,12,1,1,1,1,10
"1342",4879,24,21.4,1,3,0,0,12,0,1,1,1,7
"1343",7609,24,21.4,1,1,0,0,12,1,1,1,1,7
"1344",10782,24,21.5,1,3,0,1,14,1,7,1,1,8
"1345",907,24,21.5,1,1,0,0,11,0,0,1,1,8
"1346",695,24,21.5,1,2,0,0,13,0,0,1,1,7
"1347",9035,24,21.6,1,4,0,0,10,1,1,1,1,6
"1348",5807,24,21.6,1,4,0,0,9,1,0,1,1,10
"1349",125,24,21.6,1,4,0,0,11,0,2,1,1,7
"1350",4563,24,21.7,1,2,0,0,14,0,1,1,1,10
"1351",8935,24,21.7,1,3,0,0,12,1,2,1,1,5
"1352",10175,24,21.7,1,4,0,0,12,1,1,1,1,10
"1353",8307,24,21.7,1,3,0,0,12,1,1,1,1,8
"1354",655,24,21.7,1,4,0,0,15,0,2,1,1,8
"1355",4555,24,21.7,1,1,0,0,14,0,2,1,1,7
"1356",9261,24,21.7,1,1,0,0,14,1,1,1,1,8
"1357",2435,24,21.7,1,2,0,0,12,0,2,1,1,8
"1358",7547,24,21.7,1,4,0,0,13,1,1,1,1,5
"1359",3093,24,21.8,1,5,0,0,11,0,2,1,1,9
"1360",9037,24,21.8,1,1,0,0,15,1,1,1,1,10
"1361",2169,24,21.8,1,3,0,0,9,0,2,1,1,9
"1362",735,24,21.9,1,2,0,0,15,0,1,1,1,8
"1363",7021,24,21.9,1,4,0,0,12,1,2,1,1,6
"1364",3873,24,21.9,1,4,0,1,13,0,4,1,1,2
"1365",171,24,21.9,1,4,0,0,15,0,0,1,1,5
"1366",5393,24,21.9,1,3,0,0,12,0,2,1,1,8
"1367",10139,24,22,1,2,0,0,10,1,2,1,1,9
"1368",7343,24,22,1,3,0,0,12,1,1,1,1,10
"1369",3781,24,22,1,2,0,0,12,0,1,1,1,6
"1370",1227,24,22,1,4,0,0,15,0,2,1,1,9
"1371",10065,24,22,1,1,0,0,13,1,1,1,1,6
"1372",10223,24,22,1,5,0,0,13,1,1,1,1,7
"1373",4583,24,22,1,2,0,0,10,0,0,1,1,10
"1374",8761,24,22,1,4,0,0,9,1,2,1,1,8
"1375",5743,24,22,1,3,0,0,13,1,1,1,1,9
"1376",47,24,22.1,1,1,0,0,14,0,0,1,1,8
"1377",3035,24,22.1,1,2,0,0,12,0,1,1,1,5
"1378",3171,24,22.1,1,3,0,0,15,0,2,1,1,6
"1379",3759,24,22.2,1,1,0,0,15,0,2,1,1,9
"1380",5341,24,22.2,1,2,0,0,10,0,2,1,1,9
"1381",1545,24,22.2,1,3,0,0,15,0,2,1,1,7
"1382",4149,24,22.2,1,2,0,0,15,0,2,1,1,10
"1383",5197,24,22.2,1,4,0,1,10,0,8,1,1,1
"1384",8285,24,22.4,1,4,0,0,13,1,1,1,1,7
"1385",5694,24,22.4,1,5,0,1,9,1,7,1,1,7
"1386",5151,24,22.4,1,1,0,0,12,0,2,1,1,7
"1387",4061,24,22.4,1,5,0,0,14,0,0,1,1,6
"1388",1939,24,22.4,1,3,0,0,15,0,2,1,1,8
"1389",1585,24,22.5,1,4,0,0,11,0,1,1,1,9
"1390",3575,24,22.5,1,4,0,0,12,0,1,1,1,5
"1391",8551,24,22.5,1,3,0,0,12,1,1,1,1,10
"1392",4689,24,22.5,1,2,0,1,10,0,6,1,1,4
"1393",4109,24,22.6,1,2,0,0,9,0,1,1,1,7
"1394",3038,24,22.6,1,2,0,1,11,0,7,1,1,0
"1395",2653,24,22.6,1,2,0,0,11,0,0,1,1,5
"1396",8181,24,22.6,1,4,0,0,12,1,1,1,1,7
"1397",4183,24,22.7,1,2,0,0,14,0,2,1,1,6
"1398",5623,24,22.7,1,1,0,0,14,1,2,1,1,8
"1399",67,24,22.8,1,1,0,0,13,0,2,1,1,6
"1400",3309,24,22.8,1,1,0,0,12,0,2,1,1,5
"1401",8969,24,22.8,1,4,0,0,9,1,1,1,1,10
"1402",5033,24,22.8,1,1,0,0,12,0,0,1,1,6
"1403",7727,24,22.8,1,4,0,0,12,1,2,1,1,7
"1404",777,24,22.9,1,3,0,0,9,0,1,1,1,10
"1405",1570,24,22.9,1,2,0,1,9,0,3,1,1,4
"1406",8563,24,22.9,1,5,0,0,11,1,2,1,1,5
"1407",5509,24,22.9,1,2,0,0,10,1,0,1,1,8
"1408",2909,24,23,1,1,0,0,11,0,0,1,1,7
"1409",2249,24,23,1,1,0,0,9,0,1,1,1,6
"1410",4297,24,23.1,1,3,0,0,14,0,0,1,1,9
"1411",10741,24,23.1,1,4,0,0,14,1,1,1,1,5
"1412",4345,24,23.1,1,3,0,0,10,0,2,1,1,10
"1413",9661,24,23.1,1,5,0,0,13,1,1,1,1,7
"1414",1691,24,23.2,1,1,0,0,12,0,0,1,1,7
"1415",7951,24,23.2,1,1,0,0,9,1,1,1,1,10
"1416",8851,24,23.3,1,1,0,0,15,1,1,1,1,8
"1417",8997,24,23.3,1,3,0,0,12,1,0,1,1,8
"1418",4256,24,23.3,1,5,0,1,12,0,8,1,1,9
"1419",9321,24,23.3,1,1,0,0,12,1,0,1,1,8
"1420",9893,24,23.3,1,4,0,0,11,1,1,1,1,9
"1421",4041,24,23.3,1,4,0,0,9,0,0,1,1,10
"1422",6117,24,23.3,1,4,0,0,10,1,1,1,1,9
"1423",5323,24,23.3,1,1,0,0,15,0,0,1,1,5
"1424",7683,24,23.3,1,2,0,0,12,1,2,1,1,10
"1425",6291,24,23.3,1,1,0,0,12,1,2,1,1,8
"1426",1049,24,23.4,1,2,0,0,13,0,2,1,1,10
"1427",3241,24,23.4,1,1,0,0,11,0,1,1,1,9
"1428",5903,24,23.4,1,4,0,0,13,1,2,1,1,9
"1429",1687,24,23.4,1,5,0,0,9,0,1,1,1,10
"1430",3819,24,23.4,1,2,0,0,10,0,1,1,1,10
"1431",7841,24,23.5,1,3,0,0,10,1,1,1,1,10
"1432",887,24,23.5,1,1,0,0,11,0,2,1,1,5
"1433",10855,24,23.5,1,1,0,0,9,1,2,1,1,10
"1434",659,24,23.5,1,3,0,0,13,0,1,1,1,10
"1435",10027,24,23.5,1,2,0,0,14,1,2,1,1,6
"1436",7223,24,23.5,1,2,0,0,15,1,0,1,1,10
"1437",10127,24,23.6,1,4,0,0,9,1,1,1,1,10
"1438",3989,24,23.6,1,1,0,0,15,0,2,1,1,6
"1439",4125,24,23.6,1,1,0,0,10,0,2,1,1,5
"1440",4575,24,23.6,1,3,0,0,13,0,0,1,1,10
"1441",10015,24,23.7,1,2,0,0,15,1,0,1,1,10
"1442",5113,24,23.7,1,2,0,0,14,0,0,1,1,7
"1443",5911,24,23.7,1,5,0,1,15,1,7,1,1,3
"1444",4627,24,23.7,1,1,0,0,11,0,0,1,1,8
"1445",10117,24,23.7,1,2,0,0,12,1,0,1,1,7
"1446",647,24,23.7,1,4,0,0,14,0,1,1,1,6
"1447",2915,24,23.7,1,1,0,0,15,0,2,1,1,10
"1448",4603,24,23.7,1,1,0,0,12,0,2,1,1,8
"1449",2609,24,23.7,1,1,0,0,15,0,2,1,1,6
"1450",7573,24,23.8,1,3,0,0,15,1,0,1,1,7
"1451",7113,24,23.8,1,4,0,0,10,1,2,1,1,8
"1452",10773,24,23.8,1,3,0,1,12,1,6,1,1,1
"1453",4637,24,23.8,1,5,0,0,12,0,0,1,1,6
"1454",6565,24,23.9,1,3,0,0,12,1,1,1,1,5
"1455",10235,24,23.9,1,1,0,0,12,1,2,1,1,6
"1456",623,24,23.9,1,5,0,0,12,0,1,1,1,9
"1457",3369,24,23.9,1,5,0,0,12,0,2,1,1,5
"1458",3479,24,24,1,2,0,0,9,0,2,1,1,10
"1459",7255,24,24,1,1,0,0,12,1,1,1,1,6
"1460",424,24,24,1,1,0,1,9,0,4,1,1,3
"1461",9591,24,24,1,1,0,0,10,1,2,1,1,6
"1462",5211,24,24,1,1,0,0,10,0,2,1,1,6
"1463",473,24,24.1,1,3,0,0,12,0,0,1,1,8
"1464",6257,24,24.1,1,3,0,0,15,1,0,1,1,10
"1465",10537,24,24.1,1,2,0,0,9,1,2,1,1,7
"1466",1961,24,24.1,1,4,0,0,15,0,0,1,1,8
"1467",4249,24,24.2,1,1,0,0,9,0,2,1,1,8
"1468",9597,24,24.2,1,5,0,0,12,1,0,1,1,9
"1469",1901,24,24.2,1,2,0,0,13,0,2,1,1,7
"1470",6721,24,24.2,1,4,0,0,11,1,0,1,1,6
"1471",8301,24,24.2,1,2,0,0,9,1,2,1,1,10
"1472",5363,24,24.3,1,3,0,0,9,0,0,1,1,6
"1473",6709,24,24.3,1,1,0,0,11,1,0,1,1,10
"1474",10561,24,24.3,1,5,0,0,11,1,2,1,1,10
"1475",1047,24,24.3,1,3,0,0,13,0,2,1,1,9
"1476",1793,24,24.3,1,1,0,0,10,0,1,1,1,9
"1477",5251,24,24.3,1,2,0,0,9,0,2,1,1,9
"1478",333,24,24.3,1,4,0,0,13,0,2,1,1,6
"1479",6623,24,24.3,1,3,0,0,13,1,2,1,1,5
"1480",2005,24,24.4,1,3,0,0,12,0,2,1,1,5
"1481",729,24,24.4,1,5,0,0,9,0,2,1,1,9
"1482",2213,24,24.4,1,4,0,0,12,0,1,1,1,8
"1483",10329,24,24.5,1,2,0,0,13,1,0,1,1,5
"1484",5049,24,24.5,1,3,0,0,9,0,1,1,1,7
"1485",425,24,24.5,1,5,0,0,13,0,1,1,1,7
"1486",2861,24,24.6,1,1,0,0,12,0,2,1,1,6
"1487",9559,24,24.6,1,3,0,0,13,1,1,1,1,8
"1488",5983,24,24.6,1,3,0,0,10,1,0,1,1,10
"1489",10769,24,24.6,1,4,0,0,12,1,1,1,1,10
"1490",1309,24,24.6,1,4,0,0,9,0,2,1,1,8
"1491",1087,24,24.6,1,5,0,0,10,0,2,1,1,7
"1492",3151,24,24.7,1,2,0,0,14,0,0,1,1,5
"1493",5309,24,24.7,1,4,0,0,9,0,2,1,1,5
"1494",10644,24,24.7,1,3,0,1,13,1,2,1,1,4
"1495",9627,24,24.7,1,2,0,0,10,1,2,1,1,7
"1496",3747,24,24.7,1,2,0,0,15,0,1,1,1,6
"1497",8323,24,24.8,1,4,0,0,11,1,2,1,1,7
"1498",5067,24,24.8,1,1,0,0,15,0,2,1,1,9
"1499",9315,24,24.9,1,1,0,0,9,1,2,1,1,10
"1500",7489,24,24.9,1,1,0,0,9,1,2,1,1,6
"1501",8333,24,25,1,1,0,0,13,1,1,1,1,8
"1502",193,24,25,1,3,0,0,9,0,2,1,1,8
"1503",2750,24,25,1,4,0,1,10,0,7,1,1,4
"1504",327,24,25.1,1,2,0,0,12,0,2,1,1,7
"1505",1495,24,25.1,1,5,0,0,12,0,1,1,1,7
"1506",4395,24,25.1,1,3,0,1,15,0,3,1,1,0
"1507",7629,24,25.1,1,3,0,0,15,1,2,1,1,6
"1508",8005,24,25.1,1,1,0,0,9,1,1,1,1,8
"1509",6345,24,25.2,1,3,0,0,12,1,1,1,1,10
"1510",3973,24,25.2,1,3,0,0,10,0,1,1,1,5
"1511",6621,24,25.2,1,3,0,0,12,1,2,1,1,8
"1512",269,24,25.3,1,3,0,0,15,0,0,1,1,8
"1513",6219,24,25.3,1,3,0,0,14,1,1,1,1,5
"1514",9017,24,25.3,1,3,0,0,14,1,1,1,1,7
"1515",10405,24,25.3,1,4,0,0,12,1,2,1,1,9
"1516",9573,24,25.3,1,1,0,0,9,1,1,1,1,6
"1517",387,24,25.3,1,5,0,1,12,0,4,1,1,2
"1518",8445,24,25.4,1,5,0,0,13,1,0,1,1,8
"1519",489,24,25.4,1,3,0,0,13,0,0,1,1,10
"1520",4517,24,25.5,1,5,0,0,15,0,1,1,1,9
"1521",8485,24,25.5,1,4,0,0,9,1,1,1,1,9
"1522",5581,24,25.5,1,5,0,0,12,1,2,1,1,10
"1523",8359,24,25.5,1,1,0,0,15,1,2,1,1,6
"1524",9029,24,25.5,1,3,0,0,14,1,2,1,1,8
"1525",10087,24,25.6,1,2,0,0,10,1,0,1,1,7
"1526",6461,24,25.6,1,3,0,0,12,1,0,1,1,6
"1527",1,24,25.6,1,3,0,0,14,0,0,1,1,7
"1528",2805,24,25.6,1,1,0,0,11,0,2,1,1,7
"1529",9271,24,25.7,1,2,0,0,15,1,0,1,1,6
"1530",10229,24,25.7,1,5,0,0,13,1,1,1,1,7
"1531",3403,24,25.7,1,3,0,0,11,0,0,1,1,10
"1532",8723,24,25.7,1,2,0,0,10,1,1,1,1,10
"1533",7997,24,25.7,1,1,0,0,13,1,2,1,1,6
"1534",1873,24,25.7,1,5,0,0,9,0,0,1,1,10
"1535",6069,24,25.7,1,5,0,0,9,1,1,1,1,9
"1536",1987,24,25.8,1,2,0,0,14,0,2,1,1,6
"1537",1043,24,25.8,1,3,0,0,15,0,0,1,1,10
"1538",9949,24,25.8,1,1,0,0,10,1,2,1,1,6
"1539",8946,24,25.8,1,1,0,1,14,1,8,1,1,6
"1540",2963,24,25.9,1,2,0,0,15,0,0,1,1,9
"1541",10785,24,25.9,1,5,0,0,15,1,1,1,1,9
"1542",7419,24,25.9,1,3,0,0,12,1,1,1,1,5
"1543",599,24,25.9,1,2,0,0,13,0,0,1,1,5
"1544",2591,24,25.9,1,1,0,0,11,0,1,1,1,8
"1545",9435,24,26,1,5,0,0,10,1,1,1,1,5
"1546",10323,24,26,1,3,0,0,9,1,2,1,1,7
"1547",10169,24,26,1,1,0,0,11,1,0,1,1,8
"1548",7873,24,26,1,3,0,0,15,1,0,1,1,6
"1549",3993,24,26,1,2,0,0,9,0,1,1,1,9
"1550",7013,24,26,1,4,0,0,9,1,0,1,1,5
"1551",2357,24,26,1,4,0,0,11,0,1,1,1,7
"1552",2661,24,26,1,2,0,0,11,0,1,1,1,9
"1553",839,24,26,1,5,0,0,12,0,0,1,1,6
"1554",4911,24,26,1,4,0,0,14,0,2,1,1,10
"1555",3027,24,26.1,1,5,0,0,14,0,1,1,1,8
"1556",9240,24,26.1,1,2,0,1,12,1,7,1,1,5
"1557",4201,24,26.1,1,1,0,0,11,0,2,1,1,7
"1558",6691,24,26.1,1,3,0,0,14,1,1,1,1,6
"1559",10111,24,26.1,1,3,0,0,11,1,0,1,1,10
"1560",6545,24,26.2,1,3,0,0,14,1,0,1,1,6
"1561",383,24,26.2,1,3,0,0,9,0,0,1,1,7
"1562",7581,24,26.3,1,5,0,0,12,1,0,1,1,10
"1563",2369,24,26.3,1,5,0,0,12,0,0,1,1,6
"1564",8447,24,26.3,1,2,0,0,15,1,1,1,1,7
"1565",2029,24,26.3,1,4,0,0,12,0,0,1,1,7
"1566",9445,24,26.3,1,5,0,0,12,1,2,1,1,8
"1567",2293,24,26.3,1,1,0,0,14,0,1,1,1,8
"1568",8091,24,26.3,1,1,0,0,14,1,0,1,1,7
"1569",660,24,26.3,1,4,0,1,12,0,3,1,1,8
"1570",9489,24,26.3,1,1,0,0,12,1,2,1,1,6
"1571",7233,24,26.4,1,5,0,0,11,1,0,1,1,7
"1572",10245,24,26.4,1,2,0,0,12,1,0,1,1,7
"1573",3127,24,26.5,1,1,0,0,13,0,2,1,1,8
"1574",705,24,26.5,1,2,0,0,14,0,2,1,1,5
"1575",7009,24,26.5,1,5,0,0,11,1,0,1,1,6
"1576",4713,24,26.5,1,5,0,0,12,0,0,1,1,8
"1577",9329,24,26.6,1,2,0,0,12,1,1,1,1,5
"1578",6571,24,26.6,1,5,0,0,10,1,1,1,1,8
"1579",7717,24,26.6,1,2,0,0,14,1,2,1,1,10
"1580",5679,24,26.6,1,3,0,0,9,1,2,1,1,9
"1581",8783,24,26.6,1,1,0,0,13,1,1,1,1,8
"1582",6205,24,26.6,1,2,0,0,10,1,2,1,1,10
"1583",4547,24,26.7,1,3,0,0,11,0,1,1,1,9
"1584",6133,24,26.7,1,5,0,0,12,1,2,1,1,8
"1585",2161,24,26.7,1,1,0,0,12,0,1,1,1,10
"1586",5851,24,26.8,1,5,0,0,10,1,2,1,1,9
"1587",10425,24,26.8,1,1,0,0,13,1,1,1,1,8
"1588",9803,24,26.8,1,4,0,0,10,1,1,1,1,8
"1589",1299,24,26.8,1,5,0,0,9,0,1,1,1,6
"1590",10281,24,26.8,1,1,0,0,12,1,1,1,1,8
"1591",1125,24,26.9,1,4,0,0,13,0,2,1,1,6
"1592",7299,24,26.9,1,5,0,0,12,1,0,1,1,7
"1593",4127,24,26.9,1,2,0,0,15,0,2,1,1,8
"1594",3267,24,26.9,1,2,0,0,11,0,1,1,1,10
"1595",2925,24,27,1,5,0,0,14,0,0,1,1,6
"1596",7617,24,27,1,3,0,0,12,1,1,1,1,5
"1597",4317,24,27,1,3,0,0,12,0,2,1,1,6
"1598",4907,24,27.1,1,2,0,0,9,0,0,1,1,5
"1599",8073,24,27.1,1,2,0,0,11,1,2,1,1,8
"1600",5923,24,27.2,1,3,0,0,11,1,2,1,1,9
"1601",5601,24,27.2,1,5,0,0,10,1,0,1,1,7
"1602",1437,24,27.3,1,2,0,1,14,0,8,1,1,4
"1603",10300,24,27.4,1,5,0,1,11,1,4,1,1,8
"1604",10181,24,27.4,1,5,0,0,12,1,2,1,1,6
"1605",7887,24,27.5,1,1,0,0,9,1,2,1,1,5
"1606",1971,24,27.5,1,2,0,0,11,0,1,1,1,5
"1607",7029,24,27.5,1,4,0,0,9,1,0,1,1,7
"1608",10455,24,27.5,1,4,0,0,12,1,0,1,1,5
"1609",6471,24,27.6,1,5,0,0,9,1,1,1,1,10
"1610",7281,24,27.6,1,4,0,0,9,1,1,1,1,9
"1611",4413,24,27.6,1,2,0,0,13,0,1,1,1,5
"1612",7443,24,27.6,1,5,0,0,15,1,2,1,1,5
"1613",4895,24,27.6,1,2,0,0,10,0,0,1,1,10
"1614",6485,24,27.7,1,1,0,0,12,1,1,1,1,6
"1615",3443,24,27.7,1,1,0,0,12,0,0,1,1,6
"1616",1697,24,27.7,1,5,0,0,12,0,1,1,1,10
"1617",2443,24,27.8,1,5,0,0,9,0,1,1,1,9
"1618",9149,24,27.8,1,1,0,0,10,1,0,1,1,10
"1619",3415,24,27.8,1,4,0,0,13,0,0,1,1,9
"1620",1486,24,27.8,1,3,0,1,12,0,2,1,1,1
"1621",10759,24,27.8,1,2,0,0,15,1,1,1,1,7
"1622",1819,24,27.8,1,4,0,0,11,0,1,1,1,5
"1623",6281,24,27.9,1,1,0,0,13,1,2,1,1,8
"1624",3997,24,27.9,1,1,0,0,15,0,0,1,1,8
"1625",6353,24,27.9,1,4,0,0,11,1,1,1,1,9
"1626",7151,24,27.9,1,1,0,0,12,1,1,1,1,8
"1627",4185,24,27.9,1,2,0,0,15,0,0,1,1,6
"1628",71,24,28,1,2,0,0,12,0,0,1,1,10
"1629",3211,24,28,1,2,0,0,15,0,2,1,1,10
"1630",9807,24,28,1,1,0,0,12,1,0,1,1,9
"1631",4619,24,28.1,1,2,0,0,11,0,2,1,1,6
"1632",3402,24,28.1,1,3,0,1,9,0,4,1,1,2
"1633",10353,24,28.2,1,5,0,0,10,1,0,1,1,6
"1634",5533,24,28.3,1,1,0,0,13,1,2,1,1,6
"1635",8410,24,28.3,1,3,0,1,13,1,3,1,1,5
"1636",2215,24,28.3,1,5,0,0,12,0,1,1,1,10
"1637",4043,24,28.3,1,5,0,0,9,0,0,1,1,10
"1638",6037,24,28.3,1,2,0,0,15,1,2,1,1,7
"1639",9055,24,28.4,1,5,0,0,10,1,1,1,1,9
"1640",4277,24,28.4,1,5,0,0,12,0,0,1,1,10
"1641",8905,24,28.4,1,5,0,0,10,1,2,1,1,5
"1642",9013,24,28.5,1,2,0,0,12,1,0,1,1,6
"1643",475,24,28.5,1,3,0,0,15,0,0,1,1,9
"1644",5001,24,28.5,1,5,0,1,13,0,8,1,1,0
"1645",9587,24,28.6,1,5,0,0,12,1,0,1,1,9
"1646",9791,24,28.6,1,1,0,0,10,1,0,1,1,9
"1647",7056,24,28.6,1,2,0,1,10,1,5,1,1,7
"1648",5303,24,28.6,1,4,0,0,12,0,2,1,1,7
"1649",2711,24,28.6,1,1,0,0,12,0,0,1,1,7
"1650",587,24,28.6,1,1,0,0,13,0,2,1,1,10
"1651",287,24,28.6,1,2,0,0,10,0,2,1,1,5
"1652",5631,24,28.7,1,2,0,0,9,1,0,1,1,5
"1653",3839,24,28.7,1,3,0,0,14,0,0,1,1,5
"1654",8315,24,28.7,1,1,0,0,9,1,1,1,1,7
"1655",8525,24,28.8,1,4,0,0,10,1,2,1,1,9
"1656",711,24,28.8,1,5,0,0,15,0,1,1,1,10
"1657",1831,24,28.8,1,5,0,0,13,0,0,1,1,10
"1658",8111,24,28.8,1,2,0,0,9,1,2,1,1,6
"1659",10617,24,28.8,1,5,0,0,10,1,1,1,1,6
"1660",4523,24,28.8,1,3,0,1,11,0,5,1,1,0
"1661",6425,24,28.8,1,5,0,0,12,1,1,1,1,9
"1662",7967,24,28.9,1,3,0,0,9,1,0,1,1,6
"1663",9249,24,28.9,1,2,0,0,13,1,0,1,1,5
"1664",1181,24,28.9,1,2,0,0,10,0,0,1,1,7
"1665",2701,24,28.9,1,2,0,0,11,0,0,1,1,9
"1666",5173,24,28.9,1,5,0,0,12,0,1,1,1,5
"1667",7483,24,28.9,1,1,0,0,9,1,1,1,1,7
"1668",5825,24,28.9,1,2,0,0,9,1,0,1,1,10
"1669",455,24,29,1,2,0,0,9,0,2,1,1,8
"1670",1569,24,29,1,2,0,0,13,0,0,1,1,9
"1671",5259,24,29,1,3,0,0,10,0,1,1,1,6
"1672",5253,24,29,1,1,0,0,9,0,1,1,1,8
"1673",3737,24,29.1,1,1,0,0,13,0,1,1,1,6
"1674",603,24,29.1,1,4,0,0,15,0,2,1,1,5
"1675",4869,24,29.2,1,2,0,0,11,0,2,1,1,6
"1676",951,24,29.2,1,4,0,0,14,0,0,1,1,9
"1677",1179,24,29.3,1,2,0,0,13,0,2,1,1,9
"1678",5399,24,29.3,1,5,0,0,13,0,1,1,1,8
"1679",1823,24,29.3,1,4,0,0,10,0,0,1,1,8
"1680",81,24,29.3,1,2,0,0,12,0,1,1,1,10
"1681",8899,24,29.3,1,2,0,0,12,1,1,1,1,7
"1682",2529,24,29.4,1,5,0,0,10,0,1,1,1,5
"1683",4129,24,29.5,1,4,0,0,11,0,2,1,1,7
"1684",3225,24,29.5,1,2,0,0,10,0,1,1,1,6
"1685",4795,24,29.5,1,2,0,0,11,0,1,1,1,10
"1686",6761,24,29.5,1,2,0,0,14,1,2,1,1,10
"1687",8917,24,29.6,1,4,0,0,12,1,2,1,1,9
"1688",195,24,29.6,1,3,0,0,9,0,1,1,1,9
"1689",8151,24,29.7,1,4,0,0,9,1,1,1,1,7
"1690",7917,24,29.7,1,2,0,0,13,1,2,1,1,7
"1691",10489,24,29.7,1,2,0,0,14,1,1,1,1,6
"1692",9279,24,29.7,1,1,0,0,12,1,2,1,1,7
"1693",6985,24,29.7,1,5,0,0,15,1,1,1,1,7
"1694",2067,24,29.7,1,1,0,0,10,0,0,1,1,9
"1695",8847,24,29.7,1,1,0,0,10,1,2,1,1,8
"1696",8727,24,29.8,1,2,0,0,14,1,1,1,1,6
"1697",355,24,29.8,1,4,0,0,10,0,1,1,1,8
"1698",1821,24,29.8,1,3,0,0,12,0,0,1,1,7
"1699",1365,24,29.8,1,5,0,0,15,0,1,1,1,10
"1700",2973,24,29.8,1,2,0,0,12,0,0,1,1,9
"1701",423,24,29.8,1,5,0,0,13,0,1,1,1,6
"1702",6901,24,29.8,1,3,0,0,13,1,2,1,1,7
"1703",6767,24,29.8,1,1,0,0,14,1,0,1,1,5
"1704",8077,24,29.9,1,2,0,0,11,1,1,1,1,8
"1705",3019,24,29.9,1,1,0,1,15,0,2,1,1,0
"1706",2217,24,30,1,4,0,0,15,0,0,1,1,7
"1707",3283,25,0.1,1,3,0,1,9,0,3,1,1,1
"1708",7138,25,0.5,1,5,0,1,12,1,7,1,1,4
"1709",9158,25,0.7,1,1,0,1,10,1,4,1,1,7
"1710",1940,25,0.8,1,5,0,1,15,0,1,1,1,7
"1711",7296,25,1,1,1,0,1,12,1,7,1,1,3
"1712",5206,25,1.3,1,1,0,1,15,0,5,1,1,5
"1713",6082,25,2.1,1,4,0,1,12,1,1,1,1,0
"1714",7892,25,2.3,1,2,0,1,10,1,7,1,1,4
"1715",2800,25,2.5,1,1,0,1,12,0,6,1,1,3
"1716",3742,25,2.7,1,4,0,1,10,0,6,1,1,10
"1717",446,25,2.8,1,1,0,1,9,0,5,1,1,8
"1718",4270,25,3.9,1,5,0,1,13,0,4,1,1,1
"1719",8932,25,4.4,1,3,0,1,12,1,6,1,1,7
"1720",574,25,4.5,1,4,0,1,15,0,7,1,1,1
"1721",6324,25,4.7,1,5,0,1,14,1,4,1,1,5
"1722",8607,25,4.8,1,4,0,1,14,1,3,1,1,2
"1723",10282,25,5.3,1,5,0,1,9,1,8,1,1,10
"1724",8217,25,5.5,1,3,0,1,10,1,6,1,1,2
"1725",9910,25,5.8,1,2,0,1,15,1,1,1,1,8
"1726",7548,25,6.2,1,5,0,1,9,1,6,1,1,10
"1727",2188,25,6.5,1,2,0,1,10,0,4,1,1,2
"1728",3440,25,6.5,1,1,0,1,12,0,3,1,1,6
"1729",6114,25,6.6,1,5,0,1,10,1,3,1,1,5
"1730",5338,25,6.7,1,2,0,1,12,0,5,1,1,1
"1731",5828,25,7,1,4,0,1,12,1,7,1,1,8
"1732",5528,25,7.4,1,4,0,1,11,1,2,1,1,2
"1733",4786,25,7.5,1,3,0,1,15,0,6,1,1,7
"1734",3192,25,8.1,1,1,0,1,14,0,2,1,1,2
"1735",5090,25,8.6,1,3,0,1,9,0,3,1,1,1
"1736",7734,25,8.7,1,3,0,1,9,1,6,1,1,7
"1737",7674,25,9.2,1,4,0,1,13,1,2,1,1,0
"1738",722,25,9.2,1,2,0,1,11,0,2,1,1,8
"1739",7934,25,9.4,1,4,0,1,11,1,3,1,1,9
"1740",8742,25,10,1,2,0,1,13,1,4,1,1,3
"1741",10790,25,10.1,1,1,0,1,12,1,5,1,1,2
"1742",1500,25,10.4,1,1,0,1,15,0,1,1,1,2
"1743",9034,25,10.8,1,1,0,1,9,1,4,1,1,6
"1744",8771,25,10.9,1,4,0,1,15,1,4,1,1,0
"1745",7991,25,11.1,1,5,0,1,12,1,6,1,1,3
"1746",9275,25,11.1,1,5,0,1,13,1,4,1,1,2
"1747",967,25,11.2,1,5,0,1,12,0,7,1,1,4
"1748",9005,25,11.2,1,5,0,1,12,1,6,1,1,1
"1749",8740,25,11.6,1,1,0,1,14,1,2,1,1,3
"1750",2355,25,12.9,1,2,0,1,14,0,4,1,1,3
"1751",4913,25,13.1,1,1,0,1,14,0,7,1,1,1
"1752",4392,25,13.2,1,5,0,1,14,0,6,1,1,1
"1753",2726,25,13.4,1,5,0,1,15,0,4,1,1,2
"1754",1006,25,13.5,1,5,0,1,14,0,6,1,1,4
"1755",1223,25,13.6,1,1,0,1,11,0,4,1,1,2
"1756",6161,25,13.7,1,4,0,1,13,1,7,1,1,2
"1757",7688,25,14,1,4,0,1,13,1,1,1,1,6
"1758",8144,25,14.3,1,2,0,1,12,1,5,1,1,7
"1759",10294,25,14.4,1,4,0,1,10,1,2,1,1,6
"1760",4002,25,15,1,5,0,1,12,0,6,1,1,3
"1761",9401,25,15,1,2,0,0,13,1,1,1,1,10
"1762",9123,25,15,1,3,0,0,13,1,2,1,1,5
"1763",4051,25,15,1,1,0,0,12,0,2,1,1,6
"1764",707,25,15,1,3,0,0,10,0,0,1,1,10
"1765",7499,25,15,1,4,0,0,15,1,2,1,1,8
"1766",5269,25,15.1,1,4,0,0,14,0,1,1,1,9
"1767",5925,25,15.1,1,5,0,0,10,1,0,1,1,5
"1768",2833,25,15.1,1,3,0,0,10,0,0,1,1,8
"1769",113,25,15.1,1,3,0,0,15,0,1,1,1,6
"1770",9255,25,15.2,1,1,0,0,10,1,2,1,1,7
"1771",7173,25,15.2,1,2,0,0,10,1,1,1,1,9
"1772",10265,25,15.2,1,4,0,0,14,1,0,1,1,10
"1773",8999,25,15.2,1,3,0,0,15,1,1,1,1,7
"1774",6387,25,15.2,1,5,0,0,13,1,2,1,1,6
"1775",5588,25,15.2,1,5,0,1,12,1,5,1,1,8
"1776",9001,25,15.3,1,1,0,0,15,1,0,1,1,5
"1777",7469,25,15.3,1,5,0,0,12,1,2,1,1,6
"1778",10377,25,15.3,1,1,0,0,15,1,1,1,1,7
"1779",2329,25,15.3,1,4,0,0,9,0,1,1,1,6
"1780",7833,25,15.3,1,3,0,0,12,1,1,1,1,9
"1781",10667,25,15.3,1,2,0,1,11,1,7,1,1,1
"1782",2167,25,15.3,1,2,0,0,15,0,1,1,1,6
"1783",10441,25,15.3,1,2,0,0,15,1,1,1,1,10
"1784",4247,25,15.3,1,3,0,0,9,0,1,1,1,6
"1785",7593,25,15.4,1,1,0,0,10,1,2,1,1,7
"1786",2035,25,15.4,1,1,0,0,9,0,0,1,1,8
"1787",3807,25,15.5,1,4,0,0,14,0,2,1,1,6
"1788",7705,25,15.5,1,1,0,0,12,1,2,1,1,7
"1789",5699,25,15.5,1,5,0,0,14,1,0,1,1,8
"1790",8348,25,15.5,1,4,0,1,15,1,6,1,1,8
"1791",6087,25,15.5,1,4,0,0,12,1,2,1,1,6
"1792",19,25,15.5,1,1,0,0,12,0,1,1,1,9
"1793",2353,25,15.6,1,2,0,0,12,0,2,1,1,8
"1794",7977,25,15.6,1,2,0,0,12,1,0,1,1,9
"1795",6536,25,15.7,1,2,0,1,13,1,8,1,1,3
"1796",8197,25,15.7,1,5,0,0,12,1,1,1,1,8
"1797",2391,25,15.7,1,1,0,0,15,0,2,1,1,6
"1798",4267,25,15.7,1,3,0,0,14,0,0,1,1,8
"1799",10819,25,15.8,1,4,0,0,12,1,0,1,1,9
"1800",5659,25,15.8,1,5,0,0,13,1,2,1,1,7
"1801",10333,25,15.8,1,1,0,0,12,1,2,1,1,10
"1802",5339,25,15.9,1,3,0,1,13,0,6,1,1,2
"1803",3833,25,15.9,1,1,0,0,12,0,0,1,1,6
"1804",2407,25,15.9,1,1,0,0,10,0,2,1,1,5
"1805",8541,25,15.9,1,4,0,0,12,1,1,1,1,10
"1806",10538,25,15.9,1,5,0,1,15,1,5,1,1,2
"1807",5157,25,15.9,1,2,0,1,10,0,8,1,1,0
"1808",101,25,16,1,4,0,0,13,0,2,1,1,6
"1809",1615,25,16.1,1,5,0,0,12,0,2,1,1,6
"1810",10729,25,16.1,1,1,0,0,12,1,0,1,1,6
"1811",9441,25,16.1,1,2,0,0,9,1,2,1,1,9
"1812",9253,25,16.2,1,5,0,0,13,1,1,1,1,8
"1813",9667,25,16.2,1,5,0,0,10,1,1,1,1,6
"1814",10033,25,16.2,1,4,0,0,14,1,2,1,1,7
"1815",1431,25,16.2,1,5,0,0,13,0,2,1,1,6
"1816",3311,25,16.2,1,5,0,0,11,0,2,1,1,10
"1817",4543,25,16.2,1,5,0,0,9,0,1,1,1,8
"1818",1729,25,16.2,1,1,0,0,15,0,2,1,1,10
"1819",5459,25,16.2,1,1,0,0,12,1,0,1,1,9
"1820",8497,25,16.3,1,1,0,0,14,1,1,1,1,5
"1821",2473,25,16.3,1,5,0,0,14,0,1,1,1,10
"1822",3942,25,16.3,1,5,0,1,13,0,4,1,1,1
"1823",2503,25,16.4,1,3,0,0,12,0,2,1,1,8
"1824",2741,25,16.4,1,1,0,0,11,0,2,1,1,8
"1825",4797,25,16.4,1,2,0,0,12,0,0,1,1,7
"1826",1911,25,16.4,1,1,0,0,12,0,1,1,1,9
"1827",3327,25,16.4,1,5,0,0,13,0,0,1,1,8
"1828",4111,25,16.4,1,4,0,0,14,0,1,1,1,9
"1829",3499,25,16.5,1,5,0,0,9,0,0,1,1,9
"1830",5663,25,16.5,1,2,0,0,11,1,0,1,1,8
"1831",10757,25,16.5,1,2,0,0,15,1,0,1,1,9
"1832",10461,25,16.5,1,3,0,0,9,1,1,1,1,7
"1833",1329,25,16.5,1,5,0,0,15,0,1,1,1,8
"1834",3943,25,16.6,1,4,0,0,11,0,1,1,1,10
"1835",10491,25,16.6,1,4,0,0,15,1,1,1,1,5
"1836",6045,25,16.6,1,3,0,0,15,1,1,1,1,6
"1837",4167,25,16.7,1,4,0,0,12,0,0,1,1,10
"1838",8135,25,16.7,1,5,0,0,9,1,0,1,1,10
"1839",7587,25,16.7,1,4,0,0,10,1,2,1,1,7
"1840",8653,25,16.7,1,4,0,0,13,1,0,1,1,10
"1841",4841,25,16.8,1,4,0,0,13,0,0,1,1,10
"1842",9975,25,16.8,1,1,0,0,14,1,2,1,1,7
"1843",4819,25,16.8,1,5,0,0,10,0,0,1,1,10
"1844",8179,25,16.8,1,2,0,0,10,1,2,1,1,7
"1845",7453,25,16.9,1,5,0,0,14,1,1,1,1,8
"1846",3963,25,17,1,1,0,0,14,0,1,1,1,10
"1847",3545,25,17,1,4,0,0,11,0,2,1,1,9
"1848",909,25,17,1,2,0,0,14,0,1,1,1,5
"1849",6591,25,17,1,1,0,0,11,1,0,1,1,9
"1850",9150,25,17,1,5,0,1,13,1,4,1,1,7
"1851",3445,25,17.1,1,5,0,0,13,0,1,1,1,9
"1852",1419,25,17.1,1,4,0,0,15,0,1,1,1,10
"1853",3585,25,17.1,1,4,0,0,11,0,2,1,1,7
"1854",5819,25,17.1,1,5,0,0,12,1,2,1,1,5
"1855",133,25,17.1,1,1,0,0,11,0,2,1,1,9
"1856",8529,25,17.2,1,1,0,0,14,1,0,1,1,5
"1857",2853,25,17.2,1,3,0,0,12,0,0,1,1,6
"1858",10509,25,17.2,1,2,0,0,10,1,1,1,1,9
"1859",1115,25,17.3,1,3,0,0,13,0,1,1,1,9
"1860",1447,25,17.3,1,2,0,0,9,0,0,1,1,8
"1861",3184,25,17.3,1,4,0,1,12,0,4,1,1,1
"1862",10835,25,17.3,1,3,0,0,13,1,1,1,1,6
"1863",4119,25,17.3,1,3,0,0,13,0,0,1,1,10
"1864",7653,25,17.4,1,5,0,0,9,1,2,1,1,7
"1865",5,25,17.4,1,2,0,0,13,0,1,1,1,6
"1866",1807,25,17.4,1,1,0,0,13,0,2,1,1,7
"1867",403,25,17.4,1,2,0,0,9,0,1,1,1,5
"1868",5057,25,17.5,1,3,0,0,15,0,0,1,1,5
"1869",10823,25,17.5,1,5,0,0,12,1,0,1,1,8
"1870",10069,25,17.5,1,1,0,0,9,1,1,1,1,9
"1871",1907,25,17.5,1,1,0,0,12,0,0,1,1,8
"1872",6415,25,17.5,1,5,0,0,12,1,1,1,1,10
"1873",9647,25,17.6,1,2,0,0,12,1,2,1,1,7
"1874",8419,25,17.6,1,5,0,0,15,1,0,1,1,9
"1875",1703,25,17.6,1,3,0,0,12,0,1,1,1,7
"1876",8639,25,17.6,1,3,0,0,10,1,2,1,1,9
"1877",9323,25,17.6,1,2,0,0,13,1,0,1,1,7
"1878",7329,25,17.7,1,4,0,0,11,1,1,1,1,5
"1879",461,25,17.7,1,5,0,0,14,0,0,1,1,10
"1880",6215,25,17.7,1,2,0,0,9,1,1,1,1,6
"1881",959,25,17.7,1,3,0,0,13,0,2,1,1,9
"1882",4763,25,17.7,1,3,0,0,12,0,0,1,1,7
"1883",6713,25,17.8,1,3,0,0,9,1,0,1,1,6
"1884",7323,25,17.8,1,2,0,0,14,1,1,1,1,9
"1885",1189,25,17.8,1,3,0,0,11,0,2,1,1,8
"1886",3687,25,17.8,1,4,0,0,14,0,1,1,1,9
"1887",573,25,17.9,1,1,0,0,12,0,1,1,1,5
"1888",5405,25,17.9,1,4,0,0,10,0,0,1,1,9
"1889",8459,25,17.9,1,4,0,0,12,1,0,1,1,9
"1890",5687,25,17.9,1,3,0,0,13,1,0,1,1,8
"1891",10515,25,17.9,1,4,0,0,14,1,2,1,1,10
"1892",1711,25,17.9,1,5,0,0,9,0,1,1,1,6
"1893",7835,25,17.9,1,5,0,0,12,1,0,1,1,8
"1894",9891,25,18,1,5,0,0,11,1,0,1,1,7
"1895",5417,25,18,1,5,0,0,12,0,1,1,1,7
"1896",6949,25,18,1,4,0,0,9,1,0,1,1,9
"1897",3879,25,18.1,1,4,0,0,15,0,2,1,1,5
"1898",33,25,18.1,1,2,0,0,11,0,0,1,1,7
"1899",3013,25,18.1,1,4,0,0,9,0,1,1,1,8
"1900",10001,25,18.1,1,5,0,0,9,1,2,1,1,8
"1901",10581,25,18.1,1,4,0,0,10,1,2,1,1,5
"1902",7627,25,18.1,1,3,0,0,14,1,0,1,1,5
"1903",5531,25,18.2,1,1,0,0,9,1,0,1,1,5
"1904",5937,25,18.2,1,3,0,0,14,1,0,1,1,5
"1905",4707,25,18.2,1,2,0,0,14,0,1,1,1,9
"1906",5005,25,18.3,1,1,0,0,15,0,0,1,1,6
"1907",379,25,18.3,1,2,0,0,13,0,0,1,1,8
"1908",1055,25,18.3,1,4,0,0,9,0,0,1,1,6
"1909",8183,25,18.3,1,3,0,0,13,1,1,1,1,9
"1910",7303,25,18.3,1,1,0,0,11,1,1,1,1,7
"1911",2676,25,18.3,1,5,0,1,15,0,5,1,1,9
"1912",2769,25,18.3,1,4,0,0,10,0,1,1,1,6
"1913",1033,25,18.4,1,2,0,0,15,0,1,1,1,9
"1914",9889,25,18.4,1,1,0,0,12,1,0,1,1,10
"1915",1305,25,18.4,1,5,0,0,14,0,2,1,1,7
"1916",9045,25,18.4,1,3,0,1,12,1,6,1,1,1
"1917",8633,25,18.4,1,4,0,0,12,1,1,1,1,8
"1918",9024,25,18.4,1,1,0,1,12,1,1,1,1,1
"1919",2276,25,18.5,1,4,0,1,15,0,6,1,1,10
"1920",6131,25,18.6,1,2,0,0,15,1,1,1,1,5
"1921",6971,25,18.6,1,2,0,0,10,1,1,1,1,6
"1922",7139,25,18.6,1,5,0,0,15,1,0,1,1,9
"1923",123,25,18.6,1,2,0,0,12,0,1,1,1,10
"1924",10555,25,18.6,1,2,0,0,15,1,1,1,1,7
"1925",7373,25,18.7,1,3,0,0,14,1,2,1,1,8
"1926",5145,25,18.7,1,1,0,0,12,0,1,1,1,6
"1927",4241,25,18.8,1,1,0,0,12,0,0,1,1,8
"1928",9231,25,18.8,1,2,0,0,13,1,2,1,1,6
"1929",6607,25,18.8,1,3,0,0,13,1,0,1,1,7
"1930",1757,25,18.8,1,4,0,0,13,0,0,1,1,7
"1931",3263,25,18.8,1,4,0,0,13,0,1,1,1,5
"1932",1063,25,18.9,1,5,0,0,11,0,2,1,1,10
"1933",9171,25,18.9,1,5,0,0,9,1,2,1,1,10
"1934",5013,25,18.9,1,2,0,0,9,0,0,1,1,5
"1935",3379,25,18.9,1,5,0,0,13,0,1,1,1,10
"1936",4897,25,18.9,1,4,0,0,13,0,1,1,1,6
"1937",8943,25,19,1,5,0,0,15,1,0,1,1,7
"1938",5263,25,19,1,4,0,0,14,0,2,1,1,9
"1939",2495,25,19,1,1,0,0,10,0,2,1,1,6
"1940",10865,25,19.1,1,1,0,0,13,1,2,1,1,10
"1941",4668,25,19.1,1,1,0,1,9,0,8,1,1,5
"1942",1655,25,19.1,1,4,0,0,15,0,1,1,1,10
"1943",2297,25,19.2,1,3,0,0,10,0,2,1,1,6
"1944",3401,25,19.2,1,2,0,0,15,0,1,1,1,5
"1945",6423,25,19.2,1,4,0,0,14,1,0,1,1,6
"1946",8657,25,19.2,1,5,0,0,15,1,2,1,1,5
"1947",1369,25,19.2,1,5,0,0,11,0,2,1,1,10
"1948",8267,25,19.3,1,2,0,0,13,1,0,1,1,5
"1949",9552,25,19.3,1,1,0,1,14,1,6,1,1,5
"1950",4750,25,19.3,1,2,0,1,10,0,8,1,1,1
"1951",1601,25,19.3,1,2,0,0,10,0,2,1,1,5
"1952",5297,25,19.3,1,5,0,0,15,0,0,1,1,5
"1953",2129,25,19.3,1,5,0,0,10,0,2,1,1,9
"1954",6501,25,19.4,1,4,0,0,15,1,0,1,1,10
"1955",4915,25,19.4,1,2,0,0,15,0,1,1,1,9
"1956",6967,25,19.4,1,3,0,0,15,1,1,1,1,10
"1957",4507,25,19.4,1,2,0,0,12,0,1,1,1,6
"1958",8113,25,19.4,1,5,0,0,14,1,0,1,1,8
"1959",3273,25,19.4,1,1,0,0,14,0,0,1,1,5
"1960",3723,25,19.5,1,2,0,0,10,0,0,1,1,8
"1961",241,25,19.5,1,5,0,0,13,0,2,1,1,9
"1962",9945,25,19.5,1,5,0,0,15,1,1,1,1,6
"1963",6525,25,19.5,1,1,0,0,14,1,2,1,1,9
"1964",4709,25,19.5,1,3,0,0,9,0,1,1,1,10
"1965",2941,25,19.5,1,4,0,0,12,0,0,1,1,9
"1966",31,25,19.6,1,5,0,1,14,0,1,1,1,1
"1967",2405,25,19.6,1,1,0,0,10,0,0,1,1,6
"1968",7821,25,19.6,1,3,0,0,13,1,0,1,1,8
"1969",3495,25,19.6,1,5,0,0,10,0,0,1,1,6
"1970",8929,25,19.7,1,4,0,0,10,1,1,1,1,8
"1971",10735,25,19.7,1,5,0,0,15,1,1,1,1,5
"1972",6289,25,19.7,1,2,0,0,12,1,1,1,1,7
"1973",9903,25,19.7,1,4,0,0,11,1,0,1,1,7
"1974",7691,25,19.7,1,4,0,0,11,1,1,1,1,9
"1975",7101,25,19.8,1,4,0,0,9,1,2,1,1,9
"1976",1327,25,19.8,1,5,0,0,15,0,1,1,1,10
"1977",373,25,19.8,1,4,0,0,14,0,2,1,1,8
"1978",3527,25,19.9,1,4,0,0,12,0,2,1,1,5
"1979",9905,25,19.9,1,2,0,0,9,1,2,1,1,8
"1980",6797,25,19.9,1,4,0,0,12,1,2,1,1,6
"1981",7011,25,19.9,1,3,0,0,15,1,1,1,1,8
"1982",127,25,19.9,1,2,0,0,12,0,1,1,1,8
"1983",185,25,20,1,2,0,0,15,0,0,1,1,6
"1984",3635,25,20,1,2,0,0,12,0,1,1,1,6
"1985",4965,25,20,1,4,0,0,11,0,0,1,1,5
"1986",1106,25,20,1,1,0,1,14,0,3,1,1,10
"1987",9389,25,20,1,2,0,0,14,1,2,1,1,5
"1988",9741,25,20.1,1,3,0,0,11,1,2,1,1,9
"1989",8825,25,20.1,1,1,0,0,12,1,2,1,1,7
"1990",7405,25,20.1,1,2,0,0,11,1,1,1,1,9
"1991",181,25,20.2,1,5,0,0,12,0,1,1,1,8
"1992",10141,25,20.2,1,2,0,0,10,1,1,1,1,8
"1993",8417,25,20.2,1,5,0,0,9,1,2,1,1,9
"1994",6065,25,20.2,1,2,0,0,12,1,0,1,1,7
"1995",2449,25,20.2,1,2,0,1,12,0,4,1,1,2
"1996",3179,25,20.3,1,3,0,0,10,0,0,1,1,6
"1997",4834,25,20.3,1,3,0,1,11,0,2,1,1,9
"1998",1663,25,20.3,1,1,0,0,15,0,2,1,1,10
"1999",9691,25,20.3,1,5,0,0,13,1,2,1,1,9
"2000",10271,25,20.4,1,2,0,0,10,1,2,1,1,8
"2001",3105,25,20.5,1,2,0,1,13,0,8,1,1,0
"2002",6189,25,20.5,1,3,0,0,13,1,0,1,1,5
"2003",10289,25,20.5,1,3,0,0,10,1,1,1,1,8
"2004",5745,25,20.5,1,2,0,0,15,1,2,1,1,9
"2005",855,25,20.6,1,4,0,0,11,0,2,1,1,10
"2006",2089,25,20.6,1,2,0,0,11,0,2,1,1,10
"2007",6339,25,20.6,1,1,0,0,12,1,1,1,1,8
"2008",4273,25,20.6,1,5,0,0,13,0,1,1,1,6
"2009",5777,25,20.6,1,4,0,0,12,1,1,1,1,6
"2010",10579,25,20.6,1,4,0,0,12,1,1,1,1,6
"2011",7713,25,20.6,1,1,0,0,12,1,1,1,1,5
"2012",3955,25,20.7,1,5,0,0,13,0,2,1,1,7
"2013",7065,25,20.7,1,5,0,0,9,1,2,1,1,5
"2014",2959,25,20.7,1,1,0,0,9,0,1,1,1,9
"2015",2953,25,20.7,1,3,0,0,9,0,0,1,1,7
"2016",53,25,20.7,1,5,0,0,13,0,1,1,1,9
"2017",8859,25,20.7,1,2,0,0,13,1,0,1,1,9
"2018",237,25,20.7,1,2,0,0,12,0,1,1,1,10
"2019",2335,25,20.8,1,1,0,0,9,0,2,1,1,8
"2020",10165,25,20.8,1,3,0,0,10,1,2,1,1,7
"2021",4663,25,20.8,1,2,0,1,12,0,8,1,1,4
"2022",1539,25,20.8,1,5,0,0,14,0,2,1,1,10
"2023",8453,25,20.8,1,4,0,0,11,1,2,1,1,9
"2024",2533,25,20.8,1,3,0,0,15,0,2,1,1,9
"2025",9151,25,20.9,1,3,0,1,14,1,7,1,1,3
"2026",3801,25,21,1,2,0,0,15,0,1,1,1,10
"2027",4377,25,21,1,2,0,0,13,0,0,1,1,8
"2028",1993,25,21,1,1,0,0,14,0,1,1,1,7
"2029",1251,25,21,1,5,0,0,12,0,1,1,1,10
"2030",1625,25,21,1,4,0,0,11,0,2,1,1,5
"2031",273,25,21,1,3,0,0,12,0,2,1,1,10
"2032",4601,25,21.1,1,2,0,0,14,0,0,1,1,5
"2033",6675,25,21.1,1,5,0,0,9,1,2,1,1,5
"2034",2613,25,21.1,1,2,0,0,11,0,0,1,1,7
"2035",239,25,21.1,1,5,0,0,10,0,2,1,1,10
"2036",5407,25,21.2,1,3,0,0,12,0,0,1,1,8
"2037",4177,25,21.2,1,1,0,0,11,0,0,1,1,5
"2038",7311,25,21.2,1,3,0,0,9,1,2,1,1,5
"2039",521,25,21.2,1,5,0,0,15,0,2,1,1,8
"2040",3345,25,21.2,1,4,0,0,9,0,1,1,1,6
"2041",9475,25,21.3,1,5,0,0,11,1,0,1,1,10
"2042",4845,25,21.3,1,3,0,0,15,0,0,1,1,5
"2043",2895,25,21.3,1,1,0,0,12,0,2,1,1,5
"2044",2269,25,21.3,1,5,0,0,9,0,1,1,1,7
"2045",4505,25,21.3,1,3,0,0,13,0,0,1,1,5
"2046",5993,25,21.4,1,5,0,0,12,1,2,1,1,10
"2047",7031,25,21.4,1,2,0,0,11,1,0,1,1,8
"2048",9299,25,21.4,1,4,0,0,12,1,1,1,1,7
"2049",105,25,21.4,1,1,0,0,11,0,1,1,1,6
"2050",161,25,21.4,1,4,0,0,11,0,0,1,1,10
"2051",3561,25,21.4,1,4,0,0,13,0,0,1,1,9
"2052",1988,25,21.4,1,1,0,1,11,0,6,1,1,6
"2053",7205,25,21.4,1,2,0,0,14,1,1,1,1,9
"2054",8543,25,21.4,1,4,0,0,11,1,1,1,1,6
"2055",8271,25,21.5,1,5,0,0,12,1,0,1,1,9
"2056",8051,25,21.5,1,4,0,0,10,1,1,1,1,10
"2057",1647,25,21.6,1,5,0,0,11,0,1,1,1,10
"2058",8056,25,21.7,1,1,0,1,15,1,7,1,1,3
"2059",9137,25,21.7,1,2,0,0,14,1,1,1,1,9
"2060",7385,25,21.7,1,5,0,0,13,1,0,1,1,8
"2061",2469,25,21.7,1,3,0,0,14,0,2,1,1,9
"2062",2749,25,21.7,1,1,0,0,12,0,0,1,1,8
"2063",8741,25,21.8,1,3,0,0,14,1,2,1,1,9
"2064",2291,25,21.8,1,5,0,0,10,0,1,1,1,9
"2065",8207,25,21.8,1,5,0,0,10,1,2,1,1,5
"2066",1035,25,21.8,1,4,0,0,9,0,1,1,1,9
"2067",8559,25,21.8,1,5,0,0,13,1,1,1,1,7
"2068",5449,25,21.8,1,3,0,0,12,1,2,1,1,7
"2069",8991,25,21.9,1,4,0,0,13,1,1,1,1,8
"2070",9639,25,22,1,3,0,0,12,1,2,1,1,9
"2071",6301,25,22,1,4,0,0,9,1,1,1,1,7
"2072",7861,25,22,1,4,0,0,12,1,0,1,1,8
"2073",10619,25,22,1,5,0,0,14,1,0,1,1,5
"2074",4871,25,22,1,3,0,0,11,0,0,1,1,6
"2075",7219,25,22,1,3,0,0,12,1,0,1,1,6
"2076",91,25,22,1,5,0,0,12,0,2,1,1,8
"2077",2687,25,22,1,5,0,0,10,0,2,1,1,6
"2078",2785,25,22,1,3,0,0,10,0,1,1,1,6
"2079",3185,25,22.1,1,3,0,0,12,0,1,1,1,7
"2080",1985,25,22.1,1,3,0,0,12,0,1,1,1,7
"2081",1883,25,22.1,1,4,0,0,12,0,1,1,1,5
"2082",10657,25,22.1,1,3,0,0,13,1,0,1,1,5
"2083",833,25,22.1,1,1,0,0,13,0,1,1,1,8
"2084",9615,25,22.1,1,2,0,0,14,1,0,1,1,8
"2085",539,25,22.1,1,5,0,0,13,0,1,1,1,5
"2086",4139,25,22.2,1,4,0,0,12,0,1,1,1,6
"2087",2511,25,22.2,1,5,0,0,11,0,0,1,1,8
"2088",9305,25,22.2,1,1,0,0,12,1,0,1,1,9
"2089",3783,25,22.3,1,5,0,0,15,0,2,1,1,7
"2090",3735,25,22.3,1,2,0,0,12,0,0,1,1,8
"2091",341,25,22.3,1,5,0,0,11,0,0,1,1,9
"2092",3991,25,22.4,1,1,0,0,12,0,2,1,1,10
"2093",899,25,22.4,1,3,0,0,9,0,2,1,1,9
"2094",10397,25,22.4,1,4,0,0,10,1,2,1,1,7
"2095",6687,25,22.5,1,4,0,0,14,1,2,1,1,7
"2096",10147,25,22.5,1,3,0,0,15,1,0,1,1,7
"2097",10409,25,22.5,1,3,0,0,12,1,0,1,1,10
"2098",9211,25,22.5,1,2,0,0,12,1,0,1,1,8
"2099",7891,25,22.5,1,1,0,0,13,1,0,1,1,5
"2100",2961,25,22.5,1,5,0,0,12,0,2,1,1,7
"2101",6883,25,22.6,1,1,0,0,15,1,2,1,1,10
"2102",6330,25,22.6,1,5,0,1,9,1,6,1,1,7
"2103",3063,25,22.6,1,4,0,0,13,0,0,1,1,6
"2104",4161,25,22.6,1,2,0,0,13,0,2,1,1,8
"2105",1204,25,22.7,1,1,0,1,12,0,1,1,1,3
"2106",6841,25,22.7,1,3,0,0,14,1,2,1,1,5
"2107",6225,25,22.7,1,5,0,0,15,1,2,1,1,5
"2108",4587,25,22.7,1,3,0,0,14,0,0,1,1,6
"2109",1787,25,22.7,1,2,0,0,12,0,1,1,1,8
"2110",3869,25,22.7,1,1,0,0,12,0,1,1,1,9
"2111",5987,25,22.7,1,3,0,0,15,1,1,1,1,9
"2112",7577,25,22.7,1,2,0,0,11,1,0,1,1,5
"2113",5155,25,22.8,1,1,0,0,13,0,2,1,1,5
"2114",6689,25,22.8,1,3,0,0,12,1,2,1,1,7
"2115",559,25,22.8,1,5,0,0,11,0,0,1,1,9
"2116",10705,25,22.9,1,1,0,0,12,1,1,1,1,8
"2117",1603,25,22.9,1,4,0,0,14,0,0,1,1,5
"2118",631,25,22.9,1,1,0,0,11,0,2,1,1,10
"2119",36,25,22.9,1,2,0,1,14,0,5,1,1,1
"2120",2775,25,22.9,1,2,0,0,14,0,0,1,1,8
"2121",5955,25,22.9,1,3,0,0,10,1,1,1,1,8
"2122",5284,25,23,1,5,0,1,10,0,7,1,1,10
"2123",3856,25,23,1,1,0,1,10,0,2,1,1,7
"2124",8462,25,23,1,5,0,1,10,1,6,1,1,1
"2125",6719,25,23,1,1,0,0,10,1,1,1,1,5
"2126",8241,25,23.1,1,2,0,0,11,1,1,1,1,7
"2127",2848,25,23.1,1,2,0,1,9,0,2,1,1,2
"2128",3073,25,23.1,1,4,0,0,12,0,1,1,1,9
"2129",1533,25,23.2,1,5,0,0,13,0,2,1,1,9
"2130",5829,25,23.2,1,3,0,0,13,1,0,1,1,7
"2131",2929,25,23.2,1,5,0,0,14,0,2,1,1,9
"2132",7467,25,23.2,1,4,0,0,14,1,1,1,1,6
"2133",8399,25,23.2,1,4,0,0,12,1,0,1,1,10
"2134",5383,25,23.2,1,3,0,0,10,0,0,1,1,7
"2135",3097,25,23.3,1,5,0,0,12,0,0,1,1,9
"2136",5097,25,23.3,1,5,0,0,10,0,2,1,1,7
"2137",7171,25,23.3,1,5,0,0,15,1,1,1,1,6
"2138",4195,25,23.4,1,3,0,0,11,0,1,1,1,6
"2139",207,25,23.4,1,4,0,0,15,0,2,1,1,5
"2140",5789,25,23.4,1,4,0,0,11,1,1,1,1,9
"2141",6321,25,23.4,1,1,0,0,12,1,2,1,1,7
"2142",1855,25,23.4,1,1,0,0,10,0,1,1,1,6
"2143",7711,25,23.4,1,2,0,0,12,1,2,1,1,9
"2144",931,25,23.4,1,1,0,0,15,0,2,1,1,8
"2145",2849,25,23.4,1,2,0,0,12,0,2,1,1,9
"2146",4290,25,23.5,1,3,0,1,15,0,3,1,1,0
"2147",827,25,23.5,1,3,0,0,14,0,1,1,1,9
"2148",10711,25,23.5,1,5,0,0,14,1,1,1,1,9
"2149",3537,25,23.5,1,2,0,0,15,0,1,1,1,9
"2150",9417,25,23.5,1,5,0,0,14,1,2,1,1,9
"2151",6017,25,23.5,1,5,0,0,9,1,2,1,1,8
"2152",2445,25,23.5,1,5,0,0,11,0,1,1,1,5
"2153",5926,25,23.5,1,2,0,1,11,1,8,1,1,3
"2154",2513,25,23.5,1,1,0,0,12,0,2,1,1,8
"2155",5377,25,23.6,1,1,0,0,9,0,2,1,1,5
"2156",957,25,23.6,1,2,0,0,12,0,1,1,1,9
"2157",6991,25,23.6,1,5,0,1,12,1,6,1,1,2
"2158",7647,25,23.6,1,3,0,0,13,1,0,1,1,10
"2159",1385,25,23.6,1,1,0,0,15,0,2,1,1,5
"2160",3295,25,23.6,1,1,0,0,10,0,2,1,1,7
"2161",8185,25,23.6,1,3,0,0,15,1,1,1,1,7
"2162",9455,25,23.7,1,3,0,0,12,1,2,1,1,10
"2163",259,25,23.7,1,4,0,0,10,0,1,1,1,10
"2164",2974,25,23.7,1,5,0,1,15,0,4,1,1,5
"2165",9747,25,23.8,1,3,0,0,10,1,0,1,1,9
"2166",10781,25,23.8,1,3,0,0,15,1,0,1,1,9
"2167",9333,25,23.8,1,1,0,0,11,1,1,1,1,5
"2168",2759,25,23.8,1,2,0,1,12,0,6,1,1,4
"2169",9629,25,23.9,1,2,0,0,10,1,0,1,1,7
"2170",1953,25,23.9,1,1,0,0,15,0,1,1,1,6
"2171",4875,25,24,1,3,0,0,9,0,1,1,1,6
"2172",4339,25,24,1,1,0,0,10,0,2,1,1,10
"2173",5017,25,24,1,3,0,1,9,0,8,1,1,2
"2174",9809,25,24,1,4,0,0,11,1,1,1,1,5
"2175",7121,25,24,1,1,0,0,9,1,2,1,1,7
"2176",3953,25,24.1,1,3,0,0,15,0,1,1,1,8
"2177",10519,25,24.1,1,5,0,0,13,1,0,1,1,7
"2178",661,25,24.2,1,5,0,0,15,0,1,1,1,8
"2179",5077,25,24.2,1,3,0,0,12,0,1,1,1,7
"2180",1777,25,24.2,1,2,0,0,13,0,0,1,1,7
"2181",279,25,24.2,1,5,0,0,11,0,2,1,1,9
"2182",5217,25,24.2,1,5,0,0,15,0,0,1,1,8
"2183",8737,25,24.2,1,3,0,0,12,1,1,1,1,9
"2184",4329,25,24.2,1,2,0,0,14,0,0,1,1,6
"2185",10217,25,24.3,1,2,0,0,14,1,0,1,1,5
"2186",785,25,24.3,1,5,0,0,12,0,1,1,1,9
"2187",3338,25,24.3,1,5,0,1,12,0,3,1,1,3
"2188",6495,25,24.3,1,4,0,0,9,1,0,1,1,5
"2189",5741,25,24.3,1,3,0,0,9,1,0,1,1,7
"2190",3039,25,24.3,1,2,0,0,12,0,2,1,1,5
"2191",3800,25,24.4,1,4,0,1,12,0,4,1,1,8
"2192",9921,25,24.4,1,5,0,0,12,1,0,1,1,9
"2193",4598,25,24.4,1,5,0,1,10,0,1,1,1,1
"2194",10599,25,24.5,1,1,0,0,12,1,2,1,1,7
"2195",8927,25,24.5,1,2,0,0,10,1,1,1,1,5
"2196",7487,25,24.5,1,1,0,0,9,1,0,1,1,10
"2197",7061,25,24.5,1,2,0,0,12,1,0,1,1,7
"2198",751,25,24.5,1,1,0,0,13,0,2,1,1,5
"2199",7274,25,24.6,1,2,0,1,11,1,6,1,1,5
"2200",5495,25,24.6,1,5,0,0,10,1,1,1,1,5
"2201",3599,25,24.7,1,3,0,0,10,0,2,1,1,6
"2202",2579,25,24.7,1,1,0,0,11,0,2,1,1,9
"2203",4307,25,24.7,1,3,0,0,12,0,2,1,1,10
"2204",5129,25,24.8,1,3,0,0,13,0,0,1,1,6
"2205",5815,25,24.8,1,3,0,0,14,1,0,1,1,5
"2206",7059,25,24.8,1,4,0,0,9,1,1,1,1,5
"2207",6299,25,24.9,1,1,0,0,14,1,1,1,1,9
"2208",3025,25,24.9,1,3,0,0,12,0,2,1,1,6
"2209",3861,25,24.9,1,4,0,0,13,0,1,1,1,8
"2210",3473,25,24.9,1,3,0,0,9,0,0,1,1,6
"2211",3535,25,25,1,2,0,0,15,0,2,1,1,10
"2212",799,25,25,1,2,0,0,12,0,1,1,1,10
"2213",9335,25,25,1,5,0,0,15,1,2,1,1,5
"2214",8265,25,25,1,1,0,0,14,1,0,1,1,8
"2215",5018,25,25,1,4,0,1,11,0,1,1,1,5
"2216",4975,25,25,1,2,0,0,11,0,2,1,1,9
"2217",8391,25,25,1,3,0,0,14,1,0,1,1,9
"2218",3477,25,25,1,5,0,0,12,0,1,1,1,6
"2219",10403,25,25.1,1,2,0,0,13,1,0,1,1,7
"2220",1383,25,25.1,1,1,0,0,9,0,1,1,1,7
"2221",9025,25,25.2,1,1,0,0,12,1,1,1,1,9
"2222",7869,25,25.2,1,4,0,0,10,1,2,1,1,7
"2223",1455,25,25.2,1,4,0,0,12,0,2,1,1,10
"2224",2055,25,25.2,1,3,0,0,14,0,1,1,1,8
"2225",1086,25,25.2,1,4,0,1,14,0,2,1,1,5
"2226",3653,25,25.3,1,2,0,0,10,0,1,1,1,10
"2227",2465,25,25.3,1,1,0,0,15,0,2,1,1,8
"2228",3341,25,25.3,1,1,0,0,13,0,2,1,1,9
"2229",8223,25,25.3,1,3,0,0,14,1,0,1,1,9
"2230",985,25,25.3,1,5,0,0,14,0,1,1,1,7
"2231",8395,25,25.4,1,3,0,0,10,1,2,1,1,7
"2232",5109,25,25.4,1,4,0,0,13,0,1,1,1,6
"2233",5595,25,25.4,1,1,0,0,13,1,2,1,1,10
"2234",6049,25,25.4,1,2,0,0,9,1,0,1,1,8
"2235",4749,25,25.4,1,1,0,0,12,0,0,1,1,9
"2236",5875,25,25.4,1,3,0,0,11,1,0,1,1,9
"2237",2143,25,25.4,1,5,0,0,11,0,1,1,1,7
"2238",4631,25,25.5,1,1,0,0,14,0,1,1,1,10
"2239",1467,25,25.5,1,3,0,0,10,0,1,1,1,7
"2240",2998,25,25.5,1,2,0,1,14,0,8,1,1,8
"2241",7961,25,25.5,1,5,0,0,9,1,2,1,1,5
"2242",1071,25,25.5,1,5,0,0,9,0,2,1,1,7
"2243",8826,25,25.5,1,2,0,1,15,1,2,1,1,7
"2244",991,25,25.5,1,2,0,0,12,0,1,1,1,6
"2245",3485,25,25.5,1,4,0,0,12,0,1,1,1,7
"2246",2007,25,25.5,1,3,0,0,14,0,0,1,1,6
"2247",9961,25,25.6,1,4,0,0,9,1,1,1,1,10
"2248",10143,25,25.6,1,5,0,0,15,1,2,1,1,9
"2249",1105,25,25.7,1,3,0,0,11,0,0,1,1,8
"2250",8855,25,25.7,1,5,0,0,14,1,0,1,1,9
"2251",719,25,25.7,1,4,0,0,12,0,2,1,1,9
"2252",4263,25,25.8,1,4,0,0,13,0,1,1,1,7
"2253",9353,25,25.8,1,1,0,0,15,1,1,1,1,7
"2254",4981,25,25.8,1,5,0,0,12,0,1,1,1,10
"2255",9459,25,25.9,1,2,0,0,14,1,1,1,1,7
"2256",6861,25,25.9,1,1,0,0,9,1,2,1,1,8
"2257",8117,25,25.9,1,5,0,0,15,1,0,1,1,7
"2258",2755,25,25.9,1,3,0,0,11,0,1,1,1,10
"2259",8953,25,25.9,1,2,0,0,14,1,2,1,1,10
"2260",1387,25,25.9,1,3,0,0,10,0,2,1,1,7
"2261",7321,25,25.9,1,1,0,0,14,1,2,1,1,7
"2262",147,25,26.1,1,2,0,0,12,0,1,1,1,8
"2263",6629,25,26.1,1,5,0,0,11,1,1,1,1,6
"2264",1307,25,26.2,1,1,0,0,11,0,0,1,1,5
"2265",10319,25,26.2,1,2,0,0,15,1,0,1,1,10
"2266",4787,25,26.2,1,3,0,0,12,0,0,1,1,5
"2267",6075,25,26.2,1,1,0,0,11,1,0,1,1,6
"2268",2893,25,26.3,1,4,0,0,10,0,2,1,1,5
"2269",6221,25,26.3,1,1,0,0,12,1,1,1,1,9
"2270",4485,25,26.3,1,2,0,0,10,0,2,1,1,6
"2271",1073,25,26.4,1,5,0,0,12,0,1,1,1,5
"2272",9069,25,26.4,1,5,0,0,12,1,0,1,1,10
"2273",9177,25,26.4,1,1,0,0,15,1,1,1,1,9
"2274",1507,25,26.4,1,5,0,0,12,0,1,1,1,6
"2275",1339,25,26.4,1,5,0,0,13,0,0,1,1,8
"2276",2569,25,26.4,1,3,0,0,10,0,0,1,1,10
"2277",9551,25,26.4,1,4,0,0,13,1,0,1,1,10
"2278",405,25,26.4,1,5,0,0,11,0,2,1,1,6
"2279",5728,25,26.4,1,1,0,1,9,1,8,1,1,8
"2280",7774,25,26.5,1,5,0,1,11,1,5,1,1,10
"2281",10695,25,26.5,1,5,0,0,14,1,0,1,1,8
"2282",4759,25,26.6,1,3,0,0,14,0,2,1,1,10
"2283",7747,25,26.6,1,4,0,0,9,1,0,1,1,10
"2284",1723,25,26.6,1,5,0,0,9,0,2,1,1,6
"2285",6557,25,26.6,1,5,0,0,9,1,1,1,1,8
"2286",6651,25,26.7,1,4,0,0,11,1,0,1,1,6
"2287",4805,25,26.7,1,1,0,0,12,0,1,1,1,10
"2288",7701,25,26.7,1,5,0,0,12,1,2,1,1,7
"2289",3657,25,26.7,1,5,0,0,14,0,1,1,1,5
"2290",8313,25,26.7,1,1,0,0,12,1,2,1,1,9
"2291",7097,25,26.8,1,2,0,0,14,1,0,1,1,5
"2292",6023,25,26.8,1,3,0,0,11,1,2,1,1,7
"2293",4433,25,26.8,1,4,0,0,9,0,0,1,1,7
"2294",9995,25,26.9,1,1,0,0,13,1,0,1,1,9
"2295",2821,25,26.9,1,2,0,0,15,0,1,1,1,9
"2296",7481,25,26.9,1,3,0,0,15,1,0,1,1,8
"2297",7569,25,26.9,1,1,0,0,13,1,0,1,1,10
"2298",2907,25,26.9,1,4,0,0,11,0,1,1,1,7
"2299",1487,25,27,1,3,0,0,12,0,2,1,1,10
"2300",2839,25,27,1,5,0,0,14,0,0,1,1,6
"2301",7655,25,27,1,4,0,0,13,1,1,1,1,7
"2302",2773,25,27,1,3,0,0,10,0,2,1,1,7
"2303",9738,25,27,1,2,0,1,15,1,6,1,1,0
"2304",2777,25,27,1,2,0,0,15,0,1,1,1,6
"2305",10525,25,27.1,1,2,0,0,13,1,1,1,1,5
"2306",308,25,27.1,1,2,0,1,13,0,4,1,1,10
"2307",7785,25,27.1,1,3,0,0,14,1,2,1,1,6
"2308",3137,25,27.1,1,4,0,0,10,0,1,1,1,5
"2309",13,25,27.1,1,1,0,0,12,0,2,1,1,9
"2310",7243,25,27.1,1,3,0,0,14,1,0,1,1,9
"2311",6191,25,27.2,1,1,0,0,10,1,0,1,1,10
"2312",9537,25,27.2,1,1,0,0,14,1,1,1,1,5
"2313",4623,25,27.2,1,2,0,0,10,0,0,1,1,10
"2314",45,25,27.2,1,5,0,0,14,0,1,1,1,10
"2315",1023,25,27.2,1,1,0,0,15,0,1,1,1,6
"2316",9243,25,27.2,1,5,0,1,13,1,5,1,1,1
"2317",1899,25,27.3,1,5,0,0,12,0,1,1,1,6
"2318",1459,25,27.3,1,3,0,0,15,0,0,1,1,5
"2319",4245,25,27.3,1,4,0,0,10,0,0,1,1,9
"2320",1721,25,27.3,1,5,0,0,12,0,2,1,1,8
"2321",337,25,27.3,1,3,0,0,9,0,1,1,1,9
"2322",7919,25,27.3,1,1,0,0,14,1,2,1,1,5
"2323",6911,25,27.3,1,5,0,0,13,1,2,1,1,8
"2324",6601,25,27.4,1,3,0,0,10,1,0,1,1,9
"2325",2277,25,27.4,1,1,0,0,9,0,2,1,1,6
"2326",1235,25,27.5,1,4,0,0,11,0,1,1,1,5
"2327",2605,25,27.5,1,1,0,0,10,0,0,1,1,5
"2328",5641,25,27.5,1,1,0,0,9,1,0,1,1,10
"2329",3571,25,27.6,1,3,0,0,13,0,0,1,1,8
"2330",2025,25,27.6,1,2,0,0,9,0,0,1,1,7
"2331",1093,25,27.6,1,1,0,0,10,0,1,1,1,9
"2332",3881,25,27.7,1,1,0,0,14,0,1,1,1,10
"2333",4181,25,27.7,1,4,0,0,11,0,0,1,1,10
"2334",4717,25,27.7,1,3,0,0,11,0,1,1,1,7
"2335",10867,25,27.7,1,4,0,0,10,1,2,1,1,8
"2336",3799,25,27.7,1,1,0,0,9,0,0,1,1,7
"2337",4743,25,27.8,1,2,0,0,10,0,1,1,1,6
"2338",305,25,27.8,1,5,0,0,14,0,0,1,1,7
"2339",8773,25,27.8,1,1,0,0,13,1,1,1,1,10
"2340",8687,25,27.9,1,4,0,0,10,1,2,1,1,7
"2341",3480,25,27.9,1,4,0,1,12,0,2,1,1,1
"2342",2133,25,27.9,1,4,0,0,9,0,2,1,1,9
"2343",437,25,27.9,1,2,0,0,14,0,1,1,1,5
"2344",4647,25,27.9,1,4,0,0,15,0,1,1,1,5
"2345",10655,25,27.9,1,4,0,0,10,1,1,1,1,8
"2346",3173,25,28,1,4,0,0,14,0,1,1,1,8
"2347",3289,25,28,1,4,0,0,15,0,0,1,1,10
"2348",7485,25,28,1,3,0,0,12,1,2,1,1,8
"2349",429,25,28,1,2,0,0,9,0,0,1,1,10
"2350",7429,25,28,1,5,0,0,14,1,1,1,1,5
"2351",3931,25,28.1,1,5,0,0,9,0,0,1,1,9
"2352",4589,25,28.1,1,3,0,0,13,0,2,1,1,8
"2353",8937,25,28.1,1,3,0,0,12,1,0,1,1,5
"2354",4861,25,28.1,1,2,0,0,12,0,1,1,1,9
"2355",10861,25,28.1,1,2,0,0,12,1,1,1,1,7
"2356",4453,25,28.1,1,5,0,1,14,0,1,1,1,2
"2357",6051,25,28.2,1,5,0,0,15,1,2,1,1,5
"2358",169,25,28.2,1,2,0,0,13,0,1,1,1,5
"2359",9553,25,28.2,1,4,0,0,12,1,1,1,1,10
"2360",2333,25,28.2,1,3,0,0,13,0,0,1,1,10
"2361",6559,25,28.2,1,4,0,0,15,1,2,1,1,7
"2362",9085,25,28.2,1,3,0,0,13,1,2,1,1,9
"2363",6033,25,28.2,1,5,0,0,14,1,0,1,1,9
"2364",303,25,28.3,1,4,0,0,11,0,0,1,1,6
"2365",10134,25,28.3,1,4,0,1,9,1,4,1,1,5
"2366",2573,25,28.3,1,2,0,0,12,0,2,1,1,6
"2367",9987,25,28.3,1,4,0,0,11,1,2,1,1,5
"2368",7447,25,28.3,1,2,0,0,12,1,0,1,1,8
"2369",3175,25,28.3,1,2,0,0,14,0,2,1,1,6
"2370",2677,25,28.4,1,4,0,0,9,0,0,1,1,6
"2371",8523,25,28.4,1,1,0,0,12,1,2,1,1,5
"2372",10237,25,28.5,1,3,0,0,14,1,1,1,1,8
"2373",1871,25,28.5,1,3,0,0,12,0,1,1,1,8
"2374",10792,25,28.6,1,5,0,1,14,1,8,1,1,6
"2375",3017,25,28.6,1,1,0,0,11,0,1,1,1,9
"2376",3515,25,28.6,1,2,0,0,15,0,2,1,1,9
"2377",9819,25,28.6,1,1,0,0,15,1,1,1,1,10
"2378",861,25,28.6,1,4,0,0,10,0,0,1,1,9
"2379",8993,25,28.7,1,5,0,0,15,1,0,1,1,7
"2380",4811,25,28.7,1,2,0,0,13,0,0,1,1,5
"2381",5575,25,28.7,1,3,0,0,13,1,2,1,1,6
"2382",3419,25,28.7,1,3,0,0,12,0,1,1,1,9
"2383",2253,25,28.7,1,1,0,0,15,0,0,1,1,6
"2384",1983,25,28.8,1,2,0,0,10,0,1,1,1,8
"2385",5667,25,28.8,1,1,0,0,15,1,2,1,1,9
"2386",3453,25,28.8,1,5,0,0,12,0,0,1,1,9
"2387",10102,25,28.8,1,3,0,1,12,1,7,1,1,1
"2388",2205,25,28.8,1,1,0,0,9,0,2,1,1,6
"2389",7933,25,28.8,1,1,0,0,10,1,2,1,1,5
"2390",4667,25,28.8,1,5,0,0,13,0,0,1,1,5
"2391",10221,25,28.8,1,3,0,0,9,1,1,1,1,7
"2392",5035,25,28.8,1,2,0,0,15,0,1,1,1,6
"2393",725,25,28.9,1,2,0,0,13,0,0,1,1,8
"2394",1377,25,28.9,1,3,0,0,10,0,0,1,1,6
"2395",6701,25,28.9,1,4,0,0,13,1,0,1,1,5
"2396",2563,25,29,1,4,0,0,11,0,0,1,1,8
"2397",5281,25,29,1,3,0,0,9,0,0,1,1,9
"2398",6725,25,29,1,2,0,0,9,1,0,1,1,5
"2399",7389,25,29,1,1,0,0,12,1,1,1,1,7
"2400",5975,25,29,1,3,0,0,12,1,2,1,1,8
"2401",10527,25,29,1,2,0,0,13,1,2,1,1,7
"2402",2411,25,29,1,4,0,0,12,0,0,1,1,9
"2403",10827,25,29.1,1,1,0,0,11,1,0,1,1,5
"2404",4980,25,29.1,1,2,0,1,13,0,6,1,1,0
"2405",6025,25,29.1,1,3,0,0,14,1,2,1,1,6
"2406",9203,25,29.1,1,3,0,0,15,1,1,1,1,5
"2407",4063,25,29.1,1,4,0,0,12,0,0,1,1,7
"2408",5723,25,29.2,1,1,0,0,15,1,2,1,1,8
"2409",10493,25,29.2,1,3,0,0,12,1,2,1,1,7
"2410",10789,25,29.2,1,4,0,0,12,1,0,1,1,6
"2411",1417,25,29.2,1,5,0,0,11,0,0,1,1,6
"2412",873,25,29.2,1,2,0,0,13,0,1,1,1,5
"2413",3269,25,29.2,1,1,0,0,12,0,2,1,1,5
"2414",9023,25,29.2,1,3,0,0,12,1,2,1,1,8
"2415",9175,25,29.3,1,4,0,0,10,1,0,1,1,7
"2416",4309,25,29.3,1,1,0,0,12,0,2,1,1,8
"2417",5611,25,29.3,1,1,0,0,15,1,0,1,1,10
"2418",4625,25,29.3,1,5,0,0,13,0,0,1,1,8
"2419",8831,25,29.4,1,4,0,0,14,1,1,1,1,9
"2420",449,25,29.4,1,5,0,0,9,0,2,1,1,5
"2421",3170,25,29.4,1,5,0,1,9,0,4,1,1,1
"2422",5897,25,29.4,1,2,0,0,13,1,1,1,1,6
"2423",8867,25,29.5,1,4,0,1,15,1,5,1,1,2
"2424",7801,25,29.5,1,1,0,0,14,1,2,1,1,7
"2425",6365,25,29.5,1,3,0,0,9,1,0,1,1,10
"2426",9319,25,29.5,1,5,0,0,15,1,1,1,1,7
"2427",7779,25,29.6,1,4,0,0,12,1,1,1,1,6
"2428",2863,25,29.6,1,4,0,0,12,0,1,1,1,10
"2429",5079,25,29.6,1,2,0,0,10,0,2,1,1,8
"2430",7703,25,29.6,1,3,0,0,12,1,2,1,1,6
"2431",1045,25,29.7,1,1,0,0,14,0,1,1,1,5
"2432",7971,25,29.7,1,2,0,0,14,1,1,1,1,8
"2433",5295,25,29.7,1,3,0,0,14,0,2,1,1,6
"2434",5307,25,29.8,1,1,0,0,10,0,0,1,1,8
"2435",2991,25,29.9,1,1,0,0,9,0,2,1,1,9
"2436",4537,25,29.9,1,2,0,0,10,0,1,1,1,8
"2437",8071,25,29.9,1,5,0,0,9,1,0,1,1,5
"2438",8105,25,29.9,1,2,0,0,12,1,2,1,1,7
"2439",9239,25,29.9,1,2,0,0,12,1,2,1,1,10
"2440",4415,25,29.9,1,5,0,0,9,0,2,1,1,5
"2441",8735,25,30,1,1,0,0,12,1,2,1,1,10
"2442",529,25,30,1,2,0,0,9,0,0,1,1,9
"2445",615,26,0.8,1,3,0,1,15,0,4,1,1,2
"2446",2380,26,1.1,1,2,0,1,10,0,5,1,1,6
"2447",2102,26,1.2,1,3,0,1,15,0,6,1,1,5
"2448",2919,26,1.5,1,4,0,1,9,0,1,1,1,1
"2449",570,26,2,1,3,0,1,10,0,2,1,1,9
"2450",8578,26,2,1,2,0,1,12,1,1,1,1,10
"2451",2260,26,2.3,1,2,0,1,13,0,6,1,1,6
"2452",102,26,2.4,1,4,0,1,13,0,1,1,1,2
"2453",3462,26,3.1,1,3,0,1,12,0,5,1,1,4
"2454",9338,26,3.4,1,2,0,1,13,1,1,1,1,9
"2455",7369,26,3.7,1,2,0,1,10,1,1,1,1,1
"2456",6116,26,4.1,1,5,0,1,15,1,6,1,1,1
"2457",8382,26,4.6,1,1,0,1,12,1,4,1,1,9
"2458",533,26,5.2,1,3,0,1,11,0,4,1,1,4
"2459",4331,26,5.2,1,3,0,1,9,0,1,1,1,1
"2460",4473,26,5.7,1,1,0,1,12,0,2,1,1,4
"2461",4070,26,6.1,1,2,0,1,10,0,4,1,1,8
"2462",8176,26,6.1,1,3,0,1,13,1,3,1,1,5
"2463",5756,26,7.4,1,3,0,1,9,1,2,1,1,10
"2464",1192,26,7.5,1,2,0,1,12,0,5,1,1,2
"2465",5412,26,7.9,1,3,0,1,12,0,2,1,1,3
"2466",5036,26,7.9,1,5,0,1,14,0,5,1,1,7
"2467",826,26,8,1,2,0,1,9,0,3,1,1,6
"2468",3422,26,8.2,1,4,0,1,15,0,5,1,1,1
"2469",2371,26,8.3,1,1,0,1,13,0,6,1,1,3
"2470",9946,26,8.4,1,3,0,1,14,1,6,1,1,6
"2471",10804,26,8.5,1,1,0,1,12,1,6,1,1,2
"2472",2265,26,8.9,1,2,0,1,12,0,4,1,1,3
"2473",6164,26,8.9,1,2,0,1,9,1,1,1,1,2
"2474",8157,26,8.9,1,1,0,1,10,1,6,1,1,3
"2475",596,26,10.1,1,5,0,1,12,0,5,1,1,0
"2476",10824,26,10.4,1,3,0,1,11,1,5,1,1,10
"2477",10070,26,10.6,1,1,0,1,12,1,7,1,1,10
"2478",6611,26,10.6,1,1,0,1,15,1,7,1,1,1
"2479",2476,26,11.3,1,5,0,1,13,0,2,1,1,9
"2480",7638,26,12.4,1,4,0,1,12,1,7,1,1,0
"2481",2560,26,12.8,1,4,0,1,14,0,2,1,1,1
"2482",5583,26,13.4,1,3,0,1,12,1,2,1,1,4
"2483",7195,26,14.1,1,2,0,1,12,1,5,1,1,0
"2484",1090,26,14.2,1,3,0,1,10,0,5,1,1,9
"2485",4066,26,14.7,1,4,0,1,13,0,5,1,1,7
"2486",7199,26,14.8,1,3,0,1,12,1,2,1,1,1
"2487",727,26,15,1,3,0,0,12,0,1,1,1,7
"2488",1577,26,15,1,3,0,0,14,0,1,1,1,5
"2489",1595,26,15,1,4,0,0,10,0,1,1,1,10
"2490",6853,26,15,1,3,0,0,14,1,1,1,1,9
"2491",9423,26,15.2,1,1,0,0,15,1,1,1,1,10
"2492",1159,26,15.2,1,4,0,0,9,0,1,1,1,8
"2493",3139,26,15.2,1,4,0,0,10,0,0,1,1,5
"2494",7019,26,15.2,1,5,0,0,11,1,1,1,1,8
"2495",8567,26,15.2,1,4,0,0,15,1,2,1,1,6
"2496",2489,26,15.2,1,2,0,0,12,0,0,1,1,7
"2497",8509,26,15.3,1,1,0,0,12,1,1,1,1,9
"2498",5491,26,15.3,1,1,0,0,10,1,1,1,1,9
"2499",3123,26,15.3,1,3,0,0,9,0,1,1,1,7
"2500",6183,26,15.3,1,5,0,0,13,1,0,1,1,9
"2501",9101,26,15.3,1,1,0,0,14,1,1,1,1,10
"2502",4101,26,15.3,1,2,0,0,11,0,1,1,1,5
"2503",6773,26,15.4,1,4,0,0,14,1,1,1,1,10
"2504",803,26,15.4,1,1,0,0,14,0,1,1,1,5
"2505",1157,26,15.4,1,5,0,0,12,0,2,1,1,8
"2506",5535,26,15.4,1,5,0,0,14,1,2,1,1,6
"2507",6135,26,15.5,1,5,0,0,11,1,2,1,1,5
"2508",9697,26,15.5,1,3,0,0,11,1,1,1,1,6
"2509",5015,26,15.5,1,4,0,0,10,0,0,1,1,6
"2510",8963,26,15.5,1,1,0,0,12,1,0,1,1,8
"2511",2245,26,15.5,1,3,0,0,11,0,0,1,1,5
"2512",10549,26,15.5,1,2,0,0,14,1,0,1,1,8
"2513",9655,26,15.5,1,3,0,0,13,1,2,1,1,8
"2514",1080,26,15.6,1,1,0,1,12,0,1,1,1,0
"2515",4107,26,15.6,1,1,0,0,12,0,1,1,1,8
"2516",3793,26,15.6,1,4,0,0,10,0,2,1,1,7
"2517",4815,26,15.7,1,5,0,0,12,0,2,1,1,5
"2518",5881,26,15.7,1,3,0,0,12,1,1,1,1,7
"2519",4163,26,15.7,1,5,0,0,10,0,0,1,1,9
"2520",6891,26,15.7,1,3,0,0,13,1,1,1,1,9
"2521",5677,26,15.7,1,1,0,0,15,1,1,1,1,8
"2522",4657,26,15.7,1,3,0,1,15,0,1,1,1,3
"2523",1091,26,15.8,1,2,0,0,13,0,1,1,1,9
"2524",4883,26,15.8,1,5,0,0,14,0,1,1,1,7
"2525",5731,26,15.8,1,2,0,0,14,1,0,1,1,10
"2526",8347,26,15.9,1,2,0,0,15,1,2,1,1,10
"2527",5054,26,15.9,1,4,0,1,13,0,5,1,1,5
"2528",7657,26,15.9,1,2,0,0,12,1,2,1,1,5
"2529",3425,26,15.9,1,1,0,0,13,0,1,1,1,9
"2530",9693,26,15.9,1,1,0,0,11,1,2,1,1,9
"2531",2939,26,16,1,2,0,0,13,0,2,1,1,9
"2532",4215,26,16,1,5,0,0,9,0,0,1,1,6
"2533",7849,26,16,1,1,0,0,13,1,1,1,1,8
"2534",7239,26,16,1,3,0,0,13,1,2,1,1,7
"2535",5985,26,16,1,3,0,0,12,1,0,1,1,5
"2536",9563,26,16,1,4,0,0,12,1,0,1,1,8
"2537",6715,26,16,1,2,0,1,13,1,5,1,1,3
"2538",8651,26,16.1,1,4,0,0,12,1,0,1,1,7
"2539",6253,26,16.1,1,1,0,0,10,1,2,1,1,9
"2540",4862,26,16.1,1,3,0,1,12,0,5,1,1,9
"2541",2933,26,16.1,1,2,0,0,13,0,2,1,1,5
"2542",3613,26,16.1,1,4,0,0,14,0,1,1,1,9
"2543",4500,26,16.2,1,5,0,1,11,0,3,1,1,4
"2544",5557,26,16.2,1,2,0,0,9,1,0,1,1,8
"2545",9141,26,16.2,1,2,0,0,12,1,1,1,1,6
"2546",1535,26,16.3,1,1,0,0,11,0,0,1,1,5
"2547",3947,26,16.3,1,4,0,0,9,0,2,1,1,8
"2548",4695,26,16.3,1,1,0,0,12,0,1,1,1,5
"2549",4557,26,16.3,1,1,0,0,11,0,2,1,1,9
"2550",8537,26,16.3,1,4,0,0,13,1,2,1,1,9
"2551",5989,26,16.3,1,5,0,0,11,1,0,1,1,10
"2552",6933,26,16.4,1,2,0,0,13,1,1,1,1,9
"2553",2831,26,16.4,1,2,0,0,12,0,0,1,1,7
"2554",10187,26,16.4,1,3,0,0,14,1,0,1,1,5
"2555",10083,26,16.4,1,1,0,0,9,1,1,1,1,6
"2556",8909,26,16.4,1,1,0,0,12,1,1,1,1,10
"2557",2139,26,16.5,1,1,0,0,14,0,1,1,1,10
"2558",2593,26,16.5,1,4,0,0,12,0,2,1,1,9
"2559",8499,26,16.5,1,2,0,0,14,1,1,1,1,10
"2560",7737,26,16.5,1,3,0,1,13,1,2,1,1,2
"2561",7819,26,16.5,1,5,0,0,10,1,0,1,1,9
"2562",10359,26,16.5,1,4,0,0,11,1,1,1,1,7
"2563",1659,26,16.6,1,1,0,0,10,0,1,1,1,10
"2564",5593,26,16.6,1,4,0,0,11,1,2,1,1,9
"2565",8979,26,16.7,1,1,0,0,9,1,2,1,1,9
"2566",9811,26,16.7,1,3,0,0,11,1,0,1,1,5
"2567",10501,26,16.8,1,4,0,0,13,1,2,1,1,7
"2568",8549,26,16.8,1,3,0,0,10,1,2,1,1,7
"2569",5915,26,16.9,1,3,0,0,13,1,0,1,1,8
"2570",6535,26,16.9,1,3,0,0,12,1,2,1,1,9
"2571",8392,26,16.9,1,4,0,1,9,1,6,1,1,3
"2572",3,26,17,1,5,0,0,15,0,1,1,1,9
"2573",6095,26,17,1,5,0,0,12,1,0,1,1,7
"2574",7045,26,17.1,1,5,0,0,13,1,1,1,1,9
"2575",85,26,17.1,1,5,0,0,13,0,1,1,1,7
"2576",4049,26,17.1,1,5,0,0,15,0,0,1,1,7
"2577",4001,26,17.1,1,3,0,0,12,0,0,1,1,5
"2578",597,26,17.1,1,5,0,0,12,0,0,1,1,5
"2579",5381,26,17.1,1,3,0,0,15,0,1,1,1,10
"2580",6241,26,17.2,1,1,0,0,15,1,1,1,1,10
"2581",9447,26,17.2,1,4,0,0,12,1,1,1,1,9
"2582",9929,26,17.2,1,1,0,0,15,1,2,1,1,5
"2583",5055,26,17.2,1,2,0,1,13,0,4,1,1,4
"2584",3669,26,17.2,1,4,0,0,11,0,2,1,1,6
"2585",4327,26,17.2,1,3,0,0,9,0,1,1,1,9
"2586",9675,26,17.2,1,3,0,0,13,1,1,1,1,6
"2587",1243,26,17.2,1,2,0,0,12,0,1,1,1,6
"2588",1799,26,17.3,1,1,0,0,14,0,2,1,1,10
"2589",8699,26,17.3,1,3,0,0,10,1,2,1,1,7
"2590",4693,26,17.3,1,4,0,0,13,0,2,1,1,9
"2591",4439,26,17.4,1,3,0,0,13,0,1,1,1,8
"2592",10777,26,17.4,1,3,0,0,13,1,0,1,1,8
"2593",10775,26,17.4,1,2,0,0,11,1,2,1,1,8
"2594",6871,26,17.4,1,1,0,0,9,1,0,1,1,10
"2595",10471,26,17.4,1,1,0,0,14,1,1,1,1,6
"2596",4887,26,17.4,1,5,0,0,9,0,0,1,1,5
"2597",2439,26,17.4,1,1,0,0,15,0,2,1,1,6
"2598",9805,26,17.5,1,3,0,0,14,1,0,1,1,6
"2599",1353,26,17.5,1,4,0,0,12,0,2,1,1,7
"2600",421,26,17.5,1,1,0,0,12,0,0,1,1,8
"2601",7117,26,17.5,1,5,0,0,11,1,2,1,1,5
"2602",131,26,17.6,1,2,0,0,15,0,0,1,1,8
"2603",4019,26,17.6,1,4,0,0,15,0,0,1,1,7
"2604",5089,26,17.6,1,4,0,0,9,0,0,1,1,6
"2605",10451,26,17.6,1,3,0,0,15,1,2,1,1,6
"2606",3685,26,17.7,1,2,0,0,10,0,1,1,1,7
"2607",1313,26,17.7,1,5,0,0,15,0,1,1,1,6
"2608",8707,26,17.7,1,4,0,0,12,1,0,1,1,10
"2609",10129,26,17.7,1,3,0,0,12,1,1,1,1,6
"2610",10505,26,17.7,1,5,0,0,10,1,1,1,1,5
"2611",7889,26,17.7,1,5,0,0,15,1,0,1,1,7
"2612",1517,26,17.8,1,1,0,0,12,0,2,1,1,9
"2613",5243,26,17.9,1,4,0,0,11,0,0,1,1,10
"2614",5357,26,17.9,1,2,0,0,9,0,0,1,1,9
"2615",9963,26,17.9,1,4,0,0,10,1,0,1,1,5
"2616",3643,26,17.9,1,2,0,0,12,0,0,1,1,7
"2617",481,26,17.9,1,2,0,0,15,0,0,1,1,5
"2618",2153,26,17.9,1,2,0,0,15,0,1,1,1,5
"2619",7471,26,17.9,1,1,0,0,11,1,0,1,1,7
"2620",9041,26,17.9,1,2,0,0,10,1,0,1,1,8
"2621",6696,26,17.9,1,2,0,1,11,1,8,1,1,9
"2622",8809,26,17.9,1,1,0,0,9,1,1,1,1,5
"2623",3885,26,18,1,3,0,0,12,0,2,1,1,5
"2624",8787,26,18,1,2,0,1,12,1,1,1,1,2
"2625",6393,26,18,1,1,0,0,13,1,1,1,1,6
"2626",2985,26,18,1,4,0,0,12,0,1,1,1,9
"2627",4363,26,18,1,2,0,0,10,0,2,1,1,8
"2628",7161,26,18,1,3,0,0,9,1,0,1,1,7
"2629",4571,26,18,1,5,0,0,10,0,2,1,1,7
"2630",4929,26,18.1,1,4,0,0,11,0,1,1,1,8
"2631",3871,26,18.1,1,5,0,0,12,0,1,1,1,5
"2632",853,26,18.1,1,1,0,0,12,0,0,1,1,10
"2633",3337,26,18.1,1,3,0,0,10,0,2,1,1,6
"2634",5999,26,18.2,1,2,0,0,12,1,2,1,1,10
"2635",8023,26,18.3,1,4,0,0,14,1,2,1,1,5
"2636",1889,26,18.3,1,4,0,0,11,0,2,1,1,6
"2637",3281,26,18.3,1,5,0,0,13,0,2,1,1,7
"2638",553,26,18.4,1,3,0,0,9,0,2,1,1,8
"2639",4282,26,18.4,1,3,0,1,14,0,4,1,1,5
"2640",2019,26,18.4,1,3,0,0,12,0,2,1,1,10
"2641",8441,26,18.4,1,2,0,0,12,1,0,1,1,6
"2642",4265,26,18.4,1,4,0,0,9,0,2,1,1,7
"2643",3197,26,18.5,1,1,0,0,12,0,0,1,1,10
"2644",10739,26,18.5,1,4,0,0,10,1,0,1,1,6
"2645",8083,26,18.5,1,5,0,1,14,1,1,1,1,2
"2646",1113,26,18.5,1,5,0,0,9,0,2,1,1,5
"2647",6615,26,18.5,1,5,0,0,10,1,2,1,1,7
"2648",1833,26,18.5,1,3,0,0,15,0,0,1,1,10
"2649",7341,26,18.6,1,5,0,0,15,1,1,1,1,9
"2650",4617,26,18.6,1,2,0,0,12,0,2,1,1,8
"2651",9139,26,18.6,1,2,0,0,11,1,1,1,1,8
"2652",3009,26,18.6,1,1,0,0,10,0,0,1,1,5
"2653",2313,26,18.6,1,2,0,0,14,0,1,1,1,8
"2654",7935,26,18.7,1,3,0,0,10,1,2,1,1,5
"2655",6204,26,18.7,1,2,0,1,9,1,6,1,1,8
"2656",8193,26,18.7,1,1,0,1,14,1,2,1,1,1
"2657",10473,26,18.7,1,5,0,0,13,1,1,1,1,6
"2658",3717,26,18.7,1,1,0,0,13,0,1,1,1,10
"2659",5879,26,18.7,1,3,0,0,12,1,2,1,1,6
"2660",1391,26,18.8,1,1,0,0,12,0,2,1,1,9
"2661",9687,26,18.8,1,4,0,0,10,1,1,1,1,7
"2662",8810,26,18.8,1,4,0,1,12,1,8,1,1,2
"2663",4521,26,18.9,1,1,0,0,12,0,2,1,1,7
"2664",9979,26,18.9,1,5,0,0,12,1,1,1,1,5
"2665",7745,26,18.9,1,4,0,0,12,1,2,1,1,6
"2666",217,26,18.9,1,3,0,0,13,0,1,1,1,10
"2667",10037,26,18.9,1,5,0,0,12,1,2,1,1,10
"2668",4863,26,18.9,1,2,0,0,15,0,2,1,1,5
"2669",9882,26,19,1,2,0,1,10,1,3,1,1,3
"2670",5697,26,19,1,2,0,0,14,1,2,1,1,7
"2671",9531,26,19,1,1,0,0,14,1,2,1,1,5
"2672",9785,26,19,1,3,0,0,12,1,1,1,1,5
"2673",1667,26,19,1,3,0,0,12,0,0,1,1,5
"2674",1867,26,19,1,3,0,0,13,0,1,1,1,8
"2675",9165,26,19,1,5,0,0,9,1,1,1,1,9
"2676",201,26,19,1,2,0,0,10,0,0,1,1,6
"2677",4807,26,19,1,5,0,0,14,0,2,1,1,10
"2678",2693,26,19.1,1,4,0,0,12,0,0,1,1,9
"2679",7201,26,19.1,1,3,0,0,12,1,2,1,1,9
"2680",4333,26,19.1,1,2,0,0,14,0,0,1,1,9
"2681",5493,26,19.1,1,2,0,0,9,1,0,1,1,9
"2682",4677,26,19.1,1,3,0,0,9,0,2,1,1,10
"2683",8124,26,19.1,1,2,0,1,11,1,1,1,1,2
"2684",9799,26,19.1,1,2,0,0,15,1,2,1,1,7
"2685",5627,26,19.1,1,4,0,0,10,1,2,1,1,9
"2686",4025,26,19.2,1,3,0,0,12,0,2,1,1,8
"2687",6959,26,19.3,1,1,0,0,10,1,1,1,1,9
"2688",7953,26,19.3,1,3,0,0,9,1,1,1,1,6
"2689",4509,26,19.3,1,3,0,0,14,0,0,1,1,8
"2690",1553,26,19.3,1,3,0,0,10,0,0,1,1,5
"2691",7185,26,19.5,1,4,0,0,12,1,0,1,1,5
"2692",1959,26,19.5,1,2,0,0,12,0,0,1,1,10
"2693",4209,26,19.5,1,3,0,0,12,0,0,1,1,9
"2694",10019,26,19.6,1,4,0,0,14,1,1,1,1,8
"2695",1977,26,19.6,1,3,0,0,13,0,1,1,1,7
"2696",4417,26,19.6,1,2,0,0,15,0,0,1,1,6
"2697",7475,26,19.6,1,3,0,0,15,1,2,1,1,10
"2698",7517,26,19.6,1,1,0,0,11,1,2,1,1,6
"2699",9923,26,19.7,1,5,0,0,15,1,1,1,1,9
"2700",2559,26,19.7,1,1,0,0,15,0,2,1,1,6
"2701",7847,26,19.7,1,3,0,0,13,1,1,1,1,7
"2702",8029,26,19.7,1,5,0,0,10,1,2,1,1,6
"2703",5785,26,19.7,1,5,0,0,14,1,0,1,1,9
"2704",2363,26,19.8,1,2,0,0,11,0,2,1,1,5
"2705",3427,26,19.8,1,4,0,0,15,0,1,1,1,6
"2706",1769,26,19.8,1,4,0,0,15,0,2,1,1,5
"2707",5705,26,19.8,1,1,0,0,12,1,2,1,1,5
"2708",6335,26,19.8,1,1,0,0,12,1,2,1,1,9
"2709",3679,26,19.8,1,5,0,0,12,0,0,1,1,9
"2710",5369,26,19.9,1,4,0,0,10,0,0,1,1,8
"2711",7557,26,19.9,1,3,0,0,12,1,0,1,1,5
"2712",5941,26,20,1,4,0,0,11,1,0,1,1,7
"2713",8971,26,20,1,1,0,0,12,1,1,1,1,5
"2714",8621,26,20,1,4,0,0,12,1,2,1,1,6
"2715",2801,26,20,1,4,0,0,14,0,0,1,1,5
"2716",9387,26,20,1,4,0,0,10,1,0,1,1,5
"2717",9957,26,20,1,3,0,0,11,1,1,1,1,7
"2718",8911,26,20,1,3,0,0,12,1,2,1,1,6
"2719",4778,26,20.1,1,3,0,1,14,0,5,1,1,1
"2720",3823,26,20.1,1,2,0,0,11,0,0,1,1,7
"2721",5783,26,20.1,1,5,0,0,15,1,0,1,1,8
"2722",863,26,20.1,1,1,0,0,11,0,1,1,1,9
"2723",1863,26,20.2,1,3,0,0,10,0,2,1,1,5
"2724",10577,26,20.2,1,3,0,0,14,1,0,1,1,9
"2725",10580,26,20.2,1,4,0,1,12,1,7,1,1,9
"2726",3055,26,20.2,1,2,0,0,13,0,1,1,1,9
"2727",7047,26,20.3,1,5,0,0,11,1,1,1,1,6
"2728",4113,26,20.3,1,3,0,0,14,0,1,1,1,7
"2729",10761,26,20.3,1,3,0,0,14,1,2,1,1,5
"2730",8987,26,20.4,1,5,0,0,14,1,0,1,1,8
"2731",2441,26,20.4,1,3,0,0,13,0,1,1,1,9
"2732",10049,26,20.4,1,5,0,0,9,1,1,1,1,5
"2733",4645,26,20.4,1,3,0,0,15,0,0,1,1,9
"2734",2817,26,20.4,1,4,0,0,12,0,0,1,1,5
"2735",43,26,20.4,1,2,0,0,10,0,1,1,1,7
"2736",7041,26,20.5,1,2,0,0,14,1,2,1,1,9
"2737",6685,26,20.5,1,4,0,0,10,1,1,1,1,5
"2738",5515,26,20.5,1,5,0,0,13,1,0,1,1,9
"2739",8483,26,20.6,1,1,0,0,11,1,2,1,1,9
"2740",1237,26,20.6,1,5,0,0,13,0,1,1,1,7
"2741",8921,26,20.7,1,3,0,0,12,1,0,1,1,8
"2742",8337,26,20.7,1,5,0,0,14,1,2,1,1,10
"2743",5187,26,20.7,1,3,0,0,12,0,2,1,1,10
"2744",6107,26,20.7,1,1,0,0,11,1,0,1,1,7
"2745",4437,26,20.9,1,3,0,0,13,0,0,1,1,10
"2746",7245,26,20.9,1,5,0,0,9,1,2,1,1,10
"2747",7374,26,20.9,1,2,0,1,9,1,6,1,1,5
"2748",3649,26,21,1,4,0,0,15,0,0,1,1,9
"2749",1441,26,21,1,4,0,0,13,0,2,1,1,9
"2750",3207,26,21,1,4,0,0,13,0,2,1,1,8
"2751",10059,26,21,1,1,0,0,12,1,1,1,1,5
"2752",87,26,21.1,1,2,0,0,11,0,1,1,1,7
"2753",11,26,21.1,1,3,0,0,14,0,1,1,1,10
"2754",7371,26,21.1,1,2,0,0,9,1,2,1,1,7
"2755",5747,26,21.1,1,3,0,0,10,1,0,1,1,9
"2756",8805,26,21.1,1,3,0,0,13,1,2,1,1,7
"2757",6163,26,21.2,1,4,0,0,13,1,0,1,1,9
"2758",747,26,21.2,1,1,0,0,9,0,0,1,1,6
"2759",6885,26,21.2,1,5,0,0,11,1,1,1,1,8
"2760",1117,26,21.3,1,2,0,0,10,0,0,1,1,9
"2761",7421,26,21.3,1,1,0,0,14,1,2,1,1,7
"2762",365,26,21.3,1,1,0,0,15,0,2,1,1,8
"2763",1007,26,21.4,1,4,0,0,12,0,2,1,1,7
"2764",3373,26,21.4,1,5,0,0,15,0,2,1,1,8
"2765",4697,26,21.4,1,3,0,0,14,0,1,1,1,6
"2766",1957,26,21.4,1,2,0,0,15,0,2,1,1,10
"2767",6925,26,21.4,1,2,0,0,10,1,1,1,1,7
"2768",3357,26,21.5,1,2,0,0,13,0,2,1,1,6
"2769",10137,26,21.5,1,2,0,0,11,1,0,1,1,7
"2770",6031,26,21.5,1,4,0,0,13,1,2,1,1,7
"2771",5385,26,21.5,1,2,0,0,11,0,2,1,1,6
"2772",10023,26,21.6,1,5,0,0,12,1,2,1,1,7
"2773",10510,26,21.6,1,5,0,1,12,1,7,1,1,9
"2774",5229,26,21.7,1,5,0,0,15,0,1,1,1,5
"2775",5927,26,21.7,1,2,0,0,12,1,0,1,1,10
"2776",9049,26,21.7,1,1,0,0,9,1,0,1,1,7
"2777",8755,26,21.7,1,3,0,0,13,1,1,1,1,5
"2778",841,26,21.7,1,3,0,0,13,0,2,1,1,10
"2779",3927,26,21.7,1,3,0,0,14,0,0,1,1,7
"2780",7709,26,21.7,1,3,0,0,9,1,1,1,1,10
"2781",4455,26,21.8,1,5,0,0,12,0,1,1,1,6
"2782",8199,26,21.8,1,1,0,0,14,1,0,1,1,7
"2783",10343,26,21.9,1,4,0,0,12,1,2,1,1,8
"2784",2457,26,21.9,1,4,0,0,12,0,1,1,1,8
"2785",1205,26,21.9,1,1,0,0,9,0,1,1,1,5
"2786",4783,26,21.9,1,3,0,0,13,0,0,1,1,8
"2787",9801,26,22,1,5,0,0,12,1,0,1,1,7
"2788",1693,26,22,1,3,0,0,10,0,0,1,1,8
"2789",8177,26,22,1,5,0,0,12,1,2,1,1,8
"2790",7308,26,22,1,3,0,1,10,1,1,1,1,1
"2791",9439,26,22,1,2,0,0,9,1,2,1,1,7
"2792",6573,26,22.1,1,1,0,0,14,1,1,1,1,6
"2793",3119,26,22.1,1,2,0,0,14,0,1,1,1,6
"2794",1069,26,22.1,1,1,0,0,15,0,0,1,1,7
"2795",3053,26,22.2,1,5,0,0,11,0,2,1,1,8
"2796",3169,26,22.2,1,5,0,0,12,0,0,1,1,7
"2797",4609,26,22.2,1,5,0,0,14,0,0,1,1,8
"2798",925,26,22.3,1,5,0,0,15,0,1,1,1,9
"2799",8145,26,22.3,1,1,0,0,14,1,0,1,1,5
"2800",6465,26,22.3,1,4,0,0,10,1,0,1,1,9
"2801",8989,26,22.3,1,3,0,0,12,1,2,1,1,8
"2802",10439,26,22.3,1,4,0,0,9,1,0,1,1,8
"2803",5255,26,22.3,1,1,0,0,15,0,0,1,1,5
"2804",6449,26,22.4,1,4,0,0,15,1,0,1,1,5
"2805",5835,26,22.5,1,3,0,0,10,1,0,1,1,10
"2806",3387,26,22.5,1,3,0,0,12,0,2,1,1,6
"2807",5635,26,22.5,1,3,0,0,10,1,2,1,1,8
"2808",3507,26,22.5,1,1,0,0,13,0,0,1,1,6
"2809",6961,26,22.5,1,3,0,0,15,1,0,1,1,8
"2810",8293,26,22.5,1,1,0,0,13,1,0,1,1,7
"2811",923,26,22.5,1,3,0,0,14,0,1,1,1,7
"2812",7403,26,22.6,1,1,0,0,12,1,1,1,1,9
"2813",9943,26,22.6,1,1,0,0,9,1,2,1,1,7
"2814",567,26,22.7,1,2,0,0,12,0,0,1,1,9
"2815",8753,26,22.7,1,3,0,0,11,1,2,1,1,9
"2816",4541,26,22.7,1,1,0,0,12,0,0,1,1,8
"2817",1995,26,22.7,1,3,0,0,12,0,0,1,1,9
"2818",3983,26,22.8,1,2,0,0,9,0,0,1,1,8
"2819",7057,26,22.8,1,2,0,0,9,1,0,1,1,8
"2820",3577,26,22.8,1,2,0,0,13,0,1,1,1,6
"2821",2554,26,22.8,1,3,0,1,15,0,3,1,1,2
"2822",8259,26,22.8,1,2,0,0,12,1,0,1,1,10
"2823",8247,26,22.8,1,5,0,0,15,1,1,1,1,10
"2824",3645,26,22.8,1,3,0,0,11,0,2,1,1,7
"2825",7207,26,22.9,1,4,0,0,13,1,0,1,1,9
"2826",8239,26,22.9,1,3,0,0,10,1,1,1,1,8
"2827",8437,26,22.9,1,1,0,0,14,1,2,1,1,10
"2828",6777,26,22.9,1,1,0,0,14,1,2,1,1,10
"2829",1171,26,22.9,1,1,0,0,12,0,1,1,1,10
"2830",9899,26,23,1,2,0,0,14,1,2,1,1,9
"2831",7431,26,23,1,2,0,0,13,1,2,1,1,5
"2832",4653,26,23,1,3,0,0,11,0,1,1,1,9
"2833",8663,26,23,1,5,0,0,13,1,0,1,1,8
"2834",10135,26,23.1,1,4,0,0,12,1,0,1,1,6
"2835",7,26,23.1,1,3,0,0,15,0,1,1,1,10
"2836",7739,26,23.1,1,4,0,0,11,1,2,1,1,6
"2837",2037,26,23.1,1,3,0,0,13,0,1,1,1,10
"2838",9908,26,23.1,1,3,0,1,9,1,5,1,1,8
"2839",805,26,23.2,1,5,0,0,11,0,0,1,1,6
"2840",8215,26,23.2,1,5,0,0,12,1,2,1,1,10
"2841",8597,26,23.3,1,4,0,0,12,1,0,1,1,7
"2842",6835,26,23.3,1,2,0,0,12,1,1,1,1,7
"2843",7275,26,23.3,1,5,0,0,12,1,1,1,1,5
"2844",1275,26,23.4,1,3,0,0,10,0,1,1,1,6
"2845",4925,26,23.4,1,3,0,0,15,0,2,1,1,5
"2846",3489,26,23.4,1,3,0,0,12,0,0,1,1,5
"2847",2301,26,23.5,1,4,0,0,14,0,0,1,1,10
"2848",10251,26,23.5,1,5,0,0,12,1,2,1,1,10
"2849",5437,26,23.5,1,2,0,0,12,1,0,1,1,7
"2850",6079,26,23.5,1,3,0,0,13,1,1,1,1,7
"2851",10171,26,23.6,1,1,0,0,9,1,1,1,1,10
"2852",7875,26,23.6,1,5,0,0,12,1,0,1,1,7
"2853",5857,26,23.6,1,3,0,0,12,1,2,1,1,6
"2854",2207,26,23.6,1,1,0,0,15,0,0,1,1,9
"2855",1808,26,23.7,1,1,0,1,10,0,4,1,1,0
"2856",10291,26,23.7,1,2,0,0,9,1,1,1,1,7
"2857",3051,26,23.7,1,3,0,0,10,0,2,1,1,10
"2858",2273,26,23.8,1,3,0,0,15,0,1,1,1,6
"2859",983,26,23.8,1,3,0,0,10,0,0,1,1,6
"2860",4237,26,23.8,1,3,0,0,13,0,1,1,1,6
"2861",7565,26,23.8,1,4,0,0,13,1,2,1,1,9
"2862",5125,26,23.8,1,4,0,0,15,0,0,1,1,5
"2863",3688,26,23.8,1,1,0,1,10,0,1,1,1,5
"2864",369,26,23.8,1,3,0,0,13,0,2,1,1,8
"2865",2227,26,23.9,1,4,0,0,9,0,0,1,1,8
"2866",1165,26,24,1,5,0,0,13,0,2,1,1,9
"2867",3941,26,24,1,3,0,1,13,0,6,1,1,1
"2868",106,26,24,1,3,0,1,13,0,1,1,1,8
"2869",859,26,24.1,1,3,0,0,14,0,1,1,1,10
"2870",9161,26,24.1,1,4,0,0,11,1,1,1,1,5
"2871",6751,26,24.1,1,4,0,0,14,1,0,1,1,6
"2872",6381,26,24.1,1,4,0,0,11,1,0,1,1,10
"2873",1147,26,24.2,1,3,0,0,15,0,0,1,1,9
"2874",10303,26,24.2,1,2,0,0,13,1,2,1,1,10
"2875",8385,26,24.2,1,4,0,0,13,1,0,1,1,7
"2876",9915,26,24.3,1,2,0,0,12,1,1,1,1,10
"2877",9147,26,24.3,1,3,0,0,13,1,2,1,1,9
"2878",2393,26,24.3,1,1,0,0,12,0,1,1,1,5
"2879",6373,26,24.3,1,2,0,0,10,1,2,1,1,8
"2880",2647,26,24.3,1,1,0,0,12,0,0,1,1,5
"2881",10831,26,24.4,1,1,0,0,12,1,1,1,1,5
"2882",1193,26,24.4,1,2,0,0,9,0,0,1,1,6
"2883",5175,26,24.4,1,3,0,0,13,0,1,1,1,5
"2884",7073,26,24.4,1,2,0,0,15,1,2,1,1,7
"2885",2705,26,24.5,1,2,0,0,12,0,1,1,1,10
"2886",3471,26,24.6,1,4,0,0,10,0,0,1,1,8
"2887",8209,26,24.6,1,5,0,0,10,1,2,1,1,10
"2888",7035,26,24.6,1,4,0,0,11,1,2,1,1,7
"2889",4849,26,24.6,1,2,0,0,9,0,1,1,1,7
"2890",1445,26,24.6,1,1,0,0,15,0,0,1,1,9
"2891",8231,26,24.7,1,3,0,0,14,1,1,1,1,6
"2892",9449,26,24.7,1,4,0,0,15,1,2,1,1,8
"2893",7645,26,24.7,1,5,0,0,9,1,1,1,1,5
"2894",2243,26,24.8,1,4,0,0,14,0,1,1,1,8
"2895",5448,26,24.8,1,1,0,1,12,1,6,1,1,8
"2896",145,26,24.8,1,5,0,1,12,0,3,1,1,4
"2897",6953,26,24.8,1,4,0,0,10,1,1,1,1,6
"2898",1611,26,24.9,1,4,0,0,10,0,1,1,1,8
"2899",6845,26,24.9,1,5,0,0,9,1,0,1,1,8
"2900",4469,26,24.9,1,4,0,0,10,0,2,1,1,7
"2901",6055,26,24.9,1,3,0,0,12,1,1,1,1,10
"2902",2173,26,24.9,1,5,0,0,15,0,0,1,1,9
"2903",8981,26,24.9,1,3,0,0,11,1,2,1,1,9
"2904",6927,26,24.9,1,2,0,1,10,1,7,1,1,3
"2905",10293,26,25,1,2,0,0,13,1,2,1,1,8
"2906",2768,26,25,1,4,0,1,13,0,4,1,1,2
"2907",7943,26,25,1,2,0,0,12,1,2,1,1,7
"2908",2871,26,25,1,1,0,0,15,0,0,1,1,6
"2909",5721,26,25,1,1,0,0,13,1,1,1,1,7
"2910",8203,26,25,1,5,0,0,11,1,0,1,1,5
"2911",165,26,25.1,1,3,0,0,13,0,2,1,1,6
"2912",5949,26,25.1,1,4,0,0,14,1,1,1,1,10
"2913",7863,26,25.1,1,2,0,0,14,1,2,1,1,6
"2914",7157,26,25.1,1,3,0,0,13,1,2,1,1,6
"2915",6360,26,25.1,1,2,0,1,12,1,2,1,1,9
"2916",4075,26,25.2,1,2,0,0,14,0,1,1,1,6
"2917",6825,26,25.2,1,2,0,0,9,1,0,1,1,10
"2918",3421,26,25.2,1,3,0,0,12,0,2,1,1,5
"2919",10003,26,25.2,1,3,0,0,10,1,1,1,1,10
"2920",4954,26,25.2,1,4,0,1,15,0,8,1,1,4
"2921",5299,26,25.3,1,1,0,0,14,0,2,1,1,10
"2922",5227,26,25.3,1,3,0,0,10,0,1,1,1,9
"2923",2659,26,25.3,1,4,0,0,14,0,0,1,1,8
"2924",10047,26,25.3,1,1,0,0,15,1,2,1,1,5
"2925",7925,26,25.3,1,4,0,0,12,1,2,1,1,7
"2926",9499,26,25.4,1,3,0,0,11,1,1,1,1,10
"2927",55,26,25.4,1,4,0,0,13,0,1,1,1,10
"2928",1747,26,25.4,1,5,0,0,9,0,1,1,1,8
"2929",299,26,25.5,1,3,0,0,10,0,0,1,1,8
"2930",6633,26,25.5,1,1,0,0,11,1,1,1,1,6
"2931",95,26,25.6,1,3,0,1,12,0,2,1,1,0
"2932",151,26,25.6,1,5,0,0,10,0,2,1,1,9
"2933",4549,26,25.6,1,4,0,0,10,0,1,1,1,6
"2934",2707,26,25.6,1,5,0,0,13,0,2,1,1,10
"2935",4463,26,25.7,1,1,0,0,11,0,1,1,1,7
"2936",6759,26,25.7,1,4,0,0,10,1,2,1,1,10
"2937",4983,26,25.7,1,1,0,0,14,0,2,1,1,9
"2938",8261,26,25.8,1,4,0,0,10,1,2,1,1,6
"2939",411,26,25.8,1,3,0,0,9,0,2,1,1,6
"2940",8375,26,25.8,1,5,0,0,15,1,1,1,1,7
"2941",7752,26,25.8,1,1,0,1,9,1,1,1,1,4
"2942",1261,26,25.8,1,3,0,0,12,0,1,1,1,5
"2943",4121,26,25.8,1,4,0,0,14,0,1,1,1,5
"2944",3691,26,25.8,1,3,0,0,12,0,2,1,1,9
"2945",3913,26,25.8,1,3,0,0,12,0,2,1,1,5
"2946",6320,26,25.9,1,5,0,1,12,1,4,1,1,6
"2947",9465,26,25.9,1,5,0,1,15,1,3,1,1,1
"2948",6139,26,25.9,1,5,0,0,12,1,0,1,1,6
"2949",2851,26,25.9,1,4,0,0,9,0,2,1,1,6
"2950",6783,26,25.9,1,5,0,0,13,1,0,1,1,10
"2951",2119,26,26,1,4,0,0,15,0,0,1,1,8
"2952",2323,26,26,1,2,0,0,9,0,2,1,1,9
"2953",4955,26,26,1,3,0,0,13,0,2,1,1,8
"2954",5285,26,26,1,5,0,0,10,0,1,1,1,5
"2955",6679,26,26.1,1,1,0,0,14,1,1,1,1,7
"2956",5481,26,26.1,1,4,0,1,13,1,3,1,1,3
"2957",9453,26,26.1,1,2,0,0,12,1,2,1,1,7
"2958",10151,26,26.2,1,2,0,0,10,1,1,1,1,10
"2959",1099,26,26.2,1,3,0,0,12,0,1,1,1,5
"2960",3811,26,26.2,1,4,0,0,12,0,0,1,1,6
"2961",6869,26,26.2,1,4,0,0,12,1,0,1,1,9
"2962",3975,26,26.2,1,3,0,0,9,0,2,1,1,9
"2963",671,26,26.3,1,1,0,0,15,0,0,1,1,7
"2964",3389,26,26.3,1,3,0,0,11,0,1,1,1,8
"2965",3195,26,26.3,1,1,0,0,15,0,1,1,1,9
"2966",10201,26,26.3,1,5,0,0,10,1,0,1,1,5
"2967",8751,26,26.4,1,2,0,0,9,1,0,1,1,8
"2968",9116,26,26.4,1,5,0,1,12,1,2,1,1,0
"2969",9199,26,26.4,1,3,0,0,14,1,2,1,1,8
"2970",10299,26,26.4,1,4,0,0,12,1,0,1,1,7
"2971",6081,26,26.4,1,3,0,0,10,1,2,1,1,6
"2972",9357,26,26.5,1,1,0,0,9,1,2,1,1,6
"2973",10317,26,26.5,1,4,0,0,11,1,2,1,1,10
"2974",3722,26,26.5,1,2,0,1,13,0,5,1,1,1
"2975",1249,26,26.5,1,2,0,0,11,0,1,1,1,6
"2976",8821,26,26.5,1,2,0,0,11,1,2,1,1,7
"2977",7225,26,26.5,1,1,0,0,9,1,1,1,1,9
"2978",8504,26,26.5,1,3,0,1,13,1,3,1,1,8
"2979",6229,26,26.6,1,3,0,0,10,1,0,1,1,9
"2980",2419,26,26.6,1,4,0,0,10,0,2,1,1,7
"2981",1731,26,26.6,1,3,0,0,10,0,0,1,1,7
"2982",6963,26,26.6,1,4,0,0,12,1,0,1,1,7
"2983",7633,26,26.6,1,1,0,0,13,1,1,1,1,6
"2984",1095,26,26.6,1,1,0,0,11,0,0,1,1,9
"2985",5739,26,26.7,1,4,0,0,9,1,2,1,1,6
"2986",10787,26,26.7,1,2,0,0,14,1,2,1,1,7
"2987",4687,26,26.7,1,3,0,0,13,0,1,1,1,7
"2988",7699,26,26.7,1,1,0,0,13,1,0,1,1,6
"2989",3633,26,26.7,1,1,0,0,12,0,0,1,1,8
"2990",5455,26,26.7,1,3,0,0,11,1,2,1,1,9
"2991",5761,26,26.7,1,3,0,0,10,1,0,1,1,5
"2992",4877,26,26.8,1,5,0,0,15,0,2,1,1,8
"2993",1619,26,26.8,1,4,0,0,12,0,2,1,1,5
"2994",10197,26,26.9,1,1,0,0,11,1,1,1,1,8
"2995",10109,26,26.9,1,4,0,0,14,1,2,1,1,5
"2996",4853,26,26.9,1,5,0,0,12,0,2,1,1,7
"2997",2187,26,26.9,1,4,0,0,12,0,0,1,1,7
"2998",7077,26,26.9,1,2,0,0,12,1,1,1,1,10
"2999",8415,26,27,1,2,0,0,10,1,1,1,1,7
"3000",8335,26,27,1,4,0,0,10,1,0,1,1,8
"3001",1817,26,27,1,4,0,0,12,0,1,1,1,8
"3002",1097,26,27.1,1,3,0,0,13,0,1,1,1,5
"3003",5391,26,27.1,1,4,0,0,10,0,1,1,1,8
"3004",9121,26,27.1,1,2,0,0,11,1,2,1,1,7
"3005",4077,26,27.1,1,1,0,0,9,0,0,1,1,7
"3006",6337,26,27.1,1,2,0,0,13,1,2,1,1,6
"3007",4519,26,27.1,1,4,0,0,15,0,2,1,1,6
"3008",3015,26,27.1,1,2,0,0,12,0,0,1,1,5
"3009",7167,26,27.1,1,5,0,0,10,1,0,1,1,6
"3010",477,26,27.1,1,3,0,0,14,0,1,1,1,5
"3011",3497,26,27.2,1,1,0,0,13,0,2,1,1,7
"3012",10755,26,27.3,1,4,0,0,11,1,2,1,1,6
"3013",4155,26,27.3,1,4,0,0,13,0,1,1,1,5
"3014",8777,26,27.3,1,2,0,0,13,1,2,1,1,6
"3015",251,26,27.4,1,4,0,0,10,0,1,1,1,10
"3016",10327,26,27.4,1,5,0,0,12,1,0,1,1,5
"3017",3363,26,27.4,1,4,0,0,12,0,0,1,1,9
"3018",6635,26,27.4,1,1,0,0,10,1,2,1,1,8
"3019",2267,26,27.4,1,2,0,0,10,0,1,1,1,10
"3020",892,26,27.4,1,2,0,1,12,0,6,1,1,7
"3021",693,26,27.4,1,2,0,0,15,0,1,1,1,8
"3022",649,26,27.4,1,5,0,0,11,0,0,1,1,6
"3023",8689,26,27.4,1,1,0,0,10,1,0,1,1,10
"3024",10675,26,27.4,1,3,0,0,12,1,0,1,1,9
"3025",5649,26,27.4,1,5,0,0,14,1,0,1,1,9
"3026",2491,26,27.5,1,2,0,0,14,0,0,1,1,8
"3027",1429,26,27.5,1,3,0,0,13,0,2,1,1,7
"3028",10717,26,27.5,1,2,0,0,10,1,0,1,1,6
"3029",8585,26,27.5,1,5,0,0,11,1,1,1,1,9
"3030",4605,26,27.5,1,3,0,0,9,0,0,1,1,8
"3031",252,26,27.5,1,2,0,1,11,0,8,1,1,1
"3032",8469,26,27.5,1,5,0,0,12,1,2,1,1,7
"3033",7749,26,27.5,1,1,0,0,9,1,0,1,1,6
"3034",4611,26,27.5,1,4,0,0,12,0,2,1,1,10
"3035",3559,26,27.6,1,4,0,0,14,0,1,1,1,9
"3036",2987,26,27.6,1,5,0,0,12,0,1,1,1,9
"3037",1895,26,27.6,1,5,0,0,12,0,0,1,1,10
"3038",5207,26,27.6,1,1,0,0,14,0,1,1,1,8
"3039",7913,26,27.6,1,2,0,0,15,1,0,1,1,5
"3040",2459,26,27.7,1,3,0,0,11,0,1,1,1,7
"3041",6867,26,27.8,1,1,0,0,11,1,1,1,1,10
"3042",3847,26,27.8,1,5,0,0,10,0,2,1,1,6
"3043",3729,26,27.8,1,5,0,0,12,0,0,1,1,6
"3044",6397,26,27.8,1,1,0,0,11,1,0,1,1,6
"3045",6511,26,27.9,1,3,0,0,14,1,0,1,1,9
"3046",7147,26,27.9,1,2,0,0,15,1,0,1,1,6
"3047",4987,26,27.9,1,2,0,0,12,0,0,1,1,5
"3048",9815,26,27.9,1,2,0,0,13,1,2,1,1,9
"3049",9991,26,28,1,1,0,0,15,1,2,1,1,10
"3050",3377,26,28,1,3,0,0,10,0,1,1,1,5
"3051",6319,26,28,1,2,0,0,14,1,2,1,1,7
"3052",3777,26,28,1,4,0,0,14,0,1,1,1,9
"3053",6421,26,28.1,1,2,0,0,14,1,1,1,1,10
"3054",5325,26,28.1,1,1,0,0,10,0,2,1,1,5
"3055",3121,26,28.1,1,5,0,0,12,0,2,1,1,5
"3056",6047,26,28.1,1,3,0,0,9,1,0,1,1,5
"3057",8731,26,28.1,1,2,0,0,10,1,0,1,1,10
"3058",2229,26,28.1,1,5,0,0,12,0,1,1,1,5
"3059",4387,26,28.1,1,5,0,0,12,0,2,1,1,5
"3060",7175,26,28.1,1,1,0,0,12,1,2,1,1,9
"3061",1407,26,28.2,1,3,0,0,10,0,0,1,1,10
"3062",5795,26,28.2,1,3,0,0,11,1,1,1,1,8
"3063",2903,26,28.2,1,5,0,0,15,0,1,1,1,7
"3064",2179,26,28.2,1,2,0,0,14,0,0,1,1,6
"3065",7401,26,28.2,1,5,0,0,9,1,2,1,1,7
"3066",10663,26,28.2,1,2,0,0,12,1,0,1,1,7
"3067",1503,26,28.2,1,5,0,0,15,0,0,1,1,6
"3068",7721,26,28.2,1,4,0,0,14,1,0,1,1,5
"3069",687,26,28.2,1,4,0,0,12,0,0,1,1,9
"3070",5435,26,28.3,1,4,0,0,13,1,1,1,1,9
"3071",1013,26,28.3,1,2,0,0,13,0,2,1,1,6
"3072",254,26,28.3,1,2,0,1,14,0,3,1,1,5
"3073",10523,26,28.3,1,4,0,0,10,1,2,1,1,8
"3074",7267,26,28.4,1,1,0,0,12,1,0,1,1,6
"3075",6137,26,28.4,1,5,0,0,11,1,0,1,1,7
"3076",5729,26,28.4,1,2,0,0,9,1,1,1,1,9
"3077",9107,26,28.4,1,4,0,0,10,1,2,1,1,7
"3078",10067,26,28.4,1,4,0,0,12,1,2,1,1,6
"3079",6643,26,28.4,1,3,0,0,15,1,2,1,1,9
"3080",4775,26,28.4,1,2,0,0,14,0,1,1,1,8
"3081",2177,26,28.5,1,1,0,0,15,0,2,1,1,9
"3082",7843,26,28.5,1,1,0,0,13,1,1,1,1,6
"3083",4515,26,28.5,1,1,0,0,9,0,2,1,1,10
"3084",10569,26,28.5,1,2,0,0,12,1,2,1,1,9
"3085",457,26,28.5,1,2,0,0,12,0,1,1,1,9
"3086",6167,26,28.6,1,1,0,0,10,1,1,1,1,9
"3087",7687,26,28.6,1,4,0,0,9,1,2,1,1,7
"3088",9831,26,28.6,1,2,0,0,11,1,2,1,1,10
"3089",6377,26,28.6,1,5,0,1,12,1,3,1,1,4
"3090",9263,26,28.6,1,4,0,0,10,1,2,1,1,5
"3091",821,26,28.6,1,4,0,0,12,0,1,1,1,10
"3092",7191,26,28.6,1,5,0,0,14,1,2,1,1,5
"3093",3229,26,28.6,1,5,0,0,10,0,0,1,1,7
"3094",6315,26,28.7,1,1,0,0,12,1,1,1,1,8
"3095",10801,26,28.7,1,3,0,0,14,1,1,1,1,7
"3096",10547,26,28.8,1,4,0,0,15,1,2,1,1,9
"3097",3699,26,28.8,1,3,0,0,10,0,0,1,1,8
"3098",5717,26,28.8,1,5,0,1,14,1,1,1,1,4
"3099",9115,26,28.8,1,2,0,0,13,1,2,1,1,9
"3100",4221,26,28.8,1,5,0,0,12,0,0,1,1,5
"3101",4321,26,28.9,1,3,0,0,12,0,0,1,1,5
"3102",6593,26,28.9,1,5,0,0,13,1,0,1,1,5
"3103",10760,26,28.9,1,1,0,1,15,1,6,1,1,2
"3104",5669,26,28.9,1,2,0,0,12,1,2,1,1,10
"3105",3033,26,28.9,1,5,0,0,12,0,0,1,1,6
"3106",6435,26,28.9,1,1,0,0,10,1,1,1,1,9
"3107",5603,26,29,1,2,0,0,14,1,2,1,1,9
"3108",7417,26,29,1,4,0,0,15,1,1,1,1,9
"3109",6252,26,29,1,3,0,1,12,1,3,1,1,7
"3110",3631,26,29,1,4,0,0,14,0,1,1,1,7
"3111",2373,26,29,1,2,0,0,15,0,2,1,1,6
"3112",4313,26,29.1,1,2,0,0,12,0,0,1,1,10
"3113",485,26,29.1,1,5,0,0,15,0,2,1,1,9
"3114",8619,26,29.1,1,3,0,0,14,1,0,1,1,5
"3115",2031,26,29.2,1,1,0,0,9,0,2,1,1,8
"3116",9543,26,29.2,1,1,0,0,13,1,2,1,1,6
"3117",3129,26,29.2,1,5,0,0,12,0,1,1,1,5
"3118",8785,26,29.3,1,4,0,0,9,1,0,1,1,10
"3119",1719,26,29.3,1,5,0,1,14,0,5,1,1,3
"3120",8817,26,29.3,1,3,0,0,9,1,0,1,1,5
"3121",10648,26,29.3,1,5,0,1,14,1,1,1,1,6
"3122",8863,26,29.3,1,1,0,0,11,1,0,1,1,10
"3123",7315,26,29.3,1,5,0,0,14,1,2,1,1,9
"3124",9643,26,29.4,1,1,0,0,12,1,2,1,1,9
"3125",625,26,29.5,1,1,0,0,12,0,2,1,1,6
"3126",3411,26,29.5,1,3,0,0,14,0,0,1,1,7
"3127",3359,26,29.5,1,2,0,0,13,0,1,1,1,7
"3128",4829,26,29.5,1,3,0,0,13,0,1,1,1,7
"3129",4231,26,29.6,1,3,0,0,9,0,0,1,1,10
"3130",5222,26,29.6,1,1,0,1,10,0,7,1,1,9
"3131",8945,26,29.6,1,1,0,0,14,1,0,1,1,8
"3132",3331,26,29.6,1,1,0,0,12,0,0,1,1,7
"3133",10565,26,29.6,1,1,0,0,15,1,1,1,1,9
"3134",5579,26,29.7,1,5,0,0,13,1,0,1,1,6
"3135",9583,26,29.7,1,4,0,1,10,1,2,1,1,0
"3136",3163,26,29.7,1,5,0,0,12,0,2,1,1,10
"3137",745,26,29.7,1,1,0,0,9,0,0,1,1,7
"3138",7291,26,29.8,1,1,0,0,15,1,2,1,1,8
"3139",1631,26,29.8,1,3,0,0,15,0,1,1,1,6
"3140",1665,26,29.8,1,2,0,0,12,0,1,1,1,6
"3141",3227,26,29.8,1,3,0,0,12,0,1,1,1,8
"3142",10693,26,29.9,1,4,0,0,12,1,2,1,1,6
"3143",6791,26,29.9,1,1,0,0,13,1,0,1,1,5
"3144",5318,26,29.9,1,3,0,1,14,0,4,1,1,2
"3145",4091,26,29.9,1,1,0,0,14,0,1,1,1,7
"3146",3757,26,29.9,1,4,0,0,13,0,1,1,1,7
"3147",3435,26,29.9,1,3,0,0,9,0,1,1,1,8
"3148",8994,26,30,1,2,0,1,12,1,5,1,1,3
"3149",9227,26,30,1,3,0,0,12,1,2,1,1,10
"3150",10367,26,30,1,4,0,0,9,1,2,1,1,6
"3151",5808,27,0.2,1,3,0,1,15,1,3,1,1,10
"3155",9010,27,2.1,1,3,0,1,12,1,8,1,1,10
"3156",7040,27,2.7,1,4,0,1,15,1,4,1,1,3
"3157",9938,27,2.9,1,3,0,1,12,1,2,1,1,7
"3158",6042,27,2.9,1,2,0,1,12,1,2,1,1,10
"3159",9214,27,3.9,1,3,0,1,9,1,1,1,1,6
"3160",10598,27,4.1,1,1,0,1,13,1,7,1,1,3
"3161",9302,27,4.5,1,3,0,1,10,1,6,1,1,9
"3162",10722,27,4.7,1,1,0,1,14,1,4,1,1,3
"3163",9440,27,5.5,1,4,0,1,15,1,4,1,1,4
"3164",4884,27,6.3,1,5,0,1,12,0,2,1,1,0
"3165",6122,27,6.4,1,2,0,1,11,1,5,1,1,5
"3166",4698,27,7,1,5,0,1,13,0,3,1,1,10
"3167",4173,27,7,1,4,0,1,10,0,6,1,1,4
"3168",8320,27,7.2,1,1,0,1,11,1,8,1,1,7
"3169",4536,27,7.4,1,1,0,1,14,0,2,1,1,7
"3170",4682,27,7.4,1,2,0,1,12,0,4,1,1,4
"3171",7836,27,7.5,1,1,0,1,15,1,3,1,1,10
"3172",1648,27,8.4,1,2,0,1,11,0,8,1,1,5
"3173",3230,27,9,1,3,0,1,10,0,5,1,1,6
"3174",804,27,9.6,1,3,0,1,14,0,7,1,1,4
"3175",10214,27,9.8,1,5,0,1,10,1,7,1,1,8
"3176",10304,27,10.6,1,4,0,1,13,1,3,1,1,6
"3177",4386,27,10.7,1,4,0,1,14,0,8,1,1,0
"3178",6292,27,11,1,1,0,1,11,1,7,1,1,4
"3179",10642,27,11.8,1,2,0,1,9,1,2,1,1,2
"3180",8094,27,11.8,1,4,0,1,10,1,5,1,1,4
"3181",1557,27,11.9,1,4,0,1,10,0,3,1,1,3
"3182",10809,27,12.1,1,3,0,1,10,1,6,1,1,2
"3183",9184,27,12.2,1,2,0,1,10,1,4,1,1,8
"3184",7497,27,12.6,1,4,0,1,12,1,1,1,1,1
"3185",718,27,12.7,1,4,0,1,13,0,8,1,1,8
"3186",9442,27,13.1,1,5,0,1,9,1,6,1,1,4
"3187",5791,27,13.3,1,3,0,1,12,1,8,1,1,3
"3188",3366,27,13.4,1,1,0,1,14,0,1,1,1,7
"3189",10594,27,13.7,1,4,0,1,13,1,8,1,1,2
"3190",5952,27,14.1,1,2,0,1,15,1,8,1,1,9
"3191",1761,27,14.3,1,5,0,1,12,0,8,1,1,2
"3192",9458,27,14.3,1,4,0,1,11,1,4,1,1,9
"3193",564,27,14.6,1,3,0,1,12,0,3,1,1,8
"3194",1168,27,15,1,2,0,1,9,0,3,1,1,2
"3195",8147,27,15,1,4,0,0,12,1,2,1,1,5
"3196",9565,27,15,1,4,0,0,11,1,2,1,1,10
"3197",9245,27,15,1,4,0,0,14,1,2,1,1,7
"3198",8569,27,15,1,1,0,0,13,1,1,1,1,6
"3199",2103,27,15.1,1,3,0,0,15,0,2,1,1,6
"3200",7809,27,15.1,1,1,0,0,10,1,2,1,1,6
"3201",1297,27,15.1,1,3,0,0,11,0,2,1,1,6
"3202",5805,27,15.1,1,2,0,0,10,1,2,1,1,7
"3203",7479,27,15.1,1,2,0,0,12,1,2,1,1,6
"3204",9181,27,15.2,1,4,0,0,9,1,0,1,1,8
"3205",3135,27,15.2,1,5,0,0,13,0,1,1,1,9
"3206",10321,27,15.2,1,1,0,0,13,1,0,1,1,5
"3207",3313,27,15.3,1,2,0,0,12,0,0,1,1,6
"3208",7989,27,15.4,1,3,0,0,12,1,2,1,1,10
"3209",8473,27,15.5,1,3,0,0,12,1,0,1,1,10
"3210",6799,27,15.5,1,1,0,0,13,1,0,1,1,6
"3211",6197,27,15.5,1,4,0,1,15,1,3,1,1,4
"3212",6987,27,15.6,1,1,0,0,14,1,1,1,1,8
"3213",10257,27,15.6,1,2,0,0,9,1,0,1,1,10
"3214",5029,27,15.7,1,1,0,0,15,0,0,1,1,7
"3215",5038,27,15.7,1,4,0,1,9,0,2,1,1,9
"3216",10479,27,15.7,1,4,0,0,11,1,2,1,1,9
"3217",3049,27,15.7,1,4,0,0,11,0,0,1,1,7
"3218",5086,27,15.8,1,1,0,1,14,0,3,1,1,6
"3219",9649,27,15.8,1,2,0,0,11,1,2,1,1,7
"3220",4478,27,15.9,1,2,0,1,12,0,8,1,1,9
"3221",2791,27,15.9,1,1,0,0,14,0,2,1,1,9
"3222",9063,27,15.9,1,1,0,0,14,1,1,1,1,9
"3223",479,27,15.9,1,2,0,0,13,0,1,1,1,9
"3224",2097,27,15.9,1,3,0,0,14,0,1,1,1,8
"3225",8501,27,16,1,1,0,0,12,1,1,1,1,9
"3226",4389,27,16.1,1,5,0,0,15,0,1,1,1,6
"3227",2181,27,16.1,1,3,0,0,12,0,0,1,1,9
"3228",8317,27,16.1,1,1,0,0,13,1,2,1,1,8
"3229",2463,27,16.1,1,5,0,0,14,0,0,1,1,6
"3230",5753,27,16.1,1,4,0,0,12,1,0,1,1,6
"3231",9301,27,16.2,1,4,0,0,10,1,1,1,1,9
"3232",297,27,16.2,1,5,0,0,15,0,1,1,1,9
"3233",2966,27,16.2,1,2,0,1,11,0,5,1,1,0
"3234",669,27,16.3,1,3,0,0,12,0,1,1,1,8
"3235",9292,27,16.3,1,4,0,1,10,1,3,1,1,2
"3236",8691,27,16.3,1,2,0,0,9,1,1,1,1,6
"3237",9849,27,16.3,1,3,0,0,12,1,1,1,1,5
"3238",8765,27,16.4,1,1,0,0,11,1,1,1,1,7
"3239",581,27,16.4,1,4,0,0,12,0,2,1,1,10
"3240",813,27,16.4,1,5,0,0,12,0,2,1,1,7
"3241",4615,27,16.4,1,4,0,0,9,0,1,1,1,6
"3242",3301,27,16.4,1,5,0,0,11,0,0,1,1,6
"3243",10631,27,16.5,1,4,0,0,13,1,1,1,1,10
"3244",2679,27,16.5,1,2,0,0,15,0,2,1,1,5
"3245",5147,27,16.5,1,3,0,0,14,0,1,1,1,9
"3246",5957,27,16.5,1,2,0,1,11,1,5,1,1,3
"3247",1453,27,16.5,1,2,0,0,15,0,2,1,1,9
"3248",1317,27,16.6,1,4,0,0,10,0,1,1,1,9
"3249",4467,27,16.6,1,4,0,0,9,0,2,1,1,5
"3250",9179,27,16.6,1,2,0,0,9,1,2,1,1,10
"3251",7063,27,16.6,1,5,0,0,12,1,0,1,1,7
"3252",3979,27,16.6,1,5,0,0,9,0,0,1,1,7
"3253",2368,27,16.6,1,2,0,1,14,0,4,1,1,10
"3254",5527,27,16.7,1,5,0,0,12,1,0,1,1,10
"3255",4385,27,16.7,1,4,0,0,11,0,0,1,1,6
"3256",249,27,16.7,1,2,0,0,10,0,1,1,1,10
"3257",3883,27,16.7,1,5,0,1,12,0,6,1,1,4
"3258",2383,27,16.7,1,4,0,0,9,0,1,1,1,8
"3259",3434,27,16.7,1,5,0,1,14,0,5,1,1,9
"3260",189,27,16.7,1,4,0,0,12,0,0,1,1,6
"3261",4097,27,16.9,1,4,0,0,10,0,1,1,1,10
"3262",10103,27,16.9,1,3,0,0,12,1,1,1,1,5
"3263",8243,27,16.9,1,4,0,0,11,1,1,1,1,7
"3264",3333,27,16.9,1,2,0,0,12,0,0,1,1,7
"3265",6249,27,16.9,1,3,0,0,9,1,1,1,1,5
"3266",7105,27,16.9,1,2,0,0,13,1,0,1,1,7
"3267",9103,27,16.9,1,4,0,0,10,1,1,1,1,5
"3268",10075,27,16.9,1,5,0,0,11,1,1,1,1,6
"3269",7153,27,16.9,1,1,0,0,15,1,0,1,1,8
"3270",5137,27,17,1,2,0,0,13,0,0,1,1,9
"3271",945,27,17,1,1,0,0,12,0,2,1,1,9
"3272",6705,27,17,1,4,0,0,10,1,0,1,1,8
"3273",1289,27,17.1,1,5,0,0,11,0,1,1,1,10
"3274",10429,27,17.1,1,3,0,0,15,1,2,1,1,9
"3275",2649,27,17.1,1,3,0,0,14,0,2,1,1,5
"3276",9717,27,17.1,1,4,0,0,15,1,1,1,1,7
"3277",2725,27,17.1,1,5,0,0,15,0,2,1,1,6
"3278",3133,27,17.1,1,3,0,0,12,0,1,1,1,8
"3279",3821,27,17.1,1,4,0,0,13,0,2,1,1,6
"3280",3837,27,17.1,1,3,0,0,11,0,1,1,1,5
"3281",600,27,17.2,1,3,0,1,9,0,5,1,1,5
"3282",5591,27,17.2,1,3,0,0,10,1,1,1,1,5
"3283",6505,27,17.2,1,2,0,0,11,1,2,1,1,6
"3284",4401,27,17.2,1,4,0,0,12,0,0,1,1,8
"3285",5395,27,17.2,1,1,0,0,12,0,1,1,1,5
"3286",1709,27,17.2,1,5,0,0,9,0,0,1,1,9
"3287",6893,27,17.3,1,2,0,0,12,1,1,1,1,9
"3288",1231,27,17.3,1,3,0,0,14,0,2,1,1,6
"3289",4227,27,17.3,1,2,0,0,13,0,0,1,1,7
"3290",2255,27,17.3,1,4,0,0,15,0,0,1,1,10
"3291",5485,27,17.5,1,4,0,0,15,1,0,1,1,5
"3292",9276,27,17.5,1,2,0,1,11,1,2,1,1,6
"3293",1129,27,17.5,1,2,0,0,9,0,1,1,1,6
"3294",9529,27,17.5,1,3,0,0,13,1,2,1,1,6
"3295",755,27,17.5,1,2,0,0,12,0,1,1,1,8
"3296",7413,27,17.6,1,2,0,0,12,1,1,1,1,10
"3297",8849,27,17.6,1,3,0,0,13,1,0,1,1,8
"3298",5185,27,17.6,1,2,0,0,12,0,1,1,1,5
"3299",2977,27,17.6,1,4,0,0,12,0,2,1,1,5
"3300",5513,27,17.6,1,1,0,0,13,1,0,1,1,7
"3301",2557,27,17.7,1,2,0,0,9,0,2,1,1,5
"3302",1135,27,17.7,1,4,0,0,10,0,1,1,1,10
"3303",345,27,17.7,1,5,0,0,11,0,0,1,1,9
"3304",7751,27,17.8,1,1,0,1,15,1,3,1,1,0
"3305",3003,27,17.8,1,2,0,0,10,0,2,1,1,6
"3306",7197,27,17.8,1,1,0,0,9,1,0,1,1,9
"3307",3213,27,17.8,1,4,0,0,15,0,2,1,1,7
"3308",3555,27,17.8,1,4,0,0,10,0,2,1,1,6
"3309",9613,27,17.8,1,4,0,0,11,1,2,1,1,8
"3310",9407,27,17.9,1,5,0,0,9,1,0,1,1,7
"3311",7111,27,17.9,1,5,0,0,10,1,1,1,1,5
"3312",8845,27,17.9,1,2,0,0,15,1,1,1,1,10
"3313",6692,27,17.9,1,3,0,1,14,1,8,1,1,8
"3314",10557,27,17.9,1,2,0,0,14,1,1,1,1,8
"3315",10305,27,17.9,1,3,0,0,13,1,1,1,1,5
"3316",6245,27,17.9,1,5,0,0,15,1,2,1,1,5
"3317",6071,27,18,1,5,0,0,11,1,0,1,1,5
"3318",5331,27,18.1,1,1,0,0,14,0,2,1,1,10
"3319",4435,27,18.1,1,4,0,0,11,0,1,1,1,8
"3320",3023,27,18.2,1,4,0,0,12,0,1,1,1,7
"3321",2387,27,18.2,1,3,0,0,12,0,0,1,1,7
"3322",1947,27,18.2,1,3,0,0,9,0,1,1,1,9
"3323",1909,27,18.2,1,2,0,0,9,0,1,1,1,9
"3324",9609,27,18.2,1,5,0,0,10,1,1,1,1,10
"3325",9533,27,18.3,1,1,0,0,12,1,0,1,1,8
"3326",4135,27,18.3,1,2,0,0,12,0,1,1,1,8
"3327",5769,27,18.3,1,5,0,0,11,1,0,1,1,5
"3328",7615,27,18.3,1,1,0,0,10,1,2,1,1,7
"3329",3167,27,18.3,1,4,0,0,12,0,2,1,1,5
"3330",5619,27,18.4,1,3,0,0,10,1,0,1,1,9
"3331",8719,27,18.4,1,5,0,0,10,1,0,1,1,8
"3332",7342,27,18.4,1,4,0,1,15,1,3,1,1,2
"3333",2947,27,18.4,1,1,0,0,10,0,2,1,1,6
"3334",10383,27,18.4,1,3,0,0,12,1,0,1,1,5
"3335",4779,27,18.4,1,1,0,0,10,0,0,1,1,7
"3336",843,27,18.4,1,3,0,1,12,0,6,1,1,4
"3337",7879,27,18.4,1,4,0,0,9,1,2,1,1,6
"3338",4641,27,18.4,1,5,0,0,11,0,2,1,1,6
"3339",3659,27,18.5,1,3,0,0,10,0,0,1,1,8
"3340",1565,27,18.5,1,4,0,0,15,0,1,1,1,9
"3341",1141,27,18.5,1,5,0,0,15,0,1,1,1,5
"3342",4085,27,18.5,1,3,0,0,10,0,1,1,1,5
"3343",8377,27,18.5,1,3,0,0,12,1,0,1,1,6
"3344",2599,27,18.5,1,1,0,0,12,0,2,1,1,10
"3345",7859,27,18.5,1,5,0,0,9,1,1,1,1,6
"3346",2307,27,18.6,1,2,0,0,15,0,2,1,1,8
"3347",8636,27,18.6,1,1,0,1,12,1,2,1,1,10
"3348",6177,27,18.6,1,4,0,0,12,1,1,1,1,7
"3349",5257,27,18.6,1,2,0,0,9,0,2,1,1,5
"3350",7529,27,18.6,1,3,0,0,11,1,1,1,1,6
"3351",3965,27,18.7,1,5,0,0,12,0,0,1,1,10
"3352",4207,27,18.7,1,1,0,1,13,0,3,1,1,4
"3353",8449,27,18.7,1,3,0,0,15,1,0,1,1,7
"3354",1941,27,18.7,1,2,0,1,14,0,6,1,1,1
"3355",4357,27,18.7,1,5,0,0,12,0,2,1,1,10
"3356",419,27,18.7,1,1,0,0,9,0,1,1,1,7
"3357",1547,27,18.7,1,2,0,0,14,0,2,1,1,6
"3358",4639,27,18.7,1,1,0,0,11,0,1,1,1,10
"3359",947,27,18.8,1,3,0,0,12,0,0,1,1,6
"3360",5505,27,18.8,1,5,0,0,14,1,2,1,1,6
"3361",9451,27,18.8,1,4,0,0,14,1,2,1,1,8
"3362",2721,27,18.8,1,2,0,0,11,0,2,1,1,5
"3363",7689,27,18.8,1,2,0,0,12,1,1,1,1,9
"3364",381,27,18.8,1,1,0,0,12,0,1,1,1,6
"3365",6531,27,18.9,1,5,0,0,13,1,2,1,1,10
"3366",3303,27,18.9,1,3,0,0,14,0,0,1,1,5
"3367",831,27,18.9,1,4,0,0,11,0,1,1,1,8
"3368",4003,27,18.9,1,1,0,0,11,0,2,1,1,8
"3369",10793,27,18.9,1,1,0,0,11,1,2,1,1,7
"3370",4308,27,18.9,1,4,0,1,13,0,5,1,1,0
"3371",2105,27,18.9,1,1,0,0,12,0,0,1,1,6
"3372",871,27,18.9,1,1,0,0,12,0,2,1,1,9
"3373",2515,27,19,1,5,0,0,9,0,2,1,1,10
"3374",8119,27,19,1,1,0,0,11,1,1,1,1,5
"3375",6863,27,19,1,5,0,0,15,1,0,1,1,5
"3376",1683,27,19,1,4,0,0,12,0,1,1,1,9
"3377",10391,27,19,1,1,0,0,11,1,2,1,1,7
"3378",1913,27,19,1,3,0,0,11,0,1,1,1,10
"3379",9983,27,19,1,2,0,0,12,1,0,1,1,5
"3380",6639,27,19,1,4,0,0,13,1,1,1,1,5
"3381",8869,27,19.1,1,3,0,0,14,1,0,1,1,6
"3382",7771,27,19.1,1,1,0,0,15,1,1,1,1,6
"3383",2401,27,19.1,1,5,0,0,9,0,1,1,1,7
"3384",3647,27,19.1,1,1,0,0,14,0,2,1,1,5
"3385",9925,27,19.1,1,4,0,0,12,1,1,1,1,7
"3386",9419,27,19.2,1,4,0,0,13,1,2,1,1,8
"3387",6259,27,19.2,1,3,0,0,13,1,2,1,1,10
"3388",2261,27,19.2,1,3,0,0,15,0,1,1,1,9
"3389",8425,27,19.2,1,1,0,0,12,1,2,1,1,5
"3390",7127,27,19.2,1,3,0,0,15,1,1,1,1,6
"3391",6575,27,19.2,1,1,0,0,13,1,1,1,1,5
"3392",10767,27,19.2,1,5,0,0,13,1,0,1,1,5
"3393",3125,27,19.3,1,1,0,0,9,0,2,1,1,8
"3394",6747,27,19.3,1,2,0,0,11,1,1,1,1,8
"3395",8629,27,19.3,1,4,0,0,14,1,1,1,1,10
"3396",4355,27,19.3,1,1,0,0,13,0,2,1,1,8
"3397",1143,27,19.4,1,5,0,0,14,0,2,1,1,6
"3398",8331,27,19.4,1,2,0,0,12,1,2,1,1,6
"3399",9641,27,19.4,1,2,0,0,12,1,0,1,1,10
"3400",6041,27,19.5,1,4,0,0,9,1,1,1,1,8
"3401",7137,27,19.6,1,1,0,0,13,1,0,1,1,7
"3402",5027,27,19.6,1,5,0,0,15,0,0,1,1,7
"3403",2127,27,19.6,1,4,0,0,10,0,0,1,1,8
"3404",4083,27,19.6,1,4,0,0,13,0,0,1,1,8
"3405",1381,27,19.7,1,4,0,0,12,0,1,1,1,7
"3406",8662,27,19.8,1,4,0,1,12,1,5,1,1,7
"3407",5149,27,19.8,1,3,0,0,11,0,0,1,1,8
"3408",7599,27,19.9,1,3,0,0,9,1,1,1,1,9
"3409",7757,27,19.9,1,5,0,0,11,1,1,1,1,8
"3410",10453,27,19.9,1,5,0,0,14,1,1,1,1,5
"3411",3221,27,19.9,1,5,0,0,9,0,1,1,1,7
"3412",1509,27,19.9,1,2,0,0,11,0,1,1,1,9
"3413",5543,27,19.9,1,1,0,0,12,1,1,1,1,6
"3414",4217,27,19.9,1,5,0,0,10,0,2,1,1,8
"3415",5389,27,20,1,5,0,0,9,0,2,1,1,10
"3416",5119,27,20,1,2,0,0,12,0,2,1,1,5
"3417",7601,27,20,1,5,0,0,13,1,1,1,1,9
"3418",9567,27,20,1,2,0,0,12,1,1,1,1,7
"3419",427,27,20,1,4,0,0,11,0,1,1,1,6
"3420",107,27,20,1,5,0,0,15,0,0,1,1,9
"3421",8351,27,20,1,5,0,0,15,1,2,1,1,9
"3422",10093,27,20,1,3,0,0,13,1,0,1,1,10
"3423",8907,27,20.1,1,1,0,0,9,1,1,1,1,8
"3424",7575,27,20.1,1,3,0,0,12,1,0,1,1,6
"3425",10593,27,20.1,1,4,0,1,14,1,6,1,1,4
"3426",2183,27,20.1,1,4,0,0,10,0,0,1,1,7
"3427",4715,27,20.1,1,1,0,0,10,0,1,1,1,8
"3428",2949,27,20.2,1,4,0,1,9,0,1,1,1,0
"3429",353,27,20.2,1,1,0,0,10,0,1,1,1,8
"3430",253,27,20.2,1,3,0,0,12,0,1,1,1,7
"3431",3155,27,20.2,1,4,0,0,11,0,1,1,1,7
"3432",6763,27,20.2,1,4,0,0,12,1,1,1,1,10
"3433",3257,27,20.2,1,5,0,0,12,0,0,1,1,9
"3434",2209,27,20.2,1,5,0,0,13,0,0,1,1,8
"3435",10774,27,20.3,1,2,0,1,15,1,1,1,1,10
"3436",5081,27,20.3,1,3,0,0,13,0,0,1,1,7
"3437",4293,27,20.3,1,3,0,0,9,0,0,1,1,10
"3438",8661,27,20.3,1,3,0,0,15,1,0,1,1,6
"3439",495,27,20.4,1,3,0,0,9,0,1,1,1,10
"3440",4527,27,20.4,1,1,0,0,14,0,0,1,1,7
"3441",8397,27,20.4,1,3,0,0,14,1,2,1,1,7
"3442",5559,27,20.4,1,3,0,0,12,1,0,1,1,8
"3443",9969,27,20.4,1,5,0,0,15,1,0,1,1,8
"3444",9787,27,20.4,1,3,0,0,11,1,1,1,1,8
"3445",6375,27,20.5,1,3,0,0,12,1,2,1,1,9
"3446",7366,27,20.5,1,3,0,1,12,1,5,1,1,6
"3447",593,27,20.5,1,4,0,0,12,0,0,1,1,6
"3448",1001,27,20.5,1,4,0,0,12,0,0,1,1,7
"3449",5367,27,20.5,1,2,0,0,12,0,0,1,1,8
"3450",8973,27,20.5,1,5,0,0,12,1,2,1,1,9
"3451",7213,27,20.6,1,4,0,0,11,1,1,1,1,5
"3452",6729,27,20.6,1,1,0,0,12,1,0,1,1,8
"3453",3579,27,20.6,1,5,0,0,15,0,2,1,1,6
"3454",8035,27,20.6,1,3,0,0,9,1,2,1,1,5
"3455",7509,27,20.7,1,2,0,0,9,1,2,1,1,10
"3456",3617,27,20.7,1,3,0,0,14,0,1,1,1,10
"3457",3079,27,20.7,1,5,0,0,12,0,2,1,1,5
"3458",3047,27,20.7,1,1,0,0,13,0,0,1,1,9
"3459",9469,27,20.8,1,2,0,0,14,1,0,1,1,9
"3460",7055,27,20.8,1,2,0,0,13,1,1,1,1,9
"3461",1491,27,20.8,1,4,0,0,9,0,1,1,1,7
"3462",8811,27,20.8,1,3,0,0,12,1,0,1,1,6
"3463",1695,27,20.9,1,1,0,0,14,0,1,1,1,5
"3464",4269,27,20.9,1,5,0,0,13,0,2,1,1,9
"3465",2165,27,21,1,4,0,0,12,0,0,1,1,9
"3466",9781,27,21,1,4,0,0,12,1,2,1,1,7
"3467",4931,27,21.1,1,1,0,0,10,0,0,1,1,8
"3468",7495,27,21.1,1,1,0,0,15,1,2,1,1,7
"3469",6419,27,21.1,1,3,0,0,10,1,2,1,1,5
"3470",4365,27,21.2,1,2,0,0,13,0,2,1,1,5
"3471",9433,27,21.2,1,2,0,0,12,1,2,1,1,8
"3472",6745,27,21.2,1,2,0,0,13,1,0,1,1,9
"3473",4271,27,21.2,1,4,0,0,9,0,0,1,1,7
"3474",6555,27,21.2,1,2,0,0,12,1,1,1,1,8
"3475",9065,27,21.2,1,4,0,0,15,1,1,1,1,6
"3476",9911,27,21.2,1,3,0,0,9,1,2,1,1,8
"3477",2063,27,21.3,1,3,0,0,15,0,0,1,1,8
"3478",5444,27,21.3,1,4,0,1,10,1,5,1,1,4
"3479",2351,27,21.3,1,1,0,0,13,0,1,1,1,6
"3480",2059,27,21.3,1,2,0,0,9,0,0,1,1,8
"3481",5737,27,21.3,1,4,0,0,9,1,1,1,1,5
"3482",8729,27,21.3,1,4,0,0,10,1,2,1,1,10
"3483",8477,27,21.4,1,2,0,0,10,1,2,1,1,10
"3484",9077,27,21.4,1,3,0,0,12,1,2,1,1,10
"3485",4591,27,21.4,1,5,0,0,12,0,2,1,1,6
"3486",3835,27,21.4,1,4,0,0,11,0,1,1,1,7
"3487",6039,27,21.5,1,3,0,0,15,1,0,1,1,8
"3488",10597,27,21.5,1,4,0,0,11,1,2,1,1,10
"3489",8763,27,21.5,1,3,0,0,14,1,1,1,1,10
"3490",10548,27,21.5,1,1,0,1,9,1,1,1,1,9
"3491",9411,27,21.5,1,3,0,0,12,1,0,1,1,7
"3492",613,27,21.6,1,2,0,0,13,0,0,1,1,6
"3493",3957,27,21.6,1,4,0,0,12,0,1,1,1,8
"3494",673,27,21.6,1,4,0,0,9,0,1,1,1,10
"3495",4213,27,21.7,1,5,0,0,10,0,1,1,1,5
"3496",4495,27,21.7,1,5,0,0,9,0,0,1,1,6
"3497",3553,27,21.7,1,3,0,0,12,0,0,1,1,7
"3498",4937,27,21.7,1,4,0,0,14,0,0,1,1,10
"3499",8325,27,21.8,1,1,0,0,9,1,0,1,1,5
"3500",9761,27,21.8,1,1,0,0,10,1,2,1,1,10
"3501",7907,27,21.8,1,2,0,0,15,1,0,1,1,9
"3502",3245,27,21.8,1,4,0,0,12,0,1,1,1,8
"3503",6207,27,21.9,1,1,0,0,14,1,1,1,1,10
"3504",6467,27,21.9,1,4,0,0,11,1,0,1,1,8
"3505",10279,27,21.9,1,1,0,0,12,1,2,1,1,9
"3506",5781,27,21.9,1,4,0,0,11,1,1,1,1,9
"3507",5467,27,21.9,1,2,0,0,12,1,1,1,1,7
"3508",1575,27,21.9,1,3,0,0,13,0,1,1,1,9
"3509",4253,27,21.9,1,1,0,0,13,0,0,1,1,5
"3510",10723,27,21.9,1,2,0,0,12,1,1,1,1,9
"3511",3503,27,21.9,1,3,0,0,12,0,2,1,1,5
"3512",1499,27,21.9,1,2,0,1,12,0,4,1,1,0
"3513",7623,27,21.9,1,1,0,0,9,1,2,1,1,6
"3514",1041,27,22,1,2,0,0,11,0,0,1,1,7
"3515",10709,27,22,1,3,0,0,12,1,2,1,1,8
"3516",5291,27,22,1,1,0,0,12,0,2,1,1,9
"3517",5487,27,22.1,1,4,0,0,15,1,1,1,1,5
"3518",8679,27,22.1,1,4,0,0,9,1,0,1,1,7
"3519",10295,27,22.1,1,1,0,0,14,1,0,1,1,5
"3520",1813,27,22.1,1,3,0,0,9,0,2,1,1,8
"3521",7827,27,22.1,1,2,0,0,12,1,2,1,1,10
"3522",2837,27,22.1,1,4,0,0,15,0,2,1,1,8
"3523",709,27,22.2,1,3,0,0,12,0,1,1,1,5
"3524",7905,27,22.2,1,4,0,0,11,1,1,1,1,10
"3525",6697,27,22.2,1,2,0,0,12,1,1,1,1,5
"3526",10325,27,22.3,1,2,0,0,13,1,2,1,1,5
"3527",9663,27,22.4,1,5,0,0,12,1,0,1,1,6
"3528",10144,27,22.4,1,4,0,1,12,1,1,1,1,9
"3529",9026,27,22.4,1,4,0,1,14,1,5,1,1,1
"3530",10575,27,22.4,1,5,0,0,9,1,2,1,1,9
"3531",167,27,22.5,1,4,0,0,12,0,1,1,1,7
"3532",3888,27,22.5,1,5,0,1,12,0,5,1,1,0
"3533",6385,27,22.5,1,4,0,0,13,1,0,1,1,8
"3534",7131,27,22.5,1,4,0,0,12,1,1,1,1,9
"3535",2783,27,22.5,1,2,0,0,10,0,1,1,1,10
"3536",1767,27,22.6,1,1,0,0,9,0,2,1,1,8
"3537",9191,27,22.6,1,2,0,0,15,1,0,1,1,5
"3538",2347,27,22.7,1,5,0,0,14,0,1,1,1,8
"3539",6699,27,22.7,1,5,0,0,15,1,0,1,1,5
"3540",4957,27,22.7,1,2,0,0,12,0,1,1,1,10
"3541",4738,27,22.7,1,2,0,1,15,0,3,1,1,10
"3542",6667,27,22.7,1,2,0,0,9,1,1,1,1,5
"3543",1395,27,22.7,1,5,0,0,15,0,0,1,1,10
"3544",5715,27,22.7,1,2,0,0,11,1,1,1,1,9
"3545",8451,27,22.8,1,2,0,0,14,1,2,1,1,8
"3546",5519,27,22.8,1,4,0,0,14,1,2,1,1,10
"3547",8510,27,22.8,1,3,0,1,10,1,8,1,1,4
"3548",1123,27,22.8,1,2,0,0,13,0,1,1,1,9
"3549",4789,27,22.8,1,1,0,0,15,0,0,1,1,10
"3550",8889,27,22.9,1,2,0,0,15,1,0,1,1,7
"3551",741,27,22.9,1,4,0,0,10,0,0,1,1,10
"3552",7993,27,22.9,1,1,0,0,14,1,2,1,1,5
"3553",9665,27,22.9,1,2,0,0,13,1,2,1,1,10
"3554",9585,27,22.9,1,2,0,0,12,1,2,1,1,10
"3555",7339,27,22.9,1,2,0,0,9,1,1,1,1,9
"3556",2381,27,23,1,4,0,0,12,0,1,1,1,9
"3557",4843,27,23,1,3,0,0,13,0,0,1,1,10
"3558",8480,27,23.1,1,4,0,1,9,1,4,1,1,0
"3559",10681,27,23.1,1,4,0,0,14,1,1,1,1,7
"3560",901,27,23.1,1,2,0,0,12,0,1,1,1,6
"3561",10199,27,23.1,1,2,0,0,12,1,2,1,1,8
"3562",3899,27,23.1,1,1,0,0,11,0,2,1,1,7
"3563",5425,27,23.1,1,5,0,0,11,1,1,1,1,8
"3564",7399,27,23.2,1,2,0,0,15,1,2,1,1,7
"3565",1155,27,23.2,1,1,0,0,11,0,0,1,1,5
"3566",5865,27,23.2,1,5,0,0,15,1,2,1,1,6
"3567",6671,27,23.2,1,2,0,0,11,1,1,1,1,8
"3568",2601,27,23.2,1,3,0,0,12,0,1,1,1,6
"3569",2289,27,23.2,1,5,0,0,12,0,1,1,1,5
"3570",1197,27,23.3,1,5,0,0,12,0,2,1,1,9
"3571",10247,27,23.3,1,3,0,0,15,1,0,1,1,10
"3572",9931,27,23.4,1,2,0,0,11,1,2,1,1,10
"3573",5043,27,23.4,1,3,0,0,9,0,1,1,1,5
"3574",501,27,23.4,1,4,0,0,12,0,0,1,1,8
"3575",7899,27,23.4,1,2,0,0,11,1,1,1,1,6
"3576",9971,27,23.5,1,2,0,0,13,1,1,1,1,10
"3577",3615,27,23.5,1,4,0,0,12,0,2,1,1,10
"3578",3695,27,23.5,1,5,0,0,12,0,1,1,1,8
"3579",3364,27,23.6,1,2,0,1,12,0,4,1,1,0
"3580",6141,27,23.6,1,3,0,0,15,1,2,1,1,8
"3581",4053,27,23.6,1,5,0,0,9,0,2,1,1,8
"3582",7313,27,23.6,1,2,0,0,10,1,1,1,1,9
"3583",1869,27,23.6,1,4,0,0,10,0,2,1,1,7
"3584",6311,27,23.6,1,1,0,0,13,1,1,1,1,9
"3585",3865,27,23.6,1,5,0,0,12,0,0,1,1,10
"3586",3165,27,23.6,1,4,0,0,9,0,1,1,1,8
"3587",8017,27,23.7,1,2,0,0,12,1,2,1,1,7
"3588",8749,27,23.7,1,2,0,0,12,1,2,1,1,10
"3589",8167,27,23.7,1,5,0,0,13,1,1,1,1,8
"3590",6507,27,23.7,1,5,0,0,14,1,1,1,1,9
"3591",2341,27,23.7,1,4,0,0,14,0,2,1,1,8
"3592",3061,27,23.8,1,4,0,0,12,0,0,1,1,9
"3593",2829,27,23.8,1,3,0,0,12,0,2,1,1,10
"3594",4323,27,23.8,1,5,0,0,10,0,0,1,1,10
"3595",1225,27,23.9,1,2,0,0,14,0,1,1,1,8
"3596",2855,27,23.9,1,3,0,0,12,0,0,1,1,10
"3597",10639,27,24,1,5,0,0,11,1,2,1,1,7
"3598",10025,27,24,1,2,0,0,12,1,0,1,1,6
"3599",10853,27,24,1,2,0,0,11,1,1,1,1,5
"3600",6865,27,24,1,5,0,0,9,1,1,1,1,9
"3601",1501,27,24.1,1,3,0,0,12,0,2,1,1,10
"3602",2487,27,24.1,1,5,0,0,12,0,1,1,1,9
"3603",3449,27,24.1,1,2,0,0,13,0,0,1,1,9
"3604",4449,27,24.2,1,1,0,0,12,0,1,1,1,5
"3605",10077,27,24.3,1,1,0,0,10,1,0,1,1,10
"3606",2471,27,24.3,1,3,0,0,14,0,2,1,1,8
"3607",8711,27,24.3,1,1,0,0,11,1,2,1,1,7
"3608",6743,27,24.3,1,1,0,0,13,1,0,1,1,9
"3609",9777,27,24.3,1,5,0,0,10,1,2,1,1,6
"3610",4369,27,24.4,1,2,0,0,13,0,0,1,1,10
"3611",1567,27,24.4,1,5,0,0,12,0,2,1,1,9
"3612",471,27,24.4,1,3,0,0,14,0,2,1,1,7
"3613",2547,27,24.4,1,2,0,0,15,0,0,1,1,6
"3614",9821,27,24.4,1,2,0,0,15,1,2,1,1,6
"3615",5803,27,24.6,1,1,0,0,11,1,0,1,1,9
"3616",7143,27,24.6,1,4,0,0,12,1,2,1,1,8
"3617",10183,27,24.6,1,5,0,0,14,1,0,1,1,6
"3618",1591,27,24.6,1,3,0,0,12,0,0,1,1,6
"3619",1815,27,24.6,1,1,0,0,13,0,1,1,1,8
"3620",3921,27,24.7,1,4,0,0,10,0,2,1,1,10
"3621",6839,27,24.7,1,1,0,0,13,1,0,1,1,10
"3622",4341,27,24.7,1,2,0,0,15,0,2,1,1,10
"3623",3581,27,24.7,1,1,0,0,12,0,0,1,1,8
"3624",10603,27,24.7,1,2,0,0,9,1,1,1,1,7
"3625",4093,27,24.8,1,5,0,0,12,0,1,1,1,6
"3626",4381,27,24.8,1,1,0,0,11,0,0,1,1,8
"3627",6152,27,24.8,1,1,0,1,15,1,4,1,1,2
"3628",9965,27,24.8,1,1,0,0,13,1,0,1,1,9
"3629",323,27,24.9,1,3,0,0,13,0,1,1,1,7
"3630",8647,27,24.9,1,2,0,0,15,1,2,1,1,10
"3631",9138,27,24.9,1,5,0,1,9,1,7,1,1,7
"3632",6223,27,24.9,1,4,0,0,11,1,1,1,1,5
"3633",7263,27,24.9,1,1,0,0,11,1,2,1,1,7
"3634",1955,27,24.9,1,3,0,0,14,0,1,1,1,7
"3635",7679,27,25,1,5,0,0,12,1,0,1,1,8
"3636",10659,27,25,1,3,0,0,12,1,1,1,1,10
"3637",4179,27,25.1,1,2,0,0,13,0,0,1,1,6
"3638",9695,27,25.1,1,2,0,0,11,1,0,1,1,5
"3639",5811,27,25.1,1,3,0,0,12,1,0,1,1,10
"3640",6603,27,25.2,1,3,0,0,13,1,1,1,1,8
"3641",2331,27,25.2,1,4,0,0,14,0,0,1,1,10
"3642",1443,27,25.3,1,1,0,0,9,0,2,1,1,9
"3643",6549,27,25.3,1,5,0,0,13,1,0,1,1,5
"3644",6333,27,25.3,1,5,0,0,10,1,1,1,1,7
"3645",491,27,25.3,1,3,0,0,14,0,2,1,1,9
"3646",4489,27,25.3,1,5,0,0,13,0,2,1,1,5
"3647",9287,27,25.3,1,4,0,0,9,1,0,1,1,10
"3648",7521,27,25.4,1,1,0,0,12,1,2,1,1,7
"3649",4279,27,25.4,1,2,0,0,14,0,0,1,1,10
"3650",8667,27,25.4,1,4,0,0,9,1,1,1,1,10
"3651",10081,27,25.4,1,2,0,0,11,1,2,1,1,5
"3652",9645,27,25.4,1,4,0,0,12,1,2,1,1,7
"3653",2935,27,25.5,1,3,0,0,12,0,2,1,1,8
"3654",4299,27,25.5,1,4,0,0,9,0,0,1,1,10
"3655",4889,27,25.5,1,2,0,0,11,0,2,1,1,6
"3656",9257,27,25.5,1,2,0,0,14,1,2,1,1,8
"3657",759,27,25.5,1,2,0,0,11,0,2,1,1,7
"3658",399,27,25.6,1,2,0,0,13,0,0,1,1,10
"3659",5176,27,25.6,1,4,0,1,12,0,4,1,1,4
"3660",5091,27,25.6,1,2,0,0,10,0,1,1,1,6
"3661",10449,27,25.7,1,3,0,0,13,1,1,1,1,9
"3662",1923,27,25.7,1,2,0,0,10,0,1,1,1,6
"3663",4773,27,25.8,1,5,0,0,10,0,1,1,1,8
"3664",2535,27,25.8,1,2,0,0,14,0,0,1,1,8
"3665",617,27,25.8,1,1,0,0,10,0,2,1,1,10
"3666",3655,27,25.8,1,1,0,0,13,0,1,1,1,10
"3667",5965,27,25.8,1,1,0,0,14,1,0,1,1,7
"3668",8767,27,25.9,1,1,0,0,9,1,0,1,1,7
"3669",6313,27,25.9,1,5,0,0,15,1,2,1,1,5
"3670",1811,27,25.9,1,5,0,1,11,0,7,1,1,3
"3671",3705,27,25.9,1,2,0,0,14,0,2,1,1,5
"3672",3277,27,26,1,1,0,0,12,0,2,1,1,9
"3673",8429,27,26,1,4,0,0,12,1,0,1,1,8
"3674",3293,27,26,1,1,0,1,15,0,7,1,1,4
"3675",5031,27,26,1,5,0,0,11,0,1,1,1,8
"3676",5242,27,26,1,1,0,1,14,0,1,1,1,10
"3677",2875,27,26,1,2,0,0,10,0,2,1,1,10
"3678",10459,27,26.1,1,2,0,0,15,1,2,1,1,9
"3679",8781,27,26.1,1,1,0,0,14,1,1,1,1,5
"3680",7589,27,26.1,1,3,0,0,15,1,2,1,1,5
"3681",9595,27,26.1,1,1,0,0,12,1,1,1,1,5
"3682",8123,27,26.1,1,2,0,0,12,1,2,1,1,10
"3683",2145,27,26.1,1,5,0,0,15,0,1,1,1,6
"3684",393,27,26.1,1,5,0,0,14,0,2,1,1,7
"3685",8103,27,26.1,1,1,0,0,12,1,1,1,1,5
"3686",7729,27,26.1,1,4,0,0,11,1,0,1,1,6
"3687",7579,27,26.2,1,1,0,0,13,1,2,1,1,6
"3688",5075,27,26.3,1,1,0,0,13,0,1,1,1,5
"3689",9095,27,26.3,1,5,0,0,15,1,1,1,1,7
"3690",4565,27,26.3,1,3,0,0,12,0,0,1,1,5
"3691",6433,27,26.3,1,3,0,0,9,1,1,1,1,8
"3692",2993,27,26.3,1,1,0,0,12,0,2,1,1,5
"3693",2723,27,26.3,1,4,0,0,11,0,0,1,1,7
"3694",10097,27,26.3,1,4,0,0,11,1,2,1,1,7
"3695",5945,27,26.4,1,5,0,0,10,1,0,1,1,9
"3696",8139,27,26.4,1,4,0,0,12,1,0,1,1,10
"3697",1425,27,26.4,1,2,0,0,14,0,0,1,1,10
"3698",5039,27,26.4,1,3,0,0,13,0,1,1,1,5
"3699",2845,27,26.5,1,1,0,0,15,0,2,1,1,5
"3700",9737,27,26.5,1,2,0,0,12,1,2,1,1,8
"3701",8109,27,26.5,1,4,0,0,9,1,1,1,1,6
"3702",7375,27,26.5,1,5,0,0,10,1,0,1,1,9
"3703",1551,27,26.5,1,2,0,0,14,0,1,1,1,6
"3704",9099,27,26.5,1,5,0,0,12,1,2,1,1,10
"3705",2493,27,26.5,1,4,0,0,14,0,1,1,1,9
"3706",2083,27,26.5,1,5,0,0,12,0,2,1,1,8
"3707",743,27,26.5,1,1,0,0,15,0,1,1,1,7
"3708",9795,27,26.5,1,4,0,0,10,1,1,1,1,7
"3709",187,27,26.6,1,2,0,0,9,0,0,1,1,10
"3710",609,27,26.7,1,1,0,0,12,0,0,1,1,7
"3711",3259,27,26.7,1,5,0,0,13,0,2,1,1,6
"3712",2927,27,26.7,1,3,0,0,10,0,0,1,1,9
"3713",3523,27,26.7,1,5,0,0,11,0,0,1,1,10
"3714",3875,27,26.7,1,2,0,0,13,0,2,1,1,7
"3715",6781,27,26.8,1,4,0,0,10,1,2,1,1,5
"3716",5311,27,26.8,1,1,0,0,12,0,0,1,1,9
"3717",6703,27,26.8,1,3,0,0,12,1,2,1,1,10
"3718",545,27,26.9,1,3,0,0,13,0,2,1,1,9
"3719",5943,27,26.9,1,3,0,0,13,1,2,1,1,6
"3720",2815,27,27,1,5,0,0,9,0,1,1,1,6
"3721",2611,27,27,1,1,0,0,12,0,1,1,1,7
"3722",2159,27,27,1,4,0,0,9,0,2,1,1,9
"3723",4545,27,27,1,5,0,0,11,0,1,1,1,6
"3724",8163,27,27.1,1,5,0,0,9,1,2,1,1,6
"3725",3113,27,27.1,1,5,0,0,15,0,0,1,1,10
"3726",8055,27,27.1,1,4,0,0,12,1,0,1,1,5
"3727",9109,27,27.2,1,5,0,0,11,1,0,1,1,6
"3728",6905,27,27.2,1,2,0,0,12,1,0,1,1,9
"3729",7583,27,27.2,1,3,0,0,10,1,1,1,1,10
"3730",605,27,27.2,1,5,0,0,14,0,1,1,1,6
"3731",3395,27,27.2,1,4,0,0,13,0,1,1,1,10
"3732",6325,27,27.3,1,2,0,0,9,1,0,1,1,8
"3733",5776,27,27.3,1,1,0,1,14,1,3,1,1,1
"3734",6379,27,27.3,1,4,0,0,12,1,0,1,1,8
"3735",2713,27,27.3,1,4,0,0,12,0,1,1,1,9
"3736",4701,27,27.3,1,5,0,0,12,0,2,1,1,9
"3737",2575,27,27.3,1,1,0,0,15,0,2,1,1,10
"3738",7959,27,27.4,1,4,0,0,11,1,2,1,1,9
"3739",9901,27,27.4,1,4,0,0,9,1,1,1,1,8
"3740",4409,27,27.4,1,1,0,0,15,0,0,1,1,6
"3741",4259,27,27.4,1,1,0,0,10,0,2,1,1,7
"3742",3177,27,27.4,1,3,0,0,11,0,2,1,1,8
"3743",9087,27,27.4,1,3,0,0,12,1,2,1,1,5
"3744",9217,27,27.5,1,1,0,0,10,1,2,1,1,10
"3745",8081,27,27.5,1,1,0,0,13,1,2,1,1,10
"3746",5373,27,27.5,1,3,0,0,12,0,2,1,1,7
"3747",6957,27,27.5,1,3,0,0,12,1,0,1,1,6
"3748",507,27,27.5,1,1,0,0,12,0,0,1,1,5
"3749",4251,27,27.6,1,1,0,0,11,0,2,1,1,9
"3750",1965,27,27.7,1,5,0,0,13,0,2,1,1,9
"3751",9947,27,27.7,1,3,0,0,10,1,0,1,1,6
"3752",4441,27,27.7,1,4,0,0,13,0,0,1,1,6
"3753",51,27,27.8,1,2,0,0,11,0,0,1,1,9
"3754",487,27,27.8,1,2,0,0,13,0,1,1,1,8
"3755",2531,27,27.8,1,5,0,0,11,0,0,1,1,10
"3756",6986,27,27.8,1,1,0,1,12,1,4,1,1,9
"3757",5703,27,27.8,1,1,0,0,9,1,0,1,1,10
"3758",773,27,27.9,1,3,0,0,12,0,2,1,1,6
"3759",9702,27,27.9,1,1,0,1,11,1,5,1,1,9
"3760",1881,27,28,1,3,0,0,11,0,2,1,1,5
"3761",3525,27,28,1,5,0,0,13,0,1,1,1,6
"3762",6487,27,28,1,3,0,0,14,1,0,1,1,8
"3763",1463,27,28,1,3,0,0,12,0,0,1,1,5
"3764",3339,27,28,1,2,0,0,10,0,1,1,1,6
"3765",7357,27,28,1,4,0,1,10,1,1,1,1,0
"3766",7868,27,28.1,1,2,0,1,11,1,2,1,1,1
"3767",6361,27,28.1,1,3,0,0,13,1,1,1,1,6
"3768",4809,27,28.1,1,1,0,0,12,0,2,1,1,7
"3769",5647,27,28.1,1,3,0,0,14,1,0,1,1,7
"3770",9977,27,28.1,1,1,0,0,14,1,1,1,1,5
"3771",9167,27,28.1,1,3,0,0,12,1,1,1,1,10
"3772",10125,27,28.2,1,3,0,0,12,1,1,1,1,6
"3773",8587,27,28.2,1,4,0,0,12,1,0,1,1,5
"3774",7331,27,28.2,1,4,0,0,9,1,1,1,1,5
"3775",5265,27,28.2,1,4,0,0,9,0,2,1,1,9
"3776",513,27,28.2,1,3,0,0,12,0,1,1,1,8
"3777",1550,27,28.2,1,2,0,1,12,0,1,1,1,1
"3778",5319,27,28.2,1,5,0,0,12,0,1,1,1,10
"3779",511,27,28.2,1,2,0,1,12,0,2,1,1,3
"3780",3601,27,28.3,1,4,0,0,15,0,0,1,1,10
"3781",4873,27,28.3,1,5,0,0,11,0,2,1,1,6
"3782",1079,27,28.3,1,3,0,0,9,0,2,1,1,9
"3783",3945,27,28.3,1,5,0,0,14,0,1,1,1,9
"3784",779,27,28.3,1,4,0,0,14,0,1,1,1,5
"3785",7823,27,28.3,1,3,0,0,9,1,0,1,1,5
"3786",2945,27,28.4,1,3,0,0,13,0,2,1,1,7
"3787",2117,27,28.4,1,3,0,0,14,0,2,1,1,7
"3788",8275,27,28.4,1,3,0,0,12,1,1,1,1,7
"3789",9885,27,28.4,1,4,0,0,9,1,1,1,1,9
"3790",8015,27,28.5,1,5,0,0,12,1,1,1,1,5
"3791",5645,27,28.5,1,4,0,0,11,1,0,1,1,6
"3792",5219,27,28.5,1,2,0,0,13,0,2,1,1,9
"3793",4165,27,28.5,1,3,0,0,12,0,1,1,1,6
"3794",2049,27,28.5,1,2,0,0,13,0,2,1,1,8
"3795",1829,27,28.5,1,3,0,0,14,0,0,1,1,6
"3796",6429,27,28.5,1,3,0,0,10,1,1,1,1,5
"3797",4881,27,28.6,1,5,0,0,13,0,2,1,1,8
"3798",5607,27,28.6,1,1,0,0,12,1,2,1,1,10
"3799",3949,27,28.6,1,2,0,0,15,0,1,1,1,7
"3800",5653,27,28.6,1,5,0,0,11,1,0,1,1,10
"3801",10379,27,28.6,1,4,0,0,10,1,0,1,1,8
"3802",5287,27,28.6,1,4,0,0,9,0,2,1,1,6
"3803",2389,27,28.7,1,4,0,0,13,0,1,1,1,6
"3804",3693,27,28.7,1,4,0,0,12,0,1,1,1,8
"3805",4533,27,28.7,1,3,0,0,15,0,1,1,1,9
"3806",10771,27,28.7,1,4,0,0,11,1,2,1,1,7
"3807",8779,27,28.7,1,2,0,0,12,1,2,1,1,8
"3808",6129,27,28.7,1,2,0,0,11,1,2,1,1,5
"3809",10643,27,28.7,1,2,0,0,14,1,0,1,1,5
"3810",7335,27,28.7,1,1,0,1,11,1,7,1,1,1
"3811",3855,27,28.8,1,3,0,0,10,0,1,1,1,8
"3812",4391,27,28.8,1,2,0,0,10,0,0,1,1,5
"3813",2843,27,28.8,1,3,0,0,15,0,1,1,1,7
"3814",6809,27,28.8,1,2,0,0,9,1,2,1,1,5
"3815",8011,27,28.8,1,5,0,0,12,1,2,1,1,8
"3816",4229,27,28.8,1,3,0,0,9,0,0,1,1,7
"3817",4977,27,28.8,1,2,0,0,12,0,2,1,1,6
"3818",1839,27,28.9,1,3,0,0,13,0,1,1,1,6
"3819",3291,27,28.9,1,2,0,0,12,0,0,1,1,7
"3820",1505,27,28.9,1,5,0,0,11,0,1,1,1,8
"3821",9871,27,28.9,1,2,0,0,15,1,2,1,1,9
"3822",10701,27,28.9,1,3,0,0,12,1,0,1,1,7
"3823",9579,27,29,1,3,0,0,12,1,1,1,1,10
"3824",41,27,29,1,5,0,0,12,0,2,1,1,6
"3825",1435,27,29.1,1,2,0,0,12,0,1,1,1,10
"3826",2645,27,29.1,1,4,0,0,12,0,0,1,1,6
"3827",9421,27,29.2,1,4,0,0,9,1,2,1,1,7
"3828",10435,27,29.2,1,3,0,0,11,1,0,1,1,8
"3829",2565,27,29.2,1,2,0,0,15,0,0,1,1,7
"3830",3863,27,29.2,1,2,0,0,14,0,0,1,1,10
"3831",4597,27,29.2,1,2,0,0,13,0,2,1,1,9
"3832",1701,27,29.2,1,2,0,0,11,0,1,1,1,8
"3833",3059,27,29.2,1,3,0,0,12,0,1,1,1,9
"3834",9824,27,29.2,1,5,0,1,13,1,7,1,1,6
"3835",4153,27,29.3,1,2,0,0,12,0,1,1,1,7
"3836",2075,27,29.3,1,2,0,0,13,0,1,1,1,10
"3837",1555,27,29.3,1,2,0,0,12,0,1,1,1,7
"3838",5885,27,29.4,1,3,0,0,12,1,2,1,1,8
"3839",8355,27,29.4,1,1,0,0,11,1,0,1,1,10
"3840",4921,27,29.4,1,3,0,0,10,0,0,1,1,6
"3841",850,27,29.4,1,5,0,1,13,0,1,1,1,10
"3842",3958,27,29.5,1,2,0,1,9,0,3,1,1,5
"3843",4211,27,29.5,1,1,0,0,9,0,1,1,1,8
"3844",7273,27,29.5,1,4,0,0,12,1,0,1,1,6
"3845",3817,27,29.5,1,2,0,0,11,0,0,1,1,9
"3846",7707,27,29.6,1,5,0,0,10,1,1,1,1,8
"3847",9467,27,29.6,1,2,0,0,14,1,1,1,1,9
"3848",1085,27,29.6,1,3,0,0,9,0,0,1,1,10
"3849",6617,27,29.6,1,1,0,0,9,1,2,1,1,6
"3850",4691,27,29.6,1,2,0,0,15,0,2,1,1,6
"3851",4997,27,29.7,1,5,0,0,13,0,0,1,1,8
"3852",9311,27,29.7,1,3,0,0,12,1,0,1,1,5
"3853",5969,27,29.7,1,1,0,0,14,1,2,1,1,9
"3854",5707,27,29.7,1,2,0,0,12,1,2,1,1,5
"3855",5971,27,29.7,1,2,0,0,9,1,2,1,1,9
"3856",309,27,29.7,1,4,0,0,12,0,2,1,1,10
"3857",5143,27,29.7,1,1,0,0,15,0,1,1,1,5
"3858",5045,27,29.7,1,1,0,0,12,0,1,1,1,9
"3859",1139,27,29.7,1,3,0,0,14,0,2,1,1,7
"3860",3375,27,29.8,1,5,0,0,14,0,0,1,1,9
"3861",2709,27,29.8,1,3,0,0,15,0,0,1,1,8
"3862",5329,27,29.8,1,1,0,0,13,0,0,1,1,10
"3863",9209,27,29.8,1,2,0,0,12,1,1,1,1,5
"3864",9215,27,29.9,1,4,0,0,15,1,0,1,1,8
"3865",5817,27,29.9,1,1,0,0,12,1,2,1,1,9
"3866",915,27,29.9,1,2,0,0,15,0,0,1,1,8
"3867",2151,27,29.9,1,1,0,0,12,0,2,1,1,7
"3868",10465,27,30,1,5,0,0,12,1,1,1,1,6
"3869",3001,27,30,1,1,0,0,14,0,1,1,1,8
"3870",2643,27,30,1,4,0,0,9,0,2,1,1,5
"3877",6077,28,2.2,1,1,0,1,15,1,2,1,1,4
"3878",4214,28,3.2,1,2,0,1,12,0,8,1,1,7
"3879",5414,28,3.3,1,4,0,1,12,0,5,1,1,4
"3880",357,28,3.5,1,2,0,1,12,0,4,1,1,3
"3882",699,28,5.1,1,4,0,1,11,0,1,1,1,2
"3883",8221,28,5.3,1,4,0,1,9,1,6,1,1,2
"3884",6723,28,5.4,1,5,0,1,14,1,7,1,1,3
"3885",1464,28,5.4,1,1,0,1,10,0,2,1,1,9
"3886",4910,28,5.5,1,1,0,1,11,0,8,1,1,2
"3887",9456,28,5.9,1,5,0,1,12,1,3,1,1,0
"3888",1005,28,6,1,1,0,1,14,0,4,1,1,0
"3889",8678,28,6,1,3,0,1,15,1,1,1,1,7
"3890",3603,28,6.3,1,2,0,1,13,0,6,1,1,4
"3891",3328,28,6.8,1,2,0,1,10,0,7,1,1,5
"3892",9080,28,7.2,1,2,0,1,12,1,1,1,1,7
"3893",331,28,7.2,1,2,0,1,12,0,5,1,1,2
"3894",6015,28,7.8,1,5,0,1,10,1,7,1,1,2
"3895",6354,28,8.1,1,1,0,1,11,1,2,1,1,2
"3896",420,28,8.4,1,4,0,1,12,0,2,1,1,8
"3897",7773,28,8.8,1,4,0,1,15,1,5,1,1,1
"3898",10056,28,9.2,1,3,0,1,11,1,6,1,1,5
"3899",9972,28,9.5,1,3,0,1,9,1,8,1,1,0
"3900",6616,28,9.9,1,4,0,1,15,1,7,1,1,9
"3901",10797,28,10.7,1,3,0,1,13,1,1,1,1,3
"3902",1541,28,11,1,4,0,1,11,0,3,1,1,2
"3903",6372,28,11.4,1,2,0,1,10,1,4,1,1,6
"3904",6910,28,11.9,1,1,0,1,12,1,4,1,1,10
"3905",1180,28,13.5,1,4,0,1,12,0,6,1,1,2
"3906",644,28,13.8,1,1,0,1,11,0,4,1,1,4
"3907",2071,28,13.9,1,1,0,1,9,0,2,1,1,2
"3908",9376,28,14.2,1,3,0,1,15,1,8,1,1,8
"3909",4092,28,14.6,1,2,0,1,12,0,5,1,1,9
"3910",9210,28,14.7,1,4,0,1,10,1,4,1,1,5
"3911",6260,28,14.7,1,4,0,1,14,1,4,1,1,4
"3912",9866,28,14.8,1,5,0,1,12,1,1,1,1,9
"3913",1367,28,15,1,3,0,0,14,0,2,1,1,6
"3914",1653,28,15,1,3,0,0,12,0,2,1,1,9
"3915",199,28,15,1,1,0,0,9,0,1,1,1,6
"3916",3045,28,15.1,1,3,0,0,11,0,0,1,1,8
"3917",4375,28,15.1,1,4,0,0,12,0,1,1,1,6
"3918",1167,28,15.1,1,5,0,0,13,0,0,1,1,9
"3919",5877,28,15.1,1,3,0,0,10,1,1,1,1,7
"3920",10211,28,15.2,1,5,0,1,15,1,5,1,1,4
"3921",2641,28,15.2,1,1,0,0,9,0,1,1,1,5
"3922",5839,28,15.2,1,4,0,0,11,1,0,1,1,10
"3923",7355,28,15.2,1,1,0,0,12,1,1,1,1,10
"3924",2597,28,15.2,1,1,0,0,12,0,0,1,1,8
"3925",10309,28,15.2,1,2,0,0,13,1,2,1,1,8
"3926",2479,28,15.2,1,4,0,0,11,0,0,1,1,7
"3927",1242,28,15.2,1,4,0,1,13,0,5,1,1,2
"3928",6497,28,15.3,1,1,0,0,12,1,0,1,1,7
"3929",1003,28,15.3,1,2,0,0,12,0,2,1,1,9
"3930",3459,28,15.3,1,4,0,0,12,0,0,1,1,6
"3931",4017,28,15.3,1,3,0,0,9,0,1,1,1,5
"3932",807,28,15.3,1,2,0,0,12,0,2,1,1,8
"3933",1613,28,15.3,1,4,0,0,12,0,1,1,1,9
"3934",5127,28,15.3,1,1,0,0,13,0,2,1,1,9
"3935",9511,28,15.4,1,2,0,0,11,1,0,1,1,8
"3936",7765,28,15.5,1,1,0,0,10,1,0,1,1,10
"3937",4699,28,15.5,1,4,0,0,13,0,1,1,1,5
"3938",3189,28,15.5,1,2,0,0,14,0,2,1,1,10
"3939",9775,28,15.5,1,5,0,0,14,1,1,1,1,10
"3940",1018,28,15.5,1,1,0,1,14,0,8,1,1,10
"3941",3321,28,15.5,1,4,0,0,15,0,2,1,1,5
"3942",10665,28,15.6,1,5,0,0,12,1,0,1,1,8
"3943",9913,28,15.6,1,4,0,0,9,1,0,1,1,5
"3944",3147,28,15.6,1,5,0,0,11,0,0,1,1,8
"3945",7563,28,15.7,1,5,0,0,12,1,2,1,1,7
"3946",5463,28,15.7,1,1,0,0,12,1,1,1,1,5
"3947",8793,28,15.7,1,1,0,0,12,1,0,1,1,9
"3948",4827,28,15.7,1,3,0,0,12,0,0,1,1,7
"3949",9703,28,15.7,1,3,0,0,12,1,1,1,1,6
"3950",2598,28,15.7,1,1,0,1,10,0,4,1,1,0
"3951",5656,28,15.7,1,2,0,1,14,1,3,1,1,8
"3952",1257,28,15.7,1,1,0,0,10,0,1,1,1,10
"3953",6105,28,15.7,1,5,0,0,12,1,1,1,1,8
"3954",6109,28,15.8,1,5,0,0,14,1,2,1,1,6
"3955",7305,28,15.8,1,3,0,0,10,1,2,1,1,6
"3956",7459,28,15.8,1,4,0,0,14,1,1,1,1,9
"3957",2061,28,15.8,1,2,0,0,10,0,0,1,1,6
"3958",9483,28,15.9,1,2,0,0,15,1,1,1,1,8
"3959",6457,28,15.9,1,3,0,0,13,1,0,1,1,9
"3960",3043,28,15.9,1,4,0,1,9,0,3,1,1,3
"3961",8675,28,15.9,1,2,0,0,10,1,1,1,1,7
"3962",2989,28,16,1,2,0,0,9,0,0,1,1,10
"3963",3353,28,16,1,2,0,1,10,0,2,1,1,3
"3964",5421,28,16,1,1,0,0,14,1,2,1,1,7
"3965",10195,28,16,1,2,0,0,13,1,2,1,1,8
"3966",1679,28,16,1,1,0,0,12,0,1,1,1,7
"3967",10051,28,16,1,1,0,0,15,1,2,1,1,7
"3968",7123,28,16,1,4,0,0,11,1,2,1,1,8
"3969",8887,28,16.1,1,4,0,0,12,1,1,1,1,6
"3970",8739,28,16.1,1,5,0,1,11,1,6,1,1,1
"3971",1605,28,16.1,1,5,0,0,12,0,0,1,1,8
"3972",10423,28,16.2,1,5,0,1,12,1,8,1,1,0
"3973",6833,28,16.2,1,3,0,0,9,1,1,1,1,10
"3974",4079,28,16.2,1,5,0,0,9,0,0,1,1,7
"3975",10697,28,16.3,1,1,0,0,12,1,2,1,1,10
"3976",8033,28,16.3,1,1,0,0,12,1,1,1,1,8
"3977",10085,28,16.3,1,2,0,0,12,1,0,1,1,7
"3978",7377,28,16.3,1,2,0,0,12,1,0,1,1,6
"3979",451,28,16.3,1,1,0,0,13,0,1,1,1,5
"3980",969,28,16.4,1,1,0,0,11,0,2,1,1,9
"3981",9111,28,16.4,1,1,0,0,15,1,2,1,1,6
"3982",8524,28,16.5,1,1,0,1,15,1,4,1,1,4
"3983",1713,28,16.5,1,1,0,0,9,0,0,1,1,6
"3984",8961,28,16.5,1,3,0,0,9,1,1,1,1,9
"3985",9855,28,16.5,1,2,0,0,10,1,0,1,1,9
"3986",1727,28,16.5,1,2,0,0,12,0,1,1,1,10
"3987",10845,28,16.5,1,4,0,0,12,1,0,1,1,9
"3988",2879,28,16.5,1,5,0,0,12,0,1,1,1,7
"3989",10499,28,16.5,1,4,0,0,11,1,2,1,1,9
"3990",5489,28,16.6,1,4,0,0,14,1,2,1,1,6
"3991",10849,28,16.6,1,4,0,0,12,1,1,1,1,9
"3992",4289,28,16.6,1,4,0,0,12,0,0,1,1,10
"3993",5693,28,16.6,1,3,0,0,13,1,1,1,1,9
"3994",3209,28,16.6,1,2,0,0,15,0,0,1,1,7
"3995",913,28,16.7,1,1,0,0,13,0,0,1,1,7
"3996",8201,28,16.7,1,5,0,0,13,1,0,1,1,7
"3997",2733,28,16.7,1,5,0,0,14,0,2,1,1,6
"3998",1563,28,16.7,1,1,0,0,10,0,1,1,1,5
"3999",9431,28,16.8,1,1,0,0,15,1,0,1,1,8
"4000",179,28,16.8,1,2,0,0,11,0,0,1,1,7
"4001",9195,28,16.8,1,1,0,0,12,1,1,1,1,7
"4002",9277,28,16.8,1,5,0,0,12,1,1,1,1,5
"4003",611,28,16.8,1,2,0,0,13,0,0,1,1,6
"4004",3307,28,16.9,1,5,0,0,11,0,2,1,1,5
"4005",1581,28,16.9,1,3,0,0,10,0,2,1,1,7
"4006",3841,28,16.9,1,5,0,0,10,0,0,1,1,8
"4007",6855,28,17,1,2,0,0,14,1,2,1,1,7
"4008",8655,28,17,1,2,0,0,14,1,1,1,1,10
"4009",771,28,17,1,3,0,0,12,0,0,1,1,6
"4010",10651,28,17,1,2,0,0,12,1,0,1,1,6
"4011",5883,28,17,1,4,0,0,10,1,2,1,1,9
"4012",9731,28,17,1,5,0,0,12,1,1,1,1,7
"4013",8827,28,17,1,4,0,0,14,1,0,1,1,5
"4014",7490,28,17,1,2,0,1,12,1,3,1,1,9
"4015",2799,28,17.1,1,1,0,0,12,0,1,1,1,5
"4016",5651,28,17.1,1,2,0,0,10,1,0,1,1,8
"4017",8413,28,17.1,1,5,0,0,15,1,1,1,1,8
"4018",1490,28,17.1,1,2,0,1,12,0,8,1,1,7
"4019",4727,28,17.1,1,5,0,0,12,0,1,1,1,5
"4020",4857,28,17.1,1,1,0,0,10,0,1,1,1,10
"4021",3447,28,17.2,1,3,0,0,13,0,2,1,1,6
"4022",8435,28,17.3,1,2,0,0,15,1,0,1,1,8
"4023",3815,28,17.3,1,4,0,0,14,0,2,1,1,8
"4024",2827,28,17.3,1,4,0,0,12,0,1,1,1,5
"4025",3501,28,17.3,1,2,0,0,9,0,2,1,1,9
"4026",2781,28,17.3,1,1,0,0,12,0,1,1,1,9
"4027",8213,28,17.3,1,5,0,0,14,1,2,1,1,10
"4028",765,28,17.4,1,3,0,0,14,0,0,1,1,5
"4029",325,28,17.4,1,1,0,0,10,0,2,1,1,6
"4030",963,28,17.4,1,4,0,0,12,0,2,1,1,10
"4031",10605,28,17.5,1,5,0,0,14,1,2,1,1,7
"4032",1765,28,17.5,1,5,0,0,12,0,2,1,1,8
"4033",10799,28,17.5,1,5,0,0,12,1,0,1,1,9
"4034",1279,28,17.6,1,1,0,0,13,0,2,1,1,5
"4035",7203,28,17.6,1,5,0,0,11,1,2,1,1,5
"4036",9027,28,17.6,1,1,0,0,11,1,0,1,1,9
"4037",2869,28,17.6,1,1,0,0,11,0,2,1,1,5
"4038",2972,28,17.6,1,3,0,1,12,0,3,1,1,1
"4039",1775,28,17.6,1,4,0,0,10,0,2,1,1,9
"4040",6222,28,17.7,1,1,0,1,9,1,3,1,1,7
"4041",2199,28,17.7,1,2,0,0,13,0,2,1,1,10
"4042",1612,28,17.8,1,2,0,1,12,0,5,1,1,6
"4043",1061,28,17.8,1,5,0,0,10,0,0,1,1,6
"4044",6919,28,17.8,1,4,0,0,12,1,1,1,1,6
"4045",5689,28,17.8,1,4,0,0,12,1,0,1,1,10
"4046",3827,28,17.8,1,3,0,0,12,0,1,1,1,8
"4047",4945,28,17.9,1,3,0,0,14,0,0,1,1,5
"4048",7813,28,17.9,1,2,0,0,10,1,1,1,1,7
"4049",6247,28,17.9,1,1,0,0,15,1,1,1,1,10
"4050",10177,28,17.9,1,2,0,0,15,1,2,1,1,6
"4051",5809,28,17.9,1,3,0,0,15,1,0,1,1,5
"4052",1229,28,17.9,1,2,0,0,9,0,2,1,1,8
"4053",7135,28,18,1,2,0,0,9,1,2,1,1,6
"4054",8575,28,18,1,2,0,0,11,1,2,1,1,9
"4055",1991,28,18,1,4,0,0,13,0,0,1,1,5
"4056",6829,28,18,1,3,0,0,12,1,2,1,1,5
"4057",6169,28,18,1,5,0,0,12,1,0,1,1,7
"4058",6515,28,18.1,1,5,0,0,9,1,2,1,1,10
"4059",9897,28,18.1,1,4,0,0,13,1,1,1,1,7
"4060",8234,28,18.1,1,5,0,1,13,1,4,1,1,4
"4061",7673,28,18.1,1,4,0,0,11,1,1,1,1,6
"4062",4927,28,18.1,1,3,0,0,14,0,0,1,1,9
"4063",302,28,18.1,1,1,0,1,13,0,5,1,1,8
"4064",10031,28,18.1,1,1,0,0,13,1,2,1,1,10
"4065",3825,28,18.1,1,3,0,0,14,0,1,1,1,5
"4066",2669,28,18.2,1,3,0,0,13,0,2,1,1,5
"4067",7769,28,18.2,1,3,0,0,11,1,1,1,1,5
"4068",9837,28,18.3,1,4,0,0,13,1,2,1,1,9
"4069",385,28,18.3,1,2,0,0,11,0,0,1,1,10
"4070",9281,28,18.3,1,1,0,0,10,1,2,1,1,10
"4071",1725,28,18.4,1,1,0,0,10,0,1,1,1,7
"4072",9782,28,18.4,1,1,0,1,14,1,3,1,1,1
"4073",1209,28,18.4,1,1,0,0,10,0,2,1,1,6
"4074",4825,28,18.4,1,2,0,0,14,0,0,1,1,8
"4075",1388,28,18.5,1,3,0,1,11,0,5,1,1,4
"4076",2497,28,18.5,1,4,0,0,15,0,0,1,1,10
"4077",5847,28,18.5,1,5,0,0,11,1,2,1,1,10
"4078",8865,28,18.5,1,3,0,0,11,1,1,1,1,7
"4079",10457,28,18.6,1,2,0,0,13,1,2,1,1,9
"4080",2077,28,18.6,1,4,0,0,10,0,2,1,1,6
"4081",409,28,18.6,1,1,0,0,13,0,1,1,1,6
"4082",767,28,18.6,1,4,0,0,15,0,1,1,1,10
"4083",1397,28,18.6,1,1,0,0,10,0,2,1,1,6
"4084",10419,28,18.6,1,4,0,0,10,1,0,1,1,10
"4085",9941,28,18.7,1,1,0,0,9,1,1,1,1,8
"4086",4501,28,18.7,1,2,0,0,10,0,1,1,1,5
"4087",2377,28,18.7,1,2,0,0,12,0,1,1,1,10
"4088",2675,28,18.7,1,2,0,0,10,0,0,1,1,5
"4089",3739,28,18.8,1,1,0,0,14,0,0,1,1,10
"4090",3505,28,18.9,1,4,0,0,11,0,1,1,1,10
"4091",8367,28,18.9,1,5,0,0,12,1,1,1,1,10
"4092",1643,28,19,1,5,0,0,12,0,1,1,1,10
"4093",6951,28,19,1,3,0,0,10,1,0,1,1,6
"4094",5457,28,19.1,1,3,0,0,9,1,2,1,1,7
"4095",10365,28,19.1,1,5,0,0,11,1,0,1,1,7
"4096",7795,28,19.2,1,2,0,0,12,1,2,1,1,7
"4097",3703,28,19.3,1,4,0,0,11,0,2,1,1,6
"4098",9569,28,19.3,1,5,0,0,13,1,2,1,1,6
"4099",2309,28,19.3,1,3,0,0,10,0,0,1,1,10
"4100",8195,28,19.3,1,5,0,0,11,1,0,1,1,8
"4101",10185,28,19.4,1,2,0,0,12,1,0,1,1,9
"4102",6665,28,19.4,1,4,0,0,11,1,2,1,1,7
"4103",2171,28,19.4,1,3,0,0,11,0,1,1,1,10
"4104",377,28,19.4,1,1,0,0,11,0,2,1,1,7
"4105",57,28,19.4,1,3,0,0,9,0,2,1,1,10
"4106",5661,28,19.4,1,2,0,0,14,1,1,1,1,5
"4107",10707,28,19.5,1,4,0,0,10,1,1,1,1,6
"4108",1285,28,19.5,1,2,0,0,14,0,2,1,1,8
"4109",6403,28,19.5,1,2,0,0,9,1,1,1,1,9
"4110",4949,28,19.5,1,1,0,0,11,0,0,1,1,6
"4111",5445,28,19.6,1,5,0,0,15,1,2,1,1,9
"4112",9539,28,19.6,1,1,0,0,14,1,1,1,1,8
"4113",1217,28,19.6,1,2,0,0,14,0,2,1,1,10
"4114",8697,28,19.6,1,1,0,0,10,1,0,1,1,7
"4115",5599,28,19.6,1,2,0,0,10,1,2,1,1,5
"4116",2195,28,19.7,1,1,0,0,15,0,1,1,1,8
"4117",10805,28,19.7,1,3,0,0,12,1,2,1,1,5
"4118",8925,28,19.7,1,2,0,0,13,1,1,1,1,6
"4119",8533,28,19.7,1,5,0,0,10,1,2,1,1,7
"4120",3143,28,19.8,1,4,0,0,11,0,1,1,1,6
"4121",2011,28,19.8,1,1,0,0,11,0,0,1,1,5
"4122",10363,28,19.8,1,5,0,0,13,1,0,1,1,6
"4123",4923,28,19.9,1,5,0,0,14,0,2,1,1,5
"4124",1213,28,19.9,1,2,0,0,11,0,0,1,1,6
"4125",2883,28,19.9,1,5,0,0,15,0,1,1,1,8
"4126",8881,28,20,1,1,0,0,11,1,1,1,1,7
"4127",1323,28,20,1,2,0,0,15,0,1,1,1,5
"4128",6733,28,20,1,2,0,0,12,1,2,1,1,6
"4129",10601,28,20.1,1,5,0,0,11,1,0,1,1,5
"4130",1393,28,20.1,1,2,0,0,9,0,2,1,1,6
"4131",8571,28,20.1,1,4,0,0,12,1,1,1,1,8
"4132",5021,28,20.1,1,2,0,0,10,0,0,1,1,5
"4133",3181,28,20.1,1,1,0,0,12,0,1,1,1,7
"4134",9997,28,20.2,1,5,0,0,9,1,1,1,1,9
"4135",2417,28,20.3,1,1,0,0,10,0,1,1,1,9
"4136",9657,28,20.3,1,2,0,0,15,1,0,1,1,7
"4137",3937,28,20.3,1,1,0,0,11,0,1,1,1,7
"4138",7337,28,20.4,1,4,0,0,10,1,0,1,1,5
"4139",9221,28,20.4,1,4,0,0,15,1,2,1,1,5
"4140",7533,28,20.4,1,3,0,0,14,1,1,1,1,5
"4141",9727,28,20.4,1,3,0,0,10,1,2,1,1,5
"4142",4935,28,20.5,1,3,0,0,12,0,2,1,1,9
"4143",1103,28,20.5,1,2,0,0,11,0,2,1,1,6
"4144",6053,28,20.5,1,4,0,0,12,1,2,1,1,5
"4145",2397,28,20.6,1,5,0,0,9,0,0,1,1,10
"4146",2271,28,20.6,1,5,0,0,15,0,1,1,1,5
"4147",9173,28,20.7,1,4,0,0,13,1,0,1,1,7
"4148",2027,28,20.7,1,4,0,0,15,0,1,1,1,10
"4149",6989,28,20.7,1,2,0,0,10,1,1,1,1,10
"4150",7807,28,20.8,1,4,0,0,12,1,1,1,1,10
"4151",9223,28,20.8,1,3,0,0,14,1,2,1,1,5
"4152",5639,28,20.8,1,4,0,0,11,1,1,1,1,9
"4153",6329,28,20.8,1,4,0,0,10,1,2,1,1,5
"4154",8747,28,20.8,1,3,0,0,14,1,2,1,1,7
"4155",893,28,20.8,1,4,0,0,10,0,0,1,1,6
"4156",5713,28,20.8,1,1,0,0,12,1,0,1,1,5
"4157",1931,28,20.8,1,3,0,0,14,0,0,1,1,8
"4158",5427,28,20.8,1,1,0,0,14,1,2,1,1,9
"4159",10647,28,20.8,1,2,0,0,9,1,1,1,1,10
"4160",1597,28,21,1,1,0,0,10,0,0,1,1,10
"4161",2835,28,21,1,2,0,0,15,0,2,1,1,9
"4162",1479,28,21,1,1,0,0,12,0,2,1,1,7
"4163",5177,28,21,1,5,0,0,9,0,2,1,1,8
"4164",5011,28,21,1,4,0,0,11,0,0,1,1,8
"4165",4233,28,21.1,1,5,0,0,11,0,1,1,1,5
"4166",8503,28,21.1,1,4,0,0,11,1,0,1,1,5
"4167",6851,28,21.1,1,3,0,0,15,1,2,1,1,5
"4168",3323,28,21.1,1,3,0,0,9,0,1,1,1,5
"4169",5617,28,21.1,1,3,0,0,14,1,2,1,1,5
"4170",8535,28,21.2,1,3,0,0,12,1,0,1,1,7
"4171",9776,28,21.2,1,4,0,1,10,1,3,1,1,7
"4172",6059,28,21.2,1,3,0,0,15,1,2,1,1,9
"4173",5735,28,21.2,1,5,0,0,15,1,1,1,1,7
"4174",7039,28,21.2,1,3,0,0,14,1,2,1,1,7
"4175",1207,28,21.2,1,1,0,0,15,0,2,1,1,10
"4176",5165,28,21.2,1,3,0,0,11,0,0,1,1,5
"4177",723,28,21.2,1,1,0,0,12,0,2,1,1,5
"4178",4799,28,21.3,1,1,0,0,15,0,1,1,1,5
"4179",3765,28,21.3,1,3,0,0,14,0,1,1,1,8
"4180",5587,28,21.3,1,1,0,0,12,1,2,1,1,7
"4181",5871,28,21.4,1,2,0,0,9,1,2,1,1,7
"4182",7743,28,21.4,1,2,0,0,9,1,1,1,1,5
"4183",2587,28,21.4,1,4,0,0,14,0,2,1,1,9
"4184",543,28,21.4,1,2,0,0,13,0,2,1,1,6
"4185",5477,28,21.5,1,1,0,0,10,1,2,1,1,9
"4186",7130,28,21.5,1,1,0,1,15,1,7,1,1,5
"4187",1303,28,21.5,1,1,0,0,15,0,2,1,1,6
"4188",3795,28,21.5,1,5,0,0,11,0,2,1,1,5
"4189",8009,28,21.5,1,3,0,0,11,1,1,1,1,10
"4190",7085,28,21.5,1,1,0,0,12,1,1,1,1,10
"4191",8383,28,21.5,1,1,0,0,9,1,1,1,1,8
"4192",4419,28,21.7,1,1,0,0,12,0,1,1,1,6
"4193",9877,28,21.7,1,4,0,0,15,1,2,1,1,7
"4194",7361,28,21.8,1,1,0,0,12,1,2,1,1,10
"4195",4147,28,21.8,1,3,0,0,15,0,0,1,1,5
"4196",343,28,21.8,1,2,0,0,12,0,1,1,1,5
"4197",8581,28,21.8,1,2,0,0,10,1,1,1,1,6
"4198",6217,28,21.8,1,4,0,0,15,1,1,1,1,7
"4199",230,28,21.8,1,4,0,1,9,0,1,1,1,1
"4200",321,28,21.8,1,3,0,0,15,0,2,1,1,8
"4201",6527,28,21.9,1,5,0,0,15,1,2,1,1,8
"4202",3417,28,22,1,4,0,0,10,0,2,1,1,7
"4203",6647,28,22,1,1,0,0,9,1,2,1,1,7
"4204",6145,28,22.1,1,5,0,0,13,1,0,1,1,6
"4205",6061,28,22.1,1,3,0,0,14,1,2,1,1,5
"4206",10399,28,22.1,1,2,0,0,12,1,0,1,1,8
"4207",5475,28,22.2,1,1,0,0,14,1,2,1,1,9
"4208",10053,28,22.2,1,4,0,0,10,1,1,1,1,8
"4209",5289,28,22.2,1,3,0,0,11,0,0,1,1,8
"4210",5003,28,22.2,1,3,0,0,9,0,1,1,1,10
"4211",3247,28,22.2,1,4,0,0,15,0,2,1,1,8
"4212",37,28,22.3,1,1,0,0,9,0,0,1,1,7
"4213",10585,28,22.3,1,3,0,0,14,1,0,1,1,7
"4214",9959,28,22.4,1,1,0,0,10,1,0,1,1,8
"4215",8875,28,22.4,1,4,0,0,15,1,2,1,1,5
"4216",5551,28,22.4,1,4,0,0,9,1,0,1,1,7
"4217",3981,28,22.4,1,2,0,0,12,0,2,1,1,5
"4218",8211,28,22.5,1,2,0,0,13,1,0,1,1,8
"4219",8161,28,22.5,1,4,0,0,12,1,2,1,1,9
"4220",5919,28,22.5,1,4,0,0,11,1,2,1,1,6
"4221",5333,28,22.5,1,3,0,0,12,0,1,1,1,7
"4222",10417,28,22.5,1,2,0,0,11,1,2,1,1,5
"4223",5059,28,22.6,1,2,0,0,9,0,2,1,1,8
"4224",7445,28,22.6,1,4,0,0,9,1,2,1,1,8
"4225",3915,28,22.7,1,2,0,0,13,0,0,1,1,10
"4226",4373,28,22.7,1,4,0,0,12,0,1,1,1,9
"4227",6213,28,22.8,1,4,0,0,12,1,1,1,1,5
"4228",2303,28,22.8,1,3,0,0,13,0,2,1,1,7
"4229",697,28,22.9,1,1,0,0,12,0,1,1,1,7
"4230",9495,28,22.9,1,5,0,0,12,1,0,1,1,6
"4231",10011,28,22.9,1,4,0,0,9,1,2,1,1,8
"4232",9183,28,22.9,1,1,0,0,12,1,0,1,1,5
"4233",5887,28,22.9,1,2,0,0,9,1,2,1,1,9
"4234",549,28,22.9,1,4,0,0,12,0,2,1,1,6
"4235",8631,28,22.9,1,3,0,0,11,1,0,1,1,9
"4236",10851,28,23,1,3,0,0,12,1,1,1,1,7
"4237",5695,28,23,1,4,0,0,10,1,0,1,1,9
"4238",9325,28,23,1,4,0,0,10,1,1,1,1,10
"4239",9523,28,23,1,5,0,0,12,1,0,1,1,9
"4240",8643,28,23,1,3,0,0,11,1,2,1,1,6
"4241",1471,28,23,1,4,0,0,10,0,1,1,1,10
"4242",7888,28,23,1,5,0,1,12,1,6,1,1,4
"4243",3951,28,23.1,1,5,0,0,15,0,2,1,1,6
"4244",641,28,23.1,1,2,0,0,12,0,2,1,1,8
"4245",1325,28,23.1,1,5,0,1,15,0,3,1,1,4
"4246",10099,28,23.1,1,4,0,0,9,1,0,1,1,6
"4247",7251,28,23.2,1,2,0,1,15,1,1,1,1,1
"4248",10469,28,23.2,1,5,0,0,15,1,1,1,1,10
"4249",8115,28,23.2,1,3,0,0,13,1,0,1,1,8
"4250",5757,28,23.2,1,1,0,0,13,1,0,1,1,7
"4251",8305,28,23.2,1,4,0,0,13,1,0,1,1,10
"4252",829,28,23.3,1,4,0,0,15,0,2,1,1,5
"4253",6035,28,23.3,1,3,0,0,12,1,0,1,1,6
"4254",2013,28,23.3,1,3,0,0,11,0,1,1,1,10
"4255",6551,28,23.3,1,2,0,0,10,1,2,1,1,10
"4256",4953,28,23.3,1,2,0,0,10,0,1,1,1,5
"4257",3891,28,23.4,1,1,0,0,12,0,1,1,1,9
"4258",1025,28,23.5,1,2,0,0,10,0,0,1,1,5
"4259",7893,28,23.5,1,1,0,0,12,1,1,1,1,7
"4260",8759,28,23.5,1,5,0,0,10,1,1,1,1,9
"4261",5063,28,23.6,1,5,0,0,14,0,2,1,1,10
"4262",6455,28,23.6,1,3,0,0,13,1,0,1,1,8
"4263",9343,28,23.6,1,1,0,0,10,1,0,1,1,6
"4264",6269,28,23.6,1,4,0,0,12,1,1,1,1,6
"4265",9185,28,23.7,1,5,0,0,12,1,2,1,1,6
"4266",6941,28,23.7,1,1,0,0,14,1,2,1,1,10
"4267",3429,28,23.7,1,3,0,0,11,0,2,1,1,5
"4268",3091,28,23.7,1,1,0,0,14,0,0,1,1,9
"4269",5101,28,23.7,1,2,0,0,12,0,1,1,1,6
"4270",835,28,23.8,1,2,0,0,12,0,1,1,1,7
"4271",10483,28,23.8,1,3,0,0,11,1,0,1,1,8
"4272",5507,28,23.8,1,5,0,0,9,1,1,1,1,8
"4273",1661,28,23.8,1,3,0,0,15,0,0,1,1,6
"4274",8795,28,23.8,1,2,0,0,12,1,1,1,1,10
"4275",9581,28,23.9,1,2,0,0,12,1,2,1,1,7
"4276",8614,28,23.9,1,2,0,1,10,1,7,1,1,4
"4277",10385,28,23.9,1,5,0,0,12,1,0,1,1,8
"4278",5335,28,24,1,5,0,0,12,0,2,1,1,6
"4279",4757,28,24,1,3,0,0,15,0,1,1,1,7
"4280",7075,28,24,1,1,0,0,14,1,2,1,1,6
"4281",3279,28,24,1,5,0,0,10,0,0,1,1,10
"4282",7081,28,24,1,2,0,0,12,1,1,1,1,6
"4283",4661,28,24,1,4,0,0,12,0,0,1,1,7
"4284",8915,28,24.1,1,5,0,0,14,1,0,1,1,6
"4285",4941,28,24.1,1,3,0,0,14,0,2,1,1,10
"4286",3573,28,24.1,1,3,0,0,15,0,0,1,1,9
"4287",7543,28,24.1,1,2,0,0,11,1,2,1,1,8
"4288",8873,28,24.1,1,5,0,0,15,1,2,1,1,6
"4289",9373,28,24.1,1,2,0,0,12,1,0,1,1,9
"4290",8189,28,24.2,1,1,0,0,11,1,1,1,1,8
"4291",3697,28,24.2,1,5,0,0,9,0,2,1,1,5
"4292",6599,28,24.2,1,4,0,0,11,1,1,1,1,9
"4293",9721,28,24.2,1,1,0,0,9,1,2,1,1,5
"4294",6843,28,24.3,1,4,0,0,12,1,0,1,1,8
"4295",9683,28,24.3,1,2,0,0,10,1,1,1,1,7
"4296",7817,28,24.3,1,2,0,0,10,1,2,1,1,6
"4297",5497,28,24.3,1,2,0,0,11,1,0,1,1,8
"4298",233,28,24.3,1,5,0,0,15,0,1,1,1,10
"4299",9337,28,24.3,1,5,0,0,13,1,0,1,1,5
"4300",809,28,24.4,1,2,0,0,13,0,0,1,1,8
"4301",6085,28,24.4,1,4,0,0,15,1,2,1,1,9
"4302",6441,28,24.5,1,1,0,0,12,1,2,1,1,7
"4303",1783,28,24.5,1,3,0,0,10,0,2,1,1,5
"4304",1345,28,24.5,1,2,0,1,12,0,8,1,1,1
"4305",5139,28,24.5,1,2,0,0,10,0,2,1,1,7
"4306",8768,28,24.5,1,1,0,1,9,1,3,1,1,7
"4307",6185,28,24.5,1,3,0,0,9,1,2,1,1,9
"4308",1515,28,24.6,1,3,0,0,9,0,2,1,1,6
"4309",6805,28,24.6,1,3,0,0,11,1,1,1,1,9
"4310",877,28,24.6,1,2,0,0,12,0,2,1,1,10
"4311",753,28,24.7,1,2,0,0,12,0,0,1,1,6
"4312",7903,28,24.7,1,5,0,0,14,1,1,1,1,9
"4313",6003,28,24.8,1,3,0,0,13,1,2,1,1,7
"4314",5657,28,24.8,1,4,0,0,11,1,2,1,1,10
"4315",8947,28,24.8,1,1,0,0,11,1,1,1,1,5
"4316",9607,28,24.8,1,2,0,0,11,1,0,1,1,7
"4317",4477,28,24.9,1,1,0,0,12,0,0,1,1,8
"4318",9471,28,25,1,1,0,0,10,1,1,1,1,5
"4319",8227,28,25,1,2,0,0,12,1,2,1,1,10
"4320",865,28,25,1,5,0,0,9,0,2,1,1,9
"4321",7761,28,25,1,5,0,0,15,1,1,1,1,9
"4322",2235,28,25.1,1,5,0,0,10,0,0,1,1,5
"4323",9303,28,25.1,1,4,0,0,15,1,0,1,1,5
"4324",1389,28,25.1,1,5,0,0,10,0,0,1,1,8
"4325",5071,28,25.1,1,3,0,0,14,0,2,1,1,9
"4326",8919,28,25.1,1,3,0,0,10,1,2,1,1,6
"4327",4483,28,25.1,1,1,0,0,10,0,2,1,1,7
"4328",997,28,25.2,1,3,0,0,10,0,1,1,1,9
"4329",7805,28,25.2,1,2,0,0,13,1,1,1,1,10
"4330",8519,28,25.2,1,3,0,0,14,1,1,1,1,6
"4331",5891,28,25.2,1,2,0,0,12,1,0,1,1,8
"4332",2885,28,25.2,1,5,0,0,14,0,1,1,1,8
"4333",7181,28,25.3,1,1,0,0,12,1,2,1,1,5
"4334",7637,28,25.3,1,5,0,0,12,1,0,1,1,5
"4335",3521,28,25.4,1,4,0,0,11,0,2,1,1,9
"4336",7921,28,25.4,1,1,0,0,10,1,0,1,1,10
"4337",8957,28,25.5,1,1,0,0,13,1,1,1,1,7
"4338",9241,28,25.5,1,1,0,0,15,1,0,1,1,10
"4339",9783,28,25.5,1,3,0,0,10,1,0,1,1,9
"4340",627,28,25.6,1,3,0,0,10,0,1,1,1,10
"4341",5931,28,25.6,1,5,0,0,12,1,2,1,1,7
"4342",7643,28,25.6,1,2,0,0,11,1,0,1,1,8
"4343",8373,28,25.6,1,5,0,0,10,1,2,1,1,8
"4344",3661,28,25.7,1,3,0,0,12,0,0,1,1,6
"4345",6764,28,25.7,1,3,0,1,14,1,4,1,1,5
"4346",5115,28,25.7,1,4,0,0,10,0,0,1,1,8
"4347",1283,28,25.7,1,2,0,0,14,0,0,1,1,9
"4348",5061,28,25.7,1,5,0,0,11,0,0,1,1,8
"4349",5913,28,25.7,1,5,0,0,12,1,2,1,1,6
"4350",6753,28,25.7,1,3,0,0,10,1,1,1,1,6
"4351",129,28,25.8,1,3,0,0,13,0,1,1,1,9
"4352",245,28,25.8,1,1,0,0,14,0,2,1,1,7
"4353",1963,28,25.8,1,1,0,0,14,0,0,1,1,8
"4354",93,28,25.9,1,3,0,0,14,0,1,1,1,6
"4355",6915,28,25.9,1,2,0,0,15,1,2,1,1,8
"4356",9347,28,25.9,1,2,0,0,10,1,1,1,1,10
"4357",1481,28,25.9,1,1,0,0,12,0,0,1,1,9
"4358",5189,28,25.9,1,5,0,0,10,0,0,1,1,7
"4359",7528,28,25.9,1,5,0,1,11,1,4,1,1,1
"4360",990,28,26,1,3,0,1,11,0,8,1,1,6
"4361",2477,28,26,1,4,0,0,13,0,1,1,1,10
"4362",9067,28,26,1,1,0,0,9,1,0,1,1,8
"4363",7129,28,26.1,1,5,0,0,12,1,0,1,1,6
"4364",7053,28,26.1,1,1,0,0,13,1,2,1,1,9
"4365",6479,28,26.1,1,5,0,0,15,1,1,1,1,7
"4366",6823,28,26.1,1,4,0,0,15,1,2,1,1,8
"4367",6877,28,26.2,1,3,0,0,13,1,0,1,1,7
"4368",2995,28,26.2,1,2,0,0,10,0,2,1,1,10
"4369",2739,28,26.2,1,3,0,0,13,0,2,1,1,10
"4370",8379,28,26.2,1,5,0,0,9,1,2,1,1,9
"4371",6309,28,26.2,1,1,0,0,12,1,0,1,1,8
"4372",3641,28,26.3,1,5,0,0,10,0,1,1,1,8
"4373",2771,28,26.4,1,5,0,0,15,0,0,1,1,8
"4374",2577,28,26.4,1,5,0,0,9,0,1,1,1,5
"4375",2881,28,26.4,1,3,0,0,13,0,0,1,1,7
"4376",9608,28,26.4,1,2,0,1,14,1,3,1,1,1
"4377",3707,28,26.4,1,2,0,0,12,0,0,1,1,10
"4378",2379,28,26.4,1,1,0,0,14,0,0,1,1,8
"4379",5823,28,26.4,1,1,0,1,13,1,7,1,1,4
"4380",9405,28,26.5,1,5,0,0,12,1,2,1,1,8
"4381",7393,28,26.5,1,4,0,0,9,1,1,1,1,8
"4382",8977,28,26.6,1,1,0,0,12,1,2,1,1,8
"4383",5313,28,26.6,1,4,0,0,13,0,2,1,1,9
"4384",2475,28,26.6,1,4,0,0,12,0,1,1,1,10
"4385",8693,28,26.6,1,5,0,0,14,1,0,1,1,6
"4386",5479,28,26.6,1,5,0,0,12,1,2,1,1,10
"4387",4655,28,26.7,1,5,0,0,12,0,0,1,1,10
"4388",6785,28,26.7,1,4,0,0,12,1,2,1,1,10
"4389",10381,28,26.7,1,4,0,0,12,1,2,1,1,7
"4390",2483,28,26.7,1,4,0,0,15,0,0,1,1,6
"4391",9779,28,26.7,1,3,0,0,12,1,1,1,1,10
"4392",1519,28,26.7,1,2,0,0,12,0,2,1,1,8
"4393",2241,28,26.8,1,2,0,0,9,0,2,1,1,10
"4394",10017,28,26.8,1,4,0,0,14,1,1,1,1,9
"4395",1321,28,26.8,1,3,0,0,9,0,2,1,1,10
"4396",293,28,26.8,1,1,0,0,14,0,0,1,1,5
"4397",255,28,26.8,1,5,0,0,14,0,2,1,1,7
"4398",8801,28,26.9,1,3,0,0,11,1,0,1,1,7
"4399",9485,28,26.9,1,3,0,0,13,1,1,1,1,5
"4400",7911,28,27,1,3,0,0,14,1,1,1,1,8
"4401",8463,28,27,1,1,0,0,12,1,2,1,1,5
"4402",6255,28,27,1,3,0,1,12,1,2,1,1,0
"4403",6283,28,27.1,1,2,0,0,14,1,2,1,1,9
"4404",5095,28,27.1,1,1,0,0,12,0,1,1,1,8
"4405",1361,28,27.1,1,5,0,0,12,0,2,1,1,10
"4406",6993,28,27.1,1,3,0,0,11,1,0,1,1,9
"4407",4131,28,27.1,1,3,0,0,15,0,2,1,1,5
"4408",4197,28,27.1,1,5,0,0,15,0,2,1,1,8
"4409",9765,28,27.2,1,2,0,0,13,1,1,1,1,7
"4410",6101,28,27.2,1,3,0,0,12,1,2,1,1,8
"4411",6409,28,27.3,1,2,0,0,11,1,1,1,1,6
"4412",3725,28,27.3,1,2,0,0,12,0,0,1,1,9
"4413",9479,28,27.3,1,5,0,0,12,1,2,1,1,9
"4414",4347,28,27.4,1,1,0,0,11,0,0,1,1,7
"4415",3967,28,27.4,1,1,0,0,11,0,2,1,1,6
"4416",4047,28,27.4,1,1,0,0,10,0,2,1,1,9
"4417",5273,28,27.4,1,3,0,0,12,0,2,1,1,5
"4418",9699,28,27.4,1,3,0,1,10,1,5,1,1,0
"4419",6142,28,27.4,1,1,0,1,9,1,4,1,1,10
"4420",6975,28,27.5,1,2,0,1,15,1,4,1,1,4
"4421",5961,28,27.5,1,5,0,0,12,1,0,1,1,5
"4422",5963,28,27.5,1,5,0,0,12,1,0,1,1,9
"4423",7855,28,27.5,1,5,0,0,12,1,0,1,1,9
"4424",8465,28,27.6,1,4,0,0,9,1,1,1,1,9
"4425",378,28,27.6,1,2,0,1,9,0,3,1,1,7
"4426",3319,28,27.6,1,4,0,0,14,0,0,1,1,10
"4427",2343,28,27.6,1,4,0,1,14,0,4,1,1,4
"4428",2825,28,27.6,1,4,0,0,13,0,2,1,1,9
"4429",5288,28,27.7,1,4,0,1,15,0,8,1,1,7
"4430",721,28,27.7,1,2,0,0,13,0,1,1,1,7
"4431",6837,28,27.7,1,3,0,0,10,1,2,1,1,6
"4432",10105,28,27.8,1,2,0,0,12,1,0,1,1,7
"4433",5361,28,27.8,1,2,0,0,9,0,0,1,1,8
"4434",2901,28,27.8,1,5,0,0,14,0,2,1,1,5
"4435",9503,28,27.8,1,4,0,0,12,1,2,1,1,6
"4436",1589,28,27.8,1,2,0,0,13,0,2,1,1,7
"4437",7461,28,27.8,1,3,0,0,15,1,2,1,1,5
"4438",50,28,27.8,1,1,0,1,10,0,5,1,1,8
"4439",3785,28,27.9,1,4,0,0,10,0,1,1,1,6
"4440",10615,28,27.9,1,2,0,0,9,1,2,1,1,10
"4441",3343,28,27.9,1,5,0,0,9,0,1,1,1,6
"4442",5415,28,27.9,1,3,0,0,12,0,1,1,1,9
"4443",9251,28,27.9,1,4,0,0,12,1,1,1,1,9
"4444",5371,28,27.9,1,2,0,0,12,0,1,1,1,6
"4445",6305,28,28,1,4,0,0,10,1,1,1,1,8
"4446",1175,28,28,1,2,0,0,12,0,0,1,1,7
"4447",2299,28,28,1,1,0,0,15,0,2,1,1,7
"4448",6523,28,28,1,1,0,0,12,1,0,1,1,6
"4449",10640,28,28,1,4,0,1,10,1,6,1,1,6
"4450",633,28,28.1,1,5,0,0,12,0,2,1,1,10
"4451",3433,28,28.1,1,1,0,0,14,0,0,1,1,5
"4452",5237,28,28.1,1,2,0,0,9,0,2,1,1,5
"4453",2053,28,28.2,1,4,0,0,11,0,0,1,1,9
"4454",3191,28,28.2,1,3,0,0,15,0,0,1,1,8
"4455",1837,28,28.2,1,1,0,0,12,0,0,1,1,5
"4456",315,28,28.2,1,2,0,0,12,0,2,1,1,8
"4457",6181,28,28.3,1,2,0,0,13,1,1,1,1,8
"4458",10863,28,28.3,1,5,0,0,13,1,2,1,1,6
"4459",8279,28,28.3,1,4,0,0,13,1,0,1,1,5
"4460",2943,28,28.4,1,2,0,0,11,0,2,1,1,7
"4461",703,28,28.4,1,4,0,0,12,0,1,1,1,10
"4462",9371,28,28.4,1,3,0,0,15,1,2,1,1,6
"4463",2337,28,28.4,1,2,0,0,13,0,2,1,1,10
"4464",10571,28,28.4,1,2,0,0,10,1,2,1,1,10
"4465",3813,28,28.5,1,3,0,0,12,0,0,1,1,8
"4466",4281,28,28.5,1,1,0,0,10,0,1,1,1,6
"4467",8245,28,28.5,1,5,0,0,10,1,2,1,1,7
"4468",4579,28,28.5,1,2,0,0,11,0,2,1,1,5
"4469",7209,28,28.6,1,1,0,0,12,1,0,1,1,10
"4470",7249,28,28.6,1,4,0,0,12,1,0,1,1,5
"4471",9951,28,28.6,1,1,0,0,13,1,0,1,1,5
"4472",7957,28,28.6,1,5,0,0,12,1,1,1,1,5
"4473",987,28,28.7,1,5,0,0,10,0,1,1,1,7
"4474",5053,28,28.7,1,5,0,0,12,0,2,1,1,7
"4475",6157,28,28.8,1,5,0,0,12,1,1,1,1,5
"4476",585,28,28.8,1,3,0,0,12,0,2,1,1,6
"4477",2753,28,28.8,1,5,0,0,12,0,2,1,1,6
"4478",9033,28,28.9,1,3,0,0,14,1,2,1,1,7
"4479",8291,28,28.9,1,5,0,0,12,1,1,1,1,6
"4480",3629,28,28.9,1,3,0,0,12,0,1,1,1,7
"4481",8047,28,28.9,1,3,0,0,12,1,0,1,1,8
"4482",7439,28,28.9,1,2,0,0,9,1,0,1,1,7
"4483",8303,28,28.9,1,4,0,0,11,1,2,1,1,6
"4484",2246,28,29,1,4,0,1,12,0,7,1,1,10
"4485",7325,28,29,1,5,0,0,15,1,2,1,1,8
"4486",1281,28,29,1,1,0,0,14,0,0,1,1,7
"4487",10829,28,29.1,1,4,0,0,12,1,2,1,1,8
"4488",965,28,29.1,1,2,0,0,12,0,1,1,1,7
"4489",6439,28,29.1,1,2,0,0,12,1,0,1,1,8
"4490",7247,28,29.1,1,5,0,0,12,1,1,1,1,7
"4491",354,28,29.2,1,5,0,1,12,0,1,1,1,3
"4492",10119,28,29.2,1,2,0,0,13,1,2,1,1,8
"4493",10341,28,29.2,1,4,0,0,10,1,2,1,1,5
"4494",7937,28,29.2,1,5,0,0,15,1,2,1,1,10
"4495",7567,28,29.3,1,1,0,0,11,1,1,1,1,5
"4496",10447,28,29.3,1,4,0,0,9,1,1,1,1,8
"4497",2971,28,29.4,1,3,0,0,13,0,1,1,1,9
"4498",3607,28,29.4,1,1,0,0,14,0,1,1,1,5
"4499",851,28,29.4,1,5,0,0,12,0,1,1,1,6
"4500",1743,28,29.4,1,2,0,0,13,0,2,1,1,7
"4501",4027,28,29.5,1,2,0,0,12,0,1,1,1,10
"4502",4733,28,29.5,1,2,0,0,11,0,2,1,1,7
"4503",10632,28,29.6,1,3,0,1,11,1,3,1,1,6
"4504",5933,28,29.6,1,3,0,1,14,1,6,1,1,1
"4505",9535,28,29.6,1,3,0,0,11,1,0,1,1,6
"4506",1927,28,29.7,1,1,0,0,11,0,2,1,1,7
"4507",2221,28,29.7,1,1,0,0,9,0,2,1,1,5
"4508",4199,28,29.7,1,1,0,0,14,0,0,1,1,7
"4509",3619,28,29.7,1,5,0,0,15,0,1,1,1,5
"4510",10747,28,29.7,1,3,0,1,15,1,4,1,1,4
"4511",5471,28,29.7,1,3,0,0,12,1,2,1,1,7
"4512",4967,28,29.8,1,3,0,0,14,0,2,1,1,6
"4513",9361,28,29.8,1,2,0,0,10,1,0,1,1,6
"4514",9541,28,29.8,1,4,0,0,14,1,2,1,1,8
"4515",25,28,29.8,1,5,0,0,11,0,2,1,1,8
"4516",5701,28,29.8,1,1,0,0,13,1,2,1,1,6
"4517",7908,28,29.9,1,2,0,1,13,1,1,1,1,0
"4518",4493,28,29.9,1,5,0,0,11,0,1,1,1,5
"4519",17,28,29.9,1,2,0,0,12,0,2,1,1,6
"4520",10783,28,29.9,1,1,0,0,11,1,0,1,1,6
"4521",4015,28,29.9,1,1,0,0,9,0,2,1,1,10
"4522",265,28,29.9,1,5,0,0,12,0,1,1,1,10
"4523",5337,28,29.9,1,2,0,0,13,0,0,1,1,10
"4524",1915,28,30,1,3,0,0,13,0,1,1,1,10
"4534",4254,29,3.2,1,3,0,1,15,0,1,1,1,7
"4535",8942,29,3.2,1,4,0,1,13,1,3,1,1,7
"4538",6084,29,4.2,1,1,0,1,14,1,8,1,1,7
"4540",8626,29,4.5,1,2,0,1,12,1,3,1,1,0
"4543",220,29,5,1,5,0,1,9,0,2,1,1,2
"4544",8593,29,5.4,1,5,0,1,11,1,1,1,1,1
"4546",9166,29,5.6,1,4,0,1,9,1,8,1,1,5
"4547",2359,29,6.4,1,2,0,1,9,0,7,1,1,3
"4548",9071,29,6.5,1,4,0,1,15,1,4,1,1,4
"4549",1970,29,6.6,1,5,0,1,13,0,3,1,1,4
"4550",4361,29,6.7,1,1,0,1,15,0,8,1,1,2
"4551",5710,29,6.8,1,3,0,1,11,1,2,1,1,5
"4552",9296,29,7.6,1,1,0,1,11,1,6,1,1,2
"4553",1462,29,7.9,1,1,0,1,15,0,2,1,1,1
"4554",4676,29,8.3,1,2,0,1,13,0,3,1,1,3
"4555",2727,29,8.3,1,5,0,1,13,0,3,1,1,0
"4556",2340,29,8.8,1,4,0,1,9,0,1,1,1,4
"4557",6187,29,9,1,5,0,1,15,1,4,1,1,0
"4558",5793,29,9.2,1,4,0,1,11,1,3,1,1,0
"4559",9064,29,9.3,1,1,0,1,15,1,5,1,1,7
"4560",3193,29,10,1,1,0,1,13,0,7,1,1,3
"4561",6268,29,10.2,1,5,0,1,9,1,8,1,1,6
"4562",4613,29,10.4,1,5,0,1,12,0,6,1,1,2
"4563",1860,29,10.6,1,5,0,1,13,0,5,1,1,6
"4564",5436,29,10.9,1,3,0,1,10,1,4,1,1,10
"4565",956,29,11,1,5,0,1,12,0,2,1,1,1
"4566",8526,29,11.1,1,4,0,1,9,1,3,1,1,4
"4567",2508,29,11.2,1,2,0,1,13,0,3,1,1,1
"4568",1145,29,11.5,1,5,0,1,11,0,1,1,1,2
"4569",2636,29,12.1,1,3,0,1,11,0,1,1,1,4
"4570",7107,29,12.1,1,1,0,1,11,1,1,1,1,2
"4571",9637,29,12.3,1,5,0,1,10,1,1,1,1,2
"4572",4280,29,12.3,1,4,0,1,10,0,7,1,1,7
"4573",10684,29,12.6,1,2,0,1,14,1,1,1,1,0
"4574",10738,29,12.9,1,4,0,1,10,1,8,1,1,0
"4575",6562,29,13.1,1,5,0,1,13,1,3,1,1,10
"4576",4503,29,13.2,1,2,0,1,10,0,2,1,1,1
"4577",7008,29,13.5,1,3,0,1,10,1,5,1,1,0
"4578",5906,29,13.5,1,3,0,1,10,1,2,1,1,6
"4579",5806,29,13.7,1,2,0,1,9,1,6,1,1,1
"4580",5461,29,13.7,1,4,0,1,14,1,5,1,1,1
"4581",5861,29,13.9,1,3,0,1,13,1,5,1,1,3
"4582",10259,29,13.9,1,5,0,1,12,1,6,1,1,3
"4583",407,29,14.3,1,1,0,1,12,0,5,1,1,1
"4584",10753,29,15.2,1,3,0,1,13,1,6,1,1,1
"4585",6688,29,15.4,1,1,0,1,13,1,5,1,1,1
"4586",674,29,15.4,1,2,0,1,12,0,5,1,1,4
"4587",6695,29,15.6,1,2,0,1,9,1,1,1,1,3
"4588",2090,29,15.6,1,5,0,1,11,0,8,1,1,3
"4589",74,29,16.2,1,1,0,1,13,0,5,1,1,9
"4590",5205,29,16.2,1,3,0,1,9,0,4,1,1,3
"4591",7071,29,16.2,1,3,0,1,11,1,7,1,1,3
"4592",10012,29,16.6,1,3,0,1,11,1,4,1,1,3
"4593",7428,29,16.9,1,5,0,1,9,1,2,1,1,3
"4594",8404,29,17,1,4,0,1,10,1,3,1,1,7
"4595",10219,29,17.3,1,4,0,1,12,1,1,1,1,1
"4596",1798,29,17.4,1,2,0,1,15,0,4,1,1,6
"4597",159,29,17.5,1,1,0,1,9,0,4,1,1,3
"4598",1366,29,17.7,1,4,0,1,10,0,5,1,1,6
"4599",3206,29,17.9,1,4,0,1,12,0,4,1,1,0
"4600",3194,29,17.9,1,2,0,1,10,0,6,1,1,3
"4601",3320,29,18.1,1,4,0,1,11,0,4,1,1,2
"4602",2132,29,18.5,1,4,0,1,10,0,4,1,1,7
"4603",6736,29,18.6,1,5,0,1,12,1,5,1,1,4
"4604",3954,29,18.7,1,5,0,1,11,0,7,1,1,2
"4605",5349,29,18.9,1,2,0,1,15,0,4,1,1,1
"4606",2118,29,18.9,1,1,0,1,10,0,7,1,1,5
"4607",2180,29,19.1,1,4,0,1,15,0,8,1,1,9
"4608",6209,29,19.2,1,3,0,1,13,1,3,1,1,3
"4609",9360,29,20.2,1,3,0,1,15,1,2,1,1,1
"4610",9082,29,20.3,1,1,0,1,12,1,2,1,1,6
"4611",1707,29,21.6,1,1,0,1,9,0,4,1,1,1
"4612",7654,29,21.8,1,4,0,1,11,1,3,1,1,3
"4613",4735,29,21.9,1,3,0,1,14,0,3,1,1,1
"4614",7610,29,22,1,1,0,1,12,1,5,1,1,2
"4615",9768,29,22.4,1,1,0,1,10,1,2,1,1,10
"4616",2233,29,22.9,1,1,0,1,14,0,7,1,1,3
"4617",6460,29,23.3,1,3,0,1,12,1,2,1,1,10
"4618",8354,29,23.6,1,4,0,1,9,1,6,1,1,2
"4619",8472,29,23.7,1,3,0,1,11,1,5,1,1,6
"4620",9834,29,24,1,4,0,1,12,1,2,1,1,7
"4621",10284,29,24,1,3,0,1,12,1,6,1,1,8
"4622",6276,29,24,1,2,0,1,13,1,7,1,1,8
"4623",9383,29,24.1,1,5,0,1,15,1,7,1,1,3
"4624",1396,29,24.5,1,3,0,1,10,0,6,1,1,10
"4625",6793,29,24.7,1,1,0,1,15,1,8,1,1,3
"4626",3909,29,25,1,2,0,1,15,0,4,1,1,3
"4627",6314,29,25.1,1,5,0,1,15,1,1,1,1,7
"4628",1960,29,25.4,1,4,0,1,13,0,3,1,1,1
"4629",9926,29,25.7,1,1,0,1,10,1,3,1,1,3
"4630",9679,29,25.9,1,5,0,1,9,1,8,1,1,3
"4631",8042,29,25.9,1,5,0,1,12,1,5,1,1,8
"4632",9308,29,26.7,1,3,0,1,13,1,4,1,1,3
"4633",10542,29,27,1,2,0,1,12,1,5,1,1,10
"4634",10296,29,27.4,1,3,0,1,11,1,1,1,1,0
"4635",5235,29,27.4,1,3,0,1,11,0,3,1,1,3
"4636",2865,29,27.6,1,2,0,1,12,0,2,1,1,2
"4637",7616,29,28,1,3,0,1,10,1,8,1,1,6
"4638",7078,29,28.7,1,5,0,1,11,1,2,1,1,7
"4639",9532,29,28.8,1,3,0,1,11,1,8,1,1,9
"4640",7574,29,28.9,1,1,0,1,12,1,8,1,1,7
"4641",8750,29,29,1,2,0,1,11,1,4,1,1,3
"4642",7494,29,29,1,2,0,1,10,1,8,1,1,4
"4643",3238,29,29.5,1,5,0,1,13,0,2,1,1,2
"4644",1690,29,29.9,1,2,0,1,11,0,6,1,1,0
"4675",3010,30,6.7,1,2,0,1,5,0,1,1,1,9
"4679",6740,30,7.8,1,3,0,1,6,1,6,1,1,5
"4680",6296,30,7.9,1,5,0,1,7,1,5,1,1,10
"4681",6872,30,8,1,1,0,1,8,1,8,1,1,1
"4682",4440,30,8.3,1,5,0,1,7,0,8,1,1,0
"4683",5674,30,8.9,1,1,0,1,7,1,1,1,1,2
"4684",5402,30,9.1,1,5,0,1,7,0,8,1,1,8
"4685",140,30,9.2,1,5,0,1,7,0,6,1,1,7
"4686",3627,30,9.2,1,4,0,1,5,0,4,1,1,2
"4687",1016,30,9.3,1,5,0,1,7,0,6,1,1,5
"4688",1789,30,9.4,1,1,0,1,5,0,3,1,1,1
"4689",8922,30,10.1,1,2,0,1,5,1,4,1,1,8
"4690",7912,30,10.7,1,5,0,1,6,1,4,1,1,6
"4691",6606,30,10.8,1,3,0,1,7,1,3,1,1,2
"4692",5708,30,11.5,1,3,0,1,5,1,4,1,1,5
"4693",4246,30,11.6,1,5,0,1,7,0,8,1,1,1
"4694",5960,30,12.1,1,2,0,1,6,1,4,1,1,8
"4695",5991,30,12.2,1,5,0,1,6,1,4,1,1,2
"4696",10166,30,12.8,1,3,0,1,8,1,6,1,1,0
"4697",1233,30,12.8,1,4,0,1,6,0,2,1,1,0
"4698",3678,30,13.2,1,3,0,1,7,0,7,1,1,4
"4699",10692,30,13.6,1,2,0,1,8,1,6,1,1,5
"4700",8122,30,13.9,1,4,0,1,8,1,2,1,1,10
"4701",10130,30,14.2,1,3,0,1,5,1,8,1,1,2
"4702",7796,30,14.3,1,4,0,1,8,1,5,1,1,0
"4703",9497,30,14.6,1,1,0,1,7,1,4,1,1,0
"4704",329,30,14.8,1,2,0,1,7,0,6,1,1,2
"4705",3749,30,15.7,1,3,0,1,7,0,4,1,1,3
"4706",10026,30,15.7,1,5,0,1,6,1,5,1,1,1
"4707",1446,30,15.7,1,5,0,1,6,0,2,1,1,10
"4708",698,30,15.8,1,4,0,1,5,0,6,1,1,2
"4709",9746,30,17.1,1,2,0,1,6,1,7,1,1,8
"4710",3928,30,17.3,1,3,0,1,5,0,2,1,1,6
"4711",6396,30,18,1,3,0,1,5,1,4,1,1,8
"4712",2522,30,18,1,3,0,1,7,0,2,1,1,5
"4713",9367,30,18,1,3,0,1,8,1,4,1,1,3
"4714",5199,30,18.2,1,3,0,1,7,0,1,1,1,3
"4715",2718,30,18.2,1,2,0,1,8,0,3,1,1,4
"4716",10248,30,18.4,1,1,0,1,6,1,3,1,1,6
"4717",248,30,18.4,1,4,0,1,5,0,4,1,1,7
"4718",4260,30,18.5,1,1,0,1,5,0,1,1,1,5
"4719",9019,30,18.6,1,1,0,1,5,1,5,1,1,3
"4720",2774,30,18.8,1,4,0,1,7,0,8,1,1,3
"4721",7182,30,18.9,1,3,0,1,8,1,8,1,1,3
"4722",3744,30,19,1,1,0,1,8,0,2,1,1,4
"4723",9512,30,19,1,1,0,1,7,1,2,1,1,3
"4724",9446,30,19.3,1,1,0,1,7,1,5,1,1,4
"4725",9698,30,19.4,1,5,0,1,5,1,2,1,1,5
"4726",2302,30,19.6,1,1,0,1,8,0,8,1,1,6
"4727",6800,30,19.6,1,2,0,1,5,1,1,1,1,3
"4728",8792,30,19.9,1,4,0,1,7,1,7,1,1,10
"4729",7914,30,20.1,1,3,0,1,7,1,2,1,1,5
"4730",8128,30,20.6,1,4,0,1,5,1,2,1,1,10
"4731",9517,30,20.9,1,2,0,1,7,1,2,1,1,3
"4732",4354,30,21,1,3,0,1,7,0,4,1,1,4
"4733",3099,30,21.1,1,5,0,1,6,0,2,1,1,2
"4734",3393,30,21.3,1,5,0,1,7,0,4,1,1,3
"4735",5562,30,21.4,1,1,0,1,8,1,5,1,1,3
"4736",8701,30,21.4,1,1,0,1,5,1,6,1,1,4
"4737",8725,30,22.2,1,4,0,1,5,1,3,1,1,0
"4738",5452,30,22.2,1,5,0,1,8,1,1,1,1,4
"4739",7002,30,23.9,1,4,0,1,8,1,4,1,1,6
"4740",848,30,24,1,5,0,1,8,0,4,1,1,9
"4741",9620,30,24.2,1,4,0,1,8,1,8,1,1,4
"4742",9222,30,24.6,1,3,0,1,8,1,6,1,1,1
"4743",10386,30,24.9,1,2,0,1,7,1,7,1,1,5
"4744",7700,30,25.6,1,3,0,1,5,1,2,1,1,7
"4745",1774,30,25.6,1,3,0,1,7,0,3,1,1,7
"4746",5283,30,25.7,1,1,0,1,5,0,1,1,1,2
"4747",1945,30,26.4,1,5,0,1,5,0,5,1,1,4
"4748",262,30,27.2,1,1,0,1,5,0,3,1,1,3
"4749",4286,30,27.3,1,3,0,1,8,0,2,1,1,9
"4750",9953,30,28.2,1,4,0,1,5,1,6,1,1,4
"4751",8572,30,28.3,1,3,0,1,8,1,2,1,1,9
"4752",1866,30,28.3,1,5,0,1,8,0,5,1,1,7
"4753",3305,30,28.6,1,3,0,1,6,0,2,1,1,1
"4754",9078,30,28.7,1,4,0,1,8,1,1,1,1,7
"4755",6717,30,28.9,1,5,0,1,7,1,3,1,1,1
"4756",1318,30,28.9,1,4,0,1,5,0,7,1,1,10
"4757",782,30,29.2,1,1,0,1,8,0,3,1,1,6
"4758",2484,30,29.2,1,1,0,1,8,0,4,1,1,6
"4759",4382,30,29.2,1,4,0,1,5,0,3,1,1,4
"4760",7288,30,29.6,1,2,0,1,8,1,3,1,1,4
"4761",8837,30,29.7,1,1,0,1,8,1,7,1,1,3
"4783",3886,31,8.1,1,4,0,1,8,0,3,1,1,9
"4784",6610,31,8.9,1,4,0,1,6,1,3,1,1,1
"4785",3244,31,8.9,1,5,0,1,6,0,1,1,1,7
"4786",313,31,9.1,1,3,0,1,7,0,1,1,1,3
"4787",5804,31,9.2,1,4,0,1,8,1,1,1,1,1
"4788",6044,31,9.8,1,1,0,1,8,1,4,1,1,0
"4789",5552,31,9.9,1,2,0,1,5,1,6,1,1,0
"4790",4577,31,9.9,1,4,0,1,8,0,3,1,1,2
"4791",8601,31,10,1,5,0,1,6,1,1,1,1,1
"4792",5234,31,10.8,1,1,0,1,8,0,1,1,1,7
"4793",8568,31,10.9,1,1,0,1,5,1,3,1,1,0
"4794",10373,31,11.4,1,5,0,1,7,1,6,1,1,0
"4795",2868,31,11.5,1,5,0,1,7,0,5,1,1,9
"4796",10658,31,11.5,1,4,0,1,5,1,1,1,1,1
"4797",5646,31,11.9,1,3,0,1,6,1,2,1,1,3
"4798",3829,31,12,1,1,0,1,6,0,6,1,1,4
"4799",1065,31,13,1,5,0,1,6,0,1,1,1,2
"4800",88,31,13.6,1,5,0,1,8,0,7,1,1,0
"4801",1778,31,13.8,1,2,0,1,6,0,8,1,1,0
"4802",1256,31,14,1,4,0,1,6,0,3,1,1,1
"4803",3838,31,14.5,1,1,0,1,5,0,4,1,1,4
"4804",7015,31,14.6,1,5,0,1,6,1,1,1,1,3
"4805",10086,31,14.9,1,3,0,1,6,1,7,1,1,7
"4806",2803,31,15.2,1,4,0,1,5,0,6,1,1,0
"4807",6022,31,15.3,1,3,0,1,6,1,2,1,1,10
"4808",484,31,15.3,1,4,0,1,6,0,5,1,1,3
"4809",10649,31,16.1,1,1,0,1,8,1,6,1,1,0
"4810",4016,31,16.7,1,1,0,1,6,0,1,1,1,5
"4811",2986,31,16.8,1,1,0,1,5,0,6,1,1,7
"4812",9394,31,17.9,1,3,0,1,8,1,3,1,1,9
"4813",8556,31,18.3,1,2,0,1,6,1,1,1,1,7
"4814",7214,31,18.5,1,4,0,1,7,1,5,1,1,0
"4815",2686,31,19.4,1,5,0,1,7,0,2,1,1,5
"4816",5563,31,19.4,1,1,0,1,7,1,6,1,1,4
"4817",1482,31,19.5,1,5,0,1,7,0,1,1,1,0
"4818",3878,31,19.5,1,3,0,1,7,0,5,1,1,3
"4819",7944,31,19.5,1,3,0,1,5,1,8,1,1,3
"4820",7648,31,20.1,1,5,0,1,7,1,2,1,1,3
"4821",4160,31,20.8,1,5,0,1,5,0,1,1,1,3
"4822",1558,31,20.9,1,2,0,1,8,0,3,1,1,1
"4823",6852,31,21.1,1,3,0,1,7,1,3,1,1,6
"4824",8455,31,21.3,1,3,0,1,5,1,6,1,1,2
"4825",3464,31,22.4,1,1,0,1,6,0,4,1,1,9
"4826",10191,31,22.8,1,3,0,1,6,1,3,1,1,3
"4827",3926,31,22.9,1,5,0,1,7,0,3,1,1,6
"4828",7799,31,23,1,5,0,1,7,1,1,1,1,3
"4829",384,31,23.7,1,5,0,1,8,0,7,1,1,4
"4830",6083,31,24.1,1,1,0,1,8,1,7,1,1,2
"4831",2890,31,24.2,1,2,0,1,7,0,8,1,1,5
"4832",8956,31,24.3,1,1,0,1,8,1,1,1,1,8
"4833",7824,31,24.5,1,3,0,1,6,1,3,1,1,4
"4834",10500,31,24.5,1,1,0,1,8,1,3,1,1,7
"4835",10521,31,25.2,1,2,0,1,7,1,6,1,1,3
"4836",9238,31,25.8,1,3,0,1,7,1,1,1,1,3
"4837",5518,31,25.9,1,4,0,1,6,1,8,1,1,4
"4838",8901,31,25.9,1,2,0,1,8,1,2,1,1,2
"4839",4683,31,26,1,2,0,1,6,0,6,1,1,4
"4840",8149,31,26.1,1,1,0,1,7,1,5,1,1,2
"4841",598,31,26.4,1,3,0,1,6,0,1,1,1,5
"4842",5198,31,26.4,1,2,0,1,6,0,6,1,1,4
"4843",3068,31,26.4,1,2,0,1,8,0,3,1,1,5
"4844",2614,31,26.5,1,1,0,1,6,0,4,1,1,8
"4845",6831,31,26.8,1,1,0,1,5,1,8,1,1,3
"4846",9399,31,27.1,1,4,0,1,7,1,1,1,1,0
"4847",442,31,27.9,1,5,0,1,8,0,6,1,1,6
"4848",7262,31,28.3,1,2,0,1,7,1,4,1,1,2
"4849",1493,31,28.8,1,4,0,1,5,0,2,1,1,0
"4850",2942,31,28.8,1,4,0,1,8,0,8,1,1,8
"4851",9650,31,29.2,1,1,0,1,8,1,7,1,1,5
"4852",1822,31,29.3,1,5,0,1,5,0,4,1,1,10
"4853",4296,31,29.3,1,1,0,1,5,0,4,1,1,1
"4854",9163,31,29.6,1,2,0,1,8,1,6,1,1,3
"4855",4660,31,29.7,1,3,0,1,8,0,1,1,1,5
"4886",3196,32,10.7,1,2,0,1,5,0,4,1,1,6
"4887",3980,32,11.2,1,5,0,1,6,0,1,1,1,7
"4888",5640,32,11.3,1,1,0,1,7,1,1,1,1,5
"4889",4248,32,11.5,1,5,0,1,8,0,6,1,1,10
"4890",374,32,11.6,1,4,0,1,7,0,1,1,1,8
"4891",6884,32,11.7,1,3,0,1,7,1,6,1,1,4
"4892",8928,32,11.7,1,1,0,1,5,1,3,1,1,0
"4893",5914,32,12.7,1,1,0,1,7,1,1,1,1,1
"4894",3316,32,13.1,1,4,0,1,7,0,6,1,1,6
"4895",3454,32,13.4,1,3,0,1,7,0,8,1,1,9
"4896",9282,32,13.5,1,5,0,1,5,1,5,1,1,10
"4897",3466,32,13.7,1,5,0,1,5,0,6,1,1,7
"4898",9725,32,13.9,1,4,0,1,8,1,4,1,1,3
"4899",8820,32,14,1,2,0,1,7,1,1,1,1,10
"4900",2762,32,14,1,1,0,1,5,0,3,1,1,9
"4901",9874,32,14.5,1,2,0,1,5,1,6,1,1,4
"4902",8648,32,15.1,1,1,0,1,6,1,7,1,1,3
"4903",8321,32,16.2,1,3,0,1,7,1,4,1,1,0
"4904",2714,32,16.2,1,4,0,1,6,0,8,1,1,5
"4905",4628,32,16.8,1,2,0,1,8,0,8,1,1,8
"4906",1795,32,17.1,1,4,0,1,8,0,4,1,1,2
"4907",4760,32,17.1,1,5,0,1,8,0,1,1,1,4
"4908",6969,32,17.2,1,5,0,1,8,1,7,1,1,1
"4909",10724,32,17.3,1,4,0,1,5,1,5,1,1,6
"4910",6173,32,17.5,1,2,0,1,8,1,6,1,1,1
"4911",7558,32,17.6,1,4,0,1,6,1,2,1,1,10
"4912",4112,32,17.8,1,4,0,1,5,0,2,1,1,9
"4913",1734,32,18.1,1,3,0,1,8,0,2,1,1,4
"4914",4305,32,18.1,1,5,0,1,7,0,8,1,1,1
"4915",1635,32,18.2,1,3,0,1,6,0,5,1,1,0
"4916",3096,32,18.4,1,1,0,1,5,0,3,1,1,6
"4917",4301,32,18.4,1,4,0,1,7,0,8,1,1,3
"4918",10140,32,18.4,1,2,0,1,6,1,8,1,1,7
"4919",7406,32,18.4,1,4,0,1,7,1,4,1,1,7
"4920",4837,32,18.7,1,1,0,1,8,0,5,1,1,0
"4921",6200,32,18.8,1,4,0,1,6,1,7,1,1,5
"4922",2281,32,18.9,1,2,0,1,5,0,6,1,1,0
"4923",285,32,19.1,1,1,0,1,7,0,1,1,1,1
"4924",5981,32,19.3,1,1,0,1,6,1,6,1,1,0
"4925",361,32,19.5,1,3,0,1,6,0,5,1,1,2
"4926",6029,32,19.7,1,4,0,1,7,1,5,1,1,2
"4927",563,32,20.2,1,5,0,1,7,0,4,1,1,2
"4928",6486,32,20.8,1,1,0,1,8,1,6,1,1,0
"4929",6620,32,21.1,1,4,0,1,8,1,5,1,1,2
"4930",1720,32,21.5,1,5,0,1,5,0,7,1,1,5
"4931",5221,32,21.9,1,4,0,1,5,0,4,1,1,0
"4932",3205,32,21.9,1,1,0,1,6,0,4,1,1,2
"4933",8494,32,22.1,1,3,0,1,7,1,8,1,1,5
"4934",5275,32,22.8,1,4,0,1,5,0,7,1,1,2
"4935",6355,32,22.8,1,2,0,1,5,1,5,1,1,2
"4936",5482,32,23,1,3,0,1,8,1,6,1,1,0
"4937",6766,32,23.3,1,3,0,1,6,1,5,1,1,1
"4938",619,32,23.4,1,2,0,1,5,0,6,1,1,3
"4939",2370,32,23.4,1,2,0,1,5,0,4,1,1,4
"4940",2632,32,23.5,1,3,0,1,8,0,8,1,1,0
"4941",9028,32,23.6,1,5,0,1,7,1,8,1,1,6
"4942",10614,32,24,1,1,0,1,7,1,2,1,1,10
"4943",9135,32,24.9,1,3,0,1,5,1,3,1,1,4
"4944",2352,32,25,1,5,0,1,8,0,3,1,1,2
"4945",10082,32,25.2,1,1,0,1,6,1,5,1,1,10
"4946",4400,32,25.2,1,5,0,1,6,0,3,1,1,2
"4947",6195,32,25.8,1,3,0,1,6,1,2,1,1,1
"4948",10207,32,25.8,1,3,0,1,6,1,2,1,1,3
"4949",5978,32,26,1,5,0,1,5,1,2,1,1,6
"4950",9934,32,26.2,1,4,0,1,8,1,6,1,1,9
"4951",2922,32,26.4,1,5,0,1,8,0,3,1,1,2
"4952",10700,32,26.4,1,3,0,1,7,1,7,1,1,1
"4953",7754,32,26.5,1,3,0,1,8,1,6,1,1,8
"4954",2101,32,26.6,1,1,0,1,6,0,3,1,1,2
"4955",1372,32,27.3,1,4,0,1,7,0,4,1,1,6
"4956",10669,32,27.3,1,3,0,1,7,1,5,1,1,2
"4957",7426,32,27.5,1,3,0,1,5,1,1,1,1,10
"4958",8026,32,27.5,1,2,0,1,7,1,1,1,1,0
"4959",10388,32,28,1,4,0,1,8,1,8,1,1,2
"4960",7631,32,28.3,1,4,0,1,8,1,4,1,1,2
"4961",5332,32,28.8,1,1,0,1,7,0,3,1,1,2
"4962",9878,32,28.9,1,4,0,1,7,1,1,1,1,8
"4963",7289,32,29,1,2,0,1,8,1,6,1,1,3
"4964",8431,32,29.1,1,3,0,1,5,1,5,1,1,4
"4965",3654,32,29.2,1,2,0,1,8,0,7,1,1,5
"4966",4812,32,29.5,1,1,0,1,6,0,7,1,1,3
"4967",9406,32,29.6,1,1,0,1,6,1,7,1,1,2
"4968",3458,32,29.9,1,5,0,1,7,0,3,1,1,1
"5000",1758,33,11.5,1,3,0,1,5,0,5,1,1,8
"5003",9506,33,12,1,3,0,1,7,1,4,1,1,3
"5004",6430,33,12,1,5,0,1,5,1,2,1,1,10
"5005",386,33,12,1,3,0,1,8,0,3,1,1,8
"5006",941,33,12.6,1,4,0,1,8,0,6,1,1,0
"5007",4447,33,12.6,1,1,0,1,5,0,7,1,1,3
"5008",9229,33,12.9,1,4,0,1,8,1,3,1,1,1
"5009",3849,33,13,1,5,0,1,5,0,3,1,1,4
"5010",10578,33,13.2,1,4,0,1,7,1,4,1,1,5
"5011",1670,33,14,1,1,0,1,6,0,2,1,1,10
"5012",482,33,14.1,1,3,0,1,8,0,7,1,1,9
"5013",1710,33,14.5,1,1,0,1,8,0,3,1,1,5
"5014",8414,33,14.6,1,5,0,1,7,1,8,1,1,4
"5015",281,33,14.8,1,4,0,1,7,0,8,1,1,1
"5016",8258,33,15,1,5,0,1,7,1,8,1,1,10
"5017",1864,33,15.1,1,3,0,1,6,0,6,1,1,2
"5018",10476,33,15.7,1,4,0,1,7,1,3,1,1,1
"5019",1468,33,15.7,1,1,0,1,8,0,4,1,1,4
"5020",7918,33,16.6,1,3,0,1,8,1,4,1,1,1
"5021",10826,33,16.8,1,1,0,1,5,1,3,1,1,6
"5022",7560,33,17.1,1,2,0,1,8,1,8,1,1,6
"5023",1109,33,17.3,1,4,0,1,8,0,3,1,1,1
"5024",100,33,18.1,1,3,0,1,7,0,6,1,1,1
"5025",2432,33,18.1,1,4,0,1,7,0,5,1,1,2
"5026",6502,33,18.2,1,4,0,1,5,1,3,1,1,4
"5027",4310,33,18.2,1,1,0,1,8,0,5,1,1,0
"5028",10595,33,18.2,1,4,0,1,5,1,4,1,1,0
"5029",4844,33,18.3,1,4,0,1,5,0,6,1,1,5
"5030",9589,33,18.5,1,5,0,1,6,1,4,1,1,3
"5031",2635,33,18.9,1,4,0,1,5,0,4,1,1,4
"5032",2540,33,19.5,1,5,0,1,8,0,8,1,1,2
"5033",347,33,19.5,1,3,0,1,5,0,7,1,1,3
"5034",6080,33,19.7,1,3,0,1,6,1,6,1,1,7
"5035",8137,33,20.3,1,4,0,1,8,1,2,1,1,3
"5036",5100,33,20.7,1,1,0,1,7,0,7,1,1,3
"5037",995,33,20.8,1,4,0,1,6,0,6,1,1,4
"5038",8850,33,20.9,1,2,0,1,8,1,4,1,1,7
"5039",152,33,21,1,5,0,1,8,0,3,1,1,5
"5040",204,33,21.2,1,1,0,1,7,0,6,1,1,10
"5041",10552,33,21.2,1,5,0,1,5,1,7,1,1,8
"5042",82,33,21.3,1,4,0,1,5,0,8,1,1,6
"5043",8823,33,21.6,1,2,0,1,5,1,3,1,1,2
"5044",2631,33,22.3,1,2,0,1,5,0,3,1,1,3
"5045",7756,33,22.5,1,2,0,1,7,1,3,1,1,7
"5046",8858,33,23.7,1,2,0,1,7,1,3,1,1,2
"5047",8448,33,23.8,1,2,0,1,6,1,3,1,1,7
"5048",10036,33,23.8,1,5,0,1,8,1,5,1,1,1
"5049",5762,33,23.8,1,3,0,1,8,1,1,1,1,4
"5050",2814,33,24.3,1,2,0,1,8,0,7,1,1,8
"5051",1337,33,24.4,1,1,0,1,5,0,8,1,1,4
"5052",400,33,24.4,1,2,0,1,6,0,2,1,1,10
"5053",18,33,25,1,4,0,1,5,0,4,1,1,2
"5054",10780,33,25.1,1,2,0,1,7,1,7,1,1,5
"5055",6693,33,25.3,1,1,0,1,5,1,5,1,1,0
"5056",8726,33,26.3,1,2,0,1,5,1,6,1,1,6
"5057",5956,33,26.5,1,4,0,1,5,1,6,1,1,8
"5058",3451,33,26.7,1,1,0,1,5,0,7,1,1,4
"5059",10168,33,27.2,1,2,0,1,8,1,3,1,1,7
"5060",6125,33,27.4,1,2,0,1,7,1,5,1,1,1
"5061",4084,33,27.6,1,1,0,1,5,0,3,1,1,3
"5062",4158,33,27.8,1,3,0,1,8,0,3,1,1,7
"5063",8075,33,28.5,1,4,0,1,6,1,1,1,1,3
"5064",9981,33,28.8,1,2,0,1,5,1,4,1,1,1
"5065",2622,33,29.2,1,4,0,1,8,0,5,1,1,7
"5066",4180,33,29.3,1,1,0,1,6,0,3,1,1,0
"5067",8516,33,29.4,1,3,0,1,6,1,6,1,1,1
"5068",856,33,29.9,1,2,0,1,8,0,2,1,1,6
"5121",5685,34,12.5,1,3,0,1,8,1,1,1,1,3
"5123",2464,34,13.1,1,3,0,1,7,0,5,1,1,5
"5124",1194,34,14,1,1,0,1,8,0,1,1,1,4
"5125",10458,34,14.2,1,4,0,1,8,1,1,1,1,0
"5126",10560,34,14.4,1,4,0,1,6,1,2,1,1,4
"5127",7852,34,14.4,1,3,0,1,5,1,2,1,1,3
"5128",6978,34,14.5,1,3,0,1,7,1,5,1,1,5
"5129",7915,34,15.1,1,1,0,1,7,1,6,1,1,0
"5130",215,34,15.6,1,5,0,1,5,0,3,1,1,1
"5131",6474,34,15.7,1,3,0,1,6,1,6,1,1,2
"5132",6104,34,15.8,1,4,0,1,7,1,5,1,1,7
"5133",2086,34,16.4,1,4,0,1,6,0,8,1,1,6
"5134",1744,34,16.5,1,1,0,1,8,0,5,1,1,2
"5135",8583,34,16.8,1,1,0,1,6,1,5,1,1,4
"5136",2062,34,16.9,1,3,0,1,8,0,8,1,1,1
"5137",6298,34,17.1,1,2,0,1,8,1,8,1,1,10
"5138",5434,34,17.4,1,3,0,1,5,1,6,1,1,5
"5139",208,34,17.5,1,2,0,1,8,0,4,1,1,2
"5140",9168,34,17.6,1,4,0,1,5,1,7,1,1,7
"5141",4222,34,17.9,1,2,0,1,6,0,3,1,1,3
"5142",4939,34,18.3,1,3,0,1,6,0,5,1,1,3
"5143",5062,34,18.4,1,2,0,1,6,0,3,1,1,2
"5144",10184,34,18.7,1,3,0,1,5,1,3,1,1,6
"5145",2665,34,18.9,1,4,0,1,6,0,5,1,1,1
"5146",3745,34,19.1,1,3,0,1,8,0,5,1,1,3
"5147",8461,34,19.3,1,3,0,1,5,1,1,1,1,4
"5148",10007,34,19.4,1,2,0,1,7,1,4,1,1,3
"5149",802,34,19.6,1,3,0,1,5,0,5,1,1,2
"5150",10146,34,19.7,1,4,0,1,6,1,8,1,1,4
"5151",9651,34,19.9,1,3,0,1,5,1,5,1,1,3
"5152",10332,34,19.9,1,3,0,1,6,1,7,1,1,5
"5153",5774,34,20.5,1,2,0,1,5,1,6,1,1,4
"5154",1403,34,20.5,1,2,0,1,6,0,5,1,1,1
"5155",7186,34,20.8,1,5,0,1,5,1,6,1,1,1
"5156",9860,34,21.4,1,4,0,1,8,1,4,1,1,1
"5157",5511,34,21.4,1,3,0,1,7,1,7,1,1,0
"5158",1528,34,21.4,1,5,0,1,7,0,6,1,1,3
"5159",10106,34,21.7,1,4,0,1,6,1,6,1,1,5
"5160",2017,34,22,1,1,0,1,5,0,2,1,1,0
"5161",4226,34,22,1,3,0,1,8,0,3,1,1,0
"5162",1332,34,22.1,1,3,0,1,5,0,4,1,1,9
"5163",5246,34,22.3,1,2,0,1,7,0,4,1,1,4
"5164",2794,34,22.4,1,3,0,1,5,0,6,1,1,9
"5165",3037,34,22.4,1,3,0,1,7,0,2,1,1,1
"5166",8492,34,23.3,1,1,0,1,7,1,6,1,1,0
"5167",6227,34,23.5,1,3,0,1,8,1,2,1,1,1
"5168",8877,34,23.5,1,3,0,1,5,1,8,1,1,1
"5169",6350,34,23.8,1,2,0,1,8,1,7,1,1,4
"5170",9260,34,24.1,1,5,0,1,8,1,2,1,1,6
"5171",1336,34,24.9,1,1,0,1,8,0,7,1,1,4
"5172",6147,34,25,1,2,0,1,5,1,7,1,1,4
"5173",7217,34,25,1,5,0,1,8,1,6,1,1,3
"5174",8345,34,25.3,1,2,0,1,6,1,8,1,1,1
"5175",7356,34,25.8,1,2,0,1,5,1,4,1,1,2
"5176",8016,34,26.2,1,5,0,1,5,1,1,1,1,10
"5177",7822,34,26.6,1,2,0,1,5,1,8,1,1,9
"5178",1912,34,27.2,1,5,0,1,8,0,1,1,1,4
"5179",2018,34,27.3,1,3,0,1,8,0,4,1,1,3
"5180",4712,34,27.4,1,3,0,1,8,0,7,1,1,3
"5181",9290,34,27.5,1,1,0,1,6,1,8,1,1,1
"5182",4946,34,29,1,2,0,1,7,0,7,1,1,6
"5183",1357,34,29.2,1,4,0,1,5,0,7,1,1,2
"5184",6683,34,29.5,1,4,0,1,5,1,8,1,1,2
"5185",7524,34,29.6,1,2,0,1,8,1,3,1,1,5
"5186",9348,34,29.7,1,3,0,1,6,1,2,1,1,0


================================================
FILE: data/data_clus_2.csv
================================================
"","x"
"1",1216
"2",6844
"3",1081
"4",2156
"5",4405
"6",4006
"7",8476
"8",5730
"9",2115
"10",9182
"11",9870
"12",9701
"13",4694
"14",3322
"15",2888
"16",6702
"17",812
"18",4979
"19",3141
"20",3457
"21",4134
"22",8376
"23",6482
"24",651
"25",9322
"26",2073
"27",10234
"28",9634
"29",5892
"30",6896
"31",6570
"32",10825
"33",8284
"34",1140
"35",8283
"36",2964
"37",8380
"38",8403
"39",3494
"40",7283
"41",5759
"42",1342
"43",8579
"44",280
"45",5044
"46",5510
"47",1032
"48",117
"49",9676
"50",9286
"51",8159
"52",7004
"53",5628
"54",3384
"55",2431
"56",1958
"57",7545
"58",5293
"59",1534
"60",8175
"61",1057
"62",2950
"63",6076
"64",1260
"65",5238
"66",2892
"67",1806
"68",3256
"69",10727
"70",9654
"71",10320
"72",2106
"73",10269
"74",10852
"75",4781
"76",1382
"77",4087
"78",824
"79",5268
"80",3112
"81",5010
"82",4859
"83",3088
"84",975
"85",496
"86",3686
"87",3050
"88",8054
"89",7516
"90",5726
"91",5159
"92",10155
"93",10507
"94",8170
"95",160
"96",1320
"97",9664
"98",6503
"99",4948
"100",7716
"101",3816
"102",10390
"103",10252
"104",3890
"105",6879
"106",5218
"107",1989
"108",10400
"109",2823
"110",7332
"111",1092
"112",7455
"113",9818
"114",6657
"115",986
"116",5194
"117",6830
"118",6848
"119",6431
"120",1263
"121",10225
"122",5440
"123",5842
"124",845
"125",4072
"126",8500
"127",1776
"128",5094
"129",9850
"130",430
"131",2688
"132",4607
"133",5169
"134",1705
"135",7241
"136",683
"137",3632
"138",2668
"139",4152
"140",7338
"141",1068
"142",1592
"143",7301
"144",5279
"145",3830
"146",6066
"147",5896
"148",8584
"149",8842
"150",2467
"151",434
"152",6194
"153",1562
"154",2689
"155",5378
"156",10430
"157",4457
"158",2660
"159",554
"160",7425
"161",8452
"162",3592
"163",4556
"164",10190
"165",7005
"166",8746
"167",5212
"168",6046
"169",3272
"170",6554
"171",2045
"172",3834
"173",6446
"174",2427
"175",3750
"176",6494
"177",4351
"178",5846
"179",9592
"180",8136
"181",5470
"182",4640
"183",5411
"184",6159
"185",526
"186",1716
"187",2155
"188",3972
"189",7033
"190",10249
"191",1374
"192",3773
"193",4586
"194",242
"195",8018
"196",9800
"197",4338
"198",5164
"199",7576
"200",4972
"201",1118
"202",10609
"203",9478
"204",9334
"205",3416
"206",250
"207",1755
"208",815
"209",2036
"210",8985
"211",1330
"212",3391
"213",1626
"214",5171
"215",8780
"216",3290
"217",10242
"218",1561
"219",7830
"220",10006
"221",1062
"222",505
"223",10186
"224",6008
"225",10164
"226",5684
"227",4318
"228",5522
"229",3056
"230",10788
"231",6477
"232",2670
"233",9178
"234",645
"235",410
"236",8714
"237",3733
"238",6538
"239",7760
"240",10000
"241",9932
"242",2889
"243",621
"244",97
"245",8148
"246",7781
"247",6982
"248",7804
"249",9306
"250",9555
"251",2867
"252",6370
"253",5778
"254",9982
"255",8996
"256",8545
"257",7392
"258",4000
"259",3680
"260",7110
"261",5734
"262",4448
"263",8230
"264",5266
"265",1779
"266",8978
"267",1185
"268",949
"269",7980
"270",3748
"271",2500
"272",257
"273",3864
"274",4238
"275",8812
"276",10770
"277",4502
"278",714
"279",4711
"280",1015
"281",5174
"282",1544
"283",54
"284",6206
"285",1772
"286",5680
"287",2507
"288",8967
"289",810
"290",9154
"291",10740
"292",10362
"293",2960
"294",8450
"295",8654
"296",8191
"297",1675
"298",5245
"299",9256
"300",4969
"301",4522
"302",6489
"303",10832
"304",3767
"305",7304
"306",10856
"307",8774
"308",8389
"309",9656
"310",6598
"311",1195
"312",3218
"313",6999
"314",1432
"315",7635
"316",4959
"317",4326
"318",5260
"319",7270
"320",415
"321",6946
"322",2450
"323",6981
"324",10463
"325",7200
"326",5740
"327",3513
"328",5544
"329",9751
"330",5116
"331",6352
"332",2850
"333",444
"334",1754
"335",1908
"336",996
"337",2708
"338",8916
"339",1284
"340",8100
"341",1294
"342",10094
"343",9734
"344",10654
"345",7256
"346",3467
"347",6250
"348",10290
"349",1421
"350",5979
"351",3317
"352",9518
"353",964
"354",3804
"355",9340
"356",6779
"357",6547
"358",5771
"359",4104
"360",8252
"361",10633
"362",6862
"363",9192
"364",8434
"365",814
"366",2880
"367",1100
"368",10268
"369",4030
"370",744
"371",2328
"372",7816
"373",1780
"374",7780
"375",3920
"376",2266
"377",3226
"378",6973
"379",3152
"380",8155
"381",6092
"382",3409
"383",1176
"384",7482
"385",9130
"386",7698
"387",9770
"388",2339
"389",1924
"390",8249
"391",2932
"392",10068
"393",7566
"394",4250
"395",7954
"396",5154
"397",789
"398",2412
"399",14
"400",1771
"401",1162
"402",4510
"403",6391
"404",763
"405",7501
"406",4124
"407",2203
"408",224
"409",1449
"410",7396
"411",8646
"412",3406
"413",4148
"414",9434
"415",2720
"416",2784
"417",7588
"418",7084
"419",5002
"420",515
"421",3895
"422",10672
"423",10806
"424",8766
"425",9516
"426",4172
"427",3450
"428",9470
"429",4138
"430",8460
"431",8561
"432",7279
"433",7624
"434",3262
"435",708
"436",3021
"437",3746
"438",483
"439",3651
"440",6103
"441",1825
"442",7220
"443",5324
"444",692
"445",10650
"446",3371
"447",4982
"448",958
"449",6514
"450",4406
"451",149
"452",10839
"453",6814
"454",689
"455",8536
"456",9896
"457",1937
"458",8162
"459",9100
"460",1522
"461",9236
"462",895
"463",3014
"464",267
"465",4808
"466",4864
"467",8870
"468",359
"469",5066
"470",5167
"471",5162
"472",5464
"473",3361
"474",3108
"475",6306
"476",8032
"477",7120
"478",10316
"479",9743
"480",4360
"481",2287
"482",6589
"483",6984
"484",10618
"485",10396
"486",5577
"487",7562
"488",4866
"489",6624
"490",5547
"491",1916
"492",2978
"493",8974
"494",3810
"495",6452
"496",5267
"497",6846
"498",9742
"499",4594
"500",1800
"501",8520
"502",3552
"503",4684
"504",5328
"505",9015
"506",4934
"507",10431
"508",4608
"509",6358
"510",2232
"511",3438
"512",5492
"513",10159
"514",4491
"515",4080
"516",2116
"517",3766
"518",5714
"519",6537
"520",9810
"521",1599
"522",6950
"523",10244
"524",3233
"525",8634
"526",10331
"527",9942
"528",1416
"529",9993
"530",4940
"531",10344
"532",1130
"533",7436
"534",6728
"535",8393
"536",7378
"537",7632
"538",1178
"539",6909
"540",2466
"541",934
"542",3186
"543",5775
"544",10032
"545",4748
"546",8530
"547",1857
"548",6707
"549",2040
"550",2316
"551",2228
"552",1102
"553",922
"554",5773
"555",5798
"556",653
"557",7246
"558",32
"559",3474
"560",7950
"561",10228
"562",940
"563",9590
"564",3299
"565",10715
"566",8934
"567",3966
"568",5886
"569",9712
"570",7446
"571",3558
"572",6463
"573",1253
"574",4724
"575",8717
"576",5873
"577",2240
"578",3115
"579",962
"580",10008
"581",2736
"582",463
"583",6504
"584",820
"585",2556
"586",3052
"587",9058
"588",8152
"589",9426
"590",2198
"591",1854
"592",2042
"593",2308
"594",4508
"595",6212
"596",7001
"597",9752
"598",5274
"599",4184
"600",632
"601",7193
"602",3297
"603",2976
"604",6113
"605",8112
"606",5780
"607",700
"608",1574
"609",428
"610",4564
"611",2716
"612",10324
"613",5400
"614",7658
"615",8439
"616",1401
"617",3216
"618",275
"619",606
"620",2418
"621",8050
"622",7222
"623",3995
"624",4115
"625",1239
"626",733
"627",503
"628",6942
"629",3870
"630",4178
"631",1722
"632",3986
"633",1269
"634",4848
"635",7148
"636",175
"637",4761
"638",4642
"639",5034
"640",10253
"641",9716
"642",6392
"643",7372
"644",4157
"645",7089
"646",1017
"647",3674
"648",8274
"649",1753
"650",2910
"651",5570
"652",1918
"653",889
"654",10090
"655",453
"656",7086
"657",2038
"658",2834
"659",7541
"660",2082
"661",8745
"662",2364
"663",2438
"664",8756
"665",1818
"666",943
"667",4856
"668",3150
"669",9474
"670",4198
"671",1708
"672",8012
"673",1876
"674",9847
"675",9912
"676",3970
"677",3789
"678",2174
"679",3159
"680",10416
"681",4352
"682",6317
"683",8341
"684",4422
"685",1903
"686",7553
"687",9605
"688",3180
"689",4604
"690",8612
"691",5586
"692",10138
"693",6302
"694",10142
"695",5170
"696",8010
"697",3102
"698",9554
"699",10342
"700",7963
"701",1994
"702",3824
"703",4024
"704",291
"705",6445
"706",3708
"707",2900
"708",10591
"709",730
"710",9820
"711",2074
"712",7866
"713",5012
"714",3448
"715",7586
"716",7504
"717",8078
"718",5254
"719",4410
"720",7260
"721",10114
"722",5354
"723",10834
"724",4540
"725",2109
"726",158
"727",608
"728",10462
"729",2216
"730",9131
"731",9802
"732",3296
"733",5998
"734",8690
"735",5733
"736",9794
"737",109
"738",2640
"739",4821
"740",1549
"741",6790
"742",2092
"743",4278
"744",10116
"745",2182
"746",3884
"747",1497
"748",8824
"749",7678
"750",7530
"751",3423
"752",5797
"753",4999
"754",2624
"755",7064
"756",196
"757",5550
"758",5083
"759",2178
"760",5682
"761",5918
"762",8576
"763",5658
"764",4716
"765",9967
"766",6694
"767",9681
"768",6000
"769",3203
"770",4830
"771",3064
"772",418
"773",7897
"774",8980
"775",6686
"776",9740
"777",9264
"778",9230
"779",6972
"780",6962
"781",6120
"782",3424
"783",1288
"784",266
"785",4585
"786",7702
"787",4828
"788",9879
"789",9258
"790",3676
"791",9580
"792",2763
"793",2010
"794",3582
"795",1478
"796",2782
"797",4102
"798",2621
"799",4576
"800",2437
"801",8430
"802",7463
"803",5821
"804",772
"805",3162
"806",3682
"807",2468
"808",6440
"809",6650
"810",6179
"811",4878
"812",9713
"813",1764
"814",9481
"815",4659
"816",1208
"817",9003
"818",10210
"819",10566
"820",83
"821",2047
"822",3062
"823",448
"824",1874
"825",1652
"826",330
"827",6906
"828",7022
"829",836
"830",9808
"831",1717
"832",7597
"833",4126
"834",3664
"835",8487
"836",7208
"837",2043
"838",6576
"839",1466
"840",7964
"841",550
"842",3623
"843",2239
"844",5986
"845",9234
"846",7141
"847",8003
"848",10072
"849",1293
"850",9714
"851",3069
"852",2315
"853",6154
"854",1796
"855",9682
"856",1460
"857",9388
"858",6078
"859",8396
"860",5121
"861",10356
"862",9224
"863",10524
"864",8062
"865",7190
"866",2703
"867",6516
"868",2176
"869",8954
"870",4210
"871",8288
"872",9729
"873",6417
"874",4886
"875",5966
"876",7723
"877",1254
"878",9757
"879",4868
"880",8378
"881",9600
"882",806
"883",4785
"884",5272
"885",8277
"886",7966
"887",1735
"888",3908
"889",2015
"890",6014
"891",7570
"892",1206
"893",3190
"894",10534
"895",9487
"896",6994
"897",10078
"898",470
"899",818
"900",3157
"901",9395
"902",9838
"903",2664
"904",480
"905",3368
"906",10610
"907",4242
"908",8828
"909",8645
"910",510
"911",6903
"912",666
"913",7196
"914",2157
"915",9377
"916",166
"917",6850
"918",8800
"919",8304
"920",842
"921",4548
"922",3756
"923",10470
"924",6540
"925",2350
"926",10351
"927",4236
"928",1638
"929",6662
"930",6637
"931",10131
"932",9588
"933",3714
"934",9484
"935",7904
"936",3149
"937",870
"938",7641
"939",8912
"940",2112
"941",9174
"942",2442
"943",9344
"944",9603
"945",9133
"946",3524
"947",7900
"948",3658
"949",808
"950",1232
"951",8116
"952",9958
"953",3794
"954",5473
"955",2244
"956",7513
"957",2990
"958",1112
"959",5252
"960",4664
"961",7492
"962",10795
"963",6932
"964",1890
"965",314
"966",7359
"967",10866
"968",8087
"969",9016
"970",6539
"971",7318
"972",866
"973",6334
"974",10512
"975",4496
"976",8833
"977",5534
"978",9494
"979",3491
"980",9491
"981",5784
"982",3718
"983",8409
"984",340
"985",4322
"986",2259
"987",1634
"988",2317
"989",2578
"990",5712
"991",10764
"992",4538
"993",2326
"994",1682
"995",1286
"996",9176
"997",8843
"998",7672
"999",1529
"1000",7520
"1001",701
"1002",8512
"1003",143
"1004",5598
"1005",5215
"1006",9266
"1007",4788
"1008",8194
"1009",9339
"1010",6924
"1011",4431
"1012",988
"1013",3677
"1014",7802
"1015",7224
"1016",10330
"1017",6849
"1018",9628
"1019",6817
"1020",9390
"1021",713
"1022",7600
"1023",3740
"1024",3386
"1025",2190
"1026",213
"1027",6813
"1028",6199
"1029",6011
"1030",4984
"1031",173
"1032",5190
"1033",2870
"1034",2797
"1035",6584
"1036",2787
"1037",3788
"1038",4800
"1039",1606
"1040",10020
"1041",5150
"1042",3857
"1043",5460
"1044",6605
"1045",7551
"1046",1584
"1047",9999
"1048",1302
"1049",6462
"1050",6995
"1051",5662
"1052",7519
"1053",3228
"1054",5922
"1055",10840
"1056",4951
"1057",5678
"1058",9206
"1059",6367
"1060",5796
"1061",3176
"1062",1210
"1063",39
"1064",7508
"1065",4488
"1066",6762
"1067",4420
"1068",6295
"1069",1861
"1070",1897
"1071",4105
"1072",5609
"1073",8868
"1074",6304
"1075",2757
"1076",2225
"1077",10009
"1078",2409
"1079",7094
"1080",679
"1081",2537
"1082",6532
"1083",7828
"1084",8482
"1085",8066
"1086",4747
"1087",4058
"1088",3172
"1089",5088
"1090",5352
"1091",1244
"1092",7622
"1093",10661
"1094",2146
"1095",4258
"1096",8251
"1097",5241
"1098",670
"1099",4766
"1100",2752
"1101",184
"1102",6928
"1103",2812
"1104",2212
"1105",3504
"1106",6798
"1107",4950
"1108",8154
"1109",502
"1110",4067
"1111",6246
"1112",9114
"1113",1258
"1114",3634
"1115",10254
"1116",9950
"1117",21
"1118",8308
"1119",191
"1120",4143
"1121",320
"1122",4176
"1123",8506
"1124",4186
"1125",10616
"1126",1359
"1127",5754
"1128",6512
"1129",5634
"1130",8632
"1131",2093
"1132",5240
"1133",728
"1134",10750
"1135",3476
"1136",6110
"1137",30
"1138",1853
"1139",5521
"1140",5794
"1141",44
"1142",9718
"1143",3762
"1144",7808
"1145",10172
"1146",4344
"1147",2648
"1148",5004
"1149",5946
"1150",3487
"1151",9720
"1152",2194
"1153",5178
"1154",10216
"1155",1998
"1156",5894
"1157",3484
"1158",7119
"1159",8156
"1160",8682
"1161",9144
"1162",6966
"1163",7264
"1164",8982
"1165",4570
"1166",272
"1167",5454
"1168",4616
"1169",2238
"1170",2186
"1171",6411
"1172",9083
"1173",811
"1174",3405
"1175",6018
"1176",5183
"1177",10454
"1178",9153
"1179",3946
"1180",2766
"1181",8709
"1182",1814
"1183",2462
"1184",8539
"1185",4793
"1186",10766
"1187",10401
"1188",10514
"1189",9169
"1190",1083
"1191",6338
"1192",4082
"1193",847
"1194",3094
"1195",6923
"1196",6021
"1197",7725
"1198",6996
"1199",6089
"1200",9097
"1201",3673
"1202",7598
"1203",8350
"1204",23
"1205",1268
"1206",5530
"1207",7591
"1208",4191
"1209",9572
"1210",8702
"1211",3084
"1212",2541
"1213",7104
"1214",1131
"1215",4612
"1216",7837
"1217",5568
"1218",5830
"1219",3034
"1220",3488
"1221",7424
"1222",7697
"1223",9952
"1224",4232
"1225",7416
"1226",10518
"1227",522
"1228",8604
"1229",1238
"1230",8059
"1231",2365
"1232",4343
"1233",9863
"1234",1039
"1235",10266
"1236",9601
"1237",7726
"1238",72
"1239",3106
"1240",10860
"1241",2828
"1242",290
"1243",5340
"1244",7512
"1245",456
"1246",4704
"1247",9962
"1248",2806
"1249",1973
"1250",8566
"1251",5910
"1252",9448
"1253",5408
"1254",8838
"1255",2730
"1256",2512
"1257",10562
"1258",3880
"1259",10272
"1260",10843
"1261",4311
"1262",10060
"1263",10752
"1264",3551
"1265",10604
"1266",7038
"1267",2735
"1268",6009
"1269",8721
"1270",5523
"1271",676
"1272",3436
"1273",1228
"1274",684
"1275",3472
"1276",555
"1277",9498
"1278",3665
"1279",7272
"1280",2474
"1281",3030
"1282",4255
"1283",2192
"1284",9576
"1285",6162
"1286",577
"1287",10612
"1288",10366
"1289",3292
"1290",1763
"1291",823
"1292",2392
"1293",4839
"1294",2983
"1295",1314
"1296",7895
"1297",4723
"1298",547
"1299",6294
"1300",1662
"1301",2446
"1302",9968
"1303",4794
"1304",6389
"1305",8130
"1306",2548
"1307",7531
"1308",10122
"1309",8475
"1310",9108
"1311",7685
"1312",2674
"1313",7956
"1314",6955
"1315",2056
"1316",9242
"1317",6726
"1318",981
"1319",9317
"1320",10115
"1321",7999
"1322",5942
"1323",1733
"1324",10274
"1325",271
"1326",4376
"1327",20
"1328",65
"1329",6231
"1330",7556
"1331",1902
"1332",168
"1333",3342
"1334",2044
"1335",10222
"1336",2994
"1337",7095
"1338",4428
"1339",5638
"1340",7876
"1341",4832
"1342",7252
"1343",10349
"1344",10084
"1345",9468
"1346",9778
"1347",1785
"1348",8006
"1349",27
"1350",6416
"1351",4558
"1352",4596
"1353",1732
"1354",10162
"1355",8534
"1356",9876
"1357",7924
"1358",264
"1359",6940
"1360",8846
"1361",7062
"1362",749
"1363",682
"1364",7920
"1365",2319
"1366",7290
"1367",2926
"1368",7502
"1369",8611
"1370",3860
"1371",7753
"1372",8540
"1373",9922
"1374",5107
"1375",6732
"1376",1826
"1377",618
"1378",1477
"1379",4445
"1380",9784
"1381",3465
"1382",7091
"1383",1696
"1384",3598
"1385",1982
"1386",9956
"1387",7358
"1388",1950
"1389",10674
"1390",7488
"1391",10
"1392",3898
"1393",7036
"1394",2360
"1395",6146
"1396",194
"1397",1629
"1398",7998
"1399",1127
"1400",2408
"1401",2656
"1402",3578
"1403",1026
"1404",7696
"1405",7755
"1406",1060
"1407",3782
"1408",1300
"1409",2346
"1410",8990
"1411",2087
"1412",4320
"1413",7144
"1414",3130
"1415",4334
"1416",869
"1417",8478
"1418",178
"1419",7334
"1420",2585
"1421",1426
"1422",7176
"1423",1255
"1424",9861
"1425",2250
"1426",10756
"1427",2028
"1428",5724
"1429",565
"1430",1715
"1431",8685
"1432",9269
"1433",2517
"1434",472
"1435",8698
"1436",9425
"1437",10310
"1438",1579
"1439",6228
"1440",2651
"1441",6820
"1442",7368
"1443",8660
"1444",362
"1445",1702
"1446",7109
"1447",7740
"1448",10288
"1449",2596
"1450",6070
"1451",2824
"1452",5334
"1453",10680
"1454",1066
"1455",10628
"1456",2403
"1457",10822
"1458",10113
"1459",6974
"1460",906
"1461",10736
"1462",9722
"1463",5888
"1464",9379
"1465",5988
"1466",10590
"1467",9868
"1468",8839
"1469",9694
"1470",500
"1471",4858
"1472",3772
"1473",6734
"1474",8511
"1475",5604
"1476",2764
"1477",3518
"1478",5672
"1479",8481
"1480",5929
"1481",7244
"1482",7515
"1483",8664
"1484",4638
"1485",862
"1486",136
"1487",4995
"1488",4516
"1489",1107
"1490",5813
"1491",3702
"1492",5546
"1493",9510
"1494",8421
"1495",7916
"1496",4831
"1497",8998
"1498",9009
"1499",7347
"1500",7728
"1501",846
"1502",9140
"1503",1024
"1504",9692
"1505",1692
"1506",9125
"1507",6010
"1508",1830
"1509",9496
"1510",3000
"1511",960
"1512",3640
"1513",5567
"1514",1245
"1515",2751
"1516",2912
"1517",3468
"1518",830
"1519",7500
"1520",4021
"1521",9754
"1522",5248
"1523",7840
"1524",10256
"1525",6926
"1526",9196
"1527",4966
"1528",4903
"1529",9880
"1530",5690
"1531",6908
"1532",9313
"1533",8521
"1534",3539
"1535",9631
"1536",4384
"1537",7793
"1538",7561
"1539",4772
"1540",4204
"1541",7965
"1542",10243
"1543",5564
"1544",5664
"1545",8287
"1546",6422
"1547",5537
"1548",8070
"1549",9546
"1550",5302
"1551",6126
"1552",8596
"1553",9630
"1554",9424
"1555",5290
"1556",8730
"1557",60
"1558",2057
"1559",7116
"1560",6414
"1561",9342
"1562",3897
"1563",6546
"1564",2382
"1565",350
"1566",9750
"1567",551
"1568",52
"1569",2114
"1570",5974
"1571",2712
"1572",8883
"1573",3164
"1574",6473
"1575",9493
"1576",5358
"1577",5465
"1578",2662
"1579",6263
"1580",1560
"1581",186
"1582",75
"1583",8053
"1584",439
"1585",1865
"1586",4018
"1587",1004
"1588",9054
"1589",6060
"1590",3721
"1591",211
"1592",9814
"1593",8093
"1594",5755
"1595",7402
"1596",1694
"1597",8412
"1598",7853
"1599",9668
"1600",3668
"1601",244
"1602",9409
"1603",3771
"1604",10690
"1605",9355
"1606",9106
"1607",8836
"1608",6248
"1609",4739
"1610",3758
"1611",10079
"1612",164
"1613",6264
"1614",9382
"1615",8734
"1616",8908
"1617",7093
"1618",9059
"1619",4022
"1620",7353
"1621",688
"1622",6513
"1623",2
"1624",506
"1625",8143
"1626",7349
"1627",8260
"1628",1075
"1629",904
"1630",3806
"1631",5280
"1632",4216
"1633",4620
"1634",1158
"1635",1587
"1636",5343
"1637",8106
"1638",4552
"1639",512
"1640",7532
"1641",7990
"1642",4774
"1643",10587
"1644",352
"1645",73
"1646",2162
"1647",6024
"1648",8672
"1649",219
"1650",4896
"1651",3058
"1652",1266
"1653",6232
"1654",6786
"1655",68
"1656",3199
"1657",8848
"1658",7462
"1659",6958
"1660",9346
"1661",7642
"1662",4013
"1663",6567
"1664",6980
"1665",7166
"1666",3734
"1667",1298
"1668",6458
"1669",3251
"1670",9219
"1671",7136
"1672",162
"1673",2224
"1674",7108
"1675",212
"1676",6002
"1677",6363
"1678",3646
"1679",1781
"1680",1718
"1681",982
"1682",10238
"1683",2502
"1684",324
"1685",7985
"1686",6997
"1687",5239
"1688",289
"1689",1914
"1690",7122
"1691",738
"1692",5443
"1693",4696
"1694",1160
"1695",10726
"1696",3026
"1697",3971
"1698",8924
"1699",9514
"1700",762
"1701",7163
"1702",540
"1703",1934
"1704",10720
"1705",372
"1706",640
"1707",5250
"1708",9454
"1709",7788
"1710",282
"1711",5422
"1712",5746
"1713",3596
"1714",7367
"1715",9208
"1716",6558
"1717",3398
"1718",4224
"1719",5560
"1720",304
"1721",5480
"1722",4813
"1723",9690
"1724",1610
"1725",3905
"1726",6432
"1727",944
"1728",2366
"1729",1174
"1730",6873
"1731",10297
"1732",5691
"1733",6730
"1734",3441
"1735",8513
"1736",8074
"1737",5153
"1738",2458
"1739",5576
"1740",1404
"1741",92
"1742",5768
"1743",8627
"1744",9044
"1745",971
"1746",3922
"1747",2663
"1748",6856
"1749",4574
"1750",3842
"1751",2931
"1752",216
"1753",8872
"1754",2141
"1755",9955
"1756",6405
"1757",9314
"1758",610
"1759",7294
"1760",10064
"1761",202
"1762",6132
"1763",5374
"1764",8226
"1765",6130
"1766",7020
"1767",366
"1768",26
"1769",7511
"1770",8635
"1771",5592
"1772",9391
"1773",4403
"1774",6952
"1775",1588
"1776",3560
"1777",4740
"1778",6936
"1779",6150
"1780",1142
"1781",801
"1782",4471
"1783",590
"1784",4904
"1785",775
"1786",5317
"1787",686
"1788",5305
"1789",156
"1790",10376
"1791",8388
"1792",6518
"1793",5536
"1794",938
"1795",4962
"1796",5944
"1797",5120
"1798",10506
"1799",10460
"1800",4504
"1801",5304
"1802",4234
"1803",7322
"1804",9452
"1805",5060
"1806",4452
"1807",9410
"1808",6427
"1809",5080
"1810",880
"1811",3040
"1812",2858
"1813",8558
"1814",4122
"1815",288
"1816",9307
"1817",4480
"1818",10540
"1819",1273
"1820",5438
"1821",8126
"1822",1322
"1823",6804
"1824",7154
"1825",5744
"1826",10180
"1827",4651
"1828",4782
"1829",10307
"1830",994
"1831",668
"1832",2536
"1833",5670
"1834",7846
"1835",7534
"1836",3919
"1837",8913
"1838",2582
"1839",6842
"1840",9964
"1841",9318
"1842",493
"1843",10393
"1844",7248
"1845",8884
"1846",10751
"1847",6595
"1848",5366
"1849",902
"1850",2041
"1851",1473
"1852",1980
"1853",9622
"1854",5136
"1855",9992
"1856",4349
"1857",2891
"1858",3032
"1859",4336
"1860",2572
"1861",206
"1862",7585
"1863",312
"1864",2877
"1865",5970
"1866",5561
"1867",5106
"1868",9527
"1869",9578
"1870",10411
"1871",4059
"1872",7568
"1873",1400
"1874",3639
"1875",7881
"1876",1267
"1877",3936
"1878",1841
"1879",3048
"1880",7404
"1881",7784
"1882",2630
"1883",9710
"1884",1894
"1885",3939
"1886",371
"1887",5765
"1888",1766
"1889",8080
"1890",2590
"1891",3900
"1892",6328
"1893",4356
"1894",8022
"1895",10495
"1896",5472
"1897",3470
"1898",979
"1899",2962
"1900",8630
"1901",7464
"1902",4652
"1903",1008
"1904",2070
"1905",6677
"1906",9671
"1907",528
"1908",5387
"1909",3380
"1910",6749
"1911",4996
"1912",3516
"1913",8
"1914",10038
"1915",3840
"1916",1596
"1917",10283
"1918",561
"1919",8886
"1920",3200
"1921",8902
"1922",5716
"1923",5028
"1924",6517
"1925",1423
"1926",10776
"1927",7286
"1928",2937
"1929",3067
"1930",1276
"1931",656
"1932",3568
"1933",10539
"1934",3540
"1935",7096
"1936",740
"1937",8628
"1938",9152
"1939",6155
"1940",6824
"1941",6210
"1942",2361
"1943",7652
"1944",8182
"1945",2262
"1946",2078
"1947",7690
"1948",7044
"1949",10588
"1950",1480
"1951",2342
"1952",4484
"1953",8841
"1954",5298
"1955",4560
"1956",8349
"1957",7210
"1958",9825
"1959",5934
"1960",7128
"1961",6859
"1962",8684
"1963",6757
"1964",8788
"1965",163
"1966",5725
"1967",2242
"1968",8133
"1969",336
"1970",6180
"1971",5612
"1972",9413
"1973",8722
"1974",4590
"1975",7187
"1976",678
"1977",3526
"1978",2866
"1979",4944
"1980",1292
"1981",2122
"1982",3652
"1983",1536
"1984",6988
"1985",4171
"1986",3774
"1987",180
"1988",6654
"1989",5545
"1990",48
"1991",6680
"1992",9707
"1993",5770
"1994",6016
"1995",4588
"1996",8898
"1997",6451
"1998",3078
"1999",7582
"2000",778
"2001",557
"2002",4190
"2003",7043
"2004",9362
"2005",9040
"2006",8926
"2007",2052
"2008",1637
"2009",3236
"2010",9859
"2011",2058
"2012",2386
"2013",9669
"2014",3580
"2015",4592
"2016",4257
"2017",9869
"2018",7540
"2019",10176
"2020",10772
"2021",2936
"2022",2160
"2023",534
"2024",1730
"2025",4064
"2026",1128
"2027",2372
"2028",5548
"2029",3780
"2030",9298
"2031",3532
"2032",9244
"2033",7815
"2034",1354
"2035",6300
"2036",677
"2037",7346
"2038",172
"2039",7418
"2040",8669
"2041",5810
"2042",2210
"2043",5625
"2044",1328
"2045",1348
"2046",2295
"2047",4806
"2048",7415
"2049",10466
"2050",8724
"2051",2486
"2052",10124
"2053",8796
"2054",8893
"2055",1760
"2056",9689
"2057",6663
"2058",7602
"2059",4014
"2060",706
"2061",7365
"2062",10503
"2063",7106
"2064",5782
"2065",10803
"2066",5751
"2067",9476
"2068",6775
"2069",3530
"2070",4732
"2071",9577
"2072",3374
"2073",933
"2074",9396
"2075",8676
"2076",1122
"2077",6945
"2078",1852
"2079",9944
"2080",9053
"2081",1508
"2082",4614
"2083",6340
"2084",2992
"2085",7258
"2086",7901
"2087",8121
"2088",5379
"2089",7651
"2090",2223
"2091",6583
"2092",9004
"2093",696
"2094",6436
"2095",2048
"2096",317
"2097",5626
"2098",8286
"2099",1082
"2100",10440
"2101",8290
"2102",1436
"2103",2555
"2104",10482
"2105",7744
"2106",10004
"2107",7382
"2108",9134
"2109",3437
"2110",7054
"2111",10112
"2112",1203
"2113",896
"2114",2501
"2115",4741
"2116",4906
"2117",5968
"2118",9804
"2119",4561
"2120",3420
"2121",588
"2122",8918
"2123",794
"2124",10688
"2125",4840
"2126",10336
"2127",5192
"2128",4202
"2129",2054
"2130",3414
"2131",1489
"2132",3727
"2133",8624
"2134",2612
"2135",885
"2136",9328
"2137",10748
"2138",6201
"2139",2268
"2140",2899
"2141",3701
"2142",6437
"2143",3843
"2144",10021
"2145",6112
"2146",3288
"2147",3872
"2148",5394
"2149",7115
"2150",5346
"2151",1616
"2152",662
"2153",6674
"2154",1110
"2155",8683
"2156",3893
"2157",7844
"2158",3249
"2159",8030
"2160",888
"2161",7748
"2162",2946
"2163",3270
"2164",793
"2165",10622
"2166",10704
"2167",10104
"2168",3284
"2169",6500
"2170",5322
"2171",8798
"2172",10096
"2173",8292
"2174",6165
"2175",5330
"2176",466
"2177",3231
"2178",34
"2179",6203
"2180",1620
"2181",5854
"2182",3408
"2183",2400
"2184",2219
"2185",4081
"2186",1859
"2187",9189
"2188",9610
"2189",10395
"2190",28
"2191",2385
"2192",4993
"2193",7100
"2194",2550
"2195",6400
"2196",3683
"2197",2816
"2198",595
"2199",9560
"2200",3260
"2201",3475
"2202",5070
"2203",5688
"2204",10179
"2205",10010
"2206",2030
"2207",1802
"2208",852
"2209",142
"2210",356
"2211",1724
"2212",1801
"2213",9032
"2214",4380
"2215",3452
"2216",3566
"2217",7527
"2218",3775
"2219",5453
"2220",3103
"2221",1326
"2222",1654
"2223",7370
"2224",8620
"2225",9384
"2226",894
"2227",235
"2228",3444
"2229",6816
"2230",9806
"2231",3892
"2232",4924
"2233",2908
"2234",8923
"2235",6895
"2236",5188
"2237",3496
"2238",8605
"2239",4672
"2240",6806
"2241",2200
"2242",5606
"2243",2969
"2244",6488
"2245",10670
"2246",3385
"2247",1898
"2248",5403
"2249",9978
"2250",9534
"2251",7048
"2252",3482
"2253",2237
"2254",6412
"2255",7028
"2256",6649
"2257",8057
"2258",10161
"2259",4446
"2260",10035
"2261",10132
"2262",4770
"2263",6005
"2264",9427
"2265",9086
"2266",8356
"2267",7140
"2268",8372
"2269",514
"2270",2970
"2271",7391
"2272",3671
"2273",4175
"2274",5602
"2275",6240
"2276",9652
"2277",9614
"2278",10794
"2279",9677
"2280",5210
"2281",1794
"2282",79
"2283",4274
"2284",5118
"2285",2832
"2286",8854
"2287",10716
"2288",6939
"2289",2594
"2290",8703
"2291",3978
"2292",664
"2293",2552
"2294",1951
"2295",5504
"2296",9432
"2297",7607
"2298",5424
"2299",4004
"2300",5539
"2301",6921
"2302",4891
"2303",5864
"2304",8095
"2305",1997
"2306",6608
"2307",4275
"2308",137
"2309",10450
"2310",9110
"2311",7776
"2312",5589
"2313",9907
"2314",5146
"2315",3306
"2316",3007
"2317",1896
"2318",3858
"2319",3612
"2320",130
"2321",8890
"2322",4870
"2323",10276
"2324",7046
"2325",368
"2326",5052
"2327",382
"2328",3198
"2329",535
"2330",10691
"2331",6878
"2332",9220
"2333",6904
"2334",10407
"2335",8206
"2336",227
"2337",478
"2338",2504
"2339",7974
"2340",4434
"2341",8229
"2342",238
"2343",6386
"2344",4292
"2345",332
"2346",3569
"2347",8966
"2348",3543
"2349",8554
"2350",5375
"2351",5074
"2352",7894
"2353",8309
"2354",4372
"2355",7680
"2356",6508
"2357",10314
"2358",10089
"2359",8748
"2360",7168
"2361",3378
"2362",4991
"2363",2506
"2364",3020
"2365",4673
"2366",2349
"2367",6288
"2368",1498
"2369",2754
"2370",9336
"2371",4358
"2372",9904
"2373",882
"2374",4518
"2375",4526
"2376",1905
"2377",980
"2378",9685
"2379",2642
"2380",5406
"2381",5084
"2382",6578
"2383",6944
"2384",4880
"2385",3902
"2386",10666
"2387",1030
"2388",8489
"2389",6382
"2390",10841
"2391",8562
"2392",5160
"2393",748
"2394",10287
"2395",7390
"2396",5863
"2397",8468
"2398",5019
"2399",5940
"2400",6983
"2401",111
"2402",3667
"2403",1496
"2404",3128
"2405",9428
"2406",9914
"2407",6898
"2408",4974
"2409",6088
"2410",7926
"2411",7398
"2412",9285
"2413",7496
"2414",5042
"2415",8958
"2416",454
"2417",2362
"2418",7948
"2419",5413
"2420",7968
"2421",9048
"2422",6602
"2423",6072
"2424",8052
"2425",15
"2426",5898
"2427",1820
"2428",3912
"2429",7976
"2430",3388
"2431",9845
"2432",2742
"2433",1598
"2434",2667
"2435",10052
"2436",1470
"2437",4140
"2438",4476
"2439",6188
"2440",6342
"2441",2549
"2442",1846
"2443",2166
"2444",4629
"2445",3792
"2446",4902
"2447",9830
"2448",7644
"2449",9392
"2450",10126
"2451",10413
"2452",6218
"2453",7850
"2454",5704
"2455",3031
"2456",1910
"2457",1440
"2458",2292
"2459",4407
"2460",5124
"2461",1792
"2462",4551
"2463",4546
"2464",7778
"2465",6568
"2466",2809
"2467",9112
"2468",5409
"2469",1051
"2470",2064
"2471",10227
"2472",1316
"2473",2272
"2474",4532
"2475",2318
"2476",1259
"2477",4731
"2478",2279
"2479",8718
"2480",2859
"2481",3541
"2482",3390
"2483",76
"2484",7718
"2485",9466
"2486",6670
"2487",9902
"2488",9756
"2489",9274
"2490",1070
"2491",5767
"2492",8332
"2493",2637
"2494",2694
"2495",7230
"2496",10583
"2497",10522
"2498",6038
"2499",9646
"2500",1887
"2501",4529
"2502",3805
"2503",4220
"2504",8743
"2505",5249
"2506",3116
"2507",8710
"2508",9312
"2509",5916
"2510",3528
"2511",3404
"2512",509
"2513",9429
"2514",7450
"2515",8677
"2516",10270
"2517",1187
"2518",3769
"2519",2193
"2520",7792
"2521",8427
"2522",648
"2523",1969
"2524",3608
"2525",9142
"2526",10676
"2527",1642
"2528",5758
"2529",2254
"2530",6238
"2531",2429
"2532",3929
"2533",6390
"2534",9022
"2535",7656
"2536",2584
"2537",9403
"2538",9769
"2539",9422
"2540",7204
"2541",1144
"2542",1542
"2543",10410
"2544",2873
"2545",6614
"2546",2944
"2547",6166
"2548",10384
"2549",6655
"2550",8218
"2551",7522
"2552",2496
"2553",7178
"2554",9659
"2555",2003
"2556",8712
"2557",4736
"2558",9272
"2559",1571
"2560",4742
"2561",5032
"2562",602
"2563",8652
"2564",6454
"2565",6735
"2566",1583
"2567",5213
"2568",1215
"2569",8802
"2570",10380
"2571",7169
"2572",10536
"2573",5500
"2574",2034
"2575",1224
"2576",5082
"2577",9705
"2578",3791
"2579",761
"2580",9764
"2581",8353
"2582",9853
"2583",3304
"2584",2211
"2585",10844
"2586",1438
"2587",9050
"2588",6310
"2589",8090
"2590",5430
"2591",7179
"2592",10606
"2593",739
"2594",10596
"2595",1646
"2596",8962
"2597",10374
"2598",8602
"2599",7742
"2600",4792
"2601",3932
"2602",8896
"2603",3563
"2604",497
"2605",924
"2606",344
"2607",10046
"2608",4790
"2609",4194
"2610",10535
"2611",1149
"2612",5244
"2613",8944
"2614",3349
"2615",488
"2616",6028
"2617",7877
"2618",6262
"2619",8232
"2620",4141
"2621",9966
"2622",1373
"2623",10050
"2624",6326
"2625",3935
"2626",10188
"2627",9288
"2628",3253
"2629",6172
"2630",7018
"2631",776
"2632",10434
"2633",7292
"2634",10812
"2635",4011
"2636",3625
"2637",790
"2638",322
"2639",8208
"2640",3681
"2641",8342
"2642",4803
"2643",8298
"2644",926
"2645",6424
"2646",1674
"2647",376
"2648",198
"2649",518
"2650",4917
"2651",4681
"2652",467
"2653",2230
"2654",3751
"2655",974
"2656",7539
"2657",4076
"2658",7000
"2659",4170
"2660",5336
"2661",2793
"2662",328
"2663",4432
"2664",6007
"2665",5048
"2666",9570
"2667",8336
"2668",6271
"2669",4042
"2670",7825
"2671",2320
"2672",306
"2673",2884
"2674",7969
"2675",1164
"2676",7423
"2677",2981
"2678",5096
"2679",5204
"2680",7027
"2681",10387
"2682",2488
"2683",8941
"2684",3142
"2685",401
"2686",2979
"2687",1398
"2688",5565
"2689",6510
"2690",9
"2691",8546
"2692",1658
"2693",6459
"2694",9854
"2695",2485
"2696",1056
"2697",9833
"2698",4459
"2699",141
"2700",10136
"2701",5315
"2702",5117
"2703",10152
"2704",3694
"2705",6838
"2706",1379
"2707",4914
"2708",310
"2709",908
"2710",912
"2711",447
"2712",2628
"2713",8438
"2714",8184
"2715",868
"2716",8138
"2717",9265
"2718",1040
"2719",1469
"2720",3028
"2721",5654
"2722",6929
"2723",4368
"2724",3910
"2725",1700
"2726",1942
"2727",5014
"2728",7146
"2729",135
"2730",4008
"2731",840
"2732",3808
"2733",3074
"2734",769
"2735",3512
"2736",3214
"2737",8986
"2738",8970
"2739",1311
"2740",7189
"2741",4890
"2742",10685
"2743",9047
"2744",9906
"2745",458
"2746",7772
"2747",6308
"2748",3940
"2749",4348
"2750",9157
"2751",8623
"2752",4038
"2753",2722
"2754",2492
"2755",5912
"2756",5702
"2757",3446
"2758",3844
"2759",8474
"2760",1888
"2761",1150
"2762",292
"2763",2001
"2764",10584
"2765",3178
"2766",9739
"2767",7595
"2768",4397
"2769",8940
"2770",9755
"2771",10220
"2772",3347
"2773",7050
"2774",4665
"2775",99
"2776",10636
"2777",5852
"2778",875
"2779",8127
"2780",5675
"2781",3836
"2782",3276
"2783",3776
"2784",4466
"2785",5450
"2786",10474
"2787",8165
"2788",5359
"2789",8704
"2790",1840
"2791",4486
"2792",8952
"2793",398
"2794",3933
"2795",1240
"2796",10042
"2797",8638
"2798",42
"2799",6770
"2800",1370
"2801",1706
"2802",10340
"2803",2654
"2804",10652
"2805",1124
"2806",10108
"2807",6534
"2808",4855
"2809",4443
"2810",10728
"2811",546
"2812",5236
"2813",6625
"2814",3790
"2815",1835
"2816",6123
"2817",2404
"2818",6796
"2819",298
"2820",5860
"2821",3275
"2822",7878
"2823",58
"2824",1356
"2825",2854
"2826",6266
"2827",3460
"2828",1078
"2829",8237
"2830",8674
"2831",9686
"2832",7074
"2833",4426
"2834",10854
"2835",837
"2836",1408
"2837",3410
"2838",8031
"2839",5772
"2840",10024
"2841",2746
"2842",4328
"2843",7066
"2844",6243
"2845",9636
"2846",4700
"2847",492
"2848",1430
"2849",8339
"2850",4796
"2851",2280
"2852",6406
"2853",7132
"2854",10062
"2855",1922
"2856",3743
"2857",8608
"2858",10796
"2859",5345
"2860",5008
"2861",854
"2862",5433
"2863",9164
"2864",6036
"2865",10627
"2866",2004
"2867",4960
"2868",3188
"2869",6182
"2870",2505
"2871",10438
"2872",5959
"2873",5468
"2874",7975
"2875",263
"2876",5058
"2877",6448
"2878",5441
"2879",5030
"2880",3988
"2881",4366
"2882",8806
"2883",6012
"2884",6776
"2885",3029
"2886",6874
"2887",360
"2888",7768
"2889",1984
"2890",6402
"2891",7400
"2892",2968
"2893",4942
"2894",9916
"2895",1749
"2896",9584
"2897",2610
"2898",7712
"2899",7156
"2900",8038
"2901",9310
"2902",261
"2903",8046
"2904",5899
"2905",4350
"2906",8622
"2907",6356
"2908",7012
"2909",942
"2910",834
"2911",10734
"2912",6198
"2913",2451
"2914",1312
"2915",9704
"2916",2321
"2917",8014
"2918",260
"2919",10437
"2920",4264
"2921",1355
"2922",115
"2923",7555
"2924",5410
"2925",4804
"2926",5420
"2927",1152
"2928",4554
"2929",6064
"2930",4294
"2931",3081
"2932",284
"2933",1002
"2934",2414
"2935",2399
"2936",1786
"2937",3987
"2938",4626
"2939",7857
"2940",754
"2941",6640
"2942",6020
"2943",5630
"2944",177
"2945",857
"2946",1633
"2947",7067
"2948",952
"2949",7250
"2950",277
"2951",9416
"2952",1737
"2953",891
"2954",6992
"2955",9352
"2956",825
"2957",476
"2958",8186
"2959",1084
"2960",120
"2961",1211
"2962",7478
"2963",1704
"2964",9828
"2965",4230
"2966",2874
"2967",3948
"2968",8225
"2969",10232
"2970",5224
"2971",1034
"2972",7242
"2973",8408
"2974",1673
"2975",5133
"2976",1660
"2977",867
"2978",6371
"2979",6579
"2980",435
"2981",4506
"2982",1226
"2983",7387
"2984",4086
"2985",5540
"2986",8322
"2987",7114
"2988",4
"2989",3710
"2990",3732
"2991",7316
"2992",2482
"2993",2024
"2994",8864
"2995",9273
"2996",10375
"2997",10028
"2998",7381
"2999",8381
"3000",1748
"3001",9088
"3002",1858
"3003",8004
"3004",3962
"3005",7155
"3006",4288
"3007",8871
"3008",10433
"3009",8816
"3010",2499
"3011",7636
"3012",8822
"3013",9568
"3014",2928
"3015",8775
"3016",3822
"3017",10088
"3018",10412
"3019",9732
"3020",6564
"3021",9000
"3022",7099
"3023",5186
"3024",4618
"3025",1248
"3026",1304
"3027",3600
"3028",7052
"3029",2704
"3030",7996
"3031",8072
"3032",3286
"3033",10638
"3034",1089
"3035",1882
"3036",3588
"3037",8107
"3038",218
"3039",5009
"3040",5158
"3041",1428
"3042",6176
"3043",2440
"3044",2175
"3045",3086
"3046",724
"3047",9120
"3048",5650
"3049",10762
"3050",3520
"3051",4151
"3052",70
"3053",1170
"3054",10620
"3055",7883
"3056",8591
"3057",307
"3058",1803
"3059",9617
"3060",10170
"3061",4005
"3062",6347
"3063",3075
"3064",9895
"3065",8920
"3066",3583
"3067",5660
"3068",3570
"3069",3557
"3070",4371
"3071",1805
"3072",5853
"3073",4228
"3074",7352
"3075",9538
"3076",5103
"3077",2201
"3078",1331
"3079",334
"3080",1190
"3081",10209
"3082",7604
"3083",2107
"3084",3310
"3085",3554
"3086",5905
"3087",9159
"3088",6186
"3089",7578
"3090",9363
"3091",2456
"3092",508
"3093",8063
"3094",10034
"3095",8300
"3096",3628
"3097",8762
"3098",9736
"3099",10842
"3100",7384
"3101",8233
"3102",89
"3103",1848
"3104",3397
"3105",7664
"3106",7394
"3107",8613
"3108",6426
"3109",10570
"3110",3469
"3111",3531
"3112",4728
"3113",10334
"3114",1191
"3115",7736
"3116",4754
"3117",9246
"3118",589
"3119",10660
"3120",7608
"3121",8744
"3122",9788
"3123",2796
"3124",5128
"3125",6492
"3126",9146
"3127",1434
"3128",6834
"3129",4734
"3130",3564
"3131",4930
"3132",1116
"3133",1363
"3134",7662
"3135",8407
"3136",2033
"3137",2206
"3138",4324
"3139",10800
"3140",9548
"3141",657
"3142",10526
"3143",7460
"3144",8897
"3145",7327
"3146",7669
"3147",1809
"3148",5092
"3149",2257
"3150",3660
"3151",10613
"3152",5831
"3153",530
"3154",1222
"3155",192
"3156",937
"3157",10710
"3158",5760
"3159",9075
"3160",8398
"3161",1506
"3162",3223
"3163",6592
"3164",1736
"3165",2124
"3166",10154


================================================
FILE: data/dataiitb.csv
================================================
"","rollno","sname","gender","age","course","marks1","marks2","passfail"
"1",1,"Student_1","F",27,"btech",72,48.02,1
"2",2,"Student_2","F",21,"btech",74,62.31,1
"3",3,"Student_3","F",23,"mtech",62,59.61,1
"4",4,"Student_4","F",24,"btech",61,77.29,1
"5",5,"Student_5","F",27,"btech",57,73.4,0
"6",6,"Student_6","M",23,"mtech",51,80.22,1
"7",7,"Student_7","F",28,"phd",59,78.04,0
"8",8,"Student_8","F",23,"mtech",58,58.18,1
"9",9,"Student_9","F",26,"btech",61,57.76,0
"10",10,"Student_10","M",22,"mtech",74,65.71,0
"11",11,"Student_11","F",27,"btech",78,76.09,1
"12",12,"Student_12","M",29,"phd",72,74.94,1
"13",13,"Student_13","M",23,"btech",64,67.42,1
"14",14,"Student_14","F",21,"btech",48,77.89,0
"15",15,"Student_15","F",24,"btech",70,81.03,0
"16",16,"Student_16","F",21,"btech",78,74.16,1
"17",17,"Student_17","F",27,"btech",88,69.15,1
"18",18,"Student_18","F",23,"phd",57,67.62,1
"19",19,"Student_19","F",24,"btech",49,68.28,0
"20",20,"Student_20","F",24,"phd",60,67.37,1
"21",21,"Student_21","M",26,"phd",76,81.01,1
"22",22,"Student_22","F",25,"mtech",74,60.24,1
"23",23,"Student_23","F",26,"btech",77,67.77,1
"24",24,"Student_24","M",22,"btech",73,81.19,0
"25",25,"Student_25","F",25,"phd",57,79.93,0
"26",26,"Student_26","F",21,"btech",57,66.28,0
"27",27,"Student_27","M",20,"btech",54,71.91,1
"28",28,"Student_28","F",22,"btech",72,63.16,1
"29",29,"Student_29","F",22,"phd",50,72.05,1
"30",30,"Student_30","F",23,"btech",53,58.66,1
"31",31,"Student_31","M",27,"btech",75,70.7,1
"32",32,"Student_32","F",29,"btech",23,67.07,0
"33",33,"Student_33","F",23,"btech",66,62.47,1
"34",34,"Student_34","M",25,"btech",64,73.04,1
"35",35,"Student_35","F",28,"btech",77,86.23,1
"36",36,"Student_36","F",23,"mtech",59,58.18,1
"37",37,"Student_37","F",28,"btech",59,75.06,0
"38",38,"Student_38","F",23,"btech",71,68.84,1
"39",39,"Student_39","F",26,"mtech",60,64.68,1
"40",40,"Student_40","F",23,"mtech",63,62.5,0
"41",41,"Student_41","F",24,"mtech",51,75.63,1
"42",42,"Student_42","M",26,"btech",83,64.58,0
"43",43,"Student_43","M",28,"btech",64,72.64,1
"44",44,"Student_44","M",25,"btech",66,61.39,1
"45",45,"Student_45","F",23,"mtech",56,66.67,1
"46",46,"Student_46","F",23,"btech",62,52.29,1
"47",47,"Student_47","F",23,"btech",73,84.92,0
"48",48,"Student_48","M",29,"btech",57,59.15,1
"49",49,"Student_49","F",24,"btech",57,82.62,1
"50",50,"Student_50","M",29,"btech",55,75.44,1
"51",51,"Student_51","M",21,"mtech",78,70.28,1
"52",52,"Student_52","M",29,"btech",55,63.2,0
"53",53,"Student_53","M",29,"btech",65,77.85,0
"54",54,"Student_54","M",29,"mtech",78,61.43,1
"55",55,"Student_55","M",23,"btech",62,70.19,0
"56",56,"Student_56","F",23,"btech",87,74.22,0
"57",57,"Student_57","M",27,"btech",72,68.99,0
"58",58,"Student_58","F",22,"btech",71,55.68,0
"59",59,"Student_59","F",28,"btech",62,71.83,1
"60",60,"Student_60","F",26,"btech",68,70.89,1
"61",61,"Student_61","M",20,"btech",64,74.18,1
"62",62,"Student_62","M",21,"phd",60,78.13,0
"63",63,"Student_63","F",23,"mtech",90,63.95,0
"64",64,"Student_64","F",20,"phd",67,65.35,1
"65",65,"Student_65","F",24,"btech",57,78.74,1
"66",66,"Student_66","F",28,"btech",79,78.25,0
"67",67,"Student_67","M",28,"btech",70,60.14,1
"68",68,"Student_68","M",26,"btech",70,74.87,1
"69",69,"Student_69","F",22,"btech",50,78.05,0
"70",70,"Student_70","F",21,"btech",89,70.3,0
"71",71,"Student_71","F",23,"mtech",67,80.28,0
"72",72,"Student_72","M",28,"btech",51,68.1,0
"73",73,"Student_73","M",23,"btech",57,66.05,1
"74",74,"Student_74","M",22,"btech",65,68.47,0
"75",75,"Student_75","F",23,"btech",61,67.48,1
"76",76,"Student_76","F",23,"btech",59,67.57,1
"77",77,"Student_77","F",26,"mtech",73,69.32,0
"78",78,"Student_78","M",27,"btech",63,72.65,1
"79",79,"Student_79","F",29,"mtech",55,75.91,0
"80",80,"Student_80","F",23,"btech",52,70.84,1
"81",81,"Student_81","M",20,"btech",56,66.24,0
"82",82,"Student_82","F",22,"btech",48,67.28,1
"83",83,"Student_83","M",28,"btech",52,69.32,1
"84",84,"Student_84","M",21,"mtech",64,82.79,1
"85",85,"Student_85","M",26,"btech",54,73.78,1
"86",86,"Student_86","M",24,"btech",76,74.87,1
"87",87,"Student_87","F",20,"mtech",71,74.37,1
"88",88,"Student_88","F",25,"mtech",87,65.33,0
"89",89,"Student_89","F",21,"mtech",59,77.71,0
"90",90,"Student_90","F",28,"btech",51,75.38,0
"91",91,"Student_91","F",20,"btech",76,60.3,0
"92",92,"Student_92","F",22,"btech",59,81.98,1
"93",93,"Student_93","F",24,"btech",69,68.77,1
"94",94,"Student_94","F",26,"btech",65,73.28,0
"95",95,"Student_95","M",27,"btech",83,79.06,1
"96",96,"Student_96","M",21,"btech",66,61.22,1
"97",97,"Student_97","F",25,"phd",66,76.62,0
"98",98,"Student_98","M",23,"btech",54,77.23,0
"99",99,"Student_99","F",25,"btech",56,61.29,1
"100",100,"Student_100","F",26,"btech",73,73.21,1
"101",101,"Student_101","F",29,"mtech",66,70.64,1
"102",102,"Student_102","F",28,"btech",59,71.14,0
"103",103,"Student_103","M",20,"mtech",61,72.93,0
"104",104,"Student_104","M",25,"btech",76,59.02,1
"105",105,"Student_105","F",24,"btech",60,56.98,1
"106",106,"Student_106","M",24,"btech",83,86.87,0
"107",107,"Student_107","M",25,"btech",61,80.73,1
"108",108,"Student_108","F",23,"mtech",73,58.58,0
"109",109,"Student_109","M",22,"mtech",57,68.19,0
"110",110,"Student_110","M",24,"phd",71,64.09,1
"111",111,"Student_111","F",23,"btech",59,79.51,0
"112",112,"Student_112","M",22,"mtech",54,67.38,1
"113",113,"Student_113","M",25,"mtech",77,65.91,0
"114",114,"Student_114","F",28,"btech",66,64.87,0
"115",115,"Student_115","M",26,"btech",81,65.01,1
"116",116,"Student_116","F",23,"mtech",79,63.69,0
"117",117,"Student_117","F",28,"btech",62,74.67,0
"118",118,"Student_118","F",29,"btech",77,70.83,1
"119",119,"Student_119","F",27,"btech",84,62.43,0
"120",120,"Student_120","M",22,"mtech",63,76.64,1
"121",121,"Student_121","F",20,"mtech",49,75.75,1
"122",122,"Student_122","F",20,"btech",79,86.2,1
"123",123,"Student_123","F",29,"btech",52,65.36,1
"124",124,"Student_124","F",24,"btech",54,69.23,0
"125",125,"Student_125","F",23,"btech",38,81.81,1
"126",126,"Student_126","F",25,"btech",41,55.86,0
"127",127,"Student_127","M",23,"btech",62,68.05,1
"128",128,"Student_128","M",29,"btech",73,64.02,1
"129",129,"Student_129","F",23,"mtech",55,61.36,1
"130",130,"Student_130","M",29,"mtech",49,76.75,1
"131",131,"Student_131","F",20,"btech",68,73.95,0
"132",132,"Student_132","F",23,"btech",64,79.81,1
"133",133,"Student_133","M",27,"btech",67,63.53,1
"134",134,"Student_134","M",27,"btech",46,71.49,1
"135",135,"Student_135","F",22,"phd",69,74.52,0
"136",136,"Student_136","M",29,"mtech",71,80.23,0
"137",137,"Student_137","F",20,"btech",67,87.41,0
"138",138,"Student_138","F",25,"mtech",54,65.72,1
"139",139,"Student_139","F",24,"mtech",67,74.14,1
"140",140,"Student_140","F",28,"phd",64,72.09,1
"141",141,"Student_141","M",25,"mtech",73,69.35,1
"142",142,"Student_142","M",24,"btech",49,56.91,0
"143",143,"Student_143","F",28,"btech",58,73.96,1
"144",144,"Student_144","M",23,"btech",51,60.02,1
"145",145,"Student_145","M",20,"btech",69,84.39,1
"146",146,"Student_146","F",27,"btech",68,68.96,0
"147",147,"Student_147","F",22,"phd",65,59.38,0
"148",148,"Student_148","F",27,"phd",59,89.8,0
"149",149,"Student_149","M",23,"btech",57,81.45,0
"150",150,"Student_150","M",25,"phd",67,77.76,1
"151",151,"Student_151","F",28,"btech",77,60.25,0
"152",152,"Student_152","F",26,"btech",45,74.21,0
"153",153,"Student_153","M",25,"btech",88,87.24,1
"154",154,"Student_154","F",24,"btech",71,77.06,1
"155",155,"Student_155","F",28,"mtech",51,61.56,0
"156",156,"Student_156","M",24,"phd",78,64.03,1
"157",157,"Student_157","M",28,"btech",63,62.19,1
"158",158,"Student_158","F",25,"mtech",68,55.63,0
"159",159,"Student_159","F",24,"btech",55,73.41,1
"160",160,"Student_160","M",20,"btech",75,55.21,1
"161",161,"Student_161","M",28,"btech",66,63.69,0
"162",162,"Student_162","M",27,"btech",71,81.03,0
"163",163,"Student_163","F",23,"btech",66,71.62,1
"164",164,"Student_164","F",27,"btech",60,80.53,0
"165",165,"Student_165","M",29,"mtech",66,67.51,1
"166",166,"Student_166","F",23,"btech",53,81.04,0
"167",167,"Student_167","M",29,"btech",61,61.1,1
"168",168,"Student_168","F",27,"btech",77,74.19,1
"169",169,"Student_169","M",28,"btech",76,53.3,1
"170",170,"Student_170","F",20,"btech",69,68.2,1
"171",171,"Student_171","F",25,"btech",76,68.07,0
"172",172,"Student_172","M",28,"btech",66,65.76,0
"173",173,"Student_173","F",20,"mtech",57,60.13,0
"174",174,"Student_174","F",24,"btech",43,67.64,1
"175",175,"Student_175","F",28,"btech",60,68.92,1
"176",176,"Student_176","M",28,"btech",81,79.62,1
"177",177,"Student_177","F",23,"phd",83,71.93,1
"178",178,"Student_178","F",29,"btech",71,78.62,0
"179",179,"Student_179","F",21,"btech",50,68.65,1
"180",180,"Student_180","F",27,"btech",81,66.14,0
"181",181,"Student_181","F",27,"btech",78,70.63,0
"182",182,"Student_182","M",25,"mtech",70,71.02,1
"183",183,"Student_183","M",20,"btech",58,67.95,1
"184",184,"Student_184","F",20,"mtech",89,71.69,1
"185",185,"Student_185","F",25,"btech",60,66.51,0
"186",186,"Student_186","F",26,"phd",62,79.72,0
"187",187,"Student_187","F",22,"btech",63,71.61,0
"188",188,"Student_188","F",27,"mtech",55,67.66,1
"189",189,"Student_189","F",26,"btech",60,77.8,1
"190",190,"Student_190","F",23,"mtech",48,69.03,0
"191",191,"Student_191","F",28,"phd",69,62.46,0
"192",192,"Student_192","M",25,"btech",60,76.36,1
"193",193,"Student_193","M",29,"mtech",79,57.16,0
"194",194,"Student_194","F",26,"mtech",71,70.55,1
"195",195,"Student_195","M",21,"btech",73,65.57,1
"196",196,"Student_196","F",27,"btech",80,62.98,0
"197",197,"Student_197","F",29,"btech",53,63.94,1
"198",198,"Student_198","F",27,"phd",73,79.28,1
"199",199,"Student_199","M",26,"btech",72,83.26,1
"200",200,"Student_200","M",25,"btech",61,61.35,1
"201",201,"Student_201","F",22,"btech",60,78.5,0
"202",202,"Student_202","M",27,"btech",58,68.35,1
"203",203,"Student_203","M",26,"btech",67,70.8,0
"204",204,"Student_204","F",21,"phd",74,60.61,1
"205",205,"Student_205","M",28,"btech",65,79.86,1
"206",206,"Student_206","F",20,"mtech",55,71.2,1
"207",207,"Student_207","F",29,"btech",50,93.68,1
"208",208,"Student_208","F",21,"btech",71,64.29,1
"209",209,"Student_209","M",26,"mtech",52,76.8,1
"210",210,"Student_210","F",20,"btech",56,72.01,0
"211",211,"Student_211","M",22,"btech",54,68.36,0
"212",212,"Student_212","F",22,"btech",86,70.77,1
"213",213,"Student_213","F",29,"btech",75,77.23,0
"214",214,"Student_214","F",21,"btech",79,66.59,0
"215",215,"Student_215","F",21,"btech",68,67.99,0
"216",216,"Student_216","F",25,"phd",53,67.72,0
"217",217,"Student_217","F",26,"btech",88,64.91,0
"218",218,"Student_218","M",22,"btech",66,77.8,1
"219",219,"Student_219","M",24,"mtech",67,73.58,0
"220",220,"Student_220","M",22,"phd",68,76.45,1
"221",221,"Student_221","F",29,"mtech",57,62.8,1
"222",222,"Student_222","F",26,"btech",71,76.18,1
"223",223,"Student_223","M",28,"btech",52,61.9,1
"224",224,"Student_224","M",21,"btech",76,67.39,1
"225",225,"Student_225","F",25,"phd",49,74.68,1
"226",226,"Student_226","F",25,"btech",60,72.77,1
"227",227,"Student_227","F",22,"btech",74,60.23,1
"228",228,"Student_228","F",25,"mtech",64,57.45,1
"229",229,"Student_229","M",20,"btech",40,50.28,0
"230",230,"Student_230","F",27,"btech",44,50.73,1
"231",231,"Student_231","F",24,"btech",78,77.81,0
"232",232,"Student_232","M",25,"btech",71,69.46,1
"233",233,"Student_233","F",28,"btech",67,78.56,1
"234",234,"Student_234","F",25,"btech",66,71.74,1
"235",235,"Student_235","F",25,"mtech",51,69.01,1
"236",236,"Student_236","M",29,"btech",59,77.28,0
"237",237,"Student_237","F",25,"btech",67,63.92,0
"238",238,"Student_238","F",24,"btech",82,72.92,1
"239",239,"Student_239","F",27,"btech",65,68.98,1
"240",240,"Student_240","F",26,"btech",64,65.22,0
"241",241,"Student_241","F",25,"mtech",51,65.12,1
"242",242,"Student_242","F",25,"btech",53,65.48,1
"243",243,"Student_243","F",26,"btech",87,62.5,1
"244",244,"Student_244","F",23,"btech",67,74.24,1
"245",245,"Student_245","F",20,"btech",66,79.36,0
"246",246,"Student_246","F",21,"phd",75,70.92,0
"247",247,"Student_247","F",25,"mtech",68,70.35,1
"248",248,"Student_248","F",23,"btech",66,66.97,1
"249",249,"Student_249","F",22,"mtech",61,63.45,1
"250",250,"Student_250","F",22,"mtech",78,65.1,1
"251",251,"Student_251","M",21,"mtech",62,66.1,1
"252",252,"Student_252","F",24,"btech",65,38.63,1
"253",253,"Student_253","F",29,"btech",52,74.39,1
"254",254,"Student_254","F",21,"mtech",62,62.58,0
"255",255,"Student_255","F",24,"btech",66,74.61,0
"256",256,"Student_256","F",22,"btech",84,65.78,0
"257",257,"Student_257","M",29,"btech",71,76.66,0
"258",258,"Student_258","F",24,"phd",66,66.78,0
"259",259,"Student_259","M",20,"phd",72,74.74,0
"260",260,"Student_260","F",25,"btech",56,81.53,0
"261",261,"Student_261","F",24,"btech",65,77.54,0
"262",262,"Student_262","F",24,"btech",60,66.9,1
"263",263,"Student_263","F",23,"btech",63,71,0
"264",264,"Student_264","F",24,"btech",51,66.33,1
"265",265,"Student_265","M",27,"mtech",62,50.84,0
"266",266,"Student_266","M",24,"phd",56,75.67,1
"267",267,"Student_267","F",22,"btech",62,61.59,1
"268",268,"Student_268","F",22,"mtech",53,62.49,1
"269",269,"Student_269","M",21,"phd",75,81.07,0
"270",270,"Student_270","F",29,"mtech",75,70.42,1
"271",271,"Student_271","F",20,"btech",63,80.46,1
"272",272,"Student_272","M",25,"btech",64,74.27,1
"273",273,"Student_273","F",25,"phd",55,77.13,0
"274",274,"Student_274","F",22,"btech",75,70.49,1
"275",275,"Student_275","F",22,"phd",63,66.69,1
"276",276,"Student_276","M",25,"mtech",65,69.17,0
"277",277,"Student_277","M",20,"btech",94,76.94,0
"278",278,"Student_278","F",29,"btech",54,61.38,1
"279",279,"Student_279","F",25,"btech",70,64.34,1
"280",280,"Student_280","F",24,"btech",77,67.26,1
"281",281,"Student_281","F",21,"btech",66,65.1,1
"282",282,"Student_282","M",23,"btech",62,72.5,0
"283",283,"Student_283","F",25,"btech",65,67.47,1
"284",284,"Student_284","F",27,"btech",82,63.7,0
"285",285,"Student_285","F",21,"btech",70,71.46,0
"286",286,"Student_286","F",21,"mtech",63,73.32,0
"287",287,"Student_287","M",22,"btech",74,71.91,1
"288",288,"Student_288","M",25,"mtech",64,73.75,1
"289",289,"Student_289","M",23,"mtech",66,78.32,1
"290",290,"Student_290","M",21,"btech",67,72.3,1
"291",291,"Student_291","F",28,"btech",60,82.07,0
"292",292,"Student_292","M",25,"btech",66,66.18,1
"293",293,"Student_293","F",26,"btech",64,75.01,0
"294",294,"Student_294","F",21,"phd",78,76.04,1
"295",295,"Student_295","F",28,"btech",55,77.89,0
"296",296,"Student_296","F",25,"phd",62,66.66,1
"297",297,"Student_297","M",20,"btech",36,58.97,1
"298",298,"Student_298","F",21,"btech",75,66.95,0
"299",299,"Student_299","M",29,"mtech",93,72.78,1
"300",300,"Student_300","F",25,"phd",56,69.16,1
"301",301,"Student_301","F",23,"mtech",55,80.12,0
"302",302,"Student_302","F",28,"mtech",73,65.57,1
"303",303,"Student_303","M",25,"btech",64,72.26,1
"304",304,"Student_304","M",23,"btech",66,82.07,1
"305",305,"Student_305","M",21,"btech",67,67.87,0
"306",306,"Student_306","M",24,"btech",71,45.16,1
"307",307,"Student_307","F",29,"btech",55,73.36,1
"308",308,"Student_308","M",23,"btech",59,58.1,1
"309",309,"Student_309","F",23,"btech",74,85.58,1
"310",310,"Student_310","F",23,"btech",54,74.61,1
"311",311,"Student_311","M",27,"btech",77,66.42,1
"312",312,"Student_312","M",25,"mtech",72,64.43,0
"313",313,"Student_313","F",22,"btech",62,69.36,1
"314",314,"Student_314","F",28,"btech",64,78.44,1
"315",315,"Student_315","F",27,"btech",56,74.46,1
"316",316,"Student_316","F",28,"phd",47,67.65,1
"317",317,"Student_317","F",26,"phd",66,61.22,1
"318",318,"Student_318","F",24,"btech",94,68.72,1
"319",319,"Student_319","M",21,"mtech",67,80.13,0
"320",320,"Student_320","F",27,"btech",81,76.07,1
"321",321,"Student_321","F",29,"phd",80,64.51,0
"322",322,"Student_322","F",27,"btech",78,81.73,0
"323",323,"Student_323","M",25,"btech",70,63.3,0
"324",324,"Student_324","F",29,"btech",51,73.78,1
"325",325,"Student_325","F",29,"mtech",73,68.85,1
"326",326,"Student_326","M",27,"btech",62,58.25,1
"327",327,"Student_327","F",29,"phd",68,55.94,1
"328",328,"Student_328","F",24,"btech",49,73.22,1
"329",329,"Student_329","F",28,"btech",71,67.72,1
"330",330,"Student_330","F",26,"btech",74,79.16,1
"331",331,"Student_331","F",28,"btech",77,70.72,1
"332",332,"Student_332","M",27,"btech",80,63.2,0
"333",333,"Student_333","F",20,"mtech",73,63.71,1
"334",334,"Student_334","F",23,"btech",75,64.56,0
"335",335,"Student_335","M",24,"btech",65,77.92,0
"336",336,"Student_336","F",24,"btech",79,75.12,1
"337",337,"Student_337","M",26,"btech",68,70.84,0
"338",338,"Student_338","F",22,"btech",62,82.29,1
"339",339,"Student_339","M",28,"btech",60,69.34,1
"340",340,"Student_340","F",22,"btech",63,69.43,0
"341",341,"Student_341","M",25,"phd",92,74.51,1
"342",342,"Student_342","M",24,"mtech",54,80.91,1
"343",343,"Student_343","F",28,"btech",74,79.85,1
"344",344,"Student_344","M",29,"btech",57,59.56,1
"345",345,"Student_345","M",24,"btech",63,71.55,0
"346",346,"Student_346","F",22,"btech",49,73.97,1
"347",347,"Student_347","M",26,"btech",44,75.25,1
"348",348,"Student_348","M",27,"mtech",58,82.29,0
"349",349,"Student_349","F",22,"btech",47,82.11,1
"350",350,"Student_350","F",25,"btech",41,63.48,1
"351",351,"Student_351","F",20,"btech",51,75.71,1
"352",352,"Student_352","F",26,"phd",74,75.82,1
"353",353,"Student_353","F",21,"btech",73,73.53,0
"354",354,"Student_354","F",20,"btech",39,75.23,1
"355",355,"Student_355","F",29,"btech",54,73.23,1
"356",356,"Student_356","M",23,"phd",67,67.08,0
"357",357,"Student_357","F",23,"btech",66,80.96,1
"358",358,"Student_358","F",25,"btech",69,52.06,0
"359",359,"Student_359","F",27,"phd",59,62.49,1
"360",360,"Student_360","F",24,"btech",60,67.96,1
"361",361,"Student_361","F",22,"mtech",64,66.36,0
"362",362,"Student_362","F",20,"btech",48,88.11,1
"363",363,"Student_363","F",23,"phd",59,70.3,1
"364",364,"Student_364","M",25,"phd",78,65.88,1
"365",365,"Student_365","F",29,"mtech",72,72.68,1
"366",366,"Student_366","F",21,"btech",90,69.8,1
"367",367,"Student_367","M",22,"btech",56,79.23,0
"368",368,"Student_368","M",20,"mtech",67,57.23,0
"369",369,"Student_369","F",23,"phd",58,82.15,1
"370",370,"Student_370","M",21,"phd",79,78.78,0
"371",371,"Student_371","M",29,"btech",76,69.55,1
"372",372,"Student_372","M",22,"btech",65,67.09,1
"373",373,"Student_373","F",23,"mtech",61,61.45,0
"374",374,"Student_374","F",25,"phd",58,87.11,0
"375",375,"Student_375","M",28,"btech",62,60.79,1
"376",376,"Student_376","F",23,"btech",56,71.56,1
"377",377,"Student_377","F",27,"btech",48,79.44,0
"378",378,"Student_378","M",28,"mtech",83,73.21,0
"379",379,"Student_379","M",24,"phd",67,69.66,1
"380",380,"Student_380","F",24,"btech",82,62.99,1
"381",381,"Student_381","F",21,"mtech",61,57.46,1
"382",382,"Student_382","F",28,"mtech",70,71.27,0
"383",383,"Student_383","F",22,"btech",74,58.05,1
"384",384,"Student_384","F",23,"btech",60,78.82,0
"385",385,"Student_385","M",23,"btech",60,71.37,0
"386",386,"Student_386","M",28,"btech",66,78.98,1
"387",387,"Student_387","M",28,"btech",72,66.87,1
"388",388,"Student_388","F",24,"mtech",81,74.74,0
"389",389,"Student_389","M",28,"btech",76,69.47,1
"390",390,"Student_390","F",22,"btech",69,71.61,0
"391",391,"Student_391","F",23,"btech",70,48.3,1
"392",392,"Student_392","M",25,"phd",59,72.5,1
"393",393,"Student_393","F",27,"btech",70,69.69,1
"394",394,"Student_394","F",21,"btech",79,61.72,0
"395",395,"Student_395","F",20,"btech",62,76.17,1
"396",396,"Student_396","F",26,"btech",81,77.49,1
"397",397,"Student_397","M",28,"btech",67,72.44,0
"398",398,"Student_398","M",20,"phd",50,77.9,1
"399",399,"Student_399","F",20,"btech",59,73.5,1
"400",400,"Student_400","F",24,"btech",50,68.12,1
"401",401,"Student_401","F",27,"btech",73,69.71,0
"402",402,"Student_402","F",28,"mtech",67,60.52,1
"403",403,"Student_403","F",26,"btech",58,74.81,1
"404",404,"Student_404","M",27,"btech",73,67.41,1
"405",405,"Student_405","M",28,"mtech",64,73.84,0
"406",406,"Student_406","F",24,"mtech",70,72.06,1
"407",407,"Student_407","F",25,"mtech",76,75.92,0
"408",408,"Student_408","F",24,"phd",64,63.9,0
"409",409,"Student_409","F",27,"phd",60,68.28,0
"410",410,"Student_410","F",28,"btech",74,82.58,0
"411",411,"Student_411","F",20,"btech",63,64.2,0
"412",412,"Student_412","F",20,"btech",79,72.19,1
"413",413,"Student_413","F",20,"btech",68,66.58,0
"414",414,"Student_414","F",26,"mtech",85,81.08,0
"415",415,"Student_415","F",21,"btech",62,65.66,1
"416",416,"Student_416","F",26,"mtech",71,76.41,0
"417",417,"Student_417","F",28,"mtech",56,73.2,1
"418",418,"Student_418","F",28,"btech",64,73.82,0
"419",419,"Student_419","M",23,"btech",52,67.76,1
"420",420,"Student_420","M",29,"btech",80,85.74,0
"421",421,"Student_421","M",28,"mtech",57,67.2,1
"422",422,"Student_422","F",22,"btech",73,70.47,0
"423",423,"Student_423","F",22,"mtech",61,66.85,1
"424",424,"Student_424","F",28,"btech",66,63.05,0
"425",425,"Student_425","F",25,"btech",70,73.52,0
"426",426,"Student_426","M",20,"btech",61,57.15,0
"427",427,"Student_427","M",22,"btech",79,65.64,1
"428",428,"Student_428","F",21,"btech",68,65.73,1
"429",429,"Student_429","M",24,"mtech",62,79.09,0
"430",430,"Student_430","F",27,"btech",74,61.89,0
"431",431,"Student_431","F",21,"btech",46,73.78,0
"432",432,"Student_432","F",29,"btech",71,59.71,1
"433",433,"Student_433","F",20,"btech",63,52.67,0
"434",434,"Student_434","F",27,"btech",62,68.91,1
"435",435,"Student_435","F",29,"phd",77,63.07,0
"436",436,"Student_436","M",26,"btech",69,71.04,0
"437",437,"Student_437","M",23,"btech",53,71.46,1
"438",438,"Student_438","F",21,"btech",83,59.29,0
"439",439,"Student_439","M",20,"btech",54,81.09,1
"440",440,"Student_440","F",24,"btech",52,85.87,1
"441",441,"Student_441","F",27,"btech",63,59.35,1
"442",442,"Student_442","F",24,"btech",58,58.06,1
"443",443,"Student_443","F",26,"mtech",84,82.16,1
"444",444,"Student_444","M",23,"btech",66,87.36,1
"445",445,"Student_445","F",20,"mtech",64,57.83,0
"446",446,"Student_446","M",23,"btech",56,69.37,0
"447",447,"Student_447","M",24,"btech",62,65.34,0
"448",448,"Student_448","F",28,"btech",63,71.18,1
"449",449,"Student_449","F",25,"btech",61,62.92,1
"450",450,"Student_450","F",28,"btech",75,71.73,1
"451",451,"Student_451","M",24,"btech",69,90.39,0
"452",452,"Student_452","F",26,"mtech",65,61.28,0
"453",453,"Student_453","M",23,"btech",56,73.37,1
"454",454,"Student_454","M",29,"phd",72,71.92,1
"455",455,"Student_455","F",28,"btech",79,68.8,0
"456",456,"Student_456","F",26,"btech",80,77.91,1
"457",457,"Student_457","F",26,"btech",65,65.97,1
"458",458,"Student_458","F",21,"btech",69,81.68,0
"459",459,"Student_459","M",29,"btech",60,74.74,1
"460",460,"Student_460","F",21,"btech",75,71.18,0
"461",461,"Student_461","M",28,"btech",72,74.06,1
"462",462,"Student_462","M",29,"btech",75,67.23,0
"463",463,"Student_463","M",23,"btech",53,77.79,1
"464",464,"Student_464","F",22,"btech",65,71.09,0
"465",465,"Student_465","F",24,"btech",68,68.94,1
"466",466,"Student_466","F",26,"btech",62,65.69,1
"467",467,"Student_467","F",22,"btech",80,84.04,1
"468",468,"Student_468","F",20,"btech",86,66.52,0
"469",469,"Student_469","F",26,"mtech",80,62.52,1
"470",470,"Student_470","F",27,"btech",63,77.24,1
"471",471,"Student_471","M",24,"btech",73,74.55,0
"472",472,"Student_472","F",25,"btech",77,78.7,1
"473",473,"Student_473","F",26,"phd",55,56.66,1
"474",474,"Student_474","M",27,"btech",53,67.63,1
"475",475,"Student_475","F",28,"mtech",53,75.15,1
"476",476,"Student_476","M",22,"btech",73,77.52,1
"477",477,"Student_477","F",24,"btech",58,65.49,1
"478",478,"Student_478","M",22,"btech",72,47.96,1
"479",479,"Student_479","F",27,"btech",71,85.3,0
"480",480,"Student_480","F",22,"btech",60,75.78,0
"481",481,"Student_481","F",28,"mtech",66,77.65,1
"482",482,"Student_482","M",28,"mtech",66,56.51,1
"483",483,"Student_483","M",23,"btech",50,54.37,1
"484",484,"Student_484","F",22,"mtech",64,83.8,0
"485",485,"Student_485","F",24,"btech",87,74.74,1
"486",486,"Student_486","F",26,"btech",73,68.53,0
"487",487,"Student_487","F",25,"btech",50,63.51,0
"488",488,"Student_488","M",28,"btech",59,60.41,0
"489",489,"Student_489","F",29,"mtech",82,56.57,1
"490",490,"Student_490","F",22,"btech",61,77.52,1
"491",491,"Student_491","F",23,"mtech",70,67.67,1
"492",492,"Student_492","M",26,"btech",75,65.33,0
"493",493,"Student_493","F",25,"btech",75,70.68,0
"494",494,"Student_494","M",27,"btech",57,74.73,1
"495",495,"Student_495","F",22,"btech",68,71.97,1
"496",496,"Student_496","F",21,"btech",78,79.2,0
"497",497,"Student_497","F",20,"btech",65,77.91,1
"498",498,"Student_498","F",22,"phd",77,71.77,1
"499",499,"Student_499","M",28,"btech",40,84.12,1
"500",500,"Student_500","F",25,"btech",58,62.86,1
"501",501,"Student_501","F",21,"btech",68,76.3,1
"502",502,"Student_502","F",29,"btech",55,75.35,1
"503",503,"Student_503","F",24,"btech",68,68.76,0
"504",504,"Student_504","F",23,"btech",54,58.78,1
"505",505,"Student_505","M",24,"btech",70,56.71,1
"506",506,"Student_506","M",23,"mtech",53,60.65,0
"507",507,"Student_507","M",20,"btech",67,69.61,1
"508",508,"Student_508","M",22,"mtech",50,77.22,0
"509",509,"Student_509","M",28,"btech",67,62.75,0
"510",510,"Student_510","F",21,"btech",84,86.05,0
"511",511,"Student_511","F",22,"btech",67,58.38,1
"512",512,"Student_512","F",22,"btech",75,81.17,0
"513",513,"Student_513","M",27,"mtech",77,65.86,0
"514",514,"Student_514","F",27,"btech",63,60.74,1
"515",515,"Student_515","F",25,"btech",76,73.71,1
"516",516,"Student_516","F",21,"btech",62,73.9,1
"517",517,"Student_517","F",21,"btech",74,73.29,1
"518",518,"Student_518","M",21,"btech",61,78.09,1
"519",519,"Student_519","F",28,"mtech",82,76.16,0
"520",520,"Student_520","F",29,"phd",74,77.26,0
"521",521,"Student_521","F",24,"btech",83,83.89,1
"522",522,"Student_522","F",21,"btech",66,72.6,1
"523",523,"Student_523","F",27,"mtech",78,53.46,1
"524",524,"Student_524","M",29,"btech",54,62.42,0
"525",525,"Student_525","M",28,"btech",62,79.04,1
"526",526,"Student_526","F",23,"btech",76,86.69,1
"527",527,"Student_527","F",28,"btech",60,65.07,1
"528",528,"Student_528","F",22,"mtech",60,69.07,1
"529",529,"Student_529","F",26,"phd",69,77.26,0
"530",530,"Student_530","F",21,"btech",80,73.05,1
"531",531,"Student_531","F",21,"btech",81,78.27,1
"532",532,"Student_532","M",26,"btech",58,63.31,1
"533",533,"Student_533","F",27,"phd",73,72.09,0
"534",534,"Student_534","M",27,"btech",92,77.14,0
"535",535,"Student_535","F",20,"btech",85,77.56,0
"536",536,"Student_536","F",29,"btech",45,60.62,0
"537",537,"Student_537","F",23,"btech",72,78.91,1
"538",538,"Student_538","F",24,"btech",54,66.18,0
"539",539,"Student_539","M",29,"btech",70,66.58,0
"540",540,"Student_540","F",24,"mtech",73,78.95,1
"541",541,"Student_541","F",28,"phd",69,62.49,1
"542",542,"Student_542","M",29,"phd",50,74.24,0
"543",543,"Student_543","F",25,"btech",58,69.71,1
"544",544,"Student_544","M",27,"mtech",78,79.5,1
"545",545,"Student_545","M",27,"btech",60,75.78,0
"546",546,"Student_546","F",24,"btech",45,70.25,0
"547",547,"Student_547","F",21,"phd",66,57.28,0
"548",548,"Student_548","F",24,"phd",79,67.2,1
"549",549,"Student_549","F",28,"btech",81,71.51,0
"550",550,"Student_550","F",25,"btech",65,60.34,0
"551",551,"Student_551","F",22,"phd",81,78.1,1
"552",552,"Student_552","F",28,"mtech",59,68.16,1
"553",553,"Student_553","F",20,"btech",58,67.28,1
"554",554,"Student_554","M",25,"btech",76,62.71,1
"555",555,"Student_555","F",22,"mtech",64,59.05,0
"556",556,"Student_556","M",24,"mtech",55,66.58,1
"557",557,"Student_557","F",29,"btech",75,63.39,1
"558",558,"Student_558","M",20,"btech",62,68.81,1
"559",559,"Student_559","M",24,"btech",81,76.37,0
"560",560,"Student_560","F",20,"btech",74,67.38,1
"561",561,"Student_561","F",21,"btech",70,69.6,0
"562",562,"Student_562","F",24,"btech",66,63.44,1
"563",563,"Student_563","F",25,"btech",52,69.9,1
"564",564,"Student_564","F",24,"btech",62,67.63,1
"565",565,"Student_565","F",27,"btech",78,73.42,1
"566",566,"Student_566","F",21,"btech",75,70.05,0
"567",567,"Student_567","M",24,"phd",58,55.8,1
"568",568,"Student_568","F",27,"btech",71,76.03,1
"569",569,"Student_569","F",21,"mtech",64,64.09,0
"570",570,"Student_570","M",28,"btech",60,73.04,1
"571",571,"Student_571","F",21,"btech",73,57.3,0
"572",572,"Student_572","F",23,"btech",69,68.84,1
"573",573,"Student_573","F",23,"btech",49,65.95,1
"574",574,"Student_574","F",29,"btech",65,77.19,0
"575",575,"Student_575","M",29,"btech",62,59.74,1
"576",576,"Student_576","F",29,"mtech",54,64.29,0
"577",577,"Student_577","M",26,"btech",64,73.62,0
"578",578,"Student_578","M",24,"btech",55,65.96,0
"579",579,"Student_579","M",26,"btech",60,75.87,0
"580",580,"Student_580","F",20,"btech",62,67.86,0
"581",581,"Student_581","M",24,"btech",78,83.98,1
"582",582,"Student_582","M",25,"btech",77,63.38,1
"583",583,"Student_583","M",27,"btech",47,60.41,0
"584",584,"Student_584","M",24,"btech",78,90.29,1
"585",585,"Student_585","F",27,"btech",50,68.19,0
"586",586,"Student_586","M",28,"btech",67,55.86,0
"587",587,"Student_587","F",20,"phd",78,62.8,1
"588",588,"Student_588","F",20,"btech",59,65.2,0
"589",589,"Student_589","F",26,"btech",66,65.11,0
"590",590,"Student_590","F",29,"btech",62,77.04,1
"591",591,"Student_591","F",24,"mtech",65,64.86,0
"592",592,"Student_592","F",28,"mtech",73,69.27,0
"593",593,"Student_593","F",23,"btech",60,60.82,0
"594",594,"Student_594","F",28,"phd",73,62.13,0
"595",595,"Student_595","M",28,"btech",72,54.67,1
"596",596,"Student_596","F",22,"mtech",80,65.31,1
"597",597,"Student_597","M",20,"btech",78,60.89,0
"598",598,"Student_598","F",24,"btech",60,70.64,1
"599",599,"Student_599","F",24,"btech",69,66.39,1
"600",600,"Student_600","F",25,"mtech",80,70.56,0
"601",601,"Student_601","F",26,"btech",54,86.94,1
"602",602,"Student_602","F",23,"mtech",57,78.74,0
"603",603,"Student_603","F",21,"mtech",41,85.76,1
"604",604,"Student_604","F",22,"mtech",63,81.67,0
"605",605,"Student_605","F",28,"mtech",69,61.64,0
"606",606,"Student_606","F",28,"mtech",61,71.54,0
"607",607,"Student_607","F",26,"mtech",68,65.05,1
"608",608,"Student_608","F",25,"btech",78,59.15,1
"609",609,"Student_609","F",20,"btech",50,82.11,1
"610",610,"Student_610","F",25,"btech",71,70.97,1
"611",611,"Student_611","F",25,"btech",70,76.43,1
"612",612,"Student_612","M",29,"mtech",77,68.32,0
"613",613,"Student_613","F",25,"btech",61,73.04,1
"614",614,"Student_614","M",28,"mtech",77,79.49,0
"615",615,"Student_615","M",26,"btech",77,72.09,1
"616",616,"Student_616","M",26,"btech",66,61.14,1
"617",617,"Student_617","F",23,"btech",58,65.28,1
"618",618,"Student_618","F",25,"btech",85,67.56,0
"619",619,"Student_619","F",26,"btech",56,71.21,1
"620",620,"Student_620","M",26,"mtech",76,72.86,1
"621",621,"Student_621","F",28,"btech",60,71.37,1
"622",622,"Student_622","F",21,"btech",64,67.88,1
"623",623,"Student_623","F",26,"btech",70,70.58,1
"624",624,"Student_624","F",22,"btech",57,75.6,0
"625",625,"Student_625","M",22,"mtech",68,74.93,1
"626",626,"Student_626","M",25,"phd",57,70.24,1
"627",627,"Student_627","M",21,"mtech",49,73.76,1
"628",628,"Student_628","F",23,"btech",76,66.6,0
"629",629,"Student_629","M",29,"phd",62,90.41,1
"630",630,"Student_630","F",27,"btech",46,71,1
"631",631,"Student_631","M",20,"btech",66,62.77,0
"632",632,"Student_632","F",23,"mtech",62,84.25,1
"633",633,"Student_633","F",21,"mtech",75,65.1,0
"634",634,"Student_634","F",27,"btech",84,77.56,0
"635",635,"Student_635","M",20,"mtech",73,64.82,1
"636",636,"Student_636","M",22,"btech",60,67.15,0
"637",637,"Student_637","F",20,"btech",63,75.69,1
"638",638,"Student_638","F",25,"phd",61,52.55,0
"639",639,"Student_639","F",24,"phd",74,68.57,0
"640",640,"Student_640","F",21,"btech",75,69.79,1
"641",641,"Student_641","F",23,"mtech",64,65.08,1
"642",642,"Student_642","F",27,"mtech",74,61.7,0
"643",643,"Student_643","F",21,"btech",79,70.64,1
"644",644,"Student_644","F",20,"btech",73,67.01,1
"645",645,"Student_645","M",21,"btech",65,67.41,0
"646",646,"Student_646","F",27,"mtech",50,49.52,1
"647",647,"Student_647","F",28,"mtech",76,63.46,1
"648",648,"Student_648","F",24,"btech",59,66.54,1
"649",649,"Student_649","F",21,"btech",59,66.76,0
"650",650,"Student_650","F",22,"btech",60,78.74,1
"651",651,"Student_651","M",23,"mtech",57,71.68,1
"652",652,"Student_652","F",27,"btech",84,61.16,1
"653",653,"Student_653","M",25,"btech",66,53.26,1
"654",654,"Student_654","F",20,"btech",66,75.9,0
"655",655,"Student_655","F",24,"btech",66,63.03,1
"656",656,"Student_656","F",29,"mtech",70,53.66,1
"657",657,"Student_657","M",23,"btech",58,64.51,1
"658",658,"Student_658","M",22,"btech",76,70.64,1
"659",659,"Student_659","F",29,"mtech",66,69.7,1
"660",660,"Student_660","M",28,"btech",54,74.88,1
"661",661,"Student_661","F",27,"btech",73,71.55,0
"662",662,"Student_662","F",29,"btech",70,74.02,0
"663",663,"Student_663","M",26,"mtech",65,55.56,1
"664",664,"Student_664","F",23,"btech",52,68.71,0
"665",665,"Student_665","M",20,"btech",59,75.19,1
"666",666,"Student_666","M",21,"btech",81,75.28,1
"667",667,"Student_667","M",23,"phd",66,74.87,1
"668",668,"Student_668","F",27,"btech",54,79.02,1
"669",669,"Student_669","M",25,"btech",54,57.13,0
"670",670,"Student_670","M",23,"btech",66,72.33,0
"671",671,"Student_671","M",20,"btech",68,79.67,1
"672",672,"Student_672","M",21,"mtech",67,69.72,0
"673",673,"Student_673","F",22,"btech",54,67.14,1
"674",674,"Student_674","M",25,"btech",72,65.73,0
"675",675,"Student_675","F",27,"btech",62,64.73,0
"676",676,"Student_676","M",29,"btech",72,82.01,0
"677",677,"Student_677","F",23,"btech",76,65.14,0
"678",678,"Student_678","M",26,"btech",56,60.58,0
"679",679,"Student_679","F",27,"mtech",54,80.96,1
"680",680,"Student_680","F",27,"phd",67,76.58,0
"681",681,"Student_681","F",29,"btech",63,73.76,1
"682",682,"Student_682","M",23,"btech",85,65.71,1
"683",683,"Student_683","F",24,"btech",76,47.5,1
"684",684,"Student_684","F",27,"mtech",63,53.19,1
"685",685,"Student_685","F",22,"btech",71,69.24,1
"686",686,"Student_686","M",29,"btech",61,70.76,1
"687",687,"Student_687","F",21,"btech",55,71.52,0
"688",688,"Student_688","F",27,"btech",76,56.4,1
"689",689,"Student_689","F",22,"mtech",64,69.17,0
"690",690,"Student_690","M",21,"btech",67,68.41,0
"691",691,"Student_691","F",29,"btech",71,59.11,1
"692",692,"Student_692","M",24,"btech",79,58.75,0
"693",693,"Student_693","F",23,"btech",59,77.06,1
"694",694,"Student_694","F",29,"btech",52,73.38,1
"695",695,"Student_695","F",29,"phd",75,72.71,1
"696",696,"Student_696","F",25,"phd",58,69.51,0
"697",697,"Student_697","F",23,"btech",81,60.98,1
"698",698,"Student_698","M",24,"btech",56,78.11,1
"699",699,"Student_699","M",26,"btech",60,73.09,0
"700",700,"Student_700","F",23,"btech",60,69.67,1
"701",701,"Student_701","F",26,"btech",57,66.86,1
"702",702,"Student_702","M",21,"btech",52,70.5,1
"703",703,"Student_703","F",25,"mtech",59,72.36,0
"704",704,"Student_704","M",26,"btech",68,75.56,0
"705",705,"Student_705","F",29,"btech",68,70.85,0
"706",706,"Student_706","M",27,"mtech",52,80.42,0
"707",707,"Student_707","F",28,"phd",64,54.09,1
"708",708,"Student_708","F",25,"btech",62,67.17,1
"709",709,"Student_709","M",24,"btech",74,82.5,1
"710",710,"Student_710","F",28,"btech",71,65.52,1
"711",711,"Student_711","M",24,"mtech",57,76.1,1
"712",712,"Student_712","M",29,"phd",59,58.34,1
"713",713,"Student_713","F",25,"btech",61,69.69,1
"714",714,"Student_714","F",20,"btech",67,64.34,0
"715",715,"Student_715","F",26,"phd",62,69.79,0
"716",716,"Student_716","M",29,"btech",42,71.98,0
"717",717,"Student_717","M",28,"btech",54,61.34,1
"718",718,"Student_718","F",24,"mtech",60,74.26,1
"719",719,"Student_719","F",21,"btech",48,59.52,1
"720",720,"Student_720","M",27,"btech",67,56.74,1
"721",721,"Student_721","M",27,"phd",73,54.13,0
"722",722,"Student_722","F",27,"btech",51,80.33,1
"723",723,"Student_723","F",27,"btech",72,72.59,1
"724",724,"Student_724","M",21,"btech",56,66.56,1
"725",725,"Student_725","M",23,"btech",75,78.92,1
"726",726,"Student_726","M",24,"btech",69,79.72,1
"727",727,"Student_727","M",27,"btech",73,69.48,1
"728",728,"Student_728","F",29,"btech",71,72.01,1
"729",729,"Student_729","F",26,"mtech",59,76.26,1
"730",730,"Student_730","M",29,"btech",60,48.81,1
"731",731,"Student_731","F",26,"mtech",78,69.13,1
"732",732,"Student_732","F",24,"btech",55,61.55,1
"733",733,"Student_733","F",24,"btech",48,57.38,0
"734",734,"Student_734","F",22,"btech",63,67.06,1
"735",735,"Student_735","M",25,"btech",50,57.67,1
"736",736,"Student_736","F",24,"mtech",61,58.8,0
"737",737,"Student_737","M",21,"btech",28,74.66,0
"738",738,"Student_738","F",20,"phd",75,63.26,0
"739",739,"Student_739","F",20,"btech",49,67.83,1
"740",740,"Student_740","F",22,"phd",69,87.98,0
"741",741,"Student_741","M",20,"mtech",63,76.12,0
"742",742,"Student_742","M",27,"phd",65,62.35,0
"743",743,"Student_743","F",21,"btech",72,75.1,0
"744",744,"Student_744","F",26,"btech",54,65.05,1
"745",745,"Student_745","M",26,"btech",58,58.62,1
"746",746,"Student_746","F",27,"btech",63,52.83,0
"747",747,"Student_747","M",27,"btech",81,71.34,0
"748",748,"Student_748","M",28,"mtech",64,70.35,1
"749",749,"Student_749","M",24,"btech",79,66.4,1
"750",750,"Student_750","F",22,"mtech",63,71.06,1
"751",751,"Student_751","F",22,"btech",58,75.73,1
"752",752,"Student_752","F",26,"phd",71,78.85,0
"753",753,"Student_753","F",28,"mtech",46,73.44,0
"754",754,"Student_754","M",21,"phd",68,74.13,0
"755",755,"Student_755","F",24,"btech",65,84.93,0
"756",756,"Student_756","F",26,"mtech",69,85.24,1
"757",757,"Student_757","F",26,"btech",55,70.66,1
"758",758,"Student_758","F",29,"btech",55,60.62,1
"759",759,"Student_759","M",20,"btech",74,70.13,1
"760",760,"Student_760","F",25,"mtech",86,59.62,1
"761",761,"Student_761","M",22,"btech",67,69.35,1
"762",762,"Student_762","F",28,"btech",82,76.32,1
"763",763,"Student_763","M",21,"btech",56,75.4,0
"764",764,"Student_764","F",20,"mtech",72,76.51,1
"765",765,"Student_765","F",25,"btech",38,73.73,1
"766",766,"Student_766","F",24,"phd",87,75.93,1
"767",767,"Student_767","F",23,"mtech",51,65.03,0
"768",768,"Student_768","F",23,"btech",58,66.71,0
"769",769,"Student_769","M",29,"btech",65,42.41,1
"770",770,"Student_770","F",28,"mtech",67,61.84,0
"771",771,"Student_771","F",20,"btech",64,70.17,0
"772",772,"Student_772","M",26,"btech",58,74.35,1
"773",773,"Student_773","F",28,"btech",45,68.07,1
"774",774,"Student_774","M",28,"mtech",55,68.21,0
"775",775,"Student_775","F",23,"phd",55,61.97,1
"776",776,"Student_776","F",24,"phd",76,69.13,1
"777",777,"Student_777","F",24,"btech",57,76.72,1
"778",778,"Student_778","F",20,"mtech",60,58.83,1
"779",779,"Student_779","M",24,"mtech",56,60.05,0
"780",780,"Student_780","F",23,"btech",77,57.76,0
"781",781,"Student_781","M",20,"mtech",76,74.16,0
"782",782,"Student_782","F",24,"btech",66,53.14,1
"783",783,"Student_783","M",29,"btech",69,67.81,0
"784",784,"Student_784","F",27,"mtech",53,75.71,1
"785",785,"Student_785","M",22,"btech",70,74.41,0
"786",786,"Student_786","F",24,"btech",73,74.65,1
"787",787,"Student_787","F",26,"btech",67,62.43,0
"788",788,"Student_788","F",23,"btech",62,75.49,1
"789",789,"Student_789","F",27,"btech",63,67.03,1
"790",790,"Student_790","F",29,"btech",62,67.76,0
"791",791,"Student_791","F",27,"mtech",72,66.25,1
"792",792,"Student_792","F",23,"mtech",69,72.59,0
"793",793,"Student_793","F",24,"mtech",55,71.56,1
"794",794,"Student_794","F",29,"btech",57,69.72,1
"795",795,"Student_795","M",24,"mtech",79,84.64,1
"796",796,"Student_796","F",24,"mtech",65,70.68,1
"797",797,"Student_797","M",27,"btech",53,75.82,1
"798",798,"Student_798","F",25,"btech",50,75.2,1
"799",799,"Student_799","M",24,"btech",56,46.02,1
"800",800,"Student_800","F",26,"btech",67,73.26,0
"801",801,"Student_801","M",26,"mtech",80,72.61,1
"802",802,"Student_802","F",25,"phd",53,77.87,1
"803",803,"Student_803","F",21,"phd",73,57.15,0
"804",804,"Student_804","F",20,"mtech",68,63.96,0
"805",805,"Student_805","F",20,"btech",66,63.31,1
"806",806,"Student_806","F",24,"btech",54,79.39,1
"807",807,"Student_807","F",21,"btech",74,72.77,1
"808",808,"Student_808","M",26,"btech",53,60.15,0
"809",809,"Student_809","F",29,"btech",59,66.88,1
"810",810,"Student_810","M",20,"btech",72,74.37,1
"811",811,"Student_811","F",29,"btech",63,68.02,1
"812",812,"Student_812","F",27,"btech",79,79,1
"813",813,"Student_813","F",20,"mtech",74,88.08,1
"814",814,"Student_814","F",24,"btech",66,69,1
"815",815,"Student_815","F",27,"mtech",62,65.9,1
"816",816,"Student_816","F",27,"btech",75,80.01,0
"817",817,"Student_817","F",22,"btech",56,60.9,1
"818",818,"Student_818","M",21,"btech",53,73.12,1
"819",819,"Student_819","F",23,"mtech",63,69.62,1
"820",820,"Student_820","F",28,"btech",69,65.9,0
"821",821,"Student_821","F",21,"btech",72,74,0
"822",822,"Student_822","F",24,"mtech",69,69.23,1
"823",823,"Student_823","M",26,"btech",80,62.03,0
"824",824,"Student_824","M",28,"btech",70,73.27,1
"825",825,"Student_825","F",27,"btech",71,71.84,0
"826",826,"Student_826","F",22,"phd",70,53.21,1
"827",827,"Student_827","F",26,"btech",39,71.55,0
"828",828,"Student_828","M",28,"mtech",60,72.14,1
"829",829,"Student_829","F",20,"btech",68,55.09,0
"830",830,"Student_830","F",29,"btech",53,69.29,0
"831",831,"Student_831","F",27,"btech",67,71.53,0
"832",832,"Student_832","M",20,"phd",68,64.49,0
"833",833,"Student_833","F",21,"mtech",68,58.46,0
"834",834,"Student_834","F",22,"btech",65,68.67,1
"835",835,"Student_835","M",29,"btech",65,67.11,1
"836",836,"Student_836","F",20,"btech",53,69.77,1
"837",837,"Student_837","M",21,"btech",74,63.52,0
"838",838,"Student_838","F",23,"btech",58,74.8,1
"839",839,"Student_839","M",22,"btech",67,71.49,1
"840",840,"Student_840","F",22,"mtech",66,66.37,0
"841",841,"Student_841","M",23,"btech",54,67.78,1
"842",842,"Student_842","F",20,"btech",52,66.59,0
"843",843,"Student_843","F",25,"btech",60,71.2,1
"844",844,"Student_844","M",24,"btech",62,77.77,1
"845",845,"Student_845","F",20,"mtech",58,81.9,0
"846",846,"Student_846","M",26,"btech",60,62.83,0
"847",847,"Student_847","F",23,"phd",74,82.07,1
"848",848,"Student_848","M",20,"btech",51,71.34,0
"849",849,"Student_849","F",28,"mtech",60,74.82,0
"850",850,"Student_850","F",23,"mtech",81,78.86,1
"851",851,"Student_851","F",23,"btech",60,87.37,1
"852",852,"Student_852","M",26,"mtech",52,70.04,0
"853",853,"Student_853","F",23,"btech",61,66.73,1
"854",854,"Student_854","M",26,"btech",59,73.84,1
"855",855,"Student_855","F",20,"btech",71,69.94,0
"856",856,"Student_856","F",28,"btech",62,67.94,1
"857",857,"Student_857","F",25,"btech",66,58.93,1
"858",858,"Student_858","F",27,"btech",46,67.55,1
"859",859,"Student_859","M",22,"mtech",78,52.67,1
"860",860,"Student_860","F",20,"mtech",49,68.84,0
"861",861,"Student_861","F",24,"btech",53,77.77,0
"862",862,"Student_862","F",22,"mtech",89,72.25,0
"863",863,"Student_863","F",22,"phd",73,88.27,0
"864",864,"Student_864","F",22,"mtech",75,52.24,0
"865",865,"Student_865","F",21,"mtech",65,63.56,1
"866",866,"Student_866","F",24,"btech",61,79.51,1
"867",867,"Student_867","F",26,"btech",71,69.32,0
"868",868,"Student_868","F",25,"btech",69,74,0
"869",869,"Student_869","F",25,"btech",76,61.55,0
"870",870,"Student_870","F",20,"phd",63,73.69,1
"871",871,"Student_871","F",22,"btech",61,77.37,1
"872",872,"Student_872","F",23,"btech",70,67.62,1
"873",873,"Student_873","M",29,"btech",62,68.95,1
"874",874,"Student_874","M",20,"btech",51,63.39,1
"875",875,"Student_875","M",28,"btech",60,75.27,1
"876",876,"Student_876","F",21,"btech",76,68.21,1
"877",877,"Student_877","M",26,"btech",75,72.25,0
"878",878,"Student_878","F",26,"phd",51,77.02,0
"879",879,"Student_879","F",26,"btech",60,68.49,0
"880",880,"Student_880","M",29,"btech",61,73.08,1
"881",881,"Student_881","F",25,"btech",66,76.03,1
"882",882,"Student_882","F",22,"btech",57,65.45,1
"883",883,"Student_883","M",24,"btech",71,59.38,1
"884",884,"Student_884","F",26,"mtech",97,57.83,1
"885",885,"Student_885","M",25,"mtech",68,80.97,1
"886",886,"Student_886","F",20,"btech",66,80.78,1
"887",887,"Student_887","M",20,"btech",48,64.06,0
"888",888,"Student_888","F",23,"btech",62,67.21,1
"889",889,"Student_889","F",23,"btech",59,62.79,0
"890",890,"Student_890","F",23,"phd",59,65.69,1
"891",891,"Student_891","M",22,"mtech",82,70.51,1
"892",892,"Student_892","F",23,"btech",71,70.31,1
"893",893,"Student_893","M",23,"btech",60,61.47,1
"894",894,"Student_894","M",23,"btech",47,85.17,0
"895",895,"Student_895","F",22,"btech",49,57.62,1
"896",896,"Student_896","F",25,"mtech",60,70.85,0
"897",897,"Student_897","F",20,"btech",68,66.81,0
"898",898,"Student_898","M",21,"btech",49,78.82,1
"899",899,"Student_899","F",29,"btech",70,77.23,1
"900",900,"Student_900","F",21,"btech",60,69.35,1
"901",901,"Student_901","F",24,"phd",58,68.7,0
"902",902,"Student_902","M",21,"btech",40,70.83,0
"903",903,"Student_903","F",29,"mtech",64,71.55,0
"904",904,"Student_904","F",27,"phd",70,68.19,1
"905",905,"Student_905","F",23,"phd",53,73.72,1
"906",906,"Student_906","F",26,"btech",79,59.1,0
"907",907,"Student_907","F",23,"btech",71,47.99,0
"908",908,"Student_908","M",23,"btech",64,82.33,0
"909",909,"Student_909","F",29,"btech",71,79.68,1
"910",910,"Student_910","F",25,"mtech",62,71.48,0
"911",911,"Student_911","M",20,"mtech",57,83.56,1
"912",912,"Student_912","M",27,"btech",76,79.85,0
"913",913,"Student_913","F",22,"btech",69,57.01,1
"914",914,"Student_914","F",28,"btech",57,74.36,1
"915",915,"Student_915","M",20,"btech",66,69.51,1
"916",916,"Student_916","M",23,"mtech",67,61.7,1
"917",917,"Student_917","F",29,"mtech",63,72.4,1
"918",918,"Student_918","F",27,"mtech",74,83.47,1
"919",919,"Student_919","F",26,"btech",73,73.44,1
"920",920,"Student_920","M",29,"btech",54,72.37,1
"921",921,"Student_921","F",28,"btech",68,75.22,1
"922",922,"Student_922","F",22,"btech",73,74.4,1
"923",923,"Student_923","M",25,"btech",48,80.18,0
"924",924,"Student_924","M",26,"mtech",66,78.92,1
"925",925,"Student_925","F",25,"btech",77,74.67,1
"926",926,"Student_926","M",23,"btech",67,63.93,1
"927",927,"Student_927","F",23,"mtech",73,76.95,0
"928",928,"Student_928","M",24,"phd",56,58.65,1
"929",929,"Student_929","F",28,"mtech",54,80.43,0
"930",930,"Student_930","M",28,"btech",55,61.89,0
"931",931,"Student_931","F",23,"phd",53,93.79,0
"932",932,"Student_932","F",28,"btech",65,69.59,1
"933",933,"Student_933","M",24,"mtech",61,82.96,1
"934",934,"Student_934","M",20,"btech",60,65.65,1
"935",935,"Student_935","F",22,"btech",59,69.42,1
"936",936,"Student_936","M",25,"mtech",57,79.84,1
"937",937,"Student_937","M",23,"btech",63,74.79,1
"938",938,"Student_938","F",22,"btech",56,69.21,1
"939",939,"Student_939","M",29,"btech",61,77.98,1
"940",940,"Student_940","F",27,"btech",63,78.22,0
"941",941,"Student_941","F",28,"btech",84,68.75,1
"942",942,"Student_942","F",24,"btech",73,57.25,0
"943",943,"Student_943","F",21,"mtech",48,71.55,0
"944",944,"Student_944","F",23,"btech",70,61.86,1
"945",945,"Student_945","M",22,"mtech",59,52.23,0
"946",946,"Student_946","F",26,"btech",58,60.55,1
"947",947,"Student_947","F",21,"btech",68,80.2,1
"948",948,"Student_948","F",29,"btech",57,62.73,1
"949",949,"Student_949","F",28,"btech",57,82.14,0
"950",950,"Student_950","M",28,"btech",67,73.28,0
"951",951,"Student_951","F",26,"btech",58,84.51,1
"952",952,"Student_952","F",27,"btech",68,70.16,1
"953",953,"Student_953","M",25,"btech",67,67.45,1
"954",954,"Student_954","F",21,"btech",58,74.65,0
"955",955,"Student_955","M",23,"mtech",77,83.36,1
"956",956,"Student_956","F",22,"btech",61,74.66,1
"957",957,"Student_957","M",20,"mtech",54,68.33,1
"958",958,"Student_958","F",22,"phd",61,65.81,0
"959",959,"Student_959","F",20,"btech",66,72.56,0
"960",960,"Student_960","M",28,"mtech",79,60.08,1
"961",961,"Student_961","M",20,"btech",77,74.19,1
"962",962,"Student_962","M",23,"mtech",64,70.76,1
"963",963,"Student_963","M",21,"phd",72,77.02,0
"964",964,"Student_964","M",28,"btech",46,61.66,1
"965",965,"Student_965","F",26,"btech",65,69.16,1
"966",966,"Student_966","F",29,"btech",45,67.84,1
"967",967,"Student_967","F",29,"mtech",77,62.46,1
"968",968,"Student_968","M",27,"btech",73,80.01,0
"969",969,"Student_969","M",21,"btech",61,67.19,1
"970",970,"Student_970","F",26,"btech",54,64.29,1
"971",971,"Student_971","F",21,"btech",64,62.5,1
"972",972,"Student_972","F",28,"btech",48,59.7,0
"973",973,"Student_973","M",22,"mtech",72,54.65,1
"974",974,"Student_974","M",26,"btech",67,79.53,0
"975",975,"Student_975","F",22,"phd",66,64.5,0
"976",976,"Student_976","M",21,"btech",52,73.45,1
"977",977,"Student_977","F",29,"btech",72,79.41,0
"978",978,"Student_978","M",26,"phd",39,70.67,0
"979",979,"Student_979","M",22,"btech",89,64.52,0
"980",980,"Student_980","M",29,"btech",55,63.43,1
"981",981,"Student_981","M",29,"mtech",50,81.31,1
"982",982,"Student_982","F",27,"btech",74,63.78,0
"983",983,"Student_983","F",22,"btech",83,72.7,1
"984",984,"Student_984","M",25,"btech",55,66.96,1
"985",985,"Student_985","F",28,"phd",47,83.85,0
"986",986,"Student_986","M",28,"btech",72,83.64,1
"987",987,"Student_987","F",25,"btech",65,61.6,1
"988",988,"Student_988","F",29,"mtech",52,77.57,1
"989",989,"Student_989","M",27,"btech",65,61.52,1
"990",990,"Student_990","F",24,"phd",65,64.2,1
"991",991,"Student_991","F",23,"btech",60,68.66,1
"992",992,"Student_992","F",20,"btech",73,72.93,0
"993",993,"Student_993","F",22,"btech",62,54.24,0
"994",994,"Student_994","F",25,"btech",70,78.63,0
"995",995,"Student_995","F",29,"mtech",78,63.64,0
"996",996,"Student_996","F",29,"phd",48,70.9,0
"997",997,"Student_997","M",20,"mtech",64,60.1,1
"998",998,"Student_998","F",26,"btech",65,78.23,1
"999",999,"Student_999","F",27,"btech",62,61.29,1
"1000",1000,"Student_1000","F",29,"btech",59,79.03,1


================================================
FILE: data/dateformat1.R
================================================
# Date Formats
#https://www.r-bloggers.com/date-formats-in-r/
#http://www.statmethods.net/input/dates.html


# use as.Date( ) to convert strings to dates 
mydates <- as.Date(c("2007-06-22", "2004-02-13"))
# number of days between 6/22/07 and 2/13/04 
days <- mydates[1] - mydates[2]
Sys.Date() #returns today's date. 
date() #returns the current date and time.

as.Date(dates,
        format = "%m/%d/%y")

================================================
FILE: data/denco.csv
================================================
custname,region,partnum,revenue,cost,margin
3M COMPANY,01-East,727032005,24097.50,19851.82,4245.68
4-STATE SUPPLY,01-East,735602000,156200.00,52381.38,103818.62
4-STATE SUPPLY,01-East,777143000,34927.20,15382.08,19545.12
4-STATE SUPPLY,01-East,777142000,21989.40,12562.50,9426.90
4-STATE SUPPLY,01-East,735750000,12487.00,3686.91,8800.09
A G C INC.,01-East,791104479,78642.00,18147.40,60494.60
A G C INC.,01-East,791106280,3340.46,7614.14,-4273.68
A P S TECHNOLOGY,01-East,764521000,333.00,13.27,319.73
A W CHESTERTON COMPANY,01-East,770560000,2937.60,258.27,2679.33
A W CHESTERTON COMPANY,01-East,770568000,1029.00,89.50,939.50
A&F WHOLESALE INSULATION,01-East,770264000,95134.50,41677.18,53457.32
A&F WHOLESALE INSULATION,01-East,735602000,89103.50,26645.65,62457.85
A&F WHOLESALE INSULATION,01-East,764861000,70882.00,30089.92,40792.08
A&F WHOLESALE INSULATION,01-East,764521000,70611.00,22683.19,47927.81
A&F WHOLESALE INSULATION,01-East,770220200,69066.00,21411.45,47654.55
A&F WHOLESALE INSULATION,01-East,724281200,45570.00,24306.87,21263.13
A&F WHOLESALE INSULATION,01-East,733151500,21384.00,5113.93,16270.07
A&F WHOLESALE INSULATION,01-East,764901000,12155.00,3649.65,8505.35
A&F WHOLESALE INSULATION,01-East,770380000,12140.00,2314.54,9825.46
A&F WHOLESALE INSULATION,01-East,735602110,11028.00,3418.00,7610.00
A&F WHOLESALE INSULATION,01-East,770200000,10953.00,2403.03,8549.97
A&F WHOLESALE INSULATION,01-East,737271000,6025.70,9870.84,-3845.14
A&F WHOLESALE INSULATION,01-East,770540000,4897.38,474.88,4422.50
A&F WHOLESALE INSULATION,01-East,733525600,4692.50,868.02,3824.48
A&F WHOLESALE INSULATION,01-East,764821000,4390.00,1356.36,3033.64
A&F WHOLESALE INSULATION,01-East,738521000,2373.12,1601.27,771.85
A&F WHOLESALE INSULATION,01-East,739561200,1628.80,659.65,969.15
A&F WHOLESALE INSULATION,01-East,764781000,1594.00,408.90,1185.10
A&F WHOLESALE INSULATION,01-East,770512000,1192.55,88.67,1103.88
A&F WHOLESALE INSULATION,01-East,733211050,968.00,223.64,744.36
A&F WHOLESALE INSULATION,01-East,734851000,860.58,182.80,677.78
A&F WHOLESALE INSULATION,01-East,739541200,829.44,256.39,573.05
A&F WHOLESALE INSULATION,01-East,764902000,606.00,191.56,414.44
A&F WHOLESALE INSULATION,01-East,735750000,516.00,127.10,388.90
A&F WHOLESALE INSULATION,01-East,734611100,415.05,81.32,333.73
A&F WHOLESALE INSULATION,01-East,739561000,404.64,170.44,234.20
A&F WHOLESALE INSULATION,01-East,734311100,308.92,35.30,273.62
A&F WHOLESALE INSULATION,01-East,733141200,271.20,66.98,204.22
A&F WHOLESALE INSULATION,01-East,733464500,268.00,65.06,202.94
A&F WHOLESALE INSULATION,01-East,735752000,258.00,64.44,193.56
A.C. MILLER CONCRETE,01-East,764481000,23006.00,1404.61,21601.39
A.C. MILLER CONCRETE,01-East,764461006,776.00,54.50,721.50
"ABLE AEROSPACE, INC.",01-East,734311100,381.48,33.70,347.78
ABSORPTION CORP.,01-East,764221000,476.00,33.66,442.34
ACCEL-RF CORPORATION,01-East,733141200,520.00,66.98,453.02
ACME SPECIALTIES INC,01-East,733545700,1079.00,133.91,945.09
ACS INDUSTRIES INC.,01-East,740382101,165294.00,57490.21,107803.79
ACS INDUSTRIES INC.,01-East,733011000,4800.00,1200.36,3599.64
ADVANCE COOLING,01-East,739781100,394.88,95.21,299.67
ADVANCE THERMAL CORP,01-East,768221130,131510.40,39169.80,92340.60
ADVANCE THERMAL CORP,01-East,733201000,18278.00,4679.78,13598.22
ADVANCE THERMAL CORP,01-East,733151500,17460.00,3704.05,13755.95
ADVANCED CLIMATE TECH,01-East,770560000,597.00,89.50,507.50
ADVANCED RADIATION CORP,01-East,734211200,3785.12,369.72,3415.40
AECOM,01-East,770722000,1342.00,38.38,1303.62
AECOM,01-East,770930000,1054.50,23.77,1030.73
AERO ENGINEERING & MFG,01-East,771100000,5400.00,146.93,5253.07
AERO ENGINEERING & MFG,01-East,771102000,967.00,22.98,944.02
AERO TUBE TECHNOLOGIES,01-East,770800000,2736.00,51.10,2684.90
AGIS,01-East,739561100,56288.40,24606.26,31682.14
AGIS,01-East,740063000,38955.50,12193.45,26762.05
AGIS,01-East,764822000,15719.60,2604.25,13115.35
AGIS,01-East,734311100,12846.96,1478.00,11368.96
AGIS,01-East,777601000,4914.82,1366.94,3547.88
AGIS,01-East,734900000,4603.30,1637.23,2966.07
AGIS,01-East,764781040,3262.10,659.07,2603.03
AGIS,01-East,764482000,2740.30,636.09,2104.21
AGIS,01-East,739541100,2550.92,648.78,1902.14
AGIS,01-East,734910000,2068.55,421.22,1647.33
AGIS,01-East,764522000,1883.20,370.20,1513.00
AGIS,01-East,764762000,1677.00,454.39,1222.61
AGIS,01-East,743975000,1492.00,154.10,1337.90
AGIS,01-East,739761100,1214.15,603.28,610.87
AGIS,01-East,771170000,1119.85,75.47,1044.38
AGIS,01-East,738521000,1110.78,573.81,536.97
AGIS,01-East,739450000,756.22,390.35,365.87
AGIS,01-East,739521100,690.14,139.38,550.76
AGIS,01-East,764601000,473.55,93.00,380.55
AGIS,01-East,740023000,463.34,118.54,344.80
AGIS,01-East,764862000,429.15,78.55,350.60
AGIS,01-East,733525600,427.00,65.49,361.51
AGIS,01-East,764821000,385.40,62.62,322.78
AGIS,01-East,793330000,288.80,53.02,235.78
AGIS,01-East,793330074,139.00,48.23,90.77
AGIS,01-East,734611100,131.29,26.96,104.33
AIR LIQUIDE AT REC,01-East,734900000,203.60,37.83,165.77
AIR PRODUCTS & CHEMICALS,01-East,764761000,508.00,30.71,477.29
AIR PRODUCTS & CHEMICALS,01-East,764781000,454.00,20.45,433.55
AIR PRODUCTS & CHEMICALS,01-East,764821000,430.00,15.36,414.64
AKRO-FIREGUARD,01-East,726020019,231.50,22.61,208.89
ALBATROSS UV,01-East,764601000,326.00,13.29,312.71
ALBERTUS ENGINEERING INC.,01-East,734311100,700.00,70.60,629.40
ALCOA,01-East,724401150,28791.00,7594.88,21196.12
ALCOA,01-East,724401500,1620.00,370.66,1249.34
ALL TEMP INSULATION,01-East,735752000,23047.00,6722.23,16324.77
ALPHABET ENERGY INC,01-East,792654000,1024.00,321.90,702.10
ALPHABET ENERGY INC,01-East,792624000,666.00,86.09,579.91
ALPHABET ENERGY INC,01-East,739561200,421.12,76.67,344.45
ALPHABET ENERGY INC,01-East,739370000,364.80,138.24,226.56
ALSTOM POWER INC.,01-East,764902000,4299.75,2011.39,2288.36
ALTAIR TECHNOLOGIES,01-East,740361900,2736.00,898.74,1837.26
ALYSSA OXLEY,01-East,733423700,371.20,45.26,325.94
AMERET LLC,01-East,764261000,424.00,22.89,401.11
AMERICAN CONTROL & ENGRG,01-East,770560000,3018.00,172.18,2845.82
AMERICAN CONTROL & ENGRG,01-East,777601000,366.72,72.02,294.70
AMERICAN DESIGN &,01-East,764821000,430.00,15.36,414.64
AMERICAN NATIONAL CARBIDE,01-East,739761100,7695.36,2572.81,5122.55
AMERICAN POWER SERVICE,01-East,770500000,2043.00,83.60,1959.40
AMERICAN RAILCAR,01-East,764706120,1199.52,326.91,872.61
"AMERISAFE, INC",01-East,733725500,205.20,51.76,153.44
AMERITHERM INC.,01-East,766081010,1101.00,36.37,1064.63
AMERITHERM INC.,01-East,766121000,875.00,27.25,847.75
ANCHOR INSULATION,01-East,768144200,3070.00,526.37,2543.63
ANTHONY INTERNATIONAL,01-East,764521000,973.00,39.87,933.13
APCO SERVICES,01-East,770570000,3316.50,201.46,3115.04
APEX DISTRIBUTING INC,01-East,735602000,23968.00,5841.31,18126.69
APEX DISTRIBUTING,01-East,735602000,90480.00,25799.33,64680.67
APPLIED INDUSTRIAL TECH.,01-East,738561000,20198.72,9345.87,10852.85
APPLIED INDUSTRIAL TECH.,01-East,733525600,14175.00,2075.84,12099.16
APPLIED INDUSTRIAL TECH.,01-East,770222000,9348.15,800.79,8547.36
APPLIED INDUSTRIAL TECH.,01-East,764821000,3362.20,262.86,3099.34
APPLIED INDUSTRIAL TECH.,01-East,770522000,3337.10,205.59,3131.51
APPLIED INDUSTRIAL TECH.,01-East,770560000,1347.30,89.50,1257.80
APPLIED INDUSTRIAL TECH.,01-East,777601000,738.72,130.82,607.90
APPLIED INDUSTRIAL TECH.,01-East,724003000,591.15,81.82,509.33
APPLIED INDUSTRIAL TECH.,01-East,724281200,510.90,89.08,421.82
APPLIED INDUSTRIAL TECH.,01-East,764341000,406.00,47.97,358.03
APPLIED INDUSTRIAL TECH.,01-East,733151600,310.60,46.64,263.96
APPLIED TECHNICAL SERVICE,01-East,754067000,1935.00,617.82,1317.18
APPLIED TECHNICAL SERVICE,01-East,739450000,708.91,260.23,448.68
APPLIED TECHNICAL SERVICE,01-East,764521000,325.80,39.81,285.99
APPROVED AERONAUTICS,01-East,733464500,626.00,65.05,560.95
APPROVED AERONAUTICS,01-East,733444200,348.00,38.64,309.36
ARCHIE BRAY FOUNDATION,01-East,734351200,1890.75,143.35,1747.40
AREMCO PRODUCTS INC,01-East,734000000,11289.75,1365.74,9924.01
AREMCO PRODUCTS INC,01-East,734030000,2236.50,494.66,1741.84
AREMCO PRODUCTS INC,01-East,734040000,1912.00,484.88,1427.12
ARI - MARMADUKE,01-East,764706120,74370.24,20268.30,54101.94
ARI - MARMADUKE,01-East,764652002,34003.20,5785.32,28217.88
ARMENA REFINING USA,01-East,764481000,1758.00,86.00,1672.00
ASHLAND ADVANCED,01-East,766689000,19660.00,2386.27,17273.73
ASK CHEMICALS HI-TECH LLC,01-East,732663000,215136.00,33045.76,182090.24
ASK CHEMICALS HI-TECH LLC,01-East,732670000,11329.90,4547.46,6782.44
ASK CHEMICALS HI-TECH LLC,01-East,732675000,6236.33,2049.38,4186.95
ASK CHEMICALS HI-TECH LLC,01-East,732650000,4881.61,1944.67,2936.94
ASK CHEMICALS HI-TECH LLC,01-East,732662000,4792.50,1755.06,3037.44
ASK CHEMICALS HI-TECH LLC,01-East,732630000,4683.15,1258.91,3424.24
ASK CHEMICALS HI-TECH LLC,01-East,733141200,265.60,133.96,131.64
ASPEN PRODUCTS GROUP,01-East,739390000,364.64,161.76,202.88
ATLANTECH DISTRIBUTION,01-East,735602000,77355.00,29396.27,47958.73
ATLANTECH DISTRIBUTION,01-East,739741100,31786.40,32062.53,-276.13
ATLANTECH DISTRIBUTION,01-East,735752000,10092.00,4207.32,5884.68
ATLANTECH DISTRIBUTION,01-East,735602010,1800.00,1130.24,669.76
ATLANTECH DISTRIBUTION,01-East,734351200,1488.52,147.24,1341.28
ATLANTECH DISTRIBUTION,01-East,734751200,654.45,113.13,541.32
ATLANTECH DISTRIBUTION,01-East,738542000,640.32,574.08,66.24
ATLANTIC CASTING,01-East,734351200,1890.75,143.35,1747.40
"ATLAS HOSE & GASKET CO.,",01-East,739521100,1320.48,120.06,1200.42
ATLAS METALIZING,01-East,733586500,1857.60,181.35,1676.25
ATLAS METALIZING,01-East,734311100,381.48,33.70,347.78
AUBURN MANUFACTURING CO.,01-East,733464600,611.00,66.28,544.72
AUBURN MANUFACTURING CO.,01-East,770310000,518.00,45.47,472.53
AUBURN MANUFACTURING CO.,01-East,733151500,472.00,54.11,417.89
AUBURN MANUFACTURING CO.,01-East,733725500,286.00,51.76,234.24
AUTOMATIC MACHINE PRODUCT,01-East,740382101,4334.40,1351.23,2983.17
"AVIS INDUSTRIES, INC.",01-East,724281900,600.00,148.99,451.01
BAE SYSTEMS,01-East,738401000,681.48,321.12,360.36
BAE SYSTEMS,01-East,768021000,389.00,27.49,361.51
BAILEY POTTERY EQUIPMENT,01-East,734311100,4196.80,410.84,3785.96
BAKER DISTRIBUTING  CO.,01-East,735602000,31774.50,7934.89,23839.61
BAKER MODULES,01-East,793560000,6660.00,2649.33,4010.67
BALLARD MATERIAL PRODUCTS,01-East,764481000,2090.00,169.41,1920.59
BASF CORPORATION,01-East,724071900,23791.32,3183.37,20607.95
BASF,01-East,733141200,520.00,66.98,453.02
BATTELLE FOR THE US DOE,01-East,777601000,386.72,72.02,314.70
BATTELLE FOR THE US DOE,01-East,768111000,280.00,15.38,264.62
BAY INDUSTRIES INC.,01-East,735602000,1477184.00,497108.39,980075.61
BAY INDUSTRIES INC.,01-East,735602110,428300.00,150904.70,277395.30
BAY INDUSTRIES INC.,01-East,764631000,244921.60,80765.31,164156.29
BAY INDUSTRIES INC.,01-East,735750000,111685.00,45262.02,66422.98
BAY INDUSTRIES INC.,01-East,764822000,93744.00,22126.95,71617.05
BAY INDUSTRIES INC.,01-East,764651000,87479.04,24691.61,62787.43
BAY INDUSTRIES INC.,01-East,764901000,80118.75,33921.91,46196.84
BAY INDUSTRIES INC.,01-East,764522000,77203.00,22375.27,54827.73
BAY INDUSTRIES INC.,01-East,764821000,74253.50,28926.60,45326.90
BAY INDUSTRIES INC.,01-East,764521000,48980.00,20993.74,27986.26
BAY INDUSTRIES INC.,01-East,764706300,41077.76,17359.48,23718.28
BAY INDUSTRIES INC.,01-East,764602000,35085.00,11564.29,23520.71
BAY INDUSTRIES INC.,01-East,735752000,28110.00,9784.77,18325.23
BAY INDUSTRIES INC.,01-East,764902000,18430.00,4808.72,13621.28
BAY INDUSTRIES INC.,01-East,735862000,16374.40,7084.09,9290.31
BAY INDUSTRIES INC.,01-East,764261000,14530.00,6650.50,7879.50
BAY INDUSTRIES INC.,01-East,766162000,14175.00,8969.25,5205.75
BAY INDUSTRIES INC.,01-East,764861000,13845.00,6022.31,7822.69
BAY INDUSTRIES INC.,01-East,766121000,13800.00,5970.23,7829.77
BAY INDUSTRIES INC.,01-East,764862000,11307.50,4896.53,6410.97
BAY INDUSTRIES INC.,01-East,764706200,10386.24,3579.18,6807.06
BAY INDUSTRIES INC.,01-East,764341000,9600.00,4797.40,4802.60
BAY INDUSTRIES INC.,01-East,764901050,8960.00,2021.74,6938.26
BAY INDUSTRIES INC.,01-East,733151500,8720.00,2164.26,6555.74
BAY INDUSTRIES INC.,01-East,779988827,8169.56,4272.41,3897.15
BAY INDUSTRIES INC.,01-East,780250000,5103.36,2493.56,2609.80
BAY INDUSTRIES INC.,01-East,780401000,4239.36,2825.72,1413.64
BAY INDUSTRIES INC.,01-East,764601000,3175.00,1328.62,1846.38
BAY INDUSTRIES INC.,01-East,764561000,2053.00,926.49,1126.51
BAY INDUSTRIES INC.,01-East,766001000,1290.00,599.17,690.83
BAY INDUSTRIES INC.,01-East,766122000,1180.00,287.62,892.38
BAY INDUSTRIES INC.,01-East,734910000,571.00,102.70,468.30
BAY INDUSTRIES INC.,01-East,768144210,321.70,118.28,203.42
BAY INSULATION OF TEXAS,01-East,735750000,4740.00,1906.59,2833.41
BAY INSULATION,01-East,739781100,558.59,327.68,230.91
"BENGAL SUPPLY, LLC",01-East,735602000,27360.00,8284.44,19075.56
BIRK MANUFACTURING,01-East,733525500,752.50,70.39,682.11
BLACKSTONE-NEY ULTRASONIC,01-East,739641100,393.84,137.43,256.41
BLASCH PRECISION CERAMICS,01-East,764821000,4402.00,155.37,4246.63
BLASCH PRECISION CERAMICS,01-East,770212000,3133.00,237.35,2895.65
BLASCH PRECISION CERAMICS,01-East,770200000,2592.00,166.40,2425.60
BLASCH PRECISION PRODUCTS,01-East,770212000,2054.00,156.16,1897.84
BLASCH PRECISION PRODUCTS,01-East,770220000,942.00,54.69,887.31
BLASCH PRECISION PRODUCTS,01-East,764821000,860.00,30.72,829.28
BLASCH PRECISION PRODUCTS,01-East,779987651,815.00,95.41,719.59
BLICKMAN INC.,01-East,739541100,23069.76,3433.81,19635.95
BLOOM ENERGY CORPORATION,01-East,777907250,3930.00,2688.00,1242.00
BLOUNT BOATS INC.,01-East,768133000,2592.00,706.53,1885.47
BLOXR CORPORATION,01-East,733525500,5840.00,563.15,5276.85
BLOXR CORPORATION,01-East,733464600,2960.00,349.71,2610.29
BLOXR CORPORATION,01-East,733444200,1676.00,193.20,1482.80
BLOXR CORPORATION,01-East,733464500,1252.00,130.10,1121.90
BODY COTE,01-East,764461006,1200.00,136.24,1063.76
BOEING AEROSPACE COMPANY,01-East,733141200,432.00,59.97,372.03
BOEING COMMERCIAL,01-East,733648000,197199.00,116961.57,80237.43
BOEING COMMERCIAL,01-East,733649000,89635.00,54444.71,35190.29
BOEING COMPANY,01-East,715000000,2635.00,304.36,2330.64
BOEING COMPANY,01-East,737912200,692.00,86.72,605.28
BOEING COMPANY,01-East,737902100,462.00,67.12,394.88
BOEING COMPANY,01-East,727000140,255.20,15.84,239.36
BOLTS METALLIZING WEST,01-East,733464500,596.00,59.04,536.96
BOLTS METALLIZING WEST,01-East,764481000,346.00,16.94,329.06
BOSTON ATLANTIC CORP.,01-East,738361000,601.92,164.47,437.45
BRADCO SUPPLY INC.,01-East,735602000,13050.00,3796.01,9253.99
BRADEN MANUFACTURING,01-East,768231000,65059.00,43104.30,21954.70
BRADEN MANUFACTURING,01-East,768241000,56919.95,37832.36,19087.59
BRADEN MANUFACTURING,01-East,768080000,48538.00,37090.11,11447.89
BRADEN MANUFACTURING,01-East,771320000,44468.50,6242.28,38226.22
BRADEN MANUFACTURING,01-East,768231012,29663.00,20710.29,8952.71
BRADEN MANUFACTURING,01-East,768241102,20942.25,14417.91,6524.34
BRADEN MANUFACTURING,01-East,768231200,3120.00,1794.85,1325.15
BRADEN MANUFACTURING,01-East,768144200,975.20,213.33,761.87
BRADEN MANUFACTURING,01-East,770220000,423.90,27.35,396.55
BRAND ENERGY SERVICES LLC,01-East,733464600,298.00,31.31,266.69
BRAUER SUPPLY COMPANY,01-East,735602000,414296.00,132066.58,282229.42
BRAUER SUPPLY COMPANY,01-East,735602110,58712.00,19152.93,39559.07
BRAUER SUPPLY COMPANY,01-East,735752000,27784.00,10316.12,17467.88
BRAUER SUPPLY COMPANY,01-East,735750000,26081.00,10090.73,15990.27
BRAYTON ENERGY,01-East,740361701,1130.40,628.78,501.62
BRIGHTSOURCE CONSTRUCTION,01-East,777690062,1287662.20,981523.47,306138.73
BRIGHTSOURCE CONSTRUCTION,01-East,777692562,167520.00,159604.68,7915.32
BRIGHTSOURCE CONSTRUCTION,01-East,777692662,74214.00,71587.06,2626.94
BRIGHTSOURCE CONSTRUCTION,01-East,777699062,55212.48,42084.51,13127.97
BRIGHTSOURCE CONSTRUCTION,01-East,777691562,53806.86,41013.63,12793.23
BRIGHTSOURCE CONSTRUCTION,01-East,777690362,48810.30,37205.76,11604.54
BRIGHTSOURCE CONSTRUCTION,01-East,777692462,46068.00,43855.34,2212.66
BRIGHTSOURCE CONSTRUCTION,01-East,777690862,41884.00,31925.88,9958.12
BRIGHTSOURCE CONSTRUCTION,01-East,793550074,33637.68,25235.42,8402.26
BRIGHTSOURCE CONSTRUCTION,01-East,777691162,26386.62,14917.77,11468.85
BRIGHTSOURCE CONSTRUCTION,01-East,777691262,22967.01,12984.93,9982.08
BRIGHTSOURCE CONSTRUCTION,01-East,777692762,14812.56,14256.34,556.22
BRIGHTSOURCE CONSTRUCTION,01-East,777652674,9360.00,7134.30,2225.70
BRIGHTSOURCE CONSTRUCTION,01-East,777691862,9332.70,6996.81,2335.89
BRIGHTSOURCE CONSTRUCTION,01-East,734351200,9206.20,1495.44,7710.76
BRIGHTSOURCE CONSTRUCTION,01-East,734910000,8718.50,2413.34,6305.16
BRIGHTSOURCE CONSTRUCTION,01-East,777694062,8559.36,6524.10,2035.26
BRIGHTSOURCE CONSTRUCTION,01-East,777690162,8506.16,6483.44,2022.72
BRIGHTSOURCE CONSTRUCTION,01-East,777652362,7994.08,7693.90,300.18
BRIGHTSOURCE CONSTRUCTION,01-East,777690262,7766.72,5919.46,1847.26
BRIGHTSOURCE CONSTRUCTION,01-East,777692262,4827.45,3679.56,1147.89
BRIGHTSOURCE CONSTRUCTION,01-East,777690462,4640.40,3537.06,1103.34
BRIGHTSOURCE CONSTRUCTION,01-East,777652474,4491.50,3422.70,1068.80
BRIGHTSOURCE CONSTRUCTION,01-East,779988847,4118.40,3719.38,399.02
BRIGHTSOURCE CONSTRUCTION,01-East,777690962,4039.56,3079.12,960.44
BRIGHTSOURCE CONSTRUCTION,01-East,777690562,3168.18,2414.84,753.34
BRIGHTSOURCE CONSTRUCTION,01-East,777691062,2854.62,2175.86,678.76
BRIGHTSOURCE CONSTRUCTION,01-East,777691962,2446.05,1864.35,581.70
BRIGHTSOURCE CONSTRUCTION,01-East,777692362,2357.25,1796.85,560.40
BRIGHTSOURCE CONSTRUCTION,01-East,777692162,2129.10,1622.88,506.22
BRIGHTSOURCE CONSTRUCTION,01-East,777690662,2041.50,1556.12,485.38
BRIGHTSOURCE CONSTRUCTION,01-East,777690762,1912.50,1457.76,454.74
BRIGHTSOURCE CONSTRUCTION,01-East,777692062,1723.10,1313.36,409.74
BRIGHTSOURCE CONSTRUCTION,01-East,777691662,1636.65,1247.43,389.22
BRIGHTSOURCE CONSTRUCTION,01-East,777693062,1499.80,1143.20,356.60
BRIGHTSOURCE CONSTRUCTION,01-East,777691762,1424.70,1085.82,338.88
BRIGHTSOURCE CONSTRUCTION,01-East,777692962,1192.86,1148.00,44.86
BRIGHTSOURCE CONSTRUCTION,01-East,777652462,1175.60,1131.46,44.14
BRIGHTSOURCE CONSTRUCTION,01-East,777693162,1175.60,1131.46,44.14
BRIGHTSOURCE CONSTRUCTION,01-East,777652762,1147.32,1109.07,38.25
BRIGHTSOURCE CONSTRUCTION,01-East,777652862,1147.32,1109.07,38.25
BRIGHTSOURCE CONSTRUCTION,01-East,777652562,1132.95,876.03,256.92
BRIGHTSOURCE CONSTRUCTION,01-East,777695062,860.04,655.58,204.46
BRIGHTSOURCE CONSTRUCTION,01-East,777692862,662.70,637.78,24.92
BRIGHTSOURCE CONSTRUCTION,01-East,777693262,644.00,619.76,24.24
BRIGHTSOURCE CONSTRUCTION,01-East,777696062,587.16,447.56,139.60
BRIGHTSOURCE CONSTRUCTION,01-East,777697062,333.60,254.30,79.30
BRIGHTSOURCE CONSTRUCTION,01-East,777698062,333.60,254.30,79.30
BRUCE DIAMOND CORP,01-East,739541000,3960.00,614.63,3345.37
BRUCE DIAMOND CORP,01-East,740103000,824.88,135.47,689.41
BRUCE DIAMOND CORP,01-East,740123000,697.20,128.01,569.19
BRUCKNER SUPPLY,01-East,766121000,67600.00,15986.46,51613.54
BRUCKNER SUPPLY,01-East,770522000,49598.90,3037.44,46561.46
BRUCKNER SUPPLY,01-East,733211000,24816.00,5532.59,19283.41
BRUCKNER SUPPLY,01-East,764821000,18717.75,5145.39,13572.36
BRUCKNER SUPPLY,01-East,734970000,6428.22,2695.00,3733.22
BRUCKNER SUPPLY,01-East,734311100,3501.96,308.12,3193.84
BRUCKNER SUPPLY,01-East,733403200,3082.64,396.68,2685.96
BRUCKNER SUPPLY,01-East,740361701,2649.60,1894.86,754.74
BRUCKNER SUPPLY,01-East,770540000,1356.25,94.98,1261.27
BRUCKNER SUPPLY,01-East,740402402,815.00,193.66,621.34
BRUCKNER SUPPLY,01-East,774751000,636.00,79.14,556.86
BRUCKNER SUPPLY,01-East,764221000,476.00,33.66,442.34
BRUCKNER SUPPLY,01-East,733464500,412.00,65.05,346.95
BRUCKNER SUPPLY,01-East,774671000,408.00,48.36,359.64
BRUCKNER SUPPLY,01-East,774901000,336.00,39.57,296.43
BRUCKNER SUPPLY,01-East,764221100,276.00,89.64,186.36
BRUCKNER SUPPLY,01-East,764341000,101.60,23.98,77.62
BRYANT REFRACTORY CO.,01-East,764781040,676089.00,471699.33,204389.67
BRYANT REFRACTORY CO.,01-East,764821000,630120.00,268156.41,361963.59
BRYANT REFRACTORY CO.,01-East,764901000,348688.50,165932.95,182755.55
BRYANT REFRACTORY CO.,01-East,764901050,303447.00,143134.67,160312.33
BRYANT REFRACTORY CO.,01-East,764821030,283968.00,123668.14,160299.86
BRYANT REFRACTORY CO.,01-East,779958162,279532.50,239408.15,40124.35
BRYANT REFRACTORY CO.,01-East,764521000,258694.00,116027.89,142666.11
BRYANT REFRACTORY CO.,01-East,779956871,135368.20,82522.69,52845.51
BRYANT REFRACTORY CO.,01-East,764601000,125635.50,60782.67,64852.83
BRYANT REFRACTORY CO.,01-East,764781000,119858.80,43501.50,76357.30
BRYANT REFRACTORY CO.,01-East,755084012,108981.72,78419.81,30561.91
BRYANT REFRACTORY CO.,01-East,766121000,100401.00,29406.10,70994.90
BRYANT REFRACTORY CO.,01-East,779956872,97091.40,58656.10,38435.30
BRYANT REFRACTORY CO.,01-East,764821073,78502.50,77091.75,1410.75
BRYANT REFRACTORY CO.,01-East,764901073,68160.00,69984.00,-1824.00
BRYANT REFRACTORY CO.,01-East,777902600,66942.40,30423.72,36518.68
BRYANT REFRACTORY CO.,01-East,764861000,62798.50,29542.69,33255.81
BRYANT REFRACTORY CO.,01-East,766122000,60473.00,18929.13,41543.87
BRYANT REFRACTORY CO.,01-East,733211000,59539.00,12254.84,47284.16
BRYANT REFRACTORY CO.,01-East,779958166,58110.00,40961.06,17148.94
BRYANT REFRACTORY CO.,01-East,764522000,52800.00,34920.83,17879.17
BRYANT REFRACTORY CO.,01-East,768231000,46204.70,8821.75,37382.95
BRYANT REFRACTORY CO.,01-East,779957196,43989.88,33000.66,10989.22
BRYANT REFRACTORY CO.,01-East,764821161,41437.50,41106.00,331.50
BRYANT REFRACTORY CO.,01-East,779958163,39330.00,29511.06,9818.94
BRYANT REFRACTORY CO.,01-East,738401000,31212.00,24325.62,6886.38
BRYANT REFRACTORY CO.,01-East,733151500,29912.60,4793.77,25118.83
BRYANT REFRACTORY CO.,01-East,733525600,25490.00,3701.87,21788.13
BRYANT REFRACTORY CO.,01-East,764561000,22148.50,9722.37,12426.13
BRYANT REFRACTORY CO.,01-East,779956873,21656.96,13171.69,8485.27
BRYANT REFRACTORY CO.,01-East,739641100,19815.99,13268.06,6547.93
BRYANT REFRACTORY CO.,01-East,764521061,18910.00,19809.00,-899.00
BRYANT REFRACTORY CO.,01-East,739681100,17905.14,10064.72,7840.42
BRYANT REFRACTORY CO.,01-East,764521073,17080.00,18060.00,-980.00
BRYANT REFRACTORY CO.,01-East,779956889,17040.00,9791.02,7248.98
BRYANT REFRACTORY CO.,01-East,764261000,16775.00,7495.39,9279.61
BRYANT REFRACTORY CO.,01-East,779956888,14752.80,11805.66,2947.14
BRYANT REFRACTORY CO.,01-East,734910000,14153.10,4031.97,10121.13
BRYANT REFRACTORY CO.,01-East,738361000,12791.68,8913.41,3878.27
BRYANT REFRACTORY CO.,01-East,727000140,11168.32,1409.36,9758.96
BRYANT REFRACTORY CO.,01-East,739561000,10692.28,2993.42,7698.86
BRYANT REFRACTORY CO.,01-East,766750050,9856.00,4894.16,4961.84
BRYANT REFRACTORY CO.,01-East,766162000,9756.00,5063.70,4692.30
BRYANT REFRACTORY CO.,01-East,779956498,9360.00,6315.06,3044.94
BRYANT REFRACTORY CO.,01-East,764341000,9162.00,5108.81,4053.19
BRYANT REFRACTORY CO.,01-East,777601000,8527.59,2466.39,6061.20
BRYANT REFRACTORY CO.,01-East,734661300,8503.00,1654.31,6848.69
BRYANT REFRACTORY CO.,01-East,734900000,8109.40,4531.36,3578.04
BRYANT REFRACTORY CO.,01-East,724261200,7204.40,1336.67,5867.73
BRYANT REFRACTORY CO.,01-East,739661100,7102.08,3744.00,3358.08
BRYANT REFRACTORY CO.,01-East,779958168,7000.00,7375.04,-375.04
BRYANT REFRACTORY CO.,01-East,737912200,6980.25,1777.81,5202.44
BRYANT REFRACTORY CO.,01-East,764822000,6730.00,2235.10,4494.90
BRYANT REFRACTORY CO.,01-East,733464500,6521.80,1037.29,5484.51
BRYANT REFRACTORY CO.,01-East,764481000,5838.00,2022.90,3815.10
BRYANT REFRACTORY CO.,01-East,764901061,5680.00,5968.00,-288.00
BRYANT REFRACTORY CO.,01-East,780680000,5280.00,2004.36,3275.64
BRYANT REFRACTORY CO.,01-East,733211050,5020.00,1118.20,3901.80
BRYANT REFRACTORY CO.,01-East,724071900,4959.36,1027.67,3931.69
BRYANT REFRACTORY CO.,01-East,764902000,4649.50,1762.14,2887.36
BRYANT REFRACTORY CO.,01-East,779956874,4387.46,2381.77,2005.69
BRYANT REFRACTORY CO.,01-East,779958171,4259.08,2514.04,1745.04
BRYANT REFRACTORY CO.,01-East,768141000,4107.50,815.88,3291.62
BRYANT REFRACTORY CO.,01-East,768241000,3889.75,1580.91,2308.84
BRYANT REFRACTORY CO.,01-East,764301000,3380.00,1525.36,1854.64
BRYANT REFRACTORY CO.,01-East,780640000,3340.92,1080.00,2260.92
BRYANT REFRACTORY CO.,01-East,779988828,3203.59,1584.96,1618.63
BRYANT REFRACTORY CO.,01-East,733525400,3004.50,428.50,2576.00
BRYANT REFRACTORY CO.,01-East,777642000,2578.80,1500.95,1077.85
BRYANT REFRACTORY CO.,01-East,739450000,2552.40,1301.16,1251.24
BRYANT REFRACTORY CO.,01-East,739541000,2519.42,501.34,2018.08
BRYANT REFRACTORY CO.,01-East,768221000,2496.00,459.94,2036.06
BRYANT REFRACTORY CO.,01-East,781940000,2353.58,485.42,1868.16
BRYANT REFRACTORY CO.,01-East,779956599,2340.00,1791.22,548.78
BRYANT REFRACTORY CO.,01-East,733151550,2282.40,334.16,1948.24
BRYANT REFRACTORY CO.,01-East,770540000,2277.46,184.78,2092.68
BRYANT REFRACTORY CO.,01-East,792785000,2193.12,479.91,1713.21
BRYANT REFRACTORY CO.,01-East,733141200,1976.40,373.85,1602.55
BRYANT REFRACTORY CO.,01-East,770450000,1958.40,199.22,1759.18
BRYANT REFRACTORY CO.,01-East,737871000,1906.24,279.74,1626.50
BRYANT REFRACTORY CO.,01-East,770220000,1829.40,169.53,1659.87
BRYANT REFRACTORY CO.,01-East,754285000,1764.56,1147.22,617.34
BRYANT REFRACTORY CO.,01-East,791003500,1746.00,432.93,1313.07
BRYANT REFRACTORY CO.,01-East,766001000,1450.00,399.45,1050.55
BRYANT REFRACTORY CO.,01-East,734651200,1431.10,407.01,1024.09
BRYANT REFRACTORY CO.,01-East,737009842,1397.68,2507.20,-1109.52
BRYANT REFRACTORY CO.,01-East,770280000,1340.00,160.05,1179.95
BRYANT REFRACTORY CO.,01-East,764221000,1176.00,406.16,769.84
BRYANT REFRACTORY CO.,01-East,737009844,1166.64,756.56,410.08
BRYANT REFRACTORY CO.,01-East,781580000,1040.00,636.71,403.29
BRYANT REFRACTORY CO.,01-East,766021000,995.00,303.72,691.28
BRYANT REFRACTORY CO.,01-East,737821000,989.70,334.62,655.08
BRYANT REFRACTORY CO.,01-East,779958693,920.64,556.24,364.40
BRYANT REFRACTORY CO.,01-East,733403200,919.60,99.17,820.43
BRYANT REFRACTORY CO.,01-East,770200000,813.60,84.82,728.78
BRYANT REFRACTORY CO.,01-East,770320000,778.00,111.14,666.86
BRYANT REFRACTORY CO.,01-East,770222000,739.50,68.04,671.46
BRYANT REFRACTORY CO.,01-East,771132000,729.70,35.80,693.90
BRYANT REFRACTORY CO.,01-East,768750000,663.12,144.72,518.40
BRYANT REFRACTORY CO.,01-East,733725500,637.20,199.11,438.09
BRYANT REFRACTORY CO.,01-East,793540000,492.00,115.87,376.13
BRYANT REFRACTORY CO.,01-East,768211000,465.20,89.76,375.44
BRYANT REFRACTORY CO.,01-East,740023000,463.68,119.31,344.37
BRYANT REFRACTORY CO.,01-East,734822000,457.70,160.95,296.75
BRYANT REFRACTORY CO.,01-East,770210000,440.00,63.04,376.96
BRYANT REFRACTORY CO.,01-East,733444100,436.60,156.66,279.94
BRYANT REFRACTORY CO.,01-East,792620000,427.60,86.74,340.86
BRYANT REFRACTORY CO.,01-East,792783000,414.00,90.01,323.99
BRYANT REFRACTORY CO.,01-East,754087000,331.44,142.20,189.24
BRYANT REFRACTORY CO.,01-East,764761000,319.20,94.43,224.77
BRYANT REFRACTORY CO.,01-East,733151600,310.00,46.64,263.36
BRYANT REFRACTORY CO.,01-East,734311100,291.44,33.70,257.74
BRYANT REFRACTORY CO.,01-East,793320000,262.00,117.93,144.07
BRYANT REFRACTORY CO.,01-East,733423600,244.80,45.08,199.72
BRYANT REFRACTORY CO.,01-East,733423700,232.96,36.22,196.74
BRYANT REFRACTORY CO.,01-East,768751000,232.80,48.25,184.55
BRYANT REFRACTORY CO.,01-East,734611100,228.08,107.82,120.26
BRYANT REFRACTORY CO.,01-East,779181000,65.91,20.63,45.28
BUFFALO DENTAL,01-East,740063000,1898.40,356.52,1541.88
BURKHART REFRACTORY INST.,01-East,734311100,196.00,33.70,162.30
BURNHAM INSULATION,01-East,739541000,259.20,90.50,168.70
BURNHAM INSULATION,01-East,764901000,100.50,16.01,84.49
C & E RECLAMATION INC.,01-East,739390000,9509.76,5823.36,3686.40
C & E RECLAMATION INC.,01-East,777904301,2962.50,1174.68,1787.82
C & E RECLAMATION INC.,01-East,739370000,1138.56,414.72,723.84
C & E RECLAMATION INC.,01-East,773501000,926.10,394.18,531.92
C & E RECLAMATION INC.,01-East,773131000,690.90,138.16,552.74
C & E RECLAMATION INC.,01-East,733151500,472.00,54.11,417.89
C M FURNACE INC,01-East,738521000,15802.88,5509.46,10293.42
C M FURNACE INC,01-East,738542000,2603.76,997.55,1606.21
C M FURNACE INC,01-East,733525400,576.00,74.13,501.87
CABOT CORP,01-East,724261200,826.00,98.66,727.34
CAMERON MANUFACTURING,01-East,770372000,1078.00,44.11,1033.89
CAPITAL HARDWARE/ELGEN,01-East,735602000,82870.00,31641.00,51229.00
CAPITAL HARDWARE/ELGEN,01-East,735602110,15840.00,6426.23,9413.77
CARBOLINE COMPANY,01-East,720252303,214384.50,6059.96,208324.54
CARBOLINE COMPANY,01-East,723800000,166018.50,41260.90,124757.60
CARBOLINE COMPANY,01-East,734000008,16038.00,6792.87,9245.13
CARBONYX CARBON TECH.,01-East,764951600,75075.84,37675.72,37400.12
CARBONYX CARBON TECH.,01-East,766122000,51460.00,35665.08,15794.92
CARBONYX CARBON TECH.,01-East,764951800,32686.08,16025.28,16660.80
CARBONYX CARBON TECH.,01-East,734361300,32175.00,9647.26,22527.74
CARBONYX CARBON TECH.,01-East,764952100,20832.00,9180.24,11651.76
CARBONYX CARBON TECH.,01-East,764821000,13802.00,6327.47,7474.53
CARBONYX CARBON TECH.,01-East,764951700,12257.28,6183.20,6074.08
CARBONYX CARBON TECH.,01-East,764951900,5617.92,2837.06,2780.86
CARBONYX CARBON TECH.,01-East,734351200,4050.00,1255.14,2794.86
CARBONYX CARBON TECH.,01-East,766121000,2490.00,1634.93,855.07
CARBONYX CARBON TECH.,01-East,764822010,1273.00,591.57,681.43
CARDINDAL FG,01-East,738521000,40364.80,16199.33,24165.47
CARDINDAL FG,01-East,727000140,2312.00,167.14,2144.86
CARRIER ENTERPRISE NE LLC,01-East,735602000,70800.00,23715.15,47084.85
CARRIER ENTERPRISE NE LLC,01-East,735602010,1617.00,642.19,974.81
CARRIER ENTERPRISE NE LLC,01-East,764821000,1073.60,122.86,950.74
CARRIER ENTERPRISE NE LLC,01-East,764901000,265.50,32.28,233.22
CASCADE DESIGNS,01-East,770372000,7882.00,370.85,7511.15
CASHCO INC.,01-East,737201000,3106.56,518.48,2588.08
CASSO SOLAR,01-East,734211200,486.92,46.76,440.16
CBOL CORPORATION,01-East,733648000,51845.10,21521.81,30323.29
CBOL CORPORATION,01-East,732820000,10657.92,462.12,10195.80
CBOL CORPORATION,01-East,733151600,472.00,53.12,418.88
CDI WORLDWIDE,01-East,768241000,1457.00,70.76,1386.24
"CEI ENTERPRISES, INC.",01-East,770300000,4380.75,341.31,4039.44
"CEI ENTERPRISES, INC.",01-East,734711100,1704.96,244.80,1460.16
CENTRAL GLASS PRODUCT INC,01-East,733464600,298.00,31.31,266.69
CERALINK INC.,01-East,779989990,422.56,132.34,290.22
CERAMASPEED,01-East,727000440,220614.00,137628.01,82985.99
CERAMATERIALS,01-East,764863000,603.00,256.34,346.66
CERAMATERIALS,01-East,764827000,596.00,363.85,232.15
CERAMIC MAGNETICS,01-East,737861000,9897.84,1089.64,8808.20
CERAMIC MAGNETICS,01-East,775061000,589.00,430.10,158.90
CERAMIC MAGNETICS,01-East,776201000,124.20,54.85,69.35
CERAMIC SYSTEMS INC.,01-East,793330000,317490.00,73731.26,243758.74
CERAMIC SYSTEMS INC.,01-East,764521000,87523.00,44020.15,43502.85
CERAMIC SYSTEMS INC.,01-East,766121000,73390.00,37439.34,35950.66
CERAMIC SYSTEMS INC.,01-East,793530000,49057.92,27460.84,21597.08
CERAMIC SYSTEMS INC.,01-East,766004000,42012.00,23820.53,18191.47
CERAMIC SYSTEMS INC.,01-East,764821000,37900.00,15587.84,22312.16
CERAMIC SYSTEMS INC.,01-East,764822000,36120.00,14750.75,21369.25
CERAMIC SYSTEMS INC.,01-East,766122000,35680.00,18802.49,16877.51
CERAMIC SYSTEMS INC.,01-East,766045000,31680.00,15251.30,16428.70
CERAMIC SYSTEMS INC.,01-East,764528100,23616.00,10288.16,13327.84
CERAMIC SYSTEMS INC.,01-East,764601000,21800.00,9494.41,12305.59
CERAMIC SYSTEMS INC.,01-East,766005000,21458.00,13075.45,8382.55
CERAMIC SYSTEMS INC.,01-East,764523000,17211.00,9161.74,8049.26
CERAMIC SYSTEMS INC.,01-East,764522000,16310.00,8215.29,8094.71
CERAMIC SYSTEMS INC.,01-East,793280074,13026.00,13067.82,-41.82
CERAMIC SYSTEMS INC.,01-East,764481000,12978.00,6485.36,6492.64
CERAMIC SYSTEMS INC.,01-East,764781000,11646.00,5796.96,5849.04
CERAMIC SYSTEMS INC.,01-East,764901000,9710.00,4187.06,5522.94
CERAMIC SYSTEMS INC.,01-East,727000140,8413.20,2589.17,5824.03
CERAMIC SYSTEMS INC.,01-East,764907010,8142.50,5049.63,3092.87
CERAMIC SYSTEMS INC.,01-East,764528510,7800.00,4433.24,3366.76
CERAMIC SYSTEMS INC.,01-East,766001000,7600.00,5036.34,2563.66
CERAMIC SYSTEMS INC.,01-East,733525500,2190.00,780.35,1409.65
CERAMIC SYSTEMS INC.,01-East,766081010,1458.00,654.58,803.42
CERAMIC SYSTEMS INC.,01-East,785890000,1245.75,470.23,775.52
CERAMIC SYSTEMS INC.,01-East,764761000,1180.00,588.16,591.84
CERAMIC SYSTEMS INC.,01-East,766162000,994.00,675.71,318.29
CERAMIC SYSTEMS INC.,01-East,764341000,810.00,359.80,450.20
CERAMIC SYSTEMS INC.,01-East,764561000,288.00,167.98,120.02
CERAMIC SYSTEMS INC.,01-East,764861000,258.75,116.27,142.48
CERAMIC SYSTEMS INC.,01-East,764902000,75.00,31.93,43.07
CERAMIC SYSTEMS INC.,01-East,733464500,73.00,29.52,43.48
CERAMSOURECE INC.,01-East,770600000,3245.20,1717.90,1527.30
CERAMSOURECE INC.,01-East,770830000,1343.20,805.51,537.69
CERAMSOURECE INC.,01-East,770570000,377.40,201.46,175.94
CERAMSOURECE INC.,01-East,764785000,274.00,146.72,127.28
CHANDLER ENGINEERING,01-East,733444100,332.00,75.04,256.96
CHEF MART RESTAURANT,01-East,735602000,25440.00,8284.44,17155.56
CHIZ BROS INC,01-East,764348000,20269.20,13106.80,7162.40
CHIZ BROS INC,01-East,740402402,14520.00,3675.39,10844.61
CHIZ BROS INC,01-East,738561000,10315.20,7432.32,2882.88
CHIZ BROS INC,01-East,779440100,3762.00,605.35,3156.65
CHIZ BROS INC,01-East,770560000,2999.40,430.45,2568.95
CHIZ BROS INC,01-East,734351200,2977.00,294.47,2682.53
CHIZ BROS INC,01-East,777902700,2597.49,1141.55,1455.94
CHIZ BROS INC,01-East,779959870,1763.20,880.96,882.24
CHIZ BROS INC,01-East,737912200,918.00,272.84,645.16
CHIZ BROS INC,01-East,764862000,790.80,157.11,633.69
CHIZ BROS INC,01-East,775201000,688.00,515.80,172.20
CHIZ BROS INC,01-East,779957054,448.40,224.48,223.92
CHIZ BROS INC,01-East,734611100,262.58,53.92,208.66
CHIZ BROS INC,01-East,734651200,218.16,36.75,181.41
CHIZ BROS INC,01-East,779959135,180.00,111.60,68.40
CHIZ BROS INC,01-East,774981100,65.50,49.04,16.46
CHIZ BROS INC,01-East,767500000,32.70,17.45,15.25
CHRISTY CATALYTICS LLC,01-East,734830000,246.00,44.64,201.36
CHRISTY INDUSTRIAL SERVIC,01-East,765661000,41532.00,13318.44,28213.56
CHRISTY INDUSTRIAL SERVIC,01-East,770330000,21974.00,3034.60,18939.40
CHRISTY INDUSTRIAL SERVIC,01-East,780770000,18746.70,5877.30,12869.40
CHRISTY INDUSTRIAL SERVIC,01-East,727041000,12000.00,1187.73,10812.27
CHRISTY INDUSTRIAL SERVIC,01-East,734990000,7746.24,1978.12,5768.12
CHRISTY INDUSTRIAL SERVIC,01-East,764821000,6360.00,3071.59,3288.41
CHRISTY INDUSTRIAL SERVIC,01-East,780290000,6127.20,3495.24,2631.96
CHRISTY INDUSTRIAL SERVIC,01-East,780650000,5207.80,2322.56,2885.24
CHRISTY INDUSTRIAL SERVIC,01-East,780720000,3963.60,1115.32,2848.28
CHRISTY INDUSTRIAL SERVIC,01-East,765651000,3840.00,1311.90,2528.10
CHRISTY INDUSTRIAL SERVIC,01-East,754087000,2704.00,1810.42,893.58
CHRISTY INDUSTRIAL SERVIC,01-East,779957722,2676.00,1781.98,894.02
CHRISTY INDUSTRIAL SERVIC,01-East,754085000,2449.20,1492.72,956.48
CHRISTY INDUSTRIAL SERVIC,01-East,777642000,1722.00,475.05,1246.95
CHRISTY INDUSTRIAL SERVIC,01-East,733211000,1685.40,343.36,1342.04
CHRISTY INDUSTRIAL SERVIC,01-East,764781000,1590.00,1022.26,567.74
CHRISTY INDUSTRIAL SERVIC,01-East,737311000,1284.96,273.31,1011.65
CHRISTY INDUSTRIAL SERVIC,01-East,734810000,938.00,750.51,187.49
CHRISTY INDUSTRIAL SERVIC,01-East,766081010,868.00,145.46,722.54
CHRISTY INDUSTRIAL SERVIC,01-East,738561000,755.52,372.41,383.11
CHRISTY INDUSTRIAL SERVIC,01-East,764785000,530.00,146.72,383.28
CHRISTY INDUSTRIAL SERVIC,01-East,777650000,356.60,285.32,71.28
CHRISTY INDUSTRIAL SERVIC,01-East,780690000,354.96,127.26,227.70
CHRISTY INDUSTRIAL SERVIC,01-East,764761000,204.00,61.43,142.57
CHRISTY INDUSTRIAL SERVIC,01-East,767990000,160.50,58.98,101.52
CHRISTY INDUSTRIAL SERVIC,01-East,766121000,95.80,54.50,41.30
CHRISTY INDUSTRIAL SERVIC,01-East,779987255,56.00,23.18,32.82
CHRISTY REFRACTORIES CO,01-East,764764010,87014.40,54086.18,32928.22
CHRISTY REFRACTORIES CO,01-East,727000060,77036.00,25833.63,51202.37
CHRISTY REFRACTORIES CO,01-East,720474000,76366.25,12958.70,63407.55
CHRISTY REFRACTORIES CO,01-East,733182300,61281.84,31628.98,29652.86
CHRISTY REFRACTORIES CO,01-East,764030000,54407.20,28111.03,26296.17
CHRISTY REFRACTORIES CO,01-East,730552560,37535.00,5813.35,31721.65
CHRISTY REFRACTORIES CO,01-East,764264056,35322.00,24085.99,11236.01
CHRISTY REFRACTORIES CO,01-East,764301000,32951.90,19151.41,13800.49
CHRISTY REFRACTORIES CO,01-East,777750074,32766.72,27531.00,5235.72
CHRISTY REFRACTORIES CO,01-East,764901000,32424.30,17324.77,15099.53
CHRISTY REFRACTORIES CO,01-East,764821000,29179.00,13658.64,15520.36
CHRISTY REFRACTORIES CO,01-East,739741100,28341.51,16475.47,11866.04
CHRISTY REFRACTORIES CO,01-East,733481000,28140.00,14458.84,13681.16
CHRISTY REFRACTORIES CO,01-East,764865100,24444.00,14164.82,10279.18
CHRISTY REFRACTORIES CO,01-East,777780074,20694.80,16416.40,4278.40
CHRISTY REFRACTORIES CO,01-East,738521000,20080.78,16796.48,3284.30
CHRISTY REFRACTORIES CO,01-East,734211200,12406.61,1721.98,10684.63
CHRISTY REFRACTORIES CO,01-East,734990000,12375.36,3305.15,9070.21
CHRISTY REFRACTORIES CO,01-East,734000000,11820.60,1517.87,10302.73
CHRISTY REFRACTORIES CO,01-East,734173000,11627.35,3161.64,8465.71
CHRISTY REFRACTORIES CO,01-East,766261020,10584.00,6168.11,4415.89
CHRISTY REFRACTORIES CO,01-East,770220000,9849.00,916.61,8932.39
CHRISTY REFRACTORIES CO,01-East,733182390,9801.15,4095.04,5706.11
CHRISTY REFRACTORIES CO,01-East,733505300,9480.00,1612.13,7867.87
CHRISTY REFRACTORIES CO,01-East,733721000,9028.80,2825.41,6203.39
CHRISTY REFRACTORIES CO,01-East,732429400,8688.50,997.20,7691.30
CHRISTY REFRACTORIES CO,01-East,770560000,8373.20,692.13,7681.07
CHRISTY REFRACTORIES CO,01-East,764340523,7978.50,8074.53,-96.03
CHRISTY REFRACTORIES CO,01-East,764481000,7457.10,2826.95,4630.15
CHRISTY REFRACTORIES CO,01-East,771500000,7112.48,516.02,6596.46
CHRISTY REFRACTORIES CO,01-East,777601000,6896.90,2040.14,4856.76
CHRISTY REFRACTORIES CO,01-East,733182380,6706.05,2801.85,3904.20
CHRISTY REFRACTORIES CO,01-East,733202000,5394.48,3127.98,2266.50
CHRISTY REFRACTORIES CO,01-East,764822000,5394.40,1558.28,3836.12
CHRISTY REFRACTORIES CO,01-East,727000025,5338.50,2502.95,2835.55
CHRISTY REFRACTORIES CO,01-East,733464500,5305.50,812.07,4493.43
CHRISTY REFRACTORIES CO,01-East,734162000,5286.93,2252.97,3033.96
CHRISTY REFRACTORIES CO,01-East,754925000,5168.40,1163.38,4005.02
CHRISTY REFRACTORIES CO,01-East,777750073,5019.58,2039.90,2979.68
CHRISTY REFRACTORIES CO,01-East,764561000,4743.50,3489.38,1254.12
CHRISTY REFRACTORIES CO,01-East,764781000,4439.00,2680.51,1758.49
CHRISTY REFRACTORIES CO,01-East,768241000,4429.00,900.79,3528.21
CHRISTY REFRACTORIES CO,01-East,734311100,4371.75,515.14,3856.61
CHRISTY REFRACTORIES CO,01-East,764601000,4324.80,2816.66,1508.14
CHRISTY REFRACTORIES CO,01-East,764341000,4213.70,3133.22,1080.48
CHRISTY REFRACTORIES CO,01-East,766162000,3949.00,570.34,3378.66
CHRISTY REFRACTORIES CO,01-East,777780073,3789.20,1618.09,2171.11
CHRISTY REFRACTORIES CO,01-East,733525500,3247.50,476.67,2770.83
CHRISTY REFRACTORIES CO,01-East,782070000,3241.44,1654.68,1586.76
CHRISTY REFRACTORIES CO,01-East,764261000,2907.80,1186.32,1721.48
CHRISTY REFRACTORIES CO,01-East,766121000,2765.60,403.00,2362.60
CHRISTY REFRACTORIES CO,01-East,764821161,2680.00,2976.00,-296.00
CHRISTY REFRACTORIES CO,01-East,733383000,2593.80,491.79,2102.01
CHRISTY REFRACTORIES CO,01-East,768242007,2528.00,1017.20,1510.80
CHRISTY REFRACTORIES CO,01-East,733211000,2513.00,532.04,1980.96
CHRISTY REFRACTORIES CO,01-East,764521000,2504.00,1327.10,1176.90
CHRISTY REFRACTORIES CO,01-East,733151600,2317.60,399.03,1918.57
CHRISTY REFRACTORIES CO,01-East,764461006,2236.00,1465.52,770.48
CHRISTY REFRACTORIES CO,01-East,740123000,2226.00,640.05,1585.95
CHRISTY REFRACTORIES CO,01-East,739561100,2157.79,653.24,1504.55
CHRISTY REFRACTORIES CO,01-East,779959904,2125.44,1410.70,714.74
CHRISTY REFRACTORIES CO,01-East,770240000,1946.25,202.89,1743.36
CHRISTY REFRACTORIES CO,01-East,739561200,1804.28,596.00,1208.28
CHRISTY REFRACTORIES CO,01-East,740382201,1800.00,594.28,1205.72
CHRISTY REFRACTORIES CO,01-East,768121000,1749.60,351.09,1398.51
CHRISTY REFRACTORIES CO,01-East,768231400,1746.00,958.78,787.22
CHRISTY REFRACTORIES CO,01-East,777730022,1729.98,1380.96,349.02
CHRISTY REFRACTORIES CO,01-East,770200000,1629.00,168.02,1460.98
CHRISTY REFRACTORIES CO,01-East,764761000,1530.80,821.49,709.31
CHRISTY REFRACTORIES CO,01-East,764487500,1480.00,757.91,722.09
CHRISTY REFRACTORIES CO,01-East,734151100,1285.86,301.83,984.03
CHRISTY REFRACTORIES CO,01-East,740382150,1254.96,548.62,706.34
CHRISTY REFRACTORIES CO,01-East,764523010,1139.80,923.51,216.29
CHRISTY REFRACTORIES CO,01-East,734132000,1109.60,313.31,796.29
CHRISTY REFRACTORIES CO,01-East,737311000,990.24,273.31,716.93
CHRISTY REFRACTORIES CO,01-East,764529600,864.00,368.44,495.56
CHRISTY REFRACTORIES CO,01-East,733444200,852.00,142.71,709.29
CHRISTY REFRACTORIES CO,01-East,777642000,850.40,253.37,597.03
CHRISTY REFRACTORIES CO,01-East,733211050,816.00,397.57,418.43
CHRISTY REFRACTORIES CO,01-East,764342000,800.40,209.07,591.33
CHRISTY REFRACTORIES CO,01-East,738542000,792.00,574.08,217.92
CHRISTY REFRACTORIES CO,01-East,764523504,771.00,586.35,184.65
CHRISTY REFRACTORIES CO,01-East,755125000,721.02,149.32,571.70
CHRISTY REFRACTORIES CO,01-East,739641100,695.19,412.29,282.90
CHRISTY REFRACTORIES CO,01-East,720455000,693.60,94.87,598.73
CHRISTY REFRACTORIES CO,01-East,770230000,693.00,73.92,619.08
CHRISTY REFRACTORIES CO,01-East,768221000,672.00,210.63,461.37
CHRISTY REFRACTORIES CO,01-East,764262000,656.00,446.16,209.84
CHRISTY REFRACTORIES CO,01-East,770300000,649.00,70.74,578.26
CHRISTY REFRACTORIES CO,01-East,770392000,445.40,41.51,403.89
CHRISTY REFRACTORIES CO,01-East,733725500,410.40,103.52,306.88
CHRISTY REFRACTORIES CO,01-East,724121700,352.00,162.85,189.15
CHRISTY REFRACTORIES CO,01-East,766122000,344.00,57.52,286.48
CHRISTY REFRACTORIES CO,01-East,768122000,293.40,58.22,235.18
CHRISTY REFRACTORIES CO,01-East,764482000,268.00,68.20,199.80
CHRISTY REFRACTORIES CO,01-East,727063000,261.60,33.59,228.01
CHRISTY REFRACTORIES CO,01-East,764526610,225.75,168.15,57.60
CHRISTY REFRACTORIES CO,01-East,734711100,203.51,40.72,162.79
CHRISTY REFRACTORIES CO,01-East,733444300,195.00,46.17,148.83
CHRISTY REFRACTORIES CO,01-East,775201000,179.60,130.63,48.97
CHRISTY REFRACTORIES CO,01-East,773591000,88.56,26.53,62.03
CHRISTY REFRACTORIES CO,01-East,774981100,78.73,35.92,42.81
CIMINI & ASSOCIATES,01-East,764261000,3460.00,183.76,3276.24
CLAUDIUS PETERS,01-East,724003000,2434.00,295.77,2138.23
CLIMATECH,01-East,733572750,193129.11,135159.39,57969.72
CLIMATECH,01-East,733572800,142823.27,110203.93,32619.34
CLIMATECH,01-East,733572600,48260.16,35261.01,12999.15
CLIMATECH,01-East,733572900,13847.04,9975.45,3871.59
CLINTON POTTERY,01-East,764821000,430.00,15.36,414.64
CLYDE BERGEMANN BACHMANN,01-East,768241000,47480.50,23217.78,24262.72
CLYDE BERGEMANN BACHMANN,01-East,733725500,10216.00,2235.97,7980.03
CLYDE BERGEMANN BACHMANN,01-East,768221000,8053.00,3863.70,4189.30
CLYDE BERGEMANN BACHMANN,01-East,734751200,6891.90,2329.58,4562.32
CLYDE BERGEMANN BACHMANN,01-East,734761300,2126.30,758.78,1367.52
CLYDE BERGEMANN BACHMANN,01-East,734651200,889.20,293.98,595.22
CLYDE BERGEMANN BACHMANN,01-East,734711100,817.12,270.80,546.32
CLYDE BERGEMANN BACHMANN,01-East,768222000,643.50,322.20,321.30
CLYDE BERGEMANN BACHMANN,01-East,768242000,572.00,353.28,218.72
CLYDE BERGEMANN BACHMANN,01-East,734611100,85.08,40.44,44.64
COASTAL IGNITION &,01-East,733151600,450.00,46.64,403.36
CODY SALES & SERVICE,01-East,737912200,326.50,45.47,281.03
COLORADO STATE UNIVERSITY,01-East,764483000,421.00,89.22,331.78
COLORADO STATE UNIVERSITY,01-East,764481000,346.00,16.94,329.06
COLUMBIA HELICOPTER,01-East,734351200,1945.80,147.24,1798.56
COLUMBIA HELICOPTER,01-East,770830000,1061.00,35.87,1025.13
COLUMBIA HELICOPTER,01-East,764521000,666.00,26.54,639.46
COLUMBIA MANUFACTURING IN,01-East,734121000,455.00,83.36,371.64
COLUMBIA MANUFACTURING IN,01-East,734132000,422.00,78.33,343.67
COMMERCIAL HEATING SUPPLY,01-East,739561100,1658.16,279.96,1378.20
COMPANIA NACIONAL,01-East,764821029,121817.10,34700.01,87117.09
COMPANIA NACIONAL,01-East,727000140,51075.36,9144.99,41930.37
COMPANIA NACIONAL,01-East,764821000,27328.00,6880.38,20447.62
COMPANIA NACIONAL,01-East,733151500,23664.00,5649.40,18014.60
COMPANIA NACIONAL,01-East,764821027,22300.00,6375.74,15924.26
COMPANIA NACIONAL,01-East,734000000,22186.50,3187.33,18999.17
COMPANIA NACIONAL,01-East,770222000,13738.75,2151.10,11587.65
COMPANIA NACIONAL,01-East,770560000,12240.00,1721.79,10518.21
COMPANIA NACIONAL,01-East,733444300,8870.00,2720.87,6149.13
COMPANIA NACIONAL,01-East,764901000,6882.75,1840.40,5042.35
COMPANIA NACIONAL,01-East,770220000,5595.00,885.98,4709.02
COMPANIA NACIONAL,01-East,733151600,4140.00,932.84,3207.16
COMPANIA NACIONAL,01-East,770260000,3780.00,676.28,3103.72
COMPANIA NACIONAL,01-East,770202000,3442.50,607.59,2834.91
COMPANIA NACIONAL,01-East,733525500,3200.00,703.94,2496.06
COMPANIA NACIONAL,01-East,764521000,2784.00,851.02,1932.98
COMPANIA NACIONAL,01-East,733211000,2416.50,728.27,1688.23
COMPANIA NACIONAL,01-East,764521020,1740.00,557.56,1182.44
COMPANIA NACIONAL,01-East,766121040,1230.00,279.13,950.87
COMPANIA NACIONAL,01-East,770212000,877.50,162.38,715.12
COMPANIA NACIONAL,01-East,770240000,798.75,140.66,658.09
COMPLIANCE INSPECTION,01-East,768133000,162.25,11.04,151.21
CONSOLIDATED FABRICATORS,01-East,768241000,3700.00,2193.63,1506.37
CONSOLIDATED FABRICATORS,01-East,768231000,2494.00,1331.79,1162.21
CONSTRUCTION SPECIALTIES,01-East,737016200,346350.65,84901.39,261449.26
CONSTRUCTION SPECIALTIES,01-East,733610650,89868.12,22127.38,67740.74
CONSTRUCTION SPECIALTIES,01-East,733151600,40960.00,6637.24,34322.76
CONSTRUCTION SPECIALTIES,01-East,764787200,32943.10,6931.03,26012.07
CONSTRUCTION SPECIALTIES,01-East,733184000,19572.00,4417.97,15154.03
CONSTRUCTION SPECIALTIES,01-East,733182340,16493.40,2622.64,13870.76
CONSTRUCTION SPECIALTIES,01-East,768222000,12898.60,2726.64,10171.96
CONSTRUCTION SPECIALTIES,01-East,768221000,4386.20,919.89,3466.31
CONSTRUCTION SPECIALTIES,01-East,734910000,362.00,53.09,308.91
CONSTRUCTION SPECIALTIES,01-East,733151500,294.00,47.69,246.31
COORSTEK,01-East,779988831,8818.50,2227.99,6590.51
COORSTEK,01-East,739561000,443.52,86.19,357.33
COORSTEK,01-East,777642000,143.00,50.36,92.64
CORNING GLASS WORKS,01-East,733505200,910.00,167.67,742.33
CORNING INCORPORATED,01-East,733423600,228.48,45.08,183.40
CORNING INCORPORATED,01-East,733444100,208.60,81.62,126.98
CORNING SHARED SERVICES,01-East,734370000,12641200.00,2013467.19,10627732.81
CORNING SHARED SERVICES,01-East,761924000,52394.94,12023.40,40371.54
CORNING SHARED SERVICES,01-East,770700000,44200.00,29622.50,14577.50
CORNING SHARED SERVICES,01-East,719500000,42090.00,6819.48,35270.52
CORNING SHARED SERVICES,01-East,718501000,21856.00,4331.98,17524.02
CORNING SHARED SERVICES,01-East,761073000,18480.00,7110.73,11369.27
CORNING SHARED SERVICES,01-East,766121000,4579.50,283.25,4296.25
CORNING SHARED SERVICES,01-East,733525500,3164.00,536.32,2627.68
CORNING SHARED SERVICES,01-East,737321000,2504.16,1168.96,1335.20
CORNING SHARED SERVICES,01-East,766081010,2055.30,134.32,1920.98
CORNING SHARED SERVICES,01-East,740402301,1767.50,430.33,1337.17
CORNING SHARED SERVICES,01-East,740283401,1564.20,265.38,1298.82
CORNING SHARED SERVICES,01-East,740402402,1535.00,387.32,1147.68
CORNING SHARED SERVICES,01-East,770540000,1280.83,94.98,1185.85
CORNING SHARED SERVICES,01-East,770230000,960.75,71.08,889.67
CORNING SHARED SERVICES,01-East,733403100,944.93,122.60,822.33
CORNING SHARED SERVICES,01-East,770380000,916.00,51.37,864.63
CORNING SHARED SERVICES,01-East,764781000,480.60,39.44,441.16
CORNING SHARED SERVICES,01-East,733505200,434.00,76.44,357.56
CORNING SHARED SERVICES,01-East,777601000,287.95,58.80,229.15
CORNING SHARED SERVICES,01-East,734611100,278.32,54.36,223.96
CORNING SHARED SERVICES,01-East,727063000,139.60,15.92,123.68
CORNING SHARED SERVICES,01-East,727063016,139.60,15.84,123.76
CORNING SHARED SERVICES,01-East,726020005,132.80,14.88,117.92
CORNING SHARED SERVICES,01-East,726020015,132.80,16.37,116.43
COTRONICS INC,01-East,733586500,193643.28,29482.30,164160.98
COTRONICS INC,01-East,733566200,91497.60,16578.07,74919.53
COTRONICS INC,01-East,733543500,84780.80,20175.81,64604.99
COTRONICS INC,01-East,770260000,53118.45,5653.58,47464.87
COTRONICS INC,01-East,733543700,42262.40,7887.59,34374.81
COTRONICS INC,01-East,733505300,32689.00,5703.00,26986.00
COTRONICS INC,01-East,770222000,32621.38,3059.28,29562.10
COTRONICS INC,01-East,733543000,18140.20,4220.58,13919.62
COTRONICS INC,01-East,764461006,13803.20,2118.01,11685.19
COTRONICS INC,01-East,770212000,12680.20,1420.95,11259.25
COTRONICS INC,01-East,733586400,11583.72,1774.92,9808.80
COTRONICS INC,01-East,733566100,3693.60,870.72,2822.88
COTRONICS INC,01-East,764521000,2600.00,265.94,2334.06
COTRONICS INC,01-East,792620000,1776.00,409.36,1366.64
COTRONICS INC,01-East,770200000,390.60,40.79,349.81
CPP GUAYMAS,01-East,764609200,19215.00,6989.64,12225.36
CPP GUAYMAS,01-East,764609500,10293.75,3921.28,6372.47
CPP GUAYMAS,01-East,764609100,7320.00,2685.10,4634.90
CPP GUAYMAS,01-East,764461006,353.60,111.75,241.85
CRC INDUSTRIES,01-East,727000140,40986.00,3966.67,37019.33
CREST ULTRASONICS,01-East,739541100,15859.20,2048.23,13810.97
CROWN ENGINEERING CORP,01-East,733721000,11160.00,3750.70,7409.30
CUBE TECHNOLOGY,01-East,739641100,482.04,201.60,280.44
CUBE TECHNOLOGY,01-East,739561000,473.76,85.22,388.54
CUSTOM HEAT LLC,01-East,733464500,298.00,29.52,268.48
CUSTOM MACHINE & DESIGN,01-East,739741100,2504.32,774.88,1729.44
CYCLOTHERM OF WATERTOWN,01-East,733211050,3126.40,627.42,2498.98
D & M HYDRAULIC,01-East,770220000,471.00,27.35,443.65
"D-J ENGINEERING, INC.",01-East,764821000,894.00,31.32,862.68
"D-J ENGINEERING, INC.",01-East,777642000,143.00,50.36,92.64
D. W. MACK CO,01-East,733464400,626.00,86.74,539.26
DALE STOVER,01-East,739641100,926.64,274.86,651.78
DALE STOVER,01-East,734611100,178.72,26.96,151.76
DANNER CORPORATION,01-East,725441200,388.00,65.77,322.23
DATA CENTER RESOURCES LLC,01-East,735750110,1180.00,275.34,904.66
DATA CENTER RESOURCES LLC,01-East,735750112,945.00,290.67,654.33
DATA CENTER RESOURCES LLC,01-East,735751022,520.50,229.97,290.53
DAVID H. SUTHERLAND & CO.,01-East,770800000,2980.00,63.88,2916.12
"DAVIS-STANDARD, LLC",01-East,733444200,2324.00,266.53,2057.47
DCO ENERGY,01-East,734751200,275.45,37.39,238.06
DEKA RESEARCH,01-East,733671000,1438.08,119.66,1318.42
DELAWARE CITY REFINING CO,01-East,770240000,13627.50,963.02,12664.48
DELPHI AUTOMOTIVE SYSTEMS,01-East,768241000,364.25,17.69,346.56
DELPHI CORPORATION,01-East,768211000,374.00,23.39,350.61
DELPHI CORPORATION,01-East,768231000,262.25,13.17,249.08
DELTA REFRACTORIES,01-East,766121610,795755.36,299738.55,496016.81
DELTA REFRACTORIES,01-East,764821000,465073.00,194458.54,270614.46
DELTA REFRACTORIES,01-East,764901000,384200.00,181439.71,202760.29
DELTA REFRACTORIES,01-East,734132000,277481.50,62861.76,214619.74
DELTA REFRACTORIES,01-East,764820600,266880.00,98426.76,168453.24
DELTA REFRACTORIES,01-East,764901050,225320.50,103145.37,122175.13
DELTA REFRACTORIES,01-East,792627000,172084.77,72343.11,99741.66
DELTA REFRACTORIES,01-East,766126012,163472.00,66294.12,97177.88
DELTA REFRACTORIES,01-East,766001510,153960.00,60498.16,93461.84
DELTA REFRACTORIES,01-East,766121000,145086.50,56815.04,88271.46
DELTA REFRACTORIES,01-East,766001000,116207.00,45801.74,70405.26
DELTA REFRACTORIES,01-East,764521000,103791.00,46509.93,57281.07
DELTA REFRACTORIES,01-East,734990000,102715.60,33396.35,69319.25
DELTA REFRACTORIES,01-East,734830000,101136.00,33145.74,67990.26
DELTA REFRACTORIES,01-East,766122000,91531.00,34533.84,56997.16
DELTA REFRACTORIES,01-East,734311100,90305.48,10411.74,79893.74
DELTA REFRACTORIES,01-East,766006110,81367.00,32669.43,48697.57
DELTA REFRACTORIES,01-East,779989337,80878.08,33761.00,47117.08
DELTA REFRACTORIES,01-East,764822000,76330.00,30815.91,45514.09
DELTA REFRACTORIES,01-East,766162000,71797.00,38517.86,33279.14
DELTA REFRACTORIES,01-East,768222512,67490.00,44723.41,22766.59
DELTA REFRACTORIES,01-East,740262000,66066.00,16608.90,49457.10
DELTA REFRACTORIES,01-East,764605000,60295.00,26714.38,33580.62
DELTA REFRACTORIES,01-East,762134000,59052.84,20051.57,39001.27
DELTA REFRACTORIES,01-East,733525500,54922.50,18597.12,36325.38
DELTA REFRACTORIES,01-East,727000140,54130.40,18545.17,35585.23
DELTA REFRACTORIES,01-East,733211000,52745.70,10770.95,41974.75
DELTA REFRACTORIES,01-East,764906000,52279.00,23839.04,28439.96
DELTA REFRACTORIES,01-East,792623000,52096.10,23145.57,28950.53
DELTA REFRACTORIES,01-East,764861000,51811.00,21126.44,30684.56
DELTA REFRACTORIES,01-East,764824010,50328.00,18611.93,31716.07
DELTA REFRACTORIES,01-East,768221000,45320.00,21276.57,24043.43
DELTA REFRACTORIES,01-East,734910000,41787.30,9887.15,31900.15
DELTA REFRACTORIES,01-East,777601000,37631.65,10119.58,27512.07
DELTA REFRACTORIES,01-East,766594010,36736.00,19511.34,17224.66
DELTA REFRACTORIES,01-East,733511000,32166.25,15958.65,16207.60
DELTA REFRACTORIES,01-East,724311400,32143.00,7277.87,24865.13
DELTA REFRACTORIES,01-East,734851000,31144.60,7559.42,23585.18
DELTA REFRACTORIES,01-East,764522000,30760.00,14028.57,16731.43
DELTA REFRACTORIES,01-East,764864000,29488.00,12710.97,16777.03
DELTA REFRACTORIES,01-East,764561000,29085.00,15046.26,14038.74
DELTA REFRACTORIES,01-East,740023000,27484.80,6895.13,20589.67
DELTA REFRACTORIES,01-East,764601000,26938.75,13863.54,13075.21
DELTA REFRACTORIES,01-East,733383000,25732.08,4401.18,21330.90
DELTA REFRACTORIES,01-East,739741100,22298.88,17009.30,5289.58
DELTA REFRACTORIES,01-East,764481000,21552.00,8959.66,12592.34
DELTA REFRACTORIES,01-East,766594001,21504.00,11345.38,10158.62
DELTA REFRACTORIES,01-East,764902000,20295.00,8662.44,11632.56
DELTA REFRACTORIES,01-East,739561100,19780.99,5318.45,14462.54
DELTA REFRACTORIES,01-East,770430000,19175.00,3375.33,15799.67
DELTA REFRACTORIES,01-East,766081010,18227.00,6012.01,12214.99
DELTA REFRACTORIES,01-East,740063000,17947.44,5372.37,12575.07
DELTA REFRACTORIES,01-East,764781000,17748.00,6533.01,11214.99
DELTA REFRACTORIES,01-East,733510000,16785.00,9095.53,7689.47
DELTA REFRACTORIES,01-East,764520500,16184.00,6478.20,9705.80
DELTA REFRACTORIES,01-East,740361900,14711.04,6242.47,8468.57
DELTA REFRACTORIES,01-East,771502000,13545.00,990.96,12554.04
DELTA REFRACTORIES,01-East,779988828,13193.12,6248.62,6944.50
DELTA REFRACTORIES,01-East,766161000,11412.00,6279.69,5132.31
DELTA REFRACTORIES,01-East,734211200,11272.76,1463.76,9809.00
DELTA REFRACTORIES,01-East,766166000,9660.00,4855.52,4804.48
DELTA REFRACTORIES,01-East,777901000,9445.38,2118.82,7326.56
DELTA REFRACTORIES,01-East,740283502,9350.00,3745.96,5604.04
DELTA REFRACTORIES,01-East,764564000,9296.00,4699.99,4596.01
DELTA REFRACTORIES,01-East,768222550,9278.10,4765.43,4512.67
DELTA REFRACTORIES,01-East,779988827,9200.94,4212.69,4988.25
DELTA REFRACTORIES,01-East,777642000,9198.10,3631.14,5566.96
DELTA REFRACTORIES,01-East,779989700,8437.76,3073.90,5363.86
DELTA REFRACTORIES,01-East,733221400,7768.84,2338.09,5430.75
DELTA REFRACTORIES,01-East,770410000,7630.00,720.95,6909.05
DELTA REFRACTORIES,01-East,764526020,7408.00,2988.04,4419.96
DELTA REFRACTORIES,01-East,733444200,6712.40,1106.95,5605.45
DELTA REFRACTORIES,01-East,779988846,6226.74,2725.74,3501.00
DELTA REFRACTORIES,01-East,766541110,6156.00,3085.30,3070.70
DELTA REFRACTORIES,01-East,768211000,6156.00,2947.07,3208.93
DELTA REFRACTORIES,01-East,734351200,5254.70,2503.04,2751.66
DELTA REFRACTORIES,01-East,777903100,4900.98,1946.28,2954.70
DELTA REFRACTORIES,01-East,724281200,4537.00,1075.67,3461.33
DELTA REFRACTORIES,01-East,764821030,4424.00,1776.11,2647.89
DELTA REFRACTORIES,01-East,792620000,4160.00,2046.78,2113.22
DELTA REFRACTORIES,01-East,771320000,4155.50,684.50,3471.00
DELTA REFRACTORIES,01-East,733403300,3678.84,371.68,3307.16
DELTA REFRACTORIES,01-East,770372000,3404.80,238.55,3166.25
DELTA REFRACTORIES,01-East,739781100,3392.80,2061.29,1331.51
DELTA REFRACTORIES,01-East,764563500,3320.00,1438.28,1881.72
DELTA REFRACTORIES,01-East,768241000,3316.00,1472.53,1843.47
DELTA REFRACTORIES,01-East,739641100,3024.00,2061.41,962.59
DELTA REFRACTORIES,01-East,733586600,2563.20,378.10,2185.10
DELTA REFRACTORIES,01-East,718500000,2523.20,431.35,2091.85
DELTA REFRACTORIES,01-East,764821073,2370.00,2187.00,183.00
DELTA REFRACTORIES,01-East,738521000,2330.56,1624.59,705.97
DELTA REFRACTORIES,01-East,764901073,2295.00,2187.00,108.00
DELTA REFRACTORIES,01-East,733464600,2133.90,443.63,1690.27
DELTA REFRACTORIES,01-East,733721000,2092.00,652.35,1439.65
DELTA REFRACTORIES,01-East,770850000,2090.00,94.48,1995.52
DELTA REFRACTORIES,01-East,764261000,2040.00,918.76,1121.24
DELTA REFRACTORIES,01-East,733464500,2024.00,313.24,1710.76
DELTA REFRACTORIES,01-East,770510000,1967.50,130.98,1836.52
DELTA REFRACTORIES,01-East,770810000,1909.50,53.50,1856.00
DELTA REFRACTORIES,01-East,768127000,1700.00,285.82,1414.18
DELTA REFRACTORIES,01-East,764952100,1425.00,555.26,869.74
DELTA REFRACTORIES,01-East,764563000,1408.00,710.78,697.22
DELTA REFRACTORIES,01-East,740283401,1401.40,292.43,1108.97
DELTA REFRACTORIES,01-East,766594000,1392.00,779.69,612.31
DELTA REFRACTORIES,01-East,770540000,1279.25,94.98,1184.27
DELTA REFRACTORIES,01-East,733525600,1205.00,261.96,943.04
DELTA REFRACTORIES,01-East,792600000,1104.00,177.32,926.68
DELTA REFRACTORIES,01-East,766041000,1100.00,376.49,723.51
DELTA REFRACTORIES,01-East,734651200,1090.75,183.74,907.01
DELTA REFRACTORIES,01-East,766021000,1076.00,110.56,965.44
DELTA REFRACTORIES,01-East,764561210,1066.00,512.87,553.13
DELTA REFRACTORIES,01-East,764762000,1060.00,315.70,744.30
DELTA REFRACTORIES,01-East,792624000,1040.00,499.67,540.33
DELTA REFRACTORIES,01-East,733423600,963.20,171.14,792.06
DELTA REFRACTORIES,01-East,764605001,880.00,360.78,519.22
DELTA REFRACTORIES,01-East,738361000,761.22,617.76,143.46
DELTA REFRACTORIES,01-East,770500000,711.00,41.80,669.20
DELTA REFRACTORIES,01-East,770710000,657.00,16.81,640.19
DELTA REFRACTORIES,01-East,770380000,564.00,51.37,512.63
DELTA REFRACTORIES,01-East,777904301,518.18,234.94,283.24
DELTA REFRACTORIES,01-East,770392000,444.00,41.51,402.49
DELTA REFRACTORIES,01-East,792720000,429.00,85.41,343.59
DELTA REFRACTORIES,01-East,734751200,423.95,75.10,348.85
DELTA REFRACTORIES,01-East,764761000,416.00,122.84,293.16
DELTA REFRACTORIES,01-East,770200000,390.60,40.79,349.81
DELTA REFRACTORIES,01-East,734822000,370.50,67.47,303.03
DELTA REFRACTORIES,01-East,733141200,336.00,66.98,269.02
DELTA REFRACTORIES,01-East,777587000,317.84,18.74,299.10
DELTA REFRACTORIES,01-East,770220000,294.00,27.35,266.65
DELTA REFRACTORIES,01-East,770570000,246.67,21.63,225.04
DELTA REFRACTORIES,01-East,733423700,243.20,45.26,197.94
DELTA REFRACTORIES,01-East,733423800,243.20,48.07,195.13
DELTA REFRACTORIES,01-East,734900000,201.96,76.72,125.24
DELTA REFRACTORIES,01-East,779041000,120.20,30.16,90.04
DELTA REFRACTORIES,01-East,764461006,80.00,27.94,52.06
DENTAL CREATIONS LTD,01-East,734961000,25332.00,4643.96,20688.04
DEPT. OF THE NAVY,01-East,724311400,2346.00,376.76,1969.24
DERRINGER NEY,01-East,733151500,450.00,47.69,402.31
DETECTOR TECHNOLOGY,01-East,739541200,501.76,64.10,437.66
DFAS COLUMBUS CENTER,01-East,734010000,1720.00,180.10,1539.90
DISTRIBUTION INT'L.,01-East,735602000,690746.90,246603.50,444143.40
DISTRIBUTION INT'L.,01-East,735602110,64332.00,20826.61,43505.39
DISTRIBUTION INT'L.,01-East,724071900,44762.34,6992.37,37769.97
DISTRIBUTION INT'L.,01-East,735752000,39744.00,14795.93,24948.07
DISTRIBUTION INT'L.,01-East,734311100,33981.20,3883.17,30098.03
DISTRIBUTION INT'L.,01-East,739370000,20040.38,15579.44,4460.94
DISTRIBUTION INT'L.,01-East,734121000,13972.50,3751.06,10221.44
DISTRIBUTION INT'L.,01-East,768142074,13484.00,3496.25,9987.75
DISTRIBUTION INT'L.,01-East,739290000,13142.30,14424.48,-1282.18
DISTRIBUTION INT'L.,01-East,735750000,6030.00,2008.93,4021.07
DISTRIBUTION INT'L.,01-East,735650000,5850.00,1482.97,4367.03
DISTRIBUTION INT'L.,01-East,768144200,4354.55,447.42,3907.13
DISTRIBUTION INT'L.,01-East,768121000,2520.00,704.55,1815.45
DISTRIBUTION INT'L.,01-East,737851000,1983.62,1326.40,657.22
DISTRIBUTION INT'L.,01-East,718501000,1577.00,272.12,1304.88
DISTRIBUTION INT'L.,01-East,770830000,1390.00,71.75,1318.25
DISTRIBUTION INT'L.,01-East,770320000,1311.20,206.07,1105.13
DISTRIBUTION INT'L.,01-East,770580000,1297.00,89.66,1207.34
DISTRIBUTION INT'L.,01-East,733505100,975.00,180.19,794.81
DISTRIBUTION INT'L.,01-East,737301000,839.62,643.84,195.78
DISTRIBUTION INT'L.,01-East,733141300,779.20,209.19,570.01
DISTRIBUTION INT'L.,01-East,768133000,778.60,85.83,692.77
DISTRIBUTION INT'L.,01-East,770312000,712.50,113.53,598.97
DISTRIBUTION INT'L.,01-East,770200000,689.40,81.57,607.83
DISTRIBUTION INT'L.,01-East,733384000,675.84,141.33,534.51
DISTRIBUTION INT'L.,01-East,742546000,661.50,82.04,579.46
DISTRIBUTION INT'L.,01-East,724311200,631.00,171.33,459.67
DISTRIBUTION INT'L.,01-East,768241100,500.00,183.65,316.35
DISTRIBUTION INT'L.,01-East,770380000,498.00,51.37,446.63
DISTRIBUTION INT'L.,01-East,770392000,392.00,41.51,350.49
DISTRIBUTION INT'L.,01-East,733505300,375.00,77.58,297.42
DISTRIBUTION INT'L.,01-East,734132000,366.00,76.28,289.72
DISTRIBUTION INT'L.,01-East,777601000,271.65,72.02,199.63
DISTRIBUTION INT'L.,01-East,777642000,210.50,66.20,144.30
DISTRIBUTION INT'L.,01-East,767650000,174.00,63.10,110.90
DISTRIBUTION INT'L.,01-East,767660000,93.00,33.15,59.85
DIVERSIFIED CONTROL INC.,01-East,738401000,2725.92,1284.48,1441.44
DOOLEY GASKET & SEAL,01-East,733525600,9630.00,1174.44,8455.56
DOOLEY GASKET & SEAL,01-East,733202000,3572.00,735.60,2836.40
DOOLEY GASKET & SEAL,01-East,733201000,2690.40,563.87,2126.53
DOOLEY GASKET & SEAL,01-East,733151600,1682.40,252.63,1429.77
DOOLEY GASKET & SEAL,01-East,733211050,1562.80,323.03,1239.77
DOOLEY GASKET & SEAL,01-East,733182200,1242.60,195.30,1047.30
DOOLEY GASKET & SEAL,01-East,764521000,899.10,39.81,859.29
DOOLEY GASKET & SEAL,01-East,764601000,610.20,26.32,583.88
DOOLEY GASKET & SEAL,01-East,739541200,470.02,80.45,389.57
DOT/FAA TECHNICAL CENTER,01-East,733464500,313.00,32.53,280.47
DOW CHEMICAL COMPANY,01-East,733423800,400.00,48.07,351.93
DRAKE WATER TECHNOLOGY,01-East,764903000,2160.00,598.17,1561.83
DRAKE WATER TECHNOLOGY,01-East,739641100,463.32,137.43,325.89
DS FIBERTECH CORP.,01-East,727000140,53577.60,15424.00,38153.60
DS FIBERTECH CORP.,01-East,727063016,39641.40,11472.38,28169.02
DS FIBERTECH CORP.,01-East,715000000,27678.75,15217.97,12460.78
DS FIBERTECH CORP.,01-East,734311100,24370.80,3543.20,20827.60
DS FIBERTECH CORP.,01-East,763991000,17755.20,6354.36,11400.84
DS FIBERTECH CORP.,01-East,734173000,15138.00,3651.92,11486.08
DS FIBERTECH CORP.,01-East,764781000,12946.20,2564.86,10381.34
DS FIBERTECH CORP.,01-East,764821000,5780.00,1240.51,4539.49
DS FIBERTECH CORP.,01-East,764901000,4553.40,771.80,3781.60
DS FIBERTECH CORP.,01-East,740361900,4298.88,1797.47,2501.41
DS FIBERTECH CORP.,01-East,764761000,4294.00,1159.35,3134.65
DS FIBERTECH CORP.,01-East,733211050,3366.90,770.29,2596.61
DS FIBERTECH CORP.,01-East,777901100,2348.47,935.30,1413.17
DS FIBERTECH CORP.,01-East,733525600,2244.00,338.56,1905.44
DS FIBERTECH CORP.,01-East,770570000,2179.95,196.14,1983.81
DS FIBERTECH CORP.,01-East,764221000,1977.20,607.32,1369.88
DS FIBERTECH CORP.,01-East,777642000,121.50,50.36,71.14
DSSI,01-East,739641100,463.32,137.43,325.89
DUNCAN BOLT,01-East,733403100,1163.15,122.60,1040.55
DURR SYSTEMS INC.,01-East,782070000,40736.40,19967.55,20768.85
DURR SYSTEMS INC.,01-East,780770000,24779.46,12150.48,12628.98
DURR SYSTEMS INC.,01-East,779956216,11773.92,3633.60,8140.32
DURR SYSTEMS INC.,01-East,780561000,5148.36,2523.48,2624.88
DURR SYSTEMS INC.,01-East,777902892,4317.30,3650.40,666.90
DURR SYSTEMS INC.,01-East,779956217,3769.56,970.74,2798.82
DURR SYSTEMS INC.,01-East,777904301,2591.75,1174.68,1417.07
DURR SYSTEMS INC.,01-East,777902891,2502.00,2115.00,387.00
DURR SYSTEMS INC.,01-East,779952144,2355.84,1316.85,1038.99
DURR SYSTEMS INC.,01-East,766121000,1962.00,490.47,1471.53
DURR SYSTEMS INC.,01-East,779952145,1424.88,862.71,562.17
DURR SYSTEMS INC.,01-East,764821000,1107.00,276.45,830.55
DURR SYSTEMS INC.,01-East,779956599,669.60,537.36,132.24
DURR SYSTEMS INC.,01-East,780521000,660.00,342.67,317.33
DURR SYSTEMS INC.,01-East,779989410,652.50,441.00,211.50
DURR SYSTEMS INC.,01-East,779989510,499.50,364.50,135.00
DURR SYSTEMS INC.,01-East,777902890,453.60,388.80,64.80
DXP ENTERPRISES,01-East,764262000,1237.20,135.92,1101.28
E I DUPONT DE NEMOURS &,01-East,734900000,4144.50,863.14,3281.36
E I DUPONT DE NEMOURS &,01-East,734950000,3340.00,565.64,2774.36
E I DUPONT DE NEMOURS &,01-East,733444100,696.00,163.25,532.75
E&J METAL FABRICATORS INC,01-East,764601000,5085.00,197.37,4887.63
E&J METAL FABRICATORS INC,01-East,739741100,2471.04,657.34,1813.70
E&J METAL FABRICATORS INC,01-East,764521000,640.00,26.60,613.40
E. A. PATTEN COMPANY,01-East,770800000,3288.00,53.64,3234.36
E. O. WOOD CO,01-East,735602000,80552.00,24333.91,56218.09
E. O. WOOD CO,01-East,735602110,13750.00,4812.87,8937.13
E. O. WOOD CO,01-East,735752000,2340.00,773.28,1566.72
E.I.DUPONT DE NEMOURS&CO,01-East,733444100,332.00,75.04,256.96
E.K.S. PARTS INC.,01-East,734311100,19784.32,2965.90,16818.42
E.K.S. PARTS INC.,01-East,734900000,2632.00,1059.17,1572.83
E.K.S. PARTS INC.,01-East,770260000,612.00,67.63,544.37
E.K.S. PARTS INC.,01-East,770240000,611.25,67.63,543.62
E.K.S. PARTS INC.,01-East,770210000,560.00,63.05,496.95
E.K.S. PARTS INC.,01-East,770220000,540.00,54.70,485.30
E.K.S. PARTS INC.,01-East,739541100,428.40,83.57,344.83
E.K.S. PARTS INC.,01-East,739561100,398.16,93.32,304.84
E.K.S. PARTS INC.,01-East,739451000,284.16,116.03,168.13
E.K.S. PARTS INC.,01-East,777601000,238.16,72.02,166.14
EAGLE INSULATION,01-East,735602000,482795.00,183732.02,299062.98
EAGLE PICHER TECHNOLOGIES,01-East,724003000,1825.50,221.83,1603.67
EAGLE PICHER TECHNOLOGIES,01-East,740382101,626.40,252.83,373.57
"EAGLE ROCK SPECIALTIES,",01-East,766121000,875.00,27.25,847.75
"EAGLE ROCK SPECIALTIES,",01-East,777601000,338.76,58.80,279.96
"EAGLE ROCK SPECIALTIES,",01-East,764521000,333.00,13.27,319.73
EAST COAST METAL,01-East,735602000,19393.50,4805.20,14588.30
EASTERN REGIONAL CONTRACT,01-East,764821000,3168.00,737.18,2430.82
EASTERN REGIONAL CONTRACT,01-East,739781100,2250.72,856.89,1393.83
EASTERN REGIONAL CONTRACT,01-East,737851000,2161.60,332.22,1829.38
EASTERN REGIONAL CONTRACT,01-East,737811000,2135.76,400.30,1735.46
EASTERN REGIONAL CONTRACT,01-East,766121000,2052.00,653.98,1398.02
EASTERN REGIONAL CONTRACT,01-East,764902000,1980.00,478.89,1501.11
EASTERN REGIONAL CONTRACT,01-East,764901000,1584.00,387.46,1196.54
EASTERN REGIONAL CONTRACT,01-East,734651200,1307.34,222.00,1085.34
EASTERN REGIONAL CONTRACT,01-East,764602000,1176.00,314.55,861.45
EASTERN REGIONAL CONTRACT,01-East,734822000,792.20,160.95,631.25
EASTERN REGIONAL CONTRACT,01-East,734830000,656.00,89.29,566.71
EASTERN REGIONAL CONTRACT,01-East,734900000,100.96,38.36,62.60
ECR INTERNATIONAL INC.,01-East,734311100,2334.64,205.40,2129.24
EDLON PRODUCTS INC.,01-East,734211200,486.92,46.75,440.17
"ELECTRIC MOTION CO.,INC",01-East,739681100,906.30,327.46,578.84
ELECTRO CHEMICAL ENGR MFG,01-East,733721000,1800.00,415.91,1384.09
EMCORE CORPORATION,01-East,779985552,3365.12,1397.82,1967.30
EMPIRE INSUL. SPECIALTIES,01-East,735602000,2736.00,1008.69,1727.31
EMS LLC,01-East,764521000,666.00,26.54,639.46
ENERGYSOLUTIONS US,01-East,738521000,59647.68,37618.56,22029.12
ENERGYSOLUTIONS US,01-East,764821000,6084.00,375.72,5708.28
ENERGYSOLUTIONS US,01-East,777642000,5434.00,1913.68,3520.32
ENERGYSOLUTIONS US,01-East,779985571,386.52,111.95,274.57
ENERSYS ADVANCED SYSTEMS,01-East,733525400,2870.00,274.80,2595.20
ENERSYS ADVANCED SYSTEMS,01-East,740382201,1180.00,300.51,879.49
ENERSYS ADVANCED SYSTEMS,01-East,740402301,1107.50,219.36,888.14
ESCO TURBINE TECHNOLOGIES,01-East,764609200,224489.00,82206.88,142282.12
ESCO TURBINE TECHNOLOGIES,01-East,764609500,162258.75,64151.37,98107.38
ESCO TURBINE TECHNOLOGIES,01-East,764609100,122593.00,45426.60,77166.40
ESCO TURBINE TECHNOLOGIES,01-East,764781040,25706.00,6500.75,19205.25
ESCO TURBINE TECHNOLOGIES,01-East,764461006,4577.60,1460.64,3116.96
ESCO TURBINE TECHNOLOGIES,01-East,740402301,795.00,210.97,584.03
ESTERLINE,01-East,739541100,658.56,83.57,574.99
ESTERLINE,01-East,739452000,540.54,150.83,389.71
EVANS / EVCO,01-East,724281200,3519.00,499.18,3019.82
EVIE AULT,01-East,733423700,380.80,36.22,344.58
EXOTIC METAL FORMING,01-East,740382201,6470.00,1776.11,4693.89
EXPRESS INTEGRATED TECH.,01-East,739370000,4438.08,3179.52,1258.56
EXPRESS INTEGRATED TECH.,01-East,739741100,4073.60,3198.08,875.52
EXPRESS METAL FAB INC.,01-East,768241000,223909.50,121746.50,102163.00
EXPRESS METAL FAB INC.,01-East,779958169,194821.31,102613.49,92207.82
EXPRESS METAL FAB INC.,01-East,737912200,161657.00,56722.01,104934.99
EXPRESS METAL FAB INC.,01-East,768221000,128412.00,64711.59,63700.41
EXPRESS METAL FAB INC.,01-East,779956285,77422.50,47154.03,30268.47
EXPRESS METAL FAB INC.,01-East,768231000,40912.00,20708.52,20203.48
EXPRESS METAL FAB INC.,01-East,751348000,30465.84,19085.43,11380.41
EXPRESS METAL FAB INC.,01-East,779958170,26324.88,21598.72,4726.16
EXPRESS METAL FAB INC.,01-East,764821000,20181.00,8822.19,11358.81
EXPRESS METAL FAB INC.,01-East,766122000,16014.00,5867.47,10146.53
EXPRESS METAL FAB INC.,01-East,764901000,7984.00,3783.51,4200.49
EXPRESS METAL FAB INC.,01-East,768222000,5175.00,2561.73,2613.27
EXPRESS METAL FAB INC.,01-East,764861000,5107.00,2413.44,2693.56
EXPRESS METAL FAB INC.,01-East,739370000,4438.08,2088.90,2349.18
EXPRESS METAL FAB INC.,01-East,779956599,2580.60,2059.92,520.68
EXPRESS METAL FAB INC.,01-East,777642000,1590.00,475.05,1114.95
EXPRESS METAL FAB INC.,01-East,768121000,182.50,71.92,110.58
EXPRESSIONS ART GLASS,01-East,739741100,823.68,219.12,604.56
EXPRESSIONS ART GLASS,01-East,739561200,421.12,72.33,348.79
EYE THINK INC.,01-East,733464600,298.00,31.31,266.69
F & B MFG LLC,01-East,740361900,4708.80,1584.97,3123.83
F.S. SPERRY COMPANY,01-East,727041200,84171.60,12684.45,71487.15
F.S. SPERRY COMPANY,01-East,727041000,28350.00,11281.65,17068.35
F.S. SPERRY COMPANY,01-East,779956843,11185.68,8680.66,2505.02
F.S. SPERRY COMPANY,01-East,734890000,4761.87,1193.78,3568.09
F.S. SPERRY COMPANY,01-East,779956842,4116.00,3083.85,1032.15
F.S. SPERRY COMPANY,01-East,734990000,2776.00,552.92,2223.08
F.S. SPERRY COMPANY,01-East,779956841,2596.10,1483.72,1112.38
F.S. SPERRY COMPANY,01-East,779956840,2056.80,1229.02,827.78
F.S. SPERRY COMPANY,01-East,777601000,1629.90,432.10,1197.80
F.S. SPERRY COMPANY,01-East,764822000,980.00,153.82,826.18
F.S. SPERRY COMPANY,01-East,779261000,519.93,115.39,404.54
F.S. SPERRY COMPANY,01-East,734910000,499.80,104.44,395.36
F.S. SPERRY COMPANY,01-East,764821000,390.40,61.44,328.96
F.S. SPERRY COMPANY,01-East,770210000,285.00,31.52,253.48
FAIR RITE PRODUCTS CORP,01-East,733511000,537.50,66.56,470.94
FASTENAL COMPANY,01-East,770320000,1130.00,111.14,1018.86
FASTENAL COMPANY,01-East,770310000,544.00,47.28,496.72
FAUST ELECTRIC MOTOR,01-East,764261000,424.00,22.89,401.11
FEDERAL MOGUL WORLDWIDE,01-East,726020047,9064.00,3209.12,5854.88
FEDERAL MOGUL WORLDWIDE,01-East,726020007,1269.60,464.04,805.56
FEDERAL MOGUL WORLDWIDE,01-East,726020046,423.20,148.75,274.45
FIAS USA INC,01-East,734910000,3340.00,530.85,2809.15
FIBER GLASS INDUSTRIES,01-East,764761000,1016.00,61.43,954.57
FIBRECAST INC.,01-East,764901000,14985.00,8647.70,6337.30
FIBRECAST INC.,01-East,733403300,831.60,122.54,709.06
FIRELINE 520,01-East,764822000,29566.00,11777.94,17788.06
FIRELINE 520,01-East,764781040,25182.00,12183.17,12998.83
FIRELINE 520,01-East,764781000,400.00,204.45,195.55
FIRELINE 520,01-East,764461006,244.00,82.44,161.56
FIRELINE 520,01-East,764482000,60.00,34.10,25.90
FIRST STATE MANUFACTURING,01-East,791104475,10850.00,4964.87,5885.13
FLAME CONTROL COATINGS,01-East,720219100,2127.60,52.39,2075.21
FLANDERS ELECTRIC,01-East,770500000,2043.00,83.60,1959.40
FLANDERS ELECTRIC,01-East,739541000,1128.96,143.24,985.72
FLANDERS ELECTRIC,01-East,739561000,947.52,170.44,777.08
FLANDERS ELECTRIC,01-East,739641100,463.32,137.43,325.89
FLAREGAS CORP.,01-East,770280000,2906.25,240.08,2666.17
FLEXLINE LLC,01-East,739561100,552.72,93.32,459.40
FLEXLINE LLC,01-East,739451000,411.84,116.03,295.81
FLIGHT ENVIRONMENTS,01-East,724281400,12293.00,1937.89,10355.11
FMS LOGISTICS MANAGEMENT,01-East,770510000,1880.00,82.78,1797.22
FORESTWOOD CO. INC.,01-East,768751000,4533.84,2645.88,1887.96
FORESTWOOD CO. INC.,01-East,768753000,4533.84,2645.88,1887.96
FORESTWOOD CO. INC.,01-East,727063000,764.80,261.74,503.06
FOUR STATES GASKET & RUBB,01-East,738521000,556.48,298.56,257.92
FRANK MURKEN PRODUCTS INC,01-East,733484800,648.00,82.18,565.82
FRIC & FRAC,01-East,739641100,482.04,201.60,280.44
FRICCION Y TECNOLOGIA,01-East,727080000,362487.20,50308.56,312178.64
FRICCION Y TECNOLOGIA,01-East,779987651,119319.20,16797.77,102521.43
FRIEDMAN ELECTRIC SUPPLY,01-East,764481000,360.00,17.59,342.41
FUELCELL ENERGY,01-East,777585000,70459.55,2551.49,67908.06
FUELCELL ENERGY,01-East,770740000,48023.00,1811.40,46211.60
FUELCELL ENERGY,01-East,733151500,11934.00,1383.92,10550.08
FUELCELL ENERGY,01-East,733182010,7825.29,1761.96,6063.33
FUELCELL ENERGY,01-East,770722000,2483.00,85.76,2397.24
FUELCELL ENERGY,01-East,770850000,1230.00,47.24,1182.76
FUELCELL ENERGY,01-East,734611100,1215.28,176.31,1038.97
FUELCELL ENERGY,01-East,768221000,1014.00,51.42,962.58
FUELCELL ENERGY,01-East,734900000,673.00,191.81,481.19
FUJI INDUSTRIES CORP,01-East,733464500,626.00,65.05,560.95
"FYFE CP., LLC",01-East,727063022,55185.60,5610.80,49574.80
G.W. LISK CO,01-East,733464400,313.00,43.37,269.63
GAF,01-East,764221000,486.00,33.75,452.25
GAF,01-East,764261000,424.00,22.89,401.11
GARLOCK SEALING TECH,01-East,733151600,8684.00,1210.04,7473.96
GARLOCK SEALING TECH,01-East,770565000,7146.00,778.95,6367.05
GARLOCK SEALING TECH,01-East,770262000,5249.25,892.68,4356.57
GARLOCK SEALING TECH,01-East,770244000,3596.25,554.54,3041.71
GARLOCK SEALING TECH,01-East,770260000,3564.00,608.66,2955.34
GARLOCK SEALING TECH,01-East,770601000,3001.95,288.96,2712.99
GARLOCK SEALING TECH,01-East,770524000,2196.00,277.27,1918.73
GARLOCK SEALING TECH,01-East,770221000,1887.00,275.66,1611.34
GARLOCK SEALING TECH,01-East,770531000,1795.00,193.36,1601.64
GARLOCK SEALING TECH,01-East,770201000,1746.00,329.56,1416.44
GARLOCK SEALING TECH,01-East,770301000,1542.75,256.77,1285.98
GARLOCK SEALING TECH,01-East,770322000,1378.00,317.22,1060.78
GARLOCK SEALING TECH,01-East,770332000,1342.50,294.77,1047.73
GARLOCK SEALING TECH,01-East,770231000,1254.75,218.91,1035.84
GARLOCK SEALING TECH,01-East,770211000,1175.00,214.63,960.37
GARLOCK SEALING TECH,01-East,770571000,1076.40,100.16,976.24
GARLOCK SEALING TECH,01-East,770501000,531.00,51.44,479.56
GARLOCK SEALING TECH,01-East,770514000,515.00,82.78,432.22
GARLOCK SEALING TECH,01-East,770220000,378.00,54.69,323.31
GARLOCK SEALING TECH,01-East,770310500,364.00,90.94,273.06
GB & G DIE GASKET CO.INC.,01-East,733671000,1438.08,119.66,1318.42
GE ENERGY CERAMIC,01-East,733151500,472.00,54.11,417.89
GE GLOBAL RESEARCH,01-East,764821000,447.00,15.66,431.34
"GEMX TECHNOLOGIES, LLC",01-East,737550000,42352.60,55206.93,-12854.33
"GEMX TECHNOLOGIES, LLC",01-East,737550600,24288.70,13046.50,11242.20
"GEMX TECHNOLOGIES, LLC",01-East,779980780,21410.90,11579.26,9831.64
"GEMX TECHNOLOGIES, LLC",01-East,737550200,18258.80,10457.46,7801.34
"GEMX TECHNOLOGIES, LLC",01-East,779980760,7224.00,3220.70,4003.30
"GEMX TECHNOLOGIES, LLC",01-East,779980750,6744.00,3006.70,3737.30
"GEMX TECHNOLOGIES, LLC",01-East,737550400,4762.92,2452.91,2310.01
"GEMX TECHNOLOGIES, LLC",01-East,764461006,1000.00,108.98,891.02
"GEMX TECHNOLOGIES, LLC",01-East,734900000,915.54,171.02,744.52
GENERAL GASKET,01-East,771320000,6000.00,711.76,5288.24
GENERAL GASKET,01-East,733721000,1208.00,277.28,930.72
GENERAL GASKET,01-East,770260000,978.75,70.33,908.42
GENERAL GASKET,01-East,733525600,752.50,71.05,681.45
GENERAL TOOL & SUPPLY,01-East,770120000,600.16,37.20,562.96
GENESYS,01-East,764821000,5334.00,281.79,5052.21
GENEX TURBINE TECHNOLOGIE,01-East,740402402,3225.00,652.05,2572.95
"GEROME TECHNOLOGIES, INC",01-East,765702000,5250.00,2316.78,2933.22
GERRESHEIMER GLASS INC.,01-East,737912200,3382.00,442.06,2939.94
GHX,01-East,770512000,6704.50,266.00,6438.50
GHX,01-East,770810000,5070.00,103.12,4966.88
GHX,01-East,770830000,4040.00,123.90,3916.10
GHX,01-East,770430000,3982.50,265.64,3716.86
GHX,01-East,770522000,1963.00,102.80,1860.20
GHX,01-East,742541500,1868.75,138.40,1730.35
GHX,01-East,770520000,1812.00,96.63,1715.37
GLASTON AMERICA INC.,01-East,771320000,6343.40,1111.55,5231.85
GLENRO INC,01-East,771320000,24934.00,3477.02,21456.98
GLENRO INC,01-East,739561200,2947.84,506.32,2441.52
GLENRO INC,01-East,738521000,2183.04,884.02,1299.02
GLENRO INC,01-East,734311100,2166.78,190.16,1976.62
GLENRO INC,01-East,733525600,1220.00,130.98,1089.02
GLENRO INC,01-East,739541200,501.76,64.10,437.66
GLIDEWELL DENTAL,01-East,737301000,3230.72,1287.68,1943.04
GMS SHEETMETAL DIVISON,01-East,724003000,1899.00,245.46,1653.54
GONZALES MFG. CO.,01-East,766594010,100470.00,59529.97,40940.03
GONZALES MFG. CO.,01-East,766594000,18912.00,12162.45,6749.55
GONZALES MFG. CO.,01-East,766564001,4368.00,2615.47,1752.53
GONZALES MFG. CO.,01-East,766541110,2052.00,1119.94,932.06
GOVERNMENT SCIENTIFIC,01-East,734751200,1118.35,149.88,968.47
GRAVER TECHNOLOGIES,01-East,733505300,82980.00,18781.71,64198.29
GRAYMONT MATERIAL NY INC.,01-East,734910000,724.00,106.17,617.83
GREAT LAKES PUMP & SEAL,01-East,737321000,7180.89,1070.70,6110.19
GREAT LAKES PUMP & SEAL,01-East,737301000,3106.56,490.72,2615.84
GREAT LAKES PUMP & SEAL,01-East,742520400,2495.50,338.32,2157.18
GREAT LAKES PUMP & SEAL,01-East,737271000,1615.70,254.14,1361.56
GREAT LAKES PUMP & SEAL,01-East,770560000,1509.00,86.09,1422.91
GREENBRIER RAIL SERVICES,01-East,764821000,9166.00,324.89,8841.11
GREENBRIER RAIL SERVICES,01-East,764481000,1730.00,84.70,1645.30
GREENBRIER RAIL SERVICES,01-East,764643100,302.40,105.99,196.41
GRESCO PRODUCTS INC,01-East,724261200,786.00,78.20,707.80
GRESCO PRODUCTS INC,01-East,734990000,463.20,55.04,408.16
GSA ACCOUNT PAYABLE,01-East,734311100,3524.84,309.72,3215.12
GT MIDWEST,01-East,764222050,274964.18,158313.53,116650.65
GT MIDWEST,01-East,768122200,264011.53,163840.64,100170.89
GT MIDWEST,01-East,733586500,190153.44,32489.24,157664.20
GT MIDWEST,01-East,768241800,181005.95,92634.50,88371.45
GT MIDWEST,01-East,768550000,168251.42,91022.99,77228.43
GT MIDWEST,01-East,733201000,166744.00,41481.70,125262.30
GT MIDWEST,01-East,733211050,100596.40,27755.55,72840.85
GT MIDWEST,01-East,764522000,89235.00,35205.92,54029.08
GT MIDWEST,01-East,764781040,89080.00,34248.40,54831.60
GT MIDWEST,01-East,764521000,66995.00,14749.83,52245.17
GT MIDWEST,01-East,742520110,62213.00,14392.91,47820.09
GT MIDWEST,01-East,764481000,52302.00,14568.79,37733.21
GT MIDWEST,01-East,764762060,46008.00,19292.96,26715.04
GT MIDWEST,01-East,768225030,34248.00,17393.80,16854.20
GT MIDWEST,01-East,764902000,32626.00,8339.23,24286.77
GT MIDWEST,01-East,733182200,32202.72,12769.20,19433.52
GT MIDWEST,01-East,738561000,32136.02,14863.40,17272.62
GT MIDWEST,01-East,724281700,30440.00,12044.48,18395.52
GT MIDWEST,01-East,764461006,26817.00,7817.24,18999.76
GT MIDWEST,01-East,777904000,26536.44,9292.43,17244.01
GT MIDWEST,01-East,779980925,23097.60,7842.24,15255.36
GT MIDWEST,01-East,770830000,20653.30,890.21,19763.09
GT MIDWEST,01-East,734661300,19811.00,6118.74,13692.26
GT MIDWEST,01-East,770880000,19801.50,1034.84,18766.66
GT MIDWEST,01-East,727000140,15969.80,2019.02,13950.78
GT MIDWEST,01-East,764822000,15855.00,3627.37,12227.63
GT MIDWEST,01-East,768242011,15053.66,7569.71,7483.95
GT MIDWEST,01-East,739541200,13821.44,4739.81,9081.63
GT MIDWEST,01-East,737902100,13398.73,3312.38,10086.35
GT MIDWEST,01-East,739452000,12972.33,5499.06,7473.27
GT MIDWEST,01-East,739681100,10423.08,6067.36,4355.72
GT MIDWEST,01-East,770222000,9952.80,929.07,9023.73
GT MIDWEST,01-East,733525600,9607.50,1375.25,8232.25
GT MIDWEST,01-East,764561000,9074.00,1675.57,7398.43
GT MIDWEST,01-East,764902021,8125.26,4108.30,4016.96
GT MIDWEST,01-East,733171900,6510.00,1259.24,5250.76
GT MIDWEST,01-East,738441000,6210.00,1730.71,4479.29
GT MIDWEST,01-East,738521000,5935.68,3512.78,2422.90
GT MIDWEST,01-East,764262000,5900.00,1706.47,4193.53
GT MIDWEST,01-East,764601000,5751.80,1006.66,4745.14
GT MIDWEST,01-East,737912200,5628.85,1152.75,4476.10
GT MIDWEST,01-East,734910000,5483.10,1294.17,4188.93
GT MIDWEST,01-East,777601000,4343.20,1269.90,3073.30
GT MIDWEST,01-East,779081000,3796.06,1104.76,2691.30
GT MIDWEST,01-East,739561200,3778.04,1021.32,2756.72
GT MIDWEST,01-East,764821000,3614.00,814.07,2799.93
GT MIDWEST,01-East,734311100,3293.32,378.73,2914.59
GT MIDWEST,01-East,770720000,3148.40,117.70,3030.70
GT MIDWEST,01-East,764342000,2722.10,578.39,2143.71
GT MIDWEST,01-East,740023000,2356.36,595.01,1761.35
GT MIDWEST,01-East,768222200,2268.00,1320.64,947.36
GT MIDWEST,01-East,734711100,2116.40,433.10,1683.30
GT MIDWEST,01-East,739641100,2102.94,1154.52,948.42
GT MIDWEST,01-East,734990000,2028.88,385.00,1643.88
GT MIDWEST,01-East,764482000,1830.00,511.44,1318.56
GT MIDWEST,01-East,733151500,1627.80,270.53,1357.27
GT MIDWEST,01-East,764826800,1584.00,672.39,911.61
GT MIDWEST,01-East,764602000,1470.00,632.49,837.51
GT MIDWEST,01-East,764461000,1449.00,419.06,1029.94
GT MIDWEST,01-East,724281400,1395.80,332.79,1063.01
GT MIDWEST,01-East,739741100,1050.24,438.24,612.00
GT MIDWEST,01-East,739781100,1007.04,380.84,626.20
GT MIDWEST,01-East,779951213,865.50,320.61,544.89
GT MIDWEST,01-East,739561000,627.55,172.39,455.16
GT MIDWEST,01-East,764901000,526.00,129.15,396.85
GT MIDWEST,01-East,733725500,412.00,103.53,308.47
GT MIDWEST,01-East,724311900,344.50,109.89,234.61
H.C. STARCK INC.,01-East,770210000,440.00,30.32,409.68
H.E.I.,01-East,733464400,313.00,43.37,269.63
HAARSLEY INCORPORATED,01-East,739741100,14158.08,3944.08,10214.00
HAARSLEY INCORPORATED,01-East,734751200,550.90,74.78,476.12
HAARSLEY INCORPORATED,01-East,734711100,220.36,54.10,166.26
HAAS GROUP INTERNATIONAL,01-East,734910000,1810.00,265.43,1544.57
HAAS GROUP INTERNATIONAL,01-East,734311100,1617.44,141.21,1476.23
HAAS GROUP INTERNATIONAL,01-East,734751200,292.00,37.71,254.29
HAGEMEYER NORTH AMERICA,01-East,733505200,3095.00,313.15,2781.85
HAGEMEYER NORTH AMERICA,01-East,770500000,1075.50,51.44,1024.06
HAGEMEYER NORTH AMERICA,01-East,733525600,752.50,71.05,681.45
HANES SUPPLY,01-East,770220000,2025.00,168.43,1856.57
HANYAN-HIGGINS CO INC,01-East,737301000,7064.54,1717.52,5347.02
HANYAN-HIGGINS CO INC,01-East,734990000,2131.20,663.51,1467.69
HANYAN-HIGGINS CO INC,01-East,766121000,1623.60,163.50,1460.10
HANYAN-HIGGINS CO INC,01-East,734910000,1484.90,311.57,1173.33
HANYAN-HIGGINS CO INC,01-East,739561100,1084.35,293.24,791.11
HANYAN-HIGGINS CO INC,01-East,739561200,1073.41,289.33,784.08
HANYAN-HIGGINS CO INC,01-East,776101000,1064.25,502.81,561.44
HANYAN-HIGGINS CO INC,01-East,734651200,629.74,110.75,518.99
HANYAN-HIGGINS CO INC,01-East,733511000,619.50,133.12,486.38
HANYAN-HIGGINS CO INC,01-East,739451000,525.12,232.06,293.06
HANYAN-HIGGINS CO INC,01-East,773191000,307.00,44.38,262.62
HANYAN-HIGGINS CO INC,01-East,777642000,290.40,88.54,201.86
HANYAN-HIGGINS CO INC,01-East,764821000,268.40,30.72,237.68
HARBISON-WALKER REFR.,01-East,718501000,9945.00,1224.55,8720.45
HARCO MANUFACTURING,01-East,732429400,6672.00,466.22,6205.78
HARPER INTERNATIONAL CORP,01-East,779985557,91230.72,26635.06,64595.66
HARPER INTERNATIONAL CORP,01-East,740142000,52626.88,21129.30,31497.58
HARPER INTERNATIONAL CORP,01-East,779988973,41616.00,31612.00,10004.00
HARPER INTERNATIONAL CORP,01-East,779985552,39179.52,12967.66,26211.86
HARPER INTERNATIONAL CORP,01-East,775171000,11581.52,5533.02,6048.50
HARPER INTERNATIONAL CORP,01-East,737321000,11235.84,4496.94,6738.90
HARPER INTERNATIONAL CORP,01-East,737861000,10570.56,10581.31,-10.75
HARPER INTERNATIONAL CORP,01-East,738542000,8649.60,4799.30,3850.30
HARPER INTERNATIONAL CORP,01-East,770540000,7329.00,538.80,6790.20
HARPER INTERNATIONAL CORP,01-East,779951021,6440.00,4855.12,1584.88
HARPER INTERNATIONAL CORP,01-East,779959108,5883.92,3235.12,2648.80
HARPER INTERNATIONAL CORP,01-East,770392000,5174.00,456.92,4717.08
HARPER INTERNATIONAL CORP,01-East,770380000,4860.00,1282.51,3577.49
HARPER INTERNATIONAL CORP,01-East,779951023,3570.00,2670.54,899.46
HARPER INTERNATIONAL CORP,01-East,780690000,3393.28,1866.44,1526.84
HARPER INTERNATIONAL CORP,01-East,737871000,3045.12,839.22,2205.90
HARPER INTERNATIONAL CORP,01-East,770372000,2877.00,538.28,2338.72
HARPER INTERNATIONAL CORP,01-East,737311000,2703.36,1366.56,1336.80
HARPER INTERNATIONAL CORP,01-East,780521000,2625.28,1445.00,1180.28
HARPER INTERNATIONAL CORP,01-East,740146000,2448.72,956.86,1491.86
HARPER INTERNATIONAL CORP,01-East,737821000,2283.84,790.54,1493.30
HARPER INTERNATIONAL CORP,01-East,774981100,2265.07,1025.38,1239.69
HARPER INTERNATIONAL CORP,01-East,722413100,2230.56,207.88,2022.68
HARPER INTERNATIONAL CORP,01-East,738561000,1856.16,1541.14,315.02
HARPER INTERNATIONAL CORP,01-East,779958975,1696.32,933.10,763.22
HARPER INTERNATIONAL CORP,01-East,739681100,1570.75,1204.60,366.15
HARPER INTERNATIONAL CORP,01-East,775011000,1460.00,895.07,564.93
HARPER INTERNATIONAL CORP,01-East,764903000,1239.00,436.84,802.16
HARPER INTERNATIONAL CORP,01-East,770530000,1177.25,93.46,1083.79
HARPER INTERNATIONAL CORP,01-East,737251000,1121.76,601.38,520.38
HARPER INTERNATIONAL CORP,01-East,737261000,1119.36,559.86,559.50
HARPER INTERNATIONAL CORP,01-East,764821000,1090.00,313.10,776.90
HARPER INTERNATIONAL CORP,01-East,739541200,1029.12,256.38,772.74
HARPER INTERNATIONAL CORP,01-East,775151000,961.80,425.89,535.91
HARPER INTERNATIONAL CORP,01-East,779959993,880.64,484.42,396.22
HARPER INTERNATIONAL CORP,01-East,738381000,763.20,473.34,289.86
HARPER INTERNATIONAL CORP,01-East,764901000,676.20,129.15,547.05
HARPER INTERNATIONAL CORP,01-East,734751200,617.37,112.17,505.20
HARPER INTERNATIONAL CORP,01-East,779958963,565.44,311.04,254.40
HARPER INTERNATIONAL CORP,01-East,766121000,493.50,75.15,418.35
HARPER INTERNATIONAL CORP,01-East,737301000,470.40,245.36,225.04
HARPER INTERNATIONAL CORP,01-East,738401000,457.92,281.37,176.55
HARPER INTERNATIONAL CORP,01-East,779986495,457.92,324.72,133.20
HARPER INTERNATIONAL CORP,01-East,738521000,409.60,597.12,-187.52
HARPER INTERNATIONAL CORP,01-East,777642000,407.75,129.56,278.19
HARPER INTERNATIONAL CORP,01-East,779951061,400.00,298.86,101.14
HARPER INTERNATIONAL CORP,01-East,739561200,299.52,144.66,154.86
HARPER INTERNATIONAL CORP,01-East,764301000,69.50,18.56,50.94
HARPER INTERNATIONAL CORP,01-East,775111000,43.80,18.81,24.99
HARPER INTERNATIONAL CORP,01-East,775091000,43.04,21.34,21.70
HARVEST PRODUCTS,01-East,727000140,1950.40,130.20,1820.20
HAWAII BIOCHAR PILOR LLC,01-East,739781100,15525.22,6881.28,8643.94
HAWAII BIOCHAR PILOR LLC,01-East,777601000,819.84,117.60,702.24
HAZELETT STRIP CASTING CO,01-East,733464500,31927.00,3363.85,28563.15
HAZELETT STRIP CASTING CO,01-East,739561200,13178.88,2316.01,10862.87
HAZELETT STRIP CASTING CO,01-East,733510000,11245.00,1572.97,9672.03
HAZELETT STRIP CASTING CO,01-East,764521000,2586.00,106.33,2479.67
HAZELETT STRIP CASTING CO,01-East,777601000,1546.88,288.08,1258.80
HAZELETT STRIP CASTING CO,01-East,740402402,1010.00,217.35,792.65
HAZELETT STRIP CASTING CO,01-East,739451000,840.32,227.59,612.73
HAZELETT STRIP CASTING CO,01-East,739741100,823.68,219.12,604.56
HAZELETT STRIP CASTING CO,01-East,739781100,410.72,163.84,246.88
HEAT SHIELD INC.,01-East,764461006,404.00,27.94,376.06
HEAVY EQUIPMENT MACHINERY,01-East,764902000,2416.00,515.33,1900.67
HERAEUS CO,01-East,766081010,11362.00,333.93,11028.07
HERCULES INDUSTRIES - DEN,01-East,735602000,251657.60,74086.06,177571.54
HERCULES INDUSTRIES - DEN,01-East,735602110,18734.40,5621.39,13113.01
HERCULES INDUSTRIES,01-East,735602110,15142.40,4550.35,10592.05
HERCULES INDUSTRIES,01-East,735602000,13977.60,4034.75,9942.85
HI TEMP INSULATION INC,01-East,791106278,63673.29,14854.41,48818.88
HI TEMP INSULATION INC,01-East,779989967,21744.00,4858.10,16885.90
HI TEMP INSULATION INC,01-East,724413000,20766.00,4683.99,16082.01
HI TEMP INSULATION INC,01-East,779989966,19680.00,3151.97,16528.03
HI TEMP INSULATION INC,01-East,732771000,15563.52,975.70,14587.82
HI TEMP INSULATION INC,01-East,740283550,6712.20,1214.78,5497.42
HI TEMP INSULATION INC,01-East,740262000,4980.80,757.75,4223.05
HI TEMP INSULATION INC,01-East,779988243,4273.50,712.81,3560.69
HI TEMP INSULATION INC,01-East,724401500,3145.50,263.37,2882.13
HI TEMP INSULATION INC,01-East,733423800,2503.04,383.67,2119.37
HI TEMP INSULATION INC,01-East,764482000,1519.00,342.73,1176.27
HI TEMP INSULATION INC,01-East,724261400,1207.80,220.54,987.26
HI-TECH ENTERPRISES,01-East,770800000,1953.00,30.65,1922.35
HI-TEMP FABRICATION INC.,01-East,739561100,38141.36,9590.81,28550.55
HI-TEMP FABRICATION INC.,01-East,737871000,36618.06,11471.18,25146.88
HI-TEMP FABRICATION INC.,01-East,739541100,20704.31,4398.57,16305.74
HI-TEMP FABRICATION INC.,01-East,764821000,17233.20,2691.93,14541.27
HI-TEMP FABRICATION INC.,01-East,735750000,15510.00,6168.46,9341.54
HI-TEMP FABRICATION INC.,01-East,737851000,13427.18,3319.72,10107.46
HI-TEMP FABRICATION INC.,01-East,738521000,5956.64,3059.10,2897.54
HI-TEMP FABRICATION INC.,01-East,733211050,3900.80,869.70,3031.10
HI-TEMP FABRICATION INC.,01-East,738542000,3282.42,1421.04,1861.38
HI-TEMP FABRICATION INC.,01-East,739561200,2289.66,578.65,1711.01
HI-TEMP FABRICATION INC.,01-East,733423800,2174.08,403.24,1770.84
HI-TEMP FABRICATION INC.,01-East,764901000,1932.00,322.88,1609.12
HI-TEMP FABRICATION INC.,01-East,733151600,1863.60,279.84,1583.76
HI-TEMP FABRICATION INC.,01-East,764822000,1825.20,285.93,1539.27
HI-TEMP FABRICATION INC.,01-East,733444200,1725.20,285.40,1439.80
HI-TEMP FABRICATION INC.,01-East,793330000,1294.80,212.05,1082.75
HI-TEMP FABRICATION INC.,01-East,764902000,1005.00,161.04,843.96
HI-TEMP FABRICATION INC.,01-East,733725500,1004.00,354.38,649.62
HI-TEMP FABRICATION INC.,01-East,734211200,851.65,92.43,759.22
HI-TEMP FABRICATION INC.,01-East,733423700,734.40,135.77,598.63
HI-TEMP FABRICATION INC.,01-East,733151500,651.20,108.22,542.98
HI-TEMP FABRICATION INC.,01-East,733528000,464.34,69.06,395.28
HI-TEMP FABRICATION INC.,01-East,733511000,348.75,75.22,273.53
HI-TEMP FABRICATION INC.,01-East,739661100,303.55,150.50,153.05
HI-TEMP FABRICATION INC.,01-East,739761100,303.55,120.66,182.89
HI-TEMP FABRICATION INC.,01-East,764781000,205.60,40.89,164.71
HI-TEMP FABRICATION INC.,01-East,733464500,185.00,29.52,155.48
HICKORY INDUSTRIES,01-East,733464500,313.00,32.53,280.47
HIGBEE GASKETS & SEALING,01-East,733443000,1024.50,181.10,843.40
HIGBEE GASKETS & SEALING,01-East,733464400,943.20,158.44,784.76
HIGBEE GASKETS & SEALING,01-East,792760000,426.70,116.62,310.08
HIGBEE GASKETS & SEALING,01-East,733741000,284.40,93.19,191.21
HIGBEE GASKETS & SEALING,01-East,792702000,283.50,96.60,186.90
HIGBEE GASKETS & SEALING,01-East,733444300,255.60,41.25,214.35
HILLBOTTOM ENTERPRISES,01-East,764602000,678.00,26.50,651.50
HIMED,01-East,766121000,1750.00,54.50,1695.50
HISCO - MEXICALI,01-East,734751200,2578.35,338.40,2239.95
HOLLOWICK,01-East,733505100,430.00,90.09,339.91
HOMANS-CORPORATE OFFICE,01-East,764901000,1593.90,193.73,1400.17
HOMANS-CORPORATE OFFICE,01-East,764902000,531.30,63.85,467.45
"HONEYWELL FM&T, LLC",01-East,791006650,19820.40,10189.49,9630.91
"HONEYWELL FM&T, LLC",01-East,791006850,12197.92,5849.37,6348.55
HPI INC,01-East,764901000,850.50,32.29,818.21
HUDSON STUDIOS,01-East,734900000,305.40,56.74,248.66
HUGHES CIRCUITS INC.,01-East,733464500,3353.00,339.75,3013.25
HUTCHINSON SEAL DE,01-East,724261200,3564.00,591.96,2972.04
I SQUARED R ELEMENT CO.,01-East,733525400,7455.00,1131.80,6323.20
I SQUARED R ELEMENT CO.,01-East,733211000,251.20,53.20,198.00
I.C.M.,01-East,779958986,17289.60,10920.05,6369.55
I.C.M.,01-East,780650000,1082.46,630.14,452.32
I.C.M.,01-East,764823500,1070.00,331.88,738.12
I.C.M.,01-East,770220000,423.90,27.35,396.55
IBC ADVANCED ALLOYS,01-East,739681100,8103.24,2687.52,5415.72
IBC ADVANCED ALLOYS,01-East,733525500,3657.50,335.87,3321.63
IBC ADVANCED ALLOYS,01-East,739641100,2817.36,952.91,1864.45
IBC ADVANCED ALLOYS,01-East,739541000,2304.00,324.24,1979.76
IBC ADVANCED ALLOYS,01-East,764781000,472.00,19.72,452.28
IBC ADVANCED ALLOYS,01-East,770220000,471.00,27.35,443.65
IBC ADVANCED ALLOYS,01-East,734311100,381.48,33.70,347.78
ICO RALLY,01-East,724281200,2404.00,356.32,2047.68
IDC CORPORATION,01-East,735602000,310980.00,125992.60,184987.40
IDC CORPORATION,01-East,735602110,5112.00,2142.08,2969.92
IDC CORPORATION,01-East,768121000,4080.00,1150.67,2929.33
IDC CORPORATION,01-East,768221000,2448.00,822.76,1625.24
"IGH INTERNATIONAL, CORP.",01-East,733511000,452.50,66.56,385.94
INDUSTRIAL FURNACE CO,01-East,737871000,1458648.96,520390.74,938258.22
INDUSTRIAL FURNACE CO,01-East,737321000,1092083.52,466071.35,626012.17
INDUSTRIAL FURNACE CO,01-East,737851000,691473.60,209162.64,482310.96
INDUSTRIAL FURNACE CO,01-East,737801000,405557.28,122187.72,283369.56
INDUSTRIAL FURNACE CO,01-East,739781100,328027.20,162673.38,165353.82
INDUSTRIAL FURNACE CO,01-East,737301000,299225.60,130085.71,169139.89
INDUSTRIAL FURNACE CO,01-East,766001000,182480.00,40287.35,142192.65
INDUSTRIAL FURNACE CO,01-East,779989700,136740.48,53446.49,83293.99
INDUSTRIAL FURNACE CO,01-East,770930000,100415.00,4516.54,95898.46
INDUSTRIAL FURNACE CO,01-East,739741100,79820.80,45317.06,34503.74
INDUSTRIAL FURNACE CO,01-East,768121000,67888.80,10281.22,57607.58
INDUSTRIAL FURNACE CO,01-East,740283502,64583.20,14042.36,50540.84
INDUSTRIAL FURNACE CO,01-East,770430000,54175.00,6268.57,47906.43
INDUSTRIAL FURNACE CO,01-East,739641100,52538.04,31564.07,20973.97
INDUSTRIAL FURNACE CO,01-East,739561200,50771.20,16940.00,33831.20
INDUSTRIAL FURNACE CO,01-East,739451000,44096.00,23206.70,20889.30
INDUSTRIAL FURNACE CO,01-East,764952010,39760.00,8760.28,30999.72
INDUSTRIAL FURNACE CO,01-East,734851000,37384.74,10628.05,26756.69
INDUSTRIAL FURNACE CO,01-East,770500000,33687.00,2630.10,31056.90
INDUSTRIAL FURNACE CO,01-East,766121000,31416.00,5073.36,26342.64
INDUSTRIAL FURNACE CO,01-East,740283401,27106.20,7565.94,19540.26
INDUSTRIAL FURNACE CO,01-East,724311200,24213.00,7917.07,16295.93
INDUSTRIAL FURNACE CO,01-East,737651000,23858.01,8893.92,14964.09
INDUSTRIAL FURNACE CO,01-East,764821000,23354.00,4503.57,18850.43
INDUSTRIAL FURNACE CO,01-East,764521000,23294.30,5339.08,17955.22
INDUSTRIAL FURNACE CO,01-East,779988828,20508.44,10268.03,10240.41
INDUSTRIAL FURNACE CO,01-East,770510000,19152.50,1578.12,17574.38
INDUSTRIAL FURNACE CO,01-East,734311100,18797.39,2516.31,16281.08
INDUSTRIAL FURNACE CO,01-East,737651010,16857.00,11281.16,5575.84
INDUSTRIAL FURNACE CO,01-East,768111000,12948.00,2249.18,10698.82
INDUSTRIAL FURNACE CO,01-East,766081010,6265.00,1198.49,5066.51
INDUSTRIAL FURNACE CO,01-East,739450000,5061.60,2511.54,2550.06
INDUSTRIAL FURNACE CO,01-East,733444200,4970.00,1025.71,3944.29
INDUSTRIAL FURNACE CO,01-East,718501000,4788.00,1212.21,3575.79
INDUSTRIAL FURNACE CO,01-East,740382101,4788.00,2528.30,2259.70
INDUSTRIAL FURNACE CO,01-East,733464500,4409.00,812.07,3596.93
INDUSTRIAL FURNACE CO,01-East,739561100,4399.08,1472.81,2926.27
INDUSTRIAL FURNACE CO,01-East,737032000,4131.90,2467.54,1664.36
INDUSTRIAL FURNACE CO,01-East,764761000,3965.20,1273.04,2692.16
INDUSTRIAL FURNACE CO,01-East,740382201,3300.00,1502.56,1797.44
INDUSTRIAL FURNACE CO,01-East,737261000,3259.20,1119.72,2139.48
INDUSTRIAL FURNACE CO,01-East,733464400,2470.00,792.24,1677.76
INDUSTRIAL FURNACE CO,01-East,733444100,2250.00,1008.46,1241.54
INDUSTRIAL FURNACE CO,01-East,738561000,2122.62,868.96,1253.66
INDUSTRIAL FURNACE CO,01-East,727000140,1482.40,269.22,1213.18
INDUSTRIAL FURNACE CO,01-East,764481000,1446.70,392.89,1053.81
INDUSTRIAL FURNACE CO,01-East,733151500,1224.00,216.42,1007.58
INDUSTRIAL FURNACE CO,01-East,764781000,1002.00,245.36,756.64
INDUSTRIAL FURNACE CO,01-East,770400000,913.50,98.65,814.85
INDUSTRIAL FURNACE CO,01-East,764861000,592.95,127.38,465.57
INDUSTRIAL FURNACE CO,01-East,739452500,563.52,362.66,200.86
INDUSTRIAL FURNACE CO,01-East,793260074,481.95,122.49,359.46
INDUSTRIAL FURNACE CO,01-East,770210000,450.00,60.64,389.36
INDUSTRIAL FURNACE CO,01-East,733211000,414.80,94.78,320.02
INDUSTRIAL FURNACE CO,01-East,770392000,375.00,35.15,339.85
INDUSTRIAL FURNACE CO,01-East,739541100,356.16,83.57,272.59
INDUSTRIAL FURNACE CO,01-East,764827000,350.40,124.42,225.98
INDUSTRIAL FURNACE CO,01-East,768115000,250.20,114.68,135.52
INDUSTRIAL FURNACE CO,01-East,734711100,125.11,27.05,98.06
INDUSTRIAL FURNACE CO,01-East,764561220,81.55,29.47,52.08
INDUSTRIAL INSULATION,01-East,735602110,145146.00,47860.90,97285.10
INDUSTRIAL INSULATION,01-East,735602000,50782.00,15973.99,34808.01
INDUSTRIAL INSULATION,01-East,764821000,14208.40,1853.07,12355.33
INDUSTRIAL INSULATION,01-East,764901000,12295.59,1652.95,10642.64
INDUSTRIAL INSULATION,01-East,735752000,9863.00,3665.84,6197.16
INDUSTRIAL INSULATION,01-East,735750000,7792.00,2718.27,5073.73
INDUSTRIAL INSULATION,01-East,727000140,3906.96,367.69,3539.27
INDUSTRIAL INSULATION,01-East,771320000,3422.30,684.50,2737.80
INDUSTRIAL INSULATION,01-East,718501000,3409.00,942.82,2466.18
INDUSTRIAL INSULATION,01-East,739451000,2415.84,1021.91,1393.93
INDUSTRIAL INSULATION,01-East,734751200,2341.36,373.90,1967.46
INDUSTRIAL INSULATION,01-East,764902000,2318.50,319.27,1999.23
INDUSTRIAL INSULATION,01-East,766688000,2311.50,247.00,2064.50
INDUSTRIAL INSULATION,01-East,739561100,2113.28,559.92,1553.36
INDUSTRIAL INSULATION,01-East,764521000,2078.80,318.54,1760.26
INDUSTRIAL INSULATION,01-East,764822000,1901.20,249.13,1652.07
INDUSTRIAL INSULATION,01-East,739561000,1830.26,514.23,1316.03
INDUSTRIAL INSULATION,01-East,739370000,1631.16,635.74,995.42
INDUSTRIAL INSULATION,01-East,764261000,1114.20,206.72,907.48
INDUSTRIAL INSULATION,01-East,764262000,733.20,134.54,598.66
INDUSTRIAL INSULATION,01-East,739541000,719.42,143.24,576.18
INDUSTRIAL INSULATION,01-East,764221000,695.20,169.05,526.15
INDUSTRIAL INSULATION,01-East,718500000,572.58,85.63,486.95
INDUSTRIAL INSULATION,01-East,739561200,536.70,144.66,392.04
INDUSTRIAL INSULATION,01-East,764301000,522.78,102.05,420.73
INDUSTRIAL INSULATION,01-East,764601000,437.30,66.06,371.24
INDUSTRIAL INSULATION,01-East,738401000,418.32,321.12,97.20
INDUSTRIAL INSULATION,01-East,764342000,380.10,68.67,311.43
INDUSTRIAL INSULATION,01-East,764341000,370.50,70.90,299.60
INDUSTRIAL INSULATION,01-East,738521000,325.44,143.45,181.99
INDUSTRIAL INSULATION,01-East,739541200,319.74,64.10,255.64
INDUSTRIAL INSULATION,01-East,739450000,295.38,130.12,165.26
INDUSTRIAL INSULATION,01-East,777601000,290.04,72.02,218.02
INDUSTRIAL INSULATION,01-East,739761100,284.59,120.66,163.93
INDUSTRIAL INSULATION,01-East,739380000,263.52,227.76,35.76
INDUSTRIAL INSULATION,01-East,764781000,246.80,40.90,205.90
INDUSTRIAL INSULATION,01-East,764482000,191.00,34.80,156.20
INDUSTRIAL INSULATION,01-East,735602010,150.00,94.19,55.81
INDUSTRIAL INSULATION,01-East,764781040,123.40,20.17,103.23
INDUSTRIAL INSULATION,01-East,764561000,65.05,11.09,53.96
INDUSTRIAL SUPPLY CO INC,01-East,741001000,38760.27,5551.88,33208.39
INDUSTRIAL SUPPLY CO INC,01-East,733151500,29220.20,4663.50,24556.70
INDUSTRIAL SUPPLY CO INC,01-East,777994000,11275.92,3623.14,7652.78
INDUSTRIAL SUPPLY CO INC,01-East,733525500,5190.00,752.87,4437.13
INDUSTRIAL SUPPLY CO INC,01-East,764221000,3675.60,539.75,3135.85
INDUSTRIAL SUPPLY CO INC,01-East,770540000,3010.70,179.60,2831.10
INDUSTRIAL SUPPLY CO INC,01-East,734211200,2756.63,320.81,2435.82
INDUSTRIAL SUPPLY CO INC,01-East,770500000,2650.05,135.04,2515.01
INDUSTRIAL SUPPLY CO INC,01-East,733141200,1974.40,373.86,1600.54
INDUSTRIAL SUPPLY CO INC,01-East,770560000,1347.00,89.50,1257.50
INDUSTRIAL SUPPLY CO INC,01-East,764821000,1216.80,93.94,1122.86
INDUSTRIAL SUPPLY CO INC,01-East,764261000,1052.00,115.22,936.78
INDUSTRIAL SUPPLY CO INC,01-East,733151600,621.20,93.28,527.92
INDUSTRIAL TECHNOLOGIES,01-East,780250000,78938.49,35312.18,43626.31
INDUSTRIAL TECHNOLOGIES,01-East,780260000,31066.50,12095.64,18970.86
INDUSTRIAL TECHNOLOGIES,01-East,779957812,22621.84,11228.10,11393.74
INDUSTRIAL TECHNOLOGIES,01-East,779952300,7296.12,3911.80,3384.32
INDUSTRIAL TECHNOLOGIES,01-East,780210000,7146.66,3672.66,3474.00
INDUSTRIAL TECHNOLOGIES,01-East,780370000,7025.86,2830.10,4195.76
INDUSTRIAL TECHNOLOGIES,01-East,780230000,6790.51,3211.39,3579.12
INDUSTRIAL TECHNOLOGIES,01-East,784210000,3240.00,1205.18,2034.82
INDUSTRIAL TECHNOLOGIES,01-East,784120000,3105.32,1687.08,1418.24
INDUSTRIAL TECHNOLOGIES,01-East,777642000,1422.00,484.92,937.08
INDUSTRIAL TECHNOLOGIES,01-East,764821000,1220.00,307.16,912.84
INDUSTRIAL TECHNOLOGIES,01-East,779988890,111.84,7.84,104.00
INDUSTRIAL VALVES &,01-East,770810000,4435.00,95.60,4339.40
INDUSTRIAL VALVES &,01-East,770830000,3081.00,97.81,2983.19
INDUSTRIAL VALVES &,01-East,770850000,2684.00,84.12,2599.88
INEX CORPORATION,01-East,733525500,937.50,135.42,802.08
INEX CORPORATION,01-East,733151500,651.20,108.22,542.98
INEX CORPORATION,01-East,770210000,503.00,63.04,439.96
INFINIA,01-East,734900000,203.60,37.82,165.78
INLINE PRO SERVICES,01-East,739681100,10165.32,4554.00,5611.32
INLINE PRO SERVICES,01-East,738401000,4088.88,1926.72,2162.16
INLINE PRO SERVICES,01-East,738361000,2504.16,1235.52,1268.64
INLINE PRO SERVICES,01-East,764902000,1005.00,64.42,940.58
INLINE PRO SERVICES,01-East,739641100,964.08,403.20,560.88
INSUL-FAB,01-East,768212000,31243.00,7506.78,23736.22
INSULATION CONTRACTORS,01-East,735602000,50883.20,14865.74,36017.46
INSULATION CONTRACTORS,01-East,735750000,624.00,190.66,433.34
INSULATION FABRICATORS,01-East,727000140,1376.00,334.27,1041.73
INSULATION FABRICATORS,01-East,739541000,403.20,71.62,331.58
INSULATION MATERIALS CORP,01-East,735602000,407365.00,161133.20,246231.80
INSULATION MATERIALS CORP,01-East,764901000,136301.50,65445.72,70855.78
INSULATION MATERIALS CORP,01-East,735750000,100152.00,43357.37,56794.63
INSULATION MATERIALS CORP,01-East,764821000,86587.00,31981.32,54605.68
INSULATION MATERIALS CORP,01-East,764902000,37745.00,17978.84,19766.16
INSULATION MATERIALS CORP,01-East,764861000,29841.00,13374.31,16466.69
INSULATION MATERIALS CORP,01-East,764521000,28258.50,9910.75,18347.75
INSULATION MATERIALS CORP,01-East,777993000,24542.02,10405.46,14136.56
INSULATION MATERIALS CORP,01-East,764601000,17359.00,7840.89,9518.11
INSULATION MATERIALS CORP,01-East,768132200,12228.00,6294.18,5933.82
INSULATION MATERIALS CORP,01-East,764821161,9460.00,8184.00,1276.00
INSULATION MATERIALS CORP,01-East,764822000,9132.00,3391.88,5740.12
INSULATION MATERIALS CORP,01-East,764901061,8836.50,9623.40,-786.90
INSULATION MATERIALS CORP,01-East,766121000,8678.50,3383.12,5295.38
INSULATION MATERIALS CORP,01-East,768141020,8618.00,3491.84,5126.16
INSULATION MATERIALS CORP,01-East,735602110,8322.00,3325.26,4996.74
INSULATION MATERIALS CORP,01-East,764864000,8115.00,3544.37,4570.63
INSULATION MATERIALS CORP,01-East,733525500,7930.00,1300.60,6629.40
INSULATION MATERIALS CORP,01-East,764781000,5742.00,2201.81,3540.19
INSULATION MATERIALS CORP,01-East,764602000,5252.00,2327.66,2924.34
INSULATION MATERIALS CORP,01-East,768241000,5064.00,1684.81,3379.19
INSULATION MATERIALS CORP,01-East,764561000,4664.50,2240.61,2423.89
INSULATION MATERIALS CORP,01-East,734910000,4368.60,924.26,3444.34
INSULATION MATERIALS CORP,01-East,764521061,3750.00,3195.00,555.00
INSULATION MATERIALS CORP,01-East,764481000,3418.00,1282.10,2135.90
INSULATION MATERIALS CORP,01-East,768142000,3407.50,1313.67,2093.83
INSULATION MATERIALS CORP,01-East,735752000,3000.00,1367.98,1632.02
INSULATION MATERIALS CORP,01-East,766007000,2440.00,1009.12,1430.88
INSULATION MATERIALS CORP,01-East,768221000,2200.00,707.60,1492.40
INSULATION MATERIALS CORP,01-East,724003000,2166.00,295.76,1870.24
INSULATION MATERIALS CORP,01-East,765502000,1900.00,529.54,1370.46
INSULATION MATERIALS CORP,01-East,734311100,1748.70,202.23,1546.47
INSULATION MATERIALS CORP,01-East,734900000,1619.40,567.42,1051.98
INSULATION MATERIALS CORP,01-East,764901050,1370.00,631.79,738.21
INSULATION MATERIALS CORP,01-East,768222000,1320.00,429.60,890.40
INSULATION MATERIALS CORP,01-East,734651200,1209.11,221.24,987.87
INSULATION MATERIALS CORP,01-East,764825000,1032.00,774.63,257.37
INSULATION MATERIALS CORP,01-East,768211000,754.00,321.79,432.21
INSULATION MATERIALS CORP,01-East,733403200,739.20,131.34,607.86
INSULATION MATERIALS CORP,01-East,768121000,630.00,287.66,342.34
INSULATION MATERIALS CORP,01-East,740402402,624.00,193.66,430.34
INSULATION MATERIALS CORP,01-East,764301000,560.00,266.94,293.06
INSULATION MATERIALS CORP,01-East,764261000,477.00,206.04,270.96
INSULATION MATERIALS CORP,01-East,730710500,457.00,70.96,386.04
INSULATION MATERIALS CORP,01-East,764761000,434.00,214.99,219.01
INSULATION MATERIALS CORP,01-East,734990000,339.36,55.29,284.07
INSULATION MATERIALS CORP,01-East,739561200,182.46,72.33,110.13
INSULATION MATERIALS CORP,01-East,768122000,126.00,55.92,70.08
INSULATION MATERIALS CORP,01-East,764781050,110.00,49.38,60.62
INSULATION MATERIALS CORP,01-East,768131000,107.00,44.16,62.84
INSULATION SALES,01-East,735602000,1960.00,690.37,1269.63
INSULCON BENELUX B.V.,01-East,768242701,344050.08,277840.52,66209.56
INSULCON BENELUX B.V.,01-East,768242821,17427.76,13388.16,4039.60
INSULSHIELD TECHNOLOGY,01-East,735990100,2480.00,1214.02,1265.98
INSULSHIELD TECHNOLOGY,01-East,768222130,1468.80,669.40,799.40
INTEGRAL PRODUCTS,01-East,733464600,298.00,31.31,266.69
INTELLIFUSE TECH.,01-East,740361900,667.20,220.63,446.57
INTERNATIONAL CRYSTAL LAB,01-East,739741100,428.48,168.32,260.16
"INTERNATIONAL PAINT, INC.",01-East,723800000,939270.25,305588.85,633681.40
INTERNATIONAL TECHNIFAB,01-East,764821000,401090.00,172793.44,228296.56
INTERNATIONAL TECHNIFAB,01-East,735602000,364004.00,118504.42,245499.58
INTERNATIONAL TECHNIFAB,01-East,734173000,181632.00,72254.74,109377.26
INTERNATIONAL TECHNIFAB,01-East,764901050,172626.00,66515.38,106110.62
INTERNATIONAL TECHNIFAB,01-East,764521000,121778.00,43745.80,78032.20
INTERNATIONAL TECHNIFAB,01-East,764601000,82870.00,29104.12,53765.88
INTERNATIONAL TECHNIFAB,01-East,739452000,52202.89,31721.27,20481.62
INTERNATIONAL TECHNIFAB,01-East,764821073,51070.00,51759.00,-689.00
INTERNATIONAL TECHNIFAB,01-East,764821161,38340.00,40176.00,-1836.00
INTERNATIONAL TECHNIFAB,01-East,764781000,36021.60,10515.99,25505.61
INTERNATIONAL TECHNIFAB,01-East,764901000,33448.50,15044.76,18403.74
INTERNATIONAL TECHNIFAB,01-East,764561000,31920.00,12147.95,19772.05
INTERNATIONAL TECHNIFAB,01-East,764521073,28120.00,23865.00,4255.00
INTERNATIONAL TECHNIFAB,01-East,764827550,27264.00,10919.35,16344.65
INTERNATIONAL TECHNIFAB,01-East,764861000,24194.00,5949.81,18244.19
INTERNATIONAL TECHNIFAB,01-East,764522000,22990.00,8190.87,14799.13
INTERNATIONAL TECHNIFAB,01-East,735750000,22483.00,8454.23,14028.77
INTERNATIONAL TECHNIFAB,01-East,764605000,19768.00,6708.54,13059.46
INTERNATIONAL TECHNIFAB,01-East,739561100,18976.96,5485.15,13491.81
INTERNATIONAL TECHNIFAB,01-East,733525500,18130.00,3104.03,15025.97
INTERNATIONAL TECHNIFAB,01-East,740063000,17701.32,7744.17,9957.15
INTERNATIONAL TECHNIFAB,01-East,735602110,17688.00,6006.35,11681.65
INTERNATIONAL TECHNIFAB,01-East,754095000,13113.60,5108.65,8004.95
INTERNATIONAL TECHNIFAB,01-East,768141000,11826.00,3241.74,8584.26
INTERNATIONAL TECHNIFAB,01-East,768142000,11826.00,3354.06,8471.94
INTERNATIONAL TECHNIFAB,01-East,764602000,11280.00,3948.78,7331.22
INTERNATIONAL TECHNIFAB,01-East,735752000,10797.00,3930.84,6866.16
INTERNATIONAL TECHNIFAB,01-East,779988831,9222.00,3288.30,5933.70
INTERNATIONAL TECHNIFAB,01-East,764521061,9120.00,7668.00,1452.00
INTERNATIONAL TECHNIFAB,01-East,764862000,8153.50,2345.06,5808.44
INTERNATIONAL TECHNIFAB,01-East,780640000,7968.33,1783.64,6184.69
INTERNATIONAL TECHNIFAB,01-East,733444200,7656.40,1289.16,6367.24
INTERNATIONAL TECHNIFAB,01-East,764902000,6495.00,2887.48,3607.52
INTERNATIONAL TECHNIFAB,01-East,764901073,5880.00,5832.00,48.00
INTERNATIONAL TECHNIFAB,01-East,735862000,5088.00,2467.32,2620.68
INTERNATIONAL TECHNIFAB,01-East,733423700,4915.84,778.71,4137.13
INTERNATIONAL TECHNIFAB,01-East,764762000,4614.40,1696.40,2918.00
INTERNATIONAL TECHNIFAB,01-East,754487000,4462.75,1708.65,2754.10
INTERNATIONAL TECHNIFAB,01-East,733464500,3752.00,729.52,3022.48
INTERNATIONAL TECHNIFAB,01-East,739541100,3286.42,585.01,2701.41
INTERNATIONAL TECHNIFAB,01-East,764901061,2940.00,2984.00,-44.00
INTERNATIONAL TECHNIFAB,01-East,734990000,2395.52,442.34,1953.18
INTERNATIONAL TECHNIFAB,01-East,764900005,2352.00,918.56,1433.44
INTERNATIONAL TECHNIFAB,01-East,764562000,1831.50,717.53,1113.97
INTERNATIONAL TECHNIFAB,01-East,724281400,1022.00,178.16,843.84
INTERNATIONAL TECHNIFAB,01-East,754387000,927.96,385.13,542.83
INTERNATIONAL TECHNIFAB,01-East,733383000,912.56,163.93,748.63
INTERNATIONAL TECHNIFAB,01-East,733570000,896.40,183.35,713.05
INTERNATIONAL TECHNIFAB,01-East,754895000,885.96,193.27,692.69
INTERNATIONAL TECHNIFAB,01-East,724261200,702.00,98.66,603.34
INTERNATIONAL TECHNIFAB,01-East,734414100,698.42,106.12,592.30
INTERNATIONAL TECHNIFAB,01-East,767960000,612.04,173.18,438.86
INTERNATIONAL TECHNIFAB,01-East,734651200,584.00,73.50,510.50
INTERNATIONAL TECHNIFAB,01-East,777601000,493.76,130.82,362.94
INTERNATIONAL TECHNIFAB,01-East,733211000,490.60,100.59,390.01
INTERNATIONAL TECHNIFAB,01-East,770800000,483.00,10.22,472.78
INTERNATIONAL TECHNIFAB,01-East,767990000,481.50,176.94,304.56
INTERNATIONAL TECHNIFAB,01-East,770300000,325.05,35.37,289.68
INTERNATIONAL TECHNIFAB,01-East,767970000,231.12,71.89,159.23
INTERNATIONAL TECHNIFAB,01-East,779957257,225.00,102.09,122.91
INTERNATIONAL TECHNIFAB,01-East,734900000,47.62,18.91,28.71
INTERNATIONAL TURBING SYS,01-East,764481000,346.00,16.94,329.06
INTERTEK TESTING SERVICES,01-East,781540000,15192.00,5679.70,9512.30
INTERTEK TESTING SERVICES,01-East,779958512,4712.76,1798.24,2914.52
INTERTEK TESTING SERVICES,01-East,766121000,1744.00,383.18,1360.82
IPR/GDF SUEZ N.A.,01-East,735655000,1301.60,95.99,1205.61
"IRWIN INTERNATIONAL, INC.",01-East,733525500,8550.00,812.55,7737.45
"IRWIN INTERNATIONAL, INC.",01-East,733505200,800.00,83.83,716.17
J & M AIRCRAFT,01-East,770520000,958.50,41.85,916.65
J D SUPPLY,01-East,733721000,410.40,97.80,312.60
J T THORPE & SON INC,01-East,780670000,5830.35,2234.71,3595.64
J T THORPE & SON INC,01-East,777993000,2163.30,650.34,1512.96
J-THERM LLC,01-East,779905209,1643.52,0.00,1643.52
"J. T. THORPE & SON, INC.",01-East,770430000,2950.00,177.09,2772.91
"J. T. THORPE & SON, INC.",01-East,770720000,1010.00,27.12,982.88
J. T. THORPE,01-East,734830000,12312.00,1634.76,10677.24
J.W. DUBOIS & SONS,01-East,768241000,728.50,35.38,693.12
JAMES AVERY CRAFTSMAN INC,01-East,737301000,1615.36,643.84,971.52
JIMCO CORPORATION,01-East,734900000,1682.50,472.84,1209.66
"JM SUPPLY, INC.",01-East,735602000,8736.00,2353.60,6382.40
JOHNSON MATTHEY GPT,01-East,764821000,878.00,61.43,816.57
JOHNSON MATTHEY,01-East,766121000,460.35,23.95,436.40
JOHNSON PACKINGS & INDUST,01-East,740063000,1109.80,240.20,869.60
JOHNSON PACKINGS & INDUST,01-East,733384000,1056.00,141.33,914.67
JOHNSON PACKINGS & INDUST,01-East,740023000,680.40,118.54,561.86
JOHNSON PACKINGS & INDUST,01-East,733464500,596.00,59.04,536.96
JOHNSON PACKINGS & INDUST,01-East,733444300,302.00,41.25,260.75
JUN KANEKO STUDIOS,01-East,734311100,808.72,70.60,738.12
"K&L REFRACTORIES, INC.",01-East,733211000,2070.00,473.89,1596.11
"K.R. ANDERSON, INC.",01-East,734010000,502.25,45.30,456.95
KAISER CONSTRUCTION,01-East,735655000,676.80,47.99,628.81
KELLER TECHNOLOGY,01-East,724311200,27985.50,6041.36,21944.14
KELLER TECHNOLOGY,01-East,770512000,22335.30,1241.27,21094.03
KELLER TECHNOLOGY,01-East,779989700,7872.00,1572.45,6299.55
KELLER TECHNOLOGY,01-East,740283502,5206.08,774.57,4431.51
KELLER TECHNOLOGY,01-East,770500000,4592.70,270.10,4322.60
KELLER TECHNOLOGY,01-East,734851000,3644.56,731.20,2913.36
KEN MAXWELL,01-East,733464500,298.00,29.52,268.48
KENNAMETAL INC,01-East,766121000,1750.00,54.50,1695.50
KENNAMETAL INC,01-East,734651200,292.00,36.75,255.25
KENNETH CROSBY,01-East,770210000,440.00,30.32,409.68
KENNETH INDUSTRIAL,01-East,764482000,345.60,51.14,294.46
KEYSTONE TOOL & DIE,01-East,770500000,11452.50,498.39,10954.11
KIEWIT POWER CONSTRUCTORS,01-East,734611100,714.88,107.84,607.04
KINGSBURY INC.,01-East,733211050,2326.00,260.92,2065.08
KINGSBURY INC.,01-East,733141200,496.00,59.97,436.03
KINGSTON HOSPITAL,01-East,764521000,4020.00,265.42,3754.58
KINGSTON HOSPITAL,01-East,734351200,1945.80,147.24,1798.56
KINGSTON HOSPITAL,01-East,739541200,522.24,80.45,441.79
KIRK & BLUM,01-East,789160000,16116.48,6545.22,9571.26
KIRK & BLUM,01-East,784180000,4916.80,1773.78,3143.02
KIRK & BLUM,01-East,764521000,3820.00,530.84,3289.16
KMI SYSTEMS INC,01-East,764345052,4590.00,2024.64,2565.36
KMI SYSTEMS INC,01-East,765662000,3640.00,1132.50,2507.50
KMI SYSTEMS INC,01-East,764605000,3450.00,1293.88,2156.12
KNOWLTON TECHNOLOGIES,01-East,723960000,50111.00,11308.04,38802.96
KOSHII MAXELUM AMERICA,01-East,768241000,534.00,70.76,463.24
KREEM PRODUCT CO.,01-East,734464300,5951.55,672.58,5278.97
KREISLER INDUSTRIAL CORP,01-East,770800000,20180.00,357.66,19822.34
KTEC EQUIPMENT & SUPPLIES,01-East,727000140,510.40,31.68,478.72
KTEC EQUIPMENT & SUPPLIES,01-East,764301000,322.00,19.06,302.94
LABARGE ELECTRONICS,01-East,791104277,5457.78,2598.84,2858.94
LACO SALES INC,01-East,764482000,4979.00,430.81,4548.19
LANE REFRACTORIES,01-East,754045000,9919.80,6217.82,3701.98
LANE REFRACTORIES,01-East,764521000,2240.00,531.88,1708.12
LANE REFRACTORIES,01-East,734990000,1557.12,331.76,1225.36
LANE REFRACTORIES,01-East,766686000,1200.00,361.13,838.87
LANE REFRACTORIES,01-East,764821000,775.00,156.55,618.45
LANE REFRACTORIES,01-East,733151500,620.00,95.38,524.62
LANE REFRACTORIES,01-East,764481000,296.00,67.76,228.24
LAWRENCE ELECTRONICS CO.,01-East,733464600,313.00,34.97,278.03
LEWIS-GOETZ & CO.,01-East,770430000,1393.75,92.06,1301.69
LEWIS-GOETZ & CO.,01-East,770372000,973.00,44.11,928.89
"LIMCO AIREPAIR, INC.",01-East,733444200,664.00,69.38,594.62
LMB INDUSTRIAL SERVICES,01-East,779958687,22875.84,11924.97,10950.87
LMB INDUSTRIAL SERVICES,01-East,764821000,8960.00,1731.97,7228.03
LMB INDUSTRIAL SERVICES,01-East,784180000,7340.74,3015.44,4325.30
LMB INDUSTRIAL SERVICES,01-East,784140000,5548.60,2226.00,3322.60
LMB INDUSTRIAL SERVICES,01-East,780290000,5148.48,2407.82,2740.66
LMB INDUSTRIAL SERVICES,01-East,779958690,4876.80,2603.25,2273.55
LMB INDUSTRIAL SERVICES,01-East,784100000,4599.18,2682.89,1916.29
LMB INDUSTRIAL SERVICES,01-East,779958688,4381.92,1942.35,2439.57
LMB INDUSTRIAL SERVICES,01-East,779958689,4118.40,2175.90,1942.50
LMB INDUSTRIAL SERVICES,01-East,779958691,2322.72,1039.53,1283.19
LMB INDUSTRIAL SERVICES,01-East,780210000,2023.20,1129.62,893.58
LMB INDUSTRIAL SERVICES,01-East,779958250,1161.36,494.76,666.60
LMB INDUSTRIAL SERVICES,01-East,767990000,668.00,224.12,443.88
LMB INDUSTRIAL SERVICES,01-East,781590000,412.48,193.94,218.54
LMB INDUSTRIAL SERVICES,01-East,777642000,330.00,95.01,234.99
LOCKHEED MARTIN,01-East,779988846,800.45,267.61,532.84
LOCKHEED MARTIN,01-East,739370000,379.52,138.24,241.28
LONE STAR GASKET,01-East,733464500,626.00,65.05,560.95
"LONE STAR INDUCTION, INC",01-East,727000140,666.00,47.51,618.49
LONGHORN GASKET & SUPPLY,01-East,733141200,520.00,66.98,453.02
LUCIFER FURNACES INC,01-East,737321000,10866.24,8200.34,2665.90
LUFKIN RUBBER & GASKET,01-East,733741000,2516.40,755.83,1760.57
LUFKIN RUBBER & GASKET,01-East,777601000,1557.70,235.18,1322.52
LUVATA WATERBURY INC.,01-East,733211000,912.00,94.78,817.22
LUVATA WATERBURY INC.,01-East,733151500,450.00,47.69,402.31
LVR INC.,01-East,718501000,5525.00,680.30,4844.70
LVR INC.,01-East,718500000,1414.40,172.54,1241.86
LYDALL INC TECH PAPERS DI,01-East,722413100,47235.80,4720.90,42514.90
LYNN MANUFACTURING INC.,01-East,727000060,123198.00,53223.43,69974.57
LYNN MANUFACTURING INC.,01-East,727000100,76549.44,55157.97,21391.47
LYNN MANUFACTURING INC.,01-East,739741100,73474.31,39855.07,33619.24
LYNN MANUFACTURING INC.,01-East,726000062,67575.00,31194.05,36380.95
LYNN MANUFACTURING INC.,01-East,726020056,59598.00,26213.03,33384.97
LYNN MANUFACTURING INC.,01-East,739781100,46746.90,26491.68,20255.22
LYNN MANUFACTURING INC.,01-East,739761100,27596.43,14152.22,13444.21
LYNN MANUFACTURING INC.,01-East,734761300,17930.00,4255.19,13674.81
LYNN MANUFACTURING INC.,01-East,737321000,17078.88,6076.56,11002.32
LYNN MANUFACTURING INC.,01-East,764902000,16014.60,2755.01,13259.59
LYNN MANUFACTURING INC.,01-East,724003000,12163.65,5060.13,7103.52
LYNN MANUFACTURING INC.,01-East,737311000,11640.54,3006.45,8634.09
LYNN MANUFACTURING INC.,01-East,764821000,9824.40,1552.07,8272.33
LYNN MANUFACTURING INC.,01-East,733525500,6192.50,909.75,5282.75
LYNN MANUFACTURING INC.,01-East,739561200,4332.23,1273.03,3059.20
LYNN MANUFACTURING INC.,01-East,739761101,3718.52,1733.68,1984.84
LYNN MANUFACTURING INC.,01-East,737301000,2863.39,1134.56,1728.83
LYNN MANUFACTURING INC.,01-East,739781101,2819.72,1142.20,1677.52
LYNN MANUFACTURING INC.,01-East,726020030,2303.10,1092.17,1210.93
LYNN MANUFACTURING INC.,01-East,764761000,659.20,188.86,470.34
LYNN MANUFACTURING INC.,01-East,733464500,599.30,91.57,507.73
LYNN MANUFACTURING INC.,01-East,764781000,558.00,118.32,439.68
LYNN MANUFACTURING INC.,01-East,733423700,465.92,72.44,393.48
M CUBED TECHNOLOGIES INC,01-East,734351200,917.85,69.73,848.12
M DAVIS & SONS,01-East,734910000,362.00,53.09,308.91
M&M INSULATION,01-East,735602000,111346.00,34972.94,76373.06
M&M INSULATION,01-East,768221000,44820.00,9132.96,35687.04
M&M INSULATION,01-East,735750000,6408.00,1861.36,4546.64
M&M INSULATION,01-East,768241000,5015.00,1038.97,3976.03
M&M INSULATION,01-East,764901000,3630.00,710.33,2919.67
M&M INSULATION,01-East,735602110,2650.00,892.53,1757.47
M&M INSULATION,01-East,735752000,2340.00,644.40,1695.60
MAC ARTHUR COMPANY INC.,01-East,735602000,24000.00,8069.50,15930.50
MAC ARTHUR COMPANY INC.,01-East,735602110,24000.00,8400.65,15599.35
MAC ARTHUR COMPANY,01-East,735602000,41040.00,13117.04,27922.96
MAC ARTHUR COMPANY,01-East,735602110,10800.00,3570.13,7229.87
MAGELLAN AEROSPACE,01-East,724003000,695.50,81.82,613.68
MAGELLAN AEROSPACE,01-East,734311100,404.36,35.30,369.06
MALYN INDUSTRIAL CERAMICS,01-East,733525600,1435.00,130.98,1304.02
MALYN INDUSTRIAL CERAMICS,01-East,734211200,486.92,46.75,440.17
MARIND INDUSTRIES,01-East,727063007,1845.00,948.54,896.46
MARIND INDUSTRIES,01-East,727063005,1599.00,804.28,794.72
MARIND INDUSTRIES,01-East,727063016,426.40,217.28,209.12
MARK STEEL,01-East,764901000,11025.00,2882.57,8142.43
MARLIN ENGINEERING INC.,01-East,733464500,313.00,32.53,280.47
MATERION ADVANCED MATLS.,01-East,764821000,10208.00,850.65,9357.35
MATERION ADVANCED MATLS.,01-East,740283401,1647.80,292.43,1355.37
MATERION ADVANCED MATLS.,01-East,739681100,1208.44,481.84,726.60
MATERION ADVANCED MATLS,01-East,764821000,1908.25,156.56,1751.69
MATERION ADVANCED MATLS,01-East,739681100,628.42,414.00,214.42
MATERION BREWSTER LLC,01-East,733151500,2744.00,298.97,2445.03
MATERION BREWSTER LLC,01-East,764821000,1778.05,125.25,1652.80
MATERION BREWSTER LLC,01-East,739541000,399.74,90.50,309.24
MATRIX INDUSTRIAL,01-East,740103000,824.88,135.47,689.41
MATRIX SOLUTIONS,01-East,734351204,5832.00,858.19,4973.81
MAYNARD INC.,01-East,724121700,583.00,162.85,420.15
MCCONNELL AFB,01-East,740402402,1022.50,193.66,828.84
MCJUNKIN RED MAN CORP.,01-East,770830000,2020.00,61.94,1958.06
MCNEIL INC.,01-East,734351203,389757.00,68680.02,321076.98
MCNEIL INC.,01-East,764521000,155750.00,76230.46,79519.54
MCNEIL INC.,01-East,733525500,129525.00,31288.64,98236.36
MCNEIL INC.,01-East,754525000,117176.24,45154.66,72021.58
MCNEIL INC.,01-East,764821000,109310.00,38251.71,71058.29
MCNEIL INC.,01-East,739741100,69263.36,78273.85,-9010.49
MCNEIL INC.,01-East,734351200,67463.90,11351.75,56112.15
MCNEIL INC.,01-East,764481000,41722.20,16752.04,24970.16
MCNEIL INC.,01-East,733202000,38228.00,15639.90,22588.10
MCNEIL INC.,01-East,739561100,27534.36,11563.39,15970.97
MCNEIL INC.,01-East,764601000,24800.00,8193.87,16606.13
MCNEIL INC.,01-East,764521061,24750.00,28755.00,-4005.00
MCNEIL INC.,01-East,779988972,23782.50,19426.00,4356.50
MCNEIL INC.,01-East,754085000,21894.69,9293.31,12601.38
MCNEIL INC.,01-East,764901000,20202.64,5998.38,14204.26
MCNEIL INC.,01-East,734910000,18828.00,6297.26,12530.74
MCNEIL INC.,01-East,777601000,17372.64,4991.49,12381.15
MCNEIL INC.,01-East,734311100,16660.00,3530.13,13129.87
MCNEIL INC.,01-East,733444220,15393.60,4726.81,10666.79
MCNEIL INC.,01-East,733566100,12686.40,3766.76,8919.64
MCNEIL INC.,01-East,770850000,12348.80,546.16,11802.64
MCNEIL INC.,01-East,779959708,12215.52,3923.36,8292.16
MCNEIL INC.,01-East,739451000,12006.40,10219.16,1787.24
MCNEIL INC.,01-East,764821161,10800.00,8928.00,1872.00
MCNEIL INC.,01-East,733464400,10752.00,3983.12,6768.88
MCNEIL INC.,01-East,764221000,9195.60,4957.78,4237.82
MCNEIL INC.,01-East,764482000,8108.40,3228.29,4880.11
MCNEIL INC.,01-East,738521000,7647.54,7956.21,-308.67
MCNEIL INC.,01-East,733586500,7128.00,1632.17,5495.83
MCNEIL INC.,01-East,734900000,6440.00,3356.65,3083.35
MCNEIL INC.,01-East,734211200,6008.20,778.64,5229.56
MCNEIL INC.,01-East,734651200,5892.90,1250.97,4641.93
MCNEIL INC.,01-East,739452000,5708.64,4936.64,772.00
MCNEIL INC.,01-East,734751200,4844.68,937.91,3906.77
MCNEIL INC.,01-East,771320000,4712.58,768.70,3943.88
MCNEIL INC.,01-East,793336074,4302.00,2707.80,1594.20
MCNEIL INC.,01-East,727000140,4250.40,1153.24,3097.16
MCNEIL INC.,01-East,779959709,3995.56,1557.22,2438.34
MCNEIL INC.,01-East,793330000,3764.25,910.46,2853.79
MCNEIL INC.,01-East,779951021,3455.60,2940.82,514.78
MCNEIL INC.,01-East,733403100,3431.40,553.38,2878.02
MCNEIL INC.,01-East,739541100,3358.66,1258.00,2100.66
MCNEIL INC.,01-East,764522000,3301.00,1616.85,1684.15
MCNEIL INC.,01-East,770500000,2845.80,167.22,2678.58
MCNEIL INC.,01-East,764822000,2736.00,966.75,1769.25
MCNEIL INC.,01-East,739781100,2649.79,1977.19,672.60
MCNEIL INC.,01-East,739721000,2627.57,1840.55,787.02
MCNEIL INC.,01-East,733545700,2588.30,771.07,1817.23
MCNEIL INC.,01-East,740063000,2390.30,716.82,1673.48
MCNEIL INC.,01-East,733423700,2177.28,488.86,1688.42
MCNEIL INC.,01-East,737912200,2147.45,446.30,1701.15
MCNEIL INC.,01-East,754465000,2084.98,963.92,1121.06
MCNEIL INC.,01-East,777904301,2073.41,939.74,1133.67
MCNEIL INC.,01-East,780710000,2060.52,896.00,1164.52
MCNEIL INC.,01-East,733586400,2052.00,494.95,1557.05
MCNEIL INC.,01-East,740402402,2051.00,604.67,1446.33
MCNEIL INC.,01-East,739561000,2008.08,861.95,1146.13
MCNEIL INC.,01-East,779951023,1908.00,1617.58,290.42
MCNEIL INC.,01-East,727000440,1892.00,425.69,1466.31
MCNEIL INC.,01-East,754926000,1665.12,985.10,680.02
MCNEIL INC.,01-East,733505300,1650.00,425.73,1224.27
MCNEIL INC.,01-East,768021000,1613.50,409.11,1204.39
MCNEIL INC.,01-East,770720000,1536.80,56.55,1480.25
MCNEIL INC.,01-East,724281200,1480.00,386.57,1093.43
MCNEIL INC.,01-East,764861000,1449.88,454.62,995.26
MCNEIL INC.,01-East,764761000,1325.60,628.03,697.57
MCNEIL INC.,01-East,754065000,1130.64,527.04,603.60
MCNEIL INC.,01-East,768121000,1094.85,219.57,875.28
MCNEIL INC.,01-East,733505200,980.00,244.10,735.90
MCNEIL INC.,01-East,767990000,931.86,347.38,584.48
MCNEIL INC.,01-East,777650000,923.70,427.32,496.38
MCNEIL INC.,01-East,734851000,912.46,197.80,714.66
MCNEIL INC.,01-East,724281400,888.00,231.94,656.06
MCNEIL INC.,01-East,770830000,749.70,30.97,718.73
MCNEIL INC.,01-East,770212000,730.60,162.39,568.21
MCNEIL INC.,01-East,734990000,715.20,191.98,523.22
MCNEIL INC.,01-East,734810000,680.00,544.58,135.42
MCNEIL INC.,01-East,727000025,598.00,211.14,386.86
MCNEIL INC.,01-East,739521100,517.78,139.38,378.40
MCNEIL INC.,01-East,724003000,478.95,81.82,397.13
MCNEIL INC.,01-East,739390000,462.24,247.29,214.95
MCNEIL INC.,01-East,739450000,457.92,376.73,81.19
MCNEIL INC.,01-East,740123000,444.78,128.01,316.77
MCNEIL INC.,01-East,768750000,339.72,73.86,265.86
MCNEIL INC.,01-East,764781040,339.00,120.99,218.01
MCNEIL INC.,01-East,764561000,338.75,110.89,227.86
MCNEIL INC.,01-East,770260000,333.68,70.33,263.35
MCNEIL INC.,01-East,770270000,321.30,68.38,252.92
MCNEIL INC.,01-East,792702000,303.75,96.60,207.15
MCNEIL INC.,01-East,733525400,285.00,74.13,210.87
MCNEIL INC.,01-East,733525600,285.00,71.05,213.95
MCNEIL INC.,01-East,739541000,239.90,90.50,149.40
MCNEIL INC.,01-East,779957254,189.00,113.16,75.84
MCNEIL INC.,01-East,739681100,188.53,207.00,-18.47
MCNEIL INC.,01-East,779957258,167.04,89.70,77.34
MCNEIL INC.,01-East,770220000,151.20,28.44,122.76
MCNEIL INC.,01-East,777642000,121.60,50.36,71.24
MCNEIL INC.,01-East,733211050,114.00,55.91,58.09
MCT INDUSTRIES - GOV'T,01-East,791006850,25158.21,12064.32,13093.89
MCT INDUSTRIES - GOV'T,01-East,791006650,17177.68,8830.89,8346.79
MECHANICAL EQUIPMENT INC,01-East,734211200,3310.20,324.03,2986.17
MEGADIAMOND,01-East,777585000,295.00,9.07,285.93
"MEGTEC SYSTEMS, INC",01-East,754055000,4717.44,2791.82,1925.62
"MEGTEC SYSTEMS, INC",01-East,734651200,1219.62,74.00,1145.62
"MEGTEC SYSTEMS, INC",01-East,764522000,552.00,157.40,394.60
"MEGTEC SYSTEMS, INC",01-East,754045000,284.22,188.42,95.80
MERCER GASKET & SHIM,01-East,733202000,5107.20,1325.44,3781.76
MERIDEN MANUFACTURING INC,01-East,764461006,1196.00,83.13,1112.87
"MESTEK, INC.",01-East,724281200,500.00,89.08,410.92
METALCRAFT TECHNOLOGIES,01-East,764821000,1307.00,46.38,1260.62
METALLIZED CARBON,01-East,733725500,5204.00,1019.36,4184.64
METALLIZED CARBON,01-East,764521000,1933.00,79.77,1853.23
METAULLIC SYSTEMS DIV.,01-East,766121000,5103.50,1607.68,3495.82
METAULLIC SYSTEMS DIV.,01-East,777904301,1998.00,704.80,1293.20
METAULLIC SYSTEMS DIV.,01-East,764261000,1631.00,861.31,769.69
METAULLIC SYSTEMS DIV.,01-East,733511000,1087.50,665.61,421.89
METAULLIC SYSTEMS DIV.,01-East,764821000,962.50,355.61,606.89
METAULLIC SYSTEMS DIV.,01-East,733510000,561.25,397.57,163.68
METAULLIC SYSTEMS DIV.,01-East,764341000,516.00,287.84,228.16
METAULLIC SYSTEMS DIV.,01-East,772101000,222.00,57.90,164.10
METAULLIC SYSTEMS DIV.,01-East,733464500,130.00,59.04,70.96
METAULLIC SYSTEMS DIV.,01-East,771981000,103.20,31.30,71.90
METRO CIRCUITS,01-East,733464600,1520.00,163.88,1356.12
METRO SUPPLY CO.,01-East,735602000,216905.00,78191.65,138713.35
METRO SUPPLY CO.,01-East,735602110,9400.00,3500.26,5899.74
METRO SUPPLY CO.,01-East,764901000,8156.00,3151.78,5004.22
METRO SUPPLY CO.,01-East,735750000,6510.00,2781.80,3728.20
METRO SUPPLY CO.,01-East,764781000,4684.00,1859.60,2824.40
METRO SUPPLY CO.,01-East,764821000,3054.00,1125.98,1928.02
METRO SUPPLY CO.,01-East,764861000,1524.00,623.46,900.54
METSO PAPER USA INC.,01-East,764601000,7786.00,1806.92,5979.08
MICROCHIP TECHNOLOGY INC.,01-East,764221000,476.00,33.66,442.34
MICROCHIP TECHNOLOGY INC.,01-East,734211200,459.00,45.68,413.32
MID-TEX DISTRIBUTING INC,01-East,735602110,70122.00,20528.23,49593.77
MID-TEX DISTRIBUTING INC,01-East,735602000,18523.50,5048.33,13475.17
MIDWEST MATERIALS CO.,01-East,768141003,354256.00,161233.53,193022.47
MIDWEST MATERIALS CO.,01-East,735602000,210176.00,53275.68,156900.32
MIDWEST MATERIALS CO.,01-East,768121000,90960.00,27863.51,63096.49
MIDWEST MATERIALS CO.,01-East,768141000,15795.00,7293.85,8501.15
MIDWEST MATERIALS CO.,01-East,735750000,9408.00,3050.56,6357.44
MIDWEST MATERIALS CO.,01-East,768144200,2052.00,315.82,1736.18
MIDWEST MATERIALS CO.,01-East,733151500,636.60,101.80,534.80
MIDWEST MATERIALS CO.,01-East,733211000,614.10,201.18,412.92
MIDWEST MATERIALS CO.,01-East,779989770,560.08,0.00,560.08
MILAMAR COATING,01-East,779987651,1255.50,156.78,1098.72
MITSUBISHI POWER SYSTEMS,01-East,734611100,178.72,26.96,151.76
MODYNE,01-East,770720000,1010.00,27.12,982.88
MONTAGUE STUDIOS,01-East,739681100,444.24,120.46,323.78
MORAC MEXICO S.A. de C.V.,01-East,734761300,2039.40,379.39,1660.01
MORRISON SUPPLY COMPANY,01-East,735602000,4630.00,862.96,3767.04
MOTION INDUSTRIES,01-East,770410000,1507.50,98.81,1408.69
MOTION INDUSTRIES,01-East,770430000,1327.50,88.55,1238.95
MOTION INDUSTRIES,01-East,733211000,912.00,94.78,817.22
MOTION INDUSTRIES,01-East,766121000,910.00,23.95,886.05
MOTION INDUSTRIES,01-East,734900000,737.00,133.46,603.54
MOTION INDUSTRIES,01-East,733464600,626.00,69.94,556.06
MOTION INDUSTRIES,01-East,734830000,370.00,45.41,324.59
MOTION INDUSTRIES,01-East,734151100,326.17,43.14,283.03
MOTION INDUSTRIES,01-East,733464500,298.00,29.52,268.48
MOUNTAINVIEW MECHANICAL,01-East,780645010,100629.60,49683.31,50946.29
MOUNTAINVIEW MECHANICAL,01-East,784320000,16483.20,9567.28,6915.92
MOUNTAINVIEW MECHANICAL,01-East,764523000,3864.00,787.76,3076.24
MOUNTAINVIEW MECHANICAL,01-East,784000000,2481.84,1294.06,1187.78
MOUNTAINVIEW MECHANICAL,01-East,764522000,2144.00,419.72,1724.28
MOUNTAINVIEW MECHANICAL,01-East,780200000,1898.64,1140.24,758.40
MOUNTAINVIEW MECHANICAL,01-East,739370000,1413.44,635.75,777.69
MOUNTAINVIEW MECHANICAL,01-East,739741100,807.68,438.23,369.45
MOUNTAINVIEW MECHANICAL,01-East,764561000,102.00,22.18,79.82
MSSM,01-East,764900005,134320.00,52817.45,81502.55
MSSM,01-East,764827550,65296.00,24113.55,41182.45
MSSM,01-East,764864005,22736.00,7588.29,15147.71
MSSM,01-East,764901011,7938.75,4360.33,3578.42
MSSM,01-East,764827150,2310.00,986.90,1323.10
MSSM,01-East,764902501,1569.81,824.54,745.27
MSSM,01-East,764821073,885.50,838.35,47.15
MSSM,01-East,764781000,312.00,157.77,154.23
MSSM,01-East,764861000,256.50,116.90,139.60
MSSM,01-East,764901600,64.68,32.95,31.73
MUNNELL & SHERRILL. INC,01-East,733444200,298.00,38.64,259.36
MUNNELL & SHERRILL. INC,01-East,733464500,275.00,32.53,242.47
MURPHY COMPANY,01-East,739741100,1713.92,673.28,1040.64
MUSTANG GROUP,01-East,734611100,536.16,81.10,455.06
MYLAN TECHNOLOGIES INC.,01-East,770830200,1010.00,28.98,981.02
N2 AERO,01-East,733648000,25416.00,10632.87,14783.13
NATIONAL ELECTRIC,01-East,734910000,683.00,102.70,580.30
NATIONAL GYPSUM CO,01-East,764821000,447.00,15.66,431.34
NATIONAL PAPER CONVERTING,01-East,733525500,1330.00,130.06,1199.94
NAWC-WD/475500D,01-East,724311400,7038.00,919.50,6118.50
NEWTEX INDUSTRIES INC.,01-East,733832000,2430.00,385.42,2044.58
NIAGARA CERAMICS CORP.,01-East,764822000,1464.00,123.05,1340.95
NIAGARA SPECIALTY METALS,01-East,754924010,37941.48,7234.02,30707.46
NIAGARA SPECIALTY METALS,01-East,754904005,32485.17,6492.81,25992.36
NIAGARA SPECIALTY METALS,01-East,754907000,32324.33,6225.56,26098.77
NIAGARA SPECIALTY METALS,01-East,754927000,30559.02,5579.77,24979.25
NIAGARA SPECIALTY METALS,01-East,779957936,18014.56,5719.01,12295.55
NIAGARA SPECIALTY METALS,01-East,754877000,10244.20,2061.01,8183.19
NIAGARA SPECIALTY METALS,01-East,754874005,7643.28,1613.73,6029.55
NIAGARA SPECIALTY METALS,01-East,754867000,6985.44,1453.48,5531.96
NIAGARA SPECIALTY METALS,01-East,764821000,4970.80,555.26,4415.54
NIAGARA SPECIALTY METALS,01-East,764901000,3507.33,400.73,3106.60
NIAGARA SPECIALTY METALS,01-East,766121000,2869.50,239.49,2630.01
NIAGARA SPECIALTY METALS,01-East,738381000,2534.40,2080.80,453.60
NIPRO GLASS AMERICAS CORP,01-East,793330000,1294.50,78.04,1216.46
NIPRO GLASS AMERICAS CORP,01-East,733464400,596.00,79.22,516.78
NIPRO GLASS AMERICAS CORP,01-East,733464500,298.00,29.52,268.48
NOELLE INDUSTRIES,01-East,720219100,553.20,13.33,539.87
NORAM ENGINEERING,01-East,733182200,4719.60,585.90,4133.70
NORTH ATLANTIC ASSOCIATES,01-East,764522000,2550.40,476.10,2074.30
NORTH ATLANTIC ASSOCIATES,01-East,764861000,362.00,63.37,298.63
NORTH ATLANTIC ASSOCIATES,01-East,764521000,139.20,26.59,112.61
NORTHWEST INSULATION,01-East,735750000,17532.00,5332.02,12199.98
NORTHWEST INSULATION,01-East,735602000,10612.00,2752.53,7859.47
NORTHWEST INSULATION,01-East,764902000,2653.50,96.62,2556.88
NORTHWEST INSULATION,01-East,764602000,2034.00,79.49,1954.51
NOTHING INC.,01-East,777601000,409.92,58.80,351.12
NOV WILSON,01-East,770300000,552.75,39.97,512.78
NOVELIS OSWEGO,01-East,739451000,8683.52,2695.26,5988.26
NOVELIS OSWEGO,01-East,718501000,1052.50,134.69,917.81
NOVELIS OSWEGO,01-East,739741100,700.16,219.12,481.04
NRG/ENERGY,01-East,764461006,404.00,27.94,376.06
NTK AVIATION AMERICA INC.,01-East,770800000,1368.00,25.56,1342.44
NTK AVIATION AMERICA INC.,01-East,733383000,1117.60,163.93,953.67
NU-SOURCE INC.,01-East,733464500,4688.00,472.32,4215.68
NUTEC BICKLEY SA DE CV,01-East,779121000,691.29,93.00,598.29
O G SUPPLY,01-East,726000240,25704.00,2662.54,23041.46
OE-CAL,01-East,737902100,462.00,67.12,394.88
OECO MILWAUKIE,01-East,771132000,1217.50,35.80,1181.70
OECO MILWAUKIE,01-East,771002000,828.50,19.30,809.20
OHMITE MFG,01-East,733151500,900.00,95.38,804.62
OHMITE MFG,01-East,739561200,839.68,144.66,695.02
OLYMPIC CRANES & FABRICAT,01-East,739370000,364.80,90.82,273.98
OMNIA SALES,01-East,740283401,9609.60,1592.30,8017.30
ONQUEST INC.,01-East,779957139,568.25,395.28,172.97
ORCON - MEXICO LLC,01-East,724261200,19484.00,2731.10,16752.90
"OWENS CORNING MEXICO,",01-East,733525500,752.50,70.39,682.11
"OWENS CORNING SALES,LLC",01-East,793330000,3883.50,234.12,3649.38
OWENS CORNING,01-East,793330000,898.00,53.01,844.99
PACIFIC INSULATION CO.,01-East,735602000,320120.00,100400.76,219719.24
PACIFIC INSULATION CO.,01-East,768241000,253575.00,100795.31,152779.69
PACIFIC INSULATION CO.,01-East,735602110,76640.00,25621.10,51018.90
PACIFIC INSULATION CO.,01-East,768221000,74752.00,28829.86,45922.14
PACIFIC INSULATION CO.,01-East,726020019,39623.25,16207.45,23415.80
PACIFIC INSULATION CO.,01-East,735750000,17048.00,6031.09,11016.91
PACIFIC INSULATION CO.,01-East,735752000,6642.00,2391.10,4250.90
PACIFIC INSULATION CO.,01-East,737301000,5650.56,1472.16,4178.40
PACIFIC INSULATION CO.,01-East,764901000,2765.29,402.18,2363.11
PACIFIC INSULATION CO.,01-East,739641100,1102.76,549.70,553.06
PACIFIC INSULATION CO.,01-East,739661100,1062.44,601.98,460.46
PACIFIC INSULATION CO.,01-East,733211000,382.80,94.78,288.02
PACIFIC INSULATION CO.,01-East,764901061,339.23,111.90,227.33
PACIFIC INSULATION CO.,01-East,733464500,196.50,29.52,166.98
PACIFIC NORTHWEST NATL,01-East,764221000,248.00,16.92,231.08
PACIFIC SCIENTIFIC,01-East,733671000,1438.08,119.66,1318.42
PAC,01-East,740123000,670.32,121.37,548.95
PALL CORPORATION-H2,01-East,733505200,49590.00,9616.56,39973.44
PALL CORPORATION-H2,01-East,733444200,553.80,104.06,449.74
PALL TRINITY MICRO CORP,01-East,733505200,5538.00,917.32,4620.68
PANDO LLC,01-East,768021000,374.00,26.84,347.16
PANELCRAFT INC.,01-East,737902100,2642.00,405.82,2236.18
PARAGON PACIFIC INSULATIO,01-East,735602110,38000.00,13566.49,24433.51
PARAGON PACIFIC INSULATIO,01-East,735602000,25000.00,8405.73,16594.27
PARAGON PACIFIC INSULATIO,01-East,735750000,13320.00,4667.93,8652.07
PARAGON PACIFIC INSULATIO,01-East,735752000,185.00,68.40,116.60
PARAGON PACIFIC SPI,01-East,735602110,44000.00,15708.56,28291.44
PARAGON PACIFIC SPI,01-East,735602000,12000.00,4142.22,7857.78
PARKER AEROSPACE,01-East,779001000,275.84,36.20,239.64
PASQUALE DINUZZO LLC,01-East,764483000,421.00,89.22,331.78
PATS AIRCRAFT LLC,01-East,724121700,5176.00,1302.84,3873.16
"PATS AIRCRAFT, LLC",01-East,724121700,584.00,162.85,421.15
PAWLING CORP.,01-East,764781040,4232.00,314.99,3917.01
PENNINGTON FURNACE SUPPLY,01-East,733742000,1632.00,327.00,1305.00
PENNINGTON FURNACE SUPPLY,01-East,733721000,300.00,69.32,230.68
PENSAR,01-East,738521000,4815.36,1291.07,3524.29
PENSAR,01-East,764822000,1720.00,61.52,1658.48
PENSAR,01-East,727063016,988.80,63.34,925.46
"PERFORMANCE PLASTICS,INC.",01-East,724261400,6915.60,1140.39,5775.21
"PERFORMANCE PLASTICS,INC.",01-East,724401150,648.00,81.46,566.54
PETRO-CHEM DEVELOPMENT,01-East,768133000,833.00,42.91,790.09
PHOTONIS USA INC.,01-East,771320000,2859.00,342.25,2516.75
PHYTOTECHNOLOGY LABORATOR,01-East,726000240,441.60,29.58,412.02
PICA MFG. SOLUTIONS,01-East,733464500,262.00,29.52,232.48
PIONEER GASKET,01-East,770230000,2016.00,147.83,1868.17
PIONEER GASKET,01-East,770240000,1972.50,140.66,1831.84
PK CONTRACTING INC,01-East,768112000,269.00,19.33,249.67
PLANET TOOL,01-East,771504000,50115.00,3432.84,46682.16
PLANET TOOL,01-East,771502000,10041.00,687.06,9353.94
PLIBRICO COMPANY LLC,01-East,779987650,88963.92,25595.56,63368.36
PLIBRICO REFRACTORY,01-East,780250000,88464.74,39839.14,48625.60
PLIBRICO REFRACTORY,01-East,779980245,26094.78,20914.05,5180.73
PLIBRICO REFRACTORY,01-East,754467000,11160.22,3646.79,7513.43
PLIBRICO REFRACTORY,01-East,737271000,9920.16,8968.99,951.17
PLIBRICO REFRACTORY,01-East,764521000,7183.60,1341.56,5842.04
PLIBRICO REFRACTORY,01-East,770540000,4787.13,454.18,4332.95
PLIBRICO REFRACTORY,01-East,738401000,4706.40,4281.60,424.80
PLIBRICO REFRACTORY,01-East,779951021,4069.80,1747.83,2321.97
PLIBRICO REFRACTORY,01-East,737861000,3485.80,1771.05,1714.75
PLIBRICO REFRACTORY,01-East,742520400,3056.71,661.56,2395.15
PLIBRICO REFRACTORY,01-East,779959123,3047.31,1275.66,1771.65
PLIBRICO REFRACTORY,01-East,739450000,2656.37,1404.04,1252.33
PLIBRICO REFRACTORY,01-East,754485000,2336.16,827.82,1508.34
PLIBRICO REFRACTORY,01-East,779951023,2217.60,961.41,1256.19
PLIBRICO REFRACTORY,01-East,739641100,1890.44,1099.40,791.04
PLIBRICO REFRACTORY,01-East,754367000,1782.00,820.74,961.26
PLIBRICO REFRACTORY,01-East,739561200,1593.86,442.66,1151.20
PLIBRICO REFRACTORY,01-East,770512000,1558.83,88.67,1470.16
PLIBRICO REFRACTORY,01-East,737912200,1276.00,363.80,912.20
PLIBRICO REFRACTORY,01-East,764901000,1062.60,129.15,933.45
PLIBRICO REFRACTORY,01-East,779959124,817.62,332.68,484.94
PLIBRICO REFRACTORY,01-East,733444200,670.80,115.92,554.88
PLIBRICO REFRACTORY,01-East,739781100,620.32,422.89,197.43
PLIBRICO REFRACTORY,01-East,779959125,547.56,222.65,324.91
PLIBRICO REFRACTORY,01-East,764821000,495.00,140.91,354.09
PLIBRICO REFRACTORY,01-East,739561000,482.98,170.44,312.54
PLIBRICO REFRACTORY,01-East,733464500,412.60,65.06,347.54
PLIBRICO REFRACTORY,01-East,733211000,251.30,53.20,198.10
PLIBRICO REFRACTORY,01-East,733211050,251.30,55.91,195.39
PLIBRICO REFRACTORY,01-East,779987718,246.00,137.09,108.91
PLIBRICO REFRACTORY,01-East,764482000,79.60,17.40,62.20
POLYMERIC EXTRUDED PROD.,01-East,764481000,360.00,17.59,342.41
PRAXAIR INC.,01-East,739561000,362.23,85.22,277.01
PRAXAIR,01-East,739781100,2513.66,1310.72,1202.94
PRAXAIR,01-East,734822000,2330.00,402.38,1927.62
PRAXAIR,01-East,738561000,1700.35,496.55,1203.80
PRAXAIR,01-East,733464400,1222.00,165.96,1056.04
PRAXAIR,01-East,738521000,1171.58,430.36,741.22
PRAXAIR,01-East,777601000,996.48,189.62,806.86
PRAXAIR,01-East,738401000,975.56,461.81,513.75
PRAXAIR,01-East,738361000,896.18,473.35,422.83
PRAXAIR,01-East,734211200,787.76,93.50,694.26
PRAXAIR,01-East,733211000,780.50,106.40,674.10
PRAXAIR,01-East,734900000,499.62,95.09,404.53
PRAXAIR,01-East,738381000,475.20,346.80,128.40
PRAXAIR,01-East,740063000,471.74,120.10,351.64
PRAXAIR,01-East,764901000,425.25,16.14,409.11
PRAXAIR,01-East,764481000,346.00,16.94,329.06
PRAXAIR,01-East,739451000,327.74,111.56,216.18
PRAXAIR,01-East,733464500,298.00,29.52,268.48
PRAXAIR,01-East,739390000,268.85,82.43,186.42
PRC-DESOTO INTERNATIONAL,01-East,720252303,6185.70,145.78,6039.92
PRECISION ELECTRO MINERAL,01-East,770260000,3820.50,275.92,3544.58
PRECISION FITTING & GAUGE,01-East,734990000,715.20,82.94,632.26
PRESSCUT INDUSTRIES,01-East,733444200,664.00,69.38,594.62
PRIME MACHINE,01-East,770520000,906.00,48.32,857.68
PROCEDYNE CORP,01-East,737321000,74163.08,34479.25,39683.83
PROCEDYNE CORP,01-East,727000140,60538.00,8379.39,52158.61
PROCEDYNE CORP,01-East,754477000,51914.80,19034.55,32880.25
PROCEDYNE CORP,01-East,754527000,30667.18,10318.04,20349.14
PROCEDYNE CORP,01-East,764821000,29892.00,3411.25,26480.75
PROCEDYNE CORP,01-East,739681100,17674.00,7829.95,9844.05
PROCEDYNE CORP,01-East,734311100,14840.00,1859.92,12980.08
PROCEDYNE CORP,01-East,739781100,8942.74,3522.77,5419.97
PROCEDYNE CORP,01-East,754907000,8493.74,1710.68,6783.06
PROCEDYNE CORP,01-East,764601000,8116.00,1062.88,7053.12
PROCEDYNE CORP,01-East,764902000,6689.00,1213.22,5475.78
PROCEDYNE CORP,01-East,779958793,6090.00,2064.75,4025.25
PROCEDYNE CORP,01-East,764781000,5856.00,817.80,5038.20
PROCEDYNE CORP,01-East,777642000,4122.30,1246.55,2875.75
PROCEDYNE CORP,01-East,734711100,2309.67,515.00,1794.67
PROCEDYNE CORP,01-East,777601000,2244.26,621.74,1622.52
PROCEDYNE CORP,01-East,733525600,2070.00,273.08,1796.92
PROCEDYNE CORP,01-East,739761100,1639.14,723.93,915.21
PROCEDYNE CORP,01-East,764521000,1499.85,199.38,1300.47
PROCEDYNE CORP,01-East,764901000,1196.00,258.30,937.70
PROCEDYNE CORP,01-East,754427000,824.35,293.39,530.96
PROCEDYNE CORP,01-East,739541000,345.31,71.62,273.69
PROCEDYNE CORP,01-East,766121000,291.55,23.95,267.60
PROCEDYNE CORP,01-East,739561200,257.60,72.33,185.27
PROCEDYNE CORP,01-East,754447000,240.50,98.42,142.08
PROCEDYNE CORP,01-East,779957077,74.90,34.60,40.30
PROCESS RESEARCH PRODUCTS,01-East,777990000,4950.00,872.00,4078.00
PTI THERMAL SOLUTIONS,01-East,764827550,268742.40,95177.28,173565.12
PTI THERMAL SOLUTIONS,01-East,768121000,200741.00,96982.28,103758.72
PTI THERMAL SOLUTIONS,01-East,724281410,178670.10,101168.46,77501.64
PTI THERMAL SOLUTIONS,01-East,764821000,115398.60,43190.11,72208.49
PTI THERMAL SOLUTIONS,01-East,764521000,110633.00,39323.38,71309.62
PTI THERMAL SOLUTIONS,01-East,740361900,84115.20,46604.54,37510.66
PTI THERMAL SOLUTIONS,01-East,764901000,80419.00,38626.14,41792.86
PTI THERMAL SOLUTIONS,01-East,766121000,68140.00,26226.79,41913.21
PTI THERMAL SOLUTIONS,01-East,739457000,59365.44,37211.37,22154.07
PTI THERMAL SOLUTIONS,01-East,733525600,58721.75,9846.10,48875.65
PTI THERMAL SOLUTIONS,01-East,734251300,57087.41,5801.00,51286.41
PTI THERMAL SOLUTIONS,01-East,764900005,51624.80,21586.28,30038.52
PTI THERMAL SOLUTIONS,01-East,779988846,48199.68,21187.84,27011.84
PTI THERMAL SOLUTIONS,01-East,764901005,47880.00,19955.00,27925.00
PTI THERMAL SOLUTIONS,01-East,740382201,38174.00,19672.05,18501.95
PTI THERMAL SOLUTIONS,01-East,777601000,31598.30,8892.49,22705.81
PTI THERMAL SOLUTIONS,01-East,780680000,17560.80,6300.48,11260.32
PTI THERMAL SOLUTIONS,01-East,759821000,13578.84,4074.20,9504.64
PTI THERMAL SOLUTIONS,01-East,733211050,11389.80,3292.37,8097.43
PTI THERMAL SOLUTIONS,01-East,764902000,10772.00,5130.80,5641.20
PTI THERMAL SOLUTIONS,01-East,766162000,10674.00,4147.92,6526.08
PTI THERMAL SOLUTIONS,01-East,734351200,10269.36,1441.25,8828.11
PTI THERMAL SOLUTIONS,01-East,771400000,8450.70,299.90,8150.80
PTI THERMAL SOLUTIONS,01-East,764821161,7800.00,7440.00,360.00
PTI THERMAL SOLUTIONS,01-East,768141000,7325.05,2447.63,4877.42
PTI THERMAL SOLUTIONS,01-East,764822000,7133.90,2479.64,4654.26
PTI THERMAL SOLUTIONS,01-East,766122000,6650.00,2403.85,4246.15
PTI THERMAL SOLUTIONS,01-East,795900000,5850.00,1033.66,4816.34
PTI THERMAL SOLUTIONS,01-East,780640000,4567.23,1618.55,2948.68
PTI THERMAL SOLUTIONS,01-East,777904301,4091.43,1527.09,2564.34
PTI THERMAL SOLUTIONS,01-East,779956886,3900.00,1557.21,2342.79
PTI THERMAL SOLUTIONS,01-East,734910000,3545.40,945.11,2600.29
PTI THERMAL SOLUTIONS,01-East,733211000,3133.10,904.47,2228.63
PTI THERMAL SOLUTIONS,01-East,779986951,2700.00,1442.58,1257.42
PTI THERMAL SOLUTIONS,01-East,734830000,1951.60,312.50,1639.10
PTI THERMAL SOLUTIONS,01-East,733444200,1938.00,386.40,1551.60
PTI THERMAL SOLUTIONS,01-East,781660000,1868.52,564.32,1304.20
PTI THERMAL SOLUTIONS,01-East,779956887,1753.92,512.34,1241.58
PTI THERMAL SOLUTIONS,01-East,733484900,1707.20,347.01,1360.19
PTI THERMAL SOLUTIONS,01-East,739561000,1609.92,681.76,928.16
PTI THERMAL SOLUTIONS,01-East,738521000,1263.36,1194.24,69.12
PTI THERMAL SOLUTIONS,01-East,733444300,1162.80,277.02,885.78
PTI THERMAL SOLUTIONS,01-East,739561200,1073.40,289.32,784.08
PTI THERMAL SOLUTIONS,01-East,764482000,692.00,170.48,521.52
PTI THERMAL SOLUTIONS,01-East,770570000,640.50,95.41,545.09
PTI THERMAL SOLUTIONS,01-East,777903000,485.10,167.33,317.77
PTI THERMAL SOLUTIONS,01-East,793330000,423.30,53.01,370.29
PTI THERMAL SOLUTIONS,01-East,734990000,400.96,110.59,290.37
PTI THERMAL SOLUTIONS,01-East,779956599,264.00,171.96,92.04
PTI THERMAL SOLUTIONS,01-East,737902100,185.30,33.87,151.43
PTI THERMAL SOLUTIONS,01-East,777642000,183.00,75.54,107.46
PUGET SOUND NAVAL,01-East,733671000,2741.76,225.10,2516.66
PYROTEK INC,01-East,726020015,25617.40,20817.63,4799.77
PYROTEK INC,01-East,777902300,18566.10,9820.10,8746.00
PYROTEK INC,01-East,733151520,17118.00,7773.56,9344.44
PYROTEK INC,01-East,733525400,17020.00,7449.16,9570.84
PYROTEK INC,01-East,771320000,16332.00,4270.57,12061.43
PYROTEK INC,01-East,737321000,16104.71,5995.92,10108.79
PYROTEK INC,01-East,733464500,9484.00,4458.26,5025.74
PYROTEK INC,01-East,777903800,5287.06,1853.81,3433.25
PYROTEK INC,01-East,764821000,4316.00,1597.23,2718.77
PYROTEK INC,01-East,739681100,3555.36,3756.44,-201.08
PYROTEK INC,01-East,733211000,3370.00,2084.23,1285.77
PYROTEK INC,01-East,764901050,3042.00,1137.22,1904.78
PYROTEK INC,01-East,777902200,2489.27,1155.09,1334.18
PYROTEK INC,01-East,737301000,2314.50,981.45,1333.05
PYROTEK INC,01-East,734611100,2145.68,582.00,1563.68
PYROTEK INC,01-East,781590000,2126.52,436.37,1690.15
PYROTEK INC,01-East,754267000,2067.12,607.32,1459.80
PYROTEK INC,01-East,733510000,1669.75,1158.11,511.64
PYROTEK INC,01-East,734311100,1637.68,410.82,1226.86
PYROTEK INC,01-East,768221000,1542.40,566.08,976.32
PYROTEK INC,01-East,770260000,1474.20,202.89,1271.31
PYROTEK INC,01-East,776131000,1156.68,907.49,249.19
PYROTEK INC,01-East,768021000,1128.00,599.57,528.43
PYROTEK INC,01-East,770330000,1071.00,193.29,877.71
PYROTEK INC,01-East,773161000,1060.29,235.29,825.00
PYROTEK INC,01-East,777901100,1033.20,561.18,472.02
PYROTEK INC,01-East,754885000,1006.92,181.83,825.09
PYROTEK INC,01-East,733442000,975.00,477.95,497.05
PYROTEK INC,01-East,766121000,865.00,272.48,592.52
PYROTEK INC,01-East,733423700,761.60,452.56,309.04
PYROTEK INC,01-East,733505200,710.50,327.94,382.56
PYROTEK INC,01-East,739561000,656.64,342.83,313.81
PYROTEK INC,01-East,737261000,650.06,279.93,370.13
PYROTEK INC,01-East,764761000,648.00,368.58,279.42
PYROTEK INC,01-East,768241000,432.90,159.21,273.69
PYROTEK INC,01-East,739641100,348.48,274.86,73.62
PYROTEK INC,01-East,772101000,308.21,94.57,213.64
PYROTEK INC,01-East,764781000,308.00,143.12,164.88
PYROTEK INC,01-East,739661100,294.24,300.99,-6.75
PYROTEK INC,01-East,733151600,210.00,106.23,103.77
PYROTEK INC,01-East,734711100,203.04,54.40,148.64
PYROTEK INC,01-East,771991000,192.63,69.89,122.74
PYROTEK INC,01-East,739541000,191.81,71.62,120.19
PYROTEK INC,01-East,739561200,148.74,76.67,72.07
PYROTEK INC,01-East,773191000,99.55,22.47,77.08
PYROTEK INC,01-East,772051000,42.13,14.05,28.08
QUADNA - PHOENIX,01-East,764605000,3912.00,153.87,3758.13
QUADNA - PHOENIX,01-East,734311100,381.48,33.70,347.78
QUADNA,01-East,737321000,7095.36,3506.88,3588.48
QUADNA,01-East,737311000,5345.28,3612.48,1732.80
QUADNA,01-East,764605000,4633.60,828.08,3805.52
QUADNA,01-East,764262000,607.20,66.92,540.28
QUADNA,01-East,734211200,413.88,46.76,367.12
QUICK CUT GASKET & RUBBER,01-East,733464500,313.00,32.53,280.47
"R.L. WILLIAMS & CO., INC.",01-East,735602000,7344.00,2044.25,5299.75
RACING INDUSTRIES,01-East,730710500,4911.00,638.67,4272.33
RACING INDUSTRIES,01-East,730552560,3214.00,386.21,2827.79
RAINBOW ART GLASS INC.,01-East,733671000,3595.54,344.76,3250.78
RAMPART SUPPLY,01-East,792610000,690.00,80.93,609.07
RATH INCORPORATED,01-East,764261110,111760.00,38351.84,73408.16
RATH INCORPORATED,01-East,727031200,40867.20,12028.68,28838.52
RATH INCORPORATED,01-East,764301020,37302.50,13089.33,24213.17
RATH INCORPORATED,01-East,764821026,35980.00,8592.99,27387.01
RATH INCORPORATED,01-East,727000140,31579.20,12684.05,18895.15
RATH INCORPORATED,01-East,727063000,21135.60,8166.88,12968.72
RATH INCORPORATED,01-East,764521000,18735.00,4580.45,14154.55
RATH INCORPORATED,01-East,727063016,14461.20,5558.54,8902.66
RATH INCORPORATED,01-East,727063007,11025.00,4426.50,6598.50
RATH INCORPORATED,01-East,727063018,11025.00,4447.51,6577.49
RATH INCORPORATED,01-East,766121000,6845.00,2016.42,4828.58
RATH INCORPORATED,01-East,764601000,2753.75,397.94,2355.81
RATH INCORPORATED,01-East,764781000,456.00,121.22,334.78
RATH INCORPORATED,01-East,766162000,190.50,74.74,115.76
RAYTEQ LLC,01-East,776101000,387.00,182.84,204.16
RAYTHEON,01-East,724261200,1572.00,156.40,1415.60
RAYTHEON,01-East,764482000,692.00,34.10,657.90
RAYTHEON,01-East,739561000,473.76,85.22,388.54
RB LLC,01-East,768144200,22394.00,6662.37,15731.63
RB LLC,01-East,768133000,7630.00,2183.01,5446.99
RB LLC,01-East,768241100,4760.00,1248.80,3511.20
"RBC BEARINGS, INC.",01-East,740361900,1401.60,457.49,944.11
RBC INDUSTRIES,01-East,779987651,3324.00,408.97,2915.03
RECON,01-East,733403200,996.60,99.17,897.43
RECON,01-East,733383000,985.60,163.93,821.67
RED-RAY MFG,01-East,734311100,1851.48,202.22,1649.26
REDCO,01-East,733211000,958.00,106.40,851.60
REDCO,01-East,792760000,356.72,58.31,298.41
REFRACTORY ANCHORS INC,01-East,734351204,1836.00,278.52,1557.48
REFRACTORY ENGINEERS INC.,01-East,777730021,7482.00,4876.80,2605.20
REFRACTORY ENGINEERS INC.,01-East,793330000,6681.00,1827.48,4853.52
REFRACTORY ENGINEERS INC.,01-East,793330074,5208.00,2700.80,2507.20
REFRACTORY ENGINEERS INC.,01-East,727000440,4023.60,751.70,3271.90
REFRACTORY ENGINEERS INC.,01-East,777720000,3142.08,2253.92,888.16
REFRACTORY ENGINEERS INC.,01-East,777762000,956.48,2814.56,-1858.08
REFRACTORY ENGINEERS INC.,01-East,792614000,858.00,180.97,677.03
REFRACTORY ENGINEERS INC.,01-East,792654000,638.00,259.35,378.65
REFRACTORY MAINTENANCE CO,01-East,718500000,29539.84,6517.91,23021.93
REFRACTORY MAINTENANCE CO,01-East,724311400,12808.00,3065.01,9742.99
REFRACTORY MAINTENANCE CO,01-East,737321000,7276.54,1713.12,5563.42
REFRACTORY MAINTENANCE CO,01-East,734990000,6374.32,938.93,5435.39
REFRACTORY MAINTENANCE CO,01-East,777901100,2646.00,982.06,1663.94
REFRACTORY MAINTENANCE CO,01-East,777901300,2321.55,849.15,1472.40
REFRACTORY MAINTENANCE CO,01-East,740382201,859.00,293.77,565.23
REFRACTORY SERVICE INC.,01-East,755065000,40554.00,13761.54,26792.46
REFRACTORY SERVICE INC.,01-East,779988827,9920.18,4882.75,5037.43
REFRACTORY SERVICE INC.,01-East,734851000,2581.71,548.40,2033.31
REFRACTORY SERVICE INC.,01-East,777642000,344.40,95.01,249.39
REINHOLD INDUSTRIES INC.,01-East,733403200,1133.00,99.17,1033.83
RESTORATION SUPPLY CO.,01-East,733151500,922.00,101.80,820.20
REVERE COPPER PRODUCTS IN,01-East,770220000,635.40,82.04,553.36
REX ROTO CORP,01-East,793250074,543.00,313.78,229.22
REX ROTO CORP,01-East,733464500,152.00,29.52,122.48
RHI CANADA INC.,01-East,737301000,2469.12,2575.36,-106.24
RHI-MONOFRAX LTD.,01-East,734351200,1945.80,147.24,1798.56
RHI-MONOFRAX LTD.,01-East,734251300,1176.05,88.29,1087.76
RHI-MONOFRAX LTD.,01-East,733525500,752.50,70.39,682.11
RHI-MONOFRAX LTD.,01-East,779081000,698.56,106.23,592.33
RHI-MONOFRAX LTD.,01-East,734751200,584.00,75.41,508.59
RIVE TECHNOLOGY,01-East,733444100,332.00,75.04,256.96
ROBBIE HOLLAND WELDING,01-East,764822000,894.00,31.77,862.23
ROGERS CORP,01-East,733525500,12582.00,1364.95,11217.05
ROSEMOUNT ANALYTICAL INC.,01-East,724281200,2947.00,421.87,2525.13
"RSG AERODESIGN, LLC",01-East,724281200,601.00,89.08,511.92
RTI FABRICATION,01-East,737671000,2478.20,326.50,2151.70
S & S SUPPLIES &,01-East,771120000,8595.00,285.54,8309.46
S & S SUPPLIES &,01-East,771100000,7880.00,244.88,7635.12
S & S SUPPLIES &,01-East,771002000,3256.50,79.49,3177.01
S & S SUPPLIES &,01-East,771000000,3084.00,82.45,3001.55
S & S SUPPLIES &,01-East,734910000,362.00,53.09,308.91
S M ENGINEERING,01-East,740382101,1742.40,549.20,1193.20
S P I,01-East,735602000,33120.00,10467.90,22652.10
S P I,01-East,766161000,606.00,308.46,297.54
S W ANDERSON,01-East,735602000,45700.00,16811.46,28888.54
SAES SMART MATERIALS,01-East,733525600,717.50,65.49,652.01
SAES SMART MATERIALS,01-East,764342000,429.50,23.40,406.10
SAINT GOBAIN,01-East,777601000,1933.60,360.09,1573.51
SAINT-GOBAIN,01-East,733586600,41803.20,4004.06,37799.14
SAINT-GOBAIN,01-East,766211000,2200.00,221.56,1978.44
SAINT-GOBAIN,01-East,738561000,1817.28,796.32,1020.96
SAINT-GOBAIN,01-East,766681100,627.30,52.66,574.64
SANDELIUS INSTRUMENTS INC,01-East,766681100,612.00,49.56,562.44
SANDIA NATIONAL LABS,01-East,793330000,431.50,26.01,405.49
SANDIA NATIONAL LAB,01-East,738521000,3210.24,860.71,2349.53
SANDIA NATIONAL LAB,01-East,739541000,587.52,90.50,497.02
SANDVIK HEATING,01-East,777601000,819.84,117.60,702.24
SANDVIK THERMAL PROCESS,01-East,715000050,275511.83,147685.91,127825.92
SANDVIK THERMAL PROCESS,01-East,763991000,229252.32,78195.76,151056.56
SANDVIK THERMAL PROCESS,01-East,764787510,113725.44,36139.15,77586.29
SANDVIK THERMAL PROCESS,01-East,727000140,70621.60,20680.84,49940.76
SANDVIK THERMAL PROCESS,01-East,764761010,15552.00,4926.87,10625.13
SANDVIK THERMAL PROCESS,01-East,734251300,2551.70,264.29,2287.41
SANDVIK THERMAL PROCESS,01-East,734211200,685.12,92.43,592.69
SANDVIK THERMAL PROCESS,01-East,733525500,431.25,70.39,360.86
SANDVIK THERMAL PROCESS,01-East,777601000,287.95,58.80,229.15
SANDY VALLEY FASTENERS,01-East,771002000,1740.00,43.20,1696.80
SAXON GLASS TECH INC,01-East,727063000,56.80,16.80,40.00
SCHWEITZER-MAUDUIT INT'L,01-East,766681100,918.00,74.33,843.67
SCHWEITZER-MAUDUIT INT'L,01-East,766686000,414.00,37.54,376.46
SCREW CONVEYOR CORP,01-East,770720000,1061.00,29.43,1031.57
SDK LABORATORIES,01-East,727000140,520.80,32.55,488.25
SEALING DEVICES INC,01-East,740402402,48360.00,23428.43,24931.57
SEALING DEVICES INC,01-East,732664000,46110.00,6590.83,39519.17
SEALING DEVICES INC,01-East,733525500,10660.00,2968.57,7691.43
SEALING DEVICES INC,01-East,732675000,7632.28,2932.80,4699.48
SEALING DEVICES INC,01-East,732663000,7458.00,1146.25,6311.75
SEALING DEVICES INC,01-East,740402450,6969.34,3158.52,3810.82
SEALING DEVICES INC,01-East,764490000,6775.13,1907.30,4867.83
SEALING DEVICES INC,01-East,766121000,6312.00,320.40,5991.60
SEALING DEVICES INC,01-East,740382201,6130.00,4139.77,1990.23
SEALING DEVICES INC,01-East,764489300,2931.84,953.49,1978.35
SEALING DEVICES INC,01-East,777601000,1392.20,288.08,1104.12
SEALING DEVICES INC,01-East,732630100,1062.00,375.01,686.99
SEALING DEVICES INC,01-East,764481000,592.00,170.70,421.30
SEALING DEVICES INC,01-East,733510000,537.50,141.73,395.77
SEISLER MACH & FABRICATIO,01-East,733464400,626.00,86.74,539.26
SELAS HEAT TECHNOLOGY CO.,01-East,734311100,981.30,105.90,875.40
SELLE SUPPLY COMPANY,01-East,735602000,232198.80,68574.11,163624.69
SELLE SUPPLY COMPANY,01-East,735752000,29569.00,9872.96,19696.04
SELLE SUPPLY COMPANY,01-East,764601000,23310.88,5648.72,17662.16
SELLE SUPPLY COMPANY,01-East,735750000,12683.00,4146.30,8536.70
SELLE SUPPLY COMPANY,01-East,764521000,12285.40,3041.49,9243.91
SELLE SUPPLY COMPANY,01-East,764561000,3540.10,908.81,2631.29
SELLE SUPPLY COMPANY,01-East,735602110,2800.00,875.07,1924.93
SELLE SUPPLY COMPANY,01-East,764481000,383.00,102.95,280.05
SELTEK INT'L INC,01-East,737009817,1707.00,1535.40,171.60
SEMCOR,01-East,734910000,1086.00,159.26,926.74
SEMICONDUCTOR PACKAGING,01-East,764821000,1341.00,46.97,1294.03
SEMICONDUCTOR PACKAGING,01-East,764861000,666.50,25.98,640.52
SHANNON ENTERPRISES OF WN,01-East,764902000,12575.60,3018.00,9557.60
SHANNON ENTERPRISES OF WN,01-East,764822000,5548.00,1289.48,4258.52
SHANNON ENTERPRISES OF WN,01-East,764342000,1695.00,552.43,1142.57
SHARP TOOL,01-East,733742000,1184.00,210.15,973.85
SHARP TOOL,01-East,733741000,408.00,119.06,288.94
SHAW ENERGY & CHEMICALS,01-East,779959409,74593.75,50403.33,24190.42
SHAW ENERGY & CHEMICALS,01-East,779959407,50866.20,16488.93,34377.27
SHAW ENERGY & CHEMICALS,01-East,779980286,31154.76,28339.80,2814.96
SHAW ENERGY & CHEMICALS,01-East,779959408,25433.10,9546.09,15887.01
SHAW ENERGY & CHEMICALS,01-East,766121000,24321.00,10954.02,13366.98
SHAW ENERGY & CHEMICALS,01-East,779980285,22988.70,20906.10,2082.60
SHAW ENERGY & CHEMICALS,01-East,754924010,19972.80,12785.75,7187.05
SHAW ENERGY & CHEMICALS,01-East,779980287,13225.00,12050.00,1175.00
SHAW ENERGY & CHEMICALS,01-East,779988139,12938.64,12043.20,895.44
SHAW ENERGY & CHEMICALS,01-East,764821000,12240.00,5528.85,6711.15
SHAW ENERGY & CHEMICALS,01-East,779988136,10392.32,9450.24,942.08
SHAW ENERGY & CHEMICALS,01-East,779980284,9745.50,8855.50,890.00
SHAW ENERGY & CHEMICALS,01-East,777902888,4935.00,4494.00,441.00
SHAW ENERGY & CHEMICALS,01-East,779980289,4637.25,4232.25,405.00
SHAW ENERGY & CHEMICALS,01-East,777902887,4200.00,3750.00,450.00
SHAW ENERGY & CHEMICALS,01-East,779988145,3568.00,1008.32,2559.68
SHAW ENERGY & CHEMICALS,01-East,779956599,2460.00,2239.02,220.98
SHAW ENERGY & CHEMICALS,01-East,779988134,2406.72,2188.68,218.04
SHAW ENERGY & CHEMICALS,01-East,779980290,2014.50,3285.25,-1270.75
SHAW ENERGY & CHEMICALS,01-East,779957834,1968.75,973.83,994.92
SHAW ENERGY & CHEMICALS,01-East,779988146,1962.99,1266.67,696.32
SHAW ENERGY & CHEMICALS,01-East,779988147,1847.36,1251.36,596.00
SHAW ENERGY & CHEMICALS,01-East,779988148,1784.00,717.60,1066.40
SHAW ENERGY & CHEMICALS,01-East,779988135,1452.32,1320.86,131.46
SHAW ENERGY & CHEMICALS,01-East,777902889,1265.28,10.72,1254.56
SHAW ENERGY & CHEMICALS,01-East,777902886,1098.24,1689.60,-591.36
SHAW ENERGY & CHEMICALS,01-East,777642000,840.00,475.05,364.95
SHAW ENERGY & CHEMICALS,01-East,779988138,611.52,556.40,55.12
SHAW ENERGY & CHEMICALS,01-East,779988137,518.10,470.80,47.30
SILURIA TECHNOLOGY,01-East,764461006,1808.00,138.31,1669.69
SILURIA TECHNOLOGY,01-East,764821000,860.00,30.72,829.28
SILURIA TECHNOLOGY,01-East,764481000,346.00,16.94,329.06
SION POWER CORP,01-East,720252303,293.25,6.68,286.57
SKIDMORE COLLEGE,01-East,777642000,270.00,63.34,206.66
SL PACIFIC,01-East,733211000,319.00,47.39,271.61
SMITH - SHARPE,01-East,779957935,496.20,223.89,272.31
SMITH - SHARPE,01-East,754305000,393.75,219.57,174.18
SMITHS TUBULAR SYSTEMS-,01-East,745121000,21536.80,3533.51,18003.29
SMT INTERNATIONAL LLC,01-East,733211050,479.00,55.91,423.09
SNYDER CONTRACTORS,01-East,770220000,554.40,56.87,497.53
SNYDER CONTRACTORS,01-East,770210000,493.00,60.63,432.37
SNYDER CONTRACTORS,01-East,733423600,279.36,35.90,243.46
SNYDER CONTRACTORS,01-East,777601000,271.65,72.02,199.63
SOLAR PRODUCTS INC,01-East,734351203,106800.00,16882.55,89917.45
SOLCOAT,01-East,734030000,12388.50,2729.61,9658.89
SOLDAR DE CHIHUAHUA S.A.,01-East,777590000,409.92,122.86,287.06
SOUND TECHNOLOGIES,01-East,764521000,4662.00,1114.77,3547.23
SOUND TECHNOLOGIES,01-East,764601000,4662.00,2210.58,2451.42
SOUTH JERSEY BOILER,01-East,734414100,776.50,89.66,686.84
SOUTH JERSEY BOILER,01-East,734211200,229.68,22.84,206.84
SOUTH JERSEY BOILER,01-East,734311100,190.74,16.85,173.89
SOUTHLANDS ENGINEERING,01-East,792650000,533.00,94.66,438.34
SOUTHWEST RESEARCH INST.,01-East,768121000,503.00,28.76,474.24
SOUTHWEST RESEARCH,01-East,764821000,3256.00,124.05,3131.95
SOUTHWEST RESEARCH,01-East,768121000,1257.50,71.92,1185.58
SOUTHWEST RESEARCH,01-East,764521000,960.00,39.89,920.11
SP &I- AUBURN MA,01-East,735602000,58200.00,23024.80,35175.20
SP &I- AUBURN MA,01-East,735602110,35580.00,14483.84,21096.16
SP &I- AUBURN MA,01-East,764602000,3744.00,943.60,2800.40
SP &I- AUBURN MA,01-East,764561000,2496.00,709.66,1786.34
SP &I- AUBURN MA,01-East,764601000,2080.00,531.44,1548.56
SP &I- AUBURN MA,01-East,764822000,1128.00,246.10,881.90
SP &I- AUBURN MA,01-East,764521000,1030.00,265.94,764.06
SP &I- AUBURN MA,01-East,764522000,618.00,160.32,457.68
"SPACE EXPLORATION TECH,",01-East,777998700,18150.00,15425.00,2725.00
SPAR INC,01-East,754065000,2138.40,1282.86,855.54
SPAR INC,01-East,764901000,1640.00,645.76,994.24
SPAR INC,01-East,754085000,1188.00,712.53,475.47
SPECIAL METALS INC.,01-East,733525600,43867.75,7285.27,36582.48
SPECIAL METALS INC.,01-East,733545810,6435.00,2382.37,4052.63
SPECIAL METALS INC.,01-East,733484800,3187.20,739.63,2447.57
SPECIAL METALS INC.,01-East,733525500,1682.00,281.58,1400.42
SPECIAL METALS INC.,01-East,718501000,1630.80,270.75,1360.05
SPECIAL METALS INC.,01-East,725441100,795.40,134.51,660.89
SPECIAL METALS INC.,01-East,764861000,320.50,12.67,307.83
SPECIAL METALS INC.,01-East,764341000,214.75,11.99,202.76
SPECIALTY FOUNDRY PRODUCT,01-East,766162000,3429.00,1278.06,2150.94
SPECIALTY FOUNDRY PRODUCT,01-East,766121000,2808.00,997.54,1810.46
SPECIALTY OPTICAL SYSTEMS,01-East,740402402,2097.50,411.01,1686.49
SPECIALTY PROD & INSUL.,01-East,735602000,105650.00,32862.64,72787.36
SPECIALTY PROD & INSUL.,01-East,735750000,1485.00,618.03,866.97
SPECIALTY PROD & INSUL.,01-East,764605000,254.00,103.51,150.49
SPECIALTY PRODUCTS&INSUL,01-East,735602110,123360.00,47441.91,75918.09
SPECIALTY PRODUCTS&INSUL,01-East,735602000,64627.00,24117.36,40509.64
SPECIALTY PRODUCTS&INSUL,01-East,735752000,31690.00,11747.46,19942.54
SPECIALTY PRODUCTS&INSUL,01-East,735750000,12400.00,4653.42,7746.58
SPECIALTY PRODUCTS&INSUL,01-East,764601000,6168.00,1532.50,4635.50
SPECIALTY PRODUCTS&INSUL,01-East,735655000,3568.00,479.94,3088.06
SPECIALTY PRODUCTS&INSUL,01-East,764602000,3024.00,741.90,2282.10
SPECIALTY PRODUCTS&INSUL,01-East,764521000,2504.00,637.84,1866.16
SPECIALTY PRODUCTS&INSUL,01-East,735862000,2160.00,1233.66,926.34
SPECIALTY PRODUCTS&INSUL,01-East,764522000,1926.00,480.99,1445.01
SPECIALTY PRODUCTS&INSUL,01-East,764762000,920.00,315.70,604.30
SPECIALTY PRODUCTS,01-East,735602000,1906937.00,706984.83,1199952.17
SPECIALTY PRODUCTS,01-East,733566300,258854.40,48955.50,209898.90
SPECIALTY PRODUCTS,01-East,735602110,183056.00,73742.10,109313.90
SPECIALTY PRODUCTS,01-East,768222000,86926.00,20633.48,66292.52
SPECIALTY PRODUCTS,01-East,764602000,81655.00,35405.35,46249.65
SPECIALTY PRODUCTS,01-East,735752000,48388.00,18143.10,30244.90
SPECIALTY PRODUCTS,01-East,735750000,37271.00,14467.33,22803.67
SPECIALTY PRODUCTS,01-East,764901000,35307.50,15724.18,19583.32
SPECIALTY PRODUCTS,01-East,764522000,25061.00,10313.41,14747.59
SPECIALTY PRODUCTS,01-East,768141020,17620.00,6472.93,11147.07
SPECIALTY PRODUCTS,01-East,766161000,12932.00,9026.67,3905.33
SPECIALTY PRODUCTS,01-East,768212000,12396.00,3897.21,8498.79
SPECIALTY PRODUCTS,01-East,764601000,9808.00,3487.08,6320.92
SPECIALTY PRODUCTS,01-East,764521000,6765.00,1993.51,4771.49
SPECIALTY PRODUCTS,01-East,735602010,6009.00,4013.36,1995.64
SPECIALTY PRODUCTS,01-East,764482000,5780.00,2119.50,3660.50
SPECIALTY PRODUCTS,01-East,764902000,5560.00,1277.08,4282.92
SPECIALTY PRODUCTS,01-East,764821000,4794.00,1044.35,3749.65
SPECIALTY PRODUCTS,01-East,764822000,3948.00,861.38,3086.62
SPECIALTY PRODUCTS,01-East,735655000,3596.80,485.37,3111.43
SPECIALTY PRODUCTS,01-East,764521061,3424.00,2044.80,1379.20
SPECIALTY PRODUCTS,01-East,735657000,3168.00,768.43,2399.57
SPECIALTY PRODUCTS,01-East,768142000,3000.00,801.87,2198.13
SPECIALTY PRODUCTS,01-East,766162000,2522.00,1943.34,578.66
SPECIALTY PRODUCTS,01-East,733566200,2298.24,432.14,1866.10
SPECIALTY PRODUCTS,01-East,764561000,1560.00,443.54,1116.46
SPECIALTY PRODUCTS,01-East,733511000,331.88,66.56,265.32
SPECIALTY PRODUCTS,01-East,764761000,260.00,61.42,198.58
SPIRIT AEROSYSTEMS INC.,01-East,734211200,229.68,22.84,206.84
SPX FLOW TECHNOLOGY,01-East,720455000,1569.60,142.31,1427.29
ST GEORGE STEEL CO,01-East,777601000,677.52,117.59,559.93
ST. GEORGE STEEL,01-East,768221000,7410.00,3660.00,3750.00
ST. GEORGE STEEL,01-East,768241000,6550.00,3425.78,3124.22
STANDARD INDUSTRIAL SUPPL,01-East,770722000,1409.00,42.88,1366.12
STATE INSULATION CORP,01-East,764901000,32690.00,10977.87,21712.13
STATE OF DELAWARE,01-East,768144200,2088.00,105.28,1982.72
STATE OF DELAWARE,01-East,771810000,207.50,48.80,158.70
STEBBINS ENGINEERING & MF,01-East,733525600,1875.00,213.14,1661.86
STEBBINS ENGINEERING & MF,01-East,733525500,1390.00,130.06,1259.94
STEBBINS ENGINEERING & MF,01-East,733444100,1328.00,300.17,1027.83
STEBBINS ENGINEERING & MF,01-East,733464500,626.00,65.05,560.95
STUDIO BEL VETRO,01-East,739370000,379.52,138.24,241.28
STULLER SETTINGS INC.,01-East,770200000,666.00,42.41,623.59
SUMMIT CONTROLS INC.,01-East,734990000,4152.00,547.75,3604.25
SUNDANCE DIE CUT LLC,01-East,733151500,472.00,54.11,417.89
SUNSHINE GLASS WORKS,01-East,733464500,1207.00,121.09,1085.91
SUNSHINE GLASS WORKS,01-East,739561000,492.48,86.19,406.29
SUPER CONDUCTOR MTLS.,01-East,764821000,2631.00,93.06,2537.94
SURFACE COMBUSTION INC,01-East,770540000,1293.08,89.80,1203.28
SW ANDERSON SALES,01-East,735602000,737747.25,268120.39,469626.86
SW ANDERSON SALES,01-East,735602110,14624.00,5684.26,8939.74
SW ANDERSON SALES,01-East,768121000,3142.40,842.50,2299.90
SW ANDERSON SALES,01-East,735752000,2975.00,1095.48,1879.52
SWANSON DISTRIBUTING,01-East,733464600,924.00,101.25,822.75
TASE/HB-MEDTEK,01-East,737902100,667.00,101.30,565.70
TAYLOR DEVICES,01-East,764521000,320.00,13.30,306.70
"TAYLOR-SEIDENBACH, INC.",01-East,735602110,38668.40,10122.46,28545.94
"TAYLOR-SEIDENBACH, INC.",01-East,768241600,22410.00,7313.81,15096.19
"TAYLOR-SEIDENBACH, INC.",01-East,735602000,19547.00,4995.21,14551.79
"TAYLOR-SEIDENBACH, INC.",01-East,768241500,14960.00,4869.65,10090.35
"TAYLOR-SEIDENBACH, INC.",01-East,735750000,1896.00,508.42,1387.58
"TAYLOR-SEIDENBACH, INC.",01-East,768243600,1620.00,633.25,986.75
"TAYLOR-SEIDENBACH, INC.",01-East,735752000,948.00,257.76,690.24
TERRA INTERNATIONAL,01-East,734311100,404.36,35.30,369.06
TEXAS STREET,01-East,734990000,3321.60,438.20,2883.40
TEXCAST INC,01-East,734311100,3624.06,320.18,3303.88
TEXCAST INC,01-East,733141200,1016.00,126.95,889.05
TEXCO,01-East,718501000,5525.00,680.30,4844.70
TEXCO,01-East,774901000,427.20,48.62,378.58
TEXCO,01-East,774631000,412.80,46.56,366.24
TEXTRON FINANCE SHARED,01-East,734351200,917.85,69.73,848.12
THE BELL GROUP,01-East,734311100,1525.92,134.81,1391.11
THE BELL GROUP,01-East,727000140,888.00,63.35,824.65
THE BELL GROUP,01-East,733721000,572.00,97.79,474.21
THE BERND GROUP INC.,01-East,764481000,1038.00,50.82,987.18
THE BERND GROUP INC.,01-East,733444100,996.00,225.13,770.87
THE BERND GROUP INC.,01-East,764461006,388.00,27.25,360.75
THE CLAREMONT SALES CORP.,01-East,739561200,10783.36,2223.42,8559.94
THE CLAREMONT SALES CORP.,01-East,764482000,1728.00,255.71,1472.29
THE CLAREMONT SALES CORP.,01-East,734990000,787.44,110.08,677.36
THE CLAREMONT SALES CORP.,01-East,770260000,680.40,67.63,612.77
THE CLAREMONT SALES CORP.,01-East,739741100,350.08,109.56,240.52
THE MINE SUPPLY CO,01-East,733721000,1458.00,285.33,1172.67
"THE N.P. GROUP, INC.",01-East,779987650,4827.60,1183.63,3643.97
"THE NP GROUP, INC.",01-East,779987650,18576.00,4142.56,14433.44
"THE NP GROUP, INC.",01-East,724003000,383.50,81.82,301.68
THE R B BIRGE CO,01-East,733525600,752.50,71.05,681.45
"THE SCHAEFER GROUP, INC.",01-East,777902700,38252.34,20221.83,18030.51
"THE SCHAEFER GROUP, INC.",01-East,733211000,502.60,106.40,396.20
"THE SCHAEFER GROUP, INC.",01-East,739561200,278.91,76.67,202.24
THE TRANE CO,01-East,733525500,717.50,65.03,652.47
THERMAL NORTHWEST,01-East,724311200,366.50,94.19,272.31
THERMAL SOLUTIONS PRODUCT,01-East,726000240,9204.00,908.61,8295.39
THERMAL STRUCTURES,01-East,732820000,568241.79,44616.14,523625.65
THERMAL STRUCTURES,01-East,732830000,149046.52,13131.66,135914.86
THERMAL STRUCTURES,01-East,791104295,136666.97,38406.72,98260.25
THERMAL STRUCTURES,01-East,732771000,51728.54,3530.39,48198.15
THERMAL STRUCTURES,01-East,724101400,38064.00,8884.38,29179.62
THERMAL STRUCTURES,01-East,724261400,33455.40,6816.25,26639.15
THERMAL STRUCTURES,01-East,732905000,29559.59,2196.99,27362.60
THERMAL STRUCTURES,01-East,724281400,27616.20,7916.28,19699.92
THERMAL STRUCTURES,01-East,791106479,26849.05,6343.90,20505.15
THERMAL STRUCTURES,01-East,733525600,23351.50,3178.11,20173.39
THERMAL STRUCTURES,01-East,733444300,11139.60,2083.23,9056.37
THERMAL STRUCTURES,01-East,724121400,10462.00,3598.11,6863.89
THERMAL STRUCTURES,01-East,724401150,8048.40,1212.97,6835.43
THERMAL STRUCTURES,01-East,779989967,5431.47,1213.51,4217.96
THERMAL STRUCTURES,01-East,791104480,4452.72,1110.02,3342.70
THERMAL STRUCTURES,01-East,791104880,975.11,180.59,794.52
THERMAL STRUCTURES,01-East,733383000,910.80,163.93,746.87
THERMAL STRUCTURES,01-East,733141300,775.60,155.94,619.66
THERMAL STRUCTURES,01-East,733141200,756.80,119.94,636.86
THERMAL STRUCTURES,01-East,734611100,161.02,27.18,133.84
THERMO CONTROL HEATING,01-East,764901000,2707.50,481.73,2225.77
THERMO FISHER ENVIR.,01-East,724281200,2346.00,332.78,2013.22
THERMO FISHER SCIENTIFIC,01-East,738361000,626.04,308.88,317.16
THERMOMETRICS CORPORATION,01-East,766081010,2202.00,72.74,2129.26
THERMOMETRICS CORPORATION,01-East,727000140,265.60,16.71,248.89
THERMTREND INC.,01-East,733403200,1133.00,99.17,1033.83
THERMTREND INC.,01-East,770810000,845.00,17.19,827.81
THORPE PRODUCTS,01-East,735602000,106490.00,33828.15,72661.85
THORPE PRODUCTS,01-East,735862000,9100.00,3598.17,5501.83
THORPE PRODUCTS,01-East,768142074,6451.50,2558.12,3893.38
THORPE PRODUCTS,01-East,768241100,6200.00,2277.23,3922.77
THORPE PRODUCTS,01-East,735602110,3740.00,1071.04,2668.96
THORPE PRODUCTS,01-East,735752000,2600.00,889.19,1710.81
THORPE PRODUCTS,01-East,735750000,2180.00,755.37,1424.63
THORPE TECHNOLOGIES INC.,01-East,770340000,3075.75,305.15,2770.60
THORPE TECHNOLOGIES INC.,01-East,770330000,2130.00,202.93,1927.07
THORPE TECHNOLOGIES INC.,01-East,770410000,1585.00,102.99,1482.01
THORPE TECHNOLOGIES INC.,01-East,770140000,1177.56,76.74,1100.82
THORPE TECHNOLOGIES INC.,01-East,770520000,906.00,48.32,857.68
TIGHITCO LATINOAMERICA,01-East,733525600,5252.50,529.48,4723.02
TIGHITCO LATINOAMERICA,01-East,724281200,4195.00,1384.90,2810.10
TIGHITCO LATINOAMERICA,01-East,724101400,1134.00,229.59,904.41
TIGHITCO LATINOAMERICA,01-East,724281410,894.00,339.83,554.17
TIGHITCO LATINOAMERICA,01-East,733444300,799.20,123.75,675.45
TIGHITCO LATINOAMERICA,01-East,733423800,310.40,38.28,272.12
TIMSCO INTERNATIONAL,01-East,734830000,3420.00,454.10,2965.90
"TK HOLDINGS, INC.",01-East,733030800,255496.50,114509.22,140987.28
TOBIASON STUDIO,01-East,739561100,552.72,93.32,459.40
TOLEDO ENGINEERING CO INC,01-East,777750076,8697.60,4632.00,4065.60
TOLEDO ENGINEERING CO INC,01-East,795763000,6606.60,1950.48,4656.12
TOLEDO ENGINEERING CO INC,01-East,795762000,4340.40,1212.38,3128.02
TOLEDO ENGINEERING CO INC,01-East,777763000,2246.64,1797.84,448.80
TOLEDO ENGINEERING CO INC,01-East,793260000,728.00,159.84,568.16
TOLEDO ENGINEERING CO INC,01-East,793260074,152.00,81.66,70.34
TORTOLITA CREATIONS,01-East,733151500,958.00,54.11,903.89
TOWANDA METADYNE,01-East,764521000,1306.00,53.13,1252.87
TRACHTE BUILDING SYSTEMS,01-East,764601000,1498.80,315.80,1183.00
TRANSCO,01-East,764703110,10492.44,3665.52,6826.92
TRANSCO,01-East,764643052,4490.64,1501.95,2988.69
TREIBACHER SCHLEIFMITTEL,01-East,764821000,430.00,15.36,414.64
TRI-CITY INSULATION DIST.,01-East,735602000,113975.00,41332.68,72642.32
TRI-CITY INSULATION DIST.,01-East,735750000,1700.00,661.12,1038.88
TRI-CITY INSULATION DIST.,01-East,764601000,592.00,105.27,486.73
TRIDENT DISTRIBUTION,01-East,735602000,15960.00,4832.60,11127.40
TRIDENT DISTRIBUTION,01-East,735602110,13680.00,4284.15,9395.85
TRIDENT DISTRIBUTION,01-East,735752000,3400.00,1367.98,2032.02
TRINITY INDUSTRIES INC,01-East,764703110,2541045.92,854580.05,1686465.87
TRINITY INDUSTRIES INC,01-East,764633510,357725.36,95000.01,262725.35
TRINITY INDUSTRIES INC,01-East,764706110,22374.24,7479.56,14894.68
TRIPLEX INC,01-East,770830000,10406.00,339.14,10066.86
TRIPLEX INC,01-East,771132000,2266.00,79.02,2186.98
TRIUMPH INSULATION,01-East,733648000,29800976.28,18922952.69,10878023.59
TRIUMPH INSULATION,01-East,733649000,5645789.16,3636906.92,2008882.24
TRIUMPH INSULATION,01-East,733640000,145765.80,61147.00,84618.80
TRULITE GLASS & ALUMINUM,01-East,739741100,12854.40,5049.60,7804.80
TRULITE GLASS & ALUMINUM,01-East,734651200,584.00,73.50,510.50
TTM TECHNOLOGIES INC.,01-East,733525500,1505.00,140.78,1364.22
TURPEN & ASSOCIATES,01-East,770540000,3629.50,184.78,3444.72
TWIN CC SUPPLY,01-East,777601000,409.92,58.80,351.12
U S ARTIST SERVICE,01-East,777601000,386.72,72.02,314.70
"UNICAL AVIATION, INC.",01-East,732820000,3494.40,147.02,3347.38
UNICIRCUIT INC.,01-East,733464500,4828.00,484.36,4343.64
UNICIRCUIT INC.,01-East,733525500,2190.00,211.17,1978.83
UNILUX ADVANCED,01-East,764341000,45277.50,24555.05,20722.45
UNILUX ADVANCED,01-East,764261000,20136.00,10782.65,9353.35
UNITED CHIMNEY,01-East,734611100,178.72,26.96,151.76
UNITED MATERIALS INT'L,01-East,727000140,780.00,217.28,562.72
UNITED MECHANICAL & METAL,01-East,774191000,810.00,288.86,521.14
UNITED MECHANICAL & METAL,01-East,774401000,726.00,220.42,505.58
UNITED MECHANICAL & METAL,01-East,774141000,265.00,77.28,187.72
UNIVERSITY OF ALABAMA,01-East,764221000,223.00,16.92,206.08
UNIVERSITY OF ARKANSAS,01-East,764301000,167.50,9.28,158.22
UNIVERSITY OF CONNECTICUT,01-East,734311100,381.48,33.70,347.78
UPSTATE REFRACTORY SERVIC,01-East,764821000,100462.50,39653.64,60808.86
UPSTATE REFRACTORY SERVIC,01-East,766121000,65370.40,19817.36,45553.04
UPSTATE REFRACTORY SERVIC,01-East,764901000,36433.50,14832.31,21601.19
UPSTATE REFRACTORY SERVIC,01-East,766126500,34384.00,7774.34,26609.66
UPSTATE REFRACTORY SERVIC,01-East,779980240,34339.86,23057.79,11282.07
UPSTATE REFRACTORY SERVIC,01-East,779980223,31171.14,21368.85,9802.29
UPSTATE REFRACTORY SERVIC,01-East,766162000,25765.05,9797.80,15967.25
UPSTATE REFRACTORY SERVIC,01-East,764821073,18000.00,14580.00,3420.00
UPSTATE REFRACTORY SERVIC,01-East,781670000,16953.03,8281.68,8671.35
UPSTATE REFRACTORY SERVIC,01-East,770560000,16165.00,2678.28,13486.72
UPSTATE REFRACTORY SERVIC,01-East,777601000,15275.96,5387.01,9888.95
UPSTATE REFRACTORY SERVIC,01-East,780290000,13764.59,5681.14,8083.45
UPSTATE REFRACTORY SERVIC,01-East,764040000,13180.32,2743.44,10436.88
UPSTATE REFRACTORY SERVIC,01-East,737321000,12520.80,10520.64,2000.16
UPSTATE REFRACTORY SERVIC,01-East,739681100,11068.17,5420.70,5647.47
UPSTATE REFRACTORY SERVIC,01-East,779959551,10623.00,3469.92,7153.08
UPSTATE REFRACTORY SERVIC,01-East,764781040,10610.10,4147.41,6462.69
UPSTATE REFRACTORY SERVIC,01-East,782070000,10357.20,5412.89,4944.31
UPSTATE REFRACTORY SERVIC,01-East,733525600,9625.00,1501.94,8123.06
UPSTATE REFRACTORY SERVIC,01-East,770530000,9145.25,1455.66,7689.59
UPSTATE REFRACTORY SERVIC,01-East,779957701,8352.00,3949.47,4402.53
UPSTATE REFRACTORY SERVIC,01-East,755107000,8304.08,2404.09,5899.99
UPSTATE REFRACTORY SERVIC,01-East,733525500,8218.00,1262.38,6955.62
UPSTATE REFRACTORY SERVIC,01-East,779951021,8200.50,4161.51,4038.99
UPSTATE REFRACTORY SERVIC,01-East,764901073,7080.00,5832.00,1248.00
UPSTATE REFRACTORY SERVIC,01-East,779957700,6733.80,3585.66,3148.14
UPSTATE REFRACTORY SERVIC,01-East,733221200,6462.24,1819.95,4642.29
UPSTATE REFRACTORY SERVIC,01-East,754495000,5897.20,2875.45,3021.75
UPSTATE REFRACTORY SERVIC,01-East,777904301,5702.61,2584.30,3118.31
UPSTATE REFRACTORY SERVIC,01-East,764822000,5580.00,1928.47,3651.53
UPSTATE REFRACTORY SERVIC,01-East,733151500,5412.40,948.26,4464.14
UPSTATE REFRACTORY SERVIC,01-East,764861000,5267.25,2390.71,2876.54
UPSTATE REFRACTORY SERVIC,01-East,737902100,5142.80,1488.99,3653.81
UPSTATE REFRACTORY SERVIC,01-East,733211000,4789.80,1088.20,3701.60
UPSTATE REFRACTORY SERVIC,01-East,737912200,4772.50,1084.03,3688.47
UPSTATE REFRACTORY SERVIC,01-East,781630000,4176.00,2233.14,1942.86
UPSTATE REFRACTORY SERVIC,01-East,754286000,3886.85,1669.09,2217.76
UPSTATE REFRACTORY SERVIC,01-East,779951023,3727.50,2131.95,1595.55
UPSTATE REFRACTORY SERVIC,01-East,777903700,3692.82,1512.27,2180.55
UPSTATE REFRACTORY SERVIC,01-East,718501000,3496.50,948.31,2548.19
UPSTATE REFRACTORY SERVIC,01-East,764601000,3342.55,1074.13,2268.42
UPSTATE REFRACTORY SERVIC,01-East,718500000,3196.80,858.84,2337.96
UPSTATE REFRACTORY SERVIC,01-East,754467000,2668.08,1502.16,1165.92
UPSTATE REFRACTORY SERVIC,01-East,764902000,2655.00,961.17,1693.83
UPSTATE REFRACTORY SERVIC,01-East,764781000,2572.50,1009.41,1563.09
UPSTATE REFRACTORY SERVIC,01-East,764342110,2554.50,1582.43,972.07
UPSTATE REFRACTORY SERVIC,01-East,781590000,2523.10,1182.42,1340.68
UPSTATE REFRACTORY SERVIC,01-East,739561200,2135.04,867.98,1267.06
UPSTATE REFRACTORY SERVIC,01-East,739561100,2049.76,599.74,1450.02
UPSTATE REFRACTORY SERVIC,01-East,780370000,1958.88,776.12,1182.76
UPSTATE REFRACTORY SERVIC,01-East,733211050,1865.30,441.07,1424.23
UPSTATE REFRACTORY SERVIC,01-East,764521000,1786.15,611.37,1174.78
UPSTATE REFRACTORY SERVIC,01-East,764906000,1770.00,654.37,1115.63
UPSTATE REFRACTORY SERVIC,01-East,738361000,1366.84,657.88,708.96
UPSTATE REFRACTORY SERVIC,01-East,733510000,1239.00,283.45,955.55
UPSTATE REFRACTORY SERVIC,01-East,734990000,1149.60,273.88,875.72
UPSTATE REFRACTORY SERVIC,01-East,782030000,1130.40,544.53,585.87
UPSTATE REFRACTORY SERVIC,01-East,767990000,1035.08,442.34,592.74
UPSTATE REFRACTORY SERVIC,01-East,764601500,969.00,2036.62,-1067.62
UPSTATE REFRACTORY SERVIC,01-East,755007000,950.76,349.89,600.87
UPSTATE REFRACTORY SERVIC,01-East,734900000,798.04,530.64,267.40
UPSTATE REFRACTORY SERVIC,01-East,739641100,772.20,412.29,359.91
UPSTATE REFRACTORY SERVIC,01-East,734611100,654.89,135.90,518.99
UPSTATE REFRACTORY SERVIC,01-East,764826200,600.00,298.76,301.24
UPSTATE REFRACTORY SERVIC,01-East,742520110,585.50,165.28,420.22
UPSTATE REFRACTORY SERVIC,01-East,770260000,544.50,67.63,476.87
UPSTATE REFRACTORY SERVIC,01-East,727000140,520.00,79.18,440.82
UPSTATE REFRACTORY SERVIC,01-East,734830000,492.00,89.29,402.71
UPSTATE REFRACTORY SERVIC,01-East,733221400,416.16,128.48,287.68
UPSTATE REFRACTORY SERVIC,01-East,764482000,412.20,156.62,255.58
UPSTATE REFRACTORY SERVIC,01-East,764827061,412.00,178.43,233.57
UPSTATE REFRACTORY SERVIC,01-East,777901300,374.78,147.04,227.74
UPSTATE REFRACTORY SERVIC,01-East,777650000,307.90,142.44,165.46
UPSTATE REFRACTORY SERVIC,01-East,734810000,272.20,217.84,54.36
UPSTATE REFRACTORY SERVIC,01-East,733741000,232.40,119.06,113.34
UPSTATE REFRACTORY SERVIC,01-East,764825000,198.00,64.56,133.44
UPSTATE REFRACTORY SERVIC,01-East,733725500,168.00,73.67,94.33
UPSTATE REFRACTORY SERVIC,01-East,764341000,149.53,58.39,91.14
UPSTATE REFRACTORY SERVIC,01-East,764301000,136.65,55.67,80.98
UPSTATE REFRACTORY SERVIC,01-East,764762000,135.20,60.58,74.62
UPSTATE REFRACTORY SERVIC,01-East,764561000,129.50,42.00,87.50
UPSTATE REFRACTORY SERVIC,01-East,779957258,75.60,47.96,27.64
UPSTATE REFRACTORY SERVIC,01-East,764761000,70.40,31.86,38.54
UPSTATE REFRACTORY SERVIC,01-East,764261000,58.20,22.90,35.30
US JOINER LLC,01-East,768127000,1000.00,293.56,706.44
US JOINER LLC,01-East,768111200,580.00,157.21,422.79
"US TOOL GRINDING, INC.",01-East,739781100,1232.16,491.52,740.64
USDA FOREST SERVICE,01-East,738521000,556.48,298.56,257.92
UTLX CARROTANQUES,01-East,764643052,359.55,166.88,192.67
UTLX MANUFACTURING INC.,01-East,764643054,727058.28,339146.10,387912.18
UTLX MANUFACTURING INC.,01-East,764643052,424628.55,197089.60,227538.95
UTLX MANUFACTURING INC.,01-East,764752120,29726.40,7388.55,22337.85
UTLX MANUFACTURING,01-East,764643054,851928.72,397436.56,454492.16
UTLX MANUFACTURING,01-East,764643052,429302.70,199259.09,230043.61
UTLX MANUFACTURING,01-East,764901000,2211.25,80.07,2131.18
V-J ELECTRONIC ASSEMBLIES,01-East,740103000,792.96,115.03,677.93
V-J ELECTRONIC ASSEMBLIES,01-East,740123000,670.32,121.37,548.95
VA MEDICAL CENTER,01-East,770222000,1022.25,65.43,956.82
VA MEDICAL CENTER,01-East,770520000,958.50,41.85,916.65
VALERO OIL,01-East,734830000,6840.00,908.20,5931.80
"VAN HORN, METZ & CO",01-East,779987651,13392.50,2310.46,11082.04
"VAN HORN, METZ & CO",01-East,720219100,1208.55,45.84,1162.71
VASTEX INTERNATIONAL,01-East,733725500,300.00,73.67,226.33
VESUVIUS USA,01-East,764520500,20000.00,9265.58,10734.42
VESUVIUS USA,01-East,764521000,12000.00,6382.59,5617.41
VISHAY DALE ELECTRONICS,01-East,734030000,17464.50,3847.08,13617.42
VISUAL COMMUNICATION,01-East,777587000,4791.26,192.16,4599.10
VITRO AUTOMOTRIZ SA DE CV,01-East,733525600,1470.00,136.54,1333.46
VITRO VIDRIO Y CRISTAL,01-East,779571000,22545.15,5551.20,16993.95
W. D. EQUIPMENT CO.,01-East,738542000,13711.68,5081.61,8630.07
W. D. EQUIPMENT CO.,01-East,764526600,3960.00,1827.94,2132.06
W. D. EQUIPMENT CO.,01-East,739781100,2115.36,856.89,1258.47
W. D. EQUIPMENT CO.,01-East,764526500,1832.60,645.21,1187.39
W. D. EQUIPMENT CO.,01-East,764481000,1274.00,351.77,922.23
W. D. EQUIPMENT CO.,01-East,764521000,1225.40,292.54,932.86
W. D. EQUIPMENT CO.,01-East,764526520,965.25,346.76,618.49
W. D. EQUIPMENT CO.,01-East,766081010,837.00,109.11,727.89
W. D. EQUIPMENT CO.,01-East,739681100,793.26,361.38,431.88
W. D. EQUIPMENT CO.,01-East,738561000,708.48,248.27,460.21
W. D. EQUIPMENT CO.,01-East,774631000,486.00,52.77,433.23
W. D. EQUIPMENT CO.,01-East,764523020,457.60,111.09,346.51
W. D. EQUIPMENT CO.,01-East,774901000,453.00,59.34,393.66
W. D. EQUIPMENT CO.,01-East,737912200,444.55,86.72,357.83
W. D. EQUIPMENT CO.,01-East,739561000,292.82,86.19,206.63
W. D. EQUIPMENT CO.,01-East,739661100,265.68,150.50,115.18
W. D. EQUIPMENT CO.,01-East,764526610,114.40,52.48,61.92
WAHL REFRACTORIES,01-East,733510000,591.00,212.59,378.41
WAHOO,01-East,720252303,1150.50,26.51,1123.99
WALL COLMONOY CORP,01-East,733510000,2715.00,362.96,2352.04
WALTER MORENO,01-East,768144200,271.50,13.16,258.34
WARDELL  PUBLICATIONS INC,01-East,733671000,3071.04,351.87,2719.17
WASHINGTON MILLS,01-East,724281200,4091.00,576.48,3514.52
WASHINGTON MILLS,01-East,733464500,611.00,62.05,548.95
WASHINGTON MILLS,01-East,764781000,454.00,20.45,433.55
WASHINGTON MILLS,01-East,764901000,425.25,16.14,409.11
WASHINGTON UNIVERISTY IN,01-East,737912200,326.50,45.47,281.03
WATT FUEL CELL,01-East,732820000,3494.40,147.02,3347.38
"WELCO ELECTRONICS, INC.",01-East,770720000,3528.00,108.47,3419.53
"WELCO ELECTRONICS, INC.",01-East,771500000,2911.50,165.39,2746.11
"WELCO ELECTRONICS, INC.",01-East,734890000,2365.54,397.92,1967.62
"WELCO ELECTRONICS, INC.",01-East,764301000,1647.00,114.40,1532.60
WELSH RESTAURANT EQUIP.,01-East,735602000,4852.50,1158.30,3694.20
WENDT CORPORATION,01-East,739451000,428.48,111.56,316.92
WESCO DISTRIBUTION INC.,01-East,739641100,463.32,137.43,325.89
WESCO DISTRIBUTION INC.,01-East,777601000,386.42,72.02,314.40
WESCO DISTRIBUTION INC,01-East,770210000,440.00,30.32,409.68
WEST FRASER MILLS,01-East,770220000,15831.00,915.60,14915.40
WEST FRASER MILLS,01-East,770222000,1080.25,68.04,1012.21
WEST METAL WORKS,01-East,766162000,1822.00,51.84,1770.16
WESTERN INDUSTRIAL CERAMI,01-East,764521300,1198557.20,613886.53,584670.67
WESTERN INDUSTRIAL CERAMI,01-East,726000062,333453.00,161980.73,171472.27
WESTERN INDUSTRIAL CERAMI,01-East,768221120,275946.04,165728.29,110217.75
WESTERN INDUSTRIAL CERAMI,01-East,764901005,262352.00,147579.22,114772.78
WESTERN INDUSTRIAL CERAMI,01-East,733571500,249551.25,195969.57,53581.68
WESTERN INDUSTRIAL CERAMI,01-East,766121000,215670.00,99453.46,116216.54
WESTERN INDUSTRIAL CERAMI,01-East,764821000,184352.00,76468.68,107883.32
WESTERN INDUSTRIAL CERAMI,01-East,768251220,128644.92,72452.40,56192.52
WESTERN INDUSTRIAL CERAMI,01-East,768222000,119318.00,41060.42,78257.58
WESTERN INDUSTRIAL CERAMI,01-East,764901000,117740.00,63552.63,54187.37
WESTERN INDUSTRIAL CERAMI,01-East,768111120,102718.53,64986.48,37732.05
WESTERN INDUSTRIAL CERAMI,01-East,764902000,101480.50,54034.22,47446.28
WESTERN INDUSTRIAL CERAMI,01-East,726020039,100536.75,67551.51,32985.24
WESTERN INDUSTRIAL CERAMI,01-East,768221122,98224.00,51040.90,47183.10
WESTERN INDUSTRIAL CERAMI,01-East,734311100,94782.29,20601.96,74180.33
WESTERN INDUSTRIAL CERAMI,01-East,764902051,84972.00,40268.16,44703.84
WESTERN INDUSTRIAL CERAMI,01-East,733525600,80782.50,22872.66,57909.84
WESTERN INDUSTRIAL CERAMI,01-East,764521000,77696.00,37849.20,39846.80
WESTERN INDUSTRIAL CERAMI,01-East,768221110,73853.24,62245.57,11607.67
WESTERN INDUSTRIAL CERAMI,01-East,764482000,71689.60,29375.46,42314.14
WESTERN INDUSTRIAL CERAMI,01-East,727000140,66629.52,21043.73,45585.79
WESTERN INDUSTRIAL CERAMI,01-East,768211000,65860.00,27419.87,38440.13
WESTERN INDUSTRIAL CERAMI,01-East,768112030,65195.56,42299.81,22895.75
WESTERN INDUSTRIAL CERAMI,01-East,734173000,61518.60,14959.97,46558.63
WESTERN INDUSTRIAL CERAMI,01-East,768222530,59909.41,38340.26,21569.15
WESTERN INDUSTRIAL CERAMI,01-East,763991000,51600.24,32683.76,18916.48
WESTERN INDUSTRIAL CERAMI,01-East,764522000,50396.00,24257.22,26138.78
WESTERN INDUSTRIAL CERAMI,01-East,733525550,47906.00,18417.67,29488.33
WESTERN INDUSTRIAL CERAMI,01-East,734830000,47793.10,9366.91,38426.19
WESTERN INDUSTRIAL CERAMI,01-East,764481000,46191.60,18985.92,27205.68
WESTERN INDUSTRIAL CERAMI,01-East,764900005,46176.00,21979.10,24196.90
WESTERN INDUSTRIAL CERAMI,01-East,739457000,43930.80,34914.07,9016.73
WESTERN INDUSTRIAL CERAMI,01-East,777601000,42513.97,14658.36,27855.61
WESTERN INDUSTRIAL CERAMI,01-East,734990000,40372.80,11252.79,29120.01
WESTERN INDUSTRIAL CERAMI,01-East,737321000,35541.12,14259.28,21281.84
WESTERN INDUSTRIAL CERAMI,01-East,734211200,29172.39,4162.93,25009.46
WESTERN INDUSTRIAL CERAMI,01-East,764822000,27328.00,11317.52,16010.48
WESTERN INDUSTRIAL CERAMI,01-East,764781040,27109.00,11216.76,15892.24
WESTERN INDUSTRIAL CERAMI,01-East,740361900,25569.60,12288.69,13280.91
WESTERN INDUSTRIAL CERAMI,01-East,737251000,24447.02,5713.13,18733.89
WESTERN INDUSTRIAL CERAMI,01-East,737301000,24395.28,12570.56,11824.72
WESTERN INDUSTRIAL CERAMI,01-East,779951186,24298.56,8906.05,15392.51
WESTERN INDUSTRIAL CERAMI,01-East,734910000,23742.90,5607.19,18135.71
WESTERN INDUSTRIAL CERAMI,01-East,733151500,23491.20,5432.97,18058.23
WESTERN INDUSTRIAL CERAMI,01-East,727000060,21600.00,11913.27,9686.73
WESTERN INDUSTRIAL CERAMI,01-East,768021000,20998.00,6840.28,14157.72
WESTERN INDUSTRIAL CERAMI,01-East,764521320,20935.00,10598.80,10336.20
WESTERN INDUSTRIAL CERAMI,01-East,768222112,20467.00,14061.07,6405.93
WESTERN INDUSTRIAL CERAMI,01-East,766162000,20280.00,12477.47,7802.53
WESTERN INDUSTRIAL CERAMI,01-East,768212000,17568.00,7459.28,10108.72
WESTERN INDUSTRIAL CERAMI,01-East,726000080,16848.00,8178.50,8669.50
WESTERN INDUSTRIAL CERAMI,01-East,764222010,16632.00,7360.83,9271.17
WESTERN INDUSTRIAL CERAMI,01-East,739541000,16547.60,3676.05,12871.55
WESTERN INDUSTRIAL CERAMI,01-East,780730000,16331.29,7017.01,9314.28
WESTERN INDUSTRIAL CERAMI,01-East,764462210,16158.24,7617.71,8540.53
WESTERN INDUSTRIAL CERAMI,01-East,733211000,16118.60,8088.99,8029.61
WESTERN INDUSTRIAL CERAMI,01-East,764821073,15400.00,14580.00,820.00
WESTERN INDUSTRIAL CERAMI,01-East,733211050,15176.40,7516.61,7659.79
WESTERN INDUSTRIAL CERAMI,01-East,733484900,14825.60,2633.09,12192.51
WESTERN INDUSTRIAL CERAMI,01-East,734351200,13897.39,2506.68,11390.71
WESTERN INDUSTRIAL CERAMI,01-East,733525500,13817.50,3120.09,10697.41
WESTERN INDUSTRIAL CERAMI,01-East,740402402,13723.00,3477.58,10245.42
WESTERN INDUSTRIAL CERAMI,01-East,779988827,13144.23,6018.14,7126.09
WESTERN INDUSTRIAL CERAMI,01-East,738361000,12093.48,9017.70,3075.78
WESTERN INDUSTRIAL CERAMI,01-East,739641100,12052.51,6780.55,5271.96
WESTERN INDUSTRIAL CERAMI,01-East,738521000,11274.88,7359.09,3915.79
WESTERN INDUSTRIAL CERAMI,01-East,734890000,11205.60,3183.42,8022.18
WESTERN INDUSTRIAL CERAMI,01-East,724281200,10200.00,2319.40,7880.60
WESTERN INDUSTRIAL CERAMI,01-East,727041000,10108.80,1353.81,8754.99
WESTERN INDUSTRIAL CERAMI,01-East,715000000,10088.50,5487.52,4600.98
WESTERN INDUSTRIAL CERAMI,01-East,740382201,9726.50,3579.19,6147.31
WESTERN INDUSTRIAL CERAMI,01-East,733151600,8943.20,2035.31,6907.89
WESTERN INDUSTRIAL CERAMI,01-East,727041200,8463.36,1905.36,6558.00
WESTERN INDUSTRIAL CERAMI,01-East,764461006,7391.60,2580.56,4811.04
WESTERN INDUSTRIAL CERAMI,01-East,734251300,6790.12,701.68,6088.44
WESTERN INDUSTRIAL CERAMI,01-East,733586600,6652.80,1082.62,5570.18
WESTERN INDUSTRIAL CERAMI,01-East,737032000,6193.80,3337.20,2856.60
WESTERN INDUSTRIAL CERAMI,01-East,732620000,6008.38,1425.25,4583.13
WESTERN INDUSTRIAL CERAMI,01-East,764521073,5600.00,6450.00,-850.00
WESTERN INDUSTRIAL CERAMI,01-East,733505200,5150.00,1038.10,4111.90
WESTERN INDUSTRIAL CERAMI,01-East,764901073,4960.00,5832.00,-872.00
WESTERN INDUSTRIAL CERAMI,01-East,739741100,4916.16,3029.76,1886.40
WESTERN INDUSTRIAL CERAMI,01-East,777642000,4739.00,1846.72,2892.28
WESTERN INDUSTRIAL CERAMI,01-East,735750000,4416.00,1579.42,2836.58
WESTERN INDUSTRIAL CERAMI,01-East,738561000,4321.68,2196.15,2125.53
WESTERN INDUSTRIAL CERAMI,01-East,726000240,4248.16,2034.16,2214.00
WESTERN INDUSTRIAL CERAMI,01-East,730710500,4212.80,501.38,3711.42
WESTERN INDUSTRIAL CERAMI,01-East,734970000,3959.34,1809.26,2150.08
WESTERN INDUSTRIAL CERAMI,01-East,768131000,3900.00,1324.74,2575.26
WESTERN INDUSTRIAL CERAMI,01-East,764821161,3850.00,3720.00,130.00
WESTERN INDUSTRIAL CERAMI,01-East,792764000,3790.80,1289.34,2501.46
WESTERN INDUSTRIAL CERAMI,01-East,733383000,3650.24,655.72,2994.52
WESTERN INDUSTRIAL CERAMI,01-East,768232000,3560.00,1069.79,2490.21
WESTERN INDUSTRIAL CERAMI,01-East,733505300,3550.00,868.51,2681.49
WESTERN INDUSTRIAL CERAMI,01-East,733464500,3513.00,885.58,2627.42
WESTERN INDUSTRIAL CERAMI,01-East,768122000,3300.00,1398.18,1901.82
WESTERN INDUSTRIAL CERAMI,01-East,740063000,3238.86,949.46,2289.40
WESTERN INDUSTRIAL CERAMI,01-East,751365000,3223.20,1595.24,1627.96
WESTERN INDUSTRIAL CERAMI,01-East,764762000,3216.00,947.10,2268.90
WESTERN INDUSTRIAL CERAMI,01-East,733423600,3109.76,530.96,2578.80
WESTERN INDUSTRIAL CERAMI,01-East,768221000,3040.00,1039.44,2000.56
WESTERN INDUSTRIAL CERAMI,01-East,780630000,2946.72,1422.70,1524.02
WESTERN INDUSTRIAL CERAMI,01-East,764521061,2800.00,3195.00,-395.00
WESTERN INDUSTRIAL CERAMI,01-East,740283502,2666.62,413.99,2252.63
WESTERN INDUSTRIAL CERAMI,01-East,733141300,2545.60,824.70,1720.90
WESTERN INDUSTRIAL CERAMI,01-East,793336074,2490.00,1410.63,1079.37
WESTERN INDUSTRIAL CERAMI,01-East,737851000,2248.10,1326.40,921.70
WESTERN INDUSTRIAL CERAMI,01-East,768231000,1780.00,539.26,1240.74
WESTERN INDUSTRIAL CERAMI,01-East,764781000,1692.00,736.02,955.98
WESTERN INDUSTRIAL CERAMI,01-East,739541100,1679.32,334.28,1345.04
WESTERN INDUSTRIAL CERAMI,01-East,768241000,1589.00,479.62,1109.38
WESTERN INDUSTRIAL CERAMI,01-East,792730000,1400.70,375.36,1025.34
WESTERN INDUSTRIAL CERAMI,01-East,734751200,1234.71,224.34,1010.37
WESTERN INDUSTRIAL CERAMI,01-East,732429400,1224.10,133.82,1090.28
WESTERN INDUSTRIAL CERAMI,01-East,764261000,1105.20,276.56,828.64
WESTERN INDUSTRIAL CERAMI,01-East,780230000,1100.19,573.61,526.58
WESTERN INDUSTRIAL CERAMI,01-East,734900000,1017.40,421.98,595.42
WESTERN INDUSTRIAL CERAMI,01-East,767990000,924.48,339.72,584.76
WESTERN INDUSTRIAL CERAMI,01-East,733403200,920.04,99.17,820.87
WESTERN INDUSTRIAL CERAMI,01-East,738401000,828.90,642.24,186.66
WESTERN INDUSTRIAL CERAMI,01-East,741001000,735.93,114.14,621.79
WESTERN INDUSTRIAL CERAMI,01-East,770330000,700.25,96.65,603.60
WESTERN INDUSTRIAL CERAMI,01-East,780610000,690.75,370.02,320.73
WESTERN INDUSTRIAL CERAMI,01-East,780250000,637.92,311.69,326.23
WESTERN INDUSTRIAL CERAMI,01-East,771102000,579.70,22.98,556.72
WESTERN INDUSTRIAL CERAMI,01-East,734810000,544.40,435.66,108.74
WESTERN INDUSTRIAL CERAMI,01-East,777904301,519.18,234.94,284.24
WESTERN INDUSTRIAL CERAMI,01-East,733484800,510.40,102.72,407.68
WESTERN INDUSTRIAL CERAMI,01-East,724261200,467.60,78.20,389.40
WESTERN INDUSTRIAL CERAMI,01-East,733725500,422.00,125.43,296.57
WESTERN INDUSTRIAL CERAMI,01-East,770200000,413.10,42.41,370.69
WESTERN INDUSTRIAL CERAMI,01-East,738381000,396.00,346.80,49.20
WESTERN INDUSTRIAL CERAMI,01-East,770220000,293.40,27.35,266.05
WESTERN INDUSTRIAL CERAMI,01-East,770210000,285.00,31.52,253.48
WESTERN INDUSTRIAL CERAMI,01-East,733423700,232.96,36.22,196.74
WESTERN INDUSTRIAL CERAMI,01-East,733444200,214.00,34.69,179.31
WESTERN INDUSTRIAL CERAMI,01-East,777650000,178.00,142.44,35.56
WESTERN INDUSTRIAL CERAMI,01-East,793320074,63.25,30.73,32.52
WHITCRAFT LLC,01-East,791104481,2590.16,477.86,2112.30
WHITCRAFT LLC,01-East,791104260,2259.44,461.80,1797.64
WHITE CAP CORPORATION,01-East,768211000,2620.00,224.45,2395.55
WILT INDUSTRIES INC,01-East,737251000,16073.86,7345.62,8728.24
WILT INDUSTRIES INC,01-East,737311000,2358.72,1806.24,552.48
WILT INDUSTRIES INC,01-East,777601000,2136.00,432.12,1703.88
WIREROPE WORKS INC.,01-East,770200000,1809.00,127.24,1681.76
WIREROPE WORKS INC.,01-East,770220000,1341.00,85.31,1255.69
WOLSELEY INDUSTRIAL GROUP,01-East,770200000,11880.00,880.92,10999.08
WOLSELEY INDUSTRIAL GROUP,01-East,737311000,5444.64,2709.36,2735.28
WOLSELEY INDUSTRIAL GROUP,01-East,727000140,5364.00,475.08,4888.92
WOLSELEY INDUSTRIAL GROUP,01-East,724261200,826.00,98.66,727.34
WOLSELEY INDUSTRIAL GROUP,01-East,764481000,360.00,17.59,342.41
ZAR-TECH,01-East,764781022,374319.00,195172.03,179146.97
ZAR-TECH,01-East,764821000,330560.00,115977.26,214582.74
ZAR-TECH,01-East,740285000,330278.68,108912.50,221366.18
ZAR-TECH,01-East,740382201,206661.00,109433.07,97227.93
ZAR-TECH,01-East,766122000,140101.00,64769.53,75331.47
ZAR-TECH,01-East,740285200,119628.00,39686.80,79941.20
ZAR-TECH,01-East,764781000,116226.00,63298.07,52927.93
ZAR-TECH,01-East,740283401,115772.80,37949.75,77823.05
ZAR-TECH,01-East,766121000,111132.90,51513.89,59619.01
ZAR-TECH,01-East,734311100,106157.78,14376.34,91781.44
ZAR-TECH,01-East,764261000,83862.80,35460.88,48401.92
ZAR-TECH,01-East,777901100,71357.51,30864.85,40492.66
ZAR-TECH,01-East,739451010,69108.06,40733.22,28374.84
ZAR-TECH,01-East,764781040,57938.40,27253.42,30684.98
ZAR-TECH,01-East,726020005,56347.20,20255.64,36091.56
ZAR-TECH,01-East,739370000,53583.03,27483.60,26099.43
ZAR-TECH,01-East,733211000,53403.30,13094.45,40308.85
ZAR-TECH,01-East,777980000,47176.00,37744.00,9432.00
ZAR-TECH,01-East,733151500,46274.40,8538.25,37736.15
ZAR-TECH,01-East,733525600,45785.00,9799.71,35985.29
ZAR-TECH,01-East,739541005,45474.93,18556.49,26918.44
ZAR-TECH,01-East,779985554,45360.40,39206.89,6153.51
ZAR-TECH,01-East,768142000,42382.80,20055.68,22327.12
ZAR-TECH,01-East,768122000,40446.00,10307.71,30138.29
ZAR-TECH,01-East,738521000,36727.05,34655.40,2071.65
ZAR-TECH,01-East,766081010,35039.80,6008.08,29031.72
ZAR-TECH,01-East,792780000,31432.80,25023.04,6409.76
ZAR-TECH,01-East,739781100,31000.88,23841.26,7159.62
ZAR-TECH,01-East,739561200,30368.44,9504.37,20864.07
ZAR-TECH,01-East,715000300,29067.81,12817.18,16250.63
ZAR-TECH,01-East,727000025,28914.51,10047.09,18867.42
ZAR-TECH,01-East,777901300,27605.92,11564.69,16041.23
ZAR-TECH,01-East,739681100,26555.66,16077.29,10478.37
ZAR-TECH,01-East,733221200,26204.30,9518.86,16685.44
ZAR-TECH,01-East,739451000,24597.54,15476.50,9121.04
ZAR-TECH,01-East,739821010,24320.32,17138.24,7182.08
ZAR-TECH,01-East,764521000,24223.50,7271.58,16951.92
ZAR-TECH,01-East,727000140,22690.80,8455.68,14235.12
ZAR-TECH,01-East,764822024,22150.00,7942.54,14207.46
ZAR-TECH,01-East,737311000,22131.19,5822.77,16308.42
ZAR-TECH,01-East,768222000,21449.00,7676.17,13772.83
ZAR-TECH,01-East,737871000,21087.62,5129.18,15958.44
ZAR-TECH,01-East,733464500,20767.00,4886.10,15880.90
ZAR-TECH,01-East,777601000,20523.73,6589.13,13934.60
ZAR-TECH,01-East,764601000,19904.50,6005.22,13899.28
ZAR-TECH,01-East,733141200,16480.80,3670.68,12810.12
ZAR-TECH,01-East,754485000,15957.40,7933.25,8024.15
ZAR-TECH,01-East,777994000,15600.00,9057.84,6542.16
ZAR-TECH,01-East,793320074,15345.40,9339.40,6006.00
ZAR-TECH,01-East,777993000,14933.76,7804.09,7129.67
ZAR-TECH,01-East,730552560,14730.00,1917.37,12812.63
ZAR-TECH,01-East,763902000,14343.00,12710.70,1632.30
ZAR-TECH,01-East,764762000,14023.20,4131.73,9891.47
ZAR-TECH,01-East,764901000,13673.40,5019.01,8654.39
ZAR-TECH,01-East,768221000,12859.20,4273.76,8585.44
ZAR-TECH,01-East,737301000,12244.81,2944.32,9300.49
ZAR-TECH,01-East,764221000,12198.60,5163.15,7035.45
ZAR-TECH,01-East,768121000,12121.20,3142.17,8979.03
ZAR-TECH,01-East,739741100,12005.76,8107.28,3898.48
ZAR-TECH,01-East,724281400,11808.20,3243.83,8564.37
ZAR-TECH,01-East,764761000,11446.80,5436.52,6010.28
ZAR-TECH,01-East,733221010,10901.90,3048.74,7853.16
ZAR-TECH,01-East,737851000,10808.00,1661.10,9146.90
ZAR-TECH,01-East,764482000,10130.40,2554.99,7575.41
ZAR-TECH,01-East,779986490,9834.05,5211.53,4622.52
ZAR-TECH,01-East,734900000,9050.52,3964.99,5085.53
ZAR-TECH,01-East,739450000,8184.10,5598.02,2586.08
ZAR-TECH,01-East,733151600,8065.00,1482.09,6582.91
ZAR-TECH,01-East,739541000,7911.35,1960.39,5950.96
ZAR-TECH,01-East,737861000,7062.71,2134.26,4928.45
ZAR-TECH,01-East,739561000,7029.08,2237.16,4791.92
ZAR-TECH,01-East,734990000,6552.32,2191.03,4361.29
ZAR-TECH,01-East,734910000,6499.10,1766.72,4732.38
ZAR-TECH,01-East,793320000,6312.00,3537.96,2774.04
ZAR-TECH,01-East,781630000,6287.28,2054.50,4232.78
ZAR-TECH,01-East,718501000,6160.80,1083.00,5077.80
ZAR-TECH,01-East,738561000,6140.15,2482.75,3657.40
ZAR-TECH,01-East,764861000,5898.40,1992.13,3906.27
ZAR-TECH,01-East,732429400,5560.50,733.83,4826.67
ZAR-TECH,01-East,733725500,5430.00,2287.76,3142.24
ZAR-TECH,01-East,770510000,5296.00,358.36,4937.64
ZAR-TECH,01-East,781610000,5139.68,2887.99,2251.69
ZAR-TECH,01-East,734651200,4805.93,885.50,3920.43
ZAR-TECH,01-East,792702000,4760.00,2696.66,2063.34
ZAR-TECH,01-East,734151100,4389.08,920.16,3468.92
ZAR-TECH,01-East,739564300,4320.00,1377.59,2942.41
ZAR-TECH,01-East,770200000,4311.90,504.07,3807.83
ZAR-TECH,01-East,733403200,4183.08,592.36,3590.72
ZAR-TECH,01-East,770722000,3984.80,153.52,3831.28
ZAR-TECH,01-East,780240000,3709.36,1233.50,2475.86
ZAR-TECH,01-East,739641100,3650.40,2748.55,901.85
ZAR-TECH,01-East,780710000,3631.36,1341.90,2289.46
ZAR-TECH,01-East,734611100,3593.86,788.19,2805.67
ZAR-TECH,01-East,733510000,3451.50,850.35,2601.15
ZAR-TECH,01-East,764862000,3252.10,1202.20,2049.90
ZAR-TECH,01-East,777901000,2955.82,1059.41,1896.41
ZAR-TECH,01-East,739561100,2819.13,879.69,1939.44
ZAR-TECH,01-East,739661100,2211.84,1872.00,339.84
ZAR-TECH,01-East,764902000,2193.70,801.55,1392.15
ZAR-TECH,01-East,739541100,2182.32,501.42,1680.90
ZAR-TECH,01-East,784130000,2137.96,731.18,1406.78
ZAR-TECH,01-East,770450000,2101.20,194.68,1906.52
ZAR-TECH,01-East,771152000,2077.40,90.76,1986.64
ZAR-TECH,01-East,738361000,1540.08,986.82,553.26
ZAR-TECH,01-East,770330000,1400.25,193.29,1206.96
ZAR-TECH,01-East,770392000,1315.80,118.16,1197.64
ZAR-TECH,01-East,770710000,1315.80,33.62,1282.18
ZAR-TECH,01-East,782070000,1289.70,310.25,979.45
ZAR-TECH,01-East,770500000,1286.10,93.24,1192.86
ZAR-TECH,01-East,766162000,1211.10,776.40,434.70
ZAR-TECH,01-East,737912200,1184.40,363.80,820.60
ZAR-TECH,01-East,770570000,1143.68,100.73,1042.95
ZAR-TECH,01-East,770522000,1138.15,88.79,1049.36
ZAR-TECH,01-East,770520000,1102.50,96.64,1005.86
ZAR-TECH,01-East,770530000,1092.50,97.30,995.20
ZAR-TECH,01-East,779988827,1075.47,601.81,473.66
ZAR-TECH,01-East,770560000,1040.40,86.09,954.31
ZAR-TECH,01-East,770410000,1039.00,98.81,940.19
ZAR-TECH,01-East,770210000,1026.50,124.89,901.61
ZAR-TECH,01-East,733464400,1004.00,216.85,787.15
ZAR-TECH,01-East,730710500,914.00,141.92,772.08
ZAR-TECH,01-East,764266000,899.25,341.88,557.37
ZAR-TECH,01-East,764822000,886.00,317.70,568.30
ZAR-TECH,01-East,768211000,847.60,397.63,449.97
ZAR-TECH,01-East,764483600,840.00,381.92,458.08
ZAR-TECH,01-East,734211200,744.00,93.52,650.48
ZAR-TECH,01-East,740361701,740.88,628.78,112.10
ZAR-TECH,01-East,768133200,738.00,124.16,613.84
ZAR-TECH,01-East,734752000,730.30,181.71,548.59
ZAR-TECH,01-East,740402402,720.00,193.66,526.34
ZAR-TECH,01-East,734351200,702.14,69.73,632.41
ZAR-TECH,01-East,770372000,702.10,53.11,648.99
ZAR-TECH,01-East,733423600,697.92,107.70,590.22
ZAR-TECH,01-East,754107000,683.76,293.32,390.44
ZAR-TECH,01-East,733444200,670.80,115.92,554.88
ZAR-TECH,01-East,764483500,660.00,337.16,322.84
ZAR-TECH,01-East,770240000,647.25,67.63,579.62
ZAR-TECH,01-East,768111000,609.00,153.78,455.22
ZAR-TECH,01-East,770380000,594.80,61.29,533.51
ZAR-TECH,01-East,733141300,574.80,147.70,427.10
ZAR-TECH,01-East,754525000,566.88,236.19,330.69
ZAR-TECH,01-East,739541200,565.89,144.55,421.34
ZAR-TECH,01-East,777580000,558.79,27.00,531.79
ZAR-TECH,01-East,739452000,438.65,291.44,147.21
ZAR-TECH,01-East,734010000,397.50,90.61,306.89
ZAR-TECH,01-East,764562000,394.20,84.36,309.84
ZAR-TECH,01-East,732429300,385.40,49.22,336.18
ZAR-TECH,01-East,738381000,343.20,346.80,-3.60
ZAR-TECH,01-East,768212000,318.00,138.71,179.29
ZAR-TECH,01-East,779181000,308.25,107.25,201.00
ZAR-TECH,01-East,779986495,287.28,161.44,125.84
ZAR-TECH,01-East,768241000,282.50,85.64,196.86
ZAR-TECH,01-East,724311200,266.80,77.14,189.66
ZAR-TECH,01-East,779221000,239.16,58.84,180.32
ZAR-TECH,01-East,733444100,213.00,75.04,137.96
ZAR-TECH,01-East,764561000,197.10,42.00,155.10
ZAR-TECH,01-East,779201000,178.70,52.06,126.64
ZAR-TECH,01-East,793330000,174.30,53.02,121.28
ZAR-TECH,01-East,734830000,164.00,44.64,119.36
ZAR-TECH,01-East,727063000,120.00,31.84,88.16
ZAR-TECH,01-East,754007000,105.12,52.36,52.76
ZAR-TECH,01-East,766211000,72.50,22.61,49.89
ZIRCAR CERAMICS INC,01-East,727000046,97611.60,44203.42,53408.18
ZIRCAR CERAMICS INC,01-East,739681100,26944.56,18804.40,8140.16
ZIRCAR CERAMICS INC,01-East,739641100,20602.80,14138.05,6464.75
ZIRCAR CERAMICS INC,01-East,739781100,14172.32,8506.60,5665.72
ZIRCAR CERAMICS INC,01-East,777901100,14090.83,5611.79,8479.04
ZIRCAR CERAMICS INC,01-East,727031000,11100.00,6421.76,4678.24
ZIRCAR CERAMICS INC,01-East,764030000,9864.00,4454.10,5409.90
ZIRCAR CERAMICS INC,01-East,734311100,9060.95,1047.22,8013.73
ZIRCAR CERAMICS INC,01-East,739761100,7207.92,4281.51,2926.41
ZIRCAR CERAMICS INC,01-East,777901300,6183.42,2426.16,3757.26
ZIRCAR CERAMICS INC,01-East,734900000,5403.78,2110.44,3293.34
ZIRCAR CERAMICS INC,01-East,777901000,3152.07,1059.41,2092.66
ZIRCAR CERAMICS INC,01-East,733525600,2625.00,409.62,2215.38
ZIRCAR CERAMICS INC,01-East,763991000,2054.40,1100.66,953.74
ZIRCAR CERAMICS INC,01-East,739781101,1877.76,856.65,1021.11
ZIRCAR CERAMICS INC,01-East,734910000,1764.20,417.74,1346.46
ZIRCAR CERAMICS INC,01-East,739661100,1522.08,936.00,586.08
ZIRCAR CERAMICS INC,01-East,738521000,1415.10,1027.48,387.62
ZIRCAR CERAMICS INC,01-East,733484900,1405.60,285.71,1119.89
ZIRCAR CERAMICS INC,01-East,727031200,1100.40,504.78,595.62
ZIRCAR CERAMICS INC,01-East,793260074,924.00,398.34,525.66
ZIRCAR CERAMICS INC,01-East,733505300,910.00,170.30,739.70
ZIRCAR CERAMICS INC,01-East,734751200,654.48,113.13,541.35
ZIRCAR CERAMICS INC,01-East,733525500,448.00,70.39,377.61
ZIRCAR CERAMICS INC,01-East,733510000,302.25,79.51,222.74
ZIRCAR REFRAC. COMPOSITES,01-East,739741100,2803.84,1784.79,1019.05
ZIRCAR REFRAC. COMPOSITES,01-East,740283502,2666.62,413.99,2252.63
ZIRCAR REFRAC. COMPOSITES,01-East,738521000,2267.63,1302.71,964.92
ZIRCAR REFRAC. COMPOSITES,01-East,738561000,1547.32,762.00,785.32
ZIRCAR REFRAC. COMPOSITES,01-East,764481000,1308.80,256.70,1052.10
ZIRCAR REFRAC. COMPOSITES,01-East,739452000,1194.65,603.33,591.32
ZIRCAR REFRAC. COMPOSITES,01-East,734311100,1028.64,101.10,927.54
ZIRCAR REFRAC. COMPOSITES,01-East,793330000,996.00,212.05,783.95
ZIRCAR REFRAC. COMPOSITES,01-East,739561200,697.54,217.00,480.54
ZIRCAR REFRAC. COMPOSITES,01-East,738542000,686.40,574.08,112.32
ZIRCAR REFRAC. COMPOSITES,01-East,780240000,528.36,94.88,433.48
ZIRCAR REFRAC. COMPOSITES,01-East,793540000,492.00,115.88,376.12
ZIRCAR REFRAC. COMPOSITES,01-East,793260074,364.00,156.68,207.32
ZIRCAR REFRAC. COMPOSITES,01-East,739561100,304.92,93.32,211.60
ZIRCAR REFRAC. COMPOSITES,01-East,733511000,287.63,66.56,221.07
ZIRCAR REFRAC. COMPOSITES,01-East,739541200,277.12,64.10,213.02
ZIRCAR REFRAC. COMPOSITES,01-East,733423700,212.16,45.26,166.90
ZIRCAR REFRAC. COMPOSITES,01-East,733464600,170.30,31.31,138.99
ZIRCAR REFRAC. COMPOSITES,01-East,734900000,107.68,38.36,69.32
ZIRCAR ZIRCONIA INC.,01-East,739541000,3687.82,829.48,2858.34
ZIRCAR ZIRCONIA INC.,01-East,754287000,795.36,281.96,513.40
3-I INDUSTRIAL SUPPLY,02-Central,793450174,5827.50,1552.96,4274.54
3-I INDUSTRIAL SUPPLY,02-Central,734990000,798.40,110.59,687.81
3M COMPANY,02-Central,727032004,2514017.70,1754572.03,759445.67
3M COMPANY,02-Central,793001000,568488.00,226065.71,342422.29
4-STATE SUPPLY,02-Central,735602000,166880.00,55965.54,110914.46
4-STATE SUPPLY,02-Central,735750000,11033.00,3165.27,7867.73
4-STATE SUPPLY,02-Central,735862000,1200.00,514.02,685.98
A O SMITH,02-Central,793000000,1706.00,114.88,1591.12
A O SMITH,02-Central,726000240,662.40,44.38,618.02
A W CHESTERTON COMPANY,02-Central,770210000,826.50,92.16,734.34
A W CHESTERTON COMPANY,02-Central,770372000,660.80,53.11,607.69
"ABP INDUCTION, LLC",02-Central,733671000,16167.63,2322.05,13845.58
"ABP INDUCTION, LLC",02-Central,733403300,9407.20,921.11,8486.09
"ABP INDUCTION, LLC",02-Central,770460000,8136.50,608.69,7527.81
"ABP INDUCTION, LLC",02-Central,734311100,1414.80,134.81,1279.99
ACCELLENT ENDOSCOPY,02-Central,733211050,958.00,111.82,846.18
ACCURIDE CAMDEN,02-Central,739541000,564.48,71.62,492.86
ACME INSULATION INC,02-Central,735602000,115915.00,38635.48,77279.52
ACME INSULATION INC,02-Central,735602110,82910.00,28292.45,54617.55
ACME INSULATION INC,02-Central,735752000,29860.00,10450.74,19409.26
ACTION INDUSTRIAL GROUP,02-Central,733832000,2556.00,556.68,1999.32
ACTION INDUSTRIAL GROUP,02-Central,733721000,300.00,69.32,230.68
ADVANCE THERMAL CORP,02-Central,768221130,44990.40,13400.19,31590.21
ADVANCE THERMAL CORP,02-Central,733151500,3340.00,619.98,2720.02
ADVANCE THERMAL CORP,02-Central,768122000,1944.00,671.13,1272.87
ADVANCE THERMAL CORP,02-Central,733201000,767.60,168.85,598.75
ADVANCED TECH SERVICES,02-Central,770580000,1598.00,89.66,1508.34
ADVANCED TECH SERVICES,02-Central,739681100,462.06,207.00,255.06
ADVANCED TECH SERVICES,02-Central,739781100,410.72,163.84,246.88
ADVANCED THERMAL BATTERIE,02-Central,733464400,313.00,43.37,269.63
ADVSEAL,02-Central,770310000,518.00,45.47,472.53
AEROJET,02-Central,733403200,1133.00,99.17,1033.83
AEROJET,02-Central,733383000,1117.60,163.93,953.67
"AFTECH, LLC.",02-Central,734311100,785.84,69.00,716.84
AGROTEK SERVICES INC.,02-Central,777992000,2400.00,1300.68,1099.32
AJAX-TOCCO MAGNETHERMIC,02-Central,738521000,10058.95,3501.13,6557.82
AJAX-TOCCO MAGNETHERMIC,02-Central,733525500,2641.00,260.12,2380.88
AJAX-TOCCO MAGNETHERMIC,02-Central,733586500,1857.60,181.35,1676.25
AJAX-TOCCO MAGNETHERMIC,02-Central,777601000,1183.36,202.84,980.52
AJAX-TOCCO MAGNETHERMIC,02-Central,764461006,1125.20,81.75,1043.45
AJAX-TOCCO MAGNETHERMIC,02-Central,734711100,536.16,81.14,455.02
AJAX-TOCCO MAGNETHERMIC,02-Central,733464500,298.00,29.52,268.48
ALBEMARLE CORPORATION,02-Central,764901000,3017.00,112.46,2904.54
ALCHEMY SOUTH,02-Central,720219100,2444.70,92.16,2352.54
ALCOA INC.,02-Central,770290000,3109.50,229.76,2879.74
ALCOA TECH CENTER,02-Central,770200000,630.00,40.79,589.21
ALCOA,02-Central,793330000,11489.70,1917.22,9572.48
ALCOA,02-Central,739390000,9116.00,4044.00,5072.00
ALCOA,02-Central,733464500,1755.00,295.20,1459.80
ALCOA,02-Central,733464600,702.00,125.26,576.74
ALCOA,02-Central,766161000,512.50,51.41,461.09
ALCOA,02-Central,739370000,379.52,138.24,241.28
ALLIANCE AIRCRAFT GROUP,02-Central,770520000,958.50,41.85,916.65
ALLIANCE REFRACTORIES LTD,02-Central,764821000,370.00,153.58,216.42
ALLIED MINERAL PRODUCTS I,02-Central,779988176,66368.00,50759.89,15608.11
ALLIED MINERAL PRODUCTS I,02-Central,779981056,28774.40,20888.76,7885.64
ALLIED MINERAL PRODUCTS I,02-Central,779981057,13920.00,10024.72,3895.28
ALLIED MINERAL PRODUCTS I,02-Central,779988169,9868.80,9190.60,678.20
ALLIED MINERAL PRODUCTS I,02-Central,770360000,8703.20,578.58,8124.62
ALLIED MINERAL PRODUCTS I,02-Central,779980081,8406.00,7577.38,828.62
ALLIED MINERAL PRODUCTS I,02-Central,737301000,5481.48,1962.90,3518.58
ALLIED MINERAL PRODUCTS I,02-Central,777902700,3687.39,1630.80,2056.59
ALLIED MINERAL PRODUCTS I,02-Central,737912200,588.20,86.72,501.48
ALLIED MINERAL PRODUCTS I,02-Central,770220000,420.90,28.44,392.46
ALLIED MINERAL PRODUCTS I,02-Central,739561000,201.60,86.19,115.41
ALPHA 1,02-Central,734311100,13695.00,1203.60,12491.40
ALPHA 1,02-Central,733444300,10748.00,1449.72,9298.28
ALPHA 1,02-Central,766121000,875.00,27.25,847.75
AMCOR AMERICAN METALS,02-Central,726020031,985944.80,504525.07,481419.73
AMECO USA,02-Central,727000025,166.00,14.09,151.91
"AMERICAN FOAM CAST, INC.",02-Central,734173000,2814.00,429.92,2384.08
AMERICAN MECHANICAL,02-Central,735602000,174706.00,53103.10,121602.90
AMERICAN MECHANICAL,02-Central,735750000,7664.80,2235.41,5429.39
AMERICAN MECHANICAL,02-Central,735602010,465.00,275.22,189.78
AMERICRAFT MFG CO.,02-Central,733721000,286.20,48.90,237.30
"AMERISAFE, INC",02-Central,735602000,130460.00,44474.32,85985.68
"AMERISAFE, INC",02-Central,764901000,17545.00,4865.08,12679.92
"AMERISAFE, INC",02-Central,735602110,16430.00,6037.78,10392.22
"AMERISAFE, INC",02-Central,764861000,10493.00,2883.16,7609.84
"AMERISAFE, INC",02-Central,764821000,8240.00,2081.71,6158.29
"AMERISAFE, INC",02-Central,735750000,3192.00,1304.73,1887.27
"AMERISAFE, INC",02-Central,739561200,2122.24,451.34,1670.90
"AMERISAFE, INC",02-Central,764261000,1950.00,758.20,1191.80
"AMERISAFE, INC",02-Central,764341000,1890.00,706.97,1183.03
"AMERISAFE, INC",02-Central,724281400,800.00,178.16,621.84
"AMERISAFE, INC",02-Central,733725500,205.20,51.76,153.44
"AMERISAFE, INC",02-Central,764461006,84.00,27.94,56.06
AMES TRUE TEMPER,02-Central,739390000,8386.72,3720.48,4666.24
AMES TRUE TEMPER,02-Central,739781100,3159.04,761.68,2397.36
AMES TRUE TEMPER,02-Central,764522000,666.00,26.72,639.28
AMES TRUE TEMPER,02-Central,774201000,589.00,157.42,431.58
AMES TRUE TEMPER,02-Central,776131000,445.00,260.22,184.78
AMES TRUE TEMPER,02-Central,777601000,409.92,58.80,351.12
AMPCO METAL,02-Central,770260000,1957.50,140.66,1816.84
AMPCO METAL,02-Central,770220000,447.00,28.44,418.56
"AMSTED RAIL COMPANY, INC.",02-Central,780770000,6915.60,1091.36,5824.24
"AMSTED RAIL COMPANY, INC.",02-Central,780730000,3175.20,582.38,2592.82
ANCHOR ACQUISTION LLC,02-Central,770220000,471.00,27.35,443.65
APEX PIPELINE SERVICES,02-Central,768222000,4396.00,250.60,4145.40
APPLIED INDUSTRIAL TECH.,02-Central,733525600,14332.50,2114.76,12217.74
APPLIED INDUSTRIAL TECH.,02-Central,733525500,6675.00,974.77,5700.23
APPLIED INDUSTRIAL TECH.,02-Central,733211000,3996.20,793.13,3203.07
APPLIED INDUSTRIAL TECH.,02-Central,737912200,3363.45,541.47,2821.98
APPLIED INDUSTRIAL TECH.,02-Central,770560000,2856.30,175.59,2680.71
APPLIED INDUSTRIAL TECH.,02-Central,734890000,2010.70,397.92,1612.78
APPLIED INDUSTRIAL TECH.,02-Central,734990000,1945.48,274.67,1670.81
APPLIED INDUSTRIAL TECH.,02-Central,766121000,1602.80,109.00,1493.80
APPLIED INDUSTRIAL TECH.,02-Central,770520000,770.10,48.32,721.78
APPLIED INDUSTRIAL TECH.,02-Central,734611100,483.07,81.53,401.54
APPLIED INDUSTRIAL TECH.,02-Central,739641100,393.80,137.43,256.37
APPLIED INDUSTRIAL TECH.,02-Central,733511000,348.75,75.22,273.53
APPLIED INDUSTRIAL TECH.,02-Central,734910000,307.70,53.09,254.61
APPLIED POROUS TECH.,02-Central,740361900,18662.40,7942.53,10719.87
APPLIED POWER PRODUCTS,02-Central,733464500,596.00,59.04,536.96
APPLIED POWER PRODUCTS,02-Central,733510000,537.50,70.86,466.64
APPLIED POWER PRODUCTS,02-Central,733151600,352.00,46.64,305.36
APPLIED PRODUCTS INC.,02-Central,733403100,1107.25,92.79,1014.46
ARCELORMITTAL,02-Central,770560000,1509.00,86.09,1422.91
ARGOS CEMENT LLC,02-Central,770600000,3449.60,192.64,3256.96
ARI - MARMADUKE,02-Central,764706120,1645267.20,437262.08,1208005.12
ARI - MARMADUKE,02-Central,764652002,517090.56,88883.55,428207.01
ARI - MARMADUKE,02-Central,764643054,9828.00,2225.83,7602.17
ARI - MARMADUKE,02-Central,764262000,252.00,46.00,206.00
ARMIL / C F S INC,02-Central,734830000,79011.70,15630.42,63381.28
ARMIL / C F S INC,02-Central,793000000,71560.00,11121.19,60438.81
ARMIL / C F S INC,02-Central,771320000,70906.00,23024.60,47881.40
ARMIL / C F S INC,02-Central,754847000,65818.80,21558.67,44260.13
ARMIL / C F S INC,02-Central,764821000,61797.80,26521.73,35276.07
ARMIL / C F S INC,02-Central,737851000,55543.04,37139.20,18403.84
ARMIL / C F S INC,02-Central,718507000,55064.88,10624.61,44440.27
ARMIL / C F S INC,02-Central,718500000,54956.92,10579.03,44377.89
ARMIL / C F S INC,02-Central,781650000,44323.20,13715.94,30607.26
ARMIL / C F S INC,02-Central,781610000,42086.94,12695.80,29391.14
ARMIL / C F S INC,02-Central,766121000,41432.00,25148.89,16283.11
ARMIL / C F S INC,02-Central,726020015,40240.80,13806.34,26434.46
ARMIL / C F S INC,02-Central,737912200,40058.80,8777.68,31281.12
ARMIL / C F S INC,02-Central,770942000,38821.60,1709.27,37112.33
ARMIL / C F S INC,02-Central,733211000,36766.40,7828.79,28937.61
ARMIL / C F S INC,02-Central,734311100,35989.36,4435.63,31553.73
ARMIL / C F S INC,02-Central,780710000,34878.00,11542.35,23335.65
ARMIL / C F S INC,02-Central,777601000,33883.07,9918.19,23964.88
ARMIL / C F S INC,02-Central,792785000,27829.44,6335.87,21493.57
ARMIL / C F S INC,02-Central,768021000,24965.60,15819.03,9146.57
ARMIL / C F S INC,02-Central,733151500,24962.40,4148.08,20814.32
ARMIL / C F S INC,02-Central,780650000,24425.94,9111.62,15314.32
ARMIL / C F S INC,02-Central,733152500,22460.00,8546.96,13913.04
ARMIL / C F S INC,02-Central,739561200,21781.25,8020.07,13761.18
ARMIL / C F S INC,02-Central,781670000,20216.94,5615.64,14601.30
ARMIL / C F S INC,02-Central,777904301,19956.49,9045.00,10911.49
ARMIL / C F S INC,02-Central,770392000,19391.40,1683.33,17708.07
ARMIL / C F S INC,02-Central,764827505,17374.50,8180.85,9193.65
ARMIL / C F S INC,02-Central,792734000,16934.40,2960.64,13973.76
ARMIL / C F S INC,02-Central,740283401,16741.34,3346.87,13394.47
ARMIL / C F S INC,02-Central,764822000,16290.00,6997.21,9292.79
ARMIL / C F S INC,02-Central,780690000,16230.29,5784.42,10445.87
ARMIL / C F S INC,02-Central,733484900,15449.60,2898.03,12551.57
ARMIL / C F S INC,02-Central,733505300,15380.00,2650.94,12729.06
ARMIL / C F S INC,02-Central,782050000,14774.40,5317.55,9456.85
ARMIL / C F S INC,02-Central,739741100,14341.36,9133.13,5208.23
ARMIL / C F S INC,02-Central,740361900,14244.96,6724.31,7520.65
ARMIL / C F S INC,02-Central,781500000,13396.05,4390.38,9005.67
ARMIL / C F S INC,02-Central,727000140,12699.04,2323.30,10375.74
ARMIL / C F S INC,02-Central,764561000,12308.01,6558.78,5749.23
ARMIL / C F S INC,02-Central,777905413,12167.76,7064.59,5103.17
ARMIL / C F S INC,02-Central,764901000,11587.00,5145.32,6441.68
ARMIL / C F S INC,02-Central,733151600,10286.80,1668.65,8618.15
ARMIL / C F S INC,02-Central,781630000,10272.96,3215.72,7057.24
ARMIL / C F S INC,02-Central,770940000,10213.00,434.16,9778.84
ARMIL / C F S INC,02-Central,768121000,10167.00,4931.60,5235.40
ARMIL / C F S INC,02-Central,733141200,10121.20,2006.27,8114.93
ARMIL / C F S INC,02-Central,733484800,9960.00,1849.08,8110.92
ARMIL / C F S INC,02-Central,780250000,9919.62,4086.91,5832.71
ARMIL / C F S INC,02-Central,764827501,9093.18,4282.79,4810.39
ARMIL / C F S INC,02-Central,734142001,9076.50,3554.48,5522.02
ARMIL / C F S INC,02-Central,733423700,9006.40,1566.22,7440.18
ARMIL / C F S INC,02-Central,766121070,8969.29,5828.28,3141.01
ARMIL / C F S INC,02-Central,733464500,8559.00,1362.00,7197.00
ARMIL / C F S INC,02-Central,759661000,8480.82,1164.68,7316.14
ARMIL / C F S INC,02-Central,764827502,8364.33,3959.88,4404.45
ARMIL / C F S INC,02-Central,734173000,8329.30,2046.89,6282.41
ARMIL / C F S INC,02-Central,770560000,8131.10,698.95,7432.15
ARMIL / C F S INC,02-Central,768241030,8094.00,3449.84,4644.16
ARMIL / C F S INC,02-Central,764342000,7985.00,4519.40,3465.60
ARMIL / C F S INC,02-Central,764861000,7421.00,3586.64,3834.36
ARMIL / C F S INC,02-Central,734711100,7024.87,1477.76,5547.11
ARMIL / C F S INC,02-Central,777905414,7001.22,4292.57,2708.65
ARMIL / C F S INC,02-Central,733464400,6946.20,1463.62,5482.58
ARMIL / C F S INC,02-Central,733201000,6856.72,1389.02,5467.70
ARMIL / C F S INC,02-Central,733725500,6766.00,3026.40,3739.60
ARMIL / C F S INC,02-Central,739681100,6731.39,4080.42,2650.97
ARMIL / C F S INC,02-Central,768211000,6497.00,2983.93,3513.07
ARMIL / C F S INC,02-Central,738561000,6408.40,5222.97,1185.43
ARMIL / C F S INC,02-Central,770200000,6129.00,663.98,5465.02
ARMIL / C F S INC,02-Central,764481000,6096.00,3296.52,2799.48
ARMIL / C F S INC,02-Central,739541200,6025.46,1928.10,4097.36
ARMIL / C F S INC,02-Central,739370000,5952.83,3898.06,2054.77
ARMIL / C F S INC,02-Central,764821073,5930.00,5832.00,98.00
ARMIL / C F S INC,02-Central,777905415,5871.88,3320.66,2551.22
ARMIL / C F S INC,02-Central,792624000,5639.80,1216.06,4423.74
ARMIL / C F S INC,02-Central,793000010,5472.00,866.59,4605.41
ARMIL / C F S INC,02-Central,770210000,5454.00,618.39,4835.61
ARMIL / C F S INC,02-Central,733525600,5383.00,808.10,4574.90
ARMIL / C F S INC,02-Central,766081010,5310.00,2448.72,2861.28
ARMIL / C F S INC,02-Central,771330000,5293.08,1665.59,3627.49
ARMIL / C F S INC,02-Central,766122000,5250.00,2884.62,2365.38
ARMIL / C F S INC,02-Central,780210000,4860.00,1907.14,2952.86
ARMIL / C F S INC,02-Central,777642000,4602.70,1590.24,3012.46
ARMIL / C F S INC,02-Central,733211050,4600.00,1056.06,3543.94
ARMIL / C F S INC,02-Central,770930000,4599.00,160.70,4438.30
ARMIL / C F S INC,02-Central,764261000,4589.00,1605.55,2983.45
ARMIL / C F S INC,02-Central,764522000,4424.00,2108.43,2315.57
ARMIL / C F S INC,02-Central,764902000,4370.00,1926.87,2443.13
ARMIL / C F S INC,02-Central,764601000,4356.00,2115.55,2240.45
ARMIL / C F S INC,02-Central,764521000,4173.00,2124.92,2048.08
ARMIL / C F S INC,02-Central,733525400,4169.75,618.30,3551.45
ARMIL / C F S INC,02-Central,764827504,4003.58,1882.58,2121.00
ARMIL / C F S INC,02-Central,770380000,3981.20,369.51,3611.69
ARMIL / C F S INC,02-Central,770240000,3948.00,416.58,3531.42
ARMIL / C F S INC,02-Central,733423800,3872.64,689.91,3182.73
ARMIL / C F S INC,02-Central,764827503,3861.00,1610.21,2250.79
ARMIL / C F S INC,02-Central,764821161,3805.00,3720.00,85.00
ARMIL / C F S INC,02-Central,777905410,3602.74,2952.47,650.27
ARMIL / C F S INC,02-Central,734414100,3578.74,562.07,3016.67
ARMIL / C F S INC,02-Central,738521000,3532.13,3547.76,-15.63
ARMIL / C F S INC,02-Central,739561000,3374.63,1196.98,2177.65
ARMIL / C F S INC,02-Central,768221000,3370.00,1747.05,1622.95
ARMIL / C F S INC,02-Central,764781000,3240.00,1409.43,1830.57
ARMIL / C F S INC,02-Central,768750000,3197.20,805.67,2391.53
ARMIL / C F S INC,02-Central,777905411,3094.30,2850.01,244.29
ARMIL / C F S INC,02-Central,770270000,3086.13,328.80,2757.33
ARMIL / C F S INC,02-Central,733423600,2851.20,512.61,2338.59
ARMIL / C F S INC,02-Central,737321000,2835.84,856.56,1979.28
ARMIL / C F S INC,02-Central,780640000,2786.48,881.47,1905.01
ARMIL / C F S INC,02-Central,733525500,2764.50,422.37,2342.13
ARMIL / C F S INC,02-Central,739541100,2705.98,815.94,1890.04
ARMIL / C F S INC,02-Central,733403200,2680.48,361.85,2318.63
ARMIL / C F S INC,02-Central,770600000,2642.36,192.64,2449.72
ARMIL / C F S INC,02-Central,793452074,2561.90,1380.26,1181.64
ARMIL / C F S INC,02-Central,764602000,2553.00,1218.82,1334.18
ARMIL / C F S INC,02-Central,739781100,2490.66,1332.94,1157.72
ARMIL / C F S INC,02-Central,737902100,2365.40,608.41,1756.99
ARMIL / C F S INC,02-Central,793260074,2353.00,2034.81,318.19
ARMIL / C F S INC,02-Central,767501000,2352.24,3.00,2349.24
ARMIL / C F S INC,02-Central,767970110,2352.24,31.86,2320.38
ARMIL / C F S INC,02-Central,771500000,2258.03,165.39,2092.64
ARMIL / C F S INC,02-Central,780770000,2213.71,699.39,1514.32
ARMIL / C F S INC,02-Central,733586400,2208.96,182.86,2026.10
ARMIL / C F S INC,02-Central,793330000,2086.50,895.31,1191.19
ARMIL / C F S INC,02-Central,777905412,2065.13,1492.94,572.19
ARMIL / C F S INC,02-Central,734851000,1898.33,456.99,1441.34
ARMIL / C F S INC,02-Central,764482000,1826.00,1029.94,796.06
ARMIL / C F S INC,02-Central,739541000,1588.90,543.01,1045.89
ARMIL / C F S INC,02-Central,781570000,1478.00,482.68,995.32
ARMIL / C F S INC,02-Central,739390000,1434.72,577.02,857.70
ARMIL / C F S INC,02-Central,764903000,1367.35,509.66,857.69
ARMIL / C F S INC,02-Central,764301000,1320.00,762.68,557.32
ARMIL / C F S INC,02-Central,781590000,1298.24,417.88,880.36
ARMIL / C F S INC,02-Central,770510000,1292.00,96.40,1195.60
ARMIL / C F S INC,02-Central,770260000,1224.90,135.26,1089.64
ARMIL / C F S INC,02-Central,780360000,1209.20,373.85,835.35
ARMIL / C F S INC,02-Central,733832000,1206.00,385.42,820.58
ARMIL / C F S INC,02-Central,770520000,1190.70,96.64,1094.06
ARMIL / C F S INC,02-Central,764521061,1180.00,1278.00,-98.00
ARMIL / C F S INC,02-Central,770570000,1143.68,100.73,1042.95
ARMIL / C F S INC,02-Central,770220000,1125.60,109.39,1016.21
ARMIL / C F S INC,02-Central,733444200,1073.00,193.20,879.80
ARMIL / C F S INC,02-Central,770450000,1050.75,97.34,953.41
ARMIL / C F S INC,02-Central,768231300,1025.00,545.35,479.65
ARMIL / C F S INC,02-Central,768231000,1024.00,539.26,484.74
ARMIL / C F S INC,02-Central,793450174,1005.14,856.63,148.51
ARMIL / C F S INC,02-Central,740382201,976.50,300.51,675.99
ARMIL / C F S INC,02-Central,770303000,934.40,25.23,909.17
ARMIL / C F S INC,02-Central,792760000,914.14,233.24,680.90
ARMIL / C F S INC,02-Central,792620000,855.20,173.48,681.72
ARMIL / C F S INC,02-Central,764341000,792.00,458.70,333.30
ARMIL / C F S INC,02-Central,764346500,792.00,469.69,322.31
ARMIL / C F S INC,02-Central,764262000,788.00,460.00,328.00
ARMIL / C F S INC,02-Central,781580000,779.68,175.64,604.04
ARMIL / C F S INC,02-Central,771132000,766.25,39.51,726.74
ARMIL / C F S INC,02-Central,781835000,734.20,214.72,519.48
ARMIL / C F S INC,02-Central,742546000,714.60,78.36,636.24
ARMIL / C F S INC,02-Central,763991000,712.00,196.12,515.88
ARMIL / C F S INC,02-Central,734890000,700.35,198.96,501.39
ARMIL / C F S INC,02-Central,764761000,681.00,472.17,208.83
ARMIL / C F S INC,02-Central,770290000,643.00,76.59,566.41
ARMIL / C F S INC,02-Central,780310000,572.48,159.95,412.53
ARMIL / C F S INC,02-Central,764862000,565.00,250.28,314.72
ARMIL / C F S INC,02-Central,767970020,522.72,1.80,520.92
ARMIL / C F S INC,02-Central,723960000,476.50,176.46,300.04
ARMIL / C F S INC,02-Central,770280000,440.88,76.95,363.93
ARMIL / C F S INC,02-Central,739641100,408.24,274.85,133.39
ARMIL / C F S INC,02-Central,764781040,385.00,201.66,183.34
ARMIL / C F S INC,02-Central,780630000,339.52,118.56,220.96
ARMIL / C F S INC,02-Central,776201000,316.00,185.15,130.85
ARMIL / C F S INC,02-Central,793540000,307.50,144.84,162.66
ARMIL / C F S INC,02-Central,734990000,140.80,27.39,113.41
ARMIL / C F S INC,02-Central,734900000,96.92,38.36,58.56
ASHEVILLE RUBBER & GASKET,02-Central,733525600,7645.00,720.37,6924.63
ASHEVILLE RUBBER & GASKET,02-Central,734211200,946.28,92.43,853.85
ASSOCIATED SPRING,02-Central,739781100,789.76,190.42,599.34
ASSOCIATED SPRING,02-Central,739741100,411.84,109.56,302.28
ATA,02-Central,745471000,2262.00,293.30,1968.70
ATA,02-Central,764821000,1290.00,46.07,1243.93
ATA,02-Central,734311100,275.36,35.30,240.06
ATD ENGINEERING MACHINE,02-Central,739370000,2218.24,639.76,1578.48
ATI FABRICATED COMPONENTS,02-Central,766121000,68580.00,3269.84,65310.16
ATLANTECH DISTRIBUTION,02-Central,735602000,235807.20,91012.40,144794.80
ATLANTECH DISTRIBUTION,02-Central,733403200,182160.00,22729.65,159430.35
ATLANTECH DISTRIBUTION,02-Central,735602110,34265.00,13764.58,20500.42
ATLANTECH DISTRIBUTION,02-Central,764781000,20700.00,8504.97,12195.03
ATLANTECH DISTRIBUTION,02-Central,764821000,17268.85,3585.65,13683.20
ATLANTECH DISTRIBUTION,02-Central,739641100,16439.04,8585.27,7853.77
ATLANTECH DISTRIBUTION,02-Central,764301000,16344.90,4089.86,12255.04
ATLANTECH DISTRIBUTION,02-Central,733464600,14582.50,2476.53,12105.97
ATLANTECH DISTRIBUTION,02-Central,764521000,5172.30,1674.49,3497.81
ATLANTECH DISTRIBUTION,02-Central,735750000,4650.00,1927.06,2722.94
ATLANTECH DISTRIBUTION,02-Central,764601000,3858.75,993.27,2865.48
ATLANTECH DISTRIBUTION,02-Central,733151500,3794.40,753.88,3040.52
ATLANTECH DISTRIBUTION,02-Central,739450000,3249.18,1431.28,1817.90
ATLANTECH DISTRIBUTION,02-Central,733444200,2572.00,455.78,2116.22
ATLANTECH DISTRIBUTION,02-Central,764522000,2563.60,817.14,1746.46
ATLANTECH DISTRIBUTION,02-Central,764602000,2537.50,655.28,1882.22
ATLANTECH DISTRIBUTION,02-Central,739561200,2478.08,795.66,1682.42
ATLANTECH DISTRIBUTION,02-Central,733141200,1960.00,299.86,1660.14
ATLANTECH DISTRIBUTION,02-Central,764221000,1624.00,942.37,681.63
ATLANTECH DISTRIBUTION,02-Central,739370000,1439.36,1382.40,56.96
ATLANTECH DISTRIBUTION,02-Central,764561000,1175.80,185.57,990.23
ATLANTECH DISTRIBUTION,02-Central,739541200,815.36,225.00,590.36
ATLANTECH DISTRIBUTION,02-Central,733211050,502.60,111.82,390.78
ATLANTECH DISTRIBUTION,02-Central,733211000,490.60,100.59,390.01
ATLANTECH DISTRIBUTION,02-Central,735752000,465.00,205.20,259.80
ATLANTECH DISTRIBUTION,02-Central,733464400,399.00,86.74,312.26
ATLANTECH DISTRIBUTION,02-Central,733423700,244.80,45.26,199.54
ATLANTECH DISTRIBUTION,02-Central,764481000,186.00,50.82,135.18
ATLANTECH DISTRIBUTION,02-Central,734311100,181.96,17.65,164.31
ATLANTECH,02-Central,735602000,21805.00,7466.95,14338.05
ATLANTECH,02-Central,735602110,6272.00,2205.14,4066.86
ATLANTECH,02-Central,764521000,693.00,239.35,453.65
ATLANTECH,02-Central,733211050,688.00,223.64,464.36
ATLANTECH,02-Central,734151100,454.34,100.61,353.73
ATLANTIC COAST FIRE EQ.,02-Central,770380000,1880.00,112.66,1767.34
ATS CAROLINA INC,02-Central,737851000,3059.20,1326.40,1732.80
"ATSCOTT MFG COMPANY, INC.",02-Central,792004000,1119.68,386.27,733.41
AUBURN LEATHER,02-Central,733671000,16480.80,4320.72,12160.08
AUBURN LEATHER,02-Central,733742000,265.60,81.75,183.85
AUSCO PRODUCTS INC.,02-Central,727000140,12323.20,5524.73,6798.47
B F GOODRICH CO,02-Central,733383000,1174.80,152.13,1022.67
B&R ENGINEERING,02-Central,734990000,1430.40,165.88,1264.52
BABINGTON TECHNOLOGY LLC,02-Central,739781100,35129.60,10025.56,25104.04
BABINGTON TECHNOLOGY LLC,02-Central,739761100,4053.12,1259.80,2793.32
BABINGTON TECHNOLOGY LLC,02-Central,739741100,3739.84,1103.54,2636.30
BABINGTON TECHNOLOGY LLC,02-Central,733151500,1350.00,143.07,1206.93
BAKER DISTRIBUTING  CO.,02-Central,735602000,4136.00,1017.64,3118.36
BALTIMORE RUBBER & GASKET,02-Central,777601000,1160.16,216.06,944.10
BARON BLAKESLEE SFC,02-Central,791106668,9061.92,2376.26,6685.66
BASF CORPORATION,02-Central,740402301,9810.00,1949.09,7860.91
BASF CORPORATION,02-Central,777601000,7031.40,1440.36,5591.04
BASIC RESOURCES INC.,02-Central,733202000,25444.80,11292.36,14152.44
BASIC RESOURCES INC.,02-Central,770410000,16190.00,2030.59,14159.41
BASIC RESOURCES INC.,02-Central,733211000,12960.00,4734.53,8225.47
BASIC RESOURCES INC.,02-Central,734351200,12364.64,1859.63,10505.01
BASIC RESOURCES INC.,02-Central,733464500,5855.60,1498.67,4356.93
BASIC RESOURCES INC.,02-Central,733151500,5112.00,1947.83,3164.17
BASIC RESOURCES INC.,02-Central,733505200,1335.00,327.93,1007.07
BASIC RESOURCES INC.,02-Central,733484800,1264.00,287.63,976.37
BASIC RESOURCES INC.,02-Central,770220000,1065.00,136.73,928.27
BASIC RESOURCES INC.,02-Central,733383000,805.20,163.93,641.27
BASIC RESOURCES INC.,02-Central,777601000,697.89,216.05,481.84
BASIC RESOURCES INC.,02-Central,764781000,126.00,39.44,86.56
BASIC RUBBER & PLASTIC,02-Central,738381000,5008.32,4161.60,846.72
BASIC RUBBER & PLASTIC,02-Central,738361000,4382.28,2162.16,2220.12
BASIC RUBBER & PLASTIC,02-Central,740103000,792.96,135.47,657.49
BASIC RUBBER & PLASTIC,02-Central,740123000,670.32,128.01,542.31
BASIC RUBBER & PLASTIC,02-Central,738521000,556.48,298.56,257.92
BATTELLE,02-Central,733741000,388.00,93.19,294.81
BAY INDUSTRIES INC.,02-Central,735602000,1217970.00,430162.17,787807.83
BAY INDUSTRIES INC.,02-Central,735602110,226608.00,82947.78,143660.22
BAY INDUSTRIES INC.,02-Central,735750000,106438.00,43834.14,62603.86
BAY INDUSTRIES INC.,02-Central,735752000,6508.00,2784.58,3723.42
BAY INDUSTRIES INC.,02-Central,739561000,4596.26,1284.15,3312.11
BAY INDUSTRIES INC.,02-Central,735602010,1590.00,1100.90,489.10
BAY INDUSTRIES INC.,02-Central,764561000,967.50,332.66,634.84
BAY INDUSTRIES INC.,02-Central,733151500,632.20,95.38,536.82
BDI,02-Central,739741100,2142.40,841.60,1300.80
BDI,02-Central,739781100,2053.60,819.20,1234.40
BEARING SERVICE CO.,02-Central,770222000,3124.75,198.90,2925.85
BHC INTEGRATED SUPPORT,02-Central,735602000,2465.00,714.48,1750.52
BLUE RIDGE HOME,02-Central,739561200,424.96,76.67,348.29
BLUE STREAK FAB &,02-Central,764821000,2580.00,92.14,2487.86
BLUE STREAK FAB &,02-Central,733444300,2072.00,272.10,1799.90
BLUE STREAK FAB &,02-Central,734311100,1773.86,155.65,1618.21
BLUE STREAK FAB &,02-Central,764821073,894.00,72.90,821.10
BLUE STREAK FAB &,02-Central,727000140,693.60,50.14,643.46
BOATWRIGHT DISTRIBUTION,02-Central,735602000,373070.00,137263.30,235806.70
BOATWRIGHT DISTRIBUTION,02-Central,764261000,16129.95,3680.62,12449.33
BOATWRIGHT DISTRIBUTION,02-Central,764301000,11012.62,3604.09,7408.53
BOATWRIGHT DISTRIBUTION,02-Central,764341000,9705.00,3412.88,6292.12
BOATWRIGHT DISTRIBUTION,02-Central,735750000,6580.00,3150.74,3429.26
BOATWRIGHT DISTRIBUTION,02-Central,733403200,2760.12,297.51,2462.61
BOATWRIGHT DISTRIBUTION,02-Central,766211000,312.00,113.05,198.95
BODYCOTE HOT ISOSTATIC,02-Central,740283502,20501.80,2864.56,17637.24
BODYCOTE HOT ISOSTATIC,02-Central,764762000,1600.00,151.46,1448.54
BODYCOTE HOT ISOSTATIC,02-Central,770720000,1010.00,27.12,982.88
BODYCOTE HOT ISOSTATIC,02-Central,764781040,427.20,39.06,388.14
BOEING,02-Central,734311100,381.48,33.70,347.78
BOILER TUBE OF AMERICA,02-Central,739781100,67019.57,44284.37,22735.20
BOILER TUBE OF AMERICA,02-Central,764901000,18863.10,4118.85,14744.25
BOILER TUBE OF AMERICA,02-Central,764821000,3362.40,698.23,2664.17
BOILER TUBE OF AMERICA,02-Central,764901073,603.00,291.60,311.40
BOILER TUBE OF AMERICA,02-Central,764821073,304.20,145.80,158.40
BORBET ALABAMA INC.,02-Central,739521100,1270.08,139.38,1130.70
BORBET ALABAMA INC.,02-Central,739541100,658.56,83.57,574.99
BORG WARNER,02-Central,740382101,892.80,296.37,596.43
BOVARD STUDIO INC.,02-Central,739541200,501.76,64.10,437.66
BP-HUSKY FREFINING LLC,02-Central,734761300,23595.00,3793.90,19801.10
BP-HUSKY FREFINING LLC,02-Central,774901000,330.00,39.57,290.43
BP-HUSKY FREFINING LLC,02-Central,767970140,224.50,24.28,200.22
BP-HUSKY FREFINING LLC,02-Central,767970000,69.50,16.80,52.70
BRADEN MANUFACTURING,02-Central,768241000,153.00,102.77,50.23
BRANHAM CORP.,02-Central,770810000,2535.00,51.57,2483.43
BRANHAM CORP.,02-Central,770800000,684.00,12.78,671.22
BRANHAM CORP.,02-Central,733464600,596.00,62.62,533.38
BRAZEE STREET STUDIO,02-Central,733464500,611.00,62.05,548.95
BRC RAILCAR SERVICE,02-Central,764703110,6301.68,843.40,5458.28
BRC RAILCAR SERVICE,02-Central,764521000,3390.00,159.25,3230.75
BRI SPECIALTY PRODUCTS,02-Central,733211000,680.00,236.94,443.06
BRUCKNER SUPPLY,02-Central,718501000,117740.50,49261.35,68479.15
BRUCKNER SUPPLY,02-Central,754926000,64634.88,47295.44,17339.44
BRUCKNER SUPPLY,02-Central,779957280,24506.88,12225.02,12281.86
BRUCKNER SUPPLY,02-Central,754906000,20554.56,14179.68,6374.88
BRUCKNER SUPPLY,02-Central,785800000,15626.16,9464.63,6161.53
BRUCKNER SUPPLY,02-Central,766121000,13312.00,3164.26,10147.74
BRUCKNER SUPPLY,02-Central,754866000,10910.40,6664.98,4245.42
BRUCKNER SUPPLY,02-Central,770522000,8599.50,616.79,7982.71
BRUCKNER SUPPLY,02-Central,754826000,7916.88,4555.96,3360.92
BRUCKNER SUPPLY,02-Central,764822000,7028.00,1877.99,5150.01
BRUCKNER SUPPLY,02-Central,754886000,5370.96,3575.86,1795.10
BRUCKNER SUPPLY,02-Central,785780000,5131.80,3494.62,1637.18
BRUCKNER SUPPLY,02-Central,785740000,2974.40,1995.52,978.88
BRUCKNER SUPPLY,02-Central,785760000,2218.80,1593.06,625.74
BRUCKNER SUPPLY,02-Central,737301000,1915.20,736.08,1179.12
BRUCKNER SUPPLY,02-Central,785580000,1718.40,1393.74,324.66
BRUCKNER SUPPLY,02-Central,770510000,1705.25,96.40,1608.85
BRUCKNER SUPPLY,02-Central,785620000,1597.20,1237.95,359.25
BRUCKNER SUPPLY,02-Central,739380000,1046.88,720.92,325.96
BRUCKNER SUPPLY,02-Central,777601000,889.95,248.42,641.53
BRUCKNER SUPPLY,02-Central,777904301,740.50,234.94,505.56
BRUNNER & LAY,02-Central,724261200,786.00,78.20,707.80
BRUNNER & LAY,02-Central,733211000,456.00,47.39,408.61
BRYAN TOOL & MACHINE,02-Central,739781100,1184.64,285.63,899.01
BRYAN TOOL & MACHINE,02-Central,739380000,396.48,120.15,276.33
BRYAN TOOL & MACHINE,02-Central,739370000,364.80,90.82,273.98
BRYANT REFRACTORY CO.,02-Central,737321000,69822.72,18630.21,51192.51
BRYANT REFRACTORY CO.,02-Central,739781100,16576.00,6664.66,9911.34
BRYANT REFRACTORY CO.,02-Central,764781000,12464.00,3234.30,9229.70
BRYANT REFRACTORY CO.,02-Central,754887000,6133.92,1424.30,4709.62
BRYANT REFRACTORY CO.,02-Central,737912200,5621.60,1387.56,4234.04
BRYANT REFRACTORY CO.,02-Central,779958161,2998.00,1827.70,1170.30
BRYANT REFRACTORY CO.,02-Central,780620000,973.35,485.43,487.92
BRYANT REFRACTORY CO.,02-Central,739761100,591.84,415.20,176.64
BRYANT REFRACTORY CO.,02-Central,734651200,390.90,111.00,279.90
BRYANT REFRACTORY CO.,02-Central,777642000,172.50,47.52,124.98
BUILDING SERVICE IND,02-Central,735602110,36090.00,11244.34,24845.66
BUILDING SERVICE IND,02-Central,735752000,19164.00,8073.27,11090.73
BUILDING SERVICE IND,02-Central,735602000,14064.00,4283.48,9780.52
BUILDING SERVICE IND,02-Central,735750000,1920.00,824.04,1095.96
BULLSEYE INDUSTRIAL,02-Central,764341000,712.50,119.93,592.57
BURKHART REFRACTORY INST.,02-Central,734211200,125.00,22.84,102.16
C P FILMS INC.,02-Central,740402402,7262.50,1403.00,5859.50
C T TAKAHASHI CO.,02-Central,740382101,892.80,296.37,596.43
C.F. ROARK WELDING & ENG,02-Central,733151500,3238.00,359.49,2878.51
CAL-CHEM CO.,02-Central,723960000,50324.00,15873.77,34450.23
CAL-CHEM CO.,02-Central,720252303,2415.60,105.78,2309.82
CAL-CHEM CO.,02-Central,734030000,342.00,157.54,184.46
CARBONYX CARBON TECH.,02-Central,764951600,16343.04,8201.52,8141.52
CARBONYX CARBON TECH.,02-Central,764951800,16343.04,8012.64,8330.40
CARBONYX CARBON TECH.,02-Central,766121000,8630.00,5449.76,3180.24
CARBONYX CARBON TECH.,02-Central,734351203,4950.00,1547.57,3402.43
CARBONYX CARBON TECH.,02-Central,764952100,4032.00,1776.82,2255.18
CARBONYX CARBON TECH.,02-Central,764951700,3064.32,1545.80,1518.52
CARBONYX CARBON TECH.,02-Central,764902000,2250.00,957.81,1292.19
CARBONYX CARBON TECH.,02-Central,764951900,2042.88,1031.66,1011.22
CARDINAL FG,02-Central,739452000,9300.14,4223.37,5076.77
CARDINAL FG,02-Central,764521000,7059.00,345.31,6713.69
CARDINAL FG,02-Central,738501000,6251.90,4206.45,2045.45
CARDINAL FG,02-Central,738521000,4372.48,2008.33,2364.15
CARDINAL FG,02-Central,738601000,3430.56,1975.19,1455.37
CARDINAL FG,02-Central,739741100,2530.88,1095.58,1435.30
CARDINAL FG,02-Central,764901073,2220.00,437.40,1782.60
CARDINAL FG,02-Central,764521073,2172.00,387.00,1785.00
CARDINAL FG,02-Central,738361000,1405.44,657.88,747.56
CARDINAL FG,02-Central,792730000,547.40,62.56,484.84
CARDINAL FG,02-Central,768212000,374.00,23.12,350.88
CARLISLE BRAKE & FRICTION,02-Central,734030000,1089.90,494.66,595.24
CAROLINA GLASS & MIRROR,02-Central,733525600,717.50,65.49,652.01
CAROLINA GLASS & MIRROR,02-Central,733511000,537.50,66.56,470.94
CAST ALUMINUM SOLUTIONS,02-Central,733525600,752.50,71.05,681.45
CAT-I MFG,02-Central,734311100,404.36,35.30,369.06
CATACEL,02-Central,764221000,200.00,16.83,183.17
CECO ABATEMENT,02-Central,779957221,20929.92,10427.93,10501.99
CECO ABATEMENT,02-Central,764821000,2112.00,308.36,1803.64
CECO ABATEMENT,02-Central,779952098,1188.84,589.10,599.74
CECO ABATEMENT,02-Central,777642000,1051.50,351.26,700.24
CECO ABATEMENT,02-Central,764902000,1014.00,191.55,822.45
CECO ABATEMENT,02-Central,779952097,614.88,382.24,232.64
CECO ABATEMENT,02-Central,764901000,522.50,145.30,377.20
CECO ABATEMENT,02-Central,739781100,402.72,285.63,117.09
CECO ABATEMENT,02-Central,738561000,340.00,124.14,215.86
CECO ABATEMENT,02-Central,779956579,100.00,57.60,42.40
CECO ABATEMENT,02-Central,779957600,80.00,76.30,3.70
CENTURY ALUMINUM OF,02-Central,734311100,11650.32,1025.47,10624.85
CERAMIC SYSTEMS INC.,02-Central,733403200,17468.00,3784.75,13683.25
CERAMIC SYSTEMS INC.,02-Central,766121000,1182.00,653.98,528.02
CERAMIC SYSTEMS INC.,02-Central,764341000,672.00,275.22,396.78
CERAMIC SYSTEMS INC.,02-Central,764601000,600.00,263.16,336.84
CERAMTEC NORTH AMERICA,02-Central,770500000,1075.50,51.44,1024.06
CGR VALLEY PRODUCTS,02-Central,733870000,17690.65,6878.15,10812.50
CGR VALLEY PRODUCTS,02-Central,733867100,14175.00,8303.86,5871.14
CGR VALLEY PRODUCTS,02-Central,733586600,6000.48,730.36,5270.12
CGR VALLEY PRODUCTS,02-Central,733511000,3255.00,526.55,2728.45
CGR VALLEY PRODUCTS,02-Central,733211050,2368.80,428.64,1940.16
CGR VALLEY PRODUCTS,02-Central,733721000,278.00,69.32,208.68
CH2M HILL CONSTRUCTORS,02-Central,737301000,5085.44,2575.36,2510.08
CH2M HILL CONSTRUCTORS,02-Central,734311100,363.44,35.30,328.14
CHARLES JOHNSTON,02-Central,739380000,396.48,120.15,276.33
CHASE CORPORATION-GLOBAL,02-Central,720219100,82980.00,1999.44,80980.56
CHASE SPECIALTY COATINGS,02-Central,720219100,224201.25,5455.01,218746.24
CHEM-CLAY CORPORATION,02-Central,777601000,386.72,72.02,314.70
CHERNE CONTRACTING CORP,02-Central,737912200,4161.50,636.63,3524.87
CHEVRON,02-Central,780270000,481.70,92.75,388.95
CHIZ BROS INC,02-Central,764901000,428112.70,219814.24,208298.46
CHIZ BROS INC,02-Central,764821000,250859.60,119423.09,131436.51
CHIZ BROS INC,02-Central,742520150,212675.20,100384.32,112290.88
CHIZ BROS INC,02-Central,764822000,162763.00,48373.26,114389.74
CHIZ BROS INC,02-Central,734990000,154938.88,50417.59,104521.29
CHIZ BROS INC,02-Central,782070000,126267.98,90079.01,36188.97
CHIZ BROS INC,02-Central,718500000,112006.40,36944.89,75061.51
CHIZ BROS INC,02-Central,742541500,102420.52,15380.09,87040.43
CHIZ BROS INC,02-Central,780370000,83068.86,46496.76,36572.10
CHIZ BROS INC,02-Central,733525600,82222.50,14677.98,67544.52
CHIZ BROS INC,02-Central,764822110,79032.00,20653.87,58378.13
CHIZ BROS INC,02-Central,779952165,71359.56,32699.81,38659.75
CHIZ BROS INC,02-Central,779952102,67190.40,24858.78,42331.62
CHIZ BROS INC,02-Central,725482900,65910.00,30617.78,35292.22
CHIZ BROS INC,02-Central,733211050,63534.90,14076.51,49458.39
CHIZ BROS INC,02-Central,764827550,53558.40,14313.48,39244.92
CHIZ BROS INC,02-Central,779958876,53544.45,22331.69,31212.76
CHIZ BROS INC,02-Central,770240000,53299.50,7049.42,46250.08
CHIZ BROS INC,02-Central,742451500,51687.72,13383.10,38304.62
CHIZ BROS INC,02-Central,742451300,47387.38,12269.64,35117.74
CHIZ BROS INC,02-Central,739781100,45924.88,31668.28,14256.60
CHIZ BROS INC,02-Central,781670000,45719.10,29981.80,15737.30
CHIZ BROS INC,02-Central,779956835,42327.36,23428.68,18898.68
CHIZ BROS INC,02-Central,733525500,41270.00,7054.71,34215.29
CHIZ BROS INC,02-Central,779956823,39803.40,19593.42,20209.98
CHIZ BROS INC,02-Central,770430000,39554.75,6021.20,33533.55
CHIZ BROS INC,02-Central,733211000,37993.80,9216.43,28777.37
CHIZ BROS INC,02-Central,780770000,35739.90,23434.82,12305.08
CHIZ BROS INC,02-Central,771504000,35641.80,4673.99,30967.81
CHIZ BROS INC,02-Central,791002500,33608.59,16835.62,16772.97
CHIZ BROS INC,02-Central,779956826,33320.64,16103.52,17217.12
CHIZ BROS INC,02-Central,770500000,32860.35,2752.38,30107.97
CHIZ BROS INC,02-Central,764861000,31461.00,8243.84,23217.16
CHIZ BROS INC,02-Central,727000025,31362.57,5448.40,25914.17
CHIZ BROS INC,02-Central,733151550,31311.00,6767.89,24543.11
CHIZ BROS INC,02-Central,739561200,30830.59,8899.65,21930.94
CHIZ BROS INC,02-Central,770450000,29403.00,5077.91,24325.09
CHIZ BROS INC,02-Central,734414100,28988.88,6882.41,22106.47
CHIZ BROS INC,02-Central,770830000,25496.80,1307.75,24189.05
CHIZ BROS INC,02-Central,770372000,25033.40,3217.02,21816.38
CHIZ BROS INC,02-Central,764601000,24947.00,10283.89,14663.11
CHIZ BROS INC,02-Central,764521000,23955.00,8507.50,15447.50
CHIZ BROS INC,02-Central,734132000,23785.95,5497.92,18288.03
CHIZ BROS INC,02-Central,777601000,23784.89,6719.88,17065.01
CHIZ BROS INC,02-Central,727041200,23485.44,4180.76,19304.68
CHIZ BROS INC,02-Central,742461100,23394.00,5539.34,17854.66
CHIZ BROS INC,02-Central,770222000,23200.00,2810.62,20389.38
CHIZ BROS INC,02-Central,764781040,22092.90,7133.22,14959.68
CHIZ BROS INC,02-Central,739541020,21907.20,7671.60,14235.60
CHIZ BROS INC,02-Central,770200000,21852.00,2326.32,19525.68
CHIZ BROS INC,02-Central,733151500,21656.80,3569.34,18087.46
CHIZ BROS INC,02-Central,770810000,21267.40,638.28,20629.12
CHIZ BROS INC,02-Central,781970000,20944.44,10156.95,10787.49
CHIZ BROS INC,02-Central,739541100,20292.71,5185.87,15106.84
CHIZ BROS INC,02-Central,733511000,20226.89,4831.98,15394.91
CHIZ BROS INC,02-Central,726000062,20100.00,4565.32,15534.68
CHIZ BROS INC,02-Central,770440000,19920.00,3732.46,16187.54
CHIZ BROS INC,02-Central,793330000,19820.00,4231.18,15588.82
CHIZ BROS INC,02-Central,734211200,19708.64,2570.76,17137.88
CHIZ BROS INC,02-Central,779956824,19609.92,9782.70,9827.22
CHIZ BROS INC,02-Central,739761100,19475.76,12157.83,7317.93
CHIZ BROS INC,02-Central,718501000,17964.80,3771.28,14193.52
CHIZ BROS INC,02-Central,777904301,17826.50,8054.84,9771.66
CHIZ BROS INC,02-Central,734910000,17716.70,4187.77,13528.93
CHIZ BROS INC,02-Central,770560000,17581.20,2582.69,14998.51
CHIZ BROS INC,02-Central,734162000,17380.80,8646.82,8733.98
CHIZ BROS INC,02-Central,739641100,16204.86,8383.08,7821.78
CHIZ BROS INC,02-Central,742471100,15470.56,4252.63,11217.93
CHIZ BROS INC,02-Central,735602000,14544.00,4142.22,10401.78
CHIZ BROS INC,02-Central,768221000,14224.00,3184.22,11039.78
CHIZ BROS INC,02-Central,770512000,13837.95,1596.09,12241.86
CHIZ BROS INC,02-Central,737912200,13207.00,3276.34,9930.66
CHIZ BROS INC,02-Central,737311000,13157.76,6511.98,6645.78
CHIZ BROS INC,02-Central,766121008,12160.00,6563.55,5596.45
CHIZ BROS INC,02-Central,739681100,11784.96,5782.10,6002.86
CHIZ BROS INC,02-Central,742451000,11610.00,3006.08,8603.92
CHIZ BROS INC,02-Central,734311100,11002.52,1346.38,9656.14
CHIZ BROS INC,02-Central,766122110,10574.40,2155.32,8419.08
CHIZ BROS INC,02-Central,764781000,10292.40,2904.99,7387.41
CHIZ BROS INC,02-Central,766121000,10056.00,4525.00,5531.00
CHIZ BROS INC,02-Central,779956856,10005.12,5002.92,5002.20
CHIZ BROS INC,02-Central,764901050,9982.40,5039.64,4942.76
CHIZ BROS INC,02-Central,734251300,9631.99,968.29,8663.70
CHIZ BROS INC,02-Central,779989329,9408.00,3668.03,5739.97
CHIZ BROS INC,02-Central,733725500,9400.80,2807.33,6593.47
CHIZ BROS INC,02-Central,770260000,9333.00,1255.14,8077.86
CHIZ BROS INC,02-Central,764481000,9238.40,2486.12,6752.28
CHIZ BROS INC,02-Central,734751200,8802.10,1724.70,7077.40
CHIZ BROS INC,02-Central,771320000,8519.00,1396.26,7122.74
CHIZ BROS INC,02-Central,770392000,8384.00,1318.81,7065.19
CHIZ BROS INC,02-Central,733151600,8312.40,1425.15,6887.25
CHIZ BROS INC,02-Central,764522000,8265.00,3410.29,4854.71
CHIZ BROS INC,02-Central,779952109,8224.50,3290.04,4934.46
CHIZ BROS INC,02-Central,770530000,8165.00,1588.85,6576.15
CHIZ BROS INC,02-Central,764524010,8096.00,4369.55,3726.45
CHIZ BROS INC,02-Central,739561100,8053.59,2199.06,5854.53
CHIZ BROS INC,02-Central,733403200,7730.80,1050.74,6680.06
CHIZ BROS INC,02-Central,733671000,7460.21,1161.03,6299.18
CHIZ BROS INC,02-Central,739541200,7351.04,2086.40,5264.64
CHIZ BROS INC,02-Central,780730000,7269.40,4570.41,2698.99
CHIZ BROS INC,02-Central,781630000,7236.72,4746.28,2490.44
CHIZ BROS INC,02-Central,770510000,7020.00,1102.38,5917.62
CHIZ BROS INC,02-Central,764561000,6676.50,1540.60,5135.90
CHIZ BROS INC,02-Central,738561000,6630.88,4777.92,1852.96
CHIZ BROS INC,02-Central,766081013,6450.00,3311.23,3138.77
CHIZ BROS INC,02-Central,779958865,6093.00,3510.58,2582.42
CHIZ BROS INC,02-Central,785890000,5943.36,4715.44,1227.92
CHIZ BROS INC,02-Central,768112000,5846.40,1454.90,4391.50
CHIZ BROS INC,02-Central,782060000,5481.23,2491.40,2989.83
CHIZ BROS INC,02-Central,764821073,5478.20,6269.40,-791.20
CHIZ BROS INC,02-Central,764821027,5440.00,2748.10,2691.90
CHIZ BROS INC,02-Central,764261000,5316.00,1373.61,3942.39
CHIZ BROS INC,02-Central,795210000,5230.08,1850.32,3379.76
CHIZ BROS INC,02-Central,739741100,5161.76,3577.55,1584.21
CHIZ BROS INC,02-Central,770210000,4986.00,778.43,4207.57
CHIZ BROS INC,02-Central,770710000,4952.00,134.52,4817.48
CHIZ BROS INC,02-Central,764952010,4928.00,2365.61,2562.39
CHIZ BROS INC,02-Central,777903100,4901.86,1946.28,2955.58
CHIZ BROS INC,02-Central,734851000,4602.24,1186.82,3415.42
CHIZ BROS INC,02-Central,779958676,4551.12,2412.03,2139.09
CHIZ BROS INC,02-Central,770520000,4537.50,444.35,4093.15
CHIZ BROS INC,02-Central,779958247,4513.60,2780.60,1733.00
CHIZ BROS INC,02-Central,764341000,4492.00,1605.45,2886.55
CHIZ BROS INC,02-Central,770220000,4470.00,503.15,3966.85
CHIZ BROS INC,02-Central,782020000,4390.04,1996.84,2393.20
CHIZ BROS INC,02-Central,733423800,4288.00,767.33,3520.67
CHIZ BROS INC,02-Central,768131000,4272.85,1344.47,2928.38
CHIZ BROS INC,02-Central,777730600,4194.00,1276.20,2917.80
CHIZ BROS INC,02-Central,779957955,3987.72,2538.39,1449.33
CHIZ BROS INC,02-Central,764824000,3900.00,1896.72,2003.28
CHIZ BROS INC,02-Central,733403300,3863.20,490.18,3373.02
CHIZ BROS INC,02-Central,770202000,3645.00,911.40,2733.60
CHIZ BROS INC,02-Central,784400000,3643.20,2180.00,1463.20
CHIZ BROS INC,02-Central,740063000,3617.03,1071.46,2545.57
CHIZ BROS INC,02-Central,793550000,3570.00,841.18,2728.82
CHIZ BROS INC,02-Central,764902000,3380.00,638.54,2741.46
CHIZ BROS INC,02-Central,766261000,3310.00,1563.83,1746.17
CHIZ BROS INC,02-Central,779958862,3308.40,1876.30,1432.10
CHIZ BROS INC,02-Central,765661000,3200.00,1287.10,1912.90
CHIZ BROS INC,02-Central,779987564,3174.08,1033.36,2140.72
CHIZ BROS INC,02-Central,740142500,3147.84,2360.45,787.39
CHIZ BROS INC,02-Central,741061000,3142.82,143.71,2999.11
CHIZ BROS INC,02-Central,770522000,3094.00,710.36,2383.64
CHIZ BROS INC,02-Central,766122000,2952.00,1035.45,1916.55
CHIZ BROS INC,02-Central,742491700,2929.88,1812.66,1117.22
CHIZ BROS INC,02-Central,733444300,2845.20,570.67,2274.53
CHIZ BROS INC,02-Central,770540000,2719.50,628.60,2090.90
CHIZ BROS INC,02-Central,777902700,2597.49,1141.55,1455.94
CHIZ BROS INC,02-Central,779959183,2585.88,1801.80,784.08
CHIZ BROS INC,02-Central,771120000,2529.60,105.38,2424.22
CHIZ BROS INC,02-Central,751165000,2515.78,1509.46,1006.32
CHIZ BROS INC,02-Central,764482000,2509.20,626.48,1882.72
CHIZ BROS INC,02-Central,779958863,2345.20,1282.68,1062.52
CHIZ BROS INC,02-Central,779952104,2289.15,1030.11,1259.04
CHIZ BROS INC,02-Central,737301000,2287.36,490.72,1796.64
CHIZ BROS INC,02-Central,742548000,2275.00,284.20,1990.80
CHIZ BROS INC,02-Central,793336074,2241.00,1269.57,971.43
CHIZ BROS INC,02-Central,766231000,2100.00,719.90,1380.10
CHIZ BROS INC,02-Central,780760000,2091.60,411.27,1680.33
CHIZ BROS INC,02-Central,779261000,2010.54,560.02,1450.52
CHIZ BROS INC,02-Central,739561000,1960.27,602.39,1357.88
CHIZ BROS INC,02-Central,770300000,1955.25,226.02,1729.23
CHIZ BROS INC,02-Central,779988833,1925.42,859.30,1066.12
CHIZ BROS INC,02-Central,779959180,1829.96,1222.49,607.47
CHIZ BROS INC,02-Central,768141000,1725.00,405.21,1319.79
CHIZ BROS INC,02-Central,770212000,1716.00,468.45,1247.55
CHIZ BROS INC,02-Central,780680000,1671.12,427.81,1243.31
CHIZ BROS INC,02-Central,764761000,1660.00,720.18,939.82
CHIZ BROS INC,02-Central,734861000,1485.40,611.14,874.26
CHIZ BROS INC,02-Central,733721000,1470.00,452.45,1017.55
CHIZ BROS INC,02-Central,771132000,1459.40,71.60,1387.80
CHIZ BROS INC,02-Central,779957171,1414.80,1197.99,216.81
CHIZ BROS INC,02-Central,737211000,1336.39,289.24,1047.15
CHIZ BROS INC,02-Central,770140000,1309.72,138.91,1170.81
CHIZ BROS INC,02-Central,740283502,1302.40,193.64,1108.76
CHIZ BROS INC,02-Central,733464500,1236.00,195.16,1040.84
CHIZ BROS INC,02-Central,770600000,1231.02,271.26,959.76
CHIZ BROS INC,02-Central,764562000,1161.30,257.01,904.29
CHIZ BROS INC,02-Central,777642000,1153.05,360.61,792.44
CHIZ BROS INC,02-Central,754285000,1145.20,498.79,646.41
CHIZ BROS INC,02-Central,781800000,1124.82,596.25,528.57
CHIZ BROS INC,02-Central,733444200,1087.00,181.35,905.65
CHIZ BROS INC,02-Central,739370000,1086.40,967.68,118.72
CHIZ BROS INC,02-Central,763991000,1072.80,275.16,797.64
CHIZ BROS INC,02-Central,780690000,1032.72,254.51,778.21
CHIZ BROS INC,02-Central,770380000,1016.40,183.87,832.53
CHIZ BROS INC,02-Central,754265000,1012.20,465.21,546.99
CHIZ BROS INC,02-Central,738521000,962.72,717.26,245.46
CHIZ BROS INC,02-Central,793346074,924.00,518.98,405.02
CHIZ BROS INC,02-Central,734900000,891.48,343.64,547.84
CHIZ BROS INC,02-Central,734651200,872.65,147.00,725.65
CHIZ BROS INC,02-Central,738401000,828.90,642.24,186.66
CHIZ BROS INC,02-Central,779958864,799.50,529.02,270.48
CHIZ BROS INC,02-Central,734890000,793.73,198.96,594.77
CHIZ BROS INC,02-Central,726020005,745.12,167.16,577.96
CHIZ BROS INC,02-Central,779989944,743.80,678.50,65.30
CHIZ BROS INC,02-Central,779221000,743.66,209.48,534.18
CHIZ BROS INC,02-Central,741620310,732.00,145.82,586.18
CHIZ BROS INC,02-Central,767970010,716.50,5.56,710.94
CHIZ BROS INC,02-Central,792702000,710.00,193.20,516.80
CHIZ BROS INC,02-Central,741640310,708.00,131.71,576.29
CHIZ BROS INC,02-Central,724311200,697.90,154.28,543.62
CHIZ BROS INC,02-Central,734711100,672.21,135.55,536.66
CHIZ BROS INC,02-Central,792650000,656.00,228.70,427.30
CHIZ BROS INC,02-Central,770580000,652.00,169.46,482.54
CHIZ BROS INC,02-Central,793320074,635.00,321.38,313.62
CHIZ BROS INC,02-Central,779959182,623.90,507.37,116.53
CHIZ BROS INC,02-Central,727000440,614.40,121.92,492.48
CHIZ BROS INC,02-Central,770620100,585.00,55.28,529.72
CHIZ BROS INC,02-Central,764762000,577.60,252.56,325.04
CHIZ BROS INC,02-Central,777650000,535.20,285.32,249.88
CHIZ BROS INC,02-Central,777671000,526.40,460.50,65.90
CHIZ BROS INC,02-Central,734830000,525.00,90.82,434.18
CHIZ BROS INC,02-Central,739290000,524.16,215.31,308.85
CHIZ BROS INC,02-Central,741001000,522.73,87.94,434.79
CHIZ BROS INC,02-Central,779989946,496.80,480.90,15.90
CHIZ BROS INC,02-Central,780501000,494.40,232.71,261.69
CHIZ BROS INC,02-Central,779959804,488.25,428.95,59.30
CHIZ BROS INC,02-Central,779989945,472.90,461.70,11.20
CHIZ BROS INC,02-Central,733505300,465.00,77.58,387.42
CHIZ BROS INC,02-Central,740123000,427.14,121.37,305.77
CHIZ BROS INC,02-Central,779959204,407.58,199.73,207.85
CHIZ BROS INC,02-Central,754067000,397.08,125.08,272.00
CHIZ BROS INC,02-Central,734121000,372.00,83.56,288.44
CHIZ BROS INC,02-Central,792654000,341.00,129.68,211.32
CHIZ BROS INC,02-Central,776131000,340.20,252.08,88.12
CHIZ BROS INC,02-Central,779001000,338.94,84.71,254.23
CHIZ BROS INC,02-Central,739541000,311.04,71.62,239.42
CHIZ BROS INC,02-Central,779957933,285.93,216.60,69.33
CHIZ BROS INC,02-Central,779987255,201.60,92.70,108.90
CHIZ BROS INC,02-Central,776101000,131.40,70.80,60.60
CHIZ BROS INC,02-Central,734611100,131.30,26.96,104.34
CHIZ BROS INC,02-Central,780330000,129.45,35.90,93.55
CHIZ BROS INC,02-Central,779181000,69.87,21.45,48.42
CHIZ BROS INC,02-Central,773591000,44.10,13.41,30.69
CHIZ BROS INC,02-Central,779986450,10.80,2.77,8.03
CHRISTY CATALYTICS LLC,02-Central,777904301,259.18,117.47,141.71
CHRISTY CATALYTICS LLC,02-Central,734830000,246.00,44.64,201.36
CHRISTY INDUSTRIAL SERVIC,02-Central,780650000,6874.88,2379.87,4495.01
CHRISTY INDUSTRIAL SERVIC,02-Central,781500000,5951.88,961.64,4990.24
CHRISTY INDUSTRIAL SERVIC,02-Central,754924014,3840.00,2300.72,1539.28
CHRISTY INDUSTRIAL SERVIC,02-Central,718501000,3075.00,808.14,2266.86
CHRISTY INDUSTRIAL SERVIC,02-Central,765661000,2304.00,763.56,1540.44
CHRISTY INDUSTRIAL SERVIC,02-Central,754864023,1724.80,915.78,809.02
CHRISTY INDUSTRIAL SERVIC,02-Central,754327000,1020.96,416.10,604.86
CHRISTY INDUSTRIAL SERVIC,02-Central,754864020,915.60,392.48,523.12
CHRISTY INDUSTRIAL SERVIC,02-Central,739761100,853.92,361.97,491.95
CHRISTY REFRACTORIES CO,02-Central,768242007,194216.00,75633.28,118582.72
CHRISTY REFRACTORIES CO,02-Central,766261020,160372.00,93950.99,66421.01
CHRISTY REFRACTORIES CO,02-Central,734990000,58344.00,10782.04,47561.96
CHRISTY REFRACTORIES CO,02-Central,764529600,48400.00,20108.26,28291.74
CHRISTY REFRACTORIES CO,02-Central,733505300,47145.00,8132.54,39012.46
CHRISTY REFRACTORIES CO,02-Central,733721000,12121.20,2966.63,9154.57
CHRISTY REFRACTORIES CO,02-Central,740382150,9979.56,4150.89,5828.67
CHRISTY REFRACTORIES CO,02-Central,737912200,9197.00,3440.23,5756.77
CHRISTY REFRACTORIES CO,02-Central,770570000,5444.85,477.03,4967.82
CHRISTY REFRACTORIES CO,02-Central,734151100,4714.82,992.12,3722.70
CHRISTY REFRACTORIES CO,02-Central,770220000,4458.00,414.56,4043.44
CHRISTY REFRACTORIES CO,02-Central,734173000,4295.00,1169.08,3125.92
CHRISTY REFRACTORIES CO,02-Central,768132210,3800.00,1318.32,2481.68
CHRISTY REFRACTORIES CO,02-Central,733444200,3621.00,589.72,3031.28
CHRISTY REFRACTORIES CO,02-Central,735602000,2990.00,966.66,2023.34
CHRISTY REFRACTORIES CO,02-Central,764342000,2736.00,2246.52,489.48
CHRISTY REFRACTORIES CO,02-Central,733383000,2688.40,456.38,2232.02
CHRISTY REFRACTORIES CO,02-Central,770240000,2685.00,278.62,2406.38
CHRISTY REFRACTORIES CO,02-Central,770260000,2025.00,210.99,1814.01
CHRISTY REFRACTORIES CO,02-Central,770200000,1998.00,208.81,1789.19
CHRISTY REFRACTORIES CO,02-Central,740382101,1882.44,758.49,1123.95
CHRISTY REFRACTORIES CO,02-Central,734162000,1333.64,563.87,769.77
CHRISTY REFRACTORIES CO,02-Central,770245000,1146.79,259.92,886.87
CHRISTY REFRACTORIES CO,02-Central,774401000,1075.50,339.36,736.14
CHRISTY REFRACTORIES CO,02-Central,733211000,753.90,159.61,594.29
CHRISTY REFRACTORIES CO,02-Central,770300000,668.25,75.34,592.91
CHRISTY REFRACTORIES CO,02-Central,733151600,621.20,106.23,514.97
CHRISTY REFRACTORIES CO,02-Central,774311000,621.00,377.43,243.57
CHRISTY REFRACTORIES CO,02-Central,733211050,478.60,99.39,379.21
CHRISTY REFRACTORIES CO,02-Central,733141300,324.00,69.73,254.27
CHRISTY REFRACTORIES CO,02-Central,737902100,287.00,67.12,219.88
CHRISTY REFRACTORIES CO,02-Central,770210000,285.00,31.52,253.48
CHRISTY REFRACTORIES CO,02-Central,734751200,269.00,74.78,194.22
CHRISTY REFRACTORIES CO,02-Central,734711100,262.58,54.10,208.48
CHRISTY REFRACTORIES CO,02-Central,764482000,172.80,34.10,138.70
CLEMSON UNIVERSITY,02-Central,788200010,200.43,35.27,165.16
COLUMBUS RUBBER & GASKET,02-Central,733211000,1391.00,147.98,1243.02
COMPOSITE USA,02-Central,733510000,565.00,79.51,485.49
COMPOSITE USA,02-Central,734211200,243.46,23.38,220.08
CONDO ELECTRIC INDUSTRIAL,02-Central,733141300,496.00,69.73,426.27
CONDO ELECTRIC INDUSTRIAL,02-Central,733151600,450.00,46.64,403.36
CONSOLIDATED METAL PRODUC,02-Central,740361701,579.60,318.65,260.95
CONSUTECH SYSTEMS LLC,02-Central,777642000,7665.00,2302.55,5362.45
CORE PIPE,02-Central,724261200,786.00,78.20,707.80
CORMETECH INC,02-Central,770200000,630.00,40.79,589.21
CORVID TECHNOLOGIES,02-Central,777642000,143.00,50.36,92.64
"CPP - CLEVELAND, INC.",02-Central,764781000,22243.00,5719.18,16523.82
"CPP - CLEVELAND, INC.",02-Central,764461006,15028.00,4749.30,10278.70
CREATIVE ENGINEERS,02-Central,764901000,1275.75,48.42,1227.33
CROWN BATTERY MFG. CO.,02-Central,733151500,1372.00,149.49,1222.51
CYTEC,02-Central,723960000,98698.50,27780.73,70917.77
CYTEC,02-Central,734000008,1701.00,362.77,1338.23
CYTEC,02-Central,720219100,1339.20,39.99,1299.21
CYTEC,02-Central,734000006,1182.00,159.44,1022.56
DANSER INC,02-Central,727000140,207544.56,88726.90,118817.66
DANSER INC,02-Central,763991000,177678.80,123197.80,54481.00
DANSER INC,02-Central,734910000,41998.80,11524.33,30474.47
DANSER INC,02-Central,734830000,11152.00,1517.88,9634.12
DANSER INC,02-Central,734311100,1028.64,101.11,927.53
DANSER INC,02-Central,770220000,690.00,55.79,634.21
DANSER INC,02-Central,770200000,486.00,42.41,443.59
DARK CORNER GLASS,02-Central,737321000,1551.04,214.14,1336.90
DATA CENTER RESOURCES LLC,02-Central,735751000,2002.00,621.86,1380.14
DATA CENTER RESOURCES LLC,02-Central,735751020,704.00,260.73,443.27
DAVIS SALES AGENCY,02-Central,738561000,10529.92,3930.10,6599.82
DEAS CONSTRUCTION,02-Central,739741100,385.60,168.32,217.28
DECATUR RUBBER & GASKET,02-Central,770510000,940.00,41.39,898.61
DECATUR RUBBER & GASKET,02-Central,770200000,666.00,42.41,623.59
DECATUR RUBBER & GASKET,02-Central,733444200,332.00,34.69,297.31
DELTA REFRACTORIES,02-Central,766121000,20000.00,10899.52,9100.48
DELTA REFRACTORIES,02-Central,734950000,4540.00,1131.28,3408.72
DELTA REFRACTORIES,02-Central,764821000,4260.00,1842.95,2417.05
DELTA REFRACTORIES,02-Central,764901000,2712.00,516.60,2195.40
DELTA REFRACTORIES,02-Central,766081010,1320.00,545.48,774.52
DELTA REFRACTORIES,02-Central,740402402,815.00,193.66,621.34
DELTA REFRACTORIES,02-Central,734851000,380.23,91.40,288.83
DELTA REFRACTORIES,02-Central,733141300,336.00,77.97,258.03
DELTA REFRACTORIES,02-Central,733151600,324.00,53.12,270.88
DENTSPLY CERAMCO,02-Central,734311100,2800.24,329.75,2470.49
DENTSPLY CERAMCO,02-Central,734211200,1344.00,137.04,1206.96
DIAMOND POWER INT'L. INC.,02-Central,727000140,3241.60,284.13,2957.47
DIELECTRIC SOLUTIONS LLC,02-Central,733403100,1107.25,92.79,1014.46
DILLON SUPPLY COMPANY,02-Central,770290000,2938.50,238.94,2699.56
DILLON SUPPLY,02-Central,770290000,3918.00,318.58,3599.42
DISTRIBUTION INT'L.,02-Central,735602000,384858.40,133532.97,251325.43
DISTRIBUTION INT'L.,02-Central,735602110,163798.40,61559.86,102238.54
DISTRIBUTION INT'L.,02-Central,764521030,114990.00,66784.69,48205.31
DISTRIBUTION INT'L.,02-Central,764521000,75605.00,28435.38,47169.62
DISTRIBUTION INT'L.,02-Central,764522000,26152.00,9225.83,16926.17
DISTRIBUTION INT'L.,02-Central,768111200,17712.00,6791.47,10920.53
DISTRIBUTION INT'L.,02-Central,735750000,17623.20,7140.05,10483.15
DISTRIBUTION INT'L.,02-Central,768111220,17064.00,8544.70,8519.30
DISTRIBUTION INT'L.,02-Central,764906000,6336.00,2330.33,4005.67
DISTRIBUTION INT'L.,02-Central,734830000,4554.40,768.14,3786.26
DISTRIBUTION INT'L.,02-Central,735752000,2662.40,1031.04,1631.36
DISTRIBUTION INT'L.,02-Central,739561100,2338.56,799.66,1538.90
DISTRIBUTION INT'L.,02-Central,733211050,2035.60,621.21,1414.39
DISTRIBUTION INT'L.,02-Central,733511000,1550.00,1504.42,45.58
DISTRIBUTION INT'L.,02-Central,764601000,1224.00,425.15,798.85
DISTRIBUTION INT'L.,02-Central,733725500,1152.00,589.40,562.60
DISTRIBUTION INT'L.,02-Central,764602000,954.60,317.96,636.64
DISTRIBUTION INT'L.,02-Central,734151100,832.20,186.90,645.30
DISTRIBUTION INT'L.,02-Central,735602010,812.50,470.93,341.57
DISTRIBUTION INT'L.,02-Central,733671000,485.86,112.55,373.31
DISTRIBUTION INT'L.,02-Central,739741100,436.99,336.64,100.35
DISTRIBUTION INT'L.,02-Central,739370000,388.22,276.48,111.74
DISTRIBUTION INT'L.,02-Central,777601000,287.95,58.80,229.15
DISTRIBUTION INT'L.,02-Central,770220000,258.60,27.35,231.25
DISTRIBUTION INT'L.,02-Central,733211000,201.00,53.20,147.80
DISTRIBUTION INT'L.,02-Central,764901000,183.00,64.06,118.94
DISTRIBUTION INT'L.,02-Central,734900000,171.60,57.54,114.06
DISTRIBUTION INT'L.,02-Central,768231000,165.30,53.93,111.37
DIVERSIFIED INSULATION,02-Central,735602000,48384.00,16461.45,31922.55
DIVERSIFIED INSULATION,02-Central,768241000,44998.00,12500.72,32497.28
DIVERSIFIED INSULATION,02-Central,768231000,24155.00,6627.45,17527.55
DIVERSIFIED INSULATION,02-Central,768750000,1394.40,149.20,1245.20
DIVERSIFIED INSULATION,02-Central,735602110,840.00,262.52,577.48
DIVERSIFIED INSULATION,02-Central,764901073,140.50,72.90,67.60
DOMTAR A.W. LLC,02-Central,733525500,1460.00,140.79,1319.21
DOUGLAS MACHINES CORP.,02-Central,733211050,3740.00,422.43,3317.57
DUMORE ENTERPRISES LTD,02-Central,733211000,912.00,94.78,817.22
DURR SYSTEMS INC.,02-Central,777904301,18130.00,8222.73,9907.27
DURR SYSTEMS INC.,02-Central,784140000,8866.20,4100.52,4765.68
DURR SYSTEMS INC.,02-Central,780250000,7309.76,3710.82,3598.94
DURR SYSTEMS INC.,02-Central,779959184,1791.60,807.62,983.98
DURR SYSTEMS INC.,02-Central,764821000,1375.00,344.42,1030.58
DURR SYSTEMS INC.,02-Central,780501000,232.68,116.36,116.32
DURR SYSTEMS INC.,02-Central,779956599,35.80,28.66,7.14
DeANGELO,02-Central,770200000,4284.00,296.89,3987.11
E I DUPONT DE NEMOURS &,02-Central,724071900,12620.65,1776.07,10844.58
E I DUPONT DE NEMOURS &,02-Central,733505100,1600.00,180.18,1419.82
E TIP INC.,02-Central,733211000,456.00,47.39,408.61
E.I.DUPONT DE NEMOURS&CO,02-Central,724071900,8714.15,1172.75,7541.40
E.I.DUPONT DE NEMOURS&CO,02-Central,733505100,2295.00,247.77,2047.23
E.I.DUPONT DE NEMOURS&CO,02-Central,733464500,298.00,29.52,268.48
E.K.S. PARTS INC.,02-Central,734900000,48688.00,2269.62,46418.38
E.K.S. PARTS INC.,02-Central,734311100,9173.60,2022.18,7151.42
EAST COAST METAL,02-Central,735602000,23626.00,5878.31,17747.69
"EB AIRFOILS, LLC",02-Central,739641100,926.64,274.86,651.78
"EB AIRFOILS, LLC",02-Central,764342000,859.00,46.80,812.20
EBERBACH CORP,02-Central,764821000,430.00,15.36,414.64
EBNER FURNACES INC,02-Central,777902700,34306.86,15546.90,18759.96
ECONOMY DIE & GASKET,02-Central,733464400,4382.00,607.18,3774.82
ECONOMY DIE & GASKET,02-Central,733444100,1028.00,244.86,783.14
ECONOMY DIE & GASKET,02-Central,733423600,371.20,45.08,326.12
EDEN PARK ILLUMINATION,02-Central,739390000,2103.36,494.59,1608.77
EFFICIENT MACHINE PRODUCT,02-Central,740382101,2635.20,845.57,1789.63
EISENMANN CORPORATION,02-Central,779952186,202744.08,154554.19,48189.89
EISENMANN CORPORATION,02-Central,779952185,53820.00,34419.86,19400.14
EISENMANN CORPORATION,02-Central,764822000,30212.00,11564.35,18647.65
EISENMANN CORPORATION,02-Central,777642000,5994.00,2719.44,3274.56
EISENMANN CORPORATION,02-Central,764901000,3900.00,1921.71,1978.29
EISENMANN CORPORATION,02-Central,739781100,2820.48,1142.52,1677.96
EISENMANN CORPORATION,02-Central,739390000,2785.92,1941.12,844.80
EISENMANN CORPORATION,02-Central,779958696,1027.20,699.73,327.47
EISENMANN CORPORATION,02-Central,764561000,810.00,221.77,588.23
EISENMANN CORPORATION,02-Central,789180000,693.80,231.56,462.24
EISENMANN CORPORATION,02-Central,764601000,688.00,212.58,475.42
EISENMANN CORPORATION,02-Central,734611100,618.24,217.44,400.80
EISENMANN CORPORATION,02-Central,773450100,496.80,120.66,376.14
EISENMANN CORPORATION,02-Central,764521000,456.50,146.27,310.23
EISENMANN CORPORATION,02-Central,739451000,419.84,232.07,187.77
EISENMANN CORPORATION,02-Central,779985087,165.60,62.88,102.72
EISENMANN CORPORATION,02-Central,780190000,129.24,112.37,16.87
EISENMANN CORPORATION,02-Central,771910510,124.80,42.96,81.84
EISENMANN CORPORATION,02-Central,773450200,83.20,160.40,-77.20
ELECTRO-FERRO CENTROL SAC,02-Central,733464600,5512.00,814.17,4697.83
ELECTROCHEMICAL SYSTEMS,02-Central,738401000,655.20,140.69,514.51
EMPIRE COMFORT SYSTEM,02-Central,726020019,94264.00,52499.09,41764.91
EMPIRE COMFORT SYSTEM,02-Central,726020018,15045.00,7749.80,7295.20
EMPIRE COMFORT SYSTEM,02-Central,738601000,6308.16,4890.12,1418.04
EMPIRE COMFORT SYSTEM,02-Central,777601000,639.18,130.82,508.36
EMPIRE COMFORT SYSTEM,02-Central,777642000,281.00,82.04,198.96
EMPIRE COMFORT SYSTEM,02-Central,734900000,134.60,38.36,96.24
ENGINEERED PRODUCTS CO.,02-Central,735602000,27390.00,8573.85,18816.15
ENGINEERED PRODUCTS CO.,02-Central,764821000,1900.00,626.21,1273.79
ENGINETICS AEROSPACE,02-Central,740283502,1680.80,220.35,1460.45
ENGINETICS AEROSPACE,02-Central,733525400,1505.00,148.27,1356.73
ENGINETICS AEROSPACE,02-Central,733444200,696.00,77.28,618.72
ENSER CORPORATION,02-Central,740382201,4460.00,1202.06,3257.94
ENSER CORPORATION,02-Central,740402402,3172.50,628.36,2544.14
ENVIRACARBON,02-Central,734900000,431.60,76.72,354.88
ENVIRONEX INC,02-Central,764342000,893.00,45.78,847.22
ENVIRONEX INC,02-Central,764301000,670.00,37.11,632.89
"ENVIRONMENTAL SERVICES,",02-Central,770560000,3306.00,258.27,3047.73
ERICO INTERNATIONAL CORP.,02-Central,770218000,23760.00,3172.23,20587.77
ERIKS MIDWEST INC.,02-Central,733211000,935.00,100.59,834.41
ESCO TURBINE TECHNOLOGIES,02-Central,764781000,75408.00,20410.65,54997.35
ESCO TURBINE TECHNOLOGIES,02-Central,764461006,61591.00,19661.20,41929.80
ESSEX GROUP,02-Central,733211000,1188.00,201.19,986.81
ESSEX GROUP,02-Central,739741100,856.96,336.64,520.32
ESTERLINE,02-Central,739781100,18046.40,7071.70,10974.70
ESTERLINE,02-Central,739741100,5075.20,1784.80,3290.40
ESTERLINE,02-Central,739561200,2439.68,455.68,1984.00
ESTERLINE,02-Central,739561000,492.48,86.19,406.29
ESTERLINE,02-Central,793000000,393.60,22.98,370.62
ESTES REFRACTORY SALES &,02-Central,770560000,5988.00,358.02,5629.98
ESTES REFRACTORY SALES &,02-Central,764901000,2350.00,640.57,1709.43
ESTES REFRACTORY SALES &,02-Central,733464500,524.00,130.10,393.90
ETS SCHAEFER,02-Central,766121132,915895.60,529020.31,386875.29
ETS SCHAEFER,02-Central,766121133,350513.60,172031.13,178482.47
ETS SCHAEFER,02-Central,764521100,322646.00,153946.03,168699.97
ETS SCHAEFER,02-Central,733151500,296345.20,50672.96,245672.24
ETS SCHAEFER,02-Central,764827010,233397.00,99022.13,134374.87
ETS SCHAEFER,02-Central,768221010,187937.00,76930.97,111006.03
ETS SCHAEFER,02-Central,764903100,94403.25,41678.95,52724.30
ETS SCHAEFER,02-Central,765665100,79582.00,32508.68,47073.32
ETS SCHAEFER,02-Central,764261100,79452.80,49608.54,29844.26
ETS SCHAEFER,02-Central,764462510,61018.80,39665.25,21353.55
ETS SCHAEFER,02-Central,764785100,44280.00,24541.31,19738.69
ETS SCHAEFER,02-Central,764265110,42515.42,23941.95,18573.47
ETS SCHAEFER,02-Central,764264052,34132.00,23787.58,10344.42
ETS SCHAEFER,02-Central,770230000,31094.70,3326.38,27768.32
ETS SCHAEFER,02-Central,764901054,30766.70,13551.74,17214.96
ETS SCHAEFER,02-Central,766121007,28680.00,16932.71,11747.29
ETS SCHAEFER,02-Central,737912200,23532.51,5005.35,18527.16
ETS SCHAEFER,02-Central,764824002,22558.40,9782.96,12775.44
ETS SCHAEFER,02-Central,764828710,21196.80,8089.04,13107.76
ETS SCHAEFER,02-Central,764829200,19960.00,8532.40,11427.60
ETS SCHAEFER,02-Central,764345053,17016.80,11141.62,5875.18
ETS SCHAEFER,02-Central,793540000,14553.00,4417.06,10135.94
ETS SCHAEFER,02-Central,764266501,14254.40,8022.87,6231.53
ETS SCHAEFER,02-Central,733464500,14049.00,2156.60,11892.40
ETS SCHAEFER,02-Central,766122006,13365.00,7737.95,5627.05
ETS SCHAEFER,02-Central,764762200,12963.60,7341.68,5621.92
ETS SCHAEFER,02-Central,734900000,12038.80,4668.56,7370.24
ETS SCHAEFER,02-Central,734851000,11342.90,2421.37,8921.53
ETS SCHAEFER,02-Central,764787102,10612.80,5963.94,4648.86
ETS SCHAEFER,02-Central,766121612,10332.00,6437.90,3894.10
ETS SCHAEFER,02-Central,764902010,8952.00,3633.52,5318.48
ETS SCHAEFER,02-Central,764261610,8578.98,4921.19,3657.79
ETS SCHAEFER,02-Central,777601000,8379.56,2237.08,6142.48
ETS SCHAEFER,02-Central,733211050,8100.90,1745.61,6355.29
ETS SCHAEFER,02-Central,733211000,7084.50,1444.03,5640.47
ETS SCHAEFER,02-Central,766122000,6265.00,4026.70,2238.30
ETS SCHAEFER,02-Central,764561200,6235.00,3326.93,2908.07
ETS SCHAEFER,02-Central,764483200,5983.20,3936.67,2046.53
ETS SCHAEFER,02-Central,764601051,5482.50,3067.08,2415.42
ETS SCHAEFER,02-Central,764821000,5222.00,2191.72,3030.28
ETS SCHAEFER,02-Central,734910000,4901.65,1155.73,3745.92
ETS SCHAEFER,02-Central,764862000,4648.35,2150.54,2497.81
ETS SCHAEFER,02-Central,764261600,4377.60,2413.56,1964.04
ETS SCHAEFER,02-Central,768221000,4272.10,1731.20,2540.90
ETS SCHAEFER,02-Central,764465010,3645.60,2400.30,1245.30
ETS SCHAEFER,02-Central,766162000,2918.35,2146.00,772.35
ETS SCHAEFER,02-Central,764864006,2236.00,1069.62,1166.38
ETS SCHAEFER,02-Central,766161100,1822.00,1050.23,771.77
ETS SCHAEFER,02-Central,764762000,1702.40,969.38,733.02
ETS SCHAEFER,02-Central,733525600,1417.50,207.59,1209.91
ETS SCHAEFER,02-Central,766081010,1207.00,918.70,288.30
ETS SCHAEFER,02-Central,764341000,1147.20,767.58,379.62
ETS SCHAEFER,02-Central,793330000,1050.00,312.16,737.84
ETS SCHAEFER,02-Central,734890000,833.42,207.69,625.73
ETS SCHAEFER,02-Central,764561000,631.50,332.66,298.84
ETS SCHAEFER,02-Central,770210000,544.00,60.63,483.37
ETS SCHAEFER,02-Central,764861000,447.20,207.82,239.38
ETS SCHAEFER,02-Central,768222000,350.80,136.62,214.18
ETS SCHAEFER,02-Central,792760000,228.63,58.31,170.32
ETS SCHAEFER,02-Central,734990000,149.72,27.65,122.07
ETS SCHAEFER,02-Central,768211000,116.50,23.39,93.11
EUROFINS SCIENTIFIC,02-Central,727000140,1041.60,65.10,976.50
EVERBURN MANUFACTURING CO,02-Central,726000062,646200.00,435246.78,210953.22
EVERBURN MANUFACTURING CO,02-Central,727000060,16800.00,11913.27,4886.73
EVERBURN MANUFACTURING CO,02-Central,726000240,5443.20,3594.43,1848.77
EVERHARD PRODUCTS INC,02-Central,764461006,388.00,27.25,360.75
EXOCHEM CORPORATION,02-Central,726000062,1045093.50,533368.66,511724.84
EXOCHEM CORPORATION,02-Central,727000060,22050.00,11913.27,10136.73
F.S. SPERRY COMPANY,02-Central,764040000,307838.40,78425.97,229412.43
F.S. SPERRY COMPANY,02-Central,764821000,95722.00,20589.36,75132.64
F.S. SPERRY COMPANY,02-Central,777902700,51466.46,22831.10,28635.36
F.S. SPERRY COMPANY,02-Central,766121000,30855.00,5251.78,25603.22
F.S. SPERRY COMPANY,02-Central,768211000,24772.00,4751.64,20020.36
F.S. SPERRY COMPANY,02-Central,733525500,23437.50,3385.58,20051.92
F.S. SPERRY COMPANY,02-Central,779956844,20090.40,10762.47,9327.93
F.S. SPERRY COMPANY,02-Central,779956840,12879.36,9030.03,3849.33
F.S. SPERRY COMPANY,02-Central,779956843,12003.24,6543.06,5460.18
F.S. SPERRY COMPANY,02-Central,764901000,11498.75,2581.74,8917.01
F.S. SPERRY COMPANY,02-Central,733464500,9972.00,1521.08,8450.92
F.S. SPERRY COMPANY,02-Central,754864010,8820.00,1532.38,7287.62
F.S. SPERRY COMPANY,02-Central,764521000,7800.00,2659.41,5140.59
F.S. SPERRY COMPANY,02-Central,779959916,7184.16,2125.48,5058.68
F.S. SPERRY COMPANY,02-Central,777901100,6604.86,2805.90,3798.96
F.S. SPERRY COMPANY,02-Central,734810000,6547.20,4985.55,1561.65
F.S. SPERRY COMPANY,02-Central,727041000,6546.00,910.46,5635.54
F.S. SPERRY COMPANY,02-Central,780690000,6300.00,3011.32,3288.68
F.S. SPERRY COMPANY,02-Central,718501000,6011.20,1077.51,4933.69
F.S. SPERRY COMPANY,02-Central,766123050,5535.00,2035.46,3499.54
F.S. SPERRY COMPANY,02-Central,738361000,5460.27,2715.75,2744.52
F.S. SPERRY COMPANY,02-Central,727000140,4980.00,714.34,4265.66
F.S. SPERRY COMPANY,02-Central,734651200,4601.48,812.52,3788.96
F.S. SPERRY COMPANY,02-Central,739561200,4548.61,1093.64,3454.97
F.S. SPERRY COMPANY,02-Central,739390000,4430.02,2035.95,2394.07
F.S. SPERRY COMPANY,02-Central,733211000,4251.90,846.32,3405.58
F.S. SPERRY COMPANY,02-Central,759651000,4246.76,891.40,3355.36
F.S. SPERRY COMPANY,02-Central,779956842,4188.12,3145.53,1042.59
F.S. SPERRY COMPANY,02-Central,781610000,4019.20,2126.99,1892.21
F.S. SPERRY COMPANY,02-Central,779440100,3548.94,576.25,2972.69
F.S. SPERRY COMPANY,02-Central,766081010,3465.60,581.84,2883.76
F.S. SPERRY COMPANY,02-Central,777601000,3357.59,784.91,2572.68
F.S. SPERRY COMPANY,02-Central,777650000,2914.50,2139.90,774.60
F.S. SPERRY COMPANY,02-Central,779980915,2640.00,1243.98,1396.02
F.S. SPERRY COMPANY,02-Central,754097000,2610.72,872.95,1737.77
F.S. SPERRY COMPANY,02-Central,781670000,2610.72,1403.52,1207.20
F.S. SPERRY COMPANY,02-Central,781550000,2198.46,673.45,1525.01
F.S. SPERRY COMPANY,02-Central,788460000,1529.40,290.97,1238.43
F.S. SPERRY COMPANY,02-Central,738401000,1354.97,422.06,932.91
F.S. SPERRY COMPANY,02-Central,781570000,1300.64,386.14,914.50
F.S. SPERRY COMPANY,02-Central,754247000,1205.24,608.32,596.92
F.S. SPERRY COMPANY,02-Central,764903000,1152.50,364.04,788.46
F.S. SPERRY COMPANY,02-Central,770410000,1092.25,102.99,989.26
F.S. SPERRY COMPANY,02-Central,739661100,1006.13,468.00,538.13
F.S. SPERRY COMPANY,02-Central,759661000,811.92,158.78,653.14
F.S. SPERRY COMPANY,02-Central,770830000,749.70,30.97,718.73
F.S. SPERRY COMPANY,02-Central,734611100,672.22,135.23,536.99
F.S. SPERRY COMPANY,02-Central,767990000,668.00,224.12,443.88
F.S. SPERRY COMPANY,02-Central,770220000,599.40,55.79,543.61
F.S. SPERRY COMPANY,02-Central,764602000,592.00,105.98,486.02
F.S. SPERRY COMPANY,02-Central,734950000,514.20,113.13,401.07
F.S. SPERRY COMPANY,02-Central,754447000,340.45,190.16,150.29
F.S. SPERRY COMPANY,02-Central,739741100,309.54,168.32,141.22
F.S. SPERRY COMPANY,02-Central,770210000,285.00,31.52,253.48
F.S. SPERRY COMPANY,02-Central,734173000,257.10,54.34,202.76
F.S. SPERRY COMPANY,02-Central,781805000,238.48,122.16,116.32
F.S. SPERRY COMPANY,02-Central,777671000,230.12,184.20,45.92
F.S. SPERRY COMPANY,02-Central,764822000,202.80,31.77,171.03
F.S. SPERRY COMPANY,02-Central,734861000,109.14,87.30,21.84
FAIRMONT SUPPLY CO.,02-Central,734414100,621.20,71.72,549.48
FAIRMONT SUPPLY,02-Central,734414100,1863.60,215.18,1648.42
FASTENAL COMPANY,02-Central,777601000,2753.24,477.70,2275.54
FASTENAL COMPANY,02-Central,764262000,424.00,22.31,401.69
FASTENAL,02-Central,770380000,2844.00,173.95,2670.05
FDP VIRGINIA INC.,02-Central,726020005,217900.80,79010.85,138889.95
FEATHERLITE INC.,02-Central,764261000,3980.00,230.15,3749.85
FEDERAL MOGUL CORPORATION,02-Central,739451000,411.84,116.03,295.81
FEDERAL MOGUL WORLDWIDE,02-Central,726020047,242763.92,85795.74,156968.18
FEDERAL MOGUL WORLDWIDE,02-Central,726020005,181306.08,63174.16,118131.92
FEDERAL MOGUL WORLDWIDE,02-Central,726020046,75957.04,26503.38,49453.66
FEDERAL MOGUL WORLDWIDE,02-Central,726000240,6820.80,2573.78,4247.02
FEDERAL MOGUL WORLDWIDE,02-Central,720252301,5935.50,395.45,5540.05
FEDERAL MOGUL WORLDWIDE,02-Central,720252303,2938.50,197.04,2741.46
FEDERAL MOGUL WORLDWIDE,02-Central,726020007,950.40,340.30,610.10
FERGUSON ENTERPRISES,02-Central,770200000,1332.00,84.83,1247.17
FERRITE INTERNATIONAL,02-Central,734822000,1920.00,295.90,1624.10
FERRITE INTERNATIONAL,02-Central,718500000,1414.40,172.54,1241.86
FIRELINE INC,02-Central,793000500,202478.40,100346.78,102131.62
FIRELINE INC,02-Central,726000062,86400.00,44626.20,41773.80
FIRELINE INC,02-Central,793060330,25488.00,12407.06,13080.94
FIRELINE INC,02-Central,793060300,21240.00,10339.22,10900.78
FIRELINE INC,02-Central,727000140,1111.32,427.58,683.74
FIRELINE,02-Central,793060300,151653.60,73822.01,77831.59
FIRELINE,02-Central,726000062,98736.00,51348.41,47387.59
FIRELINE,02-Central,793000500,95040.00,48150.58,46889.42
FIRELINE,02-Central,726020031,2106.72,1027.50,1079.22
FISHER SCIENTIFIC CO,02-Central,727000140,255.20,15.84,239.36
FLEET SUPPORT SERVICES,02-Central,771002000,3282.00,86.39,3195.61
FLEET SUPPORT SERVICES,02-Central,779021000,628.96,71.47,557.49
FLOMET INC,02-Central,764341000,4809.50,258.58,4550.92
FLOMET INC,02-Central,764827000,484.50,45.48,439.02
FLORIDA PURCHASING AGENCY,02-Central,749906600,4091.93,189.64,3902.29
FLORIDA TURBINE TECHNOLOG,02-Central,768211000,389.00,22.44,366.56
FMI,02-Central,764262000,882.00,46.00,836.00
FORT RECOVERY INDUSTRIES,02-Central,734311100,3379.86,296.06,3083.80
FORT RECOVERY INDUSTRIES,02-Central,734900000,1270.40,229.09,1041.31
FOSBEL INC / CETEK LTD.,02-Central,734830000,69329.00,9368.61,59960.39
FOSBEL INC / CETEK LTD.,02-Central,734990000,34930.00,4838.09,30091.91
FOSBEL INC / CETEK LTD.,02-Central,764821000,1788.00,62.62,1725.38
FOURNIER RUBBER & SUPPLY,02-Central,770380000,964.00,61.29,902.71
FRAS-LE NORTH AMERICA INC,02-Central,726020005,72085.68,16520.72,55564.96
FRC SUPPORT EQUIPMENT,02-Central,764781000,534.00,39.44,494.56
FRC SUPPORT EQUIPMENT,02-Central,779081000,224.36,55.24,169.12
FRYE & ROWE,02-Central,737912200,326.50,45.47,281.03
FSPA,02-Central,734900000,814.40,151.32,663.08
FUELCELL ENERGY,02-Central,768241000,15180.00,7783.80,7396.20
FUELCELL ENERGY,02-Central,768221000,2346.00,1202.92,1143.08
GALA INDUSTRIES INC.,02-Central,740361701,9072.00,5038.76,4033.24
"GARRECO, INC.",02-Central,734990000,4550.40,550.87,3999.53
GASKETS INC,02-Central,733586500,4999.68,754.00,4245.68
GASKETS INC,02-Central,733201000,2537.64,506.55,2031.09
GENERAL ELECTRIC CO,02-Central,734414100,310.60,35.86,274.74
GENERAL INSULATION CO,02-Central,764901000,3470.00,128.64,3341.36
GENERAL INSULATION CO,02-Central,737912200,1997.00,268.62,1728.38
GENERAL INSULATION CO,02-Central,734311100,808.72,70.60,738.12
GENERAL INSULATION CO,02-Central,734611100,378.88,54.36,324.52
GENERAL JACKSON SHOWBOAT,02-Central,768133000,947.50,55.20,892.30
GEO. V HAMILTON INC,02-Central,735602000,86769.00,24158.90,62610.10
GEO. V HAMILTON INC,02-Central,735750000,31176.00,11264.62,19911.38
GEO. V HAMILTON INC,02-Central,735602110,2121.00,624.77,1496.23
GEO. V HAMILTON INC,02-Central,735862000,1992.00,616.83,1375.17
GEORGIA TECH,02-Central,733464400,298.00,39.61,258.39
GERARD DANIEL WORLDWIDE,02-Central,733721000,1200.00,277.28,922.72
GLASS CRAFTERS,02-Central,733444100,1044.00,244.86,799.14
GLASS CRAFTERS,02-Central,739450000,926.64,251.16,675.48
GLASS CRAFTERS,02-Central,733505200,765.00,76.44,688.56
GLASS CRAFTERS,02-Central,739641100,463.32,137.43,325.89
GLASS CRAFTERS,02-Central,733444200,332.00,34.69,297.31
GLASS CRAFTERS,02-Central,733464500,298.00,29.52,268.48
GLASSTECH INC,02-Central,733484900,11712.00,2040.91,9671.09
GLASSTECH INC,02-Central,733505300,10770.00,1627.27,9142.73
GLASSTECH INC,02-Central,724101200,7588.00,720.80,6867.20
GLASSTECH INC,02-Central,734752000,2505.19,454.02,2051.17
GLENRO INC,02-Central,739561200,7292.16,1264.34,6027.82
GLENRO INC,02-Central,738521000,6998.40,2175.10,4823.30
GLENRO INC,02-Central,733444300,3782.00,550.97,3231.03
GLENRO INC,02-Central,739451000,2520.96,682.77,1838.19
GLENRO INC,02-Central,734311100,953.70,84.26,869.44
GLENRO INC,02-Central,733505300,620.00,77.58,542.42
GLENRO INC,02-Central,739741100,411.84,109.56,302.28
GLENRO INC,02-Central,739781100,410.72,163.84,246.88
GOODMAN INDUSTRIAL,02-Central,770260000,5778.00,416.58,5361.42
GRAFTECH,02-Central,739390000,701.12,164.86,536.26
GRAFTECH,02-Central,764261000,424.00,22.90,401.10
GRAYBAR ELECTRIC CO,02-Central,733464500,31560.00,3397.50,28162.50
GRAYBAR ELECTRIC CO,02-Central,733525500,2920.00,281.57,2638.43
GREAT LAKES CHEESE,02-Central,766685000,315.00,35.46,279.54
GREAT LAKES CHEESE,02-Central,766681100,306.00,24.78,281.22
GREEN BOILER TECH.,02-Central,770260000,931.50,67.63,863.87
GREEN GLOBAL ENERGY,02-Central,739561200,421.12,72.33,348.79
GREENING ASSOCIATES INC,02-Central,733464500,313.00,32.53,280.47
GRIFFIN WHEEL CO,02-Central,780770000,5532.48,873.08,4659.40
GSA ACCOUNT PAYABLE,02-Central,734311100,404.36,35.30,369.06
GT MIDWEST,02-Central,739561200,13628.80,3703.36,9925.44
GT MIDWEST,02-Central,764522000,12870.00,6153.19,6716.81
GT MIDWEST,02-Central,764902000,811.40,127.71,683.69
GT MIDWEST,02-Central,770222000,739.50,68.04,671.46
GT MIDWEST,02-Central,739561100,704.42,186.64,517.78
GT MIDWEST,02-Central,733151500,651.20,108.21,542.99
GT MIDWEST,02-Central,733151600,325.60,53.12,272.48
GUARDIAN FIBERGLASS,02-Central,764822000,9528.00,340.42,9187.58
GUARDIAN FIBERGLASS,02-Central,764262000,1272.00,66.93,1205.07
GUARDIAN FIBERGLASS,02-Central,739741100,823.68,219.12,604.56
HAGEMEYER NORTH AMERICA,02-Central,792600000,798.00,99.32,698.68
"HAMON DELTAK, INC.",02-Central,764900005,73972.00,32769.48,41202.52
"HAMON DELTAK, INC.",02-Central,764826700,23801.60,7938.50,15863.10
"HAMON DELTAK, INC.",02-Central,777642000,67.50,15.84,51.66
HANSON BRICK PLANT 4,02-Central,764303200,7176.00,4298.84,2877.16
HARRIS COMPANIES,02-Central,733464500,298.00,29.52,268.48
HARRIS,02-Central,764521000,653.00,26.57,626.43
HARROP INDUSTRIES INC,02-Central,775171000,1013.60,396.52,617.08
HARROP INDUSTRIES INC,02-Central,773591000,757.40,185.74,571.66
HARROP INDUSTRIES INC,02-Central,774981100,185.50,69.84,115.66
HEARTH & HOME TECHNOLOGIE,02-Central,726000080,667931.52,347389.42,320542.10
HEARTH & HOME TECHNOLOGIE,02-Central,764020110,186719.00,139183.58,47535.42
HEARTH & HOME TECHNOLOGIE,02-Central,768222000,86592.00,33348.62,53243.38
HEARTH & HOME TECHNOLOGIE,02-Central,734211200,1529.20,185.93,1343.27
HEARTH & HOME TECHNOLOGIE,02-Central,777601000,304.80,58.80,246.00
HEAT & SENSOR,02-Central,764461006,4900.00,551.86,4348.14
HEAT TREATING SERVICES,02-Central,734751200,275.45,37.39,238.06
HENKEL CHEMICAL MGMT.,02-Central,734000000,1072.50,151.61,920.89
HENKEL CHEMICAL MGMT.,02-Central,727063000,84.00,16.80,67.20
HERAEUS ELECTRO - NITE CO,02-Central,726000240,89326.80,37653.21,51673.59
"HERAEUS NOBELIGHT, LLC",02-Central,739561100,1149.12,199.92,949.20
"HERAEUS NOBLELIGHT, LLC",02-Central,739561100,552.72,93.32,459.40
HERAEUS QUARTZ TECH,02-Central,764902000,3018.00,386.50,2631.50
HERAEUS QUARTZ TECH,02-Central,734890000,980.49,207.69,772.80
HERAEUS-AMERSIL INC.,02-Central,779981050,6500.16,1526.07,4974.09
HERAEUS-AMERSIL INC.,02-Central,718501000,1021.00,134.69,886.31
HERAEUS-AMERSIL INC.,02-Central,779988827,1021.00,610.34,410.66
HERAEUS-AMERSIL INC.,02-Central,738521000,338.56,298.56,40.00
HERAEUS,02-Central,726000240,198562.32,83221.87,115340.45
HERAEUS,02-Central,793000410,1728.00,773.49,954.51
HERAEUS,02-Central,793060300,1728.00,596.48,1131.52
HI-TEMP FABRICATION INC.,02-Central,793330000,1132.95,185.55,947.40
HI-TEMP FABRICATION INC.,02-Central,737912200,311.40,43.36,268.04
HIBBING INTERNATIONAL,02-Central,726000240,12398.40,836.74,11561.66
HITCO CARBON COMPOSITIES,02-Central,791104770,1946.64,760.90,1185.74
HITCO CARBON COMPOSITIES,02-Central,791104760,1892.46,735.90,1156.56
"HOMEFIRES, AQR, INC.",02-Central,733721000,300.00,69.32,230.68
HONEYWELL INC           ',02-Central,739370000,364.80,90.82,273.98
HONEYWELL,02-Central,734311100,404.36,35.30,369.06
HORIZON METALS,02-Central,739781100,1642.88,655.36,987.52
"HSS, LLC",02-Central,770722000,6150.00,214.41,5935.59
HUGHES ASSOCIATES INC.,02-Central,764522000,1332.00,53.44,1278.56
HUGHES ASSOCIATES INC.,02-Central,738561000,1164.80,248.27,916.53
HUNTSVILLE RUBBER & GASKE,02-Central,770510000,1097.50,48.20,1049.30
HUNTSVILLE RUBBER & GASKE,02-Central,733151500,472.00,54.11,417.89
HUNTSVILLE RUBBER & GASKE,02-Central,733444200,348.00,38.64,309.36
HUSSONG MANUFACTURING,02-Central,764030000,101885.28,54115.84,47769.44
HUSSONG MANUFACTURING,02-Central,764050000,46094.40,25181.31,20913.09
HYDRA-FAB,02-Central,733741000,408.00,119.06,288.94
HYPER TECH RESEARCH,02-Central,740405000,681.80,122.64,559.16
I S A SALES & SERVICE,02-Central,765651000,443457.00,235458.12,207998.88
I S A SALES & SERVICE,02-Central,777922000,335120.64,268462.49,66658.15
I S A SALES & SERVICE,02-Central,765501001,321988.50,187770.50,134218.00
I S A SALES & SERVICE,02-Central,764605020,249546.00,129502.56,120043.44
I S A SALES & SERVICE,02-Central,740025000,174323.64,114160.31,60163.33
I S A SALES & SERVICE,02-Central,733202000,141644.10,56726.36,84917.74
I S A SALES & SERVICE,02-Central,764261020,127647.60,100021.45,27626.15
I S A SALES & SERVICE,02-Central,742520400,121394.00,54999.51,66394.49
I S A SALES & SERVICE,02-Central,727041200,94288.32,13320.71,80967.61
I S A SALES & SERVICE,02-Central,764901050,61014.00,34578.52,26435.48
I S A SALES & SERVICE,02-Central,764781000,57498.00,37042.12,20455.88
I S A SALES & SERVICE,02-Central,765735000,50019.84,34873.02,15146.82
I S A SALES & SERVICE,02-Central,777921000,47737.68,38242.28,9495.40
I S A SALES & SERVICE,02-Central,765655000,41721.57,20164.75,21556.82
I S A SALES & SERVICE,02-Central,764528200,28526.00,14712.44,13813.56
I S A SALES & SERVICE,02-Central,737912200,25622.50,11043.11,14579.39
I S A SALES & SERVICE,02-Central,777904301,25155.12,11003.61,14151.51
I S A SALES & SERVICE,02-Central,766121000,24099.00,11583.69,12515.31
I S A SALES & SERVICE,02-Central,764523200,22881.00,13812.89,9068.11
I S A SALES & SERVICE,02-Central,726020037,21610.40,12315.05,9295.35
I S A SALES & SERVICE,02-Central,742431000,18287.50,7019.48,11268.02
I S A SALES & SERVICE,02-Central,764521073,17672.00,24252.00,-6580.00
I S A SALES & SERVICE,02-Central,764765000,17292.00,9994.25,7297.75
I S A SALES & SERVICE,02-Central,777907221,16907.90,8156.94,8750.96
I S A SALES & SERVICE,02-Central,779958167,15480.00,10884.12,4595.88
I S A SALES & SERVICE,02-Central,742461000,15168.00,3113.03,12054.97
I S A SALES & SERVICE,02-Central,726020039,14654.50,7776.01,6878.49
I S A SALES & SERVICE,02-Central,738071000,13502.64,10816.86,2685.78
I S A SALES & SERVICE,02-Central,724261200,12636.00,2111.38,10524.62
I S A SALES & SERVICE,02-Central,764827000,12495.00,6667.54,5827.46
I S A SALES & SERVICE,02-Central,764484000,10275.00,4066.55,6208.45
I S A SALES & SERVICE,02-Central,742451000,9881.25,2100.53,7780.72
I S A SALES & SERVICE,02-Central,739564300,9840.00,5427.33,4412.67
I S A SALES & SERVICE,02-Central,779988929,9728.68,5786.20,3942.48
I S A SALES & SERVICE,02-Central,764864000,9360.00,4953.26,4406.74
I S A SALES & SERVICE,02-Central,733211000,9040.00,4035.32,5004.68
I S A SALES & SERVICE,02-Central,764261000,7908.60,6199.16,1709.44
I S A SALES & SERVICE,02-Central,733831000,6300.00,4682.69,1617.31
I S A SALES & SERVICE,02-Central,770392000,6232.00,935.56,5296.44
I S A SALES & SERVICE,02-Central,765503112,6072.40,3308.30,2764.10
I S A SALES & SERVICE,02-Central,764822000,5770.00,2819.03,2950.97
I S A SALES & SERVICE,02-Central,734890000,5229.28,1626.61,3602.67
I S A SALES & SERVICE,02-Central,777950000,5103.36,4088.26,1015.10
I S A SALES & SERVICE,02-Central,733602010,4263.84,2193.26,2070.58
I S A SALES & SERVICE,02-Central,777601000,3952.12,1444.85,2507.27
I S A SALES & SERVICE,02-Central,770430000,3487.50,531.28,2956.22
I S A SALES & SERVICE,02-Central,770230000,3360.00,284.31,3075.69
I S A SALES & SERVICE,02-Central,764345052,3200.00,2269.86,930.14
I S A SALES & SERVICE,02-Central,764781040,2957.00,1954.93,1002.07
I S A SALES & SERVICE,02-Central,779988828,2910.68,1562.16,1348.52
I S A SALES & SERVICE,02-Central,733221050,2908.00,1310.58,1597.42
I S A SALES & SERVICE,02-Central,779988940,2901.76,2205.12,696.64
I S A SALES & SERVICE,02-Central,770220000,2790.00,448.44,2341.56
I S A SALES & SERVICE,02-Central,764821073,2418.00,2843.10,-425.10
I S A SALES & SERVICE,02-Central,777907222,2340.00,1170.00,1170.00
I S A SALES & SERVICE,02-Central,754047000,1571.04,700.91,870.13
I S A SALES & SERVICE,02-Central,738381000,1504.80,3814.80,-2310.00
I S A SALES & SERVICE,02-Central,764601000,1500.00,797.17,702.83
I S A SALES & SERVICE,02-Central,764521000,1410.00,796.26,613.74
I S A SALES & SERVICE,02-Central,770280000,1350.00,480.16,869.84
I S A SALES & SERVICE,02-Central,742411000,1340.00,365.48,974.52
I S A SALES & SERVICE,02-Central,764901000,1282.50,608.55,673.95
I S A SALES & SERVICE,02-Central,770240000,1237.50,421.98,815.52
I S A SALES & SERVICE,02-Central,733151600,992.00,345.92,646.08
I S A SALES & SERVICE,02-Central,734311100,926.76,105.90,820.86
I S A SALES & SERVICE,02-Central,777670000,789.60,729.84,59.76
I S A SALES & SERVICE,02-Central,738542000,684.00,1435.20,-751.20
I S A SALES & SERVICE,02-Central,734861000,636.60,262.44,374.16
I S A SALES & SERVICE,02-Central,734990000,518.00,191.71,326.29
I S A SALES & SERVICE,02-Central,733741000,496.00,372.76,123.24
I S A SALES & SERVICE,02-Central,724261400,468.00,60.94,407.06
I S A SALES & SERVICE,02-Central,764861000,384.00,207.82,176.18
I S A SALES & SERVICE,02-Central,779957137,367.92,398.16,-30.24
I S A SALES & SERVICE,02-Central,779956598,300.00,265.36,34.64
I S A SALES & SERVICE,02-Central,733151500,284.00,101.80,182.20
I S A SALES & SERVICE,02-Central,764522000,282.00,160.33,121.67
I S A SALES & SERVICE,02-Central,739761100,273.60,415.20,-141.60
I S A SALES & SERVICE,02-Central,733212000,240.00,92.84,147.16
I S A SALES & SERVICE,02-Central,764821000,88.00,61.44,26.56
I S A SALES & SERVICE,02-Central,767501000,30.00,19.64,10.36
I.C.M.,02-Central,779958986,7318.08,4624.64,2693.44
I.C.M.,02-Central,780650000,916.80,517.36,399.44
I.C.M.,02-Central,780690000,70.36,42.42,27.94
IBC ADVANCED ALLOYS,02-Central,739641100,964.08,403.20,560.88
"IDG USA, LLC",02-Central,779301000,460.69,49.41,411.28
IN VITRO,02-Central,733384000,2235.20,309.03,1926.17
INDUST'L FIREBRICK OF DET,02-Central,740361900,101966.40,63434.17,38532.23
INDUST'L FIREBRICK OF DET,02-Central,733526410,88372.50,14758.56,73613.94
INDUST'L FIREBRICK OF DET,02-Central,739781100,86500.52,64270.10,22230.42
INDUST'L FIREBRICK OF DET,02-Central,754267000,74259.54,31536.48,42723.06
INDUST'L FIREBRICK OF DET,02-Central,734351200,65782.56,7388.30,58394.26
INDUST'L FIREBRICK OF DET,02-Central,733525400,48407.50,9314.97,39092.53
INDUST'L FIREBRICK OF DET,02-Central,779989574,39302.40,24794.02,14508.38
INDUST'L FIREBRICK OF DET,02-Central,734121000,36691.90,9512.93,27178.97
INDUST'L FIREBRICK OF DET,02-Central,733586500,28576.80,5110.96,23465.84
INDUST'L FIREBRICK OF DET,02-Central,739951050,21144.00,13709.32,7434.68
INDUST'L FIREBRICK OF DET,02-Central,734990000,19546.64,5697.16,13849.48
INDUST'L FIREBRICK OF DET,02-Central,768141003,16660.00,4306.98,12353.02
INDUST'L FIREBRICK OF DET,02-Central,793330000,16404.00,3787.36,12616.64
INDUST'L FIREBRICK OF DET,02-Central,734251300,16221.20,1848.29,14372.91
INDUST'L FIREBRICK OF DET,02-Central,770392000,16210.00,1606.69,14603.31
INDUST'L FIREBRICK OF DET,02-Central,764521000,14670.40,3188.40,11482.00
INDUST'L FIREBRICK OF DET,02-Central,764901000,13312.00,3475.67,9836.33
INDUST'L FIREBRICK OF DET,02-Central,738521000,12048.87,8000.11,4048.76
INDUST'L FIREBRICK OF DET,02-Central,764601000,11930.20,3646.00,8284.20
INDUST'L FIREBRICK OF DET,02-Central,768221000,11210.00,2493.65,8716.35
INDUST'L FIREBRICK OF DET,02-Central,720252303,11200.50,397.55,10802.95
INDUST'L FIREBRICK OF DET,02-Central,777601000,10597.32,2832.39,7764.93
INDUST'L FIREBRICK OF DET,02-Central,739681100,10263.24,5510.74,4752.50
INDUST'L FIREBRICK OF DET,02-Central,733525500,10123.00,1603.62,8519.38
INDUST'L FIREBRICK OF DET,02-Central,724311200,9980.80,2364.54,7616.26
INDUST'L FIREBRICK OF DET,02-Central,733505100,8828.00,1892.03,6935.97
INDUST'L FIREBRICK OF DET,02-Central,770560000,8768.00,798.69,7969.31
INDUST'L FIREBRICK OF DET,02-Central,734311100,8340.42,1068.75,7271.67
INDUST'L FIREBRICK OF DET,02-Central,740283502,8069.60,1602.57,6467.03
INDUST'L FIREBRICK OF DET,02-Central,739641100,7915.68,4728.20,3187.48
INDUST'L FIREBRICK OF DET,02-Central,733511000,7865.00,1967.60,5897.40
INDUST'L FIREBRICK OF DET,02-Central,764824000,7246.80,1043.19,6203.61
INDUST'L FIREBRICK OF DET,02-Central,768241030,6936.00,1640.18,5295.82
INDUST'L FIREBRICK OF DET,02-Central,764902000,5994.60,1544.86,4449.74
INDUST'L FIREBRICK OF DET,02-Central,767540000,5956.84,2198.39,3758.45
INDUST'L FIREBRICK OF DET,02-Central,739741100,5660.48,3123.40,2537.08
INDUST'L FIREBRICK OF DET,02-Central,733832000,5616.00,1670.05,3945.95
INDUST'L FIREBRICK OF DET,02-Central,738561000,5370.56,2427.27,2943.29
INDUST'L FIREBRICK OF DET,02-Central,766121000,5163.30,953.71,4209.59
INDUST'L FIREBRICK OF DET,02-Central,738401000,4782.24,1688.24,3094.00
INDUST'L FIREBRICK OF DET,02-Central,764821000,4760.00,1242.89,3517.11
INDUST'L FIREBRICK OF DET,02-Central,737871000,4486.72,2611.52,1875.20
INDUST'L FIREBRICK OF DET,02-Central,739561000,4334.12,1201.85,3132.27
INDUST'L FIREBRICK OF DET,02-Central,740103000,4126.08,1002.00,3124.08
INDUST'L FIREBRICK OF DET,02-Central,779957613,4045.44,1817.55,2227.89
INDUST'L FIREBRICK OF DET,02-Central,754047000,3971.88,1452.67,2519.21
INDUST'L FIREBRICK OF DET,02-Central,733525600,3765.00,622.77,3142.23
INDUST'L FIREBRICK OF DET,02-Central,724281900,3428.00,1300.06,2127.94
INDUST'L FIREBRICK OF DET,02-Central,770380000,3384.80,308.22,3076.58
INDUST'L FIREBRICK OF DET,02-Central,770600000,3097.15,277.15,2820.00
INDUST'L FIREBRICK OF DET,02-Central,733671000,2909.08,457.31,2451.77
INDUST'L FIREBRICK OF DET,02-Central,738381000,2568.24,1860.54,707.70
INDUST'L FIREBRICK OF DET,02-Central,740402402,2531.00,628.36,1902.64
INDUST'L FIREBRICK OF DET,02-Central,766081010,2029.40,228.55,1800.85
INDUST'L FIREBRICK OF DET,02-Central,734173000,2003.55,485.46,1518.09
INDUST'L FIREBRICK OF DET,02-Central,754067000,1985.40,625.44,1359.96
INDUST'L FIREBRICK OF DET,02-Central,739561200,1921.02,523.67,1397.35
INDUST'L FIREBRICK OF DET,02-Central,766162000,1845.00,311.09,1533.91
INDUST'L FIREBRICK OF DET,02-Central,764822000,1549.60,254.16,1295.44
INDUST'L FIREBRICK OF DET,02-Central,734751200,1501.20,339.36,1161.84
INDUST'L FIREBRICK OF DET,02-Central,739761100,1419.84,1245.60,174.24
INDUST'L FIREBRICK OF DET,02-Central,754265000,1388.16,398.75,989.41
INDUST'L FIREBRICK OF DET,02-Central,734851000,1342.79,289.20,1053.59
INDUST'L FIREBRICK OF DET,02-Central,727000140,1340.84,146.91,1193.93
INDUST'L FIREBRICK OF DET,02-Central,737912200,1258.50,272.83,985.67
INDUST'L FIREBRICK OF DET,02-Central,754255000,1162.84,391.93,770.91
INDUST'L FIREBRICK OF DET,02-Central,777580000,1147.30,52.80,1094.50
INDUST'L FIREBRICK OF DET,02-Central,768142000,1100.00,534.58,565.42
INDUST'L FIREBRICK OF DET,02-Central,734211200,1045.09,160.94,884.15
INDUST'L FIREBRICK OF DET,02-Central,754295000,1044.80,271.45,773.35
INDUST'L FIREBRICK OF DET,02-Central,764482000,1008.00,417.66,590.34
INDUST'L FIREBRICK OF DET,02-Central,738542000,950.40,861.12,89.28
INDUST'L FIREBRICK OF DET,02-Central,772101000,942.30,372.42,569.88
INDUST'L FIREBRICK OF DET,02-Central,734910000,907.70,212.36,695.34
INDUST'L FIREBRICK OF DET,02-Central,754275000,844.60,238.85,605.75
INDUST'L FIREBRICK OF DET,02-Central,770570000,768.60,95.41,673.19
INDUST'L FIREBRICK OF DET,02-Central,754327000,736.38,299.73,436.65
INDUST'L FIREBRICK OF DET,02-Central,742442000,717.50,158.81,558.69
INDUST'L FIREBRICK OF DET,02-Central,739561100,703.92,186.64,517.28
INDUST'L FIREBRICK OF DET,02-Central,740382101,698.40,296.37,402.03
INDUST'L FIREBRICK OF DET,02-Central,733151500,652.00,108.22,543.78
INDUST'L FIREBRICK OF DET,02-Central,770240000,648.75,67.63,581.12
INDUST'L FIREBRICK OF DET,02-Central,770250000,591.25,66.16,525.09
INDUST'L FIREBRICK OF DET,02-Central,764481000,522.00,101.65,420.35
INDUST'L FIREBRICK OF DET,02-Central,733221200,521.04,130.50,390.54
INDUST'L FIREBRICK OF DET,02-Central,771991000,502.20,137.55,364.65
INDUST'L FIREBRICK OF DET,02-Central,734830000,464.00,90.82,373.18
INDUST'L FIREBRICK OF DET,02-Central,733586400,450.00,91.43,358.57
INDUST'L FIREBRICK OF DET,02-Central,733444300,426.00,82.49,343.51
INDUST'L FIREBRICK OF DET,02-Central,770520000,421.50,41.85,379.65
INDUST'L FIREBRICK OF DET,02-Central,733444100,415.60,163.24,252.36
INDUST'L FIREBRICK OF DET,02-Central,740123000,387.24,128.01,259.23
INDUST'L FIREBRICK OF DET,02-Central,754287000,363.36,140.97,222.39
INDUST'L FIREBRICK OF DET,02-Central,754155000,320.32,137.80,182.52
INDUST'L FIREBRICK OF DET,02-Central,754165000,308.74,125.97,182.77
INDUST'L FIREBRICK OF DET,02-Central,738361000,304.56,308.88,-4.32
INDUST'L FIREBRICK OF DET,02-Central,734900000,285.72,113.48,172.24
INDUST'L FIREBRICK OF DET,02-Central,754175000,275.30,107.86,167.44
INDUST'L FIREBRICK OF DET,02-Central,764761000,268.00,61.43,206.57
INDUST'L FIREBRICK OF DET,02-Central,733423800,246.40,48.07,198.33
INDUST'L FIREBRICK OF DET,02-Central,737902100,143.45,33.56,109.89
INDUST'L FIREBRICK OF DET,02-Central,764762000,132.00,30.29,101.71
INDUST'L FIREBRICK OF DET,02-Central,777642000,122.00,50.36,71.64
INDUST'L FIREBRICK OF DET,02-Central,776201000,107.44,62.94,44.50
INDUST'L FIREBRICK OF DET,02-Central,779957013,82.32,58.88,23.44
INDUST'L FIREBRICK OF DET,02-Central,779958032,77.84,55.70,22.14
INDUST'L FIREBRICK OF DET,02-Central,779958914,19.88,14.28,5.60
INDUSTRIAL FIREBRICK WHSE,02-Central,764901000,236387.20,64953.73,171433.47
INDUSTRIAL FIREBRICK WHSE,02-Central,764821000,147035.30,38615.46,108419.84
INDUSTRIAL FIREBRICK WHSE,02-Central,764862000,101713.85,25906.54,75807.31
INDUSTRIAL FIREBRICK WHSE,02-Central,727041000,95916.00,9768.08,86147.92
INDUSTRIAL FIREBRICK WHSE,02-Central,727000140,93204.96,21034.09,72170.87
INDUSTRIAL FIREBRICK WHSE,02-Central,739390000,59573.71,43165.97,16407.74
INDUSTRIAL FIREBRICK WHSE,02-Central,764902000,46516.80,11602.26,34914.54
INDUSTRIAL FIREBRICK WHSE,02-Central,764602000,33241.20,8897.53,24343.67
INDUSTRIAL FIREBRICK WHSE,02-Central,764781040,28843.20,8345.02,20498.18
INDUSTRIAL FIREBRICK WHSE,02-Central,734351200,25167.15,2514.46,22652.69
INDUSTRIAL FIREBRICK WHSE,02-Central,733525500,24117.50,4332.91,19784.59
INDUSTRIAL FIREBRICK WHSE,02-Central,777601000,18975.57,5666.18,13309.39
INDUSTRIAL FIREBRICK WHSE,02-Central,739781100,18932.82,11564.43,7368.39
INDUSTRIAL FIREBRICK WHSE,02-Central,733510000,16731.64,4651.43,12080.21
INDUSTRIAL FIREBRICK WHSE,02-Central,733384000,14092.32,2199.08,11893.24
INDUSTRIAL FIREBRICK WHSE,02-Central,764040000,13352.96,2090.12,11262.84
INDUSTRIAL FIREBRICK WHSE,02-Central,764822000,12845.60,3215.48,9630.12
INDUSTRIAL FIREBRICK WHSE,02-Central,734311100,10969.89,1260.51,9709.38
INDUSTRIAL FIREBRICK WHSE,02-Central,739380000,10817.09,9716.45,1100.64
INDUSTRIAL FIREBRICK WHSE,02-Central,733211050,10109.10,2907.24,7201.86
INDUSTRIAL FIREBRICK WHSE,02-Central,734851000,10099.11,2284.94,7814.17
INDUSTRIAL FIREBRICK WHSE,02-Central,738521000,9170.70,6870.42,2300.28
INDUSTRIAL FIREBRICK WHSE,02-Central,739741100,8430.72,5255.81,3174.91
INDUSTRIAL FIREBRICK WHSE,02-Central,768122000,7994.60,1621.89,6372.71
INDUSTRIAL FIREBRICK WHSE,02-Central,733511000,7365.16,1935.68,5429.48
INDUSTRIAL FIREBRICK WHSE,02-Central,737912200,7213.24,2172.29,5040.95
INDUSTRIAL FIREBRICK WHSE,02-Central,739561000,7187.02,2576.06,4610.96
INDUSTRIAL FIREBRICK WHSE,02-Central,734751200,6803.28,1234.19,5569.09
INDUSTRIAL FIREBRICK WHSE,02-Central,770870000,6800.00,386.06,6413.94
INDUSTRIAL FIREBRICK WHSE,02-Central,739370000,6374.65,3605.54,2769.11
INDUSTRIAL FIREBRICK WHSE,02-Central,734990000,5778.80,1099.38,4679.42
INDUSTRIAL FIREBRICK WHSE,02-Central,764261000,5652.65,2029.19,3623.46
INDUSTRIAL FIREBRICK WHSE,02-Central,733423700,5591.04,1339.78,4251.26
INDUSTRIAL FIREBRICK WHSE,02-Central,738561000,5514.92,3613.17,1901.75
INDUSTRIAL FIREBRICK WHSE,02-Central,733525600,5239.00,961.34,4277.66
INDUSTRIAL FIREBRICK WHSE,02-Central,733151500,5121.60,1113.36,4008.24
INDUSTRIAL FIREBRICK WHSE,02-Central,766121000,4563.00,2155.41,2407.59
INDUSTRIAL FIREBRICK WHSE,02-Central,764562000,3794.40,759.33,3035.07
INDUSTRIAL FIREBRICK WHSE,02-Central,733151600,3688.80,811.00,2877.80
INDUSTRIAL FIREBRICK WHSE,02-Central,739641100,3527.28,2767.31,759.97
INDUSTRIAL FIREBRICK WHSE,02-Central,770880000,3354.20,178.24,3175.96
INDUSTRIAL FIREBRICK WHSE,02-Central,739561200,3202.44,1132.69,2069.75
INDUSTRIAL FIREBRICK WHSE,02-Central,733211000,2948.80,851.28,2097.52
INDUSTRIAL FIREBRICK WHSE,02-Central,793330000,2739.30,624.33,2114.97
INDUSTRIAL FIREBRICK WHSE,02-Central,768121000,2721.10,528.41,2192.69
INDUSTRIAL FIREBRICK WHSE,02-Central,793000000,2713.20,344.64,2368.56
INDUSTRIAL FIREBRICK WHSE,02-Central,766162000,2661.00,448.47,2212.53
INDUSTRIAL FIREBRICK WHSE,02-Central,764482000,2152.00,681.90,1470.10
INDUSTRIAL FIREBRICK WHSE,02-Central,792702000,1861.50,567.27,1294.23
INDUSTRIAL FIREBRICK WHSE,02-Central,764262000,1741.20,630.16,1111.04
INDUSTRIAL FIREBRICK WHSE,02-Central,764601000,1722.20,449.94,1272.26
INDUSTRIAL FIREBRICK WHSE,02-Central,770740000,1360.00,54.66,1305.34
INDUSTRIAL FIREBRICK WHSE,02-Central,770860000,1360.00,73.56,1286.44
INDUSTRIAL FIREBRICK WHSE,02-Central,770600000,1258.15,90.42,1167.73
INDUSTRIAL FIREBRICK WHSE,02-Central,734810000,1184.30,1021.95,162.35
INDUSTRIAL FIREBRICK WHSE,02-Central,734910000,1038.45,256.75,781.70
INDUSTRIAL FIREBRICK WHSE,02-Central,733141200,950.40,239.88,710.52
INDUSTRIAL FIREBRICK WHSE,02-Central,739290000,811.00,430.64,380.36
INDUSTRIAL FIREBRICK WHSE,02-Central,739310000,584.76,285.84,298.92
INDUSTRIAL FIREBRICK WHSE,02-Central,768141000,583.76,131.60,452.16
INDUSTRIAL FIREBRICK WHSE,02-Central,739561100,566.16,199.92,366.24
INDUSTRIAL FIREBRICK WHSE,02-Central,764762000,556.00,302.93,253.07
INDUSTRIAL FIREBRICK WHSE,02-Central,792760000,456.97,116.62,340.35
INDUSTRIAL FIREBRICK WHSE,02-Central,734711100,393.87,81.15,312.72
INDUSTRIAL FIREBRICK WHSE,02-Central,739541100,324.41,83.57,240.84
INDUSTRIAL FIREBRICK WHSE,02-Central,734900000,302.88,115.08,187.80
INDUSTRIAL FIREBRICK WHSE,02-Central,739681101,219.02,118.53,100.49
INDUSTRIAL FURNACE CO,02-Central,734990000,2036.16,331.76,1704.40
INDUSTRIAL FURNACE CO,02-Central,764521000,904.80,212.75,692.05
INDUSTRIAL INSULATION,02-Central,739781100,785.52,491.52,294.00
INDUSTRIAL INSULATION,02-Central,739741100,525.12,219.12,306.00
INDUSTRIAL INSULATION,02-Central,766162000,473.00,74.74,398.26
INDUSTRIAL PARTS,02-Central,742501300,2583.00,202.76,2380.24
INDUSTRIAL PARTS,02-Central,742501400,2370.00,272.86,2097.14
INDUSTRIAL RUBBER & GASKE,02-Central,733511000,1075.00,133.12,941.88
INDUSTRIAL RUBBER & GASKE,02-Central,733444200,348.00,38.64,309.36
INDUSTRIAL SUPPLIES CO.,02-Central,793320074,2860.00,642.76,2217.24
INSULATING SERVICES INC.,02-Central,735602000,298660.00,102949.66,195710.34
INSULATING SERVICES INC.,02-Central,764521000,38188.00,11318.66,26869.34
INSULATING SERVICES INC.,02-Central,735750000,8100.00,3467.69,4632.31
INSULATING SERVICES INC.,02-Central,764901000,3767.40,839.48,2927.92
INSULATING SERVICES INC.,02-Central,764902000,3187.80,702.40,2485.40
INSULATING SERVICES INC.,02-Central,733151500,2249.20,365.93,1883.27
INSULATING SERVICES INC.,02-Central,733211000,1507.80,319.23,1188.57
INSULATING SERVICES INC.,02-Central,739561200,547.07,149.00,398.07
INSULATING SERVICES INC.,02-Central,735862000,480.00,211.45,268.55
INSULATION FABRICATORS,02-Central,735602000,494380.00,165599.17,328780.83
INSULATION FABRICATORS,02-Central,764901050,298093.00,116789.80,181303.20
INSULATION FABRICATORS,02-Central,764821000,118042.00,40525.43,77516.57
INSULATION FABRICATORS,02-Central,764605000,98847.00,31816.07,67030.93
INSULATION FABRICATORS,02-Central,764902000,96180.00,38740.42,57439.58
INSULATION FABRICATORS,02-Central,764864000,88056.00,30561.89,57494.11
INSULATION FABRICATORS,02-Central,764822000,83427.00,29303.56,54123.44
INSULATION FABRICATORS,02-Central,768241000,78478.00,19637.84,58840.16
INSULATION FABRICATORS,02-Central,764521000,66271.00,21169.77,45101.23
INSULATION FABRICATORS,02-Central,764901000,59274.00,24983.69,34290.31
INSULATION FABRICATORS,02-Central,735750000,48298.00,19321.29,28976.71
INSULATION FABRICATORS,02-Central,735602110,45680.00,15680.98,29999.02
INSULATION FABRICATORS,02-Central,764821161,42152.00,35637.60,6514.40
INSULATION FABRICATORS,02-Central,764762073,36571.18,10823.00,25748.18
INSULATION FABRICATORS,02-Central,764261000,28356.00,9874.95,18481.05
INSULATION FABRICATORS,02-Central,764341000,26043.50,8509.39,17534.11
INSULATION FABRICATORS,02-Central,764522000,25818.00,8011.84,17806.16
INSULATION FABRICATORS,02-Central,766161000,23049.50,8473.13,14576.37
INSULATION FABRICATORS,02-Central,768231000,20274.20,5018.06,15256.14
INSULATION FABRICATORS,02-Central,766121000,19470.00,5087.63,14382.37
INSULATION FABRICATORS,02-Central,768221000,17302.00,3573.74,13728.26
INSULATION FABRICATORS,02-Central,764302500,15840.00,5263.83,10576.17
INSULATION FABRICATORS,02-Central,764821073,11968.00,9914.40,2053.60
INSULATION FABRICATORS,02-Central,764862000,11362.00,4086.32,7275.68
INSULATION FABRICATORS,02-Central,735752000,10052.00,4153.64,5898.36
INSULATION FABRICATORS,02-Central,739741100,9889.60,5656.16,4233.44
INSULATION FABRICATORS,02-Central,764563000,7520.00,2734.73,4785.27
INSULATION FABRICATORS,02-Central,764781000,7494.00,1930.37,5563.63
INSULATION FABRICATORS,02-Central,768242000,6342.50,2504.64,3837.86
INSULATION FABRICATORS,02-Central,735655000,6120.00,815.89,5304.11
INSULATION FABRICATORS,02-Central,764521061,5504.00,4089.60,1414.40
INSULATION FABRICATORS,02-Central,764562000,5425.00,1950.40,3474.60
INSULATION FABRICATORS,02-Central,738561000,4605.12,2178.99,2426.13
INSULATION FABRICATORS,02-Central,764901073,4592.00,4082.40,509.60
INSULATION FABRICATORS,02-Central,733211000,4517.00,928.60,3588.40
INSULATION FABRICATORS,02-Central,733525500,4140.00,590.62,3549.38
INSULATION FABRICATORS,02-Central,768211000,3720.00,922.36,2797.64
INSULATION FABRICATORS,02-Central,764521073,3440.00,2580.00,860.00
INSULATION FABRICATORS,02-Central,764481000,2564.00,763.54,1800.46
INSULATION FABRICATORS,02-Central,764602000,2562.00,943.60,1618.40
INSULATION FABRICATORS,02-Central,764345052,2555.00,787.36,1767.64
INSULATION FABRICATORS,02-Central,739681100,2370.24,1656.00,714.24
INSULATION FABRICATORS,02-Central,770372000,1999.20,132.32,1866.88
INSULATION FABRICATORS,02-Central,727000140,1690.00,217.28,1472.72
INSULATION FABRICATORS,02-Central,739561000,1494.29,343.79,1150.50
INSULATION FABRICATORS,02-Central,735602010,1336.00,922.30,413.70
INSULATION FABRICATORS,02-Central,733151500,1311.20,203.60,1107.60
INSULATION FABRICATORS,02-Central,733671000,1294.27,119.66,1174.61
INSULATION FABRICATORS,02-Central,764564000,1200.00,422.63,777.37
INSULATION FABRICATORS,02-Central,740283502,1073.60,193.64,879.96
INSULATION FABRICATORS,02-Central,793250074,1005.00,261.49,743.51
INSULATION FABRICATORS,02-Central,733403300,920.04,92.92,827.12
INSULATION FABRICATORS,02-Central,779261000,915.34,155.38,759.96
INSULATION FABRICATORS,02-Central,734611100,905.14,270.91,634.23
INSULATION FABRICATORS,02-Central,754055000,837.76,293.27,544.49
INSULATION FABRICATORS,02-Central,793452074,738.00,295.77,442.23
INSULATION FABRICATORS,02-Central,727000060,688.00,167.43,520.57
INSULATION FABRICATORS,02-Central,770240000,637.50,70.33,567.17
INSULATION FABRICATORS,02-Central,777601000,614.88,117.60,497.28
INSULATION FABRICATORS,02-Central,733141200,324.00,59.97,264.03
INSULATION FABRICATORS,02-Central,734651200,314.60,74.00,240.60
INSULATION FABRICATORS,02-Central,764781040,280.00,80.66,199.34
INSULATION FABRICATORS,02-Central,739641100,195.12,137.43,57.69
INSULATION FABRICATORS,02-Central,764342000,63.00,23.40,39.60
INSULATION FABRICATORS,02-Central,777642000,57.35,15.84,41.51
INSULATION MATERIALS CORP,02-Central,735602000,137901.50,55221.52,82679.98
INSULATION MATERIALS CORP,02-Central,768242000,44702.00,21648.79,23053.21
INSULATION MATERIALS CORP,02-Central,771320000,25051.90,5694.09,19357.81
INSULATION MATERIALS CORP,02-Central,768222000,5133.00,2011.01,3121.99
INSULATION MATERIALS CORP,02-Central,764902000,3144.00,1532.49,1611.51
INSULATION MATERIALS CORP,02-Central,764781000,2184.00,858.69,1325.31
INSULATION MATERIALS CORP,02-Central,766121000,1596.00,653.98,942.02
INSULATION MATERIALS CORP,02-Central,735750000,1500.00,686.70,813.30
INSULATION MATERIALS CORP,02-Central,764821000,1312.00,491.46,820.54
INSULATION MATERIALS CORP,02-Central,768142000,797.50,307.45,490.05
INSULATION MATERIALS CORP,02-Central,764901000,786.00,387.46,398.54
INSULATION MATERIALS CORP,02-Central,768241000,396.00,154.16,241.84
INSULATION SALES,02-Central,735602000,277321.50,89874.73,187446.77
INSULATION SALES,02-Central,735750000,24824.40,6552.76,18271.64
INSULATION SALES,02-Central,764341000,12499.00,4577.73,7921.27
INSULATION SALES,02-Central,764521000,2971.00,876.32,2094.68
INSULATION SALES,02-Central,764901000,2510.00,640.58,1869.42
INSULATION SALES,02-Central,764301000,1906.00,762.68,1143.32
INSULATION SALES,02-Central,764261000,1426.00,505.18,920.82
INSULATION SALES,02-Central,735602110,1019.20,350.03,669.17
INSULATION SALES,02-Central,764342000,889.00,327.62,561.38
INSULATION SPECIALTIES,02-Central,727000060,776940.00,457816.86,319123.14
INSULATION SPECIALTIES,02-Central,720290010,145165.50,84880.67,60284.83
INSULATION SPECIALTIES,02-Central,720271000,119847.60,48995.04,70852.56
INSULATION SPECIALTIES,02-Central,715001000,53178.00,22389.09,30788.91
INSULATION SPECIALTIES,02-Central,763991000,19764.00,6012.04,13751.96
INSULATION SPECIALTIES,02-Central,793060000,18338.40,6106.64,12231.76
INSULATION SPECIALTIES,02-Central,764521000,16320.00,6382.59,9937.41
INSULATION SPECIALTIES,02-Central,793000000,13651.20,4962.83,8688.37
INSULATION SPECIALTIES,02-Central,727063016,11750.40,6841.28,4909.12
INSULATION SPECIALTIES,02-Central,734030000,3312.00,989.32,2322.68
INSULATION SPECIALTIES,02-Central,734000000,1561.50,455.59,1105.91
INSULFAB PLASTICS INC,02-Central,733444100,8712.00,2056.41,6655.59
INTERNATIONAL RE-ENFORCED,02-Central,727000140,45722.88,7030.76,38692.12
IPSEN CERAMICS DIV,02-Central,766162000,876.00,37.37,838.63
IRON CITY PIPE & SUPPLY,02-Central,733211050,456.00,49.70,406.30
IRON CITY PIPE & SUPPLY,02-Central,734910000,362.00,53.09,308.91
"IRWIN INTERNATIONAL, INC.",02-Central,733525500,5700.00,541.70,5158.30
"IRWIN INTERNATIONAL, INC.",02-Central,733505200,1645.00,167.66,1477.34
"IRWIN INTERNATIONAL, INC.",02-Central,733464500,626.00,65.05,560.95
J&J TRUCK EQUIPMENT CO.,02-Central,733464600,313.00,34.97,278.03
JACKSONVILLE ELECTRIC .,02-Central,770510000,1975.00,96.41,1878.59
JBC TECHNOLOGIES INC.,02-Central,768211000,294.00,140.34,153.66
JBC TECHNOLOGIES INC.,02-Central,768222000,168.00,68.31,99.69
JEANNETTE SPECIALTY GLASS,02-Central,740402402,2097.50,411.01,1686.49
JOHN HOPKINS UNIVERSITY,02-Central,733221400,4689.36,770.88,3918.48
JOHN HOPKINS UNIVERSITY,02-Central,739561200,842.24,144.66,697.58
JONATHAN ELLIS,02-Central,777642000,135.00,31.68,103.32
JONESBORO WINAIR CO #247,02-Central,735602000,11936.00,2707.75,9228.25
JOY MARK,02-Central,726000242,1891816.80,1082085.28,809731.52
JOY MARK,02-Central,726020005,315632.16,174086.81,141545.35
JOY MARK,02-Central,727000100,66203.52,67049.03,-845.51
JOY MARK,02-Central,727000060,22142.40,13342.86,8799.54
KAMAN INDUSTRIAL TECHNOLO,02-Central,770210000,460.00,31.52,428.48
KBR,02-Central,767970140,2281.50,315.58,1965.92
KBR,02-Central,767970000,972.00,302.34,669.66
KERBER SHEET METAL,02-Central,732821100,158941.44,10070.08,148871.36
KERBER SHEET METAL,02-Central,732771000,27054.72,1377.90,25676.82
KERBER SHEET METAL,02-Central,733505200,5378.00,808.77,4569.23
KERBER SHEET METAL,02-Central,733525500,5180.00,780.36,4399.64
KERDEA TECHNOLOGIES INC.,02-Central,733444200,348.00,38.64,309.36
KICZAN MANUFACTURING INC.,02-Central,733444200,680.60,73.33,607.27
KIRK & BLUM,02-Central,766594000,2980.00,162.43,2817.57
KMI SYSTEMS INC,02-Central,727000140,135594.00,57723.22,77870.78
KMI SYSTEMS INC,02-Central,763991000,86799.60,65626.92,21172.68
KMI SYSTEMS INC,02-Central,765662000,81264.00,25475.24,55788.76
KMI SYSTEMS INC,02-Central,764605000,67675.50,25889.97,41785.53
KMI SYSTEMS INC,02-Central,764345052,52778.00,24427.62,28350.38
KMI SYSTEMS INC,02-Central,734910000,30051.00,8318.36,21732.64
KMI SYSTEMS INC,02-Central,727000060,21204.00,9054.09,12149.91
KYOCERA INDUSTRIAL,02-Central,766162000,246.50,74.74,171.76
KYOCERA INDUSTRIAL,02-Central,766121000,246.00,54.50,191.50
LARRY COOPER,02-Central,768121000,100.00,14.38,85.62
LAYNE HEAVY CIVIL,02-Central,734751200,584.00,75.41,508.59
LECTRAHEAT,02-Central,764461006,3944.00,275.21,3668.79
LEE PRECISION MACHINE,02-Central,724281900,3882.00,893.92,2988.08
LILJA CORP,02-Central,770580000,1522.00,84.73,1437.27
LILJA CORP,02-Central,770560000,1509.00,86.09,1422.91
LIQTECH NA INC.,02-Central,734793020,345434.70,139724.77,205709.93
LORD CORP,02-Central,739561100,552.72,93.32,459.40
"LOTUS ENGINEERING, INC.",02-Central,764481000,706.00,34.53,671.47
LOW COUNTRY INSULATION,02-Central,735602000,13340.00,5005.19,8334.81
LPI TECHNICAL SERVICE,02-Central,768211000,748.00,46.78,701.22
LSI ADL TECHNOLOGY INC.,02-Central,733403100,5648.05,523.57,5124.48
LT ENGINEERING INC.,02-Central,768144210,1777.50,511.82,1265.68
LVR INC.,02-Central,734900000,1617.90,474.98,1142.92
LVR INC.,02-Central,733211000,479.00,53.20,425.80
LVR INC.,02-Central,734311100,404.36,35.30,369.06
LVR INC.,02-Central,777601000,386.72,72.02,314.70
M & L MECHANICAL,02-Central,734311100,404.36,35.30,369.06
M & L MECHANICAL,02-Central,734611100,404.36,27.18,377.18
M. LUKAS COMPANY,02-Central,733151500,9844.20,1282.11,8562.09
M. LUKAS COMPANY,02-Central,733211000,9764.40,1514.70,8249.70
M. LUKAS COMPANY,02-Central,792760000,609.56,116.62,492.94
MAC ARTHUR CO,02-Central,735602110,4395.00,1338.80,3056.20
MAC ARTHUR CO,02-Central,735602000,2197.50,647.22,1550.28
MACHINE TOOL,02-Central,739641100,4319.64,1750.23,2569.41
MAGNAFLEX INDUSTRIES INC.,02-Central,764765000,5040.00,2949.37,2090.63
MAGNAFLEX INDUSTRIES INC.,02-Central,764761000,4160.00,637.20,3522.80
MAGNEQUENCH INTERNATIONAL,02-Central,764821000,430.00,15.36,414.64
MAGNESITA REFRACTORIES,02-Central,763991000,1713.60,156.90,1556.70
MATHIAS DIE COMPANY INC.,02-Central,733464500,1222.00,124.09,1097.91
MATTHEWS INTERNATIONAL,02-Central,722413100,6623.60,342.00,6281.60
MCALLISTER MILLS INC.,02-Central,768133500,6625.60,3025.01,3600.59
MCALLISTER MILLS INC.,02-Central,768133400,5979.20,2666.95,3312.25
MCAULY MFG. INC.,02-Central,726000240,220.80,14.79,206.01
MCAULY MFG. INC.,02-Central,764261000,212.00,11.45,200.55
MCNEIL INC.,02-Central,754925000,97242.30,54251.58,42990.72
MCNEIL INC.,02-Central,764601000,60382.40,20387.00,39995.40
MCNEIL INC.,02-Central,795482000,32514.10,12618.76,19895.34
MCNEIL INC.,02-Central,727041200,23023.08,4663.08,18360.00
MCNEIL INC.,02-Central,754495000,20599.46,7626.18,12973.28
MCNEIL INC.,02-Central,764821000,17898.00,6295.77,11602.23
MCNEIL INC.,02-Central,764901000,16440.88,4870.86,11570.02
MCNEIL INC.,02-Central,788260020,14187.12,4174.14,10012.98
MCNEIL INC.,02-Central,739781100,11600.64,6855.08,4745.56
MCNEIL INC.,02-Central,718500000,11329.28,4568.47,6760.81
MCNEIL INC.,02-Central,754926000,11088.24,6260.30,4827.94
MCNEIL INC.,02-Central,754095000,10120.32,4291.26,5829.06
MCNEIL INC.,02-Central,788200010,8919.63,2954.01,5965.62
MCNEIL INC.,02-Central,764521000,7672.00,3828.52,3843.48
MCNEIL INC.,02-Central,738521000,7498.38,5164.26,2334.12
MCNEIL INC.,02-Central,766121000,6825.00,1930.61,4894.39
MCNEIL INC.,02-Central,734311100,6664.00,1412.04,5251.96
MCNEIL INC.,02-Central,754305000,5913.64,2371.32,3542.32
MCNEIL INC.,02-Central,739641100,5875.20,5497.08,378.12
MCNEIL INC.,02-Central,779957621,5778.72,2688.51,3090.21
MCNEIL INC.,02-Central,780310000,5538.20,2132.98,3405.22
MCNEIL INC.,02-Central,734661300,5326.20,1611.84,3714.36
MCNEIL INC.,02-Central,784140000,4448.22,2117.66,2330.56
MCNEIL INC.,02-Central,764341000,3050.25,1204.08,1846.17
MCNEIL INC.,02-Central,734990000,2725.60,717.52,2008.08
MCNEIL INC.,02-Central,734900000,2649.60,1381.03,1268.57
MCNEIL INC.,02-Central,734211200,2603.95,327.27,2276.68
MCNEIL INC.,02-Central,739390000,2049.44,1864.89,184.55
MCNEIL INC.,02-Central,734910000,1836.00,616.16,1219.84
MCNEIL INC.,02-Central,754885000,1707.36,727.32,980.04
MCNEIL INC.,02-Central,780330000,1440.10,574.46,865.64
MCNEIL INC.,02-Central,740402402,1414.00,434.70,979.30
MCNEIL INC.,02-Central,754896000,1404.96,787.76,617.20
MCNEIL INC.,02-Central,777601000,1399.16,405.67,993.49
MCNEIL INC.,02-Central,770260000,984.83,208.29,776.54
MCNEIL INC.,02-Central,734651200,925.36,184.50,740.86
MCNEIL INC.,02-Central,780250000,889.27,367.58,521.69
MCNEIL INC.,02-Central,734351200,833.00,147.24,685.76
MCNEIL INC.,02-Central,737912200,629.18,136.42,492.76
MCNEIL INC.,02-Central,733525500,550.00,130.06,419.94
MCNEIL INC.,02-Central,777642000,520.00,151.86,368.14
MCNEIL INC.,02-Central,727000140,492.80,133.71,359.09
MCNEIL INC.,02-Central,768221000,410.40,70.76,339.64
MCNEIL INC.,02-Central,739561000,386.35,170.44,215.91
MCNEIL INC.,02-Central,770392000,350.00,35.15,314.85
MCNEIL INC.,02-Central,792702000,303.75,96.60,207.15
MCNEIL INC.,02-Central,733211000,220.00,94.78,125.22
MCNEIL INC.,02-Central,733725500,164.20,51.76,112.44
"MEGTEC SYSTEMS, INC",02-Central,754055000,79930.10,46859.42,33070.68
"MEGTEC SYSTEMS, INC",02-Central,753945000,39910.72,33329.70,6581.02
"MEGTEC SYSTEMS, INC",02-Central,754045000,30718.27,19842.49,10875.78
"MEGTEC SYSTEMS, INC",02-Central,753965000,23602.83,18089.11,5513.72
"MEGTEC SYSTEMS, INC",02-Central,764821000,11351.00,2997.87,8353.13
"MEGTEC SYSTEMS, INC",02-Central,764822000,8191.20,2521.02,5670.18
"MEGTEC SYSTEMS, INC",02-Central,764522000,7503.20,2114.23,5388.97
"MEGTEC SYSTEMS, INC",02-Central,764901000,7266.00,2439.37,4826.63
"MEGTEC SYSTEMS, INC",02-Central,770540000,6659.12,549.16,6109.96
"MEGTEC SYSTEMS, INC",02-Central,734651200,6134.63,369.75,5764.88
"MEGTEC SYSTEMS, INC",02-Central,738401000,5139.00,5300.06,-161.06
"MEGTEC SYSTEMS, INC",02-Central,764861000,5045.60,1561.41,3484.19
"MEGTEC SYSTEMS, INC",02-Central,754065000,4749.00,2758.14,1990.86
"MEGTEC SYSTEMS, INC",02-Central,780650000,4320.65,2478.18,1842.47
"MEGTEC SYSTEMS, INC",02-Central,779988252,3666.00,3284.19,381.81
"MEGTEC SYSTEMS, INC",02-Central,764561000,3618.00,1419.33,2198.67
"MEGTEC SYSTEMS, INC",02-Central,764781040,3310.00,675.44,2634.56
"MEGTEC SYSTEMS, INC",02-Central,764823500,2432.20,762.56,1669.64
"MEGTEC SYSTEMS, INC",02-Central,780210000,2406.18,1270.82,1135.36
"MEGTEC SYSTEMS, INC",02-Central,764601000,2216.25,664.31,1551.94
"MEGTEC SYSTEMS, INC",02-Central,777642000,1972.80,570.06,1402.74
"MEGTEC SYSTEMS, INC",02-Central,758531000,1876.06,593.72,1282.34
"MEGTEC SYSTEMS, INC",02-Central,739641100,1609.52,1493.54,115.98
"MEGTEC SYSTEMS, INC",02-Central,770550000,1598.26,175.27,1422.99
"MEGTEC SYSTEMS, INC",02-Central,777601000,1559.32,523.26,1036.06
"MEGTEC SYSTEMS, INC",02-Central,774271000,1406.57,923.42,483.15
"MEGTEC SYSTEMS, INC",02-Central,734751200,1365.15,411.29,953.86
"MEGTEC SYSTEMS, INC",02-Central,774301000,1329.10,675.39,653.71
"MEGTEC SYSTEMS, INC",02-Central,774401000,1168.93,514.20,654.73
"MEGTEC SYSTEMS, INC",02-Central,780290000,1163.32,614.16,549.16
"MEGTEC SYSTEMS, INC",02-Central,764482000,1040.00,382.84,657.16
"MEGTEC SYSTEMS, INC",02-Central,742546000,1000.80,156.72,844.08
"MEGTEC SYSTEMS, INC",02-Central,784320000,629.43,365.84,263.59
"MEGTEC SYSTEMS, INC",02-Central,764781000,570.60,122.70,447.90
"MEGTEC SYSTEMS, INC",02-Central,738361000,548.32,926.64,-378.32
"MEGTEC SYSTEMS, INC",02-Central,734990000,509.32,109.82,399.50
"MEGTEC SYSTEMS, INC",02-Central,754295000,509.00,217.16,291.84
"MEGTEC SYSTEMS, INC",02-Central,764826500,476.80,129.32,347.48
"MEGTEC SYSTEMS, INC",02-Central,738521000,465.92,597.12,-131.20
"MEGTEC SYSTEMS, INC",02-Central,754725000,450.41,224.69,225.72
"MEGTEC SYSTEMS, INC",02-Central,754265000,440.05,199.38,240.67
"MEGTEC SYSTEMS, INC",02-Central,764521000,377.60,106.27,271.33
"MEGTEC SYSTEMS, INC",02-Central,734611100,279.48,80.88,198.60
"MEGTEC SYSTEMS, INC",02-Central,773801000,239.70,91.14,148.56
"MEGTEC SYSTEMS, INC",02-Central,764902000,184.30,63.86,120.44
MERAK NORTH AMERICA,02-Central,768021005,913.00,106.43,806.57
MERAK NORTH AMERICA,02-Central,768021000,374.00,26.83,347.17
MESSIER - BUGATTI,02-Central,779601000,23172.00,3984.62,19187.38
MESSIER - BUGATTI,02-Central,770930000,6252.00,214.28,6037.72
MESSIER - BUGATTI,02-Central,770710000,6192.00,134.52,6057.48
MESSIER-BUGATTI USA LLC,02-Central,779601000,131331.67,21395.85,109935.82
MESSIER-BUGATTI USA LLC,02-Central,770930000,6252.00,214.28,6037.72
MESSIER-BUGATTI USA LLC,02-Central,770710000,3096.00,67.25,3028.75
MESTEK INC.,02-Central,739541000,587.52,90.50,497.02
"MESTEK, FORREST CITY",02-Central,764461006,776.00,54.49,721.51
METALOR ELECTROTECHNICS,02-Central,734311100,404.36,35.30,369.06
METALSTAMP INC.,02-Central,764902000,884.50,32.21,852.29
MGM INDUSTRIAL SPLY,02-Central,734990000,4435.20,657.31,3777.89
MGM INDUSTRIAL SUPPLY CO,02-Central,734990000,59808.00,8594.63,51213.37
MHD - ROCKLAND INC.,02-Central,771102000,870.00,25.10,844.90
MHD ROCKLAND,02-Central,771102000,7573.00,229.79,7343.21
MI-T-M CORP,02-Central,764481000,346.00,16.94,329.06
MICROFLEX INC,02-Central,770230000,4032.00,295.67,3736.33
MICROFLEX INC,02-Central,770240000,937.50,67.63,869.87
MICROWAVE RESEARCH &,02-Central,739641100,1389.96,412.29,977.67
MICROWAVE RESEARCH &,02-Central,734711100,536.16,81.15,455.01
MICROWAVE RESEARCH &,02-Central,734311100,190.74,16.85,173.89
MIDSTATE INDUSTRIAL,02-Central,739641100,926.64,274.85,651.79
MIDSTATE INDUSTRIAL,02-Central,740023000,727.44,119.31,608.13
MIDSTATE INDUSTRIAL,02-Central,740063000,616.56,120.10,496.46
MIDSTATE INDUSTRIAL,02-Central,739681100,444.24,120.46,323.78
MIDWEST FABRICATION,02-Central,764601000,5568.00,225.61,5342.39
MIDWEST SERVICE CENTER,02-Central,770200000,630.00,40.79,589.21
MIKRO SYSTEMS INC.,02-Central,764521000,666.00,26.54,639.46
MILLIKEN AND COMPANY,02-Central,739380000,3171.84,961.23,2210.61
MILWAUKEE INSULATION,02-Central,777601000,1160.16,216.05,944.11
MINTEQ INTERNATIONAL INC.,02-Central,779988964,45540.00,32996.76,12543.24
MINTEQ INTERNATIONAL INC.,02-Central,777904400,27189.00,20387.43,6801.57
MITSUBISHI POWER SYSTEMS,02-Central,764821000,447.00,15.66,431.34
MITSUBISHI POWER SYSTEMS,02-Central,739641100,409.68,201.60,208.08
MODINE MFG CO,02-Central,733586400,1857.60,182.85,1674.75
MODINE MFG CO,02-Central,733566100,979.20,129.98,849.22
MONESSEN HEARTH SYSTEMS,02-Central,726020042,192497.85,152220.15,40277.70
MONESSEN HEARTH SYSTEMS,02-Central,726020041,144297.75,114581.39,29716.36
MONESSEN HEARTH SYSTEMS,02-Central,726000247,16988.40,13271.91,3716.49
MONESSEN HEARTH SYSTEMS,02-Central,726020019,3631.50,2736.50,895.00
MONESSEN HEARTH SYSTEMS,02-Central,726020005,2905.20,2008.13,897.07
MONESSEN HEARTH SYSTEMS,02-Central,726000246,807.00,605.93,201.07
MONESSEN HEARTH SYSTEMS,02-Central,777601000,441.67,144.04,297.63
MONESSEN HEARTH SYSTEMS,02-Central,777642000,381.60,201.44,180.16
MORGAN AM&T,02-Central,734990000,926.40,110.07,816.33
MOTION INDUSTRIES,02-Central,770220000,11088.00,670.50,10417.50
MOTION INDUSTRIES,02-Central,770560000,10791.00,612.86,10178.14
MOTION INDUSTRIES,02-Central,764821000,7463.00,263.78,7199.22
MOTION INDUSTRIES,02-Central,737251000,5242.32,902.07,4340.25
MOTION INDUSTRIES,02-Central,739781100,5180.96,1443.62,3737.34
MOTION INDUSTRIES,02-Central,770540000,3629.50,184.78,3444.72
MOTION INDUSTRIES,02-Central,771170000,3563.00,150.93,3412.07
MOTION INDUSTRIES,02-Central,738521000,2760.96,1337.69,1423.27
MOTION INDUSTRIES,02-Central,770200000,2592.00,166.40,2425.60
MOTION INDUSTRIES,02-Central,734890000,2424.68,406.65,2018.03
MOTION INDUSTRIES,02-Central,770330000,2130.00,202.93,1927.07
MOTION INDUSTRIES,02-Central,770600000,1916.25,96.32,1819.93
MOTION INDUSTRIES,02-Central,770522000,1868.75,88.79,1779.96
MOTION INDUSTRIES,02-Central,770210000,1320.00,90.96,1229.04
MOTION INDUSTRIES,02-Central,777601000,1206.56,189.62,1016.94
MOTION INDUSTRIES,02-Central,770222000,1080.25,68.04,1012.21
MOTION INDUSTRIES,02-Central,770230000,1065.75,71.08,994.67
MOTION INDUSTRIES,02-Central,770260000,1035.00,67.63,967.37
MOTION INDUSTRIES,02-Central,770720000,1010.00,27.12,982.88
MOTION INDUSTRIES,02-Central,770240000,986.25,70.33,915.92
MOTION INDUSTRIES,02-Central,734822000,932.00,160.95,771.05
MOTION INDUSTRIES,02-Central,766686000,845.00,73.65,771.35
MOTION INDUSTRIES,02-Central,734311100,404.36,35.30,369.06
MSC INDUSTRIAL DIRECT CO.,02-Central,734211200,82684.76,8058.43,74626.33
MSC INDUSTRIAL DIRECT CO.,02-Central,734311100,14652.44,1284.55,13367.89
MSC INDUSTRIAL DIRECT CO.,02-Central,770530000,1707.50,93.46,1614.04
MSC INDUSTRIAL DIRECT CO.,02-Central,735602000,618.50,43.15,575.35
MSC INDUSTRIAL DIRECT CO.,02-Central,777601000,409.92,58.80,351.12
MSC INDUSTRIAL DIRECT CO.,02-Central,733464500,250.40,32.53,217.87
MULTI-METALS,02-Central,770850000,1342.00,42.06,1299.94
MUNACO PACKING & RUBBER,02-Central,764482000,1384.00,68.19,1315.81
NASA - MARSHALL SPACE,02-Central,739390000,350.56,82.43,268.13
NASA GLENN RESEARCH CTR.,02-Central,739521100,1270.08,139.38,1130.70
NASA GLENN RESEARCH CTR.,02-Central,777998000,910.00,1214.32,-304.32
NASA GLENN RESEARCH CTR.,02-Central,740361900,700.80,228.74,472.06
NASA GLENN RESEARCH CTR.,02-Central,739541000,564.48,71.62,492.86
NASA GLENN RESEARCH CTR.,02-Central,740361701,529.20,318.65,210.55
NASA GLENN RESEARCH CTR.,02-Central,777998500,300.00,400.07,-100.07
NASA MSFC,02-Central,764221000,248.00,16.92,231.08
NATHAN DEKKENGA,02-Central,739390000,350.56,82.43,268.13
NATIONAL MACHINE COMPANY,02-Central,733151500,1866.00,210.01,1655.99
NAVAL RESEARCH LABORATORY,02-Central,764521000,320.00,13.30,306.70
NAVAL SURFACE WARFARE CTR,02-Central,739452000,2162.16,603.34,1558.82
NAVAL SURFACE WARFARE CTR,02-Central,734851000,1518.57,274.20,1244.37
NAVAL SURFACE WARFARE CTR,02-Central,724281400,1202.00,178.16,1023.84
NAVAL SURFACE WARFARE CTR,02-Central,739370000,1094.40,272.46,821.94
NAVAL SURFACE WARFARE CTR,02-Central,737912200,979.50,136.41,843.09
NAVY TECHNOLOGY CCENTER,02-Central,780650000,1339.68,206.95,1132.73
NEW ARTS FOUNDRY,02-Central,734910000,1730.00,309.84,1420.16
NEW ARTS FOUNDRY,02-Central,764521000,1044.00,79.77,964.23
NORANDAL,02-Central,770340000,9330.75,940.03,8390.72
NORFOLK NAVAL SHIPYARD,02-Central,726020033,408.80,33.33,375.47
NORTH AMERICAN HOGANAS,02-Central,768241000,11484.00,1273.72,10210.28
NORTHERN INDIANA PUBLIC S,02-Central,779521000,35765.68,5128.36,30637.32
NOVELIS OSWEGO,02-Central,733525600,752.50,71.05,681.45
NS INDUSTRIES INC.,02-Central,733484800,1568.00,164.36,1403.64
NS INDUSTRIES INC.,02-Central,733505200,1565.00,160.27,1404.73
NS INDUSTRIES INC.,02-Central,733510000,1130.00,159.02,970.98
NS INDUSTRIES INC.,02-Central,733741000,776.00,186.38,589.62
NUCOR STEEL BIRMINGHAM,02-Central,779988964,2226.00,3599.46,-1373.46
NUCOR-YAMATO STEEL CO.,02-Central,779980910,21250.00,9094.10,12155.90
OHIO INDUSTRIAL SALES INC,02-Central,764461006,332306.40,123177.49,209128.91
OHIO INDUSTRIAL SALES INC,02-Central,766121610,270797.60,137858.34,132939.26
OHIO INDUSTRIAL SALES INC,02-Central,764602500,113360.00,57238.81,56121.19
OHIO INDUSTRIAL SALES INC,02-Central,734761300,112661.30,20458.67,92202.63
OHIO INDUSTRIAL SALES INC,02-Central,740402450,105710.94,67205.01,38505.93
OHIO INDUSTRIAL SALES INC,02-Central,764821000,96600.00,43162.78,53437.22
OHIO INDUSTRIAL SALES INC,02-Central,764901000,96064.00,50970.77,45093.23
OHIO INDUSTRIAL SALES INC,02-Central,734251300,75399.66,16277.93,59121.73
OHIO INDUSTRIAL SALES INC,02-Central,764901073,64448.00,77274.00,-12826.00
OHIO INDUSTRIAL SALES INC,02-Central,734311100,47907.32,5512.35,42394.97
OHIO INDUSTRIAL SALES INC,02-Central,764822000,47873.00,20752.33,27120.67
OHIO INDUSTRIAL SALES INC,02-Central,718500000,47001.61,8169.94,38831.67
OHIO INDUSTRIAL SALES INC,02-Central,733202000,46642.72,9274.66,37368.06
OHIO INDUSTRIAL SALES INC,02-Central,733586500,45010.08,6786.00,38224.08
OHIO INDUSTRIAL SALES INC,02-Central,739561200,44831.11,15979.42,28851.69
OHIO INDUSTRIAL SALES INC,02-Central,766121615,43048.80,21780.71,21268.09
OHIO INDUSTRIAL SALES INC,02-Central,764264050,41396.00,25969.68,15426.32
OHIO INDUSTRIAL SALES INC,02-Central,764521000,37620.00,17532.30,20087.70
OHIO INDUSTRIAL SALES INC,02-Central,733484900,36881.60,6796.39,30085.21
OHIO INDUSTRIAL SALES INC,02-Central,733525600,36113.00,5580.13,30532.87
OHIO INDUSTRIAL SALES INC,02-Central,740361900,31337.28,13268.59,18068.69
OHIO INDUSTRIAL SALES INC,02-Central,764481000,30070.00,17050.52,13019.48
OHIO INDUSTRIAL SALES INC,02-Central,727041000,28800.00,2850.55,25949.45
OHIO INDUSTRIAL SALES INC,02-Central,764821073,23460.00,24786.00,-1326.00
OHIO INDUSTRIAL SALES INC,02-Central,764522000,23378.00,10911.89,12466.11
OHIO INDUSTRIAL SALES INC,02-Central,779988828,21594.00,11716.15,9877.85
OHIO INDUSTRIAL SALES INC,02-Central,766211000,21330.00,12036.77,9293.23
OHIO INDUSTRIAL SALES INC,02-Central,734900000,21262.62,8266.88,12995.74
OHIO INDUSTRIAL SALES INC,02-Central,733151500,18490.40,2884.37,15606.03
OHIO INDUSTRIAL SALES INC,02-Central,739741100,17064.87,11043.46,6021.41
OHIO INDUSTRIAL SALES INC,02-Central,734990000,16417.32,3062.56,13354.76
OHIO INDUSTRIAL SALES INC,02-Central,779988827,16147.51,7823.58,8323.93
OHIO INDUSTRIAL SALES INC,02-Central,764821161,15870.00,17112.00,-1242.00
OHIO INDUSTRIAL SALES INC,02-Central,737912200,15781.37,3402.11,12379.26
OHIO INDUSTRIAL SALES INC,02-Central,740382201,14721.50,5054.79,9666.71
OHIO INDUSTRIAL SALES INC,02-Central,766162000,12628.00,1818.12,10809.88
OHIO INDUSTRIAL SALES INC,02-Central,766121000,11980.80,6975.70,5005.10
OHIO INDUSTRIAL SALES INC,02-Central,771500000,11854.50,860.05,10994.45
OHIO INDUSTRIAL SALES INC,02-Central,733444300,10479.40,2040.78,8438.62
OHIO INDUSTRIAL SALES INC,02-Central,764820500,10084.80,3967.42,6117.38
OHIO INDUSTRIAL SALES INC,02-Central,733505100,9945.00,1794.43,8150.57
OHIO INDUSTRIAL SALES INC,02-Central,727000140,9237.12,1169.17,8067.95
OHIO INDUSTRIAL SALES INC,02-Central,734910000,9147.00,2756.25,6390.75
OHIO INDUSTRIAL SALES INC,02-Central,739781100,8640.48,3786.18,4854.30
OHIO INDUSTRIAL SALES INC,02-Central,737902100,8176.75,2196.88,5979.87
OHIO INDUSTRIAL SALES INC,02-Central,779988960,8060.00,5443.60,2616.40
OHIO INDUSTRIAL SALES INC,02-Central,734651200,7943.55,1401.46,6542.09
OHIO INDUSTRIAL SALES INC,02-Central,771320000,7555.00,1241.91,6313.09
OHIO INDUSTRIAL SALES INC,02-Central,739390000,7393.59,2720.22,4673.37
OHIO INDUSTRIAL SALES INC,02-Central,734211200,7230.51,1013.53,6216.98
OHIO INDUSTRIAL SALES INC,02-Central,734351200,6319.32,627.57,5691.75
OHIO INDUSTRIAL SALES INC,02-Central,764781040,6158.00,1737.90,4420.10
OHIO INDUSTRIAL SALES INC,02-Central,733141300,5629.60,1251.33,4378.27
OHIO INDUSTRIAL SALES INC,02-Central,764826700,5520.00,2186.16,3333.84
OHIO INDUSTRIAL SALES INC,02-Central,740283401,5333.68,1061.54,4272.14
OHIO INDUSTRIAL SALES INC,02-Central,739541200,5277.20,1221.72,4055.48
OHIO INDUSTRIAL SALES INC,02-Central,764482000,5022.00,2819.15,2202.85
OHIO INDUSTRIAL SALES INC,02-Central,733484700,5008.00,956.94,4051.06
OHIO INDUSTRIAL SALES INC,02-Central,777901100,4403.54,1870.60,2532.94
OHIO INDUSTRIAL SALES INC,02-Central,777601000,4204.15,1078.92,3125.23
OHIO INDUSTRIAL SALES INC,02-Central,777904301,3887.63,1762.01,2125.62
OHIO INDUSTRIAL SALES INC,02-Central,764781000,3873.60,1419.92,2453.68
OHIO INDUSTRIAL SALES INC,02-Central,733725500,3812.40,1172.74,2639.66
OHIO INDUSTRIAL SALES INC,02-Central,733211000,3709.50,768.99,2940.51
OHIO INDUSTRIAL SALES INC,02-Central,726000240,3600.00,1549.50,2050.50
OHIO INDUSTRIAL SALES INC,02-Central,770570000,3431.04,302.19,3128.85
OHIO INDUSTRIAL SALES INC,02-Central,766122000,3232.00,1538.48,1693.52
OHIO INDUSTRIAL SALES INC,02-Central,764761000,3056.00,1017.22,2038.78
OHIO INDUSTRIAL SALES INC,02-Central,766081010,3003.00,508.35,2494.65
OHIO INDUSTRIAL SALES INC,02-Central,764762000,2969.80,1226.11,1743.69
OHIO INDUSTRIAL SALES INC,02-Central,740063000,2862.37,827.49,2034.88
OHIO INDUSTRIAL SALES INC,02-Central,764341000,2824.86,1727.06,1097.80
OHIO INDUSTRIAL SALES INC,02-Central,733151600,2776.60,511.74,2264.86
OHIO INDUSTRIAL SALES INC,02-Central,770600000,2642.43,192.64,2449.79
OHIO INDUSTRIAL SALES INC,02-Central,740283502,2603.04,387.29,2215.75
OHIO INDUSTRIAL SALES INC,02-Central,737301000,2590.72,981.44,1609.28
OHIO INDUSTRIAL SALES INC,02-Central,733211050,2405.00,503.20,1901.80
OHIO INDUSTRIAL SALES INC,02-Central,764521073,2280.00,2580.00,-300.00
OHIO INDUSTRIAL SALES INC,02-Central,733464500,2181.60,330.74,1850.86
OHIO INDUSTRIAL SALES INC,02-Central,779550100,2107.04,474.56,1632.48
OHIO INDUSTRIAL SALES INC,02-Central,733403100,1889.86,245.20,1644.66
OHIO INDUSTRIAL SALES INC,02-Central,777642000,1884.00,731.00,1153.00
OHIO INDUSTRIAL SALES INC,02-Central,770210000,1750.00,220.67,1529.33
OHIO INDUSTRIAL SALES INC,02-Central,720455000,1684.00,241.84,1442.16
OHIO INDUSTRIAL SALES INC,02-Central,733511000,1327.52,266.24,1061.28
OHIO INDUSTRIAL SALES INC,02-Central,740361701,1325.52,947.43,378.09
OHIO INDUSTRIAL SALES INC,02-Central,733403300,1137.40,245.08,892.32
OHIO INDUSTRIAL SALES INC,02-Central,770450000,1102.88,101.88,1001.00
OHIO INDUSTRIAL SALES INC,02-Central,734173000,1059.50,265.70,793.80
OHIO INDUSTRIAL SALES INC,02-Central,741041000,1026.72,41.88,984.84
OHIO INDUSTRIAL SALES INC,02-Central,733141200,1002.40,193.93,808.47
OHIO INDUSTRIAL SALES INC,02-Central,738521000,976.32,430.35,545.97
OHIO INDUSTRIAL SALES INC,02-Central,734162000,952.60,396.02,556.58
OHIO INDUSTRIAL SALES INC,02-Central,733525500,915.00,130.06,784.94
OHIO INDUSTRIAL SALES INC,02-Central,733201000,889.20,187.96,701.24
OHIO INDUSTRIAL SALES INC,02-Central,733464600,825.20,139.88,685.32
OHIO INDUSTRIAL SALES INC,02-Central,764902000,756.00,386.49,369.51
OHIO INDUSTRIAL SALES INC,02-Central,770720000,749.70,27.12,722.58
OHIO INDUSTRIAL SALES INC,02-Central,770310000,680.00,94.56,585.44
OHIO INDUSTRIAL SALES INC,02-Central,740382101,664.20,252.83,411.37
OHIO INDUSTRIAL SALES INC,02-Central,768121000,660.15,132.09,528.06
OHIO INDUSTRIAL SALES INC,02-Central,733721000,615.60,146.70,468.90
OHIO INDUSTRIAL SALES INC,02-Central,733423700,465.92,72.44,393.48
OHIO INDUSTRIAL SALES INC,02-Central,733444100,426.00,150.08,275.92
OHIO INDUSTRIAL SALES INC,02-Central,764221000,300.60,84.33,216.27
OHIO INDUSTRIAL SALES INC,02-Central,768111000,234.90,46.14,188.76
OHIO INDUSTRIAL SALES INC,02-Central,733423600,232.96,35.90,197.06
OHIO INDUSTRIAL SALES INC,02-Central,764525000,198.00,103.86,94.14
OHIO INDUSTRIAL SALES INC,02-Central,764602000,104.00,52.99,51.01
OHIO STAT UNIV,02-Central,734900000,215.80,38.36,177.44
OHIO STATE UNIVERSITY,02-Central,768121000,489.00,44.03,444.97
OMI REFRACTORIES,02-Central,727041000,26956.80,2707.59,24249.21
OMI REFRACTORIES,02-Central,761051000,15789.60,10262.62,5526.98
OMI REFRACTORIES,02-Central,727041200,8961.84,1353.81,7608.03
OMI REFRACTORIES,02-Central,761071000,5518.80,3234.84,2283.96
OMI REFRACTORIES,02-Central,777642000,2986.00,734.12,2251.88
ONEX INC,02-Central,764785121,71760.00,29177.10,42582.90
ONEX INC,02-Central,764781040,7560.00,3387.84,4172.16
ONEX INC,02-Central,766542300,492.00,172.77,319.23
ONEX REFRACTORIES,02-Central,768753000,472.80,48.26,424.54
ORTON CERAMIC FOUNDATION,02-Central,777991000,1697.30,449.06,1248.24
OVERHEAD MATERIAL,02-Central,770600000,6487.60,373.47,6114.13
OWENS BROCKWAY,02-Central,766121000,5250.00,163.50,5086.50
OWENS BROCKWAY,02-Central,763991000,1236.00,137.58,1098.42
OWENS CORNING FIBERGLAS,02-Central,739641100,1928.16,806.40,1121.76
OWENS CORNING FIBERGLAS,02-Central,793260074,1366.00,163.32,1202.68
OWENS CORNING FIBERGLAS,02-Central,793330000,449.00,26.51,422.49
"OWENS CORNING SALES,LLC",02-Central,793330000,863.00,52.03,810.97
OWENS CORNING,02-Central,766121000,910.00,23.95,886.05
P.R. INTERNATIONAL INC.,02-Central,734414100,720.00,103.18,616.82
PACIFIC INDUSTRIAL,02-Central,740361701,579.60,318.65,260.95
PACOR INC.,02-Central,733525600,9805.00,3605.55,6199.45
PACOR INC,02-Central,733525600,27120.00,9964.05,17155.95
PACOR INC,02-Central,733444200,298.00,38.64,259.36
PALL CORPORATION-H2,02-Central,740361900,7284.48,4036.21,3248.27
PALL CORPORATION-H2,02-Central,740361701,688.32,637.30,51.02
PARAMOUNT DIE CORPORATION,02-Central,733211000,912.00,94.78,817.22
PARAMOUNT DIE CORPORATION,02-Central,733151500,450.00,47.69,402.31
PARSONS,02-Central,727000140,531.20,33.43,497.77
PEACHTREE DISTRIBUTING,02-Central,735602000,178480.00,66079.28,112400.72
PEACHTREE DISTRIBUTING,02-Central,735602110,1840.00,707.04,1132.96
PENN MANUFACTURING,02-Central,764822000,19656.00,1779.13,17876.87
PENN MANUFACTURING,02-Central,764862000,455.00,52.36,402.64
PERFORMANCE CONTRACTNG-KY,02-Central,735602000,1710.00,517.78,1192.22
PERFORMANCE FRICTION,02-Central,766162000,876.00,37.37,838.63
PHELPS INDUSTRIAL PRODUCT,02-Central,733721000,286.00,48.90,237.10
"PIC GROUP, INC",02-Central,739641100,1310.98,806.40,504.58
PILKINGTON N. A. INC.,02-Central,734611100,378.88,54.36,324.52
PLASMA POWER,02-Central,777601000,819.84,117.60,702.24
PLASMA POWER,02-Central,734851000,506.19,91.40,414.79
PLASTICOMP,02-Central,764821000,430.00,15.36,414.64
PLIBRICO COMPANY LLC,02-Central,764901000,51183.00,11279.99,39903.01
PLIBRICO COMPANY LLC,02-Central,764821000,42912.00,8955.18,33956.82
PLIBRICO COMPANY LLC,02-Central,768241000,32760.00,8491.42,24268.58
PLIBRICO COMPANY LLC,02-Central,733211000,31429.30,6455.34,24973.96
PLIBRICO COMPANY LLC,02-Central,779987650,29751.20,8526.35,21224.85
PLIBRICO COMPANY LLC,02-Central,792622000,13860.00,3695.53,10164.47
PLIBRICO COMPANY LLC,02-Central,768221000,12617.50,3278.74,9338.76
PLIBRICO COMPANY LLC,02-Central,766121000,10083.60,1826.53,8257.07
PLIBRICO COMPANY LLC,02-Central,764601000,8969.40,2199.10,6770.30
PLIBRICO COMPANY LLC,02-Central,734990000,4637.12,994.24,3642.88
PLIBRICO COMPANY LLC,02-Central,770560000,3279.30,268.51,3010.79
PLIBRICO COMPANY LLC,02-Central,793330000,3207.00,686.20,2520.80
PLIBRICO COMPANY LLC,02-Central,793320074,3162.50,1536.25,1626.25
PLIBRICO COMPANY LLC,02-Central,733464500,2554.50,383.75,2170.75
PLIBRICO COMPANY LLC,02-Central,770600000,2516.22,180.83,2335.39
PLIBRICO COMPANY LLC,02-Central,779181000,2254.23,697.15,1557.08
PLIBRICO COMPANY LLC,02-Central,733525500,1830.00,260.12,1569.88
PLIBRICO COMPANY LLC,02-Central,764781000,1602.00,394.42,1207.58
PLIBRICO COMPANY LLC,02-Central,779221000,1508.92,389.56,1119.36
PLIBRICO COMPANY LLC,02-Central,734121000,1450.80,333.83,1116.97
PLIBRICO COMPANY LLC,02-Central,777601000,1227.21,333.66,893.55
PLIBRICO COMPANY LLC,02-Central,764761000,1080.00,375.45,704.55
PLIBRICO COMPANY LLC,02-Central,733403300,920.04,92.92,827.12
PLIBRICO COMPANY LLC,02-Central,768211000,725.00,224.44,500.56
PLIBRICO COMPANY LLC,02-Central,764461006,547.20,222.12,325.08
PLIBRICO COMPANY LLC,02-Central,793346074,508.00,296.56,211.44
PLIBRICO COMPANY LLC,02-Central,733423700,465.92,72.44,393.48
PLIBRICO COMPANY LLC,02-Central,764902000,301.50,64.42,237.08
PLIBRICO COMPANY LLC,02-Central,733741000,284.40,93.19,191.21
PLIBRICO COMPANY LLC,02-Central,739561200,278.85,76.67,202.18
PLIBRICO COMPANY LLC,02-Central,764521000,208.80,53.18,155.62
PLIBRICO COMPANY LLC,02-Central,733725500,205.20,51.76,153.44
PLIBRICO REFRACTORY,02-Central,770200000,413.10,42.41,370.69
POOLESVILLE LAWN EQUIP.,02-Central,733464400,611.00,82.98,528.02
PORTER WALKER INC,02-Central,733211050,958.00,111.82,846.18
PORTER WARNER INDUSTRIES,02-Central,766121000,48142.00,6209.76,41932.24
PORTER WARNER INDUSTRIES,02-Central,733182200,21924.48,4331.61,17592.87
PORTER WARNER INDUSTRIES,02-Central,739741100,15776.16,8050.50,7725.66
PORTER WARNER INDUSTRIES,02-Central,734414100,11392.61,1473.98,9918.63
PORTER WARNER INDUSTRIES,02-Central,739761100,9303.84,5425.98,3877.86
PORTER WARNER INDUSTRIES,02-Central,738561000,4250.88,1489.64,2761.24
PORTER WARNER INDUSTRIES,02-Central,770220000,2543.40,164.07,2379.33
PORTER WARNER INDUSTRIES,02-Central,733525400,2223.00,280.23,1942.77
PORTER WARNER INDUSTRIES,02-Central,770260000,1719.23,137.96,1581.27
PORTER WARNER INDUSTRIES,02-Central,734311100,1388.70,151.68,1237.02
PORTER WARNER INDUSTRIES,02-Central,770392000,1315.80,118.17,1197.63
PORTER WARNER INDUSTRIES,02-Central,734651200,1307.40,222.00,1085.40
PORTER WARNER INDUSTRIES,02-Central,739641100,885.60,412.29,473.31
PORTER WARNER INDUSTRIES,02-Central,770240000,866.25,70.33,795.92
PORTER WARNER INDUSTRIES,02-Central,770210000,792.00,60.63,731.37
PORTER WARNER INDUSTRIES,02-Central,733464500,563.40,65.05,498.35
PORTER WARNER INDUSTRIES,02-Central,739561200,393.98,76.67,317.31
PORTER WARNER INDUSTRIES,02-Central,764461006,270.00,81.75,188.25
POTTER INDUSTRIES INC.,02-Central,766121000,1820.00,47.90,1772.10
POW-R-MATIC INC,02-Central,733525500,2355.00,341.24,2013.76
POW-R-MATIC INC,02-Central,733586400,1891.80,289.41,1602.39
POW-R-MATIC INC,02-Central,734414100,1085.04,140.52,944.52
POWER PLANT SERVICES,02-Central,764901000,28011.30,2509.12,25502.18
POWER PLANT SERVICES,02-Central,764821000,18510.50,1581.35,16929.15
POWER PLANT SERVICES,02-Central,739741100,14341.38,5792.60,8548.78
POWER PLANT SERVICES,02-Central,770580000,8771.50,602.97,8168.53
POWER PLANT SERVICES,02-Central,734900000,6134.96,1677.23,4457.73
POWER PLANT SERVICES,02-Central,733211000,4161.00,852.15,3308.85
POWER PLANT SERVICES,02-Central,770260000,3894.75,346.25,3548.50
POWER PLANT SERVICES,02-Central,777601000,3022.20,627.63,2394.57
POWER PLANT SERVICES,02-Central,737902100,2973.75,508.02,2465.73
POWER PLANT SERVICES,02-Central,739561100,2818.86,559.92,2258.94
POWER PLANT SERVICES,02-Central,770220000,2475.00,195.80,2279.20
POWER PLANT SERVICES,02-Central,770240000,2321.25,205.59,2115.66
POWER PLANT SERVICES,02-Central,739781100,2094.63,983.04,1111.59
POWER PLANT SERVICES,02-Central,733505300,1882.50,317.89,1564.61
POWER PLANT SERVICES,02-Central,780250000,1673.28,420.09,1253.19
POWER PLANT SERVICES,02-Central,727000140,1132.80,95.02,1037.78
POWER PLANT SERVICES,02-Central,738521000,454.78,143.45,311.33
POWER PLANT SERVICES,02-Central,737912200,294.00,43.36,250.64
POWER PLANT SERVICES,02-Central,777642000,64.35,25.18,39.17
POWER PROCESS EQUIPMENT,02-Central,724281200,4320.00,642.05,3677.95
POWER PROCESS EQUIPMENT,02-Central,764481000,2514.00,136.82,2377.18
POWER PROCESS EQUIPMENT,02-Central,733725500,1620.00,310.59,1309.41
POWER PROCESS EQUIPMENT,02-Central,764821000,804.00,31.32,772.68
POWER PROCESS EQUIPMENT,02-Central,764901000,796.00,32.02,763.98
POWER PROCESS EQUIPMENT,02-Central,777601000,737.86,117.60,620.26
PPG INDUSTRIES INC.,02-Central,768221000,6040.00,342.82,5697.18
PPG INDUSTRIES INC.,02-Central,734651200,1652.70,222.00,1430.70
PPG INDUSTRIES INC.,02-Central,733525500,717.50,65.03,652.47
PPG INDUSTRIES INC.,02-Central,718500000,707.20,86.27,620.93
PPG INDUSTRIES,02-Central,770200000,666.00,42.41,623.59
PPG INDUSTRIES,02-Central,764261000,215.00,22.89,192.11
PRECISION ART GLASS,02-Central,739741100,1252.16,387.44,864.72
PRECISION ART GLASS,02-Central,739451000,428.48,111.56,316.92
PRECISION MACHINE & TOOL,02-Central,740283401,4923.60,796.14,4127.46
PRECISION TOOLS SERVICE,02-Central,770210000,460.00,31.52,428.48
PRESCOTECH INC,02-Central,733832010,3564.00,1178.02,2385.98
PRESCOTECH INDUSTRIES INC,02-Central,733882000,9773.60,4684.90,5088.70
PRESCOTECH INDUSTRIES INC,02-Central,733510000,5440.00,1095.90,4344.10
PRIME RESINS,02-Central,734000000,1446.00,151.61,1294.39
PROLIFT INDUS EQUIP,02-Central,764521000,973.00,39.86,933.14
PROPULSION TECHNOLOGY,02-Central,771320000,5432.00,684.50,4747.50
PSP/MONOTECH INDUSTRIES,02-Central,779952600,194903.70,93570.63,101333.07
PSP/MONOTECH INDUSTRIES,02-Central,766594001,148088.00,79687.11,68400.89
PSP/MONOTECH INDUSTRIES,02-Central,766564001,61760.00,36056.11,25703.89
PSP/MONOTECH INDUSTRIES,02-Central,766541100,4480.00,2488.97,1991.03
PSP/MONOTECH INDUSTRIES,02-Central,767500000,2602.50,23.37,2579.13
PYROTEK INC,02-Central,764821000,577762.00,213509.84,364252.16
PYROTEK INC,02-Central,771320000,411715.00,107102.72,304612.28
PYROTEK INC,02-Central,727000030,363240.00,253912.34,109327.66
PYROTEK INC,02-Central,733182180,321071.80,147937.54,173134.26
PYROTEK INC,02-Central,733490500,241454.00,98968.43,142485.57
PYROTEK INC,02-Central,727000042,207051.20,143090.93,63960.27
PYROTEK INC,02-Central,734311100,184343.42,46279.22,138064.20
PYROTEK INC,02-Central,764521000,163356.65,72956.26,90400.39
PYROTEK INC,02-Central,768222000,157170.40,57720.52,99449.88
PYROTEK INC,02-Central,766211000,150895.60,104114.30,46781.30
PYROTEK INC,02-Central,733211050,126322.00,21146.12,105175.88
PYROTEK INC,02-Central,764601000,108882.00,47356.95,61525.05
PYROTEK INC,02-Central,768221000,106084.20,38807.48,67276.72
PYROTEK INC,02-Central,734910000,98252.70,40101.43,58151.27
PYROTEK INC,02-Central,733211000,90228.00,57457.51,32770.49
PYROTEK INC,02-Central,768222522,88542.08,32135.64,56406.44
PYROTEK INC,02-Central,733525500,74060.00,30378.96,43681.04
PYROTEK INC,02-Central,739781100,59640.00,53656.92,5983.08
PYROTEK INC,02-Central,734173000,53642.70,22562.60,31080.10
PYROTEK INC,02-Central,733403200,53411.60,22335.62,31075.98
PYROTEK INC,02-Central,764822000,51956.00,19326.88,32629.12
PYROTEK INC,02-Central,733525600,47395.00,19588.87,27806.13
PYROTEK INC,02-Central,733510000,45750.75,31000.16,14750.59
PYROTEK INC,02-Central,724311200,44164.50,12011.15,32153.35
PYROTEK INC,02-Central,764901050,42471.00,16054.64,26416.36
PYROTEK INC,02-Central,733151600,41964.00,20979.70,20984.30
PYROTEK INC,02-Central,766121000,40990.00,12232.75,28757.25
PYROTEK INC,02-Central,764781000,40397.60,18376.19,22021.41
PYROTEK INC,02-Central,764264054,39220.00,18512.28,20707.72
PYROTEK INC,02-Central,764261000,36920.00,19518.42,17401.58
PYROTEK INC,02-Central,733151500,36894.00,17946.12,18947.88
PYROTEK INC,02-Central,771330000,36855.00,11212.46,25642.54
PYROTEK INC,02-Central,738561000,36007.04,35669.43,337.61
PYROTEK INC,02-Central,764524000,34237.00,15617.75,18619.25
PYROTEK INC,02-Central,766212000,34121.00,23425.19,10695.81
PYROTEK INC,02-Central,734762000,32572.66,9207.42,23365.24
PYROTEK INC,02-Central,734162000,31052.30,21491.66,9560.64
PYROTEK INC,02-Central,734351200,29598.55,3568.15,26030.40
PYROTEK INC,02-Central,733511000,26762.25,17023.00,9739.25
PYROTEK INC,02-Central,733543700,26241.60,8948.62,17292.98
PYROTEK INC,02-Central,727000140,25876.80,17576.91,8299.89
PYROTEK INC,02-Central,734211200,25627.96,7182.49,18445.47
PYROTEK INC,02-Central,764262000,24829.00,12948.56,11880.44
PYROTEK INC,02-Central,768121000,21031.50,9026.36,12005.14
PYROTEK INC,02-Central,727063016,20335.92,13683.86,6652.06
PYROTEK INC,02-Central,768211000,18764.80,8696.64,10068.16
PYROTEK INC,02-Central,764605000,18380.00,7739.90,10640.10
PYROTEK INC,02-Central,737321000,18037.78,9971.40,8066.38
PYROTEK INC,02-Central,733505200,17528.00,7996.47,9531.53
PYROTEK INC,02-Central,734970000,16486.68,8386.60,8100.08
PYROTEK INC,02-Central,734414100,15287.12,4986.05,10301.07
PYROTEK INC,02-Central,764823500,14972.00,5308.33,9663.67
PYROTEK INC,02-Central,764481000,13913.00,7338.82,6574.18
PYROTEK INC,02-Central,764221000,13124.80,9138.12,3986.68
PYROTEK INC,02-Central,764781040,12946.00,5745.89,7200.11
PYROTEK INC,02-Central,764761000,12903.60,7385.93,5517.67
PYROTEK INC,02-Central,739741100,12882.38,11275.48,1606.90
PYROTEK INC,02-Central,734251300,12869.90,1582.84,11287.06
PYROTEK INC,02-Central,738361000,12043.47,13249.81,-1206.34
PYROTEK INC,02-Central,733525400,12027.50,5205.31,6822.19
PYROTEK INC,02-Central,768780000,11509.76,7094.04,4415.72
PYROTEK INC,02-Central,740402402,10766.75,3070.72,7696.03
PYROTEK INC,02-Central,733566400,10410.00,1888.10,8521.90
PYROTEK INC,02-Central,733484900,10054.40,4837.31,5217.09
PYROTEK INC,02-Central,764902000,9289.00,3588.45,5700.55
PYROTEK INC,02-Central,734761300,9215.42,2334.84,6880.58
PYROTEK INC,02-Central,733542005,8558.86,1821.81,6737.05
PYROTEK INC,02-Central,742645000,6756.00,1102.08,5653.92
PYROTEK INC,02-Central,768212000,6600.40,2985.49,3614.91
PYROTEK INC,02-Central,733202000,6566.40,3754.28,2812.12
PYROTEK INC,02-Central,733505700,5718.48,2784.81,2933.67
PYROTEK INC,02-Central,764783000,5572.00,2510.16,3061.84
PYROTEK INC,02-Central,737261000,5408.44,4955.52,452.92
PYROTEK INC,02-Central,793000000,5369.28,3652.51,1716.77
PYROTEK INC,02-Central,739541000,5332.01,2292.46,3039.55
PYROTEK INC,02-Central,733444200,5297.60,2735.99,2561.61
PYROTEK INC,02-Central,737301000,5207.62,2208.24,2999.38
PYROTEK INC,02-Central,791003400,5004.00,1308.29,3695.71
PYROTEK INC,02-Central,739452000,4878.72,3620.04,1258.68
PYROTEK INC,02-Central,733484800,4112.00,2013.45,2098.55
PYROTEK INC,02-Central,764461006,3997.80,2785.74,1212.06
PYROTEK INC,02-Central,733501550,3986.13,2952.10,1034.03
PYROTEK INC,02-Central,742723400,3711.15,423.94,3287.21
PYROTEK INC,02-Central,738521000,3085.38,4288.31,-1202.93
PYROTEK INC,02-Central,764901000,3004.50,1156.96,1847.54
PYROTEK INC,02-Central,764762000,2851.20,1609.74,1241.46
PYROTEK INC,02-Central,737311000,2652.25,2352.87,299.38
PYROTEK INC,02-Central,764522000,2624.60,1167.93,1456.67
PYROTEK INC,02-Central,764341000,2503.55,1350.81,1152.74
PYROTEK INC,02-Central,734900000,2314.00,1518.47,795.53
PYROTEK INC,02-Central,740382201,2266.50,881.31,1385.19
PYROTEK INC,02-Central,777590000,1964.68,1316.08,648.60
PYROTEK INC,02-Central,777901100,1856.61,935.30,921.31
PYROTEK INC,02-Central,777902700,1808.10,1141.55,666.55
PYROTEK INC,02-Central,768750000,1783.20,1851.76,-68.56
PYROTEK INC,02-Central,733505300,1757.00,806.08,950.92
PYROTEK INC,02-Central,770570000,1707.23,196.14,1511.09
PYROTEK INC,02-Central,777601000,1599.68,470.36,1129.32
PYROTEK INC,02-Central,737912200,1554.57,407.12,1147.45
PYROTEK INC,02-Central,779986561,1512.00,577.34,934.66
PYROTEK INC,02-Central,739541200,1363.98,512.78,851.20
PYROTEK INC,02-Central,734712000,1361.58,603.03,758.55
PYROTEK INC,02-Central,733212055,1333.80,871.70,462.10
PYROTEK INC,02-Central,779988827,1314.42,601.81,712.61
PYROTEK INC,02-Central,764602000,1200.00,524.23,675.77
PYROTEK INC,02-Central,770850000,1045.50,47.24,998.26
PYROTEK INC,02-Central,733464500,922.00,431.32,490.68
PYROTEK INC,02-Central,739681100,903.96,1242.00,-338.04
PYROTEK INC,02-Central,734611100,887.04,242.62,644.42
PYROTEK INC,02-Central,777642000,824.00,277.80,546.20
PYROTEK INC,02-Central,739561000,653.61,342.83,310.78
PYROTEK INC,02-Central,770310000,600.00,94.56,505.44
PYROTEK INC,02-Central,792730000,460.00,125.12,334.88
PYROTEK INC,02-Central,732429300,444.80,49.22,395.58
PYROTEK INC,02-Central,733721000,417.60,167.12,250.48
PYROTEK INC,02-Central,764861000,344.29,142.25,202.04
PYROTEK INC,02-Central,737902100,222.96,67.74,155.22
PYROTEK INC,02-Central,733423700,73.60,36.22,37.38
R E UPTEGRAFF MFG COMPANY,02-Central,733741000,2408.00,662.64,1745.36
R E UPTEGRAFF MFG COMPANY,02-Central,779001000,1290.48,221.56,1068.92
R K METALS LLC,02-Central,763991000,428.40,39.22,389.18
R T P  COMPANY,02-Central,734040000,2353.50,515.70,1837.80
R. G. BREWTON INC.,02-Central,770372000,15960.00,777.75,15182.25
R. G. BREWTON INC.,02-Central,764781000,7844.00,342.53,7501.47
R. G. BREWTON INC.,02-Central,764821000,430.00,15.36,414.64
RALEIGH-DURHAM RUBBER,02-Central,770200000,7217.10,706.38,6510.72
RCG OF NORTH CAROLINA LLC,02-Central,777601000,390.00,216.05,173.95
RED SEAL ELECTRIC,02-Central,738561000,3494.40,744.82,2749.58
RED SEAL ELECTRIC,02-Central,733444100,348.00,81.62,266.38
REFRACTORY ENGINEERS INC.,02-Central,764821030,305282.40,90899.92,214382.48
REFRACTORY ENGINEERS INC.,02-Central,764040000,299149.20,89159.27,209989.93
REFRACTORY ENGINEERS INC.,02-Central,764822000,171062.60,42509.70,128552.90
REFRACTORY ENGINEERS INC.,02-Central,733403500,139823.10,92270.19,47552.91
REFRACTORY ENGINEERS INC.,02-Central,734361300,122986.05,13485.23,109500.82
REFRACTORY ENGINEERS INC.,02-Central,764525000,119544.00,78252.89,41291.11
REFRACTORY ENGINEERS INC.,02-Central,764526514,108315.20,62167.15,46148.05
REFRACTORY ENGINEERS INC.,02-Central,764261000,102578.00,60992.92,41585.08
REFRACTORY ENGINEERS INC.,02-Central,733218000,93093.54,70287.85,22805.69
REFRACTORY ENGINEERS INC.,02-Central,733218300,81717.49,60364.53,21352.96
REFRACTORY ENGINEERS INC.,02-Central,766121000,79078.40,21826.23,57252.17
REFRACTORY ENGINEERS INC.,02-Central,764901000,76592.00,41228.86,35363.14
REFRACTORY ENGINEERS INC.,02-Central,739641100,61985.31,34470.58,27514.73
REFRACTORY ENGINEERS INC.,02-Central,766162000,58596.40,41842.90,16753.50
REFRACTORY ENGINEERS INC.,02-Central,764605001,56576.00,18711.79,37864.21
REFRACTORY ENGINEERS INC.,02-Central,764221000,54784.80,27624.60,27160.20
REFRACTORY ENGINEERS INC.,02-Central,733218500,51427.68,50886.00,541.68
REFRACTORY ENGINEERS INC.,02-Central,734173000,44612.20,17631.36,26980.84
REFRACTORY ENGINEERS INC.,02-Central,777741073,43531.38,23460.41,20070.97
REFRACTORY ENGINEERS INC.,02-Central,733505200,42265.00,7984.27,34280.73
REFRACTORY ENGINEERS INC.,02-Central,764521022,42018.00,24678.74,17339.26
REFRACTORY ENGINEERS INC.,02-Central,733586500,36525.60,24968.04,11557.56
REFRACTORY ENGINEERS INC.,02-Central,764601110,30993.60,15764.64,15228.96
REFRACTORY ENGINEERS INC.,02-Central,734311100,29957.56,6683.98,23273.58
REFRACTORY ENGINEERS INC.,02-Central,733403200,29821.00,16098.52,13722.48
REFRACTORY ENGINEERS INC.,02-Central,740402402,25720.00,6576.10,19143.90
REFRACTORY ENGINEERS INC.,02-Central,766125500,23587.20,7514.32,16072.88
REFRACTORY ENGINEERS INC.,02-Central,765661000,22318.70,3299.80,19018.90
REFRACTORY ENGINEERS INC.,02-Central,764521000,21120.00,10616.84,10503.16
REFRACTORY ENGINEERS INC.,02-Central,764526513,20993.60,12796.75,8196.85
REFRACTORY ENGINEERS INC.,02-Central,733201000,19822.70,8805.47,11017.23
REFRACTORY ENGINEERS INC.,02-Central,768141000,18720.00,3789.88,14930.12
REFRACTORY ENGINEERS INC.,02-Central,733211050,17952.80,8373.97,9578.83
REFRACTORY ENGINEERS INC.,02-Central,733511000,17398.00,12455.16,4942.84
REFRACTORY ENGINEERS INC.,02-Central,739781100,17036.43,8688.25,8348.18
REFRACTORY ENGINEERS INC.,02-Central,764523000,16748.00,9493.17,7254.83
REFRACTORY ENGINEERS INC.,02-Central,768221000,16126.80,6281.81,9844.99
REFRACTORY ENGINEERS INC.,02-Central,733151600,16098.00,4586.34,11511.66
REFRACTORY ENGINEERS INC.,02-Central,764524020,16000.00,8547.43,7452.57
REFRACTORY ENGINEERS INC.,02-Central,764264050,15000.00,9926.50,5073.50
REFRACTORY ENGINEERS INC.,02-Central,764827500,14595.00,4806.51,9788.49
REFRACTORY ENGINEERS INC.,02-Central,764601020,13048.00,6997.55,6050.45
REFRACTORY ENGINEERS INC.,02-Central,739741100,12876.32,8655.08,4221.24
REFRACTORY ENGINEERS INC.,02-Central,740361900,11770.56,5392.44,6378.12
REFRACTORY ENGINEERS INC.,02-Central,777710073,11758.83,4877.68,6881.15
REFRACTORY ENGINEERS INC.,02-Central,764761000,11752.60,4991.31,6761.29
REFRACTORY ENGINEERS INC.,02-Central,764821000,11520.00,7371.81,4148.19
REFRACTORY ENGINEERS INC.,02-Central,734990000,11120.28,2070.37,9049.91
REFRACTORY ENGINEERS INC.,02-Central,793330000,10795.50,2969.57,7825.93
REFRACTORY ENGINEERS INC.,02-Central,764526810,9249.60,5386.26,3863.34
REFRACTORY ENGINEERS INC.,02-Central,764523200,8960.00,6016.77,2943.23
REFRACTORY ENGINEERS INC.,02-Central,766162051,8437.50,3855.77,4581.73
REFRACTORY ENGINEERS INC.,02-Central,770560000,8323.20,688.72,7634.48
REFRACTORY ENGINEERS INC.,02-Central,770380000,8257.60,818.38,7439.22
REFRACTORY ENGINEERS INC.,02-Central,764264058,8140.00,8046.65,93.35
REFRACTORY ENGINEERS INC.,02-Central,764902000,7360.00,2554.16,4805.84
REFRACTORY ENGINEERS INC.,02-Central,793250074,7276.50,3526.89,3749.61
REFRACTORY ENGINEERS INC.,02-Central,777601000,6797.50,1937.22,4860.28
REFRACTORY ENGINEERS INC.,02-Central,764601000,6536.00,2947.43,3588.57
REFRACTORY ENGINEERS INC.,02-Central,733586505,6480.00,5293.02,1186.98
REFRACTORY ENGINEERS INC.,02-Central,777904301,6435.00,2718.36,3716.64
REFRACTORY ENGINEERS INC.,02-Central,764481000,6271.40,3385.98,2885.42
REFRACTORY ENGINEERS INC.,02-Central,777643000,5278.50,3156.09,2122.41
REFRACTORY ENGINEERS INC.,02-Central,770600000,5221.90,379.38,4842.52
REFRACTORY ENGINEERS INC.,02-Central,770810000,5089.40,147.17,4942.23
REFRACTORY ENGINEERS INC.,02-Central,727000440,4668.80,859.09,3809.71
REFRACTORY ENGINEERS INC.,02-Central,740382201,4612.50,1502.56,3109.94
REFRACTORY ENGINEERS INC.,02-Central,734851000,4190.06,951.50,3238.56
REFRACTORY ENGINEERS INC.,02-Central,793580000,4092.00,1000.02,3091.98
REFRACTORY ENGINEERS INC.,02-Central,770300000,4034.25,465.83,3568.42
REFRACTORY ENGINEERS INC.,02-Central,755087000,3706.12,601.66,3104.46
REFRACTORY ENGINEERS INC.,02-Central,739841000,3535.73,939.35,2596.38
REFRACTORY ENGINEERS INC.,02-Central,770200000,3515.40,375.22,3140.18
REFRACTORY ENGINEERS INC.,02-Central,770880000,3354.00,178.24,3175.76
REFRACTORY ENGINEERS INC.,02-Central,765541000,3275.00,1338.28,1936.72
REFRACTORY ENGINEERS INC.,02-Central,764261020,3240.00,2075.70,1164.30
REFRACTORY ENGINEERS INC.,02-Central,718501000,3155.20,544.24,2610.96
REFRACTORY ENGINEERS INC.,02-Central,770830000,3111.00,138.58,2972.42
REFRACTORY ENGINEERS INC.,02-Central,733151500,3046.00,817.18,2228.82
REFRACTORY ENGINEERS INC.,02-Central,792610000,2955.00,404.66,2550.34
REFRACTORY ENGINEERS INC.,02-Central,764781000,2847.50,867.59,1979.91
REFRACTORY ENGINEERS INC.,02-Central,733725500,2696.60,826.25,1870.35
REFRACTORY ENGINEERS INC.,02-Central,740123000,2671.20,768.06,1903.14
REFRACTORY ENGINEERS INC.,02-Central,770260000,2668.50,278.62,2389.88
REFRACTORY ENGINEERS INC.,02-Central,734651200,2571.30,517.01,2054.29
REFRACTORY ENGINEERS INC.,02-Central,764861000,2532.50,1044.37,1488.13
REFRACTORY ENGINEERS INC.,02-Central,734751200,2518.88,449.95,2068.93
REFRACTORY ENGINEERS INC.,02-Central,770540000,2443.00,179.60,2263.40
REFRACTORY ENGINEERS INC.,02-Central,733444300,2385.40,473.43,1911.97
REFRACTORY ENGINEERS INC.,02-Central,771500000,2370.75,172.01,2198.74
REFRACTORY ENGINEERS INC.,02-Central,770220000,2347.20,218.76,2128.44
REFRACTORY ENGINEERS INC.,02-Central,779958984,2189.00,961.48,1227.52
REFRACTORY ENGINEERS INC.,02-Central,766681100,2173.00,380.97,1792.03
REFRACTORY ENGINEERS INC.,02-Central,764524010,2144.00,1081.34,1062.66
REFRACTORY ENGINEERS INC.,02-Central,733525600,2137.00,546.14,1590.86
REFRACTORY ENGINEERS INC.,02-Central,734810000,1998.30,1608.24,390.06
REFRACTORY ENGINEERS INC.,02-Central,764522000,1880.00,1068.86,811.14
REFRACTORY ENGINEERS INC.,02-Central,792785000,1756.80,479.91,1276.89
REFRACTORY ENGINEERS INC.,02-Central,777740073,1752.50,849.00,903.50
REFRACTORY ENGINEERS INC.,02-Central,766081013,1710.00,794.69,915.31
REFRACTORY ENGINEERS INC.,02-Central,733464600,1674.40,1538.71,135.69
REFRACTORY ENGINEERS INC.,02-Central,764605000,1608.00,615.46,992.54
REFRACTORY ENGINEERS INC.,02-Central,764526610,1584.00,864.80,719.20
REFRACTORY ENGINEERS INC.,02-Central,753967000,1553.47,470.32,1083.15
REFRACTORY ENGINEERS INC.,02-Central,770230000,1392.30,147.83,1244.47
REFRACTORY ENGINEERS INC.,02-Central,737912200,1334.55,260.17,1074.38
REFRACTORY ENGINEERS INC.,02-Central,764483000,1263.40,724.82,538.58
REFRACTORY ENGINEERS INC.,02-Central,732429400,1224.00,133.82,1090.18
REFRACTORY ENGINEERS INC.,02-Central,777730020,1151.22,1169.83,-18.61
REFRACTORY ENGINEERS INC.,02-Central,764461006,900.00,139.69,760.31
REFRACTORY ENGINEERS INC.,02-Central,777731073,855.22,306.59,548.63
REFRACTORY ENGINEERS INC.,02-Central,770392000,850.00,70.30,779.70
REFRACTORY ENGINEERS INC.,02-Central,734970000,825.00,307.84,517.16
REFRACTORY ENGINEERS INC.,02-Central,734890000,793.73,198.96,594.77
REFRACTORY ENGINEERS INC.,02-Central,768121000,784.35,172.60,611.75
REFRACTORY ENGINEERS INC.,02-Central,770290000,675.35,79.65,595.70
REFRACTORY ENGINEERS INC.,02-Central,764561000,661.50,130.70,530.80
REFRACTORY ENGINEERS INC.,02-Central,770210000,570.50,61.84,508.66
REFRACTORY ENGINEERS INC.,02-Central,777730073,566.51,236.20,330.31
REFRACTORY ENGINEERS INC.,02-Central,739561200,557.95,153.34,404.61
REFRACTORY ENGINEERS INC.,02-Central,777590000,502.12,245.72,256.40
REFRACTORY ENGINEERS INC.,02-Central,733525500,500.00,351.97,148.03
REFRACTORY ENGINEERS INC.,02-Central,768211000,489.50,116.95,372.55
REFRACTORY ENGINEERS INC.,02-Central,733211000,476.60,142.17,334.43
REFRACTORY ENGINEERS INC.,02-Central,724261200,467.60,78.20,389.40
REFRACTORY ENGINEERS INC.,02-Central,733423600,467.20,71.80,395.40
REFRACTORY ENGINEERS INC.,02-Central,733505100,465.00,82.59,382.41
REFRACTORY ENGINEERS INC.,02-Central,733221400,454.24,128.48,325.76
REFRACTORY ENGINEERS INC.,02-Central,734162000,453.80,243.11,210.69
REFRACTORY ENGINEERS INC.,02-Central,740361701,397.08,318.65,78.43
REFRACTORY ENGINEERS INC.,02-Central,724281200,358.00,89.08,268.92
REFRACTORY ENGINEERS INC.,02-Central,725441200,357.90,68.74,289.16
REFRACTORY ENGINEERS INC.,02-Central,779261000,346.61,76.93,269.68
REFRACTORY ENGINEERS INC.,02-Central,764781040,336.00,58.59,277.41
REFRACTORY ENGINEERS INC.,02-Central,739561000,313.70,86.19,227.51
REFRACTORY ENGINEERS INC.,02-Central,734611100,302.90,67.95,234.95
REFRACTORY ENGINEERS INC.,02-Central,792783000,292.10,90.01,202.09
REFRACTORY ENGINEERS INC.,02-Central,734711100,278.32,54.40,223.92
REFRACTORY ENGINEERS INC.,02-Central,726000240,266.00,30.29,235.71
REFRACTORY ENGINEERS INC.,02-Central,780770000,172.61,37.20,135.41
REFRACTORY ENGINEERS INC.,02-Central,765502000,115.00,26.48,88.52
REFRACTORY ENGINEERS INC.,02-Central,793260074,94.10,39.17,54.93
REFRACTORY SERVICE INC.,02-Central,733211000,206490.40,58252.96,148237.44
REFRACTORY SERVICE INC.,02-Central,764821000,190347.90,62915.24,127432.66
REFRACTORY SERVICE INC.,02-Central,718500000,144103.18,26478.48,117624.70
REFRACTORY SERVICE INC.,02-Central,727041200,83680.00,17284.99,66395.01
REFRACTORY SERVICE INC.,02-Central,734990000,75720.72,14496.40,61224.32
REFRACTORY SERVICE INC.,02-Central,733525600,56067.25,10205.04,45862.21
REFRACTORY SERVICE INC.,02-Central,764822000,50181.40,16719.79,33461.61
REFRACTORY SERVICE INC.,02-Central,733586625,48886.56,6683.05,42203.51
REFRACTORY SERVICE INC.,02-Central,764481000,48495.50,15496.87,32998.63
REFRACTORY SERVICE INC.,02-Central,733525500,43667.75,7778.76,35888.99
REFRACTORY SERVICE INC.,02-Central,733151500,39769.00,8396.27,31372.73
REFRACTORY SERVICE INC.,02-Central,734173000,37997.75,9281.96,28715.79
REFRACTORY SERVICE INC.,02-Central,779956939,36479.65,18929.53,17550.12
REFRACTORY SERVICE INC.,02-Central,766121000,34854.00,6559.74,28294.26
REFRACTORY SERVICE INC.,02-Central,789360000,29590.71,18641.01,10949.70
REFRACTORY SERVICE INC.,02-Central,738561000,29332.02,10992.72,18339.30
REFRACTORY SERVICE INC.,02-Central,770220000,29284.80,7763.43,21521.37
REFRACTORY SERVICE INC.,02-Central,764522000,29283.00,11490.28,17792.72
REFRACTORY SERVICE INC.,02-Central,754085000,26585.20,12428.47,14156.73
REFRACTORY SERVICE INC.,02-Central,779958986,26176.80,14994.63,11182.17
REFRACTORY SERVICE INC.,02-Central,764821161,23352.00,17856.00,5496.00
REFRACTORY SERVICE INC.,02-Central,779952105,20785.05,12663.84,8121.21
REFRACTORY SERVICE INC.,02-Central,784260000,20321.40,8991.39,11330.01
REFRACTORY SERVICE INC.,02-Central,762116000,19815.38,6400.61,13414.77
REFRACTORY SERVICE INC.,02-Central,777888893,19423.32,19521.60,-98.28
REFRACTORY SERVICE INC.,02-Central,733202009,18146.25,5924.98,12221.27
REFRACTORY SERVICE INC.,02-Central,739561200,17979.58,4993.73,12985.85
REFRACTORY SERVICE INC.,02-Central,764787101,17848.80,11992.82,5855.98
REFRACTORY SERVICE INC.,02-Central,733202000,17826.56,5447.50,12379.06
REFRACTORY SERVICE INC.,02-Central,754045000,15982.20,9252.85,6729.35
REFRACTORY SERVICE INC.,02-Central,777888891,13680.00,13910.40,-230.40
REFRACTORY SERVICE INC.,02-Central,779952106,13305.60,6573.62,6731.98
REFRACTORY SERVICE INC.,02-Central,734311100,12715.48,1685.02,11030.46
REFRACTORY SERVICE INC.,02-Central,733505200,12618.50,2480.59,10137.91
REFRACTORY SERVICE INC.,02-Central,764602000,10302.50,3407.47,6895.03
REFRACTORY SERVICE INC.,02-Central,780210000,9583.20,5648.12,3935.08
REFRACTORY SERVICE INC.,02-Central,754065000,9456.00,5270.46,4185.54
REFRACTORY SERVICE INC.,02-Central,764861000,9166.61,1506.08,7660.53
REFRACTORY SERVICE INC.,02-Central,734651200,8845.34,626.74,8218.60
REFRACTORY SERVICE INC.,02-Central,784200000,8035.20,4236.62,3798.58
REFRACTORY SERVICE INC.,02-Central,784000000,7884.80,4473.26,3411.54
REFRACTORY SERVICE INC.,02-Central,777888895,7740.00,8668.80,-928.80
REFRACTORY SERVICE INC.,02-Central,780250000,7557.60,4189.67,3367.93
REFRACTORY SERVICE INC.,02-Central,738521000,7525.44,4373.50,3151.94
REFRACTORY SERVICE INC.,02-Central,777888892,7497.00,8396.64,-899.64
REFRACTORY SERVICE INC.,02-Central,777601000,7322.21,2381.12,4941.09
REFRACTORY SERVICE INC.,02-Central,739741100,6711.36,3201.07,3510.29
REFRACTORY SERVICE INC.,02-Central,764902000,6432.00,1915.62,4516.38
REFRACTORY SERVICE INC.,02-Central,770210000,6412.00,711.78,5700.22
REFRACTORY SERVICE INC.,02-Central,770540000,6404.15,474.90,5929.25
REFRACTORY SERVICE INC.,02-Central,770560000,6327.80,523.36,5804.44
REFRACTORY SERVICE INC.,02-Central,733610680,6137.85,1913.05,4224.80
REFRACTORY SERVICE INC.,02-Central,764561000,6060.00,1115.48,4944.52
REFRACTORY SERVICE INC.,02-Central,739561100,5635.38,1493.12,4142.26
REFRACTORY SERVICE INC.,02-Central,739781100,5538.72,2094.62,3444.10
REFRACTORY SERVICE INC.,02-Central,777888890,5508.00,6168.96,-660.96
REFRACTORY SERVICE INC.,02-Central,780280000,5380.80,2234.63,3146.17
REFRACTORY SERVICE INC.,02-Central,734851000,5370.80,1156.80,4214.00
REFRACTORY SERVICE INC.,02-Central,770260000,5336.56,557.24,4779.32
REFRACTORY SERVICE INC.,02-Central,770600000,5032.44,361.66,4670.78
REFRACTORY SERVICE INC.,02-Central,777888894,4590.00,5140.80,-550.80
REFRACTORY SERVICE INC.,02-Central,777642000,4498.40,1577.26,2921.14
REFRACTORY SERVICE INC.,02-Central,733211050,4344.00,1292.12,3051.88
REFRACTORY SERVICE INC.,02-Central,780240000,4324.32,2467.00,1857.32
REFRACTORY SERVICE INC.,02-Central,733834800,4311.77,2362.00,1949.77
REFRACTORY SERVICE INC.,02-Central,764781040,4248.00,1209.94,3038.06
REFRACTORY SERVICE INC.,02-Central,779988828,4118.91,2377.44,1741.47
REFRACTORY SERVICE INC.,02-Central,739452000,3943.17,1602.93,2340.24
REFRACTORY SERVICE INC.,02-Central,780770000,3883.20,1487.92,2395.28
REFRACTORY SERVICE INC.,02-Central,754044100,3851.50,2096.59,1754.91
REFRACTORY SERVICE INC.,02-Central,777903300,3719.10,2093.90,1625.20
REFRACTORY SERVICE INC.,02-Central,779957414,3657.00,1951.22,1705.78
REFRACTORY SERVICE INC.,02-Central,734910000,3630.40,849.36,2781.04
REFRACTORY SERVICE INC.,02-Central,764461006,3609.60,1049.19,2560.41
REFRACTORY SERVICE INC.,02-Central,780330000,3407.88,1546.32,1861.56
REFRACTORY SERVICE INC.,02-Central,733505100,3229.00,705.75,2523.25
REFRACTORY SERVICE INC.,02-Central,777888897,3160.00,1659.20,1500.80
REFRACTORY SERVICE INC.,02-Central,780370000,3031.92,1285.19,1746.73
REFRACTORY SERVICE INC.,02-Central,764901000,2760.40,579.10,2181.30
REFRACTORY SERVICE INC.,02-Central,739561000,2737.14,775.73,1961.41
REFRACTORY SERVICE INC.,02-Central,770290000,2701.40,318.58,2382.82
REFRACTORY SERVICE INC.,02-Central,777888896,2673.00,2993.76,-320.76
REFRACTORY SERVICE INC.,02-Central,770280000,2611.76,313.96,2297.80
REFRACTORY SERVICE INC.,02-Central,780310000,2506.44,1119.74,1386.70
REFRACTORY SERVICE INC.,02-Central,784100000,2443.50,1210.17,1233.33
REFRACTORY SERVICE INC.,02-Central,779201000,2431.20,624.60,1806.60
REFRACTORY SERVICE INC.,02-Central,738542000,2345.76,1430.48,915.28
REFRACTORY SERVICE INC.,02-Central,734132000,1997.50,465.89,1531.61
REFRACTORY SERVICE INC.,02-Central,770520000,1874.25,144.96,1729.29
REFRACTORY SERVICE INC.,02-Central,780270000,1864.00,927.50,936.50
REFRACTORY SERVICE INC.,02-Central,780750000,1832.40,709.38,1123.02
REFRACTORY SERVICE INC.,02-Central,777888899,1650.00,1848.00,-198.00
REFRACTORY SERVICE INC.,02-Central,777903000,1525.72,637.43,888.29
REFRACTORY SERVICE INC.,02-Central,780421000,1346.40,975.87,370.53
REFRACTORY SERVICE INC.,02-Central,779988827,1240.02,610.34,629.68
REFRACTORY SERVICE INC.,02-Central,777585000,1135.50,51.23,1084.27
REFRACTORY SERVICE INC.,02-Central,777888898,675.00,756.00,-81.00
REFRACTORY SERVICE INC.,02-Central,770372000,666.40,44.11,622.29
REFRACTORY SERVICE INC.,02-Central,770100000,568.58,42.43,526.15
REFRACTORY SERVICE INC.,02-Central,740023000,482.66,118.54,364.12
REFRACTORY SERVICE INC.,02-Central,764562000,473.00,84.36,388.64
REFRACTORY SERVICE INC.,02-Central,764781000,424.80,122.67,302.13
REFRACTORY SERVICE INC.,02-Central,768141000,394.20,81.04,313.16
REFRACTORY SERVICE INC.,02-Central,764762000,385.80,94.71,291.09
REFRACTORY SERVICE INC.,02-Central,733444100,330.00,163.24,166.76
REFRACTORY SERVICE INC.,02-Central,733725500,205.20,51.76,153.44
REFRACTORY SERVICE INC.,02-Central,768121000,195.60,44.03,151.57
REFRACTORY SERVICE INC.,02-Central,733444300,175.60,41.25,134.35
REFRACTORY SERVICE INC.,02-Central,779221000,119.72,30.60,89.12
REFRACTORY SERVICE INC.,02-Central,734611100,69.58,13.59,55.99
REFRACTORY SPECIALTIES IN,02-Central,727000060,1038585.00,797974.89,240610.11
REFRACTORY SPECIALTIES IN,02-Central,715000000,540164.75,387080.94,153083.81
REFRACTORY SPECIALTIES IN,02-Central,726000062,267039.00,209185.44,57853.56
REFRACTORY SPECIALTIES IN,02-Central,727063007,220378.50,162530.21,57848.29
REFRACTORY SPECIALTIES IN,02-Central,727000081,93066.00,69603.00,23463.00
REFRACTORY SPECIALTIES IN,02-Central,793000000,56250.00,26089.20,30160.80
REFRACTORY SPECIALTIES IN,02-Central,720252303,42075.00,2786.30,39288.70
REFRACTORY SPECIALTIES IN,02-Central,764821000,41970.00,14880.41,27089.59
REFRACTORY SPECIALTIES IN,02-Central,727000080,41535.00,35208.72,6326.28
REFRACTORY SPECIALTIES IN,02-Central,727063008,36630.00,26446.82,10183.18
REFRACTORY SPECIALTIES IN,02-Central,763994000,30564.00,1489.82,29074.18
REFRACTORY SPECIALTIES IN,02-Central,792620000,27990.00,8489.23,19500.77
REFRACTORY SPECIALTIES IN,02-Central,768750000,20155.20,15581.81,4573.39
REFRACTORY SPECIALTIES IN,02-Central,764482000,14990.00,8191.53,6798.47
REFRACTORY SPECIALTIES IN,02-Central,793060000,12150.00,5365.14,6784.86
REFRACTORY SPECIALTIES IN,02-Central,727031300,11562.00,8490.28,3071.72
REFRACTORY SPECIALTIES IN,02-Central,764521000,6670.00,2654.20,4015.80
REFRACTORY SPECIALTIES IN,02-Central,792650000,5242.00,2082.62,3159.38
REFRACTORY SPECIALTIES IN,02-Central,764821073,3560.00,2916.00,644.00
REFRACTORY SPECIALTIES IN,02-Central,764521073,2668.00,2580.00,88.00
REFRACTORY SPECIALTIES IN,02-Central,754925000,2464.00,1539.06,924.94
REFRACTORY SPECIALTIES IN,02-Central,754927000,2464.00,1455.40,1008.60
REFRACTORY SPECIALTIES IN,02-Central,764223010,2190.00,1627.81,562.19
REFRACTORY SPECIALTIES IN,02-Central,733510000,1920.00,954.17,965.83
REFRACTORY SPECIALTIES IN,02-Central,764342000,1418.00,734.52,683.48
REFRACTORY SPECIALTIES IN,02-Central,728030000,1350.00,1337.11,12.89
REFRACTORY SPECIALTIES IN,02-Central,733586600,1180.80,352.26,828.54
REFRACTORY SPECIALTIES IN,02-Central,792624000,855.00,258.26,596.74
REFRACTORY SPECIALTIES IN,02-Central,764481000,640.00,351.78,288.22
REFRACTORY SPECIALTIES IN,02-Central,764601000,516.00,212.58,303.42
REFRACTORY SPECIALTIES IN,02-Central,733525500,430.00,140.79,289.21
REFRACTORY SPECIALTIES IN,02-Central,764221000,420.00,336.56,83.44
REFRACTORY SPECIALTIES IN,02-Central,733383000,414.48,152.13,262.35
REFRACTORY SPECIALTIES IN,02-Central,764822000,335.00,123.05,211.95
REFRACTORY SPECIALTIES IN,02-Central,733525600,215.00,71.05,143.95
REFRACTORY SPECIALTIES IN,02-Central,733505200,210.00,76.44,133.56
REFRACTORY SPECIALTIES IN,02-Central,764761000,162.00,95.58,66.42
REFRACTORY SPECIALTIES IN,02-Central,764602000,129.00,52.42,76.58
REFRACTORY SPECIALTIES IN,02-Central,764341000,89.00,45.87,43.13
REFRACTORY SPECIALTIES IN,02-Central,764461006,88.00,55.87,32.13
REFRACTORY SPECIALTIES IN,02-Central,764522000,66.70,26.72,39.98
REFRACTORY SPECIALTIES IN,02-Central,764781000,47.80,19.72,28.08
RELIACHECK MANUFACTURING,02-Central,764482000,1080.00,52.20,1027.80
RENAISSANCE ROOFING,02-Central,733741000,1632.00,476.26,1155.74
RENO CONSTRUCTION CO LLC,02-Central,734910000,341.50,51.35,290.15
RENO REFRACTORIES INC.,02-Central,734910000,703.50,104.44,599.06
RESCO PRODUCTS INC,02-Central,733510000,1130.00,159.03,970.97
"RESCO PRODUCTS, INC.",02-Central,764901000,3980.25,144.13,3836.12
"RESCO PRODUCTS, INC.",02-Central,733511000,3955.00,526.55,3428.45
"RESCO PRODUCTS, INC.",02-Central,770220000,2682.00,170.62,2511.38
REX ROTO CORP,02-Central,726000062,1537696.35,1139935.62,397760.73
REX ROTO CORP,02-Central,727031200,967602.16,699312.30,268289.86
REX ROTO CORP,02-Central,764030000,848819.64,768942.56,79877.08
REX ROTO CORP,02-Central,727000046,504694.88,325675.44,179019.44
REX ROTO CORP,02-Central,793000500,246682.80,116027.75,130655.05
REX ROTO CORP,02-Central,793000200,236180.00,108914.97,127265.03
REX ROTO CORP,02-Central,715000000,133400.00,78042.83,55357.17
REX ROTO CORP,02-Central,777990000,113520.00,66969.60,46550.40
REX ROTO CORP,02-Central,734070500,82831.50,27857.47,54974.03
REX ROTO CORP,02-Central,734351200,77972.20,16724.95,61247.25
REX ROTO CORP,02-Central,724003000,55930.00,24720.74,31209.26
REX ROTO CORP,02-Central,793520000,26824.00,6663.32,20160.68
REX ROTO CORP,02-Central,733525600,21458.75,4047.27,17411.48
REX ROTO CORP,02-Central,733721000,19130.80,4325.10,14805.70
REX ROTO CORP,02-Central,792624000,14015.20,3031.71,10983.49
REX ROTO CORP,02-Central,764482000,11896.90,4591.08,7305.82
REX ROTO CORP,02-Central,733521350,11437.69,2797.97,8639.72
REX ROTO CORP,02-Central,733525500,7778.75,1472.89,6305.86
REX ROTO CORP,02-Central,777991000,5664.60,4041.50,1623.10
REX ROTO CORP,02-Central,733464600,4606.50,1027.17,3579.33
REX ROTO CORP,02-Central,734211200,4287.19,488.19,3799.00
REX ROTO CORP,02-Central,764461006,4012.60,2118.14,1894.46
REX ROTO CORP,02-Central,793250074,3879.50,2294.42,1585.08
REX ROTO CORP,02-Central,764781040,3817.90,1531.40,2286.50
REX ROTO CORP,02-Central,793320074,1885.60,1414.08,471.52
REX ROTO CORP,02-Central,764301000,1476.20,621.51,854.69
REX ROTO CORP,02-Central,793520074,1341.20,691.64,649.56
REX ROTO CORP,02-Central,793060300,1132.80,530.19,602.61
REX ROTO CORP,02-Central,764762000,879.60,440.70,438.90
REX ROTO CORP,02-Central,764484003,765.30,288.72,476.58
REX ROTO CORP,02-Central,764522000,740.10,237.57,502.53
REX ROTO CORP,02-Central,764822000,702.60,184.58,518.02
REX ROTO CORP,02-Central,734311100,561.12,141.20,419.92
REX ROTO CORP,02-Central,733464500,463.50,91.57,371.93
REX ROTO CORP,02-Central,768221000,395.20,68.56,326.64
REX ROTO CORP,02-Central,793530074,335.30,127.98,207.32
REX ROTO CORP,02-Central,766681300,217.50,99.79,117.71
REX ROTO CORP,02-Central,793580000,179.80,60.61,119.19
REX ROTO CORP,02-Central,777642000,121.00,50.36,70.64
REX ROTO CORP,02-Central,766681200,65.70,44.60,21.10
RG SMITH CO. INC,02-Central,739641100,482.04,201.60,280.44
RHI AG,02-Central,754665000,7965.72,3914.69,4051.03
RHI CANADA INC.,02-Central,777642000,124.00,63.34,60.66
ROGERS ENGINEERING,02-Central,733211050,24906.00,3677.48,21228.52
ROGERS ENGINEERING,02-Central,770210000,1965.00,185.53,1779.47
S & P INSULATION,02-Central,735602000,11200.00,3362.29,7837.71
S & S INDUSTRIES,02-Central,768250220,109882.50,65185.46,44697.04
S & S INDUSTRIES,02-Central,764781000,75978.00,26382.83,49595.17
S & S INDUSTRIES,02-Central,768251300,68155.00,38016.10,30138.90
S & S INDUSTRIES,02-Central,764487510,49764.00,22189.79,27574.21
S & S INDUSTRIES,02-Central,768250221,32150.00,19363.06,12786.94
S & S INDUSTRIES,02-Central,768250200,18240.00,11355.98,6884.02
S & S INDUSTRIES,02-Central,768251400,4770.00,2662.88,2107.12
S & S INDUSTRIES,02-Central,764762000,1870.00,770.08,1099.92
S & S INDUSTRIES,02-Central,733444200,210.00,38.64,171.36
S.C. DEPT OF AGRICULTURE,02-Central,720219100,531.90,13.10,518.80
S.P.KINNEY ENGINEERING,02-Central,770520000,958.50,41.85,916.65
SAFT AMERICA INC,02-Central,733423600,380.80,35.90,344.90
SAM DONG GEORGIA INC.,02-Central,768750000,26000.00,4824.08,21175.92
SAM DONG GEORGIA INC.,02-Central,764521000,4710.00,398.92,4311.08
SAM DONG GEORGIA INC.,02-Central,770830000,882.00,30.97,851.03
SAM DONG GEORGIA INC.,02-Central,770810000,737.00,17.19,719.81
"SAM DONG OHIO, INC.",02-Central,777601000,5189.76,843.69,4346.07
SANDERS SUPPLY,02-Central,735602000,12000.00,3407.06,8592.94
SANDY WALLIN INC.,02-Central,733464500,298.00,29.52,268.48
SAUEREISEN INC.,02-Central,720219100,4934.10,119.51,4814.59
SAVER SYSTEM,02-Central,727063007,5211.00,632.36,4578.64
"SBJ ENTERPRISES, INC.",02-Central,735602110,384.00,89.25,294.75
SCHAD BOILER SETTING CO.,02-Central,727041200,25364.76,5856.48,19508.28
SCHAD BOILER SETTING CO.,02-Central,733211000,19920.00,4687.15,15232.85
SCHAD BOILER SETTING CO.,02-Central,733151500,13228.80,2443.16,10785.64
SCHAD BOILER SETTING CO.,02-Central,777601000,7756.36,2225.24,5531.12
SCHAD BOILER SETTING CO.,02-Central,777902700,5900.58,2174.39,3726.19
SCHAD BOILER SETTING CO.,02-Central,793330000,4194.00,889.38,3304.62
SCHAD BOILER SETTING CO.,02-Central,733151600,3897.20,717.71,3179.49
SCHAD BOILER SETTING CO.,02-Central,727000140,736.00,126.70,609.30
SCHAD BOILER SETTING CO.,02-Central,737912200,629.25,136.42,492.83
SCHAD BOILER SETTING CO.,02-Central,770220000,586.80,54.69,532.11
SCHAD BOILER SETTING CO.,02-Central,764481000,586.00,224.76,361.24
SCHAD BOILER SETTING CO.,02-Central,793540000,492.00,115.87,376.13
SCHAD BOILER SETTING CO.,02-Central,734910000,428.30,102.70,325.60
SCHAD BOILER SETTING CO.,02-Central,734311100,381.48,33.70,347.78
SCHULZ XTRUDED PRODUCTS,02-Central,739681100,12013.56,5382.00,6631.56
SCHULZ XTRUDED PRODUCTS,02-Central,738361000,3756.24,1853.28,1902.96
SCHULZ XTRUTED PRODUCTS,02-Central,738361000,3033.72,966.76,2066.96
SCOT FORGE CO,02-Central,766122000,167138.00,71749.76,95388.24
SCOTT- GLENN CO.LLC,02-Central,734211200,7166.29,921.03,6245.26
SCOTT- GLENN CO.LLC,02-Central,770240000,5919.75,616.77,5302.98
SCOTT- GLENN CO.LLC,02-Central,770260000,1317.60,137.96,1179.64
SCOTT- GLENN CO.LLC,02-Central,770220000,1211.40,112.66,1098.74
SCREW CONVEYOR CORP.,02-Central,770520000,4635.00,228.66,4406.34
"SDI LAFARGA, LLC",02-Central,770830000,1061.00,35.87,1025.13
SDM & ASSOCIATES INC,02-Central,733423600,380.80,35.90,344.90
SEALTHERM CORP,02-Central,739370000,379.52,138.24,241.28
SELAS HEAT TECHNOLOGY CO.,02-Central,734311100,654.20,70.60,583.60
SELECT INDUSTRIES CORP,02-Central,724034500,30247.60,11133.24,19114.36
SELECTRODE INDUSTRIES,02-Central,727000140,531.20,33.43,497.77
SELEE CORP,02-Central,732690000,1488451.41,727047.47,761403.94
SELEE CORP,02-Central,732659400,113487.80,44143.66,69344.14
SELEE CORP,02-Central,792687000,95402.38,34102.04,61300.34
SELEE CORP,02-Central,777991000,23485.00,13696.20,9788.80
SELEE CORP,02-Central,793000410,19737.00,8035.48,11701.52
SELEE CORP,02-Central,792683000,19167.03,6714.58,12452.45
SELEE CORP,02-Central,732695000,6378.56,1725.72,4652.84
SELEE CORP,02-Central,792685000,2236.67,677.18,1559.49
SELEE CORP,02-Central,754927000,941.16,113.41,827.75
SELEE CORP,02-Central,740402301,742.50,210.97,531.53
SELEE CORP,02-Central,734651200,205.80,37.00,168.80
SERVI-SEL INC.,02-Central,734211200,243.46,23.38,220.08
SEYMORE JOHNSON AIRFORCE,02-Central,734311100,190.74,16.85,173.89
SGL CARBON GROUP,02-Central,764526600,1236.00,609.31,626.69
SGL CARBON LLC- MORGANTON,02-Central,754695000,112314.50,76577.92,35736.58
SGL CARBON LLC- MORGANTON,02-Central,779959837,22032.50,11120.03,10912.47
SGL CARBON LLC- MORGANTON,02-Central,764821000,13160.00,3697.80,9462.20
SGL CARBON LLC- MORGANTON,02-Central,764901000,12605.00,4192.23,8412.77
SGL CARBON LLC- MORGANTON,02-Central,779957232,10447.50,7271.73,3175.77
SGL CARBON LLC- MORGANTON,02-Central,754055000,9545.83,6204.57,3341.26
SGL CARBON LLC- MORGANTON,02-Central,764526600,5016.00,2270.54,2745.46
SGL CARBON LLC- MORGANTON,02-Central,764821161,2260.00,1488.00,772.00
SGL CARBON LLC- MORGANTON,02-Central,764524000,2196.00,597.32,1598.68
SGL CARBON LLC- MORGANTON,02-Central,764901061,1990.00,1492.00,498.00
SGL CARBON LLC- MORGANTON,02-Central,764821073,1130.00,729.00,401.00
SGL CARBON LLC- MORGANTON,02-Central,764901073,995.00,729.00,266.00
SGL CARBON LLC- MORGANTON,02-Central,764484000,660.00,217.14,442.86
"SGL CARBON,LLC",02-Central,734990000,11870.40,1455.96,10414.44
SHARP REFRACTORIES,02-Central,770540000,5400.50,274.58,5125.92
SHARP REFRACTORIES,02-Central,770560000,1585.00,89.50,1495.50
SHARP REFRACTORIES,02-Central,770580000,1522.00,84.73,1437.27
SHARP REFRACTORIES,02-Central,770330000,1015.00,96.65,918.35
SHOALS RUBBER & GASKET CO,02-Central,733211000,456.00,47.39,408.61
SHOOK & FLETCHER INSUL CO,02-Central,764901000,535090.15,225584.69,309505.46
SHOOK & FLETCHER INSUL CO,02-Central,735602000,351617.50,120457.30,231160.20
SHOOK & FLETCHER INSUL CO,02-Central,764821000,254016.00,84026.76,169989.24
SHOOK & FLETCHER INSUL CO,02-Central,726000240,229728.00,126361.36,103366.64
SHOOK & FLETCHER INSUL CO,02-Central,764521000,144596.00,54473.77,90122.23
SHOOK & FLETCHER INSUL CO,02-Central,738561000,61783.68,46016.36,15767.32
SHOOK & FLETCHER INSUL CO,02-Central,735602110,38540.00,14351.12,24188.88
SHOOK & FLETCHER INSUL CO,02-Central,738521000,38461.44,30210.97,8250.47
SHOOK & FLETCHER INSUL CO,02-Central,764822000,29433.60,8415.88,21017.72
SHOOK & FLETCHER INSUL CO,02-Central,737871000,21569.60,4755.60,16814.00
SHOOK & FLETCHER INSUL CO,02-Central,766121000,18870.00,4269.30,14600.70
SHOOK & FLETCHER INSUL CO,02-Central,764785000,8940.00,3649.74,5290.26
SHOOK & FLETCHER INSUL CO,02-Central,737321000,8025.60,2569.69,5455.91
SHOOK & FLETCHER INSUL CO,02-Central,768126014,7334.80,3861.89,3472.91
SHOOK & FLETCHER INSUL CO,02-Central,764902000,7118.75,2139.11,4979.64
SHOOK & FLETCHER INSUL CO,02-Central,764861000,6681.75,2816.54,3865.21
SHOOK & FLETCHER INSUL CO,02-Central,734910000,6551.00,1557.80,4993.20
SHOOK & FLETCHER INSUL CO,02-Central,764951800,6460.00,2543.85,3916.15
SHOOK & FLETCHER INSUL CO,02-Central,793260074,6426.00,2776.40,3649.60
SHOOK & FLETCHER INSUL CO,02-Central,777601000,5671.05,1510.99,4160.06
SHOOK & FLETCHER INSUL CO,02-Central,738441000,5183.04,8392.32,-3209.28
SHOOK & FLETCHER INSUL CO,02-Central,733464500,4521.20,709.03,3812.17
SHOOK & FLETCHER INSUL CO,02-Central,779980918,4380.00,2302.21,2077.79
SHOOK & FLETCHER INSUL CO,02-Central,764825000,4050.00,2168.54,1881.46
SHOOK & FLETCHER INSUL CO,02-Central,764781000,3590.20,1291.11,2299.09
SHOOK & FLETCHER INSUL CO,02-Central,779959909,2912.40,505.05,2407.35
SHOOK & FLETCHER INSUL CO,02-Central,768121000,2764.00,1150.68,1613.32
SHOOK & FLETCHER INSUL CO,02-Central,779959908,2648.70,335.07,2313.63
SHOOK & FLETCHER INSUL CO,02-Central,779980915,2640.00,1368.37,1271.63
SHOOK & FLETCHER INSUL CO,02-Central,739561200,2481.73,979.36,1502.37
SHOOK & FLETCHER INSUL CO,02-Central,764601000,2220.00,1062.89,1157.11
SHOOK & FLETCHER INSUL CO,02-Central,734611100,1801.25,353.08,1448.17
SHOOK & FLETCHER INSUL CO,02-Central,734311100,1748.70,202.23,1546.47
SHOOK & FLETCHER INSUL CO,02-Central,770200000,1652.40,169.66,1482.74
SHOOK & FLETCHER INSUL CO,02-Central,791006650,1420.00,679.30,740.70
SHOOK & FLETCHER INSUL CO,02-Central,739741100,910.40,841.60,68.80
SHOOK & FLETCHER INSUL CO,02-Central,764341000,759.00,479.74,279.26
SHOOK & FLETCHER INSUL CO,02-Central,734900000,667.10,264.80,402.30
SHOOK & FLETCHER INSUL CO,02-Central,739641100,590.76,412.28,178.48
SHOOK & FLETCHER INSUL CO,02-Central,733741000,568.80,186.38,382.42
SHOOK & FLETCHER INSUL CO,02-Central,764761000,518.40,245.72,272.68
SHOOK & FLETCHER INSUL CO,02-Central,739390000,466.08,485.28,-19.20
SHOOK & FLETCHER INSUL CO,02-Central,733525500,457.50,65.03,392.47
SHOOK & FLETCHER INSUL CO,02-Central,734851000,456.23,98.90,357.33
SHOOK & FLETCHER INSUL CO,02-Central,734651200,423.94,73.75,350.19
SHOOK & FLETCHER INSUL CO,02-Central,768122000,414.60,174.66,239.94
SHOOK & FLETCHER INSUL CO,02-Central,768222000,414.60,204.94,209.66
SHOOK & FLETCHER INSUL CO,02-Central,768241000,300.30,106.14,194.16
SHOOK & FLETCHER INSUL CO,02-Central,738542000,253.92,141.16,112.76
SHOOK & FLETCHER INSUL CO,02-Central,733211000,239.20,47.39,191.81
SHOOK & FLETCHER INSUL CO,02-Central,733423700,232.64,36.22,196.42
SHOOK & FLETCHER INSUL CO,02-Central,737912200,209.75,45.47,164.28
SHOOK & FLETCHER INSUL CO,02-Central,733511000,190.00,75.22,114.78
SHOOK & FLETCHER INSUL CO,02-Central,764762000,92.00,31.57,60.43
SHOOK & FLETCHER,02-Central,764602600,351103.20,129862.54,221240.66
SHOOK & FLETCHER,02-Central,764522030,154531.80,58930.50,95601.30
SHOOK & FLETCHER,02-Central,764901000,122386.20,51980.60,70405.60
SHOOK & FLETCHER,02-Central,764602900,78836.54,30569.70,48266.84
SHOOK & FLETCHER,02-Central,734651200,63376.50,26796.20,36580.30
SHOOK & FLETCHER,02-Central,735602000,49800.00,15318.43,34481.57
SHOOK & FLETCHER,02-Central,764529560,46598.67,18680.06,27918.61
SHOOK & FLETCHER,02-Central,764902000,31906.70,14173.98,17732.72
SHOOK & FLETCHER,02-Central,764563001,31729.60,12137.42,19592.18
SHOOK & FLETCHER,02-Central,764821000,24375.00,9672.52,14702.48
SHOOK & FLETCHER,02-Central,768241000,16376.00,5481.24,10894.76
SHOOK & FLETCHER,02-Central,734761300,8496.40,1529.26,6967.14
SHOOK & FLETCHER,02-Central,771320000,8311.30,1368.99,6942.31
SHOOK & FLETCHER,02-Central,766081010,8172.00,2181.96,5990.04
SHOOK & FLETCHER,02-Central,764521000,7004.00,2922.74,4081.26
SHOOK & FLETCHER,02-Central,764526600,2958.40,1832.26,1126.14
SHOOK & FLETCHER,02-Central,764822000,2523.80,1010.60,1513.20
SHOOK & FLETCHER,02-Central,764781040,2297.00,797.07,1499.93
SHOOK & FLETCHER,02-Central,768121000,1176.00,460.26,715.74
SHOOK & FLETCHER,02-Central,735752000,380.00,136.80,243.20
SHOOK & FLETCHER,02-Central,724281200,358.00,89.08,268.92
SHOOK & FLETCHER,02-Central,739561000,301.90,85.22,216.68
SHOOK & FLETCHER,02-Central,733211000,239.20,47.39,191.81
SIERRA LOBO,02-Central,734910000,341.50,51.35,290.15
SIMPSON LUMBER COMPANY,02-Central,770240000,5988.75,413.88,5574.87
SMITH - SHARPE,02-Central,733572200,201550.00,74088.59,127461.41
SMITH - SHARPE,02-Central,754305000,95445.00,53222.89,42222.11
SMITH - SHARPE,02-Central,733221400,84993.60,35404.38,49589.22
SMITH - SHARPE,02-Central,779952188,74654.40,29817.68,44836.72
SMITH - SHARPE,02-Central,733525500,71762.12,20100.84,51661.28
SMITH - SHARPE,02-Central,764822000,70117.60,20611.92,49505.68
SMITH - SHARPE,02-Central,764821000,58392.00,17951.57,40440.43
SMITH - SHARPE,02-Central,768241000,49620.50,19726.33,29894.17
SMITH - SHARPE,02-Central,764901000,43838.50,16882.53,26955.97
SMITH - SHARPE,02-Central,779957935,38372.80,17314.00,21058.80
SMITH - SHARPE,02-Central,764521000,35176.40,10973.28,24203.12
SMITH - SHARPE,02-Central,768221000,34470.50,12699.86,21770.64
SMITH - SHARPE,02-Central,733510000,32258.00,13305.77,18952.23
SMITH - SHARPE,02-Central,733570000,30160.80,10838.69,19322.11
SMITH - SHARPE,02-Central,733211000,29056.50,7008.09,22048.41
SMITH - SHARPE,02-Central,777601000,26901.08,7234.38,19666.70
SMITH - SHARPE,02-Central,768121000,23508.60,10957.99,12550.61
SMITH - SHARPE,02-Central,751258000,20000.00,9146.20,10853.80
SMITH - SHARPE,02-Central,733464600,18153.40,6172.24,11981.16
SMITH - SHARPE,02-Central,779952189,17940.00,7818.64,10121.36
SMITH - SHARPE,02-Central,766211000,16891.50,6133.72,10757.78
SMITH - SHARPE,02-Central,733464500,16712.00,5256.13,11455.87
SMITH - SHARPE,02-Central,768231000,16620.65,6751.66,9868.99
SMITH - SHARPE,02-Central,771400000,10440.00,623.76,9816.24
SMITH - SHARPE,02-Central,739561200,10376.11,2822.34,7553.77
SMITH - SHARPE,02-Central,764601000,10266.50,2754.30,7512.20
SMITH - SHARPE,02-Central,758422000,9797.78,3974.95,5822.83
SMITH - SHARPE,02-Central,764821161,9411.60,6844.80,2566.80
SMITH - SHARPE,02-Central,764521073,8820.00,5805.00,3015.00
SMITH - SHARPE,02-Central,739741100,8594.62,4163.22,4431.40
SMITH - SHARPE,02-Central,764602000,8438.80,2267.29,6171.51
SMITH - SHARPE,02-Central,734311100,8421.44,1128.11,7293.33
SMITH - SHARPE,02-Central,733484800,7950.40,1458.72,6491.68
SMITH - SHARPE,02-Central,754285000,7932.28,2645.19,5287.09
SMITH - SHARPE,02-Central,764901073,7919.20,6269.40,1649.80
SMITH - SHARPE,02-Central,764341000,7418.30,3694.78,3723.52
SMITH - SHARPE,02-Central,739561100,7237.88,1959.32,5278.56
SMITH - SHARPE,02-Central,780290000,6825.60,2330.16,4495.44
SMITH - SHARPE,02-Central,734751200,6795.41,1201.85,5593.56
SMITH - SHARPE,02-Central,764781040,6681.20,1126.46,5554.74
SMITH - SHARPE,02-Central,764902000,6666.40,1604.22,5062.18
SMITH - SHARPE,02-Central,764481000,6640.00,1302.98,5337.02
SMITH - SHARPE,02-Central,789180000,6381.72,2951.85,3429.87
SMITH - SHARPE,02-Central,748602000,6109.00,792.77,5316.23
SMITH - SHARPE,02-Central,734651200,6051.71,1257.04,4794.67
SMITH - SHARPE,02-Central,739541200,5823.33,1235.51,4587.82
SMITH - SHARPE,02-Central,734900000,5620.60,2218.77,3401.83
SMITH - SHARPE,02-Central,779956599,5531.40,3331.69,2199.71
SMITH - SHARPE,02-Central,726020005,5069.12,1085.89,3983.23
SMITH - SHARPE,02-Central,766681100,5020.20,1176.91,3843.29
SMITH - SHARPE,02-Central,739661100,4833.60,3064.96,1768.64
SMITH - SHARPE,02-Central,777642000,4794.50,1551.57,3242.93
SMITH - SHARPE,02-Central,767960000,4500.00,2379.20,2120.80
SMITH - SHARPE,02-Central,739761100,4260.81,3268.38,992.43
SMITH - SHARPE,02-Central,727000140,4186.20,633.42,3552.78
SMITH - SHARPE,02-Central,779951075,3435.94,1651.49,1784.45
SMITH - SHARPE,02-Central,738561000,3426.63,1930.74,1495.89
SMITH - SHARPE,02-Central,777903000,3280.90,1370.47,1910.43
SMITH - SHARPE,02-Central,741620310,3152.50,705.71,2446.79
SMITH - SHARPE,02-Central,766121000,3132.20,355.10,2777.10
SMITH - SHARPE,02-Central,764821073,3069.00,2187.00,882.00
SMITH - SHARPE,02-Central,764781000,3052.00,960.41,2091.59
SMITH - SHARPE,02-Central,741640310,3000.00,649.42,2350.58
SMITH - SHARPE,02-Central,764861000,2674.50,1119.17,1555.33
SMITH - SHARPE,02-Central,738521000,2571.36,1756.38,814.98
SMITH - SHARPE,02-Central,764521061,2370.00,1917.00,453.00
SMITH - SHARPE,02-Central,766261000,2270.00,893.62,1376.38
SMITH - SHARPE,02-Central,766686000,2015.00,825.98,1189.02
SMITH - SHARPE,02-Central,741720310,1966.50,139.30,1827.20
SMITH - SHARPE,02-Central,770400100,1958.25,259.17,1699.08
SMITH - SHARPE,02-Central,770415000,1927.50,339.78,1587.72
SMITH - SHARPE,02-Central,734414100,1844.56,281.04,1563.52
SMITH - SHARPE,02-Central,733211050,1811.10,465.90,1345.20
SMITH - SHARPE,02-Central,770381500,1645.20,245.16,1400.04
SMITH - SHARPE,02-Central,737912200,1640.90,359.57,1281.33
SMITH - SHARPE,02-Central,733444300,1581.00,618.69,962.31
SMITH - SHARPE,02-Central,777904301,1555.45,704.82,850.63
SMITH - SHARPE,02-Central,767970000,1500.00,987.68,512.32
SMITH - SHARPE,02-Central,780710000,1416.00,426.66,989.34
SMITH - SHARPE,02-Central,779956287,1374.40,662.27,712.13
SMITH - SHARPE,02-Central,779956286,1312.50,590.46,722.04
SMITH - SHARPE,02-Central,780230000,1304.40,491.66,812.74
SMITH - SHARPE,02-Central,733444200,1296.00,439.98,856.02
SMITH - SHARPE,02-Central,754085000,1292.80,380.01,912.79
SMITH - SHARPE,02-Central,768211000,1173.60,561.36,612.24
SMITH - SHARPE,02-Central,739781100,1090.96,476.05,614.91
SMITH - SHARPE,02-Central,770570000,1088.85,95.41,993.44
SMITH - SHARPE,02-Central,739641100,983.52,806.40,177.12
SMITH - SHARPE,02-Central,733151500,976.80,162.33,814.47
SMITH - SHARPE,02-Central,754267000,918.72,269.92,648.80
SMITH - SHARPE,02-Central,754255000,880.00,447.92,432.08
SMITH - SHARPE,02-Central,754485000,865.80,345.81,519.99
SMITH - SHARPE,02-Central,770392000,834.00,124.52,709.48
SMITH - SHARPE,02-Central,779956289,774.00,368.30,405.70
SMITH - SHARPE,02-Central,770520000,735.00,96.63,638.37
SMITH - SHARPE,02-Central,733423800,722.56,134.42,588.14
SMITH - SHARPE,02-Central,770300000,686.96,79.94,607.02
SMITH - SHARPE,02-Central,764561000,550.20,259.05,291.15
SMITH - SHARPE,02-Central,770392700,524.00,83.02,440.98
SMITH - SHARPE,02-Central,741600310,486.00,64.12,421.88
SMITH - SHARPE,02-Central,792614000,443.40,82.57,360.83
SMITH - SHARPE,02-Central,764762000,413.40,94.71,318.69
SMITH - SHARPE,02-Central,770290000,397.25,79.65,317.60
SMITH - SHARPE,02-Central,734910000,396.00,102.70,293.30
SMITH - SHARPE,02-Central,768751000,360.00,144.75,215.25
SMITH - SHARPE,02-Central,780531000,358.46,190.34,168.12
SMITH - SHARPE,02-Central,770380000,350.00,61.29,288.71
SMITH - SHARPE,02-Central,739541000,299.52,90.50,209.02
SMITH - SHARPE,02-Central,733505200,276.50,83.83,192.67
SMITH - SHARPE,02-Central,764482000,268.50,87.00,181.50
SMITH - SHARPE,02-Central,779956288,225.30,138.23,87.07
SMITH - SHARPE,02-Central,779301000,223.86,47.51,176.35
SMITH - SHARPE,02-Central,754287000,183.24,49.12,134.12
SMITH - SHARPE,02-Central,734211200,175.47,22.84,152.63
SMT,02-Central,739741100,428.48,168.32,260.16
SNYDER BENTLEY CO,02-Central,733671000,4247.04,351.87,3895.17
SOLBERG MFG,02-Central,734311100,363.44,35.30,328.14
SOLBERG MFG,02-Central,734211200,218.82,23.38,195.44
SOUNDTECH INC.,02-Central,768141000,2126.60,1323.70,802.90
SOUNDTECH INC.,02-Central,768241000,1400.00,707.62,692.38
SOUNDTECH INC.,02-Central,768021000,840.00,549.86,290.14
SOURLIS GLASS STUDIO,02-Central,734311100,381.48,33.70,347.78
SOUTHERN RESEARCH INSTITU,02-Central,733464600,909.00,97.60,811.40
SPAR INC,02-Central,764522000,15930.00,7161.10,8768.90
SPAR INC,02-Central,764821000,10550.00,4064.40,6485.60
SPAR INC,02-Central,764601000,7865.00,3454.39,4410.61
SPAR INC,02-Central,764901000,5670.00,2242.00,3428.00
SPAR INC,02-Central,754065000,5010.00,3294.04,1715.96
SPAR INC,02-Central,764861000,3075.00,1298.90,1776.10
SPAR INC,02-Central,766121000,1670.00,478.98,1191.02
SPAR INC,02-Central,764781000,774.00,354.98,419.02
SPAR INC,02-Central,764521000,590.00,265.94,324.06
SPECIALTY CERAMICS INC,02-Central,727000060,1484488.50,1135628.20,348860.30
SPECIALTY CERAMICS INC,02-Central,727063005,322620.00,233421.47,89198.53
SPECIALTY CERAMICS INC,02-Central,727000081,209961.00,156301.47,53659.53
SPECIALTY CERAMICS INC,02-Central,727063009,93750.00,67336.05,26413.95
SPECIALTY CERAMICS INC,02-Central,793060300,66765.60,29752.60,37013.00
SPECIALTY CERAMICS INC,02-Central,793000000,58924.80,29892.68,29032.12
SPECIALTY CERAMICS INC,02-Central,727000080,54682.50,41736.75,12945.75
SPECIALTY CERAMICS INC,02-Central,715000000,52000.00,48697.48,3302.52
SPECIALTY CERAMICS INC,02-Central,763991000,34009.20,30239.36,3769.84
SPECIALTY CERAMICS INC,02-Central,720252301,26446.50,1771.70,24674.80
SPECIALTY CERAMICS INC,02-Central,727063007,16509.00,12203.11,4305.89
SPECIALTY CERAMICS INC,02-Central,727000070,10254.00,7808.70,2445.30
SPECIALTY CERAMICS INC,02-Central,768750000,6998.40,5210.02,1788.38
SPECIALTY CERAMICS INC,02-Central,764222000,5700.00,3738.79,1961.21
SPECIALTY CERAMICS INC,02-Central,726000062,4485.00,3625.88,859.12
SPECIALTY CERAMICS INC,02-Central,727063000,3682.80,2721.13,961.67
SPECIALTY CERAMICS INC,02-Central,727031300,2835.00,2057.65,777.35
SPECIALTY CERAMICS INC,02-Central,764761000,1928.00,1137.79,790.21
SPECIALTY CERAMICS INC,02-Central,764221000,375.00,252.42,122.58
SPECIALTY FOUNDRY PRODUCT,02-Central,764821000,341490.00,160021.76,181468.24
SPECIALTY FOUNDRY PRODUCT,02-Central,764521000,54280.00,29215.00,25065.00
SPECIALTY FOUNDRY PRODUCT,02-Central,764901000,40712.50,19154.24,21558.26
SPECIALTY FOUNDRY PRODUCT,02-Central,766121000,36180.00,13839.38,22340.62
SPECIALTY FOUNDRY PRODUCT,02-Central,764761000,35030.00,14602.44,20427.56
SPECIALTY FOUNDRY PRODUCT,02-Central,733211000,26068.00,9415.85,16652.15
SPECIALTY FOUNDRY PRODUCT,02-Central,764821073,23800.00,25515.00,-1715.00
SPECIALTY FOUNDRY PRODUCT,02-Central,733151500,21200.00,5737.53,15462.47
SPECIALTY FOUNDRY PRODUCT,02-Central,764521061,13500.00,17253.00,-3753.00
SPECIALTY FOUNDRY PRODUCT,02-Central,733182300,12996.00,3614.15,9381.85
SPECIALTY FOUNDRY PRODUCT,02-Central,764905200,10950.00,5783.09,5166.91
SPECIALTY FOUNDRY PRODUCT,02-Central,733202000,9712.80,3310.16,6402.64
SPECIALTY FOUNDRY PRODUCT,02-Central,764781000,3690.00,1840.06,1849.94
SPECIALTY FOUNDRY PRODUCT,02-Central,764901073,3500.00,3645.00,-145.00
SPECIALTY FOUNDRY PRODUCT,02-Central,770290000,2799.00,229.76,2569.24
SPECIALTY FOUNDRY PRODUCT,02-Central,777642000,2714.80,860.80,1854.00
SPECIALTY FOUNDRY PRODUCT,02-Central,770400000,934.50,92.44,842.06
SPECIALTY FOUNDRY PRODUCT,02-Central,770260000,675.00,70.33,604.67
SPECIALTY FOUNDRY PRODUCT,02-Central,758431000,602.12,301.08,301.04
SPECIALTY FOUNDRY PRODUCT,02-Central,734651200,584.00,73.50,510.50
SPECIALTY FOUNDRY PRODUCT,02-Central,770392000,446.00,41.51,404.49
SPECIALTY FOUNDRY PRODUCT,02-Central,767550000,93.00,74.31,18.69
SPECIALTY FOUNDRY PRODUCT,02-Central,779958914,64.00,51.00,13.00
SPECIALTY PROD. INSULATIO,02-Central,735602000,194866.00,71720.74,123145.26
SPECIALTY PROD. INSULATIO,02-Central,764902000,39296.25,8453.87,30842.38
SPECIALTY PROD. INSULATIO,02-Central,735750000,13376.00,6102.75,7273.25
SPECIALTY PROD. INSULATIO,02-Central,735602110,10622.00,4081.19,6540.81
SPECIALTY PRODUCTS &,02-Central,735602000,209980.00,78533.57,131446.43
SPECIALTY PRODUCTS &,02-Central,735750000,23850.00,10541.24,13308.76
SPECIALTY PRODUCTS &,02-Central,768242000,4395.00,1324.83,3070.17
SPECIALTY PRODUCTS &,02-Central,735602110,3240.00,1239.07,2000.93
SPECIALTY PRODUCTS &,02-Central,735752000,604.00,257.76,346.24
SPECIALTY PRODUCTS &,02-Central,777642000,286.00,100.72,185.28
SPECIALTY PRODUCTS INSUL.,02-Central,735750000,5220.00,2287.91,2932.09
SPECIALTY PRODUCTS INSUL.,02-Central,735602000,3600.00,1344.92,2255.08
SPECIALTY PRODUCTS INSUL,02-Central,735602000,68210.00,26294.72,41915.28
SPECIALTY PRODUCTS INSUL,02-Central,764901000,9243.00,1990.44,7252.56
SPECIALTY PRODUCTS INSUL,02-Central,735655000,4453.60,634.78,3818.82
SPECIALTY PRODUCTS INSUL,02-Central,735750000,3190.00,1510.75,1679.25
SPECIALTY PRODUCTS,02-Central,724101200,2040.00,169.69,1870.31
SPI/MOBILE PULLEY WORKS,02-Central,754467000,5827.98,1288.05,4539.93
SPRINGFIELD INDUSTRIES,02-Central,734080010,39329.40,16576.68,22752.72
ST. GOBAIN  CONTAINERS,02-Central,793330000,431.50,26.01,405.49
STACK-ON PRODUCTS COMPANY,02-Central,730710500,591.50,70.96,520.54
STANDARD FORGED PRODUCTS,02-Central,777601000,819.84,117.59,702.25
STANDARD STEEL CORP,02-Central,770550000,11555.00,1042.24,10512.76
STANDARD STEEL CORP,02-Central,754067000,5037.60,1861.09,3176.51
STANDARD STEEL CORP,02-Central,770460000,3397.50,368.04,3029.46
STANDARD STEEL CORP,02-Central,734311100,308.92,35.30,273.62
STARBUCKS COFFEE COMPANY,02-Central,733202000,9538.00,1066.96,8471.04
STECO REFRACTORIES,02-Central,764901000,44650.00,12186.40,32463.60
STECO REFRACTORIES,02-Central,779988955,24850.00,15800.08,9049.92
STECO REFRACTORIES,02-Central,764821000,18220.00,4559.41,13660.59
STECO REFRACTORIES,02-Central,779980918,10080.00,3784.45,6295.55
STECO REFRACTORIES,02-Central,764521000,6400.00,2659.41,3740.59
STECO REFRACTORIES,02-Central,779980915,4521.00,2130.29,2390.71
STECO REFRACTORIES,02-Central,779988954,3000.00,1349.64,1650.36
STECO REFRACTORIES,02-Central,764901050,2350.00,631.79,1718.21
STECO REFRACTORIES,02-Central,779988910,360.00,156.30,203.70
STEEL CASE INC.,02-Central,739561100,552.72,93.32,459.40
STETSON - ROCK ISLAND,02-Central,735750000,6300.00,2472.13,3827.87
STETSON BUILDING PRODUCTS,02-Central,735602110,242304.00,85740.47,156563.53
STETSON BUILDING PRODUCTS,02-Central,735602000,170312.00,58681.47,111630.53
STETSON BUILDING PRODUCTS,02-Central,735750000,27050.00,10345.79,16704.21
STETSON BUILDING PRODUCTS,02-Central,774271000,281.50,117.63,163.87
STETSON BUILDING PRODUCTS,02-Central,774401000,159.00,55.11,103.89
STETSON BUILDING PRODUCTS,02-Central,734990000,150.80,27.39,123.41
STETSON BUILDING PRODUCTS,02-Central,764341000,66.00,11.47,54.53
STEWART ENGINEERS,02-Central,764602000,21360.00,1572.69,19787.31
STEWART ENGINEERS,02-Central,727000140,17760.00,1900.35,15859.65
STEWART ENGINEERS,02-Central,739681100,13595.04,4336.56,9258.48
STEWART ENGINEERS,02-Central,739741100,6301.44,1972.05,4329.39
STEWART ENGINEERS,02-Central,739781100,3021.12,856.89,2164.23
STEWART ENGINEERS,02-Central,739561000,2012.40,426.10,1586.30
STEWART ENGINEERS,02-Central,770200000,918.00,81.58,836.42
STEWART ENGINEERS,02-Central,739541000,479.52,71.62,407.90
STRAUCH CHEMICAL DISTRIBU,02-Central,727063000,35540.00,3669.70,31870.30
STRAUCH CHEMICAL DISTRIBU,02-Central,779987650,1264.00,175.35,1088.65
STRAUCH CHEMICAL DISTRIBU,02-Central,727070000,948.00,131.51,816.49
STRAUCH CHEMICAL DISTRIBU,02-Central,723960000,823.00,176.46,646.54
SULLIVAN SUPPLY CO,02-Central,779221000,1096.25,147.12,949.13
SUNCOAST CHEMICALS,02-Central,737912200,326.50,45.47,281.03
SUPERIOR SEALS & SERVICES,02-Central,733510000,1130.00,159.03,970.97
SUPERIOR SEALS & SERVICES,02-Central,733525500,752.50,70.39,682.11
SURFACE COMBUSTION INC,02-Central,770560000,56319.60,4379.62,51939.98
SURFACE COMBUSTION INC,02-Central,737902100,17593.20,4047.03,13546.17
SURFACE COMBUSTION INC,02-Central,739561200,13230.40,2991.64,10238.76
SURFACE COMBUSTION INC,02-Central,770580000,7839.20,598.04,7241.16
SURFACE COMBUSTION INC,02-Central,770600000,4129.93,283.06,3846.87
SURFACE COMBUSTION INC,02-Central,770830000,1666.00,71.75,1594.25
SURFACE COMBUSTION INC,02-Central,733151500,1514.00,190.76,1323.24
SURFACE COMBUSTION INC,02-Central,733141200,1136.00,193.93,942.07
SURFACE COMBUSTION INC,02-Central,770450000,1113.00,97.34,1015.66
SURFACE COMBUSTION INC,02-Central,770280000,491.25,80.03,411.22
SURFACE IGNITER LLC,02-Central,737851000,2941.44,332.22,2609.22
SUZUKI GARPHYTTAN WIRE,02-Central,738542000,1878.48,423.47,1455.01
SWANTON WELDING&MACHINING,02-Central,733464500,298.00,29.52,268.48
SWEP NORTH AMERICA,02-Central,734251300,4438.00,350.83,4087.17
SWEP NORTH AMERICA,02-Central,734351200,1945.80,147.24,1798.56
SWINDELL DRESSLER,02-Central,727000140,47096.64,23766.68,23329.96
SWINDELL DRESSLER,02-Central,777906100,43831.44,48654.00,-4822.56
SWINDELL DRESSLER,02-Central,766166000,18368.50,9555.02,8813.48
SWINDELL DRESSLER,02-Central,740283502,13469.50,6830.87,6638.63
SWINDELL DRESSLER,02-Central,765664200,2457.00,679.59,1777.41
TAMPA RUBBER & GASKET,02-Central,733721000,286.00,48.90,237.10
TBN ASSOCIATES INC.,02-Central,735602110,54780.00,17861.50,36918.50
"TECH CASTINGS, INC",02-Central,764481000,3000.00,172.65,2827.35
"TECH CASTINGS, INC",02-Central,764461006,1980.00,163.48,1816.52
"TECH CASTINGS, INC",02-Central,733383000,1117.60,163.93,953.67
THE DOW CHEMICAL COMPANY,02-Central,764781000,386.00,20.45,365.55
"THE N.P. GROUP, INC.",02-Central,779987651,1341.00,327.23,1013.77
"THE NP GROUP, INC.",02-Central,779987650,4827.60,1183.63,3643.97
THE OHIO STATE UNIVERSITY,02-Central,768121000,489.00,44.03,444.97
"THE SCHAEFER GROUP, INC.",02-Central,777902700,418042.72,209448.15,208594.57
"THE SCHAEFER GROUP, INC.",02-Central,764521000,271009.90,93593.04,177416.86
"THE SCHAEFER GROUP, INC.",02-Central,734173000,227085.60,92858.05,134227.55
"THE SCHAEFER GROUP, INC.",02-Central,754895000,139278.74,23436.58,115842.16
"THE SCHAEFER GROUP, INC.",02-Central,764821000,129143.20,22620.73,106522.47
"THE SCHAEFER GROUP, INC.",02-Central,739741100,78968.16,38810.32,40157.84
"THE SCHAEFER GROUP, INC.",02-Central,766121000,72856.80,8186.70,64670.10
"THE SCHAEFER GROUP, INC.",02-Central,764901000,44303.80,9067.36,35236.44
"THE SCHAEFER GROUP, INC.",02-Central,740402402,35462.00,8796.99,26665.01
"THE SCHAEFER GROUP, INC.",02-Central,759631000,29764.24,5618.24,24146.00
"THE SCHAEFER GROUP, INC.",02-Central,754925000,29283.63,4514.15,24769.48
"THE SCHAEFER GROUP, INC.",02-Central,739781100,26513.89,13386.50,13127.39
"THE SCHAEFER GROUP, INC.",02-Central,777903100,23799.30,12991.44,10807.86
"THE SCHAEFER GROUP, INC.",02-Central,764521073,23660.00,16770.00,6890.00
"THE SCHAEFER GROUP, INC.",02-Central,740382201,23601.50,7752.68,15848.82
"THE SCHAEFER GROUP, INC.",02-Central,733525500,22500.00,3797.23,18702.77
"THE SCHAEFER GROUP, INC.",02-Central,764521061,21600.00,19170.00,2430.00
"THE SCHAEFER GROUP, INC.",02-Central,781670000,21417.50,3965.84,17451.66
"THE SCHAEFER GROUP, INC.",02-Central,740361900,20231.04,8562.40,11668.64
"THE SCHAEFER GROUP, INC.",02-Central,733151500,20137.20,4163.69,15973.51
"THE SCHAEFER GROUP, INC.",02-Central,738561000,18292.80,8591.81,9700.99
"THE SCHAEFER GROUP, INC.",02-Central,733211000,17815.40,3769.30,14046.10
"THE SCHAEFER GROUP, INC.",02-Central,733151600,15162.00,3074.39,12087.61
"THE SCHAEFER GROUP, INC.",02-Central,777902300,14946.96,8248.88,6698.08
"THE SCHAEFER GROUP, INC.",02-Central,739761100,14154.68,8259.61,5895.07
"THE SCHAEFER GROUP, INC.",02-Central,734311100,14053.47,1620.07,12433.40
"THE SCHAEFER GROUP, INC.",02-Central,734910000,13561.40,4734.46,8826.94
"THE SCHAEFER GROUP, INC.",02-Central,754485000,13527.50,4322.66,9204.84
"THE SCHAEFER GROUP, INC.",02-Central,764461006,13456.00,3578.01,9877.99
"THE SCHAEFER GROUP, INC.",02-Central,770500000,13221.00,868.11,12352.89
"THE SCHAEFER GROUP, INC.",02-Central,738542000,13163.76,7279.33,5884.43
"THE SCHAEFER GROUP, INC.",02-Central,777904301,12698.19,5755.90,6942.29
"THE SCHAEFER GROUP, INC.",02-Central,738521000,10673.28,7692.61,2980.67
"THE SCHAEFER GROUP, INC.",02-Central,764821161,10170.00,6696.00,3474.00
"THE SCHAEFER GROUP, INC.",02-Central,777601000,10119.13,2635.44,7483.69
"THE SCHAEFER GROUP, INC.",02-Central,777903800,9974.52,3531.06,6443.46
"THE SCHAEFER GROUP, INC.",02-Central,764902000,9893.80,1414.90,8478.90
"THE SCHAEFER GROUP, INC.",02-Central,733505200,9765.00,1605.30,8159.70
"THE SCHAEFER GROUP, INC.",02-Central,739561200,9194.88,2493.99,6700.89
"THE SCHAEFER GROUP, INC.",02-Central,718551000,8925.00,2020.33,6904.67
"THE SCHAEFER GROUP, INC.",02-Central,754445000,8793.80,2864.60,5929.20
"THE SCHAEFER GROUP, INC.",02-Central,764901061,8790.00,4476.00,4314.00
"THE SCHAEFER GROUP, INC.",02-Central,764561000,8436.38,1539.53,6896.85
"THE SCHAEFER GROUP, INC.",02-Central,727000440,8366.00,2787.29,5578.71
"THE SCHAEFER GROUP, INC.",02-Central,733742000,8340.40,2142.61,6197.79
"THE SCHAEFER GROUP, INC.",02-Central,764481000,7939.20,1557.20,6382.00
"THE SCHAEFER GROUP, INC.",02-Central,724281200,7927.40,1848.81,6078.59
"THE SCHAEFER GROUP, INC.",02-Central,739561100,7728.00,2498.93,5229.07
"THE SCHAEFER GROUP, INC.",02-Central,764822000,7531.00,2118.52,5412.48
"THE SCHAEFER GROUP, INC.",02-Central,758461000,6882.12,1129.71,5752.41
"THE SCHAEFER GROUP, INC.",02-Central,733464500,6836.90,1072.28,5764.62
"THE SCHAEFER GROUP, INC.",02-Central,733484800,6323.20,1068.40,5254.80
"THE SCHAEFER GROUP, INC.",02-Central,779041000,5942.96,1476.72,4466.24
"THE SCHAEFER GROUP, INC.",02-Central,779081000,5888.07,1726.24,4161.83
"THE SCHAEFER GROUP, INC.",02-Central,734611100,5591.30,1123.19,4468.11
"THE SCHAEFER GROUP, INC.",02-Central,764781000,4842.20,877.89,3964.31
"THE SCHAEFER GROUP, INC.",02-Central,737912200,4429.81,950.73,3479.08
"THE SCHAEFER GROUP, INC.",02-Central,764261000,4400.00,2518.27,1881.73
"THE SCHAEFER GROUP, INC.",02-Central,764862000,4273.10,657.67,3615.43
"THE SCHAEFER GROUP, INC.",02-Central,771320000,4155.65,684.50,3471.15
"THE SCHAEFER GROUP, INC.",02-Central,727000140,4069.12,513.78,3555.34
"THE SCHAEFER GROUP, INC.",02-Central,737301000,3568.41,1931.52,1636.89
"THE SCHAEFER GROUP, INC.",02-Central,734830000,2785.80,544.92,2240.88
"THE SCHAEFER GROUP, INC.",02-Central,739541200,2665.47,643.57,2021.90
"THE SCHAEFER GROUP, INC.",02-Central,764601000,2591.65,554.94,2036.71
"THE SCHAEFER GROUP, INC.",02-Central,734950000,2355.50,628.72,1726.78
"THE SCHAEFER GROUP, INC.",02-Central,788100000,2165.50,539.27,1626.23
"THE SCHAEFER GROUP, INC.",02-Central,737321000,2137.60,428.28,1709.32
"THE SCHAEFER GROUP, INC.",02-Central,734990000,2119.80,410.82,1708.98
"THE SCHAEFER GROUP, INC.",02-Central,764761000,1992.00,382.32,1609.68
"THE SCHAEFER GROUP, INC.",02-Central,777901100,1868.37,982.06,886.31
"THE SCHAEFER GROUP, INC.",02-Central,779001000,1756.23,443.13,1313.10
"THE SCHAEFER GROUP, INC.",02-Central,761071000,1694.86,348.22,1346.64
"THE SCHAEFER GROUP, INC.",02-Central,754565000,1683.36,675.96,1007.40
"THE SCHAEFER GROUP, INC.",02-Central,734211200,1642.39,208.77,1433.62
"THE SCHAEFER GROUP, INC.",02-Central,777590000,1524.42,737.16,787.26
"THE SCHAEFER GROUP, INC.",02-Central,734351200,1488.52,147.24,1341.28
"THE SCHAEFER GROUP, INC.",02-Central,734900000,1334.37,515.22,819.15
"THE SCHAEFER GROUP, INC.",02-Central,770810000,1316.00,38.24,1277.76
"THE SCHAEFER GROUP, INC.",02-Central,739641100,1229.04,806.40,422.64
"THE SCHAEFER GROUP, INC.",02-Central,754905000,1004.76,161.06,843.70
"THE SCHAEFER GROUP, INC.",02-Central,733141300,952.00,139.46,812.54
"THE SCHAEFER GROUP, INC.",02-Central,764482000,889.60,173.30,716.30
"THE SCHAEFER GROUP, INC.",02-Central,770220000,882.00,84.23,797.77
"THE SCHAEFER GROUP, INC.",02-Central,734651200,823.14,148.00,675.14
"THE SCHAEFER GROUP, INC.",02-Central,779201000,810.08,208.20,601.88
"THE SCHAEFER GROUP, INC.",02-Central,733464600,719.00,195.20,523.80
"THE SCHAEFER GROUP, INC.",02-Central,768241000,688.98,119.90,569.08
"THE SCHAEFER GROUP, INC.",02-Central,770290000,675.30,79.65,595.65
"THE SCHAEFER GROUP, INC.",02-Central,770300000,668.26,75.34,592.92
"THE SCHAEFER GROUP, INC.",02-Central,770260000,591.30,67.63,523.67
"THE SCHAEFER GROUP, INC.",02-Central,724261400,467.60,60.94,406.66
"THE SCHAEFER GROUP, INC.",02-Central,741021000,460.22,50.84,409.38
"THE SCHAEFER GROUP, INC.",02-Central,733444200,447.20,77.28,369.92
"THE SCHAEFER GROUP, INC.",02-Central,764341000,371.60,71.96,299.64
"THE SCHAEFER GROUP, INC.",02-Central,733511000,348.75,75.22,273.53
"THE SCHAEFER GROUP, INC.",02-Central,770310000,338.40,47.28,291.12
"THE SCHAEFER GROUP, INC.",02-Central,767990000,334.00,112.06,221.94
"THE SCHAEFER GROUP, INC.",02-Central,733141200,324.00,59.97,264.03
"THE SCHAEFER GROUP, INC.",02-Central,779221000,225.88,58.84,167.04
"THE SCHAEFER GROUP, INC.",02-Central,764861000,146.45,25.35,121.10
"THE SCHAEFER GROUP, INC.",02-Central,737902100,143.45,33.56,109.89
"THE SCHAEFER GROUP, INC.",02-Central,779181000,131.82,41.26,90.56
"THE SCHAEFER GROUP, INC.",02-Central,734414100,118.64,17.93,100.71
THERMAL PRODUCTS INC,02-Central,724003000,624481.00,192940.15,431540.85
THERMAL PRODUCTS INC,02-Central,735602000,613467.50,221246.44,392221.06
THERMAL PRODUCTS INC,02-Central,734990000,514870.40,139607.52,375262.88
THERMAL PRODUCTS INC,02-Central,764901000,361387.50,172172.49,189215.01
THERMAL PRODUCTS INC,02-Central,727063022,319374.80,100067.28,219307.52
THERMAL PRODUCTS INC,02-Central,764821000,313300.50,147395.44,165905.06
THERMAL PRODUCTS INC,02-Central,766542300,263740.80,139310.31,124430.49
THERMAL PRODUCTS INC,02-Central,732820000,253841.28,31236.74,222604.54
THERMAL PRODUCTS INC,02-Central,764901050,224289.50,103121.25,121168.25
THERMAL PRODUCTS INC,02-Central,727070000,214497.60,56115.91,158381.69
THERMAL PRODUCTS INC,02-Central,764521000,192987.75,87110.46,105877.29
THERMAL PRODUCTS INC,02-Central,740361900,191846.40,157548.72,34297.68
THERMAL PRODUCTS INC,02-Central,764781040,181402.50,71109.45,110293.05
THERMAL PRODUCTS INC,02-Central,734351200,176135.65,33516.29,142619.36
THERMAL PRODUCTS INC,02-Central,724007000,158505.09,50879.71,107625.38
THERMAL PRODUCTS INC,02-Central,734173000,157424.50,69485.79,87938.71
THERMAL PRODUCTS INC,02-Central,733403200,121704.00,25345.97,96358.03
THERMAL PRODUCTS INC,02-Central,768231000,110808.00,55491.65,55316.35
THERMAL PRODUCTS INC,02-Central,764221000,107134.00,59227.82,47906.18
THERMAL PRODUCTS INC,02-Central,735602110,106690.00,40147.19,66542.81
THERMAL PRODUCTS INC,02-Central,764906000,103590.00,49288.84,54301.16
THERMAL PRODUCTS INC,02-Central,733525600,86310.00,25232.68,61077.32
THERMAL PRODUCTS INC,02-Central,764862500,85693.00,41586.50,44106.50
THERMAL PRODUCTS INC,02-Central,764345052,83543.50,45394.08,38149.42
THERMAL PRODUCTS INC,02-Central,764822000,79607.00,37703.39,41903.61
THERMAL PRODUCTS INC,02-Central,733586400,75852.00,25537.21,50314.79
THERMAL PRODUCTS INC,02-Central,764802000,66450.00,22271.66,44178.34
THERMAL PRODUCTS INC,02-Central,766121000,61940.50,21819.64,40120.86
THERMAL PRODUCTS INC,02-Central,735750000,61089.60,36669.95,24419.65
THERMAL PRODUCTS INC,02-Central,733202000,59751.20,23294.20,36457.00
THERMAL PRODUCTS INC,02-Central,779987650,57407.04,14745.67,42661.37
THERMAL PRODUCTS INC,02-Central,737017020,53295.00,29872.22,23422.78
THERMAL PRODUCTS INC,02-Central,764762000,49240.00,9553.92,39686.08
THERMAL PRODUCTS INC,02-Central,764781000,49176.00,19626.15,29549.85
THERMAL PRODUCTS INC,02-Central,733586600,47664.00,15234.15,32429.85
THERMAL PRODUCTS INC,02-Central,764761000,38892.00,16666.50,22225.50
THERMAL PRODUCTS INC,02-Central,733201000,38703.00,15282.16,23420.84
THERMAL PRODUCTS INC,02-Central,764602000,36552.00,18542.73,18009.27
THERMAL PRODUCTS INC,02-Central,764342000,34443.00,19067.11,15375.89
THERMAL PRODUCTS INC,02-Central,739561100,33280.80,14929.22,18351.58
THERMAL PRODUCTS INC,02-Central,739741100,32352.96,22012.12,10340.84
THERMAL PRODUCTS INC,02-Central,732771000,30320.64,2899.79,27420.85
THERMAL PRODUCTS INC,02-Central,733151600,27288.00,9058.52,18229.48
THERMAL PRODUCTS INC,02-Central,740402402,27165.00,12342.60,14822.40
THERMAL PRODUCTS INC,02-Central,735752000,26426.40,15697.32,10729.08
THERMAL PRODUCTS INC,02-Central,764522000,26079.00,11619.02,14459.98
THERMAL PRODUCTS INC,02-Central,734311100,26034.32,5779.05,20255.27
THERMAL PRODUCTS INC,02-Central,733211050,25890.00,9877.29,16012.71
THERMAL PRODUCTS INC,02-Central,727000140,25059.60,10766.14,14293.46
THERMAL PRODUCTS INC,02-Central,764262000,25037.00,13346.23,11690.77
THERMAL PRODUCTS INC,02-Central,764261000,24655.00,13289.91,11365.09
THERMAL PRODUCTS INC,02-Central,718501000,24540.00,8163.63,16376.37
THERMAL PRODUCTS INC,02-Central,733151500,23956.00,8073.31,15882.69
THERMAL PRODUCTS INC,02-Central,733211000,23714.00,8672.49,15041.51
THERMAL PRODUCTS INC,02-Central,733586500,22190.40,7358.69,14831.71
THERMAL PRODUCTS INC,02-Central,733464600,19600.00,6409.21,13190.79
THERMAL PRODUCTS INC,02-Central,740283502,19575.60,3064.87,16510.73
THERMAL PRODUCTS INC,02-Central,764526010,18063.40,7303.72,10759.68
THERMAL PRODUCTS INC,02-Central,734211200,17279.16,3699.24,13579.92
THERMAL PRODUCTS INC,02-Central,777601000,16322.64,6549.54,9773.10
THERMAL PRODUCTS INC,02-Central,733403100,14766.20,2743.76,12022.44
THERMAL PRODUCTS INC,02-Central,733403300,14696.00,2889.92,11806.08
THERMAL PRODUCTS INC,02-Central,727063000,13841.20,4335.16,9506.04
THERMAL PRODUCTS INC,02-Central,764482000,12039.00,4876.52,7162.48
THERMAL PRODUCTS INC,02-Central,739781100,11816.64,8754.54,3062.10
THERMAL PRODUCTS INC,02-Central,738521000,11458.56,6885.66,4572.90
THERMAL PRODUCTS INC,02-Central,737015102,10350.00,5949.26,4400.74
THERMAL PRODUCTS INC,02-Central,733444300,9912.00,2703.08,7208.92
THERMAL PRODUCTS INC,02-Central,733721000,9616.00,5169.28,4446.72
THERMAL PRODUCTS INC,02-Central,741101000,9364.78,852.66,8512.12
THERMAL PRODUCTS INC,02-Central,733525500,9112.50,2638.06,6474.44
THERMAL PRODUCTS INC,02-Central,724311400,9072.70,1992.26,7080.44
THERMAL PRODUCTS INC,02-Central,739541100,9072.00,3265.11,5806.89
THERMAL PRODUCTS INC,02-Central,724261200,9036.00,4121.88,4914.12
THERMAL PRODUCTS INC,02-Central,739561200,8706.56,3968.07,4738.49
THERMAL PRODUCTS INC,02-Central,722413100,8483.20,966.96,7516.24
THERMAL PRODUCTS INC,02-Central,734414100,7854.52,1670.94,6183.58
THERMAL PRODUCTS INC,02-Central,734910000,7149.00,3072.00,4077.00
THERMAL PRODUCTS INC,02-Central,740382201,7020.00,4460.49,2559.51
THERMAL PRODUCTS INC,02-Central,793320074,6643.00,4067.70,2575.30
THERMAL PRODUCTS INC,02-Central,779988828,6616.80,4754.88,1861.92
THERMAL PRODUCTS INC,02-Central,733141300,6504.00,2075.99,4428.01
THERMAL PRODUCTS INC,02-Central,733832000,6012.00,3168.82,2843.18
THERMAL PRODUCTS INC,02-Central,764605000,5115.00,2531.34,2583.66
THERMAL PRODUCTS INC,02-Central,777642000,4681.00,1955.74,2725.26
THERMAL PRODUCTS INC,02-Central,739561000,4605.12,2142.14,2462.98
THERMAL PRODUCTS INC,02-Central,733725500,4480.00,2070.56,2409.44
THERMAL PRODUCTS INC,02-Central,730710500,3810.00,1758.69,2051.31
THERMAL PRODUCTS INC,02-Central,733671000,3752.77,584.08,3168.69
THERMAL PRODUCTS INC,02-Central,733505200,3670.00,969.05,2700.95
THERMAL PRODUCTS INC,02-Central,770800000,3408.00,102.17,3305.83
THERMAL PRODUCTS INC,02-Central,732429300,3350.00,1175.13,2174.87
THERMAL PRODUCTS INC,02-Central,777580000,3165.66,206.40,2959.26
THERMAL PRODUCTS INC,02-Central,768211000,2937.60,842.04,2095.56
THERMAL PRODUCTS INC,02-Central,734900000,2885.60,1968.64,916.96
THERMAL PRODUCTS INC,02-Central,733464500,2557.00,795.13,1761.87
THERMAL PRODUCTS INC,02-Central,724311200,2477.45,574.08,1903.37
THERMAL PRODUCTS INC,02-Central,733505300,2460.00,666.03,1793.97
THERMAL PRODUCTS INC,02-Central,764461006,2362.00,1147.88,1214.12
THERMAL PRODUCTS INC,02-Central,733141200,2328.00,641.76,1686.24
THERMAL PRODUCTS INC,02-Central,741061000,2223.25,132.27,2090.98
THERMAL PRODUCTS INC,02-Central,739521100,2123.52,480.24,1643.28
THERMAL PRODUCTS INC,02-Central,733423700,2112.00,525.17,1586.83
THERMAL PRODUCTS INC,02-Central,733423600,1964.80,494.24,1470.56
THERMAL PRODUCTS INC,02-Central,764481000,1903.00,770.75,1132.25
THERMAL PRODUCTS INC,02-Central,733511000,1877.50,659.66,1217.84
THERMAL PRODUCTS INC,02-Central,733484900,1872.00,489.98,1382.02
THERMAL PRODUCTS INC,02-Central,733525400,1847.50,555.03,1292.47
THERMAL PRODUCTS INC,02-Central,733403500,1677.00,472.01,1204.99
THERMAL PRODUCTS INC,02-Central,733490600,1568.00,477.67,1090.33
THERMAL PRODUCTS INC,02-Central,764564000,1568.00,653.90,914.10
THERMAL PRODUCTS INC,02-Central,734454200,1552.60,289.40,1263.20
THERMAL PRODUCTS INC,02-Central,737912200,1515.90,446.29,1069.61
THERMAL PRODUCTS INC,02-Central,793320000,1456.00,825.51,630.49
THERMAL PRODUCTS INC,02-Central,739541000,1342.08,505.24,836.84
THERMAL PRODUCTS INC,02-Central,768221000,1295.00,619.15,675.85
THERMAL PRODUCTS INC,02-Central,734851000,1290.87,274.20,1016.67
THERMAL PRODUCTS INC,02-Central,792624000,1080.00,399.72,680.28
THERMAL PRODUCTS INC,02-Central,777904301,1036.72,469.88,566.84
THERMAL PRODUCTS INC,02-Central,771130000,1030.20,70.35,959.85
THERMAL PRODUCTS INC,02-Central,739541200,803.84,321.78,482.06
THERMAL PRODUCTS INC,02-Central,792620000,786.00,260.22,525.78
THERMAL PRODUCTS INC,02-Central,793330000,760.00,208.12,551.88
THERMAL PRODUCTS INC,02-Central,764902000,695.00,322.08,372.92
THERMAL PRODUCTS INC,02-Central,724281200,680.00,154.62,525.38
THERMAL PRODUCTS INC,02-Central,777585000,674.28,42.69,631.59
THERMAL PRODUCTS INC,02-Central,764861000,654.00,311.73,342.27
THERMAL PRODUCTS INC,02-Central,737902100,573.30,203.22,370.08
THERMAL PRODUCTS INC,02-Central,793260074,529.20,285.81,243.39
THERMAL PRODUCTS INC,02-Central,739450000,401.76,260.24,141.52
THERMAL PRODUCTS INC,02-Central,733444200,320.00,69.38,250.62
THERMAL PRODUCTS INC,02-Central,766162000,234.75,77.76,156.99
THERMAL PRODUCTS INC,02-Central,734162000,228.00,158.40,69.60
THERMAL PRODUCTS INC,02-Central,734970000,217.92,102.61,115.31
THERMAL PRODUCTS INC,02-Central,733510000,210.00,79.51,130.49
THERMAL PRODUCTS INC,02-Central,740361701,208.80,318.65,-109.85
THERMAL STRUCTURES INC.,02-Central,732820000,22105.77,1848.83,20256.94
THERMAL STRUCTURES INC.,02-Central,768144200,4260.00,648.34,3611.66
THERMAL STRUCTURES,02-Central,732820000,33348.01,2625.71,30722.30
THERMAL STRUCTURES,02-Central,732771000,10123.68,774.60,9349.08
THERMAL STRUCTURES,02-Central,792730000,547.40,62.56,484.84
THERMAL STRUCTURES,02-Central,733423700,532.16,72.44,459.72
THERMAL TECHNOLOGY INC,02-Central,735602000,6506.50,2019.01,4487.49
THERMAL TECHNOLOGY INC,02-Central,735752000,1023.00,261.72,761.28
THERMALTEK INC,02-Central,777698063,19502.00,9470.06,10031.94
THERMALTEK INC,02-Central,779988828,9840.00,7098.11,2741.89
THERMALTEK INC,02-Central,779988813,7380.00,4413.78,2966.22
THERMALTEK INC,02-Central,779988846,1934.00,1197.34,736.66
THERMCRAFT INC,02-Central,727000030,116461.80,48224.58,68237.22
THERMCRAFT INC,02-Central,764781040,55282.50,5632.33,49650.17
THERMCRAFT INC,02-Central,727000042,53278.40,21650.32,31628.08
THERMCRAFT INC,02-Central,727063020,35694.00,14374.07,21319.93
THERMCRAFT INC,02-Central,738561000,10627.20,3103.42,7523.78
THERMCRAFT INC,02-Central,738401000,9846.35,5520.24,4326.11
THERMCRAFT INC,02-Central,738381000,5082.06,2302.64,2779.42
THERMCRAFT INC,02-Central,764762000,4687.20,645.09,4042.11
THERMCRAFT INC,02-Central,715000000,4424.00,1221.97,3202.03
THERMCRAFT INC,02-Central,733525500,3891.00,471.29,3419.71
THERMCRAFT INC,02-Central,738441000,2848.07,1568.25,1279.82
THERMCRAFT INC,02-Central,763991000,2170.80,1238.24,932.56
THERMCRAFT INC,02-Central,738361000,1827.36,1235.52,591.84
THERMCRAFT INC,02-Central,777601000,1629.18,320.44,1308.74
THERMCRAFT INC,02-Central,779988828,1555.00,792.48,762.52
THERMCRAFT INC,02-Central,740402301,875.00,219.36,655.64
THERMCRAFT INC,02-Central,740382101,626.40,296.37,330.03
THERMO FISHER SCIENTIFIC,02-Central,715000500,158226.88,39806.76,118420.12
THERMO FISHER SCIENTIFIC,02-Central,764481000,18702.00,4192.42,14509.58
THERMO-TEK LTD.,02-Central,734010000,905.00,180.10,724.90
THIBADO INC.,02-Central,739561200,863.87,149.00,714.87
THOMPSON ENAMEL,02-Central,764262000,865.00,45.31,819.69
THOMPSON IG,02-Central,738361000,1252.08,617.76,634.32
THOMPSON IG,02-Central,738521000,1112.96,597.12,515.84
THOMPSON IG,02-Central,734311100,808.72,70.60,738.12
THOMPSON IG,02-Central,734611100,189.44,27.18,162.26
THORPE PRODUCTS,02-Central,735602000,52544.00,16568.92,35975.08
THORPE PRODUCTS,02-Central,768111220,8532.00,4272.35,4259.65
THORPE PRODUCTS,02-Central,768121012,8112.00,3452.00,4660.00
THORPE PRODUCTS,02-Central,771002000,1468.00,38.59,1429.41
THORPE TECHNOLOGIES INC.,02-Central,770430000,1327.50,88.55,1238.95
THORPE TECHNOLOGIES INC.,02-Central,770340000,1025.25,101.72,923.53
THORPE TECHNOLOGIES INC.,02-Central,770330000,1015.00,96.65,918.35
TIGHITCO INC.,02-Central,791107600,476434.80,247529.26,228905.54
TIGHITCO INC.,02-Central,791107850,260962.02,78958.24,182003.78
TIGHITCO INC.,02-Central,791107200,205808.94,76872.61,128936.33
TIGHITCO INC.,02-Central,724281410,62655.00,29215.97,33439.03
TIGHITCO INC.,02-Central,724261400,30795.00,10489.95,20305.05
TIGHITCO INC.,02-Central,791107400,16957.62,6742.09,10215.53
TIGHITCO INC.,02-Central,724101400,2486.00,932.14,1553.86
TIGHITCO INC.,02-Central,791004100,1734.40,1027.42,706.98
TIGHITCO INC.,02-Central,791104675,1153.67,480.25,673.42
TIGHITCO INC.,02-Central,724311400,622.00,376.76,245.24
TIGHITCO INC.,02-Central,733211000,327.50,47.39,280.11
TIGHITCO INC.,02-Central,792702000,303.75,96.60,207.15
TINKER OMEGA,02-Central,734311100,404.36,35.30,369.06
TINKER OMEGA,02-Central,734211200,243.46,23.38,220.08
TOLEDO ENGINEERING CO INC,02-Central,777761000,810.00,658.80,151.20
TOLEDO ENGINEERING CO INC,02-Central,792620000,427.60,86.74,340.86
TOLEDO ENGINEERING CO INC,02-Central,793330000,230.00,52.03,177.97
TOSOH SMD,02-Central,766121000,2625.00,81.75,2543.25
TRACHTE BUILDING SYSTEMS,02-Central,764601010,139008.80,25897.62,113111.18
TRACHTE BUILDING SYSTEMS,02-Central,764601000,34479.48,7301.95,27177.53
TRAEGERBROTHERS,02-Central,764861000,9412.00,659.08,8752.92
TRAEGERBROTHERS,02-Central,737912200,6909.00,1273.26,5635.74
TRAEGERBROTHERS,02-Central,734711100,308.92,54.10,254.82
TRANSCO PRODUCTS INC,02-Central,739741100,4530.24,1205.14,3325.10
TRANSCO PRODUCTS INC,02-Central,732429300,1310.00,94.02,1215.98
TRANSCO PRODUCTS INC,02-Central,793330000,863.00,52.02,810.98
TRANSCO PRODUCTS INC,02-Central,793346074,474.00,74.14,399.86
TREMCO INC,02-Central,768217310,17000.00,2874.46,14125.54
TREMCO INC,02-Central,768217320,17000.00,4113.35,12886.65
TRIDENT FLUID POWER,02-Central,739761100,464.16,207.60,256.56
TRIPLEX INC,02-Central,732820000,3669.12,168.08,3501.04
TRIPLEX INC,02-Central,766081010,3347.00,105.39,3241.61
TRIPLEX INC,02-Central,770580000,1598.00,89.66,1508.34
TRISTATE INDUSTRIAL,02-Central,739561100,3870.38,773.11,3097.27
TRISTATE INDUSTRIAL,02-Central,734910000,2998.80,626.60,2372.20
TRISTATE INDUSTRIAL,02-Central,764262000,1230.40,135.24,1095.16
TRISTATE INDUSTRIAL,02-Central,738561000,802.94,248.27,554.67
TRISTATE INDUSTRIAL,02-Central,733671000,722.74,119.66,603.08
TRISTATE INDUSTRIAL,02-Central,777601000,543.30,144.04,399.26
TROXLER ELECTRONIC,02-Central,734311100,381.48,33.70,347.78
TWIN COMMANDER AIRCRAFT,02-Central,770200000,666.00,42.41,623.59
U S DEPARTMENT OF ENERGY,02-Central,764861000,1923.00,76.05,1846.95
U S MARINE CORPS.,02-Central,777601000,386.72,72.02,314.70
UNIMIN CORP.,02-Central,754465000,6249.54,1364.71,4884.83
UNIMIN CORP.,02-Central,754045000,3950.64,1096.89,2853.75
UNION TANK CAR,02-Central,764643054,4467.24,2084.66,2382.58
UNION TANK CAR,02-Central,764643052,3235.95,1501.92,1734.03
UNITED SEAL & RUBBER CO,02-Central,733403300,2323.20,215.46,2107.74
UNIV. of  NORTH DAKOTA,02-Central,764261000,424.00,22.90,401.10
UNIVERSAL DYNAMICS INC.,02-Central,733741000,816.00,238.13,577.87
UNIVERSAL REFRACTORIES,02-Central,726000240,126770.40,46592.78,80177.62
UNIVERSAL REFRACTORIES,02-Central,726020005,110613.60,26962.98,83650.62
UNIVERSITY ILLINOIS,02-Central,738561000,582.40,124.14,458.26
UNIVERSITY OF ALABAMA,02-Central,792730000,547.40,62.56,484.84
UNIVERSITY OF FLORIDA,02-Central,764762000,508.00,30.29,477.71
UNIVERSITY OF MINN,02-Central,764601000,326.00,13.29,312.71
UNIVERSITY OF NOTRE DAME,02-Central,764521000,320.00,13.30,306.70
UNIVERSITY OF WISCONSIN,02-Central,793260074,328.50,39.17,289.33
UPSTATE REFRACTORY SERVIC,02-Central,777601000,223.71,72.02,151.69
VACUFORM INC.,02-Central,726000062,1211838.00,944838.76,266999.24
VACUFORM INC.,02-Central,727063007,300081.00,221460.21,78620.79
VACUFORM INC.,02-Central,764521000,133222.00,54468.93,78753.07
VACUFORM INC.,02-Central,768022000,115520.00,64239.41,51280.59
VACUFORM INC.,02-Central,764341000,66602.00,37208.36,29393.64
VACUFORM INC.,02-Central,727000060,62880.00,52418.38,10461.62
VACUFORM INC.,02-Central,793000000,43274.40,21804.26,21470.14
VACUFORM INC.,02-Central,764347010,38884.00,18700.95,20183.05
VACUFORM INC.,02-Central,764521073,29348.00,28380.00,968.00
VACUFORM INC.,02-Central,764521061,21344.00,20448.00,896.00
VACUFORM INC.,02-Central,739951051,17157.92,12256.36,4901.56
VACUFORM INC.,02-Central,766166000,9840.00,4449.48,5390.52
VACUFORM INC.,02-Central,764481000,6480.00,3596.55,2883.45
VACUFORM INC.,02-Central,726000240,2700.00,2091.85,608.15
VACUFORM INC.,02-Central,764342000,2656.00,1489.52,1166.48
VACUFORM INC.,02-Central,734851000,1352.33,570.90,781.43
VACUFORM INC.,02-Central,766122000,1086.00,345.15,740.85
VACUFORM INC.,02-Central,768211000,500.00,467.78,32.22
VACUFORM INC.,02-Central,764522000,200.10,80.16,119.94
VACUUM OVEN,02-Central,764481000,346.00,16.94,329.06
VAN PACKER COMPANY,02-Central,768540000,9055.20,2565.63,6489.57
VAN PACKER COMPANY,02-Central,768530000,8962.20,2562.32,6399.88
VAN PACKER COMPANY,02-Central,768511000,8940.00,2563.05,6376.95
VAN PACKER COMPANY,02-Central,764522000,666.00,26.72,639.28
VAN PACKER COMPANY,02-Central,779900912,232.00,0.00,232.00
VAN PACKER COMPANY,02-Central,779900911,116.00,0.00,116.00
VEITSCH-RADEX AMERICA INC,02-Central,733182300,99316.80,34104.20,65212.60
VEITSCH-RADEX AMERICA INC,02-Central,764821000,819.00,322.51,496.49
VEITSCH-RADEX AMERICA INC,02-Central,764781000,318.00,118.32,199.68
VESUVIUS USA,02-Central,726020033,1841764.80,1483084.98,358679.82
VESUVIUS USA,02-Central,726020009,1061860.80,875649.81,186210.99
VESUVIUS USA,02-Central,726020023,225735.60,179382.66,46352.94
VESUVIUS USA,02-Central,726000240,108527.04,75478.27,33048.77
VESUVIUS USA,02-Central,793000000,21572.40,11281.38,10291.02
VESUVIUS USA,02-Central,777904402,1098.00,745.52,352.48
VESUVIUS USA,02-Central,793060300,392.40,198.83,193.57
VIRGINIA TECH,02-Central,733464500,298.00,29.52,268.48
VIRGINIA-CAROLINA REF INC,02-Central,762134000,232819.16,99634.79,133184.37
VIRGINIA-CAROLINA REF INC,02-Central,762114000,36000.00,17985.68,18014.32
VIRGINIA-CAROLINA REF INC,02-Central,718500000,4114.56,1288.26,2826.30
VIRGINIA-CAROLINA REF INC,02-Central,764821000,115.00,30.72,84.28
VSP TECHNOLOGIES,02-Central,770280000,4043.75,310.89,3732.86
WAHL REFRACTORIES,02-Central,733510000,9456.00,3401.46,6054.54
WAHL REFRACTORIES,02-Central,777901100,5280.24,1870.60,3409.64
WAHL REFRACTORIES,02-Central,777902300,2640.12,982.01,1658.11
WAHL REFRACTORIES,02-Central,733525500,1319.50,130.06,1189.44
WATCO MECHANICAL SERVICES,02-Central,764643054,30406.32,3713.98,26692.34
WATCO MECHANICAL SERVICES,02-Central,764643100,3049.20,1059.90,1989.30
WATCO MECHANICAL,02-Central,764643054,5715.36,698.28,5017.08
WATCO MECHANICAL,02-Central,764706120,2222.64,326.91,1895.73
WATCO MECHANICAL,02-Central,764643052,1372.14,166.88,1205.26
"WELCO ELECTRONICS, INC.",02-Central,770720000,3528.00,108.47,3419.53
"WELCO ELECTRONICS, INC.",02-Central,771500000,2911.50,165.39,2746.11
WESCO DISTRIBUTION INC.,02-Central,734890000,2769.00,623.07,2145.93
WESCO DISTRIBUTION INC.,02-Central,768222000,1610.00,139.92,1470.08
WESTERN INDUSTRIAL CERAMI,02-Central,739741100,16803.84,5258.78,11545.06
WESTERN INDUSTRIAL CERAMI,02-Central,780650000,3682.80,1607.94,2074.86
WESTERN INDUSTRIAL CERAMI,02-Central,780630000,3192.28,1541.26,1651.02
WESTERN INDUSTRIAL CERAMI,02-Central,767990000,1848.96,679.42,1169.54
WESTERN INDUSTRIAL CERAMI,02-Central,770810000,657.90,19.12,638.78
WESTERN INDUSTRIAL CERAMI,02-Central,734211200,371.99,46.75,325.24
WESTERN INDUSTRIAL CERAMI,02-Central,764902000,119.00,63.86,55.14
WFS LTD.,02-Central,766121000,1750.00,54.50,1695.50
WFS LTD.,02-Central,734910000,1748.50,260.23,1488.27
WFS LTD.,02-Central,733151500,450.00,47.69,402.31
"WHETSTONE TECHNOLOGY, LLC",02-Central,718501000,1768.00,269.38,1498.62
WHIPMIX,02-Central,777642000,135.00,31.68,103.32
WICKS AIRCRAFT SUPPLY,02-Central,733525500,752.50,70.39,682.11
WILLIAM BONNELL CO. INC.,02-Central,733464500,596.00,59.04,536.96
WILLIAMS INDUSTRIAL SERVI,02-Central,754087000,57127.92,26276.42,30851.50
WILLIAMS INDUSTRIAL SERVI,02-Central,754067000,24933.00,12524.03,12408.97
WILLIAMS INDUSTRIAL SERVI,02-Central,754327000,21562.23,9517.71,12044.52
WILLIAMS INDUSTRIAL SERVI,02-Central,779957815,20976.24,10411.72,10564.52
WILLIAMS INDUSTRIAL SERVI,02-Central,754107000,20880.00,8799.58,12080.42
WILLIAMS INDUSTRIAL SERVI,02-Central,754127000,12216.36,5808.00,6408.36
WILLIAMS INDUSTRIAL SERVI,02-Central,754077000,10839.00,5138.53,5700.47
WILLIAMS INDUSTRIAL SERVI,02-Central,770372000,10796.80,706.61,10090.19
WILLIAMS INDUSTRIAL SERVI,02-Central,764821000,8520.75,923.88,7596.87
WILLIAMS INDUSTRIAL SERVI,02-Central,742641500,8120.00,1207.02,6912.98
WILLIAMS INDUSTRIAL SERVI,02-Central,764901000,6840.00,774.92,6065.08
WILLIAMS INDUSTRIAL SERVI,02-Central,770510000,6008.75,372.50,5636.25
WILLIAMS INDUSTRIAL SERVI,02-Central,733403200,5672.70,627.19,5045.51
WILLIAMS INDUSTRIAL SERVI,02-Central,770380000,5349.60,379.43,4970.17
WILLIAMS INDUSTRIAL SERVI,02-Central,770600000,5275.56,367.58,4907.98
WILLIAMS INDUSTRIAL SERVI,02-Central,754317000,4712.40,2018.90,2693.50
WILLIAMS INDUSTRIAL SERVI,02-Central,734851000,4675.56,1096.78,3578.78
WILLIAMS INDUSTRIAL SERVI,02-Central,754307000,4343.72,1976.52,2367.20
WILLIAMS INDUSTRIAL SERVI,02-Central,739681200,4004.64,1516.92,2487.72
WILLIAMS INDUSTRIAL SERVI,02-Central,754287000,3971.20,1669.96,2301.24
WILLIAMS INDUSTRIAL SERVI,02-Central,779951269,3450.48,1685.00,1765.48
WILLIAMS INDUSTRIAL SERVI,02-Central,754097000,3422.72,1559.05,1863.67
WILLIAMS INDUSTRIAL SERVI,02-Central,727000025,3417.00,438.56,2978.44
WILLIAMS INDUSTRIAL SERVI,02-Central,770560000,3286.20,261.68,3024.52
WILLIAMS INDUSTRIAL SERVI,02-Central,779957702,3153.92,2359.60,794.32
WILLIAMS INDUSTRIAL SERVI,02-Central,770580000,2228.85,174.39,2054.46
WILLIAMS INDUSTRIAL SERVI,02-Central,739561000,2052.00,511.32,1540.68
WILLIAMS INDUSTRIAL SERVI,02-Central,754267000,1756.80,809.76,947.04
WILLIAMS INDUSTRIAL SERVI,02-Central,753987000,1620.96,866.36,754.60
WILLIAMS INDUSTRIAL SERVI,02-Central,734611100,1552.02,350.48,1201.54
WILLIAMS INDUSTRIAL SERVI,02-Central,754047000,1431.00,876.14,554.86
WILLIAMS INDUSTRIAL SERVI,02-Central,770540000,1264.38,89.80,1174.58
WILLIAMS INDUSTRIAL SERVI,02-Central,779957703,1150.60,781.22,369.38
WILLIAMS INDUSTRIAL SERVI,02-Central,739564300,1066.82,247.97,818.85
WILLIAMS INDUSTRIAL SERVI,02-Central,733403100,999.75,122.60,877.15
WILLIAMS INDUSTRIAL SERVI,02-Central,753997000,933.52,417.54,515.98
WILLIAMS INDUSTRIAL SERVI,02-Central,779958694,850.40,412.57,437.83
WILLIAMS INDUSTRIAL SERVI,02-Central,754297000,771.60,313.46,458.14
WILLIAMS INDUSTRIAL SERVI,02-Central,727000140,726.00,83.57,642.43
WILLIAMS INDUSTRIAL SERVI,02-Central,754187000,589.44,288.50,300.94
WILLIAMS INDUSTRIAL SERVI,02-Central,770210000,560.00,60.64,499.36
WILLIAMS INDUSTRIAL SERVI,02-Central,764521000,319.50,39.81,279.69
WILLIAMS INDUSTRIAL SERVI,02-Central,770220000,303.60,27.35,276.25
WILLIAMS INDUSTRIAL SERVI,02-Central,734651200,198.50,37.00,161.50
WILLIAMS INDUSTRIAL SERVI,02-Central,754247000,164.16,91.37,72.79
WILLIAMS INDUSTRIAL SERVI,02-Central,754197000,159.68,70.99,88.69
WILLIAMS INDUSTRIAL SERVI,02-Central,753967000,158.76,78.39,80.37
WISCONSIN ALUMINUM,02-Central,777601000,1980.20,333.65,1646.55
WOLSELEY INDUSTRIAL GROUP,02-Central,770200000,2979.00,210.45,2768.55
WOLSELEY INDUSTRIAL GROUP,02-Central,737871000,2937.12,279.74,2657.38
WOLSELEY INDUSTRIAL GROUP,02-Central,739541000,587.52,90.50,497.02
WOLSELEY INDUSTRIAL GROUP,02-Central,739561000,492.48,86.19,406.29
WOLSELEY INDUSTRIAL GROUP,02-Central,739450000,482.04,125.58,356.46
"WOODSAGE INDUSTRIES, LLC",02-Central,771170000,3900.00,1094.68,2805.32
"WOODSAGE INDUSTRIES, LLC",02-Central,771172000,600.00,167.93,432.07
XPEDX,02-Central,740283502,13134.00,1989.86,11144.14
ZAR-TECH,02-Central,733151500,3766.00,667.69,3098.31
ZAR-TECH,02-Central,764902000,1336.50,483.12,853.38
ZAR-TECH,02-Central,740382101,871.20,505.66,365.54
HARBISON-WALKER REFR.,05-House,764821020,232020.00,113512.95,118507.05
HARBISON-WALKER REFR.,05-House,779989340,220320.00,153833.40,66486.60
HARBISON-WALKER REFR.,05-House,764901056,204078.00,102671.60,101406.40
HARBISON-WALKER REFR.,05-House,764521012,134010.00,73994.81,60015.19
HARBISON-WALKER REFR.,05-House,766006116,116046.01,73307.06,42738.95
HARBISON-WALKER REFR.,05-House,764864020,111888.00,59648.16,52239.84
HARBISON-WALKER REFR.,05-House,764605012,76356.00,40571.52,35784.48
HARBISON-WALKER REFR.,05-House,764901012,70625.00,35748.52,34876.48
HARBISON-WALKER REFR.,05-House,764821400,67442.40,33444.68,33997.72
HARBISON-WALKER REFR.,05-House,768242510,55796.00,24223.51,31572.49
HARBISON-WALKER REFR.,05-House,766122500,54051.84,34642.22,19409.62
HARBISON-WALKER REFR.,05-House,764906200,48816.00,24365.50,24450.50
HARBISON-WALKER REFR.,05-House,768221012,35085.00,15599.51,19485.49
HARBISON-WALKER REFR.,05-House,764528300,32901.12,17852.00,15049.12
HARBISON-WALKER REFR.,05-House,764785122,32490.00,20091.24,12398.76
HARBISON-WALKER REFR.,05-House,766001200,31320.00,24214.68,7105.32
HARBISON-WALKER REFR.,05-House,779989336,29664.00,25212.44,4451.56
HARBISON-WALKER REFR.,05-House,764601012,29325.00,15791.86,13533.14
HARBISON-WALKER REFR.,05-House,764349000,26180.00,16054.61,10125.39
HARBISON-WALKER REFR.,05-House,764261120,23870.00,13806.62,10063.38
HARBISON-WALKER REFR.,05-House,764829220,21888.00,10783.29,11104.71
HARBISON-WALKER REFR.,05-House,764901050,21696.00,12130.39,9565.61
HARBISON-WALKER REFR.,05-House,764561020,19392.00,9790.40,9601.60
HARBISON-WALKER REFR.,05-House,764345061,15912.00,9047.17,6864.83
HARBISON-WALKER REFR.,05-House,764602512,15300.00,8117.31,7182.69
HARBISON-WALKER REFR.,05-House,764901000,15255.00,8647.70,6607.30
HARBISON-WALKER REFR.,05-House,768121010,13370.00,5329.90,8040.10
HARBISON-WALKER REFR.,05-House,764862600,12144.00,5758.99,6385.01
HARBISON-WALKER REFR.,05-House,764521000,11340.00,7166.37,4173.63
HARBISON-WALKER REFR.,05-House,764481220,10950.00,7623.68,3326.32
HARBISON-WALKER REFR.,05-House,766046000,9828.00,5618.93,4209.07
HARBISON-WALKER REFR.,05-House,766162300,9100.00,6294.53,2805.47
HARBISON-WALKER REFR.,05-House,779989420,8280.00,5396.86,2883.14
HARBISON-WALKER REFR.,05-House,764862400,8064.00,4266.45,3797.55
HARBISON-WALKER REFR.,05-House,764822000,7353.00,4098.36,3254.64
HARBISON-WALKER REFR.,05-House,764781300,5544.00,3469.65,2074.35
HARBISON-WALKER REFR.,05-House,764563100,5328.00,2482.70,2845.30
HARBISON-WALKER REFR.,05-House,764902301,4972.00,2541.80,2430.20
HARBISON-WALKER REFR.,05-House,764785130,4480.00,2760.76,1719.24
HARBISON-WALKER REFR.,05-House,764821300,3420.00,1682.88,1737.12
HARBISON-WALKER REFR.,05-House,766081200,3200.00,1519.32,1680.68
HARBISON-WALKER REFR.,05-House,764803000,3150.00,1661.96,1488.04
HARBISON-WALKER REFR.,05-House,764861300,2520.00,1352.90,1167.10
HARBISON-WALKER REFR.,05-House,766081016,2304.00,1092.47,1211.53
HARBISON-WALKER REFR.,05-House,768211000,70.00,23.39,46.61
HARBISON-WALKER REFR.,05-House,768111000,44.00,15.38,28.62
A. R. THOMSON LTD.,07-Canada Direct,770210000,460.00,31.52,428.48
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754864016,491413.86,377394.44,114019.42
ALLIANCE REFRACTORIES LTD,07-Canada Direct,768221000,411732.00,159990.19,251741.81
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958212,200475.00,96348.62,104126.38
ALLIANCE REFRACTORIES LTD,07-Canada Direct,766121000,199800.00,116694.75,83105.25
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754844006,192535.20,133772.19,58763.01
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764821000,109520.00,45851.87,63668.13
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764901000,109520.00,47651.16,61868.84
ALLIANCE REFRACTORIES LTD,07-Canada Direct,793330000,98919.00,19866.01,79052.99
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779957821,83174.40,53602.44,29571.96
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754884006,72111.60,43737.31,28374.29
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764601000,67160.00,32320.98,34839.02
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754844005,62762.70,43166.04,19596.66
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754904006,53410.50,27415.80,25994.70
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959405,43036.00,44094.36,-1058.36
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958205,37350.00,18889.20,18460.80
ALLIANCE REFRACTORIES LTD,07-Canada Direct,718500000,34160.64,10960.16,23200.48
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771500000,29436.75,3612.13,25824.62
ALLIANCE REFRACTORIES LTD,07-Canada Direct,718501000,28288.00,8642.05,19645.95
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959402,27993.60,27528.60,465.00
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733211000,27060.00,10658.09,16401.91
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958200,26298.00,11825.50,14472.50
ALLIANCE REFRACTORIES LTD,07-Canada Direct,754924013,23424.00,14577.03,8846.97
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764901073,20720.00,20412.00,308.00
ALLIANCE REFRACTORIES LTD,07-Canada Direct,795551000,20240.00,14233.08,6006.92
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959403,19640.00,16458.84,3181.16
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779980291,18101.16,13629.55,4471.61
ALLIANCE REFRACTORIES LTD,07-Canada Direct,766081010,17920.00,9610.09,8309.91
ALLIANCE REFRACTORIES LTD,07-Canada Direct,768041000,16520.00,6998.83,9521.17
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764481000,13068.00,6871.85,6196.15
ALLIANCE REFRACTORIES LTD,07-Canada Direct,767970140,11875.00,4612.36,7262.64
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779957176,11815.20,10542.14,1273.06
ALLIANCE REFRACTORIES LTD,07-Canada Direct,768231000,10956.00,3481.07,7474.93
ALLIANCE REFRACTORIES LTD,07-Canada Direct,718551000,10608.00,3265.44,7342.56
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764221000,9720.00,6071.86,3648.14
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959412,9540.00,4508.63,5031.37
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771404000,9192.75,558.12,8634.63
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764821073,8880.00,8748.00,132.00
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959406,8808.80,9123.56,-314.76
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779957053,8800.00,6134.68,2665.32
ALLIANCE REFRACTORIES LTD,07-Canada Direct,793260074,7960.00,3133.48,4826.52
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779956945,6612.00,5187.55,1424.45
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764521000,6600.00,3191.29,3408.71
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779957820,5856.00,4560.16,1295.84
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958210,5662.80,4760.15,902.65
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771502000,5607.00,687.06,4919.94
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779959401,5077.44,4837.88,239.56
ALLIANCE REFRACTORIES LTD,07-Canada Direct,727000025,4789.50,2901.73,1887.77
ALLIANCE REFRACTORIES LTD,07-Canada Direct,768121000,4740.00,1761.36,2978.64
ALLIANCE REFRACTORIES LTD,07-Canada Direct,734651200,3972.00,1763.90,2208.10
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958211,3938.40,3574.84,363.56
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733464500,3920.00,1180.77,2739.23
ALLIANCE REFRACTORIES LTD,07-Canada Direct,776131000,2860.00,2602.20,257.80
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779957054,2700.00,1919.82,780.18
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779956598,2643.84,1719.54,924.30
ALLIANCE REFRACTORIES LTD,07-Canada Direct,779958201,2629.60,1374.32,1255.28
ALLIANCE REFRACTORIES LTD,07-Canada Direct,770722000,2476.00,171.52,2304.48
ALLIANCE REFRACTORIES LTD,07-Canada Direct,734311100,2432.40,529.52,1902.88
ALLIANCE REFRACTORIES LTD,07-Canada Direct,777601000,1999.84,1072.97,926.87
ALLIANCE REFRACTORIES LTD,07-Canada Direct,777642000,1984.00,1013.44,970.56
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764341000,1640.00,917.40,722.60
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771152000,1291.00,90.76,1200.24
ALLIANCE REFRACTORIES LTD,07-Canada Direct,770850000,1238.00,94.48,1143.52
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771000000,1232.00,82.45,1149.55
ALLIANCE REFRACTORIES LTD,07-Canada Direct,776101000,1180.00,914.20,265.80
ALLIANCE REFRACTORIES LTD,07-Canada Direct,734910000,1110.00,513.48,596.52
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764861000,1070.00,507.00,563.00
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771100000,719.00,44.75,674.25
ALLIANCE REFRACTORIES LTD,07-Canada Direct,771102000,719.00,45.96,673.04
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764762000,672.00,363.51,308.49
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733211050,492.00,198.78,293.22
ALLIANCE REFRACTORIES LTD,07-Canada Direct,734890000,453.56,198.96,254.60
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733423700,249.60,72.44,177.16
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733444200,204.00,69.38,134.62
ALLIANCE REFRACTORIES LTD,07-Canada Direct,764342000,168.00,91.56,76.44
ALLIANCE REFRACTORIES LTD,07-Canada Direct,733742000,136.00,81.75,54.25
ALSIPS BUILDING PRODUCTS,07-Canada Direct,735602000,63680.00,10981.19,52698.81
ALSIPS BUILDING PRODUCTS,07-Canada Direct,735890000,20700.00,4773.62,15926.38
AMERICAN RAILCAR,07-Canada Direct,764706120,2346.12,588.90,1757.22
APPLIED INDUSTRIAL,07-Canada Direct,777601000,386.72,72.02,314.70
ASHCON,07-Canada Direct,777601000,3726.60,587.95,3138.65
ATLAS FLUID,07-Canada Direct,739541100,685.44,104.83,580.61
AVANTI AEROSPACE &,07-Canada Direct,770830000,2122.00,71.74,2050.26
AVANTI AEROSPACE &,07-Canada Direct,724003000,1391.00,163.64,1227.36
AVANTI AEROSPACE &,07-Canada Direct,734311100,404.36,35.30,369.06
AVMAX SPARES,07-Canada Direct,724121200,334.00,89.77,244.23
BROCK WHITE CONSTRUCTION,07-Canada Direct,735890000,287.50,49.73,237.77
BROCKWHITE PRINCE GEORGE,07-Canada Direct,764821000,2136.00,375.72,1760.28
BROCKWHITE PRINCE GEORGE,07-Canada Direct,764901000,1408.00,256.23,1151.77
CAMBRIDGE PRO FAB INC.,07-Canada Direct,768251210,212672.25,115835.03,96837.22
CAMBRIDGE PRO FAB INC.,07-Canada Direct,768141000,65855.00,32697.61,33157.39
CAMBRIDGE PRO FAB INC.,07-Canada Direct,766594001,12430.00,6333.51,6096.49
CAMBRIDGE PRO FAB INC.,07-Canada Direct,764901000,11016.00,5188.62,5827.38
CAMBRIDGE PRO FAB INC.,07-Canada Direct,766594000,7458.00,4180.84,3277.16
CAMBRIDGE PRO FAB INC.,07-Canada Direct,766481000,3000.00,1559.97,1440.03
CENTURYVALLEN,07-Canada Direct,734311100,381.48,33.70,347.78
CLAYBURN REFRACTORIES LTD,07-Canada Direct,779958209,26785.30,6696.46,20088.84
FIBRECAST INC.,07-Canada Direct,792700000,647192.00,411296.51,235895.49
FIBRECAST INC.,07-Canada Direct,726000240,412201.20,211353.44,200847.76
FIBRECAST INC.,07-Canada Direct,726020019,260201.00,129375.64,130825.36
FIBRECAST INC.,07-Canada Direct,764821000,175854.00,68816.51,107037.49
FIBRECAST INC.,07-Canada Direct,764901050,153022.00,62219.46,90802.54
FIBRECAST INC.,07-Canada Direct,726020015,143592.00,73663.82,69928.18
FIBRECAST INC.,07-Canada Direct,754447000,131349.60,53512.89,77836.71
FIBRECAST INC.,07-Canada Direct,768221000,107836.50,39985.49,67851.01
FIBRECAST INC.,07-Canada Direct,715000000,89236.25,38120.79,51115.46
FIBRECAST INC.,07-Canada Direct,763991000,79271.20,72806.81,6464.39
FIBRECAST INC.,07-Canada Direct,768021000,77606.00,37999.67,39606.33
FIBRECAST INC.,07-Canada Direct,768141000,66240.00,27006.99,39233.01
FIBRECAST INC.,07-Canada Direct,733211000,58636.00,22105.42,36530.58
FIBRECAST INC.,07-Canada Direct,764521000,52001.50,21780.18,30221.32
FIBRECAST INC.,07-Canada Direct,768751000,42672.40,21585.13,21087.27
FIBRECAST INC.,07-Canada Direct,793330000,41661.00,9061.50,32599.50
FIBRECAST INC.,07-Canada Direct,764822000,40047.00,15897.88,24149.12
FIBRECAST INC.,07-Canada Direct,764901000,39687.00,16589.84,23097.16
FIBRECAST INC.,07-Canada Direct,740063000,37100.28,14199.26,22901.02
FIBRECAST INC.,07-Canada Direct,768241000,35130.50,10583.13,24547.37
FIBRECAST INC.,07-Canada Direct,777902700,34790.14,15927.42,18862.72
FIBRECAST INC.,07-Canada Direct,733383500,34324.40,6235.97,28088.43
FIBRECAST INC.,07-Canada Direct,733444210,33180.00,7791.53,25388.47
FIBRECAST INC.,07-Canada Direct,777601000,32561.81,11606.94,20954.87
FIBRECAST INC.,07-Canada Direct,734910000,32455.50,2124.40,30331.10
FIBRECAST INC.,07-Canada Direct,764481000,31662.00,15427.61,16234.39
FIBRECAST INC.,07-Canada Direct,792062000,31290.08,16393.94,14896.14
FIBRECAST INC.,07-Canada Direct,730710500,26862.50,11292.77,15569.73
FIBRECAST INC.,07-Canada Direct,733211050,26280.00,10374.25,15905.75
FIBRECAST INC.,07-Canada Direct,792735000,25609.51,11117.12,14492.39
FIBRECAST INC.,07-Canada Direct,733151500,24612.00,7615.62,16996.38
FIBRECAST INC.,07-Canada Direct,768121000,24540.00,11060.97,13479.03
FIBRECAST INC.,07-Canada Direct,727000140,24397.20,12993.21,11403.99
FIBRECAST INC.,07-Canada Direct,768211000,23922.00,666.96,23255.04
FIBRECAST INC.,07-Canada Direct,733151600,22326.00,6765.41,15560.59
FIBRECAST INC.,07-Canada Direct,733464510,21410.00,4338.63,17071.37
FIBRECAST INC.,07-Canada Direct,764341000,20298.00,10501.91,9796.09
FIBRECAST INC.,07-Canada Direct,768251210,19657.00,9854.86,9802.14
FIBRECAST INC.,07-Canada Direct,766121000,19625.00,7175.97,12449.03
FIBRECAST INC.,07-Canada Direct,764601000,18550.50,7611.65,10938.85
FIBRECAST INC.,07-Canada Direct,720290010,18510.00,9959.26,8550.74
FIBRECAST INC.,07-Canada Direct,734351200,18152.70,2115.23,16037.47
FIBRECAST INC.,07-Canada Direct,734990000,17952.00,82.95,17869.05
FIBRECAST INC.,07-Canada Direct,768750000,17172.00,9869.56,7302.44
FIBRECAST INC.,07-Canada Direct,754867000,16170.90,4963.92,11206.98
FIBRECAST INC.,07-Canada Direct,779951110,16140.60,4350.52,11790.08
FIBRECAST INC.,07-Canada Direct,764522000,15785.00,6580.39,9204.61
FIBRECAST INC.,07-Canada Direct,764781000,15507.00,7407.42,8099.58
FIBRECAST INC.,07-Canada Direct,734173000,14894.50,5921.36,8973.14
FIBRECAST INC.,07-Canada Direct,734311100,14757.00,2193.78,12563.22
FIBRECAST INC.,07-Canada Direct,733423710,13990.40,3665.51,10324.89
FIBRECAST INC.,07-Canada Direct,770830000,13489.00,526.55,12962.45
FIBRECAST INC.,07-Canada Direct,777902610,13350.84,6325.54,7025.30
FIBRECAST INC.,07-Canada Direct,779988283,11700.00,6950.84,4749.16
FIBRECAST INC.,07-Canada Direct,764342000,10630.00,5326.08,5303.92
FIBRECAST INC.,07-Canada Direct,764605000,9814.00,3865.77,5948.23
FIBRECAST INC.,07-Canada Direct,768031000,9570.00,4616.31,4953.69
FIBRECAST INC.,07-Canada Direct,733725500,9040.00,4077.58,4962.42
FIBRECAST INC.,07-Canada Direct,733464500,8683.00,1710.77,6972.23
FIBRECAST INC.,07-Canada Direct,728030000,8294.40,21393.71,-13099.31
FIBRECAST INC.,07-Canada Direct,779957925,7337.20,4690.84,2646.36
FIBRECAST INC.,07-Canada Direct,764261000,6907.00,3332.45,3574.55
FIBRECAST INC.,07-Canada Direct,793060000,6793.20,1812.60,4980.60
FIBRECAST INC.,07-Canada Direct,770220000,6426.00,1455.79,4970.21
FIBRECAST INC.,07-Canada Direct,732429400,6415.00,2305.22,4109.78
FIBRECAST INC.,07-Canada Direct,777902600,6387.22,2740.88,3646.34
FIBRECAST INC.,07-Canada Direct,734211200,5930.08,780.79,5149.29
FIBRECAST INC.,07-Canada Direct,764781040,5783.00,2807.22,2975.78
FIBRECAST INC.,07-Canada Direct,734830000,5437.50,1130.65,4306.85
FIBRECAST INC.,07-Canada Direct,733403230,4917.00,662.14,4254.86
FIBRECAST INC.,07-Canada Direct,770510000,4862.50,888.60,3973.90
FIBRECAST INC.,07-Canada Direct,733464400,4827.00,1327.68,3499.32
FIBRECAST INC.,07-Canada Direct,739454500,4569.60,3383.32,1186.28
FIBRECAST INC.,07-Canada Direct,740123000,4452.00,1280.10,3171.90
FIBRECAST INC.,07-Canada Direct,733423720,4211.20,1048.21,3162.99
FIBRECAST INC.,07-Canada Direct,733141200,3908.00,1089.60,2818.40
FIBRECAST INC.,07-Canada Direct,771100000,3651.00,146.94,3504.06
FIBRECAST INC.,07-Canada Direct,766122000,3562.00,1495.64,2066.36
FIBRECAST INC.,07-Canada Direct,764864000,3470.00,1466.67,2003.33
FIBRECAST INC.,07-Canada Direct,766162000,3466.00,1777.02,1688.98
FIBRECAST INC.,07-Canada Direct,777904301,3369.29,1527.08,1842.21
FIBRECAST INC.,07-Canada Direct,770280000,3338.75,938.81,2399.94
FIBRECAST INC.,07-Canada Direct,771320000,3133.00,684.50,2448.50
FIBRECAST INC.,07-Canada Direct,770260000,3100.50,760.13,2340.37
FIBRECAST INC.,07-Canada Direct,770530000,3060.00,763.04,2296.96
FIBRECAST INC.,07-Canada Direct,739457000,2998.80,1823.43,1175.37
FIBRECAST INC.,07-Canada Direct,733525500,2985.00,682.46,2302.54
FIBRECAST INC.,07-Canada Direct,770520000,2859.00,611.74,2247.26
FIBRECAST INC.,07-Canada Direct,764564000,2817.00,1172.21,1644.79
FIBRECAST INC.,07-Canada Direct,764302000,2766.00,1375.28,1390.72
FIBRECAST INC.,07-Canada Direct,733444200,2702.00,644.17,2057.83
FIBRECAST INC.,07-Canada Direct,764561000,2686.00,1106.03,1579.97
FIBRECAST INC.,07-Canada Direct,734751200,2568.60,448.68,2119.92
FIBRECAST INC.,07-Canada Direct,770410000,2568.00,592.85,1975.15
FIBRECAST INC.,07-Canada Direct,768250020,2520.00,846.94,1673.06
FIBRECAST INC.,07-Canada Direct,777903100,2382.87,973.14,1409.73
FIBRECAST INC.,07-Canada Direct,770240000,2321.25,543.74,1777.51
FIBRECAST INC.,07-Canada Direct,792785000,2289.60,531.31,1758.29
FIBRECAST INC.,07-Canada Direct,793000000,2214.00,626.14,1587.86
FIBRECAST INC.,07-Canada Direct,793060300,2214.00,596.48,1617.52
FIBRECAST INC.,07-Canada Direct,768142000,2070.00,503.10,1566.90
FIBRECAST INC.,07-Canada Direct,770850000,2042.00,94.48,1947.52
FIBRECAST INC.,07-Canada Direct,764820600,1944.00,700.07,1243.93
FIBRECAST INC.,07-Canada Direct,770440000,1932.20,314.85,1617.35
FIBRECAST INC.,07-Canada Direct,770340000,1836.00,684.06,1151.94
FIBRECAST INC.,07-Canada Direct,739561100,1753.92,759.83,994.09
FIBRECAST INC.,07-Canada Direct,740402402,1630.00,411.01,1218.99
FIBRECAST INC.,07-Canada Direct,720271000,1620.00,950.75,669.25
FIBRECAST INC.,07-Canada Direct,764221000,1490.00,945.62,544.38
FIBRECAST INC.,07-Canada Direct,734890000,1400.70,397.93,1002.77
FIBRECAST INC.,07-Canada Direct,770560000,1246.00,261.68,984.32
FIBRECAST INC.,07-Canada Direct,733742000,1120.00,256.80,863.20
FIBRECAST INC.,07-Canada Direct,792760000,1097.60,291.55,806.05
FIBRECAST INC.,07-Canada Direct,764482000,1085.00,528.48,556.52
FIBRECAST INC.,07-Canada Direct,726020009,1062.00,250.61,811.39
FIBRECAST INC.,07-Canada Direct,739452000,973.98,437.16,536.82
FIBRECAST INC.,07-Canada Direct,792702000,967.50,567.27,400.23
FIBRECAST INC.,07-Canada Direct,770540000,887.61,369.56,518.05
FIBRECAST INC.,07-Canada Direct,733525600,887.50,202.03,685.47
FIBRECAST INC.,07-Canada Direct,766081010,792.00,138.03,653.97
FIBRECAST INC.,07-Canada Direct,764461006,782.00,499.43,282.57
FIBRECAST INC.,07-Canada Direct,733444300,770.00,230.85,539.15
FIBRECAST INC.,07-Canada Direct,764301000,770.00,419.48,350.52
FIBRECAST INC.,07-Canada Direct,733182390,731.03,191.39,539.64
FIBRECAST INC.,07-Canada Direct,734900000,722.40,342.05,380.35
FIBRECAST INC.,07-Canada Direct,727063018,690.00,254.14,435.86
FIBRECAST INC.,07-Canada Direct,779956806,654.50,299.36,355.14
FIBRECAST INC.,07-Canada Direct,768222000,654.00,244.03,409.97
FIBRECAST INC.,07-Canada Direct,733721000,646.00,326.18,319.82
FIBRECAST INC.,07-Canada Direct,768212000,630.00,231.18,398.82
FIBRECAST INC.,07-Canada Direct,770810000,626.00,17.19,608.81
FIBRECAST INC.,07-Canada Direct,777583000,599.80,21.50,578.30
FIBRECAST INC.,07-Canada Direct,770210000,520.00,121.27,398.73
FIBRECAST INC.,07-Canada Direct,724003000,478.50,81.82,396.68
FIBRECAST INC.,07-Canada Direct,734711100,463.48,94.75,368.73
FIBRECAST INC.,07-Canada Direct,733141300,444.00,139.46,304.54
FIBRECAST INC.,07-Canada Direct,770400000,437.50,92.44,345.06
FIBRECAST INC.,07-Canada Direct,739541100,420.00,83.57,336.43
FIBRECAST INC.,07-Canada Direct,734030000,405.00,157.54,247.46
FIBRECAST INC.,07-Canada Direct,770200000,369.00,83.20,285.80
FIBRECAST INC.,07-Canada Direct,739541000,360.00,71.62,288.38
FIBRECAST INC.,07-Canada Direct,741021000,354.48,44.39,310.09
FIBRECAST INC.,07-Canada Direct,764862000,351.00,150.16,200.84
FIBRECAST INC.,07-Canada Direct,734040000,281.00,121.56,159.44
FIBRECAST INC.,07-Canada Direct,770372000,280.00,44.11,235.89
FIBRECAST INC.,07-Canada Direct,779951023,253.50,114.45,139.05
FIBRECAST INC.,07-Canada Direct,770380000,232.40,51.37,181.03
FIBRECAST INC.,07-Canada Direct,764906000,216.00,65.44,150.56
FIBRECAST INC.,07-Canada Direct,770392000,176.00,35.15,140.85
FIBRECAST INC.,07-Canada Direct,764761000,174.00,95.58,78.42
FIBRECAST INC.,07-Canada Direct,733423700,150.40,36.22,114.18
FIBRECAST INC.,07-Canada Direct,733423800,150.40,38.28,112.12
FIBRECAST INC.,07-Canada Direct,764527200,138.00,69.69,68.31
FIBRECAST INC.,07-Canada Direct,764861000,121.00,51.96,69.04
FIBRECAST INC.,07-Canada Direct,768224000,83.00,41.19,41.81
FIBRECAST INC.,07-Canada Direct,764525000,69.00,37.60,31.40
FIBRECAST INC.,07-Canada Direct,764602000,66.50,26.50,40.00
FIBRECAST INC.,07-Canada Direct,734162000,53.40,41.18,12.22
GARLOCK OF CANADA LTD.,07-Canada Direct,770565000,13015.00,1502.22,11512.78
GARLOCK OF CANADA LTD.,07-Canada Direct,770511000,1214.75,95.48,1119.27
GARLOCK OF CANADA LTD.,07-Canada Direct,770601000,948.15,96.32,851.83
GARLOCK OF CANADA LTD.,07-Canada Direct,770560000,746.00,89.50,656.50
GEMCAST,07-Canada Direct,726000062,21000.00,11670.09,9329.91
GOODMAN INDUSTRIAL,07-Canada Direct,770260000,1957.50,140.66,1816.84
ICC INDUSTRIAL CHIMNEY CO,07-Canada Direct,777601000,287.95,58.80,229.15
INDUST'L FIREBRICK OF DET,07-Canada Direct,733151500,1550.00,244.87,1305.13
JOEL BERMAN GLASS STUDIOS,07-Canada Direct,734311100,404.36,35.30,369.06
L & A METALWORKS INC.,07-Canada Direct,768242010,182008.00,81440.56,100567.44
L & A METALWORKS INC.,07-Canada Direct,768242000,78485.00,47824.83,30660.17
L & A METALWORKS INC.,07-Canada Direct,734751200,36897.00,14414.92,22482.08
L & A METALWORKS INC.,07-Canada Direct,768222200,15228.00,7923.84,7304.16
L & A METALWORKS INC.,07-Canada Direct,733725500,572.00,103.53,468.47
L & A METALWORKS INC.,07-Canada Direct,768211000,282.00,140.34,141.66
MAGIKIST LTD.,07-Canada Direct,733182400,7489.80,1095.03,6394.77
"MEGTEC SYSTEMS, INC",07-Canada Direct,754055000,1483.44,879.81,603.63
"MEGTEC SYSTEMS, INC",07-Canada Direct,764522000,552.00,157.41,394.59
"MEGTEC SYSTEMS, INC",07-Canada Direct,734651200,365.85,111.00,254.85
"MIURA CANADA CO., LTD.",07-Canada Direct,733151500,326.00,54.11,271.89
MOTION INDUSTRIES CANADA,07-Canada Direct,770372000,1022.00,53.11,968.89
NOVENCO CONSULTANTS LTD,07-Canada Direct,777904301,8515.75,2701.75,5814.00
NOVENCO CONSULTANTS LTD,07-Canada Direct,733151500,262.00,54.11,207.89
NOVENCO CONSULTANTS LTD,07-Canada Direct,777601000,203.26,58.80,144.46
PILKINGTON N. A. INC.,07-Canada Direct,734751200,1662.60,226.24,1436.36
PRAIRIE STAINED GLASS,07-Canada Direct,792730000,1094.80,125.12,969.68
PRAIRIE STAINED GLASS,07-Canada Direct,768121000,503.00,28.76,474.24
PRAIRIE STAINED GLASS,07-Canada Direct,734611100,178.72,26.96,151.76
PRAIRIE STAINED GLASS,07-Canada Direct,734711100,178.72,27.04,151.68
PRAXAIR,07-Canada Direct,764521000,320.00,13.30,306.70
PROCOR LTD,07-Canada Direct,764633510,2197.44,456.72,1740.72
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,726000062,969660.00,714928.10,254731.90
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764601000,56508.00,8457.08,48050.92
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,727000060,45900.00,35739.81,10160.19
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764821000,45683.00,16879.02,28803.98
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,737851000,32498.56,16918.44,15580.12
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764781000,20506.40,9210.78,11295.62
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,766121000,20291.00,4966.45,15324.55
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,737321000,15601.92,9155.16,6446.76
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764521000,13109.20,5873.97,7235.23
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764901000,12577.20,4891.16,7686.04
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,734173000,11961.00,5031.15,6929.85
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,768750000,8970.00,9269.13,-299.13
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,734211200,4655.74,1300.42,3355.32
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,739781100,3398.40,3108.60,289.80
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764822000,3012.00,1119.56,1892.44
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,768750010,2700.00,2800.31,-100.31
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,733151600,1974.00,963.87,1010.13
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,733211050,1890.00,1248.62,641.38
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764821161,1700.00,1488.00,212.00
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764901061,1690.00,1492.00,198.00
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764481000,1280.00,677.64,602.36
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,734910000,767.70,318.54,449.16
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764561000,736.00,354.84,381.16
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,727000140,708.00,483.86,224.14
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764902000,507.00,193.26,313.74
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,792620000,428.00,86.74,341.26
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,764261000,344.00,183.16,160.84
PYROTEK INC (DRUMMONDVILL,07-Canada Direct,739541000,240.48,71.62,168.86
PYROTEK INC.,07-Canada Direct,768221000,324709.80,143235.79,181474.01
PYROTEK INC.,07-Canada Direct,768211000,147384.00,79798.00,67586.00
PYROTEK INC.,07-Canada Direct,764861000,136476.62,65438.45,71038.17
PYROTEK INC.,07-Canada Direct,764821000,106955.00,39520.86,67434.14
PYROTEK INC.,07-Canada Direct,777905414,74297.74,45869.08,28428.66
PYROTEK INC.,07-Canada Direct,768222000,64732.80,28565.44,36167.36
PYROTEK INC.,07-Canada Direct,768241000,44093.60,19480.87,24612.73
PYROTEK INC.,07-Canada Direct,777905413,41344.16,25491.66,15852.50
PYROTEK INC.,07-Canada Direct,764901000,41064.00,15971.20,25092.80
PYROTEK INC.,07-Canada Direct,763902000,39333.60,26420.86,12912.74
PYROTEK INC.,07-Canada Direct,768232500,37658.52,12176.01,25482.51
PYROTEK INC.,07-Canada Direct,777901100,37361.52,18893.04,18468.48
PYROTEK INC.,07-Canada Direct,777901000,31928.28,2224.76,29703.52
PYROTEK INC.,07-Canada Direct,764781000,26778.00,12006.54,14771.46
PYROTEK INC.,07-Canada Direct,777905410,18305.04,11528.70,6776.34
PYROTEK INC.,07-Canada Direct,793330000,17764.00,4849.55,12914.45
PYROTEK INC.,07-Canada Direct,771320000,16318.14,3052.75,13265.39
PYROTEK INC.,07-Canada Direct,733211000,16236.00,10343.60,5892.40
PYROTEK INC.,07-Canada Direct,764822000,14816.00,5535.17,9280.83
PYROTEK INC.,07-Canada Direct,768121000,14674.00,7478.46,7195.54
PYROTEK INC.,07-Canada Direct,739641100,13378.91,12690.47,688.44
PYROTEK INC.,07-Canada Direct,792702000,11928.75,5707.65,6221.10
PYROTEK INC.,07-Canada Direct,777903100,11139.66,5838.84,5300.82
PYROTEK INC.,07-Canada Direct,764822020,10624.00,3985.28,6638.72
PYROTEK INC.,07-Canada Direct,792730000,10350.00,2815.20,7534.80
PYROTEK INC.,07-Canada Direct,777905412,9540.75,7109.20,2431.55
PYROTEK INC.,07-Canada Direct,733151500,9487.00,4612.16,4874.84
PYROTEK INC.,07-Canada Direct,764561000,9275.60,4369.42,4906.18
PYROTEK INC.,07-Canada Direct,777601000,9011.92,3101.30,5910.62
PYROTEK INC.,07-Canada Direct,764301000,7580.80,4398.96,3181.84
PYROTEK INC.,07-Canada Direct,777905401,7533.40,4893.58,2639.82
PYROTEK INC.,07-Canada Direct,733525500,6772.50,2784.21,3988.29
PYROTEK INC.,07-Canada Direct,764521000,6691.20,2975.22,3715.98
PYROTEK INC.,07-Canada Direct,792783000,5671.80,2160.29,3511.51
PYROTEK INC.,07-Canada Direct,770372000,5551.00,636.43,4914.57
PYROTEK INC.,07-Canada Direct,733525600,5107.50,2108.02,2999.48
PYROTEK INC.,07-Canada Direct,792760000,4198.32,1632.68,2565.64
PYROTEK INC.,07-Canada Direct,739741100,3904.70,3150.30,754.40
PYROTEK INC.,07-Canada Direct,739781100,3588.96,4217.79,-628.83
PYROTEK INC.,07-Canada Direct,739661100,3530.88,3611.91,-81.03
PYROTEK INC.,07-Canada Direct,733510000,3315.00,2255.65,1059.35
PYROTEK INC.,07-Canada Direct,733211050,3244.00,2180.45,1063.55
PYROTEK INC.,07-Canada Direct,777144000,3194.40,3546.40,-352.00
PYROTEK INC.,07-Canada Direct,792785000,2966.40,1062.62,1903.78
PYROTEK INC.,07-Canada Direct,792764000,2464.80,859.56,1605.24
PYROTEK INC.,07-Canada Direct,764261000,2408.00,1285.68,1122.32
PYROTEK INC.,07-Canada Direct,733511000,2403.00,1490.32,912.68
PYROTEK INC.,07-Canada Direct,764481000,2376.60,1255.50,1121.10
PYROTEK INC.,07-Canada Direct,733505200,2296.00,1038.06,1257.94
PYROTEK INC.,07-Canada Direct,777715000,1939.20,1100.80,838.40
PYROTEK INC.,07-Canada Direct,739561200,1842.28,1038.66,803.62
PYROTEK INC.,07-Canada Direct,764601000,1733.20,736.87,996.33
PYROTEK INC.,07-Canada Direct,777140073,1733.16,861.75,871.41
PYROTEK INC.,07-Canada Direct,770570000,1662.00,190.82,1471.18
PYROTEK INC.,07-Canada Direct,718501000,1591.50,404.07,1187.43
PYROTEK INC.,07-Canada Direct,770510000,1567.50,144.60,1422.90
PYROTEK INC.,07-Canada Direct,738521000,1514.88,1291.08,223.80
PYROTEK INC.,07-Canada Direct,739681100,1355.94,1863.00,-507.06
PYROTEK INC.,07-Canada Direct,738401000,1237.68,844.14,393.54
PYROTEK INC.,07-Canada Direct,738361000,963.87,1255.58,-291.71
PYROTEK INC.,07-Canada Direct,737251000,910.80,300.69,610.11
PYROTEK INC.,07-Canada Direct,764522000,833.80,370.20,463.60
PYROTEK INC.,07-Canada Direct,764781040,678.00,292.91,385.09
PYROTEK INC.,07-Canada Direct,739561000,602.20,344.76,257.44
PYROTEK INC.,07-Canada Direct,768231000,558.00,251.10,306.90
PYROTEK INC.,07-Canada Direct,733141200,464.00,267.92,196.08
PYROTEK INC.,07-Canada Direct,739561100,341.04,199.92,141.12
PYROTEK INC.,07-Canada Direct,738561000,183.36,124.14,59.22
PYROTEK INC.,07-Canada Direct,764461006,80.00,55.88,24.12
QUADRA INDUSTRIAL,07-Canada Direct,779984172,580.00,196.00,384.00
REFRABEC INC.,07-Canada Direct,762104000,88833.72,41705.55,47128.17
REFRABEC INC.,07-Canada Direct,718501000,16650.00,4040.67,12609.33
REFRABEC INC.,07-Canada Direct,779951086,4386.00,2193.00,2193.00
REFRABEC INC.,07-Canada Direct,779959870,1895.50,985.29,910.21
REFRABEC INC.,07-Canada Direct,779957054,1504.50,753.15,751.35
REFRACTORY ENGINEERS INC.,07-Canada Direct,734311100,1373.03,272.83,1100.20
REFRACTORY ENGINEERS INC.,07-Canada Direct,733525500,1280.00,260.12,1019.88
REFRACTORY ENGINEERS INC.,07-Canada Direct,734910000,851.40,261.96,589.44
REFRACTORY ENGINEERS INC.,07-Canada Direct,733211000,239.30,53.20,186.10
REFRACTORY ENGINEERS INC.,07-Canada Direct,779221000,112.94,29.42,83.52
REFTECH INTERNATIONAL INC,07-Canada Direct,766121000,4840.00,1915.91,2924.09
RHI CANADA INC.,07-Canada Direct,754164100,614952.36,391805.75,223146.61
RHI CANADA INC.,07-Canada Direct,781580000,410399.88,244057.44,166342.44
RHI CANADA INC.,07-Canada Direct,764821000,246768.00,104565.58,142202.42
RHI CANADA INC.,07-Canada Direct,734910000,231168.00,111034.50,120133.50
RHI CANADA INC.,07-Canada Direct,764901000,163410.00,72196.11,91213.89
RHI CANADA INC.,07-Canada Direct,764601000,156392.50,73691.19,82701.31
RHI CANADA INC.,07-Canada Direct,793330000,108495.00,25980.01,82514.99
RHI CANADA INC.,07-Canada Direct,766121000,103703.00,59651.17,44051.83
RHI CANADA INC.,07-Canada Direct,766261000,77990.00,44176.49,33813.51
RHI CANADA INC.,07-Canada Direct,764781000,75300.00,32083.84,43216.16
RHI CANADA INC.,07-Canada Direct,764861000,70604.50,31082.56,39521.94
RHI CANADA INC.,07-Canada Direct,764481000,69819.90,35820.52,33999.38
RHI CANADA INC.,07-Canada Direct,733464500,68190.00,19933.32,48256.68
RHI CANADA INC.,07-Canada Direct,777601000,60993.28,33402.71,27590.57
RHI CANADA INC.,07-Canada Direct,733211000,59064.00,23984.36,35079.64
RHI CANADA INC.,07-Canada Direct,766122000,54960.00,34514.56,20445.44
RHI CANADA INC.,07-Canada Direct,734990000,50399.60,19640.91,30758.69
RHI CANADA INC.,07-Canada Direct,715000000,48645.00,14304.88,34340.12
RHI CANADA INC.,07-Canada Direct,793000000,41344.80,17369.60,23975.20
RHI CANADA INC.,07-Canada Direct,764521000,36570.00,18126.08,18443.92
RHI CANADA INC.,07-Canada Direct,764821161,35610.00,38539.20,-2929.20
RHI CANADA INC.,07-Canada Direct,770222000,35510.50,6537.16,28973.34
RHI CANADA INC.,07-Canada Direct,779988108,30723.00,0.00,30723.00
RHI CANADA INC.,07-Canada Direct,754204010,29702.76,11835.98,17866.78
RHI CANADA INC.,07-Canada Direct,764561000,28485.00,14379.03,14105.97
RHI CANADA INC.,07-Canada Direct,718501000,25613.00,7814.71,17798.29
RHI CANADA INC.,07-Canada Direct,770220000,25329.00,4715.20,20613.80
RHI CANADA INC.,07-Canada Direct,719500000,25164.00,8986.91,16177.09
RHI CANADA INC.,07-Canada Direct,754754020,24024.00,12331.05,11692.95
RHI CANADA INC.,07-Canada Direct,770230000,23625.00,5089.05,18535.95
RHI CANADA INC.,07-Canada Direct,754727000,23338.80,8440.62,14898.18
RHI CANADA INC.,07-Canada Direct,770240000,23145.00,4733.89,18411.11
RHI CANADA INC.,07-Canada Direct,795900120,19720.80,6211.62,13509.18
RHI CANADA INC.,07-Canada Direct,764901061,18000.00,17904.00,96.00
RHI CANADA INC.,07-Canada Direct,754887000,17952.72,5094.63,12858.09
RHI CANADA INC.,07-Canada Direct,733444200,17636.00,6268.89,11367.11
RHI CANADA INC.,07-Canada Direct,724281200,16622.00,8534.80,8087.20
RHI CANADA INC.,07-Canada Direct,779984659,15675.00,7015.86,8659.14
RHI CANADA INC.,07-Canada Direct,759671000,14469.00,4988.41,9480.59
RHI CANADA INC.,07-Canada Direct,766231000,14330.00,8519.74,5810.26
RHI CANADA INC.,07-Canada Direct,754164005,13158.12,11430.59,1727.53
RHI CANADA INC.,07-Canada Direct,734173000,13120.00,6467.94,6652.06
RHI CANADA INC.,07-Canada Direct,764821073,12000.00,11664.00,336.00
RHI CANADA INC.,07-Canada Direct,733525500,11410.00,3066.43,8343.57
RHI CANADA INC.,07-Canada Direct,764341000,11217.00,6593.03,4623.97
RHI CANADA INC.,07-Canada Direct,737912200,11172.00,4456.44,6715.56
RHI CANADA INC.,07-Canada Direct,754867000,10637.81,3728.48,6909.33
RHI CANADA INC.,07-Canada Direct,734311100,10570.43,2231.35,8339.08
RHI CANADA INC.,07-Canada Direct,764781040,9930.00,4214.44,5715.56
RHI CANADA INC.,07-Canada Direct,718500000,9596.16,2911.30,6684.86
RHI CANADA INC.,07-Canada Direct,777902300,9343.32,4124.44,5218.88
RHI CANADA INC.,07-Canada Direct,733505300,8020.00,2393.58,5626.42
RHI CANADA INC.,07-Canada Direct,733525600,6815.00,1879.40,4935.60
RHI CANADA INC.,07-Canada Direct,724003000,6628.20,1113.96,5514.24
RHI CANADA INC.,07-Canada Direct,779986562,6411.00,3453.17,2957.83
RHI CANADA INC.,07-Canada Direct,764261000,6351.00,3438.54,2912.46
RHI CANADA INC.,07-Canada Direct,777992000,6332.16,2601.36,3730.80
RHI CANADA INC.,07-Canada Direct,754647000,5715.36,2596.24,3119.12
RHI CANADA INC.,07-Canada Direct,733211050,5299.00,2509.72,2789.28
RHI CANADA INC.,07-Canada Direct,777902700,5194.98,2283.11,2911.87
RHI CANADA INC.,07-Canada Direct,740382201,5060.00,3552.23,1507.77
RHI CANADA INC.,07-Canada Direct,764822000,4896.00,2091.89,2804.11
RHI CANADA INC.,07-Canada Direct,734830000,4554.00,1634.76,2919.24
RHI CANADA INC.,07-Canada Direct,764901073,4500.00,4374.00,126.00
RHI CANADA INC.,07-Canada Direct,770380000,3744.00,616.46,3127.54
RHI CANADA INC.,07-Canada Direct,780280000,3448.80,1263.72,2185.08
RHI CANADA INC.,07-Canada Direct,779984680,3225.60,1292.10,1933.50
RHI CANADA INC.,07-Canada Direct,764482000,3061.20,1576.91,1484.29
RHI CANADA INC.,07-Canada Direct,766162000,2908.50,1674.67,1233.83
RHI CANADA INC.,07-Canada Direct,734851000,2896.14,1188.20,1707.94
RHI CANADA INC.,07-Canada Direct,770560000,2888.00,433.86,2454.14
RHI CANADA INC.,07-Canada Direct,780680000,2874.00,1093.28,1780.72
RHI CANADA INC.,07-Canada Direct,767960000,2746.63,924.98,1821.65
RHI CANADA INC.,07-Canada Direct,754467000,2711.52,1067.44,1644.08
RHI CANADA INC.,07-Canada Direct,770372000,2632.00,362.77,2269.23
RHI CANADA INC.,07-Canada Direct,770026100,2262.50,363.21,1899.29
RHI CANADA INC.,07-Canada Direct,779989696,2258.40,941.37,1317.03
RHI CANADA INC.,07-Canada Direct,734611100,2088.30,1107.02,981.28
RHI CANADA INC.,07-Canada Direct,777904301,2074.00,939.75,1134.25
RHI CANADA INC.,07-Canada Direct,770260000,2052.00,421.98,1630.02
RHI CANADA INC.,07-Canada Direct,766081010,1892.00,929.06,962.94
RHI CANADA INC.,07-Canada Direct,727000025,1813.50,1098.70,714.80
RHI CANADA INC.,07-Canada Direct,779959414,1762.50,699.70,1062.80
RHI CANADA INC.,07-Canada Direct,733505200,1620.00,458.66,1161.34
RHI CANADA INC.,07-Canada Direct,770200000,1449.00,296.90,1152.10
RHI CANADA INC.,07-Canada Direct,733403100,1307.20,490.39,816.81
RHI CANADA INC.,07-Canada Direct,734211200,1293.10,302.27,990.83
RHI CANADA INC.,07-Canada Direct,764262000,1193.00,631.52,561.48
RHI CANADA INC.,07-Canada Direct,775171000,1115.00,534.68,580.32
RHI CANADA INC.,07-Canada Direct,770850000,1102.00,84.12,1017.88
RHI CANADA INC.,07-Canada Direct,775131000,1052.10,505.47,546.63
RHI CANADA INC.,07-Canada Direct,733151600,1040.00,345.92,694.08
RHI CANADA INC.,07-Canada Direct,733671000,974.40,225.10,749.30
RHI CANADA INC.,07-Canada Direct,765661000,890.00,318.15,571.85
RHI CANADA INC.,07-Canada Direct,754924010,847.08,346.12,500.96
RHI CANADA INC.,07-Canada Direct,770392000,732.00,124.53,607.47
RHI CANADA INC.,07-Canada Direct,764901050,720.00,315.90,404.10
RHI CANADA INC.,07-Canada Direct,770510000,715.00,82.78,632.22
RHI CANADA INC.,07-Canada Direct,779958220,709.31,269.80,439.51
RHI CANADA INC.,07-Canada Direct,727063000,651.12,246.69,404.43
RHI CANADA INC.,07-Canada Direct,727063016,651.12,245.43,405.69
RHI CANADA INC.,07-Canada Direct,734810000,612.00,428.86,183.14
RHI CANADA INC.,07-Canada Direct,724261200,604.00,156.40,447.60
RHI CANADA INC.,07-Canada Direct,779959413,600.00,300.81,299.19
RHI CANADA INC.,07-Canada Direct,764605000,530.00,258.78,271.22
RHI CANADA INC.,07-Canada Direct,780600000,480.96,264.67,216.29
RHI CANADA INC.,07-Canada Direct,779221000,478.92,122.40,356.52
RHI CANADA INC.,07-Canada Direct,734751200,475.50,224.34,251.16
RHI CANADA INC.,07-Canada Direct,792650000,436.00,228.70,207.30
RHI CANADA INC.,07-Canada Direct,739561100,431.76,186.64,245.12
RHI CANADA INC.,07-Canada Direct,770830000,415.00,30.97,384.03
RHI CANADA INC.,07-Canada Direct,724311200,328.00,154.28,173.72
RHI CANADA INC.,07-Canada Direct,733151500,304.00,108.22,195.78
RHI CANADA INC.,07-Canada Direct,733141200,288.00,119.94,168.06
RHI CANADA INC.,07-Canada Direct,793260074,283.50,122.49,161.01
RHI CANADA INC.,07-Canada Direct,774981100,280.00,98.09,181.91
RHI CANADA INC.,07-Canada Direct,727000440,248.00,111.25,136.75
RHI CANADA INC.,07-Canada Direct,733721000,240.00,97.79,142.21
RHI CANADA INC.,07-Canada Direct,764761000,232.00,122.86,109.14
RHI CANADA INC.,07-Canada Direct,780620000,213.50,109.18,104.32
RHI CANADA INC.,07-Canada Direct,777650000,203.70,142.66,61.04
RHI CANADA INC.,07-Canada Direct,727000140,186.00,83.57,102.43
RHI CANADA INC.,07-Canada Direct,733212000,181.50,92.84,88.66
RHI CANADA INC.,07-Canada Direct,734711100,77.83,40.72,37.11
RHI CANADA INC.,07-Canada Direct,734900000,71.40,56.73,14.67
RHI CANADA INC.,07-Canada Direct,777642000,62.00,31.67,30.33
RONSCO INC.,07-Canada Direct,764643052,8981.28,4251.43,4729.85
RONSCO INC.,07-Canada Direct,764643100,4153.97,5511.48,-1357.51
SPECIALTY GASKETS INC.,07-Canada Direct,764901000,20715.25,4793.13,15922.12
SPECIALTY GASKETS INC.,07-Canada Direct,768221000,16116.00,6063.46,10052.54
SPECIALTY GASKETS INC.,07-Canada Direct,733525600,13300.00,3352.26,9947.74
SPECIALTY GASKETS INC.,07-Canada Direct,730710500,9284.00,3122.36,6161.64
SPECIALTY GASKETS INC.,07-Canada Direct,764261000,4622.50,1433.86,3188.64
SPECIALTY GASKETS INC.,07-Canada Direct,732429400,2672.00,794.28,1877.72
SPECIALTY GASKETS INC.,07-Canada Direct,764821000,2460.00,626.21,1833.79
SPECIALTY GASKETS INC.,07-Canada Direct,764461006,2056.00,769.82,1286.18
SPECIALTY GASKETS INC.,07-Canada Direct,764521000,1184.50,305.83,878.67
SPECIALTY GASKETS INC.,07-Canada Direct,733211050,952.00,478.33,473.67
SPECIALTY GASKETS INC.,07-Canada Direct,764781000,620.00,204.45,415.55
SPECIALTY GASKETS INC.,07-Canada Direct,733151600,576.00,212.46,363.54
SPECIALTY GASKETS INC,07-Canada Direct,764461006,1120.00,415.62,704.38
STRIPE WORKS,07-Canada Direct,737851000,12236.80,5305.60,6931.20
STRIPE WORKS,07-Canada Direct,766162000,3075.00,311.10,2763.90
STRIPE WORKS,07-Canada Direct,734822000,494.00,67.47,426.53
STRIPE WORKS,07-Canada Direct,734211200,437.64,46.75,390.89
THERMAL CERAMICS,07-Canada Direct,734351200,917.85,69.73,848.12
UNIVERSITY OF ALBERTA,07-Canada Direct,737912200,523.00,86.72,436.28
VESUVIUS USA,07-Canada Direct,726020018,25500.00,12032.49,13467.51
WEATHERFORD LABS,07-Canada Direct,733484800,6320.00,780.71,5539.29
WESTERN INDUSTRIAL CERAMI,07-Canada Direct,771002000,496.40,19.30,477.10
UNIFRAX (SHANGHAI) LTD.,09-China,735657000,14256.00,10565.89,3690.11
UNIFRAX (SHANGHAI) LTD.,09-China,735655000,8325.20,6239.17,2086.03
UNIFRAX (SHANGHAI) LTD.,09-China,734990000,438.72,328.65,110.07
UNIFRAX (SHANGHAI) LTD.,09-China,734211200,31.55,23.38,8.17
UNIFRAX AUSTRALIA PTY LTD,09-China,732820000,205.97,147.02,58.95
UNIFRAX AUSTRALIA PTY LTD,09-China,777601000,72.02,72.02,0.00
ASHCON,9x-Export,777601000,2049.60,293.98,1755.62
ATLANTECH DISTRIBUTION,9x-Export,735602000,72051.00,27654.85,44396.15
AVIATION PRODUCTS INTL EK,9x-Export,733423600,800.00,90.16,709.84
BETA RESEARCH & DEV. LTD.,9x-Export,737550200,1419.60,383.16,1036.44
BETA RESEARCH & DEV. LTD.,9x-Export,779980780,920.40,271.92,648.48
BETA RESEARCH & DEV. LTD.,9x-Export,737550600,707.70,198.28,509.42
BETA RESEARCH & DEV. LTD.,9x-Export,737550000,687.92,445.77,242.15
BETA RESEARCH & DEV. LTD.,9x-Export,737550400,589.20,35.23,553.97
BETA RESEARCH & DEV. LTD.,9x-Export,779980770,273.60,143.64,129.96
BRADEN MANUFACTURING,9x-Export,768231012,2983.50,2084.16,899.34
BRADEN MANUFACTURING,9x-Export,768241102,1300.50,894.78,405.72
C.I. MILPA S.A.,9x-Export,770360000,17892.00,1301.57,16590.43
CLYDE BERGEMANN BACHMANN,9x-Export,734761300,9568.35,3414.51,6153.84
DURR SYSTEMS INC.,9x-Export,784320000,22943.52,11241.56,11701.96
DURR SYSTEMS INC.,9x-Export,784360000,21715.60,10641.82,11073.78
DURR SYSTEMS INC.,9x-Export,780690000,16376.00,8030.18,8345.82
DURR SYSTEMS INC.,9x-Export,764821000,2706.00,675.74,2030.26
DURR SYSTEMS INC.,9x-Export,779957846,691.25,356.38,334.87
ECOLOG INTERNATIONAL FZE,9x-Export,735602000,6300.00,1510.19,4789.81
F.L. SMIDTH INC.,9x-Export,770600000,1554.35,96.32,1458.03
FALEK LATINA C.I.S.A.,9x-Export,733586500,30864.96,6757.46,24107.50
FAURECIA EXHAUST MEXICANA,9x-Export,779988364,72544.50,23827.66,48716.84
FELTEX- EASTERN CAPE,9x-Export,734751200,41616.96,8669.21,32947.75
FELTEX- EASTERN CAPE,9x-Export,740402402,717.50,193.66,523.84
FELTEX- EASTERN CAPE,9x-Export,779988364,214.75,44.61,170.14
FOSECO JAPAN LTD.,9x-Export,793000010,328593.60,100675.55,227918.05
FRAS-LE S/A,9x-Export,726020007,29048.24,6836.88,22211.36
FRENOSA SOCIEDAD ANONIMA,9x-Export,727000060,56025.00,18352.93,37672.07
FRICCION Y TECNOLOGIA,9x-Export,727080000,40230.00,5100.52,35129.48
HALCO,9x-Export,724261200,1572.00,156.40,1415.60
INDUST'L FIREBRICK OF DET,9x-Export,793330000,1194.00,318.08,875.92
INSULCON BENELUX B.V.,9x-Export,768242900,37701.31,25073.30,12628.01
INTERNATIONAL AGENCIES LT,9x-Export,735602115,35208.00,9446.25,25761.75
INTERNATIONAL AGENCIES LT,9x-Export,735602000,11736.00,3026.06,8709.94
ISOLITE INSULATING PROD.,9x-Export,764040000,75142.08,48204.12,26937.96
ISOLITE INSULATING PROD.,9x-Export,719500000,38196.00,26960.76,11235.24
ISOLITE INSULATING PROD.,9x-Export,793410000,31758.72,22684.71,9074.01
ISOLITE INSULATING PROD.,9x-Export,727041000,18195.84,10261.92,7933.92
ISOLITE INSULATING PROD.,9x-Export,777650000,6879.30,4914.06,1965.24
ISOLITE INSULATING PROD.,9x-Export,733525500,1532.50,1126.96,405.54
ISOLITE INSULATING PROD.,9x-Export,733403200,897.60,396.68,500.92
ISOLITE INSULATING PROD.,9x-Export,733423600,829.44,592.76,236.68
ISOLITE INSULATING PROD.,9x-Export,792410000,719.19,513.69,205.50
ISOLITE INSULATING PROD.,9x-Export,734810000,686.22,490.14,196.08
ISOLITE INSULATING PROD.,9x-Export,792420000,667.19,476.50,190.69
ISOLITE INSULATING PROD.,9x-Export,793320074,344.40,245.80,98.60
ISOLITE INSULATING PROD.,9x-Export,733423700,252.16,181.02,71.14
ISOLITE INSULATING PROD.,9x-Export,734851000,138.65,98.90,39.75
ISOLITE INSULATING PROD.,9x-Export,734822000,94.50,67.47,27.03
"ITM CO., LTD.",9x-Export,740402402,3225.00,652.05,2572.95
"ITM CO., LTD.",9x-Export,740382201,1663.00,1188.57,474.43
KLC INTERNATIONAL LTD.,9x-Export,779181000,255.92,41.25,214.67
LIBERTY REFRACTORY,9x-Export,734173000,66636.00,20693.52,45942.48
LIBERTY REFRACTORY,9x-Export,777642000,22056.00,8772.00,13284.00
MACER LTDA,9x-Export,733221400,21402.72,5139.20,16263.52
MACER LTDA,9x-Export,733151500,6624.00,1907.68,4716.32
MACER LTDA,9x-Export,770260000,4221.45,616.77,3604.68
MACER LTDA,9x-Export,770280000,4200.00,615.64,3584.36
MACER LTDA,9x-Export,770240000,3197.26,405.78,2791.48
MACER LTDA,9x-Export,733211050,2376.00,993.92,1382.08
MACER LTDA,9x-Export,770200000,1285.20,163.14,1122.06
MACER LTDA,9x-Export,770220000,957.60,109.38,848.22
MACER LTDA,9x-Export,770210000,896.00,121.26,774.74
MCNEIL INC.,9x-Export,766041100,28032.00,16648.93,11383.07
MCNEIL INC.,9x-Export,734851000,16920.00,6580.66,10339.34
MCNEIL INC.,9x-Export,766163100,1451.52,995.98,455.54
NORTH SHORE INTERNATIONAL,9x-Export,770722000,15375.00,1072.03,14302.97
PAGLES INTERNATIONAL INC.,9x-Export,764821027,32900.00,12113.90,20786.10
PAGLES INTERNATIONAL INC.,9x-Export,774661000,3630.00,692.62,2937.38
PAGLES INTERNATIONAL INC.,9x-Export,774901000,3000.00,593.54,2406.46
PAGLES INTERNATIONAL INC.,9x-Export,777642000,1080.00,316.70,763.30
RASGAS COMPANY LIMITED,9x-Export,733444200,6976.00,850.08,6125.92
SAINT-GOBAIN AMERICA SA,9x-Export,777780074,1842.74,400.40,1442.34
SCHOTT FORMA VITRUM DE,9x-Export,739541000,564.48,71.62,492.86
SHOFU INC.,9x-Export,740382201,8700.00,2377.14,6322.86
SNS CORPORATION,9x-Export,734751200,12180.00,2634.44,9545.56
SPAR INC,9x-Export,764601000,57631.50,37812.31,19819.19
SPAR INC,9x-Export,764901000,42016.00,26863.48,15152.52
SPAR INC,9x-Export,766162000,28200.00,28103.56,96.44
SPAR INC,9x-Export,737915000,6918.75,4549.63,2369.12
UNIFRAX (SHANGHAI) LTD.,9x-Export,733525500,972.50,703.93,268.57
UNIFRAX (SHANGHAI) LTD.,9x-Export,734611100,910.00,673.91,236.09
UNIFRAX (SHANGHAI) LTD.,9x-Export,734211200,770.75,570.94,199.81
UNIFRAX (SHANGHAI) LTD.,9x-Export,793060300,91.48,68.05,23.43
UNIFRAX AUSTRALIA PTY LTD,9x-Export,735602000,26835.60,19165.06,7670.54
UNIFRAX AUSTRALIA PTY LTD,9x-Export,764902000,8046.00,5746.84,2299.16
UNIFRAX AUSTRALIA PTY LTD,9x-Export,732820000,235.20,168.08,67.12
UNIFRAX AUSTRALIA PTY LTD,9x-Export,734030000,220.50,157.54,62.96
UNIFRAX EMISSION CONTROL,9x-Export,734751200,19817.50,3770.74,16046.76
UNIFRAX EMISSION CONTROL,9x-Export,779988364,214.80,45.22,169.58
UNIFRAX FRANCE,9x-Export,735602115,17419.60,12944.86,4474.74
UNIFRAX FRANCE,9x-Export,735602116,12740.00,9410.36,3329.64
UNIFRAX FRANCE,9x-Export,735602110,3920.00,2800.22,1119.78
UNIFRAX FRANCE,9x-Export,735602000,1294.70,924.63,370.07
UNIFRAX GMBH,9x-Export,793160000,37523.26,26801.84,10721.42
UNIFRAX LTD.,9x-Export,726020007,2481.84,1694.63,787.21
UNIFRAX LTD.,9x-Export,723960000,494.00,352.92,141.08
UNIFRAX LTD.,9x-Export,720219100,202.95,145.47,57.48
UNIFRAX LTD.,9x-Export,732905000,197.23,140.83,56.40
UNIFRAX S.R.O.,9x-Export,793409000,27048.71,19320.73,7727.98
UNIFRAX S.R.O.,9x-Export,793408000,21003.18,15002.56,6000.62
UTSU CO. LTD JAPAN,9x-Export,734311100,6691.08,587.30,6103.78
VATANA PHAISAL ENGNRG CO.,9x-Export,733403200,29376.60,2677.57,26699.03
VESUVIUS MEXICO SA DE CV,9x-Export,733586600,514771.20,328196.46,186574.74
VESUVIUS MEXICO SA DE CV,9x-Export,733571000,172566.72,125708.88,46857.84
VESUVIUS MEXICO SA DE CV,9x-Export,733403300,145368.96,68065.19,77303.77
VESUVIUS MEXICO SA DE CV,9x-Export,733566300,12355.20,7137.60,5217.60
VESUVIUS MEXICO SA DE CV,9x-Export,733464900,7109.40,4933.61,2175.79
VESUVIUS MEXICO SA DE CV,9x-Export,764781000,6874.00,3867.56,3006.44
VESUVIUS MEXICO SA DE CV,9x-Export,733490600,4328.00,2388.36,1939.64
VESUVIUS MEXICO SA DE CV,9x-Export,733496000,4328.00,2388.36,1939.64
VESUVIUS MEXICO SA DE CV,9x-Export,764821000,2402.00,933.36,1468.64
VESUVIUS MEXICO SA DE CV,9x-Export,732662000,1119.02,210.44,908.58

================================================
FILE: data/dhiraj.csv
================================================
Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
5.1,3.5,1.4,0.2,setosa
4.9,3,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3,1.4,0.1,setosa
4.3,3,1.1,0.1,setosa
5.8,4,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5,3,1.6,0.2,setosa
5,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.2,setosa
5,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.6,1.4,0.1,setosa
4.4,3,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5,3.3,1.4,0.2,setosa
7,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5,2,3.5,1,versicolor
5.9,3,4.2,1.5,versicolor
6,2.2,4,1,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3,4.5,1.5,versicolor
5.8,2.7,4.1,1,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3,5,1.7,versicolor
6,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1,versicolor
5.8,2.7,3.9,1.2,versicolor
6,2.7,5.1,1.6,versicolor
5.4,3,4.5,1.5,versicolor
6,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3,4.1,1.3,versicolor
5.5,2.5,4,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3,4.6,1.4,versicolor
5.8,2.6,4,1.2,versicolor
5,2.3,3.3,1,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3,5.8,2.2,virginica
7.6,3,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3,5.5,2.1,virginica
5.7,2.5,5,2,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6,2.2,5,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2,virginica
7.7,2.8,6.7,2,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6,3,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3,5.2,2.3,virginica
6.3,2.5,5,1.9,virginica
6.5,3,5.2,2,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3,5.1,1.8,virginica


================================================
FILE: data/dtdata.csv
================================================
"","gender","married","buy"
"S1","Male","Married","Buy"
"S2","Male","Single","NotBuy"
"S3","Male","Married","Buy"
"S4","Female","Married","Buy"
"S5","Male","Married","NotBuy"
"S6","Female","Single","NotBuy"
"S7","Female","Single","Buy"
"S8","Male","Single","NotBuy"
"S9","Male","Married","Buy"
"S10","Male","Married","Buy"
"S11","Male","Married","Buy"
"S12","Male","Single","Buy"
"S13","Male","Married","NotBuy"
"S14","Male","Single","Buy"
"S15","Female","Married","Buy"
"S16","Male","Single","NotBuy"
"S17","Female","Married","NotBuy"
"S18","Female","Single","Buy"
"S19","Male","Married","Buy"
"S20","Female","Married","Buy"
"S21","Female","Married","NotBuy"
"S22","Male","Single","Buy"
"S23","Male","Married","NotBuy"
"S24","Male","Single","Buy"
"S25","Male","Married","Buy"
"S26","Male","Married","Buy"
"S27","Male","Single","Buy"
"S28","Male","Married","Buy"
"S29","Female","Married","NotBuy"
"S30","Male","Single","Buy"
"S31","Male","Married","Buy"
"S32","Male","Married","Buy"
"S33","Male","Married","NotBuy"
"S34","Male","Married","NotBuy"
"S35","Female","Married","Buy"
"S36","Male","Married","NotBuy"
"S37","Female","Married","NotBuy"
"S38","Male","Married","Buy"
"S39","Female","Married","NotBuy"
"S40","Male","Married","Buy"
"S41","Female","Married","NotBuy"
"S42","Male","Married","Buy"
"S43","Female","Married","Buy"
"S44","Male","Married","Buy"
"S45","Male","Single","NotBuy"
"S46","Female","Married","NotBuy"
"S47","Male","Married","NotBuy"
"S48","Male","Married","Buy"
"S49","Female","Single","Buy"
"S50","Male","Married","NotBuy"
"S51","Male","Married","Buy"
"S52","Female","Married","Buy"
"S53","Male","Married","NotBuy"
"S54","Male","Single","NotBuy"
"S55","Male","Married","Buy"
"S56","Male","Married","NotBuy"
"S57","Male","Married","NotBuy"
"S58","Male","Single","NotBuy"
"S59","Male","Married","NotBuy"
"S60","Male","Married","NotBuy"
"S61","Female","Single","NotBuy"
"S62","Male","Single","NotBuy"
"S63","Male","Single","NotBuy"
"S64","Male","Married","Buy"
"S65","Male","Married","NotBuy"
"S66","Male","Married","Buy"
"S67","Male","Single","Buy"
"S68","Female","Married","Buy"
"S69","Male","Married","Buy"
"S70","Female","Married","Buy"
"S71","Male","Married","Buy"
"S72","Female","Single","Buy"
"S73","Male","Married","Buy"
"S74","Male","Single","Buy"
"S75","Male","Married","Buy"
"S76","Female","Married","NotBuy"
"S77","Female","Single","Buy"
"S78","Male","Married","NotBuy"
"S79","Female","Married","Buy"
"S80","Female","Married","Buy"
"S81","Male","Married","Buy"
"S82","Female","Married","NotBuy"
"S83","Male","Married","Buy"
"S84","Male","Married","Buy"
"S85","Female","Single","Buy"
"S86","Male","Married","NotBuy"
"S87","Female","Married","NotBuy"
"S88","Male","Married","Buy"
"S89","Male","Married","NotBuy"
"S90","Male","Married","Buy"
"S91","Male","Single","NotBuy"
"S92","Male","Married","Buy"
"S93","Male","Married","Buy"
"S94","Female","Married","Buy"
"S95","Female","Single","Buy"
"S96","Female","Single","Buy"
"S97","Male","Single","Buy"
"S98","Male","Married","Buy"
"S99","Female","Married","Buy"
"S100","Male","Married","Buy"
"S101","Male","Married","Buy"
"S102","Male","Single","Buy"
"S103","Male","Married","NotBuy"
"S104","Female","Married","NotBuy"
"S105","Male","Married","Buy"
"S106","Male","Single","Buy"
"S107","Male","Married","NotBuy"
"S108","Male","Married","Buy"
"S109","Female","Single","Buy"
"S110","Male","Married","Buy"
"S111","Female","Married","Buy"
"S112","Female","Married","NotBuy"
"S113","Male","Married","Buy"
"S114","Male","Married","Buy"
"S115","Male","Married","NotBuy"
"S116","Male","Married","NotBuy"
"S117","Female","Single","NotBuy"
"S118","Male","Single","Buy"
"S119","Male","Married","NotBuy"
"S120","Male","Married","Buy"
"S121","Female","Single","Buy"
"S122","Male","Single","Buy"
"S123","Male","Single","NotBuy"
"S124","Male","Married","NotBuy"
"S125","Female","Married","Buy"
"S126","Male","Married","NotBuy"
"S127","Male","Single","Buy"
"S128","Male","Single","Buy"
"S129","Male","Single","NotBuy"
"S130","Male","Single","Buy"
"S131","Male","Married","Buy"
"S132","Male","Married","NotBuy"
"S133","Male","Married","Buy"
"S134","Male","Married","NotBuy"
"S135","Female","Married","Buy"
"S136","Male","Single","NotBuy"
"S137","Male","Single","Buy"
"S138","Male","Single","NotBuy"
"S139","Female","Married","Buy"
"S140","Male","Married","Buy"
"S141","Male","Married","Buy"
"S142","Male","Single","Buy"
"S143","Male","Married","NotBuy"
"S144","Male","Married","Buy"
"S145","Female","Single","Buy"
"S146","Male","Married","NotBuy"
"S147","Male","Single","NotBuy"
"S148","Female","Married","Buy"
"S149","Male","Married","Buy"
"S150","Female","Married","Buy"
"S151","Male","Married","Buy"
"S152","Male","Single","Buy"
"S153","Male","Single","NotBuy"
"S154","Male","Single","Buy"
"S155","Male","Married","NotBuy"
"S156","Male","Married","Buy"
"S157","Male","Married","NotBuy"
"S158","Male","Married","Buy"
"S159","Male","Married","Buy"
"S160","Male","Single","Buy"
"S161","Male","Married","NotBuy"
"S162","Female","Single","NotBuy"
"S163","Male","Single","Buy"
"S164","Female","Married","NotBuy"
"S165","Female","Married","Buy"
"S166","Male","Married","NotBuy"
"S167","Male","Married","Buy"
"S168","Male","Married","NotBuy"
"S169","Female","Married","Buy"
"S170","Male","Married","Buy"
"S171","Male","Married","NotBuy"
"S172","Female","Married","Buy"
"S173","Female","Married","Buy"
"S174","Male","Single","NotBuy"
"S175","Male","Married","Buy"
"S176","Female","Single","NotBuy"
"S177","Male","Single","Buy"
"S178","Female","Married","Buy"
"S179","Male","Married","NotBuy"
"S180","Female","Single","Buy"
"S181","Male","Married","NotBuy"
"S182","Male","Married","Buy"
"S183","Female","Married","NotBuy"
"S184","Male","Married","NotBuy"
"S185","Female","Married","Buy"
"S186","Male","Married","NotBuy"
"S187","Female","Single","Buy"
"S188","Female","Married","NotBuy"
"S189","Female","Married","Buy"
"S190","Male","Married","Buy"
"S191","Female","Married","Buy"
"S192","Male","Married","Buy"
"S193","Male","Single","NotBuy"
"S194","Female","Married","Buy"
"S195","Male","Single","Buy"
"S196","Male","Married","Buy"
"S197","Male","Single","Buy"
"S198","Female","Single","Buy"
"S199","Male","Married","Buy"
"S200","Female","Married","Buy"
"S201","Male","Married","NotBuy"
"S202","Male","Married","NotBuy"
"S203","Male","Married","NotBuy"
"S204","Male","Single","NotBuy"
"S205","Male","Single","Buy"
"S206","Male","Married","NotBuy"
"S207","Male","Married","Buy"
"S208","Male","Married","Buy"
"S209","Male","Single","Buy"
"S210","Female","Married","Buy"
"S211","Female","Married","NotBuy"
"S212","Male","Married","Buy"
"S213","Female","Married","NotBuy"
"S214","Female","Single","NotBuy"
"S215","Female","Married","NotBuy"
"S216","Male","Single","Buy"
"S217","Male","Married","Buy"
"S218","Female","Single","Buy"
"S219","Female","Single","Buy"
"S220","Male","Married","Buy"
"S221","Male","Single","Buy"
"S222","Male","Married","NotBuy"
"S223","Male","Married","Buy"
"S224","Male","Married","Buy"
"S225","Female","Married","NotBuy"
"S226","Male","Married","NotBuy"
"S227","Male","Single","NotBuy"
"S228","Male","Married","Buy"
"S229","Male","Married","NotBuy"
"S230","Female","Married","NotBuy"
"S231","Male","Single","NotBuy"
"S232","Male","Married","Buy"
"S233","Male","Single","NotBuy"
"S234","Male","Married","Buy"
"S235","Male","Single","NotBuy"
"S236","Male","Single","Buy"
"S237","Male","Single","Buy"
"S238","Male","Married","Buy"
"S239","Male","Married","Buy"
"S240","Male","Single","Buy"
"S241","Male","Married","NotBuy"
"S242","Male","Single","Buy"
"S243","Female","Single","Buy"
"S244","Male","Single","NotBuy"
"S245","Male","Single","NotBuy"
"S246","Male","Single","NotBuy"
"S247","Male","Single","Buy"
"S248","Male","Married","Buy"
"S249","Male","Married","Buy"
"S250","Female","Married","Buy"
"S251","Female","Married","NotBuy"
"S252","Female","Married","Buy"
"S253","Male","Married","Buy"
"S254","Male","Married","Buy"
"S255","Male","Married","NotBuy"
"S256","Male","Married","Buy"
"S257","Male","Married","Buy"
"S258","Male","Married","Buy"
"S259","Male","Single","Buy"
"S260","Female","Married","NotBuy"
"S261","Male","Single","NotBuy"
"S262","Male","Married","Buy"
"S263","Male","Single","NotBuy"
"S264","Female","Married","Buy"
"S265","Female","Married","NotBuy"
"S266","Male","Single","NotBuy"
"S267","Male","Single","Buy"
"S268","Female","Married","NotBuy"
"S269","Male","Single","NotBuy"
"S270","Male","Married","Buy"
"S271","Male","Single","NotBuy"
"S272","Male","Married","NotBuy"
"S273","Male","Married","NotBuy"
"S274","Male","Married","Buy"
"S275","Male","Single","NotBuy"
"S276","Male","Married","Buy"
"S277","Male","Single","NotBuy"
"S278","Male","Single","Buy"
"S279","Male","Married","Buy"
"S280","Female","Single","Buy"
"S281","Male","Single","Buy"
"S282","Male","Married","NotBuy"
"S283","Female","Married","NotBuy"
"S284","Male","Married","NotBuy"
"S285","Male","Married","Buy"
"S286","Male","Single","NotBuy"
"S287","Male","Married","Buy"
"S288","Male","Married","Buy"
"S289","Male","Single","NotBuy"
"S290","Male","Single","Buy"
"S291","Male","Married","Buy"
"S292","Male","Married","Buy"
"S293","Female","Single","NotBuy"
"S294","Male","Single","Buy"
"S295","Male","Married","NotBuy"
"S296","Male","Single","Buy"
"S297","Male","Married","Buy"
"S298","Male","Married","Buy"
"S299","Male","Married","Buy"
"S300","Female","Married","Buy"
"S301","Male","Married","NotBuy"
"S302","Male","Married","Buy"
"S303","Male","Single","Buy"
"S304","Male","Married","NotBuy"
"S305","Male","Single","NotBuy"
"S306","Female","Married","Buy"
"S307","Male","Married","Buy"
"S308","Female","Single","NotBuy"
"S309","Male","Married","NotBuy"
"S310","Male","Married","Buy"
"S311","Male","Married","Buy"
"S312","Male","Married","Buy"
"S313","Female","Single","NotBuy"
"S314","Female","Married","Buy"
"S315","Male","Single","NotBuy"
"S316","Male","Married","Buy"
"S317","Male","Single","NotBuy"
"S318","Male","Married","Buy"
"S319","Female","Married","NotBuy"
"S320","Male","Single","Buy"
"S321","Male","Married","NotBuy"
"S322","Male","Married","Buy"
"S323","Male","Married","Buy"
"S324","Female","Single","Buy"
"S325","Female","Married","Buy"
"S326","Female","Single","Buy"
"S327","Female","Married","NotBuy"
"S328","Female","Married","Buy"
"S329","Male","Single","Buy"
"S330","Female","Married","Buy"
"S331","Female","Single","Buy"
"S332","Male","Single","NotBuy"
"S333","Male","Single","NotBuy"
"S334","Female","Single","Buy"
"S335","Male","Single","Buy"
"S336","Male","Married","Buy"
"S337","Male","Married","Buy"
"S338","Male","Married","Buy"
"S339","Male","Single","NotBuy"
"S340","Female","Married","NotBuy"
"S341","Female","Married","NotBuy"
"S342","Male","Single","NotBuy"
"S343","Male","Married","NotBuy"
"S344","Male","Married","Buy"
"S345","Male","Married","Buy"
"S346","Male","Married","Buy"
"S347","Male","Married","NotBuy"
"S348","Male","Married","NotBuy"
"S349","Male","Married","Buy"
"S350","Female","Married","Buy"
"S351","Male","Married","Buy"
"S352","Male","Single","Buy"
"S353","Female","Single","Buy"
"S354","Male","Married","NotBuy"
"S355","Female","Single","Buy"
"S356","Male","Married","NotBuy"
"S357","Female","Single","NotBuy"
"S358","Male","Married","NotBuy"
"S359","Female","Married","Buy"
"S360","Male","Married","NotBuy"
"S361","Male","Married","Buy"
"S362","Male","Married","Buy"
"S363","Female","Single","Buy"
"S364","Male","Married","Buy"
"S365","Male","Married","NotBuy"
"S366","Female","Married","Buy"
"S367","Male","Single","Buy"
"S368","Female","Single","Buy"
"S369","Male","Married","NotBuy"
"S370","Male","Single","Buy"
"S371","Male","Married","NotBuy"
"S372","Male","Single","Buy"
"S373","Female","Married","Buy"
"S374","Male","Married","NotBuy"
"S375","Male","Single","NotBuy"
"S376","Male","Single","NotBuy"
"S377","Male","Married","Buy"
"S378","Female","Single","NotBuy"
"S379","Male","Single","NotBuy"
"S380","Male","Married","Buy"
"S381","Male","Married","Buy"
"S382","Male","Single","Buy"
"S383","Male","Married","Buy"
"S384","Female","Single","Buy"
"S385","Male","Married","Buy"
"S386","Male","Married","NotBuy"
"S387","Female","Married","Buy"
"S388","Female","Married","Buy"
"S389","Male","Married","NotBuy"
"S390","Female","Married","NotBuy"
"S391","Male","Married","NotBuy"
"S392","Male","Married","NotBuy"
"S393","Female","Married","Buy"
"S394","Male","Married","Buy"
"S395","Male","Married","Buy"
"S396","Male","Married","Buy"
"S397","Male","Married","Buy"
"S398","Male","Married","NotBuy"
"S399","Male","Single","Buy"
"S400","Male","Married","NotBuy"
"S401","Male","Married","NotBuy"
"S402","Male","Married","Buy"
"S403","Female","Married","Buy"
"S404","Female","Married","Buy"
"S405","Female","Married","NotBuy"
"S406","Female","Single","NotBuy"
"S407","Male","Married","Buy"
"S408","Female","Married","Buy"
"S409","Male","Married","Buy"
"S410","Female","Married","NotBuy"
"S411","Female","Single","NotBuy"
"S412","Male","Married","NotBuy"
"S413","Female","Single","Buy"
"S414","Female","Single","Buy"
"S415","Female","Married","Buy"
"S416","Male","Single","NotBuy"
"S417","Male","Married","NotBuy"
"S418","Male","Married","Buy"
"S419","Male","Married","Buy"
"S420","Female","Married","NotBuy"
"S421","Male","Single","NotBuy"
"S422","Male","Married","Buy"
"S423","Male","Married","Buy"
"S424","Male","Married","NotBuy"
"S425","Male","Single","NotBuy"
"S426","Male","Single","Buy"
"S427","Male","Married","Buy"
"S428","Female","Married","Buy"
"S429","Female","Married","Buy"
"S430","Female","Single","Buy"
"S431","Female","Married","NotBuy"
"S432","Male","Married","NotBuy"
"S433","Male","Single","NotBuy"
"S434","Male","Single","Buy"
"S435","Male","Single","Buy"
"S436","Female","Married","NotBuy"
"S437","Female","Married","Buy"
"S438","Male","Married","NotBuy"
"S439","Male","Married","NotBuy"
"S440","Female","Married","Buy"
"S441","Male","Married","NotBuy"
"S442","Male","Married","Buy"
"S443","Male","Single","NotBuy"
"S444","Male","Married","Buy"
"S445","Male","Married","Buy"
"S446","Male","Single","NotBuy"
"S447","Male","Married","NotBuy"
"S448","Female","Married","Buy"
"S449","Male","Married","Buy"
"S450","Male","Single","Buy"
"S451","Female","Single","NotBuy"
"S452","Male","Single","NotBuy"
"S453","Male","Married","Buy"
"S454","Male","Married","Buy"
"S455","Male","Married","NotBuy"
"S456","Male","Single","NotBuy"
"S457","Male","Married","Buy"
"S458","Female","Married","NotBuy"
"S459","Male","Single","NotBuy"
"S460","Male","Married","NotBuy"
"S461","Male","Married","NotBuy"
"S462","Male","Married","Buy"
"S463","Male","Married","Buy"
"S464","Male","Married","Buy"
"S465","Male","Married","Buy"
"S466","Female","Single","NotBuy"
"S467","Female","Married","Buy"
"S468","Male","Married","NotBuy"
"S469","Male","Single","Buy"
"S470","Male","Single","Buy"
"S471","Male","Married","Buy"
"S472","Female","Married","Buy"
"S473","Female","Single","Buy"
"S474","Male","Married","Buy"
"S475","Male","Single","NotBuy"
"S476","Male","Married","NotBuy"
"S477","Female","Married","Buy"
"S478","Male","Married","Buy"
"S479","Male","Married","Buy"
"S480","Male","Single","NotBuy"
"S481","Female","Married","Buy"
"S482","Male","Single","NotBuy"
"S483","Female","Married","NotBuy"
"S484","Female","Married","NotBuy"
"S485","Male","Single","NotBuy"
"S486","Male","Single","Buy"
"S487","Male","Married","NotBuy"
"S488","Male","Married","Buy"
"S489","Female","Single","NotBuy"
"S490","Male","Married","NotBuy"
"S491","Male","Single","Buy"
"S492","Female","Married","NotBuy"
"S493","Male","Married","Buy"
"S494","Male","Married","NotBuy"
"S495","Male","Married","NotBuy"
"S496","Male","Single","Buy"
"S497","Male","Single","Buy"
"S498","Male","Single","NotBuy"
"S499","Female","Single","Buy"
"S500","Male","Married","Buy"
"S501","Male","Married","NotBuy"
"S502","Male","Married","Buy"
"S503","Male","Single","Buy"
"S504","Male","Married","Buy"
"S505","Male","Married","Buy"
"S506","Female","Married","NotBuy"
"S507","Male","Married","Buy"
"S508","Female","Married","Buy"
"S509","Female","Married","NotBuy"
"S510","Male","Single","NotBuy"
"S511","Female","Married","NotBuy"
"S512","Male","Married","Buy"
"S513","Male","Married","Buy"
"S514","Female","Single","Buy"
"S515","Female","Married","Buy"
"S516","Male","Married","Buy"
"S517","Male","Married","NotBuy"
"S518","Male","Single","Buy"
"S519","Male","Married","Buy"
"S520","Male","Married","NotBuy"
"S521","Female","Married","NotBuy"
"S522","Female","Married","Buy"
"S523","Male","Single","Buy"
"S524","Male","Married","NotBuy"
"S525","Male","Married","Buy"
"S526","Female","Married","Buy"
"S527","Female","Single","Buy"
"S528","Female","Married","NotBuy"
"S529","Female","Married","Buy"
"S530","Female","Single","NotBuy"
"S531","Male","Married","Buy"
"S532","Male","Single","NotBuy"
"S533","Male","Married","NotBuy"
"S534","Male","Single","Buy"
"S535","Male","Married","NotBuy"
"S536","Male","Married","NotBuy"
"S537","Male","Married","NotBuy"
"S538","Male","Single","Buy"
"S539","Male","Single","Buy"
"S540","Male","Married","NotBuy"
"S541","Male","Married","Buy"
"S542","Female","Single","NotBuy"
"S543","Male","Single","Buy"
"S544","Male","Single","NotBuy"
"S545","Male","Single","Buy"
"S546","Male","Married","Buy"
"S547","Female","Married","Buy"
"S548","Male","Married","NotBuy"
"S549","Male","Married","Buy"
"S550","Female","Single","NotBuy"
"S551","Female","Married","NotBuy"
"S552","Male","Married","Buy"
"S553","Male","Married","Buy"
"S554","Female","Married","Buy"
"S555","Female","Single","NotBuy"
"S556","Male","Married","Buy"
"S557","Male","Married","Buy"
"S558","Male","Single","Buy"
"S559","Female","Single","Buy"
"S560","Female","Married","NotBuy"
"S561","Male","Married","NotBuy"
"S562","Male","Married","Buy"
"S563","Male","Married","NotBuy"
"S564","Female","Married","Buy"
"S565","Female","Married","NotBuy"
"S566","Male","Single","Buy"
"S567","Male","Single","Buy"
"S568","Male","Married","Buy"
"S569","Male","Single","NotBuy"
"S570","Female","Married","Buy"
"S571","Male","Single","NotBuy"
"S572","Male","Married","Buy"
"S573","Male","Married","NotBuy"
"S574","Male","Married","NotBuy"
"S575","Female","Married","Buy"
"S576","Male","Married","NotBuy"
"S577","Male","Married","NotBuy"
"S578","Male","Married","Buy"
"S579","Female","Married","Buy"
"S580","Female","Single","Buy"
"S581","Male","Single","Buy"
"S582","Female","Married","Buy"
"S583","Male","Married","NotBuy"
"S584","Male","Married","Buy"
"S585","Male","Married","Buy"
"S586","Male","Single","NotBuy"
"S587","Male","Married","Buy"
"S588","Male","Married","Buy"
"S589","Female","Married","NotBuy"
"S590","Female","Single","NotBuy"
"S591","Male","Married","Buy"
"S592","Male","Single","Buy"
"S593","Female","Married","NotBuy"
"S594","Male","Single","Buy"
"S595","Female","Married","Buy"
"S596","Male","Single","NotBuy"
"S597","Male","Married","Buy"
"S598","Male","Single","Buy"
"S599","Male","Married","Buy"
"S600","Male","Married","Buy"
"S601","Female","Married","Buy"
"S602","Female","Married","Buy"
"S603","Male","Married","Buy"
"S604","Female","Married","Buy"
"S605","Female","Married","NotBuy"
"S606","Female","Married","Buy"
"S607","Male","Married","Buy"
"S608","Male","Married","Buy"
"S609","Female","Single","NotBuy"
"S610","Male","Single","NotBuy"
"S611","Female","Single","NotBuy"
"S612","Male","Single","Buy"
"S613","Male","Married","Buy"
"S614","Male","Married","Buy"
"S615","Female","Married","NotBuy"
"S616","Male","Married","Buy"
"S617","Male","Married","NotBuy"
"S618","Male","Married","NotBuy"
"S619","Male","Married","Buy"
"S620","Male","Married","NotBuy"
"S621","Female","Married","NotBuy"
"S622","Male","Married","NotBuy"
"S623","Male","Married","Buy"
"S624","Male","Married","Buy"
"S625","Male","Single","Buy"
"S626","Male","Married","Buy"
"S627","Male","Married","Buy"
"S628","Male","Married","NotBuy"
"S629","Male","Single","Buy"
"S630","Female","Married","Buy"
"S631","Female","Married","NotBuy"
"S632","Male","Single","Buy"
"S633","Male","Single","NotBuy"
"S634","Female","Married","NotBuy"
"S635","Male","Married","Buy"
"S636","Male","Single","NotBuy"
"S637","Male","Married","Buy"
"S638","Male","Married","Buy"
"S639","Male","Single","Buy"
"S640","Male","Married","Buy"
"S641","Female","Married","Buy"
"S642","Male","Married","NotBuy"
"S643","Female","Single","NotBuy"
"S644","Male","Single","Buy"
"S645","Female","Married","NotBuy"
"S646","Male","Married","NotBuy"
"S647","Male","Married","NotBuy"
"S648","Female","Married","NotBuy"
"S649","Male","Single","NotBuy"
"S650","Female","Married","Buy"
"S651","Male","Married","NotBuy"
"S652","Female","Single","Buy"
"S653","Male","Single","NotBuy"
"S654","Male","Married","Buy"
"S655","Male","Married","NotBuy"
"S656","Male","Single","Buy"
"S657","Male","Single","NotBuy"
"S658","Male","Married","NotBuy"
"S659","Female","Single","Buy"
"S660","Female","Married","Buy"
"S661","Male","Married","NotBuy"
"S662","Female","Married","Buy"
"S663","Male","Married","Buy"
"S664","Male","Married","NotBuy"
"S665","Male","Single","Buy"
"S666","Male","Married","Buy"
"S667","Female","Married","NotBuy"
"S668","Male","Married","Buy"
"S669","Male","Married","NotBuy"
"S670","Male","Single","Buy"
"S671","Female","Single","Buy"
"S672","Male","Married","Buy"
"S673","Female","Single","NotBuy"
"S674","Male","Married","Buy"
"S675","Male","Married","Buy"
"S676","Male","Single","NotBuy"
"S677","Male","Married","NotBuy"
"S678","Male","Married","NotBuy"
"S679","Male","Single","NotBuy"
"S680","Male","Married","Buy"
"S681","Female","Single","Buy"
"S682","Female","Married","Buy"
"S683","Male","Married","NotBuy"
"S684","Male","Single","Buy"
"S685","Male","Married","Buy"
"S686","Male","Married","Buy"
"S687","Male","Married","Buy"
"S688","Male","Married","Buy"
"S689","Male","Married","NotBuy"
"S690","Female","Married","NotBuy"
"S691","Male","Married","Buy"
"S692","Male","Married","Buy"
"S693","Female","Married","Buy"
"S694","Male","Married","Buy"
"S695","Male","Single","NotBuy"
"S696","Male","Married","Buy"
"S697","Female","Married","NotBuy"
"S698","Male","Married","NotBuy"
"S699","Female","Single","NotBuy"
"S700","Male","Married","Buy"
"S701","Female","Married","NotBuy"
"S702","Female","Single","NotBuy"
"S703","Female","Married","NotBuy"
"S704","Male","Single","Buy"
"S705","Male","Married","Buy"
"S706","Male","Single","Buy"
"S707","Male","Married","NotBuy"
"S708","Male","Married","Buy"
"S709","Female","Single","NotBuy"
"S710","Female","Married","Buy"
"S711","Female","Married","NotBuy"
"S712","Male","Married","Buy"
"S713","Male","Married","Buy"
"S714","Female","Married","Buy"
"S715","Male","Married","NotBuy"
"S716","Male","Married","Buy"
"S717","Male","Married","NotBuy"
"S718","Male","Single","Buy"
"S719","Male","Married","Buy"
"S720","Female","Married","Buy"
"S721","Male","Single","NotBuy"
"S722","Male","Single","NotBuy"
"S723","Female","Single","NotBuy"
"S724","Female","Married","Buy"
"S725","Male","Married","Buy"
"S726","Female","Married","NotBuy"
"S727","Male","Married","Buy"
"S728","Male","Married","NotBuy"
"S729","Male","Married","Buy"
"S730","Male","Married","Buy"
"S731","Female","Single","Buy"
"S732","Male","Married","NotBuy"
"S733","Female","Married","Buy"
"S734","Male","Single","NotBuy"
"S735","Female","Married","NotBuy"
"S736","Male","Single","Buy"
"S737","Male","Single","Buy"
"S738","Female","Married","Buy"
"S739","Male","Married","Buy"
"S740","Male","Married","Buy"
"S741","Male","Married","Buy"
"S742","Male","Married","Buy"
"S743","Male","Single","NotBuy"
"S744","Male","Single","NotBuy"
"S745","Male","Married","Buy"
"S746","Male","Married","Buy"
"S747","Male","Married","NotBuy"
"S748","Female","Married","NotBuy"
"S749","Male","Married","Buy"
"S750","Male","Single","Buy"
"S751","Male","Married","Buy"
"S752","Female","Married","Buy"
"S753","Male","Married","Buy"
"S754","Male","Single","NotBuy"
"S755","Male","Single","NotBuy"
"S756","Male","Married","Buy"
"S757","Male","Married","Buy"
"S758","Male","Married","Buy"
"S759","Female","Married","NotBuy"
"S760","Male","Married","NotBuy"
"S761","Female","Married","Buy"
"S762","Male","Single","Buy"
"S763","Female","Married","Buy"
"S764","Male","Married","NotBuy"
"S765","Male","Married","Buy"
"S766","Male","Married","NotBuy"
"S767","Male","Married","Buy"
"S768","Female","Single","Buy"
"S769","Female","Married","NotBuy"
"S770","Female","Single","Buy"
"S771","Male","Single","Buy"
"S772","Female","Married","Buy"
"S773","Male","Single","Buy"
"S774","Female","Married","Buy"
"S775","Male","Single","Buy"
"S776","Male","Married","Buy"
"S777","Male","Married","NotBuy"
"S778","Female","Married","NotBuy"
"S779","Male","Married","NotBuy"
"S780","Female","Married","Buy"
"S781","Female","Married","Buy"
"S782","Female","Single","Buy"
"S783","Female","Married","NotBuy"
"S784","Male","Married","NotBuy"
"S785","Female","Married","NotBuy"
"S786","Male","Married","Buy"
"S787","Male","Single","NotBuy"
"S788","Male","Married","Buy"
"S789","Male","Married","Buy"
"S790","Male","Married","Buy"
"S791","Male","Married","NotBuy"
"S792","Male","Single","NotBuy"
"S793","Female","Married","Buy"
"S794","Male","Married","Buy"
"S795","Male","Single","NotBuy"
"S796","Male","Single","Buy"
"S797","Male","Married","Buy"
"S798","Male","Single","NotBuy"
"S799","Female","Married","NotBuy"
"S800","Male","Married","NotBuy"
"S801","Female","Single","Buy"
"S802","Male","Married","Buy"
"S803","Female","Single","Buy"
"S804","Female","Married","Buy"
"S805","Male","Single","Buy"
"S806","Male","Single","NotBuy"
"S807","Male","Married","Buy"
"S808","Female","Married","Buy"
"S809","Male","Married","NotBuy"
"S810","Male","Single","Buy"
"S811","Male","Single","Buy"
"S812","Male","Single","Buy"
"S813","Male","Single","Buy"
"S814","Female","Married","NotBuy"
"S815","Male","Married","Buy"
"S816","Male","Single","Buy"
"S817","Female","Married","NotBuy"
"S818","Female","Married","Buy"
"S819","Male","Single","Buy"
"S820","Female","Married","NotBuy"
"S821","Male","Married","Buy"
"S822","Male","Married","Buy"
"S823","Female","Married","Buy"
"S824","Female","Married","Buy"
"S825","Female","Married","Buy"
"S826","Male","Married","Buy"
"S827","Male","Single","Buy"
"S828","Male","Single","Buy"
"S829","Male","Married","Buy"
"S830","Female","Married","NotBuy"
"S831","Male","Single","Buy"
"S832","Female","Married","Buy"
"S833","Female","Married","NotBuy"
"S834","Female","Married","Buy"
"S835","Male","Single","Buy"
"S836","Male","Married","Buy"
"S837","Male","Married","NotBuy"
"S838","Female","Single","NotBuy"
"S839","Female","Married","NotBuy"
"S840","Male","Married","NotBuy"
"S841","Female","Married","NotBuy"
"S842","Male","Married","Buy"
"S843","Male","Married","Buy"
"S844","Male","Single","Buy"
"S845","Male","Single","NotBuy"
"S846","Male","Married","NotBuy"
"S847","Male","Married","NotBuy"
"S848","Male","Single","Buy"
"S849","Female","Single","NotBuy"
"S850","Male","Married","Buy"
"S851","Female","Married","NotBuy"
"S852","Female","Single","Buy"
"S853","Male","Married","NotBuy"
"S854","Female","Married","Buy"
"S855","Male","Married","NotBuy"
"S856","Male","Single","Buy"
"S857","Male","Married","Buy"
"S858","Female","Single","NotBuy"
"S859","Male","Married","NotBuy"
"S860","Male","Married","Buy"
"S861","Male","Married","Buy"
"S862","Male","Married","Buy"
"S863","Male","Single","Buy"
"S864","Male","Married","NotBuy"
"S865","Male","Married","Buy"
"S866","Male","Married","NotBuy"
"S867","Male","Married","NotBuy"
"S868","Male","Married","NotBuy"
"S869","Female","Married","Buy"
"S870","Male","Single","Buy"
"S871","Male","Married","Buy"
"S872","Female","Married","Buy"
"S873","Female","Married","Buy"
"S874","Male","Single","NotBuy"
"S875","Male","Married","Buy"
"S876","Male","Single","NotBuy"
"S877","Male","Married","Buy"
"S878","Male","Single","Buy"
"S879","Female","Married","Buy"
"S880","Female","Married","Buy"
"S881","Male","Single","Buy"
"S882","Female","Single","Buy"
"S883","Female","Single","Buy"
"S884","Female","Married","Buy"
"S885","Male","Married","Buy"
"S886","Female","Married","Buy"
"S887","Female","Married","NotBuy"
"S888","Male","Single","Buy"
"S889","Male","Single","Buy"
"S890","Male","Married","NotBuy"
"S891","Male","Married","Buy"
"S892","Male","Married","NotBuy"
"S893","Male","Single","Buy"
"S894","Male","Married","Buy"
"S895","Male","Married","NotBuy"
"S896","Male","Married","Buy"
"S897","Male","Married","Buy"
"S898","Male","Married","Buy"
"S899","Male","Married","NotBuy"
"S900","Male","Married","Buy"
"S901","Female","Single","Buy"
"S902","Female","Single","Buy"
"S903","Male","Married","Buy"
"S904","Male","Married","Buy"
"S905","Male","Married","Buy"
"S906","Male","Married","NotBuy"
"S907","Male","Married","Buy"
"S908","Male","Single","NotBuy"
"S909","Male","Single","NotBuy"
"S910","Male","Single","Buy"
"S911","Male","Married","Buy"
"S912","Female","Married","Buy"
"S913","Male","Married","Buy"
"S914","Male","Married","Buy"
"S915","Male","Married","NotBuy"
"S916","Male","Married","Buy"
"S917","Male","Married","NotBuy"
"S918","Female","Married","Buy"
"S919","Female","Single","Buy"
"S920","Female","Single","NotBuy"
"S921","Male","Married","Buy"
"S922","Female","Married","NotBuy"
"S923","Female","Married","Buy"
"S924","Male","Married","NotBuy"
"S925","Female","Married","Buy"
"S926","Female","Single","Buy"
"S927","Female","Married","Buy"
"S928","Male","Single","Buy"
"S929","Male","Single","NotBuy"
"S930","Male","Married","Buy"
"S931","Male","Married","NotBuy"
"S932","Female","Married","NotBuy"
"S933","Male","Single","NotBuy"
"S934","Male","Married","NotBuy"
"S935","Female","Single","Buy"
"S936","Female","Married","Buy"
"S937","Female","Married","NotBuy"
"S938","Male","Single","NotBuy"
"S939","Female","Single","Buy"
"S940","Male","Married","Buy"
"S941","Male","Married","NotBuy"
"S942","Female","Married","Buy"
"S943","Male","Single","NotBuy"
"S944","Male","Single","Buy"
"S945","Male","Married","NotBuy"
"S946","Male","Married","NotBuy"
"S947","Male","Single","Buy"
"S948","Female","Married","Buy"
"S949","Female","Single","NotBuy"
"S950","Female","Married","Buy"
"S951","Male","Single","Buy"
"S952","Male","Single","NotBuy"
"S953","Male","Married","Buy"
"S954","Female","Married","NotBuy"
"S955","Male","Single","Buy"
"S956","Male","Married","NotBuy"
"S957","Female","Single","Buy"
"S958","Male","Married","Buy"
"S959","Male","Married","Buy"
"S960","Male","Single","Buy"
"S961","Male","Married","Buy"
"S962","Male","Single","NotBuy"
"S963","Male","Married","Buy"
"S964","Male","Married","Buy"
"S965","Male","Married","Buy"
"S966","Male","Married","NotBuy"
"S967","Male","Single","Buy"
"S968","Female","Married","NotBuy"
"S969","Female","Single","NotBuy"
"S970","Female","Married","Buy"
"S971","Female","Married","Buy"
"S972","Male","Married","Buy"
"S973","Male","Married","Buy"
"S974","Female","Married","NotBuy"
"S975","Male","Single","Buy"
"S976","Male","Married","NotBuy"
"S977","Male","Single","Buy"
"S978","Male","Single","NotBuy"
"S979","Male","Married","Buy"
"S980","Female","Married","NotBuy"
"S981","Male","Married","NotBuy"
"S982","Male","Married","Buy"
"S983","Female","Single","Buy"
"S984","Male","Married","Buy"
"S985","Male","Married","NotBuy"
"S986","Male","Married","Buy"
"S987","Male","Single","Buy"
"S988","Male","Married","NotBuy"
"S989","Female","Married","NotBuy"
"S990","Male","Married","Buy"
"S991","Male","Single","Buy"
"S992","Male","Married","NotBuy"
"S993","Male","Married","Buy"
"S994","Male","Married","Buy"
"S995","Male","Married","Buy"
"S996","Female","Married","Buy"
"S997","Male","Single","Buy"
"S998","Male","Married","NotBuy"
"S999","Male","Married","Buy"
"S1000","Male","Single","Buy"


================================================
FILE: data/fintransactions.csv
================================================
"","transactionID","item"
"1",187,"NPS"
"2",22,"General Insurance"
"3",355,"PPF"
"4",329,"General Insurance"
"5",125,"FD"
"6",151,"Mobile Banking"
"7",293,"PPF"
"8",167,"Credit Card"
"9",312,"Stocks"
"10",273,"Stocks"
"11",440,"General Insurance"
"12",354,"Savings Account"
"13",366,"Car Insurance"
"14",466,"PPF"
"15",228,"PPF"
"16",296,"NRI Banking"
"17",411,"NRI Banking"
"18",113,"Stocks"
"19",206,"Bonds"
"20",20,"General Insurance"
"21",351,"Credit Card"
"22",479,"Credit Card"
"23",107,"General Insurance"
"24",331,"General Insurance"
"25",462,"Stocks"
"26",398,"Mobile Banking"
"27",36,"General Insurance"
"28",195,"Bonds"
"29",204,"General Insurance"
"30",330,"Debit Card"
"31",212,"Car Insurance"
"32",161,"NRI Banking"
"33",99,"Savings Account"
"34",82,"Savings Account"
"35",262,"FD"
"36",457,"NPS"
"37",104,"PPF"
"38",408,"NRI Banking"
"39",11,"Bonds"
"40",463,"FD"
"41",218,"PPF"
"42",222,"Debit Card"
"43",381,"NRI Banking"
"44",167,"NRI Banking"
"45",198,"Stocks"
"46",117,"Car Insurance"
"47",36,"Credit Card"
"48",457,"General Insurance"
"49",386,"Mobile Banking"
"50",54,"Mobile Banking"
"51",40,"FD"
"52",217,"Mobile Banking"
"53",340,"Debit Card"
"54",368,"Mutual Funds"
"55",227,"Debit Card"
"56",392,"Stocks"
"57",340,"Mobile Banking"
"58",260,"FD"
"59",346,"Mutual Funds"
"60",295,"Savings Account"
"61",408,"Credit Card"
"62",406,"Stocks"
"63",305,"PPF"
"64",497,"NRI Banking"
"65",422,"Credit Card"
"66",358,"FD"
"67",10,"Savings Account"
"68",153,"Savings Account"
"69",442,"Bonds"
"70",471,"General Insurance"
"71",118,"Savings Account"
"72",469,"Mobile Banking"
"73",284,"General Insurance"
"74",422,"Stocks"
"75",411,"Credit Card"
"76",140,"Debit Card"
"77",24,"Savings Account"
"78",113,"Savings Account"
"79",337,"Car Insurance"
"80",480,"Car Insurance"
"81",343,"Car Insurance"
"82",388,"Savings Account"
"83",388,"Mutual Funds"
"84",492,"PPF"
"85",6,"General Insurance"
"86",477,"NRI Banking"
"87",162,"NPS"
"88",215,"Debit Card"
"89",68,"Credit Card"
"90",10,"Mutual Funds"
"91",329,"Mobile Banking"
"92",458,"FD"
"93",358,"NRI Banking"
"94",92,"General Insurance"
"95",121,"Savings Account"
"96",419,"NPS"
"97",193,"Car Insurance"
"98",117,"Savings Account"
"99",35,"Savings Account"
"100",32,"Mobile Banking"
"101",63,"Savings Account"
"102",12,"Savings Account"
"103",196,"General Insurance"
"104",430,"Car Insurance"
"105",360,"Car Insurance"
"106",170,"Savings Account"
"107",41,"Stocks"
"108",19,"Mobile Banking"
"109",387,"Stocks"
"110",498,"Debit Card"
"111",74,"Bonds"
"112",20,"FD"
"113",283,"Mobile Banking"
"114",445,"Car Insurance"
"115",436,"Mutual Funds"
"116",491,"Mutual Funds"
"117",440,"General Insurance"
"118",256,"Mobile Banking"
"119",168,"PPF"
"120",307,"NPS"
"121",199,"Mutual Funds"
"122",70,"NPS"
"123",40,"Car Insurance"
"124",276,"Debit Card"
"125",131,"Mobile Banking"
"126",405,"Stocks"
"127",273,"Stocks"
"128",238,"Mutual Funds"
"129",332,"Debit Card"
"130",47,"Credit Card"
"131",326,"Bonds"
"132",185,"Bonds"
"133",123,"Bonds"
"134",150,"FD"
"135",280,"NPS"
"136",241,"PPF"
"137",239,"Credit Card"
"138",468,"Debit Card"
"139",236,"Credit Card"
"140",340,"Mobile Banking"
"141",468,"PPF"
"142",137,"Car Insurance"
"143",474,"Mobile Banking"
"144",157,"NPS"
"145",438,"General Insurance"
"146",84,"NRI Banking"
"147",235,"Stocks"
"148",327,"NPS"
"149",18,"Car Insurance"
"150",218,"Mobile Banking"
"151",75,"PPF"
"152",230,"PPF"
"153",310,"General Insurance"
"154",479,"General Insurance"
"155",51,"NRI Banking"
"156",114,"Savings Account"
"157",278,"Savings Account"
"158",386,"PPF"
"159",240,"NRI Banking"
"160",441,"Mutual Funds"
"161",484,"PPF"
"162",346,"PPF"
"163",434,"Mutual Funds"
"164",281,"NRI Banking"
"165",153,"Bonds"
"166",500,"PPF"
"167",147,"NRI Banking"
"168",452,"NRI Banking"
"169",21,"General Insurance"
"170",300,"Mobile Banking"
"171",341,"PPF"
"172",492,"Debit Card"
"173",252,"Bonds"
"174",372,"Stocks"
"175",456,"Debit Card"
"176",495,"Debit Card"
"177",383,"Car Insurance"
"178",411,"Car Insurance"
"179",470,"NPS"
"180",336,"Mutual Funds"
"181",454,"Bonds"
"182",382,"PPF"
"183",244,"Car Insurance"
"184",126,"Debit Card"
"185",180,"Mutual Funds"
"186",5,"FD"
"187",118,"General Insurance"
"188",54,"FD"
"189",306,"Savings Account"
"190",103,"Stocks"
"191",108,"General Insurance"
"192",9,"Savings Account"
"193",165,"NPS"
"194",135,"Car Insurance"
"195",457,"General Insurance"
"196",209,"Debit Card"
"197",346,"Mobile Banking"
"198",451,"General Insurance"
"199",104,"Credit Card"
"200",231,"Car Insurance"
"201",303,"FD"
"202",282,"Credit Card"
"203",139,"NPS"
"204",114,"Debit Card"
"205",492,"FD"
"206",50,"FD"
"207",440,"Savings Account"
"208",117,"Debit Card"
"209",387,"PPF"
"210",236,"Stocks"
"211",205,"Mutual Funds"
"212",412,"NPS"
"213",268,"General Insurance"
"214",246,"Mutual Funds"
"215",8,"Credit Card"
"216",322,"Savings Account"
"217",161,"General Insurance"
"218",268,"Car Insurance"
"219",478,"Mobile Banking"
"220",21,"Mobile Banking"
"221",143,"Stocks"
"222",246,"General Insurance"
"223",241,"NRI Banking"
"224",220,"Bonds"
"225",219,"General Insurance"
"226",94,"Mutual Funds"
"227",473,"PPF"
"228",73,"Savings Account"
"229",390,"NPS"
"230",407,"General Insurance"
"231",111,"Credit Card"
"232",159,"Mutual Funds"
"233",133,"Savings Account"
"234",261,"FD"
"235",102,"Savings Account"
"236",327,"FD"
"237",139,"Mobile Banking"
"238",205,"Stocks"
"239",419,"Savings Account"
"240",218,"Credit Card"
"241",128,"General Insurance"
"242",429,"Mobile Banking"
"243",110,"NRI Banking"
"244",94,"FD"
"245",11,"Mobile Banking"
"246",233,"Mobile Banking"
"247",149,"Savings Account"
"248",95,"Car Insurance"
"249",335,"Stocks"
"250",141,"Mobile Banking"
"251",111,"FD"
"252",246,"General Insurance"
"253",113,"NPS"
"254",411,"Stocks"
"255",192,"Savings Account"
"256",314,"Car Insurance"
"257",476,"Savings Account"
"258",389,"Savings Account"
"259",54,"Savings Account"
"260",115,"Car Insurance"
"261",109,"Bonds"
"262",347,"Car Insurance"
"263",299,"Bonds"
"264",140,"Savings Account"
"265",222,"Stocks"
"266",449,"NRI Banking"
"267",27,"PPF"
"268",333,"FD"
"269",415,"NRI Banking"
"270",376,"General Insurance"
"271",18,"Credit Card"
"272",323,"Credit Card"
"273",482,"NPS"
"274",181,"Credit Card"
"275",486,"General Insurance"
"276",246,"Mobile Banking"
"277",466,"Bonds"
"278",301,"Bonds"
"279",176,"Stocks"
"280",44,"NRI Banking"
"281",91,"Bonds"
"282",55,"Mutual Funds"
"283",184,"Credit Card"
"284",5,"Bonds"
"285",40,"Mutual Funds"
"286",150,"Bonds"
"287",294,"Bonds"
"288",352,"Bonds"
"289",375,"Car Insurance"
"290",158,"FD"
"291",194,"FD"
"292",495,"Car Insurance"
"293",341,"Bonds"
"294",445,"PPF"
"295",160,"Savings Account"
"296",232,"Mutual Funds"
"297",203,"PPF"
"298",463,"Mobile Banking"
"299",164,"Stocks"
"300",56,"Debit Card"
"301",135,"PPF"
"302",119,"Stocks"
"303",300,"NPS"
"304",105,"Credit Card"
"305",113,"Mutual Funds"
"306",281,"Credit Card"
"307",385,"NRI Banking"
"308",317,"Car Insurance"
"309",172,"NRI Banking"
"310",100,"Savings Account"
"311",2,"Mutual Funds"
"312",425,"Savings Account"
"313",340,"Bonds"
"314",102,"Mutual Funds"
"315",492,"NPS"
"316",377,"Credit Card"
"317",197,"General Insurance"
"318",46,"Savings Account"
"319",300,"NRI Banking"
"320",339,"Debit Card"
"321",258,"Savings Account"
"322",181,"NPS"
"323",327,"Debit Card"
"324",264,"FD"
"325",34,"Stocks"
"326",419,"Car Insurance"
"327",29,"NRI Banking"
"328",372,"FD"
"329",89,"Credit Card"
"330",350,"Car Insurance"
"331",391,"FD"
"332",380,"Car Insurance"
"333",109,"FD"
"334",108,"Car Insurance"
"335",51,"Car Insurance"
"336",75,"NRI Banking"
"337",250,"Debit Card"
"338",360,"Credit Card"
"339",17,"General Insurance"
"340",11,"Car Insurance"
"341",338,"Stocks"
"342",469,"Debit Card"
"343",167,"Bonds"
"344",29,"Mutual Funds"
"345",382,"Mobile Banking"
"346",465,"Mutual Funds"
"347",416,"Credit Card"
"348",90,"Credit Card"
"349",325,"NPS"
"350",336,"General Insurance"
"351",445,"General Insurance"
"352",93,"Bonds"
"353",247,"General Insurance"
"354",66,"Mobile Banking"
"355",243,"Stocks"
"356",302,"Debit Card"
"357",447,"FD"
"358",321,"Car Insurance"
"359",85,"NRI Banking"
"360",6,"Car Insurance"
"361",411,"Car Insurance"
"362",108,"Credit Card"
"363",87,"Savings Account"
"364",7,"Credit Card"
"365",273,"Debit Card"
"366",263,"Mobile Banking"
"367",355,"NRI Banking"
"368",293,"NPS"
"369",352,"NRI Banking"
"370",396,"Car Insurance"
"371",96,"Bonds"
"372",335,"Car Insurance"
"373",213,"General Insurance"
"374",259,"NRI Banking"
"375",208,"Mutual Funds"
"376",500,"Mutual Funds"
"377",210,"Car Insurance"
"378",380,"General Insurance"
"379",479,"Debit Card"
"380",70,"PPF"
"381",291,"Stocks"
"382",192,"Savings Account"
"383",348,"General Insurance"
"384",70,"Mobile Banking"
"385",464,"NRI Banking"
"386",343,"NRI Banking"
"387",322,"PPF"
"388",61,"Mobile Banking"
"389",96,"Savings Account"
"390",458,"PPF"
"391",248,"NPS"
"392",127,"NRI Banking"
"393",118,"PPF"
"394",444,"Car Insurance"
"395",488,"General Insurance"
"396",353,"Debit Card"
"397",148,"General Insurance"
"398",75,"PPF"
"399",245,"Debit Card"
"400",57,"Debit Card"
"401",218,"Stocks"
"402",413,"PPF"
"403",42,"General Insurance"
"404",435,"FD"
"405",333,"Bonds"
"406",481,"Savings Account"
"407",108,"Savings Account"
"408",414,"General Insurance"
"409",444,"General Insurance"
"410",120,"Bonds"
"411",408,"NRI Banking"
"412",288,"Mutual Funds"
"413",230,"Mobile Banking"
"414",26,"Car Insurance"
"415",309,"Mobile Banking"
"416",497,"PPF"
"417",290,"PPF"
"418",170,"Stocks"
"419",219,"General Insurance"
"420",108,"NRI Banking"
"421",469,"Savings Account"
"422",486,"General Insurance"
"423",8,"Credit Card"
"424",201,"FD"
"425",363,"Car Insurance"
"426",230,"PPF"
"427",259,"Car Insurance"
"428",415,"Bonds"
"429",452,"Savings Account"
"430",377,"Car Insurance"
"431",381,"General Insurance"
"432",420,"FD"
"433",318,"Stocks"
"434",223,"NRI Banking"
"435",235,"Debit Card"
"436",368,"NRI Banking"
"437",338,"FD"
"438",201,"Mobile Banking"
"439",450,"General Insurance"
"440",239,"General Insurance"
"441",449,"FD"
"442",484,"Stocks"
"443",304,"NPS"
"444",286,"Debit Card"
"445",227,"Bonds"
"446",485,"Debit Card"
"447",394,"FD"
"448",51,"FD"
"449",152,"Debit Card"
"450",115,"FD"
"451",172,"PPF"
"452",143,"Mutual Funds"
"453",445,"FD"
"454",276,"Bonds"
"455",267,"NPS"
"456",171,"Mutual Funds"
"457",362,"Mutual Funds"
"458",356,"General Insurance"
"459",152,"Bonds"
"460",201,"Mobile Banking"
"461",390,"Mobile Banking"
"462",218,"NPS"
"463",395,"Stocks"
"464",171,"Car Insurance"
"465",122,"NPS"
"466",278,"Bonds"
"467",441,"NRI Banking"
"468",314,"Savings Account"
"469",361,"Mobile Banking"
"470",80,"Mobile Banking"
"471",161,"Mutual Funds"
"472",178,"Mobile Banking"
"473",326,"Mobile Banking"
"474",293,"Car Insurance"
"475",149,"NPS"
"476",271,"Mutual Funds"
"477",428,"Bonds"
"478",276,"NPS"
"479",210,"FD"
"480",113,"Mobile Banking"
"481",365,"Debit Card"
"482",26,"Debit Card"
"483",146,"PPF"
"484",20,"Bonds"
"485",9,"General Insurance"
"486",350,"Bonds"
"487",300,"General Insurance"
"488",20,"Debit Card"
"489",240,"General Insurance"
"490",333,"Savings Account"
"491",128,"NRI Banking"
"492",60,"Mutual Funds"
"493",37,"PPF"
"494",331,"NPS"
"495",255,"PPF"
"496",455,"General Insurance"
"497",355,"Savings Account"
"498",121,"FD"
"499",105,"NPS"
"500",223,"Car Insurance"
"501",203,"FD"
"502",302,"Mobile Banking"
"503",181,"General Insurance"
"504",146,"FD"
"505",397,"Mobile Banking"
"506",351,"Bonds"
"507",183,"Stocks"
"508",277,"Stocks"
"509",60,"Bonds"
"510",75,"Car Insurance"
"511",76,"NPS"
"512",337,"Bonds"
"513",236,"Credit Card"
"514",142,"Credit Card"
"515",399,"Bonds"
"516",392,"Mobile Banking"
"517",76,"FD"
"518",85,"Credit Card"
"519",221,"NPS"
"520",328,"NPS"
"521",195,"NPS"
"522",377,"FD"
"523",43,"Credit Card"
"524",2,"NPS"
"525",471,"PPF"
"526",329,"Bonds"
"527",142,"Debit Card"
"528",211,"Mutual Funds"
"529",493,"Debit Card"
"530",312,"Mobile Banking"
"531",394,"Car Insurance"
"532",373,"NPS"
"533",291,"Bonds"
"534",245,"Bonds"
"535",246,"Debit Card"
"536",40,"Stocks"
"537",310,"Mutual Funds"
"538",228,"Mobile Banking"
"539",67,"NPS"
"540",35,"Savings Account"
"541",390,"NRI Banking"
"542",269,"Mobile Banking"
"543",246,"NRI Banking"
"544",213,"FD"
"545",92,"Bonds"
"546",428,"Car Insurance"
"547",328,"NPS"
"548",439,"NRI Banking"
"549",63,"Savings Account"
"550",138,"FD"
"551",288,"PPF"
"552",452,"Mobile Banking"
"553",294,"Credit Card"
"554",67,"Bonds"
"555",74,"Credit Card"
"556",357,"Mobile Banking"
"557",236,"Savings Account"
"558",299,"General Insurance"
"559",30,"Bonds"
"560",447,"Mutual Funds"
"561",61,"Car Insurance"
"562",406,"Savings Account"
"563",8,"Mutual Funds"
"564",499,"NRI Banking"
"565",317,"NPS"
"566",114,"NRI Banking"
"567",409,"Mutual Funds"
"568",115,"Mobile Banking"
"569",432,"Mobile Banking"
"570",293,"NRI Banking"
"571",36,"Mutual Funds"
"572",351,"PPF"
"573",195,"Car Insurance"
"574",406,"Mutual Funds"
"575",401,"Stocks"
"576",262,"General Insurance"
"577",50,"Bonds"
"578",175,"Stocks"
"579",189,"Mutual Funds"
"580",189,"Debit Card"
"581",180,"PPF"
"582",232,"Car Insurance"
"583",461,"FD"
"584",25,"NRI Banking"
"585",200,"Stocks"
"586",487,"Car Insurance"
"587",326,"Bonds"
"588",260,"Savings Account"
"589",351,"Bonds"
"590",397,"Debit Card"
"591",366,"Bonds"
"592",219,"Debit Card"
"593",185,"Bonds"
"594",221,"Debit Card"
"595",386,"NPS"
"596",69,"FD"
"597",189,"Car Insurance"
"598",443,"Car Insurance"
"599",204,"NRI Banking"
"600",98,"Mobile Banking"
"601",61,"Mutual Funds"
"602",264,"Debit Card"
"603",203,"NRI Banking"
"604",211,"Stocks"
"605",441,"NRI Banking"
"606",113,"Savings Account"
"607",292,"NRI Banking"
"608",171,"NRI Banking"
"609",242,"Bonds"
"610",88,"PPF"
"611",83,"Debit Card"
"612",224,"NRI Banking"
"613",359,"Car Insurance"
"614",32,"Mobile Banking"
"615",52,"Stocks"
"616",129,"Credit Card"
"617",428,"Mobile Banking"
"618",276,"Credit Card"
"619",167,"Debit Card"
"620",333,"Savings Account"
"621",298,"PPF"
"622",173,"Bonds"
"623",154,"PPF"
"624",371,"General Insurance"
"625",23,"Mobile Banking"
"626",422,"NRI Banking"
"627",350,"Savings Account"
"628",271,"PPF"
"629",471,"General Insurance"
"630",306,"Mutual Funds"
"631",4,"Stocks"
"632",129,"General Insurance"
"633",221,"Bonds"
"634",102,"NRI Banking"
"635",492,"Credit Card"
"636",145,"Savings Account"
"637",183,"Car Insurance"
"638",21,"Car Insurance"
"639",419,"Stocks"
"640",156,"Bonds"
"641",327,"Credit Card"
"642",385,"Mutual Funds"
"643",153,"General Insurance"
"644",499,"FD"
"645",74,"General Insurance"
"646",16,"PPF"
"647",260,"Stocks"
"648",1,"Car Insurance"
"649",79,"General Insurance"
"650",393,"Stocks"
"651",128,"NRI Banking"
"652",378,"FD"
"653",269,"PPF"
"654",144,"Mutual Funds"
"655",254,"NRI Banking"
"656",91,"NPS"
"657",28,"PPF"
"658",69,"Mutual Funds"
"659",343,"NRI Banking"
"660",444,"General Insurance"
"661",206,"Car Insurance"
"662",366,"Car Insurance"
"663",166,"Savings Account"
"664",441,"Mobile Banking"
"665",454,"PPF"
"666",222,"Car Insurance"
"667",160,"Debit Card"
"668",499,"Stocks"
"669",46,"Bonds"
"670",342,"PPF"
"671",477,"Mutual Funds"
"672",249,"Debit Card"
"673",353,"Mutual Funds"
"674",386,"FD"
"675",421,"Mobile Banking"
"676",123,"Stocks"
"677",317,"FD"
"678",320,"Bonds"
"679",73,"Car Insurance"
"680",144,"NPS"
"681",242,"NRI Banking"
"682",205,"Credit Card"
"683",50,"NPS"
"684",322,"NRI Banking"
"685",93,"FD"
"686",472,"Mutual Funds"
"687",354,"Savings Account"
"688",449,"Car Insurance"
"689",417,"Savings Account"
"690",186,"FD"
"691",202,"General Insurance"
"692",411,"PPF"
"693",403,"NRI Banking"
"694",240,"Bonds"
"695",125,"Mutual Funds"
"696",491,"Debit Card"
"697",155,"Car Insurance"
"698",25,"FD"
"699",484,"Stocks"
"700",30,"Credit Card"
"701",186,"PPF"
"702",198,"NRI Banking"
"703",302,"General Insurance"
"704",28,"NRI Banking"
"705",67,"Bonds"
"706",179,"Debit Card"
"707",428,"Savings Account"
"708",371,"Debit Card"
"709",291,"NRI Banking"
"710",487,"Savings Account"
"711",11,"PPF"
"712",484,"Mutual Funds"
"713",446,"Mutual Funds"
"714",468,"Credit Card"
"715",443,"Car Insurance"
"716",107,"General Insurance"
"717",391,"Mutual Funds"
"718",36,"Credit Card"
"719",40,"General Insurance"
"720",167,"General Insurance"
"721",167,"FD"
"722",56,"NRI Banking"
"723",165,"Debit Card"
"724",86,"Credit Card"
"725",456,"NPS"
"726",428,"Debit Card"
"727",71,"Car Insurance"
"728",188,"NPS"
"729",185,"Credit Card"
"730",169,"FD"
"731",387,"NRI Banking"
"732",298,"Car Insurance"
"733",489,"PPF"
"734",333,"Credit Card"
"735",482,"Bonds"
"736",450,"FD"
"737",55,"Car Insurance"
"738",271,"Car Insurance"
"739",1,"Mobile Banking"
"740",258,"NRI Banking"
"741",70,"General Insurance"
"742",70,"General Insurance"
"743",263,"NRI Banking"
"744",348,"FD"
"745",214,"Credit Card"
"746",412,"PPF"
"747",317,"NPS"
"748",122,"Mobile Banking"
"749",433,"Mutual Funds"
"750",305,"Bonds"
"751",378,"Bonds"
"752",390,"Mutual Funds"
"753",341,"PPF"
"754",144,"PPF"
"755",26,"FD"
"756",369,"Mobile Banking"
"757",125,"Mutual Funds"
"758",77,"Stocks"
"759",344,"NPS"
"760",108,"Mobile Banking"
"761",295,"Mobile Banking"
"762",78,"Mobile Banking"
"763",130,"FD"
"764",350,"Car Insurance"
"765",228,"Stocks"
"766",45,"General Insurance"
"767",350,"Mobile Banking"
"768",407,"Mobile Banking"
"769",498,"Credit Card"
"770",440,"General Insurance"
"771",354,"Car Insurance"
"772",266,"Savings Account"
"773",429,"NRI Banking"
"774",42,"NRI Banking"
"775",114,"General Insurance"
"776",115,"Savings Account"
"777",394,"Car Insurance"
"778",474,"NRI Banking"
"779",114,"NPS"
"780",483,"PPF"
"781",311,"Debit Card"
"782",65,"Bonds"
"783",454,"PPF"
"784",160,"FD"
"785",198,"Mobile Banking"
"786",207,"NRI Banking"
"787",303,"Car Insurance"
"788",216,"Mutual Funds"
"789",437,"Debit Card"
"790",18,"NRI Banking"
"791",81,"NRI Banking"
"792",52,"NRI Banking"
"793",126,"Mobile Banking"
"794",57,"Car Insurance"
"795",15,"Bonds"
"796",217,"Mobile Banking"
"797",119,"NRI Banking"
"798",224,"PPF"
"799",176,"Savings Account"
"800",157,"Mutual Funds"
"801",188,"Stocks"
"802",164,"Credit Card"
"803",10,"Stocks"
"804",10,"NPS"
"805",17,"FD"
"806",160,"Mobile Banking"
"807",335,"Mobile Banking"
"808",404,"NPS"
"809",279,"Mobile Banking"
"810",10,"Mutual Funds"
"811",103,"Savings Account"
"812",488,"Stocks"
"813",43,"FD"
"814",237,"Mobile Banking"
"815",255,"Debit Card"
"816",371,"NPS"
"817",472,"Bonds"
"818",19,"Bonds"
"819",112,"Bonds"
"820",312,"Mutual Funds"
"821",148,"Car Insurance"
"822",179,"Car Insurance"
"823",199,"Credit Card"
"824",107,"Mobile Banking"
"825",130,"FD"
"826",3,"NPS"
"827",444,"Mobile Banking"
"828",240,"Credit Card"
"829",482,"Car Insurance"
"830",305,"Car Insurance"
"831",238,"FD"
"832",456,"NPS"
"833",367,"Bonds"
"834",426,"NRI Banking"
"835",87,"Debit Card"
"836",33,"Mutual Funds"
"837",17,"Debit Card"
"838",35,"Stocks"
"839",92,"Mutual Funds"
"840",7,"Stocks"
"841",86,"NPS"
"842",302,"Bonds"
"843",101,"Mutual Funds"
"844",420,"Stocks"
"845",354,"Bonds"
"846",462,"PPF"
"847",474,"Savings Account"
"848",41,"Credit Card"
"849",129,"Credit Card"
"850",230,"General Insurance"
"851",243,"Mobile Banking"
"852",233,"General Insurance"
"853",228,"Mobile Banking"
"854",152,"Savings Account"
"855",447,"Mutual Funds"
"856",91,"Bonds"
"857",123,"Stocks"
"858",39,"PPF"
"859",61,"Mutual Funds"
"860",80,"Bonds"
"861",73,"Car Insurance"
"862",105,"NRI Banking"
"863",383,"Credit Card"
"864",36,"Mutual Funds"
"865",321,"General Insurance"
"866",391,"Savings Account"
"867",301,"Bonds"
"868",398,"NRI Banking"
"869",388,"NRI Banking"
"870",301,"Credit Card"
"871",81,"Savings Account"
"872",460,"Bonds"
"873",466,"Bonds"
"874",463,"Stocks"
"875",117,"Mobile Banking"
"876",171,"Car Insurance"
"877",270,"Savings Account"
"878",340,"Savings Account"
"879",293,"Credit Card"
"880",110,"Credit Card"
"881",349,"NPS"
"882",431,"Savings Account"
"883",192,"Bonds"
"884",256,"Mobile Banking"
"885",182,"FD"
"886",327,"Bonds"
"887",277,"Car Insurance"
"888",202,"PPF"
"889",206,"PPF"
"890",431,"Credit Card"
"891",403,"Credit Card"
"892",298,"FD"
"893",473,"FD"
"894",420,"NRI Banking"
"895",164,"Mutual Funds"
"896",211,"NPS"
"897",59,"NPS"
"898",305,"Savings Account"
"899",299,"Mobile Banking"
"900",118,"Stocks"
"901",112,"General Insurance"
"902",44,"Debit Card"
"903",174,"Debit Card"
"904",485,"Car Insurance"
"905",21,"Mobile Banking"
"906",344,"NRI Banking"
"907",302,"FD"
"908",388,"Stocks"
"909",27,"General Insurance"
"910",295,"Credit Card"
"911",227,"Debit Card"
"912",193,"NRI Banking"
"913",314,"Mutual Funds"
"914",333,"NRI Banking"
"915",15,"Savings Account"
"916",317,"Mobile Banking"
"917",160,"Mobile Banking"
"918",322,"NRI Banking"
"919",275,"FD"
"920",478,"Savings Account"
"921",65,"Credit Card"
"922",106,"General Insurance"
"923",172,"NPS"
"924",240,"FD"
"925",252,"Car Insurance"
"926",196,"Stocks"
"927",375,"General Insurance"
"928",62,"FD"
"929",38,"Mobile Banking"
"930",113,"FD"
"931",64,"Mobile Banking"
"932",118,"Car Insurance"
"933",191,"Debit Card"
"934",14,"Car Insurance"
"935",42,"Debit Card"
"936",138,"NRI Banking"
"937",11,"Stocks"
"938",49,"NPS"
"939",347,"Car Insurance"
"940",342,"Debit Card"
"941",390,"NPS"
"942",409,"Debit Card"
"943",27,"Mutual Funds"
"944",230,"FD"
"945",437,"Mobile Banking"
"946",312,"Debit Card"
"947",492,"PPF"
"948",316,"NPS"
"949",217,"NPS"
"950",276,"Debit Card"
"951",198,"Credit Card"
"952",404,"Mutual Funds"
"953",394,"NRI Banking"
"954",398,"Savings Account"
"955",162,"Credit Card"
"956",213,"PPF"
"957",83,"PPF"
"958",187,"Debit Card"
"959",160,"Car Insurance"
"960",156,"Car Insurance"
"961",415,"Stocks"
"962",32,"General Insurance"
"963",423,"Stocks"
"964",446,"NPS"
"965",481,"Savings Account"
"966",256,"NRI Banking"
"967",427,"PPF"
"968",470,"Mobile Banking"
"969",177,"Mutual Funds"
"970",439,"Stocks"
"971",348,"Mobile Banking"
"972",16,"Credit Card"
"973",72,"Mutual Funds"
"974",95,"Debit Card"
"975",222,"NPS"
"976",497,"Mobile Banking"
"977",78,"Stocks"
"978",310,"NPS"
"979",447,"Savings Account"
"980",282,"Bonds"
"981",235,"Credit Card"
"982",144,"Credit Card"
"983",105,"NRI Banking"
"984",499,"Debit Card"
"985",80,"Debit Card"
"986",219,"Debit Card"
"987",98,"Stocks"
"988",245,"FD"
"989",86,"General Insurance"
"990",470,"Credit Card"
"991",66,"Bonds"
"992",398,"Credit Card"
"993",224,"Credit Card"
"994",48,"FD"
"995",96,"NPS"
"996",471,"FD"
"997",302,"Stocks"
"998",325,"General Insurance"
"999",21,"Savings Account"
"1000",75,"Mobile Banking"


================================================
FILE: data/grades.csv
================================================
ser,student,course,batch,Internal 1 Assessment (R) (O),Internal 2 Assessment (R) (O),Assignment,Viva,Attendance,Final,Calculated Grade,Comment Faculty,Comment HOD,gender,cat,dob,backs
1,S001,BSCDS,2016,6,6,5,5,13,33,68,,,M,GEN,8-Nov-94,1
2,S002,BSCDS,2017,8,5,5,5,15,48,86,,,M,GEN,24-Jul-93,1
3,S003,BSCDS,2016,8,5,8,9,13,28,71,,,M,GEN,17-Feb-95,2
4,S004,BSCDS,2017,6,6,6,7,16,47,88,,,M,GEN,4-Mar-94,6
5,S005,BSCDS,2016,5,7,7,8,12,39,78,,,M,GEN,20-Jun-95,4
6,S006,BSCDS,2017,5,7,9,8,15,37,81,,,M,ST,8-May-94,4
7,S007,BSCDS,2016,8,7,6,9,15,44,89,,,M,GEN,7-May-94,6
8,S008,BSCDS,2017,9,9,6,9,13,33,79,,,M,GEN,15-Aug-94,0
9,S009,BSCDS,2016,9,5,5,6,12,26,63,,,M,GEN,24-Nov-94,0
10,S010,BSCDS,2017,8,9,5,8,19,29,78,,,F,GEN,25-Jan-94,4
11,S011,BSCDS,2016,9,5,8,8,16,32,78,,,M,GEN,8-Jun-94,3
12,S012,BSCDS,2017,8,8,7,7,16,49,95,,,M,OBC,24-Jun-95,5
13,S013,BSCDS,2016,8,7,6,5,13,38,77,,,M,GEN,31-Oct-94,0
14,S014,BSCDS,2017,7,6,7,5,15,26,66,,,F,GEN,21-Oct-95,0
15,S015,BSCDS,2016,9,6,9,5,19,29,77,,,F,OBC,30-Jun-96,0
16,S016,BSCDS,2017,9,7,9,9,16,29,79,Good,,F,GEN,25-Sep-94,0
17,S017,BSCDS,2016,7,9,9,8,13,33,79,,,M,GEN,16-May-94,0
18,S018,BSCDS,2017,7,7,9,7,13,41,84,,,M,GEN,24-Jun-95,1
19,S019,BSCDS,2016,7,7,8,7,19,35,83,,,F,GEN,11-Dec-93,2
20,S020,BSCDS,2017,6,5,5,8,14,35,73,,,M,GEN,30-Jun-96,1
21,S021,MSCDS,2016,9,6,5,6,14,39,79,,,F,OBC,11-Dec-93,0
22,S022,MSCDS,2017,9,6,8,9,18,27,77,,,F,GEN,6-Nov-95,0
23,S023,MSCDS,2016,5,7,8,6,15,25,66,,,F,GEN,22-Dec-93,0
24,S024,MSCDS,2017,8,9,8,6,16,46,93,,,M,SC,6-Feb-95,2
25,S025,MSCDS,2016,6,8,7,7,15,37,80,,,M,GEN,6-Jun-94,2
26,S026,MSCDS,2017,7,9,9,7,17,32,81,,,M,GEN,2-Jan-94,0
27,S027,MSCDS,2016,9,6,6,8,11,44,84,,,F,GEN,8-Apr-94,0
28,S028,MSCDS,2017,7,5,8,8,10,42,80,,,F,GEN,18-Nov-94,0
29,S029,MSCDS,2016,6,5,7,5,20,39,82,,,F,GEN,3-Jul-95,0
30,S030,MSCDS,2017,5,8,9,7,18,29,76,,,M,OBC,10-Jan-93,0
31,S031,MSCDS,2016,5,6,8,6,10,32,67,,,F,GEN,19-Dec-92,1
32,S032,MSCDS,2017,5,5,6,5,12,32,65,,,M,GEN,19-Sep-93,0
33,S033,MSCDS,2016,5,9,6,9,15,27,71,,,F,GEN,13-Aug-94,0
34,S034,MSCDS,2017,5,5,6,6,18,28,68,,,F,GEN,21-Jan-94,0
35,S035,MSCDS,2016,7,7,7,9,17,38,85,,,M,GEN,25-Feb-94,0
36,S036,MSCDS,2017,6,7,5,6,13,35,72,,,M,SC,12-Jan-96,0
37,S037,MSCDS,2016,7,7,7,5,14,44,84,,,M,GEN,27-Jul-95,3
38,S038,MSCDS,2017,7,9,7,8,13,27,71,,,M,GEN,12-Jan-96,0
39,S039,MSCDS,2016,8,7,7,8,18,31,79,,,M,SC,18-Feb-95,0
40,S040,MSCDS,2017,7,9,6,5,15,33,75,,,F,GEN,3-Mar-95,0
41,S041,MSCDS,2016,5,6,9,7,12,40,79,,,M,GEN,30-Jun-94,0
42,S042,MSCDS,2017,8,6,9,9,20,41,93,,,M,GEN,23-Oct-94,0
43,S043,MSCDS,2016,6,6,7,7,15,26,67,,,M,SC,15-Nov-93,3
44,S044,MSCDS,2017,7,7,5,6,18,29,72,,,F,GEN,11-May-93,0
45,S045,MSCDS,2016,8,5,6,5,16,36,76,,,F,GEN,2-Apr-95,0
46,S046,MSCDS,2017,8,8,8,7,13,49,93,,,F,GEN,15-Mar-94,0
47,S047,MSCDS,2016,8,9,8,6,11,29,71,,,M,ST,,11
48,S048,MSCDS,2017,7,9,9,6,12,44,87,,,M,GEN,,8
49,S049,MSCDS,2016,7,7,7,9,19,48,97,,,M,GEN,5-Apr-91,4
50,S050,MSCDS,2017,9,7,8,7,12,32,75,,,M,GEN,16-Sep-93,4

================================================
FILE: data/groceries.csv
================================================
Member_number,Date,itemDescription
1619052826499,10/22/2012,citrus fruit
1679031673299,10/03/2010,tropical fruit
1634042851699,07/07/2011,whole milk
1656022021999,03/13/2010,pip fruit
1612011328599,09/07/2013,other vegetables
1660042076699,06/06/2010,whole milk
1634062916299,02/15/2014,abrasive cleaner
1616070832799,12/30/2012,rolls/buns
1655052493299,10/24/2011,other vegetables
1615111911399,12/15/2011,liquor (appetizer)
1651110523499,11/15/2013,pot plants
1659030116999,12/17/2012,whole milk
1606041259699,08/19/2010,tropical fruit
1626091280899,07/20/2010,chocolate
1689081758199,04/10/2013,citrus fruit
1640041714599,03/05/2014,curd
1630051469899,10/22/2012,dishes
1681021693299,05/31/2013,beef
1627050218699,08/16/2014,frankfurter
1647060986799,06/19/2013,chicken
1612011328599,09/07/2013,butter
1611032908799,12/03/2012,fruit/vegetable juice
1657011295699,10/09/2013,packaged fruit/vegetables
1640041714599,03/05/2014,chocolate
1616070832799,12/30/2012,specialty bar
1616070832799,12/30/2012,other vegetables
1627050218699,08/16/2014,butter milk
1626042228999,01/17/2010,whole milk
1651110523499,11/15/2013,tropical fruit
1647060986799,06/19/2013,newspapers
1630051469899,10/22/2012,tropical fruit
1681021693299,05/31/2013,rolls/buns
1685101155599,08/04/2010,waffles
1630051469899,10/22/2012,bottled water
1656012667099,03/11/2010,yogurt
1651110523499,11/15/2013,sausage
1688081821499,10/20/2010,other vegetables
1617081511799,11/23/2014,brown bread
1692012598499,01/30/2011,newspapers
1655052493299,10/24/2011,yogurt
1670051155999,10/25/2012,hamburger meat
1677121935999,10/28/2010,bottled water
1668070593399,02/05/2013,root vegetables
1604042540799,08/09/2012,sugar
1624090693999,02/07/2014,pork
1640092051699,01/28/2011,whipped/sour cream
1615041962899,08/01/2011,beef
1609040411099,08/01/2014,pastry
1604032908399,10/12/2013,fruit/vegetable juice
1655052493299,10/24/2011,canned beer
1618072441999,05/29/2012,root vegetables
1681031185999,03/25/2013,citrus fruit
1649121242599,07/16/2010,sausage
1612022797599,06/03/2011,specialty bar
1689081758199,04/10/2013,tropical fruit
1603030743699,05/23/2014,domestic eggs
1657062009099,01/09/2012,cereals
1616070832799,12/30/2012,candy
1634031902399,05/26/2013,berries
1636082045699,11/30/2010,canned beer
1606071956499,11/11/2013,butter milk
1614020538699,12/02/2014,rolls/buns
1679041239599,05/23/2013,coffee
1675062410699,05/13/2010,pastry
1610032098599,10/13/2014,rolls/buns
1651110523499,11/15/2013,misc. beverages
1647072709099,05/12/2014,root vegetables
1696092655099,11/23/2013,whipped/sour cream
1630051469899,10/22/2012,bottled water
1657012141499,01/13/2012,sausage
1625061748499,01/03/2010,canned beer
1659101610099,05/12/2014,ham
1673111774199,03/17/2012,turkey
1612011328599,09/07/2013,domestic eggs
1697062110699,06/30/2014,baking powder
1693061749899,01/18/2013,whole milk
1676082629299,01/22/2012,soda
1651071607299,09/17/2010,whole milk
1635071847999,01/23/2012,packaged fruit/vegetables
1657011295699,10/09/2013,rolls/buns
1626042228999,01/17/2010,chocolate marshmallow
1655052493299,10/24/2011,ham
1672031502199,02/27/2012,rolls/buns
1630022141399,03/15/2013,rolls/buns
1686030782099,10/17/2010,other vegetables
1643113036899,12/02/2013,salty snack
1619071419299,06/02/2012,sausage
1635012886799,04/05/2013,sausage
1621112470199,11/24/2013,frankfurter
1685101155599,08/04/2010,sugar
1615041357399,03/17/2013,rolls/buns
1616070832799,12/30/2012,whole milk
1608031308299,06/06/2014,curd cheese
1674090103699,04/06/2013,red/blush wine
1639103003199,02/13/2013,sausage
1684072212999,11/02/2011,tropical fruit
1652021926899,04/02/2014,frozen potato products
1633092737799,09/08/2011,red/blush wine
1697072644899,07/14/2010,whole milk
1638100320499,02/17/2011,specialty chocolate
1698011240799,07/30/2011,frankfurter
1662120555499,11/14/2011,oil
1628051391899,12/16/2012,whole milk
1615111911399,12/15/2011,frozen potato products
1617081511799,11/23/2014,other vegetables
1632021738199,10/29/2013,citrus fruit
1609110573499,05/03/2011,pastry
1656012667099,03/11/2010,flour
1603111913899,01/05/2011,bottled beer
1691112207099,11/24/2010,sugar
1617081511799,11/23/2014,frozen meals
1640042088699,03/27/2010,chocolate
1669020569799,07/14/2014,root vegetables
1685101155599,08/04/2010,rolls/buns
1685010966299,08/23/2014,fruit/vegetable juice
1603020181799,03/21/2010,root vegetables
1680012240599,08/11/2013,herbs
1617081511799,11/23/2014,oil
1622120102299,07/20/2011,bottled water
1638121380799,04/07/2010,sausage
1678121622799,10/02/2013,rolls/buns
1688081821499,10/20/2010,coffee
1646033035599,06/30/2013,pork
1626042228999,01/17/2010,berries
1657011295699,10/09/2013,soda
1656012667099,03/11/2010,berries
1647032338699,09/24/2013,fruit/vegetable juice
1691032930299,12/19/2012,citrus fruit
1616070832799,12/30/2012,pork
1635040781499,09/10/2012,onions
1626042228999,01/17/2010,chocolate
1656012667099,03/11/2010,detergent
1648092897099,12/08/2012,grapes
1657020790999,01/11/2014,sausage
1642031742199,02/05/2013,fruit/vegetable juice
1659030116999,12/17/2012,shopping bags
1631011198999,03/29/2014,chicken
1637073003299,12/26/2014,root vegetables
1688122020199,12/20/2014,oil
1618090368299,10/05/2011,whole milk
1654040436999,03/29/2011,citrus fruit
1692012598499,01/30/2011,soda
1606041259699,08/19/2010,sausage
1649051357599,06/06/2013,rolls/buns
1615111911399,12/15/2011,bathroom cleaner
1626091280899,07/20/2010,frankfurter
1689070976399,06/22/2013,sausage
1689100179499,09/02/2010,fruit/vegetable juice
1642042014799,07/19/2013,photo/film
1625030342799,12/25/2012,soda
1670112104699,01/25/2011,rolls/buns
1689081758199,04/10/2013,beef
1616070832799,12/30/2012,brown bread
1684010946799,09/12/2014,whole milk
1604120185999,08/27/2014,waffles
1626091280899,07/20/2010,processed cheese
1628062981599,12/13/2013,tropical fruit
1637121923199,08/14/2013,frankfurter
1646082373499,07/03/2013,rolls/buns
1601010217699,04/18/2012,chewing gum
1627111535199,07/30/2011,packaged fruit/vegetables
1657011295699,10/09/2013,whole milk
1644052897499,06/19/2014,margarine
1667090361599,07/25/2012,berries
1611032908799,12/03/2012,citrus fruit
1612110835699,09/11/2011,tropical fruit
1619051748699,08/14/2011,hamburger meat
1636040484299,07/08/2011,bottled beer
1661012141999,08/17/2013,hamburger meat
1666121511799,03/02/2010,domestic eggs
1678032566299,09/09/2014,whole milk
1660050204499,06/10/2014,fish
1605021799199,08/20/2013,coffee
1675082691099,06/08/2014,frankfurter
1692012598499,01/30/2011,packaged fruit/vegetables
1657032771499,02/13/2014,pastry
1623042907799,05/15/2012,other vegetables
1629112281999,02/14/2014,misc. beverages
1629021743899,09/27/2013,frankfurter
1690061790499,11/06/2010,pip fruit
1643050224899,12/31/2010,domestic eggs
1668120396199,11/19/2013,misc. beverages
1684110151699,05/18/2010,sparkling wine
1699101035699,04/25/2010,specialty bar
1628030667299,10/17/2012,sausage
1692012598499,08/10/2014,curd cheese
1653060542899,09/13/2013,frankfurter
1644121952499,10/14/2011,whole milk
1649071517699,10/30/2011,shopping bags
1600031112799,07/11/2014,newspapers
1629010370399,11/04/2011,berries
1672101916699,05/30/2011,curd
1609073068699,10/23/2014,tropical fruit
1650110184599,09/10/2012,soda
1608080348699,08/09/2012,rolls/buns
1692020352899,11/15/2013,canned beer
1647050971999,04/20/2011,canned beer
1611032908799,12/03/2012,frankfurter
1672041451999,06/27/2012,margarine
1681061642999,10/26/2014,other vegetables
1685121394299,03/23/2010,whole milk
1692012598499,01/30/2011,whole milk
1644051001699,11/25/2012,citrus fruit
1665100527199,02/26/2012,oil
1658112034599,08/25/2012,frankfurter
1648010474199,08/25/2013,frankfurter
1636122888399,09/07/2011,whole milk
1602022916699,05/31/2014,sugar
1679071382799,11/06/2012,butter milk
1616051009999,12/01/2010,detergent
1668061609899,07/06/2010,hamburger meat
1606070299899,05/26/2011,bottled water
1665042331699,04/12/2011,pasta
1684011205699,05/12/2014,other vegetables
1659050492399,09/30/2012,chocolate
1655092724399,03/16/2013,sausage
1677051910899,05/28/2013,white bread
1664072426099,11/13/2014,rolls/buns
1682120680799,11/21/2011,grapes
1612011328599,09/07/2013,yogurt
1646061004499,11/25/2011,root vegetables
1625101150599,10/06/2011,beverages
1601120712199,07/21/2014,bottled beer
1680022071699,01/22/2010,coffee
1636020704499,02/21/2010,popcorn
1682082901099,02/07/2011,whole milk
1627050218699,08/16/2014,soda
1626091280899,07/20/2010,tropical fruit
1626091280899,02/12/2014,snack products
1661050632799,06/10/2013,beef
1684082440799,12/26/2014,flower soil/fertilizer
1692012598499,01/30/2011,whole milk
1610112630299,11/28/2010,frankfurter
1683082574099,10/12/2014,tropical fruit
1612011328599,09/07/2013,canned beer
1613112717499,08/11/2014,finished products
1643030485499,11/19/2011,beverages
1669012238999,09/18/2011,hamburger meat
1607030587099,04/01/2011,whole milk
1649061207499,07/13/2013,finished products
1611032908799,12/03/2012,other vegetables
1612100987299,12/14/2010,ham
1608102521399,02/15/2011,white bread
1645111803599,10/18/2013,other vegetables
1674021053099,04/12/2010,curd
1606041259699,08/19/2010,popcorn
1692012598499,07/26/2011,bottled beer
1612011328599,09/07/2013,curd
1689081758199,04/10/2013,waffles
1692012598499,01/30/2011,whole milk
1648051560299,04/14/2010,whole milk
1612012216399,01/19/2011,dessert
1679091789699,02/19/2013,sausage
1681021693299,05/31/2013,other vegetables
1600122428599,08/26/2012,shopping bags
1606041259699,08/19/2010,ham
1612020387199,02/11/2014,other vegetables
1648092066299,04/06/2012,other vegetables
1684032180399,02/13/2013,soda
1647080809499,11/11/2010,herbs


================================================
FILE: data/heart_tidy.csv
================================================
63,1,1,145,233,1,2,150,0,2.3,3,0,6,0
67,1,4,160,286,0,2,108,1,1.5,2,3,3,1
67,1,4,120,229,0,2,129,1,2.6,2,2,7,1
37,1,3,130,250,0,0,187,0,3.5,3,0,3,0
41,0,2,130,204,0,2,172,0,1.4,1,0,3,0
56,1,2,120,236,0,0,178,0,0.8,1,0,3,0
62,0,4,140,268,0,2,160,0,3.6,3,2,3,1
57,0,4,120,354,0,0,163,1,0.6,1,0,3,0
63,1,4,130,254,0,2,147,0,1.4,2,1,7,1
53,1,4,140,203,1,2,155,1,3.1,3,0,7,1
57,1,4,140,192,0,0,148,0,0.4,2,0,6,0
56,0,2,140,294,0,2,153,0,1.3,2,0,3,0
56,1,3,130,256,1,2,142,1,0.6,2,1,6,1
44,1,2,120,263,0,0,173,0,0,1,0,7,0
52,1,3,172,199,1,0,162,0,0.5,1,0,7,0
57,1,3,150,168,0,0,174,0,1.6,1,0,3,0
48,1,2,110,229,0,0,168,0,1,3,0,7,1
54,1,4,140,239,0,0,160,0,1.2,1,0,3,0
48,0,3,130,275,0,0,139,0,0.2,1,0,3,0
49,1,2,130,266,0,0,171,0,0.6,1,0,3,0
64,1,1,110,211,0,2,144,1,1.8,2,0,3,0
58,0,1,150,283,1,2,162,0,1,1,0,3,0
58,1,2,120,284,0,2,160,0,1.8,2,0,3,1
58,1,3,132,224,0,2,173,0,3.2,1,2,7,1
60,1,4,130,206,0,2,132,1,2.4,2,2,7,1
50,0,3,120,219,0,0,158,0,1.6,2,0,3,0
58,0,3,120,340,0,0,172,0,0,1,0,3,0
66,0,1,150,226,0,0,114,0,2.6,3,0,3,0
43,1,4,150,247,0,0,171,0,1.5,1,0,3,0
40,1,4,110,167,0,2,114,1,2,2,0,7,1
69,0,1,140,239,0,0,151,0,1.8,1,2,3,0
60,1,4,117,230,1,0,160,1,1.4,1,2,7,1
64,1,3,140,335,0,0,158,0,0,1,0,3,1
59,1,4,135,234,0,0,161,0,0.5,2,0,7,0
44,1,3,130,233,0,0,179,1,0.4,1,0,3,0
42,1,4,140,226,0,0,178,0,0,1,0,3,0
43,1,4,120,177,0,2,120,1,2.5,2,0,7,1
57,1,4,150,276,0,2,112,1,0.6,2,1,6,1
55,1,4,132,353,0,0,132,1,1.2,2,1,7,1
61,1,3,150,243,1,0,137,1,1,2,0,3,0
65,0,4,150,225,0,2,114,0,1,2,3,7,1
40,1,1,140,199,0,0,178,1,1.4,1,0,7,0
71,0,2,160,302,0,0,162,0,0.4,1,2,3,0
59,1,3,150,212,1,0,157,0,1.6,1,0,3,0
61,0,4,130,330,0,2,169,0,0,1,0,3,1
58,1,3,112,230,0,2,165,0,2.5,2,1,7,1
51,1,3,110,175,0,0,123,0,0.6,1,0,3,0
50,1,4,150,243,0,2,128,0,2.6,2,0,7,1
65,0,3,140,417,1,2,157,0,0.8,1,1,3,0
53,1,3,130,197,1,2,152,0,1.2,3,0,3,0
41,0,2,105,198,0,0,168,0,0,1,1,3,0
65,1,4,120,177,0,0,140,0,0.4,1,0,7,0
44,1,4,112,290,0,2,153,0,0,1,1,3,1
44,1,2,130,219,0,2,188,0,0,1,0,3,0
60,1,4,130,253,0,0,144,1,1.4,1,1,7,1
54,1,4,124,266,0,2,109,1,2.2,2,1,7,1
50,1,3,140,233,0,0,163,0,0.6,2,1,7,1
41,1,4,110,172,0,2,158,0,0,1,0,7,1
54,1,3,125,273,0,2,152,0,0.5,3,1,3,0
51,1,1,125,213,0,2,125,1,1.4,1,1,3,0
51,0,4,130,305,0,0,142,1,1.2,2,0,7,1
46,0,3,142,177,0,2,160,1,1.4,3,0,3,0
58,1,4,128,216,0,2,131,1,2.2,2,3,7,1
54,0,3,135,304,1,0,170,0,0,1,0,3,0
54,1,4,120,188,0,0,113,0,1.4,2,1,7,1
60,1,4,145,282,0,2,142,1,2.8,2,2,7,1
60,1,3,140,185,0,2,155,0,3,2,0,3,1
54,1,3,150,232,0,2,165,0,1.6,1,0,7,0
59,1,4,170,326,0,2,140,1,3.4,3,0,7,1
46,1,3,150,231,0,0,147,0,3.6,2,0,3,1
65,0,3,155,269,0,0,148,0,0.8,1,0,3,0
67,1,4,125,254,1,0,163,0,0.2,2,2,7,1
62,1,4,120,267,0,0,99,1,1.8,2,2,7,1
65,1,4,110,248,0,2,158,0,0.6,1,2,6,1
44,1,4,110,197,0,2,177,0,0,1,1,3,1
65,0,3,160,360,0,2,151,0,0.8,1,0,3,0
60,1,4,125,258,0,2,141,1,2.8,2,1,7,1
51,0,3,140,308,0,2,142,0,1.5,1,1,3,0
48,1,2,130,245,0,2,180,0,0.2,2,0,3,0
58,1,4,150,270,0,2,111,1,0.8,1,0,7,1
45,1,4,104,208,0,2,148,1,3,2,0,3,0
53,0,4,130,264,0,2,143,0,0.4,2,0,3,0
39,1,3,140,321,0,2,182,0,0,1,0,3,0
68,1,3,180,274,1,2,150,1,1.6,2,0,7,1
52,1,2,120,325,0,0,172,0,0.2,1,0,3,0
44,1,3,140,235,0,2,180,0,0,1,0,3,0
47,1,3,138,257,0,2,156,0,0,1,0,3,0
53,0,4,138,234,0,2,160,0,0,1,0,3,0
51,0,3,130,256,0,2,149,0,0.5,1,0,3,0
66,1,4,120,302,0,2,151,0,0.4,2,0,3,0
62,0,4,160,164,0,2,145,0,6.2,3,3,7,1
62,1,3,130,231,0,0,146,0,1.8,2,3,7,0
44,0,3,108,141,0,0,175,0,0.6,2,0,3,0
63,0,3,135,252,0,2,172,0,0,1,0,3,0
52,1,4,128,255,0,0,161,1,0,1,1,7,1
59,1,4,110,239,0,2,142,1,1.2,2,1,7,1
60,0,4,150,258,0,2,157,0,2.6,2,2,7,1
52,1,2,134,201,0,0,158,0,0.8,1,1,3,0
48,1,4,122,222,0,2,186,0,0,1,0,3,0
45,1,4,115,260,0,2,185,0,0,1,0,3,0
34,1,1,118,182,0,2,174,0,0,1,0,3,0
57,0,4,128,303,0,2,159,0,0,1,1,3,0
71,0,3,110,265,1,2,130,0,0,1,1,3,0
49,1,3,120,188,0,0,139,0,2,2,3,7,1
54,1,2,108,309,0,0,156,0,0,1,0,7,0
59,1,4,140,177,0,0,162,1,0,1,1,7,1
57,1,3,128,229,0,2,150,0,0.4,2,1,7,1
61,1,4,120,260,0,0,140,1,3.6,2,1,7,1
39,1,4,118,219,0,0,140,0,1.2,2,0,7,1
61,0,4,145,307,0,2,146,1,1,2,0,7,1
56,1,4,125,249,1,2,144,1,1.2,2,1,3,1
52,1,1,118,186,0,2,190,0,0,2,0,6,0
43,0,4,132,341,1,2,136,1,3,2,0,7,1
62,0,3,130,263,0,0,97,0,1.2,2,1,7,1
41,1,2,135,203,0,0,132,0,0,2,0,6,0
58,1,3,140,211,1,2,165,0,0,1,0,3,0
35,0,4,138,183,0,0,182,0,1.4,1,0,3,0
63,1,4,130,330,1,2,132,1,1.8,1,3,7,1
65,1,4,135,254,0,2,127,0,2.8,2,1,7,1
48,1,4,130,256,1,2,150,1,0,1,2,7,1
63,0,4,150,407,0,2,154,0,4,2,3,7,1
51,1,3,100,222,0,0,143,1,1.2,2,0,3,0
55,1,4,140,217,0,0,111,1,5.6,3,0,7,1
65,1,1,138,282,1,2,174,0,1.4,2,1,3,1
45,0,2,130,234,0,2,175,0,0.6,2,0,3,0
56,0,4,200,288,1,2,133,1,4,3,2,7,1
54,1,4,110,239,0,0,126,1,2.8,2,1,7,1
44,1,2,120,220,0,0,170,0,0,1,0,3,0
62,0,4,124,209,0,0,163,0,0,1,0,3,0
54,1,3,120,258,0,2,147,0,0.4,2,0,7,0
51,1,3,94,227,0,0,154,1,0,1,1,7,0
29,1,2,130,204,0,2,202,0,0,1,0,3,0
51,1,4,140,261,0,2,186,1,0,1,0,3,0
43,0,3,122,213,0,0,165,0,0.2,2,0,3,0
55,0,2,135,250,0,2,161,0,1.4,2,0,3,0
70,1,4,145,174,0,0,125,1,2.6,3,0,7,1
62,1,2,120,281,0,2,103,0,1.4,2,1,7,1
35,1,4,120,198,0,0,130,1,1.6,2,0,7,1
51,1,3,125,245,1,2,166,0,2.4,2,0,3,0
59,1,2,140,221,0,0,164,1,0,1,0,3,0
59,1,1,170,288,0,2,159,0,0.2,2,0,7,1
52,1,2,128,205,1,0,184,0,0,1,0,3,0
64,1,3,125,309,0,0,131,1,1.8,2,0,7,1
58,1,3,105,240,0,2,154,1,0.6,2,0,7,0
47,1,3,108,243,0,0,152,0,0,1,0,3,1
57,1,4,165,289,1,2,124,0,1,2,3,7,1
41,1,3,112,250,0,0,179,0,0,1,0,3,0
45,1,2,128,308,0,2,170,0,0,1,0,3,0
60,0,3,102,318,0,0,160,0,0,1,1,3,0
52,1,1,152,298,1,0,178,0,1.2,2,0,7,0
42,0,4,102,265,0,2,122,0,0.6,2,0,3,0
67,0,3,115,564,0,2,160,0,1.6,2,0,7,0
55,1,4,160,289,0,2,145,1,0.8,2,1,7,1
64,1,4,120,246,0,2,96,1,2.2,3,1,3,1
70,1,4,130,322,0,2,109,0,2.4,2,3,3,1
51,1,4,140,299,0,0,173,1,1.6,1,0,7,1
58,1,4,125,300,0,2,171,0,0,1,2,7,1
60,1,4,140,293,0,2,170,0,1.2,2,2,7,1
68,1,3,118,277,0,0,151,0,1,1,1,7,0
46,1,2,101,197,1,0,156,0,0,1,0,7,0
77,1,4,125,304,0,2,162,1,0,1,3,3,1
54,0,3,110,214,0,0,158,0,1.6,2,0,3,0
58,0,4,100,248,0,2,122,0,1,2,0,3,0
48,1,3,124,255,1,0,175,0,0,1,2,3,0
57,1,4,132,207,0,0,168,1,0,1,0,7,0
54,0,2,132,288,1,2,159,1,0,1,1,3,0
35,1,4,126,282,0,2,156,1,0,1,0,7,1
45,0,2,112,160,0,0,138,0,0,2,0,3,0
70,1,3,160,269,0,0,112,1,2.9,2,1,7,1
53,1,4,142,226,0,2,111,1,0,1,0,7,0
59,0,4,174,249,0,0,143,1,0,2,0,3,1
62,0,4,140,394,0,2,157,0,1.2,2,0,3,0
64,1,4,145,212,0,2,132,0,2,2,2,6,1
57,1,4,152,274,0,0,88,1,1.2,2,1,7,1
52,1,4,108,233,1,0,147,0,0.1,1,3,7,0
56,1,4,132,184,0,2,105,1,2.1,2,1,6,1
43,1,3,130,315,0,0,162,0,1.9,1,1,3,0
53,1,3,130,246,1,2,173,0,0,1,3,3,0
48,1,4,124,274,0,2,166,0,0.5,2,0,7,1
56,0,4,134,409,0,2,150,1,1.9,2,2,7,1
42,1,1,148,244,0,2,178,0,0.8,1,2,3,0
59,1,1,178,270,0,2,145,0,4.2,3,0,7,0
60,0,4,158,305,0,2,161,0,0,1,0,3,1
63,0,2,140,195,0,0,179,0,0,1,2,3,0
42,1,3,120,240,1,0,194,0,0.8,3,0,7,0
66,1,2,160,246,0,0,120,1,0,2,3,6,1
54,1,2,192,283,0,2,195,0,0,1,1,7,1
69,1,3,140,254,0,2,146,0,2,2,3,7,1
50,1,3,129,196,0,0,163,0,0,1,0,3,0
51,1,4,140,298,0,0,122,1,4.2,2,3,7,1
62,0,4,138,294,1,0,106,0,1.9,2,3,3,1
68,0,3,120,211,0,2,115,0,1.5,2,0,3,0
67,1,4,100,299,0,2,125,1,0.9,2,2,3,1
69,1,1,160,234,1,2,131,0,0.1,2,1,3,0
45,0,4,138,236,0,2,152,1,0.2,2,0,3,0
50,0,2,120,244,0,0,162,0,1.1,1,0,3,0
59,1,1,160,273,0,2,125,0,0,1,0,3,1
50,0,4,110,254,0,2,159,0,0,1,0,3,0
64,0,4,180,325,0,0,154,1,0,1,0,3,0
57,1,3,150,126,1,0,173,0,0.2,1,1,7,0
64,0,3,140,313,0,0,133,0,0.2,1,0,7,0
43,1,4,110,211,0,0,161,0,0,1,0,7,0
45,1,4,142,309,0,2,147,1,0,2,3,7,1
58,1,4,128,259,0,2,130,1,3,2,2,7,1
50,1,4,144,200,0,2,126,1,0.9,2,0,7,1
55,1,2,130,262,0,0,155,0,0,1,0,3,0
62,0,4,150,244,0,0,154,1,1.4,2,0,3,1
37,0,3,120,215,0,0,170,0,0,1,0,3,0
38,1,1,120,231,0,0,182,1,3.8,2,0,7,1
41,1,3,130,214,0,2,168,0,2,2,0,3,0
66,0,4,178,228,1,0,165,1,1,2,2,7,1
52,1,4,112,230,0,0,160,0,0,1,1,3,1
56,1,1,120,193,0,2,162,0,1.9,2,0,7,0
46,0,2,105,204,0,0,172,0,0,1,0,3,0
46,0,4,138,243,0,2,152,1,0,2,0,3,0
64,0,4,130,303,0,0,122,0,2,2,2,3,0
59,1,4,138,271,0,2,182,0,0,1,0,3,0
41,0,3,112,268,0,2,172,1,0,1,0,3,0
54,0,3,108,267,0,2,167,0,0,1,0,3,0
39,0,3,94,199,0,0,179,0,0,1,0,3,0
53,1,4,123,282,0,0,95,1,2,2,2,7,1
63,0,4,108,269,0,0,169,1,1.8,2,2,3,1
34,0,2,118,210,0,0,192,0,0.7,1,0,3,0
47,1,4,112,204,0,0,143,0,0.1,1,0,3,0
67,0,3,152,277,0,0,172,0,0,1,1,3,0
54,1,4,110,206,0,2,108,1,0,2,1,3,1
66,1,4,112,212,0,2,132,1,0.1,1,1,3,1
52,0,3,136,196,0,2,169,0,0.1,2,0,3,0
55,0,4,180,327,0,1,117,1,3.4,2,0,3,1
49,1,3,118,149,0,2,126,0,0.8,1,3,3,1
74,0,2,120,269,0,2,121,1,0.2,1,1,3,0
54,0,3,160,201,0,0,163,0,0,1,1,3,0
54,1,4,122,286,0,2,116,1,3.2,2,2,3,1
56,1,4,130,283,1,2,103,1,1.6,3,0,7,1
46,1,4,120,249,0,2,144,0,0.8,1,0,7,1
49,0,2,134,271,0,0,162,0,0,2,0,3,0
42,1,2,120,295,0,0,162,0,0,1,0,3,0
41,1,2,110,235,0,0,153,0,0,1,0,3,0
41,0,2,126,306,0,0,163,0,0,1,0,3,0
49,0,4,130,269,0,0,163,0,0,1,0,3,0
61,1,1,134,234,0,0,145,0,2.6,2,2,3,1
60,0,3,120,178,1,0,96,0,0,1,0,3,0
67,1,4,120,237,0,0,71,0,1,2,0,3,1
58,1,4,100,234,0,0,156,0,0.1,1,1,7,1
47,1,4,110,275,0,2,118,1,1,2,1,3,1
52,1,4,125,212,0,0,168,0,1,1,2,7,1
62,1,2,128,208,1,2,140,0,0,1,0,3,0
57,1,4,110,201,0,0,126,1,1.5,2,0,6,0
58,1,4,146,218,0,0,105,0,2,2,1,7,1
64,1,4,128,263,0,0,105,1,0.2,2,1,7,0
51,0,3,120,295,0,2,157,0,0.6,1,0,3,0
43,1,4,115,303,0,0,181,0,1.2,2,0,3,0
42,0,3,120,209,0,0,173,0,0,2,0,3,0
67,0,4,106,223,0,0,142,0,0.3,1,2,3,0
76,0,3,140,197,0,1,116,0,1.1,2,0,3,0
70,1,2,156,245,0,2,143,0,0,1,0,3,0
57,1,2,124,261,0,0,141,0,0.3,1,0,7,1
44,0,3,118,242,0,0,149,0,0.3,2,1,3,0
58,0,2,136,319,1,2,152,0,0,1,2,3,1
60,0,1,150,240,0,0,171,0,0.9,1,0,3,0
44,1,3,120,226,0,0,169,0,0,1,0,3,0
61,1,4,138,166,0,2,125,1,3.6,2,1,3,1
42,1,4,136,315,0,0,125,1,1.8,2,0,6,1
52,1,4,128,204,1,0,156,1,1,2,0,3,1
59,1,3,126,218,1,0,134,0,2.2,2,1,6,1
40,1,4,152,223,0,0,181,0,0,1,0,7,1
42,1,3,130,180,0,0,150,0,0,1,0,3,0
61,1,4,140,207,0,2,138,1,1.9,1,1,7,1
66,1,4,160,228,0,2,138,0,2.3,1,0,6,0
46,1,4,140,311,0,0,120,1,1.8,2,2,7,1
71,0,4,112,149,0,0,125,0,1.6,2,0,3,0
59,1,1,134,204,0,0,162,0,0.8,1,2,3,1
64,1,1,170,227,0,2,155,0,0.6,2,0,7,0
66,0,3,146,278,0,2,152,0,0,2,1,3,0
39,0,3,138,220,0,0,152,0,0,2,0,3,0
57,1,2,154,232,0,2,164,0,0,1,1,3,1
58,0,4,130,197,0,0,131,0,0.6,2,0,3,0
57,1,4,110,335,0,0,143,1,3,2,1,7,1
47,1,3,130,253,0,0,179,0,0,1,0,3,0
55,0,4,128,205,0,1,130,1,2,2,1,7,1
35,1,2,122,192,0,0,174,0,0,1,0,3,0
61,1,4,148,203,0,0,161,0,0,1,1,7,1
58,1,4,114,318,0,1,140,0,4.4,3,3,6,1
58,0,4,170,225,1,2,146,1,2.8,2,2,6,1
58,1,2,125,220,0,0,144,0,0.4,2,0,7,0
56,1,2,130,221,0,2,163,0,0,1,0,7,0
56,1,2,120,240,0,0,169,0,0,3,0,3,0
67,1,3,152,212,0,2,150,0,0.8,2,0,7,1
55,0,2,132,342,0,0,166,0,1.2,1,0,3,0
44,1,4,120,169,0,0,144,1,2.8,3,0,6,1
63,1,4,140,187,0,2,144,1,4,1,2,7,1
63,0,4,124,197,0,0,136,1,0,2,0,3,1
41,1,2,120,157,0,0,182,0,0,1,0,3,0
59,1,4,164,176,1,2,90,0,1,2,2,6,1
57,0,4,140,241,0,0,123,1,0.2,2,0,7,1
45,1,1,110,264,0,0,132,0,1.2,2,0,7,1
68,1,4,144,193,1,0,141,0,3.4,2,2,7,1
57,1,4,130,131,0,0,115,1,1.2,2,1,7,1
57,0,2,130,236,0,2,174,0,0,2,1,3,1
35,1,2,122,192,0,0,174,0,0,1,0,3,0


================================================
FILE: data/hhe.txt
================================================
CMAP
CBAP
CFAP
CLAP

================================================
FILE: data/iima.csv
================================================
"","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
"Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
"Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
"Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
"Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
"Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
"Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
"Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
"Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
"Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
"Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
"Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
"Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
"Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
"Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
"Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
"Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
"Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
"Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
"Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
"Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
"Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
"AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
"Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
"Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
"Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
"Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
"Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
"Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
"Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
"Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
"Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: data/iimc1.csv
================================================
rollno,sname,class,marks
1,sname1,M,20
2,sname2,F,43
3,sname3,M,23
4,sname4,M,31.66666667
5,sname5,F,33.16666667
6,sname6,M,34.66666667
7,sname7,M,36.16666667
8,sname8,F,37.66666667
9,sname9,M,39.16666667
10,sname10,M,40.66666667
11,sname11,F,42.16666667
12,sname12,M,43.66666667

================================================
FILE: data/iimtrichy.csv
================================================
"","rollno","name","gender","course","marks1","marks2","grades"
"1",1,"student1-1","Female","MBA",76,57,"B"
"2",2,"student1-2","Male","BBA",53,47,"B"
"3",3,"student1-3","Female","BBA",50,85,"A"
"4",4,"student1-4","Male","BBA",70,73,"C"
"5",5,"student1-5","Female","BBA",63,66,"B"
"6",6,"student1-6","Male","BBA",62,55,"C"
"7",7,"student1-7","Male","MBA",68,64,"C"
"8",8,"student1-8","Male","FPM",73,72,"A"
"9",9,"student1-9","Female","FPM",64,58,"A"
"10",10,"student1-10","Female","BBA",65,83,"B"
"11",11,"student1-11","Female","MBA",52,57,"A"
"12",12,"student1-12","Female","MBA",61,66,"C"
"13",13,"student1-13","Female","BBA",62,57,"A"
"14",14,"student1-14","Female","BBA",65,69,"A"
"15",15,"student1-15","Female","MBA",60,77,"C"
"16",16,"student1-16","Male","BBA",56,53,"B"
"17",17,"student1-17","Female","MBA",80,60,"B"
"18",18,"student1-18","Female","MBA",71,55,"A"
"19",19,"student1-19","Female","BBA",65,53,"C"
"20",20,"student1-20","Female","BBA",60,67,"B"
"21",21,"student1-21","Female","FPM",61,61,"B"
"22",22,"student1-22","Male","BBA",66,42,"A"
"23",23,"student1-23","Female","FPM",58,69,"A"
"24",24,"student1-24","Male","MBA",82,73,"A"
"25",25,"student1-25","Female","MBA",67,54,"B"
"26",26,"student1-26","Male","BBA",66,50,"A"
"27",27,"student1-27","Female","FPM",66,80,"A"
"28",28,"student1-28","Female","FPM",67,69,"C"
"29",29,"student1-29","Female","BBA",67,62,"A"
"30",30,"student1-30","Male","BBA",71,49,"A"
"31",31,"student1-31","Female","MBA",66,70,"A"
"32",32,"student1-32","Female","BBA",57,41,"B"
"33",33,"student1-33","Male","BBA",63,50,"A"
"34",34,"student1-34","Female","MBA",77,48,"A"
"35",35,"student1-35","Male","FPM",72,52,"A"
"36",36,"student1-36","Female","BBA",54,49,"A"
"37",37,"student1-37","Male","BBA",65,56,"C"
"38",38,"student1-38","Female","BBA",68,58,"C"
"39",39,"student1-39","Female","BBA",65,89,"A"
"40",40,"student1-40","Female","BBA",71,56,"B"
"41",41,"student1-41","Female","FPM",55,55,"C"
"42",42,"student1-42","Female","BBA",60,38,"B"
"43",43,"student1-43","Female","FPM",62,65,"B"
"44",44,"student1-44","Female","BBA",69,47,"C"
"45",45,"student1-45","Female","BBA",71,54,"B"
"46",46,"student1-46","Female","FPM",69,41,"A"
"47",47,"student1-47","Female","FPM",61,74,"A"
"48",48,"student1-48","Male","FPM",67,58,"A"
"49",49,"student1-49","Female","BBA",76,58,"A"
"50",50,"student1-50","Female","MBA",68,40,"C"
"51",51,"student1-51","Female","BBA",61,56,"C"
"52",52,"student1-52","Female","BBA",81,61,"B"
"53",53,"student1-53","Male","BBA",59,59,"A"
"54",54,"student1-54","Female","BBA",68,56,"B"
"55",55,"student1-55","Female","MBA",57,87,"B"
"56",56,"student1-56","Female","MBA",44,77,"B"
"57",57,"student1-57","Female","BBA",62,56,"A"
"58",58,"student1-58","Female","BBA",59,49,"C"
"59",59,"student1-59","Female","BBA",65,67,"C"
"60",60,"student1-60","Female","BBA",63,71,"B"


================================================
FILE: data/iitgfa.csv
================================================
"","Stock","format(Date, ""%b"")","Date","Open","High","Low","Last","Close","Qty","Turnover"
"1","ICICI","Apr",2018-04-15,281.057142857143,285.942857142857,277.5,282.197619047619,282.07380952381,24050928.047619,67084.3380952381
"2","ICICI","Aug",2018-08-16,325.792857142857,332.238095238095,322.992857142857,327.947619047619,328.290476190476,23088924.8095238,76197.1176190476
"3","ICICI","Dec",2017-12-15,309.51,312.5475,305.78,309.5475,309.55,11178511.2,34568.218
"4","ICICI","Feb",2018-02-14,326.652631578947,330.892105263158,321.776315789474,325.95,325.763157894737,15344700.2631579,50177.1942105263
"5","ICICI","Jan",2018-01-15,331.390909090909,336.034090909091,328.015909090909,332.786363636364,333.006818181818,16588079.2727273,56313.9654545455
"6","ICICI","Jul",2018-07-15,273.888636363636,277.929545454545,270.820454545455,274.738636363636,274.952272727273,22464037.6818182,62316.435
"7","ICICI","Jun",2018-06-15,289.092857142857,291.57619047619,284.192857142857,287.569047619048,287.440476190476,20901907.6666667,60026.2723809524
"8","ICICI","Mar",2018-03-15,295.221052631579,297.734210526316,289.986842105263,293.252631578947,293.092105263158,19461420.6315789,56697.7152631579
"9","ICICI","May",2018-05-16,294.995454545455,299.411363636364,291.3,295.145454545455,294.904545454545,19882321.7272727,59117.6736363636
"10","ICICI","Nov",2018-05-08,335.87619047619,339.986904761905,332.290476190476,335.591666666667,335.757142857143,18000520.1904762,61134.1404761905
"11","ICICI","Oct",2018-04-21,298.113414634146,304.439024390244,293.108536585366,299.507317073171,299.578048780488,26530527.3658537,81204.5824390244
"12","ICICI","Sep",2018-03-03,306,308.297435897436,300.878205128205,304.139743589744,304.057692307692,15207262.4871795,46365.8746153846
"13","PNB","Apr",2018-04-15,97.7071428571429,98.9166666666667,96.0880952380952,97.2809523809524,97.3238095238095,22150276.952381,21787.7357142857
"14","PNB","Aug",2018-08-16,84.3833333333333,86.002380952381,82.8452380952381,84.2047619047619,84.2714285714286,42736065.047619,36353.0195238095
"15","PNB","Dec",2017-12-15,173.255,175.755,170.345,172.9975,173.1525,16637279.95,28811.083
"16","PNB","Feb",2018-02-14,136.928947368421,140.473684210526,132.284210526316,135.526315789474,135.7,75735091.1578947,91027.0889473684
"17","PNB","Jan",2018-01-15,175.222727272727,178.713636363636,170.972727272727,174.6,174.481818181818,18646075.7727273,33048.9181818182
"18","PNB","Jul",2018-07-15,77.9954545454545,79.6340909090909,76.6522727272727,77.8636363636364,77.9545454545455,28482531.1363636,22616.3713636364
"19","PNB","Jun",2018-06-15,85.1952380952381,86.6690476190476,83.4690476190476,84.65,84.6857142857143,22741398.2857143,19226.3271428571
"20","PNB","Mar",2018-03-15,97.1578947368421,99.4026315789474,95.3605263157895,96.9157894736842,97.2236842105263,45559558.6842105,44665.6731578947
"21","PNB","May",2018-05-16,86.2545454545455,87.9113636363636,84.3090909090909,85.7113636363636,85.8136363636364,25202194.1818182,20972.7672727273
"22","PNB","Nov",2018-05-08,134.35,136.458333333333,131.232142857143,133.078571428571,133.033333333333,21876496.3333333,29859.2966666667
"23","PNB","Oct",2018-04-21,106.35,110.412195121951,104.042682926829,107.762195121951,107.543902439024,25729571.4390244,31601.7863414634
"24","PNB","Sep",2018-03-03,112.366666666667,113.365384615385,109.630769230769,110.915384615385,110.952564102564,19025502.1025641,16332.3797435897
"25","SBI","Apr",2018-04-15,249.752380952381,252.221428571429,246.069047619048,248.766666666667,248.671428571429,18434845.7142857,45987.8019047619
"26","SBI","Aug",2018-08-16,303.066666666667,307.683333333333,299.369047619048,303.697619047619,303.52380952381,29597692.4285714,90208.9980952381
"27","SBI","Dec",2017-12-15,315.88,319.1,312.12,315.25,315.3425,15417624.7,48680.3625
"28","SBI","Feb",2018-02-14,283.684210526316,287.576315789474,278.252631578947,282.723684210526,282.592105263158,26650593.4736842,74993.0978947368
"29","SBI","Jan",2018-01-15,308.654545454545,312.413636363636,304.006818181818,307.918181818182,307.663636363636,21237596.2272727,65797.5290909091
"30","SBI","Jul",2018-07-15,265.072727272727,269.202272727273,262.556818181818,266.161363636364,266.120454545455,18231484.0454545,49684.8363636364
"31","SBI","Jun",2018-06-15,271.32380952381,274.32619047619,268.130952380952,270.919047619048,270.904761904762,17631953.9047619,47938.8947619048
"32","SBI","Mar",2018-03-15,252.118421052632,256.231578947368,247.842105263158,251.605263157895,251.665789473684,25845394.4210526,64997.677368421
"33","SBI","May",2018-05-16,251.856818181818,256.327272727273,248.570454545455,252.911363636364,252.909090909091,22413778,57288.5063636364
"34","SBI","Nov",2018-05-08,307.953571428571,312.330952380952,303.082142857143,307.576190476191,307.440476190476,23616111.2142857,72652.9595238095
"35","SBI","Oct",2018-04-21,265.273170731707,270.871951219512,261.074390243902,265.784146341463,265.90487804878,29496173.2682927,83055.2080487805
"36","SBI","Sep",2018-03-03,276.528205128205,278.662820512821,271.726923076923,273.964102564103,274.108974358974,18695389.9487179,51688.7753846154


================================================
FILE: data/iris.csv
================================================
"Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
5.1,3.5,1.4,0.2,"setosa"
4.9,3,1.4,0.2,"setosa"
4.7,3.2,1.3,0.2,"setosa"
4.6,3.1,1.5,0.2,"setosa"
5,3.6,1.4,0.2,"setosa"
5.4,3.9,1.7,0.4,"setosa"
4.6,3.4,1.4,0.3,"setosa"
5,3.4,1.5,0.2,"setosa"
4.4,2.9,1.4,0.2,"setosa"
4.9,3.1,1.5,0.1,"setosa"
5.4,3.7,1.5,0.2,"setosa"
4.8,3.4,1.6,0.2,"setosa"
4.8,3,1.4,0.1,"setosa"
4.3,3,1.1,0.1,"setosa"
5.8,4,1.2,0.2,"setosa"
5.7,4.4,1.5,0.4,"setosa"
5.4,3.9,1.3,0.4,"setosa"
5.1,3.5,1.4,0.3,"setosa"
5.7,3.8,1.7,0.3,"setosa"
5.1,3.8,1.5,0.3,"setosa"
5.4,3.4,1.7,0.2,"setosa"
5.1,3.7,1.5,0.4,"setosa"
4.6,3.6,1,0.2,"setosa"
5.1,3.3,1.7,0.5,"setosa"
4.8,3.4,1.9,0.2,"setosa"
5,3,1.6,0.2,"setosa"
5,3.4,1.6,0.4,"setosa"
5.2,3.5,1.5,0.2,"setosa"
5.2,3.4,1.4,0.2,"setosa"
4.7,3.2,1.6,0.2,"setosa"
4.8,3.1,1.6,0.2,"setosa"
5.4,3.4,1.5,0.4,"setosa"
5.2,4.1,1.5,0.1,"setosa"
5.5,4.2,1.4,0.2,"setosa"
4.9,3.1,1.5,0.2,"setosa"
5,3.2,1.2,0.2,"setosa"
5.5,3.5,1.3,0.2,"setosa"
4.9,3.6,1.4,0.1,"setosa"
4.4,3,1.3,0.2,"setosa"
5.1,3.4,1.5,0.2,"setosa"
5,3.5,1.3,0.3,"setosa"
4.5,2.3,1.3,0.3,"setosa"
4.4,3.2,1.3,0.2,"setosa"
5,3.5,1.6,0.6,"setosa"
5.1,3.8,1.9,0.4,"setosa"
4.8,3,1.4,0.3,"setosa"
5.1,3.8,1.6,0.2,"setosa"
4.6,3.2,1.4,0.2,"setosa"
5.3,3.7,1.5,0.2,"setosa"
5,3.3,1.4,0.2,"setosa"
7,3.2,4.7,1.4,"versicolor"
6.4,3.2,4.5,1.5,"versicolor"
6.9,3.1,4.9,1.5,"versicolor"
5.5,2.3,4,1.3,"versicolor"
6.5,2.8,4.6,1.5,"versicolor"
5.7,2.8,4.5,1.3,"versicolor"
6.3,3.3,4.7,1.6,"versicolor"
4.9,2.4,3.3,1,"versicolor"
6.6,2.9,4.6,1.3,"versicolor"
5.2,2.7,3.9,1.4,"versicolor"
5,2,3.5,1,"versicolor"
5.9,3,4.2,1.5,"versicolor"
6,2.2,4,1,"versicolor"
6.1,2.9,4.7,1.4,"versicolor"
5.6,2.9,3.6,1.3,"versicolor"
6.7,3.1,4.4,1.4,"versicolor"
5.6,3,4.5,1.5,"versicolor"
5.8,2.7,4.1,1,"versicolor"
6.2,2.2,4.5,1.5,"versicolor"
5.6,2.5,3.9,1.1,"versicolor"
5.9,3.2,4.8,1.8,"versicolor"
6.1,2.8,4,1.3,"versicolor"
6.3,2.5,4.9,1.5,"versicolor"
6.1,2.8,4.7,1.2,"versicolor"
6.4,2.9,4.3,1.3,"versicolor"
6.6,3,4.4,1.4,"versicolor"
6.8,2.8,4.8,1.4,"versicolor"
6.7,3,5,1.7,"versicolor"
6,2.9,4.5,1.5,"versicolor"
5.7,2.6,3.5,1,"versicolor"
5.5,2.4,3.8,1.1,"versicolor"
5.5,2.4,3.7,1,"versicolor"
5.8,2.7,3.9,1.2,"versicolor"
6,2.7,5.1,1.6,"versicolor"
5.4,3,4.5,1.5,"versicolor"
6,3.4,4.5,1.6,"versicolor"
6.7,3.1,4.7,1.5,"versicolor"
6.3,2.3,4.4,1.3,"versicolor"
5.6,3,4.1,1.3,"versicolor"
5.5,2.5,4,1.3,"versicolor"
5.5,2.6,4.4,1.2,"versicolor"
6.1,3,4.6,1.4,"versicolor"
5.8,2.6,4,1.2,"versicolor"
5,2.3,3.3,1,"versicolor"
5.6,2.7,4.2,1.3,"versicolor"
5.7,3,4.2,1.2,"versicolor"
5.7,2.9,4.2,1.3,"versicolor"
6.2,2.9,4.3,1.3,"versicolor"
5.1,2.5,3,1.1,"versicolor"
5.7,2.8,4.1,1.3,"versicolor"
6.3,3.3,6,2.5,"virginica"
5.8,2.7,5.1,1.9,"virginica"
7.1,3,5.9,2.1,"virginica"
6.3,2.9,5.6,1.8,"virginica"
6.5,3,5.8,2.2,"virginica"
7.6,3,6.6,2.1,"virginica"
4.9,2.5,4.5,1.7,"virginica"
7.3,2.9,6.3,1.8,"virginica"
6.7,2.5,5.8,1.8,"virginica"
7.2,3.6,6.1,2.5,"virginica"
6.5,3.2,5.1,2,"virginica"
6.4,2.7,5.3,1.9,"virginica"
6.8,3,5.5,2.1,"virginica"
5.7,2.5,5,2,"virginica"
5.8,2.8,5.1,2.4,"virginica"
6.4,3.2,5.3,2.3,"virginica"
6.5,3,5.5,1.8,"virginica"
7.7,3.8,6.7,2.2,"virginica"
7.7,2.6,6.9,2.3,"virginica"
6,2.2,5,1.5,"virginica"
6.9,3.2,5.7,2.3,"virginica"
5.6,2.8,4.9,2,"virginica"
7.7,2.8,6.7,2,"virginica"
6.3,2.7,4.9,1.8,"virginica"
6.7,3.3,5.7,2.1,"virginica"
7.2,3.2,6,1.8,"virginica"
6.2,2.8,4.8,1.8,"virginica"
6.1,3,4.9,1.8,"virginica"
6.4,2.8,5.6,2.1,"virginica"
7.2,3,5.8,1.6,"virginica"
7.4,2.8,6.1,1.9,"virginica"
7.9,3.8,6.4,2,"virginica"
6.4,2.8,5.6,2.2,"virginica"
6.3,2.8,5.1,1.5,"virginica"
6.1,2.6,5.6,1.4,"virginica"
7.7,3,6.1,2.3,"virginica"
6.3,3.4,5.6,2.4,"virginica"
6.4,3.1,5.5,1.8,"virginica"
6,3,4.8,1.8,"virginica"
6.9,3.1,5.4,2.1,"virginica"
6.7,3.1,5.6,2.4,"virginica"
6.9,3.1,5.1,2.3,"virginica"
5.8,2.7,5.1,1.9,"virginica"
6.8,3.2,5.9,2.3,"virginica"
6.7,3.3,5.7,2.5,"virginica"
6.7,3,5.2,2.3,"virginica"
6.3,2.5,5,1.9,"virginica"
6.5,3,5.2,2,"virginica"
6.2,3.4,5.4,2.3,"virginica"
5.9,3,5.1,1.8,"virginica"


================================================
FILE: data/irisF.csv
================================================
"Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
5.1,3.5,1.4,0.2,"setosa"
4.9,3,1.4,0.2,"setosa"
4.7,3.2,1.3,0.2,"setosa"
4.6,3.1,1.5,0.2,"setosa"
5,3.6,1.4,0.2,"setosa"
5.4,3.9,1.7,0.4,"setosa"
4.6,3.4,1.4,0.3,"setosa"
5,3.4,1.5,0.2,"setosa"
4.4,2.9,1.4,0.2,"setosa"
4.9,3.1,1.5,0.1,"setosa"
5.4,3.7,1.5,0.2,"setosa"
4.8,3.4,1.6,0.2,"setosa"
4.8,3,1.4,0.1,"setosa"
4.3,3,1.1,0.1,"setosa"
5.8,4,1.2,0.2,"setosa"
5.7,4.4,1.5,0.4,"setosa"
5.4,3.9,1.3,0.4,"setosa"
5.1,3.5,1.4,0.3,"setosa"
5.7,3.8,1.7,0.3,"setosa"
5.1,3.8,1.5,0.3,"setosa"
5.4,3.4,1.7,0.2,"setosa"
5.1,3.7,1.5,0.4,"setosa"
4.6,3.6,1,0.2,"setosa"
5.1,3.3,1.7,0.5,"setosa"
4.8,3.4,1.9,0.2,"setosa"
5,3,1.6,0.2,"setosa"
5,3.4,1.6,0.4,"setosa"
5.2,3.5,1.5,0.2,"setosa"
5.2,3.4,1.4,0.2,"setosa"
4.7,3.2,1.6,0.2,"setosa"
4.8,3.1,1.6,0.2,"setosa"
5.4,3.4,1.5,0.4,"setosa"
5.2,4.1,1.5,0.1,"setosa"
5.5,4.2,1.4,0.2,"setosa"
4.9,3.1,1.5,0.2,"setosa"
5,3.2,1.2,0.2,"setosa"
5.5,3.5,1.3,0.2,"setosa"
4.9,3.6,1.4,0.1,"setosa"
4.4,3,1.3,0.2,"setosa"
5.1,3.4,1.5,0.2,"setosa"
5,3.5,1.3,0.3,"setosa"
4.5,2.3,1.3,0.3,"setosa"
4.4,3.2,1.3,0.2,"setosa"
5,3.5,1.6,0.6,"setosa"
5.1,3.8,1.9,0.4,"setosa"
4.8,3,1.4,0.3,"setosa"
5.1,3.8,1.6,0.2,"setosa"
4.6,3.2,1.4,0.2,"setosa"
5.3,3.7,1.5,0.2,"setosa"
5,3.3,1.4,0.2,"setosa"
7,3.2,4.7,1.4,"versicolor"
6.4,3.2,4.5,1.5,"versicolor"
6.9,3.1,4.9,1.5,"versicolor"
5.5,2.3,4,1.3,"versicolor"
6.5,2.8,4.6,1.5,"versicolor"
5.7,2.8,4.5,1.3,"versicolor"
6.3,3.3,4.7,1.6,"versicolor"
4.9,2.4,3.3,1,"versicolor"
6.6,2.9,4.6,1.3,"versicolor"
5.2,2.7,3.9,1.4,"versicolor"
5,2,3.5,1,"versicolor"
5.9,3,4.2,1.5,"versicolor"
6,2.2,4,1,"versicolor"
6.1,2.9,4.7,1.4,"versicolor"
5.6,2.9,3.6,1.3,"versicolor"
6.7,3.1,4.4,1.4,"versicolor"
5.6,3,4.5,1.5,"versicolor"
5.8,2.7,4.1,1,"versicolor"
6.2,2.2,4.5,1.5,"versicolor"
5.6,2.5,3.9,1.1,"versicolor"
5.9,3.2,4.8,1.8,"versicolor"
6.1,2.8,4,1.3,"versicolor"
6.3,2.5,4.9,1.5,"versicolor"
6.1,2.8,4.7,1.2,"versicolor"
6.4,2.9,4.3,1.3,"versicolor"
6.6,3,4.4,1.4,"versicolor"
6.8,2.8,4.8,1.4,"versicolor"
6.7,3,5,1.7,"versicolor"
6,2.9,4.5,1.5,"versicolor"
5.7,2.6,3.5,1,"versicolor"
5.5,2.4,3.8,1.1,"versicolor"
5.5,2.4,3.7,1,"versicolor"
5.8,2.7,3.9,1.2,"versicolor"
6,2.7,5.1,1.6,"versicolor"
5.4,3,4.5,1.5,"versicolor"
6,3.4,4.5,1.6,"versicolor"
6.7,3.1,4.7,1.5,"versicolor"
6.3,2.3,4.4,1.3,"versicolor"
5.6,3,4.1,1.3,"versicolor"
5.5,2.5,4,1.3,"versicolor"
5.5,2.6,4.4,1.2,"versicolor"
6.1,3,4.6,1.4,"versicolor"
5.8,2.6,4,1.2,"versicolor"
5,2.3,3.3,1,"versicolor"
5.6,2.7,4.2,1.3,"versicolor"
5.7,3,4.2,1.2,"versicolor"
5.7,2.9,4.2,1.3,"versicolor"
6.2,2.9,4.3,1.3,"versicolor"
5.1,2.5,3,1.1,"versicolor"
5.7,2.8,4.1,1.3,"versicolor"
6.3,3.3,6,2.5,"virginica"
5.8,2.7,5.1,1.9,"virginica"
7.1,3,5.9,2.1,"virginica"
6.3,2.9,5.6,1.8,"virginica"
6.5,3,5.8,2.2,"virginica"
7.6,3,6.6,2.1,"virginica"
4.9,2.5,4.5,1.7,"virginica"
7.3,2.9,6.3,1.8,"virginica"
6.7,2.5,5.8,1.8,"virginica"
7.2,3.6,6.1,2.5,"virginica"
6.5,3.2,5.1,2,"virginica"
6.4,2.7,5.3,1.9,"virginica"
6.8,3,5.5,2.1,"virginica"
5.7,2.5,5,2,"virginica"
5.8,2.8,5.1,2.4,"virginica"
6.4,3.2,5.3,2.3,"virginica"
6.5,3,5.5,1.8,"virginica"
7.7,3.8,6.7,2.2,"virginica"
7.7,2.6,6.9,2.3,"virginica"
6,2.2,5,1.5,"virginica"
6.9,3.2,5.7,2.3,"virginica"
5.6,2.8,4.9,2,"virginica"
7.7,2.8,6.7,2,"virginica"
6.3,2.7,4.9,1.8,"virginica"
6.7,3.3,5.7,2.1,"virginica"
7.2,3.2,6,1.8,"virginica"
6.2,2.8,4.8,1.8,"virginica"
6.1,3,4.9,1.8,"virginica"
6.4,2.8,5.6,2.1,"virginica"
7.2,3,5.8,1.6,"virginica"
7.4,2.8,6.1,1.9,"virginica"
7.9,3.8,6.4,2,"virginica"
6.4,2.8,5.6,2.2,"virginica"
6.3,2.8,5.1,1.5,"virginica"
6.1,2.6,5.6,1.4,"virginica"
7.7,3,6.1,2.3,"virginica"
6.3,3.4,5.6,2.4,"virginica"
6.4,3.1,5.5,1.8,"virginica"
6,3,4.8,1.8,"virginica"
6.9,3.1,5.4,2.1,"virginica"
6.7,3.1,5.6,2.4,"virginica"
6.9,3.1,5.1,2.3,"virginica"
5.8,2.7,5.1,1.9,"virginica"
6.8,3.2,5.9,2.3,"virginica"
6.7,3.3,5.7,2.5,"virginica"
6.7,3,5.2,2.3,"virginica"
6.3,2.5,5,1.9,"virginica"
6.5,3,5.2,2,"virginica"
6.2,3.4,5.4,2.3,"virginica"
5.9,3,5.1,1.8,"virginica"


================================================
FILE: data/irisT.csv
================================================
"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
"1",5.1,3.5,1.4,0.2,"setosa"
"2",4.9,3,1.4,0.2,"setosa"
"3",4.7,3.2,1.3,0.2,"setosa"
"4",4.6,3.1,1.5,0.2,"setosa"
"5",5,3.6,1.4,0.2,"setosa"
"6",5.4,3.9,1.7,0.4,"setosa"
"7",4.6,3.4,1.4,0.3,"setosa"
"8",5,3.4,1.5,0.2,"setosa"
"9",4.4,2.9,1.4,0.2,"setosa"
"10",4.9,3.1,1.5,0.1,"setosa"
"11",5.4,3.7,1.5,0.2,"setosa"
"12",4.8,3.4,1.6,0.2,"setosa"
"13",4.8,3,1.4,0.1,"setosa"
"14",4.3,3,1.1,0.1,"setosa"
"15",5.8,4,1.2,0.2,"setosa"
"16",5.7,4.4,1.5,0.4,"setosa"
"17",5.4,3.9,1.3,0.4,"setosa"
"18",5.1,3.5,1.4,0.3,"setosa"
"19",5.7,3.8,1.7,0.3,"setosa"
"20",5.1,3.8,1.5,0.3,"setosa"
"21",5.4,3.4,1.7,0.2,"setosa"
"22",5.1,3.7,1.5,0.4,"setosa"
"23",4.6,3.6,1,0.2,"setosa"
"24",5.1,3.3,1.7,0.5,"setosa"
"25",4.8,3.4,1.9,0.2,"setosa"
"26",5,3,1.6,0.2,"setosa"
"27",5,3.4,1.6,0.4,"setosa"
"28",5.2,3.5,1.5,0.2,"setosa"
"29",5.2,3.4,1.4,0.2,"setosa"
"30",4.7,3.2,1.6,0.2,"setosa"
"31",4.8,3.1,1.6,0.2,"setosa"
"32",5.4,3.4,1.5,0.4,"setosa"
"33",5.2,4.1,1.5,0.1,"setosa"
"34",5.5,4.2,1.4,0.2,"setosa"
"35",4.9,3.1,1.5,0.2,"setosa"
"36",5,3.2,1.2,0.2,"setosa"
"37",5.5,3.5,1.3,0.2,"setosa"
"38",4.9,3.6,1.4,0.1,"setosa"
"39",4.4,3,1.3,0.2,"setosa"
"40",5.1,3.4,1.5,0.2,"setosa"
"41",5,3.5,1.3,0.3,"setosa"
"42",4.5,2.3,1.3,0.3,"setosa"
"43",4.4,3.2,1.3,0.2,"setosa"
"44",5,3.5,1.6,0.6,"setosa"
"45",5.1,3.8,1.9,0.4,"setosa"
"46",4.8,3,1.4,0.3,"setosa"
"47",5.1,3.8,1.6,0.2,"setosa"
"48",4.6,3.2,1.4,0.2,"setosa"
"49",5.3,3.7,1.5,0.2,"setosa"
"50",5,3.3,1.4,0.2,"setosa"
"51",7,3.2,4.7,1.4,"versicolor"
"52",6.4,3.2,4.5,1.5,"versicolor"
"53",6.9,3.1,4.9,1.5,"versicolor"
"54",5.5,2.3,4,1.3,"versicolor"
"55",6.5,2.8,4.6,1.5,"versicolor"
"56",5.7,2.8,4.5,1.3,"versicolor"
"57",6.3,3.3,4.7,1.6,"versicolor"
"58",4.9,2.4,3.3,1,"versicolor"
"59",6.6,2.9,4.6,1.3,"versicolor"
"60",5.2,2.7,3.9,1.4,"versicolor"
"61",5,2,3.5,1,"versicolor"
"62",5.9,3,4.2,1.5,"versicolor"
"63",6,2.2,4,1,"versicolor"
"64",6.1,2.9,4.7,1.4,"versicolor"
"65",5.6,2.9,3.6,1.3,"versicolor"
"66",6.7,3.1,4.4,1.4,"versicolor"
"67",5.6,3,4.5,1.5,"versicolor"
"68",5.8,2.7,4.1,1,"versicolor"
"69",6.2,2.2,4.5,1.5,"versicolor"
"70",5.6,2.5,3.9,1.1,"versicolor"
"71",5.9,3.2,4.8,1.8,"versicolor"
"72",6.1,2.8,4,1.3,"versicolor"
"73",6.3,2.5,4.9,1.5,"versicolor"
"74",6.1,2.8,4.7,1.2,"versicolor"
"75",6.4,2.9,4.3,1.3,"versicolor"
"76",6.6,3,4.4,1.4,"versicolor"
"77",6.8,2.8,4.8,1.4,"versicolor"
"78",6.7,3,5,1.7,"versicolor"
"79",6,2.9,4.5,1.5,"versicolor"
"80",5.7,2.6,3.5,1,"versicolor"
"81",5.5,2.4,3.8,1.1,"versicolor"
"82",5.5,2.4,3.7,1,"versicolor"
"83",5.8,2.7,3.9,1.2,"versicolor"
"84",6,2.7,5.1,1.6,"versicolor"
"85",5.4,3,4.5,1.5,"versicolor"
"86",6,3.4,4.5,1.6,"versicolor"
"87",6.7,3.1,4.7,1.5,"versicolor"
"88",6.3,2.3,4.4,1.3,"versicolor"
"89",5.6,3,4.1,1.3,"versicolor"
"90",5.5,2.5,4,1.3,"versicolor"
"91",5.5,2.6,4.4,1.2,"versicolor"
"92",6.1,3,4.6,1.4,"versicolor"
"93",5.8,2.6,4,1.2,"versicolor"
"94",5,2.3,3.3,1,"versicolor"
"95",5.6,2.7,4.2,1.3,"versicolor"
"96",5.7,3,4.2,1.2,"versicolor"
"97",5.7,2.9,4.2,1.3,"versicolor"
"98",6.2,2.9,4.3,1.3,"versicolor"
"99",5.1,2.5,3,1.1,"versicolor"
"100",5.7,2.8,4.1,1.3,"versicolor"
"101",6.3,3.3,6,2.5,"virginica"
"102",5.8,2.7,5.1,1.9,"virginica"
"103",7.1,3,5.9,2.1,"virginica"
"104",6.3,2.9,5.6,1.8,"virginica"
"105",6.5,3,5.8,2.2,"virginica"
"106",7.6,3,6.6,2.1,"virginica"
"107",4.9,2.5,4.5,1.7,"virginica"
"108",7.3,2.9,6.3,1.8,"virginica"
"109",6.7,2.5,5.8,1.8,"virginica"
"110",7.2,3.6,6.1,2.5,"virginica"
"111",6.5,3.2,5.1,2,"virginica"
"112",6.4,2.7,5.3,1.9,"virginica"
"113",6.8,3,5.5,2.1,"virginica"
"114",5.7,2.5,5,2,"virginica"
"115",5.8,2.8,5.1,2.4,"virginica"
"116",6.4,3.2,5.3,2.3,"virginica"
"117",6.5,3,5.5,1.8,"virginica"
"118",7.7,3.8,6.7,2.2,"virginica"
"119",7.7,2.6,6.9,2.3,"virginica"
"120",6,2.2,5,1.5,"virginica"
"121",6.9,3.2,5.7,2.3,"virginica"
"122",5.6,2.8,4.9,2,"virginica"
"123",7.7,2.8,6.7,2,"virginica"
"124",6.3,2.7,4.9,1.8,"virginica"
"125",6.7,3.3,5.7,2.1,"virginica"
"126",7.2,3.2,6,1.8,"virginica"
"127",6.2,2.8,4.8,1.8,"virginica"
"128",6.1,3,4.9,1.8,"virginica"
"129",6.4,2.8,5.6,2.1,"virginica"
"130",7.2,3,5.8,1.6,"virginica"
"131",7.4,2.8,6.1,1.9,"virginica"
"132",7.9,3.8,6.4,2,"virginica"
"133",6.4,2.8,5.6,2.2,"virginica"
"134",6.3,2.8,5.1,1.5,"virginica"
"135",6.1,2.6,5.6,1.4,"virginica"
"136",7.7,3,6.1,2.3,"virginica"
"137",6.3,3.4,5.6,2.4,"virginica"
"138",6.4,3.1,5.5,1.8,"virginica"
"139",6,3,4.8,1.8,"virginica"
"140",6.9,3.1,5.4,2.1,"virginica"
"141",6.7,3.1,5.6,2.4,"virginica"
"142",6.9,3.1,5.1,2.3,"virginica"
"143",5.8,2.7,5.1,1.9,"virginica"
"144",6.8,3.2,5.9,2.3,"virginica"
"145",6.7,3.3,5.7,2.5,"virginica"
"146",6.7,3,5.2,2.3,"virginica"
"147",6.3,2.5,5,1.9,"virginica"
"148",6.5,3,5.2,2,"virginica"
"149",6.2,3.4,5.4,2.3,"virginica"
"150",5.9,3,5.1,1.8,"virginica"


================================================
FILE: data/itemlist1
================================================
I1,I2,I5
I2,I4
I2,I3
I1,I2,I4
I1,I3
I2,I3
I1,I3
I1,I2,I3,I5
I1,I2,I3


================================================
FILE: data/km5_c2.csv
================================================
"","Channel","Region","Fresh","Milk","Grocery","Frozen","Detergents_Paper","Delicassen","C3","C5"
"48",2,3,44466,54259,55571,7782,24171,6465,1,2
"57",2,3,4098,29892,26866,2616,17740,1340,1,2
"62",2,3,35942,38369,59598,3254,26701,2017,1,2
"66",2,3,85,20959,45828,36,24231,1423,1,2
"86",2,3,16117,46197,92780,1026,40827,2944,1,2
"87",2,3,22925,73498,32114,987,20070,903,1,2
"93",2,3,9198,27472,32034,3232,18906,5130,1,2
"212",2,1,12119,28326,39694,4736,19410,2870,1,2
"252",2,1,6134,23133,33586,6746,18594,5121,1,2
"334",2,2,8565,4980,67298,131,38102,1215,1,2


================================================
FILE: data/logr2.csv
================================================
userid,gender,age,salary,purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
15570769,Female,26,80000,0
15606274,Female,26,52000,0
15746139,Male,20,86000,0
15704987,Male,32,18000,0
15628972,Male,18,82000,0
15697686,Male,29,80000,0
15733883,Male,47,25000,1
15617482,Male,45,26000,1
15704583,Male,46,28000,1
15621083,Female,48,29000,1
15649487,Male,45,22000,1
15736760,Female,47,49000,1
15714658,Male,48,41000,1
15599081,Female,45,22000,1
15705113,Male,46,23000,1
15631159,Male,47,20000,1
15792818,Male,49,28000,1
15633531,Female,47,30000,1
15744529,Male,29,43000,0
15669656,Male,31,18000,0
15581198,Male,31,74000,0
15729054,Female,27,137000,1
15573452,Female,21,16000,0
15776733,Female,28,44000,0
15724858,Male,27,90000,0
15713144,Male,35,27000,0
15690188,Female,33,28000,0
15689425,Male,30,49000,0
15671766,Female,26,72000,0
15782806,Female,27,31000,0
15764419,Female,27,17000,0
15591915,Female,33,51000,0
15772798,Male,35,108000,0
15792008,Male,30,15000,0
15715541,Female,28,84000,0
15639277,Male,23,20000,0
15798850,Male,25,79000,0
15776348,Female,27,54000,0
15727696,Male,30,135000,1
15793813,Female,31,89000,0
15694395,Female,24,32000,0
15764195,Female,18,44000,0
15744919,Female,29,83000,0
15671655,Female,35,23000,0
15654901,Female,27,58000,0
15649136,Female,24,55000,0
15775562,Female,23,48000,0
15807481,Male,28,79000,0
15642885,Male,22,18000,0
15789109,Female,32,117000,0
15814004,Male,27,20000,0
15673619,Male,25,87000,0
15595135,Female,23,66000,0
15583681,Male,32,120000,1
15605000,Female,59,83000,0
15718071,Male,24,58000,0
15679760,Male,24,19000,0
15654574,Female,23,82000,0
15577178,Female,22,63000,0
15595324,Female,31,68000,0
15756932,Male,25,80000,0
15726358,Female,24,27000,0
15595228,Female,20,23000,0
15782530,Female,33,113000,0
15592877,Male,32,18000,0
15651983,Male,34,112000,1
15746737,Male,18,52000,0
15774179,Female,22,27000,0
15667265,Female,28,87000,0
15655123,Female,26,17000,0
15595917,Male,30,80000,0
15668385,Male,39,42000,0
15709476,Male,20,49000,0
15711218,Male,35,88000,0
15798659,Female,30,62000,0
15663939,Female,31,118000,1
15694946,Male,24,55000,0
15631912,Female,28,85000,0
15768816,Male,26,81000,0
15682268,Male,35,50000,0
15684801,Male,22,81000,0
15636428,Female,30,116000,0
15809823,Male,26,15000,0
15699284,Female,29,28000,0
15786993,Female,29,83000,0
15709441,Female,35,44000,0
15710257,Female,35,25000,0
15582492,Male,28,123000,1
15575694,Male,35,73000,0
15756820,Female,28,37000,0
15766289,Male,27,88000,0
15593014,Male,28,59000,0
15584545,Female,32,86000,0
15675949,Female,33,149000,1
15672091,Female,19,21000,0
15801658,Male,21,72000,0
15706185,Female,26,35000,0
15789863,Male,27,89000,0
15720943,Male,26,86000,0
15697997,Female,38,80000,0
15665416,Female,39,71000,0
15660200,Female,37,71000,0
15619653,Male,38,61000,0
15773447,Male,37,55000,0
15739160,Male,42,80000,0
15689237,Male,40,57000,0
15679297,Male,35,75000,0
15591433,Male,36,52000,0
15642725,Male,40,59000,0
15701962,Male,41,59000,0
15811613,Female,36,75000,0
15741049,Male,37,72000,0
15724423,Female,40,75000,0
15574305,Male,35,53000,0
15678168,Female,41,51000,0
15697020,Female,39,61000,0
15610801,Male,42,65000,0
15745232,Male,26,32000,0
15722758,Male,30,17000,0
15792102,Female,26,84000,0
15675185,Male,31,58000,0
15801247,Male,33,31000,0
15725660,Male,30,87000,0
15638963,Female,21,68000,0
15800061,Female,28,55000,0
15578006,Male,23,63000,0
15668504,Female,20,82000,0
15687491,Male,30,107000,1
15610403,Female,28,59000,0
15741094,Male,19,25000,0
15807909,Male,19,85000,0
15666141,Female,18,68000,0
15617134,Male,35,59000,0
15783029,Male,30,89000,0
15622833,Female,34,25000,0
15746422,Female,24,89000,0
15750839,Female,27,96000,1
15749130,Female,41,30000,0
15779862,Male,29,61000,0
15767871,Male,20,74000,0
15679651,Female,26,15000,0
15576219,Male,41,45000,0
15699247,Male,31,76000,0
15619087,Female,36,50000,0
15605327,Male,40,47000,0
15610140,Female,31,15000,0
15791174,Male,46,59000,0
15602373,Male,29,75000,0
15762605,Male,26,30000,0
15598840,Female,32,135000,1
15744279,Male,32,100000,1
15670619,Male,25,90000,0
15599533,Female,37,33000,0
15757837,Male,35,38000,0
15697574,Female,33,69000,0
15578738,Female,18,86000,0
15762228,Female,22,55000,0
15614827,Female,35,71000,0
15789815,Male,29,148000,1
15579781,Female,29,47000,0
15587013,Male,21,88000,0
15570932,Male,34,115000,0
15794661,Female,26,118000,0
15581654,Female,34,43000,0
15644296,Female,34,72000,0
15614420,Female,23,28000,0
15609653,Female,35,47000,0
15594577,Male,25,22000,0
15584114,Male,24,23000,0
15673367,Female,31,34000,0
15685576,Male,26,16000,0
15774727,Female,31,71000,0
15694288,Female,32,117000,1
15603319,Male,33,43000,0
15759066,Female,33,60000,0
15814816,Male,31,66000,0
15724402,Female,20,82000,0
15571059,Female,33,41000,0
15674206,Male,35,72000,0
15715160,Male,28,32000,0
15730448,Male,24,84000,0
15662067,Female,19,26000,0
15779581,Male,29,43000,0
15662901,Male,19,70000,0
15689751,Male,28,89000,0
15667742,Male,34,43000,0
15738448,Female,30,79000,0
15680243,Female,20,36000,0
15745083,Male,26,80000,0
15708228,Male,35,22000,0
15628523,Male,35,39000,0
15708196,Male,49,74000,0
15735549,Female,39,134000,1
15809347,Female,41,71000,0
15660866,Female,58,101000,1
15766609,Female,47,47000,0
15654230,Female,55,130000,1
15794566,Female,52,114000,0
15800890,Female,40,142000,1
15697424,Female,46,22000,0
15724536,Female,48,96000,1
15735878,Male,52,150000,1
15707596,Female,59,42000,0
15657163,Male,35,58000,0
15622478,Male,47,43000,0
15779529,Female,60,108000,1
15636023,Male,49,65000,0
15582066,Male,40,78000,0
15666675,Female,46,96000,0
15732987,Male,59,143000,1
15789432,Female,41,80000,0
15663161,Male,35,91000,1
15694879,Male,37,144000,1
15593715,Male,60,102000,1
15575002,Female,35,60000,0
15622171,Male,37,53000,0
15795224,Female,36,126000,1
15685346,Male,56,133000,1
15691808,Female,40,72000,0
15721007,Female,42,80000,1
15794253,Female,35,147000,1
15694453,Male,39,42000,0
15813113,Male,40,107000,1
15614187,Male,49,86000,1
15619407,Female,38,112000,0
15646227,Male,46,79000,1
15660541,Male,40,57000,0
15753874,Female,37,80000,0
15617877,Female,46,82000,0
15772073,Female,53,143000,1
15701537,Male,42,149000,1
15736228,Male,38,59000,0
15780572,Female,50,88000,1
15769596,Female,56,104000,1
15586996,Female,41,72000,0
15722061,Female,51,146000,1
15638003,Female,35,50000,0
15775590,Female,57,122000,1
15730688,Male,41,52000,0
15753102,Female,35,97000,1
15810075,Female,44,39000,0
15723373,Male,37,52000,0
15795298,Female,48,134000,1
15584320,Female,37,146000,1
15724161,Female,50,44000,0
15750056,Female,52,90000,1
15609637,Female,41,72000,0
15794493,Male,40,57000,0
15569641,Female,58,95000,1
15815236,Female,45,131000,1
15811177,Female,35,77000,0
15680587,Male,36,144000,1
15672821,Female,55,125000,1
15767681,Female,35,72000,0
15600379,Male,48,90000,1
15801336,Female,42,108000,1
15721592,Male,40,75000,0
15581282,Male,37,74000,0
15746203,Female,47,144000,1
15583137,Male,40,61000,0
15680752,Female,43,133000,0
15688172,Female,59,76000,1
15791373,Male,60,42000,1
15589449,Male,39,106000,1
15692819,Female,57,26000,1
15727467,Male,57,74000,1
15734312,Male,38,71000,0
15764604,Male,49,88000,1
15613014,Female,52,38000,1
15759684,Female,50,36000,1
15609669,Female,59,88000,1
15685536,Male,35,61000,0
15750447,Male,37,70000,1
15663249,Female,52,21000,1
15638646,Male,48,141000,0
15734161,Female,37,93000,1
15631070,Female,37,62000,0
15761950,Female,48,138000,1
15649668,Male,41,79000,0
15713912,Female,37,78000,1
15586757,Male,39,134000,1
15596522,Male,49,89000,1
15625395,Male,55,39000,1
15760570,Male,37,77000,0
15566689,Female,35,57000,0
15725794,Female,36,63000,0
15673539,Male,42,73000,1
15705298,Female,43,112000,1
15675791,Male,45,79000,0
15747043,Male,46,117000,1
15736397,Female,58,38000,1
15678201,Male,48,74000,1
15720745,Female,37,137000,1
15637593,Male,37,79000,1
15598070,Female,40,60000,0
15787550,Male,42,54000,0
15603942,Female,51,134000,0
15733973,Female,47,113000,1
15596761,Male,36,125000,1
15652400,Female,38,50000,0
15717893,Female,42,70000,0
15622585,Male,39,96000,1
15733964,Female,38,50000,0
15753861,Female,49,141000,1
15747097,Female,39,79000,0
15594762,Female,39,75000,1
15667417,Female,54,104000,1
15684861,Male,35,55000,0
15742204,Male,45,32000,1
15623502,Male,36,60000,0
15774872,Female,52,138000,1
15611191,Female,53,82000,1
15674331,Male,41,52000,0
15619465,Female,48,30000,1
15575247,Female,48,131000,1
15695679,Female,41,60000,0
15713463,Male,41,72000,0
15785170,Female,42,75000,0
15796351,Male,36,118000,1
15639576,Female,47,107000,1
15693264,Male,38,51000,0
15589715,Female,48,119000,1
15769902,Male,42,65000,0
15587177,Male,40,65000,0
15814553,Male,57,60000,1
15601550,Female,36,54000,0
15664907,Male,58,144000,1
15612465,Male,35,79000,0
15810800,Female,38,55000,0
15665760,Male,39,122000,1
15588080,Female,53,104000,1
15776844,Male,35,75000,0
15717560,Female,38,65000,0
15629739,Female,47,51000,1
15729908,Male,47,105000,1
15716781,Female,41,63000,0
15646936,Male,53,72000,1
15768151,Female,54,108000,1
15579212,Male,39,77000,0
15721835,Male,38,61000,0
15800515,Female,38,113000,1
15591279,Male,37,75000,0
15587419,Female,42,90000,1
15750335,Female,37,57000,0
15699619,Male,36,99000,1
15606472,Male,60,34000,1
15778368,Male,54,70000,1
15671387,Female,41,72000,0
15573926,Male,40,71000,1
15709183,Male,42,54000,0
15577514,Male,43,129000,1
15778830,Female,53,34000,1
15768072,Female,47,50000,1
15768293,Female,42,79000,0
15654456,Male,42,104000,1
15807525,Female,59,29000,1
15574372,Female,58,47000,1
15671249,Male,46,88000,1
15779744,Male,38,71000,0
15624755,Female,54,26000,1
15611430,Female,60,46000,1
15774744,Male,60,83000,1
15629885,Female,39,73000,0
15708791,Male,59,130000,1
15793890,Female,37,80000,0
15646091,Female,46,32000,1
15596984,Female,46,74000,0
15800215,Female,42,53000,0
15577806,Male,41,87000,1
15749381,Female,58,23000,1
15683758,Male,42,64000,0
15670615,Male,48,33000,1
15715622,Female,44,139000,1
15707634,Male,49,28000,1
15806901,Female,57,33000,1
15775335,Male,56,60000,1
15724150,Female,49,39000,1
15627220,Male,39,71000,0
15672330,Male,47,34000,1
15668521,Female,48,35000,1
15807837,Male,48,33000,1
15592570,Male,47,23000,1
15748589,Female,45,45000,1
15635893,Male,60,42000,1
15757632,Female,39,59000,0
15691863,Female,46,41000,1
15706071,Male,51,23000,1
15654296,Female,50,20000,1
15755018,Male,36,33000,0
15594041,Female,49,36000,1

================================================
FILE: data/msales.csv
================================================
Merchant_Id,Annual_Sales,Merchant_Location,Zone,No_of_helpers,Daily_OPEN_hours,Years_Open,Owner's_Age,Owner's_Education,Cashier_Sales_Person_Age,Merchant_Type,Shopping_Complex_Or_Standalone
mid,sales,loc,zone,helpers,hours,years ,oage,oedn,cage,mtype,mcat
247,195,NJ City,E,1,20,18,66,High School,33,Owner,Standalone
9026,417,NJ City,S,1,10,14,43,Graduation,21,Patnership,Complex
5245,147.46,Boston,N,2,23,1,46,High School,24,Patnership,Standalone
6644,500,Boston,W,2,24,17,59,Graduation,22,Franchise,Complex
4497,111.19,NJ City,N,20,11,4,76,Graduation,22,Patnership,Standalone
2571,113,NJ City,E,1,24,14,31,High School,22,Owner,Standalone
6534,447,Boston,S,1,22,12,25,Graduation,31,Patnership,Complex
7201,393,Boston,N,1,12,16,49,Graduation,21,Patnership,Standalone
9051,434,NJ City,E,2,13,14,79,Graduation,19,Owner,Standalone
4157,183,NJ City,N,3,18,6,81,High School,32,Patnership,Standalone
3746,110,NJ City,S,2,15,21,57,High School,35,Patnership,Complex
6386,381,Boston,S,3,17,10,50,Graduation,25,Patnership,Complex
6094,283.85,Boston,S,15,8,2,80,High School,22,Patnership,Complex
4090,375.26,NJ City,S,5,13,4,60,High School,28,Patnership,Complex
9536,370,NJ City,W,3,8,18,62,Graduation,23,Franchise,Complex
7047,414,Boston,E,2,16,7,61,Graduation,27,Owner,Standalone
3199,244,NJ City,E,2,21,16,46,High School,33,Owner,Standalone
3548,122,NJ City,W,2,20,6,76,High School,31,Franchise,Complex
3694,289,NJ City,S,18,21,1,54,High School,31,Patnership,Complex
6101,467,Boston,N,2,8,23,54,Graduation,28,Patnership,Standalone
8857,69.28,NJ City,N,3,20,1,45,High School,30,Patnership,Standalone
7901,379,Boston,N,2,10,19,59,Graduation,24,Patnership,Standalone
5720,420,Boston,W,2,14,17,79,Graduation,35,Franchise,Complex
8322,487,NJ City,S,3,20,7,67,Graduation,35,Patnership,Complex
2789,193,NJ City,N,3,16,6,75,High School,25,Patnership,Standalone
7970,474,Boston,S,1,20,22,28,Graduation,21,Patnership,Complex
4478,117.18,NJ City,S,25,21,3,29,No education,19,Patnership,Complex
2109,70.5,NJ City,N,15,19,4,81,Graduation,33,Patnership,Standalone
8430,77.67,NJ City,S,17,18,2,32,Graduation,30,Patnership,Complex
6569,275.81,Boston,N,12,10,2,69,No education,22,Patnership,Standalone
4965,92.68,Boston,N,8,18,1,47,Graduation,34,Patnership,Standalone
7817,387,Boston,N,3,8,10,54,Graduation,18,Patnership,Standalone
1798,299,NJ City,S,2,20,11,56,High School,31,Patnership,Complex
3433,188,NJ City,N,3,14,24,47,High School,31,Patnership,Standalone
591,284,NJ City,E,1,18,17,39,High School,22,Owner,Standalone
1863,238,NJ City,E,2,17,16,36,High School,29,Owner,Standalone
4537,23.48,NJ City,N,9,13,4,80,High School,22,Patnership,Standalone
1544,122.44,NJ City,W,12,13,3,57,No education,28,Franchise,Complex
529,470,NJ City,N,2,20,5,51,Graduation,25,Patnership,Standalone
8065,438,NJ City,N,1,20,7,31,Graduation,35,Patnership,Standalone
3427,127.88,NJ City,E,2,24,1,74,High School,26,Owner,Standalone
4624,366,NJ City,W,1,16,5,27,Graduation,21,Franchise,Complex
1816,160,NJ City,W,2,24,7,56,High School,20,Franchise,Complex
4232,462.12,NJ City,W,1,24,14,74,No education,24,Franchise,Complex
3295,252,NJ City,E,2,20,9,77,High School,30,Owner,Standalone
6953,176.16,Boston,N,1,8,2,77,No education,29,Patnership,Standalone
9907,352,NJ City,E,1,24,18,38,Graduation,23,Owner,Standalone
7869,499,Boston,N,2,18,7,55,Graduation,20,Patnership,Standalone
3405,84.91,NJ City,N,13,10,4,50,Graduation,20,Patnership,Standalone
2119,276,NJ City,E,2,23,22,48,High School,20,Owner,Standalone
4418,220,NJ City,S,2,17,24,26,High School,27,Patnership,Complex
5843,426,Boston,E,1,19,20,84,Graduation,29,Owner,Standalone
3947,86.88,NJ City,E,24,9,3,84,No education,28,Owner,Standalone
4495,434,NJ City,E,3,14,5,39,Graduation,18,Owner,Standalone
9350,358,NJ City,S,1,21,14,47,Graduation,35,Patnership,Complex
5950,482,Boston,S,2,18,23,72,Graduation,31,Patnership,Complex
7564,490,Boston,W,2,13,15,58,Graduation,25,Franchise,Complex
3541,239,NJ City,N,1,19,9,42,High School,23,Patnership,Standalone
97,100.27,NJ City,N,18,17,4,55,High School,34,Patnership,Standalone
3745,272.24,NJ City,N,17,10,2,33,High School,28,Patnership,Standalone
1415,20.59,NJ City,N,24,19,3,63,No education,19,Owner,Standalone
2198,237,NJ City,S,2,14,11,42,High School,25,Patnership,Complex
4256,227.68,NJ City,W,8,9,3,32,No education,35,Franchise,Complex
3942,150.48,NJ City,S,4,12,1,77,Graduation,34,Patnership,Complex
7751,215.34,Boston,E,8,19,3,85,No education,31,Owner,Standalone
172,318,NJ City,W,1,17,7,41,High School,20,Franchise,Complex
370,249,NJ City,S,2,13,17,38,High School,25,Patnership,Complex
5095,489,Boston,E,3,12,10,41,Graduation,18,Owner,Standalone
7605,427,Boston,N,3,22,7,71,Graduation,20,Patnership,Standalone
2056,204.08,NJ City,W,12,22,4,44,High School,34,Franchise,Complex
3073,115,NJ City,N,1,8,17,85,High School,28,Patnership,Standalone
8017,409,Boston,N,3,8,15,66,Graduation,31,Patnership,Standalone
8240,29.53,NJ City,N,21,16,4,75,No education,22,Franchise,Complex
1080,131,NJ City,W,3,18,7,27,High School,20,Franchise,Complex
6470,77.33,Boston,S,14,10,1,33,No education,23,Patnership,Complex
6181,95.11,Boston,N,9,13,3,59,High School,31,Patnership,Standalone
2392,281,NJ City,W,1,11,7,79,High School,32,Franchise,Complex
919,162,NJ City,E,3,24,12,43,High School,33,Owner,Standalone
9770,240.75,NJ City,S,8,24,3,66,No education,27,Patnership,Complex
6106,384,Boston,S,2,12,7,63,Graduation,24,Patnership,Complex
3605,270,NJ City,N,1,10,23,64,High School,26,Patnership,Standalone
8651,125.31,NJ City,E,15,18,1,83,No education,30,Owner,Standalone
8125,234.86,NJ City,N,2,12,4,55,High School,22,Patnership,Standalone
2904,178,NJ City,W,3,24,19,74,High School,35,Franchise,Complex
9211,481.81,NJ City,E,1,21,4,74,High School,22,Owner,Standalone
4538,235.56,NJ City,S,3,24,11,35,No education,18,Patnership,Complex
2401,379,NJ City,N,3,8,5,76,Graduation,26,Patnership,Standalone
8444,474,NJ City,W,1,22,19,41,Graduation,30,Franchise,Complex
1376,487,NJ City,W,2,24,5,40,Graduation,33,Franchise,Complex
6265,385,Boston,N,2,22,8,83,Graduation,34,Patnership,Standalone
2936,119.05,NJ City,W,20,18,2,72,No education,19,Franchise,Complex
7411,44.62,Boston,N,15,23,1,51,High School,28,Owner,Standalone
9900,210.94,NJ City,W,4,19,3,70,Graduation,30,Franchise,Complex
3413,267,NJ City,N,1,8,25,40,High School,22,Patnership,Standalone
1329,406.51,NJ City,N,7,8,4,64,Graduation,19,Patnership,Standalone
9989,71.1,NJ City,N,7,16,1,37,No education,25,Patnership,Standalone
6303,350,Boston,E,1,11,25,26,Graduation,20,Owner,Standalone
3453,116,NJ City,N,2,13,18,69,High School,23,Patnership,Standalone
4374,119,NJ City,S,3,12,15,48,High School,23,Patnership,Complex
1453,247.19,NJ City,N,4,11,2,33,High School,31,Patnership,Standalone
8699,455,NJ City,E,2,9,18,46,Graduation,34,Owner,Standalone
9917,416,NJ City,N,2,22,16,67,Graduation,31,Patnership,Standalone
1247,269,NJ City,E,2,22,10,36,High School,19,Owner,Standalone
2689,174,NJ City,N,1,9,21,27,High School,35,Patnership,Standalone
1086,307,NJ City,S,3,22,21,81,High School,18,Patnership,Complex
5366,496,Boston,S,2,11,23,30,Graduation,27,Patnership,Complex
8595,426,NJ City,E,3,17,5,66,Graduation,25,Owner,Standalone
1602,50.25,NJ City,S,1,21,1,25,Graduation,19,Patnership,Complex
8115,200.25,NJ City,E,1,9,2,83,Graduation,22,Owner,Standalone
1338,64.3,NJ City,N,22,14,3,33,Graduation,33,Patnership,Complex
1242,116,NJ City,S,3,24,11,41,High School,34,Patnership,Complex
3304,243,NJ City,W,3,13,8,38,High School,28,Franchise,Complex
2948,265,NJ City,W,2,18,24,67,High School,32,Franchise,Complex
79,105,Boston,E,2,16,8,85,High School,31,Owner,Standalone
2367,174,NJ City,E,3,17,22,46,High School,24,Owner,Standalone
5530,79.05,Boston,S,20,10,1,42,High School,21,Patnership,Complex
5110,449,Boston,S,2,16,18,79,Graduation,30,Patnership,Complex
1811,170,NJ City,E,3,21,14,83,High School,28,Owner,Standalone
8650,400,NJ City,S,3,22,24,80,Graduation,32,Patnership,Complex
1466,376.99,NJ City,S,2,21,19,42,No education,28,Patnership,Complex
2157,101,NJ City,N,2,14,13,83,High School,28,Patnership,Standalone
3146,247,NJ City,S,1,18,7,59,High School,19,Patnership,Complex
6659,8.39,Boston,N,2,19,2,41,No education,25,Owner,Standalone
1967,299.59,NJ City,E,16,14,3,28,No education,30,Owner,Standalone
951,165,NJ City,E,2,18,20,63,High School,24,Owner,Standalone
7642,408,Boston,S,2,13,11,32,Graduation,21,Patnership,Complex
7013,496,Boston,N,2,10,22,41,Graduation,35,Patnership,Standalone
754,179,NJ City,S,2,22,15,53,High School,24,Patnership,Complex
8290,373,NJ City,S,2,23,24,52,Graduation,30,Patnership,Complex
4363,233,NJ City,E,3,14,11,62,High School,32,Owner,Standalone
816,154,NJ City,W,1,18,18,81,High School,30,Franchise,Complex
1278,343.28,NJ City,S,24,15,3,46,Graduation,22,Patnership,Complex
4689,27.31,NJ City,N,2,9,1,44,Graduation,26,Patnership,Standalone
2814,225,NJ City,S,1,16,23,71,High School,34,Patnership,Complex
3087,54.81,NJ City,E,2,20,4,35,Graduation,22,Owner,Standalone
5810,481,Boston,S,3,18,19,85,Graduation,31,Patnership,Complex
9164,386,NJ City,W,3,10,6,80,Graduation,23,Franchise,Complex
8245,3.34,NJ City,N,9,12,3,56,High School,23,Patnership,Standalone
5098,43.74,Boston,N,13,24,1,64,High School,35,Patnership,Complex
1494,89.26,NJ City,S,10,23,2,59,Graduation,29,Patnership,Complex
6677,363,Boston,N,2,22,20,62,Graduation,27,Patnership,Standalone
1869,211,NJ City,N,1,18,20,30,High School,21,Patnership,Standalone
513,240,NJ City,N,1,17,7,39,High School,22,Patnership,Standalone
1093,316,NJ City,N,1,18,25,36,High School,26,Patnership,Standalone
6120,479,Boston,W,3,9,7,45,Graduation,30,Franchise,Complex
1474,143,NJ City,S,1,14,24,33,High School,26,Patnership,Complex
2836,142.48,NJ City,W,21,13,1,63,High School,28,Franchise,Complex
4744,177,NJ City,W,1,13,10,67,High School,30,Franchise,Complex
7054,462,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex
6203,368,Boston,E,3,10,13,84,Graduation,29,Owner,Standalone
6977,450,Boston,N,1,19,14,84,Graduation,18,Patnership,Standalone
1423,300,NJ City,E,1,20,25,52,High School,27,Owner,Standalone
9604,351,NJ City,W,2,20,18,26,Graduation,29,Franchise,Complex
8246,11.98,NJ City,N,16,21,1,40,No education,35,Patnership,Complex
9124,160.23,NJ City,W,8,19,1,47,High School,32,Franchise,Complex
3631,127.1,NJ City,E,10,21,3,48,High School,31,Owner,Standalone
4395,117,NJ City,E,2,22,18,40,High School,25,Owner,Standalone
3839,35.99,NJ City,N,22,12,3,85,No education,24,Owner,Standalone
5016,411,Boston,W,1,22,6,70,Graduation,34,Franchise,Complex
7932,170.39,Boston,W,21,8,1,82,Graduation,31,Franchise,Complex
5386,398.73,Boston,S,3,19,1,47,High School,28,Patnership,Complex
4031,147,NJ City,E,3,23,20,67,High School,24,Owner,Standalone
34,53.18,Boston,S,17,8,4,67,High School,24,Patnership,Complex
253,203,NJ City,N,3,12,20,36,High School,19,Patnership,Standalone
3057,474,NJ City,N,1,21,5,76,Graduation,34,Patnership,Standalone
9505,480.59,NJ City,N,23,18,2,37,High School,32,Patnership,Standalone
9181,435,NJ City,N,3,22,15,81,Graduation,23,Patnership,Standalone
9126,424,NJ City,S,1,12,13,59,Graduation,19,Patnership,Complex
7610,472,Boston,S,3,19,6,42,Graduation,34,Patnership,Complex
1704,161,NJ City,W,1,16,18,68,High School,24,Franchise,Complex
5949,429,Boston,N,3,21,14,64,Graduation,35,Patnership,Standalone
8070,354,NJ City,S,3,10,12,72,Graduation,35,Patnership,Complex
8592,422,NJ City,W,3,13,20,77,Graduation,29,Franchise,Complex
3524,1.56,NJ City,N,11,16,2,66,No education,34,Franchise,Complex
6745,461,Boston,N,3,21,8,78,Graduation,31,Patnership,Standalone
3385,23.42,NJ City,N,9,22,1,41,High School,20,Patnership,Standalone
1121,124.39,NJ City,N,16,16,4,58,No education,21,Patnership,Standalone
311,288,NJ City,E,1,13,18,54,High School,21,Owner,Standalone
2005,403,NJ City,N,3,11,5,59,Graduation,30,Patnership,Standalone
1825,258,NJ City,N,2,22,10,36,High School,22,Patnership,Standalone
1322,40,NJ City,N,17,12,4,25,No education,25,Patnership,Complex
6366,465,Boston,S,1,15,24,50,Graduation,25,Patnership,Complex
6449,471,Boston,N,1,20,7,29,Graduation,25,Patnership,Standalone
4797,271,NJ City,N,1,22,16,32,High School,18,Patnership,Standalone
4289,215,NJ City,N,2,22,14,63,High School,20,Patnership,Standalone
9370,412,NJ City,S,3,19,23,29,Graduation,20,Patnership,Complex
7006,497,Boston,S,3,10,24,32,Graduation,22,Patnership,Complex
1843,106.05,NJ City,E,17,23,1,57,High School,34,Owner,Standalone
8311,21.83,NJ City,N,22,8,4,77,High School,20,Owner,Standalone
9633,418,NJ City,N,2,21,23,50,Graduation,22,Patnership,Standalone
8607,422.53,NJ City,E,13,20,3,54,Graduation,28,Owner,Standalone
7894,244.72,Boston,S,23,16,3,57,High School,20,Patnership,Complex
2355,388.74,NJ City,E,1,17,10,47,No education,24,Owner,Standalone
3378,456.79,NJ City,S,9,22,2,32,Graduation,34,Patnership,Complex
5561,354,Boston,N,3,22,23,25,Graduation,26,Patnership,Standalone
9540,472,NJ City,W,2,21,20,35,Graduation,22,Franchise,Complex
8562,383,NJ City,S,3,8,13,35,Graduation,29,Patnership,Complex
4734,278,NJ City,S,1,20,20,34,High School,23,Patnership,Complex
7182,460,Boston,S,1,12,25,37,Graduation,26,Patnership,Complex
1847,123,NJ City,E,2,16,11,57,High School,35,Owner,Standalone
2812,149,NJ City,W,1,16,13,50,High School,26,Franchise,Complex
5250,387,Boston,S,1,10,16,40,Graduation,28,Patnership,Complex
1351,303,NJ City,E,1,13,10,65,High School,18,Owner,Standalone
1206,286,NJ City,S,1,18,20,52,High School,22,Patnership,Complex
6133,485,Boston,N,1,9,18,70,Graduation,34,Patnership,Standalone
9113,498,NJ City,N,1,24,14,55,Graduation,34,Patnership,Standalone
8762,382,NJ City,S,2,11,13,58,Graduation,30,Patnership,Complex
7443,419,Boston,E,3,15,22,72,Graduation,30,Owner,Standalone
4244,121.07,NJ City,W,24,15,4,50,No education,18,Franchise,Complex
6699,358,Boston,E,2,12,25,68,Graduation,21,Owner,Standalone
3756,40.89,NJ City,N,15,22,3,60,Graduation,32,Franchise,Complex
4030,385.24,NJ City,S,3,17,3,64,High School,25,Patnership,Complex
2687,196,NJ City,E,1,16,13,74,High School,20,Owner,Standalone
7206,64.95,Boston,N,25,18,1,72,Graduation,19,Patnership,Complex
4853,126,NJ City,N,2,19,13,66,High School,18,Patnership,Standalone
6325,368,Boston,N,3,20,17,71,Graduation,22,Patnership,Standalone
4028,121,NJ City,W,3,11,21,51,High School,31,Franchise,Complex
4771,224,NJ City,E,1,8,23,45,High School,32,Owner,Standalone
8479,23.19,NJ City,N,8,16,2,53,High School,23,Owner,Standalone
2560,64.33,NJ City,N,6,19,2,40,High School,19,Franchise,Complex
1835,168,NJ City,E,2,9,25,71,High School,23,Owner,Standalone
1459,99.7,NJ City,E,19,9,2,28,High School,29,Owner,Standalone
8023,402,Boston,E,3,24,20,73,Graduation,35,Owner,Standalone
7352,416,Boston,W,3,13,24,39,Graduation,25,Franchise,Complex
5889,44.94,Boston,N,25,16,4,45,Graduation,23,Patnership,Standalone
9791,44.32,NJ City,N,5,19,3,50,No education,22,Owner,Standalone
5630,35.77,Boston,N,15,8,3,58,No education,35,Patnership,Complex
3596,19.55,NJ City,N,12,13,4,43,No education,27,Franchise,Complex
7798,467,Boston,S,2,11,6,56,Graduation,25,Patnership,Complex
3187,257.52,NJ City,E,3,21,20,59,No education,20,Owner,Standalone
1314,175,NJ City,S,2,24,18,80,High School,25,Patnership,Complex
6226,369,Boston,S,3,24,5,45,Graduation,28,Patnership,Complex
7132,92.11,Boston,W,8,24,2,73,High School,18,Franchise,Complex
9353,484,NJ City,N,3,9,14,40,Graduation,29,Patnership,Standalone
867,94.19,NJ City,E,4,20,4,30,Graduation,21,Owner,Standalone
9832,366,NJ City,W,3,22,17,29,Graduation,26,Franchise,Complex
3755,156,NJ City,E,1,19,11,30,High School,32,Owner,Standalone
8397,374.58,NJ City,N,6,15,1,44,Graduation,24,Patnership,Standalone
5183,493,Boston,E,3,16,12,78,Graduation,18,Owner,Standalone
5753,62.7,Boston,N,1,14,2,37,No education,21,Patnership,Standalone
4557,74.82,NJ City,N,23,12,1,81,Graduation,28,Patnership,Standalone
9662,462,NJ City,S,3,23,10,35,Graduation,26,Patnership,Complex
9396,426,NJ City,W,2,14,13,54,Graduation,31,Franchise,Complex
8588,448,NJ City,W,2,21,12,49,Graduation,26,Franchise,Complex
1105,6.19,NJ City,N,13,15,4,58,High School,29,Patnership,Standalone
1588,162,NJ City,W,2,10,14,48,High School,19,Franchise,Complex
2664,482.34,NJ City,W,24,17,3,53,Graduation,19,Franchise,Complex
1943,191,NJ City,E,3,11,17,78,High School,23,Owner,Standalone
661,8.61,NJ City,N,17,17,2,53,High School,29,Patnership,Standalone
4125,75.13,NJ City,N,20,12,2,48,Graduation,19,Patnership,Standalone
9718,13.05,NJ City,N,14,23,1,80,High School,34,Patnership,Complex
4885,125.37,NJ City,N,11,14,2,80,High School,22,Patnership,Standalone
7256,208,Boston,W,15,17,2,76,No education,33,Franchise,Complex
6695,453,Boston,E,2,15,5,72,Graduation,22,Owner,Standalone
5767,476,Boston,E,2,16,18,37,Graduation,28,Owner,Standalone
3791,268.34,NJ City,E,24,14,3,33,No education,30,Owner,Standalone
2027,103,NJ City,E,1,16,20,53,High School,35,Owner,Standalone
2621,155,NJ City,N,2,22,19,75,High School,29,Patnership,Standalone
8568,326.99,NJ City,W,2,23,3,33,Graduation,21,Franchise,Complex
5825,26.62,Boston,N,19,19,2,65,No education,26,Patnership,Standalone
3962,241,NJ City,S,1,9,6,82,High School,19,Patnership,Complex
2303,101,NJ City,E,2,13,22,79,High School,35,Owner,Standalone
8751,394,NJ City,E,1,10,7,38,Graduation,25,Owner,Standalone
5013,43.38,Boston,N,8,15,3,56,Graduation,19,Patnership,Standalone
3648,114.86,NJ City,W,8,23,4,74,Graduation,23,Franchise,Complex
6012,79.04,Boston,W,1,18,4,74,Graduation,35,Franchise,Complex
7319,363,Boston,E,3,12,15,30,Graduation,30,Owner,Standalone
8954,397,NJ City,S,3,8,11,57,Graduation,34,Patnership,Complex
9229,499.39,NJ City,N,23,12,2,71,High School,33,Patnership,Standalone
3314,5.56,NJ City,N,20,19,3,37,No education,31,Patnership,Complex
3576,167,NJ City,W,2,17,17,70,High School,27,Franchise,Complex
6864,472,Boston,W,1,21,13,60,Graduation,27,Franchise,Complex
6558,462,Boston,S,3,14,16,36,Graduation,33,Patnership,Complex
6996,380,Boston,W,1,16,5,49,Graduation,22,Franchise,Complex
8038,361,Boston,S,1,24,14,50,Graduation,21,Patnership,Complex
3936,101.02,NJ City,W,7,9,1,85,Graduation,23,Franchise,Complex
8960,314.37,NJ City,W,13,10,3,61,No education,23,Franchise,Complex
2777,202,NJ City,N,1,18,14,35,High School,34,Patnership,Standalone
6479,482,Boston,E,1,11,22,31,Graduation,33,Owner,Standalone
5051,4.4,Boston,N,19,8,1,40,No education,35,Owner,Standalone
4532,66.67,NJ City,N,15,16,4,30,No education,21,Franchise,Complex
8443,393,NJ City,E,1,13,23,83,Graduation,32,Owner,Standalone
9943,495,NJ City,E,3,18,24,48,Graduation,34,Owner,Standalone
6503,462,Boston,E,2,19,22,61,Graduation,31,Owner,Standalone
4732,280,NJ City,W,1,19,9,52,High School,20,Franchise,Complex
8350,446,NJ City,S,3,11,23,25,Graduation,22,Patnership,Complex
1097,4.31,NJ City,N,3,12,2,44,No education,28,Patnership,Standalone
6005,361,Boston,N,3,9,19,50,Graduation,19,Patnership,Standalone
2442,45.04,NJ City,N,8,20,2,64,Graduation,25,Patnership,Complex
4585,443.5,NJ City,N,23,11,2,44,High School,25,Patnership,Standalone
7878,398,Boston,S,3,11,8,54,Graduation,24,Patnership,Complex
5060,457,Boston,W,2,19,6,57,Graduation,33,Franchise,Complex
2195,276,NJ City,E,2,15,12,64,High School,27,Owner,Standalone
2333,160.43,NJ City,N,9,10,1,60,No education,33,Patnership,Standalone
6214,409,Boston,S,2,12,11,66,Graduation,34,Patnership,Complex
6085,480,Boston,N,3,12,20,42,Graduation,29,Patnership,Standalone
4380,111.61,NJ City,W,10,15,1,38,Graduation,30,Franchise,Complex
2201,407,NJ City,N,3,15,5,41,Graduation,28,Patnership,Standalone
4384,211,NJ City,W,3,21,6,34,High School,31,Franchise,Complex
7338,454,Boston,S,3,8,22,59,Graduation,19,Patnership,Complex
6387,453,Boston,E,3,18,15,51,Graduation,31,Owner,Standalone
5281,481,Boston,N,3,8,5,59,Graduation,26,Patnership,Standalone
8100,366,NJ City,W,3,14,6,80,Graduation,30,Franchise,Complex
6733,103.2,Boston,N,14,9,4,64,High School,26,Patnership,Standalone
2885,116.99,NJ City,N,10,24,3,26,No education,22,Patnership,Standalone
2393,189.2,NJ City,N,11,22,4,79,No education,32,Patnership,Standalone
2774,389.24,NJ City,S,18,23,2,34,No education,26,Patnership,Complex
1574,165,NJ City,S,3,11,19,73,High School,27,Patnership,Complex
74,159,Boston,S,3,16,15,71,High School,23,Patnership,Complex
2356,196,NJ City,W,3,17,11,42,High School,31,Franchise,Complex
3702,314,NJ City,S,2,19,21,63,High School,21,Patnership,Complex
7543,456.35,Boston,E,10,21,4,63,High School,30,Owner,Standalone
2441,113,NJ City,N,1,10,19,47,High School,25,Patnership,Standalone
773,270,NJ City,N,1,12,16,78,High School,22,Patnership,Standalone
3259,93.28,NJ City,E,8,9,4,56,High School,29,Owner,Standalone
8267,433,NJ City,E,2,10,10,56,Graduation,20,Owner,Standalone
6031,27.79,Boston,N,25,8,2,30,High School,35,Owner,Standalone
3981,216,NJ City,N,2,22,17,68,High School,31,Patnership,Standalone
9001,462,NJ City,N,2,8,24,49,Graduation,24,Patnership,Standalone
3697,191,NJ City,N,2,18,23,47,High School,31,Patnership,Standalone
6917,449,Boston,N,3,14,23,41,Graduation,32,Patnership,Standalone
6393,25.18,Boston,N,1,15,1,75,Graduation,19,Patnership,Standalone
3127,197,NJ City,E,3,17,16,78,High School,26,Owner,Standalone
9935,460,NJ City,E,3,18,14,65,Graduation,34,Owner,Standalone
2141,312,NJ City,N,3,21,16,60,High School,25,Patnership,Standalone
6466,443,Boston,S,2,20,10,32,Graduation,29,Patnership,Complex
372,61.02,NJ City,N,11,16,4,42,Graduation,30,Franchise,Complex
2062,350,NJ City,S,2,22,5,26,Graduation,19,Patnership,Complex
6954,492.44,Boston,S,5,8,4,59,Graduation,32,Patnership,Complex
4385,233,NJ City,N,3,14,14,44,High School,35,Patnership,Standalone
4861,306.66,NJ City,N,4,16,1,39,High School,28,Patnership,Standalone
7019,474,Boston,E,3,16,5,59,Graduation,20,Owner,Standalone
1284,26.35,NJ City,N,25,13,4,48,Graduation,28,Franchise,Complex
4814,441.56,NJ City,S,22,19,2,52,No education,27,Patnership,Complex
2944,59.01,NJ City,W,16,15,4,34,High School,27,Franchise,Complex
8449,432,NJ City,N,3,13,15,39,Graduation,21,Patnership,Standalone
4995,470.17,Boston,E,11,20,4,83,Graduation,23,Owner,Standalone
3220,14.47,NJ City,N,9,23,2,41,High School,21,Franchise,Complex
1737,106.81,NJ City,N,9,23,2,74,Graduation,32,Patnership,Standalone
4645,22.47,NJ City,N,14,16,4,26,High School,19,Patnership,Standalone
4389,351.14,NJ City,N,25,13,3,77,Graduation,22,Patnership,Standalone
645,191,NJ City,N,2,15,13,27,High School,20,Patnership,Standalone
5161,88.6,Boston,N,9,10,1,56,High School,34,Patnership,Standalone
1721,237,NJ City,N,2,21,11,58,High School,31,Patnership,Standalone
2845,303.96,NJ City,N,16,10,3,81,High School,30,Patnership,Standalone
462,113.62,NJ City,S,8,11,1,58,Graduation,27,Patnership,Complex
3482,42.57,NJ City,N,23,8,1,52,No education,24,Patnership,Complex
3829,110,NJ City,N,3,22,11,33,High School,31,Patnership,Standalone
9610,372,NJ City,S,3,13,24,67,Graduation,35,Patnership,Complex
4132,96.29,NJ City,W,3,15,4,32,High School,26,Franchise,Complex
8847,453.6,NJ City,E,5,12,2,35,Graduation,35,Owner,Standalone
6252,54.52,Boston,W,18,15,2,43,Graduation,23,Franchise,Complex
1851,94.59,NJ City,E,18,14,4,42,Graduation,33,Owner,Standalone
7625,455,Boston,N,1,21,23,59,Graduation,32,Patnership,Standalone
8901,438,NJ City,N,3,10,24,76,Graduation,29,Patnership,Standalone
5222,365,Boston,S,1,11,15,79,Graduation,19,Patnership,Complex
3847,251,NJ City,E,2,17,8,68,High School,24,Owner,Standalone
1372,43.02,NJ City,N,15,19,4,73,High School,32,Franchise,Complex
8594,442,NJ City,S,1,12,21,45,Graduation,28,Patnership,Complex
9103,500,NJ City,E,2,23,21,29,Graduation,21,Owner,Standalone
4534,131,NJ City,S,2,21,15,80,High School,34,Patnership,Complex
5321,4.57,Boston,N,6,11,3,29,No education,24,Patnership,Standalone
7946,22.61,Boston,N,8,14,1,67,No education,19,Patnership,Complex
801,74.1,NJ City,N,4,8,4,53,Graduation,33,Patnership,Standalone
3873,314,NJ City,N,1,19,10,85,High School,34,Patnership,Standalone
9563,91.21,NJ City,E,13,17,1,59,No education,24,Owner,Standalone
3632,273,NJ City,W,1,19,17,31,High School,18,Franchise,Complex
6462,82.57,Boston,S,5,17,1,47,Graduation,31,Patnership,Complex
6904,403,Boston,W,3,18,20,26,Graduation,23,Franchise,Complex
9781,489,NJ City,N,3,15,10,28,Graduation,29,Patnership,Standalone
1181,228,NJ City,N,1,15,6,25,High School,34,Patnership,Standalone
5601,166.93,Boston,N,20,8,4,64,Graduation,22,Patnership,Standalone
7164,69.08,Boston,N,17,24,4,30,Graduation,21,Franchise,Complex
6456,440.99,Boston,W,1,17,4,75,Graduation,22,Franchise,Complex
9067,475,NJ City,E,2,21,17,44,Graduation,32,Owner,Standalone
797,278,NJ City,N,2,14,22,40,High School,33,Patnership,Standalone
90,121,Boston,S,1,21,20,73,High School,35,Patnership,Complex
7532,97.32,Boston,W,14,11,3,27,No education,18,Franchise,Complex
7734,426,Boston,S,3,20,7,43,Graduation,35,Patnership,Complex
681,132,NJ City,N,2,15,25,55,High School,27,Patnership,Standalone
7599,402,Boston,E,3,10,23,73,Graduation,35,Owner,Standalone
8577,432,NJ City,N,3,22,21,58,Graduation,25,Patnership,Standalone
4621,243,NJ City,N,1,10,7,67,High School,20,Patnership,Standalone
3442,274,NJ City,S,3,13,16,67,High School,18,Patnership,Complex
1399,155,NJ City,E,1,16,12,49,High School,20,Owner,Standalone
6249,486,Boston,N,3,8,6,39,Graduation,22,Patnership,Standalone
6147,371,Boston,E,2,20,13,61,Graduation,34,Owner,Standalone
4985,35.66,Boston,N,13,19,1,72,No education,26,Patnership,Standalone
4205,179,NJ City,N,2,10,6,73,High School,22,Patnership,Standalone
6477,97.37,Boston,N,23,10,2,37,Graduation,18,Patnership,Standalone
6591,398,Boston,E,1,18,9,49,Graduation,29,Owner,Standalone
3739,270,NJ City,E,3,11,8,64,High School,24,Owner,Standalone
8108,386,NJ City,W,2,13,15,56,Graduation,30,Franchise,Complex
4055,248,NJ City,E,1,10,20,76,High School,33,Owner,Standalone
4870,179,NJ City,S,1,21,21,50,High School,35,Patnership,Complex
9495,453,NJ City,E,3,24,24,32,Graduation,31,Owner,Standalone
5385,82.59,Boston,N,10,8,1,34,Graduation,25,Patnership,Standalone
2468,107.79,NJ City,W,7,12,1,28,No education,33,Franchise,Complex
8501,376,NJ City,N,2,21,24,51,Graduation,24,Patnership,Standalone
9975,427,NJ City,E,3,17,23,38,Graduation,23,Owner,Standalone
9762,108.97,NJ City,S,4,15,2,29,Graduation,28,Patnership,Complex
5955,388,Boston,E,3,19,21,75,Graduation,27,Owner,Standalone
9543,40.93,NJ City,N,9,22,1,29,Graduation,26,Owner,Standalone
4391,255,NJ City,E,2,11,13,50,High School,28,Owner,Standalone
5773,375,Boston,N,2,23,16,60,Graduation,30,Patnership,Standalone
971,394.51,NJ City,E,20,10,4,50,No education,28,Owner,Standalone
4175,196,NJ City,E,3,22,14,31,High School,29,Owner,Standalone
7423,35.09,Boston,N,23,15,1,34,High School,29,Owner,Standalone
1485,102,NJ City,N,1,8,20,28,High School,33,Patnership,Standalone
6353,88.83,Boston,N,20,15,3,29,No education,20,Patnership,Standalone
5820,382,Boston,W,3,23,11,63,Graduation,25,Franchise,Complex
3665,96.32,NJ City,N,8,8,2,33,No education,33,Patnership,Standalone
8551,31.49,NJ City,N,15,10,2,34,High School,31,Owner,Standalone
1540,127,NJ City,W,3,11,24,77,High School,24,Franchise,Complex
5999,485,Boston,E,3,8,11,32,Graduation,19,Owner,Standalone
9487,477,NJ City,E,1,8,16,59,Graduation,23,Owner,Standalone
8011,7.77,Boston,N,13,9,2,33,High School,24,Owner,Standalone
1667,49.17,NJ City,E,12,8,1,25,No education,19,Owner,Standalone
2633,220.3,NJ City,N,9,10,1,49,No education,25,Patnership,Standalone
1636,47.13,NJ City,N,24,22,1,60,High School,26,Franchise,Complex
4168,7.2,NJ City,N,3,11,3,41,High School,24,Franchise,Complex
2130,208,NJ City,S,1,11,8,76,High School,24,Patnership,Complex
162,225,NJ City,S,2,10,24,26,High School,32,Patnership,Complex
8949,452,NJ City,N,1,11,19,43,Graduation,29,Patnership,Standalone
2318,248,NJ City,S,1,9,9,58,High School,28,Patnership,Complex
2849,76.33,NJ City,N,7,21,4,57,No education,33,Patnership,Standalone
4895,285,NJ City,E,3,22,20,67,High School,28,Owner,Standalone
845,264,NJ City,N,1,17,14,54,High School,23,Patnership,Standalone
3792,441,NJ City,W,2,11,5,40,Graduation,25,Franchise,Complex
7122,73.1,Boston,N,15,12,1,49,Graduation,22,Patnership,Complex
9692,7.69,NJ City,N,6,16,1,46,No education,19,Franchise,Complex
3634,126,NJ City,S,2,11,14,40,High School,35,Patnership,Complex
4558,66.91,NJ City,N,9,10,3,50,High School,19,Patnership,Complex
5212,482,Boston,W,3,21,11,55,Graduation,31,Franchise,Complex
7152,460,Boston,W,3,15,11,58,Graduation,27,Franchise,Complex
1518,270,NJ City,S,2,13,21,76,High School,26,Patnership,Complex
9498,411.76,NJ City,S,2,12,3,81,Graduation,19,Patnership,Complex
2160,246,NJ City,W,3,8,13,46,High School,19,Franchise,Complex
5046,431,Boston,S,2,19,11,48,Graduation,25,Patnership,Complex
3462,279,NJ City,S,1,15,14,34,High School,33,Patnership,Complex
432,66.76,NJ City,N,13,14,1,48,Graduation,34,Franchise,Complex
4077,216,NJ City,N,3,24,16,66,High School,33,Patnership,Standalone
6556,372,Boston,W,2,17,17,37,Graduation,29,Franchise,Complex
3000,312.68,NJ City,W,9,15,2,85,Graduation,21,Franchise,Complex
6342,481,Boston,S,2,8,21,32,Graduation,20,Patnership,Complex
3589,104,NJ City,N,1,23,12,67,High School,24,Patnership,Standalone
2894,27.47,NJ City,N,9,18,4,43,No education,28,Patnership,Complex
8373,440,NJ City,N,2,17,19,52,Graduation,31,Patnership,Standalone
7771,365,Boston,E,2,13,24,73,Graduation,34,Owner,Standalone
5633,437,Boston,N,2,13,14,59,Graduation,34,Patnership,Standalone
8357,420,NJ City,N,2,21,25,44,Graduation,19,Patnership,Standalone
8198,362,NJ City,S,2,21,16,61,Graduation,20,Patnership,Complex
4388,297,NJ City,W,2,19,24,30,High School,26,Franchise,Complex
2286,295,NJ City,S,2,14,6,43,High School,18,Patnership,Complex
2934,121,NJ City,S,3,22,21,82,High School,27,Patnership,Complex
7841,249.56,Boston,N,18,15,2,82,No education,34,Patnership,Standalone
1037,150,NJ City,N,2,12,6,68,High School,29,Patnership,Standalone
5966,412,Boston,S,1,13,19,82,Graduation,29,Patnership,Complex
9504,118.38,NJ City,W,15,19,4,73,Graduation,34,Franchise,Complex
8711,497,NJ City,E,2,12,19,41,Graduation,30,Owner,Standalone
1652,194.09,NJ City,W,9,19,2,66,No education,29,Franchise,Complex
9358,27.58,NJ City,N,13,18,2,47,High School,26,Patnership,Complex
6383,63.95,Boston,N,4,12,3,30,No education,23,Owner,Standalone
255,143,NJ City,E,3,21,17,45,High School,18,Owner,Standalone
2428,290,NJ City,W,3,17,10,57,High School,30,Franchise,Complex
9267,13.75,NJ City,N,11,9,4,32,Graduation,23,Owner,Standalone
2371,317.82,NJ City,E,8,18,4,82,High School,34,Owner,Standalone
6935,436,Boston,E,2,11,7,73,Graduation,19,Owner,Standalone
5514,383.16,Boston,S,17,12,2,58,Graduation,23,Patnership,Complex
1337,266,NJ City,N,3,18,21,51,High School,18,Patnership,Standalone
9163,497,NJ City,E,2,18,14,85,Graduation,21,Owner,Standalone
6500,498,Boston,W,2,17,25,40,Graduation,26,Franchise,Complex
7975,498,Boston,E,3,18,25,35,Graduation,20,Owner,Standalone
789,284,NJ City,N,3,11,16,28,High School,30,Patnership,Standalone
5234,469.41,Boston,S,21,15,3,62,No education,28,Patnership,Complex
6000,14.87,Boston,N,21,13,3,64,Graduation,20,Franchise,Complex
7037,371,Boston,N,2,17,23,33,Graduation,24,Patnership,Standalone
235,111,NJ City,E,3,14,21,82,High School,18,Owner,Standalone
1750,20.82,NJ City,N,15,20,2,67,High School,21,Patnership,Complex
2339,220,NJ City,E,3,21,17,72,High School,29,Owner,Standalone
9216,352,NJ City,W,2,13,5,70,Graduation,22,Franchise,Complex
7688,183.73,Boston,W,17,8,2,38,No education,34,Franchise,Complex
3527,115.48,NJ City,E,19,12,1,73,No education,34,Owner,Standalone
2366,82.4,NJ City,S,16,22,4,83,No education,19,Patnership,Complex
4692,186,NJ City,W,3,20,23,33,High School,19,Franchise,Complex
1461,240,NJ City,N,3,21,20,68,High School,26,Patnership,Standalone
1902,111,NJ City,S,1,13,17,40,High School,21,Patnership,Complex
6849,71.01,Boston,N,20,17,2,73,Graduation,28,Patnership,Standalone
7262,290.46,Boston,S,7,16,2,52,No education,34,Patnership,Complex
6982,493,Boston,S,2,23,12,37,Graduation,24,Patnership,Complex
6379,53.2,Boston,E,12,11,3,41,High School,34,Owner,Standalone
7700,470,Boston,W,1,13,14,27,Graduation,33,Franchise,Complex
8363,404,NJ City,E,2,16,15,82,Graduation,28,Owner,Standalone
631,305.67,NJ City,E,22,24,2,63,High School,29,Owner,Standalone
2409,134,NJ City,N,1,8,20,78,High School,25,Patnership,Standalone
7670,73.96,Boston,N,9,13,3,57,No education,25,Patnership,Complex
8008,424,Boston,W,2,11,11,39,Graduation,34,Franchise,Complex
8925,376,NJ City,N,1,13,9,59,Graduation,27,Patnership,Standalone
4390,65.09,NJ City,N,9,11,3,79,High School,21,Patnership,Complex
2891,247,NJ City,E,3,16,21,79,High School,28,Owner,Standalone
4124,209,NJ City,W,1,13,8,43,High School,23,Franchise,Complex
66,197,Boston,S,2,22,17,84,High School,23,Patnership,Complex
282,168,NJ City,S,2,23,14,63,High School,28,Patnership,Complex
6362,58.35,Boston,S,22,19,4,45,No education,35,Patnership,Complex
2033,122.31,NJ City,N,19,18,4,67,No education,21,Patnership,Standalone
65,268,Boston,N,2,12,11,50,High School,18,Patnership,Standalone
7768,469,Boston,W,1,16,10,60,Graduation,31,Franchise,Complex
3003,303,NJ City,E,3,15,10,83,High School,31,Owner,Standalone
2723,262.36,NJ City,E,11,14,4,33,No education,27,Owner,Standalone
4580,213,NJ City,W,2,9,25,85,High School,34,Franchise,Complex
8821,422.86,NJ City,N,7,9,1,68,High School,32,Patnership,Standalone
9558,451,NJ City,S,2,10,14,50,Graduation,18,Patnership,Complex
7000,388,Boston,W,3,15,17,32,Graduation,33,Franchise,Complex
7099,350,Boston,E,3,10,23,65,Graduation,33,Owner,Standalone
1176,401.7,NJ City,W,24,12,2,34,Graduation,20,Franchise,Complex
5658,460,Boston,S,2,11,19,36,Graduation,24,Patnership,Complex
980,74.93,NJ City,W,17,15,4,46,No education,33,Franchise,Complex
7602,388,Boston,S,3,17,6,44,Graduation,22,Patnership,Complex
1870,232,NJ City,S,1,10,14,72,High School,23,Patnership,Complex
9534,164.4,NJ City,S,4,24,4,42,Graduation,34,Patnership,Complex
5058,473.81,Boston,S,22,23,1,76,Graduation,29,Patnership,Complex
3471,108.01,NJ City,E,10,12,2,78,Graduation,25,Owner,Standalone
1180,108,NJ City,W,3,8,8,63,High School,30,Franchise,Complex
2486,100,NJ City,S,1,23,21,36,High School,21,Patnership,Complex
1416,71.27,NJ City,N,7,20,3,46,Graduation,29,Franchise,Complex
2927,355.38,NJ City,E,25,15,3,44,No education,28,Owner,Standalone
8630,478,NJ City,S,3,15,10,60,Graduation,30,Patnership,Complex
355,99.03,NJ City,E,1,14,4,26,High School,23,Owner,Standalone
1678,196,NJ City,S,3,8,16,43,High School,32,Patnership,Complex
173,58.86,NJ City,N,19,8,1,62,No education,33,Patnership,Standalone
2623,267,NJ City,E,3,19,23,46,High School,35,Owner,Standalone
2838,100.98,NJ City,S,22,19,3,74,Graduation,21,Patnership,Complex
8945,15.49,NJ City,N,11,11,3,37,No education,30,Patnership,Standalone
375,114,NJ City,E,2,11,7,63,High School,25,Owner,Standalone
1762,149,NJ City,S,3,14,12,49,High School,26,Patnership,Complex
5558,47.43,Boston,S,12,18,3,35,No education,25,Patnership,Complex
7306,358,Boston,S,3,19,24,31,Graduation,33,Patnership,Complex
8432,486.15,NJ City,W,25,14,4,70,No education,32,Franchise,Complex
6145,447,Boston,N,3,20,11,52,Graduation,28,Patnership,Standalone
7739,478,Boston,E,3,9,9,39,Graduation,25,Owner,Standalone
3047,150,NJ City,E,2,19,7,84,High School,22,Owner,Standalone
9973,448,NJ City,N,2,22,17,26,Graduation,26,Patnership,Standalone
6491,383,Boston,E,2,17,7,47,Graduation,32,Owner,Standalone
4802,147,NJ City,S,3,15,19,54,High School,20,Patnership,Complex
9923,364,NJ City,E,3,14,19,25,Graduation,21,Owner,Standalone
1356,460.64,NJ City,W,10,16,1,68,Graduation,30,Franchise,Complex
3196,52.45,NJ City,W,4,24,1,40,High School,34,Franchise,Complex
3135,121,NJ City,E,3,13,15,72,High School,22,Owner,Standalone
7016,363.69,Boston,W,11,12,1,52,No education,19,Franchise,Complex
3840,193,NJ City,W,1,22,25,41,High School,32,Franchise,Complex
8006,350,Boston,S,2,22,8,80,Graduation,18,Patnership,Complex
5896,415,Boston,W,1,9,14,85,Graduation,33,Franchise,Complex
3251,30.96,NJ City,N,15,24,2,40,No education,31,Owner,Standalone
9148,382,NJ City,W,1,17,5,69,Graduation,22,Franchise,Complex
9376,446,NJ City,W,1,24,21,40,Graduation,34,Franchise,Complex
6744,29.82,Boston,N,25,22,1,83,Graduation,28,Franchise,Complex
6055,114.99,Boston,E,15,23,4,78,High School,34,Owner,Standalone
8211,493,NJ City,E,1,21,5,34,Graduation,26,Owner,Standalone
7801,436,Boston,N,3,24,24,37,Graduation,25,Patnership,Standalone
4567,263,NJ City,E,1,18,18,37,High School,22,Owner,Standalone
261,211,NJ City,N,3,18,8,76,High School,30,Patnership,Standalone
441,303.7,NJ City,N,25,15,4,67,Graduation,27,Patnership,Standalone
2539,108,NJ City,E,3,14,18,84,High School,23,Owner,Standalone
3417,177,NJ City,N,1,11,20,43,High School,20,Patnership,Standalone
6735,0.88,Boston,N,12,8,1,67,Graduation,20,Owner,Standalone
7112,10.83,Boston,N,11,18,4,34,No education,22,Franchise,Complex
5502,350,Boston,S,3,19,18,28,Graduation,23,Patnership,Complex
8238,127.15,NJ City,S,9,12,2,79,Graduation,25,Patnership,Complex
7631,104.8,Boston,E,15,17,3,75,No education,34,Owner,Standalone
4515,120.95,NJ City,E,8,8,1,81,Graduation,32,Owner,Standalone
7875,82.28,Boston,E,13,18,3,55,Graduation,23,Owner,Standalone
9908,370,NJ City,W,2,10,20,43,Graduation,35,Franchise,Complex
9057,78.95,NJ City,N,18,10,3,34,Graduation,31,Patnership,Standalone
332,163,NJ City,W,1,14,21,61,High School,22,Franchise,Complex
1214,138,NJ City,S,3,18,24,84,High School,34,Patnership,Complex
4874,23.54,NJ City,N,2,21,1,41,No education,28,Patnership,Complex
8533,443,NJ City,N,3,12,16,34,Graduation,22,Patnership,Standalone
6162,372,Boston,S,3,18,21,40,Graduation,20,Patnership,Complex
2786,91.49,NJ City,S,3,8,3,69,No education,31,Patnership,Complex
1899,112,NJ City,E,1,11,13,70,High School,25,Owner,Standalone
4920,81,NJ City,W,9,16,2,38,Graduation,23,Franchise,Complex
4683,287,NJ City,E,2,20,16,35,High School,22,Owner,Standalone
6842,497,Boston,S,2,10,20,53,Graduation,24,Patnership,Complex
2738,297.1,NJ City,S,17,15,4,41,No education,20,Patnership,Complex
3198,108,NJ City,S,3,23,15,37,High School,28,Patnership,Complex
1388,398,NJ City,W,2,21,5,36,Graduation,24,Franchise,Complex
7139,483,Boston,E,1,8,20,36,Graduation,20,Owner,Standalone
1015,52.52,NJ City,E,24,13,2,68,High School,33,Owner,Standalone
5995,60,Boston,E,17,23,2,76,High School,18,Owner,Standalone
5374,453,Boston,S,2,24,13,51,Graduation,23,Patnership,Complex
5723,411,Boston,E,2,21,16,78,Graduation,19,Owner,Standalone
6765,363,Boston,N,2,14,21,60,Graduation,26,Patnership,Standalone
5442,36.1,Boston,N,8,10,2,77,Graduation,31,Patnership,Complex
4784,291,NJ City,W,1,9,15,46,High School,26,Franchise,Complex
6691,192.87,Boston,E,6,12,2,70,High School,21,Owner,Standalone
7974,405,Boston,S,2,10,6,76,Graduation,30,Patnership,Complex
9304,13.99,NJ City,N,17,8,4,81,High School,28,Franchise,Complex
7535,481,Boston,E,1,13,7,82,Graduation,27,Owner,Standalone
4319,310,NJ City,E,2,22,25,35,High School,22,Owner,Standalone
5127,43.41,Boston,N,6,17,2,78,Graduation,27,Owner,Standalone
9957,389,NJ City,N,3,21,13,57,Graduation,19,Patnership,Standalone
7226,75.22,Boston,S,19,11,4,68,No education,20,Patnership,Complex
238,250,NJ City,S,1,9,14,79,High School,35,Patnership,Complex
4043,333.93,NJ City,E,4,24,1,69,No education,27,Owner,Standalone
2477,117,NJ City,N,3,15,10,52,High School,18,Patnership,Standalone
3610,26.45,NJ City,N,10,11,4,29,High School,27,Patnership,Complex
9207,125.65,NJ City,E,3,17,4,52,Graduation,35,Owner,Standalone
3312,14.33,NJ City,N,15,18,4,78,Graduation,34,Franchise,Complex
6062,481,Boston,S,3,24,5,52,Graduation,23,Patnership,Complex
9134,427.38,NJ City,S,17,19,1,66,No education,35,Patnership,Complex
3104,148,NJ City,W,3,21,19,38,High School,26,Franchise,Complex
2418,306.33,NJ City,S,14,18,3,43,Graduation,20,Patnership,Complex
5195,499,Boston,E,2,17,13,30,Graduation,28,Owner,Standalone
1012,250,NJ City,W,2,11,15,48,High School,18,Franchise,Complex
6535,11.64,Boston,N,4,17,2,42,High School,18,Owner,Standalone
6684,320.56,Boston,W,6,11,2,57,Graduation,20,Franchise,Complex
1617,221,NJ City,N,1,10,9,66,High School,20,Patnership,Standalone
9581,388,NJ City,N,3,8,22,49,Graduation,34,Patnership,Standalone
1339,276,NJ City,E,3,14,19,71,High School,35,Owner,Standalone
1644,240,NJ City,W,2,19,18,57,High School,33,Franchise,Complex
5376,85.98,Boston,W,25,8,2,27,Graduation,31,Franchise,Complex
5076,413,Boston,W,2,23,23,31,Graduation,29,Franchise,Complex
28,171,Boston,W,1,23,14,55,High School,24,Franchise,Complex
2666,205,NJ City,S,3,16,13,63,High School,30,Patnership,Complex
1343,162,NJ City,E,3,8,13,30,High School,24,Owner,Standalone
2357,271,NJ City,N,3,22,23,44,High School,32,Patnership,Standalone
7611,131.11,Boston,E,21,16,3,57,Graduation,30,Owner,Standalone
4320,246,NJ City,W,2,17,14,26,High School,20,Franchise,Complex
1925,5.64,NJ City,N,10,20,3,31,No education,24,Patnership,Standalone
2913,258,NJ City,N,1,21,21,69,High School,34,Patnership,Standalone
7898,354,Boston,S,2,17,13,57,Graduation,25,Patnership,Complex
8490,30.16,NJ City,N,14,17,3,72,Graduation,26,Patnership,Complex
7843,431,Boston,E,3,21,25,83,Graduation,22,Owner,Standalone
8853,362,NJ City,N,1,10,23,76,Graduation,19,Patnership,Standalone
2880,319,NJ City,W,1,9,11,26,High School,23,Franchise,Complex
6653,65.96,Boston,N,22,18,2,62,No education,24,Patnership,Standalone
5684,390,Boston,W,2,10,11,43,Graduation,31,Franchise,Complex
1691,271,NJ City,E,2,14,13,28,High School,20,Owner,Standalone
3164,283,NJ City,W,2,8,20,72,High School,34,Franchise,Complex
3275,372.91,NJ City,E,24,22,2,67,No education,30,Owner,Standalone
7056,74.04,Boston,W,15,15,4,78,Graduation,29,Franchise,Complex
8064,414,NJ City,W,3,23,14,32,Graduation,26,Franchise,Complex
2489,464,NJ City,N,1,20,5,34,Graduation,30,Patnership,Standalone
4404,413,NJ City,W,3,11,5,48,Graduation,26,Franchise,Complex
3062,67.07,NJ City,N,8,12,4,58,No education,24,Patnership,Complex
4147,311,NJ City,E,1,13,12,45,High School,26,Owner,Standalone
9166,408,NJ City,S,3,24,16,51,Graduation,29,Patnership,Complex
2288,183,NJ City,W,2,23,12,68,High School,32,Franchise,Complex
1156,129,NJ City,W,3,20,22,42,High School,20,Franchise,Complex
9529,491,NJ City,N,2,15,10,74,Graduation,26,Patnership,Standalone
2084,123.74,NJ City,W,9,8,2,77,No education,29,Franchise,Complex
2321,203,NJ City,N,2,24,6,59,High School,25,Patnership,Standalone
8541,14.22,NJ City,N,14,18,3,43,Graduation,33,Patnership,Standalone
9648,428,NJ City,W,1,19,12,60,Graduation,23,Franchise,Complex
9329,361,NJ City,N,3,19,5,33,Graduation,22,Patnership,Standalone
7735,491,Boston,E,3,13,24,28,Graduation,18,Owner,Standalone
4842,73.72,NJ City,N,13,12,2,59,Graduation,30,Patnership,Complex
8537,382,NJ City,N,1,13,22,85,Graduation,26,Patnership,Standalone
3534,254,NJ City,S,2,8,13,80,High School,32,Patnership,Complex
7446,108.53,Boston,S,10,14,3,79,Graduation,18,Patnership,Complex
8570,399.46,NJ City,S,5,11,1,25,No education,28,Patnership,Complex
7177,101.23,Boston,N,23,16,2,61,High School,30,Patnership,Standalone
4984,144,Boston,W,3,10,9,75,High School,26,Franchise,Complex
8892,482,NJ City,W,2,10,20,25,Graduation,33,Franchise,Complex
1654,312,NJ City,S,1,17,16,75,High School,23,Patnership,Complex
3946,124.19,NJ City,S,3,21,3,46,High School,31,Patnership,Complex
3285,117.46,NJ City,N,13,21,4,51,Graduation,20,Patnership,Standalone
9228,445,NJ City,W,1,20,8,33,Graduation,30,Franchise,Complex
3766,13.17,NJ City,N,10,20,2,51,High School,34,Patnership,Complex
5185,273.55,Boston,N,5,19,3,77,High School,29,Patnership,Standalone
7308,11.83,Boston,N,20,24,4,40,Graduation,33,Franchise,Complex
7617,482,Boston,N,1,21,21,84,Graduation,25,Patnership,Standalone
4880,124,NJ City,W,1,24,19,54,High School,27,Franchise,Complex
9305,169.03,NJ City,N,7,16,2,81,No education,26,Patnership,Standalone
7664,417,Boston,W,3,10,18,45,Graduation,19,Franchise,Complex
5980,394.28,Boston,W,11,10,2,79,High School,20,Franchise,Complex
5524,116.09,Boston,W,2,11,1,45,High School,33,Franchise,Complex
3184,106.11,NJ City,W,8,9,2,70,High School,35,Franchise,Complex
2163,282.34,NJ City,E,16,22,3,85,Graduation,20,Owner,Standalone
1287,1.38,NJ City,N,15,24,2,56,Graduation,34,Owner,Standalone
9117,37.27,NJ City,N,14,12,3,71,Graduation,20,Patnership,Standalone
8780,213.13,NJ City,W,3,18,3,39,No education,32,Franchise,Complex
9422,441,NJ City,S,1,15,16,72,Graduation,33,Patnership,Complex
3051,191,NJ City,E,3,15,23,42,High School,18,Owner,Standalone
9035,89.12,NJ City,E,3,18,2,75,No education,30,Owner,Standalone
948,75.05,NJ City,W,8,12,2,46,Graduation,29,Franchise,Complex
7669,468,Boston,N,3,23,24,26,Graduation,30,Patnership,Standalone
3944,204,NJ City,W,1,8,12,79,High School,32,Franchise,Complex
8930,415,NJ City,S,3,18,18,36,Graduation,29,Patnership,Complex
9708,396,NJ City,W,3,9,9,55,Graduation,26,Franchise,Complex
7304,30.36,Boston,N,2,16,2,64,No education,22,Franchise,Complex
1295,173,NJ City,E,1,18,15,39,High School,34,Owner,Standalone
3784,294,NJ City,W,3,22,15,35,High School,31,Franchise,Complex
436,90.41,NJ City,W,3,12,4,59,High School,32,Franchise,Complex
1553,193,NJ City,N,3,10,16,48,High School,24,Patnership,Standalone
5697,95.3,Boston,N,19,15,1,81,Graduation,26,Patnership,Standalone
5587,403,Boston,E,3,21,9,75,Graduation,28,Owner,Standalone
2749,184,NJ City,N,1,14,9,47,High School,32,Patnership,Standalone
7281,99.19,Boston,N,14,11,3,68,Graduation,33,Patnership,Standalone
5644,89.49,Boston,W,5,8,2,26,High School,19,Franchise,Complex
2261,51.58,NJ City,N,17,24,2,54,No education,19,Patnership,Standalone
4432,127,NJ City,W,1,16,24,85,High School,21,Franchise,Complex
9286,430,NJ City,S,3,10,16,35,Graduation,34,Patnership,Complex
422,140,NJ City,S,2,10,20,32,High School,22,Patnership,Complex
4923,145,NJ City,E,2,13,14,82,High School,19,Owner,Standalone
6858,429,Boston,S,3,18,14,59,Graduation,20,Patnership,Complex
8257,460,NJ City,N,2,16,19,79,Graduation,22,Patnership,Standalone
5975,481,Boston,E,3,24,19,74,Graduation,29,Owner,Standalone
5709,490.07,Boston,N,4,17,2,29,Graduation,31,Patnership,Standalone
8514,367,NJ City,S,3,20,22,34,Graduation,35,Patnership,Complex
1738,292,NJ City,S,1,19,25,49,High School,20,Patnership,Complex
570,108,NJ City,S,3,12,10,64,High School,22,Patnership,Complex
5296,457,Boston,W,1,15,5,66,Graduation,27,Franchise,Complex
9121,387,NJ City,N,2,10,21,26,Graduation,22,Patnership,Standalone
4934,147,NJ City,S,2,18,22,84,High School,21,Patnership,Complex
7662,409,Boston,S,3,19,10,41,Graduation,22,Patnership,Complex
3014,407.91,NJ City,S,14,16,1,31,No education,27,Patnership,Complex
409,239,NJ City,N,2,18,9,39,High School,31,Patnership,Standalone
1919,198,NJ City,E,1,14,11,63,High School,20,Owner,Standalone
9072,112.83,NJ City,W,25,17,1,63,Graduation,23,Franchise,Complex
4162,104,NJ City,S,1,21,10,55,High School,25,Patnership,Complex
4414,230,NJ City,S,1,12,22,71,High School,34,Patnership,Complex
891,419,NJ City,E,2,9,5,82,Graduation,25,Owner,Standalone
2331,34.07,NJ City,N,17,16,2,58,Graduation,31,Owner,Standalone
5300,481,Boston,W,1,14,13,58,Graduation,20,Franchise,Complex
8938,500,NJ City,S,3,22,20,25,Graduation,18,Patnership,Complex
9008,428,NJ City,W,1,22,11,45,Graduation,20,Franchise,Complex
6115,453,Boston,E,1,9,23,58,Graduation,26,Owner,Standalone
1562,289,NJ City,S,2,11,24,69,High School,32,Patnership,Complex
9263,156.9,NJ City,E,23,9,4,77,No education,29,Owner,Standalone
8997,50.8,NJ City,N,9,12,3,79,Graduation,30,Patnership,Standalone
9739,452,NJ City,E,1,22,22,71,Graduation,19,Owner,Standalone
8233,432,NJ City,N,1,21,15,75,Graduation,18,Patnership,Standalone
931,195,NJ City,E,3,8,9,50,High School,24,Owner,Standalone
9335,353,NJ City,E,2,20,15,76,Graduation,25,Owner,Standalone
453,300,NJ City,N,2,14,14,53,High School,28,Patnership,Standalone
8468,27.86,NJ City,N,10,19,2,66,No education,25,Franchise,Complex
8976,486,NJ City,W,2,21,17,64,Graduation,34,Franchise,Complex
7119,286,Boston,E,1,8,2,46,Graduation,20,Owner,Standalone
5579,430.29,Boston,E,1,15,2,52,No education,21,Owner,Standalone
8845,90.15,NJ City,N,6,11,1,84,High School,34,Patnership,Standalone
7860,418,Boston,W,3,14,13,34,Graduation,24,Franchise,Complex
6681,48.42,Boston,N,10,20,2,55,Graduation,24,Patnership,Standalone
3863,230,NJ City,E,3,12,7,65,High School,29,Owner,Standalone
2382,461.99,NJ City,S,2,21,4,29,Graduation,23,Patnership,Complex
4457,255,NJ City,N,3,12,24,74,High School,26,Patnership,Standalone
8675,450,NJ City,E,2,20,8,38,Graduation,18,Owner,Standalone
1254,131,NJ City,S,3,9,13,50,High School,30,Patnership,Complex
3269,98.19,NJ City,N,17,12,4,64,No education,26,Patnership,Standalone
2150,1.27,NJ City,N,17,11,2,33,No education,22,Patnership,Complex
5348,497,Boston,W,2,22,9,44,Graduation,20,Franchise,Complex
8139,394,NJ City,E,3,17,5,73,Graduation,23,Owner,Standalone
6452,88.36,Boston,W,11,10,4,67,No education,27,Franchise,Complex
2522,287,NJ City,S,3,14,23,85,High School,25,Patnership,Complex
8331,360,NJ City,E,1,24,11,60,Graduation,26,Owner,Standalone
9859,410.17,NJ City,E,5,10,4,59,High School,35,Owner,Standalone
1868,120.44,NJ City,W,12,8,1,42,No education,26,Franchise,Complex
5692,493,Boston,W,1,21,15,52,Graduation,26,Franchise,Complex
7933,6.83,Boston,N,6,22,2,54,High School,20,Patnership,Standalone
3511,233,NJ City,E,3,18,23,82,High School,30,Owner,Standalone
6933,472,Boston,N,2,15,19,38,Graduation,31,Patnership,Standalone
2003,112.48,NJ City,E,7,21,2,63,No education,34,Owner,Standalone
3924,288,NJ City,W,3,9,20,81,High School,34,Franchise,Complex
5717,453.53,Boston,N,12,16,3,48,No education,29,Patnership,Standalone
2305,251,NJ City,N,1,12,15,27,High School,21,Patnership,Standalone
4878,296,NJ City,S,1,21,13,76,High School,21,Patnership,Complex
8841,381,NJ City,N,1,14,13,29,Graduation,32,Patnership,Standalone
4091,179,NJ City,E,1,24,7,72,High School,23,Owner,Standalone
9970,111.07,NJ City,S,15,10,4,46,High School,24,Patnership,Complex
2332,303,NJ City,W,1,20,22,81,High School,32,Franchise,Complex
5329,369,Boston,N,3,21,24,37,Graduation,18,Patnership,Standalone
4944,86.42,Boston,W,25,12,2,56,Graduation,21,Franchise,Complex
2436,101,NJ City,W,3,17,19,78,High School,18,Franchise,Complex
9754,454,NJ City,S,3,11,20,85,Graduation,19,Patnership,Complex
1572,349.01,NJ City,W,4,21,2,62,Graduation,33,Franchise,Complex
3406,234,NJ City,S,1,15,18,70,High School,34,Patnership,Complex
7043,399,Boston,E,2,23,18,35,Graduation,18,Owner,Standalone
6899,389,Boston,E,3,10,13,55,Graduation,24,Owner,Standalone
6532,427.05,Boston,W,10,16,1,29,High School,31,Franchise,Complex
9860,47.89,NJ City,W,21,13,3,59,No education,32,Franchise,Complex
2886,204,NJ City,S,2,20,17,35,High School,33,Patnership,Complex
8029,487,Boston,N,1,23,9,78,Graduation,28,Patnership,Standalone
3327,73.8,NJ City,N,1,13,3,75,Graduation,33,Owner,Standalone
6986,463,Boston,S,2,23,12,57,Graduation,19,Patnership,Complex
3991,196,NJ City,E,3,21,22,32,High School,22,Owner,Standalone
152,127.29,NJ City,W,12,15,2,32,No education,22,Franchise,Complex
9458,355,NJ City,S,1,23,24,75,Graduation,24,Patnership,Complex
103,138,NJ City,E,1,24,8,39,High School,32,Owner,Standalone
1720,129,NJ City,W,1,11,25,74,High School,32,Franchise,Complex
6720,381,Boston,W,3,17,22,73,Graduation,26,Franchise,Complex
2583,132.85,NJ City,E,17,15,1,77,Graduation,22,Owner,Standalone
955,132,NJ City,E,2,22,7,33,High School,23,Owner,Standalone
9888,16.46,NJ City,N,25,8,3,77,Graduation,24,Franchise,Complex
8280,388,NJ City,W,3,22,16,65,Graduation,35,Franchise,Complex
9690,371,NJ City,S,1,17,15,38,Graduation,26,Patnership,Complex
1522,495.07,NJ City,S,15,11,3,66,High School,27,Patnership,Complex
4111,273,NJ City,E,2,15,22,60,High School,29,Owner,Standalone
4811,207,NJ City,E,2,15,25,36,High School,32,Owner,Standalone
6257,463,Boston,N,1,15,9,69,Graduation,30,Patnership,Standalone
53,111,NY City,N,3,9,6,31,High School,23,Patnership,Standalone
2750,98.87,NJ City,S,21,24,4,26,No education,23,Patnership,Complex
8069,98.56,NJ City,N,19,11,1,79,No education,24,Patnership,Standalone
8550,398,NJ City,S,3,10,17,39,Graduation,32,Patnership,Complex
5592,406,Boston,W,2,18,19,61,Graduation,22,Franchise,Complex
6054,94.07,Boston,S,22,23,3,37,Graduation,27,Patnership,Complex
9479,14.1,NJ City,N,20,9,1,34,No education,20,Owner,Standalone
4937,279,Boston,N,1,23,10,50,High School,24,Patnership,Standalone
7927,489.69,Boston,E,13,24,1,47,High School,31,Owner,Standalone
2420,37.81,NJ City,N,16,8,4,57,No education,34,Franchise,Complex
5837,414,Boston,N,1,20,21,39,Graduation,20,Patnership,Standalone
8463,376,NJ City,E,3,10,17,66,Graduation,35,Owner,Standalone
4791,61.61,NJ City,N,11,13,1,45,Graduation,29,Owner,Standalone
9462,451,NJ City,S,2,19,21,78,Graduation,31,Patnership,Complex
4589,301,NJ City,N,2,16,21,84,High School,18,Patnership,Standalone
2472,303,NJ City,W,3,8,25,44,High School,24,Franchise,Complex
9193,33.73,NJ City,N,7,18,2,59,High School,30,Patnership,Standalone
1913,46.17,NJ City,N,25,23,2,36,No education,32,Patnership,Standalone
8153,398,NJ City,N,2,14,21,34,Graduation,32,Patnership,Standalone
7321,394,Boston,N,2,22,23,51,Graduation,21,Patnership,Standalone
7014,412,Boston,S,3,13,15,71,Graduation,32,Patnership,Complex
8058,26.52,NJ City,N,12,20,1,84,Graduation,21,Patnership,Complex
7481,396.9,Boston,N,4,11,1,48,No education,32,Patnership,Standalone
3567,166,NJ City,E,2,18,21,78,High School,22,Owner,Standalone
2911,302,NJ City,E,1,11,11,73,High School,27,Owner,Standalone
7033,439,Boston,N,3,12,24,61,Graduation,22,Patnership,Standalone
970,40.56,NJ City,N,18,13,1,36,High School,20,Patnership,Complex
9960,76.95,NJ City,W,19,12,1,31,Graduation,26,Franchise,Complex
853,177,NJ City,N,1,9,24,67,High School,34,Patnership,Standalone
8236,414,NJ City,W,2,12,12,63,Graduation,34,Franchise,Complex
4364,148,NJ City,W,3,23,9,70,High School,20,Franchise,Complex
1641,21.5,NJ City,N,14,8,3,81,Graduation,22,Patnership,Standalone
5495,112.96,Boston,E,7,15,3,54,No education,20,Owner,Standalone
8039,472,Boston,E,3,15,18,57,Graduation,33,Owner,Standalone
3856,261,NJ City,W,3,22,20,26,High School,26,Franchise,Complex
1830,264.49,NJ City,S,11,10,4,40,Graduation,24,Patnership,Complex
5740,488,Boston,W,1,8,15,59,Graduation,19,Franchise,Complex
5793,414,Boston,N,1,17,14,54,Graduation,25,Patnership,Standalone
1350,128,NJ City,S,1,9,9,38,High School,20,Patnership,Complex
4850,136,NJ City,S,1,14,20,71,High School,21,Patnership,Complex
8283,407,NJ City,E,1,22,24,35,Graduation,21,Owner,Standalone
7548,497.46,Boston,W,3,20,4,79,Graduation,21,Franchise,Complex
3600,280,NJ City,W,2,14,25,28,High School,23,Franchise,Complex
6168,338.3,Boston,W,16,10,2,71,Graduation,33,Franchise,Complex
5860,89.47,Boston,W,15,8,4,39,High School,31,Franchise,Complex
8027,200.7,Boston,E,22,15,2,74,No education,33,Owner,Standalone
3614,230,NJ City,S,2,19,15,85,High School,24,Patnership,Complex
4341,165,NJ City,N,1,17,22,36,High School,29,Patnership,Standalone
6827,34.4,Boston,N,10,12,4,49,No education,28,Owner,Standalone
4540,197,NJ City,W,3,17,24,54,High School,29,Franchise,Complex
1529,135,NJ City,N,1,9,22,57,High School,24,Patnership,Standalone
3049,158,NJ City,N,3,19,15,72,High School,34,Patnership,Standalone
8401,489,NJ City,N,2,18,6,67,Graduation,19,Patnership,Standalone
4705,213,NJ City,N,1,8,22,80,High School,19,Patnership,Standalone
7234,433,Boston,S,2,20,24,27,Graduation,18,Patnership,Complex
2232,143,NJ City,W,3,14,20,51,High School,27,Franchise,Complex
963,93.7,NJ City,E,20,13,2,69,Graduation,25,Owner,Standalone
2980,228,NJ City,W,1,19,20,35,High School,29,Franchise,Complex
9658,230.37,NJ City,S,2,22,4,44,High School,25,Patnership,Complex
3007,52.42,NJ City,E,24,19,3,53,High School,19,Owner,Standalone
7409,63.06,Boston,N,21,11,1,26,No education,21,Patnership,Standalone
2657,238,NJ City,N,2,13,16,60,High School,21,Patnership,Standalone
1771,268,NJ City,E,2,19,25,33,High School,31,Owner,Standalone
22,270,NJ City,S,3,20,8,80,High School,23,Patnership,Complex
3476,261,NJ City,W,1,24,7,67,High School,20,Franchise,Complex
5857,499,Boston,N,2,22,14,30,Graduation,19,Patnership,Standalone
9858,443,NJ City,S,2,15,8,79,Graduation,34,Patnership,Complex
2607,115,NJ City,E,3,13,16,81,High School,24,Owner,Standalone
9342,89.99,NJ City,S,22,11,4,69,Graduation,26,Patnership,Complex
2965,348.12,NJ City,N,25,8,2,46,High School,28,Patnership,Standalone
5756,275.21,Boston,W,1,8,4,25,No education,19,Franchise,Complex
3653,145,NJ City,N,1,8,20,29,High School,27,Patnership,Standalone
4358,311,NJ City,S,3,13,22,57,High School,34,Patnership,Complex
487,136.23,NJ City,E,20,18,3,55,High School,20,Owner,Standalone
5140,107.15,Boston,W,3,11,4,42,High School,29,Franchise,Complex
9120,436,NJ City,W,3,20,24,46,Graduation,22,Franchise,Complex
1505,182,NJ City,N,1,20,13,46,High School,28,Patnership,Standalone
8048,353,NJ City,W,1,12,12,42,Graduation,34,Franchise,Complex
4743,486,NJ City,E,1,12,5,70,Graduation,34,Owner,Standalone
511,49.06,NJ City,E,24,15,3,44,High School,30,Owner,Standalone
6028,228.43,Boston,W,21,20,2,80,High School,31,Franchise,Complex
1164,36.09,NJ City,N,2,8,4,31,Graduation,29,Franchise,Complex
9966,387,NJ City,S,3,15,23,54,Graduation,23,Patnership,Complex
4650,326,NJ City,S,14,17,4,74,Graduation,30,Patnership,Complex
5473,319.69,Boston,N,24,13,4,52,High School,18,Patnership,Standalone
9010,123.47,NJ City,S,1,9,1,81,High School,18,Patnership,Complex
4590,82.45,NJ City,S,25,23,2,69,Graduation,22,Patnership,Complex
6388,113.32,Boston,W,9,17,2,56,High School,35,Franchise,Complex
9674,25.33,NJ City,N,7,20,4,83,No education,29,Patnership,Complex
6987,401,Boston,E,3,17,13,41,Graduation,34,Owner,Standalone
1190,275,NJ City,S,3,8,16,70,High School,34,Patnership,Complex
8488,488,NJ City,W,1,10,22,33,Graduation,21,Franchise,Complex
3721,277,NJ City,N,3,12,9,48,High School,29,Patnership,Standalone
4271,235,NJ City,E,3,13,17,33,High School,32,Owner,Standalone
2328,6.62,NJ City,N,9,13,3,53,Graduation,20,Franchise,Complex
7213,84.56,Boston,N,18,15,3,73,High School,33,Patnership,Standalone
6658,500,Boston,S,3,22,18,82,Graduation,18,Patnership,Complex
2511,311,NJ City,E,1,15,19,25,High School,29,Owner,Standalone
1298,263,NJ City,S,1,17,11,67,High School,34,Patnership,Complex
1056,250,NJ City,W,2,16,9,56,High School,34,Franchise,Complex
189,105,NJ City,N,3,11,6,34,High School,19,Patnership,Standalone
6349,373,Boston,N,2,18,22,35,Graduation,24,Patnership,Standalone
7317,421,Boston,N,1,18,14,49,Graduation,22,Patnership,Standalone
1052,252,NJ City,W,2,16,6,32,High School,35,Franchise,Complex
623,156,NJ City,E,1,18,22,44,High School,18,Owner,Standalone
1898,132,NJ City,S,3,20,19,84,High School,31,Patnership,Complex
7501,423,Boston,N,3,10,15,75,Graduation,34,Patnership,Standalone
3085,50.4,NJ City,N,12,24,1,36,High School,30,Patnership,Standalone
8109,364,NJ City,N,2,23,25,38,Graduation,35,Patnership,Standalone
7783,81.95,Boston,E,24,21,1,85,High School,32,Owner,Standalone
5993,93.95,Boston,N,15,17,3,77,No education,35,Patnership,Standalone
233,182,NJ City,N,3,16,10,48,High School,35,Patnership,Standalone
757,389.55,NJ City,N,1,16,3,77,High School,28,Patnership,Standalone
9465,497.92,NJ City,N,5,15,1,83,Graduation,35,Patnership,Standalone
1165,303,NJ City,N,2,19,19,51,High School,25,Patnership,Standalone
2580,2.99,NJ City,N,3,10,2,69,Graduation,26,Franchise,Complex
7639,92.77,Boston,E,22,16,1,52,High School,18,Owner,Standalone
4105,139,NJ City,N,3,15,15,26,High School,31,Patnership,Standalone
8893,363,NJ City,N,2,11,15,59,Graduation,30,Patnership,Standalone
7457,435,Boston,N,2,24,6,62,Graduation,31,Patnership,Standalone
8415,497,NJ City,E,3,8,21,32,Graduation,18,Owner,Standalone
4974,142.32,Boston,S,1,13,1,53,Graduation,19,Patnership,Complex
6205,444,Boston,N,3,11,6,31,Graduation,20,Patnership,Standalone
3690,0.52,NJ City,N,14,11,4,83,Graduation,19,Patnership,Complex
2848,154,NJ City,W,1,23,19,28,High School,33,Franchise,Complex
3355,295,NJ City,E,3,16,9,79,High School,27,Owner,Standalone
7920,84.93,Boston,W,16,21,4,35,Graduation,32,Franchise,Complex
8748,383,NJ City,W,1,16,5,37,Graduation,23,Franchise,Complex
3310,316,NJ City,S,3,9,12,33,High School,35,Patnership,Complex
5257,496,Boston,N,3,15,24,43,Graduation,21,Patnership,Standalone
6800,383.18,Boston,W,13,8,3,53,No education,19,Franchise,Complex
9556,462,NJ City,W,3,22,20,57,Graduation,31,Franchise,Complex
1896,164,NJ City,W,1,17,24,43,High School,30,Franchise,Complex
7327,113.94,Boston,E,18,13,3,26,High School,18,Owner,Standalone
88,169,Boston,W,3,23,15,38,High School,33,Franchise,Complex
2637,14.07,NJ City,N,22,10,2,49,Graduation,32,Patnership,Standalone
3263,274,NJ City,E,2,8,6,30,High School,23,Owner,Standalone
4948,78.43,Boston,W,19,20,4,79,High School,28,Franchise,Complex
6928,499,Boston,W,1,8,24,75,Graduation,18,Franchise,Complex
7836,453,Boston,W,1,8,5,25,Graduation,27,Franchise,Complex
7935,429,Boston,E,3,16,20,48,Graduation,20,Owner,Standalone
2778,17.7,NJ City,N,7,9,1,66,Graduation,19,Patnership,Complex
9875,103.76,NJ City,E,9,10,1,74,No education,20,Owner,Standalone
4398,59.73,NJ City,S,9,13,3,43,Graduation,20,Patnership,Complex
6036,3.84,Boston,N,21,23,4,30,Graduation,23,Franchise,Complex
9645,11.1,NJ City,N,13,16,1,38,Graduation,31,Patnership,Standalone
7415,489,Boston,E,3,22,8,50,Graduation,21,Owner,Standalone
4832,213,NJ City,W,2,12,7,39,High School,20,Franchise,Complex
843,247,NJ City,E,2,18,13,42,High School,29,Owner,Standalone
6664,360,Boston,W,1,18,20,56,Graduation,20,Franchise,Complex
8566,377.11,NJ City,S,10,8,2,27,High School,33,Patnership,Complex
6422,370,Boston,S,3,17,24,58,Graduation,31,Patnership,Complex
86,205,Boston,S,2,10,19,79,High School,33,Patnership,Complex
6217,461,Boston,N,3,20,14,59,Graduation,28,Patnership,Standalone
7528,479,Boston,W,3,8,23,46,Graduation,33,Franchise,Complex
8719,494,NJ City,E,1,22,6,77,Graduation,33,Owner,Standalone
8469,226.39,NJ City,N,8,13,1,78,Graduation,23,Patnership,Standalone
1438,6.67,NJ City,N,2,16,3,45,High School,24,Patnership,Complex
8646,464,NJ City,S,2,16,17,44,Graduation,20,Patnership,Complex
4570,187,NJ City,S,1,17,8,40,High School,35,Patnership,Complex
2922,150,NJ City,S,2,9,12,78,High School,34,Patnership,Complex
444,262,NJ City,W,2,14,25,82,High School,24,Franchise,Complex
9015,265.59,NJ City,E,17,18,3,84,Graduation,34,Owner,Standalone
5494,476,Boston,S,2,23,17,51,Graduation,35,Patnership,Complex
2858,186,NJ City,S,1,19,10,56,High School,29,Patnership,Complex
6165,22.51,Boston,N,21,22,4,42,Graduation,27,Patnership,Standalone
5671,321.86,Boston,E,9,17,4,28,High School,33,Owner,Standalone
7976,88.06,Boston,W,19,13,1,33,No education,32,Franchise,Complex
5739,350,Boston,E,3,10,16,66,Graduation,19,Owner,Standalone
2655,292,NJ City,E,3,15,12,45,High School,25,Owner,Standalone
5307,22.88,Boston,N,25,10,3,48,Graduation,18,Owner,Standalone
214,423,NJ City,S,3,22,5,25,Graduation,31,Patnership,Complex
67,132,NJ City,E,1,20,19,63,High School,29,Owner,Standalone
8647,38.94,NJ City,N,13,18,3,67,High School,21,Owner,Standalone
8786,321.08,NJ City,S,21,8,2,81,No education,20,Patnership,Complex
8890,418,NJ City,S,3,19,16,42,Graduation,27,Patnership,Complex
7570,477,Boston,S,1,18,25,28,Graduation,32,Patnership,Complex
3038,318,NJ City,S,2,21,23,31,High School,24,Patnership,Complex
4085,112,NJ City,N,2,8,12,29,High School,26,Patnership,Standalone
5984,82.51,Boston,W,22,11,3,42,No education,24,Franchise,Complex
8624,485,NJ City,W,2,9,12,26,Graduation,32,Franchise,Complex
5092,43.21,Boston,N,22,13,3,25,High School,25,Franchise,Complex
3613,494.83,NJ City,N,4,11,2,41,High School,20,Patnership,Standalone
2970,219,NJ City,S,3,22,17,37,High School,27,Patnership,Complex
1648,7.18,NJ City,N,1,18,3,43,High School,35,Franchise,Complex
158,188,NJ City,S,3,24,17,26,High School,35,Patnership,Complex
4236,216,NJ City,W,2,21,9,74,High School,21,Franchise,Complex
2656,80.92,NJ City,W,21,12,2,59,High School,32,Franchise,Complex
8975,9.66,NJ City,N,6,19,1,62,No education,32,Owner,Standalone
8119,472,NJ City,E,1,20,5,54,Graduation,20,Owner,Standalone
457,10.78,NJ City,N,4,18,2,40,High School,26,Patnership,Standalone
1090,173,NJ City,S,3,24,6,75,High School,26,Patnership,Complex
5370,458,Boston,S,2,24,10,27,Graduation,33,Patnership,Complex
5631,476,Boston,E,3,18,23,63,Graduation,28,Owner,Standalone
4872,194,NJ City,W,2,10,22,82,High School,34,Franchise,Complex
7793,16.82,Boston,N,24,17,3,28,No education,30,Patnership,Standalone
4130,166,NJ City,S,1,9,8,73,High School,35,Patnership,Complex
3158,293,NJ City,S,1,19,23,43,High School,35,Patnership,Complex
3531,274,NJ City,E,3,15,7,81,High School,25,Owner,Standalone
3794,408.87,NJ City,S,1,10,1,66,No education,20,Patnership,Complex
8137,423,NJ City,N,1,13,19,59,Graduation,29,Patnership,Standalone
4835,338.28,NJ City,E,15,22,4,51,No education,30,Owner,Standalone
1137,103,NJ City,N,2,22,9,57,High School,23,Patnership,Standalone
85,157,Boston,N,2,18,19,56,High School,22,Patnership,Standalone
5785,23.05,Boston,N,4,18,3,29,High School,23,Patnership,Standalone
3738,403.17,NJ City,S,5,9,4,63,Graduation,27,Patnership,Complex
3129,27.59,NJ City,N,12,23,4,75,Graduation,31,Patnership,Standalone
2304,269,NJ City,W,3,21,7,55,High School,28,Franchise,Complex
7953,289.83,Boston,N,1,17,3,81,Graduation,26,Patnership,Standalone
676,151,NJ City,W,2,14,17,67,High School,22,Franchise,Complex
5426,433,Boston,S,3,23,8,39,Graduation,27,Patnership,Complex
4011,106.89,NJ City,E,17,15,1,49,Graduation,18,Owner,Standalone
8338,396,NJ City,S,2,12,7,66,Graduation,31,Patnership,Complex
5849,154.13,Boston,N,9,8,4,33,No education,35,Patnership,Standalone
3896,107,NJ City,W,3,19,17,66,High School,29,Franchise,Complex
549,7.83,NJ City,N,22,9,2,61,Graduation,26,Patnership,Standalone
3545,28.33,NJ City,N,10,19,1,34,No education,28,Patnership,Standalone
9349,40.49,NJ City,N,5,22,4,47,High School,18,Patnership,Standalone
1629,84.46,NJ City,N,8,20,3,75,Graduation,21,Patnership,Standalone
7826,365,Boston,S,3,18,12,56,Graduation,34,Patnership,Complex
3751,497,NJ City,E,3,18,5,47,Graduation,19,Owner,Standalone
3163,130.21,NJ City,E,7,19,2,55,High School,25,Owner,Standalone
4927,190,NJ City,E,3,22,12,66,High School,21,Owner,Standalone
3683,130,NJ City,E,3,11,6,25,High School,35,Owner,Standalone
1125,117.97,NJ City,N,2,17,3,28,Graduation,34,Patnership,Standalone
4587,169,NJ City,E,3,20,21,65,High School,30,Owner,Standalone
4740,281,NJ City,W,3,13,13,67,High School,23,Franchise,Complex
4518,325.85,NJ City,S,2,8,3,80,Graduation,33,Patnership,Complex
5382,374,Boston,S,3,8,14,28,Graduation,27,Patnership,Complex
2578,293,NJ City,S,3,18,17,67,High School,20,Patnership,Complex
9175,107.75,NJ City,E,17,24,3,56,High School,23,Owner,Standalone
9340,125.61,NJ City,W,19,21,3,28,High School,31,Franchise,Complex
4774,278,NJ City,S,1,9,19,77,High School,28,Patnership,Complex
9627,264.21,NJ City,E,2,12,3,42,Graduation,24,Owner,Standalone
9579,15.56,NJ City,N,16,17,1,39,Graduation,26,Owner,Standalone
5569,498.03,Boston,N,23,12,1,59,High School,25,Patnership,Standalone
6510,85.6,Boston,S,9,13,2,44,Graduation,34,Patnership,Complex
1460,16.45,NJ City,N,11,17,4,41,No education,20,Franchise,Complex
9186,62.28,NJ City,N,7,9,3,60,Graduation,22,Patnership,Complex
9291,421,NJ City,E,2,12,22,27,Graduation,28,Owner,Standalone
4044,5.62,NJ City,N,4,9,1,84,Graduation,21,Franchise,Complex
1017,137,NJ City,N,3,8,14,63,High School,23,Patnership,Standalone
285,123,NJ City,N,2,10,22,50,High School,19,Patnership,Standalone
7263,103.68,Boston,E,4,15,1,36,Graduation,24,Owner,Standalone
3165,450,NJ City,N,2,23,5,61,Graduation,20,Patnership,Standalone
2807,146,NJ City,E,1,24,21,74,High School,18,Owner,Standalone
7106,43.31,Boston,N,17,22,4,63,No education,19,Patnership,Complex
7485,81.21,Boston,N,6,12,2,44,Graduation,18,Patnership,Standalone
6670,360,Boston,S,2,23,10,68,Graduation,23,Patnership,Complex
9547,476,NJ City,E,3,11,25,63,Graduation,27,Owner,Standalone
9823,479,NJ City,E,2,12,7,40,Graduation,29,Owner,Standalone
6632,115.53,Boston,W,21,24,2,30,No education,29,Franchise,Complex
5598,384,Boston,S,1,21,9,84,Graduation,28,Patnership,Complex
6483,42.54,Boston,N,9,17,3,55,Graduation,31,Owner,Standalone
3677,233,NJ City,N,2,23,18,43,High School,19,Patnership,Standalone
2187,222,NJ City,E,3,17,19,51,High School,19,Owner,Standalone
5227,482,Boston,E,3,17,18,26,Graduation,29,Owner,Standalone
2897,1.1,NJ City,W,18,15,3,49,No education,33,Patnership,Standalone
4067,62.16,NJ City,N,21,16,3,39,No education,33,Owner,Standalone
359,85.73,NJ City,E,6,15,2,66,No education,33,Owner,Standalone
8126,415,NJ City,S,1,16,21,27,Graduation,28,Patnership,Complex
7558,476,Boston,S,3,18,13,43,Graduation,19,Patnership,Complex
6918,488,Boston,S,3,12,17,25,Graduation,32,Patnership,Complex
3257,190,NJ City,N,3,24,13,49,High School,21,Patnership,Standalone
3021,434.46,NJ City,N,16,17,4,25,Graduation,29,Patnership,Standalone
8288,377,NJ City,W,1,9,9,60,Graduation,19,Franchise,Complex
6017,357,Boston,N,2,23,6,85,Graduation,19,Patnership,Standalone
4191,395,NJ City,E,2,9,5,61,Graduation,21,Owner,Standalone
2209,244,NJ City,N,2,23,23,82,High School,28,Patnership,Standalone
7143,355.1,Boston,E,10,15,4,81,Graduation,31,Owner,Standalone
20,159.61,NY City,W,12,22,1,84,No education,34,Franchise,Complex
1779,165,NJ City,E,2,10,10,32,High School,35,Owner,Standalone
1914,114,NJ City,S,2,20,20,58,High School,27,Patnership,Complex
9309,385.03,NJ City,N,19,23,3,79,Graduation,35,Patnership,Standalone
8557,452,NJ City,N,2,14,12,34,Graduation,31,Patnership,Standalone
364,39.48,NJ City,N,5,15,2,79,High School,34,Franchise,Complex
7404,364,Boston,W,2,16,5,68,Graduation,25,Franchise,Complex
2573,78.8,NJ City,N,24,23,2,37,No education,29,Patnership,Standalone
6126,352,Boston,S,2,20,15,84,Graduation,34,Patnership,Complex
8078,262.61,NJ City,S,1,13,3,46,No education,34,Patnership,Complex
8072,429,NJ City,W,3,24,7,54,Graduation,24,Franchise,Complex
7028,472,Boston,W,3,18,8,41,Graduation,29,Franchise,Complex
9436,428.44,NJ City,W,15,12,2,44,High School,31,Franchise,Complex
3748,34.58,NJ City,N,12,19,4,68,High School,27,Franchise,Complex
9834,429,NJ City,S,2,21,25,63,Graduation,26,Patnership,Complex
3885,100,NJ City,N,3,13,14,41,High School,21,Patnership,Standalone
1978,146,NJ City,S,1,8,17,65,High School,23,Patnership,Complex
4618,53.57,NJ City,S,22,19,4,40,High School,21,Patnership,Complex
5396,383,Boston,W,3,20,17,34,Graduation,30,Franchise,Complex
7484,355,Boston,W,1,21,9,36,Graduation,20,Franchise,Complex
1768,30.57,NJ City,N,22,14,2,53,High School,26,Franchise,Complex
2406,258,NJ City,S,2,15,24,25,High School,24,Patnership,Complex
5808,399,Boston,W,2,11,21,44,Graduation,22,Franchise,Complex
7513,357,Boston,N,2,24,17,59,Graduation,28,Patnership,Standalone
4506,278.48,NJ City,S,17,18,2,58,Graduation,19,Patnership,Complex
8559,372,NJ City,E,1,24,20,80,Graduation,21,Owner,Standalone
8793,440,NJ City,N,2,12,25,77,Graduation,27,Patnership,Standalone
3194,8.55,NJ City,N,4,23,1,33,No education,24,Patnership,Complex
9170,440.21,NJ City,S,1,9,2,49,No education,28,Patnership,Complex
8187,443,NJ City,E,3,15,12,44,Graduation,19,Owner,Standalone
7960,102.2,Boston,W,6,11,3,79,High School,20,Franchise,Complex
5042,357,Boston,S,1,13,18,39,Graduation,21,Patnership,Complex
8294,458,NJ City,S,3,9,14,60,Graduation,31,Patnership,Complex
5562,351,Boston,S,2,15,23,52,Graduation,21,Patnership,Complex
1209,10.92,NJ City,N,12,20,2,78,Graduation,29,Patnership,Standalone
8750,489,NJ City,S,2,19,22,38,Graduation,28,Patnership,Complex
5650,449,Boston,S,1,21,14,31,Graduation,18,Patnership,Complex
2124,208,NJ City,W,1,24,16,71,High School,20,Franchise,Complex
8591,13.37,NJ City,N,12,20,2,37,No education,18,Owner,Standalone
6288,430,Boston,W,3,17,9,39,Graduation,27,Franchise,Complex
205,115,NJ City,N,3,20,7,40,High School,31,Patnership,Standalone
7346,368,Boston,S,2,12,6,66,Graduation,24,Patnership,Complex
5686,392,Boston,S,1,18,24,54,Graduation,21,Patnership,Complex
4198,2.71,NJ City,S,8,12,1,53,High School,27,Patnership,Complex
5331,117.9,Boston,E,11,13,3,80,Graduation,23,Owner,Standalone
1405,5.33,NJ City,N,11,20,3,25,High School,26,Patnership,Standalone
2413,143,NJ City,N,2,8,11,42,High School,25,Patnership,Standalone
8700,46.69,NJ City,N,9,19,1,26,Graduation,18,Franchise,Complex
3968,288,NJ City,W,1,22,11,30,High School,29,Franchise,Complex
976,120,NJ City,W,2,19,9,51,High School,30,Franchise,Complex
6815,87.33,Boston,E,12,15,4,85,No education,24,Owner,Standalone
6332,384,Boston,W,1,15,17,42,Graduation,19,Franchise,Complex
5634,481,Boston,S,2,24,6,53,Graduation,26,Patnership,Complex
2536,113.42,NJ City,W,1,9,2,60,High School,32,Franchise,Complex
5654,368,Boston,S,2,9,7,73,Graduation,24,Patnership,Complex
7517,436,Boston,N,1,9,22,41,Graduation,25,Patnership,Standalone
7872,490,Boston,W,1,18,14,54,Graduation,26,Franchise,Complex
2492,244,NJ City,W,3,18,15,83,High School,29,Franchise,Complex
9355,383,NJ City,E,2,9,6,48,Graduation,35,Owner,Standalone
9967,377,NJ City,E,3,9,21,75,Graduation,25,Owner,Standalone
4177,42.12,NJ City,N,25,24,4,51,High School,23,Patnership,Standalone
3580,156,NJ City,W,2,11,11,27,High School,28,Franchise,Complex
687,137,NJ City,E,1,22,14,77,High School,26,Owner,Standalone
2823,238,NJ City,E,1,21,25,40,High School,21,Owner,Standalone
358,112,NJ City,S,3,9,11,52,High School,18,Patnership,Complex
4761,21.52,NJ City,N,19,17,1,46,Graduation,25,Patnership,Standalone
3825,173,NJ City,N,2,15,24,72,High School,25,Patnership,Standalone
6737,474.49,Boston,N,16,24,1,84,No education,27,Patnership,Standalone
6417,359,Boston,N,3,18,25,29,Graduation,31,Patnership,Standalone
4483,292,NJ City,E,3,16,12,83,High School,23,Owner,Standalone
4909,158,NJ City,N,1,19,23,77,High School,26,Patnership,Standalone
128,354.2,NJ City,W,24,21,2,74,No education,20,Franchise,Complex
5334,440,Boston,S,1,19,7,51,Graduation,27,Patnership,Complex
7667,354,Boston,E,3,21,18,67,Graduation,22,Owner,Standalone
7746,406,Boston,S,2,17,13,47,Graduation,23,Patnership,Complex
7866,465,Boston,S,2,12,11,67,Graduation,22,Patnership,Complex
6542,71.68,Boston,N,19,15,3,39,No education,21,Patnership,Complex
4602,43.09,NJ City,N,13,22,1,73,Graduation,19,Patnership,Complex
2185,75.91,NJ City,N,25,11,2,51,High School,26,Patnership,Standalone
190,262,NJ City,S,1,16,13,27,High School,24,Patnership,Complex
8733,90.31,NJ City,N,2,16,4,54,Graduation,20,Patnership,Standalone
818,285.31,NJ City,S,23,15,2,58,No education,35,Patnership,Complex
1585,154,NJ City,N,3,12,7,34,High School,28,Patnership,Standalone
3288,106.63,NJ City,W,22,14,1,42,Graduation,20,Franchise,Complex
1104,107,NJ City,W,1,12,20,45,High School,18,Franchise,Complex
9666,122.48,NJ City,S,25,20,2,62,Graduation,33,Patnership,Complex
4751,267,NJ City,E,2,23,19,40,High School,18,Owner,Standalone
6463,402,Boston,E,2,19,21,44,Graduation,20,Owner,Standalone
2302,295,NJ City,S,3,15,11,59,High School,22,Patnership,Complex
5865,435,Boston,N,2,15,13,74,Graduation,34,Patnership,Standalone
8262,413,NJ City,S,3,19,16,83,Graduation,23,Patnership,Complex
1134,449.74,NJ City,S,16,15,2,47,Graduation,21,Patnership,Complex
4667,115,NJ City,E,3,18,20,25,High School,32,Owner,Standalone
384,50.19,NJ City,W,12,15,3,54,Graduation,23,Franchise,Complex
8098,437,NJ City,S,2,22,23,38,Graduation,35,Patnership,Complex
9497,57.78,NJ City,N,18,22,4,51,No education,28,Patnership,Standalone
4604,36.75,NJ City,N,6,12,1,76,No education,27,Franchise,Complex
762,295.1,NJ City,S,15,21,1,26,Graduation,30,Patnership,Complex
1893,176,NJ City,N,1,12,7,27,High School,23,Patnership,Standalone
2244,197,NJ City,W,2,22,20,59,High School,21,Franchise,Complex
3916,53.69,NJ City,W,6,18,2,65,High School,18,Franchise,Complex
9730,495.28,NJ City,S,22,23,4,35,High School,29,Patnership,Complex
3077,195,NJ City,N,1,21,12,61,High School,24,Patnership,Standalone
4159,166,NJ City,E,2,18,12,35,High School,22,Owner,Standalone
2202,258,NJ City,S,1,17,23,55,High School,34,Patnership,Complex
8504,19.8,NJ City,N,4,23,3,60,No education,19,Franchise,Complex
8349,421,NJ City,N,3,15,19,63,Graduation,22,Patnership,Standalone
3551,310,NJ City,E,1,22,13,59,High School,23,Owner,Standalone
804,199,NJ City,W,3,21,6,49,High School,21,Franchise,Complex
930,214,NJ City,S,1,8,21,45,High School,32,Patnership,Complex
5310,406,Boston,S,3,10,23,38,Graduation,19,Patnership,Complex
9078,380,NJ City,S,2,20,8,51,Graduation,28,Patnership,Complex
5270,435,Boston,S,1,21,21,41,Graduation,26,Patnership,Complex
6813,84.8,Boston,N,14,13,1,62,Graduation,31,Patnership,Standalone
4403,276,NJ City,E,3,17,20,26,High School,26,Owner,Standalone
6118,405,Boston,S,3,10,14,29,Graduation,22,Patnership,Complex
9840,463,NJ City,W,1,20,18,28,Graduation,23,Franchise,Complex
900,38.64,NJ City,N,7,24,3,26,Graduation,25,Franchise,Complex
8895,429,NJ City,E,1,22,9,60,Graduation,32,Owner,Standalone
2419,488.54,NJ City,E,21,23,4,66,High School,26,Owner,Standalone
8275,475,NJ City,E,3,18,14,35,Graduation,21,Owner,Standalone
2547,290,NJ City,E,2,22,18,49,High School,21,Owner,Standalone
274,124.82,NJ City,S,21,20,3,34,High School,20,Patnership,Complex
99,115,NJ City,E,1,8,14,28,High School,30,Owner,Standalone
3105,112,NJ City,N,2,14,15,80,High School,26,Patnership,Standalone
9857,365,NJ City,N,3,23,23,63,Graduation,32,Patnership,Standalone
1094,20.49,NJ City,N,16,17,3,36,No education,34,Patnership,Complex
7686,406,Boston,S,1,24,10,39,Graduation,34,Patnership,Complex
9693,122.08,NJ City,N,25,9,3,47,Graduation,35,Patnership,Standalone
5174,70.54,Boston,N,19,11,3,57,No education,30,Patnership,Complex
8509,473,NJ City,N,1,23,6,67,Graduation,19,Patnership,Standalone
6469,27.71,Boston,N,2,21,1,78,High School,25,Patnership,Standalone
5657,89.54,Boston,N,18,10,1,62,No education,32,Patnership,Standalone
345,280,NJ City,N,3,20,12,45,High School,31,Patnership,Standalone
4012,241,NJ City,W,3,24,20,73,High School,34,Franchise,Complex
6783,367,Boston,E,2,8,5,67,Graduation,21,Owner,Standalone
5362,471,Boston,S,2,10,15,81,Graduation,31,Patnership,Complex
336,223,NJ City,W,3,13,10,76,High School,26,Franchise,Complex
8747,496,NJ City,E,1,21,9,85,Graduation,30,Owner,Standalone
6706,434,Boston,S,2,15,24,72,Graduation,21,Patnership,Complex
7721,396,Boston,N,2,22,17,28,Graduation,19,Patnership,Standalone
2989,89.42,NJ City,N,10,11,1,52,High School,25,Patnership,Standalone
3945,304,NJ City,N,1,19,11,65,High School,23,Patnership,Standalone
7534,494,Boston,S,1,15,9,49,Graduation,19,Patnership,Complex
7612,405,Boston,W,3,13,5,56,Graduation,32,Franchise,Complex
9562,387,NJ City,S,3,16,16,67,Graduation,30,Patnership,Complex
629,64.99,NJ City,N,5,22,1,70,No education,23,Patnership,Standalone
5729,447,Boston,N,2,17,7,36,Graduation,21,Patnership,Standalone
5745,379,Boston,N,2,22,21,60,Graduation,31,Patnership,Standalone
5672,477,Boston,W,2,9,16,50,Graduation,22,Franchise,Complex
7589,369,Boston,N,2,23,11,38,Graduation,25,Patnership,Standalone
7211,492,Boston,E,2,23,14,74,Graduation,28,Owner,Standalone
7698,499,Boston,S,1,13,12,49,Graduation,32,Patnership,Complex
5237,451,Boston,N,3,16,14,78,Graduation,35,Patnership,Standalone
9449,62.58,NJ City,N,7,20,3,32,No education,22,Patnership,Standalone
9994,461,NJ City,S,2,18,25,73,Graduation,30,Patnership,Complex
9,166,Maine,N,3,21,24,73,High School,27,Patnership,Standalone
4468,90.77,NJ City,W,4,12,2,60,High School,21,Franchise,Complex
392,220,NJ City,W,1,24,22,33,High School,18,Franchise,Complex
8304,184.78,NJ City,W,6,24,4,42,Graduation,25,Franchise,Complex
8815,57.86,NJ City,E,1,19,2,48,High School,32,Owner,Standalone
7813,253.67,Boston,N,22,20,3,76,High School,32,Patnership,Standalone
9238,354,NJ City,S,3,23,23,39,Graduation,26,Patnership,Complex
7919,56.64,Boston,E,4,19,3,49,No education,33,Owner,Standalone
6423,402,Boston,E,3,24,10,27,Graduation,35,Owner,Standalone
7907,123.36,Boston,E,9,8,4,53,No education,18,Owner,Standalone
9721,354,NJ City,N,3,9,20,35,Graduation,27,Patnership,Standalone
7095,440,Boston,E,3,15,21,66,Graduation,27,Owner,Standalone
4255,0.22,NJ City,N,20,13,3,46,High School,29,Owner,Standalone
188,426,NJ City,W,1,12,5,52,Graduation,27,Franchise,Complex
1866,451.84,NJ City,S,2,8,1,25,Graduation,24,Patnership,Complex
1162,19.46,NJ City,N,14,10,2,53,High School,35,Patnership,Complex
8832,78.53,NJ City,W,20,24,2,65,Graduation,26,Franchise,Complex
4623,210,NJ City,E,3,9,21,62,High School,22,Owner,Standalone
5198,380,Boston,S,3,23,19,38,Graduation,28,Patnership,Complex
4638,47.28,NJ City,N,18,8,1,44,Graduation,28,Patnership,Complex
4591,213,NJ City,E,2,23,24,28,High School,33,Owner,Standalone
4601,289,NJ City,N,2,19,6,59,High School,18,Patnership,Standalone
3388,3.47,NJ City,N,21,13,4,36,High School,23,Franchise,Complex
9919,374,NJ City,E,3,10,6,32,Graduation,32,Owner,Standalone
704,284,NJ City,W,2,24,18,34,High School,35,Franchise,Complex
2376,145,NJ City,W,1,21,12,52,High School,26,Franchise,Complex
5271,362,Boston,E,2,14,15,44,Graduation,19,Owner,Standalone
8961,440,NJ City,N,3,9,18,81,Graduation,19,Patnership,Standalone
4129,156,NJ City,N,2,11,23,67,High School,22,Patnership,Standalone
7840,104.82,Boston,W,9,23,2,72,High School,33,Franchise,Complex
2017,260,NJ City,N,3,17,6,79,High School,26,Patnership,Standalone
3446,237,NJ City,S,2,8,7,39,High School,28,Patnership,Complex
4227,281,NJ City,E,2,11,15,72,High School,24,Owner,Standalone
9699,359,NJ City,E,3,17,11,30,Graduation,20,Owner,Standalone
9930,447,NJ City,S,1,19,20,28,Graduation,31,Patnership,Complex
2928,120,NJ City,W,3,10,19,66,High School,28,Franchise,Complex
124,45.1,NJ City,N,24,16,3,45,High School,27,Franchise,Complex
27,249,Boston,E,2,10,10,84,High School,24,Owner,Standalone
8978,361,NJ City,S,2,10,12,46,Graduation,24,Patnership,Complex
3902,283.52,NJ City,S,5,19,2,26,No education,21,Patnership,Complex
7997,395,Boston,N,2,12,13,60,Graduation,22,Patnership,Standalone
6605,428.53,Boston,N,13,16,4,34,No education,33,Patnership,Standalone
9926,432,NJ City,S,3,20,8,30,Graduation,18,Patnership,Complex
4951,13.74,Boston,N,3,16,4,66,High School,34,Owner,Standalone
8584,52.95,NJ City,W,25,8,3,83,High School,22,Franchise,Complex
6768,390,Boston,W,3,19,18,80,Graduation,30,Franchise,Complex
4214,117,NJ City,S,1,17,19,72,High School,28,Patnership,Complex
7375,426,Boston,E,1,18,7,69,Graduation,22,Owner,Standalone
8281,66.26,NJ City,N,14,9,3,70,High School,32,Patnership,Standalone
3478,271,NJ City,S,3,22,9,67,High School,35,Patnership,Complex
2728,61.25,NJ City,N,15,21,2,72,High School,24,Franchise,Complex
2854,304,NJ City,S,2,8,15,76,High School,24,Patnership,Complex
5694,365,Boston,S,3,15,6,37,Graduation,22,Patnership,Complex
563,168,NJ City,E,2,21,23,68,High School,28,Owner,Standalone
559,127,NJ City,E,1,19,21,50,High School,29,Owner,Standalone
360,44.03,NJ City,N,23,19,3,72,Graduation,29,Franchise,Complex
9501,478,NJ City,N,1,11,21,64,Graduation,22,Patnership,Standalone
3470,269,NJ City,S,3,8,25,79,High School,22,Patnership,Complex
1797,427,NJ City,N,2,18,5,53,Graduation,28,Patnership,Standalone
2180,61.3,NJ City,N,25,12,4,72,No education,35,Franchise,Complex
653,137,NJ City,N,2,9,13,48,High School,25,Patnership,Standalone
6908,440.55,Boston,W,18,22,1,65,No education,21,Franchise,Complex
1071,391.36,NJ City,E,8,14,3,37,Graduation,26,Owner,Standalone
2190,24.33,NJ City,N,6,22,4,75,Graduation,30,Patnership,Complex
6034,362,Boston,S,3,19,24,25,Graduation,25,Patnership,Complex
6674,410,Boston,S,2,12,23,46,Graduation,31,Patnership,Complex
9022,378,NJ City,S,2,14,14,48,Graduation,22,Patnership,Complex
7386,410,Boston,S,1,23,8,84,Graduation,23,Patnership,Complex
7088,469,Boston,W,1,17,13,37,Graduation,30,Franchise,Complex
312,243,NJ City,W,2,16,13,59,High School,22,Franchise,Complex
3416,252,NJ City,W,2,8,17,43,High School,28,Franchise,Complex
3368,276,NJ City,W,1,21,8,80,High School,22,Franchise,Complex
6627,498,Boston,E,1,10,8,76,Graduation,35,Owner,Standalone
805,132,NJ City,N,1,16,23,58,High School,20,Patnership,Standalone
8356,492,NJ City,W,3,15,20,43,Graduation,21,Franchise,Complex
8694,298.9,NJ City,S,6,13,3,28,Graduation,21,Patnership,Complex
1958,68.72,NJ City,N,2,17,3,31,No education,35,Patnership,Complex
198,126,NJ City,S,1,11,25,31,High School,19,Patnership,Complex
7678,7.57,Boston,N,9,12,3,72,High School,22,Patnership,Complex
7050,371,Boston,S,1,13,13,67,Graduation,35,Patnership,Complex
3530,124.12,NJ City,S,11,24,4,69,No education,22,Patnership,Complex
2993,162,NJ City,N,3,16,11,28,High School,20,Patnership,Standalone
9689,389,NJ City,N,3,8,24,55,Graduation,30,Patnership,Standalone
6633,416,Boston,N,3,24,17,74,Graduation,30,Patnership,Standalone
831,229,NJ City,E,3,8,14,61,High School,22,Owner,Standalone
9778,493,NJ City,S,3,10,11,81,Graduation,30,Patnership,Complex
9615,367,NJ City,E,1,13,23,62,Graduation,27,Owner,Standalone
3823,356.7,NJ City,E,1,8,3,35,High School,29,Owner,Standalone
8759,118.95,NJ City,E,11,21,2,72,No education,19,Owner,Standalone
6431,463.72,Boston,E,15,8,1,37,No education,19,Owner,Standalone
7378,407,Boston,S,2,9,7,49,Graduation,27,Patnership,Complex
1942,145,NJ City,S,3,13,8,52,High School,24,Patnership,Complex
8805,395,NJ City,N,3,21,13,84,Graduation,20,Patnership,Standalone
3678,150,NJ City,S,1,17,6,46,High School,23,Patnership,Complex
195,488.82,NJ City,E,13,20,3,80,Graduation,20,Owner,Standalone
950,184,NJ City,S,1,10,14,65,High School,27,Patnership,Complex
1504,305,NJ City,W,1,13,22,37,High School,24,Franchise,Complex
7893,387,Boston,N,1,15,18,29,Graduation,27,Patnership,Standalone
7964,80.63,Boston,W,12,11,3,71,No education,33,Franchise,Complex
9099,350,NJ City,E,3,15,20,51,Graduation,33,Owner,Standalone
2991,291,NJ City,E,2,15,19,58,High School,21,Owner,Standalone
2808,134.03,NJ City,W,17,16,3,46,Graduation,25,Franchise,Complex
7067,450,Boston,E,1,13,6,72,Graduation,31,Owner,Standalone
8448,469,NJ City,W,2,8,8,77,Graduation,21,Franchise,Complex
1427,146,NJ City,E,2,10,8,29,High School,28,Owner,Standalone
272,152,NJ City,W,1,19,17,51,High School,19,Franchise,Complex
5509,356,Boston,N,1,17,8,48,Graduation,21,Patnership,Standalone
527,403,NJ City,E,2,21,5,41,Graduation,22,Owner,Standalone
4336,268.01,NJ City,W,16,15,1,63,High School,29,Franchise,Complex
911,288,NJ City,E,1,13,15,85,High School,21,Owner,Standalone
1981,222,NJ City,N,1,11,22,64,High School,18,Patnership,Standalone
2476,279,NJ City,W,2,16,13,37,High School,27,Franchise,Complex
2565,157,NJ City,N,1,11,24,82,High School,32,Patnership,Standalone
5238,366,Boston,S,1,11,14,56,Graduation,34,Patnership,Complex
2653,197,NJ City,N,2,14,20,70,High School,35,Patnership,Standalone
6841,98.8,Boston,N,13,20,1,54,High School,20,Patnership,Standalone
2844,234,NJ City,W,3,17,7,67,High School,26,Franchise,Complex
9681,376.28,NJ City,N,18,11,1,66,Graduation,25,Patnership,Standalone
1203,120,NJ City,E,3,17,17,77,High School,23,Owner,Standalone
774,24.25,NJ City,N,10,11,1,33,Graduation,25,Patnership,Complex
1523,266,NJ City,E,2,8,25,58,High School,35,Owner,Standalone
8409,455,NJ City,N,3,18,12,39,Graduation,21,Patnership,Standalone
1342,297,NJ City,S,1,17,7,62,High School,30,Patnership,Complex
1087,219.05,NJ City,E,4,24,2,84,High School,32,Owner,Standalone
8279,430,NJ City,E,3,14,16,35,Graduation,28,Owner,Standalone
8434,18.34,NJ City,N,2,18,4,58,High School,18,Patnership,Complex
181,240,NJ City,N,2,12,24,59,High School,22,Patnership,Standalone
9219,59.06,NJ City,E,15,18,4,50,Graduation,31,Owner,Standalone
5867,397,Boston,E,1,24,6,49,Graduation,23,Owner,Standalone
8718,459,NJ City,S,2,23,22,34,Graduation,29,Patnership,Complex
367,196,NJ City,E,3,20,17,77,High School,25,Owner,Standalone
3282,197,NJ City,S,1,12,9,55,High School,22,Patnership,Complex
7162,351,Boston,S,3,18,14,66,Graduation,32,Patnership,Complex
2915,105,NJ City,E,1,21,21,74,High School,23,Owner,Standalone
1259,371.92,NJ City,E,13,14,2,57,No education,23,Owner,Standalone
1029,241,NJ City,N,1,15,8,83,High School,23,Patnership,Standalone
517,22.49,NJ City,N,3,15,3,77,High School,32,Patnership,Standalone
3149,266,NJ City,N,1,22,16,29,High School,29,Patnership,Standalone
5777,453,Boston,N,2,12,18,49,Graduation,20,Patnership,Standalone
2816,411.98,NJ City,W,25,20,3,48,No education,35,Franchise,Complex
2070,218,NJ City,S,1,16,13,35,High School,22,Patnership,Complex
8439,371,NJ City,E,2,8,5,33,Graduation,27,Owner,Standalone
1323,231,NJ City,E,2,22,17,60,High School,27,Owner,Standalone
6812,375.11,Boston,W,4,10,2,47,No education,23,Franchise,Complex
6026,75.49,Boston,S,8,23,3,49,No education,26,Patnership,Complex
1803,69.48,NJ City,N,5,16,1,66,Graduation,25,Owner,Standalone
6502,18.17,Boston,N,9,19,3,48,High School,26,Patnership,Complex
8134,479,NJ City,S,1,14,13,84,Graduation,21,Patnership,Complex
1514,113.68,NJ City,S,21,16,3,26,No education,29,Patnership,Complex
4169,121,NJ City,N,1,18,25,41,High School,19,Patnership,Standalone
1637,57.38,NJ City,N,1,20,1,39,No education,20,Patnership,Standalone
2308,13.2,NJ City,N,10,16,3,30,High School,29,Franchise,Complex
8525,83.85,NJ City,N,22,24,3,68,No education,30,Patnership,Standalone
9801,404,NJ City,N,1,16,22,66,Graduation,26,Patnership,Standalone
1827,389,NJ City,E,2,19,5,58,Graduation,22,Owner,Standalone
5157,357,Boston,N,3,24,15,25,Graduation,18,Patnership,Standalone
1586,238,NJ City,S,1,22,6,67,High School,30,Patnership,Complex
6202,374,Boston,S,2,14,7,51,Graduation,30,Patnership,Complex
2649,155,NJ City,N,2,13,25,32,High School,35,Patnership,Standalone
8842,496,NJ City,S,2,15,9,56,Graduation,18,Patnership,Complex
4434,306,NJ City,S,3,24,17,50,High School,21,Patnership,Complex
7938,422,Boston,S,2,16,12,36,Graduation,35,Patnership,Complex
8324,72.96,NJ City,N,18,16,1,64,No education,22,Franchise,Complex
7573,430,Boston,N,2,15,6,60,Graduation,25,Patnership,Standalone
5704,46.23,Boston,N,2,21,2,81,High School,33,Franchise,Complex
4109,114,NJ City,N,2,16,17,72,High School,33,Patnership,Standalone
5647,113.12,Boston,E,11,10,3,42,High School,31,Owner,Standalone
437,3.16,NJ City,N,21,9,3,33,No education,22,Patnership,Standalone
7283,465,Boston,E,2,20,13,33,Graduation,24,Owner,Standalone
8450,474,NJ City,S,3,9,8,64,Graduation,31,Patnership,Complex
5,185,NY City,N,2,9,25,29,High School,32,Franchise,Complex
8498,437,NJ City,S,1,8,15,35,Graduation,24,Patnership,Complex
8882,396,NJ City,S,1,8,7,32,Graduation,29,Patnership,Complex
2519,282,NJ City,E,1,18,18,38,High School,30,Owner,Standalone
7723,465,Boston,E,1,20,18,53,Graduation,18,Owner,Standalone
1853,45.66,NJ City,N,15,24,2,49,No education,30,Patnership,Standalone
1364,113.4,NJ City,W,17,10,3,60,No education,29,Franchise,Complex
3938,82.32,NJ City,S,22,23,3,66,No education,19,Patnership,Complex
4484,113.87,NJ City,W,3,18,4,32,No education,25,Franchise,Complex
3088,291,NJ City,W,3,9,11,79,High School,34,Franchise,Complex
1619,241,NJ City,E,2,12,7,40,High School,18,Owner,Standalone
8888,370,NJ City,W,1,16,19,51,Graduation,22,Franchise,Complex
3851,93.4,NJ City,E,10,16,2,38,No education,33,Owner,Standalone
916,122.13,NJ City,W,11,11,4,55,High School,23,Franchise,Complex
9119,396.79,NJ City,E,7,18,4,47,No education,24,Owner,Standalone
5080,450,Boston,W,3,10,11,47,Graduation,18,Franchise,Complex
8913,348.33,NJ City,N,12,19,4,29,Graduation,31,Patnership,Standalone
3930,125,NJ City,S,3,19,11,34,High School,18,Patnership,Complex
1345,131,NJ City,N,3,23,20,73,High School,22,Patnership,Standalone
6529,493,Boston,N,2,17,10,64,Graduation,29,Patnership,Standalone
8517,398,NJ City,N,3,8,11,60,Graduation,33,Patnership,Standalone
2234,53.13,NJ City,S,23,15,4,82,No education,21,Patnership,Complex
9262,91.8,NJ City,S,3,8,3,69,High School,33,Patnership,Complex
2103,117.26,NJ City,E,6,10,1,35,Graduation,27,Owner,Standalone
2627,180,NJ City,E,2,21,13,66,High School,30,Owner,Standalone
8306,443,NJ City,S,1,15,24,83,Graduation,29,Patnership,Complex
690,172,NJ City,S,1,19,19,61,High School,20,Patnership,Complex
9639,405,NJ City,E,1,13,24,83,Graduation,31,Owner,Standalone
6384,473,Boston,W,2,12,6,59,Graduation,25,Franchise,Complex
7511,1.61,Boston,N,1,14,1,26,No education,23,Owner,Standalone
9941,351,NJ City,N,2,22,8,73,Graduation,22,Patnership,Standalone
8303,360,NJ City,E,3,24,18,73,Graduation,25,Owner,Standalone
1261,6.98,NJ City,N,19,14,1,52,High School,19,Patnership,Standalone
9598,362,NJ City,S,1,15,17,36,Graduation,26,Patnership,Complex
2340,47.35,NJ City,W,22,11,1,66,Graduation,25,Franchise,Complex
1188,130,NJ City,W,2,15,23,84,High School,29,Franchise,Complex
5738,476,Boston,S,3,10,9,70,Graduation,22,Patnership,Complex
7908,432,Boston,W,3,17,7,45,Graduation,22,Franchise,Complex
3435,78.43,NJ City,E,18,21,1,43,Graduation,27,Owner,Standalone
7161,457.67,Boston,N,18,19,2,62,Graduation,18,Patnership,Standalone
2259,88.45,NJ City,E,13,16,4,67,Graduation,24,Owner,Standalone
6135,5.47,Boston,N,17,11,3,35,Graduation,21,Owner,Standalone
6895,352,Boston,E,3,10,11,81,Graduation,34,Owner,Standalone
193,62.07,NJ City,N,1,24,1,60,High School,29,Patnership,Standalone
196,275,NJ City,W,1,8,21,45,High School,20,Franchise,Complex
5546,430,Boston,S,1,22,18,61,Graduation,33,Patnership,Complex
5208,432,Boston,W,3,19,6,66,Graduation,19,Franchise,Complex
6601,7.71,Boston,N,13,10,4,61,High School,23,Patnership,Standalone
3811,12.44,NJ City,N,13,23,2,41,High School,19,Owner,Standalone
719,28.8,NJ City,N,3,13,1,76,No education,19,Owner,Standalone
5952,264,Boston,W,5,20,4,40,Graduation,22,Franchise,Complex
2960,111,NJ City,W,2,8,23,67,High School,27,Franchise,Complex
4854,151.76,NJ City,S,14,18,2,69,Graduation,32,Patnership,Complex
3320,184,NJ City,W,2,8,23,36,High School,25,Franchise,Complex
5595,475,Boston,E,2,14,17,66,Graduation,24,Owner,Standalone
1685,272.67,NJ City,N,13,24,1,31,No education,18,Patnership,Standalone
3730,230.25,NJ City,S,18,15,4,80,High School,24,Patnership,Complex
5552,10.62,Boston,N,25,8,4,57,No education,27,Franchise,Complex
5253,353,Boston,N,1,15,13,73,Graduation,35,Patnership,Standalone
8686,382,NJ City,S,2,17,17,54,Graduation,35,Patnership,Complex
3252,258,NJ City,W,2,15,8,38,High School,20,Franchise,Complex
3526,112.73,NJ City,S,2,21,4,72,High School,33,Patnership,Complex
4677,109,NJ City,N,3,17,25,68,High School,35,Patnership,Standalone
686,218,NJ City,S,1,20,6,38,High School,28,Patnership,Complex
1707,29.98,NJ City,N,13,21,3,37,Graduation,31,Owner,Standalone
7572,105.9,Boston,W,12,12,1,26,Graduation,31,Franchise,Complex
1900,51.8,NJ City,W,5,8,4,81,High School,25,Franchise,Complex
7601,410,Boston,N,1,20,16,64,Graduation,21,Patnership,Standalone
1952,434.62,NJ City,W,20,17,3,79,No education,21,Franchise,Complex
2495,154,NJ City,E,3,13,24,55,High School,23,Owner,Standalone
8335,389,NJ City,E,2,13,9,55,Graduation,21,Owner,Standalone
3848,174,NJ City,W,1,21,20,34,High School,32,Franchise,Complex
5419,318.49,Boston,E,4,24,3,84,High School,31,Owner,Standalone
3140,16,NJ City,N,20,24,2,55,No education,24,Franchise,Complex
7023,132.94,Boston,E,10,22,4,83,Graduation,27,Owner,Standalone
6211,0.75,Boston,N,3,14,3,59,High School,32,Owner,Standalone
6347,408.52,Boston,E,12,18,2,75,No education,31,Owner,Standalone
2389,62.49,NJ City,N,24,14,3,82,High School,34,Patnership,Standalone
2175,16.71,NJ City,N,16,19,2,67,Graduation,24,Owner,Standalone
2324,28.17,NJ City,N,7,10,1,31,No education,21,Franchise,Complex
4641,244,NJ City,N,2,20,8,76,High School,27,Patnership,Standalone
7910,291.45,Boston,S,14,9,4,40,No education,31,Patnership,Complex
3358,150,NJ City,S,2,20,24,69,High School,18,Patnership,Complex
6894,79.93,Boston,S,7,18,4,70,Graduation,23,Patnership,Complex
5920,435,Boston,W,2,24,15,54,Graduation,28,Franchise,Complex
4310,38.86,NJ City,N,11,23,2,51,No education,31,Patnership,Complex
5197,52.48,Boston,N,12,18,3,34,High School,18,Patnership,Standalone
5945,6.69,Boston,N,19,23,1,43,No education,30,Patnership,Standalone
9230,475,NJ City,S,3,10,5,28,Graduation,33,Patnership,Complex
7049,413,Boston,N,1,18,14,41,Graduation,29,Patnership,Standalone
9845,372,NJ City,N,3,16,25,54,Graduation,24,Patnership,Standalone
8506,450,NJ City,S,1,12,20,38,Graduation,23,Patnership,Complex
8452,367,NJ City,W,1,10,25,81,Graduation,29,Franchise,Complex
40,166,NY City,W,3,24,14,27,High School,34,Franchise,Complex
6170,46.46,Boston,N,10,10,3,78,No education,34,Patnership,Complex
9883,438,NJ City,E,1,15,9,68,Graduation,18,Owner,Standalone
7879,398,Boston,E,3,18,20,57,Graduation,34,Owner,Standalone
2137,138,NJ City,N,1,14,6,40,High School,29,Patnership,Standalone
357,135.2,NJ City,N,3,14,4,40,Graduation,34,Patnership,Standalone
262,243.02,NJ City,S,2,24,4,49,High School,35,Patnership,Complex
7999,46.88,Boston,N,18,10,4,78,High School,34,Owner,Standalone
2708,272,NJ City,W,2,19,13,38,High School,34,Franchise,Complex
7081,464,Boston,N,3,24,13,66,Graduation,30,Patnership,Standalone
2236,63.52,NJ City,N,1,13,2,30,High School,35,Franchise,Complex
8184,495,NJ City,W,3,12,13,60,Graduation,18,Franchise,Complex
2346,185,NJ City,S,1,12,19,38,High School,20,Patnership,Complex
9851,390,NJ City,E,3,23,20,71,Graduation,27,Owner,Standalone
4569,104,NJ City,N,1,12,20,77,High School,20,Patnership,Standalone
6555,108.25,Boston,E,21,13,1,59,Graduation,30,Owner,Standalone
6276,466,Boston,W,1,20,25,61,Graduation,20,Franchise,Complex
8103,455,NJ City,E,2,19,23,26,Graduation,18,Owner,Standalone
5935,498,Boston,E,2,9,9,46,Graduation,26,Owner,Standalone
6742,34.75,Boston,N,12,10,3,42,High School,32,Patnership,Complex
8737,493,NJ City,N,2,14,20,35,Graduation,18,Patnership,Standalone
3857,20.14,NJ City,N,16,12,2,55,No education,20,Patnership,Standalone
3321,36.67,NJ City,N,11,17,4,55,Graduation,27,Patnership,Standalone
8271,382,NJ City,E,2,20,11,67,Graduation,20,Owner,Standalone
9464,434.77,NJ City,W,2,21,4,43,No education,32,Franchise,Complex
6374,424,Boston,S,2,17,20,61,Graduation,27,Patnership,Complex
9496,461,NJ City,W,3,14,18,77,Graduation,30,Franchise,Complex
5934,408,Boston,S,1,16,21,85,Graduation,27,Patnership,Complex
3359,100.33,NJ City,E,25,18,4,32,No education,24,Owner,Standalone
1931,298,NJ City,E,1,17,12,26,High School,33,Owner,Standalone
140,226,NJ City,W,1,20,17,43,High School,29,Franchise,Complex
5279,99.62,Boston,E,18,9,2,75,No education,31,Owner,Standalone
873,101,NJ City,N,1,22,16,51,High School,20,Patnership,Standalone
6281,469,Boston,N,1,20,15,41,Graduation,33,Patnership,Standalone
5247,419,Boston,E,3,12,12,52,Graduation,26,Owner,Standalone
8201,48.51,NJ City,N,14,18,1,30,No education,33,Patnership,Standalone
2001,227,NJ City,N,3,19,19,68,High School,20,Patnership,Standalone
6776,439,Boston,W,2,14,8,47,Graduation,30,Franchise,Complex
1915,290.21,NJ City,E,4,17,4,82,High School,23,Owner,Standalone
2981,17.39,NJ City,N,24,19,2,25,No education,21,Patnership,Standalone
6671,79.59,Boston,E,14,24,1,82,No education,19,Owner,Standalone
2635,200,NJ City,E,1,9,9,55,High School,33,Owner,Standalone
9484,346.37,NJ City,W,22,23,2,34,High School,29,Franchise,Complex
3844,280,NJ City,W,3,16,17,57,High School,32,Franchise,Complex
2240,258,NJ City,W,3,9,20,76,High School,23,Franchise,Complex
1554,10.64,NJ City,N,6,21,1,54,Graduation,22,Patnership,Complex
3975,57.81,NJ City,E,7,23,2,44,Graduation,34,Owner,Standalone
1632,22.45,NJ City,N,2,15,3,54,Graduation,34,Franchise,Complex
2528,54.07,NJ City,W,16,13,2,39,No education,18,Franchise,Complex
1686,481.11,NJ City,S,12,12,4,56,Graduation,25,Patnership,Complex
8141,0.37,NJ City,N,6,16,1,63,No education,21,Patnership,Standalone
1968,491.43,NJ City,W,16,17,1,76,Graduation,27,Franchise,Complex
5728,469,Boston,W,3,8,12,39,Graduation,32,Franchise,Complex
8739,37.19,NJ City,N,15,14,3,52,Graduation,29,Owner,Standalone
6122,100.32,Boston,S,23,13,2,40,No education,30,Patnership,Complex
2493,121.74,NJ City,N,20,12,4,52,Graduation,35,Patnership,Standalone
7444,42.32,Boston,N,16,14,3,33,High School,19,Franchise,Complex
9273,379,NJ City,N,3,20,20,69,Graduation,21,Patnership,Standalone
7508,34.78,Boston,N,15,23,1,85,No education,32,Franchise,Complex
3374,183,NJ City,S,2,17,15,41,High School,19,Patnership,Complex
6520,446,Boston,W,2,8,13,81,Graduation,35,Franchise,Complex
9728,431,NJ City,W,1,19,5,33,Graduation,35,Franchise,Complex
876,190,NJ City,W,2,13,11,76,High School,35,Franchise,Complex
5802,58.87,Boston,S,10,10,2,62,Graduation,19,Patnership,Complex
8583,100.13,NJ City,E,13,15,4,77,Graduation,32,Owner,Standalone
1526,287.5,NJ City,S,11,22,2,75,No education,26,Patnership,Complex
6656,434,Boston,W,3,22,20,56,Graduation,34,Franchise,Complex
7273,474,Boston,N,3,15,5,47,Graduation,26,Patnership,Standalone
1039,133.3,NJ City,E,22,23,3,82,High School,25,Owner,Standalone
8844,64.87,NJ City,N,5,13,4,75,Graduation,28,Franchise,Complex
9312,397,NJ City,W,1,24,14,31,Graduation,29,Franchise,Complex
1335,228,NJ City,E,1,21,24,28,High School,33,Owner,Standalone
3380,275,NJ City,W,3,10,25,79,High School,31,Franchise,Complex
7574,356,Boston,S,1,19,20,76,Graduation,27,Patnership,Complex
1770,91.46,NJ City,S,4,12,4,31,Graduation,22,Patnership,Complex
4068,223,NJ City,W,3,16,24,52,High School,21,Franchise,Complex
6622,121.96,Boston,S,4,24,3,83,High School,27,Patnership,Complex
3556,159,NJ City,W,2,11,13,85,High School,21,Franchise,Complex
8408,457,NJ City,W,1,24,25,56,Graduation,34,Franchise,Complex
6835,98.95,Boston,E,10,18,4,73,High School,26,Owner,Standalone
8007,495,Boston,E,2,17,17,69,Graduation,29,Owner,Standalone
4818,244,NJ City,S,2,11,8,52,High School,20,Patnership,Complex
7323,462,Boston,E,2,24,14,35,Graduation,24,Owner,Standalone
9122,115.65,NJ City,S,13,15,4,42,No education,35,Patnership,Complex
1533,149,NJ City,N,1,21,12,59,High School,24,Patnership,Standalone
7330,104.79,Boston,S,12,16,3,80,High School,26,Patnership,Complex
8819,386,NJ City,E,3,19,6,66,Graduation,18,Owner,Standalone
4963,227,Boston,E,3,11,7,76,High School,27,Owner,Standalone
972,177,NJ City,W,2,18,18,80,High School,18,Franchise,Complex
590,218,NJ City,S,1,12,11,53,High School,35,Patnership,Complex
2075,311,NJ City,E,3,10,21,68,High School,28,Owner,Standalone
5371,117.52,Boston,E,25,12,1,38,High School,22,Owner,Standalone
8146,437,NJ City,S,2,16,23,76,Graduation,25,Patnership,Complex
3283,177,NJ City,E,2,20,13,85,High School,23,Owner,Standalone
9401,416,NJ City,N,3,13,6,30,Graduation,31,Patnership,Standalone
2188,387,NJ City,W,3,16,5,59,Graduation,32,Franchise,Complex
1550,162,NJ City,S,3,22,14,58,High School,35,Patnership,Complex
1565,109,NJ City,N,3,17,20,58,High School,24,Patnership,Standalone
2698,261,NJ City,S,2,22,20,78,High School,26,Patnership,Complex
6238,398,Boston,S,3,23,16,53,Graduation,32,Patnership,Complex
4686,460.94,NJ City,S,21,20,3,74,Graduation,25,Patnership,Complex
8012,469,Boston,W,3,12,18,40,Graduation,27,Franchise,Complex
3177,359.02,NJ City,N,7,23,3,33,Graduation,32,Patnership,Standalone
872,265,NJ City,W,1,24,20,42,High School,26,Franchise,Complex
640,405,NJ City,W,3,22,5,62,Graduation,24,Franchise,Complex
3203,126.13,NJ City,E,6,24,2,82,No education,24,Owner,Standalone
9345,375,NJ City,N,3,17,17,67,Graduation,25,Patnership,Standalone
9231,98.4,NJ City,E,13,13,2,54,Graduation,26,Owner,Standalone
1075,151,NJ City,E,3,8,7,25,High School,24,Owner,Standalone
1435,303,NJ City,E,2,17,24,80,High School,28,Owner,Standalone
9466,460,NJ City,S,3,22,25,80,Graduation,32,Patnership,Complex
7684,412,Boston,W,3,12,12,78,Graduation,33,Franchise,Complex
2579,264,NJ City,E,1,23,14,50,High School,22,Owner,Standalone
423,49.69,NJ City,E,23,11,3,81,Graduation,32,Owner,Standalone
6901,40.2,Boston,N,1,8,4,27,High School,30,Patnership,Standalone
8435,52.7,NJ City,E,7,8,2,59,No education,27,Owner,Standalone
7524,36.25,Boston,N,1,16,1,28,Graduation,18,Franchise,Complex
2548,173.81,NJ City,W,2,8,4,36,High School,32,Franchise,Complex
7414,95.27,Boston,S,15,11,2,42,High School,25,Patnership,Complex
9242,482,NJ City,S,1,24,14,58,Graduation,23,Patnership,Complex
5236,364,Boston,W,1,18,23,63,Graduation,21,Franchise,Complex
715,224,NJ City,E,3,9,14,69,High School,32,Owner,Standalone
8336,490,NJ City,W,3,12,13,37,Graduation,29,Franchise,Complex
434,317,NJ City,S,2,15,18,81,High School,30,Patnership,Complex
9599,415.3,NJ City,E,1,15,4,65,No education,21,Owner,Standalone
4492,463.83,NJ City,W,9,10,3,58,High School,29,Franchise,Complex
5718,494,Boston,S,2,18,11,39,Graduation,21,Patnership,Complex
2452,481,NJ City,W,2,9,5,68,Graduation,22,Franchise,Complex
6857,490,Boston,N,1,19,18,75,Graduation,25,Patnership,Standalone
4069,478,NJ City,N,3,8,5,63,Graduation,33,Patnership,Standalone
1498,75.68,NJ City,S,6,10,3,33,High School,27,Patnership,Complex
9252,38.5,NJ City,N,1,15,1,46,Graduation,21,Franchise,Complex
6955,39.7,Boston,N,12,12,1,27,High School,32,Owner,Standalone
4803,83.08,NJ City,E,1,18,2,31,Graduation,22,Owner,Standalone
9139,391,NJ City,E,1,16,24,47,Graduation,25,Owner,Standalone
1420,112,NJ City,W,3,18,10,25,High School,27,Franchise,Complex
6957,436,Boston,N,2,19,19,49,Graduation,24,Patnership,Standalone
8151,422,NJ City,E,3,24,6,36,Graduation,23,Owner,Standalone
1373,197,NJ City,N,1,11,22,52,High School,18,Patnership,Standalone
5368,495,Boston,W,2,20,23,60,Graduation,27,Franchise,Complex
3659,4.45,NJ City,N,9,11,1,40,No education,18,Owner,Standalone
5152,297.03,Boston,W,20,10,4,51,High School,26,Franchise,Complex
1821,174,NJ City,N,3,14,10,36,High School,21,Patnership,Standalone
3461,93.09,NJ City,N,6,11,3,79,No education,29,Patnership,Standalone
1223,90.38,NJ City,E,25,8,4,64,No education,18,Owner,Standalone
6734,109.65,Boston,S,13,21,4,28,No education,28,Patnership,Complex
7473,424,Boston,N,3,16,12,42,Graduation,27,Patnership,Standalone
9918,495,NJ City,S,2,18,19,64,Graduation,29,Patnership,Complex
5024,367.56,Boston,W,7,22,1,31,No education,31,Franchise,Complex
7884,430,Boston,W,3,16,9,62,Graduation,30,Franchise,Complex
6646,463,Boston,S,1,12,16,32,Graduation,29,Patnership,Complex
1582,114,NJ City,S,3,16,9,74,High School,34,Patnership,Complex
7107,357,Boston,E,2,17,8,85,Graduation,22,Owner,Standalone
2569,293,NJ City,N,3,21,16,62,High School,24,Patnership,Standalone
6424,497,Boston,W,2,9,5,33,Graduation,19,Franchise,Complex
9043,93.36,NJ City,E,21,16,4,56,High School,27,Owner,Standalone
4706,153,NJ City,S,3,24,10,30,High School,25,Patnership,Complex
4691,205,NJ City,E,2,15,13,26,High School,22,Owner,Standalone
7464,105.22,Boston,W,15,15,2,81,Graduation,33,Franchise,Complex
3999,188,NJ City,E,1,19,19,59,High School,33,Owner,Standalone
5541,371,Boston,N,1,11,16,67,Graduation,34,Patnership,Standalone
7222,384,Boston,S,3,10,19,53,Graduation,20,Patnership,Complex
5500,394,Boston,W,1,23,15,71,Graduation,35,Franchise,Complex
4943,272,Boston,E,2,12,15,47,High School,18,Owner,Standalone
2465,303,NJ City,N,3,10,21,61,High School,33,Patnership,Standalone
1211,196.93,NJ City,E,17,18,3,53,No education,28,Owner,Standalone
2805,228,NJ City,N,1,20,11,54,High School,22,Patnership,Standalone
5852,390,Boston,W,2,15,23,41,Graduation,23,Franchise,Complex
8438,22.16,NJ City,N,14,13,3,74,No education,27,Patnership,Complex
6481,38.38,Boston,N,6,13,2,48,High School,23,Patnership,Standalone
7897,420,Boston,N,3,17,20,57,Graduation,23,Patnership,Standalone
4790,311,NJ City,S,1,14,15,47,High School,29,Patnership,Complex
379,201,NJ City,E,3,23,17,52,High School,21,Owner,Standalone
5850,406,Boston,S,3,19,20,33,Graduation,34,Patnership,Complex
605,312,NJ City,N,2,18,16,49,High School,35,Patnership,Standalone
7455,46.34,Boston,N,14,16,1,37,Graduation,35,Owner,Standalone
7402,476,Boston,S,2,8,21,27,Graduation,29,Patnership,Complex
5044,100.77,Boston,W,18,23,3,28,High School,20,Franchise,Complex
2327,6.22,NJ City,N,8,15,2,27,No education,22,Owner,Standalone
9750,429,NJ City,S,2,13,17,34,Graduation,21,Patnership,Complex
6182,96.03,Boston,S,18,23,4,51,No education,34,Patnership,Complex
5369,358,Boston,N,3,9,21,70,Graduation,26,Patnership,Standalone
6186,398,Boston,S,2,8,18,79,Graduation,33,Patnership,Complex
4481,222,NJ City,N,2,24,7,74,High School,20,Patnership,Standalone
905,148,NJ City,N,2,16,14,75,High School,31,Patnership,Standalone
1788,169,NJ City,W,1,21,7,62,High School,23,Franchise,Complex
6253,406,Boston,N,2,10,25,46,Graduation,27,Patnership,Standalone
3867,111.31,NJ City,E,25,9,3,26,Graduation,27,Owner,Standalone
7740,385,Boston,W,1,14,20,59,Graduation,34,Franchise,Complex
1091,235,NJ City,E,2,22,22,84,High School,34,Owner,Standalone
8812,487,NJ City,W,2,9,22,41,Graduation,34,Franchise,Complex
5170,417,Boston,S,2,18,16,67,Graduation,35,Patnership,Complex
5242,448,Boston,S,1,12,17,49,Graduation,33,Patnership,Complex
3652,319,NJ City,W,1,16,19,36,High School,34,Franchise,Complex
8629,87.03,NJ City,N,16,12,4,74,High School,23,Patnership,Standalone
1576,172,NJ City,W,1,8,6,76,High School,32,Franchise,Complex
2212,124,NJ City,W,1,8,11,46,High School,34,Franchise,Complex
3655,24.71,NJ City,N,15,17,3,37,High School,21,Owner,Standalone
4235,218,NJ City,E,3,18,6,25,High School,24,Owner,Standalone
1432,88.58,NJ City,W,17,24,4,51,High School,31,Franchise,Complex
9079,282.86,NJ City,E,10,19,2,44,High School,18,Owner,Standalone
875,74.62,NJ City,E,6,17,3,45,No education,24,Owner,Standalone
2533,158,NJ City,N,3,24,14,69,High School,27,Patnership,Standalone
7994,407,Boston,S,1,8,11,43,Graduation,19,Patnership,Complex
7797,462,Boston,N,1,12,11,52,Graduation,26,Patnership,Standalone
5318,498,Boston,S,1,12,5,74,Graduation,25,Patnership,Complex
7778,419,Boston,S,2,9,25,73,Graduation,34,Patnership,Complex
3173,256,NJ City,N,3,24,19,74,High School,19,Patnership,Standalone
9794,376,NJ City,S,2,21,17,25,Graduation,28,Patnership,Complex
6696,341.44,Boston,W,20,12,1,80,Graduation,31,Franchise,Complex
2727,100.26,NJ City,E,25,10,1,75,Graduation,34,Owner,Standalone
565,273,NJ City,N,3,8,12,62,High School,21,Patnership,Standalone
6229,91.88,Boston,N,3,18,4,52,High School,32,Patnership,Standalone
6199,379,Boston,E,1,19,25,35,Graduation,21,Owner,Standalone
8637,351,NJ City,N,1,19,9,29,Graduation,22,Patnership,Standalone
7228,367,Boston,W,2,8,25,71,Graduation,26,Franchise,Complex
6814,16.69,Boston,N,9,13,2,43,High School,18,Patnership,Complex
1642,102,NJ City,S,1,23,23,84,High School,27,Patnership,Complex
8919,448,NJ City,E,2,22,22,51,Graduation,19,Owner,Standalone
8254,24.33,NJ City,N,10,17,1,53,High School,35,Patnership,Complex
1888,100,NJ City,W,3,18,24,35,High School,31,Franchise,Complex
4971,114,Boston,E,3,11,17,34,High School,26,Owner,Standalone
1846,196,NJ City,S,3,24,9,73,High School,25,Patnership,Complex
4685,208,NJ City,N,1,12,13,67,High School,20,Patnership,Standalone
8016,403,Boston,W,2,17,17,73,Graduation,28,Franchise,Complex
8344,380,NJ City,W,3,20,14,45,Graduation,28,Franchise,Complex
8206,469,NJ City,S,1,14,10,59,Graduation,18,Patnership,Complex
4161,305,NJ City,N,1,19,14,80,High School,24,Patnership,Standalone
7507,454,Boston,E,3,11,15,59,Graduation,25,Owner,Standalone
1712,186,NJ City,W,2,15,8,29,High School,32,Franchise,Complex
7918,396,Boston,S,3,19,10,45,Graduation,33,Patnership,Complex
5422,464.11,Boston,S,17,20,1,47,High School,24,Patnership,Complex
5670,43.42,Boston,N,2,10,2,32,Graduation,27,Patnership,Complex
1793,209,NJ City,N,2,15,12,57,High School,29,Patnership,Standalone
5809,106.98,Boston,N,11,11,4,66,High School,27,Patnership,Standalone
7009,387,Boston,N,2,10,25,50,Graduation,27,Patnership,Standalone
1669,160,NJ City,N,3,18,23,85,High School,33,Patnership,Standalone
7972,392,Boston,W,1,23,15,74,Graduation,33,Franchise,Complex
8544,405,NJ City,W,3,9,14,46,Graduation,23,Franchise,Complex
1468,82.89,NJ City,W,14,12,4,70,High School,18,Franchise,Complex
5082,36.95,Boston,N,5,23,1,82,Graduation,29,Patnership,Complex
9278,108.64,NJ City,S,18,22,4,41,No education,34,Patnership,Complex
2917,207,NJ City,N,1,19,9,38,High School,31,Patnership,Standalone
3900,139,NJ City,W,1,16,11,79,High School,28,Franchise,Complex
3278,177,NJ City,S,1,14,10,52,High School,29,Patnership,Complex
9802,465,NJ City,S,1,21,15,70,Graduation,19,Patnership,Complex
7401,53.9,Boston,N,24,24,1,45,Graduation,22,Patnership,Standalone
7225,458,Boston,N,3,9,9,26,Graduation,26,Patnership,Standalone
3101,467.06,NJ City,N,2,9,3,54,No education,32,Patnership,Standalone
5642,412,Boston,S,1,16,6,82,Graduation,33,Patnership,Complex
1939,465,NJ City,E,1,9,5,73,Graduation,35,Owner,Standalone
9500,471,NJ City,W,1,11,7,68,Graduation,24,Franchise,Complex
7157,40.54,Boston,N,15,21,1,71,No education,24,Patnership,Standalone
2055,311,NJ City,E,1,14,23,56,High School,33,Owner,Standalone
2010,195,NJ City,S,2,17,20,36,High School,34,Patnership,Complex
4767,112.24,NJ City,E,5,19,3,65,Graduation,23,Owner,Standalone
6179,364,Boston,E,2,17,16,48,Graduation,25,Owner,Standalone
6563,485,Boston,E,1,19,21,77,Graduation,21,Owner,Standalone
7716,476,Boston,W,1,8,19,70,Graduation,31,Franchise,Complex
9901,102.97,NJ City,N,9,12,4,61,High School,21,Patnership,Standalone
3810,268,NJ City,S,1,16,24,26,High School,33,Patnership,Complex
5699,474.81,Boston,E,3,22,5,44,No education,27,Owner,Standalone
4490,164,NJ City,S,1,13,8,63,High School,32,Patnership,Complex
6979,75.43,Boston,E,24,11,1,85,High School,19,Owner,Standalone
6263,404,Boston,E,2,15,6,29,Graduation,23,Owner,Standalone
9594,19.32,NJ City,N,18,13,3,53,Graduation,34,Patnership,Complex
8936,384,NJ City,W,2,17,24,49,Graduation,31,Franchise,Complex
8874,119.71,NJ City,S,3,17,3,82,Graduation,20,Patnership,Complex
5925,394,Boston,N,2,24,19,53,Graduation,28,Patnership,Standalone
751,154,NJ City,E,1,20,9,37,High School,21,Owner,Standalone
7100,72.24,Boston,N,20,10,2,70,No education,23,Franchise,Complex
9006,92.9,NJ City,S,1,8,3,79,Graduation,24,Patnership,Complex
3982,149,NJ City,S,3,23,19,57,High School,26,Patnership,Complex
7066,435,Boston,S,3,10,10,58,Graduation,24,Patnership,Complex
3034,94.84,NJ City,S,20,24,4,34,High School,18,Patnership,Complex
8696,352,NJ City,W,3,16,24,51,Graduation,19,Franchise,Complex
7074,1,Boston,N,5,21,1,48,Graduation,33,Patnership,Complex
504,418.57,NJ City,W,25,19,4,68,Graduation,26,Franchise,Complex
3953,38.08,NJ City,N,20,18,4,41,No education,19,Patnership,Standalone
1375,274,NJ City,E,2,16,13,40,High School,27,Owner,Standalone
3874,116.45,NJ City,S,19,18,2,70,High School,35,Patnership,Complex
4516,57.96,NJ City,W,1,21,3,55,High School,26,Franchise,Complex
7609,435,Boston,N,1,21,24,65,Graduation,30,Patnership,Standalone
2422,76.96,NJ City,S,14,24,4,59,High School,25,Patnership,Complex
6976,325.61,Boston,W,5,8,4,44,High School,35,Franchise,Complex
2831,221,NJ City,E,1,11,19,45,High School,30,Owner,Standalone
5637,424,Boston,N,3,18,22,51,Graduation,34,Patnership,Standalone
5050,65.17,Boston,N,14,14,4,32,High School,18,Patnership,Complex
8773,377,NJ City,N,3,15,20,56,Graduation,25,Patnership,Standalone
7580,87.85,Boston,W,18,24,3,73,No education,24,Franchise,Complex
817,309,NJ City,N,3,22,25,62,High School,35,Patnership,Standalone
7895,116.92,Boston,E,7,15,2,48,No education,24,Owner,Standalone
8165,450.37,NJ City,N,4,14,3,71,No education,19,Patnership,Standalone
2364,260,NJ City,W,3,14,21,63,High School,29,Franchise,Complex
4298,5.72,NJ City,N,3,20,3,70,No education,33,Patnership,Complex
6784,320.35,Boston,W,3,17,4,80,High School,33,Franchise,Complex
7663,42.91,Boston,N,10,8,1,26,High School,23,Owner,Standalone
8922,455,NJ City,S,2,18,16,76,Graduation,27,Patnership,Complex
7138,436,Boston,S,3,23,14,48,Graduation,26,Patnership,Complex
4950,108.86,Boston,S,5,10,2,51,Graduation,31,Patnership,Complex
5743,406,Boston,E,3,21,6,31,Graduation,22,Owner,Standalone
239,363.95,NJ City,E,21,10,3,83,No education,18,Owner,Standalone
2222,176,NJ City,S,1,17,7,58,High School,22,Patnership,Complex
8663,494,NJ City,E,2,11,9,84,Graduation,19,Owner,Standalone
1292,104,NJ City,W,3,9,15,81,High School,19,Franchise,Complex
5094,379,Boston,S,1,9,17,72,Graduation,19,Patnership,Complex
3089,174,NJ City,N,1,12,8,78,High School,24,Patnership,Standalone
5880,399,Boston,W,3,22,13,78,Graduation,21,Franchise,Complex
3835,52.31,NJ City,E,4,8,3,66,High School,25,Owner,Standalone
7911,490,Boston,E,3,9,14,29,Graduation,33,Owner,Standalone
8628,412,NJ City,W,1,22,5,84,Graduation,22,Franchise,Complex
1531,376.27,NJ City,E,21,23,1,84,High School,21,Owner,Standalone
1777,162.51,NJ City,N,9,21,3,81,High School,24,Patnership,Standalone
1800,271,NJ City,W,2,21,11,72,High School,25,Franchise,Complex
9920,491.72,NJ City,W,7,23,1,30,No education,20,Franchise,Complex
2488,49.2,NJ City,W,17,12,4,44,High School,29,Franchise,Complex
3757,271,NJ City,N,1,22,8,32,High School,23,Patnership,Standalone
1073,12.17,NJ City,N,20,16,3,85,No education,30,Patnership,Standalone
4936,224,Boston,W,3,12,24,67,High School,28,Franchise,Complex
7322,353.24,Boston,S,12,24,2,56,No education,20,Patnership,Complex
7888,51.73,Boston,W,2,15,3,33,High School,26,Franchise,Complex
286,290,NJ City,S,2,8,23,36,High School,23,Patnership,Complex
7982,104.02,Boston,S,18,19,2,27,No education,18,Patnership,Complex
8094,392,NJ City,S,1,15,18,33,Graduation,19,Patnership,Complex
8617,190.61,NJ City,N,1,13,5,84,High School,22,Patnership,Standalone
6584,341.13,Boston,W,12,14,4,49,No education,32,Franchise,Complex
545,218,NJ City,N,2,18,6,40,High School,31,Patnership,Standalone
9538,434,NJ City,S,1,8,20,70,Graduation,23,Patnership,Complex
8178,57.68,NJ City,S,14,8,4,55,Graduation,18,Patnership,Complex
8917,466,NJ City,N,2,24,23,71,Graduation,31,Patnership,Standalone
1050,270,NJ City,S,2,18,13,51,High School,27,Patnership,Complex
9045,396,NJ City,N,3,22,10,43,Graduation,20,Patnership,Standalone
4681,231,NJ City,N,1,15,6,54,High School,26,Patnership,Standalone
8640,476,NJ City,W,2,22,9,27,Graduation,29,Franchise,Complex
9317,394,NJ City,N,3,19,18,84,Graduation,25,Patnership,Standalone
2963,214,NJ City,E,3,9,20,74,High School,24,Owner,Standalone
9951,414,NJ City,E,1,14,25,51,Graduation,31,Owner,Standalone
2227,234.07,NJ City,E,20,14,1,68,High School,18,Owner,Standalone
5138,475,Boston,S,3,24,11,46,Graduation,28,Patnership,Complex
5285,57.88,Boston,N,15,16,2,49,No education,33,Patnership,Standalone
4049,282,NJ City,N,1,16,10,70,High School,30,Patnership,Standalone
4290,254,NJ City,S,3,20,7,84,High School,34,Patnership,Complex
2712,118,NJ City,W,1,19,22,64,High School,25,Franchise,Complex
7800,408,Boston,W,1,12,6,63,Graduation,32,Franchise,Complex
8775,475,NJ City,E,2,12,5,47,Graduation,33,Owner,Standalone
2097,182,NJ City,N,1,22,19,43,High School,27,Patnership,Standalone
8599,48.16,NJ City,E,9,13,4,77,High School,34,Owner,Standalone
6988,93.91,Boston,W,23,13,4,65,High School,26,Franchise,Complex
8510,419,NJ City,S,1,17,14,50,Graduation,24,Patnership,Complex
4554,268,NJ City,S,1,23,18,41,High School,32,Patnership,Complex
6959,17.48,Boston,N,3,18,1,40,No education,23,Owner,Standalone
439,146,NJ City,E,3,15,9,26,High School,28,Owner,Standalone
2971,65.7,NJ City,N,20,23,1,35,High School,34,Owner,Standalone
4123,155,NJ City,E,3,21,17,30,High School,29,Owner,Standalone
4680,194,NJ City,W,2,20,15,55,High School,25,Franchise,Complex
9000,445,NJ City,W,2,12,13,39,Graduation,32,Franchise,Complex
3699,82.58,NJ City,E,9,16,3,59,Graduation,33,Owner,Standalone
968,292,NJ City,W,1,20,9,62,High School,27,Franchise,Complex
8091,438,NJ City,E,3,10,25,57,Graduation,26,Owner,Standalone
779,104.21,NJ City,E,1,16,4,76,No education,33,Owner,Standalone
6880,495,Boston,W,3,11,25,71,Graduation,32,Franchise,Complex
7459,424,Boston,E,3,20,17,29,Graduation,24,Owner,Standalone
8585,33.96,NJ City,N,17,20,1,83,No education,25,Patnership,Standalone
4501,244,NJ City,N,3,10,11,26,High School,35,Patnership,Standalone
6150,290.69,Boston,S,7,20,1,47,Graduation,33,Patnership,Complex
9058,89.22,NJ City,S,1,11,4,33,High School,21,Patnership,Complex
8502,42.57,NJ City,N,24,24,4,79,Graduation,34,Patnership,Complex
57,109,Maine,N,2,22,23,73,High School,35,Patnership,Standalone
8182,353,NJ City,S,3,13,13,82,Graduation,33,Patnership,Complex
7367,390,Boston,E,3,19,23,41,Graduation,29,Owner,Standalone
5651,455,Boston,E,1,22,9,68,Graduation,18,Owner,Standalone
7959,370,Boston,E,1,14,22,32,Graduation,23,Owner,Standalone
8542,239.72,NJ City,S,1,24,1,28,High School,29,Patnership,Complex
7545,303.55,Boston,N,6,15,2,71,Graduation,19,Patnership,Standalone
117,307,NJ City,N,3,17,23,35,High School,27,Patnership,Standalone
1959,192,NJ City,E,2,14,6,59,High School,32,Owner,Standalone
206,257.67,NJ City,S,10,20,1,43,No education,27,Patnership,Complex
6195,459,Boston,E,3,21,8,45,Graduation,20,Owner,Standalone
5560,87.27,Boston,W,3,18,1,41,High School,22,Franchise,Complex
2,11.64,NY City,N,20,22,4,55,No education,23,Franchise,Standalone
4264,292,NJ City,W,1,17,6,68,High School,28,Franchise,Complex
4577,77.41,NJ City,N,2,14,3,59,No education,32,Patnership,Standalone
9757,280.91,NJ City,N,18,17,1,47,High School,35,Patnership,Standalone
7341,431,Boston,N,3,11,12,78,Graduation,32,Patnership,Standalone
7389,268.24,Boston,N,8,24,4,78,Graduation,31,Patnership,Standalone
3786,62.25,NJ City,N,23,11,2,27,Graduation,30,Patnership,Complex
5015,377,Boston,E,2,24,20,41,Graduation,28,Owner,Standalone
5551,483,Boston,E,1,24,20,27,Graduation,23,Owner,Standalone
4925,122,NJ City,N,1,24,15,72,High School,27,Patnership,Standalone
546,266,NJ City,S,2,24,16,53,High School,19,Patnership,Complex
2721,421.57,NJ City,N,2,16,3,62,Graduation,18,Patnership,Standalone
3004,209,NJ City,W,3,17,13,38,High School,25,Franchise,Complex
1219,274,NJ City,E,2,23,14,71,High School,25,Owner,Standalone
9421,487,NJ City,N,2,18,20,56,Graduation,20,Patnership,Standalone
112,308,NJ City,W,3,24,16,68,High School,22,Franchise,Complex
9655,477,NJ City,E,3,10,13,65,Graduation,19,Owner,Standalone
6676,99.7,Boston,W,16,24,1,84,High School,33,Franchise,Complex
7288,381,Boston,W,1,15,13,58,Graduation,34,Franchise,Complex
9187,490,NJ City,E,1,15,22,26,Graduation,27,Owner,Standalone
1484,112,NJ City,W,3,15,10,30,High School,28,Franchise,Complex
6087,398,Boston,E,1,21,23,39,Graduation,20,Owner,Standalone
5244,7.42,Boston,N,20,24,3,53,Graduation,24,Franchise,Complex
781,121,NJ City,N,1,17,21,29,High School,19,Patnership,Standalone
4489,155,NJ City,N,3,23,9,26,High School,29,Patnership,Standalone
2581,299,NJ City,N,2,15,23,42,High School,34,Patnership,Standalone
378,224,NJ City,S,2,19,23,51,High School,32,Patnership,Complex
6557,468,Boston,N,2,17,9,74,Graduation,20,Patnership,Standalone
2953,262.1,NJ City,N,5,18,3,81,High School,21,Patnership,Standalone
8277,68.82,NJ City,N,3,9,4,39,Graduation,33,Patnership,Standalone
5145,390,Boston,N,3,21,7,59,Graduation,30,Patnership,Standalone
6925,0.7,Boston,N,1,19,1,47,High School,32,Patnership,Standalone
3886,310,NJ City,S,1,10,16,58,High School,20,Patnership,Complex
4464,77.47,NJ City,W,8,22,4,32,Graduation,20,Franchise,Complex
2869,155.34,NJ City,N,2,14,5,55,High School,35,Patnership,Standalone
6176,413.81,Boston,W,3,19,5,25,No education,35,Franchise,Complex
1780,159,NJ City,W,3,11,18,40,High School,24,Franchise,Complex
1347,316,NJ City,E,3,11,11,44,High School,35,Owner,Standalone
3123,308,NJ City,E,1,17,20,80,High School,19,Owner,Standalone
7398,384,Boston,S,3,14,20,36,Graduation,22,Patnership,Complex
5735,497.42,Boston,E,3,20,5,40,No education,22,Owner,Standalone
8169,459,NJ City,N,3,14,17,66,Graduation,32,Patnership,Standalone
584,242,NJ City,W,3,13,24,59,High School,35,Franchise,Complex
7955,81.16,Boston,E,20,8,4,85,No education,25,Owner,Standalone
8484,355.31,NJ City,W,24,16,3,59,Graduation,25,Franchise,Complex
9034,78.34,NJ City,S,13,23,3,57,High School,24,Patnership,Complex
494,262,NJ City,S,3,14,18,35,High School,34,Patnership,Complex
3044,216,NJ City,W,2,15,22,51,High School,28,Franchise,Complex
7488,415,Boston,W,2,20,24,44,Graduation,32,Franchise,Complex
2598,211,NJ City,S,1,16,15,84,High School,24,Patnership,Complex
5102,15.47,Boston,N,4,24,3,43,No education,18,Patnership,Complex
3716,383.32,NJ City,W,4,15,2,69,No education,19,Franchise,Complex
8622,387,NJ City,S,2,15,10,73,Graduation,21,Patnership,Complex
2470,148,NJ City,S,2,22,13,29,High School,31,Patnership,Complex
5861,468,Boston,N,3,24,20,32,Graduation,24,Patnership,Standalone
6428,498,Boston,W,3,22,15,51,Graduation,29,Franchise,Complex
8454,403,NJ City,S,2,8,23,73,Graduation,28,Patnership,Complex
6172,425.25,Boston,W,18,11,2,58,High School,22,Franchise,Complex
2278,132,NJ City,S,1,20,6,64,High School,23,Patnership,Complex
4260,313,NJ City,W,3,20,25,58,High School,29,Franchise,Complex
4356,217,NJ City,W,2,24,22,44,High School,30,Franchise,Complex
3564,115,NJ City,W,3,20,9,44,High School,30,Franchise,Complex
5913,491,Boston,N,1,14,9,77,Graduation,20,Patnership,Standalone
8174,424,NJ City,S,3,20,9,69,Graduation,23,Patnership,Complex
5071,156.45,Boston,E,21,23,2,52,High School,18,Owner,Standalone
363,184,NJ City,E,1,12,22,84,High School,27,Owner,Standalone
113,21.74,NJ City,N,6,21,2,55,No education,35,Patnership,Standalone
7129,368,Boston,N,1,18,22,30,Graduation,23,Patnership,Standalone
7977,48.86,Boston,N,24,21,4,82,Graduation,29,Patnership,Standalone
9510,496,NJ City,S,1,19,13,72,Graduation,34,Patnership,Complex
310,57.75,NJ City,S,17,22,4,37,High School,33,Patnership,Complex
9374,439,NJ City,S,3,8,22,30,Graduation,32,Patnership,Complex
713,150,NJ City,N,1,10,11,66,High School,26,Patnership,Standalone
6097,339.96,Boston,N,7,9,2,39,High School,21,Patnership,Standalone
8273,431,NJ City,N,3,14,12,60,Graduation,34,Patnership,Standalone
650,258.45,NJ City,S,9,22,2,78,No education,34,Patnership,Complex
4548,126.81,NJ City,W,22,8,4,79,Graduation,19,Franchise,Complex
5786,495,Boston,S,1,20,19,71,Graduation,19,Patnership,Complex
95,45.45,NJ City,N,12,12,1,48,No education,25,Owner,Standalone
896,97.24,NJ City,W,12,15,1,54,No education,18,Franchise,Complex
6103,32.7,Boston,N,21,18,2,50,High School,19,Owner,Standalone
889,63.97,NJ City,N,1,18,2,57,High School,20,Patnership,Standalone
2688,317,NJ City,W,1,18,18,85,High School,22,Franchise,Complex
7905,13.47,Boston,N,21,18,3,53,Graduation,23,Patnership,Standalone
7557,469,Boston,N,3,20,25,52,Graduation,27,Patnership,Standalone
414,50.18,NJ City,S,1,17,4,48,Graduation,26,Patnership,Complex
1864,228,NJ City,W,2,19,25,47,High School,23,Franchise,Complex
9320,355,NJ City,W,1,15,9,74,Graduation,33,Franchise,Complex
9654,443,NJ City,S,2,17,20,77,Graduation,25,Patnership,Complex
184,304,NJ City,W,3,13,22,53,High School,32,Franchise,Complex
8897,484,NJ City,N,2,15,23,56,Graduation,22,Patnership,Standalone
6278,500,Boston,S,2,22,15,37,Graduation,21,Patnership,Complex
4408,312,NJ City,W,1,24,19,57,High School,30,Franchise,Complex
464,55.19,NJ City,W,17,12,4,34,No education,32,Franchise,Complex
551,242,NJ City,E,2,19,6,75,High School,20,Owner,Standalone
6279,473,Boston,E,2,13,14,29,Graduation,23,Owner,Standalone
2134,245.84,NJ City,S,19,22,4,64,High School,25,Patnership,Complex
8112,473,NJ City,W,1,20,14,76,Graduation,24,Franchise,Complex
4186,240,NJ City,S,3,12,17,30,High School,22,Patnership,Complex
5202,393,Boston,S,3,19,19,54,Graduation,23,Patnership,Complex
6074,386,Boston,S,3,11,20,59,Graduation,32,Patnership,Complex
1132,197,NJ City,W,3,18,22,46,High School,34,Franchise,Complex
6878,101.66,Boston,S,17,22,2,74,No education,25,Patnership,Complex
9014,454,NJ City,S,2,16,22,82,Graduation,21,Patnership,Complex
5953,21.69,Boston,N,10,9,2,41,High School,23,Patnership,Standalone
4669,467.97,NJ City,N,11,12,3,56,High School,29,Patnership,Standalone
411,191,NJ City,E,2,22,11,32,High School,32,Owner,Standalone
6701,440,Boston,N,3,21,22,42,Graduation,32,Patnership,Standalone
1034,108.97,NJ City,S,21,23,1,59,No education,19,Patnership,Complex
3039,45.28,NJ City,N,10,15,3,55,Graduation,34,Owner,Standalone
3828,253,NJ City,W,1,17,19,75,High School,35,Franchise,Complex
8692,401,NJ City,W,2,11,6,78,Graduation,18,Franchise,Complex
6991,452,Boston,E,2,16,8,30,Graduation,21,Owner,Standalone
2506,74.43,NJ City,S,21,24,3,37,High School,34,Patnership,Complex
5590,415,Boston,S,3,21,11,47,Graduation,22,Patnership,Complex
6997,420,Boston,N,2,17,10,33,Graduation,21,Patnership,Standalone
465,123.63,NJ City,N,7,12,3,53,Graduation,34,Patnership,Standalone
683,235,NJ City,E,1,8,7,34,High School,24,Owner,Standalone
3779,52.32,NJ City,E,3,24,4,43,No education,29,Owner,Standalone
2809,96.8,NJ City,N,9,9,4,27,High School,29,Patnership,Standalone
9603,93.14,NJ City,E,20,16,4,28,Graduation,23,Owner,Standalone
473,63.76,NJ City,N,3,9,3,48,No education,33,Patnership,Standalone
5986,416,Boston,S,3,8,21,71,Graduation,30,Patnership,Complex
3418,276.79,NJ City,S,22,10,4,69,High School,30,Patnership,Complex
1511,244,NJ City,E,1,9,23,84,High School,34,Owner,Standalone
520,344.13,NJ City,W,8,14,3,80,High School,33,Franchise,Complex
6024,456,Boston,W,3,16,6,56,Graduation,22,Franchise,Complex
1573,183,NJ City,N,1,15,15,57,High School,32,Patnership,Standalone
2731,483.17,NJ City,E,15,23,4,69,High School,34,Owner,Standalone
5014,374,Boston,S,1,22,6,51,Graduation,19,Patnership,Complex
6467,392,Boston,E,2,8,18,72,Graduation,21,Owner,Standalone
9933,62.88,NJ City,N,9,22,1,56,Graduation,33,Patnership,Standalone
1724,484.46,NJ City,W,2,11,5,27,No education,30,Franchise,Complex
2799,247,NJ City,E,2,12,17,36,High School,29,Owner,Standalone
9431,372,NJ City,E,2,14,15,52,Graduation,22,Owner,Standalone
9905,116.82,NJ City,N,21,13,1,44,No education,18,Patnership,Standalone
9876,484,NJ City,W,2,23,25,78,Graduation,35,Franchise,Complex
2865,132,NJ City,N,3,15,14,30,High School,35,Patnership,Standalone
8180,378,NJ City,W,1,20,14,26,Graduation,32,Franchise,Complex
9277,414,NJ City,N,1,11,16,43,Graduation,35,Patnership,Standalone
7069,33.82,Boston,N,7,21,2,36,High School,34,Patnership,Standalone
4394,159,NJ City,S,2,22,15,37,High School,26,Patnership,Complex
8756,421,NJ City,W,3,14,25,83,Graduation,35,Franchise,Complex
6740,343.36,Boston,W,16,10,2,53,No education,18,Franchise,Complex
6799,411,Boston,E,2,22,10,83,Graduation,26,Owner,Standalone
320,193.56,NJ City,W,3,11,5,63,No education,28,Franchise,Complex
9425,380,NJ City,N,2,19,17,58,Graduation,18,Patnership,Standalone
6828,10.24,Boston,N,8,11,1,29,Graduation,32,Franchise,Complex
4756,283,NJ City,W,1,23,12,33,High School,33,Franchise,Complex
6310,181.5,Boston,S,4,23,4,78,High School,18,Patnership,Complex
3658,310,NJ City,S,1,23,17,53,High School,32,Patnership,Complex
1646,220,NJ City,S,1,20,16,67,High School,20,Patnership,Complex
2343,165,NJ City,E,1,17,21,70,High School,24,Owner,Standalone
9735,439,NJ City,E,1,16,17,43,Graduation,30,Owner,Standalone
4423,54.87,NJ City,E,25,9,4,75,High School,27,Owner,Standalone
8222,461,NJ City,S,2,19,21,82,Graduation,23,Patnership,Complex
2238,363,NJ City,S,2,23,5,57,Graduation,29,Patnership,Complex
8107,46.53,NJ City,N,5,20,1,46,High School,19,Owner,Standalone
4528,231,NJ City,W,3,17,6,58,High School,20,Franchise,Complex
7445,360,Boston,N,1,9,16,57,Graduation,26,Patnership,Standalone
3888,48.2,NJ City,W,24,21,3,76,Graduation,20,Franchise,Complex
6823,96.84,Boston,E,24,14,3,71,High School,21,Owner,Standalone
3870,136,NJ City,S,1,15,15,46,High School,26,Patnership,Complex
614,169,NJ City,S,2,18,12,66,High School,21,Patnership,Complex
9990,382,NJ City,S,3,11,18,81,Graduation,18,Patnership,Complex
3401,211,NJ City,N,2,14,16,36,High School,28,Patnership,Standalone
5314,72.18,Boston,N,6,10,4,43,High School,27,Patnership,Complex
4048,235,NJ City,W,3,14,24,59,High School,30,Franchise,Complex
836,143,NJ City,W,2,13,20,36,High School,19,Franchise,Complex
4999,404.87,Boston,E,5,20,4,52,High School,29,Owner,Standalone
8864,125,NJ City,W,24,10,4,39,No education,19,Franchise,Complex
11,32.81,NY City,N,18,22,1,38,No education,31,Owner,Standalone
8766,494,NJ City,S,14,12,18,61,Graduation,33,Patnership,Complex
4912,6.59,NJ City,N,6,23,1,80,High School,20,Franchise,Complex
4980,131.41,Boston,W,19,20,15,63,High School,30,Franchise,Complex
3603,59.41,NJ City,E,12,23,2,42,Graduation,33,Owner,Standalone
5498,470,Boston,S,13,10,17,41,Graduation,32,Patnership,Complex
8033,435,Boston,N,18,16,18,76,Graduation,27,Patnership,Standalone
6873,356.99,Boston,N,25,10,3,71,Graduation,28,Patnership,Standalone
8297,25.53,NJ City,N,3,23,3,65,No education,30,Patnership,Standalone
302,237,NJ City,S,1,15,15,51,High School,30,Patnership,Complex
7931,460,Boston,E,2,10,15,70,Graduation,29,Owner,Standalone
348,480.25,NJ City,W,19,9,7,65,High School,23,Franchise,Complex
9509,109.17,NJ City,N,13,21,1,28,No education,18,Patnership,Standalone
2199,388.95,NJ City,E,15,17,7,61,High School,25,Owner,Standalone
7031,390,Boston,E,4,19,21,33,Graduation,27,Owner,Standalone
8645,465,NJ City,N,8,18,11,79,Graduation,30,Patnership,Standalone
5505,126.75,Boston,N,8,16,1,46,Graduation,28,Patnership,Standalone
5655,480,Boston,E,15,18,17,74,Graduation,34,Owner,Standalone
2435,254.45,NJ City,E,11,23,3,78,No education,21,Owner,Standalone
3375,140,NJ City,E,2,8,14,47,High School,21,Owner,Standalone
6673,443,Boston,N,7,24,15,29,Graduation,35,Patnership,Standalone
8239,407,NJ City,E,1,14,19,50,Graduation,21,Owner,Standalone
6426,376,Boston,S,21,23,12,39,Graduation,26,Patnership,Complex
4279,215.8,NJ City,E,4,16,1,82,High School,27,Owner,Standalone
2910,341.76,NJ City,S,24,11,21,63,High School,20,Patnership,Complex
554,315.23,NJ City,S,24,11,4,34,No education,21,Patnership,Complex
4939,127.52,Boston,E,23,11,1,73,High School,25,Owner,Standalone
9337,498,NJ City,N,18,8,23,35,Graduation,23,Patnership,Standalone
572,124.05,NJ City,W,8,8,3,76,No education,34,Franchise,Complex
6751,499,Boston,E,12,23,23,26,Graduation,32,Owner,Standalone
9871,440,NJ City,E,15,14,19,36,Graduation,35,Owner,Standalone
1703,239.26,NJ City,E,19,23,13,78,High School,29,Owner,Standalone
7279,361,Boston,E,24,18,25,55,Graduation,35,Owner,Standalone
697,60.82,NJ City,N,4,19,3,47,High School,24,Patnership,Standalone
9235,112.11,NJ City,E,21,17,4,68,High School,22,Owner,Standalone
3695,169.53,NJ City,E,21,18,5,36,No education,21,Owner,Standalone
558,306.52,NJ City,S,22,15,4,69,Graduation,22,Patnership,Complex
2464,335.09,NJ City,W,17,16,2,44,High School,27,Franchise,Complex
1140,434.72,NJ City,W,9,16,8,58,High School,34,Franchise,Complex
3624,373.77,NJ City,W,19,11,18,26,High School,23,Franchise,Complex
9208,463,NJ City,W,19,24,18,77,Graduation,33,Franchise,Complex
6724,360,Boston,W,9,16,25,72,Graduation,24,Franchise,Complex
5073,441.9,Boston,N,18,23,3,55,Graduation,35,Patnership,Standalone
3985,253.01,NJ City,N,6,18,24,34,High School,33,Patnership,Standalone
9098,6.54,NJ City,N,9,16,2,66,No education,30,Patnership,Complex
1499,128.87,NJ City,E,23,23,13,31,High School,21,Owner,Standalone
1832,453.27,NJ City,W,19,11,8,43,High School,31,Franchise,Complex
4764,169.83,NJ City,W,20,18,18,70,High School,25,Franchise,Complex
9566,399,NJ City,S,9,21,20,61,Graduation,29,Patnership,Complex
8348,495,NJ City,W,5,14,23,73,Graduation,30,Franchise,Complex
5760,457,Boston,W,14,22,8,37,Graduation,24,Franchise,Complex
154,415.51,NJ City,S,10,23,8,64,High School,20,Patnership,Complex
5846,102.99,Boston,S,3,22,2,56,No education,31,Patnership,Complex
1802,295.83,NJ City,S,6,22,4,77,No education,21,Patnership,Complex
3328,295,NJ City,W,22,11,15,77,High School,21,Franchise,Complex
3238,127.41,NJ City,S,8,19,3,59,High School,31,Patnership,Complex
9435,385.01,NJ City,E,20,20,1,43,Graduation,18,Owner,Standalone
628,332.95,NJ City,W,9,19,3,59,High School,18,Franchise,Complex
2732,231,NJ City,W,1,14,11,59,High School,26,Franchise,Complex
6613,491,Boston,N,21,16,7,63,Graduation,32,Patnership,Standalone
2345,481.21,NJ City,N,15,11,21,73,High School,26,Patnership,Standalone
4101,187.24,NJ City,N,5,13,23,64,High School,33,Patnership,Standalone
1635,188,NJ City,E,2,17,17,25,High School,20,Owner,Standalone
6137,387,Boston,N,17,22,22,56,Graduation,29,Patnership,Standalone
2995,31.53,NJ City,N,22,18,1,75,High School,29,Owner,Standalone
6667,376,Boston,E,8,15,11,68,Graduation,25,Owner,Standalone
1407,10.46,NJ City,N,23,8,2,70,Graduation,32,Owner,Standalone
325,366.65,NJ City,N,4,15,20,85,High School,29,Patnership,Standalone
1216,441.75,NJ City,W,25,9,22,33,High School,21,Franchise,Complex
5822,431,Boston,S,25,21,24,76,Graduation,29,Patnership,Complex
5903,118.36,Boston,E,7,13,4,59,No education,18,Owner,Standalone
9737,296.53,NJ City,N,16,10,1,44,No education,19,Patnership,Standalone
7465,406,Boston,N,25,23,11,85,Graduation,20,Patnership,Standalone
6285,357,Boston,N,9,22,20,58,Graduation,23,Patnership,Standalone
1230,278,NJ City,S,2,15,13,67,High School,34,Patnership,Complex
92,462.54,NJ City,W,16,12,25,81,High School,26,Franchise,Complex
2572,383.74,NJ City,W,22,18,1,31,High School,18,Franchise,Complex
6587,104.1,Boston,E,7,11,4,57,No education,18,Owner,Standalone
4633,219.37,NJ City,N,14,9,16,31,High School,29,Patnership,Standalone
1352,413.07,NJ City,W,12,18,24,52,High School,35,Franchise,Complex
8384,471,NJ City,W,24,16,24,37,Graduation,22,Franchise,Complex
4606,447.53,NJ City,S,24,13,4,85,High School,35,Patnership,Complex
46,266.2,Boston,S,19,21,9,36,High School,26,Patnership,Complex
8050,374,NJ City,S,12,22,21,44,Graduation,33,Patnership,Complex
1385,58.04,NJ City,N,4,15,2,47,No education,32,Patnership,Standalone
2110,410.91,NJ City,S,15,8,8,75,High School,29,Patnership,Complex
323,204,NJ City,E,2,20,22,79,High School,27,Owner,Standalone
3361,453.7,NJ City,N,18,13,17,27,High School,26,Patnership,Standalone
6717,181.77,Boston,N,15,16,3,41,Graduation,24,Patnership,Standalone
7092,359.94,Boston,W,14,19,3,31,Graduation,35,Franchise,Complex
1949,218.78,NJ City,N,19,17,14,64,High School,32,Patnership,Standalone
6948,370,Boston,W,5,8,13,78,Graduation,30,Franchise,Complex
258,235.6,NJ City,S,18,22,14,40,High School,25,Patnership,Complex
2125,114.94,NJ City,N,21,11,2,69,High School,35,Patnership,Standalone
9102,350,NJ City,S,24,15,19,36,Graduation,26,Patnership,Complex
7867,373,Boston,E,19,10,14,71,Graduation,31,Owner,Standalone
8720,449,NJ City,W,21,8,25,56,Graduation,19,Franchise,Complex
6262,438,Boston,S,21,17,18,29,Graduation,29,Patnership,Complex
2901,408.31,NJ City,N,6,20,2,44,Graduation,32,Patnership,Standalone
6967,378,Boston,E,12,14,25,25,Graduation,21,Owner,Standalone
1736,317.93,NJ City,W,19,18,13,50,High School,31,Franchise,Complex
3086,94.06,NJ City,S,3,16,3,82,No education,34,Patnership,Complex
9841,59.6,NJ City,N,22,14,3,63,High School,20,Patnership,Standalone
1220,84.32,NJ City,W,19,13,3,85,No education,22,Franchise,Complex
4420,133.74,NJ City,W,20,17,3,46,High School,23,Franchise,Complex
8106,43.09,NJ City,N,25,11,3,64,Graduation,24,Patnership,Complex
6287,26.89,Boston,N,6,8,4,68,No education,31,Owner,Standalone
6738,278.34,Boston,S,24,13,3,75,Graduation,31,Patnership,Complex
9817,430,NJ City,N,12,14,11,30,Graduation,28,Patnership,Standalone
5037,358,Boston,N,2,17,9,33,Graduation,28,Patnership,Standalone
2410,63.54,NJ City,N,18,14,4,77,High School,19,Patnership,Complex
8351,408,NJ City,E,14,17,15,80,Graduation,32,Owner,Standalone
3366,79.68,NJ City,S,14,8,3,36,Graduation,22,Patnership,Complex
7497,496,Boston,N,12,20,22,72,Graduation,23,Patnership,Standalone
7432,67.96,Boston,N,10,18,1,61,High School,29,Franchise,Complex
24,101.09,Maine,W,2,12,1,59,Graduation,26,Franchise,Complex
8287,354,NJ City,E,17,14,20,30,Graduation,25,Owner,Standalone
5320,414.52,Boston,W,2,17,1,51,High School,24,Franchise,Complex
581,105.61,NJ City,N,7,18,2,55,No education,27,Patnership,Standalone
2138,304.3,NJ City,S,15,18,12,40,High School,27,Patnership,Complex
883,168.97,NJ City,E,10,21,24,49,High School,26,Owner,Standalone
2354,351.61,NJ City,S,13,14,25,38,High School,31,Patnership,Complex
3940,132.23,NJ City,W,16,10,18,70,High School,31,Franchise,Complex
6875,408,Boston,E,4,24,9,51,Graduation,26,Owner,Standalone
2228,130.29,NJ City,W,25,24,25,30,High School,26,Franchise,Complex
5971,75.62,Boston,E,22,11,3,45,High School,28,Owner,Standalone
8197,372.78,NJ City,N,6,8,5,61,High School,18,Patnership,Standalone
1676,329.22,NJ City,W,10,17,22,29,High School,32,Franchise,Complex
7145,490,Boston,N,10,9,10,44,Graduation,27,Patnership,Standalone
9545,471,NJ City,N,7,8,19,30,Graduation,21,Patnership,Standalone
4777,208.95,NJ City,N,18,17,16,71,High School,26,Patnership,Standalone
4158,482.05,NJ City,S,21,22,25,46,High School,35,Patnership,Complex
3439,100.34,NJ City,E,11,20,4,80,High School,22,Owner,Standalone
6560,487,Boston,W,9,16,24,40,Graduation,30,Franchise,Complex
5956,497,Boston,W,15,13,23,30,Graduation,20,Franchise,Complex
9075,458,NJ City,E,2,18,25,81,Graduation,30,Owner,Standalone
8445,357,NJ City,N,11,23,10,53,Graduation,32,Patnership,Standalone
3024,410,NJ City,W,8,18,5,33,Graduation,35,Franchise,Complex
9180,396,NJ City,W,14,17,17,49,Graduation,21,Franchise,Complex
6050,86.78,Boston,S,3,14,1,46,No education,20,Patnership,Complex
4615,148.33,NJ City,E,2,8,2,37,High School,20,Owner,Standalone
5554,94.26,Boston,S,17,13,3,31,High School,28,Patnership,Complex
6640,496,Boston,W,17,13,25,77,Graduation,29,Franchise,Complex
4267,309.49,NJ City,E,4,19,10,32,High School,19,Owner,Standalone
1218,300.03,NJ City,S,8,16,10,36,High School,32,Patnership,Complex
9318,368,NJ City,S,9,21,17,43,Graduation,34,Patnership,Complex
308,351.62,NJ City,W,5,19,22,77,High School,19,Franchise,Complex
5231,361,Boston,E,4,9,24,45,Graduation,23,Owner,Standalone
6618,479,Boston,S,21,24,15,75,Graduation,24,Patnership,Complex
3258,325.46,NJ City,S,6,9,12,67,High School,22,Patnership,Complex
5392,464,Boston,W,23,14,20,28,Graduation,31,Franchise,Complex
9874,325.77,NJ City,S,20,20,1,26,High School,34,Patnership,Complex
4074,311.56,NJ City,S,1,11,3,28,Graduation,29,Patnership,Complex
9569,495,NJ City,N,5,18,15,67,Graduation,35,Patnership,Standalone
4688,69.43,NJ City,N,25,15,1,36,No education,30,Franchise,Complex
7738,381,Boston,S,1,21,24,67,Graduation,34,Patnership,Complex
9997,379,NJ City,N,11,14,9,74,Graduation,28,Patnership,Standalone
4855,97.9,NJ City,E,25,12,3,47,High School,27,Owner,Standalone
5612,90.01,Boston,W,24,20,3,36,No education,30,Franchise,Complex
2544,27.21,NJ City,N,25,9,2,75,Graduation,29,Franchise,Complex
1501,381.03,NJ City,N,15,17,19,65,High School,33,Patnership,Standalone
8777,177.88,NJ City,N,21,8,4,37,No education,24,Patnership,Standalone
9844,119.08,NJ City,W,20,15,2,55,High School,34,Franchise,Complex
8953,494,NJ City,N,5,18,25,48,Graduation,23,Patnership,Standalone
5325,377,Boston,N,24,19,18,35,Graduation,19,Patnership,Standalone
7643,461.38,Boston,E,16,20,4,43,No education,34,Owner,Standalone
635,133.26,NJ City,E,4,17,25,38,High School,35,Owner,Standalone
288,471.69,NJ City,W,25,14,23,75,High School,31,Franchise,Complex
7345,39.02,Boston,N,19,15,1,42,High School,34,Patnership,Standalone
7450,2.39,Boston,S,12,13,3,34,High School,26,Patnership,Complex
660,85.24,NJ City,W,2,23,3,47,Graduation,23,Franchise,Complex
8249,469,NJ City,N,17,13,7,27,Graduation,24,Patnership,Standalone
6790,66.91,Boston,N,24,21,4,40,High School,23,Patnership,Complex
3303,48.83,NJ City,E,1,21,2,43,Graduation,30,Owner,Standalone
6410,490,Boston,S,19,23,21,48,Graduation,34,Patnership,Complex
2481,402.04,NJ City,N,24,11,2,81,Graduation,18,Patnership,Standalone
1848,408.87,NJ City,W,13,10,1,67,Graduation,29,Franchise,Complex
6981,380,Boston,N,22,8,12,79,Graduation,28,Patnership,Standalone
1710,432.69,NJ City,S,5,10,11,37,High School,27,Patnership,Complex
8935,358,NJ City,E,10,18,16,57,Graduation,34,Owner,Standalone
4625,435.63,NJ City,N,18,10,13,73,High School,25,Patnership,Standalone
679,350.16,NJ City,E,22,23,4,36,High School,29,Owner,Standalone
5544,376,Boston,W,13,24,5,28,Graduation,30,Franchise,Complex
5747,426,Boston,E,17,17,25,51,Graduation,29,Owner,Standalone
1778,442.65,NJ City,S,6,16,22,35,High School,22,Patnership,Complex
2462,317.54,NJ City,S,20,13,17,59,High School,23,Patnership,Complex
7732,94.49,Boston,W,4,18,2,58,High School,21,Franchise,Complex
1366,216.51,NJ City,S,12,11,24,66,High School,29,Patnership,Complex
4596,35.36,NJ City,N,18,24,3,48,Graduation,34,Franchise,Complex
7478,483,Boston,S,8,18,6,32,Graduation,33,Patnership,Complex
48,403,Boston,W,17,12,5,84,Graduation,24,Franchise,Complex
4337,100.91,NJ City,N,7,11,1,78,No education,32,Patnership,Standalone
3200,129.15,NJ City,W,23,9,19,66,High School,21,Franchise,Complex
1130,145,NJ City,S,2,15,16,68,High School,30,Patnership,Complex
7470,417,Boston,S,5,14,16,75,Graduation,21,Patnership,Complex
3644,116.59,NJ City,W,23,21,3,44,No education,24,Franchise,Complex
6893,429,Boston,N,24,19,15,59,Graduation,26,Patnership,Standalone
8110,454,NJ City,S,7,10,14,44,Graduation,20,Patnership,Complex
9094,175.93,NJ City,S,23,17,5,25,High School,25,Patnership,Complex
6763,465,Boston,E,1,22,21,85,Graduation,33,Owner,Standalone
9025,356,NJ City,N,24,9,15,64,Graduation,18,Patnership,Standalone
996,490.82,NJ City,W,4,13,22,41,High School,22,Franchise,Complex
5034,390,Boston,S,19,16,15,54,Graduation,24,Patnership,Complex
589,489.68,NJ City,N,19,20,13,31,High School,31,Patnership,Standalone
31,58.94,Boston,E,5,12,2,29,High School,18,Owner,Standalone
3492,52.43,NJ City,W,23,23,1,30,Graduation,33,Franchise,Complex
7588,430,Boston,W,11,24,18,74,Graduation,19,Franchise,Complex
6562,66.57,Boston,N,18,24,2,50,High School,25,Patnership,Complex
7369,448,Boston,N,13,23,23,37,Graduation,31,Patnership,Standalone
1671,216.4,NJ City,E,9,15,14,72,High School,35,Owner,Standalone
9112,90.6,NJ City,W,3,21,4,47,High School,27,Franchise,Complex
9736,96.43,NJ City,W,2,14,2,63,High School,25,Franchise,Complex
6427,418,Boston,E,11,17,9,45,Graduation,19,Owner,Standalone
9609,500,NJ City,N,1,11,5,75,Graduation,25,Patnership,Standalone
1202,212.07,NJ City,S,18,19,2,29,No education,27,Patnership,Complex
5856,418,Boston,W,23,10,12,38,Graduation,24,Franchise,Complex
3781,448.77,NJ City,N,19,18,15,76,High School,32,Patnership,Standalone
5565,434.29,Boston,N,19,8,4,52,Graduation,21,Patnership,Standalone
648,158.22,NJ City,W,14,21,17,68,High School,21,Franchise,Complex
5492,474,Boston,W,17,18,20,53,Graduation,26,Franchise,Complex
6213,409,Boston,N,18,9,13,78,Graduation,20,Patnership,Standalone
5306,415,Boston,S,12,18,13,84,Graduation,25,Patnership,Complex
4118,196.86,NJ City,S,20,13,7,51,High School,30,Patnership,Complex
4399,25.98,NJ City,N,13,14,3,50,High School,29,Owner,Standalone
2315,11.63,NJ City,N,16,11,1,59,No education,27,Owner,Standalone
3141,360.36,NJ City,N,16,19,19,30,High School,28,Patnership,Standalone
9341,34.57,NJ City,N,10,8,3,36,No education,18,Patnership,Standalone
8530,316.66,NJ City,S,24,19,4,75,High School,23,Patnership,Complex
7645,413,Boston,N,20,23,19,71,Graduation,29,Patnership,Standalone
7900,350.67,Boston,W,3,17,1,60,High School,22,Franchise,Complex
2375,111.79,NJ City,E,8,18,2,82,No education,19,Owner,Standalone
8573,436.01,NJ City,N,8,20,4,83,No education,31,Patnership,Standalone
5451,425,Boston,E,9,8,13,27,Graduation,33,Owner,Standalone
166,244,NJ City,S,3,18,19,79,High School,35,Patnership,Complex
1236,187.36,NJ City,W,17,13,24,56,High School,25,Franchise,Complex
7376,486,Boston,W,4,15,20,26,Graduation,35,Franchise,Complex
637,13.23,NJ City,N,16,15,2,35,High School,34,Patnership,Standalone
887,264.51,NJ City,E,12,18,15,82,High School,34,Owner,Standalone
1950,295.62,NJ City,S,5,10,14,38,High School,24,Patnership,Complex
8247,356,NJ City,E,7,24,16,57,Graduation,19,Owner,Standalone
6811,11.25,Boston,N,4,20,4,53,High School,26,Owner,Standalone
6183,63.51,Boston,N,7,21,4,31,Graduation,20,Owner,Standalone
5906,498,Boston,S,9,21,12,53,Graduation,31,Patnership,Complex
3808,102.13,NJ City,W,9,15,2,47,High School,21,Franchise,Complex
1354,321.13,NJ City,S,12,23,9,82,High School,34,Patnership,Complex
8608,359,NJ City,W,12,10,24,69,Graduation,35,Franchise,Complex
5877,25.2,Boston,N,11,18,3,75,Graduation,18,Patnership,Standalone
9410,410,NJ City,S,20,15,18,52,Graduation,35,Patnership,Complex
3969,117.52,NJ City,N,12,21,4,76,Graduation,24,Patnership,Standalone
9004,397,NJ City,W,23,19,20,50,Graduation,33,Franchise,Complex
4676,360.69,NJ City,W,20,11,16,65,High School,26,Franchise,Complex
6964,359.99,Boston,W,13,15,3,35,High School,21,Franchise,Complex
616,188.19,NJ City,W,6,13,22,42,High School,22,Franchise,Complex
1706,490.79,NJ City,S,22,22,4,31,No education,29,Patnership,Complex
5345,335.07,Boston,N,1,16,4,52,No education,19,Patnership,Standalone
5388,95.6,Boston,W,13,22,2,64,Graduation,25,Franchise,Complex
7027,41.06,Boston,N,1,19,4,41,High School,28,Owner,Standalone
5787,119.27,Boston,E,18,17,4,47,Graduation,21,Owner,Standalone
2978,137.92,NJ City,S,10,16,13,28,High School,21,Patnership,Complex
6264,16.49,Boston,N,4,8,4,43,Graduation,30,Franchise,Complex
4047,220.66,NJ City,E,15,18,14,85,High School,32,Owner,Standalone
9007,271.52,NJ City,E,21,14,1,61,High School,18,Owner,Standalone
4440,207,NJ City,W,3,20,20,70,High School,28,Franchise,Complex
4658,218.83,NJ City,S,5,8,5,70,No education,32,Patnership,Complex
2263,123.71,NJ City,E,25,20,4,74,High School,18,Owner,Standalone
5287,65.14,Boston,N,3,11,1,85,High School,33,Owner,Standalone
2912,168.73,NJ City,W,16,14,18,51,High School,18,Franchise,Complex
1118,182.35,NJ City,S,10,11,17,32,High School,20,Patnership,Complex
5608,467,Boston,W,18,21,23,74,Graduation,31,Franchise,Complex
745,307.13,NJ City,N,18,18,9,80,High School,20,Patnership,Standalone
9825,18.69,NJ City,N,7,16,1,61,Graduation,25,Patnership,Standalone
9399,499,NJ City,E,18,16,16,62,Graduation,29,Owner,Standalone
1307,139.32,NJ City,E,25,14,10,35,High School,34,Owner,Standalone
5169,49.1,Boston,N,24,21,2,55,Graduation,31,Patnership,Standalone
6190,396,Boston,S,13,11,21,29,Graduation,19,Patnership,Complex
17,168,Boston,N,2,8,19,52,High School,28,Patnership,Standalone
7253,484,Boston,N,19,23,8,67,Graduation,29,Patnership,Standalone
4284,168.14,NJ City,W,9,16,25,26,High School,31,Franchise,Complex
2864,359.71,NJ City,W,8,17,3,79,No education,23,Franchise,Complex
435,154.37,NJ City,E,13,21,20,26,High School,19,Owner,Standalone
3477,96.29,NJ City,N,25,24,4,61,Graduation,34,Patnership,Standalone
3322,230,NJ City,S,2,21,22,42,High School,35,Patnership,Complex
2861,347.57,NJ City,N,22,15,17,66,High School,29,Patnership,Standalone
8984,378,NJ City,W,13,23,9,39,Graduation,30,Franchise,Complex
3428,469.03,NJ City,W,13,20,15,61,High School,35,Franchise,Complex
2611,274.84,NJ City,E,8,24,16,56,High School,29,Owner,Standalone
5870,39.55,Boston,N,14,13,4,46,No education,35,Patnership,Complex
9751,190.39,NJ City,E,2,11,1,75,High School,31,Owner,Standalone
3594,48.88,NJ City,S,23,14,3,68,Graduation,25,Patnership,Complex
1920,250.34,NJ City,W,13,11,11,48,High School,19,Franchise,Complex
2785,280.62,NJ City,N,14,19,21,42,High School,30,Patnership,Standalone
9423,493,NJ City,E,4,21,13,62,Graduation,21,Owner,Standalone
5022,437,Boston,S,6,14,12,57,Graduation,18,Patnership,Complex
3782,368.74,NJ City,S,15,16,14,29,High School,28,Patnership,Complex
6533,350,Boston,N,4,12,11,76,Graduation,21,Patnership,Standalone
5119,422,Boston,E,10,18,11,52,Graduation,29,Owner,Standalone
1516,224.16,NJ City,W,6,22,23,56,High School,20,Franchise,Complex
3740,414.25,NJ City,W,8,8,5,38,No education,30,Franchise,Complex
476,219.27,NJ City,W,18,20,18,42,High School,32,Franchise,Complex
8687,491,NJ City,E,10,18,10,84,Graduation,18,Owner,Standalone
4257,294,NJ City,N,3,19,8,80,High School,28,Patnership,Standalone
727,110.63,NJ City,E,17,10,2,38,High School,20,Owner,Standalone
5074,477,Boston,S,11,10,25,78,Graduation,33,Patnership,Complex
8970,397,NJ City,S,17,20,24,37,Graduation,22,Patnership,Complex
7637,386,Boston,N,18,9,8,76,Graduation,28,Patnership,Standalone
1806,389.28,NJ City,S,24,19,21,81,High School,25,Patnership,Complex
2608,246.92,NJ City,W,20,12,1,35,High School,20,Franchise,Complex
5594,400,Boston,S,14,22,18,85,Graduation,35,Patnership,Complex
7958,426,Boston,S,13,24,8,31,Graduation,23,Patnership,Complex
215,273.56,NJ City,E,20,16,16,51,High School,35,Owner,Standalone
4154,318.04,NJ City,S,20,9,6,63,High School,19,Patnership,Complex
6961,378,Boston,N,14,10,20,59,Graduation,24,Patnership,Standalone
8309,363,NJ City,N,11,9,19,31,Graduation,28,Patnership,Standalone
6084,452,Boston,W,4,11,17,52,Graduation,27,Franchise,Complex
4082,93.21,NJ City,S,19,20,4,47,No education,21,Patnership,Complex
7585,461,Boston,N,4,24,14,34,Graduation,30,Patnership,Standalone
8158,455,NJ City,S,23,21,19,44,Graduation,22,Patnership,Complex
1628,457.02,NJ City,W,16,10,8,57,High School,29,Franchise,Complex
8508,95.16,NJ City,W,18,20,2,80,Graduation,31,Franchise,Complex
6581,475,Boston,N,21,23,7,67,Graduation,22,Patnership,Standalone
1035,347.53,NJ City,E,13,12,9,65,High School,22,Owner,Standalone
8656,472.01,NJ City,W,2,10,3,46,High School,24,Franchise,Complex
5462,460,Boston,S,4,8,6,42,Graduation,26,Patnership,Complex
3965,343.72,NJ City,N,22,21,9,76,High School,26,Patnership,Standalone
6816,411,Boston,W,25,14,18,58,Graduation,20,Franchise,Complex
1283,227.64,NJ City,E,24,23,20,36,High School,35,Owner,Standalone
2773,141.13,NJ City,N,4,9,4,68,High School,22,Patnership,Standalone
5387,354,Boston,E,7,18,6,45,Graduation,24,Owner,Standalone
488,377.9,NJ City,W,19,12,25,26,High School,18,Franchise,Complex
8854,266.68,NJ City,S,8,21,2,34,High School,27,Patnership,Complex
4240,113,NJ City,W,2,19,23,44,High School,28,Franchise,Complex
4992,425.23,Boston,W,8,20,7,75,High School,21,Franchise,Complex
9747,33.71,NJ City,N,20,15,1,49,Graduation,25,Owner,Standalone
2671,493.63,NJ City,E,25,11,16,63,High School,33,Owner,Standalone
9755,52.53,NJ City,E,9,9,1,56,No education,18,Owner,Standalone
8657,447,NJ City,N,18,18,18,75,Graduation,20,Patnership,Standalone
5453,61.38,Boston,N,18,11,1,27,No education,24,Patnership,Standalone
8200,413,NJ City,W,19,8,17,60,Graduation,25,Franchise,Complex
5713,392,Boston,N,12,12,6,32,Graduation,35,Patnership,Standalone
8513,448,NJ City,N,15,11,18,72,Graduation,26,Patnership,Standalone
5944,215.58,Boston,W,24,8,3,46,High School,23,Franchise,Complex
6119,372,Boston,E,17,22,14,81,Graduation,27,Owner,Standalone
5425,368,Boston,N,14,17,12,41,Graduation,25,Patnership,Standalone
5888,454,Boston,W,24,14,21,39,Graduation,33,Franchise,Complex
2181,293,NJ City,N,1,14,11,55,High School,26,Patnership,Standalone
3043,328.08,NJ City,E,23,14,2,43,High School,21,Owner,Standalone
9756,67.87,NJ City,N,20,9,2,42,Graduation,20,Franchise,Complex
6617,370,Boston,N,17,12,20,44,Graduation,26,Patnership,Standalone
145,398.42,NJ City,N,22,15,4,63,High School,22,Patnership,Standalone
556,491.49,NJ City,W,13,15,25,53,High School,35,Franchise,Complex
4171,483.38,NJ City,E,14,16,11,73,High School,33,Owner,Standalone
3029,207.18,NJ City,N,7,17,13,63,High School,22,Patnership,Standalone
6793,271.19,Boston,N,14,13,4,62,High School,24,Patnership,Standalone
9572,350,NJ City,W,5,18,20,25,Graduation,18,Franchise,Complex
9477,305.77,NJ City,N,2,20,4,62,Graduation,32,Patnership,Standalone
8172,376,NJ City,W,18,15,8,55,Graduation,30,Franchise,Complex
2460,206,NJ City,W,2,8,8,84,High School,30,Franchise,Complex
6803,283.67,Boston,E,6,23,3,46,No education,23,Owner,Standalone
1905,409.53,NJ City,N,11,23,3,83,Graduation,25,Patnership,Standalone
6041,395.46,Boston,N,4,12,4,43,No education,34,Patnership,Standalone
3591,315.97,NJ City,E,19,17,6,50,High School,34,Owner,Standalone
123,146.13,NJ City,E,23,19,6,40,High School,32,Owner,Standalone
6184,338.83,Boston,W,19,14,1,65,High School,32,Franchise,Complex
854,308.19,NJ City,S,10,11,6,63,High School,31,Patnership,Complex
8244,62.13,NJ City,N,11,23,2,57,Graduation,28,Franchise,Complex
4020,208,NJ City,W,2,13,9,73,High School,27,Franchise,Complex
4465,156.13,NJ City,N,17,11,8,25,High School,27,Patnership,Standalone
8301,406,NJ City,N,4,23,25,47,Graduation,22,Patnership,Standalone
6277,494,Boston,N,11,9,12,48,Graduation,28,Patnership,Standalone
3308,24.23,NJ City,N,13,10,2,73,No education,27,Franchise,Complex
9080,20.39,NJ City,N,23,24,4,45,No education,34,Franchise,Complex
8994,156.72,NJ City,S,20,18,2,83,Graduation,21,Patnership,Complex
3708,459.95,NJ City,W,8,21,16,63,High School,22,Franchise,Complex
4956,391.21,Boston,W,7,18,16,79,High School,23,Franchise,Complex
8036,18.03,Boston,N,18,16,2,56,No education,32,Franchise,Complex
8836,479,NJ City,W,23,24,22,29,Graduation,25,Franchise,Complex
8483,370,NJ City,E,6,13,9,36,Graduation,23,Owner,Standalone
3877,136,NJ City,N,18,18,10,52,High School,19,Patnership,Standalone
4063,108.84,NJ City,E,4,16,2,71,High School,31,Owner,Standalone
5355,417,Boston,E,14,17,23,46,Graduation,25,Owner,Standalone
7188,371,Boston,W,18,14,25,65,Graduation,28,Franchise,Complex
4006,238.4,NJ City,S,17,20,20,46,High School,33,Patnership,Complex
7266,366,Boston,S,24,12,17,65,Graduation,26,Patnership,Complex
4268,203.42,NJ City,W,5,21,7,70,High School,26,Franchise,Complex
9528,158.6,NJ City,W,18,12,1,59,Graduation,31,Franchise,Complex
5118,494,Boston,S,22,18,24,69,Graduation,22,Patnership,Complex
7328,10.41,Boston,N,8,17,4,58,No education,29,Franchise,Complex
420,321.2,NJ City,W,20,9,7,65,High School,20,Franchise,Complex
4402,51.01,NJ City,S,21,19,4,76,High School,32,Patnership,Complex
2873,432.61,NJ City,N,23,22,13,42,High School,22,Patnership,Standalone
9659,388,NJ City,E,17,20,23,57,Graduation,27,Owner,Standalone
8292,427,NJ City,W,16,24,24,53,Graduation,19,Franchise,Complex
9217,9.59,NJ City,N,14,8,1,60,High School,25,Patnership,Standalone
1702,188.98,NJ City,S,13,10,17,52,High School,30,Patnership,Complex
142,149.03,NJ City,S,7,21,10,77,High School,25,Patnership,Complex
4509,18.93,NJ City,N,3,13,2,41,Graduation,18,Patnership,Standalone
7759,460,Boston,E,25,9,7,36,Graduation,20,Owner,Standalone
8274,86.64,NJ City,S,11,23,3,80,Graduation,24,Patnership,Complex
6320,70.52,Boston,N,6,21,2,38,No education,32,Franchise,Complex
4720,174.44,NJ City,W,19,15,16,38,High School,22,Franchise,Complex
844,206.46,NJ City,W,22,20,3,85,High School,33,Franchise,Complex
512,207.26,NJ City,W,7,23,18,78,High School,33,Franchise,Complex
2029,68.29,NJ City,N,8,19,2,46,High School,33,Patnership,Standalone
5788,358,Boston,W,18,10,10,79,Graduation,32,Franchise,Complex
5430,94.78,Boston,S,22,18,1,26,Graduation,32,Patnership,Complex
8044,42.41,NJ City,N,10,20,4,42,High School,19,Franchise,Complex
4822,1.41,NJ City,N,10,10,3,31,High School,22,Patnership,Complex
2659,171,NJ City,E,2,9,13,29,High School,25,Owner,Standalone
2711,296.31,NJ City,E,8,16,8,65,High School,35,Owner,Standalone
5008,459,Boston,W,24,13,11,47,Graduation,24,Franchise,Complex
2205,176.7,NJ City,N,6,14,1,81,Graduation,19,Patnership,Standalone
1033,356.66,NJ City,N,7,23,22,31,High School,28,Patnership,Standalone
7045,474.63,Boston,N,23,21,1,78,High School,32,Patnership,Standalone
3517,488.37,NJ City,N,9,19,15,29,High School,23,Patnership,Standalone
8688,112.47,NJ City,W,16,22,1,79,Graduation,32,Franchise,Complex
186,215.17,NJ City,S,19,21,25,83,High School,20,Patnership,Complex
9602,449,NJ City,S,7,12,23,49,Graduation,30,Patnership,Complex
9531,385,NJ City,E,23,13,20,43,Graduation,26,Owner,Standalone
5593,472,Boston,N,13,11,22,39,Graduation,19,Patnership,Standalone
9088,258.55,NJ City,W,13,20,3,68,High School,21,Franchise,Complex
7383,95.75,Boston,E,13,9,1,31,Graduation,31,Owner,Standalone
5927,449,Boston,E,22,10,8,32,Graduation,24,Owner,Standalone
9457,453,NJ City,N,19,19,16,43,Graduation,32,Patnership,Standalone
5926,497,Boston,S,21,14,22,44,Graduation,27,Patnership,Complex
8369,399,NJ City,N,13,8,19,43,Graduation,23,Patnership,Standalone
39,234,NJ City,E,3,10,17,29,High School,33,Owner,Standalone
3219,231,NJ City,E,1,19,7,25,High School,23,Owner,Standalone
1729,20.55,NJ City,N,23,8,4,75,High School,29,Patnership,Standalone
3323,439.18,NJ City,E,5,10,24,80,High School,23,Owner,Standalone
9414,480.62,NJ City,S,1,23,4,79,Graduation,28,Patnership,Complex
571,141.07,NJ City,E,11,8,10,58,High School,23,Owner,Standalone
9046,106.75,NJ City,S,25,16,3,61,High School,34,Patnership,Complex
8068,36.59,NJ City,N,2,11,2,52,High School,21,Franchise,Complex
2093,383.54,NJ City,N,18,23,3,59,No education,31,Patnership,Standalone
8241,126.08,NJ City,N,6,10,4,67,Graduation,32,Patnership,Standalone
7051,124.05,Boston,E,8,14,4,50,High School,25,Owner,Standalone
7364,443,Boston,W,19,10,8,68,Graduation,21,Franchise,Complex
5589,18.39,Boston,N,14,24,4,65,Graduation,33,Patnership,Standalone
7818,420,Boston,S,4,11,22,55,Graduation,34,Patnership,Complex
2531,450.67,NJ City,E,22,10,14,63,High School,28,Owner,Standalone
3480,375.15,NJ City,W,16,18,11,26,High School,25,Franchise,Complex
1222,254,NJ City,S,2,22,7,66,High School,20,Patnership,Complex
2159,160.88,NJ City,E,16,21,14,84,High School,19,Owner,Standalone
8431,445,NJ City,E,19,16,15,83,Graduation,34,Owner,Standalone
1726,96.97,NJ City,S,20,20,1,55,High School,20,Patnership,Complex
6848,207.55,Boston,W,23,15,13,46,Graduation,27,Franchise,Complex
419,351.89,NJ City,E,15,21,18,26,High School,34,Owner,Standalone
7392,208.83,Boston,W,24,9,13,37,Graduation,27,Franchise,Complex
3761,371.15,NJ City,N,19,24,24,44,High School,21,Patnership,Standalone
7576,285.66,Boston,W,9,22,19,62,Graduation,27,Franchise,Complex
2116,98.42,NJ City,W,16,8,3,37,High School,34,Franchise,Complex
7176,71.52,Boston,N,7,12,2,60,Graduation,27,Franchise,Complex
2566,240,NJ City,S,1,16,7,77,High School,20,Patnership,Complex
8959,122.13,NJ City,E,9,18,2,73,High School,25,Owner,Standalone
1627,196,NJ City,E,1,21,22,81,High School,27,Owner,Standalone
646,464.58,NJ City,S,12,19,1,58,High School,20,Patnership,Complex
4565,406.35,NJ City,N,8,11,4,73,No education,35,Patnership,Standalone
9090,332.75,NJ City,S,25,24,17,68,Graduation,32,Patnership,Complex
6792,194.79,Boston,W,20,13,13,80,Graduation,32,Franchise,Complex
1098,495.57,NJ City,S,25,8,13,80,High School,29,Patnership,Complex
3131,392.09,NJ City,E,18,19,17,33,High School,35,Owner,Standalone
6642,406.89,Boston,S,15,24,24,79,Graduation,31,Patnership,Complex
2023,11.37,NJ City,N,10,16,4,70,High School,25,Owner,Standalone
8385,325.5,NJ City,N,10,11,21,34,Graduation,30,Patnership,Standalone
6580,333.56,Boston,W,10,11,3,38,High School,33,Franchise,Complex
9407,279.25,NJ City,E,11,16,5,72,No education,25,Owner,Standalone
5967,454.97,Boston,E,12,19,21,83,Graduation,20,Owner,Standalone
1590,25.79,NJ City,N,25,11,4,78,Graduation,33,Patnership,Complex
598,143.5,NJ City,S,20,23,12,32,High School,23,Patnership,Complex
9839,174.64,NJ City,E,15,22,8,27,Graduation,19,Owner,Standalone
3827,468.27,NJ City,E,16,12,12,26,High School,19,Owner,Standalone
8778,225.9,NJ City,S,6,18,7,85,Graduation,28,Patnership,Complex
9307,244.8,NJ City,E,7,17,13,81,Graduation,29,Owner,Standalone
1112,370.26,NJ City,W,1,11,3,42,No education,26,Franchise,Complex
2758,43.35,NJ City,N,15,20,3,79,High School,35,Patnership,Complex
9882,73.35,NJ City,N,15,24,4,58,Graduation,23,Patnership,Complex
2650,51.43,NJ City,S,23,24,3,85,High School,19,Patnership,Complex
9247,15.98,NJ City,N,10,10,3,44,High School,21,Owner,Standalone
3561,310.23,NJ City,N,13,22,8,85,High School,26,Patnership,Standalone
7114,24.42,Boston,N,25,16,3,38,High School,25,Patnership,Complex
8399,444.96,NJ City,E,19,8,13,78,Graduation,31,Owner,Standalone
7626,186.25,Boston,S,13,18,13,43,Graduation,28,Patnership,Complex
5356,396.32,Boston,W,18,20,21,72,Graduation,27,Franchise,Complex
3445,197.66,NJ City,N,24,13,22,45,High School,19,Patnership,Standalone
6528,460.2,Boston,W,8,21,16,78,Graduation,19,Franchise,Complex
4172,115.67,NJ City,W,10,17,2,62,No education,28,Franchise,Complex
9255,178.46,NJ City,E,17,16,23,36,Graduation,27,Owner,Standalone
7943,54.63,Boston,E,4,10,1,31,No education,31,Owner,Standalone
2165,40.62,NJ City,N,19,19,2,64,No education,19,Patnership,Standalone
1257,58.11,NJ City,N,23,10,1,31,Graduation,20,Patnership,Standalone
1670,70.01,NJ City,N,21,10,4,61,No education,20,Patnership,Complex
9587,449,NJ City,E,4,10,12,63,Graduation,28,Owner,Standalone
6363,14.71,Boston,N,6,18,2,31,Graduation,25,Owner,Standalone
8529,461.58,NJ City,N,9,15,21,61,Graduation,35,Patnership,Standalone
8941,75.83,NJ City,N,19,14,1,41,High School,31,Patnership,Standalone
7242,452.6,Boston,S,10,11,8,51,Graduation,34,Patnership,Complex
3,150.95,Maine,E,23,11,17,76,High School,34,Patnership,Complex
1625,416.59,NJ City,N,6,16,24,84,High School,29,Patnership,Standalone
8931,220.43,NJ City,E,16,9,17,41,Graduation,26,Owner,Standalone
1916,310.17,NJ City,W,15,17,23,41,High School,24,Franchise,Complex
9964,475.54,NJ City,W,12,22,18,25,Graduation,19,Franchise,Complex
969,249.67,NJ City,N,13,16,18,42,High School,23,Patnership,Standalone
664,171.82,NJ City,W,15,20,17,70,High School,25,Franchise,Complex
2974,138.97,NJ City,S,19,19,18,83,High School,28,Patnership,Complex
8731,303.95,NJ City,E,19,21,11,73,Graduation,25,Owner,Standalone
5463,340.64,Boston,E,22,9,25,35,Graduation,30,Owner,Standalone
1003,367.88,NJ City,E,22,21,4,43,High School,26,Owner,Standalone
9049,141.56,NJ City,N,22,19,14,81,Graduation,30,Patnership,Standalone
9352,459.89,NJ City,W,3,15,12,40,Graduation,33,Franchise,Complex
120,20.24,NJ City,N,1,19,4,55,Graduation,33,Franchise,Complex
8856,348.7,NJ City,W,6,14,2,79,Graduation,20,Franchise,Complex
8554,72.91,NJ City,N,12,16,1,67,High School,28,Patnership,Complex
9483,390.29,NJ City,E,12,15,13,81,Graduation,23,Owner,Standalone
4807,450.25,NJ City,E,19,18,16,43,High School,29,Owner,Standalone
8752,432.54,NJ City,W,25,22,23,71,Graduation,34,Franchise,Complex
2057,230,NJ City,N,3,14,15,38,High School,33,Patnership,Standalone
5121,441.33,Boston,N,1,23,4,69,Graduation,21,Patnership,Standalone
167,1.37,NJ City,N,8,15,2,85,No education,23,Owner,Standalone
6059,167.3,Boston,E,19,9,9,42,Graduation,28,Owner,Standalone
8728,492.68,NJ City,W,12,11,18,58,Graduation,31,Franchise,Complex
4783,212.61,NJ City,E,22,9,2,85,High School,24,Owner,Standalone
4738,297.67,NJ City,S,10,9,13,36,High School,27,Patnership,Complex
3525,19.95,NJ City,N,8,11,1,42,Graduation,20,Patnership,Standalone
7235,258.67,Boston,E,10,19,25,59,Graduation,33,Owner,Standalone
691,56.89,NJ City,E,12,9,4,78,High School,29,Owner,Standalone
8131,418.69,NJ City,E,18,19,10,33,Graduation,23,Owner,Standalone
2325,101.43,NJ City,N,20,16,3,51,Graduation,30,Patnership,Standalone
4133,308.69,NJ City,N,22,10,4,46,No education,35,Patnership,Standalone
3546,218.6,NJ City,S,4,15,13,76,High School,28,Patnership,Complex
5609,102.73,Boston,N,11,11,1,32,No education,30,Patnership,Standalone
2523,299.59,NJ City,E,13,21,6,29,High School,28,Owner,Standalone
6634,328.09,Boston,S,16,15,14,54,Graduation,32,Patnership,Complex
2638,477.64,NJ City,S,23,24,25,73,High School,26,Patnership,Complex
2484,429.39,NJ City,W,18,15,23,47,High School,28,Franchise,Complex
9669,353.34,NJ City,N,9,20,22,66,Graduation,34,Patnership,Standalone
5117,393.34,Boston,N,11,10,13,64,Graduation,26,Patnership,Standalone
7583,332.92,Boston,E,20,21,8,79,Graduation,18,Owner,Standalone
5508,202.62,Boston,W,23,15,25,46,Graduation,23,Franchise,Complex
8163,3,NJ City,N,4,13,1,29,Graduation,23,Owner,Standalone
4306,23.59,NJ City,N,4,18,1,31,High School,32,Patnership,Complex
8339,494.07,NJ City,E,9,12,20,32,Graduation,29,Owner,Standalone
4167,34.57,NJ City,N,15,19,1,83,Graduation,24,Owner,Standalone
9765,411.81,NJ City,N,15,24,6,26,Graduation,35,Patnership,Standalone
6271,389.37,Boston,E,25,9,13,35,Graduation,24,Owner,Standalone
4248,310.06,NJ City,W,11,23,12,51,High School,27,Franchise,Complex
5390,480.78,Boston,S,13,21,22,37,Graduation,31,Patnership,Complex
3193,466.5,NJ City,N,24,21,16,31,High School,32,Patnership,Standalone
3643,353.84,NJ City,E,24,15,11,50,High School,34,Owner,Standalone
7786,82.39,Boston,S,15,22,4,73,High School,27,Patnership,Complex
4307,202,NJ City,E,2,19,20,64,High School,23,Owner,Standalone
8148,418.62,NJ City,W,12,24,9,65,Graduation,31,Franchise,Complex
2162,311.94,NJ City,S,10,20,11,68,High School,21,Patnership,Complex
9398,140.04,NJ City,S,3,20,10,29,Graduation,28,Patnership,Complex
5900,252.59,Boston,W,5,13,20,38,Graduation,34,Franchise,Complex
1412,3.32,NJ City,N,1,19,2,84,No education,20,Franchise,Complex
4674,6.38,NJ City,N,4,12,4,79,Graduation,25,Patnership,Complex
2083,197.71,NJ City,E,16,24,21,76,High School,33,Owner,Standalone
9280,385.8,NJ City,W,17,9,18,70,Graduation,19,Franchise,Complex
8318,102.86,NJ City,S,14,8,1,75,No education,32,Patnership,Complex
9271,152.67,NJ City,E,8,18,20,49,Graduation,34,Owner,Standalone
105,172.22,NJ City,N,16,16,1,83,Graduation,20,Patnership,Standalone
2933,435.35,NJ City,N,11,14,8,63,High School,29,Patnership,Standalone
8199,154.47,NJ City,E,18,8,20,65,Graduation,21,Owner,Standalone
5981,194.93,Boston,N,25,23,13,26,Graduation,32,Patnership,Standalone
994,343.29,NJ City,S,13,22,18,30,High School,28,Patnership,Complex
7475,209.13,Boston,E,20,11,21,73,Graduation,28,Owner,Standalone
2115,53.3,NJ City,E,21,21,2,68,Graduation,24,Owner,Standalone
8995,11.91,NJ City,N,23,22,4,56,High School,18,Owner,Standalone
6870,14.64,Boston,N,7,18,4,68,Graduation,26,Patnership,Complex
271,275.4,NJ City,E,20,13,2,74,High School,26,Owner,Standalone
7981,281.48,Boston,N,5,18,18,77,Graduation,21,Patnership,Standalone
7765,153.52,Boston,N,7,21,11,47,Graduation,26,Patnership,Standalone
6501,71.44,Boston,N,18,24,1,64,Graduation,27,Patnership,Standalone
2699,60.47,NJ City,N,1,16,1,76,No education,34,Owner,Standalone
2992,238.66,NJ City,W,25,19,13,27,High School,34,Franchise,Complex
7644,171.2,Boston,W,17,23,2,62,Graduation,26,Franchise,Complex
1393,224,NJ City,N,2,18,18,49,High School,18,Patnership,Standalone
8794,367.79,NJ City,S,19,11,11,57,Graduation,19,Patnership,Complex
1823,457.16,NJ City,E,12,21,10,37,High School,21,Owner,Standalone
4027,416.06,NJ City,E,5,16,18,29,High School,29,Owner,Standalone
3434,396.56,NJ City,S,13,10,13,81,High School,34,Patnership,Complex
7988,444.47,Boston,W,14,21,11,30,Graduation,21,Franchise,Complex
5526,490.84,Boston,S,3,24,3,48,Graduation,31,Patnership,Complex
9922,164.11,NJ City,S,17,22,6,25,Graduation,22,Patnership,Complex
1095,150.43,NJ City,E,5,20,19,66,High School,26,Owner,Standalone
6822,198.98,Boston,S,9,22,17,44,Graduation,22,Patnership,Complex
5112,221.8,Boston,W,21,16,16,78,Graduation,32,Franchise,Complex
381,135.02,NJ City,N,1,21,3,39,Graduation,31,Patnership,Standalone
9617,299.26,NJ City,N,16,22,21,73,Graduation,32,Patnership,Standalone
4058,467.01,NJ City,S,24,12,22,25,High School,26,Patnership,Complex
4431,367.38,NJ City,E,23,18,17,83,High School,18,Owner,Standalone
9984,419.13,NJ City,W,18,24,19,61,Graduation,21,Franchise,Complex
3169,190,NJ City,N,1,17,18,40,High School,28,Patnership,Standalone
7909,184.38,Boston,N,7,16,11,59,Graduation,21,Patnership,Standalone
6614,141.13,Boston,S,5,8,18,62,Graduation,23,Patnership,Complex
2034,197.58,NJ City,S,9,24,20,27,High School,32,Patnership,Complex
4815,54.6,NJ City,E,15,19,3,62,Graduation,27,Owner,Standalone
696,106.9,NJ City,W,24,19,2,64,Graduation,20,Franchise,Complex
9962,267.36,NJ City,S,15,21,2,62,No education,20,Patnership,Complex
9565,414.84,NJ City,N,2,12,6,62,Graduation,32,Patnership,Standalone
6930,62.24,Boston,N,24,19,4,43,Graduation,32,Patnership,Complex
499,433.43,NJ City,E,19,13,8,77,High School,26,Owner,Standalone
2966,392.92,NJ City,S,6,16,19,46,High School,24,Patnership,Complex
3764,245.42,NJ City,W,7,16,16,77,High School,21,Franchise,Complex
9441,132.65,NJ City,N,17,24,12,43,Graduation,33,Patnership,Standalone
1542,482.68,NJ City,S,13,15,2,76,Graduation,35,Patnership,Complex
8947,478.26,NJ City,E,25,15,11,69,Graduation,22,Owner,Standalone
1320,98.61,NJ City,W,10,11,1,30,Graduation,18,Franchise,Complex
5667,429.89,Boston,E,2,14,10,39,Graduation,20,Owner,Standalone
1053,145,NJ City,N,1,18,16,63,High School,27,Patnership,Standalone
3264,304.88,NJ City,W,14,8,1,49,Graduation,18,Franchise,Complex
5791,337.42,Boston,E,5,20,1,45,High School,27,Owner,Standalone
2275,186.13,NJ City,E,23,14,21,47,High School,29,Owner,Standalone
8870,328.48,NJ City,S,19,14,12,30,Graduation,26,Patnership,Complex
6663,170.51,Boston,E,16,16,13,76,Graduation,32,Owner,Standalone
8996,478.59,NJ City,W,6,19,22,81,Graduation,22,Franchise,Complex
2326,54.52,NJ City,S,9,20,2,43,High School,22,Patnership,Complex
9157,41.57,NJ City,N,13,9,3,42,High School,20,Patnership,Standalone
6604,357.89,Boston,W,16,21,16,79,Graduation,35,Franchise,Complex
997,211.13,NJ City,N,17,20,24,57,High School,21,Patnership,Standalone
4859,280.27,NJ City,E,19,11,13,52,High School,26,Owner,Standalone
6923,141.6,Boston,E,23,16,14,31,Graduation,33,Owner,Standalone
4994,205.41,Boston,S,22,8,14,54,High School,24,Patnership,Complex
5456,92.45,Boston,W,3,20,2,33,No education,22,Franchise,Complex
9856,63.45,NJ City,N,22,17,3,69,High School,30,Franchise,Complex
8082,347.71,NJ City,S,3,10,3,74,Graduation,31,Patnership,Complex
1205,342.58,NJ City,N,23,23,6,38,High School,26,Patnership,Standalone
8173,178.85,NJ City,N,1,17,14,76,Graduation,34,Patnership,Standalone
7556,269.57,Boston,W,7,16,22,33,Graduation,35,Franchise,Complex
3871,437.86,NJ City,E,25,17,3,27,High School,22,Owner,Standalone
9224,113.08,NJ City,W,22,21,1,60,No education,28,Franchise,Complex
6189,327.25,Boston,N,1,22,25,51,Graduation,27,Patnership,Standalone
299,108.42,NJ City,E,24,14,2,37,No education,22,Owner,Standalone
6125,83.67,Boston,N,15,14,4,66,No education,31,Patnership,Standalone
933,399.27,NJ City,N,13,21,1,39,Graduation,18,Patnership,Standalone
397,222.39,NJ City,N,17,23,18,58,High School,26,Patnership,Standalone
2946,164.62,NJ City,S,16,20,4,74,Graduation,28,Patnership,Complex
4846,84.43,NJ City,S,9,11,1,71,High School,32,Patnership,Complex
3468,214.29,NJ City,W,7,19,19,48,High School,28,Franchise,Complex
7377,177.43,Boston,N,19,15,6,61,Graduation,25,Patnership,Standalone
8633,417.91,NJ City,N,24,9,23,60,Graduation,28,Patnership,Standalone
6007,156.99,Boston,E,14,12,3,79,High School,28,Owner,Standalone
3573,103,NJ City,N,1,19,24,41,High School,28,Patnership,Standalone
5626,412.55,Boston,S,8,21,22,39,Graduation,24,Patnership,Complex
9544,85.35,NJ City,W,14,9,2,63,High School,35,Franchise,Complex
799,4.24,NJ City,N,2,11,4,65,High School,35,Owner,Standalone
1146,102.03,NJ City,S,6,22,2,65,Graduation,34,Patnership,Complex
4793,221.99,NJ City,N,25,8,23,32,High School,31,Patnership,Standalone
2937,417.51,NJ City,N,6,23,6,47,High School,18,Patnership,Standalone
156,288,NJ City,W,2,8,21,38,High School,32,Franchise,Complex
7238,178.87,Boston,S,22,24,14,42,Graduation,29,Patnership,Complex
3806,46.77,NJ City,N,4,23,4,35,No education,27,Patnership,Complex
9253,176.35,NJ City,N,16,22,24,50,Graduation,27,Patnership,Standalone
5207,223.63,Boston,E,1,12,22,73,Graduation,35,Owner,Standalone
8904,387.32,NJ City,W,25,10,8,44,Graduation,29,Franchise,Complex
2846,160.57,NJ City,S,25,15,13,60,High School,21,Patnership,Complex
5855,9.23,Boston,N,2,12,2,80,No education,26,Owner,Standalone
8293,44.17,NJ City,N,9,22,1,84,High School,29,Patnership,Standalone
1948,177.94,NJ City,W,24,23,19,58,High School,19,Franchise,Complex
6200,471.73,Boston,W,8,8,6,56,Graduation,34,Franchise,Complex
7102,479.81,Boston,S,2,11,25,44,Graduation,26,Patnership,Complex
9560,413.33,NJ City,W,22,24,23,77,Graduation,33,Franchise,Complex
289,23.31,NJ City,N,22,8,2,28,High School,35,Patnership,Standalone
3340,51.34,NJ City,W,18,23,4,60,High School,22,Franchise,Complex
3696,183.34,NJ City,W,24,15,1,30,Graduation,22,Franchise,Complex
842,204.94,NJ City,S,16,21,21,54,High School,22,Patnership,Complex
543,364.26,NJ City,E,6,12,1,71,Graduation,29,Owner,Standalone
6995,6.32,Boston,N,19,23,1,74,No education,30,Owner,Standalone
1674,482.07,NJ City,S,19,13,23,27,High School,35,Patnership,Complex
8237,284.63,NJ City,N,15,19,18,49,Graduation,24,Patnership,Standalone
3016,217.14,NJ City,W,24,10,10,41,High School,29,Franchise,Complex
907,499.06,NJ City,E,25,23,11,35,High School,18,Owner,Standalone
6215,110.88,Boston,E,11,9,2,38,No education,19,Owner,Standalone
7767,454.88,Boston,E,20,24,13,27,Graduation,35,Owner,Standalone
9294,428.76,NJ City,S,20,23,5,47,Graduation,20,Patnership,Complex
9201,17.53,NJ City,N,9,12,2,29,Graduation,26,Patnership,Standalone
4294,344.87,NJ City,S,24,17,23,47,High School,26,Patnership,Complex
4170,32.08,NJ City,N,5,24,3,71,Graduation,27,Patnership,Complex
3838,369.37,NJ City,S,23,20,25,85,High School,29,Patnership,Complex
9204,476.74,NJ City,W,19,17,6,85,Graduation,18,Franchise,Complex
767,25.36,NJ City,N,6,18,4,59,No education,20,Owner,Standalone
988,348.11,NJ City,W,5,10,16,44,High School,25,Franchise,Complex
2919,445.08,NJ City,E,9,22,9,69,High School,26,Owner,Standalone
8666,10.01,NJ City,N,8,20,1,57,No education,34,Patnership,Complex
956,475.18,NJ City,W,19,8,18,33,High School,22,Franchise,Complex
4349,466.63,NJ City,N,13,8,19,34,High School,30,Patnership,Standalone
4736,216.67,NJ City,W,12,16,25,30,High School,31,Franchise,Complex
6128,414.55,Boston,W,11,17,21,52,Graduation,33,Franchise,Complex
3280,320.58,NJ City,W,16,16,13,47,High School,19,Franchise,Complex
8801,490.97,NJ City,N,20,18,20,39,Graduation,22,Patnership,Standalone
9031,468.22,NJ City,E,2,15,17,63,Graduation,27,Owner,Standalone
6754,237.13,Boston,S,1,16,10,84,Graduation,31,Patnership,Complex
906,321.16,NJ City,S,6,8,1,27,Graduation,28,Patnership,Complex
8863,49.55,NJ City,E,20,21,1,69,High School,27,Owner,Standalone
8194,73.98,NJ City,S,13,18,3,58,High School,31,Patnership,Complex
3963,283.42,NJ City,E,12,11,22,30,High School,22,Owner,Standalone
4745,134.34,NJ City,N,16,17,19,28,High School,32,Patnership,Standalone
9824,209.64,NJ City,W,7,20,7,27,Graduation,34,Franchise,Complex
5571,337.47,Boston,E,13,21,25,39,Graduation,31,Owner,Standalone
6588,201.39,Boston,W,6,23,22,39,Graduation,24,Franchise,Complex
9551,25.53,NJ City,N,13,17,1,79,No education,34,Owner,Standalone
7381,331.25,Boston,N,19,17,1,32,High School,31,Patnership,Standalone
5338,242.94,Boston,S,9,21,4,38,High School,22,Patnership,Complex
3241,211.02,NJ City,N,25,13,21,49,High School,35,Patnership,Standalone
3931,222.72,NJ City,E,19,24,8,35,High School,26,Owner,Standalone
7649,289.34,Boston,N,3,10,7,66,Graduation,28,Patnership,Standalone
3300,295.34,NJ City,W,20,13,23,60,High School,23,Franchise,Complex
4657,339.19,NJ City,N,25,16,3,44,High School,34,Patnership,Standalone
7353,31.08,Boston,N,10,22,4,79,Graduation,31,Patnership,Standalone
393,119.07,NJ City,N,15,18,3,42,Graduation,25,Patnership,Standalone
3092,331.85,NJ City,W,7,17,19,47,High School,23,Franchise,Complex
5606,223.63,Boston,S,11,19,19,70,Graduation,35,Patnership,Complex
5413,439.44,Boston,N,7,13,8,78,Graduation,23,Patnership,Standalone
8040,439.01,Boston,W,18,12,15,72,Graduation,28,Franchise,Complex
3490,169.48,NJ City,S,17,15,20,41,High School,32,Patnership,Complex
7456,239.19,Boston,W,11,15,16,49,Graduation,31,Franchise,Complex
2146,474.25,NJ City,S,22,9,17,79,High School,23,Patnership,Complex
380,66.62,NJ City,N,18,23,3,72,No education,32,Franchise,Complex
7714,391.7,Boston,S,7,22,6,83,Graduation,20,Patnership,Complex
6006,319.38,Boston,S,21,23,17,60,Graduation,31,Patnership,Complex
1894,454.29,NJ City,S,6,14,19,65,High School,24,Patnership,Complex
3059,274.33,NJ City,E,21,13,5,78,No education,20,Owner,Standalone
636,253.35,NJ City,W,14,14,15,36,High School,24,Franchise,Complex
810,17.95,NJ City,N,20,19,4,60,Graduation,28,Patnership,Complex
710,147.12,NJ City,S,24,22,14,45,High School,30,Patnership,Complex
4228,342.74,NJ City,W,10,11,6,64,High School,33,Franchise,Complex
5951,355.14,Boston,E,23,23,18,59,Graduation,31,Owner,Standalone
4527,42.02,NJ City,N,24,19,4,50,Graduation,28,Owner,Standalone
8549,103.18,NJ City,N,2,10,2,54,No education,35,Patnership,Standalone
9723,410.19,NJ City,E,12,18,7,79,Graduation,27,Owner,Standalone
7165,400.32,Boston,N,18,12,25,78,Graduation,24,Patnership,Standalone
877,304.76,NJ City,N,10,13,25,50,High School,30,Patnership,Standalone
1906,369.64,NJ City,S,12,8,21,32,High School,27,Patnership,Complex
7331,317.66,Boston,E,19,8,13,27,Graduation,26,Owner,Standalone
4801,117.09,NJ City,N,3,23,3,59,High School,30,Patnership,Standalone
3821,421.89,NJ City,N,16,10,2,66,No education,33,Patnership,Standalone
6707,321.71,Boston,E,12,15,21,81,Graduation,19,Owner,Standalone
5265,182.72,Boston,N,8,11,6,44,Graduation,21,Patnership,Standalone
9033,278.59,NJ City,N,25,14,11,25,Graduation,29,Patnership,Standalone
2210,419.17,NJ City,S,21,23,3,69,No education,20,Patnership,Complex
2782,156.08,NJ City,S,11,9,21,47,High School,19,Patnership,Complex
6547,139.93,Boston,E,16,23,2,85,High School,31,Owner,Standalone
5563,462.38,Boston,E,24,14,3,38,High School,21,Owner,Standalone
6161,151.44,Boston,N,16,22,9,52,Graduation,24,Patnership,Standalone
2685,287.67,NJ City,N,8,10,19,45,High School,31,Patnership,Standalone
9665,306.73,NJ City,N,5,13,14,84,Graduation,30,Patnership,Standalone
599,48.54,NJ City,E,6,24,1,82,No education,27,Owner,Standalone
2512,171.67,NJ City,W,20,24,13,53,High School,18,Franchise,Complex
2204,321.12,NJ City,W,13,19,24,32,High School,24,Franchise,Complex
538,488.76,NJ City,S,4,23,20,36,High School,21,Patnership,Complex
8535,15.43,NJ City,N,6,17,2,71,Graduation,29,Owner,Standalone
8395,412.16,NJ City,E,9,16,15,35,Graduation,33,Owner,Standalone
2126,81.96,NJ City,S,3,24,1,77,No education,29,Patnership,Complex
2431,341.47,NJ City,E,10,8,15,65,High School,30,Owner,Standalone
9911,314.37,NJ City,E,7,9,11,49,Graduation,22,Owner,Standalone
9237,237.67,NJ City,N,7,23,6,54,Graduation,22,Patnership,Standalone
4223,347.33,NJ City,E,9,20,3,42,No education,30,Owner,Standalone
1421,102.34,NJ City,N,2,14,4,77,No education,28,Patnership,Standalone
3347,19.41,NJ City,N,12,19,3,72,No education,35,Owner,Standalone
4838,127,NJ City,S,1,11,23,31,High School,30,Patnership,Complex
5939,447.95,Boston,E,2,13,22,55,Graduation,34,Owner,Standalone
9702,152.98,NJ City,S,17,19,7,74,Graduation,31,Patnership,Complex
6149,248.08,Boston,N,23,18,23,35,Graduation,32,Patnership,Standalone
815,460.59,NJ City,E,11,17,5,30,No education,22,Owner,Standalone
1714,71.92,NJ City,N,8,15,1,35,High School,29,Patnership,Complex
6879,419.98,Boston,E,14,8,12,53,Graduation,33,Owner,Standalone
6025,470.01,Boston,N,11,16,19,56,Graduation,25,Patnership,Standalone
6457,47.21,Boston,N,25,9,2,75,High School,30,Patnership,Standalone
4200,300.63,NJ City,W,4,12,15,61,High School,32,Franchise,Complex
5381,154.67,Boston,N,10,21,7,59,Graduation,29,Patnership,Standalone
9233,287.59,NJ City,N,6,11,21,26,Graduation,29,Patnership,Standalone
1598,10.83,NJ City,N,3,24,1,51,No education,33,Patnership,Complex
2317,220.95,NJ City,N,17,10,15,41,High School,19,Patnership,Standalone
800,226.39,NJ City,W,16,23,16,71,High School,34,Franchise,Complex
7709,291.84,Boston,N,21,20,23,47,Graduation,29,Patnership,Standalone
5898,298.66,Boston,S,15,12,15,53,Graduation,30,Patnership,Complex
5340,370.57,Boston,W,25,9,3,52,Graduation,34,Franchise,Complex
5103,413.8,Boston,E,15,18,10,42,Graduation,19,Owner,Standalone
4856,390.65,NJ City,W,7,9,24,47,High School,18,Franchise,Complex
5215,426.86,Boston,E,21,24,22,69,Graduation,35,Owner,Standalone
3520,290.15,NJ City,W,20,8,16,84,High School,24,Franchise,Complex
4184,379.2,NJ City,W,6,19,25,62,High School,26,Franchise,Complex
7441,321.45,Boston,N,23,9,24,39,Graduation,28,Patnership,Standalone
1849,417.73,NJ City,N,5,24,22,59,High School,18,Patnership,Standalone
7224,46.98,Boston,N,2,24,3,37,Graduation,25,Franchise,Complex
2914,99.84,NJ City,S,20,19,2,44,High School,18,Patnership,Complex
8785,370.61,NJ City,N,19,16,21,55,Graduation,20,Patnership,Standalone
4836,473.48,NJ City,W,24,20,14,82,High School,30,Franchise,Complex
3116,281.7,NJ City,W,8,16,23,68,High School,35,Franchise,Complex
9482,316.22,NJ City,S,7,12,2,26,No education,18,Patnership,Complex
9326,217.37,NJ City,S,11,12,7,38,Graduation,31,Patnership,Complex
9209,328.44,NJ City,N,15,23,4,61,No education,30,Patnership,Standalone
1782,359.41,NJ City,S,1,9,2,70,Graduation,26,Patnership,Complex
8226,446,NJ City,S,14,23,17,28,Graduation,20,Patnership,Complex
7094,449.8,Boston,S,3,15,3,78,No education,30,Patnership,Complex
752,183.43,NJ City,W,3,9,5,68,No education,31,Franchise,Complex
1615,266.65,NJ City,E,21,12,14,26,High School,20,Owner,Standalone
1154,265.85,NJ City,S,2,18,5,70,No education,25,Patnership,Complex
3012,396.81,NJ City,W,18,18,23,67,High School,33,Franchise,Complex
9393,298.15,NJ City,N,10,16,2,25,Graduation,29,Patnership,Standalone
2025,248.03,NJ City,N,6,23,5,31,Graduation,27,Patnership,Standalone
721,55.76,NJ City,N,18,13,4,66,High School,32,Patnership,Standalone
7472,345.3,Boston,W,6,9,11,42,Graduation,24,Franchise,Complex
9488,104.29,NJ City,W,24,21,3,79,No education,19,Franchise,Complex
1634,148.97,NJ City,S,19,8,24,58,High School,31,Patnership,Complex
3860,256.76,NJ City,W,14,15,19,55,High School,22,Franchise,Complex
3054,73.55,NJ City,N,2,23,1,54,Graduation,20,Patnership,Complex
1517,231.02,NJ City,N,2,17,2,29,No education,19,Patnership,Standalone
1450,457.02,NJ City,S,16,10,2,78,High School,31,Patnership,Complex
4608,100,NJ City,W,3,9,9,81,High School,33,Franchise,Complex
6033,368.37,Boston,N,15,18,19,67,Graduation,20,Patnership,Standalone
3536,474.7,NJ City,W,6,15,23,58,High School,18,Franchise,Complex
1239,101.68,NJ City,E,17,24,4,49,Graduation,28,Owner,Standalone
608,130.25,NJ City,W,19,8,5,45,No education,28,Franchise,Complex
6941,454.36,Boston,N,2,12,17,67,Graduation,35,Patnership,Standalone
3569,313.35,NJ City,N,8,19,1,81,No education,27,Patnership,Standalone
1756,392.65,NJ City,W,25,15,12,84,High School,26,Franchise,Complex
7733,198.78,Boston,N,24,24,18,32,Graduation,27,Patnership,Standalone
5722,306.42,Boston,S,11,12,16,38,Graduation,26,Patnership,Complex
5055,419.25,Boston,E,24,8,8,67,Graduation,25,Owner,Standalone
2606,301,NJ City,S,19,11,4,80,No education,20,Patnership,Complex
3772,343.62,NJ City,W,20,8,6,58,High School,34,Franchise,Complex
2996,188.76,NJ City,W,23,24,22,77,High School,26,Franchise,Complex
1321,263.17,NJ City,N,24,14,23,55,High School,33,Patnership,Standalone
3128,202.79,NJ City,W,23,20,24,36,High School,18,Franchise,Complex
4270,326.48,NJ City,S,10,15,21,49,High School,31,Patnership,Complex
2909,366.44,NJ City,N,21,19,5,50,No education,27,Patnership,Standalone
864,347.39,NJ City,W,13,15,21,46,High School,25,Franchise,Complex
4371,441.01,NJ City,E,6,19,7,33,High School,23,Owner,Standalone
8851,119.79,NJ City,E,24,11,1,85,High School,23,Owner,Standalone
3207,29.28,NJ City,N,22,12,2,36,Graduation,34,Owner,Standalone
6919,130.17,Boston,E,20,20,8,75,Graduation,30,Owner,Standalone
1022,97.88,NJ City,S,17,13,3,57,No education,24,Patnership,Complex
5976,176.9,Boston,W,16,17,18,60,Graduation,20,Franchise,Complex
6789,240.99,Boston,N,23,12,7,72,Graduation,35,Patnership,Standalone
5858,362.73,Boston,S,18,16,13,40,Graduation,19,Patnership,Complex
1178,308.93,NJ City,S,24,12,18,80,High School,33,Patnership,Complex
9298,28.91,NJ City,N,16,20,4,79,High School,32,Patnership,Complex
2599,218.02,NJ City,E,23,9,24,32,High School,23,Owner,Standalone
7533,183.11,Boston,N,20,21,6,50,Graduation,21,Patnership,Standalone
540,207.22,NJ City,W,19,11,15,32,High School,21,Franchise,Complex
4968,370,Boston,W,13,15,10,27,High School,19,Franchise,Complex
3686,279.07,NJ City,S,17,20,8,37,High School,23,Patnership,Complex
8701,314.85,NJ City,N,12,12,21,84,Graduation,27,Patnership,Standalone
9652,287.6,NJ City,W,20,12,4,38,High School,19,Franchise,Complex
3151,2.93,NJ City,E,15,13,1,54,High School,29,Owner,Standalone
8005,123.5,Boston,N,13,12,2,81,High School,23,Patnership,Standalone
6661,30.11,Boston,N,11,8,2,42,High School,30,Patnership,Standalone
471,409.74,NJ City,E,18,21,7,40,High School,33,Owner,Standalone
3139,82.35,NJ City,E,6,19,3,58,High School,35,Owner,Standalone
8104,308.03,NJ City,W,3,16,15,32,Graduation,25,Franchise,Complex
9475,29.8,NJ City,N,1,13,3,52,High School,27,Owner,Standalone
5391,333.15,Boston,E,3,10,8,28,Graduation,27,Owner,Standalone
7980,6.13,Boston,N,6,14,1,51,Graduation,28,Franchise,Complex
6761,119.05,Boston,N,19,19,3,84,No education,31,Patnership,Standalone
139,293.78,NJ City,E,12,18,16,45,High School,35,Owner,Standalone
9865,63.69,NJ City,N,2,22,4,80,High School,29,Patnership,Standalone
4189,47.2,NJ City,N,12,17,1,59,High School,30,Patnership,Standalone
1921,32.27,NJ City,N,7,11,1,84,High School,24,Patnership,Standalone
9667,244.41,NJ City,E,12,21,22,85,Graduation,19,Owner,Standalone
7065,68.16,Boston,N,2,21,2,70,Graduation,18,Patnership,Standalone
8526,484.02,NJ City,S,17,8,14,62,Graduation,22,Patnership,Complex
7215,459.25,Boston,E,7,12,21,40,Graduation,31,Owner,Standalone
7682,120.04,Boston,S,19,14,1,52,No education,29,Patnership,Complex
1212,305.7,NJ City,W,20,15,25,48,High School,22,Franchise,Complex
8111,220.82,NJ City,E,24,20,14,29,Graduation,32,Owner,Standalone
3156,230,NJ City,W,1,15,20,48,High School,19,Franchise,Complex
7764,495.89,Boston,W,11,20,11,42,Graduation,21,Franchise,Complex
2015,428.63,NJ City,E,8,9,11,49,High School,31,Owner,Standalone
8610,329.26,NJ City,S,16,20,14,32,Graduation,35,Patnership,Complex
1515,110.57,NJ City,E,21,23,2,77,Graduation,26,Owner,Standalone
8875,326,NJ City,E,15,23,18,41,Graduation,21,Owner,Standalone
9085,452.96,NJ City,N,9,9,4,36,High School,19,Patnership,Standalone
8062,433.12,NJ City,S,13,11,8,32,Graduation,28,Patnership,Complex
5135,407.62,Boston,E,22,18,14,76,Graduation,22,Owner,Standalone
4152,146.28,NJ City,W,15,13,25,41,High School,23,Franchise,Complex
2705,322.27,NJ City,N,20,10,16,57,High School,21,Patnership,Standalone
3316,182,NJ City,W,1,9,6,36,High School,35,Franchise,Complex
7339,380.54,Boston,E,5,10,2,76,High School,23,Owner,Standalone
813,65.69,NJ City,N,17,11,4,49,Graduation,25,Patnership,Standalone
1723,246.59,NJ City,E,10,23,15,27,High School,29,Owner,Standalone
6772,260.01,Boston,W,4,8,20,28,Graduation,18,Franchise,Complex
7789,72.31,Boston,N,11,19,1,61,High School,33,Patnership,Standalone
2988,283.49,NJ City,W,4,23,7,26,High School,20,Franchise,Complex
539,374.05,NJ City,E,6,10,11,30,High School,31,Owner,Standalone
8674,291.55,NJ City,S,24,9,16,49,Graduation,31,Patnership,Complex
1313,195.35,NJ City,N,17,10,5,71,No education,34,Patnership,Standalone
2114,39.61,NJ City,N,7,8,1,53,No education,24,Patnership,Complex
2007,4.5,NJ City,N,17,20,3,26,Graduation,31,Owner,Standalone
9454,309.72,NJ City,S,21,8,17,33,Graduation,18,Patnership,Complex
9866,26.99,NJ City,N,12,15,3,71,No education,20,Patnership,Complex
2073,446.72,NJ City,N,21,13,3,26,Graduation,24,Patnership,Standalone
7876,446.66,Boston,W,23,19,14,65,Graduation,35,Franchise,Complex
9623,334.87,NJ City,E,17,10,13,37,Graduation,23,Owner,Standalone
587,54.76,NJ City,E,16,10,3,37,No education,24,Owner,Standalone
1944,330.16,NJ City,W,11,9,15,58,High School,35,Franchise,Complex
1783,419.73,NJ City,E,11,15,2,67,High School,31,Owner,Standalone
3488,238.87,NJ City,W,18,13,6,78,High School,23,Franchise,Complex
3201,470.95,NJ City,N,4,21,10,42,High School,31,Patnership,Standalone
8738,7.19,NJ City,N,12,9,3,66,No education,26,Patnership,Complex
4303,477.58,NJ City,E,15,18,1,61,High School,19,Owner,Standalone
9456,396.39,NJ City,W,21,16,7,46,Graduation,33,Franchise,Complex
9418,463.02,NJ City,S,12,16,6,85,Graduation,22,Patnership,Complex
5213,100.03,Boston,N,22,8,2,83,No education,21,Patnership,Standalone
4215,64.89,NJ City,N,11,16,3,72,Graduation,19,Owner,Standalone
9898,131.6,NJ City,S,15,19,13,35,Graduation,31,Patnership,Complex
5283,105.16,Boston,E,13,13,1,38,Graduation,19,Owner,Standalone
1708,169,NJ City,W,1,23,18,37,High School,33,Franchise,Complex
1262,211.28,NJ City,S,9,11,8,59,High School,25,Patnership,Complex
6515,247.71,Boston,E,22,8,18,43,Graduation,26,Owner,Standalone
1363,40.79,NJ City,N,21,12,3,80,High School,18,Owner,Standalone
4155,311,NJ City,E,2,10,12,59,High School,35,Owner,Standalone
5983,178.83,Boston,E,25,9,14,51,Graduation,28,Owner,Standalone
6608,258.85,Boston,W,1,23,9,57,Graduation,32,Franchise,Complex
6247,103.42,Boston,E,23,11,4,84,High School,24,Owner,Standalone
1044,336.33,NJ City,W,12,9,23,56,High School,27,Franchise,Complex
823,3.53,NJ City,N,21,21,1,74,High School,30,Owner,Standalone
6030,73.09,Boston,N,10,22,1,51,Graduation,35,Patnership,Complex
8722,302.51,NJ City,S,7,12,8,52,Graduation,25,Patnership,Complex
8993,167,NJ City,N,14,14,3,69,No education,23,Patnership,Standalone
9174,196.15,NJ City,S,2,8,3,42,Graduation,25,Patnership,Complex
3122,497.64,NJ City,S,22,21,7,73,High School,20,Patnership,Complex
9597,337.12,NJ City,N,13,21,10,49,Graduation,28,Patnership,Standalone
1813,278,NJ City,N,2,17,20,46,High School,33,Patnership,Standalone
8202,224.29,NJ City,S,6,12,20,28,Graduation,18,Patnership,Complex
3498,167.99,NJ City,S,23,15,2,77,Graduation,24,Patnership,Complex
5864,310.7,Boston,W,12,24,16,30,Graduation,27,Franchise,Complex
5596,200.83,Boston,W,9,21,24,52,Graduation,20,Franchise,Complex
8121,101.23,NJ City,N,12,20,2,73,Graduation,29,Patnership,Standalone
2716,246.7,NJ City,W,24,19,3,83,High School,23,Franchise,Complex
8638,108.47,NJ City,S,1,15,4,84,High School,33,Patnership,Complex
5450,260.36,Boston,S,8,13,9,82,Graduation,28,Patnership,Complex
7506,152.72,Boston,S,13,10,9,70,Graduation,27,Patnership,Complex
309,283.52,NJ City,N,14,20,3,83,Graduation,32,Patnership,Standalone
4212,112.23,NJ City,W,6,16,4,83,Graduation,24,Franchise,Complex
5133,287.37,Boston,N,3,15,4,38,Graduation,20,Patnership,Standalone
8658,277.92,NJ City,S,16,8,12,28,Graduation,22,Patnership,Complex
3349,114.4,NJ City,N,18,8,3,37,High School,29,Patnership,Standalone
993,1.72,NJ City,N,9,13,4,52,Graduation,24,Patnership,Standalone
2667,250.98,NJ City,E,5,16,19,41,High School,33,Owner,Standalone
1520,364.28,NJ City,W,24,17,9,40,High School,31,Franchise,Complex
1997,55.65,NJ City,N,18,24,1,58,No education,19,Patnership,Standalone
521,127.93,NJ City,N,12,18,11,55,High School,30,Patnership,Standalone
9877,226.3,NJ City,N,17,8,10,34,Graduation,32,Patnership,Standalone
45,402.83,Boston,N,21,15,21,75,High School,30,Patnership,Standalone
8648,198.21,NJ City,W,20,24,24,77,Graduation,18,Franchise,Complex
9039,131.71,NJ City,E,7,19,18,74,Graduation,35,Owner,Standalone
3555,153.38,NJ City,E,5,12,2,48,Graduation,32,Owner,Standalone
4072,480.11,NJ City,W,12,19,9,72,High School,19,Franchise,Complex
5087,477.96,Boston,E,11,23,21,61,Graduation,31,Owner,Standalone
102,478.99,NJ City,S,7,10,24,44,High School,28,Patnership,Complex
5891,157.7,Boston,E,10,16,25,38,Graduation,34,Owner,Standalone
3423,98.7,NJ City,E,20,9,2,34,Graduation,24,Owner,Standalone
8586,306.44,NJ City,S,2,13,13,69,Graduation,34,Patnership,Complex
151,43.87,NJ City,N,23,22,3,66,High School,20,Owner,Standalone
9884,9.66,NJ City,N,1,24,4,25,No education,18,Franchise,Complex
3119,187.68,NJ City,E,23,19,15,38,High School,22,Owner,Standalone
5768,85.89,Boston,W,20,10,3,67,No education,34,Franchise,Complex
3309,74.32,NJ City,N,23,23,1,60,Graduation,35,Patnership,Standalone
5330,449.8,Boston,S,22,24,15,35,Graduation,23,Patnership,Complex
8729,490.09,NJ City,N,1,9,23,77,Graduation,29,Patnership,Standalone
5795,107.41,Boston,E,12,22,2,34,No education,25,Owner,Standalone
9250,457.86,NJ City,S,19,10,16,58,Graduation,28,Patnership,Complex
9784,174.08,NJ City,W,4,10,21,66,Graduation,33,Franchise,Complex
4610,137,NJ City,S,3,12,23,27,High School,21,Patnership,Complex
9248,318.13,NJ City,W,24,17,16,62,Graduation,19,Franchise,Complex
4000,127.02,NJ City,W,3,14,1,70,High School,21,Franchise,Complex
4014,184.54,NJ City,S,21,17,21,27,High School,28,Patnership,Complex
524,378.17,NJ City,W,13,15,24,40,High School,20,Franchise,Complex
3084,84.86,NJ City,W,3,15,1,25,Graduation,33,Franchise,Complex
7638,100,Boston,S,24,24,4,29,Graduation,29,Patnership,Complex
6107,333.22,Boston,E,14,17,18,77,Graduation,26,Owner,Standalone
4018,334.46,NJ City,S,20,21,2,71,High School,32,Patnership,Complex
9985,202.74,NJ City,N,9,9,24,72,Graduation,27,Patnership,Standalone
953,315.62,NJ City,N,21,19,16,73,High School,20,Patnership,Standalone
4451,40.55,NJ City,N,9,21,4,37,No education,28,Owner,Standalone
6389,51.26,Boston,N,11,9,1,40,No education,32,Patnership,Standalone
4940,408.06,Boston,W,4,14,9,40,High School,26,Franchise,Complex
6295,467.18,Boston,E,12,18,3,30,High School,25,Owner,Standalone
2920,414.54,NJ City,W,20,11,5,44,High School,32,Franchise,Complex
833,285.75,NJ City,N,12,23,11,40,High School,28,Patnership,Standalone
4612,118.29,NJ City,W,9,10,4,31,High School,34,Franchise,Complex
7870,430.93,Boston,S,1,10,8,49,Graduation,35,Patnership,Complex
2856,140.83,NJ City,W,20,9,12,83,High School,20,Franchise,Complex
6222,8.95,Boston,N,17,23,2,72,Graduation,29,Patnership,Complex
1577,29.01,NJ City,N,23,11,3,63,No education,28,Patnership,Standalone
9616,235.05,NJ City,W,11,24,22,51,Graduation,29,Franchise,Complex
6926,199.82,Boston,S,17,22,21,49,Graduation,32,Patnership,Complex
1715,169.94,NJ City,E,19,14,2,40,No education,32,Owner,Standalone
2260,472.92,NJ City,W,20,24,23,77,High School,24,Franchise,Complex
6989,358.53,Boston,N,12,23,7,83,Graduation,25,Patnership,Standalone
4401,100,NJ City,N,1,18,18,60,High School,31,Patnership,Standalone
470,472.36,NJ City,S,24,9,15,28,High School,18,Patnership,Complex
7197,494.02,Boston,N,20,18,2,49,Graduation,25,Patnership,Standalone
3270,165.57,NJ City,S,18,24,22,26,High School,31,Patnership,Complex
4762,375.32,NJ City,S,25,24,11,29,High School,32,Patnership,Complex
81,356.36,Boston,N,23,12,5,35,Graduation,29,Patnership,Standalone
5027,297.32,Boston,E,1,12,25,65,Graduation,31,Owner,Standalone
1934,296.35,NJ City,S,23,15,1,50,No education,22,Patnership,Complex
5624,34.39,Boston,N,20,14,2,65,No education,31,Franchise,Complex
2943,155,NJ City,E,1,21,17,69,High School,29,Owner,Standalone
3221,152.57,NJ City,N,25,18,15,35,High School,33,Patnership,Standalone
4176,121.77,NJ City,W,16,24,1,38,Graduation,32,Franchise,Complex
1567,469.5,NJ City,E,6,10,23,79,High School,32,Owner,Standalone
7776,196.94,Boston,W,21,10,3,32,Graduation,32,Franchise,Complex
8981,56.54,NJ City,N,14,24,4,57,No education,23,Patnership,Standalone
1719,111.58,NJ City,E,13,18,1,54,Graduation,23,Owner,Standalone
449,369.04,NJ City,N,17,11,18,59,High School,18,Patnership,Standalone
1659,296.01,NJ City,E,21,21,19,42,High School,27,Owner,Standalone
3284,80.36,NJ City,W,1,18,2,68,No education,23,Franchise,Complex
5332,318,Boston,W,14,23,23,82,Graduation,19,Franchise,Complex
7868,5.64,Boston,N,12,16,1,74,No education,21,Franchise,Complex
8746,450.48,NJ City,S,15,14,12,60,Graduation,20,Patnership,Complex
5830,385.57,Boston,S,24,13,22,58,Graduation,21,Patnership,Complex
523,461.93,NJ City,E,6,20,23,31,High School,18,Owner,Standalone
4387,68.47,NJ City,N,22,10,4,43,High School,19,Owner,Standalone
6001,373.78,Boston,N,21,19,7,29,Graduation,21,Patnership,Standalone
4779,379.83,NJ City,E,25,22,11,85,High School,19,Owner,Standalone
1903,56.97,NJ City,E,8,17,4,77,High School,27,Owner,Standalone
2249,81.14,NJ City,N,3,17,2,74,No education,22,Patnership,Standalone
4446,484.27,NJ City,S,15,20,20,71,High School,26,Patnership,Complex
7108,456.09,Boston,W,21,9,6,37,Graduation,22,Franchise,Complex
4491,41.45,NJ City,N,16,16,1,50,Graduation,23,Owner,Standalone
2434,132.95,NJ City,S,17,15,5,80,High School,22,Patnership,Complex
642,291,NJ City,S,2,16,20,78,High School,21,Patnership,Complex
2458,59.17,NJ City,S,19,18,4,25,High School,21,Patnership,Complex
8282,363.46,NJ City,S,14,13,12,53,Graduation,19,Patnership,Complex
5427,396.65,Boston,E,23,24,17,36,Graduation,18,Owner,Standalone
9979,324.52,NJ City,E,11,16,25,74,Graduation,29,Owner,Standalone
7737,295.78,Boston,N,16,15,23,66,Graduation,30,Patnership,Standalone
1502,451.77,NJ City,S,12,19,23,27,High School,18,Patnership,Complex
5957,262.64,Boston,N,23,14,12,44,Graduation,25,Patnership,Standalone
9630,190.46,NJ City,S,17,14,14,45,Graduation,19,Patnership,Complex
9192,457.16,NJ City,W,20,8,15,31,Graduation,19,Franchise,Complex
5083,332.55,Boston,E,2,16,7,37,Graduation,29,Owner,Standalone
6338,474.01,Boston,S,9,23,20,78,Graduation,20,Patnership,Complex
7584,57.68,Boston,W,24,19,2,82,Graduation,28,Franchise,Complex
7412,250.81,Boston,W,23,24,23,34,Graduation,33,Franchise,Complex
2740,11.06,NJ City,N,22,20,4,40,High School,18,Franchise,Complex
3578,155.1,NJ City,S,21,24,4,44,No education,28,Patnership,Complex
6951,230.24,Boston,E,16,8,5,80,Graduation,35,Owner,Standalone
6304,381.41,Boston,W,23,12,16,78,Graduation,32,Franchise,Complex
9836,327.13,NJ City,W,1,18,17,68,Graduation,20,Franchise,Complex
3976,377.08,NJ City,W,11,13,10,84,High School,35,Franchise,Complex
9789,29.82,NJ City,N,1,19,2,32,Graduation,28,Patnership,Standalone
2139,439.44,NJ City,E,13,15,15,35,High School,18,Owner,Standalone
2588,233.21,NJ City,W,17,9,16,66,High School,19,Franchise,Complex
2636,264.27,NJ City,W,11,23,23,41,High School,22,Franchise,Complex
1910,184.33,NJ City,S,11,12,11,82,High School,28,Patnership,Complex
4016,417.43,NJ City,W,21,19,19,45,High School,18,Franchise,Complex
857,226.38,NJ City,N,16,13,13,85,High School,29,Patnership,Standalone
6435,195.49,Boston,E,7,8,19,55,Graduation,30,Owner,Standalone
6647,391.35,Boston,E,20,16,7,71,Graduation,33,Owner,Standalone
9740,288.32,NJ City,W,25,15,12,65,Graduation,18,Franchise,Complex
5349,374.51,Boston,N,17,9,16,57,Graduation,31,Patnership,Standalone
9993,287.2,NJ City,N,1,8,8,25,Graduation,24,Patnership,Standalone
3121,11.59,NJ City,N,12,16,1,49,High School,21,Patnership,Standalone
613,494.51,NJ City,N,4,12,18,51,High School,24,Patnership,Standalone
600,420.46,NJ City,W,15,10,19,29,High School,34,Franchise,Complex
3192,33.68,NJ City,N,8,18,2,37,Graduation,26,Franchise,Complex
1833,445.21,NJ City,N,25,23,25,55,High School,26,Patnership,Standalone
8088,358.21,NJ City,W,7,8,7,85,Graduation,29,Franchise,Complex
1221,438.1,NJ City,N,21,20,20,41,High School,18,Patnership,Standalone
7115,415.8,Boston,E,23,23,15,73,Graduation,31,Owner,Standalone
3563,254.69,NJ City,E,10,14,24,55,High School,26,Owner,Standalone
2781,424.45,NJ City,N,4,19,16,61,High School,18,Patnership,Standalone
2000,453.15,NJ City,W,11,21,25,59,High School,34,Franchise,Complex
1316,72.31,NJ City,N,10,20,3,46,No education,18,Franchise,Complex
7086,152.76,Boston,S,8,17,13,44,Graduation,30,Patnership,Complex
8076,168.06,NJ City,W,23,19,1,52,Graduation,25,Franchise,Complex
4787,110.09,NJ City,E,3,24,2,68,No education,35,Owner,Standalone
4666,335.39,NJ City,S,2,24,4,67,High School,23,Patnership,Complex
4242,203.23,NJ City,S,14,13,15,41,High School,22,Patnership,Complex
1268,344.87,NJ City,W,13,10,1,57,No education,27,Franchise,Complex
6490,106.49,Boston,S,24,17,2,51,High School,19,Patnership,Complex
7616,265.33,Boston,W,10,17,21,83,Graduation,18,Franchise,Complex
9459,465.23,NJ City,E,19,20,12,52,Graduation,34,Owner,Standalone
2206,150.45,NJ City,S,16,22,9,31,High School,33,Patnership,Complex
3892,89.44,NJ City,W,24,19,3,58,High School,29,Franchise,Complex
3125,107,NJ City,N,2,16,19,68,High School,23,Patnership,Standalone
8555,154.75,NJ City,E,7,10,10,73,Graduation,25,Owner,Standalone
4530,456.5,NJ City,S,20,23,8,74,High School,18,Patnership,Complex
7780,404.55,Boston,W,10,8,22,60,Graduation,18,Franchise,Complex
2068,94.88,NJ City,W,21,8,1,46,High School,22,Franchise,Complex
8235,465.97,NJ City,E,4,23,23,54,Graduation,29,Owner,Standalone
2494,491.05,NJ City,S,25,13,1,49,High School,32,Patnership,Complex
3025,121.41,NJ City,N,4,9,4,42,High School,27,Patnership,Standalone
5201,321.81,Boston,N,16,20,21,54,Graduation,25,Patnership,Standalone
7565,482,Boston,N,12,20,1,61,No education,20,Patnership,Standalone
6861,180.93,Boston,N,19,18,6,59,Graduation,18,Patnership,Standalone
8942,469.33,NJ City,S,25,22,16,49,Graduation,32,Patnership,Complex
2695,355.07,NJ City,E,16,12,7,66,High School,29,Owner,Standalone
821,410.18,NJ City,N,8,15,16,62,High School,26,Patnership,Standalone
6371,203.67,Boston,E,12,16,5,43,No education,27,Owner,Standalone
5940,101.53,Boston,W,16,11,1,43,Graduation,34,Franchise,Complex
3222,81.09,NJ City,S,8,17,2,28,Graduation,34,Patnership,Complex
2014,55.51,NJ City,S,14,20,4,80,High School,30,Patnership,Complex
5586,22.26,Boston,N,18,17,4,46,Graduation,30,Patnership,Complex
9019,348.23,NJ City,E,24,10,22,67,Graduation,24,Owner,Standalone
2863,378.58,NJ City,E,22,14,20,68,High School,20,Owner,Standalone
622,98.16,NJ City,S,2,20,1,69,High School,35,Patnership,Complex
6177,114.07,Boston,N,4,12,2,66,Graduation,25,Patnership,Standalone
9076,482.17,NJ City,W,21,11,21,69,Graduation,25,Franchise,Complex
8266,305.32,NJ City,S,10,16,15,72,Graduation,20,Patnership,Complex
9064,404.8,NJ City,W,3,20,25,41,Graduation,28,Franchise,Complex
4199,75.75,NJ City,E,22,22,4,38,No education,25,Owner,Standalone
1083,154.2,NJ City,E,21,17,7,51,High School,26,Owner,Standalone
4588,352.86,NJ City,W,17,20,15,56,High School,34,Franchise,Complex
1767,391.67,NJ City,E,23,13,15,51,High School,35,Owner,Standalone
8223,261.41,NJ City,E,22,15,17,44,Graduation,21,Owner,Standalone
6044,213,Boston,W,25,18,1,50,No education,22,Franchise,Complex
8024,174.75,Boston,W,16,8,18,74,Graduation,19,Franchise,Complex
6499,335.88,Boston,E,2,9,13,25,Graduation,26,Owner,Standalone
5770,309.2,Boston,S,11,22,10,81,Graduation,25,Patnership,Complex
7087,80.46,Boston,E,21,21,1,63,High School,25,Owner,Standalone
9703,120.32,NJ City,E,3,10,2,40,High School,29,Owner,Standalone
8891,496.08,NJ City,E,16,14,15,82,Graduation,34,Owner,Standalone
1690,347.48,NJ City,S,23,14,21,59,High School,26,Patnership,Complex
7476,128.29,Boston,W,17,11,7,34,Graduation,18,Franchise,Complex
183,233.96,NJ City,E,22,18,17,39,High School,23,Owner,Standalone
106,319.15,NJ City,S,13,17,2,25,High School,35,Patnership,Complex
1308,67.73,NJ City,N,9,14,3,70,Graduation,22,Franchise,Complex
9595,8.22,NJ City,N,6,18,2,67,High School,20,Owner,Standalone
3543,114.66,NJ City,E,16,10,2,50,Graduation,23,Owner,Standalone
5210,53.05,Boston,S,13,10,1,48,No education,31,Patnership,Complex
4153,115.31,NJ City,N,9,10,3,41,High School,32,Patnership,Standalone
1228,189,NJ City,W,2,13,22,43,High School,30,Franchise,Complex
2761,490.65,NJ City,N,19,20,5,43,High School,34,Patnership,Standalone
8313,172.88,NJ City,N,22,14,1,71,Graduation,27,Patnership,Standalone
4933,181.4,NJ City,N,21,17,22,44,High School,31,Patnership,Standalone
9129,286.33,NJ City,N,23,22,20,41,Graduation,23,Patnership,Standalone
6482,20.4,Boston,N,17,20,1,46,No education,21,Patnership,Complex
5409,176.8,Boston,N,10,10,4,79,Graduation,21,Patnership,Standalone
4851,10.21,NJ City,N,19,10,4,34,Graduation,29,Owner,Standalone
110,17.25,NJ City,N,1,9,1,60,No education,26,Patnership,Complex
1804,257.93,NJ City,W,15,13,22,25,High School,23,Franchise,Complex
4350,208.97,NJ City,S,15,20,18,69,High School,22,Patnership,Complex
4541,257,NJ City,N,1,15,18,61,High School,27,Patnership,Standalone
5537,256.87,Boston,N,4,22,19,78,Graduation,28,Patnership,Standalone
880,126.86,NJ City,W,11,14,4,78,High School,23,Franchise,Complex
5377,289.83,Boston,N,18,20,24,37,Graduation,20,Patnership,Standalone
8156,167.94,NJ City,W,25,15,24,54,Graduation,20,Franchise,Complex
3208,122.89,NJ City,W,24,12,1,39,High School,28,Franchise,Complex
9588,265.66,NJ City,W,21,17,5,85,Graduation,21,Franchise,Complex
9771,271.4,NJ City,E,7,10,4,80,Graduation,18,Owner,Standalone
4224,164.39,NJ City,W,23,17,17,30,High School,28,Franchise,Complex
4250,222.35,NJ City,S,13,15,15,28,High School,24,Patnership,Complex
426,389.1,NJ City,S,10,18,16,32,High School,28,Patnership,Complex
6585,451.05,Boston,N,15,8,22,33,Graduation,21,Patnership,Standalone
7806,363.17,Boston,S,23,8,23,79,Graduation,22,Patnership,Complex
8774,169.04,NJ City,S,17,8,12,35,Graduation,20,Patnership,Complex
1640,79.06,NJ City,W,9,16,1,26,No education,28,Franchise,Complex
7772,82.86,Boston,W,5,20,2,78,No education,31,Franchise,Complex
1946,136.37,NJ City,S,6,18,2,81,No education,33,Patnership,Complex
9891,378.76,NJ City,E,8,20,2,25,Graduation,19,Owner,Standalone
9683,16.49,NJ City,N,7,22,2,83,No education,31,Owner,Standalone
3432,272.73,NJ City,W,22,17,21,38,High School,22,Franchise,Complex
5467,484.81,Boston,E,20,24,19,36,Graduation,35,Owner,Standalone
9974,120.86,NJ City,S,15,8,1,25,No education,32,Patnership,Complex
826,97.56,NJ City,S,6,17,2,54,High School,25,Patnership,Complex
33,122.01,Boston,N,3,12,4,53,Graduation,25,Patnership,Standalone
6713,445.32,Boston,N,2,15,12,82,Graduation,27,Patnership,Standalone
91,87.62,Boston,E,17,18,2,63,High School,20,Owner,Standalone
4424,295.17,NJ City,W,6,20,11,77,High School,28,Franchise,Complex
6480,191.7,Boston,W,17,10,21,28,Graduation,29,Franchise,Complex
2745,154,NJ City,N,1,20,18,79,High School,26,Patnership,Standalone
4806,117.6,NJ City,S,18,9,3,26,Graduation,28,Patnership,Complex
281,126.13,NJ City,N,3,18,4,54,No education,29,Patnership,Standalone
6380,260.23,Boston,W,2,20,15,71,Graduation,27,Franchise,Complex
4888,50.64,NJ City,W,19,19,3,35,High School,25,Franchise,Complex
3816,301.75,NJ City,W,13,15,19,38,High School,32,Franchise,Complex
8371,250.55,NJ City,E,21,16,13,64,Graduation,24,Owner,Standalone
3341,304.33,NJ City,N,11,8,9,60,High School,27,Patnership,Standalone
4986,227.78,Boston,S,20,8,12,61,High School,27,Patnership,Complex
1266,28.23,NJ City,N,1,16,3,81,Graduation,22,Patnership,Complex
8669,132.38,NJ City,N,25,11,25,52,Graduation,31,Patnership,Standalone
865,470.7,NJ City,N,2,10,4,74,High School,26,Patnership,Standalone
279,82.64,NJ City,E,13,22,1,52,Graduation,18,Owner,Standalone
2784,137.36,NJ City,W,3,12,1,27,Graduation,27,Franchise,Complex
6139,176.84,Boston,E,3,14,16,80,Graduation,31,Owner,Standalone
7536,124.95,Boston,W,6,20,2,67,Graduation,19,Franchise,Complex
3728,216,NJ City,W,3,22,8,62,High School,18,Franchise,Complex
2929,46.69,NJ City,N,9,20,3,47,High School,20,Patnership,Standalone
1319,339.57,NJ City,E,24,11,13,74,High School,30,Owner,Standalone
8899,387.9,NJ City,E,3,24,7,63,Graduation,25,Owner,Standalone
3744,367.73,NJ City,W,5,15,1,75,Graduation,23,Franchise,Complex
7940,276.85,Boston,W,16,11,9,46,Graduation,27,Franchise,Complex
1449,406.06,NJ City,N,20,23,19,51,High School,24,Patnership,Standalone
7362,77.88,Boston,S,24,9,4,48,Graduation,35,Patnership,Complex
9264,73.04,NJ City,N,4,21,2,85,Graduation,19,Franchise,Complex
6224,406.57,Boston,W,19,15,14,75,Graduation,21,Franchise,Complex
1396,60.21,NJ City,W,19,15,4,43,High School,29,Franchise,Complex
643,141.9,NJ City,E,8,14,5,51,High School,28,Owner,Standalone
1386,134.64,NJ City,S,5,14,20,67,High School,21,Patnership,Complex
5814,207.95,Boston,S,17,8,6,40,Graduation,26,Patnership,Complex
6256,87.63,Boston,W,12,18,2,47,High School,32,Franchise,Complex
1932,0.74,NJ City,N,3,18,3,38,Graduation,22,Franchise,Complex
3890,460.34,NJ City,S,13,13,10,37,High School,35,Patnership,Complex
1016,67.84,NJ City,N,13,10,4,72,No education,29,Franchise,Complex
6839,146.69,Boston,E,15,18,24,53,Graduation,19,Owner,Standalone
1985,355.85,NJ City,N,14,20,2,52,No education,28,Patnership,Standalone
8707,137.53,NJ City,E,22,15,2,42,High School,28,Owner,Standalone
6741,169.07,Boston,N,8,10,24,67,Graduation,35,Patnership,Standalone
270,305.74,NJ City,S,15,12,19,58,High School,18,Patnership,Complex
6909,424.88,Boston,N,8,21,22,73,Graduation,24,Patnership,Standalone
445,94.98,NJ City,N,10,9,2,46,High School,18,Patnership,Standalone
1000,48.86,NJ City,W,22,8,4,63,High School,32,Franchise,Complex
3134,78.67,NJ City,S,16,17,4,48,No education,18,Patnership,Complex
7418,392.43,Boston,S,25,20,20,72,Graduation,21,Patnership,Complex
9198,143.43,NJ City,S,18,10,9,31,Graduation,22,Patnership,Complex
9212,22,NJ City,N,24,21,4,54,No education,24,Franchise,Complex
2082,140.95,NJ City,S,21,12,16,31,High School,20,Patnership,Complex
5048,32.09,Boston,N,20,11,3,71,No education,21,Franchise,Complex
7311,118.45,Boston,E,3,23,2,34,Graduation,34,Owner,Standalone
4931,170.24,NJ City,E,10,20,13,53,High School,21,Owner,Standalone
5226,390.78,Boston,S,23,19,4,74,Graduation,30,Patnership,Complex
3076,105.37,NJ City,W,23,16,2,55,High School,34,Franchise,Complex
9928,225.17,NJ City,W,2,10,17,40,Graduation,28,Franchise,Complex
4866,62.56,NJ City,N,11,12,3,55,Graduation,30,Patnership,Complex
4145,179.1,NJ City,N,14,21,11,27,High School,24,Patnership,Standalone
615,176.19,NJ City,E,7,22,21,77,High School,29,Owner,Standalone
4477,494.94,NJ City,N,5,12,10,65,High School,34,Patnership,Standalone
4144,402.19,NJ City,W,5,22,23,34,High School,32,Franchise,Complex
9940,63.39,NJ City,N,2,9,2,77,High School,20,Franchise,Complex
5828,465.71,Boston,W,19,15,3,30,No education,25,Franchise,Complex
4021,140.05,NJ City,N,12,22,22,75,High School,35,Patnership,Standalone
2916,266.29,NJ City,W,20,21,6,51,High School,18,Franchise,Complex
1023,294.59,NJ City,E,19,12,11,40,High School,33,Owner,Standalone
878,320.14,NJ City,S,8,14,2,35,No education,20,Patnership,Complex
8896,78.39,NJ City,W,8,9,4,43,High School,33,Franchise,Complex
7430,96.33,Boston,S,7,20,3,83,No education,19,Patnership,Complex
4568,39.65,NJ City,N,25,17,1,36,No education,30,Franchise,Complex
4142,151,NJ City,S,2,8,20,38,High School,19,Patnership,Complex
9893,437.36,NJ City,N,7,13,3,70,No education,24,Patnership,Standalone
1886,57.94,NJ City,S,25,22,4,51,No education,22,Patnership,Complex
6154,461.29,Boston,S,4,11,1,31,High School,22,Patnership,Complex
5540,116.18,Boston,W,14,16,2,73,No education,26,Franchise,Complex
3552,83.23,NJ City,W,21,11,2,55,Graduation,27,Franchise,Complex
3022,368.32,NJ City,S,8,18,20,85,High School,28,Patnership,Complex
5375,44.04,Boston,N,11,19,3,77,No education,32,Owner,Standalone
2337,187.76,NJ City,N,15,24,8,64,High School,31,Patnership,Standalone
8407,388.98,NJ City,E,19,16,3,55,High School,23,Owner,Standalone
1658,496.6,NJ City,S,6,20,24,65,High School,35,Patnership,Complex
4597,412.77,NJ City,N,20,23,17,43,High School,21,Patnership,Standalone
7542,280.36,Boston,S,13,16,3,64,Graduation,34,Patnership,Complex
8472,404.96,NJ City,W,19,20,10,35,Graduation,21,Franchise,Complex
7057,73.53,Boston,N,17,13,2,47,High School,33,Patnership,Standalone
9297,190.12,NJ City,N,24,11,18,33,Graduation,31,Patnership,Standalone
5380,249.98,Boston,W,23,24,14,47,Graduation,32,Franchise,Complex
9600,181.2,NJ City,W,23,14,24,48,Graduation,31,Franchise,Complex
467,46.16,NJ City,N,25,21,2,38,No education,23,Owner,Standalone
506,266,NJ City,S,3,14,18,45,High School,18,Patnership,Complex
1820,113.24,NJ City,W,8,22,2,32,No education,28,Franchise,Complex
9021,404.23,NJ City,N,16,11,14,68,Graduation,26,Patnership,Standalone
3994,255.94,NJ City,S,14,16,6,85,High School,30,Patnership,Complex
1374,210.6,NJ City,S,15,24,8,45,High School,34,Patnership,Complex
7874,471.73,Boston,S,15,13,3,57,No education,18,Patnership,Complex
9110,362.15,NJ City,S,10,12,2,26,No education,18,Patnership,Complex
2316,289.59,NJ City,W,9,24,23,35,High School,34,Franchise,Complex
2710,164.66,NJ City,S,12,17,6,76,High School,18,Patnership,Complex
5260,79.62,Boston,W,19,23,1,34,High School,32,Franchise,Complex
7861,249.34,Boston,N,24,24,19,26,Graduation,26,Patnership,Standalone
8332,106.68,NJ City,W,23,19,1,79,High School,29,Franchise,Complex
2016,41.65,NJ City,N,20,18,1,64,Graduation,27,Franchise,Complex
7097,48.29,Boston,N,2,14,3,60,No education,25,Patnership,Standalone
4572,237.89,NJ City,W,2,8,4,70,Graduation,22,Franchise,Complex
3496,343.24,NJ City,W,5,24,6,33,High School,23,Franchise,Complex
4476,90.41,NJ City,W,25,12,4,33,Graduation,34,Franchise,Complex
8560,16.95,NJ City,N,11,24,2,70,High School,27,Franchise,Complex
8765,360.61,NJ City,N,2,9,12,30,Graduation,21,Patnership,Standalone
2388,471.84,NJ City,W,17,24,13,57,High School,34,Franchise,Complex
6293,198.09,Boston,N,25,12,21,74,Graduation,31,Patnership,Standalone
4034,52,NJ City,S,10,18,3,36,No education,19,Patnership,Complex
1998,71.3,NJ City,N,14,21,4,78,Graduation,33,Patnership,Complex
2241,430.29,NJ City,N,15,13,19,35,High School,21,Patnership,Standalone
1560,217.62,NJ City,W,14,18,8,36,High School,35,Franchise,Complex
2755,88.27,NJ City,E,5,23,4,57,High School,18,Owner,Standalone
5669,468.41,Boston,N,18,15,8,54,Graduation,31,Patnership,Standalone
798,47.03,NJ City,N,23,22,1,73,Graduation,27,Patnership,Complex
6544,95.72,Boston,W,25,9,2,81,High School,31,Franchise,Complex
8440,170.17,NJ City,W,23,9,1,77,High School,19,Franchise,Complex
1055,273.51,NJ City,E,5,8,1,77,No education,19,Owner,Standalone
604,20.31,NJ City,N,5,11,1,59,High School,20,Franchise,Complex
9392,489.06,NJ City,W,17,17,14,52,Graduation,32,Franchise,Complex
5428,42.13,Boston,N,3,22,1,46,High School,19,Franchise,Complex
9853,6.71,NJ City,N,19,8,2,35,High School,22,Patnership,Standalone
2752,69.08,NJ City,N,1,17,1,84,High School,28,Franchise,Complex
2860,478.6,NJ City,W,12,21,21,42,High School,28,Franchise,Complex
979,33.4,NJ City,N,8,22,4,62,High School,30,Owner,Standalone
3700,71.44,NJ City,N,4,20,2,40,High School,29,Franchise,Complex
1358,167.38,NJ City,S,6,23,3,40,No education,33,Patnership,Complex
6232,203.58,Boston,W,19,19,3,51,High School,32,Franchise,Complex
6914,107.97,Boston,S,7,21,2,74,No education,19,Patnership,Complex
2294,441.33,NJ City,S,8,16,17,64,High School,24,Patnership,Complex
4932,267.17,NJ City,W,10,8,4,81,Graduation,24,Franchise,Complex
14,180.44,Boston,S,20,17,23,50,High School,19,Patnership,Complex
3933,216,NJ City,N,2,10,17,25,High School,34,Patnership,Standalone
677,382.63,NJ City,N,25,21,1,78,No education,30,Patnership,Standalone
9978,441.83,NJ City,S,12,17,10,68,Graduation,28,Patnership,Complex
1845,354.35,NJ City,N,9,8,22,61,High School,30,Patnership,Standalone
3959,466.86,NJ City,E,12,15,14,37,High School,25,Owner,Standalone
9783,261.58,NJ City,E,24,10,11,57,Graduation,35,Owner,Standalone
9220,58.54,NJ City,W,19,12,4,69,High School,18,Franchise,Complex
4609,109.97,NJ City,N,7,16,3,58,High School,22,Patnership,Standalone
4964,99.19,Boston,W,10,24,3,40,No education,22,Franchise,Complex
3639,373.44,NJ City,E,10,9,3,52,Graduation,34,Owner,Standalone
7391,176.71,Boston,E,12,22,7,63,Graduation,23,Owner,Standalone
531,4.02,NJ City,N,4,21,2,38,Graduation,32,Owner,Standalone
2104,132.43,NJ City,W,12,12,15,80,High School,33,Franchise,Complex
2108,93.88,NJ City,W,22,11,2,73,No education,23,Franchise,Complex
8791,305.52,NJ City,E,13,23,10,84,Graduation,31,Owner,Standalone
9826,160.17,NJ City,S,4,16,10,55,Graduation,19,Patnership,Complex
8477,88.3,NJ City,N,21,23,4,46,No education,26,Patnership,Standalone
4520,346.52,NJ City,W,11,8,11,56,High School,24,Franchise,Complex
5268,475.89,Boston,W,23,14,14,71,Graduation,21,Franchise,Complex
438,189.77,NJ City,S,9,9,5,48,Graduation,29,Patnership,Complex
3769,297.31,NJ City,N,24,20,10,76,High School,19,Patnership,Standalone
9688,48.13,NJ City,W,25,15,2,71,High School,26,Franchise,Complex
8122,307.7,NJ City,S,22,23,7,39,Graduation,23,Patnership,Complex
8460,267.18,NJ City,W,14,17,5,82,Graduation,28,Franchise,Complex
2982,163.99,NJ City,S,8,20,5,40,Graduation,26,Patnership,Complex
1286,233,NJ City,S,2,17,20,33,High School,23,Patnership,Complex
6447,383.85,Boston,E,25,13,8,57,Graduation,29,Owner,Standalone
4869,270,NJ City,N,3,12,15,78,High School,27,Patnership,Standalone
2652,126.57,NJ City,W,5,10,4,66,Graduation,25,Franchise,Complex
4517,222.58,NJ City,N,18,13,12,73,High School,27,Patnership,Standalone
7555,330.66,Boston,E,19,20,10,80,Graduation,28,Owner,Standalone
8581,487.07,NJ City,N,15,18,12,75,Graduation,25,Patnership,Standalone
2233,311.02,NJ City,N,19,17,13,54,High School,29,Patnership,Standalone
3372,464.08,NJ City,W,21,15,10,70,High School,24,Franchise,Complex
9050,212.14,NJ City,S,20,14,10,26,Graduation,34,Patnership,Complex
9822,195.57,NJ City,S,14,24,22,49,Graduation,28,Patnership,Complex
6808,390.44,Boston,W,4,12,4,82,High School,35,Franchise,Complex
3002,158.02,NJ City,S,10,8,21,72,High School,29,Patnership,Complex
3237,493.77,NJ City,N,20,22,8,25,High School,19,Patnership,Standalone
8989,217.68,NJ City,N,8,24,17,44,Graduation,18,Patnership,Standalone
7489,451.75,Boston,N,9,23,14,44,Graduation,18,Patnership,Standalone
9450,405.08,NJ City,S,11,12,16,83,Graduation,24,Patnership,Complex
8955,260.58,NJ City,E,8,12,17,35,Graduation,30,Owner,Standalone
7373,17.65,Boston,N,13,9,4,67,No education,31,Patnership,Standalone
9419,242.41,NJ City,E,10,8,4,31,No education,19,Owner,Standalone
6960,90.8,Boston,W,20,8,2,77,Graduation,35,Franchise,Complex
6246,78.02,Boston,S,20,17,3,54,Graduation,30,Patnership,Complex
7454,483.77,Boston,S,14,22,12,69,Graduation,24,Patnership,Complex
1442,234.17,NJ City,S,18,19,4,41,No education,27,Patnership,Complex
2078,6.76,NJ City,N,1,24,4,36,No education,27,Patnership,Complex
6344,250.31,Boston,W,2,23,22,75,Graduation,22,Franchise,Complex
101,99.27,NJ City,N,8,10,3,61,No education,22,Patnership,Standalone
6690,253.14,Boston,S,16,15,25,32,Graduation,35,Patnership,Complex
9130,244.31,NJ City,S,4,18,2,29,High School,24,Patnership,Complex
9814,40.1,NJ City,N,2,19,3,61,High School,23,Patnership,Complex
6038,349.36,Boston,S,9,8,5,67,No education,34,Patnership,Complex
3881,314.82,NJ City,N,21,19,22,48,High School,28,Patnership,Standalone
2277,123.21,NJ City,N,13,18,1,78,Graduation,23,Patnership,Standalone
4634,198.25,NJ City,S,10,19,22,74,High School,33,Patnership,Complex
633,304.99,NJ City,N,22,12,13,82,High School,26,Patnership,Standalone
252,84.13,NJ City,W,1,16,1,53,Graduation,25,Franchise,Complex
2553,473.26,NJ City,N,14,13,7,35,High School,19,Patnership,Standalone
8680,27.23,NJ City,N,18,12,4,26,High School,35,Franchise,Complex
1677,239.95,NJ City,N,6,18,4,46,Graduation,34,Patnership,Standalone
6846,95.49,Boston,S,20,22,3,74,Graduation,18,Patnership,Complex
5144,58.27,Boston,W,14,11,3,81,No education,18,Franchise,Complex
8523,90.03,NJ City,E,18,16,4,49,Graduation,22,Owner,Standalone
7703,441.66,Boston,E,19,8,14,35,Graduation,24,Owner,Standalone
6459,180.78,Boston,E,14,14,2,38,Graduation,28,Owner,Standalone
7995,207.95,Boston,E,4,21,5,67,Graduation,34,Owner,Standalone
9969,370.38,NJ City,N,17,16,11,62,Graduation,23,Patnership,Standalone
6090,426.61,Boston,S,7,17,16,55,Graduation,28,Patnership,Complex
1999,440.87,NJ City,E,19,8,22,40,High School,26,Owner,Standalone
2129,317.46,NJ City,N,20,24,16,30,High School,18,Patnership,Standalone
3256,411.9,NJ City,W,25,10,25,59,High School,33,Franchise,Complex
7842,36.8,Boston,N,25,11,4,69,Graduation,29,Patnership,Complex
7195,220.23,Boston,E,3,8,15,57,Graduation,28,Owner,Standalone
9679,115.57,NJ City,E,15,16,3,38,High School,19,Owner,Standalone
793,48.38,NJ City,N,13,10,2,67,High School,18,Patnership,Standalone
7248,166.3,Boston,W,23,8,4,43,Graduation,25,Franchise,Complex
1810,497.39,NJ City,S,5,16,17,80,High School,19,Patnership,Complex
8166,454.56,NJ City,S,3,22,11,61,Graduation,30,Patnership,Complex
720,477.63,NJ City,W,19,16,3,81,Graduation,22,Franchise,Complex
5615,15.05,Boston,N,11,21,2,50,No education,25,Owner,Standalone
5361,421.83,Boston,N,19,10,7,47,Graduation,20,Patnership,Standalone
3616,146.81,NJ City,W,21,24,18,44,High School,25,Franchise,Complex
2117,75.44,NJ City,N,1,8,3,85,No education,24,Patnership,Standalone
8677,140.58,NJ City,N,14,13,5,61,High School,25,Patnership,Standalone
5643,204.92,Boston,E,11,22,8,40,Graduation,30,Owner,Standalone
6567,92.29,Boston,E,1,21,2,52,Graduation,28,Owner,Standalone
912,472.2,NJ City,W,11,17,23,61,High School,22,Franchise,Complex
7427,131.75,Boston,E,3,17,16,40,Graduation,30,Owner,Standalone
8798,457.12,NJ City,S,15,14,3,53,No education,33,Patnership,Complex
1293,271,NJ City,N,3,15,19,49,High School,33,Patnership,Standalone
1907,330.22,NJ City,E,6,16,24,39,High School,34,Owner,Standalone
3599,106.85,NJ City,E,5,24,3,30,No education,26,Owner,Standalone
4089,299.5,NJ City,N,8,8,25,27,High School,25,Patnership,Standalone
9038,26.03,NJ City,N,21,23,2,58,No education,23,Patnership,Complex
5528,9.44,Boston,N,18,23,1,73,No education,29,Franchise,Complex
2064,263.05,NJ City,W,15,22,17,60,High School,29,Franchise,Complex
7805,50.58,Boston,N,22,16,4,46,No education,18,Patnership,Standalone
8956,324.46,NJ City,W,11,8,16,25,Graduation,28,Franchise,Complex
5099,439.9,Boston,E,6,9,1,80,No education,27,Owner,Standalone
5142,345.36,Boston,S,25,24,17,61,Graduation,25,Patnership,Complex
7784,260.89,Boston,W,14,9,12,43,Graduation,31,Franchise,Complex
1546,20.77,NJ City,N,9,12,4,71,High School,19,Patnership,Complex
9726,294.27,NJ City,S,24,8,23,48,Graduation,23,Patnership,Complex
8128,286.97,NJ City,W,4,14,13,45,Graduation,25,Franchise,Complex
2099,489.39,NJ City,E,16,21,22,77,High School,33,Owner,Standalone
1537,391.47,NJ City,N,18,21,3,62,High School,27,Patnership,Standalone
8609,396.13,NJ City,N,5,22,14,65,Graduation,19,Patnership,Standalone
4187,144.73,NJ City,E,12,15,16,47,High School,35,Owner,Standalone
3911,221.28,NJ City,E,8,20,3,61,No education,21,Owner,Standalone
2810,213.31,NJ City,S,17,11,25,58,High School,35,Patnership,Complex
3765,9.85,NJ City,N,3,10,2,60,Graduation,25,Patnership,Standalone
1483,178.28,NJ City,E,13,11,21,48,High School,23,Owner,Standalone
1991,117,NJ City,E,3,20,19,33,High School,27,Owner,Standalone
6236,477.53,Boston,W,14,10,1,35,No education,23,Franchise,Complex
4843,172.17,NJ City,E,5,12,16,67,High School,22,Owner,Standalone
3799,157.24,NJ City,E,25,24,3,40,High School,31,Owner,Standalone
1976,105,NJ City,W,2,15,17,45,High School,32,Franchise,Complex
7782,10.1,Boston,N,8,8,3,68,Graduation,20,Patnership,Complex
5567,231.78,Boston,E,15,11,18,84,Graduation,22,Owner,Standalone
2678,173.88,NJ City,S,19,23,8,67,High School,32,Patnership,Complex
474,279.25,NJ City,S,14,10,20,36,High School,35,Patnership,Complex
3688,311.59,NJ City,W,12,21,16,76,High School,19,Franchise,Complex
4143,78.81,NJ City,E,8,16,2,75,Graduation,27,Owner,Standalone
7719,338.79,Boston,E,18,23,12,35,Graduation,25,Owner,Standalone
6645,248.02,Boston,N,19,8,2,80,Graduation,33,Patnership,Standalone
4512,283,NJ City,W,2,20,15,53,High School,33,Franchise,Complex
8020,23.67,Boston,N,15,12,4,40,High School,31,Franchise,Complex
5678,346.99,Boston,S,24,16,10,62,Graduation,25,Patnership,Complex
4369,354.7,NJ City,N,15,11,22,56,High School,27,Patnership,Standalone
7343,317.24,Boston,E,15,23,15,85,Graduation,27,Owner,Standalone
4576,478.75,NJ City,W,6,18,2,69,High School,30,Franchise,Complex
9793,111.71,NJ City,N,1,19,1,49,High School,22,Patnership,Standalone
2962,214.44,NJ City,S,9,17,4,44,High School,27,Patnership,Complex
6346,176.44,Boston,S,18,18,18,53,Graduation,25,Patnership,Complex
6506,218.67,Boston,S,13,11,11,50,Graduation,25,Patnership,Complex
1409,309.6,NJ City,N,6,20,5,56,No education,23,Patnership,Standalone
734,31.56,NJ City,N,6,13,4,45,No education,20,Patnership,Complex
84,176.3,Boston,W,23,20,3,37,Graduation,25,Franchise,Complex
7890,455.26,Boston,S,25,21,21,28,Graduation,30,Patnership,Complex
2038,481.57,NJ City,S,17,16,18,28,High School,22,Patnership,Complex
9426,446.09,NJ City,S,23,13,8,75,Graduation,34,Patnership,Complex
5018,478.2,Boston,S,20,8,13,76,Graduation,23,Patnership,Complex
2069,124.27,NJ City,N,2,19,2,74,No education,24,Patnership,Standalone
5662,383.67,Boston,S,18,19,5,73,High School,24,Patnership,Complex
5457,78.56,Boston,N,8,14,4,58,Graduation,27,Patnership,Standalone
3798,462.9,NJ City,S,17,9,21,36,High School,28,Patnership,Complex
2949,291.47,NJ City,N,14,8,24,65,High School,21,Patnership,Standalone
1072,62.33,NJ City,N,1,24,2,77,High School,26,Franchise,Complex
1123,17.79,NJ City,N,7,19,3,50,High School,24,Owner,Standalone
1656,403.97,NJ City,W,4,20,2,80,Graduation,21,Franchise,Complex
220,223.92,NJ City,W,4,14,9,47,High School,28,Franchise,Complex
3907,169.2,NJ City,E,4,17,16,41,High School,23,Owner,Standalone
5784,67.54,Boston,N,23,20,2,46,Graduation,20,Franchise,Complex
4901,442.36,NJ City,N,23,14,16,55,High School,29,Patnership,Standalone
7525,343.04,Boston,N,4,18,13,56,Graduation,27,Patnership,Standalone
2290,224.57,NJ City,S,4,8,17,44,High School,24,Patnership,Complex
2348,259,NJ City,W,1,15,23,75,High School,33,Franchise,Complex
9151,281.24,NJ City,E,13,13,9,69,Graduation,18,Owner,Standalone
3950,79.19,NJ City,S,19,13,3,37,No education,35,Patnership,Complex
9873,149.12,NJ City,N,22,10,12,83,Graduation,34,Patnership,Standalone
3144,389.9,NJ City,W,10,17,5,44,Graduation,28,Franchise,Complex
4555,292.08,NJ City,E,9,8,18,81,High School,20,Owner,Standalone
4718,453.22,NJ City,S,11,13,9,37,High School,31,Patnership,Complex
8612,188.31,NJ City,W,4,13,22,66,Graduation,27,Franchise,Complex
5171,25.28,Boston,N,14,18,3,34,No education,23,Owner,Standalone
7356,52.27,Boston,W,15,13,4,48,Graduation,29,Franchise,Complex
8494,262.32,NJ City,S,14,8,9,61,Graduation,30,Patnership,Complex
9448,416.55,NJ City,W,17,17,9,35,Graduation,22,Franchise,Complex
9769,435.06,NJ City,N,20,11,14,38,Graduation,29,Patnership,Standalone
76,71.73,Boston,N,18,18,3,53,High School,24,Franchise,Complex
30,78.5,Boston,S,15,19,3,55,Graduation,33,Patnership,Complex
8189,111.41,NJ City,N,24,14,4,77,No education,23,Patnership,Standalone
9218,489.42,NJ City,S,13,22,22,64,Graduation,33,Patnership,Complex
7496,94.06,Boston,W,20,13,1,68,No education,35,Franchise,Complex
490,26.23,NJ City,N,9,20,3,39,High School,31,Patnership,Complex
6969,319.56,Boston,N,4,15,20,48,Graduation,20,Patnership,Standalone
2787,216.56,NJ City,E,14,9,23,74,High School,30,Owner,Standalone
3707,233.89,NJ City,E,14,17,1,66,No education,21,Owner,Standalone
7292,402.75,Boston,W,13,11,6,60,Graduation,29,Franchise,Complex
2776,123.92,NJ City,W,8,10,3,72,High School,25,Franchise,Complex
4953,239.62,Boston,N,25,18,7,60,High School,24,Patnership,Standalone
2881,338.38,NJ City,N,7,9,25,64,High School,31,Patnership,Standalone
3743,275.4,NJ City,E,19,14,21,79,High School,22,Owner,Standalone
2835,109.97,NJ City,E,1,9,3,76,Graduation,30,Owner,Standalone
9672,407.59,NJ City,W,21,15,7,58,Graduation,32,Franchise,Complex
9284,357.91,NJ City,W,3,22,18,47,Graduation,22,Franchise,Complex
3319,323.55,NJ City,E,9,22,1,26,High School,27,Owner,Standalone
3880,164.12,NJ City,W,22,17,10,53,High School,22,Franchise,Complex
3497,320.94,NJ City,N,18,10,4,66,No education,30,Patnership,Standalone
7665,211.66,Boston,N,7,15,13,63,Graduation,27,Patnership,Standalone
5656,498.43,Boston,W,8,13,8,52,Graduation,30,Franchise,Complex
1074,253.17,NJ City,S,11,20,9,27,High School,21,Patnership,Complex
9503,443.62,NJ City,E,7,20,18,44,Graduation,28,Owner,Standalone
1592,488.82,NJ City,W,23,24,7,84,High School,27,Franchise,Complex
3906,337.42,NJ City,S,19,23,9,47,High School,29,Patnership,Complex
5081,470.57,Boston,N,24,14,17,53,Graduation,35,Patnership,Standalone
7422,65.74,Boston,N,13,12,3,65,Graduation,27,Patnership,Complex
9541,90.65,NJ City,N,22,21,4,35,High School,24,Patnership,Standalone
9097,424.22,NJ City,N,9,11,19,56,Graduation,29,Patnership,Standalone
3522,197.38,NJ City,S,18,14,25,54,High School,18,Patnership,Complex
1253,413.18,NJ City,N,16,14,17,66,High School,34,Patnership,Standalone
9546,275.66,NJ City,S,21,8,12,77,Graduation,22,Patnership,Complex
3460,90.37,NJ City,W,20,23,2,60,High School,29,Franchise,Complex
6992,10.85,Boston,N,24,15,1,43,No education,35,Franchise,Complex
4062,35.5,NJ City,N,10,19,4,67,Graduation,31,Patnership,Complex
6862,283.15,Boston,S,22,10,14,45,Graduation,25,Patnership,Complex
3307,329.09,NJ City,E,12,13,11,38,High School,34,Owner,Standalone
8814,30.36,NJ City,N,14,9,1,64,Graduation,24,Patnership,Complex
6377,261.24,Boston,N,21,11,14,80,Graduation,24,Patnership,Standalone
3430,181.05,NJ City,S,25,16,24,60,High School,18,Patnership,Complex
4141,268.63,NJ City,N,15,23,17,76,High School,27,Patnership,Standalone
334,43.16,NJ City,N,9,12,3,61,High School,22,Patnership,Complex
1929,34.31,NJ City,N,16,22,2,60,Graduation,24,Patnership,Standalone
8712,125.4,NJ City,W,7,14,3,51,Graduation,27,Franchise,Complex
6820,40.12,Boston,N,21,23,2,32,High School,34,Franchise,Complex
5515,287.24,Boston,E,19,14,8,53,Graduation,24,Owner,Standalone
8314,242.61,NJ City,S,15,21,18,39,Graduation,35,Patnership,Complex
2733,24.65,NJ City,N,23,15,3,65,Graduation,27,Patnership,Standalone
4088,358.62,NJ City,W,20,22,8,54,High School,24,Franchise,Complex
19,107.16,NJ City,E,14,17,4,48,High School,19,Owner,Standalone
2986,393.23,NJ City,S,8,20,18,63,High School,32,Patnership,Complex
2832,195.75,NJ City,W,1,17,4,41,Graduation,33,Franchise,Complex
3922,418.5,NJ City,S,25,21,23,52,High School,18,Patnership,Complex
4335,149,NJ City,E,3,22,12,25,High School,20,Owner,Standalone
4875,112.28,NJ City,E,13,10,3,39,Graduation,33,Owner,Standalone
9620,359.08,NJ City,W,16,19,10,26,Graduation,28,Franchise,Complex
5902,466.4,Boston,S,20,12,15,85,Graduation,25,Patnership,Complex
5812,449.86,Boston,W,17,14,20,58,Graduation,18,Franchise,Complex
4647,142.06,NJ City,E,4,19,19,66,High School,34,Owner,Standalone
5991,479.84,Boston,E,3,24,3,47,Graduation,26,Owner,Standalone
6419,431.44,Boston,E,24,8,15,54,Graduation,35,Owner,Standalone
6759,499.65,Boston,E,4,13,14,28,Graduation,34,Owner,Standalone
3160,208,NJ City,W,2,18,17,67,High School,33,Franchise,Complex
4293,135,NJ City,N,1,17,16,27,High School,26,Patnership,Standalone
6270,478.44,Boston,S,15,23,22,52,Graduation,34,Patnership,Complex
6448,377.47,Boston,W,4,10,15,85,Graduation,31,Franchise,Complex
3584,259.39,NJ City,W,11,16,2,41,No education,31,Franchise,Complex
6704,485.78,Boston,W,24,9,1,76,No education,25,Franchise,Complex
3419,324.93,NJ City,E,20,18,16,73,High School,32,Owner,Standalone
2081,316.47,NJ City,N,20,10,8,57,High School,27,Patnership,Standalone
1173,23.66,NJ City,N,21,19,2,55,Graduation,25,Patnership,Standalone
8346,359.85,NJ City,S,3,10,11,29,Graduation,22,Patnership,Complex
1066,295.2,NJ City,S,8,21,12,32,High School,19,Patnership,Complex
3977,234.6,NJ City,N,13,8,5,63,No education,23,Patnership,Standalone
8312,168.26,NJ City,W,15,14,17,58,Graduation,18,Franchise,Complex
6494,393.23,Boston,S,19,17,4,43,No education,20,Patnership,Complex
671,121.85,NJ City,E,19,18,4,30,No education,28,Owner,Standalone
3544,192.77,NJ City,W,12,23,2,38,High School,32,Franchise,Complex
2610,309.41,NJ City,S,19,22,19,31,High School,21,Patnership,Complex
9524,398.94,NJ City,W,13,8,11,76,Graduation,19,Franchise,Complex
8054,121.51,NJ City,S,9,21,1,38,No education,31,Patnership,Complex
7137,47.39,Boston,N,2,18,2,29,Graduation,22,Patnership,Standalone
3864,462.39,NJ City,W,10,9,21,82,High School,19,Franchise,Complex
6847,175.94,Boston,E,1,14,15,54,Graduation,31,Owner,Standalone
2297,298.52,NJ City,N,13,8,25,55,High School,29,Patnership,Standalone
9371,363.73,NJ City,E,17,23,18,32,Graduation,33,Owner,Standalone
508,319.9,NJ City,W,4,12,24,40,High School,32,Franchise,Complex
1871,297.59,NJ City,E,16,19,10,38,High School,26,Owner,Standalone
1157,443.85,NJ City,N,8,19,23,26,High School,23,Patnership,Standalone
9068,355.42,NJ City,W,6,21,17,60,Graduation,24,Franchise,Complex
5511,218.39,Boston,E,2,12,14,81,Graduation,27,Owner,Standalone
4556,13.47,NJ City,N,12,17,4,76,No education,28,Franchise,Complex
3607,107.59,NJ City,E,18,20,4,77,High School,24,Owner,Standalone
2647,411.9,NJ City,E,4,9,22,31,High School,35,Owner,Standalone
5116,348.58,Boston,W,9,9,25,35,Graduation,18,Franchise,Complex
8181,192.75,NJ City,N,11,18,10,30,Graduation,30,Patnership,Standalone
2938,259.64,NJ City,S,2,13,3,61,High School,30,Patnership,Complex
9017,415.52,NJ City,N,4,23,16,43,Graduation,22,Patnership,Standalone
927,470.83,NJ City,E,5,9,18,45,High School,19,Owner,Standalone
8987,168.63,NJ City,E,8,13,5,57,No education,21,Owner,Standalone
8183,276.58,NJ City,E,17,23,1,66,No education,22,Owner,Standalone
8779,276.1,NJ City,E,25,13,24,33,Graduation,18,Owner,Standalone
2306,91.54,NJ City,S,24,17,1,83,No education,28,Patnership,Complex
8518,469.62,NJ City,S,15,21,3,81,High School,28,Patnership,Complex
2798,180.18,NJ City,S,8,10,22,26,High School,27,Patnership,Complex
1457,19.87,NJ City,N,21,18,1,81,No education,19,Patnership,Standalone
4013,60.9,NJ City,N,22,23,2,41,No education,21,Patnership,Standalone
4794,148.52,NJ City,S,13,19,18,51,High School,25,Patnership,Complex
2843,484.48,NJ City,E,23,17,21,50,High School,34,Owner,Standalone
8754,272.05,NJ City,S,19,14,16,47,Graduation,20,Patnership,Complex
8252,297.61,NJ City,W,16,15,8,58,Graduation,22,Franchise,Complex
4847,89.62,NJ City,E,8,20,1,77,No education,28,Owner,Standalone
4629,33.01,NJ City,N,16,21,2,44,Graduation,34,Patnership,Standalone
9016,479.15,NJ City,W,8,8,12,85,Graduation,22,Franchise,Complex
5801,230.32,Boston,N,23,9,17,61,Graduation,35,Patnership,Standalone
8534,263.17,NJ City,S,6,23,21,52,Graduation,26,Patnership,Complex
4757,102.27,NJ City,N,11,13,3,55,No education,18,Patnership,Standalone
8671,480.25,NJ City,E,5,19,17,51,Graduation,18,Owner,Standalone
2759,285.83,NJ City,E,11,22,19,47,High School,23,Owner,Standalone
9748,393.26,NJ City,W,19,9,3,82,High School,25,Franchise,Complex
4668,180.13,NJ City,W,7,10,24,57,High School,22,Franchise,Complex
7815,156.19,Boston,E,2,17,21,47,Graduation,35,Owner,Standalone
2802,204.46,NJ City,S,16,12,25,57,High School,30,Patnership,Complex
4593,367.72,NJ City,N,13,21,20,54,High School,35,Patnership,Standalone
1742,151.5,NJ City,S,23,19,16,76,High School,31,Patnership,Complex
2035,374.41,NJ City,E,18,19,10,40,High School,22,Owner,Standalone
9104,421.72,NJ City,W,25,18,19,32,Graduation,34,Franchise,Complex
718,332.41,NJ City,S,19,12,23,51,High School,26,Patnership,Complex
8459,137.94,NJ City,E,5,20,20,35,Graduation,23,Owner,Standalone
4460,467.39,NJ City,W,4,11,15,63,High School,26,Franchise,Complex
1390,228.29,NJ City,S,12,9,13,30,High School,23,Patnership,Complex
3443,177.39,NJ City,E,13,15,14,28,High School,27,Owner,Standalone
6978,351.13,Boston,S,9,20,17,41,Graduation,32,Patnership,Complex
2797,449.24,NJ City,N,18,14,17,31,High School,27,Patnership,Standalone
5072,188.07,Boston,W,24,16,19,54,Graduation,28,Franchise,Complex
8659,349.68,NJ City,E,11,23,8,54,Graduation,29,Owner,Standalone
2404,63.7,NJ City,N,7,12,1,59,High School,28,Franchise,Complex
3205,69.39,NJ City,N,17,17,1,83,High School,29,Patnership,Standalone
2221,128.53,NJ City,N,6,16,17,76,High School,33,Patnership,Standalone
8631,393.14,NJ City,E,9,14,18,52,Graduation,21,Owner,Standalone
1264,104.89,NJ City,W,13,19,1,69,High School,27,Franchise,Complex
1096,223.37,NJ City,W,9,23,12,63,High School,28,Franchise,Complex
6078,70.55,Boston,N,7,12,2,27,Graduation,24,Patnership,Complex
9861,311.11,NJ City,N,25,15,7,36,Graduation,31,Patnership,Standalone
6056,102.16,Boston,W,18,13,4,33,No education,26,Franchise,Complex
7128,481.29,Boston,W,12,10,19,64,Graduation,27,Franchise,Complex
3997,240.18,NJ City,N,23,8,17,77,High School,35,Patnership,Standalone
7250,456.82,Boston,S,3,16,1,55,No education,30,Patnership,Complex
2338,122.7,NJ City,S,24,13,3,78,High School,26,Patnership,Complex
9988,223.33,NJ City,W,15,11,9,77,Graduation,33,Franchise,Complex
8392,0.22,NJ City,N,12,22,2,72,High School,21,Franchise,Complex
2255,418.17,NJ City,E,21,14,22,54,High School,21,Owner,Standalone
232,257.53,NJ City,W,2,22,2,27,High School,25,Franchise,Complex
5750,16.33,Boston,N,18,11,3,78,No education,26,Patnership,Complex
8916,141.7,NJ City,W,13,23,8,72,Graduation,22,Franchise,Complex
2164,172.47,NJ City,W,25,13,7,32,High School,28,Franchise,Complex
2871,88.03,NJ City,E,6,16,4,80,Graduation,32,Owner,Standalone
1539,233,NJ City,E,1,17,6,50,High School,33,Owner,Standalone
8353,332.03,NJ City,N,3,23,1,29,High School,24,Patnership,Standalone
8497,200.28,NJ City,N,1,10,20,80,Graduation,31,Patnership,Standalone
3080,19.17,NJ City,N,22,10,4,53,No education,27,Franchise,Complex
1862,137.64,NJ City,S,13,24,9,71,High School,33,Patnership,Complex
4116,96.11,NJ City,W,7,18,3,44,Graduation,19,Franchise,Complex
7117,318.42,Boston,N,7,10,15,70,Graduation,20,Patnership,Standalone
3305,108.29,NJ City,N,12,22,2,38,No education,26,Patnership,Standalone
1403,113.41,NJ City,E,21,19,4,65,No education,30,Owner,Standalone
2726,260.7,NJ City,S,9,8,11,35,High School,18,Patnership,Complex
6206,188.54,Boston,S,1,11,20,29,Graduation,27,Patnership,Complex
6194,117.66,Boston,S,17,15,4,37,No education,22,Patnership,Complex
9646,481.79,NJ City,S,1,8,9,69,Graduation,20,Patnership,Complex
5516,217.36,Boston,W,1,13,25,55,Graduation,30,Franchise,Complex
55,284.64,NJ City,E,11,22,3,60,High School,25,Owner,Standalone
1951,313,NJ City,E,13,15,16,34,High School,23,Owner,Standalone
4586,427.5,NJ City,S,18,21,25,71,High School,34,Patnership,Complex
264,23.93,NJ City,N,16,24,2,33,Graduation,31,Franchise,Complex
7802,376.63,Boston,S,4,13,12,52,Graduation,26,Patnership,Complex
4441,248.58,NJ City,N,22,23,21,64,High School,24,Patnership,Standalone
7916,466.41,Boston,W,9,23,23,63,Graduation,23,Franchise,Complex
6781,406.9,Boston,N,2,16,3,38,High School,26,Patnership,Standalone
1928,460.98,NJ City,W,10,22,18,52,High School,25,Franchise,Complex
3776,56.54,NJ City,W,4,17,3,47,No education,18,Franchise,Complex
7924,492.52,Boston,W,1,19,7,75,Graduation,34,Franchise,Complex
8427,237.4,NJ City,E,10,11,25,74,Graduation,32,Owner,Standalone
6049,99.12,Boston,N,12,13,2,27,High School,28,Patnership,Standalone
1607,488.52,NJ City,E,19,22,21,54,High School,28,Owner,Standalone
6504,90.77,Boston,W,14,24,1,44,Graduation,25,Franchise,Complex
3804,26.75,NJ City,N,7,18,2,67,Graduation,18,Franchise,Complex
5101,3.8,Boston,N,9,15,1,79,High School,32,Patnership,Standalone
8950,147.73,NJ City,S,20,19,16,73,Graduation,31,Patnership,Complex
9416,404.78,NJ City,W,21,18,23,78,Graduation,24,Franchise,Complex
3360,108.79,NJ City,W,25,8,4,26,Graduation,28,Franchise,Complex
6629,179.91,Boston,N,14,8,21,32,Graduation,33,Patnership,Standalone
3081,239.75,NJ City,N,11,12,3,62,Graduation,34,Patnership,Standalone
9626,106.83,NJ City,S,10,9,4,44,No education,20,Patnership,Complex
2008,320.6,NJ City,W,13,15,25,73,High School,27,Franchise,Complex
6331,75.68,Boston,E,17,24,3,50,High School,25,Owner,Standalone
5725,289.07,Boston,N,13,20,9,39,Graduation,21,Patnership,Standalone
3396,195.7,NJ City,W,11,23,13,39,High School,33,Franchise,Complex
1935,242.86,NJ City,E,21,8,24,54,High School,22,Owner,Standalone
77,316,Boston,N,1,8,15,83,High School,29,Patnership,Standalone
4470,39.01,NJ City,N,17,20,4,60,Graduation,33,Patnership,Complex
7121,50.47,Boston,N,2,22,3,67,No education,29,Patnership,Standalone
7856,82.52,Boston,W,1,12,4,53,No education,20,Franchise,Complex
4913,180.2,NJ City,N,8,15,2,69,No education,19,Patnership,Standalone
5566,259.77,Boston,S,10,18,15,40,Graduation,25,Patnership,Complex
3392,403.36,NJ City,W,20,19,11,61,High School,25,Franchise,Complex
58,155.3,Boston,S,12,24,18,33,High School,31,Patnership,Complex
2271,469.66,NJ City,E,18,9,10,69,High School,29,Owner,Standalone
216,305.51,NJ City,W,7,11,5,31,Graduation,28,Franchise,Complex
7581,51.18,Boston,N,25,20,3,37,Graduation,26,Patnership,Standalone
2225,436.47,NJ City,N,25,23,25,39,High School,20,Patnership,Standalone
6185,112.96,Boston,N,14,19,2,33,No education,18,Patnership,Standalone
69,400.58,Maine,N,21,8,11,66,High School,31,Patnership,Standalone
1775,53.45,NJ City,E,5,14,2,75,No education,33,Owner,Standalone
522,218,NJ City,S,2,14,25,82,High School,28,Patnership,Complex
8580,63.3,NJ City,N,22,9,1,43,Graduation,26,Franchise,Complex
922,144.62,NJ City,S,12,24,4,40,High School,21,Patnership,Complex
1030,9.78,NJ City,N,12,21,4,27,High School,18,Patnership,Complex
3069,472.87,NJ City,N,21,24,7,65,High School,32,Patnership,Standalone
3974,127.17,NJ City,S,15,10,2,31,No education,18,Patnership,Complex
8866,84.84,NJ City,S,11,15,3,75,High School,29,Patnership,Complex
1814,9.36,NJ City,N,10,24,1,44,No education,33,Patnership,Complex
706,126.46,NJ City,S,2,11,3,57,High School,24,Patnership,Complex
5466,159.47,Boston,S,24,11,9,76,Graduation,34,Patnership,Complex
4217,221.54,NJ City,N,14,15,5,67,No education,31,Patnership,Standalone
9041,179.97,NJ City,N,8,18,9,82,Graduation,31,Patnership,Standalone
119,102.64,NJ City,E,12,13,2,47,No education,34,Owner,Standalone
7827,313.82,Boston,E,19,11,8,82,Graduation,18,Owner,Standalone
4017,253.55,NJ City,N,6,15,14,35,High School,23,Patnership,Standalone
5150,461.66,Boston,S,12,12,25,38,Graduation,22,Patnership,Complex
738,77.62,NJ City,S,23,8,3,73,Graduation,19,Patnership,Complex
4603,201.22,NJ City,E,10,16,18,40,High School,25,Owner,Standalone
2503,360.74,NJ City,E,19,8,25,43,High School,35,Owner,Standalone
4494,388.76,NJ City,S,22,8,14,76,High School,21,Patnership,Complex
1443,484.02,NJ City,E,22,23,18,76,High School,28,Owner,Standalone
3513,471.63,NJ City,N,24,13,25,61,High School,21,Patnership,Standalone
8567,191.16,NJ City,E,14,11,18,74,Graduation,24,Owner,Standalone
8380,141.46,NJ City,W,15,15,17,26,Graduation,21,Franchise,Complex
9946,108.21,NJ City,S,3,20,1,44,High School,30,Patnership,Complex
223,264.33,NJ City,E,16,17,4,42,High School,26,Owner,Standalone
6367,51.88,Boston,E,1,17,3,38,High School,22,Owner,Standalone
6075,304.79,Boston,E,13,21,5,58,Graduation,23,Owner,Standalone
7329,138.27,Boston,N,2,20,5,75,Graduation,22,Patnership,Standalone
7136,338.23,Boston,W,24,18,10,55,Graduation,20,Franchise,Complex
1400,185.83,NJ City,W,16,13,13,54,High School,26,Franchise,Complex
7039,395.75,Boston,E,5,17,17,34,Graduation,22,Owner,Standalone
4346,239,NJ City,S,2,22,18,40,High School,33,Patnership,Complex
5052,320.35,Boston,W,13,17,22,51,Graduation,23,Franchise,Complex
9522,90.75,NJ City,S,22,16,3,79,Graduation,28,Patnership,Complex
4652,264,NJ City,W,1,11,18,26,High School,28,Franchise,Complex
5904,434.52,Boston,W,19,22,25,46,Graduation,33,Franchise,Complex
3352,102,NJ City,W,3,13,20,69,High School,19,Franchise,Complex
8877,146.96,NJ City,N,2,8,7,83,Graduation,29,Patnership,Standalone
8398,207.01,NJ City,S,5,11,17,25,Graduation,28,Patnership,Complex
2397,39.5,NJ City,N,19,16,4,55,Graduation,29,Patnership,Standalone
5379,417.83,Boston,E,23,24,7,77,Graduation,30,Owner,Standalone
178,381.88,NJ City,S,7,21,23,77,High School,26,Patnership,Complex
1325,412.02,NJ City,N,25,23,11,75,High School,30,Patnership,Standalone
5618,287.98,Boston,S,2,16,17,82,Graduation,29,Patnership,Complex
8698,101.08,NJ City,S,15,22,4,71,High School,25,Patnership,Complex
4792,354.85,NJ City,W,24,17,5,45,High School,32,Franchise,Complex
7990,106.47,Boston,S,13,16,2,75,High School,31,Patnership,Complex
2509,200.76,NJ City,N,13,24,18,55,High School,22,Patnership,Standalone
1189,152.84,NJ City,N,9,19,5,50,High School,27,Patnership,Standalone
848,160,NJ City,W,1,15,7,73,High School,31,Franchise,Complex
2076,350.74,NJ City,W,13,21,18,63,High School,31,Franchise,Complex
5219,432.34,Boston,E,6,15,11,52,Graduation,25,Owner,Standalone
9642,92.93,NJ City,S,19,16,4,36,Graduation,28,Patnership,Complex
5759,99.53,Boston,E,22,20,1,81,No education,30,Owner,Standalone
6721,451.02,Boston,N,15,17,20,25,Graduation,31,Patnership,Standalone
7654,416.13,Boston,S,25,12,14,69,Graduation,32,Patnership,Complex
1462,320.66,NJ City,S,7,16,25,37,High School,25,Patnership,Complex
4632,256,NJ City,W,2,15,24,82,High School,34,Franchise,Complex
1429,185.16,NJ City,N,16,14,8,29,High School,20,Patnership,Standalone
389,76.91,NJ City,N,10,12,4,69,No education,22,Patnership,Standalone
8943,486.51,NJ City,E,7,16,19,79,Graduation,30,Owner,Standalone
3197,372.62,NJ City,N,12,16,14,38,High School,29,Patnership,Standalone
8761,83.34,NJ City,N,20,21,2,36,High School,28,Patnership,Standalone
1716,149.32,NJ City,W,15,17,19,58,High School,33,Franchise,Complex
1241,126.36,NJ City,N,22,19,4,58,No education,24,Patnership,Standalone
902,337.39,NJ City,S,16,9,20,42,High School,30,Patnership,Complex
240,23.38,NJ City,N,6,19,1,48,Graduation,35,Franchise,Complex
9123,451.18,NJ City,E,10,8,20,81,Graduation,29,Owner,Standalone
698,191.15,NJ City,S,23,17,2,35,No education,26,Patnership,Complex
4413,84.59,NJ City,N,5,15,2,41,Graduation,25,Patnership,Standalone
2876,91.18,NJ City,W,4,12,1,79,No education,35,Franchise,Complex
941,164.31,NJ City,N,16,10,13,68,High School,26,Patnership,Standalone
5090,131.94,Boston,S,17,16,16,37,Graduation,28,Patnership,Complex
1114,106.53,NJ City,S,24,18,3,71,High School,28,Patnership,Complex
4071,258.54,NJ City,E,4,15,13,62,High School,32,Owner,Standalone
1882,158.2,NJ City,S,19,23,16,35,High School,20,Patnership,Complex
9346,107.77,NJ City,S,20,19,4,53,High School,18,Patnership,Complex
9065,286.38,NJ City,N,5,13,24,41,Graduation,27,Patnership,Standalone
5581,380,Boston,N,19,22,19,30,Graduation,30,Patnership,Standalone
4301,315.32,NJ City,N,4,10,13,38,High School,35,Patnership,Standalone
2921,355.12,NJ City,N,24,23,6,28,High School,34,Patnership,Standalone
5841,84.54,Boston,N,21,11,4,51,Graduation,32,Patnership,Standalone
9944,249.28,NJ City,W,12,10,24,40,Graduation,20,Franchise,Complex
2387,103,NJ City,E,1,19,8,80,High School,28,Owner,Standalone
4080,428.55,NJ City,W,25,12,14,31,High School,26,Franchise,Complex
1841,397.93,NJ City,N,22,14,14,74,High School,21,Patnership,Standalone
8473,195.3,NJ City,N,17,19,12,26,Graduation,22,Patnership,Standalone
866,389.2,NJ City,S,17,18,24,68,High School,26,Patnership,Complex
7987,299.52,Boston,E,15,13,8,47,Graduation,20,Owner,Standalone
3437,481.88,NJ City,N,10,15,25,42,High School,30,Patnership,Standalone
8907,474.77,NJ City,E,16,15,9,85,Graduation,23,Owner,Standalone
2148,289.09,NJ City,W,5,16,7,73,High School,31,Franchise,Complex
7503,238.06,Boston,E,21,11,4,29,Graduation,35,Owner,Standalone
9981,56.37,NJ City,N,10,23,1,63,Graduation,24,Patnership,Standalone
7406,79.79,Boston,S,10,14,1,82,No education,27,Patnership,Complex
8130,495.19,NJ City,S,16,19,9,57,Graduation,27,Patnership,Complex
4998,183.31,Boston,S,17,21,8,25,High School,29,Patnership,Complex
2307,72.52,NJ City,N,11,23,4,50,Graduation,25,Owner,Standalone
4979,306.17,Boston,E,22,8,23,43,High School,18,Owner,Standalone
1428,273.89,NJ City,W,5,20,14,44,High School,25,Franchise,Complex
408,274.87,NJ City,W,13,12,6,38,High School,21,Franchise,Complex
6309,249.81,Boston,N,9,13,9,45,Graduation,22,Patnership,Standalone
2524,140.63,NJ City,W,22,9,25,38,High School,22,Franchise,Complex
460,74.94,NJ City,W,19,15,4,81,High School,28,Franchise,Complex
5872,62.89,Boston,N,25,24,4,32,High School,27,Franchise,Complex
3110,493.22,NJ City,S,4,23,4,44,No education,26,Patnership,Complex
6834,499.81,Boston,S,10,13,1,32,Graduation,32,Patnership,Complex
8149,168.94,NJ City,N,14,8,22,33,Graduation,22,Patnership,Standalone
1285,216.73,NJ City,N,16,8,17,62,High School,34,Patnership,Standalone
5744,1.5,Boston,N,21,12,4,79,No education,19,Franchise,Complex
6413,175.52,Boston,N,24,21,20,71,Graduation,30,Patnership,Standalone
35,107.46,Boston,E,3,14,2,43,No education,26,Owner,Standalone
1681,395.62,NJ City,N,6,20,20,77,High School,33,Patnership,Standalone
9265,481.09,NJ City,N,25,23,24,27,Graduation,25,Patnership,Standalone
51,46.95,Boston,N,22,23,2,48,Graduation,25,Owner,Standalone
9221,290.73,NJ City,N,3,24,22,83,Graduation,30,Patnership,Standalone
9710,487.34,NJ City,S,15,11,14,56,Graduation,18,Patnership,Complex
5273,390.75,Boston,N,1,23,13,75,Graduation,31,Patnership,Standalone
9107,331.01,NJ City,E,19,9,15,57,Graduation,35,Owner,Standalone
8493,478.79,NJ City,N,20,21,6,58,Graduation,18,Patnership,Standalone
4334,278,NJ City,S,3,16,14,76,High School,18,Patnership,Complex
230,340.7,NJ City,S,16,12,5,33,No education,35,Patnership,Complex
7171,59.9,Boston,E,9,8,2,37,High School,18,Owner,Standalone
2679,433.45,NJ City,E,13,18,13,61,High School,25,Owner,Standalone
6523,156.82,Boston,E,25,13,17,74,Graduation,31,Owner,Standalone
6973,224.42,Boston,N,10,14,5,72,High School,29,Patnership,Standalone
7661,411.72,Boston,N,19,22,17,60,Graduation,28,Patnership,Standalone
9056,112.94,NJ City,W,8,16,2,84,No education,31,Franchise,Complex
6818,492.97,Boston,S,8,16,15,38,Graduation,25,Patnership,Complex
4991,410.05,Boston,E,12,23,14,79,High School,23,Owner,Standalone
6775,261.47,Boston,E,3,20,24,47,Graduation,31,Owner,Standalone
5153,76.68,Boston,N,12,20,3,50,No education,27,Patnership,Standalone
1213,152.17,NJ City,N,23,17,10,25,High School,23,Patnership,Standalone
8758,324.92,NJ City,S,16,15,1,72,High School,21,Patnership,Complex
8641,168.07,NJ City,N,4,22,9,75,Graduation,29,Patnership,Standalone
4614,87.44,NJ City,S,5,20,2,43,Graduation,29,Patnership,Complex
9906,50.24,NJ City,S,19,13,4,53,Graduation,18,Patnership,Complex
6454,112.81,Boston,S,21,20,4,81,High School,26,Patnership,Complex
6098,276.51,Boston,S,11,19,7,77,Graduation,19,Patnership,Complex
4571,106.61,NJ City,E,22,23,3,25,No education,24,Owner,Standalone
8145,48.68,NJ City,N,23,23,3,31,Graduation,32,Patnership,Standalone
2412,300.28,NJ City,W,22,21,12,73,High School,20,Franchise,Complex
2701,219.7,NJ City,N,5,10,5,51,High School,18,Patnership,Standalone
2651,173.6,NJ City,E,24,16,23,32,High School,27,Owner,Standalone
7567,393.01,Boston,E,20,13,24,68,Graduation,23,Owner,Standalone
3249,417.17,NJ City,N,17,17,6,49,High School,27,Patnership,Standalone
9359,450.94,NJ City,E,10,22,5,43,No education,20,Owner,Standalone
9695,200.4,NJ City,E,9,22,12,76,Graduation,32,Owner,Standalone
8940,16.76,NJ City,N,20,19,2,66,Graduation,21,Franchise,Complex
4229,106,NJ City,N,1,9,19,35,High School,32,Patnership,Standalone
1789,70.54,NJ City,N,9,21,1,28,High School,23,Patnership,Standalone
3459,11.71,NJ City,N,14,11,1,54,Graduation,28,Owner,Standalone
2361,148.94,NJ City,N,22,12,22,83,High School,19,Patnership,Standalone
5085,357.49,Boston,N,10,10,1,28,Graduation,35,Patnership,Standalone
3268,298.64,NJ City,W,22,19,17,42,High School,23,Franchise,Complex
3407,233.44,NJ City,E,6,24,24,38,High School,30,Owner,Standalone
9700,214.42,NJ City,W,19,10,9,55,Graduation,34,Franchise,Complex
8215,4.88,NJ City,N,2,17,4,61,High School,33,Owner,Standalone
7147,137.66,Boston,E,20,12,7,28,Graduation,34,Owner,Standalone
5575,270.4,Boston,E,10,8,6,58,Graduation,18,Owner,Standalone
8639,283.66,NJ City,E,16,21,17,28,Graduation,26,Owner,Standalone
6163,345.1,Boston,E,4,21,1,30,High School,24,Owner,Standalone
6723,104.78,Boston,E,14,13,2,64,Graduation,24,Owner,Standalone
3869,115.51,NJ City,N,24,11,3,84,No education,33,Patnership,Standalone
9800,173.39,NJ City,W,2,11,3,25,No education,35,Franchise,Complex
7657,176.81,Boston,N,19,8,20,65,Graduation,28,Patnership,Standalone
1147,360.81,NJ City,E,5,20,11,76,High School,27,Owner,Standalone
7677,392.53,Boston,N,12,24,4,25,Graduation,30,Patnership,Standalone
2039,213,NJ City,E,2,15,6,79,High School,29,Owner,Standalone
1700,25.37,NJ City,N,11,17,1,83,No education,30,Franchise,Complex
4665,370.8,NJ City,N,19,21,8,71,High School,26,Patnership,Standalone
3957,185.86,NJ City,N,19,13,22,33,High School,28,Patnership,Standalone
4699,121.08,NJ City,E,20,17,4,49,High School,32,Owner,Standalone
6376,377.43,Boston,W,23,12,18,63,Graduation,32,Franchise,Complex
9141,52.74,NJ City,N,8,20,4,66,Graduation,26,Patnership,Standalone
9096,224.74,NJ City,W,18,17,9,84,Graduation,28,Franchise,Complex
7883,115.38,Boston,E,7,16,1,59,No education,20,Owner,Standalone
1500,364.86,NJ City,W,6,23,12,26,High School,30,Franchise,Complex
9387,193.32,NJ City,E,4,9,22,56,Graduation,26,Owner,Standalone
7159,267.81,Boston,E,11,19,11,78,Graduation,26,Owner,Standalone
777,416.1,NJ City,N,16,21,17,40,High School,26,Patnership,Standalone
6209,28.67,Boston,N,10,14,2,54,No education,19,Patnership,Standalone
2646,385.21,NJ City,S,13,8,13,41,High School,27,Patnership,Complex
7571,73.31,Boston,N,1,11,3,29,No education,30,Owner,Standalone
5322,108.42,Boston,S,9,22,2,62,Graduation,32,Patnership,Complex
6250,209.16,Boston,S,1,17,9,59,Graduation,30,Patnership,Complex
4663,205,NJ City,E,1,8,6,74,High School,20,Owner,Standalone
8932,31.23,NJ City,N,15,14,4,83,High School,22,Franchise,Complex
1169,131,NJ City,N,1,15,19,39,High School,30,Patnership,Standalone
3485,430.37,NJ City,N,11,16,3,40,No education,22,Patnership,Standalone
3159,458.63,NJ City,E,6,20,19,59,High School,24,Owner,Standalone
8276,178.63,NJ City,W,1,12,1,63,No education,30,Franchise,Complex
6378,217.39,Boston,S,18,13,9,56,Graduation,28,Patnership,Complex
6966,157.81,Boston,S,8,10,3,44,Graduation,18,Patnership,Complex
1158,278.34,NJ City,S,13,9,5,52,Graduation,28,Patnership,Complex
3245,67.12,NJ City,N,11,18,4,65,No education,24,Patnership,Standalone
2092,213.43,NJ City,W,6,19,15,66,High School,18,Franchise,Complex
5333,314.27,Boston,N,22,24,15,34,Graduation,24,Patnership,Standalone
1909,41.39,NJ City,N,2,20,4,51,High School,19,Patnership,Standalone
3124,82.41,NJ City,W,21,10,1,55,High School,32,Franchise,Complex
386,241,NJ City,S,3,22,11,35,High School,26,Patnership,Complex
5931,488.81,Boston,E,25,14,7,58,Graduation,19,Owner,Standalone
1809,444.36,NJ City,N,4,10,17,39,High School,25,Patnership,Standalone
4817,77.73,NJ City,N,7,22,2,75,No education,19,Patnership,Standalone
8295,171.59,NJ City,E,22,19,5,54,Graduation,21,Owner,Standalone
23,302.16,NY City,E,3,8,1,57,No education,28,Owner,Standalone
7646,335.76,Boston,S,8,14,10,51,Graduation,23,Patnership,Complex
7477,328.14,Boston,N,11,19,14,45,Graduation,25,Patnership,Standalone
4865,396.75,NJ City,N,18,22,20,45,High School,19,Patnership,Standalone
3618,2.15,NJ City,S,4,16,3,43,Graduation,34,Patnership,Complex
1486,90.53,NJ City,S,10,24,4,73,High School,30,Patnership,Complex
6927,217.89,Boston,E,3,22,23,85,Graduation,29,Owner,Standalone
4719,53.85,NJ City,E,9,12,2,51,Graduation,35,Owner,Standalone
2900,414.38,NJ City,W,9,17,2,82,No education,27,Franchise,Complex
7325,286.85,Boston,N,9,12,17,76,Graduation,25,Patnership,Standalone
2179,125.62,NJ City,E,12,10,1,81,High School,23,Owner,Standalone
7693,322.52,Boston,N,3,17,3,29,High School,27,Patnership,Standalone
722,118.24,NJ City,S,17,24,2,74,No education,28,Patnership,Complex
4902,125,NJ City,S,3,15,13,46,High School,33,Patnership,Complex
9077,83.7,NJ City,N,8,19,3,39,No education,28,Patnership,Standalone
3474,126.48,NJ City,S,21,13,2,27,Graduation,29,Patnership,Complex
8009,482.54,Boston,N,13,23,21,32,Graduation,31,Patnership,Standalone
2216,157.55,NJ City,W,24,21,2,68,No education,19,Franchise,Complex
3118,249.13,NJ City,S,23,18,10,46,High School,28,Patnership,Complex
6404,2.93,Boston,W,1,19,3,39,No education,32,Franchise,Complex
8939,203.32,NJ City,E,9,13,21,35,Graduation,18,Owner,Standalone
1566,318,NJ City,S,1,19,12,79,High School,28,Patnership,Complex
9570,427.49,NJ City,S,10,11,14,69,Graduation,34,Patnership,Complex
6686,56.84,Boston,S,14,17,4,36,No education,22,Patnership,Complex
8887,89.91,NJ City,E,8,19,2,38,High School,34,Owner,Standalone
1569,332.99,NJ City,N,21,15,3,82,Graduation,25,Patnership,Standalone
3472,7.65,NJ City,N,24,10,1,51,High School,28,Franchise,Complex
7284,339.8,Boston,W,23,22,9,41,Graduation,20,Franchise,Complex
3535,318.34,NJ City,E,16,13,18,64,High School,25,Owner,Standalone
6753,130.89,Boston,N,6,12,16,65,Graduation,20,Patnership,Standalone
8660,450.36,NJ City,W,11,12,17,70,Graduation,33,Franchise,Complex
701,325.83,NJ City,N,22,17,19,25,High School,25,Patnership,Standalone
8830,66.54,NJ City,N,19,9,2,40,High School,30,Patnership,Complex
1982,323.27,NJ City,S,11,22,10,81,High School,19,Patnership,Complex
4945,25.16,Boston,N,13,14,2,69,High School,21,Patnership,Standalone
6067,316.67,Boston,E,24,23,7,42,Graduation,35,Owner,Standalone
4266,53.38,NJ City,S,16,17,3,55,Graduation,24,Patnership,Complex
2857,379.5,NJ City,N,8,22,10,63,High School,21,Patnership,Standalone
9571,155.62,NJ City,E,17,21,19,73,Graduation,19,Owner,Standalone
9048,164.95,NJ City,W,8,24,4,82,Graduation,18,Franchise,Complex
742,8.59,NJ City,N,11,16,1,37,High School,33,Patnership,Complex
5964,198.33,Boston,W,11,9,10,41,Graduation,21,Franchise,Complex
4828,27.64,NJ City,N,21,18,1,80,High School,28,Franchise,Complex
3858,258,NJ City,S,13,23,1,55,Graduation,32,Patnership,Complex
5228,444.26,Boston,W,22,23,11,61,Graduation,25,Franchise,Complex
918,421.33,NJ City,S,12,19,12,80,High School,25,Patnership,Complex
3130,246.01,NJ City,S,18,21,4,33,High School,26,Patnership,Complex
2178,280.92,NJ City,S,25,17,25,58,High School,26,Patnership,Complex
3608,166.15,NJ City,W,3,20,1,41,No education,23,Franchise,Complex
2018,209.28,NJ City,S,9,18,13,34,High School,31,Patnership,Complex
2408,99.59,NJ City,W,5,22,3,54,No education,35,Franchise,Complex
2086,453.56,NJ City,S,13,24,16,28,High School,31,Patnership,Complex
4046,262,NJ City,S,2,15,21,41,High School,27,Patnership,Complex
5435,107.31,Boston,E,6,16,4,48,No education,22,Owner,Standalone
8764,121.84,NJ City,W,12,15,2,71,High School,32,Franchise,Complex
7387,166.13,Boston,E,16,16,19,51,Graduation,18,Owner,Standalone
1897,80.32,NJ City,N,7,24,1,30,High School,35,Patnership,Standalone
4065,273.32,NJ City,N,4,19,19,33,High School,23,Patnership,Standalone
8285,304.41,NJ City,N,9,9,7,26,Graduation,31,Patnership,Standalone
2196,279.05,NJ City,W,10,19,3,41,Graduation,33,Franchise,Complex
3729,465.5,NJ City,N,20,19,21,54,High School,33,Patnership,Standalone
1006,430.87,NJ City,S,14,15,14,43,High School,30,Patnership,Complex
6408,367.14,Boston,W,24,20,22,53,Graduation,24,Franchise,Complex
3872,210.22,NJ City,W,24,8,4,51,No education,28,Franchise,Complex
2140,376.84,NJ City,W,20,23,15,57,High School,18,Franchise,Complex
306,143.04,NJ City,S,23,8,16,60,High School,31,Patnership,Complex
3598,239.36,NJ City,S,14,9,18,57,High School,29,Patnership,Complex
3226,233,NJ City,S,2,14,6,56,High School,20,Patnership,Complex
1019,37.27,NJ City,N,10,19,1,55,No education,32,Owner,Standalone
8138,136.99,NJ City,S,1,20,11,48,Graduation,35,Patnership,Complex
9961,3.44,NJ City,N,14,16,1,60,High School,29,Patnership,Standalone
2796,346.39,NJ City,W,13,23,17,36,High School,18,Franchise,Complex
6610,198.7,Boston,S,19,8,24,46,Graduation,30,Patnership,Complex
3216,294.87,NJ City,W,4,18,6,41,High School,19,Franchise,Complex
874,481.44,NJ City,S,7,10,7,75,High School,28,Patnership,Complex
8868,231.87,NJ City,W,10,13,24,50,Graduation,24,Franchise,Complex
7811,400.85,Boston,E,11,8,9,52,Graduation,19,Owner,Standalone
9872,469.37,NJ City,W,4,19,2,54,No education,22,Franchise,Complex
2702,90.37,NJ City,S,2,23,2,70,No education,32,Patnership,Complex
4712,348.07,NJ City,W,20,18,5,29,No education,21,Franchise,Complex
926,390.71,NJ City,S,22,18,12,37,High School,28,Patnership,Complex
7814,403.78,Boston,S,4,19,7,81,Graduation,21,Patnership,Complex
3692,225.83,NJ City,W,19,18,14,79,High School,18,Franchise,Complex
9696,157.26,NJ City,W,18,24,14,53,Graduation,33,Franchise,Complex
8831,187.27,NJ City,E,3,16,12,48,Graduation,26,Owner,Standalone
4935,110.52,NJ City,E,21,15,4,34,Graduation,21,Owner,Standalone
6974,387.08,Boston,S,22,22,2,33,No education,29,Patnership,Complex
2591,161,NJ City,E,3,18,22,44,High School,18,Owner,Standalone
840,451.2,NJ City,W,8,8,2,28,Graduation,28,Franchise,Complex
5009,451.35,Boston,N,22,19,14,82,Graduation,19,Patnership,Standalone
6819,206.62,Boston,E,5,15,1,42,Graduation,35,Owner,Standalone
9239,4.33,NJ City,N,22,18,3,42,No education,25,Owner,Standalone
5063,45.98,Boston,N,15,13,2,70,No education,30,Owner,Standalone
3785,58.1,NJ City,N,5,19,4,38,No education,31,Patnership,Standalone
9555,14.64,NJ City,N,15,18,1,84,Graduation,23,Owner,Standalone
4898,356.12,NJ City,S,20,8,15,74,High School,31,Patnership,Complex
8985,15.23,NJ City,N,7,20,1,83,Graduation,24,Patnership,Standalone
2066,16.96,NJ City,N,20,20,4,68,No education,34,Patnership,Complex
133,235,NJ City,N,1,21,25,69,High School,30,Patnership,Standalone
9818,55.75,NJ City,S,17,14,3,56,No education,23,Patnership,Complex
3491,480.29,NJ City,E,15,10,1,46,No education,23,Owner,Standalone
3154,127.9,NJ City,S,17,22,2,34,High School,21,Patnership,Complex
1299,173.68,NJ City,E,18,19,24,56,High School,18,Owner,Standalone
3620,100.38,NJ City,W,19,21,4,56,No education,27,Franchise,Complex
2194,89.67,NJ City,S,6,15,4,26,High School,22,Patnership,Complex
2604,344.83,NJ City,W,4,22,5,60,Graduation,32,Franchise,Complex
2853,131.92,NJ City,N,11,15,3,71,Graduation,34,Patnership,Standalone
6361,366.5,Boston,N,1,13,21,79,Graduation,21,Patnership,Standalone
1772,221.45,NJ City,W,13,9,23,26,High School,22,Franchise,Complex
2230,138.95,NJ City,S,1,10,3,75,High School,22,Patnership,Complex
135,103.04,NJ City,E,10,22,1,41,Graduation,33,Owner,Standalone
1860,69.5,NJ City,N,17,19,3,63,Graduation,30,Franchise,Complex
3597,228.48,NJ City,N,5,16,7,65,High School,27,Patnership,Standalone
6710,376.24,Boston,S,18,14,17,58,Graduation,27,Patnership,Complex
3628,446.69,NJ City,W,14,11,16,56,High School,35,Franchise,Complex
1092,460.38,NJ City,W,12,23,10,62,High School,23,Franchise,Complex
8781,360.66,NJ City,N,24,21,18,62,Graduation,31,Patnership,Standalone
2215,246.29,NJ City,E,11,10,13,60,High School,28,Owner,Standalone
6290,186.37,Boston,S,24,10,10,36,Graduation,19,Patnership,Complex
5061,329.57,Boston,N,23,19,9,26,Graduation,34,Patnership,Standalone
5114,302.57,Boston,S,4,13,17,80,Graduation,29,Patnership,Complex
3717,6.62,NJ City,N,2,13,3,68,Graduation,27,Patnership,Standalone
4181,286,NJ City,N,2,23,21,51,High School,23,Patnership,Standalone
932,439.06,NJ City,W,4,20,15,35,High School,32,Franchise,Complex
9724,232.01,NJ City,W,2,20,25,45,Graduation,27,Franchise,Complex
9879,175.64,NJ City,E,17,10,18,63,Graduation,20,Owner,Standalone
8606,172.32,NJ City,S,20,14,4,25,No education,32,Patnership,Complex
1237,416.73,NJ City,N,5,24,24,65,High School,28,Patnership,Standalone
9887,140.48,NJ City,E,9,16,10,43,Graduation,18,Owner,Standalone
2089,377.17,NJ City,N,24,11,20,25,High School,25,Patnership,Standalone
382,77.84,NJ City,S,13,20,3,29,High School,26,Patnership,Complex
1482,178.11,NJ City,S,20,21,6,26,High School,27,Patnership,Complex
6780,241.33,Boston,W,9,15,11,77,Graduation,33,Franchise,Complex
1818,51.95,NJ City,S,24,17,2,67,Graduation,22,Patnership,Complex
260,245.26,NJ City,W,25,19,14,75,High School,24,Franchise,Complex
8171,445.64,NJ City,E,18,20,13,37,Graduation,23,Owner,Standalone
6395,258.32,Boston,E,23,24,22,51,Graduation,30,Owner,Standalone
9092,205.51,NJ City,W,20,17,16,74,Graduation,25,Franchise,Complex
4311,442.73,NJ City,E,13,21,22,76,High School,24,Owner,Standalone
10000,159,NJ City,W,16,24,12,59,Graduation,35,Franchise,Complex
8299,330.28,NJ City,E,11,21,5,68,High School,33,Owner,Standalone
2136,307.01,NJ City,W,16,14,18,68,High School,22,Franchise,Complex
4075,315,NJ City,E,1,24,12,49,High School,33,Owner,Standalone
3846,263.78,NJ City,S,4,22,22,81,High School,32,Patnership,Complex
2446,166.09,NJ City,S,12,20,20,63,High School,29,Patnership,Complex
7243,299.7,Boston,E,6,21,15,67,Graduation,22,Owner,Standalone
7316,207.18,Boston,W,3,22,3,29,No education,18,Franchise,Complex
8615,472.84,NJ City,E,17,20,19,46,Graduation,18,Owner,Standalone
6420,83.73,Boston,W,22,11,2,53,Graduation,18,Franchise,Complex
1699,103.95,NJ City,E,6,18,2,85,High School,30,Owner,Standalone
7101,250.54,Boston,N,17,23,6,26,Graduation,34,Patnership,Standalone
9927,436.79,NJ City,E,13,15,4,27,Graduation,31,Owner,Standalone
2174,468.13,NJ City,S,11,9,10,33,High School,27,Patnership,Complex
2640,429.66,NJ City,W,13,18,12,57,High School,33,Franchise,Complex
8442,389.1,NJ City,S,12,13,17,71,Graduation,31,Patnership,Complex
9179,297.11,NJ City,E,20,24,7,36,Graduation,25,Owner,Standalone
4225,499.4,NJ City,N,14,8,24,68,High School,28,Patnership,Standalone
7091,438.74,Boston,E,14,17,17,78,Graduation,25,Owner,Standalone
293,20.49,NJ City,N,24,11,1,54,No education,34,Patnership,Standalone
5679,230.27,Boston,E,20,8,14,28,Graduation,23,Owner,Standalone
7881,187.2,Boston,N,24,11,24,80,Graduation,32,Patnership,Standalone
9983,302.9,NJ City,E,15,24,16,47,Graduation,22,Owner,Standalone
7676,317.61,Boston,W,3,15,20,48,Graduation,30,Franchise,Complex
9744,7.73,NJ City,N,7,22,2,28,Graduation,28,Franchise,Complex
4037,480.56,NJ City,N,10,17,22,54,High School,20,Patnership,Standalone
5661,356.8,Boston,N,6,19,17,65,Graduation,19,Patnership,Standalone
4958,229.5,Boston,S,17,11,15,33,High School,26,Patnership,Complex
822,322.28,NJ City,S,17,22,19,49,High School,24,Patnership,Complex
6313,226.89,Boston,N,6,17,4,77,High School,26,Patnership,Standalone
4709,151.29,NJ City,N,12,8,20,41,High School,25,Patnership,Standalone
2549,238.41,NJ City,N,21,15,4,80,No education,25,Patnership,Standalone
3464,86.31,NJ City,W,8,10,3,30,No education,22,Franchise,Complex
3020,169.5,NJ City,W,12,16,12,49,High School,28,Franchise,Complex
3010,63.17,NJ City,N,9,8,4,67,High School,22,Patnership,Complex
9532,58.82,NJ City,W,9,8,2,69,High School,25,Franchise,Complex
1758,403.92,NJ City,S,20,14,24,50,High School,22,Patnership,Complex
9924,470.37,NJ City,W,3,20,21,58,Graduation,29,Franchise,Complex
6434,319.36,Boston,S,11,9,7,28,Graduation,26,Patnership,Complex
2822,272.68,NJ City,S,20,21,4,66,No education,19,Patnership,Complex
350,232.05,NJ City,S,6,14,22,75,High School,24,Patnership,Complex
1579,240.23,NJ City,E,18,21,19,84,High School,35,Owner,Standalone
5404,363.74,Boston,W,12,22,15,54,Graduation,34,Franchise,Complex
5838,217.17,Boston,S,24,9,6,85,Graduation,28,Patnership,Complex
3061,47.13,NJ City,N,11,20,1,37,High School,21,Patnership,Standalone
4531,6.65,NJ City,N,2,9,1,25,High School,20,Owner,Standalone
4226,347.13,NJ City,S,19,12,14,78,High School,25,Patnership,Complex
54,212.17,Maine,S,4,22,22,60,High School,26,Patnership,Complex
5932,309.73,Boston,W,17,14,12,45,Graduation,25,Franchise,Complex
8402,143.25,NJ City,S,7,24,1,39,No education,19,Patnership,Complex
6460,352.3,Boston,W,9,11,24,58,Graduation,23,Franchise,Complex
2662,161.64,NJ City,S,9,22,3,27,High School,22,Patnership,Complex
9514,248.08,NJ City,S,23,16,21,69,Graduation,26,Patnership,Complex
6767,343.25,Boston,E,11,18,11,82,Graduation,33,Owner,Standalone
3447,174.36,NJ City,E,12,17,25,49,High School,21,Owner,Standalone
5840,108.91,Boston,W,6,16,1,34,No education,23,Franchise,Complex
127,274.88,NJ City,E,8,12,1,84,High School,24,Owner,Standalone
6860,345.22,Boston,W,7,16,23,54,Graduation,19,Franchise,Complex
685,455.08,NJ City,N,13,20,5,26,High School,23,Patnership,Standalone
1698,6.88,NJ City,N,5,24,3,27,Graduation,35,Patnership,Complex
7270,370.86,Boston,S,8,15,17,80,Graduation,19,Patnership,Complex
6898,234.26,Boston,S,1,20,16,62,Graduation,32,Patnership,Complex
5448,335.39,Boston,W,20,21,13,31,Graduation,32,Franchise,Complex
1171,424.19,NJ City,E,9,18,9,35,High School,29,Owner,Standalone
9270,378.94,NJ City,S,13,18,24,63,Graduation,29,Patnership,Complex
475,309.95,NJ City,E,14,22,22,53,High School,29,Owner,Standalone
3951,5.11,NJ City,N,19,16,2,32,Graduation,26,Owner,Standalone
5299,426.81,Boston,E,11,22,11,46,Graduation,22,Owner,Standalone
2840,278.56,NJ City,W,13,23,6,61,High School,32,Franchise,Complex
6939,478.98,Boston,E,12,8,5,71,Graduation,32,Owner,Standalone
8400,332.37,NJ City,W,25,14,2,28,Graduation,26,Franchise,Complex
7073,314.69,Boston,N,16,19,23,37,Graduation,28,Patnership,Standalone
126,425.63,NJ City,S,20,15,11,60,High School,20,Patnership,Complex
7044,34.32,Boston,N,16,17,2,59,Graduation,26,Franchise,Complex
1493,225.12,NJ City,N,5,24,22,76,High School,23,Patnership,Standalone
2736,442.98,NJ City,W,8,14,18,50,High School,30,Franchise,Complex
9256,257.29,NJ City,W,10,22,24,76,Graduation,25,Franchise,Complex
1175,274.33,NJ City,E,16,20,20,31,High School,33,Owner,Standalone
8802,270.1,NJ City,S,18,10,11,30,Graduation,27,Patnership,Complex
2094,303.65,NJ City,S,8,14,4,44,Graduation,22,Patnership,Complex
9439,66.98,NJ City,N,15,17,1,66,High School,32,Owner,Standalone
4447,363.01,NJ City,E,24,15,22,52,High School,30,Owner,Standalone
5475,448.69,Boston,E,14,8,19,42,Graduation,34,Owner,Standalone
2118,331.99,NJ City,S,20,24,10,26,High School,31,Patnership,Complex
4749,226.38,NJ City,N,6,12,2,28,Graduation,21,Patnership,Standalone
7914,154.57,Boston,S,16,15,13,54,Graduation,26,Patnership,Complex
5969,45.37,Boston,N,17,14,2,73,No education,27,Patnership,Standalone
791,108.19,NJ City,E,23,16,1,68,No education,35,Owner,Standalone
2247,380.66,NJ City,E,6,20,14,78,High School,34,Owner,Standalone
1179,330.88,NJ City,E,19,14,10,63,High School,25,Owner,Standalone
8084,301.79,NJ City,W,10,8,13,80,Graduation,33,Franchise,Complex
7436,432.01,Boston,W,8,22,21,37,Graduation,35,Franchise,Complex
7471,378.1,Boston,E,5,9,16,42,Graduation,26,Owner,Standalone
2956,50.55,NJ City,W,22,17,2,78,High School,25,Franchise,Complex
3397,48.14,NJ City,N,20,19,1,34,High School,26,Patnership,Standalone
3606,262.07,NJ City,S,22,10,4,27,Graduation,35,Patnership,Complex
6489,164.55,Boston,N,17,8,12,41,Graduation,35,Patnership,Standalone
8467,26.19,NJ City,N,12,14,3,40,High School,35,Owner,Standalone
2872,469.51,NJ City,W,6,17,10,84,High School,20,Franchise,Complex
7058,420.72,Boston,S,5,15,19,43,Graduation,19,Patnership,Complex
9215,192.44,NJ City,E,13,11,10,85,Graduation,25,Owner,Standalone
7144,37.45,Boston,N,6,22,2,56,High School,34,Franchise,Complex
243,52.42,NJ City,E,20,15,2,67,Graduation,19,Owner,Standalone
6316,53.08,Boston,W,20,11,4,30,High School,27,Franchise,Complex
3891,11.45,NJ City,N,20,10,4,54,Graduation,30,Owner,Standalone
6402,488.55,Boston,S,3,16,20,77,Graduation,31,Patnership,Complex
5602,352.84,Boston,S,9,14,14,25,Graduation,26,Patnership,Complex
7030,165.02,Boston,S,16,18,11,50,Graduation,26,Patnership,Complex
2074,109.8,NJ City,S,19,10,2,54,High School,26,Patnership,Complex
7083,195.18,Boston,E,14,22,6,57,Graduation,29,Owner,Standalone
1296,333.67,NJ City,W,8,23,6,84,High School,24,Franchise,Complex
9323,269.11,NJ City,E,16,23,9,53,Graduation,30,Owner,Standalone
2253,105.05,NJ City,N,2,9,1,44,Graduation,21,Patnership,Standalone
6319,258.56,Boston,E,14,14,15,70,Graduation,18,Owner,Standalone
1875,128.16,NJ City,E,22,18,9,26,High School,29,Owner,Standalone
3206,470.11,NJ City,S,11,16,1,81,No education,22,Patnership,Complex
9843,296.74,NJ City,E,10,13,3,51,Graduation,33,Owner,Standalone
2874,257.6,NJ City,S,12,23,6,26,High School,31,Patnership,Complex
6233,341.52,Boston,N,18,24,1,39,No education,21,Patnership,Standalone
4877,433.94,NJ City,N,8,22,13,49,High School,34,Patnership,Standalone
1580,83.39,NJ City,W,19,18,4,34,No education,25,Franchise,Complex
9319,245.03,NJ City,E,1,11,25,56,Graduation,22,Owner,Standalone
6392,268.94,Boston,W,14,11,10,47,Graduation,21,Franchise,Complex
1689,458.87,NJ City,N,17,15,4,25,Graduation,32,Patnership,Standalone
5424,237.07,Boston,W,11,22,24,72,Graduation,35,Franchise,Complex
1697,123.46,NJ City,N,10,10,3,36,No education,32,Patnership,Standalone
6152,127.88,Boston,W,10,13,2,37,No education,18,Franchise,Complex
7125,158.59,Boston,N,23,20,24,80,Graduation,35,Patnership,Standalone
8315,78.16,NJ City,E,24,12,4,76,No education,21,Owner,Standalone
2642,439.3,NJ City,S,22,13,4,60,No education,33,Patnership,Complex
1182,293.57,NJ City,S,10,18,7,48,High School,34,Patnership,Complex
9691,476.08,NJ City,E,11,15,10,26,Graduation,30,Owner,Standalone
574,75.3,NJ City,S,10,19,3,41,High School,18,Patnership,Complex
4286,158.12,NJ City,S,7,21,7,56,High School,33,Patnership,Complex
2801,322.86,NJ City,N,23,9,7,60,High School,34,Patnership,Standalone
1472,259.17,NJ City,W,13,19,21,38,High School,24,Franchise,Complex
5702,48.63,Boston,S,1,19,4,71,No education,35,Patnership,Complex
9111,103.4,NJ City,E,4,15,3,82,Graduation,29,Owner,Standalone
6464,403.26,Boston,W,11,9,22,52,Graduation,30,Franchise,Complex
8543,40.84,NJ City,N,15,14,2,35,No education,23,Owner,Standalone
4770,100.28,NJ City,S,21,20,3,60,Graduation,26,Patnership,Complex
6140,108.26,Boston,W,23,11,1,28,No education,26,Franchise,Complex
3913,270.08,NJ City,N,13,11,11,35,High School,33,Patnership,Standalone
632,101,NJ City,W,1,13,14,85,High School,22,Franchise,Complex
9758,451.61,NJ City,S,12,9,12,49,Graduation,28,Patnership,Complex
8456,194.22,NJ City,W,9,13,14,39,Graduation,31,Franchise,Complex
8080,300.54,NJ City,W,20,17,17,68,Graduation,30,Franchise,Complex
5481,368.46,Boston,N,3,20,21,29,Graduation,25,Patnership,Standalone
567,333.33,NJ City,E,13,22,11,39,High School,34,Owner,Standalone
1741,71.08,NJ City,N,23,22,4,55,High School,21,Patnership,Standalone
7413,494.21,Boston,N,23,19,15,81,Graduation,20,Patnership,Standalone
2246,10.63,NJ City,N,22,24,2,59,No education,26,Patnership,Complex
2142,467.12,NJ City,S,14,20,11,63,High School,26,Patnership,Complex
3836,246.95,NJ City,W,13,9,22,43,High School,30,Franchise,Complex
7290,436.51,Boston,S,21,13,18,35,Graduation,29,Patnership,Complex
4045,134.49,NJ City,N,16,23,1,55,High School,30,Patnership,Standalone
7172,16.87,Boston,N,16,19,1,38,No education,18,Franchise,Complex
3602,54.31,NJ City,S,19,22,1,29,No education,27,Patnership,Complex
4899,295,NJ City,E,2,20,7,26,High School,33,Owner,Standalone
3190,145.13,NJ City,S,20,11,14,32,High School,30,Patnership,Complex
8316,433.84,NJ City,W,18,16,18,73,Graduation,34,Franchise,Complex
8097,450.19,NJ City,N,13,18,6,71,Graduation,21,Patnership,Standalone
424,326.9,NJ City,W,20,14,16,25,High School,21,Franchise,Complex
2818,321.62,NJ City,S,11,15,25,42,High School,30,Patnership,Complex
3217,79.94,NJ City,N,11,11,4,32,High School,28,Patnership,Standalone
9516,82.24,NJ City,W,7,11,3,82,Graduation,27,Franchise,Complex
630,358.5,NJ City,S,15,18,15,82,High School,27,Patnership,Complex
3989,284.15,NJ City,N,12,8,20,73,High School,19,Patnership,Standalone
3142,388.98,NJ City,S,4,12,14,70,High School,21,Patnership,Complex
2629,465.23,NJ City,N,7,11,23,61,High School,20,Patnership,Standalone
8229,465.99,NJ City,N,14,15,3,77,Graduation,26,Patnership,Standalone
6651,145.57,Boston,E,15,11,23,70,Graduation,34,Owner,Standalone
6300,224.98,Boston,W,10,13,21,73,Graduation,34,Franchise,Complex
9146,390.98,NJ City,S,2,14,7,34,Graduation,33,Patnership,Complex
7494,222.36,Boston,S,11,9,11,77,Graduation,22,Patnership,Complex
3654,318.45,NJ City,S,21,23,2,43,Graduation,28,Patnership,Complex
8347,341.21,NJ City,E,3,15,12,60,Graduation,18,Owner,Standalone
659,293.83,NJ City,E,24,15,10,69,High School,21,Owner,Standalone
7007,364,Boston,E,16,22,5,45,No education,29,Owner,Standalone
9063,227.18,NJ City,E,11,12,3,80,Graduation,35,Owner,Standalone
6134,118.22,Boston,S,19,23,3,30,No education,26,Patnership,Complex
915,127.45,NJ City,E,20,13,1,46,Graduation,32,Owner,Standalone
2677,164.09,NJ City,N,9,16,6,50,High School,29,Patnership,Standalone
2540,362.99,NJ City,W,12,18,13,45,High School,29,Franchise,Complex
9024,291.94,NJ City,W,24,9,20,58,Graduation,25,Franchise,Complex
6980,397,Boston,W,9,18,20,76,Graduation,33,Franchise,Complex
1327,269.81,NJ City,E,7,23,22,60,High School,32,Owner,Standalone
4106,35.51,NJ City,N,11,24,3,47,No education,34,Patnership,Complex
7889,188.15,Boston,N,17,24,9,62,Graduation,22,Patnership,Standalone
7349,420.18,Boston,N,17,9,12,65,Graduation,27,Patnership,Standalone
2628,92.87,NJ City,W,14,22,2,52,Graduation,22,Franchise,Complex
1879,378.21,NJ City,E,10,22,5,58,High School,25,Owner,Standalone
4084,185.24,NJ City,W,21,20,25,51,High School,22,Franchise,Complex
9332,229.29,NJ City,W,2,17,5,29,No education,20,Franchise,Complex
1041,315.39,NJ City,N,17,8,6,81,High School,32,Patnership,Standalone
4054,307.76,NJ City,S,2,11,4,33,High School,25,Patnership,Complex
3519,61.97,NJ City,N,1,10,3,56,Graduation,34,Owner,Standalone
9720,5.83,NJ City,N,2,8,3,54,Graduation,33,Franchise,Complex
1608,223,NJ City,W,2,9,7,48,High School,27,Franchise,Complex
2497,128.81,NJ City,N,24,14,18,57,High School,22,Patnership,Standalone
8852,219.03,NJ City,W,14,14,7,50,Graduation,18,Franchise,Complex
6029,298.41,Boston,N,15,12,1,41,No education,32,Patnership,Standalone
3071,267.8,NJ City,E,15,22,22,40,High School,31,Owner,Standalone
5818,181.28,Boston,S,3,15,7,65,Graduation,33,Patnership,Complex
5988,257.65,Boston,W,1,16,8,75,Graduation,20,Franchise,Complex
402,329.46,NJ City,S,21,8,20,74,High School,20,Patnership,Complex
7089,0.26,Boston,N,7,21,4,57,Graduation,23,Patnership,Standalone
3611,478.64,NJ City,E,17,23,24,38,High School,19,Owner,Standalone
6596,171.56,Boston,W,24,17,22,59,Graduation,27,Franchise,Complex
712,349.37,NJ City,W,18,17,6,49,High School,31,Franchise,Complex
237,256.26,NJ City,N,9,18,6,51,High School,31,Patnership,Standalone
9254,495.62,NJ City,S,9,21,19,47,Graduation,25,Patnership,Complex
3875,258.13,NJ City,E,11,15,14,65,High School,27,Owner,Standalone
8218,150.05,NJ City,S,4,12,23,63,Graduation,33,Patnership,Complex
1271,232.62,NJ City,E,6,14,6,72,High School,25,Owner,Standalone
3410,22.52,NJ City,N,5,11,3,69,No education,21,Patnership,Complex
7526,102.5,Boston,S,24,24,1,32,No education,22,Patnership,Complex
9395,242.99,NJ City,E,25,18,21,41,Graduation,33,Owner,Standalone
2717,307.12,NJ City,N,21,11,19,41,High School,34,Patnership,Standalone
9471,336.95,NJ City,E,12,14,6,32,Graduation,26,Owner,Standalone
9362,210.92,NJ City,S,11,22,9,25,Graduation,31,Patnership,Complex
3583,95.82,NJ City,E,12,14,4,67,High School,24,Owner,Standalone
160,220.01,NJ City,W,6,24,10,74,High School,19,Franchise,Complex
2707,161.55,NJ City,E,12,14,18,71,High School,23,Owner,Standalone
515,420.27,NJ City,E,21,8,9,65,High School,19,Owner,Standalone
692,418.91,NJ City,W,4,24,23,80,High School,25,Franchise,Complex
4727,294.46,NJ City,E,6,24,10,45,High School,21,Owner,Standalone
9519,1.34,NJ City,N,3,16,2,38,Graduation,19,Owner,Standalone
8210,390.66,NJ City,S,17,13,10,79,Graduation,28,Patnership,Complex
4955,41.8,Boston,N,6,24,1,35,No education,23,Owner,Standalone
4066,229.84,NJ City,S,25,17,6,34,High School,21,Patnership,Complex
7608,199.47,Boston,W,12,20,24,43,Graduation,20,Franchise,Complex
9986,165.63,NJ City,S,1,24,4,38,No education,23,Patnership,Complex
6890,153.32,Boston,S,12,9,1,62,No education,27,Patnership,Complex
3068,44.03,NJ City,N,24,9,1,54,No education,28,Franchise,Complex
7835,25.53,Boston,N,5,15,2,44,No education,23,Owner,Standalone
7551,400.49,Boston,E,19,24,4,35,Graduation,23,Owner,Standalone
5125,129.95,Boston,N,19,23,1,66,High School,33,Patnership,Standalone
8196,308.66,NJ City,W,24,16,7,35,Graduation,18,Franchise,Complex
1277,162,NJ City,N,13,23,20,25,High School,22,Patnership,Standalone
7679,395.84,Boston,E,24,11,10,82,Graduation,23,Owner,Standalone
7495,51.28,Boston,E,8,10,4,27,High School,19,Owner,Standalone
3802,237,NJ City,S,2,15,19,58,High School,27,Patnership,Complex
9290,339.46,NJ City,S,21,23,3,55,No education,34,Patnership,Complex
461,60.74,NJ City,N,13,12,3,39,No education,21,Patnership,Standalone
7029,393.1,Boston,N,24,18,3,47,Graduation,29,Patnership,Standalone
116,339.76,NJ City,W,23,13,2,37,No education,24,Franchise,Complex
7839,132.09,Boston,E,19,12,20,55,Graduation,19,Owner,Standalone
4903,79.65,NJ City,E,18,15,2,41,High School,32,Owner,Standalone
3009,424.1,NJ City,N,5,22,13,61,High School,28,Patnership,Standalone
6451,242.73,Boston,E,4,13,25,33,Graduation,22,Owner,Standalone
1930,5.8,NJ City,N,8,24,2,62,High School,29,Patnership,Complex
1984,56.5,NJ City,W,18,16,1,65,High School,29,Franchise,Complex
5604,478.2,Boston,W,8,18,8,55,Graduation,25,Franchise,Complex
8965,387.49,NJ City,N,12,22,11,33,Graduation,33,Patnership,Standalone
9299,442.48,NJ City,E,8,21,17,49,Graduation,28,Owner,Standalone
2514,181.04,NJ City,S,25,24,6,76,High School,24,Patnership,Complex
8898,223.64,NJ City,S,25,19,9,75,Graduation,35,Patnership,Complex
8413,477,NJ City,N,23,8,1,74,High School,28,Patnership,Standalone
1368,35.27,NJ City,N,20,11,4,44,Graduation,27,Franchise,Complex
227,499.36,NJ City,E,19,20,12,29,High School,30,Owner,Standalone
49,9.25,Boston,N,3,9,2,33,High School,25,Patnership,Standalone
5455,387.07,Boston,E,18,8,15,57,Graduation,23,Owner,Standalone
5123,110.17,Boston,E,14,16,3,81,No education,23,Owner,Standalone
4452,25.56,NJ City,N,25,18,1,39,Graduation,31,Franchise,Complex
3345,419.07,NJ City,N,15,18,20,62,High School,32,Patnership,Standalone
6787,199.39,Boston,E,20,18,22,80,Graduation,29,Owner,Standalone
2293,134.4,NJ City,N,9,9,2,34,High School,23,Patnership,Standalone
5899,463.08,Boston,E,9,11,6,40,Graduation,31,Owner,Standalone
493,387.41,NJ City,N,6,23,22,83,High School,19,Patnership,Standalone
3899,150.65,NJ City,E,4,13,16,73,High School,32,Owner,Standalone
6711,142.66,Boston,E,21,17,23,48,Graduation,34,Owner,Standalone
8902,313.79,NJ City,S,17,23,1,45,High School,24,Patnership,Complex
8393,379.4,NJ City,N,20,11,22,84,Graduation,19,Patnership,Standalone
1290,191.04,NJ City,S,1,18,2,57,Graduation,32,Patnership,Complex
7269,410.4,Boston,N,16,8,13,67,Graduation,25,Patnership,Standalone
9032,471.14,NJ City,W,2,18,17,65,Graduation,26,Franchise,Complex
9322,382.32,NJ City,S,22,19,3,66,High School,31,Patnership,Complex
6465,56,Boston,N,13,18,3,65,Graduation,32,Patnership,Standalone
204,49.59,NJ City,W,24,8,2,29,Graduation,26,Franchise,Complex
560,14.73,NJ City,N,16,12,4,74,No education,33,Franchise,Complex
2383,343.43,NJ City,E,13,22,10,37,High School,24,Owner,Standalone
8538,36.66,NJ City,N,16,22,1,80,Graduation,34,Patnership,Complex
9992,394.74,NJ City,W,24,8,4,30,No education,33,Franchise,Complex
7690,316.51,Boston,S,15,19,4,35,High School,26,Patnership,Complex
7706,331.84,Boston,S,9,17,17,55,Graduation,21,Patnership,Complex
2186,449.37,NJ City,S,8,15,16,51,High School,30,Patnership,Complex
4798,133.81,NJ City,S,15,21,20,50,High School,21,Patnership,Complex
7773,435.02,Boston,N,11,18,2,44,Graduation,22,Patnership,Standalone
9489,459.71,NJ City,N,10,12,25,58,Graduation,31,Patnership,Standalone
5936,336.83,Boston,W,7,22,2,76,No education,22,Franchise,Complex
2584,433.78,NJ City,W,23,13,5,63,High School,32,Franchise,Complex
4315,229.4,NJ City,E,15,11,12,76,High School,21,Owner,Standalone
2009,105.26,NJ City,N,16,23,1,70,No education,32,Patnership,Standalone
1148,251.5,NJ City,W,5,16,25,48,High School,34,Franchise,Complex
8846,481.03,NJ City,S,13,15,9,41,Graduation,26,Patnership,Complex
7727,417.43,Boston,E,14,18,25,67,Graduation,32,Owner,Standalone
1955,114,NJ City,E,3,9,14,77,High School,35,Owner,Standalone
4372,34.89,NJ City,N,24,22,3,40,High School,20,Franchise,Complex
3394,338.94,NJ City,S,6,12,19,70,High School,18,Patnership,Complex
3710,300.41,NJ City,S,4,8,18,56,High School,24,Patnership,Complex
8634,16.9,NJ City,N,25,20,1,27,Graduation,27,Patnership,Complex
7655,71.95,Boston,N,2,16,1,29,No education,28,Owner,Standalone
7500,317.89,Boston,W,10,20,15,49,Graduation,21,Franchise,Complex
5477,252.24,Boston,N,16,12,19,27,Graduation,26,Patnership,Standalone
829,266.93,NJ City,N,5,18,15,40,High School,33,Patnership,Standalone
8087,2.94,NJ City,E,23,12,3,74,No education,33,Owner,Standalone
7758,370.98,Boston,S,10,23,18,70,Graduation,25,Patnership,Complex
3260,193.05,NJ City,W,13,20,13,77,High School,28,Franchise,Complex
2170,423.64,NJ City,S,17,9,1,79,High School,19,Patnership,Complex
1199,475.06,NJ City,E,8,22,25,57,High School,21,Owner,Standalone
6837,266.2,Boston,N,17,19,23,64,Graduation,27,Patnership,Standalone
1159,187.76,NJ City,E,10,24,6,36,High School,29,Owner,Standalone
1265,399.48,NJ City,N,7,24,20,54,High School,26,Patnership,Standalone
6180,150.9,Boston,W,25,24,18,74,Graduation,23,Franchise,Complex
338,5.02,NJ City,N,22,24,4,51,No education,25,Patnership,Complex
6450,7.76,Boston,N,20,8,1,31,Graduation,35,Patnership,Complex
8116,178.72,NJ City,W,4,12,4,52,High School,18,Franchise,Complex
9656,340.56,NJ City,W,15,15,19,61,Graduation,33,Franchise,Complex
8071,414.13,NJ City,E,10,11,4,32,High School,26,Owner,Standalone
5168,281.71,Boston,W,20,17,11,34,Graduation,33,Franchise,Complex
7554,28.09,Boston,N,5,9,1,74,Graduation,35,Patnership,Complex
7293,183.98,Boston,N,23,14,3,61,Graduation,20,Patnership,Standalone
256,431.55,NJ City,W,8,23,8,60,High School,20,Franchise,Complex
416,86.35,NJ City,W,19,8,1,75,No education,19,Franchise,Complex
2529,391.83,NJ City,N,13,24,24,50,High School,35,Patnership,Standalone
4302,281.38,NJ City,S,10,23,8,60,High School,24,Patnership,Complex
4957,215.15,Boston,N,11,23,8,29,High School,19,Patnership,Standalone
3095,78.17,NJ City,E,7,12,4,46,No education,25,Owner,Standalone
221,387.03,NJ City,N,21,16,1,44,No education,22,Patnership,Standalone
7653,59.89,Boston,N,15,9,2,29,Graduation,30,Patnership,Standalone
3878,14.23,NJ City,N,21,8,2,83,No education,31,Patnership,Complex
5461,229.25,Boston,N,1,18,3,68,High School,24,Patnership,Standalone
3604,69.51,NJ City,N,10,19,4,80,High School,24,Franchise,Complex
8755,180.84,NJ City,E,11,13,16,30,Graduation,26,Owner,Standalone
8161,358.08,NJ City,N,9,19,3,67,High School,33,Patnership,Standalone
2827,319.82,NJ City,E,19,16,4,76,High School,30,Owner,Standalone
3015,360.46,NJ City,E,17,20,1,33,Graduation,19,Owner,Standalone
7061,314.98,Boston,N,21,21,11,72,Graduation,24,Patnership,Standalone
674,167.04,NJ City,S,20,16,4,55,No education,20,Patnership,Complex
4730,34.19,NJ City,N,21,23,4,67,No education,23,Patnership,Complex
3630,59.83,NJ City,S,13,18,4,40,Graduation,34,Patnership,Complex
6485,297.45,Boston,N,16,10,14,45,Graduation,28,Patnership,Standalone
3100,8.79,NJ City,N,10,11,2,69,High School,23,Franchise,Complex
3451,308.52,NJ City,E,15,15,5,52,High School,21,Owner,Standalone
146,50.69,NJ City,S,18,10,4,75,No education,25,Patnership,Complex
6791,331.31,Boston,E,16,11,21,81,Graduation,19,Owner,Standalone
6142,66.34,Boston,N,25,24,1,50,High School,27,Patnership,Complex
4919,464.61,NJ City,E,25,21,21,28,High School,18,Owner,Standalone
8032,219.31,Boston,W,12,12,1,48,High School,31,Franchise,Complex
6292,497.54,Boston,W,14,11,24,30,Graduation,18,Franchise,Complex
3126,185.4,NJ City,S,13,14,9,50,High School,33,Patnership,Complex
1446,463.19,NJ City,S,20,9,20,64,High School,28,Patnership,Complex
1115,354.42,NJ City,E,5,15,25,59,High School,29,Owner,Standalone
385,418.97,NJ City,N,14,18,25,36,High School,24,Patnership,Standalone
4338,33.23,NJ City,N,18,21,4,71,Graduation,24,Patnership,Complex
9012,160.51,NJ City,W,3,18,19,78,Graduation,33,Franchise,Complex
6806,112.96,Boston,S,17,13,4,75,No education,32,Patnership,Complex
8437,86.48,NJ City,N,24,22,1,34,High School,24,Patnership,Standalone
7915,399.61,Boston,E,2,11,12,57,Graduation,31,Owner,Standalone
4086,302.97,NJ City,S,10,18,18,61,High School,22,Patnership,Complex
6294,164.52,Boston,S,24,9,10,80,Graduation,28,Patnership,Complex
36,407.36,NJ City,W,9,20,5,43,Graduation,33,Franchise,Complex
8859,193.63,NJ City,E,23,13,23,26,Graduation,26,Owner,Standalone
4496,253.14,NJ City,W,8,21,2,49,No education,33,Franchise,Complex
4628,489.4,NJ City,W,16,15,16,52,High School,21,Franchise,Complex
6370,2.99,Boston,N,24,8,4,76,High School,32,Patnership,Complex
6586,379.37,Boston,S,24,10,1,40,High School,30,Patnership,Complex
5483,399.16,Boston,E,23,19,11,39,Graduation,31,Owner,Standalone
9678,111.04,NJ City,S,2,8,4,82,Graduation,28,Patnership,Complex
7276,64.97,Boston,N,21,23,2,29,High School,22,Franchise,Complex
7227,11.27,Boston,N,13,8,1,57,Graduation,21,Owner,Standalone
7179,141.66,Boston,E,20,13,2,77,Graduation,21,Owner,Standalone
1776,162.82,NJ City,W,13,14,23,38,High School,35,Franchise,Complex
1153,493.86,NJ City,N,20,18,16,27,High School,21,Patnership,Standalone
6260,462.72,Boston,W,4,13,25,36,Graduation,20,Franchise,Complex
9232,446.88,NJ City,W,21,18,4,51,High School,25,Franchise,Complex
6068,230.24,Boston,W,23,18,9,60,Graduation,26,Franchise,Complex
5998,394.52,Boston,S,25,21,10,79,Graduation,32,Patnership,Complex
5930,24.14,Boston,N,17,21,1,73,No education,22,Patnership,Complex
7388,17.18,Boston,N,9,14,4,44,No education,34,Franchise,Complex
7371,248.91,Boston,E,10,19,5,85,Graduation,20,Owner,Standalone
3147,450.88,NJ City,E,24,15,25,40,High School,24,Owner,Standalone
3854,422.95,NJ City,S,18,12,5,34,No education,29,Patnership,Complex
9863,120.76,NJ City,E,13,13,2,27,No education,34,Owner,Standalone
6830,199.9,Boston,S,5,13,4,42,No education,29,Patnership,Complex
3932,455.38,NJ City,W,7,14,19,45,High School,19,Franchise,Complex
1336,205.97,NJ City,W,14,10,9,85,High School,30,Franchise,Complex
8326,244.62,NJ City,S,12,17,18,68,Graduation,35,Patnership,Complex
736,209.68,NJ City,W,19,18,9,41,High School,27,Franchise,Complex
2135,195.77,NJ City,E,25,12,1,60,No education,18,Owner,Standalone
13,73.48,Boston,N,25,15,2,70,High School,26,Patnership,Standalone
7170,459.81,Boston,S,2,19,18,31,Graduation,22,Patnership,Complex
2690,448.31,NJ City,S,17,10,25,76,High School,29,Patnership,Complex
3469,493.75,NJ City,N,25,14,12,67,High School,31,Patnership,Standalone
920,460.9,NJ City,W,8,11,3,32,No education,22,Franchise,Complex
9333,365.9,NJ City,N,18,14,13,29,Graduation,26,Patnership,Standalone
6715,105.75,Boston,E,4,20,1,30,High School,31,Owner,Standalone
5979,28.32,Boston,N,8,14,2,85,Graduation,20,Owner,Standalone
5039,238.38,Boston,E,12,18,25,54,Graduation,30,Owner,Standalone
4333,69.18,NJ City,N,13,11,3,58,High School,20,Patnership,Standalone
8865,299.85,NJ City,N,16,11,5,48,Graduation,20,Patnership,Standalone
7351,204.31,Boston,E,25,22,11,80,Graduation,26,Owner,Standalone
3883,33.55,NJ City,N,23,24,1,38,High School,33,Owner,Standalone
7514,403.09,Boston,S,10,8,14,36,Graduation,28,Patnership,Complex
4640,159.25,NJ City,W,16,15,23,48,High School,28,Franchise,Complex
8937,175.18,NJ City,N,5,24,22,80,Graduation,22,Patnership,Standalone
2672,362.69,NJ City,W,4,17,22,35,High School,19,Franchise,Complex
9827,473.36,NJ City,E,8,9,20,84,Graduation,34,Owner,Standalone
3030,157,NJ City,S,1,24,23,37,High School,20,Patnership,Complex
8516,77.68,NJ City,W,12,18,3,71,No education,19,Franchise,Complex
7729,92.63,Boston,N,7,16,1,79,High School,35,Patnership,Standalone
1201,293.82,NJ City,N,14,10,11,76,High School,23,Patnership,Standalone
4524,53.69,NJ City,W,2,20,1,51,Graduation,28,Franchise,Complex
8973,218.68,NJ City,N,1,22,16,35,Graduation,26,Patnership,Standalone
4339,319.45,NJ City,E,6,22,15,52,High School,18,Owner,Standalone
3529,122.61,NJ City,N,2,15,1,67,High School,30,Patnership,Standalone
9759,67.36,NJ City,N,24,24,3,40,Graduation,30,Owner,Standalone
8234,186.59,NJ City,S,24,18,4,31,No education,18,Patnership,Complex
8457,492.96,NJ City,N,2,24,19,53,Graduation,18,Patnership,Standalone
147,156.92,NJ City,E,21,14,4,65,Graduation,18,Owner,Standalone
3463,474.63,NJ City,E,9,9,2,47,High School,34,Owner,Standalone
5229,441.54,Boston,N,21,12,14,32,Graduation,29,Patnership,Standalone
5885,387.98,Boston,N,5,14,21,53,Graduation,24,Patnership,Standalone
5164,260.41,Boston,W,2,20,24,83,Graduation,31,Franchise,Complex
2368,194.69,NJ City,W,10,8,3,61,High School,31,Franchise,Complex
2779,297.08,NJ City,E,7,24,21,70,High School,26,Owner,Standalone
9149,55.78,NJ City,N,16,24,1,52,No education,34,Patnership,Standalone
6061,292.12,Boston,N,19,18,21,56,Graduation,27,Patnership,Standalone
1141,261.82,NJ City,N,16,22,9,81,High School,19,Patnership,Standalone
3376,173.76,NJ City,W,4,23,4,31,High School,24,Franchise,Complex
6648,467.49,Boston,W,3,22,15,43,Graduation,31,Franchise,Complex
917,323.66,NJ City,N,25,23,10,39,High School,21,Patnership,Standalone
2403,213,NJ City,E,1,16,18,61,High School,30,Owner,Standalone
9437,307.66,NJ City,N,2,15,21,36,Graduation,18,Patnership,Standalone
9158,69.6,NJ City,N,23,8,1,69,No education,23,Patnership,Complex
4622,96.62,NJ City,S,16,8,2,50,No education,27,Patnership,Complex
2313,497.94,NJ City,N,22,8,20,33,High School,27,Patnership,Standalone
9849,451.12,NJ City,N,9,8,3,36,Graduation,25,Patnership,Standalone
3425,163.86,NJ City,N,5,13,17,53,High School,33,Patnership,Standalone
5089,146.13,Boston,N,25,10,8,28,Graduation,20,Patnership,Standalone
7848,235.42,Boston,W,16,20,10,26,Graduation,29,Franchise,Complex
1289,306.32,NJ City,N,13,17,11,45,High School,20,Patnership,Standalone
6576,360.53,Boston,W,20,22,13,30,Graduation,23,Franchise,Complex
7816,21.74,Boston,N,1,12,3,65,High School,28,Franchise,Complex
9138,353.37,NJ City,S,3,24,23,43,Graduation,22,Patnership,Complex
5235,307.75,Boston,E,6,9,9,44,Graduation,29,Owner,Standalone
6836,444.5,Boston,W,12,11,1,45,No education,21,Franchise,Complex
4626,378.73,NJ City,S,7,15,15,71,High School,32,Patnership,Complex
3232,7.47,NJ City,N,7,23,1,41,High School,34,Franchise,Complex
2811,414.32,NJ City,E,5,21,20,26,High School,27,Owner,Standalone
4486,395.12,NJ City,S,13,11,9,66,High School,21,Patnership,Complex
7937,321.46,Boston,N,21,15,21,77,Graduation,34,Patnership,Standalone
8948,389.03,NJ City,W,16,8,15,45,Graduation,22,Franchise,Complex
2550,225.85,NJ City,S,18,11,19,40,High School,21,Patnership,Complex
5625,50.18,Boston,N,18,15,3,49,Graduation,30,Patnership,Standalone
7142,104.42,Boston,S,15,23,4,51,No education,20,Patnership,Complex
8632,285.45,NJ City,W,17,14,10,51,Graduation,19,Franchise,Complex
6341,186.69,Boston,N,23,16,22,71,Graduation,31,Patnership,Standalone
1079,343.36,NJ City,E,24,12,10,67,High School,28,Owner,Standalone
6207,30.21,Boston,N,13,8,3,26,Graduation,32,Owner,Standalone
8872,423.91,NJ City,W,3,17,11,35,Graduation,35,Franchise,Complex
7594,416.37,Boston,S,22,23,12,34,Graduation,27,Patnership,Complex
6844,176.38,Boston,W,20,17,4,36,High School,31,Franchise,Complex
295,414.48,NJ City,E,7,19,6,52,High School,21,Owner,Standalone
5358,189.26,Boston,S,20,21,21,33,Graduation,21,Patnership,Complex
9054,79.93,NJ City,S,2,15,2,65,Graduation,32,Patnership,Complex
1965,15,NJ City,N,2,18,2,69,Graduation,26,Patnership,Standalone
7512,378.18,Boston,W,17,11,14,82,Graduation,18,Franchise,Complex
7426,129.36,Boston,S,18,21,16,32,Graduation,32,Patnership,Complex
9463,212.85,NJ City,E,25,16,9,64,Graduation,29,Owner,Standalone
3262,295.21,NJ City,S,25,16,8,56,High School,35,Patnership,Complex
5771,207.07,Boston,E,15,9,12,80,Graduation,23,Owner,Standalone
8059,217.16,NJ City,E,16,20,19,28,Graduation,18,Owner,Standalone
3760,186,NJ City,W,3,19,23,82,High School,28,Franchise,Complex
8569,221.97,NJ City,N,17,18,24,48,Graduation,33,Patnership,Standalone
6769,430.3,Boston,N,18,21,4,66,High School,32,Patnership,Standalone
6509,437.98,Boston,N,23,11,24,32,Graduation,18,Patnership,Standalone
482,208.04,NJ City,S,21,19,18,68,High School,19,Patnership,Complex
3850,117.73,NJ City,S,21,12,4,72,High School,30,Patnership,Complex
2372,7.2,NJ City,N,25,16,2,31,No education,33,Franchise,Complex
8461,369.09,NJ City,N,23,8,10,67,Graduation,20,Patnership,Standalone
6123,271.3,Boston,E,15,11,9,69,Graduation,29,Owner,Standalone
1111,39.89,NJ City,N,16,14,3,65,High School,23,Owner,Standalone
8342,410.8,NJ City,S,11,11,5,76,No education,29,Patnership,Complex
9133,191.35,NJ City,N,13,22,25,47,Graduation,25,Patnership,Standalone
3574,440.57,NJ City,S,12,22,6,48,High School,23,Patnership,Complex
3365,108,NJ City,N,2,19,12,49,High School,22,Patnership,Standalone
1936,43.34,NJ City,N,6,22,3,48,High School,26,Franchise,Complex
8455,389.68,NJ City,E,11,22,18,77,Graduation,30,Owner,Standalone
882,210.5,NJ City,S,4,11,12,65,High School,31,Patnership,Complex
2467,24.55,NJ City,N,2,14,1,85,High School,31,Owner,Standalone
5272,2.2,Boston,W,11,22,2,31,High School,31,Franchise,Complex
547,333.19,NJ City,E,14,9,19,66,High School,34,Owner,Standalone
5732,114.1,Boston,W,13,19,2,75,No education,34,Franchise,Complex
4400,187.06,NJ City,W,4,19,2,52,No education,27,Franchise,Complex
2577,320.78,NJ City,N,4,17,14,84,High School,26,Patnership,Standalone
1645,8.04,NJ City,N,21,13,3,59,High School,32,Patnership,Standalone
7124,245.26,Boston,W,1,17,19,83,Graduation,29,Franchise,Complex
3379,175.67,NJ City,E,9,22,13,26,High School,35,Owner,Standalone
228,142.79,NJ City,W,17,23,7,72,High School,35,Franchise,Complex
3277,117.04,NJ City,N,20,9,2,25,High School,18,Patnership,Standalone
3635,160.91,NJ City,E,17,21,23,55,High School,34,Owner,Standalone
5258,128.81,Boston,S,22,19,9,31,Graduation,21,Patnership,Complex
7951,101.35,Boston,E,6,24,2,66,High School,26,Owner,Standalone
72,203.31,Maine,W,18,16,22,65,High School,20,Franchise,Complex
5275,93.27,Boston,E,18,21,4,42,High School,20,Owner,Standalone
2682,191.31,NJ City,S,16,17,2,69,Graduation,27,Patnership,Complex
7452,97.05,Boston,W,22,9,1,82,Graduation,21,Franchise,Complex
9772,121.16,NJ City,W,14,22,3,70,High School,35,Franchise,Complex
4083,101.84,NJ City,E,20,12,4,83,Graduation,23,Owner,Standalone
2841,257.78,NJ City,N,22,9,15,72,High School,21,Patnership,Standalone
6242,10.12,Boston,N,10,20,4,57,No education,31,Patnership,Complex
4993,146.59,Boston,N,21,16,13,75,High School,22,Patnership,Standalone
3961,465.25,NJ City,N,18,12,7,58,High School,29,Patnership,Standalone
7193,278.37,Boston,N,25,22,6,32,Graduation,27,Patnership,Standalone
8354,382.14,NJ City,S,11,14,24,68,Graduation,21,Patnership,Complex
8589,126.78,NJ City,N,25,10,3,31,Graduation,31,Patnership,Standalone
6468,77.25,Boston,W,16,19,3,56,Graduation,23,Franchise,Complex
4179,110.97,NJ City,E,23,20,3,46,Graduation,28,Owner,Standalone
155,300.76,NJ City,E,13,13,3,80,No education,18,Owner,Standalone
7361,155.59,Boston,N,23,16,19,71,Graduation,26,Patnership,Standalone
856,155.87,NJ City,W,25,24,23,62,High School,33,Franchise,Complex
9136,428.11,NJ City,W,7,12,7,52,Graduation,22,Franchise,Complex
870,100.88,NJ City,S,13,22,4,85,Graduation,19,Patnership,Complex
5523,287.76,Boston,E,22,15,14,30,Graduation,33,Owner,Standalone
9325,86.14,NJ City,N,1,12,4,64,High School,18,Patnership,Standalone
9472,471.02,NJ City,W,9,11,11,47,Graduation,35,Franchise,Complex
2737,311.59,NJ City,N,17,11,17,37,High School,29,Patnership,Standalone
6355,415.36,Boston,E,17,8,17,73,Graduation,23,Owner,Standalone
2143,91.16,NJ City,E,14,16,3,67,High School,29,Owner,Standalone
2601,326.98,NJ City,N,22,22,18,37,High School,34,Patnership,Standalone
904,8.18,NJ City,N,11,23,2,82,High School,22,Franchise,Complex
4355,483.67,NJ City,E,21,9,24,73,High School,25,Owner,Standalone
3996,379.36,NJ City,W,10,23,7,73,High School,30,Franchise,Complex
7196,179.97,Boston,W,20,18,10,50,Graduation,23,Franchise,Complex
6239,134.53,Boston,E,4,9,22,83,Graduation,31,Owner,Standalone
8265,390.05,NJ City,N,5,14,14,79,Graduation,26,Patnership,Standalone
1108,467.62,NJ City,W,4,21,14,42,High School,24,Franchise,Complex
6345,8.62,Boston,N,22,9,4,45,Graduation,20,Patnership,Standalone
5700,11.38,Boston,N,16,8,4,40,Graduation,28,Franchise,Complex
5470,363.32,Boston,S,6,24,18,58,Graduation,19,Patnership,Complex
9295,18.28,NJ City,N,25,13,3,75,High School,20,Owner,Standalone
9625,402.76,NJ City,N,18,17,15,85,Graduation,26,Patnership,Standalone
2958,405.8,NJ City,S,12,19,22,73,High School,34,Patnership,Complex
4704,369.07,NJ City,W,17,20,12,83,High School,30,Franchise,Complex
6002,268.71,Boston,S,20,8,20,45,Graduation,19,Patnership,Complex
7084,430.61,Boston,W,3,21,19,32,Graduation,31,Franchise,Complex
6929,140.54,Boston,N,5,18,6,79,Graduation,19,Patnership,Standalone
5504,315.31,Boston,W,24,24,21,65,Graduation,18,Franchise,Complex
8620,128.57,NJ City,W,10,8,11,65,Graduation,27,Franchise,Complex
9564,374.29,NJ City,W,7,15,24,50,Graduation,26,Franchise,Complex
627,298.77,NJ City,E,6,11,18,62,High School,35,Owner,Standalone
1831,469.72,NJ City,E,25,23,4,83,High School,28,Owner,Standalone
9518,181.31,NJ City,S,25,15,4,71,No education,35,Patnership,Complex
8298,410.26,NJ City,S,14,24,8,41,Graduation,35,Patnership,Complex
4900,124.1,NJ City,W,16,14,2,51,High School,34,Franchise,Complex
5407,138.23,Boston,E,18,18,24,62,Graduation,33,Owner,Standalone
3588,121.75,NJ City,W,23,9,3,79,Graduation,28,Franchise,Complex
1061,4.22,NJ City,N,14,20,4,66,No education,19,Patnership,Standalone
892,377.64,NJ City,W,23,24,1,46,High School,22,Franchise,Complex
4052,466.95,NJ City,W,7,8,20,28,High School,29,Franchise,Complex
8030,35.58,Boston,N,7,21,2,36,No education,22,Patnership,Complex
824,400.23,NJ City,W,21,24,14,51,High School,24,Franchise,Complex
6687,241.83,Boston,E,10,20,17,56,Graduation,18,Owner,Standalone
6643,390.33,Boston,E,25,22,17,71,Graduation,28,Owner,Standalone
2748,92.2,NJ City,W,1,16,4,44,Graduation,32,Franchise,Complex
1276,255.07,NJ City,W,21,20,3,29,High School,28,Franchise,Complex
7032,485.4,Boston,W,17,19,9,27,Graduation,22,Franchise,Complex
9086,375.14,NJ City,S,8,9,2,78,No education,30,Patnership,Complex
2463,133.93,NJ City,E,16,16,10,80,High School,33,Owner,Standalone
5783,205.7,Boston,E,25,14,7,26,Graduation,34,Owner,Standalone
4430,481.45,NJ City,S,24,11,17,67,High School,31,Patnership,Complex
9583,273.33,NJ City,E,12,10,3,54,High School,33,Owner,Standalone
5282,391.21,Boston,S,18,16,8,59,Graduation,26,Patnership,Complex
5487,482.74,Boston,E,9,24,8,59,Graduation,31,Owner,Standalone
4584,101.38,NJ City,W,20,17,2,53,Graduation,23,Franchise,Complex
7180,291.53,Boston,W,7,23,17,81,Graduation,30,Franchise,Complex
225,293.52,NJ City,N,25,23,7,59,High School,20,Patnership,Standalone
812,153,NJ City,W,1,16,7,26,High School,35,Franchise,Complex
885,111.82,NJ City,N,7,12,2,49,Graduation,19,Patnership,Standalone
7302,151.07,Boston,S,1,17,22,47,Graduation,23,Patnership,Complex
9909,154.66,NJ City,N,13,8,19,58,Graduation,28,Patnership,Standalone
5482,495.53,Boston,S,8,16,19,85,Graduation,32,Patnership,Complex
8101,277.25,NJ City,N,20,15,25,32,Graduation,26,Patnership,Standalone
9954,46.06,NJ City,N,15,18,3,28,Graduation,19,Patnership,Complex
5730,313.7,Boston,S,9,10,17,80,Graduation,25,Patnership,Complex
981,327.42,NJ City,N,20,23,7,50,High School,33,Patnership,Standalone
8,2.68,NY City,W,17,11,2,76,No education,31,Franchise,Complex
9842,408.85,NJ City,S,7,19,14,83,Graduation,31,Patnership,Complex
7127,164.24,Boston,E,1,8,14,36,Graduation,19,Owner,Standalone
3734,372.39,NJ City,S,22,16,3,82,No education,23,Patnership,Complex
7831,14.29,Boston,N,18,16,1,84,High School,24,Owner,Standalone
4263,17.48,NJ City,N,8,16,3,36,Graduation,33,Owner,Standalone
7287,107.51,Boston,E,8,15,4,49,Graduation,30,Owner,Standalone
2237,105.82,NJ City,N,22,8,2,45,No education,19,Patnership,Standalone
7447,219.86,Boston,E,3,24,12,65,Graduation,25,Owner,Standalone
6636,483.47,Boston,W,25,10,17,41,Graduation,23,Franchise,Complex
4760,47.86,NJ City,W,22,11,4,70,No education,33,Franchise,Complex
9921,235.93,NJ City,N,3,20,15,70,Graduation,30,Patnership,Standalone
2101,466.64,NJ City,N,24,23,21,37,High School,24,Patnership,Standalone
9446,482.6,NJ City,S,21,15,12,76,Graduation,30,Patnership,Complex
6020,242.03,Boston,W,3,17,15,58,Graduation,27,Franchise,Complex
3005,95.8,NJ City,N,12,10,1,50,No education,28,Patnership,Standalone
3409,209.45,NJ City,N,18,10,15,35,High School,26,Patnership,Standalone
2791,347.62,NJ City,E,19,20,4,25,High School,18,Owner,Standalone
7174,73.53,Boston,N,1,19,4,41,High School,27,Patnership,Complex
8308,358.17,NJ City,W,21,10,11,67,Graduation,31,Franchise,Complex
6689,395.76,Boston,N,23,10,6,37,Graduation,20,Patnership,Standalone
8952,229.12,NJ City,W,9,9,25,79,Graduation,28,Franchise,Complex
3023,70.84,NJ City,N,23,21,3,27,No education,19,Owner,Standalone
975,483.82,NJ City,E,10,10,25,64,High School,35,Owner,Standalone
361,214.32,NJ City,N,12,24,20,73,High School,22,Patnership,Standalone
1603,334.84,NJ City,E,24,10,20,52,High School,34,Owner,Standalone
5167,393.37,Boston,E,1,10,20,29,Graduation,35,Owner,Standalone
3980,336.12,NJ City,W,4,21,9,41,High School,32,Franchise,Complex
1250,315.7,NJ City,S,10,18,23,35,High School,35,Patnership,Complex
4967,356.71,Boston,E,4,14,4,75,No education,24,Owner,Standalone
5716,320.2,Boston,W,12,22,4,25,High School,25,Franchise,Complex
1317,82.75,NJ City,N,23,18,3,41,Graduation,26,Patnership,Standalone
5126,261.19,Boston,S,7,12,2,33,No education,25,Patnership,Complex
8579,346.77,NJ City,E,8,24,8,34,Graduation,27,Owner,Standalone
8251,161.1,NJ City,E,2,12,4,53,High School,34,Owner,Standalone
8053,446.98,NJ City,N,21,10,21,32,Graduation,29,Patnership,Standalone
1109,134.47,NJ City,N,20,18,19,25,High School,34,Patnership,Standalone
8367,68.9,NJ City,N,17,15,1,45,Graduation,25,Owner,Standalone
5088,334.8,Boston,W,20,22,13,45,Graduation,30,Franchise,Complex
9731,274.8,NJ City,E,15,13,2,39,No education,32,Owner,Standalone
2087,258.26,NJ City,E,9,21,14,82,High School,32,Owner,Standalone
3143,11.71,NJ City,N,20,14,3,84,No education,24,Owner,Standalone
89,139.1,Boston,N,19,16,17,74,High School,34,Patnership,Standalone
5685,307.75,Boston,N,2,24,8,59,Graduation,24,Patnership,Standalone
4772,291,NJ City,W,3,11,12,76,High School,31,Franchise,Complex
1769,372.41,NJ City,N,25,12,21,72,High School,24,Patnership,Standalone
2214,202.27,NJ City,S,17,16,8,66,High School,22,Patnership,Complex
8771,31.19,NJ City,N,1,22,2,60,No education,30,Owner,Standalone
5446,186.3,Boston,S,13,14,11,51,Graduation,24,Patnership,Complex
7846,423.45,Boston,S,11,14,9,70,Graduation,21,Patnership,Complex
9470,161.54,NJ City,S,7,10,24,30,Graduation,30,Patnership,Complex
6746,186.23,Boston,S,17,15,24,77,Graduation,20,Patnership,Complex
8203,151.92,NJ City,E,23,14,3,59,High School,35,Owner,Standalone
8837,63.44,NJ City,N,11,17,4,36,No education,24,Patnership,Standalone
400,464.37,NJ City,W,24,17,1,45,High School,25,Franchise,Complex
3837,59.17,NJ City,N,16,13,1,65,Graduation,23,Patnership,Standalone
3737,239.1,NJ City,N,8,16,14,34,High School,31,Patnership,Standalone
5506,305.14,Boston,S,23,12,3,55,High School,22,Patnership,Complex
768,163.18,NJ City,W,22,21,7,39,High School,25,Franchise,Complex
2373,471.64,NJ City,N,15,16,23,59,High School,28,Patnership,Standalone
4573,429.57,NJ City,N,6,8,15,68,High School,19,Patnership,Standalone
8602,44.92,NJ City,N,24,13,2,44,High School,18,Patnership,Complex
5230,428.95,Boston,S,18,8,12,60,Graduation,30,Patnership,Complex
3441,223.27,NJ City,N,9,13,11,25,High School,32,Patnership,Standalone
6042,160.86,Boston,S,14,15,4,38,Graduation,19,Patnership,Complex
4812,198,NJ City,W,2,11,18,32,High School,22,Franchise,Complex
8753,149.11,NJ City,N,7,18,25,28,Graduation,30,Patnership,Standalone
3841,10.52,NJ City,N,19,24,4,54,High School,30,Patnership,Standalone
7833,37.34,Boston,N,24,9,2,29,Graduation,19,Patnership,Standalone
3813,188.15,NJ City,N,23,19,6,33,High School,28,Patnership,Standalone
5354,257.37,Boston,S,3,15,8,76,Graduation,24,Patnership,Complex
7912,205.46,Boston,W,22,14,20,39,Graduation,30,Franchise,Complex
5309,82.07,Boston,N,1,11,1,74,No education,29,Patnership,Standalone
9259,199.43,NJ City,E,23,9,9,53,Graduation,21,Owner,Standalone
667,0.12,NJ City,N,22,12,1,77,High School,31,Owner,Standalone
8157,194.35,NJ City,N,25,18,10,85,Graduation,28,Patnership,Standalone
6757,488,Boston,N,11,18,6,82,Graduation,33,Patnership,Standalone
6498,309.64,Boston,S,12,11,5,29,Graduation,20,Patnership,Complex
2520,51.55,NJ City,W,6,15,2,59,Graduation,18,Franchise,Complex
8988,68.17,NJ City,N,12,11,4,76,Graduation,29,Franchise,Complex
2287,380.16,NJ City,E,5,22,23,43,High School,34,Owner,Standalone
8736,149.62,NJ City,W,17,23,3,49,Graduation,26,Franchise,Complex
6692,487.24,Boston,W,17,21,19,49,Graduation,18,Franchise,Complex
8790,296.69,NJ City,S,11,9,21,70,Graduation,28,Patnership,Complex
5859,361.87,Boston,E,24,15,18,40,Graduation,22,Owner,Standalone
8587,320.08,NJ City,E,14,13,2,83,High School,18,Owner,Standalone
1397,190,NJ City,N,1,8,17,37,High School,32,Patnership,Standalone
2959,116.12,NJ City,E,2,22,2,47,High School,32,Owner,Standalone
3558,55.88,NJ City,S,21,16,2,71,Graduation,26,Patnership,Complex
5105,266.49,Boston,N,1,12,24,57,Graduation,35,Patnership,Standalone
6824,14.98,Boston,N,11,18,4,40,No education,26,Franchise,Complex
6113,183.37,Boston,N,11,21,12,67,Graduation,25,Patnership,Standalone
1138,476.8,NJ City,S,15,10,6,65,High School,32,Patnership,Complex
578,400.4,NJ City,S,15,11,25,67,High School,25,Patnership,Complex
8333,152.2,NJ City,N,18,18,10,30,Graduation,31,Patnership,Standalone
7487,9.43,Boston,N,11,8,4,61,No education,25,Owner,Standalone
2219,126.87,NJ City,E,11,16,4,66,No education,34,Owner,Standalone
9455,346.86,NJ City,E,18,11,3,48,No education,31,Owner,Standalone
8770,369.28,NJ City,S,15,8,18,74,Graduation,18,Patnership,Complex
3685,278.5,NJ City,N,20,8,8,63,High School,28,Patnership,Standalone
7425,179.24,Boston,N,7,21,20,73,Graduation,27,Patnership,Standalone
4051,126,NJ City,E,2,11,8,34,High School,19,Owner,Standalone
7490,209.25,Boston,S,5,15,21,72,Graduation,30,Patnership,Complex
5868,418.61,Boston,W,16,19,2,48,Graduation,34,Franchise,Complex
8396,491.61,NJ City,W,2,18,10,62,Graduation,28,Franchise,Complex
6702,366.82,Boston,S,10,18,24,65,Graduation,20,Patnership,Complex
7416,165.92,Boston,W,16,14,21,30,Graduation,28,Franchise,Complex
8717,200.45,NJ City,N,25,20,11,85,Graduation,28,Patnership,Standalone
8386,163.98,NJ City,S,8,13,20,50,Graduation,33,Patnership,Complex
3132,13.72,NJ City,N,19,8,2,55,Graduation,21,Franchise,Complex
1584,341.93,NJ City,W,17,15,19,77,High School,25,Franchise,Complex
6971,436.62,Boston,E,2,24,3,39,No education,18,Owner,Standalone
9725,433.3,NJ City,N,20,16,13,27,Graduation,28,Patnership,Standalone
8886,396.52,NJ City,S,1,24,11,33,Graduation,27,Patnership,Complex
2931,464.57,NJ City,E,18,19,7,59,High School,24,Owner,Standalone
8142,192.1,NJ City,S,22,8,13,33,Graduation,19,Patnership,Complex
3289,172.01,NJ City,N,1,11,2,38,High School,24,Patnership,Standalone
7021,195.13,Boston,N,4,17,19,46,Graduation,24,Patnership,Standalone
203,487.82,NJ City,E,24,14,19,56,High School,29,Owner,Standalone
899,309.79,NJ City,E,15,17,10,50,High School,30,Owner,Standalone
5742,285.48,Boston,S,10,10,12,56,Graduation,20,Patnership,Complex
1380,128,NJ City,W,3,15,19,56,High School,23,Franchise,Complex
3956,328.87,NJ City,W,14,14,1,38,No education,24,Franchise,Complex
705,420.55,NJ City,N,23,19,1,60,Graduation,22,Patnership,Standalone
1945,434.32,NJ City,N,10,9,13,62,High School,25,Patnership,Standalone
1131,319.76,NJ City,E,25,22,12,56,High School,21,Owner,Standalone
8002,111.94,Boston,S,21,11,3,53,High School,26,Patnership,Complex
8129,114.59,NJ City,N,11,18,2,55,No education,23,Patnership,Standalone
5959,132.22,Boston,E,6,10,16,82,Graduation,29,Owner,Standalone
4386,230.56,NJ City,S,12,15,19,28,High School,26,Patnership,Complex
5824,160.19,Boston,W,6,19,1,69,High School,20,Franchise,Complex
7819,142.21,Boston,E,22,15,23,31,Graduation,33,Owner,Standalone
1731,74.68,NJ City,E,18,15,4,32,Graduation,21,Owner,Standalone
6680,496.49,Boston,W,9,19,21,37,Graduation,31,Franchise,Complex
5680,296.49,Boston,W,8,10,22,56,Graduation,23,Franchise,Complex
5572,125.39,Boston,W,2,9,3,51,High School,34,Franchise,Complex
2714,169.21,NJ City,S,21,20,5,60,No education,27,Patnership,Complex
6091,62.92,Boston,N,22,14,4,65,High School,21,Owner,Standalone
50,485.15,Boston,S,14,21,3,26,No education,30,Patnership,Complex
9892,157.26,NJ City,W,3,21,7,39,Graduation,29,Franchise,Complex
7903,139.97,Boston,E,5,17,15,32,Graduation,19,Owner,Standalone
9912,303.57,NJ City,W,19,19,11,44,Graduation,32,Franchise,Complex
9820,138.73,NJ City,W,2,23,4,76,High School,21,Franchise,Complex
7744,289.97,Boston,W,5,20,2,64,High School,26,Franchise,Complex
4475,195,NJ City,E,2,13,15,76,High School,31,Owner,Standalone
2362,205,NJ City,S,1,19,19,77,High School,32,Patnership,Complex
9339,379.16,NJ City,E,11,18,9,33,Graduation,24,Owner,Standalone
2426,177.56,NJ City,S,12,18,6,44,High School,33,Patnership,Complex
3272,462.16,NJ City,W,18,14,7,73,High School,28,Franchise,Complex
830,187.76,NJ City,S,7,18,18,64,High School,19,Patnership,Complex
4087,114.28,NJ City,E,25,11,4,85,High School,29,Owner,Standalone
6237,112.49,Boston,N,13,12,1,46,Graduation,23,Patnership,Standalone
5297,20.72,Boston,N,3,15,1,34,No education,24,Patnership,Standalone
9415,250.51,NJ City,E,10,8,19,52,Graduation,33,Owner,Standalone
9727,41.59,NJ City,N,12,24,4,62,High School,21,Owner,Standalone
869,91.64,NJ City,N,17,8,2,33,No education,30,Patnership,Standalone
4281,216.44,NJ City,N,23,23,13,77,High School,33,Patnership,Standalone
7598,475.77,Boston,S,23,19,22,68,Graduation,20,Patnership,Complex
6825,106.79,Boston,N,3,15,2,81,Graduation,24,Patnership,Standalone
3093,302.96,NJ City,N,12,8,18,44,High School,31,Patnership,Standalone
6088,177.75,Boston,W,14,9,12,63,Graduation,34,Franchise,Complex
111,137.49,NJ City,E,13,13,1,71,Graduation,32,Owner,Standalone
2438,118.44,NJ City,S,14,8,1,80,No education,26,Patnership,Complex
6043,484.66,Boston,E,25,19,4,83,High School,24,Owner,Standalone
7720,346.22,Boston,W,18,21,25,25,Graduation,18,Franchise,Complex
3680,184.23,NJ City,W,17,21,10,75,High School,34,Franchise,Complex
8152,14.63,NJ City,N,4,22,3,42,High School,32,Franchise,Complex
3503,461.85,NJ City,E,14,22,21,29,High School,26,Owner,Standalone
7344,45.1,Boston,N,17,10,4,54,Graduation,23,Franchise,Complex
4166,409.26,NJ City,S,11,14,24,39,High School,33,Patnership,Complex
8042,274.83,Boston,S,18,19,19,73,Graduation,23,Patnership,Complex
7504,148.43,Boston,W,12,22,10,76,Graduation,28,Franchise,Complex
668,306.34,NJ City,W,16,11,11,79,High School,30,Franchise,Complex
1807,430.72,NJ City,E,20,15,25,40,High School,34,Owner,Standalone
1792,47.02,NJ City,N,4,18,1,42,High School,23,Franchise,Complex
1786,314.04,NJ City,S,11,19,25,69,High School,23,Patnership,Complex
9810,258.69,NJ City,S,24,20,14,70,Graduation,34,Patnership,Complex
5878,252.44,Boston,S,10,10,21,37,Graduation,21,Patnership,Complex
8193,140.03,NJ City,N,1,13,14,46,Graduation,35,Patnership,Standalone
8908,334.13,NJ City,W,21,21,4,28,High School,18,Franchise,Complex
2893,483.73,NJ City,N,11,17,25,57,High School,19,Patnership,Standalone
4988,451.13,Boston,W,5,14,25,49,High School,28,Franchise,Complex
8855,306.54,NJ City,E,23,15,18,76,Graduation,30,Owner,Standalone
8924,260.15,NJ City,W,11,22,2,80,No education,20,Franchise,Complex
825,384.36,NJ City,N,20,15,25,30,High School,35,Patnership,Standalone
6743,174.44,Boston,E,18,16,25,44,Graduation,21,Owner,Standalone
716,213.27,NJ City,W,16,23,2,50,No education,35,Franchise,Complex
2323,316.68,NJ City,E,9,9,14,67,High School,33,Owner,Standalone
8678,264.24,NJ City,S,22,16,7,83,Graduation,30,Patnership,Complex
3861,230.68,NJ City,N,22,23,15,47,High School,28,Patnership,Standalone
3919,2.01,NJ City,E,15,23,4,43,High School,23,Owner,Standalone
1161,428.88,NJ City,N,8,19,8,74,High School,18,Patnership,Standalone
2045,115.55,NJ City,N,24,15,3,77,No education,33,Patnership,Standalone
9244,317.24,NJ City,W,2,15,21,25,Graduation,28,Franchise,Complex
8649,19.11,NJ City,N,5,13,4,34,Graduation,21,Patnership,Standalone
296,349.53,NJ City,W,7,15,5,72,No education,19,Franchise,Complex
6487,140.59,Boston,E,6,14,24,27,Graduation,20,Owner,Standalone
959,168.48,NJ City,E,15,13,22,77,High School,25,Owner,Standalone
6524,410.51,Boston,W,10,21,13,73,Graduation,27,Franchise,Complex
1808,46.03,NJ City,N,24,22,4,48,No education,33,Franchise,Complex
4507,104,NJ City,E,2,11,15,64,High School,24,Owner,Standalone
6531,281.32,Boston,E,19,9,14,54,Graduation,21,Owner,Standalone
8099,192.31,NJ City,E,22,16,11,62,Graduation,26,Owner,Standalone
7178,141.49,Boston,S,1,13,20,43,Graduation,19,Patnership,Complex
9753,442.38,NJ City,N,11,20,23,54,Graduation,33,Patnership,Standalone
2320,80.92,NJ City,W,19,17,4,81,High School,22,Franchise,Complex
2888,132,NJ City,W,3,21,10,60,High School,32,Franchise,Complex
7672,37.83,Boston,N,6,17,2,44,High School,26,Franchise,Complex
4285,472.12,NJ City,N,21,21,19,66,High School,26,Patnership,Standalone
7011,355.62,Boston,E,20,12,4,33,Graduation,35,Owner,Standalone
7961,319.14,Boston,N,4,11,19,48,Graduation,31,Patnership,Standalone
9963,190.08,NJ City,E,14,9,24,77,Graduation,19,Owner,Standalone
8944,2.72,NJ City,W,22,22,4,41,High School,19,Franchise,Complex
8858,84.32,NJ City,S,19,17,2,66,No education,34,Patnership,Complex
4466,145.6,NJ City,S,2,18,3,63,No education,31,Patnership,Complex
7527,423.89,Boston,E,24,24,19,66,Graduation,25,Owner,Standalone
7618,145.03,Boston,S,1,10,1,72,High School,19,Patnership,Complex
481,426.56,NJ City,N,14,17,18,84,High School,26,Patnership,Standalone
5075,320.89,Boston,E,16,22,19,28,Graduation,32,Owner,Standalone
1666,245.25,NJ City,S,10,18,3,61,High School,28,Patnership,Complex
7701,214.97,Boston,N,17,22,13,50,Graduation,19,Patnership,Standalone
1215,255,NJ City,E,3,19,7,28,High School,31,Owner,Standalone
1536,212.57,NJ City,W,16,24,12,45,High School,24,Franchise,Complex
6616,333.81,Boston,W,20,12,18,68,Graduation,25,Franchise,Complex
8920,139.59,NJ City,W,17,16,5,81,High School,19,Franchise,Complex
4321,196.77,NJ City,N,22,10,7,54,High School,24,Patnership,Standalone
5108,72.75,Boston,N,13,9,2,66,No education,26,Franchise,Complex
5701,318.12,Boston,N,8,24,10,68,Graduation,32,Patnership,Standalone
44,427.42,Boston,W,25,23,18,53,High School,29,Franchise,Complex
9632,11.14,NJ City,N,10,8,1,77,No education,33,Franchise,Complex
3231,443.56,NJ City,E,12,13,13,25,High School,25,Owner,Standalone
3403,183.38,NJ City,E,23,20,22,78,High School,18,Owner,Standalone
8272,232.89,NJ City,W,25,10,23,27,Graduation,32,Franchise,Complex
518,355.98,NJ City,S,22,10,22,29,High School,28,Patnership,Complex
5317,254.13,Boston,N,13,20,14,27,Graduation,35,Patnership,Standalone
8259,256.35,NJ City,E,5,11,2,48,Graduation,26,Owner,Standalone
4197,145.65,NJ City,N,4,11,10,61,High School,22,Patnership,Standalone
3117,143.46,NJ City,N,18,19,8,82,High School,22,Patnership,Standalone
4429,39.07,NJ City,N,18,9,1,80,High School,31,Patnership,Standalone
6897,374.35,Boston,N,18,15,24,41,Graduation,30,Patnership,Standalone
8248,45.84,NJ City,N,19,9,2,77,High School,18,Franchise,Complex
4600,493.01,NJ City,W,15,18,5,25,High School,18,Franchise,Complex
4262,290.93,NJ City,S,24,21,25,41,High School,24,Patnership,Complex
7929,309.99,Boston,N,12,16,14,69,Graduation,19,Patnership,Standalone
1311,93.61,NJ City,E,24,19,4,39,Graduation,24,Owner,Standalone
2022,260.41,NJ City,S,24,14,20,53,High School,31,Patnership,Complex
756,397.24,NJ City,W,5,16,17,56,High School,35,Franchise,Complex
9428,435.18,NJ City,W,24,9,2,67,No education,26,Franchise,Complex
7491,108.15,Boston,E,8,14,3,41,Graduation,25,Owner,Standalone
4327,94.86,NJ City,E,20,13,4,35,High School,34,Owner,Standalone
7085,23.05,Boston,N,22,21,2,57,No education,24,Patnership,Standalone
6010,104.46,Boston,S,14,18,3,70,High School,19,Patnership,Complex
7244,408.48,Boston,W,8,16,13,82,Graduation,23,Franchise,Complex
8769,489.76,NJ City,N,15,13,19,77,Graduation,28,Patnership,Standalone
1774,423.41,NJ City,S,5,21,15,31,High School,25,Patnership,Complex
2720,358.39,NJ City,W,12,15,18,76,High School,33,Franchise,Complex
7462,50.26,Boston,S,6,12,4,34,High School,21,Patnership,Complex
6668,248.99,Boston,W,1,8,24,71,Graduation,27,Franchise,Complex
1379,128.75,NJ City,E,22,10,10,81,High School,31,Owner,Standalone
5520,75.76,Boston,W,5,11,4,73,Graduation,27,Franchise,Complex
7372,73,Boston,N,21,21,2,74,High School,34,Franchise,Complex
3444,290,NJ City,W,3,12,13,42,High School,31,Franchise,Complex
5711,358.61,Boston,E,22,11,11,76,Graduation,24,Owner,Standalone
7236,326.08,Boston,W,22,13,9,74,Graduation,31,Franchise,Complex
56,6.86,NY City,N,3,23,3,74,No education,19,Franchise,Complex
9288,458.08,NJ City,W,16,23,24,49,Graduation,20,Franchise,Complex
5721,336.95,Boston,N,25,18,10,43,Graduation,23,Patnership,Standalone
6178,270.67,Boston,S,25,10,4,52,High School,28,Patnership,Complex
387,6.44,NJ City,N,18,12,2,74,Graduation,32,Owner,Standalone
7695,423.15,Boston,E,1,19,7,28,Graduation,27,Owner,Standalone
6624,249.44,Boston,W,9,23,12,27,Graduation,25,Franchise,Complex
3053,10.83,NJ City,N,8,22,4,85,No education,28,Patnership,Standalone
3204,105.72,NJ City,W,16,24,1,31,Graduation,18,Franchise,Complex
4188,235.48,NJ City,W,11,18,25,29,High School,20,Franchise,Complex
9682,98.56,NJ City,S,3,13,3,53,High School,20,Patnership,Complex
5973,145.85,Boston,N,21,8,18,66,Graduation,34,Patnership,Standalone
6060,455.26,Boston,W,5,12,19,81,Graduation,24,Franchise,Complex
7296,43.62,Boston,N,13,24,1,80,Graduation,32,Franchise,Complex
7079,404.85,Boston,E,14,8,2,30,No education,30,Owner,Standalone
8496,246.53,NJ City,W,14,21,19,27,Graduation,34,Franchise,Complex
6518,47.67,Boston,S,2,9,3,39,No education,35,Patnership,Complex
8154,211.08,NJ City,S,4,11,8,40,Graduation,35,Patnership,Complex
6782,195.07,Boston,S,4,16,2,51,No education,18,Patnership,Complex
9797,494.44,NJ City,N,23,22,4,48,No education,31,Patnership,Standalone
9767,190.1,NJ City,E,14,8,16,75,Graduation,22,Owner,Standalone
7838,276.29,Boston,S,6,14,19,51,Graduation,19,Patnership,Complex
6164,179.08,Boston,W,9,13,2,43,No education,20,Franchise,Complex
9161,63.12,NJ City,N,10,23,2,69,No education,33,Patnership,Standalone
9373,382.85,NJ City,N,11,16,3,41,High School,34,Patnership,Standalone
536,483.22,NJ City,W,25,23,8,37,High School,26,Franchise,Complex
7298,478.34,Boston,S,23,8,5,27,No education,25,Patnership,Complex
2482,440.74,NJ City,S,21,23,24,76,High School,30,Patnership,Complex
9949,16.72,NJ City,N,21,23,2,58,High School,31,Patnership,Standalone
4435,395.97,NJ City,E,25,15,8,79,High School,29,Owner,Standalone
9413,333.69,NJ City,N,22,14,9,78,Graduation,24,Patnership,Standalone
5614,369.89,Boston,S,11,9,6,36,Graduation,27,Patnership,Complex
3670,90.73,NJ City,S,25,12,1,43,High School,33,Patnership,Complex
301,25.01,NJ City,N,2,9,1,69,High School,19,Patnership,Standalone
5134,96.7,Boston,S,16,17,4,63,High School,29,Patnership,Complex
9093,497.19,NJ City,N,22,18,22,29,Graduation,24,Patnership,Standalone
194,87.41,NJ City,S,22,19,1,73,No education,35,Patnership,Complex
3651,163.15,NJ City,E,25,10,16,70,High School,23,Owner,Standalone
4362,462.38,NJ City,S,25,12,5,85,Graduation,22,Patnership,Complex
8132,351.73,NJ City,W,3,14,19,60,Graduation,35,Franchise,Complex
2756,121.09,NJ City,W,7,14,1,44,No education,18,Franchise,Complex
1361,192.43,NJ City,N,6,18,3,65,No education,21,Patnership,Standalone
2730,263.22,NJ City,S,7,15,16,84,High School,32,Patnership,Complex
9512,153.07,NJ City,W,21,17,13,66,Graduation,30,Franchise,Complex
8022,473.53,Boston,S,24,11,18,77,Graduation,23,Patnership,Complex
5100,231.24,Boston,W,17,12,19,49,Graduation,29,Franchise,Complex
7996,168.69,Boston,W,22,9,4,49,High School,24,Franchise,Complex
4769,119.35,NJ City,N,7,24,2,60,No education,29,Patnership,Standalone
5813,119.99,Boston,N,20,19,4,70,No education,34,Patnership,Standalone
1649,468.52,NJ City,N,18,18,17,29,High School,20,Patnership,Standalone
6942,467.93,Boston,S,4,16,11,64,Graduation,34,Patnership,Complex
305,50.11,NJ City,N,23,18,2,75,No education,34,Patnership,Standalone
6856,401.9,Boston,W,15,10,1,68,High School,35,Franchise,Complex
4754,268,NJ City,S,2,11,16,39,High School,29,Patnership,Complex
489,32.77,NJ City,N,25,14,1,50,Graduation,23,Patnership,Standalone
7906,173.99,Boston,S,11,11,10,63,Graduation,29,Patnership,Complex
6104,251.91,Boston,W,22,14,10,70,Graduation,33,Franchise,Complex
978,30.42,NJ City,N,14,9,3,47,Graduation,19,Patnership,Complex
6874,314.35,Boston,S,14,12,23,66,Graduation,26,Patnership,Complex
3302,51.28,NJ City,S,16,10,1,74,No education,27,Patnership,Complex
9996,283.37,NJ City,W,11,8,4,70,Graduation,29,Franchise,Complex
4448,261.08,NJ City,W,25,19,11,36,High School,22,Franchise,Complex
8527,361.45,NJ City,E,3,10,20,43,Graduation,19,Owner,Standalone
6998,312.85,Boston,S,18,17,8,35,Graduation,18,Patnership,Complex
861,125.8,NJ City,N,23,9,2,56,Graduation,33,Patnership,Standalone
2658,162.38,NJ City,S,25,18,17,51,High School,26,Patnership,Complex
9338,70.02,NJ City,N,18,19,1,52,No education,22,Patnership,Complex
4002,110,NJ City,S,3,22,9,61,High School,30,Patnership,Complex
6641,472.49,Boston,N,15,21,6,44,Graduation,25,Patnership,Standalone
7857,351.56,Boston,N,2,13,6,36,Graduation,18,Patnership,Standalone
3986,90,NJ City,S,18,13,1,37,No education,27,Patnership,Complex
989,280.97,NJ City,N,5,11,15,29,High School,24,Patnership,Standalone
7437,252.99,Boston,N,11,11,13,66,Graduation,32,Patnership,Standalone
4112,134.1,NJ City,W,9,11,14,26,High School,35,Franchise,Complex
552,461.11,NJ City,W,14,21,22,30,High School,31,Franchise,Complex
3663,358.85,NJ City,E,23,21,5,40,Graduation,25,Owner,Standalone
1492,306,NJ City,W,2,15,15,36,High School,20,Franchise,Complex
3458,256.03,NJ City,S,24,8,11,74,High School,27,Patnership,Complex
1859,290.81,NJ City,E,14,15,21,65,High School,21,Owner,Standalone
5132,283.83,Boston,W,11,18,25,26,Graduation,32,Franchise,Complex
6360,177.49,Boston,W,21,17,10,41,Graduation,22,Franchise,Complex
2352,453.64,NJ City,W,13,23,1,36,Graduation,23,Franchise,Complex
5617,181.62,Boston,N,5,17,11,66,Graduation,28,Patnership,Standalone
1512,157,NJ City,W,3,20,20,59,High School,30,Franchise,Complex
1424,360.73,NJ City,W,25,21,5,33,No education,34,Franchise,Complex
6559,129.15,Boston,E,12,15,18,38,Graduation,20,Owner,Standalone
641,251.45,NJ City,N,24,17,3,35,No education,23,Patnership,Standalone
618,227.62,NJ City,S,24,14,11,54,High School,19,Patnership,Complex
6486,408.39,Boston,S,6,13,11,74,Graduation,20,Patnership,Complex
5556,175.4,Boston,W,6,23,1,59,Graduation,35,Franchise,Complex
8604,22.22,NJ City,N,1,10,3,84,Graduation,18,Franchise,Complex
7692,228.02,Boston,W,6,24,4,73,Graduation,25,Franchise,Complex
1444,152.98,NJ City,W,24,8,16,60,High School,19,Franchise,Complex
6458,155.29,Boston,S,25,9,11,63,Graduation,35,Patnership,Complex
236,2.12,NJ City,W,20,14,2,59,No education,29,Franchise,Complex
7257,320.77,Boston,N,23,16,15,59,Graduation,34,Patnership,Standalone
8982,245.72,NJ City,S,8,11,19,53,Graduation,21,Patnership,Complex
7336,35.19,Boston,N,12,21,4,71,High School,35,Franchise,Complex
1746,249.13,NJ City,S,9,13,19,41,High School,28,Patnership,Complex
569,181.29,NJ City,N,12,21,13,28,High School,33,Patnership,Standalone
1594,446.11,NJ City,S,2,15,4,50,High School,30,Patnership,Complex
5454,44.44,Boston,N,14,9,1,36,Graduation,18,Patnership,Complex
8883,415.49,NJ City,E,24,23,16,76,Graduation,33,Owner,Standalone
3815,297.18,NJ City,E,9,15,22,55,High School,30,Owner,Standalone
9722,101.53,NJ City,S,12,10,1,63,No education,33,Patnership,Complex
7173,229.68,Boston,N,18,13,10,55,Graduation,28,Patnership,Standalone
25,332.8,Boston,N,18,11,18,75,High School,33,Patnership,Standalone
6505,115.36,Boston,N,1,19,3,68,High School,27,Patnership,Standalone
5636,298.8,Boston,W,5,24,6,82,Graduation,34,Franchise,Complex
3954,186.52,NJ City,S,15,11,18,39,High School,27,Patnership,Complex
4253,302,NJ City,N,3,14,13,67,High School,21,Patnership,Standalone
5800,328.63,Boston,W,12,19,7,58,Graduation,28,Franchise,Complex
6859,477.52,Boston,E,18,13,23,63,Graduation,30,Owner,Standalone
8458,171.03,NJ City,S,4,11,17,33,Graduation,28,Patnership,Complex
2558,79.93,NJ City,S,1,22,4,72,No education,27,Patnership,Complex
2686,474.3,NJ City,S,14,9,20,42,High School,26,Patnership,Complex
4213,291.18,NJ City,N,7,22,21,68,High School,30,Patnership,Standalone
6174,287.69,Boston,S,15,8,13,79,Graduation,28,Patnership,Complex
5559,256.9,Boston,E,25,19,22,55,Graduation,23,Owner,Standalone
2264,58.41,NJ City,W,8,23,3,58,No education,32,Franchise,Complex
5853,119.25,Boston,N,19,21,4,56,Graduation,32,Patnership,Standalone
7395,58.45,Boston,E,16,16,3,55,Graduation,32,Owner,Standalone
573,98.05,NJ City,N,4,10,3,47,Graduation,22,Patnership,Standalone
2751,276.58,NJ City,E,24,18,13,57,High School,28,Owner,Standalone
986,92.09,NJ City,S,1,18,1,33,No education,22,Patnership,Complex
4073,474.92,NJ City,N,15,8,13,35,High School,28,Patnership,Standalone
1062,196,NJ City,S,1,18,23,26,High School,21,Patnership,Complex
4360,272.98,NJ City,W,11,15,25,81,High School,29,Franchise,Complex
5914,19.98,Boston,N,10,14,1,30,High School,18,Patnership,Complex
526,92.82,NJ City,S,15,19,4,75,High School,29,Patnership,Complex
1812,357.48,NJ City,W,14,21,12,41,High School,26,Franchise,Complex
5137,44.49,Boston,N,23,23,2,75,High School,25,Patnership,Standalone
1198,104,NJ City,S,1,9,12,44,High School,24,Patnership,Complex
1760,302.06,NJ City,W,7,21,24,42,High School,33,Franchise,Complex
8268,341.25,NJ City,W,4,8,2,65,Graduation,19,Franchise,Complex
5122,55.81,Boston,S,3,19,1,69,High School,27,Patnership,Complex
936,2.09,NJ City,W,13,17,3,80,Graduation,29,Franchise,Complex
9059,188.26,NJ City,E,9,17,16,78,Graduation,24,Owner,Standalone
9622,118.79,NJ City,S,17,23,1,49,High School,32,Patnership,Complex
6289,446.69,Boston,N,18,13,20,36,Graduation,26,Patnership,Standalone
2925,223.29,NJ City,N,18,21,17,28,High School,35,Patnership,Standalone
5350,257.14,Boston,S,11,17,2,41,High School,30,Patnership,Complex
8135,342.63,NJ City,E,17,22,16,37,Graduation,19,Owner,Standalone
1957,442.33,NJ City,N,17,19,23,76,High School,18,Patnership,Standalone
5548,88.3,Boston,W,18,18,4,68,High School,29,Franchise,Complex
4038,379.81,NJ City,S,15,11,22,62,High School,20,Patnership,Complex
5512,170.67,Boston,W,24,23,3,85,High School,23,Franchise,Complex
7804,121.31,Boston,W,21,12,2,70,High School,30,Franchise,Complex
1246,409.05,NJ City,S,23,22,13,58,High School,34,Patnership,Complex
2824,41.56,NJ City,N,3,18,1,35,High School,25,Franchise,Complex
2310,481.8,NJ City,S,5,23,16,85,High School,20,Patnership,Complex
6058,227.66,Boston,S,12,16,2,70,High School,24,Patnership,Complex
3036,401.65,NJ City,W,15,20,25,65,High School,31,Franchise,Complex
903,134.49,NJ City,E,7,12,12,33,High School,34,Owner,Standalone
3083,50.25,NJ City,E,1,17,3,54,No education,25,Owner,Standalone
8388,7.22,NJ City,N,9,17,2,36,Graduation,25,Franchise,Complex
1791,233.44,NJ City,E,14,22,20,55,High School,31,Owner,Standalone
246,165.52,NJ City,S,25,17,18,61,High School,32,Patnership,Complex
2562,21.57,NJ City,N,24,21,3,45,Graduation,29,Patnership,Complex
3749,482.62,NJ City,N,6,14,19,85,High School,30,Patnership,Standalone
2350,142.02,NJ City,S,12,17,5,77,High School,31,Patnership,Complex
9671,409.42,NJ City,E,10,21,6,44,Graduation,22,Owner,Standalone
7705,283.28,Boston,N,20,13,10,33,Graduation,28,Patnership,Standalone
9013,43.15,NJ City,N,4,19,1,34,High School,19,Patnership,Standalone
2596,451.9,NJ City,W,25,17,18,25,High School,19,Franchise,Complex
4093,464.59,NJ City,N,18,18,6,79,High School,29,Patnership,Standalone
6130,224.98,Boston,S,23,23,21,27,Graduation,22,Patnership,Complex
5480,152.77,Boston,W,25,16,18,74,Graduation,23,Franchise,Complex
9714,303.31,NJ City,S,8,14,6,27,Graduation,33,Patnership,Complex
3210,71.39,NJ City,N,14,13,2,51,Graduation,29,Patnership,Complex
9596,386.69,NJ City,W,14,9,24,82,Graduation,24,Franchise,Complex
6947,290.9,Boston,E,9,12,2,52,No education,24,Owner,Standalone
8216,46.33,NJ City,N,24,10,2,42,No education,30,Franchise,Complex
3001,411.79,NJ City,N,9,18,6,57,High School,22,Patnership,Standalone
7828,105.77,Boston,W,11,19,3,37,High School,19,Franchise,Complex
2766,201.37,NJ City,S,16,24,12,65,High School,29,Patnership,Complex
8066,448.53,NJ City,S,7,9,9,30,Graduation,23,Patnership,Complex
7774,281.3,Boston,S,3,18,13,67,Graduation,27,Patnership,Complex
2850,242,NJ City,S,3,16,18,58,High School,30,Patnership,Complex
8026,350.37,Boston,S,2,22,1,45,High School,23,Patnership,Complex
6739,120.48,Boston,E,17,19,3,85,High School,34,Owner,Standalone
5922,176.61,Boston,S,20,24,5,69,Graduation,19,Patnership,Complex
138,227.21,NJ City,S,22,18,24,27,High School,28,Patnership,Complex
863,75.4,NJ City,E,1,15,1,61,No education,30,Owner,Standalone
7212,236.72,Boston,W,5,14,10,27,Graduation,19,Franchise,Complex
3937,75.64,NJ City,N,5,12,3,31,High School,25,Patnership,Standalone
7191,271.73,Boston,E,3,21,1,78,Graduation,24,Owner,Standalone
3521,78.57,NJ City,N,7,10,4,35,No education,18,Patnership,Standalone
1665,9.49,NJ City,N,14,11,3,84,Graduation,23,Patnership,Standalone
3801,190.16,NJ City,N,8,8,13,41,High School,33,Patnership,Standalone
9114,51.17,NJ City,S,9,17,2,35,Graduation,25,Patnership,Complex
1043,30.09,NJ City,N,12,21,2,66,No education,22,Owner,Standalone
9095,39.95,NJ City,N,4,18,3,69,No education,27,Owner,Standalone
5886,107.11,Boston,S,5,11,4,60,Graduation,34,Patnership,Complex
8910,11.73,NJ City,N,7,20,2,70,Graduation,26,Patnership,Complex
5360,341.95,Boston,W,13,14,3,70,No education,25,Franchise,Complex
4406,147.92,NJ City,S,17,8,2,59,No education,18,Patnership,Complex
2203,90.08,NJ City,E,1,13,2,43,High School,26,Owner,Standalone
6100,148.44,Boston,W,7,10,22,43,Graduation,28,Franchise,Complex
5901,269.07,Boston,N,16,11,2,35,Graduation,21,Patnership,Standalone
7385,237.61,Boston,N,23,13,24,52,Graduation,26,Patnership,Standalone
6013,13.21,Boston,N,12,18,4,62,High School,31,Patnership,Standalone
1252,338.79,NJ City,W,18,20,20,26,High School,19,Franchise,Complex
6910,236.96,Boston,S,13,11,21,50,Graduation,33,Patnership,Complex
3091,369.61,NJ City,E,24,21,16,75,High School,29,Owner,Standalone
1887,489.08,NJ City,E,9,9,9,30,High School,27,Owner,Standalone
6662,87.28,Boston,S,20,22,3,64,No education,31,Patnership,Complex
8414,85.27,NJ City,S,20,13,1,26,No education,23,Patnership,Complex
923,19.46,NJ City,N,22,22,3,30,No education,19,Owner,Standalone
9575,97.86,NJ City,E,15,22,3,73,No education,21,Owner,Standalone
5963,301.68,Boston,E,17,21,5,41,No education,24,Owner,Standalone
9037,425.28,NJ City,N,7,20,25,74,Graduation,33,Patnership,Standalone
846,303.53,NJ City,S,6,18,18,84,High School,25,Patnership,Complex
5104,112.01,Boston,W,16,22,2,82,High School,25,Franchise,Complex
6593,302.44,Boston,N,11,8,4,80,No education,29,Patnership,Standalone
7591,240.53,Boston,E,13,12,23,41,Graduation,34,Owner,Standalone
3812,182.33,NJ City,W,6,21,9,45,High School,28,Franchise,Complex
7017,68.24,Boston,N,21,24,4,80,Graduation,19,Patnership,Standalone
3917,365.72,NJ City,N,11,17,10,64,High School,26,Patnership,Standalone
5778,254.3,Boston,S,9,22,21,40,Graduation,30,Patnership,Complex
7075,232.33,Boston,E,6,9,6,54,Graduation,30,Owner,Standalone
1759,87.73,NJ City,E,6,12,3,75,High School,28,Owner,Standalone
2051,198.49,NJ City,E,15,21,23,64,High School,35,Owner,Standalone
4910,91.91,NJ City,S,8,12,1,31,No education,34,Patnership,Complex
4343,126.03,NJ City,E,3,19,4,71,No education,29,Owner,Standalone
7111,56.96,Boston,E,8,20,3,53,High School,21,Owner,Standalone
1028,258.07,NJ City,W,20,19,8,57,High School,22,Franchise,Complex
3915,357.89,NJ City,E,20,24,22,71,High School,18,Owner,Standalone
7310,163.08,Boston,S,20,12,15,55,Graduation,31,Patnership,Complex
3904,36.79,NJ City,N,15,23,4,46,High School,30,Franchise,Complex
8593,260.45,NJ City,N,10,14,3,41,High School,30,Patnership,Standalone
6798,35.78,Boston,N,9,16,1,76,Graduation,22,Patnership,Complex
8614,48,NJ City,S,25,17,3,65,High School,33,Patnership,Complex
15,52.26,Boston,E,11,10,3,83,Graduation,19,Owner,Standalone
3325,45.78,NJ City,N,8,21,1,81,High School,23,Patnership,Standalone
9913,461.86,NJ City,N,25,22,8,61,Graduation,25,Patnership,Standalone
315,230,NJ City,E,3,8,19,30,High School,30,Owner,Standalone
6160,145.8,Boston,W,23,8,10,54,Graduation,32,Franchise,Complex
354,259.66,NJ City,S,8,8,1,32,Graduation,27,Patnership,Complex
2242,198.63,NJ City,S,7,8,20,78,High School,23,Patnership,Complex
2987,387.73,NJ City,E,12,8,19,51,High School,31,Owner,Standalone
1527,437.2,NJ City,E,21,8,17,49,High School,24,Owner,Standalone
6495,233.81,Boston,E,14,17,3,60,Graduation,33,Owner,Standalone
5780,269.52,Boston,W,7,9,5,73,No education,33,Franchise,Complex
1988,442.49,NJ City,W,10,9,12,72,High School,27,Franchise,Complex
1341,149.65,NJ City,N,6,14,18,46,High School,18,Patnership,Standalone
1583,444.51,NJ City,E,14,19,20,80,High School,32,Owner,Standalone
291,314.07,NJ City,E,19,8,1,75,Graduation,31,Owner,Standalone
897,90.21,NJ City,N,16,17,1,59,Graduation,27,Patnership,Standalone
8359,38.63,NJ City,N,3,22,3,32,High School,26,Owner,Standalone
1966,134.46,NJ City,S,18,24,4,61,High School,24,Patnership,Complex
4525,53.55,NJ City,N,11,18,4,40,High School,34,Patnership,Standalone
1226,382.84,NJ City,S,17,17,2,74,No education,30,Patnership,Complex
8684,407.41,NJ City,W,16,19,19,76,Graduation,21,Franchise,Complex
7358,51.89,Boston,S,15,18,4,73,No education,22,Patnership,Complex
862,233.29,NJ City,S,21,16,3,73,High School,22,Patnership,Complex
8176,242.52,NJ City,W,14,24,25,39,Graduation,22,Franchise,Complex
5806,196.54,Boston,S,20,12,14,63,Graduation,28,Patnership,Complex
4544,23.17,NJ City,N,8,11,2,78,No education,33,Franchise,Complex
412,352.07,NJ City,W,12,14,3,38,High School,29,Franchise,Complex
6527,8.05,Boston,N,12,14,2,47,No education,30,Owner,Standalone
3384,39.89,NJ City,N,1,13,4,71,Graduation,27,Franchise,Complex
7366,155.59,Boston,S,14,18,15,27,Graduation,18,Patnership,Complex
2258,77.29,NJ City,S,13,20,4,80,No education,28,Patnership,Complex
7757,12.19,Boston,N,4,11,4,72,No education,19,Patnership,Standalone
3351,249.55,NJ City,E,14,11,8,37,High School,19,Owner,Standalone
6666,150.07,Boston,S,1,22,22,67,Graduation,25,Patnership,Complex
5364,225.31,Boston,W,14,11,11,41,Graduation,32,Franchise,Complex
2127,99.3,NJ City,E,6,21,3,78,Graduation,32,Owner,Standalone
2417,471.28,NJ City,N,8,11,6,79,High School,18,Patnership,Standalone
8090,189.99,NJ City,S,1,13,12,48,Graduation,22,Patnership,Complex
9904,449.54,NJ City,W,24,19,23,26,Graduation,19,Franchise,Complex
168,235.37,NJ City,W,9,17,23,75,High School,26,Franchise,Complex
7005,495.37,Boston,N,6,16,11,79,Graduation,32,Patnership,Standalone
298,368.61,NJ City,S,22,17,18,70,High School,26,Patnership,Complex
7509,149.87,Boston,N,5,21,21,52,Graduation,31,Patnership,Standalone
8511,253.38,NJ City,E,10,8,11,65,Graduation,25,Owner,Standalone
1911,273.17,NJ City,E,3,8,5,53,Graduation,25,Owner,Standalone
61,437.29,Boston,N,21,20,5,58,High School,34,Patnership,Standalone
6907,352.42,Boston,E,16,16,1,56,High School,19,Owner,Standalone
8880,215.39,NJ City,W,1,10,8,51,Graduation,28,Franchise,Complex
2564,497.75,NJ City,W,23,21,12,82,High School,33,Franchise,Complex
7342,18.67,Boston,N,18,12,1,56,High School,32,Patnership,Complex
4564,112.98,NJ City,W,13,11,4,32,High School,29,Franchise,Complex
1458,53.14,NJ City,S,21,22,4,62,Graduation,19,Patnership,Complex
2771,468.89,NJ City,E,14,12,21,39,High School,33,Owner,Standalone
2314,463.1,NJ City,S,17,19,5,65,High School,18,Patnership,Complex
555,197.87,NJ City,E,10,9,14,63,High School,35,Owner,Standalone
8803,401.43,NJ City,E,10,12,17,41,Graduation,21,Owner,Standalone
5176,445.21,Boston,W,18,12,6,43,Graduation,32,Franchise,Complex
3506,453.03,NJ City,S,25,19,16,65,High School,23,Patnership,Complex
9372,315.93,NJ City,W,24,23,7,27,Graduation,22,Franchise,Complex
4960,413.09,Boston,W,8,19,3,33,High School,30,Franchise,Complex
7335,281.15,Boston,E,21,20,14,64,Graduation,22,Owner,Standalone
7754,304.52,Boston,S,2,16,5,51,No education,18,Patnership,Complex
5313,202.39,Boston,N,11,21,11,75,Graduation,22,Patnership,Standalone
1855,6.4,NJ City,N,14,13,1,54,High School,24,Owner,Standalone
9734,126.83,NJ City,S,16,14,3,33,No education,18,Patnership,Complex
2243,203.57,NJ City,E,7,12,2,60,No education,18,Owner,Standalone
8878,265.99,NJ City,S,16,14,25,50,Graduation,19,Patnership,Complex
6750,386.42,Boston,S,14,20,10,72,Graduation,24,Patnership,Complex
63,168.98,Boston,E,13,24,8,78,High School,19,Owner,Standalone
726,382.83,NJ City,S,10,12,15,60,High School,18,Patnership,Complex
7796,425.31,Boston,W,6,8,7,50,Graduation,26,Franchise,Complex
1571,481.59,NJ City,E,14,9,5,29,No education,27,Owner,Standalone
8037,304.45,Boston,N,22,21,10,66,Graduation,26,Patnership,Standalone
7026,322.95,Boston,S,7,20,6,68,Graduation,35,Patnership,Complex
7630,362.1,Boston,S,24,17,9,67,Graduation,30,Patnership,Complex
2030,246.34,NJ City,S,19,20,16,29,High School,34,Patnership,Complex
788,391.94,NJ City,W,3,11,5,63,No education,26,Franchise,Complex
3763,313.03,NJ City,E,14,22,21,30,High School,27,Owner,Standalone
5146,106.29,Boston,S,10,21,4,33,High School,34,Patnership,Complex
1470,17.15,NJ City,N,4,9,2,50,Graduation,19,Patnership,Complex
4032,204,NJ City,W,3,24,6,81,High School,31,Franchise,Complex
7864,480.47,Boston,W,11,10,19,30,Graduation,18,Franchise,Complex
1722,162.59,NJ City,S,24,14,11,71,High School,21,Patnership,Complex
2554,469.11,NJ City,S,23,18,4,65,High School,20,Patnership,Complex
276,189.09,NJ City,W,3,24,4,34,Graduation,22,Franchise,Complex
2349,338.28,NJ City,N,13,16,18,83,High School,20,Patnership,Standalone
4989,211.96,Boston,N,21,11,4,81,Graduation,35,Patnership,Standalone
5965,396.53,Boston,N,1,17,10,70,Graduation,18,Patnership,Standalone
5398,56.17,Boston,S,11,14,1,38,High School,19,Patnership,Complex
5871,407.6,Boston,E,12,12,24,66,Graduation,33,Owner,Standalone
2692,167.32,NJ City,W,20,11,24,52,High School,32,Franchise,Complex
7479,345.59,Boston,E,23,24,24,32,Graduation,27,Owner,Standalone
9934,399.84,NJ City,S,24,15,2,49,High School,28,Patnership,Complex
8421,245.15,NJ City,N,7,24,6,47,Graduation,27,Patnership,Standalone
6730,389.98,Boston,S,18,15,9,84,Graduation,22,Patnership,Complex
992,354.49,NJ City,W,23,9,6,48,High School,20,Franchise,Complex
2429,215.17,NJ City,N,9,18,20,31,High School,23,Patnership,Standalone
9053,273.02,NJ City,N,18,23,20,72,Graduation,22,Patnership,Standalone
6571,278.2,Boston,E,2,23,8,57,Graduation,32,Owner,Standalone
3455,58.24,NJ City,E,2,15,4,52,No education,31,Owner,Standalone
2541,6.67,NJ City,N,9,20,3,66,Graduation,23,Patnership,Standalone
6990,80.34,Boston,S,17,13,1,28,Graduation,34,Patnership,Complex
6729,123.72,Boston,N,19,16,3,40,Graduation,22,Patnership,Standalone
5917,49.26,Boston,N,3,11,2,42,High School,30,Patnership,Standalone
9251,429.53,NJ City,E,16,12,20,44,Graduation,32,Owner,Standalone
1692,229.96,NJ City,W,7,11,8,41,High School,30,Franchise,Complex
3178,383.21,NJ City,S,17,20,1,27,High School,26,Patnership,Complex
6845,412.11,Boston,N,24,8,24,78,Graduation,28,Patnership,Standalone
6155,388.37,Boston,E,6,9,19,68,Graduation,28,Owner,Standalone
6368,133.37,Boston,W,1,19,24,25,Graduation,30,Franchise,Complex
7845,322.88,Boston,N,7,22,4,84,Graduation,19,Patnership,Standalone
8601,100.78,NJ City,N,19,14,2,26,Graduation,18,Patnership,Standalone
4273,350.77,NJ City,N,19,13,20,60,High School,33,Patnership,Standalone
7632,494.21,Boston,W,1,8,20,74,Graduation,35,Franchise,Complex
9293,296.04,NJ City,N,19,17,2,83,No education,29,Patnership,Standalone
7134,296.06,Boston,S,7,13,17,53,Graduation,29,Patnership,Complex
6655,275.09,Boston,E,1,18,20,71,Graduation,19,Owner,Standalone
4122,434.87,NJ City,S,16,8,14,74,High School,25,Patnership,Complex
3595,212.86,NJ City,E,11,21,6,51,High School,31,Owner,Standalone
3512,305.22,NJ City,W,20,21,21,47,High School,23,Franchise,Complex
1082,431.94,NJ City,S,24,15,15,43,High School,21,Patnership,Complex
5703,408.95,Boston,E,7,19,17,43,Graduation,24,Owner,Standalone
2994,458.12,NJ City,S,5,13,21,37,High School,25,Patnership,Complex
9411,16.13,NJ City,N,21,21,2,53,Graduation,33,Owner,Standalone
6944,388.78,Boston,W,18,17,14,39,Graduation,27,Franchise,Complex
1065,438.49,NJ City,N,11,22,12,64,High School,26,Patnership,Standalone
5162,364.55,Boston,S,20,18,25,33,Graduation,34,Patnership,Complex
9060,321.77,NJ City,W,2,13,9,63,Graduation,18,Franchise,Complex
2552,230.17,NJ City,W,12,13,19,79,High School,28,Franchise,Complex
1145,112.33,NJ City,N,7,12,4,78,No education,21,Patnership,Standalone
4209,250.64,NJ City,N,10,8,2,53,Graduation,21,Patnership,Standalone
349,53.01,NJ City,N,16,14,4,60,High School,35,Patnership,Standalone
4780,431.56,NJ City,W,19,8,4,47,High School,26,Franchise,Complex
3691,442.21,NJ City,E,23,16,21,33,High School,29,Owner,Standalone
2768,83.81,NJ City,W,14,17,4,26,No education,31,Franchise,Complex
6138,186.51,Boston,S,14,24,13,49,Graduation,30,Patnership,Complex
4917,211.96,NJ City,N,13,19,11,80,High School,31,Patnership,Standalone
5811,14.69,Boston,N,19,22,1,76,Graduation,24,Owner,Standalone
9314,193.69,NJ City,S,9,10,5,83,No education,31,Patnership,Complex
8520,349.85,NJ City,W,3,19,12,32,Graduation,34,Franchise,Complex
5109,263.55,Boston,N,16,16,24,35,Graduation,27,Patnership,Standalone
6261,351.67,Boston,N,10,13,2,81,Graduation,33,Patnership,Standalone
7681,49.01,Boston,N,25,14,4,27,High School,30,Patnership,Standalone
1297,481.73,NJ City,N,6,20,5,61,High School,32,Patnership,Standalone
8073,69.82,NJ City,N,2,23,3,74,Graduation,24,Patnership,Standalone
2020,227,NJ City,W,2,20,23,82,High School,28,Franchise,Complex
4239,412.1,NJ City,E,16,18,25,65,High School,35,Owner,Standalone
3337,287.84,NJ City,N,12,13,18,81,High School,34,Patnership,Standalone
7105,69.48,Boston,N,13,8,2,43,High School,35,Patnership,Standalone
6697,62.5,Boston,N,2,9,3,47,High School,31,Patnership,Standalone
9902,78.74,NJ City,S,12,17,1,34,No education,29,Patnership,Complex
3494,102.57,NJ City,S,14,11,4,57,No education,35,Patnership,Complex
6144,489.94,Boston,W,11,16,25,50,Graduation,27,Franchise,Complex
62,291.91,Boston,S,16,15,23,47,High School,29,Patnership,Complex
966,452.72,NJ City,S,5,8,2,66,Graduation,22,Patnership,Complex
8547,252.73,NJ City,E,23,11,19,42,Graduation,29,Owner,Standalone
9205,231.15,NJ City,N,4,16,15,51,Graduation,20,Patnership,Standalone
9589,227.81,NJ City,N,17,20,15,60,Graduation,25,Patnership,Standalone
8227,310.67,NJ City,E,13,13,14,51,Graduation,26,Owner,Standalone
9914,136.42,NJ City,S,21,16,25,81,Graduation,33,Patnership,Complex
6282,83.92,Boston,S,2,17,4,73,Graduation,18,Patnership,Complex
4982,257.65,Boston,S,24,16,18,57,High School,22,Patnership,Complex
2396,137.79,NJ City,W,6,19,25,57,High School,27,Franchise,Complex
4259,295.27,NJ City,E,8,21,11,50,High School,24,Owner,Standalone
2193,131.09,NJ City,N,13,21,2,38,Graduation,22,Patnership,Standalone
3987,488.54,NJ City,E,21,18,19,43,High School,34,Owner,Standalone
9408,234.65,NJ City,W,12,15,17,60,Graduation,31,Franchise,Complex
4412,385.45,NJ City,W,19,22,3,57,No education,20,Franchise,Complex
2248,22.27,NJ City,N,25,11,1,82,High School,22,Franchise,Complex
5584,310.51,Boston,W,9,16,21,28,Graduation,23,Franchise,Complex
2803,224.97,NJ City,E,17,18,25,48,High School,32,Owner,Standalone
492,257.71,NJ City,W,13,16,13,66,High School,24,Franchise,Complex
2377,449.43,NJ City,N,12,24,3,84,High School,27,Patnership,Standalone
6356,164.56,Boston,W,21,9,21,35,Graduation,30,Franchise,Complex
7063,182.52,Boston,E,22,8,23,46,Graduation,23,Owner,Standalone
1346,222,NJ City,S,2,21,16,25,High School,31,Patnership,Complex
5992,488.12,Boston,W,23,15,20,56,Graduation,28,Franchise,Complex
2713,82.28,NJ City,N,11,18,3,35,High School,19,Patnership,Standalone
4115,433.76,NJ City,E,13,9,17,62,High School,26,Owner,Standalone
949,70.92,NJ City,N,11,16,3,53,High School,23,Patnership,Standalone
4789,58.31,NJ City,N,7,10,4,52,High School,31,Patnership,Standalone
2457,132,NJ City,N,2,16,19,43,High School,25,Patnership,Standalone
8499,414.85,NJ City,E,16,23,11,53,Graduation,23,Owner,Standalone
8972,126.09,NJ City,W,22,13,2,75,No education,27,Franchise,Complex
4078,48.64,NJ City,S,7,14,4,70,High School,25,Patnership,Complex
2575,498.53,NJ City,E,18,17,15,56,High School,32,Owner,Standalone
3560,151.56,NJ City,W,13,14,20,28,High School,18,Franchise,Complex
3585,403.61,NJ City,N,21,13,6,82,High School,31,Patnership,Standalone
5269,419.69,Boston,N,5,24,18,34,Graduation,18,Patnership,Standalone
6649,413.99,Boston,N,12,17,11,37,Graduation,22,Patnership,Standalone
10,142.72,NJ City,S,24,15,25,31,High School,22,Patnership,Complex
2047,2.73,NJ City,E,9,15,1,62,High School,29,Owner,Standalone
8816,28.38,NJ City,N,1,11,4,59,No education,24,Franchise,Complex
9830,365.77,NJ City,S,13,20,17,69,Graduation,30,Patnership,Complex
7820,456.83,Boston,W,6,10,20,43,Graduation,33,Franchise,Complex
5691,499.75,Boston,E,16,20,7,39,Graduation,33,Owner,Standalone
4864,188.72,NJ City,W,7,15,7,26,High School,28,Franchise,Complex
5557,318.07,Boston,N,16,19,15,48,Graduation,28,Patnership,Standalone
9760,137.54,NJ City,W,2,17,25,63,Graduation,21,Franchise,Complex
5288,241.54,Boston,W,25,21,8,74,Graduation,29,Franchise,Complex
2668,304.28,NJ City,W,6,19,25,57,High School,29,Franchise,Complex
9154,150.57,NJ City,S,2,18,7,46,Graduation,20,Patnership,Complex
8426,73.82,NJ City,N,21,8,4,29,No education,19,Patnership,Complex
2011,239.86,NJ City,E,4,23,1,53,High School,33,Owner,Standalone
5875,372.76,Boston,E,13,12,15,71,Graduation,29,Owner,Standalone
682,299.64,NJ City,S,16,12,5,81,High School,33,Patnership,Complex
6921,168.61,Boston,N,13,9,18,36,Graduation,29,Patnership,Standalone
6492,160.06,Boston,W,24,19,19,76,Graduation,31,Franchise,Complex
4886,75.7,NJ City,S,21,9,2,69,No education,22,Patnership,Complex
9269,166.3,NJ City,N,8,13,2,79,No education,22,Patnership,Standalone
7620,286.89,Boston,W,3,11,23,30,Graduation,34,Franchise,Complex
2400,368.36,NJ City,W,16,17,1,35,Graduation,35,Franchise,Complex
9611,470.31,NJ City,E,10,10,7,73,Graduation,21,Owner,Standalone
9438,214.04,NJ City,S,22,16,20,83,Graduation,28,Patnership,Complex
8305,308.02,NJ City,N,24,16,14,72,Graduation,26,Patnership,Standalone
275,162.77,NJ City,E,5,13,25,41,High School,21,Owner,Standalone
8045,81.45,NJ City,N,9,9,3,61,No education,30,Patnership,Standalone
1624,411.69,NJ City,W,7,21,20,46,High School,23,Franchise,Complex
7619,443,Boston,E,11,20,16,66,Graduation,22,Owner,Standalone
1447,168.64,NJ City,E,25,19,1,53,High School,19,Owner,Standalone
5266,376.56,Boston,S,10,21,16,49,Graduation,21,Patnership,Complex
2111,269,NJ City,E,2,13,15,76,High School,20,Owner,Standalone
3905,189.53,NJ City,N,19,17,25,80,High School,31,Patnership,Standalone
3704,125.04,NJ City,W,2,14,1,65,No education,26,Franchise,Complex
3523,30.33,NJ City,N,23,24,3,70,High School,24,Owner,Standalone
6758,436.98,Boston,S,15,8,4,40,No education,25,Patnership,Complex
8334,463.92,NJ City,S,14,18,10,50,Graduation,20,Patnership,Complex
2516,476.91,NJ City,W,13,13,22,62,High School,19,Franchise,Complex
9653,143.45,NJ City,N,9,19,13,77,Graduation,31,Patnership,Standalone
4863,449.67,NJ City,E,8,8,10,68,High School,31,Owner,Standalone
4026,44.76,NJ City,N,17,10,2,71,Graduation,25,Patnership,Complex
3586,495.79,NJ City,S,22,14,14,49,High School,30,Patnership,Complex
2826,61.08,NJ City,N,11,8,2,44,Graduation,33,Patnership,Complex
2451,155.23,NJ City,E,10,20,21,50,High School,31,Owner,Standalone
6284,99.88,Boston,W,13,18,4,62,No education,28,Franchise,Complex
5933,70.94,Boston,N,8,20,4,33,No education,24,Patnership,Standalone
5259,310.52,Boston,E,13,23,1,53,Graduation,18,Owner,Standalone
850,73.33,NJ City,N,24,13,3,70,High School,28,Patnership,Complex
4498,305.51,NJ City,S,18,12,13,78,High School,26,Patnership,Complex
5616,281.08,Boston,W,22,17,15,53,Graduation,22,Franchise,Complex
6,418.18,Maine,S,9,16,6,49,High School,18,Patnership,Complex
6151,308.25,Boston,E,10,21,20,76,Graduation,18,Owner,Standalone
4100,368.71,NJ City,W,5,24,3,82,No education,30,Franchise,Complex
5664,54.89,Boston,W,10,14,1,56,Graduation,35,Franchise,Complex
7568,339.02,Boston,W,10,24,16,44,Graduation,24,Franchise,Complex
7460,175.84,Boston,W,3,11,25,25,Graduation,26,Franchise,Complex
7251,22.22,Boston,N,20,21,3,42,Graduation,20,Owner,Standalone
5408,488.91,Boston,W,10,19,15,49,Graduation,21,Franchise,Complex
8085,146.29,NJ City,N,23,12,23,36,Graduation,20,Patnership,Standalone
5255,169.76,Boston,E,15,15,11,32,Graduation,33,Owner,Standalone
938,154.98,NJ City,S,24,22,22,69,High School,22,Patnership,Complex
5136,10.85,Boston,N,3,9,2,68,Graduation,31,Franchise,Complex
9385,114.73,NJ City,N,11,16,3,44,High School,18,Patnership,Standalone
3797,189,NJ City,N,2,18,8,70,High School,28,Patnership,Standalone
5490,325.8,Boston,S,4,20,18,73,Graduation,18,Patnership,Complex
93,114.35,NJ City,N,22,15,3,78,Graduation,20,Patnership,Standalone
5154,264.46,Boston,S,3,18,12,29,Graduation,24,Patnership,Complex
3436,190.75,NJ City,W,4,24,11,80,High School,27,Franchise,Complex
990,182.98,NJ City,S,10,20,20,59,High School,18,Patnership,Complex
3626,212.7,NJ City,S,23,17,7,30,High School,32,Patnership,Complex
5583,215.24,Boston,E,4,20,1,53,Graduation,33,Owner,Standalone
2399,79.92,NJ City,E,8,18,2,25,No education,20,Owner,Standalone
4723,276.17,NJ City,E,18,11,18,50,High School,33,Owner,Standalone
9612,296.32,NJ City,W,7,21,13,38,Graduation,21,Franchise,Complex
4287,73.74,NJ City,N,9,20,1,39,Graduation,26,Owner,Standalone
3373,109.07,NJ City,N,4,13,3,80,High School,20,Patnership,Standalone
2739,321.02,NJ City,E,21,14,25,25,High School,25,Owner,Standalone
2498,305.01,NJ City,S,7,8,7,73,High School,23,Patnership,Complex
910,251,NJ City,S,2,11,17,83,High School,22,Patnership,Complex
5264,407.19,Boston,W,18,13,24,53,Graduation,26,Franchise,Complex
1126,305.04,NJ City,S,5,17,14,71,High School,18,Patnership,Complex
8818,120.67,NJ City,S,23,18,1,81,High School,29,Patnership,Complex
4607,364.59,NJ City,E,25,13,7,73,High School,31,Owner,Standalone
5507,476.68,Boston,E,23,8,16,69,Graduation,21,Owner,Standalone
2590,62.04,NJ City,N,22,9,4,83,High School,28,Patnership,Complex
3901,115.46,NJ City,N,22,11,3,55,High School,32,Patnership,Standalone
5443,181.1,Boston,E,12,11,14,26,Graduation,32,Owner,Standalone
4411,319.06,NJ City,E,10,21,9,76,High School,31,Owner,Standalone
8839,415.16,NJ City,E,18,21,14,51,Graduation,19,Owner,Standalone
2597,381.4,NJ City,N,6,19,25,71,High School,22,Patnership,Standalone
6111,307.18,Boston,E,9,15,9,79,Graduation,27,Owner,Standalone
7606,1.7,Boston,N,5,14,2,40,High School,33,Patnership,Complex
4354,121.98,NJ City,S,7,14,2,47,High School,31,Patnership,Complex
4976,251.73,Boston,W,13,20,7,62,High School,33,Franchise,Complex
7666,14.62,Boston,N,24,22,3,58,High School,34,Patnership,Complex
273,477.72,NJ City,N,5,14,2,52,Graduation,31,Patnership,Standalone
3973,340.69,NJ City,N,9,8,10,31,High School,32,Patnership,Standalone
534,335.13,NJ City,S,13,21,24,49,High School,34,Patnership,Complex
9867,341.86,NJ City,E,20,11,15,79,Graduation,35,Owner,Standalone
7711,8.97,Boston,N,18,13,2,74,High School,24,Owner,Standalone
2616,87.73,NJ City,W,13,13,1,27,Graduation,33,Franchise,Complex
3412,282.13,NJ City,W,14,10,19,25,High School,32,Franchise,Complex
9116,265.76,NJ City,W,11,19,10,40,Graduation,21,Franchise,Complex
1819,216.88,NJ City,E,7,16,12,52,High School,28,Owner,Standalone
9391,108.57,NJ City,E,14,19,3,58,High School,25,Owner,Standalone
8095,16.53,NJ City,N,12,22,1,69,High School,21,Owner,Standalone
1001,434.03,NJ City,N,14,19,25,81,High School,18,Patnership,Standalone
3656,240.89,NJ City,W,14,11,22,67,High School,30,Franchise,Complex
5181,210.75,Boston,N,8,18,12,36,Graduation,32,Patnership,Standalone
759,214.5,NJ City,E,19,18,17,59,High School,33,Owner,Standalone
9971,375.27,NJ City,E,19,17,10,62,Graduation,18,Owner,Standalone
132,275.59,NJ City,W,14,19,4,30,Graduation,32,Franchise,Complex
1924,8.58,NJ City,N,7,21,1,81,High School,33,Franchise,Complex
4858,330.01,NJ City,S,13,16,9,26,High School,33,Patnership,Complex
9234,395.92,NJ City,S,5,23,8,43,Graduation,27,Patnership,Complex
4883,394.98,NJ City,E,21,12,9,42,High School,35,Owner,Standalone
4292,292,NJ City,W,2,19,18,65,High School,19,Franchise,Complex
1163,286.52,NJ City,E,18,20,1,79,No education,21,Owner,Standalone
8270,368.23,NJ City,S,12,15,16,38,Graduation,21,Patnership,Complex
6299,31.52,Boston,N,1,20,4,77,No education,26,Owner,Standalone
7354,7.84,Boston,N,16,21,2,43,High School,29,Patnership,Complex
6568,122.34,Boston,W,8,23,1,65,High School,30,Franchise,Complex
5668,75.07,Boston,W,14,24,4,70,High School,26,Franchise,Complex
1876,220.37,NJ City,W,24,17,11,28,High School,26,Franchise,Complex
2725,425.76,NJ City,N,14,8,11,33,High School,35,Patnership,Standalone
2133,142.09,NJ City,N,4,11,24,70,High School,30,Patnership,Standalone
4821,344.38,NJ City,N,5,15,15,41,High School,23,Patnership,Standalone
1933,99.93,NJ City,N,5,18,1,33,High School,28,Patnership,Standalone
8783,326.35,NJ City,E,24,8,25,43,Graduation,21,Owner,Standalone
2877,444.74,NJ City,N,4,9,19,64,High School,32,Patnership,Standalone
1467,339.93,NJ City,E,21,23,20,59,High School,30,Owner,Standalone
5047,172.61,Boston,E,20,11,15,79,Graduation,20,Owner,Standalone
6298,376.35,Boston,S,10,15,11,42,Graduation,23,Patnership,Complex
8411,82.43,NJ City,E,11,12,3,45,No education,25,Owner,Standalone
8787,298.9,NJ City,E,20,17,16,45,Graduation,31,Owner,Standalone
8302,360.01,NJ City,S,5,12,25,78,Graduation,31,Patnership,Complex
9663,176.19,NJ City,E,6,9,7,58,Graduation,35,Owner,Standalone
8102,132.86,NJ City,S,6,22,22,83,Graduation,33,Patnership,Complex
9106,441.81,NJ City,S,13,13,3,38,High School,21,Patnership,Complex
7024,33.36,Boston,N,2,19,1,32,High School,29,Franchise,Complex
4163,195.09,NJ City,E,24,16,8,45,High School,34,Owner,Standalone
2440,245.42,NJ City,W,8,17,16,73,High School,23,Franchise,Complex
9315,47.56,NJ City,E,12,10,4,76,Graduation,28,Owner,Standalone
4834,258.3,NJ City,S,5,13,23,63,High School,35,Patnership,Complex
1255,466.34,NJ City,E,6,11,23,74,High School,34,Owner,Standalone
322,432.33,NJ City,S,24,11,4,53,High School,32,Patnership,Complex
717,482.63,NJ City,N,23,12,14,43,High School,28,Patnership,Standalone
8362,167.14,NJ City,S,7,18,2,30,High School,24,Patnership,Complex
3040,152.3,NJ City,W,16,15,17,28,High School,34,Franchise,Complex
639,73.63,NJ City,N,4,8,2,34,Graduation,20,Owner,Standalone
784,383.07,NJ City,W,14,14,22,57,High School,30,Franchise,Complex
1149,448.05,NJ City,N,6,15,19,28,High School,29,Patnership,Standalone
5727,340.73,Boston,E,15,11,12,84,Graduation,24,Owner,Standalone
7621,30.67,Boston,N,2,20,4,43,High School,32,Patnership,Standalone
5158,223.32,Boston,S,15,16,1,49,High School,33,Patnership,Complex
5472,430.28,Boston,W,16,22,2,71,Graduation,31,Franchise,Complex
619,240.47,NJ City,E,1,22,2,60,High School,34,Owner,Standalone
4906,299.06,NJ City,S,4,23,12,63,High School,28,Patnership,Complex
4221,140.94,NJ City,N,19,21,7,65,High School,34,Patnership,Standalone
6934,426.4,Boston,S,6,21,8,36,Graduation,24,Patnership,Complex
7403,31.92,Boston,N,8,22,3,72,No education,24,Owner,Standalone
9807,328.65,NJ City,E,7,19,1,58,Graduation,22,Owner,Standalone
4682,270.39,NJ City,S,19,23,17,69,High School,25,Patnership,Complex
9200,135.65,NJ City,W,14,19,21,37,Graduation,33,Franchise,Complex
5690,379.5,Boston,S,13,20,22,68,Graduation,30,Patnership,Complex
9197,245.79,NJ City,N,15,16,13,32,Graduation,25,Patnership,Standalone
5564,451.98,Boston,W,4,11,20,65,Graduation,19,Franchise,Complex
9716,47.32,NJ City,W,21,23,4,59,No education,21,Franchise,Complex
2765,182.66,NJ City,N,23,18,20,85,High School,35,Patnership,Standalone
2012,108.64,NJ City,W,15,15,1,46,No education,23,Franchise,Complex
2594,244.21,NJ City,S,23,13,10,26,High School,18,Patnership,Complex
3297,387.09,NJ City,N,20,19,3,66,Graduation,35,Patnership,Standalone
5232,439.48,Boston,W,18,17,21,70,Graduation,24,Franchise,Complex
4893,6.05,NJ City,N,1,18,1,30,Graduation,21,Patnership,Standalone
5389,233.03,Boston,N,4,20,17,79,Graduation,29,Patnership,Standalone
3048,341.63,NJ City,W,25,9,12,74,High School,28,Franchise,Complex
6826,25.34,Boston,N,5,14,4,81,High School,25,Patnership,Complex
2239,401.84,NJ City,E,23,16,14,67,High School,25,Owner,Standalone
5041,362.58,Boston,N,1,13,4,56,High School,26,Patnership,Standalone
6807,371.67,Boston,E,15,20,4,56,Graduation,25,Owner,Standalone
8114,82.43,NJ City,S,8,10,2,46,No education,21,Patnership,Complex
3172,432.46,NJ City,W,3,22,3,56,High School,29,Franchise,Complex
9167,454.5,NJ City,E,25,15,19,46,Graduation,27,Owner,Standalone
2715,36.01,NJ City,N,4,18,4,52,Graduation,32,Owner,Standalone
3852,333.16,NJ City,W,17,24,8,46,High School,35,Franchise,Complex
2794,320.31,NJ City,S,15,11,24,59,High School,19,Patnership,Complex
1730,192.17,NJ City,S,17,24,25,42,High School,30,Patnership,Complex
3978,94.17,NJ City,S,17,9,4,83,Graduation,29,Patnership,Complex
4514,252.59,NJ City,S,18,16,2,84,No education,24,Patnership,Complex
8188,136.77,NJ City,W,8,17,11,81,Graduation,34,Franchise,Complex
4437,5.51,NJ City,N,6,22,3,34,Graduation,32,Patnership,Standalone
3921,345.14,NJ City,N,25,24,24,78,High School,23,Patnership,Standalone
443,346.23,NJ City,E,7,16,24,27,High School,34,Owner,Standalone
3318,186.73,NJ City,S,17,19,19,32,High School,34,Patnership,Complex
1273,317.92,NJ City,N,14,14,8,30,High School,31,Patnership,Standalone
5465,17.42,Boston,N,11,13,4,40,No education,32,Patnership,Standalone
2724,206.86,NJ City,W,16,15,11,83,High School,29,Franchise,Complex
5741,422.79,Boston,N,8,19,8,48,Graduation,20,Patnership,Standalone
3261,156,NJ City,N,3,14,20,75,High School,34,Patnership,Standalone
9144,385.68,NJ City,W,4,9,10,37,Graduation,29,Franchise,Complex
130,135.99,NJ City,S,3,24,3,31,High School,28,Patnership,Complex
6938,255.47,Boston,S,16,18,23,46,Graduation,34,Patnership,Complex
4962,485.51,Boston,S,19,16,14,75,High School,24,Patnership,Complex
6348,11.42,Boston,N,1,23,3,45,Graduation,19,Franchise,Complex
6797,271.73,Boston,N,10,19,6,56,Graduation,27,Patnership,Standalone
3718,161.55,NJ City,S,15,21,2,83,High School,21,Patnership,Complex
4245,270.07,NJ City,N,22,17,14,75,High School,23,Patnership,Standalone
5706,67.11,Boston,N,1,9,4,30,Graduation,19,Patnership,Complex
1040,141.46,NJ City,W,20,12,9,48,High School,29,Franchise,Complex
4308,148.73,NJ City,W,13,8,25,80,High School,26,Franchise,Complex
7516,208.72,Boston,W,22,9,25,73,Graduation,20,Franchise,Complex
9833,489.17,NJ City,N,9,16,18,79,Graduation,32,Patnership,Standalone
1081,176.96,NJ City,N,19,21,5,35,High School,19,Patnership,Standalone
6070,155.07,Boston,S,4,20,18,25,Graduation,28,Patnership,Complex
7010,153.34,Boston,S,12,17,9,25,Graduation,29,Patnership,Complex
2734,52.83,NJ City,S,7,18,1,32,High School,25,Patnership,Complex
9982,444.61,NJ City,S,25,8,1,46,High School,32,Patnership,Complex
5028,350.42,Boston,W,1,10,9,68,Graduation,29,Franchise,Complex
3568,489.44,NJ City,W,14,24,20,29,High School,33,Franchise,Complex
4409,36.41,NJ City,N,24,19,4,70,No education,25,Patnership,Standalone
1509,304.84,NJ City,N,14,16,5,67,Graduation,23,Patnership,Standalone
655,353.57,NJ City,E,8,22,4,48,High School,18,Owner,Standalone
5120,332.52,Boston,W,23,8,17,76,Graduation,33,Franchise,Complex
1059,188.69,NJ City,E,2,9,3,50,Graduation,21,Owner,Standalone
5239,69.77,Boston,N,23,11,4,61,High School,23,Owner,Standalone
1117,57.84,NJ City,N,10,17,2,37,High School,24,Patnership,Standalone
3152,150.8,NJ City,W,24,14,23,72,High School,19,Franchise,Complex
9199,198.82,NJ City,E,9,18,14,48,Graduation,34,Owner,Standalone
2067,30.6,NJ City,N,3,18,4,79,Graduation,20,Owner,Standalone
2072,494.76,NJ City,W,8,11,8,28,High School,34,Franchise,Complex
1234,27.31,NJ City,N,3,23,2,31,High School,28,Patnership,Complex
2834,106.98,NJ City,S,13,17,3,43,No education,24,Patnership,Complex
5884,385.25,Boston,W,21,10,13,80,Graduation,24,Franchise,Complex
937,182.28,NJ City,N,20,9,2,58,High School,23,Patnership,Standalone
6327,135.15,Boston,E,3,9,8,79,Graduation,23,Owner,Standalone
3967,241.42,NJ City,E,18,19,12,37,High School,19,Owner,Standalone
5622,464.91,Boston,S,22,13,3,46,Graduation,30,Patnership,Complex
8563,90.98,NJ City,E,20,22,1,79,High School,30,Owner,Standalone
1954,155.72,NJ City,S,15,15,14,42,High School,20,Patnership,Complex
8204,222.04,NJ City,W,16,20,21,54,Graduation,25,Franchise,Complex
2167,164,NJ City,E,3,12,9,38,High School,21,Owner,Standalone
4543,84.28,NJ City,E,16,14,2,38,High School,35,Owner,Standalone
4687,406.1,NJ City,E,15,24,9,49,High School,26,Owner,Standalone
9150,194.15,NJ City,S,19,10,4,44,Graduation,20,Patnership,Complex
597,482.37,NJ City,N,25,11,8,61,High School,22,Patnership,Standalone
928,135.92,NJ City,W,16,17,2,73,High School,33,Franchise,Complex
9474,346.19,NJ City,S,6,17,15,79,Graduation,27,Patnership,Complex
8662,322.78,NJ City,S,13,15,9,38,Graduation,28,Patnership,Complex
9101,396.88,NJ City,N,15,22,8,67,Graduation,32,Patnership,Standalone
3032,113.77,NJ City,W,3,22,1,68,No education,21,Franchise,Complex
2645,288.28,NJ City,N,10,15,21,55,High School,33,Patnership,Standalone
765,346.95,NJ City,N,25,18,13,81,High School,30,Patnership,Standalone
1510,122.17,NJ City,S,7,16,3,26,High School,23,Patnership,Complex
8418,304.24,NJ City,S,22,11,21,84,Graduation,22,Patnership,Complex
3703,120.97,NJ City,E,6,13,4,26,High School,31,Owner,Standalone
37,162,NY City,N,1,22,11,27,High School,27,Patnership,Standalone
2485,422.42,NJ City,N,8,19,6,40,High School,22,Patnership,Standalone
5641,92.68,Boston,N,5,13,1,56,High School,28,Patnership,Standalone
2563,381.69,NJ City,E,21,20,11,46,High School,28,Owner,Standalone
4092,497.56,NJ City,W,18,21,13,78,High School,34,Franchise,Complex
1369,88.3,NJ City,N,21,8,4,54,High School,28,Patnership,Standalone
7110,11.07,Boston,N,15,19,4,80,Graduation,35,Patnership,Complex
8828,486.54,NJ City,W,15,24,4,76,No education,35,Franchise,Complex
9236,375.36,NJ City,W,12,12,14,81,Graduation,33,Franchise,Complex
4750,349.76,NJ City,S,18,9,25,62,High School,18,Patnership,Complex
4007,95.75,NJ City,E,18,16,3,28,No education,29,Owner,Standalone
4419,39.78,NJ City,N,9,9,2,43,Graduation,34,Owner,Standalone
2251,446.57,NJ City,E,8,21,16,65,High School,31,Owner,Standalone
1799,408.33,NJ City,E,9,18,2,54,No education,23,Owner,Standalone
4295,225.85,NJ City,E,17,21,18,64,High School,19,Owner,Standalone
5093,182.44,Boston,N,16,9,12,77,Graduation,24,Patnership,Standalone
6228,123.16,Boston,W,20,19,1,58,Graduation,29,Franchise,Complex
5513,473.96,Boston,N,8,22,3,67,No education,27,Patnership,Standalone
3910,438.19,NJ City,S,10,23,18,72,High School,22,Patnership,Complex
4907,70.04,NJ City,N,21,18,4,42,No education,30,Owner,Standalone
8809,391.28,NJ City,N,14,18,15,83,Graduation,30,Patnership,Standalone
4459,319.82,NJ City,E,18,14,3,80,High School,22,Owner,Standalone
7040,266.58,Boston,W,9,9,12,68,Graduation,19,Franchise,Complex
9804,118.96,NJ City,W,9,10,1,35,Graduation,34,Franchise,Complex
180,284.28,NJ City,W,5,16,18,34,High School,31,Franchise,Complex
9763,385.92,NJ City,E,2,21,24,62,Graduation,18,Owner,Standalone
9697,120.39,NJ City,N,12,22,1,26,High School,32,Patnership,Standalone
1451,116.47,NJ City,E,24,12,2,32,No education,29,Owner,Standalone
4472,18.22,NJ City,N,15,10,2,28,No education,20,Franchise,Complex
5043,111.31,Boston,E,3,19,4,60,Graduation,35,Owner,Standalone
5764,41.14,Boston,N,7,20,4,59,High School,32,Franchise,Complex
2224,124.24,NJ City,W,1,23,1,66,High School,24,Franchise,Complex
4342,271.71,NJ City,S,5,12,1,52,High School,35,Patnership,Complex
3060,497.51,NJ City,W,15,8,16,48,High School,32,Franchise,Complex
6444,321.62,Boston,W,11,15,9,45,Graduation,18,Franchise,Complex
8485,438.75,NJ City,N,15,10,15,57,Graduation,33,Patnership,Standalone
1160,191.08,NJ City,W,25,17,20,47,High School,24,Franchise,Complex
7468,497.84,Boston,W,11,22,23,68,Graduation,24,Franchise,Complex
3162,434.33,NJ City,S,5,17,24,32,High School,33,Patnership,Complex
8123,239.27,NJ City,E,5,17,14,29,Graduation,27,Owner,Standalone
9379,197.76,NJ City,E,22,20,14,81,Graduation,27,Owner,Standalone
2788,390.49,NJ City,W,10,23,15,84,High School,28,Franchise,Complex
2147,155,NJ City,E,3,20,12,29,High School,32,Owner,Standalone
4019,225.59,NJ City,E,20,10,3,58,No education,23,Owner,Standalone
944,472.37,NJ City,W,21,15,6,32,High School,30,Franchise,Complex
1990,477.53,NJ City,S,5,13,10,43,High School,32,Patnership,Complex
3674,138.98,NJ City,S,6,21,5,35,No education,20,Patnership,Complex
2028,128.13,NJ City,W,16,8,16,75,High School,30,Franchise,Complex
7651,464.18,Boston,E,14,12,18,60,Graduation,30,Owner,Standalone
4351,201.95,NJ City,E,21,15,1,80,High School,20,Owner,Standalone
3479,37.45,NJ City,N,21,15,3,47,No education,23,Owner,Standalone
7650,63.7,Boston,N,14,21,4,83,Graduation,25,Patnership,Complex
6727,141.32,Boston,E,10,11,17,77,Graduation,23,Owner,Standalone
3183,121.4,NJ City,E,14,19,4,46,Graduation,31,Owner,Standalone
7207,105.49,Boston,E,21,24,3,52,High School,23,Owner,Standalone
9850,371.21,NJ City,S,14,14,9,84,Graduation,34,Patnership,Complex
2665,97.22,NJ City,N,3,11,4,69,High School,35,Patnership,Standalone
4502,27.12,NJ City,N,12,17,1,48,No education,28,Patnership,Complex
468,230.17,NJ City,W,10,11,8,38,High School,23,Franchise,Complex
6931,46.25,Boston,N,22,14,3,70,High School,28,Owner,Standalone
541,448.27,NJ City,N,23,9,12,52,High School,18,Patnership,Standalone
7275,46.62,Boston,N,2,19,2,82,Graduation,27,Owner,Standalone
371,15.85,NJ City,N,2,22,1,66,No education,28,Owner,Standalone
7363,279.79,Boston,E,4,20,9,56,Graduation,30,Owner,Standalone
4679,359.99,NJ City,E,23,11,6,79,High School,33,Owner,Standalone
208,414.39,NJ City,W,13,8,15,65,High School,23,Franchise,Complex
1992,278,NJ City,W,3,8,10,56,High School,21,Franchise,Complex
2967,352.75,NJ City,E,11,23,9,42,High School,18,Owner,Standalone
9388,370.58,NJ City,W,1,22,3,33,High School,23,Franchise,Complex
3590,152.77,NJ City,S,8,18,8,52,High School,29,Patnership,Complex
1559,60.42,NJ City,N,19,23,3,50,No education,23,Owner,Standalone
319,262.09,NJ City,E,13,22,11,72,High School,25,Owner,Standalone
4450,134.34,NJ City,S,14,10,21,58,High School,32,Patnership,Complex
7018,94.48,Boston,S,14,8,1,31,High School,34,Patnership,Complex
6116,195.84,Boston,W,5,12,2,72,No education,34,Franchise,Complex
2644,400.81,NJ City,W,11,10,23,59,High School,26,Franchise,Complex
5124,474.01,Boston,W,14,23,21,52,Graduation,28,Franchise,Complex
2218,36.02,NJ City,N,8,24,3,33,High School,34,Patnership,Complex
1021,237.62,NJ City,N,8,20,3,83,High School,25,Patnership,Standalone
6838,358.22,Boston,S,6,23,19,65,Graduation,31,Patnership,Complex
6965,492.5,Boston,N,21,18,14,76,Graduation,34,Patnership,Standalone
4282,91.05,NJ City,S,6,22,3,33,High School,30,Patnership,Complex
1764,168.53,NJ City,W,23,16,17,67,High School,29,Franchise,Complex
8772,295.55,NJ City,W,1,13,7,31,Graduation,33,Franchise,Complex
3414,404.76,NJ City,S,7,17,1,35,Graduation,25,Patnership,Complex
4582,471.35,NJ City,S,12,21,20,31,High School,22,Patnership,Complex
5762,175.67,Boston,S,23,23,3,72,No education,25,Patnership,Complex
2291,324.87,NJ City,E,23,24,23,34,High School,22,Owner,Standalone
9852,274.84,NJ City,W,24,14,6,30,Graduation,20,Franchise,Complex
7318,352.93,Boston,S,16,10,6,73,Graduation,28,Patnership,Complex
4283,151.31,NJ City,E,10,22,25,39,High School,33,Owner,Standalone
3876,324.53,NJ City,W,13,17,6,38,High School,25,Franchise,Complex
4655,356.2,NJ City,E,4,19,10,36,High School,26,Owner,Standalone
5224,301.78,Boston,W,2,14,5,35,High School,28,Franchise,Complex
4535,375.21,NJ City,E,15,23,20,59,High School,26,Owner,Standalone
9972,466.19,NJ City,W,11,21,4,38,Graduation,23,Franchise,Complex
1371,241.1,NJ City,E,24,18,7,38,High School,34,Owner,Standalone
5017,435.87,Boston,N,14,17,3,60,High School,31,Patnership,Standalone
7973,283.15,Boston,N,10,16,22,63,Graduation,24,Patnership,Standalone
8446,88.21,NJ City,S,23,11,1,76,High School,33,Patnership,Complex
2173,306.99,NJ City,N,15,21,20,62,High School,24,Patnership,Standalone
335,140.19,NJ City,E,12,8,2,83,No education,20,Owner,Standalone
6166,162.83,Boston,S,4,14,11,82,Graduation,21,Patnership,Complex
2806,413,NJ City,S,16,21,19,81,High School,25,Patnership,Complex
3145,213.45,NJ City,N,24,10,6,45,High School,30,Patnership,Standalone
672,283.15,NJ City,W,17,22,9,77,High School,32,Franchise,Complex
2208,432.8,NJ City,W,20,13,24,84,High School,23,Franchise,Complex
1795,240.45,NJ City,E,24,18,17,54,High School,34,Owner,Standalone
1167,303,NJ City,E,3,14,6,26,High School,24,Owner,Standalone
3633,215.28,NJ City,N,12,24,23,25,High School,28,Patnership,Standalone
4539,241.19,NJ City,E,7,13,24,77,High School,19,Owner,Standalone
6539,133.42,Boston,E,1,17,4,41,No education,29,Owner,Standalone
2043,37.84,NJ City,N,2,9,3,38,Graduation,30,Owner,Standalone
8826,489.83,NJ City,S,19,12,21,54,Graduation,19,Patnership,Complex
6508,51.7,Boston,W,24,20,1,58,High School,21,Franchise,Complex
2507,482.68,NJ City,E,25,21,21,28,High School,35,Owner,Standalone
3052,108.92,NJ City,W,6,10,4,84,High School,25,Franchise,Complex
5436,76.06,Boston,W,23,13,3,82,Graduation,31,Franchise,Complex
894,446.73,NJ City,S,25,24,10,77,High School,21,Patnership,Complex
5652,400.73,Boston,W,19,20,22,41,Graduation,30,Franchise,Complex
3803,57.27,NJ City,E,20,11,2,69,No education,18,Owner,Standalone
6358,410.66,Boston,S,19,11,25,25,Graduation,20,Patnership,Complex
8963,10.21,NJ City,N,10,15,4,33,No education,24,Owner,Standalone
2525,172.44,NJ City,N,22,24,2,27,No education,21,Patnership,Standalone
943,54.97,NJ City,E,9,20,3,43,High School,32,Owner,Standalone
7968,88.91,Boston,W,24,23,2,80,Graduation,35,Franchise,Complex
1183,348.92,NJ City,E,25,16,11,30,High School,26,Owner,Standalone
6359,263.44,Boston,E,2,18,5,36,No education,27,Owner,Standalone
8545,447.28,NJ City,N,19,22,17,40,Graduation,28,Patnership,Standalone
52,103.36,NJ City,W,7,22,2,59,High School,23,Franchise,Complex
7822,447.18,Boston,S,24,20,7,84,Graduation,32,Patnership,Complex
8797,116.54,NJ City,N,17,10,3,29,High School,31,Patnership,Standalone
2450,339.62,NJ City,S,9,19,4,81,No education,31,Patnership,Complex
1051,312.07,NJ City,E,15,17,20,57,High School,29,Owner,Standalone
390,427.59,NJ City,S,16,18,3,63,Graduation,27,Patnership,Complex
1923,426.66,NJ City,E,23,16,4,56,Graduation,30,Owner,Standalone
7486,117.75,Boston,S,8,17,4,54,High School,25,Patnership,Complex
7561,283.61,Boston,N,17,12,7,38,Graduation,21,Patnership,Standalone
501,201.71,NJ City,N,21,23,6,27,High School,25,Patnership,Standalone
2945,391.9,NJ City,N,14,8,18,59,High School,34,Patnership,Standalone
2852,84.3,NJ City,W,14,11,1,60,No education,32,Franchise,Complex
7149,71.23,Boston,N,5,8,1,52,Graduation,29,Patnership,Standalone
129,328.94,NJ City,N,15,15,10,84,High School,29,Patnership,Standalone
4892,435.01,NJ City,W,20,13,9,82,High School,35,Franchise,Complex
9526,56.16,NJ City,S,8,22,2,34,High School,30,Patnership,Complex
4361,114.74,NJ City,N,9,21,4,68,No education,24,Patnership,Standalone
1605,11.85,NJ City,N,7,8,2,42,Graduation,30,Patnership,Standalone
8253,18.44,NJ City,N,11,23,2,44,Graduation,29,Patnership,Standalone
3215,12.98,NJ City,N,18,22,4,29,No education,30,Owner,Standalone
7433,411.39,Boston,N,11,14,17,70,Graduation,29,Patnership,Standalone
8792,353.72,NJ City,W,18,13,16,59,Graduation,29,Franchise,Complex
1334,481.22,NJ City,S,5,23,23,85,High School,24,Patnership,Complex
7658,310.09,Boston,S,9,10,14,45,Graduation,24,Patnership,Complex
9382,25.91,NJ City,N,25,18,2,38,High School,25,Patnership,Complex
3889,226.67,NJ City,N,19,21,24,34,High School,27,Patnership,Standalone
1328,106.89,NJ City,W,3,20,1,50,No education,31,Franchise,Complex
1244,473.76,NJ City,W,20,14,17,35,High School,19,Franchise,Complex
3918,215.39,NJ City,S,11,12,17,81,High School,30,Patnership,Complex
9044,468.74,NJ City,W,17,11,2,30,No education,34,Franchise,Complex
5199,422.52,Boston,E,14,18,10,25,Graduation,32,Owner,Standalone
7668,215.22,Boston,W,5,22,13,39,Graduation,34,Franchise,Complex
8725,354.12,NJ City,N,14,15,16,80,Graduation,18,Patnership,Standalone
9173,298.05,NJ City,N,7,23,5,44,No education,25,Patnership,Standalone
6046,164.56,Boston,S,1,16,1,36,High School,29,Patnership,Complex
9261,163,NJ City,N,15,17,23,28,Graduation,28,Patnership,Standalone
1815,195.21,NJ City,E,22,21,1,64,Graduation,32,Owner,Standalone
3636,412.84,NJ City,W,24,22,7,32,High School,24,Franchise,Complex
1953,324.44,NJ City,N,8,18,3,49,Graduation,25,Patnership,Standalone
755,5.82,NJ City,N,4,15,4,56,No education,31,Owner,Standalone
3726,384.97,NJ City,S,22,11,23,50,High School,20,Patnership,Complex
606,30.58,NJ City,N,2,19,4,36,Graduation,24,Patnership,Complex
6537,195.13,Boston,N,8,13,7,60,Graduation,35,Patnership,Standalone
1587,83.41,NJ City,E,10,9,2,31,Graduation,21,Owner,Standalone
7696,476.87,Boston,W,21,17,2,40,High School,35,Franchise,Complex
1850,18.78,NJ City,N,12,19,2,79,No education,28,Patnership,Complex
5674,445.96,Boston,S,2,10,11,59,Graduation,18,Patnership,Complex
1763,395.76,NJ City,E,21,24,25,51,High School,32,Owner,Standalone
8124,88.72,NJ City,W,1,23,2,66,Graduation,33,Franchise,Complex
4426,226.15,NJ City,S,17,11,8,84,High School,34,Patnership,Complex
1683,142.1,NJ City,E,23,14,7,80,High School,22,Owner,Standalone
8224,52.63,NJ City,W,15,23,2,78,High School,33,Franchise,Complex
6438,244.62,Boston,S,1,15,21,45,Graduation,26,Patnership,Complex
8423,111.09,NJ City,E,21,18,2,45,No education,18,Owner,Standalone
9143,290.45,NJ City,E,10,19,20,60,Graduation,21,Owner,Standalone
9585,482.53,NJ City,N,19,21,5,32,Graduation,29,Patnership,Standalone
7439,323.75,Boston,E,25,16,20,84,Graduation,35,Owner,Standalone
7777,314.46,Boston,N,2,15,8,52,Graduation,31,Patnership,Standalone
6553,395.2,Boston,N,9,22,9,82,Graduation,33,Patnership,Standalone
5280,75.29,Boston,W,19,23,4,71,Graduation,33,Franchise,Complex
8118,433.08,NJ City,S,15,10,14,34,Graduation,31,Patnership,Complex
8873,293.08,NJ City,N,23,15,9,30,Graduation,35,Patnership,Standalone
4839,414.94,NJ City,E,7,8,19,83,High School,32,Owner,Standalone
3031,277.91,NJ City,E,16,16,23,81,High School,27,Owner,Standalone
3111,443.88,NJ City,E,24,15,8,58,High School,32,Owner,Standalone
8300,343.76,NJ City,W,14,8,21,43,Graduation,23,Franchise,Complex
7340,275.42,Boston,W,8,20,18,72,Graduation,19,Franchise,Complex
5766,330.34,Boston,S,4,22,24,65,Graduation,34,Patnership,Complex
4330,435.19,NJ City,S,7,8,20,84,High School,22,Patnership,Complex
2250,384.3,NJ City,S,23,20,4,67,Graduation,29,Patnership,Complex
6418,448.41,Boston,S,23,8,20,57,Graduation,26,Patnership,Complex
9864,90.91,NJ City,W,1,21,4,52,Graduation,23,Franchise,Complex
377,115.04,NJ City,N,14,9,4,53,No education,24,Patnership,Standalone
4249,151.4,NJ City,N,8,16,22,38,High School,26,Patnership,Standalone
6398,29.22,Boston,N,22,24,4,75,No education,21,Patnership,Complex
6352,243.3,Boston,W,9,15,6,77,Graduation,31,Franchise,Complex
2425,108.83,NJ City,N,11,20,2,77,High School,26,Patnership,Standalone
421,216.69,NJ City,N,14,17,6,36,High School,30,Patnership,Standalone
6766,236.3,Boston,S,21,12,21,41,Graduation,20,Patnership,Complex
2955,241,NJ City,E,2,14,12,74,High School,23,Owner,Standalone
3510,206,NJ City,S,2,17,20,38,High School,29,Patnership,Complex
1168,232.12,NJ City,W,21,22,25,27,High School,33,Franchise,Complex
1144,309.86,NJ City,W,19,11,18,49,High School,23,Franchise,Complex
5790,460.98,Boston,S,14,16,16,70,Graduation,24,Patnership,Complex
4598,413.75,NJ City,S,17,10,20,56,High School,22,Patnership,Complex
3108,47.75,NJ City,W,11,13,3,58,Graduation,32,Franchise,Complex
1620,57.99,NJ City,W,14,9,3,84,Graduation,22,Franchise,Complex
9896,427.9,NJ City,W,15,12,3,42,No education,20,Franchise,Complex
4375,153.74,NJ City,E,19,12,21,68,High School,24,Owner,Standalone
1994,293,NJ City,S,2,22,20,78,High School,22,Patnership,Complex
2285,82.22,NJ City,N,4,17,3,26,No education,25,Patnership,Standalone
1107,200.2,NJ City,E,4,14,17,29,High School,20,Owner,Standalone
786,267.4,NJ City,S,6,14,10,25,High School,32,Patnership,Complex
6484,3.34,Boston,N,13,24,2,72,High School,27,Franchise,Complex
2042,139.48,NJ City,S,23,14,25,26,High School,18,Patnership,Complex
8840,452.19,NJ City,W,6,21,6,60,Graduation,26,Franchise,Complex
7299,9.69,Boston,N,15,9,4,76,Graduation,29,Owner,Standalone
5315,198.11,Boston,E,17,20,15,60,Graduation,29,Owner,Standalone
6108,301.64,Boston,W,6,12,1,28,Graduation,19,Franchise,Complex
8914,87.31,NJ City,S,10,11,2,62,High School,29,Patnership,Complex
8382,143.67,NJ City,S,14,14,5,72,Graduation,23,Patnership,Complex
744,267.66,NJ City,W,24,16,2,84,Graduation,18,Franchise,Complex
8979,137.95,NJ City,E,16,23,24,58,Graduation,21,Owner,Standalone
8964,204.81,NJ City,W,8,20,2,81,Graduation,26,Franchise,Complex
1479,271.89,NJ City,E,19,20,21,66,High School,18,Owner,Standalone
4871,138.71,NJ City,E,13,10,23,76,High School,25,Owner,Standalone
5254,360.24,Boston,S,11,19,10,79,Graduation,27,Patnership,Complex
8850,423.06,NJ City,S,16,10,6,69,Graduation,29,Patnership,Complex
6602,349.26,Boston,S,16,24,6,79,Graduation,35,Patnership,Complex
3078,256.5,NJ City,S,25,18,5,78,Graduation,30,Patnership,Complex
4254,29.91,NJ City,N,22,12,2,53,Graduation,21,Patnership,Complex
5021,36.54,Boston,N,12,23,2,69,No education,21,Patnership,Standalone
7652,1.3,Boston,N,18,23,1,62,No education,34,Franchise,Complex
2021,37.86,NJ City,N,8,13,4,65,No education,20,Patnership,Standalone
9592,341.14,NJ City,W,23,12,24,78,Graduation,21,Franchise,Complex
3235,173.55,NJ City,E,10,13,3,68,High School,31,Owner,Standalone
6956,394.09,Boston,W,16,24,1,70,No education,18,Franchise,Complex
159,122.92,NJ City,E,25,11,1,78,Graduation,31,Owner,Standalone
3018,397.59,NJ City,S,8,19,18,34,High School,28,Patnership,Complex
8749,41.2,NJ City,N,24,12,2,31,High School,23,Patnership,Standalone
9283,214.53,NJ City,E,5,9,11,65,Graduation,32,Owner,Standalone
1937,275.21,NJ City,N,14,18,19,81,High School,18,Patnership,Standalone
5536,19.81,Boston,N,4,16,1,75,High School,33,Franchise,Complex
2741,300.46,NJ City,N,13,20,16,46,High School,22,Patnership,Standalone
507,59.33,NJ City,E,6,20,4,79,Graduation,33,Owner,Standalone
1591,153.63,NJ City,E,10,11,2,45,High School,23,Owner,Standalone
1185,193.27,NJ City,N,21,14,3,33,Graduation,31,Patnership,Standalone
1784,438.7,NJ City,W,10,17,18,30,High School,35,Franchise,Complex
3336,348.05,NJ City,W,24,15,14,76,High School,32,Franchise,Complex
2866,258.12,NJ City,S,8,8,5,25,High School,34,Patnership,Complex
8881,215.35,NJ City,N,20,21,17,47,Graduation,26,Patnership,Standalone
4748,86.92,NJ City,W,9,20,3,85,No education,23,Franchise,Complex
5312,483.97,Boston,W,10,23,1,78,No education,27,Franchise,Complex
7770,390.76,Boston,S,1,14,10,50,Graduation,35,Patnership,Complex
5970,263.27,Boston,S,13,11,3,83,Graduation,18,Patnership,Complex
3949,229.41,NJ City,N,23,18,7,41,High School,26,Patnership,Standalone
2624,311.17,NJ City,W,6,16,9,26,High School,24,Franchise,Complex
5056,463.72,Boston,W,14,24,23,42,Graduation,26,Franchise,Complex
9513,446.18,NJ City,N,13,12,3,63,Graduation,30,Patnership,Standalone
6512,216.58,Boston,W,21,15,19,61,Graduation,23,Franchise,Complex
2449,394.5,NJ City,N,5,21,4,71,High School,29,Patnership,Standalone
4844,444.99,NJ City,W,10,10,5,41,No education,21,Franchise,Complex
7156,154.46,Boston,W,8,17,21,72,Graduation,34,Franchise,Complex
114,190.29,NJ City,S,25,24,4,41,Graduation,35,Patnership,Complex
2924,397.52,NJ City,W,19,11,8,70,High School,34,Franchise,Complex
2942,413.97,NJ City,S,3,16,1,80,No education,30,Patnership,Complex
5714,374.74,Boston,S,16,12,13,71,Graduation,32,Patnership,Complex
7209,204.47,Boston,N,23,13,17,48,Graduation,25,Patnership,Standalone
4561,291.73,NJ City,N,8,19,8,27,High School,21,Patnership,Standalone
6099,3.56,Boston,N,5,19,3,46,Graduation,35,Owner,Standalone
6167,355.17,Boston,E,4,8,21,64,Graduation,21,Owner,Standalone
4840,175.79,NJ City,W,13,19,22,61,High School,19,Franchise,Complex
8323,269.07,NJ City,E,4,19,22,44,Graduation,32,Owner,Standalone
472,348.36,NJ City,W,7,13,1,75,High School,27,Franchise,Complex
3064,280.27,NJ City,W,12,21,9,62,High School,24,Franchise,Complex
2556,243.61,NJ City,W,22,20,2,28,Graduation,21,Franchise,Complex
6946,358.26,Boston,S,18,9,9,38,Graduation,35,Patnership,Complex
3657,53.86,NJ City,N,14,23,3,79,Graduation,25,Patnership,Standalone
4702,465.29,NJ City,S,14,11,3,85,High School,30,Patnership,Complex
6076,294.75,Boston,W,25,10,24,78,Graduation,34,Franchise,Complex
3547,461.2,NJ City,E,16,9,16,59,High School,25,Owner,Standalone
3346,266.29,NJ City,S,10,18,4,48,High School,30,Patnership,Complex
2878,186.48,NJ City,S,13,16,3,37,High School,19,Patnership,Complex
5444,48.31,Boston,W,21,8,2,44,No education,25,Franchise,Complex
9936,455.42,NJ City,W,6,20,4,25,Graduation,23,Franchise,Complex
5030,86.48,Boston,S,12,16,4,80,No education,32,Patnership,Complex
5178,374.8,Boston,S,17,14,11,58,Graduation,35,Patnership,Complex
9738,294.02,NJ City,S,12,20,20,79,Graduation,24,Patnership,Complex
485,167.37,NJ City,N,14,20,19,27,High School,27,Patnership,Standalone
3592,185.16,NJ City,W,19,10,7,77,High School,35,Franchise,Complex
3539,153.08,NJ City,E,17,17,13,73,High School,32,Owner,Standalone
2024,407.66,NJ City,W,21,21,6,70,High School,22,Franchise,Complex
8167,217.58,NJ City,E,5,15,25,59,Graduation,19,Owner,Standalone
592,108.01,NJ City,W,19,17,4,66,High School,33,Franchise,Complex
3712,228.24,NJ City,W,11,19,1,40,High School,23,Franchise,Complex
9713,109.77,NJ City,N,6,9,1,33,No education,20,Patnership,Standalone
6755,18.82,Boston,N,8,9,3,76,No education,22,Owner,Standalone
8159,410.92,NJ City,E,7,18,2,49,No education,18,Owner,Standalone
4983,429.12,Boston,E,6,24,11,40,High School,23,Owner,Standalone
772,210.35,NJ City,W,7,17,20,72,High School,21,Franchise,Complex
2046,178,NJ City,S,3,12,12,52,High School,21,Patnership,Complex
1027,323.03,NJ City,E,7,19,10,43,High School,26,Owner,Standalone
403,474.13,NJ City,E,17,18,1,85,High School,20,Owner,Standalone
940,396.2,NJ City,W,16,16,8,64,High School,31,Franchise,Complex
6843,173,Boston,E,5,21,2,52,Graduation,21,Owner,Standalone
5294,38.59,Boston,N,10,21,3,46,No education,23,Patnership,Complex
5151,17.12,Boston,N,2,8,2,67,Graduation,25,Owner,Standalone
2171,269,NJ City,E,3,21,6,48,High School,31,Owner,Standalone
1431,228.82,NJ City,E,6,11,10,81,High School,32,Owner,Standalone
6330,80.08,Boston,S,2,15,1,26,Graduation,35,Patnership,Complex
8860,268.25,NJ City,W,14,24,1,52,High School,20,Franchise,Complex
5574,128.99,Boston,S,14,8,15,45,Graduation,26,Patnership,Complex
9732,418.38,NJ City,W,17,18,17,76,Graduation,32,Franchise,Complex
5724,239.79,Boston,W,2,14,11,75,Graduation,19,Franchise,Complex
952,250,NJ City,W,2,22,22,64,High School,24,Franchise,Complex
7231,159.69,Boston,E,5,14,22,53,Graduation,32,Owner,Standalone
480,377.89,NJ City,W,19,21,19,72,High School,23,Franchise,Complex
391,406.72,NJ City,E,8,23,16,47,High School,30,Owner,Standalone
9214,208.43,NJ City,S,16,8,18,31,Graduation,34,Patnership,Complex
1085,333.93,NJ City,N,2,11,1,28,No education,23,Patnership,Standalone
7060,200.79,Boston,W,2,21,18,78,Graduation,32,Franchise,Complex
7428,169.07,Boston,W,9,19,4,36,Graduation,32,Franchise,Complex
4546,414.03,NJ City,S,5,22,2,59,High School,22,Patnership,Complex
2890,328.79,NJ City,S,24,17,15,41,High School,27,Patnership,Complex
9677,394.58,NJ City,N,23,10,18,84,Graduation,23,Patnership,Standalone
2036,475.89,NJ City,W,22,18,21,77,High School,23,Franchise,Complex
2379,485.84,NJ City,E,18,21,6,58,High School,30,Owner,Standalone
1535,284.27,NJ City,E,11,20,19,70,High School,33,Owner,Standalone
5916,234.53,Boston,W,15,17,22,27,Graduation,19,Franchise,Complex
3673,64.77,NJ City,N,22,20,4,52,High School,21,Patnership,Standalone
5529,261.5,Boston,N,10,19,12,72,Graduation,24,Patnership,Standalone
5897,65.84,Boston,N,15,22,4,58,No education,22,Patnership,Standalone
2615,67.18,NJ City,N,9,22,1,62,No education,18,Owner,Standalone
8231,315.11,NJ City,E,20,17,25,57,Graduation,21,Owner,Standalone
4029,117.81,NJ City,N,14,12,1,71,Graduation,27,Patnership,Standalone
3398,290.27,NJ City,S,11,8,9,39,High School,20,Patnership,Complex
8556,232.28,NJ City,W,25,16,24,77,Graduation,35,Franchise,Complex
6156,142.22,Boston,W,24,9,15,46,Graduation,33,Franchise,Complex
9773,355.09,NJ City,N,22,24,11,65,Graduation,32,Patnership,Standalone
8539,151.56,NJ City,E,4,22,22,68,Graduation,28,Owner,Standalone
5393,296.12,Boston,N,14,22,1,75,No education,18,Patnership,Standalone
7852,403.41,Boston,W,21,9,1,58,High School,35,Franchise,Complex
4328,251,NJ City,W,3,15,6,49,High School,22,Franchise,Complex
8618,450.43,NJ City,S,7,24,6,82,Graduation,31,Patnership,Complex
3505,80.76,NJ City,N,10,12,4,65,High School,31,Patnership,Standalone
8849,305.15,NJ City,N,22,16,3,47,No education,22,Patnership,Standalone
8667,456.62,NJ City,E,12,18,11,33,Graduation,31,Owner,Standalone
7699,52.41,Boston,E,3,8,2,78,High School,22,Owner,Standalone
3741,52.08,NJ City,N,8,23,4,31,Graduation,25,Patnership,Standalone
1367,24.45,NJ City,N,23,13,2,39,No education,31,Owner,Standalone
5067,250.04,Boston,E,3,17,2,60,Graduation,35,Owner,Standalone
3771,28.17,NJ City,N,1,8,1,60,Graduation,26,Owner,Standalone
4265,217.24,NJ City,N,25,16,11,42,High School,24,Patnership,Standalone
8476,95.26,NJ City,W,8,21,1,43,High School,23,Franchise,Complex
2002,180.97,NJ City,S,16,24,22,31,High School,19,Patnership,Complex
8278,456.08,NJ City,S,7,24,25,74,Graduation,26,Patnership,Complex
2542,468.64,NJ City,S,25,9,1,82,High School,20,Patnership,Complex
265,273,NJ City,N,3,22,24,39,High School,20,Patnership,Standalone
8923,301.71,NJ City,E,17,16,18,75,Graduation,31,Owner,Standalone
7985,188.24,Boston,N,12,20,4,40,No education,20,Patnership,Standalone
2973,231.6,NJ City,N,16,19,8,54,High School,35,Patnership,Standalone
4876,296.47,NJ City,W,22,11,3,84,High School,33,Franchise,Complex
1507,311.62,NJ City,E,12,12,7,81,High School,30,Owner,Standalone
4322,190.51,NJ City,S,25,9,20,79,High School,18,Patnership,Complex
4788,64.68,NJ City,N,11,16,1,79,Graduation,30,Franchise,Complex
4726,121.7,NJ City,S,20,14,4,29,High School,18,Patnership,Complex
8086,259.75,NJ City,S,1,13,15,44,Graduation,22,Patnership,Complex
8697,165.4,NJ City,N,10,12,14,62,Graduation,25,Patnership,Standalone
9517,18.31,NJ City,N,10,9,1,82,High School,19,Patnership,Standalone
7858,340.59,Boston,S,14,15,11,36,Graduation,22,Patnership,Complex
2508,340.74,NJ City,W,4,22,1,33,Graduation,30,Franchise,Complex
3115,449.85,NJ City,E,7,15,20,48,High School,33,Owner,Standalone
6698,488.61,Boston,S,6,23,6,66,Graduation,23,Patnership,Complex
537,285.97,NJ City,N,14,15,4,58,Graduation,28,Patnership,Standalone
6158,262.24,Boston,S,18,15,6,74,Graduation,25,Patnership,Complex
5372,437.82,Boston,W,19,10,4,84,No education,21,Franchise,Complex
2903,414.52,NJ City,E,13,18,5,66,No education,23,Owner,Standalone
4095,10.57,NJ City,N,16,8,4,63,Graduation,25,Owner,Standalone
2052,91.25,NJ City,W,14,22,2,31,Graduation,34,Franchise,Complex
2703,230,NJ City,E,3,13,15,81,High School,32,Owner,Standalone
2780,122,NJ City,W,1,17,16,81,High School,24,Franchise,Complex
1600,282.7,NJ City,W,6,14,11,29,High School,25,Franchise,Complex
2744,317.11,NJ City,W,14,19,20,42,High School,28,Franchise,Complex
176,134.83,NJ City,W,19,19,1,78,No education,32,Franchise,Complex
8998,304.53,NJ City,S,12,19,9,79,Graduation,21,Patnership,Complex
6719,193.7,Boston,E,18,19,3,43,No education,21,Owner,Standalone
4768,197.28,NJ City,W,18,12,21,55,High School,25,Franchise,Complex
9745,150.65,NJ City,N,12,18,11,42,Graduation,20,Patnership,Standalone
2095,283.72,NJ City,E,5,23,4,84,High School,26,Owner,Standalone
1877,14.69,NJ City,N,4,24,2,65,No education,29,Patnership,Standalone
1679,303.67,NJ City,E,13,14,11,55,High School,20,Owner,Standalone
5206,443.87,Boston,S,6,13,15,38,Graduation,35,Patnership,Complex
1281,202.65,NJ City,N,6,22,19,68,High School,35,Patnership,Standalone
4891,63.46,NJ City,N,6,20,1,83,High School,26,Owner,Standalone
7278,379.02,Boston,S,18,14,21,79,Graduation,28,Patnership,Complex
9686,140.91,NJ City,S,9,9,11,44,Graduation,35,Patnership,Complex
6678,333.34,Boston,S,6,11,5,32,Graduation,22,Patnership,Complex
5545,159.23,Boston,N,3,18,13,64,Graduation,20,Patnership,Standalone
5547,448.04,Boston,E,10,18,14,32,Graduation,34,Owner,Standalone
9087,235.12,NJ City,E,21,16,20,51,Graduation,20,Owner,Standalone
5894,92.38,Boston,S,10,21,1,41,No education,30,Patnership,Complex
3313,11.48,NJ City,N,9,12,3,26,High School,23,Patnership,Standalone
2815,213,NJ City,E,1,21,13,68,High School,33,Owner,Standalone
207,105.2,NJ City,E,1,17,4,61,Graduation,29,Owner,Standalone
3714,247,NJ City,S,3,16,19,47,High School,23,Patnership,Complex
5096,256.28,Boston,W,25,18,25,58,Graduation,24,Franchise,Complex
2386,280,NJ City,S,16,11,16,48,High School,29,Patnership,Complex
1532,412.56,NJ City,W,24,12,23,47,High School,19,Franchise,Complex
700,293.84,NJ City,W,16,12,6,38,High School,32,Franchise,Complex
4102,432.68,NJ City,S,16,18,20,30,High School,23,Patnership,Complex
7563,52.65,Boston,E,14,9,2,39,Graduation,35,Owner,Standalone
7549,119.08,Boston,N,8,11,3,76,High School,26,Patnership,Standalone
4445,408,NJ City,N,22,14,13,36,High School,28,Patnership,Standalone
1622,453.45,NJ City,S,11,22,10,83,High School,18,Patnership,Complex
2475,114.74,NJ City,E,15,20,2,57,Graduation,25,Owner,Standalone
8900,416.79,NJ City,W,12,8,15,76,Graduation,32,Franchise,Complex
6442,388.39,Boston,S,4,9,10,83,Graduation,32,Patnership,Complex
5997,413.39,Boston,N,11,21,21,41,Graduation,22,Patnership,Standalone
9492,64.24,NJ City,N,22,16,3,78,Graduation,32,Franchise,Complex
9440,319.54,NJ City,W,25,16,16,27,Graduation,30,Franchise,Complex
4733,243.45,NJ City,N,21,11,23,55,High School,28,Patnership,Standalone
3928,464.88,NJ City,W,10,20,8,29,High School,34,Franchise,Complex
2019,341.69,NJ City,E,16,11,20,84,High School,31,Owner,Standalone
3783,76.28,NJ City,E,13,16,3,43,Graduation,28,Owner,Standalone
8358,20.55,NJ City,N,15,20,4,68,Graduation,24,Patnership,Complex
662,263,NJ City,S,3,12,10,37,High School,21,Patnership,Complex
8673,241.3,NJ City,N,10,24,6,56,Graduation,33,Patnership,Standalone
5510,168.31,Boston,S,11,20,8,65,Graduation,19,Patnership,Complex
3960,18.65,NJ City,N,4,9,4,60,Graduation,26,Franchise,Complex
5395,307.36,Boston,E,24,12,21,73,Graduation,20,Owner,Standalone
9525,427.86,NJ City,N,18,8,15,50,Graduation,28,Patnership,Standalone
3227,395.32,NJ City,E,16,24,17,67,High School,31,Owner,Standalone
3668,103.42,NJ City,W,20,10,1,80,No education,23,Franchise,Complex
9537,496.68,NJ City,N,16,8,2,44,Graduation,32,Patnership,Standalone
8031,473.93,Boston,E,2,24,23,83,Graduation,28,Owner,Standalone
6958,212.97,Boston,S,3,11,24,57,Graduation,19,Patnership,Complex
1631,201.15,NJ City,E,23,11,23,35,High School,23,Owner,Standalone
7312,10.68,Boston,N,12,8,3,53,High School,34,Franchise,Complex
7760,348.7,Boston,W,9,10,1,72,No education,29,Franchise,Complex
2500,184.85,NJ City,W,6,11,3,64,High School,32,Franchise,Complex
8429,324.79,NJ City,N,8,19,13,30,Graduation,34,Patnership,Standalone
3112,431.08,NJ City,W,18,13,4,52,High School,35,Franchise,Complex
4094,149.52,NJ City,S,12,12,11,63,High School,35,Patnership,Complex
3326,148.97,NJ City,S,13,15,18,38,High School,28,Patnership,Complex
5682,239.7,Boston,S,16,13,13,69,Graduation,30,Patnership,Complex
5978,288.03,Boston,S,2,19,16,48,Graduation,33,Patnership,Complex
5817,73.56,Boston,N,11,17,1,85,Graduation,20,Patnership,Standalone
5610,345.41,Boston,S,22,21,17,85,Graduation,23,Patnership,Complex
6251,145.63,Boston,E,20,18,7,85,Graduation,33,Owner,Standalone
6255,177.24,Boston,E,19,18,12,39,Graduation,30,Owner,Standalone
3671,89.76,NJ City,E,2,15,4,52,No education,19,Owner,Standalone
1530,497.63,NJ City,S,10,9,13,82,High School,20,Patnership,Complex
6051,126.87,Boston,E,10,21,2,36,Graduation,23,Owner,Standalone
2168,483.01,NJ City,W,17,23,4,41,No education,31,Franchise,Complex
1248,33.29,NJ City,N,12,10,2,29,Graduation,23,Franchise,Complex
5458,446.01,Boston,S,24,11,18,78,Graduation,29,Patnership,Complex
6326,496.44,Boston,S,5,16,8,34,Graduation,33,Patnership,Complex
4131,313.4,NJ City,E,24,15,8,29,High School,20,Owner,Standalone
6619,24.75,Boston,N,2,17,4,80,High School,34,Owner,Standalone
388,174,NJ City,W,1,15,10,33,High School,30,Franchise,Complex
5534,15.59,Boston,N,22,17,4,74,No education,21,Patnership,Complex
625,224.91,NJ City,N,15,22,16,47,High School,20,Patnership,Standalone
8092,417.14,NJ City,W,9,10,3,35,High School,24,Franchise,Complex
2586,366.17,NJ City,S,24,12,25,64,High School,21,Patnership,Complex
5035,251.61,Boston,E,21,17,8,35,Graduation,25,Owner,Standalone
9089,350.56,NJ City,N,4,17,21,39,Graduation,22,Patnership,Standalone
5491,355.02,Boston,E,15,15,22,25,Graduation,23,Owner,Standalone
8242,100.92,NJ City,S,7,22,2,85,High School,34,Patnership,Complex
2879,9.04,NJ City,N,21,10,1,66,No education,32,Owner,Standalone
6324,216.9,Boston,W,22,21,6,85,Graduation,34,Franchise,Complex
879,353.24,NJ City,E,1,23,2,45,Graduation,20,Owner,Standalone
5698,9.29,Boston,N,17,18,4,84,High School,27,Patnership,Complex
8225,22.53,NJ City,N,7,23,1,46,No education,23,Patnership,Standalone
1150,477.06,NJ City,S,14,17,5,53,High School,35,Patnership,Complex
4904,445.16,NJ City,W,10,21,16,64,High School,20,Franchise,Complex
9591,195.78,NJ City,E,16,14,14,45,Graduation,24,Owner,Standalone
3153,259.83,NJ City,N,12,11,13,67,High School,32,Patnership,Standalone
5518,399.96,Boston,S,8,18,10,54,Graduation,31,Patnership,Complex
491,84.38,NJ City,E,7,19,1,40,No education,29,Owner,Standalone
2402,55.65,NJ City,S,1,11,3,63,No education,28,Patnership,Complex
4776,263.81,NJ City,W,7,9,9,73,High School,31,Franchise,Complex
7691,344.67,Boston,E,25,15,1,62,No education,25,Owner,Standalone
6196,219.27,Boston,W,14,23,25,55,Graduation,29,Franchise,Complex
7219,233.82,Boston,E,15,21,19,71,Graduation,22,Owner,Standalone
3236,427.74,NJ City,W,18,19,10,25,High School,27,Franchise,Complex
6334,304.52,Boston,S,23,22,21,49,Graduation,28,Patnership,Complex
9792,106.6,NJ City,W,16,21,3,28,Graduation,35,Franchise,Complex
5937,40.43,Boston,N,14,9,4,59,Graduation,28,Patnership,Standalone
7133,292.18,Boston,N,15,19,5,27,No education,23,Patnership,Standalone
3046,270.69,NJ City,S,19,19,18,39,High School,27,Patnership,Complex
7189,252.48,Boston,N,5,16,14,26,Graduation,30,Patnership,Standalone
3909,320.58,NJ City,N,22,17,6,59,High School,35,Patnership,Standalone
6339,176.01,Boston,E,6,19,1,43,Graduation,34,Owner,Standalone
7451,443.72,Boston,E,19,18,3,72,No education,28,Owner,Standalone
3826,290.04,NJ City,S,15,12,13,33,High School,25,Patnership,Complex
7220,380.05,Boston,W,24,24,2,30,No education,18,Franchise,Complex
6902,34.74,Boston,N,24,19,2,62,No education,29,Patnership,Complex
1891,353.03,NJ City,E,10,18,18,68,High School,28,Owner,Standalone
8360,319.34,NJ City,W,12,15,12,85,Graduation,23,Franchise,Complex
8776,470.74,NJ City,W,7,14,4,68,High School,32,Franchise,Complex
2918,29.23,NJ City,N,2,24,1,41,No education,20,Patnership,Complex
3731,64.28,NJ City,N,25,24,3,39,No education,22,Owner,Standalone
2272,32.68,NJ City,N,20,14,1,32,High School,18,Franchise,Complex
7286,237.56,Boston,S,25,15,6,70,Graduation,34,Patnership,Complex
1796,412.02,NJ City,W,24,17,22,47,High School,21,Franchise,Complex
182,165.68,NJ City,S,18,24,3,34,No education,19,Patnership,Complex
7397,384.13,Boston,N,14,23,20,67,Graduation,35,Patnership,Standalone
442,167.38,NJ City,S,23,22,14,73,High School,25,Patnership,Complex
7795,233.45,Boston,E,24,20,13,57,Graduation,24,Owner,Standalone
5972,491.57,Boston,W,22,14,2,81,No education,33,Franchise,Complex
7722,387.69,Boston,S,11,8,10,32,Graduation,26,Patnership,Complex
1020,451.39,NJ City,W,20,24,25,74,High School,35,Franchise,Complex
2282,209,NJ City,S,1,16,7,71,High School,32,Patnership,Complex
1892,30.61,NJ City,N,9,13,2,34,No education,29,Franchise,Complex
7552,225.83,Boston,W,25,21,20,63,Graduation,19,Franchise,Complex
9210,58.17,NJ City,S,8,15,4,43,Graduation,25,Patnership,Complex
4713,415.38,NJ City,N,18,15,2,76,Graduation,35,Patnership,Standalone
9052,215.45,NJ City,W,3,10,9,63,Graduation,20,Franchise,Complex
1895,356.13,NJ City,E,25,20,14,54,High School,23,Owner,Standalone
2704,140.43,NJ City,W,11,8,1,37,High School,32,Franchise,Complex
9327,499.27,NJ City,E,17,15,14,57,Graduation,29,Owner,Standalone
1204,490.1,NJ City,W,5,19,1,75,High School,31,Franchise,Complex
7277,116.72,Boston,N,23,19,4,34,No education,25,Patnership,Standalone
3344,144.41,NJ City,W,12,13,12,39,High School,35,Franchise,Complex
8600,460.34,NJ City,W,2,22,14,37,Graduation,24,Franchise,Complex
3170,267.1,NJ City,S,4,12,14,76,High School,24,Patnership,Complex
7671,319.67,Boston,E,13,17,2,68,Graduation,21,Owner,Standalone
5646,197.39,Boston,S,23,11,13,65,Graduation,27,Patnership,Complex
8695,417.74,NJ City,E,7,13,4,51,High School,18,Owner,Standalone
4619,484.96,NJ City,E,12,24,16,77,High School,31,Owner,Standalone
8018,351.23,Boston,S,10,19,5,77,No education,26,Patnership,Complex
2619,249.18,NJ City,E,14,14,6,31,High School,35,Owner,Standalone
4471,460.16,NJ City,E,16,16,14,62,High School,33,Owner,Standalone
2898,150.34,NJ City,S,6,10,23,36,High School,26,Patnership,Complex
9848,369.46,NJ City,W,10,18,2,84,No education,19,Franchise,Complex
7104,392.6,Boston,W,20,9,7,66,Graduation,24,Franchise,Complex
4759,385.65,NJ City,E,15,22,25,51,High School,21,Owner,Standalone
7607,92.83,Boston,E,6,15,2,70,No education,21,Owner,Standalone
7569,433.72,Boston,N,5,20,21,43,Graduation,21,Patnership,Standalone
7969,150.81,Boston,N,22,22,10,79,Graduation,22,Patnership,Standalone
7123,122.02,Boston,E,15,8,2,34,High School,34,Owner,Standalone
1170,48.42,NJ City,S,20,24,4,34,Graduation,25,Patnership,Complex
2790,260.16,NJ City,S,22,23,21,34,High School,23,Patnership,Complex
1753,47.1,NJ City,N,18,20,3,48,High School,26,Patnership,Standalone
7498,264.32,Boston,S,2,18,14,64,Graduation,32,Patnership,Complex
2884,426.09,NJ City,W,23,22,19,84,High School,32,Franchise,Complex
2385,479.43,NJ City,N,7,21,6,83,High School,29,Patnership,Standalone
9894,245.46,NJ City,S,22,20,19,55,Graduation,35,Patnership,Complex
2631,480.04,NJ City,E,24,24,15,53,High School,22,Owner,Standalone
1471,419.31,NJ City,E,21,24,8,75,High School,34,Owner,Standalone
383,48.59,NJ City,E,25,14,3,74,No education,23,Owner,Standalone
6092,221.93,Boston,W,22,13,5,85,No education,19,Franchise,Complex
4064,145.16,NJ City,W,10,24,9,66,High School,23,Franchise,Complex
2617,208,NJ City,N,1,10,15,58,High School,34,Patnership,Standalone
440,157,NJ City,W,3,15,7,57,High School,35,Franchise,Complex
9694,234.63,NJ City,S,24,22,6,32,Graduation,20,Patnership,Complex
9854,205,NJ City,S,16,19,6,65,Graduation,35,Patnership,Complex
1761,51.71,NJ City,N,9,20,4,48,Graduation,30,Patnership,Standalone
5278,188.03,Boston,S,23,19,2,54,High School,25,Patnership,Complex
9451,224.51,NJ City,E,11,24,18,41,Graduation,27,Owner,Standalone
8492,296.54,NJ City,W,8,13,3,45,No education,20,Franchise,Complex
131,356.33,NJ City,E,8,21,11,56,High School,18,Owner,Standalone
1657,246.56,NJ City,N,20,22,23,38,High School,31,Patnership,Standalone
2319,196.39,NJ City,E,25,10,9,28,High School,29,Owner,Standalone
500,212.94,NJ City,W,11,8,17,68,High School,20,Franchise,Complex
7853,471.38,Boston,N,13,24,7,83,Graduation,26,Patnership,Standalone
4579,224.27,NJ City,E,24,13,1,83,High School,27,Owner,Standalone
1251,183.65,NJ City,E,24,10,11,31,High School,25,Owner,Standalone
3538,383.2,NJ City,S,24,23,12,78,High School,34,Patnership,Complex
6234,41.96,Boston,N,23,8,2,51,Graduation,25,Patnership,Complex
3465,435.71,NJ City,N,15,16,6,80,High School,32,Patnership,Standalone
9707,288.62,NJ City,E,3,22,24,77,Graduation,27,Owner,Standalone
7917,134.43,Boston,N,24,12,17,48,Graduation,20,Patnership,Standalone
1664,173.57,NJ City,W,14,8,5,43,No education,34,Franchise,Complex
7502,476.83,Boston,S,6,9,21,79,Graduation,35,Patnership,Complex
7223,256.01,Boston,E,2,22,18,58,Graduation,25,Owner,Standalone
6660,8.21,Boston,N,22,24,4,79,Graduation,34,Franchise,Complex
4799,17.88,NJ City,N,22,23,4,46,No education,28,Owner,Standalone
5412,270.99,Boston,W,15,17,12,36,Graduation,32,Franchise,Complex
174,61.53,NJ City,N,23,9,2,83,Graduation,18,Patnership,Complex
834,194,NJ City,S,2,13,17,52,High School,24,Patnership,Complex
6785,238.23,Boston,N,21,15,12,70,Graduation,18,Patnership,Standalone
3623,477.22,NJ City,E,5,9,25,36,High School,25,Owner,Standalone
6171,495.46,Boston,E,12,23,11,71,Graduation,19,Owner,Standalone
5066,141.81,Boston,S,22,19,2,76,No education,33,Patnership,Complex
2298,499.52,NJ City,S,10,15,13,80,High School,30,Patnership,Complex
3649,147.6,NJ City,N,13,22,12,78,High School,27,Patnership,Standalone
852,137,NJ City,W,3,14,6,25,High School,19,Franchise,Complex
2555,198.21,NJ City,E,24,20,4,43,No education,24,Owner,Standalone
6611,189.69,Boston,E,3,13,4,81,No education,35,Owner,Standalone
8503,175.54,NJ City,E,14,9,18,50,Graduation,19,Owner,Standalone
5873,466.94,Boston,N,6,23,17,76,Graduation,28,Patnership,Standalone
4208,134.86,NJ City,W,19,11,1,28,No education,19,Franchise,Complex
760,163.78,NJ City,W,25,10,3,63,High School,25,Franchise,Complex
1437,413.77,NJ City,N,25,11,8,36,High School,34,Patnership,Standalone
1395,330.61,NJ City,E,8,15,3,56,Graduation,29,Owner,Standalone
6760,203.91,Boston,W,17,19,11,85,Graduation,34,Franchise,Complex
8361,119.51,NJ City,N,3,17,4,76,Graduation,28,Patnership,Standalone
5960,422.8,Boston,W,12,21,24,52,Graduation,21,Franchise,Complex
2295,246,NJ City,E,1,15,23,45,High School,21,Owner,Standalone
8929,120.05,NJ City,N,4,8,4,45,High School,24,Patnership,Standalone
1013,87.17,NJ City,N,6,15,2,67,No education,24,Patnership,Standalone
9469,211.8,NJ City,N,23,12,4,73,High School,19,Patnership,Standalone
9461,305.26,NJ City,N,2,10,24,67,Graduation,25,Patnership,Standalone
1387,133.21,NJ City,E,21,23,3,36,High School,21,Owner,Standalone
9227,202.1,NJ City,E,8,8,5,31,No education,20,Owner,Standalone
6022,170.64,Boston,S,24,16,24,75,Graduation,33,Patnership,Complex
4947,467.35,Boston,E,19,15,18,25,High School,20,Owner,Standalone
2040,327.6,NJ City,W,14,11,4,81,Graduation,29,Franchise,Complex
934,292.86,NJ City,S,7,13,10,26,High School,24,Patnership,Complex
9354,135.77,NJ City,S,8,17,4,39,Graduation,30,Patnership,Complex
4848,316.02,NJ City,W,21,24,5,34,Graduation,25,Franchise,Complex
4521,415.87,NJ City,N,25,19,9,85,High School,21,Patnership,Standalone
5794,382.98,Boston,S,20,12,1,34,High School,19,Patnership,Complex
6867,455.27,Boston,E,15,17,13,56,Graduation,26,Owner,Standalone
4025,134,NJ City,N,1,21,12,30,High School,24,Patnership,Standalone
3393,475.85,NJ City,N,4,21,21,59,High School,29,Patnership,Standalone
4104,159.14,NJ City,W,25,10,14,38,High School,20,Franchise,Complex
7118,255.03,Boston,S,16,14,12,80,Graduation,32,Patnership,Complex
7109,53.26,Boston,N,17,12,2,63,No education,22,Patnership,Standalone
6385,242.53,Boston,N,10,19,24,38,Graduation,27,Patnership,Standalone
2979,87.3,NJ City,E,21,22,3,57,Graduation,33,Owner,Standalone
1693,9.07,NJ City,N,5,14,1,64,High School,33,Patnership,Standalone
1873,56.74,NJ City,N,1,10,4,33,High School,21,Patnership,Standalone
6297,460.85,Boston,N,16,12,4,66,Graduation,26,Patnership,Standalone
7991,57.71,Boston,E,6,18,1,67,No education,18,Owner,Standalone
4140,205.35,NJ City,W,19,13,7,45,High School,25,Franchise,Complex
5367,384.03,Boston,E,18,13,2,70,Graduation,34,Owner,Standalone
2997,126.26,NJ City,N,2,17,4,71,Graduation,32,Patnership,Standalone
8185,190.99,NJ City,N,24,12,20,69,Graduation,25,Patnership,Standalone
7420,204.31,Boston,W,18,21,24,46,Graduation,31,Franchise,Complex
9434,23.66,NJ City,N,25,21,3,53,No education,20,Patnership,Complex
9942,278.11,NJ City,S,13,22,5,31,Graduation,20,Patnership,Complex
9768,212.98,NJ City,W,2,10,8,35,Graduation,29,Franchise,Complex
9301,188.49,NJ City,N,22,21,9,66,Graduation,26,Patnership,Standalone
7,324.73,NJ City,E,5,21,24,44,High School,32,Owner,Standalone
562,391.57,NJ City,S,17,10,8,33,High School,20,Patnership,Complex
7382,299.85,Boston,S,9,18,16,84,Graduation,24,Patnership,Complex
2612,471.35,NJ City,W,10,10,13,54,High School,27,Franchise,Complex
2746,400.66,NJ City,S,22,12,18,26,High School,33,Patnership,Complex
8951,429.12,NJ City,E,24,15,5,79,No education,26,Owner,Standalone
248,144.01,NJ City,W,19,18,13,46,High School,22,Franchise,Complex
4646,223.04,NJ City,S,1,11,2,33,No education,18,Patnership,Complex
1106,150.47,NJ City,S,22,12,9,33,High School,19,Patnership,Complex
5292,197.62,Boston,W,24,23,14,31,Graduation,32,Franchise,Complex
2614,348.01,NJ City,S,3,22,1,64,High School,33,Patnership,Complex
8804,182.15,NJ City,W,17,9,1,38,No education,30,Franchise,Complex
2543,340.28,NJ City,E,12,22,19,64,High School,32,Owner,Standalone
1070,453.51,NJ City,S,9,12,16,27,High School,35,Patnership,Complex
7072,299.02,Boston,W,16,12,5,43,High School,32,Franchise,Complex
4673,405.79,NJ City,N,19,11,2,70,No education,29,Patnership,Standalone
6606,479.04,Boston,S,3,23,23,69,Graduation,30,Patnership,Complex
4594,310.32,NJ City,S,16,18,18,42,High School,27,Patnership,Complex
5961,297.97,Boston,N,2,10,6,73,Graduation,23,Patnership,Standalone
4613,177.46,NJ City,N,14,13,2,56,No education,28,Patnership,Standalone
9890,277.06,NJ City,S,20,12,9,76,Graduation,20,Patnership,Complex
1256,228.76,NJ City,W,7,19,25,58,High School,35,Franchise,Complex
8616,68.11,NJ City,N,20,13,2,60,Graduation,25,Franchise,Complex
2706,7.6,NJ City,N,25,15,4,38,Graduation,30,Patnership,Complex
2926,261.82,NJ City,S,9,12,6,41,High School,32,Patnership,Complex
7950,416.05,Boston,S,10,21,5,56,Graduation,34,Patnership,Complex
8760,349.14,NJ City,W,20,22,5,71,Graduation,23,Franchise,Complex
3243,492.65,NJ City,E,5,21,17,58,High School,25,Owner,Standalone
3075,439.36,NJ City,E,16,22,1,72,Graduation,27,Owner,Standalone
2335,207.52,NJ City,E,11,15,22,57,High School,19,Owner,Standalone
1838,162.15,NJ City,S,14,8,21,30,High School,25,Patnership,Complex
9405,87.26,NJ City,N,1,19,3,40,Graduation,30,Patnership,Standalone
1068,431.37,NJ City,W,13,9,11,72,High School,33,Franchise,Complex
5660,82.31,Boston,W,16,19,1,68,No education,25,Franchise,Complex
7925,116.76,Boston,N,14,20,1,80,No education,31,Patnership,Standalone
6082,152,Boston,S,18,19,24,79,Graduation,34,Patnership,Complex
9980,312.24,NJ City,W,5,16,20,34,Graduation,26,Franchise,Complex
3137,228.52,NJ City,N,23,8,25,72,High School,20,Patnership,Standalone
8980,246.25,NJ City,W,10,24,12,80,Graduation,27,Franchise,Complex
4693,311.81,NJ City,N,7,12,16,76,High School,31,Patnership,Standalone
838,246.24,NJ City,S,16,24,17,29,High School,31,Patnership,Complex
177,395.62,NJ City,N,17,9,16,63,High School,22,Patnership,Standalone
8822,377.71,NJ City,S,5,23,3,29,No education,33,Patnership,Complex
217,24.91,NJ City,N,22,24,1,54,High School,24,Patnership,Standalone
7707,235.25,Boston,E,11,18,10,52,Graduation,35,Owner,Standalone
1744,422.18,NJ City,W,21,14,13,44,High School,31,Franchise,Complex
2421,433.07,NJ City,N,14,20,11,49,High School,19,Patnership,Standalone
5765,332.31,Boston,N,11,8,9,43,Graduation,33,Patnership,Standalone
6786,470.66,Boston,S,18,9,7,79,Graduation,31,Patnership,Complex
4583,211.32,NJ City,E,24,10,1,29,No education,20,Owner,Standalone
6390,257.33,Boston,S,7,8,24,74,Graduation,25,Patnership,Complex
3429,94.48,NJ City,N,23,24,1,61,Graduation,19,Patnership,Standalone
3679,305.23,NJ City,E,14,17,20,76,High School,26,Owner,Standalone
945,8.12,NJ City,N,16,24,2,45,Graduation,33,Patnership,Standalone
7899,459.52,Boston,E,4,14,7,79,Graduation,29,Owner,Standalone
3842,473.27,NJ City,S,16,12,3,61,No education,26,Patnership,Complex
6999,219.87,Boston,E,11,20,1,27,Graduation,33,Owner,Standalone
7301,150.74,Boston,N,22,16,6,72,Graduation,31,Patnership,Standalone
7604,317.9,Boston,W,10,18,6,45,Graduation,20,Franchise,Complex
6023,327.53,Boston,E,21,14,13,48,Graduation,32,Owner,Standalone
2120,116,NJ City,W,1,19,9,63,High School,28,Franchise,Complex
7483,245.61,Boston,E,16,20,13,79,Graduation,20,Owner,Standalone
4700,26.74,NJ City,N,21,14,3,53,No education,30,Franchise,Complex
4458,315.69,NJ City,S,18,23,4,31,Graduation,18,Patnership,Complex
7660,272.81,Boston,W,19,19,25,40,Graduation,29,Franchise,Complex
5599,269.6,Boston,E,19,23,18,76,Graduation,32,Owner,Standalone
185,471.54,NJ City,N,10,15,8,39,High School,26,Patnership,Standalone
3722,378.64,NJ City,S,15,15,14,68,High School,24,Patnership,Complex
3107,384.41,NJ City,E,3,12,3,38,No education,33,Owner,Standalone
6080,321.5,Boston,W,8,15,12,78,Graduation,34,Franchise,Complex
4134,55.55,NJ City,S,14,16,1,25,Graduation,35,Patnership,Complex
1310,170.23,NJ City,S,8,8,8,70,High School,21,Patnership,Complex
2559,126,NJ City,E,1,16,18,83,High School,18,Owner,Standalone
2363,32.34,NJ City,N,18,24,3,43,No education,31,Owner,Standalone
5359,450.14,Boston,E,14,18,5,70,High School,23,Owner,Standalone
8083,171.44,NJ City,E,13,24,2,85,High School,28,Owner,Standalone
8093,38.27,NJ City,N,25,11,4,31,No education,35,Patnership,Standalone
7522,6.19,Boston,N,6,14,4,47,High School,35,Patnership,Complex
1570,335.03,NJ City,S,6,18,4,53,High School,18,Patnership,Complex
3339,121.07,NJ City,E,9,18,1,30,Graduation,25,Owner,Standalone
8745,273.29,NJ City,N,8,18,7,45,Graduation,25,Patnership,Standalone
2365,470.22,NJ City,N,14,11,23,67,High School,19,Patnership,Standalone
795,385.53,NJ City,E,25,24,8,36,High School,24,Owner,Standalone
6626,465.67,Boston,S,19,16,4,59,No education,29,Patnership,Complex
3364,120.24,NJ City,W,10,21,1,59,High School,32,Franchise,Complex
2527,192.73,NJ City,E,12,14,23,55,High School,24,Owner,Standalone
8789,124.99,NJ City,N,19,17,3,77,No education,33,Patnership,Standalone
9777,26.92,NJ City,N,11,23,4,82,Graduation,32,Patnership,Standalone
5336,82.91,Boston,W,8,20,3,47,No education,29,Franchise,Complex
7337,135.23,Boston,N,17,23,15,26,Graduation,35,Patnership,Standalone
2968,450.97,NJ City,W,19,21,14,55,High School,21,Franchise,Complex
1972,382.58,NJ City,W,4,16,22,50,High School,28,Franchise,Complex
3230,264.73,NJ City,S,14,19,13,54,High School,34,Patnership,Complex
3179,235.71,NJ City,E,15,22,13,38,High School,28,Owner,Standalone
5573,250.05,Boston,N,3,14,7,37,Graduation,19,Patnership,Standalone
5632,145.37,Boston,W,18,22,25,31,Graduation,32,Franchise,Complex
6439,39.52,Boston,N,22,13,1,28,High School,24,Owner,Standalone
7680,455.16,Boston,W,23,10,2,28,Graduation,23,Franchise,Complex
2480,196.85,NJ City,W,10,16,12,59,High School,34,Franchise,Complex
5414,332.02,Boston,S,11,20,8,30,Graduation,23,Patnership,Complex
5204,444.67,Boston,W,10,23,6,68,Graduation,35,Franchise,Complex
9715,89.38,NJ City,E,15,16,4,60,High School,22,Owner,Standalone
3106,263.67,NJ City,S,19,15,10,55,High School,34,Patnership,Complex
1036,317.26,NJ City,W,19,17,22,44,High School,32,Franchise,Complex
985,168,NJ City,N,3,15,20,25,High School,27,Patnership,Standalone
6235,295.33,Boston,E,18,13,16,72,Graduation,20,Owner,Standalone
808,394.77,NJ City,W,21,23,20,41,High School,27,Franchise,Complex
6373,288.29,Boston,N,19,23,6,48,Graduation,18,Patnership,Standalone
3698,162.16,NJ City,S,24,16,21,81,High School,22,Patnership,Complex
7155,447.02,Boston,E,21,20,20,69,Graduation,32,Owner,Standalone
4918,167.39,NJ City,S,23,21,9,52,High School,28,Patnership,Complex
7544,273.78,Boston,W,22,19,9,58,Graduation,19,Franchise,Complex
8521,336.72,NJ City,N,2,21,13,32,Graduation,27,Patnership,Standalone
5640,223.8,Boston,W,13,24,7,35,Graduation,18,Franchise,Complex
1970,295.56,NJ City,S,14,16,17,44,High School,18,Patnership,Complex
5688,172.77,Boston,W,10,22,18,26,Graduation,23,Franchise,Complex
5493,155.23,Boston,N,19,24,3,36,Graduation,20,Patnership,Standalone
675,8.61,NJ City,N,15,21,2,28,Graduation,22,Owner,Standalone
6525,205.63,Boston,N,17,23,20,39,Graduation,25,Patnership,Standalone
4393,422.42,NJ City,N,6,10,9,82,High School,18,Patnership,Standalone
3224,62.24,NJ City,N,19,9,1,34,No education,34,Franchise,Complex
3276,129,NJ City,W,3,10,12,44,High School,35,Franchise,Complex
2274,121,NJ City,S,2,19,4,74,Graduation,35,Patnership,Complex
9020,220.08,NJ City,W,23,18,3,76,No education,32,Franchise,Complex
5111,171.56,Boston,E,19,19,21,60,Graduation,26,Owner,Standalone
6621,28.98,Boston,N,7,14,2,47,Graduation,30,Patnership,Standalone
3754,318.53,NJ City,S,10,19,25,47,High School,33,Patnership,Complex
7380,298.56,Boston,W,12,12,11,50,Graduation,23,Franchise,Complex
6204,86.47,Boston,W,2,18,4,67,Graduation,19,Franchise,Complex
8428,334.49,NJ City,W,25,14,7,73,Graduation,27,Franchise,Complex
2065,82.27,NJ City,N,2,22,4,50,High School,33,Patnership,Standalone
7198,387.82,Boston,S,2,17,4,65,High School,26,Patnership,Complex
5187,260.88,Boston,E,5,10,15,27,Graduation,19,Owner,Standalone
9953,12.87,NJ City,N,24,23,1,25,No education,24,Patnership,Standalone
9109,368.94,NJ City,N,2,16,1,74,High School,20,Patnership,Standalone
2416,113.72,NJ City,W,14,17,3,84,High School,28,Franchise,Complex
458,130.83,NJ City,S,17,23,10,33,High School,32,Patnership,Complex
6267,195.85,Boston,E,1,22,23,55,Graduation,20,Owner,Standalone
5343,302.37,Boston,E,18,14,7,38,Graduation,25,Owner,Standalone
6305,470.08,Boston,N,10,24,24,71,Graduation,22,Patnership,Standalone
5429,329.02,Boston,N,3,20,16,71,Graduation,22,Patnership,Standalone
9742,290.71,NJ City,S,23,21,22,56,Graduation,24,Patnership,Complex
7264,335.41,Boston,W,9,15,17,78,Graduation,30,Franchise,Complex
8378,360.59,NJ City,S,11,19,17,53,Graduation,27,Patnership,Complex
6886,48.04,Boston,S,4,19,3,52,High School,23,Patnership,Complex
4128,268.73,NJ City,W,8,10,14,78,High School,33,Franchise,Complex
5411,481.2,Boston,E,6,15,14,66,Graduation,23,Owner,Standalone
2053,320.84,NJ City,N,24,20,1,36,High School,30,Patnership,Standalone
6962,497.81,Boston,S,24,12,15,59,Graduation,21,Patnership,Complex
1745,144.68,NJ City,N,17,20,22,63,High School,33,Patnership,Standalone
1503,287.28,NJ City,E,5,11,24,25,High School,19,Owner,Standalone
3807,66.38,NJ City,N,5,18,2,75,Graduation,18,Owner,Standalone
4510,352.07,NJ City,S,17,23,22,32,High School,23,Patnership,Complex
5943,208.4,Boston,E,13,15,1,57,Graduation,31,Owner,Standalone
9091,353.11,NJ City,E,15,15,17,71,Graduation,31,Owner,Standalone
6323,219.35,Boston,E,1,23,12,37,Graduation,27,Owner,Standalone
7863,60.36,Boston,E,10,23,2,79,Graduation,28,Owner,Standalone
5577,55.13,Boston,N,19,17,3,55,Graduation,25,Patnership,Standalone
1519,484.26,NJ City,E,21,14,12,67,High School,25,Owner,Standalone
7647,146.64,Boston,E,18,13,25,65,Graduation,20,Owner,Standalone
6169,494.76,Boston,N,11,11,19,42,Graduation,33,Patnership,Standalone
2453,311,NJ City,N,1,18,25,25,High School,24,Patnership,Standalone
5881,335.31,Boston,N,5,24,13,46,Graduation,33,Patnership,Standalone
5503,302.76,Boston,E,4,10,14,40,Graduation,21,Owner,Standalone
7956,275.1,Boston,W,2,17,10,84,Graduation,31,Franchise,Complex
7467,443.89,Boston,E,18,19,10,65,Graduation,27,Owner,Standalone
1612,193.04,NJ City,W,4,14,24,31,High School,34,Franchise,Complex
157,100,NJ City,N,3,24,19,82,High School,34,Patnership,Standalone
4868,272.26,NJ City,W,12,17,11,28,High School,25,Franchise,Complex
9938,374.4,NJ City,S,11,22,16,77,Graduation,24,Patnership,Complex
7560,95.95,Boston,W,4,11,1,42,Graduation,23,Franchise,Complex
6254,473.13,Boston,S,17,21,23,77,Graduation,34,Patnership,Complex
778,40.95,NJ City,N,5,11,4,76,High School,30,Patnership,Complex
1610,54.15,NJ City,S,16,19,1,40,No education,35,Patnership,Complex
9266,278.85,NJ City,S,17,9,5,26,No education,35,Patnership,Complex
783,14.62,NJ City,N,5,16,2,72,Graduation,22,Owner,Standalone
70,452,NJ City,S,15,12,23,54,High School,27,Patnership,Complex
4867,174.58,NJ City,E,11,19,23,26,High School,24,Owner,Standalone
5862,15.76,Boston,N,9,14,3,35,Graduation,27,Patnership,Complex
6032,202.57,Boston,W,13,15,13,34,Graduation,29,Franchise,Complex
1383,85.99,NJ City,E,1,18,1,66,Graduation,25,Owner,Standalone
8120,141.17,NJ City,W,19,15,7,38,Graduation,22,Franchise,Complex
2284,241.93,NJ City,W,17,22,1,31,High School,26,Franchise,Complex
2983,486.81,NJ City,E,5,9,5,64,High School,18,Owner,Standalone
1002,102.06,NJ City,S,9,20,2,48,Graduation,23,Patnership,Complex
1225,392.04,NJ City,N,19,10,18,83,High School,31,Patnership,Standalone
6425,255.67,Boston,N,3,11,10,61,Graduation,18,Patnership,Standalone
8727,449.93,NJ City,E,20,22,15,46,Graduation,21,Owner,Standalone
1880,486.35,NJ City,W,21,20,10,56,High School,34,Franchise,Complex
4278,147.91,NJ City,S,25,9,10,42,High School,27,Patnership,Complex
9073,14.86,NJ City,N,5,10,3,61,High School,29,Patnership,Standalone
4008,103.77,NJ City,W,22,23,4,30,Graduation,19,Franchise,Complex
187,329.82,NJ City,E,22,22,7,62,High School,35,Owner,Standalone
1638,497.45,NJ City,S,13,15,8,29,High School,34,Patnership,Complex
4211,429.32,NJ City,E,25,21,16,72,High School,32,Owner,Standalone
1382,60.88,NJ City,N,17,18,4,34,No education,24,Patnership,Complex
3011,268.27,NJ City,E,11,22,5,80,No education,20,Owner,Standalone
6538,220.01,Boston,S,18,18,1,63,High School,29,Patnership,Complex
7546,445.34,Boston,S,23,17,12,54,Graduation,23,Patnership,Complex
9226,197.42,NJ City,S,3,15,5,76,High School,30,Patnership,Complex
2570,406.68,NJ City,S,14,18,23,44,High School,18,Patnership,Complex
6638,248.04,Boston,S,12,20,8,57,Graduation,18,Patnership,Complex
5002,447.1,Boston,S,5,17,9,85,Graduation,25,Patnership,Complex
1473,424.97,NJ City,N,13,16,23,77,High School,35,Patnership,Standalone
3725,224,NJ City,N,2,11,21,58,High School,31,Patnership,Standalone
9225,161.92,NJ City,N,10,18,22,73,Graduation,20,Patnership,Standalone
2006,330.08,NJ City,S,10,15,15,74,High School,32,Patnership,Complex
3920,293.39,NJ City,W,10,23,23,66,High School,27,Franchise,Complex
7799,333.5,Boston,E,22,23,1,51,No education,35,Owner,Standalone
7103,342.62,Boston,E,9,12,23,36,Graduation,27,Owner,Standalone
3862,475.27,NJ City,S,19,24,19,29,High School,24,Patnership,Complex
6003,198.84,Boston,E,14,13,8,77,Graduation,25,Owner,Standalone
3389,271.67,NJ City,N,5,12,25,31,High School,29,Patnership,Standalone
1696,20.28,NJ City,N,9,17,4,76,High School,25,Franchise,Complex
8683,199.14,NJ City,E,15,23,9,73,Graduation,25,Owner,Standalone
4113,342.03,NJ City,N,13,24,13,81,High School,20,Patnership,Standalone
8635,275.51,NJ City,E,12,24,17,45,Graduation,32,Owner,Standalone
8000,475.13,Boston,W,1,21,17,44,Graduation,32,Franchise,Complex
2013,75.42,NJ City,N,13,24,2,26,Graduation,20,Patnership,Standalone
2279,341.53,NJ City,E,2,19,2,39,No education,33,Owner,Standalone
617,174.33,NJ City,N,22,23,21,63,High School,31,Patnership,Standalone
1639,245.62,NJ City,E,21,23,1,82,High School,18,Owner,Standalone
8179,211.11,NJ City,E,2,15,12,33,Graduation,33,Owner,Standalone
502,11.2,NJ City,N,24,18,2,77,High School,30,Patnership,Complex
6700,314.06,Boston,W,10,22,6,35,Graduation,24,Franchise,Complex
6273,210.03,Boston,N,25,10,19,53,Graduation,20,Patnership,Standalone
5400,291.64,Boston,W,1,24,6,67,Graduation,34,Franchise,Complex
6983,213.56,Boston,E,6,17,21,27,Graduation,31,Owner,Standalone
231,145.08,NJ City,E,15,14,11,35,High School,30,Owner,Standalone
2889,291.03,NJ City,N,9,21,9,40,High School,29,Patnership,Standalone
7808,478.2,Boston,W,14,8,11,63,Graduation,21,Franchise,Complex
9628,70.96,NJ City,N,3,12,1,58,High School,31,Franchise,Complex
702,66.69,NJ City,N,22,15,1,78,Graduation,30,Patnership,Complex
924,98.33,NJ City,W,24,15,2,48,Graduation,21,Franchise,Complex
4117,29.47,NJ City,N,16,15,3,67,High School,28,Patnership,Standalone
2128,366.04,NJ City,W,21,12,13,55,High School,31,Franchise,Complex
6726,327.59,Boston,S,13,15,25,83,Graduation,33,Patnership,Complex
1963,473.56,NJ City,E,7,18,20,56,High School,29,Owner,Standalone
4039,167.29,NJ City,E,4,11,17,76,High School,24,Owner,Standalone
8195,264.87,NJ City,E,10,11,20,63,Graduation,33,Owner,Standalone
2444,301.5,NJ City,W,7,24,4,35,No education,32,Franchise,Complex
6883,211.42,Boston,E,10,15,8,82,Graduation,30,Owner,Standalone
3487,272.79,NJ City,E,19,18,1,81,High School,33,Owner,Standalone
8653,7.94,NJ City,N,18,14,1,47,High School,35,Patnership,Standalone
4678,402.68,NJ City,S,3,22,5,56,High School,28,Patnership,Complex
431,412.49,NJ City,E,4,15,17,40,High School,25,Owner,Standalone
5006,44.29,Boston,N,25,16,3,83,No education,18,Patnership,Complex
4996,138.13,Boston,W,15,14,7,48,High School,32,Franchise,Complex
9785,85.45,NJ City,N,9,20,1,79,No education,21,Patnership,Standalone
3042,24.94,NJ City,N,15,13,4,80,Graduation,30,Patnership,Complex
100,282.19,NJ City,W,24,15,15,55,High School,21,Franchise,Complex
290,476.65,NJ City,S,16,17,25,48,High School,34,Patnership,Complex
4325,335.74,NJ City,N,5,19,16,71,High School,29,Patnership,Standalone
6809,147.9,Boston,N,18,13,18,81,Graduation,33,Patnership,Standalone
5580,19.25,Boston,N,8,19,2,78,Graduation,35,Franchise,Complex
5977,58.85,Boston,N,19,8,2,46,High School,25,Patnership,Standalone
8524,211.04,NJ City,W,1,15,17,71,Graduation,18,Franchise,Complex
2821,238.79,NJ City,N,14,14,15,48,High School,22,Patnership,Standalone
1136,8.24,NJ City,N,6,22,2,68,No education,34,Franchise,Complex
7204,378.53,Boston,W,17,9,14,59,Graduation,27,Franchise,Complex
4057,497.18,NJ City,N,13,17,20,38,High School,22,Patnership,Standalone
1191,299.67,NJ City,E,12,20,4,62,Graduation,27,Owner,Standalone
9272,461.96,NJ City,W,24,22,9,33,Graduation,27,Franchise,Complex
7992,38.81,Boston,N,2,10,2,77,Graduation,21,Franchise,Complex
8977,94.38,NJ City,N,1,24,3,74,High School,30,Patnership,Standalone
6018,285.86,Boston,S,17,11,9,57,Graduation,18,Patnership,Complex
6975,373.82,Boston,E,19,20,7,71,Graduation,34,Owner,Standalone
2059,262,NJ City,E,2,18,7,45,High School,21,Owner,Standalone
5915,193.32,Boston,E,23,15,13,62,Graduation,18,Owner,Standalone
7529,103,Boston,N,24,20,1,75,No education,19,Patnership,Standalone
3244,230.41,NJ City,W,16,13,14,60,High School,24,Franchise,Complex
9947,102.06,NJ City,E,12,24,3,39,No education,22,Owner,Standalone
1867,76.65,NJ City,E,3,17,3,33,High School,18,Owner,Standalone
5243,365.45,Boston,E,3,19,4,69,No education,35,Owner,Standalone
2895,365.13,NJ City,E,20,8,25,66,High School,30,Owner,Standalone
8337,355.1,NJ City,N,12,16,16,46,Graduation,30,Patnership,Standalone
6637,366.93,Boston,N,24,24,11,34,Graduation,26,Patnership,Standalone
5007,335.27,Boston,E,2,20,4,81,Graduation,23,Owner,Standalone
3817,77.66,NJ City,N,15,19,3,61,High School,26,Patnership,Standalone
3161,26.31,NJ City,N,15,24,3,34,No education,23,Patnership,Standalone
612,407.79,NJ City,W,23,11,2,59,Graduation,29,Franchise,Complex
5923,73.51,Boston,N,7,11,2,82,High School,28,Owner,Standalone
7775,410.04,Boston,E,16,20,24,73,Graduation,20,Owner,Standalone
1476,435.05,NJ City,W,19,15,1,56,Graduation,24,Franchise,Complex
5468,249.54,Boston,W,15,21,21,28,Graduation,28,Franchise,Complex
9948,98.85,NJ City,W,22,22,2,38,Graduation,25,Franchise,Complex
3211,345.05,NJ City,E,17,11,14,34,High School,29,Owner,Standalone
3995,334.06,NJ City,E,22,13,9,48,High School,30,Owner,Standalone
557,308.66,NJ City,N,8,15,18,43,High School,32,Patnership,Standalone
2183,392.52,NJ City,E,7,21,24,41,High School,29,Owner,Standalone
1304,217.92,NJ City,W,15,13,1,78,No education,30,Franchise,Complex
4382,58.98,NJ City,S,22,11,1,53,No education,29,Patnership,Complex
7303,68.54,Boston,N,5,10,1,38,High School,34,Owner,Standalone
6865,39.31,Boston,N,22,22,1,64,High School,21,Patnership,Standalone
1129,182.1,NJ City,N,8,18,2,37,High School,26,Patnership,Standalone
8710,47.73,NJ City,S,2,19,3,77,High School,18,Patnership,Complex
2177,148.94,NJ City,N,1,8,5,46,No education,26,Patnership,Standalone
9292,87.24,NJ City,W,17,13,3,46,High School,29,Franchise,Complex
9925,18.23,NJ City,N,22,14,3,72,High School,25,Patnership,Standalone
6774,335.73,Boston,S,21,19,14,30,Graduation,18,Patnership,Complex
5488,306.79,Boston,W,7,21,1,27,High School,32,Franchise,Complex
9159,412.82,NJ City,E,24,12,6,64,Graduation,23,Owner,Standalone
9631,473.88,NJ City,E,22,20,16,83,Graduation,31,Owner,Standalone
1353,249,NJ City,N,2,18,23,56,High School,24,Patnership,Standalone
9460,269.16,NJ City,W,8,9,2,67,High School,29,Franchise,Complex
3109,391.28,NJ City,N,8,22,17,75,High School,25,Patnership,Standalone
1564,116,NJ City,W,2,9,25,81,High School,34,Franchise,Complex
1973,178.97,NJ City,N,11,19,21,80,High School,22,Patnership,Standalone
5627,120.24,Boston,E,7,17,2,26,No education,25,Owner,Standalone
7148,17.37,Boston,N,14,17,2,27,No education,20,Franchise,Complex
3195,449.18,NJ City,E,15,8,22,46,High School,34,Owner,Standalone
6105,459.56,Boston,N,1,22,15,76,Graduation,27,Patnership,Standalone
1133,243.74,NJ City,N,15,22,16,73,High School,27,Patnership,Standalone
3612,60.37,NJ City,N,22,14,4,70,Graduation,35,Franchise,Complex
9752,168.58,NJ City,W,1,15,14,57,Graduation,21,Franchise,Complex
278,424.15,NJ City,S,23,24,8,83,High School,23,Patnership,Complex
5192,275.66,Boston,W,9,23,6,46,Graduation,32,Franchise,Complex
7715,52.69,Boston,E,2,23,3,45,No education,35,Owner,Standalone
82,155.86,Boston,S,16,24,20,27,High School,32,Patnership,Complex
3311,305.58,NJ City,E,14,13,20,69,High School,24,Owner,Standalone
6336,467.85,Boston,W,6,8,8,56,Graduation,28,Franchise,Complex
5059,168.71,Boston,E,7,19,8,69,Graduation,32,Owner,Standalone
6065,318.06,Boston,N,22,20,18,75,Graduation,29,Patnership,Standalone
6396,379.65,Boston,W,4,21,19,34,Graduation,21,Franchise,Complex
7160,51.91,Boston,W,13,16,1,48,No education,18,Franchise,Complex
427,295.75,NJ City,E,12,22,9,33,High School,24,Owner,Standalone
3941,421.07,NJ City,N,7,18,2,62,No education,23,Patnership,Standalone
163,449.89,NJ City,E,1,20,1,34,High School,20,Owner,Standalone
7752,375.68,Boston,W,4,16,12,28,Graduation,26,Franchise,Complex
4340,24.74,NJ City,N,18,12,3,53,No education,19,Franchise,Complex
6275,3.4,Boston,N,15,18,2,61,No education,23,Owner,Standalone
313,99.91,NJ City,N,22,22,4,48,High School,33,Patnership,Standalone
9404,297.41,NJ City,W,18,10,24,42,Graduation,35,Franchise,Complex
1049,426.83,NJ City,N,8,13,3,69,No education,21,Patnership,Standalone
1960,1.41,NJ City,W,4,19,1,71,High School,27,Franchise,Complex
3082,16.16,NJ City,N,9,22,1,77,High School,27,Patnership,Complex
7350,337.99,Boston,S,3,8,1,47,Graduation,24,Patnership,Complex
1194,234.8,NJ City,S,11,8,3,37,Graduation,23,Patnership,Complex
3681,8.99,NJ City,N,9,13,1,50,Graduation,23,Patnership,Standalone
6906,344.43,Boston,S,12,18,19,69,Graduation,32,Patnership,Complex
6952,94.37,Boston,W,7,19,2,34,High School,26,Franchise,Complex
6329,71.51,Boston,N,1,8,2,52,No education,25,Patnership,Standalone
4716,70.79,NJ City,N,8,14,1,25,Graduation,32,Franchise,Complex
8690,464.65,NJ City,S,11,8,8,30,Graduation,35,Patnership,Complex
3750,9.58,NJ City,N,9,15,4,79,Graduation,28,Patnership,Complex
3175,179.47,NJ City,E,16,12,6,34,High School,33,Owner,Standalone
267,98.98,NJ City,E,14,13,1,68,Graduation,25,Owner,Standalone
8730,327.43,NJ City,S,14,19,24,72,Graduation,31,Patnership,Complex
6493,339.73,Boston,N,11,19,22,26,Graduation,18,Patnership,Standalone
8990,307.61,NJ City,S,9,11,18,40,Graduation,26,Patnership,Complex
5708,116.85,Boston,W,20,24,2,79,No education,34,Franchise,Complex
2252,179.37,NJ City,W,6,12,15,68,High School,22,Franchise,Complex
5734,176.18,Boston,S,7,12,19,84,Graduation,32,Patnership,Complex
2334,102.23,NJ City,S,5,24,2,45,Graduation,29,Patnership,Complex
7823,439.38,Boston,E,25,14,15,65,Graduation,32,Owner,Standalone
5433,421.26,Boston,N,10,10,18,39,Graduation,31,Patnership,Standalone
9300,158.99,NJ City,W,8,10,3,26,Graduation,19,Franchise,Complex
5128,359.36,Boston,W,11,19,11,78,Graduation,24,Franchise,Complex
7745,313,Boston,N,24,8,24,36,Graduation,35,Patnership,Standalone
6889,442.12,Boston,N,3,22,9,71,Graduation,34,Patnership,Standalone
5648,41.72,Boston,N,9,24,3,39,No education,29,Franchise,Complex
7952,498.37,Boston,W,13,20,5,84,No education,19,Franchise,Complex
7025,129.46,Boston,N,16,16,17,47,Graduation,34,Patnership,Standalone
5188,456.46,Boston,W,22,20,11,64,Graduation,22,Franchise,Complex
477,116.76,NJ City,N,3,13,4,43,Graduation,31,Patnership,Standalone
8150,17.81,NJ City,N,24,9,1,56,No education,26,Patnership,Complex
2060,433.09,NJ City,W,12,15,18,46,High School,22,Franchise,Complex
8835,332.21,NJ City,E,17,22,4,54,Graduation,35,Owner,Standalone
2833,444.41,NJ City,N,23,18,17,64,High School,35,Patnership,Standalone
3509,493.89,NJ City,N,5,8,5,52,No education,25,Patnership,Standalone
4949,433.86,Boston,N,23,19,11,27,High School,35,Patnership,Standalone
8212,402.02,NJ City,W,20,10,6,44,Graduation,21,Franchise,Complex
6315,123.79,Boston,E,4,24,4,27,Graduation,28,Owner,Standalone
6157,167.49,Boston,N,20,22,8,49,Graduation,33,Patnership,Standalone
7166,302.66,Boston,S,25,13,2,51,No education,18,Patnership,Complex
6124,78.29,Boston,W,4,9,1,43,High School,22,Franchise,Complex
3335,389.75,NJ City,E,19,14,15,54,High School,35,Owner,Standalone
8034,6.24,Boston,N,4,14,2,63,Graduation,28,Patnership,Complex
1754,25.1,NJ City,N,24,23,1,54,No education,28,Patnership,Complex
5497,348.48,Boston,N,18,20,2,41,High School,22,Patnership,Standalone
3168,361.19,NJ City,W,9,11,2,41,Graduation,20,Franchise,Complex
2696,212.96,NJ City,W,22,23,25,74,High School,23,Franchise,Complex
2161,388.54,NJ City,N,21,16,19,31,High School,24,Patnership,Standalone
5553,94.95,Boston,N,8,16,4,44,Graduation,21,Patnership,Standalone
9369,218.34,NJ City,N,16,20,20,48,Graduation,19,Patnership,Standalone
656,235.52,NJ City,W,9,22,18,51,High School,25,Franchise,Complex
7466,186.79,Boston,S,10,15,12,40,Graduation,27,Patnership,Complex
7683,129.42,Boston,E,11,8,19,36,Graduation,32,Owner,Standalone
5910,478.41,Boston,S,7,22,10,42,Graduation,35,Patnership,Complex
5947,203.56,Boston,E,7,24,25,77,Graduation,29,Owner,Standalone
4938,433.51,Boston,S,16,18,17,67,High School,18,Patnership,Complex
7850,165.93,Boston,S,15,24,23,72,Graduation,19,Patnership,Complex
5346,404.46,Boston,S,12,9,13,47,Graduation,23,Patnership,Complex
4127,142.57,NJ City,E,8,21,9,82,High School,23,Owner,Standalone
7158,207.53,Boston,S,16,13,24,71,Graduation,22,Patnership,Complex
9952,475.18,NJ City,W,5,22,12,83,Graduation,19,Franchise,Complex
2473,376.01,NJ City,N,7,21,12,77,High School,28,Patnership,Standalone
9223,44.79,NJ City,N,7,21,1,83,High School,26,Owner,Standalone
5471,235.03,Boston,E,13,20,20,45,Graduation,19,Owner,Standalone
7896,260.37,Boston,W,16,15,10,52,Graduation,23,Franchise,Complex
611,327.35,NJ City,E,6,9,1,70,No education,22,Owner,Standalone
4135,278.55,NJ City,E,24,22,9,50,High School,33,Owner,Standalone
5342,338.81,Boston,S,12,9,1,36,No education,32,Patnership,Complex
3984,308.72,NJ City,W,21,22,2,76,Graduation,24,Franchise,Complex
5196,61.76,Boston,N,15,14,3,45,Graduation,21,Franchise,Complex
8969,85.95,NJ City,N,2,13,1,74,No education,21,Patnership,Standalone
259,428.94,NJ City,E,16,24,14,55,High School,25,Owner,Standalone
2545,11.36,NJ City,N,22,10,4,41,High School,22,Patnership,Standalone
6552,27.06,Boston,N,8,8,3,50,Graduation,26,Franchise,Complex
7730,218.42,Boston,S,24,23,24,85,Graduation,18,Patnership,Complex
3793,136.5,NJ City,N,24,20,20,61,High School,27,Patnership,Standalone
6136,85.23,Boston,W,11,16,3,69,High School,20,Franchise,Complex
5578,269.78,Boston,S,19,22,9,42,Graduation,32,Patnership,Complex
4070,435.84,NJ City,S,1,10,3,48,No education,26,Patnership,Complex
638,82.2,NJ City,S,15,8,1,70,No education,25,Patnership,Complex
8676,106.28,NJ City,W,17,8,3,51,Graduation,25,Franchise,Complex
9202,168.94,NJ City,S,18,16,15,78,Graduation,28,Patnership,Complex
7233,407.41,Boston,N,23,9,19,76,Graduation,18,Patnership,Standalone
3515,320.01,NJ City,E,15,8,13,47,High School,20,Owner,Standalone
2311,290.83,NJ City,E,12,23,24,29,High School,30,Owner,Standalone
6543,130.56,Boston,E,10,8,10,68,Graduation,32,Owner,Standalone
73,394.3,Boston,N,7,18,5,63,High School,31,Patnership,Standalone
5347,122.88,Boston,E,25,11,3,81,High School,26,Owner,Standalone
2634,254.03,NJ City,S,6,9,1,67,Graduation,29,Patnership,Complex
9183,85.31,NJ City,E,21,15,4,51,Graduation,35,Owner,Standalone
2423,158.77,NJ City,E,11,19,13,70,High School,25,Owner,Standalone
8049,102.16,NJ City,N,18,17,4,78,Graduation,28,Patnership,Standalone
4553,190.65,NJ City,N,10,8,15,62,High School,31,Patnership,Standalone
8561,122.48,NJ City,N,4,8,2,82,No education,26,Patnership,Standalone
9846,100.39,NJ City,S,10,9,2,35,Graduation,32,Patnership,Complex
748,62.77,NJ City,N,2,13,1,39,High School,35,Franchise,Complex
454,159.46,NJ City,S,19,16,12,50,High School,34,Patnership,Complex
8685,492.41,NJ City,N,19,13,9,70,Graduation,26,Patnership,Standalone
2969,389.41,NJ City,N,11,14,2,68,No education,19,Patnership,Standalone
4379,21.6,NJ City,N,10,12,1,43,No education,33,Owner,Standalone
5439,332.74,Boston,E,21,18,14,75,Graduation,26,Owner,Standalone
8704,319.67,NJ City,W,14,14,9,38,Graduation,30,Franchise,Complex
4160,435.81,NJ City,W,9,8,14,31,High School,35,Franchise,Complex
2648,101.95,NJ City,W,19,9,2,80,No education,32,Franchise,Complex
6937,473.66,Boston,N,21,19,5,44,High School,23,Patnership,Standalone
7326,335.83,Boston,S,3,21,18,25,Graduation,32,Patnership,Complex
3466,495.64,NJ City,S,15,8,7,51,High School,32,Patnership,Complex
2411,279.71,NJ City,E,7,13,23,74,High School,22,Owner,Standalone
5418,440.04,Boston,S,11,18,22,51,Graduation,24,Patnership,Complex
3571,265.78,NJ City,E,25,18,12,38,High School,33,Owner,Standalone
6519,99.8,Boston,E,11,17,1,80,Graduation,30,Owner,Standalone
5189,369.03,Boston,N,9,23,14,34,Graduation,33,Patnership,Standalone
9809,340.31,NJ City,N,12,23,10,40,Graduation,30,Patnership,Standalone
8703,123.95,NJ City,E,14,24,2,71,Graduation,28,Owner,Standalone
7407,137.1,Boston,E,14,9,6,71,Graduation,30,Owner,Standalone
8164,189.56,NJ City,W,10,14,17,46,Graduation,28,Franchise,Complex
7587,101.59,Boston,E,6,21,3,31,Graduation,32,Owner,Standalone
6436,390.33,Boston,W,19,19,19,30,Graduation,23,Franchise,Complex
1613,310.7,NJ City,N,7,19,4,67,No education,24,Patnership,Standalone
5305,4.6,Boston,N,16,15,3,40,High School,32,Patnership,Standalone
7463,402.64,Boston,E,9,17,9,47,Graduation,21,Owner,Standalone
1826,454.1,NJ City,S,6,24,23,69,High School,19,Patnership,Complex
7593,339.59,Boston,N,5,8,4,71,Graduation,24,Patnership,Standalone
6650,181.96,Boston,S,20,18,1,80,No education,34,Patnership,Complex
3367,249.91,NJ City,E,9,22,20,53,High School,32,Owner,Standalone
287,463.54,NJ City,E,19,17,2,83,No education,22,Owner,Standalone
1752,44.35,NJ City,N,6,16,2,53,Graduation,28,Franchise,Complex
3191,135.15,NJ City,E,8,12,15,37,High School,20,Owner,Standalone
1142,448.02,NJ City,S,23,8,5,71,No education,24,Patnership,Complex
9476,217.52,NJ City,W,20,21,22,54,Graduation,29,Franchise,Complex
6682,345.91,Boston,S,21,13,17,40,Graduation,35,Patnership,Complex
3334,484.74,NJ City,S,25,18,24,31,High School,30,Patnership,Complex
7200,457.66,Boston,W,10,24,12,79,Graduation,35,Franchise,Complex
9780,122.13,NJ City,W,10,16,2,44,Graduation,30,Franchise,Complex
1357,208.79,NJ City,N,6,23,4,64,High School,24,Patnership,Standalone
7865,302.3,Boston,N,24,17,16,51,Graduation,28,Patnership,Standalone
6675,400.47,Boston,E,11,24,21,83,Graduation,23,Owner,Standalone
1207,270.12,NJ City,E,8,23,25,63,High School,18,Owner,Standalone
118,168.85,NJ City,S,16,12,19,64,High School,34,Patnership,Complex
4694,235.2,NJ City,S,15,14,20,26,High School,24,Patnership,Complex
9303,279.93,NJ City,E,20,22,14,57,Graduation,28,Owner,Standalone
6570,342.19,Boston,S,24,12,20,49,Graduation,30,Patnership,Complex
64,455.93,Boston,W,23,9,17,33,High School,32,Franchise,Complex
1705,231.72,NJ City,N,10,16,8,54,High School,35,Patnership,Standalone
1618,468.26,NJ City,S,25,22,17,66,High School,21,Patnership,Complex
2526,259.23,NJ City,S,13,12,21,30,High School,23,Patnership,Complex
7390,371.2,Boston,S,8,19,8,66,Graduation,27,Patnership,Complex
6887,343.86,Boston,E,25,14,16,84,Graduation,26,Owner,Standalone
1128,316.82,NJ City,W,10,22,13,26,High School,24,Franchise,Complex
8966,408.6,NJ City,S,3,20,18,36,Graduation,19,Patnership,Complex
7945,392.19,Boston,N,4,21,13,54,Graduation,27,Patnership,Standalone
1217,242.23,NJ City,N,15,22,17,45,High School,29,Patnership,Standalone
5434,406.72,Boston,S,6,14,1,33,High School,25,Patnership,Complex
8269,408.34,NJ City,N,19,17,8,63,Graduation,28,Patnership,Standalone
1064,265.09,NJ City,W,4,16,13,29,High School,23,Franchise,Complex
787,418.57,NJ City,E,18,11,8,73,High School,22,Owner,Standalone
6173,424.77,Boston,N,14,24,14,59,Graduation,35,Patnership,Standalone
837,464.02,NJ City,N,11,12,17,42,High School,33,Patnership,Standalone
8258,116.31,NJ City,S,5,22,2,34,No education,23,Patnership,Complex
4299,90.51,NJ City,E,9,24,3,61,Graduation,25,Owner,Standalone
9855,251.71,NJ City,E,23,20,13,35,Graduation,20,Owner,Standalone
7873,94.99,Boston,N,13,22,1,58,High School,24,Patnership,Standalone
1489,124.34,NJ City,N,5,10,2,30,High School,25,Patnership,Standalone
368,383.01,NJ City,W,15,16,8,26,High School,32,Franchise,Complex
3008,56.69,NJ City,W,4,17,2,38,No education,27,Franchise,Complex
4845,346.18,NJ City,N,20,8,25,39,High School,24,Patnership,Standalone
3214,425.96,NJ City,S,4,23,21,64,High School,25,Patnership,Complex
3058,184,NJ City,S,1,19,9,73,High School,28,Patnership,Complex
7163,78.13,Boston,E,25,14,1,34,No education,32,Owner,Standalone
2309,85.26,NJ City,N,12,21,3,55,No education,30,Patnership,Standalone
3113,310.37,NJ City,N,24,13,25,70,High School,20,Patnership,Standalone
9363,214.8,NJ City,E,4,20,11,32,Graduation,32,Owner,Standalone
535,35.77,NJ City,N,11,16,1,25,High School,33,Owner,Standalone
6833,180.41,Boston,N,13,22,1,83,No education,25,Patnership,Standalone
6911,210.01,Boston,E,6,20,22,62,Graduation,27,Owner,Standalone
1993,116.24,NJ City,N,16,19,4,71,High School,35,Patnership,Standalone
6312,140.16,Boston,W,20,12,10,69,Graduation,27,Franchise,Complex
9240,461.56,NJ City,W,13,13,5,70,Graduation,25,Franchise,Complex
9313,32.56,NJ City,N,8,19,2,52,High School,33,Patnership,Standalone
4041,436.72,NJ City,N,10,24,18,64,High School,22,Patnership,Standalone
2301,242.25,NJ City,N,6,19,11,40,High School,31,Patnership,Standalone
331,123.5,NJ City,E,13,21,3,58,High School,20,Owner,Standalone
8507,350.48,NJ City,E,6,19,22,33,Graduation,32,Owner,Standalone
2384,192.66,NJ City,W,10,12,10,72,High School,33,Franchise,Complex
9507,144.96,NJ City,E,7,22,13,36,Graduation,26,Owner,Standalone
8486,363.08,NJ City,S,14,13,11,46,Graduation,19,Patnership,Complex
7168,238.78,Boston,W,4,23,15,70,Graduation,21,Franchise,Complex
199,326.07,NJ City,E,1,19,2,75,High School,32,Owner,Standalone
2722,488.48,NJ City,S,17,14,16,38,High School,20,Patnership,Complex
607,5.12,NJ City,N,2,24,1,53,High School,33,Owner,Standalone
9862,42.88,NJ City,N,4,12,2,50,High School,27,Patnership,Complex
1463,482.67,NJ City,E,18,12,4,80,No education,27,Owner,Standalone
38,41.18,Maine,N,2,23,2,33,No education,30,Patnership,Complex
1616,231.16,NJ City,W,14,16,10,73,High School,21,Franchise,Complex
7449,319.74,Boston,N,23,8,14,41,Graduation,25,Patnership,Standalone
26,28.93,Boston,N,16,16,3,31,No education,35,Patnership,Complex
1668,85.54,NJ City,W,14,12,1,55,Graduation,33,Franchise,Complex
484,393.99,NJ City,W,8,19,18,26,High School,27,Franchise,Complex
1672,19.75,NJ City,N,6,12,1,54,High School,28,Franchise,Complex
9249,439.34,NJ City,N,8,23,5,54,Graduation,20,Patnership,Standalone
5677,418.19,Boston,N,20,18,19,78,Graduation,18,Patnership,Standalone
3966,498.24,NJ City,S,16,11,7,80,High School,22,Patnership,Complex
3925,34.22,NJ City,N,25,17,3,30,High School,19,Patnership,Standalone
2795,15.71,NJ City,N,14,22,2,64,No education,32,Owner,Standalone
429,202.91,NJ City,N,14,8,16,48,High School,18,Patnership,Standalone
9380,393.42,NJ City,W,21,9,18,80,Graduation,28,Franchise,Complex
7575,264.98,Boston,E,6,14,9,34,Graduation,27,Owner,Standalone
6350,92.92,Boston,S,5,15,3,77,No education,26,Patnership,Complex
5779,103.49,Boston,E,5,21,3,60,High School,20,Owner,Standalone
4305,401.51,NJ City,N,13,20,10,32,High School,25,Patnership,Standalone
5831,60.66,Boston,N,7,20,1,69,No education,18,Owner,Standalone
3448,139.61,NJ City,W,4,23,19,49,High School,29,Franchise,Complex
9155,387.69,NJ City,E,24,23,7,83,Graduation,35,Owner,Standalone
3399,207.83,NJ City,E,22,9,24,51,High School,28,Owner,Standalone
7941,197.66,Boston,N,24,17,24,48,Graduation,34,Patnership,Standalone
7748,211.89,Boston,W,4,8,21,60,Graduation,20,Franchise,Complex
9675,47.93,NJ City,E,6,16,3,78,Graduation,25,Owner,Standalone
7154,41.85,Boston,N,14,17,4,28,No education,23,Patnership,Complex
9798,88.18,NJ City,S,22,20,3,56,Graduation,24,Patnership,Complex
849,289.84,NJ City,N,20,9,18,54,High School,25,Patnership,Standalone
2510,111.95,NJ City,S,23,9,4,82,No education,19,Patnership,Complex
316,398.81,NJ City,W,21,15,16,42,High School,26,Franchise,Complex
1398,332.92,NJ City,S,21,17,8,45,High School,29,Patnership,Complex
2954,327.44,NJ City,S,19,11,6,31,High School,22,Patnership,Complex
8991,489.46,NJ City,E,15,23,22,35,Graduation,35,Owner,Standalone
6590,378.61,Boston,S,18,21,22,73,Graduation,24,Patnership,Complex
300,368.91,NJ City,W,12,19,14,73,High School,20,Franchise,Complex
8228,241.98,NJ City,W,18,18,19,47,Graduation,22,Franchise,Complex
8377,268.62,NJ City,N,19,11,17,55,Graduation,35,Patnership,Standalone
7153,228.1,Boston,N,17,14,20,27,Graduation,33,Patnership,Standalone
6970,456.08,Boston,S,8,23,17,37,Graduation,21,Patnership,Complex
2091,297.95,NJ City,E,9,8,17,38,High School,33,Owner,Standalone
4461,417.56,NJ City,N,14,22,23,57,High School,35,Patnership,Standalone
5172,383.91,Boston,W,25,19,11,80,Graduation,23,Franchise,Complex
2964,221.03,NJ City,W,23,23,1,34,Graduation,25,Franchise,Complex
6146,245.91,Boston,S,25,15,5,66,No education,25,Patnership,Complex
1078,197.83,NJ City,S,25,10,22,68,High School,29,Patnership,Complex
9521,340.39,NJ City,N,20,11,14,28,Graduation,35,Patnership,Standalone
1961,100.35,NJ City,N,13,15,2,55,No education,31,Patnership,Standalone
2592,469.22,NJ City,W,15,16,2,68,Graduation,30,Franchise,Complex
4804,157.92,NJ City,W,6,9,14,31,High School,25,Franchise,Complex
3055,482.01,NJ City,E,16,22,20,83,High School,34,Owner,Standalone
4376,242.4,NJ City,W,11,20,6,83,High School,28,Franchise,Complex
763,366.57,NJ City,E,22,22,13,67,High School,33,Owner,Standalone
3660,359.77,NJ City,W,18,11,17,84,High School,22,Franchise,Complex
6497,106.48,Boston,N,14,11,2,58,No education,20,Patnership,Standalone
9082,457.31,NJ City,S,7,21,13,44,Graduation,27,Patnership,Complex
6198,468.91,Boston,S,16,18,23,29,Graduation,26,Patnership,Complex
8464,232.81,NJ City,W,21,22,5,26,High School,23,Franchise,Complex
2445,236.4,NJ City,N,5,20,14,37,High School,31,Patnership,Standalone
4529,232.06,NJ City,N,11,8,24,77,High School,32,Patnership,Standalone
6693,50.9,Boston,N,13,13,3,76,Graduation,33,Patnership,Standalone
7629,44.75,Boston,N,1,15,2,44,Graduation,32,Patnership,Standalone
4819,364.1,NJ City,E,11,19,4,31,High School,29,Owner,Standalone
3301,103.23,NJ City,N,4,13,1,72,High School,32,Patnership,Standalone
5499,113.95,Boston,E,2,11,4,39,Graduation,19,Owner,Standalone
4439,128.56,NJ City,E,16,9,3,47,No education,26,Owner,Standalone
8480,497.71,NJ City,W,13,14,18,48,Graduation,29,Franchise,Complex
5989,178.26,Boston,N,14,8,18,28,Graduation,32,Patnership,Standalone
3250,99.82,NJ City,S,4,8,1,74,High School,20,Patnership,Complex
7408,422.99,Boston,W,21,20,14,72,Graduation,25,Franchise,Complex
3701,431.85,NJ City,N,22,22,22,55,High School,31,Patnership,Standalone
5004,330.58,Boston,W,24,11,24,55,Graduation,30,Franchise,Complex
7267,222.35,Boston,E,15,24,23,80,Graduation,18,Owner,Standalone
6077,486.98,Boston,N,19,11,9,85,Graduation,26,Patnership,Standalone
9635,154.1,NJ City,E,6,10,8,73,Graduation,26,Owner,Standalone
5319,413.56,Boston,E,15,16,9,82,Graduation,30,Owner,Standalone
4915,1.05,NJ City,N,8,11,4,57,High School,31,Owner,Standalone
6892,2.88,Boston,W,24,12,3,40,High School,24,Franchise,Complex
3903,7.47,NJ City,N,14,21,1,69,Graduation,28,Owner,Standalone
9576,148.26,NJ City,W,3,13,6,45,Graduation,21,Franchise,Complex
6536,420.15,Boston,W,24,11,13,50,Graduation,34,Franchise,Complex
4473,14.36,NJ City,N,18,16,3,65,Graduation,32,Patnership,Standalone
6545,328.55,Boston,N,8,10,23,26,Graduation,33,Patnership,Standalone
7249,325.89,Boston,N,19,19,13,80,Graduation,25,Patnership,Standalone
5815,98.61,Boston,E,25,19,3,70,High School,20,Owner,Standalone
5295,59.43,Boston,E,4,21,3,62,Graduation,20,Owner,Standalone
8613,130.53,NJ City,N,24,12,12,78,Graduation,30,Patnership,Standalone
6876,233.89,Boston,W,5,23,5,38,Graduation,33,Franchise,Complex
5445,425.28,Boston,N,13,16,13,51,Graduation,20,Patnership,Standalone
6382,372.68,Boston,S,13,14,7,62,Graduation,29,Patnership,Complex
3735,424.7,NJ City,E,21,13,4,30,Graduation,30,Owner,Standalone
4009,468.64,NJ City,N,12,20,16,31,High School,25,Patnership,Standalone
5311,323.89,Boston,E,25,24,20,53,Graduation,30,Owner,Standalone
4241,161.25,NJ City,N,10,10,7,62,High School,24,Patnership,Standalone
8605,327.66,NJ City,N,10,19,25,34,Graduation,34,Patnership,Standalone
5823,488.58,Boston,E,12,8,16,71,Graduation,28,Owner,Standalone
4499,106.15,NJ City,E,14,24,1,38,No education,20,Owner,Standalone
9508,30.33,NJ City,N,10,15,2,42,High School,24,Franchise,Complex
3342,257.32,NJ City,S,21,20,19,61,High School,27,Patnership,Complex
4060,449.48,NJ City,W,16,18,5,40,High School,31,Franchise,Complex
376,88.29,NJ City,W,6,24,4,77,High School,34,Franchise,Complex
8422,186.99,NJ City,S,3,18,3,54,High School,31,Patnership,Complex
8491,461.97,NJ City,E,9,17,22,71,Graduation,20,Owner,Standalone
245,65.11,NJ City,N,22,15,1,82,No education,25,Patnership,Standalone
4500,305.4,NJ City,W,19,17,5,59,Graduation,27,Franchise,Complex
4192,408.05,NJ City,W,4,10,23,79,High School,20,Franchise,Complex
4831,154.77,NJ City,E,19,9,7,77,High School,25,Owner,Standalone
9009,197.9,NJ City,N,17,16,21,72,Graduation,27,Patnership,Standalone
7559,160.54,Boston,E,7,9,3,54,No education,34,Owner,Standalone
1294,115.72,NJ City,S,4,10,3,62,High School,28,Patnership,Complex
6795,176.96,Boston,E,16,9,7,81,Graduation,25,Owner,Standalone
4563,59.91,NJ City,E,17,15,1,53,Graduation,31,Owner,Standalone
1464,120.75,NJ City,W,12,21,2,43,Graduation,26,Franchise,Complex
197,118.58,NJ City,N,3,20,1,46,No education,35,Patnership,Standalone
8796,227.5,NJ City,W,4,10,21,40,Graduation,22,Franchise,Complex
4185,337.75,NJ City,N,16,18,19,61,High School,25,Patnership,Standalone
2567,333,NJ City,E,2,17,4,51,No education,26,Owner,Standalone
5695,34.62,Boston,N,25,15,3,75,High School,25,Owner,Standalone
9573,129.65,NJ City,N,23,16,20,68,Graduation,22,Patnership,Standalone
8160,49.31,NJ City,W,11,16,2,62,Graduation,29,Franchise,Complex
3369,192.02,NJ City,N,20,16,16,82,High School,32,Patnership,Standalone
4690,499.85,NJ City,S,1,17,5,78,High School,35,Patnership,Complex
6550,326.28,Boston,S,19,13,25,84,Graduation,32,Patnership,Complex
3475,48.38,NJ City,E,17,23,2,50,High School,21,Owner,Standalone
9108,208.22,NJ City,W,21,24,22,64,Graduation,27,Franchise,Complex
7309,408.08,Boston,N,15,15,1,68,High School,21,Patnership,Standalone
7347,209.2,Boston,E,7,9,18,80,Graduation,19,Owner,Standalone
3948,93.45,NJ City,W,23,16,1,53,Graduation,24,Franchise,Complex
9676,495.54,NJ City,W,7,18,6,58,Graduation,26,Franchise,Complex
7230,401.46,Boston,S,15,11,5,71,Graduation,27,Patnership,Complex
8319,240.39,NJ City,E,7,24,25,63,Graduation,32,Owner,Standalone
4637,477.95,NJ City,N,1,13,5,55,No education,20,Patnership,Standalone
3752,304.41,NJ City,W,19,11,18,36,High School,30,Franchise,Complex
1481,191.98,NJ City,N,21,24,12,36,High School,33,Patnership,Standalone
7566,411.02,Boston,S,4,18,22,36,Graduation,23,Patnership,Complex
5323,343.86,Boston,E,22,17,1,43,High School,28,Owner,Standalone
6394,307.96,Boston,S,16,9,10,69,Graduation,27,Patnership,Complex
2131,222.8,NJ City,E,10,17,19,41,High School,34,Owner,Standalone
1355,374.28,NJ City,E,18,12,5,57,No education,19,Owner,Standalone
9127,349.85,NJ City,E,3,23,9,42,Graduation,25,Owner,Standalone
3079,464.59,NJ City,E,9,20,2,79,High School,26,Owner,Standalone
5357,479.89,Boston,N,5,10,6,32,Graduation,35,Patnership,Standalone
5113,316.63,Boston,N,3,23,19,41,Graduation,24,Patnership,Standalone
5328,10.92,Boston,N,2,18,2,42,Graduation,26,Franchise,Complex
6446,441.55,Boston,S,15,21,1,53,No education,23,Patnership,Complex
8788,171.97,NJ City,W,22,11,12,76,Graduation,31,Franchise,Complex
6573,347.09,Boston,N,21,13,22,57,Graduation,28,Patnership,Standalone
4857,429.32,NJ City,N,7,23,3,60,Graduation,24,Patnership,Standalone
673,473.91,NJ City,N,4,10,8,67,High School,22,Patnership,Standalone
4463,402.01,NJ City,E,16,13,22,42,High School,20,Owner,Standalone
4433,292.43,NJ City,N,8,10,16,73,High School,31,Patnership,Standalone
4741,57.79,NJ City,N,24,22,2,52,High School,25,Patnership,Standalone
8603,64.15,NJ City,N,24,12,3,56,No education,31,Owner,Standalone
6478,413.52,Boston,S,13,16,3,55,High School,24,Patnership,Complex
9649,222.61,NJ City,N,16,8,7,48,Graduation,24,Patnership,Standalone
3006,118.48,NJ City,S,20,15,1,54,Graduation,35,Patnership,Complex
6071,143.24,Boston,E,14,17,25,85,Graduation,23,Owner,Standalone
3298,27.67,NJ City,N,4,21,3,77,High School,20,Patnership,Complex
5045,460.36,Boston,N,21,12,2,53,No education,23,Patnership,Standalone
8946,191.66,NJ City,S,25,19,7,47,Graduation,28,Patnership,Complex
961,56.05,NJ City,N,7,9,1,43,High School,19,Patnership,Standalone
369,218.92,NJ City,N,7,14,23,32,High School,27,Patnership,Standalone
5033,491.83,Boston,N,3,24,2,40,No education,33,Patnership,Standalone
6008,463.83,Boston,W,14,24,15,29,Graduation,33,Franchise,Complex
8296,191.87,NJ City,W,23,19,18,82,Graduation,26,Franchise,Complex
5774,407.27,Boston,S,24,8,17,35,Graduation,29,Patnership,Complex
294,283.91,NJ City,S,20,17,4,73,Graduation,18,Patnership,Complex
7794,438.54,Boston,S,19,12,3,73,Graduation,21,Patnership,Complex
4879,109.86,NJ City,E,21,20,4,77,High School,19,Owner,Standalone
7832,170.04,Boston,W,14,11,4,62,No education,26,Franchise,Complex
5423,281.73,Boston,E,12,12,8,51,Graduation,31,Owner,Standalone
8726,444.57,NJ City,S,21,18,7,61,Graduation,32,Patnership,Complex
5892,458.09,Boston,W,11,17,6,51,Graduation,35,Franchise,Complex
9176,379.03,NJ City,W,6,11,4,46,No education,24,Franchise,Complex
6924,393.71,Boston,W,8,10,21,75,Graduation,24,Franchise,Complex
6985,278.17,Boston,N,18,14,24,26,Graduation,21,Patnership,Standalone
1922,38.97,NJ City,N,21,15,3,69,No education,32,Patnership,Complex
680,433.81,NJ City,W,14,15,9,50,High School,21,Franchise,Complex
9011,96.59,NJ City,E,8,15,2,38,No education,34,Owner,Standalone
7169,36.61,Boston,N,16,15,1,45,No education,26,Patnership,Standalone
9638,314.18,NJ City,S,21,14,17,31,Graduation,20,Patnership,Complex
8824,55.54,NJ City,W,12,15,2,82,High School,29,Franchise,Complex
4190,365.26,NJ City,S,7,21,24,33,High School,32,Patnership,Complex
3353,151.81,NJ City,N,19,8,16,72,High School,26,Patnership,Standalone
5012,417.91,Boston,W,3,11,4,78,No education,31,Franchise,Complex
2281,264.21,NJ City,N,19,21,8,83,High School,22,Patnership,Standalone
5070,344.97,Boston,S,13,12,6,44,Graduation,33,Patnership,Complex
4438,223.69,NJ City,S,21,9,22,30,High School,23,Patnership,Complex
9705,79.41,NJ City,N,14,12,4,26,Graduation,32,Patnership,Standalone
6333,414.79,Boston,N,16,19,8,30,Graduation,21,Patnership,Standalone
6117,480.19,Boston,N,21,13,17,64,Graduation,34,Patnership,Standalone
9042,27.63,NJ City,N,12,20,2,68,Graduation,30,Patnership,Complex
6603,317.03,Boston,E,25,17,10,68,Graduation,18,Owner,Standalone
4454,317.64,NJ City,S,5,20,8,30,High School,29,Patnership,Complex
3537,287.57,NJ City,N,14,22,2,41,Graduation,35,Patnership,Standalone
2106,81.32,NJ City,S,12,15,1,85,Graduation,26,Patnership,Complex
3788,23.45,NJ City,N,13,9,3,77,No education,35,Franchise,Complex
3666,158.97,NJ City,S,13,13,8,63,High School,33,Patnership,Complex
2182,193.21,NJ City,S,25,9,21,63,High School,28,Patnership,Complex
7076,475.06,Boston,W,4,22,3,72,No education,32,Franchise,Complex
4233,380.44,NJ City,N,5,9,6,52,High School,31,Patnership,Standalone
7379,327.83,Boston,E,8,8,4,65,No education,28,Owner,Standalone
8768,212.83,NJ City,W,11,9,9,34,Graduation,19,Franchise,Complex
4533,168.6,NJ City,N,10,16,9,82,High School,25,Patnership,Standalone
1495,382.95,NJ City,E,8,8,17,68,High School,22,Owner,Standalone
8713,106,NJ City,N,8,8,3,71,High School,22,Patnership,Standalone
6400,44.49,Boston,N,20,23,1,55,High School,27,Franchise,Complex
3348,71.07,NJ City,N,9,9,3,54,Graduation,35,Franchise,Complex
3970,410.46,NJ City,S,13,11,14,36,High School,18,Patnership,Complex
9367,190.81,NJ City,E,11,22,7,56,Graduation,20,Owner,Standalone
7272,265.82,Boston,W,8,21,10,83,Graduation,30,Franchise,Complex
5942,340.16,Boston,S,11,22,4,50,No education,30,Patnership,Complex
5755,495.67,Boston,E,23,21,3,44,High School,34,Owner,Standalone
344,12.66,NJ City,N,10,21,4,55,No education,28,Franchise,Complex
1232,440.17,NJ City,W,19,22,11,43,High School,35,Franchise,Complex
6210,439.39,Boston,S,1,15,16,53,Graduation,18,Patnership,Complex
8665,44.88,NJ City,N,3,21,3,59,High School,34,Patnership,Standalone
1024,204.34,NJ City,W,4,12,1,65,High School,35,Franchise,Complex
4482,178.08,NJ City,S,14,13,14,74,High School,28,Patnership,Complex
9222,356.56,NJ City,S,13,8,19,76,Graduation,23,Patnership,Complex
7417,333.17,Boston,N,4,16,2,54,High School,23,Patnership,Standalone
3912,104.07,NJ City,W,5,19,2,39,Graduation,28,Franchise,Complex
107,412.4,NJ City,E,21,13,16,69,High School,23,Owner,Standalone
6230,220.53,Boston,S,14,12,2,50,No education,32,Patnership,Complex
2344,119.79,NJ City,W,1,9,2,52,High School,34,Franchise,Complex
7830,18.1,Boston,N,20,10,3,58,Graduation,34,Patnership,Complex
9828,284.45,NJ City,W,7,20,20,69,Graduation,31,Franchise,Complex
8489,347.45,NJ City,N,14,11,12,81,Graduation,23,Patnership,Standalone
4627,17.76,NJ City,N,5,13,4,27,High School,26,Owner,Standalone
3637,388.69,NJ City,N,10,12,14,73,High School,34,Patnership,Standalone
6896,8.46,Boston,N,18,23,2,55,No education,22,Franchise,Complex
4275,241.65,NJ City,E,24,13,2,53,Graduation,32,Owner,Standalone
2684,326.85,NJ City,W,18,14,10,58,High School,31,Franchise,Complex
2535,243.87,NJ City,E,8,21,12,59,High School,32,Owner,Standalone
585,391.41,NJ City,N,21,8,4,49,Graduation,33,Patnership,Standalone
731,132.94,NJ City,E,8,18,20,54,High School,32,Owner,Standalone
9361,115.47,NJ City,N,19,16,4,76,High School,18,Patnership,Standalone
4833,385.7,NJ City,N,6,18,1,84,Graduation,19,Patnership,Standalone
6197,201.31,Boston,N,18,15,5,45,No education,18,Patnership,Standalone
3723,15.59,NJ City,N,16,17,2,64,Graduation,34,Owner,Standalone
2474,295.67,NJ City,S,6,17,21,84,High School,34,Patnership,Complex
3090,24.9,NJ City,N,15,15,1,64,Graduation,35,Patnership,Complex
5715,487.35,Boston,E,8,24,4,72,Graduation,23,Owner,Standalone
5726,446.35,Boston,S,12,16,8,43,Graduation,25,Patnership,Complex
7140,196.65,Boston,W,5,22,1,29,Graduation,33,Franchise,Complex
5084,288.84,Boston,W,16,12,12,36,Graduation,18,Franchise,Complex
582,238.42,NJ City,S,23,14,13,78,High School,33,Patnership,Complex
1597,159.37,NJ City,N,13,15,15,25,High School,23,Patnership,Standalone
9813,380.51,NJ City,N,20,17,3,29,Graduation,33,Patnership,Standalone
6872,335.99,Boston,W,9,8,9,35,Graduation,26,Franchise,Complex
5924,324.02,Boston,W,5,9,12,81,Graduation,18,Franchise,Complex
5962,416.49,Boston,S,1,14,8,48,Graduation,29,Patnership,Complex
881,452.94,NJ City,N,24,11,7,65,High School,35,Patnership,Standalone
7384,230.16,Boston,W,13,9,14,59,Graduation,25,Franchise,Complex
2975,131.73,NJ City,E,13,23,8,48,High School,31,Owner,Standalone
1728,210.52,NJ City,W,15,8,23,39,High School,35,Franchise,Complex
1839,229.47,NJ City,E,23,18,24,26,High School,29,Owner,Standalone
3601,495.9,NJ City,N,10,14,17,33,High School,31,Patnership,Standalone
3645,466.46,NJ City,N,10,24,4,48,Graduation,26,Patnership,Standalone
8056,132.3,NJ City,W,22,18,15,75,Graduation,30,Franchise,Complex
4314,380.86,NJ City,S,18,16,17,52,High School,29,Patnership,Complex
1465,73.49,NJ City,N,13,18,3,43,High School,22,Patnership,Standalone
8320,271.03,NJ City,W,6,22,13,68,Graduation,22,Franchise,Complex
609,317.77,NJ City,N,24,17,1,27,Graduation,28,Patnership,Standalone
292,119.04,NJ City,W,4,20,1,40,High School,29,Franchise,Complex
9206,385.15,NJ City,S,9,12,13,34,Graduation,27,Patnership,Complex
9018,293.18,NJ City,S,23,11,17,53,Graduation,27,Patnership,Complex
5365,490.57,Boston,N,16,12,23,70,Graduation,25,Patnership,Standalone
5399,453.94,Boston,E,14,19,3,73,No education,32,Owner,Standalone
5246,43.38,Boston,N,15,14,2,38,No education,24,Patnership,Complex
303,2.32,NJ City,E,20,12,4,54,Graduation,18,Owner,Standalone
5479,309.04,Boston,E,11,19,20,50,Graduation,19,Owner,Standalone
3440,144.41,NJ City,W,14,13,2,72,No education,33,Franchise,Complex
1291,426.73,NJ City,E,15,22,23,28,High School,33,Owner,Standalone
7003,403.44,Boston,E,21,11,22,40,Graduation,24,Owner,Standalone
6565,94.51,Boston,N,15,16,3,29,High School,27,Patnership,Standalone
8470,335.84,NJ City,S,10,20,21,35,Graduation,19,Patnership,Complex
6854,409.68,Boston,S,6,23,13,73,Graduation,32,Patnership,Complex
3279,354.6,NJ City,E,23,23,7,45,High School,21,Owner,Standalone
3778,109,NJ City,S,1,20,9,63,High School,18,Patnership,Complex
7790,468.49,Boston,S,5,16,10,59,Graduation,29,Patnership,Complex
8741,472.8,NJ City,N,15,24,2,78,No education,34,Patnership,Standalone
1200,348.72,NJ City,W,17,24,16,45,High School,27,Franchise,Complex
7590,402.56,Boston,S,12,18,14,62,Graduation,30,Patnership,Complex
5905,107.34,Boston,N,18,22,2,55,High School,21,Patnership,Standalone
1983,443.45,NJ City,E,20,21,10,49,High School,25,Owner,Standalone
4654,262,NJ City,S,3,17,12,40,High School,32,Patnership,Complex
7187,109.99,Boston,E,3,13,3,65,No education,27,Owner,Standalone
5341,137.39,Boston,N,10,16,24,32,Graduation,35,Patnership,Standalone
6564,104.75,Boston,W,14,12,2,37,Graduation,35,Franchise,Complex
9433,281.48,NJ City,N,9,8,25,58,Graduation,19,Patnership,Standalone
3174,310,NJ City,S,1,16,10,80,High School,19,Patnership,Complex
5193,457.02,Boston,N,16,8,10,71,Graduation,33,Patnership,Standalone
4887,492.48,NJ City,E,9,17,16,79,High School,25,Owner,Standalone
6877,176.47,Boston,N,7,22,19,41,Graduation,30,Patnership,Standalone
3287,369.87,NJ City,E,13,14,16,30,High School,23,Owner,Standalone
6652,37.03,Boston,N,6,21,1,75,High School,18,Franchise,Complex
7396,242.59,Boston,W,4,14,7,62,Graduation,18,Franchise,Complex
2166,20.78,NJ City,N,6,21,2,34,Graduation,25,Patnership,Complex
2329,172.34,NJ City,N,7,22,13,31,High School,28,Patnership,Standalone
5821,204.98,Boston,N,24,13,4,61,High School,21,Patnership,Standalone
9835,107.08,NJ City,E,4,11,1,58,High School,28,Owner,Standalone
2370,317.21,NJ City,S,16,13,9,36,High School,30,Patnership,Complex
7001,99.21,Boston,N,17,21,3,66,No education,22,Patnership,Standalone
895,84.54,NJ City,E,15,16,3,51,High School,18,Owner,Standalone
4862,160.45,NJ City,S,13,9,11,76,High School,26,Patnership,Complex
5308,341.25,Boston,W,8,20,5,66,High School,21,Franchise,Complex
9796,14.84,NJ City,N,23,10,3,51,High School,21,Franchise,Complex
2098,485.46,NJ City,S,17,9,23,45,High School,22,Patnership,Complex
2585,14.5,NJ City,N,1,9,3,52,No education,33,Patnership,Standalone
9808,274.6,NJ City,W,8,20,10,54,Graduation,25,Franchise,Complex
2443,303,NJ City,E,1,12,6,56,High School,25,Owner,Standalone
1755,198.05,NJ City,E,10,17,8,81,High School,28,Owner,Standalone
498,134.84,NJ City,S,16,18,13,54,High School,28,Patnership,Complex
3955,230.87,NJ City,E,7,11,9,32,High School,24,Owner,Standalone
1392,296.67,NJ City,W,1,23,4,59,Graduation,35,Franchise,Complex
7623,121.32,Boston,E,7,13,4,69,Graduation,30,Owner,Standalone
5351,144.71,Boston,E,9,18,4,50,No education,24,Owner,Standalone
9790,301.05,NJ City,S,13,14,21,78,Graduation,24,Patnership,Complex
7297,389.24,Boston,N,12,15,12,36,Graduation,23,Patnership,Standalone
1872,246.83,NJ City,W,17,21,4,42,Graduation,29,Franchise,Complex
7541,285.49,Boston,N,24,24,6,27,Graduation,29,Patnership,Standalone
5611,235.02,Boston,E,3,20,20,33,Graduation,28,Owner,Standalone
8811,113.22,NJ City,E,19,14,3,60,Graduation,22,Owner,Standalone
2226,72.33,NJ City,N,9,23,4,35,Graduation,32,Patnership,Complex
5736,490.23,Boston,W,21,22,3,32,Graduation,26,Franchise,Complex
4551,372.08,NJ City,E,12,16,20,64,High School,24,Owner,Standalone
847,131.81,NJ City,E,15,21,13,59,High School,26,Owner,Standalone
6665,395.66,Boston,N,21,24,25,34,Graduation,31,Patnership,Standalone
8175,228.72,NJ City,E,6,20,12,68,Graduation,26,Owner,Standalone
2700,335.3,NJ City,W,21,15,14,43,High School,26,Franchise,Complex
201,438.49,NJ City,N,23,16,25,36,High School,29,Patnership,Standalone
5053,393.29,Boston,N,14,24,10,34,Graduation,31,Patnership,Standalone
5851,78.32,Boston,E,2,13,3,31,High School,26,Owner,Standalone
4234,201.37,NJ City,S,25,17,9,33,High School,31,Patnership,Complex
5597,477.47,Boston,N,23,20,1,27,No education,35,Patnership,Standalone
1912,59.77,NJ City,W,22,8,4,72,High School,23,Franchise,Complex
9409,99.07,NJ City,N,2,18,3,43,High School,25,Patnership,Standalone
898,436.15,NJ City,S,20,11,14,77,High School,25,Patnership,Complex
2414,417.55,NJ City,S,8,11,2,38,No education,30,Patnership,Complex
6430,413.12,Boston,S,13,22,16,79,Graduation,27,Patnership,Complex
4324,261.67,NJ City,W,8,17,16,43,High School,28,Franchise,Complex
3833,333.97,NJ City,N,7,18,4,61,No education,31,Patnership,Standalone
8885,387.92,NJ City,N,8,24,25,42,Graduation,27,Patnership,Standalone
143,225.18,NJ City,E,24,10,23,25,High School,35,Owner,Standalone
1962,103.41,NJ City,S,19,12,4,51,Graduation,29,Patnership,Complex
860,43.38,NJ City,N,21,23,2,26,No education,21,Franchise,Complex
9195,79.3,NJ City,E,8,12,1,36,Graduation,28,Owner,Standalone
5522,52.96,Boston,S,5,18,1,80,No education,28,Patnership,Complex
5223,9.96,Boston,N,22,8,2,76,Graduation,23,Owner,Standalone
8912,430.94,NJ City,W,4,15,14,46,Graduation,32,Franchise,Complex
7135,484.44,Boston,E,22,12,20,63,Graduation,32,Owner,Standalone
9582,234.79,NJ City,S,12,13,7,56,Graduation,18,Patnership,Complex
8926,438.23,NJ City,S,17,24,6,45,Graduation,25,Patnership,Complex
3370,449.01,NJ City,S,13,13,17,26,High School,23,Patnership,Complex
6779,398.6,Boston,E,20,10,4,55,No education,27,Owner,Standalone
8626,241.88,NJ City,S,20,14,17,52,Graduation,31,Patnership,Complex
7954,298.58,Boston,S,14,11,11,62,Graduation,24,Patnership,Complex
3037,154.42,NJ City,N,18,17,16,60,High School,21,Patnership,Standalone
8364,376.46,NJ City,W,9,10,13,60,Graduation,22,Franchise,Complex
9344,389.31,NJ City,W,20,8,12,38,Graduation,23,Franchise,Complex
2643,396.57,NJ City,E,5,18,5,34,Graduation,35,Owner,Standalone
2256,225.47,NJ City,W,16,8,21,79,High School,22,Franchise,Complex
6705,337.09,Boston,N,5,10,8,62,Graduation,30,Patnership,Standalone
1272,278.64,NJ City,W,11,8,22,85,High School,34,Franchise,Complex
2381,425.35,NJ City,N,25,12,3,59,No education,29,Patnership,Standalone
7442,471.97,Boston,S,5,17,16,28,Graduation,20,Patnership,Complex
7877,212,Boston,N,17,10,14,85,Graduation,32,Patnership,Standalone
8047,450.08,NJ City,E,20,20,25,45,Graduation,29,Owner,Standalone
6474,379.73,Boston,S,25,13,16,31,Graduation,27,Patnership,Complex
356,10.01,NJ City,N,11,19,4,54,No education,18,Franchise,Complex
4916,49.49,NJ City,W,5,12,2,60,No education,22,Franchise,Complex
3167,15.77,NJ City,N,6,11,2,81,No education,32,Owner,Standalone
579,292.4,NJ City,E,12,17,22,57,High School,30,Owner,Standalone
9910,482.88,NJ City,S,16,12,5,40,High School,30,Patnership,Complex
8261,117.51,NJ City,N,7,10,3,54,No education,21,Patnership,Standalone
5746,298.24,Boston,S,4,14,1,78,High School,21,Patnership,Complex
8536,66.02,NJ City,N,9,18,3,76,High School,20,Franchise,Complex
6912,22.72,Boston,N,19,12,3,26,Graduation,29,Franchise,Complex
9816,56.04,NJ City,W,10,17,4,35,Graduation,32,Franchise,Complex
5751,101.17,Boston,E,21,19,3,56,Graduation,34,Owner,Standalone
8644,160.29,NJ City,W,9,16,4,44,High School,19,Franchise,Complex
9071,117.15,NJ City,E,12,19,4,56,No education,33,Owner,Standalone
327,162.09,NJ City,E,4,12,11,69,High School,31,Owner,Standalone
7210,19.91,Boston,N,18,11,3,29,High School,32,Patnership,Complex
3242,173.66,NJ City,S,15,15,10,48,High School,33,Patnership,Complex
1333,163,NJ City,N,3,16,7,45,High School,28,Patnership,Standalone
1384,259.72,NJ City,W,9,16,5,38,High School,19,Franchise,Complex
2862,60.73,NJ City,N,10,20,4,25,Graduation,33,Patnership,Complex
3742,242.72,NJ City,S,20,13,12,81,High School,33,Patnership,Complex
3266,118.23,NJ City,S,4,8,1,25,No education,33,Patnership,Complex
9432,5.02,NJ City,N,14,17,2,74,Graduation,27,Franchise,Complex
4766,291.33,NJ City,S,13,12,10,57,High School,28,Patnership,Complex
2155,253.77,NJ City,E,10,20,6,63,High School,27,Owner,Standalone
9607,450.41,NJ City,E,6,14,1,70,High School,28,Owner,Standalone
3324,37.21,NJ City,N,21,18,3,35,Graduation,29,Franchise,Complex
7077,250.83,Boston,N,23,9,5,54,Graduation,22,Patnership,Standalone
8693,57.21,NJ City,N,14,8,4,43,No education,34,Patnership,Standalone
9493,209.46,NJ City,N,5,19,4,36,High School,19,Patnership,Standalone
3553,56.93,NJ City,N,19,8,1,83,High School,19,Patnership,Standalone
859,321.84,NJ City,E,16,9,18,54,High School,33,Owner,Standalone
6322,222.87,Boston,S,12,19,17,81,Graduation,27,Patnership,Complex
5525,282.35,Boston,N,24,10,23,57,Graduation,33,Patnership,Standalone
2189,262.7,NJ City,N,25,16,4,49,No education,29,Patnership,Standalone
8144,12.48,NJ City,N,6,16,3,37,No education,24,Franchise,Complex
3865,464.38,NJ City,N,10,23,13,55,High School,33,Patnership,Standalone
6421,445.77,Boston,N,6,12,11,26,Graduation,30,Patnership,Standalone
2145,399.78,NJ City,N,11,22,10,40,High School,29,Patnership,Standalone
7769,151.24,Boston,N,3,8,7,45,Graduation,26,Patnership,Standalone
8879,450.22,NJ City,E,8,11,17,40,Graduation,24,Owner,Standalone
4522,365.62,NJ City,S,25,15,17,56,High School,32,Patnership,Complex
6905,269.32,Boston,N,17,17,5,44,No education,32,Patnership,Standalone
6433,290.98,Boston,N,6,12,19,66,Graduation,34,Patnership,Standalone
1568,206,NJ City,W,1,16,22,75,High School,23,Franchise,Complex
9190,34.22,NJ City,N,18,20,4,52,High School,28,Patnership,Complex
9172,480.36,NJ City,W,25,13,12,75,Graduation,24,Franchise,Complex
3332,269.02,NJ City,W,8,18,15,31,High School,25,Franchise,Complex
1127,428.31,NJ City,E,24,21,13,51,High School,22,Owner,Standalone
1904,440.63,NJ City,W,23,9,20,73,High School,27,Franchise,Complex
4523,133.66,NJ City,E,25,9,19,45,High School,18,Owner,Standalone
6597,435.54,Boston,N,2,24,20,29,Graduation,19,Patnership,Standalone
5186,248.44,Boston,S,21,21,23,48,Graduation,29,Patnership,Complex
3641,458.36,NJ City,N,7,21,13,50,High School,29,Patnership,Standalone
9182,471.71,NJ City,S,4,16,6,69,Graduation,30,Patnership,Complex
6159,303.63,Boston,E,23,15,19,41,Graduation,26,Owner,Standalone
1727,92.02,NJ City,E,9,12,3,41,No education,22,Owner,Standalone
268,261.42,NJ City,W,17,11,25,33,High School,19,Franchise,Complex
2551,85.06,NJ City,E,3,15,1,56,High School,27,Owner,Standalone
7034,358.7,Boston,S,6,11,14,76,Graduation,26,Patnership,Complex
2432,349.36,NJ City,W,7,9,25,58,High School,27,Franchise,Complex
2269,145.22,NJ City,N,16,24,16,66,High School,31,Patnership,Standalone
7365,90.22,Boston,N,22,8,1,32,Graduation,26,Patnership,Standalone
5301,105.76,Boston,N,1,11,2,81,Graduation,30,Patnership,Standalone
6188,186.48,Boston,W,8,22,12,35,Graduation,19,Franchise,Complex
1302,235,NJ City,S,1,11,23,38,High School,27,Patnership,Complex
1578,402.71,NJ City,S,18,24,11,85,High School,32,Patnership,Complex
8574,337.81,NJ City,S,17,8,2,66,Graduation,34,Patnership,Complex
7936,450.57,Boston,W,21,9,4,65,High School,23,Franchise,Complex
4924,234.67,NJ City,W,7,9,11,85,High School,31,Franchise,Complex
5143,67.89,Boston,N,13,15,3,76,High School,31,Owner,Standalone
3240,173.11,NJ City,W,18,21,22,70,High School,29,Franchise,Complex
169,231.65,NJ City,N,12,10,8,51,High School,31,Patnership,Standalone
5761,427.8,Boston,N,20,8,1,51,High School,30,Patnership,Standalone
3415,399.54,NJ City,E,15,10,6,62,High School,31,Owner,Standalone
7702,47.76,Boston,S,3,16,4,31,High School,29,Patnership,Complex
6335,144.87,Boston,E,6,20,4,28,No education,21,Owner,Standalone
4365,26.95,NJ City,N,23,17,2,34,Graduation,24,Patnership,Standalone
9147,410.44,NJ City,E,11,10,1,82,Graduation,22,Owner,Standalone
6369,467.17,Boston,N,15,13,4,27,Graduation,24,Patnership,Standalone
5240,447.06,Boston,W,15,14,12,53,Graduation,27,Franchise,Complex
343,313,NJ City,E,2,18,6,41,High School,30,Owner,Standalone
7807,435.99,Boston,E,5,22,1,53,High School,23,Owner,Standalone
3554,211.92,NJ City,S,8,12,25,27,High School,30,Patnership,Complex
9987,495.22,NJ City,E,7,13,17,52,Graduation,25,Owner,Standalone
1031,131.51,NJ City,E,14,10,19,41,High School,27,Owner,Standalone
5719,212.94,Boston,E,4,19,17,31,Graduation,18,Owner,Standalone
4550,477.76,NJ City,S,4,10,13,38,High School,21,Patnership,Complex
2691,236.17,NJ City,E,25,22,6,53,High School,26,Owner,Standalone
6127,175.48,Boston,E,6,17,13,58,Graduation,29,Owner,Standalone
3830,74.28,NJ City,S,1,18,2,60,No education,33,Patnership,Complex
8170,71.05,NJ City,N,13,10,1,65,High School,35,Patnership,Complex
2096,194.97,NJ City,W,4,15,18,27,High School,21,Franchise,Complex
8578,463.72,NJ City,S,7,12,17,52,Graduation,30,Patnership,Complex
9241,51.23,NJ City,N,9,15,3,36,High School,31,Patnership,Standalone
4479,64.88,NJ City,N,21,17,1,70,Graduation,26,Owner,Standalone
5010,262.59,Boston,S,11,10,9,44,Graduation,28,Patnership,Complex
4505,241.26,NJ City,N,22,10,22,33,High School,35,Patnership,Standalone
466,378.14,NJ City,S,14,15,10,66,High School,20,Patnership,Complex
7547,18.03,Boston,N,16,22,4,67,No education,34,Owner,Standalone
1733,392.43,NJ City,N,11,16,23,65,High School,33,Patnership,Standalone
1436,198.97,NJ City,W,7,19,13,30,High School,20,Franchise,Complex
8060,173.62,NJ City,W,5,19,7,39,Graduation,18,Franchise,Complex
1661,405.77,NJ City,N,18,19,3,59,No education,34,Patnership,Standalone
7922,161.76,Boston,S,15,18,7,85,Graduation,24,Patnership,Complex
4036,463.32,NJ City,W,19,18,16,67,High School,27,Franchise,Complex
5406,73.97,Boston,S,17,11,2,37,Graduation,32,Patnership,Complex
5911,248.59,Boston,E,21,8,13,59,Graduation,31,Owner,Standalone
210,205,NJ City,S,3,10,9,82,High School,28,Patnership,Complex
6725,251.54,Boston,N,19,9,16,62,Graduation,20,Patnership,Standalone
6994,282.8,Boston,S,21,23,17,47,Graduation,20,Patnership,Complex
509,429.34,NJ City,N,13,22,6,73,High School,32,Patnership,Standalone
5476,288.56,Boston,W,15,8,20,63,Graduation,24,Franchise,Complex
330,44.9,NJ City,N,6,8,2,56,Graduation,35,Patnership,Complex
8096,444.62,NJ City,W,6,18,16,84,Graduation,25,Franchise,Complex
9929,373.2,NJ City,N,4,11,9,70,Graduation,27,Patnership,Standalone
6223,139.83,Boston,E,22,19,1,35,High School,28,Owner,Standalone
5639,468.8,Boston,E,17,10,18,66,Graduation,20,Owner,Standalone
4296,350.21,NJ City,W,13,24,8,48,High School,33,Franchise,Complex
6016,430.39,Boston,W,3,10,11,76,Graduation,22,Franchise,Complex
6021,37.56,Boston,N,4,21,4,29,Graduation,30,Patnership,Standalone
8352,384.72,NJ City,W,1,20,21,58,Graduation,30,Franchise,Complex
1865,185.72,NJ City,N,10,18,18,75,High School,22,Patnership,Standalone
3056,59.51,NJ City,W,6,16,4,40,No education,31,Franchise,Complex
3041,250.17,NJ City,N,9,17,22,53,High School,20,Patnership,Standalone
4258,429.76,NJ City,S,9,16,14,33,High School,30,Patnership,Complex
4908,53.11,NJ City,W,7,13,3,31,Graduation,34,Franchise,Complex
5410,229.54,Boston,S,10,10,13,46,Graduation,19,Patnership,Complex
9916,179.84,NJ City,W,14,17,1,26,High School,24,Franchise,Complex
3855,437.3,NJ City,E,10,8,24,73,High School,22,Owner,Standalone
5605,312.11,Boston,N,14,14,14,69,Graduation,28,Patnership,Standalone
7595,418.75,Boston,E,7,23,12,62,Graduation,28,Owner,Standalone
83,305.49,Boston,E,11,20,22,76,High School,29,Owner,Standalone
6066,211.73,Boston,S,13,21,24,47,Graduation,24,Patnership,Complex
4156,2.63,NJ City,W,21,18,4,60,High School,35,Franchise,Complex
1687,376.18,NJ City,E,2,21,1,43,High School,35,Owner,Standalone
469,237.41,NJ City,N,8,18,2,36,High School,20,Patnership,Standalone
496,5.99,NJ City,N,10,8,1,70,High School,33,Franchise,Complex
6015,232.42,Boston,E,12,9,22,61,Graduation,31,Owner,Standalone
1139,49.43,NJ City,E,21,9,4,60,No education,30,Owner,Standalone
8117,43.44,NJ City,N,7,18,4,44,No education,29,Patnership,Standalone
2061,267.47,NJ City,N,13,22,10,72,High School,18,Patnership,Standalone
2754,80.87,NJ City,S,17,9,2,55,Graduation,24,Patnership,Complex
4724,112.5,NJ City,W,2,10,2,40,No education,35,Franchise,Complex
9743,386.94,NJ City,E,2,10,17,51,Graduation,24,Owner,Standalone
7203,338.91,Boston,E,19,24,8,36,Graduation,18,Owner,Standalone
211,430.58,NJ City,E,20,8,10,43,High School,35,Owner,Standalone
7405,175.26,Boston,N,15,15,8,31,Graduation,23,Patnership,Standalone
2951,28.15,NJ City,N,13,12,2,73,No education,21,Owner,Standalone
9279,172.41,NJ City,E,23,16,8,73,Graduation,29,Owner,Standalone
9527,243.22,NJ City,E,7,15,18,45,Graduation,21,Owner,Standalone
5538,418.25,Boston,S,7,19,20,44,Graduation,26,Patnership,Complex
4575,295.28,NJ City,E,7,19,19,29,High School,35,Owner,Standalone
7126,454.26,Boston,S,11,13,24,64,Graduation,24,Patnership,Complex
8861,330.34,NJ City,N,11,13,9,26,Graduation,31,Patnership,Standalone
8974,229.07,NJ City,S,13,14,25,73,Graduation,27,Patnership,Complex
1315,375.14,NJ City,E,25,13,10,71,High School,33,Owner,Standalone
9749,244.71,NJ City,N,13,20,25,30,Graduation,29,Patnership,Standalone
1411,27.3,NJ City,N,8,10,2,73,High School,35,Owner,Standalone
6551,401.71,Boston,E,20,22,15,72,Graduation,33,Owner,Standalone
7499,218.26,Boston,E,20,22,22,37,Graduation,32,Owner,Standalone
6227,338.36,Boston,E,6,16,19,65,Graduation,20,Owner,Standalone
9027,474.61,NJ City,E,23,20,4,42,Graduation,20,Owner,Standalone
5469,134.54,Boston,N,22,22,21,71,Graduation,31,Patnership,Standalone
2461,440,NJ City,N,7,23,14,49,High School,35,Patnership,Standalone
7435,282.4,Boston,E,5,24,23,62,Graduation,19,Owner,Standalone
365,399.81,NJ City,N,15,13,9,42,High School,31,Patnership,Standalone
9805,180.81,NJ City,N,24,23,15,78,Graduation,25,Patnership,Standalone
2079,108.23,NJ City,E,8,11,3,84,Graduation,34,Owner,Standalone
8317,474.31,NJ City,N,7,10,17,37,Graduation,32,Patnership,Standalone
3381,382.33,NJ City,N,6,13,17,27,High School,28,Patnership,Standalone
753,7.52,NJ City,N,25,11,4,84,Graduation,35,Patnership,Standalone
8715,480.64,NJ City,E,6,12,7,38,Graduation,26,Owner,Standalone
9055,53.14,NJ City,E,7,24,4,82,High School,31,Owner,Standalone
6240,442.94,Boston,W,12,24,4,38,Graduation,19,Franchise,Complex
2398,141.05,NJ City,S,24,10,20,81,High School,28,Patnership,Complex
7540,87.83,Boston,W,6,19,1,63,High School,34,Franchise,Complex
6193,225.56,Boston,N,22,14,25,69,Graduation,34,Patnership,Standalone
8014,309.37,Boston,S,20,21,3,50,High School,35,Patnership,Complex
7205,130.61,Boston,N,24,24,17,75,Graduation,29,Patnership,Standalone
9897,164.87,NJ City,N,19,23,18,35,Graduation,27,Patnership,Standalone
6243,477.22,Boston,E,5,14,11,43,Graduation,35,Owner,Standalone
8825,217.82,NJ City,N,13,24,17,65,Graduation,29,Patnership,Standalone
802,449.62,NJ City,S,25,8,18,77,High School,23,Patnership,Complex
2632,213.14,NJ City,W,9,17,21,85,High School,30,Franchise,Complex
9803,214.32,NJ City,E,10,8,1,84,No education,30,Owner,Standalone
9779,164.35,NJ City,E,1,9,9,44,Graduation,28,Owner,Standalone
1227,471.59,NJ City,E,21,9,3,82,Graduation,28,Owner,Standalone
5954,299,Boston,S,5,21,18,68,Graduation,20,Patnership,Complex
59,128.43,Boston,E,25,23,21,60,High School,34,Owner,Standalone
2220,319.1,NJ City,W,1,24,5,54,Graduation,20,Franchise,Complex
2875,371.24,NJ City,E,6,22,6,55,High School,31,Owner,Standalone
5874,245.67,Boston,S,15,13,23,78,Graduation,28,Patnership,Complex
352,130.07,NJ City,W,16,19,10,67,High School,21,Franchise,Complex
9494,107.66,NJ City,S,20,17,2,78,No education,31,Patnership,Complex
7741,475.46,Boston,N,14,9,24,55,Graduation,18,Patnership,Standalone
8799,338.1,NJ City,E,24,10,16,77,Graduation,27,Owner,Standalone
347,466.67,NJ City,E,11,21,1,62,No education,32,Owner,Standalone
8668,359.4,NJ City,W,5,23,23,26,Graduation,25,Franchise,Complex
5274,205.33,Boston,S,22,13,15,52,Graduation,30,Patnership,Complex
5985,390.55,Boston,N,8,10,24,69,Graduation,25,Patnership,Standalone
5649,410.21,Boston,N,5,9,14,27,Graduation,27,Patnership,Standalone
406,281.21,NJ City,S,9,11,4,68,High School,30,Patnership,Complex
9336,492.91,NJ City,W,21,17,19,50,Graduation,23,Franchise,Complex
2829,265.91,NJ City,N,16,20,10,79,High School,30,Patnership,Standalone
946,40.01,NJ City,N,19,17,4,43,High School,28,Patnership,Complex
8576,492.11,NJ City,W,15,18,1,40,No education,24,Franchise,Complex
2694,215.76,NJ City,S,23,19,22,59,High School,33,Patnership,Complex
1402,35.74,NJ City,N,16,22,2,30,High School,24,Patnership,Complex
2360,234.84,NJ City,W,1,17,4,78,No education,28,Franchise,Complex
6728,346.21,Boston,W,3,13,17,62,Graduation,31,Franchise,Complex
2867,75.73,NJ City,E,6,8,2,68,No education,30,Owner,Standalone
6455,123.05,Boston,E,24,16,2,43,No education,29,Owner,Standalone
4392,252,NJ City,W,3,8,25,76,High School,32,Franchise,Complex
1193,302,NJ City,N,1,17,25,40,High School,29,Patnership,Standalone
3202,140.76,NJ City,S,13,19,25,49,High School,29,Patnership,Complex
7295,54.82,Boston,E,17,19,4,83,No education,24,Owner,Standalone
3853,173,NJ City,N,1,8,16,67,High School,28,Patnership,Standalone
1874,458.34,NJ City,S,11,15,18,38,High School,26,Patnership,Complex
9606,155.57,NJ City,S,2,21,25,29,Graduation,28,Patnership,Complex
42,10.15,Boston,N,6,20,1,65,Graduation,19,Patnership,Complex
5836,184.64,Boston,W,4,24,24,61,Graduation,25,Franchise,Complex
4035,460.41,NJ City,E,24,9,12,26,High School,23,Owner,Standalone
1549,211.57,NJ City,N,8,16,25,72,High School,21,Patnership,Standalone
7393,383.75,Boston,N,25,18,18,26,Graduation,24,Patnership,Standalone
1306,245.24,NJ City,S,19,22,24,75,High School,25,Patnership,Complex
9142,417.24,NJ City,S,3,11,12,37,Graduation,23,Patnership,Complex
9868,467.67,NJ City,W,13,18,6,30,Graduation,19,Franchise,Complex
561,355.27,NJ City,N,4,20,10,53,High School,22,Patnership,Standalone
4729,449.87,NJ City,N,17,13,15,39,High School,32,Patnership,Standalone
2156,120.22,NJ City,W,15,9,3,82,No education,19,Franchise,Complex
7113,490.4,Boston,N,18,19,8,72,Graduation,19,Patnership,Standalone
4467,5.12,NJ City,N,10,24,3,59,Graduation,28,Owner,Standalone
6522,212.15,Boston,S,14,15,7,41,Graduation,26,Patnership,Complex
9386,172.4,NJ City,S,16,9,12,39,Graduation,32,Patnership,Complex
6308,384.19,Boston,W,20,9,1,30,No education,24,Franchise,Complex
1980,44.17,NJ City,N,8,16,3,30,Graduation,29,Franchise,Complex
1695,100.77,NJ City,E,8,24,3,78,Graduation,26,Owner,Standalone
3382,303.5,NJ City,S,20,12,12,72,High School,25,Patnership,Complex
6657,123.22,Boston,N,6,22,2,27,Graduation,27,Patnership,Standalone
7080,220.25,Boston,W,24,17,5,62,Graduation,22,Franchise,Complex
6884,165.29,Boston,W,23,19,7,79,Graduation,23,Franchise,Complex
9258,409.38,NJ City,S,9,23,20,70,Graduation,29,Patnership,Complex
2407,90.88,NJ City,E,24,18,3,68,High School,28,Owner,Standalone
6511,453.34,Boston,E,24,9,22,78,Graduation,23,Owner,Standalone
6079,146.92,Boston,E,13,9,14,46,Graduation,18,Owner,Standalone
1528,242.05,NJ City,W,5,15,14,62,High School,24,Franchise,Complex
2763,468.1,NJ City,E,10,11,20,26,High School,30,Owner,Standalone
1680,170.03,NJ City,W,8,17,15,69,High School,32,Franchise,Complex
9774,330.63,NJ City,S,24,13,3,39,Graduation,19,Patnership,Complex
5883,238.34,Boston,E,20,19,20,56,Graduation,21,Owner,Standalone
8067,338.01,NJ City,E,12,16,16,64,Graduation,24,Owner,Standalone
8500,374.49,NJ City,W,4,8,14,73,Graduation,30,Franchise,Complex
3067,299.99,NJ City,E,23,8,23,60,High School,32,Owner,Standalone
3533,48.27,NJ City,N,20,21,3,33,No education,29,Patnership,Standalone
1524,34.7,NJ City,N,22,8,2,28,Graduation,18,Franchise,Complex
5757,288.22,Boston,N,25,11,16,63,Graduation,21,Patnership,Standalone
2347,400.3,NJ City,E,18,17,13,25,High School,30,Owner,Standalone
9955,210.8,NJ City,E,20,10,18,29,Graduation,26,Owner,Standalone
3072,208.62,NJ City,W,11,19,1,73,Graduation,27,Franchise,Complex
6286,242.9,Boston,S,9,9,21,70,Graduation,26,Patnership,Complex
9634,163.21,NJ City,S,14,23,23,85,Graduation,34,Patnership,Complex
7939,206.76,Boston,E,12,16,14,58,Graduation,28,Owner,Standalone
3402,385.11,NJ City,S,20,18,15,25,High School,18,Patnership,Complex
229,223.24,NJ City,N,11,10,8,39,High School,33,Patnership,Standalone
8813,178.43,NJ City,N,23,22,14,63,Graduation,29,Patnership,Standalone
1418,470.57,NJ City,S,17,21,18,64,High School,27,Patnership,Complex
9684,499.93,NJ City,W,11,13,24,70,Graduation,21,Franchise,Complex
41,140,Maine,N,2,22,23,44,High School,30,Patnership,Standalone
796,76.79,NJ City,W,7,20,3,34,High School,32,Franchise,Complex
9276,415.96,NJ City,W,3,20,2,43,Graduation,31,Franchise,Complex
4661,439.45,NJ City,N,7,18,6,79,High School,18,Patnership,Standalone
2292,158.1,NJ City,W,18,24,19,36,High School,21,Franchise,Complex
3831,173.63,NJ City,E,11,22,11,36,High School,22,Owner,Standalone
7469,473.74,Boston,N,24,9,17,46,Graduation,29,Patnership,Standalone
9641,193.24,NJ City,N,7,20,10,33,Graduation,21,Patnership,Standalone
5036,221.16,Boston,W,8,23,2,29,No education,32,Franchise,Complex
7062,457.84,Boston,S,19,19,17,30,Graduation,32,Patnership,Complex
8564,126.42,NJ City,W,11,18,4,78,No education,35,Franchise,Complex
5752,72.94,Boston,N,13,21,4,81,High School,26,Franchise,Complex
6453,103.84,Boston,N,24,11,2,77,Graduation,30,Patnership,Standalone
1842,454.4,NJ City,S,13,21,24,29,High School,19,Patnership,Complex
6296,368.35,Boston,W,12,10,21,59,Graduation,34,Franchise,Complex
4701,167.48,NJ City,N,24,13,1,33,Graduation,33,Patnership,Standalone
828,151.12,NJ City,W,4,15,18,28,High School,28,Franchise,Complex
771,372.88,NJ City,E,13,14,18,61,High School,27,Owner,Standalone
5091,310.97,Boston,E,12,18,4,56,Graduation,20,Owner,Standalone
5220,246.7,Boston,W,20,24,11,42,Graduation,28,Franchise,Complex
3120,499.63,NJ City,W,24,13,19,55,High School,27,Franchise,Complex
9084,243.3,NJ City,W,16,10,14,39,Graduation,25,Franchise,Complex
5990,399.44,Boston,S,23,15,25,60,Graduation,28,Patnership,Complex
4703,274.88,NJ City,E,6,20,16,28,High School,18,Owner,Standalone
4096,253.76,NJ City,W,22,21,6,51,High School,30,Franchise,Complex
5792,262.12,Boston,W,16,11,21,64,Graduation,24,Franchise,Complex
1986,141.68,NJ City,S,25,17,11,80,High School,22,Patnership,Complex
4329,421.83,NJ City,N,18,16,9,37,High School,20,Patnership,Standalone
2813,40.48,NJ City,N,9,23,3,50,No education,33,Patnership,Standalone
770,34.94,NJ City,N,25,16,2,76,No education,20,Patnership,Complex
967,210.66,NJ City,E,19,13,22,78,High School,28,Owner,Standalone
5539,98.88,Boston,E,12,16,2,62,High School,22,Owner,Standalone
9403,430.01,NJ City,E,1,13,4,84,High School,33,Owner,Standalone
9741,70.83,NJ City,N,12,10,4,35,Graduation,30,Patnership,Standalone
2192,223.89,NJ City,W,21,23,16,51,High School,33,Franchise,Complex
4107,127.28,NJ City,E,25,8,3,38,Graduation,28,Owner,Standalone
3689,372.84,NJ City,N,5,19,20,46,High School,19,Patnership,Standalone
6546,217.69,Boston,S,6,24,23,28,Graduation,22,Patnership,Complex
1975,395.72,NJ City,E,15,22,4,30,High School,29,Owner,Standalone
7717,158.59,Boston,N,7,9,23,65,Graduation,27,Patnership,Standalone
3662,259,NJ City,S,1,20,9,48,High School,21,Patnership,Complex
5683,475.44,Boston,E,3,11,16,83,Graduation,31,Owner,Standalone
2905,385.83,NJ City,N,12,20,3,80,High School,35,Patnership,Standalone
5666,316.68,Boston,S,20,17,20,41,Graduation,29,Patnership,Complex
6321,178.84,Boston,N,12,16,24,35,Graduation,32,Patnership,Standalone
7410,396.64,Boston,S,22,19,2,71,Graduation,30,Patnership,Complex
8003,48.29,Boston,E,24,14,4,84,No education,34,Owner,Standalone
9194,349.61,NJ City,S,4,19,1,85,No education,25,Patnership,Complex
7779,314.46,Boston,E,8,22,24,30,Graduation,24,Owner,Standalone
5879,446.87,Boston,E,13,20,17,62,Graduation,30,Owner,Standalone
161,333.62,NJ City,N,20,21,24,77,High School,32,Patnership,Standalone
987,37.91,NJ City,N,19,10,3,75,Graduation,22,Owner,Standalone
7962,142.88,Boston,S,21,24,9,50,Graduation,20,Patnership,Complex
1941,89.21,NJ City,N,5,21,4,35,Graduation,29,Patnership,Standalone
7431,13.78,Boston,N,10,13,1,42,Graduation,27,Owner,Standalone
2026,339.43,NJ City,S,7,8,8,70,High School,28,Patnership,Complex
6218,377.19,Boston,S,17,20,3,44,No education,25,Patnership,Complex
1785,304.64,NJ City,N,24,16,6,74,High School,25,Patnership,Standalone
2793,186.6,NJ City,N,16,9,8,53,High School,29,Patnership,Standalone
8208,39.45,NJ City,N,22,23,1,73,Graduation,30,Franchise,Complex
7245,177.05,Boston,N,21,8,16,36,Graduation,28,Patnership,Standalone
807,41.75,NJ City,N,25,9,3,65,Graduation,32,Owner,Standalone
2427,210.2,NJ City,E,7,21,6,78,High School,28,Owner,Standalone
947,190.35,NJ City,E,8,13,2,81,No education,21,Owner,Standalone
7214,409.19,Boston,S,13,23,18,64,Graduation,23,Patnership,Complex
1987,408.88,NJ City,E,3,15,4,49,High School,20,Owner,Standalone
7334,242.54,Boston,S,15,11,11,43,Graduation,26,Patnership,Complex
4644,225.52,NJ City,W,9,9,16,33,High School,22,Franchise,Complex
1956,328.39,NJ City,W,19,23,11,48,High School,23,Franchise,Complex
4001,75.37,NJ City,N,3,22,3,53,No education,26,Patnership,Standalone
1908,143.15,NJ City,W,25,11,5,71,Graduation,30,Franchise,Complex
1548,436.37,NJ City,W,12,23,8,34,High School,26,Franchise,Complex
1120,333.35,NJ City,W,23,19,15,73,High School,21,Franchise,Complex
5211,407.16,Boston,E,7,17,25,60,Graduation,19,Owner,Standalone
7579,266.25,Boston,E,8,8,19,53,Graduation,20,Owner,Standalone
983,446.46,NJ City,E,16,13,11,27,High School,25,Owner,Standalone
4456,47.54,NJ City,W,16,20,2,72,High School,25,Franchise,Complex
809,481.65,NJ City,N,4,21,21,67,High School,18,Patnership,Standalone
4201,66.63,NJ City,N,9,12,4,45,High School,22,Patnership,Standalone
5378,370.24,Boston,S,25,9,11,51,Graduation,25,Patnership,Complex
4396,12.17,NJ City,N,16,21,2,32,High School,23,Franchise,Complex
8394,298.23,NJ City,S,17,17,11,45,Graduation,24,Patnership,Complex
7070,366.82,Boston,S,8,18,13,25,Graduation,32,Patnership,Complex
4079,25.04,NJ City,N,11,13,1,84,No education,28,Owner,Standalone
4763,363.85,NJ City,E,4,23,18,28,High School,26,Owner,Standalone
1940,354.09,NJ City,W,17,24,18,60,High School,32,Franchise,Complex
115,463.9,NJ City,E,19,13,2,49,High School,34,Owner,Standalone
4436,341.6,NJ City,W,18,22,5,52,No education,34,Franchise,Complex
1861,415.31,NJ City,N,14,20,16,68,High School,21,Patnership,Standalone
8597,13.82,NJ City,N,10,19,2,42,No education,32,Patnership,Standalone
4882,242.6,NJ City,S,20,18,23,32,High School,25,Patnership,Complex
209,54.51,NJ City,N,7,15,3,55,No education,26,Patnership,Standalone
9520,476.37,NJ City,W,16,8,6,84,Graduation,32,Franchise,Complex
6993,376.34,Boston,N,6,17,5,75,Graduation,26,Patnership,Standalone
2254,440.13,NJ City,S,5,22,2,55,High School,28,Patnership,Complex
8918,105.14,NJ City,S,24,18,1,35,No education,19,Patnership,Complex
7641,102.77,Boston,N,16,11,2,84,Graduation,25,Patnership,Standalone
8636,177.16,NJ City,W,14,9,20,62,Graduation,31,Franchise,Complex
9486,207.51,NJ City,S,22,10,22,67,Graduation,21,Patnership,Complex
1662,263.6,NJ City,S,12,15,9,81,High School,26,Patnership,Complex
2172,186.27,NJ City,W,4,15,2,56,Graduation,24,Franchise,Complex
1947,276.92,NJ City,E,19,17,17,62,High School,24,Owner,Standalone
5799,103.09,Boston,E,10,16,3,59,Graduation,34,Owner,Standalone
7766,486.3,Boston,S,9,14,8,38,Graduation,27,Patnership,Complex
6340,413.5,Boston,W,9,18,1,71,High School,25,Franchise,Complex
525,167.95,NJ City,N,2,23,1,74,Graduation,20,Patnership,Standalone
9260,26.28,NJ City,N,20,15,1,64,No education,24,Franchise,Complex
9467,310.77,NJ City,E,5,18,9,47,Graduation,23,Owner,Standalone
8495,364.38,NJ City,E,5,24,14,65,Graduation,29,Owner,Standalone
8389,84.16,NJ City,N,10,12,4,80,High School,19,Patnership,Standalone
4050,150.99,NJ City,S,21,18,17,73,High School,33,Patnership,Complex
8365,29.58,NJ City,N,17,9,2,30,High School,32,Patnership,Standalone
6579,245.36,Boston,E,4,18,24,34,Graduation,26,Owner,Standalone
1837,348.1,NJ City,N,25,10,1,60,High School,19,Patnership,Standalone
6615,221.72,Boston,E,2,24,6,29,Graduation,31,Owner,Standalone
5184,204.64,Boston,W,15,8,22,72,Graduation,21,Franchise,Complex
448,400.23,NJ City,W,18,13,24,62,High School,31,Franchise,Complex
2004,209.22,NJ City,W,12,20,17,25,High School,31,Franchise,Complex
4204,269.42,NJ City,W,10,19,24,67,High School,22,Franchise,Complex
9390,455.03,NJ City,S,1,17,18,72,Graduation,25,Patnership,Complex
5832,321.89,Boston,W,13,13,10,32,Graduation,29,Franchise,Complex
7622,393.88,Boston,S,15,8,15,40,Graduation,31,Patnership,Complex
398,347.48,NJ City,S,12,18,4,28,No education,27,Patnership,Complex
9368,59.6,NJ City,W,16,11,3,74,No education,21,Franchise,Complex
8672,484.54,NJ City,W,2,13,24,77,Graduation,19,Franchise,Complex
4146,389.16,NJ City,S,11,24,12,50,High School,29,Patnership,Complex
2063,369.38,NJ City,E,4,9,2,29,No education,27,Owner,Standalone
8894,272.05,NJ City,S,23,21,12,41,Graduation,34,Patnership,Complex
5829,192.01,Boston,N,22,9,11,83,Graduation,32,Patnership,Standalone
75,382.05,Boston,E,24,16,22,45,High School,34,Owner,Standalone
9523,73.42,NJ City,N,21,9,4,60,High School,22,Owner,Standalone
1717,271.4,NJ City,N,5,18,16,85,High School,34,Patnership,Standalone
3882,75.49,NJ City,S,2,9,1,61,Graduation,21,Patnership,Complex
8462,74.75,NJ City,S,22,11,1,51,No education,26,Patnership,Complex
5946,337.15,Boston,S,13,17,15,62,Graduation,28,Patnership,Complex
4040,305,NJ City,W,3,21,18,48,High School,33,Franchise,Complex
9687,432.29,NJ City,E,10,20,7,32,Graduation,31,Owner,Standalone
8598,206.11,NJ City,S,20,12,21,46,Graduation,30,Patnership,Complex
7592,42.02,Boston,N,25,15,4,50,No education,28,Franchise,Complex
9549,150,NJ City,N,12,11,8,60,Graduation,30,Patnership,Standalone
7071,246.58,Boston,E,15,13,3,62,Graduation,18,Owner,Standalone
5441,182.08,Boston,N,21,14,25,29,Graduation,35,Patnership,Standalone
8205,101.72,NJ City,N,11,20,2,36,Graduation,26,Patnership,Standalone
6712,407.95,Boston,W,5,12,19,66,Graduation,25,Franchise,Complex
8481,203.44,NJ City,N,8,18,17,39,Graduation,30,Patnership,Standalone
5848,424.09,Boston,W,13,24,16,28,Graduation,35,Franchise,Complex
8884,152.48,NJ City,W,15,9,21,40,Graduation,24,Franchise,Complex
5996,322.92,Boston,W,14,19,24,83,Graduation,19,Franchise,Complex
5327,155.12,Boston,E,15,21,25,56,Graduation,29,Owner,Standalone
9162,214.74,NJ City,S,11,23,15,30,Graduation,22,Patnership,Complex
4739,435.31,NJ City,E,17,14,22,83,High School,25,Owner,Standalone
2447,165.59,NJ City,E,24,22,13,39,High School,24,Owner,Standalone
516,24.75,NJ City,N,18,11,1,31,Graduation,21,Franchise,Complex
5065,326.77,Boston,N,12,18,15,61,Graduation,22,Patnership,Standalone
4581,457.87,NJ City,N,6,18,22,78,High School,32,Patnership,Standalone
9289,472.36,NJ City,N,8,15,25,34,Graduation,34,Patnership,Standalone
6748,171.63,Boston,W,6,14,24,81,Graduation,35,Franchise,Complex
7659,496.21,Boston,E,8,10,2,40,Graduation,31,Owner,Standalone
5062,477.12,Boston,S,12,23,22,39,Graduation,21,Patnership,Complex
977,374.44,NJ City,N,14,14,11,27,High School,23,Patnership,Standalone
8307,297.64,NJ City,E,20,9,25,73,Graduation,34,Owner,Standalone
820,190.53,NJ City,W,20,20,23,78,High School,23,Franchise,Complex
418,357.1,NJ City,S,8,21,4,83,High School,27,Patnership,Complex
2265,61.76,NJ City,N,7,16,1,68,Graduation,18,Patnership,Standalone
9383,105.18,NJ City,E,4,16,3,82,No education,25,Owner,Standalone
6019,63.1,Boston,N,22,22,3,84,High School,19,Owner,Standalone
4183,188.3,NJ City,E,17,11,12,79,High School,28,Owner,Standalone
3033,497.3,NJ City,N,13,22,20,35,High School,26,Patnership,Standalone
9711,279.3,NJ City,E,7,19,25,30,Graduation,35,Owner,Standalone
2772,2.56,NJ City,W,14,24,1,49,Graduation,18,Franchise,Complex
3579,248.64,NJ City,E,14,18,12,54,High School,29,Owner,Standalone
8213,98.64,NJ City,N,13,10,4,25,No education,24,Patnership,Standalone
9998,163.82,NJ City,S,5,22,1,31,No education,24,Patnership,Complex
3225,123.95,NJ City,N,3,15,4,52,Graduation,23,Patnership,Standalone
4485,368.7,NJ City,N,11,19,24,38,High School,28,Patnership,Standalone
7315,304.62,Boston,E,21,23,11,31,Graduation,20,Owner,Standalone
1151,268.11,NJ City,E,7,13,25,35,High School,20,Owner,Standalone
6747,147.25,Boston,E,19,22,15,25,Graduation,32,Owner,Standalone
8013,317.35,Boston,N,9,24,25,73,Graduation,21,Patnership,Standalone
9542,140.29,NJ City,S,6,13,6,53,Graduation,26,Patnership,Complex
8260,254.77,NJ City,W,1,9,17,77,Graduation,23,Franchise,Complex
3528,280.88,NJ City,W,14,15,14,32,High School,23,Franchise,Complex
7880,396.33,Boston,W,5,10,8,61,Graduation,20,Franchise,Complex
200,251.59,NJ City,W,21,15,12,56,High School,21,Franchise,Complex
5478,100.76,Boston,S,6,23,3,61,Graduation,23,Patnership,Complex
9365,325.5,NJ City,N,23,10,6,81,Graduation,21,Patnership,Standalone
7871,126.17,Boston,E,15,18,4,34,No education,31,Owner,Standalone
9950,203.66,NJ City,S,23,18,14,58,Graduation,23,Patnership,Complex
769,326.34,NJ City,N,18,24,23,68,High School,33,Patnership,Standalone
224,102.43,NJ City,W,16,15,4,75,No education,24,Franchise,Complex
1235,116,NJ City,E,3,17,14,51,High School,28,Owner,Standalone
6578,351.54,Boston,S,17,8,25,61,Graduation,18,Patnership,Complex
5166,400.42,Boston,S,16,19,1,58,Graduation,27,Patnership,Complex
839,107.32,NJ City,E,20,20,2,36,No education,32,Owner,Standalone
6600,10.22,Boston,N,4,13,2,62,Graduation,31,Franchise,Complex
7520,415.03,Boston,W,13,21,24,85,Graduation,20,Franchise,Complex
8004,416.36,Boston,W,22,11,2,36,Graduation,30,Franchise,Complex
5252,307.69,Boston,W,14,11,23,72,Graduation,28,Franchise,Complex
1401,331.56,NJ City,N,10,15,20,67,High School,18,Patnership,Standalone
3542,277.17,NJ City,S,6,19,17,46,High School,22,Patnership,Complex
7141,492.73,Boston,N,10,16,5,69,High School,25,Patnership,Standalone
8376,351.13,NJ City,W,11,11,14,54,Graduation,23,Franchise,Complex
3228,378.86,NJ City,W,16,9,16,31,High School,31,Franchise,Complex
6461,407.93,Boston,N,16,21,1,57,No education,22,Patnership,Standalone
9580,341.32,NJ City,W,13,15,17,81,Graduation,28,Franchise,Complex
9356,167.29,NJ City,W,21,20,21,26,Graduation,30,Franchise,Complex
6900,102.51,Boston,W,2,10,1,50,Graduation,24,Franchise,Complex
3650,11.25,NJ City,N,6,11,4,55,No education,24,Patnership,Complex
1365,29.61,NJ City,N,23,23,3,41,Graduation,25,Patnership,Standalone
4114,328.21,NJ City,S,14,19,3,54,High School,23,Patnership,Complex
6069,167,Boston,N,6,10,11,41,Graduation,21,Patnership,Standalone
1378,289.26,NJ City,S,7,8,22,79,High School,29,Patnership,Complex
4194,247.02,NJ City,S,13,19,16,58,High School,29,Patnership,Complex
121,228.92,NJ City,N,24,12,5,69,High School,29,Patnership,Standalone
4443,42.3,NJ City,N,20,15,4,78,Graduation,28,Owner,Standalone
3627,19.5,NJ City,N,7,23,3,36,Graduation,23,Owner,Standalone
764,468.84,NJ City,W,14,22,23,60,High School,35,Franchise,Complex
1331,479.08,NJ City,E,4,18,23,57,High School,20,Owner,Standalone
7254,66.29,Boston,N,25,11,4,79,Graduation,26,Patnership,Complex
1732,460.93,NJ City,W,16,11,11,57,High School,30,Franchise,Complex
5613,105.16,Boston,N,18,22,2,66,Graduation,28,Patnership,Standalone
9023,236.92,NJ City,E,18,17,19,47,Graduation,19,Owner,Standalone
9881,362.43,NJ City,N,18,16,7,73,Graduation,21,Patnership,Standalone
413,49.9,NJ City,N,12,21,1,27,No education,23,Patnership,Standalone
7728,116.87,Boston,W,19,10,1,31,Graduation,28,Franchise,Complex
3371,206.05,NJ City,E,4,21,3,58,No education,29,Owner,Standalone
8286,354.82,NJ City,S,21,10,4,44,Graduation,30,Patnership,Complex
914,64.16,NJ City,N,25,20,4,75,No education,18,Patnership,Complex
6773,83.16,Boston,N,4,20,3,36,No education,27,Patnership,Standalone
6328,80.25,Boston,W,3,20,1,31,High School,18,Franchise,Complex
3572,176.77,NJ City,W,24,18,13,39,High School,23,Franchise,Complex
6089,58.2,Boston,N,22,9,2,60,No education,34,Patnership,Standalone
219,284.08,NJ City,E,10,18,18,78,High School,24,Owner,Standalone
6801,94.73,Boston,N,12,14,4,35,Graduation,25,Patnership,Standalone
9829,54.19,NJ City,N,3,16,4,32,High School,23,Patnership,Standalone
5057,222.29,Boston,N,20,9,20,68,Graduation,33,Patnership,Standalone
3884,338.86,NJ City,W,17,16,6,40,High School,22,Franchise,Complex
6437,434.98,Boston,N,13,20,3,48,No education,18,Patnership,Standalone
714,49.82,NJ City,S,4,10,4,45,Graduation,24,Patnership,Complex
7947,103.38,Boston,E,2,24,4,79,Graduation,19,Owner,Standalone
610,341.23,NJ City,S,24,20,7,58,High School,33,Patnership,Complex
4873,25.15,NJ City,N,11,22,3,46,High School,20,Patnership,Standalone
9311,419.65,NJ City,E,13,16,24,73,Graduation,26,Owner,Standalone
4508,30.12,NJ City,N,6,19,3,58,No education,31,Franchise,Complex
2289,334.82,NJ City,N,17,19,22,53,High School,25,Patnership,Standalone
192,126.66,NJ City,W,4,10,1,72,Graduation,31,Franchise,Complex
9491,499.17,NJ City,E,25,20,21,66,Graduation,25,Owner,Standalone
9105,52.96,NJ City,N,16,10,4,64,Graduation,19,Patnership,Standalone
6731,110.03,Boston,E,2,18,2,50,No education,30,Owner,Standalone
8721,374.74,NJ City,N,21,22,2,77,Graduation,31,Patnership,Standalone
8905,308.05,NJ City,N,9,9,21,51,Graduation,22,Patnership,Standalone
5582,143.96,Boston,S,14,11,21,83,Graduation,20,Patnership,Complex
7742,146.6,Boston,S,19,11,4,76,No education,18,Patnership,Complex
9429,362.7,NJ City,N,23,21,18,69,Graduation,27,Patnership,Standalone
3789,98.91,NJ City,N,20,13,2,74,Graduation,24,Patnership,Standalone
3421,428.73,NJ City,N,17,17,9,43,High School,35,Patnership,Standalone
4921,354.15,NJ City,N,5,13,1,25,High School,31,Patnership,Standalone
2031,368.24,NJ City,E,25,18,17,46,High School,20,Owner,Standalone
8230,386.39,NJ City,S,22,21,6,65,Graduation,32,Patnership,Complex
1673,340.95,NJ City,N,17,13,18,40,High School,20,Patnership,Standalone
1099,394.44,NJ City,E,21,11,20,26,High School,29,Owner,Standalone
7704,316.16,Boston,W,15,15,10,66,Graduation,21,Franchise,Complex
1042,246.73,NJ City,S,21,11,9,72,High School,31,Patnership,Complex
2515,35.35,NJ City,N,21,13,1,37,High School,30,Owner,Standalone
280,156.27,NJ City,W,22,21,10,33,High School,27,Franchise,Complex
8155,415.54,NJ City,E,1,19,7,45,Graduation,22,Owner,Standalone
1751,444.5,NJ City,E,3,10,2,64,No education,21,Owner,Standalone
5731,461.58,Boston,E,25,9,4,75,High School,21,Owner,Standalone
6283,460.9,Boston,E,8,15,16,85,Graduation,20,Owner,Standalone
1541,360.73,NJ City,N,13,19,16,62,High School,28,Patnership,Standalone
5895,403.73,Boston,E,22,24,1,82,Graduation,28,Owner,Standalone
7648,246.96,Boston,W,15,9,22,67,Graduation,29,Franchise,Complex
3780,164.32,NJ City,W,14,14,20,54,High School,32,Franchise,Complex
7247,22.02,Boston,N,3,11,4,31,No education,33,Owner,Standalone
8986,139.61,NJ City,S,11,9,7,49,Graduation,26,Patnership,Complex
8957,288.83,NJ City,N,12,9,23,37,Graduation,19,Patnership,Standalone
2792,241.1,NJ City,W,15,24,10,79,High School,21,Franchise,Complex
1604,363.21,NJ City,W,23,22,11,82,High School,20,Franchise,Complex
3290,41.68,NJ City,N,8,15,3,29,No education,26,Patnership,Complex
9152,295.92,NJ City,W,25,16,11,27,Graduation,20,Franchise,Complex
5665,469.5,Boston,N,11,21,4,29,High School,26,Patnership,Standalone
1878,118.1,NJ City,S,15,22,3,44,Graduation,28,Patnership,Complex
9384,428.01,NJ City,W,5,9,13,46,Graduation,19,Franchise,Complex
2681,370.77,NJ City,N,11,13,7,54,High School,19,Patnership,Standalone
6221,82.78,Boston,N,17,22,4,76,No education,26,Patnership,Standalone
5402,446.21,Boston,S,10,14,18,42,Graduation,29,Patnership,Complex
5437,471.8,Boston,N,16,18,14,52,Graduation,31,Patnership,Standalone
1046,212.54,NJ City,S,12,15,8,32,High School,19,Patnership,Complex
6598,368.35,Boston,S,6,12,3,79,High School,33,Patnership,Complex
4276,247.44,NJ City,W,5,15,23,37,High School,31,Franchise,Complex
4022,184.54,NJ City,S,19,11,18,58,High School,24,Patnership,Complex
6672,267.27,Boston,W,7,24,18,49,Graduation,33,Franchise,Complex
1410,296.5,NJ City,S,15,9,23,52,High School,21,Patnership,Complex
4317,131.32,NJ City,N,22,8,15,52,High School,33,Patnership,Standalone
553,426.47,NJ City,N,21,19,19,55,High School,22,Patnership,Standalone
9145,350.18,NJ City,N,9,10,16,51,Graduation,31,Patnership,Standalone
4345,150.55,NJ City,N,19,8,15,34,High School,34,Patnership,Standalone
9561,161.03,NJ City,N,10,11,24,51,Graduation,30,Patnership,Standalone
374,364.63,NJ City,S,11,18,24,31,High School,26,Patnership,Complex
8370,362.74,NJ City,S,4,14,6,35,Graduation,25,Patnership,Complex
3493,477.64,NJ City,N,18,13,19,38,High School,30,Patnership,Standalone
2054,322.32,NJ City,S,18,21,17,58,High School,33,Patnership,Complex
9420,198.7,NJ City,W,18,12,24,82,Graduation,22,Franchise,Complex
723,488.11,NJ City,E,8,21,6,29,High School,34,Owner,Standalone
8565,435.55,NJ City,N,20,19,24,29,Graduation,26,Patnership,Standalone
2530,9.12,NJ City,N,17,12,1,80,High School,23,Patnership,Complex
9443,376.47,NJ City,E,22,24,13,68,Graduation,33,Owner,Standalone
6718,444.29,Boston,S,5,8,3,38,High School,28,Patnership,Complex
3229,140.98,NJ City,N,11,22,7,80,High School,28,Patnership,Standalone
2669,98.56,NJ City,N,21,14,3,84,No education,33,Patnership,Standalone
2415,28.06,NJ City,N,15,9,3,38,Graduation,22,Owner,Standalone
9837,222.97,NJ City,N,17,19,14,34,Graduation,19,Patnership,Standalone
1224,311.49,NJ City,W,11,8,24,71,High School,24,Franchise,Complex
803,124.72,NJ City,E,21,10,4,72,No education,18,Owner,Standalone
6514,56.76,Boston,S,7,18,1,82,High School,31,Patnership,Complex
3518,174,NJ City,S,3,20,18,52,High School,25,Patnership,Complex
7048,213.17,Boston,W,15,13,17,44,Graduation,19,Franchise,Complex
2121,281.6,NJ City,N,3,11,3,47,Graduation,31,Patnership,Standalone
3625,423.75,NJ City,N,16,11,24,38,High School,19,Patnership,Standalone
4206,264.79,NJ City,S,25,12,10,74,High School,33,Patnership,Complex
7640,277.36,Boston,W,21,9,1,68,No education,25,Franchise,Complex
1054,382.97,NJ City,S,15,17,24,76,High School,21,Patnership,Complex
964,341.15,NJ City,W,14,10,20,43,High School,28,Franchise,Complex
6429,446.79,Boston,N,20,17,4,68,Graduation,34,Patnership,Standalone
3255,142,NJ City,E,1,17,14,84,High School,29,Owner,Standalone
342,247.12,NJ City,S,19,15,7,52,High School,29,Patnership,Complex
8217,448.87,NJ City,N,25,18,5,57,Graduation,25,Patnership,Standalone
4097,68.96,NJ City,N,4,18,1,53,No education,35,Patnership,Standalone
1101,37.73,NJ City,N,25,24,4,49,Graduation,29,Patnership,Standalone
741,393.11,NJ City,N,22,12,7,44,High School,21,Patnership,Standalone
684,125.36,NJ City,W,20,19,3,31,Graduation,32,Franchise,Complex
9903,121.69,NJ City,E,11,12,1,45,Graduation,26,Owner,Standalone
2353,69.92,NJ City,N,10,19,2,25,High School,20,Patnership,Standalone
5929,273.99,Boston,N,9,12,15,68,Graduation,35,Patnership,Standalone
304,387.86,NJ City,W,25,8,15,32,High School,34,Franchise,Complex
5179,13.67,Boston,N,3,10,1,75,High School,26,Owner,Standalone
5079,30.51,Boston,N,9,10,1,25,Graduation,26,Owner,Standalone
8515,200.62,NJ City,E,16,20,24,64,Graduation,22,Owner,Standalone
4415,102.38,NJ City,E,23,9,2,50,No education,23,Owner,Standalone
8343,28.75,NJ City,N,12,9,1,78,Graduation,31,Owner,Standalone
4954,37.64,Boston,N,21,22,4,77,High School,33,Patnership,Complex
5284,8.1,Boston,N,19,10,2,49,High School,24,Franchise,Complex
4422,409.16,NJ City,S,5,20,16,32,High School,35,Patnership,Complex
7993,115.81,Boston,N,14,11,4,83,High School,20,Patnership,Standalone
3473,104.1,NJ City,N,6,13,3,85,No education,34,Patnership,Standalone
3350,363.4,NJ City,S,11,9,18,71,High School,34,Patnership,Complex
2483,286.56,NJ City,E,23,21,15,32,High School,23,Owner,Standalone
6932,207.5,Boston,W,18,20,3,67,No education,30,Franchise,Complex
318,447.11,NJ City,S,9,13,16,26,High School,34,Patnership,Complex
4004,163,NJ City,W,3,13,21,83,High School,24,Franchise,Complex
7131,223.5,Boston,E,3,24,12,67,Graduation,21,Owner,Standalone
7736,487.17,Boston,W,14,11,5,61,No education,35,Franchise,Complex
9709,213.02,NJ City,N,2,22,19,58,Graduation,26,Patnership,Standalone
5543,113.97,Boston,E,18,22,4,28,No education,26,Owner,Standalone
222,306.29,NJ City,S,13,22,23,43,High School,24,Patnership,Complex
1545,433.03,NJ City,N,13,19,3,29,Graduation,26,Patnership,Standalone
2268,306.83,NJ City,W,20,17,19,42,High School,26,Franchise,Complex
6869,222.34,Boston,N,19,16,8,75,Graduation,31,Patnership,Standalone
4852,442.69,NJ City,W,13,14,10,57,High School,24,Franchise,Complex
8321,328.34,NJ City,N,22,13,24,35,Graduation,21,Patnership,Standalone
568,197.64,NJ City,W,15,12,4,44,High School,31,Franchise,Complex
6574,375.24,Boston,S,17,20,1,40,High School,27,Patnership,Complex
1348,398.02,NJ City,W,7,20,10,29,High School,20,Franchise,Complex
1926,445.4,NJ City,S,12,16,9,82,High School,28,Patnership,Complex
2391,206.65,NJ City,E,22,24,22,70,High School,27,Owner,Standalone
2471,316.88,NJ City,E,5,21,3,68,No education,20,Owner,Standalone
7851,329.06,Boston,E,4,8,19,63,Graduation,32,Owner,Standalone
6027,257.06,Boston,E,4,24,14,62,Graduation,35,Owner,Standalone
1766,118.37,NJ City,S,16,11,3,72,No education,35,Patnership,Complex
2283,341.53,NJ City,E,18,15,17,45,High School,24,Owner,Standalone
5339,307.69,Boston,E,8,21,24,63,Graduation,25,Owner,Standalone
5928,392.29,Boston,W,14,17,24,55,Graduation,30,Franchise,Complex
2296,465.32,NJ City,W,7,20,13,63,High School,28,Franchise,Complex
1979,244.12,NJ City,E,8,10,23,52,High School,31,Owner,Standalone
4304,131.89,NJ City,W,12,17,17,81,High School,26,Franchise,Complex
7192,440.36,Boston,W,17,19,9,29,Graduation,19,Franchise,Complex
3354,46.67,NJ City,N,14,14,1,58,Graduation,26,Patnership,Complex
6110,414.4,Boston,S,11,19,2,49,No education,20,Patnership,Complex
9939,322.31,NJ City,E,14,15,7,57,Graduation,26,Owner,Standalone
6045,343.69,Boston,N,24,8,20,30,Graduation,19,Patnership,Standalone
148,275.72,NJ City,W,5,12,14,61,High School,18,Franchise,Complex
9535,88.6,NJ City,E,24,17,1,32,High School,31,Owner,Standalone
583,450.83,NJ City,E,21,16,16,50,High School,30,Owner,Standalone
728,1.9,NJ City,N,25,10,4,71,No education,19,Franchise,Complex
1901,68.77,NJ City,N,24,14,4,54,No education,31,Patnership,Standalone
6810,295.86,Boston,S,10,8,7,31,Graduation,25,Patnership,Complex
5659,140.18,Boston,E,18,12,23,49,Graduation,35,Owner,Standalone
2231,468.71,NJ City,E,4,17,13,36,High School,34,Owner,Standalone
1725,144.32,NJ City,N,15,14,8,30,High School,31,Patnership,Standalone
6984,152.64,Boston,W,23,16,13,64,Graduation,18,Franchise,Complex
80,136,Boston,W,1,23,20,54,High School,29,Franchise,Complex
8810,334.82,NJ City,S,22,21,4,42,No education,35,Patnership,Complex
324,64.28,NJ City,N,4,23,2,36,Graduation,27,Franchise,Complex
4664,68.67,NJ City,N,17,20,4,39,No education,33,Franchise,Complex
8417,274.23,NJ City,N,18,9,8,65,Graduation,19,Patnership,Standalone
2837,29.68,NJ City,N,13,17,2,54,No education,27,Patnership,Standalone
9468,326.35,NJ City,W,23,21,8,59,Graduation,28,Franchise,Complex
1025,263.01,NJ City,N,20,8,6,43,High School,24,Patnership,Standalone
8621,355.91,NJ City,N,21,21,1,75,No education,27,Patnership,Standalone
4574,188,NJ City,S,3,21,11,70,High School,21,Patnership,Complex
5261,432.52,Boston,N,18,8,11,30,Graduation,18,Patnership,Standalone
4216,299.66,NJ City,W,7,19,7,25,High School,29,Franchise,Complex
8744,122.36,NJ City,W,11,14,2,62,No education,24,Franchise,Complex
3155,345.18,NJ City,E,12,8,16,28,High School,27,Owner,Standalone
3550,119.58,NJ City,S,9,13,4,70,High School,27,Patnership,Complex
12,269,Maine,W,3,23,14,42,High School,34,Franchise,Complex
9316,101.86,NJ City,W,10,9,4,27,High School,35,Franchise,Complex
1828,106.05,NJ City,W,1,14,2,26,High School,29,Franchise,Complex
4747,100.64,NJ City,E,8,20,4,67,High School,29,Owner,Standalone
5248,485.64,Boston,W,23,9,6,40,Graduation,27,Franchise,Complex
7821,4.81,Boston,N,3,21,2,64,Graduation,22,Patnership,Standalone
401,137.46,NJ City,N,13,19,5,80,No education,34,Patnership,Standalone
7834,306.82,Boston,S,18,21,11,79,Graduation,20,Patnership,Complex
4808,293.79,NJ City,W,23,21,23,69,High School,21,Franchise,Complex
4911,214.78,NJ City,E,15,19,21,48,High School,20,Owner,Standalone
5893,290.47,Boston,N,19,12,12,64,Graduation,34,Patnership,Standalone
5303,23.19,Boston,N,24,17,2,47,No education,32,Owner,Standalone
9746,133.98,NJ City,S,23,21,15,70,Graduation,21,Patnership,Complex
3501,125.16,NJ City,N,6,15,4,38,Graduation,25,Patnership,Standalone
3188,446.49,NJ City,W,1,22,4,74,No education,29,Franchise,Complex
3770,408.08,NJ City,S,6,18,6,48,High School,24,Patnership,Complex
7762,374.9,Boston,S,9,12,17,76,Graduation,30,Patnership,Complex
4366,269,NJ City,S,2,17,7,33,High School,34,Patnership,Complex
4010,195.97,NJ City,S,23,20,3,44,No education,35,Patnership,Complex
4651,270.4,NJ City,E,4,16,14,37,High School,19,Owner,Standalone
6488,218.04,Boston,W,16,19,23,52,Graduation,25,Franchise,Complex
6530,439.56,Boston,S,19,8,12,49,Graduation,29,Patnership,Complex
8558,91.14,NJ City,S,16,12,1,41,No education,35,Patnership,Complex
4715,378.07,NJ City,E,9,19,21,55,High School,25,Owner,Standalone
6607,96.86,Boston,E,1,18,3,80,High School,27,Owner,Standalone
7167,342.09,Boston,E,21,10,8,32,Graduation,23,Owner,Standalone
1829,82.39,NJ City,N,17,11,2,29,No education,25,Patnership,Standalone
6850,213.31,Boston,S,21,17,8,59,Graduation,32,Patnership,Complex
9717,118.83,NJ City,N,9,24,1,60,Graduation,25,Patnership,Standalone
9156,487.76,NJ City,W,8,13,3,64,Graduation,27,Franchise,Complex
3809,65.85,NJ City,N,19,19,1,49,No education,19,Patnership,Standalone
5440,396.43,Boston,W,12,21,22,50,Graduation,19,Franchise,Complex
1011,459.09,NJ City,E,3,19,1,56,Graduation,26,Owner,Standalone
7246,186.93,Boston,S,7,24,10,37,Graduation,33,Patnership,Complex
134,466.99,NJ City,S,24,24,1,72,No education,30,Patnership,Complex
6913,204.3,Boston,N,4,16,16,81,Graduation,33,Patnership,Standalone
2184,12.71,NJ City,N,3,10,2,38,Graduation,29,Franchise,Complex
1623,109.63,NJ City,E,20,20,2,70,Graduation,21,Owner,Standalone
353,487.12,NJ City,N,20,13,8,74,High School,24,Patnership,Standalone
8057,298.16,NJ City,N,6,16,16,27,Graduation,19,Patnership,Standalone
4513,297.22,NJ City,N,18,15,3,63,High School,26,Patnership,Standalone
326,98.41,NJ City,S,23,11,4,47,No education,26,Patnership,Complex
9028,221.6,NJ City,W,6,23,10,60,Graduation,21,Franchise,Complex
4781,204,NJ City,N,1,16,22,71,High School,25,Patnership,Standalone
5588,403.12,Boston,W,14,24,19,51,Graduation,35,Franchise,Complex
4178,241,NJ City,S,4,18,24,29,High School,26,Patnership,Complex
7291,478.77,Boston,E,3,11,15,44,Graduation,21,Owner,Standalone
9444,389.26,NJ City,W,9,22,25,73,Graduation,30,Franchise,Complex
2466,183,NJ City,S,1,10,15,40,High School,32,Patnership,Complex
5531,169.69,Boston,E,9,23,2,72,No education,20,Owner,Standalone
9347,311.11,NJ City,E,1,17,5,55,No education,30,Owner,Standalone
8571,82.47,NJ City,E,19,14,3,54,Graduation,23,Owner,Standalone
7241,422.38,Boston,N,12,8,21,67,Graduation,19,Patnership,Standalone
2223,316.31,NJ City,E,22,24,25,65,High School,18,Owner,Standalone
5190,438.69,Boston,S,11,24,4,66,Graduation,23,Patnership,Complex
1422,471.94,NJ City,S,11,8,20,63,High School,20,Patnership,Complex
9999,357.37,NJ City,E,5,21,17,41,Graduation,35,Owner,Standalone
7314,387.14,Boston,S,8,11,7,73,Graduation,33,Patnership,Complex
888,132.91,NJ City,W,23,10,14,66,High School,26,Franchise,Complex
8664,243.25,NJ City,W,19,8,17,69,Graduation,23,Franchise,Complex
973,230.45,NJ City,N,10,21,15,37,High School,30,Patnership,Standalone
4442,131.86,NJ City,S,11,17,22,75,High School,24,Patnership,Complex
3843,114.79,NJ City,E,8,11,4,55,Graduation,28,Owner,Standalone
6472,119.41,Boston,W,16,19,1,72,High School,29,Franchise,Complex
4714,220.37,NJ City,S,4,24,6,49,High School,28,Patnership,Complex
6507,274.74,Boston,E,20,8,16,70,Graduation,21,Owner,Standalone
4238,143.74,NJ City,S,6,9,19,62,High School,34,Patnership,Complex
3356,291.13,NJ City,W,13,18,1,77,No education,27,Franchise,Complex
5619,332.53,Boston,E,1,15,4,42,Graduation,34,Owner,Standalone
2245,253.41,NJ City,N,19,24,11,27,High School,22,Patnership,Standalone
2595,439.02,NJ City,E,12,22,20,56,High School,29,Owner,Standalone
1497,288,NJ City,N,3,22,17,43,High School,32,Patnership,Standalone
658,150.14,NJ City,S,17,11,11,48,High School,20,Patnership,Complex
8243,2.11,NJ City,E,10,20,2,65,No education,29,Owner,Standalone
2639,80.76,NJ City,E,5,18,4,37,No education,29,Owner,Standalone
4098,497.98,NJ City,S,20,11,17,33,High School,28,Patnership,Complex
3452,20.23,NJ City,N,1,9,4,76,No education,28,Franchise,Complex
7237,73.09,Boston,N,6,19,1,64,High School,28,Patnership,Standalone
841,59.49,NJ City,N,13,20,3,53,High School,26,Patnership,Standalone
5209,115.19,Boston,N,21,11,3,51,High School,33,Patnership,Standalone
3489,268.06,NJ City,N,20,8,11,77,High School,32,Patnership,Standalone
8962,392.63,NJ City,S,1,10,14,25,Graduation,31,Patnership,Complex
9838,439.83,NJ City,S,7,24,17,63,Graduation,24,Patnership,Complex
1155,268.85,NJ City,E,20,16,17,46,High School,24,Owner,Standalone
7186,52.2,Boston,S,12,13,3,65,High School,22,Patnership,Complex
3050,32.16,NJ City,N,13,9,1,62,No education,18,Patnership,Complex
1330,426.64,NJ City,S,22,19,16,75,High School,22,Patnership,Complex
3669,300.11,NJ City,N,23,13,1,78,Graduation,33,Patnership,Standalone
6630,1.53,Boston,N,14,21,2,60,Graduation,27,Patnership,Complex
6306,59.37,Boston,S,15,9,3,76,Graduation,35,Patnership,Complex
2899,154.06,NJ City,E,10,19,17,25,High School,32,Owner,Standalone
913,413.38,NJ City,N,8,11,13,33,High School,35,Patnership,Standalone
2908,383.94,NJ City,W,15,12,12,59,High School,29,Franchise,Complex
1089,83.95,NJ City,N,21,20,3,32,Graduation,23,Patnership,Standalone
6575,288.65,Boston,E,7,14,1,72,No education,29,Owner,Standalone
170,269.46,NJ City,S,24,14,2,79,No education,35,Patnership,Complex
2405,61.27,NJ City,N,11,13,3,45,No education,28,Patnership,Standalone
1749,62.63,NJ City,N,12,17,4,60,Graduation,28,Patnership,Standalone
5797,446,Boston,N,5,14,21,56,Graduation,27,Patnership,Standalone
7743,136.15,Boston,E,21,23,9,58,Graduation,18,Owner,Standalone
3687,420.74,NJ City,E,24,17,7,30,High School,18,Owner,Standalone
4695,198.81,NJ City,E,12,18,3,48,Graduation,20,Owner,Standalone
5555,376.19,Boston,E,3,12,1,68,No education,34,Owner,Standalone
3787,126.31,NJ City,E,12,22,4,80,High School,20,Owner,Standalone
2882,310.11,NJ City,S,20,13,15,68,High School,34,Patnership,Complex
7603,357.86,Boston,E,11,15,22,78,Graduation,21,Owner,Standalone
2454,139.64,NJ City,S,17,14,6,38,High School,22,Patnership,Complex
3483,63.56,NJ City,N,6,18,1,68,Graduation,32,Owner,Standalone
9608,321.35,NJ City,W,20,16,18,69,Graduation,22,Franchise,Complex
5432,225.08,Boston,W,23,16,24,31,Graduation,23,Franchise,Complex
7294,90.11,Boston,S,17,15,3,43,High School,30,Patnership,Complex
8724,312.33,NJ City,W,14,22,22,59,Graduation,29,Franchise,Complex
2972,431.88,NJ City,W,23,11,22,44,High School,22,Franchise,Complex
9577,276.66,NJ City,N,13,21,12,47,Graduation,26,Patnership,Standalone
6093,398.85,Boston,N,21,24,1,46,Graduation,35,Patnership,Standalone
1067,182.89,NJ City,E,12,22,14,67,High School,32,Owner,Standalone
4922,183.4,NJ City,S,12,23,9,66,High School,32,Patnership,Complex
3676,261.96,NJ City,W,4,17,23,44,High School,19,Franchise,Complex
4165,263.26,NJ City,N,20,11,24,37,High School,22,Patnership,Standalone
1538,118.48,NJ City,S,17,18,3,27,No education,32,Patnership,Complex
3819,276.83,NJ City,E,14,17,13,77,High School,23,Owner,Standalone
8512,27.21,NJ City,N,12,10,4,43,High School,32,Franchise,Complex
9128,390.02,NJ City,W,18,17,20,32,Graduation,23,Franchise,Complex
1305,35.41,NJ City,N,12,11,2,46,Graduation,33,Patnership,Standalone
3705,122.96,NJ City,N,10,16,2,44,Graduation,24,Patnership,Standalone
1119,14.96,NJ City,N,4,10,3,76,Graduation,25,Owner,Standalone
1184,41.78,NJ City,N,23,15,3,59,No education,19,Franchise,Complex
8867,103.54,NJ City,E,7,20,2,39,No education,20,Owner,Standalone
5689,421.79,Boston,N,24,17,3,47,High School,34,Patnership,Standalone
8643,59.83,NJ City,E,15,18,2,66,Graduation,25,Owner,Standalone
1647,346.1,NJ City,E,4,11,15,66,High School,28,Owner,Standalone
3988,287,NJ City,W,2,24,14,81,High School,23,Franchise,Complex
2961,299,NJ City,N,3,10,7,57,High School,24,Patnership,Standalone
6083,264.29,Boston,E,12,15,24,42,Graduation,18,Owner,Standalone
7921,439.33,Boston,N,13,15,13,61,Graduation,20,Patnership,Standalone
6441,296.97,Boston,N,1,8,11,38,Graduation,31,Patnership,Standalone
8691,271.48,NJ City,E,10,22,4,50,Graduation,24,Owner,Standalone
122,37.87,NJ City,N,3,17,1,33,No education,19,Patnership,Complex
4352,428.08,NJ City,W,20,12,1,80,No education,27,Franchise,Complex
9047,131.2,NJ City,E,5,16,7,63,Graduation,22,Owner,Standalone
4493,26.34,NJ City,N,16,9,1,54,No education,34,Patnership,Standalone
6153,249.7,Boston,N,1,13,6,54,Graduation,21,Patnership,Standalone
3774,306.64,NJ City,S,3,15,4,27,Graduation,25,Patnership,Complex
3136,286.48,NJ City,W,23,18,19,68,High School,18,Franchise,Complex
4800,59.91,NJ City,W,1,10,3,61,Graduation,35,Franchise,Complex
5416,113.23,Boston,W,19,12,3,68,High School,21,Franchise,Complex
9847,152.84,NJ City,E,9,19,12,45,Graduation,32,Owner,Standalone
4981,211.42,Boston,N,16,15,1,57,High School,24,Patnership,Standalone
3438,167.15,NJ City,S,11,24,5,37,Graduation,25,Patnership,Complex
4180,183.06,NJ City,W,22,23,5,66,High School,34,Franchise,Complex
1630,80.1,NJ City,S,22,19,3,44,High School,33,Patnership,Complex
6802,64.9,Boston,N,24,20,3,42,High School,18,Patnership,Complex
8582,177.91,NJ City,S,7,16,16,36,Graduation,30,Patnership,Complex
5994,299.96,Boston,S,8,9,21,75,Graduation,18,Patnership,Complex
4246,40.11,NJ City,N,25,21,4,37,High School,21,Patnership,Complex
5623,240.07,Boston,E,9,23,22,50,Graduation,20,Owner,Standalone
8391,170.25,NJ City,E,10,14,8,42,Graduation,30,Owner,Standalone
4367,281.53,NJ City,E,8,22,9,34,High School,18,Owner,Standalone
9657,292.5,NJ City,N,11,16,20,36,Graduation,25,Patnership,Standalone
7886,404.35,Boston,S,10,21,17,84,Graduation,34,Patnership,Complex
995,162.43,NJ City,E,9,23,1,78,No education,33,Owner,Standalone
8416,341.56,NJ City,W,21,24,12,59,Graduation,22,Franchise,Complex
670,413.36,NJ City,S,25,24,12,42,High School,27,Patnership,Complex
6554,455.89,Boston,S,2,15,17,49,Graduation,29,Patnership,Complex
3331,114.24,NJ City,E,10,21,4,55,High School,20,Owner,Standalone
5484,352.23,Boston,W,3,18,3,55,Graduation,26,Franchise,Complex
2144,204.99,NJ City,W,22,23,24,48,High School,22,Franchise,Complex
3570,479.96,NJ City,S,2,22,5,71,Graduation,27,Patnership,Complex
5696,392.75,Boston,W,10,14,8,69,Graduation,31,Franchise,Complex
1324,306.2,NJ City,W,11,24,11,52,High School,31,Franchise,Complex
6517,125.34,Boston,N,7,16,4,26,High School,35,Patnership,Standalone
575,263,NJ City,E,3,23,20,67,High School,24,Owner,Standalone
5591,321.27,Boston,E,13,20,10,41,Graduation,21,Owner,Standalone
5816,425.58,Boston,W,11,20,17,80,Graduation,34,Franchise,Complex
1790,394.4,NJ City,S,5,15,3,55,No education,33,Patnership,Complex
7749,186.2,Boston,N,12,13,7,33,Graduation,18,Patnership,Standalone
4672,133.1,NJ City,W,10,8,16,56,High School,25,Franchise,Complex
1135,473.6,NJ City,E,11,14,25,60,High School,19,Owner,Standalone
4975,127.93,Boston,E,20,15,1,26,High School,33,Owner,Standalone
7012,118.42,Boston,W,24,22,4,42,High School,30,Franchise,Complex
5781,96.81,Boston,N,10,14,3,48,Graduation,30,Patnership,Standalone
234,342.2,NJ City,S,9,21,1,27,Graduation,32,Patnership,Complex
1231,9.31,NJ City,N,20,18,4,40,High School,33,Owner,Standalone
1844,67.62,NJ City,N,7,17,4,55,No education,26,Franchise,Complex
8081,362.2,NJ City,N,15,15,22,81,Graduation,23,Patnership,Standalone
3166,165.51,NJ City,S,18,22,23,35,High School,19,Patnership,Complex
2902,411.96,NJ City,S,13,16,22,42,High School,24,Patnership,Complex
2998,84.51,NJ City,S,19,23,3,36,High School,26,Patnership,Complex
1448,435.58,NJ City,W,15,15,2,65,No education,24,Franchise,Complex
1480,178.79,NJ City,W,6,10,13,51,High School,35,Franchise,Complex
9324,388.09,NJ City,W,11,21,6,58,Graduation,22,Franchise,Complex
5077,220.71,Boston,N,9,23,10,46,Graduation,18,Patnership,Standalone
1663,46.62,NJ City,N,19,13,1,80,High School,20,Owner,Standalone
1781,453.31,NJ City,N,13,9,3,74,No education,21,Patnership,Standalone
7537,151.42,Boston,N,8,22,3,35,High School,34,Patnership,Standalone
3063,84.04,NJ City,E,5,13,1,31,Graduation,30,Owner,Standalone
8682,98.82,NJ City,S,24,20,4,64,Graduation,18,Patnership,Complex
962,43.52,NJ City,N,19,11,3,26,No education,27,Patnership,Complex
2673,340.96,NJ City,N,13,14,2,40,Graduation,27,Patnership,Standalone
8374,305.9,NJ City,S,20,19,4,72,High School,23,Patnership,Complex
4359,233.54,NJ City,E,17,13,12,43,High School,21,Owner,Standalone
8366,414.84,NJ City,S,25,8,4,37,No education,20,Patnership,Complex
9651,185.63,NJ City,E,25,18,19,33,Graduation,19,Owner,Standalone
6274,193.07,Boston,S,3,11,15,32,Graduation,25,Patnership,Complex
3795,186,NJ City,E,2,13,22,36,High School,34,Owner,Standalone
6476,409.84,Boston,W,15,11,24,62,Graduation,29,Franchise,Complex
8052,433.07,NJ City,W,15,16,14,34,Graduation,27,Franchise,Complex
8475,319.48,NJ City,E,25,12,12,33,Graduation,27,Owner,Standalone
8372,279.99,NJ City,W,9,18,6,58,Graduation,30,Franchise,Complex
1,205.93,NJ City,N,16,11,12,75,High School,34,Owner,Complex
6840,339.05,Boston,W,6,12,18,81,Graduation,32,Franchise,Complex
1406,352.87,NJ City,S,19,18,8,26,High School,35,Patnership,Complex
8708,458.34,NJ City,W,22,20,7,35,Graduation,18,Franchise,Complex
4595,30.32,NJ City,N,8,21,3,53,No education,34,Owner,Standalone
3979,123.26,NJ City,E,3,15,3,82,High School,21,Owner,Standalone
3516,436.84,NJ City,W,2,10,3,49,Graduation,26,Franchise,Complex
2448,51.41,NJ City,W,2,24,4,49,Graduation,28,Franchise,Complex
430,9.38,NJ City,N,9,10,4,81,High School,26,Patnership,Complex
6216,120.31,Boston,W,7,11,1,36,Graduation,18,Franchise,Complex
6141,434.1,Boston,N,11,23,15,37,Graduation,22,Patnership,Standalone
2977,100.32,NJ City,N,4,21,3,26,High School,19,Patnership,Standalone
1270,232,NJ City,S,2,9,15,81,High School,28,Patnership,Complex
7673,244.94,Boston,N,16,15,2,45,No education,27,Patnership,Standalone
1174,15.83,NJ City,N,20,16,3,65,High School,32,Patnership,Complex
351,18.98,NJ City,N,15,21,1,62,Graduation,29,Owner,Standalone
1414,212,NJ City,S,3,8,11,46,High School,34,Patnership,Complex
5097,95.69,Boston,N,14,17,3,71,Graduation,19,Patnership,Standalone
5276,458.2,Boston,W,8,11,8,74,Graduation,20,Franchise,Complex
4469,237.24,NJ City,N,4,10,13,49,High School,35,Patnership,Standalone
8328,197.24,NJ City,W,25,18,22,72,Graduation,21,Franchise,Complex
9650,440.28,NJ City,S,22,15,8,51,Graduation,21,Patnership,Complex
2105,385.16,NJ City,N,9,8,23,81,High School,25,Patnership,Standalone
9306,33.82,NJ City,N,3,15,4,66,Graduation,24,Patnership,Complex
1274,342.62,NJ City,S,22,18,3,82,No education,33,Patnership,Complex
6709,132.07,Boston,N,21,16,10,28,Graduation,19,Patnership,Standalone
9880,375.07,NJ City,W,14,16,3,76,High School,31,Franchise,Complex
8806,8.59,NJ City,N,9,20,3,34,High School,18,Patnership,Complex
4449,147.96,NJ City,N,18,11,13,33,High School,35,Patnership,Standalone
5827,304.62,Boston,E,7,10,25,67,Graduation,25,Owner,Standalone
78,285.14,Boston,S,7,11,14,73,High School,29,Patnership,Complex
958,200,NJ City,S,1,22,10,69,High School,26,Patnership,Complex
2775,141,NJ City,E,3,11,17,47,High School,29,Owner,Standalone
6301,240.37,Boston,N,11,16,17,82,Graduation,23,Patnership,Standalone
3185,37.32,NJ City,N,14,16,4,48,No education,32,Patnership,Standalone
7208,394.41,Boston,W,2,24,18,44,Graduation,20,Franchise,Complex
6628,19.37,Boston,N,20,21,2,76,High School,31,Franchise,Complex
8256,316.81,NJ City,W,24,13,14,58,Graduation,25,Franchise,Complex
3234,234.43,NJ City,S,4,14,14,72,High School,24,Patnership,Complex
8575,253.09,NJ City,E,7,21,19,78,Graduation,22,Owner,Standalone
9397,302.68,NJ City,N,23,10,9,70,Graduation,22,Patnership,Standalone
7755,301.67,Boston,E,6,18,5,63,Graduation,33,Owner,Standalone
2176,452.77,NJ City,W,2,24,3,34,High School,33,Franchise,Complex
1088,205.76,NJ City,W,13,19,9,40,High School,29,Franchise,Complex
9685,362.5,NJ City,N,17,18,21,29,Graduation,33,Patnership,Standalone
2088,285.88,NJ City,W,2,21,2,65,Graduation,28,Franchise,Complex
5486,423.78,Boston,S,2,18,7,28,Graduation,19,Patnership,Complex
3450,79.46,NJ City,S,18,10,1,59,Graduation,27,Patnership,Complex
3181,372.89,NJ City,N,14,20,18,47,High School,24,Patnership,Standalone
7825,462.6,Boston,N,22,18,5,33,High School,25,Patnership,Standalone
4444,62.66,NJ City,N,11,11,1,25,High School,31,Franchise,Complex
6943,281.14,Boston,E,16,14,3,29,High School,35,Owner,Standalone
1881,28.32,NJ City,N,15,14,3,38,Graduation,24,Patnership,Standalone
4928,339.83,NJ City,W,13,17,5,78,No education,19,Franchise,Complex
1743,108.94,NJ City,E,10,12,3,39,Graduation,25,Owner,Standalone
218,332.2,NJ City,S,25,22,14,35,High School,33,Patnership,Complex
8528,195.98,NJ City,W,4,12,2,53,No education,27,Franchise,Complex
7064,270.96,Boston,W,10,23,24,48,Graduation,24,Franchise,Complex
2683,348.2,NJ City,E,7,17,22,52,High School,33,Owner,Standalone
3070,418.53,NJ City,S,15,20,5,46,High School,19,Patnership,Complex
7218,259.22,Boston,S,14,22,19,43,Graduation,31,Patnership,Complex
4737,279.92,NJ City,N,7,18,15,43,High School,31,Patnership,Standalone
6635,412.53,Boston,E,1,19,25,45,Graduation,33,Owner,Standalone
8019,306.88,Boston,E,5,21,9,37,Graduation,31,Owner,Standalone
3709,480.04,NJ City,N,4,20,10,75,High School,30,Patnership,Standalone
7636,273.83,Boston,W,25,8,1,41,High School,20,Franchise,Complex
2479,423.28,NJ City,E,24,20,7,74,High School,28,Owner,Standalone
1787,471,NJ City,E,23,11,25,41,High School,29,Owner,Standalone
6208,350.9,Boston,W,11,13,17,42,Graduation,18,Franchise,Complex
3990,315.33,NJ City,S,11,22,7,75,High School,20,Patnership,Complex
1243,148.27,NJ City,E,18,9,7,64,High School,31,Owner,Standalone
456,159.75,NJ City,W,21,10,9,71,High School,24,Franchise,Complex
6950,346.9,Boston,S,16,23,18,52,Graduation,24,Patnership,Complex
6752,471.68,Boston,W,15,24,16,45,Graduation,28,Franchise,Complex
1477,43.05,NJ City,N,10,20,1,64,High School,21,Patnership,Standalone
5863,92.15,Boston,E,8,11,3,57,High School,22,Owner,Standalone
149,191.77,NJ City,N,8,17,19,62,High School,19,Patnership,Standalone
4261,26.42,NJ City,N,21,24,3,51,High School,27,Patnership,Standalone
8425,347.39,NJ City,N,6,22,8,43,Graduation,30,Patnership,Standalone
1005,292.39,NJ City,N,10,9,18,39,High School,20,Patnership,Standalone
2923,71.3,NJ City,N,6,12,2,74,High School,28,Owner,Standalone
8740,230.59,NJ City,W,2,23,5,44,High School,19,Franchise,Complex
4033,465.97,NJ City,N,7,22,7,72,High School,29,Patnership,Standalone
9870,325.42,NJ City,S,24,15,10,39,Graduation,28,Patnership,Complex
7792,135.36,Boston,W,12,11,8,49,Graduation,29,Franchise,Complex
9378,143.36,NJ City,S,18,24,22,83,Graduation,24,Patnership,Complex
8471,380.5,NJ City,E,19,18,11,57,Graduation,32,Owner,Standalone
6009,364.62,Boston,N,23,22,9,70,Graduation,21,Patnership,Standalone
740,109,NJ City,W,2,11,25,67,High School,27,Franchise,Complex
9706,57.84,NJ City,S,15,21,3,74,High School,28,Patnership,Complex
4616,118.6,NJ City,W,23,22,4,73,No education,23,Franchise,Complex
9590,419.31,NJ City,S,18,24,5,28,No education,27,Patnership,Complex
7320,41.39,Boston,N,17,9,2,72,Graduation,21,Franchise,Complex
4059,331.43,NJ City,E,17,18,14,42,High School,26,Owner,Standalone
6805,196.03,Boston,N,5,17,25,44,Graduation,35,Patnership,Standalone
7934,397.09,Boston,S,24,23,10,77,Graduation,33,Patnership,Complex
3426,346.5,NJ City,S,19,17,18,54,High School,33,Patnership,Complex
7199,393.7,Boston,E,20,18,3,42,No education,25,Owner,Standalone
9070,116.18,NJ City,S,14,10,2,63,High School,22,Patnership,Complex
3562,70.05,NJ City,N,2,21,2,67,High School,24,Patnership,Complex
212,186.64,NJ City,W,17,16,22,70,High School,20,Franchise,Complex
8623,193.87,NJ City,E,17,24,13,31,Graduation,22,Owner,Standalone
3593,128.85,NJ City,N,22,18,15,62,High School,26,Patnership,Standalone
1735,444.99,NJ City,E,12,23,8,66,High School,24,Owner,Standalone
6225,78.69,Boston,N,15,24,2,60,Graduation,18,Patnership,Standalone
8838,130.91,NJ City,S,24,13,14,50,Graduation,26,Patnership,Complex
8642,393.35,NJ City,S,4,9,24,36,Graduation,32,Patnership,Complex
4696,416.27,NJ City,W,9,20,24,25,High School,35,Franchise,Complex
4526,280,NJ City,S,2,18,25,74,High School,30,Patnership,Complex
9511,9.18,NJ City,N,12,13,4,28,High School,20,Owner,Standalone
5025,33.73,Boston,N,16,9,4,82,Graduation,19,Patnership,Standalone
6794,210.95,Boston,S,7,21,2,80,No education,18,Patnership,Complex
8833,191.11,NJ City,N,23,8,16,76,Graduation,19,Patnership,Standalone
8143,486.9,NJ City,E,19,17,23,75,Graduation,23,Owner,Standalone
399,476.07,NJ City,E,11,23,15,32,High School,35,Owner,Standalone
5854,99.58,Boston,S,12,22,3,31,High School,19,Patnership,Complex
1389,55.17,NJ City,N,22,22,3,66,Graduation,35,Patnership,Standalone
5147,195.95,Boston,E,19,24,21,76,Graduation,24,Owner,Standalone
5585,355.04,Boston,N,23,8,15,74,Graduation,28,Patnership,Standalone
7474,450.61,Boston,S,8,11,23,36,Graduation,26,Patnership,Complex
9168,48.58,NJ City,W,18,15,4,48,Graduation,32,Franchise,Complex
7282,56.24,Boston,S,5,18,1,42,High School,20,Patnership,Complex
5267,474.61,Boston,E,16,24,20,71,Graduation,25,Owner,Standalone
8889,114.32,NJ City,N,5,8,3,50,Graduation,28,Patnership,Standalone
999,443.44,NJ City,E,5,10,23,48,High School,28,Owner,Standalone
4397,364.73,NJ City,N,20,20,16,37,High School,19,Patnership,Standalone
3386,308.76,NJ City,S,8,20,5,27,No education,31,Patnership,Complex
1469,90.14,NJ City,N,21,16,1,71,No education,27,Patnership,Standalone
942,111.12,NJ City,S,4,14,4,33,Graduation,21,Patnership,Complex
8051,175.31,NJ City,E,23,17,23,60,Graduation,28,Owner,Standalone
1430,32.98,NJ City,N,19,20,1,73,No education,34,Patnership,Complex
4884,104.16,NJ City,W,5,10,4,50,Graduation,32,Franchise,Complex
5570,424.78,Boston,S,25,23,7,77,Graduation,35,Patnership,Complex
8136,463.39,NJ City,W,18,11,24,37,Graduation,19,Franchise,Complex
2770,255.39,NJ City,S,19,16,10,66,High School,30,Patnership,Complex
602,64.87,NJ City,N,20,12,1,41,No education,31,Patnership,Complex
1857,134.85,NJ City,N,25,24,23,69,High School,25,Patnership,Standalone
7052,20.71,Boston,N,5,19,3,64,No education,20,Franchise,Complex
4959,321.14,Boston,E,16,24,18,84,High School,24,Owner,Standalone
3587,126.73,NJ City,E,21,23,2,58,No education,18,Owner,Standalone
6936,196.44,Boston,W,15,21,4,67,Graduation,23,Franchise,Complex
8531,297.99,NJ City,E,1,18,25,42,Graduation,26,Owner,Standalone
1496,157.94,NJ City,W,2,19,3,35,No education,19,Franchise,Complex
7761,179.13,Boston,N,3,23,19,75,Graduation,27,Patnership,Standalone
5233,288.08,Boston,N,11,9,15,34,Graduation,28,Patnership,Standalone
165,219.52,NJ City,N,8,20,8,32,High School,25,Patnership,Standalone
855,377.84,NJ City,E,19,21,8,60,High School,26,Owner,Standalone
269,363.14,NJ City,N,20,10,24,47,High School,27,Patnership,Standalone
9899,221.1,NJ City,E,9,10,13,54,Graduation,27,Owner,Standalone
1426,77.32,NJ City,S,22,21,1,79,High School,33,Patnership,Complex
6375,421.51,Boston,E,12,15,20,36,Graduation,35,Owner,Standalone
5845,130.7,Boston,N,10,24,15,38,Graduation,21,Patnership,Standalone
3274,183,NJ City,S,2,18,7,78,High School,22,Patnership,Complex
3180,367.8,NJ City,W,10,12,12,68,High School,19,Franchise,Complex
794,368.69,NJ City,S,5,19,1,41,No education,21,Patnership,Complex
5191,333.39,Boston,E,13,8,23,65,Graduation,21,Owner,Standalone
3943,383.13,NJ City,E,19,11,20,51,High School,18,Owner,Standalone
2847,67.13,NJ City,N,13,14,4,71,Graduation,23,Owner,Standalone
2950,88.28,NJ City,S,1,23,1,42,High School,20,Patnership,Complex
4108,341.21,NJ City,W,13,12,14,70,High School,33,Franchise,Complex
4987,11.34,Boston,N,19,24,2,54,High School,34,Owner,Standalone
8140,440.81,NJ City,W,22,19,8,56,Graduation,27,Franchise,Complex
5304,442.82,Boston,W,11,19,6,50,Graduation,30,Franchise,Complex
9453,135.46,NJ City,N,8,8,15,29,Graduation,20,Patnership,Standalone
782,463.38,NJ City,S,15,22,13,64,High School,18,Patnership,Complex
9115,175.21,NJ City,E,9,17,18,62,Graduation,19,Owner,Standalone
8075,143.82,NJ City,E,5,22,2,47,No education,35,Owner,Standalone
7578,152.63,Boston,S,14,13,12,47,Graduation,33,Patnership,Complex
7518,387.85,Boston,S,6,18,16,37,Graduation,34,Patnership,Complex
4357,4.66,NJ City,N,22,15,4,81,High School,33,Patnership,Standalone
5982,431.23,Boston,S,24,17,18,64,Graduation,31,Patnership,Complex
3333,8.24,NJ City,N,22,14,4,37,Graduation,35,Patnership,Standalone
9364,354.85,NJ City,W,20,13,12,84,Graduation,21,Franchise,Complex
7202,386.98,Boston,S,4,10,10,70,Graduation,34,Patnership,Complex
4126,437.16,NJ City,S,5,15,23,74,High School,29,Patnership,Complex
8325,131.64,NJ City,N,12,8,25,38,Graduation,29,Patnership,Standalone
6272,98.16,Boston,W,11,23,4,46,No education,33,Franchise,Complex
8255,36.44,NJ City,N,4,20,3,41,No education,19,Owner,Standalone
5847,302.95,Boston,E,25,15,17,57,Graduation,21,Owner,Standalone
2906,132.79,NJ City,S,5,9,13,31,High School,32,Patnership,Complex
730,137.7,NJ City,S,14,21,2,43,High School,20,Patnership,Complex
175,303.86,NJ City,E,22,12,2,41,High School,26,Owner,Standalone
2456,238,NJ City,W,1,10,14,54,High School,34,Franchise,Complex
6716,342.37,Boston,W,21,13,5,64,No education,32,Franchise,Complex
241,109,NJ City,N,3,20,24,26,High School,30,Patnership,Standalone
3138,471.9,NJ City,S,8,20,24,70,High School,31,Patnership,Complex
2984,182.36,NJ City,W,16,14,4,74,No education,25,Franchise,Complex
5968,458.15,Boston,W,18,15,5,52,High School,18,Franchise,Complex
9782,304.63,NJ City,S,15,23,20,50,Graduation,26,Patnership,Complex
2517,302.74,NJ City,N,25,17,25,44,High School,23,Patnership,Standalone
3929,303.73,NJ City,N,13,14,7,53,High School,33,Patnership,Standalone
699,232.8,NJ City,E,22,16,14,39,High School,24,Owner,Standalone
7965,391.69,Boston,N,15,19,19,85,Graduation,24,Patnership,Standalone
5517,166.29,Boston,N,13,8,15,75,Graduation,23,Patnership,Standalone
7710,310.5,Boston,S,19,8,6,58,Graduation,27,Patnership,Complex
2546,498.11,NJ City,S,25,22,8,82,High School,32,Patnership,Complex
1077,23.24,NJ City,N,5,24,1,73,Graduation,32,Patnership,Standalone
2032,50.44,NJ City,W,12,16,1,70,High School,29,Franchise,Complex
8911,65.45,NJ City,N,8,16,4,76,High School,28,Owner,Standalone
9729,95.05,NJ City,N,10,10,4,48,Graduation,29,Patnership,Standalone
651,108,NJ City,E,2,23,22,47,High School,29,Owner,Standalone
1653,84.67,NJ City,N,1,11,3,40,Graduation,34,Patnership,Standalone
4103,133.88,NJ City,E,12,16,14,82,High School,32,Owner,Standalone
4,93.99,NJ City,W,19,9,3,42,High School,29,Owner,Standalone
2593,20.7,NJ City,N,4,15,2,26,High School,20,Patnership,Standalone
6268,176.2,Boston,W,12,8,20,40,Graduation,33,Franchise,Complex
7255,379.6,Boston,E,23,15,21,59,Graduation,21,Owner,Standalone
2887,357.86,NJ City,E,12,13,19,38,High School,23,Owner,Standalone
2532,466.53,NJ City,W,4,10,10,29,High School,19,Franchise,Complex
3706,489.87,NJ City,S,14,13,9,84,High School,30,Patnership,Complex
4889,344.2,NJ City,N,25,14,2,70,No education,26,Patnership,Standalone
4930,383.09,NJ City,S,16,24,11,30,High School,34,Patnership,Complex
3182,35.32,NJ City,N,19,12,2,31,No education,19,Patnership,Complex
2122,85.83,NJ City,S,25,19,1,27,High School,26,Patnership,Complex
5203,50.6,Boston,E,19,21,3,66,High School,33,Owner,Standalone
8168,426.91,NJ City,W,23,24,23,53,Graduation,24,Franchise,Complex
8035,481.89,Boston,E,24,23,14,82,Graduation,34,Owner,Standalone
5200,402.62,Boston,W,2,21,2,37,High School,33,Franchise,Complex
8915,454.71,NJ City,E,23,22,1,73,No education,23,Owner,Standalone
5987,133.74,Boston,E,19,17,13,64,Graduation,34,Owner,Standalone
4503,22.83,NJ City,N,2,18,2,62,Graduation,25,Owner,Standalone
2661,145,NJ City,N,1,18,12,47,High School,29,Patnership,Standalone
4519,330.93,NJ City,E,8,9,6,33,High School,23,Owner,Standalone
7862,225.62,Boston,S,25,14,9,38,Graduation,26,Patnership,Complex
2496,247.52,NJ City,W,11,11,4,82,Graduation,22,Franchise,Complex
3557,494.88,NJ City,N,12,23,6,33,High School,25,Patnership,Standalone
7785,453.09,Boston,N,8,19,13,50,Graduation,24,Patnership,Standalone
3329,166.55,NJ City,N,8,16,10,25,High School,19,Patnership,Standalone
2760,383.2,NJ City,W,19,16,19,71,High School,31,Franchise,Complex
7718,112.62,Boston,S,22,23,3,29,No education,30,Patnership,Complex
6583,356.85,Boston,E,4,18,8,80,Graduation,35,Owner,Standalone
8340,28.12,NJ City,N,1,18,4,46,Graduation,27,Franchise,Complex
693,78.38,NJ City,N,24,10,3,63,Graduation,28,Patnership,Standalone
4313,181.57,NJ City,N,7,9,2,54,No education,33,Patnership,Standalone
8800,123.32,NJ City,W,23,12,4,85,High School,28,Franchise,Complex
4373,233.03,NJ City,N,12,14,19,85,High School,26,Patnership,Standalone
2041,135.52,NJ City,N,19,12,11,67,High School,28,Patnership,Standalone
9302,295.3,NJ City,S,16,11,25,50,Graduation,23,Patnership,Complex
1260,115.5,NJ City,W,18,15,3,68,Graduation,24,Franchise,Complex
8848,10.83,NJ City,N,5,20,2,74,High School,18,Franchise,Complex
125,264,NJ City,N,2,19,15,70,High School,26,Patnership,Standalone
3897,313.68,NJ City,N,25,21,3,27,Graduation,29,Patnership,Standalone
2151,434.5,NJ City,E,20,17,12,40,High School,27,Owner,Standalone
3467,418.49,NJ City,E,17,12,7,83,High School,30,Owner,Standalone
1854,219.05,NJ City,S,12,16,1,40,Graduation,20,Patnership,Complex
9165,391.49,NJ City,N,19,18,15,48,Graduation,18,Patnership,Standalone
3998,166.16,NJ City,S,7,16,8,43,High School,25,Patnership,Complex
3248,98.78,NJ City,W,21,21,1,58,No education,22,Franchise,Complex
4309,433.41,NJ City,N,6,23,25,73,High School,29,Patnership,Standalone
5205,137.98,Boston,N,12,10,4,36,Graduation,20,Patnership,Standalone
8909,83.69,NJ City,N,9,8,2,31,No education,22,Patnership,Standalone
689,58.02,NJ City,N,18,8,4,37,No education,22,Patnership,Standalone
1614,352.6,NJ City,S,23,20,10,68,High School,27,Patnership,Complex
5175,414.88,Boston,E,25,14,23,28,Graduation,18,Owner,Standalone
8522,276.49,NJ City,S,11,14,1,73,No education,23,Patnership,Complex
9787,233.6,NJ City,E,6,16,20,61,Graduation,33,Owner,Standalone
893,408.8,NJ City,N,25,13,13,62,High School,35,Patnership,Standalone
5798,37.31,Boston,N,19,24,3,59,No education,28,Patnership,Complex
5629,270.73,Boston,N,22,13,23,39,Graduation,27,Patnership,Standalone
3834,416.12,NJ City,S,13,22,19,73,High School,24,Patnership,Complex
6881,16.84,Boston,N,20,13,1,48,No education,29,Patnership,Standalone
9915,73.75,NJ City,N,18,18,1,77,Graduation,26,Owner,Standalone
1995,162.72,NJ City,E,8,18,12,61,High School,23,Owner,Standalone
5474,66.97,Boston,N,8,23,2,69,No education,29,Patnership,Complex
8310,132.35,NJ City,S,5,24,18,64,Graduation,21,Patnership,Complex
9074,456.45,NJ City,S,17,17,16,76,Graduation,33,Patnership,Complex
5737,45.08,Boston,N,24,14,2,61,High School,32,Patnership,Standalone
9553,376.48,NJ City,N,5,13,19,28,Graduation,19,Patnership,Standalone
3621,16.33,NJ City,N,20,11,3,38,Graduation,35,Patnership,Standalone
2049,212.46,NJ City,N,16,19,3,31,Graduation,25,Patnership,Standalone
3296,48.27,NJ City,W,10,19,1,34,No education,19,Franchise,Complex
1552,365.07,NJ City,W,7,10,5,30,High School,32,Franchise,Complex
4914,144.16,NJ City,S,20,24,16,80,High School,34,Patnership,Complex
577,144.45,NJ City,N,14,18,13,75,High School,34,Patnership,Standalone
1506,381.87,NJ City,S,23,16,7,42,High School,22,Patnership,Complex
1047,245.43,NJ City,E,4,24,11,28,High School,27,Owner,Standalone
9481,120.22,NJ City,N,17,12,4,41,High School,23,Patnership,Standalone
6516,62.66,Boston,N,8,20,2,26,Graduation,24,Franchise,Complex
749,232.1,NJ City,N,17,23,3,35,No education,24,Patnership,Standalone
2680,458.7,NJ City,W,25,17,8,77,High School,20,Franchise,Complex
6863,20.47,Boston,N,7,20,4,38,No education,32,Owner,Standalone
851,480.87,NJ City,E,21,20,14,61,High School,35,Owner,Standalone
4405,169.41,NJ City,N,2,16,5,26,High School,27,Patnership,Standalone
1282,479.94,NJ City,S,15,19,1,74,High School,31,Patnership,Complex
2439,222.64,NJ City,E,6,14,13,79,High School,20,Owner,Standalone
1547,388.53,NJ City,E,5,15,17,68,High School,29,Owner,Standalone
3879,75.66,NJ City,E,22,17,4,59,Graduation,18,Owner,Standalone
410,277.68,NJ City,S,24,12,7,75,High School,28,Patnership,Complex
7989,139.91,Boston,N,3,24,9,63,Graduation,31,Patnership,Standalone
6039,113.41,Boston,E,25,14,4,57,Graduation,23,Owner,Standalone
4237,392.44,NJ City,N,4,19,11,44,High School,30,Patnership,Standalone
6473,393.1,Boston,N,25,24,8,25,Graduation,25,Patnership,Standalone
1765,190.81,NJ City,N,6,23,25,71,High School,24,Patnership,Standalone
5941,391.41,Boston,N,11,19,21,57,Graduation,24,Patnership,Standalone
9188,488.54,NJ City,W,18,18,21,26,Graduation,35,Franchise,Complex
663,438.86,NJ City,E,14,23,20,27,High School,33,Owner,Standalone
909,440.62,NJ City,N,13,22,17,31,High School,33,Patnership,Standalone
7239,235.33,Boston,E,12,8,1,67,Graduation,25,Owner,Standalone
1192,277.18,NJ City,W,11,9,4,53,High School,22,Franchise,Complex
9394,159.01,NJ City,S,8,15,18,42,Graduation,29,Patnership,Complex
2842,480.04,NJ City,S,16,13,18,33,High School,22,Patnership,Complex
479,297,NJ City,E,2,18,19,45,High School,28,Owner,Standalone
3759,433.1,NJ City,E,11,9,25,35,High School,20,Owner,Standalone
1010,394.77,NJ City,S,8,19,23,70,High School,30,Patnership,Complex
4269,219.28,NJ City,N,19,18,7,73,High School,33,Patnership,Standalone
9719,459.99,NJ City,E,8,13,13,69,Graduation,18,Owner,Standalone
9764,103.49,NJ City,W,17,19,4,35,No education,30,Franchise,Complex
2071,276.63,NJ City,E,7,11,22,70,High School,22,Owner,Standalone
9125,470.26,NJ City,N,25,22,3,54,No education,35,Patnership,Standalone
2828,170.93,NJ City,W,15,11,25,37,High School,34,Franchise,Complex
5139,237.29,Boston,E,8,11,19,85,Graduation,21,Owner,Standalone
5353,192.7,Boston,N,21,24,23,25,Graduation,28,Patnership,Standalone
8390,294.51,NJ City,S,16,21,2,77,No education,28,Patnership,Complex
6244,377.31,Boston,W,24,18,15,58,Graduation,32,Franchise,Complex
3343,476.18,NJ City,E,5,15,21,34,High School,25,Owner,Standalone
5148,385.01,Boston,W,8,17,24,30,Graduation,30,Franchise,Complex
5218,392.1,Boston,S,7,9,3,32,High School,23,Patnership,Complex
6040,381.23,Boston,W,15,18,16,25,Graduation,35,Franchise,Complex
644,311.36,NJ City,W,18,18,10,71,High School,18,Franchise,Complex
2217,19.5,NJ City,N,16,13,3,26,Graduation,29,Patnership,Standalone
483,409.13,NJ City,E,24,11,13,44,High School,34,Owner,Standalone
6762,444.49,Boston,S,9,24,7,51,Graduation,19,Patnership,Complex
5621,33.28,Boston,N,8,23,4,28,No education,31,Patnership,Standalone
4969,209.1,Boston,N,9,15,24,46,High School,21,Patnership,Standalone
6037,416.46,Boston,N,3,17,8,51,Graduation,28,Patnership,Standalone
4636,376.16,NJ City,W,12,21,11,35,High School,24,Franchise,Complex
4316,393.35,NJ City,W,4,19,2,42,No education,21,Franchise,Complex
250,439.52,NJ City,S,24,8,7,45,High School,32,Patnership,Complex
2505,83.57,NJ City,N,25,16,2,68,Graduation,27,Patnership,Standalone
2123,80.3,NJ City,E,5,8,4,45,No education,29,Owner,Standalone
9137,150.06,NJ City,N,20,14,21,78,Graduation,35,Patnership,Standalone
7597,97.53,Boston,N,7,16,2,54,High School,22,Patnership,Standalone
9424,455.23,NJ City,W,1,16,12,26,Graduation,34,Franchise,Complex
9040,340.41,NJ City,W,4,16,2,63,High School,27,Franchise,Complex
621,335.31,NJ City,N,20,24,8,51,High School,32,Patnership,Standalone
5289,164.23,Boston,N,24,13,1,65,Graduation,24,Patnership,Standalone
9036,0.94,NJ City,N,4,20,1,67,Graduation,28,Franchise,Complex
603,138.14,NJ City,E,14,19,1,27,Graduation,33,Owner,Standalone
9673,299.94,NJ City,N,10,20,20,50,Graduation,21,Patnership,Standalone
2341,101.93,NJ City,N,7,14,1,55,High School,30,Patnership,Standalone
7194,230.22,Boston,S,7,22,24,75,Graduation,35,Patnership,Complex
6771,250.75,Boston,E,3,14,3,83,Graduation,21,Owner,Standalone
3431,342.87,NJ City,E,22,20,17,84,High School,33,Owner,Standalone
3640,470.04,NJ City,W,6,12,22,59,High School,29,Franchise,Complex
1102,342.69,NJ City,S,18,19,2,43,High School,24,Patnership,Complex
96,494.33,NJ City,W,7,17,10,41,High School,27,Franchise,Complex
249,495.2,NJ City,N,8,15,1,62,Graduation,19,Patnership,Standalone
5003,400.73,Boston,E,7,14,23,58,Graduation,34,Owner,Standalone
8763,112.04,NJ City,E,3,10,1,77,Graduation,35,Owner,Standalone
5890,29.12,Boston,N,25,12,3,46,High School,29,Patnership,Complex
8041,129.77,Boston,N,17,21,23,63,Graduation,27,Patnership,Standalone
6688,499.76,Boston,W,25,20,20,30,Graduation,26,Franchise,Complex
5775,256.95,Boston,E,18,10,19,40,Graduation,34,Owner,Standalone
2213,179.11,NJ City,N,9,23,3,46,No education,33,Patnership,Standalone
6561,370.42,Boston,N,5,18,7,53,Graduation,19,Patnership,Standalone
8250,438.22,NJ City,S,12,12,25,51,Graduation,22,Patnership,Complex
929,207.38,NJ City,N,18,18,5,78,No education,31,Patnership,Standalone
649,289.02,NJ City,N,11,12,18,75,High School,22,Patnership,Standalone
6623,365.7,Boston,E,5,8,21,79,Graduation,23,Owner,Standalone
6916,384.71,Boston,W,25,22,23,25,Graduation,34,Franchise,Complex
3682,110.36,NJ City,S,1,11,3,55,High School,31,Patnership,Complex
4742,443.89,NJ City,S,7,9,2,40,No education,35,Patnership,Complex
6409,62.9,Boston,N,15,15,4,26,High School,18,Patnership,Standalone
2491,397.9,NJ City,E,4,23,1,43,High School,35,Owner,Standalone
9761,375.25,NJ City,N,24,18,21,77,Graduation,33,Patnership,Standalone
1116,381.07,NJ City,W,14,17,4,79,Graduation,20,Franchise,Complex
761,441.32,NJ City,N,21,24,15,26,High School,20,Patnership,Standalone
2985,92.83,NJ City,N,4,19,4,70,Graduation,31,Patnership,Standalone
3383,442.63,NJ City,E,25,17,21,44,High School,32,Owner,Standalone
7627,294.96,Boston,E,5,13,4,70,High School,32,Owner,Standalone
8670,408.72,NJ City,S,22,10,10,58,Graduation,32,Patnership,Complex
2270,493.73,NJ City,S,25,13,24,81,High School,26,Patnership,Complex
1280,191.44,NJ City,W,7,9,15,70,High School,18,Franchise,Complex
7844,275.54,Boston,W,8,9,11,84,Graduation,26,Franchise,Complex
7829,237.5,Boston,N,21,24,8,53,Graduation,32,Patnership,Standalone
7697,249.45,Boston,N,1,15,10,28,Graduation,24,Patnership,Standalone
3246,159.98,NJ City,S,8,19,2,41,Graduation,21,Patnership,Complex
5194,233.56,Boston,S,19,9,8,27,Graduation,32,Patnership,Complex
5501,239.22,Boston,N,3,20,21,56,Graduation,21,Patnership,Standalone
5464,198.85,Boston,W,8,20,20,60,Graduation,35,Franchise,Complex
4081,127.6,NJ City,N,10,21,1,45,High School,22,Patnership,Standalone
8074,293.58,NJ City,S,23,8,11,36,Graduation,32,Patnership,Complex
7713,285.88,Boston,N,21,21,20,25,Graduation,28,Patnership,Standalone
4332,121.69,NJ City,W,16,11,4,76,Graduation,18,Franchise,Complex
9243,53.99,NJ City,E,19,13,4,83,Graduation,19,Owner,Standalone
4810,355.17,NJ City,S,19,20,18,26,High School,19,Patnership,Complex
6853,106.59,Boston,N,7,21,4,53,High School,34,Patnership,Standalone
3390,119.39,NJ City,S,21,10,3,75,Graduation,26,Patnership,Complex
7059,100.92,Boston,E,1,21,1,46,Graduation,21,Owner,Standalone
3753,423.92,NJ City,N,22,22,14,45,High School,31,Patnership,Standalone
4966,157.92,Boston,S,11,23,23,48,High School,35,Patnership,Complex
6592,390.68,Boston,W,8,13,18,46,Graduation,33,Franchise,Complex
9530,124.58,NJ City,S,8,18,1,38,No education,29,Patnership,Complex
8433,498.36,NJ City,N,3,22,5,35,Graduation,35,Patnership,Standalone
9485,97.92,NJ City,N,16,15,1,31,No education,35,Patnership,Standalone
935,306,NJ City,E,1,23,12,62,High School,28,Owner,Standalone
7055,207.42,Boston,E,20,15,10,29,Graduation,30,Owner,Standalone
5438,437.04,Boston,S,4,13,24,82,Graduation,32,Patnership,Complex
5607,137.54,Boston,E,18,8,10,34,Graduation,24,Owner,Standalone
455,192.53,NJ City,E,14,19,15,47,High School,30,Owner,Standalone
5710,179.33,Boston,S,14,20,4,36,High School,23,Patnership,Complex
8742,457.66,NJ City,S,25,12,6,77,Graduation,30,Patnership,Complex
4617,244.2,NJ City,N,11,14,7,84,High School,22,Patnership,Standalone
6513,17.63,Boston,N,6,22,4,62,Graduation,18,Patnership,Standalone
4841,146.44,NJ City,N,18,19,16,66,High School,31,Patnership,Standalone
4219,308.11,NJ City,E,7,17,6,76,High School,25,Owner,Standalone
428,320.44,NJ City,W,5,17,6,73,High School,29,Franchise,Complex
6475,27.68,Boston,N,13,10,1,35,High School,29,Owner,Standalone
1172,174.45,NJ City,W,14,9,11,41,High School,26,Franchise,Complex
3926,389.92,NJ City,S,23,12,5,36,No education,33,Patnership,Complex
5948,234.85,Boston,W,15,8,21,74,Graduation,20,Franchise,Complex
9473,399.14,NJ City,N,3,12,10,29,Graduation,20,Patnership,Standalone
9931,382.82,NJ City,E,18,20,17,36,Graduation,26,Owner,Standalone
2630,66.92,NJ City,N,12,15,2,61,No education,31,Patnership,Complex
3028,341.58,NJ City,W,11,20,18,63,High School,32,Franchise,Complex
7539,216.71,Boston,E,8,14,5,61,Graduation,18,Owner,Standalone
5290,355.96,Boston,S,10,11,15,84,Graduation,22,Patnership,Complex
6540,184.3,Boston,W,23,20,15,73,Graduation,28,Franchise,Complex
3066,465.62,NJ City,S,19,18,9,76,High School,31,Patnership,Complex
746,450.09,NJ City,S,22,19,7,50,High School,30,Patnership,Complex
982,343.63,NJ City,S,17,14,5,34,High School,21,Patnership,Complex
4326,399.42,NJ City,S,17,9,16,60,High School,33,Patnership,Complex
3404,495.42,NJ City,W,6,22,23,44,High School,32,Franchise,Complex
8419,129.72,NJ City,E,23,8,5,39,High School,30,Owner,Standalone
7930,316.98,Boston,S,5,17,1,28,High School,20,Patnership,Complex
3758,283.3,NJ City,S,10,23,25,63,High School,33,Patnership,Complex
2342,249.86,NJ City,S,21,14,22,28,High School,23,Patnership,Complex
5384,305.17,Boston,W,2,16,14,70,Graduation,23,Franchise,Complex
1425,445.05,NJ City,N,10,17,10,42,High School,35,Patnership,Standalone
4765,227.31,NJ City,N,21,11,9,77,High School,35,Patnership,Standalone
7221,199.21,Boston,N,20,23,15,57,Graduation,20,Patnership,Standalone
7184,338.56,Boston,W,13,23,1,63,No education,30,Franchise,Complex
1822,144.87,NJ City,S,5,18,2,49,High School,28,Patnership,Complex
3958,128.65,NJ City,S,8,11,6,77,High School,35,Patnership,Complex
4731,19.55,NJ City,N,2,12,3,38,Graduation,21,Owner,Standalone
6736,181.23,Boston,W,25,11,16,31,Graduation,27,Franchise,Complex
7837,243.09,Boston,N,9,12,21,76,Graduation,20,Patnership,Standalone
8345,426.56,NJ City,N,6,11,14,37,Graduation,26,Patnership,Standalone
8113,402.37,NJ City,N,16,18,19,26,Graduation,31,Patnership,Standalone
6625,430.08,Boston,N,13,18,5,62,High School,35,Patnership,Standalone
2478,342.88,NJ City,S,21,13,17,54,High School,34,Patnership,Complex
3484,48.38,NJ City,W,23,9,3,33,High School,26,Franchise,Complex
3281,164,NJ City,N,2,18,12,50,High School,31,Patnership,Standalone
497,473.25,NJ City,N,6,21,21,41,High School,28,Patnership,Standalone
415,350.13,NJ City,E,8,19,8,72,High School,32,Owner,Standalone
5049,292.85,Boston,N,21,20,19,74,Graduation,24,Patnership,Standalone
7613,439.17,Boston,N,18,12,4,77,No education,29,Patnership,Standalone
1711,261.15,NJ City,E,17,16,9,73,High School,27,Owner,Standalone
8436,120.91,NJ City,W,15,21,1,25,Graduation,29,Franchise,Complex
9296,198.5,NJ City,W,24,12,22,59,Graduation,34,Franchise,Complex
3713,74.05,NJ City,N,17,21,2,69,No education,18,Patnership,Standalone
9445,273.38,NJ City,N,1,22,9,54,Graduation,22,Patnership,Standalone
7042,478.17,Boston,S,5,23,1,52,High School,35,Patnership,Complex
8714,114.85,NJ City,S,18,11,2,31,No education,32,Patnership,Complex
8341,0.88,NJ City,N,16,9,2,41,High School,22,Patnership,Standalone
5645,383.39,Boston,N,2,9,2,81,No education,34,Patnership,Standalone
5803,7.31,Boston,N,12,8,3,26,High School,26,Owner,Standalone
9618,162.76,NJ City,S,13,19,24,77,Graduation,25,Patnership,Complex
9083,76.93,NJ City,E,1,9,2,26,No education,35,Owner,Standalone
433,348.18,NJ City,N,21,22,13,35,High School,24,Patnership,Standalone
2990,7.19,NJ City,N,6,21,4,26,No education,22,Patnership,Complex
1852,29.44,NJ City,N,7,19,4,51,High School,34,Franchise,Complex
4722,10.03,NJ City,N,13,15,1,58,Graduation,24,Patnership,Complex
7983,303.35,Boston,E,13,21,18,51,Graduation,33,Owner,Standalone
9660,85.4,NJ City,W,14,9,4,64,Graduation,29,Franchise,Complex
9321,253.93,NJ City,N,7,18,20,47,Graduation,34,Patnership,Standalone
2336,185.15,NJ City,W,6,18,14,38,High School,23,Franchise,Complex
1288,261,NJ City,W,2,22,21,34,High School,19,Franchise,Complex
5909,149.63,Boston,N,3,8,22,38,Graduation,30,Patnership,Standalone
3102,256.55,NJ City,S,12,11,8,44,High School,34,Patnership,Complex
3330,431.3,NJ City,S,12,11,24,82,High School,30,Patnership,Complex
5420,193.01,Boston,W,15,24,5,65,No education,22,Franchise,Complex
8186,488.45,NJ City,S,14,22,22,32,Graduation,25,Patnership,Complex
2868,435.94,NJ City,W,9,17,19,79,High School,33,Franchise,Complex
8441,338.49,NJ City,N,21,15,11,76,Graduation,32,Patnership,Standalone
7515,4.41,Boston,N,14,19,1,59,Graduation,31,Owner,Standalone
8383,306.68,NJ City,E,9,20,7,59,Graduation,25,Owner,Standalone
3114,297.11,NJ City,S,4,22,5,30,Graduation,24,Patnership,Complex
9593,141.12,NJ City,N,9,19,3,64,No education,30,Patnership,Standalone
307,425.5,NJ City,E,13,24,21,68,High School,35,Owner,Standalone
7803,33.5,Boston,N,8,10,2,83,Graduation,34,Owner,Standalone
1152,346.29,NJ City,W,23,13,6,52,High School,34,Franchise,Complex
1312,207.5,NJ City,W,5,12,23,46,High School,25,Franchise,Complex
9287,213.14,NJ City,E,9,15,24,64,Graduation,20,Owner,Standalone
337,388.85,NJ City,N,6,20,20,71,High School,33,Patnership,Standalone
6722,123.5,Boston,S,6,24,2,37,No education,30,Patnership,Complex
9554,216.86,NJ City,S,12,17,11,39,Graduation,33,Patnership,Complex
3887,497.87,NJ City,E,17,21,15,67,High School,20,Owner,Standalone
2534,206.72,NJ City,S,17,14,23,27,High School,22,Patnership,Complex
7913,382.19,Boston,N,19,10,18,66,Graduation,31,Patnership,Standalone
505,19.86,NJ City,N,18,18,2,72,High School,21,Patnership,Standalone
4099,405.38,NJ City,E,23,16,7,64,High School,22,Owner,Standalone
9191,200.68,NJ City,E,9,20,20,85,Graduation,34,Owner,Standalone
4370,454.96,NJ City,S,11,8,11,43,High School,27,Patnership,Complex
1561,165.57,NJ City,N,7,13,19,50,High School,33,Patnership,Standalone
2058,15.98,NJ City,N,8,14,1,35,Graduation,26,Patnership,Complex
766,330.19,NJ City,S,19,24,15,45,High School,22,Patnership,Complex
1269,407.02,NJ City,N,7,10,8,82,High School,25,Patnership,Standalone
8732,163.5,NJ City,W,17,24,12,52,Graduation,28,Franchise,Complex
8654,234.57,NJ City,S,20,24,24,41,Graduation,29,Patnership,Complex
6219,27.97,Boston,N,23,16,2,62,Graduation,22,Owner,Standalone
4231,169.88,NJ City,E,4,13,22,27,High School,26,Owner,Standalone
8162,365.1,NJ City,S,4,14,12,48,Graduation,21,Patnership,Complex
6851,436.81,Boston,E,10,8,2,77,No education,25,Owner,Standalone
1300,169.96,NJ City,W,6,9,16,77,High School,29,Franchise,Complex
7849,336.06,Boston,N,5,14,24,66,Graduation,18,Patnership,Standalone
8105,27.64,NJ City,N,1,10,1,35,No education,28,Patnership,Standalone
9348,319.69,NJ City,W,10,11,19,53,Graduation,18,Franchise,Complex
5705,12.78,Boston,N,7,13,3,25,No education,28,Patnership,Standalone
7300,214.25,Boston,W,18,8,9,63,Graduation,22,Franchise,Complex
5958,412.59,Boston,S,12,22,14,48,Graduation,20,Patnership,Complex
153,364.51,NJ City,N,17,17,25,84,High School,34,Patnership,Standalone
3672,210.47,NJ City,W,19,10,18,54,High School,30,Franchise,Complex
3213,349.9,NJ City,N,10,22,19,83,High School,33,Patnership,Standalone
4427,228.57,NJ City,E,13,10,24,53,High School,33,Owner,Standalone
3582,137,NJ City,S,3,10,14,48,High School,22,Patnership,Complex
5748,94.87,Boston,W,3,15,1,38,Graduation,26,Franchise,Complex
68,259.54,NY City,W,16,22,20,31,High School,18,Franchise,Complex
4136,119,NJ City,W,3,8,18,29,High School,24,Franchise,Complex
1249,300.64,NJ City,N,18,14,2,58,High School,20,Patnership,Standalone
4182,173.87,NJ City,S,25,18,14,41,High School,25,Patnership,Complex
3820,443.92,NJ City,W,17,17,8,60,High School,26,Franchise,Complex
4753,493.49,NJ City,N,7,15,10,65,High School,25,Patnership,Standalone
2154,395.62,NJ City,S,15,23,9,37,High School,35,Patnership,Complex
6191,239.83,Boston,E,7,17,12,50,Graduation,22,Owner,Standalone
7550,473.93,Boston,S,16,22,17,71,Graduation,35,Patnership,Complex
8191,44.3,NJ City,N,15,9,1,64,High School,24,Owner,Standalone
3581,26.8,NJ City,N,10,15,2,39,No education,28,Patnership,Standalone
6831,302.68,Boston,E,16,8,21,26,Graduation,23,Owner,Standalone
6443,342.31,Boston,E,17,21,14,29,Graduation,21,Owner,Standalone
5373,32.27,Boston,N,12,18,2,79,Graduation,35,Patnership,Standalone
8025,4.48,Boston,N,11,12,3,60,Graduation,34,Patnership,Standalone
2568,365.46,NJ City,W,25,12,11,82,High School,21,Franchise,Complex
2718,439.72,NJ City,S,4,18,22,81,High School,19,Patnership,Complex
4207,205,NJ City,E,2,13,14,55,High School,25,Owner,Standalone
1757,442.62,NJ City,N,15,13,21,85,High School,22,Patnership,Standalone
5086,374.04,Boston,S,14,18,5,80,High School,31,Patnership,Complex
4042,165.55,NJ City,S,24,23,25,74,High School,32,Patnership,Complex
4827,215,NJ City,E,3,23,8,39,High School,22,Owner,Standalone
340,356.78,NJ City,W,12,24,4,83,High School,22,Franchise,Complex
1556,136.97,NJ City,W,15,13,6,55,High School,22,Franchise,Complex
6548,11.05,Boston,N,15,13,2,45,No education,33,Franchise,Complex
6248,133.33,Boston,W,7,15,6,38,Graduation,32,Franchise,Complex
2941,256.48,NJ City,N,24,16,23,68,High School,23,Patnership,Standalone
9375,301.74,NJ City,E,1,23,17,69,Graduation,25,Owner,Standalone
2048,284.43,NJ City,W,1,18,1,44,No education,26,Franchise,Complex
2100,25.76,NJ City,N,1,15,1,34,Graduation,26,Franchise,Complex
2753,207.69,NJ City,N,19,18,24,62,High School,33,Patnership,Standalone
3189,199,NJ City,N,2,9,23,62,High School,21,Patnership,Standalone
1377,26.86,NJ City,N,10,17,3,68,Graduation,32,Patnership,Standalone
486,273,NJ City,S,1,10,11,56,High School,20,Patnership,Complex
5324,426.9,Boston,W,8,13,7,82,Graduation,32,Franchise,Complex
5165,492.48,Boston,N,17,19,24,34,Graduation,29,Patnership,Standalone
9257,403.91,NJ City,N,14,21,17,49,Graduation,18,Patnership,Standalone
3457,227.15,NJ City,N,7,11,17,55,High School,27,Patnership,Standalone
164,470.5,NJ City,W,7,10,19,84,High School,28,Franchise,Complex
6415,311.92,Boston,E,13,15,14,42,Graduation,35,Owner,Standalone
6405,286.25,Boston,N,22,20,7,42,Graduation,25,Patnership,Standalone
7429,101.8,Boston,N,11,12,3,52,High School,31,Patnership,Standalone
1824,52.65,NJ City,W,9,20,2,85,Graduation,27,Franchise,Complex
9131,303.79,NJ City,E,22,20,20,41,Graduation,28,Owner,Standalone
3894,125.95,NJ City,S,10,13,1,33,Graduation,32,Patnership,Complex
4381,365.35,NJ City,N,21,24,2,31,High School,19,Patnership,Standalone
5397,306.05,Boston,N,24,12,18,82,Graduation,21,Patnership,Standalone
3822,399.7,NJ City,S,23,24,4,37,Graduation,27,Patnership,Complex
7419,441.57,Boston,E,15,21,5,34,Graduation,25,Owner,Standalone
4711,174.86,NJ City,E,6,17,2,52,High School,19,Owner,Standalone
694,205,NJ City,S,3,16,20,27,High School,28,Patnership,Complex
2262,415.62,NJ City,S,13,20,25,27,High School,29,Patnership,Complex
7324,470.66,Boston,W,18,10,20,79,Graduation,19,Franchise,Complex
3094,318.92,NJ City,S,23,19,25,79,High School,27,Patnership,Complex
9644,314.66,NJ City,W,23,23,17,25,Graduation,28,Franchise,Complex
4708,445.62,NJ City,W,17,13,21,33,High School,23,Franchise,Complex
5882,38.95,Boston,N,4,9,2,56,No education,27,Patnership,Complex
8679,357.9,NJ City,E,1,24,9,67,Graduation,29,Owner,Standalone
1748,360.21,NJ City,W,20,21,1,61,No education,33,Franchise,Complex
4825,494.81,NJ City,N,7,12,1,72,High School,20,Patnership,Standalone
1817,324.79,NJ City,N,25,13,10,39,High School,21,Patnership,Standalone
6679,426.94,Boston,E,17,19,18,55,Graduation,22,Owner,Standalone
4488,282.62,NJ City,W,23,14,6,25,High School,32,Franchise,Complex
6354,255.91,Boston,S,7,20,11,27,Graduation,23,Patnership,Complex
2158,50.03,NJ City,S,6,22,3,31,High School,33,Patnership,Complex
6414,391.82,Boston,S,15,15,16,46,Graduation,18,Patnership,Complex
586,328.22,NJ City,S,11,21,12,71,High School,31,Patnership,Complex
729,187.96,NJ City,N,16,20,10,57,High School,30,Patnership,Standalone
9733,283.66,NJ City,N,9,18,9,82,Graduation,18,Patnership,Standalone
9586,173.83,NJ City,S,20,14,16,32,Graduation,24,Patnership,Complex
8999,241.42,NJ City,E,10,9,10,75,Graduation,25,Owner,Standalone
4243,460.97,NJ City,E,6,17,24,57,High School,31,Owner,Standalone
5805,159.35,Boston,N,8,16,22,59,Graduation,25,Patnership,Standalone
954,115.64,NJ City,S,18,13,3,55,Graduation,30,Patnership,Complex
6714,193.48,Boston,S,23,16,15,26,Graduation,31,Patnership,Complex
242,475.22,NJ City,S,8,10,13,74,High School,30,Patnership,Complex
8921,370.12,NJ City,N,19,19,8,70,Graduation,26,Patnership,Standalone
4824,96.43,NJ City,W,22,16,3,67,Graduation,25,Franchise,Complex
9636,332.37,NJ City,W,21,13,8,76,Graduation,24,Franchise,Complex
1557,452.53,NJ City,N,3,16,2,72,Graduation,22,Patnership,Standalone
3299,278,NJ City,E,2,9,10,81,High School,33,Owner,Standalone
9308,11.86,NJ City,N,19,14,1,40,No education,22,Franchise,Complex
5177,329.41,Boston,N,22,11,10,71,Graduation,35,Patnership,Standalone
7694,170.75,Boston,S,16,16,19,63,Graduation,33,Patnership,Complex
6888,192.15,Boston,W,22,23,5,60,Graduation,29,Franchise,Complex
5326,293.9,Boston,S,14,13,12,35,Graduation,27,Patnership,Complex
7967,313.95,Boston,E,24,16,18,37,Graduation,19,Owner,Standalone
6852,219.15,Boston,W,15,15,4,50,Graduation,33,Franchise,Complex
87,485.04,Boston,E,9,23,9,49,High School,26,Owner,Standalone
871,376.68,NJ City,E,7,14,16,76,High School,23,Owner,Standalone
9177,60.3,NJ City,N,1,14,3,77,Graduation,20,Patnership,Standalone
5844,310.65,Boston,W,5,12,8,28,Graduation,34,Franchise,Complex
2330,458.43,NJ City,S,23,11,1,67,No education,29,Patnership,Complex
550,145.05,NJ City,S,23,10,20,35,High School,33,Patnership,Complex
9442,264.02,NJ City,S,9,10,20,41,Graduation,28,Patnership,Complex
1701,106.89,NJ City,N,22,10,4,57,Graduation,32,Patnership,Standalone
4547,166.98,NJ City,E,21,22,10,49,High School,27,Owner,Standalone
8043,299.64,Boston,E,3,20,15,34,Graduation,34,Owner,Standalone
2469,164.81,NJ City,N,8,19,15,47,High School,32,Patnership,Standalone
1834,369.91,NJ City,S,20,16,23,36,High School,21,Patnership,Complex
3622,189.66,NJ City,S,20,12,8,71,High School,18,Patnership,Complex
144,37.4,NJ City,N,20,9,1,53,Graduation,22,Franchise,Complex
5344,391.07,Boston,W,21,13,14,27,Graduation,34,Franchise,Complex
1434,243.44,NJ City,S,25,23,19,49,High School,31,Patnership,Complex
9381,487.77,NJ City,N,16,9,8,57,Graduation,30,Patnership,Standalone
9704,443.11,NJ City,W,12,22,15,82,Graduation,30,Franchise,Complex
5807,220.96,Boston,E,9,12,11,45,Graduation,30,Owner,Standalone
8133,238.33,NJ City,N,8,12,13,42,Graduation,31,Patnership,Standalone
4752,82.48,NJ City,W,20,23,3,75,Graduation,32,Franchise,Complex
3927,366.1,NJ City,E,10,18,19,64,High School,23,Owner,Standalone
1440,159.35,NJ City,W,13,23,19,46,High School,31,Franchise,Complex
4659,312.24,NJ City,E,21,17,16,37,High School,30,Owner,Standalone
7305,134.08,Boston,N,3,13,21,67,Graduation,27,Patnership,Standalone
6708,377.39,Boston,W,23,8,8,82,Graduation,26,Franchise,Complex
747,335.12,NJ City,E,11,16,20,78,High School,35,Owner,Standalone
9100,162.33,NJ City,W,1,17,3,27,High School,23,Franchise,Complex
3773,89.7,NJ City,N,19,13,1,83,No education,23,Patnership,Standalone
8933,159.26,NJ City,N,9,22,2,34,No education,29,Patnership,Standalone
5417,92.08,Boston,N,25,24,2,36,No education,23,Patnership,Standalone
9574,5.52,NJ City,N,3,23,3,82,High School,19,Patnership,Complex
6014,106.25,Boston,S,13,14,4,76,No education,34,Patnership,Complex
4148,365.13,NJ City,W,12,21,9,66,High School,32,Franchise,Complex
5040,120.71,Boston,W,8,21,2,62,Graduation,30,Franchise,Complex
4174,121.14,NJ City,S,17,17,3,72,High School,31,Patnership,Complex
2359,60.14,NJ City,E,17,9,2,31,High School,27,Owner,Standalone
7004,201.6,Boston,W,10,9,17,30,Graduation,24,Franchise,Complex
1048,376.36,NJ City,W,20,21,8,85,High School,33,Franchise,Complex
7979,449.93,Boston,E,8,22,9,66,Graduation,28,Owner,Standalone
921,424.46,NJ City,N,13,21,8,64,High School,27,Patnership,Standalone
2112,331.2,NJ City,W,14,18,1,48,Graduation,29,Franchise,Complex
5782,69.07,Boston,N,16,18,3,69,High School,28,Patnership,Complex
1360,173.44,NJ City,W,15,14,21,66,High School,20,Franchise,Complex
9680,116.44,NJ City,W,1,22,1,26,No education,33,Franchise,Complex
2152,357.94,NJ City,W,19,20,16,66,High School,31,Franchise,Complex
1989,337.41,NJ City,N,20,19,2,49,Graduation,32,Patnership,Standalone
1258,474.52,NJ City,S,7,18,18,77,High School,32,Patnership,Complex
1709,317,NJ City,N,1,22,13,84,High School,19,Patnership,Standalone
3824,154.63,NJ City,W,7,12,18,79,High School,28,Franchise,Complex
7519,312.11,Boston,E,17,8,9,25,Graduation,21,Owner,Standalone
2378,484.4,NJ City,S,25,21,11,51,High School,26,Patnership,Complex
542,175.87,NJ City,S,16,11,15,69,High School,19,Patnership,Complex
2930,293.38,NJ City,S,9,8,4,81,No education,30,Patnership,Complex
1195,16.86,NJ City,N,14,14,4,67,High School,28,Owner,Standalone
8655,371.8,NJ City,E,19,19,13,74,Graduation,28,Owner,Standalone
8055,193.38,NJ City,E,9,15,12,68,Graduation,24,Owner,Standalone
4670,300,NJ City,S,1,22,6,61,High School,26,Patnership,Complex
1009,170,NJ City,N,3,17,19,78,High School,32,Patnership,Standalone
708,15.69,NJ City,N,22,13,3,26,Graduation,23,Franchise,Complex
7333,237.38,Boston,N,3,16,12,32,Graduation,20,Patnership,Standalone
2589,190.36,NJ City,N,6,24,13,47,High School,20,Patnership,Standalone
6129,445.9,Boston,N,24,9,12,84,Graduation,34,Patnership,Standalone
6922,326.16,Boston,S,25,15,18,39,Graduation,23,Patnership,Complex
1267,142.67,NJ City,E,6,16,23,63,High School,25,Owner,Standalone
9360,126.97,NJ City,W,13,22,1,43,Graduation,29,Franchise,Complex
4786,90.34,NJ City,S,23,22,4,35,High School,19,Patnership,Complex
9559,67.88,NJ City,N,12,13,1,27,High School,22,Owner,Standalone
9584,225.14,NJ City,W,3,18,21,42,Graduation,23,Franchise,Complex
2102,376.92,NJ City,S,15,16,10,30,High School,19,Patnership,Complex
8661,104.17,NJ City,N,19,10,3,35,Graduation,33,Patnership,Standalone
451,265.54,NJ City,E,25,12,22,81,High School,29,Owner,Standalone
7634,425.55,Boston,S,11,16,14,67,Graduation,34,Patnership,Complex
1938,300,NJ City,S,3,21,22,36,High School,34,Patnership,Complex
1433,210.82,NJ City,N,4,12,20,39,High School,19,Patnership,Standalone
4562,68.37,NJ City,N,6,18,3,57,No education,18,Patnership,Complex
7260,153.73,Boston,W,9,8,23,66,Graduation,19,Franchise,Complex
3507,252.55,NJ City,E,21,20,13,38,High School,30,Owner,Standalone
1740,305.63,NJ City,W,3,10,4,53,Graduation,19,Franchise,Complex
7078,151.91,Boston,S,12,17,2,47,High School,34,Patnership,Complex
1032,389.99,NJ City,W,17,10,15,33,High School,31,Franchise,Complex
1650,197.11,NJ City,S,12,9,18,28,High School,32,Patnership,Complex
8406,219.5,NJ City,S,6,17,8,72,Graduation,24,Patnership,Complex
9427,79.63,NJ City,E,8,8,1,47,High School,23,Owner,Standalone
7348,11.59,Boston,N,5,17,1,39,High School,24,Franchise,Complex
6968,17.71,Boston,N,18,21,2,25,No education,34,Franchise,Complex
8820,354.28,NJ City,W,6,12,14,32,Graduation,33,Franchise,Complex
519,206.82,NJ City,E,11,9,12,26,High School,19,Owner,Standalone
2587,495.04,NJ City,E,8,24,3,54,High School,35,Owner,Standalone
1060,114.95,NJ City,W,11,19,2,43,High School,22,Franchise,Complex
5869,242.89,Boston,N,11,14,1,55,High School,20,Patnership,Standalone
7628,387.17,Boston,W,5,24,16,74,Graduation,20,Franchise,Complex
7624,379.57,Boston,W,14,10,24,80,Graduation,23,Franchise,Complex
3186,269.93,NJ City,S,5,21,14,42,High School,31,Patnership,Complex
3411,492.01,NJ City,E,9,9,20,84,High School,19,Owner,Standalone
3720,435.96,NJ City,W,14,22,3,47,Graduation,24,Franchise,Complex
3362,177.12,NJ City,S,6,23,21,64,High School,34,Patnership,Complex
8147,256.96,NJ City,E,15,22,13,60,Graduation,24,Owner,Standalone
8876,89.66,NJ City,W,24,20,3,29,No education,31,Franchise,Complex
9330,385.98,NJ City,S,21,14,19,85,Graduation,27,Patnership,Complex
2675,477.55,NJ City,E,5,10,10,48,High School,23,Owner,Standalone
4758,329.01,NJ City,S,11,23,13,53,High School,27,Patnership,Complex
1694,274,NJ City,S,3,19,18,75,High School,21,Patnership,Complex
5918,254.88,Boston,S,19,9,23,76,Graduation,34,Patnership,Complex
1575,28.05,NJ City,N,14,10,1,31,Graduation,30,Owner,Standalone
6915,128.7,Boston,E,19,14,9,43,Graduation,19,Owner,Standalone
4611,320.46,NJ City,E,10,19,23,38,High School,27,Owner,Standalone
4348,5.4,NJ City,N,22,10,3,42,High School,19,Franchise,Complex
8387,180.24,NJ City,E,7,8,1,83,No education,20,Owner,Standalone
5019,115.89,Boston,E,1,11,2,63,Graduation,33,Owner,Standalone
6175,376.26,Boston,E,20,13,4,49,High School,32,Owner,Standalone
7265,497.06,Boston,N,4,9,19,44,Graduation,34,Patnership,Standalone
3800,219.6,NJ City,W,14,11,19,29,High School,19,Franchise,Complex
8540,472.7,NJ City,W,10,24,2,76,No education,33,Franchise,Complex
5758,194.88,Boston,S,1,13,4,47,High School,29,Patnership,Complex
6011,27.29,Boston,N,12,22,1,70,No education,35,Owner,Standalone
3715,250,NJ City,E,6,21,8,60,High School,23,Owner,Standalone
2767,432.96,NJ City,E,5,23,15,79,High School,26,Owner,Standalone
2800,306.37,NJ City,W,9,21,14,69,High School,30,Franchise,Complex
6595,90.73,Boston,E,8,22,4,33,High School,33,Owner,Standalone
6749,319.21,Boston,N,22,12,10,36,Graduation,24,Patnership,Standalone
7957,110.27,Boston,N,14,23,3,52,High School,27,Patnership,Standalone
7229,107.08,Boston,N,10,8,3,51,No education,28,Patnership,Standalone
6764,30.93,Boston,N,21,13,4,33,No education,35,Franchise,Complex
5023,152.91,Boston,E,20,18,8,62,Graduation,29,Owner,Standalone
3098,434.32,NJ City,S,4,10,4,62,No education,24,Patnership,Complex
9030,313.89,NJ City,S,11,19,4,36,Graduation,29,Patnership,Complex
7090,16.68,Boston,N,21,11,3,40,High School,32,Patnership,Complex
1856,311.95,NJ City,W,21,9,10,69,High School,23,Franchise,Complex
6307,446.85,Boston,E,15,22,20,30,Graduation,21,Owner,Standalone
3790,130.83,NJ City,S,18,8,7,76,High School,26,Patnership,Complex
1113,83.27,NJ City,N,10,15,3,49,Graduation,18,Patnership,Standalone
4247,480.28,NJ City,E,8,24,19,63,High School,22,Owner,Standalone
4785,443.21,NJ City,N,6,13,16,75,High School,26,Patnership,Standalone
8405,162.35,NJ City,N,16,16,11,60,Graduation,25,Patnership,Standalone
3223,388.49,NJ City,E,11,11,4,53,High School,18,Owner,Standalone
1057,256.42,NJ City,N,22,14,5,61,High School,32,Patnership,Standalone
2602,264.27,NJ City,S,19,16,3,65,High School,21,Patnership,Complex
9328,56.05,NJ City,W,18,10,4,57,High School,23,Franchise,Complex
6245,44.53,Boston,N,19,14,4,40,No education,28,Patnership,Standalone
7510,146.46,Boston,S,19,19,6,71,Graduation,20,Patnership,Complex
7577,372.6,Boston,N,19,11,17,59,Graduation,29,Patnership,Standalone
4347,231.53,NJ City,E,11,15,18,78,High School,23,Owner,Standalone
329,248,NJ City,N,3,12,25,40,High School,28,Patnership,Standalone
5663,1.4,Boston,N,5,24,3,58,No education,27,Owner,Standalone
5262,372.33,Boston,S,19,8,1,41,Graduation,19,Patnership,Complex
2050,7.82,NJ City,N,25,9,1,49,High School,18,Patnership,Complex
750,299.57,NJ City,S,6,14,10,40,High School,30,Patnership,Complex
4417,344.78,NJ City,N,2,8,2,34,High School,25,Patnership,Standalone
5804,290.92,Boston,W,6,16,12,32,Graduation,26,Franchise,Complex
7041,98.07,Boston,N,8,22,2,51,Graduation,33,Patnership,Standalone
6566,317.86,Boston,S,3,23,18,29,Graduation,30,Patnership,Complex
886,141.69,NJ City,S,15,24,23,78,High School,30,Patnership,Complex
3265,235,NJ City,N,2,21,6,63,High School,27,Patnership,Standalone
5675,149.73,Boston,E,5,15,6,38,Graduation,20,Owner,Standalone
141,287,NJ City,N,3,9,13,76,High School,28,Patnership,Standalone
2504,8.41,NJ City,N,7,17,2,83,No education,27,Franchise,Complex
5401,300.5,Boston,N,1,15,22,49,Graduation,26,Patnership,Standalone
2883,239.37,NJ City,E,24,13,24,31,High School,24,Owner,Standalone
109,157.06,NJ City,N,22,22,18,57,High School,30,Patnership,Standalone
532,462.48,NJ City,W,4,15,22,79,High School,29,Franchise,Complex
5001,91.78,Boston,N,16,16,4,66,Graduation,27,Patnership,Standalone
1589,349.38,NJ City,N,19,10,12,49,High School,29,Patnership,Standalone
2374,15.15,NJ City,N,16,14,4,78,High School,25,Patnership,Complex
446,183.76,NJ City,S,16,13,5,54,No education,33,Patnership,Complex
3292,308.17,NJ City,W,10,19,2,38,High School,26,Franchise,Complex
593,488.2,NJ City,N,13,21,14,33,High School,18,Patnership,Standalone
3218,44.8,NJ City,N,14,12,4,67,No education,21,Patnership,Complex
4277,360.19,NJ City,N,23,12,13,66,High School,28,Patnership,Standalone
594,76.95,NJ City,S,25,21,2,70,Graduation,28,Patnership,Complex
1456,390.19,NJ City,W,15,9,22,46,High School,25,Franchise,Complex
2424,101.13,NJ City,W,1,16,3,65,Graduation,35,Franchise,Complex
4648,89.14,NJ City,W,2,23,4,64,High School,26,Franchise,Complex
3495,115.93,NJ City,E,13,16,4,61,Graduation,19,Owner,Standalone
4416,65.1,NJ City,N,12,12,2,78,Graduation,20,Franchise,Complex
7258,436.68,Boston,S,25,13,24,73,Graduation,32,Patnership,Complex
5834,307.71,Boston,S,8,10,10,66,Graduation,28,Patnership,Complex
136,234.76,NJ City,W,16,23,10,52,High School,19,Franchise,Complex
4121,196.17,NJ City,N,7,20,10,38,High School,25,Patnership,Standalone
4511,459.12,NJ City,E,12,11,22,80,High School,33,Owner,Standalone
8412,53.02,NJ City,W,6,24,3,34,Graduation,23,Franchise,Complex
5754,286.99,Boston,S,7,14,10,60,Graduation,25,Patnership,Complex
1885,430.96,NJ City,N,7,9,23,65,High School,32,Patnership,Standalone
9775,495.41,NJ City,E,6,17,25,43,Graduation,34,Owner,Standalone
5403,403.14,Boston,E,5,15,18,55,Graduation,30,Owner,Standalone
3895,285.32,NJ City,E,5,14,6,58,High School,18,Owner,Standalone
1883,135.02,NJ City,E,12,17,19,69,High School,34,Owner,Standalone
5638,354.78,Boston,S,23,23,9,51,Graduation,35,Patnership,Complex
9795,79.33,NJ City,E,23,24,3,38,Graduation,22,Owner,Standalone
5106,42.96,Boston,N,5,21,4,80,Graduation,30,Patnership,Complex
9878,196.36,NJ City,S,5,17,19,69,Graduation,23,Patnership,Complex
4331,194.55,NJ City,E,5,19,8,83,High School,27,Owner,Standalone
6096,164.66,Boston,W,2,18,17,47,Graduation,25,Franchise,Complex
5833,420.39,Boston,N,2,11,7,83,Graduation,24,Patnership,Standalone
4202,225.26,NJ City,S,14,9,9,38,High School,19,Patnership,Complex
8061,386.36,NJ City,N,20,9,21,77,Graduation,24,Patnership,Standalone
2613,150,NJ City,N,3,24,21,65,High School,22,Patnership,Standalone
2499,10.75,NJ City,N,17,8,2,74,Graduation,29,Owner,Standalone
6403,412.73,Boston,E,18,13,14,30,Graduation,35,Owner,Standalone
7750,117.94,Boston,S,24,14,4,35,High School,27,Patnership,Complex
8823,237.37,NJ City,E,23,14,24,69,Graduation,25,Owner,Standalone
8028,252.99,Boston,W,5,13,13,36,Graduation,19,Franchise,Complex
3027,375.16,NJ City,E,23,9,16,43,High School,31,Owner,Standalone
3391,111.05,NJ City,E,5,9,4,58,High School,18,Owner,Standalone
7038,267.11,Boston,S,4,18,6,43,Graduation,31,Patnership,Complex
9956,253.13,NJ City,W,8,21,19,85,Graduation,21,Franchise,Complex
1508,203.33,NJ City,W,22,17,14,66,High School,20,Franchise,Complex
4721,119,NJ City,N,2,13,7,35,High School,21,Patnership,Standalone
9539,353.93,NJ City,E,16,15,5,43,No education,35,Owner,Standalone
1445,168,NJ City,N,1,21,10,57,High School,33,Patnership,Standalone
8505,112.58,NJ City,N,21,23,3,40,Graduation,25,Patnership,Standalone
3500,402.13,NJ City,W,18,21,15,49,High School,22,Franchise,Complex
4973,244.02,Boston,N,23,16,14,75,High School,20,Patnership,Standalone
8590,487.01,NJ City,S,9,11,11,44,Graduation,32,Patnership,Complex
733,450.79,NJ City,N,5,20,20,41,High School,28,Patnership,Standalone
150,268.21,NJ City,S,18,11,17,43,High School,19,Patnership,Complex
2609,172.98,NJ City,N,8,8,20,28,High School,18,Patnership,Standalone
1747,294.54,NJ City,E,4,16,16,80,High School,33,Owner,Standalone
1884,150.3,NJ City,W,23,15,11,72,High School,33,Franchise,Complex
9499,447.41,NJ City,E,9,20,3,26,High School,25,Owner,Standalone
4300,323.02,NJ City,W,5,10,24,46,High School,21,Franchise,Complex
8782,448.51,NJ City,S,23,13,11,74,Graduation,34,Patnership,Complex
4318,10.02,NJ City,N,9,21,4,81,High School,24,Patnership,Complex
6612,36.76,Boston,N,10,16,2,29,Graduation,19,Franchise,Complex
4735,362.87,NJ City,E,15,14,1,54,High School,29,Owner,Standalone
7421,119.12,Boston,N,25,22,2,58,No education,28,Patnership,Standalone
3577,27.01,NJ City,N,12,10,1,85,High School,19,Patnership,Standalone
9196,427.81,NJ City,W,13,16,12,72,Graduation,23,Franchise,Complex
6121,78.01,Boston,N,21,20,4,80,High School,25,Patnership,Standalone
6399,178.98,Boston,E,16,10,4,50,Graduation,31,Owner,Standalone
7505,365.35,Boston,N,24,16,25,68,Graduation,35,Patnership,Standalone
5291,428.93,Boston,E,2,18,18,84,Graduation,20,Owner,Standalone
9799,276.96,NJ City,E,14,16,1,57,High School,18,Owner,Standalone
775,63.72,NJ City,N,21,8,3,25,High School,34,Owner,Standalone
6756,314.98,Boston,W,1,10,18,38,Graduation,20,Franchise,Complex
8474,40.05,NJ City,N,22,13,3,39,No education,33,Patnership,Complex
9932,260.53,NJ City,W,16,12,25,37,Graduation,22,Franchise,Complex
6871,414.39,Boston,E,8,24,8,85,Graduation,25,Owner,Standalone
7374,53.24,Boston,S,13,9,2,64,Graduation,22,Patnership,Complex
2719,316.02,NJ City,E,1,8,2,55,High School,26,Owner,Standalone
8403,224.16,NJ City,E,23,18,22,35,Graduation,35,Owner,Standalone
9640,102.07,NJ City,W,23,14,4,58,High School,23,Franchise,Complex
7357,347.9,Boston,N,1,20,9,65,Graduation,32,Patnership,Standalone
8329,80.09,NJ City,N,10,8,2,78,High School,22,Patnership,Standalone
9629,494.44,NJ City,N,25,23,5,80,No education,31,Patnership,Standalone
5452,353.79,Boston,W,5,11,5,45,High School,31,Franchise,Complex
2312,85.02,NJ City,W,1,16,3,55,No education,24,Franchise,Complex
1688,1.36,NJ City,N,20,15,3,45,No education,25,Franchise,Complex
5600,225.58,Boston,W,1,11,11,78,Graduation,27,Franchise,Complex
3638,186,NJ City,S,2,13,19,52,High School,35,Patnership,Complex
8519,66.42,NJ City,N,9,20,2,26,No education,20,Owner,Standalone
8807,108.01,NJ City,E,7,12,1,39,No education,26,Owner,Standalone
8869,292.7,NJ City,N,13,12,7,68,Graduation,19,Patnership,Standalone
3306,262.93,NJ City,S,25,22,22,35,High School,32,Patnership,Complex
564,44.83,NJ City,N,8,22,1,57,Graduation,29,Franchise,Complex
2907,264.2,NJ City,E,12,12,18,52,High School,34,Owner,Standalone
9621,341.24,NJ City,N,5,16,16,29,Graduation,20,Patnership,Standalone
7261,399.33,Boston,N,11,14,2,60,High School,25,Patnership,Standalone
957,354.03,NJ City,N,23,8,21,61,High School,31,Patnership,Standalone
1840,182.6,NJ City,W,8,13,22,31,High School,35,Franchise,Complex
7892,197.82,Boston,W,6,13,11,42,Graduation,34,Franchise,Complex
4639,460.22,NJ City,E,10,14,16,72,High School,25,Owner,Standalone
1836,116.36,NJ City,W,10,17,3,62,Graduation,32,Franchise,Complex
137,17.78,NJ City,N,17,19,3,63,No education,28,Patnership,Standalone
3642,18.03,NJ City,N,8,12,4,35,Graduation,28,Patnership,Complex
8834,159.76,NJ City,S,6,8,25,54,Graduation,30,Patnership,Complex
9003,391.9,NJ City,E,7,10,2,53,Graduation,30,Owner,Standalone
1858,236.4,NJ City,S,13,14,20,39,High School,23,Patnership,Complex
5449,285.42,Boston,N,16,21,8,85,Graduation,32,Patnership,Standalone
9886,66.54,NJ City,N,13,15,2,67,High School,31,Patnership,Complex
2169,104.06,NJ City,N,3,18,2,79,Graduation,25,Patnership,Standalone
1581,424.09,NJ City,N,10,11,1,85,Graduation,29,Patnership,Standalone
1534,98.88,NJ City,S,18,20,3,31,High School,30,Patnership,Complex
4881,337.02,NJ City,N,9,22,15,53,High School,27,Patnership,Standalone
739,233.51,NJ City,E,12,21,21,78,High School,27,Owner,Standalone
396,317.65,NJ City,W,15,18,9,67,High School,32,Franchise,Complex
6778,221.31,Boston,S,15,10,23,78,Graduation,19,Patnership,Complex
8089,379.75,NJ City,N,21,9,11,62,Graduation,34,Patnership,Standalone
3026,221.86,NJ City,S,1,9,1,48,No education,33,Patnership,Complex
6343,252.79,Boston,E,10,13,24,27,Graduation,22,Owner,Standalone
8221,362.7,NJ City,N,9,16,13,33,Graduation,32,Patnership,Standalone
2149,366.27,NJ City,N,21,9,24,82,High School,33,Patnership,Standalone
4487,208.2,NJ City,E,5,16,10,27,High School,35,Owner,Standalone
1332,4.87,NJ City,N,5,22,2,47,Graduation,18,Franchise,Complex
4193,196.93,NJ City,N,20,21,9,71,High School,27,Patnership,Standalone
620,91.49,NJ City,W,7,22,4,54,No education,19,Franchise,Complex
18,425.42,Boston,S,24,20,25,64,High School,27,Patnership,Complex
4970,50.83,Boston,S,3,17,4,32,No education,30,Patnership,Complex
1245,138.13,NJ City,N,5,18,22,37,High School,30,Patnership,Standalone
9895,2.12,NJ City,E,4,23,3,41,High School,35,Owner,Standalone
3805,260.27,NJ City,N,14,22,6,80,High School,20,Patnership,Standalone
6407,134.51,Boston,E,14,13,9,69,Graduation,26,Owner,Standalone
1413,47.27,NJ City,N,13,18,3,60,Graduation,32,Patnership,Standalone
1563,324.2,NJ City,E,13,14,1,65,Graduation,32,Owner,Standalone
4941,291.44,Boston,N,18,19,25,41,High School,33,Patnership,Standalone
4643,344.5,NJ City,E,25,15,19,47,High School,20,Owner,Standalone
1076,394.87,NJ City,W,19,10,17,33,High School,24,Franchise,Complex
2743,380.95,NJ City,E,14,9,9,39,High School,19,Owner,Standalone
2820,299.21,NJ City,W,12,13,8,41,High School,32,Franchise,Complex
758,455.76,NJ City,S,4,13,1,31,No education,28,Patnership,Complex
6521,352.98,Boston,N,11,21,19,65,Graduation,22,Patnership,Standalone
2839,59.39,NJ City,E,14,17,4,41,High School,24,Owner,Standalone
580,498.23,NJ City,W,20,22,15,40,High School,18,Franchise,Complex
339,156.54,NJ City,E,18,9,20,56,High School,34,Owner,Standalone
6057,85.83,Boston,N,25,11,2,47,Graduation,20,Patnership,Standalone
9081,259.7,NJ City,N,22,24,24,45,Graduation,29,Patnership,Standalone
3395,42.21,NJ City,N,10,18,3,72,No education,30,Owner,Standalone
4110,351.59,NJ City,S,10,13,23,85,High School,28,Patnership,Complex
3747,161.25,NJ City,E,14,19,10,71,High School,19,Owner,Standalone
9140,494.16,NJ City,W,15,15,24,81,Graduation,33,Franchise,Complex
5653,203.12,Boston,N,16,11,2,46,High School,23,Patnership,Standalone
7944,316.9,Boston,W,8,22,20,62,Graduation,18,Franchise,Complex
3422,23.62,NJ City,N,21,18,1,83,No education,35,Patnership,Complex
9366,157.35,NJ City,S,8,11,20,25,Graduation,32,Patnership,Complex
3099,17.02,NJ City,N,4,18,3,70,Graduation,20,Owner,Standalone
3338,327.45,NJ City,S,14,15,25,29,High School,31,Patnership,Complex
4474,273.05,NJ City,S,21,14,20,49,High School,22,Patnership,Complex
7904,310.24,Boston,W,13,14,20,34,Graduation,28,Franchise,Complex
9977,302.97,NJ City,N,21,23,4,85,No education,33,Patnership,Standalone
1240,39.11,NJ City,N,14,11,3,58,High School,24,Franchise,Complex
1303,173,NJ City,E,3,16,10,70,High School,19,Owner,Standalone
3619,73.37,NJ City,N,15,9,3,47,High School,24,Owner,Standalone
7687,50.19,Boston,E,14,22,2,52,High School,34,Owner,Standalone
4890,30.03,NJ City,N,20,21,1,78,Graduation,19,Patnership,Complex
7232,21.54,Boston,N,19,10,4,57,No education,27,Franchise,Complex
8451,390.09,NJ City,E,24,22,3,43,Graduation,29,Owner,Standalone
9275,48.62,NJ City,E,19,22,3,38,No education,29,Owner,Standalone
6095,235.33,Boston,E,23,13,21,71,Graduation,25,Owner,Standalone
8817,149.92,NJ City,N,20,13,22,55,Graduation,18,Patnership,Standalone
4542,59.22,NJ City,S,25,14,3,72,Graduation,29,Patnership,Complex
362,392.8,NJ City,S,5,15,16,75,High School,32,Patnership,Complex
9400,269.04,NJ City,W,15,19,5,65,High School,27,Franchise,Complex
1490,44.72,NJ City,N,3,22,4,67,No education,34,Patnership,Complex
2892,279.83,NJ City,W,12,22,24,43,High School,35,Franchise,Complex
4164,186.67,NJ City,W,15,24,16,71,High School,27,Franchise,Complex
7438,393.92,Boston,S,24,17,21,51,Graduation,33,Patnership,Complex
7948,101.25,Boston,W,9,10,1,82,High School,24,Franchise,Complex
3133,195.92,NJ City,N,6,14,25,52,High School,28,Patnership,Standalone
1439,209.14,NJ City,E,25,12,16,56,High School,35,Owner,Standalone
3357,274.66,NJ City,N,11,13,10,49,High School,33,Patnership,Standalone
6048,4.35,Boston,N,14,16,1,39,Graduation,31,Franchise,Complex
4053,496.53,NJ City,N,18,16,16,84,High School,19,Patnership,Standalone
9062,261.69,NJ City,S,20,23,11,63,Graduation,35,Patnership,Complex
4024,469.5,NJ City,W,24,17,1,38,High School,33,Franchise,Complex
7810,192.66,Boston,S,17,19,15,49,Graduation,24,Patnership,Complex
4592,335.85,NJ City,W,10,14,10,65,High School,27,Franchise,Complex
9184,370.79,NJ City,W,20,15,10,51,Graduation,29,Franchise,Complex
7098,122.3,Boston,S,17,18,3,44,Graduation,21,Patnership,Complex
8063,228.8,NJ City,E,6,15,17,44,Graduation,30,Owner,Standalone
8291,439.23,NJ City,E,25,24,3,68,No education,32,Owner,Standalone
1555,470.9,NJ City,E,14,16,25,67,High School,25,Owner,Standalone
3609,273.32,NJ City,N,14,15,9,75,High School,27,Patnership,Standalone
4150,28.57,NJ City,N,19,16,3,27,High School,35,Patnership,Complex
2735,111.14,NJ City,E,6,11,1,37,No education,33,Owner,Standalone
3486,269.55,NJ City,S,18,24,9,43,High School,29,Patnership,Complex
8743,352.98,NJ City,E,9,17,22,26,Graduation,21,Owner,Standalone
3377,452.97,NJ City,N,16,19,19,68,High School,28,Patnership,Standalone
5733,151.34,Boston,N,24,11,10,45,Graduation,18,Patnership,Standalone
8706,137.25,NJ City,S,10,19,3,33,Graduation,35,Patnership,Complex
3646,0.48,NJ City,N,1,12,4,31,High School,18,Patnership,Complex
2618,369.27,NJ City,S,18,9,21,77,High School,32,Patnership,Complex
6318,199.12,Boston,S,13,11,25,69,Graduation,33,Patnership,Complex
417,199.89,NJ City,N,19,19,12,42,High School,30,Patnership,Standalone
544,50.52,NJ City,W,13,12,2,80,High School,34,Franchise,Complex
5842,479.73,Boston,S,13,20,13,68,Graduation,35,Patnership,Complex
9959,1.18,NJ City,N,14,13,2,58,No education,23,Owner,Standalone
709,230.03,NJ City,N,25,22,13,85,High School,20,Patnership,Standalone
6241,33.19,Boston,N,9,24,2,34,High School,31,Patnership,Standalone
8077,182.66,NJ City,N,23,18,6,77,Graduation,23,Patnership,Standalone
3983,93.63,NJ City,E,17,19,2,47,No education,23,Owner,Standalone
7855,45.39,Boston,N,10,19,3,31,High School,24,Owner,Standalone
7633,459.38,Boston,N,3,15,4,31,High School,20,Patnership,Standalone
8482,72.98,NJ City,N,2,17,3,82,High School,32,Patnership,Complex
7963,455.62,Boston,E,22,15,3,63,High School,27,Owner,Standalone
7538,383.51,Boston,S,24,22,6,49,Graduation,23,Patnership,Complex
9619,301.39,NJ City,E,17,20,9,84,Graduation,23,Owner,Standalone
8420,487.84,NJ City,W,25,9,25,43,Graduation,30,Franchise,Complex
1684,470.95,NJ City,W,24,15,20,26,High School,35,Franchise,Complex
1488,292.57,NJ City,W,15,17,21,36,High School,35,Franchise,Complex
3868,83.84,NJ City,W,16,24,1,41,High School,25,Franchise,Complex
6365,245.55,Boston,N,3,17,7,84,Graduation,34,Patnership,Standalone
2947,262.81,NJ City,E,6,16,23,84,High School,30,Owner,Standalone
5383,262.09,Boston,E,11,20,4,36,High School,30,Owner,Standalone
790,148,NJ City,S,2,20,20,25,High School,31,Patnership,Complex
254,0.67,NJ City,N,11,17,1,32,No education,33,Patnership,Complex
4698,167.63,NJ City,S,5,22,15,84,High School,20,Patnership,Complex
4222,101.47,NJ City,S,22,14,1,29,High School,23,Patnership,Complex
666,243,NJ City,S,1,9,10,33,High School,22,Patnership,Complex
2044,307,NJ City,W,3,8,18,80,High School,28,Franchise,Complex
2080,7.8,NJ City,N,8,23,1,61,High School,31,Franchise,Complex
3273,356.62,NJ City,N,6,15,24,44,High School,25,Patnership,Standalone
8424,267.68,NJ City,W,2,13,2,47,Graduation,24,Franchise,Complex
9452,338.72,NJ City,W,7,14,24,30,Graduation,26,Franchise,Complex
5459,453.82,Boston,E,7,14,18,63,Graduation,27,Owner,Standalone
2455,222.85,NJ City,E,25,12,14,57,High School,27,Owner,Standalone
2433,292.41,NJ City,N,6,22,13,63,High School,22,Patnership,Standalone
7185,355.27,Boston,N,14,18,7,57,Graduation,32,Patnership,Standalone
2859,26.68,NJ City,N,5,21,4,72,Graduation,24,Owner,Standalone
2459,102.98,NJ City,E,25,21,4,27,No education,28,Owner,Standalone
7440,338.88,Boston,W,3,8,9,65,Graduation,25,Franchise,Complex
7190,154.56,Boston,S,1,24,21,66,Graduation,28,Patnership,Complex
8379,321.32,NJ City,E,10,16,13,33,Graduation,19,Owner,Standalone
3777,244,NJ City,N,3,11,11,40,High School,30,Patnership,Standalone
7332,6.7,Boston,N,5,14,2,46,Graduation,31,Franchise,Complex
3684,437.2,NJ City,W,8,11,2,78,Graduation,32,Franchise,Complex
5293,470.02,Boston,N,3,10,4,82,High School,30,Patnership,Standalone
7175,375.74,Boston,E,21,23,8,33,Graduation,25,Owner,Standalone
3972,405.72,NJ City,W,17,10,1,42,Graduation,25,Franchise,Complex
3424,293,NJ City,W,3,19,8,69,High School,27,Franchise,Complex
7480,275.15,Boston,W,19,16,5,78,High School,33,Franchise,Complex
8709,379.18,NJ City,N,11,20,14,56,Graduation,31,Patnership,Standalone
1004,262.09,NJ City,W,23,20,17,82,High School,21,Franchise,Complex
5054,45.8,Boston,N,4,8,2,31,No education,33,Patnership,Complex
7685,469.12,Boston,N,5,8,3,70,No education,27,Patnership,Standalone
6266,421.81,Boston,S,1,13,4,34,No education,28,Patnership,Complex
6788,72.47,Boston,N,21,20,4,65,No education,29,Franchise,Complex
1964,280.24,NJ City,W,25,10,19,71,High School,20,Franchise,Complex
3239,452.65,NJ City,E,9,16,23,26,High School,33,Owner,Standalone
6109,415.25,Boston,N,22,21,4,51,High School,28,Patnership,Standalone
108,237.69,NJ City,W,18,20,17,80,High School,32,Franchise,Complex
9945,165,NJ City,N,13,22,14,51,Graduation,26,Patnership,Standalone
3045,465.03,NJ City,N,13,20,22,56,High School,24,Patnership,Standalone
9701,208.05,NJ City,N,7,23,9,81,Graduation,20,Patnership,Standalone
6364,95.93,Boston,W,19,8,4,84,High School,20,Franchise,Complex
6683,494.72,Boston,E,24,14,9,79,Graduation,30,Owner,Standalone
6201,123.97,Boston,N,9,18,2,48,Graduation,35,Patnership,Standalone
2620,385.01,NJ City,W,10,17,23,58,High School,19,Franchise,Complex
4297,96.67,NJ City,N,23,22,1,80,High School,18,Patnership,Standalone
4642,340.09,NJ City,S,6,12,6,43,High School,31,Patnership,Complex
3566,436.53,NJ City,S,21,22,22,63,High School,30,Patnership,Complex
8934,1.66,NJ City,N,13,19,1,50,Graduation,21,Patnership,Complex
5693,179.12,Boston,N,8,14,21,28,Graduation,19,Patnership,Standalone
1521,167.6,NJ City,N,10,14,6,26,High School,20,Patnership,Standalone
7971,343.3,Boston,E,3,17,18,45,Graduation,29,Owner,Standalone
4599,200.92,NJ City,E,9,21,15,34,High School,23,Owner,Standalone
2390,128.91,NJ City,S,4,19,1,59,No education,26,Patnership,Complex
8627,269.03,NJ City,E,17,9,24,44,Graduation,24,Owner,Standalone
7582,326.31,Boston,S,24,8,15,41,Graduation,29,Patnership,Complex
2622,47.92,NJ City,S,14,10,3,84,Graduation,31,Patnership,Complex
5866,436.57,Boston,S,22,12,23,37,Graduation,20,Patnership,Complex
9069,397.03,NJ City,N,17,23,4,64,Graduation,21,Patnership,Standalone
5673,363.76,Boston,N,16,24,16,38,Graduation,21,Patnership,Standalone
7763,42.06,Boston,N,16,18,3,78,No education,32,Owner,Standalone
984,433.51,NJ City,W,22,10,6,29,High School,26,Franchise,Complex
263,308.71,NJ City,E,3,23,2,69,No education,19,Owner,Standalone
4138,29.71,NJ City,N,21,21,4,56,High School,35,Patnership,Complex
5576,54.49,Boston,W,1,23,2,61,No education,19,Franchise,Complex
4775,476.12,NJ City,E,3,13,5,79,No education,25,Owner,Standalone
9786,416.32,NJ City,S,2,10,9,84,Graduation,29,Patnership,Complex
4552,127.22,NJ City,W,20,18,1,68,High School,32,Franchise,Complex
6891,431.19,Boston,E,25,11,24,83,Graduation,34,Owner,Standalone
1143,219.11,NJ City,E,18,10,5,60,Graduation,20,Owner,Standalone
4725,274,NJ City,N,1,10,18,36,High School,35,Patnership,Standalone
4137,57.72,NJ City,N,8,13,3,57,Graduation,18,Patnership,Standalone
4455,462.22,NJ City,E,11,10,1,40,Graduation,35,Owner,Standalone
3736,49.74,NJ City,W,25,8,2,58,High School,18,Franchise,Complex
743,490.1,NJ City,E,5,14,24,56,High School,32,Owner,Standalone
8552,131.56,NJ City,W,7,10,21,49,Graduation,20,Franchise,Complex
3565,154.26,NJ City,N,20,16,16,66,High School,25,Patnership,Standalone
7008,284.01,Boston,W,10,15,17,26,Graduation,31,Franchise,Complex
5681,7.45,Boston,N,10,9,4,79,No education,21,Patnership,Standalone
1279,339.53,NJ City,E,11,21,17,35,High School,31,Owner,Standalone
4977,221.04,Boston,N,21,14,13,56,High School,21,Patnership,Standalone
2235,17.12,NJ City,N,4,10,3,61,Graduation,18,Owner,Standalone
9213,207.61,NJ City,N,22,14,23,53,Graduation,18,Patnership,Standalone
6397,190.95,Boston,N,21,8,4,38,High School,20,Patnership,Standalone
647,27.23,NJ City,N,8,17,4,62,No education,26,Owner,Standalone
9132,55.66,NJ City,W,25,12,1,61,Graduation,24,Franchise,Complex
5796,123.94,Boston,W,16,16,2,31,Graduation,24,Franchise,Complex
8355,376.71,NJ City,E,2,17,7,50,Graduation,35,Owner,Standalone
7394,6.21,Boston,N,20,14,4,35,No education,35,Patnership,Complex
4620,219.06,NJ City,W,10,21,8,84,High School,31,Franchise,Complex
3661,145.25,NJ City,N,18,21,5,39,High School,32,Patnership,Standalone
4280,52.22,NJ City,W,11,15,4,31,No education,31,Franchise,Complex
2207,385.5,NJ City,E,20,18,9,77,High School,19,Owner,Standalone
3233,202.06,NJ City,N,11,15,6,50,High School,28,Patnership,Standalone
321,66.77,NJ City,N,10,13,3,46,Graduation,18,Patnership,Standalone
2077,398.03,NJ City,N,9,12,11,46,High School,31,Patnership,Standalone
4816,29.11,NJ City,N,9,22,4,55,High School,26,Franchise,Complex
735,448.04,NJ City,E,19,21,23,69,High School,24,Owner,Standalone
1601,449.13,NJ City,N,10,18,21,70,High School,28,Patnership,Standalone
9811,186.85,NJ City,E,21,21,13,27,Graduation,29,Owner,Standalone
7725,266.51,Boston,N,9,24,20,56,Graduation,22,Patnership,Standalone
1069,112.7,NJ City,N,23,20,1,52,High School,27,Patnership,Standalone
6053,126.71,Boston,N,12,22,2,73,No education,19,Patnership,Standalone
5628,76.05,Boston,W,13,13,4,82,Graduation,22,Franchise,Complex
7978,97.78,Boston,S,18,13,4,85,High School,18,Patnership,Complex
8375,0.93,NJ City,N,25,11,2,62,No education,32,Owner,Standalone
7986,345.33,Boston,S,7,12,14,58,Graduation,21,Patnership,Complex
695,181.23,NJ City,E,17,18,10,44,High School,24,Owner,Standalone
4990,322.77,Boston,S,6,15,4,71,High School,20,Patnership,Complex
4425,307,NJ City,N,2,17,22,42,High School,35,Patnership,Standalone
5130,58.05,Boston,S,18,20,1,74,Graduation,24,Patnership,Complex
366,434.25,NJ City,S,10,23,20,75,High School,20,Patnership,Complex
478,290.06,NJ City,S,20,15,7,51,High School,34,Patnership,Complex
9502,236.16,NJ City,S,11,13,5,26,High School,25,Patnership,Complex
7787,390.22,Boston,E,4,12,24,45,Graduation,32,Owner,Standalone
6654,187.45,Boston,S,5,19,3,35,Graduation,24,Patnership,Complex
171,370.97,NJ City,E,25,10,9,49,High School,35,Owner,Standalone
2395,253.76,NJ City,E,18,13,4,85,High School,20,Owner,Standalone
1394,35.59,NJ City,N,2,19,3,54,No education,20,Patnership,Complex
5519,136.16,Boston,E,8,23,11,36,Graduation,31,Owner,Standalone
8548,400.93,NJ City,W,3,13,4,52,High School,31,Franchise,Complex
7712,242.2,Boston,W,22,16,16,62,Graduation,29,Franchise,Complex
9118,195.84,NJ City,S,6,11,22,43,Graduation,22,Patnership,Complex
7902,93.01,Boston,S,19,24,4,81,Graduation,31,Patnership,Complex
5789,14.55,Boston,N,16,8,1,40,No education,24,Patnership,Standalone
7530,196.82,Boston,S,17,24,13,83,Graduation,26,Patnership,Complex
5912,240.14,Boston,W,17,20,2,30,No education,19,Franchise,Complex
191,459.08,NJ City,E,6,9,10,84,High School,32,Owner,Standalone
5149,371.75,Boston,N,3,10,24,69,Graduation,31,Patnership,Standalone
5159,209.01,Boston,E,16,24,11,53,Graduation,19,Owner,Standalone
6620,49.34,Boston,W,7,21,4,71,No education,27,Franchise,Complex
9869,380.5,NJ City,N,21,17,19,34,Graduation,24,Patnership,Standalone
2191,47.9,NJ City,E,9,18,1,57,High School,34,Owner,Standalone
9171,208.18,NJ City,E,25,22,1,81,Graduation,29,Owner,Standalone
1609,44.63,NJ City,N,20,15,1,32,High School,28,Patnership,Standalone
1525,7.11,NJ City,N,15,19,4,68,High School,29,Patnership,Standalone
1596,283.6,NJ City,W,17,10,13,57,High School,30,Franchise,Complex
7966,246.22,Boston,S,3,17,13,55,Graduation,19,Patnership,Complex
1340,338.15,NJ City,W,12,23,14,77,High School,33,Franchise,Complex
7885,180.54,Boston,N,6,13,11,31,Graduation,33,Patnership,Standalone
3664,498.49,NJ City,W,19,23,15,54,High School,20,Franchise,Complex
1441,235.41,NJ City,N,14,21,6,57,High School,19,Patnership,Standalone
9968,147.07,NJ City,W,11,13,22,35,Graduation,20,Franchise,Complex
9506,117.15,NJ City,S,10,19,4,34,No education,20,Patnership,Complex
4377,169.73,NJ City,N,20,12,14,27,High School,26,Patnership,Standalone
6882,5.54,Boston,N,9,14,1,74,Graduation,24,Patnership,Complex
8553,150.3,NJ City,N,25,22,21,82,Graduation,20,Patnership,Standalone
1058,96.01,NJ City,S,8,14,3,49,No education,24,Patnership,Complex
2107,322.55,NJ City,E,25,17,8,47,High School,25,Owner,Standalone
4778,218,NJ City,S,3,17,23,72,High School,32,Patnership,Complex
8209,388.3,NJ City,N,21,10,15,67,Graduation,21,Patnership,Standalone
7216,205.42,Boston,W,6,8,8,30,Graduation,18,Franchise,Complex
7448,466.56,Boston,W,21,21,14,42,Graduation,29,Franchise,Complex
4120,196,NJ City,W,18,20,13,64,High School,21,Franchise,Complex
4630,254.82,NJ City,S,25,14,3,28,High School,21,Patnership,Complex
2952,11.31,NJ City,N,18,17,4,74,Graduation,28,Franchise,Complex
737,203,NJ City,N,2,21,6,64,High School,22,Patnership,Standalone
2276,469.93,NJ City,W,13,17,6,73,High School,26,Franchise,Complex
3103,234,NJ City,E,2,10,19,74,High School,34,Owner,Standalone
7847,52.1,Boston,E,4,24,4,44,No education,18,Owner,Standalone
3952,161.07,NJ City,W,23,10,5,66,High School,20,Franchise,Complex
226,21.35,NJ City,N,14,24,4,26,High School,35,Patnership,Complex
9430,138.19,NJ City,S,13,11,7,63,Graduation,29,Patnership,Complex
665,112.47,NJ City,N,5,24,3,85,No education,35,Patnership,Standalone
3291,84.28,NJ City,E,5,18,2,57,Graduation,23,Owner,Standalone
8487,354.85,NJ City,E,25,15,16,34,Graduation,34,Owner,Standalone
9668,295.56,NJ City,W,25,8,19,61,Graduation,32,Franchise,Complex
43,394.95,Boston,E,12,13,25,81,High School,25,Owner,Standalone
1309,249.31,NJ City,N,8,11,2,66,High School,23,Patnership,Standalone
5763,260.5,Boston,E,7,17,5,29,Graduation,33,Owner,Standalone
8220,401.66,NJ City,W,21,15,16,81,Graduation,23,Franchise,Complex
9377,495.24,NJ City,N,12,9,18,57,Graduation,29,Patnership,Standalone
5249,373.95,Boston,N,17,19,20,60,Graduation,24,Patnership,Standalone
3617,427.12,NJ City,N,19,20,15,45,High School,27,Patnership,Standalone
6639,347.11,Boston,E,18,23,21,53,Graduation,34,Owner,Standalone
7523,336.93,Boston,E,7,22,2,59,No education,32,Owner,Standalone
2037,262,NJ City,N,3,17,7,55,High School,19,Patnership,Standalone
9647,428.61,NJ City,E,7,19,23,42,Graduation,34,Owner,Standalone
4671,495.35,NJ City,E,25,16,19,76,High School,27,Owner,Standalone
827,290.21,NJ City,E,4,22,22,58,High School,20,Owner,Standalone
6432,199.16,Boston,W,6,8,17,69,Graduation,30,Franchise,Complex
5405,56.95,Boston,N,18,22,2,83,No education,22,Patnership,Standalone
7259,248.44,Boston,E,8,22,22,49,Graduation,28,Owner,Standalone
5277,125.3,Boston,N,9,9,4,26,Graduation,19,Patnership,Standalone
404,101.62,NJ City,W,20,12,2,80,No education,25,Franchise,Complex
7562,488.01,Boston,S,5,16,22,35,Graduation,20,Patnership,Complex
2090,311,NJ City,S,2,18,14,39,High School,18,Patnership,Complex
8829,236.03,NJ City,N,21,8,17,37,Graduation,22,Patnership,Standalone
8001,88.33,Boston,N,7,19,2,34,Graduation,35,Patnership,Standalone
1166,346.97,NJ City,S,16,9,7,43,High School,35,Patnership,Complex
6572,338.79,Boston,W,22,14,24,60,Graduation,19,Franchise,Complex
1660,319.67,NJ City,W,12,14,21,38,High School,18,Franchise,Complex
6081,263.68,Boston,N,25,11,21,80,Graduation,25,Patnership,Standalone
2626,47.63,NJ City,S,10,24,2,85,High School,23,Patnership,Complex
7854,228.51,Boston,S,5,16,21,62,Graduation,33,Patnership,Complex
1008,5.16,NJ City,N,19,15,4,49,Graduation,22,Franchise,Complex
4462,70.14,NJ City,N,18,22,3,55,High School,31,Patnership,Complex
5921,342.83,Boston,N,9,8,13,56,Graduation,19,Patnership,Standalone
60,187.42,Boston,W,8,19,16,53,High School,32,Franchise,Complex
5026,203.07,Boston,S,25,17,8,49,Graduation,30,Patnership,Complex
8289,108.47,NJ City,N,8,8,3,53,Graduation,25,Patnership,Standalone
213,9.15,NJ City,N,9,18,3,75,Graduation,20,Patnership,Standalone
6541,126.38,Boston,N,20,23,1,43,High School,35,Patnership,Standalone
1177,142,NJ City,N,3,24,12,50,High School,19,Patnership,Standalone
9490,147.78,NJ City,S,23,9,16,56,Graduation,28,Patnership,Complex
5068,354.1,Boston,W,10,10,15,66,Graduation,21,Franchise,Complex
3675,215.74,NJ City,E,22,9,2,68,Graduation,27,Owner,Standalone
3647,374.4,NJ City,E,8,19,25,36,High School,32,Owner,Standalone
9637,206.44,NJ City,N,2,24,18,54,Graduation,33,Patnership,Standalone
283,241,NJ City,E,1,10,16,27,High School,21,Owner,Standalone
5676,100.47,Boston,W,9,9,3,83,Graduation,31,Franchise,Complex
5363,216.11,Boston,E,11,20,16,48,Graduation,21,Owner,Standalone
7035,225.27,Boston,E,7,22,7,72,Graduation,31,Owner,Standalone
4559,487.7,NJ City,E,14,19,10,61,High School,33,Owner,Standalone
4015,70.09,NJ City,N,18,22,3,61,High School,31,Owner,Standalone
7285,148.53,Boston,N,5,14,21,36,Graduation,22,Patnership,Standalone
5521,340.24,Boston,N,25,16,18,52,Graduation,27,Patnership,Standalone
9806,404.17,NJ City,S,9,14,24,44,Graduation,31,Patnership,Complex
2830,342.93,NJ City,S,16,21,9,55,High School,27,Patnership,Complex
8808,409.54,NJ City,W,9,21,3,52,Graduation,26,Franchise,Complex
7307,375.79,Boston,E,11,11,1,49,No education,35,Owner,Standalone
2582,391.96,NJ City,S,12,22,20,66,High School,26,Patnership,Complex
4707,179.52,NJ City,E,10,15,25,74,High School,28,Owner,Standalone
9812,218.84,NJ City,W,10,17,19,46,Graduation,28,Franchise,Complex
3796,52.58,NJ City,W,14,14,4,31,High School,30,Franchise,Complex
7689,73.78,Boston,N,3,24,1,77,Graduation,23,Patnership,Standalone
2280,176.67,NJ City,W,9,9,18,73,High School,18,Franchise,Complex
2819,132.38,NJ City,E,23,14,23,42,High School,25,Owner,Standalone
5776,298.21,Boston,W,25,8,15,31,Graduation,28,Franchise,Complex
5919,101.65,Boston,E,12,21,2,43,Graduation,22,Owner,Standalone
7355,488.66,Boston,E,8,17,5,31,No education,32,Owner,Standalone
6577,396.22,Boston,N,8,14,5,83,High School,33,Patnership,Standalone
776,71.57,NJ City,N,9,12,1,80,No education,31,Franchise,Complex
1063,265.06,NJ City,E,16,21,11,63,High School,31,Owner,Standalone
7002,324.85,Boston,S,25,24,15,62,Graduation,23,Patnership,Complex
884,284.8,NJ City,W,25,9,1,57,No education,32,Franchise,Complex
1007,367.99,NJ City,E,20,24,1,55,No education,23,Owner,Standalone
1675,250.84,NJ City,E,5,19,17,53,High School,33,Owner,Standalone
5155,287.76,Boston,E,5,16,10,61,Graduation,32,Owner,Standalone
9815,392.18,NJ City,E,20,20,2,85,No education,29,Owner,Standalone
297,103.27,NJ City,N,18,18,3,83,Graduation,31,Patnership,Standalone
3508,61.38,NJ City,N,1,24,4,66,High School,24,Franchise,Complex
1611,105,NJ City,E,3,8,3,72,Graduation,27,Owner,Standalone
2437,8.34,NJ City,N,16,24,4,65,High School,20,Patnership,Standalone
5217,154.41,Boston,N,16,19,6,63,Graduation,21,Patnership,Standalone
8681,401.5,NJ City,N,9,12,1,48,No education,30,Patnership,Standalone
6694,163.77,Boston,S,10,22,18,58,Graduation,28,Patnership,Complex
9351,374.26,NJ City,E,13,19,10,50,Graduation,25,Owner,Standalone
678,167.65,NJ City,S,15,18,21,76,High School,31,Patnership,Complex
8079,334.87,NJ City,E,1,17,18,62,Graduation,30,Owner,Standalone
9135,186.65,NJ City,E,17,22,1,40,Graduation,24,Owner,Standalone
2257,436.17,NJ City,N,7,16,9,77,High School,29,Patnership,Standalone
510,294.96,NJ City,S,3,23,2,66,Graduation,28,Patnership,Complex
7068,391.27,Boston,W,2,24,17,62,Graduation,22,Franchise,Complex
328,167.52,NJ City,W,15,10,19,53,High School,22,Franchise,Complex
9643,480.76,NJ City,E,8,18,20,84,Graduation,18,Owner,Standalone
9402,91.23,NJ City,S,5,23,3,85,Graduation,21,Patnership,Complex
6280,177.23,Boston,W,5,23,19,52,Graduation,33,Franchise,Complex
1404,355.16,NJ City,W,23,13,10,62,High School,33,Franchise,Complex
3814,23.49,NJ City,N,2,12,2,85,High School,34,Patnership,Complex
5550,153.14,Boston,S,13,18,5,54,Graduation,34,Patnership,Complex
6372,360.38,Boston,W,11,13,19,52,Graduation,34,Franchise,Complex
528,306.76,NJ City,W,25,19,16,27,High School,28,Franchise,Complex
4353,335.84,NJ City,N,7,14,4,34,Graduation,23,Patnership,Standalone
2999,316.53,NJ City,E,15,9,20,30,High School,32,Owner,Standalone
2132,53.36,NJ City,W,23,14,2,34,No education,33,Franchise,Complex
9712,295.63,NJ City,W,7,14,12,67,Graduation,28,Franchise,Complex
7313,446.82,Boston,N,3,12,10,70,Graduation,35,Patnership,Standalone
2430,333.05,NJ City,S,14,16,11,29,High School,30,Patnership,Complex
8906,392.2,NJ City,S,22,19,6,41,Graduation,27,Patnership,Complex
4717,134,NJ City,N,1,21,15,57,High School,26,Patnership,Standalone
4813,272.23,NJ City,N,8,16,10,83,High School,29,Patnership,Standalone
6381,193.17,Boston,N,5,18,12,48,Graduation,28,Patnership,Standalone
2513,81.54,NJ City,N,12,16,4,61,No education,35,Patnership,Standalone
2574,76.77,NJ City,S,14,19,4,25,Graduation,29,Patnership,Complex
9568,237.79,NJ City,W,9,23,4,38,High School,31,Franchise,Complex
1026,462.12,NJ City,S,14,10,13,85,High School,22,Patnership,Complex
2518,212.94,NJ City,S,18,16,12,33,High School,27,Patnership,Complex
3732,410.05,NJ City,W,11,23,9,74,High School,28,Franchise,Complex
7280,387.24,Boston,W,13,19,20,62,Graduation,31,Franchise,Complex
3939,297.41,NJ City,E,5,10,24,68,High School,21,Owner,Standalone
2660,105.15,NJ City,W,23,19,2,39,No education,20,Franchise,Complex
2764,82.04,NJ City,W,24,18,2,42,High School,18,Franchise,Complex
8466,61.45,NJ City,N,18,22,4,33,Graduation,32,Patnership,Complex
4076,250.93,NJ City,W,20,11,11,70,High School,25,Franchise,Complex
1208,448.26,NJ City,W,13,18,15,30,High School,24,Franchise,Complex
1918,269.7,NJ City,S,17,14,17,26,High School,21,Patnership,Complex
6496,81.75,Boston,W,12,20,1,36,High School,18,Franchise,Complex
2729,450.52,NJ City,N,6,10,4,31,No education,28,Patnership,Standalone
6148,333.75,Boston,W,21,23,25,43,Graduation,20,Franchise,Complex
9937,33.04,NJ City,N,22,8,1,68,High School,20,Patnership,Standalone
7923,28.76,Boston,N,3,12,2,79,Graduation,33,Owner,Standalone
1718,232.64,NJ City,S,20,24,14,44,High School,28,Patnership,Complex
9245,287.98,NJ City,N,14,16,22,70,Graduation,18,Patnership,Standalone
3859,142,NJ City,E,3,22,16,54,High School,21,Owner,Standalone
1773,468.39,NJ City,N,24,20,3,35,Graduation,26,Patnership,Standalone
8652,322.5,NJ City,W,13,18,22,67,Graduation,34,Franchise,Complex
9550,246.6,NJ City,S,16,22,5,73,High School,29,Patnership,Complex
425,200,NJ City,N,1,17,22,53,High School,34,Patnership,Standalone
8177,315.62,NJ City,N,5,13,2,47,No education,20,Patnership,Standalone
8784,495.03,NJ City,W,17,18,23,34,Graduation,26,Franchise,Complex
3096,124.32,NJ City,W,16,15,3,61,Graduation,20,Franchise,Complex
576,243.98,NJ City,W,25,23,1,84,Graduation,31,Franchise,Complex
707,106.43,NJ City,E,24,14,3,62,No education,23,Owner,Standalone
9160,120.09,NJ City,W,23,24,2,51,High School,35,Franchise,Complex
8827,63.22,NJ City,N,9,21,4,36,High School,24,Owner,Standalone
2783,65.91,NJ City,N,22,24,3,47,No education,28,Owner,Standalone
1621,435.72,NJ City,N,19,11,12,79,High School,21,Patnership,Standalone
5489,74.27,Boston,N,22,23,3,70,No education,29,Patnership,Standalone
939,240.85,NJ City,E,10,18,7,67,High School,31,Owner,Standalone
1233,1.78,NJ City,N,6,13,4,43,Graduation,26,Patnership,Standalone
4578,54.1,NJ City,S,24,22,3,53,Graduation,18,Patnership,Complex
1513,155.3,NJ City,N,10,16,3,41,High School,20,Patnership,Standalone
8992,152.82,NJ City,W,14,13,24,55,Graduation,33,Franchise,Complex
6291,424.23,Boston,E,14,19,8,44,Graduation,25,Owner,Standalone
5535,436.92,Boston,E,5,11,22,36,Graduation,19,Owner,Standalone
7926,231,Boston,S,12,10,16,50,Graduation,24,Patnership,Complex
7151,434.91,Boston,E,22,14,18,55,Graduation,21,Owner,Standalone
2674,130.94,NJ City,S,10,18,21,54,High School,31,Patnership,Complex
3148,450.9,NJ City,W,22,23,17,61,High School,33,Franchise,Complex
1977,257.49,NJ City,N,13,9,13,31,High School,20,Patnership,Standalone
5078,314.89,Boston,S,12,12,15,33,Graduation,24,Patnership,Complex
991,336.02,NJ City,E,15,22,5,76,High School,30,Owner,Standalone
9885,161.06,NJ City,N,10,20,16,64,Graduation,34,Patnership,Standalone
9965,61.19,NJ City,N,25,11,1,33,No education,28,Patnership,Standalone
7615,309.51,Boston,E,3,14,15,38,Graduation,23,Owner,Standalone
5707,448.41,Boston,E,3,10,6,26,Graduation,21,Owner,Standalone
6311,161.91,Boston,E,25,11,10,78,Graduation,30,Owner,Standalone
1593,227.26,NJ City,N,13,8,20,66,High School,32,Patnership,Standalone
1452,161.66,NJ City,W,6,22,9,35,High School,24,Franchise,Complex
3540,393.68,NJ City,W,12,20,8,56,High School,26,Franchise,Complex
7859,274.71,Boston,E,3,11,9,39,Graduation,25,Owner,Standalone
5533,195.44,Boston,N,7,19,15,39,Graduation,26,Patnership,Standalone
9613,37.68,NJ City,N,12,21,2,57,High School,26,Patnership,Standalone
4897,184.22,NJ City,N,15,21,6,71,High School,35,Patnership,Standalone
5163,205.43,Boston,E,9,14,22,82,Graduation,34,Owner,Standalone
9389,203.45,NJ City,N,12,9,13,63,Graduation,21,Patnership,Standalone
2825,327.04,NJ City,N,14,8,12,58,High School,27,Patnership,Standalone
5064,446.45,Boston,W,22,11,6,34,Graduation,34,Franchise,Complex
6114,160.35,Boston,S,23,17,13,60,Graduation,19,Patnership,Complex
6220,402.38,Boston,W,2,13,3,48,High School,24,Franchise,Complex
7596,275.57,Boston,W,25,14,23,81,Graduation,20,Franchise,Complex
8968,280.82,NJ City,W,7,8,18,33,Graduation,35,Franchise,Complex
8021,143.51,Boston,N,15,11,5,58,No education,19,Patnership,Standalone
6631,346.9,Boston,E,3,10,25,68,Graduation,30,Owner,Standalone
6192,436.19,Boston,W,8,12,18,49,Graduation,33,Franchise,Complex
6594,161.33,Boston,S,7,20,15,66,Graduation,28,Patnership,Complex
595,394.15,NJ City,E,21,18,9,71,High School,35,Owner,Standalone
395,224.57,NJ City,E,25,19,5,38,No education,25,Owner,Standalone
7274,128.8,Boston,S,17,21,16,40,Graduation,22,Patnership,Complex
6732,55.33,Boston,W,23,16,2,39,Graduation,24,Franchise,Complex
9478,324.69,NJ City,S,3,15,17,70,Graduation,28,Patnership,Complex
6868,125.5,Boston,W,10,17,1,66,High School,35,Franchise,Complex
4796,18.25,NJ City,N,1,8,1,72,No education,18,Franchise,Complex
9991,311.91,NJ City,E,10,14,1,44,High School,26,Owner,Standalone
1643,164.68,NJ City,E,22,13,25,51,High School,30,Owner,Standalone
4312,110.83,NJ City,W,24,12,2,46,High School,31,Franchise,Complex
1362,355.18,NJ City,S,16,18,6,53,High School,24,Patnership,Complex
98,428.65,NJ City,S,24,15,19,35,High School,22,Patnership,Complex
3504,106.03,NJ City,W,5,23,4,78,Graduation,22,Franchise,Complex
588,33.88,NJ City,N,2,12,2,27,Graduation,23,Franchise,Complex
9976,7.06,NJ City,N,22,23,2,51,High School,30,Franchise,Complex
7022,131.93,Boston,S,7,9,2,63,No education,33,Patnership,Complex
6102,118.2,Boston,S,18,19,1,39,Graduation,27,Patnership,Complex
4605,314.34,NJ City,N,8,12,2,70,Graduation,19,Patnership,Standalone
3971,198.81,NJ City,E,13,10,4,46,No education,19,Owner,Standalone
8702,241.26,NJ City,S,4,10,1,47,No education,21,Patnership,Complex
3733,233.88,NJ City,N,12,13,25,75,High School,26,Patnership,Standalone
4545,264.76,NJ City,N,24,8,25,46,High School,32,Patnership,Standalone
2487,478.33,NJ City,E,18,16,24,71,High School,31,Owner,Standalone
394,21.31,NJ City,N,1,11,1,25,High School,35,Patnership,Complex
6855,5.22,Boston,N,24,16,1,81,Graduation,27,Owner,Standalone
6052,356.44,Boston,W,16,11,10,61,Graduation,22,Franchise,Complex
6406,34.7,Boston,N,25,15,2,39,High School,18,Patnership,Complex
624,373.09,NJ City,W,17,8,22,73,High School,30,Franchise,Complex
4151,448.33,NJ City,E,19,14,12,70,High School,21,Owner,Standalone
32,281.83,Boston,W,21,12,23,66,High School,30,Franchise,Complex
6401,96.07,Boston,N,23,11,3,80,No education,18,Patnership,Standalone
2939,261.35,NJ City,E,9,10,15,81,High School,31,Owner,Standalone
9153,420.41,NJ City,N,7,18,19,64,Graduation,26,Patnership,Standalone
7756,65.02,Boston,N,25,14,3,33,High School,34,Franchise,Complex
3667,184.73,NJ City,E,10,12,17,52,High School,32,Owner,Standalone
6777,42.68,Boston,N,11,15,2,50,Graduation,29,Patnership,Standalone
8232,376.42,NJ City,W,23,20,16,54,Graduation,21,Franchise,Complex
2817,286.17,NJ City,N,9,15,3,54,Graduation,20,Patnership,Standalone
9447,46.75,NJ City,N,17,13,3,51,Graduation,25,Owner,Standalone
3176,499.81,NJ City,W,14,14,22,72,High School,24,Franchise,Complex
6445,193,Boston,N,22,8,18,33,Graduation,23,Patnership,Standalone
8795,290.54,NJ City,E,14,21,11,31,Graduation,32,Owner,Standalone
8381,209.32,NJ City,N,16,12,24,71,Graduation,28,Patnership,Standalone
832,172.02,NJ City,W,17,12,24,42,High School,23,Franchise,Complex
8734,122.52,NJ City,S,6,12,4,51,High School,32,Patnership,Complex
251,416.86,NJ City,E,10,13,20,81,High School,34,Owner,Standalone
5485,32,Boston,N,20,9,3,40,High School,24,Patnership,Standalone
9268,105.84,NJ City,W,3,20,2,62,High School,21,Franchise,Complex
3934,229.28,NJ City,S,13,13,17,64,High School,35,Patnership,Complex
2576,405.61,NJ City,W,3,12,5,47,No education,35,Franchise,Complex
21,181.33,Maine,N,9,15,2,76,Graduation,18,Patnership,Standalone
3294,496.65,NJ City,S,17,24,20,39,High School,32,Patnership,Complex
2870,339.21,NJ City,S,8,23,9,75,High School,34,Patnership,Complex
4635,302.72,NJ City,E,17,13,24,85,High School,18,Owner,Standalone
8843,74.77,NJ City,E,15,9,3,30,No education,24,Owner,Standalone
7809,492.58,Boston,N,10,10,24,85,Graduation,28,Patnership,Standalone
9357,26.91,NJ City,N,2,11,2,73,Graduation,21,Patnership,Standalone
6903,54.62,Boston,E,6,21,1,43,Graduation,34,Owner,Standalone
8716,121.86,NJ City,W,6,11,1,63,High School,22,Franchise,Complex
9578,404.81,NJ City,S,8,24,2,82,No education,26,Patnership,Complex
407,271,NJ City,E,2,15,7,28,High School,31,Owner,Standalone
7183,13.26,Boston,N,13,9,2,49,High School,24,Owner,Standalone
5839,175.38,Boston,E,9,24,4,26,High School,35,Owner,Standalone
9661,378.03,NJ City,N,10,9,18,35,Graduation,30,Patnership,Standalone
8757,313.08,NJ City,N,10,13,17,29,Graduation,34,Patnership,Standalone
4061,293.77,NJ City,N,12,16,2,48,No education,24,Patnership,Standalone
1370,347.05,NJ City,S,4,10,1,54,No education,22,Patnership,Complex
8010,451.79,Boston,S,24,18,11,73,Graduation,22,Patnership,Complex
7724,46.34,Boston,N,1,19,1,61,No education,20,Franchise,Complex
8015,258.87,Boston,E,20,11,18,56,Graduation,24,Owner,Standalone
4196,85.61,NJ City,W,19,13,3,68,No education,33,Franchise,Complex
1417,312.76,NJ City,N,7,19,2,73,High School,18,Patnership,Standalone
5298,257.46,Boston,S,11,16,23,76,Graduation,34,Patnership,Complex
4023,324.72,NJ City,E,15,18,18,67,High School,33,Owner,Standalone
8219,465.62,NJ City,E,16,15,7,50,Graduation,27,Owner,Standalone
7268,57.47,Boston,W,23,21,2,84,No education,31,Franchise,Complex
5532,116.75,Boston,W,7,13,4,79,Graduation,23,Franchise,Complex
2358,8.28,NJ City,N,9,8,2,71,Graduation,21,Patnership,Complex
8264,389.83,NJ City,W,10,23,22,50,Graduation,26,Franchise,Complex
7791,497.98,Boston,E,16,13,23,47,Graduation,31,Owner,Standalone
5214,490.41,Boston,S,6,8,24,65,Graduation,24,Patnership,Complex
652,158.51,NJ City,W,7,10,11,80,High School,23,Franchise,Complex
5549,40.33,Boston,N,21,11,1,48,No education,31,Patnership,Standalone
7531,276.64,Boston,E,3,19,23,31,Graduation,35,Owner,Standalone
6259,106.87,Boston,E,19,23,4,52,High School,33,Owner,Standalone
3893,82.51,NJ City,N,18,23,4,73,No education,22,Patnership,Standalone
2940,284.07,NJ City,W,10,23,8,40,High School,19,Franchise,Complex
6258,222.42,Boston,S,19,23,24,48,Graduation,32,Patnership,Complex
5069,358.88,Boston,N,5,11,12,35,Graduation,28,Patnership,Standalone
965,145.23,NJ City,N,9,15,4,28,No education,27,Patnership,Standalone
2153,389.81,NJ City,N,10,15,13,67,High School,27,Patnership,Standalone
732,60.48,NJ City,N,17,8,4,55,Graduation,31,Franchise,Complex
4428,277,NJ City,W,2,12,9,76,High School,20,Franchise,Complex
6945,473.74,Boston,N,19,19,8,48,Graduation,31,Patnership,Standalone
7887,117.11,Boston,E,18,13,2,49,Graduation,18,Owner,Standalone
4005,183.47,NJ City,N,8,20,16,34,High School,25,Patnership,Standalone
3866,460.27,NJ City,S,21,12,7,51,High School,23,Patnership,Complex
8767,327.85,NJ City,E,1,23,12,82,Graduation,23,Owner,Standalone
6963,180.03,Boston,E,8,23,12,65,Graduation,33,Owner,Standalone
4946,459.13,Boston,S,20,19,14,51,High School,22,Patnership,Complex
463,286.14,NJ City,E,8,10,14,34,High School,25,Owner,Standalone
5974,6.81,Boston,N,5,12,4,60,High School,29,Patnership,Complex
7600,437.13,Boston,W,24,12,16,77,Graduation,19,Franchise,Complex
7788,199.53,Boston,W,7,11,13,79,Graduation,32,Franchise,Complex
6589,104.93,Boston,N,5,10,3,35,High School,18,Patnership,Standalone
1186,348.67,NJ City,S,17,16,5,40,High School,29,Patnership,Complex
6112,78.97,Boston,W,21,24,3,76,High School,34,Franchise,Complex
16,240.1,Boston,W,11,24,22,65,High School,31,Franchise,Complex
8478,62.51,NJ City,N,16,15,4,75,Graduation,31,Patnership,Complex
341,255.07,NJ City,N,13,17,18,39,High School,31,Patnership,Standalone
4896,67.66,NJ City,N,15,23,2,36,Graduation,28,Franchise,Complex
2394,422.26,NJ City,S,10,17,19,38,High School,25,Patnership,Complex
8410,453.47,NJ City,S,3,13,19,28,Graduation,23,Patnership,Complex
8572,205.67,NJ City,W,20,20,24,31,Graduation,31,Franchise,Complex
6866,138.22,Boston,S,25,21,5,35,No education,22,Patnership,Complex
6269,378.29,Boston,N,25,24,19,39,Graduation,24,Patnership,Standalone
5687,474.51,Boston,E,21,19,25,65,Graduation,34,Owner,Standalone
9958,253.33,NJ City,S,20,8,10,41,Graduation,28,Patnership,Complex
6609,334.21,Boston,N,23,11,17,30,Graduation,22,Patnership,Standalone
5107,379.66,Boston,E,21,11,5,70,High School,31,Owner,Standalone
3845,351.87,NJ City,N,15,12,17,35,High School,31,Patnership,Standalone
8971,273.42,NJ City,E,19,21,7,36,Graduation,30,Owner,Standalone
3035,394.56,NJ City,E,18,16,1,60,No education,29,Owner,Standalone
7949,149.86,Boston,N,10,18,9,30,Graduation,28,Patnership,Standalone
8368,243.35,NJ City,W,13,18,24,76,Graduation,32,Franchise,Complex
5251,303.34,Boston,E,25,11,6,28,Graduation,29,Owner,Standalone
4755,346.05,NJ City,E,24,14,4,56,Graduation,23,Owner,Standalone
7096,459.49,Boston,W,1,14,19,46,Graduation,18,Franchise,Complex
4675,71.68,NJ City,N,6,18,1,51,High School,25,Owner,Standalone
3549,175.39,NJ City,N,7,24,19,47,High School,19,Patnership,Standalone
6004,309.2,Boston,W,14,17,14,84,Graduation,33,Franchise,Complex
5182,241.53,Boston,S,17,10,9,52,Graduation,19,Patnership,Complex
2557,114,NJ City,N,2,15,8,75,High School,34,Patnership,Standalone
9624,220.47,NJ City,W,11,11,13,31,Graduation,20,Franchise,Complex
5568,226.59,Boston,W,15,19,16,56,Graduation,31,Franchise,Complex
5032,348.8,Boston,W,19,19,25,64,Graduation,21,Franchise,Complex
1045,110.61,NJ City,N,13,12,1,26,High School,34,Patnership,Standalone
3150,251.81,NJ City,S,23,20,7,63,High School,26,Patnership,Complex
5011,189.16,Boston,E,10,9,1,70,High School,27,Owner,Standalone
7998,182.41,Boston,S,1,11,5,50,Graduation,24,Patnership,Complex
2300,304.62,NJ City,W,22,10,20,31,High School,21,Franchise,Complex
8927,47.28,NJ City,N,4,9,1,69,No education,24,Owner,Standalone
6526,387.11,Boston,S,9,10,10,32,Graduation,21,Patnership,Complex
257,439.39,NJ City,N,14,9,6,76,High School,34,Patnership,Standalone
1974,79.16,NJ City,S,25,21,2,40,Graduation,19,Patnership,Complex
1805,124,NJ City,N,3,11,10,29,High School,30,Patnership,Standalone
71,436.56,NY City,E,24,8,11,41,High School,23,Owner,Standalone
960,153.85,NJ City,W,6,11,17,82,High School,25,Franchise,Complex
908,315.67,NJ City,W,15,22,8,41,High School,34,Franchise,Complex
3013,337.2,NJ City,N,11,22,8,35,High School,24,Patnership,Standalone
3499,420.47,NJ City,E,6,24,11,60,High School,21,Owner,Standalone
7731,220.06,Boston,E,7,23,16,82,Graduation,25,Owner,Standalone
8862,170.91,NJ City,S,6,10,22,62,Graduation,33,Patnership,Complex
4421,258.36,NJ City,N,21,16,21,60,High School,28,Patnership,Standalone
2851,456.39,NJ City,E,18,16,15,59,High School,20,Owner,Standalone
3719,166.67,NJ City,E,6,22,11,75,High School,32,Owner,Standalone
5542,204.5,Boston,S,13,11,16,30,Graduation,30,Patnership,Complex
868,147,NJ City,W,3,8,6,74,High School,34,Franchise,Complex
5907,499.17,Boston,E,6,9,15,30,Graduation,33,Owner,Standalone
1014,152.89,NJ City,S,12,16,2,30,Graduation,27,Patnership,Complex
6821,164.7,Boston,N,3,21,18,46,Graduation,18,Patnership,Standalone
1454,183.27,NJ City,S,7,15,25,47,High School,33,Patnership,Complex
4139,109.24,NJ City,E,12,12,2,71,No education,23,Owner,Standalone
4368,433.7,NJ City,W,11,13,6,38,High School,28,Franchise,Complex
835,479.58,NJ City,E,8,8,4,74,High School,26,Owner,Standalone
5286,335.28,Boston,S,14,24,12,44,Graduation,33,Patnership,Complex
9515,217.39,NJ City,E,1,22,6,79,Graduation,32,Owner,Standalone
725,430,NJ City,N,10,16,6,69,High School,31,Patnership,Standalone
8625,463.43,NJ City,N,15,21,18,68,Graduation,31,Patnership,Standalone
3775,7.02,NJ City,N,7,20,4,64,High School,24,Owner,Standalone
9331,163.97,NJ City,E,2,13,5,27,High School,28,Owner,Standalone
4929,93.78,NJ City,N,14,9,3,27,Graduation,23,Patnership,Standalone
9819,495.08,NJ City,E,24,17,19,80,Graduation,31,Owner,Standalone
9548,381.73,NJ City,W,3,8,9,31,Graduation,35,Franchise,Complex
4849,126.6,NJ City,N,6,22,2,39,High School,32,Patnership,Standalone
4119,41.52,NJ City,N,11,12,4,83,Graduation,33,Owner,Standalone
4378,481.39,NJ City,S,4,8,10,40,High School,21,Patnership,Complex
792,378.48,NJ City,W,21,20,12,82,High School,29,Franchise,Complex
7781,151.94,Boston,N,2,22,24,84,Graduation,29,Patnership,Standalone
9601,52.71,NJ City,N,16,12,3,34,High School,19,Patnership,Standalone
7020,24.42,Boston,N,1,14,4,68,Graduation,25,Franchise,Complex
3575,33.92,NJ City,N,20,13,4,67,No education,33,Owner,Standalone
5938,405.96,Boston,S,25,24,6,52,Graduation,20,Patnership,Complex
4978,235,Boston,S,3,13,16,38,High School,27,Patnership,Complex
4805,322.09,NJ City,N,15,10,14,60,High School,25,Patnership,Standalone
4220,122.78,NJ City,W,8,18,1,75,No education,29,Franchise,Complex
1419,76.86,NJ City,E,9,12,4,45,Graduation,29,Owner,Standalone
890,102.51,NJ City,S,17,17,2,41,No education,27,Patnership,Complex
3456,417.1,NJ City,W,20,19,6,62,High School,27,Franchise,Complex
9189,122.19,NJ City,N,2,12,3,81,Graduation,18,Patnership,Standalone
4773,321.97,NJ City,N,23,22,5,41,Graduation,30,Patnership,Standalone
3818,1.69,NJ City,N,6,21,3,40,No education,22,Patnership,Complex
7146,411.24,Boston,S,11,20,13,85,Graduation,29,Patnership,Complex
4323,120.2,NJ City,E,9,16,3,50,Graduation,20,Owner,Standalone
4631,432.7,NJ City,E,16,13,18,49,High School,19,Owner,Standalone
4823,473.48,NJ City,E,23,20,1,85,No education,21,Owner,Standalone
314,87.05,NJ City,S,11,22,2,72,No education,33,Patnership,Complex
8546,365.2,NJ City,S,24,18,4,36,No education,27,Patnership,Complex
3908,211.56,NJ City,W,5,15,12,81,High School,30,Franchise,Complex
4251,415.29,NJ City,E,21,11,15,53,High School,23,Owner,Standalone
9552,149.96,NJ City,W,7,23,15,79,Graduation,30,Franchise,Complex
7675,127.82,Boston,E,17,22,2,82,High School,19,Owner,Standalone
1599,318.53,NJ City,E,12,8,10,51,High School,23,Owner,Standalone
2351,312,NJ City,E,2,22,21,78,High School,27,Owner,Standalone
4809,290.74,NJ City,N,14,17,6,40,High School,18,Patnership,Standalone
450,397.16,NJ City,S,15,11,25,64,High School,18,Patnership,Complex
4274,491.52,NJ City,S,19,12,9,36,High School,21,Patnership,Complex
3559,395.75,NJ City,E,4,16,3,80,High School,27,Owner,Standalone
5256,244.04,Boston,W,17,11,12,33,Graduation,27,Franchise,Complex
9605,61.88,NJ City,N,1,12,3,54,No education,22,Patnership,Standalone
2299,136,NJ City,E,3,8,24,73,High School,18,Owner,Standalone
3271,206.63,NJ City,E,21,18,20,72,High School,18,Owner,Standalone
2976,496.86,NJ City,W,15,22,12,51,High School,33,Franchise,Complex
4383,178.44,NJ City,E,14,8,9,29,High School,34,Owner,Standalone
3762,269,NJ City,S,2,23,19,67,High School,23,Patnership,Complex
1318,11.63,NJ City,N,11,17,2,47,High School,32,Patnership,Complex
6063,432.39,Boston,E,23,18,7,41,Graduation,34,Owner,Standalone
4272,403.27,NJ City,W,15,20,16,62,High School,21,Franchise,Complex
9061,123.21,NJ City,N,3,16,2,36,High School,35,Patnership,Standalone
9005,299.61,NJ City,N,2,23,22,47,Graduation,25,Patnership,Standalone
1543,12.15,NJ City,N,9,9,2,77,High School,26,Owner,Standalone
7130,477.24,Boston,S,13,17,16,28,Graduation,27,Patnership,Complex
6047,24.52,Boston,N,15,17,4,63,No education,22,Owner,Standalone
6416,187.33,Boston,W,12,8,19,73,Graduation,31,Franchise,Complex
819,199.56,NJ City,E,10,24,2,31,Graduation,30,Owner,Standalone
4997,111,Boston,N,1,17,23,52,High School,34,Patnership,Standalone
266,347.18,NJ City,S,14,17,3,43,No education,21,Patnership,Complex
1633,11.78,NJ City,N,13,16,1,66,High School,32,Patnership,Standalone
3724,202.53,NJ City,W,20,15,1,31,High School,33,Franchise,Complex
724,201.65,NJ City,W,25,21,14,44,High School,25,Franchise,Complex
5887,260.81,Boston,E,24,9,13,65,Graduation,34,Owner,Standalone
9776,426.17,NJ City,W,3,9,21,30,Graduation,33,Franchise,Complex
5431,469.7,Boston,E,7,21,18,51,Graduation,27,Owner,Standalone
3615,61.88,NJ City,N,12,17,4,76,Graduation,29,Owner,Standalone
6086,327.55,Boston,S,13,12,11,52,Graduation,22,Patnership,Complex
9995,468.77,NJ City,E,1,14,22,82,Graduation,26,Owner,Standalone
3247,64.71,NJ City,N,21,24,3,81,High School,34,Owner,Standalone
1349,31.99,NJ City,N,15,8,1,29,No education,30,Patnership,Standalone
7036,188.41,Boston,W,8,19,7,50,Graduation,21,Franchise,Complex
7252,151.39,Boston,W,14,15,15,69,Graduation,20,Franchise,Complex
5769,429.6,Boston,N,15,20,5,83,Graduation,24,Patnership,Standalone
7614,52.68,Boston,S,14,18,2,33,Graduation,33,Patnership,Complex
8596,192.54,NJ City,W,3,22,13,57,Graduation,30,Franchise,Complex
925,237.82,NJ City,N,23,13,10,40,High School,25,Patnership,Standalone
8327,92.55,NJ City,E,10,23,2,47,No education,34,Owner,Standalone
5876,19.74,Boston,N,23,19,2,79,No education,25,Franchise,Complex
3914,499.6,NJ City,S,14,18,8,85,High School,33,Patnership,Complex
9417,430.68,NJ City,N,24,17,7,25,Graduation,22,Patnership,Standalone
2229,160.86,NJ City,N,9,17,21,84,High School,21,Patnership,Standalone
4653,424.5,NJ City,N,25,8,9,85,High School,19,Patnership,Standalone
346,331.36,NJ City,S,12,14,10,72,High School,34,Patnership,Complex
3514,298.42,NJ City,S,8,20,16,81,High School,30,Patnership,Complex
9406,369.02,NJ City,S,5,11,4,51,High School,18,Patnership,Complex
2762,48.91,NJ City,S,22,10,1,67,No education,31,Patnership,Complex
2670,275.7,NJ City,S,8,24,4,48,Graduation,32,Patnership,Complex
4003,176,NJ City,E,3,20,21,66,High School,34,Owner,Standalone
4656,169.85,NJ City,W,12,14,2,27,Graduation,25,Franchise,Complex
1739,40.06,NJ City,N,20,19,3,55,No education,26,Owner,Standalone
1100,343.79,NJ City,W,15,10,7,36,High School,24,Franchise,Complex
6391,451.38,Boston,E,13,13,25,66,Graduation,26,Owner,Standalone
5835,287.87,Boston,E,18,15,24,57,Graduation,28,Owner,Standalone
277,120.5,NJ City,N,10,16,3,27,High School,21,Patnership,Standalone
4961,485.2,Boston,N,19,16,24,56,High School,19,Patnership,Standalone
9246,168.01,NJ City,S,7,16,24,45,Graduation,34,Patnership,Complex
3502,191.74,NJ City,S,15,10,7,44,High School,26,Patnership,Complex
5216,243.73,Boston,W,25,11,10,64,Graduation,22,Franchise,Complex
6073,292.77,Boston,N,22,11,5,60,High School,28,Patnership,Standalone
5415,214.14,Boston,E,5,22,10,58,Graduation,35,Owner,Standalone
2502,62.66,NJ City,N,6,9,3,51,Graduation,24,Patnership,Complex
8465,499.86,NJ City,N,19,9,24,64,Graduation,23,Patnership,Standalone
7928,212.55,Boston,W,1,14,16,68,Graduation,25,Franchise,Complex
4549,348.94,NJ City,N,18,17,23,80,High School,28,Patnership,Standalone
7674,80.2,Boston,S,18,24,1,56,Graduation,20,Patnership,Complex
5241,167.76,Boston,N,7,14,11,40,Graduation,35,Patnership,Standalone
3363,299.35,NJ City,E,4,17,3,30,Graduation,29,Owner,Standalone
6972,107.46,Boston,W,8,16,2,46,Graduation,19,Franchise,Complex
7181,239.96,Boston,N,19,12,18,80,Graduation,19,Patnership,Standalone
5180,499.33,Boston,W,19,13,25,72,Graduation,32,Franchise,Complex
669,60.06,NJ City,N,15,19,3,32,Graduation,21,Patnership,Standalone
5749,42.63,Boston,N,13,21,2,56,High School,35,Patnership,Standalone
3727,365.06,NJ City,E,7,24,4,50,High School,22,Owner,Standalone
4697,159.24,NJ City,N,11,21,12,29,High School,25,Patnership,Standalone
1263,359.67,NJ City,E,18,23,24,59,High School,20,Owner,Standalone
3254,245.67,NJ City,S,12,18,8,50,High School,26,Patnership,Complex
9282,330.86,NJ City,S,15,20,3,73,Graduation,31,Patnership,Complex
1927,418.02,NJ City,E,21,15,17,55,High School,30,Owner,Standalone
1103,247.67,NJ City,E,20,17,24,58,High School,29,Owner,Standalone
5031,321.81,Boston,E,15,19,15,62,Graduation,19,Owner,Standalone
495,218,NJ City,E,3,19,7,40,High School,26,Owner,Standalone
1196,113.01,NJ City,W,14,19,4,39,No education,32,Franchise,Complex
3935,498.08,NJ City,E,10,22,16,38,High School,20,Owner,Standalone
5337,47.47,Boston,N,12,22,1,60,Graduation,31,Patnership,Standalone
1455,140,NJ City,E,9,23,13,62,High School,35,Owner,Standalone
7120,162.68,Boston,W,7,14,16,27,Graduation,32,Franchise,Complex
3267,398.92,NJ City,E,12,14,14,64,High School,35,Owner,Standalone
7217,295.94,Boston,N,20,17,18,63,Graduation,29,Patnership,Standalone
6317,304.77,Boston,N,3,20,6,63,Graduation,21,Patnership,Standalone
5335,415.83,Boston,E,18,18,9,56,Graduation,33,Owner,Standalone
459,77.82,NJ City,E,22,24,4,34,Graduation,34,Owner,Standalone
6337,185.03,Boston,N,10,8,23,66,Graduation,26,Patnership,Standalone
2267,471.18,NJ City,E,19,24,18,73,High School,26,Owner,Standalone
4894,302.66,NJ City,S,3,11,5,44,High School,24,Patnership,Complex
3408,332.39,NJ City,W,15,19,8,46,High School,29,Franchise,Complex
596,38.3,NJ City,N,13,11,2,41,No education,25,Franchise,Complex
7984,170.92,Boston,W,18,22,19,61,Graduation,27,Franchise,Complex
1551,233.81,NJ City,E,24,13,14,28,High School,31,Owner,Standalone
4407,148.72,NJ City,E,16,18,11,69,High School,32,Owner,Standalone
1238,342.46,NJ City,S,21,22,11,34,High School,32,Patnership,Complex
5000,338.2,Boston,W,22,18,9,70,Graduation,33,Franchise,Complex
6796,370.11,Boston,W,9,21,9,35,Graduation,21,Franchise,Complex
4566,483.5,NJ City,S,12,21,18,41,High School,28,Patnership,Complex
9281,201.03,NJ City,N,22,14,3,44,No education,18,Patnership,Standalone
7882,343.77,Boston,S,6,12,5,73,High School,35,Patnership,Complex
5527,421.19,Boston,E,8,23,23,79,Graduation,19,Owner,Standalone
2521,404.18,NJ City,N,5,8,24,60,High School,31,Patnership,Standalone
8903,134.49,NJ City,E,19,9,8,43,Graduation,28,Owner,Standalone
373,188,NJ City,N,21,16,7,67,High School,33,Patnership,Standalone
3532,247,NJ City,W,1,23,22,28,High School,27,Franchise,Complex
7492,124.3,Boston,W,20,12,3,49,High School,34,Franchise,Complex
5447,173.95,Boston,E,3,19,24,77,Graduation,25,Owner,Standalone
2697,67.65,NJ City,N,2,18,4,53,Graduation,33,Patnership,Standalone
2769,197.13,NJ City,N,11,22,13,55,High School,33,Patnership,Standalone
5421,414.46,Boston,N,17,8,5,31,Graduation,29,Patnership,Standalone
9533,447.44,NJ City,N,25,10,23,79,Graduation,24,Patnership,Standalone
6949,199.91,Boston,N,20,15,20,25,Graduation,24,Patnership,Standalone
7116,298.41,Boston,W,15,16,19,37,Graduation,25,Franchise,Complex
9567,264.32,NJ City,E,18,24,16,25,Graduation,24,Owner,Standalone
7461,153.21,Boston,N,23,16,2,56,Graduation,24,Patnership,Standalone
1475,416.02,NJ City,E,13,23,12,56,High School,23,Owner,Standalone
3449,185.05,NJ City,N,6,9,24,37,High School,29,Patnership,Standalone
5141,274.9,Boston,N,20,10,6,61,Graduation,19,Patnership,Standalone
9412,116.08,NJ City,W,18,23,1,76,High School,18,Franchise,Complex
2538,94.17,NJ City,S,1,14,2,46,Graduation,22,Patnership,Complex
3317,271.74,NJ City,N,6,11,8,52,High School,25,Patnership,Standalone
6231,358.5,Boston,E,11,21,23,51,Graduation,21,Owner,Standalone
5603,385.18,Boston,E,11,13,24,37,Graduation,29,Owner,Standalone
6817,92.95,Boston,N,19,21,2,66,High School,22,Patnership,Standalone
2380,140.11,NJ City,W,14,11,18,74,High School,34,Franchise,Complex
8611,444.22,NJ City,E,2,13,13,38,Graduation,35,Owner,Standalone
6703,198.27,Boston,E,12,11,23,68,Graduation,23,Owner,Standalone
3065,443,NJ City,N,19,22,2,55,No education,27,Patnership,Standalone
1391,196,NJ City,E,3,23,25,83,High School,21,Owner,Standalone
3420,248.71,NJ City,W,12,12,9,25,High School,30,Franchise,Complex
4173,299.33,NJ City,N,15,24,17,65,High School,29,Patnership,Standalone
1595,91.5,NJ City,E,13,16,1,30,No education,30,Owner,Standalone
9831,264.67,NJ City,E,7,14,4,79,Graduation,34,Owner,Standalone
1381,117.74,NJ City,N,15,14,4,67,High School,35,Patnership,Standalone
806,175.12,NJ City,S,5,14,1,79,No education,18,Patnership,Complex
7082,307.44,Boston,S,19,17,1,59,No education,28,Patnership,Complex
2322,96.25,NJ City,S,17,10,1,28,Graduation,28,Patnership,Complex
333,195.19,NJ City,N,11,20,4,83,Graduation,34,Patnership,Standalone
9670,230.48,NJ City,S,23,22,15,53,Graduation,28,Patnership,Complex
2501,278.94,NJ City,N,22,17,17,43,High School,18,Patnership,Standalone
8735,84.06,NJ City,E,12,11,1,31,No education,29,Owner,Standalone
2855,450.96,NJ City,E,13,9,12,34,High School,33,Owner,Standalone
2709,168.77,NJ City,N,14,16,8,81,High School,20,Patnership,Standalone
8404,186.88,NJ City,W,10,14,15,60,Graduation,22,Franchise,Complex
2600,434.99,NJ City,W,14,22,5,43,No education,18,Franchise,Complex
4056,206.03,NJ City,W,7,11,13,59,High School,31,Franchise,Complex
9178,406.25,NJ City,S,9,8,4,59,High School,32,Patnership,Complex
514,79.92,NJ City,S,11,23,1,43,High School,35,Patnership,Complex
4252,397.85,NJ City,W,14,11,15,85,High School,32,Franchise,Complex
3157,117.53,NJ City,N,16,18,2,79,High School,22,Patnership,Standalone
1038,255.38,NJ City,S,18,11,2,53,Graduation,19,Patnership,Complex
2663,314.67,NJ City,E,16,9,15,28,High School,31,Owner,Standalone
3992,93.3,NJ City,W,25,15,2,55,No education,24,Franchise,Complex
548,383.75,NJ City,W,6,12,18,36,High School,18,Franchise,Complex
5225,180.29,Boston,N,18,9,19,84,Graduation,31,Patnership,Standalone
6314,43.28,Boston,N,1,20,2,61,No education,29,Patnership,Complex
6549,213.25,Boston,N,4,17,18,84,Graduation,24,Patnership,Standalone
2757,88.97,NJ City,N,22,22,1,81,Graduation,28,Patnership,Standalone
2603,320.99,NJ City,E,25,9,20,27,High School,29,Owner,Standalone
8723,405.84,NJ City,E,21,22,6,53,Graduation,28,Owner,Standalone
711,87.82,NJ City,E,23,20,4,65,Graduation,29,Owner,Standalone
5115,441.74,Boston,E,24,14,19,34,Graduation,19,Owner,Standalone
6804,430.6,Boston,W,16,21,2,29,Graduation,30,Franchise,Complex
703,194.03,NJ City,E,13,17,12,63,High School,24,Owner,Standalone
8619,458.58,NJ City,E,12,10,21,84,Graduation,33,Owner,Standalone
1651,83.3,NJ City,E,22,16,3,80,High School,23,Owner,Standalone
9889,490.33,NJ City,N,18,22,8,55,Graduation,20,Patnership,Standalone
9002,192.08,NJ City,S,5,20,15,43,Graduation,31,Patnership,Complex
2896,345.65,NJ City,W,11,23,7,74,High School,34,Franchise,Complex
4820,103.65,NJ City,W,24,16,3,71,No education,25,Franchise,Complex
4860,22.92,NJ City,N,13,19,2,28,Graduation,18,Franchise,Complex
4972,54.36,Boston,W,14,14,1,29,High School,30,Franchise,Complex
7635,334.06,Boston,E,8,15,17,61,Graduation,31,Owner,Standalone
9310,339.53,NJ City,S,9,15,21,36,Graduation,33,Patnership,Complex
6412,238.57,Boston,W,17,21,17,64,Graduation,26,Franchise,Complex
6920,491.6,Boston,W,4,23,5,62,No education,26,Franchise,Complex
4480,266.09,NJ City,W,15,18,18,47,High School,35,Franchise,Complex
3454,307.48,NJ City,S,19,13,7,52,High School,19,Patnership,Complex
1487,235,NJ City,E,3,10,21,68,High School,19,Owner,Standalone
5173,225.59,Boston,N,5,15,3,69,High School,24,Patnership,Standalone
3017,412.77,NJ City,N,23,19,13,74,High School,23,Patnership,Standalone
6471,362.9,Boston,E,22,23,1,52,Graduation,20,Owner,Standalone
1084,22.59,NJ City,N,20,23,1,30,High School,33,Franchise,Complex
8284,332.07,NJ City,W,3,11,23,77,Graduation,27,Franchise,Complex
6072,57.9,Boston,W,9,11,4,39,Graduation,31,Franchise,Complex
6131,416.4,Boston,E,25,12,15,57,Graduation,32,Owner,Standalone
6357,52.34,Boston,N,23,23,1,31,Graduation,22,Patnership,Standalone
8532,86.89,NJ City,W,13,14,1,80,Graduation,24,Franchise,Complex
5038,476.17,Boston,S,22,13,13,69,Graduation,21,Patnership,Complex
1210,252.06,NJ City,S,6,13,24,62,High School,35,Patnership,Complex
4662,191.95,NJ City,S,10,15,23,27,High School,26,Patnership,Complex
566,106.4,NJ City,S,7,13,2,39,No education,21,Patnership,Complex
9185,248.11,NJ City,N,21,21,5,36,No education,29,Patnership,Standalone
5029,226.12,Boston,N,4,12,11,75,Graduation,34,Patnership,Standalone
7289,80.79,Boston,N,16,10,2,55,No education,31,Patnership,Standalone
6940,119.84,Boston,W,3,8,3,29,High School,20,Franchise,Complex
4504,177.24,NJ City,W,23,18,12,63,High School,33,Franchise,Complex
5460,167.43,Boston,W,24,10,24,81,Graduation,20,Franchise,Complex
447,235.92,NJ City,E,16,17,1,33,Graduation,31,Owner,Standalone
4195,206,NJ City,E,3,8,24,84,High School,33,Owner,Standalone
7240,64.89,Boston,N,8,18,4,68,High School,23,Franchise,Complex
8871,114.09,NJ City,E,23,20,3,85,Graduation,30,Owner,Standalone
6351,79.36,Boston,E,20,20,3,64,Graduation,23,Owner,Standalone
3711,349.2,NJ City,E,12,9,23,69,High School,19,Owner,Standalone
2747,292.92,NJ City,E,13,12,24,26,High School,23,Owner,Standalone
2957,293.48,NJ City,N,25,17,15,72,High School,27,Patnership,Standalone
858,229.04,NJ City,S,16,18,10,37,High School,35,Patnership,Complex
657,71.11,NJ City,N,21,17,4,63,Graduation,22,Patnership,Standalone
3832,12.93,NJ City,N,1,15,1,71,High School,20,Franchise,Complex
1917,27.76,NJ City,N,15,12,2,85,Graduation,21,Patnership,Standalone
6582,46.95,Boston,N,4,23,2,57,Graduation,30,Patnership,Complex
5316,412.13,Boston,W,8,22,13,34,Graduation,34,Franchise,Complex
179,90.72,NJ City,E,15,8,3,39,No education,19,Owner,Standalone
6885,167.22,Boston,N,5,22,12,40,Graduation,23,Patnership,Standalone
2676,243.35,NJ City,W,16,20,15,38,High School,22,Franchise,Complex
2935,169.46,NJ City,E,19,8,8,80,High School,32,Owner,Standalone
7434,82.81,Boston,S,3,23,2,51,Graduation,28,Patnership,Complex
4410,12.4,NJ City,N,19,9,3,77,Graduation,32,Patnership,Complex
7046,251.47,Boston,S,16,9,9,51,Graduation,35,Patnership,Complex
7424,447.19,Boston,W,4,24,19,31,Graduation,28,Franchise,Complex
4453,379.93,NJ City,N,12,9,2,60,High School,23,Patnership,Standalone
4218,232.61,NJ City,S,16,14,1,50,Graduation,34,Patnership,Complex
7726,393.15,Boston,S,9,11,22,58,Graduation,26,Patnership,Complex
8967,116.28,NJ City,E,9,12,4,36,Graduation,28,Owner,Standalone
1491,131.14,NJ City,E,5,19,18,59,High School,33,Owner,Standalone
1969,169.49,NJ City,N,14,10,11,65,High School,35,Patnership,Standalone
3964,170.23,NJ City,W,17,21,3,82,High School,20,Franchise,Complex
7586,43.9,Boston,N,12,11,1,80,No education,35,Patnership,Complex
2561,408.66,NJ City,N,15,22,21,70,High School,19,Patnership,Standalone
5908,258.37,Boston,W,24,12,15,66,Graduation,28,Franchise,Complex
3993,266.4,NJ City,N,8,20,2,41,Graduation,20,Patnership,Standalone
3293,304.58,NJ City,N,16,8,10,75,High School,27,Patnership,Standalone
4829,185.06,NJ City,N,22,16,21,75,High School,31,Patnership,Standalone
6669,121.66,Boston,N,18,19,4,83,Graduation,35,Patnership,Standalone
5160,347.67,Boston,W,1,10,11,29,Graduation,18,Franchise,Complex
1110,479.73,NJ City,S,17,18,12,28,High School,35,Patnership,Complex
7656,79.86,Boston,W,4,15,2,81,Graduation,24,Franchise,Complex
5221,61.35,Boston,N,14,10,1,64,High School,22,Patnership,Standalone
780,108.7,NJ City,W,18,11,1,48,Graduation,27,Franchise,Complex
9788,128.06,NJ City,W,24,24,1,79,No education,18,Franchise,Complex
4837,264.46,NJ City,N,24,22,15,61,High School,29,Patnership,Standalone
1801,466.04,NJ City,N,16,23,18,45,High School,30,Patnership,Standalone
7812,26.36,Boston,N,9,18,4,84,Graduation,18,Franchise,Complex
7015,258.79,Boston,E,22,22,8,57,Graduation,23,Owner,Standalone
3286,201.03,NJ City,S,3,23,3,54,High School,27,Patnership,Complex
1408,69.53,NJ City,N,17,14,1,31,High School,29,Franchise,Complex
7521,147.1,Boston,N,21,23,3,69,Graduation,23,Patnership,Standalone
654,73.7,NJ City,N,9,17,1,57,Graduation,33,Patnership,Complex
1229,351.47,NJ City,N,16,8,17,39,High School,18,Patnership,Standalone
3767,452.07,NJ City,E,13,22,17,62,High School,25,Owner,Standalone
4795,418.83,NJ City,E,16,18,18,77,High School,30,Owner,Standalone
3019,88.86,NJ City,E,15,15,3,61,High School,24,Owner,Standalone
5352,391.36,Boston,W,8,12,15,40,Graduation,25,Franchise,Complex
1558,417.63,NJ City,S,12,16,9,74,High School,32,Patnership,Complex
1124,390.37,NJ City,W,21,18,18,76,High School,26,Franchise,Complex
530,424.11,NJ City,S,24,16,21,37,High School,34,Patnership,Complex
8127,252.31,NJ City,E,23,14,24,37,Graduation,30,Owner,Standalone
7053,71.36,Boston,N,9,20,2,65,Graduation,24,Patnership,Standalone
2804,470.58,NJ City,W,7,20,25,72,High School,30,Franchise,Complex
94,273.94,NJ City,S,15,20,3,37,High School,31,Patnership,Complex
5772,96.14,Boston,W,20,14,1,39,Graduation,25,Franchise,Complex
2932,495.22,NJ City,W,25,15,13,62,High School,30,Franchise,Complex
1713,451.7,NJ City,N,4,17,22,25,High School,32,Patnership,Standalone
9029,329.68,NJ City,N,6,21,4,57,No education,29,Patnership,Standalone
2537,48.61,NJ City,N,2,9,1,46,No education,35,Patnership,Standalone
8453,20.85,NJ City,N,9,18,2,30,No education,30,Patnership,Standalone
1326,30.36,NJ City,N,12,18,2,26,Graduation,31,Patnership,Complex
7891,138.46,Boston,E,19,19,20,53,Graduation,27,Owner,Standalone
9334,109.28,NJ City,S,24,15,4,60,High School,20,Patnership,Complex
1996,347.02,NJ City,W,16,9,13,38,High School,31,Franchise,Complex
3253,110.84,NJ City,N,25,9,4,35,High School,30,Patnership,Standalone
4203,14.24,NJ City,N,7,20,1,48,Graduation,27,Owner,Standalone
2641,303.7,NJ City,N,13,16,10,31,High School,27,Patnership,Standalone
634,275.65,NJ City,S,9,9,15,60,High School,20,Patnership,Complex
2200,391.11,NJ City,W,8,11,22,59,High School,25,Franchise,Complex
8705,36.96,NJ City,N,16,9,1,56,No education,29,Patnership,Standalone
5819,328.44,Boston,E,12,16,3,53,No education,22,Owner,Standalone
6064,95.96,Boston,W,22,14,4,71,High School,19,Franchise,Complex
1734,155.46,NJ City,S,25,22,4,39,Graduation,26,Patnership,Complex
6599,492.82,Boston,E,3,11,11,50,Graduation,30,Owner,Standalone
1301,417.53,NJ City,N,18,13,23,33,High School,31,Patnership,Standalone
5496,327.01,Boston,W,6,13,18,52,Graduation,24,Franchise,Complex
2266,482.22,NJ City,S,19,22,9,46,High School,21,Patnership,Complex
4830,490.19,NJ City,S,17,13,25,27,High School,33,Patnership,Complex
7708,197.03,Boston,W,12,9,21,58,Graduation,24,Franchise,Complex
4560,45.69,NJ City,N,4,16,4,76,Graduation,35,Franchise,Complex
4782,422.82,NJ City,S,23,14,7,80,High School,20,Patnership,Complex
8958,295.01,NJ City,S,18,21,4,61,Graduation,21,Patnership,Complex
3315,185.28,NJ City,E,4,10,22,71,High School,18,Owner,Standalone
4660,122.11,NJ City,W,7,22,3,68,High School,20,Franchise,Complex
1889,39.84,NJ City,N,10,12,2,38,No education,26,Patnership,Standalone
974,311.05,NJ City,S,19,10,16,31,High School,19,Patnership,Complex
7824,334.66,Boston,W,22,16,20,40,Graduation,33,Franchise,Complex
4926,219,NJ City,S,3,10,13,53,High School,30,Patnership,Complex
6685,53.91,Boston,N,13,16,3,75,High School,26,Patnership,Standalone
4710,292.99,NJ City,S,9,18,3,39,Graduation,27,Patnership,Complex
9203,447.17,NJ City,E,18,18,4,81,No education,25,Owner,Standalone
8046,366.52,NJ City,S,6,13,1,46,Graduation,27,Patnership,Complex
6440,287.86,Boston,W,5,14,23,40,Graduation,27,Franchise,Complex
244,151.04,NJ City,W,24,22,18,81,High School,26,Franchise,Complex
317,312.21,NJ City,N,15,17,1,38,No education,31,Patnership,Standalone
4344,407.67,NJ City,W,10,17,13,59,High School,32,Franchise,Complex
533,390.97,NJ City,N,10,18,14,34,High School,18,Patnership,Standalone
3212,336.11,NJ City,W,17,12,20,59,High School,23,Franchise,Complex
2211,337.32,NJ City,E,1,15,3,42,Graduation,20,Owner,Standalone
4649,302.92,NJ City,N,13,12,16,63,High School,29,Patnership,Standalone
9698,15.98,NJ City,N,14,16,4,84,No education,21,Patnership,Complex
1655,480.3,NJ City,E,18,8,20,49,High School,22,Owner,Standalone
5394,26.45,Boston,N,20,18,4,52,Graduation,35,Patnership,Complex
1890,243.07,NJ City,S,21,8,23,71,High School,19,Patnership,Complex
785,424.61,NJ City,N,10,10,1,62,No education,28,Patnership,Standalone
452,298.39,NJ City,W,9,13,16,82,High School,18,Franchise,Complex
9557,311.15,NJ City,N,24,11,25,80,Graduation,34,Patnership,Standalone
5131,343.3,Boston,E,10,14,24,49,Graduation,21,Owner,Standalone
6035,499.88,Boston,E,22,11,2,74,No education,27,Owner,Standalone
4149,43.8,NJ City,N,15,18,1,61,Graduation,30,Patnership,Standalone
7271,327.63,Boston,E,25,13,21,40,Graduation,22,Owner,Standalone
5263,102.89,Boston,E,6,16,4,62,High School,35,Owner,Standalone
4288,201.89,NJ City,W,4,23,21,85,High School,35,Franchise,Complex
1682,333.76,NJ City,S,23,23,3,30,No education,24,Patnership,Complex
8928,253.05,NJ City,W,17,8,10,49,Graduation,32,Franchise,Complex
814,478.98,NJ City,S,13,22,25,46,High School,25,Patnership,Complex
7360,39.21,Boston,N,15,18,2,28,High School,30,Franchise,Complex
29,349.03,Boston,N,4,17,20,32,High School,23,Patnership,Standalone
5712,361.21,Boston,W,17,8,21,42,Graduation,22,Franchise,Complex
6411,405.54,Boston,E,1,20,19,25,Graduation,28,Owner,Standalone
3923,190.08,NJ City,E,12,21,21,67,High School,20,Owner,Standalone
4746,227.79,NJ City,S,8,10,21,78,High School,22,Patnership,Complex
5156,26.63,Boston,N,7,12,1,41,No education,19,Franchise,Complex
3481,292.16,NJ City,N,13,21,11,77,High School,21,Patnership,Standalone
7150,305.97,Boston,S,15,17,22,53,Graduation,26,Patnership,Complex
601,161.32,NJ City,N,24,10,2,82,High School,19,Patnership,Standalone
9614,263.38,NJ City,S,6,18,18,38,Graduation,29,Patnership,Complex
1359,0.3,NJ City,N,16,19,4,73,Graduation,33,Owner,Standalone
2490,259,NJ City,S,3,22,6,46,High School,18,Patnership,Complex
8330,48.85,NJ City,S,13,18,1,37,No education,27,Patnership,Complex
2197,124.59,NJ City,N,22,17,3,77,High School,31,Patnership,Standalone
104,196.29,NJ City,W,4,24,25,67,High School,21,Franchise,Complex
7370,383.6,Boston,S,3,12,16,55,Graduation,28,Patnership,Complex
8447,42.57,NJ City,N,23,15,1,38,No education,34,Owner,Standalone
1344,495.74,NJ City,W,6,23,19,49,High School,34,Franchise,Complex
3693,62.24,NJ City,N,8,19,2,81,Graduation,25,Patnership,Standalone
1018,71.84,NJ City,N,11,16,2,54,High School,33,Patnership,Complex
2605,13.65,NJ City,N,6,9,4,40,High School,20,Patnership,Standalone
3849,297.52,NJ City,N,10,17,17,77,High School,28,Patnership,Standalone
405,467.63,NJ City,N,18,16,15,28,High School,24,Patnership,Standalone
7553,8.06,Boston,N,12,21,2,81,No education,19,Patnership,Standalone
7482,13.72,Boston,N,11,21,3,49,Graduation,27,Patnership,Complex
8207,284.41,NJ City,E,13,15,5,34,No education,27,Owner,Standalone
3171,351.54,NJ City,E,19,20,21,72,High School,31,Owner,Standalone
688,402.29,NJ City,W,25,14,8,27,High School,24,Franchise,Complex
5020,139.89,Boston,W,25,11,9,79,Graduation,32,Franchise,Complex
4942,289.12,Boston,S,11,11,17,47,High School,18,Patnership,Complex
2113,463.62,NJ City,N,17,13,13,29,High School,21,Patnership,Standalone
9169,107.16,NJ City,N,9,15,4,48,High School,23,Patnership,Standalone
6212,118.9,Boston,W,5,24,1,61,No education,27,Franchise,Complex
3629,12.77,NJ City,N,20,13,4,31,No education,19,Patnership,Standalone
4826,358.19,NJ City,S,10,19,15,68,High School,28,Patnership,Complex
8689,435.57,NJ City,N,24,9,22,80,Graduation,18,Patnership,Standalone
7493,305.23,Boston,N,14,22,18,67,Graduation,30,Patnership,Standalone
1971,118,NJ City,E,1,12,17,68,High School,28,Owner,Standalone
5826,396.97,Boston,S,18,12,14,54,Graduation,25,Patnership,Complex
9480,414.59,NJ City,W,2,16,7,49,Graduation,20,Franchise,Complex
5005,204.97,Boston,N,19,8,3,83,High School,32,Patnership,Standalone
9285,0.86,NJ City,N,14,22,3,30,Graduation,29,Patnership,Standalone
7747,419.17,Boston,E,10,20,21,54,Graduation,19,Owner,Standalone
3400,225.99,NJ City,W,21,18,8,28,High School,30,Franchise,Complex
5129,380.47,Boston,N,14,8,25,42,Graduation,25,Patnership,Standalone
3387,367.35,NJ City,E,4,21,11,81,High School,24,Owner,Standalone
998,70.75,NJ City,N,11,9,2,82,No education,32,Patnership,Complex
7093,194.19,Boston,N,13,14,9,84,Graduation,32,Patnership,Standalone
7368,136.21,Boston,W,1,16,19,77,Graduation,31,Franchise,Complex
4230,37.89,NJ City,N,3,8,4,66,Graduation,30,Patnership,Complex
1626,163.63,NJ City,S,18,22,25,59,High School,33,Patnership,Complex
1122,314.96,NJ City,S,5,19,14,85,High School,22,Patnership,Complex
2742,81.05,NJ City,S,8,11,1,61,Graduation,29,Patnership,Complex
3209,302.48,NJ City,N,23,9,4,66,No education,26,Patnership,Standalone
1275,298.19,NJ City,E,10,12,6,36,High School,28,Owner,Standalone
9664,435.61,NJ City,W,2,11,9,63,Graduation,24,Franchise,Complex
47,94.1,Boston,E,6,16,2,43,No education,31,Owner,Standalone
202,50.09,NJ City,S,20,24,1,74,High School,32,Patnership,Complex
9066,143.72,NJ City,S,3,12,25,25,Graduation,31,Patnership,Complex
6187,263.65,Boston,E,8,21,4,33,High School,33,Owner,Standalone
4905,325,NJ City,N,21,12,6,49,High School,23,Patnership,Standalone
4210,316.46,NJ City,S,15,9,13,59,High School,25,Patnership,Complex
7359,95.93,Boston,E,16,11,3,84,Graduation,26,Owner,Standalone
8983,402.87,NJ City,E,25,18,11,78,Graduation,18,Owner,Standalone
7399,257.17,Boston,E,7,20,25,69,Graduation,20,Owner,Standalone
9821,64.34,NJ City,N,10,18,3,55,No education,35,Patnership,Standalone
7453,162.93,Boston,N,8,8,7,62,Graduation,26,Patnership,Standalone
4728,263.83,NJ City,W,13,16,11,42,High School,30,Franchise,Complex
9274,414.83,NJ City,S,23,14,15,66,Graduation,35,Patnership,Complex
2369,4.68,NJ City,N,14,22,3,51,No education,18,Patnership,Standalone
8192,126.48,NJ City,W,16,16,3,36,No education,32,Franchise,Complex
2625,40.33,NJ City,N,2,18,2,42,Graduation,27,Patnership,Standalone
6829,477.77,Boston,N,14,22,9,39,Graduation,33,Patnership,Standalone
8190,199.26,NJ City,S,6,10,13,57,Graduation,33,Patnership,Complex
7458,410.57,Boston,S,15,11,11,74,Graduation,21,Patnership,Complex
8263,484.48,NJ City,E,6,16,2,47,High School,19,Owner,Standalone
6132,276.31,Boston,W,17,24,10,59,Graduation,27,Franchise,Complex
8214,277.73,NJ City,S,4,11,15,35,Graduation,18,Patnership,Complex
626,123,NJ City,S,1,23,15,45,High School,27,Patnership,Complex
2654,472.16,NJ City,S,12,17,16,42,High School,35,Patnership,Complex
6832,486.14,Boston,W,8,9,10,67,Graduation,33,Franchise,Complex
1606,388.74,NJ City,S,9,12,13,46,High School,23,Patnership,Complex
1794,163.27,NJ City,S,13,19,25,25,High School,29,Patnership,Complex
3097,183.45,NJ City,N,18,23,5,32,High School,35,Patnership,Standalone
7753,36.91,Boston,N,16,18,2,42,High School,24,Patnership,Standalone
1197,314.67,NJ City,N,15,14,13,84,High School,23,Patnership,Standalone
9766,416,NJ City,S,9,23,22,36,Graduation,19,Patnership,Complex
3898,183.83,NJ City,S,13,22,15,30,High School,22,Patnership,Complex
4291,23.78,NJ City,N,21,20,2,53,High School,21,Owner,Standalone
6770,29.81,Boston,N,7,21,4,77,No education,22,Patnership,Complex
6302,4.99,Boston,N,18,11,1,80,No education,27,Patnership,Complex
5635,8.26,Boston,N,18,21,2,68,High School,31,Owner,Standalone
3074,473.93,NJ City,S,11,15,16,57,High School,19,Patnership,Complex
7400,256.82,Boston,W,2,20,9,62,Graduation,27,Franchise,Complex
503,31.38,NJ City,N,25,8,4,79,No education,32,Owner,Standalone
2693,179,NJ City,N,1,13,22,76,High School,20,Patnership,Standalone
9343,373.47,NJ City,E,2,22,15,58,Graduation,28,Owner,Standalone
5302,62.78,Boston,N,3,21,2,71,High School,23,Patnership,Complex
3768,425.43,NJ City,W,23,16,13,31,High School,24,Franchise,Complex
7942,161.77,Boston,S,13,13,1,56,High School,23,Patnership,Complex
811,453.14,NJ City,E,23,8,7,38,High School,23,Owner,Standalone
1187,196.21,NJ City,E,5,22,3,70,No education,22,Owner,Standalone
901,81.19,NJ City,N,24,17,3,60,High School,27,Patnership,Standalone
4684,11.69,NJ City,N,1,21,4,78,High School,35,Franchise,Complex
284,19.65,NJ City,N,9,11,2,68,No education,33,Franchise,Complex
4536,278,NJ City,W,1,14,20,31,High School,18,Franchise,Complex
5620,442.51,Boston,W,6,19,5,51,High School,18,Franchise,Complex
6143,219.34,Boston,E,23,23,2,74,No education,21,Owner,Standalone
2085,308.46,NJ City,N,25,8,6,81,High School,22,Patnership,Standalone
1478,125,NJ City,S,2,11,14,26,High School,31,Patnership,Complex
2273,91.63,NJ City,N,21,23,3,33,No education,18,Patnership,Standalone
4952,91.91,Boston,W,14,11,2,28,No education,25,Franchise,Complex

================================================
FILE: data/mtcars.csv
================================================
"carname","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
"Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
"Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
"Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
"Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
"Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
"Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
"Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
"Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
"Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
"Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
"Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
"Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
"Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
"Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
"Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
"Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
"Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
"Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
"Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
"Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
"Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
"AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
"Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
"Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
"Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
"Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
"Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
"Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
"Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
"Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
"Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: data/mtcars1.csv
================================================
"mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
21,6,160,110,3.9,2.62,16.46,0,1,4,4
21,6,160,110,3.9,2.875,17.02,0,1,4,4
22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
10.4,8,460,215,3,5.424,17.82,0,0,3,4
14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
15,8,301,335,3.54,3.57,14.6,0,1,5,8
21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: data/mtcarsF.csv
================================================
"mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
21,6,160,110,3.9,2.62,16.46,0,1,4,4
21,6,160,110,3.9,2.875,17.02,0,1,4,4
22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
10.4,8,460,215,3,5.424,17.82,0,0,3,4
14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
15,8,301,335,3.54,3.57,14.6,0,1,5,8
21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: data/mtcarsT.csv
================================================
"","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
"Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
"Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
"Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
"Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
"Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
"Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
"Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
"Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
"Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
"Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
"Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
"Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
"Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
"Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
"Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
"Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
"Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
"Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
"Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
"Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
"Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
"AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
"Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
"Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
"Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
"Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
"Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
"Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
"Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
"Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
"Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: data/mushrooms.csv
================================================
class,cap-shape,cap-surface,cap-color,bruises,odor,gill-attachment,gill-spacing,gill-size,gill-color,stalk-shape,stalk-root,stalk-surface-above-ring,stalk-surface-below-ring,stalk-color-above-ring,stalk-color-below-ring,veil-type,veil-color,ring-number,ring-type,spore-print-color,population,habitat
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,s,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,s,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,f,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,s,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,f,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,s,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,f,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,s,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,s,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,f,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,s,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,s,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,s,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,s,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,s,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,s,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,s,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,s,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,f,f,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,f,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,s,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,s,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,f,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,s,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,f,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,s,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,f,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,s,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,f,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,s,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,s,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,f,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,s,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
e,f,s,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
e,f,s,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,s,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,f,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,f,n,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,s,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,s,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,s,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,f,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,s,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
e,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
e,f,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,s,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,s,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,f,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
e,f,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,f,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,s,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,s,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,s,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,s,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,s,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,b,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,s,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
p,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,s,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,s,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,s,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,f,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g
e,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,y,u
e,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
e,f,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
p,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,b,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,m
e,f,s,w,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,f,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,b,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,f,n,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,y,u
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,p
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,f,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,y,u
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,f,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,b,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,b,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,g
e,s,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,f,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
p,x,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,f,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g
e,f,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,s,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
p,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,b,y,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,p
p,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,f,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
e,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,f,f,n,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,x,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p
e,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,g
e,f,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,y,n,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,b,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,s,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,p
e,f,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,b,y,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,b,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,s,y,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g
p,x,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,b,y,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,f,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d
e,f,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,f,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,g
e,x,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,s,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g
e,f,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
p,x,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p
e,f,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,s,g
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
p,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,f,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
p,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,y,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,g
e,x,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,m
e,f,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,b,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g
e,x,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,s,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,p
e,x,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,f,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
e,x,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,s,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,a,g
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,b,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
p,f,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,f,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,f,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,s,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,f,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,y,g
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,u
e,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,g,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,s,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d
e,x,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,s,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,s,n,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,g
e,x,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,a,g
e,f,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,s,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,f,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
p,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,u
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
p,f,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
p,f,y,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,s,n,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,s,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,y,y,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,g
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,f,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,a,g
p,f,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,s,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,f,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,a,g
e,f,f,w,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,s,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,w,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,n,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,a,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,s,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,a,g
e,b,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,s,n,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,s,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,f,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,s,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,s,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,p
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m
e,x,s,n,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m
e,f,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
p,f,y,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,f,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,w,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u
e,f,s,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m
e,x,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,g,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,s,g
e,f,s,w,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,g,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,f,f,n,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
p,f,y,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,b,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m
e,x,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,f,n,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,f,n,f,w,b,p,t,e,f,s,w,w,p,w,o,e,k,s,g
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,s,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
p,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,g,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,s,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,a,g
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,k,s,g
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,s,w,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,n,s,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,n,s,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
p,f,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,u
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,v,u
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,s,g
e,x,s,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,n,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,k,a,g
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,s,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,g
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
p,f,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,s,w,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
p,f,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,s,w,f,n,f,w,b,h,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,n,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,n,a,g
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,s,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,s,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,a,g
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,s,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,k,s,g
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,s,w,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,n,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,k,a,g
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
e,x,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,g,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,n,s,g
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
p,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
e,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
p,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
e,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
p,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
e,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
p,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
p,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d
p,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
e,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
p,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
e,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d
p,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
e,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
e,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
e,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
e,x,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
e,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d
p,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
e,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
e,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
e,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
p,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
e,f,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
e,k,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
e,f,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
e,k,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
e,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,k,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
e,x,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,b,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
e,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
e,k,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
e,f,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
e,f,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
e,x,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
e,f,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,f,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
e,f,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
e,x,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,b,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
e,k,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
e,k,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
p,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
e,k,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
e,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
e,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
e,k,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,x,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
e,x,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,k,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
e,k,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
e,x,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
e,k,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,k,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
e,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,k,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
e,k,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
e,k,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
e,f,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,k,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,c,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,b,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,b,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,k,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
e,x,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,f,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
e,x,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,x,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
e,x,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,f,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
e,f,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,k,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
e,k,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,k,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
e,k,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
e,x,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,k,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,k,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
e,x,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,k,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
e,k,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,k,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
e,k,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,k,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
e,k,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
e,k,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,k,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
e,k,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
e,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
e,f,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
e,x,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
e,x,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
e,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
e,k,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,f,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,b,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,f,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
e,x,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,k,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,x,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
e,x,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
e,f,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,x,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
e,k,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,f,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
e,x,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
e,k,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,k,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
e,k,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,b,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,b,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
e,f,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
e,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,x,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
e,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,k,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
e,k,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
e,x,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
e,k,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,c,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,x,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
e,x,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
e,x,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,k,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
e,k,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
e,x,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
e,f,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,f,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
e,x,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,f,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
e,x,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
e,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
e,k,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
e,f,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,f,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
e,k,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
e,x,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
e,x,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
e,k,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,f,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
e,f,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,k,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
e,k,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,f,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
e,f,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,f,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,k,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
e,k,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,x,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
e,k,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
e,k,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
e,x,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
e,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
e,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,x,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,f,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
e,f,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,k,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,k,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,x,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,f,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
e,x,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
e,x,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,k,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
e,f,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
e,x,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
e,k,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,b,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,k,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
p,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,b,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
e,k,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,k,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,x,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,x,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
e,k,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,b,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,x,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,f,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,b,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,x,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,b,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
e,k,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,k,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,f,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,x,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,f,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
p,f,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
e,x,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
e,b,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
e,b,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
e,b,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,x,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,f,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
e,k,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,x,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,b,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,c,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
e,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
e,f,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,c,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,x,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,x,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
e,x,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
e,b,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,f,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
e,b,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,x,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,c,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
e,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
e,x,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,k,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,k,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,b,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,b,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
e,k,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
e,f,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,f,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,x,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,c,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
e,b,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
e,b,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
e,k,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
e,x,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
e,b,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
e,b,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
e,x,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
e,k,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
e,b,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,x,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,x,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
e,x,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
e,b,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
e,x,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
e,k,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
e,f,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
e,x,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,f,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,b,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
e,k,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
e,x,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,x,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
e,b,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
e,x,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,x,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,b,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,x,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
e,b,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
e,b,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,y,c,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,k,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
e,k,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l

================================================
FILE: data/my_basket
================================================
1,2
1
2,3


================================================
FILE: data/myitems1.csv
================================================
"","transactionID","items"
"1",3,"milk"
"2",2,"milk"
"3",2,"bread"
"4",6,"bread"
"5",5,"milk"
"6",4,"bread"
"7",9,"butter"
"8",3,"eggs"
"9",6,"eggs"
"10",4,"milk"
"11",3,"milk"
"12",9,"milk"
"13",4,"bread"
"14",1,"milk"
"15",10,"eggs"
"16",4,"milk"
"17",4,"butter"
"18",7,"milk"
"19",8,"eggs"
"20",5,"milk"
"21",10,"bread"
"22",1,"eggs"
"23",7,"bread"
"24",9,"milk"
"25",4,"milk"
"26",10,"eggs"
"27",5,"eggs"
"28",10,"milk"
"29",4,"milk"
"30",6,"bread"
"31",9,"bread"
"32",7,"butter"
"33",5,"bread"
"34",6,"butter"
"35",10,"eggs"
"36",9,"eggs"
"37",3,"milk"
"38",9,"milk"
"39",3,"milk"
"40",4,"milk"
"41",6,"bread"
"42",2,"milk"
"43",6,"bread"
"44",10,"butter"
"45",4,"milk"
"46",4,"butter"
"47",1,"bread"
"48",9,"butter"
"49",6,"milk"
"50",9,"butter"


================================================
FILE: data/myrules1.csv
================================================
"","rules","support","confidence","lift","count"
"1","{I4} => {I2}",0.222222222222222,1,1.28571428571429,2
"2","{I5} => {I1}",0.222222222222222,1,1.5,2
"3","{I5} => {I2}",0.222222222222222,1,1.28571428571429,2
"4","{I3} => {I1}",0.444444444444444,0.666666666666667,1,4
"5","{I1} => {I3}",0.444444444444444,0.666666666666667,1,4
"6","{I3} => {I2}",0.444444444444444,0.666666666666667,0.857142857142857,4
"7","{I2} => {I3}",0.444444444444444,0.571428571428571,0.857142857142857,4
"8","{I1} => {I2}",0.444444444444444,0.666666666666667,0.857142857142857,4
"9","{I2} => {I1}",0.444444444444444,0.571428571428571,0.857142857142857,4
"10","{I1,I5} => {I2}",0.222222222222222,1,1.28571428571429,2
"11","{I2,I5} => {I1}",0.222222222222222,1,1.5,2
"12","{I1,I2} => {I5}",0.222222222222222,0.5,2.25,2
"13","{I1,I3} => {I2}",0.222222222222222,0.5,0.642857142857143,2
"14","{I2,I3} => {I1}",0.222222222222222,0.5,0.75,2
"15","{I1,I2} => {I3}",0.222222222222222,0.5,0.75,2


================================================
FILE: data/mytextcars.txt
================================================
mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
21	6	160	110	3.9	2.62	16.46	0	1	4	4
21	6	160	110	3.9	2.875	17.02	0	1	4	4
22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
18.7	8	360	175	3.15	3.44	17.02	0	0	3	2
18.1	6	225	105	2.76	3.46	20.22	1	0	3	1
14.3	8	360	245	3.21	3.57	15.84	0	0	3	4
24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
22.8	4	140.8	95	3.92	3.15	22.9	1	0	4	2
19.2	6	167.6	123	3.92	3.44	18.3	1	0	4	4
17.8	6	167.6	123	3.92	3.44	18.9	1	0	4	4
16.4	8	275.8	180	3.07	4.07	17.4	0	0	3	3
17.3	8	275.8	180	3.07	3.73	17.6	0	0	3	3
15.2	8	275.8	180	3.07	3.78	18	0	0	3	3
10.4	8	472	205	2.93	5.25	17.98	0	0	3	4
10.4	8	460	215	3	5.424	17.82	0	0	3	4
14.7	8	440	230	3.23	5.345	17.42	0	0	3	4
32.4	4	78.7	66	4.08	2.2	19.47	1	1	4	1
30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
33.9	4	71.1	65	4.22	1.835	19.9	1	1	4	1
21.5	4	120.1	97	3.7	2.465	20.01	1	0	3	1
15.5	8	318	150	2.76	3.52	16.87	0	0	3	2
15.2	8	304	150	3.15	3.435	17.3	0	0	3	2
13.3	8	350	245	3.73	3.84	15.41	0	0	3	4
19.2	8	400	175	3.08	3.845	17.05	0	0	3	2
27.3	4	79	66	4.08	1.935	18.9	1	1	4	1
26	4	120.3	91	4.43	2.14	16.7	0	1	5	2
30.4	4	95.1	113	3.77	1.513	16.9	1	1	5	2
15.8	8	351	264	4.22	3.17	14.5	0	1	5	4
19.7	6	145	175	3.62	2.77	15.5	0	1	5	6
15	8	301	335	3.54	3.57	14.6	0	1	5	8
21.4	4	121	109	4.11	2.78	18.6	1	1	4	2


================================================
FILE: data/node1.csv
================================================
"","gender","married","play"
"1","Female","Single","NotPlay"
"2","Male","Single","NotPlay"
"3","Male","Single","NotPlay"
"4","Male","Single","NotPlay"
"5","Male","Married","Play"
"6","Male","Single","NotPlay"
"7","Female","Single","NotPlay"
"8","Female","Single","NotPlay"
"9","Male","Single","NotPlay"
"10","Female","Single","NotPlay"
"11","Male","Single","Play"
"12","Female","Single","NotPlay"
"13","Female","Single","NotPlay"
"14","Male","Married","Play"
"15","Female","Single","NotPlay"
"16","Male","Married","Play"
"17","Female","Single","NotPlay"
"18","Female","Single","NotPlay"
"19","Female","Single","NotPlay"
"20","Female","Single","NotPlay"
"21","Female","Single","NotPlay"
"22","Female","Single","NotPlay"
"23","Female","Single","NotPlay"
"24","Female","Single","NotPlay"
"25","Female","Single","NotPlay"
"26","Male","Married","Play"
"27","Female","Single","NotPlay"
"28","Male","Married","Play"
"29","Male","Married","Play"
"30","Female","Single","NotPlay"


================================================
FILE: data/onsen.csv
================================================
onsen_name,rheumatism,skin_disease,feminine_problems,high_blood,hardened_arteries,gout,backache,nerve_pain,wound,gastroenteropathy,asthma,foot_ringworm,diabetes,burn_injury,eye_trouble,eczema,urinary diseases,joint_ache,muscle_ache,keratoma,dyskinesia,paralysis,liver_ailment,hemorrhoid,atopy,bruise,oversensitiveness_to_cold,astriction
onsen_1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_2,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_3,1,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_4,1,1,1,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_5,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_6,1,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_7,1,0,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_8,1,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_9,1,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_10,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
onsen_11,1,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0
onsen_12,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_13,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0
onsen_14,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_15,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_16,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0
onsen_17,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_18,1,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_19,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_20,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_21,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0
onsen_22,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_23,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
onsen_24,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_25,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0
onsen_26,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_27,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
onsen_28,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_29,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_30,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_31,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_32,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_33,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_34,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0
onsen_35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0
onsen_36,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0
onsen_37,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_38,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0
onsen_39,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_40,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_41,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
onsen_42,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_43,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_44,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_45,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_46,1,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_47,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0
onsen_48,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_49,1,1,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
onsen_50,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
onsen_51,1,0,1,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1
onsen_52,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_54,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_55,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_56,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_57,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_58,1,1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0
onsen_59,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_60,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_61,1,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_62,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_63,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_64,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
onsen_65,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_66,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0
onsen_67,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_68,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_69,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_70,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_71,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_72,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_73,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0
onsen_74,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_75,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_76,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_77,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_78,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_79,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_80,0,0,1,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_81,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_82,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_83,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_84,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_85,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_86,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_87,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0
onsen_88,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_89,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_90,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_91,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_92,1,1,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
onsen_93,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
onsen_94,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
onsen_95,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

================================================
FILE: data/pumba.csv
================================================
rollno,sname,age,course,gender,marks,married
S-1,Student1- xyz,45,MTech,M,57,FALSE
S-2,Student2- xyz,47,BTech,M,58,FALSE
S-3,Student3- xyz,26,BTech,M,67,FALSE
S-4,Student4- xyz,29,BTech,M,63,FALSE
S-5,Student5- xyz,24,Phd,F,57,FALSE
S-6,Student6- xyz,38,Phd,M,59,FALSE
S-7,Student7- xyz,41,BTech,M,64,FALSE
S-8,Student8- xyz,28,MTech,F,61,FALSE
S-9,Student9- xyz,35,MTech,M,69,FALSE
S-10,Student10- xyz,22,MTech,M,56,FALSE
S-11,Student11- xyz,33,MTech,M,60,FALSE
S-12,Student12- xyz,32,Phd,M,64,FALSE
S-13,Student13- xyz,44,BTech,M,63,FALSE
S-14,Student14- xyz,36,BTech,F,57,FALSE
S-15,Student15- xyz,36,Phd,M,62,FALSE
S-16,Student16- xyz,34,MTech,M,65,FALSE
S-17,Student17- xyz,48,MTech,F,58,TRUE
S-18,Student18- xyz,44,BTech,M,56,FALSE
S-19,Student19- xyz,44,MTech,F,65,FALSE
S-20,Student20- xyz,39,BTech,F,63,FALSE
S-21,Student21- xyz,21,MTech,M,62,FALSE
S-22,Student22- xyz,25,BTech,F,62,FALSE
S-23,Student23- xyz,43,BTech,F,53,FALSE
S-24,Student24- xyz,45,BTech,M,61,FALSE
S-25,Student25- xyz,30,BTech,F,56,FALSE
S-26,Student26- xyz,31,Phd,F,65,FALSE
S-27,Student27- xyz,29,MTech,M,58,FALSE
S-28,Student28- xyz,39,BTech,M,65,FALSE
S-29,Student29- xyz,36,MTech,M,65,FALSE
S-30,Student30- xyz,23,BTech,M,65,TRUE


================================================
FILE: data/rep2.csv
================================================
"","productID","sales.1","sales.2","sales.3","sales.4"
"1","P100",697, 58, 82, 19
"2","P101",753, 63, 98, 44
"3","P102",782, 65, 94, 39
"4","P103",708, 59, 78, 25
"5","P104",766, 64, 87, 45
"6","P105",778, 65, 90, 38
"7","P106",812, 68,104, 43
"8","P107",695, 58, 81, 37
"9","P108",720, 60, 80, 50
"10","P109",685, 57, 82, 42
"11","P110",727, 61, 86, 33
"12","P111",629, 52, 64, 17
"13","P112",682, 57, 86, 42
"14","P113",691, 58, 75, 33
"15","P114",762, 64, 93, 35
"16","P115",723, 60, 80, 42
"17","P116",580, 48, 85, 12
"18","P117",653, 54,106, 31
"19","P118",758, 63, 84, 47
"20","P119",731, 61, 96, 29


================================================
FILE: data/rep4.csv
================================================
"","productID","meanSales","maxSales","minSales","sdSales"
"1","P100",58.0833333333333,82,19,16.5554623412737
"2","P101",62.75,98,44,15.178483574994
"3","P102",65.1666666666667,94,39,16.7431578064991
"4","P103",59,78,25,13.7046773700873
"5","P104",63.8333333333333,87,45,11.9227310273911
"6","P105",64.8333333333333,90,38,15.2841768049856
"7","P106",67.6666666666667,104,43,20.5441136596497
"8","P107",57.9166666666667,81,37,13.760670790556
"9","P108",60,80,50,9.40019342160768
"10","P109",57.0833333333333,82,42,11.6264458208105
"11","P110",60.5833333333333,86,33,13.9313794483294
"12","P111",52.4166666666667,64,17,12.6092198110123
"13","P112",56.8333333333333,86,42,17.1137017584971
"14","P113",57.5833333333333,75,33,14.5880295468787
"15","P114",63.5,93,35,18.7301409983529
"16","P115",60.25,80,42,11.7328210976342
"17","P116",48.3333333333333,85,12,18.9608847766962
"18","P117",54.4166666666667,106,31,19.4723969357715
"19","P118",63.1666666666667,84,47,11.2478954260409
"20","P119",60.9166666666667,96,29,18.8605327859448


================================================
FILE: data/rep5.csv
================================================
"","month","Mean","Min","Max","SD"
"1","Jan",56.15,25,96,15.1250054371456
"2","Feb",51.4,27,77,11.997368132439
"3","Mar",53.3,33,85,14.9845534503122
"4","Apr",64.35,39,98,16.0468885326392
"5","May",62.95,39,92,13.9452878289255
"6","Jun",58.3,41,73,9.26282894152753
"7","Jul",58.4,31,91,16.3687764388565
"8","Aug",63.5,44,85,12.2839218835885
"9","Sep",63.3,33,106,17.3602692193532
"10","Oct",59.6,17,93,20.5897266156028
"11","Nov",63.3,36,88,12.490417379567
"12","Dec",62.05,12,104,18.9860752297988


================================================
FILE: data/revision1.csv
================================================
"","rollno","name","gender","marks","grades"
"1",1,"S_1","M",54,"A"
"2",2,"S_2","F",70,"C"
"3",3,"S_3","M",62,"C"
"4",4,"S_4","F",51,"A"
"5",5,"S_5","F",62,"C"


================================================
FILE: data/rules.csv
================================================
"","lhs","","rhs","support","confidence","lift","count"
"[1]","{whole milk}","=>","{hamburger meat}",0.0147432638535841,0.057699960206924,1.73541011815015,145
"[2]","{whole milk}","=>","{hygiene articles}",0.0128113879003559,0.0501392757660167,1.52197462086041,126
"[3]","{whole milk}","=>","{sugar}",0.0150482968988307,0.0588937524870672,1.73939956669762,148
"[4]","{whole milk}","=>","{long life bakery product}",0.0135231316725979,0.052924791086351,1.41444380525615,133
"[5]","{whole milk}","=>","{dessert}",0.0137264870360956,0.0537206526064465,1.44751402297096,135


================================================
FILE: data/rulesR.csv
================================================
"","rules","support","confidence","lift","count"
"1","{I4} => {I2}",0.222222222222222,1,1.28571428571429,2
"2","{I5} => {I1}",0.222222222222222,1,1.5,2
"3","{I5} => {I2}",0.222222222222222,1,1.28571428571429,2
"4","{I3} => {I1}",0.444444444444444,0.666666666666667,1,4
"5","{I1} => {I3}",0.444444444444444,0.666666666666667,1,4
"6","{I3} => {I2}",0.444444444444444,0.666666666666667,0.857142857142857,4
"7","{I2} => {I3}",0.444444444444444,0.571428571428571,0.857142857142857,4
"8","{I1} => {I2}",0.444444444444444,0.666666666666667,0.857142857142857,4
"9","{I2} => {I1}",0.444444444444444,0.571428571428571,0.857142857142857,4
"10","{I1,I5} => {I2}",0.222222222222222,1,1.28571428571429,2
"11","{I2,I5} => {I1}",0.222222222222222,1,1.5,2
"12","{I1,I2} => {I5}",0.222222222222222,0.5,2.25,2
"13","{I1,I3} => {I2}",0.222222222222222,0.5,0.642857142857143,2
"14","{I2,I3} => {I1}",0.222222222222222,0.5,0.75,2
"15","{I1,I2} => {I3}",0.222222222222222,0.5,0.75,2


================================================
FILE: data/s1.csv
================================================
"rollno","sname","marks"
1,"IIM Student 1",61.4029935979176
2,"IIM Student 2",55.4060631327715
3,"IIM Student 3",60.8511990470364
4,"IIM Student 4",62.0943624005793
5,"IIM Student 5",59.9381566681244
6,"IIM Student 6",62.8133852702911
7,"IIM Student 7",74.8762342226676
8,"IIM Student 8",65.0175192868292
9,"IIM Student 9",65.2789949768927
10,"IIM Student 10",57.5877218692895
11,"IIM Student 11",59.1810936661354
12,"IIM Student 12",51.6953971252015
13,"IIM Student 13",59.5382080909829
14,"IIM Student 14",51.3578668426692
15,"IIM Student 15",62.0241264139484
16,"IIM Student 16",69.1331135581728
17,"IIM Student 17",60.6886270511175
18,"IIM Student 18",59.492679616146
19,"IIM Student 19",53.9783190554453
20,"IIM Student 20",66.0286539356095
21,"IIM Student 21",64.4776202279535
22,"IIM Student 22",58.7736156437172
23,"IIM Student 23",53.9057134564
24,"IIM Student 24",55.6254262706981
25,"IIM Student 25",56.4407555640097
26,"IIM Student 26",59.8500316209202
27,"IIM Student 27",54.3871216314106
28,"IIM Student 28",67.4176370145796
29,"IIM Student 29",57.8025544631499
30,"IIM Student 30",62.5789813268222


================================================
FILE: data/salesdata.csv
================================================
"","productID","month","sales"
"1","P100","Jan",42
"2","P101","Jan",64
"3","P102","Jan",76
"4","P103","Jan",25
"5","P104","Jan",66
"6","P105","Jan",68
"7","P106","Jan",51
"8","P107","Jan",52
"9","P108","Jan",52
"10","P109","Jan",47
"11","P110","Jan",53
"12","P111","Jan",45
"13","P112","Jan",48
"14","P113","Jan",61
"15","P114","Jan",74
"16","P115","Jan",58
"17","P116","Jan",52
"18","P117","Jan",46
"19","P118","Jan",47
"20","P119","Jan",96
"21","P100","Feb",62
"22","P101","Feb",53
"23","P102","Feb",53
"24","P103","Feb",67
"25","P104","Feb",50
"26","P105","Feb",38
"27","P106","Feb",69
"28","P107","Feb",45
"29","P108","Feb",60
"30","P109","Feb",46
"31","P110","Feb",77
"32","P111","Feb",53
"33","P112","Feb",49
"34","P113","Feb",52
"35","P114","Feb",36
"36","P115","Feb",42
"37","P116","Feb",27
"38","P117","Feb",40
"39","P118","Feb",56
"40","P119","Feb",53
"41","P100","Mar",82
"42","P101","Mar",44
"43","P102","Mar",47
"44","P103","Mar",56
"45","P104","Mar",45
"46","P105","Mar",45
"47","P106","Mar",43
"48","P107","Mar",41
"49","P108","Mar",52
"50","P109","Mar",53
"51","P110","Mar",33
"52","P111","Mar",51
"53","P112","Mar",43
"54","P113","Mar",45
"55","P114","Mar",58
"56","P115","Mar",68
"57","P116","Mar",85
"58","P117","Mar",48
"59","P118","Mar",84
"60","P119","Mar",43
"61","P100","Apr",70
"62","P101","Apr",98
"63","P102","Apr",59
"64","P103","Apr",50
"65","P104","Apr",60
"66","P105","Apr",87
"67","P106","Apr",43
"68","P107","Apr",81
"69","P108","Apr",80
"70","P109","Apr",65
"71","P110","Apr",60
"72","P111","Apr",53
"73","P112","Apr",55
"74","P113","Apr",70
"75","P114","Apr",91
"76","P115","Apr",58
"77","P116","Apr",39
"78","P117","Apr",49
"79","P118","Apr",64
"80","P119","Apr",55
"81","P100","May",57
"82","P101","May",57
"83","P102","May",39
"84","P103","May",57
"85","P104","May",73
"86","P105","May",70
"87","P106","May",68
"88","P107","May",54
"89","P108","May",57
"90","P109","May",42
"91","P110","May",59
"92","P111","May",64
"93","P112","May",86
"94","P113","May",75
"95","P114","May",53
"96","P115","May",65
"97","P116","May",43
"98","P117","May",73
"99","P118","May",75
"100","P119","May",92
"101","P100","Jun",66
"102","P101","Jun",53
"103","P102","Jun",61
"104","P103","Jun",52
"105","P104","Jun",48
"106","P105","Jun",63
"107","P106","Jun",47
"108","P107","Jun",63
"109","P108","Jun",65
"110","P109","Jun",59
"111","P110","Jun",57
"112","P111","Jun",50
"113","P112","Jun",43
"114","P113","Jun",73
"115","P114","Jun",60
"116","P115","Jun",72
"117","P116","Jun",41
"118","P117","Jun",63
"119","P118","Jun",70
"120","P119","Jun",60
"121","P100","Jul",57
"122","P101","Jul",48
"123","P102","Jul",91
"124","P103","Jul",71
"125","P104","Jul",87
"126","P105","Jul",61
"127","P106","Jul",51
"128","P107","Jul",37
"129","P108","Jul",50
"130","P109","Jul",63
"131","P110","Jul",75
"132","P111","Jul",64
"133","P112","Jul",42
"134","P113","Jul",70
"135","P114","Jul",35
"136","P115","Jul",55
"137","P116","Jul",55
"138","P117","Jul",31
"139","P118","Jul",74
"140","P119","Jul",51
"141","P100","Aug",55
"142","P101","Aug",81
"143","P102","Aug",70
"144","P103","Aug",58
"145","P104","Aug",68
"146","P105","Aug",66
"147","P106","Aug",85
"148","P107","Aug",64
"149","P108","Aug",68
"150","P109","Aug",65
"151","P110","Aug",54
"152","P111","Aug",61
"153","P112","Aug",85
"154","P113","Aug",47
"155","P114","Aug",62
"156","P115","Aug",80
"157","P116","Aug",56
"158","P117","Aug",44
"159","P118","Aug",47
"160","P119","Aug",54
"161","P100","Sep",47
"162","P101","Sep",56
"163","P102","Sep",54
"164","P103","Sep",57
"165","P104","Sep",66
"166","P105","Sep",69
"167","P106","Sep",85
"168","P107","Sep",59
"169","P108","Sep",55
"170","P109","Sep",82
"171","P110","Sep",86
"172","P111","Sep",61
"173","P112","Sep",55
"174","P113","Sep",33
"175","P114","Sep",81
"176","P115","Sep",47
"177","P116","Sep",43
"178","P117","Sep",106
"179","P118","Sep",64
"180","P119","Sep",60
"181","P100","Oct",19
"182","P101","Oct",59
"183","P102","Oct",75
"184","P103","Oct",66
"185","P104","Oct",74
"186","P105","Oct",90
"187","P106","Oct",78
"188","P107","Oct",52
"189","P108","Oct",71
"190","P109","Oct",57
"191","P110","Oct",52
"192","P111","Oct",17
"193","P112","Oct",48
"194","P113","Oct",67
"195","P114","Oct",93
"196","P115","Oct",68
"197","P116","Oct",69
"198","P117","Oct",46
"199","P118","Oct",62
"200","P119","Oct",29
"201","P100","Nov",67
"202","P101","Nov",70
"203","P102","Nov",63
"204","P103","Nov",71
"205","P104","Nov",65
"206","P105","Nov",71
"207","P106","Nov",88
"208","P107","Nov",77
"209","P108","Nov",60
"210","P109","Nov",43
"211","P110","Nov",66
"212","P111","Nov",54
"213","P112","Nov",82
"214","P113","Nov",36
"215","P114","Nov",54
"216","P115","Nov",66
"217","P116","Nov",58
"218","P117","Nov",51
"219","P118","Nov",55
"220","P119","Nov",69
"221","P100","Dec",73
"222","P101","Dec",70
"223","P102","Dec",94
"224","P103","Dec",78
"225","P104","Dec",64
"226","P105","Dec",50
"227","P106","Dec",104
"228","P107","Dec",70
"229","P108","Dec",50
"230","P109","Dec",63
"231","P110","Dec",55
"232","P111","Dec",56
"233","P112","Dec",46
"234","P113","Dec",62
"235","P114","Dec",65
"236","P115","Dec",44
"237","P116","Dec",12
"238","P117","Dec",56
"239","P118","Dec",60
"240","P119","Dec",69


================================================
FILE: data/salesdatamonth.csv
================================================
"","productID","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"
"1","P100",42,62,82,70,57,66,57,55,47,19,67,73
"2","P101",64,53,44,98,57,53,48,81,56,59,70,70
"3","P102",76,53,47,59,39,61,91,70,54,75,63,94
"4","P103",25,67,56,50,57,52,71,58,57,66,71,78
"5","P104",66,50,45,60,73,48,87,68,66,74,65,64
"6","P105",68,38,45,87,70,63,61,66,69,90,71,50
"7","P106",51,69,43,43,68,47,51,85,85,78,88,104
"8","P107",52,45,41,81,54,63,37,64,59,52,77,70
"9","P108",52,60,52,80,57,65,50,68,55,71,60,50
"10","P109",47,46,53,65,42,59,63,65,82,57,43,63
"11","P110",53,77,33,60,59,57,75,54,86,52,66,55
"12","P111",45,53,51,53,64,50,64,61,61,17,54,56
"13","P112",48,49,43,55,86,43,42,85,55,48,82,46
"14","P113",61,52,45,70,75,73,70,47,33,67,36,62
"15","P114",74,36,58,91,53,60,35,62,81,93,54,65
"16","P115",58,42,68,58,65,72,55,80,47,68,66,44
"17","P116",52,27,85,39,43,41,55,56,43,69,58,12
"18","P117",46,40,48,49,73,63,31,44,106,46,51,56
"19","P118",47,56,84,64,75,70,74,47,64,62,55,60
"20","P119",96,53,43,55,92,60,51,54,60,29,69,69


================================================
FILE: data/salesslr.csv
================================================
X,Y
1.7,3.7
1.6,3.9
2.8,6.7
5.6,9.5
1.3,3.4
2.2,5.6
1.3,3.7
1.1,2.7
3.2,5.5
1.5,2.9
5.2,10.7
4.6,7.6
5.8,11.8
3,4.1

================================================
FILE: data/slr1.csv
================================================
X,Y
1.7,3.7
1.6,3.9
2.8,6.7
5.6,9.5
1.3,3.4
2.2,5.6
1.3,3.7
1.1,2.7
3.2,5.5
1.5,2.9
5.2,10.7
4.6,7.6
5.8,11.8
3,4.1

================================================
FILE: data/splitData1.R
================================================
# Splitting Data into 2 or more frames
data(mtcars)
mtcars
## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))
smp_size
## set the seed to make your partition reproductible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)
train_ind
train <- mtcars[train_ind, ]
train
test <- mtcars[-train_ind, ]


# Another Code ---------
bound <- floor((nrow(df)/4)*3)         #define % of training and test set

df <- df[sample(nrow(df)), ]           #sample rows 
df.train <- df[1:bound, ]              #get training set
df.test <- df[(bound+1):nrow(df), ]    #get test set





================================================
FILE: data/stock.csv
================================================
"","SBIN.NS.Close"
"1",299.600006
"2",293.899994
"3",291.100006
"4",297.649994
"5",296.299988
"6",305.299988
"7",305.299988
"8",305.549988
"9",302.75
"10",300.399994
"11",301.649994
"12",303.149994
"13",297.399994
"14",294.950012
"15",292.450012
"16",290.25
"17",286.649994
"18",288.049988
"19",285.399994
"20",281.600006
"21",280.600006
"22",287.450012
"23",293.649994
"24",284.399994
"25",283.950012
"26",284.649994
"27",289.049988
"28",287.399994
"29",286
"30",280.5
"31",275.399994
"32",267.100006
"33",262.950012
"34",259.950012
"35",262.899994
"36",267.700012
"37",268
"38",270.950012
"39",270.149994
"40",266.350006
"41",267.649994
"42",269.049988
"43",272.950012
"44",276.450012
"45",278.100006
"46",281.75
"47",281.200012
"48",287.350006
"49",286.899994
"50",293.149994
"51",291.899994
"52",298.399994
"53",298.950012
"54",303.049988
"55",303.799988
"56",298.049988
"57",294
"58",303.5
"59",308.799988
"60",319
"61",322.75
"62",329
"63",320.5
"64",321.799988
"65",317.049988
"66",312.799988
"67",314.75
"68",310.899994
"69",314.899994
"70",315.25
"71",315.600006
"72",315.75
"73",310.799988
"74",310.049988
"75",305.25
"76",310.75
"77",306.200012
"78",312.5
"79",309.950012
"80",308.149994
"81",310
"82",308.75
"83",305.450012
"84",298.25
"85",299.299988
"86",308.049988
"87",306.899994
"88",314.649994
"89",312.100006
"90",315.75
"91",319.25
"92",344.700012
"93",337.549988
"94",341.100006
"95",342.200012
"96",355.350006
"97",361.700012
"98",360.049988
"99",348.649994
"100",353.549988
"101",352.5
"102",355.450012
"103",352.399994


================================================
FILE: data/stock1.csv
================================================
"SBIN.NS.Close","sdate"
299.600006,"2019-01-01"
293.899994,"2019-01-02"
291.100006,"2019-01-03"
297.649994,"2019-01-04"
296.299988,"2019-01-07"
305.299988,"2019-01-08"
305.299988,"2019-01-09"
305.549988,"2019-01-10"
302.75,"2019-01-11"
300.399994,"2019-01-14"
301.649994,"2019-01-15"
303.149994,"2019-01-16"
297.399994,"2019-01-17"
294.950012,"2019-01-18"
292.450012,"2019-01-21"
290.25,"2019-01-22"
286.649994,"2019-01-23"
288.049988,"2019-01-24"
285.399994,"2019-01-25"
281.600006,"2019-01-28"
280.600006,"2019-01-29"
287.450012,"2019-01-30"
293.649994,"2019-01-31"
284.399994,"2019-02-01"
283.950012,"2019-02-04"
284.649994,"2019-02-05"
289.049988,"2019-02-06"
287.399994,"2019-02-07"
286,"2019-02-08"
280.5,"2019-02-11"
275.399994,"2019-02-12"
267.100006,"2019-02-14"
262.950012,"2019-02-15"
259.950012,"2019-02-18"
262.899994,"2019-02-19"
267.700012,"2019-02-20"
268,"2019-02-21"
270.950012,"2019-02-22"
270.149994,"2019-02-25"
266.350006,"2019-02-26"
267.649994,"2019-02-27"
269.049988,"2019-02-28"
272.950012,"2019-03-01"
276.450012,"2019-03-05"
278.100006,"2019-03-06"
281.75,"2019-03-07"
281.200012,"2019-03-08"
287.350006,"2019-03-11"
286.899994,"2019-03-12"
293.149994,"2019-03-13"
291.899994,"2019-03-14"
298.399994,"2019-03-15"
298.950012,"2019-03-18"
303.049988,"2019-03-19"
303.799988,"2019-03-20"
298.049988,"2019-03-22"
294,"2019-03-25"
303.5,"2019-03-26"
308.799988,"2019-03-27"
319,"2019-03-28"
322.75,"2019-04-01"
329,"2019-04-02"
320.5,"2019-04-03"
321.799988,"2019-04-04"
317.049988,"2019-04-05"
312.799988,"2019-04-08"
314.75,"2019-04-09"
310.899994,"2019-04-10"
314.899994,"2019-04-11"
315.25,"2019-04-12"
315.600006,"2019-04-15"
315.75,"2019-04-16"
310.799988,"2019-04-18"
310.049988,"2019-04-22"
305.25,"2019-04-23"
310.75,"2019-04-24"
306.200012,"2019-04-25"
312.5,"2019-04-26"
309.950012,"2019-04-30"
308.149994,"2019-05-02"
310,"2019-05-03"
308.75,"2019-05-06"
305.450012,"2019-05-07"
298.25,"2019-05-08"
299.299988,"2019-05-09"
308.049988,"2019-05-10"
306.899994,"2019-05-13"
314.649994,"2019-05-14"
312.100006,"2019-05-15"
315.75,"2019-05-16"
319.25,"2019-05-17"
344.700012,"2019-05-20"
337.549988,"2019-05-21"
341.100006,"2019-05-22"
342.200012,"2019-05-23"
355.350006,"2019-05-24"
361.700012,"2019-05-27"
360.049988,"2019-05-28"
348.649994,"2019-05-29"
353.549988,"2019-05-30"
352.5,"2019-05-31"
355.450012,"2019-06-03"
352.399994,"2019-06-04"


================================================
FILE: data/stock1.txt
================================================
"SBIN.NS.Close"
"1" 299.600006
"2" 293.899994
"3" 291.100006
"4" 297.649994
"5" 296.299988
"6" 305.299988
"7" 305.299988
"8" 305.549988
"9" 302.75
"10" 300.399994
"11" 301.649994
"12" 303.149994
"13" 297.399994
"14" 294.950012
"15" 292.450012
"16" 290.25
"17" 286.649994
"18" 288.049988
"19" 285.399994
"20" 281.600006
"21" 280.600006
"22" 287.450012
"23" 293.649994
"24" 284.399994
"25" 283.950012
"26" 284.649994
"27" 289.049988
"28" 287.399994
"29" 286
"30" 280.5
"31" 275.399994
"32" 267.100006
"33" 262.950012
"34" 259.950012
"35" 262.899994
"36" 267.700012
"37" 268
"38" 270.950012
"39" 270.149994
"40" 266.350006
"41" 267.649994
"42" 269.049988
"43" 272.950012
"44" 276.450012
"45" 278.100006
"46" 281.75
"47" 281.200012
"48" 287.350006
"49" 286.899994
"50" 293.149994
"51" 291.899994
"52" 298.399994
"53" 298.950012
"54" 303.049988
"55" 303.799988
"56" 298.049988
"57" 294
"58" 303.5
"59" 308.799988
"60" 319
"61" 322.75
"62" 329
"63" 320.5
"64" 321.799988
"65" 317.049988
"66" 312.799988
"67" 314.75
"68" 310.899994
"69" 314.899994
"70" 315.25
"71" 315.600006
"72" 315.75
"73" 310.799988
"74" 310.049988
"75" 305.25
"76" 310.75
"77" 306.200012
"78" 312.5
"79" 309.950012
"80" 308.149994
"81" 310
"82" 308.75
"83" 305.450012
"84" 298.25
"85" 299.299988
"86" 308.049988
"87" 306.899994
"88" 314.649994
"89" 312.100006
"90" 315.75
"91" 319.25
"92" 344.700012
"93" 337.549988
"94" 341.100006
"95" 342.200012
"96" 355.350006
"97" 361.700012
"98" 360.049988
"99" 348.649994
"100" 353.549988
"101" 352.5
"102" 355.450012
"103" 352.399994


================================================
FILE: data/student.csv
================================================
rollno,name,age
1,student1,23
2,student2,24
3,student3,25
4,student4,26
5,student5,27
6,student6,28
7,student7,29
8,student8,30
9,student9,31
10,student10,32
11,student11,33
12,student12,34
13,student13,35
14,student14,36
15,student15,37


================================================
FILE: data/student1.csv
================================================
rollno,feepaid,gender,cat,dob,class10,class12,sem1,sem2,attnd,btechmarks,batch,br,btechfinal,city,java,dbms,dwm,vlsi,cpp,cbnst,finalgrade
102001,35800,F,GEN,8-Nov-94,57,First,61,63,0.80,66,10-14,EC ,Pass,Gurgaon,77,56,62,42,64,56,A
102002,35800,M,GEN,24-Jul-93,78,First,67,73,0.71,69,10-14,CS,Pass,Gurgaon,92,45,63,63,62,40,A
102003,35800,M,GEN,17-Feb-95,77,Third,50,72,0.11,34,11-15,EC ,Fail,Dehra Dun,48,42,43,47,32,48,C
102004,35800,M,GEN,4-Mar-94,59,Third,67,58,0.15,36,11-15,CS,Fail,Delhi,47,31,55,49,46,50,C
102007,35800,M,GEN,20-Jun-95,68,First,53,68,0.64,65,10-14,EC ,Pass,Almora,65,50,53,50,61,58,A
102008,35800,M,GEN,8-May-94,67,First,51,78,0.64,62,10-14,EC ,Pass,Haldwani,67,54,47,65,50,58,A
102009,35800,M,ST,7-May-94,70,Second,75,66,0.33,35,11-15,CS,Fail,Delhi,65,40,31,65,42,49,C
102011,35800,M,GEN,15-Aug-94,66,Second,52,77,0.63,64,10-14,CS,Pass,Haldwani,84,65,58,51,64,50,B
102013,30800,M,GEN,24-Nov-94,61,First,67,66,0.66,68,11-15,CS,Pass,Haldwani,71,64,42,53,40,56,A
102014,35800,M,GEN,25-Jan-94,60,Second,63,68,0.54,58,11-15,EC ,Pass,Gurgaon,77,65,58,44,65,50,B
102015,35800,F,GEN,8-Jun-94,68,Second,73,79,0.56,57,10-14,CS,Pass,Dehra Dun,55,46,49,54,56,46,B
102016,35800,M,GEN,24-Jun-95,80,Second,69,52,0.34,45,10-14,EC ,Pass,Rudrapur,49,46,49,55,49,50,B
102017,35800,M,OBC,31-Oct-94,62,First,76,54,0.72,70,11-15,EC ,Pass,Noida,98,78,88,42,81,41,A
102018,3800,M,GEN,21-Oct-95,60,First,75,66,0.69,64,11-15,CS,Pass,Almora,58,54,50,65,43,64,A
102019,3800,F,GEN,30-Jun-96,63,First,69,65,0.85,67,10-14,EC ,Pass,Haldwani,77,75,88,81,83,42,A
102020,26200,F,OBC,25-Sep-94,54,First,68,78,0.81,69,10-14,EC ,Pass,Haldwani,42,65,64,43,60,63,A
102021,35800,F,GEN,16-May-94,53,First,75,65,0.76,78,11-15,CS,Pass,Noida,93,84,94,70,86,40,A
102022,35800,M,GEN,24-Jun-95,75,Second,55,75,0.27,36,10-14,CS,Fail,Haldwani,44,49,34,47,55,48,C
102023,35800,M,GEN,11-Dec-93,76,Second,67,51,0.30,45,11-15,EC ,Pass,Haldwani,49,46,49,49,50,55,B
102024,35800,F,GEN,30-Jun-96,57,First,62,77,0.81,64,10-14,CS,Pass,Haldwani,71,68,75,50,66,50,A
102025,35800,M,GEN,11-Dec-93,73,First,75,72,0.90,79,11-15,CS,Pass,Haldwani,88,89,95,91,87,47,A
102026,35800,F,OBC,6-Nov-95,53,First,63,65,0.80,63,10-14,CS,Pass,Haldwani,68,65,71,44,71,50,A
102027,35800,F,GEN,22-Dec-93,73,First,74,58,0.79,62,10-14,EC ,Pass,Delhi,70,61,68,58,76,50,A
102028,35800,F,GEN,6-Feb-95,69,First,51,64,0.74,64,10-14,CS,Pass,Noida,65,50,53,50,61,58,A
102029,35800,M,SC,6-Jun-94,71,Second,80,75,0.32,48,11-15,EC ,Pass,Gurgaon,49,46,49,46,50,55,B
102030,35800,M,GEN,2-Jan-94,62,First,66,76,0.64,61,11-15,EC ,Pass,Haldwani,65,77,71,58,72,43,A
102031,35800,M,GEN,8-Apr-94,65,Second,76,75,0.58,54,10-14,CS,Pass,Haldwani,49,46,49,54,56,62,B
102032,35800,F,GEN,18-Nov-94,70,First,58,51,0.72,68,11-15,CS,Pass,Delhi,62,62,69,63,63,42,A
102063,35800,F,GEN,3-Jul-95,74,First,53,65,0.90,72,10-14,EC ,Pass,Haldwani,40,67,47,40,61,62,A
102034,35800,F,GEN,10-Jan-93,52,First,77,60,0.81,68,11-15,CS,Pass,Haldwani,74,74,83,56,62,56,A
102035,35800,M,OBC,19-Dec-92,57,First,77,60,0.81,63,10-14,EC ,Pass,Delhi,76,58,50,65,50,64,A
102036,35800,F,GEN,19-Sep-93,68,First,61,75,0.73,68,10-14,CS,Pass,Delhi,89,58,63,70,83,41,A
102037,35800,M,GEN,13-Aug-94,55,Third,74,75,0.22,38,11-15,CS,Fail,Noida,49,46,49,23,46,50,C
102038,35800,F,GEN,21-Jan-94,63,First,60,53,0.92,76,11-15,CS,Pass,Noida,87,94,78,85,76,45,A
102039,35800,F,GEN,25-Feb-94,61,First,58,55,0.73,75,10-14,CS,Pass,Dehra Dun,82,64,72,83,73,43,B
102040,35800,M,GEN,12-Jan-96,66,Second,56,61,0.60,56,10-14,CS,Pass,Haldwani,89,77,54,56,62,45,A
102041,35800,M,SC,27-Jul-95,66,Second,79,67,0.25,42,10-14,EC ,Pass,Haldwani,49,42,46,49,46,50,B
102042,35800,M,GEN,12-Jan-96,58,Second,65,80,0.22,39,11-15,EC ,Fail,Kashipur,33,55,45,46,55,31,C
102043,35800,M,GEN,18-Feb-95,71,First,66,63,0.64,67,10-14,EC ,Pass,Delhi,47,49,63,45,56,62,A
102044,35800,M,SC,3-Mar-95,63,Second,63,53,0.33,57,10-14,CS,Pass,Haldwani,55,46,49,65,58,56,B
102045,35800,F,GEN,30-Jun-94,51,First,60,60,0.87,80,11-15,CS,Pass,Delhi,96,96,98,76,81,47,A
102047,35800,M,GEN,23-Oct-94,80,Second,54,53,0.52,54,11-15,CS,Pass,Haldwani,49,55,46,54,64,50,A
102048,35800,M,GEN,15-Nov-93,77,Third,54,66,0.08,35,10-14,EC ,Fail,Noida,42,47,65,35,41,49,C
102049,35800,M,SC,11-May-93,50,Third,61,55,0.09,35,10-14,EC ,Fail,Noida,65,65,48,35,45,35,C
102058,35800,F,GEN,2-Apr-95,67,First,75,68,0.83,71,10-14,EC ,Pass,Haldwani,62,86,79,40,76,47,A
103023,35800,F,GEN,15-Mar-94,65,First,77,62,0.90,70,11-15,EC ,Pass,Noida,93,74,81,73,80,43,A
102050,500,F,GEN,1-Jul-96,51,Third,52,58,0.04,33,10-14,EC ,Fail,Haldwani,39,65,43,65,50,36,C
102051,500,M,ST,1-Aug-96,60,Third,76,59,0.04,32,10-14,EC ,Fail,Haldwani,30,46,34,65,32,35,C
102052,35800,M,GEN,5-Apr-91,71,First,56,55,0.94,68,10-14,EC ,Pass,Haldwani,85,72,67,40,72,56,A
102033,35800,M,GEN,16-Sep-93,72,Third,62,69,0.13,36,11-15,CS,Fail,Dehra Dun,44,45,55,35,42,43,C

================================================
FILE: data/studentdata.R
================================================
# Student Data Prepare
# Split into train and test of Student 
student = read.csv('data/student1.csv')
str(student)
student$dob = as.Date(student$dob, format = "%d-%b-%y")
str(student)
student$age = round(as.numeric((Sys.Date() - student$dob)/365 ))
head(student)

## 75% of the sample size as train Data
smp_size <- floor(0.80 * nrow(student))
smp_size
## set the seed to make your partition reproductible
set.seed(123)
train_ind <- sample(seq_len(nrow(student)), size = smp_size)
train_ind
train1 <- student[train_ind, ]
head(train1)
str(train1)
test1 <- student[-train_ind, ] # rest of the data use as test
head(test1)
str(test1)
nrow(train1);nrow(test1)
# want to train the data on basis of age, class10, sem1 marks

x_train = train1[,c('age','class10','sem1')]
head(x_train)
y_train = train1$btechmarks
head(y_train)
x_test = train1[,c('age','class10','sem1')]
head(x_test)


================================================
FILE: data/studentdata.csv
================================================
"rollno","name","dob","gender","course","batch","EXCEL","MATHS","PYTHON","RPGM","STATS","age"
1,"XYZ",1994-01-01,"M","BSCDS",2016,87,92,79,42,92,23
2,"XYZ",1994-01-08,"M","BSCDS",2017,96,102,89,77,104,23
3,"XYZ",1994-01-15,"M","BSCDS",2016,87,133,89,61,99,23
4,"XYZ",1994-01-22,"M","BSCDS",2017,90,82,71,67,100,23
5,"XYZ",1994-01-29,"F","BSCDS",2016,63,102,83,51,108,23
6,"XYZ",1994-02-05,"M","BSCDS",2017,74,127,68,77,116,23
7,"XYZ",1994-02-12,"M","BSCDS",2016,102,95,60,93,126,23
8,"XYZ",1994-02-19,"M","BSCDS",2017,62,79,63,59,102,23
9,"XYZ",1994-02-26,"M","BSCDS",2016,84,83,74,69,126,23
10,"XYZ",1994-03-05,"M","BSCDS",2017,86,92,93,93,98,23
11,"XYZ",1994-03-12,"M","BSCDS",2016,95,83,76,81,117,23
12,"XYZ",1994-03-19,"M","BSCDS",2017,75,95,67,93,135,23
13,"XYZ",1994-03-26,"M","BSCDS",2016,82,92,77,70,110,23
14,"XYZ",1994-04-02,"F","BSCDS",2017,54,96,82,68,103,23
15,"XYZ",1994-04-09,"M","BSCDS",2016,85,108,62,64,110,23
16,"XYZ",1994-04-16,"F","BSCDS",2017,70,112,64,87,128,23
17,"XYZ",1994-04-23,"M","BSCDS",2016,76,134,56,55,99,23
18,"XYZ",1994-04-30,"M","BSCDS",2017,62,99,62,74,124,23
19,"XYZ",1994-05-07,"M","BSCDS",2016,86,94,66,69,123,23
20,"XYZ",1994-05-14,"M","BSCDS",2017,90,129,72,97,113,23
21,"XYZ",1994-05-21,"M","MSCDS",2016,75,134,89,63,110,23
22,"XYZ",1994-05-28,"M","MSCDS",2017,107,101,84,52,105,23
23,"XYZ",1994-06-04,"M","MSCDS",2016,90,93,78,73,106,23
24,"XYZ",1994-06-11,"M","MSCDS",2017,94,64,69,70,116,23
25,"XYZ",1994-06-18,"M","MSCDS",2016,111,128,75,60,131,22
26,"XYZ",1994-06-25,"F","MSCDS",2017,70,83,80,72,108,22
27,"XYZ",1994-07-02,"M","MSCDS",2016,92,78,70,67,96,22
28,"XYZ",1994-07-09,"F","MSCDS",2017,95,161,86,62,103,22
29,"XYZ",1994-07-16,"F","MSCDS",2016,80,105,86,76,113,22
30,"XYZ",1994-07-23,"M","MSCDS",2017,85,99,63,66,107,22
31,"XYZ",1994-07-30,"M","MSCDS",2016,65,45,68,48,108,22
32,"XYZ",1994-08-06,"M","MSCDS",2017,87,98,87,38,108,22
33,"XYZ",1994-08-13,"M","MSCDS",2016,69,120,58,63,96,22
34,"XYZ",1994-08-20,"M","MSCDS",2017,92,108,79,87,108,22
35,"XYZ",1994-08-27,"M","MSCDS",2016,68,118,77,80,119,22
36,"XYZ",1994-09-03,"M","MSCDS",2017,85,140,107,85,117,22
37,"XYZ",1994-09-10,"M","MSCDS",2016,67,123,48,82,115,22
38,"XYZ",1994-09-17,"M","MSCDS",2017,76,90,67,74,106,22
39,"XYZ",1994-09-24,"F","MSCDS",2016,57,114,82,52,108,22
40,"XYZ",1994-10-01,"F","MSCDS",2017,82,96,92,72,98,22
41,"XYZ",1994-10-08,"M","MSCDS",2016,95,89,83,89,109,22
42,"XYZ",1994-10-15,"M","MSCDS",2017,83,43,61,29,113,22
43,"XYZ",1994-10-22,"M","MSCDS",2016,54,84,97,61,127,22
44,"XYZ",1994-10-29,"M","MSCDS",2017,71,110,57,83,120,22
45,"XYZ",1994-11-05,"M","MSCDS",2016,105,143,64,76,105,22
46,"XYZ",1994-11-12,"M","MSCDS",2017,107,110,75,79,114,22
47,"XYZ",1994-11-19,"M","MSCDS",2016,62,112,97,75,99,22
48,"XYZ",1994-11-26,"M","MSCDS",2017,74,81,80,76,119,22
49,"XYZ",1994-12-03,"M","MSCDS",2016,85,103,82,70,120,22
50,"XYZ",1994-12-10,"M","MSCDS",2017,85,58,92,59,131,22
51,"XYZ",1994-12-17,"M","PGDDS",2016,71,110,85,55,114,22
52,"XYZ",1994-12-24,"M","PGDDS",2017,66,114,63,70,105,22
53,"XYZ",1994-12-31,"M","PGDDS",2016,77,104,82,65,111,22
54,"XYZ",1995-01-07,"M","PGDDS",2017,95,114,74,59,105,22
55,"XYZ",1995-01-14,"M","PGDDS",2016,80,106,93,75,102,22
56,"XYZ",1995-01-21,"M","PGDDS",2017,70,115,73,72,112,22
57,"XYZ",1995-01-28,"M","PGDDS",2016,72,137,60,88,101,22
58,"XYZ",1995-02-04,"M","PGDDS",2017,104,122,80,57,112,22
59,"XYZ",1995-02-11,"M","PGDDS",2016,73,101,79,64,114,22
60,"XYZ",1995-02-18,"F","PGDDS",2017,91,78,75,69,109,22
61,"XYZ",1995-02-25,"F","PGDDS",2016,102,108,64,53,108,22
62,"XYZ",1995-03-04,"M","PGDDS",2017,87,92,67,67,104,22
63,"XYZ",1995-03-11,"M","PGDDS",2016,74,130,75,62,99,22
64,"XYZ",1995-03-18,"M","PGDDS",2017,79,68,85,44,118,22
65,"XYZ",1995-03-25,"M","PGDDS",2016,73,92,71,67,110,22
66,"XYZ",1995-04-01,"M","BTCSDA",2017,98,108,66,64,118,22
67,"XYZ",1995-04-08,"M","BTCSDA",2016,78,97,48,84,98,22
68,"XYZ",1995-04-15,"M","BTCSDA",2017,103,88,80,63,112,22
69,"XYZ",1995-04-22,"M","BTCSDA",2016,72,94,79,77,117,22
70,"XYZ",1995-04-29,"M","BTCSDA",2017,70,113,88,72,110,22
71,"XYZ",1995-05-06,"M","BTCSDA",2016,82,118,90,55,108,22
72,"XYZ",1995-05-13,"F","BTCSDA",2017,80,113,74,74,102,22
73,"XYZ",1995-05-20,"M","BTCSDA",2016,72,145,88,69,131,22
74,"XYZ",1995-05-27,"M","BTCSDA",2017,56,123,75,72,118,22
75,"XYZ",1995-06-03,"M","BTCSDA",2016,80,106,68,78,128,22
76,"XYZ",1995-06-10,"M","BTCSDA",2017,69,87,67,71,111,22
77,"XYZ",1995-06-17,"M","BTCSDA",2016,96,158,72,89,104,21
78,"XYZ",1995-06-24,"M","BTCSDA",2017,66,114,77,71,95,21
79,"XYZ",1995-07-01,"M","BTCSDA",2016,97,86,75,55,104,21
80,"XYZ",1995-07-08,"M","BTCSDA",2017,70,104,77,79,112,21
81,"XYZ",1995-07-15,"F","BTCSDA",2016,84,94,77,55,120,21
82,"XYZ",1995-07-22,"M","BTCSDA",2017,93,95,82,47,113,21
83,"XYZ",1995-07-29,"M","BTCSDA",2016,72,81,72,60,98,21
84,"XYZ",1995-08-05,"M","BTCSDA",2017,88,102,91,61,117,21
85,"XYZ",1995-08-12,"M","BTCSDA",2016,79,106,79,63,93,21
86,"XYZ",1995-08-19,"F","BTCSDA",2017,63,78,63,89,106,21
87,"XYZ",1995-08-26,"M","BTCSDA",2016,76,35,72,90,107,21
88,"XYZ",1995-09-02,"M","BTCSDA",2017,104,95,80,79,91,21
89,"XYZ",1995-09-09,"M","BTCSDA",2016,77,101,71,55,119,21
90,"XYZ",1995-09-16,"F","BTCSDA",2017,68,112,78,70,104,21
91,"XYZ",1995-09-23,"M","BTCSDA",2016,79,101,79,76,107,21
92,"XYZ",1995-09-30,"F","BTCSDA",2017,85,108,88,68,124,21
93,"XYZ",1995-10-07,"M","BTCSDA",2016,94,78,68,72,116,21
94,"XYZ",1995-10-14,"M","BTCSDA",2017,97,114,75,59,109,21
95,"XYZ",1995-10-21,"M","BTCSDA",2016,82,114,69,55,115,21
96,"XYZ",1995-10-28,"M","BTCSDA",2017,97,105,64,87,114,21
97,"XYZ",1995-11-04,"M","BTCSDA",2016,69,127,57,73,127,21
98,"XYZ",1995-11-11,"M","BTCSDA",2017,45,108,77,65,113,21
99,"XYZ",1995-11-18,"M","BTCSDA",2016,73,105,81,74,115,21
100,"XYZ",1995-11-25,"M","BTCSDA",2017,72,105,81,95,113,21


================================================
FILE: data/studentdata2.csv
================================================
1,"BSCDS",2016,87,92,79,42,92,,
2,"BSCDS",2017,96,102,89,77,104,,
3,"BSCDS",2016,87,133,89,61,99,,
4,"BSCDS",2017,90,82,71,67,100,,
5,"BSCDS",2016,63,102,83,51,108,,
6,"BSCDS",2017,74,127,68,77,116,,
7,"BSCDS",2016,102,95,60,93,126,,
8,"BSCDS",2017,62,79,63,59,102,,
9,"BSCDS",2016,84,83,74,69,126,,
10,"BSCDS",2017,86,92,93,93,98,,
11,"BSCDS",2016,95,83,76,81,117,,
12,"BSCDS",2017,75,95,67,93,135,,
13,"BSCDS",2016,82,92,77,70,110,,
14,"BSCDS",2017,54,96,82,68,103,,
15,"BSCDS",2016,85,108,62,64,110,,
16,"BSCDS",2017,70,112,64,87,128,,
17,"BSCDS",2016,76,134,56,55,99,,
18,"BSCDS",2017,62,99,62,74,124,,
19,"BSCDS",2016,86,94,66,69,123,,
20,"BSCDS",2017,90,129,72,97,113,,
21,"MSCDS",2016,75,134,89,63,110,,
22,"MSCDS",2017,107,101,84,52,105,,
23,"MSCDS",2016,90,93,78,73,106,,
24,"MSCDS",2017,94,64,69,70,116,,
25,"MSCDS",2016,111,128,75,60,131,,
26,"MSCDS",2017,70,83,80,72,108,,
27,"MSCDS",2016,92,78,70,67,96,,
28,"MSCDS",2017,95,161,86,62,103,,
29,"MSCDS",2016,80,105,86,76,113,,
30,"MSCDS",2017,85,99,63,66,107,,
31,"MSCDS",2016,65,45,68,48,108,,
32,"MSCDS",2017,87,98,87,38,108,,
33,"MSCDS",2016,69,120,58,63,96,,
34,"MSCDS",2017,92,108,79,87,108,,
35,"MSCDS",2016,68,118,77,80,119,,
36,"MSCDS",2017,85,140,107,85,117,,
37,"MSCDS",2016,67,123,48,82,115,,
38,"MSCDS",2017,76,90,67,74,106,,
39,"MSCDS",2016,57,114,82,52,108,,
40,"MSCDS",2017,82,96,92,72,98,,
41,"MSCDS",2016,95,89,83,89,109,,
42,"MSCDS",2017,83,43,61,29,113,,
43,"MSCDS",2016,54,84,97,61,127,,
44,"MSCDS",2017,71,110,57,83,120,,
45,"MSCDS",2016,105,143,64,76,105,,
46,"MSCDS",2017,107,110,75,79,114,,
47,"MSCDS",2016,62,112,97,75,99,,
48,"MSCDS",2017,74,81,80,76,119,,
49,"MSCDS",2016,85,103,82,70,120,,
50,"MSCDS",2017,85,58,92,59,131,,
51,"PGDDS",2016,71,110,85,55,114,,
52,"PGDDS",2017,66,114,63,70,105,,
53,"PGDDS",2016,77,104,82,65,111,,
54,"PGDDS",2017,95,114,74,59,105,,
55,"PGDDS",2016,80,106,93,75,102,,
56,"PGDDS",2017,70,115,73,72,112,,
57,"PGDDS",2016,72,137,60,88,101,,
58,"PGDDS",2017,104,122,80,57,112,,
59,"PGDDS",2016,73,101,79,64,114,,
60,"PGDDS",2017,91,78,75,69,109,,
61,"PGDDS",2016,102,108,64,53,108,,
62,"PGDDS",2017,87,92,67,67,104,,
63,"PGDDS",2016,74,130,75,62,99,,
64,"PGDDS",2017,79,68,85,44,118,,
65,"PGDDS",2016,73,92,71,67,110,,
66,"BTCSDA",2017,98,108,66,64,118,,
67,"BTCSDA",2016,78,97,48,84,98,,
68,"BTCSDA",2017,103,88,80,63,112,,
69,"BTCSDA",2016,72,94,79,77,117,,
70,"BTCSDA",2017,70,113,88,72,110,,
71,"BTCSDA",2016,82,118,90,55,108,,
72,"BTCSDA",2017,80,113,74,74,102,,
73,"BTCSDA",2016,72,145,88,69,131,,
74,"BTCSDA",2017,56,123,75,72,118,,
75,"BTCSDA",2016,80,106,68,78,128,,
76,"BTCSDA",2017,69,87,67,71,111,,
77,"BTCSDA",2016,96,158,72,89,104,,
78,"BTCSDA",2017,66,114,77,71,95,,
79,"BTCSDA",2016,97,86,75,55,104,,
80,"BTCSDA",2017,70,104,77,79,112,,
81,"BTCSDA",2016,84,94,77,55,120,,
82,"BTCSDA",2017,93,95,82,47,113,,
83,"BTCSDA",2016,72,81,72,60,98,,
84,"BTCSDA",2017,88,102,91,61,117,,
85,"BTCSDA",2016,79,106,79,63,93,,
86,"BTCSDA",2017,63,78,63,89,106,,
87,"BTCSDA",2016,76,35,72,90,107,,
88,"BTCSDA",2017,104,95,80,79,91,,
89,"BTCSDA",2016,77,101,71,55,119,,
90,"BTCSDA",2017,68,112,78,70,104,,
91,"BTCSDA",2016,79,101,79,76,107,,
92,"BTCSDA",2017,85,108,88,68,124,,
93,"BTCSDA",2016,94,78,68,72,116,,
94,"BTCSDA",2017,97,114,75,59,109,,
95,"BTCSDA",2016,82,114,69,55,115,,
96,"BTCSDA",2017,97,105,64,87,114,,
97,"BTCSDA",2016,69,127,57,73,127,,
98,"BTCSDA",2017,45,108,77,65,113,,
99,"BTCSDA",2016,73,105,81,74,115,,
100,"BTCSDA",2017,72,105,81,95,113,,


================================================
FILE: data/studentdata3.txt
================================================
------------------------------------------------------------------------------------------------------------------------------------------
| rollno  |    course     |    batch    |   Lesson 1    |   Lesson 2    |    Quiz     |   Project   |   Final    | Comment   | Comment   |
------------------------------------------------------------------------------------------------------------------------------------------
|       1 | BSCDS         |        2016 |            87 |            92 |          79 |          42 |         92 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       2 | BSCDS         |        2017 |            96 |           102 |          89 |          77 |        104 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       3 | BSCDS         |        2016 |            87 |           133 |          89 |          61 |         99 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       4 | BSCDS         |        2017 |            90 |            82 |          71 |          67 |        100 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       5 | BSCDS         |        2016 |            63 |           102 |          83 |          51 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       6 | BSCDS         |        2017 |            74 |           127 |          68 |          77 |        116 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       7 | BSCDS         |        2016 |           102 |            95 |          60 |          93 |        126 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       8 | BSCDS         |        2017 |            62 |            79 |          63 |          59 |        102 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|       9 | BSCDS         |        2016 |            84 |            83 |          74 |          69 |        126 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      10 | BSCDS         |        2017 |            86 |            92 |          93 |          93 |         98 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      11 | BSCDS         |        2016 |            95 |            83 |          76 |          81 |        117 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      12 | BSCDS         |        2017 |            75 |            95 |          67 |          93 |        135 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      13 | BSCDS         |        2016 |            82 |            92 |          77 |          70 |        110 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      14 | BSCDS         |        2017 |            54 |            96 |          82 |          68 |        103 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      15 | BSCDS         |        2016 |            85 |           108 |          62 |          64 |        110 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      16 | BSCDS         |        2017 |            70 |           112 |          64 |          87 |        128 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      17 | BSCDS         |        2016 |            76 |           134 |          56 |          55 |         99 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      18 | BSCDS         |        2017 |            62 |            99 |          62 |          74 |        124 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      19 | BSCDS         |        2016 |            86 |            94 |          66 |          69 |        123 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      20 | BSCDS         |        2017 |            90 |           129 |          72 |          97 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      21 | MSCDS         |        2016 |            75 |           134 |          89 |          63 |        110 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      22 | MSCDS         |        2017 |           107 |           101 |          84 |          52 |        105 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      23 | MSCDS         |        2016 |            90 |            93 |          78 |          73 |        106 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      24 | MSCDS         |        2017 |            94 |            64 |          69 |          70 |        116 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      25 | MSCDS         |        2016 |           111 |           128 |          75 |          60 |        131 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      26 | MSCDS         |        2017 |            70 |            83 |          80 |          72 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      27 | MSCDS         |        2016 |            92 |            78 |          70 |          67 |         96 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      28 | MSCDS         |        2017 |            95 |           161 |          86 |          62 |        103 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      29 | MSCDS         |        2016 |            80 |           105 |          86 |          76 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      30 | MSCDS         |        2017 |            85 |            99 |          63 |          66 |        107 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      31 | MSCDS         |        2016 |            65 |            45 |          68 |          48 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      32 | MSCDS         |        2017 |            87 |            98 |          87 |          38 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      33 | MSCDS         |        2016 |            69 |           120 |          58 |          63 |         96 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      34 | MSCDS         |        2017 |            92 |           108 |          79 |          87 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      35 | MSCDS         |        2016 |            68 |           118 |          77 |          80 |        119 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      36 | MSCDS         |        2017 |            85 |           140 |         107 |          85 |        117 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      37 | MSCDS         |        2016 |            67 |           123 |          48 |          82 |        115 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      38 | MSCDS         |        2017 |            76 |            90 |          67 |          74 |        106 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      39 | MSCDS         |        2016 |            57 |           114 |          82 |          52 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      40 | MSCDS         |        2017 |            82 |            96 |          92 |          72 |         98 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      41 | MSCDS         |        2016 |            95 |            89 |          83 |          89 |        109 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      42 | MSCDS         |        2017 |            83 |            43 |          61 |          29 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      43 | MSCDS         |        2016 |            54 |            84 |          97 |          61 |        127 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      44 | MSCDS         |        2017 |            71 |           110 |          57 |          83 |        120 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      45 | MSCDS         |        2016 |           105 |           143 |          64 |          76 |        105 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      46 | MSCDS         |        2017 |           107 |           110 |          75 |          79 |        114 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      47 | MSCDS         |        2016 |            62 |           112 |          97 |          75 |         99 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      48 | MSCDS         |        2017 |            74 |            81 |          80 |          76 |        119 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      49 | MSCDS         |        2016 |            85 |           103 |          82 |          70 |        120 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      50 | MSCDS         |        2017 |            85 |            58 |          92 |          59 |        131 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      51 | PGDDS         |        2016 |            71 |           110 |          85 |          55 |        114 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      52 | PGDDS         |        2017 |            66 |           114 |          63 |          70 |        105 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      53 | PGDDS         |        2016 |            77 |           104 |          82 |          65 |        111 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      54 | PGDDS         |        2017 |            95 |           114 |          74 |          59 |        105 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      55 | PGDDS         |        2016 |            80 |           106 |          93 |          75 |        102 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      56 | PGDDS         |        2017 |            70 |           115 |          73 |          72 |        112 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      57 | PGDDS         |        2016 |            72 |           137 |          60 |          88 |        101 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      58 | PGDDS         |        2017 |           104 |           122 |          80 |          57 |        112 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      59 | PGDDS         |        2016 |            73 |           101 |          79 |          64 |        114 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      60 | PGDDS         |        2017 |            91 |            78 |          75 |          69 |        109 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      61 | PGDDS         |        2016 |           102 |           108 |          64 |          53 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      62 | PGDDS         |        2017 |            87 |            92 |          67 |          67 |        104 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      63 | PGDDS         |        2016 |            74 |           130 |          75 |          62 |         99 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      64 | PGDDS         |        2017 |            79 |            68 |          85 |          44 |        118 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      65 | PGDDS         |        2016 |            73 |            92 |          71 |          67 |        110 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      66 | BTCSDA        |        2017 |            98 |           108 |          66 |          64 |        118 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      67 | BTCSDA        |        2016 |            78 |            97 |          48 |          84 |         98 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      68 | BTCSDA        |        2017 |           103 |            88 |          80 |          63 |        112 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      69 | BTCSDA        |        2016 |            72 |            94 |          79 |          77 |        117 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      70 | BTCSDA        |        2017 |            70 |           113 |          88 |          72 |        110 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      71 | BTCSDA        |        2016 |            82 |           118 |          90 |          55 |        108 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      72 | BTCSDA        |        2017 |            80 |           113 |          74 |          74 |        102 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      73 | BTCSDA        |        2016 |            72 |           145 |          88 |          69 |        131 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      74 | BTCSDA        |        2017 |            56 |           123 |          75 |          72 |        118 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      75 | BTCSDA        |        2016 |            80 |           106 |          68 |          78 |        128 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      76 | BTCSDA        |        2017 |            69 |            87 |          67 |          71 |        111 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      77 | BTCSDA        |        2016 |            96 |           158 |          72 |          89 |        104 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      78 | BTCSDA        |        2017 |            66 |           114 |          77 |          71 |         95 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      79 | BTCSDA        |        2016 |            97 |            86 |          75 |          55 |        104 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      80 | BTCSDA        |        2017 |            70 |           104 |          77 |          79 |        112 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      81 | BTCSDA        |        2016 |            84 |            94 |          77 |          55 |        120 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      82 | BTCSDA        |        2017 |            93 |            95 |          82 |          47 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      83 | BTCSDA        |        2016 |            72 |            81 |          72 |          60 |         98 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      84 | BTCSDA        |        2017 |            88 |           102 |          91 |          61 |        117 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      85 | BTCSDA        |        2016 |            79 |           106 |          79 |          63 |         93 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      86 | BTCSDA        |        2017 |            63 |            78 |          63 |          89 |        106 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      87 | BTCSDA        |        2016 |            76 |            35 |          72 |          90 |        107 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      88 | BTCSDA        |        2017 |           104 |            95 |          80 |          79 |         91 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      89 | BTCSDA        |        2016 |            77 |           101 |          71 |          55 |        119 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      90 | BTCSDA        |        2017 |            68 |           112 |          78 |          70 |        104 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      91 | BTCSDA        |        2016 |            79 |           101 |          79 |          76 |        107 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      92 | BTCSDA        |        2017 |            85 |           108 |          88 |          68 |        124 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      93 | BTCSDA        |        2016 |            94 |            78 |          68 |          72 |        116 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      94 | BTCSDA        |        2017 |            97 |           114 |          75 |          59 |        109 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      95 | BTCSDA        |        2016 |            82 |           114 |          69 |          55 |        115 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      96 | BTCSDA        |        2017 |            97 |           105 |          64 |          87 |        114 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      97 | BTCSDA        |        2016 |            69 |           127 |          57 |          73 |        127 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      98 | BTCSDA        |        2017 |            45 |           108 |          77 |          65 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|      99 | BTCSDA        |        2016 |            73 |           105 |          81 |          74 |        115 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------
|     100 | BTCSDA        |        2017 |            72 |           105 |          81 |          95 |        113 |           |           |
------------------------------------------------------------------------------------------------------------------------------------------


================================================
FILE: data/studentdata4.csv
================================================
ser,student,course,batch,Internal 1 Assessment (R) (O),Internal 2 Assessment (R) (O),Assignment,Viva,Attendance,Final,Calculated Grade,Comment Faculty,Comment HOD,Gender,Cat,Date of Birth,Backs
1,S001,BSCDS,2016,7,9,7,5,18,32,78,,,M,GEN,8-Nov-94,1
2,S002,BSCDS,2017,9,5,8,5,15,31,73,,,M,GEN,24-Jul-93,1
3,S003,BSCDS,2016,8,6,5,6,11,43,79,,,M,GEN,17-Feb-95,2
4,S004,BSCDS,2017,8,5,5,8,14,32,72,,,M,GEN,4-Mar-94,6
5,S005,BSCDS,2016,5,9,9,5,16,49,93,,,M,GEN,20-Jun-95,4
6,S006,BSCDS,2017,6,6,7,8,18,45,90,,,M,ST,8-May-94,4
7,S007,BSCDS,2016,5,9,6,6,17,37,80,,,M,GEN,7-May-94,6
8,S008,BSCDS,2017,6,6,5,5,17,31,70,,,M,GEN,15-Aug-94,0
9,S009,BSCDS,2016,6,5,8,8,18,30,75,,,M,GEN,24-Nov-94,0
10,S010,BSCDS,2017,9,6,7,6,14,41,83,,,F,GEN,25-Jan-94,4
11,S011,BSCDS,2016,5,9,6,7,12,39,78,,,M,GEN,8-Jun-94,3
12,S012,BSCDS,2017,8,8,7,7,17,30,77,,,M,OBC,24-Jun-95,5
13,S013,BSCDS,2016,8,7,9,6,16,28,74,,,M,GEN,31-Oct-94,0
14,S014,BSCDS,2017,8,7,5,9,20,36,85,,,F,GEN,21-Oct-95,0
15,S015,BSCDS,2016,8,8,6,6,10,40,78,,,F,OBC,30-Jun-96,0
16,S016,BSCDS,2017,9,7,6,6,20,42,90,Good,,F,GEN,25-Sep-94,0
17,S017,BSCDS,2016,8,6,7,5,18,41,85,,,M,GEN,16-May-94,0
18,S018,BSCDS,2017,9,8,6,7,14,38,82,,,M,GEN,24-Jun-95,1
19,S019,BSCDS,2016,5,7,6,5,19,40,82,,,F,GEN,11-Dec-93,2
20,S020,BSCDS,2017,9,5,5,8,20,42,89,,,M,GEN,30-Jun-96,1
21,S021,MSCDS,2016,8,7,6,5,12,44,82,,,F,OBC,11-Dec-93,0
22,S022,MSCDS,2017,7,7,5,5,18,30,72,,,F,GEN,6-Nov-95,0
23,S023,MSCDS,2016,8,5,5,7,19,27,71,,,F,GEN,22-Dec-93,0
24,S024,MSCDS,2017,5,8,7,7,18,25,70,,,M,SC,6-Feb-95,2
25,S025,MSCDS,2016,5,5,5,8,10,47,80,,,M,GEN,6-Jun-94,2
26,S026,MSCDS,2017,7,8,6,6,19,25,71,,,M,GEN,2-Jan-94,0
27,S027,MSCDS,2016,9,9,5,7,13,40,83,,,F,GEN,8-Apr-94,0
28,S028,MSCDS,2017,6,9,9,5,16,29,74,,,F,GEN,18-Nov-94,0
29,S029,MSCDS,2016,7,5,7,5,13,26,63,,,F,GEN,3-Jul-95,0
30,S030,MSCDS,2017,6,7,9,6,11,38,77,,,M,OBC,10-Jan-93,0
31,S031,MSCDS,2016,7,5,5,8,18,39,82,,,F,GEN,19-Dec-92,1
32,S032,MSCDS,2017,9,5,8,6,17,27,72,,,M,GEN,19-Sep-93,0
33,S033,MSCDS,2016,7,8,7,8,18,46,94,,,F,GEN,13-Aug-94,0
34,S034,MSCDS,2017,8,8,8,5,14,33,76,,,F,GEN,21-Jan-94,0
35,S035,MSCDS,2016,8,5,8,6,19,25,71,,,M,GEN,25-Feb-94,0
36,S036,MSCDS,2017,6,9,5,9,10,35,74,,,M,SC,12-Jan-96,0
37,S037,MSCDS,2016,5,7,5,7,20,37,81,,,M,GEN,27-Jul-95,3
38,S038,MSCDS,2017,8,7,9,7,16,25,72,,,M,GEN,12-Jan-96,0
39,S039,MSCDS,2016,9,8,9,8,11,28,73,,,M,SC,18-Feb-95,0
40,S040,MSCDS,2017,5,6,7,5,17,38,78,,,F,GEN,3-Mar-95,0
41,S041,MSCDS,2016,6,6,6,5,14,49,86,,,M,GEN,30-Jun-94,0
42,S042,MSCDS,2017,6,8,8,9,12,26,69,,,M,GEN,23-Oct-94,0
43,S043,MSCDS,2016,6,8,9,8,15,47,93,,,M,SC,15-Nov-93,3
44,S044,MSCDS,2017,8,5,7,7,19,46,92,,,F,GEN,11-May-93,0
45,S045,MSCDS,2016,7,6,5,9,11,26,64,,,F,GEN,2-Apr-95,0
46,S046,MSCDS,2017,9,6,9,9,18,42,93,,,F,GEN,15-Mar-94,0
47,S047,MSCDS,2016,6,8,6,6,20,26,72,,,M,ST,,11
48,S048,MSCDS,2017,6,5,9,5,12,46,83,,,M,GEN,,8
49,S049,MSCDS,2016,6,8,5,8,18,48,93,,,M,GEN,5-Apr-91,4
50,S050,MSCDS,2017,7,7,6,8,15,44,87,,,M,GEN,16-Sep-93,4

================================================
FILE: data/students.csv
================================================
"rollno","name","age","marks","gender","grade"
"IIMLN-1","Dhiraj",26.26,50,"M","Ex"
"IIMLN-2","Student & 2",30.76,67,"F","Ex"
"IIMLN-3","Student & 3",29.53,71,"F","Ex"
"IIMLN-4","Student & 4",31.57,52,"M","Good"
"IIMLN-5","Student & 5",26.9,51,"M","Ex"
"IIMLN-6","Student & 6",27.89,65,"F","Ex"
"IIMLN-7","Student & 7",26.03,75,"M","Ex"
"IIMLN-8","Student & 8",27.9,43,"F","Ex"
"IIMLN-9","Student & 9",24.38,60,"M","Ex"
"IIMLN-10","Student & 10",27.85,54,"M","Ex"
"IIMLN-11","Student & 11",29.36,58,"M","Good"
"IIMLN-12","Student & 12",26.39,48,"M","Ex"
"IIMLN-13","Student & 13",31.11,47,"F","Ex"


================================================
FILE: data/students3.csv
================================================
"","rollno","name","gender","age","course","marks","married"
"1","S-1","Student1 - surname","M",32,"BTech",58,FALSE
"2","S-2","Student2 - surname","M",22,"BTech",37,FALSE
"3","S-3","Student3 - surname","M",31,"BTech",52,FALSE
"4","S-4","Student4 - surname","M",28,"MTech",80,FALSE
"5","S-5","Student5 - surname","M",25,"BTech",43,FALSE
"6","S-6","Student6 - surname","F",23,"MTech",42,FALSE
"7","S-7","Student7 - surname","M",31,"Phd",61,FALSE
"8","S-8","Student8 - surname","F",31,"BTech",57,TRUE
"9","S-9","Student9 - surname","M",28,"BTech",53,TRUE
"10","S-10","Student10 - surname","F",29,"BTech",73,TRUE
"11","S-11","Student11 - surname","M",37,"BTech",26,FALSE
"12","S-12","Student12 - surname","M",23,"MTech",68,FALSE
"13","S-13","Student13 - surname","M",30,"Phd",59,FALSE
"14","S-14","Student14 - surname","M",26,"Phd",52,FALSE
"15","S-15","Student15 - surname","M",30,"Phd",44,FALSE
"16","S-16","Student16 - surname","M",21,"BTech",44,FALSE
"17","S-17","Student17 - surname","M",20,"Phd",47,FALSE
"18","S-18","Student18 - surname","M",22,"MTech",50,FALSE
"19","S-19","Student19 - surname","F",24,"BTech",18,FALSE
"20","S-20","Student20 - surname","M",26,"BTech",71,FALSE
"21","S-21","Student21 - surname","F",34,"MTech",36,FALSE
"22","S-22","Student22 - surname","M",37,"BTech",24,TRUE
"23","S-23","Student23 - surname","F",36,"MTech",31,FALSE
"24","S-24","Student24 - surname","M",28,"BTech",43,FALSE
"25","S-25","Student25 - surname","F",33,"BTech",27,FALSE
"26","S-26","Student26 - surname","F",21,"BTech",86,FALSE
"27","S-27","Student27 - surname","M",29,"BTech",63,FALSE
"28","S-28","Student28 - surname","F",22,"BTech",70,TRUE
"29","S-29","Student29 - surname","M",25,"MTech",62,TRUE
"30","S-30","Student30 - surname","M",23,"BTech",35,FALSE


================================================
FILE: data/talltransactions.csv
================================================
"","transactionID","item"
"1",187,"item6"
"2",22,"item31"
"3",355,"item13"
"4",329,"item28"
"5",125,"item20"
"6",151,"item50"
"7",293,"item15"
"8",167,"item43"
"9",312,"item25"
"10",273,"item24"
"11",440,"item31"
"12",354,"item11"
"13",366,"item36"
"14",466,"item16"
"15",228,"item15"
"16",296,"item31"
"17",411,"item32"
"18",113,"item26"
"19",206,"item21"
"20",20,"item30"
"21",351,"item44"
"22",479,"item46"
"23",107,"item30"
"24",331,"item31"
"25",462,"item25"
"26",398,"item48"
"27",36,"item29"
"28",195,"item22"
"29",204,"item28"
"30",330,"item40"
"31",212,"item38"
"32",161,"item31"
"33",99,"item9"
"34",82,"item9"
"35",262,"item16"
"36",457,"item6"
"37",104,"item13"
"38",408,"item34"
"39",11,"item23"
"40",463,"item16"
"41",218,"item14"
"42",222,"item41"
"43",381,"item32"
"44",167,"item32"
"45",198,"item25"
"46",117,"item37"
"47",36,"item45"
"48",457,"item29"
"49",386,"item48"
"50",54,"item50"
"51",40,"item19"
"52",217,"item50"
"53",340,"item42"
"54",368,"item3"
"55",227,"item39"
"56",392,"item26"
"57",340,"item50"
"58",260,"item20"
"59",346,"item4"
"60",295,"item12"
"61",408,"item44"
"62",406,"item24"
"63",305,"item15"
"64",497,"item32"
"65",422,"item46"
"66",358,"item19"
"67",10,"item10"
"68",153,"item11"
"69",442,"item22"
"70",471,"item28"
"71",118,"item10"
"72",469,"item49"
"73",284,"item30"
"74",422,"item27"
"75",411,"item45"
"76",140,"item40"
"77",24,"item9"
"78",113,"item12"
"79",337,"item36"
"80",480,"item38"
"81",343,"item39"
"82",388,"item9"
"83",388,"item2"
"84",492,"item12"
"85",6,"item30"
"86",477,"item33"
"87",162,"item7"
"88",215,"item42"
"89",68,"item46"
"90",10,"item4"
"91",329,"item49"
"92",458,"item16"
"93",358,"item31"
"94",92,"item31"
"95",121,"item9"
"96",419,"item8"
"97",193,"item37"
"98",117,"item11"
"99",35,"item11"
"100",32,"item50"
"101",63,"item8"
"102",12,"item10"
"103",196,"item29"
"104",430,"item36"
"105",360,"item37"
"106",170,"item8"
"107",41,"item25"
"108",19,"item47"
"109",387,"item26"
"110",498,"item41"
"111",74,"item20"
"112",20,"item18"
"113",283,"item48"
"114",445,"item37"
"115",436,"item4"
"116",491,"item1"
"117",440,"item30"
"118",256,"item49"
"119",168,"item16"
"120",307,"item8"
"121",199,"item1"
"122",70,"item7"
"123",40,"item36"
"124",276,"item39"
"125",131,"item49"
"126",405,"item24"
"127",273,"item27"
"128",238,"item1"
"129",332,"item41"
"130",47,"item43"
"131",326,"item23"
"132",185,"item20"
"133",123,"item22"
"134",150,"item19"
"135",280,"item8"
"136",241,"item14"
"137",239,"item45"
"138",468,"item42"
"139",236,"item44"
"140",340,"item50"
"141",468,"item13"
"142",137,"item38"
"143",474,"item49"
"144",157,"item6"
"145",438,"item31"
"146",84,"item33"
"147",235,"item24"
"148",327,"item7"
"149",18,"item36"
"150",218,"item49"
"151",75,"item12"
"152",230,"item15"
"153",310,"item31"
"154",479,"item31"
"155",51,"item32"
"156",114,"item10"
"157",278,"item11"
"158",386,"item13"
"159",240,"item34"
"160",441,"item3"
"161",484,"item15"
"162",346,"item12"
"163",434,"item4"
"164",281,"item33"
"165",153,"item21"
"166",500,"item13"
"167",147,"item31"
"168",452,"item35"
"169",21,"item29"
"170",300,"item48"
"171",341,"item15"
"172",492,"item41"
"173",252,"item21"
"174",372,"item27"
"175",456,"item43"
"176",495,"item39"
"177",383,"item38"
"178",411,"item36"
"179",470,"item8"
"180",336,"item3"
"181",454,"item23"
"182",382,"item14"
"183",244,"item38"
"184",126,"item40"
"185",180,"item3"
"186",5,"item20"
"187",118,"item29"
"188",54,"item17"
"189",306,"item11"
"190",103,"item25"
"191",108,"item28"
"192",9,"item10"
"193",165,"item8"
"194",135,"item38"
"195",457,"item29"
"196",209,"item42"
"197",346,"item48"
"198",451,"item29"
"199",104,"item45"
"200",231,"item37"
"201",303,"item18"
"202",282,"item44"
"203",139,"item8"
"204",114,"item42"
"205",492,"item18"
"206",50,"item16"
"207",440,"item10"
"208",117,"item42"
"209",387,"item13"
"210",236,"item27"
"211",205,"item1"
"212",412,"item5"
"213",268,"item29"
"214",246,"item4"
"215",8,"item45"
"216",322,"item10"
"217",161,"item31"
"218",268,"item36"
"219",478,"item48"
"220",21,"item48"
"221",143,"item25"
"222",246,"item30"
"223",241,"item32"
"224",220,"item23"
"225",219,"item29"
"226",94,"item1"
"227",473,"item12"
"228",73,"item10"
"229",390,"item6"
"230",407,"item29"
"231",111,"item43"
"232",159,"item3"
"233",133,"item11"
"234",261,"item19"
"235",102,"item10"
"236",327,"item18"
"237",139,"item47"
"238",205,"item26"
"239",419,"item9"
"240",218,"item47"
"241",128,"item31"
"242",429,"item48"
"243",110,"item32"
"244",94,"item17"
"245",11,"item47"
"246",233,"item48"
"247",149,"item11"
"248",95,"item35"
"249",335,"item27"
"250",141,"item49"
"251",111,"item16"
"252",246,"item31"
"253",113,"item5"
"254",411,"item26"
"255",192,"item9"
"256",314,"item36"
"257",476,"item10"
"258",389,"item11"
"259",54,"item11"
"260",115,"item36"
"261",109,"item21"
"262",347,"item36"
"263",299,"item23"
"264",140,"item11"
"265",222,"item24"
"266",449,"item32"
"267",27,"item14"
"268",333,"item18"
"269",415,"item34"
"270",376,"item30"
"271",18,"item44"
"272",323,"item45"
"273",482,"item5"
"274",181,"item43"
"275",486,"item28"
"276",246,"item49"
"277",466,"item22"
"278",301,"item20"
"279",176,"item25"
"280",44,"item32"
"281",91,"item21"
"282",55,"item1"
"283",184,"item43"
"284",5,"item22"
"285",40,"item2"
"286",150,"item22"
"287",294,"item21"
"288",352,"item22"
"289",375,"item37"
"290",158,"item18"
"291",194,"item18"
"292",495,"item36"
"293",341,"item21"
"294",445,"item15"
"295",160,"item9"
"296",232,"item1"
"297",203,"item14"
"298",463,"item49"
"299",164,"item27"
"300",56,"item41"
"301",135,"item13"
"302",119,"item27"
"303",300,"item8"
"304",105,"item44"
"305",113,"item4"
"306",281,"item45"
"307",385,"item33"
"308",317,"item39"
"309",172,"item32"
"310",100,"item12"
"311",2,"item3"
"312",425,"item10"
"313",340,"item22"
"314",102,"item2"
"315",492,"item5"
"316",377,"item45"
"317",197,"item29"
"318",46,"item12"
"319",300,"item34"
"320",339,"item42"
"321",258,"item10"
"322",181,"item7"
"323",327,"item42"
"324",264,"item18"
"325",34,"item26"
"326",419,"item36"
"327",29,"item32"
"328",372,"item17"
"329",89,"item45"
"330",350,"item37"
"331",391,"item17"
"332",380,"item38"
"333",109,"item19"
"334",108,"item38"
"335",51,"item38"
"336",75,"item32"
"337",250,"item41"
"338",360,"item46"
"339",17,"item31"
"340",11,"item37"
"341",338,"item26"
"342",469,"item40"
"343",167,"item22"
"344",29,"item4"
"345",382,"item48"
"346",465,"item4"
"347",416,"item46"
"348",90,"item43"
"349",325,"item8"
"350",336,"item28"
"351",445,"item28"
"352",93,"item20"
"353",247,"item28"
"354",66,"item47"
"355",243,"item24"
"356",302,"item41"
"357",447,"item19"
"358",321,"item37"
"359",85,"item35"
"360",6,"item39"
"361",411,"item35"
"362",108,"item47"
"363",87,"item9"
"364",7,"item45"
"365",273,"item39"
"366",263,"item47"
"367",355,"item32"
"368",293,"item6"
"369",352,"item32"
"370",396,"item37"
"371",96,"item21"
"372",335,"item39"
"373",213,"item28"
"374",259,"item32"
"375",208,"item4"
"376",500,"item1"
"377",210,"item36"
"378",380,"item31"
"379",479,"item41"
"380",70,"item13"
"381",291,"item24"
"382",192,"item9"
"383",348,"item29"
"384",70,"item48"
"385",464,"item33"
"386",343,"item34"
"387",322,"item15"
"388",61,"item50"
"389",96,"item10"
"390",458,"item13"
"391",248,"item6"
"392",127,"item34"
"393",118,"item14"
"394",444,"item38"
"395",488,"item29"
"396",353,"item40"
"397",148,"item28"
"398",75,"item12"
"399",245,"item39"
"400",57,"item39"
"401",218,"item26"
"402",413,"item13"
"403",42,"item28"
"404",435,"item17"
"405",333,"item21"
"406",481,"item12"
"407",108,"item12"
"408",414,"item29"
"409",444,"item31"
"410",120,"item21"
"411",408,"item34"
"412",288,"item3"
"413",230,"item48"
"414",26,"item36"
"415",309,"item47"
"416",497,"item15"
"417",290,"item12"
"418",170,"item26"
"419",219,"item31"
"420",108,"item34"
"421",469,"item11"
"422",486,"item29"
"423",8,"item45"
"424",201,"item17"
"425",363,"item37"
"426",230,"item15"
"427",259,"item37"
"428",415,"item23"
"429",452,"item11"
"430",377,"item36"
"431",381,"item30"
"432",420,"item19"
"433",318,"item24"
"434",223,"item34"
"435",235,"item42"
"436",368,"item32"
"437",338,"item18"
"438",201,"item50"
"439",450,"item31"
"440",239,"item29"
"441",449,"item17"
"442",484,"item24"
"443",304,"item7"
"444",286,"item40"
"445",227,"item20"
"446",485,"item43"
"447",394,"item16"
"448",51,"item18"
"449",152,"item40"
"450",115,"item20"
"451",172,"item15"
"452",143,"item2"
"453",445,"item18"
"454",276,"item21"
"455",267,"item6"
"456",171,"item2"
"457",362,"item1"
"458",356,"item30"
"459",152,"item21"
"460",201,"item49"
"461",390,"item48"
"462",218,"item6"
"463",395,"item24"
"464",171,"item36"
"465",122,"item6"
"466",278,"item22"
"467",441,"item34"
"468",314,"item11"
"469",361,"item49"
"470",80,"item48"
"471",161,"item4"
"472",178,"item49"
"473",326,"item49"
"474",293,"item35"
"475",149,"item4"
"476",271,"item2"
"477",428,"item22"
"478",276,"item6"
"479",210,"item16"
"480",113,"item47"
"481",365,"item43"
"482",26,"item41"
"483",146,"item16"
"484",20,"item22"
"485",9,"item28"
"486",350,"item21"
"487",300,"item30"
"488",20,"item39"
"489",240,"item29"
"490",333,"item12"
"491",128,"item33"
"492",60,"item1"
"493",37,"item15"
"494",331,"item8"
"495",255,"item14"
"496",455,"item29"
"497",355,"item10"
"498",121,"item19"
"499",105,"item7"
"500",223,"item37"
"501",203,"item17"
"502",302,"item50"
"503",181,"item28"
"504",146,"item17"
"505",397,"item48"
"506",351,"item20"
"507",183,"item25"
"508",277,"item26"
"509",60,"item20"
"510",75,"item36"
"511",76,"item5"
"512",337,"item22"
"513",236,"item45"
"514",142,"item44"
"515",399,"item21"
"516",392,"item49"
"517",76,"item16"
"518",85,"item44"
"519",221,"item6"
"520",328,"item6"
"521",195,"item6"
"522",377,"item20"
"523",43,"item47"
"524",2,"item5"
"525",471,"item13"
"526",329,"item22"
"527",142,"item43"
"528",211,"item3"
"529",493,"item39"
"530",312,"item48"
"531",394,"item35"
"532",373,"item8"
"533",291,"item23"
"534",245,"item23"
"535",246,"item42"
"536",40,"item26"
"537",310,"item4"
"538",228,"item50"
"539",67,"item5"
"540",35,"item11"
"541",390,"item34"
"542",269,"item48"
"543",246,"item34"
"544",213,"item19"
"545",92,"item20"
"546",428,"item35"
"547",328,"item5"
"548",439,"item32"
"549",63,"item9"
"550",138,"item18"
"551",288,"item16"
"552",452,"item50"
"553",294,"item44"
"554",67,"item23"
"555",74,"item43"
"556",357,"item47"
"557",236,"item11"
"558",299,"item28"
"559",30,"item22"
"560",447,"item4"
"561",61,"item38"
"562",406,"item11"
"563",8,"item1"
"564",499,"item33"
"565",317,"item5"
"566",114,"item33"
"567",409,"item3"
"568",115,"item49"
"569",432,"item49"
"570",293,"item33"
"571",36,"item4"
"572",351,"item14"
"573",195,"item36"
"574",406,"item3"
"575",401,"item26"
"576",262,"item30"
"577",50,"item20"
"578",175,"item25"
"579",189,"item4"
"580",189,"item43"
"581",180,"item14"
"582",232,"item38"
"583",461,"item17"
"584",25,"item33"
"585",200,"item24"
"586",487,"item38"
"587",326,"item22"
"588",260,"item10"
"589",351,"item22"
"590",397,"item42"
"591",366,"item20"
"592",219,"item43"
"593",185,"item21"
"594",221,"item41"
"595",386,"item5"
"596",69,"item17"
"597",189,"item35"
"598",443,"item39"
"599",204,"item34"
"600",98,"item50"
"601",61,"item4"
"602",264,"item41"
"603",203,"item31"
"604",211,"item25"
"605",441,"item32"
"606",113,"item9"
"607",292,"item33"
"608",171,"item35"
"609",242,"item21"
"610",88,"item13"
"611",83,"item41"
"612",224,"item34"
"613",359,"item39"
"614",32,"item48"
"615",52,"item24"
"616",129,"item45"
"617",428,"item50"
"618",276,"item46"
"619",167,"item42"
"620",333,"item10"
"621",298,"item16"
"622",173,"item23"
"623",154,"item15"
"624",371,"item27"
"625",23,"item47"
"626",422,"item34"
"627",350,"item12"
"628",271,"item12"
"629",471,"item29"
"630",306,"item3"
"631",4,"item25"
"632",129,"item29"
"633",221,"item21"
"634",102,"item33"
"635",492,"item44"
"636",145,"item9"
"637",183,"item36"
"638",21,"item38"
"639",419,"item24"
"640",156,"item21"
"641",327,"item43"
"642",385,"item3"
"643",153,"item31"
"644",499,"item19"
"645",74,"item31"
"646",16,"item15"
"647",260,"item24"
"648",1,"item38"
"649",79,"item30"
"650",393,"item26"
"651",128,"item34"
"652",378,"item18"
"653",269,"item12"
"654",144,"item1"
"655",254,"item32"
"656",91,"item7"
"657",28,"item14"
"658",69,"item4"
"659",343,"item35"
"660",444,"item30"
"661",206,"item39"
"662",366,"item36"
"663",166,"item11"
"664",441,"item48"
"665",454,"item15"
"666",222,"item36"
"667",160,"item40"
"668",499,"item25"
"669",46,"item22"
"670",342,"item12"
"671",477,"item2"
"672",249,"item42"
"673",353,"item2"
"674",386,"item17"
"675",421,"item50"
"676",123,"item25"
"677",317,"item17"
"678",320,"item24"
"679",73,"item36"
"680",144,"item6"
"681",242,"item34"
"682",205,"item44"
"683",50,"item6"
"684",322,"item34"
"685",93,"item17"
"686",472,"item2"
"687",354,"item9"
"688",449,"item37"
"689",417,"item12"
"690",186,"item16"
"691",202,"item29"
"692",411,"item15"
"693",403,"item33"
"694",240,"item23"
"695",125,"item2"
"696",491,"item43"
"697",155,"item37"
"698",25,"item18"
"699",484,"item25"
"700",30,"item46"
"701",186,"item14"
"702",198,"item31"
"703",302,"item31"
"704",28,"item32"
"705",67,"item21"
"706",179,"item39"
"707",428,"item11"
"708",371,"item41"
"709",291,"item33"
"710",487,"item9"
"711",11,"item14"
"712",484,"item2"
"713",446,"item1"
"714",468,"item43"
"715",443,"item39"
"716",107,"item30"
"717",391,"item3"
"718",36,"item43"
"719",40,"item31"
"720",167,"item29"
"721",167,"item19"
"722",56,"item34"
"723",165,"item39"
"724",86,"item43"
"725",456,"item8"
"726",428,"item42"
"727",71,"item36"
"728",188,"item6"
"729",185,"item46"
"730",169,"item19"
"731",387,"item35"
"732",298,"item37"
"733",489,"item13"
"734",333,"item46"
"735",482,"item23"
"736",450,"item19"
"737",55,"item38"
"738",271,"item36"
"739",1,"item50"
"740",258,"item34"
"741",70,"item31"
"742",70,"item29"
"743",263,"item31"
"744",348,"item18"
"745",214,"item47"
"746",412,"item13"
"747",317,"item8"
"748",122,"item49"
"749",433,"item3"
"750",305,"item22"
"751",378,"item20"
"752",390,"item3"
"753",341,"item14"
"754",144,"item16"
"755",26,"item19"
"756",369,"item50"
"757",125,"item1"
"758",77,"item27"
"759",344,"item6"
"760",108,"item50"
"761",295,"item50"
"762",78,"item47"
"763",130,"item19"
"764",350,"item38"
"765",228,"item24"
"766",45,"item29"
"767",350,"item47"
"768",407,"item48"
"769",498,"item44"
"770",440,"item30"
"771",354,"item36"
"772",266,"item8"
"773",429,"item33"
"774",42,"item33"
"775",114,"item29"
"776",115,"item9"
"777",394,"item38"
"778",474,"item33"
"779",114,"item5"
"780",483,"item14"
"781",311,"item43"
"782",65,"item21"
"783",454,"item15"
"784",160,"item17"
"785",198,"item47"
"786",207,"item35"
"787",303,"item38"
"788",216,"item3"
"789",437,"item40"
"790",18,"item34"
"791",81,"item34"
"792",52,"item31"
"793",126,"item47"
"794",57,"item36"
"795",15,"item20"
"796",217,"item50"
"797",119,"item34"
"798",224,"item13"
"799",176,"item8"
"800",157,"item2"
"801",188,"item27"
"802",164,"item44"
"803",10,"item24"
"804",10,"item6"
"805",17,"item17"
"806",160,"item49"
"807",335,"item47"
"808",404,"item7"
"809",279,"item48"
"810",10,"item4"
"811",103,"item9"
"812",488,"item26"
"813",43,"item19"
"814",237,"item48"
"815",255,"item43"
"816",371,"item7"
"817",472,"item23"
"818",19,"item21"
"819",112,"item22"
"820",312,"item2"
"821",148,"item37"
"822",179,"item39"
"823",199,"item43"
"824",107,"item50"
"825",130,"item17"
"826",3,"item8"
"827",444,"item48"
"828",240,"item44"
"829",482,"item38"
"830",305,"item38"
"831",238,"item17"
"832",456,"item8"
"833",367,"item22"
"834",426,"item34"
"835",87,"item42"
"836",33,"item4"
"837",17,"item43"
"838",35,"item27"
"839",92,"item2"
"840",7,"item25"
"841",86,"item7"
"842",302,"item20"
"843",101,"item3"
"844",420,"item24"
"845",354,"item22"
"846",462,"item15"
"847",474,"item12"
"848",41,"item44"
"849",129,"item44"
"850",230,"item30"
"851",243,"item47"
"852",233,"item29"
"853",228,"item48"
"854",152,"item8"
"855",447,"item2"
"856",91,"item22"
"857",123,"item27"
"858",39,"item16"
"859",61,"item3"
"860",80,"item21"
"861",73,"item36"
"862",105,"item34"
"863",383,"item46"
"864",36,"item2"
"865",321,"item30"
"866",391,"item10"
"867",301,"item22"
"868",398,"item34"
"869",388,"item33"
"870",301,"item45"
"871",81,"item11"
"872",460,"item22"
"873",466,"item23"
"874",463,"item25"
"875",117,"item49"
"876",171,"item38"
"877",270,"item9"
"878",340,"item11"
"879",293,"item45"
"880",110,"item45"
"881",349,"item4"
"882",431,"item10"
"883",192,"item22"
"884",256,"item50"
"885",182,"item19"
"886",327,"item23"
"887",277,"item38"
"888",202,"item13"
"889",206,"item16"
"890",431,"item44"
"891",403,"item45"
"892",298,"item18"
"893",473,"item19"
"894",420,"item32"
"895",164,"item3"
"896",211,"item8"
"897",59,"item6"
"898",305,"item9"
"899",299,"item50"
"900",118,"item25"
"901",112,"item29"
"902",44,"item41"
"903",174,"item39"
"904",485,"item38"
"905",21,"item50"
"906",344,"item34"
"907",302,"item19"
"908",388,"item26"
"909",27,"item30"
"910",295,"item44"
"911",227,"item41"
"912",193,"item31"
"913",314,"item3"
"914",333,"item34"
"915",15,"item10"
"916",317,"item47"
"917",160,"item47"
"918",322,"item35"
"919",275,"item19"
"920",478,"item9"
"921",65,"item44"
"922",106,"item30"
"923",172,"item5"
"924",240,"item17"
"925",252,"item37"
"926",196,"item24"
"927",375,"item30"
"928",62,"item17"
"929",38,"item50"
"930",113,"item18"
"931",64,"item49"
"932",118,"item38"
"933",191,"item41"
"934",14,"item38"
"935",42,"item40"
"936",138,"item31"
"937",11,"item25"
"938",49,"item4"
"939",347,"item38"
"940",342,"item41"
"941",390,"item5"
"942",409,"item39"
"943",27,"item2"
"944",230,"item17"
"945",437,"item47"
"946",312,"item40"
"947",492,"item15"
"948",316,"item5"
"949",217,"item8"
"950",276,"item39"
"951",198,"item43"
"952",404,"item1"
"953",394,"item33"
"954",398,"item9"
"955",162,"item46"
"956",213,"item14"
"957",83,"item15"
"958",187,"item40"
"959",160,"item39"
"960",156,"item39"
"961",415,"item27"
"962",32,"item29"
"963",423,"item25"
"964",446,"item5"
"965",481,"item8"
"966",256,"item32"
"967",427,"item13"
"968",470,"item49"
"969",177,"item4"
"970",439,"item24"
"971",348,"item50"
"972",16,"item45"
"973",72,"item4"
"974",95,"item41"
"975",222,"item7"
"976",497,"item48"
"977",78,"item27"
"978",310,"item4"
"979",447,"item12"
"980",282,"item22"
"981",235,"item43"
"982",144,"item47"
"983",105,"item33"
"984",499,"item42"
"985",80,"item39"
"986",219,"item42"
"987",98,"item24"
"988",245,"item17"
"989",86,"item30"
"990",470,"item43"
"991",66,"item20"
"992",398,"item44"
"993",224,"item46"
"994",48,"item16"
"995",96,"item7"
"996",471,"item18"
"997",302,"item26"
"998",325,"item30"
"999",21,"item11"
"1000",75,"item50"


================================================
FILE: data/tendulkar.csv
================================================
"","Runs","Mins","BF","4s","6s","SR","Pos","Dismissal","Inns","Opposition","Ground","Start Date"
"1","15","28","24","2","0","62.50","6","bowled","2","v Pakistan","Karachi","15 Nov 1989"
"2","DNB","-","-","-","-","-","-","-","4","v Pakistan","Karachi","15 Nov 1989"
"3","59","254","172","4","0","34.30","6","lbw","1","v Pakistan","Faisalabad","23 Nov 1989"
"4","8","24","16","1","0","50.00","6","run out","3","v Pakistan","Faisalabad","23 Nov 1989"
"5","41","124","90","5","0","45.55","7","bowled","1","v Pakistan","Lahore","1 Dec 1989"
"6","35","74","51","5","0","68.62","6","lbw","1","v Pakistan","Sialkot","9 Dec 1989"
"7","57","193","134","6","0","42.53","6","caught","3","v Pakistan","Sialkot","9 Dec 1989"
"8","0","1","1","0","0","0.00","6","caught","2","v New Zealand","Christchurch","2 Feb 1990"
"9","24","50","44","3","0","54.54","6","caught","3","v New Zealand","Christchurch","2 Feb 1990"
"10","88","324","266","5","0","33.08","6","caught","1","v New Zealand","Napier","9 Feb 1990"
"11","5","15","13","1","0","38.46","6","caught","2","v New Zealand","Auckland","22 Feb 1990"
"12","DNB","-","-","-","-","-","-","-","4","v New Zealand","Auckland","22 Feb 1990"
"13","10","30","19","1","0","52.63","6","bowled","2","v England","Lord's","26 Jul 1990"
"14","27","93","65","4","0","41.53","6","caught","4","v England","Lord's","26 Jul 1990"
"15","68","216","136","8","0","50.00","6","caught","2","v England","Manchester","9 Aug 1990"
"16","119*","225","189","17","0","62.96","6","not out","4","v England","Manchester","9 Aug 1990"
"17","21","39","30","3","0","70.00","7","caught","1","v England","The Oval","23 Aug 1990"
"18","11","92","-","-","0","-","6","lbw","1","v Sri Lanka","Chandigarh","23 Nov 1990"
"19","16","47","42","3","0","38.09","6","bowled","1","v Australia","Brisbane","29 Nov 1991"
"20","7","36","25","1","0","28.00","6","caught","3","v Australia","Brisbane","29 Nov 1991"
"21","15","33","23","0","0","65.21","7","caught","1","v Australia","Melbourne","26 Dec 1991"
"22","40","122","107","5","0","37.38","7","caught","3","v Australia","Melbourne","26 Dec 1991"
"23","148*","298","213","14","0","69.48","6","not out","2","v Australia","Sydney","2 Jan 1992"
"24","6","20","8","1","0","75.00","6","lbw","2","v Australia","Adelaide","25 Jan 1992"
"25","17","36","29","2","0","58.62","4","lbw","4","v Australia","Adelaide","25 Jan 1992"
"26","114","228","161","16","0","70.80","4","caught","2","v Australia","Perth","1 Feb 1992"
"27","5","12","9","1","0","55.55","4","caught","4","v Australia","Perth","1 Feb 1992"
"28","0","4","3","0","0","0.00","4","caught","2","v Zimbabwe","Harare","18 Oct 1992"
"29","11","35","24","1","0","45.83","4","run out","2","v South Africa","Durban","13 Nov 1992"
"30","111","373","270","19","0","41.11","4","caught","2","v South Africa","Johannesburg","26 Nov 1992"
"31","1","6","5","0","0","20.00","4","lbw","4","v South Africa","Johannesburg","26 Nov 1992"
"32","6","27","17","1","0","35.29","4","caught","1","v South Africa","Port Elizabeth","26 Dec 1992"
"33","0","1","1","0","0","0.00","4","caught","3","v South Africa","Port Elizabeth","26 Dec 1992"
"34","73","272","208","8","1","35.09","5","caught","2","v South Africa","Cape Town","2 Jan 1993"
"35","DNB","-","-","-","-","-","-","-","4","v South Africa","Cape Town","2 Jan 1993"
"36","50","158","118","6","0","42.37","4","caught","1","v England","Kolkata","29 Jan 1993"
"37","9*","17","19","2","0","47.36","4","not out","4","v England","Kolkata","29 Jan 1993"
"38","165","361","296","24","1","55.74","4","caught","1","v England","Chennai","11 Feb 1993"
"39","78","285","213","10","0","36.61","4","lbw","2","v England","Mumbai","19 Feb 1993"
"40","62","128","114","7","0","54.38","4","caught","1","v Zimbabwe","Delhi","13 Mar 1993"
"41","TDNB","-","-","-","-","-","-","-","-","v Sri Lanka","Kandy","17 Jul 1993"
"42","28","74","52","5","0","53.84","4","caught","1","v Sri Lanka","Colombo (SSC)","27 Jul 1993"
"43","104*","217","161","11","1","64.59","4","not out","3","v Sri Lanka","Colombo (SSC)","27 Jul 1993"
"44","71","202","152","10","0","46.71","4","caught","2","v Sri Lanka","Colombo (PSS)","4 Aug 1993"
"45","142","260","224","22","0","63.39","4","caught","1","v Sri Lanka","Lucknow","18 Jan 1994"
"46","96","180","140","15","0","68.57","4","bowled","1","v Sri Lanka","Bengaluru","26 Jan 1994"
"47","6","25","14","1","0","42.85","4","bowled","2","v Sri Lanka","Ahmedabad","8 Feb 1994"
"48","43","55","47","7","0","91.48","4","caught","2","v New Zealand","Hamilton","19 Mar 1994"
"49","11*","57","42","0","0","26.19","4","not out","4","v New Zealand","Hamilton","19 Mar 1994"
"50","34","70","43","7","0","79.06","4","lbw","1","v West Indies","Mumbai","18 Nov 1994"
"51","85","178","130","10","1","65.38","5","caught","3","v West Indies","Mumbai","18 Nov 1994"
"52","179","414","322","24","1","55.59","4","caught","1","v West Indies","Nagpur","1 Dec 1994"
"53","54","180","138","3","0","39.13","4","caught","3","v West Indies","Nagpur","1 Dec 1994"
"54","40","72","55","9","0","72.72","4","caught","2","v West Indies","Mohali","10 Dec 1994"
"55","10","38","24","2","0","41.66","4","caught","4","v West Indies","Mohali","10 Dec 1994"
"56","4","7","4","1","0","100.00","4","caught","2","v New Zealand","Bengaluru","18 Oct 1995"
"57","0*","3","0","0","0","-","4","not out","4","v New Zealand","Bengaluru","18 Oct 1995"
"58","52*","98","88","5","0","59.09","4","not out","1","v New Zealand","Chennai","25 Oct 1995"
"59","2","8","8","0","0","25.00","4","bowled","1","v New Zealand","Cuttack","8 Nov 1995"
"60","24","71","41","4","0","58.53","4","bowled","1","v England","Birmingham","6 Jun 1996"
"61","122","263","177","19","1","68.92","4","caught","3","v England","Birmingham","6 Jun 1996"
"62","31","81","59","5","0","52.54","4","bowled","2","v England","Lord's","20 Jun 1996"
"63","177","462","360","26","0","49.16","4","caught","1","v England","Nottingham","4 Jul 1996"
"64","74","111","97","11","1","76.28","4","caught","3","v England","Nottingham","4 Jul 1996"
"65","10","12","11","2","0","90.90","4","caught","2","v Australia","Delhi","10 Oct 1996"
"66","0","6","7","0","0","0.00","4","bowled","4","v Australia","Delhi","10 Oct 1996"
"67","42","80","64","7","0","65.62","4","caught","1","v South Africa","Ahmedabad","20 Nov 1996"
"68","7","43","33","1","0","21.21","4","caught","3","v South Africa","Ahmedabad","20 Nov 1996"
"69","18","91","62","3","0","29.03","4","bowled","2","v South Africa","Kolkata","27 Nov 1996"
"70","2","28","25","0","0","8.00","4","caught","4","v South Africa","Kolkata","27 Nov 1996"
"71","61","211","173","4","1","35.26","4","caught","1","v South Africa","Kanpur","8 Dec 1996"
"72","36","129","98","4","0","36.73","5","caught","3","v South Africa","Kanpur","8 Dec 1996"
"73","15","58","45","2","0","33.33","4","bowled","2","v South Africa","Durban","26 Dec 1996"
"74","4","32","25","1","0","16.00","4","caught","4","v South Africa","Durban","26 Dec 1996"
"75","169","329","254","26","0","66.53","5","caught","2","v South Africa","Cape Town","2 Jan 1997"
"76","9","38","28","1","0","32.14","5","caught","4","v South Africa","Cape Town","2 Jan 1997"
"77","35","72","55","7","0","63.63","4","caught","1","v South Africa","Johannesburg","16 Jan 1997"
"78","9","13","11","2","0","81.81","4","caught","3","v South Africa","Johannesburg","16 Jan 1997"
"79","7","40","30","1","0","23.33","4","bowled","2","v West Indies","Kingston","6 Mar 1997"
"80","15*","43","36","1","0","41.66","4","not out","4","v West Indies","Kingston","6 Mar 1997"
"81","88","306","233","9","0","37.76","4","run out","2","v West Indies","Port of Spain","14 Mar 1997"
"82","92","222","147","14","1","62.58","4","caught","2","v West Indies","Bridgetown","27 Mar 1997"
"83","4","25","14","0","0","28.57","4","caught","4","v West Indies","Bridgetown","27 Mar 1997"
"84","DNB","-","-","-","-","-","-","-","2","v West Indies","St John's","4 Apr 1997"
"85","83","287","231","9","0","35.93","4","caught","1","v West Indies","Georgetown","17 Apr 1997"
"86","143","292","247","20","0","57.89","4","caught","1","v Sri Lanka","Colombo (RPS)","2 Aug 1997"
"87","139","397","266","16","0","52.25","4","caught","2","v Sri Lanka","Colombo (SSC)","9 Aug 1997"
"88","8","43","44","1","0","18.18","4","caught","4","v Sri Lanka","Colombo (SSC)","9 Aug 1997"
"89","23","129","93","2","0","24.73","4","caught","2","v Sri Lanka","Mohali","19 Nov 1997"
"90","15","38","20","1","1","75.00","4","bowled","1","v Sri Lanka","Nagpur","26 Nov 1997"
"91","148","320","244","20","3","60.65","5","bowled","1","v Sri Lanka","Mumbai","3 Dec 1997"
"92","13","28","16","1","0","81.25","4","caught","3","v Sri Lanka","Mumbai","3 Dec 1997"
"93","4","7","5","1","0","80.00","4","caught","1","v Australia","Chennai","6 Mar 1998"
"94","155*","286","191","14","4","81.15","4","not out","3","v Australia","Chennai","6 Mar 1998"
"95","79","109","86","12","2","91.86","4","caught","2","v Australia","Kolkata","18 Mar 1998"
"96","177","298","207","29","3","85.50","4","bowled","1","v Australia","Bengaluru","25 Mar 1998"
"97","31","75","64","4","0","48.43","4","caught","3","v Australia","Bengaluru","25 Mar 1998"
"98","34","103","68","3","0","50.00","5","caught","2","v Zimbabwe","Harare","7 Oct 1998"
"99","7","27","14","0","0","50.00","4","caught","4","v Zimbabwe","Harare","7 Oct 1998"
"100","47","95","72","7","0","65.27","5","caught","1","v New Zealand","Wellington","26 Dec 1998"
"101","113","200","151","13","2","74.83","5","caught","3","v New Zealand","Wellington","26 Dec 1998"
"102","67","118","93","9","1","72.04","4","lbw","2","v New Zealand","Hamilton","2 Jan 1999"
"103","DNB","-","-","-","-","-","-","-","4","v New Zealand","Hamilton","2 Jan 1999"
"104","0","5","3","0","0","0.00","4","caught","2","v Pakistan","Chennai","28 Jan 1999"
"105","136","405","273","18","0","49.81","4","caught","4","v Pakistan","Chennai","28 Jan 1999"
"106","6","13","11","1","0","54.54","4","lbw","1","v Pakistan","Delhi","4 Feb 1999"
"107","29","99","65","4","0","44.61","4","caught","3","v Pakistan","Delhi","4 Feb 1999"
"108","0","2","1","0","0","0.00","5","bowled","2","v Pakistan","Kolkata","16 Feb 1999"
"109","9","18","13","1","0","69.23","4","run out","4","v Pakistan","Kolkata","16 Feb 1999"
"110","53","94","54","10","0","98.14","4","caught","1","v Sri Lanka","Colombo (SSC)","24 Feb 1999"
"111","124*","308","235","10","1","52.76","4","not out","3","v Sri Lanka","Colombo (SSC)","24 Feb 1999"
"112","18","61","34","3","0","52.94","4","bowled","1","v New Zealand","Mohali","10 Oct 1999"
"113","126*","397","248","14","0","50.80","4","not out","3","v New Zealand","Mohali","10 Oct 1999"
"114","15","76","54","3","0","27.77","4","caught","2","v New Zealand","Kanpur","22 Oct 1999"
"115","44*","58","39","8","0","112.82","4","not out","4","v New Zealand","Kanpur","22 Oct 1999"
"116","217","494","344","29","0","63.08","4","caught","1","v New Zealand","Ahmedabad","29 Oct 1999"
"117","15","16","10","3","0","150.00","2","bowled","3","v New Zealand","Ahmedabad","29 Oct 1999"
"118","61","181","133","8","0","45.86","5","caught","2","v Australia","Adelaide","10 Dec 1999"
"119","0","6","5","0","0","0.00","5","lbw","4","v Australia","Adelaide","10 Dec 1999"
"120","116","283","191","9","1","60.73","4","caught","2","v Australia","Melbourne","26 Dec 1999"
"121","52","162","122","4","0","42.62","4","lbw","4","v Australia","Melbourne","26 Dec 1999"
"122","45","82","53","8","0","84.90","4","lbw","1","v Australia","Sydney","2 Jan 2000"
"123","4","6","4","1","0","100.00","4","caught","3","v Australia","Sydney","2 Jan 2000"
"124","97","208","163","12","2","59.50","4","caught","1","v South Africa","Mumbai","24 Feb 2000"
"125","8","20","11","2","0","72.72","4","lbw","3","v South Africa","Mumbai","24 Feb 2000"
"126","21","120","76","3","0","27.63","4","caught","1","v South Africa","Bengaluru","2 Mar 2000"
"127","20","76","53","2","0","37.73","4","caught","3","v South Africa","Bengaluru","2 Mar 2000"
"128","18","63","41","1","0","43.90","5","caught","2","v Bangladesh","Dhaka","10 Nov 2000"
"129","DNB","-","-","-","-","-","-","-","4","v Bangladesh","Dhaka","10 Nov 2000"
"130","122","283","233","19","0","52.36","4","caught","2","v Zimbabwe","Delhi","18 Nov 2000"
"131","39","45","39","8","0","100.00","4","caught","4","v Zimbabwe","Delhi","18 Nov 2000"
"132","201*","392","281","27","0","71.53","4","not out","1","v Zimbabwe","Nagpur","25 Nov 2000"
"133","76","138","114","13","0","66.66","4","caught","1","v Australia","Mumbai","27 Feb 2001"
"134","65","153","107","11","0","60.74","5","caught","3","v Australia","Mumbai","27 Feb 2001"
"135","10","25","18","2","0","55.55","4","lbw","2","v Australia","Kolkata","11 Mar 2001"
"136","10","26","23","2","0","43.47","4","caught","3","v Australia","Kolkata","11 Mar 2001"
"137","126","346","230","15","2","54.78","4","caught","2","v Australia","Chennai","18 Mar 2001"
"138","17","27","17","3","0","100.00","4","caught","4","v Australia","Chennai","18 Mar 2001"
"139","74","179","128","12","0","57.81","4","caught","2","v Zimbabwe","Bulawayo","7 Jun 2001"
"140","36*","41","49","4","0","73.46","4","not out","4","v Zimbabwe","Bulawayo","7 Jun 2001"
"141","20","78","46","2","1","43.47","4","bowled","1","v Zimbabwe","Harare","15 Jun 2001"
"142","69","185","135","9","0","51.11","4","caught","3","v Zimbabwe","Harare","15 Jun 2001"
"143","155","233","184","23","1","84.23","4","caught","1","v South Africa","Bloemfontein","3 Nov 2001"
"144","15","60","35","2","0","42.85","4","caught","3","v South Africa","Bloemfontein","3 Nov 2001"
"145","1","10","4","0","0","25.00","4","caught","2","v South Africa","Port Elizabeth","16 Nov 2001"
"146","22*","54","45","4","0","48.88","4","not out","4","v South Africa","Port Elizabeth","16 Nov 2001"
"147","88","229","144","13","0","61.11","5","caught","2","v England","Mohali","3 Dec 2001"
"148","DNB","-","-","-","-","-","-","-","4","v England","Mohali","3 Dec 2001"
"149","103","254","197","12","1","52.28","4","caught","2","v England","Ahmedabad","11 Dec 2001"
"150","26","87","81","4","0","32.09","4","caught","4","v England","Ahmedabad","11 Dec 2001"
"151","90","263","198","13","0","45.45","4","stumped","2","v England","Bengaluru","19 Dec 2001"
"152","176","440","316","23","0","55.69","4","caught","2","v Zimbabwe","Nagpur","21 Feb 2002"
"153","36","124","119","5","0","30.25","4","lbw","2","v Zimbabwe","Delhi","28 Feb 2002"
"154","42","78","52","7","1","80.76","5","lbw","4","v Zimbabwe","Delhi","28 Feb 2002"
"155","79","191","136","13","0","58.08","4","lbw","2","v West Indies","Georgetown","11 Apr 2002"
"156","117","356","260","14","0","45.00","4","lbw","1","v West Indies","Port of Spain","19 Apr 2002"
"157","0","16","4","0","0","0.00","4","lbw","3","v West Indies","Port of Spain","19 Apr 2002"
"158","0","4","2","0","0","0.00","4","caught","1","v West Indies","Bridgetown","2 May 2002"
"159","8","32","17","1","0","47.05","4","lbw","3","v West Indies","Bridgetown","2 May 2002"
"160","0","2","1","0","0","0.00","4","caught","1","v West Indies","St John's","10 May 2002"
"161","41","94","63","7","0","65.07","4","bowled","2","v West Indies","Kingston","18 May 2002"
"162","86","162","139","13","0","61.87","4","bowled","4","v West Indies","Kingston","18 May 2002"
"163","16","94","61","2","0","26.22","5","caught","2","v England","Lord's","25 Jul 2002"
"164","12","51","35","2","0","34.28","4","bowled","4","v England","Lord's","25 Jul 2002"
"165","34","90","68","6","0","50.00","4","bowled","1","v England","Nottingham","8 Aug 2002"
"166","92","160","113","17","0","81.41","4","bowled","3","v England","Nottingham","8 Aug 2002"
"167","193","434","330","19","3","58.48","4","lbw","1","v England","Leeds","22 Aug 2002"
"168","54","114","89","10","0","60.67","4","lbw","2","v England","The Oval","5 Sep 2002"
"169","35","96","88","5","0","39.77","4","caught","1","v West Indies","Mumbai","9 Oct 2002"
"170","43","156","113","5","0","38.05","4","bowled","2","v West Indies","Chennai","17 Oct 2002"
"171","16*","15","20","0","0","80.00","4","not out","4","v West Indies","Chennai","17 Oct 2002"
"172","36","85","65","7","0","55.38","4","caught","1","v West Indies","Kolkata","30 Oct 2002"
"173","176","419","298","26","0","59.06","4","caught","3","v West Indies","Kolkata","30 Oct 2002"
"174","8","47","26","2","0","30.76","4","lbw","1","v New Zealand","Wellington","12 Dec 2002"
"175","51","120","74","7","0","68.91","4","bowled","3","v New Zealand","Wellington","12 Dec 2002"
"176","9","40","31","1","0","29.03","4","caught","1","v New Zealand","Hamilton","19 Dec 2002"
"177","32","55","48","5","0","66.66","4","bowled","3","v New Zealand","Hamilton","19 Dec 2002"
"178","8","54","38","1","0","21.05","4","caught","1","v New Zealand","Ahmedabad","8 Oct 2003"
"179","7","19","15","0","0","46.66","4","caught","3","v New Zealand","Ahmedabad","8 Oct 2003"
"180","55","238","175","6","0","31.42","4","caught","2","v New Zealand","Mohali","16 Oct 2003"
"181","1","15","10","0","0","10.00","4","bowled","3","v New Zealand","Mohali","16 Oct 2003"
"182","0","3","3","0","0","0.00","4","lbw","2","v Australia","Brisbane","4 Dec 2003"
"183","DNB","-","-","-","-","-","-","-","4","v Australia","Brisbane","4 Dec 2003"
"184","1","15","6","0","0","16.66","4","caught","2","v Australia","Adelaide","12 Dec 2003"
"185","37","79","59","5","0","62.71","4","lbw","4","v Australia","Adelaide","12 Dec 2003"
"186","0","3","1","0","0","0.00","4","caught","1","v Australia","Melbourne","26 Dec 2003"
"187","44","107","79","5","0","55.69","5","caught","3","v Australia","Melbourne","26 Dec 2003"
"188","241*","613","436","33","0","55.27","4","not out","1","v Australia","Sydney","2 Jan 2004"
"189","60*","108","89","5","0","67.41","4","not out","3","v Australia","Sydney","2 Jan 2004"
"190","194*","493","348","21","0","55.74","4","not out","1","v Pakistan","Multan","28 Mar 2004"
"191","2","10","6","0","0","33.33","4","lbw","1","v Pakistan","Lahore","5 Apr 2004"
"192","8","17","11","1","0","72.72","4","lbw","3","v Pakistan","Lahore","5 Apr 2004"
"193","1","6","3","0","0","33.33","4","caught","2","v Pakistan","Rawalpindi","13 Apr 2004"
"194","8","49","36","0","0","22.22","4","lbw","2","v Australia","Nagpur","26 Oct 2004"
"195","2","18","14","0","0","14.28","4","caught","4","v Australia","Nagpur","26 Oct 2004"
"196","5","43","35","0","0","14.28","4","caught","1","v Australia","Mumbai","3 Nov 2004"
"197","55","120","83","6","1","66.26","4","caught","3","v Australia","Mumbai","3 Nov 2004"
"198","3","21","18","0","0","16.66","4","bowled","2","v South Africa","Kanpur","20 Nov 2004"
"199","20","82","54","3","0","37.03","4","bowled","2","v South Africa","Kolkata","28 Nov 2004"
"200","32*","73","64","3","1","50.00","4","not out","4","v South Africa","Kolkata","28 Nov 2004"
"201","248*","552","379","35","0","65.43","4","not out","2","v Bangladesh","Dhaka","10 Dec 2004"
"202","36","79","55","6","0","65.45","4","lbw","1","v Bangladesh","Chittagong","17 Dec 2004"
"203","94","301","202","11","0","46.53","4","caught","2","v Pakistan","Mohali","8 Mar 2005"
"204","DNB","-","-","-","-","-","-","-","4","v Pakistan","Mohali","8 Mar 2005"
"205","52","147","102","9","0","50.98","4","caught","1","v Pakistan","Kolkata","16 Mar 2005"
"206","52","117","91","9","0","57.14","4","caught","3","v Pakistan","Kolkata","16 Mar 2005"
"207","41","78","71","7","0","57.74","4","caught","2","v Pakistan","Bengaluru","24 Mar 2005"
"208","16","140","98","2","0","16.32","4","caught","4","v Pakistan","Bengaluru","24 Mar 2005"
"209","22","157","126","2","0","17.46","4","lbw","1","v Sri Lanka","Chennai","2 Dec 2005"
"210","109","310","196","14","1","55.61","4","lbw","1","v Sri Lanka","Delhi","10 Dec 2005"
"211","16","42","28","3","0","57.14","4","lbw","3","v Sri Lanka","Delhi","10 Dec 2005"
"212","23","85","66","2","0","34.84","4","caught","1","v Sri Lanka","Ahmedabad","18 Dec 2005"
"213","19","40","29","3","0","65.51","4","lbw","3","v Sri Lanka","Ahmedabad","18 Dec 2005"
"214","DNB","-","-","-","-","-","-","-","2","v Pakistan","Lahore","13 Jan 2006"
"215","14","64","33","3","0","42.42","4","caught","2","v Pakistan","Faisalabad","21 Jan 2006"
"216","DNB","-","-","-","-","-","-","-","4","v Pakistan","Faisalabad","21 Jan 2006"
"217","23","49","29","5","0","79.31","4","bowled","2","v Pakistan","Karachi","29 Jan 2006"
"218","26","74","47","5","0","55.31","4","bowled","4","v Pakistan","Karachi","29 Jan 2006"
"219","16","84","45","2","0","35.55","4","lbw","2","v England","Nagpur","1 Mar 2006"
"220","28*","38","19","5","0","147.36","6","not out","4","v England","Nagpur","1 Mar 2006"
"221","4","20","15","0","0","26.66","4","caught","2","v England","Mohali","9 Mar 2006"
"222","DNB","-","-","-","-","-","-","-","4","v England","Mohali","9 Mar 2006"
"223","1","33","21","0","0","4.76","4","caught","2","v England","Mumbai","18 Mar 2006"
"224","34","77","57","5","0","59.64","5","caught","4","v England","Mumbai","18 Mar 2006"
"225","44","113","89","7","0","49.43","4","caught","1","v South Africa","Johannesburg","15 Dec 2006"
"226","14","43","22","3","0","63.63","4","bowled","3","v South Africa","Johannesburg","15 Dec 2006"
"227","63","173","115","11","0","54.78","4","caught","2","v South Africa","Durban","26 Dec 2006"
"228","0","13","7","0","0","0.00","4","lbw","4","v South Africa","Durban","26 Dec 2006"
"229","64","187","130","11","0","49.23","4","caught","1","v South Africa","Cape Town","2 Jan 2007"
"230","14","88","62","1","0","22.58","5","lbw","3","v South Africa","Cape Town","2 Jan 2007"
"231","101","274","169","9","0","59.76","4","caught","1","v Bangladesh","Chittagong","18 May 2007"
"232","31","69","50","2","0","62.00","4","bowled","3","v Bangladesh","Chittagong","18 May 2007"
"233","122*","354","226","8","1","53.98","4","not out","1","v Bangladesh","Dhaka","25 May 2007"
"234","37","104","78","6","0","47.43","4","lbw","2","v England","Lord's","19 Jul 2007"
"235","16","42","35","3","0","45.71","4","lbw","4","v England","Lord's","19 Jul 2007"
"236","91","259","197","12","0","46.19","4","lbw","2","v England","Nottingham","27 Jul 2007"
"237","1","17","15","0","0","6.66","4","caught","4","v England","Nottingham","27 Jul 2007"
"238","82","298","192","11","0","42.70","4","caught","1","v England","The Oval","9 Aug 2007"
"239","1","13","11","0","0","9.09","4","bowled","3","v England","The Oval","9 Aug 2007"
"240","1","2","1","0","0","100.00","4","run out","2","v Pakistan","Delhi","22 Nov 2007"
"241","56*","178","110","9","0","50.90","4","not out","4","v Pakistan","Delhi","22 Nov 2007"
"242","82","146","109","12","0","75.22","4","bowled","1","v Pakistan","Kolkata","30 Nov 2007"
"243","DNB","-","-","-","-","-","-","-","3","v Pakistan","Kolkata","30 Nov 2007"
"244","62","113","77","7","1","80.51","4","bowled","2","v Australia","Melbourne","26 Dec 2007"
"245","15","31","21","1","0","71.42","4","caught","4","v Australia","Melbourne","26 Dec 2007"
"246","154*","429","243","14","1","63.37","4","not out","2","v Australia","Sydney","2 Jan 2008"
"247","12","33","16","3","0","75.00","4","bowled","4","v Australia","Sydney","2 Jan 2008"
"248","71","175","128","9","0","55.46","4","lbw","1","v Australia","Perth","16 Jan 2008"
"249","13","33","25","2","0","52.00","5","lbw","3","v Australia","Perth","16 Jan 2008"
"250","153","342","205","13","3","74.63","4","caught","1","v Australia","Adelaide","24 Jan 2008"
"251","13","67","36","1","0","36.11","4","run out","3","v Australia","Adelaide","24 Jan 2008"
"252","0","9","5","0","0","0.00","4","caught","2","v South Africa","Chennai","26 Mar 2008"
"253","27","60","46","3","0","58.69","4","bowled","2","v Sri Lanka","Colombo (SSC)","23 Jul 2008"
"254","12","42","30","1","0","40.00","4","caught","3","v Sri Lanka","Colombo (SSC)","23 Jul 2008"
"255","5","5","3","1","0","166.66","4","lbw","1","v Sri Lanka","Galle","31 Jul 2008"
"256","31","54","42","5","0","73.80","4","caught","3","v Sri Lanka","Galle","31 Jul 2008"
"257","6","18","12","0","0","50.00","4","lbw","1","v Sri Lanka","Colombo (PSS)","8 Aug 2008"
"258","14","26","22","1","0","63.63","6","lbw","3","v Sri Lanka","Colombo (PSS)","8 Aug 2008"
"259","13","26","18","3","0","72.22","4","caught","2","v Australia","Bengaluru","9 Oct 2008"
"260","49","177","126","4","0","38.88","4","caught","4","v Australia","Bengaluru","9 Oct 2008"
"261","88","186","111","10","0","79.27","4","caught","1","v Australia","Mohali","17 Oct 2008"
"262","10*","16","12","1","0","83.33","5","not out","3","v Australia","Mohali","17 Oct 2008"
"263","68","188","126","11","0","53.96","4","caught","1","v Australia","Delhi","29 Oct 2008"
"264","47","159","120","4","0","39.16","5","caught","3","v Australia","Delhi","29 Oct 2008"
"265","109","274","188","12","0","57.97","4","lbw","1","v Australia","Nagpur","6 Nov 2008"
"266","12","90","55","1","0","21.81","4","run out","3","v Australia","Nagpur","6 Nov 2008"
"267","37","90","48","4","1","77.08","4","caught","2","v England","Chennai","11 Dec 2008"
"268","103*","317","196","9","0","52.55","4","not out","4","v England","Chennai","11 Dec 2008"
"269","11","45","26","1","0","42.30","4","lbw","1","v England","Mohali","19 Dec 2008"
"270","5","41","22","1","0","22.72","4","caught","3","v England","Mohali","19 Dec 2008"
"271","160","397","260","26","0","61.53","4","caught","2","v New Zealand","Hamilton","18 Mar 2009"
"272","DNB","-","-","-","-","-","-","-","4","v New Zealand","Hamilton","18 Mar 2009"
"273","49","98","65","7","1","75.38","5","caught","2","v New Zealand","Napier","26 Mar 2009"
"274","64","167","131","9","1","48.85","4","caught","3","v New Zealand","Napier","26 Mar 2009"
"275","62","108","85","11","0","72.94","4","caught","1","v New Zealand","Wellington","3 Apr 2009"
"276","9","25","20","0","1","45.00","4","caught","3","v New Zealand","Wellington","3 Apr 2009"
"277","4","3","3","1","0","133.33","4","bowled","1","v Sri Lanka","Ahmedabad","16 Nov 2009"
"278","100*","298","211","11","0","47.39","5","not out","3","v Sri Lanka","Ahmedabad","16 Nov 2009"
"279","40","122","91","0","1","43.95","4","caught","1","v Sri Lanka","Kanpur","24 Nov 2009"
"280","53","139","103","6","0","51.45","4","bowled","2","v Sri Lanka","Mumbai (BS)","2 Dec 2009"
"281","105*","246","166","11","2","63.25","4","not out","1","v Bangladesh","Chittagong","17 Jan 2010"
"282","16","47","26","2","0","61.53","5","lbw","3","v Bangladesh","Chittagong","17 Jan 2010"
"283","143","259","182","13","1","78.57","4","caught","2","v Bangladesh","Dhaka","24 Jan 2010"
"284","DNB","-","-","-","-","-","-","-","4","v Bangladesh","Dhaka","24 Jan 2010"
"285","7","25","16","1","0","43.75","4","caught","2","v South Africa","Nagpur","6 Feb 2010"
"286","100","275","179","13","0","55.86","4","bowled","3","v South Africa","Nagpur","6 Feb 2010"
"287","106","266","206","12","0","51.45","4","caught","2","v South Africa","Kolkata","14 Feb 2010"
"288","8","29","21","1","0","38.09","4","lbw","2","v Sri Lanka","Galle","18 Jul 2010"
"289","84","168","142","11","1","59.15","4","lbw","3","v Sri Lanka","Galle","18 Jul 2010"
"290","203","516","347","23","1","58.50","4","caught","2","v Sri Lanka","Colombo (SSC)","26 Jul 2010"
"291","41","88","72","5","0","56.94","4","caught","2","v Sri Lanka","Colombo (PSS)","3 Aug 2010"
"292","54","186","122","3","0","44.26","4","caught","4","v Sri Lanka","Colombo (PSS)","3 Aug 2010"
"293","98","251","189","13","0","51.85","5","lbw","2","v Australia","Mohali","1 Oct 2010"
"294","38","106","64","5","0","59.37","4","caught","4","v Australia","Mohali","1 Oct 2010"
"295","214","547","363","22","2","58.95","4","bowled","2","v Australia","Bengaluru","9 Oct 2010"
"296","53*","125","77","5","2","68.83","4","not out","4","v Australia","Bengaluru","9 Oct 2010"
"297","40","168","133","5","0","30.07","4","caught","1","v New Zealand","Ahmedabad","4 Nov 2010"
"298","12","27","25","2","0","48.00","4","bowled","3","v New Zealand","Ahmedabad","4 Nov 2010"
"299","13","52","28","1","0","46.42","4","caught","2","v New Zealand","Hyderabad (Deccan)","12 Nov 2010"
"300","DNB","-","-","-","-","-","-","-","4","v New Zealand","Hyderabad (Deccan)","12 Nov 2010"
"301","61","162","129","8","0","47.28","4","caught","2","v New Zealand","Nagpur","20 Nov 2010"
"302","36","61","34","8","0","105.88","4","lbw","1","v South Africa","Centurion","16 Dec 2010"
"303","111*","327","241","13","1","46.05","5","not out","3","v South Africa","Centurion","16 Dec 2010"
"304","13","42","22","3","0","59.09","4","caught","1","v South Africa","Durban","26 Dec 2010"
"305","6","22","10","1","0","60.00","4","caught","3","v South Africa","Durban","26 Dec 2010"
"306","146","465","314","17","2","46.49","4","bowled","2","v South Africa","Cape Town","2 Jan 2011"
"307","14*","146","91","0","0","15.38","4","not out","4","v South Africa","Cape Town","2 Jan 2011"
"308","34","90","58","6","0","58.62","4","caught","2","v England","Lord's","21 Jul 2011"
"309","12","84","68","1","0","17.64","5","lbw","4","v England","Lord's","21 Jul 2011"
"310","16","33","34","3","0","47.05","4","caught","2","v England","Nottingham","29 Jul 2011"
"311","56","136","86","8","0","65.11","4","lbw","4","v England","Nottingham","29 Jul 2011"
"312","1","10","8","0","0","12.50","4","caught","1","v England","Birmingham","10 Aug 2011"
"313","40","92","60","8","0","66.66","4","run out","3","v England","Birmingham","10 Aug 2011"
"314","23","65","34","4","0","67.64","4","caught","2","v England","The Oval","18 Aug 2011"
"315","91","235","172","11","0","52.90","4","lbw","3","v England","The Oval","18 Aug 2011"
"316","7","25","18","1","0","38.88","4","lbw","2","v West Indies","Delhi","6 Nov 2011"
"317","76","200","148","10","0","51.35","4","lbw","4","v West Indies","Delhi","6 Nov 2011"
"318","38","74","71","5","0","53.52","4","caught","1","v West Indies","Kolkata","14 Nov 2011"
"319","94","219","153","8","2","61.43","4","caught","2","v West Indies","Mumbai","22 Nov 2011"
"320","3","6","7","0","0","42.85","4","caught","4","v West Indies","Mumbai","22 Nov 2011"
"321","73","151","98","8","1","74.48","4","bowled","2","v Australia","Melbourne","26 Dec 2011"
"322","32","73","46","4","0","69.56","4","caught","4","v Australia","Melbourne","26 Dec 2011"
"323","41","133","89","8","0","46.06","4","bowled","1","v Australia","Sydney","3 Jan 2012"
"324","80","213","141","9","0","56.73","4","caught","3","v Australia","Sydney","3 Jan 2012"
"325","15","46","25","3","0","60.00","4","lbw","1","v Australia","Perth","13 Jan 2012"
"326","8","22","16","1","0","50.00","4","lbw","3","v Australia","Perth","13 Jan 2012"
"327","25","94","69","3","0","36.23","4","caught","2","v Australia","Adelaide","24 Jan 2012"
"328","13","46","34","2","0","38.23","4","caught","4","v Australia","Adelaide","24 Jan 2012"
"329","19","86","62","2","0","30.64","4","bowled","1","v New Zealand","Hyderabad (Deccan)","23 Aug 2012"
"330","17","61","50","3","0","34.00","4","bowled","2","v New Zealand","Bengaluru","31 Aug 2012"
"331","27","78","34","5","0","79.41","4","bowled","4","v New Zealand","Bengaluru","31 Aug 2012"
"332","13","25","18","2","0","72.22","4","caught","1","v England","Ahmedabad","15 Nov 2012"
"333","DNB","-","-","-","-","-","-","-","4","v England","Ahmedabad","15 Nov 2012"
"334","8","14","12","1","0","66.66","4","bowled","1","v England","Mumbai","23 Nov 2012"
"335","8","28","19","2","0","42.10","4","lbw","3","v England","Mumbai","23 Nov 2012"
"336","76","202","155","13","0","49.03","4","caught","1","v England","Kolkata","5 Dec 2012"
"337","5","17","6","1","0","83.33","4","caught","3","v England","Kolkata","5 Dec 2012"
"338","2","25","13","0","0","15.38","4","bowled","2","v England","Nagpur","13 Dec 2012"
"339","81","257","159","7","0","50.94","4","bowled","2","v Australia","Chennai","22 Feb 2013"
"340","13*","10","10","0","2","130.00","4","not out","4","v Australia","Chennai","22 Feb 2013"
"341","7","32","15","1","0","46.66","4","caught","2","v Australia","Hyderabad (Deccan)","2 Mar 2013"
"342","37","125","81","5","0","45.67","4","caught","2","v Australia","Mohali","14 Mar 2013"
"343","21","71","23","2","0","91.30","4","run out","4","v Australia","Mohali","14 Mar 2013"
"344","32","99","53","5","0","60.37","4","lbw","2","v Australia","Delhi","22 Mar 2013"
"345","1","8","5","0","0","20.00","4","lbw","4","v Australia","Delhi","22 Mar 2013"
"346","10","41","24","2","0","41.66","4","lbw","2","v West Indies","Kolkata","6 Nov 2013"
"347","74","150","118","12","0","62.71","4","caught","2","v West Indies","Mumbai","14 Nov 2013"


================================================
FILE: data/titanic.csv
================================================
"","Class","Sex","Age","Survived"
"1","3rd","Male","Child","No"
"2","3rd","Male","Child","No"
"3","3rd","Male","Child","No"
"4","3rd","Male","Child","No"
"5","3rd","Male","Child","No"
"6","3rd","Male","Child","No"
"7","3rd","Male","Child","No"
"8","3rd","Male","Child","No"
"9","3rd","Male","Child","No"
"10","3rd","Male","Child","No"
"11","3rd","Male","Child","No"
"12","3rd","Male","Child","No"
"13","3rd","Male","Child","No"
"14","3rd","Male","Child","No"
"15","3rd","Male","Child","No"
"16","3rd","Male","Child","No"
"17","3rd","Male","Child","No"
"18","3rd","Male","Child","No"
"19","3rd","Male","Child","No"
"20","3rd","Male","Child","No"
"21","3rd","Male","Child","No"
"22","3rd","Male","Child","No"
"23","3rd","Male","Child","No"
"24","3rd","Male","Child","No"
"25","3rd","Male","Child","No"
"26","3rd","Male","Child","No"
"27","3rd","Male","Child","No"
"28","3rd","Male","Child","No"
"29","3rd","Male","Child","No"
"30","3rd","Male","Child","No"
"31","3rd","Male","Child","No"
"32","3rd","Male","Child","No"
"33","3rd","Male","Child","No"
"34","3rd","Male","Child","No"
"35","3rd","Male","Child","No"
"36","3rd","Female","Child","No"
"37","3rd","Female","Child","No"
"38","3rd","Female","Child","No"
"39","3rd","Female","Child","No"
"40","3rd","Female","Child","No"
"41","3rd","Female","Child","No"
"42","3rd","Female","Child","No"
"43","3rd","Female","Child","No"
"44","3rd","Female","Child","No"
"45","3rd","Female","Child","No"
"46","3rd","Female","Child","No"
"47","3rd","Female","Child","No"
"48","3rd","Female","Child","No"
"49","3rd","Female","Child","No"
"50","3rd","Female","Child","No"
"51","3rd","Female","Child","No"
"52","3rd","Female","Child","No"
"53","1st","Male","Adult","No"
"54","1st","Male","Adult","No"
"55","1st","Male","Adult","No"
"56","1st","Male","Adult","No"
"57","1st","Male","Adult","No"
"58","1st","Male","Adult","No"
"59","1st","Male","Adult","No"
"60","1st","Male","Adult","No"
"61","1st","Male","Adult","No"
"62","1st","Male","Adult","No"
"63","1st","Male","Adult","No"
"64","1st","Male","Adult","No"
"65","1st","Male","Adult","No"
"66","1st","Male","Adult","No"
"67","1st","Male","Adult","No"
"68","1st","Male","Adult","No"
"69","1st","Male","Adult","No"
"70","1st","Male","Adult","No"
"71","1st","Male","Adult","No"
"72","1st","Male","Adult","No"
"73","1st","Male","Adult","No"
"74","1st","Male","Adult","No"
"75","1st","Male","Adult","No"
"76","1st","Male","Adult","No"
"77","1st","Male","Adult","No"
"78","1st","Male","Adult","No"
"79","1st","Male","Adult","No"
"80","1st","Male","Adult","No"
"81","1st","Male","Adult","No"
"82","1st","Male","Adult","No"
"83","1st","Male","Adult","No"
"84","1st","Male","Adult","No"
"85","1st","Male","Adult","No"
"86","1st","Male","Adult","No"
"87","1st","Male","Adult","No"
"88","1st","Male","Adult","No"
"89","1st","Male","Adult","No"
"90","1st","Male","Adult","No"
"91","1st","Male","Adult","No"
"92","1st","Male","Adult","No"
"93","1st","Male","Adult","No"
"94","1st","Male","Adult","No"
"95","1st","Male","Adult","No"
"96","1st","Male","Adult","No"
"97","1st","Male","Adult","No"
"98","1st","Male","Adult","No"
"99","1st","Male","Adult","No"
"100","1st","Male","Adult","No"
"101","1st","Male","Adult","No"
"102","1st","Male","Adult","No"
"103","1st","Male","Adult","No"
"104","1st","Male","Adult","No"
"105","1st","Male","Adult","No"
"106","1st","Male","Adult","No"
"107","1st","Male","Adult","No"
"108","1st","Male","Adult","No"
"109","1st","Male","Adult","No"
"110","1st","Male","Adult","No"
"111","1st","Male","Adult","No"
"112","1st","Male","Adult","No"
"113","1st","Male","Adult","No"
"114","1st","Male","Adult","No"
"115","1st","Male","Adult","No"
"116","1st","Male","Adult","No"
"117","1st","Male","Adult","No"
"118","1st","Male","Adult","No"
"119","1st","Male","Adult","No"
"120","1st","Male","Adult","No"
"121","1st","Male","Adult","No"
"122","1st","Male","Adult","No"
"123","1st","Male","Adult","No"
"124","1st","Male","Adult","No"
"125","1st","Male","Adult","No"
"126","1st","Male","Adult","No"
"127","1st","Male","Adult","No"
"128","1st","Male","Adult","No"
"129","1st","Male","Adult","No"
"130","1st","Male","Adult","No"
"131","1st","Male","Adult","No"
"132","1st","Male","Adult","No"
"133","1st","Male","Adult","No"
"134","1st","Male","Adult","No"
"135","1st","Male","Adult","No"
"136","1st","Male","Adult","No"
"137","1st","Male","Adult","No"
"138","1st","Male","Adult","No"
"139","1st","Male","Adult","No"
"140","1st","Male","Adult","No"
"141","1st","Male","Adult","No"
"142","1st","Male","Adult","No"
"143","1st","Male","Adult","No"
"144","1st","Male","Adult","No"
"145","1st","Male","Adult","No"
"146","1st","Male","Adult","No"
"147","1st","Male","Adult","No"
"148","1st","Male","Adult","No"
"149","1st","Male","Adult","No"
"150","1st","Male","Adult","No"
"151","1st","Male","Adult","No"
"152","1st","Male","Adult","No"
"153","1st","Male","Adult","No"
"154","1st","Male","Adult","No"
"155","1st","Male","Adult","No"
"156","1st","Male","Adult","No"
"157","1st","Male","Adult","No"
"158","1st","Male","Adult","No"
"159","1st","Male","Adult","No"
"160","1st","Male","Adult","No"
"161","1st","Male","Adult","No"
"162","1st","Male","Adult","No"
"163","1st","Male","Adult","No"
"164","1st","Male","Adult","No"
"165","1st","Male","Adult","No"
"166","1st","Male","Adult","No"
"167","1st","Male","Adult","No"
"168","1st","Male","Adult","No"
"169","1st","Male","Adult","No"
"170","1st","Male","Adult","No"
"171","2nd","Male","Adult","No"
"172","2nd","Male","Adult","No"
"173","2nd","Male","Adult","No"
"174","2nd","Male","Adult","No"
"175","2nd","Male","Adult","No"
"176","2nd","Male","Adult","No"
"177","2nd","Male","Adult","No"
"178","2nd","Male","Adult","No"
"179","2nd","Male","Adult","No"
"180","2nd","Male","Adult","No"
"181","2nd","Male","Adult","No"
"182","2nd","Male","Adult","No"
"183","2nd","Male","Adult","No"
"184","2nd","Male","Adult","No"
"185","2nd","Male","Adult","No"
"186","2nd","Male","Adult","No"
"187","2nd","Male","Adult","No"
"188","2nd","Male","Adult","No"
"189","2nd","Male","Adult","No"
"190","2nd","Male","Adult","No"
"191","2nd","Male","Adult","No"
"192","2nd","Male","Adult","No"
"193","2nd","Male","Adult","No"
"194","2nd","Male","Adult","No"
"195","2nd","Male","Adult","No"
"196","2nd","Male","Adult","No"
"197","2nd","Male","Adult","No"
"198","2nd","Male","Adult","No"
"199","2nd","Male","Adult","No"
"200","2nd","Male","Adult","No"
"201","2nd","Male","Adult","No"
"202","2nd","Male","Adult","No"
"203","2nd","Male","Adult","No"
"204","2nd","Male","Adult","No"
"205","2nd","Male","Adult","No"
"206","2nd","Male","Adult","No"
"207","2nd","Male","Adult","No"
"208","2nd","Male","Adult","No"
"209","2nd","Male","Adult","No"
"210","2nd","Male","Adult","No"
"211","2nd","Male","Adult","No"
"212","2nd","Male","Adult","No"
"213","2nd","Male","Adult","No"
"214","2nd","Male","Adult","No"
"215","2nd","Male","Adult","No"
"216","2nd","Male","Adult","No"
"217","2nd","Male","Adult","No"
"218","2nd","Male","Adult","No"
"219","2nd","Male","Adult","No"
"220","2nd","Male","Adult","No"
"221","2nd","Male","Adult","No"
"222","2nd","Male","Adult","No"
"223","2nd","Male","Adult","No"
"224","2nd","Male","Adult","No"
"225","2nd","Male","Adult","No"
"226","2nd","Male","Adult","No"
"227","2nd","Male","Adult","No"
"228","2nd","Male","Adult","No"
"229","2nd","Male","Adult","No"
"230","2nd","Male","Adult","No"
"231","2nd","Male","Adult","No"
"232","2nd","Male","Adult","No"
"233","2nd","Male","Adult","No"
"234","2nd","Male","Adult","No"
"235","2nd","Male","Adult","No"
"236","2nd","Male","Adult","No"
"237","2nd","Male","Adult","No"
"238","2nd","Male","Adult","No"
"239","2nd","Male","Adult","No"
"240","2nd","Male","Adult","No"
"241","2nd","Male","Adult","No"
"242","2nd","Male","Adult","No"
"243","2nd","Male","Adult","No"
"244","2nd","Male","Adult","No"
"245","2nd","Male","Adult","No"
"246","2nd","Male","Adult","No"
"247","2nd","Male","Adult","No"
"248","2nd","Male","Adult","No"
"249","2nd","Male","Adult","No"
"250","2nd","Male","Adult","No"
"251","2nd","Male","Adult","No"
"252","2nd","Male","Adult","No"
"253","2nd","Male","Adult","No"
"254","2nd","Male","Adult","No"
"255","2nd","Male","Adult","No"
"256","2nd","Male","Adult","No"
"257","2nd","Male","Adult","No"
"258","2nd","Male","Adult","No"
"259","2nd","Male","Adult","No"
"260","2nd","Male","Adult","No"
"261","2nd","Male","Adult","No"
"262","2nd","Male","Adult","No"
"263","2nd","Male","Adult","No"
"264","2nd","Male","Adult","No"
"265","2nd","Male","Adult","No"
"266","2nd","Male","Adult","No"
"267","2nd","Male","Adult","No"
"268","2nd","Male","Adult","No"
"269","2nd","Male","Adult","No"
"270","2nd","Male","Adult","No"
"271","2nd","Male","Adult","No"
"272","2nd","Male","Adult","No"
"273","2nd","Male","Adult","No"
"274","2nd","Male","Adult","No"
"275","2nd","Male","Adult","No"
"276","2nd","Male","Adult","No"
"277","2nd","Male","Adult","No"
"278","2nd","Male","Adult","No"
"279","2nd","Male","Adult","No"
"280","2nd","Male","Adult","No"
"281","2nd","Male","Adult","No"
"282","2nd","Male","Adult","No"
"283","2nd","Male","Adult","No"
"284","2nd","Male","Adult","No"
"285","2nd","Male","Adult","No"
"286","2nd","Male","Adult","No"
"287","2nd","Male","Adult","No"
"288","2nd","Male","Adult","No"
"289","2nd","Male","Adult","No"
"290","2nd","Male","Adult","No"
"291","2nd","Male","Adult","No"
"292","2nd","Male","Adult","No"
"293","2nd","Male","Adult","No"
"294","2nd","Male","Adult","No"
"295","2nd","Male","Adult","No"
"296","2nd","Male","Adult","No"
"297","2nd","Male","Adult","No"
"298","2nd","Male","Adult","No"
"299","2nd","Male","Adult","No"
"300","2nd","Male","Adult","No"
"301","2nd","Male","Adult","No"
"302","2nd","Male","Adult","No"
"303","2nd","Male","Adult","No"
"304","2nd","Male","Adult","No"
"305","2nd","Male","Adult","No"
"306","2nd","Male","Adult","No"
"307","2nd","Male","Adult","No"
"308","2nd","Male","Adult","No"
"309","2nd","Male","Adult","No"
"310","2nd","Male","Adult","No"
"311","2nd","Male","Adult","No"
"312","2nd","Male","Adult","No"
"313","2nd","Male","Adult","No"
"314","2nd","Male","Adult","No"
"315","2nd","Male","Adult","No"
"316","2nd","Male","Adult","No"
"317","2nd","Male","Adult","No"
"318","2nd","Male","Adult","No"
"319","2nd","Male","Adult","No"
"320","2nd","Male","Adult","No"
"321","2nd","Male","Adult","No"
"322","2nd","Male","Adult","No"
"323","2nd","Male","Adult","No"
"324","2nd","Male","Adult","No"
"325","3rd","Male","Adult","No"
"326","3rd","Male","Adult","No"
"327","3rd","Male","Adult","No"
"328","3rd","Male","Adult","No"
"329","3rd","Male","Adult","No"
"330","3rd","Male","Adult","No"
"331","3rd","Male","Adult","No"
"332","3rd","Male","Adult","No"
"333","3rd","Male","Adult","No"
"334","3rd","Male","Adult","No"
"335","3rd","Male","Adult","No"
"336","3rd","Male","Adult","No"
"337","3rd","Male","Adult","No"
"338","3rd","Male","Adult","No"
"339","3rd","Male","Adult","No"
"340","3rd","Male","Adult","No"
"341","3rd","Male","Adult","No"
"342","3rd","Male","Adult","No"
"343","3rd","Male","Adult","No"
"344","3rd","Male","Adult","No"
"345","3rd","Male","Adult","No"
"346","3rd","Male","Adult","No"
"347","3rd","Male","Adult","No"
"348","3rd","Male","Adult","No"
"349","3rd","Male","Adult","No"
"350","3rd","Male","Adult","No"
"351","3rd","Male","Adult","No"
"352","3rd","Male","Adult","No"
"353","3rd","Male","Adult","No"
"354","3rd","Male","Adult","No"
"355","3rd","Male","Adult","No"
"356","3rd","Male","Adult","No"
"357","3rd","Male","Adult","No"
"358","3rd","Male","Adult","No"
"359","3rd","Male","Adult","No"
"360","3rd","Male","Adult","No"
"361","3rd","Male","Adult","No"
"362","3rd","Male","Adult","No"
"363","3rd","Male","Adult","No"
"364","3rd","Male","Adult","No"
"365","3rd","Male","Adult","No"
"366","3rd","Male","Adult","No"
"367","3rd","Male","Adult","No"
"368","3rd","Male","Adult","No"
"369","3rd","Male","Adult","No"
"370","3rd","Male","Adult","No"
"371","3rd","Male","Adult","No"
"372","3rd","Male","Adult","No"
"373","3rd","Male","Adult","No"
"374","3rd","Male","Adult","No"
"375","3rd","Male","Adult","No"
"376","3rd","Male","Adult","No"
"377","3rd","Male","Adult","No"
"378","3rd","Male","Adult","No"
"379","3rd","Male","Adult","No"
"380","3rd","Male","Adult","No"
"381","3rd","Male","Adult","No"
"382","3rd","Male","Adult","No"
"383","3rd","Male","Adult","No"
"384","3rd","Male","Adult","No"
"385","3rd","Male","Adult","No"
"386","3rd","Male","Adult","No"
"387","3rd","Male","Adult","No"
"388","3rd","Male","Adult","No"
"389","3rd","Male","Adult","No"
"390","3rd","Male","Adult","No"
"391","3rd","Male","Adult","No"
"392","3rd","Male","Adult","No"
"393","3rd","Male","Adult","No"
"394","3rd","Male","Adult","No"
"395","3rd","Male","Adult","No"
"396","3rd","Male","Adult","No"
"397","3rd","Male","Adult","No"
"398","3rd","Male","Adult","No"
"399","3rd","Male","Adult","No"
"400","3rd","Male","Adult","No"
"401","3rd","Male","Adult","No"
"402","3rd","Male","Adult","No"
"403","3rd","Male","Adult","No"
"404","3rd","Male","Adult","No"
"405","3rd","Male","Adult","No"
"406","3rd","Male","Adult","No"
"407","3rd","Male","Adult","No"
"408","3rd","Male","Adult","No"
"409","3rd","Male","Adult","No"
"410","3rd","Male","Adult","No"
"411","3rd","Male","Adult","No"
"412","3rd","Male","Adult","No"
"413","3rd","Male","Adult","No"
"414","3rd","Male","Adult","No"
"415","3rd","Male","Adult","No"
"416","3rd","Male","Adult","No"
"417","3rd","Male","Adult","No"
"418","3rd","Male","Adult","No"
"419","3rd","Male","Adult","No"
"420","3rd","Male","Adult","No"
"421","3rd","Male","Adult","No"
"422","3rd","Male","Adult","No"
"423","3rd","Male","Adult","No"
"424","3rd","Male","Adult","No"
"425","3rd","Male","Adult","No"
"426","3rd","Male","Adult","No"
"427","3rd","Male","Adult","No"
"428","3rd","Male","Adult","No"
"429","3rd","Male","Adult","No"
"430","3rd","Male","Adult","No"
"431","3rd","Male","Adult","No"
"432","3rd","Male","Adult","No"
"433","3rd","Male","Adult","No"
"434","3rd","Male","Adult","No"
"435","3rd","Male","Adult","No"
"436","3rd","Male","Adult","No"
"437","3rd","Male","Adult","No"
"438","3rd","Male","Adult","No"
"439","3rd","Male","Adult","No"
"440","3rd","Male","Adult","No"
"441","3rd","Male","Adult","No"
"442","3rd","Male","Adult","No"
"443","3rd","Male","Adult","No"
"444","3rd","Male","Adult","No"
"445","3rd","Male","Adult","No"
"446","3rd","Male","Adult","No"
"447","3rd","Male","Adult","No"
"448","3rd","Male","Adult","No"
"449","3rd","Male","Adult","No"
"450","3rd","Male","Adult","No"
"451","3rd","Male","Adult","No"
"452","3rd","Male","Adult","No"
"453","3rd","Male","Adult","No"
"454","3rd","Male","Adult","No"
"455","3rd","Male","Adult","No"
"456","3rd","Male","Adult","No"
"457","3rd","Male","Adult","No"
"458","3rd","Male","Adult","No"
"459","3rd","Male","Adult","No"
"460","3rd","Male","Adult","No"
"461","3rd","Male","Adult","No"
"462","3rd","Male","Adult","No"
"463","3rd","Male","Adult","No"
"464","3rd","Male","Adult","No"
"465","3rd","Male","Adult","No"
"466","3rd","Male","Adult","No"
"467","3rd","Male","Adult","No"
"468","3rd","Male","Adult","No"
"469","3rd","Male","Adult","No"
"470","3rd","Male","Adult","No"
"471","3rd","Male","Adult","No"
"472","3rd","Male","Adult","No"
"473","3rd","Male","Adult","No"
"474","3rd","Male","Adult","No"
"475","3rd","Male","Adult","No"
"476","3rd","Male","Adult","No"
"477","3rd","Male","Adult","No"
"478","3rd","Male","Adult","No"
"479","3rd","Male","Adult","No"
"480","3rd","Male","Adult","No"
"481","3rd","Male","Adult","No"
"482","3rd","Male","Adult","No"
"483","3rd","Male","Adult","No"
"484","3rd","Male","Adult","No"
"485","3rd","Male","Adult","No"
"486","3rd","Male","Adult","No"
"487","3rd","Male","Adult","No"
"488","3rd","Male","Adult","No"
"489","3rd","Male","Adult","No"
"490","3rd","Male","Adult","No"
"491","3rd","Male","Adult","No"
"492","3rd","Male","Adult","No"
"493","3rd","Male","Adult","No"
"494","3rd","Male","Adult","No"
"495","3rd","Male","Adult","No"
"496","3rd","Male","Adult","No"
"497","3rd","Male","Adult","No"
"498","3rd","Male","Adult","No"
"499","3rd","Male","Adult","No"
"500","3rd","Male","Adult","No"
"501","3rd","Male","Adult","No"
"502","3rd","Male","Adult","No"
"503","3rd","Male","Adult","No"
"504","3rd","Male","Adult","No"
"505","3rd","Male","Adult","No"
"506","3rd","Male","Adult","No"
"507","3rd","Male","Adult","No"
"508","3rd","Male","Adult","No"
"509","3rd","Male","Adult","No"
"510","3rd","Male","Adult","No"
"511","3rd","Male","Adult","No"
"512","3rd","Male","Adult","No"
"513","3rd","Male","Adult","No"
"514","3rd","Male","Adult","No"
"515","3rd","Male","Adult","No"
"516","3rd","Male","Adult","No"
"517","3rd","Male","Adult","No"
"518","3rd","Male","Adult","No"
"519","3rd","Male","Adult","No"
"520","3rd","Male","Adult","No"
"521","3rd","Male","Adult","No"
"522","3rd","Male","Adult","No"
"523","3rd","Male","Adult","No"
"524","3rd","Male","Adult","No"
"525","3rd","Male","Adult","No"
"526","3rd","Male","Adult","No"
"527","3rd","Male","Adult","No"
"528","3rd","Male","Adult","No"
"529","3rd","Male","Adult","No"
"530","3rd","Male","Adult","No"
"531","3rd","Male","Adult","No"
"532","3rd","Male","Adult","No"
"533","3rd","Male","Adult","No"
"534","3rd","Male","Adult","No"
"535","3rd","Male","Adult","No"
"536","3rd","Male","Adult","No"
"537","3rd","Male","Adult","No"
"538","3rd","Male","Adult","No"
"539","3rd","Male","Adult","No"
"540","3rd","Male","Adult","No"
"541","3rd","Male","Adult","No"
"542","3rd","Male","Adult","No"
"543","3rd","Male","Adult","No"
"544","3rd","Male","Adult","No"
"545","3rd","Male","Adult","No"
"546","3rd","Male","Adult","No"
"547","3rd","Male","Adult","No"
"548","3rd","Male","Adult","No"
"549","3rd","Male","Adult","No"
"550","3rd","Male","Adult","No"
"551","3rd","Male","Adult","No"
"552","3rd","Male","Adult","No"
"553","3rd","Male","Adult","No"
"554","3rd","Male","Adult","No"
"555","3rd","Male","Adult","No"
"556","3rd","Male","Adult","No"
"557","3rd","Male","Adult","No"
"558","3rd","Male","Adult","No"
"559","3rd","Male","Adult","No"
"560","3rd","Male","Adult","No"
"561","3rd","Male","Adult","No"
"562","3rd","Male","Adult","No"
"563","3rd","Male","Adult","No"
"564","3rd","Male","Adult","No"
"565","3rd","Male","Adult","No"
"566","3rd","Male","Adult","No"
"567","3rd","Male","Adult","No"
"568","3rd","Male","Adult","No"
"569","3rd","Male","Adult","No"
"570","3rd","Male","Adult","No"
"571","3rd","Male","Adult","No"
"572","3rd","Male","Adult","No"
"573","3rd","Male","Adult","No"
"574","3rd","Male","Adult","No"
"575","3rd","Male","Adult","No"
"576","3rd","Male","Adult","No"
"577","3rd","Male","Adult","No"
"578","3rd","Male","Adult","No"
"579","3rd","Male","Adult","No"
"580","3rd","Male","Adult","No"
"581","3rd","Male","Adult","No"
"582","3rd","Male","Adult","No"
"583","3rd","Male","Adult","No"
"584","3rd","Male","Adult","No"
"585","3rd","Male","Adult","No"
"586","3rd","Male","Adult","No"
"587","3rd","Male","Adult","No"
"588","3rd","Male","Adult","No"
"589","3rd","Male","Adult","No"
"590","3rd","Male","Adult","No"
"591","3rd","Male","Adult","No"
"592","3rd","Male","Adult","No"
"593","3rd","Male","Adult","No"
"594","3rd","Male","Adult","No"
"595","3rd","Male","Adult","No"
"596","3rd","Male","Adult","No"
"597","3rd","Male","Adult","No"
"598","3rd","Male","Adult","No"
"599","3rd","Male","Adult","No"
"600","3rd","Male","Adult","No"
"601","3rd","Male","Adult","No"
"602","3rd","Male","Adult","No"
"603","3rd","Male","Adult","No"
"604","3rd","Male","Adult","No"
"605","3rd","Male","Adult","No"
"606","3rd","Male","Adult","No"
"607","3rd","Male","Adult","No"
"608","3rd","Male","Adult","No"
"609","3rd","Male","Adult","No"
"610","3rd","Male","Adult","No"
"611","3rd","Male","Adult","No"
"612","3rd","Male","Adult","No"
"613","3rd","Male","Adult","No"
"614","3rd","Male","Adult","No"
"615","3rd","Male","Adult","No"
"616","3rd","Male","Adult","No"
"617","3rd","Male","Adult","No"
"618","3rd","Male","Adult","No"
"619","3rd","Male","Adult","No"
"620","3rd","Male","Adult","No"
"621","3rd","Male","Adult","No"
"622","3rd","Male","Adult","No"
"623","3rd","Male","Adult","No"
"624","3rd","Male","Adult","No"
"625","3rd","Male","Adult","No"
"626","3rd","Male","Adult","No"
"627","3rd","Male","Adult","No"
"628","3rd","Male","Adult","No"
"629","3rd","Male","Adult","No"
"630","3rd","Male","Adult","No"
"631","3rd","Male","Adult","No"
"632","3rd","Male","Adult","No"
"633","3rd","Male","Adult","No"
"634","3rd","Male","Adult","No"
"635","3rd","Male","Adult","No"
"636","3rd","Male","Adult","No"
"637","3rd","Male","Adult","No"
"638","3rd","Male","Adult","No"
"639","3rd","Male","Adult","No"
"640","3rd","Male","Adult","No"
"641","3rd","Male","Adult","No"
"642","3rd","Male","Adult","No"
"643","3rd","Male","Adult","No"
"644","3rd","Male","Adult","No"
"645","3rd","Male","Adult","No"
"646","3rd","Male","Adult","No"
"647","3rd","Male","Adult","No"
"648","3rd","Male","Adult","No"
"649","3rd","Male","Adult","No"
"650","3rd","Male","Adult","No"
"651","3rd","Male","Adult","No"
"652","3rd","Male","Adult","No"
"653","3rd","Male","Adult","No"
"654","3rd","Male","Adult","No"
"655","3rd","Male","Adult","No"
"656","3rd","Male","Adult","No"
"657","3rd","Male","Adult","No"
"658","3rd","Male","Adult","No"
"659","3rd","Male","Adult","No"
"660","3rd","Male","Adult","No"
"661","3rd","Male","Adult","No"
"662","3rd","Male","Adult","No"
"663","3rd","Male","Adult","No"
"664","3rd","Male","Adult","No"
"665","3rd","Male","Adult","No"
"666","3rd","Male","Adult","No"
"667","3rd","Male","Adult","No"
"668","3rd","Male","Adult","No"
"669","3rd","Male","Adult","No"
"670","3rd","Male","Adult","No"
"671","3rd","Male","Adult","No"
"672","3rd","Male","Adult","No"
"673","3rd","Male","Adult","No"
"674","3rd","Male","Adult","No"
"675","3rd","Male","Adult","No"
"676","3rd","Male","Adult","No"
"677","3rd","Male","Adult","No"
"678","3rd","Male","Adult","No"
"679","3rd","Male","Adult","No"
"680","3rd","Male","Adult","No"
"681","3rd","Male","Adult","No"
"682","3rd","Male","Adult","No"
"683","3rd","Male","Adult","No"
"684","3rd","Male","Adult","No"
"685","3rd","Male","Adult","No"
"686","3rd","Male","Adult","No"
"687","3rd","Male","Adult","No"
"688","3rd","Male","Adult","No"
"689","3rd","Male","Adult","No"
"690","3rd","Male","Adult","No"
"691","3rd","Male","Adult","No"
"692","3rd","Male","Adult","No"
"693","3rd","Male","Adult","No"
"694","3rd","Male","Adult","No"
"695","3rd","Male","Adult","No"
"696","3rd","Male","Adult","No"
"697","3rd","Male","Adult","No"
"698","3rd","Male","Adult","No"
"699","3rd","Male","Adult","No"
"700","3rd","Male","Adult","No"
"701","3rd","Male","Adult","No"
"702","3rd","Male","Adult","No"
"703","3rd","Male","Adult","No"
"704","3rd","Male","Adult","No"
"705","3rd","Male","Adult","No"
"706","3rd","Male","Adult","No"
"707","3rd","Male","Adult","No"
"708","3rd","Male","Adult","No"
"709","3rd","Male","Adult","No"
"710","3rd","Male","Adult","No"
"711","3rd","Male","Adult","No"
"712","Crew","Male","Adult","No"
"713","Crew","Male","Adult","No"
"714","Crew","Male","Adult","No"
"715","Crew","Male","Adult","No"
"716","Crew","Male","Adult","No"
"717","Crew","Male","Adult","No"
"718","Crew","Male","Adult","No"
"719","Crew","Male","Adult","No"
"720","Crew","Male","Adult","No"
"721","Crew","Male","Adult","No"
"722","Crew","Male","Adult","No"
"723","Crew","Male","Adult","No"
"724","Crew","Male","Adult","No"
"725","Crew","Male","Adult","No"
"726","Crew","Male","Adult","No"
"727","Crew","Male","Adult","No"
"728","Crew","Male","Adult","No"
"729","Crew","Male","Adult","No"
"730","Crew","Male","Adult","No"
"731","Crew","Male","Adult","No"
"732","Crew","Male","Adult","No"
"733","Crew","Male","Adult","No"
"734","Crew","Male","Adult","No"
"735","Crew","Male","Adult","No"
"736","Crew","Male","Adult","No"
"737","Crew","Male","Adult","No"
"738","Crew","Male","Adult","No"
"739","Crew","Male","Adult","No"
"740","Crew","Male","Adult","No"
"741","Crew","Male","Adult","No"
"742","Crew","Male","Adult","No"
"743","Crew","Male","Adult","No"
"744","Crew","Male","Adult","No"
"745","Crew","Male","Adult","No"
"746","Crew","Male","Adult","No"
"747","Crew","Male","Adult","No"
"748","Crew","Male","Adult","No"
"749","Crew","Male","Adult","No"
"750","Crew","Male","Adult","No"
"751","Crew","Male","Adult","No"
"752","Crew","Male","Adult","No"
"753","Crew","Male","Adult","No"
"754","Crew","Male","Adult","No"
"755","Crew","Male","Adult","No"
"756","Crew","Male","Adult","No"
"757","Crew","Male","Adult","No"
"758","Crew","Male","Adult","No"
"759","Crew","Male","Adult","No"
"760","Crew","Male","Adult","No"
"761","Crew","Male","Adult","No"
"762","Crew","Male","Adult","No"
"763","Crew","Male","Adult","No"
"764","Crew","Male","Adult","No"
"765","Crew","Male","Adult","No"
"766","Crew","Male","Adult","No"
"767","Crew","Male","Adult","No"
"768","Crew","Male","Adult","No"
"769","Crew","Male","Adult","No"
"770","Crew","Male","Adult","No"
"771","Crew","Male","Adult","No"
"772","Crew","Male","Adult","No"
"773","Crew","Male","Adult","No"
"774","Crew","Male","Adult","No"
"775","Crew","Male","Adult","No"
"776","Crew","Male","Adult","No"
"777","Crew","Male","Adult","No"
"778","Crew","Male","Adult","No"
"779","Crew","Male","Adult","No"
"780","Crew","Male","Adult","No"
"781","Crew","Male","Adult","No"
"782","Crew","Male","Adult","No"
"783","Crew","Male","Adult","No"
"784","Crew","Male","Adult","No"
"785","Crew","Male","Adult","No"
"786","Crew","Male","Adult","No"
"787","Crew","Male","Adult","No"
"788","Crew","Male","Adult","No"
"789","Crew","Male","Adult","No"
"790","Crew","Male","Adult","No"
"791","Crew","Male","Adult","No"
"792","Crew","Male","Adult","No"
"793","Crew","Male","Adult","No"
"794","Crew","Male","Adult","No"
"795","Crew","Male","Adult","No"
"796","Crew","Male","Adult","No"
"797","Crew","Male","Adult","No"
"798","Crew","Male","Adult","No"
"799","Crew","Male","Adult","No"
"800","Crew","Male","Adult","No"
"801","Crew","Male","Adult","No"
"802","Crew","Male","Adult","No"
"803","Crew","Male","Adult","No"
"804","Crew","Male","Adult","No"
"805","Crew","Male","Adult","No"
"806","Crew","Male","Adult","No"
"807","Crew","Male","Adult","No"
"808","Crew","Male","Adult","No"
"809","Crew","Male","Adult","No"
"810","Crew","Male","Adult","No"
"811","Crew","Male","Adult","No"
"812","Crew","Male","Adult","No"
"813","Crew","Male","Adult","No"
"814","Crew","Male","Adult","No"
"815","Crew","Male","Adult","No"
"816","Crew","Male","Adult","No"
"817","Crew","Male","Adult","No"
"818","Crew","Male","Adult","No"
"819","Crew","Male","Adult","No"
"820","Crew","Male","Adult","No"
"821","Crew","Male","Adult","No"
"822","Crew","Male","Adult","No"
"823","Crew","Male","Adult","No"
"824","Crew","Male","Adult","No"
"825","Crew","Male","Adult","No"
"826","Crew","Male","Adult","No"
"827","Crew","Male","Adult","No"
"828","Crew","Male","Adult","No"
"829","Crew","Male","Adult","No"
"830","Crew","Male","Adult","No"
"831","Crew","Male","Adult","No"
"832","Crew","Male","Adult","No"
"833","Crew","Male","Adult","No"
"834","Crew","Male","Adult","No"
"835","Crew","Male","Adult","No"
"836","Crew","Male","Adult","No"
"837","Crew","Male","Adult","No"
"838","Crew","Male","Adult","No"
"839","Crew","Male","Adult","No"
"840","Crew","Male","Adult","No"
"841","Crew","Male","Adult","No"
"842","Crew","Male","Adult","No"
"843","Crew","Male","Adult","No"
"844","Crew","Male","Adult","No"
"845","Crew","Male","Adult","No"
"846","Crew","Male","Adult","No"
"847","Crew","Male","Adult","No"
"848","Crew","Male","Adult","No"
"849","Crew","Male","Adult","No"
"850","Crew","Male","Adult","No"
"851","Crew","Male","Adult","No"
"852","Crew","Male","Adult","No"
"853","Crew","Male","Adult","No"
"854","Crew","Male","Adult","No"
"855","Crew","Male","Adult","No"
"856","Crew","Male","Adult","No"
"857","Crew","Male","Adult","No"
"858","Crew","Male","Adult","No"
"859","Crew","Male","Adult","No"
"860","Crew","Male","Adult","No"
"861","Crew","Male","Adult","No"
"862","Crew","Male","Adult","No"
"863","Crew","Male","Adult","No"
"864","Crew","Male","Adult","No"
"865","Crew","Male","Adult","No"
"866","Crew","Male","Adult","No"
"867","Crew","Male","Adult","No"
"868","Crew","Male","Adult","No"
"869","Crew","Male","Adult","No"
"870","Crew","Male","Adult","No"
"871","Crew","Male","Adult","No"
"872","Crew","Male","Adult","No"
"873","Crew","Male","Adult","No"
"874","Crew","Male","Adult","No"
"875","Crew","Male","Adult","No"
"876","Crew","Male","Adult","No"
"877","Crew","Male","Adult","No"
"878","Crew","Male","Adult","No"
"879","Crew","Male","Adult","No"
"880","Crew","Male","Adult","No"
"881","Crew","Male","Adult","No"
"882","Crew","Male","Adult","No"
"883","Crew","Male","Adult","No"
"884","Crew","Male","Adult","No"
"885","Crew","Male","Adult","No"
"886","Crew","Male","Adult","No"
"887","Crew","Male","Adult","No"
"888","Crew","Male","Adult","No"
"889","Crew","Male","Adult","No"
"890","Crew","Male","Adult","No"
"891","Crew","Male","Adult","No"
"892","Crew","Male","Adult","No"
"893","Crew","Male","Adult","No"
"894","Crew","Male","Adult","No"
"895","Crew","Male","Adult","No"
"896","Crew","Male","Adult","No"
"897","Crew","Male","Adult","No"
"898","Crew","Male","Adult","No"
"899","Crew","Male","Adult","No"
"900","Crew","Male","Adult","No"
"901","Crew","Male","Adult","No"
"902","Crew","Male","Adult","No"
"903","Crew","Male","Adult","No"
"904","Crew","Male","Adult","No"
"905","Crew","Male","Adult","No"
"906","Crew","Male","Adult","No"
"907","Crew","Male","Adult","No"
"908","Crew","Male","Adult","No"
"909","Crew","Male","Adult","No"
"910","Crew","Male","Adult","No"
"911","Crew","Male","Adult","No"
"912","Crew","Male","Adult","No"
"913","Crew","Male","Adult","No"
"914","Crew","Male","Adult","No"
"915","Crew","Male","Adult","No"
"916","Crew","Male","Adult","No"
"917","Crew","Male","Adult","No"
"918","Crew","Male","Adult","No"
"919","Crew","Male","Adult","No"
"920","Crew","Male","Adult","No"
"921","Crew","Male","Adult","No"
"922","Crew","Male","Adult","No"
"923","Crew","Male","Adult","No"
"924","Crew","Male","Adult","No"
"925","Crew","Male","Adult","No"
"926","Crew","Male","Adult","No"
"927","Crew","Male","Adult","No"
"928","Crew","Male","Adult","No"
"929","Crew","Male","Adult","No"
"930","Crew","Male","Adult","No"
"931","Crew","Male","Adult","No"
"932","Crew","Male","Adult","No"
"933","Crew","Male","Adult","No"
"934","Crew","Male","Adult","No"
"935","Crew","Male","Adult","No"
"936","Crew","Male","Adult","No"
"937","Crew","Male","Adult","No"
"938","Crew","Male","Adult","No"
"939","Crew","Male","Adult","No"
"940","Crew","Male","Adult","No"
"941","Crew","Male","Adult","No"
"942","Crew","Male","Adult","No"
"943","Crew","Male","Adult","No"
"944","Crew","Male","Adult","No"
"945","Crew","Male","Adult","No"
"946","Crew","Male","Adult","No"
"947","Crew","Male","Adult","No"
"948","Crew","Male","Adult","No"
"949","Crew","Male","Adult","No"
"950","Crew","Male","Adult","No"
"951","Crew","Male","Adult","No"
"952","Crew","Male","Adult","No"
"953","Crew","Male","Adult","No"
"954","Crew","Male","Adult","No"
"955","Crew","Male","Adult","No"
"956","Crew","Male","Adult","No"
"957","Crew","Male","Adult","No"
"958","Crew","Male","Adult","No"
"959","Crew","Male","Adult","No"
"960","Crew","Male","Adult","No"
"961","Crew","Male","Adult","No"
"962","Crew","Male","Adult","No"
"963","Crew","Male","Adult","No"
"964","Crew","Male","Adult","No"
"965","Crew","Male","Adult","No"
"966","Crew","Male","Adult","No"
"967","Crew","Male","Adult","No"
"968","Crew","Male","Adult","No"
"969","Crew","Male","Adult","No"
"970","Crew","Male","Adult","No"
"971","Crew","Male","Adult","No"
"972","Crew","Male","Adult","No"
"973","Crew","Male","Adult","No"
"974","Crew","Male","Adult","No"
"975","Crew","Male","Adult","No"
"976","Crew","Male","Adult","No"
"977","Crew","Male","Adult","No"
"978","Crew","Male","Adult","No"
"979","Crew","Male","Adult","No"
"980","Crew","Male","Adult","No"
"981","Crew","Male","Adult","No"
"982","Crew","Male","Adult","No"
"983","Crew","Male","Adult","No"
"984","Crew","Male","Adult","No"
"985","Crew","Male","Adult","No"
"986","Crew","Male","Adult","No"
"987","Crew","Male","Adult","No"
"988","Crew","Male","Adult","No"
"989","Crew","Male","Adult","No"
"990","Crew","Male","Adult","No"
"991","Crew","Male","Adult","No"
"992","Crew","Male","Adult","No"
"993","Crew","Male","Adult","No"
"994","Crew","Male","Adult","No"
"995","Crew","Male","Adult","No"
"996","Crew","Male","Adult","No"
"997","Crew","Male","Adult","No"
"998","Crew","Male","Adult","No"
"999","Crew","Male","Adult","No"
"1000","Crew","Male","Adult","No"
"1001","Crew","Male","Adult","No"
"1002","Crew","Male","Adult","No"
"1003","Crew","Male","Adult","No"
"1004","Crew","Male","Adult","No"
"1005","Crew","Male","Adult","No"
"1006","Crew","Male","Adult","No"
"1007","Crew","Male","Adult","No"
"1008","Crew","Male","Adult","No"
"1009","Crew","Male","Adult","No"
"1010","Crew","Male","Adult","No"
"1011","Crew","Male","Adult","No"
"1012","Crew","Male","Adult","No"
"1013","Crew","Male","Adult","No"
"1014","Crew","Male","Adult","No"
"1015","Crew","Male","Adult","No"
"1016","Crew","Male","Adult","No"
"1017","Crew","Male","Adult","No"
"1018","Crew","Male","Adult","No"
"1019","Crew","Male","Adult","No"
"1020","Crew","Male","Adult","No"
"1021","Crew","Male","Adult","No"
"1022","Crew","Male","Adult","No"
"1023","Crew","Male","Adult","No"
"1024","Crew","Male","Adult","No"
"1025","Crew","Male","Adult","No"
"1026","Crew","Male","Adult","No"
"1027","Crew","Male","Adult","No"
"1028","Crew","Male","Adult","No"
"1029","Crew","Male","Adult","No"
"1030","Crew","Male","Adult","No"
"1031","Crew","Male","Adult","No"
"1032","Crew","Male","Adult","No"
"1033","Crew","Male","Adult","No"
"1034","Crew","Male","Adult","No"
"1035","Crew","Male","Adult","No"
"1036","Crew","Male","Adult","No"
"1037","Crew","Male","Adult","No"
"1038","Crew","Male","Adult","No"
"1039","Crew","Male","Adult","No"
"1040","Crew","Male","Adult","No"
"1041","Crew","Male","Adult","No"
"1042","Crew","Male","Adult","No"
"1043","Crew","Male","Adult","No"
"1044","Crew","Male","Adult","No"
"1045","Crew","Male","Adult","No"
"1046","Crew","Male","Adult","No"
"1047","Crew","Male","Adult","No"
"1048","Crew","Male","Adult","No"
"1049","Crew","Male","Adult","No"
"1050","Crew","Male","Adult","No"
"1051","Crew","Male","Adult","No"
"1052","Crew","Male","Adult","No"
"1053","Crew","Male","Adult","No"
"1054","Crew","Male","Adult","No"
"1055","Crew","Male","Adult","No"
"1056","Crew","Male","Adult","No"
"1057","Crew","Male","Adult","No"
"1058","Crew","Male","Adult","No"
"1059","Crew","Male","Adult","No"
"1060","Crew","Male","Adult","No"
"1061","Crew","Male","Adult","No"
"1062","Crew","Male","Adult","No"
"1063","Crew","Male","Adult","No"
"1064","Crew","Male","Adult","No"
"1065","Crew","Male","Adult","No"
"1066","Crew","Male","Adult","No"
"1067","Crew","Male","Adult","No"
"1068","Crew","Male","Adult","No"
"1069","Crew","Male","Adult","No"
"1070","Crew","Male","Adult","No"
"1071","Crew","Male","Adult","No"
"1072","Crew","Male","Adult","No"
"1073","Crew","Male","Adult","No"
"1074","Crew","Male","Adult","No"
"1075","Crew","Male","Adult","No"
"1076","Crew","Male","Adult","No"
"1077","Crew","Male","Adult","No"
"1078","Crew","Male","Adult","No"
"1079","Crew","Male","Adult","No"
"1080","Crew","Male","Adult","No"
"1081","Crew","Male","Adult","No"
"1082","Crew","Male","Adult","No"
"1083","Crew","Male","Adult","No"
"1084","Crew","Male","Adult","No"
"1085","Crew","Male","Adult","No"
"1086","Crew","Male","Adult","No"
"1087","Crew","Male","Adult","No"
"1088","Crew","Male","Adult","No"
"1089","Crew","Male","Adult","No"
"1090","Crew","Male","Adult","No"
"1091","Crew","Male","Adult","No"
"1092","Crew","Male","Adult","No"
"1093","Crew","Male","Adult","No"
"1094","Crew","Male","Adult","No"
"1095","Crew","Male","Adult","No"
"1096","Crew","Male","Adult","No"
"1097","Crew","Male","Adult","No"
"1098","Crew","Male","Adult","No"
"1099","Crew","Male","Adult","No"
"1100","Crew","Male","Adult","No"
"1101","Crew","Male","Adult","No"
"1102","Crew","Male","Adult","No"
"1103","Crew","Male","Adult","No"
"1104","Crew","Male","Adult","No"
"1105","Crew","Male","Adult","No"
"1106","Crew","Male","Adult","No"
"1107","Crew","Male","Adult","No"
"1108","Crew","Male","Adult","No"
"1109","Crew","Male","Adult","No"
"1110","Crew","Male","Adult","No"
"1111","Crew","Male","Adult","No"
"1112","Crew","Male","Adult","No"
"1113","Crew","Male","Adult","No"
"1114","Crew","Male","Adult","No"
"1115","Crew","Male","Adult","No"
"1116","Crew","Male","Adult","No"
"1117","Crew","Male","Adult","No"
"1118","Crew","Male","Adult","No"
"1119","Crew","Male","Adult","No"
"1120","Crew","Male","Adult","No"
"1121","Crew","Male","Adult","No"
"1122","Crew","Male","Adult","No"
"1123","Crew","Male","Adult","No"
"1124","Crew","Male","Adult","No"
"1125","Crew","Male","Adult","No"
"1126","Crew","Male","Adult","No"
"1127","Crew","Male","Adult","No"
"1128","Crew","Male","Adult","No"
"1129","Crew","Male","Adult","No"
"1130","Crew","Male","Adult","No"
"1131","Crew","Male","Adult","No"
"1132","Crew","Male","Adult","No"
"1133","Crew","Male","Adult","No"
"1134","Crew","Male","Adult","No"
"1135","Crew","Male","Adult","No"
"1136","Crew","Male","Adult","No"
"1137","Crew","Male","Adult","No"
"1138","Crew","Male","Adult","No"
"1139","Crew","Male","Adult","No"
"1140","Crew","Male","Adult","No"
"1141","Crew","Male","Adult","No"
"1142","Crew","Male","Adult","No"
"1143","Crew","Male","Adult","No"
"1144","Crew","Male","Adult","No"
"1145","Crew","Male","Adult","No"
"1146","Crew","Male","Adult","No"
"1147","Crew","Male","Adult","No"
"1148","Crew","Male","Adult","No"
"1149","Crew","Male","Adult","No"
"1150","Crew","Male","Adult","No"
"1151","Crew","Male","Adult","No"
"1152","Crew","Male","Adult","No"
"1153","Crew","Male","Adult","No"
"1154","Crew","Male","Adult","No"
"1155","Crew","Male","Adult","No"
"1156","Crew","Male","Adult","No"
"1157","Crew","Male","Adult","No"
"1158","Crew","Male","Adult","No"
"1159","Crew","Male","Adult","No"
"1160","Crew","Male","Adult","No"
"1161","Crew","Male","Adult","No"
"1162","Crew","Male","Adult","No"
"1163","Crew","Male","Adult","No"
"1164","Crew","Male","Adult","No"
"1165","Crew","Male","Adult","No"
"1166","Crew","Male","Adult","No"
"1167","Crew","Male","Adult","No"
"1168","Crew","Male","Adult","No"
"1169","Crew","Male","Adult","No"
"1170","Crew","Male","Adult","No"
"1171","Crew","Male","Adult","No"
"1172","Crew","Male","Adult","No"
"1173","Crew","Male","Adult","No"
"1174","Crew","Male","Adult","No"
"1175","Crew","Male","Adult","No"
"1176","Crew","Male","Adult","No"
"1177","Crew","Male","Adult","No"
"1178","Crew","Male","Adult","No"
"1179","Crew","Male","Adult","No"
"1180","Crew","Male","Adult","No"
"1181","Crew","Male","Adult","No"
"1182","Crew","Male","Adult","No"
"1183","Crew","Male","Adult","No"
"1184","Crew","Male","Adult","No"
"1185","Crew","Male","Adult","No"
"1186","Crew","Male","Adult","No"
"1187","Crew","Male","Adult","No"
"1188","Crew","Male","Adult","No"
"1189","Crew","Male","Adult","No"
"1190","Crew","Male","Adult","No"
"1191","Crew","Male","Adult","No"
"1192","Crew","Male","Adult","No"
"1193","Crew","Male","Adult","No"
"1194","Crew","Male","Adult","No"
"1195","Crew","Male","Adult","No"
"1196","Crew","Male","Adult","No"
"1197","Crew","Male","Adult","No"
"1198","Crew","Male","Adult","No"
"1199","Crew","Male","Adult","No"
"1200","Crew","Male","Adult","No"
"1201","Crew","Male","Adult","No"
"1202","Crew","Male","Adult","No"
"1203","Crew","Male","Adult","No"
"1204","Crew","Male","Adult","No"
"1205","Crew","Male","Adult","No"
"1206","Crew","Male","Adult","No"
"1207","Crew","Male","Adult","No"
"1208","Crew","Male","Adult","No"
"1209","Crew","Male","Adult","No"
"1210","Crew","Male","Adult","No"
"1211","Crew","Male","Adult","No"
"1212","Crew","Male","Adult","No"
"1213","Crew","Male","Adult","No"
"1214","Crew","Male","Adult","No"
"1215","Crew","Male","Adult","No"
"1216","Crew","Male","Adult","No"
"1217","Crew","Male","Adult","No"
"1218","Crew","Male","Adult","No"
"1219","Crew","Male","Adult","No"
"1220","Crew","Male","Adult","No"
"1221","Crew","Male","Adult","No"
"1222","Crew","Male","Adult","No"
"1223","Crew","Male","Adult","No"
"1224","Crew","Male","Adult","No"
"1225","Crew","Male","Adult","No"
"1226","Crew","Male","Adult","No"
"1227","Crew","Male","Adult","No"
"1228","Crew","Male","Adult","No"
"1229","Crew","Male","Adult","No"
"1230","Crew","Male","Adult","No"
"1231","Crew","Male","Adult","No"
"1232","Crew","Male","Adult","No"
"1233","Crew","Male","Adult","No"
"1234","Crew","Male","Adult","No"
"1235","Crew","Male","Adult","No"
"1236","Crew","Male","Adult","No"
"1237","Crew","Male","Adult","No"
"1238","Crew","Male","Adult","No"
"1239","Crew","Male","Adult","No"
"1240","Crew","Male","Adult","No"
"1241","Crew","Male","Adult","No"
"1242","Crew","Male","Adult","No"
"1243","Crew","Male","Adult","No"
"1244","Crew","Male","Adult","No"
"1245","Crew","Male","Adult","No"
"1246","Crew","Male","Adult","No"
"1247","Crew","Male","Adult","No"
"1248","Crew","Male","Adult","No"
"1249","Crew","Male","Adult","No"
"1250","Crew","Male","Adult","No"
"1251","Crew","Male","Adult","No"
"1252","Crew","Male","Adult","No"
"1253","Crew","Male","Adult","No"
"1254","Crew","Male","Adult","No"
"1255","Crew","Male","Adult","No"
"1256","Crew","Male","Adult","No"
"1257","Crew","Male","Adult","No"
"1258","Crew","Male","Adult","No"
"1259","Crew","Male","Adult","No"
"1260","Crew","Male","Adult","No"
"1261","Crew","Male","Adult","No"
"1262","Crew","Male","Adult","No"
"1263","Crew","Male","Adult","No"
"1264","Crew","Male","Adult","No"
"1265","Crew","Male","Adult","No"
"1266","Crew","Male","Adult","No"
"1267","Crew","Male","Adult","No"
"1268","Crew","Male","Adult","No"
"1269","Crew","Male","Adult","No"
"1270","Crew","Male","Adult","No"
"1271","Crew","Male","Adult","No"
"1272","Crew","Male","Adult","No"
"1273","Crew","Male","Adult","No"
"1274","Crew","Male","Adult","No"
"1275","Crew","Male","Adult","No"
"1276","Crew","Male","Adult","No"
"1277","Crew","Male","Adult","No"
"1278","Crew","Male","Adult","No"
"1279","Crew","Male","Adult","No"
"1280","Crew","Male","Adult","No"
"1281","Crew","Male","Adult","No"
"1282","Crew","Male","Adult","No"
"1283","Crew","Male","Adult","No"
"1284","Crew","Male","Adult","No"
"1285","Crew","Male","Adult","No"
"1286","Crew","Male","Adult","No"
"1287","Crew","Male","Adult","No"
"1288","Crew","Male","Adult","No"
"1289","Crew","Male","Adult","No"
"1290","Crew","Male","Adult","No"
"1291","Crew","Male","Adult","No"
"1292","Crew","Male","Adult","No"
"1293","Crew","Male","Adult","No"
"1294","Crew","Male","Adult","No"
"1295","Crew","Male","Adult","No"
"1296","Crew","Male","Adult","No"
"1297","Crew","Male","Adult","No"
"1298","Crew","Male","Adult","No"
"1299","Crew","Male","Adult","No"
"1300","Crew","Male","Adult","No"
"1301","Crew","Male","Adult","No"
"1302","Crew","Male","Adult","No"
"1303","Crew","Male","Adult","No"
"1304","Crew","Male","Adult","No"
"1305","Crew","Male","Adult","No"
"1306","Crew","Male","Adult","No"
"1307","Crew","Male","Adult","No"
"1308","Crew","Male","Adult","No"
"1309","Crew","Male","Adult","No"
"1310","Crew","Male","Adult","No"
"1311","Crew","Male","Adult","No"
"1312","Crew","Male","Adult","No"
"1313","Crew","Male","Adult","No"
"1314","Crew","Male","Adult","No"
"1315","Crew","Male","Adult","No"
"1316","Crew","Male","Adult","No"
"1317","Crew","Male","Adult","No"
"1318","Crew","Male","Adult","No"
"1319","Crew","Male","Adult","No"
"1320","Crew","Male","Adult","No"
"1321","Crew","Male","Adult","No"
"1322","Crew","Male","Adult","No"
"1323","Crew","Male","Adult","No"
"1324","Crew","Male","Adult","No"
"1325","Crew","Male","Adult","No"
"1326","Crew","Male","Adult","No"
"1327","Crew","Male","Adult","No"
"1328","Crew","Male","Adult","No"
"1329","Crew","Male","Adult","No"
"1330","Crew","Male","Adult","No"
"1331","Crew","Male","Adult","No"
"1332","Crew","Male","Adult","No"
"1333","Crew","Male","Adult","No"
"1334","Crew","Male","Adult","No"
"1335","Crew","Male","Adult","No"
"1336","Crew","Male","Adult","No"
"1337","Crew","Male","Adult","No"
"1338","Crew","Male","Adult","No"
"1339","Crew","Male","Adult","No"
"1340","Crew","Male","Adult","No"
"1341","Crew","Male","Adult","No"
"1342","Crew","Male","Adult","No"
"1343","Crew","Male","Adult","No"
"1344","Crew","Male","Adult","No"
"1345","Crew","Male","Adult","No"
"1346","Crew","Male","Adult","No"
"1347","Crew","Male","Adult","No"
"1348","Crew","Male","Adult","No"
"1349","Crew","Male","Adult","No"
"1350","Crew","Male","Adult","No"
"1351","Crew","Male","Adult","No"
"1352","Crew","Male","Adult","No"
"1353","Crew","Male","Adult","No"
"1354","Crew","Male","Adult","No"
"1355","Crew","Male","Adult","No"
"1356","Crew","Male","Adult","No"
"1357","Crew","Male","Adult","No"
"1358","Crew","Male","Adult","No"
"1359","Crew","Male","Adult","No"
"1360","Crew","Male","Adult","No"
"1361","Crew","Male","Adult","No"
"1362","Crew","Male","Adult","No"
"1363","Crew","Male","Adult","No"
"1364","Crew","Male","Adult","No"
"1365","Crew","Male","Adult","No"
"1366","Crew","Male","Adult","No"
"1367","Crew","Male","Adult","No"
"1368","Crew","Male","Adult","No"
"1369","Crew","Male","Adult","No"
"1370","Crew","Male","Adult","No"
"1371","Crew","Male","Adult","No"
"1372","Crew","Male","Adult","No"
"1373","Crew","Male","Adult","No"
"1374","Crew","Male","Adult","No"
"1375","Crew","Male","Adult","No"
"1376","Crew","Male","Adult","No"
"1377","Crew","Male","Adult","No"
"1378","Crew","Male","Adult","No"
"1379","Crew","Male","Adult","No"
"1380","Crew","Male","Adult","No"
"1381","Crew","Male","Adult","No"
"1382","1st","Female","Adult","No"
"1383","1st","Female","Adult","No"
"1384","1st","Female","Adult","No"
"1385","1st","Female","Adult","No"
"1386","2nd","Female","Adult","No"
"1387","2nd","Female","Adult","No"
"1388","2nd","Female","Adult","No"
"1389","2nd","Female","Adult","No"
"1390","2nd","Female","Adult","No"
"1391","2nd","Female","Adult","No"
"1392","2nd","Female","Adult","No"
"1393","2nd","Female","Adult","No"
"1394","2nd","Female","Adult","No"
"1395","2nd","Female","Adult","No"
"1396","2nd","Female","Adult","No"
"1397","2nd","Female","Adult","No"
"1398","2nd","Female","Adult","No"
"1399","3rd","Female","Adult","No"
"1400","3rd","Female","Adult","No"
"1401","3rd","Female","Adult","No"
"1402","3rd","Female","Adult","No"
"1403","3rd","Female","Adult","No"
"1404","3rd","Female","Adult","No"
"1405","3rd","Female","Adult","No"
"1406","3rd","Female","Adult","No"
"1407","3rd","Female","Adult","No"
"1408","3rd","Female","Adult","No"
"1409","3rd","Female","Adult","No"
"1410","3rd","Female","Adult","No"
"1411","3rd","Female","Adult","No"
"1412","3rd","Female","Adult","No"
"1413","3rd","Female","Adult","No"
"1414","3rd","Female","Adult","No"
"1415","3rd","Female","Adult","No"
"1416","3rd","Female","Adult","No"
"1417","3rd","Female","Adult","No"
"1418","3rd","Female","Adult","No"
"1419","3rd","Female","Adult","No"
"1420","3rd","Female","Adult","No"
"1421","3rd","Female","Adult","No"
"1422","3rd","Female","Adult","No"
"1423","3rd","Female","Adult","No"
"1424","3rd","Female","Adult","No"
"1425","3rd","Female","Adult","No"
"1426","3rd","Female","Adult","No"
"1427","3rd","Female","Adult","No"
"1428","3rd","Female","Adult","No"
"1429","3rd","Female","Adult","No"
"1430","3rd","Female","Adult","No"
"1431","3rd","Female","Adult","No"
"1432","3rd","Female","Adult","No"
"1433","3rd","Female","Adult","No"
"1434","3rd","Female","Adult","No"
"1435","3rd","Female","Adult","No"
"1436","3rd","Female","Adult","No"
"1437","3rd","Female","Adult","No"
"1438","3rd","Female","Adult","No"
"1439","3rd","Female","Adult","No"
"1440","3rd","Female","Adult","No"
"1441","3rd","Female","Adult","No"
"1442","3rd","Female","Adult","No"
"1443","3rd","Female","Adult","No"
"1444","3rd","Female","Adult","No"
"1445","3rd","Female","Adult","No"
"1446","3rd","Female","Adult","No"
"1447","3rd","Female","Adult","No"
"1448","3rd","Female","Adult","No"
"1449","3rd","Female","Adult","No"
"1450","3rd","Female","Adult","No"
"1451","3rd","Female","Adult","No"
"1452","3rd","Female","Adult","No"
"1453","3rd","Female","Adult","No"
"1454","3rd","Female","Adult","No"
"1455","3rd","Female","Adult","No"
"1456","3rd","Female","Adult","No"
"1457","3rd","Female","Adult","No"
"1458","3rd","Female","Adult","No"
"1459","3rd","Female","Adult","No"
"1460","3rd","Female","Adult","No"
"1461","3rd","Female","Adult","No"
"1462","3rd","Female","Adult","No"
"1463","3rd","Female","Adult","No"
"1464","3rd","Female","Adult","No"
"1465","3rd","Female","Adult","No"
"1466","3rd","Female","Adult","No"
"1467","3rd","Female","Adult","No"
"1468","3rd","Female","Adult","No"
"1469","3rd","Female","Adult","No"
"1470","3rd","Female","Adult","No"
"1471","3rd","Female","Adult","No"
"1472","3rd","Female","Adult","No"
"1473","3rd","Female","Adult","No"
"1474","3rd","Female","Adult","No"
"1475","3rd","Female","Adult","No"
"1476","3rd","Female","Adult","No"
"1477","3rd","Female","Adult","No"
"1478","3rd","Female","Adult","No"
"1479","3rd","Female","Adult","No"
"1480","3rd","Female","Adult","No"
"1481","3rd","Female","Adult","No"
"1482","3rd","Female","Adult","No"
"1483","3rd","Female","Adult","No"
"1484","3rd","Female","Adult","No"
"1485","3rd","Female","Adult","No"
"1486","3rd","Female","Adult","No"
"1487","3rd","Female","Adult","No"
"1488","Crew","Female","Adult","No"
"1489","Crew","Female","Adult","No"
"1490","Crew","Female","Adult","No"
"1491","1st","Male","Child","Yes"
"1492","1st","Male","Child","Yes"
"1493","1st","Male","Child","Yes"
"1494","1st","Male","Child","Yes"
"1495","1st","Male","Child","Yes"
"1496","2nd","Male","Child","Yes"
"1497","2nd","Male","Child","Yes"
"1498","2nd","Male","Child","Yes"
"1499","2nd","Male","Child","Yes"
"1500","2nd","Male","Child","Yes"
"1501","2nd","Male","Child","Yes"
"1502","2nd","Male","Child","Yes"
"1503","2nd","Male","Child","Yes"
"1504","2nd","Male","Child","Yes"
"1505","2nd","Male","Child","Yes"
"1506","2nd","Male","Child","Yes"
"1507","3rd","Male","Child","Yes"
"1508","3rd","Male","Child","Yes"
"1509","3rd","Male","Child","Yes"
"1510","3rd","Male","Child","Yes"
"1511","3rd","Male","Child","Yes"
"1512","3rd","Male","Child","Yes"
"1513","3rd","Male","Child","Yes"
"1514","3rd","Male","Child","Yes"
"1515","3rd","Male","Child","Yes"
"1516","3rd","Male","Child","Yes"
"1517","3rd","Male","Child","Yes"
"1518","3rd","Male","Child","Yes"
"1519","3rd","Male","Child","Yes"
"1520","1st","Female","Child","Yes"
"1521","2nd","Female","Child","Yes"
"1522","2nd","Female","Child","Yes"
"1523","2nd","Female","Child","Yes"
"1524","2nd","Female","Child","Yes"
"1525","2nd","Female","Child","Yes"
"1526","2nd","Female","Child","Yes"
"1527","2nd","Female","Child","Yes"
"1528","2nd","Female","Child","Yes"
"1529","2nd","Female","Child","Yes"
"1530","2nd","Female","Child","Yes"
"1531","2nd","Female","Child","Yes"
"1532","2nd","Female","Child","Yes"
"1533","2nd","Female","Child","Yes"
"1534","3rd","Female","Child","Yes"
"1535","3rd","Female","Child","Yes"
"1536","3rd","Female","Child","Yes"
"1537","3rd","Female","Child","Yes"
"1538","3rd","Female","Child","Yes"
"1539","3rd","Female","Child","Yes"
"1540","3rd","Female","Child","Yes"
"1541","3rd","Female","Child","Yes"
"1542","3rd","Female","Child","Yes"
"1543","3rd","Female","Child","Yes"
"1544","3rd","Female","Child","Yes"
"1545","3rd","Female","Child","Yes"
"1546","3rd","Female","Child","Yes"
"1547","3rd","Female","Child","Yes"
"1548","1st","Male","Adult","Yes"
"1549","1st","Male","Adult","Yes"
"1550","1st","Male","Adult","Yes"
"1551","1st","Male","Adult","Yes"
"1552","1st","Male","Adult","Yes"
"1553","1st","Male","Adult","Yes"
"1554","1st","Male","Adult","Yes"
"1555","1st","Male","Adult","Yes"
"1556","1st","Male","Adult","Yes"
"1557","1st","Male","Adult","Yes"
"1558","1st","Male","Adult","Yes"
"1559","1st","Male","Adult","Yes"
"1560","1st","Male","Adult","Yes"
"1561","1st","Male","Adult","Yes"
"1562","1st","Male","Adult","Yes"
"1563","1st","Male","Adult","Yes"
"1564","1st","Male","Adult","Yes"
"1565","1st","Male","Adult","Yes"
"1566","1st","Male","Adult","Yes"
"1567","1st","Male","Adult","Yes"
"1568","1st","Male","Adult","Yes"
"1569","1st","Male","Adult","Yes"
"1570","1st","Male","Adult","Yes"
"1571","1st","Male","Adult","Yes"
"1572","1st","Male","Adult","Yes"
"1573","1st","Male","Adult","Yes"
"1574","1st","Male","Adult","Yes"
"1575","1st","Male","Adult","Yes"
"1576","1st","Male","Adult","Yes"
"1577","1st","Male","Adult","Yes"
"1578","1st","Male","Adult","Yes"
"1579","1st","Male","Adult","Yes"
"1580","1st","Male","Adult","Yes"
"1581","1st","Male","Adult","Yes"
"1582","1st","Male","Adult","Yes"
"1583","1st","Male","Adult","Yes"
"1584","1st","Male","Adult","Yes"
"1585","1st","Male","Adult","Yes"
"1586","1st","Male","Adult","Yes"
"1587","1st","Male","Adult","Yes"
"1588","1st","Male","Adult","Yes"
"1589","1st","Male","Adult","Yes"
"1590","1st","Male","Adult","Yes"
"1591","1st","Male","Adult","Yes"
"1592","1st","Male","Adult","Yes"
"1593","1st","Male","Adult","Yes"
"1594","1st","Male","Adult","Yes"
"1595","1st","Male","Adult","Yes"
"1596","1st","Male","Adult","Yes"
"1597","1st","Male","Adult","Yes"
"1598","1st","Male","Adult","Yes"
"1599","1st","Male","Adult","Yes"
"1600","1st","Male","Adult","Yes"
"1601","1st","Male","Adult","Yes"
"1602","1st","Male","Adult","Yes"
"1603","1st","Male","Adult","Yes"
"1604","1st","Male","Adult","Yes"
"1605","2nd","Male","Adult","Yes"
"1606","2nd","Male","Adult","Yes"
"1607","2nd","Male","Adult","Yes"
"1608","2nd","Male","Adult","Yes"
"1609","2nd","Male","Adult","Yes"
"1610","2nd","Male","Adult","Yes"
"1611","2nd","Male","Adult","Yes"
"1612","2nd","Male","Adult","Yes"
"1613","2nd","Male","Adult","Yes"
"1614","2nd","Male","Adult","Yes"
"1615","2nd","Male","Adult","Yes"
"1616","2nd","Male","Adult","Yes"
"1617","2nd","Male","Adult","Yes"
"1618","2nd","Male","Adult","Yes"
"1619","3rd","Male","Adult","Yes"
"1620","3rd","Male","Adult","Yes"
"1621","3rd","Male","Adult","Yes"
"1622","3rd","Male","Adult","Yes"
"1623","3rd","Male","Adult","Yes"
"1624","3rd","Male","Adult","Yes"
"1625","3rd","Male","Adult","Yes"
"1626","3rd","Male","Adult","Yes"
"1627","3rd","Male","Adult","Yes"
"1628","3rd","Male","Adult","Yes"
"1629","3rd","Male","Adult","Yes"
"1630","3rd","Male","Adult","Yes"
"1631","3rd","Male","Adult","Yes"
"1632","3rd","Male","Adult","Yes"
"1633","3rd","Male","Adult","Yes"
"1634","3rd","Male","Adult","Yes"
"1635","3rd","Male","Adult","Yes"
"1636","3rd","Male","Adult","Yes"
"1637","3rd","Male","Adult","Yes"
"1638","3rd","Male","Adult","Yes"
"1639","3rd","Male","Adult","Yes"
"1640","3rd","Male","Adult","Yes"
"1641","3rd","Male","Adult","Yes"
"1642","3rd","Male","Adult","Yes"
"1643","3rd","Male","Adult","Yes"
"1644","3rd","Male","Adult","Yes"
"1645","3rd","Male","Adult","Yes"
"1646","3rd","Male","Adult","Yes"
"1647","3rd","Male","Adult","Yes"
"1648","3rd","Male","Adult","Yes"
"1649","3rd","Male","Adult","Yes"
"1650","3rd","Male","Adult","Yes"
"1651","3rd","Male","Adult","Yes"
"1652","3rd","Male","Adult","Yes"
"1653","3rd","Male","Adult","Yes"
"1654","3rd","Male","Adult","Yes"
"1655","3rd","Male","Adult","Yes"
"1656","3rd","Male","Adult","Yes"
"1657","3rd","Male","Adult","Yes"
"1658","3rd","Male","Adult","Yes"
"1659","3rd","Male","Adult","Yes"
"1660","3rd","Male","Adult","Yes"
"1661","3rd","Male","Adult","Yes"
"1662","3rd","Male","Adult","Yes"
"1663","3rd","Male","Adult","Yes"
"1664","3rd","Male","Adult","Yes"
"1665","3rd","Male","Adult","Yes"
"1666","3rd","Male","Adult","Yes"
"1667","3rd","Male","Adult","Yes"
"1668","3rd","Male","Adult","Yes"
"1669","3rd","Male","Adult","Yes"
"1670","3rd","Male","Adult","Yes"
"1671","3rd","Male","Adult","Yes"
"1672","3rd","Male","Adult","Yes"
"1673","3rd","Male","Adult","Yes"
"1674","3rd","Male","Adult","Yes"
"1675","3rd","Male","Adult","Yes"
"1676","3rd","Male","Adult","Yes"
"1677","3rd","Male","Adult","Yes"
"1678","3rd","Male","Adult","Yes"
"1679","3rd","Male","Adult","Yes"
"1680","3rd","Male","Adult","Yes"
"1681","3rd","Male","Adult","Yes"
"1682","3rd","Male","Adult","Yes"
"1683","3rd","Male","Adult","Yes"
"1684","3rd","Male","Adult","Yes"
"1685","3rd","Male","Adult","Yes"
"1686","3rd","Male","Adult","Yes"
"1687","3rd","Male","Adult","Yes"
"1688","3rd","Male","Adult","Yes"
"1689","3rd","Male","Adult","Yes"
"1690","3rd","Male","Adult","Yes"
"1691","3rd","Male","Adult","Yes"
"1692","3rd","Male","Adult","Yes"
"1693","3rd","Male","Adult","Yes"
"1694","Crew","Male","Adult","Yes"
"1695","Crew","Male","Adult","Yes"
"1696","Crew","Male","Adult","Yes"
"1697","Crew","Male","Adult","Yes"
"1698","Crew","Male","Adult","Yes"
"1699","Crew","Male","Adult","Yes"
"1700","Crew","Male","Adult","Yes"
"1701","Crew","Male","Adult","Yes"
"1702","Crew","Male","Adult","Yes"
"1703","Crew","Male","Adult","Yes"
"1704","Crew","Male","Adult","Yes"
"1705","Crew","Male","Adult","Yes"
"1706","Crew","Male","Adult","Yes"
"1707","Crew","Male","Adult","Yes"
"1708","Crew","Male","Adult","Yes"
"1709","Crew","Male","Adult","Yes"
"1710","Crew","Male","Adult","Yes"
"1711","Crew","Male","Adult","Yes"
"1712","Crew","Male","Adult","Yes"
"1713","Crew","Male","Adult","Yes"
"1714","Crew","Male","Adult","Yes"
"1715","Crew","Male","Adult","Yes"
"1716","Crew","Male","Adult","Yes"
"1717","Crew","Male","Adult","Yes"
"1718","Crew","Male","Adult","Yes"
"1719","Crew","Male","Adult","Yes"
"1720","Crew","Male","Adult","Yes"
"1721","Crew","Male","Adult","Yes"
"1722","Crew","Male","Adult","Yes"
"1723","Crew","Male","Adult","Yes"
"1724","Crew","Male","Adult","Yes"
"1725","Crew","Male","Adult","Yes"
"1726","Crew","Male","Adult","Yes"
"1727","Crew","Male","Adult","Yes"
"1728","Crew","Male","Adult","Yes"
"1729","Crew","Male","Adult","Yes"
"1730","Crew","Male","Adult","Yes"
"1731","Crew","Male","Adult","Yes"
"1732","Crew","Male","Adult","Yes"
"1733","Crew","Male","Adult","Yes"
"1734","Crew","Male","Adult","Yes"
"1735","Crew","Male","Adult","Yes"
"1736","Crew","Male","Adult","Yes"
"1737","Crew","Male","Adult","Yes"
"1738","Crew","Male","Adult","Yes"
"1739","Crew","Male","Adult","Yes"
"1740","Crew","Male","Adult","Yes"
"1741","Crew","Male","Adult","Yes"
"1742","Crew","Male","Adult","Yes"
"1743","Crew","Male","Adult","Yes"
"1744","Crew","Male","Adult","Yes"
"1745","Crew","Male","Adult","Yes"
"1746","Crew","Male","Adult","Yes"
"1747","Crew","Male","Adult","Yes"
"1748","Crew","Male","Adult","Yes"
"1749","Crew","Male","Adult","Yes"
"1750","Crew","Male","Adult","Yes"
"1751","Crew","Male","Adult","Yes"
"1752","Crew","Male","Adult","Yes"
"1753","Crew","Male","Adult","Yes"
"1754","Crew","Male","Adult","Yes"
"1755","Crew","Male","Adult","Yes"
"1756","Crew","Male","Adult","Yes"
"1757","Crew","Male","Adult","Yes"
"1758","Crew","Male","Adult","Yes"
"1759","Crew","Male","Adult","Yes"
"1760","Crew","Male","Adult","Yes"
"1761","Crew","Male","Adult","Yes"
"1762","Crew","Male","Adult","Yes"
"1763","Crew","Male","Adult","Yes"
"1764","Crew","Male","Adult","Yes"
"1765","Crew","Male","Adult","Yes"
"1766","Crew","Male","Adult","Yes"
"1767","Crew","Male","Adult","Yes"
"1768","Crew","Male","Adult","Yes"
"1769","Crew","Male","Adult","Yes"
"1770","Crew","Male","Adult","Yes"
"1771","Crew","Male","Adult","Yes"
"1772","Crew","Male","Adult","Yes"
"1773","Crew","Male","Adult","Yes"
"1774","Crew","Male","Adult","Yes"
"1775","Crew","Male","Adult","Yes"
"1776","Crew","Male","Adult","Yes"
"1777","Crew","Male","Adult","Yes"
"1778","Crew","Male","Adult","Yes"
"1779","Crew","Male","Adult","Yes"
"1780","Crew","Male","Adult","Yes"
"1781","Crew","Male","Adult","Yes"
"1782","Crew","Male","Adult","Yes"
"1783","Crew","Male","Adult","Yes"
"1784","Crew","Male","Adult","Yes"
"1785","Crew","Male","Adult","Yes"
"1786","Crew","Male","Adult","Yes"
"1787","Crew","Male","Adult","Yes"
"1788","Crew","Male","Adult","Yes"
"1789","Crew","Male","Adult","Yes"
"1790","Crew","Male","Adult","Yes"
"1791","Crew","Male","Adult","Yes"
"1792","Crew","Male","Adult","Yes"
"1793","Crew","Male","Adult","Yes"
"1794","Crew","Male","Adult","Yes"
"1795","Crew","Male","Adult","Yes"
"1796","Crew","Male","Adult","Yes"
"1797","Crew","Male","Adult","Yes"
"1798","Crew","Male","Adult","Yes"
"1799","Crew","Male","Adult","Yes"
"1800","Crew","Male","Adult","Yes"
"1801","Crew","Male","Adult","Yes"
"1802","Crew","Male","Adult","Yes"
"1803","Crew","Male","Adult","Yes"
"1804","Crew","Male","Adult","Yes"
"1805","Crew","Male","Adult","Yes"
"1806","Crew","Male","Adult","Yes"
"1807","Crew","Male","Adult","Yes"
"1808","Crew","Male","Adult","Yes"
"1809","Crew","Male","Adult","Yes"
"1810","Crew","Male","Adult","Yes"
"1811","Crew","Male","Adult","Yes"
"1812","Crew","Male","Adult","Yes"
"1813","Crew","Male","Adult","Yes"
"1814","Crew","Male","Adult","Yes"
"1815","Crew","Male","Adult","Yes"
"1816","Crew","Male","Adult","Yes"
"1817","Crew","Male","Adult","Yes"
"1818","Crew","Male","Adult","Yes"
"1819","Crew","Male","Adult","Yes"
"1820","Crew","Male","Adult","Yes"
"1821","Crew","Male","Adult","Yes"
"1822","Crew","Male","Adult","Yes"
"1823","Crew","Male","Adult","Yes"
"1824","Crew","Male","Adult","Yes"
"1825","Crew","Male","Adult","Yes"
"1826","Crew","Male","Adult","Yes"
"1827","Crew","Male","Adult","Yes"
"1828","Crew","Male","Adult","Yes"
"1829","Crew","Male","Adult","Yes"
"1830","Crew","Male","Adult","Yes"
"1831","Crew","Male","Adult","Yes"
"1832","Crew","Male","Adult","Yes"
"1833","Crew","Male","Adult","Yes"
"1834","Crew","Male","Adult","Yes"
"1835","Crew","Male","Adult","Yes"
"1836","Crew","Male","Adult","Yes"
"1837","Crew","Male","Adult","Yes"
"1838","Crew","Male","Adult","Yes"
"1839","Crew","Male","Adult","Yes"
"1840","Crew","Male","Adult","Yes"
"1841","Crew","Male","Adult","Yes"
"1842","Crew","Male","Adult","Yes"
"1843","Crew","Male","Adult","Yes"
"1844","Crew","Male","Adult","Yes"
"1845","Crew","Male","Adult","Yes"
"1846","Crew","Male","Adult","Yes"
"1847","Crew","Male","Adult","Yes"
"1848","Crew","Male","Adult","Yes"
"1849","Crew","Male","Adult","Yes"
"1850","Crew","Male","Adult","Yes"
"1851","Crew","Male","Adult","Yes"
"1852","Crew","Male","Adult","Yes"
"1853","Crew","Male","Adult","Yes"
"1854","Crew","Male","Adult","Yes"
"1855","Crew","Male","Adult","Yes"
"1856","Crew","Male","Adult","Yes"
"1857","Crew","Male","Adult","Yes"
"1858","Crew","Male","Adult","Yes"
"1859","Crew","Male","Adult","Yes"
"1860","Crew","Male","Adult","Yes"
"1861","Crew","Male","Adult","Yes"
"1862","Crew","Male","Adult","Yes"
"1863","Crew","Male","Adult","Yes"
"1864","Crew","Male","Adult","Yes"
"1865","Crew","Male","Adult","Yes"
"1866","Crew","Male","Adult","Yes"
"1867","Crew","Male","Adult","Yes"
"1868","Crew","Male","Adult","Yes"
"1869","Crew","Male","Adult","Yes"
"1870","Crew","Male","Adult","Yes"
"1871","Crew","Male","Adult","Yes"
"1872","Crew","Male","Adult","Yes"
"1873","Crew","Male","Adult","Yes"
"1874","Crew","Male","Adult","Yes"
"1875","Crew","Male","Adult","Yes"
"1876","Crew","Male","Adult","Yes"
"1877","Crew","Male","Adult","Yes"
"1878","Crew","Male","Adult","Yes"
"1879","Crew","Male","Adult","Yes"
"1880","Crew","Male","Adult","Yes"
"1881","Crew","Male","Adult","Yes"
"1882","Crew","Male","Adult","Yes"
"1883","Crew","Male","Adult","Yes"
"1884","Crew","Male","Adult","Yes"
"1885","Crew","Male","Adult","Yes"
"1886","1st","Female","Adult","Yes"
"1887","1st","Female","Adult","Yes"
"1888","1st","Female","Adult","Yes"
"1889","1st","Female","Adult","Yes"
"1890","1st","Female","Adult","Yes"
"1891","1st","Female","Adult","Yes"
"1892","1st","Female","Adult","Yes"
"1893","1st","Female","Adult","Yes"
"1894","1st","Female","Adult","Yes"
"1895","1st","Female","Adult","Yes"
"1896","1st","Female","Adult","Yes"
"1897","1st","Female","Adult","Yes"
"1898","1st","Female","Adult","Yes"
"1899","1st","Female","Adult","Yes"
"1900","1st","Female","Adult","Yes"
"1901","1st","Female","Adult","Yes"
"1902","1st","Female","Adult","Yes"
"1903","1st","Female","Adult","Yes"
"1904","1st","Female","Adult","Yes"
"1905","1st","Female","Adult","Yes"
"1906","1st","Female","Adult","Yes"
"1907","1st","Female","Adult","Yes"
"1908","1st","Female","Adult","Yes"
"1909","1st","Female","Adult","Yes"
"1910","1st","Female","Adult","Yes"
"1911","1st","Female","Adult","Yes"
"1912","1st","Female","Adult","Yes"
"1913","1st","Female","Adult","Yes"
"1914","1st","Female","Adult","Yes"
"1915","1st","Female","Adult","Yes"
"1916","1st","Female","Adult","Yes"
"1917","1st","Female","Adult","Yes"
"1918","1st","Female","Adult","Yes"
"1919","1st","Female","Adult","Yes"
"1920","1st","Female","Adult","Yes"
"1921","1st","Female","Adult","Yes"
"1922","1st","Female","Adult","Yes"
"1923","1st","Female","Adult","Yes"
"1924","1st","Female","Adult","Yes"
"1925","1st","Female","Adult","Yes"
"1926","1st","Female","Adult","Yes"
"1927","1st","Female","Adult","Yes"
"1928","1st","Female","Adult","Yes"
"1929","1st","Female","Adult","Yes"
"1930","1st","Female","Adult","Yes"
"1931","1st","Female","Adult","Yes"
"1932","1st","Female","Adult","Yes"
"1933","1st","Female","Adult","Yes"
"1934","1st","Female","Adult","Yes"
"1935","1st","Female","Adult","Yes"
"1936","1st","Female","Adult","Yes"
"1937","1st","Female","Adult","Yes"
"1938","1st","Female","Adult","Yes"
"1939","1st","Female","Adult","Yes"
"1940","1st","Female","Adult","Yes"
"1941","1st","Female","Adult","Yes"
"1942","1st","Female","Adult","Yes"
"1943","1st","Female","Adult","Yes"
"1944","1st","Female","Adult","Yes"
"1945","1st","Female","Adult","Yes"
"1946","1st","Female","Adult","Yes"
"1947","1st","Female","Adult","Yes"
"1948","1st","Female","Adult","Yes"
"1949","1st","Female","Adult","Yes"
"1950","1st","Female","Adult","Yes"
"1951","1st","Female","Adult","Yes"
"1952","1st","Female","Adult","Yes"
"1953","1st","Female","Adult","Yes"
"1954","1st","Female","Adult","Yes"
"1955","1st","Female","Adult","Yes"
"1956","1st","Female","Adult","Yes"
"1957","1st","Female","Adult","Yes"
"1958","1st","Female","Adult","Yes"
"1959","1st","Female","Adult","Yes"
"1960","1st","Female","Adult","Yes"
"1961","1st","Female","Adult","Yes"
"1962","1st","Female","Adult","Yes"
"1963","1st","Female","Adult","Yes"
"1964","1st","Female","Adult","Yes"
"1965","1st","Female","Adult","Yes"
"1966","1st","Female","Adult","Yes"
"1967","1st","Female","Adult","Yes"
"1968","1st","Female","Adult","Yes"
"1969","1st","Female","Adult","Yes"
"1970","1st","Female","Adult","Yes"
"1971","1st","Female","Adult","Yes"
"1972","1st","Female","Adult","Yes"
"1973","1st","Female","Adult","Yes"
"1974","1st","Female","Adult","Yes"
"1975","1st","Female","Adult","Yes"
"1976","1st","Female","Adult","Yes"
"1977","1st","Female","Adult","Yes"
"1978","1st","Female","Adult","Yes"
"1979","1st","Female","Adult","Yes"
"1980","1st","Female","Adult","Yes"
"1981","1st","Female","Adult","Yes"
"1982","1st","Female","Adult","Yes"
"1983","1st","Female","Adult","Yes"
"1984","1st","Female","Adult","Yes"
"1985","1st","Female","Adult","Yes"
"1986","1st","Female","Adult","Yes"
"1987","1st","Female","Adult","Yes"
"1988","1st","Female","Adult","Yes"
"1989","1st","Female","Adult","Yes"
"1990","1st","Female","Adult","Yes"
"1991","1st","Female","Adult","Yes"
"1992","1st","Female","Adult","Yes"
"1993","1st","Female","Adult","Yes"
"1994","1st","Female","Adult","Yes"
"1995","1st","Female","Adult","Yes"
"1996","1st","Female","Adult","Yes"
"1997","1st","Female","Adult","Yes"
"1998","1st","Female","Adult","Yes"
"1999","1st","Female","Adult","Yes"
"2000","1st","Female","Adult","Yes"
"2001","1st","Female","Adult","Yes"
"2002","1st","Female","Adult","Yes"
"2003","1st","Female","Adult","Yes"
"2004","1st","Female","Adult","Yes"
"2005","1st","Female","Adult","Yes"
"2006","1st","Female","Adult","Yes"
"2007","1st","Female","Adult","Yes"
"2008","1st","Female","Adult","Yes"
"2009","1st","Female","Adult","Yes"
"2010","1st","Female","Adult","Yes"
"2011","1st","Female","Adult","Yes"
"2012","1st","Female","Adult","Yes"
"2013","1st","Female","Adult","Yes"
"2014","1st","Female","Adult","Yes"
"2015","1st","Female","Adult","Yes"
"2016","1st","Female","Adult","Yes"
"2017","1st","Female","Adult","Yes"
"2018","1st","Female","Adult","Yes"
"2019","1st","Female","Adult","Yes"
"2020","1st","Female","Adult","Yes"
"2021","1st","Female","Adult","Yes"
"2022","1st","Female","Adult","Yes"
"2023","1st","Female","Adult","Yes"
"2024","1st","Female","Adult","Yes"
"2025","1st","Female","Adult","Yes"
"2026","2nd","Female","Adult","Yes"
"2027","2nd","Female","Adult","Yes"
"2028","2nd","Female","Adult","Yes"
"2029","2nd","Female","Adult","Yes"
"2030","2nd","Female","Adult","Yes"
"2031","2nd","Female","Adult","Yes"
"2032","2nd","Female","Adult","Yes"
"2033","2nd","Female","Adult","Yes"
"2034","2nd","Female","Adult","Yes"
"2035","2nd","Female","Adult","Yes"
"2036","2nd","Female","Adult","Yes"
"2037","2nd","Female","Adult","Yes"
"2038","2nd","Female","Adult","Yes"
"2039","2nd","Female","Adult","Yes"
"2040","2nd","Female","Adult","Yes"
"2041","2nd","Female","Adult","Yes"
"2042","2nd","Female","Adult","Yes"
"2043","2nd","Female","Adult","Yes"
"2044","2nd","Female","Adult","Yes"
"2045","2nd","Female","Adult","Yes"
"2046","2nd","Female","Adult","Yes"
"2047","2nd","Female","Adult","Yes"
"2048","2nd","Female","Adult","Yes"
"2049","2nd","Female","Adult","Yes"
"2050","2nd","Female","Adult","Yes"
"2051","2nd","Female","Adult","Yes"
"2052","2nd","Female","Adult","Yes"
"2053","2nd","Female","Adult","Yes"
"2054","2nd","Female","Adult","Yes"
"2055","2nd","Female","Adult","Yes"
"2056","2nd","Female","Adult","Yes"
"2057","2nd","Female","Adult","Yes"
"2058","2nd","Female","Adult","Yes"
"2059","2nd","Female","Adult","Yes"
"2060","2nd","Female","Adult","Yes"
"2061","2nd","Female","Adult","Yes"
"2062","2nd","Female","Adult","Yes"
"2063","2nd","Female","Adult","Yes"
"2064","2nd","Female","Adult","Yes"
"2065","2nd","Female","Adult","Yes"
"2066","2nd","Female","Adult","Yes"
"2067","2nd","Female","Adult","Yes"
"2068","2nd","Female","Adult","Yes"
"2069","2nd","Female","Adult","Yes"
"2070","2nd","Female","Adult","Yes"
"2071","2nd","Female","Adult","Yes"
"2072","2nd","Female","Adult","Yes"
"2073","2nd","Female","Adult","Yes"
"2074","2nd","Female","Adult","Yes"
"2075","2nd","Female","Adult","Yes"
"2076","2nd","Female","Adult","Yes"
"2077","2nd","Female","Adult","Yes"
"2078","2nd","Female","Adult","Yes"
"2079","2nd","Female","Adult","Yes"
"2080","2nd","Female","Adult","Yes"
"2081","2nd","Female","Adult","Yes"
"2082","2nd","Female","Adult","Yes"
"2083","2nd","Female","Adult","Yes"
"2084","2nd","Female","Adult","Yes"
"2085","2nd","Female","Adult","Yes"
"2086","2nd","Female","Adult","Yes"
"2087","2nd","Female","Adult","Yes"
"2088","2nd","Female","Adult","Yes"
"2089","2nd","Female","Adult","Yes"
"2090","2nd","Female","Adult","Yes"
"2091","2nd","Female","Adult","Yes"
"2092","2nd","Female","Adult","Yes"
"2093","2nd","Female","Adult","Yes"
"2094","2nd","Female","Adult","Yes"
"2095","2nd","Female","Adult","Yes"
"2096","2nd","Female","Adult","Yes"
"2097","2nd","Female","Adult","Yes"
"2098","2nd","Female","Adult","Yes"
"2099","2nd","Female","Adult","Yes"
"2100","2nd","Female","Adult","Yes"
"2101","2nd","Female","Adult","Yes"
"2102","2nd","Female","Adult","Yes"
"2103","2nd","Female","Adult","Yes"
"2104","2nd","Female","Adult","Yes"
"2105","2nd","Female","Adult","Yes"
"2106","3rd","Female","Adult","Yes"
"2107","3rd","Female","Adult","Yes"
"2108","3rd","Female","Adult","Yes"
"2109","3rd","Female","Adult","Yes"
"2110","3rd","Female","Adult","Yes"
"2111","3rd","Female","Adult","Yes"
"2112","3rd","Female","Adult","Yes"
"2113","3rd","Female","Adult","Yes"
"2114","3rd","Female","Adult","Yes"
"2115","3rd","Female","Adult","Yes"
"2116","3rd","Female","Adult","Yes"
"2117","3rd","Female","Adult","Yes"
"2118","3rd","Female","Adult","Yes"
"2119","3rd","Female","Adult","Yes"
"2120","3rd","Female","Adult","Yes"
"2121","3rd","Female","Adult","Yes"
"2122","3rd","Female","Adult","Yes"
"2123","3rd","Female","Adult","Yes"
"2124","3rd","Female","Adult","Yes"
"2125","3rd","Female","Adult","Yes"
"2126","3rd","Female","Adult","Yes"
"2127","3rd","Female","Adult","Yes"
"2128","3rd","Female","Adult","Yes"
"2129","3rd","Female","Adult","Yes"
"2130","3rd","Female","Adult","Yes"
"2131","3rd","Female","Adult","Yes"
"2132","3rd","Female","Adult","Yes"
"2133","3rd","Female","Adult","Yes"
"2134","3rd","Female","Adult","Yes"
"2135","3rd","Female","Adult","Yes"
"2136","3rd","Female","Adult","Yes"
"2137","3rd","Female","Adult","Yes"
"2138","3rd","Female","Adult","Yes"
"2139","3rd","Female","Adult","Yes"
"2140","3rd","Female","Adult","Yes"
"2141","3rd","Female","Adult","Yes"
"2142","3rd","Female","Adult","Yes"
"2143","3rd","Female","Adult","Yes"
"2144","3rd","Female","Adult","Yes"
"2145","3rd","Female","Adult","Yes"
"2146","3rd","Female","Adult","Yes"
"2147","3rd","Female","Adult","Yes"
"2148","3rd","Female","Adult","Yes"
"2149","3rd","Female","Adult","Yes"
"2150","3rd","Female","Adult","Yes"
"2151","3rd","Female","Adult","Yes"
"2152","3rd","Female","Adult","Yes"
"2153","3rd","Female","Adult","Yes"
"2154","3rd","Female","Adult","Yes"
"2155","3rd","Female","Adult","Yes"
"2156","3rd","Female","Adult","Yes"
"2157","3rd","Female","Adult","Yes"
"2158","3rd","Female","Adult","Yes"
"2159","3rd","Female","Adult","Yes"
"2160","3rd","Female","Adult","Yes"
"2161","3rd","Female","Adult","Yes"
"2162","3rd","Female","Adult","Yes"
"2163","3rd","Female","Adult","Yes"
"2164","3rd","Female","Adult","Yes"
"2165","3rd","Female","Adult","Yes"
"2166","3rd","Female","Adult","Yes"
"2167","3rd","Female","Adult","Yes"
"2168","3rd","Female","Adult","Yes"
"2169","3rd","Female","Adult","Yes"
"2170","3rd","Female","Adult","Yes"
"2171","3rd","Female","Adult","Yes"
"2172","3rd","Female","Adult","Yes"
"2173","3rd","Female","Adult","Yes"
"2174","3rd","Female","Adult","Yes"
"2175","3rd","Female","Adult","Yes"
"2176","3rd","Female","Adult","Yes"
"2177","3rd","Female","Adult","Yes"
"2178","3rd","Female","Adult","Yes"
"2179","3rd","Female","Adult","Yes"
"2180","3rd","Female","Adult","Yes"
"2181","3rd","Female","Adult","Yes"
"2182","Crew","Female","Adult","Yes"
"2183","Crew","Female","Adult","Yes"
"2184","Crew","Female","Adult","Yes"
"2185","Crew","Female","Adult","Yes"
"2186","Crew","Female","Adult","Yes"
"2187","Crew","Female","Adult","Yes"
"2188","Crew","Female","Adult","Yes"
"2189","Crew","Female","Adult","Yes"
"2190","Crew","Female","Adult","Yes"
"2191","Crew","Female","Adult","Yes"
"2192","Crew","Female","Adult","Yes"
"2193","Crew","Female","Adult","Yes"
"2194","Crew","Female","Adult","Yes"
"2195","Crew","Female","Adult","Yes"
"2196","Crew","Female","Adult","Yes"
"2197","Crew","Female","Adult","Yes"
"2198","Crew","Female","Adult","Yes"
"2199","Crew","Female","Adult","Yes"
"2200","Crew","Female","Adult","Yes"
"2201","Crew","Female","Adult","Yes"


================================================
FILE: data/ximb.csv
================================================
"","gender","spl","age","experience","grade","placement"
"1","M","Finance",28.77,5.37,"A","no"
"2","F","Marketing",27.73,3.71,"B","yes"
"3","M","Finance",29.46,4.55,"B","yes"
"4","M","HR",29.28,4.35,"A","yes"
"5","M","Finance",22.44,4.75,"B","yes"
"6","M","HR",28.89,4.59,"D","yes"
"7","F","HR",27.44,3.22,"A","yes"
"8","M","Finance",25,5.76,"A","no"
"9","F","Marketing",23.96,2.37,"C","yes"
"10","M","Finance",23.65,3.68,"A","no"
"11","M","HR",25.21,4.17,"C","no"
"12","M","HR",22.26,4.56,"A","yes"
"13","M","Finance",29.59,4.42,"B","yes"
"14","M","HR",25.43,3.28,"A","yes"
"15","M","Finance",29.47,4.04,"B","yes"
"16","F","Marketing",24.67,4.16,"D","no"
"17","F","Finance",22.99,4,"B","yes"
"18","F","Finance",26.06,5.1,"C","yes"
"19","F","Finance",24.96,4.84,"C","yes"
"20","F","Finance",23.46,3.89,"C","yes"
"21","M","Finance",25.68,3.9,"C","no"
"22","M","HR",27.62,2.59,"B","yes"
"23","M","Finance",22.37,3.96,"A","yes"
"24","M","HR",27.86,3.24,"B","yes"
"25","M","Finance",22.26,3.79,"B","yes"
"26","M","HR",28.73,4.15,"C","yes"
"27","M","HR",24.78,3.35,"C","yes"
"28","F","Marketing",29.89,2.12,"B","no"
"29","F","Finance",23.39,4.38,"D","yes"
"30","M","Finance",26.21,3.64,"B","yes"
"31","M","HR",22.99,2.99,"C","no"
"32","M","Finance",23.54,2.72,"C","yes"
"33","M","Finance",23.18,5.56,"B","yes"
"34","F","HR",25.22,4.44,"B","no"
"35","F","HR",22.24,4.09,"D","yes"
"36","F","Marketing",21.47,2.77,"B","yes"
"37","F","Marketing",25.59,5.09,"A","yes"
"38","M","Finance",25.97,5.56,"A","yes"
"39","F","Marketing",25.29,4.38,"A","yes"
"40","F","Finance",24.02,3.39,"D","yes"
"41","F","Marketing",24.14,4.85,"B","yes"
"42","M","HR",26.92,4.39,"D","no"
"43","M","HR",23.34,3.35,"B","yes"
"44","M","HR",24.55,3.26,"A","no"
"45","F","Finance",26.83,5.49,"A","yes"
"46","M","HR",21.75,4.39,"B","no"
"47","M","Finance",21.93,3.55,"A","yes"
"48","F","Marketing",25.63,2.44,"C","no"
"49","M","Finance",27.48,3.01,"B","yes"
"50","M","Finance",21.79,3.44,"B","yes"
"51","M","Marketing",27.06,2.36,"C","yes"
"52","M","Finance",28.34,2.62,"C","yes"
"53","M","HR",23.54,4.7,"C","no"
"54","F","Finance",21.3,4.09,"A","yes"
"55","F","Marketing",21.23,2.85,"B","yes"
"56","M","Finance",24.99,4.48,"A","yes"
"57","F","HR",25.13,1.91,"A","no"
"58","M","Finance",27.09,6.32,"B","yes"
"59","F","HR",21.39,5.51,"B","no"
"60","F","HR",21.58,5.38,"A","yes"
"61","F","Finance",26.83,4.74,"B","yes"
"62","M","Marketing",29.27,3.53,"C","yes"
"63","F","Finance",26.83,4.74,"A","yes"
"64","M","HR",22.69,3.72,"C","no"
"65","F","HR",22.36,4.01,"A","yes"
"66","F","Marketing",23.76,4.6,"A","yes"
"67","F","Finance",25.51,4.05,"D","yes"
"68","M","Finance",26.08,4.4,"C","yes"
"69","F","Finance",23.41,4.8,"A","yes"
"70","M","HR",24.31,3.01,"B","yes"
"71","M","Finance",25.72,3.49,"A","no"
"72","M","Marketing",22.35,3.35,"A","yes"
"73","M","HR",23.1,3.03,"C","yes"
"74","M","Finance",26.93,3.17,"B","yes"
"75","M","HR",21.92,5.08,"A","yes"
"76","F","Marketing",23.7,4.58,"C","no"
"77","M","Marketing",23.69,4.47,"C","yes"
"78","M","Marketing",27.24,4.9,"D","yes"
"79","M","Marketing",26.08,4.08,"A","no"
"80","M","Finance",23.76,4.75,"A","yes"
"81","F","HR",22.89,3.86,"D","yes"
"82","F","Finance",22.52,5.11,"A","yes"
"83","M","Finance",26.82,5.27,"A","no"
"84","M","Marketing",25.16,2.77,"B","no"
"85","M","HR",25.73,3.91,"A","yes"
"86","F","HR",27.68,4.28,"B","yes"
"87","M","Marketing",26.4,3.29,"B","yes"
"88","M","HR",26.36,4.31,"A","yes"
"89","F","HR",29.26,5.63,"B","yes"
"90","M","HR",25.29,3.29,"A","yes"
"91","M","Marketing",24.93,2.67,"B","no"
"92","F","Finance",21.23,2.79,"A","yes"
"93","M","HR",27.19,5.11,"A","yes"
"94","F","Finance",29.34,3.01,"A","yes"
"95","M","Marketing",27.96,4.8,"B","yes"
"96","F","Finance",25.36,4.36,"B","yes"
"97","M","Finance",24.07,4.66,"B","no"
"98","M","Marketing",22.74,2.39,"A","yes"
"99","M","Finance",22.4,2.92,"D","yes"
"100","M","Finance",24.78,3.45,"B","yes"


================================================
FILE: dates/11-date1.R
================================================
#Data Format in R 
#When we import data into R, dates and times are usually stored as character or factor by default due to symbols such as “-”, “:” and “/”
cdate1='15/August/1947'
cdate1
cdate2 = c('15-Aug-1947', "26-Jan-1950")
cdate2

class(cdate1) ; class(cdate2)
#convert to Dates
#Default Format of Date in R
?as.Date()

#See/ Convert Date format
as.Date('2018-09-01')
#Default Format : Year-Month-Date : %Y-%m-%d
(date3= as.Date('2018-09-01',format='%Y-%m-%d'))
class(date3)

#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan) %d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

#convert cdate1 cdate2 to date formats
cdate1 #"15/August/1947
date1 = as.Date(cdate1, format='%d/%B/%y')
date1
#
cdate2 #"15-Aug-1947" "26-Jan-1950"
date2 = as.Date(cdate2, format='%d-%b-%Y')
date2
#

#one more practise on date format
date3 =as.Date('30Apr18',format("%d%b%y"))
class(date3)

#Date to Characters
(cdate2 = as.character(date3))
class(cdate2)


#The “Date” class means dates are stored as the number of days since January 1, 1970, with negative values for earlier dates. We can use the as.numeric function to view the raw values.

date1
as.numeric(date1)
date2
as.numeric(date2)


================================================
FILE: dates/12-dates-seq.R
================================================
#Create Sequence of Dates

#Next day after certain date
as.Date('2018-04-29') + 1
#Sequence of Dates
as.Date('2018-09-16') + 1:15
#alternative Dates
as.Date('2018-09-16') + seq(1,10,2)


#system date
Sys.Date()
#next 15 days after todays date
Sys.Date() + 1:15

##increment/ decrement dates 
(startdt = as.Date("2018-11-30", format="%Y-%m-%d"))
(next20days = startdt + 1:20) # start : 20 days course
startdt - 1
(prev20days = startdt - c(1:20)) # before : 20 days course
rev(prev20days)


================================================
FILE: dates/12c-dates-seq2.R
================================================
# sequence of dates

(indepdate = as.Date("1947-08-15"))

#add 30days to each previous date
(days30= indepdate + seq(0,200,30))
length(days30)

as.numeric((Sys.Date() - days30)/365)  #: Years

#by years : from start date to end date
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), by="years")

# find next 5 yrs date from a date
seq(as.Date("2016/1/1"), length.out=5, by="years")

## quarters : 4 qtrs in a year
seq(as.Date("2017/1/1"), as.Date("2018/1/1"), by = "quarter")


# find next months date from a date 
seq(as.Date("2016/1/1"), as.Date("2018/5/1"), by="months")

## by month : 6 months
seq(as.Date("2017/1/1"), by = "month", length.out = 6)
seq(as.Date("2017/1/1"), by = "month", along.with = c(3,2,43,5,6,7))

# find next few days after certain date
seq(as.Date("2017/1/1"), by = "days", length.out = 45)


# using  along.with in seq
(x = LETTERS[1:10])
seq(as.Date("2017/1/1"), by = "month", along.with = x)  # 
seq(as.Date("2017/1/1"), by = "quarter", along.with = x)  # 
seq(as.Date("2017/1/1"), by = "month", length.out = length(c(3,2,43))) 



#More selective Dates

#Find 7th of all months between two dates in fwd direction
(dateseq1 = seq(as.Date("2017/1/7"), as.Date("2018/1/7"), by = "1 month"))

# dates in opposite order : note -2
(dateseq2 = seq(as.Date("2018/1/7"), as.Date("2017/1/7"), by = "-2 month")) 

# then reverse them
rev(dateseq2)# correct the order
format(rev(dateseq2), "%A : %d-%b")

#another Sequence : using another package : mm-dd-yyyy format
chron::seq.dates("01/04/2010", "12/31/2017", by = "years")
chron::seq.dates("05/01/2017", "12/31/2017", by = "months")
chron::seq.dates("01/01/2017", "12/31/2017", by = "weeks")
chron::seq.dates("01/01/2017", "12/31/2017", by = "days")


================================================
FILE: dates/13-dates-format.R
================================================
# Format the date for printing/ display
format(Sys.Date(), format="%A: %d %B")

#Format Types ----
#%Y: 4-digit year (1982),%y: 2-digit year (82)
#%m: 2-digit month (01)
#%B: month (January), %b: abbreviated month (Jan)
#, %d: 2-digit day of the month (13)
#%A: weekday (Wednesday), %a: abbreviated weekday (Wed)

#Display Day of the week for todays date
format(Sys.Date(), "%A")


#Common formats to see other date formats
format(Sys.Date(), format="%A %a %d  %m %b %B %y %Y") # another format

cdate4 = c('09-07-1993', '08-08-1994', '04-11-1994')
class(cdate4)  #character
(date4 = as.Date(cdate4, '%d-%m-%Y'))
format(date4, '%A')

#character to date and then display Day of the week
format(as.Date('18-04-1994', '%d-%m-%Y'), '%A')


format(date4, format=('%d-%m-%Y')) # print in different format
format(date4, format=('%d / %B - %Y')) # print in different format
format(date4, format=('%d / %b - %Y')) # print in different format




================================================
FILE: dates/14-dates-format2.R
================================================
#Other formats of displaying dates

(startdt = as.Date("2018-11-30", format="%Y-%m-%d"))
#create 45 days course starting from above date
(course = startdt + 1:45)
length(course)

#display 
format(course, "%d-%b")
#display without quote
cat(format(course, format="%B-%d"))
#Month-date

#standard values
months(course)
weekdays(course)
quarters(course)


================================================
FILE: dates/15-date-subset-arithmetic.R
================================================
# date Arithmetic and subsets

#start course on 25 Oct 2018 for 30 days
(course1 = as.Date("2018-10-25") + 0:29)

#Difference in dates
course1
length(course1)  #no of days
min(course1)  # first date
max(course1)  # last date
range(course1) # start to end
mean(course1)  # center of the course period
median(course1) # middle date

sd(course1) #no meaning

#subset / select dates
course1
#1st and 5th dates
course[c(1,5)] # 1st & 5th date 
#1st and 5th dates
course[1] ; course[5]

#duration from 8th date to first date
(duration1 = course[8] - course[1])
#total duration
(duration2 = max(course) - min(course))

#duration since independence
(independencedays = Sys.Date() - as.Date('15-08-1947', '%d-%m-%Y'))
independencedays/365  #years




================================================
FILE: dates/17-time-chron.R
================================================
#Dealing with Time 

time = c("00:00:01", "01:02:00", "09:30:01", "14:15:25")

library(chron)
x = chron(times=time)
x

#difference
diff(x)
x[2] - x[1]


#chron objects store the values internally as a fraction of seconds per day. Thus 1 second is equivalent to 1/(60*60*24), or 1/86400, i.e. 1.157407e-05.

#to add 1 second to all times
x + 1/86400


================================================
FILE: dates/17c-time-posixt.R
================================================
# Time 

time = c("00:00:01","01:02:00","09:30:01","14:15:25")
(converted.time = as.difftime(time, units = "mins")) #"difftime" class
(secss <- as.numeric(converted.time, units = "secs"))
(hourss <- as.numeric(converted.time, units = "hours"))
(dayss <- as.numeric(converted.time, units="days"))

(w = strptime(x = time, format = "%H:%M:%S")) #"POSIXlt" "POSIXt" class
# this is the format which can store date and time
#but cannot do any arithmetic


================================================
FILE: dates/19-datetime-lubridate.R
================================================
# Package Lubridate https://data.library.virginia.edu/working-with-dates-and-time-in-r-using-the-lubridate-package/
# Date Functions - Deal with Date & Time
#lubridate provides a series of functions that are a permutation of the letters “m”, “d” and “y” to represent the ordering of month, day and year. For example, if our data has a column of dates such as May 11, 1996, our dates are ordered month-day-year. Therefore we would use the mdy function to transform the column to a date object. If our dates were in the order of, say, year-month-day, we would use the ymd function. lubridate provides functions for every permutation of “m”, “d”, “y”.


#Eg1
library(lubridate)

(date8a = lubridate::ymd("20110604"))
(date8 = ymd("20110604"))

class(date8)

date8 + 1:10

#date in different format seq
mdy("06-04-2011") #mon-date-year
dmy("04/06/2011") #date-mon-year

#Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

begin = c("May 11, 1996", "September 12, 2001", "July 1, 1988")
end = c("7/8/97","10/23/02","1/4/91")
class(begin)  ## [1] "character"
class(end) ## [1] "character"

(begin = mdy(begin))
## [1] "1996-05-11" "2001-09-12" "1988-07-01"

(end = mdy(end))
## [1] "1997-07-08" "2002-10-23" "1991-01-04"

class(begin) ; class(end)## [1] "Date"


#If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.

begin1 = c("May 11, 1996 12:05", "September 12, 2001 1:00", "July 1, 1988 3:32")
end1 = c("7/8/97 8:00","10/23/02: 12:00","1/4/91 2:05")
(begin1a = mdy_hm(begin1))
(end1a = mdy_hm(end1))

class(begin1a) ; class(end1a) ## [1] "POSIXct" "POSIXt"
begin1a ; as.numeric(begin1a)
end1a ; as.numeric(end1a)

#class is now “POSIXct”. “POSIXct” represents the number of seconds since the beginning of 1970. If a date is before 1970, the number of seconds is negative. 

#Notice also the the letters “UTC” have been appended to the date-times. UTC is short for Universal Coordinated Time. it’s basically the time standard by which the world regulates clocks. If we prefer we can specify a time zone when formatting dates by using the tz argument. Here’s how we can specify the Eastern Time Zone in the United States when formatting our dates.

(begin1b = mdy_hm(begin1, tz = "US/Eastern"))
(begin1c = mdy_hm(begin1, tz = "Asia/Calcutta"))

#use the OlsonNames function to see a character vector of all time zone names
OlsonNames()
Sys.timezone()

#read in times without dates using the functions ms, hm, or hms, where again “h”, “m”, and “s” stand for “hours”, “minutes”, and “seconds”

time1 = c("1:13", "0:58", "1:01")
time2 = c("12:23:11", "09:45:31", "12:05:22")
time3 = c("2:14", "2:16", "3:35")

(time1a <- ms(time1)) ## [1] "1M 13S" "58S"    "1M 1S"
(time2a <- hms(time2)) ## [1] "12H 23M 11S" "9H 45M 31S"  "12H 5M 22S"
(time3a <- hm(time3)) ## [1] "2H 14M 0S" "2H 16M 0S" "3H 35M 0S"



arrive <- ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
arrive
leave <- ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
leave

leave - arrive  # Time Difference

#Setting and Extracting information
#Functions

#Eg
second(arrive)
second(arrive) = 25  #change
second(arrive)
arrive
minute(arrive)
hour(arrive)
day(arrive)
wday(arrive)
wday(arrive)
wday(arrive, label = TRUE)

week(arrive)

month(arrive)
year(arrive)
tz(arrive)

#Time Intervals
#save an interval of time as an Interval class object
(student1 <- interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                     ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                         ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))


================================================
FILE: dates/19c-datetime-lubridate.R
================================================
#Lubridate 2 - date time functions
#more functions

arrive = ymd_hms("2018-04-27 18:40:15", tz = "Asia/Calcutta")
leave = ymd_hms("2018-04-29 22:00:00", tz = "Asia/Calcutta")
arrive ; leave

#days between
leave - arrive  # Time Difference in days

#Setting and Extracting information
arrive
second(arrive)
second(arrive) = 25  #change
arrive ; second(arrive)
arrive



day(arrive) #27
month(arrive) #4
year(arrive) #2018
wday(arrive)  #6
wday(arrive, label = TRUE)  #with levels and Fri
week(arrive)  #17

hour(arrive) #18
minute(arrive) #40
second(arrive) #25
tz(arrive)  #asia/calcutta



================================================
FILE: dates/19d-datetime-lubridate.R
================================================
#lubridate 3

#interval
#-----------------
#Time Intervals
#save an interval of time as an Interval class object
(student1 = interval(arrive, leave))
(student2 <- arrive %--% leave)
(student3 = interval(ymd(20180320, tz = "Asia/Calcutta"),
                     ymd(20180327, tz = "Asia/Calcutta")))

(courseperiod = interval(ymd(20180421, tz = "Asia/Calcutta"),
                         ymd(20180425, tz = "Asia/Calcutta")))

int_overlaps(student1, courseperiod)
#TRUE
int_overlaps(student3, courseperiod)

setdiff(student2, courseperiod)
setdiff(student3, courseperiod)

?setdiff
#2017-07-05 IST--2017-07-15 IST

#Other Functions
#int_start, int_end, int_flip, int_shift, int_aligns, union, intersect, and %within%.
int_start(courseperiod)
int_end(courseperiod)
int_flip(courseperiod)
int_aligns(student1, courseperiod)  #share end point

dates = now() + days(1:10)
dates
int_diff(dates)

# Years Betw ----------
ref_date <- as.Date('20/04/08',format='%d/%m/%y')
today <- as.Date(Sys.Date(), format='%d/%m/%y')
year(arrive) = 2015
arrive
arrive2 = as.Date(arrive, format='%d/%m/%y')
(yrsbetw = year(today)-year(ref_date))
(yrsbetw = year(today)-year(arrive2))


================================================
FILE: dates/19e-datetime-lubridate.R
================================================
#Durations, Intervals and Periods
#lubridate provides three classes, or three different ways, to distinguish between different types of time spans.
#Duration ,Interval, Period

#Understanding these classes will help you get the most out of lubridate.

#The most simple is Duration. This is simply a span of time measured in seconds. There is no start date.

#An Interval is also measured in seconds but has an associated start date. An Interval measures elapsed seconds between two specific points in time.

#A Period records a time span in units larger than seconds, such as years or months. Unlike seconds, years and months differ in time. June has 30 days while July has 31 days. February has 28 days except for leap years when it has 29 days. With the Period class, we can add 1 month to February 1 and get March 1. It allows us to perform calculations in calendar or clock time as opposed to absolute number of seconds.

#Let’s see these three classes in action. Below we define two dates in the US Eastern time zone. The start day is March 11, 2017 at 5:21 AM. The end day is March 12, 2017 at the same time. 

(start = mdy_hm("3-11-2017 5:21"))
(end = mdy_hm("3-12-2017 5:21"))

#Since we’re dealing with elapsed time between two dates, let’s start with Intervals. We can define an Interval using the %--% operator.

(time.interval = start %--% end)
## [1] 2017-03-11 05:21:00 EST--2017-03-12 05:21:00 EDT

str(time.interval)

#To create a Duration between these two dates, we can use the as.duration function.
(time.duration = as.duration(time.interval))

str(time.duration)

#We can create a Period from an Interval using the as.period function.

(time.period <- as.period(time.interval))
#A Period prints elapsed time as integers in the form of years, months, weeks, days and so on. Notice this Period is 1 day long. 

str(time.period)


#summary
An Interval is elapsed time in seconds between two specific dates. (If no time is provided, the time for each date is assumed to be 00:00:00, or midnight.)
A Duration is elapsed time in seconds independent of a start date.
A Period is elapsed time in “calendar” or “clock” time (4 weeks, 2 months, etc) independent of a start date.

================================================
FILE: dates/19f-datetime-lubridate.R
================================================
#Lubridate : Calculations and conversions

#Once we format dates and define our time span we often want to do some calculations and conversions. For example, we may want to calculate the mean elapsed time in weeks for different groups.

#Let’s create some data and demonstrate. First we enter arbitrary start and end dates and define an Interval

start = c("2012-08-21", "2012-09-01", "2012-08-15", "2012-09-18")
end = c("2012-09-16", "2012-09-06", "2012-08-22", "2012-10-11")
(elapsed.time = start %--% end)

#If we view the elapsed.time object we’ll just see date ranges. We can use as.duration or even as.numeric to view the elapsed time in seconds but that’s not very useful in this case. It would be better if we converted seconds to another unit of time such as weeks or days. Fortunately lubridate makes this easy.

#The trick is to convert intervals to durations and then divide the duration by a duration object in the units we desire. That’s a mouthful but easy to demonstrate. Below we demonstrate how to convert to weeks. 
#First we convert our interval to a duration, and then we divide by dweeks(1). The function call dweeks(1) generates a duration of one week in seconds, which is 604800. Dividing that into our duration returns number of weeks.

dweeks(1)
ddays(1)
dyears(1)
dhours(1)
dhours(2)
ddays(1)

as.duration(elapsed.time) / dweeks(1)
## [1] 3.7142857 0.7142857 1.0000000 3.2857143

#We can do the same with hours, days, minutes and years.

as.duration(elapsed.time) / dhours(1)
## [1] 624 120 168 552
as.duration(elapsed.time) / ddays(1)
## [1] 26  5  7 23
as.duration(elapsed.time) / dminutes(1)
## [1] 37440  7200 10080 33120
as.duration(elapsed.time) / dyears(1)
## [1] 0.07123288 0.01369863 0.01917808 0.06301370

#Once we have the durations in the units we want, we can then do things like find the mean.

mean(as.duration(elapsed.time) / dweeks(1))
## [1] 2.178571

#----------

As another example, consider the following vector of character data summarizing a duration of time. “12w” means 12 weeks and “4d” means 4 days.

StudyTime <- c("12w 4d", "11w", "10w 5d", NA, "12w 6d")
What if we wanted to convert that to numeric weeks? First we’ll give the R code and them explain how it works.

as.duration(period(StudyTime, units = c("week","day"))) / dweeks(1)
## [1] 12.57143 11.00000 10.71429       NA 12.85714
First we use the period function to define a Period using our data. The units argument says the first part of our data represents weeks and the second part represents days. That is then converted to a Duration object that stores time in seconds. Finally we divide by dweeks(1) to convert seconds to weeks. Notice how the NA remains NA and that “11w” converts to 11 just fine even though it had no days appended to it.

================================================
FILE: dates/30-datetime-zzz.R
================================================

# Split Data into parts

#Split date and time seperately
#M1----
(tdatetime = Sys.time())
#date part
(splitDate <- sapply(strsplit(as.character(tdatetime), " "), "[", 1))  
#time part
(splitTime <- sapply(strsplit(as.character(tdatetime), " "), "[", 2))

#  M2----

(df <- data.frame(datetime = c( "13:11:2013 15:39" , "13:11:2013 16:15" , "13:11:2013 17:52" ) ))
str(df)

#split the datetime : delim by space
(new <- do.call( rbind , strsplit( as.character( df$datetime ) , " " ) ))


#M3----
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%H:%M:%S")
format(as.POSIXct(tdatetime,format="%Y:%m:%d %H:%M:%S"),"%Y:%m:%d")

#Time Stamp to POSIXT
td = 1377986400
as.POSIXct(1377986400,origin="1970-01-01")
(td1 = as.POSIXct(as.numeric(td),origin="1970-01-01"))


datetime2 <- as.POSIXct('4/17/2008 16:01', format='%m/%d/%Y %H:%M') 
(datetime2split <- strsplit(format(datetime2, "%d %m %Y %H %M"), ' ') )

(x2 = as.POSIXct(td1, format='%m/%d/%Y %H:%M'))
(y2 <- strsplit(format(x2, "%d %m %Y %H %M"), ' ') )

#-----
# In R, there are two basic data types around date and time in R. One is Date, which contains only the date information like 2016–01–10. Another is POSIXct, which has both date and time information like 2016–01–10 10:45:20.

library(dplyr)
dateposix = seq(1377986400, 1377986420, by=1)
df = data.frame(dateposix)
(df$datetime = as.POSIXct(as.numeric(df$dateposix),origin="1970-01-01"))
library(lubridate)
head(df)
df$dateposix2 = format(unclass(df$datetime), scientific=F)
df <- df %>% mutate(year=year(datetime), month = month(datetime), day= day(datetime), hour=hour(datetime), min=minute(datetime))
head(format(df, scientific=F))

split1 =strsplit(format(df$datetime, "%d %m %Y %H %M"), ' ') #list form
matrix(as.numeric(unlist(split1)), ncol=5, byrow = T) 
do.call(rbind, split1)
as.Date(with(df$datetime, ISOdate(Year, Month, Day)))

#-----

do.call(rbind, spl) 

# using seperate in tidyr
library(dplyr)
library(tidyr)
head(df)
df1 = df['datetime']
head(df1)

df1 %>% separate(datetime, into=c('Date', 'Time'), sep=' ')
(year=as.numeric (format(df1$datetime,"%Y")))
(month=as.numeric (format(df1$datetime,"%m")))
(day=as.numeric (format(df1$datetime,"%d")))
(hour=as.numeric (format(df1$datetime,"%H")))
(min =as.numeric (format(df1$datetime,"%M")))
data.frame(df1,cbind(year, month, day, hour, min))

#method
(dt = df$datetime)  #vector
(df2 = df['datetime'])
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_144') 
library(qdap)
df2a = colSplit(df2$datetime, c("Date", "Time"), ",")
df2a

#-----
head(df2)
list <- strsplit(df2$datetime, " ")
library("plyr")
df <- ldply(list)
colnames(df2) <- c('datetime', 'date', 'time')




#-----
DF <- readr::read_csv(
  'ID,Date1,Date2
  1,"21-03-16 8:36","22-03-16 12:36"
  1,"23-03-16 9:36","24-03-16 01:36"
  1,"22-03-16 10:36","25-03-16 11:46"
  1,"23-03-16 11:36","28-03-16 10:16"'
)
DF

library(dplyr)
library(lubridate)
library(hms)
DF
DF %>%  mutate_at(vars(Date1, Date2), dmy_hm) %>% 
  mutate_at(vars(Date1, Date2), funs("date" = date(.), "time" = as.hms(.)))

DF %>%  mutate_at(vars(Date1, Date2), dmy_hm) %>% 
  mutate_at(vars(Date1, Date2), funs("date" = date(.), "time" = as.hms(.))) %>%   select(-Date1, -Date2)


================================================
FILE: download/fms.txt
================================================
The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.



But much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.



FMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.



Corporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.



We invite you to discover FMS for yourself.

================================================
FILE: download/iris.csv
================================================
"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
"1",5.1,3.5,1.4,0.2,"setosa"
"2",4.9,3,1.4,0.2,"setosa"
"3",4.7,3.2,1.3,0.2,"setosa"
"4",4.6,3.1,1.5,0.2,"setosa"
"5",5,3.6,1.4,0.2,"setosa"
"6",5.4,3.9,1.7,0.4,"setosa"
"7",4.6,3.4,1.4,0.3,"setosa"
"8",5,3.4,1.5,0.2,"setosa"
"9",4.4,2.9,1.4,0.2,"setosa"
"10",4.9,3.1,1.5,0.1,"setosa"
"11",5.4,3.7,1.5,0.2,"setosa"
"12",4.8,3.4,1.6,0.2,"setosa"
"13",4.8,3,1.4,0.1,"setosa"
"14",4.3,3,1.1,0.1,"setosa"
"15",5.8,4,1.2,0.2,"setosa"
"16",5.7,4.4,1.5,0.4,"setosa"
"17",5.4,3.9,1.3,0.4,"setosa"
"18",5.1,3.5,1.4,0.3,"setosa"
"19",5.7,3.8,1.7,0.3,"setosa"
"20",5.1,3.8,1.5,0.3,"setosa"
"21",5.4,3.4,1.7,0.2,"setosa"
"22",5.1,3.7,1.5,0.4,"setosa"
"23",4.6,3.6,1,0.2,"setosa"
"24",5.1,3.3,1.7,0.5,"setosa"
"25",4.8,3.4,1.9,0.2,"setosa"
"26",5,3,1.6,0.2,"setosa"
"27",5,3.4,1.6,0.4,"setosa"
"28",5.2,3.5,1.5,0.2,"setosa"
"29",5.2,3.4,1.4,0.2,"setosa"
"30",4.7,3.2,1.6,0.2,"setosa"
"31",4.8,3.1,1.6,0.2,"setosa"
"32",5.4,3.4,1.5,0.4,"setosa"
"33",5.2,4.1,1.5,0.1,"setosa"
"34",5.5,4.2,1.4,0.2,"setosa"
"35",4.9,3.1,1.5,0.2,"setosa"
"36",5,3.2,1.2,0.2,"setosa"
"37",5.5,3.5,1.3,0.2,"setosa"
"38",4.9,3.6,1.4,0.1,"setosa"
"39",4.4,3,1.3,0.2,"setosa"
"40",5.1,3.4,1.5,0.2,"setosa"
"41",5,3.5,1.3,0.3,"setosa"
"42",4.5,2.3,1.3,0.3,"setosa"
"43",4.4,3.2,1.3,0.2,"setosa"
"44",5,3.5,1.6,0.6,"setosa"
"45",5.1,3.8,1.9,0.4,"setosa"
"46",4.8,3,1.4,0.3,"setosa"
"47",5.1,3.8,1.6,0.2,"setosa"
"48",4.6,3.2,1.4,0.2,"setosa"
"49",5.3,3.7,1.5,0.2,"setosa"
"50",5,3.3,1.4,0.2,"setosa"
"51",7,3.2,4.7,1.4,"versicolor"
"52",6.4,3.2,4.5,1.5,"versicolor"
"53",6.9,3.1,4.9,1.5,"versicolor"
"54",5.5,2.3,4,1.3,"versicolor"
"55",6.5,2.8,4.6,1.5,"versicolor"
"56",5.7,2.8,4.5,1.3,"versicolor"
"57",6.3,3.3,4.7,1.6,"versicolor"
"58",4.9,2.4,3.3,1,"versicolor"
"59",6.6,2.9,4.6,1.3,"versicolor"
"60",5.2,2.7,3.9,1.4,"versicolor"
"61",5,2,3.5,1,"versicolor"
"62",5.9,3,4.2,1.5,"versicolor"
"63",6,2.2,4,1,"versicolor"
"64",6.1,2.9,4.7,1.4,"versicolor"
"65",5.6,2.9,3.6,1.3,"versicolor"
"66",6.7,3.1,4.4,1.4,"versicolor"
"67",5.6,3,4.5,1.5,"versicolor"
"68",5.8,2.7,4.1,1,"versicolor"
"69",6.2,2.2,4.5,1.5,"versicolor"
"70",5.6,2.5,3.9,1.1,"versicolor"
"71",5.9,3.2,4.8,1.8,"versicolor"
"72",6.1,2.8,4,1.3,"versicolor"
"73",6.3,2.5,4.9,1.5,"versicolor"
"74",6.1,2.8,4.7,1.2,"versicolor"
"75",6.4,2.9,4.3,1.3,"versicolor"
"76",6.6,3,4.4,1.4,"versicolor"
"77",6.8,2.8,4.8,1.4,"versicolor"
"78",6.7,3,5,1.7,"versicolor"
"79",6,2.9,4.5,1.5,"versicolor"
"80",5.7,2.6,3.5,1,"versicolor"
"81",5.5,2.4,3.8,1.1,"versicolor"
"82",5.5,2.4,3.7,1,"versicolor"
"83",5.8,2.7,3.9,1.2,"versicolor"
"84",6,2.7,5.1,1.6,"versicolor"
"85",5.4,3,4.5,1.5,"versicolor"
"86",6,3.4,4.5,1.6,"versicolor"
"87",6.7,3.1,4.7,1.5,"versicolor"
"88",6.3,2.3,4.4,1.3,"versicolor"
"89",5.6,3,4.1,1.3,"versicolor"
"90",5.5,2.5,4,1.3,"versicolor"
"91",5.5,2.6,4.4,1.2,"versicolor"
"92",6.1,3,4.6,1.4,"versicolor"
"93",5.8,2.6,4,1.2,"versicolor"
"94",5,2.3,3.3,1,"versicolor"
"95",5.6,2.7,4.2,1.3,"versicolor"
"96",5.7,3,4.2,1.2,"versicolor"
"97",5.7,2.9,4.2,1.3,"versicolor"
"98",6.2,2.9,4.3,1.3,"versicolor"
"99",5.1,2.5,3,1.1,"versicolor"
"100",5.7,2.8,4.1,1.3,"versicolor"
"101",6.3,3.3,6,2.5,"virginica"
"102",5.8,2.7,5.1,1.9,"virginica"
"103",7.1,3,5.9,2.1,"virginica"
"104",6.3,2.9,5.6,1.8,"virginica"
"105",6.5,3,5.8,2.2,"virginica"
"106",7.6,3,6.6,2.1,"virginica"
"107",4.9,2.5,4.5,1.7,"virginica"
"108",7.3,2.9,6.3,1.8,"virginica"
"109",6.7,2.5,5.8,1.8,"virginica"
"110",7.2,3.6,6.1,2.5,"virginica"
"111",6.5,3.2,5.1,2,"virginica"
"112",6.4,2.7,5.3,1.9,"virginica"
"113",6.8,3,5.5,2.1,"virginica"
"114",5.7,2.5,5,2,"virginica"
"115",5.8,2.8,5.1,2.4,"virginica"
"116",6.4,3.2,5.3,2.3,"virginica"
"117",6.5,3,5.5,1.8,"virginica"
"118",7.7,3.8,6.7,2.2,"virginica"
"119",7.7,2.6,6.9,2.3,"virginica"
"120",6,2.2,5,1.5,"virginica"
"121",6.9,3.2,5.7,2.3,"virginica"
"122",5.6,2.8,4.9,2,"virginica"
"123",7.7,2.8,6.7,2,"virginica"
"124",6.3,2.7,4.9,1.8,"virginica"
"125",6.7,3.3,5.7,2.1,"virginica"
"126",7.2,3.2,6,1.8,"virginica"
"127",6.2,2.8,4.8,1.8,"virginica"
"128",6.1,3,4.9,1.8,"virginica"
"129",6.4,2.8,5.6,2.1,"virginica"
"130",7.2,3,5.8,1.6,"virginica"
"131",7.4,2.8,6.1,1.9,"virginica"
"132",7.9,3.8,6.4,2,"virginica"
"133",6.4,2.8,5.6,2.2,"virginica"
"134",6.3,2.8,5.1,1.5,"virginica"
"135",6.1,2.6,5.6,1.4,"virginica"
"136",7.7,3,6.1,2.3,"virginica"
"137",6.3,3.4,5.6,2.4,"virginica"
"138",6.4,3.1,5.5,1.8,"virginica"
"139",6,3,4.8,1.8,"virginica"
"140",6.9,3.1,5.4,2.1,"virginica"
"141",6.7,3.1,5.6,2.4,"virginica"
"142",6.9,3.1,5.1,2.3,"virginica"
"143",5.8,2.7,5.1,1.9,"virginica"
"144",6.8,3.2,5.9,2.3,"virginica"
"145",6.7,3.3,5.7,2.5,"virginica"
"146",6.7,3,5.2,2.3,"virginica"
"147",6.3,2.5,5,1.9,"virginica"
"148",6.5,3,5.2,2,"virginica"
"149",6.2,3.4,5.4,2.3,"virginica"
"150",5.9,3,5.1,1.8,"virginica"


================================================
FILE: download/rowling.txt
================================================
President Faust, members of the Harvard Corporation and the Board of Overseers, members of the faculty, proud parents, and, above all, graduates.



The first thing I would like to say is ‘thank you.’ Not only has Harvard given me an extraordinary honour, but the weeks of fear and nausea I have endured at the thought of giving this commencement address have made me lose weight. A win-win situation! Now all I have to do is take deep breaths, squint at the red banners and convince myself that I am at the world’s largest Gryffindor reunion.

Delivering a commencement address is a great responsibility; or so I thought until I cast my mind back to my own graduation. The commencement speaker that day was the distinguished British philosopher Baroness Mary Warnock. Reflecting on her speech has helped me enormously in writing this one, because it turns out that I can’t remember a single word she said. This liberating discovery enables me to proceed without any fear that I might inadvertently influence you to abandon promising careers in business, the law or politics for the giddy delights of becoming a gay wizard.



You see? If all you remember in years to come is the ‘gay wizard’ joke, I’ve come out ahead of Baroness Mary Warnock. Achievable goals: the first step to self improvement.



Actually, I have wracked my mind and heart for what I ought to say to you today. I have asked myself what I wish I had known at my own graduation, and what important lessons I have learned in the 21 years that have expired between that day and this.



I have come up with two answers. On this wonderful day when we are gathered together to celebrate your academic success, I have decided to talk to you about the benefits of failure. And as you stand on the threshold of what is sometimes called ‘real life’, I want to extol the crucial importance of imagination.



These may seem quixotic or paradoxical choices, but please bear with me.



Looking back at the 21-year-old that I was at graduation, is a slightly uncomfortable experience for the 42-year-old that she has become. Half my lifetime ago, I was striking an uneasy balance between the ambition I had for myself, and what those closest to me expected of me.



I was convinced that the only thing I wanted to do, ever, was to write novels. However, my parents, both of whom came from impoverished backgrounds and neither of whom had been to college, took the view that my overactive imagination was an amusing personal quirk that would never pay a mortgage, or secure a pension. I know that the irony strikes with the force of a cartoon anvil, now.



So they hoped that I would take a vocational degree; I wanted to study English Literature. A compromise was reached that in retrospect satisfied nobody, and I went up to study Modern Languages. Hardly had my parents’ car rounded the corner at the end of the road than I ditched German and scuttled off down the Classics corridor.



I cannot remember telling my parents that I was studying Classics; they might well have found out for the first time on graduation day. Of all the subjects on this planet, I think they would have been hard put to name one less useful than Greek mythology when it came to securing the keys to an executive bathroom.



I would like to make it clear, in parenthesis, that I do not blame my parents for their point of view. There is an expiry date on blaming your parents for steering you in the wrong direction; the moment you are old enough to take the wheel, responsibility lies with you. What is more, I cannot criticise my parents for hoping that I would never experience poverty. They had been poor themselves, and I have since been poor, and I quite agree with them that it is not an ennobling experience. Poverty entails fear, and stress, and sometimes depression; it means a thousand petty humiliations and hardships. Climbing out of poverty by your own efforts, that is indeed something on which to pride yourself, but poverty itself is romanticised only by fools.



What I feared most for myself at your age was not poverty, but failure.

At your age, in spite of a distinct lack of motivation at university, where I had spent far too long in the coffee bar writing stories, and far too little time at lectures, I had a knack for passing examinations, and that, for years, had been the measure of success in my life and that of my peers.



I am not dull enough to suppose that because you are young, gifted and well-educated, you have never known hardship or heartbreak. Talent and intelligence never yet inoculated anyone against the caprice of the Fates, and I do not for a moment suppose that everyone here has enjoyed an existence of unruffled privilege and contentment.



However, the fact that you are graduating from Harvard suggests that you are not very well-acquainted with failure. You might be driven by a fear of failure quite as much as a desire for success. Indeed, your conception of failure might not be too far from the average person’s idea of success, so high have you already flown.



Ultimately, we all have to decide for ourselves what constitutes failure, but the world is quite eager to give you a set of criteria if you let it. So I think it fair to say that by any conventional measure, a mere seven years after my graduation day, I had failed on an epic scale. An exceptionally short-lived marriage had imploded, and I was jobless, a lone parent, and as poor as it is possible to be in modern Britain, without being homeless. The fears that my parents had had for me, and that I had had for myself, had both come to pass, and by every usual standard, I was the biggest failure I knew.



Now, I am not going to stand here and tell you that failure is fun. That period of my life was a dark one, and I had no idea that there was going to be what the press has since represented as a kind of fairy tale resolution. I had no idea then how far the tunnel extended, and for a long time, any light at the end of it was a hope rather than a reality.



So why do I talk about the benefits of failure? Simply because failure meant a stripping away of the inessential. I stopped pretending to myself that I was anything other than what I was, and began to direct all my energy into finishing the only work that mattered to me. Had I really succeeded at anything else, I might never have found the determination to succeed in the one arena I believed I truly belonged. I was set free, because my greatest fear had been realised, and I was still alive, and I still had a daughter whom I adored, and I had an old typewriter and a big idea. And so rock bottom became the solid foundation on which I rebuilt my life.



You might never fail on the scale I did, but some failure in life is inevitable. It is impossible to live without failing at something, unless you live so cautiously that you might as well not have lived at all – in which case, you fail by default.



Failure gave me an inner security that I had never attained by passing examinations. Failure taught me things about myself that I could have learned no other way. I discovered that I had a strong will, and more discipline than I had suspected; I also found out that I had friends whose value was truly above the price of rubies.



The knowledge that you have emerged wiser and stronger from setbacks means that you are, ever after, secure in your ability to survive. You will never truly know yourself, or the strength of your relationships, until both have been tested by adversity. Such knowledge is a true gift, for all that it is painfully won, and it has been worth more than any qualification I ever earned.



================================================
FILE: download/vector.R
================================================






<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="utf-8">
  <link rel="dns-prefetch" href="https://assets-cdn.github.com">
  <link rel="dns-prefetch" href="https://avatars0.githubusercontent.com">
  <link rel="dns-prefetch" href="https://avatars1.githubusercontent.com">
  <link rel="dns-prefetch" href="https://avatars2.githubusercontent.com">
  <link rel="dns-prefetch" href="https://avatars3.githubusercontent.com">
  <link rel="dns-prefetch" href="https://github-cloud.s3.amazonaws.com">
  <link rel="dns-prefetch" href="https://user-images.githubusercontent.com/">



  <link crossorigin="anonymous" media="all" integrity="sha512-qQ+v+W1uJYfDMrQ/cwCVI+AGTsn1yi4rCU6KX45obe52BoF+WiHNeQ11u63iJA05vyivY57xNbhAsyK4/j1ZIQ==" rel="stylesheet" href="https://assets-cdn.github.com/assets/frameworks-01356238c65ce56a395237b592b58668.css" />
  <link crossorigin="anonymous" media="all" integrity="sha512-MvjOFVG9G6K0C5Y8lXI60HDgJqbmLdOSVWxxV2ekgmwhBJPfVCmpuIauiReTQMNFOZqPuan4eT4tguke0l2M3g==" rel="stylesheet" href="https://assets-cdn.github.com/assets/github-3a00484da6571761791f22bb115f2d6c.css" />
  
  
  <link crossorigin="anonymous" media="all" integrity="sha512-2jE+5s+6LV2ckEshC+YTwb0A/IhES9iLMcwfkkybRMMGemXcEjAx8wdd2ZbbVr1dYBJt+fkCaR+UvXiybFr/sA==" rel="stylesheet" href="https://assets-cdn.github.com/assets/site-99075769314a73391cea9aa2907a29f9.css" />
  

  <meta name="viewport" content="width=device-width">
  
  <title>analytics/20a-vectors.R at master · dupadhyaya/analytics · GitHub</title>
    <meta name="description" content="My work on Analytics and Research in Data Science">
    <link rel="search" type="application/opensearchdescription+xml" href="/opensearch.xml" title="GitHub">
  <link rel="fluid-icon" href="https://github.com/fluidicon.png" title="GitHub">
  <meta property="fb:app_id" content="1401488693436528">

    
    <meta property="og:image" content="https://avatars0.githubusercontent.com/u/6006907?s=400&amp;v=4" /><meta property="og:site_name" content="GitHub" /><meta property="og:type" content="object" /><meta property="og:title" content="dupadhyaya/analytics" /><meta property="og:url" content="https://github.com/dupadhyaya/analytics" /><meta property="og:description" content="analytics - My work on Analytics and Research in Data Science" />

  <link rel="assets" href="https://assets-cdn.github.com/">
  
  <meta name="pjax-timeout" content="1000">
  
  <meta name="request-id" content="CDCA:5EC6:E6DF3A:1A93D9A:5B63342F" data-pjax-transient>


  

  <meta name="selected-link" value="repo_source" data-pjax-transient>

    <meta name="google-site-verification" content="KT5gs8h0wvaagLKAVWq8bbeNwnZZK1r1XQysX3xurLU">
  <meta name="google-site-verification" content="ZzhVyEFwb7w3e0-uOTltm8Jsck2F5StVihD0exw2fsA">
  <meta name="google-site-verification" content="GXs5KoUUkNCoaAZn7wPN-t01Pywp9M3sEjnt_3_ZWPc">
    <meta name="google-analytics" content="UA-3769691-2">

<meta name="octolytics-host" content="collector.githubapp.com" /><meta name="octolytics-app-id" content="github" /><meta name="octolytics-event-url" content="https://collector.githubapp.com/github-external/browser_event" /><meta name="octolytics-dimension-request_id" content="CDCA:5EC6:E6DF3A:1A93D9A:5B63342F" /><meta name="octolytics-dimension-region_edge" content="iad" /><meta name="octolytics-dimension-region_render" content="iad" />
<meta name="analytics-location" content="/&lt;user-name&gt;/&lt;repo-name&gt;/blob/show" data-pjax-transient="true" />




<meta class="js-ga-set" name="dimension1" content="Logged Out">


  

      <meta name="hostname" content="github.com">
    <meta name="user-login" content="">

      <meta name="expected-hostname" content="github.com">
    <meta name="js-proxy-site-detection-payload" content="Y2ExNTBjNmQxYTU3OGEyM2RlYjNmNTk5OTFmNzk4ZTA5MWVlZjA2N2RkYWJkMTlmNDI3MzNiMDEzNjg4MjFjN3x7InJlbW90ZV9hZGRyZXNzIjoiMTIyLjE3Ny4xNTEuMjIiLCJyZXF1ZXN0X2lkIjoiQ0RDQTo1RUM2OkU2REYzQToxQTkzRDlBOjVCNjMzNDJGIiwidGltZXN0YW1wIjoxNTMzMjI4MDgzLCJob3N0IjoiZ2l0aHViLmNvbSJ9">

    <meta name="enabled-features" content="DASHBOARD_V2_LAYOUT_OPT_IN,EXPLORE_DISCOVER_REPOSITORIES,UNIVERSE_BANNER,FREE_TRIALS,MARKETPLACE_INSIGHTS,MARKETPLACE_PLAN_RESTRICTION_EDITOR,MARKETPLACE_SEARCH,MARKETPLACE_INSIGHTS_CONVERSION_PERCENTAGES">

  <meta name="html-safe-nonce" content="2e9da7e776ad9d51aff45b9475086a0423b168df">

  <meta http-equiv="x-pjax-version" content="b7335acc108d21d59e28fb95072c86fb">
  

      <link href="https://github.com/dupadhyaya/analytics/commits/master.atom" rel="alternate" title="Recent Commits to analytics:master" type="application/atom+xml">

  <meta name="go-import" content="github.com/dupadhyaya/analytics git https://github.com/dupadhyaya/analytics.git">

  <meta name="octolytics-dimension-user_id" content="6006907" /><meta name="octolytics-dimension-user_login" content="dupadhyaya" /><meta name="octolytics-dimension-repository_id" content="123219793" /><meta name="octolytics-dimension-repository_nwo" content="dupadhyaya/analytics" /><meta name="octolytics-dimension-repository_public" content="true" /><meta name="octolytics-dimension-repository_is_fork" content="false" /><meta name="octolytics-dimension-repository_network_root_id" content="123219793" /><meta name="octolytics-dimension-repository_network_root_nwo" content="dupadhyaya/analytics" /><meta name="octolytics-dimension-repository_explore_github_marketplace_ci_cta_shown" content="false" />


    <link rel="canonical" href="https://github.com/dupadhyaya/analytics/blob/master/15a-DS/20a-vectors.R" data-pjax-transient>


  <meta name="browser-stats-url" content="https://api.github.com/_private/browser/stats">

  <meta name="browser-errors-url" content="https://api.github.com/_private/browser/errors">

  <link rel="mask-icon" href="https://assets-cdn.github.com/pinned-octocat.svg" color="#000000">
  <link rel="icon" type="image/x-icon" class="js-site-favicon" href="https://assets-cdn.github.com/favicon.ico">

<meta name="theme-color" content="#1e2327">



<link rel="manifest" href="/manifest.json" crossOrigin="use-credentials">

  </head>

  <body class="logged-out env-production page-blob">
    

  <div class="position-relative js-header-wrapper ">
    <a href="#start-of-content" tabindex="1" class="px-2 py-4 bg-blue text-white show-on-focus js-skip-to-content">Skip to content</a>
    <div id="js-pjax-loader-bar" class="pjax-loader-bar"><div class="progress"></div></div>

    
    
    



        


  <header class="Header header-logged-out  position-relative f4 py-3" role="banner" >
    <div class="container-lg d-flex px-3">
      <div class="d-flex flex-justify-between flex-items-center">
        <a class="header-logo-invertocat my-0" href="https://github.com/" aria-label="Homepage" data-ga-click="(Logged out) Header, go to homepage, icon:logo-wordmark; experiment:site_header_dropdowns; group:control">
          <svg height="32" class="octicon octicon-mark-github" viewBox="0 0 16 16" version="1.1" width="32" aria-hidden="true"><path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"/></svg>
        </a>

      </div>

      <div class="HeaderMenu d-flex flex-justify-between flex-auto">
          <nav class="mt-0">
            <ul class="d-flex list-style-none">
                <li class="ml-2">
                  <a class="js-selected-navigation-item HeaderNavlink px-0 py-2 m-0" data-ga-click="Header, click, Nav menu - item:features; experiment:site_header_dropdowns; group:control" data-selected-links="/features /features/project-management /features/code-review /features/project-management /features/integrations /features" href="/features">
                    Features
</a>                </li>
                <li class="ml-4">
                  <a class="js-selected-navigation-item HeaderNavlink px-0 py-2 m-0" data-ga-click="Header, click, Nav menu - item:business; experiment:site_header_dropdowns; group:control" data-selected-links="/business /business/security /business/customers /business" href="/business">
                    Business
</a>                </li>

                <li class="ml-4">
                  <a class="js-selected-navigation-item HeaderNavlink px-0 py-2 m-0" data-ga-click="Header, click, Nav menu - item:explore; experiment:site_header_dropdowns; group:control" data-selected-links="/explore /trending /trending/developers /integrations /integrations/feature/code /integrations/feature/collaborate /integrations/feature/ship showcases showcases_search showcases_landing /explore" href="/explore">
                    Explore
</a>                </li>

                <li class="ml-4">
                      <a class="js-selected-navigation-item HeaderNavlink px-0 py-2 m-0" data-ga-click="Header, click, Nav menu - item:marketplace; experiment:site_header_dropdowns; group:control" data-selected-links=" /marketplace" href="/marketplace">
                        Marketplace
</a>                </li>
                <li class="ml-4">
                  <a class="js-selected-navigation-item HeaderNavlink px-0 py-2 m-0" data-ga-click="Header, click, Nav menu - item:pricing; experiment:site_header_dropdowns; group:control" data-selected-links="/pricing /pricing/developer /pricing/team /pricing/business-hosted /pricing/business-enterprise /pricing" href="/pricing">
                    Pricing
</a>                </li>
            </ul>
          </nav>

        <div class="d-flex">
            <div class="d-lg-flex flex-items-center mr-3">
              <div class="header-search scoped-search site-scoped-search js-site-search position-relative js-jump-to"
  role="search combobox"
  aria-owns="jump-to-results"
  aria-label="Search or jump to"
  aria-haspopup="listbox"
  aria-expanded="true"
>
  <div class="position-relative">
    <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="js-site-search-form" data-scope-type="Repository" data-scope-id="123219793" data-scoped-search-url="/dupadhyaya/analytics/search" data-unscoped-search-url="/search" action="/dupadhyaya/analytics/search" accept-charset="UTF-8" method="get"><input name="utf8" type="hidden" value="&#x2713;" />
      <label class="form-control header-search-wrapper header-search-wrapper-jump-to position-relative d-flex flex-justify-between flex-items-center js-chromeless-input-container">
        <input type="text"
          class="form-control header-search-input jump-to-field js-jump-to-field js-site-search-focus js-site-search-field is-clearable"
          data-hotkey="s,/"
          name="q"
          value=""
          placeholder="Search"
          data-unscoped-placeholder="Search GitHub"
          data-scoped-placeholder="Search"
          autocapitalize="off"
          aria-autocomplete="list"
          aria-controls="jump-to-results"
          data-jump-to-suggestions-path="/_graphql/GetSuggestedNavigationDestinations#csrf-token=CwsrKajgEXbzwZZ/nUQDv2bW1/bnerIrUXIXi9TA+HaQa2nRmnpqXiwBGYWbSx1AoIPvi39HPauR8beGErN67w=="
          spellcheck="false"
          autocomplete="off"
          >
          <input type="hidden" class="js-site-search-type-field" name="type" >
            <img src="https://assets-cdn.github.com/images/search-shortcut-hint.svg" alt="" class="mr-2 header-search-key-slash">

            <div class="Box position-absolute overflow-hidden d-none jump-to-suggestions js-jump-to-suggestions-container">
              <ul class="d-none js-jump-to-suggestions-template-container">
                <li class="d-flex flex-justify-start flex-items-center p-0 f5 navigation-item js-navigation-item">
                  <a tabindex="-1" class="no-underline d-flex flex-auto flex-items-center p-2 jump-to-suggestions-path js-jump-to-suggestion-path js-navigation-open" href="">
                    <div class="jump-to-octicon js-jump-to-octicon mr-2 text-center d-none"></div>
                    <img class="avatar mr-2 flex-shrink-0 js-jump-to-suggestion-avatar" alt="" aria-label="Team" src="" width="28" height="28">

                    <div class="jump-to-suggestion-name js-jump-to-suggestion-name flex-auto overflow-hidden text-left no-wrap css-truncate css-truncate-target">
                    </div>

                    <div class="border rounded-1 flex-shrink-0 bg-gray px-1 text-gray-light ml-1 f6 d-none js-jump-to-badge-search">
                      <span class="js-jump-to-badge-search-text-default d-none" aria-label="in this repository">
                        In this repository
                      </span>
                      <span class="js-jump-to-badge-search-text-global d-none" aria-label="in all of GitHub">
                        All GitHub
                      </span>
                      <span aria-hidden="true" class="d-inline-block ml-1 v-align-middle">↵</span>
                    </div>

                    <div aria-hidden="true" class="border rounded-1 flex-shrink-0 bg-gray px-1 text-gray-light ml-1 f6 d-none d-on-nav-focus js-jump-to-badge-jump">
                      Jump to
                      <span class="d-inline-block ml-1 v-align-middle">↵</span>
                    </div>
                  </a>
                </li>
                <svg height="16" width="16" class="octicon octicon-repo flex-shrink-0 js-jump-to-repo-octicon-template" title="Repository" aria-label="Repository" viewBox="0 0 12 16" version="1.1" role="img"><path fill-rule="evenodd" d="M4 9H3V8h1v1zm0-3H3v1h1V6zm0-2H3v1h1V4zm0-2H3v1h1V2zm8-1v12c0 .55-.45 1-1 1H6v2l-1.5-1.5L3 16v-2H1c-.55 0-1-.45-1-1V1c0-.55.45-1 1-1h10c.55 0 1 .45 1 1zm-1 10H1v2h2v-1h3v1h5v-2zm0-10H2v9h9V1z"/></svg>
                <svg height="16" width="16" class="octicon octicon-project flex-shrink-0 js-jump-to-project-octicon-template" title="Project" aria-label="Project" viewBox="0 0 15 16" version="1.1" role="img"><path fill-rule="evenodd" d="M10 12h3V2h-3v10zm-4-2h3V2H6v8zm-4 4h3V2H2v12zm-1 1h13V1H1v14zM14 0H1a1 1 0 0 0-1 1v14a1 1 0 0 0 1 1h13a1 1 0 0 0 1-1V1a1 1 0 0 0-1-1z"/></svg>
                <svg height="16" width="16" class="octicon octicon-search flex-shrink-0 js-jump-to-search-octicon-template" title="Search" aria-label="Search" viewBox="0 0 16 16" version="1.1" role="img"><path fill-rule="evenodd" d="M15.7 13.3l-3.81-3.83A5.93 5.93 0 0 0 13 6c0-3.31-2.69-6-6-6S1 2.69 1 6s2.69 6 6 6c1.3 0 2.48-.41 3.47-1.11l3.83 3.81c.19.2.45.3.7.3.25 0 .52-.09.7-.3a.996.996 0 0 0 0-1.41v.01zM7 10.7c-2.59 0-4.7-2.11-4.7-4.7 0-2.59 2.11-4.7 4.7-4.7 2.59 0 4.7 2.11 4.7 4.7 0 2.59-2.11 4.7-4.7 4.7z"/></svg>
              </ul>
              <ul class="d-none js-jump-to-no-results-template-container">
                <li class="d-flex flex-justify-center flex-items-center p-3 f5 d-none">
                  <span class="text-gray">No suggested jump to results</span>
                </li>
              </ul>

              <ul id="jump-to-results" class="js-navigation-container jump-to-suggestions-results-container js-jump-to-suggestions-results-container" >
                <li class="d-flex flex-justify-center flex-items-center p-0 f5">
                  <img src="https://assets-cdn.github.com/images/spinners/octocat-spinner-128.gif" alt="Octocat Spinner Icon" class="m-2" width="28">
                </li>
              </ul>
            </div>
      </label>
</form>  </div>
</div>

            </div>

          <span class="d-inline-block">
              <div class="HeaderNavlink px-0 py-2 m-0">
                <a class="text-bold text-white no-underline" href="/login?return_to=%2Fdupadhyaya%2Fanalytics%2Fblob%2Fmaster%2F15a-DS%2F20a-vectors.R" data-ga-click="(Logged out) Header, clicked Sign in, text:sign-in; experiment:site_header_dropdowns; group:control">Sign in</a>
                  <span class="text-gray">or</span>
                  <a class="text-bold text-white no-underline" href="/join?source=header-repo" data-ga-click="(Logged out) Header, clicked Sign up, text:sign-up; experiment:site_header_dropdowns; group:control">Sign up</a>
              </div>
          </span>
        </div>
      </div>
    </div>
  </header>

  </div>

  <div id="start-of-content" class="show-on-focus"></div>

    <div id="js-flash-container">


</div>



  <div role="main" class="application-main ">
        <div itemscope itemtype="http://schema.org/SoftwareSourceCode" class="">
    <div id="js-repo-pjax-container" data-pjax-container >
      







  <div class="pagehead repohead instapaper_ignore readability-menu experiment-repo-nav  ">
    <div class="repohead-details-container clearfix container">

      <ul class="pagehead-actions">
  <li>
      <a href="/login?return_to=%2Fdupadhyaya%2Fanalytics"
    class="btn btn-sm btn-with-count tooltipped tooltipped-n"
    aria-label="You must be signed in to watch a repository" rel="nofollow">
    <svg class="octicon octicon-eye v-align-text-bottom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/></svg>
    Watch
  </a>
  <a class="social-count" href="/dupadhyaya/analytics/watchers"
     aria-label="2 users are watching this repository">
    2
  </a>

  </li>

  <li>
      <a href="/login?return_to=%2Fdupadhyaya%2Fanalytics"
    class="btn btn-sm btn-with-count tooltipped tooltipped-n"
    aria-label="You must be signed in to star a repository" rel="nofollow">
    <svg class="octicon octicon-star v-align-text-bottom" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M14 6l-4.9-.64L7 1 4.9 5.36 0 6l3.6 3.26L2.67 14 7 11.67 11.33 14l-.93-4.74L14 6z"/></svg>
    Star
  </a>

    <a class="social-count js-social-count" href="/dupadhyaya/analytics/stargazers"
      aria-label="139 users starred this repository">
      139
    </a>

  </li>

  <li>
      <a href="/login?return_to=%2Fdupadhyaya%2Fanalytics"
        class="btn btn-sm btn-with-count tooltipped tooltipped-n"
        aria-label="You must be signed in to fork a repository" rel="nofollow">
        <svg class="octicon octicon-repo-forked v-align-text-bottom" viewBox="0 0 10 16" version="1.1" width="10" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8 1a1.993 1.993 0 0 0-1 3.72V6L5 8 3 6V4.72A1.993 1.993 0 0 0 2 1a1.993 1.993 0 0 0-1 3.72V6.5l3 3v1.78A1.993 1.993 0 0 0 5 15a1.993 1.993 0 0 0 1-3.72V9.5l3-3V4.72A1.993 1.993 0 0 0 8 1zM2 4.2C1.34 4.2.8 3.65.8 3c0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2 0 .65-.55 1.2-1.2 1.2zm3 10c-.66 0-1.2-.55-1.2-1.2 0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2 0 .65-.55 1.2-1.2 1.2zm3-10c-.66 0-1.2-.55-1.2-1.2 0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2 0 .65-.55 1.2-1.2 1.2z"/></svg>
        Fork
      </a>

    <a href="/dupadhyaya/analytics/network/members" class="social-count"
       aria-label="11 users forked this repository">
      11
    </a>
  </li>
</ul>

      <h1 class="public ">
  <svg class="octicon octicon-repo" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M4 9H3V8h1v1zm0-3H3v1h1V6zm0-2H3v1h1V4zm0-2H3v1h1V2zm8-1v12c0 .55-.45 1-1 1H6v2l-1.5-1.5L3 16v-2H1c-.55 0-1-.45-1-1V1c0-.55.45-1 1-1h10c.55 0 1 .45 1 1zm-1 10H1v2h2v-1h3v1h5v-2zm0-10H2v9h9V1z"/></svg>
  <span class="author" itemprop="author"><a class="url fn" rel="author" href="/dupadhyaya">dupadhyaya</a></span><!--
--><span class="path-divider">/</span><!--
--><strong itemprop="name"><a data-pjax="#js-repo-pjax-container" href="/dupadhyaya/analytics">analytics</a></strong>

</h1>

    </div>
    
<nav class="reponav js-repo-nav js-sidenav-container-pjax container"
     itemscope
     itemtype="http://schema.org/BreadcrumbList"
     role="navigation"
     data-pjax="#js-repo-pjax-container">

  <span itemscope itemtype="http://schema.org/ListItem" itemprop="itemListElement">
    <a class="js-selected-navigation-item selected reponav-item" itemprop="url" data-hotkey="g c" data-selected-links="repo_source repo_downloads repo_commits repo_releases repo_tags repo_branches repo_packages /dupadhyaya/analytics" href="/dupadhyaya/analytics">
      <svg class="octicon octicon-code" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M9.5 3L8 4.5 11.5 8 8 11.5 9.5 13 14 8 9.5 3zm-5 0L0 8l4.5 5L6 11.5 2.5 8 6 4.5 4.5 3z"/></svg>
      <span itemprop="name">Code</span>
      <meta itemprop="position" content="1">
</a>  </span>

    <span itemscope itemtype="http://schema.org/ListItem" itemprop="itemListElement">
      <a itemprop="url" data-hotkey="g i" class="js-selected-navigation-item reponav-item" data-selected-links="repo_issues repo_labels repo_milestones /dupadhyaya/analytics/issues" href="/dupadhyaya/analytics/issues">
        <svg class="octicon octicon-issue-opened" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"/></svg>
        <span itemprop="name">Issues</span>
        <span class="Counter">0</span>
        <meta itemprop="position" content="2">
</a>    </span>

  <span itemscope itemtype="http://schema.org/ListItem" itemprop="itemListElement">
    <a data-hotkey="g p" itemprop="url" class="js-selected-navigation-item reponav-item" data-selected-links="repo_pulls checks /dupadhyaya/analytics/pulls" href="/dupadhyaya/analytics/pulls">
      <svg class="octicon octicon-git-pull-request" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M11 11.28V5c-.03-.78-.34-1.47-.94-2.06C9.46 2.35 8.78 2.03 8 2H7V0L4 3l3 3V4h1c.27.02.48.11.69.31.21.2.3.42.31.69v6.28A1.993 1.993 0 0 0 10 15a1.993 1.993 0 0 0 1-3.72zm-1 2.92c-.66 0-1.2-.55-1.2-1.2 0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2 0 .65-.55 1.2-1.2 1.2zM4 3c0-1.11-.89-2-2-2a1.993 1.993 0 0 0-1 3.72v6.56A1.993 1.993 0 0 0 2 15a1.993 1.993 0 0 0 1-3.72V4.72c.59-.34 1-.98 1-1.72zm-.8 10c0 .66-.55 1.2-1.2 1.2-.65 0-1.2-.55-1.2-1.2 0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2zM2 4.2C1.34 4.2.8 3.65.8 3c0-.65.55-1.2 1.2-1.2.65 0 1.2.55 1.2 1.2 0 .65-.55 1.2-1.2 1.2z"/></svg>
      <span itemprop="name">Pull requests</span>
      <span class="Counter">0</span>
      <meta itemprop="position" content="3">
</a>  </span>

    <a data-hotkey="g b" class="js-selected-navigation-item reponav-item" data-selected-links="repo_projects new_repo_project repo_project /dupadhyaya/analytics/projects" href="/dupadhyaya/analytics/projects">
      <svg class="octicon octicon-project" viewBox="0 0 15 16" version="1.1" width="15" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M10 12h3V2h-3v10zm-4-2h3V2H6v8zm-4 4h3V2H2v12zm-1 1h13V1H1v14zM14 0H1a1 1 0 0 0-1 1v14a1 1 0 0 0 1 1h13a1 1 0 0 0 1-1V1a1 1 0 0 0-1-1z"/></svg>
      Projects
      <span class="Counter" >0</span>
</a>


  <a class="js-selected-navigation-item reponav-item" data-selected-links="repo_graphs repo_contributors dependency_graph pulse /dupadhyaya/analytics/pulse" href="/dupadhyaya/analytics/pulse">
    <svg class="octicon octicon-graph" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M16 14v1H0V0h1v14h15zM5 13H3V8h2v5zm4 0H7V3h2v10zm4 0h-2V6h2v7z"/></svg>
    Insights
</a>

</nav>


  </div>

<div class="container new-discussion-timeline experiment-repo-nav  ">
  <div class="repository-content ">

    
  <a class="d-none js-permalink-shortcut" data-hotkey="y" href="/dupadhyaya/analytics/blob/2942702c592fc6b2a19c9a65a32b5812e30573bb/15a-DS/20a-vectors.R">Permalink</a>

  <!-- blob contrib key: blob_contributors:v21:435a7eeb59b387982b97c92d5c7485a7 -->

      <div class="signup-prompt-bg rounded-1">
      <div class="signup-prompt p-4 text-center mb-4 rounded-1">
        <div class="position-relative">
          <!-- '"` --><!-- </textarea></xmp> --></option></form><form action="/site/dismiss_signup_prompt" accept-charset="UTF-8" method="post"><input name="utf8" type="hidden" value="&#x2713;" /><input type="hidden" name="authenticity_token" value="dmltOZJYKOGypISdQbrPiiudQTaZFjdDpfKJdz4IjTpB6BY8djEfKc/iOeY2Tb7aESnUXa4PQ7YtYtiaOq++lA==" />
            <button type="submit" class="position-absolute top-0 right-0 btn-link link-gray" data-ga-click="(Logged out) Sign up prompt, clicked Dismiss, text:dismiss">
              Dismiss
            </button>
</form>          <h3 class="pt-2">Join GitHub today</h3>
          <p class="col-6 mx-auto">GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.</p>
          <a class="btn btn-primary" href="/join?source=prompt-blob-show" data-ga-click="(Logged out) Sign up prompt, clicked Sign up, text:sign-up">Sign up</a>
        </div>
      </div>
    </div>


  <div class="file-navigation">
    
<div class="select-menu branch-select-menu js-menu-container js-select-menu float-left">
  <button class=" btn btn-sm select-menu-button js-menu-target css-truncate" data-hotkey="w"
    
    type="button" aria-label="Switch branches or tags" aria-expanded="false" aria-haspopup="true">
      <i>Branch:</i>
      <span class="js-select-button css-truncate-target">master</span>
  </button>

  <div class="select-menu-modal-holder js-menu-content js-navigation-container" data-pjax>

    <div class="select-menu-modal">
      <div class="select-menu-header">
        <svg class="octicon octicon-x js-menu-close" role="img" aria-label="Close" viewBox="0 0 12 16" version="1.1" width="12" height="16"><path fill-rule="evenodd" d="M7.48 8l3.75 3.75-1.48 1.48L6 9.48l-3.75 3.75-1.48-1.48L4.52 8 .77 4.25l1.48-1.48L6 6.52l3.75-3.75 1.48 1.48L7.48 8z"/></svg>
        <span class="select-menu-title">Switch branches/tags</span>
      </div>

      <div class="select-menu-filters">
        <div class="select-menu-text-filter">
          <input type="text" aria-label="Filter branches/tags" id="context-commitish-filter-field" class="form-control js-filterable-field js-navigation-enable" placeholder="Filter branches/tags">
        </div>
        <div class="select-menu-tabs">
          <ul>
            <li class="select-menu-tab">
              <a href="#" data-tab-filter="branches" data-filter-placeholder="Filter branches/tags" class="js-select-menu-tab" role="tab">Branches</a>
            </li>
            <li class="select-menu-tab">
              <a href="#" data-tab-filter="tags" data-filter-placeholder="Find a tag…" class="js-select-menu-tab" role="tab">Tags</a>
            </li>
          </ul>
        </div>
      </div>

      <div class="select-menu-list select-menu-tab-bucket js-select-menu-tab-bucket" data-tab-filter="branches" role="menu">

        <div data-filterable-for="context-commitish-filter-field" data-filterable-type="substring">


            <a class="select-menu-item js-navigation-item js-navigation-open selected"
               href="/dupadhyaya/analytics/blob/master/15a-DS/20a-vectors.R"
               data-name="master"
               data-skip-pjax="true"
               rel="nofollow">
              <svg class="octicon octicon-check select-menu-item-icon" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M12 5l-8 8-4-4 1.5-1.5L4 10l6.5-6.5L12 5z"/></svg>
              <span class="select-menu-item-text css-truncate-target js-select-menu-filter-text">
                master
              </span>
            </a>
        </div>

          <div class="select-menu-no-results">Nothing to show</div>
      </div>

      <div class="select-menu-list select-menu-tab-bucket js-select-menu-tab-bucket" data-tab-filter="tags">
        <div data-filterable-for="context-commitish-filter-field" data-filterable-type="substring">


        </div>

        <div class="select-menu-no-results">Nothing to show</div>
      </div>

    </div>
  </div>
</div>

    <div class="BtnGroup float-right">
      <a href="/dupadhyaya/analytics/find/master"
            class="js-pjax-capture-input btn btn-sm BtnGroup-item"
            data-pjax
            data-hotkey="t">
        Find file
      </a>
      <clipboard-copy for="blob-path" class="btn btn-sm BtnGroup-item">
        Copy path
      </clipboard-copy>
    </div>
    <div id="blob-path" class="breadcrumb">
      <span class="repo-root js-repo-root"><span class="js-path-segment"><a data-pjax="true" href="/dupadhyaya/analytics"><span>analytics</span></a></span></span><span class="separator">/</span><span class="js-path-segment"><a data-pjax="true" href="/dupadhyaya/analytics/tree/master/15a-DS"><span>15a-DS</span></a></span><span class="separator">/</span><strong class="final-path">20a-vectors.R</strong>
    </div>
  </div>


  
  <div class="commit-tease">
      <span class="float-right">
        <a class="commit-tease-sha" href="/dupadhyaya/analytics/commit/f00388103dfd96b55c593008341d039bdbcc4861" data-pjax>
          f003881
        </a>
        <relative-time datetime="2018-07-20T07:08:00Z">Jul 20, 2018</relative-time>
      </span>
      <div>
        <a rel="author" data-skip-pjax="true" data-hovercard-user-id="6006907" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="/dupadhyaya"><img class="avatar" src="https://avatars1.githubusercontent.com/u/6006907?s=40&amp;v=4" width="20" height="20" alt="@dupadhyaya" /></a>
        <a class="user-mention" rel="author" data-hovercard-user-id="6006907" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="/dupadhyaya">dupadhyaya</a>
          <a data-pjax="true" title="19 Jul" class="message" href="/dupadhyaya/analytics/commit/f00388103dfd96b55c593008341d039bdbcc4861">19 Jul</a>
      </div>

    <div class="commit-tease-contributors">
      
<details class="details-reset details-overlay details-overlay-dark lh-default text-gray-dark float-left mr-2" id="blob_contributors_box">
  <summary class="btn-link" aria-haspopup="dialog" >
    
    <span><strong>1</strong> contributor</span>
  </summary>
  <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast " aria-label="Users who have contributed to this file">
    <div class="Box-header">
      <button class="Box-btn-octicon btn-octicon float-right" type="button" aria-label="Close dialog" data-close-dialog>
        <svg class="octicon octicon-x" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.48 8l3.75 3.75-1.48 1.48L6 9.48l-3.75 3.75-1.48-1.48L4.52 8 .77 4.25l1.48-1.48L6 6.52l3.75-3.75 1.48 1.48L7.48 8z"/></svg>
      </button>
      <h3 class="Box-title">Users who have contributed to this file</h3>
    </div>
    
        <ul class="list-style-none overflow-auto">
            <li class="Box-row">
              <a class="link-gray-dark no-underline" href="/dupadhyaya">
                <img class="avatar mr-2" alt="" src="https://avatars1.githubusercontent.com/u/6006907?s=40&amp;v=4" width="20" height="20" />
                dupadhyaya
</a>            </li>
        </ul>

  </details-dialog>
</details>
      
    </div>
  </div>



  <div class="file">
    <div class="file-header">
  <div class="file-actions">

    <div class="BtnGroup">
      <a id="raw-url" class="btn btn-sm BtnGroup-item" href="/dupadhyaya/analytics/raw/master/15a-DS/20a-vectors.R">Raw</a>
        <a class="btn btn-sm js-update-url-with-hash BtnGroup-item" data-hotkey="b" href="/dupadhyaya/analytics/blame/master/15a-DS/20a-vectors.R">Blame</a>
      <a rel="nofollow" class="btn btn-sm BtnGroup-item" href="/dupadhyaya/analytics/commits/master/15a-DS/20a-vectors.R">History</a>
    </div>


        <button type="button" class="btn-octicon disabled tooltipped tooltipped-nw"
          aria-label="You must be signed in to make or propose changes">
          <svg class="octicon octicon-pencil" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M0 12v3h3l8-8-3-3-8 8zm3 2H1v-2h1v1h1v1zm10.3-9.3L12 6 9 3l1.3-1.3a.996.996 0 0 1 1.41 0l1.59 1.59c.39.39.39 1.02 0 1.41z"/></svg>
        </button>
        <button type="button" class="btn-octicon btn-octicon-danger disabled tooltipped tooltipped-nw"
          aria-label="You must be signed in to make or propose changes">
          <svg class="octicon octicon-trashcan" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M11 2H9c0-.55-.45-1-1-1H5c-.55 0-1 .45-1 1H2c-.55 0-1 .45-1 1v1c0 .55.45 1 1 1v9c0 .55.45 1 1 1h7c.55 0 1-.45 1-1V5c.55 0 1-.45 1-1V3c0-.55-.45-1-1-1zm-1 12H3V5h1v8h1V5h1v8h1V5h1v8h1V5h1v9zm1-10H2V3h9v1z"/></svg>
        </button>
  </div>

  <div class="file-info">
      138 lines (91 sloc)
      <span class="file-info-divider"></span>
    2.48 KB
  </div>
</div>

    

  <div itemprop="text" class="blob-wrapper data type-r">
      <table class="highlight tab-size js-file-line-container" data-tab-size="8">
      <tr>
        <td id="L1" class="blob-num js-line-number" data-line-number="1"></td>
        <td id="LC1" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span> Vectors</span></td>
      </tr>
      <tr>
        <td id="L2" class="blob-num js-line-number" data-line-number="2"></td>
        <td id="LC2" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L3" class="blob-num js-line-number" data-line-number="3"></td>
        <td id="LC3" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>sequence of data elements of the same basic type</span></td>
      </tr>
      <tr>
        <td id="L4" class="blob-num js-line-number" data-line-number="4"></td>
        <td id="LC4" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L5" class="blob-num js-line-number" data-line-number="5"></td>
        <td id="LC5" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Scalar</span></td>
      </tr>
      <tr>
        <td id="L6" class="blob-num js-line-number" data-line-number="6"></td>
        <td id="LC6" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L7" class="blob-num js-line-number" data-line-number="7"></td>
        <td id="LC7" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector----</span></td>
      </tr>
      <tr>
        <td id="L8" class="blob-num js-line-number" data-line-number="8"></td>
        <td id="LC8" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> c(<span class="pl-c1">1</span>, <span class="pl-c1">5</span>, <span class="pl-c1">4</span>, <span class="pl-c1">9</span>, <span class="pl-c1">0</span>)</td>
      </tr>
      <tr>
        <td id="L9" class="blob-num js-line-number" data-line-number="9"></td>
        <td id="LC9" class="blob-code blob-code-inner js-file-line">typeof(<span class="pl-smi">x</span>)  <span class="pl-c"><span class="pl-c">#</span>[1] &quot;double&quot;</span></td>
      </tr>
      <tr>
        <td id="L10" class="blob-num js-line-number" data-line-number="10"></td>
        <td id="LC10" class="blob-code blob-code-inner js-file-line">length(<span class="pl-smi">x</span>)   <span class="pl-c"><span class="pl-c">#</span>[1] 5</span></td>
      </tr>
      <tr>
        <td id="L11" class="blob-num js-line-number" data-line-number="11"></td>
        <td id="LC11" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L12" class="blob-num js-line-number" data-line-number="12"></td>
        <td id="LC12" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector using : operator</span></td>
      </tr>
      <tr>
        <td id="L13" class="blob-num js-line-number" data-line-number="13"></td>
        <td id="LC13" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> <span class="pl-c1">1</span><span class="pl-k">:</span><span class="pl-c1">7</span>; <span class="pl-smi">x</span>   <span class="pl-c"><span class="pl-c">#</span>[1] 1 2 3 4 5 6 7</span></td>
      </tr>
      <tr>
        <td id="L14" class="blob-num js-line-number" data-line-number="14"></td>
        <td id="LC14" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">y</span> <span class="pl-k">&lt;-</span> <span class="pl-c1">2</span><span class="pl-k">:</span><span class="pl-k">-</span><span class="pl-c1">2</span>; <span class="pl-smi">y</span>  <span class="pl-c"><span class="pl-c">#</span>[1]  2  1  0 -1 -2</span></td>
      </tr>
      <tr>
        <td id="L15" class="blob-num js-line-number" data-line-number="15"></td>
        <td id="LC15" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L16" class="blob-num js-line-number" data-line-number="16"></td>
        <td id="LC16" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector using seq command</span></td>
      </tr>
      <tr>
        <td id="L17" class="blob-num js-line-number" data-line-number="17"></td>
        <td id="LC17" class="blob-code blob-code-inner js-file-line">seq(<span class="pl-c1">1</span>, <span class="pl-c1">3</span>, <span class="pl-v">by</span><span class="pl-k">=</span><span class="pl-c1">0.2</span>)          <span class="pl-c"><span class="pl-c">#</span> specify step size</span></td>
      </tr>
      <tr>
        <td id="L18" class="blob-num js-line-number" data-line-number="18"></td>
        <td id="LC18" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>[1] 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0</span></td>
      </tr>
      <tr>
        <td id="L19" class="blob-num js-line-number" data-line-number="19"></td>
        <td id="LC19" class="blob-code blob-code-inner js-file-line">seq(<span class="pl-c1">1</span>, <span class="pl-c1">5</span>, <span class="pl-v">length.out</span><span class="pl-k">=</span><span class="pl-c1">4</span>)    <span class="pl-c"><span class="pl-c">#</span> specify length of the vector 4parts from 5</span></td>
      </tr>
      <tr>
        <td id="L20" class="blob-num js-line-number" data-line-number="20"></td>
        <td id="LC20" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>[1] 1.00 2.33 3.667 5.00</span></td>
      </tr>
      <tr>
        <td id="L21" class="blob-num js-line-number" data-line-number="21"></td>
        <td id="LC21" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L22" class="blob-num js-line-number" data-line-number="22"></td>
        <td id="LC22" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L23" class="blob-num js-line-number" data-line-number="23"></td>
        <td id="LC23" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Character/ String ----</span></td>
      </tr>
      <tr>
        <td id="L24" class="blob-num js-line-number" data-line-number="24"></td>
        <td id="LC24" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L25" class="blob-num js-line-number" data-line-number="25"></td>
        <td id="LC25" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Numeric ----</span></td>
      </tr>
      <tr>
        <td id="L26" class="blob-num js-line-number" data-line-number="26"></td>
        <td id="LC26" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L27" class="blob-num js-line-number" data-line-number="27"></td>
        <td id="LC27" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L28" class="blob-num js-line-number" data-line-number="28"></td>
        <td id="LC28" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Logical----</span></td>
      </tr>
      <tr>
        <td id="L29" class="blob-num js-line-number" data-line-number="29"></td>
        <td id="LC29" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L30" class="blob-num js-line-number" data-line-number="30"></td>
        <td id="LC30" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L31" class="blob-num js-line-number" data-line-number="31"></td>
        <td id="LC31" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L32" class="blob-num js-line-number" data-line-number="32"></td>
        <td id="LC32" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L33" class="blob-num js-line-number" data-line-number="33"></td>
        <td id="LC33" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L34" class="blob-num js-line-number" data-line-number="34"></td>
        <td id="LC34" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L35" class="blob-num js-line-number" data-line-number="35"></td>
        <td id="LC35" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span> Combining vectors with c()----</span></td>
      </tr>
      <tr>
        <td id="L36" class="blob-num js-line-number" data-line-number="36"></td>
        <td id="LC36" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L37" class="blob-num js-line-number" data-line-number="37"></td>
        <td id="LC37" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L38" class="blob-num js-line-number" data-line-number="38"></td>
        <td id="LC38" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L39" class="blob-num js-line-number" data-line-number="39"></td>
        <td id="LC39" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L40" class="blob-num js-line-number" data-line-number="40"></td>
        <td id="LC40" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector Coercian ----</span></td>
      </tr>
      <tr>
        <td id="L41" class="blob-num js-line-number" data-line-number="41"></td>
        <td id="LC41" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> c(<span class="pl-c1">1</span>, <span class="pl-c1">5.4</span>, <span class="pl-c1">TRUE</span>, <span class="pl-s"><span class="pl-pds">&quot;</span>hello<span class="pl-pds">&quot;</span></span>)</td>
      </tr>
      <tr>
        <td id="L42" class="blob-num js-line-number" data-line-number="42"></td>
        <td id="LC42" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">x</span>  <span class="pl-c"><span class="pl-c">#</span>[1] &quot;1&quot;     &quot;5.4&quot;   &quot;TRUE&quot;  &quot;hello&quot;</span></td>
      </tr>
      <tr>
        <td id="L43" class="blob-num js-line-number" data-line-number="43"></td>
        <td id="LC43" class="blob-code blob-code-inner js-file-line">typeof(<span class="pl-smi">x</span>)  <span class="pl-c"><span class="pl-c">#</span>[1] &quot;character&quot;</span></td>
      </tr>
      <tr>
        <td id="L44" class="blob-num js-line-number" data-line-number="44"></td>
        <td id="LC44" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L45" class="blob-num js-line-number" data-line-number="45"></td>
        <td id="LC45" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L46" class="blob-num js-line-number" data-line-number="46"></td>
        <td id="LC46" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector Recycling ----</span></td>
      </tr>
      <tr>
        <td id="L47" class="blob-num js-line-number" data-line-number="47"></td>
        <td id="LC47" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>If two vectors are of unequal length, the shorter one will be recycled in order to match the longer vector. Recycle values of u</span></td>
      </tr>
      <tr>
        <td id="L48" class="blob-num js-line-number" data-line-number="48"></td>
        <td id="LC48" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L49" class="blob-num js-line-number" data-line-number="49"></td>
        <td id="LC49" class="blob-code blob-code-inner js-file-line"><span class="pl-v">u</span> <span class="pl-k">=</span> c(<span class="pl-c1">10</span>, <span class="pl-c1">20</span>, <span class="pl-c1">30</span>) </td>
      </tr>
      <tr>
        <td id="L50" class="blob-num js-line-number" data-line-number="50"></td>
        <td id="LC50" class="blob-code blob-code-inner js-file-line"><span class="pl-v">v</span> <span class="pl-k">=</span> c(<span class="pl-c1">1</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">4</span>, <span class="pl-c1">5</span>, <span class="pl-c1">6</span>, <span class="pl-c1">7</span>, <span class="pl-c1">8</span>, <span class="pl-c1">9</span>) </td>
      </tr>
      <tr>
        <td id="L51" class="blob-num js-line-number" data-line-number="51"></td>
        <td id="LC51" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">u</span> <span class="pl-k">+</span> <span class="pl-smi">v</span> </td>
      </tr>
      <tr>
        <td id="L52" class="blob-num js-line-number" data-line-number="52"></td>
        <td id="LC52" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>[1] 11 22 33 14 25 36 17 28 39</span></td>
      </tr>
      <tr>
        <td id="L53" class="blob-num js-line-number" data-line-number="53"></td>
        <td id="LC53" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>1+10, 2+20, 3+30, 4+10, 5+20, 6+30, 7+10 ...</span></td>
      </tr>
      <tr>
        <td id="L54" class="blob-num js-line-number" data-line-number="54"></td>
        <td id="LC54" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L55" class="blob-num js-line-number" data-line-number="55"></td>
        <td id="LC55" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector Index ----</span></td>
      </tr>
      <tr>
        <td id="L56" class="blob-num js-line-number" data-line-number="56"></td>
        <td id="LC56" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">u</span>[<span class="pl-c1">1</span>]</td>
      </tr>
      <tr>
        <td id="L57" class="blob-num js-line-number" data-line-number="57"></td>
        <td id="LC57" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L58" class="blob-num js-line-number" data-line-number="58"></td>
        <td id="LC58" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">u</span>[<span class="pl-k">-</span><span class="pl-c1">3</span>]  <span class="pl-c"><span class="pl-c">#</span>negative index :strip the member whose position has the same absolute value as the negative index.</span></td>
      </tr>
      <tr>
        <td id="L59" class="blob-num js-line-number" data-line-number="59"></td>
        <td id="LC59" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L60" class="blob-num js-line-number" data-line-number="60"></td>
        <td id="LC60" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Numeric Index Vector ----</span></td>
      </tr>
      <tr>
        <td id="L61" class="blob-num js-line-number" data-line-number="61"></td>
        <td id="LC61" class="blob-code blob-code-inner js-file-line"><span class="pl-v">s</span> <span class="pl-k">=</span> c(<span class="pl-s"><span class="pl-pds">&quot;</span>aa<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>bb<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>cc<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>dd<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>ee<span class="pl-pds">&quot;</span></span>) </td>
      </tr>
      <tr>
        <td id="L62" class="blob-num js-line-number" data-line-number="62"></td>
        <td id="LC62" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[c(<span class="pl-c1">2</span>, <span class="pl-c1">3</span>)] </td>
      </tr>
      <tr>
        <td id="L63" class="blob-num js-line-number" data-line-number="63"></td>
        <td id="LC63" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[<span class="pl-k">-</span>c(<span class="pl-c1">4</span>,<span class="pl-c1">5</span>)]</td>
      </tr>
      <tr>
        <td id="L64" class="blob-num js-line-number" data-line-number="64"></td>
        <td id="LC64" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L65" class="blob-num js-line-number" data-line-number="65"></td>
        <td id="LC65" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>cannot mix + &amp; -</span></td>
      </tr>
      <tr>
        <td id="L66" class="blob-num js-line-number" data-line-number="66"></td>
        <td id="LC66" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[c(<span class="pl-k">-</span><span class="pl-c1">2</span>,<span class="pl-c1">3</span>)]</td>
      </tr>
      <tr>
        <td id="L67" class="blob-num js-line-number" data-line-number="67"></td>
        <td id="LC67" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L68" class="blob-num js-line-number" data-line-number="68"></td>
        <td id="LC68" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Duplicate Index</span></td>
      </tr>
      <tr>
        <td id="L69" class="blob-num js-line-number" data-line-number="69"></td>
        <td id="LC69" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[c(<span class="pl-c1">2</span>,<span class="pl-c1">3</span>,<span class="pl-c1">2</span>)]</td>
      </tr>
      <tr>
        <td id="L70" class="blob-num js-line-number" data-line-number="70"></td>
        <td id="LC70" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L71" class="blob-num js-line-number" data-line-number="71"></td>
        <td id="LC71" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Range Index</span></td>
      </tr>
      <tr>
        <td id="L72" class="blob-num js-line-number" data-line-number="72"></td>
        <td id="LC72" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[c(<span class="pl-c1">2</span><span class="pl-k">:</span><span class="pl-c1">5</span>)]</td>
      </tr>
      <tr>
        <td id="L73" class="blob-num js-line-number" data-line-number="73"></td>
        <td id="LC73" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[<span class="pl-c1">2</span><span class="pl-k">:</span><span class="pl-c1">5</span>]</td>
      </tr>
      <tr>
        <td id="L74" class="blob-num js-line-number" data-line-number="74"></td>
        <td id="LC74" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Out of Range Index</span></td>
      </tr>
      <tr>
        <td id="L75" class="blob-num js-line-number" data-line-number="75"></td>
        <td id="LC75" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[c(<span class="pl-c1">10</span>)]</td>
      </tr>
      <tr>
        <td id="L76" class="blob-num js-line-number" data-line-number="76"></td>
        <td id="LC76" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L77" class="blob-num js-line-number" data-line-number="77"></td>
        <td id="LC77" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Logical Index Vector</span></td>
      </tr>
      <tr>
        <td id="L78" class="blob-num js-line-number" data-line-number="78"></td>
        <td id="LC78" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise.</span></td>
      </tr>
      <tr>
        <td id="L79" class="blob-num js-line-number" data-line-number="79"></td>
        <td id="LC79" class="blob-code blob-code-inner js-file-line"><span class="pl-v">s</span> <span class="pl-k">=</span> c(<span class="pl-s"><span class="pl-pds">&quot;</span>aa<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>bb<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>cc<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>dd<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>ee<span class="pl-pds">&quot;</span></span>) <span class="pl-c"><span class="pl-c">#</span>retrieve 2nd &amp; 4th member</span></td>
      </tr>
      <tr>
        <td id="L80" class="blob-num js-line-number" data-line-number="80"></td>
        <td id="LC80" class="blob-code blob-code-inner js-file-line"><span class="pl-v">L</span> <span class="pl-k">=</span> c(<span class="pl-c1">FALSE</span>, <span class="pl-c1">TRUE</span>, <span class="pl-c1">FALSE</span>, <span class="pl-c1">TRUE</span>, <span class="pl-c1">FALSE</span>) </td>
      </tr>
      <tr>
        <td id="L81" class="blob-num js-line-number" data-line-number="81"></td>
        <td id="LC81" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">s</span>[<span class="pl-smi">L</span>] </td>
      </tr>
      <tr>
        <td id="L82" class="blob-num js-line-number" data-line-number="82"></td>
        <td id="LC82" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>[1] &quot;bb&quot; &quot;dd&quot;</span></td>
      </tr>
      <tr>
        <td id="L83" class="blob-num js-line-number" data-line-number="83"></td>
        <td id="LC83" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L84" class="blob-num js-line-number" data-line-number="84"></td>
        <td id="LC84" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> <span class="pl-c1">1</span><span class="pl-k">:</span><span class="pl-c1">10</span></td>
      </tr>
      <tr>
        <td id="L85" class="blob-num js-line-number" data-line-number="85"></td>
        <td id="LC85" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">x</span>[<span class="pl-smi">x</span><span class="pl-k">&lt;</span><span class="pl-c1">5</span>]  <span class="pl-c"><span class="pl-c">#</span>elements satisfying condition</span></td>
      </tr>
      <tr>
        <td id="L86" class="blob-num js-line-number" data-line-number="86"></td>
        <td id="LC86" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L87" class="blob-num js-line-number" data-line-number="87"></td>
        <td id="LC87" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L88" class="blob-num js-line-number" data-line-number="88"></td>
        <td id="LC88" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Named Vector Members ----</span></td>
      </tr>
      <tr>
        <td id="L89" class="blob-num js-line-number" data-line-number="89"></td>
        <td id="LC89" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>We can assign names to vector members.</span></td>
      </tr>
      <tr>
        <td id="L90" class="blob-num js-line-number" data-line-number="90"></td>
        <td id="LC90" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">v</span> <span class="pl-k">=</span> c(<span class="pl-s"><span class="pl-pds">&quot;</span>Mary<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>Sue<span class="pl-pds">&quot;</span></span>) )</td>
      </tr>
      <tr>
        <td id="L91" class="blob-num js-line-number" data-line-number="91"></td>
        <td id="LC91" class="blob-code blob-code-inner js-file-line">names(<span class="pl-smi">v</span>) <span class="pl-k">=</span> c(<span class="pl-s"><span class="pl-pds">&quot;</span>First<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>Last<span class="pl-pds">&quot;</span></span>)  <span class="pl-c"><span class="pl-c">#</span>Name the members</span></td>
      </tr>
      <tr>
        <td id="L92" class="blob-num js-line-number" data-line-number="92"></td>
        <td id="LC92" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">v</span> </td>
      </tr>
      <tr>
        <td id="L93" class="blob-num js-line-number" data-line-number="93"></td>
        <td id="LC93" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Retrieve the first member by its name.</span></td>
      </tr>
      <tr>
        <td id="L94" class="blob-num js-line-number" data-line-number="94"></td>
        <td id="LC94" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">v</span>[<span class="pl-s"><span class="pl-pds">&quot;</span>First<span class="pl-pds">&quot;</span></span>]   <span class="pl-c"><span class="pl-c">#</span>[1] &quot;Mary&quot;</span></td>
      </tr>
      <tr>
        <td id="L95" class="blob-num js-line-number" data-line-number="95"></td>
        <td id="LC95" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>everse the order with a character string index vector.</span></td>
      </tr>
      <tr>
        <td id="L96" class="blob-num js-line-number" data-line-number="96"></td>
        <td id="LC96" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">v</span>[c(<span class="pl-s"><span class="pl-pds">&quot;</span>Last<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>First<span class="pl-pds">&quot;</span></span>)] </td>
      </tr>
      <tr>
        <td id="L97" class="blob-num js-line-number" data-line-number="97"></td>
        <td id="LC97" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L98" class="blob-num js-line-number" data-line-number="98"></td>
        <td id="LC98" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L99" class="blob-num js-line-number" data-line-number="99"></td>
        <td id="LC99" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>DELETE Vector----</span></td>
      </tr>
      <tr>
        <td id="L100" class="blob-num js-line-number" data-line-number="100"></td>
        <td id="LC100" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">x</span> <span class="pl-k">=</span> <span class="pl-c1">1</span><span class="pl-k">:</span><span class="pl-c1">10</span>)</td>
      </tr>
      <tr>
        <td id="L101" class="blob-num js-line-number" data-line-number="101"></td>
        <td id="LC101" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> <span class="pl-c1">NULL</span>  <span class="pl-c"><span class="pl-c">#</span>delete</span></td>
      </tr>
      <tr>
        <td id="L102" class="blob-num js-line-number" data-line-number="102"></td>
        <td id="LC102" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">x</span></td>
      </tr>
      <tr>
        <td id="L103" class="blob-num js-line-number" data-line-number="103"></td>
        <td id="LC103" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L104" class="blob-num js-line-number" data-line-number="104"></td>
        <td id="LC104" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L105" class="blob-num js-line-number" data-line-number="105"></td>
        <td id="LC105" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L106" class="blob-num js-line-number" data-line-number="106"></td>
        <td id="LC106" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Vector ARITHMETIC----</span></td>
      </tr>
      <tr>
        <td id="L107" class="blob-num js-line-number" data-line-number="107"></td>
        <td id="LC107" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span> Create two vectors.</span></td>
      </tr>
      <tr>
        <td id="L108" class="blob-num js-line-number" data-line-number="108"></td>
        <td id="LC108" class="blob-code blob-code-inner js-file-line"><span class="pl-v">v1</span> <span class="pl-k">=</span> c(<span class="pl-c1">3</span>,<span class="pl-c1">8</span>,<span class="pl-c1">4</span>,<span class="pl-c1">5</span>,<span class="pl-c1">0</span>,<span class="pl-c1">11</span>)</td>
      </tr>
      <tr>
        <td id="L109" class="blob-num js-line-number" data-line-number="109"></td>
        <td id="LC109" class="blob-code blob-code-inner js-file-line"><span class="pl-v">v2</span> <span class="pl-k">=</span> c(<span class="pl-c1">4</span>,<span class="pl-c1">11</span>,<span class="pl-c1">0</span>,<span class="pl-c1">8</span>,<span class="pl-c1">1</span>,<span class="pl-c1">2</span>)</td>
      </tr>
      <tr>
        <td id="L110" class="blob-num js-line-number" data-line-number="110"></td>
        <td id="LC110" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L111" class="blob-num js-line-number" data-line-number="111"></td>
        <td id="LC111" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">add.result</span> <span class="pl-k">=</span> <span class="pl-smi">v1</span><span class="pl-k">+</span><span class="pl-smi">v2</span>) <span class="pl-c"><span class="pl-c">#</span> Vector addition.</span></td>
      </tr>
      <tr>
        <td id="L112" class="blob-num js-line-number" data-line-number="112"></td>
        <td id="LC112" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">sub.result</span> <span class="pl-k">=</span> <span class="pl-smi">v1</span><span class="pl-k">-</span><span class="pl-smi">v2</span>) <span class="pl-c"><span class="pl-c">#</span> Vector subtraction.</span></td>
      </tr>
      <tr>
        <td id="L113" class="blob-num js-line-number" data-line-number="113"></td>
        <td id="LC113" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">multi.result</span> <span class="pl-k">=</span> <span class="pl-smi">v1</span><span class="pl-k">*</span><span class="pl-smi">v2</span>) <span class="pl-c"><span class="pl-c">#</span> Vector multiplication.</span></td>
      </tr>
      <tr>
        <td id="L114" class="blob-num js-line-number" data-line-number="114"></td>
        <td id="LC114" class="blob-code blob-code-inner js-file-line">(<span class="pl-v">divi.result</span> <span class="pl-k">=</span> <span class="pl-smi">v1</span><span class="pl-k">/</span><span class="pl-smi">v2</span>) <span class="pl-c"><span class="pl-c">#</span> Vector division.</span></td>
      </tr>
      <tr>
        <td id="L115" class="blob-num js-line-number" data-line-number="115"></td>
        <td id="LC115" class="blob-code blob-code-inner js-file-line">(<span class="pl-smi">v1</span><span class="pl-k">/</span><span class="pl-c1">2</span>)</td>
      </tr>
      <tr>
        <td id="L116" class="blob-num js-line-number" data-line-number="116"></td>
        <td id="LC116" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L117" class="blob-num js-line-number" data-line-number="117"></td>
        <td id="LC117" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L118" class="blob-num js-line-number" data-line-number="118"></td>
        <td id="LC118" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Sort</span></td>
      </tr>
      <tr>
        <td id="L119" class="blob-num js-line-number" data-line-number="119"></td>
        <td id="LC119" class="blob-code blob-code-inner js-file-line"><span class="pl-v">v</span> <span class="pl-k">=</span> c(<span class="pl-c1">3</span>,<span class="pl-c1">8</span>,<span class="pl-c1">4</span>,<span class="pl-c1">5</span>,<span class="pl-c1">0</span>,<span class="pl-c1">11</span>, <span class="pl-k">-</span><span class="pl-c1">9</span>, <span class="pl-c1">304</span>)</td>
      </tr>
      <tr>
        <td id="L120" class="blob-num js-line-number" data-line-number="120"></td>
        <td id="LC120" class="blob-code blob-code-inner js-file-line">sort(<span class="pl-smi">v</span>)</td>
      </tr>
      <tr>
        <td id="L121" class="blob-num js-line-number" data-line-number="121"></td>
        <td id="LC121" class="blob-code blob-code-inner js-file-line">sort(<span class="pl-smi">v</span>, <span class="pl-v">decreasing</span><span class="pl-k">=</span><span class="pl-c1">T</span>)</td>
      </tr>
      <tr>
        <td id="L122" class="blob-num js-line-number" data-line-number="122"></td>
        <td id="LC122" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L123" class="blob-num js-line-number" data-line-number="123"></td>
        <td id="LC123" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L124" class="blob-num js-line-number" data-line-number="124"></td>
        <td id="LC124" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L125" class="blob-num js-line-number" data-line-number="125"></td>
        <td id="LC125" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>READ values</span></td>
      </tr>
      <tr>
        <td id="L126" class="blob-num js-line-number" data-line-number="126"></td>
        <td id="LC126" class="blob-code blob-code-inner js-file-line"><span class="pl-v">nums</span> <span class="pl-k">=</span> scan()  <span class="pl-c"><span class="pl-c">#</span>type the numbers with space</span></td>
      </tr>
      <tr>
        <td id="L127" class="blob-num js-line-number" data-line-number="127"></td>
        <td id="LC127" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">nums</span></td>
      </tr>
      <tr>
        <td id="L128" class="blob-num js-line-number" data-line-number="128"></td>
        <td id="LC128" class="blob-code blob-code-inner js-file-line"><span class="pl-v">names</span> <span class="pl-k">=</span> scan(<span class="pl-v">what</span><span class="pl-k">=</span><span class="pl-s"><span class="pl-pds">&#39;</span>character<span class="pl-pds">&#39;</span></span>)</td>
      </tr>
      <tr>
        <td id="L129" class="blob-num js-line-number" data-line-number="129"></td>
        <td id="LC129" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">names</span></td>
      </tr>
      <tr>
        <td id="L130" class="blob-num js-line-number" data-line-number="130"></td>
        <td id="LC130" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L131" class="blob-num js-line-number" data-line-number="131"></td>
        <td id="LC131" class="blob-code blob-code-inner js-file-line">
</td>
      </tr>
      <tr>
        <td id="L132" class="blob-num js-line-number" data-line-number="132"></td>
        <td id="LC132" class="blob-code blob-code-inner js-file-line"><span class="pl-c"><span class="pl-c">#</span>Missing Values</span></td>
      </tr>
      <tr>
        <td id="L133" class="blob-num js-line-number" data-line-number="133"></td>
        <td id="LC133" class="blob-code blob-code-inner js-file-line"><span class="pl-v">x</span> <span class="pl-k">=</span> c(<span class="pl-c1">1</span>,<span class="pl-c1">4</span>,<span class="pl-c1">7</span>,<span class="pl-c1">NA</span>,<span class="pl-c1">12</span>,<span class="pl-c1">19</span>,<span class="pl-c1">15</span>,<span class="pl-c1">21</span>,<span class="pl-c1">20</span>)</td>
      </tr>
      <tr>
        <td id="L134" class="blob-num js-line-number" data-line-number="134"></td>
        <td id="LC134" class="blob-code blob-code-inner js-file-line">mean(<span class="pl-smi">x</span>)</td>
      </tr>
      <tr>
        <td id="L135" class="blob-num js-line-number" data-line-number="135"></td>
        <td id="LC135" class="blob-code blob-code-inner js-file-line">mean(<span class="pl-smi">x</span>, <span class="pl-v">na.rm</span><span class="pl-k">=</span><span class="pl-c1">T</span>)</td>
      </tr>
      <tr>
        <td id="L136" class="blob-num js-line-number" data-line-number="136"></td>
        <td id="LC136" class="blob-code blob-code-inner js-file-line">is.na(<span class="pl-smi">x</span>)</td>
      </tr>
      <tr>
        <td id="L137" class="blob-num js-line-number" data-line-number="137"></td>
        <td id="LC137" class="blob-code blob-code-inner js-file-line"><span class="pl-smi">x</span>[<span class="pl-k">!</span>is.na(<span class="pl-smi">x</span>)] <span class="pl-c"><span class="pl-c">#</span>non missing values</span></td>
      </tr>
</table>

  <details class="details-reset details-overlay BlobToolbar position-absolute js-file-line-actions dropdown d-none" aria-hidden="true">
    <summary class="btn-octicon ml-0 px-2 p-0 bg-white border border-gray-dark rounded-1" aria-label="Inline file action toolbar">
      <svg class="octicon octicon-kebab-horizontal" viewBox="0 0 13 16" version="1.1" width="13" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3zm5 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3zM13 7.5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0z"/></svg>
    </summary>
    <details-menu>
      <ul class="BlobToolbar-dropdown dropdown-menu dropdown-menu-se mt-2">
        <li><clipboard-copy role="menuitem" class="dropdown-item" id="js-copy-lines" style="cursor:pointer;" data-original-text="Copy lines">Copy lines</clipboard-copy></li>
        <li><clipboard-copy role="menuitem" class="dropdown-item" id="js-copy-permalink" style="cursor:pointer;" data-original-text="Copy permalink">Copy permalink</clipboard-copy></li>
        <li><a class="dropdown-item js-update-url-with-hash" id="js-view-git-blame" role="menuitem" href="/dupadhyaya/analytics/blame/2942702c592fc6b2a19c9a65a32b5812e30573bb/15a-DS/20a-vectors.R">View git blame</a></li>
          <li><a class="dropdown-item" id="js-new-issue" role="menuitem" href="/dupadhyaya/analytics/issues/new">Open new issue</a></li>
      </ul>
    </details-menu>
  </details>

  </div>

  </div>

  <details class="details-reset details-overlay details-overlay-dark">
    <summary data-hotkey="l" aria-label="Jump to line"></summary>
    <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast linejump" aria-label="Jump to line">
      <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="js-jump-to-line-form Box-body d-flex" action="" accept-charset="UTF-8" method="get"><input name="utf8" type="hidden" value="&#x2713;" />
        <input class="form-control flex-auto mr-3 linejump-input js-jump-to-line-field" type="text" placeholder="Jump to line&hellip;" aria-label="Jump to line" autofocus>
        <button type="submit" class="btn" data-close-dialog>Go</button>
</form>    </details-dialog>
  </details>


  </div>
  <div class="modal-backdrop js-touch-events"></div>
</div>

    </div>
  </div>

  </div>

        
<div class="footer container-lg px-3" role="contentinfo">
  <div class="position-relative d-flex flex-justify-between pt-6 pb-2 mt-6 f6 text-gray border-top border-gray-light ">
    <ul class="list-style-none d-flex flex-wrap ">
      <li class="mr-3">&copy; 2018 <span title="0.71328s from unicorn-3568333827-k9thg">GitHub</span>, Inc.</li>
        <li class="mr-3"><a data-ga-click="Footer, go to terms, text:terms" href="https://github.com/site/terms">Terms</a></li>
        <li class="mr-3"><a data-ga-click="Footer, go to privacy, text:privacy" href="https://github.com/site/privacy">Privacy</a></li>
        <li class="mr-3"><a href="https://help.github.com/articles/github-security/" data-ga-click="Footer, go to security, text:security">Security</a></li>
        <li class="mr-3"><a href="https://status.github.com/" data-ga-click="Footer, go to status, text:status">Status</a></li>
        <li><a data-ga-click="Footer, go to help, text:help" href="https://help.github.com">Help</a></li>
    </ul>

    <a aria-label="Homepage" title="GitHub" class="footer-octicon" href="https://github.com">
      <svg height="24" class="octicon octicon-mark-github" viewBox="0 0 16 16" version="1.1" width="24" aria-hidden="true"><path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"/></svg>
</a>
   <ul class="list-style-none d-flex flex-wrap ">
        <li class="mr-3"><a data-ga-click="Footer, go to contact, text:contact" href="https://github.com/contact">Contact GitHub</a></li>
      <li class="mr-3"><a href="https://developer.github.com" data-ga-click="Footer, go to api, text:api">API</a></li>
      <li class="mr-3"><a href="https://training.github.com" data-ga-click="Footer, go to training, text:training">Training</a></li>
      <li class="mr-3"><a href="https://shop.github.com" data-ga-click="Footer, go to shop, text:shop">Shop</a></li>
        <li class="mr-3"><a href="https://blog.github.com" data-ga-click="Footer, go to blog, text:blog">Blog</a></li>
        <li><a data-ga-click="Footer, go to about, text:about" href="https://github.com/about">About</a></li>

    </ul>
  </div>
  <div class="d-flex flex-justify-center pb-6">
    <span class="f6 text-gray-light"></span>
  </div>
</div>



  <div id="ajax-error-message" class="ajax-error-message flash flash-error">
    <svg class="octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 0 0 0 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 0 0 .01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"/></svg>
    <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error">
      <svg class="octicon octicon-x" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.48 8l3.75 3.75-1.48 1.48L6 9.48l-3.75 3.75-1.48-1.48L4.52 8 .77 4.25l1.48-1.48L6 6.52l3.75-3.75 1.48 1.48L7.48 8z"/></svg>
    </button>
    You can’t perform that action at this time.
  </div>


    <script crossorigin="anonymous" integrity="sha512-2VdGgXQE8W5ONZ4OsrbEo/noennUZaqXkevD9R8juTHiCsjT0HFTTF6MoBfySUc8G+eFqUcDgd7v+CAu8Gjxlg==" type="application/javascript" src="https://assets-cdn.github.com/assets/compat-f849c975b0ffaa01d6ca305e48417d08.js"></script>
    <script crossorigin="anonymous" integrity="sha512-ZCuUuaCERG6/AdCPvg2qDAGDVKP7KsHnKx3EuTwcIZSLeqJpyqaZZBUmbK7Wbtgar1y1iAlO66Vstl53f/EFOg==" type="application/javascript" src="https://assets-cdn.github.com/assets/frameworks-b83c5ea8e6d3b8ce4839ec986855c7bf.js"></script>
    
    <script crossorigin="anonymous" async="async" integrity="sha512-C9OozuIafvm8zbohi7zhm2zh0/+VjfgBUy7/luGeY/wZLu66/4nHWZ+vCFjCWSMR0jNHuF3KNdIsZ/aOR91JZQ==" type="application/javascript" src="https://assets-cdn.github.com/assets/github-6e44a44d20af579fa9707fe7a0d263e2.js"></script>
    
    
    
  <div class="js-stale-session-flash stale-session-flash flash flash-warn flash-banner d-none">
    <svg class="octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 0 0 0 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 0 0 .01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"/></svg>
    <span class="signed-in-tab-flash">You signed in with another tab or window. <a href="">Reload</a> to refresh your session.</span>
    <span class="signed-out-tab-flash">You signed out in another tab or window. <a href="">Reload</a> to refresh your session.</span>
  </div>
  <div class="facebox" id="facebox" style="display:none;">
  <div class="facebox-popup">
    <div class="facebox-content" role="dialog" aria-labelledby="facebox-header" aria-describedby="facebox-description">
    </div>
    <button type="button" class="facebox-close js-facebox-close" aria-label="Close modal">
      <svg class="octicon octicon-x" viewBox="0 0 12 16" version="1.1" width="12" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.48 8l3.75 3.75-1.48 1.48L6 9.48l-3.75 3.75-1.48-1.48L4.52 8 .77 4.25l1.48-1.48L6 6.52l3.75-3.75 1.48 1.48L7.48 8z"/></svg>
    </button>
  </div>
</div>

  <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;" tabindex="0">
  <div class="Popover-message Popover-message--bottom-left Popover-message--large Box box-shadow-large" style="width:360px;">
  </div>
</div>

<div id="hovercard-aria-description" class="sr-only">
  Press h to open a hovercard with more details.
</div>


  </body>
</html>



================================================
FILE: iim.txt
================================================
The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.

But much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.

FMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.

Corporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.

We invite you to discover FMS for yourself.


================================================
FILE: misc/1-ds.R
================================================
# Data Structures

#vectors----
v1 = 1:100 #create vector from 1 to 100
v2 = c(1,4,5,10)
class(v1)
class(v2)
v3 = c('a','Dhiraj','Ashish')
v3   #print the vector
class(v3)
v4 = c(TRUE, FALSE, T, F, T)
class(v4)

#summary on vectors
mean(v1)
median(v1)
sd(v1)
var(v1)
hist(v1)
hist(women$height)
v2
v2[v2>=5]

x = rnorm(60, mean=60, sd=10)
x
plot(x)
hist(x)
plot(density(x))
abline(v=60)
#rectangles and density together
hist(x, freq=F)
lines(density(x))

hist(x, breaks=10, col=1:10)
length(x)
sd(x)

?sample
x1 = LETTERS[5:20]
x1

set.seed(1234)
y1 = sample(x1)
y1

set.seed(53)
(y2= sample(x1, size=5))

(gender=sample(c('M','F'), size=1000000, replace=TRUE, prob=c(.3,.7)))
(t1=table(gender))
prop.table(t1)
pie(t1)
barplot(t1, col=1:2, horiz=T)
#


#matrix----
(m1 = matrix(1:24, nrow=4))
(m2 = matrix(1:24, nrow=4, byrow=T))
(m3 = matrix(1:24, ncol=4, byrow=T))
(x=trunc(runif(60,60,100)))
#round, trunc, ceiling, floor
plot(density(x))
(m4 = matrix(x, ncol=6))
colSums(m4)
rowSums(m4)
rowMeans(m4)
colMeans(m4)
m4[m4> 67 & m4 < 86]
m4[8:10, ]
m4[8:10, c(1,3,5) ]
rowSums(m4[8:10, c(1,3,5) ])
m4[ ,c(1,5,6) ]
#
#array----


#data.frame
#rollno, name, gender, course, marks1, marks2,grades
(rollno = 1:60)
(name=paste('student1',1:60,sep='-'))
(gender=sample(c('Male','Female'), size=60, replace=T,prob=c(.3,.7)))
(course=sample(c('BBA','MBA','FPM'), size=60, replace=T, prob=c(.4,.2,.1)))
(marks1 = ceiling(rnorm(60, mean=65, sd=7)))
(marks2 = ceiling(rnorm(60, mean=60, sd=11)))
(grades = sample(c('A','B','C'), size=60, replace=T))
students = data.frame(rollno, name,gender, course, marks1, marks2, grades, stringsAsFactors = F)
class(students )

summary(students)
students[, c('name')]
students[students$gender == 'Male', c('rollno','gender','marks1') ]
students[students$gender == 'Male' & students$grades == 'C', c('rollno','gender','marks1') ]
students[students$marks1 > 55 | students$marks1 < 75, c('name','marks1') ]
students$gender
t1=table(students$gender)
barplot(table(students$course), ylim=c(0,50), col=1:3)
text(1:3,table(students$course)+5,table(students$course) )
str(students)
nrow(students)
names(students)
dim(students)
head(students)
tail(students)
head(students,n=7)
#
#avg marks scored by each gender in marks1
#gender, marks1
aggregate(students$marks1, by=list(students$gender), FUN=mean)
aggregate(students$marks2, by=list(students$course), FUN=max)
aggregate(students$marks2, by=list(students$course, students$gender), FUN=mean)

#dplyr
library(dplyr)
students %>% group_by(gender) %>% summarise(mean(marks1))
students %>% group_by(course,gender) %>% summarise(meanmarks1= mean(marks1), min(marks2),max(marks2)) %>% arrange(desc(meanmarks1))
students %>% arrange(desc(marks1)) %>% filter(gender=='Male') %>% head(n=3)
students %>% sample_frac(.1) %>% arrange(course) %>% select(name, gender)
students %>% sample_n(2) 
students %>% arrange(desc(course), gender, marks1)
students %>% arrange(course, grades,marks1) %>% select(course, grades, marks1) %>% filter(course=='BBA')
#
students %>% group_by(course, gender) %>% arrange(marks1) %>% top_n(n=1)


#factor
names(students)
students$gender = factor(students$gender)
summary(students$gender)
summary(students$course)
students$course = factor(students$course,ordered=T)
summary(students$course)
students$course = factor(students$course,ordered=T, levels=c('FPM','MBA','BBA'))
summary(students$course)
students$grades
# C, A, B
students$grades = factor(students$grades, ordered=T, levels=c('C','A','B'))
students$grades
table(students$grades)
barplot(table(students$grades))

#

students
write.csv(students,'./data/iimtrichy.csv' )
students2 = read.csv('./data/iimtrichy.csv')
head(students2)
students3 = read.csv(file.choose())
head(students3)
students3 = students3[,-1]
head(students3)
#






#extra commands
name[1:10]
name[15:20]
name[c(15,20,37)]
name[-c(1:10)]
rev(name)
name[60:1]
name[-c(1:10, 35:40)]


mtcars
plot(women)
women
install.packages('car')
?women
?mean
x = 1:100
x
y = seq(1,100, by=2)
y = seq(1,100, by=2)



================================================
FILE: misc/cswr.R
================================================
library(csvwr)

d = data.frame(x=c('a','b','c'), y=1:3)
d
s = derive_table_schema(d)
s

m = create_metadata(tables=list(list(url='table.csv', tableSchema=s)))
m

j = jsonlite::toJSON(m)
j
cat(j, file='metadata.json')



================================================
FILE: misc/fms.txt
================================================
The Faculty of Management Studies focuses on management education more than just business management. The commitment is thought leadership with a deep understanding of business. The approach to pedagogy combines fieldwork, case studies and instrumented feedback with a strong emphasis on concepts and theory. The intent is to encourage intellectual curiosity and open minds to the adventure of ideas.

But much about the school is not just about what is taught within its confines. FMS has the unique privilege to be part of one of the premier universities world – the University of Delhi - with some of the finest departments in Economics, Law, Sociology, Commerce and Operations Research. Our collaborative approach involves inputs from various departments which gives the benefit of a much wider view and deeper understanding. This is indicated by the success of our alumni who are in positions of leadership in industries & governments across the world. The network of more than 10000 alumni gives students unmatched access to information, mentors and careers.

FMS rewards initiative, novelty and thinking outside the box. Our students are individualists of enormous intellectual energy with a talent for collaboration and teamwork. We are a diverse lot, possessing flair and dynamism that develops in a metropolis like Delhi and thrives on challenges both inside and outside the classroom.

Corporate recruiters value our graduates for their intellectual abilities, their collaborative mind-set, their individuality and their ability to hit the ground running.

We invite you to discover FMS for yourself.

================================================
FILE: misc/importcsv.R
================================================
#import from csv

df1 = read.csv('pdpu.csv')
df1
head(df1)
names(df1)
#avg marks gender wise
aggregate(cbind(df1$marks1,df1$marks2), by=list(df1$gender), FUN=mean)
t1=table(df1$gender)
table(df1$batch)
barplot(table(df1$gender), col=1:2)
barplot(t1, col=c('red','green'))
pie(t1)


write.csv(df1,'pdpu2.csv')
write.csv(t1,'pdpu3.csv')


mtcars
str(mtcars)
df2 = mtcars
df3=rbind(df2,mtcars)
dim(df3)


================================================
FILE: misc/mysqlR.R
================================================
#mysql
#install.packages('RMySQL')
library(RMySQL)

#Connecting to MySQL:  Once the RMySQL library is installed create a database connection object.

mydb = dbConnect(MySQL(), user='du', password='mysql123', dbname='dulms', host='localhost')

#Listing Tables and Fields: Now that a connection has been made we list the tables and fields in the database we connected to.

dbListTables(mydb)

#This will return a list of the tables in our connection. 

dbListFields(mydb, 'pd8')
rs = dbSendQuery(mydb, "select * from pd8")

dbGetQuery(mydb, paste("select rollno, name, marks1 from pd8","where marks1 > 60 order by fees", "limit 5"))

data = fetch(rs, n=-1)
data
data2= dbReadTable(mydb, "pd8")
data2
dbWriteTable(mydb, name='mtcars', value=mtcars)


library(RODBC)
## tell it to map names to l/case
channel <- odbcConnect("mysql", uid="du", case="tolower")
sqlTables(channel)
sqlFetch(channel, "mtcars")

#https://www.dezyre.com/data-science-in-r-programming-tutorial/r-tutorial-importing-data-from-relational-database
#https://cran.r-project.org/doc/manuals/r-devel/R-data.html
#https://rpubs.com/williamsurles/290693



================================================
FILE: misc/nest.R
================================================


library(dplyr)
gearnested <- mtcars %>% group_by(gear) %>% nest()
gearnested
gearnested[[1]]
gearnested[[1,'data']]
gearnested[[2,'data']]
gearnested[[3,'data']]
gearnested$data[[1]]
gearnested[['data']] #all
gearnested[['data']][[1]]

gearnested %>% unnest()



================================================
FILE: misc/timeszones.R
================================================
#Ranjan data import issue - T/F

library(readxl)
list.files('E:/data/students')
data = readxl::read_excel('E:/data/students/Sirifort.xlsx')
head(data)
head(data$`From Date`)
Sys.timezone()
str(OlsonNames())
OlsonNames()
Sys.setenv(TZ = "UTC")
Sys.setenv(TZ="Asia/Calcutta")
options(tz='UTC')
options(tz="Asia/Calcutta")

getOption('tz')
data$`From Date` = as.POSIXct(data$`From Date`, '%d-%m-%Y %H:%M', tz=getOption("tz"))
data$`To Date` = as.POSIXct(data$`To Date`)

str(data)
dim(data)
colSums(is.na(data))
?read_excel
(colClasses1 = c(rep('character',2), rep('numeric',39)))
data = readxl::read_excel('E:/data/students/Sirifort.xlsx', col_types=colClasses1)


================================================
FILE: munaz.csv
================================================
"rollno","name","age"
"S01","Dhiraj",55
"S02","Munaz",42


================================================
FILE: mycars.csv
================================================
"mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
21,6,160,110,3.9,2.62,16.46,0,1,4,4
21,6,160,110,3.9,2.875,17.02,0,1,4,4
22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
10.4,8,460,215,3,5.424,17.82,0,0,3,4
14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
15,8,301,335,3.54,3.57,14.6,0,1,5,8
21.4,4,121,109,4.11,2.78,18.6,1,1,4,2


================================================
FILE: myrules1.csv
================================================
"","rules","support","confidence","coverage","lift","count"
"1","{I4} => {I2}",0.222222222222222,1,0.222222222222222,1.28571428571429,2
"2","{I5} => {I1}",0.222222222222222,1,0.222222222222222,1.5,2
"3","{I5} => {I2}",0.222222222222222,1,0.222222222222222,1.28571428571429,2
"4","{I3} => {I1}",0.444444444444444,0.666666666666667,0.666666666666667,1,4
"5","{I1} => {I3}",0.444444444444444,0.666666666666667,0.666666666666667,1,4
"6","{I3} => {I2}",0.444444444444444,0.666666666666667,0.666666666666667,0.857142857142857,4
"7","{I2} => {I3}",0.444444444444444,0.571428571428571,0.777777777777778,0.857142857142857,4
"8","{I1} => {I2}",0.444444444444444,0.666666666666667,0.666666666666667,0.857142857142857,4
"9","{I2} => {I1}",0.444444444444444,0.571428571428571,0.777777777777778,0.857142857142857,4
"10","{I1,I5} => {I2}",0.222222222222222,1,0.222222222222222,1.28571428571429,2
"11","{I2,I5} => {I1}",0.222222222222222,1,0.222222222222222,1.5,2
"12","{I1,I2} => {I5}",0.222222222222222,0.5,0.444444444444444,2.25,2
"13","{I1,I3} => {I2}",0.222222222222222,0.5,0.444444444444444,0.642857142857143,2
"14","{I2,I3} => {I1}",0.222222222222222,0.5,0.444444444444444,0.75,2
"15","{I1,I2} => {I3}",0.222222222222222,0.5,0.444444444444444,0.75,2


================================================
FILE: practise1.R
================================================


library(rpart)
library(rpart.plot)

url = 'https://raw.githubusercontent.com/DUanalytics/datasets/master/csv/pima-indians-diabetes.csv'
df = read.csv(url)
df
names(df) = c('pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label')
df
table(df$label)
model = rpart(label ~ ., data = df)
model
rpart.plot(model, cex=.8)
printcp(model)

model1 = prune(model, cp=.04)
model1
rpart.plot(model1, type=2)


================================================
FILE: rAnalytics.Rproj
================================================
Version: 1.0
ProjectId: 2f57f095-ba72-4233-be5d-1ddd2ca189f7

RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8

RnwWeave: Sweave
LaTeX: pdfLaTeX


================================================
FILE: report/knit2.R
================================================
library(knitr)
(f = system.file("examples", "knitr-minimal.Rnw", package = "knitr"))
knit(f)  # compile to tex

purl(f)  # tangle R code
purl(f, documentation = 0)  # extract R code only
purl(f, documentation = 2)  # also include documentation


================================================
FILE: report/knitr-minimal.R
================================================
#' %% LyX 2.2.1 created this file.  For more info, see http://www.lyx.org/.
#' %% Do not edit unless you really know what you are doing.
#' \documentclass{article}
#' \usepackage[sc]{mathpazo}
#' \usepackage[T1]{fontenc}
#' \usepackage{geometry}
#' \geometry{verbose,tmargin=2.5cm,bmargin=2.5cm,lmargin=2.5cm,rmargin=2.5cm}
#' \setcounter{secnumdepth}{2}
#' \setcounter{tocdepth}{2}
#' \usepackage{url}
#' \usepackage[unicode=true,pdfusetitle,
#'  bookmarks=true,bookmarksnumbered=true,bookmarksopen=true,bookmarksopenlevel=2,
#'  breaklinks=false,pdfborder={0 0 1},backref=false,colorlinks=false]
#'  {hyperref}
#' \hypersetup{
#'  pdfstartview={XYZ null null 1}}
#' \usepackage{breakurl}
#' \begin{document}
## ----setup, include=FALSE, cache=FALSE-----------------------------------
library(knitr)
# set global chunk options
opts_chunk$set(fig.path='figure/minimal-', fig.align='center', fig.show='hold')
options(formatR.arrow=TRUE,width=90)

#' 
#' \title{A Minimal Demo of knitr}
#' 
#' \author{Yihui Xie}
#' 
#' \maketitle
#' You can test if \textbf{knitr} works with this minimal demo. OK, let's
#' get started with some boring random numbers:
#' 
## ----boring-random-------------------------------------------------------
set.seed(1121)
(x=rnorm(20))
mean(x);var(x)

#' 
#' The first element of \texttt{x} is \Sexpr{x[1]}. Boring boxplots
#' and histograms recorded by the PDF device:
#' 
## ----boring-plots, fig.width=4, fig.height=4, out.width='.4\\linewidth'----
## two plots side by side (option fig.show='hold')
par(mar=c(4,4,.1,.1),cex.lab=.95,cex.axis=.9,mgp=c(2,.7,0),tcl=-.3,las=1)
boxplot(x)
hist(x,main='')

#' 
#' Do the above chunks work? You should be able to compile the \TeX{}
#' document and get a PDF file like this one: \url{https://github.com/yihui/knitr/releases/download/doc/knitr-minimal.pdf}.
#' The Rnw source of this document is at \url{https://github.com/yihui/knitr/blob/master/inst/examples/knitr-minimal.Rnw}.
#' \end{document}


================================================
FILE: report/sample1.R
================================================
#sample for reports


mtcars

women

library('knitr')
knitr::kable(head(mtcars))

#rmarkdown::render('.\\report\\sample1.R')

================================================
FILE: report/sample2.R
================================================
# Sample 2


AirPassengers

airquality


================================================
FILE: trg/bennett1.R
================================================
#first file - comment
#day 1 and day2
mtcars
colSums(mtcars)
barplot(table(mtcars$gear), col=1:4)

#to execute a line - Control + Enter
mtcars
?mtcars
#?  # this context sensitive help
?table
mean(mtcars$mpg) #mean mpg of all cars
table(mtcars$cyl) #no of cars with different gears
colMeans(mtcars)  #mean of all features

#libraries enhance the features of Base R
#install and then load
#install can be done GUI or by command
#GUI - Bottom Right Pane - Packages - Install - give the name of package
#command : install.packages(dplyr)
library(dplyr)  #loading a package / library : control + enter
#all functions in the library gets available for use
#no of cars of each cylinder type
mtcars %>% group_by(cyl) %>% count()
mtcars %>% group_by(cyl) %>% tally()
mtcars %>% group_by(cyl) %>% summarise(n=n())


#student properties
#marks, gender, rollno
marks = c(70,60,73.6)  # continuous
marks
mean(marks)
gender = c('M','F','M')  #category
?c
#mean(gender)
table(gender)
rollno = c(100, 101, 102) #character/ number
length(rollno)
#scales of measurement

#-----

#student data-----
x=10 #assign
x #print
(y=20) #assign and print
#rollno, name, age, gender, course, marks1, marks2
(n=100)  #assign and print in the screen
(rollno = c(100:(n+100-1)))
length(rollno)
(sname = paste('Student ', 1:n, sep='- '))
set.seed(1234) #gets the same pattern, any positive no
(age = runif(n=n, min=19, max=24))
plot(density(age))
mean(age)

set.seed(1234)
(gender = sample(c('M','F'), size=n, replace=TRUE, prob=c(.7,.3)))
table(gender)
prop.table(table(gender))

set.seed(1234)
(course = sample(c('BBA','BA','BTECH'), size=n, replace=T, prob=c(.6,.25,.15)))
table(course)

set.seed(1234)
(marks1 = rnorm(n=n, mean=65, sd=10))
mean(marks1); sd(marks1)
?distributions
set.seed(1234)
(marks2 = rnorm(n=n, mean=60, sd=12))
mean(marks2); sd(marks2)

#see the output
rollno; sname; gender; course ; age; marks1 ; marks2
length(rollno)

students = data.frame(rollno, sname, gender, course, age, marks1, marks2)
head(students)

library(dplyr) #using pipe 
students %>% group_by(gender) %>% tally()
students %>% group_by(course, gender) %>% tally()
students %>% group_by(course) %>% summarise(mage = mean(age), mmarks1 = min(marks1), Mmarks1 = max(marks2)) %>% arrange(mage)

#Day2 : Bennett


================================================
FILE: trg/bennett3.R
================================================
#Bennett3

#import from csv-----
head(mtcars) # first 6 rows
dim(mtcars) #rows and columns
write.csv(mtcars, 'cardata.csv')  #writing data to csv file
read1 = read.csv('cardata.csv')
head(read1)
head(women)
write.csv(women, 'women.csv', row.names = F)
read1a = read.csv('women.csv')
head(read1a)

read2 = read.csv(file=file.choose())
head(read2)

#import from google sheet----
#install.packages('gsheet')
library(gsheet)

readgg = "https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534"

read3 = as.data.frame(gsheet2tbl(readgg))
head(read3)


#linear regression----
#DV ~ IV : y = b0 + b1x1 + b2x2 .....
women
?women
head(women)
#weight proportional to height 
cor(women)  #strength and direction of relationship +/-,0-1
cov(women)  #direction 

lm1 = lm( weight ~ height, data=women)
summary(lm1)
anova(lm1)
#does the model exist : F Stats p value - < .05 (yes)
#there is linear relationship between Ht & Wt
#strength : Rsq or AdjRsq : SLR -Multiple Rsq - .991
#99% variation in Wt is due to variations in Ht
#https://statisticsbyjim.com/regression/interpret-f-test-overall-significance-regression/ y = mx + c : m slope, c -intercetp
# weight = -87 + 3.4 * height
range(women$height)
head(women)
women[c(1,5),]
-87.51 + 58 * 3.45
plot(women)
abline(lm1, col='red')
abline(v=c(63,70), h=c(129, 135, 140))
ndata1 = data.frame(height=c(63.5, 67.6))
ndata1
p1 = predict(lm1, newdata = ndata1)
cbind(ndata1, p1)
women

#multiple linear regression
lm2 = lm(mpg ~ hp + disp, data = mtcars)
summary(lm2)
library(dplyr)
mtcars %>% sample_n(2)
ndata2 = data.frame(hp=210, disp= 400)
predict(lm2, newdata = ndata2)


#logistic regression----
library(ISLR)
head(Default)
?Default
dim(Default)
# How many people actual default?
(tmp = table(Default$default))
333/10000

#what is the probability of default for a certain case ?

#Multiple Logistic Regression
logit1 = glm(default ~ income + balance + student, family='binomial', data=Default)
summary(logit1)
logit2 = glm(default ~ balance + student, family='binomial', data=Default)
summary(logit2)

library(dplyr)
slice(Default,c(1,500,1000))
seq(from=1,to=10000,by=500)
(ndata = (slice(Default, seq(1,n(),500))))
ndata
(fitted.results = predict(logit2, newdata=ndata,type='response'))
(fitted.results = round(fitted.results,4))
ndata
(ifelse(fitted.results < .5, 'No','Yes'))
p2 <- ndata %>% mutate(predictnew = ifelse(fitted.results < 0.5, 'No','Yes'))
cbind(p2,fitted.results)
ifelse(fitted.results < 0.05, 0,1)
xtabs( ~ default + student, data=Default)
table(Default$default, Default$student)
ftable(Default$default, Default$student)
#-----
#Accuracy of Model
library(caret)
set.seed(3456)
str(Default)
trainIndex <- createDataPartition(Default$default, p = .67, list = FALSE,   times = 1)

Train <- Default[ trainIndex,]
Test  <- Default[-trainIndex,]
head(Train)
head(Test)

# Logistic Regression Model
model = glm(default ~ student, data=Default, family='binomial')
Test$model_prob <- predict(model, Test, type = "response")
head(Test)
Test <- Test  %>% mutate(default_pred = ifelse(model_prob > .5,'Yes','No'))
head(Test)
Test <- Test %>% mutate(accurate = 1*(default == default_pred))
sum(Test$accurate)/nrow(Test)                         
#96% Accuracy


#decision tree-----

library(rpart)
library(rpart.plot)

#DataSet
str(iris)
head(iris)

table(iris$Species)

library(dplyr)
sample_n(iris, 3)
sample_frac(iris, .1)

# Classification Tree
summary(iris)
set.seed(1234)
#Predict Species
names(iris)
ctree = rpart(Species ~ ., method='class', data=iris)
ctree
rpart.plot(ctree, main='Classification Tree', nn=T, type=4, cex=1,extra=104)
printcp(ctree)
ctreeprune = prune(ctree, cp=0.44)
ctreeprune
rpart.plot(ctreeprune, main='Classification Tree', nn=T, type=4, extra=104)
# Regression Tree - Predict Continuous Value Length
#Predict Sepal.Length
rtree = rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, method="anova", data=iris )
rtree
rpart.plot(rtree, main='Regression Tree', nn=T, type=2, cex=1)

#another way to plot
plot(rtree, uniform=TRUE, main="Regression Tree for Sepal Length")
text(rtree, use.n=TRUE, cex = 1)
rtree

#CP
printcp(rtree)
rsq.rpart(rtree)  #r2 on anova only

rtreeprune = prune(rtree, cp=0.023)
rpart.plot(rtreeprune, main='Pruned Regression Tree', nn=T, type=2, cex=1)



#clustering
#segmentation
#group by similarities 
#student group themselves
#eating habits, study, games, humour, financial status, introvert, extrovert
marks1 = c(6,7,8,2,3,5,3,6,3,7)
marks2 = c(16,15,18,18,13,19,13,16,13,17)

student = data.frame(marks1, marks2)
student
km1 = kmeans(student, centers=3)
km1
km1$cluster
cbind(student, km1$cluster)
#student grouping
#market segmentation , customer segmentation



================================================
FILE: trg/d1-fmssrcc.R
================================================
#my first r File
#comment by starting #
women
#control + enter to execute command
?women
?mean
#? before function to seek help
mtcars
#? before function to seek help
mtcars
mean(mtcars$mpg)
names(mtcars)
names(mtcars)
mean(mtcars$hp)
colMeans(mtcars)
rowMeans(mtcars)
head(mtcars)
head(mtcars,1)
head(mtcars,-1)
head(mtcars)
tail(mtcars)
tail(mtcars,1)  # last row
mean(mtcars$mpg)
names(mtcars)
mean(mtcars$hp)
colMeans(mtcars)
rowMeans(mtcars)
head(mtcars)
hist(mtcars$mpg)
hist(mtcars$mpg, breaks=8)
hist(mtcars$mpg, breaks=3)
x = c(1,2,5,7)
x
class(x)
x1 =c(2L, 8L)
class(x1)
class(mtcars)
x3 = c('a','B','dhiraj')
x3
x3 = c("a",'B','dhiraj')
x3
class(x3)
x4 = c(TRUE, FALSE, T, F)
x4
x5 = 1:100000
x5
(x5 = 1:100000)
x6 <- 2
x7 = rnorm(100)
(x7 = rnorm(100))
mean(x7)
sd(x7)
length(x7)
plot(density(x7))
hist(x7)
plot(density(x7))
hist(x7, freq=F)
points(density(x7))
hist(x7, freq=F)
points(density(x7))
(x8 = rnorm(100, mean = 60, sd=10))

library(e1071)
?kurtosis
kurtosis(x8)
skewness(x8)
hist(x8)
points(density(x8))
hist(x8, freq=F)
points(density(x8))
x9 = runif(100, 30, 90)
?runif
x9
trunc(x9)
round(x9,1)
floor(x9)
ceiling(x9)
x10 = ceiling(x9)
x10
x9
mean(x10)
median(x10)
sort(x10)
sort(x10, decreasing=T)
x10[x10 > 60]
x10
x10[1:10]
x10[20:30]
x10[ -1:20]
x10[ -c(1:20)]
x10[c(1,5,7)]
x10
x10
x10
#matrices
m1 = matrix(1:24 , nrow=6)
m1
dim(m1)
m2 = matrix(1:100, ncol=8) #error due non divisibility
m2 = matrix(1:56, ncol=8)
m2
m3 = matrix(1:24, ncol=6, byrow = T)
m3
colnames(m3)=month.abb[1:6]
m3
rownames(m3)= paste('Product',1:4, sep='_')
m3
rowMeans(m3)
m1
m1[ , 1:2]
m3
m3[ , 1:2]
m3[c(1,3), ]
colMeans(m3[c(1,3), ])
m3[ , 1:3]
m3[ , c('Apr',' Jun')]
m3[ , c('Apr','Jun')]
colMeans(m3[c(1,3), ])
m3[ , c('Apr','Jun')]
max(m3)
min(m3)
range(m3)

#data.frame----
n=30
rollno = 1:30
(rollno = paste('F',1:30,sep='-'))
(sname = paste('Student',1:30, sep='&'))

set.seed(1234)
(gender = sample(c('M','F'), size=n, replace=T, prob=c(.7,.3)))
table(gender)
(t1=table(gender))
prop.table(t1)

set.seed(12)
(college = sample(c('SRCC','FMS','IIM'), size=n, replace=T, prob=c(.4,.3,.3)))
(t2=table(college))
prop.table(t2)
n=30
(rollno = paste('F',1:30,sep='-'))
(sname = paste('Student',1:30, sep='&'))
set.seed(1234)
(gender = sample(c('M','F'), size=n, replace=T, prob=c(.7,.3)))
(t1=table(gender))
prop.table(t1)
set.seed(12)
(college = sample(c('SRCC','FMS','IIM'), size=n, replace=T, prob=c(.4,.3,.3)))
(t2=table(college))
prop.table(t2)

(marks1 = round(rnorm(n=n, mean=60,sd=10),0) )
(marks2 = round(rnorm(n=n, mean=55,sd=15),0) )
rollno; sname; gender; college;marks1;marks2
length(gender)
students = data.frame(rollno, sname, gender, college, marks1, marks2)
class(students)
students
head(students)
students$rollno
students[,5:6]
students[,c(2,5:6)]
students[,c('sname','marks1','marks2')]
str(students)
summary(students)
quantile(students$marks1)
quantile(students$marks1, probs=seq(0,1,.1))
seq(0,1,.1)

quantile(students$marks1)
quantile(students$marks1, probs=c(0,.25,.5,.75,1))
seq(1,100)
seq(1,100,2)
seq(1,100,3)
quantile(students$marks1, probs=seq(0,1,.1))
quantile(students$marks1, probs=seq(0,1,.01))
seq(from=1,to=100,by=3)
quantile(students$marks1, probs=seq(0,1,.25))
summary(students)
str(students)
students$rollno =as.character(students$rollno)
students$sname =as.character(students$sname)
str(students)
summary(students)
str(students)
head(students)
write.csv(students, 'fms.csv', row.names = F)
students2 = read.csv('fms.csv')
head(students2)
students3 = read.csv(file.choose())
head(students3)

#students
head(students)
library(dplyr)
head(students)
#querf - avg marks of females from SRCC
students[students$marks1 > 60 , ]
students[students$gender =='F' , ]
students[students$gender =='F' & students$college == 'SRCC' , ]
#highest from all college
#using dplyr
students %>% filter(gender=='M' & marks1 > 60)
#all students with gender = M, marks1> 60

students %>% group_by(gender) %>% summarise(mean(marks1), mean(marks2))
students %>% tally()
students %>% group_by(gender, college) %>% summarise(countTotal=n(), mean(marks1), max(marks2)) 
students %>% mutate(totalMarks = marks1 + 1.2 * marks2) %>% arrange(-totalMarks) %>% head(n=2)
students %>% mutate(totalMarks = marks1 + 1.2 * marks2) %>% filter(totalMarks == max(totalMarks))
head(students)
#
students %>% slice(seq(1,30,2))  #every alternate row
students %>% sample_n(5)  #random 5 rows
students %>% sample_frac(.2)  #random 20%

students %>% group_by(college) %>% summarise(max(marks1), max(marks2))
students %>% filter(college=='FMS') %>% select(marks1, marks2)

students


================================================
FILE: trg/d2a-fmssrcc.R
================================================
# Read Data into R Environment

#CSV Files----
#Read from CSV file in PC
head(iris)
write.csv(iris, "./data/iris.csv", row.names=F)
read1 = read.csv(file="./data/iris.csv", header = TRUE,sep = ",")
read1
read1 = read.csv(file="./data/dhiraj.csv", header = TRUE,sep = ",")
head(read1)
str(read1)
class(read1)
head(read1)
read2 = read.table(file="./data/iris.csv", header = TRUE,sep = ",")
str(read2); class(read2)
head(read2)
read3 = read.delim(file="./data/iris.csv", header = TRUE,sep = ",")
str(read3) ; class(read3)
head(read3)
#difference is use of specify delimeter(read.csv takes default as comma)
#or location is different from Project Folders, or want to search for the file
read4 = read.csv(file=file.choose())
str(read4)
head(read4)


# From URL : Read CSV from Web----
read_web1 = read.csv('http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat')
head(read_web1)
library(data.table)
read_web2 = fread("http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv")
head(read_web2)
class(read_web2)

#Text file from Web-----
read_txt = read.table("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/test.txt", header = FALSE)
head(read_txt)

#Google Sheets-----
library(gsheet) #install it#
#install.packages('gsheet')
library(gsheet)
url_gsheet = "https://docs.google.com/spreadsheets/d/1QogGSuEab5SZyZIw1Q8h-0yrBNs1Z_eEBJG7oRESW5k/edit#gid=107865534"
df_gsheet = as.data.frame(gsheet2tbl(url_gsheet))
head(df_gsheet)

#graphs

mtcars
names(mtcars)
table(mtcars$cyl)
table(mtcars$cyl, mtcars$am)
mtcars$mpg
#continuous data - histogram, boxplot
hist(mtcars$mpg)
boxplot(mtcars$mpg, horizontal = T)
boxplot( mpg ~ gear, data=mtcars, col=1:3)
t1 = table(mtcars$gear)
t1
barplot(t1, col=1:3)
students
t2 = table(students$college)
barplot(t2)
t3 = table(students$gender)
barplot(t3)
title('This is bar plot', sub = 'Subtitle')
pie(t3)


================================================
FILE: trg/d2b-fmssrcc.R
================================================
#Modeling
#linear Regression - Simple, Multiple
#y ~ x1 (SLR); y~ x1 + x2 ... (MLR)
#y - Dependent Variable, x - Independent

head(women)
#y - weight, x = height
cor(women$height, women$weight)
#strength and direction of relationship
cov(women$height, women$weight) #direction +ve
#height increases, weight increases
plot(women$height, women$weight)
#perform linear modeling
fit1 =lm(weight ~ height, data=women)
summary(fit1)
#F Stats Pvalue << 0.05 : Linear Model exist
#at least one IV is significant in predicting DV
#Multiple Rsquare = .991 Coeff of Determination
#Multiple when you have one IV, otherwise take Adj Rsq
#99% of the variation in Y is explained by Xs
#y = mx + C : y = -87 + 3.45 * height
range(women$height)
#only do interpolation not exterpolation 
women$weight
women$height
(y = -87 + 3.45 * women$height) 
#predicted weights for actual heights
fitted(fit1) 
residuals(fit1) #difference between predicted and acutal weights
summary(fit1) #summary of the model
summary(residuals(fit1))
summary(students[,5:6])
(newdata1 = data.frame(height=c(60.4,59.9,77)))
?predict
(p1 =predict(fit1, newdata = newdata1, type='response'))
cbind(newdata1, p1)
#
#check for assumptions of Linear Regression
plot(fit1)


fit2 = lm(weight ~ height, data=women)
summary(fit2)
predict(fit2, newdata=data.frame(height=c(61,72)), type='response')


================================================
FILE: trg/iima-d1.R
================================================
# Title: DataStructures in R
#IIMA

#vectors----
cv = c('dhiraj','a', 'Arun')
nv = c(12,45,26)
lv = c(TRUE, FALSE, T)
cv
nv
(x = 1:100000)
set.seed(100)
(x = trunc(rnorm(100000, mean=60, sd=15)))

x
(x1=sort(x))
x1
x
# values between 60 and 70
x[ x >= 60 & x <= 70]
length(x)
length(x[ x >= 60 & x <= 70])
x
mean(x)
median(x)
mode(x)  #this is not the actual mode
xt = table(x)
xt
sort(xt, decreasing = T)[1:2]
summary(x)
quantile(x)
quantile(x, c(0,.25, .5,.75,.1))

quantile(x, c(.1,.5,.9))
quantile(x, seq(0,1,.01))
seq(0,1,.01)

#boxplot - 5 no summary
boxplot(x, notch=T)
?boxplot
text(1,30, labels='Dhiraj')

x3 = rnorm(50,mean=20,sd=3)
stem(x3)
hist(x3, breaks=12, col=1:5)
hist(x3, freq=F)
lines(density(x3))


x2 = rnorm(1000, mean=10, sd=2)
plot(density(x2))
abline(v=10, h=.1)

x3=seq(1, 100, by=2)
x3
(x3=seq(1, 100, by=2))

#

#matrix----
(m1 = matrix(c(1:24), nrow=6, byrow=F))
m1[1]; m1[10:14]
x=1:24
(m2 = matrix(x, ncol=4, byrow=T))
m2[1]; m2[10:14]
m1; m2
m3=cbind(m1,m2)
rbind(m1,m2)
m3
colSums(m1); rowSums(m1)
colMeans(m1); rowMeans(m1)
#subset col/ row, combination of R & C
m1
m1[1,] ; m1[,2] ; m1[1,2]
m1[1:2,3:4] ; m1[c(1,4), c(2,3)]
m1[m1 > 10 & m1 < 15]
m1 > 10 & m1 < 15
m1-m2
m1 + m2

t(m1) # transpose
m1
sweep(m1, MARGIN = 1, STATS = c(2,3,4,5,6,7), FUN="+" ) #rowise
sweep(m1, MARGIN = 2, STATS = c(2,3,4,5), FUN="*" ) #colwise
sweep(m1, MARGIN = 2, STATS = c(5,1,1,1), FUN="*" ) #colwise
sweep(m1[,c(1,2)], MARGIN = 2, STATS = c(5), FUN="*" ) #colwise
#

#dataframe----
#like excel, every col can of different data type
n=30
(rollno = paste('A',1:n,sep=''))
(name = paste('Student',1:n, sep='@'))
set.seed(101)
(gender = sample(c('M','F'), size=n, replace=T, prob=c(.7,.3)))
table(gender)
?rnorm
set.seed(104)
(marks1 = round(rnorm(n=n,mean=70, sd=7),2))
set.seed(105)
(marks2 = round(rnorm(n=n, mean=65, sd=10),2))
set.seed(106)
(marks3 = sample(c('A','B','C','D'), size=n, replace=T, prob=c(.3,.2,.3,.2)))
table(marks3)
#df = data.frame(rollno, name, gender, marks1, marks2, marks3)

df = data.frame(rollno, name, gender, marks1, marks2, marks3, stringsAsFactors = F)
df
summary(df)
str(df)
df$gender = factor(df$gender)
df$mark3 = factor(df$marks3)
df$rollno
df
#queries  ?
#filter
df[1:5, 1:2]
df[1:5, c('rollno','name','marks1')]
df[df$marks1 > 70 & gender=='M', c('rollno','name', 'gender', 'marks1')]
df$course = sample(c('MBA','BBA'),size=n, replace=T, prob=c(.6,.4))
head(df)
tail(df)
str(df)
#factors-----

df$marks3 = factor(df$marks3,ordered=T, levels=c('B','D','C','A'))
df$course = factor(df$course)
str(df)
boxplot(marks2 ~ marks3, data=df)
boxplot(marks2 ~ (course + gender), data=df)

aggregate(marks2 ~ course, data=df, FUN=mean)
aggregate(marks2 ~ course + gender, data=df, FUN=mean)
aggregate(marks2 ~ course + gender + marks3, data=df, FUN=mean)

#dplyr
#install.packages('dplyr')
library(dplyr)
#x %>% f(y); f(x, y)

df %>% count() ; count(df)

#group by course, mean of mark2
aggregate(marks2 ~ course, data=df, FUN=mean)
df %>% group_by(course) %>% summarise(mean(marks2))
df %>% group_by(course, gender) %>% summarise(mean(marks2), mean(marks1))
df %>% group_by(course) %>% select(marks1) %>% top_n(2)
df %>% filter(gender=='F' & marks3=='A') %>% tally()
df %>% filter(gender=='F' & marks3=='A') %>% summarise(n())
df %>% sample_frac(.2)
df %>% sample_n(3)


================================================
FILE: trg/iima-d3.R
================================================

fit = lm(weight ~ height, data = women)
summary(fit)

#R2
#coefficients
#does linear model exists between Y & Xs
#pvalue of Stats : Exists < 0.05

#R2 : Multiple & Adjusted
#here we used Multiple becos of 1 IV ie height
#.991 what does it mean - Good Model  : > .6/ .7

#coefficients
#y = mx + c : c=-87, m=3.45, 
x=0; y = 3.45 * x + -87; y
range(women$height)
x=58 
(y = 3.45 * x + -87)
women [ women$height < 60, ]
#Pr value < 0.05  : Then coeff are significant : used in the model

#residual = actual - predicted
women$weight
fitted(fit)
cbind(women, fitted(fit), resid(fit))
#y = 3.45 * x - 87
resid(fit)
summary(resid(fit))

#predict : height = 60.5
women
ndata = data.frame(height=c(60.5, 70.5))
p1=predict(fit, newdata= ndata)
cbind(ndata, p1)

#assumptions of linear regression
#linearity, autocollinearity, multicollinearity, normality, homosodescity (equal variance), outliers 
#linked with residuals.... 
plot(fit)


plot(density(resid(fit)))
plot(fit, which=2)

par(mfrow=c(2,2))
plot(fit)


#normality
#autocollinearity
#multicollinarrity
#outliers



#cor
#plot
#linear model
#R2, F-pvalue, coeff
#predict for unknown input
#assumptions using plots


================================================
FILE: trg/iima-d4.R
================================================
#iima - Day4

#missing values

x = c(1,3, NA)
is.na(x)
sum(is.na(x))

library(VIM)
data(sleep)
summary(sleep)
#analyse the data for missing values



#logistic regression------
#binary classification
set.seed(1234)
(price = sort(runif(100, min=20, max=100)))
(buy = factor(rep(c('Buy','NotBuy'), times=c(55,45))))
purchase = data.frame(price, buy)
head(purchase)
purchase
plot(x=price, y=buy, data=purchase)
str(purchase)
#log regression
log_fit = glm(buy ~ price, data=purchase, family='binomial') 
summary(log_fit)

(ndata1= data.frame(price=c(30,50,70,90,98)))
p1=predict(log_fit, newdata=ndata1, type='response')
?predict
p1
options(scipen=999)
cbind(ndata1, p1)
cbind(ndata1, p1, BUY=ifelse(p1 > .5,'No','Yes'))

#decision trees


#extras
(x = c(1,2,6))
(x = 1:10)
(x = seq(1,100,2))
(x = rnorm(10, mean=60, sd=10))
(x = runif(10, min=60, max=100))
x
sort(x)
plot(density(x))




================================================
FILE: trg/iimkpg-d6.R
================================================
#IIM Kpv -6th Day

#Review
#R, RStudio, Git
#R Env
#Data Structures
(v1 = c(77,4,5,6,8,4,11,15,7,32,55,23)) ; length(v1)
(m1 = matrix(v1, ncol=4))
m1[,2:3]
(df1 = data.frame(product=c('Maggi','Top Ramen','Cadbury'), sales=c(100,120,68), type=c('T1','T1','T2')))
head(df1)
df1$type = factor(df1$type)
#list and array as HW
#dplyr package
library(dplyr)
df1 %>% group_by(type) %>% summarise(mean(sales))
#import & export data
df1
write.csv(df1,'mydata.csv', row.names = F)
mydf = read.csv('mydata.csv')
mydf1 = read.csv(file.choose())
head(mydf)
head(mydf1)


mydf = read.csv('mydata.csv', header=F, skip=2)
head(mydf)
is.na(mydf)
sum(is.na(mydf))
?read.csv
str(mydf)
#-------------
#linear Regression
fit = lm(y ~ x1 + x2, data=...)
summary(fit)
predict(fit, newdata=.. )
plot(fit)

#logistic Regression
fit2 = glm(y ~ x1 + x2 + ... data = .., family='binomial')
summary(fit2)
predict(fit2, newdata=..., type='response')
ifelse(p > .5, 0,1)
confusionmatrix

#partition data into train and test
index=sample(length(df), size= , replace=F)
train = df[index,]
test = df[-index,]


#Decision Tree
#what type of tree - classification or regression
#choose relevant algo or packages
#check of missing values in target variable Y
#divide dataset into train and test
#create decision tree model using train set
#rpart : use cp to prune the tree
#ctree : cp not reqd, auto pruning
#predict for test set
#confusion matrix for classification tree
#rmse value for regression
sqrt(mean(actual_Y - predictedY)^2)

index = sample(nrow(women), size=.7 * nrow(women))
train = women[index,]
test = women[-index,]
fit = lm(weight ~ height, data=train)
summary(fit)
(pwt = predict(fit, newdata=test))
cbind(test, pwt)
library(ModelMetrics)
rmse(test$weight, pwt)  

(test$weight-pwt)^2
sqrt(mean((test$weight-pwt)^2))


#Clustering
#kmeans
#noofclusters
#---------

#Types - Classification & Regression Trees
#algo - CART and ctree
#classification : predict a class / category
#regression : predict a continuous value eg. Sales
#CART - gini/ entropy / rin variance to select most important variable
#ctree - stats significance ie. pvalue
#CART - cp for pruning : to avoid growing the tree very large
#cp should be such that xerror is the least

#clustering
#group /segmentation 
#kmeans : no of clusters : optimal no of clusters NbClust


#Last Class - AR 
#data to be transcation format Tnos and items in each Tr
#libraries - arules, arulesviz
#finding frequent itemset 
#finding association rules - minconf, minsp, minlength and maxlength
#interpret the rules
#

================================================
FILE: trg/iimkpv-d1.R
================================================
#Data Structures - I

#Vectors -----
#control+enter when you are in the line to execute
# Vectors-----
x=1:10   #create seq of nos from 1 to 10
x
x1  #not created so far, hence error
(x1 <- 1:20)
(x1=1:30)
(x2=c(1,2,13,4,5))
class(x2)
(x3=letters[1:10])
class(x3)
LETTERS[1:26]
(x3b = c('a',"Henry","4"))
class(x3b)

(x4=c(T,FALSE,TRUE,T,F))
class(x4)

x5=c(3L,5L)
class(x5)
x5a = c(3,5)
class(x5a)
(x5b = c(1, 'a',T, 4L))
class(x5b)

#access elements
(x6 = seq(0,100,by=3))
methods(class='numeric')
?seq
#[1]  0  2  4  6  8 10
ls()  #variables in my environment
x6
length(x6)
x6[20]
x6[3]  # access 3rd element
#[1] 4
x6[c(2, 4)]     # access 2nd and 4th element
x6[-1]          # access all but 1st element
x6[-c(1:10, 15:20)]
x6[c(2, -4)]    # cannot mix positive and negative integers
#Error in x[c(2, -4)] : only 0's may be mixed with negative subscripts
x6[-c(1,5,20)]

x = x[1:4]; x      # truncate x to first 4 elements

#sort 
x6 ; rev(x6)
sort(x6) #ascending order
sort(x6, decreasing=T)  #Descending Order


#[1] 5 0 5 0
#print values & number of such values in x6 vector from 30 to 40
x6[ x6 > 10 & x6 < 40]
length(x6[ x6 > 10 & x6 < 40])
#print last 5 positions of vector x6
tail(x6)
?tail
tail(x6, n=5)
#find the middle value by position
x6[length(x6)/2]
#replace this value to 99
x6[length(x6)/2] =99
x6[length(x6)/2]
x6[1:5] = 99
head(x6)

#Matrices : rows X columns : it can have only type data either numeric or character/ logical
(x=100:111); length(x)
matrix(1, nrow=4, ncol=2)
(m1 = matrix(100:111, nrow=4)) #ncol = 3
(m2 = matrix(x, ncol=3, byrow=T))
matrix(x, ncol=6)
class(m1)
attributes(m1)
dim(m1)
m1

# access elements of matrix
m1[1,2:3]
m1[c(1,3),]
m1[,-c(1,3), drop=F]
#find values as per conditions
m2[m2 > 105 & m2 < 110]
m1
colnames(m1)=c('C1','C2','C3')
rownames(m1)=c('R1','R2','R3','R4')
colMeans(m1) ; rowMeans(m1)
colSums(m1) ; rowSums(m1)
min(m1); max(m1);sd(m1)
pie(colSums(m1))
barplot(rowSums(m1), col=1:4, ylim=c(0,200))

#Data Frame & Factor
#create Vectors to be combined into DF
(rollno = 1:30)
(sname = paste('student',1:30,sep=''))
(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
(marks1 = floor(rnorm(30,mean= 50,sd=10)))
(marks2 = ceiling(rnorm(30,40,5)))
(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))
(grades = sample(c(LETTERS[1:5]), size=30, replace=T))
rollno; sname; gender ; marks1 ; marks2; course; grades

#create DF
df1= data.frame(rollno, sname, gender, marks1, marks2, course, grades, stringsAsFactors = F)
head(df1) #top 6 rows
str(df1) #structure of DF
head(df1,n=3) #top 3 rows
tail(df1) #last 6 rows
class(df1) # DF
summary(df1) #summary
df1
head(df1)
df1$sname
#convert few columns to Factor(Categories)
df1$gender = factor(df1$gender)
df1$course = factor(df1$course)
df1$grades = factor(df1$grades, ordered=T, levels=c('E','D','C','B','A'))
#now see the summary
str(df1)
summary(df1)

df1  #full data
df1$gender  # one column
df1[1:3 , c(2,4)] #multiple columns

#as per condition
df1[ marks2 < 50 & gender=='F', c('rollno', 'sname','gender', 'marks1')]
range(df1$marks2)
df1[ marks1 > 50 & gender=='F', c(1,2)]
df1[ marks1 > 50 | gender=='F', ]
names(df1)  # names of columns
dim(df1)  #Dimensions

aggregate(df1$marks1, by=list(df1$gender), FUN=mean)
aggregate(marks1 ~ gender, data=df1, FUN=mean)
aggregate(cbind(marks1, marks2) ~ gender + course, data=df1, FUN=mean)
?aggregate
#Sort DF
df1[ order(df1$gender, df1$marks1),]
#Course, grades - decre 
df1$grades
df1[ order(df1$course, df1$gender),]
df1[ order(df1$course, desc(df1$grades)),]
df1[ order(df1$course, df1$grades),]

#
fivenum(df1$marks1); summary(df1$marks1)
boxplot(df1$marks1)
abline(h=fivenum(df1$marks1), col=1:5,lwd=2)
boxplot(marks1 ~ gender, data=df1)
boxplot(marks2 ~ course, data=df1)
boxplot(marks2 ~ course, data=df1, col=c('red','green'))
title("Box Plot of Marks2 vs Course")
boxplot(marks2 ~ grades, data=df1, col=1:5)
aggregate(cbind(marks1, marks2) ~ gender, data=df1, FUN=mean)


#install.packages('dplyr') #after install comment
#install only once in ur system; load it whenever required
library(dplyr)
head(df1)
df1 %>% select(sname, gender, marks1) %>% head
df1 %>% filter(marks2 > 45)
df1 %>% filter(marks1 > 45 & gender == 'F')

names(df1)
head(df1)
df1 %>% group_by(course,gender) %>% summarise(MEAN1 = mean(marks1), MAX2 = max(marks2))
df1 %>% group_by(course,gender, grades) %>% count

df1 %>% sample_n(2)
df1 %>% sample_frac(.3)

#Stratied Sampling
out2 <- df1 %>%  group_by(gender) %>%  sample_frac(.2)
out2
table(df1$gender)

df1 %>% arrange(gender, course, desc(marks1)) %>% select(gender, course, marks1)



================================================
FILE: trg/iimkpv-d4.R
================================================
#Day 4 - IIM Revision



(gender = sample(c('M','F'), size=3, replace=T))

(gender = sample(c('M','F'), size=30, replace=T, prob=c(.7,.3)))
t1= table(gender)
prop.table(t1)

(course = sample(c('BBA','MBA'), size=30, replace=T, prob=c(.5,.5)))
summary(course)
courseF = factor(course)
summary(course)
summary(courseF)

(grades = sample(c(LETTERS[1:5]), size=30, replace=T))
summary(grades)
gradesF = factor(grades)
summary(gradesF)
# Ex, Good, Sat, Poor
#Poor < Sat < Good < Ex
gradesOF = factor(grades, ordered=T, levels=c('E','D','A','C','B'))
summary(gradesOF)
gradesOF
levels(gradesOF)
levels(gradesOF)[1]


================================================
FILE: trg/ximb-r.R
================================================
# Practise Exercise - XIMB

#Create a 100 row DF with following Variables
#gender, spl, age, experience, grade, placement
head(students)
str(students)
#


(gender = sample(c('M','F'), size=100, replace=T, prob=c(.6,.4)))
(spl = sample(c('Marketing','Finance','HR'), size=100, replace=T, prob=c(.3,.4,.3)))
(age = round(runif(100, 21, 30),2))
(experience = round(rnorm(100, 4, 1),2))
(grade = sample(c('A','B','C','D'), size=100, replace=T, prob=c(.4,.3,.2,.1)))
(placement = sample(c('yes','no'), size=100, replace=T, prob=c(.7,.3)))

students = data.frame(gender, spl, age, experience, grade, placement)
str(students)
summary(students)

#summarise the data in various ways using dplyr
library(dplyr)
names(students)
students %>% group_by(placement, gender) %>% summarise(mean(experience), max(experience), mean(age))

students %>% filter(spl=='Marketing') %>% summarise(mean(age), mean(experience))
students %>% filter(spl=='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))
students %>% filter(spl!='Marketing') %>% group_by(spl) %>% summarise(mean(age), mean(experience))
students %>% filter(spl=='Marketing'& gender=='F' & grade=='A') %>% group_by(spl) %>% summarise(mean(age), mean(experience))


#draw few graphs to understand the distribution of data

hist(students$age)
t1=table(students$placement)
barplot(t1, col=1:2)
boxplot(students$age)
pairs(students[,c('age','experience')])
pie(t1)

par(mfrow=c(2,2))
pie(table(students$gender))
pie(table(students$placement))
pie(table(students$grade))
pie(table(students$spl))
par(mfrow=c(1,1))
table(students$gender, students$placement, students$spl)



#find students having largest experience in each spl for each gender

students %>% group_by(spl,gender) %>% arrange(spl, gender,experience) %>% top_n(1, experience) 



#how many were placed : draw pie plot



write.csv(students, './data/ximb.csv')

students2 = read.csv('./data/ximb.csv')
head(students2)


#clustering
km3 = kmeans(students[,c('age','experience')], centers=3)
km3
km3$centers
plot(students[,c('age','experience')], col=km3$cluster)

#decision tree
library(rpart)
library(rpart.plot)

tree = rpart(placement ~ . , data=students)
tree
rpart.plot(tree, nn=T, cex=1)
printcp(tree)
prune(tree, cp=.03)

ndata = sample_n(students, 5)
ndata
predict(tree, newdata= ndata, type='class')
predict(tree, newdata= ndata, type='prob')


#logistic Regression
logitmodel1 = glm(placement ~ . , data =students, family='binomial')
summary(logitmodel1)
logitmodel1a = glm(placement ~ age , data =students, family='binomial')
summary(logitmodel1a)
logitmodel2 = glm(placement ~ age + gender , data =students, family='binomial')
summary(logitmodel2)

#linear regression
linear1 = lm(age ~ . , data=students)
summary(linear1)


#iimkpv

#vectors
x = 1:100
class(x)
x = c(1,3,5.5)
class(x)
x = LETTERS[1:10]
class(x)
x = c(T,F,T,F,F)
class(x)
#matrix
m = matrix(1:24, ncol=6)
m
class(m)
#dataframes
head(mtcars)
class(mtcars)

#datastructures
#filter, sort, delete, missingvalues, stats operations
colMeans(mtcars)
colMeans(m)
?colMeans

#manipulation / summarisation
library(dplyr)
names(mtcars)
mtcars %>% group_by(cyl) %>% summarise(mean(mpg), max(wt), n())

plot(y=mtcars$mpg, x=mtcars$wt)
abline(lm(mpg ~ wt, data=mtcars))
#visualisation

#Modeling
fit = lm(mpg ~ wt + hp, data=mtcars)
summary(fit)
#linear regression, logistic regression, decision tree, clustering, market basket analysis

#Time Series 
#stock data, sales data
#Financial Analytics, Business 

#Dates are handled in the R
#create TS objects in R
#manipulate, Plot, Forecasting
#decomp

# Decision Trees

#types - Regression  & Classification
iris
head(iris)

library(rpart)
library(rpart.plot)
#regression tree
#DV - Sepal.Length
rtree = rpart(Sepal.Length ~ . , data= iris, method='anova')
rtree
rpart.plot(rtree,  nn=T, cex=.8, fallen.leaves = T)
#classification tree
#DV - Species
ctree = rpart(Species ~ . ,data = iris, method='class')
ctree
rpart.plot(ctree, cex=.8, nn=T, extra=104)
printcp(ctree)
ctreeprune1 = prune(ctree, cp=.44)
ctreeprune1
rpart.plot(ctreeprune1, cex=.8, nn=T, extra=104)
(ndata = sample_n(iris,3))
predict(ctree,newdata=ndata, type='prob')


library(gsheet)
url= "https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA"
df = as.data.frame(gsheet2tbl(url))
head(df)

url2= "https://docs.google.com/spreadsheets/d/1FTlwpywOynI9dXbgewKCf4WhBc2PwoQmHzlqnAEisuA/edit#gid=1123010023"
df2 = as.data.frame(gsheet2tbl(url2))
head(df2)


df3 = read.csv('./data/iimc1.csv')
head(df3)